[
  {
    "path": ".github/workflows/actinos_workflow.yml",
    "content": "name: Deployment Workflow\non:\n  push:\n    branches: [main]\n  \njobs:\n  first_job:\n    name: Deploy Calliar Website\n    runs-on: ubuntu-latest\n    steps:\n    - name: SSH Remote Commands\n      uses: appleboy/ssh-action@v0.1.4\n      with:\n        host: calliar.arbml.org\n        username: arabicmachinelearning\n        key: ${{ secrets.SECRET_SSHKEY }}\n        port: 22\n        script: |\n          cd ~\n\n          if [ ! -d \"atmatah\" ];\n            then\n              echo cloning atamatah as it does not exist\n              git clone https://github.com/ARBML/atmatah.git\n              cd atmatah\n            else\n              echo pulling latest changes from atmatah\n              cd atmatah\n              git pull\n          fi\n\n          echo creating venv if not exist and install requirements\n          if [ ! -d \"venv\" ];\n            then\n              python3 -m venv venv\n          fi\n          venv/bin/python3 -m pip install -r requirements.txt\n          venv/bin/ansible-galaxy install -r requirements.yml\n\n          echo prepare for deploy\n          mkdir -p .config\n          echo ${{ secrets.ANS_PASS }} > .config/ansible_pass.password\n\n          echo deploy calliar app\n          venv/bin/ansible-playbook -i apps/calliar/inventory apps/calliar/app.yml --vault-password-file=.config/ansible_pass.password --connection=local\n\n          echo cleanup\n          cd ~\n    "
  },
  {
    "path": ".gitignore",
    "content": "# https://github.com/github/gitignore/blob/main/Python.gitignore\n# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$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/\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/\ncover/\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\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n#   For a library or package, you might want to ignore these files since the code is\n#   intended to run in multiple environments; otherwise, check them in:\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# poetry\n#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n#   This is especially recommended for binary packages to ensure reproducibility, and is more\n#   commonly ignored for libraries.\n#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n#   in version control.\n#   https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\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/page/index.html\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n#  and can be added to the global gitignore or merged into this file.  For a more nuclear\n#  option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n\n\n#---------------------custom--------------------------_#\n\n*.pyc\nvenv/*\n*.svg\n!media/*\n.vscode\n.ipynb_checkpoints\n*/.ipynb_checkpoints/*\ncalliar_server/calliar_server/__pycache__/*.pyc\ncalliar_server/server/__pycache__/views.cpython-36.pyc\ncalliar_server/db.sqlite3\ncalliar_server/server/__pycache__/urls.cpython-36.pyc\ncalliar_server/server/__pycache__/views.cpython-36.pyc\n.vscode/*\ncalliar_server/calliar_server/local_settings.py\ncalliar_server/db.sqlite3\n"
  },
  {
    "path": "LICENSE",
    "content": "MIT License\n\nCopyright (c) 2023 ARBML\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n"
  },
  {
    "path": "README.md",
    "content": "# Calliar\nCalliar is a dataset for Arabic calligraphy. The dataset consists of 2500 json files that contain strokes manually annotated for Arabic calligraphy. This repository contains the dataset for the following paper : \n\n> **Calliar: An Online Handwritten Dataset for Arabic Calligraphy**<br>\n> Zaid Alyafeai, Maged S. Al-shaibani, Mustafa Ghaleb, Yousif Ahmed Al-Wajih<br>\n> https://arxiv.org/abs/2106.10745\n>\n> **Abstract:** *Calligraphy is an essential part of the Arabic heritage and culture. It has been used in the past for the decoration of houses and mosques. Usually, such calligraphy is designed manually by experts with aesthetic insights. In the past few years, there has been a considerable effort to digitize such type of art by either taking a photo of decorated buildings or drawing them using digital devices. The latter is considered an online form where the drawing is tracked by recording the apparatus movement, an electronic pen for instance, on a screen. In the literature, there are many offline datasets collected with a diversity of Arabic styles for calligraphy. However, there is no available online dataset for Arabic calligraphy. In this paper, we illustrate our approach for the collection and annotation of an online dataset for Arabic calligraphy called Calliar that consists of 2,500 sentences. Calliar is annotated for stroke, character, word and sentence level prediction.*\n\n## Stats \n\n| Dataset | # of Samples | # of Words | # of Chars | # of Strokes | \n---------|---------------|-----------|------------|---------------\n| Train | 2,000 | 6,065 | 24,722 | 36,561 |\n| Valid | 250 | 738 | 2,946 | 4,410 |\n| Test | 250 | 753 |3,052 | 4,601 |\n\n## Dataset Formats \nMainly, we have two basic formats. \n### .json \n\nEach `.json` file contains a list of strokes. Each list is a dictionary of the stroke character and the list of points. Each composite character like `ت` is mapped into a list of primitive strokes i.e `..ٮ `. Refer to the paper and to `chars.py` for more details on the mapping. \n\n![](media/data_format_colored.png)\n\n### .npz \n\nThe compressed format of the dataset `dataset.npz` is only 8.6 MB and uses the Ramer-Douglas-Peucker Algorithm to decrease the number of points per stroke. The python library [rdp](https://github.com/fhirschmann/rdp) was used for such task. The `.npz` format follows the same approach as [QuickDraw](https://github.com/googlecreativelab/quickdraw-dataset). \n\n\n## Visualization \n\nThe `vis.py` file contains a list of python methods for easily visualizing the dataset. Here are two examples for drawing a sample json file and creating an animation.  <a href=\"https://colab.research.google.com/github/ARBML/Calliar/blob/main/demo.ipynb\">\n    <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" >\n    </a>\n\n```python\nimport glob\nimport matplotlib.pyplot as plt \nimport json \nfrom IPython.core.display import display, HTML, Video\nfrom vis import *\n\n## show an image of the strokes \ndrawing = json.load(open(json_path))\nprint(get_annotation(json_path))\ndata = convert_3d(drawing)\ndraw_strokes(data, stroke_width = 2, crop = True)\n\n## create an animation. \ncreate_animation(json_path)\nVideo(\"tmp/video.mp4\")\n```\n\n## Samples \n![sample_calliar_image_3](media/sample_calliar_image_3.png)\n\n\n## Annotation Server \nFirst `pip install django`, then traverse to the directory `calliar_server` and run \n\n```basch\npython manage.py runserver\n```\n\n## Animation\n\n\nhttps://user-images.githubusercontent.com/15667714/122690255-1acff980-d231-11eb-9752-730d8024041e.mp4\n\n\n\nhttps://user-images.githubusercontent.com/15667714/122690258-1c012680-d231-11eb-98d0-4c89afd8aba3.mp4\n\n\n\nhttps://user-images.githubusercontent.com/15667714/122690261-1d325380-d231-11eb-99ab-158455483561.mp4\n\n\n\nhttps://user-images.githubusercontent.com/15667714/122690265-1efc1700-d231-11eb-9830-328725a82c11.mp4\n\n\n## Citation \n\n```\n@misc{alyafeai2021calliar,\n      title={Calliar: An Online Handwritten Dataset for Arabic Calligraphy}, \n      author={Zaid Alyafeai and Maged S. Al-shaibani and Mustafa Ghaleb and Yousif Ahmed Al-Wajih},\n      year={2021},\n      eprint={2106.10745},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n```\n\n\n\n\n\n"
  },
  {
    "path": "calliar_server/calliar_server/__init__.py",
    "content": ""
  },
  {
    "path": "calliar_server/calliar_server/asgi.py",
    "content": "\"\"\"\nASGI config for calliar_server project.\n\nIt exposes the ASGI callable as a module-level variable named ``application``.\n\nFor more information on this file, see\nhttps://docs.djangoproject.com/en/3.2/howto/deployment/asgi/\n\"\"\"\n\nimport os\n\nfrom django.core.asgi import get_asgi_application\n\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"calliar_server.settings\")\n\napplication = get_asgi_application()\n"
  },
  {
    "path": "calliar_server/calliar_server/settings.py",
    "content": "\"\"\"\nDjango settings for calliar_server project.\n\nGenerated by 'django-admin startproject' using Django 3.2.2.\n\nFor more information on this file, see\nhttps://docs.djangoproject.com/en/3.2/topics/settings/\n\nFor the full list of settings and their values, see\nhttps://docs.djangoproject.com/en/3.2/ref/settings/\n\"\"\"\n\nfrom pathlib import Path\nimport os\n\n# Build paths inside the project like this: BASE_DIR / 'subdir'.\nBASE_DIR = Path(__file__).resolve().parent.parent\n\n\n# Quick-start development settings - unsuitable for production\n# See https://docs.djangoproject.com/en/3.2/howto/deployment/checklist/\n\n# SECURITY WARNING: keep the secret key used in production secret!\nSECRET_KEY = \"django-insecure-c0eh)tcsdj2x#_3rcs7fokahj0vk*7&!3n!zi60%0_lqf1e-u^\"\n\n# SECURITY WARNING: don't run with debug turned on in production!\nDEBUG = True\n\n# ALLOWED_HOSTS = []\nALLOWED_HOSTS = [\"*\"]\n\n\n# Application definition\n\nINSTALLED_APPS = [\n    \"django.contrib.admin\",\n    \"django.contrib.auth\",\n    \"django.contrib.contenttypes\",\n    \"django.contrib.sessions\",\n    \"django.contrib.messages\",\n    \"django.contrib.staticfiles\",\n    \"server\",\n]\n\nMIDDLEWARE = [\n    \"django.middleware.security.SecurityMiddleware\",\n    \"django.contrib.sessions.middleware.SessionMiddleware\",\n    \"django.middleware.common.CommonMiddleware\",\n    \"django.middleware.csrf.CsrfViewMiddleware\",\n    \"django.contrib.auth.middleware.AuthenticationMiddleware\",\n    \"django.contrib.messages.middleware.MessageMiddleware\",\n    \"django.middleware.clickjacking.XFrameOptionsMiddleware\",\n]\n\nROOT_URLCONF = \"calliar_server.urls\"\n\nTEMPLATES = [\n    {\n        \"BACKEND\": \"django.template.backends.django.DjangoTemplates\",\n        \"DIRS\": [],\n        \"APP_DIRS\": True,\n        \"OPTIONS\": {\n            \"context_processors\": [\n                \"django.template.context_processors.debug\",\n                \"django.template.context_processors.request\",\n                \"django.contrib.auth.context_processors.auth\",\n                \"django.contrib.messages.context_processors.messages\",\n            ],\n        },\n    },\n]\n\nWSGI_APPLICATION = \"calliar_server.wsgi.application\"\n\n\n# Database\n# https://docs.djangoproject.com/en/3.2/ref/settings/#databases\n\nDATABASES = {\n    \"default\": {\n        \"ENGINE\": \"django.db.backends.sqlite3\",\n        \"NAME\": BASE_DIR / \"db.sqlite3\",\n    }\n}\n\n\n# Password validation\n# https://docs.djangoproject.com/en/3.2/ref/settings/#auth-password-validators\n\nAUTH_PASSWORD_VALIDATORS = [\n    {\n        \"NAME\": \"django.contrib.auth.password_validation.UserAttributeSimilarityValidator\",\n    },\n    {\"NAME\": \"django.contrib.auth.password_validation.MinimumLengthValidator\",},\n    {\"NAME\": \"django.contrib.auth.password_validation.CommonPasswordValidator\",},\n    {\"NAME\": \"django.contrib.auth.password_validation.NumericPasswordValidator\",},\n]\n\n\n# Internationalization\n# https://docs.djangoproject.com/en/3.2/topics/i18n/\n\nLANGUAGE_CODE = \"en-us\"\n\nTIME_ZONE = \"UTC\"\n\nUSE_I18N = True\n\nUSE_L10N = True\n\nUSE_TZ = True\n\n\n# Static files (CSS, JavaScript, Images)\n# https://docs.djangoproject.com/en/3.2/howto/static-files/\n\nSTATIC_URL = \"/static/\"\n\n# Default primary key field type\n# https://docs.djangoproject.com/en/3.2/ref/settings/#default-auto-field\n\nDEFAULT_AUTO_FIELD = \"django.db.models.BigAutoField\"\n\nCORS_ALLOW_ALL_ORIGINS = True\nMEDIA_ROOT = os.path.join(BASE_DIR, \"../media\")\nMEDIA_URL = \"/media/\"\n\n\n#---------------- CUSTOM ENV VARS -------------\nIMAGES_DIR = '../media/calliar_images'\n\n\ntry:\n    from .local_settings import *\nexcept Exception as e:\n    print('local settings file cannot be imported')\n    \n# create these dirs if they do not exist, \n\nos.system(f'mkdir -p {IMAGES_DIR}/annotations')\nos.system(f'mkdir -p {IMAGES_DIR}/annoated_images')\n\n# assert required directories within IMAGES_DIR exists\n\nassert os.path.isdir(f'{IMAGES_DIR}/annotations'), 'annotations folder does not exist'\nassert os.path.isdir(f'{IMAGES_DIR}/images'), 'images folder does not exist'\nassert os.path.isdir(f'{IMAGES_DIR}/annoated_images'), 'processed_images folder does not exist'\n"
  },
  {
    "path": "calliar_server/calliar_server/urls.py",
    "content": "\"\"\"calliar_server URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/3.2/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.urls import include, path\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path, include\nfrom django.conf import settings\nfrom django.conf.urls.static import static\n\n\nurlpatterns = [path(\"admin/\", admin.site.urls), path(\"\", include(\"server.urls\"))]\nurlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n"
  },
  {
    "path": "calliar_server/calliar_server/wsgi.py",
    "content": "\"\"\"\nWSGI config for calliar_server project.\n\nIt exposes the WSGI callable as a module-level variable named ``application``.\n\nFor more information on this file, see\nhttps://docs.djangoproject.com/en/3.2/howto/deployment/wsgi/\n\"\"\"\n\nimport os\n\nfrom django.core.wsgi import get_wsgi_application\n\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"calliar_server.settings\")\n\napplication = get_wsgi_application()\n"
  },
  {
    "path": "calliar_server/manage.py",
    "content": "#!/usr/bin/env python\n\"\"\"Django's command-line utility for administrative tasks.\"\"\"\nimport os\nimport sys\n\n\ndef main():\n    \"\"\"Run administrative tasks.\"\"\"\n    os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"calliar_server.settings\")\n    try:\n        from django.core.management import execute_from_command_line\n    except ImportError as exc:\n        raise ImportError(\n            \"Couldn't import Django. Are you sure it's installed and \"\n            \"available on your PYTHONPATH environment variable? Did you \"\n            \"forget to activate a virtual environment?\"\n        ) from exc\n    execute_from_command_line(sys.argv)\n\n\nif __name__ == \"__main__\":\n    main()\n"
  },
  {
    "path": "calliar_server/server/__init__.py",
    "content": ""
  },
  {
    "path": "calliar_server/server/admin.py",
    "content": "from django.contrib import admin\n\n# Register your models here.\n"
  },
  {
    "path": "calliar_server/server/apps.py",
    "content": "from django.apps import AppConfig\n\n\nclass ServerConfig(AppConfig):\n    default_auto_field = 'django.db.models.BigAutoField'\n    name = 'server'\n"
  },
  {
    "path": "calliar_server/server/migrations/__init__.py",
    "content": ""
  },
  {
    "path": "calliar_server/server/models.py",
    "content": "from django.db import models\n\n# Create your models here.\n"
  },
  {
    "path": "calliar_server/server/static/explore.js",
    "content": "/*\nvariables\n*/\nvar canvas;\nvar paper; \nvar curr_img;\nvar strokeWidth = 3;\nvar currJsonId = 0;\nvar Strokeindex = 0;\nvar paths = [];\nvar colors = ['#7fc97f', '#beaed4', '#fdc086', '#008ecc', '#386cb0', '#f0027f', '#bf5b16', '#666666']\nvar w = 600;\nvar h = 600;\nvar drawing = false;\nvar readonlyInput;\nvar speed = 2;\n\n\nvar canvas = Raphael('canvas', '600px', '600px');\ncanvas.setViewBox(0,0,w,h);\n\nvar animatePath = function(paths) {\n    color = colors[randomNumber(0, colors.length)]\n    var line = canvas.path(paths[0]).attr({\n        stroke: color,\n        'stroke-opacity': 0,\n    });\n  \n    var rand = Date.now();\n    line.node.id = 'path'+rand;\n    $('#'+line.node.id).css(\"transform\", \"translate(30,7)\");\n    var length = line.getTotalLength();\n    var prev_path;\n    $('#'+line.node.id).animate({\n        'to': 1}, {\n        duration: parseInt(length*speed),\n        step: function(pos, fx) {\n            var offset = length * fx.pos;\n            \n            var subpath = line.getSubpath(0, offset);\n            if (prev_path != null)\n            {\n                prev_path.remove();\n            }\n            \n            prev_path = canvas.path(subpath).attr({\n                'stroke-width': strokeWidth,\n                stroke: color\n            });\n\n\n        },\n        complete: function(){\n            if (paths.length > 0){\n            animatePath(paths.slice(1))\n            }\n            else{\n                enableBtns()\n            drawing = false\n            }\n            \n            Strokeindex += 1;\n            readonlyInput.focus();\n            readonlyInput.setSelectionRange(0, 2*Strokeindex);\n        }\n    });\n\n};\nfunction randomNumber(min, max) {\n  return Math.floor(Math.random() * (max - min) + min);\n}\n\nfunction getJsonList(){\n    var xmlHttp = new XMLHttpRequest();\n    xmlHttp.open( \"GET\", '/explore/list-json?', false ); // false for synchronous request\n    xmlHttp.send( null );\n    response = JSON.parse(xmlHttp.response)\n    return response.json_names\n}\n\nfunction getJsonUrl(id = undefined){\n\n    if(id){\n        currJsonId = id\n    }\n\n    var xmlHttp = new XMLHttpRequest();\n    xmlHttp.open( \"GET\", '/explore/next-json?id='+currJsonId, false ); // false for synchronous request\n    xmlHttp.send( null );\n    response = JSON.parse(xmlHttp.response)\n    currJsonId = parseInt(response.id)\n    return response.json_path\n}\nfunction disableBtns(){  \n    $('button').prop('disabled', true);\n    $(':radio').prop('disabled', true);\n}\n\nfunction enableBtns(){\n    $('button').prop('disabled', false);\n    $(':radio').prop('disabled', false);\n}\nfunction generateNext(){\n    currJsonId += 1\n    generate()\n}\n\nfunction generatePrev(){\n    currJsonId -= 1\n    generate()\n}\n\nfunction setImage(w, h){\n    const scale = 600;\n    if (w > h){\n        h = parseInt((h/w) * scale);\n        w = scale;\n    }else{\n        w = parseInt((w/h) * scale);\n        h = scale; \n    }\n\n    cx = 300\n    cy = 300 \n\n    canvas.image(\"/media/calliar_images/processed_images/\"+imageName, cx - w/2, cy - h/2, w, h).attr({\n        opacity: .3,\n    });\n}\nfunction addRaster(imageName)\n{\n    image_url = \"/media/calliar_images/processed_images/\"+imageName;\n    const img = new Image();\n    img.src = image_url\n    img.onload = function() { setImage(this.width, this.height); }\n}\n\nfunction generate(id = undefined) {\n\n    canvas.clear();\n    Strokeindex = 0;\n\n    disableBtns()\n    drawing = true\n    json_path = getJsonUrl(id = id)\n    json_name = json_path.split('.json')[0]\n    text = preprocess(json_name)[1]\n    readonlyInput.value  = text;\n\n    imageName = json_name+'.jpg' \n    addRaster(imageName)\n\n    $.getJSON('/media/calliar_images/annotations/'+json_path , function(data) {\n        paths = []\n        for (var char in data){\n            for (var key in data[char]){\n                paths.push(data[char][key]); \n            }\n        }\n        animatePath(paths)\n    });\n}\n\nasync function start() {\n\n    generate()\n\n};\n\n\nfunction onClick(id){\n    generate(id)\n}\nfunction createBtn(content, id = 0){\n    return `<button id = ${id} type=\"button\" class=\"btn btn-light\" onclick=\"onClick(this.id)\">${content}</span>`\n}\n\nwindow.onload = (event) => {\n    readonlyInput = document.getElementById(\"text-readonly\");\n    strokeWidth = document.getElementById('slider').value\n    canvas.clear();\n\n    $( \"#slider\" ).on('change', function move(){\n            strokeWidth = parseInt(this.value);\n     });\n\n    console.log('page is fully loaded');\n    const jsonList = getJsonList()\n\n    for (var i=0; i<jsonList.length; i++) {\n        document.querySelector('#jsonList').innerHTML += createBtn(jsonList[i], id = i)\n    }\n\n};\n\nfunction speedUp(){\n    speed = 1 \n}\n\nfunction speedDown(){\n    speed = 4\n}\n\nfunction speedReset(){\n    speed = 2\n}\n"
  },
  {
    "path": "calliar_server/server/static/fabric.js",
    "content": "/* build: `node build.js modules=ALL exclude=gestures,accessors requirejs minifier=uglifyjs` */\n/*! Fabric.js Copyright 2008-2015, Printio (Juriy Zaytsev, Maxim Chernyak) */\n\nvar fabric = fabric || { version: '2.3.1' };\nif (typeof exports !== 'undefined') {\n  exports.fabric = fabric;\n}\n/* _AMD_START_ */\nelse if (typeof define === 'function' && define.amd) {\n  define([], function() { return fabric; });\n}\n/* _AMD_END_ */\nif (typeof document !== 'undefined' && typeof window !== 'undefined') {\n  fabric.document = document;\n  fabric.window = window;\n}\nelse {\n  // assume we're running under node.js when document/window are not present\n  fabric.document = require('jsdom')\n    .jsdom(\n      decodeURIComponent('%3C!DOCTYPE%20html%3E%3Chtml%3E%3Chead%3E%3C%2Fhead%3E%3Cbody%3E%3C%2Fbody%3E%3C%2Fhtml%3E'),\n      { features: {\n        FetchExternalResources: ['img']\n      }\n      });\n  fabric.jsdomImplForWrapper = require('jsdom/lib/jsdom/living/generated/utils').implForWrapper;\n  fabric.nodeCanvas = require('jsdom/lib/jsdom/utils').Canvas;\n  fabric.window = fabric.document.defaultView;\n  DOMParser = require('xmldom').DOMParser;\n}\n\n/**\n * True when in environment that supports touch events\n * @type boolean\n */\nfabric.isTouchSupported = 'ontouchstart' in fabric.window;\n\n/**\n * True when in environment that's probably Node.js\n * @type boolean\n */\nfabric.isLikelyNode = typeof Buffer !== 'undefined' &&\n                      typeof window === 'undefined';\n\n/* _FROM_SVG_START_ */\n/**\n * Attributes parsed from all SVG elements\n * @type array\n */\nfabric.SHARED_ATTRIBUTES = [\n  \"display\",\n  \"transform\",\n  \"fill\", \"fill-opacity\", \"fill-rule\",\n  \"opacity\",\n  \"stroke\", \"stroke-dasharray\", \"stroke-linecap\",\n  \"stroke-linejoin\", \"stroke-miterlimit\",\n  \"stroke-opacity\", \"stroke-width\",\n  \"id\", \"paint-order\",\n  \"instantiated_by_use\"\n];\n/* _FROM_SVG_END_ */\n\n/**\n * Pixel per Inch as a default value set to 96. Can be changed for more realistic conversion.\n */\nfabric.DPI = 96;\nfabric.reNum = '(?:[-+]?(?:\\\\d+|\\\\d*\\\\.\\\\d+)(?:e[-+]?\\\\d+)?)';\nfabric.fontPaths = { };\nfabric.iMatrix = [1, 0, 0, 1, 0, 0];\nfabric.canvasModule = 'canvas';\n\n/**\n * Pixel limit for cache canvases. 1Mpx , 4Mpx should be fine.\n * @since 1.7.14\n * @type Number\n * @default\n */\nfabric.perfLimitSizeTotal = 2097152;\n\n/**\n * Pixel limit for cache canvases width or height. IE fixes the maximum at 5000\n * @since 1.7.14\n * @type Number\n * @default\n */\nfabric.maxCacheSideLimit = 4096;\n\n/**\n * Lowest pixel limit for cache canvases, set at 256PX\n * @since 1.7.14\n * @type Number\n * @default\n */\nfabric.minCacheSideLimit = 256;\n\n/**\n * Cache Object for widths of chars in text rendering.\n */\nfabric.charWidthsCache = { };\n\n/**\n * if webgl is enabled and available, textureSize will determine the size\n * of the canvas backend\n * @since 2.0.0\n * @type Number\n * @default\n */\nfabric.textureSize = 2048;\n\n/**\n * Enable webgl for filtering picture is available\n * A filtering backend will be initialized, this will both take memory and\n * time since a default 2048x2048 canvas will be created for the gl context\n * @since 2.0.0\n * @type Boolean\n * @default\n */\nfabric.enableGLFiltering = true;\n\n/**\n * Device Pixel Ratio\n * @see https://developer.apple.com/library/safari/documentation/AudioVideo/Conceptual/HTML-canvas-guide/SettingUptheCanvas/SettingUptheCanvas.html\n */\nfabric.devicePixelRatio = fabric.window.devicePixelRatio ||\n                          fabric.window.webkitDevicePixelRatio ||\n                          fabric.window.mozDevicePixelRatio ||\n                          1;\n/**\n * Browser-specific constant to adjust CanvasRenderingContext2D.shadowBlur value,\n * which is unitless and not rendered equally across browsers.\n *\n * Values that work quite well (as of October 2017) are:\n * - Chrome: 1.5\n * - Edge: 1.75\n * - Firefox: 0.9\n * - Safari: 0.95\n *\n * @since 2.0.0\n * @type Number\n * @default 1\n */\nfabric.browserShadowBlurConstant = 1;\n\nfabric.initFilterBackend = function() {\n  if (fabric.enableGLFiltering && fabric.isWebglSupported && fabric.isWebglSupported(fabric.textureSize)) {\n    console.log('max texture size: ' + fabric.maxTextureSize);\n    return (new fabric.WebglFilterBackend({ tileSize: fabric.textureSize }));\n  }\n  else if (fabric.Canvas2dFilterBackend) {\n    return (new fabric.Canvas2dFilterBackend());\n  }\n};\n\n\nif (typeof document !== 'undefined' && typeof window !== 'undefined') {\n  // ensure globality even if entire library were function wrapped (as in Meteor.js packaging system)\n  window.fabric = fabric;\n}\n\n\n(function() {\n\n  /**\n   * @private\n   * @param {String} eventName\n   * @param {Function} handler\n   */\n  function _removeEventListener(eventName, handler) {\n    if (!this.__eventListeners[eventName]) {\n      return;\n    }\n    var eventListener = this.__eventListeners[eventName];\n    if (handler) {\n      eventListener[eventListener.indexOf(handler)] = false;\n    }\n    else {\n      fabric.util.array.fill(eventListener, false);\n    }\n  }\n\n  /**\n   * Observes specified event\n   * @deprecated `observe` deprecated since 0.8.34 (use `on` instead)\n   * @memberOf fabric.Observable\n   * @alias on\n   * @param {String|Object} eventName Event name (eg. 'after:render') or object with key/value pairs (eg. {'after:render': handler, 'selection:cleared': handler})\n   * @param {Function} handler Function that receives a notification when an event of the specified type occurs\n   * @return {Self} thisArg\n   * @chainable\n   */\n  function observe(eventName, handler) {\n    if (!this.__eventListeners) {\n      this.__eventListeners = { };\n    }\n    // one object with key/value pairs was passed\n    if (arguments.length === 1) {\n      for (var prop in eventName) {\n        this.on(prop, eventName[prop]);\n      }\n    }\n    else {\n      if (!this.__eventListeners[eventName]) {\n        this.__eventListeners[eventName] = [];\n      }\n      this.__eventListeners[eventName].push(handler);\n    }\n    return this;\n  }\n\n  /**\n   * Stops event observing for a particular event handler. Calling this method\n   * without arguments removes all handlers for all events\n   * @deprecated `stopObserving` deprecated since 0.8.34 (use `off` instead)\n   * @memberOf fabric.Observable\n   * @alias off\n   * @param {String|Object} eventName Event name (eg. 'after:render') or object with key/value pairs (eg. {'after:render': handler, 'selection:cleared': handler})\n   * @param {Function} handler Function to be deleted from EventListeners\n   * @return {Self} thisArg\n   * @chainable\n   */\n  function stopObserving(eventName, handler) {\n    if (!this.__eventListeners) {\n      return;\n    }\n\n    // remove all key/value pairs (event name -> event handler)\n    if (arguments.length === 0) {\n      for (eventName in this.__eventListeners) {\n        _removeEventListener.call(this, eventName);\n      }\n    }\n    // one object with key/value pairs was passed\n    else if (arguments.length === 1 && typeof arguments[0] === 'object') {\n      for (var prop in eventName) {\n        _removeEventListener.call(this, prop, eventName[prop]);\n      }\n    }\n    else {\n      _removeEventListener.call(this, eventName, handler);\n    }\n    return this;\n  }\n\n  /**\n   * Fires event with an optional options object\n   * @deprecated `fire` deprecated since 1.0.7 (use `trigger` instead)\n   * @memberOf fabric.Observable\n   * @alias trigger\n   * @param {String} eventName Event name to fire\n   * @param {Object} [options] Options object\n   * @return {Self} thisArg\n   * @chainable\n   */\n  function fire(eventName, options) {\n    if (!this.__eventListeners) {\n      return;\n    }\n\n    var listenersForEvent = this.__eventListeners[eventName];\n    if (!listenersForEvent) {\n      return;\n    }\n\n    for (var i = 0, len = listenersForEvent.length; i < len; i++) {\n      listenersForEvent[i] && listenersForEvent[i].call(this, options || { });\n    }\n    this.__eventListeners[eventName] = listenersForEvent.filter(function(value) {\n      return value !== false;\n    });\n    return this;\n  }\n\n  /**\n   * @namespace fabric.Observable\n   * @tutorial {@link http://fabricjs.com/fabric-intro-part-2#events}\n   * @see {@link http://fabricjs.com/events|Events demo}\n   */\n  fabric.Observable = {\n    observe: observe,\n    stopObserving: stopObserving,\n    fire: fire,\n\n    on: observe,\n    off: stopObserving,\n    trigger: fire\n  };\n})();\n\n\n/**\n * @namespace fabric.Collection\n */\nfabric.Collection = {\n\n  _objects: [],\n\n  /**\n   * Adds objects to collection, Canvas or Group, then renders canvas\n   * (if `renderOnAddRemove` is not `false`).\n   * in case of Group no changes to bounding box are made.\n   * Objects should be instances of (or inherit from) fabric.Object\n   * Use of this function is highly discouraged for groups.\n   * you can add a bunch of objects with the add method but then you NEED\n   * to run a addWithUpdate call for the Group class or position/bbox will be wrong.\n   * @param {...fabric.Object} object Zero or more fabric instances\n   * @return {Self} thisArg\n   * @chainable\n   */\n  add: function () {\n    this._objects.push.apply(this._objects, arguments);\n    if (this._onObjectAdded) {\n      for (var i = 0, length = arguments.length; i < length; i++) {\n        this._onObjectAdded(arguments[i]);\n      }\n    }\n    this.renderOnAddRemove && this.requestRenderAll();\n    return this;\n  },\n\n  /**\n   * Inserts an object into collection at specified index, then renders canvas (if `renderOnAddRemove` is not `false`)\n   * An object should be an instance of (or inherit from) fabric.Object\n   * Use of this function is highly discouraged for groups.\n   * you can add a bunch of objects with the insertAt method but then you NEED\n   * to run a addWithUpdate call for the Group class or position/bbox will be wrong.\n   * @param {Object} object Object to insert\n   * @param {Number} index Index to insert object at\n   * @param {Boolean} nonSplicing When `true`, no splicing (shifting) of objects occurs\n   * @return {Self} thisArg\n   * @chainable\n   */\n  insertAt: function (object, index, nonSplicing) {\n    var objects = this.getObjects();\n    if (nonSplicing) {\n      objects[index] = object;\n    }\n    else {\n      objects.splice(index, 0, object);\n    }\n    this._onObjectAdded && this._onObjectAdded(object);\n    this.renderOnAddRemove && this.requestRenderAll();\n    return this;\n  },\n\n  /**\n   * Removes objects from a collection, then renders canvas (if `renderOnAddRemove` is not `false`)\n   * @param {...fabric.Object} object Zero or more fabric instances\n   * @return {Self} thisArg\n   * @chainable\n   */\n  remove: function() {\n    var objects = this.getObjects(),\n        index, somethingRemoved = false;\n\n    for (var i = 0, length = arguments.length; i < length; i++) {\n      index = objects.indexOf(arguments[i]);\n\n      // only call onObjectRemoved if an object was actually removed\n      if (index !== -1) {\n        somethingRemoved = true;\n        objects.splice(index, 1);\n        this._onObjectRemoved && this._onObjectRemoved(arguments[i]);\n      }\n    }\n\n    this.renderOnAddRemove && somethingRemoved && this.requestRenderAll();\n    return this;\n  },\n\n  /**\n   * Executes given function for each object in this group\n   * @param {Function} callback\n   *                   Callback invoked with current object as first argument,\n   *                   index - as second and an array of all objects - as third.\n   *                   Callback is invoked in a context of Global Object (e.g. `window`)\n   *                   when no `context` argument is given\n   *\n   * @param {Object} context Context (aka thisObject)\n   * @return {Self} thisArg\n   * @chainable\n   */\n  forEachObject: function(callback, context) {\n    var objects = this.getObjects();\n    for (var i = 0, len = objects.length; i < len; i++) {\n      callback.call(context, objects[i], i, objects);\n    }\n    return this;\n  },\n\n  /**\n   * Returns an array of children objects of this instance\n   * Type parameter introduced in 1.3.10\n   * @param {String} [type] When specified, only objects of this type are returned\n   * @return {Array}\n   */\n  getObjects: function(type) {\n    if (typeof type === 'undefined') {\n      return this._objects;\n    }\n    return this._objects.filter(function(o) {\n      return o.type === type;\n    });\n  },\n\n  /**\n   * Returns object at specified index\n   * @param {Number} index\n   * @return {Self} thisArg\n   */\n  item: function (index) {\n    return this.getObjects()[index];\n  },\n\n  /**\n   * Returns true if collection contains no objects\n   * @return {Boolean} true if collection is empty\n   */\n  isEmpty: function () {\n    return this.getObjects().length === 0;\n  },\n\n  /**\n   * Returns a size of a collection (i.e: length of an array containing its objects)\n   * @return {Number} Collection size\n   */\n  size: function() {\n    return this.getObjects().length;\n  },\n\n  /**\n   * Returns true if collection contains an object\n   * @param {Object} object Object to check against\n   * @return {Boolean} `true` if collection contains an object\n   */\n  contains: function(object) {\n    return this.getObjects().indexOf(object) > -1;\n  },\n\n  /**\n   * Returns number representation of a collection complexity\n   * @return {Number} complexity\n   */\n  complexity: function () {\n    return this.getObjects().reduce(function (memo, current) {\n      memo += current.complexity ? current.complexity() : 0;\n      return memo;\n    }, 0);\n  }\n};\n\n\n/**\n * @namespace fabric.CommonMethods\n */\nfabric.CommonMethods = {\n\n  /**\n   * Sets object's properties from options\n   * @param {Object} [options] Options object\n   */\n  _setOptions: function(options) {\n    for (var prop in options) {\n      this.set(prop, options[prop]);\n    }\n  },\n\n  /**\n   * @private\n   * @param {Object} [filler] Options object\n   * @param {String} [property] property to set the Gradient to\n   */\n  _initGradient: function(filler, property) {\n    if (filler && filler.colorStops && !(filler instanceof fabric.Gradient)) {\n      this.set(property, new fabric.Gradient(filler));\n    }\n  },\n\n  /**\n   * @private\n   * @param {Object} [filler] Options object\n   * @param {String} [property] property to set the Pattern to\n   * @param {Function} [callback] callback to invoke after pattern load\n   */\n  _initPattern: function(filler, property, callback) {\n    if (filler && filler.source && !(filler instanceof fabric.Pattern)) {\n      this.set(property, new fabric.Pattern(filler, callback));\n    }\n    else {\n      callback && callback();\n    }\n  },\n\n  /**\n   * @private\n   * @param {Object} [options] Options object\n   */\n  _initClipping: function(options) {\n    if (!options.clipTo || typeof options.clipTo !== 'string') {\n      return;\n    }\n\n    var functionBody = fabric.util.getFunctionBody(options.clipTo);\n    if (typeof functionBody !== 'undefined') {\n      this.clipTo = new Function('ctx', functionBody);\n    }\n  },\n\n  /**\n   * @private\n   */\n  _setObject: function(obj) {\n    for (var prop in obj) {\n      this._set(prop, obj[prop]);\n    }\n  },\n\n  /**\n   * Sets property to a given value. When changing position/dimension -related properties (left, top, scale, angle, etc.) `set` does not update position of object's borders/controls. If you need to update those, call `setCoords()`.\n   * @param {String|Object} key Property name or object (if object, iterate over the object properties)\n   * @param {Object|Function} value Property value (if function, the value is passed into it and its return value is used as a new one)\n   * @return {fabric.Object} thisArg\n   * @chainable\n   */\n  set: function(key, value) {\n    if (typeof key === 'object') {\n      this._setObject(key);\n    }\n    else {\n      if (typeof value === 'function' && key !== 'clipTo') {\n        this._set(key, value(this.get(key)));\n      }\n      else {\n        this._set(key, value);\n      }\n    }\n    return this;\n  },\n\n  _set: function(key, value) {\n    this[key] = value;\n  },\n\n  /**\n   * Toggles specified property from `true` to `false` or from `false` to `true`\n   * @param {String} property Property to toggle\n   * @return {fabric.Object} thisArg\n   * @chainable\n   */\n  toggle: function(property) {\n    var value = this.get(property);\n    if (typeof value === 'boolean') {\n      this.set(property, !value);\n    }\n    return this;\n  },\n\n  /**\n   * Basic getter\n   * @param {String} property Property name\n   * @return {*} value of a property\n   */\n  get: function(property) {\n    return this[property];\n  }\n};\n\n\n(function(global) {\n\n  var sqrt = Math.sqrt,\n      atan2 = Math.atan2,\n      pow = Math.pow,\n      abs = Math.abs,\n      PiBy180 = Math.PI / 180,\n      PiBy2 = Math.PI / 2;\n\n  /**\n   * @namespace fabric.util\n   */\n  fabric.util = {\n\n    /**\n     * Calculate the cos of an angle, avoiding returning floats for known results\n     * @static\n     * @memberOf fabric.util\n     * @param {Number} angle the angle in radians or in degree\n     * @return {Number}\n     */\n    cos: function(angle) {\n      if (angle === 0) { return 1; }\n      if (angle < 0) {\n        // cos(a) = cos(-a)\n        angle = -angle;\n      }\n      var angleSlice = angle / PiBy2;\n      switch (angleSlice) {\n        case 1: case 3: return 0;\n        case 2: return -1;\n      }\n      return Math.cos(angle);\n    },\n\n    /**\n     * Calculate the sin of an angle, avoiding returning floats for known results\n     * @static\n     * @memberOf fabric.util\n     * @param {Number} angle the angle in radians or in degree\n     * @return {Number}\n     */\n    sin: function(angle) {\n      if (angle === 0) { return 0; }\n      var angleSlice = angle / PiBy2, sign = 1;\n      if (angle < 0) {\n        // sin(-a) = -sin(a)\n        sign = -1;\n      }\n      switch (angleSlice) {\n        case 1: return sign;\n        case 2: return 0;\n        case 3: return -sign;\n      }\n      return Math.sin(angle);\n    },\n\n    /**\n     * Removes value from an array.\n     * Presence of value (and its position in an array) is determined via `Array.prototype.indexOf`\n     * @static\n     * @memberOf fabric.util\n     * @param {Array} array\n     * @param {*} value\n     * @return {Array} original array\n     */\n    removeFromArray: function(array, value) {\n      var idx = array.indexOf(value);\n      if (idx !== -1) {\n        array.splice(idx, 1);\n      }\n      return array;\n    },\n\n    /**\n     * Returns random number between 2 specified ones.\n     * @static\n     * @memberOf fabric.util\n     * @param {Number} min lower limit\n     * @param {Number} max upper limit\n     * @return {Number} random value (between min and max)\n     */\n    getRandomInt: function(min, max) {\n      return Math.floor(Math.random() * (max - min + 1)) + min;\n    },\n\n    /**\n     * Transforms degrees to radians.\n     * @static\n     * @memberOf fabric.util\n     * @param {Number} degrees value in degrees\n     * @return {Number} value in radians\n     */\n    degreesToRadians: function(degrees) {\n      return degrees * PiBy180;\n    },\n\n    /**\n     * Transforms radians to degrees.\n     * @static\n     * @memberOf fabric.util\n     * @param {Number} radians value in radians\n     * @return {Number} value in degrees\n     */\n    radiansToDegrees: function(radians) {\n      return radians / PiBy180;\n    },\n\n    /**\n     * Rotates `point` around `origin` with `radians`\n     * @static\n     * @memberOf fabric.util\n     * @param {fabric.Point} point The point to rotate\n     * @param {fabric.Point} origin The origin of the rotation\n     * @param {Number} radians The radians of the angle for the rotation\n     * @return {fabric.Point} The new rotated point\n     */\n    rotatePoint: function(point, origin, radians) {\n      point.subtractEquals(origin);\n      var v = fabric.util.rotateVector(point, radians);\n      return new fabric.Point(v.x, v.y).addEquals(origin);\n    },\n\n    /**\n     * Rotates `vector` with `radians`\n     * @static\n     * @memberOf fabric.util\n     * @param {Object} vector The vector to rotate (x and y)\n     * @param {Number} radians The radians of the angle for the rotation\n     * @return {Object} The new rotated point\n     */\n    rotateVector: function(vector, radians) {\n      var sin = fabric.util.sin(radians),\n          cos = fabric.util.cos(radians),\n          rx = vector.x * cos - vector.y * sin,\n          ry = vector.x * sin + vector.y * cos;\n      return {\n        x: rx,\n        y: ry\n      };\n    },\n\n    /**\n     * Apply transform t to point p\n     * @static\n     * @memberOf fabric.util\n     * @param  {fabric.Point} p The point to transform\n     * @param  {Array} t The transform\n     * @param  {Boolean} [ignoreOffset] Indicates that the offset should not be applied\n     * @return {fabric.Point} The transformed point\n     */\n    transformPoint: function(p, t, ignoreOffset) {\n      if (ignoreOffset) {\n        return new fabric.Point(\n          t[0] * p.x + t[2] * p.y,\n          t[1] * p.x + t[3] * p.y\n        );\n      }\n      return new fabric.Point(\n        t[0] * p.x + t[2] * p.y + t[4],\n        t[1] * p.x + t[3] * p.y + t[5]\n      );\n    },\n\n    /**\n     * Returns coordinates of points's bounding rectangle (left, top, width, height)\n     * @param {Array} points 4 points array\n     * @return {Object} Object with left, top, width, height properties\n     */\n    makeBoundingBoxFromPoints: function(points) {\n      var xPoints = [points[0].x, points[1].x, points[2].x, points[3].x],\n          minX = fabric.util.array.min(xPoints),\n          maxX = fabric.util.array.max(xPoints),\n          width = maxX - minX,\n          yPoints = [points[0].y, points[1].y, points[2].y, points[3].y],\n          minY = fabric.util.array.min(yPoints),\n          maxY = fabric.util.array.max(yPoints),\n          height = maxY - minY;\n\n      return {\n        left: minX,\n        top: minY,\n        width: width,\n        height: height\n      };\n    },\n\n    /**\n     * Invert transformation t\n     * @static\n     * @memberOf fabric.util\n     * @param {Array} t The transform\n     * @return {Array} The inverted transform\n     */\n    invertTransform: function(t) {\n      var a = 1 / (t[0] * t[3] - t[1] * t[2]),\n          r = [a * t[3], -a * t[1], -a * t[2], a * t[0]],\n          o = fabric.util.transformPoint({ x: t[4], y: t[5] }, r, true);\n      r[4] = -o.x;\n      r[5] = -o.y;\n      return r;\n    },\n\n    /**\n     * A wrapper around Number#toFixed, which contrary to native method returns number, not string.\n     * @static\n     * @memberOf fabric.util\n     * @param {Number|String} number number to operate on\n     * @param {Number} fractionDigits number of fraction digits to \"leave\"\n     * @return {Number}\n     */\n    toFixed: function(number, fractionDigits) {\n      return parseFloat(Number(number).toFixed(fractionDigits));\n    },\n\n    /**\n     * Converts from attribute value to pixel value if applicable.\n     * Returns converted pixels or original value not converted.\n     * @param {Number|String} value number to operate on\n     * @param {Number} fontSize\n     * @return {Number|String}\n     */\n    parseUnit: function(value, fontSize) {\n      var unit = /\\D{0,2}$/.exec(value),\n          number = parseFloat(value);\n      if (!fontSize) {\n        fontSize = fabric.Text.DEFAULT_SVG_FONT_SIZE;\n      }\n      switch (unit[0]) {\n        case 'mm':\n          return number * fabric.DPI / 25.4;\n\n        case 'cm':\n          return number * fabric.DPI / 2.54;\n\n        case 'in':\n          return number * fabric.DPI;\n\n        case 'pt':\n          return number * fabric.DPI / 72; // or * 4 / 3\n\n        case 'pc':\n          return number * fabric.DPI / 72 * 12; // or * 16\n\n        case 'em':\n          return number * fontSize;\n\n        default:\n          return number;\n      }\n    },\n\n    /**\n     * Function which always returns `false`.\n     * @static\n     * @memberOf fabric.util\n     * @return {Boolean}\n     */\n    falseFunction: function() {\n      return false;\n    },\n\n    /**\n     * Returns klass \"Class\" object of given namespace\n     * @memberOf fabric.util\n     * @param {String} type Type of object (eg. 'circle')\n     * @param {String} namespace Namespace to get klass \"Class\" object from\n     * @return {Object} klass \"Class\"\n     */\n    getKlass: function(type, namespace) {\n      // capitalize first letter only\n      type = fabric.util.string.camelize(type.charAt(0).toUpperCase() + type.slice(1));\n      return fabric.util.resolveNamespace(namespace)[type];\n    },\n\n    /**\n     * Returns array of attributes for given svg that fabric parses\n     * @memberOf fabric.util\n     * @param {String} type Type of svg element (eg. 'circle')\n     * @return {Array} string names of supported attributes\n     */\n    getSvgAttributes: function(type) {\n      var attributes = [\n        'instantiated_by_use',\n        'style',\n        'id',\n        'class'\n      ];\n      switch (type) {\n        case 'linearGradient':\n          attributes = attributes.concat(['x1', 'y1', 'x2', 'y2', 'gradientUnits', 'gradientTransform']);\n          break;\n        case 'radialGradient':\n          attributes = attributes.concat(['gradientUnits', 'gradientTransform', 'cx', 'cy', 'r', 'fx', 'fy', 'fr']);\n          break;\n        case 'stop':\n          attributes = attributes.concat(['offset', 'stop-color', 'stop-opacity']);\n          break;\n      }\n      return attributes;\n    },\n\n    /**\n     * Returns object of given namespace\n     * @memberOf fabric.util\n     * @param {String} namespace Namespace string e.g. 'fabric.Image.filter' or 'fabric'\n     * @return {Object} Object for given namespace (default fabric)\n     */\n    resolveNamespace: function(namespace) {\n      if (!namespace) {\n        return fabric;\n      }\n\n      var parts = namespace.split('.'),\n          len = parts.length, i,\n          obj = global || fabric.window;\n\n      for (i = 0; i < len; ++i) {\n        obj = obj[parts[i]];\n      }\n\n      return obj;\n    },\n\n    /**\n     * Loads image element from given url and passes it to a callback\n     * @memberOf fabric.util\n     * @param {String} url URL representing an image\n     * @param {Function} callback Callback; invoked with loaded image\n     * @param {*} [context] Context to invoke callback in\n     * @param {Object} [crossOrigin] crossOrigin value to set image element to\n     */\n    loadImage: function(url, callback, context, crossOrigin) {\n      if (!url) {\n        callback && callback.call(context, url);\n        return;\n      }\n\n      var img = fabric.util.createImage();\n\n      /** @ignore */\n      var onLoadCallback = function () {\n        callback && callback.call(context, img);\n        img = img.onload = img.onerror = null;\n      };\n\n      img.onload = onLoadCallback;\n      /** @ignore */\n      img.onerror = function() {\n        fabric.log('Error loading ' + img.src);\n        callback && callback.call(context, null, true);\n        img = img.onload = img.onerror = null;\n      };\n\n      // data-urls appear to be buggy with crossOrigin\n      // https://github.com/kangax/fabric.js/commit/d0abb90f1cd5c5ef9d2a94d3fb21a22330da3e0a#commitcomment-4513767\n      // see https://code.google.com/p/chromium/issues/detail?id=315152\n      //     https://bugzilla.mozilla.org/show_bug.cgi?id=935069\n      if (url.indexOf('data') !== 0 && crossOrigin) {\n        img.crossOrigin = crossOrigin;\n      }\n\n      // IE10 / IE11-Fix: SVG contents from data: URI\n      // will only be available if the IMG is present\n      // in the DOM (and visible)\n      if (url.substring(0,14) === 'data:image/svg') {\n        img.onload = null;\n        fabric.util.loadImageInDom(img, onLoadCallback);\n      }\n\n      img.src = url;\n    },\n\n    /**\n     * Attaches SVG image with data: URL to the dom\n     * @memberOf fabric.util\n     * @param {Object} img Image object with data:image/svg src\n     * @param {Function} callback Callback; invoked with loaded image\n     * @return {Object} DOM element (div containing the SVG image)\n     */\n    loadImageInDom: function(img, onLoadCallback) {\n      var div = fabric.document.createElement('div');\n      div.style.width = div.style.height = '1px';\n      div.style.left = div.style.top = '-100%';\n      div.style.position = 'absolute';\n      div.appendChild(img);\n      fabric.document.querySelector('body').appendChild(div);\n      /**\n       * Wrap in function to:\n       *   1. Call existing callback\n       *   2. Cleanup DOM\n       */\n      img.onload = function () {\n        onLoadCallback();\n        div.parentNode.removeChild(div);\n        div = null;\n      };\n    },\n\n    /**\n     * Creates corresponding fabric instances from their object representations\n     * @static\n     * @memberOf fabric.util\n     * @param {Array} objects Objects to enliven\n     * @param {Function} callback Callback to invoke when all objects are created\n     * @param {String} namespace Namespace to get klass \"Class\" object from\n     * @param {Function} reviver Method for further parsing of object elements,\n     * called after each fabric object created.\n     */\n    enlivenObjects: function(objects, callback, namespace, reviver) {\n      objects = objects || [];\n\n      function onLoaded() {\n        if (++numLoadedObjects === numTotalObjects) {\n          callback && callback(enlivenedObjects);\n        }\n      }\n\n      var enlivenedObjects = [],\n          numLoadedObjects = 0,\n          numTotalObjects = objects.length;\n\n      if (!numTotalObjects) {\n        callback && callback(enlivenedObjects);\n        return;\n      }\n\n      objects.forEach(function (o, index) {\n        // if sparse array\n        if (!o || !o.type) {\n          onLoaded();\n          return;\n        }\n        var klass = fabric.util.getKlass(o.type, namespace);\n        klass.fromObject(o, function (obj, error) {\n          error || (enlivenedObjects[index] = obj);\n          reviver && reviver(o, obj, error);\n          onLoaded();\n        });\n      });\n    },\n\n    /**\n     * Create and wait for loading of patterns\n     * @static\n     * @memberOf fabric.util\n     * @param {Array} patterns Objects to enliven\n     * @param {Function} callback Callback to invoke when all objects are created\n     * called after each fabric object created.\n     */\n    enlivenPatterns: function(patterns, callback) {\n      patterns = patterns || [];\n\n      function onLoaded() {\n        if (++numLoadedPatterns === numPatterns) {\n          callback && callback(enlivenedPatterns);\n        }\n      }\n\n      var enlivenedPatterns = [],\n          numLoadedPatterns = 0,\n          numPatterns = patterns.length;\n\n      if (!numPatterns) {\n        callback && callback(enlivenedPatterns);\n        return;\n      }\n\n      patterns.forEach(function (p, index) {\n        if (p && p.source) {\n          new fabric.Pattern(p, function(pattern) {\n            enlivenedPatterns[index] = pattern;\n            onLoaded();\n          });\n        }\n        else {\n          enlivenedPatterns[index] = p;\n          onLoaded();\n        }\n      });\n    },\n\n    /**\n     * Groups SVG elements (usually those retrieved from SVG document)\n     * @static\n     * @memberOf fabric.util\n     * @param {Array} elements SVG elements to group\n     * @param {Object} [options] Options object\n     * @param {String} path Value to set sourcePath to\n     * @return {fabric.Object|fabric.Group}\n     */\n    groupSVGElements: function(elements, options, path) {\n      var object;\n      if (elements.length === 1) {\n        return elements[0];\n      }\n      if (options) {\n        if (options.width && options.height) {\n          options.centerPoint = {\n            x: options.width / 2,\n            y: options.height / 2\n          };\n        }\n        else {\n          delete options.width;\n          delete options.height;\n        }\n      }\n      object = new fabric.Group(elements, options);\n      if (typeof path !== 'undefined') {\n        object.sourcePath = path;\n      }\n      return object;\n    },\n\n    /**\n     * Populates an object with properties of another object\n     * @static\n     * @memberOf fabric.util\n     * @param {Object} source Source object\n     * @param {Object} destination Destination object\n     * @return {Array} properties Properties names to include\n     */\n    populateWithProperties: function(source, destination, properties) {\n      if (properties && Object.prototype.toString.call(properties) === '[object Array]') {\n        for (var i = 0, len = properties.length; i < len; i++) {\n          if (properties[i] in source) {\n            destination[properties[i]] = source[properties[i]];\n          }\n        }\n      }\n    },\n\n    /**\n     * Draws a dashed line between two points\n     *\n     * This method is used to draw dashed line around selection area.\n     * See <a href=\"http://stackoverflow.com/questions/4576724/dotted-stroke-in-canvas\">dotted stroke in canvas</a>\n     *\n     * @param {CanvasRenderingContext2D} ctx context\n     * @param {Number} x  start x coordinate\n     * @param {Number} y start y coordinate\n     * @param {Number} x2 end x coordinate\n     * @param {Number} y2 end y coordinate\n     * @param {Array} da dash array pattern\n     */\n    drawDashedLine: function(ctx, x, y, x2, y2, da) {\n      var dx = x2 - x,\n          dy = y2 - y,\n          len = sqrt(dx * dx + dy * dy),\n          rot = atan2(dy, dx),\n          dc = da.length,\n          di = 0,\n          draw = true;\n\n      ctx.save();\n      ctx.translate(x, y);\n      ctx.moveTo(0, 0);\n      ctx.rotate(rot);\n\n      x = 0;\n      while (len > x) {\n        x += da[di++ % dc];\n        if (x > len) {\n          x = len;\n        }\n        ctx[draw ? 'lineTo' : 'moveTo'](x, 0);\n        draw = !draw;\n      }\n\n      ctx.restore();\n    },\n\n    /**\n     * Creates canvas element\n     * @static\n     * @memberOf fabric.util\n     * @return {CanvasElement} initialized canvas element\n     */\n    createCanvasElement: function() {\n      return fabric.document.createElement('canvas');\n    },\n\n    /**\n     * Creates image element (works on client and node)\n     * @static\n     * @memberOf fabric.util\n     * @return {HTMLImageElement} HTML image element\n     */\n    createImage: function() {\n      return fabric.document.createElement('img');\n    },\n\n    /**\n     * @static\n     * @memberOf fabric.util\n     * @deprecated since 2.0.0\n     * @param {fabric.Object} receiver Object implementing `clipTo` method\n     * @param {CanvasRenderingContext2D} ctx Context to clip\n     */\n    clipContext: function(receiver, ctx) {\n      ctx.save();\n      ctx.beginPath();\n      receiver.clipTo(ctx);\n      ctx.clip();\n    },\n\n    /**\n     * Multiply matrix A by matrix B to nest transformations\n     * @static\n     * @memberOf fabric.util\n     * @param  {Array} a First transformMatrix\n     * @param  {Array} b Second transformMatrix\n     * @param  {Boolean} is2x2 flag to multiply matrices as 2x2 matrices\n     * @return {Array} The product of the two transform matrices\n     */\n    multiplyTransformMatrices: function(a, b, is2x2) {\n      // Matrix multiply a * b\n      return [\n        a[0] * b[0] + a[2] * b[1],\n        a[1] * b[0] + a[3] * b[1],\n        a[0] * b[2] + a[2] * b[3],\n        a[1] * b[2] + a[3] * b[3],\n        is2x2 ? 0 : a[0] * b[4] + a[2] * b[5] + a[4],\n        is2x2 ? 0 : a[1] * b[4] + a[3] * b[5] + a[5]\n      ];\n    },\n\n    /**\n     * Decomposes standard 2x2 matrix into transform componentes\n     * @static\n     * @memberOf fabric.util\n     * @param  {Array} a transformMatrix\n     * @return {Object} Components of transform\n     */\n    qrDecompose: function(a) {\n      var angle = atan2(a[1], a[0]),\n          denom = pow(a[0], 2) + pow(a[1], 2),\n          scaleX = sqrt(denom),\n          scaleY = (a[0] * a[3] - a[2] * a [1]) / scaleX,\n          skewX = atan2(a[0] * a[2] + a[1] * a [3], denom);\n      return {\n        angle: angle  / PiBy180,\n        scaleX: scaleX,\n        scaleY: scaleY,\n        skewX: skewX / PiBy180,\n        skewY: 0,\n        translateX: a[4],\n        translateY: a[5]\n      };\n    },\n\n    customTransformMatrix: function(scaleX, scaleY, skewX) {\n      var skewMatrixX = [1, 0, abs(Math.tan(skewX * PiBy180)), 1],\n          scaleMatrix = [abs(scaleX), 0, 0, abs(scaleY)];\n      return fabric.util.multiplyTransformMatrices(scaleMatrix, skewMatrixX, true);\n    },\n\n    resetObjectTransform: function (target) {\n      target.scaleX = 1;\n      target.scaleY = 1;\n      target.skewX = 0;\n      target.skewY = 0;\n      target.flipX = false;\n      target.flipY = false;\n      target.rotate(0);\n    },\n\n    /**\n     * Returns string representation of function body\n     * @param {Function} fn Function to get body of\n     * @return {String} Function body\n     */\n    getFunctionBody: function(fn) {\n      return (String(fn).match(/function[^{]*\\{([\\s\\S]*)\\}/) || {})[1];\n    },\n\n    /**\n     * Returns true if context has transparent pixel\n     * at specified location (taking tolerance into account)\n     * @param {CanvasRenderingContext2D} ctx context\n     * @param {Number} x x coordinate\n     * @param {Number} y y coordinate\n     * @param {Number} tolerance Tolerance\n     */\n    isTransparent: function(ctx, x, y, tolerance) {\n\n      // If tolerance is > 0 adjust start coords to take into account.\n      // If moves off Canvas fix to 0\n      if (tolerance > 0) {\n        if (x > tolerance) {\n          x -= tolerance;\n        }\n        else {\n          x = 0;\n        }\n        if (y > tolerance) {\n          y -= tolerance;\n        }\n        else {\n          y = 0;\n        }\n      }\n\n      var _isTransparent = true, i, temp,\n          imageData = ctx.getImageData(x, y, (tolerance * 2) || 1, (tolerance * 2) || 1),\n          l = imageData.data.length;\n\n      // Split image data - for tolerance > 1, pixelDataSize = 4;\n      for (i = 3; i < l; i += 4) {\n        temp = imageData.data[i];\n        _isTransparent = temp <= 0;\n        if (_isTransparent === false) {\n          break; // Stop if colour found\n        }\n      }\n\n      imageData = null;\n\n      return _isTransparent;\n    },\n\n    /**\n     * Parse preserveAspectRatio attribute from element\n     * @param {string} attribute to be parsed\n     * @return {Object} an object containing align and meetOrSlice attribute\n     */\n    parsePreserveAspectRatioAttribute: function(attribute) {\n      var meetOrSlice = 'meet', alignX = 'Mid', alignY = 'Mid',\n          aspectRatioAttrs = attribute.split(' '), align;\n\n      if (aspectRatioAttrs && aspectRatioAttrs.length) {\n        meetOrSlice = aspectRatioAttrs.pop();\n        if (meetOrSlice !== 'meet' && meetOrSlice !== 'slice') {\n          align = meetOrSlice;\n          meetOrSlice = 'meet';\n        }\n        else if (aspectRatioAttrs.length) {\n          align = aspectRatioAttrs.pop();\n        }\n      }\n      //divide align in alignX and alignY\n      alignX = align !== 'none' ? align.slice(1, 4) : 'none';\n      alignY = align !== 'none' ? align.slice(5, 8) : 'none';\n      return {\n        meetOrSlice: meetOrSlice,\n        alignX: alignX,\n        alignY: alignY\n      };\n    },\n\n    /**\n     * Clear char widths cache for a font family.\n     * @memberOf fabric.util\n     * @param {String} [fontFamily] font family to clear\n     */\n    clearFabricFontCache: function(fontFamily) {\n      if (!fontFamily) {\n        fabric.charWidthsCache = { };\n      }\n      else if (fabric.charWidthsCache[fontFamily]) {\n        delete fabric.charWidthsCache[fontFamily];\n      }\n    },\n\n    /**\n     * Clear char widths cache for a font family.\n     * @memberOf fabric.util\n     * @param {Number} ar aspect ratio\n     * @param {Number} maximumArea Maximum area you want to achieve\n     * @return {Object.x} Limited dimensions by X\n     * @return {Object.y} Limited dimensions by Y\n     */\n    limitDimsByArea: function(ar, maximumArea) {\n      var roughWidth = Math.sqrt(maximumArea * ar),\n          perfLimitSizeY = Math.floor(maximumArea / roughWidth);\n      return { x: Math.floor(roughWidth), y: perfLimitSizeY };\n    },\n\n    capValue: function(min, value, max) {\n      return Math.max(min, Math.min(value, max));\n    },\n\n    findScaleToFit: function(source, destination) {\n      return Math.min(destination.width / source.width, destination.height / source.height);\n    },\n\n    findScaleToCover: function(source, destination) {\n      return Math.max(destination.width / source.width, destination.height / source.height);\n    }\n  };\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function() {\n\n  var arcToSegmentsCache = { },\n      segmentToBezierCache = { },\n      boundsOfCurveCache = { },\n      _join = Array.prototype.join;\n\n  /* Adapted from http://dxr.mozilla.org/mozilla-central/source/content/svg/content/src/nsSVGPathDataParser.cpp\n   * by Andrea Bogazzi code is under MPL. if you don't have a copy of the license you can take it here\n   * http://mozilla.org/MPL/2.0/\n   */\n  function arcToSegments(toX, toY, rx, ry, large, sweep, rotateX) {\n    var argsString = _join.call(arguments);\n    if (arcToSegmentsCache[argsString]) {\n      return arcToSegmentsCache[argsString];\n    }\n\n    var PI = Math.PI, th = rotateX * PI / 180,\n        sinTh = fabric.util.sin(th),\n        cosTh = fabric.util.cos(th),\n        fromX = 0, fromY = 0;\n\n    rx = Math.abs(rx);\n    ry = Math.abs(ry);\n\n    var px = -cosTh * toX * 0.5 - sinTh * toY * 0.5,\n        py = -cosTh * toY * 0.5 + sinTh * toX * 0.5,\n        rx2 = rx * rx, ry2 = ry * ry, py2 = py * py, px2 = px * px,\n        pl = rx2 * ry2 - rx2 * py2 - ry2 * px2,\n        root = 0;\n\n    if (pl < 0) {\n      var s = Math.sqrt(1 - pl / (rx2 * ry2));\n      rx *= s;\n      ry *= s;\n    }\n    else {\n      root = (large === sweep ? -1.0 : 1.0) *\n              Math.sqrt( pl / (rx2 * py2 + ry2 * px2));\n    }\n\n    var cx = root * rx * py / ry,\n        cy = -root * ry * px / rx,\n        cx1 = cosTh * cx - sinTh * cy + toX * 0.5,\n        cy1 = sinTh * cx + cosTh * cy + toY * 0.5,\n        mTheta = calcVectorAngle(1, 0, (px - cx) / rx, (py - cy) / ry),\n        dtheta = calcVectorAngle((px - cx) / rx, (py - cy) / ry, (-px - cx) / rx, (-py - cy) / ry);\n\n    if (sweep === 0 && dtheta > 0) {\n      dtheta -= 2 * PI;\n    }\n    else if (sweep === 1 && dtheta < 0) {\n      dtheta += 2 * PI;\n    }\n\n    // Convert into cubic bezier segments <= 90deg\n    var segments = Math.ceil(Math.abs(dtheta / PI * 2)),\n        result = [], mDelta = dtheta / segments,\n        mT = 8 / 3 * Math.sin(mDelta / 4) * Math.sin(mDelta / 4) / Math.sin(mDelta / 2),\n        th3 = mTheta + mDelta;\n\n    for (var i = 0; i < segments; i++) {\n      result[i] = segmentToBezier(mTheta, th3, cosTh, sinTh, rx, ry, cx1, cy1, mT, fromX, fromY);\n      fromX = result[i][4];\n      fromY = result[i][5];\n      mTheta = th3;\n      th3 += mDelta;\n    }\n    arcToSegmentsCache[argsString] = result;\n    return result;\n  }\n\n  function segmentToBezier(th2, th3, cosTh, sinTh, rx, ry, cx1, cy1, mT, fromX, fromY) {\n    var argsString2 = _join.call(arguments);\n    if (segmentToBezierCache[argsString2]) {\n      return segmentToBezierCache[argsString2];\n    }\n\n    var costh2 = fabric.util.cos(th2),\n        sinth2 = fabric.util.sin(th2),\n        costh3 = fabric.util.cos(th3),\n        sinth3 = fabric.util.sin(th3),\n        toX = cosTh * rx * costh3 - sinTh * ry * sinth3 + cx1,\n        toY = sinTh * rx * costh3 + cosTh * ry * sinth3 + cy1,\n        cp1X = fromX + mT * ( -cosTh * rx * sinth2 - sinTh * ry * costh2),\n        cp1Y = fromY + mT * ( -sinTh * rx * sinth2 + cosTh * ry * costh2),\n        cp2X = toX + mT * ( cosTh * rx * sinth3 + sinTh * ry * costh3),\n        cp2Y = toY + mT * ( sinTh * rx * sinth3 - cosTh * ry * costh3);\n\n    segmentToBezierCache[argsString2] = [\n      cp1X, cp1Y,\n      cp2X, cp2Y,\n      toX, toY\n    ];\n    return segmentToBezierCache[argsString2];\n  }\n\n  /*\n   * Private\n   */\n  function calcVectorAngle(ux, uy, vx, vy) {\n    var ta = Math.atan2(uy, ux),\n        tb = Math.atan2(vy, vx);\n    if (tb >= ta) {\n      return tb - ta;\n    }\n    else {\n      return 2 * Math.PI - (ta - tb);\n    }\n  }\n\n  /**\n   * Draws arc\n   * @param {CanvasRenderingContext2D} ctx\n   * @param {Number} fx\n   * @param {Number} fy\n   * @param {Array} coords\n   */\n  fabric.util.drawArc = function(ctx, fx, fy, coords) {\n    var rx = coords[0],\n        ry = coords[1],\n        rot = coords[2],\n        large = coords[3],\n        sweep = coords[4],\n        tx = coords[5],\n        ty = coords[6],\n        segs = [[], [], [], []],\n        segsNorm = arcToSegments(tx - fx, ty - fy, rx, ry, large, sweep, rot);\n\n    for (var i = 0, len = segsNorm.length; i < len; i++) {\n      segs[i][0] = segsNorm[i][0] + fx;\n      segs[i][1] = segsNorm[i][1] + fy;\n      segs[i][2] = segsNorm[i][2] + fx;\n      segs[i][3] = segsNorm[i][3] + fy;\n      segs[i][4] = segsNorm[i][4] + fx;\n      segs[i][5] = segsNorm[i][5] + fy;\n      ctx.bezierCurveTo.apply(ctx, segs[i]);\n    }\n  };\n\n  /**\n   * Calculate bounding box of a elliptic-arc\n   * @param {Number} fx start point of arc\n   * @param {Number} fy\n   * @param {Number} rx horizontal radius\n   * @param {Number} ry vertical radius\n   * @param {Number} rot angle of horizontal axe\n   * @param {Number} large 1 or 0, whatever the arc is the big or the small on the 2 points\n   * @param {Number} sweep 1 or 0, 1 clockwise or counterclockwise direction\n   * @param {Number} tx end point of arc\n   * @param {Number} ty\n   */\n  fabric.util.getBoundsOfArc = function(fx, fy, rx, ry, rot, large, sweep, tx, ty) {\n\n    var fromX = 0, fromY = 0, bound, bounds = [],\n        segs = arcToSegments(tx - fx, ty - fy, rx, ry, large, sweep, rot);\n\n    for (var i = 0, len = segs.length; i < len; i++) {\n      bound = getBoundsOfCurve(fromX, fromY, segs[i][0], segs[i][1], segs[i][2], segs[i][3], segs[i][4], segs[i][5]);\n      bounds.push({ x: bound[0].x + fx, y: bound[0].y + fy });\n      bounds.push({ x: bound[1].x + fx, y: bound[1].y + fy });\n      fromX = segs[i][4];\n      fromY = segs[i][5];\n    }\n    return bounds;\n  };\n\n  /**\n   * Calculate bounding box of a beziercurve\n   * @param {Number} x0 starting point\n   * @param {Number} y0\n   * @param {Number} x1 first control point\n   * @param {Number} y1\n   * @param {Number} x2 secondo control point\n   * @param {Number} y2\n   * @param {Number} x3 end of beizer\n   * @param {Number} y3\n   */\n  // taken from http://jsbin.com/ivomiq/56/edit  no credits available for that.\n  function getBoundsOfCurve(x0, y0, x1, y1, x2, y2, x3, y3) {\n    var argsString = _join.call(arguments);\n    if (boundsOfCurveCache[argsString]) {\n      return boundsOfCurveCache[argsString];\n    }\n\n    var sqrt = Math.sqrt,\n        min = Math.min, max = Math.max,\n        abs = Math.abs, tvalues = [],\n        bounds = [[], []],\n        a, b, c, t, t1, t2, b2ac, sqrtb2ac;\n\n    b = 6 * x0 - 12 * x1 + 6 * x2;\n    a = -3 * x0 + 9 * x1 - 9 * x2 + 3 * x3;\n    c = 3 * x1 - 3 * x0;\n\n    for (var i = 0; i < 2; ++i) {\n      if (i > 0) {\n        b = 6 * y0 - 12 * y1 + 6 * y2;\n        a = -3 * y0 + 9 * y1 - 9 * y2 + 3 * y3;\n        c = 3 * y1 - 3 * y0;\n      }\n\n      if (abs(a) < 1e-12) {\n        if (abs(b) < 1e-12) {\n          continue;\n        }\n        t = -c / b;\n        if (0 < t && t < 1) {\n          tvalues.push(t);\n        }\n        continue;\n      }\n      b2ac = b * b - 4 * c * a;\n      if (b2ac < 0) {\n        continue;\n      }\n      sqrtb2ac = sqrt(b2ac);\n      t1 = (-b + sqrtb2ac) / (2 * a);\n      if (0 < t1 && t1 < 1) {\n        tvalues.push(t1);\n      }\n      t2 = (-b - sqrtb2ac) / (2 * a);\n      if (0 < t2 && t2 < 1) {\n        tvalues.push(t2);\n      }\n    }\n\n    var x, y, j = tvalues.length, jlen = j, mt;\n    while (j--) {\n      t = tvalues[j];\n      mt = 1 - t;\n      x = (mt * mt * mt * x0) + (3 * mt * mt * t * x1) + (3 * mt * t * t * x2) + (t * t * t * x3);\n      bounds[0][j] = x;\n\n      y = (mt * mt * mt * y0) + (3 * mt * mt * t * y1) + (3 * mt * t * t * y2) + (t * t * t * y3);\n      bounds[1][j] = y;\n    }\n\n    bounds[0][jlen] = x0;\n    bounds[1][jlen] = y0;\n    bounds[0][jlen + 1] = x3;\n    bounds[1][jlen + 1] = y3;\n    var result = [\n      {\n        x: min.apply(null, bounds[0]),\n        y: min.apply(null, bounds[1])\n      },\n      {\n        x: max.apply(null, bounds[0]),\n        y: max.apply(null, bounds[1])\n      }\n    ];\n    boundsOfCurveCache[argsString] = result;\n    return result;\n  }\n\n  fabric.util.getBoundsOfCurve = getBoundsOfCurve;\n\n})();\n\n\n(function() {\n\n  var slice = Array.prototype.slice;\n\n  /**\n   * Invokes method on all items in a given array\n   * @memberOf fabric.util.array\n   * @param {Array} array Array to iterate over\n   * @param {String} method Name of a method to invoke\n   * @return {Array}\n   */\n  function invoke(array, method) {\n    var args = slice.call(arguments, 2), result = [];\n    for (var i = 0, len = array.length; i < len; i++) {\n      result[i] = args.length ? array[i][method].apply(array[i], args) : array[i][method].call(array[i]);\n    }\n    return result;\n  }\n\n  /**\n   * Finds maximum value in array (not necessarily \"first\" one)\n   * @memberOf fabric.util.array\n   * @param {Array} array Array to iterate over\n   * @param {String} byProperty\n   * @return {*}\n   */\n  function max(array, byProperty) {\n    return find(array, byProperty, function(value1, value2) {\n      return value1 >= value2;\n    });\n  }\n\n  /**\n   * Finds minimum value in array (not necessarily \"first\" one)\n   * @memberOf fabric.util.array\n   * @param {Array} array Array to iterate over\n   * @param {String} byProperty\n   * @return {*}\n   */\n  function min(array, byProperty) {\n    return find(array, byProperty, function(value1, value2) {\n      return value1 < value2;\n    });\n  }\n\n  /**\n   * @private\n   */\n  function fill(array, value) {\n    var k = array.length;\n    while (k--) {\n      array[k] = value;\n    }\n    return array;\n  }\n\n  /**\n   * @private\n   */\n  function find(array, byProperty, condition) {\n    if (!array || array.length === 0) {\n      return;\n    }\n\n    var i = array.length - 1,\n        result = byProperty ? array[i][byProperty] : array[i];\n    if (byProperty) {\n      while (i--) {\n        if (condition(array[i][byProperty], result)) {\n          result = array[i][byProperty];\n        }\n      }\n    }\n    else {\n      while (i--) {\n        if (condition(array[i], result)) {\n          result = array[i];\n        }\n      }\n    }\n    return result;\n  }\n\n  /**\n   * @namespace fabric.util.array\n   */\n  fabric.util.array = {\n    fill: fill,\n    invoke: invoke,\n    min: min,\n    max: max\n  };\n\n})();\n\n\n(function() {\n  /**\n   * Copies all enumerable properties of one js object to another\n   * Does not clone or extend fabric.Object subclasses.\n   * @memberOf fabric.util.object\n   * @param {Object} destination Where to copy to\n   * @param {Object} source Where to copy from\n   * @return {Object}\n   */\n\n  function extend(destination, source, deep) {\n    // JScript DontEnum bug is not taken care of\n    // the deep clone is for internal use, is not meant to avoid\n    // javascript traps or cloning html element or self referenced objects.\n    if (deep) {\n      if (!fabric.isLikelyNode && source instanceof Element) {\n        // avoid cloning deep images, canvases,\n        destination = source;\n      }\n      else if (source instanceof Array) {\n        destination = [];\n        for (var i = 0, len = source.length; i < len; i++) {\n          destination[i] = extend({ }, source[i], deep);\n        }\n      }\n      else if (source && typeof source === 'object') {\n        for (var property in source) {\n          if (source.hasOwnProperty(property)) {\n            destination[property] = extend({ }, source[property], deep);\n          }\n        }\n      }\n      else {\n        // this sounds odd for an extend but is ok for recursive use\n        destination = source;\n      }\n    }\n    else {\n      for (var property in source) {\n        destination[property] = source[property];\n      }\n    }\n    return destination;\n  }\n\n  /**\n   * Creates an empty object and copies all enumerable properties of another object to it\n   * @memberOf fabric.util.object\n   * TODO: this function return an empty object if you try to clone null\n   * @param {Object} object Object to clone\n   * @return {Object}\n   */\n  function clone(object, deep) {\n    return extend({ }, object, deep);\n  }\n\n  /** @namespace fabric.util.object */\n  fabric.util.object = {\n    extend: extend,\n    clone: clone\n  };\n  fabric.util.object.extend(fabric.util, fabric.Observable);\n})();\n\n\n(function() {\n\n  /**\n   * Camelizes a string\n   * @memberOf fabric.util.string\n   * @param {String} string String to camelize\n   * @return {String} Camelized version of a string\n   */\n  function camelize(string) {\n    return string.replace(/-+(.)?/g, function(match, character) {\n      return character ? character.toUpperCase() : '';\n    });\n  }\n\n  /**\n   * Capitalizes a string\n   * @memberOf fabric.util.string\n   * @param {String} string String to capitalize\n   * @param {Boolean} [firstLetterOnly] If true only first letter is capitalized\n   * and other letters stay untouched, if false first letter is capitalized\n   * and other letters are converted to lowercase.\n   * @return {String} Capitalized version of a string\n   */\n  function capitalize(string, firstLetterOnly) {\n    return string.charAt(0).toUpperCase() +\n      (firstLetterOnly ? string.slice(1) : string.slice(1).toLowerCase());\n  }\n\n  /**\n   * Escapes XML in a string\n   * @memberOf fabric.util.string\n   * @param {String} string String to escape\n   * @return {String} Escaped version of a string\n   */\n  function escapeXml(string) {\n    return string.replace(/&/g, '&amp;')\n      .replace(/\"/g, '&quot;')\n      .replace(/'/g, '&apos;')\n      .replace(/</g, '&lt;')\n      .replace(/>/g, '&gt;');\n  }\n\n  /**\n   * Divide a string in the user perceived single units\n   * @memberOf fabric.util.string\n   * @param {String} textstring String to escape\n   * @return {Array} array containing the graphemes\n   */\n  function graphemeSplit(textstring) {\n    var i = 0, chr, graphemes = [];\n    for (i = 0, chr; i < textstring.length; i++) {\n      if ((chr = getWholeChar(textstring, i)) === false) {\n        continue;\n      }\n      graphemes.push(chr);\n    }\n    return graphemes;\n  }\n\n  // taken from mdn in the charAt doc page.\n  function getWholeChar(str, i) {\n    var code = str.charCodeAt(i);\n\n    if (isNaN(code)) {\n      return ''; // Position not found\n    }\n    if (code < 0xD800 || code > 0xDFFF) {\n      return str.charAt(i);\n    }\n\n    // High surrogate (could change last hex to 0xDB7F to treat high private\n    // surrogates as single characters)\n    if (0xD800 <= code && code <= 0xDBFF) {\n      if (str.length <= (i + 1)) {\n        throw 'High surrogate without following low surrogate';\n      }\n      var next = str.charCodeAt(i + 1);\n      if (0xDC00 > next || next > 0xDFFF) {\n        throw 'High surrogate without following low surrogate';\n      }\n      return str.charAt(i) + str.charAt(i + 1);\n    }\n    // Low surrogate (0xDC00 <= code && code <= 0xDFFF)\n    if (i === 0) {\n      throw 'Low surrogate without preceding high surrogate';\n    }\n    var prev = str.charCodeAt(i - 1);\n\n    // (could change last hex to 0xDB7F to treat high private\n    // surrogates as single characters)\n    if (0xD800 > prev || prev > 0xDBFF) {\n      throw 'Low surrogate without preceding high surrogate';\n    }\n    // We can pass over low surrogates now as the second component\n    // in a pair which we have already processed\n    return false;\n  }\n\n\n  /**\n   * String utilities\n   * @namespace fabric.util.string\n   */\n  fabric.util.string = {\n    camelize: camelize,\n    capitalize: capitalize,\n    escapeXml: escapeXml,\n    graphemeSplit: graphemeSplit\n  };\n})();\n\n\n(function() {\n\n  var slice = Array.prototype.slice, emptyFunction = function() { },\n\n      IS_DONTENUM_BUGGY = (function() {\n        for (var p in { toString: 1 }) {\n          if (p === 'toString') {\n            return false;\n          }\n        }\n        return true;\n      })(),\n\n      /** @ignore */\n      addMethods = function(klass, source, parent) {\n        for (var property in source) {\n\n          if (property in klass.prototype &&\n              typeof klass.prototype[property] === 'function' &&\n              (source[property] + '').indexOf('callSuper') > -1) {\n\n            klass.prototype[property] = (function(property) {\n              return function() {\n\n                var superclass = this.constructor.superclass;\n                this.constructor.superclass = parent;\n                var returnValue = source[property].apply(this, arguments);\n                this.constructor.superclass = superclass;\n\n                if (property !== 'initialize') {\n                  return returnValue;\n                }\n              };\n            })(property);\n          }\n          else {\n            klass.prototype[property] = source[property];\n          }\n\n          if (IS_DONTENUM_BUGGY) {\n            if (source.toString !== Object.prototype.toString) {\n              klass.prototype.toString = source.toString;\n            }\n            if (source.valueOf !== Object.prototype.valueOf) {\n              klass.prototype.valueOf = source.valueOf;\n            }\n          }\n        }\n      };\n\n  function Subclass() { }\n\n  function callSuper(methodName) {\n    var parentMethod = null,\n        _this = this;\n\n    // climb prototype chain to find method not equal to callee's method\n    while (_this.constructor.superclass) {\n      var superClassMethod = _this.constructor.superclass.prototype[methodName];\n      if (_this[methodName] !== superClassMethod) {\n        parentMethod = superClassMethod;\n        break;\n      }\n      // eslint-disable-next-line\n      _this = _this.constructor.superclass.prototype;\n    }\n\n    if (!parentMethod) {\n      return console.log('tried to callSuper ' + methodName + ', method not found in prototype chain', this);\n    }\n\n    return (arguments.length > 1)\n      ? parentMethod.apply(this, slice.call(arguments, 1))\n      : parentMethod.call(this);\n  }\n\n  /**\n   * Helper for creation of \"classes\".\n   * @memberOf fabric.util\n   * @param {Function} [parent] optional \"Class\" to inherit from\n   * @param {Object} [properties] Properties shared by all instances of this class\n   *                  (be careful modifying objects defined here as this would affect all instances)\n   */\n  function createClass() {\n    var parent = null,\n        properties = slice.call(arguments, 0);\n\n    if (typeof properties[0] === 'function') {\n      parent = properties.shift();\n    }\n    function klass() {\n      this.initialize.apply(this, arguments);\n    }\n\n    klass.superclass = parent;\n    klass.subclasses = [];\n\n    if (parent) {\n      Subclass.prototype = parent.prototype;\n      klass.prototype = new Subclass();\n      parent.subclasses.push(klass);\n    }\n    for (var i = 0, length = properties.length; i < length; i++) {\n      addMethods(klass, properties[i], parent);\n    }\n    if (!klass.prototype.initialize) {\n      klass.prototype.initialize = emptyFunction;\n    }\n    klass.prototype.constructor = klass;\n    klass.prototype.callSuper = callSuper;\n    return klass;\n  }\n\n  fabric.util.createClass = createClass;\n})();\n\n\n(function () {\n\n  var unknown = 'unknown';\n\n  /* EVENT HANDLING */\n\n  function areHostMethods(object) {\n    var methodNames = Array.prototype.slice.call(arguments, 1),\n        t, i, len = methodNames.length;\n    for (i = 0; i < len; i++) {\n      t = typeof object[methodNames[i]];\n      if (!(/^(?:function|object|unknown)$/).test(t)) {\n        return false;\n      }\n    }\n    return true;\n  }\n\n  /** @ignore */\n  var getElement,\n      setElement,\n      getUniqueId = (function () {\n        var uid = 0;\n        return function (element) {\n          return element.__uniqueID || (element.__uniqueID = 'uniqueID__' + uid++);\n        };\n      })();\n\n  (function () {\n    var elements = { };\n    /** @ignore */\n    getElement = function (uid) {\n      return elements[uid];\n    };\n    /** @ignore */\n    setElement = function (uid, element) {\n      elements[uid] = element;\n    };\n  })();\n\n  function createListener(uid, handler) {\n    return {\n      handler: handler,\n      wrappedHandler: createWrappedHandler(uid, handler)\n    };\n  }\n\n  function createWrappedHandler(uid, handler) {\n    return function (e) {\n      handler.call(getElement(uid), e || fabric.window.event);\n    };\n  }\n\n  function createDispatcher(uid, eventName) {\n    return function (e) {\n      if (handlers[uid] && handlers[uid][eventName]) {\n        var handlersForEvent = handlers[uid][eventName];\n        for (var i = 0, len = handlersForEvent.length; i < len; i++) {\n          handlersForEvent[i].call(this, e || fabric.window.event);\n        }\n      }\n    };\n  }\n\n  var shouldUseAddListenerRemoveListener = (\n        areHostMethods(fabric.document.documentElement, 'addEventListener', 'removeEventListener') &&\n        areHostMethods(fabric.window, 'addEventListener', 'removeEventListener')),\n\n      shouldUseAttachEventDetachEvent = (\n        areHostMethods(fabric.document.documentElement, 'attachEvent', 'detachEvent') &&\n        areHostMethods(fabric.window, 'attachEvent', 'detachEvent')),\n\n      // IE branch\n      listeners = { },\n\n      // DOM L0 branch\n      handlers = { },\n\n      addListener, removeListener;\n\n  if (shouldUseAddListenerRemoveListener) {\n    /** @ignore */\n    addListener = function (element, eventName, handler, options) {\n      // since ie10 or ie9 can use addEventListener but they do not support options, i need to check\n      element && element.addEventListener(eventName, handler, shouldUseAttachEventDetachEvent ? false : options);\n    };\n    /** @ignore */\n    removeListener = function (element, eventName, handler, options) {\n      element && element.removeEventListener(eventName, handler, shouldUseAttachEventDetachEvent ? false : options);\n    };\n  }\n\n  else if (shouldUseAttachEventDetachEvent) {\n    /** @ignore */\n    addListener = function (element, eventName, handler) {\n      if (!element) {\n        return;\n      }\n      var uid = getUniqueId(element);\n      setElement(uid, element);\n      if (!listeners[uid]) {\n        listeners[uid] = { };\n      }\n      if (!listeners[uid][eventName]) {\n        listeners[uid][eventName] = [];\n\n      }\n      var listener = createListener(uid, handler);\n      listeners[uid][eventName].push(listener);\n      element.attachEvent('on' + eventName, listener.wrappedHandler);\n    };\n    /** @ignore */\n    removeListener = function (element, eventName, handler) {\n      if (!element) {\n        return;\n      }\n      var uid = getUniqueId(element), listener;\n      if (listeners[uid] && listeners[uid][eventName]) {\n        for (var i = 0, len = listeners[uid][eventName].length; i < len; i++) {\n          listener = listeners[uid][eventName][i];\n          if (listener && listener.handler === handler) {\n            element.detachEvent('on' + eventName, listener.wrappedHandler);\n            listeners[uid][eventName][i] = null;\n          }\n        }\n      }\n    };\n  }\n  else {\n    /** @ignore */\n    addListener = function (element, eventName, handler) {\n      if (!element) {\n        return;\n      }\n      var uid = getUniqueId(element);\n      if (!handlers[uid]) {\n        handlers[uid] = { };\n      }\n      if (!handlers[uid][eventName]) {\n        handlers[uid][eventName] = [];\n        var existingHandler = element['on' + eventName];\n        if (existingHandler) {\n          handlers[uid][eventName].push(existingHandler);\n        }\n        element['on' + eventName] = createDispatcher(uid, eventName);\n      }\n      handlers[uid][eventName].push(handler);\n    };\n    /** @ignore */\n    removeListener = function (element, eventName, handler) {\n      if (!element) {\n        return;\n      }\n      var uid = getUniqueId(element);\n      if (handlers[uid] && handlers[uid][eventName]) {\n        var handlersForEvent = handlers[uid][eventName];\n        for (var i = 0, len = handlersForEvent.length; i < len; i++) {\n          if (handlersForEvent[i] === handler) {\n            handlersForEvent.splice(i, 1);\n          }\n        }\n      }\n    };\n  }\n\n  /**\n   * Adds an event listener to an element\n   * @function\n   * @memberOf fabric.util\n   * @param {HTMLElement} element\n   * @param {String} eventName\n   * @param {Function} handler\n   */\n  fabric.util.addListener = addListener;\n\n  /**\n   * Removes an event listener from an element\n   * @function\n   * @memberOf fabric.util\n   * @param {HTMLElement} element\n   * @param {String} eventName\n   * @param {Function} handler\n   */\n  fabric.util.removeListener = removeListener;\n\n  /**\n   * Cross-browser wrapper for getting event's coordinates\n   * @memberOf fabric.util\n   * @param {Event} event Event object\n   */\n  function getPointer(event) {\n    event || (event = fabric.window.event);\n\n    var element = event.target ||\n                  (typeof event.srcElement !== unknown ? event.srcElement : null),\n\n        scroll = fabric.util.getScrollLeftTop(element);\n    return {\n      x: pointerX(event) + scroll.left,\n      y: pointerY(event) + scroll.top\n    };\n  }\n\n  var pointerX = function(event) {\n        return event.clientX;\n      },\n\n      pointerY = function(event) {\n        return event.clientY;\n      };\n\n  function _getPointer(event, pageProp, clientProp) {\n    var touchProp = event.type === 'touchend' ? 'changedTouches' : 'touches';\n    var pointer, eventTouchProp = event[touchProp];\n\n    if (eventTouchProp && eventTouchProp[0]) {\n      pointer = eventTouchProp[0][clientProp];\n    }\n\n    if (typeof pointer === 'undefined') {\n      pointer = event[clientProp];\n    }\n\n    return pointer;\n  }\n\n  if (fabric.isTouchSupported) {\n    pointerX = function(event) {\n      return _getPointer(event, 'pageX', 'clientX');\n    };\n    pointerY = function(event) {\n      return _getPointer(event, 'pageY', 'clientY');\n    };\n  }\n\n  fabric.util.getPointer = getPointer;\n\n})();\n\n\n(function () {\n\n  /**\n   * Cross-browser wrapper for setting element's style\n   * @memberOf fabric.util\n   * @param {HTMLElement} element\n   * @param {Object} styles\n   * @return {HTMLElement} Element that was passed as a first argument\n   */\n  function setStyle(element, styles) {\n    var elementStyle = element.style;\n    if (!elementStyle) {\n      return element;\n    }\n    if (typeof styles === 'string') {\n      element.style.cssText += ';' + styles;\n      return styles.indexOf('opacity') > -1\n        ? setOpacity(element, styles.match(/opacity:\\s*(\\d?\\.?\\d*)/)[1])\n        : element;\n    }\n    for (var property in styles) {\n      if (property === 'opacity') {\n        setOpacity(element, styles[property]);\n      }\n      else {\n        var normalizedProperty = (property === 'float' || property === 'cssFloat')\n          ? (typeof elementStyle.styleFloat === 'undefined' ? 'cssFloat' : 'styleFloat')\n          : property;\n        elementStyle[normalizedProperty] = styles[property];\n      }\n    }\n    return element;\n  }\n\n  var parseEl = fabric.document.createElement('div'),\n      supportsOpacity = typeof parseEl.style.opacity === 'string',\n      supportsFilters = typeof parseEl.style.filter === 'string',\n      reOpacity = /alpha\\s*\\(\\s*opacity\\s*=\\s*([^\\)]+)\\)/,\n\n      /** @ignore */\n      setOpacity = function (element) { return element; };\n\n  if (supportsOpacity) {\n    /** @ignore */\n    setOpacity = function(element, value) {\n      element.style.opacity = value;\n      return element;\n    };\n  }\n  else if (supportsFilters) {\n    /** @ignore */\n    setOpacity = function(element, value) {\n      var es = element.style;\n      if (element.currentStyle && !element.currentStyle.hasLayout) {\n        es.zoom = 1;\n      }\n      if (reOpacity.test(es.filter)) {\n        value = value >= 0.9999 ? '' : ('alpha(opacity=' + (value * 100) + ')');\n        es.filter = es.filter.replace(reOpacity, value);\n      }\n      else {\n        es.filter += ' alpha(opacity=' + (value * 100) + ')';\n      }\n      return element;\n    };\n  }\n\n  fabric.util.setStyle = setStyle;\n\n})();\n\n\n(function() {\n\n  var _slice = Array.prototype.slice;\n\n  /**\n   * Takes id and returns an element with that id (if one exists in a document)\n   * @memberOf fabric.util\n   * @param {String|HTMLElement} id\n   * @return {HTMLElement|null}\n   */\n  function getById(id) {\n    return typeof id === 'string' ? fabric.document.getElementById(id) : id;\n  }\n\n  var sliceCanConvertNodelists,\n      /**\n       * Converts an array-like object (e.g. arguments or NodeList) to an array\n       * @memberOf fabric.util\n       * @param {Object} arrayLike\n       * @return {Array}\n       */\n      toArray = function(arrayLike) {\n        return _slice.call(arrayLike, 0);\n      };\n\n  try {\n    sliceCanConvertNodelists = toArray(fabric.document.childNodes) instanceof Array;\n  }\n  catch (err) { }\n\n  if (!sliceCanConvertNodelists) {\n    toArray = function(arrayLike) {\n      var arr = new Array(arrayLike.length), i = arrayLike.length;\n      while (i--) {\n        arr[i] = arrayLike[i];\n      }\n      return arr;\n    };\n  }\n\n  /**\n   * Creates specified element with specified attributes\n   * @memberOf fabric.util\n   * @param {String} tagName Type of an element to create\n   * @param {Object} [attributes] Attributes to set on an element\n   * @return {HTMLElement} Newly created element\n   */\n  function makeElement(tagName, attributes) {\n    var el = fabric.document.createElement(tagName);\n    for (var prop in attributes) {\n      if (prop === 'class') {\n        el.className = attributes[prop];\n      }\n      else if (prop === 'for') {\n        el.htmlFor = attributes[prop];\n      }\n      else {\n        el.setAttribute(prop, attributes[prop]);\n      }\n    }\n    return el;\n  }\n\n  /**\n   * Adds class to an element\n   * @memberOf fabric.util\n   * @param {HTMLElement} element Element to add class to\n   * @param {String} className Class to add to an element\n   */\n  function addClass(element, className) {\n    if (element && (' ' + element.className + ' ').indexOf(' ' + className + ' ') === -1) {\n      element.className += (element.className ? ' ' : '') + className;\n    }\n  }\n\n  /**\n   * Wraps element with another element\n   * @memberOf fabric.util\n   * @param {HTMLElement} element Element to wrap\n   * @param {HTMLElement|String} wrapper Element to wrap with\n   * @param {Object} [attributes] Attributes to set on a wrapper\n   * @return {HTMLElement} wrapper\n   */\n  function wrapElement(element, wrapper, attributes) {\n    if (typeof wrapper === 'string') {\n      wrapper = makeElement(wrapper, attributes);\n    }\n    if (element.parentNode) {\n      element.parentNode.replaceChild(wrapper, element);\n    }\n    wrapper.appendChild(element);\n    return wrapper;\n  }\n\n  /**\n   * Returns element scroll offsets\n   * @memberOf fabric.util\n   * @param {HTMLElement} element Element to operate on\n   * @return {Object} Object with left/top values\n   */\n  function getScrollLeftTop(element) {\n\n    var left = 0,\n        top = 0,\n        docElement = fabric.document.documentElement,\n        body = fabric.document.body || {\n          scrollLeft: 0, scrollTop: 0\n        };\n\n    // While loop checks (and then sets element to) .parentNode OR .host\n    //  to account for ShadowDOM. We still want to traverse up out of ShadowDOM,\n    //  but the .parentNode of a root ShadowDOM node will always be null, instead\n    //  it should be accessed through .host. See http://stackoverflow.com/a/24765528/4383938\n    while (element && (element.parentNode || element.host)) {\n\n      // Set element to element parent, or 'host' in case of ShadowDOM\n      element = element.parentNode || element.host;\n\n      if (element === fabric.document) {\n        left = body.scrollLeft || docElement.scrollLeft || 0;\n        top = body.scrollTop ||  docElement.scrollTop || 0;\n      }\n      else {\n        left += element.scrollLeft || 0;\n        top += element.scrollTop || 0;\n      }\n\n      if (element.nodeType === 1 && element.style.position === 'fixed') {\n        break;\n      }\n    }\n\n    return { left: left, top: top };\n  }\n\n  /**\n   * Returns offset for a given element\n   * @function\n   * @memberOf fabric.util\n   * @param {HTMLElement} element Element to get offset for\n   * @return {Object} Object with \"left\" and \"top\" properties\n   */\n  function getElementOffset(element) {\n    var docElem,\n        doc = element && element.ownerDocument,\n        box = { left: 0, top: 0 },\n        offset = { left: 0, top: 0 },\n        scrollLeftTop,\n        offsetAttributes = {\n          borderLeftWidth: 'left',\n          borderTopWidth:  'top',\n          paddingLeft:     'left',\n          paddingTop:      'top'\n        };\n\n    if (!doc) {\n      return offset;\n    }\n\n    for (var attr in offsetAttributes) {\n      offset[offsetAttributes[attr]] += parseInt(getElementStyle(element, attr), 10) || 0;\n    }\n\n    docElem = doc.documentElement;\n    if ( typeof element.getBoundingClientRect !== 'undefined' ) {\n      box = element.getBoundingClientRect();\n    }\n\n    scrollLeftTop = getScrollLeftTop(element);\n\n    return {\n      left: box.left + scrollLeftTop.left - (docElem.clientLeft || 0) + offset.left,\n      top: box.top + scrollLeftTop.top - (docElem.clientTop || 0)  + offset.top\n    };\n  }\n\n  /**\n   * Returns style attribute value of a given element\n   * @memberOf fabric.util\n   * @param {HTMLElement} element Element to get style attribute for\n   * @param {String} attr Style attribute to get for element\n   * @return {String} Style attribute value of the given element.\n   */\n  var getElementStyle;\n  if (fabric.document.defaultView && fabric.document.defaultView.getComputedStyle) {\n    getElementStyle = function(element, attr) {\n      var style = fabric.document.defaultView.getComputedStyle(element, null);\n      return style ? style[attr] : undefined;\n    };\n  }\n  else {\n    getElementStyle = function(element, attr) {\n      var value = element.style[attr];\n      if (!value && element.currentStyle) {\n        value = element.currentStyle[attr];\n      }\n      return value;\n    };\n  }\n\n  (function () {\n    var style = fabric.document.documentElement.style,\n        selectProp = 'userSelect' in style\n          ? 'userSelect'\n          : 'MozUserSelect' in style\n            ? 'MozUserSelect'\n            : 'WebkitUserSelect' in style\n              ? 'WebkitUserSelect'\n              : 'KhtmlUserSelect' in style\n                ? 'KhtmlUserSelect'\n                : '';\n\n    /**\n     * Makes element unselectable\n     * @memberOf fabric.util\n     * @param {HTMLElement} element Element to make unselectable\n     * @return {HTMLElement} Element that was passed in\n     */\n    function makeElementUnselectable(element) {\n      if (typeof element.onselectstart !== 'undefined') {\n        element.onselectstart = fabric.util.falseFunction;\n      }\n      if (selectProp) {\n        element.style[selectProp] = 'none';\n      }\n      else if (typeof element.unselectable === 'string') {\n        element.unselectable = 'on';\n      }\n      return element;\n    }\n\n    /**\n     * Makes element selectable\n     * @memberOf fabric.util\n     * @param {HTMLElement} element Element to make selectable\n     * @return {HTMLElement} Element that was passed in\n     */\n    function makeElementSelectable(element) {\n      if (typeof element.onselectstart !== 'undefined') {\n        element.onselectstart = null;\n      }\n      if (selectProp) {\n        element.style[selectProp] = '';\n      }\n      else if (typeof element.unselectable === 'string') {\n        element.unselectable = '';\n      }\n      return element;\n    }\n\n    fabric.util.makeElementUnselectable = makeElementUnselectable;\n    fabric.util.makeElementSelectable = makeElementSelectable;\n  })();\n\n  (function() {\n\n    /**\n     * Inserts a script element with a given url into a document; invokes callback, when that script is finished loading\n     * @memberOf fabric.util\n     * @param {String} url URL of a script to load\n     * @param {Function} callback Callback to execute when script is finished loading\n     */\n    function getScript(url, callback) {\n      var headEl = fabric.document.getElementsByTagName('head')[0],\n          scriptEl = fabric.document.createElement('script'),\n          loading = true;\n\n      /** @ignore */\n      scriptEl.onload = /** @ignore */ scriptEl.onreadystatechange = function(e) {\n        if (loading) {\n          if (typeof this.readyState === 'string' &&\n              this.readyState !== 'loaded' &&\n              this.readyState !== 'complete') {\n            return;\n          }\n          loading = false;\n          callback(e || fabric.window.event);\n          scriptEl = scriptEl.onload = scriptEl.onreadystatechange = null;\n        }\n      };\n      scriptEl.src = url;\n      headEl.appendChild(scriptEl);\n      // causes issue in Opera\n      // headEl.removeChild(scriptEl);\n    }\n\n    fabric.util.getScript = getScript;\n  })();\n\n  function getNodeCanvas(element) {\n    var impl = fabric.jsdomImplForWrapper(element);\n    return impl._canvas || impl._image;\n  };\n\n  fabric.util.getById = getById;\n  fabric.util.toArray = toArray;\n  fabric.util.makeElement = makeElement;\n  fabric.util.addClass = addClass;\n  fabric.util.wrapElement = wrapElement;\n  fabric.util.getScrollLeftTop = getScrollLeftTop;\n  fabric.util.getElementOffset = getElementOffset;\n  fabric.util.getElementStyle = getElementStyle;\n  fabric.util.getNodeCanvas = getNodeCanvas;\n\n})();\n\n\n(function() {\n\n  function addParamToUrl(url, param) {\n    return url + (/\\?/.test(url) ? '&' : '?') + param;\n  }\n\n  var makeXHR = (function() {\n    var factories = [\n      function() { return new ActiveXObject('Microsoft.XMLHTTP'); },\n      function() { return new ActiveXObject('Msxml2.XMLHTTP'); },\n      function() { return new ActiveXObject('Msxml2.XMLHTTP.3.0'); },\n      function() { return new XMLHttpRequest(); }\n    ];\n    for (var i = factories.length; i--; ) {\n      try {\n        var req = factories[i]();\n        if (req) {\n          return factories[i];\n        }\n      }\n      catch (err) { }\n    }\n  })();\n\n  function emptyFn() { }\n\n  /**\n   * Cross-browser abstraction for sending XMLHttpRequest\n   * @memberOf fabric.util\n   * @param {String} url URL to send XMLHttpRequest to\n   * @param {Object} [options] Options object\n   * @param {String} [options.method=\"GET\"]\n   * @param {String} [options.parameters] parameters to append to url in GET or in body\n   * @param {String} [options.body] body to send with POST or PUT request\n   * @param {Function} options.onComplete Callback to invoke when request is completed\n   * @return {XMLHttpRequest} request\n   */\n  function request(url, options) {\n\n    options || (options = { });\n\n    var method = options.method ? options.method.toUpperCase() : 'GET',\n        onComplete = options.onComplete || function() { },\n        xhr = makeXHR(),\n        body = options.body || options.parameters;\n\n    /** @ignore */\n    xhr.onreadystatechange = function() {\n      if (xhr.readyState === 4) {\n        onComplete(xhr);\n        xhr.onreadystatechange = emptyFn;\n      }\n    };\n\n    if (method === 'GET') {\n      body = null;\n      if (typeof options.parameters === 'string') {\n        url = addParamToUrl(url, options.parameters);\n      }\n    }\n\n    xhr.open(method, url, true);\n\n    if (method === 'POST' || method === 'PUT') {\n      xhr.setRequestHeader('Content-Type', 'application/x-www-form-urlencoded');\n    }\n\n    xhr.send(body);\n    return xhr;\n  }\n\n  fabric.util.request = request;\n})();\n\n\n/**\n * Wrapper around `console.log` (when available)\n * @param {*} [values] Values to log\n */\nfabric.log = function() { };\n\n/**\n * Wrapper around `console.warn` (when available)\n * @param {*} [values] Values to log as a warning\n */\nfabric.warn = function() { };\n\n/* eslint-disable */\nif (typeof console !== 'undefined') {\n\n  ['log', 'warn'].forEach(function(methodName) {\n\n    if (typeof console[methodName] !== 'undefined' &&\n        typeof console[methodName].apply === 'function') {\n\n      fabric[methodName] = function() {\n        return console[methodName].apply(console, arguments);\n      };\n    }\n  });\n}\n/* eslint-enable */\n\n\n(function() {\n\n  function noop() {\n    return false;\n  }\n\n  /**\n   * Changes value from one to another within certain period of time, invoking callbacks as value is being changed.\n   * @memberOf fabric.util\n   * @param {Object} [options] Animation options\n   * @param {Function} [options.onChange] Callback; invoked on every value change\n   * @param {Function} [options.onComplete] Callback; invoked when value change is completed\n   * @param {Number} [options.startValue=0] Starting value\n   * @param {Number} [options.endValue=100] Ending value\n   * @param {Number} [options.byValue=100] Value to modify the property by\n   * @param {Function} [options.easing] Easing function\n   * @param {Number} [options.duration=500] Duration of change (in ms)\n   */\n  function animate(options) {\n\n    requestAnimFrame(function(timestamp) {\n      options || (options = { });\n\n      var start = timestamp || +new Date(),\n          duration = options.duration || 500,\n          finish = start + duration, time,\n          onChange = options.onChange || noop,\n          abort = options.abort || noop,\n          onComplete = options.onComplete || noop,\n          easing = options.easing || function(t, b, c, d) {return -c * Math.cos(t / d * (Math.PI / 2)) + c + b;},\n          startValue = 'startValue' in options ? options.startValue : 0,\n          endValue = 'endValue' in options ? options.endValue : 100,\n          byValue = options.byValue || endValue - startValue;\n\n      options.onStart && options.onStart();\n\n      (function tick(ticktime) {\n        if (abort()) {\n          onComplete(endValue, 1, 1);\n          return;\n        }\n        time = ticktime || +new Date();\n        var currentTime = time > finish ? duration : (time - start),\n            timePerc = currentTime / duration,\n            current = easing(currentTime, startValue, byValue, duration),\n            valuePerc = Math.abs((current - startValue) / byValue);\n        onChange(current, valuePerc, timePerc);\n        if (time > finish) {\n          options.onComplete && options.onComplete();\n          return;\n        }\n        requestAnimFrame(tick);\n      })(start);\n    });\n\n  }\n\n  var _requestAnimFrame = fabric.window.requestAnimationFrame       ||\n                          fabric.window.webkitRequestAnimationFrame ||\n                          fabric.window.mozRequestAnimationFrame    ||\n                          fabric.window.oRequestAnimationFrame      ||\n                          fabric.window.msRequestAnimationFrame     ||\n                          function(callback) {\n                            return fabric.window.setTimeout(callback, 1000 / 60);\n                          };\n\n  var _cancelAnimFrame = fabric.window.cancelAnimationFrame || fabric.window.clearTimeout;\n\n  /**\n   * requestAnimationFrame polyfill based on http://paulirish.com/2011/requestanimationframe-for-smart-animating/\n   * In order to get a precise start time, `requestAnimFrame` should be called as an entry into the method\n   * @memberOf fabric.util\n   * @param {Function} callback Callback to invoke\n   * @param {DOMElement} element optional Element to associate with animation\n   */\n  function requestAnimFrame() {\n    return _requestAnimFrame.apply(fabric.window, arguments);\n  }\n\n  function cancelAnimFrame() {\n    return _cancelAnimFrame.apply(fabric.window, arguments);\n  }\n\n  fabric.util.animate = animate;\n  fabric.util.requestAnimFrame = requestAnimFrame;\n  fabric.util.cancelAnimFrame = cancelAnimFrame;\n})();\n\n\n(function() {\n  // Calculate an in-between color. Returns a \"rgba()\" string.\n  // Credit: Edwin Martin <edwin@bitstorm.org>\n  //         http://www.bitstorm.org/jquery/color-animation/jquery.animate-colors.js\n  function calculateColor(begin, end, pos) {\n    var color = 'rgba('\n        + parseInt((begin[0] + pos * (end[0] - begin[0])), 10) + ','\n        + parseInt((begin[1] + pos * (end[1] - begin[1])), 10) + ','\n        + parseInt((begin[2] + pos * (end[2] - begin[2])), 10);\n\n    color += ',' + (begin && end ? parseFloat(begin[3] + pos * (end[3] - begin[3])) : 1);\n    color += ')';\n    return color;\n  }\n\n  /**\n   * Changes the color from one to another within certain period of time, invoking callbacks as value is being changed.\n   * @memberOf fabric.util\n   * @param {String} fromColor The starting color in hex or rgb(a) format.\n   * @param {String} toColor The starting color in hex or rgb(a) format.\n   * @param {Number} [duration] Duration of change (in ms).\n   * @param {Object} [options] Animation options\n   * @param {Function} [options.onChange] Callback; invoked on every value change\n   * @param {Function} [options.onComplete] Callback; invoked when value change is completed\n   * @param {Function} [options.colorEasing] Easing function. Note that this function only take two arguments (currentTime, duration). Thus the regular animation easing functions cannot be used.\n   */\n  function animateColor(fromColor, toColor, duration, options) {\n    var startColor = new fabric.Color(fromColor).getSource(),\n        endColor = new fabric.Color(toColor).getSource();\n\n    options = options || {};\n\n    fabric.util.animate(fabric.util.object.extend(options, {\n      duration: duration || 500,\n      startValue: startColor,\n      endValue: endColor,\n      byValue: endColor,\n      easing: function (currentTime, startValue, byValue, duration) {\n        var posValue = options.colorEasing\n          ? options.colorEasing(currentTime, duration)\n          : 1 - Math.cos(currentTime / duration * (Math.PI / 2));\n        return calculateColor(startValue, byValue, posValue);\n      }\n    }));\n  }\n\n  fabric.util.animateColor = animateColor;\n\n})();\n\n\n(function() {\n\n  function normalize(a, c, p, s) {\n    if (a < Math.abs(c)) {\n      a = c;\n      s = p / 4;\n    }\n    else {\n      //handle the 0/0 case:\n      if (c === 0 && a === 0) {\n        s = p / (2 * Math.PI) * Math.asin(1);\n      }\n      else {\n        s = p / (2 * Math.PI) * Math.asin(c / a);\n      }\n    }\n    return { a: a, c: c, p: p, s: s };\n  }\n\n  function elastic(opts, t, d) {\n    return opts.a *\n      Math.pow(2, 10 * (t -= 1)) *\n      Math.sin( (t * d - opts.s) * (2 * Math.PI) / opts.p );\n  }\n\n  /**\n   * Cubic easing out\n   * @memberOf fabric.util.ease\n   */\n  function easeOutCubic(t, b, c, d) {\n    return c * ((t = t / d - 1) * t * t + 1) + b;\n  }\n\n  /**\n   * Cubic easing in and out\n   * @memberOf fabric.util.ease\n   */\n  function easeInOutCubic(t, b, c, d) {\n    t /= d / 2;\n    if (t < 1) {\n      return c / 2 * t * t * t + b;\n    }\n    return c / 2 * ((t -= 2) * t * t + 2) + b;\n  }\n\n  /**\n   * Quartic easing in\n   * @memberOf fabric.util.ease\n   */\n  function easeInQuart(t, b, c, d) {\n    return c * (t /= d) * t * t * t + b;\n  }\n\n  /**\n   * Quartic easing out\n   * @memberOf fabric.util.ease\n   */\n  function easeOutQuart(t, b, c, d) {\n    return -c * ((t = t / d - 1) * t * t * t - 1) + b;\n  }\n\n  /**\n   * Quartic easing in and out\n   * @memberOf fabric.util.ease\n   */\n  function easeInOutQuart(t, b, c, d) {\n    t /= d / 2;\n    if (t < 1) {\n      return c / 2 * t * t * t * t + b;\n    }\n    return -c / 2 * ((t -= 2) * t * t * t - 2) + b;\n  }\n\n  /**\n   * Quintic easing in\n   * @memberOf fabric.util.ease\n   */\n  function easeInQuint(t, b, c, d) {\n    return c * (t /= d) * t * t * t * t + b;\n  }\n\n  /**\n   * Quintic easing out\n   * @memberOf fabric.util.ease\n   */\n  function easeOutQuint(t, b, c, d) {\n    return c * ((t = t / d - 1) * t * t * t * t + 1) + b;\n  }\n\n  /**\n   * Quintic easing in and out\n   * @memberOf fabric.util.ease\n   */\n  function easeInOutQuint(t, b, c, d) {\n    t /= d / 2;\n    if (t < 1) {\n      return c / 2 * t * t * t * t * t + b;\n    }\n    return c / 2 * ((t -= 2) * t * t * t * t + 2) + b;\n  }\n\n  /**\n   * Sinusoidal easing in\n   * @memberOf fabric.util.ease\n   */\n  function easeInSine(t, b, c, d) {\n    return -c * Math.cos(t / d * (Math.PI / 2)) + c + b;\n  }\n\n  /**\n   * Sinusoidal easing out\n   * @memberOf fabric.util.ease\n   */\n  function easeOutSine(t, b, c, d) {\n    return c * Math.sin(t / d * (Math.PI / 2)) + b;\n  }\n\n  /**\n   * Sinusoidal easing in and out\n   * @memberOf fabric.util.ease\n   */\n  function easeInOutSine(t, b, c, d) {\n    return -c / 2 * (Math.cos(Math.PI * t / d) - 1) + b;\n  }\n\n  /**\n   * Exponential easing in\n   * @memberOf fabric.util.ease\n   */\n  function easeInExpo(t, b, c, d) {\n    return (t === 0) ? b : c * Math.pow(2, 10 * (t / d - 1)) + b;\n  }\n\n  /**\n   * Exponential easing out\n   * @memberOf fabric.util.ease\n   */\n  function easeOutExpo(t, b, c, d) {\n    return (t === d) ? b + c : c * (-Math.pow(2, -10 * t / d) + 1) + b;\n  }\n\n  /**\n   * Exponential easing in and out\n   * @memberOf fabric.util.ease\n   */\n  function easeInOutExpo(t, b, c, d) {\n    if (t === 0) {\n      return b;\n    }\n    if (t === d) {\n      return b + c;\n    }\n    t /= d / 2;\n    if (t < 1) {\n      return c / 2 * Math.pow(2, 10 * (t - 1)) + b;\n    }\n    return c / 2 * (-Math.pow(2, -10 * --t) + 2) + b;\n  }\n\n  /**\n   * Circular easing in\n   * @memberOf fabric.util.ease\n   */\n  function easeInCirc(t, b, c, d) {\n    return -c * (Math.sqrt(1 - (t /= d) * t) - 1) + b;\n  }\n\n  /**\n   * Circular easing out\n   * @memberOf fabric.util.ease\n   */\n  function easeOutCirc(t, b, c, d) {\n    return c * Math.sqrt(1 - (t = t / d - 1) * t) + b;\n  }\n\n  /**\n   * Circular easing in and out\n   * @memberOf fabric.util.ease\n   */\n  function easeInOutCirc(t, b, c, d) {\n    t /= d / 2;\n    if (t < 1) {\n      return -c / 2 * (Math.sqrt(1 - t * t) - 1) + b;\n    }\n    return c / 2 * (Math.sqrt(1 - (t -= 2) * t) + 1) + b;\n  }\n\n  /**\n   * Elastic easing in\n   * @memberOf fabric.util.ease\n   */\n  function easeInElastic(t, b, c, d) {\n    var s = 1.70158, p = 0, a = c;\n    if (t === 0) {\n      return b;\n    }\n    t /= d;\n    if (t === 1) {\n      return b + c;\n    }\n    if (!p) {\n      p = d * 0.3;\n    }\n    var opts = normalize(a, c, p, s);\n    return -elastic(opts, t, d) + b;\n  }\n\n  /**\n   * Elastic easing out\n   * @memberOf fabric.util.ease\n   */\n  function easeOutElastic(t, b, c, d) {\n    var s = 1.70158, p = 0, a = c;\n    if (t === 0) {\n      return b;\n    }\n    t /= d;\n    if (t === 1) {\n      return b + c;\n    }\n    if (!p) {\n      p = d * 0.3;\n    }\n    var opts = normalize(a, c, p, s);\n    return opts.a * Math.pow(2, -10 * t) * Math.sin((t * d - opts.s) * (2 * Math.PI) / opts.p ) + opts.c + b;\n  }\n\n  /**\n   * Elastic easing in and out\n   * @memberOf fabric.util.ease\n   */\n  function easeInOutElastic(t, b, c, d) {\n    var s = 1.70158, p = 0, a = c;\n    if (t === 0) {\n      return b;\n    }\n    t /= d / 2;\n    if (t === 2) {\n      return b + c;\n    }\n    if (!p) {\n      p = d * (0.3 * 1.5);\n    }\n    var opts = normalize(a, c, p, s);\n    if (t < 1) {\n      return -0.5 * elastic(opts, t, d) + b;\n    }\n    return opts.a * Math.pow(2, -10 * (t -= 1)) *\n      Math.sin((t * d - opts.s) * (2 * Math.PI) / opts.p ) * 0.5 + opts.c + b;\n  }\n\n  /**\n   * Backwards easing in\n   * @memberOf fabric.util.ease\n   */\n  function easeInBack(t, b, c, d, s) {\n    if (s === undefined) {\n      s = 1.70158;\n    }\n    return c * (t /= d) * t * ((s + 1) * t - s) + b;\n  }\n\n  /**\n   * Backwards easing out\n   * @memberOf fabric.util.ease\n   */\n  function easeOutBack(t, b, c, d, s) {\n    if (s === undefined) {\n      s = 1.70158;\n    }\n    return c * ((t = t / d - 1) * t * ((s + 1) * t + s) + 1) + b;\n  }\n\n  /**\n   * Backwards easing in and out\n   * @memberOf fabric.util.ease\n   */\n  function easeInOutBack(t, b, c, d, s) {\n    if (s === undefined) {\n      s = 1.70158;\n    }\n    t /= d / 2;\n    if (t < 1) {\n      return c / 2 * (t * t * (((s *= (1.525)) + 1) * t - s)) + b;\n    }\n    return c / 2 * ((t -= 2) * t * (((s *= (1.525)) + 1) * t + s) + 2) + b;\n  }\n\n  /**\n   * Bouncing easing in\n   * @memberOf fabric.util.ease\n   */\n  function easeInBounce(t, b, c, d) {\n    return c - easeOutBounce (d - t, 0, c, d) + b;\n  }\n\n  /**\n   * Bouncing easing out\n   * @memberOf fabric.util.ease\n   */\n  function easeOutBounce(t, b, c, d) {\n    if ((t /= d) < (1 / 2.75)) {\n      return c * (7.5625 * t * t) + b;\n    }\n    else if (t < (2 / 2.75)) {\n      return c * (7.5625 * (t -= (1.5 / 2.75)) * t + 0.75) + b;\n    }\n    else if (t < (2.5 / 2.75)) {\n      return c * (7.5625 * (t -= (2.25 / 2.75)) * t + 0.9375) + b;\n    }\n    else {\n      return c * (7.5625 * (t -= (2.625 / 2.75)) * t + 0.984375) + b;\n    }\n  }\n\n  /**\n   * Bouncing easing in and out\n   * @memberOf fabric.util.ease\n   */\n  function easeInOutBounce(t, b, c, d) {\n    if (t < d / 2) {\n      return easeInBounce (t * 2, 0, c, d) * 0.5 + b;\n    }\n    return easeOutBounce(t * 2 - d, 0, c, d) * 0.5 + c * 0.5 + b;\n  }\n\n  /**\n   * Easing functions\n   * See <a href=\"http://gizma.com/easing/\">Easing Equations by Robert Penner</a>\n   * @namespace fabric.util.ease\n   */\n  fabric.util.ease = {\n\n    /**\n     * Quadratic easing in\n     * @memberOf fabric.util.ease\n     */\n    easeInQuad: function(t, b, c, d) {\n      return c * (t /= d) * t + b;\n    },\n\n    /**\n     * Quadratic easing out\n     * @memberOf fabric.util.ease\n     */\n    easeOutQuad: function(t, b, c, d) {\n      return -c * (t /= d) * (t - 2) + b;\n    },\n\n    /**\n     * Quadratic easing in and out\n     * @memberOf fabric.util.ease\n     */\n    easeInOutQuad: function(t, b, c, d) {\n      t /= (d / 2);\n      if (t < 1) {\n        return c / 2 * t * t + b;\n      }\n      return -c / 2 * ((--t) * (t - 2) - 1) + b;\n    },\n\n    /**\n     * Cubic easing in\n     * @memberOf fabric.util.ease\n     */\n    easeInCubic: function(t, b, c, d) {\n      return c * (t /= d) * t * t + b;\n    },\n\n    easeOutCubic: easeOutCubic,\n    easeInOutCubic: easeInOutCubic,\n    easeInQuart: easeInQuart,\n    easeOutQuart: easeOutQuart,\n    easeInOutQuart: easeInOutQuart,\n    easeInQuint: easeInQuint,\n    easeOutQuint: easeOutQuint,\n    easeInOutQuint: easeInOutQuint,\n    easeInSine: easeInSine,\n    easeOutSine: easeOutSine,\n    easeInOutSine: easeInOutSine,\n    easeInExpo: easeInExpo,\n    easeOutExpo: easeOutExpo,\n    easeInOutExpo: easeInOutExpo,\n    easeInCirc: easeInCirc,\n    easeOutCirc: easeOutCirc,\n    easeInOutCirc: easeInOutCirc,\n    easeInElastic: easeInElastic,\n    easeOutElastic: easeOutElastic,\n    easeInOutElastic: easeInOutElastic,\n    easeInBack: easeInBack,\n    easeOutBack: easeOutBack,\n    easeInOutBack: easeInOutBack,\n    easeInBounce: easeInBounce,\n    easeOutBounce: easeOutBounce,\n    easeInOutBounce: easeInOutBounce\n  };\n\n})();\n\n\n(function(global) {\n\n  'use strict';\n\n  /**\n   * @name fabric\n   * @namespace\n   */\n\n  var fabric = global.fabric || (global.fabric = { }),\n      extend = fabric.util.object.extend,\n      clone = fabric.util.object.clone,\n      toFixed = fabric.util.toFixed,\n      parseUnit = fabric.util.parseUnit,\n      multiplyTransformMatrices = fabric.util.multiplyTransformMatrices,\n\n      svgValidTagNames = ['path', 'circle', 'polygon', 'polyline', 'ellipse', 'rect', 'line',\n        'image', 'text', 'linearGradient', 'radialGradient', 'stop'],\n      svgViewBoxElements = ['symbol', 'image', 'marker', 'pattern', 'view', 'svg'],\n      svgInvalidAncestors = ['pattern', 'defs', 'symbol', 'metadata', 'clipPath', 'mask', 'desc'],\n      svgValidParents = ['symbol', 'g', 'a', 'svg'],\n\n      attributesMap = {\n        cx:                   'left',\n        x:                    'left',\n        r:                    'radius',\n        cy:                   'top',\n        y:                    'top',\n        display:              'visible',\n        visibility:           'visible',\n        transform:            'transformMatrix',\n        'fill-opacity':       'fillOpacity',\n        'fill-rule':          'fillRule',\n        'font-family':        'fontFamily',\n        'font-size':          'fontSize',\n        'font-style':         'fontStyle',\n        'font-weight':        'fontWeight',\n        'letter-spacing':     'charSpacing',\n        'paint-order':        'paintFirst',\n        'stroke-dasharray':   'strokeDashArray',\n        'stroke-linecap':     'strokeLineCap',\n        'stroke-linejoin':    'strokeLineJoin',\n        'stroke-miterlimit':  'strokeMiterLimit',\n        'stroke-opacity':     'strokeOpacity',\n        'stroke-width':       'strokeWidth',\n        'text-decoration':    'textDecoration',\n        'text-anchor':        'textAnchor',\n        opacity:              'opacity'\n      },\n\n      colorAttributes = {\n        stroke: 'strokeOpacity',\n        fill:   'fillOpacity'\n      };\n\n  fabric.svgValidTagNamesRegEx = getSvgRegex(svgValidTagNames);\n  fabric.svgViewBoxElementsRegEx = getSvgRegex(svgViewBoxElements);\n  fabric.svgInvalidAncestorsRegEx = getSvgRegex(svgInvalidAncestors);\n  fabric.svgValidParentsRegEx = getSvgRegex(svgValidParents);\n\n  fabric.cssRules = { };\n  fabric.gradientDefs = { };\n\n  function normalizeAttr(attr) {\n    // transform attribute names\n    if (attr in attributesMap) {\n      return attributesMap[attr];\n    }\n    return attr;\n  }\n\n  function normalizeValue(attr, value, parentAttributes, fontSize) {\n    var isArray = Object.prototype.toString.call(value) === '[object Array]',\n        parsed;\n\n    if ((attr === 'fill' || attr === 'stroke') && value === 'none') {\n      value = '';\n    }\n    else if (attr === 'strokeDashArray') {\n      if (value === 'none') {\n        value = null;\n      }\n      else {\n        value = value.replace(/,/g, ' ').split(/\\s+/).map(function(n) {\n          return parseFloat(n);\n        });\n      }\n    }\n    else if (attr === 'transformMatrix') {\n      if (parentAttributes && parentAttributes.transformMatrix) {\n        value = multiplyTransformMatrices(\n          parentAttributes.transformMatrix, fabric.parseTransformAttribute(value));\n      }\n      else {\n        value = fabric.parseTransformAttribute(value);\n      }\n    }\n    else if (attr === 'visible') {\n      value = value !== 'none' && value !== 'hidden';\n      // display=none on parent element always takes precedence over child element\n      if (parentAttributes && parentAttributes.visible === false) {\n        value = false;\n      }\n    }\n    else if (attr === 'opacity') {\n      value = parseFloat(value);\n      if (parentAttributes && typeof parentAttributes.opacity !== 'undefined') {\n        value *= parentAttributes.opacity;\n      }\n    }\n    else if (attr === 'textAnchor' /* text-anchor */) {\n      value = value === 'start' ? 'left' : value === 'end' ? 'right' : 'center';\n    }\n    else if (attr === 'charSpacing') {\n      // parseUnit returns px and we convert it to em\n      parsed = parseUnit(value, fontSize) / fontSize * 1000;\n    }\n    else if (attr === 'paintFirst') {\n      var fillIndex = value.indexOf('fill');\n      var strokeIndex = value.indexOf('stroke');\n      var value = 'fill';\n      if (fillIndex > -1 && strokeIndex > -1 && strokeIndex < fillIndex) {\n        value = 'stroke';\n      }\n      else if (fillIndex === -1 && strokeIndex > -1) {\n        value = 'stroke';\n      }\n    }\n    else {\n      parsed = isArray ? value.map(parseUnit) : parseUnit(value, fontSize);\n    }\n\n    return (!isArray && isNaN(parsed) ? value : parsed);\n  }\n\n  /**\n    * @private\n    */\n  function getSvgRegex(arr) {\n    return new RegExp('^(' + arr.join('|') + ')\\\\b', 'i');\n  }\n\n  /**\n   * @private\n   * @param {Object} attributes Array of attributes to parse\n   */\n  function _setStrokeFillOpacity(attributes) {\n    for (var attr in colorAttributes) {\n\n      if (typeof attributes[colorAttributes[attr]] === 'undefined' || attributes[attr] === '') {\n        continue;\n      }\n\n      if (typeof attributes[attr] === 'undefined') {\n        if (!fabric.Object.prototype[attr]) {\n          continue;\n        }\n        attributes[attr] = fabric.Object.prototype[attr];\n      }\n\n      if (attributes[attr].indexOf('url(') === 0) {\n        continue;\n      }\n\n      var color = new fabric.Color(attributes[attr]);\n      attributes[attr] = color.setAlpha(toFixed(color.getAlpha() * attributes[colorAttributes[attr]], 2)).toRgba();\n    }\n    return attributes;\n  }\n\n  /**\n   * @private\n   */\n  function _getMultipleNodes(doc, nodeNames) {\n    var nodeName, nodeArray = [], nodeList, i, len;\n    for (i = 0, len = nodeNames.length; i < len; i++) {\n      nodeName = nodeNames[i];\n      nodeList = doc.getElementsByTagName(nodeName);\n      nodeArray = nodeArray.concat(Array.prototype.slice.call(nodeList));\n    }\n    return nodeArray;\n  }\n\n  /**\n   * Parses \"transform\" attribute, returning an array of values\n   * @static\n   * @function\n   * @memberOf fabric\n   * @param {String} attributeValue String containing attribute value\n   * @return {Array} Array of 6 elements representing transformation matrix\n   */\n  fabric.parseTransformAttribute = (function() {\n    function rotateMatrix(matrix, args) {\n      var cos = fabric.util.cos(args[0]), sin = fabric.util.sin(args[0]),\n          x = 0, y = 0;\n      if (args.length === 3) {\n        x = args[1];\n        y = args[2];\n      }\n\n      matrix[0] = cos;\n      matrix[1] = sin;\n      matrix[2] = -sin;\n      matrix[3] = cos;\n      matrix[4] = x - (cos * x - sin * y);\n      matrix[5] = y - (sin * x + cos * y);\n    }\n\n    function scaleMatrix(matrix, args) {\n      var multiplierX = args[0],\n          multiplierY = (args.length === 2) ? args[1] : args[0];\n\n      matrix[0] = multiplierX;\n      matrix[3] = multiplierY;\n    }\n\n    function skewMatrix(matrix, args, pos) {\n      matrix[pos] = Math.tan(fabric.util.degreesToRadians(args[0]));\n    }\n\n    function translateMatrix(matrix, args) {\n      matrix[4] = args[0];\n      if (args.length === 2) {\n        matrix[5] = args[1];\n      }\n    }\n\n    // identity matrix\n    var iMatrix = [\n          1, // a\n          0, // b\n          0, // c\n          1, // d\n          0, // e\n          0  // f\n        ],\n\n        // == begin transform regexp\n        number = fabric.reNum,\n\n        commaWsp = '(?:\\\\s+,?\\\\s*|,\\\\s*)',\n\n        skewX = '(?:(skewX)\\\\s*\\\\(\\\\s*(' + number + ')\\\\s*\\\\))',\n\n        skewY = '(?:(skewY)\\\\s*\\\\(\\\\s*(' + number + ')\\\\s*\\\\))',\n\n        rotate = '(?:(rotate)\\\\s*\\\\(\\\\s*(' + number + ')(?:' +\n                    commaWsp + '(' + number + ')' +\n                    commaWsp + '(' + number + '))?\\\\s*\\\\))',\n\n        scale = '(?:(scale)\\\\s*\\\\(\\\\s*(' + number + ')(?:' +\n                    commaWsp + '(' + number + '))?\\\\s*\\\\))',\n\n        translate = '(?:(translate)\\\\s*\\\\(\\\\s*(' + number + ')(?:' +\n                    commaWsp + '(' + number + '))?\\\\s*\\\\))',\n\n        matrix = '(?:(matrix)\\\\s*\\\\(\\\\s*' +\n                  '(' + number + ')' + commaWsp +\n                  '(' + number + ')' + commaWsp +\n                  '(' + number + ')' + commaWsp +\n                  '(' + number + ')' + commaWsp +\n                  '(' + number + ')' + commaWsp +\n                  '(' + number + ')' +\n                  '\\\\s*\\\\))',\n\n        transform = '(?:' +\n                    matrix + '|' +\n                    translate + '|' +\n                    scale + '|' +\n                    rotate + '|' +\n                    skewX + '|' +\n                    skewY +\n                    ')',\n\n        transforms = '(?:' + transform + '(?:' + commaWsp + '*' + transform + ')*' + ')',\n\n        transformList = '^\\\\s*(?:' + transforms + '?)\\\\s*$',\n\n        // http://www.w3.org/TR/SVG/coords.html#TransformAttribute\n        reTransformList = new RegExp(transformList),\n        // == end transform regexp\n\n        reTransform = new RegExp(transform, 'g');\n\n    return function(attributeValue) {\n\n      // start with identity matrix\n      var matrix = iMatrix.concat(),\n          matrices = [];\n\n      // return if no argument was given or\n      // an argument does not match transform attribute regexp\n      if (!attributeValue || (attributeValue && !reTransformList.test(attributeValue))) {\n        return matrix;\n      }\n\n      attributeValue.replace(reTransform, function(match) {\n\n        var m = new RegExp(transform).exec(match).filter(function (match) {\n              // match !== '' && match != null\n              return (!!match);\n            }),\n            operation = m[1],\n            args = m.slice(2).map(parseFloat);\n\n        switch (operation) {\n          case 'translate':\n            translateMatrix(matrix, args);\n            break;\n          case 'rotate':\n            args[0] = fabric.util.degreesToRadians(args[0]);\n            rotateMatrix(matrix, args);\n            break;\n          case 'scale':\n            scaleMatrix(matrix, args);\n            break;\n          case 'skewX':\n            skewMatrix(matrix, args, 2);\n            break;\n          case 'skewY':\n            skewMatrix(matrix, args, 1);\n            break;\n          case 'matrix':\n            matrix = args;\n            break;\n        }\n\n        // snapshot current matrix into matrices array\n        matrices.push(matrix.concat());\n        // reset\n        matrix = iMatrix.concat();\n      });\n\n      var combinedMatrix = matrices[0];\n      while (matrices.length > 1) {\n        matrices.shift();\n        combinedMatrix = fabric.util.multiplyTransformMatrices(combinedMatrix, matrices[0]);\n      }\n      return combinedMatrix;\n    };\n  })();\n\n  /**\n   * @private\n   */\n  function parseStyleString(style, oStyle) {\n    var attr, value;\n    style.replace(/;\\s*$/, '').split(';').forEach(function (chunk) {\n      var pair = chunk.split(':');\n\n      attr = pair[0].trim().toLowerCase();\n      value =  pair[1].trim();\n\n      oStyle[attr] = value;\n    });\n  }\n\n  /**\n   * @private\n   */\n  function parseStyleObject(style, oStyle) {\n    var attr, value;\n    for (var prop in style) {\n      if (typeof style[prop] === 'undefined') {\n        continue;\n      }\n\n      attr = prop.toLowerCase();\n      value = style[prop];\n\n      oStyle[attr] = value;\n    }\n  }\n\n  /**\n   * @private\n   */\n  function getGlobalStylesForElement(element, svgUid) {\n    var styles = { };\n    for (var rule in fabric.cssRules[svgUid]) {\n      if (elementMatchesRule(element, rule.split(' '))) {\n        for (var property in fabric.cssRules[svgUid][rule]) {\n          styles[property] = fabric.cssRules[svgUid][rule][property];\n        }\n      }\n    }\n    return styles;\n  }\n\n  /**\n   * @private\n   */\n  function elementMatchesRule(element, selectors) {\n    var firstMatching, parentMatching = true;\n    //start from rightmost selector.\n    firstMatching = selectorMatches(element, selectors.pop());\n    if (firstMatching && selectors.length) {\n      parentMatching = doesSomeParentMatch(element, selectors);\n    }\n    return firstMatching && parentMatching && (selectors.length === 0);\n  }\n\n  function doesSomeParentMatch(element, selectors) {\n    var selector, parentMatching = true;\n    while (element.parentNode && element.parentNode.nodeType === 1 && selectors.length) {\n      if (parentMatching) {\n        selector = selectors.pop();\n      }\n      element = element.parentNode;\n      parentMatching = selectorMatches(element, selector);\n    }\n    return selectors.length === 0;\n  }\n\n  /**\n   * @private\n   */\n  function selectorMatches(element, selector) {\n    var nodeName = element.nodeName,\n        classNames = element.getAttribute('class'),\n        id = element.getAttribute('id'), matcher, i;\n    // i check if a selector matches slicing away part from it.\n    // if i get empty string i should match\n    matcher = new RegExp('^' + nodeName, 'i');\n    selector = selector.replace(matcher, '');\n    if (id && selector.length) {\n      matcher = new RegExp('#' + id + '(?![a-zA-Z\\\\-]+)', 'i');\n      selector = selector.replace(matcher, '');\n    }\n    if (classNames && selector.length) {\n      classNames = classNames.split(' ');\n      for (i = classNames.length; i--;) {\n        matcher = new RegExp('\\\\.' + classNames[i] + '(?![a-zA-Z\\\\-]+)', 'i');\n        selector = selector.replace(matcher, '');\n      }\n    }\n    return selector.length === 0;\n  }\n\n  /**\n   * @private\n   * to support IE8 missing getElementById on SVGdocument\n   */\n  function elementById(doc, id) {\n    var el;\n    doc.getElementById && (el = doc.getElementById(id));\n    if (el) {\n      return el;\n    }\n    var node, i, len, nodelist = doc.getElementsByTagName('*');\n    for (i = 0, len = nodelist.length; i < len; i++) {\n      node = nodelist[i];\n      if (id === node.getAttribute('id')) {\n        return node;\n      }\n    }\n  }\n\n  /**\n   * @private\n   */\n  function parseUseDirectives(doc) {\n    var nodelist = _getMultipleNodes(doc, ['use', 'svg:use']), i = 0;\n\n    while (nodelist.length && i < nodelist.length) {\n      var el = nodelist[i],\n          xlink = el.getAttribute('xlink:href').substr(1),\n          x = el.getAttribute('x') || 0,\n          y = el.getAttribute('y') || 0,\n          el2 = elementById(doc, xlink).cloneNode(true),\n          currentTrans = (el2.getAttribute('transform') || '') + ' translate(' + x + ', ' + y + ')',\n          parentNode, oldLength = nodelist.length, attr, j, attrs, len;\n\n      applyViewboxTransform(el2);\n      if (/^svg$/i.test(el2.nodeName)) {\n        var el3 = el2.ownerDocument.createElement('g');\n        for (j = 0, attrs = el2.attributes, len = attrs.length; j < len; j++) {\n          attr = attrs.item(j);\n          el3.setAttribute(attr.nodeName, attr.nodeValue);\n        }\n        // el2.firstChild != null\n        while (el2.firstChild) {\n          el3.appendChild(el2.firstChild);\n        }\n        el2 = el3;\n      }\n\n      for (j = 0, attrs = el.attributes, len = attrs.length; j < len; j++) {\n        attr = attrs.item(j);\n        if (attr.nodeName === 'x' || attr.nodeName === 'y' || attr.nodeName === 'xlink:href') {\n          continue;\n        }\n\n        if (attr.nodeName === 'transform') {\n          currentTrans = attr.nodeValue + ' ' + currentTrans;\n        }\n        else {\n          el2.setAttribute(attr.nodeName, attr.nodeValue);\n        }\n      }\n\n      el2.setAttribute('transform', currentTrans);\n      el2.setAttribute('instantiated_by_use', '1');\n      el2.removeAttribute('id');\n      parentNode = el.parentNode;\n      parentNode.replaceChild(el2, el);\n      // some browsers do not shorten nodelist after replaceChild (IE8)\n      if (nodelist.length === oldLength) {\n        i++;\n      }\n    }\n  }\n\n  // http://www.w3.org/TR/SVG/coords.html#ViewBoxAttribute\n  // matches, e.g.: +14.56e-12, etc.\n  var reViewBoxAttrValue = new RegExp(\n    '^' +\n    '\\\\s*(' + fabric.reNum + '+)\\\\s*,?' +\n    '\\\\s*(' + fabric.reNum + '+)\\\\s*,?' +\n    '\\\\s*(' + fabric.reNum + '+)\\\\s*,?' +\n    '\\\\s*(' + fabric.reNum + '+)\\\\s*' +\n    '$'\n  );\n\n  /**\n   * Add a <g> element that envelop all child elements and makes the viewbox transformMatrix descend on all elements\n   */\n  function applyViewboxTransform(element) {\n\n    var viewBoxAttr = element.getAttribute('viewBox'),\n        scaleX = 1,\n        scaleY = 1,\n        minX = 0,\n        minY = 0,\n        viewBoxWidth, viewBoxHeight, matrix, el,\n        widthAttr = element.getAttribute('width'),\n        heightAttr = element.getAttribute('height'),\n        x = element.getAttribute('x') || 0,\n        y = element.getAttribute('y') || 0,\n        preserveAspectRatio = element.getAttribute('preserveAspectRatio') || '',\n        missingViewBox = (!viewBoxAttr || !fabric.svgViewBoxElementsRegEx.test(element.nodeName)\n                           || !(viewBoxAttr = viewBoxAttr.match(reViewBoxAttrValue))),\n        missingDimAttr = (!widthAttr || !heightAttr || widthAttr === '100%' || heightAttr === '100%'),\n        toBeParsed = missingViewBox && missingDimAttr,\n        parsedDim = { }, translateMatrix = '', widthDiff = 0, heightDiff = 0;\n\n    parsedDim.width = 0;\n    parsedDim.height = 0;\n    parsedDim.toBeParsed = toBeParsed;\n\n    if (toBeParsed) {\n      return parsedDim;\n    }\n\n    if (missingViewBox) {\n      parsedDim.width = parseUnit(widthAttr);\n      parsedDim.height = parseUnit(heightAttr);\n      return parsedDim;\n    }\n\n    minX = -parseFloat(viewBoxAttr[1]);\n    minY = -parseFloat(viewBoxAttr[2]);\n    viewBoxWidth = parseFloat(viewBoxAttr[3]);\n    viewBoxHeight = parseFloat(viewBoxAttr[4]);\n\n    if (!missingDimAttr) {\n      parsedDim.width = parseUnit(widthAttr);\n      parsedDim.height = parseUnit(heightAttr);\n      scaleX = parsedDim.width / viewBoxWidth;\n      scaleY = parsedDim.height / viewBoxHeight;\n    }\n    else {\n      parsedDim.width = viewBoxWidth;\n      parsedDim.height = viewBoxHeight;\n    }\n\n    // default is to preserve aspect ratio\n    preserveAspectRatio = fabric.util.parsePreserveAspectRatioAttribute(preserveAspectRatio);\n    if (preserveAspectRatio.alignX !== 'none') {\n      //translate all container for the effect of Mid, Min, Max\n      if (preserveAspectRatio.meetOrSlice === 'meet') {\n        scaleY = scaleX = (scaleX > scaleY ? scaleY : scaleX);\n        // calculate additional translation to move the viewbox\n      }\n      if (preserveAspectRatio.meetOrSlice === 'slice') {\n        scaleY = scaleX = (scaleX > scaleY ? scaleX : scaleY);\n        // calculate additional translation to move the viewbox\n      }\n      widthDiff = parsedDim.width - viewBoxWidth * scaleX;\n      heightDiff = parsedDim.height - viewBoxHeight * scaleX;\n      if (preserveAspectRatio.alignX === 'Mid') {\n        widthDiff /= 2;\n      }\n      if (preserveAspectRatio.alignY === 'Mid') {\n        heightDiff /= 2;\n      }\n      if (preserveAspectRatio.alignX === 'Min') {\n        widthDiff = 0;\n      }\n      if (preserveAspectRatio.alignY === 'Min') {\n        heightDiff = 0;\n      }\n    }\n\n    if (scaleX === 1 && scaleY === 1 && minX === 0 && minY === 0 && x === 0 && y === 0) {\n      return parsedDim;\n    }\n\n    if (x || y) {\n      translateMatrix = ' translate(' + parseUnit(x) + ' ' + parseUnit(y) + ') ';\n    }\n\n    matrix = translateMatrix + ' matrix(' + scaleX +\n                  ' 0' +\n                  ' 0 ' +\n                  scaleY + ' ' +\n                  (minX * scaleX + widthDiff) + ' ' +\n                  (minY * scaleY + heightDiff) + ') ';\n\n    if (element.nodeName === 'svg') {\n      el = element.ownerDocument.createElement('g');\n      // element.firstChild != null\n      while (element.firstChild) {\n        el.appendChild(element.firstChild);\n      }\n      element.appendChild(el);\n    }\n    else {\n      el = element;\n      matrix = el.getAttribute('transform') + matrix;\n    }\n\n    el.setAttribute('transform', matrix);\n    return parsedDim;\n  }\n\n  function hasAncestorWithNodeName(element, nodeName) {\n    while (element && (element = element.parentNode)) {\n      if (element.nodeName && nodeName.test(element.nodeName.replace('svg:', ''))\n        && !element.getAttribute('instantiated_by_use')) {\n        return true;\n      }\n    }\n    return false;\n  }\n\n  /**\n   * Parses an SVG document, converts it to an array of corresponding fabric.* instances and passes them to a callback\n   * @static\n   * @function\n   * @memberOf fabric\n   * @param {SVGDocument} doc SVG document to parse\n   * @param {Function} callback Callback to call when parsing is finished;\n   * It's being passed an array of elements (parsed from a document).\n   * @param {Function} [reviver] Method for further parsing of SVG elements, called after each fabric object created.\n   * @param {Object} [parsingOptions] options for parsing document\n   * @param {String} [parsingOptions.crossOrigin] crossOrigin settings\n   */\n  fabric.parseSVGDocument = function(doc, callback, reviver, parsingOptions) {\n    if (!doc) {\n      return;\n    }\n\n    parseUseDirectives(doc);\n\n    var svgUid =  fabric.Object.__uid++, i, len,\n        options = applyViewboxTransform(doc),\n        descendants = fabric.util.toArray(doc.getElementsByTagName('*'));\n    options.crossOrigin = parsingOptions && parsingOptions.crossOrigin;\n    options.svgUid = svgUid;\n\n    if (descendants.length === 0 && fabric.isLikelyNode) {\n      // we're likely in node, where \"o3-xml\" library fails to gEBTN(\"*\")\n      // https://github.com/ajaxorg/node-o3-xml/issues/21\n      descendants = doc.selectNodes('//*[name(.)!=\"svg\"]');\n      var arr = [];\n      for (i = 0, len = descendants.length; i < len; i++) {\n        arr[i] = descendants[i];\n      }\n      descendants = arr;\n    }\n\n    var elements = descendants.filter(function(el) {\n      applyViewboxTransform(el);\n      return fabric.svgValidTagNamesRegEx.test(el.nodeName.replace('svg:', '')) &&\n            !hasAncestorWithNodeName(el, fabric.svgInvalidAncestorsRegEx); // http://www.w3.org/TR/SVG/struct.html#DefsElement\n    });\n\n    if (!elements || (elements && !elements.length)) {\n      callback && callback([], {});\n      return;\n    }\n\n    fabric.gradientDefs[svgUid] = fabric.getGradientDefs(doc);\n    fabric.cssRules[svgUid] = fabric.getCSSRules(doc);\n    // Precedence of rules:   style > class > attribute\n    fabric.parseElements(elements, function(instances, elements) {\n      if (callback) {\n        callback(instances, options, elements, descendants);\n      }\n    }, clone(options), reviver, parsingOptions);\n  };\n\n  var reFontDeclaration = new RegExp(\n    '(normal|italic)?\\\\s*(normal|small-caps)?\\\\s*' +\n    '(normal|bold|bolder|lighter|100|200|300|400|500|600|700|800|900)?\\\\s*(' +\n      fabric.reNum +\n    '(?:px|cm|mm|em|pt|pc|in)*)(?:\\\\/(normal|' + fabric.reNum + '))?\\\\s+(.*)');\n\n  extend(fabric, {\n    /**\n     * Parses a short font declaration, building adding its properties to a style object\n     * @static\n     * @function\n     * @memberOf fabric\n     * @param {String} value font declaration\n     * @param {Object} oStyle definition\n     */\n    parseFontDeclaration: function(value, oStyle) {\n      var match = value.match(reFontDeclaration);\n\n      if (!match) {\n        return;\n      }\n      var fontStyle = match[1],\n          // font variant is not used\n          // fontVariant = match[2],\n          fontWeight = match[3],\n          fontSize = match[4],\n          lineHeight = match[5],\n          fontFamily = match[6];\n\n      if (fontStyle) {\n        oStyle.fontStyle = fontStyle;\n      }\n      if (fontWeight) {\n        oStyle.fontWeight = isNaN(parseFloat(fontWeight)) ? fontWeight : parseFloat(fontWeight);\n      }\n      if (fontSize) {\n        oStyle.fontSize = parseUnit(fontSize);\n      }\n      if (fontFamily) {\n        oStyle.fontFamily = fontFamily;\n      }\n      if (lineHeight) {\n        oStyle.lineHeight = lineHeight === 'normal' ? 1 : lineHeight;\n      }\n    },\n\n    /**\n     * Parses an SVG document, returning all of the gradient declarations found in it\n     * @static\n     * @function\n     * @memberOf fabric\n     * @param {SVGDocument} doc SVG document to parse\n     * @return {Object} Gradient definitions; key corresponds to element id, value -- to gradient definition element\n     */\n    getGradientDefs: function(doc) {\n      var tagArray = [\n            'linearGradient',\n            'radialGradient',\n            'svg:linearGradient',\n            'svg:radialGradient'],\n          elList = _getMultipleNodes(doc, tagArray),\n          el, j = 0, id, xlink,\n          gradientDefs = { }, idsToXlinkMap = { };\n      j = elList.length;\n\n      while (j--) {\n        el = elList[j];\n        xlink = el.getAttribute('xlink:href');\n        id = el.getAttribute('id');\n        if (xlink) {\n          idsToXlinkMap[id] = xlink.substr(1);\n        }\n        gradientDefs[id] = el;\n      }\n\n      for (id in idsToXlinkMap) {\n        var el2 = gradientDefs[idsToXlinkMap[id]].cloneNode(true);\n        el = gradientDefs[id];\n        while (el2.firstChild) {\n          el.appendChild(el2.firstChild);\n        }\n      }\n      return gradientDefs;\n    },\n\n    /**\n     * Returns an object of attributes' name/value, given element and an array of attribute names;\n     * Parses parent \"g\" nodes recursively upwards.\n     * @static\n     * @memberOf fabric\n     * @param {DOMElement} element Element to parse\n     * @param {Array} attributes Array of attributes to parse\n     * @return {Object} object containing parsed attributes' names/values\n     */\n    parseAttributes: function(element, attributes, svgUid) {\n\n      if (!element) {\n        return;\n      }\n\n      var value,\n          parentAttributes = { },\n          fontSize;\n\n      if (typeof svgUid === 'undefined') {\n        svgUid = element.getAttribute('svgUid');\n      }\n      // if there's a parent container (`g` or `a` or `symbol` node), parse its attributes recursively upwards\n      if (element.parentNode && fabric.svgValidParentsRegEx.test(element.parentNode.nodeName)) {\n        parentAttributes = fabric.parseAttributes(element.parentNode, attributes, svgUid);\n      }\n      fontSize = (parentAttributes && parentAttributes.fontSize ) ||\n                 element.getAttribute('font-size') || fabric.Text.DEFAULT_SVG_FONT_SIZE;\n\n      var ownAttributes = attributes.reduce(function(memo, attr) {\n        value = element.getAttribute(attr);\n        if (value) { // eslint-disable-line\n          memo[attr] = value;\n        }\n        return memo;\n      }, { });\n      // add values parsed from style, which take precedence over attributes\n      // (see: http://www.w3.org/TR/SVG/styling.html#UsingPresentationAttributes)\n      ownAttributes = extend(ownAttributes,\n        extend(getGlobalStylesForElement(element, svgUid), fabric.parseStyleAttribute(element)));\n\n      var normalizedAttr, normalizedValue, normalizedStyle = {};\n      for (var attr in ownAttributes) {\n        normalizedAttr = normalizeAttr(attr);\n        normalizedValue = normalizeValue(normalizedAttr, ownAttributes[attr], parentAttributes, fontSize);\n        normalizedStyle[normalizedAttr] = normalizedValue;\n      }\n      if (normalizedStyle && normalizedStyle.font) {\n        fabric.parseFontDeclaration(normalizedStyle.font, normalizedStyle);\n      }\n      var mergedAttrs = extend(parentAttributes, normalizedStyle);\n      return fabric.svgValidParentsRegEx.test(element.nodeName) ? mergedAttrs : _setStrokeFillOpacity(mergedAttrs);\n    },\n\n    /**\n     * Transforms an array of svg elements to corresponding fabric.* instances\n     * @static\n     * @memberOf fabric\n     * @param {Array} elements Array of elements to parse\n     * @param {Function} callback Being passed an array of fabric instances (transformed from SVG elements)\n     * @param {Object} [options] Options object\n     * @param {Function} [reviver] Method for further parsing of SVG elements, called after each fabric object created.\n     */\n    parseElements: function(elements, callback, options, reviver, parsingOptions) {\n      new fabric.ElementsParser(elements, callback, options, reviver, parsingOptions).parse();\n    },\n\n    /**\n     * Parses \"style\" attribute, retuning an object with values\n     * @static\n     * @memberOf fabric\n     * @param {SVGElement} element Element to parse\n     * @return {Object} Objects with values parsed from style attribute of an element\n     */\n    parseStyleAttribute: function(element) {\n      var oStyle = { },\n          style = element.getAttribute('style');\n\n      if (!style) {\n        return oStyle;\n      }\n\n      if (typeof style === 'string') {\n        parseStyleString(style, oStyle);\n      }\n      else {\n        parseStyleObject(style, oStyle);\n      }\n\n      return oStyle;\n    },\n\n    /**\n     * Parses \"points\" attribute, returning an array of values\n     * @static\n     * @memberOf fabric\n     * @param {String} points points attribute string\n     * @return {Array} array of points\n     */\n    parsePointsAttribute: function(points) {\n\n      // points attribute is required and must not be empty\n      if (!points) {\n        return null;\n      }\n\n      // replace commas with whitespace and remove bookending whitespace\n      points = points.replace(/,/g, ' ').trim();\n\n      points = points.split(/\\s+/);\n      var parsedPoints = [], i, len;\n\n      for (i = 0, len = points.length; i < len; i += 2) {\n        parsedPoints.push({\n          x: parseFloat(points[i]),\n          y: parseFloat(points[i + 1])\n        });\n      }\n\n      // odd number of points is an error\n      // if (parsedPoints.length % 2 !== 0) {\n      //   return null;\n      // }\n\n      return parsedPoints;\n    },\n\n    /**\n     * Returns CSS rules for a given SVG document\n     * @static\n     * @function\n     * @memberOf fabric\n     * @param {SVGDocument} doc SVG document to parse\n     * @return {Object} CSS rules of this document\n     */\n    getCSSRules: function(doc) {\n      var styles = doc.getElementsByTagName('style'), i, len,\n          allRules = { }, rules;\n\n      // very crude parsing of style contents\n      for (i = 0, len = styles.length; i < len; i++) {\n        // IE9 doesn't support textContent, but provides text instead.\n        var styleContents = styles[i].textContent || styles[i].text;\n\n        // remove comments\n        styleContents = styleContents.replace(/\\/\\*[\\s\\S]*?\\*\\//g, '');\n        if (styleContents.trim() === '') {\n          continue;\n        }\n        rules = styleContents.match(/[^{]*\\{[\\s\\S]*?\\}/g);\n        rules = rules.map(function(rule) { return rule.trim(); });\n        // eslint-disable-next-line no-loop-func\n        rules.forEach(function(rule) {\n\n          var match = rule.match(/([\\s\\S]*?)\\s*\\{([^}]*)\\}/),\n              ruleObj = { }, declaration = match[2].trim(),\n              propertyValuePairs = declaration.replace(/;$/, '').split(/\\s*;\\s*/);\n\n          for (i = 0, len = propertyValuePairs.length; i < len; i++) {\n            var pair = propertyValuePairs[i].split(/\\s*:\\s*/),\n                property = pair[0],\n                value = pair[1];\n            ruleObj[property] = value;\n          }\n          rule = match[1];\n          rule.split(',').forEach(function(_rule) {\n            _rule = _rule.replace(/^svg/i, '').trim();\n            if (_rule === '') {\n              return;\n            }\n            if (allRules[_rule]) {\n              fabric.util.object.extend(allRules[_rule], ruleObj);\n            }\n            else {\n              allRules[_rule] = fabric.util.object.clone(ruleObj);\n            }\n          });\n        });\n      }\n      return allRules;\n    },\n\n    /**\n     * Takes url corresponding to an SVG document, and parses it into a set of fabric objects.\n     * Note that SVG is fetched via XMLHttpRequest, so it needs to conform to SOP (Same Origin Policy)\n     * @memberOf fabric\n     * @param {String} url\n     * @param {Function} callback\n     * @param {Function} [reviver] Method for further parsing of SVG elements, called after each fabric object created.\n     * @param {Object} [options] Object containing options for parsing\n     * @param {String} [options.crossOrigin] crossOrigin crossOrigin setting to use for external resources\n     */\n    loadSVGFromURL: function(url, callback, reviver, options) {\n\n      url = url.replace(/^\\n\\s*/, '').trim();\n      new fabric.util.request(url, {\n        method: 'get',\n        onComplete: onComplete\n      });\n\n      function onComplete(r) {\n\n        var xml = r.responseXML;\n        if (xml && !xml.documentElement && fabric.window.ActiveXObject && r.responseText) {\n          xml = new ActiveXObject('Microsoft.XMLDOM');\n          xml.async = 'false';\n          //IE chokes on DOCTYPE\n          xml.loadXML(r.responseText.replace(/<!DOCTYPE[\\s\\S]*?(\\[[\\s\\S]*\\])*?>/i, ''));\n        }\n        if (!xml || !xml.documentElement) {\n          callback && callback(null);\n        }\n\n        fabric.parseSVGDocument(xml.documentElement, function (results, _options, elements, allElements) {\n          callback && callback(results, _options, elements, allElements);\n        }, reviver, options);\n      }\n    },\n\n    /**\n     * Takes string corresponding to an SVG document, and parses it into a set of fabric objects\n     * @memberOf fabric\n     * @param {String} string\n     * @param {Function} callback\n     * @param {Function} [reviver] Method for further parsing of SVG elements, called after each fabric object created.\n     * @param {Object} [options] Object containing options for parsing\n     * @param {String} [options.crossOrigin] crossOrigin crossOrigin setting to use for external resources\n     */\n    loadSVGFromString: function(string, callback, reviver, options) {\n      string = string.trim();\n      var doc;\n      if (typeof DOMParser !== 'undefined') {\n        var parser = new DOMParser();\n        if (parser && parser.parseFromString) {\n          doc = parser.parseFromString(string, 'text/xml');\n        }\n      }\n      else if (fabric.window.ActiveXObject) {\n        doc = new ActiveXObject('Microsoft.XMLDOM');\n        doc.async = 'false';\n        // IE chokes on DOCTYPE\n        doc.loadXML(string.replace(/<!DOCTYPE[\\s\\S]*?(\\[[\\s\\S]*\\])*?>/i, ''));\n      }\n\n      fabric.parseSVGDocument(doc.documentElement, function (results, _options, elements, allElements) {\n        callback(results, _options, elements, allElements);\n      }, reviver, options);\n    }\n  });\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\nfabric.ElementsParser = function(elements, callback, options, reviver, parsingOptions) {\n  this.elements = elements;\n  this.callback = callback;\n  this.options = options;\n  this.reviver = reviver;\n  this.svgUid = (options && options.svgUid) || 0;\n  this.parsingOptions = parsingOptions;\n  this.regexUrl = /^url\\(['\"]?#([^'\"]+)['\"]?\\)/g;\n};\n\nfabric.ElementsParser.prototype.parse = function() {\n  this.instances = new Array(this.elements.length);\n  this.numElements = this.elements.length;\n\n  this.createObjects();\n};\n\nfabric.ElementsParser.prototype.createObjects = function() {\n  for (var i = 0, len = this.elements.length; i < len; i++) {\n    this.elements[i].setAttribute('svgUid', this.svgUid);\n    (function(_obj, i) {\n      setTimeout(function() {\n        _obj.createObject(_obj.elements[i], i);\n      }, 0);\n    })(this, i);\n  }\n};\n\nfabric.ElementsParser.prototype.createObject = function(el, index) {\n  var klass = fabric[fabric.util.string.capitalize(el.tagName.replace('svg:', ''))];\n  if (klass && klass.fromElement) {\n    try {\n      this._createObject(klass, el, index);\n    }\n    catch (err) {\n      fabric.log(err);\n    }\n  }\n  else {\n    this.checkIfDone();\n  }\n};\n\nfabric.ElementsParser.prototype._createObject = function(klass, el, index) {\n  klass.fromElement(el, this.createCallback(index, el), this.options);\n};\n\nfabric.ElementsParser.prototype.createCallback = function(index, el) {\n  var _this = this;\n  return function(obj) {\n    var _options;\n    _this.resolveGradient(obj, 'fill');\n    _this.resolveGradient(obj, 'stroke');\n    if (obj instanceof fabric.Image) {\n      _options = obj.parsePreserveAspectRatioAttribute(el);\n    }\n    obj._removeTransformMatrix(_options);\n    _this.reviver && _this.reviver(el, obj);\n    _this.instances[index] = obj;\n    _this.checkIfDone();\n  };\n};\n\nfabric.ElementsParser.prototype.resolveGradient = function(obj, property) {\n\n  var instanceFillValue = obj[property];\n  if (!(/^url\\(/).test(instanceFillValue)) {\n    return;\n  }\n  var gradientId = this.regexUrl.exec(instanceFillValue)[1];\n  this.regexUrl.lastIndex = 0;\n  if (fabric.gradientDefs[this.svgUid][gradientId]) {\n    obj.set(property,\n      fabric.Gradient.fromElement(fabric.gradientDefs[this.svgUid][gradientId], obj));\n  }\n};\n\nfabric.ElementsParser.prototype.checkIfDone = function() {\n  if (--this.numElements === 0) {\n    this.instances = this.instances.filter(function(el) {\n      // eslint-disable-next-line no-eq-null, eqeqeq\n      return el != null;\n    });\n    this.callback(this.instances, this.elements);\n  }\n};\n\n\n(function(global) {\n\n  'use strict';\n\n  /* Adaptation of work of Kevin Lindsey (kevin@kevlindev.com) */\n\n  var fabric = global.fabric || (global.fabric = { });\n\n  if (fabric.Point) {\n    fabric.warn('fabric.Point is already defined');\n    return;\n  }\n\n  fabric.Point = Point;\n\n  /**\n   * Point class\n   * @class fabric.Point\n   * @memberOf fabric\n   * @constructor\n   * @param {Number} x\n   * @param {Number} y\n   * @return {fabric.Point} thisArg\n   */\n  function Point(x, y) {\n    this.x = x;\n    this.y = y;\n  }\n\n  Point.prototype = /** @lends fabric.Point.prototype */ {\n\n    type: 'point',\n\n    constructor: Point,\n\n    /**\n     * Adds another point to this one and returns another one\n     * @param {fabric.Point} that\n     * @return {fabric.Point} new Point instance with added values\n     */\n    add: function (that) {\n      return new Point(this.x + that.x, this.y + that.y);\n    },\n\n    /**\n     * Adds another point to this one\n     * @param {fabric.Point} that\n     * @return {fabric.Point} thisArg\n     * @chainable\n     */\n    addEquals: function (that) {\n      this.x += that.x;\n      this.y += that.y;\n      return this;\n    },\n\n    /**\n     * Adds value to this point and returns a new one\n     * @param {Number} scalar\n     * @return {fabric.Point} new Point with added value\n     */\n    scalarAdd: function (scalar) {\n      return new Point(this.x + scalar, this.y + scalar);\n    },\n\n    /**\n     * Adds value to this point\n     * @param {Number} scalar\n     * @return {fabric.Point} thisArg\n     * @chainable\n     */\n    scalarAddEquals: function (scalar) {\n      this.x += scalar;\n      this.y += scalar;\n      return this;\n    },\n\n    /**\n     * Subtracts another point from this point and returns a new one\n     * @param {fabric.Point} that\n     * @return {fabric.Point} new Point object with subtracted values\n     */\n    subtract: function (that) {\n      return new Point(this.x - that.x, this.y - that.y);\n    },\n\n    /**\n     * Subtracts another point from this point\n     * @param {fabric.Point} that\n     * @return {fabric.Point} thisArg\n     * @chainable\n     */\n    subtractEquals: function (that) {\n      this.x -= that.x;\n      this.y -= that.y;\n      return this;\n    },\n\n    /**\n     * Subtracts value from this point and returns a new one\n     * @param {Number} scalar\n     * @return {fabric.Point}\n     */\n    scalarSubtract: function (scalar) {\n      return new Point(this.x - scalar, this.y - scalar);\n    },\n\n    /**\n     * Subtracts value from this point\n     * @param {Number} scalar\n     * @return {fabric.Point} thisArg\n     * @chainable\n     */\n    scalarSubtractEquals: function (scalar) {\n      this.x -= scalar;\n      this.y -= scalar;\n      return this;\n    },\n\n    /**\n     * Multiplies this point by a value and returns a new one\n     * TODO: rename in scalarMultiply in 2.0\n     * @param {Number} scalar\n     * @return {fabric.Point}\n     */\n    multiply: function (scalar) {\n      return new Point(this.x * scalar, this.y * scalar);\n    },\n\n    /**\n     * Multiplies this point by a value\n     * TODO: rename in scalarMultiplyEquals in 2.0\n     * @param {Number} scalar\n     * @return {fabric.Point} thisArg\n     * @chainable\n     */\n    multiplyEquals: function (scalar) {\n      this.x *= scalar;\n      this.y *= scalar;\n      return this;\n    },\n\n    /**\n     * Divides this point by a value and returns a new one\n     * TODO: rename in scalarDivide in 2.0\n     * @param {Number} scalar\n     * @return {fabric.Point}\n     */\n    divide: function (scalar) {\n      return new Point(this.x / scalar, this.y / scalar);\n    },\n\n    /**\n     * Divides this point by a value\n     * TODO: rename in scalarDivideEquals in 2.0\n     * @param {Number} scalar\n     * @return {fabric.Point} thisArg\n     * @chainable\n     */\n    divideEquals: function (scalar) {\n      this.x /= scalar;\n      this.y /= scalar;\n      return this;\n    },\n\n    /**\n     * Returns true if this point is equal to another one\n     * @param {fabric.Point} that\n     * @return {Boolean}\n     */\n    eq: function (that) {\n      return (this.x === that.x && this.y === that.y);\n    },\n\n    /**\n     * Returns true if this point is less than another one\n     * @param {fabric.Point} that\n     * @return {Boolean}\n     */\n    lt: function (that) {\n      return (this.x < that.x && this.y < that.y);\n    },\n\n    /**\n     * Returns true if this point is less than or equal to another one\n     * @param {fabric.Point} that\n     * @return {Boolean}\n     */\n    lte: function (that) {\n      return (this.x <= that.x && this.y <= that.y);\n    },\n\n    /**\n\n     * Returns true if this point is greater another one\n     * @param {fabric.Point} that\n     * @return {Boolean}\n     */\n    gt: function (that) {\n      return (this.x > that.x && this.y > that.y);\n    },\n\n    /**\n     * Returns true if this point is greater than or equal to another one\n     * @param {fabric.Point} that\n     * @return {Boolean}\n     */\n    gte: function (that) {\n      return (this.x >= that.x && this.y >= that.y);\n    },\n\n    /**\n     * Returns new point which is the result of linear interpolation with this one and another one\n     * @param {fabric.Point} that\n     * @param {Number} t , position of interpolation, between 0 and 1 default 0.5\n     * @return {fabric.Point}\n     */\n    lerp: function (that, t) {\n      if (typeof t === 'undefined') {\n        t = 0.5;\n      }\n      t = Math.max(Math.min(1, t), 0);\n      return new Point(this.x + (that.x - this.x) * t, this.y + (that.y - this.y) * t);\n    },\n\n    /**\n     * Returns distance from this point and another one\n     * @param {fabric.Point} that\n     * @return {Number}\n     */\n    distanceFrom: function (that) {\n      var dx = this.x - that.x,\n          dy = this.y - that.y;\n      return Math.sqrt(dx * dx + dy * dy);\n    },\n\n    /**\n     * Returns the point between this point and another one\n     * @param {fabric.Point} that\n     * @return {fabric.Point}\n     */\n    midPointFrom: function (that) {\n      return this.lerp(that);\n    },\n\n    /**\n     * Returns a new point which is the min of this and another one\n     * @param {fabric.Point} that\n     * @return {fabric.Point}\n     */\n    min: function (that) {\n      return new Point(Math.min(this.x, that.x), Math.min(this.y, that.y));\n    },\n\n    /**\n     * Returns a new point which is the max of this and another one\n     * @param {fabric.Point} that\n     * @return {fabric.Point}\n     */\n    max: function (that) {\n      return new Point(Math.max(this.x, that.x), Math.max(this.y, that.y));\n    },\n\n    /**\n     * Returns string representation of this point\n     * @return {String}\n     */\n    toString: function () {\n      return this.x + ',' + this.y;\n    },\n\n    /**\n     * Sets x/y of this point\n     * @param {Number} x\n     * @param {Number} y\n     * @chainable\n     */\n    setXY: function (x, y) {\n      this.x = x;\n      this.y = y;\n      return this;\n    },\n\n    /**\n     * Sets x of this point\n     * @param {Number} x\n     * @chainable\n     */\n    setX: function (x) {\n      this.x = x;\n      return this;\n    },\n\n    /**\n     * Sets y of this point\n     * @param {Number} y\n     * @chainable\n     */\n    setY: function (y) {\n      this.y = y;\n      return this;\n    },\n\n    /**\n     * Sets x/y of this point from another point\n     * @param {fabric.Point} that\n     * @chainable\n     */\n    setFromPoint: function (that) {\n      this.x = that.x;\n      this.y = that.y;\n      return this;\n    },\n\n    /**\n     * Swaps x/y of this point and another point\n     * @param {fabric.Point} that\n     */\n    swap: function (that) {\n      var x = this.x,\n          y = this.y;\n      this.x = that.x;\n      this.y = that.y;\n      that.x = x;\n      that.y = y;\n    },\n\n    /**\n     * return a cloned instance of the point\n     * @return {fabric.Point}\n     */\n    clone: function () {\n      return new Point(this.x, this.y);\n    }\n  };\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function(global) {\n\n  'use strict';\n\n  /* Adaptation of work of Kevin Lindsey (kevin@kevlindev.com) */\n  var fabric = global.fabric || (global.fabric = { });\n\n  if (fabric.Intersection) {\n    fabric.warn('fabric.Intersection is already defined');\n    return;\n  }\n\n  /**\n   * Intersection class\n   * @class fabric.Intersection\n   * @memberOf fabric\n   * @constructor\n   */\n  function Intersection(status) {\n    this.status = status;\n    this.points = [];\n  }\n\n  fabric.Intersection = Intersection;\n\n  fabric.Intersection.prototype = /** @lends fabric.Intersection.prototype */ {\n\n    constructor: Intersection,\n\n    /**\n     * Appends a point to intersection\n     * @param {fabric.Point} point\n     * @return {fabric.Intersection} thisArg\n     * @chainable\n     */\n    appendPoint: function (point) {\n      this.points.push(point);\n      return this;\n    },\n\n    /**\n     * Appends points to intersection\n     * @param {Array} points\n     * @return {fabric.Intersection} thisArg\n     * @chainable\n     */\n    appendPoints: function (points) {\n      this.points = this.points.concat(points);\n      return this;\n    }\n  };\n\n  /**\n   * Checks if one line intersects another\n   * TODO: rename in intersectSegmentSegment\n   * @static\n   * @param {fabric.Point} a1\n   * @param {fabric.Point} a2\n   * @param {fabric.Point} b1\n   * @param {fabric.Point} b2\n   * @return {fabric.Intersection}\n   */\n  fabric.Intersection.intersectLineLine = function (a1, a2, b1, b2) {\n    var result,\n        uaT = (b2.x - b1.x) * (a1.y - b1.y) - (b2.y - b1.y) * (a1.x - b1.x),\n        ubT = (a2.x - a1.x) * (a1.y - b1.y) - (a2.y - a1.y) * (a1.x - b1.x),\n        uB = (b2.y - b1.y) * (a2.x - a1.x) - (b2.x - b1.x) * (a2.y - a1.y);\n    if (uB !== 0) {\n      var ua = uaT / uB,\n          ub = ubT / uB;\n      if (0 <= ua && ua <= 1 && 0 <= ub && ub <= 1) {\n        result = new Intersection('Intersection');\n        result.appendPoint(new fabric.Point(a1.x + ua * (a2.x - a1.x), a1.y + ua * (a2.y - a1.y)));\n      }\n      else {\n        result = new Intersection();\n      }\n    }\n    else {\n      if (uaT === 0 || ubT === 0) {\n        result = new Intersection('Coincident');\n      }\n      else {\n        result = new Intersection('Parallel');\n      }\n    }\n    return result;\n  };\n\n  /**\n   * Checks if line intersects polygon\n   * TODO: rename in intersectSegmentPolygon\n   * fix detection of coincident\n   * @static\n   * @param {fabric.Point} a1\n   * @param {fabric.Point} a2\n   * @param {Array} points\n   * @return {fabric.Intersection}\n   */\n  fabric.Intersection.intersectLinePolygon = function(a1, a2, points) {\n    var result = new Intersection(),\n        length = points.length,\n        b1, b2, inter, i;\n\n    for (i = 0; i < length; i++) {\n      b1 = points[i];\n      b2 = points[(i + 1) % length];\n      inter = Intersection.intersectLineLine(a1, a2, b1, b2);\n\n      result.appendPoints(inter.points);\n    }\n    if (result.points.length > 0) {\n      result.status = 'Intersection';\n    }\n    return result;\n  };\n\n  /**\n   * Checks if polygon intersects another polygon\n   * @static\n   * @param {Array} points1\n   * @param {Array} points2\n   * @return {fabric.Intersection}\n   */\n  fabric.Intersection.intersectPolygonPolygon = function (points1, points2) {\n    var result = new Intersection(),\n        length = points1.length, i;\n\n    for (i = 0; i < length; i++) {\n      var a1 = points1[i],\n          a2 = points1[(i + 1) % length],\n          inter = Intersection.intersectLinePolygon(a1, a2, points2);\n\n      result.appendPoints(inter.points);\n    }\n    if (result.points.length > 0) {\n      result.status = 'Intersection';\n    }\n    return result;\n  };\n\n  /**\n   * Checks if polygon intersects rectangle\n   * @static\n   * @param {Array} points\n   * @param {fabric.Point} r1\n   * @param {fabric.Point} r2\n   * @return {fabric.Intersection}\n   */\n  fabric.Intersection.intersectPolygonRectangle = function (points, r1, r2) {\n    var min = r1.min(r2),\n        max = r1.max(r2),\n        topRight = new fabric.Point(max.x, min.y),\n        bottomLeft = new fabric.Point(min.x, max.y),\n        inter1 = Intersection.intersectLinePolygon(min, topRight, points),\n        inter2 = Intersection.intersectLinePolygon(topRight, max, points),\n        inter3 = Intersection.intersectLinePolygon(max, bottomLeft, points),\n        inter4 = Intersection.intersectLinePolygon(bottomLeft, min, points),\n        result = new Intersection();\n\n    result.appendPoints(inter1.points);\n    result.appendPoints(inter2.points);\n    result.appendPoints(inter3.points);\n    result.appendPoints(inter4.points);\n\n    if (result.points.length > 0) {\n      result.status = 'Intersection';\n    }\n    return result;\n  };\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric = global.fabric || (global.fabric = { });\n\n  if (fabric.Color) {\n    fabric.warn('fabric.Color is already defined.');\n    return;\n  }\n\n  /**\n   * Color class\n   * The purpose of {@link fabric.Color} is to abstract and encapsulate common color operations;\n   * {@link fabric.Color} is a constructor and creates instances of {@link fabric.Color} objects.\n   *\n   * @class fabric.Color\n   * @param {String} color optional in hex or rgb(a) or hsl format or from known color list\n   * @return {fabric.Color} thisArg\n   * @tutorial {@link http://fabricjs.com/fabric-intro-part-2/#colors}\n   */\n  function Color(color) {\n    if (!color) {\n      this.setSource([0, 0, 0, 1]);\n    }\n    else {\n      this._tryParsingColor(color);\n    }\n  }\n\n  fabric.Color = Color;\n\n  fabric.Color.prototype = /** @lends fabric.Color.prototype */ {\n\n    /**\n     * @private\n     * @param {String|Array} color Color value to parse\n     */\n    _tryParsingColor: function(color) {\n      var source;\n\n      if (color in Color.colorNameMap) {\n        color = Color.colorNameMap[color];\n      }\n\n      if (color === 'transparent') {\n        source = [255, 255, 255, 0];\n      }\n\n      if (!source) {\n        source = Color.sourceFromHex(color);\n      }\n      if (!source) {\n        source = Color.sourceFromRgb(color);\n      }\n      if (!source) {\n        source = Color.sourceFromHsl(color);\n      }\n      if (!source) {\n        //if color is not recognize let's make black as canvas does\n        source = [0, 0, 0, 1];\n      }\n      if (source) {\n        this.setSource(source);\n      }\n    },\n\n    /**\n     * Adapted from <a href=\"https://rawgithub.com/mjijackson/mjijackson.github.com/master/2008/02/rgb-to-hsl-and-rgb-to-hsv-color-model-conversion-algorithms-in-javascript.html\">https://github.com/mjijackson</a>\n     * @private\n     * @param {Number} r Red color value\n     * @param {Number} g Green color value\n     * @param {Number} b Blue color value\n     * @return {Array} Hsl color\n     */\n    _rgbToHsl: function(r, g, b) {\n      r /= 255; g /= 255; b /= 255;\n\n      var h, s, l,\n          max = fabric.util.array.max([r, g, b]),\n          min = fabric.util.array.min([r, g, b]);\n\n      l = (max + min) / 2;\n\n      if (max === min) {\n        h = s = 0; // achromatic\n      }\n      else {\n        var d = max - min;\n        s = l > 0.5 ? d / (2 - max - min) : d / (max + min);\n        switch (max) {\n          case r:\n            h = (g - b) / d + (g < b ? 6 : 0);\n            break;\n          case g:\n            h = (b - r) / d + 2;\n            break;\n          case b:\n            h = (r - g) / d + 4;\n            break;\n        }\n        h /= 6;\n      }\n\n      return [\n        Math.round(h * 360),\n        Math.round(s * 100),\n        Math.round(l * 100)\n      ];\n    },\n\n    /**\n     * Returns source of this color (where source is an array representation; ex: [200, 200, 100, 1])\n     * @return {Array}\n     */\n    getSource: function() {\n      return this._source;\n    },\n\n    /**\n     * Sets source of this color (where source is an array representation; ex: [200, 200, 100, 1])\n     * @param {Array} source\n     */\n    setSource: function(source) {\n      this._source = source;\n    },\n\n    /**\n     * Returns color representation in RGB format\n     * @return {String} ex: rgb(0-255,0-255,0-255)\n     */\n    toRgb: function() {\n      var source = this.getSource();\n      return 'rgb(' + source[0] + ',' + source[1] + ',' + source[2] + ')';\n    },\n\n    /**\n     * Returns color representation in RGBA format\n     * @return {String} ex: rgba(0-255,0-255,0-255,0-1)\n     */\n    toRgba: function() {\n      var source = this.getSource();\n      return 'rgba(' + source[0] + ',' + source[1] + ',' + source[2] + ',' + source[3] + ')';\n    },\n\n    /**\n     * Returns color representation in HSL format\n     * @return {String} ex: hsl(0-360,0%-100%,0%-100%)\n     */\n    toHsl: function() {\n      var source = this.getSource(),\n          hsl = this._rgbToHsl(source[0], source[1], source[2]);\n\n      return 'hsl(' + hsl[0] + ',' + hsl[1] + '%,' + hsl[2] + '%)';\n    },\n\n    /**\n     * Returns color representation in HSLA format\n     * @return {String} ex: hsla(0-360,0%-100%,0%-100%,0-1)\n     */\n    toHsla: function() {\n      var source = this.getSource(),\n          hsl = this._rgbToHsl(source[0], source[1], source[2]);\n\n      return 'hsla(' + hsl[0] + ',' + hsl[1] + '%,' + hsl[2] + '%,' + source[3] + ')';\n    },\n\n    /**\n     * Returns color representation in HEX format\n     * @return {String} ex: FF5555\n     */\n    toHex: function() {\n      var source = this.getSource(), r, g, b;\n\n      r = source[0].toString(16);\n      r = (r.length === 1) ? ('0' + r) : r;\n\n      g = source[1].toString(16);\n      g = (g.length === 1) ? ('0' + g) : g;\n\n      b = source[2].toString(16);\n      b = (b.length === 1) ? ('0' + b) : b;\n\n      return r.toUpperCase() + g.toUpperCase() + b.toUpperCase();\n    },\n\n    /**\n     * Returns color representation in HEXA format\n     * @return {String} ex: FF5555CC\n     */\n    toHexa: function() {\n      var source = this.getSource(), a;\n\n      a = Math.round(source[3] * 255);\n      a = a.toString(16);\n      a = (a.length === 1) ? ('0' + a) : a;\n\n      return this.toHex() + a.toUpperCase();\n    },\n\n    /**\n     * Gets value of alpha channel for this color\n     * @return {Number} 0-1\n     */\n    getAlpha: function() {\n      return this.getSource()[3];\n    },\n\n    /**\n     * Sets value of alpha channel for this color\n     * @param {Number} alpha Alpha value 0-1\n     * @return {fabric.Color} thisArg\n     */\n    setAlpha: function(alpha) {\n      var source = this.getSource();\n      source[3] = alpha;\n      this.setSource(source);\n      return this;\n    },\n\n    /**\n     * Transforms color to its grayscale representation\n     * @return {fabric.Color} thisArg\n     */\n    toGrayscale: function() {\n      var source = this.getSource(),\n          average = parseInt((source[0] * 0.3 + source[1] * 0.59 + source[2] * 0.11).toFixed(0), 10),\n          currentAlpha = source[3];\n      this.setSource([average, average, average, currentAlpha]);\n      return this;\n    },\n\n    /**\n     * Transforms color to its black and white representation\n     * @param {Number} threshold\n     * @return {fabric.Color} thisArg\n     */\n    toBlackWhite: function(threshold) {\n      var source = this.getSource(),\n          average = (source[0] * 0.3 + source[1] * 0.59 + source[2] * 0.11).toFixed(0),\n          currentAlpha = source[3];\n\n      threshold = threshold || 127;\n\n      average = (Number(average) < Number(threshold)) ? 0 : 255;\n      this.setSource([average, average, average, currentAlpha]);\n      return this;\n    },\n\n    /**\n     * Overlays color with another color\n     * @param {String|fabric.Color} otherColor\n     * @return {fabric.Color} thisArg\n     */\n    overlayWith: function(otherColor) {\n      if (!(otherColor instanceof Color)) {\n        otherColor = new Color(otherColor);\n      }\n\n      var result = [],\n          alpha = this.getAlpha(),\n          otherAlpha = 0.5,\n          source = this.getSource(),\n          otherSource = otherColor.getSource(), i;\n\n      for (i = 0; i < 3; i++) {\n        result.push(Math.round((source[i] * (1 - otherAlpha)) + (otherSource[i] * otherAlpha)));\n      }\n\n      result[3] = alpha;\n      this.setSource(result);\n      return this;\n    }\n  };\n\n  /**\n   * Regex matching color in RGB or RGBA formats (ex: rgb(0, 0, 0), rgba(255, 100, 10, 0.5), rgba( 255 , 100 , 10 , 0.5 ), rgb(1,1,1), rgba(100%, 60%, 10%, 0.5))\n   * @static\n   * @field\n   * @memberOf fabric.Color\n   */\n  // eslint-disable-next-line max-len\n  fabric.Color.reRGBa = /^rgba?\\(\\s*(\\d{1,3}(?:\\.\\d+)?\\%?)\\s*,\\s*(\\d{1,3}(?:\\.\\d+)?\\%?)\\s*,\\s*(\\d{1,3}(?:\\.\\d+)?\\%?)\\s*(?:\\s*,\\s*((?:\\d*\\.?\\d+)?)\\s*)?\\)$/;\n\n  /**\n   * Regex matching color in HSL or HSLA formats (ex: hsl(200, 80%, 10%), hsla(300, 50%, 80%, 0.5), hsla( 300 , 50% , 80% , 0.5 ))\n   * @static\n   * @field\n   * @memberOf fabric.Color\n   */\n  fabric.Color.reHSLa = /^hsla?\\(\\s*(\\d{1,3})\\s*,\\s*(\\d{1,3}\\%)\\s*,\\s*(\\d{1,3}\\%)\\s*(?:\\s*,\\s*(\\d+(?:\\.\\d+)?)\\s*)?\\)$/;\n\n  /**\n   * Regex matching color in HEX format (ex: #FF5544CC, #FF5555, 010155, aff)\n   * @static\n   * @field\n   * @memberOf fabric.Color\n   */\n  fabric.Color.reHex = /^#?([0-9a-f]{8}|[0-9a-f]{6}|[0-9a-f]{4}|[0-9a-f]{3})$/i;\n\n  /**\n   * Map of the 148 color names with HEX code\n   * @static\n   * @field\n   * @memberOf fabric.Color\n   * @see: https://www.w3.org/TR/css3-color/#svg-color\n   */\n  fabric.Color.colorNameMap = {\n    aliceblue:            '#F0F8FF',\n    antiquewhite:         '#FAEBD7',\n    aqua:                 '#00FFFF',\n    aquamarine:           '#7FFFD4',\n    azure:                '#F0FFFF',\n    beige:                '#F5F5DC',\n    bisque:               '#FFE4C4',\n    black:                '#000000',\n    blanchedalmond:       '#FFEBCD',\n    blue:                 '#0000FF',\n    blueviolet:           '#8A2BE2',\n    brown:                '#A52A2A',\n    burlywood:            '#DEB887',\n    cadetblue:            '#5F9EA0',\n    chartreuse:           '#7FFF00',\n    chocolate:            '#D2691E',\n    coral:                '#FF7F50',\n    cornflowerblue:       '#6495ED',\n    cornsilk:             '#FFF8DC',\n    crimson:              '#DC143C',\n    cyan:                 '#00FFFF',\n    darkblue:             '#00008B',\n    darkcyan:             '#008B8B',\n    darkgoldenrod:        '#B8860B',\n    darkgray:             '#A9A9A9',\n    darkgrey:             '#A9A9A9',\n    darkgreen:            '#006400',\n    darkkhaki:            '#BDB76B',\n    darkmagenta:          '#8B008B',\n    darkolivegreen:       '#556B2F',\n    darkorange:           '#FF8C00',\n    darkorchid:           '#9932CC',\n    darkred:              '#8B0000',\n    darksalmon:           '#E9967A',\n    darkseagreen:         '#8FBC8F',\n    darkslateblue:        '#483D8B',\n    darkslategray:        '#2F4F4F',\n    darkslategrey:        '#2F4F4F',\n    darkturquoise:        '#00CED1',\n    darkviolet:           '#9400D3',\n    deeppink:             '#FF1493',\n    deepskyblue:          '#00BFFF',\n    dimgray:              '#696969',\n    dimgrey:              '#696969',\n    dodgerblue:           '#1E90FF',\n    firebrick:            '#B22222',\n    floralwhite:          '#FFFAF0',\n    forestgreen:          '#228B22',\n    fuchsia:              '#FF00FF',\n    gainsboro:            '#DCDCDC',\n    ghostwhite:           '#F8F8FF',\n    gold:                 '#FFD700',\n    goldenrod:            '#DAA520',\n    gray:                 '#808080',\n    grey:                 '#808080',\n    green:                '#008000',\n    greenyellow:          '#ADFF2F',\n    honeydew:             '#F0FFF0',\n    hotpink:              '#FF69B4',\n    indianred:            '#CD5C5C',\n    indigo:               '#4B0082',\n    ivory:                '#FFFFF0',\n    khaki:                '#F0E68C',\n    lavender:             '#E6E6FA',\n    lavenderblush:        '#FFF0F5',\n    lawngreen:            '#7CFC00',\n    lemonchiffon:         '#FFFACD',\n    lightblue:            '#ADD8E6',\n    lightcoral:           '#F08080',\n    lightcyan:            '#E0FFFF',\n    lightgoldenrodyellow: '#FAFAD2',\n    lightgray:            '#D3D3D3',\n    lightgrey:            '#D3D3D3',\n    lightgreen:           '#90EE90',\n    lightpink:            '#FFB6C1',\n    lightsalmon:          '#FFA07A',\n    lightseagreen:        '#20B2AA',\n    lightskyblue:         '#87CEFA',\n    lightslategray:       '#778899',\n    lightslategrey:       '#778899',\n    lightsteelblue:       '#B0C4DE',\n    lightyellow:          '#FFFFE0',\n    lime:                 '#00FF00',\n    limegreen:            '#32CD32',\n    linen:                '#FAF0E6',\n    magenta:              '#FF00FF',\n    maroon:               '#800000',\n    mediumaquamarine:     '#66CDAA',\n    mediumblue:           '#0000CD',\n    mediumorchid:         '#BA55D3',\n    mediumpurple:         '#9370DB',\n    mediumseagreen:       '#3CB371',\n    mediumslateblue:      '#7B68EE',\n    mediumspringgreen:    '#00FA9A',\n    mediumturquoise:      '#48D1CC',\n    mediumvioletred:      '#C71585',\n    midnightblue:         '#191970',\n    mintcream:            '#F5FFFA',\n    mistyrose:            '#FFE4E1',\n    moccasin:             '#FFE4B5',\n    navajowhite:          '#FFDEAD',\n    navy:                 '#000080',\n    oldlace:              '#FDF5E6',\n    olive:                '#808000',\n    olivedrab:            '#6B8E23',\n    orange:               '#FFA500',\n    orangered:            '#FF4500',\n    orchid:               '#DA70D6',\n    palegoldenrod:        '#EEE8AA',\n    palegreen:            '#98FB98',\n    paleturquoise:        '#AFEEEE',\n    palevioletred:        '#DB7093',\n    papayawhip:           '#FFEFD5',\n    peachpuff:            '#FFDAB9',\n    peru:                 '#CD853F',\n    pink:                 '#FFC0CB',\n    plum:                 '#DDA0DD',\n    powderblue:           '#B0E0E6',\n    purple:               '#800080',\n    rebeccapurple:        '#663399',\n    red:                  '#FF0000',\n    rosybrown:            '#BC8F8F',\n    royalblue:            '#4169E1',\n    saddlebrown:          '#8B4513',\n    salmon:               '#FA8072',\n    sandybrown:           '#F4A460',\n    seagreen:             '#2E8B57',\n    seashell:             '#FFF5EE',\n    sienna:               '#A0522D',\n    silver:               '#C0C0C0',\n    skyblue:              '#87CEEB',\n    slateblue:            '#6A5ACD',\n    slategray:            '#708090',\n    slategrey:            '#708090',\n    snow:                 '#FFFAFA',\n    springgreen:          '#00FF7F',\n    steelblue:            '#4682B4',\n    tan:                  '#D2B48C',\n    teal:                 '#008080',\n    thistle:              '#D8BFD8',\n    tomato:               '#FF6347',\n    turquoise:            '#40E0D0',\n    violet:               '#EE82EE',\n    wheat:                '#F5DEB3',\n    white:                '#FFFFFF',\n    whitesmoke:           '#F5F5F5',\n    yellow:               '#FFFF00',\n    yellowgreen:          '#9ACD32'\n  };\n\n  /**\n   * @private\n   * @param {Number} p\n   * @param {Number} q\n   * @param {Number} t\n   * @return {Number}\n   */\n  function hue2rgb(p, q, t) {\n    if (t < 0) {\n      t += 1;\n    }\n    if (t > 1) {\n      t -= 1;\n    }\n    if (t < 1 / 6) {\n      return p + (q - p) * 6 * t;\n    }\n    if (t < 1 / 2) {\n      return q;\n    }\n    if (t < 2 / 3) {\n      return p + (q - p) * (2 / 3 - t) * 6;\n    }\n    return p;\n  }\n\n  /**\n   * Returns new color object, when given a color in RGB format\n   * @memberOf fabric.Color\n   * @param {String} color Color value ex: rgb(0-255,0-255,0-255)\n   * @return {fabric.Color}\n   */\n  fabric.Color.fromRgb = function(color) {\n    return Color.fromSource(Color.sourceFromRgb(color));\n  };\n\n  /**\n   * Returns array representation (ex: [100, 100, 200, 1]) of a color that's in RGB or RGBA format\n   * @memberOf fabric.Color\n   * @param {String} color Color value ex: rgb(0-255,0-255,0-255), rgb(0%-100%,0%-100%,0%-100%)\n   * @return {Array} source\n   */\n  fabric.Color.sourceFromRgb = function(color) {\n    var match = color.match(Color.reRGBa);\n    if (match) {\n      var r = parseInt(match[1], 10) / (/%$/.test(match[1]) ? 100 : 1) * (/%$/.test(match[1]) ? 255 : 1),\n          g = parseInt(match[2], 10) / (/%$/.test(match[2]) ? 100 : 1) * (/%$/.test(match[2]) ? 255 : 1),\n          b = parseInt(match[3], 10) / (/%$/.test(match[3]) ? 100 : 1) * (/%$/.test(match[3]) ? 255 : 1);\n\n      return [\n        parseInt(r, 10),\n        parseInt(g, 10),\n        parseInt(b, 10),\n        match[4] ? parseFloat(match[4]) : 1\n      ];\n    }\n  };\n\n  /**\n   * Returns new color object, when given a color in RGBA format\n   * @static\n   * @function\n   * @memberOf fabric.Color\n   * @param {String} color\n   * @return {fabric.Color}\n   */\n  fabric.Color.fromRgba = Color.fromRgb;\n\n  /**\n   * Returns new color object, when given a color in HSL format\n   * @param {String} color Color value ex: hsl(0-260,0%-100%,0%-100%)\n   * @memberOf fabric.Color\n   * @return {fabric.Color}\n   */\n  fabric.Color.fromHsl = function(color) {\n    return Color.fromSource(Color.sourceFromHsl(color));\n  };\n\n  /**\n   * Returns array representation (ex: [100, 100, 200, 1]) of a color that's in HSL or HSLA format.\n   * Adapted from <a href=\"https://rawgithub.com/mjijackson/mjijackson.github.com/master/2008/02/rgb-to-hsl-and-rgb-to-hsv-color-model-conversion-algorithms-in-javascript.html\">https://github.com/mjijackson</a>\n   * @memberOf fabric.Color\n   * @param {String} color Color value ex: hsl(0-360,0%-100%,0%-100%) or hsla(0-360,0%-100%,0%-100%, 0-1)\n   * @return {Array} source\n   * @see http://http://www.w3.org/TR/css3-color/#hsl-color\n   */\n  fabric.Color.sourceFromHsl = function(color) {\n    var match = color.match(Color.reHSLa);\n    if (!match) {\n      return;\n    }\n\n    var h = (((parseFloat(match[1]) % 360) + 360) % 360) / 360,\n        s = parseFloat(match[2]) / (/%$/.test(match[2]) ? 100 : 1),\n        l = parseFloat(match[3]) / (/%$/.test(match[3]) ? 100 : 1),\n        r, g, b;\n\n    if (s === 0) {\n      r = g = b = l;\n    }\n    else {\n      var q = l <= 0.5 ? l * (s + 1) : l + s - l * s,\n          p = l * 2 - q;\n\n      r = hue2rgb(p, q, h + 1 / 3);\n      g = hue2rgb(p, q, h);\n      b = hue2rgb(p, q, h - 1 / 3);\n    }\n\n    return [\n      Math.round(r * 255),\n      Math.round(g * 255),\n      Math.round(b * 255),\n      match[4] ? parseFloat(match[4]) : 1\n    ];\n  };\n\n  /**\n   * Returns new color object, when given a color in HSLA format\n   * @static\n   * @function\n   * @memberOf fabric.Color\n   * @param {String} color\n   * @return {fabric.Color}\n   */\n  fabric.Color.fromHsla = Color.fromHsl;\n\n  /**\n   * Returns new color object, when given a color in HEX format\n   * @static\n   * @memberOf fabric.Color\n   * @param {String} color Color value ex: FF5555\n   * @return {fabric.Color}\n   */\n  fabric.Color.fromHex = function(color) {\n    return Color.fromSource(Color.sourceFromHex(color));\n  };\n\n  /**\n   * Returns array representation (ex: [100, 100, 200, 1]) of a color that's in HEX format\n   * @static\n   * @memberOf fabric.Color\n   * @param {String} color ex: FF5555 or FF5544CC (RGBa)\n   * @return {Array} source\n   */\n  fabric.Color.sourceFromHex = function(color) {\n    if (color.match(Color.reHex)) {\n      var value = color.slice(color.indexOf('#') + 1),\n          isShortNotation = (value.length === 3 || value.length === 4),\n          isRGBa = (value.length === 8 || value.length === 4),\n          r = isShortNotation ? (value.charAt(0) + value.charAt(0)) : value.substring(0, 2),\n          g = isShortNotation ? (value.charAt(1) + value.charAt(1)) : value.substring(2, 4),\n          b = isShortNotation ? (value.charAt(2) + value.charAt(2)) : value.substring(4, 6),\n          a = isRGBa ? (isShortNotation ? (value.charAt(3) + value.charAt(3)) : value.substring(6, 8)) : 'FF';\n\n      return [\n        parseInt(r, 16),\n        parseInt(g, 16),\n        parseInt(b, 16),\n        parseFloat((parseInt(a, 16) / 255).toFixed(2))\n      ];\n    }\n  };\n\n  /**\n   * Returns new color object, when given color in array representation (ex: [200, 100, 100, 0.5])\n   * @static\n   * @memberOf fabric.Color\n   * @param {Array} source\n   * @return {fabric.Color}\n   */\n  fabric.Color.fromSource = function(source) {\n    var oColor = new Color();\n    oColor.setSource(source);\n    return oColor;\n  };\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function() {\n\n  /* _FROM_SVG_START_ */\n  function getColorStop(el) {\n    var style = el.getAttribute('style'),\n        offset = el.getAttribute('offset') || 0,\n        color, colorAlpha, opacity, i;\n\n    // convert percents to absolute values\n    offset = parseFloat(offset) / (/%$/.test(offset) ? 100 : 1);\n    offset = offset < 0 ? 0 : offset > 1 ? 1 : offset;\n    if (style) {\n      var keyValuePairs = style.split(/\\s*;\\s*/);\n\n      if (keyValuePairs[keyValuePairs.length - 1] === '') {\n        keyValuePairs.pop();\n      }\n\n      for (i = keyValuePairs.length; i--; ) {\n\n        var split = keyValuePairs[i].split(/\\s*:\\s*/),\n            key = split[0].trim(),\n            value = split[1].trim();\n\n        if (key === 'stop-color') {\n          color = value;\n        }\n        else if (key === 'stop-opacity') {\n          opacity = value;\n        }\n      }\n    }\n\n    if (!color) {\n      color = el.getAttribute('stop-color') || 'rgb(0,0,0)';\n    }\n    if (!opacity) {\n      opacity = el.getAttribute('stop-opacity');\n    }\n\n    color = new fabric.Color(color);\n    colorAlpha = color.getAlpha();\n    opacity = isNaN(parseFloat(opacity)) ? 1 : parseFloat(opacity);\n    opacity *= colorAlpha;\n\n    return {\n      offset: offset,\n      color: color.toRgb(),\n      opacity: opacity\n    };\n  }\n\n  function getLinearCoords(el) {\n    return {\n      x1: el.getAttribute('x1') || 0,\n      y1: el.getAttribute('y1') || 0,\n      x2: el.getAttribute('x2') || '100%',\n      y2: el.getAttribute('y2') || 0\n    };\n  }\n\n  function getRadialCoords(el) {\n    return {\n      x1: el.getAttribute('fx') || el.getAttribute('cx') || '50%',\n      y1: el.getAttribute('fy') || el.getAttribute('cy') || '50%',\n      r1: 0,\n      x2: el.getAttribute('cx') || '50%',\n      y2: el.getAttribute('cy') || '50%',\n      r2: el.getAttribute('r') || '50%'\n    };\n  }\n  /* _FROM_SVG_END_ */\n\n  var clone = fabric.util.object.clone;\n\n  /**\n   * Gradient class\n   * @class fabric.Gradient\n   * @tutorial {@link http://fabricjs.com/fabric-intro-part-2#gradients}\n   * @see {@link fabric.Gradient#initialize} for constructor definition\n   */\n  fabric.Gradient = fabric.util.createClass(/** @lends fabric.Gradient.prototype */ {\n\n    /**\n     * Horizontal offset for aligning gradients coming from SVG when outside pathgroups\n     * @type Number\n     * @default 0\n     */\n    offsetX: 0,\n\n    /**\n     * Vertical offset for aligning gradients coming from SVG when outside pathgroups\n     * @type Number\n     * @default 0\n     */\n    offsetY: 0,\n\n    /**\n     * Constructor\n     * @param {Object} [options] Options object with type, coords, gradientUnits and colorStops\n     * @return {fabric.Gradient} thisArg\n     */\n    initialize: function(options) {\n      options || (options = { });\n\n      var coords = { };\n\n      this.id = fabric.Object.__uid++;\n      this.type = options.type || 'linear';\n\n      coords = {\n        x1: options.coords.x1 || 0,\n        y1: options.coords.y1 || 0,\n        x2: options.coords.x2 || 0,\n        y2: options.coords.y2 || 0\n      };\n\n      if (this.type === 'radial') {\n        coords.r1 = options.coords.r1 || 0;\n        coords.r2 = options.coords.r2 || 0;\n      }\n      this.coords = coords;\n      this.colorStops = options.colorStops.slice();\n      if (options.gradientTransform) {\n        this.gradientTransform = options.gradientTransform;\n      }\n      this.offsetX = options.offsetX || this.offsetX;\n      this.offsetY = options.offsetY || this.offsetY;\n    },\n\n    /**\n     * Adds another colorStop\n     * @param {Object} colorStop Object with offset and color\n     * @return {fabric.Gradient} thisArg\n     */\n    addColorStop: function(colorStops) {\n      for (var position in colorStops) {\n        var color = new fabric.Color(colorStops[position]);\n        this.colorStops.push({\n          offset: parseFloat(position),\n          color: color.toRgb(),\n          opacity: color.getAlpha()\n        });\n      }\n      return this;\n    },\n\n    /**\n     * Returns object representation of a gradient\n     * @param {Array} [propertiesToInclude] Any properties that you might want to additionally include in the output\n     * @return {Object}\n     */\n    toObject: function(propertiesToInclude) {\n      var object = {\n        type: this.type,\n        coords: this.coords,\n        colorStops: this.colorStops,\n        offsetX: this.offsetX,\n        offsetY: this.offsetY,\n        gradientTransform: this.gradientTransform ? this.gradientTransform.concat() : this.gradientTransform\n      };\n      fabric.util.populateWithProperties(this, object, propertiesToInclude);\n\n      return object;\n    },\n\n    /* _TO_SVG_START_ */\n    /**\n     * Returns SVG representation of an gradient\n     * @param {Object} object Object to create a gradient for\n     * @return {String} SVG representation of an gradient (linear/radial)\n     */\n    toSVG: function(object) {\n      var coords = clone(this.coords, true), i, len,\n          markup, commonAttributes, colorStops = clone(this.colorStops, true),\n          needsSwap = coords.r1 > coords.r2,\n          offsetX = object.width / 2, offsetY = object.height / 2;\n      // colorStops must be sorted ascending\n      colorStops.sort(function(a, b) {\n        return a.offset - b.offset;\n      });\n      if (object.type === 'path') {\n        offsetX -= object.pathOffset.x;\n        offsetY -= object.pathOffset.y;\n      }\n      for (var prop in coords) {\n        if (prop === 'x1' || prop === 'x2') {\n          coords[prop] += this.offsetX - offsetX;\n        }\n        else if (prop === 'y1' || prop === 'y2') {\n          coords[prop] += this.offsetY - offsetY;\n        }\n      }\n\n      commonAttributes = 'id=\"SVGID_' + this.id +\n                     '\" gradientUnits=\"userSpaceOnUse\"';\n      if (this.gradientTransform) {\n        commonAttributes += ' gradientTransform=\"matrix(' + this.gradientTransform.join(' ') + ')\" ';\n      }\n      if (this.type === 'linear') {\n        markup = [\n          '<linearGradient ',\n          commonAttributes,\n          ' x1=\"', coords.x1,\n          '\" y1=\"', coords.y1,\n          '\" x2=\"', coords.x2,\n          '\" y2=\"', coords.y2,\n          '\">\\n'\n        ];\n      }\n      else if (this.type === 'radial') {\n        // svg radial gradient has just 1 radius. the biggest.\n        markup = [\n          '<radialGradient ',\n          commonAttributes,\n          ' cx=\"', needsSwap ? coords.x1 : coords.x2,\n          '\" cy=\"', needsSwap ? coords.y1 : coords.y2,\n          '\" r=\"', needsSwap ? coords.r1 : coords.r2,\n          '\" fx=\"', needsSwap ? coords.x2 : coords.x1,\n          '\" fy=\"', needsSwap ? coords.y2 : coords.y1,\n          '\">\\n'\n        ];\n      }\n\n      if (this.type === 'radial') {\n        if (needsSwap) {\n          // svg goes from internal to external radius. if radius are inverted, swap color stops.\n          colorStops = colorStops.concat();\n          colorStops.reverse();\n          for (i = 0, len = colorStops.length; i < len; i++) {\n            colorStops[i].offset = 1 - colorStops[i].offset;\n          }\n        }\n        var minRadius = Math.min(coords.r1, coords.r2);\n        if (minRadius > 0) {\n          // i have to shift all colorStops and add new one in 0.\n          var maxRadius = Math.max(coords.r1, coords.r2),\n              percentageShift = minRadius / maxRadius;\n          for (i = 0, len = colorStops.length; i < len; i++) {\n            colorStops[i].offset += percentageShift * (1 - colorStops[i].offset);\n          }\n        }\n      }\n\n      for (i = 0, len = colorStops.length; i < len; i++) {\n        var colorStop = colorStops[i];\n        markup.push(\n          '<stop ',\n          'offset=\"', (colorStop.offset * 100) + '%',\n          '\" style=\"stop-color:', colorStop.color,\n          (typeof colorStop.opacity !== 'undefined' ? ';stop-opacity: ' + colorStop.opacity : ';'),\n          '\"/>\\n'\n        );\n      }\n\n      markup.push((this.type === 'linear' ? '</linearGradient>\\n' : '</radialGradient>\\n'));\n\n      return markup.join('');\n    },\n    /* _TO_SVG_END_ */\n\n    /**\n     * Returns an instance of CanvasGradient\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     * @return {CanvasGradient}\n     */\n    toLive: function(ctx) {\n      var gradient, coords = fabric.util.object.clone(this.coords), i, len;\n\n      if (!this.type) {\n        return;\n      }\n\n      if (this.type === 'linear') {\n        gradient = ctx.createLinearGradient(\n          coords.x1, coords.y1, coords.x2, coords.y2);\n      }\n      else if (this.type === 'radial') {\n        gradient = ctx.createRadialGradient(\n          coords.x1, coords.y1, coords.r1, coords.x2, coords.y2, coords.r2);\n      }\n\n      for (i = 0, len = this.colorStops.length; i < len; i++) {\n        var color = this.colorStops[i].color,\n            opacity = this.colorStops[i].opacity,\n            offset = this.colorStops[i].offset;\n\n        if (typeof opacity !== 'undefined') {\n          color = new fabric.Color(color).setAlpha(opacity).toRgba();\n        }\n        gradient.addColorStop(offset, color);\n      }\n\n      return gradient;\n    }\n  });\n\n  fabric.util.object.extend(fabric.Gradient, {\n\n    /* _FROM_SVG_START_ */\n    /**\n     * Returns {@link fabric.Gradient} instance from an SVG element\n     * @static\n     * @memberOf fabric.Gradient\n     * @param {SVGGradientElement} el SVG gradient element\n     * @param {fabric.Object} instance\n     * @return {fabric.Gradient} Gradient instance\n     * @see http://www.w3.org/TR/SVG/pservers.html#LinearGradientElement\n     * @see http://www.w3.org/TR/SVG/pservers.html#RadialGradientElement\n     */\n    fromElement: function(el, instance) {\n      /**\n       *  @example:\n       *\n       *  <linearGradient id=\"linearGrad1\">\n       *    <stop offset=\"0%\" stop-color=\"white\"/>\n       *    <stop offset=\"100%\" stop-color=\"black\"/>\n       *  </linearGradient>\n       *\n       *  OR\n       *\n       *  <linearGradient id=\"linearGrad2\">\n       *    <stop offset=\"0\" style=\"stop-color:rgb(255,255,255)\"/>\n       *    <stop offset=\"1\" style=\"stop-color:rgb(0,0,0)\"/>\n       *  </linearGradient>\n       *\n       *  OR\n       *\n       *  <radialGradient id=\"radialGrad1\">\n       *    <stop offset=\"0%\" stop-color=\"white\" stop-opacity=\"1\" />\n       *    <stop offset=\"50%\" stop-color=\"black\" stop-opacity=\"0.5\" />\n       *    <stop offset=\"100%\" stop-color=\"white\" stop-opacity=\"1\" />\n       *  </radialGradient>\n       *\n       *  OR\n       *\n       *  <radialGradient id=\"radialGrad2\">\n       *    <stop offset=\"0\" stop-color=\"rgb(255,255,255)\" />\n       *    <stop offset=\"0.5\" stop-color=\"rgb(0,0,0)\" />\n       *    <stop offset=\"1\" stop-color=\"rgb(255,255,255)\" />\n       *  </radialGradient>\n       *\n       */\n\n      var colorStopEls = el.getElementsByTagName('stop'),\n          type,\n          gradientUnits = el.getAttribute('gradientUnits') || 'objectBoundingBox',\n          gradientTransform = el.getAttribute('gradientTransform'),\n          colorStops = [],\n          coords, ellipseMatrix, i;\n\n      if (el.nodeName === 'linearGradient' || el.nodeName === 'LINEARGRADIENT') {\n        type = 'linear';\n      }\n      else {\n        type = 'radial';\n      }\n\n      if (type === 'linear') {\n        coords = getLinearCoords(el);\n      }\n      else if (type === 'radial') {\n        coords = getRadialCoords(el);\n      }\n\n      for (i = colorStopEls.length; i--; ) {\n        colorStops.push(getColorStop(colorStopEls[i]));\n      }\n\n      ellipseMatrix = _convertPercentUnitsToValues(instance, coords, gradientUnits);\n\n      var gradient = new fabric.Gradient({\n        type: type,\n        coords: coords,\n        colorStops: colorStops,\n        offsetX: -instance.left,\n        offsetY: -instance.top\n      });\n\n      if (gradientTransform || ellipseMatrix !== '') {\n        gradient.gradientTransform = fabric.parseTransformAttribute((gradientTransform || '') + ellipseMatrix);\n      }\n\n      return gradient;\n    },\n    /* _FROM_SVG_END_ */\n\n    /**\n     * Returns {@link fabric.Gradient} instance from its object representation\n     * @static\n     * @memberOf fabric.Gradient\n     * @param {Object} obj\n     * @param {Object} [options] Options object\n     */\n    forObject: function(obj, options) {\n      options || (options = { });\n      _convertPercentUnitsToValues(obj, options.coords, 'userSpaceOnUse');\n      return new fabric.Gradient(options);\n    }\n  });\n\n  /**\n   * @private\n   */\n  function _convertPercentUnitsToValues(object, options, gradientUnits) {\n    var propValue, addFactor = 0, multFactor = 1, ellipseMatrix = '';\n    for (var prop in options) {\n      if (options[prop] === 'Infinity') {\n        options[prop] = 1;\n      }\n      else if (options[prop] === '-Infinity') {\n        options[prop] = 0;\n      }\n      propValue = parseFloat(options[prop], 10);\n      if (typeof options[prop] === 'string' && /^(\\d+\\.\\d+)%|(\\d+)%$/.test(options[prop])) {\n        multFactor = 0.01;\n      }\n      else {\n        multFactor = 1;\n      }\n      if (prop === 'x1' || prop === 'x2' || prop === 'r2') {\n        multFactor *= gradientUnits === 'objectBoundingBox' ? object.width : 1;\n        addFactor = gradientUnits === 'objectBoundingBox' ? object.left || 0 : 0;\n      }\n      else if (prop === 'y1' || prop === 'y2') {\n        multFactor *= gradientUnits === 'objectBoundingBox' ? object.height : 1;\n        addFactor = gradientUnits === 'objectBoundingBox' ? object.top || 0 : 0;\n      }\n      options[prop] = propValue * multFactor + addFactor;\n    }\n    if (object.type === 'ellipse' &&\n        options.r2 !== null &&\n        gradientUnits === 'objectBoundingBox' &&\n        object.rx !== object.ry) {\n\n      var scaleFactor = object.ry / object.rx;\n      ellipseMatrix = ' scale(1, ' + scaleFactor + ')';\n      if (options.y1) {\n        options.y1 /= scaleFactor;\n      }\n      if (options.y2) {\n        options.y2 /= scaleFactor;\n      }\n    }\n    return ellipseMatrix;\n  }\n})();\n\n\n(function() {\n\n  'use strict';\n\n  var toFixed = fabric.util.toFixed;\n\n  /**\n   * Pattern class\n   * @class fabric.Pattern\n   * @see {@link http://fabricjs.com/patterns|Pattern demo}\n   * @see {@link http://fabricjs.com/dynamic-patterns|DynamicPattern demo}\n   * @see {@link fabric.Pattern#initialize} for constructor definition\n   */\n\n\n  fabric.Pattern = fabric.util.createClass(/** @lends fabric.Pattern.prototype */ {\n\n    /**\n     * Repeat property of a pattern (one of repeat, repeat-x, repeat-y or no-repeat)\n     * @type String\n     * @default\n     */\n    repeat: 'repeat',\n\n    /**\n     * Pattern horizontal offset from object's left/top corner\n     * @type Number\n     * @default\n     */\n    offsetX: 0,\n\n    /**\n     * Pattern vertical offset from object's left/top corner\n     * @type Number\n     * @default\n     */\n    offsetY: 0,\n\n    /**\n     * crossOrigin value (one of \"\", \"anonymous\", \"use-credentials\")\n     * @see https://developer.mozilla.org/en-US/docs/HTML/CORS_settings_attributes\n     * @type String\n     * @default\n     */\n    crossOrigin: '',\n\n    /**\n     * transform matrix to change the pattern, imported from svgs.\n     * @type Array\n     * @default\n     */\n    patternTransform: null,\n\n    /**\n     * Constructor\n     * @param {Object} [options] Options object\n     * @param {Function} [callback] function to invoke after callback init.\n     * @return {fabric.Pattern} thisArg\n     */\n    initialize: function(options, callback) {\n      options || (options = { });\n\n      this.id = fabric.Object.__uid++;\n      this.setOptions(options);\n      if (!options.source || (options.source && typeof options.source !== 'string')) {\n        callback && callback(this);\n        return;\n      }\n      // function string\n      if (typeof fabric.util.getFunctionBody(options.source) !== 'undefined') {\n        this.source = new Function(fabric.util.getFunctionBody(options.source));\n        callback && callback(this);\n      }\n      else {\n        // img src string\n        var _this = this;\n        this.source = fabric.util.createImage();\n        fabric.util.loadImage(options.source, function(img) {\n          _this.source = img;\n          callback && callback(_this);\n        }, null, this.crossOrigin);\n      }\n    },\n\n    /**\n     * Returns object representation of a pattern\n     * @param {Array} [propertiesToInclude] Any properties that you might want to additionally include in the output\n     * @return {Object} Object representation of a pattern instance\n     */\n    toObject: function(propertiesToInclude) {\n      var NUM_FRACTION_DIGITS = fabric.Object.NUM_FRACTION_DIGITS,\n          source, object;\n\n      // callback\n      if (typeof this.source === 'function') {\n        source = String(this.source);\n      }\n      // <img> element\n      else if (typeof this.source.src === 'string') {\n        source = this.source.src;\n      }\n      // <canvas> element\n      else if (typeof this.source === 'object' && this.source.toDataURL) {\n        source = this.source.toDataURL();\n      }\n\n      object = {\n        type: 'pattern',\n        source: source,\n        repeat: this.repeat,\n        crossOrigin: this.crossOrigin,\n        offsetX: toFixed(this.offsetX, NUM_FRACTION_DIGITS),\n        offsetY: toFixed(this.offsetY, NUM_FRACTION_DIGITS),\n        patternTransform: this.patternTransform ? this.patternTransform.concat() : null\n      };\n      fabric.util.populateWithProperties(this, object, propertiesToInclude);\n\n      return object;\n    },\n\n    /* _TO_SVG_START_ */\n    /**\n     * Returns SVG representation of a pattern\n     * @param {fabric.Object} object\n     * @return {String} SVG representation of a pattern\n     */\n    toSVG: function(object) {\n      var patternSource = typeof this.source === 'function' ? this.source() : this.source,\n          patternWidth = patternSource.width / object.width,\n          patternHeight = patternSource.height / object.height,\n          patternOffsetX = this.offsetX / object.width,\n          patternOffsetY = this.offsetY / object.height,\n          patternImgSrc = '';\n      if (this.repeat === 'repeat-x' || this.repeat === 'no-repeat') {\n        patternHeight = 1;\n      }\n      if (this.repeat === 'repeat-y' || this.repeat === 'no-repeat') {\n        patternWidth = 1;\n      }\n      if (patternSource.src) {\n        patternImgSrc = patternSource.src;\n      }\n      else if (patternSource.toDataURL) {\n        patternImgSrc = patternSource.toDataURL();\n      }\n\n      return '<pattern id=\"SVGID_' + this.id +\n                    '\" x=\"' + patternOffsetX +\n                    '\" y=\"' + patternOffsetY +\n                    '\" width=\"' + patternWidth +\n                    '\" height=\"' + patternHeight + '\">\\n' +\n               '<image x=\"0\" y=\"0\"' +\n                      ' width=\"' + patternSource.width +\n                      '\" height=\"' + patternSource.height +\n                      '\" xlink:href=\"' + patternImgSrc +\n               '\"></image>\\n' +\n             '</pattern>\\n';\n    },\n    /* _TO_SVG_END_ */\n\n    setOptions: function(options) {\n      for (var prop in options) {\n        this[prop] = options[prop];\n      }\n    },\n\n    /**\n     * Returns an instance of CanvasPattern\n     * @param {CanvasRenderingContext2D} ctx Context to create pattern\n     * @return {CanvasPattern}\n     */\n    toLive: function(ctx) {\n      var source = typeof this.source === 'function' ? this.source() : this.source;\n\n      // if the image failed to load, return, and allow rest to continue loading\n      if (!source) {\n        return '';\n      }\n\n      // if an image\n      if (typeof source.src !== 'undefined') {\n        if (!source.complete) {\n          return '';\n        }\n        if (source.naturalWidth === 0 || source.naturalHeight === 0) {\n          return '';\n        }\n      }\n      return ctx.createPattern(source, this.repeat);\n    }\n  });\n})();\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric = global.fabric || (global.fabric = { }),\n      toFixed = fabric.util.toFixed;\n\n  if (fabric.Shadow) {\n    fabric.warn('fabric.Shadow is already defined.');\n    return;\n  }\n\n  /**\n   * Shadow class\n   * @class fabric.Shadow\n   * @see {@link http://fabricjs.com/shadows|Shadow demo}\n   * @see {@link fabric.Shadow#initialize} for constructor definition\n   */\n  fabric.Shadow = fabric.util.createClass(/** @lends fabric.Shadow.prototype */ {\n\n    /**\n     * Shadow color\n     * @type String\n     * @default\n     */\n    color: 'rgb(0,0,0)',\n\n    /**\n     * Shadow blur\n     * @type Number\n     */\n    blur: 0,\n\n    /**\n     * Shadow horizontal offset\n     * @type Number\n     * @default\n     */\n    offsetX: 0,\n\n    /**\n     * Shadow vertical offset\n     * @type Number\n     * @default\n     */\n    offsetY: 0,\n\n    /**\n     * Whether the shadow should affect stroke operations\n     * @type Boolean\n     * @default\n     */\n    affectStroke: false,\n\n    /**\n     * Indicates whether toObject should include default values\n     * @type Boolean\n     * @default\n     */\n    includeDefaultValues: true,\n\n    /**\n     * Constructor\n     * @param {Object|String} [options] Options object with any of color, blur, offsetX, offsetY properties or string (e.g. \"rgba(0,0,0,0.2) 2px 2px 10px\")\n     * @return {fabric.Shadow} thisArg\n     */\n    initialize: function(options) {\n\n      if (typeof options === 'string') {\n        options = this._parseShadow(options);\n      }\n\n      for (var prop in options) {\n        this[prop] = options[prop];\n      }\n\n      this.id = fabric.Object.__uid++;\n    },\n\n    /**\n     * @private\n     * @param {String} shadow Shadow value to parse\n     * @return {Object} Shadow object with color, offsetX, offsetY and blur\n     */\n    _parseShadow: function(shadow) {\n      var shadowStr = shadow.trim(),\n          offsetsAndBlur = fabric.Shadow.reOffsetsAndBlur.exec(shadowStr) || [],\n          color = shadowStr.replace(fabric.Shadow.reOffsetsAndBlur, '') || 'rgb(0,0,0)';\n\n      return {\n        color: color.trim(),\n        offsetX: parseInt(offsetsAndBlur[1], 10) || 0,\n        offsetY: parseInt(offsetsAndBlur[2], 10) || 0,\n        blur: parseInt(offsetsAndBlur[3], 10) || 0\n      };\n    },\n\n    /**\n     * Returns a string representation of an instance\n     * @see http://www.w3.org/TR/css-text-decor-3/#text-shadow\n     * @return {String} Returns CSS3 text-shadow declaration\n     */\n    toString: function() {\n      return [this.offsetX, this.offsetY, this.blur, this.color].join('px ');\n    },\n\n    /* _TO_SVG_START_ */\n    /**\n     * Returns SVG representation of a shadow\n     * @param {fabric.Object} object\n     * @return {String} SVG representation of a shadow\n     */\n    toSVG: function(object) {\n      var fBoxX = 40, fBoxY = 40, NUM_FRACTION_DIGITS = fabric.Object.NUM_FRACTION_DIGITS,\n          offset = fabric.util.rotateVector(\n            { x: this.offsetX, y: this.offsetY },\n            fabric.util.degreesToRadians(-object.angle)),\n          BLUR_BOX = 20, color = new fabric.Color(this.color);\n\n      if (object.width && object.height) {\n        //http://www.w3.org/TR/SVG/filters.html#FilterEffectsRegion\n        // we add some extra space to filter box to contain the blur ( 20 )\n        fBoxX = toFixed((Math.abs(offset.x) + this.blur) / object.width, NUM_FRACTION_DIGITS) * 100 + BLUR_BOX;\n        fBoxY = toFixed((Math.abs(offset.y) + this.blur) / object.height, NUM_FRACTION_DIGITS) * 100 + BLUR_BOX;\n      }\n      if (object.flipX) {\n        offset.x *= -1;\n      }\n      if (object.flipY) {\n        offset.y *= -1;\n      }\n\n      return (\n        '<filter id=\"SVGID_' + this.id + '\" y=\"-' + fBoxY + '%\" height=\"' + (100 + 2 * fBoxY) + '%\" ' +\n          'x=\"-' + fBoxX + '%\" width=\"' + (100 + 2 * fBoxX) + '%\" ' + '>\\n' +\n          '\\t<feGaussianBlur in=\"SourceAlpha\" stdDeviation=\"' +\n            toFixed(this.blur ? this.blur / 2 : 0, NUM_FRACTION_DIGITS) + '\"></feGaussianBlur>\\n' +\n          '\\t<feOffset dx=\"' + toFixed(offset.x, NUM_FRACTION_DIGITS) +\n          '\" dy=\"' + toFixed(offset.y, NUM_FRACTION_DIGITS) + '\" result=\"oBlur\" ></feOffset>\\n' +\n          '\\t<feFlood flood-color=\"' + color.toRgb() + '\" flood-opacity=\"' + color.getAlpha() + '\"/>\\n' +\n          '\\t<feComposite in2=\"oBlur\" operator=\"in\" />\\n' +\n          '\\t<feMerge>\\n' +\n            '\\t\\t<feMergeNode></feMergeNode>\\n' +\n            '\\t\\t<feMergeNode in=\"SourceGraphic\"></feMergeNode>\\n' +\n          '\\t</feMerge>\\n' +\n        '</filter>\\n');\n    },\n    /* _TO_SVG_END_ */\n\n    /**\n     * Returns object representation of a shadow\n     * @return {Object} Object representation of a shadow instance\n     */\n    toObject: function() {\n      if (this.includeDefaultValues) {\n        return {\n          color: this.color,\n          blur: this.blur,\n          offsetX: this.offsetX,\n          offsetY: this.offsetY,\n          affectStroke: this.affectStroke\n        };\n      }\n      var obj = { }, proto = fabric.Shadow.prototype;\n\n      ['color', 'blur', 'offsetX', 'offsetY', 'affectStroke'].forEach(function(prop) {\n        if (this[prop] !== proto[prop]) {\n          obj[prop] = this[prop];\n        }\n      }, this);\n\n      return obj;\n    }\n  });\n\n  /**\n   * Regex matching shadow offsetX, offsetY and blur (ex: \"2px 2px 10px rgba(0,0,0,0.2)\", \"rgb(0,255,0) 2px 2px\")\n   * @static\n   * @field\n   * @memberOf fabric.Shadow\n   */\n  // eslint-disable-next-line max-len\n  fabric.Shadow.reOffsetsAndBlur = /(?:\\s|^)(-?\\d+(?:px)?(?:\\s?|$))?(-?\\d+(?:px)?(?:\\s?|$))?(\\d+(?:px)?)?(?:\\s?|$)(?:$|\\s)/;\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function () {\n\n  'use strict';\n\n  if (fabric.StaticCanvas) {\n    fabric.warn('fabric.StaticCanvas is already defined.');\n    return;\n  }\n\n  // aliases for faster resolution\n  var extend = fabric.util.object.extend,\n      getElementOffset = fabric.util.getElementOffset,\n      removeFromArray = fabric.util.removeFromArray,\n      toFixed = fabric.util.toFixed,\n      transformPoint = fabric.util.transformPoint,\n      invertTransform = fabric.util.invertTransform,\n\n      CANVAS_INIT_ERROR = new Error('Could not initialize `canvas` element');\n\n  /**\n   * Static canvas class\n   * @class fabric.StaticCanvas\n   * @mixes fabric.Collection\n   * @mixes fabric.Observable\n   * @see {@link http://fabricjs.com/static_canvas|StaticCanvas demo}\n   * @see {@link fabric.StaticCanvas#initialize} for constructor definition\n   * @fires before:render\n   * @fires after:render\n   * @fires canvas:cleared\n   * @fires object:added\n   * @fires object:removed\n   */\n  fabric.StaticCanvas = fabric.util.createClass(fabric.CommonMethods, /** @lends fabric.StaticCanvas.prototype */ {\n\n    /**\n     * Constructor\n     * @param {HTMLElement | String} el &lt;canvas> element to initialize instance on\n     * @param {Object} [options] Options object\n     * @return {Object} thisArg\n     */\n    initialize: function(el, options) {\n      options || (options = { });\n      this.renderAndResetBound = this.renderAndReset.bind(this);\n      this.requestRenderAllBound = this.requestRenderAll.bind(this);\n      this._initStatic(el, options);\n    },\n\n    /**\n     * Background color of canvas instance.\n     * Should be set via {@link fabric.StaticCanvas#setBackgroundColor}.\n     * @type {(String|fabric.Pattern)}\n     * @default\n     */\n    backgroundColor: '',\n\n    /**\n     * Background image of canvas instance.\n     * Should be set via {@link fabric.StaticCanvas#setBackgroundImage}.\n     * <b>Backwards incompatibility note:</b> The \"backgroundImageOpacity\"\n     * and \"backgroundImageStretch\" properties are deprecated since 1.3.9.\n     * Use {@link fabric.Image#opacity}, {@link fabric.Image#width} and {@link fabric.Image#height}.\n     * @type fabric.Image\n     * @default\n     */\n    backgroundImage: null,\n\n    /**\n     * Overlay color of canvas instance.\n     * Should be set via {@link fabric.StaticCanvas#setOverlayColor}\n     * @since 1.3.9\n     * @type {(String|fabric.Pattern)}\n     * @default\n     */\n    overlayColor: '',\n\n    /**\n     * Overlay image of canvas instance.\n     * Should be set via {@link fabric.StaticCanvas#setOverlayImage}.\n     * <b>Backwards incompatibility note:</b> The \"overlayImageLeft\"\n     * and \"overlayImageTop\" properties are deprecated since 1.3.9.\n     * Use {@link fabric.Image#left} and {@link fabric.Image#top}.\n     * @type fabric.Image\n     * @default\n     */\n    overlayImage: null,\n\n    /**\n     * Indicates whether toObject/toDatalessObject should include default values\n     * @type Boolean\n     * @default\n     */\n    includeDefaultValues: true,\n\n    /**\n     * Indicates whether objects' state should be saved\n     * @type Boolean\n     * @default\n     */\n    stateful: false,\n\n    /**\n     * Indicates whether {@link fabric.Collection.add}, {@link fabric.Collection.insertAt} and {@link fabric.Collection.remove},\n     * {@link fabric.StaticCanvas.moveTo}, {@link fabric.StaticCanvas.clear} and many more, should also re-render canvas.\n     * Disabling this option will not give a performance boost when adding/removing a lot of objects to/from canvas at once\n     * since the renders are quequed and executed one per frame.\n     * Disabling is suggested anyway and managing the renders of the app manually is not a big effort ( canvas.requestRenderAll() )\n     * Left default to true to do not break documentation and old app, fiddles.\n     * @type Boolean\n     * @default\n     */\n    renderOnAddRemove: true,\n\n    /**\n     * Function that determines clipping of entire canvas area\n     * Being passed context as first argument. See clipping canvas area in {@link https://github.com/kangax/fabric.js/wiki/FAQ}\n     * @deprecated since 2.0.0\n     * @type Function\n     * @default\n     */\n    clipTo: null,\n\n    /**\n     * Indicates whether object controls (borders/controls) are rendered above overlay image\n     * @type Boolean\n     * @default\n     */\n    controlsAboveOverlay: false,\n\n    /**\n     * Indicates whether the browser can be scrolled when using a touchscreen and dragging on the canvas\n     * @type Boolean\n     * @default\n     */\n    allowTouchScrolling: false,\n\n    /**\n     * Indicates whether this canvas will use image smoothing, this is on by default in browsers\n     * @type Boolean\n     * @default\n     */\n    imageSmoothingEnabled: true,\n\n    /**\n     * The transformation (in the format of Canvas transform) which focuses the viewport\n     * @type Array\n     * @default\n     */\n    viewportTransform: fabric.iMatrix.concat(),\n\n    /**\n     * if set to false background image is not affected by viewport transform\n     * @since 1.6.3\n     * @type Boolean\n     * @default\n     */\n    backgroundVpt: true,\n\n    /**\n     * if set to false overlya image is not affected by viewport transform\n     * @since 1.6.3\n     * @type Boolean\n     * @default\n     */\n    overlayVpt: true,\n\n    /**\n     * Callback; invoked right before object is about to be scaled/rotated\n     * @deprecated since 2.3.0\n     * Use before:transform event\n     */\n    onBeforeScaleRotate: function () {\n      /* NOOP */\n    },\n\n    /**\n     * When true, canvas is scaled by devicePixelRatio for better rendering on retina screens\n     * @type Boolean\n     * @default\n     */\n    enableRetinaScaling: true,\n\n    /**\n     * Describe canvas element extension over design\n     * properties are tl,tr,bl,br.\n     * if canvas is not zoomed/panned those points are the four corner of canvas\n     * if canvas is viewportTransformed you those points indicate the extension\n     * of canvas element in plain untrasformed coordinates\n     * The coordinates get updated with @method calcViewportBoundaries.\n     * @memberOf fabric.StaticCanvas.prototype\n     */\n    vptCoords: { },\n\n    /**\n     * Based on vptCoords and object.aCoords, skip rendering of objects that\n     * are not included in current viewport.\n     * May greatly help in applications with crowded canvas and use of zoom/pan\n     * If One of the corner of the bounding box of the object is on the canvas\n     * the objects get rendered.\n     * @memberOf fabric.StaticCanvas.prototype\n     * @type Boolean\n     * @default\n     */\n    skipOffscreen: true,\n\n    /**\n     * @private\n     * @param {HTMLElement | String} el &lt;canvas> element to initialize instance on\n     * @param {Object} [options] Options object\n     */\n    _initStatic: function(el, options) {\n      var cb = this.requestRenderAllBound;\n      this._objects = [];\n      this._createLowerCanvas(el);\n      this._initOptions(options);\n      this._setImageSmoothing();\n      // only initialize retina scaling once\n      if (!this.interactive) {\n        this._initRetinaScaling();\n      }\n\n      if (options.overlayImage) {\n        this.setOverlayImage(options.overlayImage, cb);\n      }\n      if (options.backgroundImage) {\n        this.setBackgroundImage(options.backgroundImage, cb);\n      }\n      if (options.backgroundColor) {\n        this.setBackgroundColor(options.backgroundColor, cb);\n      }\n      if (options.overlayColor) {\n        this.setOverlayColor(options.overlayColor, cb);\n      }\n      this.calcOffset();\n    },\n\n    /**\n     * @private\n     */\n    _isRetinaScaling: function() {\n      return (fabric.devicePixelRatio !== 1 && this.enableRetinaScaling);\n    },\n\n    /**\n     * @private\n     * @return {Number} retinaScaling if applied, otherwise 1;\n     */\n    getRetinaScaling: function() {\n      return this._isRetinaScaling() ? fabric.devicePixelRatio : 1;\n    },\n\n    /**\n     * @private\n     */\n    _initRetinaScaling: function() {\n      if (!this._isRetinaScaling()) {\n        return;\n      }\n      this.lowerCanvasEl.setAttribute('width', this.width * fabric.devicePixelRatio);\n      this.lowerCanvasEl.setAttribute('height', this.height * fabric.devicePixelRatio);\n\n      this.contextContainer.scale(fabric.devicePixelRatio, fabric.devicePixelRatio);\n    },\n\n    /**\n     * Calculates canvas element offset relative to the document\n     * This method is also attached as \"resize\" event handler of window\n     * @return {fabric.Canvas} instance\n     * @chainable\n     */\n    calcOffset: function () {\n      this._offset = getElementOffset(this.lowerCanvasEl);\n      return this;\n    },\n\n    /**\n     * Sets {@link fabric.StaticCanvas#overlayImage|overlay image} for this canvas\n     * @param {(fabric.Image|String)} image fabric.Image instance or URL of an image to set overlay to\n     * @param {Function} callback callback to invoke when image is loaded and set as an overlay\n     * @param {Object} [options] Optional options to set for the {@link fabric.Image|overlay image}.\n     * @return {fabric.Canvas} thisArg\n     * @chainable\n     * @see {@link http://jsfiddle.net/fabricjs/MnzHT/|jsFiddle demo}\n     * @example <caption>Normal overlayImage with left/top = 0</caption>\n     * canvas.setOverlayImage('http://fabricjs.com/assets/jail_cell_bars.png', canvas.renderAll.bind(canvas), {\n     *   // Needed to position overlayImage at 0/0\n     *   originX: 'left',\n     *   originY: 'top'\n     * });\n     * @example <caption>overlayImage with different properties</caption>\n     * canvas.setOverlayImage('http://fabricjs.com/assets/jail_cell_bars.png', canvas.renderAll.bind(canvas), {\n     *   opacity: 0.5,\n     *   angle: 45,\n     *   left: 400,\n     *   top: 400,\n     *   originX: 'left',\n     *   originY: 'top'\n     * });\n     * @example <caption>Stretched overlayImage #1 - width/height correspond to canvas width/height</caption>\n     * fabric.Image.fromURL('http://fabricjs.com/assets/jail_cell_bars.png', function(img) {\n     *    img.set({width: canvas.width, height: canvas.height, originX: 'left', originY: 'top'});\n     *    canvas.setOverlayImage(img, canvas.renderAll.bind(canvas));\n     * });\n     * @example <caption>Stretched overlayImage #2 - width/height correspond to canvas width/height</caption>\n     * canvas.setOverlayImage('http://fabricjs.com/assets/jail_cell_bars.png', canvas.renderAll.bind(canvas), {\n     *   width: canvas.width,\n     *   height: canvas.height,\n     *   // Needed to position overlayImage at 0/0\n     *   originX: 'left',\n     *   originY: 'top'\n     * });\n     * @example <caption>overlayImage loaded from cross-origin</caption>\n     * canvas.setOverlayImage('http://fabricjs.com/assets/jail_cell_bars.png', canvas.renderAll.bind(canvas), {\n     *   opacity: 0.5,\n     *   angle: 45,\n     *   left: 400,\n     *   top: 400,\n     *   originX: 'left',\n     *   originY: 'top',\n     *   crossOrigin: 'anonymous'\n     * });\n     */\n    setOverlayImage: function (image, callback, options) {\n      return this.__setBgOverlayImage('overlayImage', image, callback, options);\n    },\n\n    /**\n     * Sets {@link fabric.StaticCanvas#backgroundImage|background image} for this canvas\n     * @param {(fabric.Image|String)} image fabric.Image instance or URL of an image to set background to\n     * @param {Function} callback Callback to invoke when image is loaded and set as background\n     * @param {Object} [options] Optional options to set for the {@link fabric.Image|background image}.\n     * @return {fabric.Canvas} thisArg\n     * @chainable\n     * @see {@link http://jsfiddle.net/djnr8o7a/28/|jsFiddle demo}\n     * @example <caption>Normal backgroundImage with left/top = 0</caption>\n     * canvas.setBackgroundImage('http://fabricjs.com/assets/honey_im_subtle.png', canvas.renderAll.bind(canvas), {\n     *   // Needed to position backgroundImage at 0/0\n     *   originX: 'left',\n     *   originY: 'top'\n     * });\n     * @example <caption>backgroundImage with different properties</caption>\n     * canvas.setBackgroundImage('http://fabricjs.com/assets/honey_im_subtle.png', canvas.renderAll.bind(canvas), {\n     *   opacity: 0.5,\n     *   angle: 45,\n     *   left: 400,\n     *   top: 400,\n     *   originX: 'left',\n     *   originY: 'top'\n     * });\n     * @example <caption>Stretched backgroundImage #1 - width/height correspond to canvas width/height</caption>\n     * fabric.Image.fromURL('http://fabricjs.com/assets/honey_im_subtle.png', function(img) {\n     *    img.set({width: canvas.width, height: canvas.height, originX: 'left', originY: 'top'});\n     *    canvas.setBackgroundImage(img, canvas.renderAll.bind(canvas));\n     * });\n     * @example <caption>Stretched backgroundImage #2 - width/height correspond to canvas width/height</caption>\n     * canvas.setBackgroundImage('http://fabricjs.com/assets/honey_im_subtle.png', canvas.renderAll.bind(canvas), {\n     *   width: canvas.width,\n     *   height: canvas.height,\n     *   // Needed to position backgroundImage at 0/0\n     *   originX: 'left',\n     *   originY: 'top'\n     * });\n     * @example <caption>backgroundImage loaded from cross-origin</caption>\n     * canvas.setBackgroundImage('http://fabricjs.com/assets/honey_im_subtle.png', canvas.renderAll.bind(canvas), {\n     *   opacity: 0.5,\n     *   angle: 45,\n     *   left: 400,\n     *   top: 400,\n     *   originX: 'left',\n     *   originY: 'top',\n     *   crossOrigin: 'anonymous'\n     * });\n     */\n    setBackgroundImage: function (image, callback, options) {\n      return this.__setBgOverlayImage('backgroundImage', image, callback, options);\n    },\n\n    /**\n     * Sets {@link fabric.StaticCanvas#overlayColor|foreground color} for this canvas\n     * @param {(String|fabric.Pattern)} overlayColor Color or pattern to set foreground color to\n     * @param {Function} callback Callback to invoke when foreground color is set\n     * @return {fabric.Canvas} thisArg\n     * @chainable\n     * @see {@link http://jsfiddle.net/fabricjs/pB55h/|jsFiddle demo}\n     * @example <caption>Normal overlayColor - color value</caption>\n     * canvas.setOverlayColor('rgba(255, 73, 64, 0.6)', canvas.renderAll.bind(canvas));\n     * @example <caption>fabric.Pattern used as overlayColor</caption>\n     * canvas.setOverlayColor({\n     *   source: 'http://fabricjs.com/assets/escheresque_ste.png'\n     * }, canvas.renderAll.bind(canvas));\n     * @example <caption>fabric.Pattern used as overlayColor with repeat and offset</caption>\n     * canvas.setOverlayColor({\n     *   source: 'http://fabricjs.com/assets/escheresque_ste.png',\n     *   repeat: 'repeat',\n     *   offsetX: 200,\n     *   offsetY: 100\n     * }, canvas.renderAll.bind(canvas));\n     */\n    setOverlayColor: function(overlayColor, callback) {\n      return this.__setBgOverlayColor('overlayColor', overlayColor, callback);\n    },\n\n    /**\n     * Sets {@link fabric.StaticCanvas#backgroundColor|background color} for this canvas\n     * @param {(String|fabric.Pattern)} backgroundColor Color or pattern to set background color to\n     * @param {Function} callback Callback to invoke when background color is set\n     * @return {fabric.Canvas} thisArg\n     * @chainable\n     * @see {@link http://jsfiddle.net/fabricjs/hXzvk/|jsFiddle demo}\n     * @example <caption>Normal backgroundColor - color value</caption>\n     * canvas.setBackgroundColor('rgba(255, 73, 64, 0.6)', canvas.renderAll.bind(canvas));\n     * @example <caption>fabric.Pattern used as backgroundColor</caption>\n     * canvas.setBackgroundColor({\n     *   source: 'http://fabricjs.com/assets/escheresque_ste.png'\n     * }, canvas.renderAll.bind(canvas));\n     * @example <caption>fabric.Pattern used as backgroundColor with repeat and offset</caption>\n     * canvas.setBackgroundColor({\n     *   source: 'http://fabricjs.com/assets/escheresque_ste.png',\n     *   repeat: 'repeat',\n     *   offsetX: 200,\n     *   offsetY: 100\n     * }, canvas.renderAll.bind(canvas));\n     */\n    setBackgroundColor: function(backgroundColor, callback) {\n      return this.__setBgOverlayColor('backgroundColor', backgroundColor, callback);\n    },\n\n    /**\n     * @private\n     * @see {@link http://www.whatwg.org/specs/web-apps/current-work/multipage/the-canvas-element.html#dom-context-2d-imagesmoothingenabled|WhatWG Canvas Standard}\n     */\n    _setImageSmoothing: function() {\n      var ctx = this.getContext();\n\n      ctx.imageSmoothingEnabled = ctx.imageSmoothingEnabled || ctx.webkitImageSmoothingEnabled\n        || ctx.mozImageSmoothingEnabled || ctx.msImageSmoothingEnabled || ctx.oImageSmoothingEnabled;\n      ctx.imageSmoothingEnabled = this.imageSmoothingEnabled;\n    },\n\n    /**\n     * @private\n     * @param {String} property Property to set ({@link fabric.StaticCanvas#backgroundImage|backgroundImage}\n     * or {@link fabric.StaticCanvas#overlayImage|overlayImage})\n     * @param {(fabric.Image|String|null)} image fabric.Image instance, URL of an image or null to set background or overlay to\n     * @param {Function} callback Callback to invoke when image is loaded and set as background or overlay\n     * @param {Object} [options] Optional options to set for the {@link fabric.Image|image}.\n     */\n    __setBgOverlayImage: function(property, image, callback, options) {\n      if (typeof image === 'string') {\n        fabric.util.loadImage(image, function(img) {\n          img && (this[property] = new fabric.Image(img, options));\n          callback && callback(img);\n        }, this, options && options.crossOrigin);\n      }\n      else {\n        options && image.setOptions(options);\n        this[property] = image;\n        callback && callback(image);\n      }\n\n      return this;\n    },\n\n    /**\n     * @private\n     * @param {String} property Property to set ({@link fabric.StaticCanvas#backgroundColor|backgroundColor}\n     * or {@link fabric.StaticCanvas#overlayColor|overlayColor})\n     * @param {(Object|String|null)} color Object with pattern information, color value or null\n     * @param {Function} [callback] Callback is invoked when color is set\n     */\n    __setBgOverlayColor: function(property, color, callback) {\n      this[property] = color;\n      this._initGradient(color, property);\n      this._initPattern(color, property, callback);\n      return this;\n    },\n\n    /**\n     * @private\n     */\n    _createCanvasElement: function() {\n      var element = fabric.util.createCanvasElement();\n      if (!element) {\n        throw CANVAS_INIT_ERROR;\n      }\n      if (!element.style) {\n        element.style = { };\n      }\n      if (typeof element.getContext === 'undefined') {\n        throw CANVAS_INIT_ERROR;\n      }\n      return element;\n    },\n\n    /**\n     * @private\n     * @param {Object} [options] Options object\n     */\n    _initOptions: function (options) {\n      this._setOptions(options);\n\n      this.width = this.width || parseInt(this.lowerCanvasEl.width, 10) || 0;\n      this.height = this.height || parseInt(this.lowerCanvasEl.height, 10) || 0;\n\n      if (!this.lowerCanvasEl.style) {\n        return;\n      }\n\n      this.lowerCanvasEl.width = this.width;\n      this.lowerCanvasEl.height = this.height;\n\n      this.lowerCanvasEl.style.width = this.width + 'px';\n      this.lowerCanvasEl.style.height = this.height + 'px';\n\n      this.viewportTransform = this.viewportTransform.slice();\n    },\n\n    /**\n     * Creates a bottom canvas\n     * @private\n     * @param {HTMLElement} [canvasEl]\n     */\n    _createLowerCanvas: function (canvasEl) {\n      // canvasEl === 'HTMLCanvasElement' does not work on jsdom/node\n      if (canvasEl && canvasEl.getContext) {\n        this.lowerCanvasEl = canvasEl;\n      }\n      else {\n        this.lowerCanvasEl = fabric.util.getById(canvasEl) || this._createCanvasElement();\n      }\n\n      fabric.util.addClass(this.lowerCanvasEl, 'lower-canvas');\n\n      if (this.interactive) {\n        this._applyCanvasStyle(this.lowerCanvasEl);\n      }\n\n      this.contextContainer = this.lowerCanvasEl.getContext('2d');\n    },\n\n    /**\n     * Returns canvas width (in px)\n     * @return {Number}\n     */\n    getWidth: function () {\n      return this.width;\n    },\n\n    /**\n     * Returns canvas height (in px)\n     * @return {Number}\n     */\n    getHeight: function () {\n      return this.height;\n    },\n\n    /**\n     * Sets width of this canvas instance\n     * @param {Number|String} value                         Value to set width to\n     * @param {Object}        [options]                     Options object\n     * @param {Boolean}       [options.backstoreOnly=false] Set the given dimensions only as canvas backstore dimensions\n     * @param {Boolean}       [options.cssOnly=false]       Set the given dimensions only as css dimensions\n     * @return {fabric.Canvas} instance\n     * @chainable true\n     */\n    setWidth: function (value, options) {\n      return this.setDimensions({ width: value }, options);\n    },\n\n    /**\n     * Sets height of this canvas instance\n     * @param {Number|String} value                         Value to set height to\n     * @param {Object}        [options]                     Options object\n     * @param {Boolean}       [options.backstoreOnly=false] Set the given dimensions only as canvas backstore dimensions\n     * @param {Boolean}       [options.cssOnly=false]       Set the given dimensions only as css dimensions\n     * @return {fabric.Canvas} instance\n     * @chainable true\n     */\n    setHeight: function (value, options) {\n      return this.setDimensions({ height: value }, options);\n    },\n\n    /**\n     * Sets dimensions (width, height) of this canvas instance. when options.cssOnly flag active you should also supply the unit of measure (px/%/em)\n     * @param {Object}        dimensions                    Object with width/height properties\n     * @param {Number|String} [dimensions.width]            Width of canvas element\n     * @param {Number|String} [dimensions.height]           Height of canvas element\n     * @param {Object}        [options]                     Options object\n     * @param {Boolean}       [options.backstoreOnly=false] Set the given dimensions only as canvas backstore dimensions\n     * @param {Boolean}       [options.cssOnly=false]       Set the given dimensions only as css dimensions\n     * @return {fabric.Canvas} thisArg\n     * @chainable\n     */\n    setDimensions: function (dimensions, options) {\n      var cssValue;\n\n      options = options || {};\n\n      for (var prop in dimensions) {\n        cssValue = dimensions[prop];\n\n        if (!options.cssOnly) {\n          this._setBackstoreDimension(prop, dimensions[prop]);\n          cssValue += 'px';\n          this.hasLostContext = true;\n        }\n\n        if (!options.backstoreOnly) {\n          this._setCssDimension(prop, cssValue);\n        }\n      }\n      if (this._isCurrentlyDrawing) {\n        this.freeDrawingBrush && this.freeDrawingBrush._setBrushStyles();\n      }\n      this._initRetinaScaling();\n      this._setImageSmoothing();\n      this.calcOffset();\n\n      if (!options.cssOnly) {\n        this.requestRenderAll();\n      }\n\n      return this;\n    },\n\n    /**\n     * Helper for setting width/height\n     * @private\n     * @param {String} prop property (width|height)\n     * @param {Number} value value to set property to\n     * @return {fabric.Canvas} instance\n     * @chainable true\n     */\n    _setBackstoreDimension: function (prop, value) {\n      this.lowerCanvasEl[prop] = value;\n\n      if (this.upperCanvasEl) {\n        this.upperCanvasEl[prop] = value;\n      }\n\n      if (this.cacheCanvasEl) {\n        this.cacheCanvasEl[prop] = value;\n      }\n\n      this[prop] = value;\n\n      return this;\n    },\n\n    /**\n     * Helper for setting css width/height\n     * @private\n     * @param {String} prop property (width|height)\n     * @param {String} value value to set property to\n     * @return {fabric.Canvas} instance\n     * @chainable true\n     */\n    _setCssDimension: function (prop, value) {\n      this.lowerCanvasEl.style[prop] = value;\n\n      if (this.upperCanvasEl) {\n        this.upperCanvasEl.style[prop] = value;\n      }\n\n      if (this.wrapperEl) {\n        this.wrapperEl.style[prop] = value;\n      }\n\n      return this;\n    },\n\n    /**\n     * Returns canvas zoom level\n     * @return {Number}\n     */\n    getZoom: function () {\n      return this.viewportTransform[0];\n    },\n\n    /**\n     * Sets viewport transform of this canvas instance\n     * @param {Array} vpt the transform in the form of context.transform\n     * @return {fabric.Canvas} instance\n     * @chainable true\n     */\n    setViewportTransform: function (vpt) {\n      var activeObject = this._activeObject, object, ignoreVpt = false, skipAbsolute = true, i, len;\n      this.viewportTransform = vpt;\n      for (i = 0, len = this._objects.length; i < len; i++) {\n        object = this._objects[i];\n        object.group || object.setCoords(ignoreVpt, skipAbsolute);\n      }\n      if (activeObject && activeObject.type === 'activeSelection') {\n        activeObject.setCoords(ignoreVpt, skipAbsolute);\n      }\n      this.calcViewportBoundaries();\n      this.renderOnAddRemove && this.requestRenderAll();\n      return this;\n    },\n\n    /**\n     * Sets zoom level of this canvas instance, zoom centered around point\n     * @param {fabric.Point} point to zoom with respect to\n     * @param {Number} value to set zoom to, less than 1 zooms out\n     * @return {fabric.Canvas} instance\n     * @chainable true\n     */\n    zoomToPoint: function (point, value) {\n      // TODO: just change the scale, preserve other transformations\n      var before = point, vpt = this.viewportTransform.slice(0);\n      point = transformPoint(point, invertTransform(this.viewportTransform));\n      vpt[0] = value;\n      vpt[3] = value;\n      var after = transformPoint(point, vpt);\n      vpt[4] += before.x - after.x;\n      vpt[5] += before.y - after.y;\n      return this.setViewportTransform(vpt);\n    },\n\n    /**\n     * Sets zoom level of this canvas instance\n     * @param {Number} value to set zoom to, less than 1 zooms out\n     * @return {fabric.Canvas} instance\n     * @chainable true\n     */\n    setZoom: function (value) {\n      this.zoomToPoint(new fabric.Point(0, 0), value);\n      return this;\n    },\n\n    /**\n     * Pan viewport so as to place point at top left corner of canvas\n     * @param {fabric.Point} point to move to\n     * @return {fabric.Canvas} instance\n     * @chainable true\n     */\n    absolutePan: function (point) {\n      var vpt = this.viewportTransform.slice(0);\n      vpt[4] = -point.x;\n      vpt[5] = -point.y;\n      return this.setViewportTransform(vpt);\n    },\n\n    /**\n     * Pans viewpoint relatively\n     * @param {fabric.Point} point (position vector) to move by\n     * @return {fabric.Canvas} instance\n     * @chainable true\n     */\n    relativePan: function (point) {\n      return this.absolutePan(new fabric.Point(\n        -point.x - this.viewportTransform[4],\n        -point.y - this.viewportTransform[5]\n      ));\n    },\n\n    /**\n     * Returns &lt;canvas> element corresponding to this instance\n     * @return {HTMLCanvasElement}\n     */\n    getElement: function () {\n      return this.lowerCanvasEl;\n    },\n\n    /**\n     * @private\n     * @param {fabric.Object} obj Object that was added\n     */\n    _onObjectAdded: function(obj) {\n      this.stateful && obj.setupState();\n      obj._set('canvas', this);\n      obj.setCoords();\n      this.fire('object:added', { target: obj });\n      obj.fire('added');\n    },\n\n    /**\n     * @private\n     * @param {fabric.Object} obj Object that was removed\n     */\n    _onObjectRemoved: function(obj) {\n      this.fire('object:removed', { target: obj });\n      obj.fire('removed');\n      delete obj.canvas;\n    },\n\n    /**\n     * Clears specified context of canvas element\n     * @param {CanvasRenderingContext2D} ctx Context to clear\n     * @return {fabric.Canvas} thisArg\n     * @chainable\n     */\n    clearContext: function(ctx) {\n      ctx.clearRect(0, 0, this.width, this.height);\n      return this;\n    },\n\n    /**\n     * Returns context of canvas where objects are drawn\n     * @return {CanvasRenderingContext2D}\n     */\n    getContext: function () {\n      return this.contextContainer;\n    },\n\n    /**\n     * Clears all contexts (background, main, top) of an instance\n     * @return {fabric.Canvas} thisArg\n     * @chainable\n     */\n    clear: function () {\n      this._objects.length = 0;\n      this.backgroundImage = null;\n      this.overlayImage = null;\n      this.backgroundColor = '';\n      this.overlayColor = '';\n      if (this._hasITextHandlers) {\n        this.off('mouse:up', this._mouseUpITextHandler);\n        this._iTextInstances = null;\n        this._hasITextHandlers = false;\n      }\n      this.clearContext(this.contextContainer);\n      this.fire('canvas:cleared');\n      this.renderOnAddRemove && this.requestRenderAll();\n      return this;\n    },\n\n    /**\n     * Renders the canvas\n     * @return {fabric.Canvas} instance\n     * @chainable\n     */\n    renderAll: function () {\n      var canvasToDrawOn = this.contextContainer;\n      this.renderCanvas(canvasToDrawOn, this._objects);\n      return this;\n    },\n\n    /**\n     * Function created to be instance bound at initialization\n     * used in requestAnimationFrame rendering\n     * @return {fabric.Canvas} instance\n     * @chainable\n     */\n    renderAndReset: function() {\n      this.isRendering = 0;\n      this.renderAll();\n    },\n\n    /**\n     * Append a renderAll request to next animation frame.\n     * a boolean flag will avoid appending more.\n     * @return {fabric.Canvas} instance\n     * @chainable\n     */\n    requestRenderAll: function () {\n      if (!this.isRendering) {\n        this.isRendering = fabric.util.requestAnimFrame(this.renderAndResetBound);\n      }\n      return this;\n    },\n\n    /**\n     * Calculate the position of the 4 corner of canvas with current viewportTransform.\n     * helps to determinate when an object is in the current rendering viewport using\n     * object absolute coordinates ( aCoords )\n     * @return {Object} points.tl\n     * @chainable\n     */\n    calcViewportBoundaries: function() {\n      var points = { }, width = this.width, height = this.height,\n          iVpt = invertTransform(this.viewportTransform);\n      points.tl = transformPoint({ x: 0, y: 0 }, iVpt);\n      points.br = transformPoint({ x: width, y: height }, iVpt);\n      points.tr = new fabric.Point(points.br.x, points.tl.y);\n      points.bl = new fabric.Point(points.tl.x, points.br.y);\n      this.vptCoords = points;\n      return points;\n    },\n\n    /**\n     * Renders background, objects, overlay and controls.\n     * @param {CanvasRenderingContext2D} ctx\n     * @param {Array} objects to render\n     * @return {fabric.Canvas} instance\n     * @chainable\n     */\n    renderCanvas: function(ctx, objects) {\n      var v = this.viewportTransform;\n      if (this.isRendering) {\n        fabric.util.cancelAnimFrame(this.isRendering);\n        this.isRendering = 0;\n      }\n      this.calcViewportBoundaries();\n      this.clearContext(ctx);\n      this.fire('before:render');\n      if (this.clipTo) {\n        fabric.util.clipContext(this, ctx);\n      }\n      this._renderBackground(ctx);\n\n      ctx.save();\n      //apply viewport transform once for all rendering process\n      ctx.transform(v[0], v[1], v[2], v[3], v[4], v[5]);\n      this._renderObjects(ctx, objects);\n      ctx.restore();\n      if (!this.controlsAboveOverlay && this.interactive) {\n        this.drawControls(ctx);\n      }\n      if (this.clipTo) {\n        ctx.restore();\n      }\n      this._renderOverlay(ctx);\n      if (this.controlsAboveOverlay && this.interactive) {\n        this.drawControls(ctx);\n      }\n      this.fire('after:render');\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     * @param {Array} objects to render\n     */\n    _renderObjects: function(ctx, objects) {\n      var i, len;\n      for (i = 0, len = objects.length; i < len; ++i) {\n        objects[i] && objects[i].render(ctx);\n      }\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     * @param {string} property 'background' or 'overlay'\n     */\n    _renderBackgroundOrOverlay: function(ctx, property) {\n      var object = this[property + 'Color'], v;\n      if (object) {\n        ctx.fillStyle = object.toLive\n          ? object.toLive(ctx, this)\n          : object;\n\n        ctx.fillRect(\n          object.offsetX || 0,\n          object.offsetY || 0,\n          this.width,\n          this.height);\n      }\n      object = this[property + 'Image'];\n      if (object) {\n        if (this[property + 'Vpt']) {\n          v = this.viewportTransform;\n          ctx.save();\n          ctx.transform(v[0], v[1], v[2], v[3], v[4], v[5]);\n        }\n        object.render(ctx);\n        this[property + 'Vpt'] && ctx.restore();\n      }\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    _renderBackground: function(ctx) {\n      this._renderBackgroundOrOverlay(ctx, 'background');\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    _renderOverlay: function(ctx) {\n      this._renderBackgroundOrOverlay(ctx, 'overlay');\n    },\n\n    /**\n     * Returns coordinates of a center of canvas.\n     * Returned value is an object with top and left properties\n     * @return {Object} object with \"top\" and \"left\" number values\n     */\n    getCenter: function () {\n      return {\n        top: this.height / 2,\n        left: this.width / 2\n      };\n    },\n\n    /**\n     * Centers object horizontally in the canvas\n     * @param {fabric.Object} object Object to center horizontally\n     * @return {fabric.Canvas} thisArg\n     */\n    centerObjectH: function (object) {\n      return this._centerObject(object, new fabric.Point(this.getCenter().left, object.getCenterPoint().y));\n    },\n\n    /**\n     * Centers object vertically in the canvas\n     * @param {fabric.Object} object Object to center vertically\n     * @return {fabric.Canvas} thisArg\n     * @chainable\n     */\n    centerObjectV: function (object) {\n      return this._centerObject(object, new fabric.Point(object.getCenterPoint().x, this.getCenter().top));\n    },\n\n    /**\n     * Centers object vertically and horizontally in the canvas\n     * @param {fabric.Object} object Object to center vertically and horizontally\n     * @return {fabric.Canvas} thisArg\n     * @chainable\n     */\n    centerObject: function(object) {\n      var center = this.getCenter();\n\n      return this._centerObject(object, new fabric.Point(center.left, center.top));\n    },\n\n    /**\n     * Centers object vertically and horizontally in the viewport\n     * @param {fabric.Object} object Object to center vertically and horizontally\n     * @return {fabric.Canvas} thisArg\n     * @chainable\n     */\n    viewportCenterObject: function(object) {\n      var vpCenter = this.getVpCenter();\n\n      return this._centerObject(object, vpCenter);\n    },\n\n    /**\n     * Centers object horizontally in the viewport, object.top is unchanged\n     * @param {fabric.Object} object Object to center vertically and horizontally\n     * @return {fabric.Canvas} thisArg\n     * @chainable\n     */\n    viewportCenterObjectH: function(object) {\n      var vpCenter = this.getVpCenter();\n      this._centerObject(object, new fabric.Point(vpCenter.x, object.getCenterPoint().y));\n      return this;\n    },\n\n    /**\n     * Centers object Vertically in the viewport, object.top is unchanged\n     * @param {fabric.Object} object Object to center vertically and horizontally\n     * @return {fabric.Canvas} thisArg\n     * @chainable\n     */\n    viewportCenterObjectV: function(object) {\n      var vpCenter = this.getVpCenter();\n\n      return this._centerObject(object, new fabric.Point(object.getCenterPoint().x, vpCenter.y));\n    },\n\n    /**\n     * Calculate the point in canvas that correspond to the center of actual viewport.\n     * @return {fabric.Point} vpCenter, viewport center\n     * @chainable\n     */\n    getVpCenter: function() {\n      var center = this.getCenter(),\n          iVpt = invertTransform(this.viewportTransform);\n      return transformPoint({ x: center.left, y: center.top }, iVpt);\n    },\n\n    /**\n     * @private\n     * @param {fabric.Object} object Object to center\n     * @param {fabric.Point} center Center point\n     * @return {fabric.Canvas} thisArg\n     * @chainable\n     */\n    _centerObject: function(object, center) {\n      object.setPositionByOrigin(center, 'center', 'center');\n      object.setCoords();\n      this.renderOnAddRemove && this.requestRenderAll();\n      return this;\n    },\n\n    /**\n     * Returs dataless JSON representation of canvas\n     * @param {Array} [propertiesToInclude] Any properties that you might want to additionally include in the output\n     * @return {String} json string\n     */\n    toDatalessJSON: function (propertiesToInclude) {\n      return this.toDatalessObject(propertiesToInclude);\n    },\n\n    /**\n     * Returns object representation of canvas\n     * @param {Array} [propertiesToInclude] Any properties that you might want to additionally include in the output\n     * @return {Object} object representation of an instance\n     */\n    toObject: function (propertiesToInclude) {\n      return this._toObjectMethod('toObject', propertiesToInclude);\n    },\n\n    /**\n     * Returns dataless object representation of canvas\n     * @param {Array} [propertiesToInclude] Any properties that you might want to additionally include in the output\n     * @return {Object} object representation of an instance\n     */\n    toDatalessObject: function (propertiesToInclude) {\n      return this._toObjectMethod('toDatalessObject', propertiesToInclude);\n    },\n\n    /**\n     * @private\n     */\n    _toObjectMethod: function (methodName, propertiesToInclude) {\n\n      var data = {\n        version: fabric.version,\n        objects: this._toObjects(methodName, propertiesToInclude)\n      };\n\n      extend(data, this.__serializeBgOverlay(methodName, propertiesToInclude));\n\n      fabric.util.populateWithProperties(this, data, propertiesToInclude);\n\n      return data;\n    },\n\n    /**\n     * @private\n     */\n    _toObjects: function(methodName, propertiesToInclude) {\n      return this.getObjects().filter(function(object) {\n        return !object.excludeFromExport;\n      }).map(function(instance) {\n        return this._toObject(instance, methodName, propertiesToInclude);\n      }, this);\n    },\n\n    /**\n     * @private\n     */\n    _toObject: function(instance, methodName, propertiesToInclude) {\n      var originalValue;\n\n      if (!this.includeDefaultValues) {\n        originalValue = instance.includeDefaultValues;\n        instance.includeDefaultValues = false;\n      }\n\n      var object = instance[methodName](propertiesToInclude);\n      if (!this.includeDefaultValues) {\n        instance.includeDefaultValues = originalValue;\n      }\n      return object;\n    },\n\n    /**\n     * @private\n     */\n    __serializeBgOverlay: function(methodName, propertiesToInclude) {\n      var data = { }, bgImage = this.backgroundImage, overlay = this.overlayImage;\n\n      if (this.backgroundColor) {\n        data.background = this.backgroundColor.toObject\n          ? this.backgroundColor.toObject(propertiesToInclude)\n          : this.backgroundColor;\n      }\n\n      if (this.overlayColor) {\n        data.overlay = this.overlayColor.toObject\n          ? this.overlayColor.toObject(propertiesToInclude)\n          : this.overlayColor;\n      }\n      if (bgImage && !bgImage.excludeFromExport) {\n        data.backgroundImage = this._toObject(bgImage, methodName, propertiesToInclude);\n      }\n      if (overlay && !overlay.excludeFromExport) {\n        data.overlayImage = this._toObject(overlay, methodName, propertiesToInclude);\n      }\n\n      return data;\n    },\n\n    /* _TO_SVG_START_ */\n    /**\n     * When true, getSvgTransform() will apply the StaticCanvas.viewportTransform to the SVG transformation. When true,\n     * a zoomed canvas will then produce zoomed SVG output.\n     * @type Boolean\n     * @default\n     */\n    svgViewportTransformation: true,\n\n    /**\n     * Returns SVG representation of canvas\n     * @function\n     * @param {Object} [options] Options object for SVG output\n     * @param {Boolean} [options.suppressPreamble=false] If true xml tag is not included\n     * @param {Object} [options.viewBox] SVG viewbox object\n     * @param {Number} [options.viewBox.x] x-cooridnate of viewbox\n     * @param {Number} [options.viewBox.y] y-coordinate of viewbox\n     * @param {Number} [options.viewBox.width] Width of viewbox\n     * @param {Number} [options.viewBox.height] Height of viewbox\n     * @param {String} [options.encoding=UTF-8] Encoding of SVG output\n     * @param {String} [options.width] desired width of svg with or without units\n     * @param {String} [options.height] desired height of svg with or without units\n     * @param {Function} [reviver] Method for further parsing of svg elements, called after each fabric object converted into svg representation.\n     * @return {String} SVG string\n     * @tutorial {@link http://fabricjs.com/fabric-intro-part-3#serialization}\n     * @see {@link http://jsfiddle.net/fabricjs/jQ3ZZ/|jsFiddle demo}\n     * @example <caption>Normal SVG output</caption>\n     * var svg = canvas.toSVG();\n     * @example <caption>SVG output without preamble (without &lt;?xml ../>)</caption>\n     * var svg = canvas.toSVG({suppressPreamble: true});\n     * @example <caption>SVG output with viewBox attribute</caption>\n     * var svg = canvas.toSVG({\n     *   viewBox: {\n     *     x: 100,\n     *     y: 100,\n     *     width: 200,\n     *     height: 300\n     *   }\n     * });\n     * @example <caption>SVG output with different encoding (default: UTF-8)</caption>\n     * var svg = canvas.toSVG({encoding: 'ISO-8859-1'});\n     * @example <caption>Modify SVG output with reviver function</caption>\n     * var svg = canvas.toSVG(null, function(svg) {\n     *   return svg.replace('stroke-dasharray: ; stroke-linecap: butt; stroke-linejoin: miter; stroke-miterlimit: 10; ', '');\n     * });\n     */\n    toSVG: function(options, reviver) {\n      options || (options = { });\n\n      var markup = [];\n\n      this._setSVGPreamble(markup, options);\n      this._setSVGHeader(markup, options);\n\n      this._setSVGBgOverlayColor(markup, 'backgroundColor');\n      this._setSVGBgOverlayImage(markup, 'backgroundImage', reviver);\n\n      this._setSVGObjects(markup, reviver);\n\n      this._setSVGBgOverlayColor(markup, 'overlayColor');\n      this._setSVGBgOverlayImage(markup, 'overlayImage', reviver);\n\n      markup.push('</svg>');\n\n      return markup.join('');\n    },\n\n    /**\n     * @private\n     */\n    _setSVGPreamble: function(markup, options) {\n      if (options.suppressPreamble) {\n        return;\n      }\n      markup.push(\n        '<?xml version=\"1.0\" encoding=\"', (options.encoding || 'UTF-8'), '\" standalone=\"no\" ?>\\n',\n        '<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\" ',\n        '\"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\\n'\n      );\n    },\n\n    /**\n     * @private\n     */\n    _setSVGHeader: function(markup, options) {\n      var width = options.width || this.width,\n          height = options.height || this.height,\n          vpt, viewBox = 'viewBox=\"0 0 ' + this.width + ' ' + this.height + '\" ',\n          NUM_FRACTION_DIGITS = fabric.Object.NUM_FRACTION_DIGITS;\n\n      if (options.viewBox) {\n        viewBox = 'viewBox=\"' +\n                options.viewBox.x + ' ' +\n                options.viewBox.y + ' ' +\n                options.viewBox.width + ' ' +\n                options.viewBox.height + '\" ';\n      }\n      else {\n        if (this.svgViewportTransformation) {\n          vpt = this.viewportTransform;\n          viewBox = 'viewBox=\"' +\n                  toFixed(-vpt[4] / vpt[0], NUM_FRACTION_DIGITS) + ' ' +\n                  toFixed(-vpt[5] / vpt[3], NUM_FRACTION_DIGITS) + ' ' +\n                  toFixed(this.width / vpt[0], NUM_FRACTION_DIGITS) + ' ' +\n                  toFixed(this.height / vpt[3], NUM_FRACTION_DIGITS) + '\" ';\n        }\n      }\n\n      markup.push(\n        '<svg ',\n        'xmlns=\"http://www.w3.org/2000/svg\" ',\n        'xmlns:xlink=\"http://www.w3.org/1999/xlink\" ',\n        'version=\"1.1\" ',\n        'width=\"', width, '\" ',\n        'height=\"', height, '\" ',\n        viewBox,\n        'xml:space=\"preserve\">\\n',\n        '<desc>Created with Fabric.js ', fabric.version, '</desc>\\n',\n        '<defs>\\n',\n        this.createSVGFontFacesMarkup(),\n        this.createSVGRefElementsMarkup(),\n        '</defs>\\n'\n      );\n    },\n\n    /**\n     * Creates markup containing SVG referenced elements like patterns, gradients etc.\n     * @return {String}\n     */\n    createSVGRefElementsMarkup: function() {\n      var _this = this,\n          markup = ['backgroundColor', 'overlayColor'].map(function(prop) {\n            var fill = _this[prop];\n            if (fill && fill.toLive) {\n              return fill.toSVG(_this, false);\n            }\n          });\n      return markup.join('');\n    },\n\n    /**\n     * Creates markup containing SVG font faces,\n     * font URLs for font faces must be collected by developers\n     * and are not extracted from the DOM by fabricjs\n     * @param {Array} objects Array of fabric objects\n     * @return {String}\n     */\n    createSVGFontFacesMarkup: function() {\n      var markup = '', fontList = { }, obj, fontFamily,\n          style, row, rowIndex, _char, charIndex, i, len,\n          fontPaths = fabric.fontPaths, objects = this.getObjects();\n\n      for (i = 0, len = objects.length; i < len; i++) {\n        obj = objects[i];\n        fontFamily = obj.fontFamily;\n        if (obj.type.indexOf('text') === -1 || fontList[fontFamily] || !fontPaths[fontFamily]) {\n          continue;\n        }\n        fontList[fontFamily] = true;\n        if (!obj.styles) {\n          continue;\n        }\n        style = obj.styles;\n        for (rowIndex in style) {\n          row = style[rowIndex];\n          for (charIndex in row) {\n            _char = row[charIndex];\n            fontFamily = _char.fontFamily;\n            if (!fontList[fontFamily] && fontPaths[fontFamily]) {\n              fontList[fontFamily] = true;\n            }\n          }\n        }\n      }\n\n      for (var j in fontList) {\n        markup += [\n          '\\t\\t@font-face {\\n',\n          '\\t\\t\\tfont-family: \\'', j, '\\';\\n',\n          '\\t\\t\\tsrc: url(\\'', fontPaths[j], '\\');\\n',\n          '\\t\\t}\\n'\n        ].join('');\n      }\n\n      if (markup) {\n        markup = [\n          '\\t<style type=\"text/css\">',\n          '<![CDATA[\\n',\n          markup,\n          ']]>',\n          '</style>\\n'\n        ].join('');\n      }\n\n      return markup;\n    },\n\n    /**\n     * @private\n     */\n    _setSVGObjects: function(markup, reviver) {\n      var instance, i, len, objects = this.getObjects();\n      for (i = 0, len = objects.length; i < len; i++) {\n        instance = objects[i];\n        if (instance.excludeFromExport) {\n          continue;\n        }\n        this._setSVGObject(markup, instance, reviver);\n      }\n    },\n\n    /**\n     * @private\n     */\n    _setSVGObject: function(markup, instance, reviver) {\n      markup.push(instance.toSVG(reviver));\n    },\n\n    /**\n     * @private\n     */\n    _setSVGBgOverlayImage: function(markup, property, reviver) {\n      if (this[property] && this[property].toSVG) {\n        markup.push(this[property].toSVG(reviver));\n      }\n    },\n\n    /**\n     * @private\n     */\n    _setSVGBgOverlayColor: function(markup, property) {\n      var filler = this[property], vpt = this.viewportTransform, finalWidth = this.width / vpt[0],\n          finalHeight = this.height / vpt[3];\n      if (!filler) {\n        return;\n      }\n      if (filler.toLive) {\n        var repeat = filler.repeat;\n        markup.push(\n          '<rect transform=\"translate(', finalWidth / 2, ',', finalHeight / 2, ')\"',\n          ' x=\"', filler.offsetX - finalWidth / 2, '\" y=\"', filler.offsetY - finalHeight / 2, '\" ',\n          'width=\"',\n          (repeat === 'repeat-y' || repeat === 'no-repeat'\n            ? filler.source.width\n            : finalWidth ),\n          '\" height=\"',\n          (repeat === 'repeat-x' || repeat === 'no-repeat'\n            ? filler.source.height\n            : finalHeight),\n          '\" fill=\"url(#SVGID_' + filler.id + ')\"',\n          '></rect>\\n'\n        );\n      }\n      else {\n        markup.push(\n          '<rect x=\"0\" y=\"0\" width=\"100%\" height=\"100%\" ',\n          'fill=\"', this[property], '\"',\n          '></rect>\\n'\n        );\n      }\n    },\n    /* _TO_SVG_END_ */\n\n    /**\n     * Moves an object or the objects of a multiple selection\n     * to the bottom of the stack of drawn objects\n     * @param {fabric.Object} object Object to send to back\n     * @return {fabric.Canvas} thisArg\n     * @chainable\n     */\n    sendToBack: function (object) {\n      if (!object) {\n        return this;\n      }\n      var activeSelection = this._activeObject,\n          i, obj, objs;\n      if (object === activeSelection && object.type === 'activeSelection') {\n        objs = activeSelection._objects;\n        for (i = objs.length; i--;) {\n          obj = objs[i];\n          removeFromArray(this._objects, obj);\n          this._objects.unshift(obj);\n        }\n      }\n      else {\n        removeFromArray(this._objects, object);\n        this._objects.unshift(object);\n      }\n      this.renderOnAddRemove && this.requestRenderAll();\n      return this;\n    },\n\n    /**\n     * Moves an object or the objects of a multiple selection\n     * to the top of the stack of drawn objects\n     * @param {fabric.Object} object Object to send\n     * @return {fabric.Canvas} thisArg\n     * @chainable\n     */\n    bringToFront: function (object) {\n      if (!object) {\n        return this;\n      }\n      var activeSelection = this._activeObject,\n          i, obj, objs;\n      if (object === activeSelection && object.type === 'activeSelection') {\n        objs = activeSelection._objects;\n        for (i = 0; i < objs.length; i++) {\n          obj = objs[i];\n          removeFromArray(this._objects, obj);\n          this._objects.push(obj);\n        }\n      }\n      else {\n        removeFromArray(this._objects, object);\n        this._objects.push(object);\n      }\n      this.renderOnAddRemove && this.requestRenderAll();\n      return this;\n    },\n\n    /**\n     * Moves an object or a selection down in stack of drawn objects\n     * An optional paramter, intersecting allowes to move the object in behind\n     * the first intersecting object. Where intersection is calculated with\n     * bounding box. If no intersection is found, there will not be change in the\n     * stack.\n     * @param {fabric.Object} object Object to send\n     * @param {Boolean} [intersecting] If `true`, send object behind next lower intersecting object\n     * @return {fabric.Canvas} thisArg\n     * @chainable\n     */\n    sendBackwards: function (object, intersecting) {\n      if (!object) {\n        return this;\n      }\n      var activeSelection = this._activeObject,\n          i, obj, idx, newIdx, objs, objsMoved = 0;\n\n      if (object === activeSelection && object.type === 'activeSelection') {\n        objs = activeSelection._objects;\n        for (i = 0; i < objs.length; i++) {\n          obj = objs[i];\n          idx = this._objects.indexOf(obj);\n          if (idx > 0 + objsMoved) {\n            newIdx = idx - 1;\n            removeFromArray(this._objects, obj);\n            this._objects.splice(newIdx, 0, obj);\n          }\n          objsMoved++;\n        }\n      }\n      else {\n        idx = this._objects.indexOf(object);\n        if (idx !== 0) {\n          // if object is not on the bottom of stack\n          newIdx = this._findNewLowerIndex(object, idx, intersecting);\n          removeFromArray(this._objects, object);\n          this._objects.splice(newIdx, 0, object);\n        }\n      }\n      this.renderOnAddRemove && this.requestRenderAll();\n      return this;\n    },\n\n    /**\n     * @private\n     */\n    _findNewLowerIndex: function(object, idx, intersecting) {\n      var newIdx, i;\n\n      if (intersecting) {\n        newIdx = idx;\n\n        // traverse down the stack looking for the nearest intersecting object\n        for (i = idx - 1; i >= 0; --i) {\n\n          var isIntersecting = object.intersectsWithObject(this._objects[i]) ||\n                               object.isContainedWithinObject(this._objects[i]) ||\n                               this._objects[i].isContainedWithinObject(object);\n\n          if (isIntersecting) {\n            newIdx = i;\n            break;\n          }\n        }\n      }\n      else {\n        newIdx = idx - 1;\n      }\n\n      return newIdx;\n    },\n\n    /**\n     * Moves an object or a selection up in stack of drawn objects\n     * An optional paramter, intersecting allowes to move the object in front\n     * of the first intersecting object. Where intersection is calculated with\n     * bounding box. If no intersection is found, there will not be change in the\n     * stack.\n     * @param {fabric.Object} object Object to send\n     * @param {Boolean} [intersecting] If `true`, send object in front of next upper intersecting object\n     * @return {fabric.Canvas} thisArg\n     * @chainable\n     */\n    bringForward: function (object, intersecting) {\n      if (!object) {\n        return this;\n      }\n      var activeSelection = this._activeObject,\n          i, obj, idx, newIdx, objs, objsMoved = 0;\n\n      if (object === activeSelection && object.type === 'activeSelection') {\n        objs = activeSelection._objects;\n        for (i = objs.length; i--;) {\n          obj = objs[i];\n          idx = this._objects.indexOf(obj);\n          if (idx < this._objects.length - 1 - objsMoved) {\n            newIdx = idx + 1;\n            removeFromArray(this._objects, obj);\n            this._objects.splice(newIdx, 0, obj);\n          }\n          objsMoved++;\n        }\n      }\n      else {\n        idx = this._objects.indexOf(object);\n        if (idx !== this._objects.length - 1) {\n          // if object is not on top of stack (last item in an array)\n          newIdx = this._findNewUpperIndex(object, idx, intersecting);\n          removeFromArray(this._objects, object);\n          this._objects.splice(newIdx, 0, object);\n        }\n      }\n      this.renderOnAddRemove && this.requestRenderAll();\n      return this;\n    },\n\n    /**\n     * @private\n     */\n    _findNewUpperIndex: function(object, idx, intersecting) {\n      var newIdx, i, len;\n\n      if (intersecting) {\n        newIdx = idx;\n\n        // traverse up the stack looking for the nearest intersecting object\n        for (i = idx + 1, len = this._objects.length; i < len; ++i) {\n\n          var isIntersecting = object.intersectsWithObject(this._objects[i]) ||\n                               object.isContainedWithinObject(this._objects[i]) ||\n                               this._objects[i].isContainedWithinObject(object);\n\n          if (isIntersecting) {\n            newIdx = i;\n            break;\n          }\n        }\n      }\n      else {\n        newIdx = idx + 1;\n      }\n\n      return newIdx;\n    },\n\n    /**\n     * Moves an object to specified level in stack of drawn objects\n     * @param {fabric.Object} object Object to send\n     * @param {Number} index Position to move to\n     * @return {fabric.Canvas} thisArg\n     * @chainable\n     */\n    moveTo: function (object, index) {\n      removeFromArray(this._objects, object);\n      this._objects.splice(index, 0, object);\n      return this.renderOnAddRemove && this.requestRenderAll();\n    },\n\n    /**\n     * Clears a canvas element and dispose objects\n     * @return {fabric.Canvas} thisArg\n     * @chainable\n     */\n    dispose: function () {\n      // cancel eventually ongoing renders\n      if (this.isRendering) {\n        fabric.util.cancelAnimFrame(this.isRendering);\n        this.isRendering = 0;\n      }\n      this.forEachObject(function(object) {\n        object.dispose && object.dispose();\n      });\n      this._objects = [];\n      this.backgroundImage = null;\n      this.overlayImage = null;\n      this._iTextInstances = null;\n      this.lowerCanvasEl = null;\n      this.contextContainer = null;\n      return this;\n    },\n\n    /**\n     * Returns a string representation of an instance\n     * @return {String} string representation of an instance\n     */\n    toString: function () {\n      return '#<fabric.Canvas (' + this.complexity() + '): ' +\n               '{ objects: ' + this.getObjects().length + ' }>';\n    }\n  });\n\n  extend(fabric.StaticCanvas.prototype, fabric.Observable);\n  extend(fabric.StaticCanvas.prototype, fabric.Collection);\n  extend(fabric.StaticCanvas.prototype, fabric.DataURLExporter);\n\n  extend(fabric.StaticCanvas, /** @lends fabric.StaticCanvas */ {\n\n    /**\n     * @static\n     * @type String\n     * @default\n     */\n    EMPTY_JSON: '{\"objects\": [], \"background\": \"white\"}',\n\n    /**\n     * Provides a way to check support of some of the canvas methods\n     * (either those of HTMLCanvasElement itself, or rendering context)\n     *\n     * @param {String} methodName Method to check support for;\n     *                            Could be one of \"getImageData\", \"toDataURL\", \"toDataURLWithQuality\" or \"setLineDash\"\n     * @return {Boolean | null} `true` if method is supported (or at least exists),\n     *                          `null` if canvas element or context can not be initialized\n     */\n    supports: function (methodName) {\n      var el = fabric.util.createCanvasElement();\n\n      if (!el || !el.getContext) {\n        return null;\n      }\n\n      var ctx = el.getContext('2d');\n      if (!ctx) {\n        return null;\n      }\n\n      switch (methodName) {\n\n        case 'getImageData':\n          return typeof ctx.getImageData !== 'undefined';\n\n        case 'setLineDash':\n          return typeof ctx.setLineDash !== 'undefined';\n\n        case 'toDataURL':\n          return typeof el.toDataURL !== 'undefined';\n\n        case 'toDataURLWithQuality':\n          try {\n            el.toDataURL('image/jpeg', 0);\n            return true;\n          }\n          catch (e) { }\n          return false;\n\n        default:\n          return null;\n      }\n    }\n  });\n\n  /**\n   * Returns JSON representation of canvas\n   * @function\n   * @param {Array} [propertiesToInclude] Any properties that you might want to additionally include in the output\n   * @return {String} JSON string\n   * @tutorial {@link http://fabricjs.com/fabric-intro-part-3#serialization}\n   * @see {@link http://jsfiddle.net/fabricjs/pec86/|jsFiddle demo}\n   * @example <caption>JSON without additional properties</caption>\n   * var json = canvas.toJSON();\n   * @example <caption>JSON with additional properties included</caption>\n   * var json = canvas.toJSON(['lockMovementX', 'lockMovementY', 'lockRotation', 'lockScalingX', 'lockScalingY', 'lockUniScaling']);\n   * @example <caption>JSON without default values</caption>\n   * canvas.includeDefaultValues = false;\n   * var json = canvas.toJSON();\n   */\n  fabric.StaticCanvas.prototype.toJSON = fabric.StaticCanvas.prototype.toObject;\n\n  if (fabric.isLikelyNode) {\n    fabric.StaticCanvas.prototype.createPNGStream = function() {\n      var impl = fabric.util.getNodeCanvas(this.lowerCanvasEl);\n      return impl && impl.createPNGStream();\n    };\n    fabric.StaticCanvas.prototype.createJPEGStream = function(opts) {\n      var impl = fabric.util.getNodeCanvas(this.lowerCanvasEl);\n      return impl && impl.createJPEGStream(opts);\n    };\n  }\n})();\n\n\n/**\n * BaseBrush class\n * @class fabric.BaseBrush\n * @see {@link http://fabricjs.com/freedrawing|Freedrawing demo}\n */\nfabric.BaseBrush = fabric.util.createClass(/** @lends fabric.BaseBrush.prototype */ {\n\n  /**\n   * Color of a brush\n   * @type String\n   * @default\n   */\n  color: 'rgb(0, 0, 0)',\n\n  /**\n   * Width of a brush, has to be a Number, no string literals\n   * @type Number\n   * @default\n   */\n  width: 1,\n\n  /**\n   * Shadow object representing shadow of this shape.\n   * <b>Backwards incompatibility note:</b> This property replaces \"shadowColor\" (String), \"shadowOffsetX\" (Number),\n   * \"shadowOffsetY\" (Number) and \"shadowBlur\" (Number) since v1.2.12\n   * @type fabric.Shadow\n   * @default\n   */\n  shadow: null,\n\n  /**\n   * Line endings style of a brush (one of \"butt\", \"round\", \"square\")\n   * @type String\n   * @default\n   */\n  strokeLineCap: 'round',\n\n  /**\n   * Corner style of a brush (one of \"bevel\", \"round\", \"miter\")\n   * @type String\n   * @default\n   */\n  strokeLineJoin: 'round',\n\n  /**\n   * Maximum miter length (used for strokeLineJoin = \"miter\") of a brush's\n   * @type Number\n   * @default\n   */\n  strokeMiterLimit:         10,\n\n  /**\n   * Stroke Dash Array.\n   * @type Array\n   * @default\n   */\n  strokeDashArray: null,\n\n  /**\n   * Sets shadow of an object\n   * @param {Object|String} [options] Options object or string (e.g. \"2px 2px 10px rgba(0,0,0,0.2)\")\n   * @return {fabric.Object} thisArg\n   * @chainable\n   */\n  setShadow: function(options) {\n    this.shadow = new fabric.Shadow(options);\n    return this;\n  },\n\n  /**\n   * Sets brush styles\n   * @private\n   */\n  _setBrushStyles: function() {\n    var ctx = this.canvas.contextTop;\n    ctx.strokeStyle = this.color;\n    ctx.lineWidth = this.width;\n    ctx.lineCap = this.strokeLineCap;\n    ctx.miterLimit = this.strokeMiterLimit;\n    ctx.lineJoin = this.strokeLineJoin;\n    if (fabric.StaticCanvas.supports('setLineDash')) {\n      ctx.setLineDash(this.strokeDashArray || []);\n    }\n  },\n\n  /**\n   * Sets the transformation on given context\n   * @param {RenderingContext2d} ctx context to render on\n   * @private\n   */\n  _saveAndTransform: function(ctx) {\n    var v = this.canvas.viewportTransform;\n    ctx.save();\n    ctx.transform(v[0], v[1], v[2], v[3], v[4], v[5]);\n  },\n\n  /**\n   * Sets brush shadow styles\n   * @private\n   */\n  _setShadow: function() {\n    if (!this.shadow) {\n      return;\n    }\n\n    var ctx = this.canvas.contextTop,\n        zoom = this.canvas.getZoom();\n\n    ctx.shadowColor = this.shadow.color;\n    ctx.shadowBlur = this.shadow.blur * zoom;\n    ctx.shadowOffsetX = this.shadow.offsetX * zoom;\n    ctx.shadowOffsetY = this.shadow.offsetY * zoom;\n  },\n\n  /**\n   * Removes brush shadow styles\n   * @private\n   */\n  _resetShadow: function() {\n    var ctx = this.canvas.contextTop;\n\n    ctx.shadowColor = '';\n    ctx.shadowBlur = ctx.shadowOffsetX = ctx.shadowOffsetY = 0;\n  }\n});\n\n\n(function() {\n\n  /**\n   * PencilBrush class\n   * @class fabric.PencilBrush\n   * @extends fabric.BaseBrush\n   */\n  fabric.PencilBrush = fabric.util.createClass(fabric.BaseBrush, /** @lends fabric.PencilBrush.prototype */ {\n\n    /**\n     * Constructor\n     * @param {fabric.Canvas} canvas\n     * @return {fabric.PencilBrush} Instance of a pencil brush\n     */\n    initialize: function(canvas) {\n      this.canvas = canvas;\n      this._points = [];\n    },\n\n    /**\n     * Invoked inside on mouse down and mouse move\n     * @param {Object} pointer\n     */\n    _drawSegment: function (ctx, p1, p2) {\n      var midPoint = p1.midPointFrom(p2);\n      ctx.quadraticCurveTo(p1.x, p1.y, midPoint.x, midPoint.y);\n      return midPoint;\n    },\n\n    /**\n     * Inovoked on mouse down\n     * @param {Object} pointer\n     */\n    onMouseDown: function(pointer) {\n      this._prepareForDrawing(pointer);\n      // capture coordinates immediately\n      // this allows to draw dots (when movement never occurs)\n      this._captureDrawingPath(pointer);\n      this._render();\n    },\n\n    /**\n     * Inovoked on mouse move\n     * @param {Object} pointer\n     */\n    onMouseMove: function(pointer) {\n      if (this._captureDrawingPath(pointer) && this._points.length > 1) {\n        if (this.needsFullRender) {\n          // redraw curve\n          // clear top canvas\n          this.canvas.clearContext(this.canvas.contextTop);\n          this._render();\n        }\n        else {\n          var points = this._points, length = points.length, ctx = this.canvas.contextTop;\n          // draw the curve update\n          this._saveAndTransform(ctx);\n          if (this.oldEnd) {\n            ctx.beginPath();\n            ctx.moveTo(this.oldEnd.x, this.oldEnd.y);\n          }\n          this.oldEnd = this._drawSegment(ctx, points[length - 2], points[length - 1], true);\n          ctx.stroke();\n          ctx.restore();\n        }\n      }\n    },\n\n    /**\n     * Invoked on mouse up\n     */\n    onMouseUp: function() {\n      this.oldEnd = undefined;\n      this._finalizeAndAddPath();\n    },\n\n    /**\n     * @private\n     * @param {Object} pointer Actual mouse position related to the canvas.\n     */\n    _prepareForDrawing: function(pointer) {\n\n      var p = new fabric.Point(pointer.x, pointer.y);\n\n      this._reset();\n      this._addPoint(p);\n      this.canvas.contextTop.moveTo(p.x, p.y);\n    },\n\n    /**\n     * @private\n     * @param {fabric.Point} point Point to be added to points array\n     */\n    _addPoint: function(point) {\n      if (this._points.length > 1 && point.eq(this._points[this._points.length - 1])) {\n        return false;\n      }\n      this._points.push(point);\n      return true;\n    },\n\n    /**\n     * Clear points array and set contextTop canvas style.\n     * @private\n     */\n    _reset: function() {\n      this._points.length = 0;\n      this._setBrushStyles();\n      var color = new fabric.Color(this.color);\n      this.needsFullRender = (color.getAlpha() < 1);\n      this._setShadow();\n    },\n\n    /**\n     * @private\n     * @param {Object} pointer Actual mouse position related to the canvas.\n     */\n    _captureDrawingPath: function(pointer) {\n      var pointerPoint = new fabric.Point(pointer.x, pointer.y);\n      return this._addPoint(pointerPoint);\n    },\n\n    /**\n     * Draw a smooth path on the topCanvas using quadraticCurveTo\n     * @private\n     */\n    _render: function() {\n      var ctx  = this.canvas.contextTop, i, len,\n          p1 = this._points[0],\n          p2 = this._points[1];\n\n      this._saveAndTransform(ctx);\n      ctx.beginPath();\n      //if we only have 2 points in the path and they are the same\n      //it means that the user only clicked the canvas without moving the mouse\n      //then we should be drawing a dot. A path isn't drawn between two identical dots\n      //that's why we set them apart a bit\n      if (this._points.length === 2 && p1.x === p2.x && p1.y === p2.y) {\n        var width = this.width / 1000;\n        p1 = new fabric.Point(p1.x, p1.y);\n        p2 = new fabric.Point(p2.x, p2.y);\n        p1.x -= width;\n        p2.x += width;\n      }\n      ctx.moveTo(p1.x, p1.y);\n\n      for (i = 1, len = this._points.length; i < len; i++) {\n        // we pick the point between pi + 1 & pi + 2 as the\n        // end point and p1 as our control point.\n        this._drawSegment(ctx, p1, p2);\n        p1 = this._points[i];\n        p2 = this._points[i + 1];\n      }\n      // Draw last line as a straight line while\n      // we wait for the next point to be able to calculate\n      // the bezier control point\n      ctx.lineTo(p1.x, p1.y);\n      ctx.stroke();\n      ctx.restore();\n    },\n\n    /**\n     * Converts points to SVG path\n     * @param {Array} points Array of points\n     * @return {String} SVG path\n     */\n    convertPointsToSVGPath: function(points) {\n      var path = [], i, width = this.width / 1000,\n          p1 = new fabric.Point(points[0].x, points[0].y),\n          p2 = new fabric.Point(points[1].x, points[1].y),\n          len = points.length, multSignX = 1, multSignY = 1, manyPoints = len > 2;\n\n      if (manyPoints) {\n        multSignX = points[2].x < p2.x ? -1 : points[2].x === p2.x ? 0 : 1;\n        multSignY = points[2].y < p2.y ? -1 : points[2].y === p2.y ? 0 : 1;\n      }\n      path.push('M ', p1.x - multSignX * width, ' ', p1.y - multSignY * width, ' ');\n      for (i = 1; i < len; i++) {\n        if (!p1.eq(p2)) {\n          var midPoint = p1.midPointFrom(p2);\n          // p1 is our bezier control point\n          // midpoint is our endpoint\n          // start point is p(i-1) value.\n          path.push('Q ', p1.x, ' ', p1.y, ' ', midPoint.x, ' ', midPoint.y, ' ');\n        }\n        p1 = points[i];\n        if ((i + 1) < points.length) {\n          p2 = points[i + 1];\n        }\n      }\n      if (manyPoints) {\n        multSignX = p1.x > points[i - 2].x ? 1 : p1.x === points[i - 2].x ? 0 : -1;\n        multSignY = p1.y > points[i - 2].y ? 1 : p1.y === points[i - 2].y ? 0 : -1;\n      }\n      path.push('L ', p1.x + multSignX * width, ' ', p1.y + multSignY * width);\n      return path;\n    },\n\n    /**\n     * Creates fabric.Path object to add on canvas\n     * @param {String} pathData Path data\n     * @return {fabric.Path} Path to add on canvas\n     */\n    createPath: function(pathData) {\n      var path = new fabric.Path(pathData, {\n        fill: null,\n        stroke: this.color,\n        strokeWidth: this.width,\n        strokeLineCap: this.strokeLineCap,\n        strokeMiterLimit: this.strokeMiterLimit,\n        strokeLineJoin: this.strokeLineJoin,\n        strokeDashArray: this.strokeDashArray,\n      });\n      var position = new fabric.Point(path.left + path.width / 2, path.top + path.height / 2);\n      position = path.translateToGivenOrigin(position, 'center', 'center', path.originX, path.originY);\n      path.top = position.y;\n      path.left = position.x;\n      if (this.shadow) {\n        this.shadow.affectStroke = true;\n        path.setShadow(this.shadow);\n      }\n\n      return path;\n    },\n\n    /**\n     * On mouseup after drawing the path on contextTop canvas\n     * we use the points captured to create an new fabric path object\n     * and add it to the fabric canvas.\n     */\n    _finalizeAndAddPath: function() {\n      var ctx = this.canvas.contextTop;\n      ctx.closePath();\n\n      var pathData = this.convertPointsToSVGPath(this._points).join('');\n      if (pathData === 'M 0 0 Q 0 0 0 0 L 0 0') {\n        // do not create 0 width/height paths, as they are\n        // rendered inconsistently across browsers\n        // Firefox 4, for example, renders a dot,\n        // whereas Chrome 10 renders nothing\n        this.canvas.requestRenderAll();\n        return;\n      }\n\n      var path = this.createPath(pathData);\n      this.canvas.clearContext(this.canvas.contextTop);\n      this.canvas.add(path);\n      this.canvas.renderAll();\n      path.setCoords();\n      this._resetShadow();\n\n\n      // fire event 'path' created\n      this.canvas.fire('path:created', { path: path });\n    }\n  });\n})();\n\n\n/**\n * CircleBrush class\n * @class fabric.CircleBrush\n */\nfabric.CircleBrush = fabric.util.createClass(fabric.BaseBrush, /** @lends fabric.CircleBrush.prototype */ {\n\n  /**\n   * Width of a brush\n   * @type Number\n   * @default\n   */\n  width: 10,\n\n  /**\n   * Constructor\n   * @param {fabric.Canvas} canvas\n   * @return {fabric.CircleBrush} Instance of a circle brush\n   */\n  initialize: function(canvas) {\n    this.canvas = canvas;\n    this.points = [];\n  },\n\n  /**\n   * Invoked inside on mouse down and mouse move\n   * @param {Object} pointer\n   */\n  drawDot: function(pointer) {\n    var point = this.addPoint(pointer),\n        ctx = this.canvas.contextTop;\n    this._saveAndTransform(ctx);\n    ctx.fillStyle = point.fill;\n    ctx.beginPath();\n    ctx.arc(point.x, point.y, point.radius, 0, Math.PI * 2, false);\n    ctx.closePath();\n    ctx.fill();\n\n    ctx.restore();\n  },\n\n  /**\n   * Invoked on mouse down\n   */\n  onMouseDown: function(pointer) {\n    this.points.length = 0;\n    this.canvas.clearContext(this.canvas.contextTop);\n    this._setShadow();\n    this.drawDot(pointer);\n  },\n\n  /**\n   * Render the full state of the brush\n   * @private\n   */\n  _render: function() {\n    var ctx  = this.canvas.contextTop, i, len,\n        points = this.points, point;\n    this._saveAndTransform(ctx);\n    for (i = 0, len = points.length; i < len; i++) {\n      point = points[i];\n      ctx.fillStyle = point.fill;\n      ctx.beginPath();\n      ctx.arc(point.x, point.y, point.radius, 0, Math.PI * 2, false);\n      ctx.closePath();\n      ctx.fill();\n    }\n    ctx.restore();\n  },\n\n  /**\n   * Invoked on mouse move\n   * @param {Object} pointer\n   */\n  onMouseMove: function(pointer) {\n    this.drawDot(pointer);\n  },\n\n  /**\n   * Invoked on mouse up\n   */\n  onMouseUp: function() {\n    var originalRenderOnAddRemove = this.canvas.renderOnAddRemove, i, len;\n    this.canvas.renderOnAddRemove = false;\n\n    var circles = [];\n\n    for (i = 0, len = this.points.length; i < len; i++) {\n      var point = this.points[i],\n          circle = new fabric.Circle({\n            radius: point.radius,\n            left: point.x,\n            top: point.y,\n            originX: 'center',\n            originY: 'center',\n            fill: point.fill\n          });\n\n      this.shadow && circle.setShadow(this.shadow);\n\n      circles.push(circle);\n    }\n    var group = new fabric.Group(circles, { originX: 'center', originY: 'center' });\n    group.canvas = this.canvas;\n\n    this.canvas.add(group);\n    this.canvas.fire('path:created', { path: group });\n\n    this.canvas.clearContext(this.canvas.contextTop);\n    this._resetShadow();\n    this.canvas.renderOnAddRemove = originalRenderOnAddRemove;\n    this.canvas.requestRenderAll();\n  },\n\n  /**\n   * @param {Object} pointer\n   * @return {fabric.Point} Just added pointer point\n   */\n  addPoint: function(pointer) {\n    var pointerPoint = new fabric.Point(pointer.x, pointer.y),\n\n        circleRadius = fabric.util.getRandomInt(\n          Math.max(0, this.width - 20), this.width + 20) / 2,\n\n        circleColor = new fabric.Color(this.color)\n          .setAlpha(fabric.util.getRandomInt(0, 100) / 100)\n          .toRgba();\n\n    pointerPoint.radius = circleRadius;\n    pointerPoint.fill = circleColor;\n\n    this.points.push(pointerPoint);\n\n    return pointerPoint;\n  }\n});\n\n\n/**\n * SprayBrush class\n * @class fabric.SprayBrush\n */\nfabric.SprayBrush = fabric.util.createClass( fabric.BaseBrush, /** @lends fabric.SprayBrush.prototype */ {\n\n  /**\n   * Width of a spray\n   * @type Number\n   * @default\n   */\n  width:              10,\n\n  /**\n   * Density of a spray (number of dots per chunk)\n   * @type Number\n   * @default\n   */\n  density:            20,\n\n  /**\n   * Width of spray dots\n   * @type Number\n   * @default\n   */\n  dotWidth:           1,\n\n  /**\n   * Width variance of spray dots\n   * @type Number\n   * @default\n   */\n  dotWidthVariance:   1,\n\n  /**\n   * Whether opacity of a dot should be random\n   * @type Boolean\n   * @default\n   */\n  randomOpacity:        false,\n\n  /**\n   * Whether overlapping dots (rectangles) should be removed (for performance reasons)\n   * @type Boolean\n   * @default\n   */\n  optimizeOverlapping:  true,\n\n  /**\n   * Constructor\n   * @param {fabric.Canvas} canvas\n   * @return {fabric.SprayBrush} Instance of a spray brush\n   */\n  initialize: function(canvas) {\n    this.canvas = canvas;\n    this.sprayChunks = [];\n  },\n\n  /**\n   * Invoked on mouse down\n   * @param {Object} pointer\n   */\n  onMouseDown: function(pointer) {\n    this.sprayChunks.length = 0;\n    this.canvas.clearContext(this.canvas.contextTop);\n    this._setShadow();\n\n    this.addSprayChunk(pointer);\n    this.render(this.sprayChunkPoints);\n  },\n\n  /**\n   * Invoked on mouse move\n   * @param {Object} pointer\n   */\n  onMouseMove: function(pointer) {\n    this.addSprayChunk(pointer);\n    this.render(this.sprayChunkPoints);\n  },\n\n  /**\n   * Invoked on mouse up\n   */\n  onMouseUp: function() {\n    var originalRenderOnAddRemove = this.canvas.renderOnAddRemove;\n    this.canvas.renderOnAddRemove = false;\n\n    var rects = [];\n\n    for (var i = 0, ilen = this.sprayChunks.length; i < ilen; i++) {\n      var sprayChunk = this.sprayChunks[i];\n\n      for (var j = 0, jlen = sprayChunk.length; j < jlen; j++) {\n\n        var rect = new fabric.Rect({\n          width: sprayChunk[j].width,\n          height: sprayChunk[j].width,\n          left: sprayChunk[j].x + 1,\n          top: sprayChunk[j].y + 1,\n          originX: 'center',\n          originY: 'center',\n          fill: this.color\n        });\n        rects.push(rect);\n      }\n    }\n\n    if (this.optimizeOverlapping) {\n      rects = this._getOptimizedRects(rects);\n    }\n\n    var group = new fabric.Group(rects, { originX: 'center', originY: 'center' });\n    this.shadow && group.setShadow(this.shadow);\n    this.canvas.add(group);\n    this.canvas.fire('path:created', { path: group });\n\n    this.canvas.clearContext(this.canvas.contextTop);\n    this._resetShadow();\n    this.canvas.renderOnAddRemove = originalRenderOnAddRemove;\n    this.canvas.requestRenderAll();\n  },\n\n  /**\n   * @private\n   * @param {Array} rects\n   */\n  _getOptimizedRects: function(rects) {\n\n    // avoid creating duplicate rects at the same coordinates\n    var uniqueRects = { }, key, i, len;\n\n    for (i = 0, len = rects.length; i < len; i++) {\n      key = rects[i].left + '' + rects[i].top;\n      if (!uniqueRects[key]) {\n        uniqueRects[key] = rects[i];\n      }\n    }\n    var uniqueRectsArray = [];\n    for (key in uniqueRects) {\n      uniqueRectsArray.push(uniqueRects[key]);\n    }\n\n    return uniqueRectsArray;\n  },\n\n  /**\n   * Render new chunk of spray brush\n   */\n  render: function(sprayChunk) {\n    var ctx = this.canvas.contextTop, i, len;\n    ctx.fillStyle = this.color;\n\n    this._saveAndTransform(ctx);\n\n    for (i = 0, len = sprayChunk.length; i < len; i++) {\n      var point = sprayChunk[i];\n      if (typeof point.opacity !== 'undefined') {\n        ctx.globalAlpha = point.opacity;\n      }\n      ctx.fillRect(point.x, point.y, point.width, point.width);\n    }\n    ctx.restore();\n  },\n\n  /**\n   * Render all spray chunks\n   */\n  _render: function() {\n    var ctx = this.canvas.contextTop, i, ilen;\n    ctx.fillStyle = this.color;\n\n    this._saveAndTransform(ctx);\n\n    for (i = 0, ilen = this.sprayChunks.length; i < ilen; i++) {\n      this.render(this.sprayChunks[i]);\n    }\n    ctx.restore();\n  },\n\n  /**\n   * @param {Object} pointer\n   */\n  addSprayChunk: function(pointer) {\n    this.sprayChunkPoints = [];\n\n    var x, y, width, radius = this.width / 2, i;\n\n    for (i = 0; i < this.density; i++) {\n\n      x = fabric.util.getRandomInt(pointer.x - radius, pointer.x + radius);\n      y = fabric.util.getRandomInt(pointer.y - radius, pointer.y + radius);\n\n      if (this.dotWidthVariance) {\n        width = fabric.util.getRandomInt(\n          // bottom clamp width to 1\n          Math.max(1, this.dotWidth - this.dotWidthVariance),\n          this.dotWidth + this.dotWidthVariance);\n      }\n      else {\n        width = this.dotWidth;\n      }\n\n      var point = new fabric.Point(x, y);\n      point.width = width;\n\n      if (this.randomOpacity) {\n        point.opacity = fabric.util.getRandomInt(0, 100) / 100;\n      }\n\n      this.sprayChunkPoints.push(point);\n    }\n\n    this.sprayChunks.push(this.sprayChunkPoints);\n  }\n});\n\n\n/**\n * PatternBrush class\n * @class fabric.PatternBrush\n * @extends fabric.BaseBrush\n */\nfabric.PatternBrush = fabric.util.createClass(fabric.PencilBrush, /** @lends fabric.PatternBrush.prototype */ {\n\n  getPatternSrc: function() {\n\n    var dotWidth = 20,\n        dotDistance = 5,\n        patternCanvas = fabric.document.createElement('canvas'),\n        patternCtx = patternCanvas.getContext('2d');\n\n    patternCanvas.width = patternCanvas.height = dotWidth + dotDistance;\n\n    patternCtx.fillStyle = this.color;\n    patternCtx.beginPath();\n    patternCtx.arc(dotWidth / 2, dotWidth / 2, dotWidth / 2, 0, Math.PI * 2, false);\n    patternCtx.closePath();\n    patternCtx.fill();\n\n    return patternCanvas;\n  },\n\n  getPatternSrcFunction: function() {\n    return String(this.getPatternSrc).replace('this.color', '\"' + this.color + '\"');\n  },\n\n  /**\n   * Creates \"pattern\" instance property\n   */\n  getPattern: function() {\n    return this.canvas.contextTop.createPattern(this.source || this.getPatternSrc(), 'repeat');\n  },\n\n  /**\n   * Sets brush styles\n   */\n  _setBrushStyles: function() {\n    this.callSuper('_setBrushStyles');\n    this.canvas.contextTop.strokeStyle = this.getPattern();\n  },\n\n  /**\n   * Creates path\n   */\n  createPath: function(pathData) {\n    var path = this.callSuper('createPath', pathData),\n        topLeft = path._getLeftTopCoords().scalarAdd(path.strokeWidth / 2);\n\n    path.stroke = new fabric.Pattern({\n      source: this.source || this.getPatternSrcFunction(),\n      offsetX: -topLeft.x,\n      offsetY: -topLeft.y\n    });\n    return path;\n  }\n});\n\n\n(function() {\n\n  var getPointer = fabric.util.getPointer,\n      degreesToRadians = fabric.util.degreesToRadians,\n      radiansToDegrees = fabric.util.radiansToDegrees,\n      atan2 = Math.atan2,\n      abs = Math.abs,\n      supportLineDash = fabric.StaticCanvas.supports('setLineDash'),\n\n      STROKE_OFFSET = 0.5;\n\n  /**\n   * Canvas class\n   * @class fabric.Canvas\n   * @extends fabric.StaticCanvas\n   * @tutorial {@link http://fabricjs.com/fabric-intro-part-1#canvas}\n   * @see {@link fabric.Canvas#initialize} for constructor definition\n   *\n   * @fires object:modified\n   * @fires object:rotated\n   * @fires object:scaled\n   * @fires object:moved\n   * @fires object:skewed\n   * @fires object:rotating\n   * @fires object:scaling\n   * @fires object:moving\n   * @fires object:skewing\n   * @fires object:selected this event is deprecated. use selection:created\n   *\n   * @fires before:transform\n   * @fires before:selection:cleared\n   * @fires selection:cleared\n   * @fires selection:updated\n   * @fires selection:created\n   *\n   * @fires path:created\n   * @fires mouse:down\n   * @fires mouse:move\n   * @fires mouse:up\n   * @fires mouse:down:before\n   * @fires mouse:move:before\n   * @fires mouse:up:before\n   * @fires mouse:over\n   * @fires mouse:out\n   * @fires mouse:dblclick\n   *\n   * @fires dragover\n   * @fires dragenter\n   * @fires dragleave\n   * @fires drop\n   *\n   */\n  fabric.Canvas = fabric.util.createClass(fabric.StaticCanvas, /** @lends fabric.Canvas.prototype */ {\n\n    /**\n     * Constructor\n     * @param {HTMLElement | String} el &lt;canvas> element to initialize instance on\n     * @param {Object} [options] Options object\n     * @return {Object} thisArg\n     */\n    initialize: function(el, options) {\n      options || (options = { });\n      this.renderAndResetBound = this.renderAndReset.bind(this);\n      this._initStatic(el, options);\n      this._initInteractive();\n      this._createCacheCanvas();\n    },\n\n    /**\n     * When true, objects can be transformed by one side (unproportionally)\n     * @type Boolean\n     * @default\n     */\n    uniScaleTransform:      false,\n\n    /**\n     * Indicates which key enable unproportional scaling\n     * values: 'altKey', 'shiftKey', 'ctrlKey'.\n     * If `null` or 'none' or any other string that is not a modifier key\n     * feature is disabled feature disabled.\n     * @since 1.6.2\n     * @type String\n     * @default\n     */\n    uniScaleKey:           'shiftKey',\n\n    /**\n     * When true, objects use center point as the origin of scale transformation.\n     * <b>Backwards incompatibility note:</b> This property replaces \"centerTransform\" (Boolean).\n     * @since 1.3.4\n     * @type Boolean\n     * @default\n     */\n    centeredScaling:        false,\n\n    /**\n     * When true, objects use center point as the origin of rotate transformation.\n     * <b>Backwards incompatibility note:</b> This property replaces \"centerTransform\" (Boolean).\n     * @since 1.3.4\n     * @type Boolean\n     * @default\n     */\n    centeredRotation:       false,\n\n    /**\n     * Indicates which key enable centered Transform\n     * values: 'altKey', 'shiftKey', 'ctrlKey'.\n     * If `null` or 'none' or any other string that is not a modifier key\n     * feature is disabled feature disabled.\n     * @since 1.6.2\n     * @type String\n     * @default\n     */\n    centeredKey:           'altKey',\n\n    /**\n     * Indicates which key enable alternate action on corner\n     * values: 'altKey', 'shiftKey', 'ctrlKey'.\n     * If `null` or 'none' or any other string that is not a modifier key\n     * feature is disabled feature disabled.\n     * @since 1.6.2\n     * @type String\n     * @default\n     */\n    altActionKey:           'shiftKey',\n\n    /**\n     * Indicates that canvas is interactive. This property should not be changed.\n     * @type Boolean\n     * @default\n     */\n    interactive:            true,\n\n    /**\n     * Indicates whether group selection should be enabled\n     * @type Boolean\n     * @default\n     */\n    selection:              true,\n\n    /**\n     * Indicates which key or keys enable multiple click selection\n     * Pass value as a string or array of strings\n     * values: 'altKey', 'shiftKey', 'ctrlKey'.\n     * If `null` or empty or containing any other string that is not a modifier key\n     * feature is disabled.\n     * @since 1.6.2\n     * @type String|Array\n     * @default\n     */\n    selectionKey:           'shiftKey',\n\n    /**\n     * Indicates which key enable alternative selection\n     * in case of target overlapping with active object\n     * values: 'altKey', 'shiftKey', 'ctrlKey'.\n     * For a series of reason that come from the general expectations on how\n     * things should work, this feature works only for preserveObjectStacking true.\n     * If `null` or 'none' or any other string that is not a modifier key\n     * feature is disabled.\n     * @since 1.6.5\n     * @type null|String\n     * @default\n     */\n    altSelectionKey:           null,\n\n    /**\n     * Color of selection\n     * @type String\n     * @default\n     */\n    selectionColor:         'rgba(100, 100, 255, 0.3)', // blue\n\n    /**\n     * Default dash array pattern\n     * If not empty the selection border is dashed\n     * @type Array\n     */\n    selectionDashArray:     [],\n\n    /**\n     * Color of the border of selection (usually slightly darker than color of selection itself)\n     * @type String\n     * @default\n     */\n    selectionBorderColor:   'rgba(255, 255, 255, 0.3)',\n\n    /**\n     * Width of a line used in object/group selection\n     * @type Number\n     * @default\n     */\n    selectionLineWidth:     1,\n\n    /**\n     * Select only shapes that are fully contained in the dragged selection rectangle.\n     * @type Boolean\n     * @default\n     */\n    selectionFullyContained: false,\n\n    /**\n     * Default cursor value used when hovering over an object on canvas\n     * @type String\n     * @default\n     */\n    hoverCursor:            'move',\n\n    /**\n     * Default cursor value used when moving an object on canvas\n     * @type String\n     * @default\n     */\n    moveCursor:             'move',\n\n    /**\n     * Default cursor value used for the entire canvas\n     * @type String\n     * @default\n     */\n    defaultCursor:          'default',\n\n    /**\n     * Cursor value used during free drawing\n     * @type String\n     * @default\n     */\n    freeDrawingCursor:      'crosshair',\n\n    /**\n     * Cursor value used for rotation point\n     * @type String\n     * @default\n     */\n    rotationCursor:         'crosshair',\n\n    /**\n     * Cursor value used for disabled elements ( corners with disabled action )\n     * @type String\n     * @since 2.0.0\n     * @default\n     */\n    notAllowedCursor:         'not-allowed',\n\n    /**\n     * Default element class that's given to wrapper (div) element of canvas\n     * @type String\n     * @default\n     */\n    containerClass:         'canvas-container',\n\n    /**\n     * When true, object detection happens on per-pixel basis rather than on per-bounding-box\n     * @type Boolean\n     * @default\n     */\n    perPixelTargetFind:     false,\n\n    /**\n     * Number of pixels around target pixel to tolerate (consider active) during object detection\n     * @type Number\n     * @default\n     */\n    targetFindTolerance:    0,\n\n    /**\n     * When true, target detection is skipped when hovering over canvas. This can be used to improve performance.\n     * @type Boolean\n     * @default\n     */\n    skipTargetFind:         false,\n\n    /**\n     * When true, mouse events on canvas (mousedown/mousemove/mouseup) result in free drawing.\n     * After mousedown, mousemove creates a shape,\n     * and then mouseup finalizes it and adds an instance of `fabric.Path` onto canvas.\n     * @tutorial {@link http://fabricjs.com/fabric-intro-part-4#free_drawing}\n     * @type Boolean\n     * @default\n     */\n    isDrawingMode:          false,\n\n    /**\n     * Indicates whether objects should remain in current stack position when selected.\n     * When false objects are brought to top and rendered as part of the selection group\n     * @type Boolean\n     * @default\n     */\n    preserveObjectStacking: false,\n\n    /**\n     * Indicates the angle that an object will lock to while rotating.\n     * @type Number\n     * @since 1.6.7\n     * @default\n     */\n    snapAngle: 0,\n\n    /**\n     * Indicates the distance from the snapAngle the rotation will lock to the snapAngle.\n     * When `null`, the snapThreshold will default to the snapAngle.\n     * @type null|Number\n     * @since 1.6.7\n     * @default\n     */\n    snapThreshold: null,\n\n    /**\n     * Indicates if the right click on canvas can output the context menu or not\n     * @type Boolean\n     * @since 1.6.5\n     * @default\n     */\n    stopContextMenu: false,\n\n    /**\n     * Indicates if the canvas can fire right click events\n     * @type Boolean\n     * @since 1.6.5\n     * @default\n     */\n    fireRightClick: false,\n\n    /**\n     * Indicates if the canvas can fire middle click events\n     * @type Boolean\n     * @since 1.7.8\n     * @default\n     */\n    fireMiddleClick: false,\n\n    /**\n     * @private\n     */\n    _initInteractive: function() {\n      this._currentTransform = null;\n      this._groupSelector = null;\n      this._initWrapperElement();\n      this._createUpperCanvas();\n      this._initEventListeners();\n\n      this._initRetinaScaling();\n\n      this.freeDrawingBrush = fabric.PencilBrush && new fabric.PencilBrush(this);\n\n      this.calcOffset();\n    },\n\n    /**\n     * Divides objects in two groups, one to render immediately\n     * and one to render as activeGroup.\n     * @return {Array} objects to render immediately and pushes the other in the activeGroup.\n     */\n    _chooseObjectsToRender: function() {\n      var activeObjects = this.getActiveObjects(),\n          object, objsToRender, activeGroupObjects;\n\n      if (activeObjects.length > 0 && !this.preserveObjectStacking) {\n        objsToRender = [];\n        activeGroupObjects = [];\n        for (var i = 0, length = this._objects.length; i < length; i++) {\n          object = this._objects[i];\n          if (activeObjects.indexOf(object) === -1 ) {\n            objsToRender.push(object);\n          }\n          else {\n            activeGroupObjects.push(object);\n          }\n        }\n        if (activeObjects.length > 1) {\n          this._activeObject._objects = activeGroupObjects;\n        }\n        objsToRender.push.apply(objsToRender, activeGroupObjects);\n      }\n      else {\n        objsToRender = this._objects;\n      }\n      return objsToRender;\n    },\n\n    /**\n     * Renders both the top canvas and the secondary container canvas.\n     * @return {fabric.Canvas} instance\n     * @chainable\n     */\n    renderAll: function () {\n      if (this.contextTopDirty && !this._groupSelector && !this.isDrawingMode) {\n        this.clearContext(this.contextTop);\n        this.contextTopDirty = false;\n      }\n      if (this.hasLostContext) {\n        this.renderTopLayer(this.contextTop);\n      }\n      var canvasToDrawOn = this.contextContainer;\n      this.renderCanvas(canvasToDrawOn, this._chooseObjectsToRender());\n      return this;\n    },\n\n    renderTopLayer: function(ctx) {\n      if (this.isDrawingMode && this._isCurrentlyDrawing) {\n        this.freeDrawingBrush && this.freeDrawingBrush._render();\n      }\n      // we render the top context - last object\n      if (this.selection && this._groupSelector) {\n        this._drawSelection(ctx);\n      }\n    },\n\n    /**\n     * Method to render only the top canvas.\n     * Also used to render the group selection box.\n     * @return {fabric.Canvas} thisArg\n     * @chainable\n     */\n    renderTop: function () {\n      var ctx = this.contextTop;\n      this.clearContext(ctx);\n      this.renderTopLayer(ctx);\n      this.fire('after:render');\n      this.contextTopDirty = true;\n      return this;\n    },\n\n    /**\n     * Resets the current transform to its original values and chooses the type of resizing based on the event\n     * @private\n     */\n    _resetCurrentTransform: function() {\n      var t = this._currentTransform;\n\n      t.target.set({\n        scaleX: t.original.scaleX,\n        scaleY: t.original.scaleY,\n        skewX: t.original.skewX,\n        skewY: t.original.skewY,\n        left: t.original.left,\n        top: t.original.top\n      });\n\n      if (this._shouldCenterTransform(t.target)) {\n        if (t.originX !== 'center') {\n          if (t.originX === 'right') {\n            t.mouseXSign = -1;\n          }\n          else {\n            t.mouseXSign = 1;\n          }\n        }\n        if (t.originY !== 'center') {\n          if (t.originY === 'bottom') {\n            t.mouseYSign = -1;\n          }\n          else {\n            t.mouseYSign = 1;\n          }\n        }\n\n        t.originX = 'center';\n        t.originY = 'center';\n      }\n      else {\n        t.originX = t.original.originX;\n        t.originY = t.original.originY;\n      }\n    },\n\n    /**\n     * Checks if point is contained within an area of given object\n     * @param {Event} e Event object\n     * @param {fabric.Object} target Object to test against\n     * @param {Object} [point] x,y object of point coordinates we want to check.\n     * @return {Boolean} true if point is contained within an area of given object\n     */\n    containsPoint: function (e, target, point) {\n      var ignoreZoom = true,\n          pointer = point || this.getPointer(e, ignoreZoom),\n          xy;\n\n      if (target.group && target.group === this._activeObject && target.group.type === 'activeSelection') {\n        xy = this._normalizePointer(target.group, pointer);\n      }\n      else {\n        xy = { x: pointer.x, y: pointer.y };\n      }\n      // http://www.geog.ubc.ca/courses/klink/gis.notes/ncgia/u32.html\n      // http://idav.ucdavis.edu/~okreylos/TAship/Spring2000/PointInPolygon.html\n      return (target.containsPoint(xy) || target._findTargetCorner(pointer));\n    },\n\n    /**\n     * @private\n     */\n    _normalizePointer: function (object, pointer) {\n      var m = object.calcTransformMatrix(),\n          invertedM = fabric.util.invertTransform(m),\n          vptPointer = this.restorePointerVpt(pointer);\n      return fabric.util.transformPoint(vptPointer, invertedM);\n    },\n\n    /**\n     * Returns true if object is transparent at a certain location\n     * @param {fabric.Object} target Object to check\n     * @param {Number} x Left coordinate\n     * @param {Number} y Top coordinate\n     * @return {Boolean}\n     */\n    isTargetTransparent: function (target, x, y) {\n      if (target.shouldCache() && target._cacheCanvas) {\n        var normalizedPointer = this._normalizePointer(target, {x: x, y: y}),\n            targetRelativeX = target.cacheTranslationX + (normalizedPointer.x * target.zoomX),\n            targetRelativeY = target.cacheTranslationY + (normalizedPointer.y * target.zoomY);\n\n        var isTransparent = fabric.util.isTransparent(\n          target._cacheContext, targetRelativeX, targetRelativeY, this.targetFindTolerance);\n\n        return isTransparent;\n      }\n\n      var ctx = this.contextCache,\n          originalColor = target.selectionBackgroundColor, v = this.viewportTransform;\n\n      target.selectionBackgroundColor = '';\n\n      this.clearContext(ctx);\n\n      ctx.save();\n      ctx.transform(v[0], v[1], v[2], v[3], v[4], v[5]);\n      target.render(ctx);\n      ctx.restore();\n\n      target === this._activeObject && target._renderControls(ctx, {\n        hasBorders: false,\n        transparentCorners: false\n      }, {\n        hasBorders: false,\n      });\n\n      target.selectionBackgroundColor = originalColor;\n\n      var isTransparent = fabric.util.isTransparent(\n        ctx, x, y, this.targetFindTolerance);\n\n      return isTransparent;\n    },\n\n    /**\n     * takes an event and determins if selection key has been pressed\n     * @private\n     * @param {Event} e Event object\n     */\n    _isSelectionKeyPressed: function(e) {\n      var selectionKeyPressed = false;\n\n      if (Object.prototype.toString.call(this.selectionKey) === '[object Array]') {\n        selectionKeyPressed = !!this.selectionKey.find(function(key) { return e[key] === true; });\n      }\n      else {\n        selectionKeyPressed = e[this.selectionKey];\n      }\n\n      return selectionKeyPressed;\n    },\n\n    /**\n     * @private\n     * @param {Event} e Event object\n     * @param {fabric.Object} target\n     */\n    _shouldClearSelection: function (e, target) {\n      var activeObjects = this.getActiveObjects(),\n          activeObject = this._activeObject;\n\n      return (\n        !target\n        ||\n        (target &&\n          activeObject &&\n          activeObjects.length > 1 &&\n          activeObjects.indexOf(target) === -1 &&\n          activeObject !== target &&\n          !this._isSelectionKeyPressed(e))\n        ||\n        (target && !target.evented)\n        ||\n        (target &&\n          !target.selectable &&\n          activeObject &&\n          activeObject !== target)\n      );\n    },\n\n    /**\n     * centeredScaling from object can't override centeredScaling from canvas.\n     * this should be fixed, since object setting should take precedence over canvas.\n     * @private\n     * @param {fabric.Object} target\n     */\n    _shouldCenterTransform: function (target) {\n      if (!target) {\n        return;\n      }\n\n      var t = this._currentTransform,\n          centerTransform;\n\n      if (t.action === 'scale' || t.action === 'scaleX' || t.action === 'scaleY') {\n        centerTransform = this.centeredScaling || target.centeredScaling;\n      }\n      else if (t.action === 'rotate') {\n        centerTransform = this.centeredRotation || target.centeredRotation;\n      }\n\n      return centerTransform ? !t.altKey : t.altKey;\n    },\n\n    /**\n     * @private\n     */\n    _getOriginFromCorner: function(target, corner) {\n      var origin = {\n        x: target.originX,\n        y: target.originY\n      };\n\n      if (corner === 'ml' || corner === 'tl' || corner === 'bl') {\n        origin.x = 'right';\n      }\n      else if (corner === 'mr' || corner === 'tr' || corner === 'br') {\n        origin.x = 'left';\n      }\n\n      if (corner === 'tl' || corner === 'mt' || corner === 'tr') {\n        origin.y = 'bottom';\n      }\n      else if (corner === 'bl' || corner === 'mb' || corner === 'br') {\n        origin.y = 'top';\n      }\n\n      return origin;\n    },\n\n    /**\n     * @private\n     */\n    _getActionFromCorner: function(target, corner, e) {\n      if (!corner) {\n        return 'drag';\n      }\n\n      switch (corner) {\n        case 'mtr':\n          return 'rotate';\n        case 'ml':\n        case 'mr':\n          return e[this.altActionKey] ? 'skewY' : 'scaleX';\n        case 'mt':\n        case 'mb':\n          return e[this.altActionKey] ? 'skewX' : 'scaleY';\n        default:\n          return 'scale';\n      }\n    },\n\n    /**\n     * @private\n     * @param {Event} e Event object\n     * @param {fabric.Object} target\n     */\n    _setupCurrentTransform: function (e, target) {\n      if (!target) {\n        return;\n      }\n\n      var pointer = this.getPointer(e),\n          corner = target._findTargetCorner(this.getPointer(e, true)),\n          action = this._getActionFromCorner(target, corner, e),\n          origin = this._getOriginFromCorner(target, corner);\n\n      this._currentTransform = {\n        target: target,\n        action: action,\n        corner: corner,\n        scaleX: target.scaleX,\n        scaleY: target.scaleY,\n        skewX: target.skewX,\n        skewY: target.skewY,\n        // used by transation\n        offsetX: pointer.x - target.left,\n        offsetY: pointer.y - target.top,\n        originX: origin.x,\n        originY: origin.y,\n        ex: pointer.x,\n        ey: pointer.y,\n        lastX: pointer.x,\n        lastY: pointer.y,\n        // unsure they are usefull anymore.\n        // left: target.left,\n        // top: target.top,\n        theta: degreesToRadians(target.angle),\n        // end of unsure\n        width: target.width * target.scaleX,\n        mouseXSign: 1,\n        mouseYSign: 1,\n        shiftKey: e.shiftKey,\n        altKey: e[this.centeredKey]\n      };\n\n      this._currentTransform.original = {\n        left: target.left,\n        top: target.top,\n        scaleX: target.scaleX,\n        scaleY: target.scaleY,\n        skewX: target.skewX,\n        skewY: target.skewY,\n        originX: origin.x,\n        originY: origin.y\n      };\n\n      this._resetCurrentTransform();\n      this._beforeTransform(e);\n    },\n\n    /**\n     * Translates object by \"setting\" its left/top\n     * @private\n     * @param {Number} x pointer's x coordinate\n     * @param {Number} y pointer's y coordinate\n     * @return {Boolean} true if the translation occurred\n     */\n    _translateObject: function (x, y) {\n      var transform = this._currentTransform,\n          target = transform.target,\n          newLeft = x - transform.offsetX,\n          newTop = y - transform.offsetY,\n          moveX = !target.get('lockMovementX') && target.left !== newLeft,\n          moveY = !target.get('lockMovementY') && target.top !== newTop;\n\n      moveX && target.set('left', newLeft);\n      moveY && target.set('top', newTop);\n      return moveX || moveY;\n    },\n\n    /**\n     * Check if we are increasing a positive skew or lower it,\n     * checking mouse direction and pressed corner.\n     * @private\n     */\n    _changeSkewTransformOrigin: function(mouseMove, t, by) {\n      var property = 'originX', origins = { 0: 'center' },\n          skew = t.target.skewX, originA = 'left', originB = 'right',\n          corner = t.corner === 'mt' || t.corner === 'ml' ? 1 : -1,\n          flipSign = 1;\n\n      mouseMove = mouseMove > 0 ? 1 : -1;\n      if (by === 'y') {\n        skew = t.target.skewY;\n        originA = 'top';\n        originB = 'bottom';\n        property = 'originY';\n      }\n      origins[-1] = originA;\n      origins[1] = originB;\n\n      t.target.flipX && (flipSign *= -1);\n      t.target.flipY && (flipSign *= -1);\n\n      if (skew === 0) {\n        t.skewSign = -corner * mouseMove * flipSign;\n        t[property] = origins[-mouseMove];\n      }\n      else {\n        skew = skew > 0 ? 1 : -1;\n        t.skewSign = skew;\n        t[property] = origins[skew * corner * flipSign];\n      }\n    },\n\n    /**\n     * Skew object by mouse events\n     * @private\n     * @param {Number} x pointer's x coordinate\n     * @param {Number} y pointer's y coordinate\n     * @param {String} by Either 'x' or 'y'\n     * @return {Boolean} true if the skewing occurred\n     */\n    _skewObject: function (x, y, by) {\n      var t = this._currentTransform,\n          target = t.target, skewed = false,\n          lockSkewingX = target.get('lockSkewingX'),\n          lockSkewingY = target.get('lockSkewingY');\n\n      if ((lockSkewingX && by === 'x') || (lockSkewingY && by === 'y')) {\n        return false;\n      }\n\n      // Get the constraint point\n      var center = target.getCenterPoint(),\n          actualMouseByCenter = target.toLocalPoint(new fabric.Point(x, y), 'center', 'center')[by],\n          lastMouseByCenter = target.toLocalPoint(new fabric.Point(t.lastX, t.lastY), 'center', 'center')[by],\n          actualMouseByOrigin, constraintPosition, dim = target._getTransformedDimensions();\n\n      this._changeSkewTransformOrigin(actualMouseByCenter - lastMouseByCenter, t, by);\n      actualMouseByOrigin = target.toLocalPoint(new fabric.Point(x, y), t.originX, t.originY)[by];\n      constraintPosition = target.translateToOriginPoint(center, t.originX, t.originY);\n      // Actually skew the object\n      skewed = this._setObjectSkew(actualMouseByOrigin, t, by, dim);\n      t.lastX = x;\n      t.lastY = y;\n      // Make sure the constraints apply\n      target.setPositionByOrigin(constraintPosition, t.originX, t.originY);\n      return skewed;\n    },\n\n    /**\n     * Set object skew\n     * @private\n     * @return {Boolean} true if the skewing occurred\n     */\n    _setObjectSkew: function(localMouse, transform, by, _dim) {\n      var target = transform.target, newValue, skewed = false,\n          skewSign = transform.skewSign, newDim, dimNoSkew,\n          otherBy, _otherBy, _by, newDimMouse, skewX, skewY;\n\n      if (by === 'x') {\n        otherBy = 'y';\n        _otherBy = 'Y';\n        _by = 'X';\n        skewX = 0;\n        skewY = target.skewY;\n      }\n      else {\n        otherBy = 'x';\n        _otherBy = 'X';\n        _by = 'Y';\n        skewX = target.skewX;\n        skewY = 0;\n      }\n\n      dimNoSkew = target._getTransformedDimensions(skewX, skewY);\n      newDimMouse = 2 * Math.abs(localMouse) - dimNoSkew[by];\n      if (newDimMouse <= 2) {\n        newValue = 0;\n      }\n      else {\n        newValue = skewSign * Math.atan((newDimMouse / target['scale' + _by]) /\n                                        (dimNoSkew[otherBy] / target['scale' + _otherBy]));\n        newValue = fabric.util.radiansToDegrees(newValue);\n      }\n      skewed = target['skew' + _by] !== newValue;\n      target.set('skew' + _by, newValue);\n      if (target['skew' + _otherBy] !== 0) {\n        newDim = target._getTransformedDimensions();\n        newValue = (_dim[otherBy] / newDim[otherBy]) * target['scale' + _otherBy];\n        target.set('scale' + _otherBy, newValue);\n      }\n      return skewed;\n    },\n\n    /**\n     * Scales object by invoking its scaleX/scaleY methods\n     * @private\n     * @param {Number} x pointer's x coordinate\n     * @param {Number} y pointer's y coordinate\n     * @param {String} by Either 'x' or 'y' - specifies dimension constraint by which to scale an object.\n     *                    When not provided, an object is scaled by both dimensions equally\n     * @return {Boolean} true if the scaling occurred\n     */\n    _scaleObject: function (x, y, by) {\n      var t = this._currentTransform,\n          target = t.target,\n          lockScalingX = target.lockScalingX,\n          lockScalingY = target.lockScalingY,\n          lockScalingFlip = target.lockScalingFlip;\n\n      if (lockScalingX && lockScalingY) {\n        return false;\n      }\n\n      // Get the constraint point\n      var constraintPosition = target.translateToOriginPoint(target.getCenterPoint(), t.originX, t.originY),\n          localMouse = target.toLocalPoint(new fabric.Point(x, y), t.originX, t.originY),\n          dim = target._getTransformedDimensions(), scaled = false;\n\n      this._setLocalMouse(localMouse, t);\n\n      // Actually scale the object\n      scaled = this._setObjectScale(localMouse, t, lockScalingX, lockScalingY, by, lockScalingFlip, dim);\n\n      // Make sure the constraints apply\n      target.setPositionByOrigin(constraintPosition, t.originX, t.originY);\n      return scaled;\n    },\n\n    /**\n     * @private\n     * @return {Boolean} true if the scaling occurred\n     */\n    _setObjectScale: function(localMouse, transform, lockScalingX, lockScalingY, by, lockScalingFlip, _dim) {\n      var target = transform.target, forbidScalingX = false, forbidScalingY = false, scaled = false,\n          changeX, changeY, scaleX, scaleY;\n\n      scaleX = localMouse.x * target.scaleX / _dim.x;\n      scaleY = localMouse.y * target.scaleY / _dim.y;\n      changeX = target.scaleX !== scaleX;\n      changeY = target.scaleY !== scaleY;\n\n      if (lockScalingFlip && scaleX <= 0 && scaleX < target.scaleX) {\n        forbidScalingX = true;\n        localMouse.x = 0;\n      }\n\n      if (lockScalingFlip && scaleY <= 0 && scaleY < target.scaleY) {\n        forbidScalingY = true;\n        localMouse.y = 0;\n      }\n\n      if (by === 'equally' && !lockScalingX && !lockScalingY) {\n        scaled = this._scaleObjectEqually(localMouse, target, transform, _dim);\n      }\n      else if (!by) {\n        forbidScalingX || lockScalingX || (target.set('scaleX', scaleX) && (scaled = scaled || changeX));\n        forbidScalingY || lockScalingY || (target.set('scaleY', scaleY) && (scaled = scaled || changeY));\n      }\n      else if (by === 'x' && !target.get('lockUniScaling')) {\n        forbidScalingX || lockScalingX || (target.set('scaleX', scaleX) && (scaled = scaled || changeX));\n      }\n      else if (by === 'y' && !target.get('lockUniScaling')) {\n        forbidScalingY || lockScalingY || (target.set('scaleY', scaleY) && (scaled = scaled || changeY));\n      }\n      transform.newScaleX = scaleX;\n      transform.newScaleY = scaleY;\n      forbidScalingX || forbidScalingY || this._flipObject(transform, by);\n      return scaled;\n    },\n\n    /**\n     * @private\n     * @return {Boolean} true if the scaling occurred\n     */\n    _scaleObjectEqually: function(localMouse, target, transform, _dim) {\n\n      var dist = localMouse.y + localMouse.x,\n          lastDist = _dim.y * transform.original.scaleY / target.scaleY +\n                     _dim.x * transform.original.scaleX / target.scaleX,\n          scaled, signX = localMouse.x < 0 ? -1 : 1,\n          signY = localMouse.y < 0 ? -1 : 1;\n\n      // We use transform.scaleX/Y instead of target.scaleX/Y\n      // because the object may have a min scale and we'll loose the proportions\n      transform.newScaleX = signX * Math.abs(transform.original.scaleX * dist / lastDist);\n      transform.newScaleY = signY * Math.abs(transform.original.scaleY * dist / lastDist);\n      scaled = transform.newScaleX !== target.scaleX || transform.newScaleY !== target.scaleY;\n      target.set('scaleX', transform.newScaleX);\n      target.set('scaleY', transform.newScaleY);\n      return scaled;\n    },\n\n    /**\n     * @private\n     */\n    _flipObject: function(transform, by) {\n      if (transform.newScaleX < 0 && by !== 'y') {\n        if (transform.originX === 'left') {\n          transform.originX = 'right';\n        }\n        else if (transform.originX === 'right') {\n          transform.originX = 'left';\n        }\n      }\n\n      if (transform.newScaleY < 0 && by !== 'x') {\n        if (transform.originY === 'top') {\n          transform.originY = 'bottom';\n        }\n        else if (transform.originY === 'bottom') {\n          transform.originY = 'top';\n        }\n      }\n    },\n\n    /**\n     * @private\n     */\n    _setLocalMouse: function(localMouse, t) {\n      var target = t.target, zoom = this.getZoom(),\n          padding = target.padding / zoom;\n\n      if (t.originX === 'right') {\n        localMouse.x *= -1;\n      }\n      else if (t.originX === 'center') {\n        localMouse.x *= t.mouseXSign * 2;\n        if (localMouse.x < 0) {\n          t.mouseXSign = -t.mouseXSign;\n        }\n      }\n\n      if (t.originY === 'bottom') {\n        localMouse.y *= -1;\n      }\n      else if (t.originY === 'center') {\n        localMouse.y *= t.mouseYSign * 2;\n        if (localMouse.y < 0) {\n          t.mouseYSign = -t.mouseYSign;\n        }\n      }\n\n      // adjust the mouse coordinates when dealing with padding\n      if (abs(localMouse.x) > padding) {\n        if (localMouse.x < 0) {\n          localMouse.x += padding;\n        }\n        else {\n          localMouse.x -= padding;\n        }\n      }\n      else { // mouse is within the padding, set to 0\n        localMouse.x = 0;\n      }\n\n      if (abs(localMouse.y) > padding) {\n        if (localMouse.y < 0) {\n          localMouse.y += padding;\n        }\n        else {\n          localMouse.y -= padding;\n        }\n      }\n      else {\n        localMouse.y = 0;\n      }\n    },\n\n    /**\n     * Rotates object by invoking its rotate method\n     * @private\n     * @param {Number} x pointer's x coordinate\n     * @param {Number} y pointer's y coordinate\n     * @return {Boolean} true if the rotation occurred\n     */\n    _rotateObject: function (x, y) {\n\n      var t = this._currentTransform,\n          target = t.target, constraintPosition,\n          constraintPosition = target.translateToOriginPoint(target.getCenterPoint(), t.originX, t.originY);\n\n      if (target.lockRotation) {\n        return false;\n      }\n\n      var lastAngle = atan2(t.ey - constraintPosition.y, t.ex - constraintPosition.x),\n          curAngle = atan2(y - constraintPosition.y, x - constraintPosition.x),\n          angle = radiansToDegrees(curAngle - lastAngle + t.theta),\n          hasRotated = true;\n\n      if (target.snapAngle > 0) {\n        var snapAngle  = target.snapAngle,\n            snapThreshold  = target.snapThreshold || snapAngle,\n            rightAngleLocked = Math.ceil(angle / snapAngle) * snapAngle,\n            leftAngleLocked = Math.floor(angle / snapAngle) * snapAngle;\n\n        if (Math.abs(angle - leftAngleLocked) < snapThreshold) {\n          angle = leftAngleLocked;\n        }\n        else if (Math.abs(angle - rightAngleLocked) < snapThreshold) {\n          angle = rightAngleLocked;\n        }\n      }\n\n      // normalize angle to positive value\n      if (angle < 0) {\n        angle = 360 + angle;\n      }\n      angle %= 360;\n\n      if (target.angle === angle) {\n        hasRotated = false;\n      }\n      else {\n        // rotation only happen here\n        target.angle = angle;\n        // Make sure the constraints apply\n        target.setPositionByOrigin(constraintPosition, t.originX, t.originY);\n      }\n\n      return hasRotated;\n    },\n\n    /**\n     * Set the cursor type of the canvas element\n     * @param {String} value Cursor type of the canvas element.\n     * @see http://www.w3.org/TR/css3-ui/#cursor\n     */\n    setCursor: function (value) {\n      this.upperCanvasEl.style.cursor = value;\n    },\n\n    /**\n     * @param {fabric.Object} target to reset transform\n     * @private\n     */\n    _resetObjectTransform: function (target) {\n      target.scaleX = 1;\n      target.scaleY = 1;\n      target.skewX = 0;\n      target.skewY = 0;\n      target.rotate(0);\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx to draw the selection on\n     */\n    _drawSelection: function (ctx) {\n      var groupSelector = this._groupSelector,\n          left = groupSelector.left,\n          top = groupSelector.top,\n          aleft = abs(left),\n          atop = abs(top);\n\n      if (this.selectionColor) {\n        ctx.fillStyle = this.selectionColor;\n\n        ctx.fillRect(\n          groupSelector.ex - ((left > 0) ? 0 : -left),\n          groupSelector.ey - ((top > 0) ? 0 : -top),\n          aleft,\n          atop\n        );\n      }\n\n      if (!this.selectionLineWidth || !this.selectionBorderColor) {\n        return;\n      }\n      ctx.lineWidth = this.selectionLineWidth;\n      ctx.strokeStyle = this.selectionBorderColor;\n\n      // selection border\n      if (this.selectionDashArray.length > 1 && !supportLineDash) {\n\n        var px = groupSelector.ex + STROKE_OFFSET - ((left > 0) ? 0 : aleft),\n            py = groupSelector.ey + STROKE_OFFSET - ((top > 0) ? 0 : atop);\n\n        ctx.beginPath();\n\n        fabric.util.drawDashedLine(ctx, px, py, px + aleft, py, this.selectionDashArray);\n        fabric.util.drawDashedLine(ctx, px, py + atop - 1, px + aleft, py + atop - 1, this.selectionDashArray);\n        fabric.util.drawDashedLine(ctx, px, py, px, py + atop, this.selectionDashArray);\n        fabric.util.drawDashedLine(ctx, px + aleft - 1, py, px + aleft - 1, py + atop, this.selectionDashArray);\n\n        ctx.closePath();\n        ctx.stroke();\n      }\n      else {\n        fabric.Object.prototype._setLineDash.call(this, ctx, this.selectionDashArray);\n        ctx.strokeRect(\n          groupSelector.ex + STROKE_OFFSET - ((left > 0) ? 0 : aleft),\n          groupSelector.ey + STROKE_OFFSET - ((top > 0) ? 0 : atop),\n          aleft,\n          atop\n        );\n      }\n    },\n\n    /**\n     * Method that determines what object we are clicking on\n     * the skipGroup parameter is for internal use, is needed for shift+click action\n     * @param {Event} e mouse event\n     * @param {Boolean} skipGroup when true, activeGroup is skipped and only objects are traversed through\n     */\n    findTarget: function (e, skipGroup) {\n      if (this.skipTargetFind) {\n        return;\n      }\n\n      var ignoreZoom = true,\n          pointer = this.getPointer(e, ignoreZoom),\n          activeObject = this._activeObject,\n          aObjects = this.getActiveObjects(),\n          activeTarget, activeTargetSubs;\n\n      // first check current group (if one exists)\n      // active group does not check sub targets like normal groups.\n      // if active group just exits.\n      this.targets = [];\n\n      if (aObjects.length > 1 && !skipGroup && activeObject === this._searchPossibleTargets([activeObject], pointer)) {\n        return activeObject;\n      }\n      // if we hit the corner of an activeObject, let's return that.\n      if (aObjects.length === 1 && activeObject._findTargetCorner(pointer)) {\n        return activeObject;\n      }\n      if (aObjects.length === 1 &&\n        activeObject === this._searchPossibleTargets([activeObject], pointer)) {\n        if (!this.preserveObjectStacking) {\n          return activeObject;\n        }\n        else {\n          activeTarget = activeObject;\n          activeTargetSubs = this.targets;\n          this.targets = [];\n        }\n      }\n      var target = this._searchPossibleTargets(this._objects, pointer);\n      if (e[this.altSelectionKey] && target && activeTarget && target !== activeTarget) {\n        target = activeTarget;\n        this.targets = activeTargetSubs;\n      }\n      return target;\n    },\n\n    /**\n     * @private\n     */\n    _checkTarget: function(pointer, obj) {\n      if (obj &&\n          obj.visible &&\n          obj.evented &&\n          this.containsPoint(null, obj, pointer)){\n        if ((this.perPixelTargetFind || obj.perPixelTargetFind) && !obj.isEditing) {\n          var isTransparent = this.isTargetTransparent(obj, pointer.x, pointer.y);\n          if (!isTransparent) {\n            return true;\n          }\n        }\n        else {\n          return true;\n        }\n      }\n    },\n\n    /**\n     * @private\n     */\n    _searchPossibleTargets: function(objects, pointer) {\n\n      // Cache all targets where their bounding box contains point.\n      var target, i = objects.length, normalizedPointer, subTarget;\n      // Do not check for currently grouped objects, since we check the parent group itself.\n      // until we call this function specifically to search inside the activeGroup\n      while (i--) {\n        if (this._checkTarget(pointer, objects[i])) {\n          target = objects[i];\n          if (target.subTargetCheck && target instanceof fabric.Group) {\n            normalizedPointer = this._normalizePointer(target, pointer);\n            subTarget = this._searchPossibleTargets(target._objects, normalizedPointer);\n            subTarget && this.targets.push(subTarget);\n          }\n          break;\n        }\n      }\n      return target;\n    },\n\n    /**\n     * Returns pointer coordinates without the effect of the viewport\n     * @param {Object} pointer with \"x\" and \"y\" number values\n     * @return {Object} object with \"x\" and \"y\" number values\n     */\n    restorePointerVpt: function(pointer) {\n      return fabric.util.transformPoint(\n        pointer,\n        fabric.util.invertTransform(this.viewportTransform)\n      );\n    },\n\n    /**\n     * Returns pointer coordinates relative to canvas.\n     * Can return coordinates with or without viewportTransform.\n     * ignoreZoom false gives back coordinates that represent\n     * the point clicked on canvas element.\n     * ignoreZoom true gives back coordinates after being processed\n     * by the viewportTransform ( sort of coordinates of what is displayed\n     * on the canvas where you are clicking.\n     * ignoreZoom true = HTMLElement coordinates relative to top,left\n     * ignoreZoom false, default = fabric space coordinates, the same used for shape position\n     * To interact with your shapes top and left you want to use ignoreZoom true\n     * most of the time, while ignoreZoom false will give you coordinates\n     * compatible with the object.oCoords system.\n     * of the time.\n     * @param {Event} e\n     * @param {Boolean} ignoreZoom\n     * @return {Object} object with \"x\" and \"y\" number values\n     */\n    getPointer: function (e, ignoreZoom) {\n      // return cached values if we are in the event processing chain\n      if (this._absolutePointer && !ignoreZoom) {\n        return this._absolutePointer;\n      }\n      if (this._pointer && ignoreZoom) {\n        return this._pointer;\n      }\n\n      var pointer = getPointer(e),\n          upperCanvasEl = this.upperCanvasEl,\n          bounds = upperCanvasEl.getBoundingClientRect(),\n          boundsWidth = bounds.width || 0,\n          boundsHeight = bounds.height || 0,\n          cssScale;\n\n      if (!boundsWidth || !boundsHeight ) {\n        if ('top' in bounds && 'bottom' in bounds) {\n          boundsHeight = Math.abs( bounds.top - bounds.bottom );\n        }\n        if ('right' in bounds && 'left' in bounds) {\n          boundsWidth = Math.abs( bounds.right - bounds.left );\n        }\n      }\n\n      this.calcOffset();\n      pointer.x = pointer.x - this._offset.left;\n      pointer.y = pointer.y - this._offset.top;\n      if (!ignoreZoom) {\n        pointer = this.restorePointerVpt(pointer);\n      }\n\n      if (boundsWidth === 0 || boundsHeight === 0) {\n        // If bounds are not available (i.e. not visible), do not apply scale.\n        cssScale = { width: 1, height: 1 };\n      }\n      else {\n        cssScale = {\n          width: upperCanvasEl.width / boundsWidth,\n          height: upperCanvasEl.height / boundsHeight\n        };\n      }\n\n      return {\n        x: pointer.x * cssScale.width,\n        y: pointer.y * cssScale.height\n      };\n    },\n\n    /**\n     * @private\n     * @throws {CANVAS_INIT_ERROR} If canvas can not be initialized\n     */\n    _createUpperCanvas: function () {\n      var lowerCanvasClass = this.lowerCanvasEl.className.replace(/\\s*lower-canvas\\s*/, '');\n\n      // there is no need to create a new upperCanvas element if we have already one.\n      if (this.upperCanvasEl) {\n        this.upperCanvasEl.className = '';\n      }\n      else {\n        this.upperCanvasEl = this._createCanvasElement();\n      }\n      fabric.util.addClass(this.upperCanvasEl, 'upper-canvas ' + lowerCanvasClass);\n\n      this.wrapperEl.appendChild(this.upperCanvasEl);\n\n      this._copyCanvasStyle(this.lowerCanvasEl, this.upperCanvasEl);\n      this._applyCanvasStyle(this.upperCanvasEl);\n      this.contextTop = this.upperCanvasEl.getContext('2d');\n    },\n\n    /**\n     * @private\n     */\n    _createCacheCanvas: function () {\n      this.cacheCanvasEl = this._createCanvasElement();\n      this.cacheCanvasEl.setAttribute('width', this.width);\n      this.cacheCanvasEl.setAttribute('height', this.height);\n      this.contextCache = this.cacheCanvasEl.getContext('2d');\n    },\n\n    /**\n     * @private\n     */\n    _initWrapperElement: function () {\n      this.wrapperEl = fabric.util.wrapElement(this.lowerCanvasEl, 'div', {\n        'class': this.containerClass\n      });\n      fabric.util.setStyle(this.wrapperEl, {\n        width: this.width + 'px',\n        height: this.height + 'px',\n        position: 'relative'\n      });\n      fabric.util.makeElementUnselectable(this.wrapperEl);\n    },\n\n    /**\n     * @private\n     * @param {HTMLElement} element canvas element to apply styles on\n     */\n    _applyCanvasStyle: function (element) {\n      var width = this.width || element.width,\n          height = this.height || element.height;\n\n      fabric.util.setStyle(element, {\n        position: 'absolute',\n        width: width + 'px',\n        height: height + 'px',\n        left: 0,\n        top: 0,\n        'touch-action': this.allowTouchScrolling ? 'manipulation' : 'none'\n      });\n      element.width = width;\n      element.height = height;\n      fabric.util.makeElementUnselectable(element);\n    },\n\n    /**\n     * Copy the entire inline style from one element (fromEl) to another (toEl)\n     * @private\n     * @param {Element} fromEl Element style is copied from\n     * @param {Element} toEl Element copied style is applied to\n     */\n    _copyCanvasStyle: function (fromEl, toEl) {\n      toEl.style.cssText = fromEl.style.cssText;\n    },\n\n    /**\n     * Returns context of canvas where object selection is drawn\n     * @return {CanvasRenderingContext2D}\n     */\n    getSelectionContext: function() {\n      return this.contextTop;\n    },\n\n    /**\n     * Returns &lt;canvas> element on which object selection is drawn\n     * @return {HTMLCanvasElement}\n     */\n    getSelectionElement: function () {\n      return this.upperCanvasEl;\n    },\n\n    /**\n     * Returns currently active object\n     * @return {fabric.Object} active object\n     */\n    getActiveObject: function () {\n      return this._activeObject;\n    },\n\n    /**\n     * Returns an array with the current selected objects\n     * @return {fabric.Object} active object\n     */\n    getActiveObjects: function () {\n      var active = this._activeObject;\n      if (active) {\n        if (active.type === 'activeSelection' && active._objects) {\n          return active._objects.slice(0);\n        }\n        else {\n          return [active];\n        }\n      }\n      return [];\n    },\n\n    /**\n     * @private\n     * @param {fabric.Object} obj Object that was removed\n     */\n    _onObjectRemoved: function(obj) {\n      // removing active object should fire \"selection:cleared\" events\n      if (obj === this._activeObject) {\n        this.fire('before:selection:cleared', { target: obj });\n        this._discardActiveObject();\n        this.fire('selection:cleared', { target: obj });\n        obj.fire('deselected');\n      }\n      if (this._hoveredTarget === obj) {\n        this._hoveredTarget = null;\n      }\n      this.callSuper('_onObjectRemoved', obj);\n    },\n\n    /**\n     * @private\n     * Compares the old activeObject with the current one and fires correct events\n     * @param {fabric.Object} obj old activeObject\n     */\n    _fireSelectionEvents: function(oldObjects, e) {\n      var somethingChanged = false, objects = this.getActiveObjects(),\n          added = [], removed = [], opt = { e: e };\n      oldObjects.forEach(function(oldObject) {\n        if (objects.indexOf(oldObject) === -1) {\n          somethingChanged = true;\n          oldObject.fire('deselected', opt);\n          removed.push(oldObject);\n        }\n      });\n      objects.forEach(function(object) {\n        if (oldObjects.indexOf(object) === -1) {\n          somethingChanged = true;\n          object.fire('selected', opt);\n          added.push(object);\n        }\n      });\n      if (oldObjects.length > 0 && objects.length > 0) {\n        opt.selected = added;\n        opt.deselected = removed;\n        // added for backward compatibility\n        opt.updated = added[0] || removed[0];\n        opt.target = this._activeObject;\n        somethingChanged && this.fire('selection:updated', opt);\n      }\n      else if (objects.length > 0) {\n        // deprecated event\n        if (objects.length === 1) {\n          opt.target = added[0];\n          this.fire('object:selected', opt);\n        }\n        opt.selected = added;\n        // added for backward compatibility\n        opt.target = this._activeObject;\n        this.fire('selection:created', opt);\n      }\n      else if (oldObjects.length > 0) {\n        opt.deselected = removed;\n        this.fire('selection:cleared', opt);\n      }\n    },\n\n    /**\n     * Sets given object as the only active object on canvas\n     * @param {fabric.Object} object Object to set as an active one\n     * @param {Event} [e] Event (passed along when firing \"object:selected\")\n     * @return {fabric.Canvas} thisArg\n     * @chainable\n     */\n    setActiveObject: function (object, e) {\n      var currentActives = this.getActiveObjects();\n      this._setActiveObject(object, e);\n      this._fireSelectionEvents(currentActives, e);\n      return this;\n    },\n\n    /**\n     * @private\n     * @param {Object} object to set as active\n     * @param {Event} [e] Event (passed along when firing \"object:selected\")\n     * @return {Boolean} true if the selection happened\n     */\n    _setActiveObject: function(object, e) {\n      if (this._activeObject === object) {\n        return false;\n      }\n      if (!this._discardActiveObject(e, object)) {\n        return false;\n      }\n      if (object.onSelect({ e: e })) {\n        return false;\n      }\n      this._activeObject = object;\n      return true;\n    },\n\n    /**\n     * @private\n     */\n    _discardActiveObject: function(e, object) {\n      var obj = this._activeObject;\n      if (obj) {\n        // onDeselect return TRUE to cancel selection;\n        if (obj.onDeselect({ e: e, object: object })) {\n          return false;\n        }\n        this._activeObject = null;\n      }\n      return true;\n    },\n\n    /**\n     * Discards currently active object and fire events. If the function is called by fabric\n     * as a consequence of a mouse event, the event is passed as a parameter and\n     * sent to the fire function for the custom events. When used as a method the\n     * e param does not have any application.\n     * @param {event} e\n     * @return {fabric.Canvas} thisArg\n     * @chainable\n     */\n    discardActiveObject: function (e) {\n      var currentActives = this.getActiveObjects();\n      if (currentActives.length) {\n        this.fire('before:selection:cleared', { target: currentActives[0], e: e });\n      }\n      this._discardActiveObject(e);\n      this._fireSelectionEvents(currentActives, e);\n      return this;\n    },\n\n    /**\n     * Clears a canvas element and removes all event listeners\n     * @return {fabric.Canvas} thisArg\n     * @chainable\n     */\n    dispose: function () {\n      var wrapper = this.wrapperEl;\n      this.removeListeners();\n      wrapper.removeChild(this.upperCanvasEl);\n      wrapper.removeChild(this.lowerCanvasEl);\n      this.upperCanvasEl = null;\n      this.cacheCanvasEl = null;\n      this.contextCache = null;\n      this.contextTop = null;\n      if (wrapper.parentNode) {\n        wrapper.parentNode.replaceChild(this.lowerCanvasEl, this.wrapperEl);\n      }\n      delete this.wrapperEl;\n      fabric.StaticCanvas.prototype.dispose.call(this);\n      return this;\n    },\n\n    /**\n     * Clears all contexts (background, main, top) of an instance\n     * @return {fabric.Canvas} thisArg\n     * @chainable\n     */\n    clear: function () {\n      // this.discardActiveGroup();\n      this.discardActiveObject();\n      this.clearContext(this.contextTop);\n      return this.callSuper('clear');\n    },\n\n    /**\n     * Draws objects' controls (borders/controls)\n     * @param {CanvasRenderingContext2D} ctx Context to render controls on\n     */\n    drawControls: function(ctx) {\n      var activeObject = this._activeObject;\n\n      if (activeObject) {\n        activeObject._renderControls(ctx);\n      }\n    },\n\n    /**\n     * @private\n     */\n    _toObject: function(instance, methodName, propertiesToInclude) {\n      //If the object is part of the current selection group, it should\n      //be transformed appropriately\n      //i.e. it should be serialised as it would appear if the selection group\n      //were to be destroyed.\n      var originalProperties = this._realizeGroupTransformOnObject(instance),\n          object = this.callSuper('_toObject', instance, methodName, propertiesToInclude);\n      //Undo the damage we did by changing all of its properties\n      this._unwindGroupTransformOnObject(instance, originalProperties);\n      return object;\n    },\n\n    /**\n     * Realises an object's group transformation on it\n     * @private\n     * @param {fabric.Object} [instance] the object to transform (gets mutated)\n     * @returns the original values of instance which were changed\n     */\n    _realizeGroupTransformOnObject: function(instance) {\n      if (instance.group && instance.group.type === 'activeSelection' && this._activeObject === instance.group) {\n        var layoutProps = ['angle', 'flipX', 'flipY', 'left', 'scaleX', 'scaleY', 'skewX', 'skewY', 'top'];\n        //Copy all the positionally relevant properties across now\n        var originalValues = {};\n        layoutProps.forEach(function(prop) {\n          originalValues[prop] = instance[prop];\n        });\n        this._activeObject.realizeTransform(instance);\n        return originalValues;\n      }\n      else {\n        return null;\n      }\n    },\n\n    /**\n     * Restores the changed properties of instance\n     * @private\n     * @param {fabric.Object} [instance] the object to un-transform (gets mutated)\n     * @param {Object} [originalValues] the original values of instance, as returned by _realizeGroupTransformOnObject\n     */\n    _unwindGroupTransformOnObject: function(instance, originalValues) {\n      if (originalValues) {\n        instance.set(originalValues);\n      }\n    },\n\n    /**\n     * @private\n     */\n    _setSVGObject: function(markup, instance, reviver) {\n      //If the object is in a selection group, simulate what would happen to that\n      //object when the group is deselected\n      var originalProperties = this._realizeGroupTransformOnObject(instance);\n      this.callSuper('_setSVGObject', markup, instance, reviver);\n      this._unwindGroupTransformOnObject(instance, originalProperties);\n    },\n  });\n\n  // copying static properties manually to work around Opera's bug,\n  // where \"prototype\" property is enumerable and overrides existing prototype\n  for (var prop in fabric.StaticCanvas) {\n    if (prop !== 'prototype') {\n      fabric.Canvas[prop] = fabric.StaticCanvas[prop];\n    }\n  }\n\n  if (fabric.isTouchSupported) {\n    /** @ignore */\n    fabric.Canvas.prototype._setCursorFromEvent = function() { };\n  }\n})();\n\n\n(function() {\n\n  var cursorOffset = {\n        mt: 0, // n\n        tr: 1, // ne\n        mr: 2, // e\n        br: 3, // se\n        mb: 4, // s\n        bl: 5, // sw\n        ml: 6, // w\n        tl: 7 // nw\n      },\n      addListener = fabric.util.addListener,\n      removeListener = fabric.util.removeListener,\n      RIGHT_CLICK = 3, MIDDLE_CLICK = 2, LEFT_CLICK = 1,\n      addEventOptions = { passive: false };\n\n  function checkClick(e, value) {\n    return 'which' in e ? e.which === value : e.button === value - 1;\n  }\n\n  fabric.util.object.extend(fabric.Canvas.prototype, /** @lends fabric.Canvas.prototype */ {\n\n    /**\n     * Map of cursor style values for each of the object controls\n     * @private\n     */\n    cursorMap: [\n      'n-resize',\n      'ne-resize',\n      'e-resize',\n      'se-resize',\n      's-resize',\n      'sw-resize',\n      'w-resize',\n      'nw-resize'\n    ],\n\n    /**\n     * Adds mouse listeners to canvas\n     * @private\n     */\n    _initEventListeners: function () {\n      // in case we initialized the class twice. This should not happen normally\n      // but in some kind of applications where the canvas element may be changed\n      // this is a workaround to having double listeners.\n      this.removeListeners();\n      this._bindEvents();\n\n      addListener(fabric.window, 'resize', this._onResize);\n\n      // mouse events\n      addListener(this.upperCanvasEl, 'mousedown', this._onMouseDown);\n      addListener(this.upperCanvasEl, 'dblclick', this._onDoubleClick);\n      addListener(this.upperCanvasEl, 'mousemove', this._onMouseMove, addEventOptions);\n      addListener(this.upperCanvasEl, 'mouseout', this._onMouseOut);\n      addListener(this.upperCanvasEl, 'mouseenter', this._onMouseEnter);\n      addListener(this.upperCanvasEl, 'wheel', this._onMouseWheel);\n      addListener(this.upperCanvasEl, 'contextmenu', this._onContextMenu);\n      addListener(this.upperCanvasEl, 'dragover', this._onDragOver);\n      addListener(this.upperCanvasEl, 'dragenter', this._onDragEnter);\n      addListener(this.upperCanvasEl, 'dragleave', this._onDragLeave);\n      addListener(this.upperCanvasEl, 'drop', this._onDrop);\n      // touch events\n      addListener(this.upperCanvasEl, 'touchstart', this._onMouseDown, addEventOptions);\n      addListener(this.upperCanvasEl, 'touchmove', this._onMouseMove, addEventOptions);\n\n      if (typeof eventjs !== 'undefined' && 'add' in eventjs) {\n        eventjs.add(this.upperCanvasEl, 'gesture', this._onGesture);\n        eventjs.add(this.upperCanvasEl, 'drag', this._onDrag);\n        eventjs.add(this.upperCanvasEl, 'orientation', this._onOrientationChange);\n        eventjs.add(this.upperCanvasEl, 'shake', this._onShake);\n        eventjs.add(this.upperCanvasEl, 'longpress', this._onLongPress);\n      }\n    },\n\n    /**\n     * @private\n     */\n    _bindEvents: function() {\n      if (this.eventsBound) {\n        // for any reason we pass here twice we do not want to bind events twice.\n        return;\n      }\n      this._onMouseDown = this._onMouseDown.bind(this);\n      this._onMouseMove = this._onMouseMove.bind(this);\n      this._onMouseUp = this._onMouseUp.bind(this);\n      this._onResize = this._onResize.bind(this);\n      this._onGesture = this._onGesture.bind(this);\n      this._onDrag = this._onDrag.bind(this);\n      this._onShake = this._onShake.bind(this);\n      this._onLongPress = this._onLongPress.bind(this);\n      this._onOrientationChange = this._onOrientationChange.bind(this);\n      this._onMouseWheel = this._onMouseWheel.bind(this);\n      this._onMouseOut = this._onMouseOut.bind(this);\n      this._onMouseEnter = this._onMouseEnter.bind(this);\n      this._onContextMenu = this._onContextMenu.bind(this);\n      this._onDoubleClick = this._onDoubleClick.bind(this);\n      this._onDragOver = this._onDragOver.bind(this);\n      this._onDragEnter = this._simpleEventHandler.bind(this, 'dragenter');\n      this._onDragLeave = this._simpleEventHandler.bind(this, 'dragleave');\n      this._onDrop = this._simpleEventHandler.bind(this, 'drop');\n      this.eventsBound = true;\n    },\n\n    /**\n     * Removes all event listeners\n     */\n    removeListeners: function() {\n      removeListener(fabric.window, 'resize', this._onResize);\n\n      removeListener(this.upperCanvasEl, 'mousedown', this._onMouseDown);\n      removeListener(this.upperCanvasEl, 'mousemove', this._onMouseMove);\n      removeListener(this.upperCanvasEl, 'mouseout', this._onMouseOut);\n      removeListener(this.upperCanvasEl, 'mouseenter', this._onMouseEnter);\n      removeListener(this.upperCanvasEl, 'wheel', this._onMouseWheel);\n      removeListener(this.upperCanvasEl, 'contextmenu', this._onContextMenu);\n      removeListener(this.upperCanvasEl, 'doubleclick', this._onDoubleClick);\n      removeListener(this.upperCanvasEl, 'touchstart', this._onMouseDown);\n      removeListener(this.upperCanvasEl, 'touchmove', this._onMouseMove);\n      removeListener(this.upperCanvasEl, 'dragover', this._onDragOver);\n      removeListener(this.upperCanvasEl, 'dragenter', this._onDragEnter);\n      removeListener(this.upperCanvasEl, 'dragleave', this._onDragLeave);\n      removeListener(this.upperCanvasEl, 'drop', this._onDrop);\n\n      if (typeof eventjs !== 'undefined' && 'remove' in eventjs) {\n        eventjs.remove(this.upperCanvasEl, 'gesture', this._onGesture);\n        eventjs.remove(this.upperCanvasEl, 'drag', this._onDrag);\n        eventjs.remove(this.upperCanvasEl, 'orientation', this._onOrientationChange);\n        eventjs.remove(this.upperCanvasEl, 'shake', this._onShake);\n        eventjs.remove(this.upperCanvasEl, 'longpress', this._onLongPress);\n      }\n    },\n\n    /**\n     * @private\n     * @param {Event} [e] Event object fired on Event.js gesture\n     * @param {Event} [self] Inner Event object\n     */\n    _onGesture: function(e, self) {\n      this.__onTransformGesture && this.__onTransformGesture(e, self);\n    },\n\n    /**\n     * @private\n     * @param {Event} [e] Event object fired on Event.js drag\n     * @param {Event} [self] Inner Event object\n     */\n    _onDrag: function(e, self) {\n      this.__onDrag && this.__onDrag(e, self);\n    },\n\n    /**\n     * @private\n     * @param {Event} [e] Event object fired on wheel event\n     */\n    _onMouseWheel: function(e) {\n      this.__onMouseWheel(e);\n    },\n\n    /**\n     * @private\n     * @param {Event} e Event object fired on mousedown\n     */\n    _onMouseOut: function(e) {\n      var target = this._hoveredTarget;\n      this.fire('mouse:out', { target: target, e: e });\n      this._hoveredTarget = null;\n      target && target.fire('mouseout', { e: e });\n      if (this._iTextInstances) {\n        this._iTextInstances.forEach(function(obj) {\n          if (obj.isEditing) {\n            obj.hiddenTextarea.focus();\n          }\n        });\n      }\n    },\n\n    /**\n     * @private\n     * @param {Event} e Event object fired on mouseenter\n     */\n    _onMouseEnter: function(e) {\n      if (!this.findTarget(e)) {\n        this.fire('mouse:over', { target: null, e: e });\n        this._hoveredTarget = null;\n      }\n    },\n\n    /**\n     * @private\n     * @param {Event} [e] Event object fired on Event.js orientation change\n     * @param {Event} [self] Inner Event object\n     */\n    _onOrientationChange: function(e, self) {\n      this.__onOrientationChange && this.__onOrientationChange(e, self);\n    },\n\n    /**\n     * @private\n     * @param {Event} [e] Event object fired on Event.js shake\n     * @param {Event} [self] Inner Event object\n     */\n    _onShake: function(e, self) {\n      this.__onShake && this.__onShake(e, self);\n    },\n\n    /**\n     * @private\n     * @param {Event} [e] Event object fired on Event.js shake\n     * @param {Event} [self] Inner Event object\n     */\n    _onLongPress: function(e, self) {\n      this.__onLongPress && this.__onLongPress(e, self);\n    },\n\n    /**\n     * prevent default to allow drop event to be fired\n     * @private\n     * @param {Event} [e] Event object fired on Event.js shake\n     */\n    _onDragOver: function(e) {\n      e.preventDefault();\n      var target = this._simpleEventHandler('dragover', e);\n      this._fireEnterLeaveEvents(target, e);\n    },\n\n    /**\n     * @private\n     * @param {Event} e Event object fired on mousedown\n     */\n    _onContextMenu: function (e) {\n      if (this.stopContextMenu) {\n        e.stopPropagation();\n        e.preventDefault();\n      }\n      return false;\n    },\n\n    /**\n     * @private\n     * @param {Event} e Event object fired on mousedown\n     */\n    _onDoubleClick: function (e) {\n      this._cacheTransformEventData(e);\n      this._handleEvent(e, 'dblclick');\n      this._resetTransformEventData(e);\n    },\n\n    /**\n     * @private\n     * @param {Event} e Event object fired on mousedown\n     */\n    _onMouseDown: function (e) {\n      this.__onMouseDown(e);\n      this._resetTransformEventData();\n      addListener(fabric.document, 'touchend', this._onMouseUp, addEventOptions);\n      addListener(fabric.document, 'touchmove', this._onMouseMove, addEventOptions);\n\n      removeListener(this.upperCanvasEl, 'mousemove', this._onMouseMove);\n      removeListener(this.upperCanvasEl, 'touchmove', this._onMouseMove);\n\n      if (e.type === 'touchstart') {\n        // Unbind mousedown to prevent double triggers from touch devices\n        removeListener(this.upperCanvasEl, 'mousedown', this._onMouseDown);\n      }\n      else {\n        addListener(fabric.document, 'mouseup', this._onMouseUp);\n        addListener(fabric.document, 'mousemove', this._onMouseMove);\n      }\n    },\n\n    /**\n     * @private\n     * @param {Event} e Event object fired on mouseup\n     */\n    _onMouseUp: function (e) {\n      this.__onMouseUp(e);\n      this._resetTransformEventData();\n      removeListener(fabric.document, 'mouseup', this._onMouseUp);\n      removeListener(fabric.document, 'touchend', this._onMouseUp, addEventOptions);\n\n      removeListener(fabric.document, 'mousemove', this._onMouseMove);\n      removeListener(fabric.document, 'touchmove', this._onMouseMove);\n\n      addListener(this.upperCanvasEl, 'mousemove', this._onMouseMove, addEventOptions);\n      addListener(this.upperCanvasEl, 'touchmove', this._onMouseMove, addEventOptions);\n\n      if (e.type === 'touchend') {\n        // Wait 400ms before rebinding mousedown to prevent double triggers\n        // from touch devices\n        var _this = this;\n        setTimeout(function() {\n          addListener(_this.upperCanvasEl, 'mousedown', _this._onMouseDown);\n        }, 400);\n      }\n    },\n\n    /**\n     * @private\n     * @param {Event} e Event object fired on mousemove\n     */\n    _onMouseMove: function (e) {\n      !this.allowTouchScrolling && e.preventDefault && e.preventDefault();\n      this.__onMouseMove(e);\n    },\n\n    /**\n     * @private\n     */\n    _onResize: function () {\n      this.calcOffset();\n    },\n\n    /**\n     * Decides whether the canvas should be redrawn in mouseup and mousedown events.\n     * @private\n     * @param {Object} target\n     * @param {Object} pointer\n     */\n    _shouldRender: function(target, pointer) {\n      var activeObject = this._activeObject;\n\n      if (activeObject && activeObject.isEditing && target === activeObject) {\n        // if we mouse up/down over a editing textbox a cursor change,\n        // there is no need to re render\n        return false;\n      }\n      return !!(\n        (target && (\n          target.isMoving ||\n          target !== activeObject))\n        ||\n        (!target && !!activeObject)\n        ||\n        (!target && !activeObject && !this._groupSelector)\n        ||\n        (pointer &&\n          this._previousPointer &&\n          this.selection && (\n            pointer.x !== this._previousPointer.x ||\n          pointer.y !== this._previousPointer.y))\n      );\n    },\n\n    /**\n     * Method that defines the actions when mouse is released on canvas.\n     * The method resets the currentTransform parameters, store the image corner\n     * position in the image object and render the canvas on top.\n     * @private\n     * @param {Event} e Event object fired on mouseup\n     */\n    __onMouseUp: function (e) {\n      var target, transform = this._currentTransform,\n          groupSelector = this._groupSelector,\n          isClick = (!groupSelector || (groupSelector.left === 0 && groupSelector.top === 0));\n      this._cacheTransformEventData(e);\n      target = this._target;\n      this._handleEvent(e, 'up:before');\n      // if right/middle click just fire events and return\n      // target undefined will make the _handleEvent search the target\n      if (checkClick(e, RIGHT_CLICK)) {\n        if (this.fireRightClick) {\n          this._handleEvent(e, 'up', RIGHT_CLICK, isClick);\n        }\n        return;\n      }\n\n      if (checkClick(e, MIDDLE_CLICK)) {\n        if (this.fireMiddleClick) {\n          this._handleEvent(e, 'up', MIDDLE_CLICK, isClick);\n        }\n        this._resetTransformEventData();\n        return;\n      }\n\n      if (this.isDrawingMode && this._isCurrentlyDrawing) {\n        this._onMouseUpInDrawingMode(e);\n        return;\n      }\n\n      if (transform) {\n        this._finalizeCurrentTransform(e);\n      }\n\n      var shouldRender = this._shouldRender(target, this._absolutePointer);\n\n      if (target || !isClick) {\n        this._maybeGroupObjects(e);\n      }\n      if (target) {\n        target.isMoving = false;\n      }\n      this._setCursorFromEvent(e, target);\n      this._handleEvent(e, 'up', LEFT_CLICK, isClick);\n      this._groupSelector = null;\n      this._currentTransform = null;\n      target && (target.__corner = 0);\n      shouldRender && this.requestRenderAll();\n    },\n\n    /**\n     * @private\n     * Handle event firing for target and subtargets\n     * @param {Event} e event from mouse\n     * @param {String} eventType event to fire (up, down or move)\n     * @return {Fabric.Object} target return the the target found, for internal reasons.\n     */\n    _simpleEventHandler: function(eventType, e) {\n      var target = this.findTarget(e),\n          targets = this.targets,\n          options = {\n            e: e,\n            target: target,\n            subTargets: targets,\n          };\n      this.fire(eventType, options);\n      target && target.fire(eventType, options);\n      if (!targets) {\n        return target;\n      }\n      for (var i = 0; i < targets.length; i++) {\n        targets[i].fire(eventType, options);\n      }\n      return target;\n    },\n\n    /**\n     * @private\n     * Handle event firing for target and subtargets\n     * @param {Event} e event from mouse\n     * @param {String} eventType event to fire (up, down or move)\n     * @param {fabric.Object} targetObj receiving event\n     * @param {Number} [button] button used in the event 1 = left, 2 = middle, 3 = right\n     * @param {Boolean} isClick for left button only, indicates that the mouse up happened without move.\n     */\n    _handleEvent: function(e, eventType, button, isClick) {\n      var target = this._target,\n          targets = this.targets || [],\n          options = {\n            e: e,\n            target: target,\n            subTargets: targets,\n            button: button || LEFT_CLICK,\n            isClick: isClick || false,\n            pointer: this._pointer,\n            absolutePointer: this._absolutePointer,\n            transform: this._currentTransform\n          };\n      this.fire('mouse:' + eventType, options);\n      target && target.fire('mouse' + eventType, options);\n      for (var i = 0; i < targets.length; i++) {\n        targets[i].fire('mouse' + eventType, options);\n      }\n    },\n\n    /**\n     * @private\n     * @param {Event} e send the mouse event that generate the finalize down, so it can be used in the event\n     */\n    _finalizeCurrentTransform: function(e) {\n\n      var transform = this._currentTransform,\n          target = transform.target,\n          eventName,\n          options = {\n            e: e,\n            target: target,\n            transform: transform,\n          };\n\n      if (target._scaling) {\n        target._scaling = false;\n      }\n\n      target.setCoords();\n\n      if (transform.actionPerformed || (this.stateful && target.hasStateChanged())) {\n        if (transform.actionPerformed) {\n          eventName = this._addEventOptions(options, transform);\n          this._fire(eventName, options);\n        }\n        this._fire('modified', options);\n      }\n    },\n\n    /**\n     * Mutate option object in order to add by property and give back the event name.\n     * @private\n     * @param {Object} options to mutate\n     * @param {Object} transform to inspect action from\n     */\n    _addEventOptions: function(options, transform) {\n      // we can probably add more details at low cost\n      // scale change, rotation changes, translation changes\n      var eventName, by;\n      switch (transform.action) {\n        case 'scaleX':\n          eventName = 'scaled';\n          by = 'x';\n          break;\n        case 'scaleY':\n          eventName = 'scaled';\n          by = 'y';\n          break;\n        case 'skewX':\n          eventName = 'skewed';\n          by = 'x';\n          break;\n        case 'skewY':\n          eventName = 'skewed';\n          by = 'y';\n          break;\n        case 'scale':\n          eventName = 'scaled';\n          by = 'equally';\n          break;\n        case 'rotate':\n          eventName = 'rotated';\n          break;\n        case 'drag':\n          eventName = 'moved';\n          break;\n      }\n      options.by = by;\n      return eventName;\n    },\n\n    /**\n     * @private\n     * @param {Event} e Event object fired on mousedown\n     */\n    _onMouseDownInDrawingMode: function(e) {\n      this._isCurrentlyDrawing = true;\n      if (this.getActiveObject()) {\n        this.discardActiveObject(e).requestRenderAll();\n      }\n      if (this.clipTo) {\n        fabric.util.clipContext(this, this.contextTop);\n      }\n      var pointer = this.getPointer(e);\n      this.freeDrawingBrush.onMouseDown(pointer);\n      this._handleEvent(e, 'down');\n    },\n\n    /**\n     * @private\n     * @param {Event} e Event object fired on mousemove\n     */\n    _onMouseMoveInDrawingMode: function(e) {\n      if (this._isCurrentlyDrawing) {\n        var pointer = this.getPointer(e);\n        this.freeDrawingBrush.onMouseMove(pointer);\n      }\n      this.setCursor(this.freeDrawingCursor);\n      this._handleEvent(e, 'move');\n    },\n\n    /**\n     * @private\n     * @param {Event} e Event object fired on mouseup\n     */\n    _onMouseUpInDrawingMode: function(e) {\n      this._isCurrentlyDrawing = false;\n      if (this.clipTo) {\n        this.contextTop.restore();\n      }\n      this.freeDrawingBrush.onMouseUp();\n      this._handleEvent(e, 'up');\n    },\n\n    /**\n     * Method that defines the actions when mouse is clicked on canvas.\n     * The method inits the currentTransform parameters and renders all the\n     * canvas so the current image can be placed on the top canvas and the rest\n     * in on the container one.\n     * @private\n     * @param {Event} e Event object fired on mousedown\n     */\n    __onMouseDown: function (e) {\n      this._cacheTransformEventData(e);\n      this._handleEvent(e, 'down:before');\n      var target = this._target;\n      // if right click just fire events\n      if (checkClick(e, RIGHT_CLICK)) {\n        if (this.fireRightClick) {\n          this._handleEvent(e, 'down', RIGHT_CLICK);\n        }\n        return;\n      }\n\n      if (checkClick(e, MIDDLE_CLICK)) {\n        if (this.fireMiddleClick) {\n          this._handleEvent(e, 'down', MIDDLE_CLICK);\n        }\n        return;\n      }\n\n      if (this.isDrawingMode) {\n        this._onMouseDownInDrawingMode(e);\n        return;\n      }\n\n      // ignore if some object is being transformed at this moment\n      if (this._currentTransform) {\n        return;\n      }\n\n      var pointer = this._pointer;\n      // save pointer for check in __onMouseUp event\n      this._previousPointer = pointer;\n      var shouldRender = this._shouldRender(target, pointer),\n          shouldGroup = this._shouldGroup(e, target);\n      if (this._shouldClearSelection(e, target)) {\n        this.discardActiveObject(e);\n      }\n      else if (shouldGroup) {\n        this._handleGrouping(e, target);\n        target = this._activeObject;\n      }\n\n      if (this.selection && (!target ||\n        (!target.selectable && !target.isEditing && target !== this._activeObject))) {\n        this._groupSelector = {\n          ex: pointer.x,\n          ey: pointer.y,\n          top: 0,\n          left: 0\n        };\n      }\n\n      if (target) {\n        if (target.selectable) {\n          this.setActiveObject(target, e);\n        }\n        if (target === this._activeObject && (target.__corner || !shouldGroup)) {\n          this._setupCurrentTransform(e, target);\n        }\n      }\n      this._handleEvent(e, 'down');\n      // we must renderAll so that we update the visuals\n      shouldRender && this.requestRenderAll();\n    },\n\n    /**\n     * reset cache form common information needed during event processing\n     * @private\n     */\n    _resetTransformEventData: function() {\n      this._target = null;\n      this._pointer = null;\n      this._absolutePointer = null;\n    },\n\n    /**\n     * Cache common information needed during event processing\n     * @private\n     * @param {Event} e Event object fired on event\n     */\n    _cacheTransformEventData: function(e) {\n      // reset in order to avoid stale caching\n      this._resetTransformEventData();\n      this._pointer = this.getPointer(e, true);\n      this._absolutePointer = this.restorePointerVpt(this._pointer);\n      this._target = this._currentTransform ? this._currentTransform.target : this.findTarget(e) || null;\n    },\n\n    /**\n     * @private\n     */\n    _beforeTransform: function(e) {\n      var t = this._currentTransform;\n      this.stateful && t.target.saveState();\n      this.fire('before:transform', {\n        e: e,\n        transform: t,\n      });\n      // determine if it's a drag or rotate case\n      if (t.corner) {\n        this.onBeforeScaleRotate(t.target);\n      }\n    },\n\n    /**\n     * Method that defines the actions when mouse is hovering the canvas.\n     * The currentTransform parameter will definde whether the user is rotating/scaling/translating\n     * an image or neither of them (only hovering). A group selection is also possible and would cancel\n     * all any other type of action.\n     * In case of an image transformation only the top canvas will be rendered.\n     * @private\n     * @param {Event} e Event object fired on mousemove\n     */\n    __onMouseMove: function (e) {\n      this._handleEvent(e, 'move:before');\n      this._cacheTransformEventData(e);\n      var target, pointer;\n\n      if (this.isDrawingMode) {\n        this._onMouseMoveInDrawingMode(e);\n        return;\n      }\n      if (typeof e.touches !== 'undefined' && e.touches.length > 1) {\n        return;\n      }\n\n      var groupSelector = this._groupSelector;\n\n      // We initially clicked in an empty area, so we draw a box for multiple selection\n      if (groupSelector) {\n        pointer = this._pointer;\n\n        groupSelector.left = pointer.x - groupSelector.ex;\n        groupSelector.top = pointer.y - groupSelector.ey;\n\n        this.renderTop();\n      }\n      else if (!this._currentTransform) {\n        target = this.findTarget(e) || null;\n        this._setCursorFromEvent(e, target);\n        this._fireOverOutEvents(target, e);\n      }\n      else {\n        this._transformObject(e);\n      }\n      this._handleEvent(e, 'move');\n      this._resetTransformEventData();\n    },\n\n    /**\n     * Manage the mouseout, mouseover events for the fabric object on the canvas\n     * @param {Fabric.Object} target the target where the target from the mousemove event\n     * @param {Event} e Event object fired on mousemove\n     * @private\n     */\n    _fireOverOutEvents: function(target, e) {\n      this.fireSynteticInOutEvents(target, e, {\n        targetName: '_hoveredTarget',\n        canvasEvtOut: 'mouse:out',\n        evtOut: 'mouseout',\n        canvasEvtIn: 'mouse:over',\n        evtIn: 'mouseover',\n      });\n    },\n\n    /**\n     * Manage the dragEnter, dragLeave events for the fabric objects on the canvas\n     * @param {Fabric.Object} target the target where the target from the onDrag event\n     * @param {Event} e Event object fired on ondrag\n     * @private\n     */\n    _fireEnterLeaveEvents: function(target, e) {\n      this.fireSynteticInOutEvents(target, e, {\n        targetName: '_draggedoverTarget',\n        evtOut: 'dragleave',\n        evtIn: 'dragenter',\n      });\n    },\n\n    /**\n     * Manage the syntetic in/out events for the fabric objects on the canvas\n     * @param {Fabric.Object} target the target where the target from the supported events\n     * @param {Event} e Event object fired\n     * @param {Object} config configuration for the function to work\n     * @param {String} config.targetName property on the canvas where the old target is stored\n     * @param {String} [config.canvasEvtOut] name of the event to fire at canvas level for out\n     * @param {String} config.evtOut name of the event to fire for out\n     * @param {String} [config.canvasEvtIn] name of the event to fire at canvas level for in\n     * @param {String} config.evtIn name of the event to fire for in\n     * @private\n     */\n    fireSynteticInOutEvents: function(target, e, config) {\n      var inOpt, outOpt, oldTarget = this[config.targetName], outFires, inFires,\n          targetChanged = oldTarget !== target, canvasEvtIn = config.canvasEvtIn, canvasEvtOut = config.canvasEvtOut;\n      if (targetChanged) {\n        inOpt = { e: e, target: target, previousTarget: oldTarget };\n        outOpt = { e: e, target: oldTarget, nextTarget: target };\n        this[config.targetName] = target;\n      }\n      inFires = target && targetChanged;\n      outFires = oldTarget && targetChanged;\n      if (outFires) {\n        canvasEvtOut && this.fire(canvasEvtOut, outOpt);\n        oldTarget.fire(config.evtOut, outOpt);\n      }\n      if (inFires) {\n        canvasEvtIn && this.fire(canvasEvtIn, inOpt);\n        target.fire(config.evtIn, inOpt);\n      }\n    },\n\n    /**\n     * Method that defines actions when an Event Mouse Wheel\n     * @param {Event} e Event object fired on mouseup\n     */\n    __onMouseWheel: function(e) {\n      this._cacheTransformEventData(e);\n      this._handleEvent(e, 'wheel');\n      this._resetTransformEventData();\n    },\n\n    /**\n     * @private\n     * @param {Event} e Event fired on mousemove\n     */\n    _transformObject: function(e) {\n      var pointer = this.getPointer(e),\n          transform = this._currentTransform;\n\n      transform.reset = false;\n      transform.target.isMoving = true;\n      transform.shiftKey = e.shiftKey;\n      transform.altKey = e[this.centeredKey];\n\n      this._beforeScaleTransform(e, transform);\n      this._performTransformAction(e, transform, pointer);\n\n      transform.actionPerformed && this.requestRenderAll();\n    },\n\n    /**\n     * @private\n     */\n    _performTransformAction: function(e, transform, pointer) {\n      var x = pointer.x,\n          y = pointer.y,\n          action = transform.action,\n          actionPerformed = false,\n          options = {\n            target: transform.target,\n            e: e,\n            transform: transform,\n            pointer: pointer\n          };\n\n      if (action === 'rotate') {\n        (actionPerformed = this._rotateObject(x, y)) && this._fire('rotating', options);\n      }\n      else if (action === 'scale') {\n        (actionPerformed = this._onScale(e, transform, x, y)) && this._fire('scaling', options);\n      }\n      else if (action === 'scaleX') {\n        (actionPerformed = this._scaleObject(x, y, 'x')) && this._fire('scaling', options);\n      }\n      else if (action === 'scaleY') {\n        (actionPerformed = this._scaleObject(x, y, 'y')) && this._fire('scaling', options);\n      }\n      else if (action === 'skewX') {\n        (actionPerformed = this._skewObject(x, y, 'x')) && this._fire('skewing', options);\n      }\n      else if (action === 'skewY') {\n        (actionPerformed = this._skewObject(x, y, 'y')) && this._fire('skewing', options);\n      }\n      else {\n        actionPerformed = this._translateObject(x, y);\n        if (actionPerformed) {\n          this._fire('moving', options);\n          this.setCursor(options.target.moveCursor || this.moveCursor);\n        }\n      }\n      transform.actionPerformed = transform.actionPerformed || actionPerformed;\n    },\n\n    /**\n     * @private\n     */\n    _fire: function(eventName, options) {\n      this.fire('object:' + eventName, options);\n      options.target.fire(eventName, options);\n    },\n\n    /**\n     * @private\n     */\n    _beforeScaleTransform: function(e, transform) {\n      if (transform.action === 'scale' || transform.action === 'scaleX' || transform.action === 'scaleY') {\n        var centerTransform = this._shouldCenterTransform(transform.target);\n\n        // Switch from a normal resize to center-based\n        if ((centerTransform && (transform.originX !== 'center' || transform.originY !== 'center')) ||\n           // Switch from center-based resize to normal one\n           (!centerTransform && transform.originX === 'center' && transform.originY === 'center')\n        ) {\n          this._resetCurrentTransform();\n          transform.reset = true;\n        }\n      }\n    },\n\n    /**\n     * @private\n     * @param {Event} e Event object\n     * @param {Object} transform current tranform\n     * @param {Number} x mouse position x from origin\n     * @param {Number} y mouse poistion y from origin\n     * @return {Boolean} true if the scaling occurred\n     */\n    _onScale: function(e, transform, x, y) {\n      if (this._isUniscalePossible(e, transform.target)) {\n        transform.currentAction = 'scale';\n        return this._scaleObject(x, y);\n      }\n      else {\n        // Switch from a normal resize to proportional\n        if (!transform.reset && transform.currentAction === 'scale') {\n          this._resetCurrentTransform();\n        }\n\n        transform.currentAction = 'scaleEqually';\n        return this._scaleObject(x, y, 'equally');\n      }\n    },\n\n    /**\n     * @private\n     * @param {Event} e Event object\n     * @param {fabric.Object} target current target\n     * @return {Boolean} true if unproportional scaling is possible\n     */\n    _isUniscalePossible: function(e, target) {\n      return (e[this.uniScaleKey] || this.uniScaleTransform) && !target.get('lockUniScaling');\n    },\n\n    /**\n     * Sets the cursor depending on where the canvas is being hovered.\n     * Note: very buggy in Opera\n     * @param {Event} e Event object\n     * @param {Object} target Object that the mouse is hovering, if so.\n     */\n    _setCursorFromEvent: function (e, target) {\n      if (!target) {\n        this.setCursor(this.defaultCursor);\n        return false;\n      }\n\n      var hoverCursor = target.hoverCursor || this.hoverCursor,\n          activeSelection = this._activeObject && this._activeObject.type === 'activeSelection' ?\n            this._activeObject : null,\n          // only show proper corner when group selection is not active\n          corner = (!activeSelection || !activeSelection.contains(target))\n                    && target._findTargetCorner(this.getPointer(e, true));\n\n      if (!corner) {\n        this.setCursor(hoverCursor);\n      }\n      else {\n        this.setCursor(this.getCornerCursor(corner, target, e));\n      }\n    },\n\n    /**\n     * @private\n     */\n    getCornerCursor: function(corner, target, e) {\n      if (this.actionIsDisabled(corner, target, e)) {\n        return this.notAllowedCursor;\n      }\n      else if (corner in cursorOffset) {\n        return this._getRotatedCornerCursor(corner, target, e);\n      }\n      else if (corner === 'mtr' && target.hasRotatingPoint) {\n        return this.rotationCursor;\n      }\n      else {\n        return this.defaultCursor;\n      }\n    },\n\n    actionIsDisabled: function(corner, target, e) {\n      if (corner === 'mt' || corner === 'mb') {\n        return e[this.altActionKey] ? target.lockSkewingX : target.lockScalingY;\n      }\n      else if (corner === 'ml' || corner === 'mr') {\n        return e[this.altActionKey] ? target.lockSkewingY : target.lockScalingX;\n      }\n      else if (corner === 'mtr') {\n        return target.lockRotation;\n      }\n      else {\n        return this._isUniscalePossible(e, target) ?\n          target.lockScalingX && target.lockScalingY : target.lockScalingX || target.lockScalingY;\n      }\n    },\n\n    /**\n     * @private\n     */\n    _getRotatedCornerCursor: function(corner, target, e) {\n      var n = Math.round((target.angle % 360) / 45);\n\n      if (n < 0) {\n        n += 8; // full circle ahead\n      }\n      n += cursorOffset[corner];\n      if (e[this.altActionKey] && cursorOffset[corner] % 2 === 0) {\n        //if we are holding shift and we are on a mx corner...\n        n += 2;\n      }\n      // normalize n to be from 0 to 7\n      n %= 8;\n\n      return this.cursorMap[n];\n    }\n  });\n})();\n\n\n(function() {\n\n  var min = Math.min,\n      max = Math.max;\n\n  fabric.util.object.extend(fabric.Canvas.prototype, /** @lends fabric.Canvas.prototype */ {\n\n    /**\n     * @private\n     * @param {Event} e Event object\n     * @param {fabric.Object} target\n     * @return {Boolean}\n     */\n    _shouldGroup: function(e, target) {\n      var activeObject = this._activeObject;\n\n      return activeObject && this._isSelectionKeyPressed(e) && target && target.selectable && this.selection &&\n            (activeObject !== target || activeObject.type === 'activeSelection');\n    },\n\n    /**\n     * @private\n     * @param {Event} e Event object\n     * @param {fabric.Object} target\n     */\n    _handleGrouping: function (e, target) {\n      var activeObject = this._activeObject;\n      if (activeObject.__corner) {\n        return;\n      }\n      if (target === activeObject) {\n        // if it's a group, find target again, using activeGroup objects\n        target = this.findTarget(e, true);\n        // if even object is not found or we are on activeObjectCorner, bail out\n        if (!target) {\n          return;\n        }\n      }\n      if (activeObject && activeObject.type === 'activeSelection') {\n        this._updateActiveSelection(target, e);\n      }\n      else {\n        this._createActiveSelection(target, e);\n      }\n    },\n\n    /**\n     * @private\n     */\n    _updateActiveSelection: function(target, e) {\n      var activeSelection = this._activeObject,\n          currentActiveObjects = activeSelection._objects.slice(0);\n      if (activeSelection.contains(target)) {\n        activeSelection.removeWithUpdate(target);\n        this._hoveredTarget = target;\n        if (activeSelection.size() === 1) {\n          // activate last remaining object\n          this._setActiveObject(activeSelection.item(0), e);\n        }\n      }\n      else {\n        activeSelection.addWithUpdate(target);\n        this._hoveredTarget = activeSelection;\n      }\n      this._fireSelectionEvents(currentActiveObjects, e);\n    },\n\n    /**\n     * @private\n     */\n    _createActiveSelection: function(target, e) {\n      var currentActives = this.getActiveObjects(), group = this._createGroup(target);\n      this._hoveredTarget = group;\n      this._setActiveObject(group, e);\n      this._fireSelectionEvents(currentActives, e);\n    },\n\n    /**\n     * @private\n     * @param {Object} target\n     */\n    _createGroup: function(target) {\n      var objects = this.getObjects(),\n          isActiveLower = objects.indexOf(this._activeObject) < objects.indexOf(target),\n          groupObjects = isActiveLower\n            ? [this._activeObject, target]\n            : [target, this._activeObject];\n      this._activeObject.isEditing && this._activeObject.exitEditing();\n      return new fabric.ActiveSelection(groupObjects, {\n        canvas: this\n      });\n    },\n\n    /**\n     * @private\n     * @param {Event} e mouse event\n     */\n    _groupSelectedObjects: function (e) {\n\n      var group = this._collectObjects(),\n          aGroup;\n\n      // do not create group for 1 element only\n      if (group.length === 1) {\n        this.setActiveObject(group[0], e);\n      }\n      else if (group.length > 1) {\n        aGroup = new fabric.ActiveSelection(group.reverse(), {\n          canvas: this\n        });\n        this.setActiveObject(aGroup, e);\n      }\n    },\n\n    /**\n     * @private\n     */\n    _collectObjects: function() {\n      var group = [],\n          currentObject,\n          x1 = this._groupSelector.ex,\n          y1 = this._groupSelector.ey,\n          x2 = x1 + this._groupSelector.left,\n          y2 = y1 + this._groupSelector.top,\n          selectionX1Y1 = new fabric.Point(min(x1, x2), min(y1, y2)),\n          selectionX2Y2 = new fabric.Point(max(x1, x2), max(y1, y2)),\n          allowIntersect = !this.selectionFullyContained,\n          isClick = x1 === x2 && y1 === y2;\n      // we iterate reverse order to collect top first in case of click.\n      for (var i = this._objects.length; i--; ) {\n        currentObject = this._objects[i];\n\n        if (!currentObject || !currentObject.selectable || !currentObject.visible) {\n          continue;\n        }\n\n        if ((allowIntersect && currentObject.intersectsWithRect(selectionX1Y1, selectionX2Y2)) ||\n            currentObject.isContainedWithinRect(selectionX1Y1, selectionX2Y2) ||\n            (allowIntersect && currentObject.containsPoint(selectionX1Y1)) ||\n            (allowIntersect && currentObject.containsPoint(selectionX2Y2))\n        ) {\n          group.push(currentObject);\n\n          // only add one object if it's a click\n          if (isClick) {\n            break;\n          }\n        }\n      }\n\n      return group;\n    },\n\n    /**\n     * @private\n     */\n    _maybeGroupObjects: function(e) {\n      if (this.selection && this._groupSelector) {\n        this._groupSelectedObjects(e);\n      }\n      this.setCursor(this.defaultCursor);\n      // clear selection and current transformation\n      this._groupSelector = null;\n      this._currentTransform = null;\n    }\n  });\n\n})();\n\n\n(function () {\n\n  var supportQuality = fabric.StaticCanvas.supports('toDataURLWithQuality');\n\n  fabric.util.object.extend(fabric.StaticCanvas.prototype, /** @lends fabric.StaticCanvas.prototype */ {\n\n    /**\n     * Exports canvas element to a dataurl image. Note that when multiplier is used, cropping is scaled appropriately\n     * @param {Object} [options] Options object\n     * @param {String} [options.format=png] The format of the output image. Either \"jpeg\" or \"png\"\n     * @param {Number} [options.quality=1] Quality level (0..1). Only used for jpeg.\n     * @param {Number} [options.multiplier=1] Multiplier to scale by, to have consistent\n     * @param {Number} [options.left] Cropping left offset. Introduced in v1.2.14\n     * @param {Number} [options.top] Cropping top offset. Introduced in v1.2.14\n     * @param {Number} [options.width] Cropping width. Introduced in v1.2.14\n     * @param {Number} [options.height] Cropping height. Introduced in v1.2.14\n     * @param {Boolean} [options.enableRetinaScaling] Enable retina scaling for clone image. Introduce in 2.0.0\n     * @return {String} Returns a data: URL containing a representation of the object in the format specified by options.format\n     * @see {@link http://jsfiddle.net/fabricjs/NfZVb/|jsFiddle demo}\n     * @example <caption>Generate jpeg dataURL with lower quality</caption>\n     * var dataURL = canvas.toDataURL({\n     *   format: 'jpeg',\n     *   quality: 0.8\n     * });\n     * @example <caption>Generate cropped png dataURL (clipping of canvas)</caption>\n     * var dataURL = canvas.toDataURL({\n     *   format: 'png',\n     *   left: 100,\n     *   top: 100,\n     *   width: 200,\n     *   height: 200\n     * });\n     * @example <caption>Generate double scaled png dataURL</caption>\n     * var dataURL = canvas.toDataURL({\n     *   format: 'png',\n     *   multiplier: 2\n     * });\n     */\n    toDataURL: function (options) {\n      options || (options = { });\n\n      var format = options.format || 'png',\n          quality = options.quality || 1,\n          multiplier = (options.multiplier || 1) * (options.enableRetinaScaling ? 1 : 1 / this.getRetinaScaling()),\n          cropping = {\n            left: options.left || 0,\n            top: options.top || 0,\n            width: options.width || 0,\n            height: options.height || 0,\n          };\n      return this.__toDataURLWithMultiplier(format, quality, cropping, multiplier);\n    },\n\n    /**\n     * @private\n     */\n    __toDataURLWithMultiplier: function(format, quality, cropping, multiplier) {\n\n      var origWidth = this.width,\n          origHeight = this.height,\n          scaledWidth = (cropping.width || this.width) * multiplier,\n          scaledHeight = (cropping.height || this.height) * multiplier,\n          zoom = this.getZoom(),\n          newZoom = zoom * multiplier,\n          vp = this.viewportTransform,\n          translateX = (vp[4] - cropping.left) * multiplier,\n          translateY = (vp[5] - cropping.top) * multiplier,\n          newVp = [newZoom, 0, 0, newZoom, translateX, translateY],\n          originalInteractive = this.interactive,\n          originalSkipOffScreen = this.skipOffscreen,\n          needsResize = origWidth !== scaledWidth || origHeight !== scaledHeight;\n\n      this.viewportTransform = newVp;\n      this.skipOffscreen = false;\n      // setting interactive to false avoid exporting controls\n      this.interactive = false;\n      if (needsResize) {\n        this.setDimensions({ width: scaledWidth, height: scaledHeight }, { backstoreOnly: true });\n      }\n      // call a renderAll to force sync update. This will cancel the scheduled requestRenderAll\n      // from setDimensions\n      this.renderAll();\n      var data = this.__toDataURL(format, quality, cropping);\n      this.interactive = originalInteractive;\n      this.skipOffscreen = originalSkipOffScreen;\n      this.viewportTransform = vp;\n      //setDimensions with no option object is taking care of:\n      //this.width, this.height, this.requestRenderAll()\n      if (needsResize) {\n        this.setDimensions({ width: origWidth, height: origHeight }, { backstoreOnly: true });\n      }\n      this.renderAll();\n      return data;\n    },\n\n    /**\n     * @private\n     */\n    __toDataURL: function(format, quality) {\n\n      var canvasEl = this.contextContainer.canvas;\n      // to avoid common confusion https://github.com/kangax/fabric.js/issues/806\n      if (format === 'jpg') {\n        format = 'jpeg';\n      }\n\n      var data = supportQuality\n        ? canvasEl.toDataURL('image/' + format, quality)\n        : canvasEl.toDataURL('image/' + format);\n\n      return data;\n    },\n  });\n\n})();\n\n\nfabric.util.object.extend(fabric.StaticCanvas.prototype, /** @lends fabric.StaticCanvas.prototype */ {\n\n  /**\n   * Populates canvas with data from the specified dataless JSON.\n   * JSON format must conform to the one of {@link fabric.Canvas#toDatalessJSON}\n   * @deprecated since 1.2.2\n   * @param {String|Object} json JSON string or object\n   * @param {Function} callback Callback, invoked when json is parsed\n   *                            and corresponding objects (e.g: {@link fabric.Image})\n   *                            are initialized\n   * @param {Function} [reviver] Method for further parsing of JSON elements, called after each fabric object created.\n   * @return {fabric.Canvas} instance\n   * @chainable\n   * @tutorial {@link http://fabricjs.com/fabric-intro-part-3#deserialization}\n   */\n  loadFromDatalessJSON: function (json, callback, reviver) {\n    return this.loadFromJSON(json, callback, reviver);\n  },\n\n  /**\n   * Populates canvas with data from the specified JSON.\n   * JSON format must conform to the one of {@link fabric.Canvas#toJSON}\n   * @param {String|Object} json JSON string or object\n   * @param {Function} callback Callback, invoked when json is parsed\n   *                            and corresponding objects (e.g: {@link fabric.Image})\n   *                            are initialized\n   * @param {Function} [reviver] Method for further parsing of JSON elements, called after each fabric object created.\n   * @return {fabric.Canvas} instance\n   * @chainable\n   * @tutorial {@link http://fabricjs.com/fabric-intro-part-3#deserialization}\n   * @see {@link http://jsfiddle.net/fabricjs/fmgXt/|jsFiddle demo}\n   * @example <caption>loadFromJSON</caption>\n   * canvas.loadFromJSON(json, canvas.renderAll.bind(canvas));\n   * @example <caption>loadFromJSON with reviver</caption>\n   * canvas.loadFromJSON(json, canvas.renderAll.bind(canvas), function(o, object) {\n   *   // `o` = json object\n   *   // `object` = fabric.Object instance\n   *   // ... do some stuff ...\n   * });\n   */\n  loadFromJSON: function (json, callback, reviver) {\n    if (!json) {\n      return;\n    }\n\n    // serialize if it wasn't already\n    var serialized = (typeof json === 'string')\n      ? JSON.parse(json)\n      : fabric.util.object.clone(json);\n\n    var _this = this,\n        renderOnAddRemove = this.renderOnAddRemove;\n    this.renderOnAddRemove = false;\n\n    this._enlivenObjects(serialized.objects, function (enlivenedObjects) {\n      _this.clear();\n      _this._setBgOverlay(serialized, function () {\n        enlivenedObjects.forEach(function(obj, index) {\n          // we splice the array just in case some custom classes restored from JSON\n          // will add more object to canvas at canvas init.\n          _this.insertAt(obj, index);\n        });\n        _this.renderOnAddRemove = renderOnAddRemove;\n        // remove parts i cannot set as options\n        delete serialized.objects;\n        delete serialized.backgroundImage;\n        delete serialized.overlayImage;\n        delete serialized.background;\n        delete serialized.overlay;\n        // this._initOptions does too many things to just\n        // call it. Normally loading an Object from JSON\n        // create the Object instance. Here the Canvas is\n        // already an instance and we are just loading things over it\n        _this._setOptions(serialized);\n        _this.renderAll();\n        callback && callback();\n      });\n    }, reviver);\n    return this;\n  },\n\n  /**\n   * @private\n   * @param {Object} serialized Object with background and overlay information\n   * @param {Function} callback Invoked after all background and overlay images/patterns loaded\n   */\n  _setBgOverlay: function(serialized, callback) {\n    var loaded = {\n      backgroundColor: false,\n      overlayColor: false,\n      backgroundImage: false,\n      overlayImage: false\n    };\n\n    if (!serialized.backgroundImage && !serialized.overlayImage && !serialized.background && !serialized.overlay) {\n      callback && callback();\n      return;\n    }\n\n    var cbIfLoaded = function () {\n      if (loaded.backgroundImage && loaded.overlayImage && loaded.backgroundColor && loaded.overlayColor) {\n        callback && callback();\n      }\n    };\n\n    this.__setBgOverlay('backgroundImage', serialized.backgroundImage, loaded, cbIfLoaded);\n    this.__setBgOverlay('overlayImage', serialized.overlayImage, loaded, cbIfLoaded);\n    this.__setBgOverlay('backgroundColor', serialized.background, loaded, cbIfLoaded);\n    this.__setBgOverlay('overlayColor', serialized.overlay, loaded, cbIfLoaded);\n  },\n\n  /**\n   * @private\n   * @param {String} property Property to set (backgroundImage, overlayImage, backgroundColor, overlayColor)\n   * @param {(Object|String)} value Value to set\n   * @param {Object} loaded Set loaded property to true if property is set\n   * @param {Object} callback Callback function to invoke after property is set\n   */\n  __setBgOverlay: function(property, value, loaded, callback) {\n    var _this = this;\n\n    if (!value) {\n      loaded[property] = true;\n      callback && callback();\n      return;\n    }\n\n    if (property === 'backgroundImage' || property === 'overlayImage') {\n      fabric.util.enlivenObjects([value], function(enlivedObject){\n        _this[property] = enlivedObject[0];\n        loaded[property] = true;\n        callback && callback();\n      });\n    }\n    else {\n      this['set' + fabric.util.string.capitalize(property, true)](value, function() {\n        loaded[property] = true;\n        callback && callback();\n      });\n    }\n  },\n\n  /**\n   * @private\n   * @param {Array} objects\n   * @param {Function} callback\n   * @param {Function} [reviver]\n   */\n  _enlivenObjects: function (objects, callback, reviver) {\n    if (!objects || objects.length === 0) {\n      callback && callback([]);\n      return;\n    }\n\n    fabric.util.enlivenObjects(objects, function(enlivenedObjects) {\n      callback && callback(enlivenedObjects);\n    }, null, reviver);\n  },\n\n  /**\n   * @private\n   * @param {String} format\n   * @param {Function} callback\n   */\n  _toDataURL: function (format, callback) {\n    this.clone(function (clone) {\n      callback(clone.toDataURL(format));\n    });\n  },\n\n  /**\n   * @private\n   * @param {String} format\n   * @param {Number} multiplier\n   * @param {Function} callback\n   */\n  _toDataURLWithMultiplier: function (format, multiplier, callback) {\n    this.clone(function (clone) {\n      callback(clone.toDataURLWithMultiplier(format, multiplier));\n    });\n  },\n\n  /**\n   * Clones canvas instance\n   * @param {Object} [callback] Receives cloned instance as a first argument\n   * @param {Array} [properties] Array of properties to include in the cloned canvas and children\n   */\n  clone: function (callback, properties) {\n    var data = JSON.stringify(this.toJSON(properties));\n    this.cloneWithoutData(function(clone) {\n      clone.loadFromJSON(data, function() {\n        callback && callback(clone);\n      });\n    });\n  },\n\n  /**\n   * Clones canvas instance without cloning existing data.\n   * This essentially copies canvas dimensions, clipping properties, etc.\n   * but leaves data empty (so that you can populate it with your own)\n   * @param {Object} [callback] Receives cloned instance as a first argument\n   */\n  cloneWithoutData: function(callback) {\n    var el = fabric.document.createElement('canvas');\n\n    el.width = this.width;\n    el.height = this.height;\n\n    var clone = new fabric.Canvas(el);\n    clone.clipTo = this.clipTo;\n    if (this.backgroundImage) {\n      clone.setBackgroundImage(this.backgroundImage.src, function() {\n        clone.renderAll();\n        callback && callback(clone);\n      });\n      clone.backgroundImageOpacity = this.backgroundImageOpacity;\n      clone.backgroundImageStretch = this.backgroundImageStretch;\n    }\n    else {\n      callback && callback(clone);\n    }\n  }\n});\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric = global.fabric || (global.fabric = { }),\n      extend = fabric.util.object.extend,\n      clone = fabric.util.object.clone,\n      toFixed = fabric.util.toFixed,\n      capitalize = fabric.util.string.capitalize,\n      degreesToRadians = fabric.util.degreesToRadians,\n      supportsLineDash = fabric.StaticCanvas.supports('setLineDash'),\n      objectCaching = !fabric.isLikelyNode,\n      ALIASING_LIMIT = 2;\n\n  if (fabric.Object) {\n    return;\n  }\n\n  /**\n   * Root object class from which all 2d shape classes inherit from\n   * @class fabric.Object\n   * @tutorial {@link http://fabricjs.com/fabric-intro-part-1#objects}\n   * @see {@link fabric.Object#initialize} for constructor definition\n   *\n   * @fires added\n   * @fires removed\n   *\n   * @fires selected\n   * @fires deselected\n   * @fires modified\n   * @fires modified\n   * @fires moved\n   * @fires scaled\n   * @fires rotated\n   * @fires skewed\n   *\n   * @fires rotating\n   * @fires scaling\n   * @fires moving\n   * @fires skewing\n   *\n   * @fires mousedown\n   * @fires mouseup\n   * @fires mouseover\n   * @fires mouseout\n   * @fires mousewheel\n   * @fires mousedblclick\n   *\n   * @fires dragover\n   * @fires dragenter\n   * @fires dragleave\n   * @fires drop\n   */\n  fabric.Object = fabric.util.createClass(fabric.CommonMethods, /** @lends fabric.Object.prototype */ {\n\n    /**\n     * Type of an object (rect, circle, path, etc.).\n     * Note that this property is meant to be read-only and not meant to be modified.\n     * If you modify, certain parts of Fabric (such as JSON loading) won't work correctly.\n     * @type String\n     * @default\n     */\n    type:                     'object',\n\n    /**\n     * Horizontal origin of transformation of an object (one of \"left\", \"right\", \"center\")\n     * See http://jsfiddle.net/1ow02gea/244/ on how originX/originY affect objects in groups\n     * @type String\n     * @default\n     */\n    originX:                  'left',\n\n    /**\n     * Vertical origin of transformation of an object (one of \"top\", \"bottom\", \"center\")\n     * See http://jsfiddle.net/1ow02gea/244/ on how originX/originY affect objects in groups\n     * @type String\n     * @default\n     */\n    originY:                  'top',\n\n    /**\n     * Top position of an object. Note that by default it's relative to object top. You can change this by setting originY={top/center/bottom}\n     * @type Number\n     * @default\n     */\n    top:                      0,\n\n    /**\n     * Left position of an object. Note that by default it's relative to object left. You can change this by setting originX={left/center/right}\n     * @type Number\n     * @default\n     */\n    left:                     0,\n\n    /**\n     * Object width\n     * @type Number\n     * @default\n     */\n    width:                    0,\n\n    /**\n     * Object height\n     * @type Number\n     * @default\n     */\n    height:                   0,\n\n    /**\n     * Object scale factor (horizontal)\n     * @type Number\n     * @default\n     */\n    scaleX:                   1,\n\n    /**\n     * Object scale factor (vertical)\n     * @type Number\n     * @default\n     */\n    scaleY:                   1,\n\n    /**\n     * When true, an object is rendered as flipped horizontally\n     * @type Boolean\n     * @default\n     */\n    flipX:                    false,\n\n    /**\n     * When true, an object is rendered as flipped vertically\n     * @type Boolean\n     * @default\n     */\n    flipY:                    false,\n\n    /**\n     * Opacity of an object\n     * @type Number\n     * @default\n     */\n    opacity:                  1,\n\n    /**\n     * Angle of rotation of an object (in degrees)\n     * @type Number\n     * @default\n     */\n    angle:                    0,\n\n    /**\n     * Angle of skew on x axes of an object (in degrees)\n     * @type Number\n     * @default\n     */\n    skewX:                    0,\n\n    /**\n     * Angle of skew on y axes of an object (in degrees)\n     * @type Number\n     * @default\n     */\n    skewY:                    0,\n\n    /**\n     * Size of object's controlling corners (in pixels)\n     * @type Number\n     * @default\n     */\n    cornerSize:               13,\n\n    /**\n     * When true, object's controlling corners are rendered as transparent inside (i.e. stroke instead of fill)\n     * @type Boolean\n     * @default\n     */\n    transparentCorners:       true,\n\n    /**\n     * Default cursor value used when hovering over this object on canvas\n     * @type String\n     * @default\n     */\n    hoverCursor:              null,\n\n    /**\n     * Default cursor value used when moving this object on canvas\n     * @type String\n     * @default\n     */\n    moveCursor:               null,\n\n    /**\n     * Padding between object and its controlling borders (in pixels)\n     * @type Number\n     * @default\n     */\n    padding:                  0,\n\n    /**\n     * Color of controlling borders of an object (when it's active)\n     * @type String\n     * @default\n     */\n    borderColor:              'rgba(102,153,255,0.75)',\n\n    /**\n     * Array specifying dash pattern of an object's borders (hasBorder must be true)\n     * @since 1.6.2\n     * @type Array\n     */\n    borderDashArray:          null,\n\n    /**\n     * Color of controlling corners of an object (when it's active)\n     * @type String\n     * @default\n     */\n    cornerColor:              'rgba(102,153,255,0.5)',\n\n    /**\n     * Color of controlling corners of an object (when it's active and transparentCorners false)\n     * @since 1.6.2\n     * @type String\n     * @default\n     */\n    cornerStrokeColor:        null,\n\n    /**\n     * Specify style of control, 'rect' or 'circle'\n     * @since 1.6.2\n     * @type String\n     */\n    cornerStyle:          'rect',\n\n    /**\n     * Array specifying dash pattern of an object's control (hasBorder must be true)\n     * @since 1.6.2\n     * @type Array\n     */\n    cornerDashArray:          null,\n\n    /**\n     * When true, this object will use center point as the origin of transformation\n     * when being scaled via the controls.\n     * <b>Backwards incompatibility note:</b> This property replaces \"centerTransform\" (Boolean).\n     * @since 1.3.4\n     * @type Boolean\n     * @default\n     */\n    centeredScaling:          false,\n\n    /**\n     * When true, this object will use center point as the origin of transformation\n     * when being rotated via the controls.\n     * <b>Backwards incompatibility note:</b> This property replaces \"centerTransform\" (Boolean).\n     * @since 1.3.4\n     * @type Boolean\n     * @default\n     */\n    centeredRotation:         true,\n\n    /**\n     * Color of object's fill\n     * takes css colors https://www.w3.org/TR/css-color-3/\n     * @type String\n     * @default\n     */\n    fill:                     'rgb(0,0,0)',\n\n    /**\n     * Fill rule used to fill an object\n     * accepted values are nonzero, evenodd\n     * <b>Backwards incompatibility note:</b> This property was used for setting globalCompositeOperation until v1.4.12 (use `fabric.Object#globalCompositeOperation` instead)\n     * @type String\n     * @default\n     */\n    fillRule:                 'nonzero',\n\n    /**\n     * Composite rule used for canvas globalCompositeOperation\n     * @type String\n     * @default\n     */\n    globalCompositeOperation: 'source-over',\n\n    /**\n     * Background color of an object.\n     * takes css colors https://www.w3.org/TR/css-color-3/\n     * @type String\n     * @default\n     */\n    backgroundColor:          '',\n\n    /**\n     * Selection Background color of an object. colored layer behind the object when it is active.\n     * does not mix good with globalCompositeOperation methods.\n     * @type String\n     * @default\n     */\n    selectionBackgroundColor:          '',\n\n    /**\n     * When defined, an object is rendered via stroke and this property specifies its color\n     * takes css colors https://www.w3.org/TR/css-color-3/\n     * @type String\n     * @default\n     */\n    stroke:                   null,\n\n    /**\n     * Width of a stroke used to render this object\n     * @type Number\n     * @default\n     */\n    strokeWidth:              1,\n\n    /**\n     * Array specifying dash pattern of an object's stroke (stroke must be defined)\n     * @type Array\n     */\n    strokeDashArray:          null,\n\n    /**\n     * Line endings style of an object's stroke (one of \"butt\", \"round\", \"square\")\n     * @type String\n     * @default\n     */\n    strokeLineCap:            'butt',\n\n    /**\n     * Corner style of an object's stroke (one of \"bevil\", \"round\", \"miter\")\n     * @type String\n     * @default\n     */\n    strokeLineJoin:           'miter',\n\n    /**\n     * Maximum miter length (used for strokeLineJoin = \"miter\") of an object's stroke\n     * @type Number\n     * @default\n     */\n    strokeMiterLimit:         4,\n\n    /**\n     * Shadow object representing shadow of this shape\n     * @type fabric.Shadow\n     * @default\n     */\n    shadow:                   null,\n\n    /**\n     * Opacity of object's controlling borders when object is active and moving\n     * @type Number\n     * @default\n     */\n    borderOpacityWhenMoving:  0.4,\n\n    /**\n     * Scale factor of object's controlling borders\n     * @type Number\n     * @default\n     */\n    borderScaleFactor:        1,\n\n    /**\n     * Transform matrix (similar to SVG's transform matrix)\n     * @type Array\n     */\n    transformMatrix:          null,\n\n    /**\n     * Minimum allowed scale value of an object\n     * @type Number\n     * @default\n     */\n    minScaleLimit:            0,\n\n    /**\n     * When set to `false`, an object can not be selected for modification (using either point-click-based or group-based selection).\n     * But events still fire on it.\n     * @type Boolean\n     * @default\n     */\n    selectable:               true,\n\n    /**\n     * When set to `false`, an object can not be a target of events. All events propagate through it. Introduced in v1.3.4\n     * @type Boolean\n     * @default\n     */\n    evented:                  true,\n\n    /**\n     * When set to `false`, an object is not rendered on canvas\n     * @type Boolean\n     * @default\n     */\n    visible:                  true,\n\n    /**\n     * When set to `false`, object's controls are not displayed and can not be used to manipulate object\n     * @type Boolean\n     * @default\n     */\n    hasControls:              true,\n\n    /**\n     * When set to `false`, object's controlling borders are not rendered\n     * @type Boolean\n     * @default\n     */\n    hasBorders:               true,\n\n    /**\n     * When set to `false`, object's controlling rotating point will not be visible or selectable\n     * @type Boolean\n     * @default\n     */\n    hasRotatingPoint:         true,\n\n    /**\n     * Offset for object's controlling rotating point (when enabled via `hasRotatingPoint`)\n     * @type Number\n     * @default\n     */\n    rotatingPointOffset:      40,\n\n    /**\n     * When set to `true`, objects are \"found\" on canvas on per-pixel basis rather than according to bounding box\n     * @type Boolean\n     * @default\n     */\n    perPixelTargetFind:       false,\n\n    /**\n     * When `false`, default object's values are not included in its serialization\n     * @type Boolean\n     * @default\n     */\n    includeDefaultValues:     true,\n\n    /**\n     * Function that determines clipping of an object (context is passed as a first argument)\n     * Note that context origin is at the object's center point (not left/top corner)\n     * @deprecated since 2.0.0\n     * @type Function\n     */\n    clipTo:                   null,\n\n    /**\n     * When `true`, object horizontal movement is locked\n     * @type Boolean\n     * @default\n     */\n    lockMovementX:            false,\n\n    /**\n     * When `true`, object vertical movement is locked\n     * @type Boolean\n     * @default\n     */\n    lockMovementY:            false,\n\n    /**\n     * When `true`, object rotation is locked\n     * @type Boolean\n     * @default\n     */\n    lockRotation:             false,\n\n    /**\n     * When `true`, object horizontal scaling is locked\n     * @type Boolean\n     * @default\n     */\n    lockScalingX:             false,\n\n    /**\n     * When `true`, object vertical scaling is locked\n     * @type Boolean\n     * @default\n     */\n    lockScalingY:             false,\n\n    /**\n     * When `true`, object non-uniform scaling is locked\n     * @type Boolean\n     * @default\n     */\n    lockUniScaling:           false,\n\n    /**\n     * When `true`, object horizontal skewing is locked\n     * @type Boolean\n     * @default\n     */\n    lockSkewingX:             false,\n\n    /**\n     * When `true`, object vertical skewing is locked\n     * @type Boolean\n     * @default\n     */\n    lockSkewingY:             false,\n\n    /**\n     * When `true`, object cannot be flipped by scaling into negative values\n     * @type Boolean\n     * @default\n     */\n    lockScalingFlip:          false,\n\n    /**\n     * When `true`, object is not exported in OBJECT/JSON\n     * since 1.6.3\n     * @type Boolean\n     * @default\n     */\n    excludeFromExport:        false,\n\n    /**\n     * When `true`, object is cached on an additional canvas.\n     * default to true\n     * since 1.7.0\n     * @type Boolean\n     * @default true\n     */\n    objectCaching:            objectCaching,\n\n    /**\n     * When `true`, object properties are checked for cache invalidation. In some particular\n     * situation you may want this to be disabled ( spray brush, very big, groups)\n     * or if your application does not allow you to modify properties for groups child you want\n     * to disable it for groups.\n     * default to false\n     * since 1.7.0\n     * @type Boolean\n     * @default false\n     */\n    statefullCache:            false,\n\n    /**\n     * When `true`, cache does not get updated during scaling. The picture will get blocky if scaled\n     * too much and will be redrawn with correct details at the end of scaling.\n     * this setting is performance and application dependant.\n     * default to true\n     * since 1.7.0\n     * @type Boolean\n     * @default true\n     */\n    noScaleCache:              true,\n\n    /**\n     * When set to `true`, object's cache will be rerendered next render call.\n     * since 1.7.0\n     * @type Boolean\n     * @default true\n     */\n    dirty:                true,\n\n    /**\n     * keeps the value of the last hovered coner during mouse move.\n     * 0 is no corner, or 'mt', 'ml', 'mtr' etc..\n     * It should be private, but there is no harm in using it as\n     * a read-only property.\n     * @type number|string|any\n     * @default 0\n     */\n    __corner: 0,\n\n    /**\n     * Determins if the fill or the stroke is drawn first (one of \"fill\" or \"stroke\")\n     * @type String\n     * @default\n     */\n    paintFirst:           'fill',\n\n    /**\n     * List of properties to consider when checking if state\n     * of an object is changed (fabric.Object#hasStateChanged)\n     * as well as for history (undo/redo) purposes\n     * @type Array\n     */\n    stateProperties: (\n      'top left width height scaleX scaleY flipX flipY originX originY transformMatrix ' +\n      'stroke strokeWidth strokeDashArray strokeLineCap strokeLineJoin strokeMiterLimit ' +\n      'angle opacity fill globalCompositeOperation shadow clipTo visible backgroundColor ' +\n      'skewX skewY fillRule paintFirst'\n    ).split(' '),\n\n    /**\n     * List of properties to consider when checking if cache needs refresh\n     * @type Array\n     */\n    cacheProperties: (\n      'fill stroke strokeWidth strokeDashArray width height paintFirst' +\n      ' strokeLineCap strokeLineJoin strokeMiterLimit backgroundColor'\n    ).split(' '),\n\n    /**\n     * Constructor\n     * @param {Object} [options] Options object\n     */\n    initialize: function(options) {\n      if (options) {\n        this.setOptions(options);\n      }\n    },\n\n    /**\n     * Create a the canvas used to keep the cached copy of the object\n     * @private\n     */\n    _createCacheCanvas: function() {\n      this._cacheProperties = {};\n      this._cacheCanvas = fabric.document.createElement('canvas');\n      this._cacheContext = this._cacheCanvas.getContext('2d');\n      this._updateCacheCanvas();\n    },\n\n    /**\n     * Limit the cache dimensions so that X * Y do not cross fabric.perfLimitSizeTotal\n     * and each side do not cross fabric.cacheSideLimit\n     * those numbers are configurable so that you can get as much detail as you want\n     * making bargain with performances.\n     * @param {Object} dims\n     * @param {Object} dims.width width of canvas\n     * @param {Object} dims.height height of canvas\n     * @param {Object} dims.zoomX zoomX zoom value to unscale the canvas before drawing cache\n     * @param {Object} dims.zoomY zoomY zoom value to unscale the canvas before drawing cache\n     * @return {Object}.width width of canvas\n     * @return {Object}.height height of canvas\n     * @return {Object}.zoomX zoomX zoom value to unscale the canvas before drawing cache\n     * @return {Object}.zoomY zoomY zoom value to unscale the canvas before drawing cache\n     */\n    _limitCacheSize: function(dims) {\n      var perfLimitSizeTotal = fabric.perfLimitSizeTotal,\n          width = dims.width, height = dims.height,\n          max = fabric.maxCacheSideLimit, min = fabric.minCacheSideLimit;\n      if (width <= max && height <= max && width * height <= perfLimitSizeTotal) {\n        if (width < min) {\n          dims.width = min;\n        }\n        if (height < min) {\n          dims.height = min;\n        }\n        return dims;\n      }\n      var ar = width / height, limitedDims = fabric.util.limitDimsByArea(ar, perfLimitSizeTotal),\n          capValue = fabric.util.capValue,\n          x = capValue(min, limitedDims.x, max),\n          y = capValue(min, limitedDims.y, max);\n      if (width > x) {\n        dims.zoomX /= width / x;\n        dims.width = x;\n        dims.capped = true;\n      }\n      if (height > y) {\n        dims.zoomY /= height / y;\n        dims.height = y;\n        dims.capped = true;\n      }\n      return dims;\n    },\n\n    /**\n     * Return the dimension and the zoom level needed to create a cache canvas\n     * big enough to host the object to be cached.\n     * @private\n     * @param {Object} dim.x width of object to be cached\n     * @param {Object} dim.y height of object to be cached\n     * @return {Object}.width width of canvas\n     * @return {Object}.height height of canvas\n     * @return {Object}.zoomX zoomX zoom value to unscale the canvas before drawing cache\n     * @return {Object}.zoomY zoomY zoom value to unscale the canvas before drawing cache\n     */\n    _getCacheCanvasDimensions: function() {\n      var zoom = this.canvas && this.canvas.getZoom() || 1,\n          objectScale = this.getObjectScaling(),\n          retina = this.canvas && this.canvas._isRetinaScaling() ? fabric.devicePixelRatio : 1,\n          dim = this._getNonTransformedDimensions(),\n          zoomX = objectScale.scaleX * zoom * retina,\n          zoomY = objectScale.scaleY * zoom * retina,\n          width = dim.x * zoomX,\n          height = dim.y * zoomY;\n      return {\n        // for sure this ALIASING_LIMIT is slightly crating problem\n        // in situation in wich the cache canvas gets an upper limit\n        width: width + ALIASING_LIMIT,\n        height: height + ALIASING_LIMIT,\n        zoomX: zoomX,\n        zoomY: zoomY,\n        x: dim.x,\n        y: dim.y\n      };\n    },\n\n    /**\n     * Update width and height of the canvas for cache\n     * returns true or false if canvas needed resize.\n     * @private\n     * @return {Boolean} true if the canvas has been resized\n     */\n    _updateCacheCanvas: function() {\n      if (this.noScaleCache && this.canvas && this.canvas._currentTransform) {\n        var target = this.canvas._currentTransform.target,\n            action = this.canvas._currentTransform.action;\n        if (this === target && action.slice && action.slice(0, 5) === 'scale') {\n          return false;\n        }\n      }\n      var canvas = this._cacheCanvas,\n          dims = this._limitCacheSize(this._getCacheCanvasDimensions()),\n          minCacheSize = fabric.minCacheSideLimit,\n          width = dims.width, height = dims.height, drawingWidth, drawingHeight,\n          zoomX = dims.zoomX, zoomY = dims.zoomY,\n          dimensionsChanged = width !== this.cacheWidth || height !== this.cacheHeight,\n          zoomChanged = this.zoomX !== zoomX || this.zoomY !== zoomY,\n          shouldRedraw = dimensionsChanged || zoomChanged,\n          additionalWidth = 0, additionalHeight = 0, shouldResizeCanvas = false;\n      if (dimensionsChanged) {\n        var canvasWidth = this._cacheCanvas.width,\n            canvasHeight = this._cacheCanvas.height,\n            sizeGrowing = width > canvasWidth || height > canvasHeight,\n            sizeShrinking = (width < canvasWidth * 0.9 || height < canvasHeight * 0.9) &&\n              canvasWidth > minCacheSize && canvasHeight > minCacheSize;\n        shouldResizeCanvas = sizeGrowing || sizeShrinking;\n        if (sizeGrowing && !dims.capped && (width > minCacheSize || height > minCacheSize)) {\n          additionalWidth = width * 0.1;\n          additionalHeight = height * 0.1;\n        }\n      }\n      if (shouldRedraw) {\n        if (shouldResizeCanvas) {\n          canvas.width = Math.ceil(width + additionalWidth);\n          canvas.height = Math.ceil(height + additionalHeight);\n        }\n        else {\n          this._cacheContext.setTransform(1, 0, 0, 1, 0, 0);\n          this._cacheContext.clearRect(0, 0, canvas.width, canvas.height);\n        }\n        drawingWidth = dims.x * zoomX / 2;\n        drawingHeight = dims.y * zoomY / 2;\n        this.cacheTranslationX = Math.round(canvas.width / 2 - drawingWidth) + drawingWidth;\n        this.cacheTranslationY = Math.round(canvas.height / 2 - drawingHeight) + drawingHeight;\n        this.cacheWidth = width;\n        this.cacheHeight = height;\n        this._cacheContext.translate(this.cacheTranslationX, this.cacheTranslationY);\n        this._cacheContext.scale(zoomX, zoomY);\n        this.zoomX = zoomX;\n        this.zoomY = zoomY;\n        return true;\n      }\n      return false;\n    },\n\n    /**\n     * Sets object's properties from options\n     * @param {Object} [options] Options object\n     */\n    setOptions: function(options) {\n      this._setOptions(options);\n      this._initGradient(options.fill, 'fill');\n      this._initGradient(options.stroke, 'stroke');\n      this._initClipping(options);\n      this._initPattern(options.fill, 'fill');\n      this._initPattern(options.stroke, 'stroke');\n    },\n\n    /**\n     * Transforms context when rendering an object\n     * @param {CanvasRenderingContext2D} ctx Context\n     */\n    transform: function(ctx) {\n      var m;\n      if (this.group && !this.group._transformDone) {\n        m = this.calcTransformMatrix();\n      }\n      else {\n        m = this.calcOwnMatrix();\n      }\n      ctx.transform(m[0], m[1], m[2], m[3], m[4], m[5]);\n    },\n\n    /**\n     * Returns an object representation of an instance\n     * @param {Array} [propertiesToInclude] Any properties that you might want to additionally include in the output\n     * @return {Object} Object representation of an instance\n     */\n    toObject: function(propertiesToInclude) {\n      var NUM_FRACTION_DIGITS = fabric.Object.NUM_FRACTION_DIGITS,\n\n          object = {\n            type:                     this.type,\n            version:                  fabric.version,\n            originX:                  this.originX,\n            originY:                  this.originY,\n            left:                     toFixed(this.left, NUM_FRACTION_DIGITS),\n            top:                      toFixed(this.top, NUM_FRACTION_DIGITS),\n            width:                    toFixed(this.width, NUM_FRACTION_DIGITS),\n            height:                   toFixed(this.height, NUM_FRACTION_DIGITS),\n            fill:                     (this.fill && this.fill.toObject) ? this.fill.toObject() : this.fill,\n            stroke:                   (this.stroke && this.stroke.toObject) ? this.stroke.toObject() : this.stroke,\n            strokeWidth:              toFixed(this.strokeWidth, NUM_FRACTION_DIGITS),\n            strokeDashArray:          this.strokeDashArray ? this.strokeDashArray.concat() : this.strokeDashArray,\n            strokeLineCap:            this.strokeLineCap,\n            strokeLineJoin:           this.strokeLineJoin,\n            strokeMiterLimit:         toFixed(this.strokeMiterLimit, NUM_FRACTION_DIGITS),\n            scaleX:                   toFixed(this.scaleX, NUM_FRACTION_DIGITS),\n            scaleY:                   toFixed(this.scaleY, NUM_FRACTION_DIGITS),\n            angle:                    toFixed(this.angle, NUM_FRACTION_DIGITS),\n            flipX:                    this.flipX,\n            flipY:                    this.flipY,\n            opacity:                  toFixed(this.opacity, NUM_FRACTION_DIGITS),\n            shadow:                   (this.shadow && this.shadow.toObject) ? this.shadow.toObject() : this.shadow,\n            visible:                  this.visible,\n            clipTo:                   this.clipTo && String(this.clipTo),\n            backgroundColor:          this.backgroundColor,\n            fillRule:                 this.fillRule,\n            paintFirst:               this.paintFirst,\n            globalCompositeOperation: this.globalCompositeOperation,\n            transformMatrix:          this.transformMatrix ? this.transformMatrix.concat() : null,\n            skewX:                    toFixed(this.skewX, NUM_FRACTION_DIGITS),\n            skewY:                    toFixed(this.skewY, NUM_FRACTION_DIGITS)\n          };\n\n      fabric.util.populateWithProperties(this, object, propertiesToInclude);\n      if (!this.includeDefaultValues) {\n        object = this._removeDefaultValues(object);\n      }\n\n      return object;\n    },\n\n    /**\n     * Returns (dataless) object representation of an instance\n     * @param {Array} [propertiesToInclude] Any properties that you might want to additionally include in the output\n     * @return {Object} Object representation of an instance\n     */\n    toDatalessObject: function(propertiesToInclude) {\n      // will be overwritten by subclasses\n      return this.toObject(propertiesToInclude);\n    },\n\n    /**\n     * @private\n     * @param {Object} object\n     */\n    _removeDefaultValues: function(object) {\n      var prototype = fabric.util.getKlass(object.type).prototype,\n          stateProperties = prototype.stateProperties;\n      stateProperties.forEach(function(prop) {\n        if (object[prop] === prototype[prop]) {\n          delete object[prop];\n        }\n        var isArray = Object.prototype.toString.call(object[prop]) === '[object Array]' &&\n                      Object.prototype.toString.call(prototype[prop]) === '[object Array]';\n\n        // basically a check for [] === []\n        if (isArray && object[prop].length === 0 && prototype[prop].length === 0) {\n          delete object[prop];\n        }\n      });\n\n      return object;\n    },\n\n    /**\n     * Returns a string representation of an instance\n     * @return {String}\n     */\n    toString: function() {\n      return '#<fabric.' + capitalize(this.type) + '>';\n    },\n\n    /**\n     * Return the object scale factor counting also the group scaling\n     * @return {Object} object with scaleX and scaleY properties\n     */\n    getObjectScaling: function() {\n      var scaleX = this.scaleX, scaleY = this.scaleY;\n      if (this.group) {\n        var scaling = this.group.getObjectScaling();\n        scaleX *= scaling.scaleX;\n        scaleY *= scaling.scaleY;\n      }\n      return { scaleX: scaleX, scaleY: scaleY };\n    },\n\n    /**\n     * Return the object opacity counting also the group property\n     * @return {Number}\n     */\n    getObjectOpacity: function() {\n      var opacity = this.opacity;\n      if (this.group) {\n        opacity *= this.group.getObjectOpacity();\n      }\n      return opacity;\n    },\n\n    /**\n     * @private\n     * @param {String} key\n     * @param {*} value\n     * @return {fabric.Object} thisArg\n     */\n    _set: function(key, value) {\n      var shouldConstrainValue = (key === 'scaleX' || key === 'scaleY'),\n          isChanged = this[key] !== value, groupNeedsUpdate = false;\n\n      if (shouldConstrainValue) {\n        value = this._constrainScale(value);\n      }\n      if (key === 'scaleX' && value < 0) {\n        this.flipX = !this.flipX;\n        value *= -1;\n      }\n      else if (key === 'scaleY' && value < 0) {\n        this.flipY = !this.flipY;\n        value *= -1;\n      }\n      else if (key === 'shadow' && value && !(value instanceof fabric.Shadow)) {\n        value = new fabric.Shadow(value);\n      }\n      else if (key === 'dirty' && this.group) {\n        this.group.set('dirty', value);\n      }\n\n      this[key] = value;\n\n      if (isChanged) {\n        groupNeedsUpdate = this.group && this.group.isOnACache();\n        if (this.cacheProperties.indexOf(key) > -1) {\n          this.dirty = true;\n          groupNeedsUpdate && this.group.set('dirty', true);\n        }\n        else if (groupNeedsUpdate && this.stateProperties.indexOf(key) > -1) {\n          this.group.set('dirty', true);\n        }\n      }\n\n      return this;\n    },\n\n    /**\n     * This callback function is called by the parent group of an object every\n     * time a non-delegated property changes on the group. It is passed the key\n     * and value as parameters. Not adding in this function's signature to avoid\n     * Travis build error about unused variables.\n     */\n    setOnGroup: function() {\n      // implemented by sub-classes, as needed.\n    },\n\n    /**\n     * Retrieves viewportTransform from Object's canvas if possible\n     * @method getViewportTransform\n     * @memberOf fabric.Object.prototype\n     * @return {Boolean}\n     */\n    getViewportTransform: function() {\n      if (this.canvas && this.canvas.viewportTransform) {\n        return this.canvas.viewportTransform;\n      }\n      return fabric.iMatrix.concat();\n    },\n\n    /*\n     * @private\n     * return if the object would be visible in rendering\n     * @memberOf fabric.Object.prototype\n     * @return {Boolean}\n     */\n    isNotVisible: function() {\n      return this.opacity === 0 || (this.width === 0 && this.height === 0) || !this.visible;\n    },\n\n    /**\n     * Renders an object on a specified context\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    render: function(ctx) {\n      // do not render if width/height are zeros or object is not visible\n      if (this.isNotVisible()) {\n        return;\n      }\n      if (this.canvas && this.canvas.skipOffscreen && !this.group && !this.isOnScreen()) {\n        return;\n      }\n      ctx.save();\n      this._setupCompositeOperation(ctx);\n      this.drawSelectionBackground(ctx);\n      this.transform(ctx);\n      this._setOpacity(ctx);\n      this._setShadow(ctx, this);\n      if (this.transformMatrix) {\n        ctx.transform.apply(ctx, this.transformMatrix);\n      }\n      this.clipTo && fabric.util.clipContext(this, ctx);\n      if (this.shouldCache()) {\n        if (!this._cacheCanvas) {\n          this._createCacheCanvas();\n        }\n        if (this.isCacheDirty()) {\n          this.statefullCache && this.saveState({ propertySet: 'cacheProperties' });\n          this.drawObject(this._cacheContext);\n          this.dirty = false;\n        }\n        this.drawCacheOnCanvas(ctx);\n      }\n      else {\n        this._removeCacheCanvas();\n        this.dirty = false;\n        this.drawObject(ctx);\n        if (this.objectCaching && this.statefullCache) {\n          this.saveState({ propertySet: 'cacheProperties' });\n        }\n      }\n      this.clipTo && ctx.restore();\n      ctx.restore();\n    },\n\n    /**\n     * Remove cacheCanvas and its dimensions from the objects\n     */\n    _removeCacheCanvas: function() {\n      this._cacheCanvas = null;\n      this.cacheWidth = 0;\n      this.cacheHeight = 0;\n    },\n\n    /**\n     * When set to `true`, force the object to have its own cache, even if it is inside a group\n     * it may be needed when your object behave in a particular way on the cache and always needs\n     * its own isolated canvas to render correctly.\n     * Created to be overridden\n     * since 1.7.12\n     * @returns false\n     */\n    needsItsOwnCache: function() {\n      if (this.paintFirst === 'stroke' && typeof this.shadow === 'object') {\n        return true;\n      }\n      return false;\n    },\n\n    /**\n     * Decide if the object should cache or not. Create its own cache level\n     * objectCaching is a global flag, wins over everything\n     * needsItsOwnCache should be used when the object drawing method requires\n     * a cache step. None of the fabric classes requires it.\n     * Generally you do not cache objects in groups because the group outside is cached.\n     * @return {Boolean}\n     */\n    shouldCache: function() {\n      this.ownCaching = this.objectCaching &&\n      (!this.group || this.needsItsOwnCache() || !this.group.isOnACache());\n      return this.ownCaching;\n    },\n\n    /**\n     * Check if this object or a child object will cast a shadow\n     * used by Group.shouldCache to know if child has a shadow recursively\n     * @return {Boolean}\n     */\n    willDrawShadow: function() {\n      return !!this.shadow && (this.shadow.offsetX !== 0 || this.shadow.offsetY !== 0);\n    },\n\n    /**\n     * Execute the drawing operation for an object on a specified context\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    drawObject: function(ctx) {\n      this._renderBackground(ctx);\n      this._setStrokeStyles(ctx, this);\n      this._setFillStyles(ctx, this);\n      this._render(ctx);\n    },\n\n    /**\n     * Paint the cached copy of the object on the target context.\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    drawCacheOnCanvas: function(ctx) {\n      ctx.scale(1 / this.zoomX, 1 / this.zoomY);\n      ctx.drawImage(this._cacheCanvas, -this.cacheTranslationX, -this.cacheTranslationY);\n    },\n\n    /**\n     * Check if cache is dirty\n     * @param {Boolean} skipCanvas skip canvas checks because this object is painted\n     * on parent canvas.\n     */\n    isCacheDirty: function(skipCanvas) {\n      if (this.isNotVisible()) {\n        return false;\n      }\n      if (this._cacheCanvas && !skipCanvas && this._updateCacheCanvas()) {\n        // in this case the context is already cleared.\n        return true;\n      }\n      else {\n        if (this.dirty || (this.statefullCache && this.hasStateChanged('cacheProperties'))) {\n          if (this._cacheCanvas && !skipCanvas) {\n            var width = this.cacheWidth / this.zoomX;\n            var height = this.cacheHeight / this.zoomY;\n            this._cacheContext.clearRect(-width / 2, -height / 2, width, height);\n          }\n          return true;\n        }\n      }\n      return false;\n    },\n\n    /**\n     * Draws a background for the object big as its untrasformed dimensions\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    _renderBackground: function(ctx) {\n      if (!this.backgroundColor) {\n        return;\n      }\n      var dim = this._getNonTransformedDimensions();\n      ctx.fillStyle = this.backgroundColor;\n\n      ctx.fillRect(\n        -dim.x / 2,\n        -dim.y / 2,\n        dim.x,\n        dim.y\n      );\n      // if there is background color no other shadows\n      // should be casted\n      this._removeShadow(ctx);\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    _setOpacity: function(ctx) {\n      if (this.group && !this.group._transformDone) {\n        ctx.globalAlpha = this.getObjectOpacity();\n      }\n      else {\n        ctx.globalAlpha *= this.opacity;\n      }\n    },\n\n    _setStrokeStyles: function(ctx, decl) {\n      if (decl.stroke) {\n        ctx.lineWidth = decl.strokeWidth;\n        ctx.lineCap = decl.strokeLineCap;\n        ctx.lineJoin = decl.strokeLineJoin;\n        ctx.miterLimit = decl.strokeMiterLimit;\n        ctx.strokeStyle = decl.stroke.toLive\n          ? decl.stroke.toLive(ctx, this)\n          : decl.stroke;\n      }\n    },\n\n    _setFillStyles: function(ctx, decl) {\n      if (decl.fill) {\n        ctx.fillStyle = decl.fill.toLive\n          ? decl.fill.toLive(ctx, this)\n          : decl.fill;\n      }\n    },\n\n    /**\n     * @private\n     * Sets line dash\n     * @param {CanvasRenderingContext2D} ctx Context to set the dash line on\n     * @param {Array} dashArray array representing dashes\n     * @param {Function} alternative function to call if browaser does not support lineDash\n     */\n    _setLineDash: function(ctx, dashArray, alternative) {\n      if (!dashArray) {\n        return;\n      }\n      // Spec requires the concatenation of two copies the dash list when the number of elements is odd\n      if (1 & dashArray.length) {\n        dashArray.push.apply(dashArray, dashArray);\n      }\n      if (supportsLineDash) {\n        ctx.setLineDash(dashArray);\n      }\n      else {\n        alternative && alternative(ctx);\n      }\n    },\n\n    /**\n     * Renders controls and borders for the object\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     * @param {Object} [styleOverride] properties to override the object style\n     */\n    _renderControls: function(ctx, styleOverride) {\n      var vpt = this.getViewportTransform(),\n          matrix = this.calcTransformMatrix(),\n          options, drawBorders, drawControls;\n      styleOverride = styleOverride || { };\n      drawBorders = typeof styleOverride.hasBorders !== 'undefined' ? styleOverride.hasBorders : this.hasBorders;\n      drawControls = typeof styleOverride.hasControls !== 'undefined' ? styleOverride.hasControls : this.hasControls;\n      matrix = fabric.util.multiplyTransformMatrices(vpt, matrix);\n      options = fabric.util.qrDecompose(matrix);\n      ctx.save();\n      ctx.translate(options.translateX, options.translateY);\n      ctx.lineWidth = 1 * this.borderScaleFactor;\n      if (!this.group) {\n        ctx.globalAlpha = this.isMoving ? this.borderOpacityWhenMoving : 1;\n      }\n      if (styleOverride.forActiveSelection) {\n        ctx.rotate(degreesToRadians(options.angle));\n        drawBorders && this.drawBordersInGroup(ctx, options, styleOverride);\n      }\n      else {\n        ctx.rotate(degreesToRadians(this.angle));\n        drawBorders && this.drawBorders(ctx, styleOverride);\n      }\n      drawControls && this.drawControls(ctx, styleOverride);\n      ctx.restore();\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    _setShadow: function(ctx) {\n      if (!this.shadow) {\n        return;\n      }\n\n      var multX = (this.canvas && this.canvas.viewportTransform[0]) || 1,\n          multY = (this.canvas && this.canvas.viewportTransform[3]) || 1,\n          scaling = this.getObjectScaling();\n      if (this.canvas && this.canvas._isRetinaScaling()) {\n        multX *= fabric.devicePixelRatio;\n        multY *= fabric.devicePixelRatio;\n      }\n      ctx.shadowColor = this.shadow.color;\n      ctx.shadowBlur = this.shadow.blur * fabric.browserShadowBlurConstant *\n        (multX + multY) * (scaling.scaleX + scaling.scaleY) / 4;\n      ctx.shadowOffsetX = this.shadow.offsetX * multX * scaling.scaleX;\n      ctx.shadowOffsetY = this.shadow.offsetY * multY * scaling.scaleY;\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    _removeShadow: function(ctx) {\n      if (!this.shadow) {\n        return;\n      }\n\n      ctx.shadowColor = '';\n      ctx.shadowBlur = ctx.shadowOffsetX = ctx.shadowOffsetY = 0;\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     * @param {Object} filler fabric.Pattern or fabric.Gradient\n     */\n    _applyPatternGradientTransform: function(ctx, filler) {\n      if (!filler || !filler.toLive) {\n        return { offsetX: 0, offsetY: 0 };\n      }\n      var t = filler.gradientTransform || filler.patternTransform;\n      var offsetX = -this.width / 2 + filler.offsetX || 0,\n          offsetY = -this.height / 2 + filler.offsetY || 0;\n      ctx.translate(offsetX, offsetY);\n      if (t) {\n        ctx.transform(t[0], t[1], t[2], t[3], t[4], t[5]);\n      }\n      return { offsetX: offsetX, offsetY: offsetY };\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    _renderPaintInOrder: function(ctx) {\n      if (this.paintFirst === 'stroke') {\n        this._renderStroke(ctx);\n        this._renderFill(ctx);\n      }\n      else {\n        this._renderFill(ctx);\n        this._renderStroke(ctx);\n      }\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    _renderFill: function(ctx) {\n      if (!this.fill) {\n        return;\n      }\n\n      ctx.save();\n      this._applyPatternGradientTransform(ctx, this.fill);\n      if (this.fillRule === 'evenodd') {\n        ctx.fill('evenodd');\n      }\n      else {\n        ctx.fill();\n      }\n      ctx.restore();\n    },\n\n    _renderStroke: function(ctx) {\n      if (!this.stroke || this.strokeWidth === 0) {\n        return;\n      }\n\n      if (this.shadow && !this.shadow.affectStroke) {\n        this._removeShadow(ctx);\n      }\n\n      ctx.save();\n      this._setLineDash(ctx, this.strokeDashArray, this._renderDashedStroke);\n      this._applyPatternGradientTransform(ctx, this.stroke);\n      ctx.stroke();\n      ctx.restore();\n    },\n\n    /**\n     * This function is an helper for svg import. it returns the center of the object in the svg\n     * untransformed coordinates\n     * @private\n     * @return {Object} center point from element coordinates\n     */\n    _findCenterFromElement: function() {\n      return { x: this.left + this.width / 2, y: this.top + this.height / 2 };\n    },\n\n    /**\n     * This function is an helper for svg import. it decoompose the transformMatrix\n     * and assign properties to object.\n     * untransformed coordinates\n     * @private\n     * @chainable\n     */\n    _assignTransformMatrixProps: function() {\n      if (this.transformMatrix) {\n        var options = fabric.util.qrDecompose(this.transformMatrix);\n        this.flipX = false;\n        this.flipY = false;\n        this.set('scaleX', options.scaleX);\n        this.set('scaleY', options.scaleY);\n        this.angle = options.angle;\n        this.skewX = options.skewX;\n        this.skewY = 0;\n      }\n    },\n\n    /**\n     * This function is an helper for svg import. it removes the transform matrix\n     * and set to object properties that fabricjs can handle\n     * @private\n     * @param {Object} preserveAspectRatioOptions\n     * @return {thisArg}\n     */\n    _removeTransformMatrix: function(preserveAspectRatioOptions) {\n      var center = this._findCenterFromElement();\n      if (this.transformMatrix) {\n        this._assignTransformMatrixProps();\n        center = fabric.util.transformPoint(center, this.transformMatrix);\n      }\n      this.transformMatrix = null;\n      if (preserveAspectRatioOptions) {\n        this.scaleX *= preserveAspectRatioOptions.scaleX;\n        this.scaleY *= preserveAspectRatioOptions.scaleY;\n        this.cropX = preserveAspectRatioOptions.cropX;\n        this.cropY = preserveAspectRatioOptions.cropY;\n        center.x += preserveAspectRatioOptions.offsetLeft;\n        center.y += preserveAspectRatioOptions.offsetTop;\n        this.width = preserveAspectRatioOptions.width;\n        this.height = preserveAspectRatioOptions.height;\n      }\n      this.setPositionByOrigin(center, 'center', 'center');\n    },\n\n    /**\n     * Clones an instance, using a callback method will work for every object.\n     * @param {Function} callback Callback is invoked with a clone as a first argument\n     * @param {Array} [propertiesToInclude] Any properties that you might want to additionally include in the output\n     */\n    clone: function(callback, propertiesToInclude) {\n      var objectForm = this.toObject(propertiesToInclude);\n      if (this.constructor.fromObject) {\n        this.constructor.fromObject(objectForm, callback);\n      }\n      else {\n        fabric.Object._fromObject('Object', objectForm, callback);\n      }\n    },\n\n    /**\n     * Creates an instance of fabric.Image out of an object\n     * @param {Function} callback callback, invoked with an instance as a first argument\n     * @param {Object} [options] for clone as image, passed to toDataURL\n     * @param {Boolean} [options.enableRetinaScaling] enable retina scaling for the cloned image\n     * @return {fabric.Object} thisArg\n     */\n    cloneAsImage: function(callback, options) {\n      var dataUrl = this.toDataURL(options);\n      fabric.util.loadImage(dataUrl, function(img) {\n        if (callback) {\n          callback(new fabric.Image(img));\n        }\n      });\n      return this;\n    },\n\n    /**\n     * Converts an object into a data-url-like string\n     * @param {Object} options Options object\n     * @param {String} [options.format=png] The format of the output image. Either \"jpeg\" or \"png\"\n     * @param {Number} [options.quality=1] Quality level (0..1). Only used for jpeg.\n     * @param {Number} [options.multiplier=1] Multiplier to scale by\n     * @param {Number} [options.left] Cropping left offset. Introduced in v1.2.14\n     * @param {Number} [options.top] Cropping top offset. Introduced in v1.2.14\n     * @param {Number} [options.width] Cropping width. Introduced in v1.2.14\n     * @param {Number} [options.height] Cropping height. Introduced in v1.2.14\n     * @param {Boolean} [options.enableRetinaScaling] Enable retina scaling for clone image. Introduce in 1.6.4\n     * @return {String} Returns a data: URL containing a representation of the object in the format specified by options.format\n     */\n    toDataURL: function(options) {\n      options || (options = { });\n\n      var el = fabric.util.createCanvasElement(),\n          boundingRect = this.getBoundingRect();\n\n      el.width = boundingRect.width;\n      el.height = boundingRect.height;\n      fabric.util.wrapElement(el, 'div');\n      var canvas = new fabric.StaticCanvas(el, {\n        enableRetinaScaling: options.enableRetinaScaling,\n        renderOnAddRemove: false,\n        skipOffscreen: false,\n      });\n      // to avoid common confusion https://github.com/kangax/fabric.js/issues/806\n      if (options.format === 'jpg') {\n        options.format = 'jpeg';\n      }\n\n      if (options.format === 'jpeg') {\n        canvas.backgroundColor = '#fff';\n      }\n\n      var origParams = {\n        left: this.left,\n        top: this.top\n      };\n\n      this.setPositionByOrigin(new fabric.Point(canvas.width / 2, canvas.height / 2), 'center', 'center');\n\n      var originalCanvas = this.canvas;\n      canvas.add(this);\n      var data = canvas.toDataURL(options);\n      this.set(origParams).setCoords();\n      this.canvas = originalCanvas;\n      // canvas.dispose will call image.dispose that will nullify the elements\n      // since this canvas is a simple element for the process, we remove references\n      // to objects in this way in order to avoid object trashing.\n      canvas._objects = [];\n      canvas.dispose();\n      canvas = null;\n\n      return data;\n    },\n\n    /**\n     * Returns true if specified type is identical to the type of an instance\n     * @param {String} type Type to check against\n     * @return {Boolean}\n     */\n    isType: function(type) {\n      return this.type === type;\n    },\n\n    /**\n     * Returns complexity of an instance\n     * @return {Number} complexity of this instance (is 1 unless subclassed)\n     */\n    complexity: function() {\n      return 1;\n    },\n\n    /**\n     * Returns a JSON representation of an instance\n     * @param {Array} [propertiesToInclude] Any properties that you might want to additionally include in the output\n     * @return {Object} JSON\n     */\n    toJSON: function(propertiesToInclude) {\n      // delegate, not alias\n      return this.toObject(propertiesToInclude);\n    },\n\n    /**\n     * Sets gradient (fill or stroke) of an object\n     * <b>Backwards incompatibility note:</b> This method was named \"setGradientFill\" until v1.1.0\n     * @param {String} property Property name 'stroke' or 'fill'\n     * @param {Object} [options] Options object\n     * @param {String} [options.type] Type of gradient 'radial' or 'linear'\n     * @param {Number} [options.x1=0] x-coordinate of start point\n     * @param {Number} [options.y1=0] y-coordinate of start point\n     * @param {Number} [options.x2=0] x-coordinate of end point\n     * @param {Number} [options.y2=0] y-coordinate of end point\n     * @param {Number} [options.r1=0] Radius of start point (only for radial gradients)\n     * @param {Number} [options.r2=0] Radius of end point (only for radial gradients)\n     * @param {Object} [options.colorStops] Color stops object eg. {0: 'ff0000', 1: '000000'}\n     * @param {Object} [options.gradientTransform] transforMatrix for gradient\n     * @return {fabric.Object} thisArg\n     * @chainable\n     * @see {@link http://jsfiddle.net/fabricjs/58y8b/|jsFiddle demo}\n     * @example <caption>Set linear gradient</caption>\n     * object.setGradient('fill', {\n     *   type: 'linear',\n     *   x1: -object.width / 2,\n     *   y1: 0,\n     *   x2: object.width / 2,\n     *   y2: 0,\n     *   colorStops: {\n     *     0: 'red',\n     *     0.5: '#005555',\n     *     1: 'rgba(0,0,255,0.5)'\n     *   }\n     * });\n     * canvas.renderAll();\n     * @example <caption>Set radial gradient</caption>\n     * object.setGradient('fill', {\n     *   type: 'radial',\n     *   x1: 0,\n     *   y1: 0,\n     *   x2: 0,\n     *   y2: 0,\n     *   r1: object.width / 2,\n     *   r2: 10,\n     *   colorStops: {\n     *     0: 'red',\n     *     0.5: '#005555',\n     *     1: 'rgba(0,0,255,0.5)'\n     *   }\n     * });\n     * canvas.renderAll();\n     */\n    setGradient: function(property, options) {\n      options || (options = { });\n\n      var gradient = { colorStops: [] };\n\n      gradient.type = options.type || (options.r1 || options.r2 ? 'radial' : 'linear');\n      gradient.coords = {\n        x1: options.x1,\n        y1: options.y1,\n        x2: options.x2,\n        y2: options.y2\n      };\n\n      if (options.r1 || options.r2) {\n        gradient.coords.r1 = options.r1;\n        gradient.coords.r2 = options.r2;\n      }\n\n      gradient.gradientTransform = options.gradientTransform;\n      fabric.Gradient.prototype.addColorStop.call(gradient, options.colorStops);\n\n      return this.set(property, fabric.Gradient.forObject(this, gradient));\n    },\n\n    /**\n     * Sets pattern fill of an object\n     * @param {Object} options Options object\n     * @param {(String|HTMLImageElement)} options.source Pattern source\n     * @param {String} [options.repeat=repeat] Repeat property of a pattern (one of repeat, repeat-x, repeat-y or no-repeat)\n     * @param {Number} [options.offsetX=0] Pattern horizontal offset from object's left/top corner\n     * @param {Number} [options.offsetY=0] Pattern vertical offset from object's left/top corner\n     * @return {fabric.Object} thisArg\n     * @chainable\n     * @see {@link http://jsfiddle.net/fabricjs/QT3pa/|jsFiddle demo}\n     * @example <caption>Set pattern</caption>\n     * fabric.util.loadImage('http://fabricjs.com/assets/escheresque_ste.png', function(img) {\n     *   object.setPatternFill({\n     *     source: img,\n     *     repeat: 'repeat'\n     *   });\n     *   canvas.renderAll();\n     * });\n     */\n    setPatternFill: function(options) {\n      return this.set('fill', new fabric.Pattern(options));\n    },\n\n    /**\n     * Sets {@link fabric.Object#shadow|shadow} of an object\n     * @param {Object|String} [options] Options object or string (e.g. \"2px 2px 10px rgba(0,0,0,0.2)\")\n     * @param {String} [options.color=rgb(0,0,0)] Shadow color\n     * @param {Number} [options.blur=0] Shadow blur\n     * @param {Number} [options.offsetX=0] Shadow horizontal offset\n     * @param {Number} [options.offsetY=0] Shadow vertical offset\n     * @return {fabric.Object} thisArg\n     * @chainable\n     * @see {@link http://jsfiddle.net/fabricjs/7gvJG/|jsFiddle demo}\n     * @example <caption>Set shadow with string notation</caption>\n     * object.setShadow('2px 2px 10px rgba(0,0,0,0.2)');\n     * canvas.renderAll();\n     * @example <caption>Set shadow with object notation</caption>\n     * object.setShadow({\n     *   color: 'red',\n     *   blur: 10,\n     *   offsetX: 20,\n     *   offsetY: 20\n     * });\n     * canvas.renderAll();\n     */\n    setShadow: function(options) {\n      return this.set('shadow', options ? new fabric.Shadow(options) : null);\n    },\n\n    /**\n     * Sets \"color\" of an instance (alias of `set('fill', &hellip;)`)\n     * @param {String} color Color value\n     * @return {fabric.Object} thisArg\n     * @chainable\n     */\n    setColor: function(color) {\n      this.set('fill', color);\n      return this;\n    },\n\n    /**\n     * Sets \"angle\" of an instance with centered rotation\n     * @param {Number} angle Angle value (in degrees)\n     * @return {fabric.Object} thisArg\n     * @chainable\n     */\n    rotate: function(angle) {\n      var shouldCenterOrigin = (this.originX !== 'center' || this.originY !== 'center') && this.centeredRotation;\n\n      if (shouldCenterOrigin) {\n        this._setOriginToCenter();\n      }\n\n      this.set('angle', angle);\n\n      if (shouldCenterOrigin) {\n        this._resetOrigin();\n      }\n\n      return this;\n    },\n\n    /**\n     * Centers object horizontally on canvas to which it was added last.\n     * You might need to call `setCoords` on an object after centering, to update controls area.\n     * @return {fabric.Object} thisArg\n     * @chainable\n     */\n    centerH: function () {\n      this.canvas && this.canvas.centerObjectH(this);\n      return this;\n    },\n\n    /**\n     * Centers object horizontally on current viewport of canvas to which it was added last.\n     * You might need to call `setCoords` on an object after centering, to update controls area.\n     * @return {fabric.Object} thisArg\n     * @chainable\n     */\n    viewportCenterH: function () {\n      this.canvas && this.canvas.viewportCenterObjectH(this);\n      return this;\n    },\n\n    /**\n     * Centers object vertically on canvas to which it was added last.\n     * You might need to call `setCoords` on an object after centering, to update controls area.\n     * @return {fabric.Object} thisArg\n     * @chainable\n     */\n    centerV: function () {\n      this.canvas && this.canvas.centerObjectV(this);\n      return this;\n    },\n\n    /**\n     * Centers object vertically on current viewport of canvas to which it was added last.\n     * You might need to call `setCoords` on an object after centering, to update controls area.\n     * @return {fabric.Object} thisArg\n     * @chainable\n     */\n    viewportCenterV: function () {\n      this.canvas && this.canvas.viewportCenterObjectV(this);\n      return this;\n    },\n\n    /**\n     * Centers object vertically and horizontally on canvas to which is was added last\n     * You might need to call `setCoords` on an object after centering, to update controls area.\n     * @return {fabric.Object} thisArg\n     * @chainable\n     */\n    center: function () {\n      this.canvas && this.canvas.centerObject(this);\n      return this;\n    },\n\n    /**\n     * Centers object on current viewport of canvas to which it was added last.\n     * You might need to call `setCoords` on an object after centering, to update controls area.\n     * @return {fabric.Object} thisArg\n     * @chainable\n     */\n    viewportCenter: function () {\n      this.canvas && this.canvas.viewportCenterObject(this);\n      return this;\n    },\n\n    /**\n     * Returns coordinates of a pointer relative to an object\n     * @param {Event} e Event to operate upon\n     * @param {Object} [pointer] Pointer to operate upon (instead of event)\n     * @return {Object} Coordinates of a pointer (x, y)\n     */\n    getLocalPointer: function(e, pointer) {\n      pointer = pointer || this.canvas.getPointer(e);\n      var pClicked = new fabric.Point(pointer.x, pointer.y),\n          objectLeftTop = this._getLeftTopCoords();\n      if (this.angle) {\n        pClicked = fabric.util.rotatePoint(\n          pClicked, objectLeftTop, degreesToRadians(-this.angle));\n      }\n      return {\n        x: pClicked.x - objectLeftTop.x,\n        y: pClicked.y - objectLeftTop.y\n      };\n    },\n\n    /**\n     * Sets canvas globalCompositeOperation for specific object\n     * custom composition operation for the particular object can be specifed using globalCompositeOperation property\n     * @param {CanvasRenderingContext2D} ctx Rendering canvas context\n     */\n    _setupCompositeOperation: function (ctx) {\n      if (this.globalCompositeOperation) {\n        ctx.globalCompositeOperation = this.globalCompositeOperation;\n      }\n    }\n  });\n\n  fabric.util.createAccessors && fabric.util.createAccessors(fabric.Object);\n\n  extend(fabric.Object.prototype, fabric.Observable);\n\n  /**\n   * Defines the number of fraction digits to use when serializing object values.\n   * You can use it to increase/decrease precision of such values like left, top, scaleX, scaleY, etc.\n   * @static\n   * @memberOf fabric.Object\n   * @constant\n   * @type Number\n   */\n  fabric.Object.NUM_FRACTION_DIGITS = 2;\n\n  fabric.Object._fromObject = function(className, object, callback, extraParam) {\n    var klass = fabric[className];\n    object = clone(object, true);\n    fabric.util.enlivenPatterns([object.fill, object.stroke], function(patterns) {\n      if (typeof patterns[0] !== 'undefined') {\n        object.fill = patterns[0];\n      }\n      if (typeof patterns[1] !== 'undefined') {\n        object.stroke = patterns[1];\n      }\n      var instance = extraParam ? new klass(object[extraParam], object) : new klass(object);\n      callback && callback(instance);\n    });\n  };\n\n  /**\n   * Unique id used internally when creating SVG elements\n   * @static\n   * @memberOf fabric.Object\n   * @type Number\n   */\n  fabric.Object.__uid = 0;\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function() {\n\n  var degreesToRadians = fabric.util.degreesToRadians,\n      originXOffset = {\n        left: -0.5,\n        center: 0,\n        right: 0.5\n      },\n      originYOffset = {\n        top: -0.5,\n        center: 0,\n        bottom: 0.5\n      };\n\n  fabric.util.object.extend(fabric.Object.prototype, /** @lends fabric.Object.prototype */ {\n\n    /**\n     * Translates the coordinates from a set of origin to another (based on the object's dimensions)\n     * @param {fabric.Point} point The point which corresponds to the originX and originY params\n     * @param {String} fromOriginX Horizontal origin: 'left', 'center' or 'right'\n     * @param {String} fromOriginY Vertical origin: 'top', 'center' or 'bottom'\n     * @param {String} toOriginX Horizontal origin: 'left', 'center' or 'right'\n     * @param {String} toOriginY Vertical origin: 'top', 'center' or 'bottom'\n     * @return {fabric.Point}\n     */\n    translateToGivenOrigin: function(point, fromOriginX, fromOriginY, toOriginX, toOriginY) {\n      var x = point.x,\n          y = point.y,\n          offsetX, offsetY, dim;\n\n      if (typeof fromOriginX === 'string') {\n        fromOriginX = originXOffset[fromOriginX];\n      }\n      else {\n        fromOriginX -= 0.5;\n      }\n\n      if (typeof toOriginX === 'string') {\n        toOriginX = originXOffset[toOriginX];\n      }\n      else {\n        toOriginX -= 0.5;\n      }\n\n      offsetX = toOriginX - fromOriginX;\n\n      if (typeof fromOriginY === 'string') {\n        fromOriginY = originYOffset[fromOriginY];\n      }\n      else {\n        fromOriginY -= 0.5;\n      }\n\n      if (typeof toOriginY === 'string') {\n        toOriginY = originYOffset[toOriginY];\n      }\n      else {\n        toOriginY -= 0.5;\n      }\n\n      offsetY = toOriginY - fromOriginY;\n\n      if (offsetX || offsetY) {\n        dim = this._getTransformedDimensions();\n        x = point.x + offsetX * dim.x;\n        y = point.y + offsetY * dim.y;\n      }\n\n      return new fabric.Point(x, y);\n    },\n\n    /**\n     * Translates the coordinates from origin to center coordinates (based on the object's dimensions)\n     * @param {fabric.Point} point The point which corresponds to the originX and originY params\n     * @param {String} originX Horizontal origin: 'left', 'center' or 'right'\n     * @param {String} originY Vertical origin: 'top', 'center' or 'bottom'\n     * @return {fabric.Point}\n     */\n    translateToCenterPoint: function(point, originX, originY) {\n      var p = this.translateToGivenOrigin(point, originX, originY, 'center', 'center');\n      if (this.angle) {\n        return fabric.util.rotatePoint(p, point, degreesToRadians(this.angle));\n      }\n      return p;\n    },\n\n    /**\n     * Translates the coordinates from center to origin coordinates (based on the object's dimensions)\n     * @param {fabric.Point} center The point which corresponds to center of the object\n     * @param {String} originX Horizontal origin: 'left', 'center' or 'right'\n     * @param {String} originY Vertical origin: 'top', 'center' or 'bottom'\n     * @return {fabric.Point}\n     */\n    translateToOriginPoint: function(center, originX, originY) {\n      var p = this.translateToGivenOrigin(center, 'center', 'center', originX, originY);\n      if (this.angle) {\n        return fabric.util.rotatePoint(p, center, degreesToRadians(this.angle));\n      }\n      return p;\n    },\n\n    /**\n     * Returns the real center coordinates of the object\n     * @return {fabric.Point}\n     */\n    getCenterPoint: function() {\n      var leftTop = new fabric.Point(this.left, this.top);\n      return this.translateToCenterPoint(leftTop, this.originX, this.originY);\n    },\n\n    /**\n     * Returns the coordinates of the object based on center coordinates\n     * @param {fabric.Point} point The point which corresponds to the originX and originY params\n     * @return {fabric.Point}\n     */\n    // getOriginPoint: function(center) {\n    //   return this.translateToOriginPoint(center, this.originX, this.originY);\n    // },\n\n    /**\n     * Returns the coordinates of the object as if it has a different origin\n     * @param {String} originX Horizontal origin: 'left', 'center' or 'right'\n     * @param {String} originY Vertical origin: 'top', 'center' or 'bottom'\n     * @return {fabric.Point}\n     */\n    getPointByOrigin: function(originX, originY) {\n      var center = this.getCenterPoint();\n      return this.translateToOriginPoint(center, originX, originY);\n    },\n\n    /**\n     * Returns the point in local coordinates\n     * @param {fabric.Point} point The point relative to the global coordinate system\n     * @param {String} originX Horizontal origin: 'left', 'center' or 'right'\n     * @param {String} originY Vertical origin: 'top', 'center' or 'bottom'\n     * @return {fabric.Point}\n     */\n    toLocalPoint: function(point, originX, originY) {\n      var center = this.getCenterPoint(),\n          p, p2;\n\n      if (typeof originX !== 'undefined' && typeof originY !== 'undefined' ) {\n        p = this.translateToGivenOrigin(center, 'center', 'center', originX, originY);\n      }\n      else {\n        p = new fabric.Point(this.left, this.top);\n      }\n\n      p2 = new fabric.Point(point.x, point.y);\n      if (this.angle) {\n        p2 = fabric.util.rotatePoint(p2, center, -degreesToRadians(this.angle));\n      }\n      return p2.subtractEquals(p);\n    },\n\n    /**\n     * Returns the point in global coordinates\n     * @param {fabric.Point} The point relative to the local coordinate system\n     * @return {fabric.Point}\n     */\n    // toGlobalPoint: function(point) {\n    //   return fabric.util.rotatePoint(point, this.getCenterPoint(), degreesToRadians(this.angle)).addEquals(new fabric.Point(this.left, this.top));\n    // },\n\n    /**\n     * Sets the position of the object taking into consideration the object's origin\n     * @param {fabric.Point} pos The new position of the object\n     * @param {String} originX Horizontal origin: 'left', 'center' or 'right'\n     * @param {String} originY Vertical origin: 'top', 'center' or 'bottom'\n     * @return {void}\n     */\n    setPositionByOrigin: function(pos, originX, originY) {\n      var center = this.translateToCenterPoint(pos, originX, originY),\n          position = this.translateToOriginPoint(center, this.originX, this.originY);\n      this.set('left', position.x);\n      this.set('top', position.y);\n    },\n\n    /**\n     * @param {String} to One of 'left', 'center', 'right'\n     */\n    adjustPosition: function(to) {\n      var angle = degreesToRadians(this.angle),\n          hypotFull = this.getScaledWidth(),\n          xFull = fabric.util.cos(angle) * hypotFull,\n          yFull = fabric.util.sin(angle) * hypotFull,\n          offsetFrom, offsetTo;\n\n      //TODO: this function does not consider mixed situation like top, center.\n      if (typeof this.originX === 'string') {\n        offsetFrom = originXOffset[this.originX];\n      }\n      else {\n        offsetFrom = this.originX - 0.5;\n      }\n      if (typeof to === 'string') {\n        offsetTo = originXOffset[to];\n      }\n      else {\n        offsetTo = to - 0.5;\n      }\n      this.left += xFull * (offsetTo - offsetFrom);\n      this.top += yFull * (offsetTo - offsetFrom);\n      this.setCoords();\n      this.originX = to;\n    },\n\n    /**\n     * Sets the origin/position of the object to it's center point\n     * @private\n     * @return {void}\n     */\n    _setOriginToCenter: function() {\n      this._originalOriginX = this.originX;\n      this._originalOriginY = this.originY;\n\n      var center = this.getCenterPoint();\n\n      this.originX = 'center';\n      this.originY = 'center';\n\n      this.left = center.x;\n      this.top = center.y;\n    },\n\n    /**\n     * Resets the origin/position of the object to it's original origin\n     * @private\n     * @return {void}\n     */\n    _resetOrigin: function() {\n      var originPoint = this.translateToOriginPoint(\n        this.getCenterPoint(),\n        this._originalOriginX,\n        this._originalOriginY);\n\n      this.originX = this._originalOriginX;\n      this.originY = this._originalOriginY;\n\n      this.left = originPoint.x;\n      this.top = originPoint.y;\n\n      this._originalOriginX = null;\n      this._originalOriginY = null;\n    },\n\n    /**\n     * @private\n     */\n    _getLeftTopCoords: function() {\n      return this.translateToOriginPoint(this.getCenterPoint(), 'left', 'top');\n    },\n\n    /**\n    * Callback; invoked right before object is about to go from active to inactive\n    */\n    onDeselect: function() {\n      /* NOOP */\n    }\n  });\n\n})();\n\n\n(function() {\n\n  function getCoords(coords) {\n    return [\n      new fabric.Point(coords.tl.x, coords.tl.y),\n      new fabric.Point(coords.tr.x, coords.tr.y),\n      new fabric.Point(coords.br.x, coords.br.y),\n      new fabric.Point(coords.bl.x, coords.bl.y)\n    ];\n  }\n\n  var degreesToRadians = fabric.util.degreesToRadians,\n      multiplyMatrices = fabric.util.multiplyTransformMatrices,\n      transformPoint = fabric.util.transformPoint;\n\n  fabric.util.object.extend(fabric.Object.prototype, /** @lends fabric.Object.prototype */ {\n\n    /**\n     * Describe object's corner position in canvas element coordinates.\n     * properties are tl,mt,tr,ml,mr,bl,mb,br,mtr for the main controls.\n     * each property is an object with x, y and corner.\n     * The `corner` property contains in a similar manner the 4 points of the\n     * interactive area of the corner.\n     * The coordinates depends from this properties: width, height, scaleX, scaleY\n     * skewX, skewY, angle, strokeWidth, viewportTransform, top, left, padding.\n     * The coordinates get updated with @method setCoords.\n     * You can calculate them without updating with @method calcCoords;\n     * @memberOf fabric.Object.prototype\n     */\n    oCoords: null,\n\n    /**\n     * Describe object's corner position in canvas object absolute coordinates\n     * properties are tl,tr,bl,br and describe the four main corner.\n     * each property is an object with x, y, instance of Fabric.Point.\n     * The coordinates depends from this properties: width, height, scaleX, scaleY\n     * skewX, skewY, angle, strokeWidth, top, left.\n     * Those coordinates are usefull to understand where an object is. They get updated\n     * with oCoords but they do not need to be updated when zoom or panning change.\n     * The coordinates get updated with @method setCoords.\n     * You can calculate them without updating with @method calcCoords(true);\n     * @memberOf fabric.Object.prototype\n     */\n    aCoords: null,\n\n    /**\n     * storage for object transform matrix\n     */\n    ownMatrixCache: null,\n\n    /**\n     * storage for object full transform matrix\n     */\n    matrixCache: null,\n\n    /**\n     * return correct set of coordinates for intersection\n     */\n    getCoords: function(absolute, calculate) {\n      if (!this.oCoords) {\n        this.setCoords();\n      }\n      var coords = absolute ? this.aCoords : this.oCoords;\n      return getCoords(calculate ? this.calcCoords(absolute) : coords);\n    },\n\n    /**\n     * Checks if object intersects with an area formed by 2 points\n     * @param {Object} pointTL top-left point of area\n     * @param {Object} pointBR bottom-right point of area\n     * @param {Boolean} [absolute] use coordinates without viewportTransform\n     * @param {Boolean} [calculate] use coordinates of current position instead of .oCoords\n     * @return {Boolean} true if object intersects with an area formed by 2 points\n     */\n    intersectsWithRect: function(pointTL, pointBR, absolute, calculate) {\n      var coords = this.getCoords(absolute, calculate),\n          intersection = fabric.Intersection.intersectPolygonRectangle(\n            coords,\n            pointTL,\n            pointBR\n          );\n      return intersection.status === 'Intersection';\n    },\n\n    /**\n     * Checks if object intersects with another object\n     * @param {Object} other Object to test\n     * @param {Boolean} [absolute] use coordinates without viewportTransform\n     * @param {Boolean} [calculate] use coordinates of current position instead of .oCoords\n     * @return {Boolean} true if object intersects with another object\n     */\n    intersectsWithObject: function(other, absolute, calculate) {\n      var intersection = fabric.Intersection.intersectPolygonPolygon(\n        this.getCoords(absolute, calculate),\n        other.getCoords(absolute, calculate)\n      );\n\n      return intersection.status === 'Intersection'\n        || other.isContainedWithinObject(this, absolute, calculate)\n        || this.isContainedWithinObject(other, absolute, calculate);\n    },\n\n    /**\n     * Checks if object is fully contained within area of another object\n     * @param {Object} other Object to test\n     * @param {Boolean} [absolute] use coordinates without viewportTransform\n     * @param {Boolean} [calculate] use coordinates of current position instead of .oCoords\n     * @return {Boolean} true if object is fully contained within area of another object\n     */\n    isContainedWithinObject: function(other, absolute, calculate) {\n      var points = this.getCoords(absolute, calculate),\n          i = 0, lines = other._getImageLines(\n            calculate ? other.calcCoords(absolute) : absolute ? other.aCoords : other.oCoords\n          );\n      for (; i < 4; i++) {\n        if (!other.containsPoint(points[i], lines)) {\n          return false;\n        }\n      }\n      return true;\n    },\n\n    /**\n     * Checks if object is fully contained within area formed by 2 points\n     * @param {Object} pointTL top-left point of area\n     * @param {Object} pointBR bottom-right point of area\n     * @param {Boolean} [absolute] use coordinates without viewportTransform\n     * @param {Boolean} [calculate] use coordinates of current position instead of .oCoords\n     * @return {Boolean} true if object is fully contained within area formed by 2 points\n     */\n    isContainedWithinRect: function(pointTL, pointBR, absolute, calculate) {\n      var boundingRect = this.getBoundingRect(absolute, calculate);\n\n      return (\n        boundingRect.left >= pointTL.x &&\n        boundingRect.left + boundingRect.width <= pointBR.x &&\n        boundingRect.top >= pointTL.y &&\n        boundingRect.top + boundingRect.height <= pointBR.y\n      );\n    },\n\n    /**\n     * Checks if point is inside the object\n     * @param {fabric.Point} point Point to check against\n     * @param {Object} [lines] object returned from @method _getImageLines\n     * @param {Boolean} [absolute] use coordinates without viewportTransform\n     * @param {Boolean} [calculate] use coordinates of current position instead of .oCoords\n     * @return {Boolean} true if point is inside the object\n     */\n    containsPoint: function(point, lines, absolute, calculate) {\n      var lines = lines || this._getImageLines(\n            calculate ? this.calcCoords(absolute) : absolute ? this.aCoords : this.oCoords\n          ),\n          xPoints = this._findCrossPoints(point, lines);\n\n      // if xPoints is odd then point is inside the object\n      return (xPoints !== 0 && xPoints % 2 === 1);\n    },\n\n    /**\n     * Checks if object is contained within the canvas with current viewportTransform\n     * the check is done stopping at first point that appears on screen\n     * @param {Boolean} [calculate] use coordinates of current position instead of .oCoords\n     * @return {Boolean} true if object is fully or partially contained within canvas\n     */\n    isOnScreen: function(calculate) {\n      if (!this.canvas) {\n        return false;\n      }\n      var pointTL = this.canvas.vptCoords.tl, pointBR = this.canvas.vptCoords.br;\n      var points = this.getCoords(true, calculate), point;\n      for (var i = 0; i < 4; i++) {\n        point = points[i];\n        if (point.x <= pointBR.x && point.x >= pointTL.x && point.y <= pointBR.y && point.y >= pointTL.y) {\n          return true;\n        }\n      }\n      // no points on screen, check intersection with absolute coordinates\n      if (this.intersectsWithRect(pointTL, pointBR, true, calculate)) {\n        return true;\n      }\n      return this._containsCenterOfCanvas(pointTL, pointBR, calculate);\n    },\n\n    /**\n     * Checks if the object contains the midpoint between canvas extremities\n     * Does not make sense outside the context of isOnScreen and isPartiallyOnScreen\n     * @private\n     * @param {Fabric.Point} pointTL Top Left point\n     * @param {Fabric.Point} pointBR Top Right point\n     * @param {Boolean} calculate use coordinates of current position instead of .oCoords\n     * @return {Boolean} true if the objects containe the point\n     */\n    _containsCenterOfCanvas: function(pointTL, pointBR, calculate) {\n      // worst case scenario the object is so big that contains the screen\n      var centerPoint = { x: (pointTL.x + pointBR.x) / 2, y: (pointTL.y + pointBR.y) / 2 };\n      if (this.containsPoint(centerPoint, null, true, calculate)) {\n        return true;\n      }\n      return false;\n    },\n\n    /**\n     * Checks if object is partially contained within the canvas with current viewportTransform\n     * @param {Boolean} [calculate] use coordinates of current position instead of .oCoords\n     * @return {Boolean} true if object is partially contained within canvas\n     */\n    isPartiallyOnScreen: function(calculate) {\n      if (!this.canvas) {\n        return false;\n      }\n      var pointTL = this.canvas.vptCoords.tl, pointBR = this.canvas.vptCoords.br;\n      if (this.intersectsWithRect(pointTL, pointBR, true, calculate)) {\n        return true;\n      }\n      return this._containsCenterOfCanvas(pointTL, pointBR, calculate);\n    },\n\n    /**\n     * Method that returns an object with the object edges in it, given the coordinates of the corners\n     * @private\n     * @param {Object} oCoords Coordinates of the object corners\n     */\n    _getImageLines: function(oCoords) {\n      return {\n        topline: {\n          o: oCoords.tl,\n          d: oCoords.tr\n        },\n        rightline: {\n          o: oCoords.tr,\n          d: oCoords.br\n        },\n        bottomline: {\n          o: oCoords.br,\n          d: oCoords.bl\n        },\n        leftline: {\n          o: oCoords.bl,\n          d: oCoords.tl\n        }\n      };\n    },\n\n    /**\n     * Helper method to determine how many cross points are between the 4 object edges\n     * and the horizontal line determined by a point on canvas\n     * @private\n     * @param {fabric.Point} point Point to check\n     * @param {Object} lines Coordinates of the object being evaluated\n     */\n    // remove yi, not used but left code here just in case.\n    _findCrossPoints: function(point, lines) {\n      var b1, b2, a1, a2, xi, // yi,\n          xcount = 0,\n          iLine;\n\n      for (var lineKey in lines) {\n        iLine = lines[lineKey];\n        // optimisation 1: line below point. no cross\n        if ((iLine.o.y < point.y) && (iLine.d.y < point.y)) {\n          continue;\n        }\n        // optimisation 2: line above point. no cross\n        if ((iLine.o.y >= point.y) && (iLine.d.y >= point.y)) {\n          continue;\n        }\n        // optimisation 3: vertical line case\n        if ((iLine.o.x === iLine.d.x) && (iLine.o.x >= point.x)) {\n          xi = iLine.o.x;\n          // yi = point.y;\n        }\n        // calculate the intersection point\n        else {\n          b1 = 0;\n          b2 = (iLine.d.y - iLine.o.y) / (iLine.d.x - iLine.o.x);\n          a1 = point.y - b1 * point.x;\n          a2 = iLine.o.y - b2 * iLine.o.x;\n\n          xi = -(a1 - a2) / (b1 - b2);\n          // yi = a1 + b1 * xi;\n        }\n        // dont count xi < point.x cases\n        if (xi >= point.x) {\n          xcount += 1;\n        }\n        // optimisation 4: specific for square images\n        if (xcount === 2) {\n          break;\n        }\n      }\n      return xcount;\n    },\n\n    /**\n     * Returns coordinates of object's bounding rectangle (left, top, width, height)\n     * the box is intented as aligned to axis of canvas.\n     * @param {Boolean} [absolute] use coordinates without viewportTransform\n     * @param {Boolean} [calculate] use coordinates of current position instead of .oCoords / .aCoords\n     * @return {Object} Object with left, top, width, height properties\n     */\n    getBoundingRect: function(absolute, calculate) {\n      var coords = this.getCoords(absolute, calculate);\n      return fabric.util.makeBoundingBoxFromPoints(coords);\n    },\n\n    /**\n     * Returns width of an object bounding box counting transformations\n     * before 2.0 it was named getWidth();\n     * @return {Number} width value\n     */\n    getScaledWidth: function() {\n      return this._getTransformedDimensions().x;\n    },\n\n    /**\n     * Returns height of an object bounding box counting transformations\n     * before 2.0 it was named getHeight();\n     * @return {Number} height value\n     */\n    getScaledHeight: function() {\n      return this._getTransformedDimensions().y;\n    },\n\n    /**\n     * Makes sure the scale is valid and modifies it if necessary\n     * @private\n     * @param {Number} value\n     * @return {Number}\n     */\n    _constrainScale: function(value) {\n      if (Math.abs(value) < this.minScaleLimit) {\n        if (value < 0) {\n          return -this.minScaleLimit;\n        }\n        else {\n          return this.minScaleLimit;\n        }\n      }\n      else if (value === 0) {\n        return 0.0001;\n      }\n      return value;\n    },\n\n    /**\n     * Scales an object (equally by x and y)\n     * @param {Number} value Scale factor\n     * @return {fabric.Object} thisArg\n     * @chainable\n     */\n    scale: function(value) {\n      this._set('scaleX', value);\n      this._set('scaleY', value);\n      return this.setCoords();\n    },\n\n    /**\n     * Scales an object to a given width, with respect to bounding box (scaling by x/y equally)\n     * @param {Number} value New width value\n     * @param {Boolean} absolute ignore viewport\n     * @return {fabric.Object} thisArg\n     * @chainable\n     */\n    scaleToWidth: function(value, absolute) {\n      // adjust to bounding rect factor so that rotated shapes would fit as well\n      var boundingRectFactor = this.getBoundingRect(absolute).width / this.getScaledWidth();\n      return this.scale(value / this.width / boundingRectFactor);\n    },\n\n    /**\n     * Scales an object to a given height, with respect to bounding box (scaling by x/y equally)\n     * @param {Number} value New height value\n     * @param {Boolean} absolute ignore viewport\n     * @return {fabric.Object} thisArg\n     * @chainable\n     */\n    scaleToHeight: function(value, absolute) {\n      // adjust to bounding rect factor so that rotated shapes would fit as well\n      var boundingRectFactor = this.getBoundingRect(absolute).height / this.getScaledHeight();\n      return this.scale(value / this.height / boundingRectFactor);\n    },\n\n    /**\n     * Calculate and returns the .coords of an object.\n     * @return {Object} Object with tl, tr, br, bl ....\n     * @chainable\n     */\n    calcCoords: function(absolute) {\n      var rotateMatrix = this._calcRotateMatrix(),\n          translateMatrix = this._calcTranslateMatrix(),\n          startMatrix = multiplyMatrices(translateMatrix, rotateMatrix),\n          vpt = this.getViewportTransform(),\n          finalMatrix = absolute ? startMatrix : multiplyMatrices(vpt, startMatrix),\n          dim = this._getTransformedDimensions(),\n          w = dim.x / 2, h = dim.y / 2,\n          tl = transformPoint({ x: -w, y: -h }, finalMatrix),\n          tr = transformPoint({ x: w, y: -h }, finalMatrix),\n          bl = transformPoint({ x: -w, y: h }, finalMatrix),\n          br = transformPoint({ x: w, y: h }, finalMatrix);\n      if (!absolute) {\n        var padding = this.padding, angle = degreesToRadians(this.angle),\n            cos = fabric.util.cos(angle), sin = fabric.util.sin(angle),\n            cosP = cos * padding, sinP = sin * padding, cosPSinP = cosP + sinP,\n            cosPMinusSinP = cosP - sinP;\n        if (padding) {\n          tl.x -= cosPMinusSinP;\n          tl.y -= cosPSinP;\n          tr.x += cosPSinP;\n          tr.y -= cosPMinusSinP;\n          bl.x -= cosPSinP;\n          bl.y += cosPMinusSinP;\n          br.x += cosPMinusSinP;\n          br.y += cosPSinP;\n        }\n        var ml  = new fabric.Point((tl.x + bl.x) / 2, (tl.y + bl.y) / 2),\n            mt  = new fabric.Point((tr.x + tl.x) / 2, (tr.y + tl.y) / 2),\n            mr  = new fabric.Point((br.x + tr.x) / 2, (br.y + tr.y) / 2),\n            mb  = new fabric.Point((br.x + bl.x) / 2, (br.y + bl.y) / 2),\n            mtr = new fabric.Point(mt.x + sin * this.rotatingPointOffset, mt.y - cos * this.rotatingPointOffset);\n      }\n\n      // if (!absolute) {\n      //   var canvas = this.canvas;\n      //   setTimeout(function() {\n      //     canvas.contextTop.clearRect(0, 0, 700, 700);\n      //     canvas.contextTop.fillStyle = 'green';\n      //     canvas.contextTop.fillRect(mb.x, mb.y, 3, 3);\n      //     canvas.contextTop.fillRect(bl.x, bl.y, 3, 3);\n      //     canvas.contextTop.fillRect(br.x, br.y, 3, 3);\n      //     canvas.contextTop.fillRect(tl.x, tl.y, 3, 3);\n      //     canvas.contextTop.fillRect(tr.x, tr.y, 3, 3);\n      //     canvas.contextTop.fillRect(ml.x, ml.y, 3, 3);\n      //     canvas.contextTop.fillRect(mr.x, mr.y, 3, 3);\n      //     canvas.contextTop.fillRect(mt.x, mt.y, 3, 3);\n      //     canvas.contextTop.fillRect(mtr.x, mtr.y, 3, 3);\n      //   }, 50);\n      // }\n\n      var coords = {\n        // corners\n        tl: tl, tr: tr, br: br, bl: bl,\n      };\n      if (!absolute) {\n        // middle\n        coords.ml = ml;\n        coords.mt = mt;\n        coords.mr = mr;\n        coords.mb = mb;\n        // rotating point\n        coords.mtr = mtr;\n      }\n      return coords;\n    },\n\n    /**\n     * Sets corner position coordinates based on current angle, width and height\n     * See https://github.com/kangax/fabric.js/wiki/When-to-call-setCoords\n     * @param {Boolean} [ignoreZoom] set oCoords with or without the viewport transform.\n     * @param {Boolean} [skipAbsolute] skip calculation of aCoords, usefull in setViewportTransform\n     * @return {fabric.Object} thisArg\n     * @chainable\n     */\n    setCoords: function(ignoreZoom, skipAbsolute) {\n      this.oCoords = this.calcCoords(ignoreZoom);\n      if (!skipAbsolute) {\n        this.aCoords = this.calcCoords(true);\n      }\n\n      // set coordinates of the draggable boxes in the corners used to scale/rotate the image\n      ignoreZoom || (this._setCornerCoords && this._setCornerCoords());\n\n      return this;\n    },\n\n    /**\n     * calculate rotation matrix of an object\n     * @return {Array} rotation matrix for the object\n     */\n    _calcRotateMatrix: function() {\n      if (this.angle) {\n        var theta = degreesToRadians(this.angle), cos = fabric.util.cos(theta), sin = fabric.util.sin(theta);\n        return [cos, sin, -sin, cos, 0, 0];\n      }\n      return fabric.iMatrix.concat();\n    },\n\n    /**\n     * calculate the translation matrix for an object transform\n     * @return {Array} rotation matrix for the object\n     */\n    _calcTranslateMatrix: function() {\n      var center = this.getCenterPoint();\n      return [1, 0, 0, 1, center.x, center.y];\n    },\n\n    transformMatrixKey: function(skipGroup) {\n      var sep = '_', prefix = '';\n      if (!skipGroup && this.group) {\n        prefix = this.group.transformMatrixKey(skipGroup) + sep;\n      };\n      return prefix + this.top + sep + this.left + sep + this.scaleX + sep + this.scaleY +\n        sep + this.skewX + sep + this.skewY + sep + this.angle + sep + this.originX + sep + this.originY +\n        sep + this.width + sep + this.height + sep + this.strokeWidth + this.flipX + this.flipY;\n    },\n\n    /**\n     * calculate trasform Matrix that represent current transformation from\n     * object properties.\n     * @param {Boolean} [skipGroup] return transformMatrix for object and not go upward with parents\n     * @return {Array} matrix Transform Matrix for the object\n     */\n    calcTransformMatrix: function(skipGroup) {\n      if (skipGroup) {\n        return this.calcOwnMatrix();\n      }\n      var key = this.transformMatrixKey(), cache = this.matrixCache || (this.matrixCache = {});\n      if (cache.key === key) {\n        return cache.value;\n      }\n      var matrix = this.calcOwnMatrix();\n      if (this.group) {\n        matrix = multiplyMatrices(this.group.calcTransformMatrix(), matrix);\n      }\n      cache.key = key;\n      cache.value = matrix;\n      return matrix;\n    },\n\n    calcOwnMatrix: function() {\n      var key = this.transformMatrixKey(true), cache = this.ownMatrixCache || (this.ownMatrixCache = {});\n      if (cache.key === key) {\n        return cache.value;\n      }\n      var matrix = this._calcTranslateMatrix(),\n          rotateMatrix,\n          dimensionMatrix = this._calcDimensionsTransformMatrix(this.skewX, this.skewY, true);\n      if (this.angle) {\n        rotateMatrix = this._calcRotateMatrix();\n        matrix = multiplyMatrices(matrix, rotateMatrix);\n      }\n      matrix = multiplyMatrices(matrix, dimensionMatrix);\n      cache.key = key;\n      cache.value = matrix;\n      return matrix;\n    },\n\n    _calcDimensionsTransformMatrix: function(skewX, skewY, flipping) {\n      var skewMatrix,\n          scaleX = this.scaleX * (flipping && this.flipX ? -1 : 1),\n          scaleY = this.scaleY * (flipping && this.flipY ? -1 : 1),\n          scaleMatrix = [scaleX, 0, 0, scaleY, 0, 0];\n      if (skewX) {\n        skewMatrix = [1, 0, Math.tan(degreesToRadians(skewX)), 1];\n        scaleMatrix = multiplyMatrices(scaleMatrix, skewMatrix, true);\n      }\n      if (skewY) {\n        skewMatrix = [1, Math.tan(degreesToRadians(skewY)), 0, 1];\n        scaleMatrix = multiplyMatrices(scaleMatrix, skewMatrix, true);\n      }\n      return scaleMatrix;\n    },\n\n\n    /*\n     * Calculate object dimensions from its properties\n     * @private\n     * @return {Object} .x width dimension\n     * @return {Object} .y height dimension\n     */\n    _getNonTransformedDimensions: function() {\n      var strokeWidth = this.strokeWidth,\n          w = this.width + strokeWidth,\n          h = this.height + strokeWidth;\n      return { x: w, y: h };\n    },\n\n    /*\n     * Calculate object bounding boxdimensions from its properties scale, skew.\n     * @private\n     * @return {Object} .x width dimension\n     * @return {Object} .y height dimension\n     */\n    _getTransformedDimensions: function(skewX, skewY) {\n      if (typeof skewX === 'undefined') {\n        skewX = this.skewX;\n      }\n      if (typeof skewY === 'undefined') {\n        skewY = this.skewY;\n      }\n      var dimensions = this._getNonTransformedDimensions();\n      if (skewX === 0 && skewY === 0) {\n        return { x: dimensions.x * this.scaleX, y: dimensions.y * this.scaleY };\n      }\n      var dimX = dimensions.x / 2, dimY = dimensions.y / 2,\n          points = [\n            {\n              x: -dimX,\n              y: -dimY\n            },\n            {\n              x: dimX,\n              y: -dimY\n            },\n            {\n              x: -dimX,\n              y: dimY\n            },\n            {\n              x: dimX,\n              y: dimY\n            }],\n          i, transformMatrix = this._calcDimensionsTransformMatrix(skewX, skewY, false),\n          bbox;\n      for (i = 0; i < points.length; i++) {\n        points[i] = fabric.util.transformPoint(points[i], transformMatrix);\n      }\n      bbox = fabric.util.makeBoundingBoxFromPoints(points);\n      return { x: bbox.width, y: bbox.height };\n    },\n\n    /*\n     * Calculate object dimensions for controls. include padding and canvas zoom\n     * private\n     */\n    _calculateCurrentDimensions: function()  {\n      var vpt = this.getViewportTransform(),\n          dim = this._getTransformedDimensions(),\n          p = fabric.util.transformPoint(dim, vpt, true);\n\n      return p.scalarAdd(2 * this.padding);\n    },\n  });\n})();\n\n\nfabric.util.object.extend(fabric.Object.prototype, /** @lends fabric.Object.prototype */ {\n\n  /**\n   * Moves an object to the bottom of the stack of drawn objects\n   * @return {fabric.Object} thisArg\n   * @chainable\n   */\n  sendToBack: function() {\n    if (this.group) {\n      fabric.StaticCanvas.prototype.sendToBack.call(this.group, this);\n    }\n    else {\n      this.canvas.sendToBack(this);\n    }\n    return this;\n  },\n\n  /**\n   * Moves an object to the top of the stack of drawn objects\n   * @return {fabric.Object} thisArg\n   * @chainable\n   */\n  bringToFront: function() {\n    if (this.group) {\n      fabric.StaticCanvas.prototype.bringToFront.call(this.group, this);\n    }\n    else {\n      this.canvas.bringToFront(this);\n    }\n    return this;\n  },\n\n  /**\n   * Moves an object down in stack of drawn objects\n   * @param {Boolean} [intersecting] If `true`, send object behind next lower intersecting object\n   * @return {fabric.Object} thisArg\n   * @chainable\n   */\n  sendBackwards: function(intersecting) {\n    if (this.group) {\n      fabric.StaticCanvas.prototype.sendBackwards.call(this.group, this, intersecting);\n    }\n    else {\n      this.canvas.sendBackwards(this, intersecting);\n    }\n    return this;\n  },\n\n  /**\n   * Moves an object up in stack of drawn objects\n   * @param {Boolean} [intersecting] If `true`, send object in front of next upper intersecting object\n   * @return {fabric.Object} thisArg\n   * @chainable\n   */\n  bringForward: function(intersecting) {\n    if (this.group) {\n      fabric.StaticCanvas.prototype.bringForward.call(this.group, this, intersecting);\n    }\n    else {\n      this.canvas.bringForward(this, intersecting);\n    }\n    return this;\n  },\n\n  /**\n   * Moves an object to specified level in stack of drawn objects\n   * @param {Number} index New position of object\n   * @return {fabric.Object} thisArg\n   * @chainable\n   */\n  moveTo: function(index) {\n    if (this.group && this.group.type !== 'activeSelection') {\n      fabric.StaticCanvas.prototype.moveTo.call(this.group, this, index);\n    }\n    else {\n      this.canvas.moveTo(this, index);\n    }\n    return this;\n  }\n});\n\n\n/* _TO_SVG_START_ */\n(function() {\n  function getSvgColorString(prop, value) {\n    if (!value) {\n      return prop + ': none; ';\n    }\n    else if (value.toLive) {\n      return prop + ': url(#SVGID_' + value.id + '); ';\n    }\n    else {\n      var color = new fabric.Color(value),\n          str = prop + ': ' + color.toRgb() + '; ',\n          opacity = color.getAlpha();\n      if (opacity !== 1) {\n        //change the color in rgb + opacity\n        str += prop + '-opacity: ' + opacity.toString() + '; ';\n      }\n      return str;\n    }\n  }\n\n  var toFixed = fabric.util.toFixed;\n\n  fabric.util.object.extend(fabric.Object.prototype, /** @lends fabric.Object.prototype */ {\n    /**\n     * Returns styles-string for svg-export\n     * @param {Boolean} skipShadow a boolean to skip shadow filter output\n     * @return {String}\n     */\n    getSvgStyles: function(skipShadow) {\n\n      var fillRule = this.fillRule,\n          strokeWidth = this.strokeWidth ? this.strokeWidth : '0',\n          strokeDashArray = this.strokeDashArray ? this.strokeDashArray.join(' ') : 'none',\n          strokeLineCap = this.strokeLineCap ? this.strokeLineCap : 'butt',\n          strokeLineJoin = this.strokeLineJoin ? this.strokeLineJoin : 'miter',\n          strokeMiterLimit = this.strokeMiterLimit ? this.strokeMiterLimit : '4',\n          opacity = typeof this.opacity !== 'undefined' ? this.opacity : '1',\n          visibility = this.visible ? '' : ' visibility: hidden;',\n          filter = skipShadow ? '' : this.getSvgFilter(),\n          fill = getSvgColorString('fill', this.fill),\n          stroke = getSvgColorString('stroke', this.stroke);\n\n      return [\n        stroke,\n        'stroke-width: ', strokeWidth, '; ',\n        'stroke-dasharray: ', strokeDashArray, '; ',\n        'stroke-linecap: ', strokeLineCap, '; ',\n        'stroke-linejoin: ', strokeLineJoin, '; ',\n        'stroke-miterlimit: ', strokeMiterLimit, '; ',\n        fill,\n        'fill-rule: ', fillRule, '; ',\n        'opacity: ', opacity, ';',\n        filter,\n        visibility\n      ].join('');\n    },\n\n    /**\n     * Returns styles-string for svg-export\n     * @param {Object} style the object from which to retrieve style properties\n     * @param {Boolean} useWhiteSpace a boolean to include an additional attribute in the style.\n     * @return {String}\n     */\n    getSvgSpanStyles: function(style, useWhiteSpace) {\n      var term = '; ';\n      var fontFamily = style.fontFamily ?\n        'font-family: ' + (((style.fontFamily.indexOf('\\'') === -1 && style.fontFamily.indexOf('\"') === -1) ?\n          '\\'' + style.fontFamily  + '\\'' : style.fontFamily)) + term : '';\n      var strokeWidth = style.strokeWidth ? 'stroke-width: ' + style.strokeWidth + term : '',\n          fontFamily = fontFamily,\n          fontSize = style.fontSize ? 'font-size: ' + style.fontSize + 'px' + term : '',\n          fontStyle = style.fontStyle ? 'font-style: ' + style.fontStyle + term : '',\n          fontWeight = style.fontWeight ? 'font-weight: ' + style.fontWeight + term : '',\n          fill = style.fill ? getSvgColorString('fill', style.fill) : '',\n          stroke = style.stroke ? getSvgColorString('stroke', style.stroke) : '',\n          textDecoration = this.getSvgTextDecoration(style),\n          deltaY = style.deltaY ? 'baseline-shift: ' + (-style.deltaY) + '; ' : '';\n      if (textDecoration) {\n        textDecoration = 'text-decoration: ' + textDecoration + term;\n      }\n\n      return [\n        stroke,\n        strokeWidth,\n        fontFamily,\n        fontSize,\n        fontStyle,\n        fontWeight,\n        textDecoration,\n        fill,\n        deltaY,\n        useWhiteSpace ? 'white-space: pre; ' : ''\n      ].join('');\n    },\n\n    /**\n     * Returns text-decoration property for svg-export\n     * @param {Object} style the object from which to retrieve style properties\n     * @return {String}\n     */\n    getSvgTextDecoration: function(style) {\n      if ('overline' in style || 'underline' in style || 'linethrough' in style) {\n        return (style.overline ? 'overline ' : '') +\n          (style.underline ? 'underline ' : '') + (style.linethrough ? 'line-through ' : '');\n      }\n      return '';\n    },\n\n    /**\n     * Returns filter for svg shadow\n     * @return {String}\n     */\n    getSvgFilter: function() {\n      return this.shadow ? 'filter: url(#SVGID_' + this.shadow.id + ');' : '';\n    },\n\n    /**\n     * Returns id attribute for svg output\n     * @return {String}\n     */\n    getSvgId: function() {\n      return this.id ? 'id=\"' + this.id + '\" ' : '';\n    },\n\n    /**\n     * Returns transform-string for svg-export\n     * @return {String}\n     */\n    getSvgTransform: function() {\n      var angle = this.angle,\n          skewX = (this.skewX % 360),\n          skewY = (this.skewY % 360),\n          center = this.getCenterPoint(),\n\n          NUM_FRACTION_DIGITS = fabric.Object.NUM_FRACTION_DIGITS,\n\n          translatePart = 'translate(' +\n                            toFixed(center.x, NUM_FRACTION_DIGITS) +\n                            ' ' +\n                            toFixed(center.y, NUM_FRACTION_DIGITS) +\n                          ')',\n\n          anglePart = angle !== 0\n            ? (' rotate(' + toFixed(angle, NUM_FRACTION_DIGITS) + ')')\n            : '',\n\n          scalePart = (this.scaleX === 1 && this.scaleY === 1)\n            ? '' :\n            (' scale(' +\n              toFixed(this.scaleX, NUM_FRACTION_DIGITS) +\n              ' ' +\n              toFixed(this.scaleY, NUM_FRACTION_DIGITS) +\n            ')'),\n\n          skewXPart = skewX !== 0 ? ' skewX(' + toFixed(skewX, NUM_FRACTION_DIGITS) + ')' : '',\n\n          skewYPart = skewY !== 0 ? ' skewY(' + toFixed(skewY, NUM_FRACTION_DIGITS) + ')' : '',\n\n          flipXPart = this.flipX ? ' matrix(-1 0 0 1 0 0) ' : '',\n\n          flipYPart = this.flipY ? ' matrix(1 0 0 -1 0 0)' : '';\n\n      return [\n        translatePart, anglePart, scalePart, flipXPart, flipYPart, skewXPart, skewYPart\n      ].join('');\n    },\n\n    /**\n     * Returns transform-string for svg-export from the transform matrix of single elements\n     * @return {String}\n     */\n    getSvgTransformMatrix: function() {\n      return this.transformMatrix ? ' matrix(' + this.transformMatrix.join(' ') + ') ' : '';\n    },\n\n    _setSVGBg: function(textBgRects) {\n      if (this.backgroundColor) {\n        var NUM_FRACTION_DIGITS = fabric.Object.NUM_FRACTION_DIGITS;\n        textBgRects.push(\n          '\\t\\t<rect ',\n          this._getFillAttributes(this.backgroundColor),\n          ' x=\"',\n          toFixed(-this.width / 2, NUM_FRACTION_DIGITS),\n          '\" y=\"',\n          toFixed(-this.height / 2, NUM_FRACTION_DIGITS),\n          '\" width=\"',\n          toFixed(this.width, NUM_FRACTION_DIGITS),\n          '\" height=\"',\n          toFixed(this.height, NUM_FRACTION_DIGITS),\n          '\"></rect>\\n');\n      }\n    },\n\n    /**\n     * @private\n     */\n    _createBaseSVGMarkup: function() {\n      var markup = [];\n\n      if (this.fill && this.fill.toLive) {\n        markup.push(this.fill.toSVG(this, false));\n      }\n      if (this.stroke && this.stroke.toLive) {\n        markup.push(this.stroke.toSVG(this, false));\n      }\n      if (this.shadow) {\n        markup.push(this.shadow.toSVG(this));\n      }\n      return markup;\n    },\n\n    addPaintOrder: function() {\n      return this.paintFirst !== 'fill' ? ' paint-order=\"' + this.paintFirst + '\" ' : '';\n    }\n  });\n})();\n/* _TO_SVG_END_ */\n\n\n(function() {\n\n  var extend = fabric.util.object.extend,\n      originalSet = 'stateProperties';\n\n  /*\n    Depends on `stateProperties`\n  */\n  function saveProps(origin, destination, props) {\n    var tmpObj = { }, deep = true;\n    props.forEach(function(prop) {\n      tmpObj[prop] = origin[prop];\n    });\n    extend(origin[destination], tmpObj, deep);\n  }\n\n  function _isEqual(origValue, currentValue, firstPass) {\n    if (origValue === currentValue) {\n      // if the objects are identical, return\n      return true;\n    }\n    else if (Array.isArray(origValue)) {\n      if (!Array.isArray(currentValue) || origValue.length !== currentValue.length) {\n        return false;\n      }\n      for (var i = 0, len = origValue.length; i < len; i++) {\n        if (!_isEqual(origValue[i], currentValue[i])) {\n          return false;\n        }\n      }\n      return true;\n    }\n    else if (origValue && typeof origValue === 'object') {\n      var keys = Object.keys(origValue), key;\n      if (!currentValue ||\n          typeof currentValue !== 'object' ||\n          (!firstPass && keys.length !== Object.keys(currentValue).length)\n      ) {\n        return false;\n      }\n      for (var i = 0, len = keys.length; i < len; i++) {\n        key = keys[i];\n        if (!_isEqual(origValue[key], currentValue[key])) {\n          return false;\n        }\n      }\n      return true;\n    }\n  }\n\n\n  fabric.util.object.extend(fabric.Object.prototype, /** @lends fabric.Object.prototype */ {\n\n    /**\n     * Returns true if object state (one of its state properties) was changed\n     * @param {String} [propertySet] optional name for the set of property we want to save\n     * @return {Boolean} true if instance' state has changed since `{@link fabric.Object#saveState}` was called\n     */\n    hasStateChanged: function(propertySet) {\n      propertySet = propertySet || originalSet;\n      var dashedPropertySet = '_' + propertySet;\n      if (Object.keys(this[dashedPropertySet]).length < this[propertySet].length) {\n        return true;\n      }\n      return !_isEqual(this[dashedPropertySet], this, true);\n    },\n\n    /**\n     * Saves state of an object\n     * @param {Object} [options] Object with additional `stateProperties` array to include when saving state\n     * @return {fabric.Object} thisArg\n     */\n    saveState: function(options) {\n      var propertySet = options && options.propertySet || originalSet,\n          destination = '_' + propertySet;\n      if (!this[destination]) {\n        return this.setupState(options);\n      }\n      saveProps(this, destination, this[propertySet]);\n      if (options && options.stateProperties) {\n        saveProps(this, destination, options.stateProperties);\n      }\n      return this;\n    },\n\n    /**\n     * Setups state of an object\n     * @param {Object} [options] Object with additional `stateProperties` array to include when saving state\n     * @return {fabric.Object} thisArg\n     */\n    setupState: function(options) {\n      options = options || { };\n      var propertySet = options.propertySet || originalSet;\n      options.propertySet = propertySet;\n      this['_' + propertySet] = { };\n      this.saveState(options);\n      return this;\n    }\n  });\n})();\n\n\n(function() {\n\n  var degreesToRadians = fabric.util.degreesToRadians;\n\n  fabric.util.object.extend(fabric.Object.prototype, /** @lends fabric.Object.prototype */ {\n\n    /**\n     * The object interactivity controls.\n     * @private\n     */\n    _controlsVisibility: null,\n\n    /**\n     * Determines which corner has been clicked\n     * @private\n     * @param {Object} pointer The pointer indicating the mouse position\n     * @return {String|Boolean} corner code (tl, tr, bl, br, etc.), or false if nothing is found\n     */\n    _findTargetCorner: function(pointer) {\n      // objects in group, anykind, are not self modificable,\n      // must not return an hovered corner.\n      if (!this.hasControls || this.group || (!this.canvas || this.canvas._activeObject !== this)) {\n        return false;\n      }\n\n      var ex = pointer.x,\n          ey = pointer.y,\n          xPoints,\n          lines;\n      this.__corner = 0;\n      for (var i in this.oCoords) {\n\n        if (!this.isControlVisible(i)) {\n          continue;\n        }\n\n        if (i === 'mtr' && !this.hasRotatingPoint) {\n          continue;\n        }\n\n        if (this.get('lockUniScaling') &&\n           (i === 'mt' || i === 'mr' || i === 'mb' || i === 'ml')) {\n          continue;\n        }\n\n        lines = this._getImageLines(this.oCoords[i].corner);\n\n        // debugging\n\n        // canvas.contextTop.fillRect(lines.bottomline.d.x, lines.bottomline.d.y, 2, 2);\n        // canvas.contextTop.fillRect(lines.bottomline.o.x, lines.bottomline.o.y, 2, 2);\n\n        // canvas.contextTop.fillRect(lines.leftline.d.x, lines.leftline.d.y, 2, 2);\n        // canvas.contextTop.fillRect(lines.leftline.o.x, lines.leftline.o.y, 2, 2);\n\n        // canvas.contextTop.fillRect(lines.topline.d.x, lines.topline.d.y, 2, 2);\n        // canvas.contextTop.fillRect(lines.topline.o.x, lines.topline.o.y, 2, 2);\n\n        // canvas.contextTop.fillRect(lines.rightline.d.x, lines.rightline.d.y, 2, 2);\n        // canvas.contextTop.fillRect(lines.rightline.o.x, lines.rightline.o.y, 2, 2);\n\n        xPoints = this._findCrossPoints({ x: ex, y: ey }, lines);\n        if (xPoints !== 0 && xPoints % 2 === 1) {\n          this.__corner = i;\n          return i;\n        }\n      }\n      return false;\n    },\n\n    /**\n     * Sets the coordinates of the draggable boxes in the corners of\n     * the image used to scale/rotate it.\n     * @private\n     */\n    _setCornerCoords: function() {\n      var coords = this.oCoords,\n          newTheta = degreesToRadians(45 - this.angle),\n          /* Math.sqrt(2 * Math.pow(this.cornerSize, 2)) / 2, */\n          /* 0.707106 stands for sqrt(2)/2 */\n          cornerHypotenuse = this.cornerSize * 0.707106,\n          cosHalfOffset = cornerHypotenuse * fabric.util.cos(newTheta),\n          sinHalfOffset = cornerHypotenuse * fabric.util.sin(newTheta),\n          x, y;\n\n      for (var point in coords) {\n        x = coords[point].x;\n        y = coords[point].y;\n        coords[point].corner = {\n          tl: {\n            x: x - sinHalfOffset,\n            y: y - cosHalfOffset\n          },\n          tr: {\n            x: x + cosHalfOffset,\n            y: y - sinHalfOffset\n          },\n          bl: {\n            x: x - cosHalfOffset,\n            y: y + sinHalfOffset\n          },\n          br: {\n            x: x + sinHalfOffset,\n            y: y + cosHalfOffset\n          }\n        };\n      }\n    },\n\n    /**\n     * Draws a colored layer behind the object, inside its selection borders.\n     * Requires public options: padding, selectionBackgroundColor\n     * this function is called when the context is transformed\n     * has checks to be skipped when the object is on a staticCanvas\n     * @param {CanvasRenderingContext2D} ctx Context to draw on\n     * @return {fabric.Object} thisArg\n     * @chainable\n     */\n    drawSelectionBackground: function(ctx) {\n      if (!this.selectionBackgroundColor ||\n        (this.canvas && !this.canvas.interactive) ||\n        (this.canvas && this.canvas._activeObject !== this)\n      ) {\n        return this;\n      }\n      ctx.save();\n      var center = this.getCenterPoint(), wh = this._calculateCurrentDimensions(),\n          vpt = this.canvas.viewportTransform;\n      ctx.translate(center.x, center.y);\n      ctx.scale(1 / vpt[0], 1 / vpt[3]);\n      ctx.rotate(degreesToRadians(this.angle));\n      ctx.fillStyle = this.selectionBackgroundColor;\n      ctx.fillRect(-wh.x / 2, -wh.y / 2, wh.x, wh.y);\n      ctx.restore();\n      return this;\n    },\n\n    /**\n     * Draws borders of an object's bounding box.\n     * Requires public properties: width, height\n     * Requires public options: padding, borderColor\n     * @param {CanvasRenderingContext2D} ctx Context to draw on\n     * @param {Object} styleOverride object to override the object style\n     * @return {fabric.Object} thisArg\n     * @chainable\n     */\n    drawBorders: function(ctx, styleOverride) {\n      styleOverride = styleOverride || {};\n      var wh = this._calculateCurrentDimensions(),\n          strokeWidth = 1 / this.borderScaleFactor,\n          width = wh.x + strokeWidth,\n          height = wh.y + strokeWidth,\n          drawRotatingPoint = typeof styleOverride.hasRotatingPoint !== 'undefined' ?\n            styleOverride.hasRotatingPoint : this.hasRotatingPoint,\n          hasControls = typeof styleOverride.hasControls !== 'undefined' ?\n            styleOverride.hasControls : this.hasControls,\n          rotatingPointOffset = typeof styleOverride.rotatingPointOffset !== 'undefined' ?\n            styleOverride.rotatingPointOffset : this.rotatingPointOffset;\n\n      ctx.save();\n      ctx.strokeStyle = styleOverride.borderColor || this.borderColor;\n      this._setLineDash(ctx, styleOverride.borderDashArray || this.borderDashArray, null);\n\n      ctx.strokeRect(\n        -width / 2,\n        -height / 2,\n        width,\n        height\n      );\n\n      if (drawRotatingPoint && this.isControlVisible('mtr') && hasControls) {\n\n        var rotateHeight = -height / 2;\n\n        ctx.beginPath();\n        ctx.moveTo(0, rotateHeight);\n        ctx.lineTo(0, rotateHeight - rotatingPointOffset);\n        ctx.stroke();\n      }\n\n      ctx.restore();\n      return this;\n    },\n\n    /**\n     * Draws borders of an object's bounding box when it is inside a group.\n     * Requires public properties: width, height\n     * Requires public options: padding, borderColor\n     * @param {CanvasRenderingContext2D} ctx Context to draw on\n     * @param {object} options object representing current object parameters\n     * @param {Object} styleOverride object to override the object style\n     * @return {fabric.Object} thisArg\n     * @chainable\n     */\n    drawBordersInGroup: function(ctx, options, styleOverride) {\n      styleOverride = styleOverride || {};\n      var p = this._getNonTransformedDimensions(),\n          matrix = fabric.util.customTransformMatrix(options.scaleX, options.scaleY, options.skewX),\n          wh = fabric.util.transformPoint(p, matrix),\n          strokeWidth = 1 / this.borderScaleFactor,\n          width = wh.x + strokeWidth,\n          height = wh.y + strokeWidth;\n\n      ctx.save();\n      this._setLineDash(ctx, styleOverride.borderDashArray || this.borderDashArray, null);\n      ctx.strokeStyle = styleOverride.borderColor || this.borderColor;\n\n      ctx.strokeRect(\n        -width / 2,\n        -height / 2,\n        width,\n        height\n      );\n\n      ctx.restore();\n      return this;\n    },\n\n    /**\n     * Draws corners of an object's bounding box.\n     * Requires public properties: width, height\n     * Requires public options: cornerSize, padding\n     * @param {CanvasRenderingContext2D} ctx Context to draw on\n     * @param {Object} styleOverride object to override the object style\n     * @return {fabric.Object} thisArg\n     * @chainable\n     */\n    drawControls: function(ctx, styleOverride) {\n      styleOverride = styleOverride || {};\n      var wh = this._calculateCurrentDimensions(),\n          width = wh.x,\n          height = wh.y,\n          scaleOffset = styleOverride.cornerSize || this.cornerSize,\n          left = -(width + scaleOffset) / 2,\n          top = -(height + scaleOffset) / 2,\n          transparentCorners = typeof styleOverride.transparentCorners !== 'undefined' ?\n            styleOverride.transparentCorners : this.transparentCorners,\n          hasRotatingPoint = typeof styleOverride.hasRotatingPoint !== 'undefined' ?\n            styleOverride.hasRotatingPoint : this.hasRotatingPoint,\n          methodName = transparentCorners ? 'stroke' : 'fill';\n\n      ctx.save();\n      ctx.strokeStyle = ctx.fillStyle = styleOverride.cornerColor || this.cornerColor;\n      if (!this.transparentCorners) {\n        ctx.strokeStyle = styleOverride.cornerStrokeColor || this.cornerStrokeColor;\n      }\n      this._setLineDash(ctx, styleOverride.cornerDashArray || this.cornerDashArray, null);\n\n      // top-left\n      this._drawControl('tl', ctx, methodName,\n        left,\n        top, styleOverride);\n\n      // top-right\n      this._drawControl('tr', ctx, methodName,\n        left + width,\n        top, styleOverride);\n\n      // bottom-left\n      this._drawControl('bl', ctx, methodName,\n        left,\n        top + height, styleOverride);\n\n      // bottom-right\n      this._drawControl('br', ctx, methodName,\n        left + width,\n        top + height, styleOverride);\n\n      if (!this.get('lockUniScaling')) {\n\n        // middle-top\n        this._drawControl('mt', ctx, methodName,\n          left + width / 2,\n          top, styleOverride);\n\n        // middle-bottom\n        this._drawControl('mb', ctx, methodName,\n          left + width / 2,\n          top + height, styleOverride);\n\n        // middle-right\n        this._drawControl('mr', ctx, methodName,\n          left + width,\n          top + height / 2, styleOverride);\n\n        // middle-left\n        this._drawControl('ml', ctx, methodName,\n          left,\n          top + height / 2, styleOverride);\n      }\n\n      // middle-top-rotate\n      if (hasRotatingPoint) {\n        this._drawControl('mtr', ctx, methodName,\n          left + width / 2,\n          top - this.rotatingPointOffset, styleOverride);\n      }\n\n      ctx.restore();\n\n      return this;\n    },\n\n    /**\n     * @private\n     */\n    _drawControl: function(control, ctx, methodName, left, top, styleOverride) {\n      styleOverride = styleOverride || {};\n      if (!this.isControlVisible(control)) {\n        return;\n      }\n      var size = this.cornerSize, stroke = !this.transparentCorners && this.cornerStrokeColor;\n      switch (styleOverride.cornerStyle || this.cornerStyle) {\n        case 'circle':\n          ctx.beginPath();\n          ctx.arc(left + size / 2, top + size / 2, size / 2, 0, 2 * Math.PI, false);\n          ctx[methodName]();\n          if (stroke) {\n            ctx.stroke();\n          }\n          break;\n        default:\n          this.transparentCorners || ctx.clearRect(left, top, size, size);\n          ctx[methodName + 'Rect'](left, top, size, size);\n          if (stroke) {\n            ctx.strokeRect(left, top, size, size);\n          }\n      }\n    },\n\n    /**\n     * Returns true if the specified control is visible, false otherwise.\n     * @param {String} controlName The name of the control. Possible values are 'tl', 'tr', 'br', 'bl', 'ml', 'mt', 'mr', 'mb', 'mtr'.\n     * @returns {Boolean} true if the specified control is visible, false otherwise\n     */\n    isControlVisible: function(controlName) {\n      return this._getControlsVisibility()[controlName];\n    },\n\n    /**\n     * Sets the visibility of the specified control.\n     * @param {String} controlName The name of the control. Possible values are 'tl', 'tr', 'br', 'bl', 'ml', 'mt', 'mr', 'mb', 'mtr'.\n     * @param {Boolean} visible true to set the specified control visible, false otherwise\n     * @return {fabric.Object} thisArg\n     * @chainable\n     */\n    setControlVisible: function(controlName, visible) {\n      this._getControlsVisibility()[controlName] = visible;\n      return this;\n    },\n\n    /**\n     * Sets the visibility state of object controls.\n     * @param {Object} [options] Options object\n     * @param {Boolean} [options.bl] true to enable the bottom-left control, false to disable it\n     * @param {Boolean} [options.br] true to enable the bottom-right control, false to disable it\n     * @param {Boolean} [options.mb] true to enable the middle-bottom control, false to disable it\n     * @param {Boolean} [options.ml] true to enable the middle-left control, false to disable it\n     * @param {Boolean} [options.mr] true to enable the middle-right control, false to disable it\n     * @param {Boolean} [options.mt] true to enable the middle-top control, false to disable it\n     * @param {Boolean} [options.tl] true to enable the top-left control, false to disable it\n     * @param {Boolean} [options.tr] true to enable the top-right control, false to disable it\n     * @param {Boolean} [options.mtr] true to enable the middle-top-rotate control, false to disable it\n     * @return {fabric.Object} thisArg\n     * @chainable\n     */\n    setControlsVisibility: function(options) {\n      options || (options = { });\n\n      for (var p in options) {\n        this.setControlVisible(p, options[p]);\n      }\n      return this;\n    },\n\n    /**\n     * Returns the instance of the control visibility set for this object.\n     * @private\n     * @returns {Object}\n     */\n    _getControlsVisibility: function() {\n      if (!this._controlsVisibility) {\n        this._controlsVisibility = {\n          tl: true,\n          tr: true,\n          br: true,\n          bl: true,\n          ml: true,\n          mt: true,\n          mr: true,\n          mb: true,\n          mtr: true\n        };\n      }\n      return this._controlsVisibility;\n    },\n\n    /**\n     * This callback function is called every time _discardActiveObject or _setActiveObject\n     * try to to deselect this object. If the function returns true, the process is cancelled\n     * @param {Object} [options] options sent from the upper functions\n     * @param {Event} [options.e] event if the process is generated by an event\n     */\n    onDeselect: function() {\n      // implemented by sub-classes, as needed.\n    },\n\n\n    /**\n     * This callback function is called every time _discardActiveObject or _setActiveObject\n     * try to to select this object. If the function returns true, the process is cancelled\n     * @param {Object} [options] options sent from the upper functions\n     * @param {Event} [options.e] event if the process is generated by an event\n     */\n    onSelect: function() {\n      // implemented by sub-classes, as needed.\n    }\n  });\n})();\n\n\nfabric.util.object.extend(fabric.StaticCanvas.prototype, /** @lends fabric.StaticCanvas.prototype */ {\n\n  /**\n   * Animation duration (in ms) for fx* methods\n   * @type Number\n   * @default\n   */\n  FX_DURATION: 500,\n\n  /**\n   * Centers object horizontally with animation.\n   * @param {fabric.Object} object Object to center\n   * @param {Object} [callbacks] Callbacks object with optional \"onComplete\" and/or \"onChange\" properties\n   * @param {Function} [callbacks.onComplete] Invoked on completion\n   * @param {Function} [callbacks.onChange] Invoked on every step of animation\n   * @return {fabric.Canvas} thisArg\n   * @chainable\n   */\n  fxCenterObjectH: function (object, callbacks) {\n    callbacks = callbacks || { };\n\n    var empty = function() { },\n        onComplete = callbacks.onComplete || empty,\n        onChange = callbacks.onChange || empty,\n        _this = this;\n\n    fabric.util.animate({\n      startValue: object.left,\n      endValue: this.getCenter().left,\n      duration: this.FX_DURATION,\n      onChange: function(value) {\n        object.set('left', value);\n        _this.requestRenderAll();\n        onChange();\n      },\n      onComplete: function() {\n        object.setCoords();\n        onComplete();\n      }\n    });\n\n    return this;\n  },\n\n  /**\n   * Centers object vertically with animation.\n   * @param {fabric.Object} object Object to center\n   * @param {Object} [callbacks] Callbacks object with optional \"onComplete\" and/or \"onChange\" properties\n   * @param {Function} [callbacks.onComplete] Invoked on completion\n   * @param {Function} [callbacks.onChange] Invoked on every step of animation\n   * @return {fabric.Canvas} thisArg\n   * @chainable\n   */\n  fxCenterObjectV: function (object, callbacks) {\n    callbacks = callbacks || { };\n\n    var empty = function() { },\n        onComplete = callbacks.onComplete || empty,\n        onChange = callbacks.onChange || empty,\n        _this = this;\n\n    fabric.util.animate({\n      startValue: object.top,\n      endValue: this.getCenter().top,\n      duration: this.FX_DURATION,\n      onChange: function(value) {\n        object.set('top', value);\n        _this.requestRenderAll();\n        onChange();\n      },\n      onComplete: function() {\n        object.setCoords();\n        onComplete();\n      }\n    });\n\n    return this;\n  },\n\n  /**\n   * Same as `fabric.Canvas#remove` but animated\n   * @param {fabric.Object} object Object to remove\n   * @param {Object} [callbacks] Callbacks object with optional \"onComplete\" and/or \"onChange\" properties\n   * @param {Function} [callbacks.onComplete] Invoked on completion\n   * @param {Function} [callbacks.onChange] Invoked on every step of animation\n   * @return {fabric.Canvas} thisArg\n   * @chainable\n   */\n  fxRemove: function (object, callbacks) {\n    callbacks = callbacks || { };\n\n    var empty = function() { },\n        onComplete = callbacks.onComplete || empty,\n        onChange = callbacks.onChange || empty,\n        _this = this;\n\n    fabric.util.animate({\n      startValue: object.opacity,\n      endValue: 0,\n      duration: this.FX_DURATION,\n      onChange: function(value) {\n        object.set('opacity', value);\n        _this.requestRenderAll();\n        onChange();\n      },\n      onComplete: function () {\n        _this.remove(object);\n        onComplete();\n      }\n    });\n\n    return this;\n  }\n});\n\nfabric.util.object.extend(fabric.Object.prototype, /** @lends fabric.Object.prototype */ {\n  /**\n   * Animates object's properties\n   * @param {String|Object} property Property to animate (if string) or properties to animate (if object)\n   * @param {Number|Object} value Value to animate property to (if string was given first) or options object\n   * @return {fabric.Object} thisArg\n   * @tutorial {@link http://fabricjs.com/fabric-intro-part-2#animation}\n   * @chainable\n   *\n   * As object — multiple properties\n   *\n   * object.animate({ left: ..., top: ... });\n   * object.animate({ left: ..., top: ... }, { duration: ... });\n   *\n   * As string — one property\n   *\n   * object.animate('left', ...);\n   * object.animate('left', { duration: ... });\n   *\n   */\n  animate: function() {\n    if (arguments[0] && typeof arguments[0] === 'object') {\n      var propsToAnimate = [], prop, skipCallbacks;\n      for (prop in arguments[0]) {\n        propsToAnimate.push(prop);\n      }\n      for (var i = 0, len = propsToAnimate.length; i < len; i++) {\n        prop = propsToAnimate[i];\n        skipCallbacks = i !== len - 1;\n        this._animate(prop, arguments[0][prop], arguments[1], skipCallbacks);\n      }\n    }\n    else {\n      this._animate.apply(this, arguments);\n    }\n    return this;\n  },\n\n  /**\n   * @private\n   * @param {String} property Property to animate\n   * @param {String} to Value to animate to\n   * @param {Object} [options] Options object\n   * @param {Boolean} [skipCallbacks] When true, callbacks like onchange and oncomplete are not invoked\n   */\n  _animate: function(property, to, options, skipCallbacks) {\n    var _this = this, propPair;\n\n    to = to.toString();\n\n    if (!options) {\n      options = { };\n    }\n    else {\n      options = fabric.util.object.clone(options);\n    }\n\n    if (~property.indexOf('.')) {\n      propPair = property.split('.');\n    }\n\n    var currentValue = propPair\n      ? this.get(propPair[0])[propPair[1]]\n      : this.get(property);\n\n    if (!('from' in options)) {\n      options.from = currentValue;\n    }\n\n    if (~to.indexOf('=')) {\n      to = currentValue + parseFloat(to.replace('=', ''));\n    }\n    else {\n      to = parseFloat(to);\n    }\n\n    fabric.util.animate({\n      startValue: options.from,\n      endValue: to,\n      byValue: options.by,\n      easing: options.easing,\n      duration: options.duration,\n      abort: options.abort && function() {\n        return options.abort.call(_this);\n      },\n      onChange: function(value, valueProgress, timeProgress) {\n        if (propPair) {\n          _this[propPair[0]][propPair[1]] = value;\n        }\n        else {\n          _this.set(property, value);\n        }\n        if (skipCallbacks) {\n          return;\n        }\n        options.onChange && options.onChange(value, valueProgress, timeProgress);\n      },\n      onComplete: function(value, valueProgress, timeProgress) {\n        if (skipCallbacks) {\n          return;\n        }\n\n        _this.setCoords();\n        options.onComplete && options.onComplete(value, valueProgress, timeProgress);\n      }\n    });\n  }\n});\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric = global.fabric || (global.fabric = { }),\n      extend = fabric.util.object.extend,\n      clone = fabric.util.object.clone,\n      coordProps = { x1: 1, x2: 1, y1: 1, y2: 1 },\n      supportsLineDash = fabric.StaticCanvas.supports('setLineDash');\n\n  if (fabric.Line) {\n    fabric.warn('fabric.Line is already defined');\n    return;\n  }\n\n  /**\n   * Line class\n   * @class fabric.Line\n   * @extends fabric.Object\n   * @see {@link fabric.Line#initialize} for constructor definition\n   */\n  fabric.Line = fabric.util.createClass(fabric.Object, /** @lends fabric.Line.prototype */ {\n\n    /**\n     * Type of an object\n     * @type String\n     * @default\n     */\n    type: 'line',\n\n    /**\n     * x value or first line edge\n     * @type Number\n     * @default\n     */\n    x1: 0,\n\n    /**\n     * y value or first line edge\n     * @type Number\n     * @default\n     */\n    y1: 0,\n\n    /**\n     * x value or second line edge\n     * @type Number\n     * @default\n     */\n    x2: 0,\n\n    /**\n     * y value or second line edge\n     * @type Number\n     * @default\n     */\n    y2: 0,\n\n    cacheProperties: fabric.Object.prototype.cacheProperties.concat('x1', 'x2', 'y1', 'y2'),\n\n    /**\n     * Constructor\n     * @param {Array} [points] Array of points\n     * @param {Object} [options] Options object\n     * @return {fabric.Line} thisArg\n     */\n    initialize: function(points, options) {\n      if (!points) {\n        points = [0, 0, 0, 0];\n      }\n\n      this.callSuper('initialize', options);\n\n      this.set('x1', points[0]);\n      this.set('y1', points[1]);\n      this.set('x2', points[2]);\n      this.set('y2', points[3]);\n\n      this._setWidthHeight(options);\n    },\n\n    /**\n     * @private\n     * @param {Object} [options] Options\n     */\n    _setWidthHeight: function(options) {\n      options || (options = { });\n\n      this.width = Math.abs(this.x2 - this.x1);\n      this.height = Math.abs(this.y2 - this.y1);\n\n      this.left = 'left' in options\n        ? options.left\n        : this._getLeftToOriginX();\n\n      this.top = 'top' in options\n        ? options.top\n        : this._getTopToOriginY();\n    },\n\n    /**\n     * @private\n     * @param {String} key\n     * @param {*} value\n     */\n    _set: function(key, value) {\n      this.callSuper('_set', key, value);\n      if (typeof coordProps[key] !== 'undefined') {\n        this._setWidthHeight();\n      }\n      return this;\n    },\n\n    /**\n     * @private\n     * @return {Number} leftToOriginX Distance from left edge of canvas to originX of Line.\n     */\n    _getLeftToOriginX: makeEdgeToOriginGetter(\n      { // property names\n        origin: 'originX',\n        axis1: 'x1',\n        axis2: 'x2',\n        dimension: 'width'\n      },\n      { // possible values of origin\n        nearest: 'left',\n        center: 'center',\n        farthest: 'right'\n      }\n    ),\n\n    /**\n     * @private\n     * @return {Number} topToOriginY Distance from top edge of canvas to originY of Line.\n     */\n    _getTopToOriginY: makeEdgeToOriginGetter(\n      { // property names\n        origin: 'originY',\n        axis1: 'y1',\n        axis2: 'y2',\n        dimension: 'height'\n      },\n      { // possible values of origin\n        nearest: 'top',\n        center: 'center',\n        farthest: 'bottom'\n      }\n    ),\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    _render: function(ctx) {\n      ctx.beginPath();\n\n      if (!this.strokeDashArray || this.strokeDashArray && supportsLineDash) {\n        // move from center (of virtual box) to its left/top corner\n        // we can't assume x1, y1 is top left and x2, y2 is bottom right\n        var p = this.calcLinePoints();\n        ctx.moveTo(p.x1, p.y1);\n        ctx.lineTo(p.x2, p.y2);\n      }\n\n      ctx.lineWidth = this.strokeWidth;\n\n      // TODO: test this\n      // make sure setting \"fill\" changes color of a line\n      // (by copying fillStyle to strokeStyle, since line is stroked, not filled)\n      var origStrokeStyle = ctx.strokeStyle;\n      ctx.strokeStyle = this.stroke || ctx.fillStyle;\n      this.stroke && this._renderStroke(ctx);\n      ctx.strokeStyle = origStrokeStyle;\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    _renderDashedStroke: function(ctx) {\n      var p = this.calcLinePoints();\n\n      ctx.beginPath();\n      fabric.util.drawDashedLine(ctx, p.x1, p.y1, p.x2, p.y2, this.strokeDashArray);\n      ctx.closePath();\n    },\n\n    /**\n     * This function is an helper for svg import. it returns the center of the object in the svg\n     * untransformed coordinates\n     * @private\n     * @return {Object} center point from element coordinates\n     */\n    _findCenterFromElement: function() {\n      return {\n        x: (this.x1 + this.x2) / 2,\n        y: (this.y1 + this.y2) / 2,\n      };\n    },\n\n    /**\n     * Returns object representation of an instance\n     * @methd toObject\n     * @param {Array} [propertiesToInclude] Any properties that you might want to additionally include in the output\n     * @return {Object} object representation of an instance\n     */\n    toObject: function(propertiesToInclude) {\n      return extend(this.callSuper('toObject', propertiesToInclude), this.calcLinePoints());\n    },\n\n    /*\n     * Calculate object dimensions from its properties\n     * @private\n     */\n    _getNonTransformedDimensions: function() {\n      var dim = this.callSuper('_getNonTransformedDimensions');\n      if (this.strokeLineCap === 'butt') {\n        if (this.width === 0) {\n          dim.y -= this.strokeWidth;\n        }\n        if (this.height === 0) {\n          dim.x -= this.strokeWidth;\n        }\n      }\n      return dim;\n    },\n\n    /**\n     * Recalculates line points given width and height\n     * @private\n     */\n    calcLinePoints: function() {\n      var xMult = this.x1 <= this.x2 ? -1 : 1,\n          yMult = this.y1 <= this.y2 ? -1 : 1,\n          x1 = (xMult * this.width * 0.5),\n          y1 = (yMult * this.height * 0.5),\n          x2 = (xMult * this.width * -0.5),\n          y2 = (yMult * this.height * -0.5);\n\n      return {\n        x1: x1,\n        x2: x2,\n        y1: y1,\n        y2: y2\n      };\n    },\n\n    /* _TO_SVG_START_ */\n    /**\n     * Returns SVG representation of an instance\n     * @param {Function} [reviver] Method for further parsing of svg representation.\n     * @return {String} svg representation of an instance\n     */\n    toSVG: function(reviver) {\n      var markup = this._createBaseSVGMarkup(),\n          p = this.calcLinePoints();\n      markup.push(\n        '<line ', this.getSvgId(),\n        'x1=\"', p.x1,\n        '\" y1=\"', p.y1,\n        '\" x2=\"', p.x2,\n        '\" y2=\"', p.y2,\n        '\" style=\"', this.getSvgStyles(),\n        '\" transform=\"', this.getSvgTransform(),\n        this.getSvgTransformMatrix(),\n        '\"/>\\n'\n      );\n\n      return reviver ? reviver(markup.join('')) : markup.join('');\n    },\n    /* _TO_SVG_END_ */\n  });\n\n  /* _FROM_SVG_START_ */\n  /**\n   * List of attribute names to account for when parsing SVG element (used by {@link fabric.Line.fromElement})\n   * @static\n   * @memberOf fabric.Line\n   * @see http://www.w3.org/TR/SVG/shapes.html#LineElement\n   */\n  fabric.Line.ATTRIBUTE_NAMES = fabric.SHARED_ATTRIBUTES.concat('x1 y1 x2 y2'.split(' '));\n\n  /**\n   * Returns fabric.Line instance from an SVG element\n   * @static\n   * @memberOf fabric.Line\n   * @param {SVGElement} element Element to parse\n   * @param {Object} [options] Options object\n   * @param {Function} [callback] callback function invoked after parsing\n   */\n  fabric.Line.fromElement = function(element, callback, options) {\n    options = options || { };\n    var parsedAttributes = fabric.parseAttributes(element, fabric.Line.ATTRIBUTE_NAMES),\n        points = [\n          parsedAttributes.x1 || 0,\n          parsedAttributes.y1 || 0,\n          parsedAttributes.x2 || 0,\n          parsedAttributes.y2 || 0\n        ];\n    callback(new fabric.Line(points, extend(parsedAttributes, options)));\n  };\n  /* _FROM_SVG_END_ */\n\n  /**\n   * Returns fabric.Line instance from an object representation\n   * @static\n   * @memberOf fabric.Line\n   * @param {Object} object Object to create an instance from\n   * @param {function} [callback] invoked with new instance as first argument\n   */\n  fabric.Line.fromObject = function(object, callback) {\n    function _callback(instance) {\n      delete instance.points;\n      callback && callback(instance);\n    };\n    var options = clone(object, true);\n    options.points = [object.x1, object.y1, object.x2, object.y2];\n    fabric.Object._fromObject('Line', options, _callback, 'points');\n  };\n\n  /**\n   * Produces a function that calculates distance from canvas edge to Line origin.\n   */\n  function makeEdgeToOriginGetter(propertyNames, originValues) {\n    var origin = propertyNames.origin,\n        axis1 = propertyNames.axis1,\n        axis2 = propertyNames.axis2,\n        dimension = propertyNames.dimension,\n        nearest = originValues.nearest,\n        center = originValues.center,\n        farthest = originValues.farthest;\n\n    return function() {\n      switch (this.get(origin)) {\n        case nearest:\n          return Math.min(this.get(axis1), this.get(axis2));\n        case center:\n          return Math.min(this.get(axis1), this.get(axis2)) + (0.5 * this.get(dimension));\n        case farthest:\n          return Math.max(this.get(axis1), this.get(axis2));\n      }\n    };\n\n  }\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric = global.fabric || (global.fabric = { }),\n      pi = Math.PI;\n\n  if (fabric.Circle) {\n    fabric.warn('fabric.Circle is already defined.');\n    return;\n  }\n\n  /**\n   * Circle class\n   * @class fabric.Circle\n   * @extends fabric.Object\n   * @see {@link fabric.Circle#initialize} for constructor definition\n   */\n  fabric.Circle = fabric.util.createClass(fabric.Object, /** @lends fabric.Circle.prototype */ {\n\n    /**\n     * Type of an object\n     * @type String\n     * @default\n     */\n    type: 'circle',\n\n    /**\n     * Radius of this circle\n     * @type Number\n     * @default\n     */\n    radius: 0,\n\n    /**\n     * Start angle of the circle, moving clockwise\n     * deprectated type, this should be in degree, this was an oversight.\n     * probably will change to degrees in next major version\n     * @type Number\n     * @default 0\n     */\n    startAngle: 0,\n\n    /**\n     * End angle of the circle\n     * deprectated type, this should be in degree, this was an oversight.\n     * probably will change to degrees in next major version\n     * @type Number\n     * @default 2Pi\n     */\n    endAngle: pi * 2,\n\n    cacheProperties: fabric.Object.prototype.cacheProperties.concat('radius', 'startAngle', 'endAngle'),\n\n    /**\n     * @private\n     * @param {String} key\n     * @param {*} value\n     * @return {fabric.Circle} thisArg\n     */\n    _set: function(key, value) {\n      this.callSuper('_set', key, value);\n\n      if (key === 'radius') {\n        this.setRadius(value);\n      }\n\n      return this;\n    },\n\n    /**\n     * Returns object representation of an instance\n     * @param {Array} [propertiesToInclude] Any properties that you might want to additionally include in the output\n     * @return {Object} object representation of an instance\n     */\n    toObject: function(propertiesToInclude) {\n      return this.callSuper('toObject', ['radius', 'startAngle', 'endAngle'].concat(propertiesToInclude));\n    },\n\n    /* _TO_SVG_START_ */\n    /**\n     * Returns svg representation of an instance\n     * @param {Function} [reviver] Method for further parsing of svg representation.\n     * @return {String} svg representation of an instance\n     */\n    toSVG: function(reviver) {\n      var markup = this._createBaseSVGMarkup(), x = 0, y = 0,\n          angle = (this.endAngle - this.startAngle) % ( 2 * pi);\n\n      if (angle === 0) {\n        markup.push(\n          '<circle ', this.getSvgId(),\n          'cx=\"' + x + '\" cy=\"' + y + '\" ',\n          'r=\"', this.radius,\n          '\" style=\"', this.getSvgStyles(),\n          '\" transform=\"', this.getSvgTransform(),\n          ' ', this.getSvgTransformMatrix(), '\"',\n          this.addPaintOrder(),\n          '/>\\n'\n        );\n      }\n      else {\n        var startX = fabric.util.cos(this.startAngle) * this.radius,\n            startY = fabric.util.sin(this.startAngle) * this.radius,\n            endX = fabric.util.cos(this.endAngle) * this.radius,\n            endY = fabric.util.sin(this.endAngle) * this.radius,\n            largeFlag = angle > pi ? '1' : '0';\n\n        markup.push(\n          '<path d=\"M ' + startX + ' ' + startY,\n          ' A ' + this.radius + ' ' + this.radius,\n          ' 0 ', +largeFlag + ' 1', ' ' + endX + ' ' + endY,\n          '\" style=\"', this.getSvgStyles(),\n          '\" transform=\"', this.getSvgTransform(),\n          ' ', this.getSvgTransformMatrix(), '\"',\n          this.addPaintOrder(),\n          '\"/>\\n'\n        );\n      }\n\n      return reviver ? reviver(markup.join('')) : markup.join('');\n    },\n    /* _TO_SVG_END_ */\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx context to render on\n     */\n    _render: function(ctx) {\n      ctx.beginPath();\n      ctx.arc(\n        0,\n        0,\n        this.radius,\n        this.startAngle,\n        this.endAngle, false);\n      this._renderPaintInOrder(ctx);\n    },\n\n    /**\n     * Returns horizontal radius of an object (according to how an object is scaled)\n     * @return {Number}\n     */\n    getRadiusX: function() {\n      return this.get('radius') * this.get('scaleX');\n    },\n\n    /**\n     * Returns vertical radius of an object (according to how an object is scaled)\n     * @return {Number}\n     */\n    getRadiusY: function() {\n      return this.get('radius') * this.get('scaleY');\n    },\n\n    /**\n     * Sets radius of an object (and updates width accordingly)\n     * @return {fabric.Circle} thisArg\n     */\n    setRadius: function(value) {\n      this.radius = value;\n      return this.set('width', value * 2).set('height', value * 2);\n    },\n  });\n\n  /* _FROM_SVG_START_ */\n  /**\n   * List of attribute names to account for when parsing SVG element (used by {@link fabric.Circle.fromElement})\n   * @static\n   * @memberOf fabric.Circle\n   * @see: http://www.w3.org/TR/SVG/shapes.html#CircleElement\n   */\n  fabric.Circle.ATTRIBUTE_NAMES = fabric.SHARED_ATTRIBUTES.concat('cx cy r'.split(' '));\n\n  /**\n   * Returns {@link fabric.Circle} instance from an SVG element\n   * @static\n   * @memberOf fabric.Circle\n   * @param {SVGElement} element Element to parse\n   * @param {Function} [callback] Options callback invoked after parsing is finished\n   * @param {Object} [options] Options object\n   * @throws {Error} If value of `r` attribute is missing or invalid\n   */\n  fabric.Circle.fromElement = function(element, callback) {\n    var parsedAttributes = fabric.parseAttributes(element, fabric.Circle.ATTRIBUTE_NAMES);\n\n    if (!isValidRadius(parsedAttributes)) {\n      throw new Error('value of `r` attribute is required and can not be negative');\n    }\n\n    parsedAttributes.left = (parsedAttributes.left || 0) - parsedAttributes.radius;\n    parsedAttributes.top = (parsedAttributes.top || 0) - parsedAttributes.radius;\n    callback(new fabric.Circle(parsedAttributes));\n  };\n\n  /**\n   * @private\n   */\n  function isValidRadius(attributes) {\n    return (('radius' in attributes) && (attributes.radius >= 0));\n  }\n  /* _FROM_SVG_END_ */\n\n  /**\n   * Returns {@link fabric.Circle} instance from an object representation\n   * @static\n   * @memberOf fabric.Circle\n   * @param {Object} object Object to create an instance from\n   * @param {function} [callback] invoked with new instance as first argument\n   * @return {Object} Instance of fabric.Circle\n   */\n  fabric.Circle.fromObject = function(object, callback) {\n    return fabric.Object._fromObject('Circle', object, callback);\n  };\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric = global.fabric || (global.fabric = { });\n\n  if (fabric.Triangle) {\n    fabric.warn('fabric.Triangle is already defined');\n    return;\n  }\n\n  /**\n   * Triangle class\n   * @class fabric.Triangle\n   * @extends fabric.Object\n   * @return {fabric.Triangle} thisArg\n   * @see {@link fabric.Triangle#initialize} for constructor definition\n   */\n  fabric.Triangle = fabric.util.createClass(fabric.Object, /** @lends fabric.Triangle.prototype */ {\n\n    /**\n     * Type of an object\n     * @type String\n     * @default\n     */\n    type: 'triangle',\n\n    /**\n     * Width is set to 100 to compensate the old initialize code that was setting it to 100\n     * @type Number\n     * @default\n     */\n    width: 100,\n\n    /**\n     * Height is set to 100 to compensate the old initialize code that was setting it to 100\n     * @type Number\n     * @default\n     */\n    height: 100,\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    _render: function(ctx) {\n      var widthBy2 = this.width / 2,\n          heightBy2 = this.height / 2;\n\n      ctx.beginPath();\n      ctx.moveTo(-widthBy2, heightBy2);\n      ctx.lineTo(0, -heightBy2);\n      ctx.lineTo(widthBy2, heightBy2);\n      ctx.closePath();\n\n      this._renderPaintInOrder(ctx);\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    _renderDashedStroke: function(ctx) {\n      var widthBy2 = this.width / 2,\n          heightBy2 = this.height / 2;\n\n      ctx.beginPath();\n      fabric.util.drawDashedLine(ctx, -widthBy2, heightBy2, 0, -heightBy2, this.strokeDashArray);\n      fabric.util.drawDashedLine(ctx, 0, -heightBy2, widthBy2, heightBy2, this.strokeDashArray);\n      fabric.util.drawDashedLine(ctx, widthBy2, heightBy2, -widthBy2, heightBy2, this.strokeDashArray);\n      ctx.closePath();\n    },\n\n    /* _TO_SVG_START_ */\n    /**\n     * Returns SVG representation of an instance\n     * @param {Function} [reviver] Method for further parsing of svg representation.\n     * @return {String} svg representation of an instance\n     */\n    toSVG: function(reviver) {\n      var markup = this._createBaseSVGMarkup(),\n          widthBy2 = this.width / 2,\n          heightBy2 = this.height / 2,\n          points = [\n            -widthBy2 + ' ' + heightBy2,\n            '0 ' + -heightBy2,\n            widthBy2 + ' ' + heightBy2\n          ]\n            .join(',');\n\n      markup.push(\n        '<polygon ', this.getSvgId(),\n        'points=\"', points,\n        '\" style=\"', this.getSvgStyles(),\n        '\" transform=\"', this.getSvgTransform(), '\"',\n        this.addPaintOrder(),\n        '/>'\n      );\n\n      return reviver ? reviver(markup.join('')) : markup.join('');\n    },\n    /* _TO_SVG_END_ */\n  });\n\n  /**\n   * Returns {@link fabric.Triangle} instance from an object representation\n   * @static\n   * @memberOf fabric.Triangle\n   * @param {Object} object Object to create an instance from\n   * @param {function} [callback] invoked with new instance as first argument\n   */\n  fabric.Triangle.fromObject = function(object, callback) {\n    return fabric.Object._fromObject('Triangle', object, callback);\n  };\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric = global.fabric || (global.fabric = { }),\n      piBy2   = Math.PI * 2;\n\n  if (fabric.Ellipse) {\n    fabric.warn('fabric.Ellipse is already defined.');\n    return;\n  }\n\n  /**\n   * Ellipse class\n   * @class fabric.Ellipse\n   * @extends fabric.Object\n   * @return {fabric.Ellipse} thisArg\n   * @see {@link fabric.Ellipse#initialize} for constructor definition\n   */\n  fabric.Ellipse = fabric.util.createClass(fabric.Object, /** @lends fabric.Ellipse.prototype */ {\n\n    /**\n     * Type of an object\n     * @type String\n     * @default\n     */\n    type: 'ellipse',\n\n    /**\n     * Horizontal radius\n     * @type Number\n     * @default\n     */\n    rx:   0,\n\n    /**\n     * Vertical radius\n     * @type Number\n     * @default\n     */\n    ry:   0,\n\n    cacheProperties: fabric.Object.prototype.cacheProperties.concat('rx', 'ry'),\n\n    /**\n     * Constructor\n     * @param {Object} [options] Options object\n     * @return {fabric.Ellipse} thisArg\n     */\n    initialize: function(options) {\n      this.callSuper('initialize', options);\n      this.set('rx', options && options.rx || 0);\n      this.set('ry', options && options.ry || 0);\n    },\n\n    /**\n     * @private\n     * @param {String} key\n     * @param {*} value\n     * @return {fabric.Ellipse} thisArg\n     */\n    _set: function(key, value) {\n      this.callSuper('_set', key, value);\n      switch (key) {\n\n        case 'rx':\n          this.rx = value;\n          this.set('width', value * 2);\n          break;\n\n        case 'ry':\n          this.ry = value;\n          this.set('height', value * 2);\n          break;\n\n      }\n      return this;\n    },\n\n    /**\n     * Returns horizontal radius of an object (according to how an object is scaled)\n     * @return {Number}\n     */\n    getRx: function() {\n      return this.get('rx') * this.get('scaleX');\n    },\n\n    /**\n     * Returns Vertical radius of an object (according to how an object is scaled)\n     * @return {Number}\n     */\n    getRy: function() {\n      return this.get('ry') * this.get('scaleY');\n    },\n\n    /**\n     * Returns object representation of an instance\n     * @param {Array} [propertiesToInclude] Any properties that you might want to additionally include in the output\n     * @return {Object} object representation of an instance\n     */\n    toObject: function(propertiesToInclude) {\n      return this.callSuper('toObject', ['rx', 'ry'].concat(propertiesToInclude));\n    },\n\n    /* _TO_SVG_START_ */\n    /**\n     * Returns svg representation of an instance\n     * @param {Function} [reviver] Method for further parsing of svg representation.\n     * @return {String} svg representation of an instance\n     */\n    toSVG: function(reviver) {\n      var markup = this._createBaseSVGMarkup();\n      markup.push(\n        '<ellipse ', this.getSvgId(),\n        'cx=\"0\" cy=\"0\" ',\n        'rx=\"', this.rx,\n        '\" ry=\"', this.ry,\n        '\" style=\"', this.getSvgStyles(),\n        '\" transform=\"', this.getSvgTransform(),\n        this.getSvgTransformMatrix(), '\"',\n        this.addPaintOrder(),\n        '/>\\n'\n      );\n\n      return reviver ? reviver(markup.join('')) : markup.join('');\n    },\n    /* _TO_SVG_END_ */\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx context to render on\n     */\n    _render: function(ctx) {\n      ctx.beginPath();\n      ctx.save();\n      ctx.transform(1, 0, 0, this.ry / this.rx, 0, 0);\n      ctx.arc(\n        0,\n        0,\n        this.rx,\n        0,\n        piBy2,\n        false);\n      ctx.restore();\n      this._renderPaintInOrder(ctx);\n    },\n  });\n\n  /* _FROM_SVG_START_ */\n  /**\n   * List of attribute names to account for when parsing SVG element (used by {@link fabric.Ellipse.fromElement})\n   * @static\n   * @memberOf fabric.Ellipse\n   * @see http://www.w3.org/TR/SVG/shapes.html#EllipseElement\n   */\n  fabric.Ellipse.ATTRIBUTE_NAMES = fabric.SHARED_ATTRIBUTES.concat('cx cy rx ry'.split(' '));\n\n  /**\n   * Returns {@link fabric.Ellipse} instance from an SVG element\n   * @static\n   * @memberOf fabric.Ellipse\n   * @param {SVGElement} element Element to parse\n   * @param {Function} [callback] Options callback invoked after parsing is finished\n   * @return {fabric.Ellipse}\n   */\n  fabric.Ellipse.fromElement = function(element, callback) {\n\n    var parsedAttributes = fabric.parseAttributes(element, fabric.Ellipse.ATTRIBUTE_NAMES);\n\n    parsedAttributes.left = (parsedAttributes.left || 0) - parsedAttributes.rx;\n    parsedAttributes.top = (parsedAttributes.top || 0) - parsedAttributes.ry;\n    callback(new fabric.Ellipse(parsedAttributes));\n  };\n  /* _FROM_SVG_END_ */\n\n  /**\n   * Returns {@link fabric.Ellipse} instance from an object representation\n   * @static\n   * @memberOf fabric.Ellipse\n   * @param {Object} object Object to create an instance from\n   * @param {function} [callback] invoked with new instance as first argument\n   * @return {fabric.Ellipse}\n   */\n  fabric.Ellipse.fromObject = function(object, callback) {\n    return fabric.Object._fromObject('Ellipse', object, callback);\n  };\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric = global.fabric || (global.fabric = { }),\n      extend = fabric.util.object.extend;\n\n  if (fabric.Rect) {\n    fabric.warn('fabric.Rect is already defined');\n    return;\n  }\n\n  /**\n   * Rectangle class\n   * @class fabric.Rect\n   * @extends fabric.Object\n   * @return {fabric.Rect} thisArg\n   * @see {@link fabric.Rect#initialize} for constructor definition\n   */\n  fabric.Rect = fabric.util.createClass(fabric.Object, /** @lends fabric.Rect.prototype */ {\n\n    /**\n     * List of properties to consider when checking if state of an object is changed ({@link fabric.Object#hasStateChanged})\n     * as well as for history (undo/redo) purposes\n     * @type Array\n     */\n    stateProperties: fabric.Object.prototype.stateProperties.concat('rx', 'ry'),\n\n    /**\n     * Type of an object\n     * @type String\n     * @default\n     */\n    type: 'rect',\n\n    /**\n     * Horizontal border radius\n     * @type Number\n     * @default\n     */\n    rx:   0,\n\n    /**\n     * Vertical border radius\n     * @type Number\n     * @default\n     */\n    ry:   0,\n\n    cacheProperties: fabric.Object.prototype.cacheProperties.concat('rx', 'ry'),\n\n    /**\n     * Constructor\n     * @param {Object} [options] Options object\n     * @return {Object} thisArg\n     */\n    initialize: function(options) {\n      this.callSuper('initialize', options);\n      this._initRxRy();\n    },\n\n    /**\n     * Initializes rx/ry attributes\n     * @private\n     */\n    _initRxRy: function() {\n      if (this.rx && !this.ry) {\n        this.ry = this.rx;\n      }\n      else if (this.ry && !this.rx) {\n        this.rx = this.ry;\n      }\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    _render: function(ctx) {\n\n      // optimize 1x1 case (used in spray brush)\n      if (this.width === 1 && this.height === 1) {\n        ctx.fillRect(-0.5, -0.5, 1, 1);\n        return;\n      }\n\n      var rx = this.rx ? Math.min(this.rx, this.width / 2) : 0,\n          ry = this.ry ? Math.min(this.ry, this.height / 2) : 0,\n          w = this.width,\n          h = this.height,\n          x = -this.width / 2,\n          y = -this.height / 2,\n          isRounded = rx !== 0 || ry !== 0,\n          /* \"magic number\" for bezier approximations of arcs (http://itc.ktu.lt/itc354/Riskus354.pdf) */\n          k = 1 - 0.5522847498;\n      ctx.beginPath();\n\n      ctx.moveTo(x + rx, y);\n\n      ctx.lineTo(x + w - rx, y);\n      isRounded && ctx.bezierCurveTo(x + w - k * rx, y, x + w, y + k * ry, x + w, y + ry);\n\n      ctx.lineTo(x + w, y + h - ry);\n      isRounded && ctx.bezierCurveTo(x + w, y + h - k * ry, x + w - k * rx, y + h, x + w - rx, y + h);\n\n      ctx.lineTo(x + rx, y + h);\n      isRounded && ctx.bezierCurveTo(x + k * rx, y + h, x, y + h - k * ry, x, y + h - ry);\n\n      ctx.lineTo(x, y + ry);\n      isRounded && ctx.bezierCurveTo(x, y + k * ry, x + k * rx, y, x + rx, y);\n\n      ctx.closePath();\n\n      this._renderPaintInOrder(ctx);\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    _renderDashedStroke: function(ctx) {\n      var x = -this.width / 2,\n          y = -this.height / 2,\n          w = this.width,\n          h = this.height;\n\n      ctx.beginPath();\n      fabric.util.drawDashedLine(ctx, x, y, x + w, y, this.strokeDashArray);\n      fabric.util.drawDashedLine(ctx, x + w, y, x + w, y + h, this.strokeDashArray);\n      fabric.util.drawDashedLine(ctx, x + w, y + h, x, y + h, this.strokeDashArray);\n      fabric.util.drawDashedLine(ctx, x, y + h, x, y, this.strokeDashArray);\n      ctx.closePath();\n    },\n\n    /**\n     * Returns object representation of an instance\n     * @param {Array} [propertiesToInclude] Any properties that you might want to additionally include in the output\n     * @return {Object} object representation of an instance\n     */\n    toObject: function(propertiesToInclude) {\n      return this.callSuper('toObject', ['rx', 'ry'].concat(propertiesToInclude));\n    },\n\n    /* _TO_SVG_START_ */\n    /**\n     * Returns svg representation of an instance\n     * @param {Function} [reviver] Method for further parsing of svg representation.\n     * @return {String} svg representation of an instance\n     */\n    toSVG: function(reviver) {\n      var markup = this._createBaseSVGMarkup(), x = -this.width / 2, y = -this.height / 2;\n      markup.push(\n        '<rect ', this.getSvgId(),\n        'x=\"', x, '\" y=\"', y,\n        '\" rx=\"', this.get('rx'), '\" ry=\"', this.get('ry'),\n        '\" width=\"', this.width, '\" height=\"', this.height,\n        '\" style=\"', this.getSvgStyles(),\n        '\" transform=\"', this.getSvgTransform(),\n        this.getSvgTransformMatrix(), '\"',\n        this.addPaintOrder(),\n        '/>\\n');\n\n      return reviver ? reviver(markup.join('')) : markup.join('');\n    },\n    /* _TO_SVG_END_ */\n  });\n\n  /* _FROM_SVG_START_ */\n  /**\n   * List of attribute names to account for when parsing SVG element (used by `fabric.Rect.fromElement`)\n   * @static\n   * @memberOf fabric.Rect\n   * @see: http://www.w3.org/TR/SVG/shapes.html#RectElement\n   */\n  fabric.Rect.ATTRIBUTE_NAMES = fabric.SHARED_ATTRIBUTES.concat('x y rx ry width height'.split(' '));\n\n  /**\n   * Returns {@link fabric.Rect} instance from an SVG element\n   * @static\n   * @memberOf fabric.Rect\n   * @param {SVGElement} element Element to parse\n   * @param {Function} callback callback function invoked after parsing\n   * @param {Object} [options] Options object\n   */\n  fabric.Rect.fromElement = function(element, callback, options) {\n    if (!element) {\n      return callback(null);\n    }\n    options = options || { };\n\n    var parsedAttributes = fabric.parseAttributes(element, fabric.Rect.ATTRIBUTE_NAMES);\n\n    parsedAttributes.left = parsedAttributes.left || 0;\n    parsedAttributes.top  = parsedAttributes.top  || 0;\n    var rect = new fabric.Rect(extend((options ? fabric.util.object.clone(options) : { }), parsedAttributes));\n    rect.visible = rect.visible && rect.width > 0 && rect.height > 0;\n    callback(rect);\n  };\n  /* _FROM_SVG_END_ */\n\n  /**\n   * Returns {@link fabric.Rect} instance from an object representation\n   * @static\n   * @memberOf fabric.Rect\n   * @param {Object} object Object to create an instance from\n   * @param {Function} [callback] Callback to invoke when an fabric.Rect instance is created\n   */\n  fabric.Rect.fromObject = function(object, callback) {\n    return fabric.Object._fromObject('Rect', object, callback);\n  };\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric = global.fabric || (global.fabric = { }),\n      extend = fabric.util.object.extend,\n      min = fabric.util.array.min,\n      max = fabric.util.array.max,\n      toFixed = fabric.util.toFixed;\n\n  if (fabric.Polyline) {\n    fabric.warn('fabric.Polyline is already defined');\n    return;\n  }\n\n  /**\n   * Polyline class\n   * @class fabric.Polyline\n   * @extends fabric.Object\n   * @see {@link fabric.Polyline#initialize} for constructor definition\n   */\n  fabric.Polyline = fabric.util.createClass(fabric.Object, /** @lends fabric.Polyline.prototype */ {\n\n    /**\n     * Type of an object\n     * @type String\n     * @default\n     */\n    type: 'polyline',\n\n    /**\n     * Points array\n     * @type Array\n     * @default\n     */\n    points: null,\n\n    cacheProperties: fabric.Object.prototype.cacheProperties.concat('points'),\n\n    /**\n     * Constructor\n     * @param {Array} points Array of points (where each point is an object with x and y)\n     * @param {Object} [options] Options object\n     * @return {fabric.Polyline} thisArg\n     * @example\n     * var poly = new fabric.Polyline([\n     *     { x: 10, y: 10 },\n     *     { x: 50, y: 30 },\n     *     { x: 40, y: 70 },\n     *     { x: 60, y: 50 },\n     *     { x: 100, y: 150 },\n     *     { x: 40, y: 100 }\n     *   ], {\n     *   stroke: 'red',\n     *   left: 100,\n     *   top: 100\n     * });\n     */\n    initialize: function(points, options) {\n      options = options || {};\n      this.points = points || [];\n      this.callSuper('initialize', options);\n      var calcDim = this._calcDimensions();\n      if (typeof options.left === 'undefined') {\n        this.left = calcDim.left;\n      }\n      if (typeof options.top === 'undefined') {\n        this.top = calcDim.top;\n      }\n      this.width = calcDim.width;\n      this.height = calcDim.height;\n      this.pathOffset = {\n        x: calcDim.left + this.width / 2,\n        y: calcDim.top + this.height / 2\n      };\n    },\n\n    /**\n     * Calculate the polygon min and max point from points array,\n     * returning an object with left, top, widht, height to measure the\n     * polygon size\n     * @return {Object} object.left X coordinate of the polygon leftmost point\n     * @return {Object} object.top Y coordinate of the polygon topmost point\n     * @return {Object} object.width distance between X coordinates of the polygon leftmost and rightmost point\n     * @return {Object} object.height distance between Y coordinates of the polygon topmost and bottommost point\n     * @private\n     */\n    _calcDimensions: function() {\n\n      var points = this.points,\n          minX = min(points, 'x') || 0,\n          minY = min(points, 'y') || 0,\n          maxX = max(points, 'x') || 0,\n          maxY = max(points, 'y') || 0,\n          width = (maxX - minX),\n          height = (maxY - minY);\n\n      return {\n        left: minX,\n        top: minY,\n        width: width,\n        height: height\n      };\n    },\n\n    /**\n     * Returns object representation of an instance\n     * @param {Array} [propertiesToInclude] Any properties that you might want to additionally include in the output\n     * @return {Object} Object representation of an instance\n     */\n    toObject: function(propertiesToInclude) {\n      return extend(this.callSuper('toObject', propertiesToInclude), {\n        points: this.points.concat()\n      });\n    },\n\n    /* _TO_SVG_START_ */\n    /**\n     * Returns svg representation of an instance\n     * @param {Function} [reviver] Method for further parsing of svg representation.\n     * @return {String} svg representation of an instance\n     */\n    toSVG: function(reviver) {\n      var points = [], diffX = this.pathOffset.x, diffY = this.pathOffset.y,\n          markup = this._createBaseSVGMarkup(),\n          NUM_FRACTION_DIGITS = fabric.Object.NUM_FRACTION_DIGITS;\n\n      for (var i = 0, len = this.points.length; i < len; i++) {\n        points.push(\n          toFixed(this.points[i].x - diffX, NUM_FRACTION_DIGITS), ',',\n          toFixed(this.points[i].y - diffY, NUM_FRACTION_DIGITS), ' '\n        );\n      }\n      markup.push(\n        '<', this.type, ' ', this.getSvgId(),\n        'points=\"', points.join(''),\n        '\" style=\"', this.getSvgStyles(),\n        '\" transform=\"', this.getSvgTransform(),\n        ' ', this.getSvgTransformMatrix(), '\"',\n        this.addPaintOrder(),\n        '/>\\n'\n      );\n\n      return reviver ? reviver(markup.join('')) : markup.join('');\n    },\n    /* _TO_SVG_END_ */\n\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    commonRender: function(ctx) {\n      var point, len = this.points.length,\n          x = this.pathOffset.x,\n          y = this.pathOffset.y;\n\n      if (!len || isNaN(this.points[len - 1].y)) {\n        // do not draw if no points or odd points\n        // NaN comes from parseFloat of a empty string in parser\n        return false;\n      }\n      ctx.beginPath();\n      ctx.moveTo(this.points[0].x - x, this.points[0].y - y);\n      for (var i = 0; i < len; i++) {\n        point = this.points[i];\n        ctx.lineTo(point.x - x, point.y - y);\n      }\n      return true;\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    _render: function(ctx) {\n      if (!this.commonRender(ctx)) {\n        return;\n      }\n      this._renderPaintInOrder(ctx);\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    _renderDashedStroke: function(ctx) {\n      var p1, p2;\n\n      ctx.beginPath();\n      for (var i = 0, len = this.points.length; i < len; i++) {\n        p1 = this.points[i];\n        p2 = this.points[i + 1] || p1;\n        fabric.util.drawDashedLine(ctx, p1.x, p1.y, p2.x, p2.y, this.strokeDashArray);\n      }\n    },\n\n    /**\n     * Returns complexity of an instance\n     * @return {Number} complexity of this instance\n     */\n    complexity: function() {\n      return this.get('points').length;\n    }\n  });\n\n  /* _FROM_SVG_START_ */\n  /**\n   * List of attribute names to account for when parsing SVG element (used by {@link fabric.Polyline.fromElement})\n   * @static\n   * @memberOf fabric.Polyline\n   * @see: http://www.w3.org/TR/SVG/shapes.html#PolylineElement\n   */\n  fabric.Polyline.ATTRIBUTE_NAMES = fabric.SHARED_ATTRIBUTES.concat();\n\n  /**\n   * Returns fabric.Polyline instance from an SVG element\n   * @static\n   * @memberOf fabric.Polyline\n   * @param {SVGElement} element Element to parser\n   * @param {Function} callback callback function invoked after parsing\n   * @param {Object} [options] Options object\n   */\n  fabric.Polyline.fromElement = function(element, callback, options) {\n    if (!element) {\n      return callback(null);\n    }\n    options || (options = { });\n\n    var points = fabric.parsePointsAttribute(element.getAttribute('points')),\n        parsedAttributes = fabric.parseAttributes(element, fabric.Polyline.ATTRIBUTE_NAMES);\n\n    callback(new fabric.Polyline(points, fabric.util.object.extend(parsedAttributes, options)));\n  };\n  /* _FROM_SVG_END_ */\n\n  /**\n   * Returns fabric.Polyline instance from an object representation\n   * @static\n   * @memberOf fabric.Polyline\n   * @param {Object} object Object to create an instance from\n   * @param {Function} [callback] Callback to invoke when an fabric.Path instance is created\n   */\n  fabric.Polyline.fromObject = function(object, callback) {\n    return fabric.Object._fromObject('Polyline', object, callback, 'points');\n  };\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric = global.fabric || (global.fabric = { }),\n      extend = fabric.util.object.extend;\n\n  if (fabric.Polygon) {\n    fabric.warn('fabric.Polygon is already defined');\n    return;\n  }\n\n  /**\n   * Polygon class\n   * @class fabric.Polygon\n   * @extends fabric.Polyline\n   * @see {@link fabric.Polygon#initialize} for constructor definition\n   */\n  fabric.Polygon = fabric.util.createClass(fabric.Polyline, /** @lends fabric.Polygon.prototype */ {\n\n    /**\n     * Type of an object\n     * @type String\n     * @default\n     */\n    type: 'polygon',\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    _render: function(ctx) {\n      if (!this.commonRender(ctx)) {\n        return;\n      }\n      ctx.closePath();\n      this._renderPaintInOrder(ctx);\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    _renderDashedStroke: function(ctx) {\n      this.callSuper('_renderDashedStroke', ctx);\n      ctx.closePath();\n    },\n  });\n\n  /* _FROM_SVG_START_ */\n  /**\n   * List of attribute names to account for when parsing SVG element (used by `fabric.Polygon.fromElement`)\n   * @static\n   * @memberOf fabric.Polygon\n   * @see: http://www.w3.org/TR/SVG/shapes.html#PolygonElement\n   */\n  fabric.Polygon.ATTRIBUTE_NAMES = fabric.SHARED_ATTRIBUTES.concat();\n\n  /**\n   * Returns {@link fabric.Polygon} instance from an SVG element\n   * @static\n   * @memberOf fabric.Polygon\n   * @param {SVGElement} element Element to parse\n   * @param {Function} callback callback function invoked after parsing\n   * @param {Object} [options] Options object\n   */\n  fabric.Polygon.fromElement = function(element, callback, options) {\n    if (!element) {\n      return callback(null);\n    }\n\n    options || (options = { });\n\n    var points = fabric.parsePointsAttribute(element.getAttribute('points')),\n        parsedAttributes = fabric.parseAttributes(element, fabric.Polygon.ATTRIBUTE_NAMES);\n\n    callback(new fabric.Polygon(points, extend(parsedAttributes, options)));\n  };\n  /* _FROM_SVG_END_ */\n\n  /**\n   * Returns fabric.Polygon instance from an object representation\n   * @static\n   * @memberOf fabric.Polygon\n   * @param {Object} object Object to create an instance from\n   * @param {Function} [callback] Callback to invoke when an fabric.Path instance is created\n   */\n  fabric.Polygon.fromObject = function(object, callback) {\n    return fabric.Object._fromObject('Polygon', object, callback, 'points');\n  };\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric = global.fabric || (global.fabric = { }),\n      min = fabric.util.array.min,\n      max = fabric.util.array.max,\n      extend = fabric.util.object.extend,\n      _toString = Object.prototype.toString,\n      drawArc = fabric.util.drawArc,\n      commandLengths = {\n        m: 2,\n        l: 2,\n        h: 1,\n        v: 1,\n        c: 6,\n        s: 4,\n        q: 4,\n        t: 2,\n        a: 7\n      },\n      repeatedCommands = {\n        m: 'l',\n        M: 'L'\n      };\n\n  if (fabric.Path) {\n    fabric.warn('fabric.Path is already defined');\n    return;\n  }\n\n  /**\n   * Path class\n   * @class fabric.Path\n   * @extends fabric.Object\n   * @tutorial {@link http://fabricjs.com/fabric-intro-part-1#path_and_pathgroup}\n   * @see {@link fabric.Path#initialize} for constructor definition\n   */\n  fabric.Path = fabric.util.createClass(fabric.Object, /** @lends fabric.Path.prototype */ {\n\n    /**\n     * Type of an object\n     * @type String\n     * @default\n     */\n    type: 'path',\n\n    /**\n     * Array of path points\n     * @type Array\n     * @default\n     */\n    path: null,\n\n    cacheProperties: fabric.Object.prototype.cacheProperties.concat('path', 'fillRule'),\n\n    stateProperties: fabric.Object.prototype.stateProperties.concat('path'),\n\n    /**\n     * Constructor\n     * @param {Array|String} path Path data (sequence of coordinates and corresponding \"command\" tokens)\n     * @param {Object} [options] Options object\n     * @return {fabric.Path} thisArg\n     */\n    initialize: function(path, options) {\n      options = options || { };\n      this.callSuper('initialize', options);\n\n      if (!path) {\n        path = [];\n      }\n\n      var fromArray = _toString.call(path) === '[object Array]';\n\n      this.path = fromArray\n        ? path\n        // one of commands (m,M,l,L,q,Q,c,C,etc.) followed by non-command characters (i.e. command values)\n        : path.match && path.match(/[mzlhvcsqta][^mzlhvcsqta]*/gi);\n\n      if (!this.path) {\n        return;\n      }\n\n      if (!fromArray) {\n        this.path = this._parsePath();\n      }\n\n      this._setPositionDimensions(options);\n    },\n\n    /**\n     * @private\n     * @param {Object} options Options object\n     */\n    _setPositionDimensions: function(options) {\n      var calcDim = this._parseDimensions();\n\n      this.width = calcDim.width;\n      this.height = calcDim.height;\n\n      if (typeof options.left === 'undefined') {\n        this.left = calcDim.left;\n      }\n\n      if (typeof options.top === 'undefined') {\n        this.top = calcDim.top;\n      }\n\n      this.pathOffset = this.pathOffset || {\n        x: calcDim.left + this.width / 2,\n        y: calcDim.top + this.height / 2\n      };\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx context to render path on\n     */\n    _renderPathCommands: function(ctx) {\n      var current, // current instruction\n          previous = null,\n          subpathStartX = 0,\n          subpathStartY = 0,\n          x = 0, // current x\n          y = 0, // current y\n          controlX = 0, // current control point x\n          controlY = 0, // current control point y\n          tempX,\n          tempY,\n          l = -this.pathOffset.x,\n          t = -this.pathOffset.y;\n\n      ctx.beginPath();\n\n      for (var i = 0, len = this.path.length; i < len; ++i) {\n\n        current = this.path[i];\n\n        switch (current[0]) { // first letter\n\n          case 'l': // lineto, relative\n            x += current[1];\n            y += current[2];\n            ctx.lineTo(x + l, y + t);\n            break;\n\n          case 'L': // lineto, absolute\n            x = current[1];\n            y = current[2];\n            ctx.lineTo(x + l, y + t);\n            break;\n\n          case 'h': // horizontal lineto, relative\n            x += current[1];\n            ctx.lineTo(x + l, y + t);\n            break;\n\n          case 'H': // horizontal lineto, absolute\n            x = current[1];\n            ctx.lineTo(x + l, y + t);\n            break;\n\n          case 'v': // vertical lineto, relative\n            y += current[1];\n            ctx.lineTo(x + l, y + t);\n            break;\n\n          case 'V': // verical lineto, absolute\n            y = current[1];\n            ctx.lineTo(x + l, y + t);\n            break;\n\n          case 'm': // moveTo, relative\n            x += current[1];\n            y += current[2];\n            subpathStartX = x;\n            subpathStartY = y;\n            ctx.moveTo(x + l, y + t);\n            break;\n\n          case 'M': // moveTo, absolute\n            x = current[1];\n            y = current[2];\n            subpathStartX = x;\n            subpathStartY = y;\n            ctx.moveTo(x + l, y + t);\n            break;\n\n          case 'c': // bezierCurveTo, relative\n            tempX = x + current[5];\n            tempY = y + current[6];\n            controlX = x + current[3];\n            controlY = y + current[4];\n            ctx.bezierCurveTo(\n              x + current[1] + l, // x1\n              y + current[2] + t, // y1\n              controlX + l, // x2\n              controlY + t, // y2\n              tempX + l,\n              tempY + t\n            );\n            x = tempX;\n            y = tempY;\n            break;\n\n          case 'C': // bezierCurveTo, absolute\n            x = current[5];\n            y = current[6];\n            controlX = current[3];\n            controlY = current[4];\n            ctx.bezierCurveTo(\n              current[1] + l,\n              current[2] + t,\n              controlX + l,\n              controlY + t,\n              x + l,\n              y + t\n            );\n            break;\n\n          case 's': // shorthand cubic bezierCurveTo, relative\n\n            // transform to absolute x,y\n            tempX = x + current[3];\n            tempY = y + current[4];\n\n            if (previous[0].match(/[CcSs]/) === null) {\n              // If there is no previous command or if the previous command was not a C, c, S, or s,\n              // the control point is coincident with the current point\n              controlX = x;\n              controlY = y;\n            }\n            else {\n              // calculate reflection of previous control points\n              controlX = 2 * x - controlX;\n              controlY = 2 * y - controlY;\n            }\n\n            ctx.bezierCurveTo(\n              controlX + l,\n              controlY + t,\n              x + current[1] + l,\n              y + current[2] + t,\n              tempX + l,\n              tempY + t\n            );\n            // set control point to 2nd one of this command\n            // \"... the first control point is assumed to be\n            // the reflection of the second control point on\n            // the previous command relative to the current point.\"\n            controlX = x + current[1];\n            controlY = y + current[2];\n\n            x = tempX;\n            y = tempY;\n            break;\n\n          case 'S': // shorthand cubic bezierCurveTo, absolute\n            tempX = current[3];\n            tempY = current[4];\n            if (previous[0].match(/[CcSs]/) === null) {\n              // If there is no previous command or if the previous command was not a C, c, S, or s,\n              // the control point is coincident with the current point\n              controlX = x;\n              controlY = y;\n            }\n            else {\n              // calculate reflection of previous control points\n              controlX = 2 * x - controlX;\n              controlY = 2 * y - controlY;\n            }\n            ctx.bezierCurveTo(\n              controlX + l,\n              controlY + t,\n              current[1] + l,\n              current[2] + t,\n              tempX + l,\n              tempY + t\n            );\n            x = tempX;\n            y = tempY;\n\n            // set control point to 2nd one of this command\n            // \"... the first control point is assumed to be\n            // the reflection of the second control point on\n            // the previous command relative to the current point.\"\n            controlX = current[1];\n            controlY = current[2];\n\n            break;\n\n          case 'q': // quadraticCurveTo, relative\n            // transform to absolute x,y\n            tempX = x + current[3];\n            tempY = y + current[4];\n\n            controlX = x + current[1];\n            controlY = y + current[2];\n\n            ctx.quadraticCurveTo(\n              controlX + l,\n              controlY + t,\n              tempX + l,\n              tempY + t\n            );\n            x = tempX;\n            y = tempY;\n            break;\n\n          case 'Q': // quadraticCurveTo, absolute\n            tempX = current[3];\n            tempY = current[4];\n\n            ctx.quadraticCurveTo(\n              current[1] + l,\n              current[2] + t,\n              tempX + l,\n              tempY + t\n            );\n            x = tempX;\n            y = tempY;\n            controlX = current[1];\n            controlY = current[2];\n            break;\n\n          case 't': // shorthand quadraticCurveTo, relative\n\n            // transform to absolute x,y\n            tempX = x + current[1];\n            tempY = y + current[2];\n\n            if (previous[0].match(/[QqTt]/) === null) {\n              // If there is no previous command or if the previous command was not a Q, q, T or t,\n              // assume the control point is coincident with the current point\n              controlX = x;\n              controlY = y;\n            }\n            else {\n              // calculate reflection of previous control point\n              controlX = 2 * x - controlX;\n              controlY = 2 * y - controlY;\n            }\n\n            ctx.quadraticCurveTo(\n              controlX + l,\n              controlY + t,\n              tempX + l,\n              tempY + t\n            );\n            x = tempX;\n            y = tempY;\n\n            break;\n\n          case 'T':\n            tempX = current[1];\n            tempY = current[2];\n\n            if (previous[0].match(/[QqTt]/) === null) {\n              // If there is no previous command or if the previous command was not a Q, q, T or t,\n              // assume the control point is coincident with the current point\n              controlX = x;\n              controlY = y;\n            }\n            else {\n              // calculate reflection of previous control point\n              controlX = 2 * x - controlX;\n              controlY = 2 * y - controlY;\n            }\n            ctx.quadraticCurveTo(\n              controlX + l,\n              controlY + t,\n              tempX + l,\n              tempY + t\n            );\n            x = tempX;\n            y = tempY;\n            break;\n\n          case 'a':\n            // TODO: optimize this\n            drawArc(ctx, x + l, y + t, [\n              current[1],\n              current[2],\n              current[3],\n              current[4],\n              current[5],\n              current[6] + x + l,\n              current[7] + y + t\n            ]);\n            x += current[6];\n            y += current[7];\n            break;\n\n          case 'A':\n            // TODO: optimize this\n            drawArc(ctx, x + l, y + t, [\n              current[1],\n              current[2],\n              current[3],\n              current[4],\n              current[5],\n              current[6] + l,\n              current[7] + t\n            ]);\n            x = current[6];\n            y = current[7];\n            break;\n\n          case 'z':\n          case 'Z':\n            x = subpathStartX;\n            y = subpathStartY;\n            ctx.closePath();\n            break;\n        }\n        previous = current;\n      }\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx context to render path on\n     */\n    _render: function(ctx) {\n      this._renderPathCommands(ctx);\n      this._renderPaintInOrder(ctx);\n    },\n\n    /**\n     * Returns string representation of an instance\n     * @return {String} string representation of an instance\n     */\n    toString: function() {\n      return '#<fabric.Path (' + this.complexity() +\n        '): { \"top\": ' + this.top + ', \"left\": ' + this.left + ' }>';\n    },\n\n    /**\n     * Returns object representation of an instance\n     * @param {Array} [propertiesToInclude] Any properties that you might want to additionally include in the output\n     * @return {Object} object representation of an instance\n     */\n    toObject: function(propertiesToInclude) {\n      var o = extend(this.callSuper('toObject', propertiesToInclude), {\n        path: this.path.map(function(item) { return item.slice(); }),\n        top: this.top,\n        left: this.left,\n      });\n      return o;\n    },\n\n    /**\n     * Returns dataless object representation of an instance\n     * @param {Array} [propertiesToInclude] Any properties that you might want to additionally include in the output\n     * @return {Object} object representation of an instance\n     */\n    toDatalessObject: function(propertiesToInclude) {\n      var o = this.toObject(['sourcePath'].concat(propertiesToInclude));\n      if (o.sourcePath) {\n        delete o.path;\n      }\n      return o;\n    },\n\n    /* _TO_SVG_START_ */\n    /**\n     * Returns svg representation of an instance\n     * @param {Function} [reviver] Method for further parsing of svg representation.\n     * @return {String} svg representation of an instance\n     */\n    toSVG: function(reviver) {\n      var chunks = [],\n          markup = this._createBaseSVGMarkup(), addTransform = '';\n\n      for (var i = 0, len = this.path.length; i < len; i++) {\n        chunks.push(this.path[i].join(' '));\n      }\n      var path = chunks.join(' ');\n      addTransform = ' translate(' + (-this.pathOffset.x) + ', ' + (-this.pathOffset.y) + ') ';\n      markup.push(\n        '<path ', this.getSvgId(),\n        'd=\"', path,\n        '\" style=\"', this.getSvgStyles(),\n        '\" transform=\"', this.getSvgTransform(), addTransform,\n        this.getSvgTransformMatrix(), '\" stroke-linecap=\"round\" ',\n        this.addPaintOrder(),\n        '/>\\n'\n      );\n\n      return reviver ? reviver(markup.join('')) : markup.join('');\n    },\n    /* _TO_SVG_END_ */\n\n    /**\n     * Returns number representation of an instance complexity\n     * @return {Number} complexity of this instance\n     */\n    complexity: function() {\n      return this.path.length;\n    },\n\n    /**\n     * @private\n     */\n    _parsePath: function() {\n      var result = [],\n          coords = [],\n          currentPath,\n          parsed,\n          re = /([-+]?((\\d+\\.\\d+)|((\\d+)|(\\.\\d+)))(?:e[-+]?\\d+)?)/ig,\n          match,\n          coordsStr;\n\n      for (var i = 0, coordsParsed, len = this.path.length; i < len; i++) {\n        currentPath = this.path[i];\n\n        coordsStr = currentPath.slice(1).trim();\n        coords.length = 0;\n\n        while ((match = re.exec(coordsStr))) {\n          coords.push(match[0]);\n        }\n\n        coordsParsed = [currentPath.charAt(0)];\n\n        for (var j = 0, jlen = coords.length; j < jlen; j++) {\n          parsed = parseFloat(coords[j]);\n          if (!isNaN(parsed)) {\n            coordsParsed.push(parsed);\n          }\n        }\n\n        var command = coordsParsed[0],\n            commandLength = commandLengths[command.toLowerCase()],\n            repeatedCommand = repeatedCommands[command] || command;\n\n        if (coordsParsed.length - 1 > commandLength) {\n          for (var k = 1, klen = coordsParsed.length; k < klen; k += commandLength) {\n            result.push([command].concat(coordsParsed.slice(k, k + commandLength)));\n            command = repeatedCommand;\n          }\n        }\n        else {\n          result.push(coordsParsed);\n        }\n      }\n\n      return result;\n    },\n\n    /**\n     * @private\n     */\n    _parseDimensions: function() {\n\n      var aX = [],\n          aY = [],\n          current, // current instruction\n          previous = null,\n          subpathStartX = 0,\n          subpathStartY = 0,\n          x = 0, // current x\n          y = 0, // current y\n          controlX = 0, // current control point x\n          controlY = 0, // current control point y\n          tempX,\n          tempY,\n          bounds;\n\n      for (var i = 0, len = this.path.length; i < len; ++i) {\n\n        current = this.path[i];\n\n        switch (current[0]) { // first letter\n\n          case 'l': // lineto, relative\n            x += current[1];\n            y += current[2];\n            bounds = [];\n            break;\n\n          case 'L': // lineto, absolute\n            x = current[1];\n            y = current[2];\n            bounds = [];\n            break;\n\n          case 'h': // horizontal lineto, relative\n            x += current[1];\n            bounds = [];\n            break;\n\n          case 'H': // horizontal lineto, absolute\n            x = current[1];\n            bounds = [];\n            break;\n\n          case 'v': // vertical lineto, relative\n            y += current[1];\n            bounds = [];\n            break;\n\n          case 'V': // verical lineto, absolute\n            y = current[1];\n            bounds = [];\n            break;\n\n          case 'm': // moveTo, relative\n            x += current[1];\n            y += current[2];\n            subpathStartX = x;\n            subpathStartY = y;\n            bounds = [];\n            break;\n\n          case 'M': // moveTo, absolute\n            x = current[1];\n            y = current[2];\n            subpathStartX = x;\n            subpathStartY = y;\n            bounds = [];\n            break;\n\n          case 'c': // bezierCurveTo, relative\n            tempX = x + current[5];\n            tempY = y + current[6];\n            controlX = x + current[3];\n            controlY = y + current[4];\n            bounds = fabric.util.getBoundsOfCurve(x, y,\n              x + current[1], // x1\n              y + current[2], // y1\n              controlX, // x2\n              controlY, // y2\n              tempX,\n              tempY\n            );\n            x = tempX;\n            y = tempY;\n            break;\n\n          case 'C': // bezierCurveTo, absolute\n            controlX = current[3];\n            controlY = current[4];\n            bounds = fabric.util.getBoundsOfCurve(x, y,\n              current[1],\n              current[2],\n              controlX,\n              controlY,\n              current[5],\n              current[6]\n            );\n            x = current[5];\n            y = current[6];\n            break;\n\n          case 's': // shorthand cubic bezierCurveTo, relative\n\n            // transform to absolute x,y\n            tempX = x + current[3];\n            tempY = y + current[4];\n\n            if (previous[0].match(/[CcSs]/) === null) {\n              // If there is no previous command or if the previous command was not a C, c, S, or s,\n              // the control point is coincident with the current point\n              controlX = x;\n              controlY = y;\n            }\n            else {\n              // calculate reflection of previous control points\n              controlX = 2 * x - controlX;\n              controlY = 2 * y - controlY;\n            }\n\n            bounds = fabric.util.getBoundsOfCurve(x, y,\n              controlX,\n              controlY,\n              x + current[1],\n              y + current[2],\n              tempX,\n              tempY\n            );\n            // set control point to 2nd one of this command\n            // \"... the first control point is assumed to be\n            // the reflection of the second control point on\n            // the previous command relative to the current point.\"\n            controlX = x + current[1];\n            controlY = y + current[2];\n            x = tempX;\n            y = tempY;\n            break;\n\n          case 'S': // shorthand cubic bezierCurveTo, absolute\n            tempX = current[3];\n            tempY = current[4];\n            if (previous[0].match(/[CcSs]/) === null) {\n              // If there is no previous command or if the previous command was not a C, c, S, or s,\n              // the control point is coincident with the current point\n              controlX = x;\n              controlY = y;\n            }\n            else {\n              // calculate reflection of previous control points\n              controlX = 2 * x - controlX;\n              controlY = 2 * y - controlY;\n            }\n            bounds = fabric.util.getBoundsOfCurve(x, y,\n              controlX,\n              controlY,\n              current[1],\n              current[2],\n              tempX,\n              tempY\n            );\n            x = tempX;\n            y = tempY;\n            // set control point to 2nd one of this command\n            // \"... the first control point is assumed to be\n            // the reflection of the second control point on\n            // the previous command relative to the current point.\"\n            controlX = current[1];\n            controlY = current[2];\n            break;\n\n          case 'q': // quadraticCurveTo, relative\n            // transform to absolute x,y\n            tempX = x + current[3];\n            tempY = y + current[4];\n            controlX = x + current[1];\n            controlY = y + current[2];\n            bounds = fabric.util.getBoundsOfCurve(x, y,\n              controlX,\n              controlY,\n              controlX,\n              controlY,\n              tempX,\n              tempY\n            );\n            x = tempX;\n            y = tempY;\n            break;\n\n          case 'Q': // quadraticCurveTo, absolute\n            controlX = current[1];\n            controlY = current[2];\n            bounds = fabric.util.getBoundsOfCurve(x, y,\n              controlX,\n              controlY,\n              controlX,\n              controlY,\n              current[3],\n              current[4]\n            );\n            x = current[3];\n            y = current[4];\n            break;\n\n          case 't': // shorthand quadraticCurveTo, relative\n            // transform to absolute x,y\n            tempX = x + current[1];\n            tempY = y + current[2];\n            if (previous[0].match(/[QqTt]/) === null) {\n              // If there is no previous command or if the previous command was not a Q, q, T or t,\n              // assume the control point is coincident with the current point\n              controlX = x;\n              controlY = y;\n            }\n            else {\n              // calculate reflection of previous control point\n              controlX = 2 * x - controlX;\n              controlY = 2 * y - controlY;\n            }\n\n            bounds = fabric.util.getBoundsOfCurve(x, y,\n              controlX,\n              controlY,\n              controlX,\n              controlY,\n              tempX,\n              tempY\n            );\n            x = tempX;\n            y = tempY;\n\n            break;\n\n          case 'T':\n            tempX = current[1];\n            tempY = current[2];\n\n            if (previous[0].match(/[QqTt]/) === null) {\n              // If there is no previous command or if the previous command was not a Q, q, T or t,\n              // assume the control point is coincident with the current point\n              controlX = x;\n              controlY = y;\n            }\n            else {\n              // calculate reflection of previous control point\n              controlX = 2 * x - controlX;\n              controlY = 2 * y - controlY;\n            }\n            bounds = fabric.util.getBoundsOfCurve(x, y,\n              controlX,\n              controlY,\n              controlX,\n              controlY,\n              tempX,\n              tempY\n            );\n            x = tempX;\n            y = tempY;\n            break;\n\n          case 'a':\n            // TODO: optimize this\n            bounds = fabric.util.getBoundsOfArc(x, y,\n              current[1],\n              current[2],\n              current[3],\n              current[4],\n              current[5],\n              current[6] + x,\n              current[7] + y\n            );\n            x += current[6];\n            y += current[7];\n            break;\n\n          case 'A':\n            // TODO: optimize this\n            bounds = fabric.util.getBoundsOfArc(x, y,\n              current[1],\n              current[2],\n              current[3],\n              current[4],\n              current[5],\n              current[6],\n              current[7]\n            );\n            x = current[6];\n            y = current[7];\n            break;\n\n          case 'z':\n          case 'Z':\n            x = subpathStartX;\n            y = subpathStartY;\n            break;\n        }\n        previous = current;\n        bounds.forEach(function (point) {\n          aX.push(point.x);\n          aY.push(point.y);\n        });\n        aX.push(x);\n        aY.push(y);\n      }\n\n      var minX = min(aX) || 0,\n          minY = min(aY) || 0,\n          maxX = max(aX) || 0,\n          maxY = max(aY) || 0,\n          deltaX = maxX - minX,\n          deltaY = maxY - minY,\n\n          o = {\n            left: minX,\n            top: minY,\n            width: deltaX,\n            height: deltaY\n          };\n\n      return o;\n    }\n  });\n\n  /**\n   * Creates an instance of fabric.Path from an object\n   * @static\n   * @memberOf fabric.Path\n   * @param {Object} object\n   * @param {Function} [callback] Callback to invoke when an fabric.Path instance is created\n   */\n  fabric.Path.fromObject = function(object, callback) {\n    if (typeof object.sourcePath === 'string') {\n      var pathUrl = object.sourcePath;\n      fabric.loadSVGFromURL(pathUrl, function (elements) {\n        var path = elements[0];\n        path.setOptions(object);\n        callback && callback(path);\n      });\n    }\n    else {\n      fabric.Object._fromObject('Path', object, callback, 'path');\n    }\n  };\n\n  /* _FROM_SVG_START_ */\n  /**\n   * List of attribute names to account for when parsing SVG element (used by `fabric.Path.fromElement`)\n   * @static\n   * @memberOf fabric.Path\n   * @see http://www.w3.org/TR/SVG/paths.html#PathElement\n   */\n  fabric.Path.ATTRIBUTE_NAMES = fabric.SHARED_ATTRIBUTES.concat(['d']);\n\n  /**\n   * Creates an instance of fabric.Path from an SVG <path> element\n   * @static\n   * @memberOf fabric.Path\n   * @param {SVGElement} element to parse\n   * @param {Function} callback Callback to invoke when an fabric.Path instance is created\n   * @param {Object} [options] Options object\n   * @param {Function} [callback] Options callback invoked after parsing is finished\n   */\n  fabric.Path.fromElement = function(element, callback, options) {\n    var parsedAttributes = fabric.parseAttributes(element, fabric.Path.ATTRIBUTE_NAMES);\n    callback(new fabric.Path(parsedAttributes.d, extend(parsedAttributes, options)));\n  };\n  /* _FROM_SVG_END_ */\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric = global.fabric || (global.fabric = { }),\n      extend = fabric.util.object.extend,\n      min = fabric.util.array.min,\n      max = fabric.util.array.max;\n\n  if (fabric.Group) {\n    return;\n  }\n\n  /**\n   * Group class\n   * @class fabric.Group\n   * @extends fabric.Object\n   * @mixes fabric.Collection\n   * @tutorial {@link http://fabricjs.com/fabric-intro-part-3#groups}\n   * @see {@link fabric.Group#initialize} for constructor definition\n   */\n  fabric.Group = fabric.util.createClass(fabric.Object, fabric.Collection, /** @lends fabric.Group.prototype */ {\n\n    /**\n     * Type of an object\n     * @type String\n     * @default\n     */\n    type: 'group',\n\n    /**\n     * Width of stroke\n     * @type Number\n     * @default\n     */\n    strokeWidth: 0,\n\n    /**\n     * Indicates if click events should also check for subtargets\n     * @type Boolean\n     * @default\n     */\n    subTargetCheck: false,\n\n    /**\n     * Groups are container, do not render anything on theyr own, ence no cache properties\n     * @type Array\n     * @default\n     */\n    cacheProperties: [],\n\n    /**\n     * setOnGroup is a method used for TextBox that is no more used since 2.0.0 The behavior is still\n     * available setting this boolean to true.\n     * @type Boolean\n     * @since 2.0.0\n     * @default\n     */\n    useSetOnGroup: false,\n\n    /**\n     * Constructor\n     * @param {Object} objects Group objects\n     * @param {Object} [options] Options object\n     * @param {Boolean} [isAlreadyGrouped] if true, objects have been grouped already.\n     * @return {Object} thisArg\n     */\n    initialize: function(objects, options, isAlreadyGrouped) {\n      options = options || {};\n      this._objects = [];\n      // if objects enclosed in a group have been grouped already,\n      // we cannot change properties of objects.\n      // Thus we need to set options to group without objects,\n      isAlreadyGrouped && this.callSuper('initialize', options);\n      this._objects = objects || [];\n      for (var i = this._objects.length; i--; ) {\n        this._objects[i].group = this;\n      }\n\n      if (options.originX) {\n        this.originX = options.originX;\n      }\n      if (options.originY) {\n        this.originY = options.originY;\n      }\n\n      if (!isAlreadyGrouped) {\n        var center = options && options.centerPoint;\n        // if coming from svg i do not want to calc bounds.\n        // i assume width and height are passed along options\n        center || this._calcBounds();\n        this._updateObjectsCoords(center);\n        delete options.centerPoint;\n        this.callSuper('initialize', options);\n      }\n      else {\n        this._updateObjectsACoords();\n      }\n\n      this.setCoords();\n    },\n\n    /**\n     * @private\n     * @param {Boolean} [skipCoordsChange] if true, coordinates of objects enclosed in a group do not change\n     */\n    _updateObjectsACoords: function() {\n      var ignoreZoom = true, skipAbsolute = true;\n      for (var i = this._objects.length; i--; ){\n        this._objects[i].setCoords(ignoreZoom, skipAbsolute);\n      }\n    },\n\n    /**\n     * @private\n     * @param {Boolean} [skipCoordsChange] if true, coordinates of objects enclosed in a group do not change\n     */\n    _updateObjectsCoords: function(center) {\n      var center = center || this.getCenterPoint();\n      for (var i = this._objects.length; i--; ){\n        this._updateObjectCoords(this._objects[i], center);\n      }\n    },\n\n    /**\n     * @private\n     * @param {Object} object\n     * @param {fabric.Point} center, current center of group.\n     */\n    _updateObjectCoords: function(object, center) {\n      var objectLeft = object.left,\n          objectTop = object.top,\n          ignoreZoom = true, skipAbsolute = true;\n\n      object.set({\n        left: objectLeft - center.x,\n        top: objectTop - center.y\n      });\n      object.group = this;\n      object.setCoords(ignoreZoom, skipAbsolute);\n    },\n\n    /**\n     * Returns string represenation of a group\n     * @return {String}\n     */\n    toString: function() {\n      return '#<fabric.Group: (' + this.complexity() + ')>';\n    },\n\n    /**\n     * Adds an object to a group; Then recalculates group's dimension, position.\n     * @param {Object} object\n     * @return {fabric.Group} thisArg\n     * @chainable\n     */\n    addWithUpdate: function(object) {\n      this._restoreObjectsState();\n      fabric.util.resetObjectTransform(this);\n      if (object) {\n        this._objects.push(object);\n        object.group = this;\n        object._set('canvas', this.canvas);\n      }\n      this._calcBounds();\n      this._updateObjectsCoords();\n      this.setCoords();\n      this.dirty = true;\n      return this;\n    },\n\n    /**\n     * Removes an object from a group; Then recalculates group's dimension, position.\n     * @param {Object} object\n     * @return {fabric.Group} thisArg\n     * @chainable\n     */\n    removeWithUpdate: function(object) {\n      this._restoreObjectsState();\n      fabric.util.resetObjectTransform(this);\n\n      this.remove(object);\n      this._calcBounds();\n      this._updateObjectsCoords();\n      this.setCoords();\n      this.dirty = true;\n      return this;\n    },\n\n    /**\n     * @private\n     */\n    _onObjectAdded: function(object) {\n      this.dirty = true;\n      object.group = this;\n      object._set('canvas', this.canvas);\n    },\n\n    /**\n     * @private\n     */\n    _onObjectRemoved: function(object) {\n      this.dirty = true;\n      delete object.group;\n    },\n\n    /**\n     * @private\n     */\n    _set: function(key, value) {\n      var i = this._objects.length;\n      if (this.useSetOnGroup) {\n        while (i--) {\n          this._objects[i].setOnGroup(key, value);\n        }\n      }\n      if (key === 'canvas') {\n        i = this._objects.length;\n        while (i--) {\n          this._objects[i]._set(key, value);\n        }\n      }\n      this.callSuper('_set', key, value);\n    },\n\n    /**\n     * Returns object representation of an instance\n     * @param {Array} [propertiesToInclude] Any properties that you might want to additionally include in the output\n     * @return {Object} object representation of an instance\n     */\n    toObject: function(propertiesToInclude) {\n      var objsToObject = this.getObjects().map(function(obj) {\n        var originalDefaults = obj.includeDefaultValues;\n        obj.includeDefaultValues = obj.group.includeDefaultValues;\n        var _obj = obj.toObject(propertiesToInclude);\n        obj.includeDefaultValues = originalDefaults;\n        return _obj;\n      });\n      return extend(this.callSuper('toObject', propertiesToInclude), {\n        objects: objsToObject\n      });\n    },\n\n    /**\n     * Returns object representation of an instance, in dataless mode.\n     * @param {Array} [propertiesToInclude] Any properties that you might want to additionally include in the output\n     * @return {Object} object representation of an instance\n     */\n    toDatalessObject: function(propertiesToInclude) {\n      var objsToObject, sourcePath = this.sourcePath;\n      if (sourcePath) {\n        objsToObject = sourcePath;\n      }\n      else {\n        objsToObject = this.getObjects().map(function(obj) {\n          var originalDefaults = obj.includeDefaultValues;\n          obj.includeDefaultValues = obj.group.includeDefaultValues;\n          var _obj = obj.toDatalessObject(propertiesToInclude);\n          obj.includeDefaultValues = originalDefaults;\n          return _obj;\n        });\n      }\n      return extend(this.callSuper('toDatalessObject', propertiesToInclude), {\n        objects: objsToObject\n      });\n    },\n\n    /**\n     * Renders instance on a given context\n     * @param {CanvasRenderingContext2D} ctx context to render instance on\n     */\n    render: function(ctx) {\n      this._transformDone = true;\n      this.callSuper('render', ctx);\n      this._transformDone = false;\n    },\n\n    /**\n     * Decide if the object should cache or not. Create its own cache level\n     * objectCaching is a global flag, wins over everything\n     * needsItsOwnCache should be used when the object drawing method requires\n     * a cache step. None of the fabric classes requires it.\n     * Generally you do not cache objects in groups because the group outside is cached.\n     * @return {Boolean}\n     */\n    shouldCache: function() {\n      var ownCache = this.objectCaching && (!this.group || this.needsItsOwnCache() || !this.group.isOnACache());\n      this.ownCaching = ownCache;\n      if (ownCache) {\n        for (var i = 0, len = this._objects.length; i < len; i++) {\n          if (this._objects[i].willDrawShadow()) {\n            this.ownCaching = false;\n            return false;\n          }\n        }\n      }\n      return ownCache;\n    },\n\n    /**\n     * Check if this object or a child object will cast a shadow\n     * @return {Boolean}\n     */\n    willDrawShadow: function() {\n      if (this.shadow) {\n        return this.callSuper('willDrawShadow');\n      }\n      for (var i = 0, len = this._objects.length; i < len; i++) {\n        if (this._objects[i].willDrawShadow()) {\n          return true;\n        }\n      }\n      return false;\n    },\n\n    /**\n     * Check if this group or its parent group are caching, recursively up\n     * @return {Boolean}\n     */\n    isOnACache: function() {\n      return this.ownCaching || (this.group && this.group.isOnACache());\n    },\n\n    /**\n     * Execute the drawing operation for an object on a specified context\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    drawObject: function(ctx) {\n      for (var i = 0, len = this._objects.length; i < len; i++) {\n        this._objects[i].render(ctx);\n      }\n    },\n\n    /**\n     * Check if cache is dirty\n     */\n    isCacheDirty: function() {\n      if (this.callSuper('isCacheDirty')) {\n        return true;\n      }\n      if (!this.statefullCache) {\n        return false;\n      }\n      for (var i = 0, len = this._objects.length; i < len; i++) {\n        if (this._objects[i].isCacheDirty(true)) {\n          if (this._cacheCanvas) {\n            // if this group has not a cache canvas there is nothing to clean\n            var x = this.cacheWidth / this.zoomX, y = this.cacheHeight / this.zoomY;\n            this._cacheContext.clearRect(-x / 2, -y / 2, x, y);\n          }\n          return true;\n        }\n      }\n      return false;\n    },\n\n    /**\n     * Retores original state of each of group objects (original state is that which was before group was created).\n     * @private\n     * @return {fabric.Group} thisArg\n     * @chainable\n     */\n    _restoreObjectsState: function() {\n      this._objects.forEach(this._restoreObjectState, this);\n      return this;\n    },\n\n    /**\n     * Realises the transform from this group onto the supplied object\n     * i.e. it tells you what would happen if the supplied object was in\n     * the group, and then the group was destroyed. It mutates the supplied\n     * object.\n     * @param {fabric.Object} object\n     * @return {fabric.Object} transformedObject\n     */\n    realizeTransform: function(object) {\n      var matrix = object.calcTransformMatrix(),\n          options = fabric.util.qrDecompose(matrix),\n          center = new fabric.Point(options.translateX, options.translateY);\n      object.flipX = false;\n      object.flipY = false;\n      object.set('scaleX', options.scaleX);\n      object.set('scaleY', options.scaleY);\n      object.skewX = options.skewX;\n      object.skewY = options.skewY;\n      object.angle = options.angle;\n      object.setPositionByOrigin(center, 'center', 'center');\n      return object;\n    },\n\n    /**\n     * Restores original state of a specified object in group\n     * @private\n     * @param {fabric.Object} object\n     * @return {fabric.Group} thisArg\n     */\n    _restoreObjectState: function(object) {\n      this.realizeTransform(object);\n      object.setCoords();\n      delete object.group;\n      return this;\n    },\n\n    /**\n     * Destroys a group (restoring state of its objects)\n     * @return {fabric.Group} thisArg\n     * @chainable\n     */\n    destroy: function() {\n      // when group is destroyed objects needs to get a repaint to be eventually\n      // displayed on canvas.\n      this._objects.forEach(function(object) {\n        object.set('dirty', true);\n      });\n      return this._restoreObjectsState();\n    },\n\n    /**\n     * make a group an active selection, remove the group from canvas\n     * the group has to be on canvas for this to work.\n     * @return {fabric.ActiveSelection} thisArg\n     * @chainable\n     */\n    toActiveSelection: function() {\n      if (!this.canvas) {\n        return;\n      }\n      var objects = this._objects, canvas = this.canvas;\n      this._objects = [];\n      var options = this.toObject();\n      delete options.objects;\n      var activeSelection = new fabric.ActiveSelection([]);\n      activeSelection.set(options);\n      activeSelection.type = 'activeSelection';\n      canvas.remove(this);\n      objects.forEach(function(object) {\n        object.group = activeSelection;\n        object.dirty = true;\n        canvas.add(object);\n      });\n      activeSelection.canvas = canvas;\n      activeSelection._objects = objects;\n      canvas._activeObject = activeSelection;\n      activeSelection.setCoords();\n      return activeSelection;\n    },\n\n    /**\n     * Destroys a group (restoring state of its objects)\n     * @return {fabric.Group} thisArg\n     * @chainable\n     */\n    ungroupOnCanvas: function() {\n      return this._restoreObjectsState();\n    },\n\n    /**\n     * Sets coordinates of all objects inside group\n     * @return {fabric.Group} thisArg\n     * @chainable\n     */\n    setObjectsCoords: function() {\n      var ignoreZoom = true, skipAbsolute = true;\n      this.forEachObject(function(object) {\n        object.setCoords(ignoreZoom, skipAbsolute);\n      });\n      return this;\n    },\n\n    /**\n     * @private\n     */\n    _calcBounds: function(onlyWidthHeight) {\n      var aX = [],\n          aY = [],\n          o, prop,\n          props = ['tr', 'br', 'bl', 'tl'],\n          i = 0, iLen = this._objects.length,\n          j, jLen = props.length,\n          ignoreZoom = true;\n\n      for ( ; i < iLen; ++i) {\n        o = this._objects[i];\n        o.setCoords(ignoreZoom);\n        for (j = 0; j < jLen; j++) {\n          prop = props[j];\n          aX.push(o.oCoords[prop].x);\n          aY.push(o.oCoords[prop].y);\n        }\n      }\n\n      this.set(this._getBounds(aX, aY, onlyWidthHeight));\n    },\n\n    /**\n     * @private\n     */\n    _getBounds: function(aX, aY, onlyWidthHeight) {\n      var minXY = new fabric.Point(min(aX), min(aY)),\n          maxXY = new fabric.Point(max(aX), max(aY)),\n          obj = {\n            width: (maxXY.x - minXY.x) || 0,\n            height: (maxXY.y - minXY.y) || 0\n          };\n\n      if (!onlyWidthHeight) {\n        obj.left = minXY.x || 0;\n        obj.top = minXY.y || 0;\n        if (this.originX === 'center') {\n          obj.left += obj.width / 2;\n        }\n        if (this.originX === 'right') {\n          obj.left += obj.width;\n        }\n        if (this.originY === 'center') {\n          obj.top += obj.height / 2;\n        }\n        if (this.originY === 'bottom') {\n          obj.top += obj.height;\n        }\n      }\n      return obj;\n    },\n\n    /* _TO_SVG_START_ */\n    /**\n     * Returns svg representation of an instance\n     * @param {Function} [reviver] Method for further parsing of svg representation.\n     * @return {String} svg representation of an instance\n     */\n    toSVG: function(reviver) {\n      var markup = this._createBaseSVGMarkup();\n      markup.push(\n        '<g ', this.getSvgId(), 'transform=\"',\n        /* avoiding styles intentionally */\n        this.getSvgTransform(),\n        this.getSvgTransformMatrix(),\n        '\" style=\"',\n        this.getSvgFilter(),\n        '\">\\n'\n      );\n\n      for (var i = 0, len = this._objects.length; i < len; i++) {\n        markup.push('\\t', this._objects[i].toSVG(reviver));\n      }\n\n      markup.push('</g>\\n');\n\n      return reviver ? reviver(markup.join('')) : markup.join('');\n    },\n    /* _TO_SVG_END_ */\n  });\n\n  /**\n   * Returns {@link fabric.Group} instance from an object representation\n   * @static\n   * @memberOf fabric.Group\n   * @param {Object} object Object to create a group from\n   * @param {Function} [callback] Callback to invoke when an group instance is created\n   */\n  fabric.Group.fromObject = function(object, callback) {\n    fabric.util.enlivenObjects(object.objects, function(enlivenedObjects) {\n      var options = fabric.util.object.clone(object, true);\n      delete options.objects;\n      callback && callback(new fabric.Group(enlivenedObjects, options, true));\n    });\n  };\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric = global.fabric || (global.fabric = { });\n\n  if (fabric.ActiveSelection) {\n    return;\n  }\n\n  /**\n   * Group class\n   * @class fabric.ActiveSelection\n   * @extends fabric.Group\n   * @tutorial {@link http://fabricjs.com/fabric-intro-part-3#groups}\n   * @see {@link fabric.ActiveSelection#initialize} for constructor definition\n   */\n  fabric.ActiveSelection = fabric.util.createClass(fabric.Group, /** @lends fabric.ActiveSelection.prototype */ {\n\n    /**\n     * Type of an object\n     * @type String\n     * @default\n     */\n    type: 'activeSelection',\n\n    /**\n     * Constructor\n     * @param {Object} objects ActiveSelection objects\n     * @param {Object} [options] Options object\n     * @return {Object} thisArg\n     */\n    initialize: function(objects, options) {\n      options = options || {};\n      this._objects = objects || [];\n      for (var i = this._objects.length; i--; ) {\n        this._objects[i].group = this;\n      }\n\n      if (options.originX) {\n        this.originX = options.originX;\n      }\n      if (options.originY) {\n        this.originY = options.originY;\n      }\n      this._calcBounds();\n      this._updateObjectsCoords();\n      fabric.Object.prototype.initialize.call(this, options);\n      this.setCoords();\n    },\n\n    /**\n     * Change te activeSelection to a normal group,\n     * High level function that automatically adds it to canvas as\n     * active object. no events fired.\n     * @since 2.0.0\n     * @return {fabric.Group}\n     */\n    toGroup: function() {\n      var objects = this._objects;\n      this._objects = [];\n      var options = this.toObject();\n      var newGroup = new fabric.Group([]);\n      delete options.objects;\n      newGroup.set(options);\n      newGroup.type = 'group';\n      objects.forEach(function(object) {\n        object.group = newGroup;\n        object.canvas.remove(object);\n      });\n      newGroup._objects = objects;\n      if (!this.canvas) {\n        return newGroup;\n      }\n      var canvas = this.canvas;\n      canvas.add(newGroup);\n      canvas._activeObject = newGroup;\n      newGroup.setCoords();\n      return newGroup;\n    },\n\n    /**\n     * If returns true, deselection is cancelled.\n     * @since 2.0.0\n     * @return {Boolean} [cancel]\n     */\n    onDeselect: function() {\n      this.destroy();\n      return false;\n    },\n\n    /**\n     * Returns string representation of a group\n     * @return {String}\n     */\n    toString: function() {\n      return '#<fabric.ActiveSelection: (' + this.complexity() + ')>';\n    },\n\n    /**\n     * @private\n     */\n    _set: function(key, value) {\n      var i = this._objects.length;\n      if (key === 'canvas') {\n        while (i--) {\n          this._objects[i].set(key, value);\n        }\n      }\n      if (this.useSetOnGroup) {\n        while (i--) {\n          this._objects[i].setOnGroup(key, value);\n        }\n      }\n      fabric.Object.prototype._set.call(this, key, value);\n    },\n\n    /**\n     * Decide if the object should cache or not. Create its own cache level\n     * objectCaching is a global flag, wins over everything\n     * needsItsOwnCache should be used when the object drawing method requires\n     * a cache step. None of the fabric classes requires it.\n     * Generally you do not cache objects in groups because the group outside is cached.\n     * @return {Boolean}\n     */\n    shouldCache: function() {\n      return false;\n    },\n\n    /**\n     * Check if this object or a child object will cast a shadow\n     * @return {Boolean}\n     */\n    willDrawShadow: function() {\n      if (this.shadow) {\n        return this.callSuper('willDrawShadow');\n      }\n      for (var i = 0, len = this._objects.length; i < len; i++) {\n        if (this._objects[i].willDrawShadow()) {\n          return true;\n        }\n      }\n      return false;\n    },\n\n    /**\n     * Check if this group or its parent group are caching, recursively up\n     * @return {Boolean}\n     */\n    isOnACache: function() {\n      return false;\n    },\n\n    /**\n     * Renders controls and borders for the object\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     * @param {Object} [styleOverride] properties to override the object style\n     * @param {Object} [childrenOverride] properties to override the children overrides\n     */\n    _renderControls: function(ctx, styleOverride, childrenOverride) {\n      ctx.save();\n      ctx.globalAlpha = this.isMoving ? this.borderOpacityWhenMoving : 1;\n      this.callSuper('_renderControls', ctx, styleOverride);\n      childrenOverride = childrenOverride || { };\n      if (typeof childrenOverride.hasControls === 'undefined') {\n        childrenOverride.hasControls = false;\n      }\n      if (typeof childrenOverride.hasRotatingPoint === 'undefined') {\n        childrenOverride.hasRotatingPoint = false;\n      }\n      childrenOverride.forActiveSelection = true;\n      for (var i = 0, len = this._objects.length; i < len; i++) {\n        this._objects[i]._renderControls(ctx, childrenOverride);\n      }\n      ctx.restore();\n    },\n  });\n\n  /**\n   * Returns {@link fabric.ActiveSelection} instance from an object representation\n   * @static\n   * @memberOf fabric.ActiveSelection\n   * @param {Object} object Object to create a group from\n   * @param {Function} [callback] Callback to invoke when an ActiveSelection instance is created\n   */\n  fabric.ActiveSelection.fromObject = function(object, callback) {\n    fabric.util.enlivenObjects(object.objects, function(enlivenedObjects) {\n      delete object.objects;\n      callback && callback(new fabric.ActiveSelection(enlivenedObjects, object, true));\n    });\n  };\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function(global) {\n\n  'use strict';\n\n  var extend = fabric.util.object.extend;\n\n  if (!global.fabric) {\n    global.fabric = { };\n  }\n\n  if (global.fabric.Image) {\n    fabric.warn('fabric.Image is already defined.');\n    return;\n  }\n\n  /**\n   * Image class\n   * @class fabric.Image\n   * @extends fabric.Object\n   * @tutorial {@link http://fabricjs.com/fabric-intro-part-1#images}\n   * @see {@link fabric.Image#initialize} for constructor definition\n   */\n  fabric.Image = fabric.util.createClass(fabric.Object, /** @lends fabric.Image.prototype */ {\n\n    /**\n     * Type of an object\n     * @type String\n     * @default\n     */\n    type: 'image',\n\n    /**\n     * crossOrigin value (one of \"\", \"anonymous\", \"use-credentials\")\n     * @see https://developer.mozilla.org/en-US/docs/HTML/CORS_settings_attributes\n     * @type String\n     * @default\n     */\n    crossOrigin: '',\n\n    /**\n     * Width of a stroke.\n     * For image quality a stroke multiple of 2 gives better results.\n     * @type Number\n     * @default\n     */\n    strokeWidth: 0,\n\n    /**\n     * private\n     * contains last value of scaleX to detect\n     * if the Image got resized after the last Render\n     * @type Number\n     */\n    _lastScaleX: 1,\n\n    /**\n     * private\n     * contains last value of scaleY to detect\n     * if the Image got resized after the last Render\n     * @type Number\n     */\n    _lastScaleY: 1,\n\n    /**\n     * private\n     * contains last value of scaling applied by the apply filter chain\n     * @type Number\n     */\n    _filterScalingX: 1,\n\n    /**\n     * private\n     * contains last value of scaling applied by the apply filter chain\n     * @type Number\n     */\n    _filterScalingY: 1,\n\n    /**\n     * minimum scale factor under which any resizeFilter is triggered to resize the image\n     * 0 will disable the automatic resize. 1 will trigger automatically always.\n     * number bigger than 1 are not implemented yet.\n     * @type Number\n     */\n    minimumScaleTrigger: 0.5,\n\n    /**\n     * List of properties to consider when checking if\n     * state of an object is changed ({@link fabric.Object#hasStateChanged})\n     * as well as for history (undo/redo) purposes\n     * @type Array\n     */\n    stateProperties: fabric.Object.prototype.stateProperties.concat('cropX', 'cropY'),\n\n    /**\n     * When `true`, object is cached on an additional canvas.\n     * default to false for images\n     * since 1.7.0\n     * @type Boolean\n     * @default\n     */\n    objectCaching: false,\n\n    /**\n     * key used to retrieve the texture representing this image\n     * since 2.0.0\n     * @type String\n     * @default\n     */\n    cacheKey: '',\n\n    /**\n     * Image crop in pixels from original image size.\n     * since 2.0.0\n     * @type Number\n     * @default\n     */\n    cropX: 0,\n\n    /**\n     * Image crop in pixels from original image size.\n     * since 2.0.0\n     * @type Number\n     * @default\n     */\n    cropY: 0,\n\n    /**\n     * Constructor\n     * @param {HTMLImageElement | String} element Image element\n     * @param {Object} [options] Options object\n     * @param {function} [callback] callback function to call after eventual filters applied.\n     * @return {fabric.Image} thisArg\n     */\n    initialize: function(element, options) {\n      options || (options = { });\n      this.filters = [];\n      this.cacheKey = 'texture' + fabric.Object.__uid++;\n      this.callSuper('initialize', options);\n      this._initElement(element, options);\n    },\n\n    /**\n     * Returns image element which this instance if based on\n     * @return {HTMLImageElement} Image element\n     */\n    getElement: function() {\n      return this._element;\n    },\n\n    /**\n     * Sets image element for this instance to a specified one.\n     * If filters defined they are applied to new image.\n     * You might need to call `canvas.renderAll` and `object.setCoords` after replacing, to render new image and update controls area.\n     * @param {HTMLImageElement} element\n     * @param {Object} [options] Options object\n     * @return {fabric.Image} thisArg\n     * @chainable\n     */\n    setElement: function(element, options) {\n      var backend = fabric.filterBackend;\n      if (backend && backend.evictCachesForKey) {\n        backend.evictCachesForKey(this.cacheKey);\n        backend.evictCachesForKey(this.cacheKey + '_filtered');\n      }\n      this._element = element;\n      this._originalElement = element;\n      this._initConfig(options);\n      if (this.resizeFilter) {\n        this.applyResizeFilters();\n      }\n      if (this.filters.length !== 0) {\n        this.applyFilters();\n      }\n      return this;\n    },\n\n    /**\n     * Delete cacheKey if we have a webGlBackend\n     * delete reference to image elements\n     */\n    dispose: function() {\n      var backend = fabric.filterBackend;\n      if (backend && backend.evictCachesForKey) {\n        backend.evictCachesForKey(this.cacheKey);\n        backend.evictCachesForKey(this.cacheKey + '_filtered');\n      }\n      this._originalElement = undefined;\n      this._element = undefined;\n      this._filteredEl = undefined;\n    },\n\n    /**\n     * Sets crossOrigin value (on an instance and corresponding image element)\n     * @return {fabric.Image} thisArg\n     * @chainable\n     */\n    setCrossOrigin: function(value) {\n      this.crossOrigin = value;\n      this._element.crossOrigin = value;\n\n      return this;\n    },\n\n    /**\n     * Returns original size of an image\n     * @return {Object} Object with \"width\" and \"height\" properties\n     */\n    getOriginalSize: function() {\n      var element = this.getElement();\n      return {\n        width: element.width,\n        height: element.height\n      };\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    _stroke: function(ctx) {\n      if (!this.stroke || this.strokeWidth === 0) {\n        return;\n      }\n      var w = this.width / 2, h = this.height / 2;\n      ctx.beginPath();\n      ctx.moveTo(-w, -h);\n      ctx.lineTo(w, -h);\n      ctx.lineTo(w, h);\n      ctx.lineTo(-w, h);\n      ctx.lineTo(-w, -h);\n      ctx.closePath();\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    _renderDashedStroke: function(ctx) {\n      var x = -this.width / 2,\n          y = -this.height / 2,\n          w = this.width,\n          h = this.height;\n\n      ctx.save();\n      this._setStrokeStyles(ctx, this);\n\n      ctx.beginPath();\n      fabric.util.drawDashedLine(ctx, x, y, x + w, y, this.strokeDashArray);\n      fabric.util.drawDashedLine(ctx, x + w, y, x + w, y + h, this.strokeDashArray);\n      fabric.util.drawDashedLine(ctx, x + w, y + h, x, y + h, this.strokeDashArray);\n      fabric.util.drawDashedLine(ctx, x, y + h, x, y, this.strokeDashArray);\n      ctx.closePath();\n      ctx.restore();\n    },\n\n    /**\n     * Returns object representation of an instance\n     * @param {Array} [propertiesToInclude] Any properties that you might want to additionally include in the output\n     * @return {Object} Object representation of an instance\n     */\n    toObject: function(propertiesToInclude) {\n      var filters = [];\n\n      this.filters.forEach(function(filterObj) {\n        if (filterObj) {\n          filters.push(filterObj.toObject());\n        }\n      });\n      var object = extend(\n        this.callSuper(\n          'toObject',\n          ['crossOrigin', 'cropX', 'cropY'].concat(propertiesToInclude)\n        ), {\n          src: this.getSrc(),\n          filters: filters,\n        });\n      if (this.resizeFilter) {\n        object.resizeFilter = this.resizeFilter.toObject();\n      }\n      return object;\n    },\n\n    /**\n     * Returns true if an image has crop applied, inspecting values of cropX,cropY,width,hight.\n     * @return {Boolean}\n     */\n    hasCrop: function() {\n      return this.cropX || this.cropY || this.width < this._element.width || this.height < this._element.height;\n    },\n\n    /* _TO_SVG_START_ */\n    /**\n     * Returns SVG representation of an instance\n     * @param {Function} [reviver] Method for further parsing of svg representation.\n     * @return {String} svg representation of an instance\n     */\n    toSVG: function(reviver) {\n      var markup = this._createBaseSVGMarkup(), x = -this.width / 2, y = -this.height / 2, clipPath = '';\n      if (this.hasCrop()) {\n        var clipPathId = fabric.Object.__uid++;\n        markup.push(\n          '<clipPath id=\"imageCrop_' + clipPathId + '\">\\n',\n          '\\t<rect x=\"' + x + '\" y=\"' + y + '\" width=\"' + this.width + '\" height=\"' + this.height + '\" />\\n',\n          '</clipPath>\\n'\n        );\n        clipPath = ' clip-path=\"url(#imageCrop_' + clipPathId + ')\" ';\n      }\n      markup.push('<g transform=\"', this.getSvgTransform(), this.getSvgTransformMatrix(), '\">\\n');\n      var imageMarkup = ['\\t<image ', this.getSvgId(), 'xlink:href=\"', this.getSvgSrc(true),\n        '\" x=\"', x - this.cropX, '\" y=\"', y - this.cropY,\n        '\" style=\"', this.getSvgStyles(),\n        // we're essentially moving origin of transformation from top/left corner to the center of the shape\n        // by wrapping it in container <g> element with actual transformation, then offsetting object to the top/left\n        // so that object's center aligns with container's left/top\n        '\" width=\"', this._element.width || this._element.naturalWidth,\n        '\" height=\"', this._element.height || this._element.height,\n        '\"', clipPath,\n        '></image>\\n'];\n      if (this.paintFirst === 'fill') {\n        Array.prototype.push.apply(markup, imageMarkup);\n      }\n      if (this.stroke || this.strokeDashArray) {\n        var origFill = this.fill;\n        this.fill = null;\n        markup.push(\n          '\\t<rect ',\n          'x=\"', x, '\" y=\"', y,\n          '\" width=\"', this.width, '\" height=\"', this.height,\n          '\" style=\"', this.getSvgStyles(),\n          '\"/>\\n'\n        );\n        this.fill = origFill;\n      }\n      if (this.paintFirst !== 'fill') {\n        Array.prototype.push.apply(markup, imageMarkup);\n      }\n      markup.push('</g>\\n');\n\n      return reviver ? reviver(markup.join('')) : markup.join('');\n    },\n    /* _TO_SVG_END_ */\n\n    /**\n     * Returns source of an image\n     * @param {Boolean} filtered indicates if the src is needed for svg\n     * @return {String} Source of an image\n     */\n    getSrc: function(filtered) {\n      var element = filtered ? this._element : this._originalElement;\n      if (element) {\n        if (element.toDataURL) {\n          return element.toDataURL();\n        }\n        return element.src;\n      }\n      else {\n        return this.src || '';\n      }\n    },\n\n    /**\n     * Sets source of an image\n     * @param {String} src Source string (URL)\n     * @param {Function} [callback] Callback is invoked when image has been loaded (and all filters have been applied)\n     * @param {Object} [options] Options object\n     * @return {fabric.Image} thisArg\n     * @chainable\n     */\n    setSrc: function(src, callback, options) {\n      fabric.util.loadImage(src, function(img) {\n        this.setElement(img, options);\n        this._setWidthHeight();\n        callback(this);\n      }, this, options && options.crossOrigin);\n      return this;\n    },\n\n    /**\n     * Returns string representation of an instance\n     * @return {String} String representation of an instance\n     */\n    toString: function() {\n      return '#<fabric.Image: { src: \"' + this.getSrc() + '\" }>';\n    },\n\n    applyResizeFilters: function() {\n      var filter = this.resizeFilter,\n          retinaScaling = this.canvas ? this.canvas.getRetinaScaling() : 1,\n          minimumScale = this.minimumScaleTrigger,\n          scaleX = this.scaleX * retinaScaling,\n          scaleY = this.scaleY * retinaScaling,\n          elementToFilter = this._filteredEl || this._originalElement;\n      if (this.group) {\n        this.set('dirty', true);\n      }\n      if (!filter || (scaleX > minimumScale && scaleY > minimumScale)) {\n        this._element = elementToFilter;\n        this._filterScalingX = 1;\n        this._filterScalingY = 1;\n        return;\n      }\n      if (!fabric.filterBackend) {\n        fabric.filterBackend = fabric.initFilterBackend();\n      }\n      var canvasEl = fabric.util.createCanvasElement(),\n          cacheKey = this._filteredEl ? this.cacheKey : (this.cacheKey + '_filtered'),\n          sourceWidth = elementToFilter.width, sourceHeight = elementToFilter.height;\n      canvasEl.width = sourceWidth;\n      canvasEl.height = sourceHeight;\n      this._element = canvasEl;\n      filter.scaleX = scaleX;\n      filter.scaleY = scaleY;\n      fabric.filterBackend.applyFilters(\n        [filter], elementToFilter, sourceWidth, sourceHeight, this._element, cacheKey);\n      this._filterScalingX = canvasEl.width / this._originalElement.width;\n      this._filterScalingY = canvasEl.height / this._originalElement.height;\n    },\n\n    /**\n     * Applies filters assigned to this image (from \"filters\" array) or from filter param\n     * @method applyFilters\n     * @param {Array} filters to be applied\n     * @param {Boolean} forResizing specify if the filter operation is a resize operation\n     * @return {thisArg} return the fabric.Image object\n     * @chainable\n     */\n    applyFilters: function(filters) {\n\n      filters = filters || this.filters || [];\n      filters = filters.filter(function(filter) { return filter; });\n      if (this.group) {\n        this.set('dirty', true);\n      }\n      if (filters.length === 0) {\n        this._element = this._originalElement;\n        this._filteredEl = null;\n        this._filterScalingX = 1;\n        this._filterScalingY = 1;\n        return this;\n      }\n\n      var imgElement = this._originalElement,\n          sourceWidth = imgElement.naturalWidth || imgElement.width,\n          sourceHeight = imgElement.naturalHeight || imgElement.height;\n\n      if (this._element === this._originalElement) {\n        // if the element is the same we need to create a new element\n        var canvasEl = fabric.util.createCanvasElement();\n        canvasEl.width = sourceWidth;\n        canvasEl.height = sourceHeight;\n        this._element = canvasEl;\n        this._filteredEl = canvasEl;\n      }\n      else {\n        // clear the existing element to get new filter data\n        this._element.getContext('2d').clearRect(0, 0, sourceWidth, sourceHeight);\n      }\n      if (!fabric.filterBackend) {\n        fabric.filterBackend = fabric.initFilterBackend();\n      }\n      fabric.filterBackend.applyFilters(\n        filters, this._originalElement, sourceWidth, sourceHeight, this._element, this.cacheKey);\n      if (this._originalElement.width !== this._element.width ||\n        this._originalElement.height !== this._element.height) {\n        this._filterScalingX = this._element.width / this._originalElement.width;\n        this._filterScalingY = this._element.height / this._originalElement.height;\n      }\n      return this;\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    _render: function(ctx) {\n      if (this.isMoving === false && this.resizeFilter && this._needsResize()) {\n        this._lastScaleX = this.scaleX;\n        this._lastScaleY = this.scaleY;\n        this.applyResizeFilters();\n      }\n      this._stroke(ctx);\n      this._renderPaintInOrder(ctx);\n    },\n\n    _renderFill: function(ctx) {\n      var w = this.width, h = this.height, sW = w * this._filterScalingX, sH = h * this._filterScalingY,\n          x = -w / 2, y = -h / 2, elementToDraw = this._element;\n      elementToDraw && ctx.drawImage(elementToDraw,\n        this.cropX * this._filterScalingX,\n        this.cropY * this._filterScalingY,\n        sW,\n        sH,\n        x, y, w, h);\n    },\n\n    /**\n     * @private, needed to check if image needs resize\n     */\n    _needsResize: function() {\n      return (this.scaleX !== this._lastScaleX || this.scaleY !== this._lastScaleY);\n    },\n\n    /**\n     * @private\n     */\n    _resetWidthHeight: function() {\n      var element = this.getElement();\n\n      this.set('width', element.width);\n      this.set('height', element.height);\n    },\n\n    /**\n     * The Image class's initialization method. This method is automatically\n     * called by the constructor.\n     * @private\n     * @param {HTMLImageElement|String} element The element representing the image\n     * @param {Object} [options] Options object\n     */\n    _initElement: function(element, options) {\n      this.setElement(fabric.util.getById(element), options);\n      fabric.util.addClass(this.getElement(), fabric.Image.CSS_CANVAS);\n    },\n\n    /**\n     * @private\n     * @param {Object} [options] Options object\n     */\n    _initConfig: function(options) {\n      options || (options = { });\n      this.setOptions(options);\n      this._setWidthHeight(options);\n      if (this._element && this.crossOrigin) {\n        this._element.crossOrigin = this.crossOrigin;\n      }\n    },\n\n    /**\n     * @private\n     * @param {Array} filters to be initialized\n     * @param {Function} callback Callback to invoke when all fabric.Image.filters instances are created\n     */\n    _initFilters: function(filters, callback) {\n      if (filters && filters.length) {\n        fabric.util.enlivenObjects(filters, function(enlivenedObjects) {\n          callback && callback(enlivenedObjects);\n        }, 'fabric.Image.filters');\n      }\n      else {\n        callback && callback();\n      }\n    },\n\n    /**\n     * @private\n     * @param {Object} [options] Object with width/height properties\n     */\n    _setWidthHeight: function(options) {\n      this.width = options && ('width' in options)\n        ? options.width\n        : (this.getElement()\n          ? this.getElement().width || 0\n          : 0);\n\n      this.height = options && ('height' in options)\n        ? options.height\n        : (this.getElement()\n          ? this.getElement().height || 0\n          : 0);\n    },\n\n    /**\n     * Calculate offset for center and scale factor for the image in order to respect\n     * the preserveAspectRatio attribute\n     * @private\n     * @return {Object}\n     */\n    parsePreserveAspectRatioAttribute: function() {\n      var pAR = fabric.util.parsePreserveAspectRatioAttribute(this.preserveAspectRatio || ''),\n          rWidth = this._element.width, rHeight = this._element.height,\n          scaleX = 1, scaleY = 1, offsetLeft = 0, offsetTop = 0, cropX = 0, cropY = 0,\n          offset, pWidth = this.width, pHeight = this.height, parsedAttributes = { width: pWidth, height: pHeight };\n      if (pAR && (pAR.alignX !== 'none' || pAR.alignY !== 'none')) {\n        if (pAR.meetOrSlice === 'meet') {\n          scaleX = scaleY = fabric.util.findScaleToFit(this._element, parsedAttributes);\n          offset = (pWidth - rWidth * scaleX) / 2;\n          if (pAR.alignX === 'Min') {\n            offsetLeft = -offset;\n          }\n          if (pAR.alignX === 'Max') {\n            offsetLeft = offset;\n          }\n          offset = (pHeight - rHeight * scaleY) / 2;\n          if (pAR.alignY === 'Min') {\n            offsetTop = -offset;\n          }\n          if (pAR.alignY === 'Max') {\n            offsetTop = offset;\n          }\n        }\n        if (pAR.meetOrSlice === 'slice') {\n          scaleX = scaleY = fabric.util.findScaleToCover(this._element, parsedAttributes);\n          offset = rWidth - pWidth / scaleX;\n          if (pAR.alignX === 'Mid') {\n            cropX = offset / 2;\n          }\n          if (pAR.alignX === 'Max') {\n            cropX = offset;\n          }\n          offset = rHeight - pHeight / scaleY;\n          if (pAR.alignY === 'Mid') {\n            cropY = offset / 2;\n          }\n          if (pAR.alignY === 'Max') {\n            cropY = offset;\n          }\n          rWidth = pWidth / scaleX;\n          rHeight = pHeight / scaleY;\n        }\n      }\n      else {\n        scaleX = pWidth / rWidth;\n        scaleY = pHeight / rHeight;\n      }\n      return {\n        width: rWidth,\n        height: rHeight,\n        scaleX: scaleX,\n        scaleY: scaleY,\n        offsetLeft: offsetLeft,\n        offsetTop: offsetTop,\n        cropX: cropX,\n        cropY: cropY\n      };\n    }\n  });\n\n  /**\n   * Default CSS class name for canvas\n   * @static\n   * @type String\n   * @default\n   */\n  fabric.Image.CSS_CANVAS = 'canvas-img';\n\n  /**\n   * Alias for getSrc\n   * @static\n   */\n  fabric.Image.prototype.getSvgSrc = fabric.Image.prototype.getSrc;\n\n  /**\n   * Creates an instance of fabric.Image from its object representation\n   * @static\n   * @param {Object} object Object to create an instance from\n   * @param {Function} callback Callback to invoke when an image instance is created\n   */\n  fabric.Image.fromObject = function(_object, callback) {\n    var object = fabric.util.object.clone(_object);\n    fabric.util.loadImage(object.src, function(img, error) {\n      if (error) {\n        callback && callback(null, error);\n        return;\n      }\n      fabric.Image.prototype._initFilters.call(object, object.filters, function(filters) {\n        object.filters = filters || [];\n        fabric.Image.prototype._initFilters.call(object, [object.resizeFilter], function(resizeFilters) {\n          object.resizeFilter = resizeFilters[0];\n          var image = new fabric.Image(img, object);\n          callback(image);\n        });\n      });\n    }, null, object.crossOrigin);\n  };\n\n  /**\n   * Creates an instance of fabric.Image from an URL string\n   * @static\n   * @param {String} url URL to create an image from\n   * @param {Function} [callback] Callback to invoke when image is created (newly created image is passed as a first argument)\n   * @param {Object} [imgOptions] Options object\n   */\n  fabric.Image.fromURL = function(url, callback, imgOptions) {\n    fabric.util.loadImage(url, function(img) {\n      callback && callback(new fabric.Image(img, imgOptions));\n    }, null, imgOptions && imgOptions.crossOrigin);\n  };\n\n  /* _FROM_SVG_START_ */\n  /**\n   * List of attribute names to account for when parsing SVG element (used by {@link fabric.Image.fromElement})\n   * @static\n   * @see {@link http://www.w3.org/TR/SVG/struct.html#ImageElement}\n   */\n  fabric.Image.ATTRIBUTE_NAMES =\n    fabric.SHARED_ATTRIBUTES.concat('x y width height preserveAspectRatio xlink:href crossOrigin'.split(' '));\n\n  /**\n   * Returns {@link fabric.Image} instance from an SVG element\n   * @static\n   * @param {SVGElement} element Element to parse\n   * @param {Object} [options] Options object\n   * @param {Function} callback Callback to execute when fabric.Image object is created\n   * @return {fabric.Image} Instance of fabric.Image\n   */\n  fabric.Image.fromElement = function(element, callback, options) {\n    var parsedAttributes = fabric.parseAttributes(element, fabric.Image.ATTRIBUTE_NAMES);\n    fabric.Image.fromURL(parsedAttributes['xlink:href'], callback,\n      extend((options ? fabric.util.object.clone(options) : { }), parsedAttributes));\n  };\n  /* _FROM_SVG_END_ */\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\nfabric.util.object.extend(fabric.Object.prototype, /** @lends fabric.Object.prototype */ {\n\n  /**\n   * @private\n   * @return {Number} angle value\n   */\n  _getAngleValueForStraighten: function() {\n    var angle = this.angle % 360;\n    if (angle > 0) {\n      return Math.round((angle - 1) / 90) * 90;\n    }\n    return Math.round(angle / 90) * 90;\n  },\n\n  /**\n   * Straightens an object (rotating it from current angle to one of 0, 90, 180, 270, etc. depending on which is closer)\n   * @return {fabric.Object} thisArg\n   * @chainable\n   */\n  straighten: function() {\n    this.rotate(this._getAngleValueForStraighten());\n    return this;\n  },\n\n  /**\n   * Same as {@link fabric.Object.prototype.straighten} but with animation\n   * @param {Object} callbacks Object with callback functions\n   * @param {Function} [callbacks.onComplete] Invoked on completion\n   * @param {Function} [callbacks.onChange] Invoked on every step of animation\n   * @return {fabric.Object} thisArg\n   * @chainable\n   */\n  fxStraighten: function(callbacks) {\n    callbacks = callbacks || { };\n\n    var empty = function() { },\n        onComplete = callbacks.onComplete || empty,\n        onChange = callbacks.onChange || empty,\n        _this = this;\n\n    fabric.util.animate({\n      startValue: this.get('angle'),\n      endValue: this._getAngleValueForStraighten(),\n      duration: this.FX_DURATION,\n      onChange: function(value) {\n        _this.rotate(value);\n        onChange();\n      },\n      onComplete: function() {\n        _this.setCoords();\n        onComplete();\n      },\n    });\n\n    return this;\n  }\n});\n\nfabric.util.object.extend(fabric.StaticCanvas.prototype, /** @lends fabric.StaticCanvas.prototype */ {\n\n  /**\n   * Straightens object, then rerenders canvas\n   * @param {fabric.Object} object Object to straighten\n   * @return {fabric.Canvas} thisArg\n   * @chainable\n   */\n  straightenObject: function (object) {\n    object.straighten();\n    this.requestRenderAll();\n    return this;\n  },\n\n  /**\n   * Same as {@link fabric.Canvas.prototype.straightenObject}, but animated\n   * @param {fabric.Object} object Object to straighten\n   * @return {fabric.Canvas} thisArg\n   * @chainable\n   */\n  fxStraightenObject: function (object) {\n    object.fxStraighten({\n      onChange: this.requestRenderAllBound\n    });\n    return this;\n  }\n});\n\n\n(function() {\n\n  'use strict';\n\n  /**\n   * Tests if webgl supports certain precision\n   * @param {WebGL} Canvas WebGL context to test on\n   * @param {String} Precision to test can be any of following: 'lowp', 'mediump', 'highp'\n   * @returns {Boolean} Whether the user's browser WebGL supports given precision.\n   */\n  function testPrecision(gl, precision){\n    var fragmentSource = 'precision ' + precision + ' float;\\nvoid main(){}';\n    var fragmentShader = gl.createShader(gl.FRAGMENT_SHADER);\n    gl.shaderSource(fragmentShader, fragmentSource);\n    gl.compileShader(fragmentShader);\n    if (!gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS)) {\n      return false;\n    }\n    return true;\n  }\n\n  /**\n   * Indicate whether this filtering backend is supported by the user's browser.\n   * @param {Number} tileSize check if the tileSize is supported\n   * @returns {Boolean} Whether the user's browser supports WebGL.\n   */\n  fabric.isWebglSupported = function(tileSize) {\n    if (fabric.isLikelyNode) {\n      return false;\n    }\n    tileSize = tileSize || fabric.WebglFilterBackend.prototype.tileSize;\n    var canvas = document.createElement('canvas');\n    var gl = canvas.getContext('webgl') || canvas.getContext('experimental-webgl');\n    var isSupported = false;\n    // eslint-disable-next-line\n    if (gl) {\n      fabric.maxTextureSize = gl.getParameter(gl.MAX_TEXTURE_SIZE);\n      isSupported = fabric.maxTextureSize >= tileSize;\n      var precisions = ['highp', 'mediump', 'lowp'];\n      for (var i = 0; i < 3; i++){\n        if (testPrecision(gl, precisions[i])){\n          fabric.webGlPrecision = precisions[i];\n          break;\n        };\n      }\n    }\n    this.isSupported = isSupported;\n    return isSupported;\n  };\n\n  fabric.WebglFilterBackend = WebglFilterBackend;\n\n  /**\n   * WebGL filter backend.\n   */\n  function WebglFilterBackend(options) {\n    if (options && options.tileSize) {\n      this.tileSize = options.tileSize;\n    }\n    this.setupGLContext(this.tileSize, this.tileSize);\n    this.captureGPUInfo();\n  };\n\n  WebglFilterBackend.prototype = /** @lends fabric.WebglFilterBackend.prototype */ {\n\n    tileSize: 2048,\n\n    /**\n     * Experimental. This object is a sort of repository of help layers used to avoid\n     * of recreating them during frequent filtering. If you are previewing a filter with\n     * a slider you problably do not want to create help layers every filter step.\n     * in this object there will be appended some canvases, created once, resized sometimes\n     * cleared never. Clearing is left to the developer.\n     **/\n    resources: {\n\n    },\n\n    /**\n     * Setup a WebGL context suitable for filtering, and bind any needed event handlers.\n     */\n    setupGLContext: function(width, height) {\n      this.dispose();\n      this.createWebGLCanvas(width, height);\n      // eslint-disable-next-line\n      this.aPosition = new Float32Array([0, 0, 0, 1, 1, 0, 1, 1]);\n      this.chooseFastestCopyGLTo2DMethod(width, height);\n    },\n\n    /**\n     * Pick a method to copy data from GL context to 2d canvas.  In some browsers using\n     * putImageData is faster than drawImage for that specific operation.\n     */\n    chooseFastestCopyGLTo2DMethod: function(width, height) {\n      var canMeasurePerf = typeof window.performance !== 'undefined';\n      var canUseImageData;\n      try {\n        new ImageData(1, 1);\n        canUseImageData = true;\n      }\n      catch (e) {\n        canUseImageData = false;\n      }\n      // eslint-disable-next-line no-undef\n      var canUseArrayBuffer = typeof ArrayBuffer !== 'undefined';\n      // eslint-disable-next-line no-undef\n      var canUseUint8Clamped = typeof Uint8ClampedArray !== 'undefined';\n\n      if (!(canMeasurePerf && canUseImageData && canUseArrayBuffer && canUseUint8Clamped)) {\n        return;\n      }\n\n      var targetCanvas = fabric.util.createCanvasElement();\n      // eslint-disable-next-line no-undef\n      var imageBuffer = new ArrayBuffer(width * height * 4);\n      var testContext = {\n        imageBuffer: imageBuffer,\n        destinationWidth: width,\n        destinationHeight: height,\n        targetCanvas: targetCanvas\n      };\n      var startTime, drawImageTime, putImageDataTime;\n      targetCanvas.width = width;\n      targetCanvas.height = height;\n\n      startTime = window.performance.now();\n      copyGLTo2DDrawImage.call(testContext, this.gl, testContext);\n      drawImageTime = window.performance.now() - startTime;\n\n      startTime = window.performance.now();\n      copyGLTo2DPutImageData.call(testContext, this.gl, testContext);\n      putImageDataTime = window.performance.now() - startTime;\n\n      if (drawImageTime > putImageDataTime) {\n        this.imageBuffer = imageBuffer;\n        this.copyGLTo2D = copyGLTo2DPutImageData;\n      }\n      else {\n        this.copyGLTo2D = copyGLTo2DDrawImage;\n      }\n    },\n\n    /**\n     * Create a canvas element and associated WebGL context and attaches them as\n     * class properties to the GLFilterBackend class.\n     */\n    createWebGLCanvas: function(width, height) {\n      var canvas = fabric.util.createCanvasElement();\n      canvas.width = width;\n      canvas.height = height;\n      var glOptions = {\n            alpha: true,\n            premultipliedAlpha: false,\n            depth: false,\n            stencil: false,\n            antialias: false\n          },\n          gl = canvas.getContext('webgl', glOptions);\n      if (!gl) {\n        gl = canvas.getContext('experimental-webgl', glOptions);\n      }\n      if (!gl) {\n        return;\n      }\n      gl.clearColor(0, 0, 0, 0);\n      // this canvas can fire webglcontextlost and webglcontextrestored\n      this.canvas = canvas;\n      this.gl = gl;\n    },\n\n    /**\n     * Attempts to apply the requested filters to the source provided, drawing the filtered output\n     * to the provided target canvas.\n     *\n     * @param {Array} filters The filters to apply.\n     * @param {HTMLImageElement|HTMLCanvasElement} source The source to be filtered.\n     * @param {Number} width The width of the source input.\n     * @param {Number} height The height of the source input.\n     * @param {HTMLCanvasElement} targetCanvas The destination for filtered output to be drawn.\n     * @param {String|undefined} cacheKey A key used to cache resources related to the source. If\n     * omitted, caching will be skipped.\n     */\n    applyFilters: function(filters, source, width, height, targetCanvas, cacheKey) {\n      var gl = this.gl;\n      var cachedTexture;\n      if (cacheKey) {\n        cachedTexture = this.getCachedTexture(cacheKey, source);\n      }\n      var pipelineState = {\n        originalWidth: source.width || source.originalWidth,\n        originalHeight: source.height || source.originalHeight,\n        sourceWidth: width,\n        sourceHeight: height,\n        destinationWidth: width,\n        destinationHeight: height,\n        context: gl,\n        sourceTexture: this.createTexture(gl, width, height, !cachedTexture && source),\n        targetTexture: this.createTexture(gl, width, height),\n        originalTexture: cachedTexture ||\n          this.createTexture(gl, width, height, !cachedTexture && source),\n        passes: filters.length,\n        webgl: true,\n        aPosition: this.aPosition,\n        programCache: this.programCache,\n        pass: 0,\n        filterBackend: this,\n        targetCanvas: targetCanvas\n      };\n      var tempFbo = gl.createFramebuffer();\n      gl.bindFramebuffer(gl.FRAMEBUFFER, tempFbo);\n      filters.forEach(function(filter) { filter && filter.applyTo(pipelineState); });\n      resizeCanvasIfNeeded(pipelineState);\n      this.copyGLTo2D(gl, pipelineState);\n      gl.bindTexture(gl.TEXTURE_2D, null);\n      gl.deleteTexture(pipelineState.sourceTexture);\n      gl.deleteTexture(pipelineState.targetTexture);\n      gl.deleteFramebuffer(tempFbo);\n      targetCanvas.getContext('2d').setTransform(1, 0, 0, 1, 0, 0);\n      return pipelineState;\n    },\n\n    /**\n     * The same as the applyFilter method but with additional logging of WebGL\n     * errors.\n     */\n    applyFiltersDebug: function(filters, source, width, height, targetCanvas, cacheKey) {\n      // The following code is useful when debugging a specific issue but adds ~10x slowdown.\n      var gl = this.gl;\n      var ret = this.applyFilters(filters, source, width, height, targetCanvas, cacheKey);\n      var glError = gl.getError();\n      if (glError !== gl.NO_ERROR) {\n        var errorString = this.glErrorToString(gl, glError);\n        var error = new Error('WebGL Error ' + errorString);\n        error.glErrorCode = glError;\n        throw error;\n      }\n      return ret;\n    },\n\n    glErrorToString: function(context, errorCode) {\n      if (!context) {\n        return 'Context undefined for error code: ' + errorCode;\n      }\n      else if (typeof errorCode !== 'number') {\n        return 'Error code is not a number';\n      }\n      switch (errorCode) {\n        case context.NO_ERROR:\n          return 'NO_ERROR';\n        case context.INVALID_ENUM:\n          return 'INVALID_ENUM';\n        case context.INVALID_VALUE:\n          return 'INVALID_VALUE';\n        case context.INVALID_OPERATION:\n          return 'INVALID_OPERATION';\n        case context.INVALID_FRAMEBUFFER_OPERATION:\n          return 'INVALID_FRAMEBUFFER_OPERATION';\n        case context.OUT_OF_MEMORY:\n          return 'OUT_OF_MEMORY';\n        case context.CONTEXT_LOST_WEBGL:\n          return 'CONTEXT_LOST_WEBGL';\n        default:\n          return 'UNKNOWN_ERROR';\n      }\n    },\n\n    /**\n     * Detach event listeners, remove references, and clean up caches.\n     */\n    dispose: function() {\n      if (this.canvas) {\n        this.canvas = null;\n        this.gl = null;\n      }\n      this.clearWebGLCaches();\n    },\n\n    /**\n     * Wipe out WebGL-related caches.\n     */\n    clearWebGLCaches: function() {\n      this.programCache = {};\n      this.textureCache = {};\n    },\n\n    /**\n     * Create a WebGL texture object.\n     *\n     * Accepts specific dimensions to initialize the textuer to or a source image.\n     *\n     * @param {WebGLRenderingContext} gl The GL context to use for creating the texture.\n     * @param {Number} width The width to initialize the texture at.\n     * @param {Number} height The height to initialize the texture.\n     * @param {HTMLImageElement|HTMLCanvasElement} textureImageSource A source for the texture data.\n     * @returns {WebGLTexture}\n     */\n    createTexture: function(gl, width, height, textureImageSource) {\n      var texture = gl.createTexture();\n      gl.bindTexture(gl.TEXTURE_2D, texture);\n      gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n      gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n      gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n      gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n      if (textureImageSource) {\n        gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, textureImageSource);\n      }\n      else {\n        gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n      }\n      return texture;\n    },\n\n    /**\n     * Can be optionally used to get a texture from the cache array\n     *\n     * If an existing texture is not found, a new texture is created and cached.\n     *\n     * @param {String} uniqueId A cache key to use to find an existing texture.\n     * @param {HTMLImageElement|HTMLCanvasElement} textureImageSource A source to use to create the\n     * texture cache entry if one does not already exist.\n     */\n    getCachedTexture: function(uniqueId, textureImageSource) {\n      if (this.textureCache[uniqueId]) {\n        return this.textureCache[uniqueId];\n      }\n      else {\n        var texture = this.createTexture(\n          this.gl, textureImageSource.width, textureImageSource.height, textureImageSource);\n        this.textureCache[uniqueId] = texture;\n        return texture;\n      }\n    },\n\n    /**\n     * Clear out cached resources related to a source image that has been\n     * filtered previously.\n     *\n     * @param {String} cacheKey The cache key provided when the source image was filtered.\n     */\n    evictCachesForKey: function(cacheKey) {\n      if (this.textureCache[cacheKey]) {\n        this.gl.deleteTexture(this.textureCache[cacheKey]);\n        delete this.textureCache[cacheKey];\n      }\n    },\n\n    copyGLTo2D: copyGLTo2DDrawImage,\n\n    /**\n     * Attempt to extract GPU information strings from a WebGL context.\n     *\n     * Useful information when debugging or blacklisting specific GPUs.\n     *\n     * @returns {Object} A GPU info object with renderer and vendor strings.\n     */\n    captureGPUInfo: function() {\n      if (this.gpuInfo) {\n        return this.gpuInfo;\n      }\n      var gl = this.gl;\n      var ext = gl.getExtension('WEBGL_debug_renderer_info');\n      var gpuInfo = { renderer: '', vendor: '' };\n      if (ext) {\n        var renderer = gl.getParameter(ext.UNMASKED_RENDERER_WEBGL);\n        var vendor = gl.getParameter(ext.UNMASKED_VENDOR_WEBGL);\n        if (renderer) {\n          gpuInfo.renderer = renderer.toLowerCase();\n        }\n        if (vendor) {\n          gpuInfo.vendor = vendor.toLowerCase();\n        }\n      }\n      this.gpuInfo = gpuInfo;\n      return gpuInfo;\n    },\n  };\n})();\n\nfunction resizeCanvasIfNeeded(pipelineState) {\n  var targetCanvas = pipelineState.targetCanvas,\n      width = targetCanvas.width, height = targetCanvas.height,\n      dWidth = pipelineState.destinationWidth,\n      dHeight = pipelineState.destinationHeight;\n\n  if (width !== dWidth || height !== dHeight) {\n    targetCanvas.width = dWidth;\n    targetCanvas.height = dHeight;\n  }\n}\n\n/**\n * Copy an input WebGL canvas on to an output 2D canvas.\n *\n * The WebGL canvas is assumed to be upside down, with the top-left pixel of the\n * desired output image appearing in the bottom-left corner of the WebGL canvas.\n *\n * @param {WebGLRenderingContext} sourceContext The WebGL context to copy from.\n * @param {HTMLCanvasElement} targetCanvas The 2D target canvas to copy on to.\n * @param {Object} pipelineState The 2D target canvas to copy on to.\n */\nfunction copyGLTo2DDrawImage(gl, pipelineState) {\n  var glCanvas = gl.canvas, targetCanvas = pipelineState.targetCanvas,\n      ctx = targetCanvas.getContext('2d');\n  ctx.translate(0, targetCanvas.height); // move it down again\n  ctx.scale(1, -1); // vertical flip\n  // where is my image on the big glcanvas?\n  var sourceY = glCanvas.height - targetCanvas.height;\n  ctx.drawImage(glCanvas, 0, sourceY, targetCanvas.width, targetCanvas.height, 0, 0,\n    targetCanvas.width, targetCanvas.height);\n}\n\n/**\n * Copy an input WebGL canvas on to an output 2D canvas using 2d canvas' putImageData\n * API. Measurably faster than using ctx.drawImage in Firefox (version 54 on OSX Sierra).\n *\n * @param {WebGLRenderingContext} sourceContext The WebGL context to copy from.\n * @param {HTMLCanvasElement} targetCanvas The 2D target canvas to copy on to.\n * @param {Object} pipelineState The 2D target canvas to copy on to.\n */\nfunction copyGLTo2DPutImageData(gl, pipelineState) {\n  var targetCanvas = pipelineState.targetCanvas, ctx = targetCanvas.getContext('2d'),\n      dWidth = pipelineState.destinationWidth,\n      dHeight = pipelineState.destinationHeight,\n      numBytes = dWidth * dHeight * 4;\n\n  // eslint-disable-next-line no-undef\n  var u8 = new Uint8Array(this.imageBuffer, 0, numBytes);\n  // eslint-disable-next-line no-undef\n  var u8Clamped = new Uint8ClampedArray(this.imageBuffer, 0, numBytes);\n\n  gl.readPixels(0, 0, dWidth, dHeight, gl.RGBA, gl.UNSIGNED_BYTE, u8);\n  var imgData = new ImageData(u8Clamped, dWidth, dHeight);\n  ctx.putImageData(imgData, 0, 0);\n}\n\n\n(function() {\n\n  'use strict';\n\n  var noop = function() {};\n\n  fabric.Canvas2dFilterBackend = Canvas2dFilterBackend;\n\n  /**\n   * Canvas 2D filter backend.\n   */\n  function Canvas2dFilterBackend() {};\n\n  Canvas2dFilterBackend.prototype = /** @lends fabric.Canvas2dFilterBackend.prototype */ {\n    evictCachesForKey: noop,\n    dispose: noop,\n    clearWebGLCaches: noop,\n\n    /**\n     * Experimental. This object is a sort of repository of help layers used to avoid\n     * of recreating them during frequent filtering. If you are previewing a filter with\n     * a slider you probably do not want to create help layers every filter step.\n     * in this object there will be appended some canvases, created once, resized sometimes\n     * cleared never. Clearing is left to the developer.\n     **/\n    resources: {\n\n    },\n\n    /**\n     * Apply a set of filters against a source image and draw the filtered output\n     * to the provided destination canvas.\n     *\n     * @param {EnhancedFilter} filters The filter to apply.\n     * @param {HTMLImageElement|HTMLCanvasElement} sourceElement The source to be filtered.\n     * @param {Number} sourceWidth The width of the source input.\n     * @param {Number} sourceHeight The height of the source input.\n     * @param {HTMLCanvasElement} targetCanvas The destination for filtered output to be drawn.\n     */\n    applyFilters: function(filters, sourceElement, sourceWidth, sourceHeight, targetCanvas) {\n      var ctx = targetCanvas.getContext('2d');\n      ctx.drawImage(sourceElement, 0, 0, sourceWidth, sourceHeight);\n      var imageData = ctx.getImageData(0, 0, sourceWidth, sourceHeight);\n      var originalImageData = ctx.getImageData(0, 0, sourceWidth, sourceHeight);\n      var pipelineState = {\n        sourceWidth: sourceWidth,\n        sourceHeight: sourceHeight,\n        imageData: imageData,\n        originalEl: sourceElement,\n        originalImageData: originalImageData,\n        canvasEl: targetCanvas,\n        ctx: ctx,\n        filterBackend: this,\n      };\n      filters.forEach(function(filter) { filter.applyTo(pipelineState); });\n      if (pipelineState.imageData.width !== sourceWidth || pipelineState.imageData.height !== sourceHeight) {\n        targetCanvas.width = pipelineState.imageData.width;\n        targetCanvas.height = pipelineState.imageData.height;\n      }\n      ctx.putImageData(pipelineState.imageData, 0, 0);\n      return pipelineState;\n    },\n\n  };\n})();\n\n\n/**\n * @namespace fabric.Image.filters\n * @memberOf fabric.Image\n * @tutorial {@link http://fabricjs.com/fabric-intro-part-2#image_filters}\n * @see {@link http://fabricjs.com/image-filters|ImageFilters demo}\n */\nfabric.Image.filters = fabric.Image.filters || { };\n\n/**\n * Root filter class from which all filter classes inherit from\n * @class fabric.Image.filters.BaseFilter\n * @memberOf fabric.Image.filters\n */\nfabric.Image.filters.BaseFilter = fabric.util.createClass(/** @lends fabric.Image.filters.BaseFilter.prototype */ {\n\n  /**\n   * Filter type\n   * @param {String} type\n   * @default\n   */\n  type: 'BaseFilter',\n\n  /**\n   * Array of attributes to send with buffers. do not modify\n   * @private\n   */\n\n  vertexSource: 'attribute vec2 aPosition;\\n' +\n    'varying vec2 vTexCoord;\\n' +\n    'void main() {\\n' +\n      'vTexCoord = aPosition;\\n' +\n      'gl_Position = vec4(aPosition * 2.0 - 1.0, 0.0, 1.0);\\n' +\n    '}',\n\n  fragmentSource: 'precision highp float;\\n' +\n    'varying vec2 vTexCoord;\\n' +\n    'uniform sampler2D uTexture;\\n' +\n    'void main() {\\n' +\n      'gl_FragColor = texture2D(uTexture, vTexCoord);\\n' +\n    '}',\n\n  /**\n   * Constructor\n   * @param {Object} [options] Options object\n   */\n  initialize: function(options) {\n    if (options) {\n      this.setOptions(options);\n    }\n  },\n\n  /**\n   * Sets filter's properties from options\n   * @param {Object} [options] Options object\n   */\n  setOptions: function(options) {\n    for (var prop in options) {\n      this[prop] = options[prop];\n    }\n  },\n\n  /**\n   * Compile this filter's shader program.\n   *\n   * @param {WebGLRenderingContext} gl The GL canvas context to use for shader compilation.\n   * @param {String} fragmentSource fragmentShader source for compilation\n   * @param {String} vertexSource vertexShader source for compilation\n   */\n  createProgram: function(gl, fragmentSource, vertexSource) {\n    fragmentSource = fragmentSource || this.fragmentSource;\n    vertexSource = vertexSource || this.vertexSource;\n    if (fabric.webGlPrecision !== 'highp'){\n      fragmentSource = fragmentSource.replace(\n        /precision highp float/g,\n        'precision ' + fabric.webGlPrecision + ' float'\n      );\n    }\n    var vertexShader = gl.createShader(gl.VERTEX_SHADER);\n    gl.shaderSource(vertexShader, vertexSource);\n    gl.compileShader(vertexShader);\n    if (!gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS)) {\n      throw new Error(\n        // eslint-disable-next-line prefer-template\n        'Vertex shader compile error for ' + this.type + ': ' +\n        gl.getShaderInfoLog(vertexShader)\n      );\n    }\n\n    var fragmentShader = gl.createShader(gl.FRAGMENT_SHADER);\n    gl.shaderSource(fragmentShader, fragmentSource);\n    gl.compileShader(fragmentShader);\n    if (!gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS)) {\n      throw new Error(\n        // eslint-disable-next-line prefer-template\n        'Fragment shader compile error for ' + this.type + ': ' +\n        gl.getShaderInfoLog(fragmentShader)\n      );\n    }\n\n    var program = gl.createProgram();\n    gl.attachShader(program, vertexShader);\n    gl.attachShader(program, fragmentShader);\n    gl.linkProgram(program);\n    if (!gl.getProgramParameter(program, gl.LINK_STATUS)) {\n      throw new Error(\n        // eslint-disable-next-line prefer-template\n        'Shader link error for \"${this.type}\" ' +\n        gl.getProgramInfoLog(program)\n      );\n    }\n\n    var attributeLocations = this.getAttributeLocations(gl, program);\n    var uniformLocations = this.getUniformLocations(gl, program) || { };\n    uniformLocations.uStepW = gl.getUniformLocation(program, 'uStepW');\n    uniformLocations.uStepH = gl.getUniformLocation(program, 'uStepH');\n    return {\n      program: program,\n      attributeLocations: attributeLocations,\n      uniformLocations: uniformLocations\n    };\n  },\n\n  /**\n   * Return a map of attribute names to WebGLAttributeLocation objects.\n   *\n   * @param {WebGLRenderingContext} gl The canvas context used to compile the shader program.\n   * @param {WebGLShaderProgram} program The shader program from which to take attribute locations.\n   * @returns {Object} A map of attribute names to attribute locations.\n   */\n  getAttributeLocations: function(gl, program) {\n    return {\n      aPosition: gl.getAttribLocation(program, 'aPosition'),\n    };\n  },\n\n  /**\n   * Return a map of uniform names to WebGLUniformLocation objects.\n   *\n   * Intended to be overridden by subclasses.\n   *\n   * @param {WebGLRenderingContext} gl The canvas context used to compile the shader program.\n   * @param {WebGLShaderProgram} program The shader program from which to take uniform locations.\n   * @returns {Object} A map of uniform names to uniform locations.\n   */\n  getUniformLocations: function (/* gl, program */) {\n    // in case i do not need any special uniform i need to return an empty object\n    return { };\n  },\n\n  /**\n   * Send attribute data from this filter to its shader program on the GPU.\n   *\n   * @param {WebGLRenderingContext} gl The canvas context used to compile the shader program.\n   * @param {Object} attributeLocations A map of shader attribute names to their locations.\n   */\n  sendAttributeData: function(gl, attributeLocations, aPositionData) {\n    var attributeLocation = attributeLocations.aPosition;\n    var buffer = gl.createBuffer();\n    gl.bindBuffer(gl.ARRAY_BUFFER, buffer);\n    gl.enableVertexAttribArray(attributeLocation);\n    gl.vertexAttribPointer(attributeLocation, 2, gl.FLOAT, false, 0, 0);\n    gl.bufferData(gl.ARRAY_BUFFER, aPositionData, gl.STATIC_DRAW);\n  },\n\n  _setupFrameBuffer: function(options) {\n    var gl = options.context, width, height;\n    if (options.passes > 1) {\n      width = options.destinationWidth;\n      height = options.destinationHeight;\n      if (options.sourceWidth !== width || options.sourceHeight !== height) {\n        gl.deleteTexture(options.targetTexture);\n        options.targetTexture = options.filterBackend.createTexture(gl, width, height);\n      }\n      gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D,\n        options.targetTexture, 0);\n    }\n    else {\n      // draw last filter on canvas and not to framebuffer.\n      gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n      gl.finish();\n    }\n  },\n\n  _swapTextures: function(options) {\n    options.passes--;\n    options.pass++;\n    var temp = options.targetTexture;\n    options.targetTexture = options.sourceTexture;\n    options.sourceTexture = temp;\n  },\n\n  /**\n   * Intentionally left blank, to be overridden in custom filters\n   * @param {Object} options\n   **/\n  isNeutralState: function(/* options */) {\n    return false;\n  },\n\n  /**\n   * Apply this filter to the input image data provided.\n   *\n   * Determines whether to use WebGL or Canvas2D based on the options.webgl flag.\n   *\n   * @param {Object} options\n   * @param {Number} options.passes The number of filters remaining to be executed\n   * @param {Boolean} options.webgl Whether to use webgl to render the filter.\n   * @param {WebGLTexture} options.sourceTexture The texture setup as the source to be filtered.\n   * @param {WebGLTexture} options.targetTexture The texture where filtered output should be drawn.\n   * @param {WebGLRenderingContext} options.context The GL context used for rendering.\n   * @param {Object} options.programCache A map of compiled shader programs, keyed by filter type.\n   */\n  applyTo: function(options) {\n    if (options.webgl) {\n      if (options.passes > 1 && this.isNeutralState(options)) {\n        // avoid doing something that we do not need\n        return;\n      }\n      this._setupFrameBuffer(options);\n      this.applyToWebGL(options);\n      this._swapTextures(options);\n    }\n    else if (!this.isNeutralState()) {\n      this.applyTo2d(options);\n    }\n  },\n\n  /**\n   * Retrieves the cached shader.\n   * @param {Object} options\n   * @param {WebGLRenderingContext} options.context The GL context used for rendering.\n   * @param {Object} options.programCache A map of compiled shader programs, keyed by filter type.\n   */\n  retrieveShader: function(options) {\n    if (!options.programCache.hasOwnProperty(this.type)) {\n      options.programCache[this.type] = this.createProgram(options.context);\n    }\n    return options.programCache[this.type];\n  },\n\n  /**\n   * Apply this filter using webgl.\n   *\n   * @param {Object} options\n   * @param {Number} options.passes The number of filters remaining to be executed\n   * @param {Boolean} options.webgl Whether to use webgl to render the filter.\n   * @param {WebGLTexture} options.originalTexture The texture of the original input image.\n   * @param {WebGLTexture} options.sourceTexture The texture setup as the source to be filtered.\n   * @param {WebGLTexture} options.targetTexture The texture where filtered output should be drawn.\n   * @param {WebGLRenderingContext} options.context The GL context used for rendering.\n   * @param {Object} options.programCache A map of compiled shader programs, keyed by filter type.\n   */\n  applyToWebGL: function(options) {\n    var gl = options.context;\n    var shader = this.retrieveShader(options);\n    if (options.pass === 0 && options.originalTexture) {\n      gl.bindTexture(gl.TEXTURE_2D, options.originalTexture);\n    }\n    else {\n      gl.bindTexture(gl.TEXTURE_2D, options.sourceTexture);\n    }\n    gl.useProgram(shader.program);\n    this.sendAttributeData(gl, shader.attributeLocations, options.aPosition);\n\n    gl.uniform1f(shader.uniformLocations.uStepW, 1 / options.sourceWidth);\n    gl.uniform1f(shader.uniformLocations.uStepH, 1 / options.sourceHeight);\n\n    this.sendUniformData(gl, shader.uniformLocations);\n    gl.viewport(0, 0, options.destinationWidth, options.destinationHeight);\n    gl.drawArrays(gl.TRIANGLE_STRIP, 0, 4);\n  },\n\n  bindAdditionalTexture: function(gl, texture, textureUnit) {\n    gl.activeTexture(textureUnit);\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    // reset active texture to 0 as usual\n    gl.activeTexture(gl.TEXTURE0);\n  },\n\n  unbindAdditionalTexture: function(gl, textureUnit) {\n    gl.activeTexture(textureUnit);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.activeTexture(gl.TEXTURE0);\n  },\n\n  getMainParameter: function() {\n    return this[this.mainParameter];\n  },\n\n  setMainParameter: function(value) {\n    this[this.mainParameter] = value;\n  },\n\n  /**\n   * Send uniform data from this filter to its shader program on the GPU.\n   *\n   * Intended to be overridden by subclasses.\n   *\n   * @param {WebGLRenderingContext} gl The canvas context used to compile the shader program.\n   * @param {Object} uniformLocations A map of shader uniform names to their locations.\n   */\n  sendUniformData: function(/* gl, uniformLocations */) {\n    // Intentionally left blank.  Override me in subclasses.\n  },\n\n  /**\n   * If needed by a 2d filter, this functions can create an helper canvas to be used\n   * remember that options.targetCanvas is available for use till end of chain.\n   */\n  createHelpLayer: function(options) {\n    if (!options.helpLayer) {\n      var helpLayer = document.createElement('canvas');\n      helpLayer.width = options.sourceWidth;\n      helpLayer.height = options.sourceHeight;\n      options.helpLayer = helpLayer;\n    }\n  },\n\n  /**\n   * Returns object representation of an instance\n   * @return {Object} Object representation of an instance\n   */\n  toObject: function() {\n    var object = { type: this.type }, mainP = this.mainParameter;\n    if (mainP) {\n      object[mainP] = this[mainP];\n    }\n    return object;\n  },\n\n  /**\n   * Returns a JSON representation of an instance\n   * @return {Object} JSON\n   */\n  toJSON: function() {\n    // delegate, not alias\n    return this.toObject();\n  }\n});\n\nfabric.Image.filters.BaseFilter.fromObject = function(object, callback) {\n  var filter = new fabric.Image.filters[object.type](object);\n  callback && callback(filter);\n  return filter;\n};\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric  = global.fabric || (global.fabric = { }),\n      filters = fabric.Image.filters,\n      createClass = fabric.util.createClass;\n\n  /**\n   * Color Matrix filter class\n   * @class fabric.Image.filters.ColorMatrix\n   * @memberOf fabric.Image.filters\n   * @extends fabric.Image.filters.BaseFilter\n   * @see {@link fabric.Image.filters.ColorMatrix#initialize} for constructor definition\n   * @see {@link http://fabricjs.com/image-filters|ImageFilters demo}\n   * @see {@Link http://www.webwasp.co.uk/tutorials/219/Color_Matrix_Filter.php}\n   * @see {@Link http://phoboslab.org/log/2013/11/fast-image-filters-with-webgl}\n   * @example <caption>Kodachrome filter</caption>\n   * var filter = new fabric.Image.filters.ColorMatrix({\n   *  matrix: [\n       1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n       -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n       -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n       0, 0, 0, 1, 0\n      ]\n   * });\n   * object.filters.push(filter);\n   * object.applyFilters();\n   */\n  filters.ColorMatrix = createClass(filters.BaseFilter, /** @lends fabric.Image.filters.ColorMatrix.prototype */ {\n\n    /**\n     * Filter type\n     * @param {String} type\n     * @default\n     */\n    type: 'ColorMatrix',\n\n    fragmentSource: 'precision highp float;\\n' +\n      'uniform sampler2D uTexture;\\n' +\n      'varying vec2 vTexCoord;\\n' +\n      'uniform mat4 uColorMatrix;\\n' +\n      'uniform vec4 uConstants;\\n' +\n      'void main() {\\n' +\n        'vec4 color = texture2D(uTexture, vTexCoord);\\n' +\n        'color *= uColorMatrix;\\n' +\n        'color += uConstants;\\n' +\n        'gl_FragColor = color;\\n' +\n      '}',\n\n    /**\n     * Colormatrix for pixels.\n     * array of 20 floats. Numbers in positions 4, 9, 14, 19 loose meaning\n     * outside the -1, 1 range.\n     * 0.0039215686 is the part of 1 that get translated to 1 in 2d\n     * @param {Array} matrix array of 20 numbers.\n     * @default\n     */\n    matrix: [\n      1, 0, 0, 0, 0,\n      0, 1, 0, 0, 0,\n      0, 0, 1, 0, 0,\n      0, 0, 0, 1, 0\n    ],\n\n    mainParameter: 'matrix',\n\n    /**\n     * Lock the colormatrix on the color part, skipping alpha, manly for non webgl scenario\n     * to save some calculation\n     */\n    colorsOnly: true,\n\n    /**\n     * Constructor\n     * @param {Object} [options] Options object\n     */\n    initialize: function(options) {\n      this.callSuper('initialize', options);\n      // create a new array instead mutating the prototype with push\n      this.matrix = this.matrix.slice(0);\n    },\n\n    /**\n     * Intentionally left blank, to be overridden in custom filters\n     * @param {Object} options\n     **/\n    isNeutralState: function(/* options */) {\n      var _class = filters.ColorMatrix;\n      for (var i = 20; i--;) {\n        if (this.matrix[i] !== _class.prototype.matrix[i]) {\n          return false;\n        }\n      }\n      return true;\n    },\n\n    /**\n     * Apply the ColorMatrix operation to a Uint8Array representing the pixels of an image.\n     *\n     * @param {Object} options\n     * @param {ImageData} options.imageData The Uint8Array to be filtered.\n     */\n    applyTo2d: function(options) {\n      var imageData = options.imageData,\n          data = imageData.data,\n          iLen = data.length,\n          m = this.matrix,\n          r, g, b, a, i, colorsOnly = this.colorsOnly;\n\n      for (i = 0; i < iLen; i += 4) {\n        r = data[i];\n        g = data[i + 1];\n        b = data[i + 2];\n        if (colorsOnly) {\n          data[i] = r * m[0] + g * m[1] + b * m[2] + m[4] * 255;\n          data[i + 1] = r * m[5] + g * m[6] + b * m[7] + m[9] * 255;\n          data[i + 2] = r * m[10] + g * m[11] + b * m[12] + m[14] * 255;\n        }\n        else {\n          a = data[i + 3];\n          data[i] = r * m[0] + g * m[1] + b * m[2] + a * m[3] + m[4] * 255;\n          data[i + 1] = r * m[5] + g * m[6] + b * m[7] + a * m[8] + m[9] * 255;\n          data[i + 2] = r * m[10] + g * m[11] + b * m[12] + a * m[13] + m[14] * 255;\n          data[i + 3] = r * m[15] + g * m[16] + b * m[17] + a * m[18] + m[19] * 255;\n        }\n      }\n    },\n\n    /**\n     * Return WebGL uniform locations for this filter's shader.\n     *\n     * @param {WebGLRenderingContext} gl The GL canvas context used to compile this filter's shader.\n     * @param {WebGLShaderProgram} program This filter's compiled shader program.\n     */\n    getUniformLocations: function(gl, program) {\n      return {\n        uColorMatrix: gl.getUniformLocation(program, 'uColorMatrix'),\n        uConstants: gl.getUniformLocation(program, 'uConstants'),\n      };\n    },\n\n    /**\n     * Send data from this filter to its shader program's uniforms.\n     *\n     * @param {WebGLRenderingContext} gl The GL canvas context used to compile this filter's shader.\n     * @param {Object} uniformLocations A map of string uniform names to WebGLUniformLocation objects\n     */\n    sendUniformData: function(gl, uniformLocations) {\n      var m = this.matrix,\n          matrix = [\n            m[0], m[1], m[2], m[3],\n            m[5], m[6], m[7], m[8],\n            m[10], m[11], m[12], m[13],\n            m[15], m[16], m[17], m[18]\n          ],\n          constants = [m[4], m[9], m[14], m[19]];\n      gl.uniformMatrix4fv(uniformLocations.uColorMatrix, false, matrix);\n      gl.uniform4fv(uniformLocations.uConstants, constants);\n    },\n  });\n\n  /**\n   * Returns filter instance from an object representation\n   * @static\n   * @param {Object} object Object to create an instance from\n   * @param {function} [callback] function to invoke after filter creation\n   * @return {fabric.Image.filters.ColorMatrix} Instance of fabric.Image.filters.ColorMatrix\n   */\n  fabric.Image.filters.ColorMatrix.fromObject = fabric.Image.filters.BaseFilter.fromObject;\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric  = global.fabric || (global.fabric = { }),\n      filters = fabric.Image.filters,\n      createClass = fabric.util.createClass;\n\n  /**\n   * Brightness filter class\n   * @class fabric.Image.filters.Brightness\n   * @memberOf fabric.Image.filters\n   * @extends fabric.Image.filters.BaseFilter\n   * @see {@link fabric.Image.filters.Brightness#initialize} for constructor definition\n   * @see {@link http://fabricjs.com/image-filters|ImageFilters demo}\n   * @example\n   * var filter = new fabric.Image.filters.Brightness({\n   *   brightness: 0.05\n   * });\n   * object.filters.push(filter);\n   * object.applyFilters();\n   */\n  filters.Brightness = createClass(filters.BaseFilter, /** @lends fabric.Image.filters.Brightness.prototype */ {\n\n    /**\n     * Filter type\n     * @param {String} type\n     * @default\n     */\n    type: 'Brightness',\n\n    /**\n     * Fragment source for the brightness program\n     */\n    fragmentSource: 'precision highp float;\\n' +\n      'uniform sampler2D uTexture;\\n' +\n      'uniform float uBrightness;\\n' +\n      'varying vec2 vTexCoord;\\n' +\n      'void main() {\\n' +\n        'vec4 color = texture2D(uTexture, vTexCoord);\\n' +\n        'color.rgb += uBrightness;\\n' +\n        'gl_FragColor = color;\\n' +\n      '}',\n\n    /**\n     * Brightness value, from -1 to 1.\n     * translated to -255 to 255 for 2d\n     * 0.0039215686 is the part of 1 that get translated to 1 in 2d\n     * @param {Number} brightness\n     * @default\n     */\n    brightness: 0,\n\n    /**\n     * Describe the property that is the filter parameter\n     * @param {String} m\n     * @default\n     */\n    mainParameter: 'brightness',\n\n    /**\n    * Apply the Brightness operation to a Uint8ClampedArray representing the pixels of an image.\n    *\n    * @param {Object} options\n    * @param {ImageData} options.imageData The Uint8ClampedArray to be filtered.\n    */\n    applyTo2d: function(options) {\n      if (this.brightness === 0) {\n        return;\n      }\n      var imageData = options.imageData,\n          data = imageData.data, i, len = data.length,\n          brightness = Math.round(this.brightness * 255);\n      for (i = 0; i < len; i += 4) {\n        data[i] = data[i] + brightness;\n        data[i + 1] = data[i + 1] + brightness;\n        data[i + 2] = data[i + 2] + brightness;\n      }\n    },\n\n    /**\n     * Return WebGL uniform locations for this filter's shader.\n     *\n     * @param {WebGLRenderingContext} gl The GL canvas context used to compile this filter's shader.\n     * @param {WebGLShaderProgram} program This filter's compiled shader program.\n     */\n    getUniformLocations: function(gl, program) {\n      return {\n        uBrightness: gl.getUniformLocation(program, 'uBrightness'),\n      };\n    },\n\n    /**\n     * Send data from this filter to its shader program's uniforms.\n     *\n     * @param {WebGLRenderingContext} gl The GL canvas context used to compile this filter's shader.\n     * @param {Object} uniformLocations A map of string uniform names to WebGLUniformLocation objects\n     */\n    sendUniformData: function(gl, uniformLocations) {\n      gl.uniform1f(uniformLocations.uBrightness, this.brightness);\n    },\n  });\n\n  /**\n   * Returns filter instance from an object representation\n   * @static\n   * @param {Object} object Object to create an instance from\n   * @param {function} [callback] to be invoked after filter creation\n   * @return {fabric.Image.filters.Brightness} Instance of fabric.Image.filters.Brightness\n   */\n  fabric.Image.filters.Brightness.fromObject = fabric.Image.filters.BaseFilter.fromObject;\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric  = global.fabric || (global.fabric = { }),\n      extend = fabric.util.object.extend,\n      filters = fabric.Image.filters,\n      createClass = fabric.util.createClass;\n\n  /**\n   * Adapted from <a href=\"http://www.html5rocks.com/en/tutorials/canvas/imagefilters/\">html5rocks article</a>\n   * @class fabric.Image.filters.Convolute\n   * @memberOf fabric.Image.filters\n   * @extends fabric.Image.filters.BaseFilter\n   * @see {@link fabric.Image.filters.Convolute#initialize} for constructor definition\n   * @see {@link http://fabricjs.com/image-filters|ImageFilters demo}\n   * @example <caption>Sharpen filter</caption>\n   * var filter = new fabric.Image.filters.Convolute({\n   *   matrix: [ 0, -1,  0,\n   *            -1,  5, -1,\n   *             0, -1,  0 ]\n   * });\n   * object.filters.push(filter);\n   * object.applyFilters();\n   * canvas.renderAll();\n   * @example <caption>Blur filter</caption>\n   * var filter = new fabric.Image.filters.Convolute({\n   *   matrix: [ 1/9, 1/9, 1/9,\n   *             1/9, 1/9, 1/9,\n   *             1/9, 1/9, 1/9 ]\n   * });\n   * object.filters.push(filter);\n   * object.applyFilters();\n   * canvas.renderAll();\n   * @example <caption>Emboss filter</caption>\n   * var filter = new fabric.Image.filters.Convolute({\n   *   matrix: [ 1,   1,  1,\n   *             1, 0.7, -1,\n   *            -1,  -1, -1 ]\n   * });\n   * object.filters.push(filter);\n   * object.applyFilters();\n   * canvas.renderAll();\n   * @example <caption>Emboss filter with opaqueness</caption>\n   * var filter = new fabric.Image.filters.Convolute({\n   *   opaque: true,\n   *   matrix: [ 1,   1,  1,\n   *             1, 0.7, -1,\n   *            -1,  -1, -1 ]\n   * });\n   * object.filters.push(filter);\n   * object.applyFilters();\n   * canvas.renderAll();\n   */\n  filters.Convolute = createClass(filters.BaseFilter, /** @lends fabric.Image.filters.Convolute.prototype */ {\n\n    /**\n     * Filter type\n     * @param {String} type\n     * @default\n     */\n    type: 'Convolute',\n\n    /*\n     * Opaque value (true/false)\n     */\n    opaque: false,\n\n    /*\n     * matrix for the filter, max 9x9\n     */\n    matrix: [0, 0, 0, 0, 1, 0, 0, 0, 0],\n\n    /**\n     * Fragment source for the brightness program\n     */\n    fragmentSource: {\n      Convolute_3_1: 'precision highp float;\\n' +\n        'uniform sampler2D uTexture;\\n' +\n        'uniform float uMatrix[9];\\n' +\n        'uniform float uStepW;\\n' +\n        'uniform float uStepH;\\n' +\n        'varying vec2 vTexCoord;\\n' +\n        'void main() {\\n' +\n          'vec4 color = vec4(0, 0, 0, 0);\\n' +\n          'for (float h = 0.0; h < 3.0; h+=1.0) {\\n' +\n            'for (float w = 0.0; w < 3.0; w+=1.0) {\\n' +\n              'vec2 matrixPos = vec2(uStepW * (w - 1), uStepH * (h - 1));\\n' +\n              'color += texture2D(uTexture, vTexCoord + matrixPos) * uMatrix[int(h * 3.0 + w)];\\n' +\n            '}\\n' +\n          '}\\n' +\n          'gl_FragColor = color;\\n' +\n        '}',\n      Convolute_3_0: 'precision highp float;\\n' +\n        'uniform sampler2D uTexture;\\n' +\n        'uniform float uMatrix[9];\\n' +\n        'uniform float uStepW;\\n' +\n        'uniform float uStepH;\\n' +\n        'varying vec2 vTexCoord;\\n' +\n        'void main() {\\n' +\n          'vec4 color = vec4(0, 0, 0, 1);\\n' +\n          'for (float h = 0.0; h < 3.0; h+=1.0) {\\n' +\n            'for (float w = 0.0; w < 3.0; w+=1.0) {\\n' +\n              'vec2 matrixPos = vec2(uStepW * (w - 1.0), uStepH * (h - 1.0));\\n' +\n              'color.rgb += texture2D(uTexture, vTexCoord + matrixPos).rgb * uMatrix[int(h * 3.0 + w)];\\n' +\n            '}\\n' +\n          '}\\n' +\n          'float alpha = texture2D(uTexture, vTexCoord).a;\\n' +\n          'gl_FragColor = color;\\n' +\n          'gl_FragColor.a = alpha;\\n' +\n        '}',\n      Convolute_5_1: 'precision highp float;\\n' +\n        'uniform sampler2D uTexture;\\n' +\n        'uniform float uMatrix[25];\\n' +\n        'uniform float uStepW;\\n' +\n        'uniform float uStepH;\\n' +\n        'varying vec2 vTexCoord;\\n' +\n        'void main() {\\n' +\n          'vec4 color = vec4(0, 0, 0, 0);\\n' +\n          'for (float h = 0.0; h < 5.0; h+=1.0) {\\n' +\n            'for (float w = 0.0; w < 5.0; w+=1.0) {\\n' +\n              'vec2 matrixPos = vec2(uStepW * (w - 2.0), uStepH * (h - 2.0));\\n' +\n              'color += texture2D(uTexture, vTexCoord + matrixPos) * uMatrix[int(h * 5.0 + w)];\\n' +\n            '}\\n' +\n          '}\\n' +\n          'gl_FragColor = color;\\n' +\n        '}',\n      Convolute_5_0: 'precision highp float;\\n' +\n        'uniform sampler2D uTexture;\\n' +\n        'uniform float uMatrix[25];\\n' +\n        'uniform float uStepW;\\n' +\n        'uniform float uStepH;\\n' +\n        'varying vec2 vTexCoord;\\n' +\n        'void main() {\\n' +\n          'vec4 color = vec4(0, 0, 0, 1);\\n' +\n          'for (float h = 0.0; h < 5.0; h+=1.0) {\\n' +\n            'for (float w = 0.0; w < 5.0; w+=1.0) {\\n' +\n              'vec2 matrixPos = vec2(uStepW * (w - 2.0), uStepH * (h - 2.0));\\n' +\n              'color.rgb += texture2D(uTexture, vTexCoord + matrixPos).rgb * uMatrix[int(h * 5.0 + w)];\\n' +\n            '}\\n' +\n          '}\\n' +\n          'float alpha = texture2D(uTexture, vTexCoord).a;\\n' +\n          'gl_FragColor = color;\\n' +\n          'gl_FragColor.a = alpha;\\n' +\n        '}',\n      Convolute_7_1: 'precision highp float;\\n' +\n        'uniform sampler2D uTexture;\\n' +\n        'uniform float uMatrix[49];\\n' +\n        'uniform float uStepW;\\n' +\n        'uniform float uStepH;\\n' +\n        'varying vec2 vTexCoord;\\n' +\n        'void main() {\\n' +\n          'vec4 color = vec4(0, 0, 0, 0);\\n' +\n          'for (float h = 0.0; h < 7.0; h+=1.0) {\\n' +\n            'for (float w = 0.0; w < 7.0; w+=1.0) {\\n' +\n              'vec2 matrixPos = vec2(uStepW * (w - 3.0), uStepH * (h - 3.0));\\n' +\n              'color += texture2D(uTexture, vTexCoord + matrixPos) * uMatrix[int(h * 7.0 + w)];\\n' +\n            '}\\n' +\n          '}\\n' +\n          'gl_FragColor = color;\\n' +\n        '}',\n      Convolute_7_0: 'precision highp float;\\n' +\n        'uniform sampler2D uTexture;\\n' +\n        'uniform float uMatrix[49];\\n' +\n        'uniform float uStepW;\\n' +\n        'uniform float uStepH;\\n' +\n        'varying vec2 vTexCoord;\\n' +\n        'void main() {\\n' +\n          'vec4 color = vec4(0, 0, 0, 1);\\n' +\n          'for (float h = 0.0; h < 7.0; h+=1.0) {\\n' +\n            'for (float w = 0.0; w < 7.0; w+=1.0) {\\n' +\n              'vec2 matrixPos = vec2(uStepW * (w - 3.0), uStepH * (h - 3.0));\\n' +\n              'color.rgb += texture2D(uTexture, vTexCoord + matrixPos).rgb * uMatrix[int(h * 7.0 + w)];\\n' +\n            '}\\n' +\n          '}\\n' +\n          'float alpha = texture2D(uTexture, vTexCoord).a;\\n' +\n          'gl_FragColor = color;\\n' +\n          'gl_FragColor.a = alpha;\\n' +\n        '}',\n      Convolute_9_1: 'precision highp float;\\n' +\n        'uniform sampler2D uTexture;\\n' +\n        'uniform float uMatrix[81];\\n' +\n        'uniform float uStepW;\\n' +\n        'uniform float uStepH;\\n' +\n        'varying vec2 vTexCoord;\\n' +\n        'void main() {\\n' +\n          'vec4 color = vec4(0, 0, 0, 0);\\n' +\n          'for (float h = 0.0; h < 9.0; h+=1.0) {\\n' +\n            'for (float w = 0.0; w < 9.0; w+=1.0) {\\n' +\n              'vec2 matrixPos = vec2(uStepW * (w - 4.0), uStepH * (h - 4.0));\\n' +\n              'color += texture2D(uTexture, vTexCoord + matrixPos) * uMatrix[int(h * 9.0 + w)];\\n' +\n            '}\\n' +\n          '}\\n' +\n          'gl_FragColor = color;\\n' +\n        '}',\n      Convolute_9_0: 'precision highp float;\\n' +\n        'uniform sampler2D uTexture;\\n' +\n        'uniform float uMatrix[81];\\n' +\n        'uniform float uStepW;\\n' +\n        'uniform float uStepH;\\n' +\n        'varying vec2 vTexCoord;\\n' +\n        'void main() {\\n' +\n          'vec4 color = vec4(0, 0, 0, 1);\\n' +\n          'for (float h = 0.0; h < 9.0; h+=1.0) {\\n' +\n            'for (float w = 0.0; w < 9.0; w+=1.0) {\\n' +\n              'vec2 matrixPos = vec2(uStepW * (w - 4.0), uStepH * (h - 4.0));\\n' +\n              'color.rgb += texture2D(uTexture, vTexCoord + matrixPos).rgb * uMatrix[int(h * 9.0 + w)];\\n' +\n            '}\\n' +\n          '}\\n' +\n          'float alpha = texture2D(uTexture, vTexCoord).a;\\n' +\n          'gl_FragColor = color;\\n' +\n          'gl_FragColor.a = alpha;\\n' +\n        '}',\n    },\n\n    /**\n     * Constructor\n     * @memberOf fabric.Image.filters.Convolute.prototype\n     * @param {Object} [options] Options object\n     * @param {Boolean} [options.opaque=false] Opaque value (true/false)\n     * @param {Array} [options.matrix] Filter matrix\n     */\n\n\n    /**\n    * Retrieves the cached shader.\n    * @param {Object} options\n    * @param {WebGLRenderingContext} options.context The GL context used for rendering.\n    * @param {Object} options.programCache A map of compiled shader programs, keyed by filter type.\n    */\n    retrieveShader: function(options) {\n      var size = Math.sqrt(this.matrix.length);\n      var cacheKey = this.type + '_' + size + '_' + (this.opaque ? 1 : 0);\n      var shaderSource = this.fragmentSource[cacheKey];\n      if (!options.programCache.hasOwnProperty(cacheKey)) {\n        options.programCache[cacheKey] = this.createProgram(options.context, shaderSource);\n      }\n      return options.programCache[cacheKey];\n    },\n\n    /**\n     * Apply the Brightness operation to a Uint8ClampedArray representing the pixels of an image.\n     *\n     * @param {Object} options\n     * @param {ImageData} options.imageData The Uint8ClampedArray to be filtered.\n     */\n    applyTo2d: function(options) {\n      var imageData = options.imageData,\n          data = imageData.data,\n          weights = this.matrix,\n          side = Math.round(Math.sqrt(weights.length)),\n          halfSide = Math.floor(side / 2),\n          sw = imageData.width,\n          sh = imageData.height,\n          output = options.ctx.createImageData(sw, sh),\n          dst = output.data,\n          // go through the destination image pixels\n          alphaFac = this.opaque ? 1 : 0,\n          r, g, b, a, dstOff,\n          scx, scy, srcOff, wt,\n          x, y, cx, cy;\n\n      for (y = 0; y < sh; y++) {\n        for (x = 0; x < sw; x++) {\n          dstOff = (y * sw + x) * 4;\n          // calculate the weighed sum of the source image pixels that\n          // fall under the convolution matrix\n          r = 0; g = 0; b = 0; a = 0;\n\n          for (cy = 0; cy < side; cy++) {\n            for (cx = 0; cx < side; cx++) {\n              scy = y + cy - halfSide;\n              scx = x + cx - halfSide;\n\n              // eslint-disable-next-line max-depth\n              if (scy < 0 || scy > sh || scx < 0 || scx > sw) {\n                continue;\n              }\n\n              srcOff = (scy * sw + scx) * 4;\n              wt = weights[cy * side + cx];\n\n              r += data[srcOff] * wt;\n              g += data[srcOff + 1] * wt;\n              b += data[srcOff + 2] * wt;\n              // eslint-disable-next-line max-depth\n              if (!alphaFac) {\n                a += data[srcOff + 3] * wt;\n              }\n            }\n          }\n          dst[dstOff] = r;\n          dst[dstOff + 1] = g;\n          dst[dstOff + 2] = b;\n          if (!alphaFac) {\n            dst[dstOff + 3] = a;\n          }\n          else {\n            dst[dstOff + 3] = data[dstOff + 3];\n          }\n        }\n      }\n      options.imageData = output;\n    },\n\n    /**\n     * Return WebGL uniform locations for this filter's shader.\n     *\n     * @param {WebGLRenderingContext} gl The GL canvas context used to compile this filter's shader.\n     * @param {WebGLShaderProgram} program This filter's compiled shader program.\n     */\n    getUniformLocations: function(gl, program) {\n      return {\n        uMatrix: gl.getUniformLocation(program, 'uMatrix'),\n        uOpaque: gl.getUniformLocation(program, 'uOpaque'),\n        uHalfSize: gl.getUniformLocation(program, 'uHalfSize'),\n        uSize: gl.getUniformLocation(program, 'uSize'),\n      };\n    },\n\n    /**\n     * Send data from this filter to its shader program's uniforms.\n     *\n     * @param {WebGLRenderingContext} gl The GL canvas context used to compile this filter's shader.\n     * @param {Object} uniformLocations A map of string uniform names to WebGLUniformLocation objects\n     */\n    sendUniformData: function(gl, uniformLocations) {\n      gl.uniform1fv(uniformLocations.uMatrix, this.matrix);\n    },\n\n    /**\n     * Returns object representation of an instance\n     * @return {Object} Object representation of an instance\n     */\n    toObject: function() {\n      return extend(this.callSuper('toObject'), {\n        opaque: this.opaque,\n        matrix: this.matrix\n      });\n    }\n  });\n\n  /**\n   * Returns filter instance from an object representation\n   * @static\n   * @param {Object} object Object to create an instance from\n   * @param {function} [callback] to be invoked after filter creation\n   * @return {fabric.Image.filters.Convolute} Instance of fabric.Image.filters.Convolute\n   */\n  fabric.Image.filters.Convolute.fromObject = fabric.Image.filters.BaseFilter.fromObject;\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric  = global.fabric || (global.fabric = { }),\n      filters = fabric.Image.filters,\n      createClass = fabric.util.createClass;\n\n  /**\n   * Grayscale image filter class\n   * @class fabric.Image.filters.Grayscale\n   * @memberOf fabric.Image.filters\n   * @extends fabric.Image.filters.BaseFilter\n   * @see {@link http://fabricjs.com/image-filters|ImageFilters demo}\n   * @example\n   * var filter = new fabric.Image.filters.Grayscale();\n   * object.filters.push(filter);\n   * object.applyFilters();\n   */\n  filters.Grayscale = createClass(filters.BaseFilter, /** @lends fabric.Image.filters.Grayscale.prototype */ {\n\n    /**\n     * Filter type\n     * @param {String} type\n     * @default\n     */\n    type: 'Grayscale',\n\n    fragmentSource: {\n      average: 'precision highp float;\\n' +\n        'uniform sampler2D uTexture;\\n' +\n        'varying vec2 vTexCoord;\\n' +\n        'void main() {\\n' +\n          'vec4 color = texture2D(uTexture, vTexCoord);\\n' +\n          'float average = (color.r + color.b + color.g) / 3.0;\\n' +\n          'gl_FragColor = vec4(average, average, average, color.a);\\n' +\n        '}',\n      lightness: 'precision highp float;\\n' +\n        'uniform sampler2D uTexture;\\n' +\n        'uniform int uMode;\\n' +\n        'varying vec2 vTexCoord;\\n' +\n        'void main() {\\n' +\n          'vec4 col = texture2D(uTexture, vTexCoord);\\n' +\n          'float average = (max(max(col.r, col.g),col.b) + min(min(col.r, col.g),col.b)) / 2.0;\\n' +\n          'gl_FragColor = vec4(average, average, average, col.a);\\n' +\n        '}',\n      luminosity: 'precision highp float;\\n' +\n        'uniform sampler2D uTexture;\\n' +\n        'uniform int uMode;\\n' +\n        'varying vec2 vTexCoord;\\n' +\n        'void main() {\\n' +\n          'vec4 col = texture2D(uTexture, vTexCoord);\\n' +\n          'float average = 0.21 * col.r + 0.72 * col.g + 0.07 * col.b;\\n' +\n          'gl_FragColor = vec4(average, average, average, col.a);\\n' +\n        '}',\n    },\n\n\n    /**\n     * Grayscale mode, between 'average', 'lightness', 'luminosity'\n     * @param {String} type\n     * @default\n     */\n    mode: 'average',\n\n    mainParameter: 'mode',\n\n    /**\n     * Apply the Grayscale operation to a Uint8Array representing the pixels of an image.\n     *\n     * @param {Object} options\n     * @param {ImageData} options.imageData The Uint8Array to be filtered.\n     */\n    applyTo2d: function(options) {\n      var imageData = options.imageData,\n          data = imageData.data, i,\n          len = data.length, value,\n          mode = this.mode;\n      for (i = 0; i < len; i += 4) {\n        if (mode === 'average') {\n          value = (data[i] + data[i + 1] + data[i + 2]) / 3;\n        }\n        else if (mode === 'lightness') {\n          value = (Math.min(data[i], data[i + 1], data[i + 2]) +\n            Math.max(data[i], data[i + 1], data[i + 2])) / 2;\n        }\n        else if (mode === 'luminosity') {\n          value = 0.21 * data[i] + 0.72 * data[i + 1] + 0.07 * data[i + 2];\n        }\n        data[i] = value;\n        data[i + 1] = value;\n        data[i + 2] = value;\n      }\n    },\n\n    /**\n     * Retrieves the cached shader.\n     * @param {Object} options\n     * @param {WebGLRenderingContext} options.context The GL context used for rendering.\n     * @param {Object} options.programCache A map of compiled shader programs, keyed by filter type.\n     */\n    retrieveShader: function(options) {\n      var cacheKey = this.type + '_' + this.mode;\n      if (!options.programCache.hasOwnProperty(cacheKey)) {\n        var shaderSource = this.fragmentSource[this.mode];\n        options.programCache[cacheKey] = this.createProgram(options.context, shaderSource);\n      }\n      return options.programCache[cacheKey];\n    },\n\n    /**\n     * Return WebGL uniform locations for this filter's shader.\n     *\n     * @param {WebGLRenderingContext} gl The GL canvas context used to compile this filter's shader.\n     * @param {WebGLShaderProgram} program This filter's compiled shader program.\n     */\n    getUniformLocations: function(gl, program) {\n      return {\n        uMode: gl.getUniformLocation(program, 'uMode'),\n      };\n    },\n\n    /**\n     * Send data from this filter to its shader program's uniforms.\n     *\n     * @param {WebGLRenderingContext} gl The GL canvas context used to compile this filter's shader.\n     * @param {Object} uniformLocations A map of string uniform names to WebGLUniformLocation objects\n     */\n    sendUniformData: function(gl, uniformLocations) {\n      // default average mode.\n      var mode = 1;\n      gl.uniform1i(uniformLocations.uMode, mode);\n    },\n  });\n\n  /**\n   * Returns filter instance from an object representation\n   * @static\n   * @param {Object} object Object to create an instance from\n   * @param {function} [callback] to be invoked after filter creation\n   * @return {fabric.Image.filters.Grayscale} Instance of fabric.Image.filters.Grayscale\n   */\n  fabric.Image.filters.Grayscale.fromObject = fabric.Image.filters.BaseFilter.fromObject;\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric  = global.fabric || (global.fabric = { }),\n      filters = fabric.Image.filters,\n      createClass = fabric.util.createClass;\n\n  /**\n   * Invert filter class\n   * @class fabric.Image.filters.Invert\n   * @memberOf fabric.Image.filters\n   * @extends fabric.Image.filters.BaseFilter\n   * @see {@link http://fabricjs.com/image-filters|ImageFilters demo}\n   * @example\n   * var filter = new fabric.Image.filters.Invert();\n   * object.filters.push(filter);\n   * object.applyFilters(canvas.renderAll.bind(canvas));\n   */\n  filters.Invert = createClass(filters.BaseFilter, /** @lends fabric.Image.filters.Invert.prototype */ {\n\n    /**\n     * Filter type\n     * @param {String} type\n     * @default\n     */\n    type: 'Invert',\n\n    fragmentSource: 'precision highp float;\\n' +\n      'uniform sampler2D uTexture;\\n' +\n      'uniform int uInvert;\\n' +\n      'varying vec2 vTexCoord;\\n' +\n      'void main() {\\n' +\n        'vec4 color = texture2D(uTexture, vTexCoord);\\n' +\n        'if (uInvert == 1) {\\n' +\n          'gl_FragColor = vec4(1.0 - color.r,1.0 -color.g,1.0 -color.b,color.a);\\n' +\n        '} else {\\n' +\n          'gl_FragColor = color;\\n' +\n        '}\\n' +\n      '}',\n\n    /**\n     * Filter invert. if false, does nothing\n     * @param {Boolean} invert\n     * @default\n     */\n    invert: true,\n\n    mainParameter: 'invert',\n\n    /**\n     * Apply the Invert operation to a Uint8Array representing the pixels of an image.\n     *\n     * @param {Object} options\n     * @param {ImageData} options.imageData The Uint8Array to be filtered.\n     */\n    applyTo2d: function(options) {\n      if (!this.invert) {\n        return;\n      }\n      var imageData = options.imageData,\n          data = imageData.data, i,\n          len = data.length;\n      for (i = 0; i < len; i += 4) {\n        data[i] = 255 - data[i];\n        data[i + 1] = 255 - data[i + 1];\n        data[i + 2] = 255 - data[i + 2];\n      }\n    },\n\n    /**\n     * Return WebGL uniform locations for this filter's shader.\n     *\n     * @param {WebGLRenderingContext} gl The GL canvas context used to compile this filter's shader.\n     * @param {WebGLShaderProgram} program This filter's compiled shader program.\n     */\n    getUniformLocations: function(gl, program) {\n      return {\n        uInvert: gl.getUniformLocation(program, 'uInvert'),\n      };\n    },\n\n    /**\n     * Send data from this filter to its shader program's uniforms.\n     *\n     * @param {WebGLRenderingContext} gl The GL canvas context used to compile this filter's shader.\n     * @param {Object} uniformLocations A map of string uniform names to WebGLUniformLocation objects\n     */\n    sendUniformData: function(gl, uniformLocations) {\n      gl.uniform1i(uniformLocations.uInvert, this.invert);\n    },\n  });\n\n  /**\n   * Returns filter instance from an object representation\n   * @static\n   * @param {Object} object Object to create an instance from\n   * @param {function} [callback] to be invoked after filter creation\n   * @return {fabric.Image.filters.Invert} Instance of fabric.Image.filters.Invert\n   */\n  fabric.Image.filters.Invert.fromObject = fabric.Image.filters.BaseFilter.fromObject;\n\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric  = global.fabric || (global.fabric = { }),\n      extend = fabric.util.object.extend,\n      filters = fabric.Image.filters,\n      createClass = fabric.util.createClass;\n\n  /**\n   * Noise filter class\n   * @class fabric.Image.filters.Noise\n   * @memberOf fabric.Image.filters\n   * @extends fabric.Image.filters.BaseFilter\n   * @see {@link fabric.Image.filters.Noise#initialize} for constructor definition\n   * @see {@link http://fabricjs.com/image-filters|ImageFilters demo}\n   * @example\n   * var filter = new fabric.Image.filters.Noise({\n   *   noise: 700\n   * });\n   * object.filters.push(filter);\n   * object.applyFilters();\n   * canvas.renderAll();\n   */\n  filters.Noise = createClass(filters.BaseFilter, /** @lends fabric.Image.filters.Noise.prototype */ {\n\n    /**\n     * Filter type\n     * @param {String} type\n     * @default\n     */\n    type: 'Noise',\n\n    /**\n     * Fragment source for the noise program\n     */\n    fragmentSource: 'precision highp float;\\n' +\n      'uniform sampler2D uTexture;\\n' +\n      'uniform float uStepH;\\n' +\n      'uniform float uNoise;\\n' +\n      'uniform float uSeed;\\n' +\n      'varying vec2 vTexCoord;\\n' +\n      'float rand(vec2 co, float seed, float vScale) {\\n' +\n        'return fract(sin(dot(co.xy * vScale ,vec2(12.9898 , 78.233))) * 43758.5453 * (seed + 0.01) / 2.0);\\n' +\n      '}\\n' +\n      'void main() {\\n' +\n        'vec4 color = texture2D(uTexture, vTexCoord);\\n' +\n        'color.rgb += (0.5 - rand(vTexCoord, uSeed, 0.1 / uStepH)) * uNoise;\\n' +\n        'gl_FragColor = color;\\n' +\n      '}',\n\n    /**\n     * Describe the property that is the filter parameter\n     * @param {String} m\n     * @default\n     */\n    mainParameter: 'noise',\n\n    /**\n     * Noise value, from\n     * @param {Number} noise\n     * @default\n     */\n    noise: 0,\n\n    /**\n     * Apply the Brightness operation to a Uint8ClampedArray representing the pixels of an image.\n     *\n     * @param {Object} options\n     * @param {ImageData} options.imageData The Uint8ClampedArray to be filtered.\n     */\n    applyTo2d: function(options) {\n      if (this.noise === 0) {\n        return;\n      }\n      var imageData = options.imageData,\n          data = imageData.data, i, len = data.length,\n          noise = this.noise, rand;\n\n      for (i = 0, len = data.length; i < len; i += 4) {\n\n        rand = (0.5 - Math.random()) * noise;\n\n        data[i] += rand;\n        data[i + 1] += rand;\n        data[i + 2] += rand;\n      }\n    },\n\n    /**\n     * Return WebGL uniform locations for this filter's shader.\n     *\n     * @param {WebGLRenderingContext} gl The GL canvas context used to compile this filter's shader.\n     * @param {WebGLShaderProgram} program This filter's compiled shader program.\n     */\n    getUniformLocations: function(gl, program) {\n      return {\n        uNoise: gl.getUniformLocation(program, 'uNoise'),\n        uSeed: gl.getUniformLocation(program, 'uSeed'),\n      };\n    },\n\n    /**\n     * Send data from this filter to its shader program's uniforms.\n     *\n     * @param {WebGLRenderingContext} gl The GL canvas context used to compile this filter's shader.\n     * @param {Object} uniformLocations A map of string uniform names to WebGLUniformLocation objects\n     */\n    sendUniformData: function(gl, uniformLocations) {\n      gl.uniform1f(uniformLocations.uNoise, this.noise / 255);\n      gl.uniform1f(uniformLocations.uSeed, Math.random());\n    },\n\n    /**\n     * Returns object representation of an instance\n     * @return {Object} Object representation of an instance\n     */\n    toObject: function() {\n      return extend(this.callSuper('toObject'), {\n        noise: this.noise\n      });\n    }\n  });\n\n  /**\n   * Returns filter instance from an object representation\n   * @static\n   * @param {Object} object Object to create an instance from\n   * @param {Function} [callback] to be invoked after filter creation\n   * @return {fabric.Image.filters.Noise} Instance of fabric.Image.filters.Noise\n   */\n  fabric.Image.filters.Noise.fromObject = fabric.Image.filters.BaseFilter.fromObject;\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric  = global.fabric || (global.fabric = { }),\n      filters = fabric.Image.filters,\n      createClass = fabric.util.createClass;\n\n  /**\n   * Pixelate filter class\n   * @class fabric.Image.filters.Pixelate\n   * @memberOf fabric.Image.filters\n   * @extends fabric.Image.filters.BaseFilter\n   * @see {@link fabric.Image.filters.Pixelate#initialize} for constructor definition\n   * @see {@link http://fabricjs.com/image-filters|ImageFilters demo}\n   * @example\n   * var filter = new fabric.Image.filters.Pixelate({\n   *   blocksize: 8\n   * });\n   * object.filters.push(filter);\n   * object.applyFilters();\n   */\n  filters.Pixelate = createClass(filters.BaseFilter, /** @lends fabric.Image.filters.Pixelate.prototype */ {\n\n    /**\n     * Filter type\n     * @param {String} type\n     * @default\n     */\n    type: 'Pixelate',\n\n    blocksize: 4,\n\n    mainParameter: 'blocksize',\n\n    /**\n     * Fragment source for the Pixelate program\n     */\n    fragmentSource: 'precision highp float;\\n' +\n      'uniform sampler2D uTexture;\\n' +\n      'uniform float uBlocksize;\\n' +\n      'uniform float uStepW;\\n' +\n      'uniform float uStepH;\\n' +\n      'varying vec2 vTexCoord;\\n' +\n      'void main() {\\n' +\n        'float blockW = uBlocksize * uStepW;\\n' +\n        'float blockH = uBlocksize * uStepW;\\n' +\n        'int posX = int(vTexCoord.x / blockW);\\n' +\n        'int posY = int(vTexCoord.y / blockH);\\n' +\n        'float fposX = float(posX);\\n' +\n        'float fposY = float(posY);\\n' +\n        'vec2 squareCoords = vec2(fposX * blockW, fposY * blockH);\\n' +\n        'vec4 color = texture2D(uTexture, squareCoords);\\n' +\n        'gl_FragColor = color;\\n' +\n      '}',\n\n    /**\n     * Apply the Pixelate operation to a Uint8ClampedArray representing the pixels of an image.\n     *\n     * @param {Object} options\n     * @param {ImageData} options.imageData The Uint8ClampedArray to be filtered.\n     */\n    applyTo2d: function(options) {\n      if (this.blocksize === 1) {\n        return;\n      }\n      var imageData = options.imageData,\n          data = imageData.data,\n          iLen = imageData.height,\n          jLen = imageData.width,\n          index, i, j, r, g, b, a,\n          _i, _j, _iLen, _jLen;\n\n      for (i = 0; i < iLen; i += this.blocksize) {\n        for (j = 0; j < jLen; j += this.blocksize) {\n\n          index = (i * 4) * jLen + (j * 4);\n\n          r = data[index];\n          g = data[index + 1];\n          b = data[index + 2];\n          a = data[index + 3];\n\n          _iLen = Math.min(i + this.blocksize, iLen);\n          _jLen = Math.min(j + this.blocksize, jLen);\n          for (_i = i; _i < _iLen; _i++) {\n            for (_j = j; _j < _jLen; _j++) {\n              index = (_i * 4) * jLen + (_j * 4);\n              data[index] = r;\n              data[index + 1] = g;\n              data[index + 2] = b;\n              data[index + 3] = a;\n            }\n          }\n        }\n      }\n    },\n\n    /**\n     * Indicate when the filter is not gonna apply changes to the image\n     **/\n    isNeutralState: function() {\n      return this.blocksize === 1;\n    },\n\n    /**\n     * Return WebGL uniform locations for this filter's shader.\n     *\n     * @param {WebGLRenderingContext} gl The GL canvas context used to compile this filter's shader.\n     * @param {WebGLShaderProgram} program This filter's compiled shader program.\n     */\n    getUniformLocations: function(gl, program) {\n      return {\n        uBlocksize: gl.getUniformLocation(program, 'uBlocksize'),\n        uStepW: gl.getUniformLocation(program, 'uStepW'),\n        uStepH: gl.getUniformLocation(program, 'uStepH'),\n      };\n    },\n\n    /**\n     * Send data from this filter to its shader program's uniforms.\n     *\n     * @param {WebGLRenderingContext} gl The GL canvas context used to compile this filter's shader.\n     * @param {Object} uniformLocations A map of string uniform names to WebGLUniformLocation objects\n     */\n    sendUniformData: function(gl, uniformLocations) {\n      gl.uniform1f(uniformLocations.uBlocksize, this.blocksize);\n    },\n  });\n\n  /**\n   * Returns filter instance from an object representation\n   * @static\n   * @param {Object} object Object to create an instance from\n   * @param {Function} [callback] to be invoked after filter creation\n   * @return {fabric.Image.filters.Pixelate} Instance of fabric.Image.filters.Pixelate\n   */\n  fabric.Image.filters.Pixelate.fromObject = fabric.Image.filters.BaseFilter.fromObject;\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric  = global.fabric || (global.fabric = { }),\n      extend = fabric.util.object.extend,\n      filters = fabric.Image.filters,\n      createClass = fabric.util.createClass;\n\n  /**\n   * Remove white filter class\n   * @class fabric.Image.filters.RemoveColor\n   * @memberOf fabric.Image.filters\n   * @extends fabric.Image.filters.BaseFilter\n   * @see {@link fabric.Image.filters.RemoveColor#initialize} for constructor definition\n   * @see {@link http://fabricjs.com/image-filters|ImageFilters demo}\n   * @example\n   * var filter = new fabric.Image.filters.RemoveColor({\n   *   threshold: 0.2,\n   * });\n   * object.filters.push(filter);\n   * object.applyFilters();\n   * canvas.renderAll();\n   */\n  filters.RemoveColor = createClass(filters.BaseFilter, /** @lends fabric.Image.filters.RemoveColor.prototype */ {\n\n    /**\n     * Filter type\n     * @param {String} type\n     * @default\n     */\n    type: 'RemoveColor',\n\n    /**\n     * Color to remove, in any format understood by fabric.Color.\n     * @param {String} type\n     * @default\n     */\n    color: '#FFFFFF',\n\n    /**\n     * Fragment source for the brightness program\n     */\n    fragmentSource: 'precision highp float;\\n' +\n      'uniform sampler2D uTexture;\\n' +\n      'uniform vec4 uLow;\\n' +\n      'uniform vec4 uHigh;\\n' +\n      'varying vec2 vTexCoord;\\n' +\n      'void main() {\\n' +\n        'gl_FragColor = texture2D(uTexture, vTexCoord);\\n' +\n        'if(all(greaterThan(gl_FragColor.rgb,uLow.rgb)) && all(greaterThan(uHigh.rgb,gl_FragColor.rgb))) {\\n' +\n          'gl_FragColor.a = 0.0;\\n' +\n        '}\\n' +\n      '}',\n\n    /**\n     * distance to actual color, as value up or down from each r,g,b\n     * between 0 and 1\n     **/\n    distance: 0.02,\n\n    /**\n     * For color to remove inside distance, use alpha channel for a smoother deletion\n     * NOT IMPLEMENTED YET\n     **/\n    useAlpha: false,\n\n    /**\n     * Constructor\n     * @memberOf fabric.Image.filters.RemoveWhite.prototype\n     * @param {Object} [options] Options object\n     * @param {Number} [options.color=#RRGGBB] Threshold value\n     * @param {Number} [options.distance=10] Distance value\n     */\n\n    /**\n     * Applies filter to canvas element\n     * @param {Object} canvasEl Canvas element to apply filter to\n     */\n    applyTo2d: function(options) {\n      var imageData = options.imageData,\n          data = imageData.data, i,\n          distance = this.distance * 255,\n          r, g, b,\n          source = new fabric.Color(this.color).getSource(),\n          lowC = [\n            source[0] - distance,\n            source[1] - distance,\n            source[2] - distance,\n          ],\n          highC = [\n            source[0] + distance,\n            source[1] + distance,\n            source[2] + distance,\n          ];\n\n\n      for (i = 0; i < data.length; i += 4) {\n        r = data[i];\n        g = data[i + 1];\n        b = data[i + 2];\n\n        if (r > lowC[0] &&\n            g > lowC[1] &&\n            b > lowC[2] &&\n            r < highC[0] &&\n            g < highC[1] &&\n            b < highC[2]) {\n          data[i + 3] = 0;\n        }\n      }\n    },\n\n    /**\n     * Return WebGL uniform locations for this filter's shader.\n     *\n     * @param {WebGLRenderingContext} gl The GL canvas context used to compile this filter's shader.\n     * @param {WebGLShaderProgram} program This filter's compiled shader program.\n     */\n    getUniformLocations: function(gl, program) {\n      return {\n        uLow: gl.getUniformLocation(program, 'uLow'),\n        uHigh: gl.getUniformLocation(program, 'uHigh'),\n      };\n    },\n\n    /**\n     * Send data from this filter to its shader program's uniforms.\n     *\n     * @param {WebGLRenderingContext} gl The GL canvas context used to compile this filter's shader.\n     * @param {Object} uniformLocations A map of string uniform names to WebGLUniformLocation objects\n     */\n    sendUniformData: function(gl, uniformLocations) {\n      var source = new fabric.Color(this.color).getSource(),\n          distance = parseFloat(this.distance),\n          lowC = [\n            0 + source[0] / 255 - distance,\n            0 + source[1] / 255 - distance,\n            0 + source[2] / 255 - distance,\n            1\n          ],\n          highC = [\n            source[0] / 255 + distance,\n            source[1] / 255 + distance,\n            source[2] / 255 + distance,\n            1\n          ];\n      gl.uniform4fv(uniformLocations.uLow, lowC);\n      gl.uniform4fv(uniformLocations.uHigh, highC);\n    },\n\n    /**\n     * Returns object representation of an instance\n     * @return {Object} Object representation of an instance\n     */\n    toObject: function() {\n      return extend(this.callSuper('toObject'), {\n        color: this.color,\n        distance: this.distance\n      });\n    }\n  });\n\n  /**\n   * Returns filter instance from an object representation\n   * @static\n   * @param {Object} object Object to create an instance from\n   * @param {Function} [callback] to be invoked after filter creation\n   * @return {fabric.Image.filters.RemoveColor} Instance of fabric.Image.filters.RemoveWhite\n   */\n  fabric.Image.filters.RemoveColor.fromObject = fabric.Image.filters.BaseFilter.fromObject;\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric  = global.fabric || (global.fabric = { }),\n      filters = fabric.Image.filters,\n      createClass = fabric.util.createClass;\n\n  var matrices = {\n    Brownie: [\n      0.59970,0.34553,-0.27082,0,0.186,\n      -0.03770,0.86095,0.15059,0,-0.1449,\n      0.24113,-0.07441,0.44972,0,-0.02965,\n      0,0,0,1,0\n    ],\n    Vintage: [\n      0.62793,0.32021,-0.03965,0,0.03784,\n      0.02578,0.64411,0.03259,0,0.02926,\n      0.04660,-0.08512,0.52416,0,0.02023,\n      0,0,0,1,0\n    ],\n    Kodachrome: [\n      1.12855,-0.39673,-0.03992,0,0.24991,\n      -0.16404,1.08352,-0.05498,0,0.09698,\n      -0.16786,-0.56034,1.60148,0,0.13972,\n      0,0,0,1,0\n    ],\n    Technicolor: [\n      1.91252,-0.85453,-0.09155,0,0.04624,\n      -0.30878,1.76589,-0.10601,0,-0.27589,\n      -0.23110,-0.75018,1.84759,0,0.12137,\n      0,0,0,1,0\n    ],\n    Polaroid: [\n      1.438,-0.062,-0.062,0,0,\n      -0.122,1.378,-0.122,0,0,\n      -0.016,-0.016,1.483,0,0,\n      0,0,0,1,0\n    ],\n    Sepia: [\n      0.393, 0.769, 0.189, 0, 0,\n      0.349, 0.686, 0.168, 0, 0,\n      0.272, 0.534, 0.131, 0, 0,\n      0, 0, 0, 1, 0\n    ],\n    BlackWhite: [\n      1.5, 1.5, 1.5, 0, -1,\n      1.5, 1.5, 1.5, 0, -1,\n      1.5, 1.5, 1.5, 0, -1,\n      0, 0, 0, 1, 0,\n    ]\n  };\n\n  for (var key in matrices) {\n    filters[key] = createClass(filters.ColorMatrix, /** @lends fabric.Image.filters.Sepia.prototype */ {\n\n      /**\n       * Filter type\n       * @param {String} type\n       * @default\n       */\n      type: key,\n\n      /**\n       * Colormatrix for the effect\n       * array of 20 floats. Numbers in positions 4, 9, 14, 19 loose meaning\n       * outside the -1, 1 range.\n       * @param {Array} matrix array of 20 numbers.\n       * @default\n       */\n      matrix: matrices[key],\n\n      /**\n       * Lock the matrix export for this kind of static, parameter less filters.\n       */\n      mainParameter: false,\n      /**\n       * Lock the colormatrix on the color part, skipping alpha\n       */\n      colorsOnly: true,\n\n    });\n    fabric.Image.filters[key].fromObject = fabric.Image.filters.BaseFilter.fromObject;\n  }\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function(global) {\n  'use strict';\n\n  var fabric = global.fabric,\n      filters = fabric.Image.filters,\n      createClass = fabric.util.createClass;\n\n  /**\n   * Color Blend filter class\n   * @class fabric.Image.filter.BlendColor\n   * @memberOf fabric.Image.filters\n   * @extends fabric.Image.filters.BaseFilter\n   * @example\n   * var filter = new fabric.Image.filters.BlendColor({\n   *  color: '#000',\n   *  mode: 'multiply'\n   * });\n   *\n   * var filter = new fabric.Image.filters.BlendImage({\n   *  image: fabricImageObject,\n   *  mode: 'multiply',\n   *  alpha: 0.5\n   * });\n   * object.filters.push(filter);\n   * object.applyFilters();\n   * canvas.renderAll();\n   */\n\n  filters.BlendColor = createClass(filters.BaseFilter, /** @lends fabric.Image.filters.Blend.prototype */ {\n    type: 'BlendColor',\n\n    /**\n     * Color to make the blend operation with. default to a reddish color since black or white\n     * gives always strong result.\n     **/\n    color: '#F95C63',\n\n    /**\n     * Blend mode for the filter: one of multiply, add, diff, screen, subtract,\n     * darken, lighten, overlay, exclusion, tint.\n     **/\n    mode: 'multiply',\n\n    /**\n     * alpha value. represent the strength of the blend color operation.\n     **/\n    alpha: 1,\n\n    /**\n     * Fragment source for the Multiply program\n     */\n    fragmentSource: {\n      multiply: 'gl_FragColor.rgb *= uColor.rgb;\\n',\n      screen: 'gl_FragColor.rgb = 1.0 - (1.0 - gl_FragColor.rgb) * (1.0 - uColor.rgb);\\n',\n      add: 'gl_FragColor.rgb += uColor.rgb;\\n',\n      diff: 'gl_FragColor.rgb = abs(gl_FragColor.rgb - uColor.rgb);\\n',\n      subtract: 'gl_FragColor.rgb -= uColor.rgb;\\n',\n      lighten: 'gl_FragColor.rgb = max(gl_FragColor.rgb, uColor.rgb);\\n',\n      darken: 'gl_FragColor.rgb = min(gl_FragColor.rgb, uColor.rgb);\\n',\n      exclusion: 'gl_FragColor.rgb += uColor.rgb - 2.0 * (uColor.rgb * gl_FragColor.rgb);\\n',\n      overlay: 'if (uColor.r < 0.5) {\\n' +\n          'gl_FragColor.r *= 2.0 * uColor.r;\\n' +\n        '} else {\\n' +\n          'gl_FragColor.r = 1.0 - 2.0 * (1.0 - gl_FragColor.r) * (1.0 - uColor.r);\\n' +\n        '}\\n' +\n        'if (uColor.g < 0.5) {\\n' +\n          'gl_FragColor.g *= 2.0 * uColor.g;\\n' +\n        '} else {\\n' +\n          'gl_FragColor.g = 1.0 - 2.0 * (1.0 - gl_FragColor.g) * (1.0 - uColor.g);\\n' +\n        '}\\n' +\n        'if (uColor.b < 0.5) {\\n' +\n          'gl_FragColor.b *= 2.0 * uColor.b;\\n' +\n        '} else {\\n' +\n          'gl_FragColor.b = 1.0 - 2.0 * (1.0 - gl_FragColor.b) * (1.0 - uColor.b);\\n' +\n        '}\\n',\n      tint: 'gl_FragColor.rgb *= (1.0 - uColor.a);\\n' +\n        'gl_FragColor.rgb += uColor.rgb;\\n',\n    },\n\n    /**\n     * build the fragment source for the filters, joining the common part with\n     * the specific one.\n     * @param {String} mode the mode of the filter, a key of this.fragmentSource\n     * @return {String} the source to be compiled\n     * @private\n     */\n    buildSource: function(mode) {\n      return 'precision highp float;\\n' +\n        'uniform sampler2D uTexture;\\n' +\n        'uniform vec4 uColor;\\n' +\n        'varying vec2 vTexCoord;\\n' +\n        'void main() {\\n' +\n          'vec4 color = texture2D(uTexture, vTexCoord);\\n' +\n          'gl_FragColor = color;\\n' +\n          'if (color.a > 0.0) {\\n' +\n            this.fragmentSource[mode] +\n          '}\\n' +\n        '}';\n    },\n\n    /**\n     * Retrieves the cached shader.\n     * @param {Object} options\n     * @param {WebGLRenderingContext} options.context The GL context used for rendering.\n     * @param {Object} options.programCache A map of compiled shader programs, keyed by filter type.\n     */\n    retrieveShader: function(options) {\n      var cacheKey = this.type + '_' + this.mode, shaderSource;\n      if (!options.programCache.hasOwnProperty(cacheKey)) {\n        shaderSource = this.buildSource(this.mode);\n        options.programCache[cacheKey] = this.createProgram(options.context, shaderSource);\n      }\n      return options.programCache[cacheKey];\n    },\n\n    /**\n     * Apply the Blend operation to a Uint8ClampedArray representing the pixels of an image.\n     *\n     * @param {Object} options\n     * @param {ImageData} options.imageData The Uint8ClampedArray to be filtered.\n     */\n    applyTo2d: function(options) {\n      var imageData = options.imageData,\n          data = imageData.data, iLen = data.length,\n          tr, tg, tb,\n          r, g, b,\n          source, alpha1 = 1 - this.alpha;\n\n      source = new fabric.Color(this.color).getSource();\n      tr = source[0] * this.alpha;\n      tg = source[1] * this.alpha;\n      tb = source[2] * this.alpha;\n\n      for (var i = 0; i < iLen; i += 4) {\n\n        r = data[i];\n        g = data[i + 1];\n        b = data[i + 2];\n\n        switch (this.mode) {\n          case 'multiply':\n            data[i] = r * tr / 255;\n            data[i + 1] = g * tg / 255;\n            data[i + 2] = b * tb / 255;\n            break;\n          case 'screen':\n            data[i] = 255 - (255 - r) * (255 - tr) / 255;\n            data[i + 1] = 255 - (255 - g) * (255 - tg) / 255;\n            data[i + 2] = 255 - (255 - b) * (255 - tb) / 255;\n            break;\n          case 'add':\n            data[i] = r + tr;\n            data[i + 1] = g + tg;\n            data[i + 2] = b + tb;\n            break;\n          case 'diff':\n          case 'difference':\n            data[i] = Math.abs(r - tr);\n            data[i + 1] = Math.abs(g - tg);\n            data[i + 2] = Math.abs(b - tb);\n            break;\n          case 'subtract':\n            data[i] = r - tr;\n            data[i + 1] = g - tg;\n            data[i + 2] = b - tb;\n            break;\n          case 'darken':\n            data[i] = Math.min(r, tr);\n            data[i + 1] = Math.min(g, tg);\n            data[i + 2] = Math.min(b, tb);\n            break;\n          case 'lighten':\n            data[i] = Math.max(r, tr);\n            data[i + 1] = Math.max(g, tg);\n            data[i + 2] = Math.max(b, tb);\n            break;\n          case 'overlay':\n            data[i] = tr < 128 ? (2 * r * tr / 255) : (255 - 2 * (255 - r) * (255 - tr) / 255);\n            data[i + 1] = tg < 128 ? (2 * g * tg / 255) : (255 - 2 * (255 - g) * (255 - tg) / 255);\n            data[i + 2] = tb < 128 ? (2 * b * tb / 255) : (255 - 2 * (255 - b) * (255 - tb) / 255);\n            break;\n          case 'exclusion':\n            data[i] = tr + r - ((2 * tr * r) / 255);\n            data[i + 1] = tg + g - ((2 * tg * g) / 255);\n            data[i + 2] = tb + b - ((2 * tb * b) / 255);\n            break;\n          case 'tint':\n            data[i] = tr + r * alpha1;\n            data[i + 1] = tg + g * alpha1;\n            data[i + 2] = tb + b * alpha1;\n        }\n      }\n    },\n\n    /**\n     * Return WebGL uniform locations for this filter's shader.\n     *\n     * @param {WebGLRenderingContext} gl The GL canvas context used to compile this filter's shader.\n     * @param {WebGLShaderProgram} program This filter's compiled shader program.\n     */\n    getUniformLocations: function(gl, program) {\n      return {\n        uColor: gl.getUniformLocation(program, 'uColor'),\n      };\n    },\n\n    /**\n     * Send data from this filter to its shader program's uniforms.\n     *\n     * @param {WebGLRenderingContext} gl The GL canvas context used to compile this filter's shader.\n     * @param {Object} uniformLocations A map of string uniform names to WebGLUniformLocation objects\n     */\n    sendUniformData: function(gl, uniformLocations) {\n      var source = new fabric.Color(this.color).getSource();\n      source[0] = this.alpha * source[0] / 255;\n      source[1] = this.alpha * source[1] / 255;\n      source[2] = this.alpha * source[2] / 255;\n      source[3] = this.alpha;\n      gl.uniform4fv(uniformLocations.uColor, source);\n    },\n\n    /**\n     * Returns object representation of an instance\n     * @return {Object} Object representation of an instance\n     */\n    toObject: function() {\n      return {\n        type: this.type,\n        color: this.color,\n        mode: this.mode,\n        alpha: this.alpha\n      };\n    }\n  });\n\n  /**\n   * Returns filter instance from an object representation\n   * @static\n   * @param {Object} object Object to create an instance from\n   * @param {function} [callback] to be invoked after filter creation\n   * @return {fabric.Image.filters.BlendColor} Instance of fabric.Image.filters.BlendColor\n   */\n  fabric.Image.filters.BlendColor.fromObject = fabric.Image.filters.BaseFilter.fromObject;\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function(global) {\n  'use strict';\n\n  var fabric = global.fabric,\n      filters = fabric.Image.filters,\n      createClass = fabric.util.createClass;\n\n  /**\n   * Image Blend filter class\n   * @class fabric.Image.filter.BlendImage\n   * @memberOf fabric.Image.filters\n   * @extends fabric.Image.filters.BaseFilter\n   * @example\n   * var filter = new fabric.Image.filters.BlendColor({\n   *  color: '#000',\n   *  mode: 'multiply'\n   * });\n   *\n   * var filter = new fabric.Image.filters.BlendImage({\n   *  image: fabricImageObject,\n   *  mode: 'multiply',\n   *  alpha: 0.5\n   * });\n   * object.filters.push(filter);\n   * object.applyFilters();\n   * canvas.renderAll();\n   */\n\n  filters.BlendImage = createClass(filters.BaseFilter, /** @lends fabric.Image.filters.BlendImage.prototype */ {\n    type: 'BlendImage',\n\n    /**\n     * Color to make the blend operation with. default to a reddish color since black or white\n     * gives always strong result.\n     **/\n    image: null,\n\n    /**\n     * Blend mode for the filter: one of multiply, add, diff, screen, subtract,\n     * darken, lighten, overlay, exclusion, tint.\n     **/\n    mode: 'multiply',\n\n    /**\n     * alpha value. represent the strength of the blend color operation.\n     **/\n    alpha: 1,\n\n    vertexSource: 'attribute vec2 aPosition;\\n' +\n      'varying vec2 vTexCoord;\\n' +\n      'varying vec2 vTexCoord2;\\n' +\n      'uniform mat3 uTransformMatrix;\\n' +\n      'void main() {\\n' +\n        'vTexCoord = aPosition;\\n' +\n        'vTexCoord2 = (uTransformMatrix * vec3(aPosition, 1.0)).xy;\\n' +\n        'gl_Position = vec4(aPosition * 2.0 - 1.0, 0.0, 1.0);\\n' +\n      '}',\n\n    /**\n     * Fragment source for the Multiply program\n     */\n    fragmentSource: {\n      multiply: 'precision highp float;\\n' +\n        'uniform sampler2D uTexture;\\n' +\n        'uniform sampler2D uImage;\\n' +\n        'uniform vec4 uColor;\\n' +\n        'varying vec2 vTexCoord;\\n' +\n        'varying vec2 vTexCoord2;\\n' +\n        'void main() {\\n' +\n          'vec4 color = texture2D(uTexture, vTexCoord);\\n' +\n          'vec4 color2 = texture2D(uImage, vTexCoord2);\\n' +\n          'color.rgba *= color2.rgba;\\n' +\n          'gl_FragColor = color;\\n' +\n        '}',\n      mask: 'precision highp float;\\n' +\n        'uniform sampler2D uTexture;\\n' +\n        'uniform sampler2D uImage;\\n' +\n        'uniform vec4 uColor;\\n' +\n        'varying vec2 vTexCoord;\\n' +\n        'varying vec2 vTexCoord2;\\n' +\n        'void main() {\\n' +\n          'vec4 color = texture2D(uTexture, vTexCoord);\\n' +\n          'vec4 color2 = texture2D(uImage, vTexCoord2);\\n' +\n          'color.a = color2.a;\\n' +\n          'gl_FragColor = color;\\n' +\n        '}',\n    },\n\n    /**\n     * Retrieves the cached shader.\n     * @param {Object} options\n     * @param {WebGLRenderingContext} options.context The GL context used for rendering.\n     * @param {Object} options.programCache A map of compiled shader programs, keyed by filter type.\n     */\n    retrieveShader: function(options) {\n      var cacheKey = this.type + '_' + this.mode;\n      var shaderSource = this.fragmentSource[this.mode];\n      if (!options.programCache.hasOwnProperty(cacheKey)) {\n        options.programCache[cacheKey] = this.createProgram(options.context, shaderSource);\n      }\n      return options.programCache[cacheKey];\n    },\n\n    applyToWebGL: function(options) {\n      // load texture to blend.\n      var gl = options.context,\n          texture = this.createTexture(options.filterBackend, this.image);\n      this.bindAdditionalTexture(gl, texture, gl.TEXTURE1);\n      this.callSuper('applyToWebGL', options);\n      this.unbindAdditionalTexture(gl, gl.TEXTURE1);\n    },\n\n    createTexture: function(backend, image) {\n      return backend.getCachedTexture(image.cacheKey, image._element);\n    },\n\n    /**\n     * Calculate a transformMatrix to adapt the image to blend over\n     * @param {Object} options\n     * @param {WebGLRenderingContext} options.context The GL context used for rendering.\n     * @param {Object} options.programCache A map of compiled shader programs, keyed by filter type.\n     */\n    calculateMatrix: function() {\n      var image = this.image,\n          width = image._element.width,\n          height = image._element.height;\n      return [\n        1 / image.scaleX, 0, 0,\n        0, 1 / image.scaleY, 0,\n        -image.left / width, -image.top / height, 1\n      ];\n    },\n\n    /**\n     * Apply the Blend operation to a Uint8ClampedArray representing the pixels of an image.\n     *\n     * @param {Object} options\n     * @param {ImageData} options.imageData The Uint8ClampedArray to be filtered.\n     */\n    applyTo2d: function(options) {\n      var imageData = options.imageData,\n          resources = options.filterBackend.resources,\n          data = imageData.data, iLen = data.length,\n          width = options.imageData.width,\n          height = options.imageData.height,\n          tr, tg, tb, ta,\n          r, g, b, a,\n          canvas1, context, image = this.image, blendData;\n\n      if (!resources.blendImage) {\n        resources.blendImage = document.createElement('canvas');\n      }\n      canvas1 = resources.blendImage;\n      if (canvas1.width !== width || canvas1.height !== height) {\n        canvas1.width = width;\n        canvas1.height = height;\n      }\n      context = canvas1.getContext('2d');\n      context.setTransform(image.scaleX, 0, 0, image.scaleY, image.left, image.top);\n      context.drawImage(image._element, 0, 0, width, height);\n      blendData = context.getImageData(0, 0, width, height).data;\n      for (var i = 0; i < iLen; i += 4) {\n\n        r = data[i];\n        g = data[i + 1];\n        b = data[i + 2];\n        a = data[i + 3];\n\n        tr = blendData[i];\n        tg = blendData[i + 1];\n        tb = blendData[i + 2];\n        ta = blendData[i + 3];\n\n        switch (this.mode) {\n          case 'multiply':\n            data[i] = r * tr / 255;\n            data[i + 1] = g * tg / 255;\n            data[i + 2] = b * tb / 255;\n            data[i + 3] = a * ta / 255;\n            break;\n          case 'mask':\n            data[i + 3] = ta;\n            break;\n        }\n      }\n    },\n\n    /**\n     * Return WebGL uniform locations for this filter's shader.\n     *\n     * @param {WebGLRenderingContext} gl The GL canvas context used to compile this filter's shader.\n     * @param {WebGLShaderProgram} program This filter's compiled shader program.\n     */\n    getUniformLocations: function(gl, program) {\n      return {\n        uTransformMatrix: gl.getUniformLocation(program, 'uTransformMatrix'),\n        uImage: gl.getUniformLocation(program, 'uImage'),\n      };\n    },\n\n    /**\n     * Send data from this filter to its shader program's uniforms.\n     *\n     * @param {WebGLRenderingContext} gl The GL canvas context used to compile this filter's shader.\n     * @param {Object} uniformLocations A map of string uniform names to WebGLUniformLocation objects\n     */\n    sendUniformData: function(gl, uniformLocations) {\n      var matrix = this.calculateMatrix();\n      gl.uniform1i(uniformLocations.uImage, 1); // texture unit 1.\n      gl.uniformMatrix3fv(uniformLocations.uTransformMatrix, false, matrix);\n    },\n\n    /**\n     * Returns object representation of an instance\n     * @return {Object} Object representation of an instance\n     */\n    toObject: function() {\n      return {\n        type: this.type,\n        image: this.image && this.image.toObject(),\n        mode: this.mode,\n        alpha: this.alpha\n      };\n    }\n  });\n\n  /**\n   * Returns filter instance from an object representation\n   * @static\n   * @param {Object} object Object to create an instance from\n   * @param {function} callback to be invoked after filter creation\n   * @return {fabric.Image.filters.BlendImage} Instance of fabric.Image.filters.BlendImage\n   */\n  fabric.Image.filters.BlendImage.fromObject = function(object, callback) {\n    fabric.Image.fromObject(object.image, function(image) {\n      var options = fabric.util.object.clone(object);\n      options.image = image;\n      callback(new fabric.Image.filters.BlendImage(options));\n    });\n  };\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric  = global.fabric || (global.fabric = { }), pow = Math.pow, floor = Math.floor,\n      sqrt = Math.sqrt, abs = Math.abs, round = Math.round, sin = Math.sin,\n      ceil = Math.ceil,\n      filters = fabric.Image.filters,\n      createClass = fabric.util.createClass;\n\n  /**\n   * Resize image filter class\n   * @class fabric.Image.filters.Resize\n   * @memberOf fabric.Image.filters\n   * @extends fabric.Image.filters.BaseFilter\n   * @see {@link http://fabricjs.com/image-filters|ImageFilters demo}\n   * @example\n   * var filter = new fabric.Image.filters.Resize();\n   * object.filters.push(filter);\n   * object.applyFilters(canvas.renderAll.bind(canvas));\n   */\n  filters.Resize = createClass(filters.BaseFilter, /** @lends fabric.Image.filters.Resize.prototype */ {\n\n    /**\n     * Filter type\n     * @param {String} type\n     * @default\n     */\n    type: 'Resize',\n\n    /**\n     * Resize type\n     * @param {String} resizeType\n     * @default\n     */\n    resizeType: 'hermite',\n\n    /**\n     * Scale factor for resizing, x axis\n     * @param {Number} scaleX\n     * @default\n     */\n    scaleX: 0,\n\n    /**\n     * Scale factor for resizing, y axis\n     * @param {Number} scaleY\n     * @default\n     */\n    scaleY: 0,\n\n    /**\n     * LanczosLobes parameter for lanczos filter\n     * @param {Number} lanczosLobes\n     * @default\n     */\n    lanczosLobes: 3,\n\n\n    /**\n     * Return WebGL uniform locations for this filter's shader.\n     *\n     * @param {WebGLRenderingContext} gl The GL canvas context used to compile this filter's shader.\n     * @param {WebGLShaderProgram} program This filter's compiled shader program.\n     */\n    getUniformLocations: function(gl, program) {\n      return {\n        uDelta: gl.getUniformLocation(program, 'uDelta'),\n        uTaps: gl.getUniformLocation(program, 'uTaps'),\n      };\n    },\n\n    /**\n     * Send data from this filter to its shader program's uniforms.\n     *\n     * @param {WebGLRenderingContext} gl The GL canvas context used to compile this filter's shader.\n     * @param {Object} uniformLocations A map of string uniform names to WebGLUniformLocation objects\n     */\n    sendUniformData: function(gl, uniformLocations) {\n      gl.uniform2fv(uniformLocations.uDelta, this.horizontal ? [1 / this.width, 0] : [0, 1 / this.height]);\n      gl.uniform1fv(uniformLocations.uTaps, this.taps);\n    },\n\n    /**\n     * Retrieves the cached shader.\n     * @param {Object} options\n     * @param {WebGLRenderingContext} options.context The GL context used for rendering.\n     * @param {Object} options.programCache A map of compiled shader programs, keyed by filter type.\n     */\n    retrieveShader: function(options) {\n      var filterWindow = this.getFilterWindow(), cacheKey = this.type + '_' + filterWindow;\n      if (!options.programCache.hasOwnProperty(cacheKey)) {\n        var fragmentShader = this.generateShader(filterWindow);\n        options.programCache[cacheKey] = this.createProgram(options.context, fragmentShader);\n      }\n      return options.programCache[cacheKey];\n    },\n\n    getFilterWindow: function() {\n      var scale = this.tempScale;\n      return Math.ceil(this.lanczosLobes / scale);\n    },\n\n    getTaps: function() {\n      var lobeFunction = this.lanczosCreate(this.lanczosLobes), scale = this.tempScale,\n          filterWindow = this.getFilterWindow(), taps = new Array(filterWindow);\n      for (var i = 1; i <= filterWindow; i++) {\n        taps[i - 1] = lobeFunction(i * scale);\n      }\n      return taps;\n    },\n\n    /**\n     * Generate vertex and shader sources from the necessary steps numbers\n     * @param {Number} filterWindow\n     */\n    generateShader: function(filterWindow) {\n      var offsets = new Array(filterWindow),\n          fragmentShader = this.fragmentSourceTOP, filterWindow;\n\n      for (var i = 1; i <= filterWindow; i++) {\n        offsets[i - 1] = i + '.0 * uDelta';\n      }\n\n      fragmentShader += 'uniform float uTaps[' + filterWindow + '];\\n';\n      fragmentShader += 'void main() {\\n';\n      fragmentShader += '  vec4 color = texture2D(uTexture, vTexCoord);\\n';\n      fragmentShader += '  float sum = 1.0;\\n';\n\n      offsets.forEach(function(offset, i) {\n        fragmentShader += '  color += texture2D(uTexture, vTexCoord + ' + offset + ') * uTaps[' + i + '];\\n';\n        fragmentShader += '  color += texture2D(uTexture, vTexCoord - ' + offset + ') * uTaps[' + i + '];\\n';\n        fragmentShader += '  sum += 2.0 * uTaps[' + i + '];\\n';\n      });\n      fragmentShader += '  gl_FragColor = color / sum;\\n';\n      fragmentShader += '}';\n      return fragmentShader;\n    },\n\n    fragmentSourceTOP: 'precision highp float;\\n' +\n      'uniform sampler2D uTexture;\\n' +\n      'uniform vec2 uDelta;\\n' +\n      'varying vec2 vTexCoord;\\n',\n\n    /**\n     * Apply the resize filter to the image\n     * Determines whether to use WebGL or Canvas2D based on the options.webgl flag.\n     *\n     * @param {Object} options\n     * @param {Number} options.passes The number of filters remaining to be executed\n     * @param {Boolean} options.webgl Whether to use webgl to render the filter.\n     * @param {WebGLTexture} options.sourceTexture The texture setup as the source to be filtered.\n     * @param {WebGLTexture} options.targetTexture The texture where filtered output should be drawn.\n     * @param {WebGLRenderingContext} options.context The GL context used for rendering.\n     * @param {Object} options.programCache A map of compiled shader programs, keyed by filter type.\n     */\n    applyTo: function(options) {\n      if (options.webgl) {\n        if (options.passes > 1 && this.isNeutralState(options)) {\n          // avoid doing something that we do not need\n          return;\n        }\n        options.passes++;\n        this.width = options.sourceWidth;\n        this.horizontal = true;\n        this.dW = Math.round(this.width * this.scaleX);\n        this.dH = options.sourceHeight;\n        this.tempScale = this.dW / this.width;\n        this.taps = this.getTaps();\n        options.destinationWidth = this.dW;\n        this._setupFrameBuffer(options);\n        this.applyToWebGL(options);\n        this._swapTextures(options);\n        options.sourceWidth = options.destinationWidth;\n\n        this.height = options.sourceHeight;\n        this.horizontal = false;\n        this.dH = Math.round(this.height * this.scaleY);\n        this.tempScale = this.dH / this.height;\n        this.taps = this.getTaps();\n        options.destinationHeight = this.dH;\n        this._setupFrameBuffer(options);\n        this.applyToWebGL(options);\n        this._swapTextures(options);\n        options.sourceHeight = options.destinationHeight;\n      }\n      else if (!this.isNeutralState(options)) {\n        this.applyTo2d(options);\n      }\n    },\n\n    isNeutralState: function(options) {\n      var scaleX = options.scaleX || this.scaleX,\n          scaleY = options.scaleY || this.scaleY;\n      return scaleX === 1 && scaleY === 1;\n    },\n\n    lanczosCreate: function(lobes) {\n      return function(x) {\n        if (x >= lobes || x <= -lobes) {\n          return 0.0;\n        }\n        if (x < 1.19209290E-07 && x > -1.19209290E-07) {\n          return 1.0;\n        }\n        x *= Math.PI;\n        var xx = x / lobes;\n        return (sin(x) / x) * sin(xx) / xx;\n      };\n    },\n\n    /**\n     * Applies filter to canvas element\n     * @memberOf fabric.Image.filters.Resize.prototype\n     * @param {Object} canvasEl Canvas element to apply filter to\n     * @param {Number} scaleX\n     * @param {Number} scaleY\n     */\n    applyTo2d: function(options) {\n      var imageData = options.imageData,\n          scaleX = this.scaleX,\n          scaleY = this.scaleY;\n\n      this.rcpScaleX = 1 / scaleX;\n      this.rcpScaleY = 1 / scaleY;\n\n      var oW = imageData.width, oH = imageData.height,\n          dW = round(oW * scaleX), dH = round(oH * scaleY),\n          newData;\n\n      if (this.resizeType === 'sliceHack') {\n        newData = this.sliceByTwo(options, oW, oH, dW, dH);\n      }\n      else if (this.resizeType === 'hermite') {\n        newData = this.hermiteFastResize(options, oW, oH, dW, dH);\n      }\n      else if (this.resizeType === 'bilinear') {\n        newData = this.bilinearFiltering(options, oW, oH, dW, dH);\n      }\n      else if (this.resizeType === 'lanczos') {\n        newData = this.lanczosResize(options, oW, oH, dW, dH);\n      }\n      options.imageData = newData;\n    },\n\n    /**\n     * Filter sliceByTwo\n     * @param {Object} canvasEl Canvas element to apply filter to\n     * @param {Number} oW Original Width\n     * @param {Number} oH Original Height\n     * @param {Number} dW Destination Width\n     * @param {Number} dH Destination Height\n     * @returns {ImageData}\n     */\n    sliceByTwo: function(options, oW, oH, dW, dH) {\n      var imageData = options.imageData,\n          mult = 0.5, doneW = false, doneH = false, stepW = oW * mult,\n          stepH = oH * mult, resources = fabric.filterBackend.resources,\n          tmpCanvas, ctx, sX = 0, sY = 0, dX = oW, dY = 0;\n      if (!resources.sliceByTwo) {\n        resources.sliceByTwo = document.createElement('canvas');\n      }\n      tmpCanvas = resources.sliceByTwo;\n      if (tmpCanvas.width < oW * 1.5 || tmpCanvas.height < oH) {\n        tmpCanvas.width = oW * 1.5;\n        tmpCanvas.height = oH;\n      }\n      ctx = tmpCanvas.getContext('2d');\n      ctx.clearRect(0, 0, oW * 1.5, oH);\n      ctx.putImageData(imageData, 0, 0);\n\n      dW = floor(dW);\n      dH = floor(dH);\n\n      while (!doneW || !doneH) {\n        oW = stepW;\n        oH = stepH;\n        if (dW < floor(stepW * mult)) {\n          stepW = floor(stepW * mult);\n        }\n        else {\n          stepW = dW;\n          doneW = true;\n        }\n        if (dH < floor(stepH * mult)) {\n          stepH = floor(stepH * mult);\n        }\n        else {\n          stepH = dH;\n          doneH = true;\n        }\n        ctx.drawImage(tmpCanvas, sX, sY, oW, oH, dX, dY, stepW, stepH);\n        sX = dX;\n        sY = dY;\n        dY += stepH;\n      }\n      return ctx.getImageData(sX, sY, dW, dH);\n    },\n\n    /**\n     * Filter lanczosResize\n     * @param {Object} canvasEl Canvas element to apply filter to\n     * @param {Number} oW Original Width\n     * @param {Number} oH Original Height\n     * @param {Number} dW Destination Width\n     * @param {Number} dH Destination Height\n     * @returns {ImageData}\n     */\n    lanczosResize: function(options, oW, oH, dW, dH) {\n\n      function process(u) {\n        var v, i, weight, idx, a, red, green,\n            blue, alpha, fX, fY;\n        center.x = (u + 0.5) * ratioX;\n        icenter.x = floor(center.x);\n        for (v = 0; v < dH; v++) {\n          center.y = (v + 0.5) * ratioY;\n          icenter.y = floor(center.y);\n          a = 0; red = 0; green = 0; blue = 0; alpha = 0;\n          for (i = icenter.x - range2X; i <= icenter.x + range2X; i++) {\n            if (i < 0 || i >= oW) {\n              continue;\n            }\n            fX = floor(1000 * abs(i - center.x));\n            if (!cacheLanc[fX]) {\n              cacheLanc[fX] = { };\n            }\n            for (var j = icenter.y - range2Y; j <= icenter.y + range2Y; j++) {\n              if (j < 0 || j >= oH) {\n                continue;\n              }\n              fY = floor(1000 * abs(j - center.y));\n              if (!cacheLanc[fX][fY]) {\n                cacheLanc[fX][fY] = lanczos(sqrt(pow(fX * rcpRatioX, 2) + pow(fY * rcpRatioY, 2)) / 1000);\n              }\n              weight = cacheLanc[fX][fY];\n              if (weight > 0) {\n                idx = (j * oW + i) * 4;\n                a += weight;\n                red += weight * srcData[idx];\n                green += weight * srcData[idx + 1];\n                blue += weight * srcData[idx + 2];\n                alpha += weight * srcData[idx + 3];\n              }\n            }\n          }\n          idx = (v * dW + u) * 4;\n          destData[idx] = red / a;\n          destData[idx + 1] = green / a;\n          destData[idx + 2] = blue / a;\n          destData[idx + 3] = alpha / a;\n        }\n\n        if (++u < dW) {\n          return process(u);\n        }\n        else {\n          return destImg;\n        }\n      }\n\n      var srcData = options.imageData.data,\n          destImg = options.ctx.createImageData(dW, dH),\n          destData = destImg.data,\n          lanczos = this.lanczosCreate(this.lanczosLobes),\n          ratioX = this.rcpScaleX, ratioY = this.rcpScaleY,\n          rcpRatioX = 2 / this.rcpScaleX, rcpRatioY = 2 / this.rcpScaleY,\n          range2X = ceil(ratioX * this.lanczosLobes / 2),\n          range2Y = ceil(ratioY * this.lanczosLobes / 2),\n          cacheLanc = { }, center = { }, icenter = { };\n\n      return process(0);\n    },\n\n    /**\n     * bilinearFiltering\n     * @param {Object} canvasEl Canvas element to apply filter to\n     * @param {Number} oW Original Width\n     * @param {Number} oH Original Height\n     * @param {Number} dW Destination Width\n     * @param {Number} dH Destination Height\n     * @returns {ImageData}\n     */\n    bilinearFiltering: function(options, oW, oH, dW, dH) {\n      var a, b, c, d, x, y, i, j, xDiff, yDiff, chnl,\n          color, offset = 0, origPix, ratioX = this.rcpScaleX,\n          ratioY = this.rcpScaleY,\n          w4 = 4 * (oW - 1), img = options.imageData,\n          pixels = img.data, destImage = options.ctx.createImageData(dW, dH),\n          destPixels = destImage.data;\n      for (i = 0; i < dH; i++) {\n        for (j = 0; j < dW; j++) {\n          x = floor(ratioX * j);\n          y = floor(ratioY * i);\n          xDiff = ratioX * j - x;\n          yDiff = ratioY * i - y;\n          origPix = 4 * (y * oW + x);\n\n          for (chnl = 0; chnl < 4; chnl++) {\n            a = pixels[origPix + chnl];\n            b = pixels[origPix + 4 + chnl];\n            c = pixels[origPix + w4 + chnl];\n            d = pixels[origPix + w4 + 4 + chnl];\n            color = a * (1 - xDiff) * (1 - yDiff) + b * xDiff * (1 - yDiff) +\n                    c * yDiff * (1 - xDiff) + d * xDiff * yDiff;\n            destPixels[offset++] = color;\n          }\n        }\n      }\n      return destImage;\n    },\n\n    /**\n     * hermiteFastResize\n     * @param {Object} canvasEl Canvas element to apply filter to\n     * @param {Number} oW Original Width\n     * @param {Number} oH Original Height\n     * @param {Number} dW Destination Width\n     * @param {Number} dH Destination Height\n     * @returns {ImageData}\n     */\n    hermiteFastResize: function(options, oW, oH, dW, dH) {\n      var ratioW = this.rcpScaleX, ratioH = this.rcpScaleY,\n          ratioWHalf = ceil(ratioW / 2),\n          ratioHHalf = ceil(ratioH / 2),\n          img = options.imageData, data = img.data,\n          img2 = options.ctx.createImageData(dW, dH), data2 = img2.data;\n      for (var j = 0; j < dH; j++) {\n        for (var i = 0; i < dW; i++) {\n          var x2 = (i + j * dW) * 4, weight = 0, weights = 0, weightsAlpha = 0,\n              gxR = 0, gxG = 0, gxB = 0, gxA = 0, centerY = (j + 0.5) * ratioH;\n          for (var yy = floor(j * ratioH); yy < (j + 1) * ratioH; yy++) {\n            var dy = abs(centerY - (yy + 0.5)) / ratioHHalf,\n                centerX = (i + 0.5) * ratioW, w0 = dy * dy;\n            for (var xx = floor(i * ratioW); xx < (i + 1) * ratioW; xx++) {\n              var dx = abs(centerX - (xx + 0.5)) / ratioWHalf,\n                  w = sqrt(w0 + dx * dx);\n              /* eslint-disable max-depth */\n              if (w > 1 && w < -1) {\n                continue;\n              }\n              //hermite filter\n              weight = 2 * w * w * w - 3 * w * w + 1;\n              if (weight > 0) {\n                dx = 4 * (xx + yy * oW);\n                //alpha\n                gxA += weight * data[dx + 3];\n                weightsAlpha += weight;\n                //colors\n                if (data[dx + 3] < 255) {\n                  weight = weight * data[dx + 3] / 250;\n                }\n                gxR += weight * data[dx];\n                gxG += weight * data[dx + 1];\n                gxB += weight * data[dx + 2];\n                weights += weight;\n              }\n              /* eslint-enable max-depth */\n            }\n          }\n          data2[x2] = gxR / weights;\n          data2[x2 + 1] = gxG / weights;\n          data2[x2 + 2] = gxB / weights;\n          data2[x2 + 3] = gxA / weightsAlpha;\n        }\n      }\n      return img2;\n    },\n\n    /**\n     * Returns object representation of an instance\n     * @return {Object} Object representation of an instance\n     */\n    toObject: function() {\n      return {\n        type: this.type,\n        scaleX: this.scaleX,\n        scaleY: this.scaleY,\n        resizeType: this.resizeType,\n        lanczosLobes: this.lanczosLobes\n      };\n    }\n  });\n\n  /**\n   * Returns filter instance from an object representation\n   * @static\n   * @param {Object} object Object to create an instance from\n   * @param {Function} [callback] to be invoked after filter creation\n   * @return {fabric.Image.filters.Resize} Instance of fabric.Image.filters.Resize\n   */\n  fabric.Image.filters.Resize.fromObject = fabric.Image.filters.BaseFilter.fromObject;\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric  = global.fabric || (global.fabric = { }),\n      filters = fabric.Image.filters,\n      createClass = fabric.util.createClass;\n\n  /**\n   * Contrast filter class\n   * @class fabric.Image.filters.Contrast\n   * @memberOf fabric.Image.filters\n   * @extends fabric.Image.filters.BaseFilter\n   * @see {@link fabric.Image.filters.Contrast#initialize} for constructor definition\n   * @see {@link http://fabricjs.com/image-filters|ImageFilters demo}\n   * @example\n   * var filter = new fabric.Image.filters.Contrast({\n   *   contrast: 40\n   * });\n   * object.filters.push(filter);\n   * object.applyFilters();\n   */\n  filters.Contrast = createClass(filters.BaseFilter, /** @lends fabric.Image.filters.Contrast.prototype */ {\n\n    /**\n     * Filter type\n     * @param {String} type\n     * @default\n     */\n    type: 'Contrast',\n\n    fragmentSource: 'precision highp float;\\n' +\n      'uniform sampler2D uTexture;\\n' +\n      'uniform float uContrast;\\n' +\n      'varying vec2 vTexCoord;\\n' +\n      'void main() {\\n' +\n        'vec4 color = texture2D(uTexture, vTexCoord);\\n' +\n        'float contrastF = 1.015 * (uContrast + 1.0) / (1.0 * (1.015 - uContrast));\\n' +\n        'color.rgb = contrastF * (color.rgb - 0.5) + 0.5;\\n' +\n        'gl_FragColor = color;\\n' +\n      '}',\n\n    contrast: 0,\n\n    mainParameter: 'contrast',\n\n    /**\n     * Constructor\n     * @memberOf fabric.Image.filters.Contrast.prototype\n     * @param {Object} [options] Options object\n     * @param {Number} [options.contrast=0] Value to contrast the image up (-1...1)\n     */\n\n    /**\n      * Apply the Contrast operation to a Uint8Array representing the pixels of an image.\n      *\n      * @param {Object} options\n      * @param {ImageData} options.imageData The Uint8Array to be filtered.\n      */\n    applyTo2d: function(options) {\n      if (this.contrast === 0) {\n        return;\n      }\n      var imageData = options.imageData, i, len,\n          data = imageData.data, len = data.length,\n          contrast = Math.floor(this.contrast * 255),\n          contrastF = 259 * (contrast + 255) / (255 * (259 - contrast));\n\n      for (i = 0; i < len; i += 4) {\n        data[i] = contrastF * (data[i] - 128) + 128;\n        data[i + 1] = contrastF * (data[i + 1] - 128) + 128;\n        data[i + 2] = contrastF * (data[i + 2] - 128) + 128;\n      }\n    },\n\n    /**\n     * Return WebGL uniform locations for this filter's shader.\n     *\n     * @param {WebGLRenderingContext} gl The GL canvas context used to compile this filter's shader.\n     * @param {WebGLShaderProgram} program This filter's compiled shader program.\n     */\n    getUniformLocations: function(gl, program) {\n      return {\n        uContrast: gl.getUniformLocation(program, 'uContrast'),\n      };\n    },\n\n    /**\n     * Send data from this filter to its shader program's uniforms.\n     *\n     * @param {WebGLRenderingContext} gl The GL canvas context used to compile this filter's shader.\n     * @param {Object} uniformLocations A map of string uniform names to WebGLUniformLocation objects\n     */\n    sendUniformData: function(gl, uniformLocations) {\n      gl.uniform1f(uniformLocations.uContrast, this.contrast);\n    },\n  });\n\n  /**\n   * Returns filter instance from an object representation\n   * @static\n   * @param {Object} object Object to create an instance from\n   * @param {function} [callback] to be invoked after filter creation\n   * @return {fabric.Image.filters.Contrast} Instance of fabric.Image.filters.Contrast\n   */\n  fabric.Image.filters.Contrast.fromObject = fabric.Image.filters.BaseFilter.fromObject;\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric  = global.fabric || (global.fabric = { }),\n      filters = fabric.Image.filters,\n      createClass = fabric.util.createClass;\n\n  /**\n   * Saturate filter class\n   * @class fabric.Image.filters.Saturation\n   * @memberOf fabric.Image.filters\n   * @extends fabric.Image.filters.BaseFilter\n   * @see {@link fabric.Image.filters.Saturation#initialize} for constructor definition\n   * @see {@link http://fabricjs.com/image-filters|ImageFilters demo}\n   * @example\n   * var filter = new fabric.Image.filters.Saturation({\n   *   saturation: 100\n   * });\n   * object.filters.push(filter);\n   * object.applyFilters();\n   */\n  filters.Saturation = createClass(filters.BaseFilter, /** @lends fabric.Image.filters.Saturation.prototype */ {\n\n    /**\n     * Filter type\n     * @param {String} type\n     * @default\n     */\n    type: 'Saturation',\n\n    fragmentSource: 'precision highp float;\\n' +\n      'uniform sampler2D uTexture;\\n' +\n      'uniform float uSaturation;\\n' +\n      'varying vec2 vTexCoord;\\n' +\n      'void main() {\\n' +\n        'vec4 color = texture2D(uTexture, vTexCoord);\\n' +\n        'float rgMax = max(color.r, color.g);\\n' +\n        'float rgbMax = max(rgMax, color.b);\\n' +\n        'color.r += rgbMax != color.r ? (rgbMax - color.r) * uSaturation : 0.00;\\n' +\n        'color.g += rgbMax != color.g ? (rgbMax - color.g) * uSaturation : 0.00;\\n' +\n        'color.b += rgbMax != color.b ? (rgbMax - color.b) * uSaturation : 0.00;\\n' +\n        'gl_FragColor = color;\\n' +\n      '}',\n\n    saturation: 0,\n\n    mainParameter: 'saturation',\n\n    /**\n     * Constructor\n     * @memberOf fabric.Image.filters.Saturate.prototype\n     * @param {Object} [options] Options object\n     * @param {Number} [options.saturate=0] Value to saturate the image (-1...1)\n     */\n\n    /**\n     * Apply the Saturation operation to a Uint8ClampedArray representing the pixels of an image.\n     *\n     * @param {Object} options\n     * @param {ImageData} options.imageData The Uint8ClampedArray to be filtered.\n     */\n    applyTo2d: function(options) {\n      if (this.saturation === 0) {\n        return;\n      }\n      var imageData = options.imageData,\n          data = imageData.data, len = data.length,\n          adjust = -this.saturation, i, max;\n\n      for (i = 0; i < len; i += 4) {\n        max = Math.max(data[i], data[i + 1], data[i + 2]);\n        data[i] += max !== data[i] ? (max - data[i]) * adjust : 0;\n        data[i + 1] += max !== data[i + 1] ? (max - data[i + 1]) * adjust : 0;\n        data[i + 2] += max !== data[i + 2] ? (max - data[i + 2]) * adjust : 0;\n      }\n    },\n\n    /**\n     * Return WebGL uniform locations for this filter's shader.\n     *\n     * @param {WebGLRenderingContext} gl The GL canvas context used to compile this filter's shader.\n     * @param {WebGLShaderProgram} program This filter's compiled shader program.\n     */\n    getUniformLocations: function(gl, program) {\n      return {\n        uSaturation: gl.getUniformLocation(program, 'uSaturation'),\n      };\n    },\n\n    /**\n     * Send data from this filter to its shader program's uniforms.\n     *\n     * @param {WebGLRenderingContext} gl The GL canvas context used to compile this filter's shader.\n     * @param {Object} uniformLocations A map of string uniform names to WebGLUniformLocation objects\n     */\n    sendUniformData: function(gl, uniformLocations) {\n      gl.uniform1f(uniformLocations.uSaturation, -this.saturation);\n    },\n  });\n\n  /**\n   * Returns filter instance from an object representation\n   * @static\n   * @param {Object} object Object to create an instance from\n   * @param {Function} [callback] to be invoked after filter creation\n   * @return {fabric.Image.filters.Saturation} Instance of fabric.Image.filters.Saturate\n   */\n  fabric.Image.filters.Saturation.fromObject = fabric.Image.filters.BaseFilter.fromObject;\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric  = global.fabric || (global.fabric = { }),\n      filters = fabric.Image.filters,\n      createClass = fabric.util.createClass;\n\n  /**\n   * Blur filter class\n   * @class fabric.Image.filters.Blur\n   * @memberOf fabric.Image.filters\n   * @extends fabric.Image.filters.BaseFilter\n   * @see {@link fabric.Image.filters.Blur#initialize} for constructor definition\n   * @see {@link http://fabricjs.com/image-filters|ImageFilters demo}\n   * @example\n   * var filter = new fabric.Image.filters.Blur({\n   *   blur: 0.5\n   * });\n   * object.filters.push(filter);\n   * object.applyFilters();\n   * canvas.renderAll();\n   */\n  filters.Blur = createClass(filters.BaseFilter, /** @lends fabric.Image.filters.Blur.prototype */ {\n\n    type: 'Blur',\n\n    /*\n'gl_FragColor = vec4(0.0);',\n'gl_FragColor += texture2D(texture, vTexCoord + -7 * uDelta)*0.0044299121055113265;',\n'gl_FragColor += texture2D(texture, vTexCoord + -6 * uDelta)*0.00895781211794;',\n'gl_FragColor += texture2D(texture, vTexCoord + -5 * uDelta)*0.0215963866053;',\n'gl_FragColor += texture2D(texture, vTexCoord + -4 * uDelta)*0.0443683338718;',\n'gl_FragColor += texture2D(texture, vTexCoord + -3 * uDelta)*0.0776744219933;',\n'gl_FragColor += texture2D(texture, vTexCoord + -2 * uDelta)*0.115876621105;',\n'gl_FragColor += texture2D(texture, vTexCoord + -1 * uDelta)*0.147308056121;',\n'gl_FragColor += texture2D(texture, vTexCoord              )*0.159576912161;',\n'gl_FragColor += texture2D(texture, vTexCoord + 1 * uDelta)*0.147308056121;',\n'gl_FragColor += texture2D(texture, vTexCoord + 2 * uDelta)*0.115876621105;',\n'gl_FragColor += texture2D(texture, vTexCoord + 3 * uDelta)*0.0776744219933;',\n'gl_FragColor += texture2D(texture, vTexCoord + 4 * uDelta)*0.0443683338718;',\n'gl_FragColor += texture2D(texture, vTexCoord + 5 * uDelta)*0.0215963866053;',\n'gl_FragColor += texture2D(texture, vTexCoord + 6 * uDelta)*0.00895781211794;',\n'gl_FragColor += texture2D(texture, vTexCoord + 7 * uDelta)*0.0044299121055113265;',\n*/\n\n    /* eslint-disable max-len */\n    fragmentSource: 'precision highp float;\\n' +\n      'uniform sampler2D uTexture;\\n' +\n      'uniform vec2 uDelta;\\n' +\n      'varying vec2 vTexCoord;\\n' +\n      'const float nSamples = 15.0;\\n' +\n      'vec3 v3offset = vec3(12.9898, 78.233, 151.7182);\\n' +\n      'float random(vec3 scale) {\\n' +\n        /* use the fragment position for a different seed per-pixel */\n        'return fract(sin(dot(gl_FragCoord.xyz, scale)) * 43758.5453);\\n' +\n      '}\\n' +\n      'void main() {\\n' +\n        'vec4 color = vec4(0.0);\\n' +\n        'float total = 0.0;\\n' +\n        'float offset = random(v3offset);\\n' +\n        'for (float t = -nSamples; t <= nSamples; t++) {\\n' +\n          'float percent = (t + offset - 0.5) / nSamples;\\n' +\n          'float weight = 1.0 - abs(percent);\\n' +\n          'color += texture2D(uTexture, vTexCoord + uDelta * percent) * weight;\\n' +\n          'total += weight;\\n' +\n        '}\\n' +\n        'gl_FragColor = color / total;\\n' +\n      '}',\n    /* eslint-enable max-len */\n\n    /**\n     * blur value, in percentage of image dimensions.\n     * specific to keep the image blur constant at different resolutions\n     * range bewteen 0 and 1.\n     */\n    blur: 0,\n\n    mainParameter: 'blur',\n\n    applyTo: function(options) {\n      if (options.webgl) {\n        // this aspectRatio is used to give the same blur to vertical and horizontal\n        this.aspectRatio = options.sourceWidth / options.sourceHeight;\n        options.passes++;\n        this._setupFrameBuffer(options);\n        this.horizontal = true;\n        this.applyToWebGL(options);\n        this._swapTextures(options);\n        this._setupFrameBuffer(options);\n        this.horizontal = false;\n        this.applyToWebGL(options);\n        this._swapTextures(options);\n      }\n      else {\n        this.applyTo2d(options);\n      }\n    },\n\n    applyTo2d: function(options) {\n      // paint canvasEl with current image data.\n      //options.ctx.putImageData(options.imageData, 0, 0);\n      options.imageData = this.simpleBlur(options);\n    },\n\n    simpleBlur: function(options) {\n      var resources = options.filterBackend.resources, canvas1, canvas2,\n          width = options.imageData.width,\n          height = options.imageData.height;\n\n      if (!resources.blurLayer1) {\n        resources.blurLayer1 = fabric.util.createCanvasElement();\n        resources.blurLayer2 = fabric.util.createCanvasElement();\n      }\n      canvas1 = resources.blurLayer1;\n      canvas2 = resources.blurLayer2;\n      if (canvas1.width !== width || canvas1.height !== height) {\n        canvas2.width = canvas1.width = width;\n        canvas2.height = canvas1.height = height;\n      }\n      var ctx1 = canvas1.getContext('2d'),\n          ctx2 = canvas2.getContext('2d'),\n          nSamples = 15,\n          random, percent, j, i,\n          blur = this.blur * 0.06 * 0.5;\n\n      // load first canvas\n      ctx1.putImageData(options.imageData, 0, 0);\n      ctx2.clearRect(0, 0, width, height);\n\n      for (i = -nSamples; i <= nSamples; i++) {\n        random = (Math.random() - 0.5) / 4;\n        percent = i / nSamples;\n        j = blur * percent * width + random;\n        ctx2.globalAlpha = 1 - Math.abs(percent);\n        ctx2.drawImage(canvas1, j, random);\n        ctx1.drawImage(canvas2, 0, 0);\n        ctx2.globalAlpha = 1;\n        ctx2.clearRect(0, 0, canvas2.width, canvas2.height);\n      }\n      for (i = -nSamples; i <= nSamples; i++) {\n        random = (Math.random() - 0.5) / 4;\n        percent = i / nSamples;\n        j = blur * percent * height + random;\n        ctx2.globalAlpha = 1 - Math.abs(percent);\n        ctx2.drawImage(canvas1, random, j);\n        ctx1.drawImage(canvas2, 0, 0);\n        ctx2.globalAlpha = 1;\n        ctx2.clearRect(0, 0, canvas2.width, canvas2.height);\n      }\n      options.ctx.drawImage(canvas1, 0, 0);\n      var newImageData = options.ctx.getImageData(0, 0, canvas1.width, canvas1.height);\n      ctx1.globalAlpha = 1;\n      ctx1.clearRect(0, 0, canvas1.width, canvas1.height);\n      return newImageData;\n    },\n\n    /**\n     * Return WebGL uniform locations for this filter's shader.\n     *\n     * @param {WebGLRenderingContext} gl The GL canvas context used to compile this filter's shader.\n     * @param {WebGLShaderProgram} program This filter's compiled shader program.\n     */\n    getUniformLocations: function(gl, program) {\n      return {\n        delta: gl.getUniformLocation(program, 'uDelta'),\n      };\n    },\n\n    /**\n     * Send data from this filter to its shader program's uniforms.\n     *\n     * @param {WebGLRenderingContext} gl The GL canvas context used to compile this filter's shader.\n     * @param {Object} uniformLocations A map of string uniform names to WebGLUniformLocation objects\n     */\n    sendUniformData: function(gl, uniformLocations) {\n      var delta = this.chooseRightDelta();\n      gl.uniform2fv(uniformLocations.delta, delta);\n    },\n\n    /**\n     * choose right value of image percentage to blur with\n     * @returns {Array} a numeric array with delta values\n     */\n    chooseRightDelta: function() {\n      var blurScale = 1, delta = [0, 0], blur;\n      if (this.horizontal) {\n        if (this.aspectRatio > 1) {\n          // image is wide, i want to shrink radius horizontal\n          blurScale = 1 / this.aspectRatio;\n        }\n      }\n      else {\n        if (this.aspectRatio < 1) {\n          // image is tall, i want to shrink radius vertical\n          blurScale = this.aspectRatio;\n        }\n      }\n      blur = blurScale * this.blur * 0.12;\n      if (this.horizontal) {\n        delta[0] = blur;\n      }\n      else {\n        delta[1] = blur;\n      }\n      return delta;\n    },\n  });\n\n  /**\n   * Deserialize a JSON definition of a BlurFilter into a concrete instance.\n   */\n  filters.Blur.fromObject = fabric.Image.filters.BaseFilter.fromObject;\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric  = global.fabric || (global.fabric = { }),\n      filters = fabric.Image.filters,\n      createClass = fabric.util.createClass;\n\n  /**\n   * Gamma filter class\n   * @class fabric.Image.filters.Gamma\n   * @memberOf fabric.Image.filters\n   * @extends fabric.Image.filters.BaseFilter\n   * @see {@link fabric.Image.filters.Gamma#initialize} for constructor definition\n   * @see {@link http://fabricjs.com/image-filters|ImageFilters demo}\n   * @example\n   * var filter = new fabric.Image.filters.Gamma({\n   *   brightness: 200\n   * });\n   * object.filters.push(filter);\n   * object.applyFilters();\n   */\n  filters.Gamma = createClass(filters.BaseFilter, /** @lends fabric.Image.filters.Gamma.prototype */ {\n\n    /**\n     * Filter type\n     * @param {String} type\n     * @default\n     */\n    type: 'Gamma',\n\n    fragmentSource: 'precision highp float;\\n' +\n      'uniform sampler2D uTexture;\\n' +\n      'uniform vec3 uGamma;\\n' +\n      'varying vec2 vTexCoord;\\n' +\n      'void main() {\\n' +\n        'vec4 color = texture2D(uTexture, vTexCoord);\\n' +\n        'vec3 correction = (1.0 / uGamma);\\n' +\n        'color.r = pow(color.r, correction.r);\\n' +\n        'color.g = pow(color.g, correction.g);\\n' +\n        'color.b = pow(color.b, correction.b);\\n' +\n        'gl_FragColor = color;\\n' +\n        'gl_FragColor.rgb *= color.a;\\n' +\n      '}',\n\n    /**\n     * Gamma array value, from 0.01 to 2.2.\n     * @param {Array} gamma\n     * @default\n     */\n    gamma: [1, 1, 1],\n\n    /**\n     * Describe the property that is the filter parameter\n     * @param {String} m\n     * @default\n     */\n    mainParameter: 'gamma',\n\n    /**\n     * Apply the Gamma operation to a Uint8Array representing the pixels of an image.\n     *\n     * @param {Object} options\n     * @param {ImageData} options.imageData The Uint8Array to be filtered.\n     */\n    applyTo2d: function(options) {\n      var imageData = options.imageData, data = imageData.data,\n          gamma = this.gamma, len = data.length,\n          rInv = 1 / gamma[0], gInv = 1 / gamma[1],\n          bInv = 1 / gamma[2], i;\n\n      if (!this.rVals) {\n        // eslint-disable-next-line\n        this.rVals = new Uint8Array(256);\n        // eslint-disable-next-line\n        this.gVals = new Uint8Array(256);\n        // eslint-disable-next-line\n        this.bVals = new Uint8Array(256);\n      }\n\n      // This is an optimization - pre-compute a look-up table for each color channel\n      // instead of performing these pow calls for each pixel in the image.\n      for (i = 0, len = 256; i < len; i++) {\n        this.rVals[i] = Math.pow(i / 255, rInv) * 255;\n        this.gVals[i] = Math.pow(i / 255, gInv) * 255;\n        this.bVals[i] = Math.pow(i / 255, bInv) * 255;\n      }\n      for (i = 0, len = data.length; i < len; i += 4) {\n        data[i] = this.rVals[data[i]];\n        data[i + 1] = this.gVals[data[i + 1]];\n        data[i + 2] = this.bVals[data[i + 2]];\n      }\n    },\n\n    /**\n     * Return WebGL uniform locations for this filter's shader.\n     *\n     * @param {WebGLRenderingContext} gl The GL canvas context used to compile this filter's shader.\n     * @param {WebGLShaderProgram} program This filter's compiled shader program.\n     */\n    getUniformLocations: function(gl, program) {\n      return {\n        uGamma: gl.getUniformLocation(program, 'uGamma'),\n      };\n    },\n\n    /**\n     * Send data from this filter to its shader program's uniforms.\n     *\n     * @param {WebGLRenderingContext} gl The GL canvas context used to compile this filter's shader.\n     * @param {Object} uniformLocations A map of string uniform names to WebGLUniformLocation objects\n     */\n    sendUniformData: function(gl, uniformLocations) {\n      gl.uniform3fv(uniformLocations.uGamma, this.gamma);\n    },\n  });\n\n  /**\n   * Returns filter instance from an object representation\n   * @static\n   * @param {Object} object Object to create an instance from\n   * @param {function} [callback] to be invoked after filter creation\n   * @return {fabric.Image.filters.Gamma} Instance of fabric.Image.filters.Gamma\n   */\n  fabric.Image.filters.Gamma.fromObject = fabric.Image.filters.BaseFilter.fromObject;\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric  = global.fabric || (global.fabric = { }),\n      filters = fabric.Image.filters,\n      createClass = fabric.util.createClass;\n\n  /**\n   * A container class that knows how to apply a sequence of filters to an input image.\n   */\n  filters.Composed = createClass(filters.BaseFilter, /** @lends fabric.Image.filters.Composed.prototype */ {\n\n    type: 'Composed',\n\n    /**\n     * A non sparse array of filters to apply\n     */\n    subFilters: [],\n\n    /**\n     * Constructor\n     * @param {Object} [options] Options object\n     */\n    initialize: function(options) {\n      this.callSuper('initialize', options);\n      // create a new array instead mutating the prototype with push\n      this.subFilters = this.subFilters.slice(0);\n    },\n\n    /**\n     * Apply this container's filters to the input image provided.\n     *\n     * @param {Object} options\n     * @param {Number} options.passes The number of filters remaining to be applied.\n     */\n    applyTo: function(options) {\n      options.passes += this.subFilters.length - 1;\n      this.subFilters.forEach(function(filter) {\n        filter.applyTo(options);\n      });\n    },\n\n    /**\n     * Serialize this filter into JSON.\n     *\n     * @returns {Object} A JSON representation of this filter.\n     */\n    toObject: function() {\n      return fabric.util.object.extend(this.callSuper('toObject'), {\n        subFilters: this.subFilters.map(function(filter) { return filter.toObject(); }),\n      });\n    },\n  });\n\n  /**\n   * Deserialize a JSON definition of a ComposedFilter into a concrete instance.\n   */\n  fabric.Image.filters.Composed.fromObject = function(object, callback) {\n    var filters = object.subFilters || [],\n        subFilters = filters.map(function(filter) {\n          return new fabric.Image.filters[filter.type](filter);\n        }),\n        instance = new fabric.Image.filters.Composed({ subFilters: subFilters });\n    callback && callback(instance);\n    return instance;\n  };\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric  = global.fabric || (global.fabric = { }),\n      filters = fabric.Image.filters,\n      createClass = fabric.util.createClass;\n\n  /**\n   * HueRotation filter class\n   * @class fabric.Image.filters.HueRotation\n   * @memberOf fabric.Image.filters\n   * @extends fabric.Image.filters.BaseFilter\n   * @see {@link fabric.Image.filters.HueRotation#initialize} for constructor definition\n   * @see {@link http://fabricjs.com/image-filters|ImageFilters demo}\n   * @example\n   * var filter = new fabric.Image.filters.HueRotation({\n   *   rotation: -0.5\n   * });\n   * object.filters.push(filter);\n   * object.applyFilters();\n   */\n  filters.HueRotation = createClass(filters.ColorMatrix, /** @lends fabric.Image.filters.HueRotation.prototype */ {\n\n    /**\n     * Filter type\n     * @param {String} type\n     * @default\n     */\n    type: 'HueRotation',\n\n    /**\n     * HueRotation value, from -1 to 1.\n     * the unit is radians\n     * @param {Number} myParameter\n     * @default\n     */\n    rotation: 0,\n\n    /**\n     * Describe the property that is the filter parameter\n     * @param {String} m\n     * @default\n     */\n    mainParameter: 'rotation',\n\n    calculateMatrix: function() {\n      var rad = this.rotation * Math.PI, cos = fabric.util.cos(rad), sin = fabric.util.sin(rad),\n          aThird = 1 / 3, aThirdSqtSin = Math.sqrt(aThird) * sin, OneMinusCos = 1 - cos;\n      this.matrix = [\n        1, 0, 0, 0, 0,\n        0, 1, 0, 0, 0,\n        0, 0, 1, 0, 0,\n        0, 0, 0, 1, 0\n      ];\n      this.matrix[0] = cos + OneMinusCos / 3;\n      this.matrix[1] = aThird * OneMinusCos - aThirdSqtSin;\n      this.matrix[2] = aThird * OneMinusCos + aThirdSqtSin;\n      this.matrix[5] = aThird * OneMinusCos + aThirdSqtSin;\n      this.matrix[6] = cos + aThird * OneMinusCos;\n      this.matrix[7] = aThird * OneMinusCos - aThirdSqtSin;\n      this.matrix[10] = aThird * OneMinusCos - aThirdSqtSin;\n      this.matrix[11] = aThird * OneMinusCos + aThirdSqtSin;\n      this.matrix[12] = cos + aThird * OneMinusCos;\n    },\n\n    /**\n     * Apply this filter to the input image data provided.\n     *\n     * Determines whether to use WebGL or Canvas2D based on the options.webgl flag.\n     *\n     * @param {Object} options\n     * @param {Number} options.passes The number of filters remaining to be executed\n     * @param {Boolean} options.webgl Whether to use webgl to render the filter.\n     * @param {WebGLTexture} options.sourceTexture The texture setup as the source to be filtered.\n     * @param {WebGLTexture} options.targetTexture The texture where filtered output should be drawn.\n     * @param {WebGLRenderingContext} options.context The GL context used for rendering.\n     * @param {Object} options.programCache A map of compiled shader programs, keyed by filter type.\n     */\n    applyTo: function(options) {\n      this.calculateMatrix();\n      fabric.Image.filters.BaseFilter.prototype.applyTo.call(this, options);\n    },\n\n  });\n\n  /**\n   * Returns filter instance from an object representation\n   * @static\n   * @param {Object} object Object to create an instance from\n   * @param {function} [callback] to be invoked after filter creation\n   * @return {fabric.Image.filters.HueRotation} Instance of fabric.Image.filters.HueRotation\n   */\n  fabric.Image.filters.HueRotation.fromObject = fabric.Image.filters.BaseFilter.fromObject;\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric = global.fabric || (global.fabric = { }),\n      clone = fabric.util.object.clone;\n\n  if (fabric.Text) {\n    fabric.warn('fabric.Text is already defined');\n    return;\n  }\n\n  /**\n   * Text class\n   * @class fabric.Text\n   * @extends fabric.Object\n   * @return {fabric.Text} thisArg\n   * @tutorial {@link http://fabricjs.com/fabric-intro-part-2#text}\n   * @see {@link fabric.Text#initialize} for constructor definition\n   */\n  fabric.Text = fabric.util.createClass(fabric.Object, /** @lends fabric.Text.prototype */ {\n\n    /**\n     * Properties which when set cause object to change dimensions\n     * @type Array\n     * @private\n     */\n    _dimensionAffectingProps: [\n      'fontSize',\n      'fontWeight',\n      'fontFamily',\n      'fontStyle',\n      'lineHeight',\n      'text',\n      'charSpacing',\n      'textAlign',\n      'styles',\n    ],\n\n    /**\n     * @private\n     */\n    _reNewline: /\\r?\\n/,\n\n    /**\n     * Use this regular expression to filter for whitespaces that is not a new line.\n     * Mostly used when text is 'justify' aligned.\n     * @private\n     */\n    _reSpacesAndTabs: /[ \\t\\r]/g,\n\n    /**\n     * Use this regular expression to filter for whitespace that is not a new line.\n     * Mostly used when text is 'justify' aligned.\n     * @private\n     */\n    _reSpaceAndTab: /[ \\t\\r]/,\n\n    /**\n     * Use this regular expression to filter consecutive groups of non spaces.\n     * Mostly used when text is 'justify' aligned.\n     * @private\n     */\n    _reWords: /\\S+/g,\n\n    /**\n     * Type of an object\n     * @type String\n     * @default\n     */\n    type:                 'text',\n\n    /**\n     * Font size (in pixels)\n     * @type Number\n     * @default\n     */\n    fontSize:             40,\n\n    /**\n     * Font weight (e.g. bold, normal, 400, 600, 800)\n     * @type {(Number|String)}\n     * @default\n     */\n    fontWeight:           'normal',\n\n    /**\n     * Font family\n     * @type String\n     * @default\n     */\n    fontFamily:           'Times New Roman',\n\n    /**\n     * Text decoration underline.\n     * @type String\n     * @default\n     */\n    underline:       false,\n\n    /**\n     * Text decoration overline.\n     * @type String\n     * @default\n     */\n    overline:       false,\n\n    /**\n     * Text decoration linethrough.\n     * @type String\n     * @default\n     */\n    linethrough:       false,\n\n    /**\n     * Text alignment. Possible values: \"left\", \"center\", \"right\", \"justify\",\n     * \"justify-left\", \"justify-center\" or \"justify-right\".\n     * @type String\n     * @default\n     */\n    textAlign:            'left',\n\n    /**\n     * Font style . Possible values: \"\", \"normal\", \"italic\" or \"oblique\".\n     * @type String\n     * @default\n     */\n    fontStyle:            'normal',\n\n    /**\n     * Line height\n     * @type Number\n     * @default\n     */\n    lineHeight:           1.16,\n\n    /**\n     * Superscript schema object (minimum overlap)\n     * @type {Object}\n     * @default\n     */\n    superscript: {\n      size:      0.60, // fontSize factor\n      baseline: -0.35  // baseline-shift factor (upwards)\n    },\n\n    /**\n     * Subscript schema object (minimum overlap)\n     * @type {Object}\n     * @default\n     */\n    subscript: {\n      size:      0.60, // fontSize factor\n      baseline:  0.11  // baseline-shift factor (downwards)\n    },\n\n    /**\n     * Background color of text lines\n     * @type String\n     * @default\n     */\n    textBackgroundColor:  '',\n\n    /**\n     * List of properties to consider when checking if\n     * state of an object is changed ({@link fabric.Object#hasStateChanged})\n     * as well as for history (undo/redo) purposes\n     * @type Array\n     */\n    stateProperties: fabric.Object.prototype.stateProperties.concat('fontFamily',\n      'fontWeight',\n      'fontSize',\n      'text',\n      'underline',\n      'overline',\n      'linethrough',\n      'textAlign',\n      'fontStyle',\n      'lineHeight',\n      'textBackgroundColor',\n      'charSpacing',\n      'styles'),\n\n    /**\n     * List of properties to consider when checking if cache needs refresh\n     * @type Array\n     */\n    cacheProperties: fabric.Object.prototype.cacheProperties.concat('fontFamily',\n      'fontWeight',\n      'fontSize',\n      'text',\n      'underline',\n      'overline',\n      'linethrough',\n      'textAlign',\n      'fontStyle',\n      'lineHeight',\n      'textBackgroundColor',\n      'charSpacing',\n      'styles'),\n\n    /**\n     * When defined, an object is rendered via stroke and this property specifies its color.\n     * <b>Backwards incompatibility note:</b> This property was named \"strokeStyle\" until v1.1.6\n     * @type String\n     * @default\n     */\n    stroke:               null,\n\n    /**\n     * Shadow object representing shadow of this shape.\n     * <b>Backwards incompatibility note:</b> This property was named \"textShadow\" (String) until v1.2.11\n     * @type fabric.Shadow\n     * @default\n     */\n    shadow:               null,\n\n    /**\n     * @private\n     */\n    _fontSizeFraction: 0.222,\n\n    /**\n     * @private\n     */\n    offsets: {\n      underline: 0.10,\n      linethrough: -0.315,\n      overline: -0.88\n    },\n\n    /**\n     * Text Line proportion to font Size (in pixels)\n     * @type Number\n     * @default\n     */\n    _fontSizeMult:             1.13,\n\n    /**\n     * additional space between characters\n     * expressed in thousands of em unit\n     * @type Number\n     * @default\n     */\n    charSpacing:             0,\n\n    /**\n     * Object containing character styles - top-level properties -> line numbers,\n     * 2nd-level properties - charater numbers\n     * @type Object\n     * @default\n     */\n    styles: null,\n\n    /**\n     * Reference to a context to measure text char or couple of chars\n     * the cacheContext of the canvas will be used or a freshly created one if the object is not on canvas\n     * once created it will be referenced on fabric._measuringContext to avoide creating a canvas for every\n     * text object created.\n     * @type {CanvasRenderingContext2D}\n     * @default\n     */\n    _measuringContext: null,\n\n    /**\n     * Baseline shift, stlyes only, keep at 0 for the main text object\n     * @type {Number}\n     * @default\n     */\n    deltaY: 0,\n\n    /**\n     * Array of properties that define a style unit (of 'styles').\n     * @type {Array}\n     * @default\n     */\n    _styleProperties: [\n      'stroke',\n      'strokeWidth',\n      'fill',\n      'fontFamily',\n      'fontSize',\n      'fontWeight',\n      'fontStyle',\n      'underline',\n      'overline',\n      'linethrough',\n      'deltaY',\n      'textBackgroundColor',\n    ],\n\n    /**\n     * contains characters bounding boxes\n     */\n    __charBounds: [],\n\n    /**\n     * use this size when measuring text. To avoid IE11 rounding errors\n     * @type {Number}\n     * @default\n     * @readonly\n     * @private\n     */\n    CACHE_FONT_SIZE: 400,\n\n    /**\n     * contains the min text width to avoid getting 0\n     * @type {Number}\n     * @default\n     */\n    MIN_TEXT_WIDTH: 2,\n\n    /**\n     * Constructor\n     * @param {String} text Text string\n     * @param {Object} [options] Options object\n     * @return {fabric.Text} thisArg\n     */\n    initialize: function(text, options) {\n      this.styles = options ? (options.styles || { }) : { };\n      this.text = text;\n      this.__skipDimension = true;\n      this.callSuper('initialize', options);\n      this.__skipDimension = false;\n      this.initDimensions();\n      this.setCoords();\n      this.setupState({ propertySet: '_dimensionAffectingProps' });\n    },\n\n    /**\n     * Return a contex for measurement of text string.\n     * if created it gets stored for reuse\n     * @param {String} text Text string\n     * @param {Object} [options] Options object\n     * @return {fabric.Text} thisArg\n     */\n    getMeasuringContext: function() {\n      // if we did not return we have to measure something.\n      if (!fabric._measuringContext) {\n        fabric._measuringContext = this.canvas && this.canvas.contextCache ||\n          fabric.util.createCanvasElement().getContext('2d');\n      }\n      return fabric._measuringContext;\n    },\n\n    /**\n     * @private\n     * Divides text into lines of text and lines of graphemes.\n     */\n    _splitText: function() {\n      var newLines = this._splitTextIntoLines(this.text);\n      this.textLines = newLines.lines;\n      this._textLines = newLines.graphemeLines;\n      this._unwrappedTextLines = newLines._unwrappedLines;\n      this._text = newLines.graphemeText;\n      return newLines;\n    },\n\n    /**\n     * Initialize or update text dimensions.\n     * Updates this.width and this.height with the proper values.\n     * Does not return dimensions.\n     */\n    initDimensions: function() {\n      if (this.__skipDimension) {\n        return;\n      }\n      this._splitText();\n      this._clearCache();\n      this.width = this.calcTextWidth() || this.cursorWidth || this.MIN_TEXT_WIDTH;\n      if (this.textAlign.indexOf('justify') !== -1) {\n        // once text is measured we need to make space fatter to make justified text.\n        this.enlargeSpaces();\n      }\n      this.height = this.calcTextHeight();\n      this.saveState({ propertySet: '_dimensionAffectingProps' });\n    },\n\n    /**\n     * Enlarge space boxes and shift the others\n     */\n    enlargeSpaces: function() {\n      var diffSpace, currentLineWidth, numberOfSpaces, accumulatedSpace, line, charBound, spaces;\n      for (var i = 0, len = this._textLines.length; i < len; i++) {\n        if (this.textAlign !== 'justify' && (i === len - 1 || this.isEndOfWrapping(i))) {\n          continue;\n        }\n        accumulatedSpace = 0;\n        line = this._textLines[i];\n        currentLineWidth = this.getLineWidth(i);\n        if (currentLineWidth < this.width && (spaces = this.textLines[i].match(this._reSpacesAndTabs))) {\n          numberOfSpaces = spaces.length;\n          diffSpace = (this.width - currentLineWidth) / numberOfSpaces;\n          for (var j = 0, jlen = line.length; j <= jlen; j++) {\n            charBound = this.__charBounds[i][j];\n            if (this._reSpaceAndTab.test(line[j])) {\n              charBound.width += diffSpace;\n              charBound.kernedWidth += diffSpace;\n              charBound.left += accumulatedSpace;\n              accumulatedSpace += diffSpace;\n            }\n            else {\n              charBound.left += accumulatedSpace;\n            }\n          }\n        }\n      }\n    },\n\n    /**\n     * Detect if the text line is ended with an hard break\n     * text and itext do not have wrapping, return false\n     * @return {Boolean}\n     */\n    isEndOfWrapping: function(lineIndex) {\n      return lineIndex === this._textLines.length - 1;\n    },\n\n    /**\n     * Returns string representation of an instance\n     * @return {String} String representation of text object\n     */\n    toString: function() {\n      return '#<fabric.Text (' + this.complexity() +\n        '): { \"text\": \"' + this.text + '\", \"fontFamily\": \"' + this.fontFamily + '\" }>';\n    },\n\n    /**\n     * Return the dimension and the zoom level needed to create a cache canvas\n     * big enough to host the object to be cached.\n     * @private\n     * @param {Object} dim.x width of object to be cached\n     * @param {Object} dim.y height of object to be cached\n     * @return {Object}.width width of canvas\n     * @return {Object}.height height of canvas\n     * @return {Object}.zoomX zoomX zoom value to unscale the canvas before drawing cache\n     * @return {Object}.zoomY zoomY zoom value to unscale the canvas before drawing cache\n     */\n    _getCacheCanvasDimensions: function() {\n      var dims = this.callSuper('_getCacheCanvasDimensions');\n      var fontSize = this.fontSize;\n      dims.width += fontSize * dims.zoomX;\n      dims.height += fontSize * dims.zoomY;\n      return dims;\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    _render: function(ctx) {\n      this._setTextStyles(ctx);\n      this._renderTextLinesBackground(ctx);\n      this._renderTextDecoration(ctx, 'underline');\n      this._renderText(ctx);\n      this._renderTextDecoration(ctx, 'overline');\n      this._renderTextDecoration(ctx, 'linethrough');\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    _renderText: function(ctx) {\n      if (this.paintFirst === 'stroke') {\n        this._renderTextStroke(ctx);\n        this._renderTextFill(ctx);\n      }\n      else {\n        this._renderTextFill(ctx);\n        this._renderTextStroke(ctx);\n      }\n    },\n\n    /**\n     * Set the font parameter of the context with the object properties or with charStyle\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     * @param {Object} [charStyle] object with font style properties\n     * @param {String} [charStyle.fontFamily] Font Family\n     * @param {Number} [charStyle.fontSize] Font size in pixels. ( without px suffix )\n     * @param {String} [charStyle.fontWeight] Font weight\n     * @param {String} [charStyle.fontStyle] Font style (italic|normal)\n     */\n    _setTextStyles: function(ctx, charStyle, forMeasuring) {\n      ctx.textBaseline = 'alphabetic';\n      ctx.font = this._getFontDeclaration(charStyle, forMeasuring);\n    },\n\n    /**\n     * calculate and return the text Width measuring each line.\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     * @return {Number} Maximum width of fabric.Text object\n     */\n    calcTextWidth: function() {\n      var maxWidth = this.getLineWidth(0);\n\n      for (var i = 1, len = this._textLines.length; i < len; i++) {\n        var currentLineWidth = this.getLineWidth(i);\n        if (currentLineWidth > maxWidth) {\n          maxWidth = currentLineWidth;\n        }\n      }\n      return maxWidth;\n    },\n\n    /**\n     * @private\n     * @param {String} method Method name (\"fillText\" or \"strokeText\")\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     * @param {String} line Text to render\n     * @param {Number} left Left position of text\n     * @param {Number} top Top position of text\n     * @param {Number} lineIndex Index of a line in a text\n     */\n    _renderTextLine: function(method, ctx, line, left, top, lineIndex) {\n      this._renderChars(method, ctx, line, left, top, lineIndex);\n    },\n\n    /**\n     * Renders the text background for lines, taking care of style\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    _renderTextLinesBackground: function(ctx) {\n      if (!this.textBackgroundColor && !this.styleHas('textBackgroundColor')) {\n        return;\n      }\n      var lineTopOffset = 0, heightOfLine,\n          lineLeftOffset, originalFill = ctx.fillStyle,\n          line, lastColor,\n          leftOffset = this._getLeftOffset(),\n          topOffset = this._getTopOffset(),\n          boxStart = 0, boxWidth = 0, charBox, currentColor;\n\n      for (var i = 0, len = this._textLines.length; i < len; i++) {\n        heightOfLine = this.getHeightOfLine(i);\n        if (!this.textBackgroundColor && !this.styleHas('textBackgroundColor', i)) {\n          lineTopOffset += heightOfLine;\n          continue;\n        }\n        line = this._textLines[i];\n        lineLeftOffset = this._getLineLeftOffset(i);\n        boxWidth = 0;\n        boxStart = 0;\n        lastColor = this.getValueOfPropertyAt(i, 0, 'textBackgroundColor');\n        for (var j = 0, jlen = line.length; j < jlen; j++) {\n          charBox = this.__charBounds[i][j];\n          currentColor = this.getValueOfPropertyAt(i, j, 'textBackgroundColor');\n          if (currentColor !== lastColor) {\n            ctx.fillStyle = lastColor;\n            lastColor && ctx.fillRect(\n              leftOffset + lineLeftOffset + boxStart,\n              topOffset + lineTopOffset,\n              boxWidth,\n              heightOfLine / this.lineHeight\n            );\n            boxStart = charBox.left;\n            boxWidth = charBox.width;\n            lastColor = currentColor;\n          }\n          else {\n            boxWidth += charBox.kernedWidth;\n          }\n        }\n        if (currentColor) {\n          ctx.fillStyle = currentColor;\n          ctx.fillRect(\n            leftOffset + lineLeftOffset + boxStart,\n            topOffset + lineTopOffset,\n            boxWidth,\n            heightOfLine / this.lineHeight\n          );\n        }\n        lineTopOffset += heightOfLine;\n      }\n      ctx.fillStyle = originalFill;\n      // if there is text background color no\n      // other shadows should be casted\n      this._removeShadow(ctx);\n    },\n\n    /**\n     * @private\n     * @param {Object} decl style declaration for cache\n     * @param {String} decl.fontFamily fontFamily\n     * @param {String} decl.fontStyle fontStyle\n     * @param {String} decl.fontWeight fontWeight\n     * @return {Object} reference to cache\n     */\n    getFontCache: function(decl) {\n      var fontFamily = decl.fontFamily.toLowerCase();\n      if (!fabric.charWidthsCache[fontFamily]) {\n        fabric.charWidthsCache[fontFamily] = { };\n      }\n      var cache = fabric.charWidthsCache[fontFamily],\n          cacheProp = decl.fontStyle.toLowerCase() + '_' + (decl.fontWeight + '').toLowerCase();\n      if (!cache[cacheProp]) {\n        cache[cacheProp] = { };\n      }\n      return cache[cacheProp];\n    },\n\n    /**\n     * apply all the character style to canvas for rendering\n     * @private\n     * @param {String} _char\n     * @param {Number} lineIndex\n     * @param {Number} charIndex\n     * @param {Object} [decl]\n     */\n    _applyCharStyles: function(method, ctx, lineIndex, charIndex, styleDeclaration) {\n\n      this._setFillStyles(ctx, styleDeclaration);\n      this._setStrokeStyles(ctx, styleDeclaration);\n\n      ctx.font = this._getFontDeclaration(styleDeclaration);\n    },\n\n    /**\n     * measure and return the width of a single character.\n     * possibly overridden to accommodate different measure logic or\n     * to hook some external lib for character measurement\n     * @private\n     * @param {String} char to be measured\n     * @param {Object} charStyle style of char to be measured\n     * @param {String} [previousChar] previous char\n     * @param {Object} [prevCharStyle] style of previous char\n     */\n    _measureChar: function(_char, charStyle, previousChar, prevCharStyle) {\n      // first i try to return from cache\n      var fontCache = this.getFontCache(charStyle), fontDeclaration = this._getFontDeclaration(charStyle),\n          previousFontDeclaration = this._getFontDeclaration(prevCharStyle), couple = previousChar + _char,\n          stylesAreEqual = fontDeclaration === previousFontDeclaration, width, coupleWidth, previousWidth,\n          fontMultiplier = charStyle.fontSize / this.CACHE_FONT_SIZE, kernedWidth;\n\n      if (previousChar && fontCache[previousChar]) {\n        previousWidth = fontCache[previousChar];\n      }\n      if (fontCache[_char]) {\n        kernedWidth = width = fontCache[_char];\n      }\n      if (stylesAreEqual && fontCache[couple]) {\n        coupleWidth = fontCache[couple];\n        kernedWidth = coupleWidth - previousWidth;\n      }\n      if (!width || !previousWidth || !coupleWidth) {\n        var ctx = this.getMeasuringContext();\n        // send a TRUE to specify measuring font size CACHE_FONT_SIZE\n        this._setTextStyles(ctx, charStyle, true);\n      }\n      if (!width) {\n        kernedWidth = width = ctx.measureText(_char).width;\n        fontCache[_char] = width;\n      }\n      if (!previousWidth && stylesAreEqual && previousChar) {\n        previousWidth = ctx.measureText(previousChar).width;\n        fontCache[previousChar] = previousWidth;\n      }\n      if (stylesAreEqual && !coupleWidth) {\n        // we can measure the kerning couple and subtract the width of the previous character\n        coupleWidth = ctx.measureText(couple).width;\n        fontCache[couple] = coupleWidth;\n        kernedWidth = coupleWidth - previousWidth;\n      }\n      return { width: width * fontMultiplier, kernedWidth: kernedWidth * fontMultiplier };\n    },\n\n    /**\n     * Computes height of character at given position\n     * @param {Number} line the line number\n     * @param {Number} char the character number\n     * @return {Number} fontSize of the character\n     */\n    getHeightOfChar: function(line, char) {\n      return this.getValueOfPropertyAt(line, char, 'fontSize');\n    },\n\n    /**\n     * measure a text line measuring all characters.\n     * @param {Number} lineIndex line number\n     * @return {Number} Line width\n     */\n    measureLine: function(lineIndex) {\n      var lineInfo = this._measureLine(lineIndex);\n      if (this.charSpacing !== 0) {\n        lineInfo.width -= this._getWidthOfCharSpacing();\n      }\n      if (lineInfo.width < 0) {\n        lineInfo.width = 0;\n      }\n      return lineInfo;\n    },\n\n    /**\n     * measure every grapheme of a line, populating __charBounds\n     * @param {Number} lineIndex\n     * @return {Object} object.width total width of characters\n     * @return {Object} object.widthOfSpaces length of chars that match this._reSpacesAndTabs\n     */\n    _measureLine: function(lineIndex) {\n      var width = 0, i, grapheme, line = this._textLines[lineIndex], prevGrapheme,\n          graphemeInfo, numOfSpaces = 0, lineBounds = new Array(line.length);\n\n      this.__charBounds[lineIndex] = lineBounds;\n      for (i = 0; i < line.length; i++) {\n        grapheme = line[i];\n        graphemeInfo = this._getGraphemeBox(grapheme, lineIndex, i, prevGrapheme);\n        lineBounds[i] = graphemeInfo;\n        width += graphemeInfo.kernedWidth;\n        prevGrapheme = grapheme;\n      }\n      // this latest bound box represent the last character of the line\n      // to simplify cursor handling in interactive mode.\n      lineBounds[i] = {\n        left: graphemeInfo ? graphemeInfo.left + graphemeInfo.width : 0,\n        width: 0,\n        kernedWidth: 0,\n        height: this.fontSize\n      };\n      return { width: width, numOfSpaces: numOfSpaces };\n    },\n\n    /**\n     * Measure and return the info of a single grapheme.\n     * needs the the info of previous graphemes already filled\n     * @private\n     * @param {String} grapheme to be measured\n     * @param {Number} lineIndex index of the line where the char is\n     * @param {Number} charIndex position in the line\n     * @param {String} [prevGrapheme] character preceding the one to be measured\n     */\n    _getGraphemeBox: function(grapheme, lineIndex, charIndex, prevGrapheme, skipLeft) {\n      var style = this.getCompleteStyleDeclaration(lineIndex, charIndex),\n          prevStyle = prevGrapheme ? this.getCompleteStyleDeclaration(lineIndex, charIndex - 1) : { },\n          info = this._measureChar(grapheme, style, prevGrapheme, prevStyle),\n          kernedWidth = info.kernedWidth,\n          width = info.width, charSpacing;\n\n      if (this.charSpacing !== 0) {\n        charSpacing = this._getWidthOfCharSpacing();\n        width += charSpacing;\n        kernedWidth += charSpacing;\n      }\n\n      var box = {\n        width: width,\n        left: 0,\n        height: style.fontSize,\n        kernedWidth: kernedWidth,\n        deltaY: style.deltaY,\n      };\n      if (charIndex > 0 && !skipLeft) {\n        var previousBox = this.__charBounds[lineIndex][charIndex - 1];\n        box.left = previousBox.left + previousBox.width + info.kernedWidth - info.width;\n      }\n      return box;\n    },\n\n    /**\n     * Calculate height of line at 'lineIndex'\n     * @param {Number} lineIndex index of line to calculate\n     * @return {Number}\n     */\n    getHeightOfLine: function(lineIndex) {\n      if (this.__lineHeights[lineIndex]) {\n        return this.__lineHeights[lineIndex];\n      }\n\n      var line = this._textLines[lineIndex],\n          // char 0 is measured before the line cycle because it nneds to char\n          // emptylines\n          maxHeight = this.getHeightOfChar(lineIndex, 0);\n      for (var i = 1, len = line.length; i < len; i++) {\n        maxHeight = Math.max(this.getHeightOfChar(lineIndex, i), maxHeight);\n      }\n\n      return this.__lineHeights[lineIndex] = maxHeight * this.lineHeight * this._fontSizeMult;\n    },\n\n    /**\n     * Calculate text box height\n     */\n    calcTextHeight: function() {\n      var lineHeight, height = 0;\n      for (var i = 0, len = this._textLines.length; i < len; i++) {\n        lineHeight = this.getHeightOfLine(i);\n        height += (i === len - 1 ? lineHeight / this.lineHeight : lineHeight);\n      }\n      return height;\n    },\n\n    /**\n     * @private\n     * @return {Number} Left offset\n     */\n    _getLeftOffset: function() {\n      return -this.width / 2;\n    },\n\n    /**\n     * @private\n     * @return {Number} Top offset\n     */\n    _getTopOffset: function() {\n      return -this.height / 2;\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     * @param {String} method Method name (\"fillText\" or \"strokeText\")\n     */\n    _renderTextCommon: function(ctx, method) {\n      ctx.save();\n      var lineHeights = 0, left = this._getLeftOffset(), top = this._getTopOffset(),\n          offsets = this._applyPatternGradientTransform(ctx, method === 'fillText' ? this.fill : this.stroke);\n      for (var i = 0, len = this._textLines.length; i < len; i++) {\n        var heightOfLine = this.getHeightOfLine(i),\n            maxHeight = heightOfLine / this.lineHeight,\n            leftOffset = this._getLineLeftOffset(i);\n        this._renderTextLine(\n          method,\n          ctx,\n          this._textLines[i],\n          left + leftOffset - offsets.offsetX,\n          top + lineHeights + maxHeight - offsets.offsetY,\n          i\n        );\n        lineHeights += heightOfLine;\n      }\n      ctx.restore();\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    _renderTextFill: function(ctx) {\n      if (!this.fill && !this.styleHas('fill')) {\n        return;\n      }\n\n      this._renderTextCommon(ctx, 'fillText');\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    _renderTextStroke: function(ctx) {\n      if ((!this.stroke || this.strokeWidth === 0) && this.isEmptyStyles()) {\n        return;\n      }\n\n      if (this.shadow && !this.shadow.affectStroke) {\n        this._removeShadow(ctx);\n      }\n\n      ctx.save();\n      this._setLineDash(ctx, this.strokeDashArray);\n      ctx.beginPath();\n      this._renderTextCommon(ctx, 'strokeText');\n      ctx.closePath();\n      ctx.restore();\n    },\n\n    /**\n     * @private\n     * @param {String} method\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     * @param {String} line Content of the line\n     * @param {Number} left\n     * @param {Number} top\n     * @param {Number} lineIndex\n     * @param {Number} charOffset\n     */\n    _renderChars: function(method, ctx, line, left, top, lineIndex) {\n      // set proper line offset\n      var lineHeight = this.getHeightOfLine(lineIndex),\n          isJustify = this.textAlign.indexOf('justify') !== -1,\n          actualStyle,\n          nextStyle,\n          charsToRender = '',\n          charBox,\n          boxWidth = 0,\n          timeToRender,\n          shortCut = !isJustify && this.charSpacing === 0 && this.isEmptyStyles(lineIndex);\n\n      ctx.save();\n      top -= lineHeight * this._fontSizeFraction / this.lineHeight;\n      if (shortCut) {\n        // render all the line in one pass without checking\n        this._renderChar(method, ctx, lineIndex, 0, this.textLines[lineIndex], left, top, lineHeight);\n        ctx.restore();\n        return;\n      }\n      for (var i = 0, len = line.length - 1; i <= len; i++) {\n        timeToRender = i === len || this.charSpacing;\n        charsToRender += line[i];\n        charBox = this.__charBounds[lineIndex][i];\n        if (boxWidth === 0) {\n          left += charBox.kernedWidth - charBox.width;\n          boxWidth += charBox.width;\n        }\n        else {\n          boxWidth += charBox.kernedWidth;\n        }\n        if (isJustify && !timeToRender) {\n          if (this._reSpaceAndTab.test(line[i])) {\n            timeToRender = true;\n          }\n        }\n        if (!timeToRender) {\n          // if we have charSpacing, we render char by char\n          actualStyle = actualStyle || this.getCompleteStyleDeclaration(lineIndex, i);\n          nextStyle = this.getCompleteStyleDeclaration(lineIndex, i + 1);\n          timeToRender = this._hasStyleChanged(actualStyle, nextStyle);\n        }\n        if (timeToRender) {\n          this._renderChar(method, ctx, lineIndex, i, charsToRender, left, top, lineHeight);\n          charsToRender = '';\n          actualStyle = nextStyle;\n          left += boxWidth;\n          boxWidth = 0;\n        }\n      }\n      ctx.restore();\n    },\n\n    /**\n     * @private\n     * @param {String} method\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     * @param {Number} lineIndex\n     * @param {Number} charIndex\n     * @param {String} _char\n     * @param {Number} left Left coordinate\n     * @param {Number} top Top coordinate\n     * @param {Number} lineHeight Height of the line\n     */\n    _renderChar: function(method, ctx, lineIndex, charIndex, _char, left, top) {\n      var decl = this._getStyleDeclaration(lineIndex, charIndex),\n          fullDecl = this.getCompleteStyleDeclaration(lineIndex, charIndex),\n          shouldFill = method === 'fillText' && fullDecl.fill,\n          shouldStroke = method === 'strokeText' && fullDecl.stroke && fullDecl.strokeWidth;\n\n      if (!shouldStroke && !shouldFill) {\n        return;\n      }\n      decl && ctx.save();\n\n      this._applyCharStyles(method, ctx, lineIndex, charIndex, fullDecl);\n\n      if (decl && decl.textBackgroundColor) {\n        this._removeShadow(ctx);\n      }\n      if (decl && decl.deltaY) {\n        top += decl.deltaY;\n      }\n\n      shouldFill && ctx.fillText(_char, left, top);\n      shouldStroke && ctx.strokeText(_char, left, top);\n      decl && ctx.restore();\n    },\n\n    /**\n     * Turns the character into a 'superior figure' (i.e. 'superscript')\n     * @param {Number} start selection start\n     * @param {Number} end selection end\n     * @returns {fabric.Text} thisArg\n     * @chainable\n     */\n    setSuperscript: function(start, end) {\n      return this._setScript(start, end, this.superscript);\n    },\n\n    /**\n     * Turns the character into an 'inferior figure' (i.e. 'subscript')\n     * @param {Number} start selection start\n     * @param {Number} end selection end\n     * @returns {fabric.Text} thisArg\n     * @chainable\n     */\n    setSubscript: function(start, end) {\n      return this._setScript(start, end, this.subscript);\n    },\n\n    /**\n     * Applies 'schema' at given position\n     * @private\n     * @param {Number} start selection start\n     * @param {Number} end selection end\n     * @param {Number} schema\n     * @returns {fabric.Text} thisArg\n     * @chainable\n     */\n    _setScript: function(start, end, schema) {\n      var loc = this.get2DCursorLocation(start, true),\n          fontSize = this.getValueOfPropertyAt(loc.lineIndex, loc.charIndex, 'fontSize'),\n          dy = this.getValueOfPropertyAt(loc.lineIndex, loc.charIndex, 'deltaY'),\n          style = { fontSize: fontSize * schema.size, deltaY: dy + fontSize * schema.baseline };\n      this.setSelectionStyles(style, start, end);\n      return this;\n    },\n\n    /**\n     * @private\n     * @param {Object} prevStyle\n     * @param {Object} thisStyle\n     */\n    _hasStyleChanged: function(prevStyle, thisStyle) {\n      return prevStyle.fill !== thisStyle.fill ||\n              prevStyle.stroke !== thisStyle.stroke ||\n              prevStyle.strokeWidth !== thisStyle.strokeWidth ||\n              prevStyle.fontSize !== thisStyle.fontSize ||\n              prevStyle.fontFamily !== thisStyle.fontFamily ||\n              prevStyle.fontWeight !== thisStyle.fontWeight ||\n              prevStyle.fontStyle !== thisStyle.fontStyle ||\n              prevStyle.deltaY !== thisStyle.deltaY;\n    },\n\n    /**\n     * @private\n     * @param {Object} prevStyle\n     * @param {Object} thisStyle\n     */\n    _hasStyleChangedForSvg: function(prevStyle, thisStyle) {\n      return this._hasStyleChanged(prevStyle, thisStyle) ||\n        prevStyle.overline !== thisStyle.overline ||\n        prevStyle.underline !== thisStyle.underline ||\n        prevStyle.linethrough !== thisStyle.linethrough;\n    },\n\n    /**\n     * @private\n     * @param {Number} lineIndex index text line\n     * @return {Number} Line left offset\n     */\n    _getLineLeftOffset: function(lineIndex) {\n      var lineWidth = this.getLineWidth(lineIndex);\n      if (this.textAlign === 'center') {\n        return (this.width - lineWidth) / 2;\n      }\n      if (this.textAlign === 'right') {\n        return this.width - lineWidth;\n      }\n      if (this.textAlign === 'justify-center' && this.isEndOfWrapping(lineIndex)) {\n        return (this.width - lineWidth) / 2;\n      }\n      if (this.textAlign === 'justify-right' && this.isEndOfWrapping(lineIndex)) {\n        return this.width - lineWidth;\n      }\n      return 0;\n    },\n\n    /**\n     * @private\n     */\n    _clearCache: function() {\n      this.__lineWidths = [];\n      this.__lineHeights = [];\n      this.__charBounds = [];\n    },\n\n    /**\n     * @private\n     */\n    _shouldClearDimensionCache: function() {\n      var shouldClear = this._forceClearCache;\n      shouldClear || (shouldClear = this.hasStateChanged('_dimensionAffectingProps'));\n      if (shouldClear) {\n        this.dirty = true;\n        this._forceClearCache = false;\n      }\n      return shouldClear;\n    },\n\n    /**\n     * Measure a single line given its index. Used to calculate the initial\n     * text bounding box. The values are calculated and stored in __lineWidths cache.\n     * @private\n     * @param {Number} lineIndex line number\n     * @return {Number} Line width\n     */\n    getLineWidth: function(lineIndex) {\n      if (this.__lineWidths[lineIndex]) {\n        return this.__lineWidths[lineIndex];\n      }\n\n      var width, line = this._textLines[lineIndex], lineInfo;\n\n      if (line === '') {\n        width = 0;\n      }\n      else {\n        lineInfo = this.measureLine(lineIndex);\n        width = lineInfo.width;\n      }\n      this.__lineWidths[lineIndex] = width;\n      return width;\n    },\n\n    _getWidthOfCharSpacing: function() {\n      if (this.charSpacing !== 0) {\n        return this.fontSize * this.charSpacing / 1000;\n      }\n      return 0;\n    },\n\n    /**\n     * Retrieves the value of property at given character position\n     * @param {Number} lineIndex the line number\n     * @param {Number} charIndex the charater number\n     * @param {String} property the property name\n     * @returns the value of 'property'\n     */\n    getValueOfPropertyAt: function(lineIndex, charIndex, property) {\n      var charStyle = this._getStyleDeclaration(lineIndex, charIndex);\n      if (charStyle && typeof charStyle[property] !== 'undefined') {\n        return charStyle[property];\n      }\n      return this[property];\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    _renderTextDecoration: function(ctx, type) {\n      if (!this[type] && !this.styleHas(type)) {\n        return;\n      }\n      var heightOfLine, size, _size,\n          lineLeftOffset, dy, _dy,\n          line, lastDecoration,\n          leftOffset = this._getLeftOffset(),\n          topOffset = this._getTopOffset(), top,\n          boxStart, boxWidth, charBox, currentDecoration,\n          maxHeight, currentFill, lastFill;\n\n      for (var i = 0, len = this._textLines.length; i < len; i++) {\n        heightOfLine = this.getHeightOfLine(i);\n        if (!this[type] && !this.styleHas(type, i)) {\n          topOffset += heightOfLine;\n          continue;\n        }\n        line = this._textLines[i];\n        maxHeight = heightOfLine / this.lineHeight;\n        lineLeftOffset = this._getLineLeftOffset(i);\n        boxStart = 0;\n        boxWidth = 0;\n        lastDecoration = this.getValueOfPropertyAt(i, 0, type);\n        lastFill = this.getValueOfPropertyAt(i, 0, 'fill');\n        top = topOffset + maxHeight * (1 - this._fontSizeFraction);\n        size = this.getHeightOfChar(i, 0);\n        dy = this.getValueOfPropertyAt(i, 0, 'deltaY');\n        for (var j = 0, jlen = line.length; j < jlen; j++) {\n          charBox = this.__charBounds[i][j];\n          currentDecoration = this.getValueOfPropertyAt(i, j, type);\n          currentFill = this.getValueOfPropertyAt(i, j, 'fill');\n          _size = this.getHeightOfChar(i, j);\n          _dy = this.getValueOfPropertyAt(i, j, 'deltaY');\n          if ((currentDecoration !== lastDecoration || currentFill !== lastFill || _size !== size || _dy !== dy) &&\n              boxWidth > 0) {\n            ctx.fillStyle = lastFill;\n            lastDecoration && lastFill && ctx.fillRect(\n              leftOffset + lineLeftOffset + boxStart,\n              top + this.offsets[type] * size + dy,\n              boxWidth,\n              this.fontSize / 15\n            );\n            boxStart = charBox.left;\n            boxWidth = charBox.width;\n            lastDecoration = currentDecoration;\n            lastFill = currentFill;\n            size = _size;\n            dy = _dy;\n          }\n          else {\n            boxWidth += charBox.kernedWidth;\n          }\n        }\n        ctx.fillStyle = currentFill;\n        currentDecoration && currentFill && ctx.fillRect(\n          leftOffset + lineLeftOffset + boxStart,\n          top + this.offsets[type] * size + dy,\n          boxWidth,\n          this.fontSize / 15\n        );\n        topOffset += heightOfLine;\n      }\n      // if there is text background color no\n      // other shadows should be casted\n      this._removeShadow(ctx);\n    },\n\n    /**\n     * return font declaration string for canvas context\n     * @param {Object} [styleObject] object\n     * @returns {String} font declaration formatted for canvas context.\n     */\n    _getFontDeclaration: function(styleObject, forMeasuring) {\n      var style = styleObject || this;\n      var fontFamily = style.fontFamily === undefined ||\n      style.fontFamily.indexOf('\\'') > -1 ||\n      style.fontFamily.indexOf('\"') > -1\n        ? style.fontFamily : '\"' + style.fontFamily + '\"';\n      return [\n        // node-canvas needs \"weight style\", while browsers need \"style weight\"\n        (fabric.isLikelyNode ? style.fontWeight : style.fontStyle),\n        (fabric.isLikelyNode ? style.fontStyle : style.fontWeight),\n        forMeasuring ? this.CACHE_FONT_SIZE + 'px' : style.fontSize + 'px',\n        fontFamily\n      ].join(' ');\n    },\n\n    /**\n     * Renders text instance on a specified context\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    render: function(ctx) {\n      // do not render if object is not visible\n      if (!this.visible) {\n        return;\n      }\n      if (this.canvas && this.canvas.skipOffscreen && !this.group && !this.isOnScreen()) {\n        return;\n      }\n      if (this._shouldClearDimensionCache()) {\n        this.initDimensions();\n      }\n      this.callSuper('render', ctx);\n    },\n\n    /**\n     * Returns the text as an array of lines.\n     * @param {String} text text to split\n     * @returns {Array} Lines in the text\n     */\n    _splitTextIntoLines: function(text) {\n      var lines = text.split(this._reNewline),\n          newLines = new Array(lines.length),\n          newLine = ['\\n'],\n          newText = [];\n      for (var i = 0; i < lines.length; i++) {\n        newLines[i] = fabric.util.string.graphemeSplit(lines[i]);\n        newText = newText.concat(newLines[i], newLine);\n      }\n      newText.pop();\n      return { _unwrappedLines: newLines, lines: lines, graphemeText: newText, graphemeLines: newLines };\n    },\n\n    /**\n     * Returns object representation of an instance\n     * @param {Array} [propertiesToInclude] Any properties that you might want to additionally include in the output\n     * @return {Object} Object representation of an instance\n     */\n    toObject: function(propertiesToInclude) {\n      var additionalProperties = [\n        'text',\n        'fontSize',\n        'fontWeight',\n        'fontFamily',\n        'fontStyle',\n        'lineHeight',\n        'underline',\n        'overline',\n        'linethrough',\n        'textAlign',\n        'textBackgroundColor',\n        'charSpacing',\n      ].concat(propertiesToInclude);\n      var obj = this.callSuper('toObject', additionalProperties);\n      obj.styles = clone(this.styles, true);\n      return obj;\n    },\n\n    /**\n     * Sets property to a given value. When changing position/dimension -related properties (left, top, scale, angle, etc.) `set` does not update position of object's borders/controls. If you need to update those, call `setCoords()`.\n     * @param {String|Object} key Property name or object (if object, iterate over the object properties)\n     * @param {Object|Function} value Property value (if function, the value is passed into it and its return value is used as a new one)\n     * @return {fabric.Object} thisArg\n     * @chainable\n     */\n    set: function(key, value) {\n      this.callSuper('set', key, value);\n      var needsDims = false;\n      if (typeof key === 'object') {\n        for (var _key in key) {\n          needsDims = needsDims || this._dimensionAffectingProps.indexOf(_key) !== -1;\n        }\n      }\n      else {\n        needsDims = this._dimensionAffectingProps.indexOf(key) !== -1;\n      }\n      if (needsDims) {\n        this.initDimensions();\n        this.setCoords();\n      }\n      return this;\n    },\n\n    /**\n     * Returns complexity of an instance\n     * @return {Number} complexity\n     */\n    complexity: function() {\n      return 1;\n    }\n  });\n\n  /* _FROM_SVG_START_ */\n  /**\n   * List of attribute names to account for when parsing SVG element (used by {@link fabric.Text.fromElement})\n   * @static\n   * @memberOf fabric.Text\n   * @see: http://www.w3.org/TR/SVG/text.html#TextElement\n   */\n  fabric.Text.ATTRIBUTE_NAMES = fabric.SHARED_ATTRIBUTES.concat(\n    'x y dx dy font-family font-style font-weight font-size letter-spacing text-decoration text-anchor'.split(' '));\n\n  /**\n   * Default SVG font size\n   * @static\n   * @memberOf fabric.Text\n   */\n  fabric.Text.DEFAULT_SVG_FONT_SIZE = 16;\n\n  /**\n   * Returns fabric.Text instance from an SVG element (<b>not yet implemented</b>)\n   * @static\n   * @memberOf fabric.Text\n   * @param {SVGElement} element Element to parse\n   * @param {Function} callback callback function invoked after parsing\n   * @param {Object} [options] Options object\n   */\n  fabric.Text.fromElement = function(element, callback, options) {\n    if (!element) {\n      return callback(null);\n    }\n\n    var parsedAttributes = fabric.parseAttributes(element, fabric.Text.ATTRIBUTE_NAMES),\n        parsedAnchor = parsedAttributes.textAnchor || 'left';\n    options = fabric.util.object.extend((options ? clone(options) : { }), parsedAttributes);\n\n    options.top = options.top || 0;\n    options.left = options.left || 0;\n    if (parsedAttributes.textDecoration) {\n      var textDecoration = parsedAttributes.textDecoration;\n      if (textDecoration.indexOf('underline') !== -1) {\n        options.underline = true;\n      }\n      if (textDecoration.indexOf('overline') !== -1) {\n        options.overline = true;\n      }\n      if (textDecoration.indexOf('line-through') !== -1) {\n        options.linethrough = true;\n      }\n      delete options.textDecoration;\n    }\n    if ('dx' in parsedAttributes) {\n      options.left += parsedAttributes.dx;\n    }\n    if ('dy' in parsedAttributes) {\n      options.top += parsedAttributes.dy;\n    }\n    if (!('fontSize' in options)) {\n      options.fontSize = fabric.Text.DEFAULT_SVG_FONT_SIZE;\n    }\n\n    var textContent = '';\n\n    // The XML is not properly parsed in IE9 so a workaround to get\n    // textContent is through firstChild.data. Another workaround would be\n    // to convert XML loaded from a file to be converted using DOMParser (same way loadSVGFromString() does)\n    if (!('textContent' in element)) {\n      if ('firstChild' in element && element.firstChild !== null) {\n        if ('data' in element.firstChild && element.firstChild.data !== null) {\n          textContent = element.firstChild.data;\n        }\n      }\n    }\n    else {\n      textContent = element.textContent;\n    }\n\n    textContent = textContent.replace(/^\\s+|\\s+$|\\n+/g, '').replace(/\\s+/g, ' ');\n    var originalStrokeWidth = options.strokeWidth;\n    options.strokeWidth = 0;\n\n    var text = new fabric.Text(textContent, options),\n        textHeightScaleFactor = text.getScaledHeight() / text.height,\n        lineHeightDiff = (text.height + text.strokeWidth) * text.lineHeight - text.height,\n        scaledDiff = lineHeightDiff * textHeightScaleFactor,\n        textHeight = text.getScaledHeight() + scaledDiff,\n        offX = 0;\n    /*\n      Adjust positioning:\n        x/y attributes in SVG correspond to the bottom-left corner of text bounding box\n        fabric output by default at top, left.\n    */\n    if (parsedAnchor === 'center') {\n      offX = text.getScaledWidth() / 2;\n    }\n    if (parsedAnchor === 'right') {\n      offX = text.getScaledWidth();\n    }\n    text.set({\n      left: text.left - offX,\n      top: text.top - (textHeight - text.fontSize * (0.07 + text._fontSizeFraction)) / text.lineHeight,\n      strokeWidth: typeof originalStrokeWidth !== 'undefined' ? originalStrokeWidth : 1,\n    });\n    callback(text);\n  };\n  /* _FROM_SVG_END_ */\n\n  /**\n   * Returns fabric.Text instance from an object representation\n   * @static\n   * @memberOf fabric.Text\n   * @param {Object} object Object to create an instance from\n   * @param {Function} [callback] Callback to invoke when an fabric.Text instance is created\n   */\n  fabric.Text.fromObject = function(object, callback) {\n    return fabric.Object._fromObject('Text', object, callback, 'text');\n  };\n\n  fabric.util.createAccessors && fabric.util.createAccessors(fabric.Text);\n\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function() {\n  fabric.util.object.extend(fabric.Text.prototype, /** @lends fabric.Text.prototype */ {\n    /**\n     * Returns true if object has no styling or no styling in a line\n     * @param {Number} lineIndex , lineIndex is on wrapped lines.\n     * @return {Boolean}\n     */\n    isEmptyStyles: function(lineIndex) {\n      if (!this.styles) {\n        return true;\n      }\n      if (typeof lineIndex !== 'undefined' && !this.styles[lineIndex]) {\n        return true;\n      }\n      var obj = typeof lineIndex === 'undefined' ? this.styles : { line: this.styles[lineIndex] };\n      for (var p1 in obj) {\n        for (var p2 in obj[p1]) {\n          // eslint-disable-next-line no-unused-vars\n          for (var p3 in obj[p1][p2]) {\n            return false;\n          }\n        }\n      }\n      return true;\n    },\n\n    /**\n     * Returns true if object has a style property or has it ina specified line\n     * @param {Number} lineIndex\n     * @return {Boolean}\n     */\n    styleHas: function(property, lineIndex) {\n      if (!this.styles || !property || property === '') {\n        return false;\n      }\n      if (typeof lineIndex !== 'undefined' && !this.styles[lineIndex]) {\n        return false;\n      }\n      var obj = typeof lineIndex === 'undefined' ? this.styles : { line: this.styles[lineIndex] };\n      // eslint-disable-next-line\n      for (var p1 in obj) {\n        // eslint-disable-next-line\n        for (var p2 in obj[p1]) {\n          if (typeof obj[p1][p2][property] !== 'undefined') {\n            return true;\n          }\n        }\n      }\n      return false;\n    },\n\n    /**\n     * Check if characters in a text have a value for a property\n     * whose value matches the textbox's value for that property.  If so,\n     * the character-level property is deleted.  If the character\n     * has no other properties, then it is also deleted.  Finally,\n     * if the line containing that character has no other characters\n     * then it also is deleted.\n     *\n     * @param {string} property The property to compare between characters and text.\n     */\n    cleanStyle: function(property) {\n      if (!this.styles || !property || property === '') {\n        return false;\n      }\n      var obj = this.styles, stylesCount = 0, letterCount, stylePropertyValue,\n          allStyleObjectPropertiesMatch = true, graphemeCount = 0, styleObject;\n      // eslint-disable-next-line\n      for (var p1 in obj) {\n        letterCount = 0;\n        // eslint-disable-next-line\n        for (var p2 in obj[p1]) {\n          var styleObject = obj[p1][p2],\n              stylePropertyHasBeenSet = styleObject.hasOwnProperty(property);\n\n          stylesCount++;\n\n          if (stylePropertyHasBeenSet) {\n            if (!stylePropertyValue) {\n              stylePropertyValue = styleObject[property];\n            }\n            else if (styleObject[property] !== stylePropertyValue) {\n              allStyleObjectPropertiesMatch = false;\n            }\n\n            if (styleObject[property] === this[property]) {\n              delete styleObject[property];\n            }\n          }\n          else {\n            allStyleObjectPropertiesMatch = false;\n          }\n\n          if (Object.keys(styleObject).length !== 0) {\n            letterCount++;\n          }\n          else {\n            delete obj[p1][p2];\n          }\n        }\n\n        if (letterCount === 0) {\n          delete obj[p1];\n        }\n      }\n      // if every grapheme has the same style set then\n      // delete those styles and set it on the parent\n      for (var i = 0; i < this._textLines.length; i++) {\n        graphemeCount += this._textLines[i].length;\n      }\n      if (allStyleObjectPropertiesMatch && stylesCount === graphemeCount) {\n        this[property] = stylePropertyValue;\n        this.removeStyle(property);\n      }\n    },\n\n    /**\n     * Remove a style property or properties from all individual character styles\n     * in a text object.  Deletes the character style object if it contains no other style\n     * props.  Deletes a line style object if it contains no other character styles.\n     *\n     * @param {String} props The property to remove from character styles.\n     */\n    removeStyle: function(property) {\n      if (!this.styles || !property || property === '') {\n        return;\n      }\n      var obj = this.styles, line, lineNum, charNum;\n      for (lineNum in obj) {\n        line = obj[lineNum];\n        for (charNum in line) {\n          delete line[charNum][property];\n          if (Object.keys(line[charNum]).length === 0) {\n            delete line[charNum];\n          }\n        }\n        if (Object.keys(line).length === 0) {\n          delete obj[lineNum];\n        }\n      }\n    },\n\n    /**\n     * @private\n     */\n    _extendStyles: function(index, styles) {\n      var loc = this.get2DCursorLocation(index);\n\n      if (!this._getLineStyle(loc.lineIndex)) {\n        this._setLineStyle(loc.lineIndex, {});\n      }\n\n      if (!this._getStyleDeclaration(loc.lineIndex, loc.charIndex)) {\n        this._setStyleDeclaration(loc.lineIndex, loc.charIndex, {});\n      }\n\n      fabric.util.object.extend(this._getStyleDeclaration(loc.lineIndex, loc.charIndex), styles);\n    },\n\n    /**\n     * Returns 2d representation (lineIndex and charIndex) of cursor (or selection start)\n     * @param {Number} [selectionStart] Optional index. When not given, current selectionStart is used.\n     * @param {Boolean} [skipWrapping] consider the location for unwrapped lines. usefull to manage styles.\n     */\n    get2DCursorLocation: function(selectionStart, skipWrapping) {\n      if (typeof selectionStart === 'undefined') {\n        selectionStart = this.selectionStart;\n      }\n      var lines = skipWrapping ? this._unwrappedTextLines : this._textLines;\n      var len = lines.length;\n      for (var i = 0; i < len; i++) {\n        if (selectionStart <= lines[i].length) {\n          return {\n            lineIndex: i,\n            charIndex: selectionStart\n          };\n        }\n        selectionStart -= lines[i].length + 1;\n      }\n      return {\n        lineIndex: i - 1,\n        charIndex: lines[i - 1].length < selectionStart ? lines[i - 1].length : selectionStart\n      };\n    },\n\n    /**\n     * Gets style of a current selection/cursor (at the start position)\n     * if startIndex or endIndex are not provided, slectionStart or selectionEnd will be used.\n     * @param {Number} [startIndex] Start index to get styles at\n     * @param {Number} [endIndex] End index to get styles at, if not specified selectionEnd or startIndex + 1\n     * @param {Boolean} [complete] get full style or not\n     * @return {Array} styles an array with one, zero or more Style objects\n     */\n    getSelectionStyles: function(startIndex, endIndex, complete) {\n      if (typeof startIndex === 'undefined') {\n        startIndex = this.selectionStart || 0;\n      }\n      if (typeof endIndex === 'undefined') {\n        endIndex = this.selectionEnd || startIndex;\n      }\n      var styles = [];\n      for (var i = startIndex; i < endIndex; i++) {\n        styles.push(this.getStyleAtPosition(i, complete));\n      }\n      return styles;\n    },\n\n    /**\n     * Gets style of a current selection/cursor position\n     * @param {Number} position  to get styles at\n     * @param {Boolean} [complete] full style if true\n     * @return {Object} style Style object at a specified index\n     * @private\n     */\n    getStyleAtPosition: function(position, complete) {\n      var loc = this.get2DCursorLocation(position),\n          style = complete ? this.getCompleteStyleDeclaration(loc.lineIndex, loc.charIndex) :\n            this._getStyleDeclaration(loc.lineIndex, loc.charIndex);\n      return style || {};\n    },\n\n    /**\n     * Sets style of a current selection, if no selection exist, do not set anything.\n     * @param {Object} [styles] Styles object\n     * @param {Number} [startIndex] Start index to get styles at\n     * @param {Number} [endIndex] End index to get styles at, if not specified selectionEnd or startIndex + 1\n     * @return {fabric.IText} thisArg\n     * @chainable\n     */\n    setSelectionStyles: function(styles, startIndex, endIndex) {\n      if (typeof startIndex === 'undefined') {\n        startIndex = this.selectionStart || 0;\n      }\n      if (typeof endIndex === 'undefined') {\n        endIndex = this.selectionEnd || startIndex;\n      }\n      for (var i = startIndex; i < endIndex; i++) {\n        this._extendStyles(i, styles);\n      }\n      /* not included in _extendStyles to avoid clearing cache more than once */\n      this._forceClearCache = true;\n      return this;\n    },\n\n    /**\n     * get the reference, not a clone, of the style object for a given character\n     * @param {Number} lineIndex\n     * @param {Number} charIndex\n     * @return {Object} style object\n     */\n    _getStyleDeclaration: function(lineIndex, charIndex) {\n      var lineStyle = this.styles && this.styles[lineIndex];\n      if (!lineStyle) {\n        return null;\n      }\n      return lineStyle[charIndex];\n    },\n\n    /**\n     * return a new object that contains all the style property for a character\n     * the object returned is newly created\n     * @param {Number} lineIndex of the line where the character is\n     * @param {Number} charIndex position of the character on the line\n     * @return {Object} style object\n     */\n    getCompleteStyleDeclaration: function(lineIndex, charIndex) {\n      var style = this._getStyleDeclaration(lineIndex, charIndex) || { },\n          styleObject = { }, prop;\n      for (var i = 0; i < this._styleProperties.length; i++) {\n        prop = this._styleProperties[i];\n        styleObject[prop] = typeof style[prop] === 'undefined' ? this[prop] : style[prop];\n      }\n      return styleObject;\n    },\n\n    /**\n     * @param {Number} lineIndex\n     * @param {Number} charIndex\n     * @param {Object} style\n     * @private\n     */\n    _setStyleDeclaration: function(lineIndex, charIndex, style) {\n      this.styles[lineIndex][charIndex] = style;\n    },\n\n    /**\n     *\n     * @param {Number} lineIndex\n     * @param {Number} charIndex\n     * @private\n     */\n    _deleteStyleDeclaration: function(lineIndex, charIndex) {\n      delete this.styles[lineIndex][charIndex];\n    },\n\n    /**\n     * @param {Number} lineIndex\n     * @private\n     */\n    _getLineStyle: function(lineIndex) {\n      return this.styles[lineIndex];\n    },\n\n    /**\n     * @param {Number} lineIndex\n     * @param {Object} style\n     * @private\n     */\n    _setLineStyle: function(lineIndex, style) {\n      this.styles[lineIndex] = style;\n    },\n\n    /**\n     * @param {Number} lineIndex\n     * @private\n     */\n    _deleteLineStyle: function(lineIndex) {\n      delete this.styles[lineIndex];\n    }\n  });\n})();\n\n\n(function() {\n\n  function parseDecoration(object) {\n    if (object.textDecoration) {\n      object.textDecoration.indexOf('underline') > -1 && (object.underline = true);\n      object.textDecoration.indexOf('line-through') > -1 && (object.linethrough = true);\n      object.textDecoration.indexOf('overline') > -1 && (object.overline = true);\n      delete object.textDecoration;\n    }\n  }\n\n  /**\n   * IText class (introduced in <b>v1.4</b>) Events are also fired with \"text:\"\n   * prefix when observing canvas.\n   * @class fabric.IText\n   * @extends fabric.Text\n   * @mixes fabric.Observable\n   *\n   * @fires changed\n   * @fires selection:changed\n   * @fires editing:entered\n   * @fires editing:exited\n   *\n   * @return {fabric.IText} thisArg\n   * @see {@link fabric.IText#initialize} for constructor definition\n   *\n   * <p>Supported key combinations:</p>\n   * <pre>\n   *   Move cursor:                    left, right, up, down\n   *   Select character:               shift + left, shift + right\n   *   Select text vertically:         shift + up, shift + down\n   *   Move cursor by word:            alt + left, alt + right\n   *   Select words:                   shift + alt + left, shift + alt + right\n   *   Move cursor to line start/end:  cmd + left, cmd + right or home, end\n   *   Select till start/end of line:  cmd + shift + left, cmd + shift + right or shift + home, shift + end\n   *   Jump to start/end of text:      cmd + up, cmd + down\n   *   Select till start/end of text:  cmd + shift + up, cmd + shift + down or shift + pgUp, shift + pgDown\n   *   Delete character:               backspace\n   *   Delete word:                    alt + backspace\n   *   Delete line:                    cmd + backspace\n   *   Forward delete:                 delete\n   *   Copy text:                      ctrl/cmd + c\n   *   Paste text:                     ctrl/cmd + v\n   *   Cut text:                       ctrl/cmd + x\n   *   Select entire text:             ctrl/cmd + a\n   *   Quit editing                    tab or esc\n   * </pre>\n   *\n   * <p>Supported mouse/touch combination</p>\n   * <pre>\n   *   Position cursor:                click/touch\n   *   Create selection:               click/touch & drag\n   *   Create selection:               click & shift + click\n   *   Select word:                    double click\n   *   Select line:                    triple click\n   * </pre>\n   */\n  fabric.IText = fabric.util.createClass(fabric.Text, fabric.Observable, /** @lends fabric.IText.prototype */ {\n\n    /**\n     * Type of an object\n     * @type String\n     * @default\n     */\n    type: 'i-text',\n\n    /**\n     * Index where text selection starts (or where cursor is when there is no selection)\n     * @type Number\n     * @default\n     */\n    selectionStart: 0,\n\n    /**\n     * Index where text selection ends\n     * @type Number\n     * @default\n     */\n    selectionEnd: 0,\n\n    /**\n     * Color of text selection\n     * @type String\n     * @default\n     */\n    selectionColor: 'rgba(17,119,255,0.3)',\n\n    /**\n     * Indicates whether text is in editing mode\n     * @type Boolean\n     * @default\n     */\n    isEditing: false,\n\n    /**\n     * Indicates whether a text can be edited\n     * @type Boolean\n     * @default\n     */\n    editable: true,\n\n    /**\n     * Border color of text object while it's in editing mode\n     * @type String\n     * @default\n     */\n    editingBorderColor: 'rgba(102,153,255,0.25)',\n\n    /**\n     * Width of cursor (in px)\n     * @type Number\n     * @default\n     */\n    cursorWidth: 2,\n\n    /**\n     * Color of default cursor (when not overwritten by character style)\n     * @type String\n     * @default\n     */\n    cursorColor: '#333',\n\n    /**\n     * Delay between cursor blink (in ms)\n     * @type Number\n     * @default\n     */\n    cursorDelay: 1000,\n\n    /**\n     * Duration of cursor fadein (in ms)\n     * @type Number\n     * @default\n     */\n    cursorDuration: 600,\n\n    /**\n     * Indicates whether internal text char widths can be cached\n     * @type Boolean\n     * @default\n     */\n    caching: true,\n\n    /**\n     * @private\n     */\n    _reSpace: /\\s|\\n/,\n\n    /**\n     * @private\n     */\n    _currentCursorOpacity: 0,\n\n    /**\n     * @private\n     */\n    _selectionDirection: null,\n\n    /**\n     * @private\n     */\n    _abortCursorAnimation: false,\n\n    /**\n     * @private\n     */\n    __widthOfSpace: [],\n\n    /**\n     * Helps determining when the text is in composition, so that the cursor\n     * rendering is altered.\n     */\n    inCompositionMode: false,\n\n    /**\n     * Constructor\n     * @param {String} text Text string\n     * @param {Object} [options] Options object\n     * @return {fabric.IText} thisArg\n     */\n    initialize: function(text, options) {\n      this.callSuper('initialize', text, options);\n      this.initBehavior();\n    },\n\n    /**\n     * Sets selection start (left boundary of a selection)\n     * @param {Number} index Index to set selection start to\n     */\n    setSelectionStart: function(index) {\n      index = Math.max(index, 0);\n      this._updateAndFire('selectionStart', index);\n    },\n\n    /**\n     * Sets selection end (right boundary of a selection)\n     * @param {Number} index Index to set selection end to\n     */\n    setSelectionEnd: function(index) {\n      index = Math.min(index, this.text.length);\n      this._updateAndFire('selectionEnd', index);\n    },\n\n    /**\n     * @private\n     * @param {String} property 'selectionStart' or 'selectionEnd'\n     * @param {Number} index new position of property\n     */\n    _updateAndFire: function(property, index) {\n      if (this[property] !== index) {\n        this._fireSelectionChanged();\n        this[property] = index;\n      }\n      this._updateTextarea();\n    },\n\n    /**\n     * Fires the even of selection changed\n     * @private\n     */\n    _fireSelectionChanged: function() {\n      this.fire('selection:changed');\n      this.canvas && this.canvas.fire('text:selection:changed', { target: this });\n    },\n\n    /**\n     * Initialize text dimensions. Render all text on given context\n     * or on a offscreen canvas to get the text width with measureText.\n     * Updates this.width and this.height with the proper values.\n     * Does not return dimensions.\n     * @private\n     */\n    initDimensions: function() {\n      this.isEditing && this.initDelayedCursor();\n      this.clearContextTop();\n      this.callSuper('initDimensions');\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    render: function(ctx) {\n      this.clearContextTop();\n      this.callSuper('render', ctx);\n      // clear the cursorOffsetCache, so we ensure to calculate once per renderCursor\n      // the correct position but not at every cursor animation.\n      this.cursorOffsetCache = { };\n      this.renderCursorOrSelection();\n    },\n\n    /**\n     * @private\n     * @param {CanvasRenderingContext2D} ctx Context to render on\n     */\n    _render: function(ctx) {\n      this.callSuper('_render', ctx);\n    },\n\n    /**\n     * Prepare and clean the contextTop\n     */\n    clearContextTop: function(skipRestore) {\n      if (!this.isEditing) {\n        return;\n      }\n      if (this.canvas && this.canvas.contextTop) {\n        var ctx = this.canvas.contextTop, v = this.canvas.viewportTransform;\n        ctx.save();\n        ctx.transform(v[0], v[1], v[2], v[3], v[4], v[5]);\n        this.transform(ctx);\n        this.transformMatrix && ctx.transform.apply(ctx, this.transformMatrix);\n        this._clearTextArea(ctx);\n        skipRestore || ctx.restore();\n      }\n    },\n\n    /**\n     * Renders cursor or selection (depending on what exists)\n     */\n    renderCursorOrSelection: function() {\n      if (!this.isEditing || !this.canvas) {\n        return;\n      }\n      var boundaries = this._getCursorBoundaries(), ctx;\n      if (this.canvas && this.canvas.contextTop) {\n        ctx = this.canvas.contextTop;\n        this.clearContextTop(true);\n      }\n      else {\n        ctx = this.canvas.contextContainer;\n        ctx.save();\n      }\n      if (this.selectionStart === this.selectionEnd) {\n        this.renderCursor(boundaries, ctx);\n      }\n      else {\n        this.renderSelection(boundaries, ctx);\n      }\n      ctx.restore();\n    },\n\n    _clearTextArea: function(ctx) {\n      // we add 4 pixel, to be sure to do not leave any pixel out\n      var width = this.width + 4, height = this.height + 4;\n      ctx.clearRect(-width / 2, -height / 2, width, height);\n    },\n\n    /**\n     * Returns cursor boundaries (left, top, leftOffset, topOffset)\n     * @private\n     * @param {Array} chars Array of characters\n     * @param {String} typeOfBoundaries\n     */\n    _getCursorBoundaries: function(position) {\n\n      // left/top are left/top of entire text box\n      // leftOffset/topOffset are offset from that left/top point of a text box\n\n      if (typeof position === 'undefined') {\n        position = this.selectionStart;\n      }\n\n      var left = this._getLeftOffset(),\n          top = this._getTopOffset(),\n          offsets = this._getCursorBoundariesOffsets(position);\n\n      return {\n        left: left,\n        top: top,\n        leftOffset: offsets.left,\n        topOffset: offsets.top\n      };\n    },\n\n    /**\n     * @private\n     */\n    _getCursorBoundariesOffsets: function(position) {\n      if (this.cursorOffsetCache && 'top' in this.cursorOffsetCache) {\n        return this.cursorOffsetCache;\n      }\n      var lineLeftOffset,\n          lineIndex = 0,\n          charIndex = 0,\n          topOffset = 0,\n          leftOffset = 0,\n          boundaries,\n          cursorPosition = this.get2DCursorLocation(position);\n      for (var i = 0; i < cursorPosition.lineIndex; i++) {\n        topOffset += this.getHeightOfLine(i);\n      }\n\n      lineLeftOffset = this._getLineLeftOffset(cursorPosition.lineIndex);\n      var bound = this.__charBounds[cursorPosition.lineIndex][cursorPosition.charIndex];\n      bound && (leftOffset = bound.left);\n      if (this.charSpacing !== 0 && charIndex === this._textLines[lineIndex].length) {\n        leftOffset -= this._getWidthOfCharSpacing();\n      }\n      boundaries = {\n        top: topOffset,\n        left: lineLeftOffset + (leftOffset > 0 ? leftOffset : 0),\n      };\n      this.cursorOffsetCache = boundaries;\n      return this.cursorOffsetCache;\n    },\n\n    /**\n     * Renders cursor\n     * @param {Object} boundaries\n     * @param {CanvasRenderingContext2D} ctx transformed context to draw on\n     */\n    renderCursor: function(boundaries, ctx) {\n      var cursorLocation = this.get2DCursorLocation(),\n          lineIndex = cursorLocation.lineIndex,\n          charIndex = cursorLocation.charIndex > 0 ? cursorLocation.charIndex - 1 : 0,\n          charHeight = this.getValueOfPropertyAt(lineIndex, charIndex, 'fontSize'),\n          multiplier = this.scaleX * this.canvas.getZoom(),\n          cursorWidth = this.cursorWidth / multiplier,\n          topOffset = boundaries.topOffset,\n          dy = this.getValueOfPropertyAt(lineIndex, charIndex, 'deltaY');\n\n      topOffset += (1 - this._fontSizeFraction) * this.getHeightOfLine(lineIndex) / this.lineHeight\n        - charHeight * (1 - this._fontSizeFraction);\n\n      if (this.inCompositionMode) {\n        this.renderSelection(boundaries, ctx);\n      }\n\n      ctx.fillStyle = this.getValueOfPropertyAt(lineIndex, charIndex, 'fill');\n      ctx.globalAlpha = this.__isMousedown ? 1 : this._currentCursorOpacity;\n      ctx.fillRect(\n        boundaries.left + boundaries.leftOffset - cursorWidth / 2,\n        topOffset + boundaries.top + dy,\n        cursorWidth,\n        charHeight);\n    },\n\n    /**\n     * Renders text selection\n     * @param {Object} boundaries Object with left/top/leftOffset/topOffset\n     * @param {CanvasRenderingContext2D} ctx transformed context to draw on\n     */\n    renderSelection: function(boundaries, ctx) {\n\n      var selectionStart = this.inCompositionMode ? this.hiddenTextarea.selectionStart : this.selectionStart,\n          selectionEnd = this.inCompositionMode ? this.hiddenTextarea.selectionEnd : this.selectionEnd,\n          isJustify = this.textAlign.indexOf('justify') !== -1,\n          start = this.get2DCursorLocation(selectionStart),\n          end = this.get2DCursorLocation(selectionEnd),\n          startLine = start.lineIndex,\n          endLine = end.lineIndex,\n          startChar = start.charIndex < 0 ? 0 : start.charIndex,\n          endChar = end.charIndex < 0 ? 0 : end.charIndex;\n\n      for (var i = startLine; i <= endLine; i++) {\n        var lineOffset = this._getLineLeftOffset(i) || 0,\n            lineHeight = this.getHeightOfLine(i),\n            realLineHeight = 0, boxStart = 0, boxEnd = 0;\n\n        if (i === startLine) {\n          boxStart = this.__charBounds[startLine][startChar].left;\n        }\n        if (i >= startLine && i < endLine) {\n          boxEnd = isJustify && !this.isEndOfWrapping(i) ? this.width : this.getLineWidth(i) || 5; // WTF is this 5?\n        }\n        else if (i === endLine) {\n          if (endChar === 0) {\n            boxEnd = this.__charBounds[endLine][endChar].left;\n          }\n          else {\n            boxEnd = this.__charBounds[endLine][endChar - 1].left + this.__charBounds[endLine][endChar - 1].width;\n          }\n        }\n        realLineHeight = lineHeight;\n        if (this.lineHeight < 1 || (i === endLine && this.lineHeight > 1)) {\n          lineHeight /= this.lineHeight;\n        }\n        if (this.inCompositionMode) {\n          ctx.fillStyle = this.compositionColor || 'black';\n          ctx.fillRect(\n            boundaries.left + lineOffset + boxStart,\n            boundaries.top + boundaries.topOffset + lineHeight,\n            boxEnd - boxStart,\n            1);\n        }\n        else {\n          ctx.fillStyle = this.selectionColor;\n          ctx.fillRect(\n            boundaries.left + lineOffset + boxStart,\n            boundaries.top + boundaries.topOffset,\n            boxEnd - boxStart,\n            lineHeight);\n        }\n\n\n        boundaries.topOffset += realLineHeight;\n      }\n    },\n\n    /**\n     * High level function to know the height of the cursor.\n     * the currentChar is the one that precedes the cursor\n     * Returns fontSize of char at the current cursor\n     * @return {Number} Character font size\n     */\n    getCurrentCharFontSize: function() {\n      var cp = this._getCurrentCharIndex();\n      return this.getValueOfPropertyAt(cp.l, cp.c, 'fontSize');\n    },\n\n    /**\n     * High level function to know the color of the cursor.\n     * the currentChar is the one that precedes the cursor\n     * Returns color (fill) of char at the current cursor\n     * @return {String} Character color (fill)\n     */\n    getCurrentCharColor: function() {\n      var cp = this._getCurrentCharIndex();\n      return this.getValueOfPropertyAt(cp.l, cp.c, 'fill');\n    },\n\n    /**\n     * Returns the cursor position for the getCurrent.. functions\n     * @private\n     */\n    _getCurrentCharIndex: function() {\n      var cursorPosition = this.get2DCursorLocation(this.selectionStart, true),\n          charIndex = cursorPosition.charIndex > 0 ? cursorPosition.charIndex - 1 : 0;\n      return { l: cursorPosition.lineIndex, c: charIndex };\n    }\n  });\n\n  /**\n   * Returns fabric.IText instance from an object representation\n   * @static\n   * @memberOf fabric.IText\n   * @param {Object} object Object to create an instance from\n   * @param {function} [callback] invoked with new instance as argument\n   */\n  fabric.IText.fromObject = function(object, callback) {\n    parseDecoration(object);\n    if (object.styles) {\n      for (var i in object.styles) {\n        for (var j in object.styles[i]) {\n          parseDecoration(object.styles[i][j]);\n        }\n      }\n    }\n    fabric.Object._fromObject('IText', object, callback, 'text');\n  };\n})();\n\n\n(function() {\n\n  var clone = fabric.util.object.clone;\n\n  fabric.util.object.extend(fabric.IText.prototype, /** @lends fabric.IText.prototype */ {\n\n    /**\n     * Initializes all the interactive behavior of IText\n     */\n    initBehavior: function() {\n      this.initAddedHandler();\n      this.initRemovedHandler();\n      this.initCursorSelectionHandlers();\n      this.initDoubleClickSimulation();\n      this.mouseMoveHandler = this.mouseMoveHandler.bind(this);\n    },\n\n    onDeselect: function(options) {\n      this.isEditing && this.exitEditing();\n      this.selected = false;\n      fabric.Object.prototype.onDeselect.call(this, options);\n    },\n\n    /**\n     * Initializes \"added\" event handler\n     */\n    initAddedHandler: function() {\n      var _this = this;\n      this.on('added', function() {\n        var canvas = _this.canvas;\n        if (canvas) {\n          if (!canvas._hasITextHandlers) {\n            canvas._hasITextHandlers = true;\n            _this._initCanvasHandlers(canvas);\n          }\n          canvas._iTextInstances = canvas._iTextInstances || [];\n          canvas._iTextInstances.push(_this);\n        }\n      });\n    },\n\n    initRemovedHandler: function() {\n      var _this = this;\n      this.on('removed', function() {\n        var canvas = _this.canvas;\n        if (canvas) {\n          canvas._iTextInstances = canvas._iTextInstances || [];\n          fabric.util.removeFromArray(canvas._iTextInstances, _this);\n          if (canvas._iTextInstances.length === 0) {\n            canvas._hasITextHandlers = false;\n            _this._removeCanvasHandlers(canvas);\n          }\n        }\n      });\n    },\n\n    /**\n     * register canvas event to manage exiting on other instances\n     * @private\n     */\n    _initCanvasHandlers: function(canvas) {\n      canvas._mouseUpITextHandler = (function() {\n        if (canvas._iTextInstances) {\n          canvas._iTextInstances.forEach(function(obj) {\n            obj.__isMousedown = false;\n          });\n        }\n      }).bind(this);\n      canvas.on('mouse:up', canvas._mouseUpITextHandler);\n    },\n\n    /**\n     * remove canvas event to manage exiting on other instances\n     * @private\n     */\n    _removeCanvasHandlers: function(canvas) {\n      canvas.off('mouse:up', canvas._mouseUpITextHandler);\n    },\n\n    /**\n     * @private\n     */\n    _tick: function() {\n      this._currentTickState = this._animateCursor(this, 1, this.cursorDuration, '_onTickComplete');\n    },\n\n    /**\n     * @private\n     */\n    _animateCursor: function(obj, targetOpacity, duration, completeMethod) {\n\n      var tickState;\n\n      tickState = {\n        isAborted: false,\n        abort: function() {\n          this.isAborted = true;\n        },\n      };\n\n      obj.animate('_currentCursorOpacity', targetOpacity, {\n        duration: duration,\n        onComplete: function() {\n          if (!tickState.isAborted) {\n            obj[completeMethod]();\n          }\n        },\n        onChange: function() {\n          // we do not want to animate a selection, only cursor\n          if (obj.canvas && obj.selectionStart === obj.selectionEnd) {\n            obj.renderCursorOrSelection();\n          }\n        },\n        abort: function() {\n          return tickState.isAborted;\n        }\n      });\n      return tickState;\n    },\n\n    /**\n     * @private\n     */\n    _onTickComplete: function() {\n\n      var _this = this;\n\n      if (this._cursorTimeout1) {\n        clearTimeout(this._cursorTimeout1);\n      }\n      this._cursorTimeout1 = setTimeout(function() {\n        _this._currentTickCompleteState = _this._animateCursor(_this, 0, this.cursorDuration / 2, '_tick');\n      }, 100);\n    },\n\n    /**\n     * Initializes delayed cursor\n     */\n    initDelayedCursor: function(restart) {\n      var _this = this,\n          delay = restart ? 0 : this.cursorDelay;\n\n      this.abortCursorAnimation();\n      this._currentCursorOpacity = 1;\n      this._cursorTimeout2 = setTimeout(function() {\n        _this._tick();\n      }, delay);\n    },\n\n    /**\n     * Aborts cursor animation and clears all timeouts\n     */\n    abortCursorAnimation: function() {\n      var shouldClear = this._currentTickState || this._currentTickCompleteState,\n          canvas = this.canvas;\n      this._currentTickState && this._currentTickState.abort();\n      this._currentTickCompleteState && this._currentTickCompleteState.abort();\n\n      clearTimeout(this._cursorTimeout1);\n      clearTimeout(this._cursorTimeout2);\n\n      this._currentCursorOpacity = 0;\n      // to clear just itext area we need to transform the context\n      // it may not be worth it\n      if (shouldClear && canvas) {\n        canvas.clearContext(canvas.contextTop || canvas.contextContainer);\n      }\n\n    },\n\n    /**\n     * Selects entire text\n     * @return {fabric.IText} thisArg\n     * @chainable\n     */\n    selectAll: function() {\n      this.selectionStart = 0;\n      this.selectionEnd = this._text.length;\n      this._fireSelectionChanged();\n      this._updateTextarea();\n      return this;\n    },\n\n    /**\n     * Returns selected text\n     * @return {String}\n     */\n    getSelectedText: function() {\n      return this._text.slice(this.selectionStart, this.selectionEnd).join('');\n    },\n\n    /**\n     * Find new selection index representing start of current word according to current selection index\n     * @param {Number} startFrom Surrent selection index\n     * @return {Number} New selection index\n     */\n    findWordBoundaryLeft: function(startFrom) {\n      var offset = 0, index = startFrom - 1;\n\n      // remove space before cursor first\n      if (this._reSpace.test(this._text[index])) {\n        while (this._reSpace.test(this._text[index])) {\n          offset++;\n          index--;\n        }\n      }\n      while (/\\S/.test(this._text[index]) && index > -1) {\n        offset++;\n        index--;\n      }\n\n      return startFrom - offset;\n    },\n\n    /**\n     * Find new selection index representing end of current word according to current selection index\n     * @param {Number} startFrom Current selection index\n     * @return {Number} New selection index\n     */\n    findWordBoundaryRight: function(startFrom) {\n      var offset = 0, index = startFrom;\n\n      // remove space after cursor first\n      if (this._reSpace.test(this._text[index])) {\n        while (this._reSpace.test(this._text[index])) {\n          offset++;\n          index++;\n        }\n      }\n      while (/\\S/.test(this._text[index]) && index < this.text.length) {\n        offset++;\n        index++;\n      }\n\n      return startFrom + offset;\n    },\n\n    /**\n     * Find new selection index representing start of current line according to current selection index\n     * @param {Number} startFrom Current selection index\n     * @return {Number} New selection index\n     */\n    findLineBoundaryLeft: function(startFrom) {\n      var offset = 0, index = startFrom - 1;\n\n      while (!/\\n/.test(this._text[index]) && index > -1) {\n        offset++;\n        index--;\n      }\n\n      return startFrom - offset;\n    },\n\n    /**\n     * Find new selection index representing end of current line according to current selection index\n     * @param {Number} startFrom Current selection index\n     * @return {Number} New selection index\n     */\n    findLineBoundaryRight: function(startFrom) {\n      var offset = 0, index = startFrom;\n\n      while (!/\\n/.test(this._text[index]) && index < this.text.length) {\n        offset++;\n        index++;\n      }\n\n      return startFrom + offset;\n    },\n\n    /**\n     * Finds index corresponding to beginning or end of a word\n     * @param {Number} selectionStart Index of a character\n     * @param {Number} direction 1 or -1\n     * @return {Number} Index of the beginning or end of a word\n     */\n    searchWordBoundary: function(selectionStart, direction) {\n      var index     = this._reSpace.test(this.text.charAt(selectionStart)) ? selectionStart - 1 : selectionStart,\n          _char     = this.text.charAt(index),\n          reNonWord = /[ \\n\\.,;!\\?\\-]/;\n\n      while (!reNonWord.test(_char) && index > 0 && index < this.text.length) {\n        index += direction;\n        _char = this.text.charAt(index);\n      }\n      if (reNonWord.test(_char) && _char !== '\\n') {\n        index += direction === 1 ? 0 : 1;\n      }\n      return index;\n    },\n\n    /**\n     * Selects a word based on the index\n     * @param {Number} selectionStart Index of a character\n     */\n    selectWord: function(selectionStart) {\n      selectionStart = selectionStart || this.selectionStart;\n      var newSelectionStart = this.searchWordBoundary(selectionStart, -1), /* search backwards */\n          newSelectionEnd = this.searchWordBoundary(selectionStart, 1); /* search forward */\n\n      this.selectionStart = newSelectionStart;\n      this.selectionEnd = newSelectionEnd;\n      this._fireSelectionChanged();\n      this._updateTextarea();\n      this.renderCursorOrSelection();\n    },\n\n    /**\n     * Selects a line based on the index\n     * @param {Number} selectionStart Index of a character\n     * @return {fabric.IText} thisArg\n     * @chainable\n     */\n    selectLine: function(selectionStart) {\n      selectionStart = selectionStart || this.selectionStart;\n      var newSelectionStart = this.findLineBoundaryLeft(selectionStart),\n          newSelectionEnd = this.findLineBoundaryRight(selectionStart);\n\n      this.selectionStart = newSelectionStart;\n      this.selectionEnd = newSelectionEnd;\n      this._fireSelectionChanged();\n      this._updateTextarea();\n      return this;\n    },\n\n    /**\n     * Enters editing state\n     * @return {fabric.IText} thisArg\n     * @chainable\n     */\n    enterEditing: function(e) {\n      if (this.isEditing || !this.editable) {\n        return;\n      }\n\n      if (this.canvas) {\n        this.canvas.calcOffset();\n        this.exitEditingOnOthers(this.canvas);\n      }\n\n      this.isEditing = true;\n\n      this.initHiddenTextarea(e);\n      this.hiddenTextarea.focus();\n      this.hiddenTextarea.value = this.text;\n      this._updateTextarea();\n      this._saveEditingProps();\n      this._setEditingProps();\n      this._textBeforeEdit = this.text;\n\n      this._tick();\n      this.fire('editing:entered');\n      this._fireSelectionChanged();\n      if (!this.canvas) {\n        return this;\n      }\n      this.canvas.fire('text:editing:entered', { target: this });\n      this.initMouseMoveHandler();\n      this.canvas.requestRenderAll();\n      return this;\n    },\n\n    exitEditingOnOthers: function(canvas) {\n      if (canvas._iTextInstances) {\n        canvas._iTextInstances.forEach(function(obj) {\n          obj.selected = false;\n          if (obj.isEditing) {\n            obj.exitEditing();\n          }\n        });\n      }\n    },\n\n    /**\n     * Initializes \"mousemove\" event handler\n     */\n    initMouseMoveHandler: function() {\n      this.canvas.on('mouse:move', this.mouseMoveHandler);\n    },\n\n    /**\n     * @private\n     */\n    mouseMoveHandler: function(options) {\n      if (!this.__isMousedown || !this.isEditing) {\n        return;\n      }\n\n      var newSelectionStart = this.getSelectionStartFromPointer(options.e),\n          currentStart = this.selectionStart,\n          currentEnd = this.selectionEnd;\n      if (\n        (newSelectionStart !== this.__selectionStartOnMouseDown || currentStart === currentEnd)\n        &&\n        (currentStart === newSelectionStart || currentEnd === newSelectionStart)\n      ) {\n        return;\n      }\n      if (newSelectionStart > this.__selectionStartOnMouseDown) {\n        this.selectionStart = this.__selectionStartOnMouseDown;\n        this.selectionEnd = newSelectionStart;\n      }\n      else {\n        this.selectionStart = newSelectionStart;\n        this.selectionEnd = this.__selectionStartOnMouseDown;\n      }\n      if (this.selectionStart !== currentStart || this.selectionEnd !== currentEnd) {\n        this.restartCursorIfNeeded();\n        this._fireSelectionChanged();\n        this._updateTextarea();\n        this.renderCursorOrSelection();\n      }\n    },\n\n    /**\n     * @private\n     */\n    _setEditingProps: function() {\n      this.hoverCursor = 'text';\n\n      if (this.canvas) {\n        this.canvas.defaultCursor = this.canvas.moveCursor = 'text';\n      }\n\n      this.borderColor = this.editingBorderColor;\n\n      this.hasControls = this.selectable = false;\n      this.lockMovementX = this.lockMovementY = true;\n    },\n\n    /**\n     * convert from textarea to grapheme indexes\n     */\n    fromStringToGraphemeSelection: function(start, end, text) {\n      var smallerTextStart = text.slice(0, start),\n          graphemeStart = fabric.util.string.graphemeSplit(smallerTextStart).length;\n      if (start === end) {\n        return { selectionStart: graphemeStart, selectionEnd: graphemeStart };\n      }\n      var smallerTextEnd = text.slice(start, end),\n          graphemeEnd = fabric.util.string.graphemeSplit(smallerTextEnd).length;\n      return { selectionStart: graphemeStart, selectionEnd: graphemeStart + graphemeEnd };\n    },\n\n    /**\n     * convert from fabric to textarea values\n     */\n    fromGraphemeToStringSelection: function(start, end, _text) {\n      var smallerTextStart = _text.slice(0, start),\n          graphemeStart = smallerTextStart.join('').length;\n      if (start === end) {\n        return { selectionStart: graphemeStart, selectionEnd: graphemeStart };\n      }\n      var smallerTextEnd = _text.slice(start, end),\n          graphemeEnd = smallerTextEnd.join('').length;\n      return { selectionStart: graphemeStart, selectionEnd: graphemeStart + graphemeEnd };\n    },\n\n    /**\n     * @private\n     */\n    _updateTextarea: function() {\n      this.cursorOffsetCache = { };\n      if (!this.hiddenTextarea) {\n        return;\n      }\n      if (!this.inCompositionMode) {\n        var newSelection = this.fromGraphemeToStringSelection(this.selectionStart, this.selectionEnd, this._text);\n        this.hiddenTextarea.selectionStart = newSelection.selectionStart;\n        this.hiddenTextarea.selectionEnd = newSelection.selectionEnd;\n      }\n      this.updateTextareaPosition();\n    },\n\n    /**\n     * @private\n     */\n    updateFromTextArea: function() {\n      if (!this.hiddenTextarea) {\n        return;\n      }\n      this.cursorOffsetCache = { };\n      this.text = this.hiddenTextarea.value;\n      if (this._shouldClearDimensionCache()) {\n        this.initDimensions();\n        this.setCoords();\n      }\n      var newSelection = this.fromStringToGraphemeSelection(\n        this.hiddenTextarea.selectionStart, this.hiddenTextarea.selectionEnd, this.hiddenTextarea.value);\n      this.selectionEnd = this.selectionStart = newSelection.selectionEnd;\n      if (!this.inCompositionMode) {\n        this.selectionStart = newSelection.selectionStart;\n      }\n      this.updateTextareaPosition();\n    },\n\n    /**\n     * @private\n     */\n    updateTextareaPosition: function() {\n      if (this.selectionStart === this.selectionEnd) {\n        var style = this._calcTextareaPosition();\n        this.hiddenTextarea.style.left = style.left;\n        this.hiddenTextarea.style.top = style.top;\n      }\n    },\n\n    /**\n     * @private\n     * @return {Object} style contains style for hiddenTextarea\n     */\n    _calcTextareaPosition: function() {\n      if (!this.canvas) {\n        return { x: 1, y: 1 };\n      }\n      var desiredPostion = this.inCompositionMode ? this.compositionStart : this.selectionStart,\n          boundaries = this._getCursorBoundaries(desiredPostion),\n          cursorLocation = this.get2DCursorLocation(desiredPostion),\n          lineIndex = cursorLocation.lineIndex,\n          charIndex = cursorLocation.charIndex,\n          charHeight = this.getValueOfPropertyAt(lineIndex, charIndex, 'fontSize') * this.lineHeight,\n          leftOffset = boundaries.leftOffset,\n          m = this.calcTransformMatrix(),\n          p = {\n            x: boundaries.left + leftOffset,\n            y: boundaries.top + boundaries.topOffset + charHeight\n          },\n          upperCanvas = this.canvas.upperCanvasEl,\n          upperCanvasWidth = upperCanvas.width,\n          upperCanvasHeight = upperCanvas.height,\n          maxWidth = upperCanvasWidth - charHeight,\n          maxHeight = upperCanvasHeight - charHeight,\n          scaleX = upperCanvas.clientWidth / upperCanvasWidth,\n          scaleY = upperCanvas.clientHeight / upperCanvasHeight;\n\n      p = fabric.util.transformPoint(p, m);\n      p = fabric.util.transformPoint(p, this.canvas.viewportTransform);\n      p.x *= scaleX;\n      p.y *= scaleY;\n      if (p.x < 0) {\n        p.x = 0;\n      }\n      if (p.x > maxWidth) {\n        p.x = maxWidth;\n      }\n      if (p.y < 0) {\n        p.y = 0;\n      }\n      if (p.y > maxHeight) {\n        p.y = maxHeight;\n      }\n\n      // add canvas offset on document\n      p.x += this.canvas._offset.left;\n      p.y += this.canvas._offset.top;\n\n      return { left: p.x + 'px', top: p.y + 'px', fontSize: charHeight + 'px', charHeight: charHeight };\n    },\n\n    /**\n     * @private\n     */\n    _saveEditingProps: function() {\n      this._savedProps = {\n        hasControls: this.hasControls,\n        borderColor: this.borderColor,\n        lockMovementX: this.lockMovementX,\n        lockMovementY: this.lockMovementY,\n        hoverCursor: this.hoverCursor,\n        defaultCursor: this.canvas && this.canvas.defaultCursor,\n        moveCursor: this.canvas && this.canvas.moveCursor\n      };\n    },\n\n    /**\n     * @private\n     */\n    _restoreEditingProps: function() {\n      if (!this._savedProps) {\n        return;\n      }\n\n      this.hoverCursor = this._savedProps.hoverCursor;\n      this.hasControls = this._savedProps.hasControls;\n      this.borderColor = this._savedProps.borderColor;\n      this.lockMovementX = this._savedProps.lockMovementX;\n      this.lockMovementY = this._savedProps.lockMovementY;\n\n      if (this.canvas) {\n        this.canvas.defaultCursor = this._savedProps.defaultCursor;\n        this.canvas.moveCursor = this._savedProps.moveCursor;\n      }\n    },\n\n    /**\n     * Exits from editing state\n     * @return {fabric.IText} thisArg\n     * @chainable\n     */\n    exitEditing: function() {\n      var isTextChanged = (this._textBeforeEdit !== this.text);\n      this.selected = false;\n      this.isEditing = false;\n      this.selectable = true;\n\n      this.selectionEnd = this.selectionStart;\n\n      if (this.hiddenTextarea) {\n        this.hiddenTextarea.blur && this.hiddenTextarea.blur();\n        this.canvas && this.hiddenTextarea.parentNode.removeChild(this.hiddenTextarea);\n        this.hiddenTextarea = null;\n      }\n\n      this.abortCursorAnimation();\n      this._restoreEditingProps();\n      this._currentCursorOpacity = 0;\n      if (this._shouldClearDimensionCache()) {\n        this.initDimensions();\n        this.setCoords();\n      }\n      this.fire('editing:exited');\n      isTextChanged && this.fire('modified');\n      if (this.canvas) {\n        this.canvas.off('mouse:move', this.mouseMoveHandler);\n        this.canvas.fire('text:editing:exited', { target: this });\n        isTextChanged && this.canvas.fire('object:modified', { target: this });\n      }\n      return this;\n    },\n\n    /**\n     * @private\n     */\n    _removeExtraneousStyles: function() {\n      for (var prop in this.styles) {\n        if (!this._textLines[prop]) {\n          delete this.styles[prop];\n        }\n      }\n    },\n\n    /**\n     * remove and reflow a style block from start to end.\n     * @param {Number} start linear start position for removal (included in removal)\n     * @param {Number} end linear end position for removal ( excluded from removal )\n     */\n    removeStyleFromTo: function(start, end) {\n      var cursorStart = this.get2DCursorLocation(start, true),\n          cursorEnd = this.get2DCursorLocation(end, true),\n          lineStart = cursorStart.lineIndex,\n          charStart = cursorStart.charIndex,\n          lineEnd = cursorEnd.lineIndex,\n          charEnd = cursorEnd.charIndex,\n          i, styleObj;\n      if (lineStart !== lineEnd) {\n        // step1 remove the trailing of lineStart\n        if (this.styles[lineStart]) {\n          for (i = charStart; i < this._unwrappedTextLines[lineStart].length; i++) {\n            delete this.styles[lineStart][i];\n          }\n        }\n        // step2 move the trailing of lineEnd to lineStart if needed\n        if (this.styles[lineEnd]) {\n          for (i = charEnd; i < this._unwrappedTextLines[lineEnd].length; i++) {\n            styleObj = this.styles[lineEnd][i];\n            if (styleObj) {\n              this.styles[lineStart] || (this.styles[lineStart] = { });\n              this.styles[lineStart][charStart + i - charEnd] = styleObj;\n            }\n          }\n        }\n        // step3 detects lines will be completely removed.\n        for (i = lineStart + 1; i <= lineEnd; i++) {\n          delete this.styles[i];\n        }\n        // step4 shift remaining lines.\n        this.shiftLineStyles(lineEnd, lineStart - lineEnd);\n      }\n      else {\n        // remove and shift left on the same line\n        if (this.styles[lineStart]) {\n          styleObj = this.styles[lineStart];\n          var diff = charEnd - charStart, numericChar, _char;\n          for (i = charStart; i < charEnd; i++) {\n            delete styleObj[i];\n          }\n          for (_char in this.styles[lineStart]) {\n            numericChar = parseInt(_char, 10);\n            if (numericChar >= charEnd) {\n              styleObj[numericChar - diff] = styleObj[_char];\n              delete styleObj[_char];\n            }\n          }\n        }\n      }\n    },\n\n    /**\n     * Shifts line styles up or down\n     * @param {Number} lineIndex Index of a line\n     * @param {Number} offset Can any number?\n     */\n    shiftLineStyles: function(lineIndex, offset) {\n      // shift all line styles by offset upward or downward\n      // do not clone deep. we need new array, not new style objects\n      var clonedStyles = clone(this.styles);\n      for (var line in this.styles) {\n        var numericLine = parseInt(line, 10);\n        if (numericLine > lineIndex) {\n          this.styles[numericLine + offset] = clonedStyles[numericLine];\n          if (!clonedStyles[numericLine - offset]) {\n            delete this.styles[numericLine];\n          }\n        }\n      }\n    },\n\n    restartCursorIfNeeded: function() {\n      if (!this._currentTickState || this._currentTickState.isAborted\n        || !this._currentTickCompleteState || this._currentTickCompleteState.isAborted\n      ) {\n        this.initDelayedCursor();\n      }\n    },\n\n    /**\n     * Inserts new style object\n     * @param {Number} lineIndex Index of a line\n     * @param {Number} charIndex Index of a char\n     * @param {Number} qty number of lines to add\n     * @param {Array} copiedStyle Array of objects styles\n     */\n    insertNewlineStyleObject: function(lineIndex, charIndex, qty, copiedStyle) {\n      var currentCharStyle,\n          newLineStyles = {},\n          somethingAdded = false;\n\n      qty || (qty = 1);\n      this.shiftLineStyles(lineIndex, qty);\n      if (this.styles[lineIndex]) {\n        currentCharStyle = this.styles[lineIndex][charIndex === 0 ? charIndex : charIndex - 1];\n      }\n\n      // we clone styles of all chars\n      // after cursor onto the current line\n      for (var index in this.styles[lineIndex]) {\n        var numIndex = parseInt(index, 10);\n        if (numIndex >= charIndex) {\n          somethingAdded = true;\n          newLineStyles[numIndex - charIndex] = this.styles[lineIndex][index];\n          // remove lines from the previous line since they're on a new line now\n          delete this.styles[lineIndex][index];\n        }\n      }\n      if (somethingAdded) {\n        this.styles[lineIndex + qty] = newLineStyles;\n      }\n      else {\n        delete this.styles[lineIndex + qty];\n      }\n      // for the other lines\n      // we clone current char style onto the next (otherwise empty) line\n      while (qty > 1) {\n        qty--;\n        if (copiedStyle && copiedStyle[qty]) {\n          this.styles[lineIndex + qty] = { 0: clone(copiedStyle[qty]) };\n        }\n        else if (currentCharStyle) {\n          this.styles[lineIndex + qty] = { 0: clone(currentCharStyle) };\n        }\n        else {\n          delete this.styles[lineIndex + qty];\n        }\n      }\n      this._forceClearCache = true;\n    },\n\n    /**\n     * Inserts style object for a given line/char index\n     * @param {Number} lineIndex Index of a line\n     * @param {Number} charIndex Index of a char\n     * @param {Number} quantity number Style object to insert, if given\n     * @param {Array} copiedStyle array of style objecs\n     */\n    insertCharStyleObject: function(lineIndex, charIndex, quantity, copiedStyle) {\n      if (!this.styles) {\n        this.styles = {};\n      }\n      var currentLineStyles       = this.styles[lineIndex],\n          currentLineStylesCloned = currentLineStyles ? clone(currentLineStyles) : {};\n\n      quantity || (quantity = 1);\n      // shift all char styles by quantity forward\n      // 0,1,2,3 -> (charIndex=2) -> 0,1,3,4 -> (insert 2) -> 0,1,2,3,4\n      for (var index in currentLineStylesCloned) {\n        var numericIndex = parseInt(index, 10);\n        if (numericIndex >= charIndex) {\n          currentLineStyles[numericIndex + quantity] = currentLineStylesCloned[numericIndex];\n          // only delete the style if there was nothing moved there\n          if (!currentLineStylesCloned[numericIndex - quantity]) {\n            delete currentLineStyles[numericIndex];\n          }\n        }\n      }\n      this._forceClearCache = true;\n      if (copiedStyle) {\n        while (quantity--) {\n          if (!Object.keys(copiedStyle[quantity]).length) {\n            continue;\n          }\n          if (!this.styles[lineIndex]) {\n            this.styles[lineIndex] = {};\n          }\n          this.styles[lineIndex][charIndex + quantity] = clone(copiedStyle[quantity]);\n        }\n        return;\n      }\n      if (!currentLineStyles) {\n        return;\n      }\n      var newStyle = currentLineStyles[charIndex ? charIndex - 1 : 1];\n      while (newStyle && quantity--) {\n        this.styles[lineIndex][charIndex + quantity] = clone(newStyle);\n      }\n    },\n\n    /**\n     * Inserts style object(s)\n     * @param {Array} insertedText Characters at the location where style is inserted\n     * @param {Number} start cursor index for inserting style\n     * @param {Array} [copiedStyle] array of style objects to insert.\n     */\n    insertNewStyleBlock: function(insertedText, start, copiedStyle) {\n      var cursorLoc = this.get2DCursorLocation(start, true),\n          addedLines = [0], linesLenght = 0;\n      for (var i = 0; i < insertedText.length; i++) {\n        if (insertedText[i] === '\\n') {\n          linesLenght++;\n          addedLines[linesLenght] = 0;\n        }\n        else {\n          addedLines[linesLenght]++;\n        }\n      }\n      if (addedLines[0] > 0) {\n        this.insertCharStyleObject(cursorLoc.lineIndex, cursorLoc.charIndex, addedLines[0], copiedStyle);\n        copiedStyle = copiedStyle && copiedStyle.slice(addedLines[0] + 1);\n      }\n      linesLenght && this.insertNewlineStyleObject(\n        cursorLoc.lineIndex, cursorLoc.charIndex + addedLines[0], linesLenght);\n      for (var i = 1; i < linesLenght; i++) {\n        if (addedLines[i] > 0) {\n          this.insertCharStyleObject(cursorLoc.lineIndex + i, 0, addedLines[i], copiedStyle);\n        }\n        else if (copiedStyle) {\n          this.styles[cursorLoc.lineIndex + i][0] = copiedStyle[0];\n        }\n        copiedStyle = copiedStyle && copiedStyle.slice(addedLines[i] + 1);\n      }\n      // we use i outside the loop to get it like linesLength\n      if (addedLines[i] > 0) {\n        this.insertCharStyleObject(cursorLoc.lineIndex + i, 0, addedLines[i], copiedStyle);\n      }\n    },\n\n    /**\n     * Set the selectionStart and selectionEnd according to the ne postion of cursor\n     * mimic the key - mouse navigation when shift is pressed.\n     */\n    setSelectionStartEndWithShift: function(start, end, newSelection) {\n      if (newSelection <= start) {\n        if (end === start) {\n          this._selectionDirection = 'left';\n        }\n        else if (this._selectionDirection === 'right') {\n          this._selectionDirection = 'left';\n          this.selectionEnd = start;\n        }\n        this.selectionStart = newSelection;\n      }\n      else if (newSelection > start && newSelection < end) {\n        if (this._selectionDirection === 'right') {\n          this.selectionEnd = newSelection;\n        }\n        else {\n          this.selectionStart = newSelection;\n        }\n      }\n      else {\n        // newSelection is > selection start and end\n        if (end === start) {\n          this._selectionDirection = 'right';\n        }\n        else if (this._selectionDirection === 'left') {\n          this._selectionDirection = 'right';\n          this.selectionStart = end;\n        }\n        this.selectionEnd = newSelection;\n      }\n    },\n\n    setSelectionInBoundaries: function() {\n      var length = this.text.length;\n      if (this.selectionStart > length) {\n        this.selectionStart = length;\n      }\n      else if (this.selectionStart < 0) {\n        this.selectionStart = 0;\n      }\n      if (this.selectionEnd > length) {\n        this.selectionEnd = length;\n      }\n      else if (this.selectionEnd < 0) {\n        this.selectionEnd = 0;\n      }\n    }\n  });\n})();\n\n\nfabric.util.object.extend(fabric.IText.prototype, /** @lends fabric.IText.prototype */ {\n  /**\n   * Initializes \"dbclick\" event handler\n   */\n  initDoubleClickSimulation: function() {\n\n    // for double click\n    this.__lastClickTime = +new Date();\n\n    // for triple click\n    this.__lastLastClickTime = +new Date();\n\n    this.__lastPointer = { };\n\n    this.on('mousedown', this.onMouseDown.bind(this));\n  },\n\n  /**\n   * Default event handler to simulate triple click\n   * @private\n   */\n  onMouseDown: function(options) {\n    if (!this.canvas) {\n      return;\n    }\n    this.__newClickTime = +new Date();\n    var newPointer = this.canvas.getPointer(options.e);\n    if (this.isTripleClick(newPointer)) {\n      this.fire('tripleclick', options);\n      this._stopEvent(options.e);\n    }\n    this.__lastLastClickTime = this.__lastClickTime;\n    this.__lastClickTime = this.__newClickTime;\n    this.__lastPointer = newPointer;\n    this.__lastIsEditing = this.isEditing;\n    this.__lastSelected = this.selected;\n  },\n\n  isTripleClick: function(newPointer) {\n    return this.__newClickTime - this.__lastClickTime < 500 &&\n        this.__lastClickTime - this.__lastLastClickTime < 500 &&\n        this.__lastPointer.x === newPointer.x &&\n        this.__lastPointer.y === newPointer.y;\n  },\n\n  /**\n   * @private\n   */\n  _stopEvent: function(e) {\n    e.preventDefault && e.preventDefault();\n    e.stopPropagation && e.stopPropagation();\n  },\n\n  /**\n   * Initializes event handlers related to cursor or selection\n   */\n  initCursorSelectionHandlers: function() {\n    this.initMousedownHandler();\n    this.initMouseupHandler();\n    this.initClicks();\n  },\n\n  /**\n   * Initializes double and triple click event handlers\n   */\n  initClicks: function() {\n    this.on('mousedblclick', function(options) {\n      this.selectWord(this.getSelectionStartFromPointer(options.e));\n    });\n    this.on('tripleclick', function(options) {\n      this.selectLine(this.getSelectionStartFromPointer(options.e));\n    });\n  },\n\n  /**\n   * Default event handler for the basic functionalities needed on _mouseDown\n   * can be overridden to do something different.\n   * Scope of this implementation is: find the click position, set selectionStart\n   * find selectionEnd, initialize the drawing of either cursor or selection area\n   */\n  _mouseDownHandler: function(options) {\n    if (!this.canvas || !this.editable || (options.e.button && options.e.button !== 1)) {\n      return;\n    }\n    var pointer = this.canvas.getPointer(options.e);\n\n    this.__mousedownX = pointer.x;\n    this.__mousedownY = pointer.y;\n    this.__isMousedown = true;\n\n    if (this.selected) {\n      this.setCursorByClick(options.e);\n    }\n\n    if (this.isEditing) {\n      this.__selectionStartOnMouseDown = this.selectionStart;\n      if (this.selectionStart === this.selectionEnd) {\n        this.abortCursorAnimation();\n      }\n      this.renderCursorOrSelection();\n    }\n  },\n\n  /**\n   * Initializes \"mousedown\" event handler\n   */\n  initMousedownHandler: function() {\n    this.on('mousedown', this._mouseDownHandler);\n  },\n\n  /**\n   * detect if object moved\n   * @private\n   */\n  _isObjectMoved: function(e) {\n    var pointer = this.canvas.getPointer(e);\n\n    return this.__mousedownX !== pointer.x ||\n           this.__mousedownY !== pointer.y;\n  },\n\n  /**\n   * Initializes \"mouseup\" event handler\n   */\n  initMouseupHandler: function() {\n    this.on('mouseup', this.mouseUpHandler);\n  },\n\n  /**\n   * standard hander for mouse up, overridable\n   * @private\n   */\n  mouseUpHandler: function(options) {\n    this.__isMousedown = false;\n    if (!this.editable || this._isObjectMoved(options.e) || (options.e.button && options.e.button !== 1)) {\n      return;\n    }\n\n    if (this.__lastSelected && !this.__corner) {\n      this.enterEditing(options.e);\n      if (this.selectionStart === this.selectionEnd) {\n        this.initDelayedCursor(true);\n      }\n      else {\n        this.renderCursorOrSelection();\n      }\n    }\n    this.selected = true;\n  },\n\n  /**\n   * Changes cursor location in a text depending on passed pointer (x/y) object\n   * @param {Event} e Event object\n   */\n  setCursorByClick: function(e) {\n    var newSelection = this.getSelectionStartFromPointer(e),\n        start = this.selectionStart, end = this.selectionEnd;\n    if (e.shiftKey) {\n      this.setSelectionStartEndWithShift(start, end, newSelection);\n    }\n    else {\n      this.selectionStart = newSelection;\n      this.selectionEnd = newSelection;\n    }\n    if (this.isEditing) {\n      this._fireSelectionChanged();\n      this._updateTextarea();\n    }\n  },\n\n  /**\n   * Returns index of a character corresponding to where an object was clicked\n   * @param {Event} e Event object\n   * @return {Number} Index of a character\n   */\n  getSelectionStartFromPointer: function(e) {\n    var mouseOffset = this.getLocalPointer(e),\n        prevWidth = 0,\n        width = 0,\n        height = 0,\n        charIndex = 0,\n        lineIndex = 0,\n        lineLeftOffset,\n        line;\n\n    for (var i = 0, len = this._textLines.length; i < len; i++) {\n      if (height <= mouseOffset.y) {\n        height += this.getHeightOfLine(i) * this.scaleY;\n        lineIndex = i;\n        if (i > 0) {\n          charIndex += this._textLines[i - 1].length + 1;\n        }\n      }\n      else {\n        break;\n      }\n    }\n    lineLeftOffset = this._getLineLeftOffset(lineIndex);\n    width = lineLeftOffset * this.scaleX;\n    line = this._textLines[lineIndex];\n    for (var j = 0, jlen = line.length; j < jlen; j++) {\n      prevWidth = width;\n      // i removed something about flipX here, check.\n      width += this.__charBounds[lineIndex][j].kernedWidth * this.scaleX;\n      if (width <= mouseOffset.x) {\n        charIndex++;\n      }\n      else {\n        break;\n      }\n    }\n    return this._getNewSelectionStartFromOffset(mouseOffset, prevWidth, width, charIndex, jlen);\n  },\n\n  /**\n   * @private\n   */\n  _getNewSelectionStartFromOffset: function(mouseOffset, prevWidth, width, index, jlen) {\n    // we need Math.abs because when width is after the last char, the offset is given as 1, while is 0\n    var distanceBtwLastCharAndCursor = mouseOffset.x - prevWidth,\n        distanceBtwNextCharAndCursor = width - mouseOffset.x,\n        offset = distanceBtwNextCharAndCursor > distanceBtwLastCharAndCursor ||\n          distanceBtwNextCharAndCursor < 0 ? 0 : 1,\n        newSelectionStart = index + offset;\n    // if object is horizontally flipped, mirror cursor location from the end\n    if (this.flipX) {\n      newSelectionStart = jlen - newSelectionStart;\n    }\n\n    if (newSelectionStart > this._text.length) {\n      newSelectionStart = this._text.length;\n    }\n\n    return newSelectionStart;\n  }\n});\n\n\nfabric.util.object.extend(fabric.IText.prototype, /** @lends fabric.IText.prototype */ {\n\n  /**\n   * Initializes hidden textarea (needed to bring up keyboard in iOS)\n   */\n  initHiddenTextarea: function() {\n    this.hiddenTextarea = fabric.document.createElement('textarea');\n    this.hiddenTextarea.setAttribute('autocapitalize', 'off');\n    this.hiddenTextarea.setAttribute('autocorrect', 'off');\n    this.hiddenTextarea.setAttribute('autocomplete', 'off');\n    this.hiddenTextarea.setAttribute('spellcheck', 'false');\n    this.hiddenTextarea.setAttribute('data-fabric-hiddentextarea', '');\n    this.hiddenTextarea.setAttribute('wrap', 'off');\n    var style = this._calcTextareaPosition();\n    this.hiddenTextarea.style.cssText = 'position: absolute; top: ' + style.top +\n    '; left: ' + style.left + '; z-index: -999; opacity: 0; width: 1px; height: 1px; font-size: 1px;' +\n    ' line-height: 1px; paddingｰtop: ' + style.fontSize + ';';\n    fabric.document.body.appendChild(this.hiddenTextarea);\n\n    fabric.util.addListener(this.hiddenTextarea, 'keydown', this.onKeyDown.bind(this));\n    fabric.util.addListener(this.hiddenTextarea, 'keyup', this.onKeyUp.bind(this));\n    fabric.util.addListener(this.hiddenTextarea, 'input', this.onInput.bind(this));\n    fabric.util.addListener(this.hiddenTextarea, 'copy', this.copy.bind(this));\n    fabric.util.addListener(this.hiddenTextarea, 'cut', this.copy.bind(this));\n    fabric.util.addListener(this.hiddenTextarea, 'paste', this.paste.bind(this));\n    fabric.util.addListener(this.hiddenTextarea, 'compositionstart', this.onCompositionStart.bind(this));\n    fabric.util.addListener(this.hiddenTextarea, 'compositionupdate', this.onCompositionUpdate.bind(this));\n    fabric.util.addListener(this.hiddenTextarea, 'compositionend', this.onCompositionEnd.bind(this));\n\n    if (!this._clickHandlerInitialized && this.canvas) {\n      fabric.util.addListener(this.canvas.upperCanvasEl, 'click', this.onClick.bind(this));\n      this._clickHandlerInitialized = true;\n    }\n  },\n\n  /**\n   * For functionalities on keyDown\n   * Map a special key to a function of the instance/prototype\n   * If you need different behaviour for ESC or TAB or arrows, you have to change\n   * this map setting the name of a function that you build on the fabric.Itext or\n   * your prototype.\n   * the map change will affect all Instances unless you need for only some text Instances\n   * in that case you have to clone this object and assign your Instance.\n   * this.keysMap = fabric.util.object.clone(this.keysMap);\n   * The function must be in fabric.Itext.prototype.myFunction And will receive event as args[0]\n   */\n  keysMap: {\n    9:  'exitEditing',\n    27: 'exitEditing',\n    33: 'moveCursorUp',\n    34: 'moveCursorDown',\n    35: 'moveCursorRight',\n    36: 'moveCursorLeft',\n    37: 'moveCursorLeft',\n    38: 'moveCursorUp',\n    39: 'moveCursorRight',\n    40: 'moveCursorDown',\n  },\n\n  /**\n   * For functionalities on keyUp + ctrl || cmd\n   */\n  ctrlKeysMapUp: {\n    67: 'copy',\n    88: 'cut'\n  },\n\n  /**\n   * For functionalities on keyDown + ctrl || cmd\n   */\n  ctrlKeysMapDown: {\n    65: 'selectAll'\n  },\n\n  onClick: function() {\n    // No need to trigger click event here, focus is enough to have the keyboard appear on Android\n    this.hiddenTextarea && this.hiddenTextarea.focus();\n  },\n\n  /**\n   * Handles keyup event\n   * @param {Event} e Event object\n   */\n  onKeyDown: function(e) {\n    if (!this.isEditing || this.inCompositionMode) {\n      return;\n    }\n    if (e.keyCode in this.keysMap) {\n      this[this.keysMap[e.keyCode]](e);\n    }\n    else if ((e.keyCode in this.ctrlKeysMapDown) && (e.ctrlKey || e.metaKey)) {\n      this[this.ctrlKeysMapDown[e.keyCode]](e);\n    }\n    else {\n      return;\n    }\n    e.stopImmediatePropagation();\n    e.preventDefault();\n    if (e.keyCode >= 33 && e.keyCode <= 40) {\n      // if i press an arrow key just update selection\n      this.clearContextTop();\n      this.renderCursorOrSelection();\n    }\n    else {\n      this.canvas && this.canvas.requestRenderAll();\n    }\n  },\n\n  /**\n   * Handles keyup event\n   * We handle KeyUp because ie11 and edge have difficulties copy/pasting\n   * if a copy/cut event fired, keyup is dismissed\n   * @param {Event} e Event object\n   */\n  onKeyUp: function(e) {\n    if (!this.isEditing || this._copyDone || this.inCompositionMode) {\n      this._copyDone = false;\n      return;\n    }\n    if ((e.keyCode in this.ctrlKeysMapUp) && (e.ctrlKey || e.metaKey)) {\n      this[this.ctrlKeysMapUp[e.keyCode]](e);\n    }\n    else {\n      return;\n    }\n    e.stopImmediatePropagation();\n    e.preventDefault();\n    this.canvas && this.canvas.requestRenderAll();\n  },\n\n  /**\n   * Handles onInput event\n   * @param {Event} e Event object\n   */\n  onInput: function(e) {\n    var fromPaste = this.fromPaste;\n    this.fromPaste = false;\n    e && e.stopPropagation();\n    if (!this.isEditing) {\n      return;\n    }\n    // decisions about style changes.\n    var nextText = this._splitTextIntoLines(this.hiddenTextarea.value).graphemeText,\n        charCount = this._text.length,\n        nextCharCount = nextText.length,\n        removedText, insertedText,\n        charDiff = nextCharCount - charCount;\n    if (this.hiddenTextarea.value === '') {\n      this.styles = { };\n      this.updateFromTextArea();\n      this.fire('changed');\n      if (this.canvas) {\n        this.canvas.fire('text:changed', { target: this });\n        this.canvas.requestRenderAll();\n      }\n      return;\n    }\n\n    var textareaSelection = this.fromStringToGraphemeSelection(\n      this.hiddenTextarea.selectionStart,\n      this.hiddenTextarea.selectionEnd,\n      this.hiddenTextarea.value\n    );\n    var backDelete = this.selectionStart > textareaSelection.selectionStart;\n\n    if (this.selectionStart !== this.selectionEnd) {\n      removedText = this._text.slice(this.selectionStart, this.selectionEnd);\n      charDiff += this.selectionEnd - this.selectionStart;\n    }\n    else if (nextCharCount < charCount) {\n      if (backDelete) {\n        removedText = this._text.slice(this.selectionEnd + charDiff, this.selectionEnd);\n      }\n      else {\n        removedText = this._text.slice(this.selectionStart, this.selectionStart - charDiff);\n      }\n    }\n    insertedText = nextText.slice(textareaSelection.selectionEnd - charDiff, textareaSelection.selectionEnd);\n    if (removedText && removedText.length) {\n      if (this.selectionStart !== this.selectionEnd) {\n        this.removeStyleFromTo(this.selectionStart, this.selectionEnd);\n      }\n      else if (backDelete) {\n        // detect differencies between forwardDelete and backDelete\n        this.removeStyleFromTo(this.selectionEnd - removedText.length, this.selectionEnd);\n      }\n      else {\n        this.removeStyleFromTo(this.selectionEnd, this.selectionEnd + removedText.length);\n      }\n    }\n    if (insertedText.length) {\n      if (fromPaste && insertedText.join('') === fabric.copiedText) {\n        this.insertNewStyleBlock(insertedText, this.selectionStart, fabric.copiedTextStyle);\n      }\n      else {\n        this.insertNewStyleBlock(insertedText, this.selectionStart);\n      }\n    }\n    this.updateFromTextArea();\n    this.fire('changed');\n    if (this.canvas) {\n      this.canvas.fire('text:changed', { target: this });\n      this.canvas.requestRenderAll();\n    }\n  },\n  /**\n   * Composition start\n   */\n  onCompositionStart: function() {\n    this.inCompositionMode = true;\n  },\n\n  /**\n   * Composition end\n   */\n  onCompositionEnd: function() {\n    this.inCompositionMode = false;\n  },\n\n  // /**\n  //  * Composition update\n  //  */\n  onCompositionUpdate: function(e) {\n    this.compositionStart = e.target.selectionStart;\n    this.compositionEnd = e.target.selectionEnd;\n    this.updateTextareaPosition();\n  },\n\n  /**\n   * Copies selected text\n   * @param {Event} e Event object\n   */\n  copy: function() {\n    if (this.selectionStart === this.selectionEnd) {\n      //do not cut-copy if no selection\n      return;\n    }\n\n    fabric.copiedText = this.getSelectedText();\n    fabric.copiedTextStyle = this.getSelectionStyles(this.selectionStart, this.selectionEnd, true);\n    this._copyDone = true;\n  },\n\n  /**\n   * Pastes text\n   * @param {Event} e Event object\n   */\n  paste: function() {\n    this.fromPaste = true;\n  },\n\n  /**\n   * @private\n   * @param {Event} e Event object\n   * @return {Object} Clipboard data object\n   */\n  _getClipboardData: function(e) {\n    return (e && e.clipboardData) || fabric.window.clipboardData;\n  },\n\n  /**\n   * Finds the width in pixels before the cursor on the same line\n   * @private\n   * @param {Number} lineIndex\n   * @param {Number} charIndex\n   * @return {Number} widthBeforeCursor width before cursor\n   */\n  _getWidthBeforeCursor: function(lineIndex, charIndex) {\n    var widthBeforeCursor = this._getLineLeftOffset(lineIndex), bound;\n\n    if (charIndex > 0) {\n      bound = this.__charBounds[lineIndex][charIndex - 1];\n      widthBeforeCursor += bound.left + bound.width;\n    }\n    return widthBeforeCursor;\n  },\n\n  /**\n   * Gets start offset of a selection\n   * @param {Event} e Event object\n   * @param {Boolean} isRight\n   * @return {Number}\n   */\n  getDownCursorOffset: function(e, isRight) {\n    var selectionProp = this._getSelectionForOffset(e, isRight),\n        cursorLocation = this.get2DCursorLocation(selectionProp),\n        lineIndex = cursorLocation.lineIndex;\n    // if on last line, down cursor goes to end of line\n    if (lineIndex === this._textLines.length - 1 || e.metaKey || e.keyCode === 34) {\n      // move to the end of a text\n      return this._text.length - selectionProp;\n    }\n    var charIndex = cursorLocation.charIndex,\n        widthBeforeCursor = this._getWidthBeforeCursor(lineIndex, charIndex),\n        indexOnOtherLine = this._getIndexOnLine(lineIndex + 1, widthBeforeCursor),\n        textAfterCursor = this._textLines[lineIndex].slice(charIndex);\n    return textAfterCursor.length + indexOnOtherLine + 2;\n  },\n\n  /**\n   * private\n   * Helps finding if the offset should be counted from Start or End\n   * @param {Event} e Event object\n   * @param {Boolean} isRight\n   * @return {Number}\n   */\n  _getSelectionForOffset: function(e, isRight) {\n    if (e.shiftKey && this.selectionStart !== this.selectionEnd && isRight) {\n      return this.selectionEnd;\n    }\n    else {\n      return this.selectionStart;\n    }\n  },\n\n  /**\n   * @param {Event} e Event object\n   * @param {Boolean} isRight\n   * @return {Number}\n   */\n  getUpCursorOffset: function(e, isRight) {\n    var selectionProp = this._getSelectionForOffset(e, isRight),\n        cursorLocation = this.get2DCursorLocation(selectionProp),\n        lineIndex = cursorLocation.lineIndex;\n    if (lineIndex === 0 || e.metaKey || e.keyCode === 33) {\n      // if on first line, up cursor goes to start of line\n      return -selectionProp;\n    }\n    var charIndex = cursorLocation.charIndex,\n        widthBeforeCursor = this._getWidthBeforeCursor(lineIndex, charIndex),\n        indexOnOtherLine = this._getIndexOnLine(lineIndex - 1, widthBeforeCursor),\n        textBeforeCursor = this._textLines[lineIndex].slice(0, charIndex);\n    // return a negative offset\n    return -this._textLines[lineIndex - 1].length + indexOnOtherLine - textBeforeCursor.length;\n  },\n\n  /**\n   * for a given width it founds the matching character.\n   * @private\n   */\n  _getIndexOnLine: function(lineIndex, width) {\n\n    var line = this._textLines[lineIndex],\n        lineLeftOffset = this._getLineLeftOffset(lineIndex),\n        widthOfCharsOnLine = lineLeftOffset,\n        indexOnLine = 0, charWidth, foundMatch;\n\n    for (var j = 0, jlen = line.length; j < jlen; j++) {\n      charWidth = this.__charBounds[lineIndex][j].width;\n      widthOfCharsOnLine += charWidth;\n      if (widthOfCharsOnLine > width) {\n        foundMatch = true;\n        var leftEdge = widthOfCharsOnLine - charWidth,\n            rightEdge = widthOfCharsOnLine,\n            offsetFromLeftEdge = Math.abs(leftEdge - width),\n            offsetFromRightEdge = Math.abs(rightEdge - width);\n\n        indexOnLine = offsetFromRightEdge < offsetFromLeftEdge ? j : (j - 1);\n        break;\n      }\n    }\n\n    // reached end\n    if (!foundMatch) {\n      indexOnLine = line.length - 1;\n    }\n\n    return indexOnLine;\n  },\n\n\n  /**\n   * Moves cursor down\n   * @param {Event} e Event object\n   */\n  moveCursorDown: function(e) {\n    if (this.selectionStart >= this._text.length && this.selectionEnd >= this._text.length) {\n      return;\n    }\n    this._moveCursorUpOrDown('Down', e);\n  },\n\n  /**\n   * Moves cursor up\n   * @param {Event} e Event object\n   */\n  moveCursorUp: function(e) {\n    if (this.selectionStart === 0 && this.selectionEnd === 0) {\n      return;\n    }\n    this._moveCursorUpOrDown('Up', e);\n  },\n\n  /**\n   * Moves cursor up or down, fires the events\n   * @param {String} direction 'Up' or 'Down'\n   * @param {Event} e Event object\n   */\n  _moveCursorUpOrDown: function(direction, e) {\n    // getUpCursorOffset\n    // getDownCursorOffset\n    var action = 'get' + direction + 'CursorOffset',\n        offset = this[action](e, this._selectionDirection === 'right');\n    if (e.shiftKey) {\n      this.moveCursorWithShift(offset);\n    }\n    else {\n      this.moveCursorWithoutShift(offset);\n    }\n    if (offset !== 0) {\n      this.setSelectionInBoundaries();\n      this.abortCursorAnimation();\n      this._currentCursorOpacity = 1;\n      this.initDelayedCursor();\n      this._fireSelectionChanged();\n      this._updateTextarea();\n    }\n  },\n\n  /**\n   * Moves cursor with shift\n   * @param {Number} offset\n   */\n  moveCursorWithShift: function(offset) {\n    var newSelection = this._selectionDirection === 'left'\n      ? this.selectionStart + offset\n      : this.selectionEnd + offset;\n    this.setSelectionStartEndWithShift(this.selectionStart, this.selectionEnd, newSelection);\n    return offset !== 0;\n  },\n\n  /**\n   * Moves cursor up without shift\n   * @param {Number} offset\n   */\n  moveCursorWithoutShift: function(offset) {\n    if (offset < 0) {\n      this.selectionStart += offset;\n      this.selectionEnd = this.selectionStart;\n    }\n    else {\n      this.selectionEnd += offset;\n      this.selectionStart = this.selectionEnd;\n    }\n    return offset !== 0;\n  },\n\n  /**\n   * Moves cursor left\n   * @param {Event} e Event object\n   */\n  moveCursorLeft: function(e) {\n    if (this.selectionStart === 0 && this.selectionEnd === 0) {\n      return;\n    }\n    this._moveCursorLeftOrRight('Left', e);\n  },\n\n  /**\n   * @private\n   * @return {Boolean} true if a change happened\n   */\n  _move: function(e, prop, direction) {\n    var newValue;\n    if (e.altKey) {\n      newValue = this['findWordBoundary' + direction](this[prop]);\n    }\n    else if (e.metaKey || e.keyCode === 35 ||  e.keyCode === 36 ) {\n      newValue = this['findLineBoundary' + direction](this[prop]);\n    }\n    else {\n      this[prop] += direction === 'Left' ? -1 : 1;\n      return true;\n    }\n    if (typeof newValue !== undefined && this[prop] !== newValue) {\n      this[prop] = newValue;\n      return true;\n    }\n  },\n\n  /**\n   * @private\n   */\n  _moveLeft: function(e, prop) {\n    return this._move(e, prop, 'Left');\n  },\n\n  /**\n   * @private\n   */\n  _moveRight: function(e, prop) {\n    return this._move(e, prop, 'Right');\n  },\n\n  /**\n   * Moves cursor left without keeping selection\n   * @param {Event} e\n   */\n  moveCursorLeftWithoutShift: function(e) {\n    var change = true;\n    this._selectionDirection = 'left';\n\n    // only move cursor when there is no selection,\n    // otherwise we discard it, and leave cursor on same place\n    if (this.selectionEnd === this.selectionStart && this.selectionStart !== 0) {\n      change = this._moveLeft(e, 'selectionStart');\n\n    }\n    this.selectionEnd = this.selectionStart;\n    return change;\n  },\n\n  /**\n   * Moves cursor left while keeping selection\n   * @param {Event} e\n   */\n  moveCursorLeftWithShift: function(e) {\n    if (this._selectionDirection === 'right' && this.selectionStart !== this.selectionEnd) {\n      return this._moveLeft(e, 'selectionEnd');\n    }\n    else if (this.selectionStart !== 0){\n      this._selectionDirection = 'left';\n      return this._moveLeft(e, 'selectionStart');\n    }\n  },\n\n  /**\n   * Moves cursor right\n   * @param {Event} e Event object\n   */\n  moveCursorRight: function(e) {\n    if (this.selectionStart >= this._text.length && this.selectionEnd >= this._text.length) {\n      return;\n    }\n    this._moveCursorLeftOrRight('Right', e);\n  },\n\n  /**\n   * Moves cursor right or Left, fires event\n   * @param {String} direction 'Left', 'Right'\n   * @param {Event} e Event object\n   */\n  _moveCursorLeftOrRight: function(direction, e) {\n    var actionName = 'moveCursor' + direction + 'With';\n    this._currentCursorOpacity = 1;\n\n    if (e.shiftKey) {\n      actionName += 'Shift';\n    }\n    else {\n      actionName += 'outShift';\n    }\n    if (this[actionName](e)) {\n      this.abortCursorAnimation();\n      this.initDelayedCursor();\n      this._fireSelectionChanged();\n      this._updateTextarea();\n    }\n  },\n\n  /**\n   * Moves cursor right while keeping selection\n   * @param {Event} e\n   */\n  moveCursorRightWithShift: function(e) {\n    if (this._selectionDirection === 'left' && this.selectionStart !== this.selectionEnd) {\n      return this._moveRight(e, 'selectionStart');\n    }\n    else if (this.selectionEnd !== this._text.length) {\n      this._selectionDirection = 'right';\n      return this._moveRight(e, 'selectionEnd');\n    }\n  },\n\n  /**\n   * Moves cursor right without keeping selection\n   * @param {Event} e Event object\n   */\n  moveCursorRightWithoutShift: function(e) {\n    var changed = true;\n    this._selectionDirection = 'right';\n\n    if (this.selectionStart === this.selectionEnd) {\n      changed = this._moveRight(e, 'selectionStart');\n      this.selectionEnd = this.selectionStart;\n    }\n    else {\n      this.selectionStart = this.selectionEnd;\n    }\n    return changed;\n  },\n\n  /**\n   * Removes characters from start/end\n   * start/end ar per grapheme position in _text array.\n   *\n   * @param {Number} start\n   * @param {Number} end default to start + 1\n   */\n  removeChars: function(start, end) {\n    if (typeof end === 'undefined') {\n      end = start + 1;\n    }\n    this.removeStyleFromTo(start, end);\n    this._text.splice(start, end - start);\n    this.text = this._text.join('');\n    this.set('dirty', true);\n    if (this._shouldClearDimensionCache()) {\n      this.initDimensions();\n      this.setCoords();\n    }\n    this._removeExtraneousStyles();\n  },\n\n  /**\n   * insert characters at start position, before start position.\n   * start  equal 1 it means the text get inserted between actual grapheme 0 and 1\n   * if style array is provided, it must be as the same length of text in graphemes\n   * if end is provided and is bigger than start, old text is replaced.\n   * start/end ar per grapheme position in _text array.\n   *\n   * @param {String} text text to insert\n   * @param {Array} style array of style objects\n   * @param {Number} start\n   * @param {Number} end default to start + 1\n   */\n  insertChars: function(text, style, start, end) {\n    if (typeof end === 'undefined') {\n      end = start;\n    }\n    if (end > start) {\n      this.removeStyleFromTo(start, end);\n    }\n    var graphemes = fabric.util.string.graphemeSplit(text);\n    this.insertNewStyleBlock(graphemes, start, style);\n    this._text = [].concat(this._text.slice(0, start), graphemes, this._text.slice(end));\n    this.text = this._text.join('');\n    this.set('dirty', true);\n    if (this._shouldClearDimensionCache()) {\n      this.initDimensions();\n      this.setCoords();\n    }\n    this._removeExtraneousStyles();\n  },\n\n});\n\n\n/* _TO_SVG_START_ */\n(function() {\n  var toFixed = fabric.util.toFixed,\n      multipleSpacesRegex = /  +/g;\n\n  fabric.util.object.extend(fabric.Text.prototype, /** @lends fabric.Text.prototype */ {\n\n    /**\n     * Returns SVG representation of an instance\n     * @param {Function} [reviver] Method for further parsing of svg representation.\n     * @return {String} svg representation of an instance\n     */\n    toSVG: function(reviver) {\n      var markup = this._createBaseSVGMarkup(),\n          offsets = this._getSVGLeftTopOffsets(),\n          textAndBg = this._getSVGTextAndBg(offsets.textTop, offsets.textLeft);\n      this._wrapSVGTextAndBg(markup, textAndBg);\n\n      return reviver ? reviver(markup.join('')) : markup.join('');\n    },\n\n    /**\n     * @private\n     */\n    _getSVGLeftTopOffsets: function() {\n      return {\n        textLeft: -this.width / 2,\n        textTop: -this.height / 2,\n        lineTop: this.getHeightOfLine(0)\n      };\n    },\n\n    /**\n     * @private\n     */\n    _wrapSVGTextAndBg: function(markup, textAndBg) {\n      var noShadow = true, filter = this.getSvgFilter(),\n          style = filter === '' ? '' : ' style=\"' + filter + '\"',\n          textDecoration = this.getSvgTextDecoration(this);\n      markup.push(\n        '\\t<g ', this.getSvgId(), 'transform=\"', this.getSvgTransform(), this.getSvgTransformMatrix(), '\"',\n        style, '>\\n',\n        textAndBg.textBgRects.join(''),\n        '\\t\\t<text xml:space=\"preserve\" ',\n        (this.fontFamily ? 'font-family=\"' + this.fontFamily.replace(/\"/g, '\\'') + '\" ' : ''),\n        (this.fontSize ? 'font-size=\"' + this.fontSize + '\" ' : ''),\n        (this.fontStyle ? 'font-style=\"' + this.fontStyle + '\" ' : ''),\n        (this.fontWeight ? 'font-weight=\"' + this.fontWeight + '\" ' : ''),\n        (textDecoration ? 'text-decoration=\"' + textDecoration + '\" ' : ''),\n        'style=\"', this.getSvgStyles(noShadow), '\"', this.addPaintOrder(), ' >',\n        textAndBg.textSpans.join(''),\n        '</text>\\n',\n        '\\t</g>\\n'\n      );\n    },\n\n    /**\n     * @private\n     * @param {Number} textTopOffset Text top offset\n     * @param {Number} textLeftOffset Text left offset\n     * @return {Object}\n     */\n    _getSVGTextAndBg: function(textTopOffset, textLeftOffset) {\n      var textSpans = [],\n          textBgRects = [],\n          height = textTopOffset, lineOffset;\n      // bounding-box background\n      this._setSVGBg(textBgRects);\n\n      // text and text-background\n      for (var i = 0, len = this._textLines.length; i < len; i++) {\n        lineOffset = this._getLineLeftOffset(i);\n        if (this.textBackgroundColor || this.styleHas('textBackgroundColor', i)) {\n          this._setSVGTextLineBg(textBgRects, i, textLeftOffset + lineOffset, height);\n        }\n        this._setSVGTextLineText(textSpans, i, textLeftOffset + lineOffset, height);\n        height += this.getHeightOfLine(i);\n      }\n\n      return {\n        textSpans: textSpans,\n        textBgRects: textBgRects\n      };\n    },\n\n    /**\n     * @private\n     */\n    _createTextCharSpan: function(_char, styleDecl, left, top) {\n      var shouldUseWhitespace = _char !== _char.trim() || _char.match(multipleSpacesRegex),\n          styleProps = this.getSvgSpanStyles(styleDecl, shouldUseWhitespace),\n          fillStyles = styleProps ? 'style=\"' + styleProps + '\"' : '',\n          dy = styleDecl.deltaY, dySpan = '',\n          NUM_FRACTION_DIGITS = fabric.Object.NUM_FRACTION_DIGITS;\n      if (dy) {\n        dySpan = ' dy=\"' + toFixed(dy, NUM_FRACTION_DIGITS) + '\" ';\n      }\n      return [\n        '<tspan x=\"', toFixed(left, NUM_FRACTION_DIGITS), '\" y=\"',\n        toFixed(top, NUM_FRACTION_DIGITS), '\" ', dySpan,\n        fillStyles, '>',\n        fabric.util.string.escapeXml(_char),\n        '</tspan>'\n      ].join('');\n    },\n\n    _setSVGTextLineText: function(textSpans, lineIndex, textLeftOffset, textTopOffset) {\n      // set proper line offset\n      var lineHeight = this.getHeightOfLine(lineIndex),\n          isJustify = this.textAlign.indexOf('justify') !== -1,\n          actualStyle,\n          nextStyle,\n          charsToRender = '',\n          charBox, style,\n          boxWidth = 0,\n          line = this._textLines[lineIndex],\n          timeToRender;\n\n      textTopOffset += lineHeight * (1 - this._fontSizeFraction) / this.lineHeight;\n      for (var i = 0, len = line.length - 1; i <= len; i++) {\n        timeToRender = i === len || this.charSpacing;\n        charsToRender += line[i];\n        charBox = this.__charBounds[lineIndex][i];\n        if (boxWidth === 0) {\n          textLeftOffset += charBox.kernedWidth - charBox.width;\n          boxWidth += charBox.width;\n        }\n        else {\n          boxWidth += charBox.kernedWidth;\n        }\n        if (isJustify && !timeToRender) {\n          if (this._reSpaceAndTab.test(line[i])) {\n            timeToRender = true;\n          }\n        }\n        if (!timeToRender) {\n          // if we have charSpacing, we render char by char\n          actualStyle = actualStyle || this.getCompleteStyleDeclaration(lineIndex, i);\n          nextStyle = this.getCompleteStyleDeclaration(lineIndex, i + 1);\n          timeToRender = this._hasStyleChangedForSvg(actualStyle, nextStyle);\n        }\n        if (timeToRender) {\n          style = this._getStyleDeclaration(lineIndex, i) || { };\n          textSpans.push(this._createTextCharSpan(charsToRender, style, textLeftOffset, textTopOffset));\n          charsToRender = '';\n          actualStyle = nextStyle;\n          textLeftOffset += boxWidth;\n          boxWidth = 0;\n        }\n      }\n    },\n\n    _pushTextBgRect: function(textBgRects, color, left, top, width, height) {\n      var NUM_FRACTION_DIGITS = fabric.Object.NUM_FRACTION_DIGITS;\n      textBgRects.push(\n        '\\t\\t<rect ',\n        this._getFillAttributes(color),\n        ' x=\"',\n        toFixed(left, NUM_FRACTION_DIGITS),\n        '\" y=\"',\n        toFixed(top, NUM_FRACTION_DIGITS),\n        '\" width=\"',\n        toFixed(width, NUM_FRACTION_DIGITS),\n        '\" height=\"',\n        toFixed(height, NUM_FRACTION_DIGITS),\n        '\"></rect>\\n');\n    },\n\n    _setSVGTextLineBg: function(textBgRects, i, leftOffset, textTopOffset) {\n      var line = this._textLines[i],\n          heightOfLine = this.getHeightOfLine(i) / this.lineHeight,\n          boxWidth = 0,\n          boxStart = 0,\n          charBox, currentColor,\n          lastColor = this.getValueOfPropertyAt(i, 0, 'textBackgroundColor');\n      for (var j = 0, jlen = line.length; j < jlen; j++) {\n        charBox = this.__charBounds[i][j];\n        currentColor = this.getValueOfPropertyAt(i, j, 'textBackgroundColor');\n        if (currentColor !== lastColor) {\n          lastColor && this._pushTextBgRect(textBgRects, lastColor, leftOffset + boxStart,\n            textTopOffset, boxWidth, heightOfLine);\n          boxStart = charBox.left;\n          boxWidth = charBox.width;\n          lastColor = currentColor;\n        }\n        else {\n          boxWidth += charBox.kernedWidth;\n        }\n      }\n      currentColor && this._pushTextBgRect(textBgRects, currentColor, leftOffset + boxStart,\n        textTopOffset, boxWidth, heightOfLine);\n    },\n\n    /**\n     * Adobe Illustrator (at least CS5) is unable to render rgba()-based fill values\n     * we work around it by \"moving\" alpha channel into opacity attribute and setting fill's alpha to 1\n     *\n     * @private\n     * @param {*} value\n     * @return {String}\n     */\n    _getFillAttributes: function(value) {\n      var fillColor = (value && typeof value === 'string') ? new fabric.Color(value) : '';\n      if (!fillColor || !fillColor.getSource() || fillColor.getAlpha() === 1) {\n        return 'fill=\"' + value + '\"';\n      }\n      return 'opacity=\"' + fillColor.getAlpha() + '\" fill=\"' + fillColor.setAlpha(1).toRgb() + '\"';\n    },\n\n    /**\n     * @private\n     */\n    _getSVGLineTopOffset: function(lineIndex) {\n      var lineTopOffset = 0, lastHeight = 0;\n      for (var j = 0; j < lineIndex; j++) {\n        lineTopOffset += this.getHeightOfLine(j);\n      }\n      lastHeight = this.getHeightOfLine(j);\n      return {\n        lineTop: lineTopOffset,\n        offset: (this._fontSizeMult - this._fontSizeFraction) * lastHeight / (this.lineHeight * this._fontSizeMult)\n      };\n    },\n\n    /**\n     * Returns styles-string for svg-export\n     * @param {Boolean} skipShadow a boolean to skip shadow filter output\n     * @return {String}\n     */\n    getSvgStyles: function(skipShadow) {\n      var svgStyle = fabric.Object.prototype.getSvgStyles.call(this, skipShadow);\n      return svgStyle + ' white-space: pre;';\n    },\n  });\n})();\n/* _TO_SVG_END_ */\n\n\n(function(global) {\n\n  'use strict';\n\n  var fabric = global.fabric || (global.fabric = {});\n\n  /**\n   * Textbox class, based on IText, allows the user to resize the text rectangle\n   * and wraps lines automatically. Textboxes have their Y scaling locked, the\n   * user can only change width. Height is adjusted automatically based on the\n   * wrapping of lines.\n   * @class fabric.Textbox\n   * @extends fabric.IText\n   * @mixes fabric.Observable\n   * @return {fabric.Textbox} thisArg\n   * @see {@link fabric.Textbox#initialize} for constructor definition\n   */\n  fabric.Textbox = fabric.util.createClass(fabric.IText, fabric.Observable, {\n\n    /**\n     * Type of an object\n     * @type String\n     * @default\n     */\n    type: 'textbox',\n\n    /**\n     * Minimum width of textbox, in pixels.\n     * @type Number\n     * @default\n     */\n    minWidth: 20,\n\n    /**\n     * Minimum calculated width of a textbox, in pixels.\n     * fixed to 2 so that an empty textbox cannot go to 0\n     * and is still selectable without text.\n     * @type Number\n     * @default\n     */\n    dynamicMinWidth: 2,\n\n    /**\n     * Cached array of text wrapping.\n     * @type Array\n     */\n    __cachedLines: null,\n\n    /**\n     * Override standard Object class values\n     */\n    lockScalingFlip: true,\n\n    /**\n     * Override standard Object class values\n     * Textbox needs this on false\n     */\n    noScaleCache: false,\n\n    /**\n     * Properties which when set cause object to change dimensions\n     * @type Object\n     * @private\n     */\n    _dimensionAffectingProps: fabric.Text.prototype._dimensionAffectingProps.concat('width'),\n\n    /**\n     * Unlike superclass's version of this function, Textbox does not update\n     * its width.\n     * @private\n     * @override\n     */\n    initDimensions: function() {\n      if (this.__skipDimension) {\n        return;\n      }\n      this.isEditing && this.initDelayedCursor();\n      this.clearContextTop();\n      this._clearCache();\n      // clear dynamicMinWidth as it will be different after we re-wrap line\n      this.dynamicMinWidth = 0;\n      // wrap lines\n      this._styleMap = this._generateStyleMap(this._splitText());\n      // if after wrapping, the width is smaller than dynamicMinWidth, change the width and re-wrap\n      if (this.dynamicMinWidth > this.width) {\n        this._set('width', this.dynamicMinWidth);\n      }\n      if (this.textAlign.indexOf('justify') !== -1) {\n        // once text is measured we need to make space fatter to make justified text.\n        this.enlargeSpaces();\n      }\n      // clear cache and re-calculate height\n      this.height = this.calcTextHeight();\n      this.saveState({ propertySet: '_dimensionAffectingProps' });\n    },\n\n    /**\n     * Generate an object that translates the style object so that it is\n     * broken up by visual lines (new lines and automatic wrapping).\n     * The original text styles object is broken up by actual lines (new lines only),\n     * which is only sufficient for Text / IText\n     * @private\n     */\n    _generateStyleMap: function(textInfo) {\n      var realLineCount     = 0,\n          realLineCharCount = 0,\n          charCount         = 0,\n          map               = {};\n\n      for (var i = 0; i < textInfo.graphemeLines.length; i++) {\n        if (textInfo.graphemeText[charCount] === '\\n' && i > 0) {\n          realLineCharCount = 0;\n          charCount++;\n          realLineCount++;\n        }\n        else if (this._reSpaceAndTab.test(textInfo.graphemeText[charCount]) && i > 0) {\n          // this case deals with space's that are removed from end of lines when wrapping\n          realLineCharCount++;\n          charCount++;\n        }\n\n        map[i] = { line: realLineCount, offset: realLineCharCount };\n\n        charCount += textInfo.graphemeLines[i].length;\n        realLineCharCount += textInfo.graphemeLines[i].length;\n      }\n\n      return map;\n    },\n\n    /**\n     * Returns true if object has a style property or has it ina specified line\n     * @param {Number} lineIndex\n     * @return {Boolean}\n     */\n    styleHas: function(property, lineIndex) {\n      if (this._styleMap && !this.isWrapping) {\n        var map = this._styleMap[lineIndex];\n        if (map) {\n          lineIndex = map.line;\n        }\n      }\n      return fabric.Text.prototype.styleHas.call(this, property, lineIndex);\n    },\n\n    /**\n     * Returns true if object has no styling or no styling in a line\n     * @param {Number} lineIndex , lineIndex is on wrapped lines.\n     * @return {Boolean}\n     */\n    isEmptyStyles: function(lineIndex) {\n      var offset = 0, nextLineIndex = lineIndex + 1, nextOffset, obj, shouldLimit = false;\n      var map = this._styleMap[lineIndex];\n      var mapNextLine = this._styleMap[lineIndex + 1];\n      if (map) {\n        lineIndex = map.line;\n        offset = map.offset;\n      }\n      if (mapNextLine) {\n        nextLineIndex = mapNextLine.line;\n        shouldLimit = nextLineIndex === lineIndex;\n        nextOffset = mapNextLine.offset;\n      }\n      obj = typeof lineIndex === 'undefined' ? this.styles : { line: this.styles[lineIndex] };\n      for (var p1 in obj) {\n        for (var p2 in obj[p1]) {\n          if (p2 >= offset && (!shouldLimit || p2 < nextOffset)) {\n            // eslint-disable-next-line no-unused-vars\n            for (var p3 in obj[p1][p2]) {\n              return false;\n            }\n          }\n        }\n      }\n      return true;\n    },\n\n    /**\n     * @param {Number} lineIndex\n     * @param {Number} charIndex\n     * @private\n     */\n    _getStyleDeclaration: function(lineIndex, charIndex) {\n      if (this._styleMap && !this.isWrapping) {\n        var map = this._styleMap[lineIndex];\n        if (!map) {\n          return null;\n        }\n        lineIndex = map.line;\n        charIndex = map.offset + charIndex;\n      }\n      return this.callSuper('_getStyleDeclaration', lineIndex, charIndex);\n    },\n\n    /**\n     * @param {Number} lineIndex\n     * @param {Number} charIndex\n     * @param {Object} style\n     * @private\n     */\n    _setStyleDeclaration: function(lineIndex, charIndex, style) {\n      var map = this._styleMap[lineIndex];\n      lineIndex = map.line;\n      charIndex = map.offset + charIndex;\n\n      this.styles[lineIndex][charIndex] = style;\n    },\n\n    /**\n     * @param {Number} lineIndex\n     * @param {Number} charIndex\n     * @private\n     */\n    _deleteStyleDeclaration: function(lineIndex, charIndex) {\n      var map = this._styleMap[lineIndex];\n      lineIndex = map.line;\n      charIndex = map.offset + charIndex;\n\n      delete this.styles[lineIndex][charIndex];\n    },\n\n    /**\n    * probably broken need a fix\n     * @param {Number} lineIndex\n     * @private\n     */\n    _getLineStyle: function(lineIndex) {\n      var map = this._styleMap[lineIndex];\n      return this.styles[map.line];\n    },\n\n    /**\n     * probably broken need a fix\n     * @param {Number} lineIndex\n     * @param {Object} style\n     * @private\n     */\n    _setLineStyle: function(lineIndex, style) {\n      var map = this._styleMap[lineIndex];\n      this.styles[map.line] = style;\n    },\n\n    /**\n     * probably broken need a fix\n     * @param {Number} lineIndex\n     * @private\n     */\n    _deleteLineStyle: function(lineIndex) {\n      var map = this._styleMap[lineIndex];\n      delete this.styles[map.line];\n    },\n\n    /**\n     * Wraps text using the 'width' property of Textbox. First this function\n     * splits text on newlines, so we preserve newlines entered by the user.\n     * Then it wraps each line using the width of the Textbox by calling\n     * _wrapLine().\n     * @param {Array} lines The string array of text that is split into lines\n     * @param {Number} desiredWidth width you want to wrap to\n     * @returns {Array} Array of lines\n     */\n    _wrapText: function(lines, desiredWidth) {\n      var wrapped = [], i;\n      this.isWrapping = true;\n      for (i = 0; i < lines.length; i++) {\n        wrapped = wrapped.concat(this._wrapLine(lines[i], i, desiredWidth));\n      }\n      this.isWrapping = false;\n      return wrapped;\n    },\n\n    /**\n     * Helper function to measure a string of text, given its lineIndex and charIndex offset\n     * it gets called when charBounds are not available yet.\n     * @param {CanvasRenderingContext2D} ctx\n     * @param {String} text\n     * @param {number} lineIndex\n     * @param {number} charOffset\n     * @returns {number}\n     * @private\n     */\n    _measureWord: function(word, lineIndex, charOffset) {\n      var width = 0, prevGrapheme, skipLeft = true;\n      charOffset = charOffset || 0;\n      for (var i = 0, len = word.length; i < len; i++) {\n        var box = this._getGraphemeBox(word[i], lineIndex, i + charOffset, prevGrapheme, skipLeft);\n        width += box.kernedWidth;\n        prevGrapheme = word[i];\n      }\n      return width;\n    },\n\n    /**\n     * Wraps a line of text using the width of the Textbox and a context.\n     * @param {Array} line The grapheme array that represent the line\n     * @param {Number} lineIndex\n     * @param {Number} desiredWidth width you want to wrap the line to\n     * @param {Number} reservedSpace space to remove from wrapping for custom functionalities\n     * @returns {Array} Array of line(s) into which the given text is wrapped\n     * to.\n     */\n    _wrapLine: function(_line, lineIndex, desiredWidth, reservedSpace) {\n      var lineWidth        = 0,\n          graphemeLines    = [],\n          line             = [],\n          // spaces in different languges?\n          words            = _line.split(this._reSpaceAndTab),\n          word             = '',\n          offset           = 0,\n          infix            = ' ',\n          wordWidth        = 0,\n          infixWidth       = 0,\n          largestWordWidth = 0,\n          lineJustStarted = true,\n          additionalSpace = this._getWidthOfCharSpacing(),\n          reservedSpace = reservedSpace || 0;\n\n      desiredWidth -= reservedSpace;\n      for (var i = 0; i < words.length; i++) {\n        // i would avoid resplitting the graphemes\n        word = fabric.util.string.graphemeSplit(words[i]);\n        wordWidth = this._measureWord(word, lineIndex, offset);\n        offset += word.length;\n\n        lineWidth += infixWidth + wordWidth - additionalSpace;\n\n        if (lineWidth >= desiredWidth && !lineJustStarted) {\n          graphemeLines.push(line);\n          line = [];\n          lineWidth = wordWidth;\n          lineJustStarted = true;\n        }\n        else {\n          lineWidth += additionalSpace;\n        }\n\n        if (!lineJustStarted) {\n          line.push(infix);\n        }\n        line = line.concat(word);\n\n        infixWidth = this._measureWord([infix], lineIndex, offset);\n        offset++;\n        lineJustStarted = false;\n        // keep track of largest word\n        if (wordWidth > largestWordWidth) {\n          largestWordWidth = wordWidth;\n        }\n      }\n\n      i && graphemeLines.push(line);\n\n      if (largestWordWidth + reservedSpace > this.dynamicMinWidth) {\n        this.dynamicMinWidth = largestWordWidth - additionalSpace + reservedSpace;\n      }\n\n      return graphemeLines;\n    },\n\n    /**\n     * Detect if the text line is ended with an hard break\n     * text and itext do not have wrapping, return false\n     * @param {Number} lineIndex text to split\n     * @return {Boolean}\n     */\n    isEndOfWrapping: function(lineIndex) {\n      if (!this._styleMap[lineIndex + 1]) {\n        // is last line, return true;\n        return true;\n      }\n      if (this._styleMap[lineIndex + 1].line !== this._styleMap[lineIndex].line) {\n        // this is last line before a line break, return true;\n        return true;\n      }\n      return false;\n    },\n\n    /**\n    * Gets lines of text to render in the Textbox. This function calculates\n    * text wrapping on the fly every time it is called.\n    * @param {String} text text to split\n    * @returns {Array} Array of lines in the Textbox.\n    * @override\n    */\n    _splitTextIntoLines: function(text) {\n      var newText = fabric.Text.prototype._splitTextIntoLines.call(this, text),\n          graphemeLines = this._wrapText(newText.lines, this.width),\n          lines = new Array(graphemeLines.length);\n\n      for (var i = 0; i < graphemeLines.length; i++) {\n        lines[i] = graphemeLines[i].join('');\n      }\n      newText.lines = lines;\n      newText.graphemeLines = graphemeLines;\n      return newText;\n    },\n\n    getMinWidth: function() {\n      return Math.max(this.minWidth, this.dynamicMinWidth);\n    },\n\n    /**\n     * Returns object representation of an instance\n     * @method toObject\n     * @param {Array} [propertiesToInclude] Any properties that you might want to additionally include in the output\n     * @return {Object} object representation of an instance\n     */\n    toObject: function(propertiesToInclude) {\n      return this.callSuper('toObject', ['minWidth'].concat(propertiesToInclude));\n    }\n  });\n\n  /**\n   * Returns fabric.Textbox instance from an object representation\n   * @static\n   * @memberOf fabric.Textbox\n   * @param {Object} object Object to create an instance from\n   * @param {Function} [callback] Callback to invoke when an fabric.Textbox instance is created\n   */\n  fabric.Textbox.fromObject = function(object, callback) {\n    return fabric.Object._fromObject('Textbox', object, callback, 'text');\n  };\n})(typeof exports !== 'undefined' ? exports : this);\n\n\n(function() {\n\n  /**\n   * Override _setObjectScale and add Textbox specific resizing behavior. Resizing\n   * a Textbox doesn't scale text, it only changes width and makes text wrap automatically.\n   */\n  var setObjectScaleOverridden = fabric.Canvas.prototype._setObjectScale;\n\n  fabric.Canvas.prototype._setObjectScale = function(localMouse, transform,\n    lockScalingX, lockScalingY, by, lockScalingFlip, _dim) {\n\n    var t = transform.target;\n    if (by === 'x' && t instanceof fabric.Textbox) {\n      var tw = t._getTransformedDimensions().x;\n      var w = t.width * (localMouse.x / tw);\n      if (w >= t.getMinWidth()) {\n        t.set('width', w);\n        return true;\n      }\n    }\n    else {\n      return setObjectScaleOverridden.call(fabric.Canvas.prototype, localMouse, transform,\n        lockScalingX, lockScalingY, by, lockScalingFlip, _dim);\n    }\n  };\n\n  fabric.util.object.extend(fabric.Textbox.prototype, /** @lends fabric.IText.prototype */ {\n    /**\n     * @private\n     */\n    _removeExtraneousStyles: function() {\n      for (var prop in this._styleMap) {\n        if (!this._textLines[prop]) {\n          delete this.styles[this._styleMap[prop].line];\n        }\n      }\n    },\n\n  });\n})();\n"
  },
  {
    "path": "calliar_server/server/static/keyboard.css",
    "content": "#keyboardInputMaster {\n  position:absolute;\n  border:2px groove #dddddd;\n  color:#000000;\n  background-color:#dddddd;\n  text-align:left;\n  z-index:1000000;\n  width:auto;\n}\n\n#keyboardInputMaster thead tr th {\n  text-align:left;\n  padding:2px 5px 2px 4px;\n  background-color:inherit;\n  border:0px none;\n}\n#keyboardInputMaster thead tr th select,\n#keyboardInputMaster thead tr th label {\n  color:#000000;\n  font:normal 11px Arial,sans-serif;\n}\n#keyboardInputMaster thead tr td {\n  text-align:right;\n  padding:2px 4px 2px 5px;\n  background-color:inherit;\n  border:0px none;\n}\n#keyboardInputMaster thead tr td span {\n  padding:1px 4px;\n  font:bold 11px Arial,sans-serif;\n  border:1px outset #aaaaaa;\n  background-color:#cccccc;\n  cursor:pointer;\n}\n#keyboardInputMaster thead tr td span.pressed {\n  border:1px inset #999999;\n  background-color:#bbbbbb;\n}\n\n#keyboardInputMaster tbody tr td {\n  text-align:left;\n  margin:0px;\n  padding:0px 4px 3px 4px;\n}\n#keyboardInputMaster tbody tr td div {\n  text-align:center;\n  position:relative;\n  height:0px;\n}\n#keyboardInputMaster tbody tr td div#keyboardInputLayout {\n  height:auto;\n}\n#keyboardInputMaster tbody tr td div#keyboardInputLayout table {\n  height:20px;\n  white-space:nowrap;\n  width:100%;\n  border-collapse:separate;\n}\n#keyboardInputMaster tbody tr td div#keyboardInputLayout table.keyboardInputCenter {\n  width:auto;\n  margin:0px auto;\n}\n#keyboardInputMaster tbody tr td div#keyboardInputLayout table tbody tr td {\n  vertical-align:middle;\n  padding:0px 5px 0px 5px;\n  white-space:pre;\n  font:normal 11px 'Lucida Console',monospace;\n  border-top:1px solid #e5e5e5;\n  border-right:1px solid #5d5d5d;\n  border-bottom:1px solid #5d5d5d;\n  border-left:1px solid #e5e5e5;\n  background-color:#eeeeee;\n  cursor:default;\n}\n#keyboardInputMaster tbody tr td div#keyboardInputLayout table tbody tr td.last {\n  width:99%;\n}\n#keyboardInputMaster tbody tr td div#keyboardInputLayout table tbody tr td.alive {\n  background-color:#ccccdd;\n}\n#keyboardInputMaster tbody tr td div#keyboardInputLayout table tbody tr td.target {\n  background-color:#ddddcc;\n}\n#keyboardInputMaster tbody tr td div#keyboardInputLayout table tbody tr td.hover {\n  border-top:1px solid #d5d5d5;\n  border-right:1px solid #555555;\n  border-bottom:1px solid #555555;\n  border-left:1px solid #d5d5d5;\n  background-color:#cccccc;\n}\n#keyboardInputMaster tbody tr td div#keyboardInputLayout table tbody tr td.pressed,\n#keyboardInputMaster tbody tr td div#keyboardInputLayout table tbody tr td.dead {\n  border-top:1px solid #555555;\n  border-right:1px solid #d5d5d5;\n  border-bottom:1px solid #d5d5d5;\n  border-left:1px solid #555555;\n  background-color:#cccccc;\n}\n\n#keyboardInputMaster tbody tr td div var {\n  position:absolute;\n  bottom:0px;\n  right:0px;\n  font:bold italic 11px Arial,sans-serif;\n  color:#444444;\n}\n\n.keyboardInputInitiator {\n  margin-left:3px;\n  vertical-align:middle;\n  cursor:pointer;\n}"
  },
  {
    "path": "calliar_server/server/static/keyboard.js",
    "content": "/* ********************************************************************\n **********************************************************************\n * HTML Virtual Keyboard Interface Script - v1.10\n *   Copyright (c) 2008 - GreyWyvern\n *\n *  - Licenced for free distribution under the BSDL\n *          http://www.opensource.org/licenses/bsd-license.php\n *\n * Add a script-driven keyboard interface to text fields, password\n * fields and textareas.\n *\n * See http://www.arabic-keyboard.org/keyboard for examples and\n * usage instructions.\n * Version 1.3 - July 30, 2007\n *   - Interaction styling changes (Alt, AltGr, Shift)\n *   - Justified keys - last key expands to fit width\n *   - If no dead keys in layout, dead key checkbox is hidden\n *   - Option to disable dead keys per keyboard\n *   - Added the Number Pad layout\n *   - Pulled all variations of script up to same version number\n *\n * Keyboard Credits\n *   - Arabic keyboard layout added by Srinivas Reddy\n *\n */\n\nfunction VKI_buildKeyboardInputs() {\n  var self = this;\n  this.VKI_version = \"V 1.10\";\n  this.VKI_target = this.VKI_visible = \"\";\n  this.VKI_shift = this.VKI_capslock = this.VKI_alternate = this.VKI_dead = false;\n  this.VKI_deadkeysOn = false;\n  this.VKI_kt = \"Arabic\";  // Default keyboard layout\n  this.VKI_range = false;\n  this.VKI_keyCenter = 3;\n\n\n  /* ***** Create keyboards **************************************** */\n  this.VKI_layout = new Object();\n  this.VKI_layoutDDK = new Object();\n\n \n\n  this.VKI_layout.Arabic = [ // Arabic Keyboard\n    [[\"\\u0630\", \"\\u0651 \"], [\"1\", \"!\", \"\\u00a1\", \"\\u00b9\"], [\"2\", \"@\", \"\\u00b2\"], [\"3\", \"#\", \"\\u00b3\"], [\"4\", \"$\", \"\\u00a4\", \"\\u00a3\"], [\"5\", \"%\", \"\\u20ac\"], [\"6\", \"^\", \"\\u00bc\"], [\"7\", \"&\", \"\\u00bd\"], [\"8\", \"*\", \"\\u00be\"], [\"9\", \"(\", \"\\u2018\"], [\"0\", \")\", \"\\u2019\"], [\"-\", \"_\", \"\\u00a5\"], [\"=\", \"+\", \"\\u00d7\", \"\\u00f7\"], [\"Bksp\", \"Bksp\"]],\n    [[\"Tab\", \"Tab\"], [\"\\u0636\", \"\\u064e\"], [\"\\u0635\", \"\\u064b\"], [\"\\u062b\", \"\\u064f\"], [\"\\u0642\", \"\\u064c\"], [\"\\u0641\", \"\\u0644\"], [\"\\u063a\", \"\\u0625\"], [\"\\u0639\", \"\\u2018\"], [\"\\u0647\", \"\\u00f7\"], [\"\\u062e\", \"\\u00d7\"], [\"\\u062d\", \"\\u061b\"], [\"\\u062c\", \"\\u003c\"], [\"\\u062f\", \"\\u003e\"], [\"\\u005c\", \"\\u007c\"]],\n    [[\"Caps\", \"Caps\"], [\"\\u0634\", \"\\u0650\"], [\"\\u0633\", \"\\u064d\"], [\"\\u064a\", \"\\u005d\"], [\"\\u0628\", \"\\u005b\"], [\"\\u0644\", \"\\u0644\"], [\"\\u0627\", \"\\u0623\"], [\"\\u062a\", \"\\u0640\"], [\"\\u0646\", \"\\u060c\"], [\"\\u0645\", \"\\u002f\"], [\"\\u0643\", \"\\u003a\"], [\"\\u0637\", \"\\u0022\"], [\"Enter\", \"Enter\"]],\n    [[\"Shift\", \"Shift\"], [\"\\u0626\", \"\\u007e\"], [\"\\u0621\", \"\\u0652\"], [\"\\u0624\", \"\\u007d\"], [\"\\u0631\", \"\\u007b\"], [\"\\u0644\", \"\\u0644\"], [\"\\u0649\", \"\\u0622\"], [\"\\u0629\", \"\\u2019\"], [\"\\u0648\", \"\\u002c\"], [\"\\u0632\", \"\\u002e\"], [\"\\u0638\", \"\\u061f\"], [\"Shift\", \"Shift\"]],\n    [[\" \", \" \", \" \", \" \"], [\"Alt\", \"Alt\"]]\n  ];\n\n\n  \n\n  this.VKI_layout.Numpad = [ // Number pad\n    [[\"$\"], [\"\\u00a3\"], [\"\\u20ac\"], [\"\\u00a5\"], [\"/\"], [\"^\"], [\"Bksp\", \"Bksp\"]],\n    [[\".\"], [\"7\"], [\"8\"], [\"9\"], [\"*\"], [\"<\"], [\"(\"], [\"[\"]],\n    [[\"=\"], [\"4\"], [\"5\"], [\"6\"], [\"-\"], [\">\"], [\")\"], [\"]\"]],\n    [[\"0\"], [\"1\"], [\"2\"], [\"3\"], [\"+\"], [\"Enter\", \"Enter\"]],\n    [[\" \"]]\n  ];\n  this.VKI_layoutDDK.Numpad = true;\n\n  \n  /* ***** Define Dead Keys **************************************** */\n  this.VKI_deadkey = new Object();\n\n  /* ***** Find tagged input & textarea elements ******************* */\n  var inputElems = [\n    document.getElementsByTagName('input'),\n    document.getElementsByTagName('textarea'),\n  ]\n  for (var x = 0, inputCount = 0, elem; elem = inputElems[x++];) {\n    if (elem) {\n      for (var y = 0, keyid = \"\", ex; ex = elem[y++];) {\n        if ((ex.nodeName == \"TEXTAREA\" || ex.type == \"text\" || ex.type == \"password\") && ex.className.indexOf(\"keyboardInput\") > -1) {\n          if (!ex.id) {\n            do { keyid = 'keyboardInputInitiator' + inputCount++; } while (document.getElementById(keyid));\n            ex.id = keyid;\n          } else keyid = ex.id;\n          var keybut = document.createElement('img');\n              keybut.src = \"http://www.arabic-keyboard.org/keyboard/keyboard.png\";\n              keybut.alt = \"Keyboard interface\";\n              keybut.className = \"keyboardInputInitiator\";\n              keybut.title = \"Display graphical keyboard interface\";\n              keybut.onclick = (function(a) { return function() { self.VKI_show(a); }; })(keyid);\n          ex.parentNode.insertBefore(keybut, ex.nextSibling);\n          if (!window.sidebar && !window.opera) {\n            ex.onclick = ex.onkeyup = ex.onselect = function() {\n              if (self.VKI_target.createTextRange) self.VKI_range = document.selection.createRange();\n            };\n          }\n        }\n      }\n    }\n  }\n\n\n  /* ***** Build the keyboard interface **************************** */\n  this.VKI_keyboard = document.createElement('table');\n  this.VKI_keyboard.id = \"keyboardInputMaster\";\n  this.VKI_keyboard.cellSpacing = this.VKI_keyboard.cellPadding = this.VKI_keyboard.border = \"0\";\n\n  var layouts = 0;\n  for (ktype in this.VKI_layout) if (typeof this.VKI_layout[ktype] == \"object\") layouts++;\n\n  var thead = document.createElement('thead');\n    var tr = document.createElement('tr');\n      var th = document.createElement('th');\n        if (layouts > 1) {\n          var kblist = document.createElement('select');\n            for (ktype in this.VKI_layout) {\n              if (typeof this.VKI_layout[ktype] == \"object\") {\n                var opt = document.createElement('option');\n                    opt.value = ktype;\n                    opt.appendChild(document.createTextNode(ktype));\n                  kblist.appendChild(opt);\n              }\n            }\n              kblist.value = this.VKI_kt;\n              kblist.onchange = function() {\n                self.VKI_kt = this.value;\n                self.VKI_buildKeys();\n                self.VKI_position();\n              };\n          th.appendChild(kblist);\n        }\n\n          var label = document.createElement('label');\n            var checkbox = document.createElement('input');\n                checkbox.type = \"checkbox\";\n                checkbox.checked = this.VKI_deadkeysOn;\n                checkbox.title = \"Toggle dead key input\";\n                checkbox.onclick = function() {\n                  self.VKI_deadkeysOn = this.checked;\n                  this.nextSibling.nodeValue = \" Dead keys: \" + ((this.checked) ? \"On\" : \"Off\");\n                  self.VKI_modify(\"\");\n                  return true;\n                };\n              label.appendChild(checkbox);\n              label.appendChild(document.createTextNode(\" Dead keys: \" + ((checkbox.checked) ? \"On\" : \"Off\")))\n          th.appendChild(label);\n        tr.appendChild(th);\n\n      var td = document.createElement('td');\n\t\n var logo = document.createElement('span');\n            logo.id = \"keyboardInputlogo\";\n            logo.appendChild(document.createTextNode(\"Arabic keyboard  \"));\n            logo.title = \"www.arabic-keyboard.org\";\n            logo.onmousedown = function() { this.className = \"pressed\"; };\n            logo.onmouseup = function() { this.className = \"\"; };          \n\t\tlogo.onclick = function () { location.href='http://www.arabic-keyboard.org';}; \n\n          td.appendChild(logo);\n\n\n        var clearer = document.createElement('span');\n            clearer.id = \"keyboardInputClear\";\n            clearer.appendChild(document.createTextNode(\"Clear\"));\n            clearer.title = \"Clear this input\";\n            clearer.onmousedown = function() { this.className = \"pressed\"; };\n            clearer.onmouseup = function() { this.className = \"\"; };\n            clearer.onclick = function() {\n              self.VKI_target.value = \"\";\n              self.VKI_target.focus();\n              return false;\n            };\n          td.appendChild(clearer);\n      \n\n\n        var closer = document.createElement('span');\n            closer.id = \"keyboardInputClose\";\n            closer.appendChild(document.createTextNode('X'));\n            closer.title = \"Close this window\";\n            closer.onmousedown = function() { this.className = \"pressed\"; };\n            closer.onmouseup = function() { this.className = \"\"; };\n            closer.onclick = function() { self.VKI_close(); };\n          td.appendChild(closer);\n\n        tr.appendChild(td);\n      thead.appendChild(tr);\n  this.VKI_keyboard.appendChild(thead);\n\n  var tbody = document.createElement('tbody');\n    var tr = document.createElement('tr');\n      var td = document.createElement('td');\n          td.colSpan = \"2\";\n        var div = document.createElement('div');\n            div.id = \"keyboardInputLayout\";\n          td.appendChild(div);\n        var div = document.createElement('div');\n          var ver = document.createElement('var');\n              ver.appendChild(document.createTextNode(this.VKI_version));\n            div.appendChild(ver);\n          td.appendChild(div);\n        tr.appendChild(td);\n      tbody.appendChild(tr);\n  this.VKI_keyboard.appendChild(tbody);      \n\n\n\n  /* ***** Functions ************************************************ */\n  /* ******************************************************************\n   * Build or rebuild the keyboard keys\n   *\n   */\n  this.VKI_buildKeys = function() {\n    this.VKI_shift = this.VKI_capslock = this.VKI_alternate = this.VKI_dead = false;\n    this.VKI_deadkeysOn = (this.VKI_layoutDDK[this.VKI_kt]) ? false : this.VKI_keyboard.getElementsByTagName('label')[0].getElementsByTagName('input')[0].checked;\n\n    var container = this.VKI_keyboard.tBodies[0].getElementsByTagName('div')[0];\n    while (container.firstChild) container.removeChild(container.firstChild);\n\n    for (var x = 0, hasDeadKey = false, lyt; lyt = this.VKI_layout[this.VKI_kt][x++];) {\n      var table = document.createElement('table');\n          table.cellSpacing = table.cellPadding = table.border = \"0\";\n      if (lyt.length <= this.VKI_keyCenter) table.className = \"keyboardInputCenter\";\n        var tbody = document.createElement('tbody');\n          var tr = document.createElement('tr');\n          for (var y = 0, lkey; lkey = lyt[y++];) {\n            if (!this.VKI_layoutDDK[this.VKI_kt] && !hasDeadKey)\n              for (var z = 0; z < lkey.length; z++)\n                if (this.VKI_deadkey[lkey[z]]) hasDeadKey = true;\n\n            var td = document.createElement('td');\n                td.appendChild(document.createTextNode(lkey[0]));\n\n              var alive = false;\n              if (this.VKI_deadkeysOn) for (key in this.VKI_deadkey) if (key === lkey[0]) alive = true;\n                td.className = (alive) ? \"alive\" : \"\";\n              if (lyt.length > this.VKI_keyCenter && y == lyt.length)\n                td.className += \" last\";\n\n              if (lkey[0] == \" \")\n                td.style.paddingLeft = td.style.paddingRight = \"50px\";\n                td.onmouseover = function() { if (this.className != \"dead\" && this.firstChild.nodeValue != \"\\xa0\") this.className += \" hover\"; };\n                td.onmouseout = function() { if (this.className != \"dead\") this.className = this.className.replace(/ ?(hover|pressed)/g, \"\"); };\n                td.onmousedown = function() { if (this.className != \"dead\" && this.firstChild.nodeValue != \"\\xa0\") this.className += \" pressed\"; };\n                td.onmouseup = function() { if (this.className != \"dead\") this.className = this.className.replace(/ ?pressed/g, \"\"); };\n                td.ondblclick = function() { return false; };\n\n              switch (lkey[1]) {\n                case \"Caps\":\n                case \"Shift\":\n                case \"Alt\":\n                case \"AltGr\":\n                  td.onclick = (function(type) { return function() { self.VKI_modify(type); return false; }})(lkey[1]);\n                  break;\n                case \"Tab\":\n                  td.onclick = function() { self.VKI_insert(\"\\t\"); return false; };\n                  break;\n                case \"Bksp\":\n                  td.onclick = function() {\n                    self.VKI_target.focus();\n                    if (self.VKI_target.setSelectionRange) {\n                      var srt = self.VKI_target.selectionStart;\n                      var len = self.VKI_target.selectionEnd;\n                      if (srt < len) srt++;\n                      self.VKI_target.value = self.VKI_target.value.substr(0, srt - 1) + self.VKI_target.value.substr(len);\n                      self.VKI_target.setSelectionRange(srt - 1, srt - 1);\n                    } else if (self.VKI_target.createTextRange) {\n                      try { self.VKI_range.select(); } catch(e) {}\n                      self.VKI_range = document.selection.createRange();\n                      if (!self.VKI_range.text.length) self.VKI_range.moveStart('character', -1);\n                      self.VKI_range.text = \"\";\n                    } else self.VKI_target.value = self.VKI_target.value.substr(0, self.VKI_target.value.length - 1);\n                    if (self.VKI_shift) self.VKI_modify(\"Shift\");\n                    if (self.VKI_alternate) self.VKI_modify(\"AltGr\");\n                    return true;\n                  };\n                  break;\n                case \"Enter\":\n                  td.onclick = function() {\n                    if (self.VKI_target.nodeName == \"TEXTAREA\") { self.VKI_insert(\"\\n\"); } else self.VKI_close();\n                    return true;\n                  };\n                  break;\n                default:\n                  td.onclick = function() {\n                    if (self.VKI_deadkeysOn && self.VKI_dead) {\n                      if (self.VKI_dead != this.firstChild.nodeValue) {\n                        for (key in self.VKI_deadkey) {\n                          if (key == self.VKI_dead) {\n                            if (this.firstChild.nodeValue != \" \") {\n                              for (var z = 0, rezzed = false, dk; dk = self.VKI_deadkey[key][z++];) {\n                                if (dk[0] == this.firstChild.nodeValue) {\n                                  self.VKI_insert(dk[1]);\n                                  rezzed = true;\n                                  break;\n                                }\n                              }\n                            } else {\n                              self.VKI_insert(self.VKI_dead);\n                              rezzed = true;\n                            }\n                            break;\n                          }\n                        }\n                      } else rezzed = true;\n                    }\n                    self.VKI_dead = false;\n\n                    if (!rezzed && this.firstChild.nodeValue != \"\\xa0\") {\n                      if (self.VKI_deadkeysOn) {\n                        for (key in self.VKI_deadkey) {\n                          if (key == this.firstChild.nodeValue) {\n                            self.VKI_dead = key;\n                            this.className = \"dead\";\n                            if (self.VKI_shift) self.VKI_modify(\"Shift\");\n                            if (self.VKI_alternate) self.VKI_modify(\"AltGr\");\n                            break;\n                          }\n                        }\n                        if (!self.VKI_dead) self.VKI_insert(this.firstChild.nodeValue);\n                      } else self.VKI_insert(this.firstChild.nodeValue);\n                    }\n\n                    self.VKI_modify(\"\");\n                    return false;\n                  };\n\n              }\n              tr.appendChild(td);\n            tbody.appendChild(tr);\n          table.appendChild(tbody);\n\n          for (var z = lkey.length; z < 4; z++) lkey[z] = \"\\xa0\";\n      }\n      container.appendChild(table);\n    }\n    this.VKI_keyboard.getElementsByTagName('label')[0].style.display = (hasDeadKey) ? \"inline\" : \"none\";\n  };\n\n  this.VKI_buildKeys();\n  VKI_disableSelection(this.VKI_keyboard);\n\n\n  /* ******************************************************************\n   * Controls modifier keys\n   *\n   */\n  this.VKI_modify = function(type) {\n    switch (type) {\n      case \"Alt\":\n      case \"AltGr\": this.VKI_alternate = !this.VKI_alternate; break;\n      case \"Caps\": this.VKI_capslock = !this.VKI_capslock; break;\n      case \"Shift\": this.VKI_shift = !this.VKI_shift; break;\n    }\n    var vchar = 0;\n    if (!this.VKI_shift != !this.VKI_capslock) vchar += 1;\n\n    var tables = this.VKI_keyboard.getElementsByTagName('table');\n    for (var x = 0; x < tables.length; x++) {\n      var tds = tables[x].getElementsByTagName('td');\n      for (var y = 0; y < tds.length; y++) {\n        var dead = alive = target = false;\n        var lkey = this.VKI_layout[this.VKI_kt][x][y];\n\n        switch (lkey[1]) {\n          case \"Alt\":\n          case \"AltGr\":\n            if (this.VKI_alternate) dead = true;\n            break;\n          case \"Shift\":\n            if (this.VKI_shift) dead = true;\n            break;\n          case \"Caps\":\n            if (this.VKI_capslock) dead = true;\n            break;\n          case \"Tab\": case \"Enter\": case \"Bksp\": break;\n          default:\n            if (type) tds[y].firstChild.nodeValue = lkey[vchar + ((this.VKI_alternate && lkey.length == 4) ? 2 : 0)];\n            if (this.VKI_deadkeysOn) {\n              var char = tds[y].firstChild.nodeValue;\n              if (this.VKI_dead) {\n                if (char == this.VKI_dead) dead = true;\n                for (var z = 0; z < this.VKI_deadkey[this.VKI_dead].length; z++)\n                  if (char == this.VKI_deadkey[this.VKI_dead][z][0]) { target = true; break; }\n              }\n              for (key in this.VKI_deadkey) if (key === char) { alive = true; break; }\n            }\n        }\n\n        tds[y].className = (dead) ? \"dead\" : ((target) ? \"target\" : ((alive) ? \"alive\" : \"\"));\n        if (y == tds.length - 1 && tds.length > this.VKI_keyCenter) tds[y].className += \" last\";\n      }\n    }\n    this.VKI_target.focus();\n  };\n\n\n  /* ******************************************************************\n   * Insert text at the cursor\n   *\n   */\n  this.VKI_insert = function(text) {\n    this.VKI_target.focus();\n    if (this.VKI_target.setSelectionRange) {\n      var srt = this.VKI_target.selectionStart;\n      var len = this.VKI_target.selectionEnd;\n      this.VKI_target.value = this.VKI_target.value.substr(0, srt) + text + this.VKI_target.value.substr(len);\n      if (text == \"\\n\" && window.opera) srt++;\n      this.VKI_target.setSelectionRange(srt + text.length, srt + text.length);\n    } else if (this.VKI_target.createTextRange) {\n      try { this.VKI_range.select(); } catch(e) {}\n      this.VKI_range = document.selection.createRange();\n      this.VKI_range.text = text;\n      this.VKI_range.collapse(true);\n      this.VKI_range.select();\n    } else this.VKI_target.value += text;\n    if (this.VKI_shift) this.VKI_modify(\"Shift\");\n    if (this.VKI_alternate) this.VKI_modify(\"AltGr\");\n    this.VKI_target.focus();\n  };\n\n\n  /* ******************************************************************\n   * Show the keyboard interface\n   *\n   */\n  this.VKI_show = function(id) {\n    if (this.VKI_target = document.getElementById(id)) {\n      if (this.VKI_visible != id) {\n        this.VKI_range = \"\";\n        try { this.VKI_keyboard.parentNode.removeChild(this.VKI_keyboard); } catch (e) {}\n\n        var elem = this.VKI_target;\n        this.VKI_target.keyboardPosition = \"absolute\";\n        do {\n          if (VKI_getStyle(elem, \"position\") == \"fixed\") {\n            this.VKI_target.keyboardPosition = \"fixed\";\n            break;\n          }\n        } while (elem = elem.offsetParent);\n\n        this.VKI_keyboard.style.top = this.VKI_keyboard.style.right = this.VKI_keyboard.style.bottom = this.VKI_keyboard.style.left = \"auto\";\n        this.VKI_keyboard.style.position = this.VKI_target.keyboardPosition;\n        document.body.appendChild(this.VKI_keyboard);\n\n        this.VKI_visible = this.VKI_target.id;\n        this.VKI_position();\n        this.VKI_target.focus();\n      } else this.VKI_close();\n    }\n  };\n\n\n  /* ******************************************************************\n   * Position the keyboard\n   *\n   */\n  this.VKI_position = function() {\n    if (self.VKI_visible != \"\") {\n      var inputElemPos = VKI_findPos(self.VKI_target);\n      self.VKI_keyboard.style.top = inputElemPos[1] - ((self.VKI_target.keyboardPosition == \"fixed\") ? document.body.scrollTop : 0) + self.VKI_target.offsetHeight + 3 + \"px\";\n      self.VKI_keyboard.style.left = Math.min(VKI_innerDimensions()[0] - self.VKI_keyboard.offsetWidth - 15, inputElemPos[0]) + \"px\";\n    }\n  };\n\n\n  if (window.addEventListener) {\n    window.addEventListener('resize', this.VKI_position, false); \n  } else if (window.attachEvent)\n    window.attachEvent('onresize', this.VKI_position);\n\n\n  /* ******************************************************************\n   * Close the keyboard interface\n   *\n   */\n  this.VKI_close = function() {\n    try { this.VKI_keyboard.parentNode.removeChild(this.VKI_keyboard); } catch (e) {}\n    this.VKI_visible = \"\";\n    this.VKI_target.focus();\n    this.VKI_target = \"\";\n  };\n}\n\n\n/* ***** Attach this script to the onload event ******************** */\nif (window.addEventListener) {\n  window.addEventListener('load', VKI_buildKeyboardInputs, false); \n} else if (window.attachEvent)\n  window.attachEvent('onload', VKI_buildKeyboardInputs);\n\n\nfunction VKI_findPos(obj) {\n  var curleft = curtop = 0;\n  do {\n    curleft += obj.offsetLeft;\n    curtop += obj.offsetTop;\n  } while (obj = obj.offsetParent);    \n  return [curleft, curtop];\n}\n\nfunction VKI_innerDimensions() {\n  if (self.innerHeight) {\n    return [self.innerWidth, self.innerHeight];\n  } else if (document.documentElement && document.documentElement.clientHeight) {\n    return [document.documentElement.clientWidth, document.documentElement.clientHeight];\n  } else if (document.body)\n    return [document.body.clientWidth, document.body.clientHeight];\n  return [0, 0];\n}\n\nfunction VKI_getStyle(obj, styleProp) {\n  if (obj.currentStyle) {\n    var y = obj.currentStyle[styleProp];\n  } else if (window.getComputedStyle)\n    var y = window.getComputedStyle(obj, null)[styleProp];\n  return y;\n}\n\nfunction VKI_disableSelection(elem) {\n  elem.onselectstart = function() { return false; };\n  elem.unselectable = \"on\";\n  elem.style.MozUserSelect = \"none\";\n  elem.style.cursor = \"default\";\n  if (window.opera) elem.onmousedown = function() { return false; };\n}"
  },
  {
    "path": "calliar_server/server/static/main.js",
    "content": "/*\nvariables\n*/\n\nvar canvas;\nvar mousePressed = false;\nvar curr_img;\nvar currStroke = [];\nvar currSketch = [];\nconst colors = ['black', 'red', 'blue', 'green', 'orange', 'brown', 'purple']\nvar oldImageName;\nvar newImageName;\nvar numImages;\nvar procNumImages;\nvar readonlyInput;\nvar input;\nvar ctr = 0; \n\n/*\nrecord the current drawing coordinates\n*/\nfunction recordCoor(event) {\n    var pointer = canvas.getPointer(event.e);\n    var x = pointer.x;\n    var y = pointer.y;\n    if (x >= 0 && y >= 0 && mousePressed) {\n        currStroke.push([x, y])\n    }\n}\n\nfunction preprocess(name)\n{\n    var text = name;\n    const diacritics = \"[ًٌٍَُِّْ]\"\n    const numbers = '0123456789'\n    for (i = 0; i < diacritics.length; i++) \n    {\n        text = text.replace(diacritics[i], '')\n    }\n\n    for (i = 0; i < numbers.length; i++) \n    {\n        text = text.replaceAll(numbers[i], '')\n    }\n    var outText = \"\"\n    for (i = 0; i < text.length; i++) \n    {\n        if (text[i] == \" \")\n            continue\n\n            if (text[i] in map_chars)\n            if (i < text.length - 1 && text[i] == \"\\u0643\") // if we get kaf at the end \n            {\n                if (text[i+1] != ' ')\n                    outText += '\\uFEDB'\n                else\n                outText += map_chars[text[i]].join(\" \") \n            }\n            else\n                outText += map_chars[text[i]].join(\" \")\n        else{\n\n                outText += text[i]\n        }            \n        if (i != text.length - 1)\n            outText +=  \" \"\n            \n    }\n\n    return [text, outText]\n}\nfunction addImage(imageName)\n{\n    fabric.Image.fromURL(\"/static/images/\"+imageName, function(img) {\n        img.opacity = 0.5\n        img.set({\n            left: 0,\n            top: 0,\n          });\n        \n        var w, h;\n        h = img.height;\n        w = img.width;\n\n        const scale = 600;\n        if (img.width > img.height){\n            h = parseInt((h/w) * scale);\n            w = scale;\n        }else{\n            w = parseInt((w/h) * scale);\n            h = scale; \n        }\n        \n        img.scaleToHeight(h);\n        img.scaleToWidth(w);\n        canvas.add(img)\n        canvas.setHeight(h);\n        canvas.setWidth(w);\n        curr_img = img\n        var text =((imageName).split('.')[0]).trim()\n        newImageName = text+'.jpg'\n        input.value  = text;\n        text = preprocess(text)[1]\n        readonlyInput.value  = text;\n        \n\n    });\n}\n/*\nload the model\n*/\n\n\nfunction getImageUrl(){\n    var xmlHttp = new XMLHttpRequest();\n    xmlHttp.open( \"GET\", '/endpoint/next-image', false ); // false for synchronous request\n    xmlHttp.send( null );\n    response = JSON.parse(xmlHttp.response)\n    numImages = response.num_images\n    procNumImages = response.proc_num_images\n    document.getElementById(\"ctr\").innerHTML = 'You processed '+ctr+ ', remaining images '+numImages+', total processed images '+procNumImages;\n    return response.image_path\n}\n\nasync function start() {\n   \n    canvas = new fabric.Canvas('canvas');\n    canvas.backgroundColor = '#ffffff';\n    canvas.isDrawingMode = 0;\n    canvas.freeDrawingBrush.color = \"black\";\n    canvas.freeDrawingBrush.width = 10;\n\n    canvas.renderAll();\n\n    // addImage('https://www.namearabic.com/thumbs/Thuluth/Aysha-462-400.jpg')\n    oldImageName = getImageUrl()\n    addImage(oldImageName)\n\n    //setup listeners \n    canvas.on('mouse:up', function(e) {\n        mousePressed = false\n        const currChar = readonlyInput.value.charAt(currSketch.length * 2)\n        if (currChar == \"\")\n        {\n            const objects = canvas.getObjects();\n            const lastStrokeIdx = objects.length - 1;\n            if (objects.length > 1){\n                canvas.remove(objects[lastStrokeIdx]);\n            }\n            alert(\"cannot add empty characters!\")\n        }\n        else{\n            currSketch.push({[currChar]:currStroke})\n            canvas.freeDrawingBrush.color = colors[currSketch.length % colors.length];\n            readonlyInput.focus();\n            readonlyInput.setSelectionRange(0, currSketch.length * 2);\n            currStroke =[]\n            console.log(currSketch)\n\n        }\n    });\n    canvas.on('mouse:down', function(e) {\n        mousePressed = true\n        recordCoor(e)\n    });\n    canvas.on('mouse:move', function(e) {\n        recordCoor(e)\n    });\n\n    canvas.isDrawingMode = 1;\n    var slider = document.getElementById('myRange');\n    slider.onchange  = function() {\n        canvas.freeDrawingBrush.width = this.value;\n    };\n\n    readonlyInput = document.getElementById(\"text-readonly\");\n    input = document.getElementById(\"text\");\n\n    $(input).change(function(e){\n        text = input.value.trim()\n        newImageName = text+'.jpg'\n        text = preprocess(text)[1]\n        readonlyInput.value  = text;\n    })\n\n    \n}\n\nfunction undo() {\n    const objects = canvas.getObjects();\n    const lastStrokeIdx = objects.length - 1;\n    if (objects.length > 1){\n        canvas.remove(objects[lastStrokeIdx]);\n        currSketch.splice(-1, 1)\n    }\n    console.log(currSketch)\n    // update selection\n    readonlyInput.focus();\n    readonlyInput.setSelectionRange(0, currSketch.length * 2);\n        \n}\n\nfunction save() {\n    if(currSketch.length != readonlyInput.value.split(\" \").length)\n    {\n        console.log(readonlyInput.value.split(\" \"))\n        console.log(currSketch)\n        alert('sketch size is not the same as the text chars!')\n        return \n    }\n    var xhr = new XMLHttpRequest();\n    xhr.open(\"POST\", 'http://172.16.100.199:8000/endpoint/', false);\n    xhr.setRequestHeader('Content-Type', 'application/json');\n    xhr.send(JSON.stringify({\n        sketch: currSketch,\n        oldImageName:oldImageName,\n        newImageName:newImageName\n    }));\n    const response = JSON.parse(xhr.response)\n\n    if (response.result == true)\n    {\n        next()\n        ctr += 1;\n        document.getElementById(\"ctr\").innerHTML = 'You processed '+ctr+ ', remaining images '+numImages+', total processed images '+procNumImages;\n    }\n    else{\n        alert('something is wrong!')\n    }\n    currStroke = []\n    currSketch = []\n}\n\nfunction clearCanvas()\n{\n    canvas.clear();\n    canvas.backgroundColor = '#ffffff';\n    currStroke = []\n    currSketch = []\n}\nfunction next() {\n    clearCanvas();\n    oldImageName = getImageUrl()\n    addImage(oldImageName)\n}\n\n/*\nclear the canvs \n*/\nfunction erase() {\n    clearCanvas();\n    canvas.add(curr_img);\n}"
  },
  {
    "path": "calliar_server/server/static/main2.js",
    "content": "/*\nvariables\n*/\n\nvar canvas;\nvar mousePressed = false;\nvar curr_img;\nvar currStroke = [];\nvar currSketch = [];\nconst colors = ['black', 'red', 'blue', 'green', 'orange', 'brown', 'purple']\nvar imageName;\nvar input;\n\n/*\nrecord the current drawing coordinates\n*/\nfunction recordCoor(event) {\n    var pointer = canvas.getPointer(event.e);\n    var x = pointer.x;\n    var y = pointer.y;\n    // console.log(x)\n    // document.getElementById(\"x\").innerHTML =\"\"+x;\n    // document.getElementById(\"y\").innerHTML =\"\"+y;\n\n    if (x >= 0 && y >= 0 && mousePressed) {\n        currStroke.push([x, y])\n    }\n}\n\nfunction addImage(imageName)\n{\n    fabric.Image.fromURL(\"/static/images_non_annotated/\"+imageName, function(img) {\n        img.opacity = 0.5\n        img.set({\n            left: 0,\n            top: 0,\n          });\n        curr_img = img\n        canvas.add(curr_img)\n        canvas.setHeight(img.height);\n        canvas.setWidth(img.width);\n\n    });\n}\n/*\nload the model\n*/\n\n\nfunction getImageUrl(){\n    var xmlHttp = new XMLHttpRequest();\n    xmlHttp.open( \"GET\", '/endpoint2/next-image2', false ); // false for synchronous request\n    xmlHttp.send( null );\n    response = JSON.parse(xmlHttp.response)\n    return response.image_path\n}\n\nasync function start() {\n   \n    canvas = new fabric.Canvas('canvas');\n    canvas.backgroundColor = '#ffffff';\n\n    canvas.renderAll();\n\n    // addImage('https://www.namearabic.com/thumbs/Thuluth/Aysha-462-400.jpg')\n    imageName = getImageUrl()\n    addImage(imageName)\n    input = document.getElementById(\"text\");\n\n    input.addEventListener(\"keyup\", function(event) {\n        if (event.keyCode === 13) {\n         event.preventDefault();\n         document.getElementById(\"save\").click();\n        }\n    });\n}\n\nfunction save() {\n    text = document.getElementById('text').value\n    var xhr = new XMLHttpRequest();\n    xhr.open(\"POST\", 'http://172.16.100.199:8000/endpoint2/', false);\n    xhr.setRequestHeader('Content-Type', 'application/json');\n    xhr.send(JSON.stringify({\n        text: text,\n        imageName: imageName\n    }));\n    const response = JSON.parse(xhr.response)\n\n    if (response.result == true)\n    {\n        next()\n    }\n    else{\n        alert('something is wrong!')\n    }\n    currStroke = []\n    currSketch = []\n}\n\nfunction clearCanvas()\n{\n    canvas.clear();\n    canvas.backgroundColor = '#ffffff';\n    currStroke = []\n    currSketch = []\n}\nfunction next() {\n    input.value = \"\"\n    clearCanvas();\n    imageName = getImageUrl()\n    addImage(imageName)\n    input.focus()\n    \n}\n\n/*\nclear the canvs \n*/\nfunction erase() {\n    clearCanvas();\n    canvas.add(curr_img);\n}"
  },
  {
    "path": "calliar_server/server/static/paper-full.js",
    "content": "/*!\n * Paper.js v0.12.15 - The Swiss Army Knife of Vector Graphics Scripting.\n * http://paperjs.org/\n *\n * Copyright (c) 2011 - 2020, Jürg Lehni & Jonathan Puckey\n * http://juerglehni.com/ & https://puckey.studio/\n *\n * Distributed under the MIT license. See LICENSE file for details.\n *\n * All rights reserved.\n *\n * Date: Wed Mar 17 10:49:48 2021 +0100\n *\n ***\n *\n * Straps.js - Class inheritance library with support for bean-style accessors\n *\n * Copyright (c) 2006 - 2020 Jürg Lehni\n * http://juerglehni.com/\n *\n * Distributed under the MIT license.\n *\n ***\n *\n * Acorn.js\n * https://marijnhaverbeke.nl/acorn/\n *\n * Acorn is a tiny, fast JavaScript parser written in JavaScript,\n * created by Marijn Haverbeke and released under an MIT license.\n *\n */\n\nvar paper = function(self, undefined) {\n\nself = self || require('./node/self.js');\nvar window = self.window,\n\tdocument = self.document;\n\nvar Base = new function() {\n\tvar hidden = /^(statics|enumerable|beans|preserve)$/,\n\t\tarray = [],\n\t\tslice = array.slice,\n\t\tcreate = Object.create,\n\t\tdescribe = Object.getOwnPropertyDescriptor,\n\t\tdefine = Object.defineProperty,\n\n\t\tforEach = array.forEach || function(iter, bind) {\n\t\t\tfor (var i = 0, l = this.length; i < l; i++) {\n\t\t\t\titer.call(bind, this[i], i, this);\n\t\t\t}\n\t\t},\n\n\t\tforIn = function(iter, bind) {\n\t\t\tfor (var i in this) {\n\t\t\t\tif (this.hasOwnProperty(i))\n\t\t\t\t\titer.call(bind, this[i], i, this);\n\t\t\t}\n\t\t},\n\n\t\tset = Object.assign || function(dst) {\n\t\t\tfor (var i = 1, l = arguments.length; i < l; i++) {\n\t\t\t\tvar src = arguments[i];\n\t\t\t\tfor (var key in src) {\n\t\t\t\t\tif (src.hasOwnProperty(key))\n\t\t\t\t\t\tdst[key] = src[key];\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn dst;\n\t\t},\n\n\t\teach = function(obj, iter, bind) {\n\t\t\tif (obj) {\n\t\t\t\tvar desc = describe(obj, 'length');\n\t\t\t\t(desc && typeof desc.value === 'number' ? forEach : forIn)\n\t\t\t\t\t.call(obj, iter, bind = bind || obj);\n\t\t\t}\n\t\t\treturn bind;\n\t\t};\n\n\tfunction inject(dest, src, enumerable, beans, preserve) {\n\t\tvar beansNames = {};\n\n\t\tfunction field(name, val) {\n\t\t\tval = val || (val = describe(src, name))\n\t\t\t\t\t&& (val.get ? val : val.value);\n\t\t\tif (typeof val === 'string' && val[0] === '#')\n\t\t\t\tval = dest[val.substring(1)] || val;\n\t\t\tvar isFunc = typeof val === 'function',\n\t\t\t\tres = val,\n\t\t\t\tprev = preserve || isFunc && !val.base\n\t\t\t\t\t\t? (val && val.get ? name in dest : dest[name])\n\t\t\t\t\t\t: null,\n\t\t\t\tbean;\n\t\t\tif (!preserve || !prev) {\n\t\t\t\tif (isFunc && prev)\n\t\t\t\t\tval.base = prev;\n\t\t\t\tif (isFunc && beans !== false\n\t\t\t\t\t\t&& (bean = name.match(/^([gs]et|is)(([A-Z])(.*))$/)))\n\t\t\t\t\tbeansNames[bean[3].toLowerCase() + bean[4]] = bean[2];\n\t\t\t\tif (!res || isFunc || !res.get || typeof res.get !== 'function'\n\t\t\t\t\t\t|| !Base.isPlainObject(res)) {\n\t\t\t\t\tres = { value: res, writable: true };\n\t\t\t\t}\n\t\t\t\tif ((describe(dest, name)\n\t\t\t\t\t\t|| { configurable: true }).configurable) {\n\t\t\t\t\tres.configurable = true;\n\t\t\t\t\tres.enumerable = enumerable != null ? enumerable : !bean;\n\t\t\t\t}\n\t\t\t\tdefine(dest, name, res);\n\t\t\t}\n\t\t}\n\t\tif (src) {\n\t\t\tfor (var name in src) {\n\t\t\t\tif (src.hasOwnProperty(name) && !hidden.test(name))\n\t\t\t\t\tfield(name);\n\t\t\t}\n\t\t\tfor (var name in beansNames) {\n\t\t\t\tvar part = beansNames[name],\n\t\t\t\t\tset = dest['set' + part],\n\t\t\t\t\tget = dest['get' + part] || set && dest['is' + part];\n\t\t\t\tif (get && (beans === true || get.length === 0))\n\t\t\t\t\tfield(name, { get: get, set: set });\n\t\t\t}\n\t\t}\n\t\treturn dest;\n\t}\n\n\tfunction Base() {\n\t\tfor (var i = 0, l = arguments.length; i < l; i++) {\n\t\t\tvar src = arguments[i];\n\t\t\tif (src)\n\t\t\t\tset(this, src);\n\t\t}\n\t\treturn this;\n\t}\n\n\treturn inject(Base, {\n\t\tinject: function(src) {\n\t\t\tif (src) {\n\t\t\t\tvar statics = src.statics === true ? src : src.statics,\n\t\t\t\t\tbeans = src.beans,\n\t\t\t\t\tpreserve = src.preserve;\n\t\t\t\tif (statics !== src)\n\t\t\t\t\tinject(this.prototype, src, src.enumerable, beans, preserve);\n\t\t\t\tinject(this, statics, null, beans, preserve);\n\t\t\t}\n\t\t\tfor (var i = 1, l = arguments.length; i < l; i++)\n\t\t\t\tthis.inject(arguments[i]);\n\t\t\treturn this;\n\t\t},\n\n\t\textend: function() {\n\t\t\tvar base = this,\n\t\t\t\tctor,\n\t\t\t\tproto;\n\t\t\tfor (var i = 0, obj, l = arguments.length;\n\t\t\t\t\ti < l && !(ctor && proto); i++) {\n\t\t\t\tobj = arguments[i];\n\t\t\t\tctor = ctor || obj.initialize;\n\t\t\t\tproto = proto || obj.prototype;\n\t\t\t}\n\t\t\tctor = ctor || function() {\n\t\t\t\tbase.apply(this, arguments);\n\t\t\t};\n\t\t\tproto = ctor.prototype = proto || create(this.prototype);\n\t\t\tdefine(proto, 'constructor',\n\t\t\t\t\t{ value: ctor, writable: true, configurable: true });\n\t\t\tinject(ctor, this);\n\t\t\tif (arguments.length)\n\t\t\t\tthis.inject.apply(ctor, arguments);\n\t\t\tctor.base = base;\n\t\t\treturn ctor;\n\t\t}\n\t}).inject({\n\t\tenumerable: false,\n\n\t\tinitialize: Base,\n\n\t\tset: Base,\n\n\t\tinject: function() {\n\t\t\tfor (var i = 0, l = arguments.length; i < l; i++) {\n\t\t\t\tvar src = arguments[i];\n\t\t\t\tif (src) {\n\t\t\t\t\tinject(this, src, src.enumerable, src.beans, src.preserve);\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn this;\n\t\t},\n\n\t\textend: function() {\n\t\t\tvar res = create(this);\n\t\t\treturn res.inject.apply(res, arguments);\n\t\t},\n\n\t\teach: function(iter, bind) {\n\t\t\treturn each(this, iter, bind);\n\t\t},\n\n\t\tclone: function() {\n\t\t\treturn new this.constructor(this);\n\t\t},\n\n\t\tstatics: {\n\t\t\tset: set,\n\t\t\teach: each,\n\t\t\tcreate: create,\n\t\t\tdefine: define,\n\t\t\tdescribe: describe,\n\n\t\t\tclone: function(obj) {\n\t\t\t\treturn set(new obj.constructor(), obj);\n\t\t\t},\n\n\t\t\tisPlainObject: function(obj) {\n\t\t\t\tvar ctor = obj != null && obj.constructor;\n\t\t\t\treturn ctor && (ctor === Object || ctor === Base\n\t\t\t\t\t\t|| ctor.name === 'Object');\n\t\t\t},\n\n\t\t\tpick: function(a, b) {\n\t\t\t\treturn a !== undefined ? a : b;\n\t\t\t},\n\n\t\t\tslice: function(list, begin, end) {\n\t\t\t\treturn slice.call(list, begin, end);\n\t\t\t}\n\t\t}\n\t});\n};\n\nif (typeof module !== 'undefined')\n\tmodule.exports = Base;\n\nBase.inject({\n\tenumerable: false,\n\n\ttoString: function() {\n\t\treturn this._id != null\n\t\t\t?  (this._class || 'Object') + (this._name\n\t\t\t\t? \" '\" + this._name + \"'\"\n\t\t\t\t: ' @' + this._id)\n\t\t\t: '{ ' + Base.each(this, function(value, key) {\n\t\t\t\tif (!/^_/.test(key)) {\n\t\t\t\t\tvar type = typeof value;\n\t\t\t\t\tthis.push(key + ': ' + (type === 'number'\n\t\t\t\t\t\t\t? Formatter.instance.number(value)\n\t\t\t\t\t\t\t: type === 'string' ? \"'\" + value + \"'\" : value));\n\t\t\t\t}\n\t\t\t}, []).join(', ') + ' }';\n\t},\n\n\tgetClassName: function() {\n\t\treturn this._class || '';\n\t},\n\n\timportJSON: function(json) {\n\t\treturn Base.importJSON(json, this);\n\t},\n\n\texportJSON: function(options) {\n\t\treturn Base.exportJSON(this, options);\n\t},\n\n\ttoJSON: function() {\n\t\treturn Base.serialize(this);\n\t},\n\n\tset: function(props, exclude) {\n\t\tif (props)\n\t\t\tBase.filter(this, props, exclude, this._prioritize);\n\t\treturn this;\n\t}\n}, {\n\nbeans: false,\nstatics: {\n\texports: {},\n\n\textend: function extend() {\n\t\tvar res = extend.base.apply(this, arguments),\n\t\t\tname = res.prototype._class;\n\t\tif (name && !Base.exports[name])\n\t\t\tBase.exports[name] = res;\n\t\treturn res;\n\t},\n\n\tequals: function(obj1, obj2) {\n\t\tif (obj1 === obj2)\n\t\t\treturn true;\n\t\tif (obj1 && obj1.equals)\n\t\t\treturn obj1.equals(obj2);\n\t\tif (obj2 && obj2.equals)\n\t\t\treturn obj2.equals(obj1);\n\t\tif (obj1 && obj2\n\t\t\t\t&& typeof obj1 === 'object' && typeof obj2 === 'object') {\n\t\t\tif (Array.isArray(obj1) && Array.isArray(obj2)) {\n\t\t\t\tvar length = obj1.length;\n\t\t\t\tif (length !== obj2.length)\n\t\t\t\t\treturn false;\n\t\t\t\twhile (length--) {\n\t\t\t\t\tif (!Base.equals(obj1[length], obj2[length]))\n\t\t\t\t\t\treturn false;\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tvar keys = Object.keys(obj1),\n\t\t\t\t\tlength = keys.length;\n\t\t\t\tif (length !== Object.keys(obj2).length)\n\t\t\t\t\treturn false;\n\t\t\t\twhile (length--) {\n\t\t\t\t\tvar key = keys[length];\n\t\t\t\t\tif (!(obj2.hasOwnProperty(key)\n\t\t\t\t\t\t\t&& Base.equals(obj1[key], obj2[key])))\n\t\t\t\t\t\treturn false;\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn true;\n\t\t}\n\t\treturn false;\n\t},\n\n\tread: function(list, start, options, amount) {\n\t\tif (this === Base) {\n\t\t\tvar value = this.peek(list, start);\n\t\t\tlist.__index++;\n\t\t\treturn value;\n\t\t}\n\t\tvar proto = this.prototype,\n\t\t\treadIndex = proto._readIndex,\n\t\t\tbegin = start || readIndex && list.__index || 0,\n\t\t\tlength = list.length,\n\t\t\tobj = list[begin];\n\t\tamount = amount || length - begin;\n\t\tif (obj instanceof this\n\t\t\t|| options && options.readNull && obj == null && amount <= 1) {\n\t\t\tif (readIndex)\n\t\t\t\tlist.__index = begin + 1;\n\t\t\treturn obj && options && options.clone ? obj.clone() : obj;\n\t\t}\n\t\tobj = Base.create(proto);\n\t\tif (readIndex)\n\t\t\tobj.__read = true;\n\t\tobj = obj.initialize.apply(obj, begin > 0 || begin + amount < length\n\t\t\t\t? Base.slice(list, begin, begin + amount)\n\t\t\t\t: list) || obj;\n\t\tif (readIndex) {\n\t\t\tlist.__index = begin + obj.__read;\n\t\t\tvar filtered = obj.__filtered;\n\t\t\tif (filtered) {\n\t\t\t\tlist.__filtered = filtered;\n\t\t\t\tobj.__filtered = undefined;\n\t\t\t}\n\t\t\tobj.__read = undefined;\n\t\t}\n\t\treturn obj;\n\t},\n\n\tpeek: function(list, start) {\n\t\treturn list[list.__index = start || list.__index || 0];\n\t},\n\n\tremain: function(list) {\n\t\treturn list.length - (list.__index || 0);\n\t},\n\n\treadList: function(list, start, options, amount) {\n\t\tvar res = [],\n\t\t\tentry,\n\t\t\tbegin = start || 0,\n\t\t\tend = amount ? begin + amount : list.length;\n\t\tfor (var i = begin; i < end; i++) {\n\t\t\tres.push(Array.isArray(entry = list[i])\n\t\t\t\t\t? this.read(entry, 0, options)\n\t\t\t\t\t: this.read(list, i, options, 1));\n\t\t}\n\t\treturn res;\n\t},\n\n\treadNamed: function(list, name, start, options, amount) {\n\t\tvar value = this.getNamed(list, name),\n\t\t\thasValue = value !== undefined;\n\t\tif (hasValue) {\n\t\t\tvar filtered = list.__filtered;\n\t\t\tif (!filtered) {\n\t\t\t\tvar source = this.getSource(list);\n\t\t\t\tfiltered = list.__filtered = Base.create(source);\n\t\t\t\tfiltered.__unfiltered = source;\n\t\t\t}\n\t\t\tfiltered[name] = undefined;\n\t\t}\n\t\treturn this.read(hasValue ? [value] : list, start, options, amount);\n\t},\n\n\treadSupported: function(list, dest) {\n\t\tvar source = this.getSource(list),\n\t\t\tthat = this,\n\t\t\tread = false;\n\t\tif (source) {\n\t\t\tObject.keys(source).forEach(function(key) {\n\t\t\t\tif (key in dest) {\n\t\t\t\t\tvar value = that.readNamed(list, key);\n\t\t\t\t\tif (value !== undefined) {\n\t\t\t\t\t\tdest[key] = value;\n\t\t\t\t\t}\n\t\t\t\t\tread = true;\n\t\t\t\t}\n\t\t\t});\n\t\t}\n\t\treturn read;\n\t},\n\n\tgetSource: function(list) {\n\t\tvar source = list.__source;\n\t\tif (source === undefined) {\n\t\t\tvar arg = list.length === 1 && list[0];\n\t\t\tsource = list.__source = arg && Base.isPlainObject(arg)\n\t\t\t\t? arg : null;\n\t\t}\n\t\treturn source;\n\t},\n\n\tgetNamed: function(list, name) {\n\t\tvar source = this.getSource(list);\n\t\tif (source) {\n\t\t\treturn name ? source[name] : list.__filtered || source;\n\t\t}\n\t},\n\n\thasNamed: function(list, name) {\n\t\treturn !!this.getNamed(list, name);\n\t},\n\n\tfilter: function(dest, source, exclude, prioritize) {\n\t\tvar processed;\n\n\t\tfunction handleKey(key) {\n\t\t\tif (!(exclude && key in exclude) &&\n\t\t\t\t!(processed && key in processed)) {\n\t\t\t\tvar value = source[key];\n\t\t\t\tif (value !== undefined)\n\t\t\t\t\tdest[key] = value;\n\t\t\t}\n\t\t}\n\n\t\tif (prioritize) {\n\t\t\tvar keys = {};\n\t\t\tfor (var i = 0, key, l = prioritize.length; i < l; i++) {\n\t\t\t\tif ((key = prioritize[i]) in source) {\n\t\t\t\t\thandleKey(key);\n\t\t\t\t\tkeys[key] = true;\n\t\t\t\t}\n\t\t\t}\n\t\t\tprocessed = keys;\n\t\t}\n\n\t\tObject.keys(source.__unfiltered || source).forEach(handleKey);\n\t\treturn dest;\n\t},\n\n\tisPlainValue: function(obj, asString) {\n\t\treturn Base.isPlainObject(obj) || Array.isArray(obj)\n\t\t\t\t|| asString && typeof obj === 'string';\n\t},\n\n\tserialize: function(obj, options, compact, dictionary) {\n\t\toptions = options || {};\n\n\t\tvar isRoot = !dictionary,\n\t\t\tres;\n\t\tif (isRoot) {\n\t\t\toptions.formatter = new Formatter(options.precision);\n\t\t\tdictionary = {\n\t\t\t\tlength: 0,\n\t\t\t\tdefinitions: {},\n\t\t\t\treferences: {},\n\t\t\t\tadd: function(item, create) {\n\t\t\t\t\tvar id = '#' + item._id,\n\t\t\t\t\t\tref = this.references[id];\n\t\t\t\t\tif (!ref) {\n\t\t\t\t\t\tthis.length++;\n\t\t\t\t\t\tvar res = create.call(item),\n\t\t\t\t\t\t\tname = item._class;\n\t\t\t\t\t\tif (name && res[0] !== name)\n\t\t\t\t\t\t\tres.unshift(name);\n\t\t\t\t\t\tthis.definitions[id] = res;\n\t\t\t\t\t\tref = this.references[id] = [id];\n\t\t\t\t\t}\n\t\t\t\t\treturn ref;\n\t\t\t\t}\n\t\t\t};\n\t\t}\n\t\tif (obj && obj._serialize) {\n\t\t\tres = obj._serialize(options, dictionary);\n\t\t\tvar name = obj._class;\n\t\t\tif (name && !obj._compactSerialize && (isRoot || !compact)\n\t\t\t\t\t&& res[0] !== name) {\n\t\t\t\tres.unshift(name);\n\t\t\t}\n\t\t} else if (Array.isArray(obj)) {\n\t\t\tres = [];\n\t\t\tfor (var i = 0, l = obj.length; i < l; i++)\n\t\t\t\tres[i] = Base.serialize(obj[i], options, compact, dictionary);\n\t\t} else if (Base.isPlainObject(obj)) {\n\t\t\tres = {};\n\t\t\tvar keys = Object.keys(obj);\n\t\t\tfor (var i = 0, l = keys.length; i < l; i++) {\n\t\t\t\tvar key = keys[i];\n\t\t\t\tres[key] = Base.serialize(obj[key], options, compact,\n\t\t\t\t\t\tdictionary);\n\t\t\t}\n\t\t} else if (typeof obj === 'number') {\n\t\t\tres = options.formatter.number(obj, options.precision);\n\t\t} else {\n\t\t\tres = obj;\n\t\t}\n\t\treturn isRoot && dictionary.length > 0\n\t\t\t\t? [['dictionary', dictionary.definitions], res]\n\t\t\t\t: res;\n\t},\n\n\tdeserialize: function(json, create, _data, _setDictionary, _isRoot) {\n\t\tvar res = json,\n\t\t\tisFirst = !_data,\n\t\t\thasDictionary = isFirst && json && json.length\n\t\t\t\t&& json[0][0] === 'dictionary';\n\t\t_data = _data || {};\n\t\tif (Array.isArray(json)) {\n\t\t\tvar type = json[0],\n\t\t\t\tisDictionary = type === 'dictionary';\n\t\t\tif (json.length == 1 && /^#/.test(type)) {\n\t\t\t\treturn _data.dictionary[type];\n\t\t\t}\n\t\t\ttype = Base.exports[type];\n\t\t\tres = [];\n\t\t\tfor (var i = type ? 1 : 0, l = json.length; i < l; i++) {\n\t\t\t\tres.push(Base.deserialize(json[i], create, _data,\n\t\t\t\t\t\tisDictionary, hasDictionary));\n\t\t\t}\n\t\t\tif (type) {\n\t\t\t\tvar args = res;\n\t\t\t\tif (create) {\n\t\t\t\t\tres = create(type, args, isFirst || _isRoot);\n\t\t\t\t} else {\n\t\t\t\t\tres = new type(args);\n\t\t\t\t}\n\t\t\t}\n\t\t} else if (Base.isPlainObject(json)) {\n\t\t\tres = {};\n\t\t\tif (_setDictionary)\n\t\t\t\t_data.dictionary = res;\n\t\t\tfor (var key in json)\n\t\t\t\tres[key] = Base.deserialize(json[key], create, _data);\n\t\t}\n\t\treturn hasDictionary ? res[1] : res;\n\t},\n\n\texportJSON: function(obj, options) {\n\t\tvar json = Base.serialize(obj, options);\n\t\treturn options && options.asString == false\n\t\t\t\t? json\n\t\t\t\t: JSON.stringify(json);\n\t},\n\n\timportJSON: function(json, target) {\n\t\treturn Base.deserialize(\n\t\t\t\ttypeof json === 'string' ? JSON.parse(json) : json,\n\t\t\t\tfunction(ctor, args, isRoot) {\n\t\t\t\t\tvar useTarget = isRoot && target\n\t\t\t\t\t\t\t&& target.constructor === ctor,\n\t\t\t\t\t\tobj = useTarget ? target\n\t\t\t\t\t\t\t: Base.create(ctor.prototype);\n\t\t\t\t\tif (args.length === 1 && obj instanceof Item\n\t\t\t\t\t\t\t&& (useTarget || !(obj instanceof Layer))) {\n\t\t\t\t\t\tvar arg = args[0];\n\t\t\t\t\t\tif (Base.isPlainObject(arg)) {\n\t\t\t\t\t\t\targ.insert = false;\n\t\t\t\t\t\t\tif (useTarget) {\n\t\t\t\t\t\t\t\targs = args.concat([{ insert: true }]);\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\t(useTarget ? obj.set : ctor).apply(obj, args);\n\t\t\t\t\tif (useTarget)\n\t\t\t\t\t\ttarget = null;\n\t\t\t\t\treturn obj;\n\t\t\t\t});\n\t},\n\n\tpush: function(list, items) {\n\t\tvar itemsLength = items.length;\n\t\tif (itemsLength < 4096) {\n\t\t\tlist.push.apply(list, items);\n\t\t} else {\n\t\t\tvar startLength = list.length;\n\t\t\tlist.length += itemsLength;\n\t\t\tfor (var i = 0; i < itemsLength; i++) {\n\t\t\t\tlist[startLength + i] = items[i];\n\t\t\t}\n\t\t}\n\t\treturn list;\n\t},\n\n\tsplice: function(list, items, index, remove) {\n\t\tvar amount = items && items.length,\n\t\t\tappend = index === undefined;\n\t\tindex = append ? list.length : index;\n\t\tif (index > list.length)\n\t\t\tindex = list.length;\n\t\tfor (var i = 0; i < amount; i++)\n\t\t\titems[i]._index = index + i;\n\t\tif (append) {\n\t\t\tBase.push(list, items);\n\t\t\treturn [];\n\t\t} else {\n\t\t\tvar args = [index, remove];\n\t\t\tif (items)\n\t\t\t\tBase.push(args, items);\n\t\t\tvar removed = list.splice.apply(list, args);\n\t\t\tfor (var i = 0, l = removed.length; i < l; i++)\n\t\t\t\tremoved[i]._index = undefined;\n\t\t\tfor (var i = index + amount, l = list.length; i < l; i++)\n\t\t\t\tlist[i]._index = i;\n\t\t\treturn removed;\n\t\t}\n\t},\n\n\tcapitalize: function(str) {\n\t\treturn str.replace(/\\b[a-z]/g, function(match) {\n\t\t\treturn match.toUpperCase();\n\t\t});\n\t},\n\n\tcamelize: function(str) {\n\t\treturn str.replace(/-(.)/g, function(match, chr) {\n\t\t\treturn chr.toUpperCase();\n\t\t});\n\t},\n\n\thyphenate: function(str) {\n\t\treturn str.replace(/([a-z])([A-Z])/g, '$1-$2').toLowerCase();\n\t}\n}});\n\nvar Emitter = {\n\ton: function(type, func) {\n\t\tif (typeof type !== 'string') {\n\t\t\tBase.each(type, function(value, key) {\n\t\t\t\tthis.on(key, value);\n\t\t\t}, this);\n\t\t} else {\n\t\t\tvar types = this._eventTypes,\n\t\t\t\tentry = types && types[type],\n\t\t\t\thandlers = this._callbacks = this._callbacks || {};\n\t\t\thandlers = handlers[type] = handlers[type] || [];\n\t\t\tif (handlers.indexOf(func) === -1) {\n\t\t\t\thandlers.push(func);\n\t\t\t\tif (entry && entry.install && handlers.length === 1)\n\t\t\t\t\tentry.install.call(this, type);\n\t\t\t}\n\t\t}\n\t\treturn this;\n\t},\n\n\toff: function(type, func) {\n\t\tif (typeof type !== 'string') {\n\t\t\tBase.each(type, function(value, key) {\n\t\t\t\tthis.off(key, value);\n\t\t\t}, this);\n\t\t\treturn;\n\t\t}\n\t\tvar types = this._eventTypes,\n\t\t\tentry = types && types[type],\n\t\t\thandlers = this._callbacks && this._callbacks[type],\n\t\t\tindex;\n\t\tif (handlers) {\n\t\t\tif (!func || (index = handlers.indexOf(func)) !== -1\n\t\t\t\t\t&& handlers.length === 1) {\n\t\t\t\tif (entry && entry.uninstall)\n\t\t\t\t\tentry.uninstall.call(this, type);\n\t\t\t\tdelete this._callbacks[type];\n\t\t\t} else if (index !== -1) {\n\t\t\t\thandlers.splice(index, 1);\n\t\t\t}\n\t\t}\n\t\treturn this;\n\t},\n\n\tonce: function(type, func) {\n\t\treturn this.on(type, function handler() {\n\t\t\tfunc.apply(this, arguments);\n\t\t\tthis.off(type, handler);\n\t\t});\n\t},\n\n\temit: function(type, event) {\n\t\tvar handlers = this._callbacks && this._callbacks[type];\n\t\tif (!handlers)\n\t\t\treturn false;\n\t\tvar args = Base.slice(arguments, 1),\n\t\t\tsetTarget = event && event.target && !event.currentTarget;\n\t\thandlers = handlers.slice();\n\t\tif (setTarget)\n\t\t\tevent.currentTarget = this;\n\t\tfor (var i = 0, l = handlers.length; i < l; i++) {\n\t\t\tif (handlers[i].apply(this, args) == false) {\n\t\t\t\tif (event && event.stop)\n\t\t\t\t\tevent.stop();\n\t\t\t\tbreak;\n\t\t   }\n\t\t}\n\t\tif (setTarget)\n\t\t\tdelete event.currentTarget;\n\t\treturn true;\n\t},\n\n\tresponds: function(type) {\n\t\treturn !!(this._callbacks && this._callbacks[type]);\n\t},\n\n\tattach: '#on',\n\tdetach: '#off',\n\tfire: '#emit',\n\n\t_installEvents: function(install) {\n\t\tvar types = this._eventTypes,\n\t\t\thandlers = this._callbacks,\n\t\t\tkey = install ? 'install' : 'uninstall';\n\t\tif (types) {\n\t\t\tfor (var type in handlers) {\n\t\t\t\tif (handlers[type].length > 0) {\n\t\t\t\t\tvar entry = types[type],\n\t\t\t\t\t\tfunc = entry && entry[key];\n\t\t\t\t\tif (func)\n\t\t\t\t\t\tfunc.call(this, type);\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t},\n\n\tstatics: {\n\t\tinject: function inject(src) {\n\t\t\tvar events = src._events;\n\t\t\tif (events) {\n\t\t\t\tvar types = {};\n\t\t\t\tBase.each(events, function(entry, key) {\n\t\t\t\t\tvar isString = typeof entry === 'string',\n\t\t\t\t\t\tname = isString ? entry : key,\n\t\t\t\t\t\tpart = Base.capitalize(name),\n\t\t\t\t\t\ttype = name.substring(2).toLowerCase();\n\t\t\t\t\ttypes[type] = isString ? {} : entry;\n\t\t\t\t\tname = '_' + name;\n\t\t\t\t\tsrc['get' + part] = function() {\n\t\t\t\t\t\treturn this[name];\n\t\t\t\t\t};\n\t\t\t\t\tsrc['set' + part] = function(func) {\n\t\t\t\t\t\tvar prev = this[name];\n\t\t\t\t\t\tif (prev)\n\t\t\t\t\t\t\tthis.off(type, prev);\n\t\t\t\t\t\tif (func)\n\t\t\t\t\t\t\tthis.on(type, func);\n\t\t\t\t\t\tthis[name] = func;\n\t\t\t\t\t};\n\t\t\t\t});\n\t\t\t\tsrc._eventTypes = types;\n\t\t\t}\n\t\t\treturn inject.base.apply(this, arguments);\n\t\t}\n\t}\n};\n\nvar PaperScope = Base.extend({\n\t_class: 'PaperScope',\n\n\tinitialize: function PaperScope() {\n\t\tpaper = this;\n\t\tthis.settings = new Base({\n\t\t\tapplyMatrix: true,\n\t\t\tinsertItems: true,\n\t\t\thandleSize: 4,\n\t\t\thitTolerance: 0\n\t\t});\n\t\tthis.project = null;\n\t\tthis.projects = [];\n\t\tthis.tools = [];\n\t\tthis._id = PaperScope._id++;\n\t\tPaperScope._scopes[this._id] = this;\n\t\tvar proto = PaperScope.prototype;\n\t\tif (!this.support) {\n\t\t\tvar ctx = CanvasProvider.getContext(1, 1) || {};\n\t\t\tproto.support = {\n\t\t\t\tnativeDash: 'setLineDash' in ctx || 'mozDash' in ctx,\n\t\t\t\tnativeBlendModes: BlendMode.nativeModes\n\t\t\t};\n\t\t\tCanvasProvider.release(ctx);\n\t\t}\n\t\tif (!this.agent) {\n\t\t\tvar user = self.navigator.userAgent.toLowerCase(),\n\t\t\t\tos = (/(darwin|win|mac|linux|freebsd|sunos)/.exec(user)||[])[0],\n\t\t\t\tplatform = os === 'darwin' ? 'mac' : os,\n\t\t\t\tagent = proto.agent = proto.browser = { platform: platform };\n\t\t\tif (platform)\n\t\t\t\tagent[platform] = true;\n\t\t\tuser.replace(\n\t\t\t\t/(opera|chrome|safari|webkit|firefox|msie|trident|atom|node|jsdom)\\/?\\s*([.\\d]+)(?:.*version\\/([.\\d]+))?(?:.*rv\\:v?([.\\d]+))?/g,\n\t\t\t\tfunction(match, n, v1, v2, rv) {\n\t\t\t\t\tif (!agent.chrome) {\n\t\t\t\t\t\tvar v = n === 'opera' ? v2 :\n\t\t\t\t\t\t\t\t/^(node|trident)$/.test(n) ? rv : v1;\n\t\t\t\t\t\tagent.version = v;\n\t\t\t\t\t\tagent.versionNumber = parseFloat(v);\n\t\t\t\t\t\tn = { trident: 'msie', jsdom: 'node' }[n] || n;\n\t\t\t\t\t\tagent.name = n;\n\t\t\t\t\t\tagent[n] = true;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t);\n\t\t\tif (agent.chrome)\n\t\t\t\tdelete agent.webkit;\n\t\t\tif (agent.atom)\n\t\t\t\tdelete agent.chrome;\n\t\t}\n\t},\n\n\tversion: \"0.12.15\",\n\n\tgetView: function() {\n\t\tvar project = this.project;\n\t\treturn project && project._view;\n\t},\n\n\tgetPaper: function() {\n\t\treturn this;\n\t},\n\n\texecute: function(code, options) {\n\t\t\tvar exports = paper.PaperScript.execute(code, this, options);\n\t\t\tView.updateFocus();\n\t\t\treturn exports;\n\t},\n\n\tinstall: function(scope) {\n\t\tvar that = this;\n\t\tBase.each(['project', 'view', 'tool'], function(key) {\n\t\t\tBase.define(scope, key, {\n\t\t\t\tconfigurable: true,\n\t\t\t\tget: function() {\n\t\t\t\t\treturn that[key];\n\t\t\t\t}\n\t\t\t});\n\t\t});\n\t\tfor (var key in this)\n\t\t\tif (!/^_/.test(key) && this[key])\n\t\t\t\tscope[key] = this[key];\n\t},\n\n\tsetup: function(element) {\n\t\tpaper = this;\n\t\tthis.project = new Project(element);\n\t\treturn this;\n\t},\n\n\tcreateCanvas: function(width, height) {\n\t\treturn CanvasProvider.getCanvas(width, height);\n\t},\n\n\tactivate: function() {\n\t\tpaper = this;\n\t},\n\n\tclear: function() {\n\t\tvar projects = this.projects,\n\t\t\ttools = this.tools;\n\t\tfor (var i = projects.length - 1; i >= 0; i--)\n\t\t\tprojects[i].remove();\n\t\tfor (var i = tools.length - 1; i >= 0; i--)\n\t\t\ttools[i].remove();\n\t},\n\n\tremove: function() {\n\t\tthis.clear();\n\t\tdelete PaperScope._scopes[this._id];\n\t},\n\n\tstatics: new function() {\n\t\tfunction handleAttribute(name) {\n\t\t\tname += 'Attribute';\n\t\t\treturn function(el, attr) {\n\t\t\t\treturn el[name](attr) || el[name]('data-paper-' + attr);\n\t\t\t};\n\t\t}\n\n\t\treturn {\n\t\t\t_scopes: {},\n\t\t\t_id: 0,\n\n\t\t\tget: function(id) {\n\t\t\t\treturn this._scopes[id] || null;\n\t\t\t},\n\n\t\t\tgetAttribute: handleAttribute('get'),\n\t\t\thasAttribute: handleAttribute('has')\n\t\t};\n\t}\n});\n\nvar PaperScopeItem = Base.extend(Emitter, {\n\n\tinitialize: function(activate) {\n\t\tthis._scope = paper;\n\t\tthis._index = this._scope[this._list].push(this) - 1;\n\t\tif (activate || !this._scope[this._reference])\n\t\t\tthis.activate();\n\t},\n\n\tactivate: function() {\n\t\tif (!this._scope)\n\t\t\treturn false;\n\t\tvar prev = this._scope[this._reference];\n\t\tif (prev && prev !== this)\n\t\t\tprev.emit('deactivate');\n\t\tthis._scope[this._reference] = this;\n\t\tthis.emit('activate', prev);\n\t\treturn true;\n\t},\n\n\tisActive: function() {\n\t\treturn this._scope[this._reference] === this;\n\t},\n\n\tremove: function() {\n\t\tif (this._index == null)\n\t\t\treturn false;\n\t\tBase.splice(this._scope[this._list], null, this._index, 1);\n\t\tif (this._scope[this._reference] == this)\n\t\t\tthis._scope[this._reference] = null;\n\t\tthis._scope = null;\n\t\treturn true;\n\t},\n\n\tgetView: function() {\n\t\treturn this._scope.getView();\n\t}\n});\n\nvar CollisionDetection = {\n\tfindItemBoundsCollisions: function(items1, items2, tolerance) {\n\t\tfunction getBounds(items) {\n\t\t\tvar bounds = new Array(items.length);\n\t\t\tfor (var i = 0; i < items.length; i++) {\n\t\t\t\tvar rect = items[i].getBounds();\n\t\t\t\tbounds[i] = [rect.left, rect.top, rect.right, rect.bottom];\n\t\t\t}\n\t\t\treturn bounds;\n\t\t}\n\n\t\tvar bounds1 = getBounds(items1),\n\t\t\tbounds2 = !items2 || items2 === items1\n\t\t\t\t? bounds1\n\t\t\t\t: getBounds(items2);\n\t\treturn this.findBoundsCollisions(bounds1, bounds2, tolerance || 0);\n\t},\n\n\tfindCurveBoundsCollisions: function(curves1, curves2, tolerance, bothAxis) {\n\t\tfunction getBounds(curves) {\n\t\t\tvar min = Math.min,\n\t\t\t\tmax = Math.max,\n\t\t\t\tbounds = new Array(curves.length);\n\t\t\tfor (var i = 0; i < curves.length; i++) {\n\t\t\t\tvar v = curves[i];\n\t\t\t\tbounds[i] = [\n\t\t\t\t\tmin(v[0], v[2], v[4], v[6]),\n\t\t\t\t\tmin(v[1], v[3], v[5], v[7]),\n\t\t\t\t\tmax(v[0], v[2], v[4], v[6]),\n\t\t\t\t\tmax(v[1], v[3], v[5], v[7])\n\t\t\t\t];\n\t\t\t}\n\t\t\treturn bounds;\n\t\t}\n\n\t\tvar bounds1 = getBounds(curves1),\n\t\t\tbounds2 = !curves2 || curves2 === curves1\n\t\t\t\t? bounds1\n\t\t\t\t: getBounds(curves2);\n\t\tif (bothAxis) {\n\t\t\tvar hor = this.findBoundsCollisions(\n\t\t\t\t\tbounds1, bounds2, tolerance || 0, false, true),\n\t\t\t\tver = this.findBoundsCollisions(\n\t\t\t\t\tbounds1, bounds2, tolerance || 0, true, true),\n\t\t\t\tlist = [];\n\t\t\tfor (var i = 0, l = hor.length; i < l; i++) {\n\t\t\t\tlist[i] = { hor: hor[i], ver: ver[i] };\n\t\t\t}\n\t\t\treturn list;\n\t\t}\n\t\treturn this.findBoundsCollisions(bounds1, bounds2, tolerance || 0);\n\t},\n\n\tfindBoundsCollisions: function(boundsA, boundsB, tolerance,\n\t\tsweepVertical, onlySweepAxisCollisions) {\n\t\tvar self = !boundsB || boundsA === boundsB,\n\t\t\tallBounds = self ? boundsA : boundsA.concat(boundsB),\n\t\t\tlengthA = boundsA.length,\n\t\t\tlengthAll = allBounds.length;\n\n\t\tfunction binarySearch(indices, coord, value) {\n\t\t\tvar lo = 0,\n\t\t\t\thi = indices.length;\n\t\t\twhile (lo < hi) {\n\t\t\t\tvar mid = (hi + lo) >>> 1;\n\t\t\t\tif (allBounds[indices[mid]][coord] < value) {\n\t\t\t\t\tlo = mid + 1;\n\t\t\t\t} else {\n\t\t\t\t\thi = mid;\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn lo - 1;\n\t\t}\n\n\t\tvar pri0 = sweepVertical ? 1 : 0,\n\t\t\tpri1 = pri0 + 2,\n\t\t\tsec0 = sweepVertical ? 0 : 1,\n\t\t\tsec1 = sec0 + 2;\n\t\tvar allIndicesByPri0 = new Array(lengthAll);\n\t\tfor (var i = 0; i < lengthAll; i++) {\n\t\t\tallIndicesByPri0[i] = i;\n\t\t}\n\t\tallIndicesByPri0.sort(function(i1, i2) {\n\t\t\treturn allBounds[i1][pri0] - allBounds[i2][pri0];\n\t\t});\n\t\tvar activeIndicesByPri1 = [],\n\t\t\tallCollisions = new Array(lengthA);\n\t\tfor (var i = 0; i < lengthAll; i++) {\n\t\t\tvar curIndex = allIndicesByPri0[i],\n\t\t\t\tcurBounds = allBounds[curIndex],\n\t\t\t\torigIndex = self ? curIndex : curIndex - lengthA,\n\t\t\t\tisCurrentA = curIndex < lengthA,\n\t\t\t\tisCurrentB = self || !isCurrentA,\n\t\t\t\tcurCollisions = isCurrentA ? [] : null;\n\t\t\tif (activeIndicesByPri1.length) {\n\t\t\t\tvar pruneCount = binarySearch(activeIndicesByPri1, pri1,\n\t\t\t\t\t\tcurBounds[pri0] - tolerance) + 1;\n\t\t\t\tactiveIndicesByPri1.splice(0, pruneCount);\n\t\t\t\tif (self && onlySweepAxisCollisions) {\n\t\t\t\t\tcurCollisions = curCollisions.concat(activeIndicesByPri1);\n\t\t\t\t\tfor (var j = 0; j < activeIndicesByPri1.length; j++) {\n\t\t\t\t\t\tvar activeIndex = activeIndicesByPri1[j];\n\t\t\t\t\t\tallCollisions[activeIndex].push(origIndex);\n\t\t\t\t\t}\n\t\t\t\t} else {\n\t\t\t\t\tvar curSec1 = curBounds[sec1],\n\t\t\t\t\t\tcurSec0 = curBounds[sec0];\n\t\t\t\t\tfor (var j = 0; j < activeIndicesByPri1.length; j++) {\n\t\t\t\t\t\tvar activeIndex = activeIndicesByPri1[j],\n\t\t\t\t\t\t\tactiveBounds = allBounds[activeIndex],\n\t\t\t\t\t\t\tisActiveA = activeIndex < lengthA,\n\t\t\t\t\t\t\tisActiveB = self || activeIndex >= lengthA;\n\n\t\t\t\t\t\tif (\n\t\t\t\t\t\t\tonlySweepAxisCollisions ||\n\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\tisCurrentA && isActiveB ||\n\t\t\t\t\t\t\t\tisCurrentB && isActiveA\n\t\t\t\t\t\t\t) && (\n\t\t\t\t\t\t\t\tcurSec1 >= activeBounds[sec0] - tolerance &&\n\t\t\t\t\t\t\t\tcurSec0 <= activeBounds[sec1] + tolerance\n\t\t\t\t\t\t\t)\n\t\t\t\t\t\t) {\n\t\t\t\t\t\t\tif (isCurrentA && isActiveB) {\n\t\t\t\t\t\t\t\tcurCollisions.push(\n\t\t\t\t\t\t\t\t\tself ? activeIndex : activeIndex - lengthA);\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\tif (isCurrentB && isActiveA) {\n\t\t\t\t\t\t\t\tallCollisions[activeIndex].push(origIndex);\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\t\t\t}\n\t\t\tif (isCurrentA) {\n\t\t\t\tif (boundsA === boundsB) {\n\t\t\t\t\tcurCollisions.push(curIndex);\n\t\t\t\t}\n\t\t\t\tallCollisions[curIndex] = curCollisions;\n\t\t\t}\n\t\t\tif (activeIndicesByPri1.length) {\n\t\t\t\tvar curPri1 = curBounds[pri1],\n\t\t\t\t\tindex = binarySearch(activeIndicesByPri1, pri1, curPri1);\n\t\t\t\tactiveIndicesByPri1.splice(index + 1, 0, curIndex);\n\t\t\t} else {\n\t\t\t\tactiveIndicesByPri1.push(curIndex);\n\t\t\t}\n\t\t}\n\t\tfor (var i = 0; i < allCollisions.length; i++) {\n\t\t\tvar collisions = allCollisions[i];\n\t\t\tif (collisions) {\n\t\t\t\tcollisions.sort(function(i1, i2) { return i1 - i2; });\n\t\t\t}\n\t\t}\n\t\treturn allCollisions;\n\t}\n};\n\nvar Formatter = Base.extend({\n\tinitialize: function(precision) {\n\t\tthis.precision = Base.pick(precision, 5);\n\t\tthis.multiplier = Math.pow(10, this.precision);\n\t},\n\n\tnumber: function(val) {\n\t\treturn this.precision < 16\n\t\t\t\t? Math.round(val * this.multiplier) / this.multiplier : val;\n\t},\n\n\tpair: function(val1, val2, separator) {\n\t\treturn this.number(val1) + (separator || ',') + this.number(val2);\n\t},\n\n\tpoint: function(val, separator) {\n\t\treturn this.number(val.x) + (separator || ',') + this.number(val.y);\n\t},\n\n\tsize: function(val, separator) {\n\t\treturn this.number(val.width) + (separator || ',')\n\t\t\t\t+ this.number(val.height);\n\t},\n\n\trectangle: function(val, separator) {\n\t\treturn this.point(val, separator) + (separator || ',')\n\t\t\t\t+ this.size(val, separator);\n\t}\n});\n\nFormatter.instance = new Formatter();\n\nvar Numerical = new function() {\n\n\tvar abscissas = [\n\t\t[  0.5773502691896257645091488],\n\t\t[0,0.7745966692414833770358531],\n\t\t[  0.3399810435848562648026658,0.8611363115940525752239465],\n\t\t[0,0.5384693101056830910363144,0.9061798459386639927976269],\n\t\t[  0.2386191860831969086305017,0.6612093864662645136613996,0.9324695142031520278123016],\n\t\t[0,0.4058451513773971669066064,0.7415311855993944398638648,0.9491079123427585245261897],\n\t\t[  0.1834346424956498049394761,0.5255324099163289858177390,0.7966664774136267395915539,0.9602898564975362316835609],\n\t\t[0,0.3242534234038089290385380,0.6133714327005903973087020,0.8360311073266357942994298,0.9681602395076260898355762],\n\t\t[  0.1488743389816312108848260,0.4333953941292471907992659,0.6794095682990244062343274,0.8650633666889845107320967,0.9739065285171717200779640],\n\t\t[0,0.2695431559523449723315320,0.5190961292068118159257257,0.7301520055740493240934163,0.8870625997680952990751578,0.9782286581460569928039380],\n\t\t[  0.1252334085114689154724414,0.3678314989981801937526915,0.5873179542866174472967024,0.7699026741943046870368938,0.9041172563704748566784659,0.9815606342467192506905491],\n\t\t[0,0.2304583159551347940655281,0.4484927510364468528779129,0.6423493394403402206439846,0.8015780907333099127942065,0.9175983992229779652065478,0.9841830547185881494728294],\n\t\t[  0.1080549487073436620662447,0.3191123689278897604356718,0.5152486363581540919652907,0.6872929048116854701480198,0.8272013150697649931897947,0.9284348836635735173363911,0.9862838086968123388415973],\n\t\t[0,0.2011940939974345223006283,0.3941513470775633698972074,0.5709721726085388475372267,0.7244177313601700474161861,0.8482065834104272162006483,0.9372733924007059043077589,0.9879925180204854284895657],\n\t\t[  0.0950125098376374401853193,0.2816035507792589132304605,0.4580167776572273863424194,0.6178762444026437484466718,0.7554044083550030338951012,0.8656312023878317438804679,0.9445750230732325760779884,0.9894009349916499325961542]\n\t];\n\n\tvar weights = [\n\t\t[1],\n\t\t[0.8888888888888888888888889,0.5555555555555555555555556],\n\t\t[0.6521451548625461426269361,0.3478548451374538573730639],\n\t\t[0.5688888888888888888888889,0.4786286704993664680412915,0.2369268850561890875142640],\n\t\t[0.4679139345726910473898703,0.3607615730481386075698335,0.1713244923791703450402961],\n\t\t[0.4179591836734693877551020,0.3818300505051189449503698,0.2797053914892766679014678,0.1294849661688696932706114],\n\t\t[0.3626837833783619829651504,0.3137066458778872873379622,0.2223810344533744705443560,0.1012285362903762591525314],\n\t\t[0.3302393550012597631645251,0.3123470770400028400686304,0.2606106964029354623187429,0.1806481606948574040584720,0.0812743883615744119718922],\n\t\t[0.2955242247147528701738930,0.2692667193099963550912269,0.2190863625159820439955349,0.1494513491505805931457763,0.0666713443086881375935688],\n\t\t[0.2729250867779006307144835,0.2628045445102466621806889,0.2331937645919904799185237,0.1862902109277342514260976,0.1255803694649046246346943,0.0556685671161736664827537],\n\t\t[0.2491470458134027850005624,0.2334925365383548087608499,0.2031674267230659217490645,0.1600783285433462263346525,0.1069393259953184309602547,0.0471753363865118271946160],\n\t\t[0.2325515532308739101945895,0.2262831802628972384120902,0.2078160475368885023125232,0.1781459807619457382800467,0.1388735102197872384636018,0.0921214998377284479144218,0.0404840047653158795200216],\n\t\t[0.2152638534631577901958764,0.2051984637212956039659241,0.1855383974779378137417166,0.1572031671581935345696019,0.1215185706879031846894148,0.0801580871597602098056333,0.0351194603317518630318329],\n\t\t[0.2025782419255612728806202,0.1984314853271115764561183,0.1861610000155622110268006,0.1662692058169939335532009,0.1395706779261543144478048,0.1071592204671719350118695,0.0703660474881081247092674,0.0307532419961172683546284],\n\t\t[0.1894506104550684962853967,0.1826034150449235888667637,0.1691565193950025381893121,0.1495959888165767320815017,0.1246289712555338720524763,0.0951585116824927848099251,0.0622535239386478928628438,0.0271524594117540948517806]\n\t];\n\n\tvar abs = Math.abs,\n\t\tsqrt = Math.sqrt,\n\t\tpow = Math.pow,\n\t\tlog2 = Math.log2 || function(x) {\n\t\t\treturn Math.log(x) * Math.LOG2E;\n\t\t},\n\t\tEPSILON = 1e-12,\n\t\tMACHINE_EPSILON = 1.12e-16;\n\n\tfunction clamp(value, min, max) {\n\t\treturn value < min ? min : value > max ? max : value;\n\t}\n\n\tfunction getDiscriminant(a, b, c) {\n\t\tfunction split(v) {\n\t\t\tvar x = v * 134217729,\n\t\t\t\ty = v - x,\n\t\t\t\thi = y + x,\n\t\t\t\tlo = v - hi;\n\t\t\treturn [hi, lo];\n\t\t}\n\n\t\tvar D = b * b - a * c,\n\t\t\tE = b * b + a * c;\n\t\tif (abs(D) * 3 < E) {\n\t\t\tvar ad = split(a),\n\t\t\t\tbd = split(b),\n\t\t\t\tcd = split(c),\n\t\t\t\tp = b * b,\n\t\t\t\tdp = (bd[0] * bd[0] - p + 2 * bd[0] * bd[1]) + bd[1] * bd[1],\n\t\t\t\tq = a * c,\n\t\t\t\tdq = (ad[0] * cd[0] - q + ad[0] * cd[1] + ad[1] * cd[0])\n\t\t\t\t\t\t+ ad[1] * cd[1];\n\t\t\tD = (p - q) + (dp - dq);\n\t\t}\n\t\treturn D;\n\t}\n\n\tfunction getNormalizationFactor() {\n\t\tvar norm = Math.max.apply(Math, arguments);\n\t\treturn norm && (norm < 1e-8 || norm > 1e8)\n\t\t\t\t? pow(2, -Math.round(log2(norm)))\n\t\t\t\t: 0;\n\t}\n\n\treturn {\n\t\tEPSILON: EPSILON,\n\t\tMACHINE_EPSILON: MACHINE_EPSILON,\n\t\tCURVETIME_EPSILON: 1e-8,\n\t\tGEOMETRIC_EPSILON: 1e-7,\n\t\tTRIGONOMETRIC_EPSILON: 1e-8,\n\t\tKAPPA: 4 * (sqrt(2) - 1) / 3,\n\n\t\tisZero: function(val) {\n\t\t\treturn val >= -EPSILON && val <= EPSILON;\n\t\t},\n\n\t\tisMachineZero: function(val) {\n\t\t\treturn val >= -MACHINE_EPSILON && val <= MACHINE_EPSILON;\n\t\t},\n\n\t\tclamp: clamp,\n\n\t\tintegrate: function(f, a, b, n) {\n\t\t\tvar x = abscissas[n - 2],\n\t\t\t\tw = weights[n - 2],\n\t\t\t\tA = (b - a) * 0.5,\n\t\t\t\tB = A + a,\n\t\t\t\ti = 0,\n\t\t\t\tm = (n + 1) >> 1,\n\t\t\t\tsum = n & 1 ? w[i++] * f(B) : 0;\n\t\t\twhile (i < m) {\n\t\t\t\tvar Ax = A * x[i];\n\t\t\t\tsum += w[i++] * (f(B + Ax) + f(B - Ax));\n\t\t\t}\n\t\t\treturn A * sum;\n\t\t},\n\n\t\tfindRoot: function(f, df, x, a, b, n, tolerance) {\n\t\t\tfor (var i = 0; i < n; i++) {\n\t\t\t\tvar fx = f(x),\n\t\t\t\t\tdx = fx / df(x),\n\t\t\t\t\tnx = x - dx;\n\t\t\t\tif (abs(dx) < tolerance) {\n\t\t\t\t\tx = nx;\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t\tif (fx > 0) {\n\t\t\t\t\tb = x;\n\t\t\t\t\tx = nx <= a ? (a + b) * 0.5 : nx;\n\t\t\t\t} else {\n\t\t\t\t\ta = x;\n\t\t\t\t\tx = nx >= b ? (a + b) * 0.5 : nx;\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn clamp(x, a, b);\n\t\t},\n\n\t\tsolveQuadratic: function(a, b, c, roots, min, max) {\n\t\t\tvar x1, x2 = Infinity;\n\t\t\tif (abs(a) < EPSILON) {\n\t\t\t\tif (abs(b) < EPSILON)\n\t\t\t\t\treturn abs(c) < EPSILON ? -1 : 0;\n\t\t\t\tx1 = -c / b;\n\t\t\t} else {\n\t\t\t\tb *= -0.5;\n\t\t\t\tvar D = getDiscriminant(a, b, c);\n\t\t\t\tif (D && abs(D) < MACHINE_EPSILON) {\n\t\t\t\t\tvar f = getNormalizationFactor(abs(a), abs(b), abs(c));\n\t\t\t\t\tif (f) {\n\t\t\t\t\t\ta *= f;\n\t\t\t\t\t\tb *= f;\n\t\t\t\t\t\tc *= f;\n\t\t\t\t\t\tD = getDiscriminant(a, b, c);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tif (D >= -MACHINE_EPSILON) {\n\t\t\t\t\tvar Q = D < 0 ? 0 : sqrt(D),\n\t\t\t\t\t\tR = b + (b < 0 ? -Q : Q);\n\t\t\t\t\tif (R === 0) {\n\t\t\t\t\t\tx1 = c / a;\n\t\t\t\t\t\tx2 = -x1;\n\t\t\t\t\t} else {\n\t\t\t\t\t\tx1 = R / a;\n\t\t\t\t\t\tx2 = c / R;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t\tvar count = 0,\n\t\t\t\tboundless = min == null,\n\t\t\t\tminB = min - EPSILON,\n\t\t\t\tmaxB = max + EPSILON;\n\t\t\tif (isFinite(x1) && (boundless || x1 > minB && x1 < maxB))\n\t\t\t\troots[count++] = boundless ? x1 : clamp(x1, min, max);\n\t\t\tif (x2 !== x1\n\t\t\t\t\t&& isFinite(x2) && (boundless || x2 > minB && x2 < maxB))\n\t\t\t\troots[count++] = boundless ? x2 : clamp(x2, min, max);\n\t\t\treturn count;\n\t\t},\n\n\t\tsolveCubic: function(a, b, c, d, roots, min, max) {\n\t\t\tvar f = getNormalizationFactor(abs(a), abs(b), abs(c), abs(d)),\n\t\t\t\tx, b1, c2, qd, q;\n\t\t\tif (f) {\n\t\t\t\ta *= f;\n\t\t\t\tb *= f;\n\t\t\t\tc *= f;\n\t\t\t\td *= f;\n\t\t\t}\n\n\t\t\tfunction evaluate(x0) {\n\t\t\t\tx = x0;\n\t\t\t\tvar tmp = a * x;\n\t\t\t\tb1 = tmp + b;\n\t\t\t\tc2 = b1 * x + c;\n\t\t\t\tqd = (tmp + b1) * x + c2;\n\t\t\t\tq = c2 * x + d;\n\t\t\t}\n\n\t\t\tif (abs(a) < EPSILON) {\n\t\t\t\ta = b;\n\t\t\t\tb1 = c;\n\t\t\t\tc2 = d;\n\t\t\t\tx = Infinity;\n\t\t\t} else if (abs(d) < EPSILON) {\n\t\t\t\tb1 = b;\n\t\t\t\tc2 = c;\n\t\t\t\tx = 0;\n\t\t\t} else {\n\t\t\t\tevaluate(-(b / a) / 3);\n\t\t\t\tvar t = q / a,\n\t\t\t\t\tr = pow(abs(t), 1/3),\n\t\t\t\t\ts = t < 0 ? -1 : 1,\n\t\t\t\t\ttd = -qd / a,\n\t\t\t\t\trd = td > 0 ? 1.324717957244746 * Math.max(r, sqrt(td)) : r,\n\t\t\t\t\tx0 = x - s * rd;\n\t\t\t\tif (x0 !== x) {\n\t\t\t\t\tdo {\n\t\t\t\t\t\tevaluate(x0);\n\t\t\t\t\t\tx0 = qd === 0 ? x : x - q / qd / (1 + MACHINE_EPSILON);\n\t\t\t\t\t} while (s * x0 > s * x);\n\t\t\t\t\tif (abs(a) * x * x > abs(d / x)) {\n\t\t\t\t\t\tc2 = -d / x;\n\t\t\t\t\t\tb1 = (c2 - c) / x;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t\tvar count = Numerical.solveQuadratic(a, b1, c2, roots, min, max),\n\t\t\t\tboundless = min == null;\n\t\t\tif (isFinite(x) && (count === 0\n\t\t\t\t\t|| count > 0 && x !== roots[0] && x !== roots[1])\n\t\t\t\t\t&& (boundless || x > min - EPSILON && x < max + EPSILON))\n\t\t\t\troots[count++] = boundless ? x : clamp(x, min, max);\n\t\t\treturn count;\n\t\t}\n\t};\n};\n\nvar UID = {\n\t_id: 1,\n\t_pools: {},\n\n\tget: function(name) {\n\t\tif (name) {\n\t\t\tvar pool = this._pools[name];\n\t\t\tif (!pool)\n\t\t\t\tpool = this._pools[name] = { _id: 1 };\n\t\t\treturn pool._id++;\n\t\t} else {\n\t\t\treturn this._id++;\n\t\t}\n\t}\n};\n\nvar Point = Base.extend({\n\t_class: 'Point',\n\t_readIndex: true,\n\n\tinitialize: function Point(arg0, arg1) {\n\t\tvar type = typeof arg0,\n\t\t\treading = this.__read,\n\t\t\tread = 0;\n\t\tif (type === 'number') {\n\t\t\tvar hasY = typeof arg1 === 'number';\n\t\t\tthis._set(arg0, hasY ? arg1 : arg0);\n\t\t\tif (reading)\n\t\t\t\tread = hasY ? 2 : 1;\n\t\t} else if (type === 'undefined' || arg0 === null) {\n\t\t\tthis._set(0, 0);\n\t\t\tif (reading)\n\t\t\t\tread = arg0 === null ? 1 : 0;\n\t\t} else {\n\t\t\tvar obj = type === 'string' ? arg0.split(/[\\s,]+/) || [] : arg0;\n\t\t\tread = 1;\n\t\t\tif (Array.isArray(obj)) {\n\t\t\t\tthis._set(+obj[0], +(obj.length > 1 ? obj[1] : obj[0]));\n\t\t\t} else if ('x' in obj) {\n\t\t\t\tthis._set(obj.x || 0, obj.y || 0);\n\t\t\t} else if ('width' in obj) {\n\t\t\t\tthis._set(obj.width || 0, obj.height || 0);\n\t\t\t} else if ('angle' in obj) {\n\t\t\t\tthis._set(obj.length || 0, 0);\n\t\t\t\tthis.setAngle(obj.angle || 0);\n\t\t\t} else {\n\t\t\t\tthis._set(0, 0);\n\t\t\t\tread = 0;\n\t\t\t}\n\t\t}\n\t\tif (reading)\n\t\t\tthis.__read = read;\n\t\treturn this;\n\t},\n\n\tset: '#initialize',\n\n\t_set: function(x, y) {\n\t\tthis.x = x;\n\t\tthis.y = y;\n\t\treturn this;\n\t},\n\n\tequals: function(point) {\n\t\treturn this === point || point\n\t\t\t\t&& (this.x === point.x && this.y === point.y\n\t\t\t\t\t|| Array.isArray(point)\n\t\t\t\t\t\t&& this.x === point[0] && this.y === point[1])\n\t\t\t\t|| false;\n\t},\n\n\tclone: function() {\n\t\treturn new Point(this.x, this.y);\n\t},\n\n\ttoString: function() {\n\t\tvar f = Formatter.instance;\n\t\treturn '{ x: ' + f.number(this.x) + ', y: ' + f.number(this.y) + ' }';\n\t},\n\n\t_serialize: function(options) {\n\t\tvar f = options.formatter;\n\t\treturn [f.number(this.x), f.number(this.y)];\n\t},\n\n\tgetLength: function() {\n\t\treturn Math.sqrt(this.x * this.x + this.y * this.y);\n\t},\n\n\tsetLength: function(length) {\n\t\tif (this.isZero()) {\n\t\t\tvar angle = this._angle || 0;\n\t\t\tthis._set(\n\t\t\t\tMath.cos(angle) * length,\n\t\t\t\tMath.sin(angle) * length\n\t\t\t);\n\t\t} else {\n\t\t\tvar scale = length / this.getLength();\n\t\t\tif (Numerical.isZero(scale))\n\t\t\t\tthis.getAngle();\n\t\t\tthis._set(\n\t\t\t\tthis.x * scale,\n\t\t\t\tthis.y * scale\n\t\t\t);\n\t\t}\n\t},\n\tgetAngle: function() {\n\t\treturn this.getAngleInRadians.apply(this, arguments) * 180 / Math.PI;\n\t},\n\n\tsetAngle: function(angle) {\n\t\tthis.setAngleInRadians.call(this, angle * Math.PI / 180);\n\t},\n\n\tgetAngleInDegrees: '#getAngle',\n\tsetAngleInDegrees: '#setAngle',\n\n\tgetAngleInRadians: function() {\n\t\tif (!arguments.length) {\n\t\t\treturn this.isZero()\n\t\t\t\t\t? this._angle || 0\n\t\t\t\t\t: this._angle = Math.atan2(this.y, this.x);\n\t\t} else {\n\t\t\tvar point = Point.read(arguments),\n\t\t\t\tdiv = this.getLength() * point.getLength();\n\t\t\tif (Numerical.isZero(div)) {\n\t\t\t\treturn NaN;\n\t\t\t} else {\n\t\t\t\tvar a = this.dot(point) / div;\n\t\t\t\treturn Math.acos(a < -1 ? -1 : a > 1 ? 1 : a);\n\t\t\t}\n\t\t}\n\t},\n\n\tsetAngleInRadians: function(angle) {\n\t\tthis._angle = angle;\n\t\tif (!this.isZero()) {\n\t\t\tvar length = this.getLength();\n\t\t\tthis._set(\n\t\t\t\tMath.cos(angle) * length,\n\t\t\t\tMath.sin(angle) * length\n\t\t\t);\n\t\t}\n\t},\n\n\tgetQuadrant: function() {\n\t\treturn this.x >= 0 ? this.y >= 0 ? 1 : 4 : this.y >= 0 ? 2 : 3;\n\t}\n}, {\n\tbeans: false,\n\n\tgetDirectedAngle: function() {\n\t\tvar point = Point.read(arguments);\n\t\treturn Math.atan2(this.cross(point), this.dot(point)) * 180 / Math.PI;\n\t},\n\n\tgetDistance: function() {\n\t\tvar args = arguments,\n\t\t\tpoint = Point.read(args),\n\t\t\tx = point.x - this.x,\n\t\t\ty = point.y - this.y,\n\t\t\td = x * x + y * y,\n\t\t\tsquared = Base.read(args);\n\t\treturn squared ? d : Math.sqrt(d);\n\t},\n\n\tnormalize: function(length) {\n\t\tif (length === undefined)\n\t\t\tlength = 1;\n\t\tvar current = this.getLength(),\n\t\t\tscale = current !== 0 ? length / current : 0,\n\t\t\tpoint = new Point(this.x * scale, this.y * scale);\n\t\tif (scale >= 0)\n\t\t\tpoint._angle = this._angle;\n\t\treturn point;\n\t},\n\n\trotate: function(angle, center) {\n\t\tif (angle === 0)\n\t\t\treturn this.clone();\n\t\tangle = angle * Math.PI / 180;\n\t\tvar point = center ? this.subtract(center) : this,\n\t\t\tsin = Math.sin(angle),\n\t\t\tcos = Math.cos(angle);\n\t\tpoint = new Point(\n\t\t\tpoint.x * cos - point.y * sin,\n\t\t\tpoint.x * sin + point.y * cos\n\t\t);\n\t\treturn center ? point.add(center) : point;\n\t},\n\n\ttransform: function(matrix) {\n\t\treturn matrix ? matrix._transformPoint(this) : this;\n\t},\n\n\tadd: function() {\n\t\tvar point = Point.read(arguments);\n\t\treturn new Point(this.x + point.x, this.y + point.y);\n\t},\n\n\tsubtract: function() {\n\t\tvar point = Point.read(arguments);\n\t\treturn new Point(this.x - point.x, this.y - point.y);\n\t},\n\n\tmultiply: function() {\n\t\tvar point = Point.read(arguments);\n\t\treturn new Point(this.x * point.x, this.y * point.y);\n\t},\n\n\tdivide: function() {\n\t\tvar point = Point.read(arguments);\n\t\treturn new Point(this.x / point.x, this.y / point.y);\n\t},\n\n\tmodulo: function() {\n\t\tvar point = Point.read(arguments);\n\t\treturn new Point(this.x % point.x, this.y % point.y);\n\t},\n\n\tnegate: function() {\n\t\treturn new Point(-this.x, -this.y);\n\t},\n\n\tisInside: function() {\n\t\treturn Rectangle.read(arguments).contains(this);\n\t},\n\n\tisClose: function() {\n\t\tvar args = arguments,\n\t\t\tpoint = Point.read(args),\n\t\t\ttolerance = Base.read(args);\n\t\treturn this.getDistance(point) <= tolerance;\n\t},\n\n\tisCollinear: function() {\n\t\tvar point = Point.read(arguments);\n\t\treturn Point.isCollinear(this.x, this.y, point.x, point.y);\n\t},\n\n\tisColinear: '#isCollinear',\n\n\tisOrthogonal: function() {\n\t\tvar point = Point.read(arguments);\n\t\treturn Point.isOrthogonal(this.x, this.y, point.x, point.y);\n\t},\n\n\tisZero: function() {\n\t\tvar isZero = Numerical.isZero;\n\t\treturn isZero(this.x) && isZero(this.y);\n\t},\n\n\tisNaN: function() {\n\t\treturn isNaN(this.x) || isNaN(this.y);\n\t},\n\n\tisInQuadrant: function(q) {\n\t\treturn this.x * (q > 1 && q < 4 ? -1 : 1) >= 0\n\t\t\t&& this.y * (q > 2 ? -1 : 1) >= 0;\n\t},\n\n\tdot: function() {\n\t\tvar point = Point.read(arguments);\n\t\treturn this.x * point.x + this.y * point.y;\n\t},\n\n\tcross: function() {\n\t\tvar point = Point.read(arguments);\n\t\treturn this.x * point.y - this.y * point.x;\n\t},\n\n\tproject: function() {\n\t\tvar point = Point.read(arguments),\n\t\t\tscale = point.isZero() ? 0 : this.dot(point) / point.dot(point);\n\t\treturn new Point(\n\t\t\tpoint.x * scale,\n\t\t\tpoint.y * scale\n\t\t);\n\t},\n\n\tstatics: {\n\t\tmin: function() {\n\t\t\tvar args = arguments,\n\t\t\t\tpoint1 = Point.read(args),\n\t\t\t\tpoint2 = Point.read(args);\n\t\t\treturn new Point(\n\t\t\t\tMath.min(point1.x, point2.x),\n\t\t\t\tMath.min(point1.y, point2.y)\n\t\t\t);\n\t\t},\n\n\t\tmax: function() {\n\t\t\tvar args = arguments,\n\t\t\t\tpoint1 = Point.read(args),\n\t\t\t\tpoint2 = Point.read(args);\n\t\t\treturn new Point(\n\t\t\t\tMath.max(point1.x, point2.x),\n\t\t\t\tMath.max(point1.y, point2.y)\n\t\t\t);\n\t\t},\n\n\t\trandom: function() {\n\t\t\treturn new Point(Math.random(), Math.random());\n\t\t},\n\n\t\tisCollinear: function(x1, y1, x2, y2) {\n\t\t\treturn Math.abs(x1 * y2 - y1 * x2)\n\t\t\t\t\t<= Math.sqrt((x1 * x1 + y1 * y1) * (x2 * x2 + y2 * y2))\n\t\t\t\t\t\t* 1e-8;\n\t\t},\n\n\t\tisOrthogonal: function(x1, y1, x2, y2) {\n\t\t\treturn Math.abs(x1 * x2 + y1 * y2)\n\t\t\t\t\t<= Math.sqrt((x1 * x1 + y1 * y1) * (x2 * x2 + y2 * y2))\n\t\t\t\t\t\t* 1e-8;\n\t\t}\n\t}\n}, Base.each(['round', 'ceil', 'floor', 'abs'], function(key) {\n\tvar op = Math[key];\n\tthis[key] = function() {\n\t\treturn new Point(op(this.x), op(this.y));\n\t};\n}, {}));\n\nvar LinkedPoint = Point.extend({\n\tinitialize: function Point(x, y, owner, setter) {\n\t\tthis._x = x;\n\t\tthis._y = y;\n\t\tthis._owner = owner;\n\t\tthis._setter = setter;\n\t},\n\n\t_set: function(x, y, _dontNotify) {\n\t\tthis._x = x;\n\t\tthis._y = y;\n\t\tif (!_dontNotify)\n\t\t\tthis._owner[this._setter](this);\n\t\treturn this;\n\t},\n\n\tgetX: function() {\n\t\treturn this._x;\n\t},\n\n\tsetX: function(x) {\n\t\tthis._x = x;\n\t\tthis._owner[this._setter](this);\n\t},\n\n\tgetY: function() {\n\t\treturn this._y;\n\t},\n\n\tsetY: function(y) {\n\t\tthis._y = y;\n\t\tthis._owner[this._setter](this);\n\t},\n\n\tisSelected: function() {\n\t\treturn !!(this._owner._selection & this._getSelection());\n\t},\n\n\tsetSelected: function(selected) {\n\t\tthis._owner._changeSelection(this._getSelection(), selected);\n\t},\n\n\t_getSelection: function() {\n\t\treturn this._setter === 'setPosition' ? 4 : 0;\n\t}\n});\n\nvar Size = Base.extend({\n\t_class: 'Size',\n\t_readIndex: true,\n\n\tinitialize: function Size(arg0, arg1) {\n\t\tvar type = typeof arg0,\n\t\t\treading = this.__read,\n\t\t\tread = 0;\n\t\tif (type === 'number') {\n\t\t\tvar hasHeight = typeof arg1 === 'number';\n\t\t\tthis._set(arg0, hasHeight ? arg1 : arg0);\n\t\t\tif (reading)\n\t\t\t\tread = hasHeight ? 2 : 1;\n\t\t} else if (type === 'undefined' || arg0 === null) {\n\t\t\tthis._set(0, 0);\n\t\t\tif (reading)\n\t\t\t\tread = arg0 === null ? 1 : 0;\n\t\t} else {\n\t\t\tvar obj = type === 'string' ? arg0.split(/[\\s,]+/) || [] : arg0;\n\t\t\tread = 1;\n\t\t\tif (Array.isArray(obj)) {\n\t\t\t\tthis._set(+obj[0], +(obj.length > 1 ? obj[1] : obj[0]));\n\t\t\t} else if ('width' in obj) {\n\t\t\t\tthis._set(obj.width || 0, obj.height || 0);\n\t\t\t} else if ('x' in obj) {\n\t\t\t\tthis._set(obj.x || 0, obj.y || 0);\n\t\t\t} else {\n\t\t\t\tthis._set(0, 0);\n\t\t\t\tread = 0;\n\t\t\t}\n\t\t}\n\t\tif (reading)\n\t\t\tthis.__read = read;\n\t\treturn this;\n\t},\n\n\tset: '#initialize',\n\n\t_set: function(width, height) {\n\t\tthis.width = width;\n\t\tthis.height = height;\n\t\treturn this;\n\t},\n\n\tequals: function(size) {\n\t\treturn size === this || size && (this.width === size.width\n\t\t\t\t&& this.height === size.height\n\t\t\t\t|| Array.isArray(size) && this.width === size[0]\n\t\t\t\t\t&& this.height === size[1]) || false;\n\t},\n\n\tclone: function() {\n\t\treturn new Size(this.width, this.height);\n\t},\n\n\ttoString: function() {\n\t\tvar f = Formatter.instance;\n\t\treturn '{ width: ' + f.number(this.width)\n\t\t\t\t+ ', height: ' + f.number(this.height) + ' }';\n\t},\n\n\t_serialize: function(options) {\n\t\tvar f = options.formatter;\n\t\treturn [f.number(this.width),\n\t\t\t\tf.number(this.height)];\n\t},\n\n\tadd: function() {\n\t\tvar size = Size.read(arguments);\n\t\treturn new Size(this.width + size.width, this.height + size.height);\n\t},\n\n\tsubtract: function() {\n\t\tvar size = Size.read(arguments);\n\t\treturn new Size(this.width - size.width, this.height - size.height);\n\t},\n\n\tmultiply: function() {\n\t\tvar size = Size.read(arguments);\n\t\treturn new Size(this.width * size.width, this.height * size.height);\n\t},\n\n\tdivide: function() {\n\t\tvar size = Size.read(arguments);\n\t\treturn new Size(this.width / size.width, this.height / size.height);\n\t},\n\n\tmodulo: function() {\n\t\tvar size = Size.read(arguments);\n\t\treturn new Size(this.width % size.width, this.height % size.height);\n\t},\n\n\tnegate: function() {\n\t\treturn new Size(-this.width, -this.height);\n\t},\n\n\tisZero: function() {\n\t\tvar isZero = Numerical.isZero;\n\t\treturn isZero(this.width) && isZero(this.height);\n\t},\n\n\tisNaN: function() {\n\t\treturn isNaN(this.width) || isNaN(this.height);\n\t},\n\n\tstatics: {\n\t\tmin: function(size1, size2) {\n\t\t\treturn new Size(\n\t\t\t\tMath.min(size1.width, size2.width),\n\t\t\t\tMath.min(size1.height, size2.height));\n\t\t},\n\n\t\tmax: function(size1, size2) {\n\t\t\treturn new Size(\n\t\t\t\tMath.max(size1.width, size2.width),\n\t\t\t\tMath.max(size1.height, size2.height));\n\t\t},\n\n\t\trandom: function() {\n\t\t\treturn new Size(Math.random(), Math.random());\n\t\t}\n\t}\n}, Base.each(['round', 'ceil', 'floor', 'abs'], function(key) {\n\tvar op = Math[key];\n\tthis[key] = function() {\n\t\treturn new Size(op(this.width), op(this.height));\n\t};\n}, {}));\n\nvar LinkedSize = Size.extend({\n\tinitialize: function Size(width, height, owner, setter) {\n\t\tthis._width = width;\n\t\tthis._height = height;\n\t\tthis._owner = owner;\n\t\tthis._setter = setter;\n\t},\n\n\t_set: function(width, height, _dontNotify) {\n\t\tthis._width = width;\n\t\tthis._height = height;\n\t\tif (!_dontNotify)\n\t\t\tthis._owner[this._setter](this);\n\t\treturn this;\n\t},\n\n\tgetWidth: function() {\n\t\treturn this._width;\n\t},\n\n\tsetWidth: function(width) {\n\t\tthis._width = width;\n\t\tthis._owner[this._setter](this);\n\t},\n\n\tgetHeight: function() {\n\t\treturn this._height;\n\t},\n\n\tsetHeight: function(height) {\n\t\tthis._height = height;\n\t\tthis._owner[this._setter](this);\n\t}\n});\n\nvar Rectangle = Base.extend({\n\t_class: 'Rectangle',\n\t_readIndex: true,\n\tbeans: true,\n\n\tinitialize: function Rectangle(arg0, arg1, arg2, arg3) {\n\t\tvar args = arguments,\n\t\t\ttype = typeof arg0,\n\t\t\tread;\n\t\tif (type === 'number') {\n\t\t\tthis._set(arg0, arg1, arg2, arg3);\n\t\t\tread = 4;\n\t\t} else if (type === 'undefined' || arg0 === null) {\n\t\t\tthis._set(0, 0, 0, 0);\n\t\t\tread = arg0 === null ? 1 : 0;\n\t\t} else if (args.length === 1) {\n\t\t\tif (Array.isArray(arg0)) {\n\t\t\t\tthis._set.apply(this, arg0);\n\t\t\t\tread = 1;\n\t\t\t} else if (arg0.x !== undefined || arg0.width !== undefined) {\n\t\t\t\tthis._set(arg0.x || 0, arg0.y || 0,\n\t\t\t\t\t\targ0.width || 0, arg0.height || 0);\n\t\t\t\tread = 1;\n\t\t\t} else if (arg0.from === undefined && arg0.to === undefined) {\n\t\t\t\tthis._set(0, 0, 0, 0);\n\t\t\t\tif (Base.readSupported(args, this)) {\n\t\t\t\t\tread = 1;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\tif (read === undefined) {\n\t\t\tvar frm = Point.readNamed(args, 'from'),\n\t\t\t\tnext = Base.peek(args),\n\t\t\t\tx = frm.x,\n\t\t\t\ty = frm.y,\n\t\t\t\twidth,\n\t\t\t\theight;\n\t\t\tif (next && next.x !== undefined || Base.hasNamed(args, 'to')) {\n\t\t\t\tvar to = Point.readNamed(args, 'to');\n\t\t\t\twidth = to.x - x;\n\t\t\t\theight = to.y - y;\n\t\t\t\tif (width < 0) {\n\t\t\t\t\tx = to.x;\n\t\t\t\t\twidth = -width;\n\t\t\t\t}\n\t\t\t\tif (height < 0) {\n\t\t\t\t\ty = to.y;\n\t\t\t\t\theight = -height;\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tvar size = Size.read(args);\n\t\t\t\twidth = size.width;\n\t\t\t\theight = size.height;\n\t\t\t}\n\t\t\tthis._set(x, y, width, height);\n\t\t\tread = args.__index;\n\t\t}\n\t\tvar filtered = args.__filtered;\n\t\tif (filtered)\n\t\t\tthis.__filtered = filtered;\n\t\tif (this.__read)\n\t\t\tthis.__read = read;\n\t\treturn this;\n\t},\n\n\tset: '#initialize',\n\n\t_set: function(x, y, width, height) {\n\t\tthis.x = x;\n\t\tthis.y = y;\n\t\tthis.width = width;\n\t\tthis.height = height;\n\t\treturn this;\n\t},\n\n\tclone: function() {\n\t\treturn new Rectangle(this.x, this.y, this.width, this.height);\n\t},\n\n\tequals: function(rect) {\n\t\tvar rt = Base.isPlainValue(rect)\n\t\t\t\t? Rectangle.read(arguments)\n\t\t\t\t: rect;\n\t\treturn rt === this\n\t\t\t\t|| rt && this.x === rt.x && this.y === rt.y\n\t\t\t\t\t&& this.width === rt.width && this.height === rt.height\n\t\t\t\t|| false;\n\t},\n\n\ttoString: function() {\n\t\tvar f = Formatter.instance;\n\t\treturn '{ x: ' + f.number(this.x)\n\t\t\t\t+ ', y: ' + f.number(this.y)\n\t\t\t\t+ ', width: ' + f.number(this.width)\n\t\t\t\t+ ', height: ' + f.number(this.height)\n\t\t\t\t+ ' }';\n\t},\n\n\t_serialize: function(options) {\n\t\tvar f = options.formatter;\n\t\treturn [f.number(this.x),\n\t\t\t\tf.number(this.y),\n\t\t\t\tf.number(this.width),\n\t\t\t\tf.number(this.height)];\n\t},\n\n\tgetPoint: function(_dontLink) {\n\t\tvar ctor = _dontLink ? Point : LinkedPoint;\n\t\treturn new ctor(this.x, this.y, this, 'setPoint');\n\t},\n\n\tsetPoint: function() {\n\t\tvar point = Point.read(arguments);\n\t\tthis.x = point.x;\n\t\tthis.y = point.y;\n\t},\n\n\tgetSize: function(_dontLink) {\n\t\tvar ctor = _dontLink ? Size : LinkedSize;\n\t\treturn new ctor(this.width, this.height, this, 'setSize');\n\t},\n\n\t_fw: 1,\n\t_fh: 1,\n\n\tsetSize: function() {\n\t\tvar size = Size.read(arguments),\n\t\t\tsx = this._sx,\n\t\t\tsy = this._sy,\n\t\t\tw = size.width,\n\t\t\th = size.height;\n\t\tif (sx) {\n\t\t\tthis.x += (this.width - w) * sx;\n\t\t}\n\t\tif (sy) {\n\t\t\tthis.y += (this.height - h) * sy;\n\t\t}\n\t\tthis.width = w;\n\t\tthis.height = h;\n\t\tthis._fw = this._fh = 1;\n\t},\n\n\tgetLeft: function() {\n\t\treturn this.x;\n\t},\n\n\tsetLeft: function(left) {\n\t\tif (!this._fw) {\n\t\t\tvar amount = left - this.x;\n\t\t\tthis.width -= this._sx === 0.5 ? amount * 2 : amount;\n\t\t}\n\t\tthis.x = left;\n\t\tthis._sx = this._fw = 0;\n\t},\n\n\tgetTop: function() {\n\t\treturn this.y;\n\t},\n\n\tsetTop: function(top) {\n\t\tif (!this._fh) {\n\t\t\tvar amount = top - this.y;\n\t\t\tthis.height -= this._sy === 0.5 ? amount * 2 : amount;\n\t\t}\n\t\tthis.y = top;\n\t\tthis._sy = this._fh = 0;\n\t},\n\n\tgetRight: function() {\n\t\treturn this.x + this.width;\n\t},\n\n\tsetRight: function(right) {\n\t\tif (!this._fw) {\n\t\t\tvar amount = right - this.x;\n\t\t\tthis.width = this._sx === 0.5 ? amount * 2 : amount;\n\t\t}\n\t\tthis.x = right - this.width;\n\t\tthis._sx = 1;\n\t\tthis._fw = 0;\n\t},\n\n\tgetBottom: function() {\n\t\treturn this.y + this.height;\n\t},\n\n\tsetBottom: function(bottom) {\n\t\tif (!this._fh) {\n\t\t\tvar amount = bottom - this.y;\n\t\t\tthis.height = this._sy === 0.5 ? amount * 2 : amount;\n\t\t}\n\t\tthis.y = bottom - this.height;\n\t\tthis._sy = 1;\n\t\tthis._fh = 0;\n\t},\n\n\tgetCenterX: function() {\n\t\treturn this.x + this.width / 2;\n\t},\n\n\tsetCenterX: function(x) {\n\t\tif (this._fw || this._sx === 0.5) {\n\t\t\tthis.x = x - this.width / 2;\n\t\t} else {\n\t\t\tif (this._sx) {\n\t\t\t\tthis.x += (x - this.x) * 2 * this._sx;\n\t\t\t}\n\t\t\tthis.width = (x - this.x) * 2;\n\t\t}\n\t\tthis._sx = 0.5;\n\t\tthis._fw = 0;\n\t},\n\n\tgetCenterY: function() {\n\t\treturn this.y + this.height / 2;\n\t},\n\n\tsetCenterY: function(y) {\n\t\tif (this._fh || this._sy === 0.5) {\n\t\t\tthis.y = y - this.height / 2;\n\t\t} else {\n\t\t\tif (this._sy) {\n\t\t\t\tthis.y += (y - this.y) * 2 * this._sy;\n\t\t\t}\n\t\t\tthis.height = (y - this.y) * 2;\n\t\t}\n\t\tthis._sy = 0.5;\n\t\tthis._fh = 0;\n\t},\n\n\tgetCenter: function(_dontLink) {\n\t\tvar ctor = _dontLink ? Point : LinkedPoint;\n\t\treturn new ctor(this.getCenterX(), this.getCenterY(), this, 'setCenter');\n\t},\n\n\tsetCenter: function() {\n\t\tvar point = Point.read(arguments);\n\t\tthis.setCenterX(point.x);\n\t\tthis.setCenterY(point.y);\n\t\treturn this;\n\t},\n\n\tgetArea: function() {\n\t\treturn this.width * this.height;\n\t},\n\n\tisEmpty: function() {\n\t\treturn this.width === 0 || this.height === 0;\n\t},\n\n\tcontains: function(arg) {\n\t\treturn arg && arg.width !== undefined\n\t\t\t\t|| (Array.isArray(arg) ? arg : arguments).length === 4\n\t\t\t\t? this._containsRectangle(Rectangle.read(arguments))\n\t\t\t\t: this._containsPoint(Point.read(arguments));\n\t},\n\n\t_containsPoint: function(point) {\n\t\tvar x = point.x,\n\t\t\ty = point.y;\n\t\treturn x >= this.x && y >= this.y\n\t\t\t\t&& x <= this.x + this.width\n\t\t\t\t&& y <= this.y + this.height;\n\t},\n\n\t_containsRectangle: function(rect) {\n\t\tvar x = rect.x,\n\t\t\ty = rect.y;\n\t\treturn x >= this.x && y >= this.y\n\t\t\t\t&& x + rect.width <= this.x + this.width\n\t\t\t\t&& y + rect.height <= this.y + this.height;\n\t},\n\n\tintersects: function() {\n\t\tvar rect = Rectangle.read(arguments),\n\t\t\tepsilon = Base.read(arguments) || 0;\n\t\treturn rect.x + rect.width > this.x - epsilon\n\t\t\t\t&& rect.y + rect.height > this.y - epsilon\n\t\t\t\t&& rect.x < this.x + this.width + epsilon\n\t\t\t\t&& rect.y < this.y + this.height + epsilon;\n\t},\n\n\tintersect: function() {\n\t\tvar rect = Rectangle.read(arguments),\n\t\t\tx1 = Math.max(this.x, rect.x),\n\t\t\ty1 = Math.max(this.y, rect.y),\n\t\t\tx2 = Math.min(this.x + this.width, rect.x + rect.width),\n\t\t\ty2 = Math.min(this.y + this.height, rect.y + rect.height);\n\t\treturn new Rectangle(x1, y1, x2 - x1, y2 - y1);\n\t},\n\n\tunite: function() {\n\t\tvar rect = Rectangle.read(arguments),\n\t\t\tx1 = Math.min(this.x, rect.x),\n\t\t\ty1 = Math.min(this.y, rect.y),\n\t\t\tx2 = Math.max(this.x + this.width, rect.x + rect.width),\n\t\t\ty2 = Math.max(this.y + this.height, rect.y + rect.height);\n\t\treturn new Rectangle(x1, y1, x2 - x1, y2 - y1);\n\t},\n\n\tinclude: function() {\n\t\tvar point = Point.read(arguments);\n\t\tvar x1 = Math.min(this.x, point.x),\n\t\t\ty1 = Math.min(this.y, point.y),\n\t\t\tx2 = Math.max(this.x + this.width, point.x),\n\t\t\ty2 = Math.max(this.y + this.height, point.y);\n\t\treturn new Rectangle(x1, y1, x2 - x1, y2 - y1);\n\t},\n\n\texpand: function() {\n\t\tvar amount = Size.read(arguments),\n\t\t\thor = amount.width,\n\t\t\tver = amount.height;\n\t\treturn new Rectangle(this.x - hor / 2, this.y - ver / 2,\n\t\t\t\tthis.width + hor, this.height + ver);\n\t},\n\n\tscale: function(hor, ver) {\n\t\treturn this.expand(this.width * hor - this.width,\n\t\t\t\tthis.height * (ver === undefined ? hor : ver) - this.height);\n\t}\n}, Base.each([\n\t\t['Top', 'Left'], ['Top', 'Right'],\n\t\t['Bottom', 'Left'], ['Bottom', 'Right'],\n\t\t['Left', 'Center'], ['Top', 'Center'],\n\t\t['Right', 'Center'], ['Bottom', 'Center']\n\t],\n\tfunction(parts, index) {\n\t\tvar part = parts.join(''),\n\t\t\txFirst = /^[RL]/.test(part);\n\t\tif (index >= 4)\n\t\t\tparts[1] += xFirst ? 'Y' : 'X';\n\t\tvar x = parts[xFirst ? 0 : 1],\n\t\t\ty = parts[xFirst ? 1 : 0],\n\t\t\tgetX = 'get' + x,\n\t\t\tgetY = 'get' + y,\n\t\t\tsetX = 'set' + x,\n\t\t\tsetY = 'set' + y,\n\t\t\tget = 'get' + part,\n\t\t\tset = 'set' + part;\n\t\tthis[get] = function(_dontLink) {\n\t\t\tvar ctor = _dontLink ? Point : LinkedPoint;\n\t\t\treturn new ctor(this[getX](), this[getY](), this, set);\n\t\t};\n\t\tthis[set] = function() {\n\t\t\tvar point = Point.read(arguments);\n\t\t\tthis[setX](point.x);\n\t\t\tthis[setY](point.y);\n\t\t};\n\t}, {\n\t\tbeans: true\n\t}\n));\n\nvar LinkedRectangle = Rectangle.extend({\n\tinitialize: function Rectangle(x, y, width, height, owner, setter) {\n\t\tthis._set(x, y, width, height, true);\n\t\tthis._owner = owner;\n\t\tthis._setter = setter;\n\t},\n\n\t_set: function(x, y, width, height, _dontNotify) {\n\t\tthis._x = x;\n\t\tthis._y = y;\n\t\tthis._width = width;\n\t\tthis._height = height;\n\t\tif (!_dontNotify)\n\t\t\tthis._owner[this._setter](this);\n\t\treturn this;\n\t}\n},\nnew function() {\n\tvar proto = Rectangle.prototype;\n\n\treturn Base.each(['x', 'y', 'width', 'height'], function(key) {\n\t\tvar part = Base.capitalize(key),\n\t\t\tinternal = '_' + key;\n\t\tthis['get' + part] = function() {\n\t\t\treturn this[internal];\n\t\t};\n\n\t\tthis['set' + part] = function(value) {\n\t\t\tthis[internal] = value;\n\t\t\tif (!this._dontNotify)\n\t\t\t\tthis._owner[this._setter](this);\n\t\t};\n\t}, Base.each(['Point', 'Size', 'Center',\n\t\t\t'Left', 'Top', 'Right', 'Bottom', 'CenterX', 'CenterY',\n\t\t\t'TopLeft', 'TopRight', 'BottomLeft', 'BottomRight',\n\t\t\t'LeftCenter', 'TopCenter', 'RightCenter', 'BottomCenter'],\n\t\tfunction(key) {\n\t\t\tvar name = 'set' + key;\n\t\t\tthis[name] = function() {\n\t\t\t\tthis._dontNotify = true;\n\t\t\t\tproto[name].apply(this, arguments);\n\t\t\t\tthis._dontNotify = false;\n\t\t\t\tthis._owner[this._setter](this);\n\t\t\t};\n\t\t}, {\n\t\t\tisSelected: function() {\n\t\t\t\treturn !!(this._owner._selection & 2);\n\t\t\t},\n\n\t\t\tsetSelected: function(selected) {\n\t\t\t\tvar owner = this._owner;\n\t\t\t\tif (owner._changeSelection) {\n\t\t\t\t\towner._changeSelection(2, selected);\n\t\t\t\t}\n\t\t\t}\n\t\t})\n\t);\n});\n\nvar Matrix = Base.extend({\n\t_class: 'Matrix',\n\n\tinitialize: function Matrix(arg, _dontNotify) {\n\t\tvar args = arguments,\n\t\t\tcount = args.length,\n\t\t\tok = true;\n\t\tif (count >= 6) {\n\t\t\tthis._set.apply(this, args);\n\t\t} else if (count === 1 || count === 2) {\n\t\t\tif (arg instanceof Matrix) {\n\t\t\t\tthis._set(arg._a, arg._b, arg._c, arg._d, arg._tx, arg._ty,\n\t\t\t\t\t\t_dontNotify);\n\t\t\t} else if (Array.isArray(arg)) {\n\t\t\t\tthis._set.apply(this,\n\t\t\t\t\t\t_dontNotify ? arg.concat([_dontNotify]) : arg);\n\t\t\t} else {\n\t\t\t\tok = false;\n\t\t\t}\n\t\t} else if (!count) {\n\t\t\tthis.reset();\n\t\t} else {\n\t\t\tok = false;\n\t\t}\n\t\tif (!ok) {\n\t\t\tthrow new Error('Unsupported matrix parameters');\n\t\t}\n\t\treturn this;\n\t},\n\n\tset: '#initialize',\n\n\t_set: function(a, b, c, d, tx, ty, _dontNotify) {\n\t\tthis._a = a;\n\t\tthis._b = b;\n\t\tthis._c = c;\n\t\tthis._d = d;\n\t\tthis._tx = tx;\n\t\tthis._ty = ty;\n\t\tif (!_dontNotify)\n\t\t\tthis._changed();\n\t\treturn this;\n\t},\n\n\t_serialize: function(options, dictionary) {\n\t\treturn Base.serialize(this.getValues(), options, true, dictionary);\n\t},\n\n\t_changed: function() {\n\t\tvar owner = this._owner;\n\t\tif (owner) {\n\t\t\tif (owner._applyMatrix) {\n\t\t\t\towner.transform(null, true);\n\t\t\t} else {\n\t\t\t\towner._changed(25);\n\t\t\t}\n\t\t}\n\t},\n\n\tclone: function() {\n\t\treturn new Matrix(this._a, this._b, this._c, this._d,\n\t\t\t\tthis._tx, this._ty);\n\t},\n\n\tequals: function(mx) {\n\t\treturn mx === this || mx && this._a === mx._a && this._b === mx._b\n\t\t\t\t&& this._c === mx._c && this._d === mx._d\n\t\t\t\t&& this._tx === mx._tx && this._ty === mx._ty;\n\t},\n\n\ttoString: function() {\n\t\tvar f = Formatter.instance;\n\t\treturn '[[' + [f.number(this._a), f.number(this._c),\n\t\t\t\t\tf.number(this._tx)].join(', ') + '], ['\n\t\t\t\t+ [f.number(this._b), f.number(this._d),\n\t\t\t\t\tf.number(this._ty)].join(', ') + ']]';\n\t},\n\n\treset: function(_dontNotify) {\n\t\tthis._a = this._d = 1;\n\t\tthis._b = this._c = this._tx = this._ty = 0;\n\t\tif (!_dontNotify)\n\t\t\tthis._changed();\n\t\treturn this;\n\t},\n\n\tapply: function(recursively, _setApplyMatrix) {\n\t\tvar owner = this._owner;\n\t\tif (owner) {\n\t\t\towner.transform(null, Base.pick(recursively, true), _setApplyMatrix);\n\t\t\treturn this.isIdentity();\n\t\t}\n\t\treturn false;\n\t},\n\n\ttranslate: function() {\n\t\tvar point = Point.read(arguments),\n\t\t\tx = point.x,\n\t\t\ty = point.y;\n\t\tthis._tx += x * this._a + y * this._c;\n\t\tthis._ty += x * this._b + y * this._d;\n\t\tthis._changed();\n\t\treturn this;\n\t},\n\n\tscale: function() {\n\t\tvar args = arguments,\n\t\t\tscale = Point.read(args),\n\t\t\tcenter = Point.read(args, 0, { readNull: true });\n\t\tif (center)\n\t\t\tthis.translate(center);\n\t\tthis._a *= scale.x;\n\t\tthis._b *= scale.x;\n\t\tthis._c *= scale.y;\n\t\tthis._d *= scale.y;\n\t\tif (center)\n\t\t\tthis.translate(center.negate());\n\t\tthis._changed();\n\t\treturn this;\n\t},\n\n\trotate: function(angle ) {\n\t\tangle *= Math.PI / 180;\n\t\tvar center = Point.read(arguments, 1),\n\t\t\tx = center.x,\n\t\t\ty = center.y,\n\t\t\tcos = Math.cos(angle),\n\t\t\tsin = Math.sin(angle),\n\t\t\ttx = x - x * cos + y * sin,\n\t\t\tty = y - x * sin - y * cos,\n\t\t\ta = this._a,\n\t\t\tb = this._b,\n\t\t\tc = this._c,\n\t\t\td = this._d;\n\t\tthis._a = cos * a + sin * c;\n\t\tthis._b = cos * b + sin * d;\n\t\tthis._c = -sin * a + cos * c;\n\t\tthis._d = -sin * b + cos * d;\n\t\tthis._tx += tx * a + ty * c;\n\t\tthis._ty += tx * b + ty * d;\n\t\tthis._changed();\n\t\treturn this;\n\t},\n\n\tshear: function() {\n\t\tvar args = arguments,\n\t\t\tshear = Point.read(args),\n\t\t\tcenter = Point.read(args, 0, { readNull: true });\n\t\tif (center)\n\t\t\tthis.translate(center);\n\t\tvar a = this._a,\n\t\t\tb = this._b;\n\t\tthis._a += shear.y * this._c;\n\t\tthis._b += shear.y * this._d;\n\t\tthis._c += shear.x * a;\n\t\tthis._d += shear.x * b;\n\t\tif (center)\n\t\t\tthis.translate(center.negate());\n\t\tthis._changed();\n\t\treturn this;\n\t},\n\n\tskew: function() {\n\t\tvar args = arguments,\n\t\t\tskew = Point.read(args),\n\t\t\tcenter = Point.read(args, 0, { readNull: true }),\n\t\t\ttoRadians = Math.PI / 180,\n\t\t\tshear = new Point(Math.tan(skew.x * toRadians),\n\t\t\t\tMath.tan(skew.y * toRadians));\n\t\treturn this.shear(shear, center);\n\t},\n\n\tappend: function(mx, _dontNotify) {\n\t\tif (mx) {\n\t\t\tvar a1 = this._a,\n\t\t\t\tb1 = this._b,\n\t\t\t\tc1 = this._c,\n\t\t\t\td1 = this._d,\n\t\t\t\ta2 = mx._a,\n\t\t\t\tb2 = mx._c,\n\t\t\t\tc2 = mx._b,\n\t\t\t\td2 = mx._d,\n\t\t\t\ttx2 = mx._tx,\n\t\t\t\tty2 = mx._ty;\n\t\t\tthis._a = a2 * a1 + c2 * c1;\n\t\t\tthis._c = b2 * a1 + d2 * c1;\n\t\t\tthis._b = a2 * b1 + c2 * d1;\n\t\t\tthis._d = b2 * b1 + d2 * d1;\n\t\t\tthis._tx += tx2 * a1 + ty2 * c1;\n\t\t\tthis._ty += tx2 * b1 + ty2 * d1;\n\t\t\tif (!_dontNotify)\n\t\t\t\tthis._changed();\n\t\t}\n\t\treturn this;\n\t},\n\n\tprepend: function(mx, _dontNotify) {\n\t\tif (mx) {\n\t\t\tvar a1 = this._a,\n\t\t\t\tb1 = this._b,\n\t\t\t\tc1 = this._c,\n\t\t\t\td1 = this._d,\n\t\t\t\ttx1 = this._tx,\n\t\t\t\tty1 = this._ty,\n\t\t\t\ta2 = mx._a,\n\t\t\t\tb2 = mx._c,\n\t\t\t\tc2 = mx._b,\n\t\t\t\td2 = mx._d,\n\t\t\t\ttx2 = mx._tx,\n\t\t\t\tty2 = mx._ty;\n\t\t\tthis._a = a2 * a1 + b2 * b1;\n\t\t\tthis._c = a2 * c1 + b2 * d1;\n\t\t\tthis._b = c2 * a1 + d2 * b1;\n\t\t\tthis._d = c2 * c1 + d2 * d1;\n\t\t\tthis._tx = a2 * tx1 + b2 * ty1 + tx2;\n\t\t\tthis._ty = c2 * tx1 + d2 * ty1 + ty2;\n\t\t\tif (!_dontNotify)\n\t\t\t\tthis._changed();\n\t\t}\n\t\treturn this;\n\t},\n\n\tappended: function(mx) {\n\t\treturn this.clone().append(mx);\n\t},\n\n\tprepended: function(mx) {\n\t\treturn this.clone().prepend(mx);\n\t},\n\n\tinvert: function() {\n\t\tvar a = this._a,\n\t\t\tb = this._b,\n\t\t\tc = this._c,\n\t\t\td = this._d,\n\t\t\ttx = this._tx,\n\t\t\tty = this._ty,\n\t\t\tdet = a * d - b * c,\n\t\t\tres = null;\n\t\tif (det && !isNaN(det) && isFinite(tx) && isFinite(ty)) {\n\t\t\tthis._a = d / det;\n\t\t\tthis._b = -b / det;\n\t\t\tthis._c = -c / det;\n\t\t\tthis._d = a / det;\n\t\t\tthis._tx = (c * ty - d * tx) / det;\n\t\t\tthis._ty = (b * tx - a * ty) / det;\n\t\t\tres = this;\n\t\t}\n\t\treturn res;\n\t},\n\n\tinverted: function() {\n\t\treturn this.clone().invert();\n\t},\n\n\tconcatenate: '#append',\n\tpreConcatenate: '#prepend',\n\tchain: '#appended',\n\n\t_shiftless: function() {\n\t\treturn new Matrix(this._a, this._b, this._c, this._d, 0, 0);\n\t},\n\n\t_orNullIfIdentity: function() {\n\t\treturn this.isIdentity() ? null : this;\n\t},\n\n\tisIdentity: function() {\n\t\treturn this._a === 1 && this._b === 0 && this._c === 0 && this._d === 1\n\t\t\t\t&& this._tx === 0 && this._ty === 0;\n\t},\n\n\tisInvertible: function() {\n\t\tvar det = this._a * this._d - this._c * this._b;\n\t\treturn det && !isNaN(det) && isFinite(this._tx) && isFinite(this._ty);\n\t},\n\n\tisSingular: function() {\n\t\treturn !this.isInvertible();\n\t},\n\n\ttransform: function( src, dst, count) {\n\t\treturn arguments.length < 3\n\t\t\t? this._transformPoint(Point.read(arguments))\n\t\t\t: this._transformCoordinates(src, dst, count);\n\t},\n\n\t_transformPoint: function(point, dest, _dontNotify) {\n\t\tvar x = point.x,\n\t\t\ty = point.y;\n\t\tif (!dest)\n\t\t\tdest = new Point();\n\t\treturn dest._set(\n\t\t\t\tx * this._a + y * this._c + this._tx,\n\t\t\t\tx * this._b + y * this._d + this._ty,\n\t\t\t\t_dontNotify);\n\t},\n\n\t_transformCoordinates: function(src, dst, count) {\n\t\tfor (var i = 0, max = 2 * count; i < max; i += 2) {\n\t\t\tvar x = src[i],\n\t\t\t\ty = src[i + 1];\n\t\t\tdst[i] = x * this._a + y * this._c + this._tx;\n\t\t\tdst[i + 1] = x * this._b + y * this._d + this._ty;\n\t\t}\n\t\treturn dst;\n\t},\n\n\t_transformCorners: function(rect) {\n\t\tvar x1 = rect.x,\n\t\t\ty1 = rect.y,\n\t\t\tx2 = x1 + rect.width,\n\t\t\ty2 = y1 + rect.height,\n\t\t\tcoords = [ x1, y1, x2, y1, x2, y2, x1, y2 ];\n\t\treturn this._transformCoordinates(coords, coords, 4);\n\t},\n\n\t_transformBounds: function(bounds, dest, _dontNotify) {\n\t\tvar coords = this._transformCorners(bounds),\n\t\t\tmin = coords.slice(0, 2),\n\t\t\tmax = min.slice();\n\t\tfor (var i = 2; i < 8; i++) {\n\t\t\tvar val = coords[i],\n\t\t\t\tj = i & 1;\n\t\t\tif (val < min[j]) {\n\t\t\t\tmin[j] = val;\n\t\t\t} else if (val > max[j]) {\n\t\t\t\tmax[j] = val;\n\t\t\t}\n\t\t}\n\t\tif (!dest)\n\t\t\tdest = new Rectangle();\n\t\treturn dest._set(min[0], min[1], max[0] - min[0], max[1] - min[1],\n\t\t\t\t_dontNotify);\n\t},\n\n\tinverseTransform: function() {\n\t\treturn this._inverseTransform(Point.read(arguments));\n\t},\n\n\t_inverseTransform: function(point, dest, _dontNotify) {\n\t\tvar a = this._a,\n\t\t\tb = this._b,\n\t\t\tc = this._c,\n\t\t\td = this._d,\n\t\t\ttx = this._tx,\n\t\t\tty = this._ty,\n\t\t\tdet = a * d - b * c,\n\t\t\tres = null;\n\t\tif (det && !isNaN(det) && isFinite(tx) && isFinite(ty)) {\n\t\t\tvar x = point.x - this._tx,\n\t\t\t\ty = point.y - this._ty;\n\t\t\tif (!dest)\n\t\t\t\tdest = new Point();\n\t\t\tres = dest._set(\n\t\t\t\t\t(x * d - y * c) / det,\n\t\t\t\t\t(y * a - x * b) / det,\n\t\t\t\t\t_dontNotify);\n\t\t}\n\t\treturn res;\n\t},\n\n\tdecompose: function() {\n\t\tvar a = this._a,\n\t\t\tb = this._b,\n\t\t\tc = this._c,\n\t\t\td = this._d,\n\t\t\tdet = a * d - b * c,\n\t\t\tsqrt = Math.sqrt,\n\t\t\tatan2 = Math.atan2,\n\t\t\tdegrees = 180 / Math.PI,\n\t\t\trotate,\n\t\t\tscale,\n\t\t\tskew;\n\t\tif (a !== 0 || b !== 0) {\n\t\t\tvar r = sqrt(a * a + b * b);\n\t\t\trotate = Math.acos(a / r) * (b > 0 ? 1 : -1);\n\t\t\tscale = [r, det / r];\n\t\t\tskew = [atan2(a * c + b * d, r * r), 0];\n\t\t} else if (c !== 0 || d !== 0) {\n\t\t\tvar s = sqrt(c * c + d * d);\n\t\t\trotate = Math.asin(c / s)  * (d > 0 ? 1 : -1);\n\t\t\tscale = [det / s, s];\n\t\t\tskew = [0, atan2(a * c + b * d, s * s)];\n\t\t} else {\n\t\t\trotate = 0;\n\t\t\tskew = scale = [0, 0];\n\t\t}\n\t\treturn {\n\t\t\ttranslation: this.getTranslation(),\n\t\t\trotation: rotate * degrees,\n\t\t\tscaling: new Point(scale),\n\t\t\tskewing: new Point(skew[0] * degrees, skew[1] * degrees)\n\t\t};\n\t},\n\n\tgetValues: function() {\n\t\treturn [ this._a, this._b, this._c, this._d, this._tx, this._ty ];\n\t},\n\n\tgetTranslation: function() {\n\t\treturn new Point(this._tx, this._ty);\n\t},\n\n\tgetScaling: function() {\n\t\treturn this.decompose().scaling;\n\t},\n\n\tgetRotation: function() {\n\t\treturn this.decompose().rotation;\n\t},\n\n\tapplyToContext: function(ctx) {\n\t\tif (!this.isIdentity()) {\n\t\t\tctx.transform(this._a, this._b, this._c, this._d,\n\t\t\t\t\tthis._tx, this._ty);\n\t\t}\n\t}\n}, Base.each(['a', 'b', 'c', 'd', 'tx', 'ty'], function(key) {\n\tvar part = Base.capitalize(key),\n\t\tprop = '_' + key;\n\tthis['get' + part] = function() {\n\t\treturn this[prop];\n\t};\n\tthis['set' + part] = function(value) {\n\t\tthis[prop] = value;\n\t\tthis._changed();\n\t};\n}, {}));\n\nvar Line = Base.extend({\n\t_class: 'Line',\n\n\tinitialize: function Line(arg0, arg1, arg2, arg3, arg4) {\n\t\tvar asVector = false;\n\t\tif (arguments.length >= 4) {\n\t\t\tthis._px = arg0;\n\t\t\tthis._py = arg1;\n\t\t\tthis._vx = arg2;\n\t\t\tthis._vy = arg3;\n\t\t\tasVector = arg4;\n\t\t} else {\n\t\t\tthis._px = arg0.x;\n\t\t\tthis._py = arg0.y;\n\t\t\tthis._vx = arg1.x;\n\t\t\tthis._vy = arg1.y;\n\t\t\tasVector = arg2;\n\t\t}\n\t\tif (!asVector) {\n\t\t\tthis._vx -= this._px;\n\t\t\tthis._vy -= this._py;\n\t\t}\n\t},\n\n\tgetPoint: function() {\n\t\treturn new Point(this._px, this._py);\n\t},\n\n\tgetVector: function() {\n\t\treturn new Point(this._vx, this._vy);\n\t},\n\n\tgetLength: function() {\n\t\treturn this.getVector().getLength();\n\t},\n\n\tintersect: function(line, isInfinite) {\n\t\treturn Line.intersect(\n\t\t\t\tthis._px, this._py, this._vx, this._vy,\n\t\t\t\tline._px, line._py, line._vx, line._vy,\n\t\t\t\ttrue, isInfinite);\n\t},\n\n\tgetSide: function(point, isInfinite) {\n\t\treturn Line.getSide(\n\t\t\t\tthis._px, this._py, this._vx, this._vy,\n\t\t\t\tpoint.x, point.y, true, isInfinite);\n\t},\n\n\tgetDistance: function(point) {\n\t\treturn Math.abs(this.getSignedDistance(point));\n\t},\n\n\tgetSignedDistance: function(point) {\n\t\treturn Line.getSignedDistance(this._px, this._py, this._vx, this._vy,\n\t\t\t\tpoint.x, point.y, true);\n\t},\n\n\tisCollinear: function(line) {\n\t\treturn Point.isCollinear(this._vx, this._vy, line._vx, line._vy);\n\t},\n\n\tisOrthogonal: function(line) {\n\t\treturn Point.isOrthogonal(this._vx, this._vy, line._vx, line._vy);\n\t},\n\n\tstatics: {\n\t\tintersect: function(p1x, p1y, v1x, v1y, p2x, p2y, v2x, v2y, asVector,\n\t\t\t\tisInfinite) {\n\t\t\tif (!asVector) {\n\t\t\t\tv1x -= p1x;\n\t\t\t\tv1y -= p1y;\n\t\t\t\tv2x -= p2x;\n\t\t\t\tv2y -= p2y;\n\t\t\t}\n\t\t\tvar cross = v1x * v2y - v1y * v2x;\n\t\t\tif (!Numerical.isMachineZero(cross)) {\n\t\t\t\tvar dx = p1x - p2x,\n\t\t\t\t\tdy = p1y - p2y,\n\t\t\t\t\tu1 = (v2x * dy - v2y * dx) / cross,\n\t\t\t\t\tu2 = (v1x * dy - v1y * dx) / cross,\n\t\t\t\t\tepsilon = 1e-12,\n\t\t\t\t\tuMin = -epsilon,\n\t\t\t\t\tuMax = 1 + epsilon;\n\t\t\t\tif (isInfinite\n\t\t\t\t\t\t|| uMin < u1 && u1 < uMax && uMin < u2 && u2 < uMax) {\n\t\t\t\t\tif (!isInfinite) {\n\t\t\t\t\t\tu1 = u1 <= 0 ? 0 : u1 >= 1 ? 1 : u1;\n\t\t\t\t\t}\n\t\t\t\t\treturn new Point(\n\t\t\t\t\t\t\tp1x + u1 * v1x,\n\t\t\t\t\t\t\tp1y + u1 * v1y);\n\t\t\t\t}\n\t\t\t}\n\t\t},\n\n\t\tgetSide: function(px, py, vx, vy, x, y, asVector, isInfinite) {\n\t\t\tif (!asVector) {\n\t\t\t\tvx -= px;\n\t\t\t\tvy -= py;\n\t\t\t}\n\t\t\tvar v2x = x - px,\n\t\t\t\tv2y = y - py,\n\t\t\t\tccw = v2x * vy - v2y * vx;\n\t\t\tif (!isInfinite && Numerical.isMachineZero(ccw)) {\n\t\t\t\tccw = (v2x * vx + v2x * vx) / (vx * vx + vy * vy);\n\t\t\t\tif (ccw >= 0 && ccw <= 1)\n\t\t\t\t\tccw = 0;\n\t\t\t}\n\t\t\treturn ccw < 0 ? -1 : ccw > 0 ? 1 : 0;\n\t\t},\n\n\t\tgetSignedDistance: function(px, py, vx, vy, x, y, asVector) {\n\t\t\tif (!asVector) {\n\t\t\t\tvx -= px;\n\t\t\t\tvy -= py;\n\t\t\t}\n\t\t\t  return  vx === 0 ? (vy > 0 ? x - px : px - x)\n\t\t\t\t\t: vy === 0 ? (vx < 0 ? y - py : py - y)\n\t\t\t\t\t: ((x - px) * vy - (y - py) * vx) / (\n\t\t\t\t\t\tvy > vx\n\t\t\t\t\t\t\t? vy * Math.sqrt(1 + (vx * vx) / (vy * vy))\n\t\t\t\t\t\t\t: vx * Math.sqrt(1 + (vy * vy) / (vx * vx))\n\t\t\t\t\t);\n\t\t},\n\n\t\tgetDistance: function(px, py, vx, vy, x, y, asVector) {\n\t\t\treturn Math.abs(\n\t\t\t\t\tLine.getSignedDistance(px, py, vx, vy, x, y, asVector));\n\t\t}\n\t}\n});\n\nvar Project = PaperScopeItem.extend({\n\t_class: 'Project',\n\t_list: 'projects',\n\t_reference: 'project',\n\t_compactSerialize: true,\n\n\tinitialize: function Project(element) {\n\t\tPaperScopeItem.call(this, true);\n\t\tthis._children = [];\n\t\tthis._namedChildren = {};\n\t\tthis._activeLayer = null;\n\t\tthis._currentStyle = new Style(null, null, this);\n\t\tthis._view = View.create(this,\n\t\t\t\telement || CanvasProvider.getCanvas(1, 1));\n\t\tthis._selectionItems = {};\n\t\tthis._selectionCount = 0;\n\t\tthis._updateVersion = 0;\n\t},\n\n\t_serialize: function(options, dictionary) {\n\t\treturn Base.serialize(this._children, options, true, dictionary);\n\t},\n\n\t_changed: function(flags, item) {\n\t\tif (flags & 1) {\n\t\t\tvar view = this._view;\n\t\t\tif (view) {\n\t\t\t\tview._needsUpdate = true;\n\t\t\t\tif (!view._requested && view._autoUpdate)\n\t\t\t\t\tview.requestUpdate();\n\t\t\t}\n\t\t}\n\t\tvar changes = this._changes;\n\t\tif (changes && item) {\n\t\t\tvar changesById = this._changesById,\n\t\t\t\tid = item._id,\n\t\t\t\tentry = changesById[id];\n\t\t\tif (entry) {\n\t\t\t\tentry.flags |= flags;\n\t\t\t} else {\n\t\t\t\tchanges.push(changesById[id] = { item: item, flags: flags });\n\t\t\t}\n\t\t}\n\t},\n\n\tclear: function() {\n\t\tvar children = this._children;\n\t\tfor (var i = children.length - 1; i >= 0; i--)\n\t\t\tchildren[i].remove();\n\t},\n\n\tisEmpty: function() {\n\t\treturn !this._children.length;\n\t},\n\n\tremove: function remove() {\n\t\tif (!remove.base.call(this))\n\t\t\treturn false;\n\t\tif (this._view)\n\t\t\tthis._view.remove();\n\t\treturn true;\n\t},\n\n\tgetView: function() {\n\t\treturn this._view;\n\t},\n\n\tgetCurrentStyle: function() {\n\t\treturn this._currentStyle;\n\t},\n\n\tsetCurrentStyle: function(style) {\n\t\tthis._currentStyle.set(style);\n\t},\n\n\tgetIndex: function() {\n\t\treturn this._index;\n\t},\n\n\tgetOptions: function() {\n\t\treturn this._scope.settings;\n\t},\n\n\tgetLayers: function() {\n\t\treturn this._children;\n\t},\n\n\tgetActiveLayer: function() {\n\t\treturn this._activeLayer || new Layer({ project: this, insert: true });\n\t},\n\n\tgetSymbolDefinitions: function() {\n\t\tvar definitions = [],\n\t\t\tids = {};\n\t\tthis.getItems({\n\t\t\tclass: SymbolItem,\n\t\t\tmatch: function(item) {\n\t\t\t\tvar definition = item._definition,\n\t\t\t\t\tid = definition._id;\n\t\t\t\tif (!ids[id]) {\n\t\t\t\t\tids[id] = true;\n\t\t\t\t\tdefinitions.push(definition);\n\t\t\t\t}\n\t\t\t\treturn false;\n\t\t\t}\n\t\t});\n\t\treturn definitions;\n\t},\n\n\tgetSymbols: 'getSymbolDefinitions',\n\n\tgetSelectedItems: function() {\n\t\tvar selectionItems = this._selectionItems,\n\t\t\titems = [];\n\t\tfor (var id in selectionItems) {\n\t\t\tvar item = selectionItems[id],\n\t\t\t\tselection = item._selection;\n\t\t\tif ((selection & 1) && item.isInserted()) {\n\t\t\t\titems.push(item);\n\t\t\t} else if (!selection) {\n\t\t\t\tthis._updateSelection(item);\n\t\t\t}\n\t\t}\n\t\treturn items;\n\t},\n\n\t_updateSelection: function(item) {\n\t\tvar id = item._id,\n\t\t\tselectionItems = this._selectionItems;\n\t\tif (item._selection) {\n\t\t\tif (selectionItems[id] !== item) {\n\t\t\t\tthis._selectionCount++;\n\t\t\t\tselectionItems[id] = item;\n\t\t\t}\n\t\t} else if (selectionItems[id] === item) {\n\t\t\tthis._selectionCount--;\n\t\t\tdelete selectionItems[id];\n\t\t}\n\t},\n\n\tselectAll: function() {\n\t\tvar children = this._children;\n\t\tfor (var i = 0, l = children.length; i < l; i++)\n\t\t\tchildren[i].setFullySelected(true);\n\t},\n\n\tdeselectAll: function() {\n\t\tvar selectionItems = this._selectionItems;\n\t\tfor (var i in selectionItems)\n\t\t\tselectionItems[i].setFullySelected(false);\n\t},\n\n\taddLayer: function(layer) {\n\t\treturn this.insertLayer(undefined, layer);\n\t},\n\n\tinsertLayer: function(index, layer) {\n\t\tif (layer instanceof Layer) {\n\t\t\tlayer._remove(false, true);\n\t\t\tBase.splice(this._children, [layer], index, 0);\n\t\t\tlayer._setProject(this, true);\n\t\t\tvar name = layer._name;\n\t\t\tif (name)\n\t\t\t\tlayer.setName(name);\n\t\t\tif (this._changes)\n\t\t\t\tlayer._changed(5);\n\t\t\tif (!this._activeLayer)\n\t\t\t\tthis._activeLayer = layer;\n\t\t} else {\n\t\t\tlayer = null;\n\t\t}\n\t\treturn layer;\n\t},\n\n\t_insertItem: function(index, item, _created) {\n\t\titem = this.insertLayer(index, item)\n\t\t\t\t|| (this._activeLayer || this._insertItem(undefined,\n\t\t\t\t\t\tnew Layer(Item.NO_INSERT), true))\n\t\t\t\t\t\t.insertChild(index, item);\n\t\tif (_created && item.activate)\n\t\t\titem.activate();\n\t\treturn item;\n\t},\n\n\tgetItems: function(options) {\n\t\treturn Item._getItems(this, options);\n\t},\n\n\tgetItem: function(options) {\n\t\treturn Item._getItems(this, options, null, null, true)[0] || null;\n\t},\n\n\timportJSON: function(json) {\n\t\tthis.activate();\n\t\tvar layer = this._activeLayer;\n\t\treturn Base.importJSON(json, layer && layer.isEmpty() && layer);\n\t},\n\n\tremoveOn: function(type) {\n\t\tvar sets = this._removeSets;\n\t\tif (sets) {\n\t\t\tif (type === 'mouseup')\n\t\t\t\tsets.mousedrag = null;\n\t\t\tvar set = sets[type];\n\t\t\tif (set) {\n\t\t\t\tfor (var id in set) {\n\t\t\t\t\tvar item = set[id];\n\t\t\t\t\tfor (var key in sets) {\n\t\t\t\t\t\tvar other = sets[key];\n\t\t\t\t\t\tif (other && other != set)\n\t\t\t\t\t\t\tdelete other[item._id];\n\t\t\t\t\t}\n\t\t\t\t\titem.remove();\n\t\t\t\t}\n\t\t\t\tsets[type] = null;\n\t\t\t}\n\t\t}\n\t},\n\n\tdraw: function(ctx, matrix, pixelRatio) {\n\t\tthis._updateVersion++;\n\t\tctx.save();\n\t\tmatrix.applyToContext(ctx);\n\t\tvar children = this._children,\n\t\t\tparam = new Base({\n\t\t\t\toffset: new Point(0, 0),\n\t\t\t\tpixelRatio: pixelRatio,\n\t\t\t\tviewMatrix: matrix.isIdentity() ? null : matrix,\n\t\t\t\tmatrices: [new Matrix()],\n\t\t\t\tupdateMatrix: true\n\t\t\t});\n\t\tfor (var i = 0, l = children.length; i < l; i++) {\n\t\t\tchildren[i].draw(ctx, param);\n\t\t}\n\t\tctx.restore();\n\n\t\tif (this._selectionCount > 0) {\n\t\t\tctx.save();\n\t\t\tctx.strokeWidth = 1;\n\t\t\tvar items = this._selectionItems,\n\t\t\t\tsize = this._scope.settings.handleSize,\n\t\t\t\tversion = this._updateVersion;\n\t\t\tfor (var id in items) {\n\t\t\t\titems[id]._drawSelection(ctx, matrix, size, items, version);\n\t\t\t}\n\t\t\tctx.restore();\n\t\t}\n\t}\n});\n\nvar Item = Base.extend(Emitter, {\n\tstatics: {\n\t\textend: function extend(src) {\n\t\t\tif (src._serializeFields)\n\t\t\t\tsrc._serializeFields = Base.set({},\n\t\t\t\t\tthis.prototype._serializeFields, src._serializeFields);\n\t\t\treturn extend.base.apply(this, arguments);\n\t\t},\n\n\t\tNO_INSERT: { insert: false }\n\t},\n\n\t_class: 'Item',\n\t_name: null,\n\t_applyMatrix: true,\n\t_canApplyMatrix: true,\n\t_canScaleStroke: false,\n\t_pivot: null,\n\t_visible: true,\n\t_blendMode: 'normal',\n\t_opacity: 1,\n\t_locked: false,\n\t_guide: false,\n\t_clipMask: false,\n\t_selection: 0,\n\t_selectBounds: true,\n\t_selectChildren: false,\n\t_serializeFields: {\n\t\tname: null,\n\t\tapplyMatrix: null,\n\t\tmatrix: new Matrix(),\n\t\tpivot: null,\n\t\tvisible: true,\n\t\tblendMode: 'normal',\n\t\topacity: 1,\n\t\tlocked: false,\n\t\tguide: false,\n\t\tclipMask: false,\n\t\tselected: false,\n\t\tdata: {}\n\t},\n\t_prioritize: ['applyMatrix']\n},\nnew function() {\n\tvar handlers = ['onMouseDown', 'onMouseUp', 'onMouseDrag', 'onClick',\n\t\t\t'onDoubleClick', 'onMouseMove', 'onMouseEnter', 'onMouseLeave'];\n\treturn Base.each(handlers,\n\t\tfunction(name) {\n\t\t\tthis._events[name] = {\n\t\t\t\tinstall: function(type) {\n\t\t\t\t\tthis.getView()._countItemEvent(type, 1);\n\t\t\t\t},\n\n\t\t\t\tuninstall: function(type) {\n\t\t\t\t\tthis.getView()._countItemEvent(type, -1);\n\t\t\t\t}\n\t\t\t};\n\t\t}, {\n\t\t\t_events: {\n\t\t\t\tonFrame: {\n\t\t\t\t\tinstall: function() {\n\t\t\t\t\t\tthis.getView()._animateItem(this, true);\n\t\t\t\t\t},\n\n\t\t\t\t\tuninstall: function() {\n\t\t\t\t\t\tthis.getView()._animateItem(this, false);\n\t\t\t\t\t}\n\t\t\t\t},\n\n\t\t\t\tonLoad: {},\n\t\t\t\tonError: {}\n\t\t\t},\n\t\t\tstatics: {\n\t\t\t\t_itemHandlers: handlers\n\t\t\t}\n\t\t}\n\t);\n}, {\n\tinitialize: function Item() {\n\t},\n\n\t_initialize: function(props, point) {\n\t\tvar hasProps = props && Base.isPlainObject(props),\n\t\t\tinternal = hasProps && props.internal === true,\n\t\t\tmatrix = this._matrix = new Matrix(),\n\t\t\tproject = hasProps && props.project || paper.project,\n\t\t\tsettings = paper.settings;\n\t\tthis._id = internal ? null : UID.get();\n\t\tthis._parent = this._index = null;\n\t\tthis._applyMatrix = this._canApplyMatrix && settings.applyMatrix;\n\t\tif (point)\n\t\t\tmatrix.translate(point);\n\t\tmatrix._owner = this;\n\t\tthis._style = new Style(project._currentStyle, this, project);\n\t\tif (internal || hasProps && props.insert == false\n\t\t\t|| !settings.insertItems && !(hasProps && props.insert === true)) {\n\t\t\tthis._setProject(project);\n\t\t} else {\n\t\t\t(hasProps && props.parent || project)\n\t\t\t\t\t._insertItem(undefined, this, true);\n\t\t}\n\t\tif (hasProps && props !== Item.NO_INSERT) {\n\t\t\tthis.set(props, {\n\t\t\t\tinternal: true, insert: true, project: true, parent: true\n\t\t\t});\n\t\t}\n\t\treturn hasProps;\n\t},\n\n\t_serialize: function(options, dictionary) {\n\t\tvar props = {},\n\t\t\tthat = this;\n\n\t\tfunction serialize(fields) {\n\t\t\tfor (var key in fields) {\n\t\t\t\tvar value = that[key];\n\t\t\t\tif (!Base.equals(value, key === 'leading'\n\t\t\t\t\t\t? fields.fontSize * 1.2 : fields[key])) {\n\t\t\t\t\tprops[key] = Base.serialize(value, options,\n\t\t\t\t\t\t\tkey !== 'data', dictionary);\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\tserialize(this._serializeFields);\n\t\tif (!(this instanceof Group))\n\t\t\tserialize(this._style._defaults);\n\t\treturn [ this._class, props ];\n\t},\n\n\t_changed: function(flags) {\n\t\tvar symbol = this._symbol,\n\t\t\tcacheParent = this._parent || symbol,\n\t\t\tproject = this._project;\n\t\tif (flags & 8) {\n\t\t\tthis._bounds = this._position = this._decomposed = undefined;\n\t\t}\n\t\tif (flags & 16) {\n\t\t\tthis._globalMatrix = undefined;\n\t\t}\n\t\tif (cacheParent\n\t\t\t\t&& (flags & 72)) {\n\t\t\tItem._clearBoundsCache(cacheParent);\n\t\t}\n\t\tif (flags & 2) {\n\t\t\tItem._clearBoundsCache(this);\n\t\t}\n\t\tif (project)\n\t\t\tproject._changed(flags, this);\n\t\tif (symbol)\n\t\t\tsymbol._changed(flags);\n\t},\n\n\tgetId: function() {\n\t\treturn this._id;\n\t},\n\n\tgetName: function() {\n\t\treturn this._name;\n\t},\n\n\tsetName: function(name) {\n\n\t\tif (this._name)\n\t\t\tthis._removeNamed();\n\t\tif (name === (+name) + '')\n\t\t\tthrow new Error(\n\t\t\t\t\t'Names consisting only of numbers are not supported.');\n\t\tvar owner = this._getOwner();\n\t\tif (name && owner) {\n\t\t\tvar children = owner._children,\n\t\t\t\tnamedChildren = owner._namedChildren;\n\t\t\t(namedChildren[name] = namedChildren[name] || []).push(this);\n\t\t\tif (!(name in children))\n\t\t\t\tchildren[name] = this;\n\t\t}\n\t\tthis._name = name || undefined;\n\t\tthis._changed(256);\n\t},\n\n\tgetStyle: function() {\n\t\treturn this._style;\n\t},\n\n\tsetStyle: function(style) {\n\t\tthis.getStyle().set(style);\n\t}\n}, Base.each(['locked', 'visible', 'blendMode', 'opacity', 'guide'],\n\tfunction(name) {\n\t\tvar part = Base.capitalize(name),\n\t\t\tkey = '_' + name,\n\t\t\tflags = {\n\t\t\t\tlocked: 256,\n\t\t\t\tvisible: 265\n\t\t\t};\n\t\tthis['get' + part] = function() {\n\t\t\treturn this[key];\n\t\t};\n\t\tthis['set' + part] = function(value) {\n\t\t\tif (value != this[key]) {\n\t\t\t\tthis[key] = value;\n\t\t\t\tthis._changed(flags[name] || 257);\n\t\t\t}\n\t\t};\n\t},\n{}), {\n\tbeans: true,\n\n\tgetSelection: function() {\n\t\treturn this._selection;\n\t},\n\n\tsetSelection: function(selection) {\n\t\tif (selection !== this._selection) {\n\t\t\tthis._selection = selection;\n\t\t\tvar project = this._project;\n\t\t\tif (project) {\n\t\t\t\tproject._updateSelection(this);\n\t\t\t\tthis._changed(257);\n\t\t\t}\n\t\t}\n\t},\n\n\t_changeSelection: function(flag, selected) {\n\t\tvar selection = this._selection;\n\t\tthis.setSelection(selected ? selection | flag : selection & ~flag);\n\t},\n\n\tisSelected: function() {\n\t\tif (this._selectChildren) {\n\t\t\tvar children = this._children;\n\t\t\tfor (var i = 0, l = children.length; i < l; i++)\n\t\t\t\tif (children[i].isSelected())\n\t\t\t\t\treturn true;\n\t\t}\n\t\treturn !!(this._selection & 1);\n\t},\n\n\tsetSelected: function(selected) {\n\t\tif (this._selectChildren) {\n\t\t\tvar children = this._children;\n\t\t\tfor (var i = 0, l = children.length; i < l; i++)\n\t\t\t\tchildren[i].setSelected(selected);\n\t\t}\n\t\tthis._changeSelection(1, selected);\n\t},\n\n\tisFullySelected: function() {\n\t\tvar children = this._children,\n\t\t\tselected = !!(this._selection & 1);\n\t\tif (children && selected) {\n\t\t\tfor (var i = 0, l = children.length; i < l; i++)\n\t\t\t\tif (!children[i].isFullySelected())\n\t\t\t\t\treturn false;\n\t\t\treturn true;\n\t\t}\n\t\treturn selected;\n\t},\n\n\tsetFullySelected: function(selected) {\n\t\tvar children = this._children;\n\t\tif (children) {\n\t\t\tfor (var i = 0, l = children.length; i < l; i++)\n\t\t\t\tchildren[i].setFullySelected(selected);\n\t\t}\n\t\tthis._changeSelection(1, selected);\n\t},\n\n\tisClipMask: function() {\n\t\treturn this._clipMask;\n\t},\n\n\tsetClipMask: function(clipMask) {\n\t\tif (this._clipMask != (clipMask = !!clipMask)) {\n\t\t\tthis._clipMask = clipMask;\n\t\t\tif (clipMask) {\n\t\t\t\tthis.setFillColor(null);\n\t\t\t\tthis.setStrokeColor(null);\n\t\t\t}\n\t\t\tthis._changed(257);\n\t\t\tif (this._parent)\n\t\t\t\tthis._parent._changed(2048);\n\t\t}\n\t},\n\n\tgetData: function() {\n\t\tif (!this._data)\n\t\t\tthis._data = {};\n\t\treturn this._data;\n\t},\n\n\tsetData: function(data) {\n\t\tthis._data = data;\n\t},\n\n\tgetPosition: function(_dontLink) {\n\t\tvar ctor = _dontLink ? Point : LinkedPoint;\n\t\tvar position = this._position ||\n\t\t\t(this._position = this._getPositionFromBounds());\n\t\treturn new ctor(position.x, position.y, this, 'setPosition');\n\t},\n\n\tsetPosition: function() {\n\t\tthis.translate(Point.read(arguments).subtract(this.getPosition(true)));\n\t},\n\n\t_getPositionFromBounds: function(bounds) {\n\t\treturn this._pivot\n\t\t\t\t? this._matrix._transformPoint(this._pivot)\n\t\t\t\t: (bounds || this.getBounds()).getCenter(true);\n\t},\n\n\tgetPivot: function() {\n\t\tvar pivot = this._pivot;\n\t\treturn pivot\n\t\t\t\t? new LinkedPoint(pivot.x, pivot.y, this, 'setPivot')\n\t\t\t\t: null;\n\t},\n\n\tsetPivot: function() {\n\t\tthis._pivot = Point.read(arguments, 0, { clone: true, readNull: true });\n\t\tthis._position = undefined;\n\t}\n}, Base.each({\n\t\tgetStrokeBounds: { stroke: true },\n\t\tgetHandleBounds: { handle: true },\n\t\tgetInternalBounds: { internal: true }\n\t},\n\tfunction(options, key) {\n\t\tthis[key] = function(matrix) {\n\t\t\treturn this.getBounds(matrix, options);\n\t\t};\n\t},\n{\n\tbeans: true,\n\n\tgetBounds: function(matrix, options) {\n\t\tvar hasMatrix = options || matrix instanceof Matrix,\n\t\t\topts = Base.set({}, hasMatrix ? options : matrix,\n\t\t\t\t\tthis._boundsOptions);\n\t\tif (!opts.stroke || this.getStrokeScaling())\n\t\t\topts.cacheItem = this;\n\t\tvar rect = this._getCachedBounds(hasMatrix && matrix, opts).rect;\n\t\treturn !arguments.length\n\t\t\t\t? new LinkedRectangle(rect.x, rect.y, rect.width, rect.height,\n\t\t\t\t\tthis, 'setBounds')\n\t\t\t\t: rect;\n\t},\n\n\tsetBounds: function() {\n\t\tvar rect = Rectangle.read(arguments),\n\t\t\tbounds = this.getBounds(),\n\t\t\t_matrix = this._matrix,\n\t\t\tmatrix = new Matrix(),\n\t\t\tcenter = rect.getCenter();\n\t\tmatrix.translate(center);\n\t\tif (rect.width != bounds.width || rect.height != bounds.height) {\n\t\t\tif (!_matrix.isInvertible()) {\n\t\t\t\t_matrix.set(_matrix._backup\n\t\t\t\t\t\t|| new Matrix().translate(_matrix.getTranslation()));\n\t\t\t\tbounds = this.getBounds();\n\t\t\t}\n\t\t\tmatrix.scale(\n\t\t\t\t\tbounds.width !== 0 ? rect.width / bounds.width : 0,\n\t\t\t\t\tbounds.height !== 0 ? rect.height / bounds.height : 0);\n\t\t}\n\t\tcenter = bounds.getCenter();\n\t\tmatrix.translate(-center.x, -center.y);\n\t\tthis.transform(matrix);\n\t},\n\n\t_getBounds: function(matrix, options) {\n\t\tvar children = this._children;\n\t\tif (!children || !children.length)\n\t\t\treturn new Rectangle();\n\t\tItem._updateBoundsCache(this, options.cacheItem);\n\t\treturn Item._getBounds(children, matrix, options);\n\t},\n\n\t_getBoundsCacheKey: function(options, internal) {\n\t\treturn [\n\t\t\toptions.stroke ? 1 : 0,\n\t\t\toptions.handle ? 1 : 0,\n\t\t\tinternal ? 1 : 0\n\t\t].join('');\n\t},\n\n\t_getCachedBounds: function(matrix, options, noInternal) {\n\t\tmatrix = matrix && matrix._orNullIfIdentity();\n\t\tvar internal = options.internal && !noInternal,\n\t\t\tcacheItem = options.cacheItem,\n\t\t\t_matrix = internal ? null : this._matrix._orNullIfIdentity(),\n\t\t\tcacheKey = cacheItem && (!matrix || matrix.equals(_matrix))\n\t\t\t\t&& this._getBoundsCacheKey(options, internal),\n\t\t\tbounds = this._bounds;\n\t\tItem._updateBoundsCache(this._parent || this._symbol, cacheItem);\n\t\tif (cacheKey && bounds && cacheKey in bounds) {\n\t\t\tvar cached = bounds[cacheKey];\n\t\t\treturn {\n\t\t\t\trect: cached.rect.clone(),\n\t\t\t\tnonscaling: cached.nonscaling\n\t\t\t};\n\t\t}\n\t\tvar res = this._getBounds(matrix || _matrix, options),\n\t\t\trect = res.rect || res,\n\t\t\tstyle = this._style,\n\t\t\tnonscaling = res.nonscaling || style.hasStroke()\n\t\t\t\t&& !style.getStrokeScaling();\n\t\tif (cacheKey) {\n\t\t\tif (!bounds) {\n\t\t\t\tthis._bounds = bounds = {};\n\t\t\t}\n\t\t\tvar cached = bounds[cacheKey] = {\n\t\t\t\trect: rect.clone(),\n\t\t\t\tnonscaling: nonscaling,\n\t\t\t\tinternal: internal\n\t\t\t};\n\t\t}\n\t\treturn {\n\t\t\trect: rect,\n\t\t\tnonscaling: nonscaling\n\t\t};\n\t},\n\n\t_getStrokeMatrix: function(matrix, options) {\n\t\tvar parent = this.getStrokeScaling() ? null\n\t\t\t\t: options && options.internal ? this\n\t\t\t\t\t: this._parent || this._symbol && this._symbol._item,\n\t\t\tmx = parent ? parent.getViewMatrix().invert() : matrix;\n\t\treturn mx && mx._shiftless();\n\t},\n\n\tstatics: {\n\t\t_updateBoundsCache: function(parent, item) {\n\t\t\tif (parent && item) {\n\t\t\t\tvar id = item._id,\n\t\t\t\t\tref = parent._boundsCache = parent._boundsCache || {\n\t\t\t\t\t\tids: {},\n\t\t\t\t\t\tlist: []\n\t\t\t\t\t};\n\t\t\t\tif (!ref.ids[id]) {\n\t\t\t\t\tref.list.push(item);\n\t\t\t\t\tref.ids[id] = item;\n\t\t\t\t}\n\t\t\t}\n\t\t},\n\n\t\t_clearBoundsCache: function(item) {\n\t\t\tvar cache = item._boundsCache;\n\t\t\tif (cache) {\n\t\t\t\titem._bounds = item._position = item._boundsCache = undefined;\n\t\t\t\tfor (var i = 0, list = cache.list, l = list.length; i < l; i++){\n\t\t\t\t\tvar other = list[i];\n\t\t\t\t\tif (other !== item) {\n\t\t\t\t\t\tother._bounds = other._position = undefined;\n\t\t\t\t\t\tif (other._boundsCache)\n\t\t\t\t\t\t\tItem._clearBoundsCache(other);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t},\n\n\t\t_getBounds: function(items, matrix, options) {\n\t\t\tvar x1 = Infinity,\n\t\t\t\tx2 = -x1,\n\t\t\t\ty1 = x1,\n\t\t\t\ty2 = x2,\n\t\t\t\tnonscaling = false;\n\t\t\toptions = options || {};\n\t\t\tfor (var i = 0, l = items.length; i < l; i++) {\n\t\t\t\tvar item = items[i];\n\t\t\t\tif (item._visible && !item.isEmpty(true)) {\n\t\t\t\t\tvar bounds = item._getCachedBounds(\n\t\t\t\t\t\tmatrix && matrix.appended(item._matrix), options, true),\n\t\t\t\t\t\trect = bounds.rect;\n\t\t\t\t\tx1 = Math.min(rect.x, x1);\n\t\t\t\t\ty1 = Math.min(rect.y, y1);\n\t\t\t\t\tx2 = Math.max(rect.x + rect.width, x2);\n\t\t\t\t\ty2 = Math.max(rect.y + rect.height, y2);\n\t\t\t\t\tif (bounds.nonscaling)\n\t\t\t\t\t\tnonscaling = true;\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn {\n\t\t\t\trect: isFinite(x1)\n\t\t\t\t\t? new Rectangle(x1, y1, x2 - x1, y2 - y1)\n\t\t\t\t\t: new Rectangle(),\n\t\t\t\tnonscaling: nonscaling\n\t\t\t};\n\t\t}\n\t}\n\n}), {\n\tbeans: true,\n\n\t_decompose: function() {\n\t\treturn this._applyMatrix\n\t\t\t? null\n\t\t\t: this._decomposed || (this._decomposed = this._matrix.decompose());\n\t},\n\n\tgetRotation: function() {\n\t\tvar decomposed = this._decompose();\n\t\treturn decomposed ? decomposed.rotation : 0;\n\t},\n\n\tsetRotation: function(rotation) {\n\t\tvar current = this.getRotation();\n\t\tif (current != null && rotation != null) {\n\t\t\tvar decomposed = this._decomposed;\n\t\t\tthis.rotate(rotation - current);\n\t\t\tif (decomposed) {\n\t\t\t\tdecomposed.rotation = rotation;\n\t\t\t\tthis._decomposed = decomposed;\n\t\t\t}\n\t\t}\n\t},\n\n\tgetScaling: function() {\n\t\tvar decomposed = this._decompose(),\n\t\t\ts = decomposed && decomposed.scaling;\n\t\treturn new LinkedPoint(s ? s.x : 1, s ? s.y : 1, this, 'setScaling');\n\t},\n\n\tsetScaling: function() {\n\t\tvar current = this.getScaling(),\n\t\t\tscaling = Point.read(arguments, 0, { clone: true, readNull: true });\n\t\tif (current && scaling && !current.equals(scaling)) {\n\t\t\tvar rotation = this.getRotation(),\n\t\t\t\tdecomposed = this._decomposed,\n\t\t\t\tmatrix = new Matrix(),\n\t\t\t\tisZero = Numerical.isZero;\n\t\t\tif (isZero(current.x) || isZero(current.y)) {\n\t\t\t\tmatrix.translate(decomposed.translation);\n\t\t\t\tif (rotation) {\n\t\t\t\t\tmatrix.rotate(rotation);\n\t\t\t\t}\n\t\t\t\tmatrix.scale(scaling.x, scaling.y);\n\t\t\t\tthis._matrix.set(matrix);\n\t\t\t} else {\n\t\t\t\tvar center = this.getPosition(true);\n\t\t\t\tmatrix.translate(center);\n\t\t\t\tif (rotation)\n\t\t\t\t\tmatrix.rotate(rotation);\n\t\t\t\tmatrix.scale(scaling.x / current.x, scaling.y / current.y);\n\t\t\t\tif (rotation)\n\t\t\t\t\tmatrix.rotate(-rotation);\n\t\t\t\tmatrix.translate(center.negate());\n\t\t\t\tthis.transform(matrix);\n\t\t\t}\n\t\t\tif (decomposed) {\n\t\t\t\tdecomposed.scaling = scaling;\n\t\t\t\tthis._decomposed = decomposed;\n\t\t\t}\n\t\t}\n\t},\n\n\tgetMatrix: function() {\n\t\treturn this._matrix;\n\t},\n\n\tsetMatrix: function() {\n\t\tvar matrix = this._matrix;\n\t\tmatrix.set.apply(matrix, arguments);\n\t},\n\n\tgetGlobalMatrix: function(_dontClone) {\n\t\tvar matrix = this._globalMatrix;\n\t\tif (matrix) {\n\t\t\tvar parent = this._parent;\n\t\t\tvar parents = [];\n\t\t\twhile (parent) {\n\t\t\t\tif (!parent._globalMatrix) {\n\t\t\t\t\tmatrix = null;\n\t\t\t\t\tfor (var i = 0, l = parents.length; i < l; i++) {\n\t\t\t\t\t\tparents[i]._globalMatrix = null;\n\t\t\t\t\t}\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t\tparents.push(parent);\n\t\t\t\tparent = parent._parent;\n\t\t\t}\n\t\t}\n\t\tif (!matrix) {\n\t\t\tmatrix = this._globalMatrix = this._matrix.clone();\n\t\t\tvar parent = this._parent;\n\t\t\tif (parent)\n\t\t\t\tmatrix.prepend(parent.getGlobalMatrix(true));\n\t\t}\n\t\treturn _dontClone ? matrix : matrix.clone();\n\t},\n\n\tgetViewMatrix: function() {\n\t\treturn this.getGlobalMatrix().prepend(this.getView()._matrix);\n\t},\n\n\tgetApplyMatrix: function() {\n\t\treturn this._applyMatrix;\n\t},\n\n\tsetApplyMatrix: function(apply) {\n\t\tif (this._applyMatrix = this._canApplyMatrix && !!apply)\n\t\t\tthis.transform(null, true);\n\t},\n\n\tgetTransformContent: '#getApplyMatrix',\n\tsetTransformContent: '#setApplyMatrix',\n}, {\n\tgetProject: function() {\n\t\treturn this._project;\n\t},\n\n\t_setProject: function(project, installEvents) {\n\t\tif (this._project !== project) {\n\t\t\tif (this._project)\n\t\t\t\tthis._installEvents(false);\n\t\t\tthis._project = project;\n\t\t\tvar children = this._children;\n\t\t\tfor (var i = 0, l = children && children.length; i < l; i++)\n\t\t\t\tchildren[i]._setProject(project);\n\t\t\tinstallEvents = true;\n\t\t}\n\t\tif (installEvents)\n\t\t\tthis._installEvents(true);\n\t},\n\n\tgetView: function() {\n\t\treturn this._project._view;\n\t},\n\n\t_installEvents: function _installEvents(install) {\n\t\t_installEvents.base.call(this, install);\n\t\tvar children = this._children;\n\t\tfor (var i = 0, l = children && children.length; i < l; i++)\n\t\t\tchildren[i]._installEvents(install);\n\t},\n\n\tgetLayer: function() {\n\t\tvar parent = this;\n\t\twhile (parent = parent._parent) {\n\t\t\tif (parent instanceof Layer)\n\t\t\t\treturn parent;\n\t\t}\n\t\treturn null;\n\t},\n\n\tgetParent: function() {\n\t\treturn this._parent;\n\t},\n\n\tsetParent: function(item) {\n\t\treturn item.addChild(this);\n\t},\n\n\t_getOwner: '#getParent',\n\n\tgetChildren: function() {\n\t\treturn this._children;\n\t},\n\n\tsetChildren: function(items) {\n\t\tthis.removeChildren();\n\t\tthis.addChildren(items);\n\t},\n\n\tgetFirstChild: function() {\n\t\treturn this._children && this._children[0] || null;\n\t},\n\n\tgetLastChild: function() {\n\t\treturn this._children && this._children[this._children.length - 1]\n\t\t\t\t|| null;\n\t},\n\n\tgetNextSibling: function() {\n\t\tvar owner = this._getOwner();\n\t\treturn owner && owner._children[this._index + 1] || null;\n\t},\n\n\tgetPreviousSibling: function() {\n\t\tvar owner = this._getOwner();\n\t\treturn owner && owner._children[this._index - 1] || null;\n\t},\n\n\tgetIndex: function() {\n\t\treturn this._index;\n\t},\n\n\tequals: function(item) {\n\t\treturn item === this || item && this._class === item._class\n\t\t\t\t&& this._style.equals(item._style)\n\t\t\t\t&& this._matrix.equals(item._matrix)\n\t\t\t\t&& this._locked === item._locked\n\t\t\t\t&& this._visible === item._visible\n\t\t\t\t&& this._blendMode === item._blendMode\n\t\t\t\t&& this._opacity === item._opacity\n\t\t\t\t&& this._clipMask === item._clipMask\n\t\t\t\t&& this._guide === item._guide\n\t\t\t\t&& this._equals(item)\n\t\t\t\t|| false;\n\t},\n\n\t_equals: function(item) {\n\t\treturn Base.equals(this._children, item._children);\n\t},\n\n\tclone: function(options) {\n\t\tvar copy = new this.constructor(Item.NO_INSERT),\n\t\t\tchildren = this._children,\n\t\t\tinsert = Base.pick(options ? options.insert : undefined,\n\t\t\t\t\toptions === undefined || options === true),\n\t\t\tdeep = Base.pick(options ? options.deep : undefined, true);\n\t\tif (children)\n\t\t\tcopy.copyAttributes(this);\n\t\tif (!children || deep)\n\t\t\tcopy.copyContent(this);\n\t\tif (!children)\n\t\t\tcopy.copyAttributes(this);\n\t\tif (insert)\n\t\t\tcopy.insertAbove(this);\n\t\tvar name = this._name,\n\t\t\tparent = this._parent;\n\t\tif (name && parent) {\n\t\t\tvar children = parent._children,\n\t\t\t\torig = name,\n\t\t\t\ti = 1;\n\t\t\twhile (children[name])\n\t\t\t\tname = orig + ' ' + (i++);\n\t\t\tif (name !== orig)\n\t\t\t\tcopy.setName(name);\n\t\t}\n\t\treturn copy;\n\t},\n\n\tcopyContent: function(source) {\n\t\tvar children = source._children;\n\t\tfor (var i = 0, l = children && children.length; i < l; i++) {\n\t\t\tthis.addChild(children[i].clone(false), true);\n\t\t}\n\t},\n\n\tcopyAttributes: function(source, excludeMatrix) {\n\t\tthis.setStyle(source._style);\n\t\tvar keys = ['_locked', '_visible', '_blendMode', '_opacity',\n\t\t\t\t'_clipMask', '_guide'];\n\t\tfor (var i = 0, l = keys.length; i < l; i++) {\n\t\t\tvar key = keys[i];\n\t\t\tif (source.hasOwnProperty(key))\n\t\t\t\tthis[key] = source[key];\n\t\t}\n\t\tif (!excludeMatrix)\n\t\t\tthis._matrix.set(source._matrix, true);\n\t\tthis.setApplyMatrix(source._applyMatrix);\n\t\tthis.setPivot(source._pivot);\n\t\tthis.setSelection(source._selection);\n\t\tvar data = source._data,\n\t\t\tname = source._name;\n\t\tthis._data = data ? Base.clone(data) : null;\n\t\tif (name)\n\t\t\tthis.setName(name);\n\t},\n\n\trasterize: function(arg0, arg1) {\n\t\tvar resolution,\n\t\t\tinsert,\n\t\t\traster;\n\t\tif (Base.isPlainObject(arg0)) {\n\t\t\tresolution = arg0.resolution;\n\t\t\tinsert = arg0.insert;\n\t\t\traster = arg0.raster;\n\t\t} else {\n\t\t\tresolution = arg0;\n\t\t\tinsert = arg1;\n\t\t}\n\t\tif (raster) {\n\t\t\traster.matrix.reset(true);\n\t\t} else {\n\t\t\traster = new Raster(Item.NO_INSERT);\n\t\t}\n\t\tvar bounds = this.getStrokeBounds(),\n\t\t\tscale = (resolution || this.getView().getResolution()) / 72,\n\t\t\ttopLeft = bounds.getTopLeft().floor(),\n\t\t\tbottomRight = bounds.getBottomRight().ceil(),\n\t\t\tboundsSize = new Size(bottomRight.subtract(topLeft)),\n\t\t\trasterSize = boundsSize.multiply(scale);\n\t\traster.setSize(rasterSize, true);\n\n\t\tif (!rasterSize.isZero()) {\n\t\t\tvar ctx = raster.getContext(true),\n\t\t\t\tmatrix = new Matrix().scale(scale).translate(topLeft.negate());\n\t\t\tctx.save();\n\t\t\tmatrix.applyToContext(ctx);\n\t\t\tthis.draw(ctx, new Base({ matrices: [matrix] }));\n\t\t\tctx.restore();\n\t\t}\n\t\traster.transform(\n\t\t\tnew Matrix()\n\t\t\t\t.translate(topLeft.add(boundsSize.divide(2)))\n\t\t\t\t.scale(1 / scale)\n\t\t);\n\t\tif (insert === undefined || insert) {\n\t\t\traster.insertAbove(this);\n\t\t}\n\t\treturn raster;\n\t},\n\n\tcontains: function() {\n\t\tvar matrix = this._matrix;\n\t\treturn (\n\t\t\tmatrix.isInvertible() &&\n\t\t\t!!this._contains(matrix._inverseTransform(Point.read(arguments)))\n\t\t);\n\t},\n\n\t_contains: function(point) {\n\t\tvar children = this._children;\n\t\tif (children) {\n\t\t\tfor (var i = children.length - 1; i >= 0; i--) {\n\t\t\t\tif (children[i].contains(point))\n\t\t\t\t\treturn true;\n\t\t\t}\n\t\t\treturn false;\n\t\t}\n\t\treturn point.isInside(this.getInternalBounds());\n\t},\n\n\tisInside: function() {\n\t\treturn Rectangle.read(arguments).contains(this.getBounds());\n\t},\n\n\t_asPathItem: function() {\n\t\treturn new Path.Rectangle({\n\t\t\trectangle: this.getInternalBounds(),\n\t\t\tmatrix: this._matrix,\n\t\t\tinsert: false,\n\t\t});\n\t},\n\n\tintersects: function(item, _matrix) {\n\t\tif (!(item instanceof Item))\n\t\t\treturn false;\n\t\treturn this._asPathItem().getIntersections(item._asPathItem(), null,\n\t\t\t\t_matrix, true).length > 0;\n\t}\n},\nnew function() {\n\tfunction hitTest() {\n\t\tvar args = arguments;\n\t\treturn this._hitTest(\n\t\t\t\tPoint.read(args),\n\t\t\t\tHitResult.getOptions(args));\n\t}\n\n\tfunction hitTestAll() {\n\t\tvar args = arguments,\n\t\t\tpoint = Point.read(args),\n\t\t\toptions = HitResult.getOptions(args),\n\t\t\tall = [];\n\t\tthis._hitTest(point, new Base({ all: all }, options));\n\t\treturn all;\n\t}\n\n\tfunction hitTestChildren(point, options, viewMatrix, _exclude) {\n\t\tvar children = this._children;\n\t\tif (children) {\n\t\t\tfor (var i = children.length - 1; i >= 0; i--) {\n\t\t\t\tvar child = children[i];\n\t\t\t\tvar res = child !== _exclude && child._hitTest(point, options,\n\t\t\t\t\t\tviewMatrix);\n\t\t\t\tif (res && !options.all)\n\t\t\t\t\treturn res;\n\t\t\t}\n\t\t}\n\t\treturn null;\n\t}\n\n\tProject.inject({\n\t\thitTest: hitTest,\n\t\thitTestAll: hitTestAll,\n\t\t_hitTest: hitTestChildren\n\t});\n\n\treturn {\n\t\thitTest: hitTest,\n\t\thitTestAll: hitTestAll,\n\t\t_hitTestChildren: hitTestChildren,\n\t};\n}, {\n\n\t_hitTest: function(point, options, parentViewMatrix) {\n\t\tif (this._locked || !this._visible || this._guide && !options.guides\n\t\t\t\t|| this.isEmpty()) {\n\t\t\treturn null;\n\t\t}\n\n\t\tvar matrix = this._matrix,\n\t\t\tviewMatrix = parentViewMatrix\n\t\t\t\t\t? parentViewMatrix.appended(matrix)\n\t\t\t\t\t: this.getGlobalMatrix().prepend(this.getView()._matrix),\n\t\t\ttolerance = Math.max(options.tolerance, 1e-12),\n\t\t\ttolerancePadding = options._tolerancePadding = new Size(\n\t\t\t\t\tPath._getStrokePadding(tolerance,\n\t\t\t\t\t\tmatrix._shiftless().invert()));\n\t\tpoint = matrix._inverseTransform(point);\n\t\tif (!point || !this._children &&\n\t\t\t!this.getBounds({ internal: true, stroke: true, handle: true })\n\t\t\t\t.expand(tolerancePadding.multiply(2))._containsPoint(point)) {\n\t\t\treturn null;\n\t\t}\n\n\t\tvar checkSelf = !(options.guides && !this._guide\n\t\t\t\t|| options.selected && !this.isSelected()\n\t\t\t\t|| options.type && options.type !== Base.hyphenate(this._class)\n\t\t\t\t|| options.class && !(this instanceof options.class)),\n\t\t\tmatch = options.match,\n\t\t\tthat = this,\n\t\t\tbounds,\n\t\t\tres;\n\n\t\tfunction filter(hit) {\n\t\t\tif (hit && match && !match(hit))\n\t\t\t\thit = null;\n\t\t\tif (hit && options.all)\n\t\t\t\toptions.all.push(hit);\n\t\t\treturn hit;\n\t\t}\n\n\t\tfunction checkPoint(type, part) {\n\t\t\tvar pt = part ? bounds['get' + part]() : that.getPosition();\n\t\t\tif (point.subtract(pt).divide(tolerancePadding).length <= 1) {\n\t\t\t\treturn new HitResult(type, that, {\n\t\t\t\t\tname: part ? Base.hyphenate(part) : type,\n\t\t\t\t\tpoint: pt\n\t\t\t\t});\n\t\t\t}\n\t\t}\n\n\t\tvar checkPosition = options.position,\n\t\t\tcheckCenter = options.center,\n\t\t\tcheckBounds = options.bounds;\n\t\tif (checkSelf && this._parent\n\t\t\t\t&& (checkPosition || checkCenter || checkBounds)) {\n\t\t\tif (checkCenter || checkBounds) {\n\t\t\t\tbounds = this.getInternalBounds();\n\t\t\t}\n\t\t\tres = checkPosition && checkPoint('position') ||\n\t\t\t\t\tcheckCenter && checkPoint('center', 'Center');\n\t\t\tif (!res && checkBounds) {\n\t\t\t\tvar points = [\n\t\t\t\t\t'TopLeft', 'TopRight', 'BottomLeft', 'BottomRight',\n\t\t\t\t\t'LeftCenter', 'TopCenter', 'RightCenter', 'BottomCenter'\n\t\t\t\t];\n\t\t\t\tfor (var i = 0; i < 8 && !res; i++) {\n\t\t\t\t\tres = checkPoint('bounds', points[i]);\n\t\t\t\t}\n\t\t\t}\n\t\t\tres = filter(res);\n\t\t}\n\n\t\tif (!res) {\n\t\t\tres = this._hitTestChildren(point, options, viewMatrix)\n\t\t\t\t|| checkSelf\n\t\t\t\t\t&& filter(this._hitTestSelf(point, options, viewMatrix,\n\t\t\t\t\t\tthis.getStrokeScaling() ? null\n\t\t\t\t\t\t\t: viewMatrix._shiftless().invert()))\n\t\t\t\t|| null;\n\t\t}\n\t\tif (res && res.point) {\n\t\t\tres.point = matrix.transform(res.point);\n\t\t}\n\t\treturn res;\n\t},\n\n\t_hitTestSelf: function(point, options) {\n\t\tif (options.fill && this.hasFill() && this._contains(point))\n\t\t\treturn new HitResult('fill', this);\n\t},\n\n\tmatches: function(name, compare) {\n\t\tfunction matchObject(obj1, obj2) {\n\t\t\tfor (var i in obj1) {\n\t\t\t\tif (obj1.hasOwnProperty(i)) {\n\t\t\t\t\tvar val1 = obj1[i],\n\t\t\t\t\t\tval2 = obj2[i];\n\t\t\t\t\tif (Base.isPlainObject(val1) && Base.isPlainObject(val2)) {\n\t\t\t\t\t\tif (!matchObject(val1, val2))\n\t\t\t\t\t\t\treturn false;\n\t\t\t\t\t} else if (!Base.equals(val1, val2)) {\n\t\t\t\t\t\treturn false;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn true;\n\t\t}\n\t\tvar type = typeof name;\n\t\tif (type === 'object') {\n\t\t\tfor (var key in name) {\n\t\t\t\tif (name.hasOwnProperty(key) && !this.matches(key, name[key]))\n\t\t\t\t\treturn false;\n\t\t\t}\n\t\t\treturn true;\n\t\t} else if (type === 'function') {\n\t\t\treturn name(this);\n\t\t} else if (name === 'match') {\n\t\t\treturn compare(this);\n\t\t} else {\n\t\t\tvar value = /^(empty|editable)$/.test(name)\n\t\t\t\t\t? this['is' + Base.capitalize(name)]()\n\t\t\t\t\t: name === 'type'\n\t\t\t\t\t\t? Base.hyphenate(this._class)\n\t\t\t\t\t\t: this[name];\n\t\t\tif (name === 'class') {\n\t\t\t\tif (typeof compare === 'function')\n\t\t\t\t\treturn this instanceof compare;\n\t\t\t\tvalue = this._class;\n\t\t\t}\n\t\t\tif (typeof compare === 'function') {\n\t\t\t\treturn !!compare(value);\n\t\t\t} else if (compare) {\n\t\t\t\tif (compare.test) {\n\t\t\t\t\treturn compare.test(value);\n\t\t\t\t} else if (Base.isPlainObject(compare)) {\n\t\t\t\t\treturn matchObject(compare, value);\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn Base.equals(value, compare);\n\t\t}\n\t},\n\n\tgetItems: function(options) {\n\t\treturn Item._getItems(this, options, this._matrix);\n\t},\n\n\tgetItem: function(options) {\n\t\treturn Item._getItems(this, options, this._matrix, null, true)[0]\n\t\t\t\t|| null;\n\t},\n\n\tstatics: {\n\t\t_getItems: function _getItems(item, options, matrix, param, firstOnly) {\n\t\t\tif (!param) {\n\t\t\t\tvar obj = typeof options === 'object' && options,\n\t\t\t\t\toverlapping = obj && obj.overlapping,\n\t\t\t\t\tinside = obj && obj.inside,\n\t\t\t\t\tbounds = overlapping || inside,\n\t\t\t\t\trect = bounds && Rectangle.read([bounds]);\n\t\t\t\tparam = {\n\t\t\t\t\titems: [],\n\t\t\t\t\trecursive: obj && obj.recursive !== false,\n\t\t\t\t\tinside: !!inside,\n\t\t\t\t\toverlapping: !!overlapping,\n\t\t\t\t\trect: rect,\n\t\t\t\t\tpath: overlapping && new Path.Rectangle({\n\t\t\t\t\t\trectangle: rect,\n\t\t\t\t\t\tinsert: false\n\t\t\t\t\t})\n\t\t\t\t};\n\t\t\t\tif (obj) {\n\t\t\t\t\toptions = Base.filter({}, options, {\n\t\t\t\t\t\trecursive: true, inside: true, overlapping: true\n\t\t\t\t\t});\n\t\t\t\t}\n\t\t\t}\n\t\t\tvar children = item._children,\n\t\t\t\titems = param.items,\n\t\t\t\trect = param.rect;\n\t\t\tmatrix = rect && (matrix || new Matrix());\n\t\t\tfor (var i = 0, l = children && children.length; i < l; i++) {\n\t\t\t\tvar child = children[i],\n\t\t\t\t\tchildMatrix = matrix && matrix.appended(child._matrix),\n\t\t\t\t\tadd = true;\n\t\t\t\tif (rect) {\n\t\t\t\t\tvar bounds = child.getBounds(childMatrix);\n\t\t\t\t\tif (!rect.intersects(bounds))\n\t\t\t\t\t\tcontinue;\n\t\t\t\t\tif (!(rect.contains(bounds)\n\t\t\t\t\t\t\t|| param.overlapping && (bounds.contains(rect)\n\t\t\t\t\t\t\t\t|| param.path.intersects(child, childMatrix))))\n\t\t\t\t\t\tadd = false;\n\t\t\t\t}\n\t\t\t\tif (add && child.matches(options)) {\n\t\t\t\t\titems.push(child);\n\t\t\t\t\tif (firstOnly)\n\t\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t\tif (param.recursive !== false) {\n\t\t\t\t\t_getItems(child, options, childMatrix, param, firstOnly);\n\t\t\t\t}\n\t\t\t\tif (firstOnly && items.length > 0)\n\t\t\t\t\tbreak;\n\t\t\t}\n\t\t\treturn items;\n\t\t}\n\t}\n}, {\n\n\timportJSON: function(json) {\n\t\tvar res = Base.importJSON(json, this);\n\t\treturn res !== this ? this.addChild(res) : res;\n\t},\n\n\taddChild: function(item) {\n\t\treturn this.insertChild(undefined, item);\n\t},\n\n\tinsertChild: function(index, item) {\n\t\tvar res = item ? this.insertChildren(index, [item]) : null;\n\t\treturn res && res[0];\n\t},\n\n\taddChildren: function(items) {\n\t\treturn this.insertChildren(this._children.length, items);\n\t},\n\n\tinsertChildren: function(index, items) {\n\t\tvar children = this._children;\n\t\tif (children && items && items.length > 0) {\n\t\t\titems = Base.slice(items);\n\t\t\tvar inserted = {};\n\t\t\tfor (var i = items.length - 1; i >= 0; i--) {\n\t\t\t\tvar item = items[i],\n\t\t\t\t\tid = item && item._id;\n\t\t\t\tif (!item || inserted[id]) {\n\t\t\t\t\titems.splice(i, 1);\n\t\t\t\t} else {\n\t\t\t\t\titem._remove(false, true);\n\t\t\t\t\tinserted[id] = true;\n\t\t\t\t}\n\t\t\t}\n\t\t\tBase.splice(children, items, index, 0);\n\t\t\tvar project = this._project,\n\t\t\t\tnotifySelf = project._changes;\n\t\t\tfor (var i = 0, l = items.length; i < l; i++) {\n\t\t\t\tvar item = items[i],\n\t\t\t\t\tname = item._name;\n\t\t\t\titem._parent = this;\n\t\t\t\titem._setProject(project, true);\n\t\t\t\tif (name)\n\t\t\t\t\titem.setName(name);\n\t\t\t\tif (notifySelf)\n\t\t\t\t\titem._changed(5);\n\t\t\t}\n\t\t\tthis._changed(11);\n\t\t} else {\n\t\t\titems = null;\n\t\t}\n\t\treturn items;\n\t},\n\n\t_insertItem: '#insertChild',\n\n\t_insertAt: function(item, offset) {\n\t\tvar owner = item && item._getOwner(),\n\t\t\tres = item !== this && owner ? this : null;\n\t\tif (res) {\n\t\t\tres._remove(false, true);\n\t\t\towner._insertItem(item._index + offset, res);\n\t\t}\n\t\treturn res;\n\t},\n\n\tinsertAbove: function(item) {\n\t\treturn this._insertAt(item, 1);\n\t},\n\n\tinsertBelow: function(item) {\n\t\treturn this._insertAt(item, 0);\n\t},\n\n\tsendToBack: function() {\n\t\tvar owner = this._getOwner();\n\t\treturn owner ? owner._insertItem(0, this) : null;\n\t},\n\n\tbringToFront: function() {\n\t\tvar owner = this._getOwner();\n\t\treturn owner ? owner._insertItem(undefined, this) : null;\n\t},\n\n\tappendTop: '#addChild',\n\n\tappendBottom: function(item) {\n\t\treturn this.insertChild(0, item);\n\t},\n\n\tmoveAbove: '#insertAbove',\n\n\tmoveBelow: '#insertBelow',\n\n\taddTo: function(owner) {\n\t\treturn owner._insertItem(undefined, this);\n\t},\n\n\tcopyTo: function(owner) {\n\t\treturn this.clone(false).addTo(owner);\n\t},\n\n\treduce: function(options) {\n\t\tvar children = this._children;\n\t\tif (children && children.length === 1) {\n\t\t\tvar child = children[0].reduce(options);\n\t\t\tif (this._parent) {\n\t\t\t\tchild.insertAbove(this);\n\t\t\t\tthis.remove();\n\t\t\t} else {\n\t\t\t\tchild.remove();\n\t\t\t}\n\t\t\treturn child;\n\t\t}\n\t\treturn this;\n\t},\n\n\t_removeNamed: function() {\n\t\tvar owner = this._getOwner();\n\t\tif (owner) {\n\t\t\tvar children = owner._children,\n\t\t\t\tnamedChildren = owner._namedChildren,\n\t\t\t\tname = this._name,\n\t\t\t\tnamedArray = namedChildren[name],\n\t\t\t\tindex = namedArray ? namedArray.indexOf(this) : -1;\n\t\t\tif (index !== -1) {\n\t\t\t\tif (children[name] == this)\n\t\t\t\t\tdelete children[name];\n\t\t\t\tnamedArray.splice(index, 1);\n\t\t\t\tif (namedArray.length) {\n\t\t\t\t\tchildren[name] = namedArray[0];\n\t\t\t\t} else {\n\t\t\t\t\tdelete namedChildren[name];\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t},\n\n\t_remove: function(notifySelf, notifyParent) {\n\t\tvar owner = this._getOwner(),\n\t\t\tproject = this._project,\n\t\t\tindex = this._index;\n\t\tif (this._style)\n\t\t\tthis._style._dispose();\n\t\tif (owner) {\n\t\t\tif (this._name)\n\t\t\t\tthis._removeNamed();\n\t\t\tif (index != null) {\n\t\t\t\tif (project._activeLayer === this)\n\t\t\t\t\tproject._activeLayer = this.getNextSibling()\n\t\t\t\t\t\t\t|| this.getPreviousSibling();\n\t\t\t\tBase.splice(owner._children, null, index, 1);\n\t\t\t}\n\t\t\tthis._installEvents(false);\n\t\t\tif (notifySelf && project._changes)\n\t\t\t\tthis._changed(5);\n\t\t\tif (notifyParent)\n\t\t\t\towner._changed(11, this);\n\t\t\tthis._parent = null;\n\t\t\treturn true;\n\t\t}\n\t\treturn false;\n\t},\n\n\tremove: function() {\n\t\treturn this._remove(true, true);\n\t},\n\n\treplaceWith: function(item) {\n\t\tvar ok = item && item.insertBelow(this);\n\t\tif (ok)\n\t\t\tthis.remove();\n\t\treturn ok;\n\t},\n\n\tremoveChildren: function(start, end) {\n\t\tif (!this._children)\n\t\t\treturn null;\n\t\tstart = start || 0;\n\t\tend = Base.pick(end, this._children.length);\n\t\tvar removed = Base.splice(this._children, null, start, end - start);\n\t\tfor (var i = removed.length - 1; i >= 0; i--) {\n\t\t\tremoved[i]._remove(true, false);\n\t\t}\n\t\tif (removed.length > 0)\n\t\t\tthis._changed(11);\n\t\treturn removed;\n\t},\n\n\tclear: '#removeChildren',\n\n\treverseChildren: function() {\n\t\tif (this._children) {\n\t\t\tthis._children.reverse();\n\t\t\tfor (var i = 0, l = this._children.length; i < l; i++)\n\t\t\t\tthis._children[i]._index = i;\n\t\t\tthis._changed(11);\n\t\t}\n\t},\n\n\tisEmpty: function(recursively) {\n\t\tvar children = this._children;\n\t\tvar numChildren = children ? children.length : 0;\n\t\tif (recursively) {\n\t\t\tfor (var i = 0; i < numChildren; i++) {\n\t\t\t\tif (!children[i].isEmpty(recursively)) {\n\t\t\t\t\treturn false;\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn true;\n\t\t}\n\t\treturn !numChildren;\n\t},\n\n\tisEditable: function() {\n\t\tvar item = this;\n\t\twhile (item) {\n\t\t\tif (!item._visible || item._locked)\n\t\t\t\treturn false;\n\t\t\titem = item._parent;\n\t\t}\n\t\treturn true;\n\t},\n\n\thasFill: function() {\n\t\treturn this.getStyle().hasFill();\n\t},\n\n\thasStroke: function() {\n\t\treturn this.getStyle().hasStroke();\n\t},\n\n\thasShadow: function() {\n\t\treturn this.getStyle().hasShadow();\n\t},\n\n\t_getOrder: function(item) {\n\t\tfunction getList(item) {\n\t\t\tvar list = [];\n\t\t\tdo {\n\t\t\t\tlist.unshift(item);\n\t\t\t} while (item = item._parent);\n\t\t\treturn list;\n\t\t}\n\t\tvar list1 = getList(this),\n\t\t\tlist2 = getList(item);\n\t\tfor (var i = 0, l = Math.min(list1.length, list2.length); i < l; i++) {\n\t\t\tif (list1[i] != list2[i]) {\n\t\t\t\treturn list1[i]._index < list2[i]._index ? 1 : -1;\n\t\t\t}\n\t\t}\n\t\treturn 0;\n\t},\n\n\thasChildren: function() {\n\t\treturn this._children && this._children.length > 0;\n\t},\n\n\tisInserted: function() {\n\t\treturn this._parent ? this._parent.isInserted() : false;\n\t},\n\n\tisAbove: function(item) {\n\t\treturn this._getOrder(item) === -1;\n\t},\n\n\tisBelow: function(item) {\n\t\treturn this._getOrder(item) === 1;\n\t},\n\n\tisParent: function(item) {\n\t\treturn this._parent === item;\n\t},\n\n\tisChild: function(item) {\n\t\treturn item && item._parent === this;\n\t},\n\n\tisDescendant: function(item) {\n\t\tvar parent = this;\n\t\twhile (parent = parent._parent) {\n\t\t\tif (parent === item)\n\t\t\t\treturn true;\n\t\t}\n\t\treturn false;\n\t},\n\n\tisAncestor: function(item) {\n\t\treturn item ? item.isDescendant(this) : false;\n\t},\n\n\tisSibling: function(item) {\n\t\treturn this._parent === item._parent;\n\t},\n\n\tisGroupedWith: function(item) {\n\t\tvar parent = this._parent;\n\t\twhile (parent) {\n\t\t\tif (parent._parent\n\t\t\t\t&& /^(Group|Layer|CompoundPath)$/.test(parent._class)\n\t\t\t\t&& item.isDescendant(parent))\n\t\t\t\t\treturn true;\n\t\t\tparent = parent._parent;\n\t\t}\n\t\treturn false;\n\t},\n\n}, Base.each(['rotate', 'scale', 'shear', 'skew'], function(key) {\n\tvar rotate = key === 'rotate';\n\tthis[key] = function() {\n\t\tvar args = arguments,\n\t\t\tvalue = (rotate ? Base : Point).read(args),\n\t\t\tcenter = Point.read(args, 0, { readNull: true });\n\t\treturn this.transform(new Matrix()[key](value,\n\t\t\t\tcenter || this.getPosition(true)));\n\t};\n}, {\n\ttranslate: function() {\n\t\tvar mx = new Matrix();\n\t\treturn this.transform(mx.translate.apply(mx, arguments));\n\t},\n\n\ttransform: function(matrix, _applyRecursively, _setApplyMatrix) {\n\t\tvar _matrix = this._matrix,\n\t\t\ttransformMatrix = matrix && !matrix.isIdentity(),\n\t\t\tapplyMatrix = (\n\t\t\t\t_setApplyMatrix && this._canApplyMatrix ||\n\t\t\t\tthis._applyMatrix && (\n\t\t\t\t\ttransformMatrix || !_matrix.isIdentity() ||\n\t\t\t\t\t_applyRecursively && this._children\n\t\t\t\t)\n\t\t\t);\n\t\tif (!transformMatrix && !applyMatrix)\n\t\t\treturn this;\n\t\tif (transformMatrix) {\n\t\t\tif (!matrix.isInvertible() && _matrix.isInvertible())\n\t\t\t\t_matrix._backup = _matrix.getValues();\n\t\t\t_matrix.prepend(matrix, true);\n\t\t\tvar style = this._style,\n\t\t\t\tfillColor = style.getFillColor(true),\n\t\t\t\tstrokeColor = style.getStrokeColor(true);\n\t\t\tif (fillColor)\n\t\t\t\tfillColor.transform(matrix);\n\t\t\tif (strokeColor)\n\t\t\t\tstrokeColor.transform(matrix);\n\t\t}\n\n\t\tif (applyMatrix && (applyMatrix = this._transformContent(\n\t\t\t\t_matrix, _applyRecursively, _setApplyMatrix))) {\n\t\t\tvar pivot = this._pivot;\n\t\t\tif (pivot)\n\t\t\t\t_matrix._transformPoint(pivot, pivot, true);\n\t\t\t_matrix.reset(true);\n\t\t\tif (_setApplyMatrix && this._canApplyMatrix)\n\t\t\t\tthis._applyMatrix = true;\n\t\t}\n\t\tvar bounds = this._bounds,\n\t\t\tposition = this._position;\n\t\tif (transformMatrix || applyMatrix) {\n\t\t\tthis._changed(25);\n\t\t}\n\t\tvar decomp = transformMatrix && bounds && matrix.decompose();\n\t\tif (decomp && decomp.skewing.isZero() && decomp.rotation % 90 === 0) {\n\t\t\tfor (var key in bounds) {\n\t\t\t\tvar cache = bounds[key];\n\t\t\t\tif (cache.nonscaling) {\n\t\t\t\t\tdelete bounds[key];\n\t\t\t\t} else if (applyMatrix || !cache.internal) {\n\t\t\t\t\tvar rect = cache.rect;\n\t\t\t\t\tmatrix._transformBounds(rect, rect);\n\t\t\t\t}\n\t\t\t}\n\t\t\tthis._bounds = bounds;\n\t\t\tvar cached = bounds[this._getBoundsCacheKey(\n\t\t\t\tthis._boundsOptions || {})];\n\t\t\tif (cached) {\n\t\t\t\tthis._position = this._getPositionFromBounds(cached.rect);\n\t\t\t}\n\t\t} else if (transformMatrix && position && this._pivot) {\n\t\t\tthis._position = matrix._transformPoint(position, position);\n\t\t}\n\t\treturn this;\n\t},\n\n\t_transformContent: function(matrix, applyRecursively, setApplyMatrix) {\n\t\tvar children = this._children;\n\t\tif (children) {\n\t\t\tfor (var i = 0, l = children.length; i < l; i++) {\n\t\t\t\tchildren[i].transform(matrix, applyRecursively, setApplyMatrix);\n\t\t\t}\n\t\t\treturn true;\n\t\t}\n\t},\n\n\tglobalToLocal: function() {\n\t\treturn this.getGlobalMatrix(true)._inverseTransform(\n\t\t\t\tPoint.read(arguments));\n\t},\n\n\tlocalToGlobal: function() {\n\t\treturn this.getGlobalMatrix(true)._transformPoint(\n\t\t\t\tPoint.read(arguments));\n\t},\n\n\tparentToLocal: function() {\n\t\treturn this._matrix._inverseTransform(Point.read(arguments));\n\t},\n\n\tlocalToParent: function() {\n\t\treturn this._matrix._transformPoint(Point.read(arguments));\n\t},\n\n\tfitBounds: function(rectangle, fill) {\n\t\trectangle = Rectangle.read(arguments);\n\t\tvar bounds = this.getBounds(),\n\t\t\titemRatio = bounds.height / bounds.width,\n\t\t\trectRatio = rectangle.height / rectangle.width,\n\t\t\tscale = (fill ? itemRatio > rectRatio : itemRatio < rectRatio)\n\t\t\t\t\t? rectangle.width / bounds.width\n\t\t\t\t\t: rectangle.height / bounds.height,\n\t\t\tnewBounds = new Rectangle(new Point(),\n\t\t\t\t\tnew Size(bounds.width * scale, bounds.height * scale));\n\t\tnewBounds.setCenter(rectangle.getCenter());\n\t\tthis.setBounds(newBounds);\n\t}\n}), {\n\n\t_setStyles: function(ctx, param, viewMatrix) {\n\t\tvar style = this._style,\n\t\t\tmatrix = this._matrix;\n\t\tif (style.hasFill()) {\n\t\t\tctx.fillStyle = style.getFillColor().toCanvasStyle(ctx, matrix);\n\t\t}\n\t\tif (style.hasStroke()) {\n\t\t\tctx.strokeStyle = style.getStrokeColor().toCanvasStyle(ctx, matrix);\n\t\t\tctx.lineWidth = style.getStrokeWidth();\n\t\t\tvar strokeJoin = style.getStrokeJoin(),\n\t\t\t\tstrokeCap = style.getStrokeCap(),\n\t\t\t\tmiterLimit = style.getMiterLimit();\n\t\t\tif (strokeJoin)\n\t\t\t\tctx.lineJoin = strokeJoin;\n\t\t\tif (strokeCap)\n\t\t\t\tctx.lineCap = strokeCap;\n\t\t\tif (miterLimit)\n\t\t\t\tctx.miterLimit = miterLimit;\n\t\t\tif (paper.support.nativeDash) {\n\t\t\t\tvar dashArray = style.getDashArray(),\n\t\t\t\t\tdashOffset = style.getDashOffset();\n\t\t\t\tif (dashArray && dashArray.length) {\n\t\t\t\t\tif ('setLineDash' in ctx) {\n\t\t\t\t\t\tctx.setLineDash(dashArray);\n\t\t\t\t\t\tctx.lineDashOffset = dashOffset;\n\t\t\t\t\t} else {\n\t\t\t\t\t\tctx.mozDash = dashArray;\n\t\t\t\t\t\tctx.mozDashOffset = dashOffset;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\tif (style.hasShadow()) {\n\t\t\tvar pixelRatio = param.pixelRatio || 1,\n\t\t\t\tmx = viewMatrix._shiftless().prepend(\n\t\t\t\t\tnew Matrix().scale(pixelRatio, pixelRatio)),\n\t\t\t\tblur = mx.transform(new Point(style.getShadowBlur(), 0)),\n\t\t\t\toffset = mx.transform(this.getShadowOffset());\n\t\t\tctx.shadowColor = style.getShadowColor().toCanvasStyle(ctx);\n\t\t\tctx.shadowBlur = blur.getLength();\n\t\t\tctx.shadowOffsetX = offset.x;\n\t\t\tctx.shadowOffsetY = offset.y;\n\t\t}\n\t},\n\n\tdraw: function(ctx, param, parentStrokeMatrix) {\n\t\tvar updateVersion = this._updateVersion = this._project._updateVersion;\n\t\tif (!this._visible || this._opacity === 0)\n\t\t\treturn;\n\t\tvar matrices = param.matrices,\n\t\t\tviewMatrix = param.viewMatrix,\n\t\t\tmatrix = this._matrix,\n\t\t\tglobalMatrix = matrices[matrices.length - 1].appended(matrix);\n\t\tif (!globalMatrix.isInvertible())\n\t\t\treturn;\n\n\t\tviewMatrix = viewMatrix ? viewMatrix.appended(globalMatrix)\n\t\t\t\t: globalMatrix;\n\n\t\tmatrices.push(globalMatrix);\n\t\tif (param.updateMatrix) {\n\t\t\tthis._globalMatrix = globalMatrix;\n\t\t}\n\n\t\tvar blendMode = this._blendMode,\n\t\t\topacity = Numerical.clamp(this._opacity, 0, 1),\n\t\t\tnormalBlend = blendMode === 'normal',\n\t\t\tnativeBlend = BlendMode.nativeModes[blendMode],\n\t\t\tdirect = normalBlend && opacity === 1\n\t\t\t\t\t|| param.dontStart\n\t\t\t\t\t|| param.clip\n\t\t\t\t\t|| (nativeBlend || normalBlend && opacity < 1)\n\t\t\t\t\t\t&& this._canComposite(),\n\t\t\tpixelRatio = param.pixelRatio || 1,\n\t\t\tmainCtx, itemOffset, prevOffset;\n\t\tif (!direct) {\n\t\t\tvar bounds = this.getStrokeBounds(viewMatrix);\n\t\t\tif (!bounds.width || !bounds.height) {\n\t\t\t\tmatrices.pop();\n\t\t\t\treturn;\n\t\t\t}\n\t\t\tprevOffset = param.offset;\n\t\t\titemOffset = param.offset = bounds.getTopLeft().floor();\n\t\t\tmainCtx = ctx;\n\t\t\tctx = CanvasProvider.getContext(bounds.getSize().ceil().add(1)\n\t\t\t\t\t.multiply(pixelRatio));\n\t\t\tif (pixelRatio !== 1)\n\t\t\t\tctx.scale(pixelRatio, pixelRatio);\n\t\t}\n\t\tctx.save();\n\t\tvar strokeMatrix = parentStrokeMatrix\n\t\t\t\t? parentStrokeMatrix.appended(matrix)\n\t\t\t\t: this._canScaleStroke && !this.getStrokeScaling(true)\n\t\t\t\t\t&& viewMatrix,\n\t\t\tclip = !direct && param.clipItem,\n\t\t\ttransform = !strokeMatrix || clip;\n\t\tif (direct) {\n\t\t\tctx.globalAlpha = opacity;\n\t\t\tif (nativeBlend)\n\t\t\t\tctx.globalCompositeOperation = blendMode;\n\t\t} else if (transform) {\n\t\t\tctx.translate(-itemOffset.x, -itemOffset.y);\n\t\t}\n\t\tif (transform) {\n\t\t\t(direct ? matrix : viewMatrix).applyToContext(ctx);\n\t\t}\n\t\tif (clip) {\n\t\t\tparam.clipItem.draw(ctx, param.extend({ clip: true }));\n\t\t}\n\t\tif (strokeMatrix) {\n\t\t\tctx.setTransform(pixelRatio, 0, 0, pixelRatio, 0, 0);\n\t\t\tvar offset = param.offset;\n\t\t\tif (offset)\n\t\t\t\tctx.translate(-offset.x, -offset.y);\n\t\t}\n\t\tthis._draw(ctx, param, viewMatrix, strokeMatrix);\n\t\tctx.restore();\n\t\tmatrices.pop();\n\t\tif (param.clip && !param.dontFinish) {\n\t\t\tctx.clip(this.getFillRule());\n\t\t}\n\t\tif (!direct) {\n\t\t\tBlendMode.process(blendMode, ctx, mainCtx, opacity,\n\t\t\t\t\titemOffset.subtract(prevOffset).multiply(pixelRatio));\n\t\t\tCanvasProvider.release(ctx);\n\t\t\tparam.offset = prevOffset;\n\t\t}\n\t},\n\n\t_isUpdated: function(updateVersion) {\n\t\tvar parent = this._parent;\n\t\tif (parent instanceof CompoundPath)\n\t\t\treturn parent._isUpdated(updateVersion);\n\t\tvar updated = this._updateVersion === updateVersion;\n\t\tif (!updated && parent && parent._visible\n\t\t\t\t&& parent._isUpdated(updateVersion)) {\n\t\t\tthis._updateVersion = updateVersion;\n\t\t\tupdated = true;\n\t\t}\n\t\treturn updated;\n\t},\n\n\t_drawSelection: function(ctx, matrix, size, selectionItems, updateVersion) {\n\t\tvar selection = this._selection,\n\t\t\titemSelected = selection & 1,\n\t\t\tboundsSelected = selection & 2\n\t\t\t\t\t|| itemSelected && this._selectBounds,\n\t\t\tpositionSelected = selection & 4;\n\t\tif (!this._drawSelected)\n\t\t\titemSelected = false;\n\t\tif ((itemSelected || boundsSelected || positionSelected)\n\t\t\t\t&& this._isUpdated(updateVersion)) {\n\t\t\tvar layer,\n\t\t\t\tcolor = this.getSelectedColor(true) || (layer = this.getLayer())\n\t\t\t\t\t&& layer.getSelectedColor(true),\n\t\t\t\tmx = matrix.appended(this.getGlobalMatrix(true)),\n\t\t\t\thalf = size / 2;\n\t\t\tctx.strokeStyle = ctx.fillStyle = color\n\t\t\t\t\t? color.toCanvasStyle(ctx) : '#009dec';\n\t\t\tif (itemSelected)\n\t\t\t\tthis._drawSelected(ctx, mx, selectionItems);\n\t\t\tif (positionSelected) {\n\t\t\t\tvar pos = this.getPosition(true),\n\t\t\t\t\tparent = this._parent,\n\t\t\t\t\tpoint = parent ? parent.localToGlobal(pos) : pos,\n\t\t\t\t\tx = point.x,\n\t\t\t\t\ty = point.y;\n\t\t\t\tctx.beginPath();\n\t\t\t\tctx.arc(x, y, half, 0, Math.PI * 2, true);\n\t\t\t\tctx.stroke();\n\t\t\t\tvar deltas = [[0, -1], [1, 0], [0, 1], [-1, 0]],\n\t\t\t\t\tstart = half,\n\t\t\t\t\tend = size + 1;\n\t\t\t\tfor (var i = 0; i < 4; i++) {\n\t\t\t\t\tvar delta = deltas[i],\n\t\t\t\t\t\tdx = delta[0],\n\t\t\t\t\t\tdy = delta[1];\n\t\t\t\t\tctx.moveTo(x + dx * start, y + dy * start);\n\t\t\t\t\tctx.lineTo(x + dx * end, y + dy * end);\n\t\t\t\t\tctx.stroke();\n\t\t\t\t}\n\t\t\t}\n\t\t\tif (boundsSelected) {\n\t\t\t\tvar coords = mx._transformCorners(this.getInternalBounds());\n\t\t\t\tctx.beginPath();\n\t\t\t\tfor (var i = 0; i < 8; i++) {\n\t\t\t\t\tctx[!i ? 'moveTo' : 'lineTo'](coords[i], coords[++i]);\n\t\t\t\t}\n\t\t\t\tctx.closePath();\n\t\t\t\tctx.stroke();\n\t\t\t\tfor (var i = 0; i < 8; i++) {\n\t\t\t\t\tctx.fillRect(coords[i] - half, coords[++i] - half,\n\t\t\t\t\t\t\tsize, size);\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t},\n\n\t_canComposite: function() {\n\t\treturn false;\n\t}\n}, Base.each(['down', 'drag', 'up', 'move'], function(key) {\n\tthis['removeOn' + Base.capitalize(key)] = function() {\n\t\tvar hash = {};\n\t\thash[key] = true;\n\t\treturn this.removeOn(hash);\n\t};\n}, {\n\n\tremoveOn: function(obj) {\n\t\tfor (var name in obj) {\n\t\t\tif (obj[name]) {\n\t\t\t\tvar key = 'mouse' + name,\n\t\t\t\t\tproject = this._project,\n\t\t\t\t\tsets = project._removeSets = project._removeSets || {};\n\t\t\t\tsets[key] = sets[key] || {};\n\t\t\t\tsets[key][this._id] = this;\n\t\t\t}\n\t\t}\n\t\treturn this;\n\t}\n}), {\n\ttween: function(from, to, options) {\n\t\tif (!options) {\n\t\t\toptions = to;\n\t\t\tto = from;\n\t\t\tfrom = null;\n\t\t\tif (!options) {\n\t\t\t\toptions = to;\n\t\t\t\tto = null;\n\t\t\t}\n\t\t}\n\t\tvar easing = options && options.easing,\n\t\t\tstart = options && options.start,\n\t\t\tduration = options != null && (\n\t\t\t\ttypeof options === 'number' ? options : options.duration\n\t\t\t),\n\t\t\ttween = new Tween(this, from, to, duration, easing, start);\n\t\tfunction onFrame(event) {\n\t\t\ttween._handleFrame(event.time * 1000);\n\t\t\tif (!tween.running) {\n\t\t\t\tthis.off('frame', onFrame);\n\t\t\t}\n\t\t}\n\t\tif (duration) {\n\t\t\tthis.on('frame', onFrame);\n\t\t}\n\t\treturn tween;\n\t},\n\n\ttweenTo: function(to, options) {\n\t\treturn this.tween(null, to, options);\n\t},\n\n\ttweenFrom: function(from, options) {\n\t\treturn this.tween(from, null, options);\n\t}\n});\n\nvar Group = Item.extend({\n\t_class: 'Group',\n\t_selectBounds: false,\n\t_selectChildren: true,\n\t_serializeFields: {\n\t\tchildren: []\n\t},\n\n\tinitialize: function Group(arg) {\n\t\tthis._children = [];\n\t\tthis._namedChildren = {};\n\t\tif (!this._initialize(arg))\n\t\t\tthis.addChildren(Array.isArray(arg) ? arg : arguments);\n\t},\n\n\t_changed: function _changed(flags) {\n\t\t_changed.base.call(this, flags);\n\t\tif (flags & 2050) {\n\t\t\tthis._clipItem = undefined;\n\t\t}\n\t},\n\n\t_getClipItem: function() {\n\t\tvar clipItem = this._clipItem;\n\t\tif (clipItem === undefined) {\n\t\t\tclipItem = null;\n\t\t\tvar children = this._children;\n\t\t\tfor (var i = 0, l = children.length; i < l; i++) {\n\t\t\t\tif (children[i]._clipMask) {\n\t\t\t\t\tclipItem = children[i];\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t\tthis._clipItem = clipItem;\n\t\t}\n\t\treturn clipItem;\n\t},\n\n\tisClipped: function() {\n\t\treturn !!this._getClipItem();\n\t},\n\n\tsetClipped: function(clipped) {\n\t\tvar child = this.getFirstChild();\n\t\tif (child)\n\t\t\tchild.setClipMask(clipped);\n\t},\n\n\t_getBounds: function _getBounds(matrix, options) {\n\t\tvar clipItem = this._getClipItem();\n\t\treturn clipItem\n\t\t\t? clipItem._getCachedBounds(clipItem._matrix.prepended(matrix),\n\t\t\t\tBase.set({}, options, { stroke: false }))\n\t\t\t: _getBounds.base.call(this, matrix, options);\n\t},\n\n\t_hitTestChildren: function _hitTestChildren(point, options, viewMatrix) {\n\t\tvar clipItem = this._getClipItem();\n\t\treturn (!clipItem || clipItem.contains(point))\n\t\t\t\t&& _hitTestChildren.base.call(this, point, options, viewMatrix,\n\t\t\t\t\tclipItem);\n\t},\n\n\t_draw: function(ctx, param) {\n\t\tvar clip = param.clip,\n\t\t\tclipItem = !clip && this._getClipItem();\n\t\tparam = param.extend({ clipItem: clipItem, clip: false });\n\t\tif (clip) {\n\t\t\tctx.beginPath();\n\t\t\tparam.dontStart = param.dontFinish = true;\n\t\t} else if (clipItem) {\n\t\t\tclipItem.draw(ctx, param.extend({ clip: true }));\n\t\t}\n\t\tvar children = this._children;\n\t\tfor (var i = 0, l = children.length; i < l; i++) {\n\t\t\tvar item = children[i];\n\t\t\tif (item !== clipItem)\n\t\t\t\titem.draw(ctx, param);\n\t\t}\n\t}\n});\n\nvar Layer = Group.extend({\n\t_class: 'Layer',\n\n\tinitialize: function Layer() {\n\t\tGroup.apply(this, arguments);\n\t},\n\n\t_getOwner: function() {\n\t\treturn this._parent || this._index != null && this._project;\n\t},\n\n\tisInserted: function isInserted() {\n\t\treturn this._parent ? isInserted.base.call(this) : this._index != null;\n\t},\n\n\tactivate: function() {\n\t\tthis._project._activeLayer = this;\n\t},\n\n\t_hitTestSelf: function() {\n\t}\n});\n\nvar Shape = Item.extend({\n\t_class: 'Shape',\n\t_applyMatrix: false,\n\t_canApplyMatrix: false,\n\t_canScaleStroke: true,\n\t_serializeFields: {\n\t\ttype: null,\n\t\tsize: null,\n\t\tradius: null\n\t},\n\n\tinitialize: function Shape(props, point) {\n\t\tthis._initialize(props, point);\n\t},\n\n\t_equals: function(item) {\n\t\treturn this._type === item._type\n\t\t\t&& this._size.equals(item._size)\n\t\t\t&& Base.equals(this._radius, item._radius);\n\t},\n\n\tcopyContent: function(source) {\n\t\tthis.setType(source._type);\n\t\tthis.setSize(source._size);\n\t\tthis.setRadius(source._radius);\n\t},\n\n\tgetType: function() {\n\t\treturn this._type;\n\t},\n\n\tsetType: function(type) {\n\t\tthis._type = type;\n\t},\n\n\tgetShape: '#getType',\n\tsetShape: '#setType',\n\n\tgetSize: function() {\n\t\tvar size = this._size;\n\t\treturn new LinkedSize(size.width, size.height, this, 'setSize');\n\t},\n\n\tsetSize: function() {\n\t\tvar size = Size.read(arguments);\n\t\tif (!this._size) {\n\t\t\tthis._size = size.clone();\n\t\t} else if (!this._size.equals(size)) {\n\t\t\tvar type = this._type,\n\t\t\t\twidth = size.width,\n\t\t\t\theight = size.height;\n\t\t\tif (type === 'rectangle') {\n\t\t\t\tthis._radius.set(Size.min(this._radius, size.divide(2).abs()));\n\t\t\t} else if (type === 'circle') {\n\t\t\t\twidth = height = (width + height) / 2;\n\t\t\t\tthis._radius = width / 2;\n\t\t\t} else if (type === 'ellipse') {\n\t\t\t\tthis._radius._set(width / 2, height / 2);\n\t\t\t}\n\t\t\tthis._size._set(width, height);\n\t\t\tthis._changed(9);\n\t\t}\n\t},\n\n\tgetRadius: function() {\n\t\tvar rad = this._radius;\n\t\treturn this._type === 'circle'\n\t\t\t\t? rad\n\t\t\t\t: new LinkedSize(rad.width, rad.height, this, 'setRadius');\n\t},\n\n\tsetRadius: function(radius) {\n\t\tvar type = this._type;\n\t\tif (type === 'circle') {\n\t\t\tif (radius === this._radius)\n\t\t\t\treturn;\n\t\t\tvar size = radius * 2;\n\t\t\tthis._radius = radius;\n\t\t\tthis._size._set(size, size);\n\t\t} else {\n\t\t\tradius = Size.read(arguments);\n\t\t\tif (!this._radius) {\n\t\t\t\tthis._radius = radius.clone();\n\t\t\t} else {\n\t\t\t\tif (this._radius.equals(radius))\n\t\t\t\t\treturn;\n\t\t\t\tthis._radius.set(radius);\n\t\t\t\tif (type === 'rectangle') {\n\t\t\t\t\tvar size = Size.max(this._size, radius.multiply(2));\n\t\t\t\t\tthis._size.set(size);\n\t\t\t\t} else if (type === 'ellipse') {\n\t\t\t\t\tthis._size._set(radius.width * 2, radius.height * 2);\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\tthis._changed(9);\n\t},\n\n\tisEmpty: function() {\n\t\treturn false;\n\t},\n\n\ttoPath: function(insert) {\n\t\tvar path = new Path[Base.capitalize(this._type)]({\n\t\t\tcenter: new Point(),\n\t\t\tsize: this._size,\n\t\t\tradius: this._radius,\n\t\t\tinsert: false\n\t\t});\n\t\tpath.copyAttributes(this);\n\t\tif (paper.settings.applyMatrix)\n\t\t\tpath.setApplyMatrix(true);\n\t\tif (insert === undefined || insert)\n\t\t\tpath.insertAbove(this);\n\t\treturn path;\n\t},\n\n\ttoShape: '#clone',\n\n\t_asPathItem: function() {\n\t\treturn this.toPath(false);\n\t},\n\n\t_draw: function(ctx, param, viewMatrix, strokeMatrix) {\n\t\tvar style = this._style,\n\t\t\thasFill = style.hasFill(),\n\t\t\thasStroke = style.hasStroke(),\n\t\t\tdontPaint = param.dontFinish || param.clip,\n\t\t\tuntransformed = !strokeMatrix;\n\t\tif (hasFill || hasStroke || dontPaint) {\n\t\t\tvar type = this._type,\n\t\t\t\tradius = this._radius,\n\t\t\t\tisCircle = type === 'circle';\n\t\t\tif (!param.dontStart)\n\t\t\t\tctx.beginPath();\n\t\t\tif (untransformed && isCircle) {\n\t\t\t\tctx.arc(0, 0, radius, 0, Math.PI * 2, true);\n\t\t\t} else {\n\t\t\t\tvar rx = isCircle ? radius : radius.width,\n\t\t\t\t\try = isCircle ? radius : radius.height,\n\t\t\t\t\tsize = this._size,\n\t\t\t\t\twidth = size.width,\n\t\t\t\t\theight = size.height;\n\t\t\t\tif (untransformed && type === 'rectangle' && rx === 0 && ry === 0) {\n\t\t\t\t\tctx.rect(-width / 2, -height / 2, width, height);\n\t\t\t\t} else {\n\t\t\t\t\tvar x = width / 2,\n\t\t\t\t\t\ty = height / 2,\n\t\t\t\t\t\tkappa = 1 - 0.5522847498307936,\n\t\t\t\t\t\tcx = rx * kappa,\n\t\t\t\t\t\tcy = ry * kappa,\n\t\t\t\t\t\tc = [\n\t\t\t\t\t\t\t-x, -y + ry,\n\t\t\t\t\t\t\t-x, -y + cy,\n\t\t\t\t\t\t\t-x + cx, -y,\n\t\t\t\t\t\t\t-x + rx, -y,\n\t\t\t\t\t\t\tx - rx, -y,\n\t\t\t\t\t\t\tx - cx, -y,\n\t\t\t\t\t\t\tx, -y + cy,\n\t\t\t\t\t\t\tx, -y + ry,\n\t\t\t\t\t\t\tx, y - ry,\n\t\t\t\t\t\t\tx, y - cy,\n\t\t\t\t\t\t\tx - cx, y,\n\t\t\t\t\t\t\tx - rx, y,\n\t\t\t\t\t\t\t-x + rx, y,\n\t\t\t\t\t\t\t-x + cx, y,\n\t\t\t\t\t\t\t-x, y - cy,\n\t\t\t\t\t\t\t-x, y - ry\n\t\t\t\t\t\t];\n\t\t\t\t\tif (strokeMatrix)\n\t\t\t\t\t\tstrokeMatrix.transform(c, c, 32);\n\t\t\t\t\tctx.moveTo(c[0], c[1]);\n\t\t\t\t\tctx.bezierCurveTo(c[2], c[3], c[4], c[5], c[6], c[7]);\n\t\t\t\t\tif (x !== rx)\n\t\t\t\t\t\tctx.lineTo(c[8], c[9]);\n\t\t\t\t\tctx.bezierCurveTo(c[10], c[11], c[12], c[13], c[14], c[15]);\n\t\t\t\t\tif (y !== ry)\n\t\t\t\t\t\tctx.lineTo(c[16], c[17]);\n\t\t\t\t\tctx.bezierCurveTo(c[18], c[19], c[20], c[21], c[22], c[23]);\n\t\t\t\t\tif (x !== rx)\n\t\t\t\t\t\tctx.lineTo(c[24], c[25]);\n\t\t\t\t\tctx.bezierCurveTo(c[26], c[27], c[28], c[29], c[30], c[31]);\n\t\t\t\t}\n\t\t\t}\n\t\t\tctx.closePath();\n\t\t}\n\t\tif (!dontPaint && (hasFill || hasStroke)) {\n\t\t\tthis._setStyles(ctx, param, viewMatrix);\n\t\t\tif (hasFill) {\n\t\t\t\tctx.fill(style.getFillRule());\n\t\t\t\tctx.shadowColor = 'rgba(0,0,0,0)';\n\t\t\t}\n\t\t\tif (hasStroke)\n\t\t\t\tctx.stroke();\n\t\t}\n\t},\n\n\t_canComposite: function() {\n\t\treturn !(this.hasFill() && this.hasStroke());\n\t},\n\n\t_getBounds: function(matrix, options) {\n\t\tvar rect = new Rectangle(this._size).setCenter(0, 0),\n\t\t\tstyle = this._style,\n\t\t\tstrokeWidth = options.stroke && style.hasStroke()\n\t\t\t\t\t&& style.getStrokeWidth();\n\t\tif (matrix)\n\t\t\trect = matrix._transformBounds(rect);\n\t\treturn strokeWidth\n\t\t\t\t? rect.expand(Path._getStrokePadding(strokeWidth,\n\t\t\t\t\tthis._getStrokeMatrix(matrix, options)))\n\t\t\t\t: rect;\n\t}\n},\nnew function() {\n\tfunction getCornerCenter(that, point, expand) {\n\t\tvar radius = that._radius;\n\t\tif (!radius.isZero()) {\n\t\t\tvar halfSize = that._size.divide(2);\n\t\t\tfor (var q = 1; q <= 4; q++) {\n\t\t\t\tvar dir = new Point(q > 1 && q < 4 ? -1 : 1, q > 2 ? -1 : 1),\n\t\t\t\t\tcorner = dir.multiply(halfSize),\n\t\t\t\t\tcenter = corner.subtract(dir.multiply(radius)),\n\t\t\t\t\trect = new Rectangle(\n\t\t\t\t\t\t\texpand ? corner.add(dir.multiply(expand)) : corner,\n\t\t\t\t\t\t\tcenter);\n\t\t\t\tif (rect.contains(point))\n\t\t\t\t\treturn { point: center, quadrant: q };\n\t\t\t}\n\t\t}\n\t}\n\n\tfunction isOnEllipseStroke(point, radius, padding, quadrant) {\n\t\tvar vector = point.divide(radius);\n\t\treturn (!quadrant || vector.isInQuadrant(quadrant)) &&\n\t\t\t\tvector.subtract(vector.normalize()).multiply(radius)\n\t\t\t\t\t.divide(padding).length <= 1;\n\t}\n\n\treturn {\n\t\t_contains: function _contains(point) {\n\t\t\tif (this._type === 'rectangle') {\n\t\t\t\tvar center = getCornerCenter(this, point);\n\t\t\t\treturn center\n\t\t\t\t\t\t? point.subtract(center.point).divide(this._radius)\n\t\t\t\t\t\t\t.getLength() <= 1\n\t\t\t\t\t\t: _contains.base.call(this, point);\n\t\t\t} else {\n\t\t\t\treturn point.divide(this.size).getLength() <= 0.5;\n\t\t\t}\n\t\t},\n\n\t\t_hitTestSelf: function _hitTestSelf(point, options, viewMatrix,\n\t\t\t\tstrokeMatrix) {\n\t\t\tvar hit = false,\n\t\t\t\tstyle = this._style,\n\t\t\t\thitStroke = options.stroke && style.hasStroke(),\n\t\t\t\thitFill = options.fill && style.hasFill();\n\t\t\tif (hitStroke || hitFill) {\n\t\t\t\tvar type = this._type,\n\t\t\t\t\tradius = this._radius,\n\t\t\t\t\tstrokeRadius = hitStroke ? style.getStrokeWidth() / 2 : 0,\n\t\t\t\t\tstrokePadding = options._tolerancePadding.add(\n\t\t\t\t\t\tPath._getStrokePadding(strokeRadius,\n\t\t\t\t\t\t\t!style.getStrokeScaling() && strokeMatrix));\n\t\t\t\tif (type === 'rectangle') {\n\t\t\t\t\tvar padding = strokePadding.multiply(2),\n\t\t\t\t\t\tcenter = getCornerCenter(this, point, padding);\n\t\t\t\t\tif (center) {\n\t\t\t\t\t\thit = isOnEllipseStroke(point.subtract(center.point),\n\t\t\t\t\t\t\t\tradius, strokePadding, center.quadrant);\n\t\t\t\t\t} else {\n\t\t\t\t\t\tvar rect = new Rectangle(this._size).setCenter(0, 0),\n\t\t\t\t\t\t\touter = rect.expand(padding),\n\t\t\t\t\t\t\tinner = rect.expand(padding.negate());\n\t\t\t\t\t\thit = outer._containsPoint(point)\n\t\t\t\t\t\t\t\t&& !inner._containsPoint(point);\n\t\t\t\t\t}\n\t\t\t\t} else {\n\t\t\t\t\thit = isOnEllipseStroke(point, radius, strokePadding);\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn hit ? new HitResult(hitStroke ? 'stroke' : 'fill', this)\n\t\t\t\t\t: _hitTestSelf.base.apply(this, arguments);\n\t\t}\n\t};\n}, {\n\nstatics: new function() {\n\tfunction createShape(type, point, size, radius, args) {\n\t\tvar item = Base.create(Shape.prototype);\n\t\titem._type = type;\n\t\titem._size = size;\n\t\titem._radius = radius;\n\t\titem._initialize(Base.getNamed(args), point);\n\t\treturn item;\n\t}\n\n\treturn {\n\t\tCircle: function() {\n\t\t\tvar args = arguments,\n\t\t\t\tcenter = Point.readNamed(args, 'center'),\n\t\t\t\tradius = Base.readNamed(args, 'radius');\n\t\t\treturn createShape('circle', center, new Size(radius * 2), radius,\n\t\t\t\t\targs);\n\t\t},\n\n\t\tRectangle: function() {\n\t\t\tvar args = arguments,\n\t\t\t\trect = Rectangle.readNamed(args, 'rectangle'),\n\t\t\t\tradius = Size.min(Size.readNamed(args, 'radius'),\n\t\t\t\t\t\trect.getSize(true).divide(2));\n\t\t\treturn createShape('rectangle', rect.getCenter(true),\n\t\t\t\t\trect.getSize(true), radius, args);\n\t\t},\n\n\t\tEllipse: function() {\n\t\t\tvar args = arguments,\n\t\t\t\tellipse = Shape._readEllipse(args),\n\t\t\t\tradius = ellipse.radius;\n\t\t\treturn createShape('ellipse', ellipse.center, radius.multiply(2),\n\t\t\t\t\tradius, args);\n\t\t},\n\n\t\t_readEllipse: function(args) {\n\t\t\tvar center,\n\t\t\t\tradius;\n\t\t\tif (Base.hasNamed(args, 'radius')) {\n\t\t\t\tcenter = Point.readNamed(args, 'center');\n\t\t\t\tradius = Size.readNamed(args, 'radius');\n\t\t\t} else {\n\t\t\t\tvar rect = Rectangle.readNamed(args, 'rectangle');\n\t\t\t\tcenter = rect.getCenter(true);\n\t\t\t\tradius = rect.getSize(true).divide(2);\n\t\t\t}\n\t\t\treturn { center: center, radius: radius };\n\t\t}\n\t};\n}});\n\nvar Raster = Item.extend({\n\t_class: 'Raster',\n\t_applyMatrix: false,\n\t_canApplyMatrix: false,\n\t_boundsOptions: { stroke: false, handle: false },\n\t_serializeFields: {\n\t\tcrossOrigin: null,\n\t\tsource: null\n\t},\n\t_prioritize: ['crossOrigin'],\n\t_smoothing: 'low',\n\tbeans: true,\n\n\tinitialize: function Raster(source, position) {\n\t\tif (!this._initialize(source,\n\t\t\t\tposition !== undefined && Point.read(arguments))) {\n\t\t\tvar image,\n\t\t\t\ttype = typeof source,\n\t\t\t\tobject = type === 'string'\n\t\t\t\t\t? document.getElementById(source)\n\t\t\t\t\t: type  === 'object'\n\t\t\t\t\t\t? source\n\t\t\t\t\t\t: null;\n\t\t\tif (object && object !== Item.NO_INSERT) {\n\t\t\t\tif (object.getContext || object.naturalHeight != null) {\n\t\t\t\t\timage = object;\n\t\t\t\t} else if (object) {\n\t\t\t\t\tvar size = Size.read(arguments);\n\t\t\t\t\tif (!size.isZero()) {\n\t\t\t\t\t\timage = CanvasProvider.getCanvas(size);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t\tif (image) {\n\t\t\t\tthis.setImage(image);\n\t\t\t} else {\n\t\t\t\tthis.setSource(source);\n\t\t\t}\n\t\t}\n\t\tif (!this._size) {\n\t\t\tthis._size = new Size();\n\t\t\tthis._loaded = false;\n\t\t}\n\t},\n\n\t_equals: function(item) {\n\t\treturn this.getSource() === item.getSource();\n\t},\n\n\tcopyContent: function(source) {\n\t\tvar image = source._image,\n\t\t\tcanvas = source._canvas;\n\t\tif (image) {\n\t\t\tthis._setImage(image);\n\t\t} else if (canvas) {\n\t\t\tvar copyCanvas = CanvasProvider.getCanvas(source._size);\n\t\t\tcopyCanvas.getContext('2d').drawImage(canvas, 0, 0);\n\t\t\tthis._setImage(copyCanvas);\n\t\t}\n\t\tthis._crossOrigin = source._crossOrigin;\n\t},\n\n\tgetSize: function() {\n\t\tvar size = this._size;\n\t\treturn new LinkedSize(size ? size.width : 0, size ? size.height : 0,\n\t\t\t\tthis, 'setSize');\n\t},\n\n\tsetSize: function(_size, _clear) {\n\t\tvar size = Size.read(arguments);\n\t\tif (!size.equals(this._size)) {\n\t\t\tif (size.width > 0 && size.height > 0) {\n\t\t\t\tvar element = !_clear && this.getElement();\n\t\t\t\tthis._setImage(CanvasProvider.getCanvas(size));\n\t\t\t\tif (element) {\n\t\t\t\t\tthis.getContext(true).drawImage(element, 0, 0,\n\t\t\t\t\t\t\tsize.width, size.height);\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tif (this._canvas)\n\t\t\t\t\tCanvasProvider.release(this._canvas);\n\t\t\t\tthis._size = size.clone();\n\t\t\t}\n\t\t} else if (_clear) {\n\t\t\tthis.clear();\n\t\t}\n\t},\n\n\tgetWidth: function() {\n\t\treturn this._size ? this._size.width : 0;\n\t},\n\n\tsetWidth: function(width) {\n\t\tthis.setSize(width, this.getHeight());\n\t},\n\n\tgetHeight: function() {\n\t\treturn this._size ? this._size.height : 0;\n\t},\n\n\tsetHeight: function(height) {\n\t\tthis.setSize(this.getWidth(), height);\n\t},\n\n\tgetLoaded: function() {\n\t\treturn this._loaded;\n\t},\n\n\tisEmpty: function() {\n\t\tvar size = this._size;\n\t\treturn !size || size.width === 0 && size.height === 0;\n\t},\n\n\tgetResolution: function() {\n\t\tvar matrix = this._matrix,\n\t\t\torig = new Point(0, 0).transform(matrix),\n\t\t\tu = new Point(1, 0).transform(matrix).subtract(orig),\n\t\t\tv = new Point(0, 1).transform(matrix).subtract(orig);\n\t\treturn new Size(\n\t\t\t72 / u.getLength(),\n\t\t\t72 / v.getLength()\n\t\t);\n\t},\n\n\tgetPpi: '#getResolution',\n\n\tgetImage: function() {\n\t\treturn this._image;\n\t},\n\n\tsetImage: function(image) {\n\t\tvar that = this;\n\n\t\tfunction emit(event) {\n\t\t\tvar view = that.getView(),\n\t\t\t\ttype = event && event.type || 'load';\n\t\t\tif (view && that.responds(type)) {\n\t\t\t\tpaper = view._scope;\n\t\t\t\tthat.emit(type, new Event(event));\n\t\t\t}\n\t\t}\n\n\t\tthis._setImage(image);\n\t\tif (this._loaded) {\n\t\t\tsetTimeout(emit, 0);\n\t\t} else if (image) {\n\t\t\tDomEvent.add(image, {\n\t\t\t\tload: function(event) {\n\t\t\t\t\tthat._setImage(image);\n\t\t\t\t\temit(event);\n\t\t\t\t},\n\t\t\t\terror: emit\n\t\t\t});\n\t\t}\n\t},\n\n\t_setImage: function(image) {\n\t\tif (this._canvas)\n\t\t\tCanvasProvider.release(this._canvas);\n\t\tif (image && image.getContext) {\n\t\t\tthis._image = null;\n\t\t\tthis._canvas = image;\n\t\t\tthis._loaded = true;\n\t\t} else {\n\t\t\tthis._image = image;\n\t\t\tthis._canvas = null;\n\t\t\tthis._loaded = !!(image && image.src && image.complete);\n\t\t}\n\t\tthis._size = new Size(\n\t\t\t\timage ? image.naturalWidth || image.width : 0,\n\t\t\t\timage ? image.naturalHeight || image.height : 0);\n\t\tthis._context = null;\n\t\tthis._changed(1033);\n\t},\n\n\tgetCanvas: function() {\n\t\tif (!this._canvas) {\n\t\t\tvar ctx = CanvasProvider.getContext(this._size);\n\t\t\ttry {\n\t\t\t\tif (this._image)\n\t\t\t\t\tctx.drawImage(this._image, 0, 0);\n\t\t\t\tthis._canvas = ctx.canvas;\n\t\t\t} catch (e) {\n\t\t\t\tCanvasProvider.release(ctx);\n\t\t\t}\n\t\t}\n\t\treturn this._canvas;\n\t},\n\n\tsetCanvas: '#setImage',\n\n\tgetContext: function(_change) {\n\t\tif (!this._context)\n\t\t\tthis._context = this.getCanvas().getContext('2d');\n\t\tif (_change) {\n\t\t\tthis._image = null;\n\t\t\tthis._changed(1025);\n\t\t}\n\t\treturn this._context;\n\t},\n\n\tsetContext: function(context) {\n\t\tthis._context = context;\n\t},\n\n\tgetSource: function() {\n\t\tvar image = this._image;\n\t\treturn image && image.src || this.toDataURL();\n\t},\n\n\tsetSource: function(src) {\n\t\tvar image = new self.Image(),\n\t\t\tcrossOrigin = this._crossOrigin;\n\t\tif (crossOrigin)\n\t\t\timage.crossOrigin = crossOrigin;\n\t\tif (src)\n\t\t\timage.src = src;\n\t\tthis.setImage(image);\n\t},\n\n\tgetCrossOrigin: function() {\n\t\tvar image = this._image;\n\t\treturn image && image.crossOrigin || this._crossOrigin || '';\n\t},\n\n\tsetCrossOrigin: function(crossOrigin) {\n\t\tthis._crossOrigin = crossOrigin;\n\t\tvar image = this._image;\n\t\tif (image)\n\t\t\timage.crossOrigin = crossOrigin;\n\t},\n\n\tgetSmoothing: function() {\n\t\treturn this._smoothing;\n\t},\n\n\tsetSmoothing: function(smoothing) {\n\t\tthis._smoothing = typeof smoothing === 'string'\n\t\t\t? smoothing\n\t\t\t: smoothing ? 'low' : 'off';\n\t\tthis._changed(257);\n\t},\n\n\tgetElement: function() {\n\t\treturn this._canvas || this._loaded && this._image;\n\t}\n}, {\n\tbeans: false,\n\n\tgetSubCanvas: function() {\n\t\tvar rect = Rectangle.read(arguments),\n\t\t\tctx = CanvasProvider.getContext(rect.getSize());\n\t\tctx.drawImage(this.getCanvas(), rect.x, rect.y,\n\t\t\t\trect.width, rect.height, 0, 0, rect.width, rect.height);\n\t\treturn ctx.canvas;\n\t},\n\n\tgetSubRaster: function() {\n\t\tvar rect = Rectangle.read(arguments),\n\t\t\traster = new Raster(Item.NO_INSERT);\n\t\traster._setImage(this.getSubCanvas(rect));\n\t\traster.translate(rect.getCenter().subtract(this.getSize().divide(2)));\n\t\traster._matrix.prepend(this._matrix);\n\t\traster.insertAbove(this);\n\t\treturn raster;\n\t},\n\n\ttoDataURL: function() {\n\t\tvar image = this._image,\n\t\t\tsrc = image && image.src;\n\t\tif (/^data:/.test(src))\n\t\t\treturn src;\n\t\tvar canvas = this.getCanvas();\n\t\treturn canvas ? canvas.toDataURL.apply(canvas, arguments) : null;\n\t},\n\n\tdrawImage: function(image ) {\n\t\tvar point = Point.read(arguments, 1);\n\t\tthis.getContext(true).drawImage(image, point.x, point.y);\n\t},\n\n\tgetAverageColor: function(object) {\n\t\tvar bounds, path;\n\t\tif (!object) {\n\t\t\tbounds = this.getBounds();\n\t\t} else if (object instanceof PathItem) {\n\t\t\tpath = object;\n\t\t\tbounds = object.getBounds();\n\t\t} else if (typeof object === 'object') {\n\t\t\tif ('width' in object) {\n\t\t\t\tbounds = new Rectangle(object);\n\t\t\t} else if ('x' in object) {\n\t\t\t\tbounds = new Rectangle(object.x - 0.5, object.y - 0.5, 1, 1);\n\t\t\t}\n\t\t}\n\t\tif (!bounds)\n\t\t\treturn null;\n\t\tvar sampleSize = 32,\n\t\t\twidth = Math.min(bounds.width, sampleSize),\n\t\t\theight = Math.min(bounds.height, sampleSize);\n\t\tvar ctx = Raster._sampleContext;\n\t\tif (!ctx) {\n\t\t\tctx = Raster._sampleContext = CanvasProvider.getContext(\n\t\t\t\t\tnew Size(sampleSize));\n\t\t} else {\n\t\t\tctx.clearRect(0, 0, sampleSize + 1, sampleSize + 1);\n\t\t}\n\t\tctx.save();\n\t\tvar matrix = new Matrix()\n\t\t\t\t.scale(width / bounds.width, height / bounds.height)\n\t\t\t\t.translate(-bounds.x, -bounds.y);\n\t\tmatrix.applyToContext(ctx);\n\t\tif (path)\n\t\t\tpath.draw(ctx, new Base({ clip: true, matrices: [matrix] }));\n\t\tthis._matrix.applyToContext(ctx);\n\t\tvar element = this.getElement(),\n\t\t\tsize = this._size;\n\t\tif (element)\n\t\t\tctx.drawImage(element, -size.width / 2, -size.height / 2);\n\t\tctx.restore();\n\t\tvar pixels = ctx.getImageData(0.5, 0.5, Math.ceil(width),\n\t\t\t\tMath.ceil(height)).data,\n\t\t\tchannels = [0, 0, 0],\n\t\t\ttotal = 0;\n\t\tfor (var i = 0, l = pixels.length; i < l; i += 4) {\n\t\t\tvar alpha = pixels[i + 3];\n\t\t\ttotal += alpha;\n\t\t\talpha /= 255;\n\t\t\tchannels[0] += pixels[i] * alpha;\n\t\t\tchannels[1] += pixels[i + 1] * alpha;\n\t\t\tchannels[2] += pixels[i + 2] * alpha;\n\t\t}\n\t\tfor (var i = 0; i < 3; i++)\n\t\t\tchannels[i] /= total;\n\t\treturn total ? Color.read(channels) : null;\n\t},\n\n\tgetPixel: function() {\n\t\tvar point = Point.read(arguments);\n\t\tvar data = this.getContext().getImageData(point.x, point.y, 1, 1).data;\n\t\treturn new Color('rgb', [data[0] / 255, data[1] / 255, data[2] / 255],\n\t\t\t\tdata[3] / 255);\n\t},\n\n\tsetPixel: function() {\n\t\tvar args = arguments,\n\t\t\tpoint = Point.read(args),\n\t\t\tcolor = Color.read(args),\n\t\t\tcomponents = color._convert('rgb'),\n\t\t\talpha = color._alpha,\n\t\t\tctx = this.getContext(true),\n\t\t\timageData = ctx.createImageData(1, 1),\n\t\t\tdata = imageData.data;\n\t\tdata[0] = components[0] * 255;\n\t\tdata[1] = components[1] * 255;\n\t\tdata[2] = components[2] * 255;\n\t\tdata[3] = alpha != null ? alpha * 255 : 255;\n\t\tctx.putImageData(imageData, point.x, point.y);\n\t},\n\n\tclear: function() {\n\t\tvar size = this._size;\n\t\tthis.getContext(true).clearRect(0, 0, size.width + 1, size.height + 1);\n\t},\n\n\tcreateImageData: function() {\n\t\tvar size = Size.read(arguments);\n\t\treturn this.getContext().createImageData(size.width, size.height);\n\t},\n\n\tgetImageData: function() {\n\t\tvar rect = Rectangle.read(arguments);\n\t\tif (rect.isEmpty())\n\t\t\trect = new Rectangle(this._size);\n\t\treturn this.getContext().getImageData(rect.x, rect.y,\n\t\t\t\trect.width, rect.height);\n\t},\n\n\tsetImageData: function(data ) {\n\t\tvar point = Point.read(arguments, 1);\n\t\tthis.getContext(true).putImageData(data, point.x, point.y);\n\t},\n\n\t_getBounds: function(matrix, options) {\n\t\tvar rect = new Rectangle(this._size).setCenter(0, 0);\n\t\treturn matrix ? matrix._transformBounds(rect) : rect;\n\t},\n\n\t_hitTestSelf: function(point) {\n\t\tif (this._contains(point)) {\n\t\t\tvar that = this;\n\t\t\treturn new HitResult('pixel', that, {\n\t\t\t\toffset: point.add(that._size.divide(2)).round(),\n\t\t\t\tcolor: {\n\t\t\t\t\tget: function() {\n\t\t\t\t\t\treturn that.getPixel(this.offset);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t});\n\t\t}\n\t},\n\n\t_draw: function(ctx, param, viewMatrix) {\n\t\tvar element = this.getElement();\n\t\tif (element && element.width > 0 && element.height > 0) {\n\t\t\tctx.globalAlpha = Numerical.clamp(this._opacity, 0, 1);\n\n\t\t\tthis._setStyles(ctx, param, viewMatrix);\n\n\t\t\tvar smoothing = this._smoothing,\n\t\t\t\tdisabled = smoothing === 'off';\n\t\t\tDomElement.setPrefixed(\n\t\t\t\tctx,\n\t\t\t\tdisabled ? 'imageSmoothingEnabled' : 'imageSmoothingQuality',\n\t\t\t\tdisabled ? false : smoothing\n\t\t\t);\n\n\t\t\tctx.drawImage(element,\n\t\t\t\t\t-this._size.width / 2, -this._size.height / 2);\n\t\t}\n\t},\n\n\t_canComposite: function() {\n\t\treturn true;\n\t}\n});\n\nvar SymbolItem = Item.extend({\n\t_class: 'SymbolItem',\n\t_applyMatrix: false,\n\t_canApplyMatrix: false,\n\t_boundsOptions: { stroke: true },\n\t_serializeFields: {\n\t\tsymbol: null\n\t},\n\n\tinitialize: function SymbolItem(arg0, arg1) {\n\t\tif (!this._initialize(arg0,\n\t\t\t\targ1 !== undefined && Point.read(arguments, 1)))\n\t\t\tthis.setDefinition(arg0 instanceof SymbolDefinition ?\n\t\t\t\t\targ0 : new SymbolDefinition(arg0));\n\t},\n\n\t_equals: function(item) {\n\t\treturn this._definition === item._definition;\n\t},\n\n\tcopyContent: function(source) {\n\t\tthis.setDefinition(source._definition);\n\t},\n\n\tgetDefinition: function() {\n\t\treturn this._definition;\n\t},\n\n\tsetDefinition: function(definition) {\n\t\tthis._definition = definition;\n\t\tthis._changed(9);\n\t},\n\n\tgetSymbol: '#getDefinition',\n\tsetSymbol: '#setDefinition',\n\n\tisEmpty: function() {\n\t\treturn this._definition._item.isEmpty();\n\t},\n\n\t_getBounds: function(matrix, options) {\n\t\tvar item = this._definition._item;\n\t\treturn item._getCachedBounds(item._matrix.prepended(matrix), options);\n\t},\n\n\t_hitTestSelf: function(point, options, viewMatrix) {\n\t\tvar opts = options.extend({ all: false });\n\t\tvar res = this._definition._item._hitTest(point, opts, viewMatrix);\n\t\tif (res)\n\t\t\tres.item = this;\n\t\treturn res;\n\t},\n\n\t_draw: function(ctx, param) {\n\t\tthis._definition._item.draw(ctx, param);\n\t}\n\n});\n\nvar SymbolDefinition = Base.extend({\n\t_class: 'SymbolDefinition',\n\n\tinitialize: function SymbolDefinition(item, dontCenter) {\n\t\tthis._id = UID.get();\n\t\tthis.project = paper.project;\n\t\tif (item)\n\t\t\tthis.setItem(item, dontCenter);\n\t},\n\n\t_serialize: function(options, dictionary) {\n\t\treturn dictionary.add(this, function() {\n\t\t\treturn Base.serialize([this._class, this._item],\n\t\t\t\t\toptions, false, dictionary);\n\t\t});\n\t},\n\n\t_changed: function(flags) {\n\t\tif (flags & 8)\n\t\t\tItem._clearBoundsCache(this);\n\t\tif (flags & 1)\n\t\t\tthis.project._changed(flags);\n\t},\n\n\tgetItem: function() {\n\t\treturn this._item;\n\t},\n\n\tsetItem: function(item, _dontCenter) {\n\t\tif (item._symbol)\n\t\t\titem = item.clone();\n\t\tif (this._item)\n\t\t\tthis._item._symbol = null;\n\t\tthis._item = item;\n\t\titem.remove();\n\t\titem.setSelected(false);\n\t\tif (!_dontCenter)\n\t\t\titem.setPosition(new Point());\n\t\titem._symbol = this;\n\t\tthis._changed(9);\n\t},\n\n\tgetDefinition: '#getItem',\n\tsetDefinition: '#setItem',\n\n\tplace: function(position) {\n\t\treturn new SymbolItem(this, position);\n\t},\n\n\tclone: function() {\n\t\treturn new SymbolDefinition(this._item.clone(false));\n\t},\n\n\tequals: function(symbol) {\n\t\treturn symbol === this\n\t\t\t\t|| symbol && this._item.equals(symbol._item)\n\t\t\t\t|| false;\n\t}\n});\n\nvar HitResult = Base.extend({\n\t_class: 'HitResult',\n\n\tinitialize: function HitResult(type, item, values) {\n\t\tthis.type = type;\n\t\tthis.item = item;\n\t\tif (values)\n\t\t\tthis.inject(values);\n\t},\n\n\tstatics: {\n\t\tgetOptions: function(args) {\n\t\t\tvar options = args && Base.read(args);\n\t\t\treturn new Base({\n\t\t\t\ttype: null,\n\t\t\t\ttolerance: paper.settings.hitTolerance,\n\t\t\t\tfill: !options,\n\t\t\t\tstroke: !options,\n\t\t\t\tsegments: !options,\n\t\t\t\thandles: false,\n\t\t\t\tends: false,\n\t\t\t\tposition: false,\n\t\t\t\tcenter: false,\n\t\t\t\tbounds: false,\n\t\t\t\tguides: false,\n\t\t\t\tselected: false\n\t\t\t}, options);\n\t\t}\n\t}\n});\n\nvar Segment = Base.extend({\n\t_class: 'Segment',\n\tbeans: true,\n\t_selection: 0,\n\n\tinitialize: function Segment(arg0, arg1, arg2, arg3, arg4, arg5) {\n\t\tvar count = arguments.length,\n\t\t\tpoint, handleIn, handleOut, selection;\n\t\tif (count > 0) {\n\t\t\tif (arg0 == null || typeof arg0 === 'object') {\n\t\t\t\tif (count === 1 && arg0 && 'point' in arg0) {\n\t\t\t\t\tpoint = arg0.point;\n\t\t\t\t\thandleIn = arg0.handleIn;\n\t\t\t\t\thandleOut = arg0.handleOut;\n\t\t\t\t\tselection = arg0.selection;\n\t\t\t\t} else {\n\t\t\t\t\tpoint = arg0;\n\t\t\t\t\thandleIn = arg1;\n\t\t\t\t\thandleOut = arg2;\n\t\t\t\t\tselection = arg3;\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tpoint = [ arg0, arg1 ];\n\t\t\t\thandleIn = arg2 !== undefined ? [ arg2, arg3 ] : null;\n\t\t\t\thandleOut = arg4 !== undefined ? [ arg4, arg5 ] : null;\n\t\t\t}\n\t\t}\n\t\tnew SegmentPoint(point, this, '_point');\n\t\tnew SegmentPoint(handleIn, this, '_handleIn');\n\t\tnew SegmentPoint(handleOut, this, '_handleOut');\n\t\tif (selection)\n\t\t\tthis.setSelection(selection);\n\t},\n\n\t_serialize: function(options, dictionary) {\n\t\tvar point = this._point,\n\t\t\tselection = this._selection,\n\t\t\tobj = selection || this.hasHandles()\n\t\t\t\t\t? [point, this._handleIn, this._handleOut]\n\t\t\t\t\t: point;\n\t\tif (selection)\n\t\t\tobj.push(selection);\n\t\treturn Base.serialize(obj, options, true, dictionary);\n\t},\n\n\t_changed: function(point) {\n\t\tvar path = this._path;\n\t\tif (!path)\n\t\t\treturn;\n\t\tvar curves = path._curves,\n\t\t\tindex = this._index,\n\t\t\tcurve;\n\t\tif (curves) {\n\t\t\tif ((!point || point === this._point || point === this._handleIn)\n\t\t\t\t\t&& (curve = index > 0 ? curves[index - 1] : path._closed\n\t\t\t\t\t\t? curves[curves.length - 1] : null))\n\t\t\t\tcurve._changed();\n\t\t\tif ((!point || point === this._point || point === this._handleOut)\n\t\t\t\t\t&& (curve = curves[index]))\n\t\t\t\tcurve._changed();\n\t\t}\n\t\tpath._changed(41);\n\t},\n\n\tgetPoint: function() {\n\t\treturn this._point;\n\t},\n\n\tsetPoint: function() {\n\t\tthis._point.set(Point.read(arguments));\n\t},\n\n\tgetHandleIn: function() {\n\t\treturn this._handleIn;\n\t},\n\n\tsetHandleIn: function() {\n\t\tthis._handleIn.set(Point.read(arguments));\n\t},\n\n\tgetHandleOut: function() {\n\t\treturn this._handleOut;\n\t},\n\n\tsetHandleOut: function() {\n\t\tthis._handleOut.set(Point.read(arguments));\n\t},\n\n\thasHandles: function() {\n\t\treturn !this._handleIn.isZero() || !this._handleOut.isZero();\n\t},\n\n\tisSmooth: function() {\n\t\tvar handleIn = this._handleIn,\n\t\t\thandleOut = this._handleOut;\n\t\treturn !handleIn.isZero() && !handleOut.isZero()\n\t\t\t\t&& handleIn.isCollinear(handleOut);\n\t},\n\n\tclearHandles: function() {\n\t\tthis._handleIn._set(0, 0);\n\t\tthis._handleOut._set(0, 0);\n\t},\n\n\tgetSelection: function() {\n\t\treturn this._selection;\n\t},\n\n\tsetSelection: function(selection) {\n\t\tvar oldSelection = this._selection,\n\t\t\tpath = this._path;\n\t\tthis._selection = selection = selection || 0;\n\t\tif (path && selection !== oldSelection) {\n\t\t\tpath._updateSelection(this, oldSelection, selection);\n\t\t\tpath._changed(257);\n\t\t}\n\t},\n\n\t_changeSelection: function(flag, selected) {\n\t\tvar selection = this._selection;\n\t\tthis.setSelection(selected ? selection | flag : selection & ~flag);\n\t},\n\n\tisSelected: function() {\n\t\treturn !!(this._selection & 7);\n\t},\n\n\tsetSelected: function(selected) {\n\t\tthis._changeSelection(7, selected);\n\t},\n\n\tgetIndex: function() {\n\t\treturn this._index !== undefined ? this._index : null;\n\t},\n\n\tgetPath: function() {\n\t\treturn this._path || null;\n\t},\n\n\tgetCurve: function() {\n\t\tvar path = this._path,\n\t\t\tindex = this._index;\n\t\tif (path) {\n\t\t\tif (index > 0 && !path._closed\n\t\t\t\t\t&& index === path._segments.length - 1)\n\t\t\t\tindex--;\n\t\t\treturn path.getCurves()[index] || null;\n\t\t}\n\t\treturn null;\n\t},\n\n\tgetLocation: function() {\n\t\tvar curve = this.getCurve();\n\t\treturn curve\n\t\t\t\t? new CurveLocation(curve, this === curve._segment1 ? 0 : 1)\n\t\t\t\t: null;\n\t},\n\n\tgetNext: function() {\n\t\tvar segments = this._path && this._path._segments;\n\t\treturn segments && (segments[this._index + 1]\n\t\t\t\t|| this._path._closed && segments[0]) || null;\n\t},\n\n\tsmooth: function(options, _first, _last) {\n\t\tvar opts = options || {},\n\t\t\ttype = opts.type,\n\t\t\tfactor = opts.factor,\n\t\t\tprev = this.getPrevious(),\n\t\t\tnext = this.getNext(),\n\t\t\tp0 = (prev || this)._point,\n\t\t\tp1 = this._point,\n\t\t\tp2 = (next || this)._point,\n\t\t\td1 = p0.getDistance(p1),\n\t\t\td2 = p1.getDistance(p2);\n\t\tif (!type || type === 'catmull-rom') {\n\t\t\tvar a = factor === undefined ? 0.5 : factor,\n\t\t\t\td1_a = Math.pow(d1, a),\n\t\t\t\td1_2a = d1_a * d1_a,\n\t\t\t\td2_a = Math.pow(d2, a),\n\t\t\t\td2_2a = d2_a * d2_a;\n\t\t\tif (!_first && prev) {\n\t\t\t\tvar A = 2 * d2_2a + 3 * d2_a * d1_a + d1_2a,\n\t\t\t\t\tN = 3 * d2_a * (d2_a + d1_a);\n\t\t\t\tthis.setHandleIn(N !== 0\n\t\t\t\t\t? new Point(\n\t\t\t\t\t\t(d2_2a * p0._x + A * p1._x - d1_2a * p2._x) / N - p1._x,\n\t\t\t\t\t\t(d2_2a * p0._y + A * p1._y - d1_2a * p2._y) / N - p1._y)\n\t\t\t\t\t: new Point());\n\t\t\t}\n\t\t\tif (!_last && next) {\n\t\t\t\tvar A = 2 * d1_2a + 3 * d1_a * d2_a + d2_2a,\n\t\t\t\t\tN = 3 * d1_a * (d1_a + d2_a);\n\t\t\t\tthis.setHandleOut(N !== 0\n\t\t\t\t\t? new Point(\n\t\t\t\t\t\t(d1_2a * p2._x + A * p1._x - d2_2a * p0._x) / N - p1._x,\n\t\t\t\t\t\t(d1_2a * p2._y + A * p1._y - d2_2a * p0._y) / N - p1._y)\n\t\t\t\t\t: new Point());\n\t\t\t}\n\t\t} else if (type === 'geometric') {\n\t\t\tif (prev && next) {\n\t\t\t\tvar vector = p0.subtract(p2),\n\t\t\t\t\tt = factor === undefined ? 0.4 : factor,\n\t\t\t\t\tk = t * d1 / (d1 + d2);\n\t\t\t\tif (!_first)\n\t\t\t\t\tthis.setHandleIn(vector.multiply(k));\n\t\t\t\tif (!_last)\n\t\t\t\t\tthis.setHandleOut(vector.multiply(k - t));\n\t\t\t}\n\t\t} else {\n\t\t\tthrow new Error('Smoothing method \\'' + type + '\\' not supported.');\n\t\t}\n\t},\n\n\tgetPrevious: function() {\n\t\tvar segments = this._path && this._path._segments;\n\t\treturn segments && (segments[this._index - 1]\n\t\t\t\t|| this._path._closed && segments[segments.length - 1]) || null;\n\t},\n\n\tisFirst: function() {\n\t\treturn !this._index;\n\t},\n\n\tisLast: function() {\n\t\tvar path = this._path;\n\t\treturn path && this._index === path._segments.length - 1 || false;\n\t},\n\n\treverse: function() {\n\t\tvar handleIn = this._handleIn,\n\t\t\thandleOut = this._handleOut,\n\t\t\ttmp = handleIn.clone();\n\t\thandleIn.set(handleOut);\n\t\thandleOut.set(tmp);\n\t},\n\n\treversed: function() {\n\t\treturn new Segment(this._point, this._handleOut, this._handleIn);\n\t},\n\n\tremove: function() {\n\t\treturn this._path ? !!this._path.removeSegment(this._index) : false;\n\t},\n\n\tclone: function() {\n\t\treturn new Segment(this._point, this._handleIn, this._handleOut);\n\t},\n\n\tequals: function(segment) {\n\t\treturn segment === this || segment && this._class === segment._class\n\t\t\t\t&& this._point.equals(segment._point)\n\t\t\t\t&& this._handleIn.equals(segment._handleIn)\n\t\t\t\t&& this._handleOut.equals(segment._handleOut)\n\t\t\t\t|| false;\n\t},\n\n\ttoString: function() {\n\t\tvar parts = [ 'point: ' + this._point ];\n\t\tif (!this._handleIn.isZero())\n\t\t\tparts.push('handleIn: ' + this._handleIn);\n\t\tif (!this._handleOut.isZero())\n\t\t\tparts.push('handleOut: ' + this._handleOut);\n\t\treturn '{ ' + parts.join(', ') + ' }';\n\t},\n\n\ttransform: function(matrix) {\n\t\tthis._transformCoordinates(matrix, new Array(6), true);\n\t\tthis._changed();\n\t},\n\n\tinterpolate: function(from, to, factor) {\n\t\tvar u = 1 - factor,\n\t\t\tv = factor,\n\t\t\tpoint1 = from._point,\n\t\t\tpoint2 = to._point,\n\t\t\thandleIn1 = from._handleIn,\n\t\t\thandleIn2 = to._handleIn,\n\t\t\thandleOut2 = to._handleOut,\n\t\t\thandleOut1 = from._handleOut;\n\t\tthis._point._set(\n\t\t\t\tu * point1._x + v * point2._x,\n\t\t\t\tu * point1._y + v * point2._y, true);\n\t\tthis._handleIn._set(\n\t\t\t\tu * handleIn1._x + v * handleIn2._x,\n\t\t\t\tu * handleIn1._y + v * handleIn2._y, true);\n\t\tthis._handleOut._set(\n\t\t\t\tu * handleOut1._x + v * handleOut2._x,\n\t\t\t\tu * handleOut1._y + v * handleOut2._y, true);\n\t\tthis._changed();\n\t},\n\n\t_transformCoordinates: function(matrix, coords, change) {\n\t\tvar point = this._point,\n\t\t\thandleIn = !change || !this._handleIn.isZero()\n\t\t\t\t\t? this._handleIn : null,\n\t\t\thandleOut = !change || !this._handleOut.isZero()\n\t\t\t\t\t? this._handleOut : null,\n\t\t\tx = point._x,\n\t\t\ty = point._y,\n\t\t\ti = 2;\n\t\tcoords[0] = x;\n\t\tcoords[1] = y;\n\t\tif (handleIn) {\n\t\t\tcoords[i++] = handleIn._x + x;\n\t\t\tcoords[i++] = handleIn._y + y;\n\t\t}\n\t\tif (handleOut) {\n\t\t\tcoords[i++] = handleOut._x + x;\n\t\t\tcoords[i++] = handleOut._y + y;\n\t\t}\n\t\tif (matrix) {\n\t\t\tmatrix._transformCoordinates(coords, coords, i / 2);\n\t\t\tx = coords[0];\n\t\t\ty = coords[1];\n\t\t\tif (change) {\n\t\t\t\tpoint._x = x;\n\t\t\t\tpoint._y = y;\n\t\t\t\ti = 2;\n\t\t\t\tif (handleIn) {\n\t\t\t\t\thandleIn._x = coords[i++] - x;\n\t\t\t\t\thandleIn._y = coords[i++] - y;\n\t\t\t\t}\n\t\t\t\tif (handleOut) {\n\t\t\t\t\thandleOut._x = coords[i++] - x;\n\t\t\t\t\thandleOut._y = coords[i++] - y;\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tif (!handleIn) {\n\t\t\t\t\tcoords[i++] = x;\n\t\t\t\t\tcoords[i++] = y;\n\t\t\t\t}\n\t\t\t\tif (!handleOut) {\n\t\t\t\t\tcoords[i++] = x;\n\t\t\t\t\tcoords[i++] = y;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\treturn coords;\n\t}\n});\n\nvar SegmentPoint = Point.extend({\n\tinitialize: function SegmentPoint(point, owner, key) {\n\t\tvar x, y,\n\t\t\tselected;\n\t\tif (!point) {\n\t\t\tx = y = 0;\n\t\t} else if ((x = point[0]) !== undefined) {\n\t\t\ty = point[1];\n\t\t} else {\n\t\t\tvar pt = point;\n\t\t\tif ((x = pt.x) === undefined) {\n\t\t\t\tpt = Point.read(arguments);\n\t\t\t\tx = pt.x;\n\t\t\t}\n\t\t\ty = pt.y;\n\t\t\tselected = pt.selected;\n\t\t}\n\t\tthis._x = x;\n\t\tthis._y = y;\n\t\tthis._owner = owner;\n\t\towner[key] = this;\n\t\tif (selected)\n\t\t\tthis.setSelected(true);\n\t},\n\n\t_set: function(x, y) {\n\t\tthis._x = x;\n\t\tthis._y = y;\n\t\tthis._owner._changed(this);\n\t\treturn this;\n\t},\n\n\tgetX: function() {\n\t\treturn this._x;\n\t},\n\n\tsetX: function(x) {\n\t\tthis._x = x;\n\t\tthis._owner._changed(this);\n\t},\n\n\tgetY: function() {\n\t\treturn this._y;\n\t},\n\n\tsetY: function(y) {\n\t\tthis._y = y;\n\t\tthis._owner._changed(this);\n\t},\n\n\tisZero: function() {\n\t\tvar isZero = Numerical.isZero;\n\t\treturn isZero(this._x) && isZero(this._y);\n\t},\n\n\tisSelected: function() {\n\t\treturn !!(this._owner._selection & this._getSelection());\n\t},\n\n\tsetSelected: function(selected) {\n\t\tthis._owner._changeSelection(this._getSelection(), selected);\n\t},\n\n\t_getSelection: function() {\n\t\tvar owner = this._owner;\n\t\treturn this === owner._point ? 1\n\t\t\t: this === owner._handleIn ? 2\n\t\t\t: this === owner._handleOut ? 4\n\t\t\t: 0;\n\t}\n});\n\nvar Curve = Base.extend({\n\t_class: 'Curve',\n\tbeans: true,\n\n\tinitialize: function Curve(arg0, arg1, arg2, arg3, arg4, arg5, arg6, arg7) {\n\t\tvar count = arguments.length,\n\t\t\tseg1, seg2,\n\t\t\tpoint1, point2,\n\t\t\thandle1, handle2;\n\t\tif (count === 3) {\n\t\t\tthis._path = arg0;\n\t\t\tseg1 = arg1;\n\t\t\tseg2 = arg2;\n\t\t} else if (!count) {\n\t\t\tseg1 = new Segment();\n\t\t\tseg2 = new Segment();\n\t\t} else if (count === 1) {\n\t\t\tif ('segment1' in arg0) {\n\t\t\t\tseg1 = new Segment(arg0.segment1);\n\t\t\t\tseg2 = new Segment(arg0.segment2);\n\t\t\t} else if ('point1' in arg0) {\n\t\t\t\tpoint1 = arg0.point1;\n\t\t\t\thandle1 = arg0.handle1;\n\t\t\t\thandle2 = arg0.handle2;\n\t\t\t\tpoint2 = arg0.point2;\n\t\t\t} else if (Array.isArray(arg0)) {\n\t\t\t\tpoint1 = [arg0[0], arg0[1]];\n\t\t\t\tpoint2 = [arg0[6], arg0[7]];\n\t\t\t\thandle1 = [arg0[2] - arg0[0], arg0[3] - arg0[1]];\n\t\t\t\thandle2 = [arg0[4] - arg0[6], arg0[5] - arg0[7]];\n\t\t\t}\n\t\t} else if (count === 2) {\n\t\t\tseg1 = new Segment(arg0);\n\t\t\tseg2 = new Segment(arg1);\n\t\t} else if (count === 4) {\n\t\t\tpoint1 = arg0;\n\t\t\thandle1 = arg1;\n\t\t\thandle2 = arg2;\n\t\t\tpoint2 = arg3;\n\t\t} else if (count === 8) {\n\t\t\tpoint1 = [arg0, arg1];\n\t\t\tpoint2 = [arg6, arg7];\n\t\t\thandle1 = [arg2 - arg0, arg3 - arg1];\n\t\t\thandle2 = [arg4 - arg6, arg5 - arg7];\n\t\t}\n\t\tthis._segment1 = seg1 || new Segment(point1, null, handle1);\n\t\tthis._segment2 = seg2 || new Segment(point2, handle2, null);\n\t},\n\n\t_serialize: function(options, dictionary) {\n\t\treturn Base.serialize(this.hasHandles()\n\t\t\t\t? [this.getPoint1(), this.getHandle1(), this.getHandle2(),\n\t\t\t\t\tthis.getPoint2()]\n\t\t\t\t: [this.getPoint1(), this.getPoint2()],\n\t\t\t\toptions, true, dictionary);\n\t},\n\n\t_changed: function() {\n\t\tthis._length = this._bounds = undefined;\n\t},\n\n\tclone: function() {\n\t\treturn new Curve(this._segment1, this._segment2);\n\t},\n\n\ttoString: function() {\n\t\tvar parts = [ 'point1: ' + this._segment1._point ];\n\t\tif (!this._segment1._handleOut.isZero())\n\t\t\tparts.push('handle1: ' + this._segment1._handleOut);\n\t\tif (!this._segment2._handleIn.isZero())\n\t\t\tparts.push('handle2: ' + this._segment2._handleIn);\n\t\tparts.push('point2: ' + this._segment2._point);\n\t\treturn '{ ' + parts.join(', ') + ' }';\n\t},\n\n\tclassify: function() {\n\t\treturn Curve.classify(this.getValues());\n\t},\n\n\tremove: function() {\n\t\tvar removed = false;\n\t\tif (this._path) {\n\t\t\tvar segment2 = this._segment2,\n\t\t\t\thandleOut = segment2._handleOut;\n\t\t\tremoved = segment2.remove();\n\t\t\tif (removed)\n\t\t\t\tthis._segment1._handleOut.set(handleOut);\n\t\t}\n\t\treturn removed;\n\t},\n\n\tgetPoint1: function() {\n\t\treturn this._segment1._point;\n\t},\n\n\tsetPoint1: function() {\n\t\tthis._segment1._point.set(Point.read(arguments));\n\t},\n\n\tgetPoint2: function() {\n\t\treturn this._segment2._point;\n\t},\n\n\tsetPoint2: function() {\n\t\tthis._segment2._point.set(Point.read(arguments));\n\t},\n\n\tgetHandle1: function() {\n\t\treturn this._segment1._handleOut;\n\t},\n\n\tsetHandle1: function() {\n\t\tthis._segment1._handleOut.set(Point.read(arguments));\n\t},\n\n\tgetHandle2: function() {\n\t\treturn this._segment2._handleIn;\n\t},\n\n\tsetHandle2: function() {\n\t\tthis._segment2._handleIn.set(Point.read(arguments));\n\t},\n\n\tgetSegment1: function() {\n\t\treturn this._segment1;\n\t},\n\n\tgetSegment2: function() {\n\t\treturn this._segment2;\n\t},\n\n\tgetPath: function() {\n\t\treturn this._path;\n\t},\n\n\tgetIndex: function() {\n\t\treturn this._segment1._index;\n\t},\n\n\tgetNext: function() {\n\t\tvar curves = this._path && this._path._curves;\n\t\treturn curves && (curves[this._segment1._index + 1]\n\t\t\t\t|| this._path._closed && curves[0]) || null;\n\t},\n\n\tgetPrevious: function() {\n\t\tvar curves = this._path && this._path._curves;\n\t\treturn curves && (curves[this._segment1._index - 1]\n\t\t\t\t|| this._path._closed && curves[curves.length - 1]) || null;\n\t},\n\n\tisFirst: function() {\n\t\treturn !this._segment1._index;\n\t},\n\n\tisLast: function() {\n\t\tvar path = this._path;\n\t\treturn path && this._segment1._index === path._curves.length - 1\n\t\t\t\t|| false;\n\t},\n\n\tisSelected: function() {\n\t\treturn this.getPoint1().isSelected()\n\t\t\t\t&& this.getHandle1().isSelected()\n\t\t\t\t&& this.getHandle2().isSelected()\n\t\t\t\t&& this.getPoint2().isSelected();\n\t},\n\n\tsetSelected: function(selected) {\n\t\tthis.getPoint1().setSelected(selected);\n\t\tthis.getHandle1().setSelected(selected);\n\t\tthis.getHandle2().setSelected(selected);\n\t\tthis.getPoint2().setSelected(selected);\n\t},\n\n\tgetValues: function(matrix) {\n\t\treturn Curve.getValues(this._segment1, this._segment2, matrix);\n\t},\n\n\tgetPoints: function() {\n\t\tvar coords = this.getValues(),\n\t\t\tpoints = [];\n\t\tfor (var i = 0; i < 8; i += 2)\n\t\t\tpoints.push(new Point(coords[i], coords[i + 1]));\n\t\treturn points;\n\t}\n}, {\n\tgetLength: function() {\n\t\tif (this._length == null)\n\t\t\tthis._length = Curve.getLength(this.getValues(), 0, 1);\n\t\treturn this._length;\n\t},\n\n\tgetArea: function() {\n\t\treturn Curve.getArea(this.getValues());\n\t},\n\n\tgetLine: function() {\n\t\treturn new Line(this._segment1._point, this._segment2._point);\n\t},\n\n\tgetPart: function(from, to) {\n\t\treturn new Curve(Curve.getPart(this.getValues(), from, to));\n\t},\n\n\tgetPartLength: function(from, to) {\n\t\treturn Curve.getLength(this.getValues(), from, to);\n\t},\n\n\tdivideAt: function(location) {\n\t\treturn this.divideAtTime(location && location.curve === this\n\t\t\t\t? location.time : this.getTimeAt(location));\n\t},\n\n\tdivideAtTime: function(time, _setHandles) {\n\t\tvar tMin = 1e-8,\n\t\t\ttMax = 1 - tMin,\n\t\t\tres = null;\n\t\tif (time >= tMin && time <= tMax) {\n\t\t\tvar parts = Curve.subdivide(this.getValues(), time),\n\t\t\t\tleft = parts[0],\n\t\t\t\tright = parts[1],\n\t\t\t\tsetHandles = _setHandles || this.hasHandles(),\n\t\t\t\tseg1 = this._segment1,\n\t\t\t\tseg2 = this._segment2,\n\t\t\t\tpath = this._path;\n\t\t\tif (setHandles) {\n\t\t\t\tseg1._handleOut._set(left[2] - left[0], left[3] - left[1]);\n\t\t\t\tseg2._handleIn._set(right[4] - right[6],right[5] - right[7]);\n\t\t\t}\n\t\t\tvar x = left[6], y = left[7],\n\t\t\t\tsegment = new Segment(new Point(x, y),\n\t\t\t\t\t\tsetHandles && new Point(left[4] - x, left[5] - y),\n\t\t\t\t\t\tsetHandles && new Point(right[2] - x, right[3] - y));\n\t\t\tif (path) {\n\t\t\t\tpath.insert(seg1._index + 1, segment);\n\t\t\t\tres = this.getNext();\n\t\t\t} else {\n\t\t\t\tthis._segment2 = segment;\n\t\t\t\tthis._changed();\n\t\t\t\tres = new Curve(segment, seg2);\n\t\t\t}\n\t\t}\n\t\treturn res;\n\t},\n\n\tsplitAt: function(location) {\n\t\tvar path = this._path;\n\t\treturn path ? path.splitAt(location) : null;\n\t},\n\n\tsplitAtTime: function(time) {\n\t\treturn this.splitAt(this.getLocationAtTime(time));\n\t},\n\n\tdivide: function(offset, isTime) {\n\t\treturn this.divideAtTime(offset === undefined ? 0.5 : isTime ? offset\n\t\t\t\t: this.getTimeAt(offset));\n\t},\n\n\tsplit: function(offset, isTime) {\n\t\treturn this.splitAtTime(offset === undefined ? 0.5 : isTime ? offset\n\t\t\t\t: this.getTimeAt(offset));\n\t},\n\n\treversed: function() {\n\t\treturn new Curve(this._segment2.reversed(), this._segment1.reversed());\n\t},\n\n\tclearHandles: function() {\n\t\tthis._segment1._handleOut._set(0, 0);\n\t\tthis._segment2._handleIn._set(0, 0);\n\t},\n\nstatics: {\n\tgetValues: function(segment1, segment2, matrix, straight) {\n\t\tvar p1 = segment1._point,\n\t\t\th1 = segment1._handleOut,\n\t\t\th2 = segment2._handleIn,\n\t\t\tp2 = segment2._point,\n\t\t\tx1 = p1.x, y1 = p1.y,\n\t\t\tx2 = p2.x, y2 = p2.y,\n\t\t\tvalues = straight\n\t\t\t\t? [ x1, y1, x1, y1, x2, y2, x2, y2 ]\n\t\t\t\t: [\n\t\t\t\t\tx1, y1,\n\t\t\t\t\tx1 + h1._x, y1 + h1._y,\n\t\t\t\t\tx2 + h2._x, y2 + h2._y,\n\t\t\t\t\tx2, y2\n\t\t\t\t];\n\t\tif (matrix)\n\t\t\tmatrix._transformCoordinates(values, values, 4);\n\t\treturn values;\n\t},\n\n\tsubdivide: function(v, t) {\n\t\tvar x0 = v[0], y0 = v[1],\n\t\t\tx1 = v[2], y1 = v[3],\n\t\t\tx2 = v[4], y2 = v[5],\n\t\t\tx3 = v[6], y3 = v[7];\n\t\tif (t === undefined)\n\t\t\tt = 0.5;\n\t\tvar u = 1 - t,\n\t\t\tx4 = u * x0 + t * x1, y4 = u * y0 + t * y1,\n\t\t\tx5 = u * x1 + t * x2, y5 = u * y1 + t * y2,\n\t\t\tx6 = u * x2 + t * x3, y6 = u * y2 + t * y3,\n\t\t\tx7 = u * x4 + t * x5, y7 = u * y4 + t * y5,\n\t\t\tx8 = u * x5 + t * x6, y8 = u * y5 + t * y6,\n\t\t\tx9 = u * x7 + t * x8, y9 = u * y7 + t * y8;\n\t\treturn [\n\t\t\t[x0, y0, x4, y4, x7, y7, x9, y9],\n\t\t\t[x9, y9, x8, y8, x6, y6, x3, y3]\n\t\t];\n\t},\n\n\tgetMonoCurves: function(v, dir) {\n\t\tvar curves = [],\n\t\t\tio = dir ? 0 : 1,\n\t\t\to0 = v[io + 0],\n\t\t\to1 = v[io + 2],\n\t\t\to2 = v[io + 4],\n\t\t\to3 = v[io + 6];\n\t\tif ((o0 >= o1) === (o1 >= o2) && (o1 >= o2) === (o2 >= o3)\n\t\t\t\t|| Curve.isStraight(v)) {\n\t\t\tcurves.push(v);\n\t\t} else {\n\t\t\tvar a = 3 * (o1 - o2) - o0 + o3,\n\t\t\t\tb = 2 * (o0 + o2) - 4 * o1,\n\t\t\t\tc = o1 - o0,\n\t\t\t\ttMin = 1e-8,\n\t\t\t\ttMax = 1 - tMin,\n\t\t\t\troots = [],\n\t\t\t\tn = Numerical.solveQuadratic(a, b, c, roots, tMin, tMax);\n\t\t\tif (!n) {\n\t\t\t\tcurves.push(v);\n\t\t\t} else {\n\t\t\t\troots.sort();\n\t\t\t\tvar t = roots[0],\n\t\t\t\t\tparts = Curve.subdivide(v, t);\n\t\t\t\tcurves.push(parts[0]);\n\t\t\t\tif (n > 1) {\n\t\t\t\t\tt = (roots[1] - t) / (1 - t);\n\t\t\t\t\tparts = Curve.subdivide(parts[1], t);\n\t\t\t\t\tcurves.push(parts[0]);\n\t\t\t\t}\n\t\t\t\tcurves.push(parts[1]);\n\t\t\t}\n\t\t}\n\t\treturn curves;\n\t},\n\n\tsolveCubic: function (v, coord, val, roots, min, max) {\n\t\tvar v0 = v[coord],\n\t\t\tv1 = v[coord + 2],\n\t\t\tv2 = v[coord + 4],\n\t\t\tv3 = v[coord + 6],\n\t\t\tres = 0;\n\t\tif (  !(v0 < val && v3 < val && v1 < val && v2 < val ||\n\t\t\t\tv0 > val && v3 > val && v1 > val && v2 > val)) {\n\t\t\tvar c = 3 * (v1 - v0),\n\t\t\t\tb = 3 * (v2 - v1) - c,\n\t\t\t\ta = v3 - v0 - c - b;\n\t\t\tres = Numerical.solveCubic(a, b, c, v0 - val, roots, min, max);\n\t\t}\n\t\treturn res;\n\t},\n\n\tgetTimeOf: function(v, point) {\n\t\tvar p0 = new Point(v[0], v[1]),\n\t\t\tp3 = new Point(v[6], v[7]),\n\t\t\tepsilon = 1e-12,\n\t\t\tgeomEpsilon = 1e-7,\n\t\t\tt = point.isClose(p0, epsilon) ? 0\n\t\t\t  : point.isClose(p3, epsilon) ? 1\n\t\t\t  : null;\n\t\tif (t === null) {\n\t\t\tvar coords = [point.x, point.y],\n\t\t\t\troots = [];\n\t\t\tfor (var c = 0; c < 2; c++) {\n\t\t\t\tvar count = Curve.solveCubic(v, c, coords[c], roots, 0, 1);\n\t\t\t\tfor (var i = 0; i < count; i++) {\n\t\t\t\t\tvar u = roots[i];\n\t\t\t\t\tif (point.isClose(Curve.getPoint(v, u), geomEpsilon))\n\t\t\t\t\t\treturn u;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\treturn point.isClose(p0, geomEpsilon) ? 0\n\t\t\t : point.isClose(p3, geomEpsilon) ? 1\n\t\t\t : null;\n\t},\n\n\tgetNearestTime: function(v, point) {\n\t\tif (Curve.isStraight(v)) {\n\t\t\tvar x0 = v[0], y0 = v[1],\n\t\t\t\tx3 = v[6], y3 = v[7],\n\t\t\t\tvx = x3 - x0, vy = y3 - y0,\n\t\t\t\tdet = vx * vx + vy * vy;\n\t\t\tif (det === 0)\n\t\t\t\treturn 0;\n\t\t\tvar u = ((point.x - x0) * vx + (point.y - y0) * vy) / det;\n\t\t\treturn u < 1e-12 ? 0\n\t\t\t\t : u > 0.999999999999 ? 1\n\t\t\t\t : Curve.getTimeOf(v,\n\t\t\t\t\tnew Point(x0 + u * vx, y0 + u * vy));\n\t\t}\n\n\t\tvar count = 100,\n\t\t\tminDist = Infinity,\n\t\t\tminT = 0;\n\n\t\tfunction refine(t) {\n\t\t\tif (t >= 0 && t <= 1) {\n\t\t\t\tvar dist = point.getDistance(Curve.getPoint(v, t), true);\n\t\t\t\tif (dist < minDist) {\n\t\t\t\t\tminDist = dist;\n\t\t\t\t\tminT = t;\n\t\t\t\t\treturn true;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\tfor (var i = 0; i <= count; i++)\n\t\t\trefine(i / count);\n\n\t\tvar step = 1 / (count * 2);\n\t\twhile (step > 1e-8) {\n\t\t\tif (!refine(minT - step) && !refine(minT + step))\n\t\t\t\tstep /= 2;\n\t\t}\n\t\treturn minT;\n\t},\n\n\tgetPart: function(v, from, to) {\n\t\tvar flip = from > to;\n\t\tif (flip) {\n\t\t\tvar tmp = from;\n\t\t\tfrom = to;\n\t\t\tto = tmp;\n\t\t}\n\t\tif (from > 0)\n\t\t\tv = Curve.subdivide(v, from)[1];\n\t\tif (to < 1)\n\t\t\tv = Curve.subdivide(v, (to - from) / (1 - from))[0];\n\t\treturn flip\n\t\t\t\t? [v[6], v[7], v[4], v[5], v[2], v[3], v[0], v[1]]\n\t\t\t\t: v;\n\t},\n\n\tisFlatEnough: function(v, flatness) {\n\t\tvar x0 = v[0], y0 = v[1],\n\t\t\tx1 = v[2], y1 = v[3],\n\t\t\tx2 = v[4], y2 = v[5],\n\t\t\tx3 = v[6], y3 = v[7],\n\t\t\tux = 3 * x1 - 2 * x0 - x3,\n\t\t\tuy = 3 * y1 - 2 * y0 - y3,\n\t\t\tvx = 3 * x2 - 2 * x3 - x0,\n\t\t\tvy = 3 * y2 - 2 * y3 - y0;\n\t\treturn Math.max(ux * ux, vx * vx) + Math.max(uy * uy, vy * vy)\n\t\t\t\t<= 16 * flatness * flatness;\n\t},\n\n\tgetArea: function(v) {\n\t\tvar x0 = v[0], y0 = v[1],\n\t\t\tx1 = v[2], y1 = v[3],\n\t\t\tx2 = v[4], y2 = v[5],\n\t\t\tx3 = v[6], y3 = v[7];\n\t\treturn 3 * ((y3 - y0) * (x1 + x2) - (x3 - x0) * (y1 + y2)\n\t\t\t\t+ y1 * (x0 - x2) - x1 * (y0 - y2)\n\t\t\t\t+ y3 * (x2 + x0 / 3) - x3 * (y2 + y0 / 3)) / 20;\n\t},\n\n\tgetBounds: function(v) {\n\t\tvar min = v.slice(0, 2),\n\t\t\tmax = min.slice(),\n\t\t\troots = [0, 0];\n\t\tfor (var i = 0; i < 2; i++)\n\t\t\tCurve._addBounds(v[i], v[i + 2], v[i + 4], v[i + 6],\n\t\t\t\t\ti, 0, min, max, roots);\n\t\treturn new Rectangle(min[0], min[1], max[0] - min[0], max[1] - min[1]);\n\t},\n\n\t_addBounds: function(v0, v1, v2, v3, coord, padding, min, max, roots) {\n\t\tfunction add(value, padding) {\n\t\t\tvar left = value - padding,\n\t\t\t\tright = value + padding;\n\t\t\tif (left < min[coord])\n\t\t\t\tmin[coord] = left;\n\t\t\tif (right > max[coord])\n\t\t\t\tmax[coord] = right;\n\t\t}\n\n\t\tpadding /= 2;\n\t\tvar minPad = min[coord] + padding,\n\t\t\tmaxPad = max[coord] - padding;\n\t\tif (    v0 < minPad || v1 < minPad || v2 < minPad || v3 < minPad ||\n\t\t\t\tv0 > maxPad || v1 > maxPad || v2 > maxPad || v3 > maxPad) {\n\t\t\tif (v1 < v0 != v1 < v3 && v2 < v0 != v2 < v3) {\n\t\t\t\tadd(v0, 0);\n\t\t\t\tadd(v3, 0);\n\t\t\t} else {\n\t\t\t\tvar a = 3 * (v1 - v2) - v0 + v3,\n\t\t\t\t\tb = 2 * (v0 + v2) - 4 * v1,\n\t\t\t\t\tc = v1 - v0,\n\t\t\t\t\tcount = Numerical.solveQuadratic(a, b, c, roots),\n\t\t\t\t\ttMin = 1e-8,\n\t\t\t\t\ttMax = 1 - tMin;\n\t\t\t\tadd(v3, 0);\n\t\t\t\tfor (var i = 0; i < count; i++) {\n\t\t\t\t\tvar t = roots[i],\n\t\t\t\t\t\tu = 1 - t;\n\t\t\t\t\tif (tMin <= t && t <= tMax)\n\t\t\t\t\t\tadd(u * u * u * v0\n\t\t\t\t\t\t\t+ 3 * u * u * t * v1\n\t\t\t\t\t\t\t+ 3 * u * t * t * v2\n\t\t\t\t\t\t\t+ t * t * t * v3,\n\t\t\t\t\t\t\tpadding);\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n}}, Base.each(\n\t['getBounds', 'getStrokeBounds', 'getHandleBounds'],\n\tfunction(name) {\n\t\tthis[name] = function() {\n\t\t\tif (!this._bounds)\n\t\t\t\tthis._bounds = {};\n\t\t\tvar bounds = this._bounds[name];\n\t\t\tif (!bounds) {\n\t\t\t\tbounds = this._bounds[name] = Path[name](\n\t\t\t\t\t\t[this._segment1, this._segment2], false, this._path);\n\t\t\t}\n\t\t\treturn bounds.clone();\n\t\t};\n\t},\n{\n\n}), Base.each({\n\tisStraight: function(p1, h1, h2, p2) {\n\t\tif (h1.isZero() && h2.isZero()) {\n\t\t\treturn true;\n\t\t} else {\n\t\t\tvar v = p2.subtract(p1);\n\t\t\tif (v.isZero()) {\n\t\t\t\treturn false;\n\t\t\t} else if (v.isCollinear(h1) && v.isCollinear(h2)) {\n\t\t\t\tvar l = new Line(p1, p2),\n\t\t\t\t\tepsilon = 1e-7;\n\t\t\t\tif (l.getDistance(p1.add(h1)) < epsilon &&\n\t\t\t\t\tl.getDistance(p2.add(h2)) < epsilon) {\n\t\t\t\t\tvar div = v.dot(v),\n\t\t\t\t\t\ts1 = v.dot(h1) / div,\n\t\t\t\t\t\ts2 = v.dot(h2) / div;\n\t\t\t\t\treturn s1 >= 0 && s1 <= 1 && s2 <= 0 && s2 >= -1;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\treturn false;\n\t},\n\n\tisLinear: function(p1, h1, h2, p2) {\n\t\tvar third = p2.subtract(p1).divide(3);\n\t\treturn h1.equals(third) && h2.negate().equals(third);\n\t}\n}, function(test, name) {\n\tthis[name] = function(epsilon) {\n\t\tvar seg1 = this._segment1,\n\t\t\tseg2 = this._segment2;\n\t\treturn test(seg1._point, seg1._handleOut, seg2._handleIn, seg2._point,\n\t\t\t\tepsilon);\n\t};\n\n\tthis.statics[name] = function(v, epsilon) {\n\t\tvar x0 = v[0], y0 = v[1],\n\t\t\tx3 = v[6], y3 = v[7];\n\t\treturn test(\n\t\t\t\tnew Point(x0, y0),\n\t\t\t\tnew Point(v[2] - x0, v[3] - y0),\n\t\t\t\tnew Point(v[4] - x3, v[5] - y3),\n\t\t\t\tnew Point(x3, y3), epsilon);\n\t};\n}, {\n\tstatics: {},\n\n\thasHandles: function() {\n\t\treturn !this._segment1._handleOut.isZero()\n\t\t\t\t|| !this._segment2._handleIn.isZero();\n\t},\n\n\thasLength: function(epsilon) {\n\t\treturn (!this.getPoint1().equals(this.getPoint2()) || this.hasHandles())\n\t\t\t\t&& this.getLength() > (epsilon || 0);\n\t},\n\n\tisCollinear: function(curve) {\n\t\treturn curve && this.isStraight() && curve.isStraight()\n\t\t\t\t&& this.getLine().isCollinear(curve.getLine());\n\t},\n\n\tisHorizontal: function() {\n\t\treturn this.isStraight() && Math.abs(this.getTangentAtTime(0.5).y)\n\t\t\t\t< 1e-8;\n\t},\n\n\tisVertical: function() {\n\t\treturn this.isStraight() && Math.abs(this.getTangentAtTime(0.5).x)\n\t\t\t\t< 1e-8;\n\t}\n}), {\n\tbeans: false,\n\n\tgetLocationAt: function(offset, _isTime) {\n\t\treturn this.getLocationAtTime(\n\t\t\t\t_isTime ? offset : this.getTimeAt(offset));\n\t},\n\n\tgetLocationAtTime: function(t) {\n\t\treturn t != null && t >= 0 && t <= 1\n\t\t\t\t? new CurveLocation(this, t)\n\t\t\t\t: null;\n\t},\n\n\tgetTimeAt: function(offset, start) {\n\t\treturn Curve.getTimeAt(this.getValues(), offset, start);\n\t},\n\n\tgetParameterAt: '#getTimeAt',\n\n\tgetTimesWithTangent: function () {\n\t\tvar tangent = Point.read(arguments);\n\t\treturn tangent.isZero()\n\t\t\t\t? []\n\t\t\t\t: Curve.getTimesWithTangent(this.getValues(), tangent);\n\t},\n\n\tgetOffsetAtTime: function(t) {\n\t\treturn this.getPartLength(0, t);\n\t},\n\n\tgetLocationOf: function() {\n\t\treturn this.getLocationAtTime(this.getTimeOf(Point.read(arguments)));\n\t},\n\n\tgetOffsetOf: function() {\n\t\tvar loc = this.getLocationOf.apply(this, arguments);\n\t\treturn loc ? loc.getOffset() : null;\n\t},\n\n\tgetTimeOf: function() {\n\t\treturn Curve.getTimeOf(this.getValues(), Point.read(arguments));\n\t},\n\n\tgetParameterOf: '#getTimeOf',\n\n\tgetNearestLocation: function() {\n\t\tvar point = Point.read(arguments),\n\t\t\tvalues = this.getValues(),\n\t\t\tt = Curve.getNearestTime(values, point),\n\t\t\tpt = Curve.getPoint(values, t);\n\t\treturn new CurveLocation(this, t, pt, null, point.getDistance(pt));\n\t},\n\n\tgetNearestPoint: function() {\n\t\tvar loc = this.getNearestLocation.apply(this, arguments);\n\t\treturn loc ? loc.getPoint() : loc;\n\t}\n\n},\nnew function() {\n\tvar methods = ['getPoint', 'getTangent', 'getNormal', 'getWeightedTangent',\n\t\t'getWeightedNormal', 'getCurvature'];\n\treturn Base.each(methods,\n\t\tfunction(name) {\n\t\t\tthis[name + 'At'] = function(location, _isTime) {\n\t\t\t\tvar values = this.getValues();\n\t\t\t\treturn Curve[name](values, _isTime ? location\n\t\t\t\t\t\t: Curve.getTimeAt(values, location));\n\t\t\t};\n\n\t\t\tthis[name + 'AtTime'] = function(time) {\n\t\t\t\treturn Curve[name](this.getValues(), time);\n\t\t\t};\n\t\t}, {\n\t\t\tstatics: {\n\t\t\t\t_evaluateMethods: methods\n\t\t\t}\n\t\t}\n\t);\n},\nnew function() {\n\n\tfunction getLengthIntegrand(v) {\n\t\tvar x0 = v[0], y0 = v[1],\n\t\t\tx1 = v[2], y1 = v[3],\n\t\t\tx2 = v[4], y2 = v[5],\n\t\t\tx3 = v[6], y3 = v[7],\n\n\t\t\tax = 9 * (x1 - x2) + 3 * (x3 - x0),\n\t\t\tbx = 6 * (x0 + x2) - 12 * x1,\n\t\t\tcx = 3 * (x1 - x0),\n\n\t\t\tay = 9 * (y1 - y2) + 3 * (y3 - y0),\n\t\t\tby = 6 * (y0 + y2) - 12 * y1,\n\t\t\tcy = 3 * (y1 - y0);\n\n\t\treturn function(t) {\n\t\t\tvar dx = (ax * t + bx) * t + cx,\n\t\t\t\tdy = (ay * t + by) * t + cy;\n\t\t\treturn Math.sqrt(dx * dx + dy * dy);\n\t\t};\n\t}\n\n\tfunction getIterations(a, b) {\n\t\treturn Math.max(2, Math.min(16, Math.ceil(Math.abs(b - a) * 32)));\n\t}\n\n\tfunction evaluate(v, t, type, normalized) {\n\t\tif (t == null || t < 0 || t > 1)\n\t\t\treturn null;\n\t\tvar x0 = v[0], y0 = v[1],\n\t\t\tx1 = v[2], y1 = v[3],\n\t\t\tx2 = v[4], y2 = v[5],\n\t\t\tx3 = v[6], y3 = v[7],\n\t\t\tisZero = Numerical.isZero;\n\t\tif (isZero(x1 - x0) && isZero(y1 - y0)) {\n\t\t\tx1 = x0;\n\t\t\ty1 = y0;\n\t\t}\n\t\tif (isZero(x2 - x3) && isZero(y2 - y3)) {\n\t\t\tx2 = x3;\n\t\t\ty2 = y3;\n\t\t}\n\t\tvar cx = 3 * (x1 - x0),\n\t\t\tbx = 3 * (x2 - x1) - cx,\n\t\t\tax = x3 - x0 - cx - bx,\n\t\t\tcy = 3 * (y1 - y0),\n\t\t\tby = 3 * (y2 - y1) - cy,\n\t\t\tay = y3 - y0 - cy - by,\n\t\t\tx, y;\n\t\tif (type === 0) {\n\t\t\tx = t === 0 ? x0 : t === 1 ? x3\n\t\t\t\t\t: ((ax * t + bx) * t + cx) * t + x0;\n\t\t\ty = t === 0 ? y0 : t === 1 ? y3\n\t\t\t\t\t: ((ay * t + by) * t + cy) * t + y0;\n\t\t} else {\n\t\t\tvar tMin = 1e-8,\n\t\t\t\ttMax = 1 - tMin;\n\t\t\tif (t < tMin) {\n\t\t\t\tx = cx;\n\t\t\t\ty = cy;\n\t\t\t} else if (t > tMax) {\n\t\t\t\tx = 3 * (x3 - x2);\n\t\t\t\ty = 3 * (y3 - y2);\n\t\t\t} else {\n\t\t\t\tx = (3 * ax * t + 2 * bx) * t + cx;\n\t\t\t\ty = (3 * ay * t + 2 * by) * t + cy;\n\t\t\t}\n\t\t\tif (normalized) {\n\t\t\t\tif (x === 0 && y === 0 && (t < tMin || t > tMax)) {\n\t\t\t\t\tx = x2 - x1;\n\t\t\t\t\ty = y2 - y1;\n\t\t\t\t}\n\t\t\t\tvar len = Math.sqrt(x * x + y * y);\n\t\t\t\tif (len) {\n\t\t\t\t\tx /= len;\n\t\t\t\t\ty /= len;\n\t\t\t\t}\n\t\t\t}\n\t\t\tif (type === 3) {\n\t\t\t\tvar x2 = 6 * ax * t + 2 * bx,\n\t\t\t\t\ty2 = 6 * ay * t + 2 * by,\n\t\t\t\t\td = Math.pow(x * x + y * y, 3 / 2);\n\t\t\t\tx = d !== 0 ? (x * y2 - y * x2) / d : 0;\n\t\t\t\ty = 0;\n\t\t\t}\n\t\t}\n\t\treturn type === 2 ? new Point(y, -x) : new Point(x, y);\n\t}\n\n\treturn { statics: {\n\n\t\tclassify: function(v) {\n\n\t\t\tvar x0 = v[0], y0 = v[1],\n\t\t\t\tx1 = v[2], y1 = v[3],\n\t\t\t\tx2 = v[4], y2 = v[5],\n\t\t\t\tx3 = v[6], y3 = v[7],\n\t\t\t\ta1 = x0 * (y3 - y2) + y0 * (x2 - x3) + x3 * y2 - y3 * x2,\n\t\t\t\ta2 = x1 * (y0 - y3) + y1 * (x3 - x0) + x0 * y3 - y0 * x3,\n\t\t\t\ta3 = x2 * (y1 - y0) + y2 * (x0 - x1) + x1 * y0 - y1 * x0,\n\t\t\t\td3 = 3 * a3,\n\t\t\t\td2 = d3 - a2,\n\t\t\t\td1 = d2 - a2 + a1,\n\t\t\t\tl = Math.sqrt(d1 * d1 + d2 * d2 + d3 * d3),\n\t\t\t\ts = l !== 0 ? 1 / l : 0,\n\t\t\t\tisZero = Numerical.isZero,\n\t\t\t\tserpentine = 'serpentine';\n\t\t\td1 *= s;\n\t\t\td2 *= s;\n\t\t\td3 *= s;\n\n\t\t\tfunction type(type, t1, t2) {\n\t\t\t\tvar hasRoots = t1 !== undefined,\n\t\t\t\t\tt1Ok = hasRoots && t1 > 0 && t1 < 1,\n\t\t\t\t\tt2Ok = hasRoots && t2 > 0 && t2 < 1;\n\t\t\t\tif (hasRoots && (!(t1Ok || t2Ok)\n\t\t\t\t\t\t|| type === 'loop' && !(t1Ok && t2Ok))) {\n\t\t\t\t\ttype = 'arch';\n\t\t\t\t\tt1Ok = t2Ok = false;\n\t\t\t\t}\n\t\t\t\treturn {\n\t\t\t\t\ttype: type,\n\t\t\t\t\troots: t1Ok || t2Ok\n\t\t\t\t\t\t\t? t1Ok && t2Ok\n\t\t\t\t\t\t\t\t? t1 < t2 ? [t1, t2] : [t2, t1]\n\t\t\t\t\t\t\t\t: [t1Ok ? t1 : t2]\n\t\t\t\t\t\t\t: null\n\t\t\t\t};\n\t\t\t}\n\n\t\t\tif (isZero(d1)) {\n\t\t\t\treturn isZero(d2)\n\t\t\t\t\t\t? type(isZero(d3) ? 'line' : 'quadratic')\n\t\t\t\t\t\t: type(serpentine, d3 / (3 * d2));\n\t\t\t}\n\t\t\tvar d = 3 * d2 * d2 - 4 * d1 * d3;\n\t\t\tif (isZero(d)) {\n\t\t\t\treturn type('cusp', d2 / (2 * d1));\n\t\t\t}\n\t\t\tvar f1 = d > 0 ? Math.sqrt(d / 3) : Math.sqrt(-d),\n\t\t\t\tf2 = 2 * d1;\n\t\t\treturn type(d > 0 ? serpentine : 'loop',\n\t\t\t\t\t(d2 + f1) / f2,\n\t\t\t\t\t(d2 - f1) / f2);\n\t\t},\n\n\t\tgetLength: function(v, a, b, ds) {\n\t\t\tif (a === undefined)\n\t\t\t\ta = 0;\n\t\t\tif (b === undefined)\n\t\t\t\tb = 1;\n\t\t\tif (Curve.isStraight(v)) {\n\t\t\t\tvar c = v;\n\t\t\t\tif (b < 1) {\n\t\t\t\t\tc = Curve.subdivide(c, b)[0];\n\t\t\t\t\ta /= b;\n\t\t\t\t}\n\t\t\t\tif (a > 0) {\n\t\t\t\t\tc = Curve.subdivide(c, a)[1];\n\t\t\t\t}\n\t\t\t\tvar dx = c[6] - c[0],\n\t\t\t\t\tdy = c[7] - c[1];\n\t\t\t\treturn Math.sqrt(dx * dx + dy * dy);\n\t\t\t}\n\t\t\treturn Numerical.integrate(ds || getLengthIntegrand(v), a, b,\n\t\t\t\t\tgetIterations(a, b));\n\t\t},\n\n\t\tgetTimeAt: function(v, offset, start) {\n\t\t\tif (start === undefined)\n\t\t\t\tstart = offset < 0 ? 1 : 0;\n\t\t\tif (offset === 0)\n\t\t\t\treturn start;\n\t\t\tvar abs = Math.abs,\n\t\t\t\tepsilon = 1e-12,\n\t\t\t\tforward = offset > 0,\n\t\t\t\ta = forward ? start : 0,\n\t\t\t\tb = forward ? 1 : start,\n\t\t\t\tds = getLengthIntegrand(v),\n\t\t\t\trangeLength = Curve.getLength(v, a, b, ds),\n\t\t\t\tdiff = abs(offset) - rangeLength;\n\t\t\tif (abs(diff) < epsilon) {\n\t\t\t\treturn forward ? b : a;\n\t\t\t} else if (diff > epsilon) {\n\t\t\t\treturn null;\n\t\t\t}\n\t\t\tvar guess = offset / rangeLength,\n\t\t\t\tlength = 0;\n\t\t\tfunction f(t) {\n\t\t\t\tlength += Numerical.integrate(ds, start, t,\n\t\t\t\t\t\tgetIterations(start, t));\n\t\t\t\tstart = t;\n\t\t\t\treturn length - offset;\n\t\t\t}\n\t\t\treturn Numerical.findRoot(f, ds, start + guess, a, b, 32,\n\t\t\t\t\t1e-12);\n\t\t},\n\n\t\tgetPoint: function(v, t) {\n\t\t\treturn evaluate(v, t, 0, false);\n\t\t},\n\n\t\tgetTangent: function(v, t) {\n\t\t\treturn evaluate(v, t, 1, true);\n\t\t},\n\n\t\tgetWeightedTangent: function(v, t) {\n\t\t\treturn evaluate(v, t, 1, false);\n\t\t},\n\n\t\tgetNormal: function(v, t) {\n\t\t\treturn evaluate(v, t, 2, true);\n\t\t},\n\n\t\tgetWeightedNormal: function(v, t) {\n\t\t\treturn evaluate(v, t, 2, false);\n\t\t},\n\n\t\tgetCurvature: function(v, t) {\n\t\t\treturn evaluate(v, t, 3, false).x;\n\t\t},\n\n\t\tgetPeaks: function(v) {\n\t\t\tvar x0 = v[0], y0 = v[1],\n\t\t\t\tx1 = v[2], y1 = v[3],\n\t\t\t\tx2 = v[4], y2 = v[5],\n\t\t\t\tx3 = v[6], y3 = v[7],\n\t\t\t\tax =     -x0 + 3 * x1 - 3 * x2 + x3,\n\t\t\t\tbx =  3 * x0 - 6 * x1 + 3 * x2,\n\t\t\t\tcx = -3 * x0 + 3 * x1,\n\t\t\t\tay =     -y0 + 3 * y1 - 3 * y2 + y3,\n\t\t\t\tby =  3 * y0 - 6 * y1 + 3 * y2,\n\t\t\t\tcy = -3 * y0 + 3 * y1,\n\t\t\t\ttMin = 1e-8,\n\t\t\t\ttMax = 1 - tMin,\n\t\t\t\troots = [];\n\t\t\tNumerical.solveCubic(\n\t\t\t\t\t9 * (ax * ax + ay * ay),\n\t\t\t\t\t9 * (ax * bx + by * ay),\n\t\t\t\t\t2 * (bx * bx + by * by) + 3 * (cx * ax + cy * ay),\n\t\t\t\t\t(cx * bx + by * cy),\n\t\t\t\t\troots, tMin, tMax);\n\t\t\treturn roots.sort();\n\t\t}\n\t}};\n},\nnew function() {\n\n\tfunction addLocation(locations, include, c1, t1, c2, t2, overlap) {\n\t\tvar excludeStart = !overlap && c1.getPrevious() === c2,\n\t\t\texcludeEnd = !overlap && c1 !== c2 && c1.getNext() === c2,\n\t\t\ttMin = 1e-8,\n\t\t\ttMax = 1 - tMin;\n\t\tif (t1 !== null && t1 >= (excludeStart ? tMin : 0) &&\n\t\t\tt1 <= (excludeEnd ? tMax : 1)) {\n\t\t\tif (t2 !== null && t2 >= (excludeEnd ? tMin : 0) &&\n\t\t\t\tt2 <= (excludeStart ? tMax : 1)) {\n\t\t\t\tvar loc1 = new CurveLocation(c1, t1, null, overlap),\n\t\t\t\t\tloc2 = new CurveLocation(c2, t2, null, overlap);\n\t\t\t\tloc1._intersection = loc2;\n\t\t\t\tloc2._intersection = loc1;\n\t\t\t\tif (!include || include(loc1)) {\n\t\t\t\t\tCurveLocation.insert(locations, loc1, true);\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\tfunction addCurveIntersections(v1, v2, c1, c2, locations, include, flip,\n\t\t\trecursion, calls, tMin, tMax, uMin, uMax) {\n\t\tif (++calls >= 4096 || ++recursion >= 40)\n\t\t\treturn calls;\n\t\tvar fatLineEpsilon = 1e-9,\n\t\t\tq0x = v2[0], q0y = v2[1], q3x = v2[6], q3y = v2[7],\n\t\t\tgetSignedDistance = Line.getSignedDistance,\n\t\t\td1 = getSignedDistance(q0x, q0y, q3x, q3y, v2[2], v2[3]),\n\t\t\td2 = getSignedDistance(q0x, q0y, q3x, q3y, v2[4], v2[5]),\n\t\t\tfactor = d1 * d2 > 0 ? 3 / 4 : 4 / 9,\n\t\t\tdMin = factor * Math.min(0, d1, d2),\n\t\t\tdMax = factor * Math.max(0, d1, d2),\n\t\t\tdp0 = getSignedDistance(q0x, q0y, q3x, q3y, v1[0], v1[1]),\n\t\t\tdp1 = getSignedDistance(q0x, q0y, q3x, q3y, v1[2], v1[3]),\n\t\t\tdp2 = getSignedDistance(q0x, q0y, q3x, q3y, v1[4], v1[5]),\n\t\t\tdp3 = getSignedDistance(q0x, q0y, q3x, q3y, v1[6], v1[7]),\n\t\t\thull = getConvexHull(dp0, dp1, dp2, dp3),\n\t\t\ttop = hull[0],\n\t\t\tbottom = hull[1],\n\t\t\ttMinClip,\n\t\t\ttMaxClip;\n\t\tif (d1 === 0 && d2 === 0\n\t\t\t\t&& dp0 === 0 && dp1 === 0 && dp2 === 0 && dp3 === 0\n\t\t\t|| (tMinClip = clipConvexHull(top, bottom, dMin, dMax)) == null\n\t\t\t|| (tMaxClip = clipConvexHull(top.reverse(), bottom.reverse(),\n\t\t\t\tdMin, dMax)) == null)\n\t\t\treturn calls;\n\t\tvar tMinNew = tMin + (tMax - tMin) * tMinClip,\n\t\t\ttMaxNew = tMin + (tMax - tMin) * tMaxClip;\n\t\tif (Math.max(uMax - uMin, tMaxNew - tMinNew) < fatLineEpsilon) {\n\t\t\tvar t = (tMinNew + tMaxNew) / 2,\n\t\t\t\tu = (uMin + uMax) / 2;\n\t\t\taddLocation(locations, include,\n\t\t\t\t\tflip ? c2 : c1, flip ? u : t,\n\t\t\t\t\tflip ? c1 : c2, flip ? t : u);\n\t\t} else {\n\t\t\tv1 = Curve.getPart(v1, tMinClip, tMaxClip);\n\t\t\tvar uDiff = uMax - uMin;\n\t\t\tif (tMaxClip - tMinClip > 0.8) {\n\t\t\t\tif (tMaxNew - tMinNew > uDiff) {\n\t\t\t\t\tvar parts = Curve.subdivide(v1, 0.5),\n\t\t\t\t\t\tt = (tMinNew + tMaxNew) / 2;\n\t\t\t\t\tcalls = addCurveIntersections(\n\t\t\t\t\t\t\tv2, parts[0], c2, c1, locations, include, !flip,\n\t\t\t\t\t\t\trecursion, calls, uMin, uMax, tMinNew, t);\n\t\t\t\t\tcalls = addCurveIntersections(\n\t\t\t\t\t\t\tv2, parts[1], c2, c1, locations, include, !flip,\n\t\t\t\t\t\t\trecursion, calls, uMin, uMax, t, tMaxNew);\n\t\t\t\t} else {\n\t\t\t\t\tvar parts = Curve.subdivide(v2, 0.5),\n\t\t\t\t\t\tu = (uMin + uMax) / 2;\n\t\t\t\t\tcalls = addCurveIntersections(\n\t\t\t\t\t\t\tparts[0], v1, c2, c1, locations, include, !flip,\n\t\t\t\t\t\t\trecursion, calls, uMin, u, tMinNew, tMaxNew);\n\t\t\t\t\tcalls = addCurveIntersections(\n\t\t\t\t\t\t\tparts[1], v1, c2, c1, locations, include, !flip,\n\t\t\t\t\t\t\trecursion, calls, u, uMax, tMinNew, tMaxNew);\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tif (uDiff === 0 || uDiff >= fatLineEpsilon) {\n\t\t\t\t\tcalls = addCurveIntersections(\n\t\t\t\t\t\t\tv2, v1, c2, c1, locations, include, !flip,\n\t\t\t\t\t\t\trecursion, calls, uMin, uMax, tMinNew, tMaxNew);\n\t\t\t\t} else {\n\t\t\t\t\tcalls = addCurveIntersections(\n\t\t\t\t\t\t\tv1, v2, c1, c2, locations, include, flip,\n\t\t\t\t\t\t\trecursion, calls, tMinNew, tMaxNew, uMin, uMax);\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\treturn calls;\n\t}\n\n\tfunction getConvexHull(dq0, dq1, dq2, dq3) {\n\t\tvar p0 = [ 0, dq0 ],\n\t\t\tp1 = [ 1 / 3, dq1 ],\n\t\t\tp2 = [ 2 / 3, dq2 ],\n\t\t\tp3 = [ 1, dq3 ],\n\t\t\tdist1 = dq1 - (2 * dq0 + dq3) / 3,\n\t\t\tdist2 = dq2 - (dq0 + 2 * dq3) / 3,\n\t\t\thull;\n\t\tif (dist1 * dist2 < 0) {\n\t\t\thull = [[p0, p1, p3], [p0, p2, p3]];\n\t\t} else {\n\t\t\tvar distRatio = dist1 / dist2;\n\t\t\thull = [\n\t\t\t\tdistRatio >= 2 ? [p0, p1, p3]\n\t\t\t\t: distRatio <= 0.5 ? [p0, p2, p3]\n\t\t\t\t: [p0, p1, p2, p3],\n\t\t\t\t[p0, p3]\n\t\t\t];\n\t\t}\n\t\treturn (dist1 || dist2) < 0 ? hull.reverse() : hull;\n\t}\n\n\tfunction clipConvexHull(hullTop, hullBottom, dMin, dMax) {\n\t\tif (hullTop[0][1] < dMin) {\n\t\t\treturn clipConvexHullPart(hullTop, true, dMin);\n\t\t} else if (hullBottom[0][1] > dMax) {\n\t\t\treturn clipConvexHullPart(hullBottom, false, dMax);\n\t\t} else {\n\t\t\treturn hullTop[0][0];\n\t\t}\n\t}\n\n\tfunction clipConvexHullPart(part, top, threshold) {\n\t\tvar px = part[0][0],\n\t\t\tpy = part[0][1];\n\t\tfor (var i = 1, l = part.length; i < l; i++) {\n\t\t\tvar qx = part[i][0],\n\t\t\t\tqy = part[i][1];\n\t\t\tif (top ? qy >= threshold : qy <= threshold) {\n\t\t\t\treturn qy === threshold ? qx\n\t\t\t\t\t\t: px + (threshold - py) * (qx - px) / (qy - py);\n\t\t\t}\n\t\t\tpx = qx;\n\t\t\tpy = qy;\n\t\t}\n\t\treturn null;\n\t}\n\n\tfunction getCurveLineIntersections(v, px, py, vx, vy) {\n\t\tvar isZero = Numerical.isZero;\n\t\tif (isZero(vx) && isZero(vy)) {\n\t\t\tvar t = Curve.getTimeOf(v, new Point(px, py));\n\t\t\treturn t === null ? [] : [t];\n\t\t}\n\t\tvar angle = Math.atan2(-vy, vx),\n\t\t\tsin = Math.sin(angle),\n\t\t\tcos = Math.cos(angle),\n\t\t\trv = [],\n\t\t\troots = [];\n\t\tfor (var i = 0; i < 8; i += 2) {\n\t\t\tvar x = v[i] - px,\n\t\t\t\ty = v[i + 1] - py;\n\t\t\trv.push(\n\t\t\t\tx * cos - y * sin,\n\t\t\t\tx * sin + y * cos);\n\t\t}\n\t\tCurve.solveCubic(rv, 1, 0, roots, 0, 1);\n\t\treturn roots;\n\t}\n\n\tfunction addCurveLineIntersections(v1, v2, c1, c2, locations, include,\n\t\t\tflip) {\n\t\tvar x1 = v2[0], y1 = v2[1],\n\t\t\tx2 = v2[6], y2 = v2[7],\n\t\t\troots = getCurveLineIntersections(v1, x1, y1, x2 - x1, y2 - y1);\n\t\tfor (var i = 0, l = roots.length; i < l; i++) {\n\t\t\tvar t1 = roots[i],\n\t\t\t\tp1 = Curve.getPoint(v1, t1),\n\t\t\t\tt2 = Curve.getTimeOf(v2, p1);\n\t\t\tif (t2 !== null) {\n\t\t\t\taddLocation(locations, include,\n\t\t\t\t\t\tflip ? c2 : c1, flip ? t2 : t1,\n\t\t\t\t\t\tflip ? c1 : c2, flip ? t1 : t2);\n\t\t\t}\n\t\t}\n\t}\n\n\tfunction addLineIntersection(v1, v2, c1, c2, locations, include) {\n\t\tvar pt = Line.intersect(\n\t\t\t\tv1[0], v1[1], v1[6], v1[7],\n\t\t\t\tv2[0], v2[1], v2[6], v2[7]);\n\t\tif (pt) {\n\t\t\taddLocation(locations, include,\n\t\t\t\t\tc1, Curve.getTimeOf(v1, pt),\n\t\t\t\t\tc2, Curve.getTimeOf(v2, pt));\n\t\t}\n\t}\n\n\tfunction getCurveIntersections(v1, v2, c1, c2, locations, include) {\n\t\tvar epsilon = 1e-12,\n\t\t\tmin = Math.min,\n\t\t\tmax = Math.max;\n\n\t\tif (max(v1[0], v1[2], v1[4], v1[6]) + epsilon >\n\t\t\tmin(v2[0], v2[2], v2[4], v2[6]) &&\n\t\t\tmin(v1[0], v1[2], v1[4], v1[6]) - epsilon <\n\t\t\tmax(v2[0], v2[2], v2[4], v2[6]) &&\n\t\t\tmax(v1[1], v1[3], v1[5], v1[7]) + epsilon >\n\t\t\tmin(v2[1], v2[3], v2[5], v2[7]) &&\n\t\t\tmin(v1[1], v1[3], v1[5], v1[7]) - epsilon <\n\t\t\tmax(v2[1], v2[3], v2[5], v2[7])) {\n\t\t\tvar overlaps = getOverlaps(v1, v2);\n\t\t\tif (overlaps) {\n\t\t\t\tfor (var i = 0; i < 2; i++) {\n\t\t\t\t\tvar overlap = overlaps[i];\n\t\t\t\t\taddLocation(locations, include,\n\t\t\t\t\t\t\tc1, overlap[0],\n\t\t\t\t\t\t\tc2, overlap[1], true);\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tvar straight1 = Curve.isStraight(v1),\n\t\t\t\t\tstraight2 = Curve.isStraight(v2),\n\t\t\t\t\tstraight = straight1 && straight2,\n\t\t\t\t\tflip = straight1 && !straight2,\n\t\t\t\t\tbefore = locations.length;\n\t\t\t\t(straight\n\t\t\t\t\t? addLineIntersection\n\t\t\t\t\t: straight1 || straight2\n\t\t\t\t\t\t? addCurveLineIntersections\n\t\t\t\t\t\t: addCurveIntersections)(\n\t\t\t\t\t\t\tflip ? v2 : v1, flip ? v1 : v2,\n\t\t\t\t\t\t\tflip ? c2 : c1, flip ? c1 : c2,\n\t\t\t\t\t\t\tlocations, include, flip,\n\t\t\t\t\t\t\t0, 0, 0, 1, 0, 1);\n\t\t\t\tif (!straight || locations.length === before) {\n\t\t\t\t\tfor (var i = 0; i < 4; i++) {\n\t\t\t\t\t\tvar t1 = i >> 1,\n\t\t\t\t\t\t\tt2 = i & 1,\n\t\t\t\t\t\t\ti1 = t1 * 6,\n\t\t\t\t\t\t\ti2 = t2 * 6,\n\t\t\t\t\t\t\tp1 = new Point(v1[i1], v1[i1 + 1]),\n\t\t\t\t\t\t\tp2 = new Point(v2[i2], v2[i2 + 1]);\n\t\t\t\t\t\tif (p1.isClose(p2, epsilon)) {\n\t\t\t\t\t\t\taddLocation(locations, include,\n\t\t\t\t\t\t\t\t\tc1, t1,\n\t\t\t\t\t\t\t\t\tc2, t2);\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\treturn locations;\n\t}\n\n\tfunction getSelfIntersection(v1, c1, locations, include) {\n\t\tvar info = Curve.classify(v1);\n\t\tif (info.type === 'loop') {\n\t\t\tvar roots = info.roots;\n\t\t\taddLocation(locations, include,\n\t\t\t\t\tc1, roots[0],\n\t\t\t\t\tc1, roots[1]);\n\t\t}\n\t  return locations;\n\t}\n\n\tfunction getIntersections(curves1, curves2, include, matrix1, matrix2,\n\t\t\t_returnFirst) {\n\t\tvar epsilon = 1e-7,\n\t\t\tself = !curves2;\n\t\tif (self)\n\t\t\tcurves2 = curves1;\n\t\tvar length1 = curves1.length,\n\t\t\tlength2 = curves2.length,\n\t\t\tvalues1 = new Array(length1),\n\t\t\tvalues2 = self ? values1 : new Array(length2),\n\t\t\tlocations = [];\n\n\t\tfor (var i = 0; i < length1; i++) {\n\t\t\tvalues1[i] = curves1[i].getValues(matrix1);\n\t\t}\n\t\tif (!self) {\n\t\t\tfor (var i = 0; i < length2; i++) {\n\t\t\t\tvalues2[i] = curves2[i].getValues(matrix2);\n\t\t\t}\n\t\t}\n\t\tvar boundsCollisions = CollisionDetection.findCurveBoundsCollisions(\n\t\t\t\tvalues1, values2, epsilon);\n\t\tfor (var index1 = 0; index1 < length1; index1++) {\n\t\t\tvar curve1 = curves1[index1],\n\t\t\t\tv1 = values1[index1];\n\t\t\tif (self) {\n\t\t\t\tgetSelfIntersection(v1, curve1, locations, include);\n\t\t\t}\n\t\t\tvar collisions1 = boundsCollisions[index1];\n\t\t\tif (collisions1) {\n\t\t\t\tfor (var j = 0; j < collisions1.length; j++) {\n\t\t\t\t\tif (_returnFirst && locations.length)\n\t\t\t\t\t\treturn locations;\n\t\t\t\t\tvar index2 = collisions1[j];\n\t\t\t\t\tif (!self || index2 > index1) {\n\t\t\t\t\t\tvar curve2 = curves2[index2],\n\t\t\t\t\t\t\tv2 = values2[index2];\n\t\t\t\t\t\tgetCurveIntersections(\n\t\t\t\t\t\t\t\tv1, v2, curve1, curve2, locations, include);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\treturn locations;\n\t}\n\n\tfunction getOverlaps(v1, v2) {\n\n\t\tfunction getSquaredLineLength(v) {\n\t\t\tvar x = v[6] - v[0],\n\t\t\t\ty = v[7] - v[1];\n\t\t\treturn x * x + y * y;\n\t\t}\n\n\t\tvar abs = Math.abs,\n\t\t\tgetDistance = Line.getDistance,\n\t\t\ttimeEpsilon = 1e-8,\n\t\t\tgeomEpsilon = 1e-7,\n\t\t\tstraight1 = Curve.isStraight(v1),\n\t\t\tstraight2 = Curve.isStraight(v2),\n\t\t\tstraightBoth = straight1 && straight2,\n\t\t\tflip = getSquaredLineLength(v1) < getSquaredLineLength(v2),\n\t\t\tl1 = flip ? v2 : v1,\n\t\t\tl2 = flip ? v1 : v2,\n\t\t\tpx = l1[0], py = l1[1],\n\t\t\tvx = l1[6] - px, vy = l1[7] - py;\n\t\tif (getDistance(px, py, vx, vy, l2[0], l2[1], true) < geomEpsilon &&\n\t\t\tgetDistance(px, py, vx, vy, l2[6], l2[7], true) < geomEpsilon) {\n\t\t\tif (!straightBoth &&\n\t\t\t\tgetDistance(px, py, vx, vy, l1[2], l1[3], true) < geomEpsilon &&\n\t\t\t\tgetDistance(px, py, vx, vy, l1[4], l1[5], true) < geomEpsilon &&\n\t\t\t\tgetDistance(px, py, vx, vy, l2[2], l2[3], true) < geomEpsilon &&\n\t\t\t\tgetDistance(px, py, vx, vy, l2[4], l2[5], true) < geomEpsilon) {\n\t\t\t\tstraight1 = straight2 = straightBoth = true;\n\t\t\t}\n\t\t} else if (straightBoth) {\n\t\t\treturn null;\n\t\t}\n\t\tif (straight1 ^ straight2) {\n\t\t\treturn null;\n\t\t}\n\n\t\tvar v = [v1, v2],\n\t\t\tpairs = [];\n\t\tfor (var i = 0; i < 4 && pairs.length < 2; i++) {\n\t\t\tvar i1 = i & 1,\n\t\t\t\ti2 = i1 ^ 1,\n\t\t\t\tt1 = i >> 1,\n\t\t\t\tt2 = Curve.getTimeOf(v[i1], new Point(\n\t\t\t\t\tv[i2][t1 ? 6 : 0],\n\t\t\t\t\tv[i2][t1 ? 7 : 1]));\n\t\t\tif (t2 != null) {\n\t\t\t\tvar pair = i1 ? [t1, t2] : [t2, t1];\n\t\t\t\tif (!pairs.length ||\n\t\t\t\t\tabs(pair[0] - pairs[0][0]) > timeEpsilon &&\n\t\t\t\t\tabs(pair[1] - pairs[0][1]) > timeEpsilon) {\n\t\t\t\t\tpairs.push(pair);\n\t\t\t\t}\n\t\t\t}\n\t\t\tif (i > 2 && !pairs.length)\n\t\t\t\tbreak;\n\t\t}\n\t\tif (pairs.length !== 2) {\n\t\t\tpairs = null;\n\t\t} else if (!straightBoth) {\n\t\t\tvar o1 = Curve.getPart(v1, pairs[0][0], pairs[1][0]),\n\t\t\t\to2 = Curve.getPart(v2, pairs[0][1], pairs[1][1]);\n\t\t\tif (abs(o2[2] - o1[2]) > geomEpsilon ||\n\t\t\t\tabs(o2[3] - o1[3]) > geomEpsilon ||\n\t\t\t\tabs(o2[4] - o1[4]) > geomEpsilon ||\n\t\t\t\tabs(o2[5] - o1[5]) > geomEpsilon)\n\t\t\t\tpairs = null;\n\t\t}\n\t\treturn pairs;\n\t}\n\n\tfunction getTimesWithTangent(v, tangent) {\n\t\tvar x0 = v[0], y0 = v[1],\n\t\t\tx1 = v[2], y1 = v[3],\n\t\t\tx2 = v[4], y2 = v[5],\n\t\t\tx3 = v[6], y3 = v[7],\n\t\t\tnormalized = tangent.normalize(),\n\t\t\ttx = normalized.x,\n\t\t\tty = normalized.y,\n\t\t\tax = 3 * x3 - 9 * x2 + 9 * x1 - 3 * x0,\n\t\t\tay = 3 * y3 - 9 * y2 + 9 * y1 - 3 * y0,\n\t\t\tbx = 6 * x2 - 12 * x1 + 6 * x0,\n\t\t\tby = 6 * y2 - 12 * y1 + 6 * y0,\n\t\t\tcx = 3 * x1 - 3 * x0,\n\t\t\tcy = 3 * y1 - 3 * y0,\n\t\t\tden = 2 * ax * ty - 2 * ay * tx,\n\t\t\ttimes = [];\n\t\tif (Math.abs(den) < Numerical.CURVETIME_EPSILON) {\n\t\t\tvar num = ax * cy - ay * cx,\n\t\t\t\tden = ax * by - ay * bx;\n\t\t\tif (den != 0) {\n\t\t\t\tvar t = -num / den;\n\t\t\t\tif (t >= 0 && t <= 1) times.push(t);\n\t\t\t}\n\t\t} else {\n\t\t\tvar delta = (bx * bx - 4 * ax * cx) * ty * ty +\n\t\t\t\t(-2 * bx * by + 4 * ay * cx + 4 * ax * cy) * tx * ty +\n\t\t\t\t(by * by - 4 * ay * cy) * tx * tx,\n\t\t\t\tk = bx * ty - by * tx;\n\t\t\tif (delta >= 0 && den != 0) {\n\t\t\t\tvar d = Math.sqrt(delta),\n\t\t\t\t\tt0 = -(k + d) / den,\n\t\t\t\t\tt1 = (-k + d) / den;\n\t\t\t\tif (t0 >= 0 && t0 <= 1) times.push(t0);\n\t\t\t\tif (t1 >= 0 && t1 <= 1) times.push(t1);\n\t\t\t}\n\t\t}\n\t\treturn times;\n\t}\n\n\treturn {\n\t\tgetIntersections: function(curve) {\n\t\t\tvar v1 = this.getValues(),\n\t\t\t\tv2 = curve && curve !== this && curve.getValues();\n\t\t\treturn v2 ? getCurveIntersections(v1, v2, this, curve, [])\n\t\t\t\t\t  : getSelfIntersection(v1, this, []);\n\t\t},\n\n\t\tstatics: {\n\t\t\tgetOverlaps: getOverlaps,\n\t\t\tgetIntersections: getIntersections,\n\t\t\tgetCurveLineIntersections: getCurveLineIntersections,\n\t\t\tgetTimesWithTangent: getTimesWithTangent\n\t\t}\n\t};\n});\n\nvar CurveLocation = Base.extend({\n\t_class: 'CurveLocation',\n\n\tinitialize: function CurveLocation(curve, time, point, _overlap, _distance) {\n\t\tif (time >= 0.99999999) {\n\t\t\tvar next = curve.getNext();\n\t\t\tif (next) {\n\t\t\t\ttime = 0;\n\t\t\t\tcurve = next;\n\t\t\t}\n\t\t}\n\t\tthis._setCurve(curve);\n\t\tthis._time = time;\n\t\tthis._point = point || curve.getPointAtTime(time);\n\t\tthis._overlap = _overlap;\n\t\tthis._distance = _distance;\n\t\tthis._intersection = this._next = this._previous = null;\n\t},\n\n\t_setPath: function(path) {\n\t\tthis._path = path;\n\t\tthis._version = path ? path._version : 0;\n\t},\n\n\t_setCurve: function(curve) {\n\t\tthis._setPath(curve._path);\n\t\tthis._curve = curve;\n\t\tthis._segment = null;\n\t\tthis._segment1 = curve._segment1;\n\t\tthis._segment2 = curve._segment2;\n\t},\n\n\t_setSegment: function(segment) {\n\t\tvar curve = segment.getCurve();\n\t\tif (curve) {\n\t\t\tthis._setCurve(curve);\n\t\t} else {\n\t\t\tthis._setPath(segment._path);\n\t\t\tthis._segment1 = segment;\n\t\t\tthis._segment2 = null;\n\t\t}\n\t\tthis._segment = segment;\n\t\tthis._time = segment === this._segment1 ? 0 : 1;\n\t\tthis._point = segment._point.clone();\n\t},\n\n\tgetSegment: function() {\n\t\tvar segment = this._segment;\n\t\tif (!segment) {\n\t\t\tvar curve = this.getCurve(),\n\t\t\t\ttime = this.getTime();\n\t\t\tif (time === 0) {\n\t\t\t\tsegment = curve._segment1;\n\t\t\t} else if (time === 1) {\n\t\t\t\tsegment = curve._segment2;\n\t\t\t} else if (time != null) {\n\t\t\t\tsegment = curve.getPartLength(0, time)\n\t\t\t\t\t< curve.getPartLength(time, 1)\n\t\t\t\t\t\t? curve._segment1\n\t\t\t\t\t\t: curve._segment2;\n\t\t\t}\n\t\t\tthis._segment = segment;\n\t\t}\n\t\treturn segment;\n\t},\n\n\tgetCurve: function() {\n\t\tvar path = this._path,\n\t\t\tthat = this;\n\t\tif (path && path._version !== this._version) {\n\t\t\tthis._time = this._offset = this._curveOffset = this._curve = null;\n\t\t}\n\n\t\tfunction trySegment(segment) {\n\t\t\tvar curve = segment && segment.getCurve();\n\t\t\tif (curve && (that._time = curve.getTimeOf(that._point)) != null) {\n\t\t\t\tthat._setCurve(curve);\n\t\t\t\treturn curve;\n\t\t\t}\n\t\t}\n\n\t\treturn this._curve\n\t\t\t|| trySegment(this._segment)\n\t\t\t|| trySegment(this._segment1)\n\t\t\t|| trySegment(this._segment2.getPrevious());\n\t},\n\n\tgetPath: function() {\n\t\tvar curve = this.getCurve();\n\t\treturn curve && curve._path;\n\t},\n\n\tgetIndex: function() {\n\t\tvar curve = this.getCurve();\n\t\treturn curve && curve.getIndex();\n\t},\n\n\tgetTime: function() {\n\t\tvar curve = this.getCurve(),\n\t\t\ttime = this._time;\n\t\treturn curve && time == null\n\t\t\t? this._time = curve.getTimeOf(this._point)\n\t\t\t: time;\n\t},\n\n\tgetParameter: '#getTime',\n\n\tgetPoint: function() {\n\t\treturn this._point;\n\t},\n\n\tgetOffset: function() {\n\t\tvar offset = this._offset;\n\t\tif (offset == null) {\n\t\t\toffset = 0;\n\t\t\tvar path = this.getPath(),\n\t\t\t\tindex = this.getIndex();\n\t\t\tif (path && index != null) {\n\t\t\t\tvar curves = path.getCurves();\n\t\t\t\tfor (var i = 0; i < index; i++)\n\t\t\t\t\toffset += curves[i].getLength();\n\t\t\t}\n\t\t\tthis._offset = offset += this.getCurveOffset();\n\t\t}\n\t\treturn offset;\n\t},\n\n\tgetCurveOffset: function() {\n\t\tvar offset = this._curveOffset;\n\t\tif (offset == null) {\n\t\t\tvar curve = this.getCurve(),\n\t\t\t\ttime = this.getTime();\n\t\t\tthis._curveOffset = offset = time != null && curve\n\t\t\t\t\t&& curve.getPartLength(0, time);\n\t\t}\n\t\treturn offset;\n\t},\n\n\tgetIntersection: function() {\n\t\treturn this._intersection;\n\t},\n\n\tgetDistance: function() {\n\t\treturn this._distance;\n\t},\n\n\tdivide: function() {\n\t\tvar curve = this.getCurve(),\n\t\t\tres = curve && curve.divideAtTime(this.getTime());\n\t\tif (res) {\n\t\t\tthis._setSegment(res._segment1);\n\t\t}\n\t\treturn res;\n\t},\n\n\tsplit: function() {\n\t\tvar curve = this.getCurve(),\n\t\t\tpath = curve._path,\n\t\t\tres = curve && curve.splitAtTime(this.getTime());\n\t\tif (res) {\n\t\t\tthis._setSegment(path.getLastSegment());\n\t\t}\n\t\treturn  res;\n\t},\n\n\tequals: function(loc, _ignoreOther) {\n\t\tvar res = this === loc;\n\t\tif (!res && loc instanceof CurveLocation) {\n\t\t\tvar c1 = this.getCurve(),\n\t\t\t\tc2 = loc.getCurve(),\n\t\t\t\tp1 = c1._path,\n\t\t\t\tp2 = c2._path;\n\t\t\tif (p1 === p2) {\n\t\t\t\tvar abs = Math.abs,\n\t\t\t\t\tepsilon = 1e-7,\n\t\t\t\t\tdiff = abs(this.getOffset() - loc.getOffset()),\n\t\t\t\t\ti1 = !_ignoreOther && this._intersection,\n\t\t\t\t\ti2 = !_ignoreOther && loc._intersection;\n\t\t\t\tres = (diff < epsilon\n\t\t\t\t\t\t|| p1 && abs(p1.getLength() - diff) < epsilon)\n\t\t\t\t\t&& (!i1 && !i2 || i1 && i2 && i1.equals(i2, true));\n\t\t\t}\n\t\t}\n\t\treturn res;\n\t},\n\n\ttoString: function() {\n\t\tvar parts = [],\n\t\t\tpoint = this.getPoint(),\n\t\t\tf = Formatter.instance;\n\t\tif (point)\n\t\t\tparts.push('point: ' + point);\n\t\tvar index = this.getIndex();\n\t\tif (index != null)\n\t\t\tparts.push('index: ' + index);\n\t\tvar time = this.getTime();\n\t\tif (time != null)\n\t\t\tparts.push('time: ' + f.number(time));\n\t\tif (this._distance != null)\n\t\t\tparts.push('distance: ' + f.number(this._distance));\n\t\treturn '{ ' + parts.join(', ') + ' }';\n\t},\n\n\tisTouching: function() {\n\t\tvar inter = this._intersection;\n\t\tif (inter && this.getTangent().isCollinear(inter.getTangent())) {\n\t\t\tvar curve1 = this.getCurve(),\n\t\t\t\tcurve2 = inter.getCurve();\n\t\t\treturn !(curve1.isStraight() && curve2.isStraight()\n\t\t\t\t\t&& curve1.getLine().intersect(curve2.getLine()));\n\t\t}\n\t\treturn false;\n\t},\n\n\tisCrossing: function() {\n\t\tvar inter = this._intersection;\n\t\tif (!inter)\n\t\t\treturn false;\n\t\tvar t1 = this.getTime(),\n\t\t\tt2 = inter.getTime(),\n\t\t\ttMin = 1e-8,\n\t\t\ttMax = 1 - tMin,\n\t\t\tt1Inside = t1 >= tMin && t1 <= tMax,\n\t\t\tt2Inside = t2 >= tMin && t2 <= tMax;\n\t\tif (t1Inside && t2Inside)\n\t\t\treturn !this.isTouching();\n\t\tvar c2 = this.getCurve(),\n\t\t\tc1 = c2 && t1 < tMin ? c2.getPrevious() : c2,\n\t\t\tc4 = inter.getCurve(),\n\t\t\tc3 = c4 && t2 < tMin ? c4.getPrevious() : c4;\n\t\tif (t1 > tMax)\n\t\t\tc2 = c2.getNext();\n\t\tif (t2 > tMax)\n\t\t\tc4 = c4.getNext();\n\t\tif (!c1 || !c2 || !c3 || !c4)\n\t\t\treturn false;\n\n\t\tvar offsets = [];\n\n\t\tfunction addOffsets(curve, end) {\n\t\t\tvar v = curve.getValues(),\n\t\t\t\troots = Curve.classify(v).roots || Curve.getPeaks(v),\n\t\t\t\tcount = roots.length,\n\t\t\t\toffset = Curve.getLength(v,\n\t\t\t\t\tend && count ? roots[count - 1] : 0,\n\t\t\t\t\t!end && count ? roots[0] : 1);\n\t\t\toffsets.push(count ? offset : offset / 32);\n\t\t}\n\n\t\tfunction isInRange(angle, min, max) {\n\t\t\treturn min < max\n\t\t\t\t\t? angle > min && angle < max\n\t\t\t\t\t: angle > min || angle < max;\n\t\t}\n\n\t\tif (!t1Inside) {\n\t\t\taddOffsets(c1, true);\n\t\t\taddOffsets(c2, false);\n\t\t}\n\t\tif (!t2Inside) {\n\t\t\taddOffsets(c3, true);\n\t\t\taddOffsets(c4, false);\n\t\t}\n\t\tvar pt = this.getPoint(),\n\t\t\toffset = Math.min.apply(Math, offsets),\n\t\t\tv2 = t1Inside ? c2.getTangentAtTime(t1)\n\t\t\t\t\t: c2.getPointAt(offset).subtract(pt),\n\t\t\tv1 = t1Inside ? v2.negate()\n\t\t\t\t\t: c1.getPointAt(-offset).subtract(pt),\n\t\t\tv4 = t2Inside ? c4.getTangentAtTime(t2)\n\t\t\t\t\t: c4.getPointAt(offset).subtract(pt),\n\t\t\tv3 = t2Inside ? v4.negate()\n\t\t\t\t\t: c3.getPointAt(-offset).subtract(pt),\n\t\t\ta1 = v1.getAngle(),\n\t\t\ta2 = v2.getAngle(),\n\t\t\ta3 = v3.getAngle(),\n\t\t\ta4 = v4.getAngle();\n\t\treturn !!(t1Inside\n\t\t\t\t? (isInRange(a1, a3, a4) ^ isInRange(a2, a3, a4)) &&\n\t\t\t\t  (isInRange(a1, a4, a3) ^ isInRange(a2, a4, a3))\n\t\t\t\t: (isInRange(a3, a1, a2) ^ isInRange(a4, a1, a2)) &&\n\t\t\t\t  (isInRange(a3, a2, a1) ^ isInRange(a4, a2, a1)));\n\t},\n\n\thasOverlap: function() {\n\t\treturn !!this._overlap;\n\t}\n}, Base.each(Curve._evaluateMethods, function(name) {\n\tvar get = name + 'At';\n\tthis[name] = function() {\n\t\tvar curve = this.getCurve(),\n\t\t\ttime = this.getTime();\n\t\treturn time != null && curve && curve[get](time, true);\n\t};\n}, {\n\tpreserve: true\n}),\nnew function() {\n\n\tfunction insert(locations, loc, merge) {\n\t\tvar length = locations.length,\n\t\t\tl = 0,\n\t\t\tr = length - 1;\n\n\t\tfunction search(index, dir) {\n\t\t\tfor (var i = index + dir; i >= -1 && i <= length; i += dir) {\n\t\t\t\tvar loc2 = locations[((i % length) + length) % length];\n\t\t\t\tif (!loc.getPoint().isClose(loc2.getPoint(),\n\t\t\t\t\t\t1e-7))\n\t\t\t\t\tbreak;\n\t\t\t\tif (loc.equals(loc2))\n\t\t\t\t\treturn loc2;\n\t\t\t}\n\t\t\treturn null;\n\t\t}\n\n\t\twhile (l <= r) {\n\t\t\tvar m = (l + r) >>> 1,\n\t\t\t\tloc2 = locations[m],\n\t\t\t\tfound;\n\t\t\tif (merge && (found = loc.equals(loc2) ? loc2\n\t\t\t\t\t: (search(m, -1) || search(m, 1)))) {\n\t\t\t\tif (loc._overlap) {\n\t\t\t\t\tfound._overlap = found._intersection._overlap = true;\n\t\t\t\t}\n\t\t\t\treturn found;\n\t\t\t}\n\t\tvar path1 = loc.getPath(),\n\t\t\tpath2 = loc2.getPath(),\n\t\t\tdiff = path1 !== path2\n\t\t\t\t? path1._id - path2._id\n\t\t\t\t: (loc.getIndex() + loc.getTime())\n\t\t\t\t- (loc2.getIndex() + loc2.getTime());\n\t\t\tif (diff < 0) {\n\t\t\t\tr = m - 1;\n\t\t\t} else {\n\t\t\t\tl = m + 1;\n\t\t\t}\n\t\t}\n\t\tlocations.splice(l, 0, loc);\n\t\treturn loc;\n\t}\n\n\treturn { statics: {\n\t\tinsert: insert,\n\n\t\texpand: function(locations) {\n\t\t\tvar expanded = locations.slice();\n\t\t\tfor (var i = locations.length - 1; i >= 0; i--) {\n\t\t\t\tinsert(expanded, locations[i]._intersection, false);\n\t\t\t}\n\t\t\treturn expanded;\n\t\t}\n\t}};\n});\n\nvar PathItem = Item.extend({\n\t_class: 'PathItem',\n\t_selectBounds: false,\n\t_canScaleStroke: true,\n\tbeans: true,\n\n\tinitialize: function PathItem() {\n\t},\n\n\tstatics: {\n\t\tcreate: function(arg) {\n\t\t\tvar data,\n\t\t\t\tsegments,\n\t\t\t\tcompound;\n\t\t\tif (Base.isPlainObject(arg)) {\n\t\t\t\tsegments = arg.segments;\n\t\t\t\tdata = arg.pathData;\n\t\t\t} else if (Array.isArray(arg)) {\n\t\t\t\tsegments = arg;\n\t\t\t} else if (typeof arg === 'string') {\n\t\t\t\tdata = arg;\n\t\t\t}\n\t\t\tif (segments) {\n\t\t\t\tvar first = segments[0];\n\t\t\t\tcompound = first && Array.isArray(first[0]);\n\t\t\t} else if (data) {\n\t\t\t\tcompound = (data.match(/m/gi) || []).length > 1\n\t\t\t\t\t\t|| /z\\s*\\S+/i.test(data);\n\t\t\t}\n\t\t\tvar ctor = compound ? CompoundPath : Path;\n\t\t\treturn new ctor(arg);\n\t\t}\n\t},\n\n\t_asPathItem: function() {\n\t\treturn this;\n\t},\n\n\tisClockwise: function() {\n\t\treturn this.getArea() >= 0;\n\t},\n\n\tsetClockwise: function(clockwise) {\n\t\tif (this.isClockwise() != (clockwise = !!clockwise))\n\t\t\tthis.reverse();\n\t},\n\n\tsetPathData: function(data) {\n\n\t\tvar parts = data && data.match(/[mlhvcsqtaz][^mlhvcsqtaz]*/ig),\n\t\t\tcoords,\n\t\t\trelative = false,\n\t\t\tprevious,\n\t\t\tcontrol,\n\t\t\tcurrent = new Point(),\n\t\t\tstart = new Point();\n\n\t\tfunction getCoord(index, coord) {\n\t\t\tvar val = +coords[index];\n\t\t\tif (relative)\n\t\t\t\tval += current[coord];\n\t\t\treturn val;\n\t\t}\n\n\t\tfunction getPoint(index) {\n\t\t\treturn new Point(\n\t\t\t\tgetCoord(index, 'x'),\n\t\t\t\tgetCoord(index + 1, 'y')\n\t\t\t);\n\t\t}\n\n\t\tthis.clear();\n\n\t\tfor (var i = 0, l = parts && parts.length; i < l; i++) {\n\t\t\tvar part = parts[i],\n\t\t\t\tcommand = part[0],\n\t\t\t\tlower = command.toLowerCase();\n\t\t\tcoords = part.match(/[+-]?(?:\\d*\\.\\d+|\\d+\\.?)(?:[eE][+-]?\\d+)?/g);\n\t\t\tvar length = coords && coords.length;\n\t\t\trelative = command === lower;\n\t\t\tif (previous === 'z' && !/[mz]/.test(lower))\n\t\t\t\tthis.moveTo(current);\n\t\t\tswitch (lower) {\n\t\t\tcase 'm':\n\t\t\tcase 'l':\n\t\t\t\tvar move = lower === 'm';\n\t\t\t\tfor (var j = 0; j < length; j += 2) {\n\t\t\t\t\tthis[move ? 'moveTo' : 'lineTo'](current = getPoint(j));\n\t\t\t\t\tif (move) {\n\t\t\t\t\t\tstart = current;\n\t\t\t\t\t\tmove = false;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tcontrol = current;\n\t\t\t\tbreak;\n\t\t\tcase 'h':\n\t\t\tcase 'v':\n\t\t\t\tvar coord = lower === 'h' ? 'x' : 'y';\n\t\t\t\tcurrent = current.clone();\n\t\t\t\tfor (var j = 0; j < length; j++) {\n\t\t\t\t\tcurrent[coord] = getCoord(j, coord);\n\t\t\t\t\tthis.lineTo(current);\n\t\t\t\t}\n\t\t\t\tcontrol = current;\n\t\t\t\tbreak;\n\t\t\tcase 'c':\n\t\t\t\tfor (var j = 0; j < length; j += 6) {\n\t\t\t\t\tthis.cubicCurveTo(\n\t\t\t\t\t\t\tgetPoint(j),\n\t\t\t\t\t\t\tcontrol = getPoint(j + 2),\n\t\t\t\t\t\t\tcurrent = getPoint(j + 4));\n\t\t\t\t}\n\t\t\t\tbreak;\n\t\t\tcase 's':\n\t\t\t\tfor (var j = 0; j < length; j += 4) {\n\t\t\t\t\tthis.cubicCurveTo(\n\t\t\t\t\t\t\t/[cs]/.test(previous)\n\t\t\t\t\t\t\t\t\t? current.multiply(2).subtract(control)\n\t\t\t\t\t\t\t\t\t: current,\n\t\t\t\t\t\t\tcontrol = getPoint(j),\n\t\t\t\t\t\t\tcurrent = getPoint(j + 2));\n\t\t\t\t\tprevious = lower;\n\t\t\t\t}\n\t\t\t\tbreak;\n\t\t\tcase 'q':\n\t\t\t\tfor (var j = 0; j < length; j += 4) {\n\t\t\t\t\tthis.quadraticCurveTo(\n\t\t\t\t\t\t\tcontrol = getPoint(j),\n\t\t\t\t\t\t\tcurrent = getPoint(j + 2));\n\t\t\t\t}\n\t\t\t\tbreak;\n\t\t\tcase 't':\n\t\t\t\tfor (var j = 0; j < length; j += 2) {\n\t\t\t\t\tthis.quadraticCurveTo(\n\t\t\t\t\t\t\tcontrol = (/[qt]/.test(previous)\n\t\t\t\t\t\t\t\t\t? current.multiply(2).subtract(control)\n\t\t\t\t\t\t\t\t\t: current),\n\t\t\t\t\t\t\tcurrent = getPoint(j));\n\t\t\t\t\tprevious = lower;\n\t\t\t\t}\n\t\t\t\tbreak;\n\t\t\tcase 'a':\n\t\t\t\tfor (var j = 0; j < length; j += 7) {\n\t\t\t\t\tthis.arcTo(current = getPoint(j + 5),\n\t\t\t\t\t\t\tnew Size(+coords[j], +coords[j + 1]),\n\t\t\t\t\t\t\t+coords[j + 2], +coords[j + 4], +coords[j + 3]);\n\t\t\t\t}\n\t\t\t\tbreak;\n\t\t\tcase 'z':\n\t\t\t\tthis.closePath(1e-12);\n\t\t\t\tcurrent = start;\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tprevious = lower;\n\t\t}\n\t},\n\n\t_canComposite: function() {\n\t\treturn !(this.hasFill() && this.hasStroke());\n\t},\n\n\t_contains: function(point) {\n\t\tvar winding = point.isInside(\n\t\t\t\tthis.getBounds({ internal: true, handle: true }))\n\t\t\t\t\t? this._getWinding(point)\n\t\t\t\t\t: {};\n\t\treturn winding.onPath || !!(this.getFillRule() === 'evenodd'\n\t\t\t\t? winding.windingL & 1 || winding.windingR & 1\n\t\t\t\t: winding.winding);\n\t},\n\n\tgetIntersections: function(path, include, _matrix, _returnFirst) {\n\t\tvar self = this === path || !path,\n\t\t\tmatrix1 = this._matrix._orNullIfIdentity(),\n\t\t\tmatrix2 = self ? matrix1\n\t\t\t\t: (_matrix || path._matrix)._orNullIfIdentity();\n\t\treturn self || this.getBounds(matrix1).intersects(\n\t\t\t\tpath.getBounds(matrix2), 1e-12)\n\t\t\t\t? Curve.getIntersections(\n\t\t\t\t\t\tthis.getCurves(), !self && path.getCurves(), include,\n\t\t\t\t\t\tmatrix1, matrix2, _returnFirst)\n\t\t\t\t: [];\n\t},\n\n\tgetCrossings: function(path) {\n\t\treturn this.getIntersections(path, function(inter) {\n\t\t\treturn inter.isCrossing();\n\t\t});\n\t},\n\n\tgetNearestLocation: function() {\n\t\tvar point = Point.read(arguments),\n\t\t\tcurves = this.getCurves(),\n\t\t\tminDist = Infinity,\n\t\t\tminLoc = null;\n\t\tfor (var i = 0, l = curves.length; i < l; i++) {\n\t\t\tvar loc = curves[i].getNearestLocation(point);\n\t\t\tif (loc._distance < minDist) {\n\t\t\t\tminDist = loc._distance;\n\t\t\t\tminLoc = loc;\n\t\t\t}\n\t\t}\n\t\treturn minLoc;\n\t},\n\n\tgetNearestPoint: function() {\n\t\tvar loc = this.getNearestLocation.apply(this, arguments);\n\t\treturn loc ? loc.getPoint() : loc;\n\t},\n\n\tinterpolate: function(from, to, factor) {\n\t\tvar isPath = !this._children,\n\t\t\tname = isPath ? '_segments' : '_children',\n\t\t\titemsFrom = from[name],\n\t\t\titemsTo = to[name],\n\t\t\titems = this[name];\n\t\tif (!itemsFrom || !itemsTo || itemsFrom.length !== itemsTo.length) {\n\t\t\tthrow new Error('Invalid operands in interpolate() call: ' +\n\t\t\t\t\tfrom + ', ' + to);\n\t\t}\n\t\tvar current = items.length,\n\t\t\tlength = itemsTo.length;\n\t\tif (current < length) {\n\t\t\tvar ctor = isPath ? Segment : Path;\n\t\t\tfor (var i = current; i < length; i++) {\n\t\t\t\tthis.add(new ctor());\n\t\t\t}\n\t\t} else if (current > length) {\n\t\t\tthis[isPath ? 'removeSegments' : 'removeChildren'](length, current);\n\t\t}\n\t\tfor (var i = 0; i < length; i++) {\n\t\t\titems[i].interpolate(itemsFrom[i], itemsTo[i], factor);\n\t\t}\n\t\tif (isPath) {\n\t\t\tthis.setClosed(from._closed);\n\t\t\tthis._changed(9);\n\t\t}\n\t},\n\n\tcompare: function(path) {\n\t\tvar ok = false;\n\t\tif (path) {\n\t\t\tvar paths1 = this._children || [this],\n\t\t\t\tpaths2 = path._children ? path._children.slice() : [path],\n\t\t\t\tlength1 = paths1.length,\n\t\t\t\tlength2 = paths2.length,\n\t\t\t\tmatched = [],\n\t\t\t\tcount = 0;\n\t\t\tok = true;\n\t\t\tvar boundsOverlaps = CollisionDetection.findItemBoundsCollisions(paths1, paths2, Numerical.GEOMETRIC_EPSILON);\n\t\t\tfor (var i1 = length1 - 1; i1 >= 0 && ok; i1--) {\n\t\t\t\tvar path1 = paths1[i1];\n\t\t\t\tok = false;\n\t\t\t\tvar pathBoundsOverlaps = boundsOverlaps[i1];\n\t\t\t\tif (pathBoundsOverlaps) {\n\t\t\t\t\tfor (var i2 = pathBoundsOverlaps.length - 1; i2 >= 0 && !ok; i2--) {\n\t\t\t\t\t\tif (path1.compare(paths2[pathBoundsOverlaps[i2]])) {\n\t\t\t\t\t\t\tif (!matched[pathBoundsOverlaps[i2]]) {\n\t\t\t\t\t\t\t\tmatched[pathBoundsOverlaps[i2]] = true;\n\t\t\t\t\t\t\t\tcount++;\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\tok = true;\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t\tok = ok && count === length2;\n\t\t}\n\t\treturn ok;\n\t},\n\n});\n\nvar Path = PathItem.extend({\n\t_class: 'Path',\n\t_serializeFields: {\n\t\tsegments: [],\n\t\tclosed: false\n\t},\n\n\tinitialize: function Path(arg) {\n\t\tthis._closed = false;\n\t\tthis._segments = [];\n\t\tthis._version = 0;\n\t\tvar args = arguments,\n\t\t\tsegments = Array.isArray(arg)\n\t\t\t? typeof arg[0] === 'object'\n\t\t\t\t? arg\n\t\t\t\t: args\n\t\t\t: arg && (arg.size === undefined && (arg.x !== undefined\n\t\t\t\t\t|| arg.point !== undefined))\n\t\t\t\t? args\n\t\t\t\t: null;\n\t\tif (segments && segments.length > 0) {\n\t\t\tthis.setSegments(segments);\n\t\t} else {\n\t\t\tthis._curves = undefined;\n\t\t\tthis._segmentSelection = 0;\n\t\t\tif (!segments && typeof arg === 'string') {\n\t\t\t\tthis.setPathData(arg);\n\t\t\t\targ = null;\n\t\t\t}\n\t\t}\n\t\tthis._initialize(!segments && arg);\n\t},\n\n\t_equals: function(item) {\n\t\treturn this._closed === item._closed\n\t\t\t\t&& Base.equals(this._segments, item._segments);\n\t},\n\n\tcopyContent: function(source) {\n\t\tthis.setSegments(source._segments);\n\t\tthis._closed = source._closed;\n\t},\n\n\t_changed: function _changed(flags) {\n\t\t_changed.base.call(this, flags);\n\t\tif (flags & 8) {\n\t\t\tthis._length = this._area = undefined;\n\t\t\tif (flags & 32) {\n\t\t\t\tthis._version++;\n\t\t\t} else if (this._curves) {\n\t\t\t   for (var i = 0, l = this._curves.length; i < l; i++)\n\t\t\t\t\tthis._curves[i]._changed();\n\t\t\t}\n\t\t} else if (flags & 64) {\n\t\t\tthis._bounds = undefined;\n\t\t}\n\t},\n\n\tgetStyle: function() {\n\t\tvar parent = this._parent;\n\t\treturn (parent instanceof CompoundPath ? parent : this)._style;\n\t},\n\n\tgetSegments: function() {\n\t\treturn this._segments;\n\t},\n\n\tsetSegments: function(segments) {\n\t\tvar fullySelected = this.isFullySelected(),\n\t\t\tlength = segments && segments.length;\n\t\tthis._segments.length = 0;\n\t\tthis._segmentSelection = 0;\n\t\tthis._curves = undefined;\n\t\tif (length) {\n\t\t\tvar last = segments[length - 1];\n\t\t\tif (typeof last === 'boolean') {\n\t\t\t\tthis.setClosed(last);\n\t\t\t\tlength--;\n\t\t\t}\n\t\t\tthis._add(Segment.readList(segments, 0, {}, length));\n\t\t}\n\t\tif (fullySelected)\n\t\t\tthis.setFullySelected(true);\n\t},\n\n\tgetFirstSegment: function() {\n\t\treturn this._segments[0];\n\t},\n\n\tgetLastSegment: function() {\n\t\treturn this._segments[this._segments.length - 1];\n\t},\n\n\tgetCurves: function() {\n\t\tvar curves = this._curves,\n\t\t\tsegments = this._segments;\n\t\tif (!curves) {\n\t\t\tvar length = this._countCurves();\n\t\t\tcurves = this._curves = new Array(length);\n\t\t\tfor (var i = 0; i < length; i++)\n\t\t\t\tcurves[i] = new Curve(this, segments[i],\n\t\t\t\t\tsegments[i + 1] || segments[0]);\n\t\t}\n\t\treturn curves;\n\t},\n\n\tgetFirstCurve: function() {\n\t\treturn this.getCurves()[0];\n\t},\n\n\tgetLastCurve: function() {\n\t\tvar curves = this.getCurves();\n\t\treturn curves[curves.length - 1];\n\t},\n\n\tisClosed: function() {\n\t\treturn this._closed;\n\t},\n\n\tsetClosed: function(closed) {\n\t\tif (this._closed != (closed = !!closed)) {\n\t\t\tthis._closed = closed;\n\t\t\tif (this._curves) {\n\t\t\t\tvar length = this._curves.length = this._countCurves();\n\t\t\t\tif (closed)\n\t\t\t\t\tthis._curves[length - 1] = new Curve(this,\n\t\t\t\t\t\tthis._segments[length - 1], this._segments[0]);\n\t\t\t}\n\t\t\tthis._changed(41);\n\t\t}\n\t}\n}, {\n\tbeans: true,\n\n\tgetPathData: function(_matrix, _precision) {\n\t\tvar segments = this._segments,\n\t\t\tlength = segments.length,\n\t\t\tf = new Formatter(_precision),\n\t\t\tcoords = new Array(6),\n\t\t\tfirst = true,\n\t\t\tcurX, curY,\n\t\t\tprevX, prevY,\n\t\t\tinX, inY,\n\t\t\toutX, outY,\n\t\t\tparts = [];\n\n\t\tfunction addSegment(segment, skipLine) {\n\t\t\tsegment._transformCoordinates(_matrix, coords);\n\t\t\tcurX = coords[0];\n\t\t\tcurY = coords[1];\n\t\t\tif (first) {\n\t\t\t\tparts.push('M' + f.pair(curX, curY));\n\t\t\t\tfirst = false;\n\t\t\t} else {\n\t\t\t\tinX = coords[2];\n\t\t\t\tinY = coords[3];\n\t\t\t\tif (inX === curX && inY === curY\n\t\t\t\t\t\t&& outX === prevX && outY === prevY) {\n\t\t\t\t\tif (!skipLine) {\n\t\t\t\t\t\tvar dx = curX - prevX,\n\t\t\t\t\t\t\tdy = curY - prevY;\n\t\t\t\t\t\tparts.push(\n\t\t\t\t\t\t\t  dx === 0 ? 'v' + f.number(dy)\n\t\t\t\t\t\t\t: dy === 0 ? 'h' + f.number(dx)\n\t\t\t\t\t\t\t: 'l' + f.pair(dx, dy));\n\t\t\t\t\t}\n\t\t\t\t} else {\n\t\t\t\t\tparts.push('c' + f.pair(outX - prevX, outY - prevY)\n\t\t\t\t\t\t\t + ' ' + f.pair( inX - prevX,  inY - prevY)\n\t\t\t\t\t\t\t + ' ' + f.pair(curX - prevX, curY - prevY));\n\t\t\t\t}\n\t\t\t}\n\t\t\tprevX = curX;\n\t\t\tprevY = curY;\n\t\t\toutX = coords[4];\n\t\t\toutY = coords[5];\n\t\t}\n\n\t\tif (!length)\n\t\t\treturn '';\n\n\t\tfor (var i = 0; i < length; i++)\n\t\t\taddSegment(segments[i]);\n\t\tif (this._closed && length > 0) {\n\t\t\taddSegment(segments[0], true);\n\t\t\tparts.push('z');\n\t\t}\n\t\treturn parts.join('');\n\t},\n\n\tisEmpty: function() {\n\t\treturn !this._segments.length;\n\t},\n\n\t_transformContent: function(matrix) {\n\t\tvar segments = this._segments,\n\t\t\tcoords = new Array(6);\n\t\tfor (var i = 0, l = segments.length; i < l; i++)\n\t\t\tsegments[i]._transformCoordinates(matrix, coords, true);\n\t\treturn true;\n\t},\n\n\t_add: function(segs, index) {\n\t\tvar segments = this._segments,\n\t\t\tcurves = this._curves,\n\t\t\tamount = segs.length,\n\t\t\tappend = index == null,\n\t\t\tindex = append ? segments.length : index;\n\t\tfor (var i = 0; i < amount; i++) {\n\t\t\tvar segment = segs[i];\n\t\t\tif (segment._path)\n\t\t\t\tsegment = segs[i] = segment.clone();\n\t\t\tsegment._path = this;\n\t\t\tsegment._index = index + i;\n\t\t\tif (segment._selection)\n\t\t\t\tthis._updateSelection(segment, 0, segment._selection);\n\t\t}\n\t\tif (append) {\n\t\t\tBase.push(segments, segs);\n\t\t} else {\n\t\t\tsegments.splice.apply(segments, [index, 0].concat(segs));\n\t\t\tfor (var i = index + amount, l = segments.length; i < l; i++)\n\t\t\t\tsegments[i]._index = i;\n\t\t}\n\t\tif (curves) {\n\t\t\tvar total = this._countCurves(),\n\t\t\t\tstart = index > 0 && index + amount - 1 === total ? index - 1\n\t\t\t\t\t: index,\n\t\t\t\tinsert = start,\n\t\t\t\tend = Math.min(start + amount, total);\n\t\t\tif (segs._curves) {\n\t\t\t\tcurves.splice.apply(curves, [start, 0].concat(segs._curves));\n\t\t\t\tinsert += segs._curves.length;\n\t\t\t}\n\t\t\tfor (var i = insert; i < end; i++)\n\t\t\t\tcurves.splice(i, 0, new Curve(this, null, null));\n\t\t\tthis._adjustCurves(start, end);\n\t\t}\n\t\tthis._changed(41);\n\t\treturn segs;\n\t},\n\n\t_adjustCurves: function(start, end) {\n\t\tvar segments = this._segments,\n\t\t\tcurves = this._curves,\n\t\t\tcurve;\n\t\tfor (var i = start; i < end; i++) {\n\t\t\tcurve = curves[i];\n\t\t\tcurve._path = this;\n\t\t\tcurve._segment1 = segments[i];\n\t\t\tcurve._segment2 = segments[i + 1] || segments[0];\n\t\t\tcurve._changed();\n\t\t}\n\t\tif (curve = curves[this._closed && !start ? segments.length - 1\n\t\t\t\t: start - 1]) {\n\t\t\tcurve._segment2 = segments[start] || segments[0];\n\t\t\tcurve._changed();\n\t\t}\n\t\tif (curve = curves[end]) {\n\t\t\tcurve._segment1 = segments[end];\n\t\t\tcurve._changed();\n\t\t}\n\t},\n\n\t_countCurves: function() {\n\t\tvar length = this._segments.length;\n\t\treturn !this._closed && length > 0 ? length - 1 : length;\n\t},\n\n\tadd: function(segment1 ) {\n\t\tvar args = arguments;\n\t\treturn args.length > 1 && typeof segment1 !== 'number'\n\t\t\t? this._add(Segment.readList(args))\n\t\t\t: this._add([ Segment.read(args) ])[0];\n\t},\n\n\tinsert: function(index, segment1 ) {\n\t\tvar args = arguments;\n\t\treturn args.length > 2 && typeof segment1 !== 'number'\n\t\t\t? this._add(Segment.readList(args, 1), index)\n\t\t\t: this._add([ Segment.read(args, 1) ], index)[0];\n\t},\n\n\taddSegment: function() {\n\t\treturn this._add([ Segment.read(arguments) ])[0];\n\t},\n\n\tinsertSegment: function(index ) {\n\t\treturn this._add([ Segment.read(arguments, 1) ], index)[0];\n\t},\n\n\taddSegments: function(segments) {\n\t\treturn this._add(Segment.readList(segments));\n\t},\n\n\tinsertSegments: function(index, segments) {\n\t\treturn this._add(Segment.readList(segments), index);\n\t},\n\n\tremoveSegment: function(index) {\n\t\treturn this.removeSegments(index, index + 1)[0] || null;\n\t},\n\n\tremoveSegments: function(start, end, _includeCurves) {\n\t\tstart = start || 0;\n\t\tend = Base.pick(end, this._segments.length);\n\t\tvar segments = this._segments,\n\t\t\tcurves = this._curves,\n\t\t\tcount = segments.length,\n\t\t\tremoved = segments.splice(start, end - start),\n\t\t\tamount = removed.length;\n\t\tif (!amount)\n\t\t\treturn removed;\n\t\tfor (var i = 0; i < amount; i++) {\n\t\t\tvar segment = removed[i];\n\t\t\tif (segment._selection)\n\t\t\t\tthis._updateSelection(segment, segment._selection, 0);\n\t\t\tsegment._index = segment._path = null;\n\t\t}\n\t\tfor (var i = start, l = segments.length; i < l; i++)\n\t\t\tsegments[i]._index = i;\n\t\tif (curves) {\n\t\t\tvar index = start > 0 && end === count + (this._closed ? 1 : 0)\n\t\t\t\t\t? start - 1\n\t\t\t\t\t: start,\n\t\t\t\tcurves = curves.splice(index, amount);\n\t\t\tfor (var i = curves.length - 1; i >= 0; i--)\n\t\t\t\tcurves[i]._path = null;\n\t\t\tif (_includeCurves)\n\t\t\t\tremoved._curves = curves.slice(1);\n\t\t\tthis._adjustCurves(index, index);\n\t\t}\n\t\tthis._changed(41);\n\t\treturn removed;\n\t},\n\n\tclear: '#removeSegments',\n\n\thasHandles: function() {\n\t\tvar segments = this._segments;\n\t\tfor (var i = 0, l = segments.length; i < l; i++) {\n\t\t\tif (segments[i].hasHandles())\n\t\t\t\treturn true;\n\t\t}\n\t\treturn false;\n\t},\n\n\tclearHandles: function() {\n\t\tvar segments = this._segments;\n\t\tfor (var i = 0, l = segments.length; i < l; i++)\n\t\t\tsegments[i].clearHandles();\n\t},\n\n\tgetLength: function() {\n\t\tif (this._length == null) {\n\t\t\tvar curves = this.getCurves(),\n\t\t\t\tlength = 0;\n\t\t\tfor (var i = 0, l = curves.length; i < l; i++)\n\t\t\t\tlength += curves[i].getLength();\n\t\t\tthis._length = length;\n\t\t}\n\t\treturn this._length;\n\t},\n\n\tgetArea: function() {\n\t\tvar area = this._area;\n\t\tif (area == null) {\n\t\t\tvar segments = this._segments,\n\t\t\t\tclosed = this._closed;\n\t\t\tarea = 0;\n\t\t\tfor (var i = 0, l = segments.length; i < l; i++) {\n\t\t\t\tvar last = i + 1 === l;\n\t\t\t\tarea += Curve.getArea(Curve.getValues(\n\t\t\t\t\t\tsegments[i], segments[last ? 0 : i + 1],\n\t\t\t\t\t\tnull, last && !closed));\n\t\t\t}\n\t\t\tthis._area = area;\n\t\t}\n\t\treturn area;\n\t},\n\n\tisFullySelected: function() {\n\t\tvar length = this._segments.length;\n\t\treturn this.isSelected() && length > 0 && this._segmentSelection\n\t\t\t\t=== length * 7;\n\t},\n\n\tsetFullySelected: function(selected) {\n\t\tif (selected)\n\t\t\tthis._selectSegments(true);\n\t\tthis.setSelected(selected);\n\t},\n\n\tsetSelection: function setSelection(selection) {\n\t\tif (!(selection & 1))\n\t\t\tthis._selectSegments(false);\n\t\tsetSelection.base.call(this, selection);\n\t},\n\n\t_selectSegments: function(selected) {\n\t\tvar segments = this._segments,\n\t\t\tlength = segments.length,\n\t\t\tselection = selected ? 7 : 0;\n\t\tthis._segmentSelection = selection * length;\n\t\tfor (var i = 0; i < length; i++)\n\t\t\tsegments[i]._selection = selection;\n\t},\n\n\t_updateSelection: function(segment, oldSelection, newSelection) {\n\t\tsegment._selection = newSelection;\n\t\tvar selection = this._segmentSelection += newSelection - oldSelection;\n\t\tif (selection > 0)\n\t\t\tthis.setSelected(true);\n\t},\n\n\tdivideAt: function(location) {\n\t\tvar loc = this.getLocationAt(location),\n\t\t\tcurve;\n\t\treturn loc && (curve = loc.getCurve().divideAt(loc.getCurveOffset()))\n\t\t\t\t? curve._segment1\n\t\t\t\t: null;\n\t},\n\n\tsplitAt: function(location) {\n\t\tvar loc = this.getLocationAt(location),\n\t\t\tindex = loc && loc.index,\n\t\t\ttime = loc && loc.time,\n\t\t\ttMin = 1e-8,\n\t\t\ttMax = 1 - tMin;\n\t\tif (time > tMax) {\n\t\t\tindex++;\n\t\t\ttime = 0;\n\t\t}\n\t\tvar curves = this.getCurves();\n\t\tif (index >= 0 && index < curves.length) {\n\t\t\tif (time >= tMin) {\n\t\t\t\tcurves[index++].divideAtTime(time);\n\t\t\t}\n\t\t\tvar segs = this.removeSegments(index, this._segments.length, true),\n\t\t\t\tpath;\n\t\t\tif (this._closed) {\n\t\t\t\tthis.setClosed(false);\n\t\t\t\tpath = this;\n\t\t\t} else {\n\t\t\t\tpath = new Path(Item.NO_INSERT);\n\t\t\t\tpath.insertAbove(this);\n\t\t\t\tpath.copyAttributes(this);\n\t\t\t}\n\t\t\tpath._add(segs, 0);\n\t\t\tthis.addSegment(segs[0]);\n\t\t\treturn path;\n\t\t}\n\t\treturn null;\n\t},\n\n\tsplit: function(index, time) {\n\t\tvar curve,\n\t\t\tlocation = time === undefined ? index\n\t\t\t\t: (curve = this.getCurves()[index])\n\t\t\t\t\t&& curve.getLocationAtTime(time);\n\t\treturn location != null ? this.splitAt(location) : null;\n\t},\n\n\tjoin: function(path, tolerance) {\n\t\tvar epsilon = tolerance || 0;\n\t\tif (path && path !== this) {\n\t\t\tvar segments = path._segments,\n\t\t\t\tlast1 = this.getLastSegment(),\n\t\t\t\tlast2 = path.getLastSegment();\n\t\t\tif (!last2)\n\t\t\t\treturn this;\n\t\t\tif (last1 && last1._point.isClose(last2._point, epsilon))\n\t\t\t\tpath.reverse();\n\t\t\tvar first2 = path.getFirstSegment();\n\t\t\tif (last1 && last1._point.isClose(first2._point, epsilon)) {\n\t\t\t\tlast1.setHandleOut(first2._handleOut);\n\t\t\t\tthis._add(segments.slice(1));\n\t\t\t} else {\n\t\t\t\tvar first1 = this.getFirstSegment();\n\t\t\t\tif (first1 && first1._point.isClose(first2._point, epsilon))\n\t\t\t\t\tpath.reverse();\n\t\t\t\tlast2 = path.getLastSegment();\n\t\t\t\tif (first1 && first1._point.isClose(last2._point, epsilon)) {\n\t\t\t\t\tfirst1.setHandleIn(last2._handleIn);\n\t\t\t\t\tthis._add(segments.slice(0, segments.length - 1), 0);\n\t\t\t\t} else {\n\t\t\t\t\tthis._add(segments.slice());\n\t\t\t\t}\n\t\t\t}\n\t\t\tif (path._closed)\n\t\t\t\tthis._add([segments[0]]);\n\t\t\tpath.remove();\n\t\t}\n\t\tvar first = this.getFirstSegment(),\n\t\t\tlast = this.getLastSegment();\n\t\tif (first !== last && first._point.isClose(last._point, epsilon)) {\n\t\t\tfirst.setHandleIn(last._handleIn);\n\t\t\tlast.remove();\n\t\t\tthis.setClosed(true);\n\t\t}\n\t\treturn this;\n\t},\n\n\treduce: function(options) {\n\t\tvar curves = this.getCurves(),\n\t\t\tsimplify = options && options.simplify,\n\t\t\ttolerance = simplify ? 1e-7 : 0;\n\t\tfor (var i = curves.length - 1; i >= 0; i--) {\n\t\t\tvar curve = curves[i];\n\t\t\tif (!curve.hasHandles() && (!curve.hasLength(tolerance)\n\t\t\t\t\t|| simplify && curve.isCollinear(curve.getNext())))\n\t\t\t\tcurve.remove();\n\t\t}\n\t\treturn this;\n\t},\n\n\treverse: function() {\n\t\tthis._segments.reverse();\n\t\tfor (var i = 0, l = this._segments.length; i < l; i++) {\n\t\t\tvar segment = this._segments[i];\n\t\t\tvar handleIn = segment._handleIn;\n\t\t\tsegment._handleIn = segment._handleOut;\n\t\t\tsegment._handleOut = handleIn;\n\t\t\tsegment._index = i;\n\t\t}\n\t\tthis._curves = null;\n\t\tthis._changed(9);\n\t},\n\n\tflatten: function(flatness) {\n\t\tvar flattener = new PathFlattener(this, flatness || 0.25, 256, true),\n\t\t\tparts = flattener.parts,\n\t\t\tlength = parts.length,\n\t\t\tsegments = [];\n\t\tfor (var i = 0; i < length; i++) {\n\t\t\tsegments.push(new Segment(parts[i].curve.slice(0, 2)));\n\t\t}\n\t\tif (!this._closed && length > 0) {\n\t\t\tsegments.push(new Segment(parts[length - 1].curve.slice(6)));\n\t\t}\n\t\tthis.setSegments(segments);\n\t},\n\n\tsimplify: function(tolerance) {\n\t\tvar segments = new PathFitter(this).fit(tolerance || 2.5);\n\t\tif (segments)\n\t\t\tthis.setSegments(segments);\n\t\treturn !!segments;\n\t},\n\n\tsmooth: function(options) {\n\t\tvar that = this,\n\t\t\topts = options || {},\n\t\t\ttype = opts.type || 'asymmetric',\n\t\t\tsegments = this._segments,\n\t\t\tlength = segments.length,\n\t\t\tclosed = this._closed;\n\n\t\tfunction getIndex(value, _default) {\n\t\t\tvar index = value && value.index;\n\t\t\tif (index != null) {\n\t\t\t\tvar path = value.path;\n\t\t\t\tif (path && path !== that)\n\t\t\t\t\tthrow new Error(value._class + ' ' + index + ' of ' + path\n\t\t\t\t\t\t\t+ ' is not part of ' + that);\n\t\t\t\tif (_default && value instanceof Curve)\n\t\t\t\t\tindex++;\n\t\t\t} else {\n\t\t\t\tindex = typeof value === 'number' ? value : _default;\n\t\t\t}\n\t\t\treturn Math.min(index < 0 && closed\n\t\t\t\t\t? index % length\n\t\t\t\t\t: index < 0 ? index + length : index, length - 1);\n\t\t}\n\n\t\tvar loop = closed && opts.from === undefined && opts.to === undefined,\n\t\t\tfrom = getIndex(opts.from, 0),\n\t\t\tto = getIndex(opts.to, length - 1);\n\n\t\tif (from > to) {\n\t\t\tif (closed) {\n\t\t\t\tfrom -= length;\n\t\t\t} else {\n\t\t\t\tvar tmp = from;\n\t\t\t\tfrom = to;\n\t\t\t\tto = tmp;\n\t\t\t}\n\t\t}\n\t\tif (/^(?:asymmetric|continuous)$/.test(type)) {\n\t\t\tvar asymmetric = type === 'asymmetric',\n\t\t\t\tmin = Math.min,\n\t\t\t\tamount = to - from + 1,\n\t\t\t\tn = amount - 1,\n\t\t\t\tpadding = loop ? min(amount, 4) : 1,\n\t\t\t\tpaddingLeft = padding,\n\t\t\t\tpaddingRight = padding,\n\t\t\t\tknots = [];\n\t\t\tif (!closed) {\n\t\t\t\tpaddingLeft = min(1, from);\n\t\t\t\tpaddingRight = min(1, length - to - 1);\n\t\t\t}\n\t\t\tn += paddingLeft + paddingRight;\n\t\t\tif (n <= 1)\n\t\t\t\treturn;\n\t\t\tfor (var i = 0, j = from - paddingLeft; i <= n; i++, j++) {\n\t\t\t\tknots[i] = segments[(j < 0 ? j + length : j) % length]._point;\n\t\t\t}\n\n\t\t\tvar x = knots[0]._x + 2 * knots[1]._x,\n\t\t\t\ty = knots[0]._y + 2 * knots[1]._y,\n\t\t\t\tf = 2,\n\t\t\t\tn_1 = n - 1,\n\t\t\t\trx = [x],\n\t\t\t\try = [y],\n\t\t\t\trf = [f],\n\t\t\t\tpx = [],\n\t\t\t\tpy = [];\n\t\t\tfor (var i = 1; i < n; i++) {\n\t\t\t\tvar internal = i < n_1,\n\t\t\t\t\ta = internal ? 1 : asymmetric ? 1 : 2,\n\t\t\t\t\tb = internal ? 4 : asymmetric ? 2 : 7,\n\t\t\t\t\tu = internal ? 4 : asymmetric ? 3 : 8,\n\t\t\t\t\tv = internal ? 2 : asymmetric ? 0 : 1,\n\t\t\t\t\tm = a / f;\n\t\t\t\tf = rf[i] = b - m;\n\t\t\t\tx = rx[i] = u * knots[i]._x + v * knots[i + 1]._x - m * x;\n\t\t\t\ty = ry[i] = u * knots[i]._y + v * knots[i + 1]._y - m * y;\n\t\t\t}\n\n\t\t\tpx[n_1] = rx[n_1] / rf[n_1];\n\t\t\tpy[n_1] = ry[n_1] / rf[n_1];\n\t\t\tfor (var i = n - 2; i >= 0; i--) {\n\t\t\t\tpx[i] = (rx[i] - px[i + 1]) / rf[i];\n\t\t\t\tpy[i] = (ry[i] - py[i + 1]) / rf[i];\n\t\t\t}\n\t\t\tpx[n] = (3 * knots[n]._x - px[n_1]) / 2;\n\t\t\tpy[n] = (3 * knots[n]._y - py[n_1]) / 2;\n\n\t\t\tfor (var i = paddingLeft, max = n - paddingRight, j = from;\n\t\t\t\t\ti <= max; i++, j++) {\n\t\t\t\tvar segment = segments[j < 0 ? j + length : j],\n\t\t\t\t\tpt = segment._point,\n\t\t\t\t\thx = px[i] - pt._x,\n\t\t\t\t\thy = py[i] - pt._y;\n\t\t\t\tif (loop || i < max)\n\t\t\t\t\tsegment.setHandleOut(hx, hy);\n\t\t\t\tif (loop || i > paddingLeft)\n\t\t\t\t\tsegment.setHandleIn(-hx, -hy);\n\t\t\t}\n\t\t} else {\n\t\t\tfor (var i = from; i <= to; i++) {\n\t\t\t\tsegments[i < 0 ? i + length : i].smooth(opts,\n\t\t\t\t\t\t!loop && i === from, !loop && i === to);\n\t\t\t}\n\t\t}\n\t},\n\n\ttoShape: function(insert) {\n\t\tif (!this._closed)\n\t\t\treturn null;\n\n\t\tvar segments = this._segments,\n\t\t\ttype,\n\t\t\tsize,\n\t\t\tradius,\n\t\t\ttopCenter;\n\n\t\tfunction isCollinear(i, j) {\n\t\t\tvar seg1 = segments[i],\n\t\t\t\tseg2 = seg1.getNext(),\n\t\t\t\tseg3 = segments[j],\n\t\t\t\tseg4 = seg3.getNext();\n\t\t\treturn seg1._handleOut.isZero() && seg2._handleIn.isZero()\n\t\t\t\t\t&& seg3._handleOut.isZero() && seg4._handleIn.isZero()\n\t\t\t\t\t&& seg2._point.subtract(seg1._point).isCollinear(\n\t\t\t\t\t\tseg4._point.subtract(seg3._point));\n\t\t}\n\n\t\tfunction isOrthogonal(i) {\n\t\t\tvar seg2 = segments[i],\n\t\t\t\tseg1 = seg2.getPrevious(),\n\t\t\t\tseg3 = seg2.getNext();\n\t\t\treturn seg1._handleOut.isZero() && seg2._handleIn.isZero()\n\t\t\t\t\t&& seg2._handleOut.isZero() && seg3._handleIn.isZero()\n\t\t\t\t\t&& seg2._point.subtract(seg1._point).isOrthogonal(\n\t\t\t\t\t\tseg3._point.subtract(seg2._point));\n\t\t}\n\n\t\tfunction isArc(i) {\n\t\t\tvar seg1 = segments[i],\n\t\t\t\tseg2 = seg1.getNext(),\n\t\t\t\thandle1 = seg1._handleOut,\n\t\t\t\thandle2 = seg2._handleIn,\n\t\t\t\tkappa = 0.5522847498307936;\n\t\t\tif (handle1.isOrthogonal(handle2)) {\n\t\t\t\tvar pt1 = seg1._point,\n\t\t\t\t\tpt2 = seg2._point,\n\t\t\t\t\tcorner = new Line(pt1, handle1, true).intersect(\n\t\t\t\t\t\t\tnew Line(pt2, handle2, true), true);\n\t\t\t\treturn corner && Numerical.isZero(handle1.getLength() /\n\t\t\t\t\t\tcorner.subtract(pt1).getLength() - kappa)\n\t\t\t\t\t&& Numerical.isZero(handle2.getLength() /\n\t\t\t\t\t\tcorner.subtract(pt2).getLength() - kappa);\n\t\t\t}\n\t\t\treturn false;\n\t\t}\n\n\t\tfunction getDistance(i, j) {\n\t\t\treturn segments[i]._point.getDistance(segments[j]._point);\n\t\t}\n\n\t\tif (!this.hasHandles() && segments.length === 4\n\t\t\t\t&& isCollinear(0, 2) && isCollinear(1, 3) && isOrthogonal(1)) {\n\t\t\ttype = Shape.Rectangle;\n\t\t\tsize = new Size(getDistance(0, 3), getDistance(0, 1));\n\t\t\ttopCenter = segments[1]._point.add(segments[2]._point).divide(2);\n\t\t} else if (segments.length === 8 && isArc(0) && isArc(2) && isArc(4)\n\t\t\t\t&& isArc(6) && isCollinear(1, 5) && isCollinear(3, 7)) {\n\t\t\ttype = Shape.Rectangle;\n\t\t\tsize = new Size(getDistance(1, 6), getDistance(0, 3));\n\t\t\tradius = size.subtract(new Size(getDistance(0, 7),\n\t\t\t\t\tgetDistance(1, 2))).divide(2);\n\t\t\ttopCenter = segments[3]._point.add(segments[4]._point).divide(2);\n\t\t} else if (segments.length === 4\n\t\t\t\t&& isArc(0) && isArc(1) && isArc(2) && isArc(3)) {\n\t\t\tif (Numerical.isZero(getDistance(0, 2) - getDistance(1, 3))) {\n\t\t\t\ttype = Shape.Circle;\n\t\t\t\tradius = getDistance(0, 2) / 2;\n\t\t\t} else {\n\t\t\t\ttype = Shape.Ellipse;\n\t\t\t\tradius = new Size(getDistance(2, 0) / 2, getDistance(3, 1) / 2);\n\t\t\t}\n\t\t\ttopCenter = segments[1]._point;\n\t\t}\n\n\t\tif (type) {\n\t\t\tvar center = this.getPosition(true),\n\t\t\t\tshape = new type({\n\t\t\t\t\tcenter: center,\n\t\t\t\t\tsize: size,\n\t\t\t\t\tradius: radius,\n\t\t\t\t\tinsert: false\n\t\t\t\t});\n\t\t\tshape.copyAttributes(this, true);\n\t\t\tshape._matrix.prepend(this._matrix);\n\t\t\tshape.rotate(topCenter.subtract(center).getAngle() + 90);\n\t\t\tif (insert === undefined || insert)\n\t\t\t\tshape.insertAbove(this);\n\t\t\treturn shape;\n\t\t}\n\t\treturn null;\n\t},\n\n\ttoPath: '#clone',\n\n\tcompare: function compare(path) {\n\t\tif (!path || path instanceof CompoundPath)\n\t\t\treturn compare.base.call(this, path);\n\t\tvar curves1 = this.getCurves(),\n\t\t\tcurves2 = path.getCurves(),\n\t\t\tlength1 = curves1.length,\n\t\t\tlength2 = curves2.length;\n\t\tif (!length1 || !length2) {\n\t\t\treturn length1 == length2;\n\t\t}\n\t\tvar v1 = curves1[0].getValues(),\n\t\t\tvalues2 = [],\n\t\t\tpos1 = 0, pos2,\n\t\t\tend1 = 0, end2;\n\t\tfor (var i = 0; i < length2; i++) {\n\t\t\tvar v2 = curves2[i].getValues();\n\t\t\tvalues2.push(v2);\n\t\t\tvar overlaps = Curve.getOverlaps(v1, v2);\n\t\t\tif (overlaps) {\n\t\t\t\tpos2 = !i && overlaps[0][0] > 0 ? length2 - 1 : i;\n\t\t\t\tend2 = overlaps[0][1];\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\t\tvar abs = Math.abs,\n\t\t\tepsilon = 1e-8,\n\t\t\tv2 = values2[pos2],\n\t\t\tstart2;\n\t\twhile (v1 && v2) {\n\t\t\tvar overlaps = Curve.getOverlaps(v1, v2);\n\t\t\tif (overlaps) {\n\t\t\t\tvar t1 = overlaps[0][0];\n\t\t\t\tif (abs(t1 - end1) < epsilon) {\n\t\t\t\t\tend1 = overlaps[1][0];\n\t\t\t\t\tif (end1 === 1) {\n\t\t\t\t\t\tv1 = ++pos1 < length1 ? curves1[pos1].getValues() : null;\n\t\t\t\t\t\tend1 = 0;\n\t\t\t\t\t}\n\t\t\t\t\tvar t2 = overlaps[0][1];\n\t\t\t\t\tif (abs(t2 - end2) < epsilon) {\n\t\t\t\t\t\tif (!start2)\n\t\t\t\t\t\t\tstart2 = [pos2, t2];\n\t\t\t\t\t\tend2 = overlaps[1][1];\n\t\t\t\t\t\tif (end2 === 1) {\n\t\t\t\t\t\t\tif (++pos2 >= length2)\n\t\t\t\t\t\t\t\tpos2 = 0;\n\t\t\t\t\t\t\tv2 = values2[pos2] || curves2[pos2].getValues();\n\t\t\t\t\t\t\tend2 = 0;\n\t\t\t\t\t\t}\n\t\t\t\t\t\tif (!v1) {\n\t\t\t\t\t\t\treturn start2[0] === pos2 && start2[1] === end2;\n\t\t\t\t\t\t}\n\t\t\t\t\t\tcontinue;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t\tbreak;\n\t\t}\n\t\treturn false;\n\t},\n\n\t_hitTestSelf: function(point, options, viewMatrix, strokeMatrix) {\n\t\tvar that = this,\n\t\t\tstyle = this.getStyle(),\n\t\t\tsegments = this._segments,\n\t\t\tnumSegments = segments.length,\n\t\t\tclosed = this._closed,\n\t\t\ttolerancePadding = options._tolerancePadding,\n\t\t\tstrokePadding = tolerancePadding,\n\t\t\tjoin, cap, miterLimit,\n\t\t\tarea, loc, res,\n\t\t\thitStroke = options.stroke && style.hasStroke(),\n\t\t\thitFill = options.fill && style.hasFill(),\n\t\t\thitCurves = options.curves,\n\t\t\tstrokeRadius = hitStroke\n\t\t\t\t\t? style.getStrokeWidth() / 2\n\t\t\t\t\t: hitFill && options.tolerance > 0 || hitCurves\n\t\t\t\t\t\t? 0 : null;\n\t\tif (strokeRadius !== null) {\n\t\t\tif (strokeRadius > 0) {\n\t\t\t\tjoin = style.getStrokeJoin();\n\t\t\t\tcap = style.getStrokeCap();\n\t\t\t\tmiterLimit = style.getMiterLimit();\n\t\t\t\tstrokePadding = strokePadding.add(\n\t\t\t\t\tPath._getStrokePadding(strokeRadius, strokeMatrix));\n\t\t\t} else {\n\t\t\t\tjoin = cap = 'round';\n\t\t\t}\n\t\t}\n\n\t\tfunction isCloseEnough(pt, padding) {\n\t\t\treturn point.subtract(pt).divide(padding).length <= 1;\n\t\t}\n\n\t\tfunction checkSegmentPoint(seg, pt, name) {\n\t\t\tif (!options.selected || pt.isSelected()) {\n\t\t\t\tvar anchor = seg._point;\n\t\t\t\tif (pt !== anchor)\n\t\t\t\t\tpt = pt.add(anchor);\n\t\t\t\tif (isCloseEnough(pt, strokePadding)) {\n\t\t\t\t\treturn new HitResult(name, that, {\n\t\t\t\t\t\tsegment: seg,\n\t\t\t\t\t\tpoint: pt\n\t\t\t\t\t});\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\tfunction checkSegmentPoints(seg, ends) {\n\t\t\treturn (ends || options.segments)\n\t\t\t\t&& checkSegmentPoint(seg, seg._point, 'segment')\n\t\t\t\t|| (!ends && options.handles) && (\n\t\t\t\t\tcheckSegmentPoint(seg, seg._handleIn, 'handle-in') ||\n\t\t\t\t\tcheckSegmentPoint(seg, seg._handleOut, 'handle-out'));\n\t\t}\n\n\t\tfunction addToArea(point) {\n\t\t\tarea.add(point);\n\t\t}\n\n\t\tfunction checkSegmentStroke(segment) {\n\t\t\tvar isJoin = closed || segment._index > 0\n\t\t\t\t\t&& segment._index < numSegments - 1;\n\t\t\tif ((isJoin ? join : cap) === 'round') {\n\t\t\t\treturn isCloseEnough(segment._point, strokePadding);\n\t\t\t} else {\n\t\t\t\tarea = new Path({ internal: true, closed: true });\n\t\t\t\tif (isJoin) {\n\t\t\t\t\tif (!segment.isSmooth()) {\n\t\t\t\t\t\tPath._addBevelJoin(segment, join, strokeRadius,\n\t\t\t\t\t\t\t   miterLimit, null, strokeMatrix, addToArea, true);\n\t\t\t\t\t}\n\t\t\t\t} else if (cap === 'square') {\n\t\t\t\t\tPath._addSquareCap(segment, cap, strokeRadius, null,\n\t\t\t\t\t\t\tstrokeMatrix, addToArea, true);\n\t\t\t\t}\n\t\t\t\tif (!area.isEmpty()) {\n\t\t\t\t\tvar loc;\n\t\t\t\t\treturn area.contains(point)\n\t\t\t\t\t\t|| (loc = area.getNearestLocation(point))\n\t\t\t\t\t\t\t&& isCloseEnough(loc.getPoint(), tolerancePadding);\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\tif (options.ends && !options.segments && !closed) {\n\t\t\tif (res = checkSegmentPoints(segments[0], true)\n\t\t\t\t\t|| checkSegmentPoints(segments[numSegments - 1], true))\n\t\t\t\treturn res;\n\t\t} else if (options.segments || options.handles) {\n\t\t\tfor (var i = 0; i < numSegments; i++)\n\t\t\t\tif (res = checkSegmentPoints(segments[i]))\n\t\t\t\t\treturn res;\n\t\t}\n\t\tif (strokeRadius !== null) {\n\t\t\tloc = this.getNearestLocation(point);\n\t\t\tif (loc) {\n\t\t\t\tvar time = loc.getTime();\n\t\t\t\tif (time === 0 || time === 1 && numSegments > 1) {\n\t\t\t\t\tif (!checkSegmentStroke(loc.getSegment()))\n\t\t\t\t\t\tloc = null;\n\t\t\t\t} else if (!isCloseEnough(loc.getPoint(), strokePadding)) {\n\t\t\t\t\tloc = null;\n\t\t\t\t}\n\t\t\t}\n\t\t\tif (!loc && join === 'miter' && numSegments > 1) {\n\t\t\t\tfor (var i = 0; i < numSegments; i++) {\n\t\t\t\t\tvar segment = segments[i];\n\t\t\t\t\tif (point.getDistance(segment._point)\n\t\t\t\t\t\t\t<= miterLimit * strokeRadius\n\t\t\t\t\t\t\t&& checkSegmentStroke(segment)) {\n\t\t\t\t\t\tloc = segment.getLocation();\n\t\t\t\t\t\tbreak;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\treturn !loc && hitFill && this._contains(point)\n\t\t\t\t|| loc && !hitStroke && !hitCurves\n\t\t\t\t\t? new HitResult('fill', this)\n\t\t\t\t\t: loc\n\t\t\t\t\t\t? new HitResult(hitStroke ? 'stroke' : 'curve', this, {\n\t\t\t\t\t\t\tlocation: loc,\n\t\t\t\t\t\t\tpoint: loc.getPoint()\n\t\t\t\t\t\t})\n\t\t\t\t\t\t: null;\n\t}\n\n}, Base.each(Curve._evaluateMethods,\n\tfunction(name) {\n\t\tthis[name + 'At'] = function(offset) {\n\t\t\tvar loc = this.getLocationAt(offset);\n\t\t\treturn loc && loc[name]();\n\t\t};\n\t},\n{\n\tbeans: false,\n\n\tgetLocationOf: function() {\n\t\tvar point = Point.read(arguments),\n\t\t\tcurves = this.getCurves();\n\t\tfor (var i = 0, l = curves.length; i < l; i++) {\n\t\t\tvar loc = curves[i].getLocationOf(point);\n\t\t\tif (loc)\n\t\t\t\treturn loc;\n\t\t}\n\t\treturn null;\n\t},\n\n\tgetOffsetOf: function() {\n\t\tvar loc = this.getLocationOf.apply(this, arguments);\n\t\treturn loc ? loc.getOffset() : null;\n\t},\n\n\tgetLocationAt: function(offset) {\n\t\tif (typeof offset === 'number') {\n\t\t\tvar curves = this.getCurves(),\n\t\t\t\tlength = 0;\n\t\t\tfor (var i = 0, l = curves.length; i < l; i++) {\n\t\t\t\tvar start = length,\n\t\t\t\t\tcurve = curves[i];\n\t\t\t\tlength += curve.getLength();\n\t\t\t\tif (length > offset) {\n\t\t\t\t\treturn curve.getLocationAt(offset - start);\n\t\t\t\t}\n\t\t\t}\n\t\t\tif (curves.length > 0 && offset <= this.getLength()) {\n\t\t\t\treturn new CurveLocation(curves[curves.length - 1], 1);\n\t\t\t}\n\t\t} else if (offset && offset.getPath && offset.getPath() === this) {\n\t\t\treturn offset;\n\t\t}\n\t\treturn null;\n\t},\n\n\tgetOffsetsWithTangent: function() {\n\t\tvar tangent = Point.read(arguments);\n\t\tif (tangent.isZero()) {\n\t\t\treturn [];\n\t\t}\n\n\t\tvar offsets = [];\n\t\tvar curveStart = 0;\n\t\tvar curves = this.getCurves();\n\t\tfor (var i = 0, l = curves.length; i < l; i++) {\n\t\t\tvar curve = curves[i];\n\t\t\tvar curveTimes = curve.getTimesWithTangent(tangent);\n\t\t\tfor (var j = 0, m = curveTimes.length; j < m; j++) {\n\t\t\t\tvar offset = curveStart + curve.getOffsetAtTime(curveTimes[j]);\n\t\t\t\tif (offsets.indexOf(offset) < 0) {\n\t\t\t\t\toffsets.push(offset);\n\t\t\t\t}\n\t\t\t}\n\t\t\tcurveStart += curve.length;\n\t\t}\n\t\treturn offsets;\n\t}\n}),\nnew function() {\n\n\tfunction drawHandles(ctx, segments, matrix, size) {\n\t\tif (size <= 0) return;\n\n\t\tvar half = size / 2,\n\t\t\tminiSize = size - 2,\n\t\t\tminiHalf = half - 1,\n\t\t\tcoords = new Array(6),\n\t\t\tpX, pY;\n\n\t\tfunction drawHandle(index) {\n\t\t\tvar hX = coords[index],\n\t\t\t\thY = coords[index + 1];\n\t\t\tif (pX != hX || pY != hY) {\n\t\t\t\tctx.beginPath();\n\t\t\t\tctx.moveTo(pX, pY);\n\t\t\t\tctx.lineTo(hX, hY);\n\t\t\t\tctx.stroke();\n\t\t\t\tctx.beginPath();\n\t\t\t\tctx.arc(hX, hY, half, 0, Math.PI * 2, true);\n\t\t\t\tctx.fill();\n\t\t\t}\n\t\t}\n\n\t\tfor (var i = 0, l = segments.length; i < l; i++) {\n\t\t\tvar segment = segments[i],\n\t\t\t\tselection = segment._selection;\n\t\t\tsegment._transformCoordinates(matrix, coords);\n\t\t\tpX = coords[0];\n\t\t\tpY = coords[1];\n\t\t\tif (selection & 2)\n\t\t\t\tdrawHandle(2);\n\t\t\tif (selection & 4)\n\t\t\t\tdrawHandle(4);\n\t\t\tctx.fillRect(pX - half, pY - half, size, size);\n\t\t\tif (miniSize > 0 && !(selection & 1)) {\n\t\t\t\tvar fillStyle = ctx.fillStyle;\n\t\t\t\tctx.fillStyle = '#ffffff';\n\t\t\t\tctx.fillRect(pX - miniHalf, pY - miniHalf, miniSize, miniSize);\n\t\t\t\tctx.fillStyle = fillStyle;\n\t\t\t}\n\t\t}\n\t}\n\n\tfunction drawSegments(ctx, path, matrix) {\n\t\tvar segments = path._segments,\n\t\t\tlength = segments.length,\n\t\t\tcoords = new Array(6),\n\t\t\tfirst = true,\n\t\t\tcurX, curY,\n\t\t\tprevX, prevY,\n\t\t\tinX, inY,\n\t\t\toutX, outY;\n\n\t\tfunction drawSegment(segment) {\n\t\t\tif (matrix) {\n\t\t\t\tsegment._transformCoordinates(matrix, coords);\n\t\t\t\tcurX = coords[0];\n\t\t\t\tcurY = coords[1];\n\t\t\t} else {\n\t\t\t\tvar point = segment._point;\n\t\t\t\tcurX = point._x;\n\t\t\t\tcurY = point._y;\n\t\t\t}\n\t\t\tif (first) {\n\t\t\t\tctx.moveTo(curX, curY);\n\t\t\t\tfirst = false;\n\t\t\t} else {\n\t\t\t\tif (matrix) {\n\t\t\t\t\tinX = coords[2];\n\t\t\t\t\tinY = coords[3];\n\t\t\t\t} else {\n\t\t\t\t\tvar handle = segment._handleIn;\n\t\t\t\t\tinX = curX + handle._x;\n\t\t\t\t\tinY = curY + handle._y;\n\t\t\t\t}\n\t\t\t\tif (inX === curX && inY === curY\n\t\t\t\t\t\t&& outX === prevX && outY === prevY) {\n\t\t\t\t\tctx.lineTo(curX, curY);\n\t\t\t\t} else {\n\t\t\t\t\tctx.bezierCurveTo(outX, outY, inX, inY, curX, curY);\n\t\t\t\t}\n\t\t\t}\n\t\t\tprevX = curX;\n\t\t\tprevY = curY;\n\t\t\tif (matrix) {\n\t\t\t\toutX = coords[4];\n\t\t\t\toutY = coords[5];\n\t\t\t} else {\n\t\t\t\tvar handle = segment._handleOut;\n\t\t\t\toutX = prevX + handle._x;\n\t\t\t\toutY = prevY + handle._y;\n\t\t\t}\n\t\t}\n\n\t\tfor (var i = 0; i < length; i++)\n\t\t\tdrawSegment(segments[i]);\n\t\tif (path._closed && length > 0)\n\t\t\tdrawSegment(segments[0]);\n\t}\n\n\treturn {\n\t\t_draw: function(ctx, param, viewMatrix, strokeMatrix) {\n\t\t\tvar dontStart = param.dontStart,\n\t\t\t\tdontPaint = param.dontFinish || param.clip,\n\t\t\t\tstyle = this.getStyle(),\n\t\t\t\thasFill = style.hasFill(),\n\t\t\t\thasStroke = style.hasStroke(),\n\t\t\t\tdashArray = style.getDashArray(),\n\t\t\t\tdashLength = !paper.support.nativeDash && hasStroke\n\t\t\t\t\t\t&& dashArray && dashArray.length;\n\n\t\t\tif (!dontStart)\n\t\t\t\tctx.beginPath();\n\n\t\t\tif (hasFill || hasStroke && !dashLength || dontPaint) {\n\t\t\t\tdrawSegments(ctx, this, strokeMatrix);\n\t\t\t\tif (this._closed)\n\t\t\t\t\tctx.closePath();\n\t\t\t}\n\n\t\t\tfunction getOffset(i) {\n\t\t\t\treturn dashArray[((i % dashLength) + dashLength) % dashLength];\n\t\t\t}\n\n\t\t\tif (!dontPaint && (hasFill || hasStroke)) {\n\t\t\t\tthis._setStyles(ctx, param, viewMatrix);\n\t\t\t\tif (hasFill) {\n\t\t\t\t\tctx.fill(style.getFillRule());\n\t\t\t\t\tctx.shadowColor = 'rgba(0,0,0,0)';\n\t\t\t\t}\n\t\t\t\tif (hasStroke) {\n\t\t\t\t\tif (dashLength) {\n\t\t\t\t\t\tif (!dontStart)\n\t\t\t\t\t\t\tctx.beginPath();\n\t\t\t\t\t\tvar flattener = new PathFlattener(this, 0.25, 32, false,\n\t\t\t\t\t\t\t\tstrokeMatrix),\n\t\t\t\t\t\t\tlength = flattener.length,\n\t\t\t\t\t\t\tfrom = -style.getDashOffset(), to,\n\t\t\t\t\t\t\ti = 0;\n\t\t\t\t\t\twhile (from > 0) {\n\t\t\t\t\t\t\tfrom -= getOffset(i--) + getOffset(i--);\n\t\t\t\t\t\t}\n\t\t\t\t\t\twhile (from < length) {\n\t\t\t\t\t\t\tto = from + getOffset(i++);\n\t\t\t\t\t\t\tif (from > 0 || to > 0)\n\t\t\t\t\t\t\t\tflattener.drawPart(ctx,\n\t\t\t\t\t\t\t\t\t\tMath.max(from, 0), Math.max(to, 0));\n\t\t\t\t\t\t\tfrom = to + getOffset(i++);\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tctx.stroke();\n\t\t\t\t}\n\t\t\t}\n\t\t},\n\n\t\t_drawSelected: function(ctx, matrix) {\n\t\t\tctx.beginPath();\n\t\t\tdrawSegments(ctx, this, matrix);\n\t\t\tctx.stroke();\n\t\t\tdrawHandles(ctx, this._segments, matrix, paper.settings.handleSize);\n\t\t}\n\t};\n},\nnew function() {\n\tfunction getCurrentSegment(that) {\n\t\tvar segments = that._segments;\n\t\tif (!segments.length)\n\t\t\tthrow new Error('Use a moveTo() command first');\n\t\treturn segments[segments.length - 1];\n\t}\n\n\treturn {\n\t\tmoveTo: function() {\n\t\t\tvar segments = this._segments;\n\t\t\tif (segments.length === 1)\n\t\t\t\tthis.removeSegment(0);\n\t\t\tif (!segments.length)\n\t\t\t\tthis._add([ new Segment(Point.read(arguments)) ]);\n\t\t},\n\n\t\tmoveBy: function() {\n\t\t\tthrow new Error('moveBy() is unsupported on Path items.');\n\t\t},\n\n\t\tlineTo: function() {\n\t\t\tthis._add([ new Segment(Point.read(arguments)) ]);\n\t\t},\n\n\t\tcubicCurveTo: function() {\n\t\t\tvar args = arguments,\n\t\t\t\thandle1 = Point.read(args),\n\t\t\t\thandle2 = Point.read(args),\n\t\t\t\tto = Point.read(args),\n\t\t\t\tcurrent = getCurrentSegment(this);\n\t\t\tcurrent.setHandleOut(handle1.subtract(current._point));\n\t\t\tthis._add([ new Segment(to, handle2.subtract(to)) ]);\n\t\t},\n\n\t\tquadraticCurveTo: function() {\n\t\t\tvar args = arguments,\n\t\t\t\thandle = Point.read(args),\n\t\t\t\tto = Point.read(args),\n\t\t\t\tcurrent = getCurrentSegment(this)._point;\n\t\t\tthis.cubicCurveTo(\n\t\t\t\thandle.add(current.subtract(handle).multiply(1 / 3)),\n\t\t\t\thandle.add(to.subtract(handle).multiply(1 / 3)),\n\t\t\t\tto\n\t\t\t);\n\t\t},\n\n\t\tcurveTo: function() {\n\t\t\tvar args = arguments,\n\t\t\t\tthrough = Point.read(args),\n\t\t\t\tto = Point.read(args),\n\t\t\t\tt = Base.pick(Base.read(args), 0.5),\n\t\t\t\tt1 = 1 - t,\n\t\t\t\tcurrent = getCurrentSegment(this)._point,\n\t\t\t\thandle = through.subtract(current.multiply(t1 * t1))\n\t\t\t\t\t.subtract(to.multiply(t * t)).divide(2 * t * t1);\n\t\t\tif (handle.isNaN())\n\t\t\t\tthrow new Error(\n\t\t\t\t\t'Cannot put a curve through points with parameter = ' + t);\n\t\t\tthis.quadraticCurveTo(handle, to);\n\t\t},\n\n\t\tarcTo: function() {\n\t\t\tvar args = arguments,\n\t\t\t\tabs = Math.abs,\n\t\t\t\tsqrt = Math.sqrt,\n\t\t\t\tcurrent = getCurrentSegment(this),\n\t\t\t\tfrom = current._point,\n\t\t\t\tto = Point.read(args),\n\t\t\t\tthrough,\n\t\t\t\tpeek = Base.peek(args),\n\t\t\t\tclockwise = Base.pick(peek, true),\n\t\t\t\tcenter, extent, vector, matrix;\n\t\t\tif (typeof clockwise === 'boolean') {\n\t\t\t\tvar middle = from.add(to).divide(2),\n\t\t\t\tthrough = middle.add(middle.subtract(from).rotate(\n\t\t\t\t\t\tclockwise ? -90 : 90));\n\t\t\t} else if (Base.remain(args) <= 2) {\n\t\t\t\tthrough = to;\n\t\t\t\tto = Point.read(args);\n\t\t\t} else if (!from.equals(to)) {\n\t\t\t\tvar radius = Size.read(args),\n\t\t\t\t\tisZero = Numerical.isZero;\n\t\t\t\tif (isZero(radius.width) || isZero(radius.height))\n\t\t\t\t\treturn this.lineTo(to);\n\t\t\t\tvar rotation = Base.read(args),\n\t\t\t\t\tclockwise = !!Base.read(args),\n\t\t\t\t\tlarge = !!Base.read(args),\n\t\t\t\t\tmiddle = from.add(to).divide(2),\n\t\t\t\t\tpt = from.subtract(middle).rotate(-rotation),\n\t\t\t\t\tx = pt.x,\n\t\t\t\t\ty = pt.y,\n\t\t\t\t\trx = abs(radius.width),\n\t\t\t\t\try = abs(radius.height),\n\t\t\t\t\trxSq = rx * rx,\n\t\t\t\t\trySq = ry * ry,\n\t\t\t\t\txSq = x * x,\n\t\t\t\t\tySq = y * y;\n\t\t\t\tvar factor = sqrt(xSq / rxSq + ySq / rySq);\n\t\t\t\tif (factor > 1) {\n\t\t\t\t\trx *= factor;\n\t\t\t\t\try *= factor;\n\t\t\t\t\trxSq = rx * rx;\n\t\t\t\t\trySq = ry * ry;\n\t\t\t\t}\n\t\t\t\tfactor = (rxSq * rySq - rxSq * ySq - rySq * xSq) /\n\t\t\t\t\t\t(rxSq * ySq + rySq * xSq);\n\t\t\t\tif (abs(factor) < 1e-12)\n\t\t\t\t\tfactor = 0;\n\t\t\t\tif (factor < 0)\n\t\t\t\t\tthrow new Error(\n\t\t\t\t\t\t\t'Cannot create an arc with the given arguments');\n\t\t\t\tcenter = new Point(rx * y / ry, -ry * x / rx)\n\t\t\t\t\t\t.multiply((large === clockwise ? -1 : 1) * sqrt(factor))\n\t\t\t\t\t\t.rotate(rotation).add(middle);\n\t\t\t\tmatrix = new Matrix().translate(center).rotate(rotation)\n\t\t\t\t\t\t.scale(rx, ry);\n\t\t\t\tvector = matrix._inverseTransform(from);\n\t\t\t\textent = vector.getDirectedAngle(matrix._inverseTransform(to));\n\t\t\t\tif (!clockwise && extent > 0)\n\t\t\t\t\textent -= 360;\n\t\t\t\telse if (clockwise && extent < 0)\n\t\t\t\t\textent += 360;\n\t\t\t}\n\t\t\tif (through) {\n\t\t\t\tvar l1 = new Line(from.add(through).divide(2),\n\t\t\t\t\t\t\tthrough.subtract(from).rotate(90), true),\n\t\t\t\t\tl2 = new Line(through.add(to).divide(2),\n\t\t\t\t\t\t\tto.subtract(through).rotate(90), true),\n\t\t\t\t\tline = new Line(from, to),\n\t\t\t\t\tthroughSide = line.getSide(through);\n\t\t\t\tcenter = l1.intersect(l2, true);\n\t\t\t\tif (!center) {\n\t\t\t\t\tif (!throughSide)\n\t\t\t\t\t\treturn this.lineTo(to);\n\t\t\t\t\tthrow new Error(\n\t\t\t\t\t\t\t'Cannot create an arc with the given arguments');\n\t\t\t\t}\n\t\t\t\tvector = from.subtract(center);\n\t\t\t\textent = vector.getDirectedAngle(to.subtract(center));\n\t\t\t\tvar centerSide = line.getSide(center, true);\n\t\t\t\tif (centerSide === 0) {\n\t\t\t\t\textent = throughSide * abs(extent);\n\t\t\t\t} else if (throughSide === centerSide) {\n\t\t\t\t\textent += extent < 0 ? 360 : -360;\n\t\t\t\t}\n\t\t\t}\n\t\t\tif (extent) {\n\t\t\t\tvar epsilon = 1e-7,\n\t\t\t\t\text = abs(extent),\n\t\t\t\t\tcount = ext >= 360 ? 4 : Math.ceil((ext - epsilon) / 90),\n\t\t\t\t\tinc = extent / count,\n\t\t\t\t\thalf = inc * Math.PI / 360,\n\t\t\t\t\tz = 4 / 3 * Math.sin(half) / (1 + Math.cos(half)),\n\t\t\t\t\tsegments = [];\n\t\t\t\tfor (var i = 0; i <= count; i++) {\n\t\t\t\t\tvar pt = to,\n\t\t\t\t\t\tout = null;\n\t\t\t\t\tif (i < count) {\n\t\t\t\t\t\tout = vector.rotate(90).multiply(z);\n\t\t\t\t\t\tif (matrix) {\n\t\t\t\t\t\t\tpt = matrix._transformPoint(vector);\n\t\t\t\t\t\t\tout = matrix._transformPoint(vector.add(out))\n\t\t\t\t\t\t\t\t\t.subtract(pt);\n\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\tpt = center.add(vector);\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tif (!i) {\n\t\t\t\t\t\tcurrent.setHandleOut(out);\n\t\t\t\t\t} else {\n\t\t\t\t\t\tvar _in = vector.rotate(-90).multiply(z);\n\t\t\t\t\t\tif (matrix) {\n\t\t\t\t\t\t\t_in = matrix._transformPoint(vector.add(_in))\n\t\t\t\t\t\t\t\t\t.subtract(pt);\n\t\t\t\t\t\t}\n\t\t\t\t\t\tsegments.push(new Segment(pt, _in, out));\n\t\t\t\t\t}\n\t\t\t\t\tvector = vector.rotate(inc);\n\t\t\t\t}\n\t\t\t\tthis._add(segments);\n\t\t\t}\n\t\t},\n\n\t\tlineBy: function() {\n\t\t\tvar to = Point.read(arguments),\n\t\t\t\tcurrent = getCurrentSegment(this)._point;\n\t\t\tthis.lineTo(current.add(to));\n\t\t},\n\n\t\tcurveBy: function() {\n\t\t\tvar args = arguments,\n\t\t\t\tthrough = Point.read(args),\n\t\t\t\tto = Point.read(args),\n\t\t\t\tparameter = Base.read(args),\n\t\t\t\tcurrent = getCurrentSegment(this)._point;\n\t\t\tthis.curveTo(current.add(through), current.add(to), parameter);\n\t\t},\n\n\t\tcubicCurveBy: function() {\n\t\t\tvar args = arguments,\n\t\t\t\thandle1 = Point.read(args),\n\t\t\t\thandle2 = Point.read(args),\n\t\t\t\tto = Point.read(args),\n\t\t\t\tcurrent = getCurrentSegment(this)._point;\n\t\t\tthis.cubicCurveTo(current.add(handle1), current.add(handle2),\n\t\t\t\t\tcurrent.add(to));\n\t\t},\n\n\t\tquadraticCurveBy: function() {\n\t\t\tvar args = arguments,\n\t\t\t\thandle = Point.read(args),\n\t\t\t\tto = Point.read(args),\n\t\t\t\tcurrent = getCurrentSegment(this)._point;\n\t\t\tthis.quadraticCurveTo(current.add(handle), current.add(to));\n\t\t},\n\n\t\tarcBy: function() {\n\t\t\tvar args = arguments,\n\t\t\t\tcurrent = getCurrentSegment(this)._point,\n\t\t\t\tpoint = current.add(Point.read(args)),\n\t\t\t\tclockwise = Base.pick(Base.peek(args), true);\n\t\t\tif (typeof clockwise === 'boolean') {\n\t\t\t\tthis.arcTo(point, clockwise);\n\t\t\t} else {\n\t\t\t\tthis.arcTo(point, current.add(Point.read(args)));\n\t\t\t}\n\t\t},\n\n\t\tclosePath: function(tolerance) {\n\t\t\tthis.setClosed(true);\n\t\t\tthis.join(this, tolerance);\n\t\t}\n\t};\n}, {\n\n\t_getBounds: function(matrix, options) {\n\t\tvar method = options.handle\n\t\t\t\t? 'getHandleBounds'\n\t\t\t\t: options.stroke\n\t\t\t\t? 'getStrokeBounds'\n\t\t\t\t: 'getBounds';\n\t\treturn Path[method](this._segments, this._closed, this, matrix, options);\n\t},\n\nstatics: {\n\tgetBounds: function(segments, closed, path, matrix, options, strokePadding) {\n\t\tvar first = segments[0];\n\t\tif (!first)\n\t\t\treturn new Rectangle();\n\t\tvar coords = new Array(6),\n\t\t\tprevCoords = first._transformCoordinates(matrix, new Array(6)),\n\t\t\tmin = prevCoords.slice(0, 2),\n\t\t\tmax = min.slice(),\n\t\t\troots = new Array(2);\n\n\t\tfunction processSegment(segment) {\n\t\t\tsegment._transformCoordinates(matrix, coords);\n\t\t\tfor (var i = 0; i < 2; i++) {\n\t\t\t\tCurve._addBounds(\n\t\t\t\t\tprevCoords[i],\n\t\t\t\t\tprevCoords[i + 4],\n\t\t\t\t\tcoords[i + 2],\n\t\t\t\t\tcoords[i],\n\t\t\t\t\ti, strokePadding ? strokePadding[i] : 0, min, max, roots);\n\t\t\t}\n\t\t\tvar tmp = prevCoords;\n\t\t\tprevCoords = coords;\n\t\t\tcoords = tmp;\n\t\t}\n\n\t\tfor (var i = 1, l = segments.length; i < l; i++)\n\t\t\tprocessSegment(segments[i]);\n\t\tif (closed)\n\t\t\tprocessSegment(first);\n\t\treturn new Rectangle(min[0], min[1], max[0] - min[0], max[1] - min[1]);\n\t},\n\n\tgetStrokeBounds: function(segments, closed, path, matrix, options) {\n\t\tvar style = path.getStyle(),\n\t\t\tstroke = style.hasStroke(),\n\t\t\tstrokeWidth = style.getStrokeWidth(),\n\t\t\tstrokeMatrix = stroke && path._getStrokeMatrix(matrix, options),\n\t\t\tstrokePadding = stroke && Path._getStrokePadding(strokeWidth,\n\t\t\t\tstrokeMatrix),\n\t\t\tbounds = Path.getBounds(segments, closed, path, matrix, options,\n\t\t\t\tstrokePadding);\n\t\tif (!stroke)\n\t\t\treturn bounds;\n\t\tvar strokeRadius = strokeWidth / 2,\n\t\t\tjoin = style.getStrokeJoin(),\n\t\t\tcap = style.getStrokeCap(),\n\t\t\tmiterLimit = style.getMiterLimit(),\n\t\t\tjoinBounds = new Rectangle(new Size(strokePadding));\n\n\t\tfunction addPoint(point) {\n\t\t\tbounds = bounds.include(point);\n\t\t}\n\n\t\tfunction addRound(segment) {\n\t\t\tbounds = bounds.unite(\n\t\t\t\t\tjoinBounds.setCenter(segment._point.transform(matrix)));\n\t\t}\n\n\t\tfunction addJoin(segment, join) {\n\t\t\tif (join === 'round' || segment.isSmooth()) {\n\t\t\t\taddRound(segment);\n\t\t\t} else {\n\t\t\t\tPath._addBevelJoin(segment, join, strokeRadius, miterLimit,\n\t\t\t\t\t\tmatrix, strokeMatrix, addPoint);\n\t\t\t}\n\t\t}\n\n\t\tfunction addCap(segment, cap) {\n\t\t\tif (cap === 'round') {\n\t\t\t\taddRound(segment);\n\t\t\t} else {\n\t\t\t\tPath._addSquareCap(segment, cap, strokeRadius, matrix,\n\t\t\t\t\t\tstrokeMatrix, addPoint);\n\t\t\t}\n\t\t}\n\n\t\tvar length = segments.length - (closed ? 0 : 1);\n\t\tif (length > 0) {\n\t\t\tfor (var i = 1; i < length; i++) {\n\t\t\t\taddJoin(segments[i], join);\n\t\t\t}\n\t\t\tif (closed) {\n\t\t\t\taddJoin(segments[0], join);\n\t\t\t} else {\n\t\t\t\taddCap(segments[0], cap);\n\t\t\t\taddCap(segments[segments.length - 1], cap);\n\t\t\t}\n\t\t}\n\t\treturn bounds;\n\t},\n\n\t_getStrokePadding: function(radius, matrix) {\n\t\tif (!matrix)\n\t\t\treturn [radius, radius];\n\t\tvar hor = new Point(radius, 0).transform(matrix),\n\t\t\tver = new Point(0, radius).transform(matrix),\n\t\t\tphi = hor.getAngleInRadians(),\n\t\t\ta = hor.getLength(),\n\t\t\tb = ver.getLength();\n\t\tvar sin = Math.sin(phi),\n\t\t\tcos = Math.cos(phi),\n\t\t\ttan = Math.tan(phi),\n\t\t\ttx = Math.atan2(b * tan, a),\n\t\t\tty = Math.atan2(b, tan * a);\n\t\treturn [Math.abs(a * Math.cos(tx) * cos + b * Math.sin(tx) * sin),\n\t\t\t\tMath.abs(b * Math.sin(ty) * cos + a * Math.cos(ty) * sin)];\n\t},\n\n\t_addBevelJoin: function(segment, join, radius, miterLimit, matrix,\n\t\t\tstrokeMatrix, addPoint, isArea) {\n\t\tvar curve2 = segment.getCurve(),\n\t\t\tcurve1 = curve2.getPrevious(),\n\t\t\tpoint = curve2.getPoint1().transform(matrix),\n\t\t\tnormal1 = curve1.getNormalAtTime(1).multiply(radius)\n\t\t\t\t.transform(strokeMatrix),\n\t\t\tnormal2 = curve2.getNormalAtTime(0).multiply(radius)\n\t\t\t\t.transform(strokeMatrix),\n\t\t\t\tangle = normal1.getDirectedAngle(normal2);\n\t\tif (angle < 0 || angle >= 180) {\n\t\t\tnormal1 = normal1.negate();\n\t\t\tnormal2 = normal2.negate();\n\t\t}\n\t\tif (isArea)\n\t\t\taddPoint(point);\n\t\taddPoint(point.add(normal1));\n\t\tif (join === 'miter') {\n\t\t\tvar corner = new Line(point.add(normal1),\n\t\t\t\t\tnew Point(-normal1.y, normal1.x), true\n\t\t\t\t).intersect(new Line(point.add(normal2),\n\t\t\t\t\tnew Point(-normal2.y, normal2.x), true\n\t\t\t\t), true);\n\t\t\tif (corner && point.getDistance(corner) <= miterLimit * radius) {\n\t\t\t\taddPoint(corner);\n\t\t\t}\n\t\t}\n\t\taddPoint(point.add(normal2));\n\t},\n\n\t_addSquareCap: function(segment, cap, radius, matrix, strokeMatrix,\n\t\t\taddPoint, isArea) {\n\t\tvar point = segment._point.transform(matrix),\n\t\t\tloc = segment.getLocation(),\n\t\t\tnormal = loc.getNormal()\n\t\t\t\t\t.multiply(loc.getTime() === 0 ? radius : -radius)\n\t\t\t\t\t.transform(strokeMatrix);\n\t\tif (cap === 'square') {\n\t\t\tif (isArea) {\n\t\t\t\taddPoint(point.subtract(normal));\n\t\t\t\taddPoint(point.add(normal));\n\t\t\t}\n\t\t\tpoint = point.add(normal.rotate(-90));\n\t\t}\n\t\taddPoint(point.add(normal));\n\t\taddPoint(point.subtract(normal));\n\t},\n\n\tgetHandleBounds: function(segments, closed, path, matrix, options) {\n\t\tvar style = path.getStyle(),\n\t\t\tstroke = options.stroke && style.hasStroke(),\n\t\t\tstrokePadding,\n\t\t\tjoinPadding;\n\t\tif (stroke) {\n\t\t\tvar strokeMatrix = path._getStrokeMatrix(matrix, options),\n\t\t\t\tstrokeRadius = style.getStrokeWidth() / 2,\n\t\t\t\tjoinRadius = strokeRadius;\n\t\t\tif (style.getStrokeJoin() === 'miter')\n\t\t\t\tjoinRadius = strokeRadius * style.getMiterLimit();\n\t\t\tif (style.getStrokeCap() === 'square')\n\t\t\t\tjoinRadius = Math.max(joinRadius, strokeRadius * Math.SQRT2);\n\t\t\tstrokePadding = Path._getStrokePadding(strokeRadius, strokeMatrix);\n\t\t\tjoinPadding = Path._getStrokePadding(joinRadius, strokeMatrix);\n\t\t}\n\t\tvar coords = new Array(6),\n\t\t\tx1 = Infinity,\n\t\t\tx2 = -x1,\n\t\t\ty1 = x1,\n\t\t\ty2 = x2;\n\t\tfor (var i = 0, l = segments.length; i < l; i++) {\n\t\t\tvar segment = segments[i];\n\t\t\tsegment._transformCoordinates(matrix, coords);\n\t\t\tfor (var j = 0; j < 6; j += 2) {\n\t\t\t\tvar padding = !j ? joinPadding : strokePadding,\n\t\t\t\t\tpaddingX = padding ? padding[0] : 0,\n\t\t\t\t\tpaddingY = padding ? padding[1] : 0,\n\t\t\t\t\tx = coords[j],\n\t\t\t\t\ty = coords[j + 1],\n\t\t\t\t\txn = x - paddingX,\n\t\t\t\t\txx = x + paddingX,\n\t\t\t\t\tyn = y - paddingY,\n\t\t\t\t\tyx = y + paddingY;\n\t\t\t\tif (xn < x1) x1 = xn;\n\t\t\t\tif (xx > x2) x2 = xx;\n\t\t\t\tif (yn < y1) y1 = yn;\n\t\t\t\tif (yx > y2) y2 = yx;\n\t\t\t}\n\t\t}\n\t\treturn new Rectangle(x1, y1, x2 - x1, y2 - y1);\n\t}\n}});\n\nPath.inject({ statics: new function() {\n\n\tvar kappa = 0.5522847498307936,\n\t\tellipseSegments = [\n\t\t\tnew Segment([-1, 0], [0, kappa ], [0, -kappa]),\n\t\t\tnew Segment([0, -1], [-kappa, 0], [kappa, 0 ]),\n\t\t\tnew Segment([1, 0], [0, -kappa], [0, kappa ]),\n\t\t\tnew Segment([0, 1], [kappa, 0 ], [-kappa, 0])\n\t\t];\n\n\tfunction createPath(segments, closed, args) {\n\t\tvar props = Base.getNamed(args),\n\t\t\tpath = new Path(props && props.insert == false && Item.NO_INSERT);\n\t\tpath._add(segments);\n\t\tpath._closed = closed;\n\t\treturn path.set(props, { insert: true });\n\t}\n\n\tfunction createEllipse(center, radius, args) {\n\t\tvar segments = new Array(4);\n\t\tfor (var i = 0; i < 4; i++) {\n\t\t\tvar segment = ellipseSegments[i];\n\t\t\tsegments[i] = new Segment(\n\t\t\t\tsegment._point.multiply(radius).add(center),\n\t\t\t\tsegment._handleIn.multiply(radius),\n\t\t\t\tsegment._handleOut.multiply(radius)\n\t\t\t);\n\t\t}\n\t\treturn createPath(segments, true, args);\n\t}\n\n\treturn {\n\t\tLine: function() {\n\t\t\tvar args = arguments;\n\t\t\treturn createPath([\n\t\t\t\tnew Segment(Point.readNamed(args, 'from')),\n\t\t\t\tnew Segment(Point.readNamed(args, 'to'))\n\t\t\t], false, args);\n\t\t},\n\n\t\tCircle: function() {\n\t\t\tvar args = arguments,\n\t\t\t\tcenter = Point.readNamed(args, 'center'),\n\t\t\t\tradius = Base.readNamed(args, 'radius');\n\t\t\treturn createEllipse(center, new Size(radius), args);\n\t\t},\n\n\t\tRectangle: function() {\n\t\t\tvar args = arguments,\n\t\t\t\trect = Rectangle.readNamed(args, 'rectangle'),\n\t\t\t\tradius = Size.readNamed(args, 'radius', 0,\n\t\t\t\t\t\t{ readNull: true }),\n\t\t\t\tbl = rect.getBottomLeft(true),\n\t\t\t\ttl = rect.getTopLeft(true),\n\t\t\t\ttr = rect.getTopRight(true),\n\t\t\t\tbr = rect.getBottomRight(true),\n\t\t\t\tsegments;\n\t\t\tif (!radius || radius.isZero()) {\n\t\t\t\tsegments = [\n\t\t\t\t\tnew Segment(bl),\n\t\t\t\t\tnew Segment(tl),\n\t\t\t\t\tnew Segment(tr),\n\t\t\t\t\tnew Segment(br)\n\t\t\t\t];\n\t\t\t} else {\n\t\t\t\tradius = Size.min(radius, rect.getSize(true).divide(2));\n\t\t\t\tvar rx = radius.width,\n\t\t\t\t\try = radius.height,\n\t\t\t\t\thx = rx * kappa,\n\t\t\t\t\thy = ry * kappa;\n\t\t\t\tsegments = [\n\t\t\t\t\tnew Segment(bl.add(rx, 0), null, [-hx, 0]),\n\t\t\t\t\tnew Segment(bl.subtract(0, ry), [0, hy]),\n\t\t\t\t\tnew Segment(tl.add(0, ry), null, [0, -hy]),\n\t\t\t\t\tnew Segment(tl.add(rx, 0), [-hx, 0], null),\n\t\t\t\t\tnew Segment(tr.subtract(rx, 0), null, [hx, 0]),\n\t\t\t\t\tnew Segment(tr.add(0, ry), [0, -hy], null),\n\t\t\t\t\tnew Segment(br.subtract(0, ry), null, [0, hy]),\n\t\t\t\t\tnew Segment(br.subtract(rx, 0), [hx, 0])\n\t\t\t\t];\n\t\t\t}\n\t\t\treturn createPath(segments, true, args);\n\t\t},\n\n\t\tRoundRectangle: '#Rectangle',\n\n\t\tEllipse: function() {\n\t\t\tvar args = arguments,\n\t\t\t\tellipse = Shape._readEllipse(args);\n\t\t\treturn createEllipse(ellipse.center, ellipse.radius, args);\n\t\t},\n\n\t\tOval: '#Ellipse',\n\n\t\tArc: function() {\n\t\t\tvar args = arguments,\n\t\t\t\tfrom = Point.readNamed(args, 'from'),\n\t\t\t\tthrough = Point.readNamed(args, 'through'),\n\t\t\t\tto = Point.readNamed(args, 'to'),\n\t\t\t\tprops = Base.getNamed(args),\n\t\t\t\tpath = new Path(props && props.insert == false\n\t\t\t\t\t\t&& Item.NO_INSERT);\n\t\t\tpath.moveTo(from);\n\t\t\tpath.arcTo(through, to);\n\t\t\treturn path.set(props);\n\t\t},\n\n\t\tRegularPolygon: function() {\n\t\t\tvar args = arguments,\n\t\t\t\tcenter = Point.readNamed(args, 'center'),\n\t\t\t\tsides = Base.readNamed(args, 'sides'),\n\t\t\t\tradius = Base.readNamed(args, 'radius'),\n\t\t\t\tstep = 360 / sides,\n\t\t\t\tthree = sides % 3 === 0,\n\t\t\t\tvector = new Point(0, three ? -radius : radius),\n\t\t\t\toffset = three ? -1 : 0.5,\n\t\t\t\tsegments = new Array(sides);\n\t\t\tfor (var i = 0; i < sides; i++)\n\t\t\t\tsegments[i] = new Segment(center.add(\n\t\t\t\t\tvector.rotate((i + offset) * step)));\n\t\t\treturn createPath(segments, true, args);\n\t\t},\n\n\t\tStar: function() {\n\t\t\tvar args = arguments,\n\t\t\t\tcenter = Point.readNamed(args, 'center'),\n\t\t\t\tpoints = Base.readNamed(args, 'points') * 2,\n\t\t\t\tradius1 = Base.readNamed(args, 'radius1'),\n\t\t\t\tradius2 = Base.readNamed(args, 'radius2'),\n\t\t\t\tstep = 360 / points,\n\t\t\t\tvector = new Point(0, -1),\n\t\t\t\tsegments = new Array(points);\n\t\t\tfor (var i = 0; i < points; i++)\n\t\t\t\tsegments[i] = new Segment(center.add(vector.rotate(step * i)\n\t\t\t\t\t\t.multiply(i % 2 ? radius2 : radius1)));\n\t\t\treturn createPath(segments, true, args);\n\t\t}\n\t};\n}});\n\nvar CompoundPath = PathItem.extend({\n\t_class: 'CompoundPath',\n\t_serializeFields: {\n\t\tchildren: []\n\t},\n\tbeans: true,\n\n\tinitialize: function CompoundPath(arg) {\n\t\tthis._children = [];\n\t\tthis._namedChildren = {};\n\t\tif (!this._initialize(arg)) {\n\t\t\tif (typeof arg === 'string') {\n\t\t\t\tthis.setPathData(arg);\n\t\t\t} else {\n\t\t\t\tthis.addChildren(Array.isArray(arg) ? arg : arguments);\n\t\t\t}\n\t\t}\n\t},\n\n\tinsertChildren: function insertChildren(index, items) {\n\t\tvar list = items,\n\t\t\tfirst = list[0];\n\t\tif (first && typeof first[0] === 'number')\n\t\t\tlist = [list];\n\t\tfor (var i = items.length - 1; i >= 0; i--) {\n\t\t\tvar item = list[i];\n\t\t\tif (list === items && !(item instanceof Path))\n\t\t\t\tlist = Base.slice(list);\n\t\t\tif (Array.isArray(item)) {\n\t\t\t\tlist[i] = new Path({ segments: item, insert: false });\n\t\t\t} else if (item instanceof CompoundPath) {\n\t\t\t\tlist.splice.apply(list, [i, 1].concat(item.removeChildren()));\n\t\t\t\titem.remove();\n\t\t\t}\n\t\t}\n\t\treturn insertChildren.base.call(this, index, list);\n\t},\n\n\treduce: function reduce(options) {\n\t\tvar children = this._children;\n\t\tfor (var i = children.length - 1; i >= 0; i--) {\n\t\t\tvar path = children[i].reduce(options);\n\t\t\tif (path.isEmpty())\n\t\t\t\tpath.remove();\n\t\t}\n\t\tif (!children.length) {\n\t\t\tvar path = new Path(Item.NO_INSERT);\n\t\t\tpath.copyAttributes(this);\n\t\t\tpath.insertAbove(this);\n\t\t\tthis.remove();\n\t\t\treturn path;\n\t\t}\n\t\treturn reduce.base.call(this);\n\t},\n\n\tisClosed: function() {\n\t\tvar children = this._children;\n\t\tfor (var i = 0, l = children.length; i < l; i++) {\n\t\t\tif (!children[i]._closed)\n\t\t\t\treturn false;\n\t\t}\n\t\treturn true;\n\t},\n\n\tsetClosed: function(closed) {\n\t\tvar children = this._children;\n\t\tfor (var i = 0, l = children.length; i < l; i++) {\n\t\t\tchildren[i].setClosed(closed);\n\t\t}\n\t},\n\n\tgetFirstSegment: function() {\n\t\tvar first = this.getFirstChild();\n\t\treturn first && first.getFirstSegment();\n\t},\n\n\tgetLastSegment: function() {\n\t\tvar last = this.getLastChild();\n\t\treturn last && last.getLastSegment();\n\t},\n\n\tgetCurves: function() {\n\t\tvar children = this._children,\n\t\t\tcurves = [];\n\t\tfor (var i = 0, l = children.length; i < l; i++) {\n\t\t\tBase.push(curves, children[i].getCurves());\n\t\t}\n\t\treturn curves;\n\t},\n\n\tgetFirstCurve: function() {\n\t\tvar first = this.getFirstChild();\n\t\treturn first && first.getFirstCurve();\n\t},\n\n\tgetLastCurve: function() {\n\t\tvar last = this.getLastChild();\n\t\treturn last && last.getLastCurve();\n\t},\n\n\tgetArea: function() {\n\t\tvar children = this._children,\n\t\t\tarea = 0;\n\t\tfor (var i = 0, l = children.length; i < l; i++)\n\t\t\tarea += children[i].getArea();\n\t\treturn area;\n\t},\n\n\tgetLength: function() {\n\t\tvar children = this._children,\n\t\t\tlength = 0;\n\t\tfor (var i = 0, l = children.length; i < l; i++)\n\t\t\tlength += children[i].getLength();\n\t\treturn length;\n\t},\n\n\tgetPathData: function(_matrix, _precision) {\n\t\tvar children = this._children,\n\t\t\tpaths = [];\n\t\tfor (var i = 0, l = children.length; i < l; i++) {\n\t\t\tvar child = children[i],\n\t\t\t\tmx = child._matrix;\n\t\t\tpaths.push(child.getPathData(_matrix && !mx.isIdentity()\n\t\t\t\t\t? _matrix.appended(mx) : _matrix, _precision));\n\t\t}\n\t\treturn paths.join('');\n\t},\n\n\t_hitTestChildren: function _hitTestChildren(point, options, viewMatrix) {\n\t\treturn _hitTestChildren.base.call(this, point,\n\t\t\t\toptions.class === Path || options.type === 'path' ? options\n\t\t\t\t\t: Base.set({}, options, { fill: false }),\n\t\t\t\tviewMatrix);\n\t},\n\n\t_draw: function(ctx, param, viewMatrix, strokeMatrix) {\n\t\tvar children = this._children;\n\t\tif (!children.length)\n\t\t\treturn;\n\n\t\tparam = param.extend({ dontStart: true, dontFinish: true });\n\t\tctx.beginPath();\n\t\tfor (var i = 0, l = children.length; i < l; i++)\n\t\t\tchildren[i].draw(ctx, param, strokeMatrix);\n\n\t\tif (!param.clip) {\n\t\t\tthis._setStyles(ctx, param, viewMatrix);\n\t\t\tvar style = this._style;\n\t\t\tif (style.hasFill()) {\n\t\t\t\tctx.fill(style.getFillRule());\n\t\t\t\tctx.shadowColor = 'rgba(0,0,0,0)';\n\t\t\t}\n\t\t\tif (style.hasStroke())\n\t\t\t\tctx.stroke();\n\t\t}\n\t},\n\n\t_drawSelected: function(ctx, matrix, selectionItems) {\n\t\tvar children = this._children;\n\t\tfor (var i = 0, l = children.length; i < l; i++) {\n\t\t\tvar child = children[i],\n\t\t\t\tmx = child._matrix;\n\t\t\tif (!selectionItems[child._id]) {\n\t\t\t\tchild._drawSelected(ctx, mx.isIdentity() ? matrix\n\t\t\t\t\t\t: matrix.appended(mx));\n\t\t\t}\n\t\t}\n\t}\n},\nnew function() {\n\tfunction getCurrentPath(that, check) {\n\t\tvar children = that._children;\n\t\tif (check && !children.length)\n\t\t\tthrow new Error('Use a moveTo() command first');\n\t\treturn children[children.length - 1];\n\t}\n\n\treturn Base.each(['lineTo', 'cubicCurveTo', 'quadraticCurveTo', 'curveTo',\n\t\t\t'arcTo', 'lineBy', 'cubicCurveBy', 'quadraticCurveBy', 'curveBy',\n\t\t\t'arcBy'],\n\t\tfunction(key) {\n\t\t\tthis[key] = function() {\n\t\t\t\tvar path = getCurrentPath(this, true);\n\t\t\t\tpath[key].apply(path, arguments);\n\t\t\t};\n\t\t}, {\n\t\t\tmoveTo: function() {\n\t\t\t\tvar current = getCurrentPath(this),\n\t\t\t\t\tpath = current && current.isEmpty() ? current\n\t\t\t\t\t\t\t: new Path(Item.NO_INSERT);\n\t\t\t\tif (path !== current)\n\t\t\t\t\tthis.addChild(path);\n\t\t\t\tpath.moveTo.apply(path, arguments);\n\t\t\t},\n\n\t\t\tmoveBy: function() {\n\t\t\t\tvar current = getCurrentPath(this, true),\n\t\t\t\t\tlast = current && current.getLastSegment(),\n\t\t\t\t\tpoint = Point.read(arguments);\n\t\t\t\tthis.moveTo(last ? point.add(last._point) : point);\n\t\t\t},\n\n\t\t\tclosePath: function(tolerance) {\n\t\t\t\tgetCurrentPath(this, true).closePath(tolerance);\n\t\t\t}\n\t\t}\n\t);\n}, Base.each(['reverse', 'flatten', 'simplify', 'smooth'], function(key) {\n\tthis[key] = function(param) {\n\t\tvar children = this._children,\n\t\t\tres;\n\t\tfor (var i = 0, l = children.length; i < l; i++) {\n\t\t\tres = children[i][key](param) || res;\n\t\t}\n\t\treturn res;\n\t};\n}, {}));\n\nPathItem.inject(new function() {\n\tvar min = Math.min,\n\t\tmax = Math.max,\n\t\tabs = Math.abs,\n\t\toperators = {\n\t\t\tunite:     { '1': true, '2': true },\n\t\t\tintersect: { '2': true },\n\t\t\tsubtract:  { '1': true },\n\t\t\texclude:   { '1': true, '-1': true }\n\t\t};\n\n\tfunction getPaths(path) {\n\t\treturn path._children || [path];\n\t}\n\n\tfunction preparePath(path, resolve) {\n\t\tvar res = path\n\t\t\t.clone(false)\n\t\t\t.reduce({ simplify: true })\n\t\t\t.transform(null, true, true);\n\t\tif (resolve) {\n\t\t\tvar paths = getPaths(res);\n\t\t\tfor (var i = 0, l = paths.length; i < l; i++) {\n\t\t\t\tvar path = paths[i];\n\t\t\t\tif (!path._closed && !path.isEmpty()) {\n\t\t\t\t\tpath.closePath(1e-12);\n\t\t\t\t\tpath.getFirstSegment().setHandleIn(0, 0);\n\t\t\t\t\tpath.getLastSegment().setHandleOut(0, 0);\n\t\t\t\t}\n\t\t\t}\n\t\t\tres = res\n\t\t\t\t.resolveCrossings()\n\t\t\t\t.reorient(res.getFillRule() === 'nonzero', true);\n\t\t}\n\t\treturn res;\n\t}\n\n\tfunction createResult(paths, simplify, path1, path2, options) {\n\t\tvar result = new CompoundPath(Item.NO_INSERT);\n\t\tresult.addChildren(paths, true);\n\t\tresult = result.reduce({ simplify: simplify });\n\t\tif (!(options && options.insert == false)) {\n\t\t\tresult.insertAbove(path2 && path1.isSibling(path2)\n\t\t\t\t\t&& path1.getIndex() < path2.getIndex() ? path2 : path1);\n\t\t}\n\t\tresult.copyAttributes(path1, true);\n\t\treturn result;\n\t}\n\n\tfunction filterIntersection(inter) {\n\t\treturn inter.hasOverlap() || inter.isCrossing();\n\t}\n\n\tfunction traceBoolean(path1, path2, operation, options) {\n\t\tif (options && (options.trace == false || options.stroke) &&\n\t\t\t\t/^(subtract|intersect)$/.test(operation))\n\t\t\treturn splitBoolean(path1, path2, operation);\n\t\tvar _path1 = preparePath(path1, true),\n\t\t\t_path2 = path2 && path1 !== path2 && preparePath(path2, true),\n\t\t\toperator = operators[operation];\n\t\toperator[operation] = true;\n\t\tif (_path2 && (operator.subtract || operator.exclude)\n\t\t\t\t^ (_path2.isClockwise() ^ _path1.isClockwise()))\n\t\t\t_path2.reverse();\n\t\tvar crossings = divideLocations(CurveLocation.expand(\n\t\t\t\t_path1.getIntersections(_path2, filterIntersection))),\n\t\t\tpaths1 = getPaths(_path1),\n\t\t\tpaths2 = _path2 && getPaths(_path2),\n\t\t\tsegments = [],\n\t\t\tcurves = [],\n\t\t\tpaths;\n\n\t\tfunction collectPaths(paths) {\n\t\t\tfor (var i = 0, l = paths.length; i < l; i++) {\n\t\t\t\tvar path = paths[i];\n\t\t\t\tBase.push(segments, path._segments);\n\t\t\t\tBase.push(curves, path.getCurves());\n\t\t\t\tpath._overlapsOnly = true;\n\t\t\t}\n\t\t}\n\n\t\tfunction getCurves(indices) {\n\t\t\tvar list = [];\n\t\t\tfor (var i = 0, l = indices && indices.length; i < l; i++) {\n\t\t\t\tlist.push(curves[indices[i]]);\n\t\t\t}\n\t\t\treturn list;\n\t\t}\n\n\t\tif (crossings.length) {\n\t\t\tcollectPaths(paths1);\n\t\t\tif (paths2)\n\t\t\t\tcollectPaths(paths2);\n\n\t\t\tvar curvesValues = new Array(curves.length);\n\t\t\tfor (var i = 0, l = curves.length; i < l; i++) {\n\t\t\t\tcurvesValues[i] = curves[i].getValues();\n\t\t\t}\n\t\t\tvar curveCollisions = CollisionDetection.findCurveBoundsCollisions(\n\t\t\t\t\tcurvesValues, curvesValues, 0, true);\n\t\t\tvar curveCollisionsMap = {};\n\t\t\tfor (var i = 0; i < curves.length; i++) {\n\t\t\t\tvar curve = curves[i],\n\t\t\t\t\tid = curve._path._id,\n\t\t\t\t\tmap = curveCollisionsMap[id] = curveCollisionsMap[id] || {};\n\t\t\t\tmap[curve.getIndex()] = {\n\t\t\t\t\thor: getCurves(curveCollisions[i].hor),\n\t\t\t\t\tver: getCurves(curveCollisions[i].ver)\n\t\t\t\t};\n\t\t\t}\n\n\t\t\tfor (var i = 0, l = crossings.length; i < l; i++) {\n\t\t\t\tpropagateWinding(crossings[i]._segment, _path1, _path2,\n\t\t\t\t\t\tcurveCollisionsMap, operator);\n\t\t\t}\n\t\t\tfor (var i = 0, l = segments.length; i < l; i++) {\n\t\t\t\tvar segment = segments[i],\n\t\t\t\t\tinter = segment._intersection;\n\t\t\t\tif (!segment._winding) {\n\t\t\t\t\tpropagateWinding(segment, _path1, _path2,\n\t\t\t\t\t\t\tcurveCollisionsMap, operator);\n\t\t\t\t}\n\t\t\t\tif (!(inter && inter._overlap))\n\t\t\t\t\tsegment._path._overlapsOnly = false;\n\t\t\t}\n\t\t\tpaths = tracePaths(segments, operator);\n\t\t} else {\n\t\t\tpaths = reorientPaths(\n\t\t\t\t\tpaths2 ? paths1.concat(paths2) : paths1.slice(),\n\t\t\t\t\tfunction(w) {\n\t\t\t\t\t\treturn !!operator[w];\n\t\t\t\t\t});\n\t\t}\n\t\treturn createResult(paths, true, path1, path2, options);\n\t}\n\n\tfunction splitBoolean(path1, path2, operation) {\n\t\tvar _path1 = preparePath(path1),\n\t\t\t_path2 = preparePath(path2),\n\t\t\tcrossings = _path1.getIntersections(_path2, filterIntersection),\n\t\t\tsubtract = operation === 'subtract',\n\t\t\tdivide = operation === 'divide',\n\t\t\tadded = {},\n\t\t\tpaths = [];\n\n\t\tfunction addPath(path) {\n\t\t\tif (!added[path._id] && (divide ||\n\t\t\t\t\t_path2.contains(path.getPointAt(path.getLength() / 2))\n\t\t\t\t\t\t^ subtract)) {\n\t\t\t\tpaths.unshift(path);\n\t\t\t\treturn added[path._id] = true;\n\t\t\t}\n\t\t}\n\n\t\tfor (var i = crossings.length - 1; i >= 0; i--) {\n\t\t\tvar path = crossings[i].split();\n\t\t\tif (path) {\n\t\t\t\tif (addPath(path))\n\t\t\t\t\tpath.getFirstSegment().setHandleIn(0, 0);\n\t\t\t\t_path1.getLastSegment().setHandleOut(0, 0);\n\t\t\t}\n\t\t}\n\t\taddPath(_path1);\n\t\treturn createResult(paths, false, path1, path2);\n\t}\n\n\tfunction linkIntersections(from, to) {\n\t\tvar prev = from;\n\t\twhile (prev) {\n\t\t\tif (prev === to)\n\t\t\t\treturn;\n\t\t\tprev = prev._previous;\n\t\t}\n\t\twhile (from._next && from._next !== to)\n\t\t\tfrom = from._next;\n\t\tif (!from._next) {\n\t\t\twhile (to._previous)\n\t\t\t\tto = to._previous;\n\t\t\tfrom._next = to;\n\t\t\tto._previous = from;\n\t\t}\n\t}\n\n\tfunction clearCurveHandles(curves) {\n\t\tfor (var i = curves.length - 1; i >= 0; i--)\n\t\t\tcurves[i].clearHandles();\n\t}\n\n\tfunction reorientPaths(paths, isInside, clockwise) {\n\t\tvar length = paths && paths.length;\n\t\tif (length) {\n\t\t\tvar lookup = Base.each(paths, function (path, i) {\n\t\t\t\t\tthis[path._id] = {\n\t\t\t\t\t\tcontainer: null,\n\t\t\t\t\t\twinding: path.isClockwise() ? 1 : -1,\n\t\t\t\t\t\tindex: i\n\t\t\t\t\t};\n\t\t\t\t}, {}),\n\t\t\t\tsorted = paths.slice().sort(function (a, b) {\n\t\t\t\t\treturn abs(b.getArea()) - abs(a.getArea());\n\t\t\t\t}),\n\t\t\t\tfirst = sorted[0];\n\t\t\tvar collisions = CollisionDetection.findItemBoundsCollisions(sorted,\n\t\t\t\t\tnull, Numerical.GEOMETRIC_EPSILON);\n\t\t\tif (clockwise == null)\n\t\t\t\tclockwise = first.isClockwise();\n\t\t\tfor (var i = 0; i < length; i++) {\n\t\t\t\tvar path1 = sorted[i],\n\t\t\t\t\tentry1 = lookup[path1._id],\n\t\t\t\t\tcontainerWinding = 0,\n\t\t\t\t\tindices = collisions[i];\n\t\t\t\tif (indices) {\n\t\t\t\t\tvar point = null;\n\t\t\t\t\tfor (var j = indices.length - 1; j >= 0; j--) {\n\t\t\t\t\t\tif (indices[j] < i) {\n\t\t\t\t\t\t\tpoint = point || path1.getInteriorPoint();\n\t\t\t\t\t\t\tvar path2 = sorted[indices[j]];\n\t\t\t\t\t\t\tif (path2.contains(point)) {\n\t\t\t\t\t\t\t\tvar entry2 = lookup[path2._id];\n\t\t\t\t\t\t\t\tcontainerWinding = entry2.winding;\n\t\t\t\t\t\t\t\tentry1.winding += containerWinding;\n\t\t\t\t\t\t\t\tentry1.container = entry2.exclude\n\t\t\t\t\t\t\t\t\t? entry2.container : path2;\n\t\t\t\t\t\t\t\tbreak;\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\t\t\t\tif (isInside(entry1.winding) === isInside(containerWinding)) {\n\t\t\t\t\tentry1.exclude = true;\n\t\t\t\t\tpaths[entry1.index] = null;\n\t\t\t\t} else {\n\t\t\t\t\tvar container = entry1.container;\n\t\t\t\t\tpath1.setClockwise(\n\t\t\t\t\t\t\tcontainer ? !container.isClockwise() : clockwise);\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\treturn paths;\n\t}\n\n\tfunction divideLocations(locations, include, clearLater) {\n\t\tvar results = include && [],\n\t\t\ttMin = 1e-8,\n\t\t\ttMax = 1 - tMin,\n\t\t\tclearHandles = false,\n\t\t\tclearCurves = clearLater || [],\n\t\t\tclearLookup = clearLater && {},\n\t\t\trenormalizeLocs,\n\t\t\tprevCurve,\n\t\t\tprevTime;\n\n\t\tfunction getId(curve) {\n\t\t\treturn curve._path._id + '.' + curve._segment1._index;\n\t\t}\n\n\t\tfor (var i = (clearLater && clearLater.length) - 1; i >= 0; i--) {\n\t\t\tvar curve = clearLater[i];\n\t\t\tif (curve._path)\n\t\t\t\tclearLookup[getId(curve)] = true;\n\t\t}\n\n\t\tfor (var i = locations.length - 1; i >= 0; i--) {\n\t\t\tvar loc = locations[i],\n\t\t\t\ttime = loc._time,\n\t\t\t\torigTime = time,\n\t\t\t\texclude = include && !include(loc),\n\t\t\t\tcurve = loc._curve,\n\t\t\t\tsegment;\n\t\t\tif (curve) {\n\t\t\t\tif (curve !== prevCurve) {\n\t\t\t\t\tclearHandles = !curve.hasHandles()\n\t\t\t\t\t\t\t|| clearLookup && clearLookup[getId(curve)];\n\t\t\t\t\trenormalizeLocs = [];\n\t\t\t\t\tprevTime = null;\n\t\t\t\t\tprevCurve = curve;\n\t\t\t\t} else if (prevTime >= tMin) {\n\t\t\t\t\ttime /= prevTime;\n\t\t\t\t}\n\t\t\t}\n\t\t\tif (exclude) {\n\t\t\t\tif (renormalizeLocs)\n\t\t\t\t\trenormalizeLocs.push(loc);\n\t\t\t\tcontinue;\n\t\t\t} else if (include) {\n\t\t\t\tresults.unshift(loc);\n\t\t\t}\n\t\t\tprevTime = origTime;\n\t\t\tif (time < tMin) {\n\t\t\t\tsegment = curve._segment1;\n\t\t\t} else if (time > tMax) {\n\t\t\t\tsegment = curve._segment2;\n\t\t\t} else {\n\t\t\t\tvar newCurve = curve.divideAtTime(time, true);\n\t\t\t\tif (clearHandles)\n\t\t\t\t\tclearCurves.push(curve, newCurve);\n\t\t\t\tsegment = newCurve._segment1;\n\t\t\t\tfor (var j = renormalizeLocs.length - 1; j >= 0; j--) {\n\t\t\t\t\tvar l = renormalizeLocs[j];\n\t\t\t\t\tl._time = (l._time - time) / (1 - time);\n\t\t\t\t}\n\t\t\t}\n\t\t\tloc._setSegment(segment);\n\t\t\tvar inter = segment._intersection,\n\t\t\t\tdest = loc._intersection;\n\t\t\tif (inter) {\n\t\t\t\tlinkIntersections(inter, dest);\n\t\t\t\tvar other = inter;\n\t\t\t\twhile (other) {\n\t\t\t\t\tlinkIntersections(other._intersection, inter);\n\t\t\t\t\tother = other._next;\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tsegment._intersection = dest;\n\t\t\t}\n\t\t}\n\t\tif (!clearLater)\n\t\t\tclearCurveHandles(clearCurves);\n\t\treturn results || locations;\n\t}\n\n\tfunction getWinding(point, curves, dir, closed, dontFlip) {\n\t\tvar curvesList = Array.isArray(curves)\n\t\t\t? curves\n\t\t\t: curves[dir ? 'hor' : 'ver'];\n\t\tvar ia = dir ? 1 : 0,\n\t\t\tio = ia ^ 1,\n\t\t\tpv = [point.x, point.y],\n\t\t\tpa = pv[ia],\n\t\t\tpo = pv[io],\n\t\t\twindingEpsilon = 1e-9,\n\t\t\tqualityEpsilon = 1e-6,\n\t\t\tpaL = pa - windingEpsilon,\n\t\t\tpaR = pa + windingEpsilon,\n\t\t\twindingL = 0,\n\t\t\twindingR = 0,\n\t\t\tpathWindingL = 0,\n\t\t\tpathWindingR = 0,\n\t\t\tonPath = false,\n\t\t\tonAnyPath = false,\n\t\t\tquality = 1,\n\t\t\troots = [],\n\t\t\tvPrev,\n\t\t\tvClose;\n\n\t\tfunction addWinding(v) {\n\t\t\tvar o0 = v[io + 0],\n\t\t\t\to3 = v[io + 6];\n\t\t\tif (po < min(o0, o3) || po > max(o0, o3)) {\n\t\t\t\treturn;\n\t\t\t}\n\t\t\tvar a0 = v[ia + 0],\n\t\t\t\ta1 = v[ia + 2],\n\t\t\t\ta2 = v[ia + 4],\n\t\t\t\ta3 = v[ia + 6];\n\t\t\tif (o0 === o3) {\n\t\t\t\tif (a0 < paR && a3 > paL || a3 < paR && a0 > paL) {\n\t\t\t\t\tonPath = true;\n\t\t\t\t}\n\t\t\t\treturn;\n\t\t\t}\n\t\t\tvar t =   po === o0 ? 0\n\t\t\t\t\t: po === o3 ? 1\n\t\t\t\t\t: paL > max(a0, a1, a2, a3) || paR < min(a0, a1, a2, a3)\n\t\t\t\t\t? 1\n\t\t\t\t\t: Curve.solveCubic(v, io, po, roots, 0, 1) > 0\n\t\t\t\t\t\t? roots[0]\n\t\t\t\t\t\t: 1,\n\t\t\t\ta =   t === 0 ? a0\n\t\t\t\t\t: t === 1 ? a3\n\t\t\t\t\t: Curve.getPoint(v, t)[dir ? 'y' : 'x'],\n\t\t\t\twinding = o0 > o3 ? 1 : -1,\n\t\t\t\twindingPrev = vPrev[io] > vPrev[io + 6] ? 1 : -1,\n\t\t\t\ta3Prev = vPrev[ia + 6];\n\t\t\tif (po !== o0) {\n\t\t\t\tif (a < paL) {\n\t\t\t\t\tpathWindingL += winding;\n\t\t\t\t} else if (a > paR) {\n\t\t\t\t\tpathWindingR += winding;\n\t\t\t\t} else {\n\t\t\t\t\tonPath = true;\n\t\t\t\t}\n\t\t\t\tif (a > pa - qualityEpsilon && a < pa + qualityEpsilon)\n\t\t\t\t\tquality /= 2;\n\t\t\t} else {\n\t\t\t\tif (winding !== windingPrev) {\n\t\t\t\t\tif (a0 < paL) {\n\t\t\t\t\t\tpathWindingL += winding;\n\t\t\t\t\t} else if (a0 > paR) {\n\t\t\t\t\t\tpathWindingR += winding;\n\t\t\t\t\t}\n\t\t\t\t} else if (a0 != a3Prev) {\n\t\t\t\t\tif (a3Prev < paR && a > paR) {\n\t\t\t\t\t\tpathWindingR += winding;\n\t\t\t\t\t\tonPath = true;\n\t\t\t\t\t} else if (a3Prev > paL && a < paL) {\n\t\t\t\t\t\tpathWindingL += winding;\n\t\t\t\t\t\tonPath = true;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tquality /= 4;\n\t\t\t}\n\t\t\tvPrev = v;\n\t\t\treturn !dontFlip && a > paL && a < paR\n\t\t\t\t\t&& Curve.getTangent(v, t)[dir ? 'x' : 'y'] === 0\n\t\t\t\t\t&& getWinding(point, curves, !dir, closed, true);\n\t\t}\n\n\t\tfunction handleCurve(v) {\n\t\t\tvar o0 = v[io + 0],\n\t\t\t\to1 = v[io + 2],\n\t\t\t\to2 = v[io + 4],\n\t\t\t\to3 = v[io + 6];\n\t\t\tif (po <= max(o0, o1, o2, o3) && po >= min(o0, o1, o2, o3)) {\n\t\t\t\tvar a0 = v[ia + 0],\n\t\t\t\t\ta1 = v[ia + 2],\n\t\t\t\t\ta2 = v[ia + 4],\n\t\t\t\t\ta3 = v[ia + 6],\n\t\t\t\t\tmonoCurves = paL > max(a0, a1, a2, a3) ||\n\t\t\t\t\t\t\t\t paR < min(a0, a1, a2, a3)\n\t\t\t\t\t\t\t? [v] : Curve.getMonoCurves(v, dir),\n\t\t\t\t\tres;\n\t\t\t\tfor (var i = 0, l = monoCurves.length; i < l; i++) {\n\t\t\t\t\tif (res = addWinding(monoCurves[i]))\n\t\t\t\t\t\treturn res;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\tfor (var i = 0, l = curvesList.length; i < l; i++) {\n\t\t\tvar curve = curvesList[i],\n\t\t\t\tpath = curve._path,\n\t\t\t\tv = curve.getValues(),\n\t\t\t\tres;\n\t\t\tif (!i || curvesList[i - 1]._path !== path) {\n\t\t\t\tvPrev = null;\n\t\t\t\tif (!path._closed) {\n\t\t\t\t\tvClose = Curve.getValues(\n\t\t\t\t\t\t\tpath.getLastCurve().getSegment2(),\n\t\t\t\t\t\t\tcurve.getSegment1(),\n\t\t\t\t\t\t\tnull, !closed);\n\t\t\t\t\tif (vClose[io] !== vClose[io + 6]) {\n\t\t\t\t\t\tvPrev = vClose;\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\tif (!vPrev) {\n\t\t\t\t\tvPrev = v;\n\t\t\t\t\tvar prev = path.getLastCurve();\n\t\t\t\t\twhile (prev && prev !== curve) {\n\t\t\t\t\t\tvar v2 = prev.getValues();\n\t\t\t\t\t\tif (v2[io] !== v2[io + 6]) {\n\t\t\t\t\t\t\tvPrev = v2;\n\t\t\t\t\t\t\tbreak;\n\t\t\t\t\t\t}\n\t\t\t\t\t\tprev = prev.getPrevious();\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tif (res = handleCurve(v))\n\t\t\t\treturn res;\n\n\t\t\tif (i + 1 === l || curvesList[i + 1]._path !== path) {\n\t\t\t\tif (vClose && (res = handleCurve(vClose)))\n\t\t\t\t\treturn res;\n\t\t\t\tif (onPath && !pathWindingL && !pathWindingR) {\n\t\t\t\t\tpathWindingL = pathWindingR = path.isClockwise(closed) ^ dir\n\t\t\t\t\t\t\t? 1 : -1;\n\t\t\t\t}\n\t\t\t\twindingL += pathWindingL;\n\t\t\t\twindingR += pathWindingR;\n\t\t\t\tpathWindingL = pathWindingR = 0;\n\t\t\t\tif (onPath) {\n\t\t\t\t\tonAnyPath = true;\n\t\t\t\t\tonPath = false;\n\t\t\t\t}\n\t\t\t\tvClose = null;\n\t\t\t}\n\t\t}\n\t\twindingL = abs(windingL);\n\t\twindingR = abs(windingR);\n\t\treturn {\n\t\t\twinding: max(windingL, windingR),\n\t\t\twindingL: windingL,\n\t\t\twindingR: windingR,\n\t\t\tquality: quality,\n\t\t\tonPath: onAnyPath\n\t\t};\n\t}\n\n\tfunction propagateWinding(segment, path1, path2, curveCollisionsMap,\n\t\t\toperator) {\n\t\tvar chain = [],\n\t\t\tstart = segment,\n\t\t\ttotalLength = 0,\n\t\t\twinding;\n\t\tdo {\n\t\t\tvar curve = segment.getCurve();\n\t\t\tif (curve) {\n\t\t\t\tvar length = curve.getLength();\n\t\t\t\tchain.push({ segment: segment, curve: curve, length: length });\n\t\t\t\ttotalLength += length;\n\t\t\t}\n\t\t\tsegment = segment.getNext();\n\t\t} while (segment && !segment._intersection && segment !== start);\n\t\tvar offsets = [0.5, 0.25, 0.75],\n\t\t\twinding = { winding: 0, quality: -1 },\n\t\t\ttMin = 1e-3,\n\t\t\ttMax = 1 - tMin;\n\t\tfor (var i = 0; i < offsets.length && winding.quality < 0.5; i++) {\n\t\t\tvar length = totalLength * offsets[i];\n\t\t\tfor (var j = 0, l = chain.length; j < l; j++) {\n\t\t\t\tvar entry = chain[j],\n\t\t\t\t\tcurveLength = entry.length;\n\t\t\t\tif (length <= curveLength) {\n\t\t\t\t\tvar curve = entry.curve,\n\t\t\t\t\t\tpath = curve._path,\n\t\t\t\t\t\tparent = path._parent,\n\t\t\t\t\t\toperand = parent instanceof CompoundPath ? parent : path,\n\t\t\t\t\t\tt = Numerical.clamp(curve.getTimeAt(length), tMin, tMax),\n\t\t\t\t\t\tpt = curve.getPointAtTime(t),\n\t\t\t\t\t\tdir = abs(curve.getTangentAtTime(t).y) < Math.SQRT1_2;\n\t\t\t\t\tvar wind = null;\n\t\t\t\t\tif (operator.subtract && path2) {\n\t\t\t\t\t\tvar otherPath = operand === path1 ? path2 : path1,\n\t\t\t\t\t\t\tpathWinding = otherPath._getWinding(pt, dir, true);\n\t\t\t\t\t\tif (operand === path1 && pathWinding.winding ||\n\t\t\t\t\t\t\toperand === path2 && !pathWinding.winding) {\n\t\t\t\t\t\t\tif (pathWinding.quality < 1) {\n\t\t\t\t\t\t\t\tcontinue;\n\t\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t\twind = { winding: 0, quality: 1 };\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\twind =  wind || getWinding(\n\t\t\t\t\t\t\tpt, curveCollisionsMap[path._id][curve.getIndex()],\n\t\t\t\t\t\t\tdir, true);\n\t\t\t\t\tif (wind.quality > winding.quality)\n\t\t\t\t\t\twinding = wind;\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t\tlength -= curveLength;\n\t\t\t}\n\t\t}\n\t\tfor (var j = chain.length - 1; j >= 0; j--) {\n\t\t\tchain[j].segment._winding = winding;\n\t\t}\n\t}\n\n\tfunction tracePaths(segments, operator) {\n\t\tvar paths = [],\n\t\t\tstarts;\n\n\t\tfunction isValid(seg) {\n\t\t\tvar winding;\n\t\t\treturn !!(seg && !seg._visited && (!operator\n\t\t\t\t\t|| operator[(winding = seg._winding || {}).winding]\n\t\t\t\t\t\t&& !(operator.unite && winding.winding === 2\n\t\t\t\t\t\t\t&& winding.windingL && winding.windingR)));\n\t\t}\n\n\t\tfunction isStart(seg) {\n\t\t\tif (seg) {\n\t\t\t\tfor (var i = 0, l = starts.length; i < l; i++) {\n\t\t\t\t\tif (seg === starts[i])\n\t\t\t\t\t\treturn true;\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn false;\n\t\t}\n\n\t\tfunction visitPath(path) {\n\t\t\tvar segments = path._segments;\n\t\t\tfor (var i = 0, l = segments.length; i < l; i++) {\n\t\t\t\tsegments[i]._visited = true;\n\t\t\t}\n\t\t}\n\n\t\tfunction getCrossingSegments(segment, collectStarts) {\n\t\t\tvar inter = segment._intersection,\n\t\t\t\tstart = inter,\n\t\t\t\tcrossings = [];\n\t\t\tif (collectStarts)\n\t\t\t\tstarts = [segment];\n\n\t\t\tfunction collect(inter, end) {\n\t\t\t\twhile (inter && inter !== end) {\n\t\t\t\t\tvar other = inter._segment,\n\t\t\t\t\t\tpath = other && other._path;\n\t\t\t\t\tif (path) {\n\t\t\t\t\t\tvar next = other.getNext() || path.getFirstSegment(),\n\t\t\t\t\t\t\tnextInter = next._intersection;\n\t\t\t\t\t\tif (other !== segment && (isStart(other)\n\t\t\t\t\t\t\t|| isStart(next)\n\t\t\t\t\t\t\t|| next && (isValid(other) && (isValid(next)\n\t\t\t\t\t\t\t\t|| nextInter && isValid(nextInter._segment))))\n\t\t\t\t\t\t) {\n\t\t\t\t\t\t\tcrossings.push(other);\n\t\t\t\t\t\t}\n\t\t\t\t\t\tif (collectStarts)\n\t\t\t\t\t\t\tstarts.push(other);\n\t\t\t\t\t}\n\t\t\t\t\tinter = inter._next;\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tif (inter) {\n\t\t\t\tcollect(inter);\n\t\t\t\twhile (inter && inter._previous)\n\t\t\t\t\tinter = inter._previous;\n\t\t\t\tcollect(inter, start);\n\t\t\t}\n\t\t\treturn crossings;\n\t\t}\n\n\t\tsegments.sort(function(seg1, seg2) {\n\t\t\tvar inter1 = seg1._intersection,\n\t\t\t\tinter2 = seg2._intersection,\n\t\t\t\tover1 = !!(inter1 && inter1._overlap),\n\t\t\t\tover2 = !!(inter2 && inter2._overlap),\n\t\t\t\tpath1 = seg1._path,\n\t\t\t\tpath2 = seg2._path;\n\t\t\treturn over1 ^ over2\n\t\t\t\t\t? over1 ? 1 : -1\n\t\t\t\t\t: !inter1 ^ !inter2\n\t\t\t\t\t\t? inter1 ? 1 : -1\n\t\t\t\t\t\t: path1 !== path2\n\t\t\t\t\t\t\t? path1._id - path2._id\n\t\t\t\t\t\t\t: seg1._index - seg2._index;\n\t\t});\n\n\t\tfor (var i = 0, l = segments.length; i < l; i++) {\n\t\t\tvar seg = segments[i],\n\t\t\t\tvalid = isValid(seg),\n\t\t\t\tpath = null,\n\t\t\t\tfinished = false,\n\t\t\t\tclosed = true,\n\t\t\t\tbranches = [],\n\t\t\t\tbranch,\n\t\t\t\tvisited,\n\t\t\t\thandleIn;\n\t\t\tif (valid && seg._path._overlapsOnly) {\n\t\t\t\tvar path1 = seg._path,\n\t\t\t\t\tpath2 = seg._intersection._segment._path;\n\t\t\t\tif (path1.compare(path2)) {\n\t\t\t\t\tif (path1.getArea())\n\t\t\t\t\t\tpaths.push(path1.clone(false));\n\t\t\t\t\tvisitPath(path1);\n\t\t\t\t\tvisitPath(path2);\n\t\t\t\t\tvalid = false;\n\t\t\t\t}\n\t\t\t}\n\t\t\twhile (valid) {\n\t\t\t\tvar first = !path,\n\t\t\t\t\tcrossings = getCrossingSegments(seg, first),\n\t\t\t\t\tother = crossings.shift(),\n\t\t\t\t\tfinished = !first && (isStart(seg) || isStart(other)),\n\t\t\t\t\tcross = !finished && other;\n\t\t\t\tif (first) {\n\t\t\t\t\tpath = new Path(Item.NO_INSERT);\n\t\t\t\t\tbranch = null;\n\t\t\t\t}\n\t\t\t\tif (finished) {\n\t\t\t\t\tif (seg.isFirst() || seg.isLast())\n\t\t\t\t\t\tclosed = seg._path._closed;\n\t\t\t\t\tseg._visited = true;\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t\tif (cross && branch) {\n\t\t\t\t\tbranches.push(branch);\n\t\t\t\t\tbranch = null;\n\t\t\t\t}\n\t\t\t\tif (!branch) {\n\t\t\t\t\tif (cross)\n\t\t\t\t\t\tcrossings.push(seg);\n\t\t\t\t\tbranch = {\n\t\t\t\t\t\tstart: path._segments.length,\n\t\t\t\t\t\tcrossings: crossings,\n\t\t\t\t\t\tvisited: visited = [],\n\t\t\t\t\t\thandleIn: handleIn\n\t\t\t\t\t};\n\t\t\t\t}\n\t\t\t\tif (cross)\n\t\t\t\t\tseg = other;\n\t\t\t\tif (!isValid(seg)) {\n\t\t\t\t\tpath.removeSegments(branch.start);\n\t\t\t\t\tfor (var j = 0, k = visited.length; j < k; j++) {\n\t\t\t\t\t\tvisited[j]._visited = false;\n\t\t\t\t\t}\n\t\t\t\t\tvisited.length = 0;\n\t\t\t\t\tdo {\n\t\t\t\t\t\tseg = branch && branch.crossings.shift();\n\t\t\t\t\t\tif (!seg || !seg._path) {\n\t\t\t\t\t\t\tseg = null;\n\t\t\t\t\t\t\tbranch = branches.pop();\n\t\t\t\t\t\t\tif (branch) {\n\t\t\t\t\t\t\t\tvisited = branch.visited;\n\t\t\t\t\t\t\t\thandleIn = branch.handleIn;\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t} while (branch && !isValid(seg));\n\t\t\t\t\tif (!seg)\n\t\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t\tvar next = seg.getNext();\n\t\t\t\tpath.add(new Segment(seg._point, handleIn,\n\t\t\t\t\t\tnext && seg._handleOut));\n\t\t\t\tseg._visited = true;\n\t\t\t\tvisited.push(seg);\n\t\t\t\tseg = next || seg._path.getFirstSegment();\n\t\t\t\thandleIn = next && next._handleIn;\n\t\t\t}\n\t\t\tif (finished) {\n\t\t\t\tif (closed) {\n\t\t\t\t\tpath.getFirstSegment().setHandleIn(handleIn);\n\t\t\t\t\tpath.setClosed(closed);\n\t\t\t\t}\n\t\t\t\tif (path.getArea() !== 0) {\n\t\t\t\t\tpaths.push(path);\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\treturn paths;\n\t}\n\n\treturn {\n\t\t_getWinding: function(point, dir, closed) {\n\t\t\treturn getWinding(point, this.getCurves(), dir, closed);\n\t\t},\n\n\t\tunite: function(path, options) {\n\t\t\treturn traceBoolean(this, path, 'unite', options);\n\t\t},\n\n\t\tintersect: function(path, options) {\n\t\t\treturn traceBoolean(this, path, 'intersect', options);\n\t\t},\n\n\t\tsubtract: function(path, options) {\n\t\t\treturn traceBoolean(this, path, 'subtract', options);\n\t\t},\n\n\t\texclude: function(path, options) {\n\t\t\treturn traceBoolean(this, path, 'exclude', options);\n\t\t},\n\n\t\tdivide: function(path, options) {\n\t\t\treturn options && (options.trace == false || options.stroke)\n\t\t\t\t\t? splitBoolean(this, path, 'divide')\n\t\t\t\t\t: createResult([\n\t\t\t\t\t\tthis.subtract(path, options),\n\t\t\t\t\t\tthis.intersect(path, options)\n\t\t\t\t\t], true, this, path, options);\n\t\t},\n\n\t\tresolveCrossings: function() {\n\t\t\tvar children = this._children,\n\t\t\t\tpaths = children || [this];\n\n\t\t\tfunction hasOverlap(seg, path) {\n\t\t\t\tvar inter = seg && seg._intersection;\n\t\t\t\treturn inter && inter._overlap && inter._path === path;\n\t\t\t}\n\n\t\t\tvar hasOverlaps = false,\n\t\t\t\thasCrossings = false,\n\t\t\t\tintersections = this.getIntersections(null, function(inter) {\n\t\t\t\t\treturn inter.hasOverlap() && (hasOverlaps = true) ||\n\t\t\t\t\t\t\tinter.isCrossing() && (hasCrossings = true);\n\t\t\t\t}),\n\t\t\t\tclearCurves = hasOverlaps && hasCrossings && [];\n\t\t\tintersections = CurveLocation.expand(intersections);\n\t\t\tif (hasOverlaps) {\n\t\t\t\tvar overlaps = divideLocations(intersections, function(inter) {\n\t\t\t\t\treturn inter.hasOverlap();\n\t\t\t\t}, clearCurves);\n\t\t\t\tfor (var i = overlaps.length - 1; i >= 0; i--) {\n\t\t\t\t\tvar overlap = overlaps[i],\n\t\t\t\t\t\tpath = overlap._path,\n\t\t\t\t\t\tseg = overlap._segment,\n\t\t\t\t\t\tprev = seg.getPrevious(),\n\t\t\t\t\t\tnext = seg.getNext();\n\t\t\t\t\tif (hasOverlap(prev, path) && hasOverlap(next, path)) {\n\t\t\t\t\t\tseg.remove();\n\t\t\t\t\t\tprev._handleOut._set(0, 0);\n\t\t\t\t\t\tnext._handleIn._set(0, 0);\n\t\t\t\t\t\tif (prev !== seg && !prev.getCurve().hasLength()) {\n\t\t\t\t\t\t\tnext._handleIn.set(prev._handleIn);\n\t\t\t\t\t\t\tprev.remove();\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t\tif (hasCrossings) {\n\t\t\t\tdivideLocations(intersections, hasOverlaps && function(inter) {\n\t\t\t\t\tvar curve1 = inter.getCurve(),\n\t\t\t\t\t\tseg1 = inter.getSegment(),\n\t\t\t\t\t\tother = inter._intersection,\n\t\t\t\t\t\tcurve2 = other._curve,\n\t\t\t\t\t\tseg2 = other._segment;\n\t\t\t\t\tif (curve1 && curve2 && curve1._path && curve2._path)\n\t\t\t\t\t\treturn true;\n\t\t\t\t\tif (seg1)\n\t\t\t\t\t\tseg1._intersection = null;\n\t\t\t\t\tif (seg2)\n\t\t\t\t\t\tseg2._intersection = null;\n\t\t\t\t}, clearCurves);\n\t\t\t\tif (clearCurves)\n\t\t\t\t\tclearCurveHandles(clearCurves);\n\t\t\t\tpaths = tracePaths(Base.each(paths, function(path) {\n\t\t\t\t\tBase.push(this, path._segments);\n\t\t\t\t}, []));\n\t\t\t}\n\t\t\tvar length = paths.length,\n\t\t\t\titem;\n\t\t\tif (length > 1 && children) {\n\t\t\t\tif (paths !== children)\n\t\t\t\t\tthis.setChildren(paths);\n\t\t\t\titem = this;\n\t\t\t} else if (length === 1 && !children) {\n\t\t\t\tif (paths[0] !== this)\n\t\t\t\t\tthis.setSegments(paths[0].removeSegments());\n\t\t\t\titem = this;\n\t\t\t}\n\t\t\tif (!item) {\n\t\t\t\titem = new CompoundPath(Item.NO_INSERT);\n\t\t\t\titem.addChildren(paths);\n\t\t\t\titem = item.reduce();\n\t\t\t\titem.copyAttributes(this);\n\t\t\t\tthis.replaceWith(item);\n\t\t\t}\n\t\t\treturn item;\n\t\t},\n\n\t\treorient: function(nonZero, clockwise) {\n\t\t\tvar children = this._children;\n\t\t\tif (children && children.length) {\n\t\t\t\tthis.setChildren(reorientPaths(this.removeChildren(),\n\t\t\t\t\t\tfunction(w) {\n\t\t\t\t\t\t\treturn !!(nonZero ? w : w & 1);\n\t\t\t\t\t\t},\n\t\t\t\t\t\tclockwise));\n\t\t\t} else if (clockwise !== undefined) {\n\t\t\t\tthis.setClockwise(clockwise);\n\t\t\t}\n\t\t\treturn this;\n\t\t},\n\n\t\tgetInteriorPoint: function() {\n\t\t\tvar bounds = this.getBounds(),\n\t\t\t\tpoint = bounds.getCenter(true);\n\t\t\tif (!this.contains(point)) {\n\t\t\t\tvar curves = this.getCurves(),\n\t\t\t\t\ty = point.y,\n\t\t\t\t\tintercepts = [],\n\t\t\t\t\troots = [];\n\t\t\t\tfor (var i = 0, l = curves.length; i < l; i++) {\n\t\t\t\t\tvar v = curves[i].getValues(),\n\t\t\t\t\t\to0 = v[1],\n\t\t\t\t\t\to1 = v[3],\n\t\t\t\t\t\to2 = v[5],\n\t\t\t\t\t\to3 = v[7];\n\t\t\t\t\tif (y >= min(o0, o1, o2, o3) && y <= max(o0, o1, o2, o3)) {\n\t\t\t\t\t\tvar monoCurves = Curve.getMonoCurves(v);\n\t\t\t\t\t\tfor (var j = 0, m = monoCurves.length; j < m; j++) {\n\t\t\t\t\t\t\tvar mv = monoCurves[j],\n\t\t\t\t\t\t\t\tmo0 = mv[1],\n\t\t\t\t\t\t\t\tmo3 = mv[7];\n\t\t\t\t\t\t\tif ((mo0 !== mo3) &&\n\t\t\t\t\t\t\t\t(y >= mo0 && y <= mo3 || y >= mo3 && y <= mo0)){\n\t\t\t\t\t\t\t\tvar x = y === mo0 ? mv[0]\n\t\t\t\t\t\t\t\t\t: y === mo3 ? mv[6]\n\t\t\t\t\t\t\t\t\t: Curve.solveCubic(mv, 1, y, roots, 0, 1)\n\t\t\t\t\t\t\t\t\t\t=== 1\n\t\t\t\t\t\t\t\t\t\t? Curve.getPoint(mv, roots[0]).x\n\t\t\t\t\t\t\t\t\t\t: (mv[0] + mv[6]) / 2;\n\t\t\t\t\t\t\t\tintercepts.push(x);\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\t\t\t\tif (intercepts.length > 1) {\n\t\t\t\t\tintercepts.sort(function(a, b) { return a - b; });\n\t\t\t\t\tpoint.x = (intercepts[0] + intercepts[1]) / 2;\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn point;\n\t\t}\n\t};\n});\n\nvar PathFlattener = Base.extend({\n\t_class: 'PathFlattener',\n\n\tinitialize: function(path, flatness, maxRecursion, ignoreStraight, matrix) {\n\t\tvar curves = [],\n\t\t\tparts = [],\n\t\t\tlength = 0,\n\t\t\tminSpan = 1 / (maxRecursion || 32),\n\t\t\tsegments = path._segments,\n\t\t\tsegment1 = segments[0],\n\t\t\tsegment2;\n\n\t\tfunction addCurve(segment1, segment2) {\n\t\t\tvar curve = Curve.getValues(segment1, segment2, matrix);\n\t\t\tcurves.push(curve);\n\t\t\tcomputeParts(curve, segment1._index, 0, 1);\n\t\t}\n\n\t\tfunction computeParts(curve, index, t1, t2) {\n\t\t\tif ((t2 - t1) > minSpan\n\t\t\t\t\t&& !(ignoreStraight && Curve.isStraight(curve))\n\t\t\t\t\t&& !Curve.isFlatEnough(curve, flatness || 0.25)) {\n\t\t\t\tvar halves = Curve.subdivide(curve, 0.5),\n\t\t\t\t\ttMid = (t1 + t2) / 2;\n\t\t\t\tcomputeParts(halves[0], index, t1, tMid);\n\t\t\t\tcomputeParts(halves[1], index, tMid, t2);\n\t\t\t} else {\n\t\t\t\tvar dx = curve[6] - curve[0],\n\t\t\t\t\tdy = curve[7] - curve[1],\n\t\t\t\t\tdist = Math.sqrt(dx * dx + dy * dy);\n\t\t\t\tif (dist > 0) {\n\t\t\t\t\tlength += dist;\n\t\t\t\t\tparts.push({\n\t\t\t\t\t\toffset: length,\n\t\t\t\t\t\tcurve: curve,\n\t\t\t\t\t\tindex: index,\n\t\t\t\t\t\ttime: t2,\n\t\t\t\t\t});\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\tfor (var i = 1, l = segments.length; i < l; i++) {\n\t\t\tsegment2 = segments[i];\n\t\t\taddCurve(segment1, segment2);\n\t\t\tsegment1 = segment2;\n\t\t}\n\t\tif (path._closed)\n\t\t\taddCurve(segment2 || segment1, segments[0]);\n\t\tthis.curves = curves;\n\t\tthis.parts = parts;\n\t\tthis.length = length;\n\t\tthis.index = 0;\n\t},\n\n\t_get: function(offset) {\n\t\tvar parts = this.parts,\n\t\t\tlength = parts.length,\n\t\t\tstart,\n\t\t\ti, j = this.index;\n\t\tfor (;;) {\n\t\t\ti = j;\n\t\t\tif (!j || parts[--j].offset < offset)\n\t\t\t\tbreak;\n\t\t}\n\t\tfor (; i < length; i++) {\n\t\t\tvar part = parts[i];\n\t\t\tif (part.offset >= offset) {\n\t\t\t\tthis.index = i;\n\t\t\t\tvar prev = parts[i - 1],\n\t\t\t\t\tprevTime = prev && prev.index === part.index ? prev.time : 0,\n\t\t\t\t\tprevOffset = prev ? prev.offset : 0;\n\t\t\t\treturn {\n\t\t\t\t\tindex: part.index,\n\t\t\t\t\ttime: prevTime + (part.time - prevTime)\n\t\t\t\t\t\t* (offset - prevOffset) / (part.offset - prevOffset)\n\t\t\t\t};\n\t\t\t}\n\t\t}\n\t\treturn {\n\t\t\tindex: parts[length - 1].index,\n\t\t\ttime: 1\n\t\t};\n\t},\n\n\tdrawPart: function(ctx, from, to) {\n\t\tvar start = this._get(from),\n\t\t\tend = this._get(to);\n\t\tfor (var i = start.index, l = end.index; i <= l; i++) {\n\t\t\tvar curve = Curve.getPart(this.curves[i],\n\t\t\t\t\ti === start.index ? start.time : 0,\n\t\t\t\t\ti === end.index ? end.time : 1);\n\t\t\tif (i === start.index)\n\t\t\t\tctx.moveTo(curve[0], curve[1]);\n\t\t\tctx.bezierCurveTo.apply(ctx, curve.slice(2));\n\t\t}\n\t}\n}, Base.each(Curve._evaluateMethods,\n\tfunction(name) {\n\t\tthis[name + 'At'] = function(offset) {\n\t\t\tvar param = this._get(offset);\n\t\t\treturn Curve[name](this.curves[param.index], param.time);\n\t\t};\n\t}, {})\n);\n\nvar PathFitter = Base.extend({\n\tinitialize: function(path) {\n\t\tvar points = this.points = [],\n\t\t\tsegments = path._segments,\n\t\t\tclosed = path._closed;\n\t\tfor (var i = 0, prev, l = segments.length; i < l; i++) {\n\t\t\tvar point = segments[i].point;\n\t\t\tif (!prev || !prev.equals(point)) {\n\t\t\t\tpoints.push(prev = point.clone());\n\t\t\t}\n\t\t}\n\t\tif (closed) {\n\t\t\tpoints.unshift(points[points.length - 1]);\n\t\t\tpoints.push(points[1]);\n\t\t}\n\t\tthis.closed = closed;\n\t},\n\n\tfit: function(error) {\n\t\tvar points = this.points,\n\t\t\tlength = points.length,\n\t\t\tsegments = null;\n\t\tif (length > 0) {\n\t\t\tsegments = [new Segment(points[0])];\n\t\t\tif (length > 1) {\n\t\t\t\tthis.fitCubic(segments, error, 0, length - 1,\n\t\t\t\t\t\tpoints[1].subtract(points[0]),\n\t\t\t\t\t\tpoints[length - 2].subtract(points[length - 1]));\n\t\t\t\tif (this.closed) {\n\t\t\t\t\tsegments.shift();\n\t\t\t\t\tsegments.pop();\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\treturn segments;\n\t},\n\n\tfitCubic: function(segments, error, first, last, tan1, tan2) {\n\t\tvar points = this.points;\n\t\tif (last - first === 1) {\n\t\t\tvar pt1 = points[first],\n\t\t\t\tpt2 = points[last],\n\t\t\t\tdist = pt1.getDistance(pt2) / 3;\n\t\t\tthis.addCurve(segments, [pt1, pt1.add(tan1.normalize(dist)),\n\t\t\t\t\tpt2.add(tan2.normalize(dist)), pt2]);\n\t\t\treturn;\n\t\t}\n\t\tvar uPrime = this.chordLengthParameterize(first, last),\n\t\t\tmaxError = Math.max(error, error * error),\n\t\t\tsplit,\n\t\t\tparametersInOrder = true;\n\t\tfor (var i = 0; i <= 4; i++) {\n\t\t\tvar curve = this.generateBezier(first, last, uPrime, tan1, tan2);\n\t\t\tvar max = this.findMaxError(first, last, curve, uPrime);\n\t\t\tif (max.error < error && parametersInOrder) {\n\t\t\t\tthis.addCurve(segments, curve);\n\t\t\t\treturn;\n\t\t\t}\n\t\t\tsplit = max.index;\n\t\t\tif (max.error >= maxError)\n\t\t\t\tbreak;\n\t\t\tparametersInOrder = this.reparameterize(first, last, uPrime, curve);\n\t\t\tmaxError = max.error;\n\t\t}\n\t\tvar tanCenter = points[split - 1].subtract(points[split + 1]);\n\t\tthis.fitCubic(segments, error, first, split, tan1, tanCenter);\n\t\tthis.fitCubic(segments, error, split, last, tanCenter.negate(), tan2);\n\t},\n\n\taddCurve: function(segments, curve) {\n\t\tvar prev = segments[segments.length - 1];\n\t\tprev.setHandleOut(curve[1].subtract(curve[0]));\n\t\tsegments.push(new Segment(curve[3], curve[2].subtract(curve[3])));\n\t},\n\n\tgenerateBezier: function(first, last, uPrime, tan1, tan2) {\n\t\tvar epsilon = 1e-12,\n\t\t\tabs = Math.abs,\n\t\t\tpoints = this.points,\n\t\t\tpt1 = points[first],\n\t\t\tpt2 = points[last],\n\t\t\tC = [[0, 0], [0, 0]],\n\t\t\tX = [0, 0];\n\n\t\tfor (var i = 0, l = last - first + 1; i < l; i++) {\n\t\t\tvar u = uPrime[i],\n\t\t\t\tt = 1 - u,\n\t\t\t\tb = 3 * u * t,\n\t\t\t\tb0 = t * t * t,\n\t\t\t\tb1 = b * t,\n\t\t\t\tb2 = b * u,\n\t\t\t\tb3 = u * u * u,\n\t\t\t\ta1 = tan1.normalize(b1),\n\t\t\t\ta2 = tan2.normalize(b2),\n\t\t\t\ttmp = points[first + i]\n\t\t\t\t\t.subtract(pt1.multiply(b0 + b1))\n\t\t\t\t\t.subtract(pt2.multiply(b2 + b3));\n\t\t\tC[0][0] += a1.dot(a1);\n\t\t\tC[0][1] += a1.dot(a2);\n\t\t\tC[1][0] = C[0][1];\n\t\t\tC[1][1] += a2.dot(a2);\n\t\t\tX[0] += a1.dot(tmp);\n\t\t\tX[1] += a2.dot(tmp);\n\t\t}\n\n\t\tvar detC0C1 = C[0][0] * C[1][1] - C[1][0] * C[0][1],\n\t\t\talpha1,\n\t\t\talpha2;\n\t\tif (abs(detC0C1) > epsilon) {\n\t\t\tvar detC0X = C[0][0] * X[1]    - C[1][0] * X[0],\n\t\t\t\tdetXC1 = X[0]    * C[1][1] - X[1]    * C[0][1];\n\t\t\talpha1 = detXC1 / detC0C1;\n\t\t\talpha2 = detC0X / detC0C1;\n\t\t} else {\n\t\t\tvar c0 = C[0][0] + C[0][1],\n\t\t\t\tc1 = C[1][0] + C[1][1];\n\t\t\talpha1 = alpha2 = abs(c0) > epsilon ? X[0] / c0\n\t\t\t\t\t\t\t: abs(c1) > epsilon ? X[1] / c1\n\t\t\t\t\t\t\t: 0;\n\t\t}\n\n\t\tvar segLength = pt2.getDistance(pt1),\n\t\t\teps = epsilon * segLength,\n\t\t\thandle1,\n\t\t\thandle2;\n\t\tif (alpha1 < eps || alpha2 < eps) {\n\t\t\talpha1 = alpha2 = segLength / 3;\n\t\t} else {\n\t\t\tvar line = pt2.subtract(pt1);\n\t\t\thandle1 = tan1.normalize(alpha1);\n\t\t\thandle2 = tan2.normalize(alpha2);\n\t\t\tif (handle1.dot(line) - handle2.dot(line) > segLength * segLength) {\n\t\t\t\talpha1 = alpha2 = segLength / 3;\n\t\t\t\thandle1 = handle2 = null;\n\t\t\t}\n\t\t}\n\n\t\treturn [pt1,\n\t\t\t\tpt1.add(handle1 || tan1.normalize(alpha1)),\n\t\t\t\tpt2.add(handle2 || tan2.normalize(alpha2)),\n\t\t\t\tpt2];\n\t},\n\n\treparameterize: function(first, last, u, curve) {\n\t\tfor (var i = first; i <= last; i++) {\n\t\t\tu[i - first] = this.findRoot(curve, this.points[i], u[i - first]);\n\t\t}\n\t\tfor (var i = 1, l = u.length; i < l; i++) {\n\t\t\tif (u[i] <= u[i - 1])\n\t\t\t\treturn false;\n\t\t}\n\t\treturn true;\n\t},\n\n\tfindRoot: function(curve, point, u) {\n\t\tvar curve1 = [],\n\t\t\tcurve2 = [];\n\t\tfor (var i = 0; i <= 2; i++) {\n\t\t\tcurve1[i] = curve[i + 1].subtract(curve[i]).multiply(3);\n\t\t}\n\t\tfor (var i = 0; i <= 1; i++) {\n\t\t\tcurve2[i] = curve1[i + 1].subtract(curve1[i]).multiply(2);\n\t\t}\n\t\tvar pt = this.evaluate(3, curve, u),\n\t\t\tpt1 = this.evaluate(2, curve1, u),\n\t\t\tpt2 = this.evaluate(1, curve2, u),\n\t\t\tdiff = pt.subtract(point),\n\t\t\tdf = pt1.dot(pt1) + diff.dot(pt2);\n\t\treturn Numerical.isMachineZero(df) ? u : u - diff.dot(pt1) / df;\n\t},\n\n\tevaluate: function(degree, curve, t) {\n\t\tvar tmp = curve.slice();\n\t\tfor (var i = 1; i <= degree; i++) {\n\t\t\tfor (var j = 0; j <= degree - i; j++) {\n\t\t\t\ttmp[j] = tmp[j].multiply(1 - t).add(tmp[j + 1].multiply(t));\n\t\t\t}\n\t\t}\n\t\treturn tmp[0];\n\t},\n\n\tchordLengthParameterize: function(first, last) {\n\t\tvar u = [0];\n\t\tfor (var i = first + 1; i <= last; i++) {\n\t\t\tu[i - first] = u[i - first - 1]\n\t\t\t\t\t+ this.points[i].getDistance(this.points[i - 1]);\n\t\t}\n\t\tfor (var i = 1, m = last - first; i <= m; i++) {\n\t\t\tu[i] /= u[m];\n\t\t}\n\t\treturn u;\n\t},\n\n\tfindMaxError: function(first, last, curve, u) {\n\t\tvar index = Math.floor((last - first + 1) / 2),\n\t\t\tmaxDist = 0;\n\t\tfor (var i = first + 1; i < last; i++) {\n\t\t\tvar P = this.evaluate(3, curve, u[i - first]);\n\t\t\tvar v = P.subtract(this.points[i]);\n\t\t\tvar dist = v.x * v.x + v.y * v.y;\n\t\t\tif (dist >= maxDist) {\n\t\t\t\tmaxDist = dist;\n\t\t\t\tindex = i;\n\t\t\t}\n\t\t}\n\t\treturn {\n\t\t\terror: maxDist,\n\t\t\tindex: index\n\t\t};\n\t}\n});\n\nvar TextItem = Item.extend({\n\t_class: 'TextItem',\n\t_applyMatrix: false,\n\t_canApplyMatrix: false,\n\t_serializeFields: {\n\t\tcontent: null\n\t},\n\t_boundsOptions: { stroke: false, handle: false },\n\n\tinitialize: function TextItem(arg) {\n\t\tthis._content = '';\n\t\tthis._lines = [];\n\t\tvar hasProps = arg && Base.isPlainObject(arg)\n\t\t\t\t&& arg.x === undefined && arg.y === undefined;\n\t\tthis._initialize(hasProps && arg, !hasProps && Point.read(arguments));\n\t},\n\n\t_equals: function(item) {\n\t\treturn this._content === item._content;\n\t},\n\n\tcopyContent: function(source) {\n\t\tthis.setContent(source._content);\n\t},\n\n\tgetContent: function() {\n\t\treturn this._content;\n\t},\n\n\tsetContent: function(content) {\n\t\tthis._content = '' + content;\n\t\tthis._lines = this._content.split(/\\r\\n|\\n|\\r/mg);\n\t\tthis._changed(521);\n\t},\n\n\tisEmpty: function() {\n\t\treturn !this._content;\n\t},\n\n\tgetCharacterStyle: '#getStyle',\n\tsetCharacterStyle: '#setStyle',\n\n\tgetParagraphStyle: '#getStyle',\n\tsetParagraphStyle: '#setStyle'\n});\n\nvar PointText = TextItem.extend({\n\t_class: 'PointText',\n\n\tinitialize: function PointText() {\n\t\tTextItem.apply(this, arguments);\n\t},\n\n\tgetPoint: function() {\n\t\tvar point = this._matrix.getTranslation();\n\t\treturn new LinkedPoint(point.x, point.y, this, 'setPoint');\n\t},\n\n\tsetPoint: function() {\n\t\tvar point = Point.read(arguments);\n\t\tthis.translate(point.subtract(this._matrix.getTranslation()));\n\t},\n\n\t_draw: function(ctx, param, viewMatrix) {\n\t\tif (!this._content)\n\t\t\treturn;\n\t\tthis._setStyles(ctx, param, viewMatrix);\n\t\tvar lines = this._lines,\n\t\t\tstyle = this._style,\n\t\t\thasFill = style.hasFill(),\n\t\t\thasStroke = style.hasStroke(),\n\t\t\tleading = style.getLeading(),\n\t\t\tshadowColor = ctx.shadowColor;\n\t\tctx.font = style.getFontStyle();\n\t\tctx.textAlign = style.getJustification();\n\t\tfor (var i = 0, l = lines.length; i < l; i++) {\n\t\t\tctx.shadowColor = shadowColor;\n\t\t\tvar line = lines[i];\n\t\t\tif (hasFill) {\n\t\t\t\tctx.fillText(line, 0, 0);\n\t\t\t\tctx.shadowColor = 'rgba(0,0,0,0)';\n\t\t\t}\n\t\t\tif (hasStroke)\n\t\t\t\tctx.strokeText(line, 0, 0);\n\t\t\tctx.translate(0, leading);\n\t\t}\n\t},\n\n\t_getBounds: function(matrix, options) {\n\t\tvar style = this._style,\n\t\t\tlines = this._lines,\n\t\t\tnumLines = lines.length,\n\t\t\tjustification = style.getJustification(),\n\t\t\tleading = style.getLeading(),\n\t\t\twidth = this.getView().getTextWidth(style.getFontStyle(), lines),\n\t\t\tx = 0;\n\t\tif (justification !== 'left')\n\t\t\tx -= width / (justification === 'center' ? 2: 1);\n\t\tvar rect = new Rectangle(x,\n\t\t\t\t\tnumLines ? - 0.75 * leading : 0,\n\t\t\t\t\twidth, numLines * leading);\n\t\treturn matrix ? matrix._transformBounds(rect, rect) : rect;\n\t}\n});\n\nvar Color = Base.extend(new function() {\n\tvar types = {\n\t\tgray: ['gray'],\n\t\trgb: ['red', 'green', 'blue'],\n\t\thsb: ['hue', 'saturation', 'brightness'],\n\t\thsl: ['hue', 'saturation', 'lightness'],\n\t\tgradient: ['gradient', 'origin', 'destination', 'highlight']\n\t};\n\n\tvar componentParsers = {},\n\t\tnamedColors = {\n\t\t\ttransparent: [0, 0, 0, 0]\n\t\t},\n\t\tcolorCtx;\n\n\tfunction fromCSS(string) {\n\t\tvar match = string.match(\n\t\t\t\t/^#([\\da-f]{2})([\\da-f]{2})([\\da-f]{2})([\\da-f]{2})?$/i\n\t\t\t) || string.match(\n\t\t\t\t/^#([\\da-f])([\\da-f])([\\da-f])([\\da-f])?$/i\n\t\t\t),\n\t\t\ttype = 'rgb',\n\t\t\tcomponents;\n\t\tif (match) {\n\t\t\tvar amount = match[4] ? 4 : 3;\n\t\t\tcomponents = new Array(amount);\n\t\t\tfor (var i = 0; i < amount; i++) {\n\t\t\t\tvar value = match[i + 1];\n\t\t\t\tcomponents[i] = parseInt(value.length == 1\n\t\t\t\t\t\t? value + value : value, 16) / 255;\n\t\t\t}\n\t\t} else if (match = string.match(/^(rgb|hsl)a?\\((.*)\\)$/)) {\n\t\t\ttype = match[1];\n\t\t\tcomponents = match[2].trim().split(/[,\\s]+/g);\n\t\t\tvar isHSL = type === 'hsl';\n\t\t\tfor (var i = 0, l = Math.min(components.length, 4); i < l; i++) {\n\t\t\t\tvar component = components[i];\n\t\t\t\tvar value = parseFloat(component);\n\t\t\t\tif (isHSL) {\n\t\t\t\t\tif (i === 0) {\n\t\t\t\t\t\tvar unit = component.match(/([a-z]*)$/)[1];\n\t\t\t\t\t\tvalue *= ({\n\t\t\t\t\t\t\tturn: 360,\n\t\t\t\t\t\t\trad: 180 / Math.PI,\n\t\t\t\t\t\t\tgrad: 0.9\n\t\t\t\t\t\t}[unit] || 1);\n\t\t\t\t\t} else if (i < 3) {\n\t\t\t\t\t\tvalue /= 100;\n\t\t\t\t\t}\n\t\t\t\t} else if (i < 3) {\n\t\t\t\t\tvalue /= /%$/.test(component) ? 100 : 255;\n\t\t\t\t}\n\t\t\t\tcomponents[i] = value;\n\t\t\t}\n\t\t} else {\n\t\t\tvar color = namedColors[string];\n\t\t\tif (!color) {\n\t\t\t\tif (window) {\n\t\t\t\t\tif (!colorCtx) {\n\t\t\t\t\t\tcolorCtx = CanvasProvider.getContext(1, 1);\n\t\t\t\t\t\tcolorCtx.globalCompositeOperation = 'copy';\n\t\t\t\t\t}\n\t\t\t\t\tcolorCtx.fillStyle = 'rgba(0,0,0,0)';\n\t\t\t\t\tcolorCtx.fillStyle = string;\n\t\t\t\t\tcolorCtx.fillRect(0, 0, 1, 1);\n\t\t\t\t\tvar data = colorCtx.getImageData(0, 0, 1, 1).data;\n\t\t\t\t\tcolor = namedColors[string] = [\n\t\t\t\t\t\tdata[0] / 255,\n\t\t\t\t\t\tdata[1] / 255,\n\t\t\t\t\t\tdata[2] / 255\n\t\t\t\t\t];\n\t\t\t\t} else {\n\t\t\t\t\tcolor = [0, 0, 0];\n\t\t\t\t}\n\t\t\t}\n\t\t\tcomponents = color.slice();\n\t\t}\n\t\treturn [type, components];\n\t}\n\n\tvar hsbIndices = [\n\t\t[0, 3, 1],\n\t\t[2, 0, 1],\n\t\t[1, 0, 3],\n\t\t[1, 2, 0],\n\t\t[3, 1, 0],\n\t\t[0, 1, 2]\n\t];\n\n\tvar converters = {\n\t\t'rgb-hsb': function(r, g, b) {\n\t\t\tvar max = Math.max(r, g, b),\n\t\t\t\tmin = Math.min(r, g, b),\n\t\t\t\tdelta = max - min,\n\t\t\t\th = delta === 0 ? 0\n\t\t\t\t\t:   ( max == r ? (g - b) / delta + (g < b ? 6 : 0)\n\t\t\t\t\t\t: max == g ? (b - r) / delta + 2\n\t\t\t\t\t\t:            (r - g) / delta + 4) * 60;\n\t\t\treturn [h, max === 0 ? 0 : delta / max, max];\n\t\t},\n\n\t\t'hsb-rgb': function(h, s, b) {\n\t\t\th = (((h / 60) % 6) + 6) % 6;\n\t\t\tvar i = Math.floor(h),\n\t\t\t\tf = h - i,\n\t\t\t\ti = hsbIndices[i],\n\t\t\t\tv = [\n\t\t\t\t\tb,\n\t\t\t\t\tb * (1 - s),\n\t\t\t\t\tb * (1 - s * f),\n\t\t\t\t\tb * (1 - s * (1 - f))\n\t\t\t\t];\n\t\t\treturn [v[i[0]], v[i[1]], v[i[2]]];\n\t\t},\n\n\t\t'rgb-hsl': function(r, g, b) {\n\t\t\tvar max = Math.max(r, g, b),\n\t\t\t\tmin = Math.min(r, g, b),\n\t\t\t\tdelta = max - min,\n\t\t\t\tachromatic = delta === 0,\n\t\t\t\th = achromatic ? 0\n\t\t\t\t\t:   ( max == r ? (g - b) / delta + (g < b ? 6 : 0)\n\t\t\t\t\t\t: max == g ? (b - r) / delta + 2\n\t\t\t\t\t\t:            (r - g) / delta + 4) * 60,\n\t\t\t\tl = (max + min) / 2,\n\t\t\t\ts = achromatic ? 0 : l < 0.5\n\t\t\t\t\t\t? delta / (max + min)\n\t\t\t\t\t\t: delta / (2 - max - min);\n\t\t\treturn [h, s, l];\n\t\t},\n\n\t\t'hsl-rgb': function(h, s, l) {\n\t\t\th = (((h / 360) % 1) + 1) % 1;\n\t\t\tif (s === 0)\n\t\t\t\treturn [l, l, l];\n\t\t\tvar t3s = [ h + 1 / 3, h, h - 1 / 3 ],\n\t\t\t\tt2 = l < 0.5 ? l * (1 + s) : l + s - l * s,\n\t\t\t\tt1 = 2 * l - t2,\n\t\t\t\tc = [];\n\t\t\tfor (var i = 0; i < 3; i++) {\n\t\t\t\tvar t3 = t3s[i];\n\t\t\t\tif (t3 < 0) t3 += 1;\n\t\t\t\tif (t3 > 1) t3 -= 1;\n\t\t\t\tc[i] = 6 * t3 < 1\n\t\t\t\t\t? t1 + (t2 - t1) * 6 * t3\n\t\t\t\t\t: 2 * t3 < 1\n\t\t\t\t\t\t? t2\n\t\t\t\t\t\t: 3 * t3 < 2\n\t\t\t\t\t\t\t? t1 + (t2 - t1) * ((2 / 3) - t3) * 6\n\t\t\t\t\t\t\t: t1;\n\t\t\t}\n\t\t\treturn c;\n\t\t},\n\n\t\t'rgb-gray': function(r, g, b) {\n\t\t\treturn [r * 0.2989 + g * 0.587 + b * 0.114];\n\t\t},\n\n\t\t'gray-rgb': function(g) {\n\t\t\treturn [g, g, g];\n\t\t},\n\n\t\t'gray-hsb': function(g) {\n\t\t\treturn [0, 0, g];\n\t\t},\n\n\t\t'gray-hsl': function(g) {\n\t\t\treturn [0, 0, g];\n\t\t},\n\n\t\t'gradient-rgb': function() {\n\t\t\treturn [];\n\t\t},\n\n\t\t'rgb-gradient': function() {\n\t\t\treturn [];\n\t\t}\n\n\t};\n\n\treturn Base.each(types, function(properties, type) {\n\t\tcomponentParsers[type] = [];\n\t\tBase.each(properties, function(name, index) {\n\t\t\tvar part = Base.capitalize(name),\n\t\t\t\thasOverlap = /^(hue|saturation)$/.test(name),\n\t\t\t\tparser = componentParsers[type][index] = type === 'gradient'\n\t\t\t\t\t? name === 'gradient'\n\t\t\t\t\t\t? function(value) {\n\t\t\t\t\t\t\tvar current = this._components[0];\n\t\t\t\t\t\t\tvalue = Gradient.read(\n\t\t\t\t\t\t\t\tArray.isArray(value)\n\t\t\t\t\t\t\t\t\t? value\n\t\t\t\t\t\t\t\t\t: arguments, 0, { readNull: true }\n\t\t\t\t\t\t\t);\n\t\t\t\t\t\t\tif (current !== value) {\n\t\t\t\t\t\t\t\tif (current)\n\t\t\t\t\t\t\t\t\tcurrent._removeOwner(this);\n\t\t\t\t\t\t\t\tif (value)\n\t\t\t\t\t\t\t\t\tvalue._addOwner(this);\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\treturn value;\n\t\t\t\t\t\t}\n\t\t\t\t\t\t: function() {\n\t\t\t\t\t\t\treturn Point.read(arguments, 0, {\n\t\t\t\t\t\t\t\t\treadNull: name === 'highlight',\n\t\t\t\t\t\t\t\t\tclone: true\n\t\t\t\t\t\t\t});\n\t\t\t\t\t\t}\n\t\t\t\t\t: function(value) {\n\t\t\t\t\t\treturn value == null || isNaN(value) ? 0 : +value;\n\t\t\t\t\t};\n\t\t\tthis['get' + part] = function() {\n\t\t\t\treturn this._type === type\n\t\t\t\t\t|| hasOverlap && /^hs[bl]$/.test(this._type)\n\t\t\t\t\t\t? this._components[index]\n\t\t\t\t\t\t: this._convert(type)[index];\n\t\t\t};\n\n\t\t\tthis['set' + part] = function(value) {\n\t\t\t\tif (this._type !== type\n\t\t\t\t\t\t&& !(hasOverlap && /^hs[bl]$/.test(this._type))) {\n\t\t\t\t\tthis._components = this._convert(type);\n\t\t\t\t\tthis._properties = types[type];\n\t\t\t\t\tthis._type = type;\n\t\t\t\t}\n\t\t\t\tthis._components[index] = parser.call(this, value);\n\t\t\t\tthis._changed();\n\t\t\t};\n\t\t}, this);\n\t}, {\n\t\t_class: 'Color',\n\t\t_readIndex: true,\n\n\t\tinitialize: function Color(arg) {\n\t\t\tvar args = arguments,\n\t\t\t\treading = this.__read,\n\t\t\t\tread = 0,\n\t\t\t\ttype,\n\t\t\t\tcomponents,\n\t\t\t\talpha,\n\t\t\t\tvalues;\n\t\t\tif (Array.isArray(arg)) {\n\t\t\t\targs = arg;\n\t\t\t\targ = args[0];\n\t\t\t}\n\t\t\tvar argType = arg != null && typeof arg;\n\t\t\tif (argType === 'string' && arg in types) {\n\t\t\t\ttype = arg;\n\t\t\t\targ = args[1];\n\t\t\t\tif (Array.isArray(arg)) {\n\t\t\t\t\tcomponents = arg;\n\t\t\t\t\talpha = args[2];\n\t\t\t\t} else {\n\t\t\t\t\tif (reading)\n\t\t\t\t\t\tread = 1;\n\t\t\t\t\targs = Base.slice(args, 1);\n\t\t\t\t\targType = typeof arg;\n\t\t\t\t}\n\t\t\t}\n\t\t\tif (!components) {\n\t\t\t\tvalues = argType === 'number'\n\t\t\t\t\t\t? args\n\t\t\t\t\t\t: argType === 'object' && arg.length != null\n\t\t\t\t\t\t\t? arg\n\t\t\t\t\t\t\t: null;\n\t\t\t\tif (values) {\n\t\t\t\t\tif (!type)\n\t\t\t\t\t\ttype = values.length >= 3\n\t\t\t\t\t\t\t\t? 'rgb'\n\t\t\t\t\t\t\t\t: 'gray';\n\t\t\t\t\tvar length = types[type].length;\n\t\t\t\t\talpha = values[length];\n\t\t\t\t\tif (reading) {\n\t\t\t\t\t\tread += values === arguments\n\t\t\t\t\t\t\t? length + (alpha != null ? 1 : 0)\n\t\t\t\t\t\t\t: 1;\n\t\t\t\t\t}\n\t\t\t\t\tif (values.length > length)\n\t\t\t\t\t\tvalues = Base.slice(values, 0, length);\n\t\t\t\t} else if (argType === 'string') {\n\t\t\t\t\tvar converted = fromCSS(arg);\n\t\t\t\t\ttype = converted[0];\n\t\t\t\t\tcomponents = converted[1];\n\t\t\t\t\tif (components.length === 4) {\n\t\t\t\t\t\talpha = components[3];\n\t\t\t\t\t\tcomponents.length--;\n\t\t\t\t\t}\n\t\t\t\t} else if (argType === 'object') {\n\t\t\t\t\tif (arg.constructor === Color) {\n\t\t\t\t\t\ttype = arg._type;\n\t\t\t\t\t\tcomponents = arg._components.slice();\n\t\t\t\t\t\talpha = arg._alpha;\n\t\t\t\t\t\tif (type === 'gradient') {\n\t\t\t\t\t\t\tfor (var i = 1, l = components.length; i < l; i++) {\n\t\t\t\t\t\t\t\tvar point = components[i];\n\t\t\t\t\t\t\t\tif (point)\n\t\t\t\t\t\t\t\t\tcomponents[i] = point.clone();\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t} else if (arg.constructor === Gradient) {\n\t\t\t\t\t\ttype = 'gradient';\n\t\t\t\t\t\tvalues = args;\n\t\t\t\t\t} else {\n\t\t\t\t\t\ttype = 'hue' in arg\n\t\t\t\t\t\t\t? 'lightness' in arg\n\t\t\t\t\t\t\t\t? 'hsl'\n\t\t\t\t\t\t\t\t: 'hsb'\n\t\t\t\t\t\t\t: 'gradient' in arg || 'stops' in arg\n\t\t\t\t\t\t\t\t\t|| 'radial' in arg\n\t\t\t\t\t\t\t\t? 'gradient'\n\t\t\t\t\t\t\t\t: 'gray' in arg\n\t\t\t\t\t\t\t\t\t? 'gray'\n\t\t\t\t\t\t\t\t\t: 'rgb';\n\t\t\t\t\t\tvar properties = types[type],\n\t\t\t\t\t\t\tparsers = componentParsers[type];\n\t\t\t\t\t\tthis._components = components = [];\n\t\t\t\t\t\tfor (var i = 0, l = properties.length; i < l; i++) {\n\t\t\t\t\t\t\tvar value = arg[properties[i]];\n\t\t\t\t\t\t\tif (value == null && !i && type === 'gradient'\n\t\t\t\t\t\t\t\t\t&& 'stops' in arg) {\n\t\t\t\t\t\t\t\tvalue = {\n\t\t\t\t\t\t\t\t\tstops: arg.stops,\n\t\t\t\t\t\t\t\t\tradial: arg.radial\n\t\t\t\t\t\t\t\t};\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\tvalue = parsers[i].call(this, value);\n\t\t\t\t\t\t\tif (value != null)\n\t\t\t\t\t\t\t\tcomponents[i] = value;\n\t\t\t\t\t\t}\n\t\t\t\t\t\talpha = arg.alpha;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tif (reading && type)\n\t\t\t\t\tread = 1;\n\t\t\t}\n\t\t\tthis._type = type || 'rgb';\n\t\t\tif (!components) {\n\t\t\t\tthis._components = components = [];\n\t\t\t\tvar parsers = componentParsers[this._type];\n\t\t\t\tfor (var i = 0, l = parsers.length; i < l; i++) {\n\t\t\t\t\tvar value = parsers[i].call(this, values && values[i]);\n\t\t\t\t\tif (value != null)\n\t\t\t\t\t\tcomponents[i] = value;\n\t\t\t\t}\n\t\t\t}\n\t\t\tthis._components = components;\n\t\t\tthis._properties = types[this._type];\n\t\t\tthis._alpha = alpha;\n\t\t\tif (reading)\n\t\t\t\tthis.__read = read;\n\t\t\treturn this;\n\t\t},\n\n\t\tset: '#initialize',\n\n\t\t_serialize: function(options, dictionary) {\n\t\t\tvar components = this.getComponents();\n\t\t\treturn Base.serialize(\n\t\t\t\t\t/^(gray|rgb)$/.test(this._type)\n\t\t\t\t\t\t? components\n\t\t\t\t\t\t: [this._type].concat(components),\n\t\t\t\t\toptions, true, dictionary);\n\t\t},\n\n\t\t_changed: function() {\n\t\t\tthis._canvasStyle = null;\n\t\t\tif (this._owner) {\n\t\t\t\tif (this._setter) {\n\t\t\t\t\tthis._owner[this._setter](this);\n\t\t\t\t} else {\n\t\t\t\t\tthis._owner._changed(129);\n\t\t\t\t}\n\t\t\t}\n\t\t},\n\n\t\t_convert: function(type) {\n\t\t\tvar converter;\n\t\t\treturn this._type === type\n\t\t\t\t\t? this._components.slice()\n\t\t\t\t\t: (converter = converters[this._type + '-' + type])\n\t\t\t\t\t\t? converter.apply(this, this._components)\n\t\t\t\t\t\t: converters['rgb-' + type].apply(this,\n\t\t\t\t\t\t\tconverters[this._type + '-rgb'].apply(this,\n\t\t\t\t\t\t\t\tthis._components));\n\t\t},\n\n\t\tconvert: function(type) {\n\t\t\treturn new Color(type, this._convert(type), this._alpha);\n\t\t},\n\n\t\tgetType: function() {\n\t\t\treturn this._type;\n\t\t},\n\n\t\tsetType: function(type) {\n\t\t\tthis._components = this._convert(type);\n\t\t\tthis._properties = types[type];\n\t\t\tthis._type = type;\n\t\t},\n\n\t\tgetComponents: function() {\n\t\t\tvar components = this._components.slice();\n\t\t\tif (this._alpha != null)\n\t\t\t\tcomponents.push(this._alpha);\n\t\t\treturn components;\n\t\t},\n\n\t\tgetAlpha: function() {\n\t\t\treturn this._alpha != null ? this._alpha : 1;\n\t\t},\n\n\t\tsetAlpha: function(alpha) {\n\t\t\tthis._alpha = alpha == null ? null : Math.min(Math.max(alpha, 0), 1);\n\t\t\tthis._changed();\n\t\t},\n\n\t\thasAlpha: function() {\n\t\t\treturn this._alpha != null;\n\t\t},\n\n\t\tequals: function(color) {\n\t\t\tvar col = Base.isPlainValue(color, true)\n\t\t\t\t\t? Color.read(arguments)\n\t\t\t\t\t: color;\n\t\t\treturn col === this || col && this._class === col._class\n\t\t\t\t\t&& this._type === col._type\n\t\t\t\t\t&& this.getAlpha() === col.getAlpha()\n\t\t\t\t\t&& Base.equals(this._components, col._components)\n\t\t\t\t\t|| false;\n\t\t},\n\n\t\ttoString: function() {\n\t\t\tvar properties = this._properties,\n\t\t\t\tparts = [],\n\t\t\t\tisGradient = this._type === 'gradient',\n\t\t\t\tf = Formatter.instance;\n\t\t\tfor (var i = 0, l = properties.length; i < l; i++) {\n\t\t\t\tvar value = this._components[i];\n\t\t\t\tif (value != null)\n\t\t\t\t\tparts.push(properties[i] + ': '\n\t\t\t\t\t\t\t+ (isGradient ? value : f.number(value)));\n\t\t\t}\n\t\t\tif (this._alpha != null)\n\t\t\t\tparts.push('alpha: ' + f.number(this._alpha));\n\t\t\treturn '{ ' + parts.join(', ') + ' }';\n\t\t},\n\n\t\ttoCSS: function(hex) {\n\t\t\tvar components = this._convert('rgb'),\n\t\t\t\talpha = hex || this._alpha == null ? 1 : this._alpha;\n\t\t\tfunction convert(val) {\n\t\t\t\treturn Math.round((val < 0 ? 0 : val > 1 ? 1 : val) * 255);\n\t\t\t}\n\t\t\tcomponents = [\n\t\t\t\tconvert(components[0]),\n\t\t\t\tconvert(components[1]),\n\t\t\t\tconvert(components[2])\n\t\t\t];\n\t\t\tif (alpha < 1)\n\t\t\t\tcomponents.push(alpha < 0 ? 0 : alpha);\n\t\t\treturn hex\n\t\t\t\t\t? '#' + ((1 << 24) + (components[0] << 16)\n\t\t\t\t\t\t+ (components[1] << 8)\n\t\t\t\t\t\t+ components[2]).toString(16).slice(1)\n\t\t\t\t\t: (components.length == 4 ? 'rgba(' : 'rgb(')\n\t\t\t\t\t\t+ components.join(',') + ')';\n\t\t},\n\n\t\ttoCanvasStyle: function(ctx, matrix) {\n\t\t\tif (this._canvasStyle)\n\t\t\t\treturn this._canvasStyle;\n\t\t\tif (this._type !== 'gradient')\n\t\t\t\treturn this._canvasStyle = this.toCSS();\n\t\t\tvar components = this._components,\n\t\t\t\tgradient = components[0],\n\t\t\t\tstops = gradient._stops,\n\t\t\t\torigin = components[1],\n\t\t\t\tdestination = components[2],\n\t\t\t\thighlight = components[3],\n\t\t\t\tinverse = matrix && matrix.inverted(),\n\t\t\t\tcanvasGradient;\n\t\t\tif (inverse) {\n\t\t\t\torigin = inverse._transformPoint(origin);\n\t\t\t\tdestination = inverse._transformPoint(destination);\n\t\t\t\tif (highlight)\n\t\t\t\t\thighlight = inverse._transformPoint(highlight);\n\t\t\t}\n\t\t\tif (gradient._radial) {\n\t\t\t\tvar radius = destination.getDistance(origin);\n\t\t\t\tif (highlight) {\n\t\t\t\t\tvar vector = highlight.subtract(origin);\n\t\t\t\t\tif (vector.getLength() > radius)\n\t\t\t\t\t\thighlight = origin.add(vector.normalize(radius - 0.1));\n\t\t\t\t}\n\t\t\t\tvar start = highlight || origin;\n\t\t\t\tcanvasGradient = ctx.createRadialGradient(start.x, start.y,\n\t\t\t\t\t\t0, origin.x, origin.y, radius);\n\t\t\t} else {\n\t\t\t\tcanvasGradient = ctx.createLinearGradient(origin.x, origin.y,\n\t\t\t\t\t\tdestination.x, destination.y);\n\t\t\t}\n\t\t\tfor (var i = 0, l = stops.length; i < l; i++) {\n\t\t\t\tvar stop = stops[i],\n\t\t\t\t\toffset = stop._offset;\n\t\t\t\tcanvasGradient.addColorStop(\n\t\t\t\t\t\toffset == null ? i / (l - 1) : offset,\n\t\t\t\t\t\tstop._color.toCanvasStyle());\n\t\t\t}\n\t\t\treturn this._canvasStyle = canvasGradient;\n\t\t},\n\n\t\ttransform: function(matrix) {\n\t\t\tif (this._type === 'gradient') {\n\t\t\t\tvar components = this._components;\n\t\t\t\tfor (var i = 1, l = components.length; i < l; i++) {\n\t\t\t\t\tvar point = components[i];\n\t\t\t\t\tmatrix._transformPoint(point, point, true);\n\t\t\t\t}\n\t\t\t\tthis._changed();\n\t\t\t}\n\t\t},\n\n\t\tstatics: {\n\t\t\t_types: types,\n\n\t\t\trandom: function() {\n\t\t\t\tvar random = Math.random;\n\t\t\t\treturn new Color(random(), random(), random());\n\t\t\t},\n\n\t\t\t_setOwner: function(color, owner, setter) {\n\t\t\t\tif (color) {\n\t\t\t\t\tif (color._owner && owner && color._owner !== owner) {\n\t\t\t\t\t\tcolor = color.clone();\n\t\t\t\t\t}\n\t\t\t\t\tif (!color._owner ^ !owner) {\n\t\t\t\t\t\tcolor._owner = owner || null;\n\t\t\t\t\t\tcolor._setter = setter || null;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\treturn color;\n\t\t\t}\n\t\t}\n\t});\n},\nnew function() {\n\tvar operators = {\n\t\tadd: function(a, b) {\n\t\t\treturn a + b;\n\t\t},\n\n\t\tsubtract: function(a, b) {\n\t\t\treturn a - b;\n\t\t},\n\n\t\tmultiply: function(a, b) {\n\t\t\treturn a * b;\n\t\t},\n\n\t\tdivide: function(a, b) {\n\t\t\treturn a / b;\n\t\t}\n\t};\n\n\treturn Base.each(operators, function(operator, name) {\n\t\tthis[name] = function(color) {\n\t\t\tcolor = Color.read(arguments);\n\t\t\tvar type = this._type,\n\t\t\t\tcomponents1 = this._components,\n\t\t\t\tcomponents2 = color._convert(type);\n\t\t\tfor (var i = 0, l = components1.length; i < l; i++)\n\t\t\t\tcomponents2[i] = operator(components1[i], components2[i]);\n\t\t\treturn new Color(type, components2,\n\t\t\t\t\tthis._alpha != null\n\t\t\t\t\t\t\t? operator(this._alpha, color.getAlpha())\n\t\t\t\t\t\t\t: null);\n\t\t};\n\t}, {\n\t});\n});\n\nvar Gradient = Base.extend({\n\t_class: 'Gradient',\n\n\tinitialize: function Gradient(stops, radial) {\n\t\tthis._id = UID.get();\n\t\tif (stops && Base.isPlainObject(stops)) {\n\t\t\tthis.set(stops);\n\t\t\tstops = radial = null;\n\t\t}\n\t\tif (this._stops == null) {\n\t\t\tthis.setStops(stops || ['white', 'black']);\n\t\t}\n\t\tif (this._radial == null) {\n\t\t\tthis.setRadial(typeof radial === 'string' && radial === 'radial'\n\t\t\t\t\t|| radial || false);\n\t\t}\n\t},\n\n\t_serialize: function(options, dictionary) {\n\t\treturn dictionary.add(this, function() {\n\t\t\treturn Base.serialize([this._stops, this._radial],\n\t\t\t\t\toptions, true, dictionary);\n\t\t});\n\t},\n\n\t_changed: function() {\n\t\tfor (var i = 0, l = this._owners && this._owners.length; i < l; i++) {\n\t\t\tthis._owners[i]._changed();\n\t\t}\n\t},\n\n\t_addOwner: function(color) {\n\t\tif (!this._owners)\n\t\t\tthis._owners = [];\n\t\tthis._owners.push(color);\n\t},\n\n\t_removeOwner: function(color) {\n\t\tvar index = this._owners ? this._owners.indexOf(color) : -1;\n\t\tif (index != -1) {\n\t\t\tthis._owners.splice(index, 1);\n\t\t\tif (!this._owners.length)\n\t\t\t\tthis._owners = undefined;\n\t\t}\n\t},\n\n\tclone: function() {\n\t\tvar stops = [];\n\t\tfor (var i = 0, l = this._stops.length; i < l; i++) {\n\t\t\tstops[i] = this._stops[i].clone();\n\t\t}\n\t\treturn new Gradient(stops, this._radial);\n\t},\n\n\tgetStops: function() {\n\t\treturn this._stops;\n\t},\n\n\tsetStops: function(stops) {\n\t\tif (stops.length < 2) {\n\t\t\tthrow new Error(\n\t\t\t\t\t'Gradient stop list needs to contain at least two stops.');\n\t\t}\n\t\tvar _stops = this._stops;\n\t\tif (_stops) {\n\t\t\tfor (var i = 0, l = _stops.length; i < l; i++)\n\t\t\t\t_stops[i]._owner = undefined;\n\t\t}\n\t\t_stops = this._stops = GradientStop.readList(stops, 0, { clone: true });\n\t\tfor (var i = 0, l = _stops.length; i < l; i++)\n\t\t\t_stops[i]._owner = this;\n\t\tthis._changed();\n\t},\n\n\tgetRadial: function() {\n\t\treturn this._radial;\n\t},\n\n\tsetRadial: function(radial) {\n\t\tthis._radial = radial;\n\t\tthis._changed();\n\t},\n\n\tequals: function(gradient) {\n\t\tif (gradient === this)\n\t\t\treturn true;\n\t\tif (gradient && this._class === gradient._class) {\n\t\t\tvar stops1 = this._stops,\n\t\t\t\tstops2 = gradient._stops,\n\t\t\t\tlength = stops1.length;\n\t\t\tif (length === stops2.length) {\n\t\t\t\tfor (var i = 0; i < length; i++) {\n\t\t\t\t\tif (!stops1[i].equals(stops2[i]))\n\t\t\t\t\t\treturn false;\n\t\t\t\t}\n\t\t\t\treturn true;\n\t\t\t}\n\t\t}\n\t\treturn false;\n\t}\n});\n\nvar GradientStop = Base.extend({\n\t_class: 'GradientStop',\n\n\tinitialize: function GradientStop(arg0, arg1) {\n\t\tvar color = arg0,\n\t\t\toffset = arg1;\n\t\tif (typeof arg0 === 'object' && arg1 === undefined) {\n\t\t\tif (Array.isArray(arg0) && typeof arg0[0] !== 'number') {\n\t\t\t\tcolor = arg0[0];\n\t\t\t\toffset = arg0[1];\n\t\t\t} else if ('color' in arg0 || 'offset' in arg0\n\t\t\t\t\t|| 'rampPoint' in arg0) {\n\t\t\t\tcolor = arg0.color;\n\t\t\t\toffset = arg0.offset || arg0.rampPoint || 0;\n\t\t\t}\n\t\t}\n\t\tthis.setColor(color);\n\t\tthis.setOffset(offset);\n\t},\n\n\tclone: function() {\n\t\treturn new GradientStop(this._color.clone(), this._offset);\n\t},\n\n\t_serialize: function(options, dictionary) {\n\t\tvar color = this._color,\n\t\t\toffset = this._offset;\n\t\treturn Base.serialize(offset == null ? [color] : [color, offset],\n\t\t\t\toptions, true, dictionary);\n\t},\n\n\t_changed: function() {\n\t\tif (this._owner)\n\t\t\tthis._owner._changed(129);\n\t},\n\n\tgetOffset: function() {\n\t\treturn this._offset;\n\t},\n\n\tsetOffset: function(offset) {\n\t\tthis._offset = offset;\n\t\tthis._changed();\n\t},\n\n\tgetRampPoint: '#getOffset',\n\tsetRampPoint: '#setOffset',\n\n\tgetColor: function() {\n\t\treturn this._color;\n\t},\n\n\tsetColor: function() {\n\t\tColor._setOwner(this._color, null);\n\t\tthis._color = Color._setOwner(Color.read(arguments, 0), this,\n\t\t\t\t'setColor');\n\t\tthis._changed();\n\t},\n\n\tequals: function(stop) {\n\t\treturn stop === this || stop && this._class === stop._class\n\t\t\t\t&& this._color.equals(stop._color)\n\t\t\t\t&& this._offset == stop._offset\n\t\t\t\t|| false;\n\t}\n});\n\nvar Style = Base.extend(new function() {\n\tvar itemDefaults = {\n\t\tfillColor: null,\n\t\tfillRule: 'nonzero',\n\t\tstrokeColor: null,\n\t\tstrokeWidth: 1,\n\t\tstrokeCap: 'butt',\n\t\tstrokeJoin: 'miter',\n\t\tstrokeScaling: true,\n\t\tmiterLimit: 10,\n\t\tdashOffset: 0,\n\t\tdashArray: [],\n\t\tshadowColor: null,\n\t\tshadowBlur: 0,\n\t\tshadowOffset: new Point(),\n\t\tselectedColor: null\n\t},\n\tgroupDefaults = Base.set({}, itemDefaults, {\n\t\tfontFamily: 'sans-serif',\n\t\tfontWeight: 'normal',\n\t\tfontSize: 12,\n\t\tleading: null,\n\t\tjustification: 'left'\n\t}),\n\ttextDefaults = Base.set({}, groupDefaults, {\n\t\tfillColor: new Color()\n\t}),\n\tflags = {\n\t\tstrokeWidth: 193,\n\t\tstrokeCap: 193,\n\t\tstrokeJoin: 193,\n\t\tstrokeScaling: 201,\n\t\tmiterLimit: 193,\n\t\tfontFamily: 9,\n\t\tfontWeight: 9,\n\t\tfontSize: 9,\n\t\tfont: 9,\n\t\tleading: 9,\n\t\tjustification: 9\n\t},\n\titem = {\n\t\tbeans: true\n\t},\n\tfields = {\n\t\t_class: 'Style',\n\t\tbeans: true,\n\n\t\tinitialize: function Style(style, _owner, _project) {\n\t\t\tthis._values = {};\n\t\t\tthis._owner = _owner;\n\t\t\tthis._project = _owner && _owner._project || _project\n\t\t\t\t\t|| paper.project;\n\t\t\tthis._defaults = !_owner || _owner instanceof Group ? groupDefaults\n\t\t\t\t\t: _owner instanceof TextItem ? textDefaults\n\t\t\t\t\t: itemDefaults;\n\t\t\tif (style)\n\t\t\t\tthis.set(style);\n\t\t}\n\t};\n\n\tBase.each(groupDefaults, function(value, key) {\n\t\tvar isColor = /Color$/.test(key),\n\t\t\tisPoint = key === 'shadowOffset',\n\t\t\tpart = Base.capitalize(key),\n\t\t\tflag = flags[key],\n\t\t\tset = 'set' + part,\n\t\t\tget = 'get' + part;\n\n\t\tfields[set] = function(value) {\n\t\t\tvar owner = this._owner,\n\t\t\t\tchildren = owner && owner._children,\n\t\t\t\tapplyToChildren = children && children.length > 0\n\t\t\t\t\t&& !(owner instanceof CompoundPath);\n\t\t\tif (applyToChildren) {\n\t\t\t\tfor (var i = 0, l = children.length; i < l; i++)\n\t\t\t\t\tchildren[i]._style[set](value);\n\t\t\t}\n\t\t\tif ((key === 'selectedColor' || !applyToChildren)\n\t\t\t\t\t&& key in this._defaults) {\n\t\t\t\tvar old = this._values[key];\n\t\t\t\tif (old !== value) {\n\t\t\t\t\tif (isColor) {\n\t\t\t\t\t\tif (old) {\n\t\t\t\t\t\t\tColor._setOwner(old, null);\n\t\t\t\t\t\t\told._canvasStyle = null;\n\t\t\t\t\t\t}\n\t\t\t\t\t\tif (value && value.constructor === Color) {\n\t\t\t\t\t\t\tvalue = Color._setOwner(value, owner,\n\t\t\t\t\t\t\t\t\tapplyToChildren && set);\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tthis._values[key] = value;\n\t\t\t\t\tif (owner)\n\t\t\t\t\t\towner._changed(flag || 129);\n\t\t\t\t}\n\t\t\t}\n\t\t};\n\n\t\tfields[get] = function(_dontMerge) {\n\t\t\tvar owner = this._owner,\n\t\t\t\tchildren = owner && owner._children,\n\t\t\t\tapplyToChildren = children && children.length > 0\n\t\t\t\t\t&& !(owner instanceof CompoundPath),\n\t\t\t\tvalue;\n\t\t\tif (applyToChildren && !_dontMerge) {\n\t\t\t\tfor (var i = 0, l = children.length; i < l; i++) {\n\t\t\t\t\tvar childValue = children[i]._style[get]();\n\t\t\t\t\tif (!i) {\n\t\t\t\t\t\tvalue = childValue;\n\t\t\t\t\t} else if (!Base.equals(value, childValue)) {\n\t\t\t\t\t\treturn undefined;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t} else if (key in this._defaults) {\n\t\t\t\tvar value = this._values[key];\n\t\t\t\tif (value === undefined) {\n\t\t\t\t\tvalue = this._defaults[key];\n\t\t\t\t\tif (value && value.clone) {\n\t\t\t\t\t\tvalue = value.clone();\n\t\t\t\t\t}\n\t\t\t\t} else {\n\t\t\t\t\tvar ctor = isColor ? Color : isPoint ? Point : null;\n\t\t\t\t\tif (ctor && !(value && value.constructor === ctor)) {\n\t\t\t\t\t\tthis._values[key] = value = ctor.read([value], 0,\n\t\t\t\t\t\t\t\t{ readNull: true, clone: true });\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t\tif (value && isColor) {\n\t\t\t\tvalue = Color._setOwner(value, owner, applyToChildren && set);\n\t\t\t}\n\t\t\treturn value;\n\t\t};\n\n\t\titem[get] = function(_dontMerge) {\n\t\t\treturn this._style[get](_dontMerge);\n\t\t};\n\n\t\titem[set] = function(value) {\n\t\t\tthis._style[set](value);\n\t\t};\n\t});\n\n\tBase.each({\n\t\tFont: 'FontFamily',\n\t\tWindingRule: 'FillRule'\n\t}, function(value, key) {\n\t\tvar get = 'get' + key,\n\t\t\tset = 'set' + key;\n\t\tfields[get] = item[get] = '#get' + value;\n\t\tfields[set] = item[set] = '#set' + value;\n\t});\n\n\tItem.inject(item);\n\treturn fields;\n}, {\n\tset: function(style) {\n\t\tvar isStyle = style instanceof Style,\n\t\t\tvalues = isStyle ? style._values : style;\n\t\tif (values) {\n\t\t\tfor (var key in values) {\n\t\t\t\tif (key in this._defaults) {\n\t\t\t\t\tvar value = values[key];\n\t\t\t\t\tthis[key] = value && isStyle && value.clone\n\t\t\t\t\t\t\t? value.clone() : value;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t},\n\n\tequals: function(style) {\n\t\tfunction compare(style1, style2, secondary) {\n\t\t\tvar values1 = style1._values,\n\t\t\t\tvalues2 = style2._values,\n\t\t\t\tdefaults2 = style2._defaults;\n\t\t\tfor (var key in values1) {\n\t\t\t\tvar value1 = values1[key],\n\t\t\t\t\tvalue2 = values2[key];\n\t\t\t\tif (!(secondary && key in values2) && !Base.equals(value1,\n\t\t\t\t\t\tvalue2 === undefined ? defaults2[key] : value2))\n\t\t\t\t\treturn false;\n\t\t\t}\n\t\t\treturn true;\n\t\t}\n\n\t\treturn style === this || style && this._class === style._class\n\t\t\t\t&& compare(this, style)\n\t\t\t\t&& compare(style, this, true)\n\t\t\t\t|| false;\n\t},\n\n\t_dispose: function() {\n\t\tvar color;\n\t\tcolor = this.getFillColor();\n\t\tif (color) color._canvasStyle = null;\n\t\tcolor = this.getStrokeColor();\n\t\tif (color) color._canvasStyle = null;\n\t\tcolor = this.getShadowColor();\n\t\tif (color) color._canvasStyle = null;\n\t},\n\n\thasFill: function() {\n\t\tvar color = this.getFillColor();\n\t\treturn !!color && color.alpha > 0;\n\t},\n\n\thasStroke: function() {\n\t\tvar color = this.getStrokeColor();\n\t\treturn !!color && color.alpha > 0 && this.getStrokeWidth() > 0;\n\t},\n\n\thasShadow: function() {\n\t\tvar color = this.getShadowColor();\n\t\treturn !!color && color.alpha > 0 && (this.getShadowBlur() > 0\n\t\t\t\t|| !this.getShadowOffset().isZero());\n\t},\n\n\tgetView: function() {\n\t\treturn this._project._view;\n\t},\n\n\tgetFontStyle: function() {\n\t\tvar fontSize = this.getFontSize();\n\t\treturn this.getFontWeight()\n\t\t\t\t+ ' ' + fontSize + (/[a-z]/i.test(fontSize + '') ? ' ' : 'px ')\n\t\t\t\t+ this.getFontFamily();\n\t},\n\n\tgetFont: '#getFontFamily',\n\tsetFont: '#setFontFamily',\n\n\tgetLeading: function getLeading() {\n\t\tvar leading = getLeading.base.call(this),\n\t\t\tfontSize = this.getFontSize();\n\t\tif (/pt|em|%|px/.test(fontSize))\n\t\t\tfontSize = this.getView().getPixelSize(fontSize);\n\t\treturn leading != null ? leading : fontSize * 1.2;\n\t}\n\n});\n\nvar DomElement = new function() {\n\tfunction handlePrefix(el, name, set, value) {\n\t\tvar prefixes = ['', 'webkit', 'moz', 'Moz', 'ms', 'o'],\n\t\t\tsuffix = name[0].toUpperCase() + name.substring(1);\n\t\tfor (var i = 0; i < 6; i++) {\n\t\t\tvar prefix = prefixes[i],\n\t\t\t\tkey = prefix ? prefix + suffix : name;\n\t\t\tif (key in el) {\n\t\t\t\tif (set) {\n\t\t\t\t\tel[key] = value;\n\t\t\t\t} else {\n\t\t\t\t\treturn el[key];\n\t\t\t\t}\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\t}\n\n\treturn {\n\t\tgetStyles: function(el) {\n\t\t\tvar doc = el && el.nodeType !== 9 ? el.ownerDocument : el,\n\t\t\t\tview = doc && doc.defaultView;\n\t\t\treturn view && view.getComputedStyle(el, '');\n\t\t},\n\n\t\tgetBounds: function(el, viewport) {\n\t\t\tvar doc = el.ownerDocument,\n\t\t\t\tbody = doc.body,\n\t\t\t\thtml = doc.documentElement,\n\t\t\t\trect;\n\t\t\ttry {\n\t\t\t\trect = el.getBoundingClientRect();\n\t\t\t} catch (e) {\n\t\t\t\trect = { left: 0, top: 0, width: 0, height: 0 };\n\t\t\t}\n\t\t\tvar x = rect.left - (html.clientLeft || body.clientLeft || 0),\n\t\t\t\ty = rect.top - (html.clientTop || body.clientTop || 0);\n\t\t\tif (!viewport) {\n\t\t\t\tvar view = doc.defaultView;\n\t\t\t\tx += view.pageXOffset || html.scrollLeft || body.scrollLeft;\n\t\t\t\ty += view.pageYOffset || html.scrollTop || body.scrollTop;\n\t\t\t}\n\t\t\treturn new Rectangle(x, y, rect.width, rect.height);\n\t\t},\n\n\t\tgetViewportBounds: function(el) {\n\t\t\tvar doc = el.ownerDocument,\n\t\t\t\tview = doc.defaultView,\n\t\t\t\thtml = doc.documentElement;\n\t\t\treturn new Rectangle(0, 0,\n\t\t\t\tview.innerWidth || html.clientWidth,\n\t\t\t\tview.innerHeight || html.clientHeight\n\t\t\t);\n\t\t},\n\n\t\tgetOffset: function(el, viewport) {\n\t\t\treturn DomElement.getBounds(el, viewport).getPoint();\n\t\t},\n\n\t\tgetSize: function(el) {\n\t\t\treturn DomElement.getBounds(el, true).getSize();\n\t\t},\n\n\t\tisInvisible: function(el) {\n\t\t\treturn DomElement.getSize(el).equals(new Size(0, 0));\n\t\t},\n\n\t\tisInView: function(el) {\n\t\t\treturn !DomElement.isInvisible(el)\n\t\t\t\t\t&& DomElement.getViewportBounds(el).intersects(\n\t\t\t\t\t\tDomElement.getBounds(el, true));\n\t\t},\n\n\t\tisInserted: function(el) {\n\t\t\treturn document.body.contains(el);\n\t\t},\n\n\t\tgetPrefixed: function(el, name) {\n\t\t\treturn el && handlePrefix(el, name);\n\t\t},\n\n\t\tsetPrefixed: function(el, name, value) {\n\t\t\tif (typeof name === 'object') {\n\t\t\t\tfor (var key in name)\n\t\t\t\t\thandlePrefix(el, key, true, name[key]);\n\t\t\t} else {\n\t\t\t\thandlePrefix(el, name, true, value);\n\t\t\t}\n\t\t}\n\t};\n};\n\nvar DomEvent = {\n\tadd: function(el, events) {\n\t\tif (el) {\n\t\t\tfor (var type in events) {\n\t\t\t\tvar func = events[type],\n\t\t\t\t\tparts = type.split(/[\\s,]+/g);\n\t\t\t\tfor (var i = 0, l = parts.length; i < l; i++) {\n\t\t\t\t\tvar name = parts[i];\n\t\t\t\t\tvar options = (\n\t\t\t\t\t\tel === document\n\t\t\t\t\t\t&& (name === 'touchstart' || name === 'touchmove')\n\t\t\t\t\t) ? { passive: false } : false;\n\t\t\t\t\tel.addEventListener(name, func, options);\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t},\n\n\tremove: function(el, events) {\n\t\tif (el) {\n\t\t\tfor (var type in events) {\n\t\t\t\tvar func = events[type],\n\t\t\t\t\tparts = type.split(/[\\s,]+/g);\n\t\t\t\tfor (var i = 0, l = parts.length; i < l; i++)\n\t\t\t\t\tel.removeEventListener(parts[i], func, false);\n\t\t\t}\n\t\t}\n\t},\n\n\tgetPoint: function(event) {\n\t\tvar pos = event.targetTouches\n\t\t\t\t? event.targetTouches.length\n\t\t\t\t\t? event.targetTouches[0]\n\t\t\t\t\t: event.changedTouches[0]\n\t\t\t\t: event;\n\t\treturn new Point(\n\t\t\tpos.pageX || pos.clientX + document.documentElement.scrollLeft,\n\t\t\tpos.pageY || pos.clientY + document.documentElement.scrollTop\n\t\t);\n\t},\n\n\tgetTarget: function(event) {\n\t\treturn event.target || event.srcElement;\n\t},\n\n\tgetRelatedTarget: function(event) {\n\t\treturn event.relatedTarget || event.toElement;\n\t},\n\n\tgetOffset: function(event, target) {\n\t\treturn DomEvent.getPoint(event).subtract(DomElement.getOffset(\n\t\t\t\ttarget || DomEvent.getTarget(event)));\n\t}\n};\n\nDomEvent.requestAnimationFrame = new function() {\n\tvar nativeRequest = DomElement.getPrefixed(window, 'requestAnimationFrame'),\n\t\trequested = false,\n\t\tcallbacks = [],\n\t\ttimer;\n\n\tfunction handleCallbacks() {\n\t\tvar functions = callbacks;\n\t\tcallbacks = [];\n\t\tfor (var i = 0, l = functions.length; i < l; i++)\n\t\t\tfunctions[i]();\n\t\trequested = nativeRequest && callbacks.length;\n\t\tif (requested)\n\t\t\tnativeRequest(handleCallbacks);\n\t}\n\n\treturn function(callback) {\n\t\tcallbacks.push(callback);\n\t\tif (nativeRequest) {\n\t\t\tif (!requested) {\n\t\t\t\tnativeRequest(handleCallbacks);\n\t\t\t\trequested = true;\n\t\t\t}\n\t\t} else if (!timer) {\n\t\t\ttimer = setInterval(handleCallbacks, 1000 / 60);\n\t\t}\n\t};\n};\n\nvar View = Base.extend(Emitter, {\n\t_class: 'View',\n\n\tinitialize: function View(project, element) {\n\n\t\tfunction getSize(name) {\n\t\t\treturn element[name] || parseInt(element.getAttribute(name), 10);\n\t\t}\n\n\t\tfunction getCanvasSize() {\n\t\t\tvar size = DomElement.getSize(element);\n\t\t\treturn size.isNaN() || size.isZero()\n\t\t\t\t\t? new Size(getSize('width'), getSize('height'))\n\t\t\t\t\t: size;\n\t\t}\n\n\t\tvar size;\n\t\tif (window && element) {\n\t\t\tthis._id = element.getAttribute('id');\n\t\t\tif (this._id == null)\n\t\t\t\telement.setAttribute('id', this._id = 'paper-view-' + View._id++);\n\t\t\tDomEvent.add(element, this._viewEvents);\n\t\t\tvar none = 'none';\n\t\t\tDomElement.setPrefixed(element.style, {\n\t\t\t\tuserDrag: none,\n\t\t\t\tuserSelect: none,\n\t\t\t\ttouchCallout: none,\n\t\t\t\tcontentZooming: none,\n\t\t\t\ttapHighlightColor: 'rgba(0,0,0,0)'\n\t\t\t});\n\n\t\t\tif (PaperScope.hasAttribute(element, 'resize')) {\n\t\t\t\tvar that = this;\n\t\t\t\tDomEvent.add(window, this._windowEvents = {\n\t\t\t\t\tresize: function() {\n\t\t\t\t\t\tthat.setViewSize(getCanvasSize());\n\t\t\t\t\t}\n\t\t\t\t});\n\t\t\t}\n\n\t\t\tsize = getCanvasSize();\n\n\t\t\tif (PaperScope.hasAttribute(element, 'stats')\n\t\t\t\t\t&& typeof Stats !== 'undefined') {\n\t\t\t\tthis._stats = new Stats();\n\t\t\t\tvar stats = this._stats.domElement,\n\t\t\t\t\tstyle = stats.style,\n\t\t\t\t\toffset = DomElement.getOffset(element);\n\t\t\t\tstyle.position = 'absolute';\n\t\t\t\tstyle.left = offset.x + 'px';\n\t\t\t\tstyle.top = offset.y + 'px';\n\t\t\t\tdocument.body.appendChild(stats);\n\t\t\t}\n\t\t} else {\n\t\t\tsize = new Size(element);\n\t\t\telement = null;\n\t\t}\n\t\tthis._project = project;\n\t\tthis._scope = project._scope;\n\t\tthis._element = element;\n\t\tif (!this._pixelRatio)\n\t\t\tthis._pixelRatio = window && window.devicePixelRatio || 1;\n\t\tthis._setElementSize(size.width, size.height);\n\t\tthis._viewSize = size;\n\t\tView._views.push(this);\n\t\tView._viewsById[this._id] = this;\n\t\t(this._matrix = new Matrix())._owner = this;\n\t\tif (!View._focused)\n\t\t\tView._focused = this;\n\t\tthis._frameItems = {};\n\t\tthis._frameItemCount = 0;\n\t\tthis._itemEvents = { native: {}, virtual: {} };\n\t\tthis._autoUpdate = !paper.agent.node;\n\t\tthis._needsUpdate = false;\n\t},\n\n\tremove: function() {\n\t\tif (!this._project)\n\t\t\treturn false;\n\t\tif (View._focused === this)\n\t\t\tView._focused = null;\n\t\tView._views.splice(View._views.indexOf(this), 1);\n\t\tdelete View._viewsById[this._id];\n\t\tvar project = this._project;\n\t\tif (project._view === this)\n\t\t\tproject._view = null;\n\t\tDomEvent.remove(this._element, this._viewEvents);\n\t\tDomEvent.remove(window, this._windowEvents);\n\t\tthis._element = this._project = null;\n\t\tthis.off('frame');\n\t\tthis._animate = false;\n\t\tthis._frameItems = {};\n\t\treturn true;\n\t},\n\n\t_events: Base.each(\n\t\tItem._itemHandlers.concat(['onResize', 'onKeyDown', 'onKeyUp']),\n\t\tfunction(name) {\n\t\t\tthis[name] = {};\n\t\t}, {\n\t\t\tonFrame: {\n\t\t\t\tinstall: function() {\n\t\t\t\t\tthis.play();\n\t\t\t\t},\n\n\t\t\t\tuninstall: function() {\n\t\t\t\t\tthis.pause();\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t),\n\n\t_animate: false,\n\t_time: 0,\n\t_count: 0,\n\n\tgetAutoUpdate: function() {\n\t\treturn this._autoUpdate;\n\t},\n\n\tsetAutoUpdate: function(autoUpdate) {\n\t\tthis._autoUpdate = autoUpdate;\n\t\tif (autoUpdate)\n\t\t\tthis.requestUpdate();\n\t},\n\n\tupdate: function() {\n\t},\n\n\tdraw: function() {\n\t\tthis.update();\n\t},\n\n\trequestUpdate: function() {\n\t\tif (!this._requested) {\n\t\t\tvar that = this;\n\t\t\tDomEvent.requestAnimationFrame(function() {\n\t\t\t\tthat._requested = false;\n\t\t\t\tif (that._animate) {\n\t\t\t\t\tthat.requestUpdate();\n\t\t\t\t\tvar element = that._element;\n\t\t\t\t\tif ((!DomElement.getPrefixed(document, 'hidden')\n\t\t\t\t\t\t\t|| PaperScope.getAttribute(element, 'keepalive')\n\t\t\t\t\t\t\t\t=== 'true') && DomElement.isInView(element)) {\n\t\t\t\t\t\tthat._handleFrame();\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tif (that._autoUpdate)\n\t\t\t\t\tthat.update();\n\t\t\t});\n\t\t\tthis._requested = true;\n\t\t}\n\t},\n\n\tplay: function() {\n\t\tthis._animate = true;\n\t\tthis.requestUpdate();\n\t},\n\n\tpause: function() {\n\t\tthis._animate = false;\n\t},\n\n\t_handleFrame: function() {\n\t\tpaper = this._scope;\n\t\tvar now = Date.now() / 1000,\n\t\t\tdelta = this._last ? now - this._last : 0;\n\t\tthis._last = now;\n\t\tthis.emit('frame', new Base({\n\t\t\tdelta: delta,\n\t\t\ttime: this._time += delta,\n\t\t\tcount: this._count++\n\t\t}));\n\t\tif (this._stats)\n\t\t\tthis._stats.update();\n\t},\n\n\t_animateItem: function(item, animate) {\n\t\tvar items = this._frameItems;\n\t\tif (animate) {\n\t\t\titems[item._id] = {\n\t\t\t\titem: item,\n\t\t\t\ttime: 0,\n\t\t\t\tcount: 0\n\t\t\t};\n\t\t\tif (++this._frameItemCount === 1)\n\t\t\t\tthis.on('frame', this._handleFrameItems);\n\t\t} else {\n\t\t\tdelete items[item._id];\n\t\t\tif (--this._frameItemCount === 0) {\n\t\t\t\tthis.off('frame', this._handleFrameItems);\n\t\t\t}\n\t\t}\n\t},\n\n\t_handleFrameItems: function(event) {\n\t\tfor (var i in this._frameItems) {\n\t\t\tvar entry = this._frameItems[i];\n\t\t\tentry.item.emit('frame', new Base(event, {\n\t\t\t\ttime: entry.time += event.delta,\n\t\t\t\tcount: entry.count++\n\t\t\t}));\n\t\t}\n\t},\n\n\t_changed: function() {\n\t\tthis._project._changed(4097);\n\t\tthis._bounds = this._decomposed = undefined;\n\t},\n\n\tgetElement: function() {\n\t\treturn this._element;\n\t},\n\n\tgetPixelRatio: function() {\n\t\treturn this._pixelRatio;\n\t},\n\n\tgetResolution: function() {\n\t\treturn this._pixelRatio * 72;\n\t},\n\n\tgetViewSize: function() {\n\t\tvar size = this._viewSize;\n\t\treturn new LinkedSize(size.width, size.height, this, 'setViewSize');\n\t},\n\n\tsetViewSize: function() {\n\t\tvar size = Size.read(arguments),\n\t\t\tdelta = size.subtract(this._viewSize);\n\t\tif (delta.isZero())\n\t\t\treturn;\n\t\tthis._setElementSize(size.width, size.height);\n\t\tthis._viewSize.set(size);\n\t\tthis._changed();\n\t\tthis.emit('resize', { size: size, delta: delta });\n\t\tif (this._autoUpdate) {\n\t\t\tthis.update();\n\t\t}\n\t},\n\n\t_setElementSize: function(width, height) {\n\t\tvar element = this._element;\n\t\tif (element) {\n\t\t\tif (element.width !== width)\n\t\t\t\telement.width = width;\n\t\t\tif (element.height !== height)\n\t\t\t\telement.height = height;\n\t\t}\n\t},\n\n\tgetBounds: function() {\n\t\tif (!this._bounds)\n\t\t\tthis._bounds = this._matrix.inverted()._transformBounds(\n\t\t\t\t\tnew Rectangle(new Point(), this._viewSize));\n\t\treturn this._bounds;\n\t},\n\n\tgetSize: function() {\n\t\treturn this.getBounds().getSize();\n\t},\n\n\tisVisible: function() {\n\t\treturn DomElement.isInView(this._element);\n\t},\n\n\tisInserted: function() {\n\t\treturn DomElement.isInserted(this._element);\n\t},\n\n\tgetPixelSize: function(size) {\n\t\tvar element = this._element,\n\t\t\tpixels;\n\t\tif (element) {\n\t\t\tvar parent = element.parentNode,\n\t\t\t\ttemp = document.createElement('div');\n\t\t\ttemp.style.fontSize = size;\n\t\t\tparent.appendChild(temp);\n\t\t\tpixels = parseFloat(DomElement.getStyles(temp).fontSize);\n\t\t\tparent.removeChild(temp);\n\t\t} else {\n\t\t\tpixels = parseFloat(pixels);\n\t\t}\n\t\treturn pixels;\n\t},\n\n\tgetTextWidth: function(font, lines) {\n\t\treturn 0;\n\t}\n}, Base.each(['rotate', 'scale', 'shear', 'skew'], function(key) {\n\tvar rotate = key === 'rotate';\n\tthis[key] = function() {\n\t\tvar args = arguments,\n\t\t\tvalue = (rotate ? Base : Point).read(args),\n\t\t\tcenter = Point.read(args, 0, { readNull: true });\n\t\treturn this.transform(new Matrix()[key](value,\n\t\t\t\tcenter || this.getCenter(true)));\n\t};\n}, {\n\t_decompose: function() {\n\t\treturn this._decomposed || (this._decomposed = this._matrix.decompose());\n\t},\n\n\ttranslate: function() {\n\t\tvar mx = new Matrix();\n\t\treturn this.transform(mx.translate.apply(mx, arguments));\n\t},\n\n\tgetCenter: function() {\n\t\treturn this.getBounds().getCenter();\n\t},\n\n\tsetCenter: function() {\n\t\tvar center = Point.read(arguments);\n\t\tthis.translate(this.getCenter().subtract(center));\n\t},\n\n\tgetZoom: function() {\n\t\tvar scaling = this._decompose().scaling;\n\t\treturn (scaling.x + scaling.y) / 2;\n\t},\n\n\tsetZoom: function(zoom) {\n\t\tthis.transform(new Matrix().scale(zoom / this.getZoom(),\n\t\t\tthis.getCenter()));\n\t},\n\n\tgetRotation: function() {\n\t\treturn this._decompose().rotation;\n\t},\n\n\tsetRotation: function(rotation) {\n\t\tvar current = this.getRotation();\n\t\tif (current != null && rotation != null) {\n\t\t\tthis.rotate(rotation - current);\n\t\t}\n\t},\n\n\tgetScaling: function() {\n\t\tvar scaling = this._decompose().scaling;\n\t\treturn new LinkedPoint(scaling.x, scaling.y, this, 'setScaling');\n\t},\n\n\tsetScaling: function() {\n\t\tvar current = this.getScaling(),\n\t\t\tscaling = Point.read(arguments, 0, { clone: true, readNull: true });\n\t\tif (current && scaling) {\n\t\t\tthis.scale(scaling.x / current.x, scaling.y / current.y);\n\t\t}\n\t},\n\n\tgetMatrix: function() {\n\t\treturn this._matrix;\n\t},\n\n\tsetMatrix: function() {\n\t\tvar matrix = this._matrix;\n\t\tmatrix.set.apply(matrix, arguments);\n\t},\n\n\ttransform: function(matrix) {\n\t\tthis._matrix.append(matrix);\n\t},\n\n\tscrollBy: function() {\n\t\tthis.translate(Point.read(arguments).negate());\n\t}\n}), {\n\n\tprojectToView: function() {\n\t\treturn this._matrix._transformPoint(Point.read(arguments));\n\t},\n\n\tviewToProject: function() {\n\t\treturn this._matrix._inverseTransform(Point.read(arguments));\n\t},\n\n\tgetEventPoint: function(event) {\n\t\treturn this.viewToProject(DomEvent.getOffset(event, this._element));\n\t},\n\n}, {\n\tstatics: {\n\t\t_views: [],\n\t\t_viewsById: {},\n\t\t_id: 0,\n\n\t\tcreate: function(project, element) {\n\t\t\tif (document && typeof element === 'string')\n\t\t\t\telement = document.getElementById(element);\n\t\t\tvar ctor = window ? CanvasView : View;\n\t\t\treturn new ctor(project, element);\n\t\t}\n\t}\n},\nnew function() {\n\tif (!window)\n\t\treturn;\n\tvar prevFocus,\n\t\ttempFocus,\n\t\tdragging = false,\n\t\tmouseDown = false;\n\n\tfunction getView(event) {\n\t\tvar target = DomEvent.getTarget(event);\n\t\treturn target.getAttribute && View._viewsById[\n\t\t\t\ttarget.getAttribute('id')];\n\t}\n\n\tfunction updateFocus() {\n\t\tvar view = View._focused;\n\t\tif (!view || !view.isVisible()) {\n\t\t\tfor (var i = 0, l = View._views.length; i < l; i++) {\n\t\t\t\tif ((view = View._views[i]).isVisible()) {\n\t\t\t\t\tView._focused = tempFocus = view;\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\tfunction handleMouseMove(view, event, point) {\n\t\tview._handleMouseEvent('mousemove', event, point);\n\t}\n\n\tvar navigator = window.navigator,\n\t\tmousedown, mousemove, mouseup;\n\tif (navigator.pointerEnabled || navigator.msPointerEnabled) {\n\t\tmousedown = 'pointerdown MSPointerDown';\n\t\tmousemove = 'pointermove MSPointerMove';\n\t\tmouseup = 'pointerup pointercancel MSPointerUp MSPointerCancel';\n\t} else {\n\t\tmousedown = 'touchstart';\n\t\tmousemove = 'touchmove';\n\t\tmouseup = 'touchend touchcancel';\n\t\tif (!('ontouchstart' in window && navigator.userAgent.match(\n\t\t\t\t/mobile|tablet|ip(ad|hone|od)|android|silk/i))) {\n\t\t\tmousedown += ' mousedown';\n\t\t\tmousemove += ' mousemove';\n\t\t\tmouseup += ' mouseup';\n\t\t}\n\t}\n\n\tvar viewEvents = {},\n\t\tdocEvents = {\n\t\t\tmouseout: function(event) {\n\t\t\t\tvar view = View._focused,\n\t\t\t\t\ttarget = DomEvent.getRelatedTarget(event);\n\t\t\t\tif (view && (!target || target.nodeName === 'HTML')) {\n\t\t\t\t\tvar offset = DomEvent.getOffset(event, view._element),\n\t\t\t\t\t\tx = offset.x,\n\t\t\t\t\t\tabs = Math.abs,\n\t\t\t\t\t\tax = abs(x),\n\t\t\t\t\t\tmax = 1 << 25,\n\t\t\t\t\t\tdiff = ax - max;\n\t\t\t\t\toffset.x = abs(diff) < ax ? diff * (x < 0 ? -1 : 1) : x;\n\t\t\t\t\thandleMouseMove(view, event, view.viewToProject(offset));\n\t\t\t\t}\n\t\t\t},\n\n\t\t\tscroll: updateFocus\n\t\t};\n\n\tviewEvents[mousedown] = function(event) {\n\t\tvar view = View._focused = getView(event);\n\t\tif (!dragging) {\n\t\t\tdragging = true;\n\t\t\tview._handleMouseEvent('mousedown', event);\n\t\t}\n\t};\n\n\tdocEvents[mousemove] = function(event) {\n\t\tvar view = View._focused;\n\t\tif (!mouseDown) {\n\t\t\tvar target = getView(event);\n\t\t\tif (target) {\n\t\t\t\tif (view !== target) {\n\t\t\t\t\tif (view)\n\t\t\t\t\t\thandleMouseMove(view, event);\n\t\t\t\t\tif (!prevFocus)\n\t\t\t\t\t\tprevFocus = view;\n\t\t\t\t\tview = View._focused = tempFocus = target;\n\t\t\t\t}\n\t\t\t} else if (tempFocus && tempFocus === view) {\n\t\t\t\tif (prevFocus && !prevFocus.isInserted())\n\t\t\t\t\tprevFocus = null;\n\t\t\t\tview = View._focused = prevFocus;\n\t\t\t\tprevFocus = null;\n\t\t\t\tupdateFocus();\n\t\t\t}\n\t\t}\n\t\tif (view)\n\t\t\thandleMouseMove(view, event);\n\t};\n\n\tdocEvents[mousedown] = function() {\n\t\tmouseDown = true;\n\t};\n\n\tdocEvents[mouseup] = function(event) {\n\t\tvar view = View._focused;\n\t\tif (view && dragging)\n\t\t\tview._handleMouseEvent('mouseup', event);\n\t\tmouseDown = dragging = false;\n\t};\n\n\tDomEvent.add(document, docEvents);\n\n\tDomEvent.add(window, {\n\t\tload: updateFocus\n\t});\n\n\tvar called = false,\n\t\tprevented = false,\n\t\tfallbacks = {\n\t\t\tdoubleclick: 'click',\n\t\t\tmousedrag: 'mousemove'\n\t\t},\n\t\twasInView = false,\n\t\toverView,\n\t\tdownPoint,\n\t\tlastPoint,\n\t\tdownItem,\n\t\toverItem,\n\t\tdragItem,\n\t\tclickItem,\n\t\tclickTime,\n\t\tdblClick;\n\n\tfunction emitMouseEvent(obj, target, type, event, point, prevPoint,\n\t\t\tstopItem) {\n\t\tvar stopped = false,\n\t\t\tmouseEvent;\n\n\t\tfunction emit(obj, type) {\n\t\t\tif (obj.responds(type)) {\n\t\t\t\tif (!mouseEvent) {\n\t\t\t\t\tmouseEvent = new MouseEvent(type, event, point,\n\t\t\t\t\t\t\ttarget || obj,\n\t\t\t\t\t\t\tprevPoint ? point.subtract(prevPoint) : null);\n\t\t\t\t}\n\t\t\t\tif (obj.emit(type, mouseEvent)) {\n\t\t\t\t\tcalled = true;\n\t\t\t\t\tif (mouseEvent.prevented)\n\t\t\t\t\t\tprevented = true;\n\t\t\t\t\tif (mouseEvent.stopped)\n\t\t\t\t\t\treturn stopped = true;\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tvar fallback = fallbacks[type];\n\t\t\t\tif (fallback)\n\t\t\t\t\treturn emit(obj, fallback);\n\t\t\t}\n\t\t}\n\n\t\twhile (obj && obj !== stopItem) {\n\t\t\tif (emit(obj, type))\n\t\t\t\tbreak;\n\t\t\tobj = obj._parent;\n\t\t}\n\t\treturn stopped;\n\t}\n\n\tfunction emitMouseEvents(view, hitItem, type, event, point, prevPoint) {\n\t\tview._project.removeOn(type);\n\t\tprevented = called = false;\n\t\treturn (dragItem && emitMouseEvent(dragItem, null, type, event,\n\t\t\t\t\tpoint, prevPoint)\n\t\t\t|| hitItem && hitItem !== dragItem\n\t\t\t\t&& !hitItem.isDescendant(dragItem)\n\t\t\t\t&& emitMouseEvent(hitItem, null, type === 'mousedrag' ?\n\t\t\t\t\t'mousemove' : type, event, point, prevPoint, dragItem)\n\t\t\t|| emitMouseEvent(view, dragItem || hitItem || view, type, event,\n\t\t\t\t\tpoint, prevPoint));\n\t}\n\n\tvar itemEventsMap = {\n\t\tmousedown: {\n\t\t\tmousedown: 1,\n\t\t\tmousedrag: 1,\n\t\t\tclick: 1,\n\t\t\tdoubleclick: 1\n\t\t},\n\t\tmouseup: {\n\t\t\tmouseup: 1,\n\t\t\tmousedrag: 1,\n\t\t\tclick: 1,\n\t\t\tdoubleclick: 1\n\t\t},\n\t\tmousemove: {\n\t\t\tmousedrag: 1,\n\t\t\tmousemove: 1,\n\t\t\tmouseenter: 1,\n\t\t\tmouseleave: 1\n\t\t}\n\t};\n\n\treturn {\n\t\t_viewEvents: viewEvents,\n\n\t\t_handleMouseEvent: function(type, event, point) {\n\t\t\tvar itemEvents = this._itemEvents,\n\t\t\t\thitItems = itemEvents.native[type],\n\t\t\t\tnativeMove = type === 'mousemove',\n\t\t\t\ttool = this._scope.tool,\n\t\t\t\tview = this;\n\n\t\t\tfunction responds(type) {\n\t\t\t\treturn itemEvents.virtual[type] || view.responds(type)\n\t\t\t\t\t\t|| tool && tool.responds(type);\n\t\t\t}\n\n\t\t\tif (nativeMove && dragging && responds('mousedrag'))\n\t\t\t\ttype = 'mousedrag';\n\t\t\tif (!point)\n\t\t\t\tpoint = this.getEventPoint(event);\n\n\t\t\tvar inView = this.getBounds().contains(point),\n\t\t\t\thit = hitItems && inView && view._project.hitTest(point, {\n\t\t\t\t\ttolerance: 0,\n\t\t\t\t\tfill: true,\n\t\t\t\t\tstroke: true\n\t\t\t\t}),\n\t\t\t\thitItem = hit && hit.item || null,\n\t\t\t\thandle = false,\n\t\t\t\tmouse = {};\n\t\t\tmouse[type.substr(5)] = true;\n\n\t\t\tif (hitItems && hitItem !== overItem) {\n\t\t\t\tif (overItem) {\n\t\t\t\t\temitMouseEvent(overItem, null, 'mouseleave', event, point);\n\t\t\t\t}\n\t\t\t\tif (hitItem) {\n\t\t\t\t\temitMouseEvent(hitItem, null, 'mouseenter', event, point);\n\t\t\t\t}\n\t\t\t\toverItem = hitItem;\n\t\t\t}\n\t\t\tif (wasInView ^ inView) {\n\t\t\t\temitMouseEvent(this, null, inView ? 'mouseenter' : 'mouseleave',\n\t\t\t\t\t\tevent, point);\n\t\t\t\toverView = inView ? this : null;\n\t\t\t\thandle = true;\n\t\t\t}\n\t\t\tif ((inView || mouse.drag) && !point.equals(lastPoint)) {\n\t\t\t\temitMouseEvents(this, hitItem, nativeMove ? type : 'mousemove',\n\t\t\t\t\t\tevent, point, lastPoint);\n\t\t\t\thandle = true;\n\t\t\t}\n\t\t\twasInView = inView;\n\t\t\tif (mouse.down && inView || mouse.up && downPoint) {\n\t\t\t\temitMouseEvents(this, hitItem, type, event, point, downPoint);\n\t\t\t\tif (mouse.down) {\n\t\t\t\t\tdblClick = hitItem === clickItem\n\t\t\t\t\t\t&& (Date.now() - clickTime < 300);\n\t\t\t\t\tdownItem = clickItem = hitItem;\n\t\t\t\t\tif (!prevented && hitItem) {\n\t\t\t\t\t\tvar item = hitItem;\n\t\t\t\t\t\twhile (item && !item.responds('mousedrag'))\n\t\t\t\t\t\t\titem = item._parent;\n\t\t\t\t\t\tif (item)\n\t\t\t\t\t\t\tdragItem = hitItem;\n\t\t\t\t\t}\n\t\t\t\t\tdownPoint = point;\n\t\t\t\t} else if (mouse.up) {\n\t\t\t\t\tif (!prevented && hitItem === downItem) {\n\t\t\t\t\t\tclickTime = Date.now();\n\t\t\t\t\t\temitMouseEvents(this, hitItem, dblClick ? 'doubleclick'\n\t\t\t\t\t\t\t\t: 'click', event, point, downPoint);\n\t\t\t\t\t\tdblClick = false;\n\t\t\t\t\t}\n\t\t\t\t\tdownItem = dragItem = null;\n\t\t\t\t}\n\t\t\t\twasInView = false;\n\t\t\t\thandle = true;\n\t\t\t}\n\t\t\tlastPoint = point;\n\t\t\tif (handle && tool) {\n\t\t\t\tcalled = tool._handleMouseEvent(type, event, point, mouse)\n\t\t\t\t\t|| called;\n\t\t\t}\n\n\t\t\tif (\n\t\t\t\tevent.cancelable !== false\n\t\t\t\t&& (called && !mouse.move || mouse.down && responds('mouseup'))\n\t\t\t) {\n\t\t\t\tevent.preventDefault();\n\t\t\t}\n\t\t},\n\n\t\t_handleKeyEvent: function(type, event, key, character) {\n\t\t\tvar scope = this._scope,\n\t\t\t\ttool = scope.tool,\n\t\t\t\tkeyEvent;\n\n\t\t\tfunction emit(obj) {\n\t\t\t\tif (obj.responds(type)) {\n\t\t\t\t\tpaper = scope;\n\t\t\t\t\tobj.emit(type, keyEvent = keyEvent\n\t\t\t\t\t\t\t|| new KeyEvent(type, event, key, character));\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tif (this.isVisible()) {\n\t\t\t\temit(this);\n\t\t\t\tif (tool && tool.responds(type))\n\t\t\t\t\temit(tool);\n\t\t\t}\n\t\t},\n\n\t\t_countItemEvent: function(type, sign) {\n\t\t\tvar itemEvents = this._itemEvents,\n\t\t\t\tnative = itemEvents.native,\n\t\t\t\tvirtual = itemEvents.virtual;\n\t\t\tfor (var key in itemEventsMap) {\n\t\t\t\tnative[key] = (native[key] || 0)\n\t\t\t\t\t\t+ (itemEventsMap[key][type] || 0) * sign;\n\t\t\t}\n\t\t\tvirtual[type] = (virtual[type] || 0) + sign;\n\t\t},\n\n\t\tstatics: {\n\t\t\tupdateFocus: updateFocus,\n\n\t\t\t_resetState: function() {\n\t\t\t\tdragging = mouseDown = called = wasInView = false;\n\t\t\t\tprevFocus = tempFocus = overView = downPoint = lastPoint =\n\t\t\t\t\tdownItem = overItem = dragItem = clickItem = clickTime =\n\t\t\t\t\tdblClick = null;\n\t\t\t}\n\t\t}\n\t};\n});\n\nvar CanvasView = View.extend({\n\t_class: 'CanvasView',\n\n\tinitialize: function CanvasView(project, canvas) {\n\t\tif (!(canvas instanceof window.HTMLCanvasElement)) {\n\t\t\tvar size = Size.read(arguments, 1);\n\t\t\tif (size.isZero())\n\t\t\t\tthrow new Error(\n\t\t\t\t\t\t'Cannot create CanvasView with the provided argument: '\n\t\t\t\t\t\t+ Base.slice(arguments, 1));\n\t\t\tcanvas = CanvasProvider.getCanvas(size);\n\t\t}\n\t\tvar ctx = this._context = canvas.getContext('2d');\n\t\tctx.save();\n\t\tthis._pixelRatio = 1;\n\t\tif (!/^off|false$/.test(PaperScope.getAttribute(canvas, 'hidpi'))) {\n\t\t\tvar deviceRatio = window.devicePixelRatio || 1,\n\t\t\t\tbackingStoreRatio = DomElement.getPrefixed(ctx,\n\t\t\t\t\t\t'backingStorePixelRatio') || 1;\n\t\t\tthis._pixelRatio = deviceRatio / backingStoreRatio;\n\t\t}\n\t\tView.call(this, project, canvas);\n\t\tthis._needsUpdate = true;\n\t},\n\n\tremove: function remove() {\n\t\tthis._context.restore();\n\t\treturn remove.base.call(this);\n\t},\n\n\t_setElementSize: function _setElementSize(width, height) {\n\t\tvar pixelRatio = this._pixelRatio;\n\t\t_setElementSize.base.call(this, width * pixelRatio, height * pixelRatio);\n\t\tif (pixelRatio !== 1) {\n\t\t\tvar element = this._element,\n\t\t\t\tctx = this._context;\n\t\t\tif (!PaperScope.hasAttribute(element, 'resize')) {\n\t\t\t\tvar style = element.style;\n\t\t\t\tstyle.width = width + 'px';\n\t\t\t\tstyle.height = height + 'px';\n\t\t\t}\n\t\t\tctx.restore();\n\t\t\tctx.save();\n\t\t\tctx.scale(pixelRatio, pixelRatio);\n\t\t}\n\t},\n\n\tgetContext: function() {\n\t\treturn this._context;\n\t},\n\n\tgetPixelSize: function getPixelSize(size) {\n\t\tvar agent = paper.agent,\n\t\t\tpixels;\n\t\tif (agent && agent.firefox) {\n\t\t\tpixels = getPixelSize.base.call(this, size);\n\t\t} else {\n\t\t\tvar ctx = this._context,\n\t\t\t\tprevFont = ctx.font;\n\t\t\tctx.font = size + ' serif';\n\t\t\tpixels = parseFloat(ctx.font);\n\t\t\tctx.font = prevFont;\n\t\t}\n\t\treturn pixels;\n\t},\n\n\tgetTextWidth: function(font, lines) {\n\t\tvar ctx = this._context,\n\t\t\tprevFont = ctx.font,\n\t\t\twidth = 0;\n\t\tctx.font = font;\n\t\tfor (var i = 0, l = lines.length; i < l; i++)\n\t\t\twidth = Math.max(width, ctx.measureText(lines[i]).width);\n\t\tctx.font = prevFont;\n\t\treturn width;\n\t},\n\n\tupdate: function() {\n\t\tif (!this._needsUpdate)\n\t\t\treturn false;\n\t\tvar project = this._project,\n\t\t\tctx = this._context,\n\t\t\tsize = this._viewSize;\n\t\tctx.clearRect(0, 0, size.width + 1, size.height + 1);\n\t\tif (project)\n\t\t\tproject.draw(ctx, this._matrix, this._pixelRatio);\n\t\tthis._needsUpdate = false;\n\t\treturn true;\n\t}\n});\n\nvar Event = Base.extend({\n\t_class: 'Event',\n\n\tinitialize: function Event(event) {\n\t\tthis.event = event;\n\t\tthis.type = event && event.type;\n\t},\n\n\tprevented: false,\n\tstopped: false,\n\n\tpreventDefault: function() {\n\t\tthis.prevented = true;\n\t\tthis.event.preventDefault();\n\t},\n\n\tstopPropagation: function() {\n\t\tthis.stopped = true;\n\t\tthis.event.stopPropagation();\n\t},\n\n\tstop: function() {\n\t\tthis.stopPropagation();\n\t\tthis.preventDefault();\n\t},\n\n\tgetTimeStamp: function() {\n\t\treturn this.event.timeStamp;\n\t},\n\n\tgetModifiers: function() {\n\t\treturn Key.modifiers;\n\t}\n});\n\nvar KeyEvent = Event.extend({\n\t_class: 'KeyEvent',\n\n\tinitialize: function KeyEvent(type, event, key, character) {\n\t\tthis.type = type;\n\t\tthis.event = event;\n\t\tthis.key = key;\n\t\tthis.character = character;\n\t},\n\n\ttoString: function() {\n\t\treturn \"{ type: '\" + this.type\n\t\t\t\t+ \"', key: '\" + this.key\n\t\t\t\t+ \"', character: '\" + this.character\n\t\t\t\t+ \"', modifiers: \" + this.getModifiers()\n\t\t\t\t+ \" }\";\n\t}\n});\n\nvar Key = new function() {\n\tvar keyLookup = {\n\t\t\t'\\t': 'tab',\n\t\t\t' ': 'space',\n\t\t\t'\\b': 'backspace',\n\t\t\t'\\x7f': 'delete',\n\t\t\t'Spacebar': 'space',\n\t\t\t'Del': 'delete',\n\t\t\t'Win': 'meta',\n\t\t\t'Esc': 'escape'\n\t\t},\n\n\t\tcharLookup = {\n\t\t\t'tab': '\\t',\n\t\t\t'space': ' ',\n\t\t\t'enter': '\\r'\n\t\t},\n\n\t\tkeyMap = {},\n\t\tcharMap = {},\n\t\tmetaFixMap,\n\t\tdownKey,\n\n\t\tmodifiers = new Base({\n\t\t\tshift: false,\n\t\t\tcontrol: false,\n\t\t\talt: false,\n\t\t\tmeta: false,\n\t\t\tcapsLock: false,\n\t\t\tspace: false\n\t\t}).inject({\n\t\t\toption: {\n\t\t\t\tget: function() {\n\t\t\t\t\treturn this.alt;\n\t\t\t\t}\n\t\t\t},\n\n\t\t\tcommand: {\n\t\t\t\tget: function() {\n\t\t\t\t\tvar agent = paper && paper.agent;\n\t\t\t\t\treturn agent && agent.mac ? this.meta : this.control;\n\t\t\t\t}\n\t\t\t}\n\t\t});\n\n\tfunction getKey(event) {\n\t\tvar key = event.key || event.keyIdentifier;\n\t\tkey = /^U\\+/.test(key)\n\t\t\t\t? String.fromCharCode(parseInt(key.substr(2), 16))\n\t\t\t\t: /^Arrow[A-Z]/.test(key) ? key.substr(5)\n\t\t\t\t: key === 'Unidentified'  || key === undefined\n\t\t\t\t\t? String.fromCharCode(event.keyCode)\n\t\t\t\t\t: key;\n\t\treturn keyLookup[key] ||\n\t\t\t\t(key.length > 1 ? Base.hyphenate(key) : key.toLowerCase());\n\t}\n\n\tfunction handleKey(down, key, character, event) {\n\t\tvar type = down ? 'keydown' : 'keyup',\n\t\t\tview = View._focused,\n\t\t\tname;\n\t\tkeyMap[key] = down;\n\t\tif (down) {\n\t\t\tcharMap[key] = character;\n\t\t} else {\n\t\t\tdelete charMap[key];\n\t\t}\n\t\tif (key.length > 1 && (name = Base.camelize(key)) in modifiers) {\n\t\t\tmodifiers[name] = down;\n\t\t\tvar agent = paper && paper.agent;\n\t\t\tif (name === 'meta' && agent && agent.mac) {\n\t\t\t\tif (down) {\n\t\t\t\t\tmetaFixMap = {};\n\t\t\t\t} else {\n\t\t\t\t\tfor (var k in metaFixMap) {\n\t\t\t\t\t\tif (k in charMap)\n\t\t\t\t\t\t\thandleKey(false, k, metaFixMap[k], event);\n\t\t\t\t\t}\n\t\t\t\t\tmetaFixMap = null;\n\t\t\t\t}\n\t\t\t}\n\t\t} else if (down && metaFixMap) {\n\t\t\tmetaFixMap[key] = character;\n\t\t}\n\t\tif (view) {\n\t\t\tview._handleKeyEvent(down ? 'keydown' : 'keyup', event, key,\n\t\t\t\t\tcharacter);\n\t\t}\n\t}\n\n\tDomEvent.add(document, {\n\t\tkeydown: function(event) {\n\t\t\tvar key = getKey(event),\n\t\t\t\tagent = paper && paper.agent;\n\t\t\tif (key.length > 1 || agent && (agent.chrome && (event.altKey\n\t\t\t\t\t\t|| agent.mac && event.metaKey\n\t\t\t\t\t\t|| !agent.mac && event.ctrlKey))) {\n\t\t\t\thandleKey(true, key,\n\t\t\t\t\t\tcharLookup[key] || (key.length > 1 ? '' : key), event);\n\t\t\t} else {\n\t\t\t\tdownKey = key;\n\t\t\t}\n\t\t},\n\n\t\tkeypress: function(event) {\n\t\t\tif (downKey) {\n\t\t\t\tvar key = getKey(event),\n\t\t\t\t\tcode = event.charCode,\n\t\t\t\t\tcharacter = code >= 32 ? String.fromCharCode(code)\n\t\t\t\t\t\t: key.length > 1 ? '' : key;\n\t\t\t\tif (key !== downKey) {\n\t\t\t\t\tkey = character.toLowerCase();\n\t\t\t\t}\n\t\t\t\thandleKey(true, key, character, event);\n\t\t\t\tdownKey = null;\n\t\t\t}\n\t\t},\n\n\t\tkeyup: function(event) {\n\t\t\tvar key = getKey(event);\n\t\t\tif (key in charMap)\n\t\t\t\thandleKey(false, key, charMap[key], event);\n\t\t}\n\t});\n\n\tDomEvent.add(window, {\n\t\tblur: function(event) {\n\t\t\tfor (var key in charMap)\n\t\t\t\thandleKey(false, key, charMap[key], event);\n\t\t}\n\t});\n\n\treturn {\n\t\tmodifiers: modifiers,\n\n\t\tisDown: function(key) {\n\t\t\treturn !!keyMap[key];\n\t\t}\n\t};\n};\n\nvar MouseEvent = Event.extend({\n\t_class: 'MouseEvent',\n\n\tinitialize: function MouseEvent(type, event, point, target, delta) {\n\t\tthis.type = type;\n\t\tthis.event = event;\n\t\tthis.point = point;\n\t\tthis.target = target;\n\t\tthis.delta = delta;\n\t},\n\n\ttoString: function() {\n\t\treturn \"{ type: '\" + this.type\n\t\t\t\t+ \"', point: \" + this.point\n\t\t\t\t+ ', target: ' + this.target\n\t\t\t\t+ (this.delta ? ', delta: ' + this.delta : '')\n\t\t\t\t+ ', modifiers: ' + this.getModifiers()\n\t\t\t\t+ ' }';\n\t}\n});\n\nvar ToolEvent = Event.extend({\n\t_class: 'ToolEvent',\n\t_item: null,\n\n\tinitialize: function ToolEvent(tool, type, event) {\n\t\tthis.tool = tool;\n\t\tthis.type = type;\n\t\tthis.event = event;\n\t},\n\n\t_choosePoint: function(point, toolPoint) {\n\t\treturn point ? point : toolPoint ? toolPoint.clone() : null;\n\t},\n\n\tgetPoint: function() {\n\t\treturn this._choosePoint(this._point, this.tool._point);\n\t},\n\n\tsetPoint: function(point) {\n\t\tthis._point = point;\n\t},\n\n\tgetLastPoint: function() {\n\t\treturn this._choosePoint(this._lastPoint, this.tool._lastPoint);\n\t},\n\n\tsetLastPoint: function(lastPoint) {\n\t\tthis._lastPoint = lastPoint;\n\t},\n\n\tgetDownPoint: function() {\n\t\treturn this._choosePoint(this._downPoint, this.tool._downPoint);\n\t},\n\n\tsetDownPoint: function(downPoint) {\n\t\tthis._downPoint = downPoint;\n\t},\n\n\tgetMiddlePoint: function() {\n\t\tif (!this._middlePoint && this.tool._lastPoint) {\n\t\t\treturn this.tool._point.add(this.tool._lastPoint).divide(2);\n\t\t}\n\t\treturn this._middlePoint;\n\t},\n\n\tsetMiddlePoint: function(middlePoint) {\n\t\tthis._middlePoint = middlePoint;\n\t},\n\n\tgetDelta: function() {\n\t\treturn !this._delta && this.tool._lastPoint\n\t\t\t\t? this.tool._point.subtract(this.tool._lastPoint)\n\t\t\t\t: this._delta;\n\t},\n\n\tsetDelta: function(delta) {\n\t\tthis._delta = delta;\n\t},\n\n\tgetCount: function() {\n\t\treturn this.tool[/^mouse(down|up)$/.test(this.type)\n\t\t\t\t? '_downCount' : '_moveCount'];\n\t},\n\n\tsetCount: function(count) {\n\t\tthis.tool[/^mouse(down|up)$/.test(this.type) ? 'downCount' : 'count']\n\t\t\t= count;\n\t},\n\n\tgetItem: function() {\n\t\tif (!this._item) {\n\t\t\tvar result = this.tool._scope.project.hitTest(this.getPoint());\n\t\t\tif (result) {\n\t\t\t\tvar item = result.item,\n\t\t\t\t\tparent = item._parent;\n\t\t\t\twhile (/^(Group|CompoundPath)$/.test(parent._class)) {\n\t\t\t\t\titem = parent;\n\t\t\t\t\tparent = parent._parent;\n\t\t\t\t}\n\t\t\t\tthis._item = item;\n\t\t\t}\n\t\t}\n\t\treturn this._item;\n\t},\n\n\tsetItem: function(item) {\n\t\tthis._item = item;\n\t},\n\n\ttoString: function() {\n\t\treturn '{ type: ' + this.type\n\t\t\t\t+ ', point: ' + this.getPoint()\n\t\t\t\t+ ', count: ' + this.getCount()\n\t\t\t\t+ ', modifiers: ' + this.getModifiers()\n\t\t\t\t+ ' }';\n\t}\n});\n\nvar Tool = PaperScopeItem.extend({\n\t_class: 'Tool',\n\t_list: 'tools',\n\t_reference: 'tool',\n\t_events: ['onMouseDown', 'onMouseUp', 'onMouseDrag', 'onMouseMove',\n\t\t\t'onActivate', 'onDeactivate', 'onEditOptions', 'onKeyDown',\n\t\t\t'onKeyUp'],\n\n\tinitialize: function Tool(props) {\n\t\tPaperScopeItem.call(this);\n\t\tthis._moveCount = -1;\n\t\tthis._downCount = -1;\n\t\tthis.set(props);\n\t},\n\n\tgetMinDistance: function() {\n\t\treturn this._minDistance;\n\t},\n\n\tsetMinDistance: function(minDistance) {\n\t\tthis._minDistance = minDistance;\n\t\tif (minDistance != null && this._maxDistance != null\n\t\t\t\t&& minDistance > this._maxDistance) {\n\t\t\tthis._maxDistance = minDistance;\n\t\t}\n\t},\n\n\tgetMaxDistance: function() {\n\t\treturn this._maxDistance;\n\t},\n\n\tsetMaxDistance: function(maxDistance) {\n\t\tthis._maxDistance = maxDistance;\n\t\tif (this._minDistance != null && maxDistance != null\n\t\t\t\t&& maxDistance < this._minDistance) {\n\t\t\tthis._minDistance = maxDistance;\n\t\t}\n\t},\n\n\tgetFixedDistance: function() {\n\t\treturn this._minDistance == this._maxDistance\n\t\t\t? this._minDistance : null;\n\t},\n\n\tsetFixedDistance: function(distance) {\n\t\tthis._minDistance = this._maxDistance = distance;\n\t},\n\n\t_handleMouseEvent: function(type, event, point, mouse) {\n\t\tpaper = this._scope;\n\t\tif (mouse.drag && !this.responds(type))\n\t\t\ttype = 'mousemove';\n\t\tvar move = mouse.move || mouse.drag,\n\t\t\tresponds = this.responds(type),\n\t\t\tminDistance = this.minDistance,\n\t\t\tmaxDistance = this.maxDistance,\n\t\t\tcalled = false,\n\t\t\ttool = this;\n\t\tfunction update(minDistance, maxDistance) {\n\t\t\tvar pt = point,\n\t\t\t\ttoolPoint = move ? tool._point : (tool._downPoint || pt);\n\t\t\tif (move) {\n\t\t\t\tif (tool._moveCount >= 0 && pt.equals(toolPoint)) {\n\t\t\t\t\treturn false;\n\t\t\t\t}\n\t\t\t\tif (toolPoint && (minDistance != null || maxDistance != null)) {\n\t\t\t\t\tvar vector = pt.subtract(toolPoint),\n\t\t\t\t\t\tdistance = vector.getLength();\n\t\t\t\t\tif (distance < (minDistance || 0))\n\t\t\t\t\t\treturn false;\n\t\t\t\t\tif (maxDistance) {\n\t\t\t\t\t\tpt = toolPoint.add(vector.normalize(\n\t\t\t\t\t\t\t\tMath.min(distance, maxDistance)));\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\ttool._moveCount++;\n\t\t\t}\n\t\t\ttool._point = pt;\n\t\t\ttool._lastPoint = toolPoint || pt;\n\t\t\tif (mouse.down) {\n\t\t\t\ttool._moveCount = -1;\n\t\t\t\ttool._downPoint = pt;\n\t\t\t\ttool._downCount++;\n\t\t\t}\n\t\t\treturn true;\n\t\t}\n\n\t\tfunction emit() {\n\t\t\tif (responds) {\n\t\t\t\tcalled = tool.emit(type, new ToolEvent(tool, type, event))\n\t\t\t\t\t\t|| called;\n\t\t\t}\n\t\t}\n\n\t\tif (mouse.down) {\n\t\t\tupdate();\n\t\t\temit();\n\t\t} else if (mouse.up) {\n\t\t\tupdate(null, maxDistance);\n\t\t\temit();\n\t\t} else if (responds) {\n\t\t\twhile (update(minDistance, maxDistance))\n\t\t\t\temit();\n\t\t}\n\t\treturn called;\n\t}\n\n});\n\nvar Tween = Base.extend(Emitter, {\n\t_class: 'Tween',\n\n\tstatics: {\n\t\teasings: new Base({\n\t\t\tlinear: function(t) {\n\t\t\t\treturn t;\n\t\t\t},\n\n\t\t\teaseInQuad: function(t) {\n\t\t\t\treturn t * t;\n\t\t\t},\n\n\t\t\teaseOutQuad: function(t) {\n\t\t\t\treturn t * (2 - t);\n\t\t\t},\n\n\t\t\teaseInOutQuad: function(t) {\n\t\t\t\treturn t < 0.5\n\t\t\t\t\t? 2 * t * t\n\t\t\t\t\t: -1 + 2 * (2 - t) * t;\n\t\t\t},\n\n\t\t\teaseInCubic: function(t) {\n\t\t\t\treturn t * t * t;\n\t\t\t},\n\n\t\t\teaseOutCubic: function(t) {\n\t\t\t\treturn --t * t * t + 1;\n\t\t\t},\n\n\t\t\teaseInOutCubic: function(t) {\n\t\t\t\treturn t < 0.5\n\t\t\t\t\t? 4 * t * t * t\n\t\t\t\t\t: (t - 1) * (2 * t - 2) * (2 * t - 2) + 1;\n\t\t\t},\n\n\t\t\teaseInQuart: function(t) {\n\t\t\t\treturn t * t * t * t;\n\t\t\t},\n\n\t\t\teaseOutQuart: function(t) {\n\t\t\t\treturn 1 - (--t) * t * t * t;\n\t\t\t},\n\n\t\t\teaseInOutQuart: function(t) {\n\t\t\t\treturn t < 0.5\n\t\t\t\t\t? 8 * t * t * t * t\n\t\t\t\t\t: 1 - 8 * (--t) * t * t * t;\n\t\t\t},\n\n\t\t\teaseInQuint: function(t) {\n\t\t\t\treturn t * t * t * t * t;\n\t\t\t},\n\n\t\t\teaseOutQuint: function(t) {\n\t\t\t\treturn 1 + --t * t * t * t * t;\n\t\t\t},\n\n\t\t\teaseInOutQuint: function(t) {\n\t\t\t\treturn t < 0.5\n\t\t\t\t\t? 16 * t * t * t * t * t\n\t\t\t\t\t: 1 + 16 * (--t) * t * t * t * t;\n\t\t\t}\n\t\t})\n\t},\n\n\tinitialize: function Tween(object, from, to, duration, easing, start) {\n\t\tthis.object = object;\n\t\tvar type = typeof easing;\n\t\tvar isFunction = type === 'function';\n\t\tthis.type = isFunction\n\t\t\t? type\n\t\t\t: type === 'string'\n\t\t\t\t? easing\n\t\t\t\t: 'linear';\n\t\tthis.easing = isFunction ? easing : Tween.easings[this.type];\n\t\tthis.duration = duration;\n\t\tthis.running = false;\n\n\t\tthis._then = null;\n\t\tthis._startTime = null;\n\t\tvar state = from || to;\n\t\tthis._keys = state ? Object.keys(state) : [];\n\t\tthis._parsedKeys = this._parseKeys(this._keys);\n\t\tthis._from = state && this._getState(from);\n\t\tthis._to = state && this._getState(to);\n\t\tif (start !== false) {\n\t\t\tthis.start();\n\t\t}\n\t},\n\n\tthen: function(then) {\n\t\tthis._then = then;\n\t\treturn this;\n\t},\n\n\tstart: function() {\n\t\tthis._startTime = null;\n\t\tthis.running = true;\n\t\treturn this;\n\t},\n\n\tstop: function() {\n\t\tthis.running = false;\n\t\treturn this;\n\t},\n\n\tupdate: function(progress) {\n\t\tif (this.running) {\n\t\t\tif (progress >= 1) {\n\t\t\t\tprogress = 1;\n\t\t\t\tthis.running = false;\n\t\t\t}\n\n\t\t\tvar factor = this.easing(progress),\n\t\t\t\tkeys = this._keys,\n\t\t\t\tgetValue = function(value) {\n\t\t\t\t\treturn typeof value === 'function'\n\t\t\t\t\t\t? value(factor, progress)\n\t\t\t\t\t\t: value;\n\t\t\t\t};\n\t\t\tfor (var i = 0, l = keys && keys.length; i < l; i++) {\n\t\t\t\tvar key = keys[i],\n\t\t\t\t\tfrom = getValue(this._from[key]),\n\t\t\t\t\tto = getValue(this._to[key]),\n\t\t\t\t\tvalue = (from && to && from.__add && to.__add)\n\t\t\t\t\t\t? to.__subtract(from).__multiply(factor).__add(from)\n\t\t\t\t\t\t: ((to - from) * factor) + from;\n\t\t\t\tthis._setProperty(this._parsedKeys[key], value);\n\t\t\t}\n\n\t\t\tif (this.responds('update')) {\n\t\t\t\tthis.emit('update', new Base({\n\t\t\t\t\tprogress: progress,\n\t\t\t\t\tfactor: factor\n\t\t\t\t}));\n\t\t\t}\n\t\t\tif (!this.running && this._then) {\n\t\t\t\tthis._then(this.object);\n\t\t\t}\n\t\t}\n\t\treturn this;\n\t},\n\n\t_events: {\n\t\tonUpdate: {}\n\t},\n\n\t_handleFrame: function(time) {\n\t\tvar startTime = this._startTime,\n\t\t\tprogress = startTime\n\t\t\t\t? (time - startTime) / this.duration\n\t\t\t\t: 0;\n\t\tif (!startTime) {\n\t\t\tthis._startTime = time;\n\t\t}\n\t\tthis.update(progress);\n\t},\n\n\t_getState: function(state) {\n\t\tvar keys = this._keys,\n\t\t\tresult = {};\n\t\tfor (var i = 0, l = keys.length; i < l; i++) {\n\t\t\tvar key = keys[i],\n\t\t\t\tpath = this._parsedKeys[key],\n\t\t\t\tcurrent = this._getProperty(path),\n\t\t\t\tvalue;\n\t\t\tif (state) {\n\t\t\t\tvar resolved = this._resolveValue(current, state[key]);\n\t\t\t\tthis._setProperty(path, resolved);\n\t\t\t\tvalue = this._getProperty(path);\n\t\t\t\tvalue = value && value.clone ? value.clone() : value;\n\t\t\t\tthis._setProperty(path, current);\n\t\t\t} else {\n\t\t\t\tvalue = current && current.clone ? current.clone() : current;\n\t\t\t}\n\t\t\tresult[key] = value;\n\t\t}\n\t\treturn result;\n\t},\n\n\t_resolveValue: function(current, value) {\n\t\tif (value) {\n\t\t\tif (Array.isArray(value) && value.length === 2) {\n\t\t\t\tvar operator = value[0];\n\t\t\t\treturn (\n\t\t\t\t\toperator &&\n\t\t\t\t\toperator.match &&\n\t\t\t\t\toperator.match(/^[+\\-\\*\\/]=/)\n\t\t\t\t)\n\t\t\t\t\t? this._calculate(current, operator[0], value[1])\n\t\t\t\t\t: value;\n\t\t\t} else if (typeof value === 'string') {\n\t\t\t\tvar match = value.match(/^[+\\-*/]=(.*)/);\n\t\t\t\tif (match) {\n\t\t\t\t\tvar parsed = JSON.parse(match[1].replace(\n\t\t\t\t\t\t/(['\"])?([a-zA-Z0-9_]+)(['\"])?:/g,\n\t\t\t\t\t\t'\"$2\": '\n\t\t\t\t\t));\n\t\t\t\t\treturn this._calculate(current, value[0], parsed);\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\treturn value;\n\t},\n\n\t_calculate: function(left, operator, right) {\n\t\treturn paper.PaperScript.calculateBinary(left, operator, right);\n\t},\n\n\t_parseKeys: function(keys) {\n\t\tvar parsed = {};\n\t\tfor (var i = 0, l = keys.length; i < l; i++) {\n\t\t\tvar key = keys[i],\n\t\t\t\tpath = key\n\t\t\t\t\t.replace(/\\.([^.]*)/g, '/$1')\n\t\t\t\t\t.replace(/\\[['\"]?([^'\"\\]]*)['\"]?\\]/g, '/$1');\n\t\t\tparsed[key] = path.split('/');\n\t\t}\n\t\treturn parsed;\n\t},\n\n\t_getProperty: function(path, offset) {\n\t\tvar obj = this.object;\n\t\tfor (var i = 0, l = path.length - (offset || 0); i < l && obj; i++) {\n\t\t\tobj = obj[path[i]];\n\t\t}\n\t\treturn obj;\n\t},\n\n\t_setProperty: function(path, value) {\n\t\tvar dest = this._getProperty(path, 1);\n\t\tif (dest) {\n\t\t\tdest[path[path.length - 1]] = value;\n\t\t}\n\t}\n});\n\nvar Http = {\n\trequest: function(options) {\n\t\tvar xhr = new self.XMLHttpRequest();\n\t\txhr.open((options.method || 'get').toUpperCase(), options.url,\n\t\t\t\tBase.pick(options.async, true));\n\t\tif (options.mimeType)\n\t\t\txhr.overrideMimeType(options.mimeType);\n\t\txhr.onload = function() {\n\t\t\tvar status = xhr.status;\n\t\t\tif (status === 0 || status === 200) {\n\t\t\t\tif (options.onLoad) {\n\t\t\t\t\toptions.onLoad.call(xhr, xhr.responseText);\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\txhr.onerror();\n\t\t\t}\n\t\t};\n\t\txhr.onerror = function() {\n\t\t\tvar status = xhr.status,\n\t\t\t\tmessage = 'Could not load \"' + options.url + '\" (Status: '\n\t\t\t\t\t\t+ status + ')';\n\t\t\tif (options.onError) {\n\t\t\t\toptions.onError(message, status);\n\t\t\t} else {\n\t\t\t\tthrow new Error(message);\n\t\t\t}\n\t\t};\n\t\treturn xhr.send(null);\n\t}\n};\n\nvar CanvasProvider = Base.exports.CanvasProvider = {\n\tcanvases: [],\n\n\tgetCanvas: function(width, height) {\n\t\tif (!window)\n\t\t\treturn null;\n\t\tvar canvas,\n\t\t\tclear = true;\n\t\tif (typeof width === 'object') {\n\t\t\theight = width.height;\n\t\t\twidth = width.width;\n\t\t}\n\t\tif (this.canvases.length) {\n\t\t\tcanvas = this.canvases.pop();\n\t\t} else {\n\t\t\tcanvas = document.createElement('canvas');\n\t\t\tclear = false;\n\t\t}\n\t\tvar ctx = canvas.getContext('2d');\n\t\tif (!ctx) {\n\t\t\tthrow new Error('Canvas ' + canvas +\n\t\t\t\t\t' is unable to provide a 2D context.');\n\t\t}\n\t\tif (canvas.width === width && canvas.height === height) {\n\t\t\tif (clear)\n\t\t\t\tctx.clearRect(0, 0, width + 1, height + 1);\n\t\t} else {\n\t\t\tcanvas.width = width;\n\t\t\tcanvas.height = height;\n\t\t}\n\t\tctx.save();\n\t\treturn canvas;\n\t},\n\n\tgetContext: function(width, height) {\n\t\tvar canvas = this.getCanvas(width, height);\n\t\treturn canvas ? canvas.getContext('2d') : null;\n\t},\n\n\trelease: function(obj) {\n\t\tvar canvas = obj && obj.canvas ? obj.canvas : obj;\n\t\tif (canvas && canvas.getContext) {\n\t\t\tcanvas.getContext('2d').restore();\n\t\t\tthis.canvases.push(canvas);\n\t\t}\n\t}\n};\n\nvar BlendMode = new function() {\n\tvar min = Math.min,\n\t\tmax = Math.max,\n\t\tabs = Math.abs,\n\t\tsr, sg, sb, sa,\n\t\tbr, bg, bb, ba,\n\t\tdr, dg, db;\n\n\tfunction getLum(r, g, b) {\n\t\treturn 0.2989 * r + 0.587 * g + 0.114 * b;\n\t}\n\n\tfunction setLum(r, g, b, l) {\n\t\tvar d = l - getLum(r, g, b);\n\t\tdr = r + d;\n\t\tdg = g + d;\n\t\tdb = b + d;\n\t\tvar l = getLum(dr, dg, db),\n\t\t\tmn = min(dr, dg, db),\n\t\t\tmx = max(dr, dg, db);\n\t\tif (mn < 0) {\n\t\t\tvar lmn = l - mn;\n\t\t\tdr = l + (dr - l) * l / lmn;\n\t\t\tdg = l + (dg - l) * l / lmn;\n\t\t\tdb = l + (db - l) * l / lmn;\n\t\t}\n\t\tif (mx > 255) {\n\t\t\tvar ln = 255 - l,\n\t\t\t\tmxl = mx - l;\n\t\t\tdr = l + (dr - l) * ln / mxl;\n\t\t\tdg = l + (dg - l) * ln / mxl;\n\t\t\tdb = l + (db - l) * ln / mxl;\n\t\t}\n\t}\n\n\tfunction getSat(r, g, b) {\n\t\treturn max(r, g, b) - min(r, g, b);\n\t}\n\n\tfunction setSat(r, g, b, s) {\n\t\tvar col = [r, g, b],\n\t\t\tmx = max(r, g, b),\n\t\t\tmn = min(r, g, b),\n\t\t\tmd;\n\t\tmn = mn === r ? 0 : mn === g ? 1 : 2;\n\t\tmx = mx === r ? 0 : mx === g ? 1 : 2;\n\t\tmd = min(mn, mx) === 0 ? max(mn, mx) === 1 ? 2 : 1 : 0;\n\t\tif (col[mx] > col[mn]) {\n\t\t\tcol[md] = (col[md] - col[mn]) * s / (col[mx] - col[mn]);\n\t\t\tcol[mx] = s;\n\t\t} else {\n\t\t\tcol[md] = col[mx] = 0;\n\t\t}\n\t\tcol[mn] = 0;\n\t\tdr = col[0];\n\t\tdg = col[1];\n\t\tdb = col[2];\n\t}\n\n\tvar modes = {\n\t\tmultiply: function() {\n\t\t\tdr = br * sr / 255;\n\t\t\tdg = bg * sg / 255;\n\t\t\tdb = bb * sb / 255;\n\t\t},\n\n\t\tscreen: function() {\n\t\t\tdr = br + sr - (br * sr / 255);\n\t\t\tdg = bg + sg - (bg * sg / 255);\n\t\t\tdb = bb + sb - (bb * sb / 255);\n\t\t},\n\n\t\toverlay: function() {\n\t\t\tdr = br < 128 ? 2 * br * sr / 255 : 255 - 2 * (255 - br) * (255 - sr) / 255;\n\t\t\tdg = bg < 128 ? 2 * bg * sg / 255 : 255 - 2 * (255 - bg) * (255 - sg) / 255;\n\t\t\tdb = bb < 128 ? 2 * bb * sb / 255 : 255 - 2 * (255 - bb) * (255 - sb) / 255;\n\t\t},\n\n\t\t'soft-light': function() {\n\t\t\tvar t = sr * br / 255;\n\t\t\tdr = t + br * (255 - (255 - br) * (255 - sr) / 255 - t) / 255;\n\t\t\tt = sg * bg / 255;\n\t\t\tdg = t + bg * (255 - (255 - bg) * (255 - sg) / 255 - t) / 255;\n\t\t\tt = sb * bb / 255;\n\t\t\tdb = t + bb * (255 - (255 - bb) * (255 - sb) / 255 - t) / 255;\n\t\t},\n\n\t\t'hard-light': function() {\n\t\t\tdr = sr < 128 ? 2 * sr * br / 255 : 255 - 2 * (255 - sr) * (255 - br) / 255;\n\t\t\tdg = sg < 128 ? 2 * sg * bg / 255 : 255 - 2 * (255 - sg) * (255 - bg) / 255;\n\t\t\tdb = sb < 128 ? 2 * sb * bb / 255 : 255 - 2 * (255 - sb) * (255 - bb) / 255;\n\t\t},\n\n\t\t'color-dodge': function() {\n\t\t\tdr = br === 0 ? 0 : sr === 255 ? 255 : min(255, 255 * br / (255 - sr));\n\t\t\tdg = bg === 0 ? 0 : sg === 255 ? 255 : min(255, 255 * bg / (255 - sg));\n\t\t\tdb = bb === 0 ? 0 : sb === 255 ? 255 : min(255, 255 * bb / (255 - sb));\n\t\t},\n\n\t\t'color-burn': function() {\n\t\t\tdr = br === 255 ? 255 : sr === 0 ? 0 : max(0, 255 - (255 - br) * 255 / sr);\n\t\t\tdg = bg === 255 ? 255 : sg === 0 ? 0 : max(0, 255 - (255 - bg) * 255 / sg);\n\t\t\tdb = bb === 255 ? 255 : sb === 0 ? 0 : max(0, 255 - (255 - bb) * 255 / sb);\n\t\t},\n\n\t\tdarken: function() {\n\t\t\tdr = br < sr ? br : sr;\n\t\t\tdg = bg < sg ? bg : sg;\n\t\t\tdb = bb < sb ? bb : sb;\n\t\t},\n\n\t\tlighten: function() {\n\t\t\tdr = br > sr ? br : sr;\n\t\t\tdg = bg > sg ? bg : sg;\n\t\t\tdb = bb > sb ? bb : sb;\n\t\t},\n\n\t\tdifference: function() {\n\t\t\tdr = br - sr;\n\t\t\tif (dr < 0)\n\t\t\t\tdr = -dr;\n\t\t\tdg = bg - sg;\n\t\t\tif (dg < 0)\n\t\t\t\tdg = -dg;\n\t\t\tdb = bb - sb;\n\t\t\tif (db < 0)\n\t\t\t\tdb = -db;\n\t\t},\n\n\t\texclusion: function() {\n\t\t\tdr = br + sr * (255 - br - br) / 255;\n\t\t\tdg = bg + sg * (255 - bg - bg) / 255;\n\t\t\tdb = bb + sb * (255 - bb - bb) / 255;\n\t\t},\n\n\t\thue: function() {\n\t\t\tsetSat(sr, sg, sb, getSat(br, bg, bb));\n\t\t\tsetLum(dr, dg, db, getLum(br, bg, bb));\n\t\t},\n\n\t\tsaturation: function() {\n\t\t\tsetSat(br, bg, bb, getSat(sr, sg, sb));\n\t\t\tsetLum(dr, dg, db, getLum(br, bg, bb));\n\t\t},\n\n\t\tluminosity: function() {\n\t\t\tsetLum(br, bg, bb, getLum(sr, sg, sb));\n\t\t},\n\n\t\tcolor: function() {\n\t\t\tsetLum(sr, sg, sb, getLum(br, bg, bb));\n\t\t},\n\n\t\tadd: function() {\n\t\t\tdr = min(br + sr, 255);\n\t\t\tdg = min(bg + sg, 255);\n\t\t\tdb = min(bb + sb, 255);\n\t\t},\n\n\t\tsubtract: function() {\n\t\t\tdr = max(br - sr, 0);\n\t\t\tdg = max(bg - sg, 0);\n\t\t\tdb = max(bb - sb, 0);\n\t\t},\n\n\t\taverage: function() {\n\t\t\tdr = (br + sr) / 2;\n\t\t\tdg = (bg + sg) / 2;\n\t\t\tdb = (bb + sb) / 2;\n\t\t},\n\n\t\tnegation: function() {\n\t\t\tdr = 255 - abs(255 - sr - br);\n\t\t\tdg = 255 - abs(255 - sg - bg);\n\t\t\tdb = 255 - abs(255 - sb - bb);\n\t\t}\n\t};\n\n\tvar nativeModes = this.nativeModes = Base.each([\n\t\t'source-over', 'source-in', 'source-out', 'source-atop',\n\t\t'destination-over', 'destination-in', 'destination-out',\n\t\t'destination-atop', 'lighter', 'darker', 'copy', 'xor'\n\t], function(mode) {\n\t\tthis[mode] = true;\n\t}, {});\n\n\tvar ctx = CanvasProvider.getContext(1, 1);\n\tif (ctx) {\n\t\tBase.each(modes, function(func, mode) {\n\t\t\tvar darken = mode === 'darken',\n\t\t\t\tok = false;\n\t\t\tctx.save();\n\t\t\ttry {\n\t\t\t\tctx.fillStyle = darken ? '#300' : '#a00';\n\t\t\t\tctx.fillRect(0, 0, 1, 1);\n\t\t\t\tctx.globalCompositeOperation = mode;\n\t\t\t\tif (ctx.globalCompositeOperation === mode) {\n\t\t\t\t\tctx.fillStyle = darken ? '#a00' : '#300';\n\t\t\t\t\tctx.fillRect(0, 0, 1, 1);\n\t\t\t\t\tok = ctx.getImageData(0, 0, 1, 1).data[0] !== darken\n\t\t\t\t\t\t\t? 170 : 51;\n\t\t\t\t}\n\t\t\t} catch (e) {}\n\t\t\tctx.restore();\n\t\t\tnativeModes[mode] = ok;\n\t\t});\n\t\tCanvasProvider.release(ctx);\n\t}\n\n\tthis.process = function(mode, srcContext, dstContext, alpha, offset) {\n\t\tvar srcCanvas = srcContext.canvas,\n\t\t\tnormal = mode === 'normal';\n\t\tif (normal || nativeModes[mode]) {\n\t\t\tdstContext.save();\n\t\t\tdstContext.setTransform(1, 0, 0, 1, 0, 0);\n\t\t\tdstContext.globalAlpha = alpha;\n\t\t\tif (!normal)\n\t\t\t\tdstContext.globalCompositeOperation = mode;\n\t\t\tdstContext.drawImage(srcCanvas, offset.x, offset.y);\n\t\t\tdstContext.restore();\n\t\t} else {\n\t\t\tvar process = modes[mode];\n\t\t\tif (!process)\n\t\t\t\treturn;\n\t\t\tvar dstData = dstContext.getImageData(offset.x, offset.y,\n\t\t\t\t\tsrcCanvas.width, srcCanvas.height),\n\t\t\t\tdst = dstData.data,\n\t\t\t\tsrc = srcContext.getImageData(0, 0,\n\t\t\t\t\tsrcCanvas.width, srcCanvas.height).data;\n\t\t\tfor (var i = 0, l = dst.length; i < l; i += 4) {\n\t\t\t\tsr = src[i];\n\t\t\t\tbr = dst[i];\n\t\t\t\tsg = src[i + 1];\n\t\t\t\tbg = dst[i + 1];\n\t\t\t\tsb = src[i + 2];\n\t\t\t\tbb = dst[i + 2];\n\t\t\t\tsa = src[i + 3];\n\t\t\t\tba = dst[i + 3];\n\t\t\t\tprocess();\n\t\t\t\tvar a1 = sa * alpha / 255,\n\t\t\t\t\ta2 = 1 - a1;\n\t\t\t\tdst[i] = a1 * dr + a2 * br;\n\t\t\t\tdst[i + 1] = a1 * dg + a2 * bg;\n\t\t\t\tdst[i + 2] = a1 * db + a2 * bb;\n\t\t\t\tdst[i + 3] = sa * alpha + a2 * ba;\n\t\t\t}\n\t\t\tdstContext.putImageData(dstData, offset.x, offset.y);\n\t\t}\n\t};\n};\n\nvar SvgElement = new function() {\n\tvar svg = 'http://www.w3.org/2000/svg',\n\t\txmlns = 'http://www.w3.org/2000/xmlns',\n\t\txlink = 'http://www.w3.org/1999/xlink',\n\t\tattributeNamespace = {\n\t\t\thref: xlink,\n\t\t\txlink: xmlns,\n\t\t\txmlns: xmlns + '/',\n\t\t\t'xmlns:xlink': xmlns + '/'\n\t\t};\n\n\tfunction create(tag, attributes, formatter) {\n\t\treturn set(document.createElementNS(svg, tag), attributes, formatter);\n\t}\n\n\tfunction get(node, name) {\n\t\tvar namespace = attributeNamespace[name],\n\t\t\tvalue = namespace\n\t\t\t\t? node.getAttributeNS(namespace, name)\n\t\t\t\t: node.getAttribute(name);\n\t\treturn value === 'null' ? null : value;\n\t}\n\n\tfunction set(node, attributes, formatter) {\n\t\tfor (var name in attributes) {\n\t\t\tvar value = attributes[name],\n\t\t\t\tnamespace = attributeNamespace[name];\n\t\t\tif (typeof value === 'number' && formatter)\n\t\t\t\tvalue = formatter.number(value);\n\t\t\tif (namespace) {\n\t\t\t\tnode.setAttributeNS(namespace, name, value);\n\t\t\t} else {\n\t\t\t\tnode.setAttribute(name, value);\n\t\t\t}\n\t\t}\n\t\treturn node;\n\t}\n\n\treturn {\n\t\tsvg: svg,\n\t\txmlns: xmlns,\n\t\txlink: xlink,\n\n\t\tcreate: create,\n\t\tget: get,\n\t\tset: set\n\t};\n};\n\nvar SvgStyles = Base.each({\n\tfillColor: ['fill', 'color'],\n\tfillRule: ['fill-rule', 'string'],\n\tstrokeColor: ['stroke', 'color'],\n\tstrokeWidth: ['stroke-width', 'number'],\n\tstrokeCap: ['stroke-linecap', 'string'],\n\tstrokeJoin: ['stroke-linejoin', 'string'],\n\tstrokeScaling: ['vector-effect', 'lookup', {\n\t\ttrue: 'none',\n\t\tfalse: 'non-scaling-stroke'\n\t}, function(item, value) {\n\t\treturn !value\n\t\t\t\t&& (item instanceof PathItem\n\t\t\t\t\t|| item instanceof Shape\n\t\t\t\t\t|| item instanceof TextItem);\n\t}],\n\tmiterLimit: ['stroke-miterlimit', 'number'],\n\tdashArray: ['stroke-dasharray', 'array'],\n\tdashOffset: ['stroke-dashoffset', 'number'],\n\tfontFamily: ['font-family', 'string'],\n\tfontWeight: ['font-weight', 'string'],\n\tfontSize: ['font-size', 'number'],\n\tjustification: ['text-anchor', 'lookup', {\n\t\tleft: 'start',\n\t\tcenter: 'middle',\n\t\tright: 'end'\n\t}],\n\topacity: ['opacity', 'number'],\n\tblendMode: ['mix-blend-mode', 'style']\n}, function(entry, key) {\n\tvar part = Base.capitalize(key),\n\t\tlookup = entry[2];\n\tthis[key] = {\n\t\ttype: entry[1],\n\t\tproperty: key,\n\t\tattribute: entry[0],\n\t\ttoSVG: lookup,\n\t\tfromSVG: lookup && Base.each(lookup, function(value, name) {\n\t\t\tthis[value] = name;\n\t\t}, {}),\n\t\texportFilter: entry[3],\n\t\tget: 'get' + part,\n\t\tset: 'set' + part\n\t};\n}, {});\n\nnew function() {\n\tvar formatter;\n\n\tfunction getTransform(matrix, coordinates, center) {\n\t\tvar attrs = new Base(),\n\t\t\ttrans = matrix.getTranslation();\n\t\tif (coordinates) {\n\t\t\tvar point;\n\t\t\tif (matrix.isInvertible()) {\n\t\t\t\tmatrix = matrix._shiftless();\n\t\t\t\tpoint = matrix._inverseTransform(trans);\n\t\t\t\ttrans = null;\n\t\t\t} else {\n\t\t\t\tpoint = new Point();\n\t\t\t}\n\t\t\tattrs[center ? 'cx' : 'x'] = point.x;\n\t\t\tattrs[center ? 'cy' : 'y'] = point.y;\n\t\t}\n\t\tif (!matrix.isIdentity()) {\n\t\t\tvar decomposed = matrix.decompose();\n\t\t\tif (decomposed) {\n\t\t\t\tvar parts = [],\n\t\t\t\t\tangle = decomposed.rotation,\n\t\t\t\t\tscale = decomposed.scaling,\n\t\t\t\t\tskew = decomposed.skewing;\n\t\t\t\tif (trans && !trans.isZero())\n\t\t\t\t\tparts.push('translate(' + formatter.point(trans) + ')');\n\t\t\t\tif (angle)\n\t\t\t\t\tparts.push('rotate(' + formatter.number(angle) + ')');\n\t\t\t\tif (!Numerical.isZero(scale.x - 1)\n\t\t\t\t\t\t|| !Numerical.isZero(scale.y - 1))\n\t\t\t\t\tparts.push('scale(' + formatter.point(scale) +')');\n\t\t\t\tif (skew.x)\n\t\t\t\t\tparts.push('skewX(' + formatter.number(skew.x) + ')');\n\t\t\t\tif (skew.y)\n\t\t\t\t\tparts.push('skewY(' + formatter.number(skew.y) + ')');\n\t\t\t\tattrs.transform = parts.join(' ');\n\t\t\t} else {\n\t\t\t\tattrs.transform = 'matrix(' + matrix.getValues().join(',') + ')';\n\t\t\t}\n\t\t}\n\t\treturn attrs;\n\t}\n\n\tfunction exportGroup(item, options) {\n\t\tvar attrs = getTransform(item._matrix),\n\t\t\tchildren = item._children;\n\t\tvar node = SvgElement.create('g', attrs, formatter);\n\t\tfor (var i = 0, l = children.length; i < l; i++) {\n\t\t\tvar child = children[i];\n\t\t\tvar childNode = exportSVG(child, options);\n\t\t\tif (childNode) {\n\t\t\t\tif (child.isClipMask()) {\n\t\t\t\t\tvar clip = SvgElement.create('clipPath');\n\t\t\t\t\tclip.appendChild(childNode);\n\t\t\t\t\tsetDefinition(child, clip, 'clip');\n\t\t\t\t\tSvgElement.set(node, {\n\t\t\t\t\t\t'clip-path': 'url(#' + clip.id + ')'\n\t\t\t\t\t});\n\t\t\t\t} else {\n\t\t\t\t\tnode.appendChild(childNode);\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\treturn node;\n\t}\n\n\tfunction exportRaster(item, options) {\n\t\tvar attrs = getTransform(item._matrix, true),\n\t\t\tsize = item.getSize(),\n\t\t\timage = item.getImage();\n\t\tattrs.x -= size.width / 2;\n\t\tattrs.y -= size.height / 2;\n\t\tattrs.width = size.width;\n\t\tattrs.height = size.height;\n\t\tattrs.href = options.embedImages == false && image && image.src\n\t\t\t\t|| item.toDataURL();\n\t\treturn SvgElement.create('image', attrs, formatter);\n\t}\n\n\tfunction exportPath(item, options) {\n\t\tvar matchShapes = options.matchShapes;\n\t\tif (matchShapes) {\n\t\t\tvar shape = item.toShape(false);\n\t\t\tif (shape)\n\t\t\t\treturn exportShape(shape, options);\n\t\t}\n\t\tvar segments = item._segments,\n\t\t\tlength = segments.length,\n\t\t\ttype,\n\t\t\tattrs = getTransform(item._matrix);\n\t\tif (matchShapes && length >= 2 && !item.hasHandles()) {\n\t\t\tif (length > 2) {\n\t\t\t\ttype = item._closed ? 'polygon' : 'polyline';\n\t\t\t\tvar parts = [];\n\t\t\t\tfor (var i = 0; i < length; i++) {\n\t\t\t\t\tparts.push(formatter.point(segments[i]._point));\n\t\t\t\t}\n\t\t\t\tattrs.points = parts.join(' ');\n\t\t\t} else {\n\t\t\t\ttype = 'line';\n\t\t\t\tvar start = segments[0]._point,\n\t\t\t\t\tend = segments[1]._point;\n\t\t\t\tattrs.set({\n\t\t\t\t\tx1: start.x,\n\t\t\t\t\ty1: start.y,\n\t\t\t\t\tx2: end.x,\n\t\t\t\t\ty2: end.y\n\t\t\t\t});\n\t\t\t}\n\t\t} else {\n\t\t\ttype = 'path';\n\t\t\tattrs.d = item.getPathData(null, options.precision);\n\t\t}\n\t\treturn SvgElement.create(type, attrs, formatter);\n\t}\n\n\tfunction exportShape(item) {\n\t\tvar type = item._type,\n\t\t\tradius = item._radius,\n\t\t\tattrs = getTransform(item._matrix, true, type !== 'rectangle');\n\t\tif (type === 'rectangle') {\n\t\t\ttype = 'rect';\n\t\t\tvar size = item._size,\n\t\t\t\twidth = size.width,\n\t\t\t\theight = size.height;\n\t\t\tattrs.x -= width / 2;\n\t\t\tattrs.y -= height / 2;\n\t\t\tattrs.width = width;\n\t\t\tattrs.height = height;\n\t\t\tif (radius.isZero())\n\t\t\t\tradius = null;\n\t\t}\n\t\tif (radius) {\n\t\t\tif (type === 'circle') {\n\t\t\t\tattrs.r = radius;\n\t\t\t} else {\n\t\t\t\tattrs.rx = radius.width;\n\t\t\t\tattrs.ry = radius.height;\n\t\t\t}\n\t\t}\n\t\treturn SvgElement.create(type, attrs, formatter);\n\t}\n\n\tfunction exportCompoundPath(item, options) {\n\t\tvar attrs = getTransform(item._matrix);\n\t\tvar data = item.getPathData(null, options.precision);\n\t\tif (data)\n\t\t\tattrs.d = data;\n\t\treturn SvgElement.create('path', attrs, formatter);\n\t}\n\n\tfunction exportSymbolItem(item, options) {\n\t\tvar attrs = getTransform(item._matrix, true),\n\t\t\tdefinition = item._definition,\n\t\t\tnode = getDefinition(definition, 'symbol'),\n\t\t\tdefinitionItem = definition._item,\n\t\t\tbounds = definitionItem.getStrokeBounds();\n\t\tif (!node) {\n\t\t\tnode = SvgElement.create('symbol', {\n\t\t\t\tviewBox: formatter.rectangle(bounds)\n\t\t\t});\n\t\t\tnode.appendChild(exportSVG(definitionItem, options));\n\t\t\tsetDefinition(definition, node, 'symbol');\n\t\t}\n\t\tattrs.href = '#' + node.id;\n\t\tattrs.x += bounds.x;\n\t\tattrs.y += bounds.y;\n\t\tattrs.width = bounds.width;\n\t\tattrs.height = bounds.height;\n\t\tattrs.overflow = 'visible';\n\t\treturn SvgElement.create('use', attrs, formatter);\n\t}\n\n\tfunction exportGradient(color) {\n\t\tvar gradientNode = getDefinition(color, 'color');\n\t\tif (!gradientNode) {\n\t\t\tvar gradient = color.getGradient(),\n\t\t\t\tradial = gradient._radial,\n\t\t\t\torigin = color.getOrigin(),\n\t\t\t\tdestination = color.getDestination(),\n\t\t\t\tattrs;\n\t\t\tif (radial) {\n\t\t\t\tattrs = {\n\t\t\t\t\tcx: origin.x,\n\t\t\t\t\tcy: origin.y,\n\t\t\t\t\tr: origin.getDistance(destination)\n\t\t\t\t};\n\t\t\t\tvar highlight = color.getHighlight();\n\t\t\t\tif (highlight) {\n\t\t\t\t\tattrs.fx = highlight.x;\n\t\t\t\t\tattrs.fy = highlight.y;\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tattrs = {\n\t\t\t\t\tx1: origin.x,\n\t\t\t\t\ty1: origin.y,\n\t\t\t\t\tx2: destination.x,\n\t\t\t\t\ty2: destination.y\n\t\t\t\t};\n\t\t\t}\n\t\t\tattrs.gradientUnits = 'userSpaceOnUse';\n\t\t\tgradientNode = SvgElement.create((radial ? 'radial' : 'linear')\n\t\t\t\t\t+ 'Gradient', attrs, formatter);\n\t\t\tvar stops = gradient._stops;\n\t\t\tfor (var i = 0, l = stops.length; i < l; i++) {\n\t\t\t\tvar stop = stops[i],\n\t\t\t\t\tstopColor = stop._color,\n\t\t\t\t\talpha = stopColor.getAlpha(),\n\t\t\t\t\toffset = stop._offset;\n\t\t\t\tattrs = {\n\t\t\t\t\toffset: offset == null ? i / (l - 1) : offset\n\t\t\t\t};\n\t\t\t\tif (stopColor)\n\t\t\t\t\tattrs['stop-color'] = stopColor.toCSS(true);\n\t\t\t\tif (alpha < 1)\n\t\t\t\t\tattrs['stop-opacity'] = alpha;\n\t\t\t\tgradientNode.appendChild(\n\t\t\t\t\t\tSvgElement.create('stop', attrs, formatter));\n\t\t\t}\n\t\t\tsetDefinition(color, gradientNode, 'color');\n\t\t}\n\t\treturn 'url(#' + gradientNode.id + ')';\n\t}\n\n\tfunction exportText(item) {\n\t\tvar node = SvgElement.create('text', getTransform(item._matrix, true),\n\t\t\t\tformatter);\n\t\tnode.textContent = item._content;\n\t\treturn node;\n\t}\n\n\tvar exporters = {\n\t\tGroup: exportGroup,\n\t\tLayer: exportGroup,\n\t\tRaster: exportRaster,\n\t\tPath: exportPath,\n\t\tShape: exportShape,\n\t\tCompoundPath: exportCompoundPath,\n\t\tSymbolItem: exportSymbolItem,\n\t\tPointText: exportText\n\t};\n\n\tfunction applyStyle(item, node, isRoot) {\n\t\tvar attrs = {},\n\t\t\tparent = !isRoot && item.getParent(),\n\t\t\tstyle = [];\n\n\t\tif (item._name != null)\n\t\t\tattrs.id = item._name;\n\n\t\tBase.each(SvgStyles, function(entry) {\n\t\t\tvar get = entry.get,\n\t\t\t\ttype = entry.type,\n\t\t\t\tvalue = item[get]();\n\t\t\tif (entry.exportFilter\n\t\t\t\t\t? entry.exportFilter(item, value)\n\t\t\t\t\t: !parent || !Base.equals(parent[get](), value)) {\n\t\t\t\tif (type === 'color' && value != null) {\n\t\t\t\t\tvar alpha = value.getAlpha();\n\t\t\t\t\tif (alpha < 1)\n\t\t\t\t\t\tattrs[entry.attribute + '-opacity'] = alpha;\n\t\t\t\t}\n\t\t\t\tif (type === 'style') {\n\t\t\t\t\tstyle.push(entry.attribute + ': ' + value);\n\t\t\t\t} else {\n\t\t\t\t\tattrs[entry.attribute] = value == null ? 'none'\n\t\t\t\t\t\t\t: type === 'color' ? value.gradient\n\t\t\t\t\t\t\t\t? exportGradient(value, item)\n\t\t\t\t\t\t\t\t: value.toCSS(true)\n\t\t\t\t\t\t\t: type === 'array' ? value.join(',')\n\t\t\t\t\t\t\t: type === 'lookup' ? entry.toSVG[value]\n\t\t\t\t\t\t\t: value;\n\t\t\t\t}\n\t\t\t}\n\t\t});\n\n\t\tif (style.length)\n\t\t\tattrs.style = style.join(';');\n\n\t\tif (attrs.opacity === 1)\n\t\t\tdelete attrs.opacity;\n\n\t\tif (!item._visible)\n\t\t\tattrs.visibility = 'hidden';\n\n\t\treturn SvgElement.set(node, attrs, formatter);\n\t}\n\n\tvar definitions;\n\tfunction getDefinition(item, type) {\n\t\tif (!definitions)\n\t\t\tdefinitions = { ids: {}, svgs: {} };\n\t\treturn item && definitions.svgs[type + '-'\n\t\t\t\t+ (item._id || item.__id || (item.__id = UID.get('svg')))];\n\t}\n\n\tfunction setDefinition(item, node, type) {\n\t\tif (!definitions)\n\t\t\tgetDefinition();\n\t\tvar typeId = definitions.ids[type] = (definitions.ids[type] || 0) + 1;\n\t\tnode.id = type + '-' + typeId;\n\t\tdefinitions.svgs[type + '-' + (item._id || item.__id)] = node;\n\t}\n\n\tfunction exportDefinitions(node, options) {\n\t\tvar svg = node,\n\t\t\tdefs = null;\n\t\tif (definitions) {\n\t\t\tsvg = node.nodeName.toLowerCase() === 'svg' && node;\n\t\t\tfor (var i in definitions.svgs) {\n\t\t\t\tif (!defs) {\n\t\t\t\t\tif (!svg) {\n\t\t\t\t\t\tsvg = SvgElement.create('svg');\n\t\t\t\t\t\tsvg.appendChild(node);\n\t\t\t\t\t}\n\t\t\t\t\tdefs = svg.insertBefore(SvgElement.create('defs'),\n\t\t\t\t\t\t\tsvg.firstChild);\n\t\t\t\t}\n\t\t\t\tdefs.appendChild(definitions.svgs[i]);\n\t\t\t}\n\t\t\tdefinitions = null;\n\t\t}\n\t\treturn options.asString\n\t\t\t\t? new self.XMLSerializer().serializeToString(svg)\n\t\t\t\t: svg;\n\t}\n\n\tfunction exportSVG(item, options, isRoot) {\n\t\tvar exporter = exporters[item._class],\n\t\t\tnode = exporter && exporter(item, options);\n\t\tif (node) {\n\t\t\tvar onExport = options.onExport;\n\t\t\tif (onExport)\n\t\t\t\tnode = onExport(item, node, options) || node;\n\t\t\tvar data = JSON.stringify(item._data);\n\t\t\tif (data && data !== '{}' && data !== 'null')\n\t\t\t\tnode.setAttribute('data-paper-data', data);\n\t\t}\n\t\treturn node && applyStyle(item, node, isRoot);\n\t}\n\n\tfunction setOptions(options) {\n\t\tif (!options)\n\t\t\toptions = {};\n\t\tformatter = new Formatter(options.precision);\n\t\treturn options;\n\t}\n\n\tItem.inject({\n\t\texportSVG: function(options) {\n\t\t\toptions = setOptions(options);\n\t\t\treturn exportDefinitions(exportSVG(this, options, true), options);\n\t\t}\n\t});\n\n\tProject.inject({\n\t\texportSVG: function(options) {\n\t\t\toptions = setOptions(options);\n\t\t\tvar children = this._children,\n\t\t\t\tview = this.getView(),\n\t\t\t\tbounds = Base.pick(options.bounds, 'view'),\n\t\t\t\tmx = options.matrix || bounds === 'view' && view._matrix,\n\t\t\t\tmatrix = mx && Matrix.read([mx]),\n\t\t\t\trect = bounds === 'view'\n\t\t\t\t\t? new Rectangle([0, 0], view.getViewSize())\n\t\t\t\t\t: bounds === 'content'\n\t\t\t\t\t\t? Item._getBounds(children, matrix, { stroke: true })\n\t\t\t\t\t\t\t.rect\n\t\t\t\t\t\t: Rectangle.read([bounds], 0, { readNull: true }),\n\t\t\t\tattrs = {\n\t\t\t\t\tversion: '1.1',\n\t\t\t\t\txmlns: SvgElement.svg,\n\t\t\t\t\t'xmlns:xlink': SvgElement.xlink,\n\t\t\t\t};\n\t\t\tif (rect) {\n\t\t\t\tattrs.width = rect.width;\n\t\t\t\tattrs.height = rect.height;\n\t\t\t\tif (rect.x || rect.x === 0 || rect.y || rect.y === 0)\n\t\t\t\t\tattrs.viewBox = formatter.rectangle(rect);\n\t\t\t}\n\t\t\tvar node = SvgElement.create('svg', attrs, formatter),\n\t\t\t\tparent = node;\n\t\t\tif (matrix && !matrix.isIdentity()) {\n\t\t\t\tparent = node.appendChild(SvgElement.create('g',\n\t\t\t\t\t\tgetTransform(matrix), formatter));\n\t\t\t}\n\t\t\tfor (var i = 0, l = children.length; i < l; i++) {\n\t\t\t\tparent.appendChild(exportSVG(children[i], options, true));\n\t\t\t}\n\t\t\treturn exportDefinitions(node, options);\n\t\t}\n\t});\n};\n\nnew function() {\n\n\tvar definitions = {},\n\t\trootSize;\n\n\tfunction getValue(node, name, isString, allowNull, allowPercent,\n\t\t\tdefaultValue) {\n\t\tvar value = SvgElement.get(node, name) || defaultValue,\n\t\t\tres = value == null\n\t\t\t\t? allowNull\n\t\t\t\t\t? null\n\t\t\t\t\t: isString ? '' : 0\n\t\t\t\t: isString\n\t\t\t\t\t? value\n\t\t\t\t\t: parseFloat(value);\n\t\treturn /%\\s*$/.test(value)\n\t\t\t? (res / 100) * (allowPercent ? 1\n\t\t\t\t: rootSize[/x|^width/.test(name) ? 'width' : 'height'])\n\t\t\t: res;\n\t}\n\n\tfunction getPoint(node, x, y, allowNull, allowPercent, defaultX, defaultY) {\n\t\tx = getValue(node, x || 'x', false, allowNull, allowPercent, defaultX);\n\t\ty = getValue(node, y || 'y', false, allowNull, allowPercent, defaultY);\n\t\treturn allowNull && (x == null || y == null) ? null\n\t\t\t\t: new Point(x, y);\n\t}\n\n\tfunction getSize(node, w, h, allowNull, allowPercent) {\n\t\tw = getValue(node, w || 'width', false, allowNull, allowPercent);\n\t\th = getValue(node, h || 'height', false, allowNull, allowPercent);\n\t\treturn allowNull && (w == null || h == null) ? null\n\t\t\t\t: new Size(w, h);\n\t}\n\n\tfunction convertValue(value, type, lookup) {\n\t\treturn value === 'none' ? null\n\t\t\t\t: type === 'number' ? parseFloat(value)\n\t\t\t\t: type === 'array' ?\n\t\t\t\t\tvalue ? value.split(/[\\s,]+/g).map(parseFloat) : []\n\t\t\t\t: type === 'color' ? getDefinition(value) || value\n\t\t\t\t: type === 'lookup' ? lookup[value]\n\t\t\t\t: value;\n\t}\n\n\tfunction importGroup(node, type, options, isRoot) {\n\t\tvar nodes = node.childNodes,\n\t\t\tisClip = type === 'clippath',\n\t\t\tisDefs = type === 'defs',\n\t\t\titem = new Group(),\n\t\t\tproject = item._project,\n\t\t\tcurrentStyle = project._currentStyle,\n\t\t\tchildren = [];\n\t\tif (!isClip && !isDefs) {\n\t\t\titem = applyAttributes(item, node, isRoot);\n\t\t\tproject._currentStyle = item._style.clone();\n\t\t}\n\t\tif (isRoot) {\n\t\t\tvar defs = node.querySelectorAll('defs');\n\t\t\tfor (var i = 0, l = defs.length; i < l; i++) {\n\t\t\t\timportNode(defs[i], options, false);\n\t\t\t}\n\t\t}\n\t\tfor (var i = 0, l = nodes.length; i < l; i++) {\n\t\t\tvar childNode = nodes[i],\n\t\t\t\tchild;\n\t\t\tif (childNode.nodeType === 1\n\t\t\t\t\t&& !/^defs$/i.test(childNode.nodeName)\n\t\t\t\t\t&& (child = importNode(childNode, options, false))\n\t\t\t\t\t&& !(child instanceof SymbolDefinition))\n\t\t\t\tchildren.push(child);\n\t\t}\n\t\titem.addChildren(children);\n\t\tif (isClip)\n\t\t\titem = applyAttributes(item.reduce(), node, isRoot);\n\t\tproject._currentStyle = currentStyle;\n\t\tif (isClip || isDefs) {\n\t\t\titem.remove();\n\t\t\titem = null;\n\t\t}\n\t\treturn item;\n\t}\n\n\tfunction importPoly(node, type) {\n\t\tvar coords = node.getAttribute('points').match(\n\t\t\t\t\t/[+-]?(?:\\d*\\.\\d+|\\d+\\.?)(?:[eE][+-]?\\d+)?/g),\n\t\t\tpoints = [];\n\t\tfor (var i = 0, l = coords.length; i < l; i += 2)\n\t\t\tpoints.push(new Point(\n\t\t\t\t\tparseFloat(coords[i]),\n\t\t\t\t\tparseFloat(coords[i + 1])));\n\t\tvar path = new Path(points);\n\t\tif (type === 'polygon')\n\t\t\tpath.closePath();\n\t\treturn path;\n\t}\n\n\tfunction importPath(node) {\n\t\treturn PathItem.create(node.getAttribute('d'));\n\t}\n\n\tfunction importGradient(node, type) {\n\t\tvar id = (getValue(node, 'href', true) || '').substring(1),\n\t\t\tradial = type === 'radialgradient',\n\t\t\tgradient;\n\t\tif (id) {\n\t\t\tgradient = definitions[id].getGradient();\n\t\t\tif (gradient._radial ^ radial) {\n\t\t\t\tgradient = gradient.clone();\n\t\t\t\tgradient._radial = radial;\n\t\t\t}\n\t\t} else {\n\t\t\tvar nodes = node.childNodes,\n\t\t\t\tstops = [];\n\t\t\tfor (var i = 0, l = nodes.length; i < l; i++) {\n\t\t\t\tvar child = nodes[i];\n\t\t\t\tif (child.nodeType === 1)\n\t\t\t\t\tstops.push(applyAttributes(new GradientStop(), child));\n\t\t\t}\n\t\t\tgradient = new Gradient(stops, radial);\n\t\t}\n\t\tvar origin, destination, highlight,\n\t\t\tscaleToBounds = getValue(node, 'gradientUnits', true) !==\n\t\t\t\t'userSpaceOnUse';\n\t\tif (radial) {\n\t\t\torigin = getPoint(node, 'cx', 'cy', false, scaleToBounds,\n\t\t\t\t'50%', '50%');\n\t\t\tdestination = origin.add(\n\t\t\t\tgetValue(node, 'r', false, false, scaleToBounds, '50%'), 0);\n\t\t\thighlight = getPoint(node, 'fx', 'fy', true, scaleToBounds);\n\t\t} else {\n\t\t\torigin = getPoint(node, 'x1', 'y1', false, scaleToBounds,\n\t\t\t\t'0%', '0%');\n\t\t\tdestination = getPoint(node, 'x2', 'y2', false, scaleToBounds,\n\t\t\t\t'100%', '0%');\n\t\t}\n\t\tvar color = applyAttributes(\n\t\t\t\tnew Color(gradient, origin, destination, highlight), node);\n\t\tcolor._scaleToBounds = scaleToBounds;\n\t\treturn null;\n\t}\n\n\tvar importers = {\n\t\t'#document': function (node, type, options, isRoot) {\n\t\t\tvar nodes = node.childNodes;\n\t\t\tfor (var i = 0, l = nodes.length; i < l; i++) {\n\t\t\t\tvar child = nodes[i];\n\t\t\t\tif (child.nodeType === 1)\n\t\t\t\t\treturn importNode(child, options, isRoot);\n\t\t\t}\n\t\t},\n\t\tg: importGroup,\n\t\tsvg: importGroup,\n\t\tclippath: importGroup,\n\t\tpolygon: importPoly,\n\t\tpolyline: importPoly,\n\t\tpath: importPath,\n\t\tlineargradient: importGradient,\n\t\tradialgradient: importGradient,\n\n\t\timage: function (node) {\n\t\t\tvar raster = new Raster(getValue(node, 'href', true));\n\t\t\traster.on('load', function() {\n\t\t\t\tvar size = getSize(node);\n\t\t\t\tthis.setSize(size);\n\t\t\t\tvar center = getPoint(node).add(size.divide(2));\n\t\t\t\tthis._matrix.append(new Matrix().translate(center));\n\t\t\t});\n\t\t\treturn raster;\n\t\t},\n\n\t\tsymbol: function(node, type, options, isRoot) {\n\t\t\treturn new SymbolDefinition(\n\t\t\t\t\timportGroup(node, type, options, isRoot), true);\n\t\t},\n\n\t\tdefs: importGroup,\n\n\t\tuse: function(node) {\n\t\t\tvar id = (getValue(node, 'href', true) || '').substring(1),\n\t\t\t\tdefinition = definitions[id],\n\t\t\t\tpoint = getPoint(node);\n\t\t\treturn definition\n\t\t\t\t\t? definition instanceof SymbolDefinition\n\t\t\t\t\t\t? definition.place(point)\n\t\t\t\t\t\t: definition.clone().translate(point)\n\t\t\t\t\t: null;\n\t\t},\n\n\t\tcircle: function(node) {\n\t\t\treturn new Shape.Circle(\n\t\t\t\t\tgetPoint(node, 'cx', 'cy'),\n\t\t\t\t\tgetValue(node, 'r'));\n\t\t},\n\n\t\tellipse: function(node) {\n\t\t\treturn new Shape.Ellipse({\n\t\t\t\tcenter: getPoint(node, 'cx', 'cy'),\n\t\t\t\tradius: getSize(node, 'rx', 'ry')\n\t\t\t});\n\t\t},\n\n\t\trect: function(node) {\n\t\t\treturn new Shape.Rectangle(new Rectangle(\n\t\t\t\t\t\tgetPoint(node),\n\t\t\t\t\t\tgetSize(node)\n\t\t\t\t\t), getSize(node, 'rx', 'ry'));\n\t\t\t},\n\n\t\tline: function(node) {\n\t\t\treturn new Path.Line(\n\t\t\t\t\tgetPoint(node, 'x1', 'y1'),\n\t\t\t\t\tgetPoint(node, 'x2', 'y2'));\n\t\t},\n\n\t\ttext: function(node) {\n\t\t\tvar text = new PointText(getPoint(node).add(\n\t\t\t\t\tgetPoint(node, 'dx', 'dy')));\n\t\t\ttext.setContent(node.textContent.trim() || '');\n\t\t\treturn text;\n\t\t},\n\n\t\tswitch: importGroup\n\t};\n\n\tfunction applyTransform(item, value, name, node) {\n\t\tif (item.transform) {\n\t\t\tvar transforms = (node.getAttribute(name) || '').split(/\\)\\s*/g),\n\t\t\t\tmatrix = new Matrix();\n\t\t\tfor (var i = 0, l = transforms.length; i < l; i++) {\n\t\t\t\tvar transform = transforms[i];\n\t\t\t\tif (!transform)\n\t\t\t\t\tbreak;\n\t\t\t\tvar parts = transform.split(/\\(\\s*/),\n\t\t\t\t\tcommand = parts[0],\n\t\t\t\t\tv = parts[1].split(/[\\s,]+/g);\n\t\t\t\tfor (var j = 0, m = v.length; j < m; j++)\n\t\t\t\t\tv[j] = parseFloat(v[j]);\n\t\t\t\tswitch (command) {\n\t\t\t\tcase 'matrix':\n\t\t\t\t\tmatrix.append(\n\t\t\t\t\t\t\tnew Matrix(v[0], v[1], v[2], v[3], v[4], v[5]));\n\t\t\t\t\tbreak;\n\t\t\t\tcase 'rotate':\n\t\t\t\t\tmatrix.rotate(v[0], v[1] || 0, v[2] || 0);\n\t\t\t\t\tbreak;\n\t\t\t\tcase 'translate':\n\t\t\t\t\tmatrix.translate(v[0], v[1] || 0);\n\t\t\t\t\tbreak;\n\t\t\t\tcase 'scale':\n\t\t\t\t\tmatrix.scale(v);\n\t\t\t\t\tbreak;\n\t\t\t\tcase 'skewX':\n\t\t\t\t\tmatrix.skew(v[0], 0);\n\t\t\t\t\tbreak;\n\t\t\t\tcase 'skewY':\n\t\t\t\t\tmatrix.skew(0, v[0]);\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t\titem.transform(matrix);\n\t\t}\n\t}\n\n\tfunction applyOpacity(item, value, name) {\n\t\tvar key = name === 'fill-opacity' ? 'getFillColor' : 'getStrokeColor',\n\t\t\tcolor = item[key] && item[key]();\n\t\tif (color)\n\t\t\tcolor.setAlpha(parseFloat(value));\n\t}\n\n\tvar attributes = Base.set(Base.each(SvgStyles, function(entry) {\n\t\tthis[entry.attribute] = function(item, value) {\n\t\t\tif (item[entry.set]) {\n\t\t\t\titem[entry.set](convertValue(value, entry.type, entry.fromSVG));\n\t\t\t\tif (entry.type === 'color') {\n\t\t\t\t\tvar color = item[entry.get]();\n\t\t\t\t\tif (color) {\n\t\t\t\t\t\tif (color._scaleToBounds) {\n\t\t\t\t\t\t\tvar bounds = item.getBounds();\n\t\t\t\t\t\t\tcolor.transform(new Matrix()\n\t\t\t\t\t\t\t\t.translate(bounds.getPoint())\n\t\t\t\t\t\t\t\t.scale(bounds.getSize()));\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t};\n\t}, {}), {\n\t\tid: function(item, value) {\n\t\t\tdefinitions[value] = item;\n\t\t\tif (item.setName)\n\t\t\t\titem.setName(value);\n\t\t},\n\n\t\t'clip-path': function(item, value) {\n\t\t\tvar clip = getDefinition(value);\n\t\t\tif (clip) {\n\t\t\t\tclip = clip.clone();\n\t\t\t\tclip.setClipMask(true);\n\t\t\t\tif (item instanceof Group) {\n\t\t\t\t\titem.insertChild(0, clip);\n\t\t\t\t} else {\n\t\t\t\t\treturn new Group(clip, item);\n\t\t\t\t}\n\t\t\t}\n\t\t},\n\n\t\tgradientTransform: applyTransform,\n\t\ttransform: applyTransform,\n\n\t\t'fill-opacity': applyOpacity,\n\t\t'stroke-opacity': applyOpacity,\n\n\t\tvisibility: function(item, value) {\n\t\t\tif (item.setVisible)\n\t\t\t\titem.setVisible(value === 'visible');\n\t\t},\n\n\t\tdisplay: function(item, value) {\n\t\t\tif (item.setVisible)\n\t\t\t\titem.setVisible(value !== null);\n\t\t},\n\n\t\t'stop-color': function(item, value) {\n\t\t\tif (item.setColor)\n\t\t\t\titem.setColor(value);\n\t\t},\n\n\t\t'stop-opacity': function(item, value) {\n\t\t\tif (item._color)\n\t\t\t\titem._color.setAlpha(parseFloat(value));\n\t\t},\n\n\t\toffset: function(item, value) {\n\t\t\tif (item.setOffset) {\n\t\t\t\tvar percent = value.match(/(.*)%$/);\n\t\t\t\titem.setOffset(percent ? percent[1] / 100 : parseFloat(value));\n\t\t\t}\n\t\t},\n\n\t\tviewBox: function(item, value, name, node, styles) {\n\t\t\tvar rect = new Rectangle(convertValue(value, 'array')),\n\t\t\t\tsize = getSize(node, null, null, true),\n\t\t\t\tgroup,\n\t\t\t\tmatrix;\n\t\t\tif (item instanceof Group) {\n\t\t\t\tvar scale = size ? size.divide(rect.getSize()) : 1,\n\t\t\t\tmatrix = new Matrix().scale(scale)\n\t\t\t\t\t\t.translate(rect.getPoint().negate());\n\t\t\t\tgroup = item;\n\t\t\t} else if (item instanceof SymbolDefinition) {\n\t\t\t\tif (size)\n\t\t\t\t\trect.setSize(size);\n\t\t\t\tgroup = item._item;\n\t\t\t}\n\t\t\tif (group)  {\n\t\t\t\tif (getAttribute(node, 'overflow', styles) !== 'visible') {\n\t\t\t\t\tvar clip = new Shape.Rectangle(rect);\n\t\t\t\t\tclip.setClipMask(true);\n\t\t\t\t\tgroup.addChild(clip);\n\t\t\t\t}\n\t\t\t\tif (matrix)\n\t\t\t\t\tgroup.transform(matrix);\n\t\t\t}\n\t\t}\n\t});\n\n\tfunction getAttribute(node, name, styles) {\n\t\tvar attr = node.attributes[name],\n\t\t\tvalue = attr && attr.value;\n\t\tif (!value && node.style) {\n\t\t\tvar style = Base.camelize(name);\n\t\t\tvalue = node.style[style];\n\t\t\tif (!value && styles.node[style] !== styles.parent[style])\n\t\t\t\tvalue = styles.node[style];\n\t\t}\n\t\treturn !value ? undefined\n\t\t\t\t: value === 'none' ? null\n\t\t\t\t: value;\n\t}\n\n\tfunction applyAttributes(item, node, isRoot) {\n\t\tvar parent = node.parentNode,\n\t\t\tstyles = {\n\t\t\t\tnode: DomElement.getStyles(node) || {},\n\t\t\t\tparent: !isRoot && !/^defs$/i.test(parent.tagName)\n\t\t\t\t\t\t&& DomElement.getStyles(parent) || {}\n\t\t\t};\n\t\tBase.each(attributes, function(apply, name) {\n\t\t\tvar value = getAttribute(node, name, styles);\n\t\t\titem = value !== undefined\n\t\t\t\t\t&& apply(item, value, name, node, styles) || item;\n\t\t});\n\t\treturn item;\n\t}\n\n\tfunction getDefinition(value) {\n\t\tvar match = value && value.match(/\\((?:[\"'#]*)([^\"')]+)/),\n\t\t\tname = match && match[1],\n\t\t\tres = name && definitions[window\n\t\t\t\t\t? name.replace(window.location.href.split('#')[0] + '#', '')\n\t\t\t\t\t: name];\n\t\tif (res && res._scaleToBounds) {\n\t\t\tres = res.clone();\n\t\t\tres._scaleToBounds = true;\n\t\t}\n\t\treturn res;\n\t}\n\n\tfunction importNode(node, options, isRoot) {\n\t\tvar type = node.nodeName.toLowerCase(),\n\t\t\tisElement = type !== '#document',\n\t\t\tbody = document.body,\n\t\t\tcontainer,\n\t\t\tparent,\n\t\t\tnext;\n\t\tif (isRoot && isElement) {\n\t\t\trootSize = paper.getView().getSize();\n\t\t\trootSize = getSize(node, null, null, true) || rootSize;\n\t\t\tcontainer = SvgElement.create('svg', {\n\t\t\t\tstyle: 'stroke-width: 1px; stroke-miterlimit: 10'\n\t\t\t});\n\t\t\tparent = node.parentNode;\n\t\t\tnext = node.nextSibling;\n\t\t\tcontainer.appendChild(node);\n\t\t\tbody.appendChild(container);\n\t\t}\n\t\tvar settings = paper.settings,\n\t\t\tapplyMatrix = settings.applyMatrix,\n\t\t\tinsertItems = settings.insertItems;\n\t\tsettings.applyMatrix = false;\n\t\tsettings.insertItems = false;\n\t\tvar importer = importers[type],\n\t\t\titem = importer && importer(node, type, options, isRoot) || null;\n\t\tsettings.insertItems = insertItems;\n\t\tsettings.applyMatrix = applyMatrix;\n\t\tif (item) {\n\t\t\tif (isElement && !(item instanceof Group))\n\t\t\t\titem = applyAttributes(item, node, isRoot);\n\t\t\tvar onImport = options.onImport,\n\t\t\t\tdata = isElement && node.getAttribute('data-paper-data');\n\t\t\tif (onImport)\n\t\t\t\titem = onImport(node, item, options) || item;\n\t\t\tif (options.expandShapes && item instanceof Shape) {\n\t\t\t\titem.remove();\n\t\t\t\titem = item.toPath();\n\t\t\t}\n\t\t\tif (data)\n\t\t\t\titem._data = JSON.parse(data);\n\t\t}\n\t\tif (container) {\n\t\t\tbody.removeChild(container);\n\t\t\tif (parent) {\n\t\t\t\tif (next) {\n\t\t\t\t\tparent.insertBefore(node, next);\n\t\t\t\t} else {\n\t\t\t\t\tparent.appendChild(node);\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\tif (isRoot) {\n\t\t\tdefinitions = {};\n\t\t\tif (item && Base.pick(options.applyMatrix, applyMatrix))\n\t\t\t\titem.matrix.apply(true, true);\n\t\t}\n\t\treturn item;\n\t}\n\n\tfunction importSVG(source, options, owner) {\n\t\tif (!source)\n\t\t\treturn null;\n\t\toptions = typeof options === 'function' ? { onLoad: options }\n\t\t\t\t: options || {};\n\t\tvar scope = paper,\n\t\t\titem = null;\n\n\t\tfunction onLoad(svg) {\n\t\t\ttry {\n\t\t\t\tvar node = typeof svg === 'object'\n\t\t\t\t\t? svg\n\t\t\t\t\t: new self.DOMParser().parseFromString(\n\t\t\t\t\t\tsvg.trim(),\n\t\t\t\t\t\t'image/svg+xml'\n\t\t\t\t\t);\n\t\t\t\tif (!node.nodeName) {\n\t\t\t\t\tnode = null;\n\t\t\t\t\tthrow new Error('Unsupported SVG source: ' + source);\n\t\t\t\t}\n\t\t\t\tpaper = scope;\n\t\t\t\titem = importNode(node, options, true);\n\t\t\t\tif (!options || options.insert !== false) {\n\t\t\t\t\towner._insertItem(undefined, item);\n\t\t\t\t}\n\t\t\t\tvar onLoad = options.onLoad;\n\t\t\t\tif (onLoad)\n\t\t\t\t\tonLoad(item, svg);\n\t\t\t} catch (e) {\n\t\t\t\tonError(e);\n\t\t\t}\n\t\t}\n\n\t\tfunction onError(message, status) {\n\t\t\tvar onError = options.onError;\n\t\t\tif (onError) {\n\t\t\t\tonError(message, status);\n\t\t\t} else {\n\t\t\t\tthrow new Error(message);\n\t\t\t}\n\t\t}\n\n\t\tif (typeof source === 'string' && !/^[\\s\\S]*</.test(source)) {\n\t\t\tvar node = document.getElementById(source);\n\t\t\tif (node) {\n\t\t\t\tonLoad(node);\n\t\t\t} else {\n\t\t\t\tHttp.request({\n\t\t\t\t\turl: source,\n\t\t\t\t\tasync: true,\n\t\t\t\t\tonLoad: onLoad,\n\t\t\t\t\tonError: onError\n\t\t\t\t});\n\t\t\t}\n\t\t} else if (typeof File !== 'undefined' && source instanceof File) {\n\t\t\tvar reader = new FileReader();\n\t\t\treader.onload = function() {\n\t\t\t\tonLoad(reader.result);\n\t\t\t};\n\t\t\treader.onerror = function() {\n\t\t\t\tonError(reader.error);\n\t\t\t};\n\t\t\treturn reader.readAsText(source);\n\t\t} else {\n\t\t\tonLoad(source);\n\t\t}\n\n\t\treturn item;\n\t}\n\n\tItem.inject({\n\t\timportSVG: function(node, options) {\n\t\t\treturn importSVG(node, options, this);\n\t\t}\n\t});\n\n\tProject.inject({\n\t\timportSVG: function(node, options) {\n\t\t\tthis.activate();\n\t\t\treturn importSVG(node, options, this);\n\t\t}\n\t});\n};\n\nBase.exports.PaperScript = function() {\n\tvar global = this,\n\t\tacorn = global.acorn;\n\tif (!acorn && typeof require !== 'undefined') {\n\t\ttry { acorn = require('acorn'); } catch(e) {}\n\t}\n\tif (!acorn) {\n\t\tvar exports, module;\n\t\tacorn = exports = module = {};\n\n(function(root, mod) {\n  if (typeof exports == \"object\" && typeof module == \"object\") return mod(exports);\n  if (typeof define == \"function\" && define.amd) return define([\"exports\"], mod);\n  mod(root.acorn || (root.acorn = {}));\n})(this, function(exports) {\n  \"use strict\";\n\n  exports.version = \"0.5.0\";\n\n  var options, input, inputLen, sourceFile;\n\n  exports.parse = function(inpt, opts) {\n\tinput = String(inpt); inputLen = input.length;\n\tsetOptions(opts);\n\tinitTokenState();\n\treturn parseTopLevel(options.program);\n  };\n\n  var defaultOptions = exports.defaultOptions = {\n\tecmaVersion: 5,\n\tstrictSemicolons: false,\n\tallowTrailingCommas: true,\n\tforbidReserved: false,\n\tallowReturnOutsideFunction: false,\n\tlocations: false,\n\tonComment: null,\n\tranges: false,\n\tprogram: null,\n\tsourceFile: null,\n\tdirectSourceFile: null\n  };\n\n  function setOptions(opts) {\n\toptions = opts || {};\n\tfor (var opt in defaultOptions) if (!Object.prototype.hasOwnProperty.call(options, opt))\n\t  options[opt] = defaultOptions[opt];\n\tsourceFile = options.sourceFile || null;\n  }\n\n  var getLineInfo = exports.getLineInfo = function(input, offset) {\n\tfor (var line = 1, cur = 0;;) {\n\t  lineBreak.lastIndex = cur;\n\t  var match = lineBreak.exec(input);\n\t  if (match && match.index < offset) {\n\t\t++line;\n\t\tcur = match.index + match[0].length;\n\t  } else break;\n\t}\n\treturn {line: line, column: offset - cur};\n  };\n\n  exports.tokenize = function(inpt, opts) {\n\tinput = String(inpt); inputLen = input.length;\n\tsetOptions(opts);\n\tinitTokenState();\n\n\tvar t = {};\n\tfunction getToken(forceRegexp) {\n\t  lastEnd = tokEnd;\n\t  readToken(forceRegexp);\n\t  t.start = tokStart; t.end = tokEnd;\n\t  t.startLoc = tokStartLoc; t.endLoc = tokEndLoc;\n\t  t.type = tokType; t.value = tokVal;\n\t  return t;\n\t}\n\tgetToken.jumpTo = function(pos, reAllowed) {\n\t  tokPos = pos;\n\t  if (options.locations) {\n\t\ttokCurLine = 1;\n\t\ttokLineStart = lineBreak.lastIndex = 0;\n\t\tvar match;\n\t\twhile ((match = lineBreak.exec(input)) && match.index < pos) {\n\t\t  ++tokCurLine;\n\t\t  tokLineStart = match.index + match[0].length;\n\t\t}\n\t  }\n\t  tokRegexpAllowed = reAllowed;\n\t  skipSpace();\n\t};\n\treturn getToken;\n  };\n\n  var tokPos;\n\n  var tokStart, tokEnd;\n\n  var tokStartLoc, tokEndLoc;\n\n  var tokType, tokVal;\n\n  var tokRegexpAllowed;\n\n  var tokCurLine, tokLineStart;\n\n  var lastStart, lastEnd, lastEndLoc;\n\n  var inFunction, labels, strict;\n\n  function raise(pos, message) {\n\tvar loc = getLineInfo(input, pos);\n\tmessage += \" (\" + loc.line + \":\" + loc.column + \")\";\n\tvar err = new SyntaxError(message);\n\terr.pos = pos; err.loc = loc; err.raisedAt = tokPos;\n\tthrow err;\n  }\n\n  var empty = [];\n\n  var _num = {type: \"num\"}, _regexp = {type: \"regexp\"}, _string = {type: \"string\"};\n  var _name = {type: \"name\"}, _eof = {type: \"eof\"};\n\n  var _break = {keyword: \"break\"}, _case = {keyword: \"case\", beforeExpr: true}, _catch = {keyword: \"catch\"};\n  var _continue = {keyword: \"continue\"}, _debugger = {keyword: \"debugger\"}, _default = {keyword: \"default\"};\n  var _do = {keyword: \"do\", isLoop: true}, _else = {keyword: \"else\", beforeExpr: true};\n  var _finally = {keyword: \"finally\"}, _for = {keyword: \"for\", isLoop: true}, _function = {keyword: \"function\"};\n  var _if = {keyword: \"if\"}, _return = {keyword: \"return\", beforeExpr: true}, _switch = {keyword: \"switch\"};\n  var _throw = {keyword: \"throw\", beforeExpr: true}, _try = {keyword: \"try\"}, _var = {keyword: \"var\"};\n  var _while = {keyword: \"while\", isLoop: true}, _with = {keyword: \"with\"}, _new = {keyword: \"new\", beforeExpr: true};\n  var _this = {keyword: \"this\"};\n\n  var _null = {keyword: \"null\", atomValue: null}, _true = {keyword: \"true\", atomValue: true};\n  var _false = {keyword: \"false\", atomValue: false};\n\n  var _in = {keyword: \"in\", binop: 7, beforeExpr: true};\n\n  var keywordTypes = {\"break\": _break, \"case\": _case, \"catch\": _catch,\n\t\t\t\t\t  \"continue\": _continue, \"debugger\": _debugger, \"default\": _default,\n\t\t\t\t\t  \"do\": _do, \"else\": _else, \"finally\": _finally, \"for\": _for,\n\t\t\t\t\t  \"function\": _function, \"if\": _if, \"return\": _return, \"switch\": _switch,\n\t\t\t\t\t  \"throw\": _throw, \"try\": _try, \"var\": _var, \"while\": _while, \"with\": _with,\n\t\t\t\t\t  \"null\": _null, \"true\": _true, \"false\": _false, \"new\": _new, \"in\": _in,\n\t\t\t\t\t  \"instanceof\": {keyword: \"instanceof\", binop: 7, beforeExpr: true}, \"this\": _this,\n\t\t\t\t\t  \"typeof\": {keyword: \"typeof\", prefix: true, beforeExpr: true},\n\t\t\t\t\t  \"void\": {keyword: \"void\", prefix: true, beforeExpr: true},\n\t\t\t\t\t  \"delete\": {keyword: \"delete\", prefix: true, beforeExpr: true}};\n\n  var _bracketL = {type: \"[\", beforeExpr: true}, _bracketR = {type: \"]\"}, _braceL = {type: \"{\", beforeExpr: true};\n  var _braceR = {type: \"}\"}, _parenL = {type: \"(\", beforeExpr: true}, _parenR = {type: \")\"};\n  var _comma = {type: \",\", beforeExpr: true}, _semi = {type: \";\", beforeExpr: true};\n  var _colon = {type: \":\", beforeExpr: true}, _dot = {type: \".\"}, _question = {type: \"?\", beforeExpr: true};\n\n  var _slash = {binop: 10, beforeExpr: true}, _eq = {isAssign: true, beforeExpr: true};\n  var _assign = {isAssign: true, beforeExpr: true};\n  var _incDec = {postfix: true, prefix: true, isUpdate: true}, _prefix = {prefix: true, beforeExpr: true};\n  var _logicalOR = {binop: 1, beforeExpr: true};\n  var _logicalAND = {binop: 2, beforeExpr: true};\n  var _bitwiseOR = {binop: 3, beforeExpr: true};\n  var _bitwiseXOR = {binop: 4, beforeExpr: true};\n  var _bitwiseAND = {binop: 5, beforeExpr: true};\n  var _equality = {binop: 6, beforeExpr: true};\n  var _relational = {binop: 7, beforeExpr: true};\n  var _bitShift = {binop: 8, beforeExpr: true};\n  var _plusMin = {binop: 9, prefix: true, beforeExpr: true};\n  var _multiplyModulo = {binop: 10, beforeExpr: true};\n\n  exports.tokTypes = {bracketL: _bracketL, bracketR: _bracketR, braceL: _braceL, braceR: _braceR,\n\t\t\t\t\t  parenL: _parenL, parenR: _parenR, comma: _comma, semi: _semi, colon: _colon,\n\t\t\t\t\t  dot: _dot, question: _question, slash: _slash, eq: _eq, name: _name, eof: _eof,\n\t\t\t\t\t  num: _num, regexp: _regexp, string: _string};\n  for (var kw in keywordTypes) exports.tokTypes[\"_\" + kw] = keywordTypes[kw];\n\n  function makePredicate(words) {\n\twords = words.split(\" \");\n\tvar f = \"\", cats = [];\n\tout: for (var i = 0; i < words.length; ++i) {\n\t  for (var j = 0; j < cats.length; ++j)\n\t\tif (cats[j][0].length == words[i].length) {\n\t\t  cats[j].push(words[i]);\n\t\t  continue out;\n\t\t}\n\t  cats.push([words[i]]);\n\t}\n\tfunction compareTo(arr) {\n\t  if (arr.length == 1) return f += \"return str === \" + JSON.stringify(arr[0]) + \";\";\n\t  f += \"switch(str){\";\n\t  for (var i = 0; i < arr.length; ++i) f += \"case \" + JSON.stringify(arr[i]) + \":\";\n\t  f += \"return true}return false;\";\n\t}\n\n\tif (cats.length > 3) {\n\t  cats.sort(function(a, b) {return b.length - a.length;});\n\t  f += \"switch(str.length){\";\n\t  for (var i = 0; i < cats.length; ++i) {\n\t\tvar cat = cats[i];\n\t\tf += \"case \" + cat[0].length + \":\";\n\t\tcompareTo(cat);\n\t  }\n\t  f += \"}\";\n\n\t} else {\n\t  compareTo(words);\n\t}\n\treturn new Function(\"str\", f);\n  }\n\n  var isReservedWord3 = makePredicate(\"abstract boolean byte char class double enum export extends final float goto implements import int interface long native package private protected public short static super synchronized throws transient volatile\");\n\n  var isReservedWord5 = makePredicate(\"class enum extends super const export import\");\n\n  var isStrictReservedWord = makePredicate(\"implements interface let package private protected public static yield\");\n\n  var isStrictBadIdWord = makePredicate(\"eval arguments\");\n\n  var isKeyword = makePredicate(\"break case catch continue debugger default do else finally for function if return switch throw try var while with null true false instanceof typeof void delete new in this\");\n\n  var nonASCIIwhitespace = /[\\u1680\\u180e\\u2000-\\u200a\\u202f\\u205f\\u3000\\ufeff]/;\n  var nonASCIIidentifierStartChars = \"\\xaa\\xb5\\xba\\xc0-\\xd6\\xd8-\\xf6\\xf8-\\u02c1\\u02c6-\\u02d1\\u02e0-\\u02e4\\u02ec\\u02ee\\u0370-\\u0374\\u0376\\u0377\\u037a-\\u037d\\u0386\\u0388-\\u038a\\u038c\\u038e-\\u03a1\\u03a3-\\u03f5\\u03f7-\\u0481\\u048a-\\u0527\\u0531-\\u0556\\u0559\\u0561-\\u0587\\u05d0-\\u05ea\\u05f0-\\u05f2\\u0620-\\u064a\\u066e\\u066f\\u0671-\\u06d3\\u06d5\\u06e5\\u06e6\\u06ee\\u06ef\\u06fa-\\u06fc\\u06ff\\u0710\\u0712-\\u072f\\u074d-\\u07a5\\u07b1\\u07ca-\\u07ea\\u07f4\\u07f5\\u07fa\\u0800-\\u0815\\u081a\\u0824\\u0828\\u0840-\\u0858\\u08a0\\u08a2-\\u08ac\\u0904-\\u0939\\u093d\\u0950\\u0958-\\u0961\\u0971-\\u0977\\u0979-\\u097f\\u0985-\\u098c\\u098f\\u0990\\u0993-\\u09a8\\u09aa-\\u09b0\\u09b2\\u09b6-\\u09b9\\u09bd\\u09ce\\u09dc\\u09dd\\u09df-\\u09e1\\u09f0\\u09f1\\u0a05-\\u0a0a\\u0a0f\\u0a10\\u0a13-\\u0a28\\u0a2a-\\u0a30\\u0a32\\u0a33\\u0a35\\u0a36\\u0a38\\u0a39\\u0a59-\\u0a5c\\u0a5e\\u0a72-\\u0a74\\u0a85-\\u0a8d\\u0a8f-\\u0a91\\u0a93-\\u0aa8\\u0aaa-\\u0ab0\\u0ab2\\u0ab3\\u0ab5-\\u0ab9\\u0abd\\u0ad0\\u0ae0\\u0ae1\\u0b05-\\u0b0c\\u0b0f\\u0b10\\u0b13-\\u0b28\\u0b2a-\\u0b30\\u0b32\\u0b33\\u0b35-\\u0b39\\u0b3d\\u0b5c\\u0b5d\\u0b5f-\\u0b61\\u0b71\\u0b83\\u0b85-\\u0b8a\\u0b8e-\\u0b90\\u0b92-\\u0b95\\u0b99\\u0b9a\\u0b9c\\u0b9e\\u0b9f\\u0ba3\\u0ba4\\u0ba8-\\u0baa\\u0bae-\\u0bb9\\u0bd0\\u0c05-\\u0c0c\\u0c0e-\\u0c10\\u0c12-\\u0c28\\u0c2a-\\u0c33\\u0c35-\\u0c39\\u0c3d\\u0c58\\u0c59\\u0c60\\u0c61\\u0c85-\\u0c8c\\u0c8e-\\u0c90\\u0c92-\\u0ca8\\u0caa-\\u0cb3\\u0cb5-\\u0cb9\\u0cbd\\u0cde\\u0ce0\\u0ce1\\u0cf1\\u0cf2\\u0d05-\\u0d0c\\u0d0e-\\u0d10\\u0d12-\\u0d3a\\u0d3d\\u0d4e\\u0d60\\u0d61\\u0d7a-\\u0d7f\\u0d85-\\u0d96\\u0d9a-\\u0db1\\u0db3-\\u0dbb\\u0dbd\\u0dc0-\\u0dc6\\u0e01-\\u0e30\\u0e32\\u0e33\\u0e40-\\u0e46\\u0e81\\u0e82\\u0e84\\u0e87\\u0e88\\u0e8a\\u0e8d\\u0e94-\\u0e97\\u0e99-\\u0e9f\\u0ea1-\\u0ea3\\u0ea5\\u0ea7\\u0eaa\\u0eab\\u0ead-\\u0eb0\\u0eb2\\u0eb3\\u0ebd\\u0ec0-\\u0ec4\\u0ec6\\u0edc-\\u0edf\\u0f00\\u0f40-\\u0f47\\u0f49-\\u0f6c\\u0f88-\\u0f8c\\u1000-\\u102a\\u103f\\u1050-\\u1055\\u105a-\\u105d\\u1061\\u1065\\u1066\\u106e-\\u1070\\u1075-\\u1081\\u108e\\u10a0-\\u10c5\\u10c7\\u10cd\\u10d0-\\u10fa\\u10fc-\\u1248\\u124a-\\u124d\\u1250-\\u1256\\u1258\\u125a-\\u125d\\u1260-\\u1288\\u128a-\\u128d\\u1290-\\u12b0\\u12b2-\\u12b5\\u12b8-\\u12be\\u12c0\\u12c2-\\u12c5\\u12c8-\\u12d6\\u12d8-\\u1310\\u1312-\\u1315\\u1318-\\u135a\\u1380-\\u138f\\u13a0-\\u13f4\\u1401-\\u166c\\u166f-\\u167f\\u1681-\\u169a\\u16a0-\\u16ea\\u16ee-\\u16f0\\u1700-\\u170c\\u170e-\\u1711\\u1720-\\u1731\\u1740-\\u1751\\u1760-\\u176c\\u176e-\\u1770\\u1780-\\u17b3\\u17d7\\u17dc\\u1820-\\u1877\\u1880-\\u18a8\\u18aa\\u18b0-\\u18f5\\u1900-\\u191c\\u1950-\\u196d\\u1970-\\u1974\\u1980-\\u19ab\\u19c1-\\u19c7\\u1a00-\\u1a16\\u1a20-\\u1a54\\u1aa7\\u1b05-\\u1b33\\u1b45-\\u1b4b\\u1b83-\\u1ba0\\u1bae\\u1baf\\u1bba-\\u1be5\\u1c00-\\u1c23\\u1c4d-\\u1c4f\\u1c5a-\\u1c7d\\u1ce9-\\u1cec\\u1cee-\\u1cf1\\u1cf5\\u1cf6\\u1d00-\\u1dbf\\u1e00-\\u1f15\\u1f18-\\u1f1d\\u1f20-\\u1f45\\u1f48-\\u1f4d\\u1f50-\\u1f57\\u1f59\\u1f5b\\u1f5d\\u1f5f-\\u1f7d\\u1f80-\\u1fb4\\u1fb6-\\u1fbc\\u1fbe\\u1fc2-\\u1fc4\\u1fc6-\\u1fcc\\u1fd0-\\u1fd3\\u1fd6-\\u1fdb\\u1fe0-\\u1fec\\u1ff2-\\u1ff4\\u1ff6-\\u1ffc\\u2071\\u207f\\u2090-\\u209c\\u2102\\u2107\\u210a-\\u2113\\u2115\\u2119-\\u211d\\u2124\\u2126\\u2128\\u212a-\\u212d\\u212f-\\u2139\\u213c-\\u213f\\u2145-\\u2149\\u214e\\u2160-\\u2188\\u2c00-\\u2c2e\\u2c30-\\u2c5e\\u2c60-\\u2ce4\\u2ceb-\\u2cee\\u2cf2\\u2cf3\\u2d00-\\u2d25\\u2d27\\u2d2d\\u2d30-\\u2d67\\u2d6f\\u2d80-\\u2d96\\u2da0-\\u2da6\\u2da8-\\u2dae\\u2db0-\\u2db6\\u2db8-\\u2dbe\\u2dc0-\\u2dc6\\u2dc8-\\u2dce\\u2dd0-\\u2dd6\\u2dd8-\\u2dde\\u2e2f\\u3005-\\u3007\\u3021-\\u3029\\u3031-\\u3035\\u3038-\\u303c\\u3041-\\u3096\\u309d-\\u309f\\u30a1-\\u30fa\\u30fc-\\u30ff\\u3105-\\u312d\\u3131-\\u318e\\u31a0-\\u31ba\\u31f0-\\u31ff\\u3400-\\u4db5\\u4e00-\\u9fcc\\ua000-\\ua48c\\ua4d0-\\ua4fd\\ua500-\\ua60c\\ua610-\\ua61f\\ua62a\\ua62b\\ua640-\\ua66e\\ua67f-\\ua697\\ua6a0-\\ua6ef\\ua717-\\ua71f\\ua722-\\ua788\\ua78b-\\ua78e\\ua790-\\ua793\\ua7a0-\\ua7aa\\ua7f8-\\ua801\\ua803-\\ua805\\ua807-\\ua80a\\ua80c-\\ua822\\ua840-\\ua873\\ua882-\\ua8b3\\ua8f2-\\ua8f7\\ua8fb\\ua90a-\\ua925\\ua930-\\ua946\\ua960-\\ua97c\\ua984-\\ua9b2\\ua9cf\\uaa00-\\uaa28\\uaa40-\\uaa42\\uaa44-\\uaa4b\\uaa60-\\uaa76\\uaa7a\\uaa80-\\uaaaf\\uaab1\\uaab5\\uaab6\\uaab9-\\uaabd\\uaac0\\uaac2\\uaadb-\\uaadd\\uaae0-\\uaaea\\uaaf2-\\uaaf4\\uab01-\\uab06\\uab09-\\uab0e\\uab11-\\uab16\\uab20-\\uab26\\uab28-\\uab2e\\uabc0-\\uabe2\\uac00-\\ud7a3\\ud7b0-\\ud7c6\\ud7cb-\\ud7fb\\uf900-\\ufa6d\\ufa70-\\ufad9\\ufb00-\\ufb06\\ufb13-\\ufb17\\ufb1d\\ufb1f-\\ufb28\\ufb2a-\\ufb36\\ufb38-\\ufb3c\\ufb3e\\ufb40\\ufb41\\ufb43\\ufb44\\ufb46-\\ufbb1\\ufbd3-\\ufd3d\\ufd50-\\ufd8f\\ufd92-\\ufdc7\\ufdf0-\\ufdfb\\ufe70-\\ufe74\\ufe76-\\ufefc\\uff21-\\uff3a\\uff41-\\uff5a\\uff66-\\uffbe\\uffc2-\\uffc7\\uffca-\\uffcf\\uffd2-\\uffd7\\uffda-\\uffdc\";\n  var nonASCIIidentifierChars = \"\\u0300-\\u036f\\u0483-\\u0487\\u0591-\\u05bd\\u05bf\\u05c1\\u05c2\\u05c4\\u05c5\\u05c7\\u0610-\\u061a\\u0620-\\u0649\\u0672-\\u06d3\\u06e7-\\u06e8\\u06fb-\\u06fc\\u0730-\\u074a\\u0800-\\u0814\\u081b-\\u0823\\u0825-\\u0827\\u0829-\\u082d\\u0840-\\u0857\\u08e4-\\u08fe\\u0900-\\u0903\\u093a-\\u093c\\u093e-\\u094f\\u0951-\\u0957\\u0962-\\u0963\\u0966-\\u096f\\u0981-\\u0983\\u09bc\\u09be-\\u09c4\\u09c7\\u09c8\\u09d7\\u09df-\\u09e0\\u0a01-\\u0a03\\u0a3c\\u0a3e-\\u0a42\\u0a47\\u0a48\\u0a4b-\\u0a4d\\u0a51\\u0a66-\\u0a71\\u0a75\\u0a81-\\u0a83\\u0abc\\u0abe-\\u0ac5\\u0ac7-\\u0ac9\\u0acb-\\u0acd\\u0ae2-\\u0ae3\\u0ae6-\\u0aef\\u0b01-\\u0b03\\u0b3c\\u0b3e-\\u0b44\\u0b47\\u0b48\\u0b4b-\\u0b4d\\u0b56\\u0b57\\u0b5f-\\u0b60\\u0b66-\\u0b6f\\u0b82\\u0bbe-\\u0bc2\\u0bc6-\\u0bc8\\u0bca-\\u0bcd\\u0bd7\\u0be6-\\u0bef\\u0c01-\\u0c03\\u0c46-\\u0c48\\u0c4a-\\u0c4d\\u0c55\\u0c56\\u0c62-\\u0c63\\u0c66-\\u0c6f\\u0c82\\u0c83\\u0cbc\\u0cbe-\\u0cc4\\u0cc6-\\u0cc8\\u0cca-\\u0ccd\\u0cd5\\u0cd6\\u0ce2-\\u0ce3\\u0ce6-\\u0cef\\u0d02\\u0d03\\u0d46-\\u0d48\\u0d57\\u0d62-\\u0d63\\u0d66-\\u0d6f\\u0d82\\u0d83\\u0dca\\u0dcf-\\u0dd4\\u0dd6\\u0dd8-\\u0ddf\\u0df2\\u0df3\\u0e34-\\u0e3a\\u0e40-\\u0e45\\u0e50-\\u0e59\\u0eb4-\\u0eb9\\u0ec8-\\u0ecd\\u0ed0-\\u0ed9\\u0f18\\u0f19\\u0f20-\\u0f29\\u0f35\\u0f37\\u0f39\\u0f41-\\u0f47\\u0f71-\\u0f84\\u0f86-\\u0f87\\u0f8d-\\u0f97\\u0f99-\\u0fbc\\u0fc6\\u1000-\\u1029\\u1040-\\u1049\\u1067-\\u106d\\u1071-\\u1074\\u1082-\\u108d\\u108f-\\u109d\\u135d-\\u135f\\u170e-\\u1710\\u1720-\\u1730\\u1740-\\u1750\\u1772\\u1773\\u1780-\\u17b2\\u17dd\\u17e0-\\u17e9\\u180b-\\u180d\\u1810-\\u1819\\u1920-\\u192b\\u1930-\\u193b\\u1951-\\u196d\\u19b0-\\u19c0\\u19c8-\\u19c9\\u19d0-\\u19d9\\u1a00-\\u1a15\\u1a20-\\u1a53\\u1a60-\\u1a7c\\u1a7f-\\u1a89\\u1a90-\\u1a99\\u1b46-\\u1b4b\\u1b50-\\u1b59\\u1b6b-\\u1b73\\u1bb0-\\u1bb9\\u1be6-\\u1bf3\\u1c00-\\u1c22\\u1c40-\\u1c49\\u1c5b-\\u1c7d\\u1cd0-\\u1cd2\\u1d00-\\u1dbe\\u1e01-\\u1f15\\u200c\\u200d\\u203f\\u2040\\u2054\\u20d0-\\u20dc\\u20e1\\u20e5-\\u20f0\\u2d81-\\u2d96\\u2de0-\\u2dff\\u3021-\\u3028\\u3099\\u309a\\ua640-\\ua66d\\ua674-\\ua67d\\ua69f\\ua6f0-\\ua6f1\\ua7f8-\\ua800\\ua806\\ua80b\\ua823-\\ua827\\ua880-\\ua881\\ua8b4-\\ua8c4\\ua8d0-\\ua8d9\\ua8f3-\\ua8f7\\ua900-\\ua909\\ua926-\\ua92d\\ua930-\\ua945\\ua980-\\ua983\\ua9b3-\\ua9c0\\uaa00-\\uaa27\\uaa40-\\uaa41\\uaa4c-\\uaa4d\\uaa50-\\uaa59\\uaa7b\\uaae0-\\uaae9\\uaaf2-\\uaaf3\\uabc0-\\uabe1\\uabec\\uabed\\uabf0-\\uabf9\\ufb20-\\ufb28\\ufe00-\\ufe0f\\ufe20-\\ufe26\\ufe33\\ufe34\\ufe4d-\\ufe4f\\uff10-\\uff19\\uff3f\";\n  var nonASCIIidentifierStart = new RegExp(\"[\" + nonASCIIidentifierStartChars + \"]\");\n  var nonASCIIidentifier = new RegExp(\"[\" + nonASCIIidentifierStartChars + nonASCIIidentifierChars + \"]\");\n\n  var newline = /[\\n\\r\\u2028\\u2029]/;\n\n  var lineBreak = /\\r\\n|[\\n\\r\\u2028\\u2029]/g;\n\n  var isIdentifierStart = exports.isIdentifierStart = function(code) {\n\tif (code < 65) return code === 36;\n\tif (code < 91) return true;\n\tif (code < 97) return code === 95;\n\tif (code < 123)return true;\n\treturn code >= 0xaa && nonASCIIidentifierStart.test(String.fromCharCode(code));\n  };\n\n  var isIdentifierChar = exports.isIdentifierChar = function(code) {\n\tif (code < 48) return code === 36;\n\tif (code < 58) return true;\n\tif (code < 65) return false;\n\tif (code < 91) return true;\n\tif (code < 97) return code === 95;\n\tif (code < 123)return true;\n\treturn code >= 0xaa && nonASCIIidentifier.test(String.fromCharCode(code));\n  };\n\n  function line_loc_t() {\n\tthis.line = tokCurLine;\n\tthis.column = tokPos - tokLineStart;\n  }\n\n  function initTokenState() {\n\ttokCurLine = 1;\n\ttokPos = tokLineStart = 0;\n\ttokRegexpAllowed = true;\n\tskipSpace();\n  }\n\n  function finishToken(type, val) {\n\ttokEnd = tokPos;\n\tif (options.locations) tokEndLoc = new line_loc_t;\n\ttokType = type;\n\tskipSpace();\n\ttokVal = val;\n\ttokRegexpAllowed = type.beforeExpr;\n  }\n\n  function skipBlockComment() {\n\tvar startLoc = options.onComment && options.locations && new line_loc_t;\n\tvar start = tokPos, end = input.indexOf(\"*/\", tokPos += 2);\n\tif (end === -1) raise(tokPos - 2, \"Unterminated comment\");\n\ttokPos = end + 2;\n\tif (options.locations) {\n\t  lineBreak.lastIndex = start;\n\t  var match;\n\t  while ((match = lineBreak.exec(input)) && match.index < tokPos) {\n\t\t++tokCurLine;\n\t\ttokLineStart = match.index + match[0].length;\n\t  }\n\t}\n\tif (options.onComment)\n\t  options.onComment(true, input.slice(start + 2, end), start, tokPos,\n\t\t\t\t\t\tstartLoc, options.locations && new line_loc_t);\n  }\n\n  function skipLineComment() {\n\tvar start = tokPos;\n\tvar startLoc = options.onComment && options.locations && new line_loc_t;\n\tvar ch = input.charCodeAt(tokPos+=2);\n\twhile (tokPos < inputLen && ch !== 10 && ch !== 13 && ch !== 8232 && ch !== 8233) {\n\t  ++tokPos;\n\t  ch = input.charCodeAt(tokPos);\n\t}\n\tif (options.onComment)\n\t  options.onComment(false, input.slice(start + 2, tokPos), start, tokPos,\n\t\t\t\t\t\tstartLoc, options.locations && new line_loc_t);\n  }\n\n  function skipSpace() {\n\twhile (tokPos < inputLen) {\n\t  var ch = input.charCodeAt(tokPos);\n\t  if (ch === 32) {\n\t\t++tokPos;\n\t  } else if (ch === 13) {\n\t\t++tokPos;\n\t\tvar next = input.charCodeAt(tokPos);\n\t\tif (next === 10) {\n\t\t  ++tokPos;\n\t\t}\n\t\tif (options.locations) {\n\t\t  ++tokCurLine;\n\t\t  tokLineStart = tokPos;\n\t\t}\n\t  } else if (ch === 10 || ch === 8232 || ch === 8233) {\n\t\t++tokPos;\n\t\tif (options.locations) {\n\t\t  ++tokCurLine;\n\t\t  tokLineStart = tokPos;\n\t\t}\n\t  } else if (ch > 8 && ch < 14) {\n\t\t++tokPos;\n\t  } else if (ch === 47) {\n\t\tvar next = input.charCodeAt(tokPos + 1);\n\t\tif (next === 42) {\n\t\t  skipBlockComment();\n\t\t} else if (next === 47) {\n\t\t  skipLineComment();\n\t\t} else break;\n\t  } else if (ch === 160) {\n\t\t++tokPos;\n\t  } else if (ch >= 5760 && nonASCIIwhitespace.test(String.fromCharCode(ch))) {\n\t\t++tokPos;\n\t  } else {\n\t\tbreak;\n\t  }\n\t}\n  }\n\n  function readToken_dot() {\n\tvar next = input.charCodeAt(tokPos + 1);\n\tif (next >= 48 && next <= 57) return readNumber(true);\n\t++tokPos;\n\treturn finishToken(_dot);\n  }\n\n  function readToken_slash() {\n\tvar next = input.charCodeAt(tokPos + 1);\n\tif (tokRegexpAllowed) {++tokPos; return readRegexp();}\n\tif (next === 61) return finishOp(_assign, 2);\n\treturn finishOp(_slash, 1);\n  }\n\n  function readToken_mult_modulo() {\n\tvar next = input.charCodeAt(tokPos + 1);\n\tif (next === 61) return finishOp(_assign, 2);\n\treturn finishOp(_multiplyModulo, 1);\n  }\n\n  function readToken_pipe_amp(code) {\n\tvar next = input.charCodeAt(tokPos + 1);\n\tif (next === code) return finishOp(code === 124 ? _logicalOR : _logicalAND, 2);\n\tif (next === 61) return finishOp(_assign, 2);\n\treturn finishOp(code === 124 ? _bitwiseOR : _bitwiseAND, 1);\n  }\n\n  function readToken_caret() {\n\tvar next = input.charCodeAt(tokPos + 1);\n\tif (next === 61) return finishOp(_assign, 2);\n\treturn finishOp(_bitwiseXOR, 1);\n  }\n\n  function readToken_plus_min(code) {\n\tvar next = input.charCodeAt(tokPos + 1);\n\tif (next === code) {\n\t  if (next == 45 && input.charCodeAt(tokPos + 2) == 62 &&\n\t\t  newline.test(input.slice(lastEnd, tokPos))) {\n\t\ttokPos += 3;\n\t\tskipLineComment();\n\t\tskipSpace();\n\t\treturn readToken();\n\t  }\n\t  return finishOp(_incDec, 2);\n\t}\n\tif (next === 61) return finishOp(_assign, 2);\n\treturn finishOp(_plusMin, 1);\n  }\n\n  function readToken_lt_gt(code) {\n\tvar next = input.charCodeAt(tokPos + 1);\n\tvar size = 1;\n\tif (next === code) {\n\t  size = code === 62 && input.charCodeAt(tokPos + 2) === 62 ? 3 : 2;\n\t  if (input.charCodeAt(tokPos + size) === 61) return finishOp(_assign, size + 1);\n\t  return finishOp(_bitShift, size);\n\t}\n\tif (next == 33 && code == 60 && input.charCodeAt(tokPos + 2) == 45 &&\n\t\tinput.charCodeAt(tokPos + 3) == 45) {\n\t  tokPos += 4;\n\t  skipLineComment();\n\t  skipSpace();\n\t  return readToken();\n\t}\n\tif (next === 61)\n\t  size = input.charCodeAt(tokPos + 2) === 61 ? 3 : 2;\n\treturn finishOp(_relational, size);\n  }\n\n  function readToken_eq_excl(code) {\n\tvar next = input.charCodeAt(tokPos + 1);\n\tif (next === 61) return finishOp(_equality, input.charCodeAt(tokPos + 2) === 61 ? 3 : 2);\n\treturn finishOp(code === 61 ? _eq : _prefix, 1);\n  }\n\n  function getTokenFromCode(code) {\n\tswitch(code) {\n\tcase 46:\n\t  return readToken_dot();\n\n\tcase 40: ++tokPos; return finishToken(_parenL);\n\tcase 41: ++tokPos; return finishToken(_parenR);\n\tcase 59: ++tokPos; return finishToken(_semi);\n\tcase 44: ++tokPos; return finishToken(_comma);\n\tcase 91: ++tokPos; return finishToken(_bracketL);\n\tcase 93: ++tokPos; return finishToken(_bracketR);\n\tcase 123: ++tokPos; return finishToken(_braceL);\n\tcase 125: ++tokPos; return finishToken(_braceR);\n\tcase 58: ++tokPos; return finishToken(_colon);\n\tcase 63: ++tokPos; return finishToken(_question);\n\n\tcase 48:\n\t  var next = input.charCodeAt(tokPos + 1);\n\t  if (next === 120 || next === 88) return readHexNumber();\n\tcase 49: case 50: case 51: case 52: case 53: case 54: case 55: case 56: case 57:\n\t  return readNumber(false);\n\n\tcase 34: case 39:\n\t  return readString(code);\n\n\tcase 47:\n\t  return readToken_slash(code);\n\n\tcase 37: case 42:\n\t  return readToken_mult_modulo();\n\n\tcase 124: case 38:\n\t  return readToken_pipe_amp(code);\n\n\tcase 94:\n\t  return readToken_caret();\n\n\tcase 43: case 45:\n\t  return readToken_plus_min(code);\n\n\tcase 60: case 62:\n\t  return readToken_lt_gt(code);\n\n\tcase 61: case 33:\n\t  return readToken_eq_excl(code);\n\n\tcase 126:\n\t  return finishOp(_prefix, 1);\n\t}\n\n\treturn false;\n  }\n\n  function readToken(forceRegexp) {\n\tif (!forceRegexp) tokStart = tokPos;\n\telse tokPos = tokStart + 1;\n\tif (options.locations) tokStartLoc = new line_loc_t;\n\tif (forceRegexp) return readRegexp();\n\tif (tokPos >= inputLen) return finishToken(_eof);\n\n\tvar code = input.charCodeAt(tokPos);\n\tif (isIdentifierStart(code) || code === 92 ) return readWord();\n\n\tvar tok = getTokenFromCode(code);\n\n\tif (tok === false) {\n\t  var ch = String.fromCharCode(code);\n\t  if (ch === \"\\\\\" || nonASCIIidentifierStart.test(ch)) return readWord();\n\t  raise(tokPos, \"Unexpected character '\" + ch + \"'\");\n\t}\n\treturn tok;\n  }\n\n  function finishOp(type, size) {\n\tvar str = input.slice(tokPos, tokPos + size);\n\ttokPos += size;\n\tfinishToken(type, str);\n  }\n\n  function readRegexp() {\n\tvar content = \"\", escaped, inClass, start = tokPos;\n\tfor (;;) {\n\t  if (tokPos >= inputLen) raise(start, \"Unterminated regular expression\");\n\t  var ch = input.charAt(tokPos);\n\t  if (newline.test(ch)) raise(start, \"Unterminated regular expression\");\n\t  if (!escaped) {\n\t\tif (ch === \"[\") inClass = true;\n\t\telse if (ch === \"]\" && inClass) inClass = false;\n\t\telse if (ch === \"/\" && !inClass) break;\n\t\tescaped = ch === \"\\\\\";\n\t  } else escaped = false;\n\t  ++tokPos;\n\t}\n\tvar content = input.slice(start, tokPos);\n\t++tokPos;\n\tvar mods = readWord1();\n\tif (mods && !/^[gmsiy]*$/.test(mods)) raise(start, \"Invalid regexp flag\");\n\ttry {\n\t  var value = new RegExp(content, mods);\n\t} catch (e) {\n\t  if (e instanceof SyntaxError) raise(start, e.message);\n\t  raise(e);\n\t}\n\treturn finishToken(_regexp, value);\n  }\n\n  function readInt(radix, len) {\n\tvar start = tokPos, total = 0;\n\tfor (var i = 0, e = len == null ? Infinity : len; i < e; ++i) {\n\t  var code = input.charCodeAt(tokPos), val;\n\t  if (code >= 97) val = code - 97 + 10;\n\t  else if (code >= 65) val = code - 65 + 10;\n\t  else if (code >= 48 && code <= 57) val = code - 48;\n\t  else val = Infinity;\n\t  if (val >= radix) break;\n\t  ++tokPos;\n\t  total = total * radix + val;\n\t}\n\tif (tokPos === start || len != null && tokPos - start !== len) return null;\n\n\treturn total;\n  }\n\n  function readHexNumber() {\n\ttokPos += 2;\n\tvar val = readInt(16);\n\tif (val == null) raise(tokStart + 2, \"Expected hexadecimal number\");\n\tif (isIdentifierStart(input.charCodeAt(tokPos))) raise(tokPos, \"Identifier directly after number\");\n\treturn finishToken(_num, val);\n  }\n\n  function readNumber(startsWithDot) {\n\tvar start = tokPos, isFloat = false, octal = input.charCodeAt(tokPos) === 48;\n\tif (!startsWithDot && readInt(10) === null) raise(start, \"Invalid number\");\n\tif (input.charCodeAt(tokPos) === 46) {\n\t  ++tokPos;\n\t  readInt(10);\n\t  isFloat = true;\n\t}\n\tvar next = input.charCodeAt(tokPos);\n\tif (next === 69 || next === 101) {\n\t  next = input.charCodeAt(++tokPos);\n\t  if (next === 43 || next === 45) ++tokPos;\n\t  if (readInt(10) === null) raise(start, \"Invalid number\");\n\t  isFloat = true;\n\t}\n\tif (isIdentifierStart(input.charCodeAt(tokPos))) raise(tokPos, \"Identifier directly after number\");\n\n\tvar str = input.slice(start, tokPos), val;\n\tif (isFloat) val = parseFloat(str);\n\telse if (!octal || str.length === 1) val = parseInt(str, 10);\n\telse if (/[89]/.test(str) || strict) raise(start, \"Invalid number\");\n\telse val = parseInt(str, 8);\n\treturn finishToken(_num, val);\n  }\n\n  function readString(quote) {\n\ttokPos++;\n\tvar out = \"\";\n\tfor (;;) {\n\t  if (tokPos >= inputLen) raise(tokStart, \"Unterminated string constant\");\n\t  var ch = input.charCodeAt(tokPos);\n\t  if (ch === quote) {\n\t\t++tokPos;\n\t\treturn finishToken(_string, out);\n\t  }\n\t  if (ch === 92) {\n\t\tch = input.charCodeAt(++tokPos);\n\t\tvar octal = /^[0-7]+/.exec(input.slice(tokPos, tokPos + 3));\n\t\tif (octal) octal = octal[0];\n\t\twhile (octal && parseInt(octal, 8) > 255) octal = octal.slice(0, -1);\n\t\tif (octal === \"0\") octal = null;\n\t\t++tokPos;\n\t\tif (octal) {\n\t\t  if (strict) raise(tokPos - 2, \"Octal literal in strict mode\");\n\t\t  out += String.fromCharCode(parseInt(octal, 8));\n\t\t  tokPos += octal.length - 1;\n\t\t} else {\n\t\t  switch (ch) {\n\t\t  case 110: out += \"\\n\"; break;\n\t\t  case 114: out += \"\\r\"; break;\n\t\t  case 120: out += String.fromCharCode(readHexChar(2)); break;\n\t\t  case 117: out += String.fromCharCode(readHexChar(4)); break;\n\t\t  case 85: out += String.fromCharCode(readHexChar(8)); break;\n\t\t  case 116: out += \"\\t\"; break;\n\t\t  case 98: out += \"\\b\"; break;\n\t\t  case 118: out += \"\\u000b\"; break;\n\t\t  case 102: out += \"\\f\"; break;\n\t\t  case 48: out += \"\\0\"; break;\n\t\t  case 13: if (input.charCodeAt(tokPos) === 10) ++tokPos;\n\t\t  case 10:\n\t\t\tif (options.locations) { tokLineStart = tokPos; ++tokCurLine; }\n\t\t\tbreak;\n\t\t  default: out += String.fromCharCode(ch); break;\n\t\t  }\n\t\t}\n\t  } else {\n\t\tif (ch === 13 || ch === 10 || ch === 8232 || ch === 8233) raise(tokStart, \"Unterminated string constant\");\n\t\tout += String.fromCharCode(ch);\n\t\t++tokPos;\n\t  }\n\t}\n  }\n\n  function readHexChar(len) {\n\tvar n = readInt(16, len);\n\tif (n === null) raise(tokStart, \"Bad character escape sequence\");\n\treturn n;\n  }\n\n  var containsEsc;\n\n  function readWord1() {\n\tcontainsEsc = false;\n\tvar word, first = true, start = tokPos;\n\tfor (;;) {\n\t  var ch = input.charCodeAt(tokPos);\n\t  if (isIdentifierChar(ch)) {\n\t\tif (containsEsc) word += input.charAt(tokPos);\n\t\t++tokPos;\n\t  } else if (ch === 92) {\n\t\tif (!containsEsc) word = input.slice(start, tokPos);\n\t\tcontainsEsc = true;\n\t\tif (input.charCodeAt(++tokPos) != 117)\n\t\t  raise(tokPos, \"Expecting Unicode escape sequence \\\\uXXXX\");\n\t\t++tokPos;\n\t\tvar esc = readHexChar(4);\n\t\tvar escStr = String.fromCharCode(esc);\n\t\tif (!escStr) raise(tokPos - 1, \"Invalid Unicode escape\");\n\t\tif (!(first ? isIdentifierStart(esc) : isIdentifierChar(esc)))\n\t\t  raise(tokPos - 4, \"Invalid Unicode escape\");\n\t\tword += escStr;\n\t  } else {\n\t\tbreak;\n\t  }\n\t  first = false;\n\t}\n\treturn containsEsc ? word : input.slice(start, tokPos);\n  }\n\n  function readWord() {\n\tvar word = readWord1();\n\tvar type = _name;\n\tif (!containsEsc && isKeyword(word))\n\t  type = keywordTypes[word];\n\treturn finishToken(type, word);\n  }\n\n  function next() {\n\tlastStart = tokStart;\n\tlastEnd = tokEnd;\n\tlastEndLoc = tokEndLoc;\n\treadToken();\n  }\n\n  function setStrict(strct) {\n\tstrict = strct;\n\ttokPos = tokStart;\n\tif (options.locations) {\n\t  while (tokPos < tokLineStart) {\n\t\ttokLineStart = input.lastIndexOf(\"\\n\", tokLineStart - 2) + 1;\n\t\t--tokCurLine;\n\t  }\n\t}\n\tskipSpace();\n\treadToken();\n  }\n\n  function node_t() {\n\tthis.type = null;\n\tthis.start = tokStart;\n\tthis.end = null;\n  }\n\n  function node_loc_t() {\n\tthis.start = tokStartLoc;\n\tthis.end = null;\n\tif (sourceFile !== null) this.source = sourceFile;\n  }\n\n  function startNode() {\n\tvar node = new node_t();\n\tif (options.locations)\n\t  node.loc = new node_loc_t();\n\tif (options.directSourceFile)\n\t  node.sourceFile = options.directSourceFile;\n\tif (options.ranges)\n\t  node.range = [tokStart, 0];\n\treturn node;\n  }\n\n  function startNodeFrom(other) {\n\tvar node = new node_t();\n\tnode.start = other.start;\n\tif (options.locations) {\n\t  node.loc = new node_loc_t();\n\t  node.loc.start = other.loc.start;\n\t}\n\tif (options.ranges)\n\t  node.range = [other.range[0], 0];\n\n\treturn node;\n  }\n\n  function finishNode(node, type) {\n\tnode.type = type;\n\tnode.end = lastEnd;\n\tif (options.locations)\n\t  node.loc.end = lastEndLoc;\n\tif (options.ranges)\n\t  node.range[1] = lastEnd;\n\treturn node;\n  }\n\n  function isUseStrict(stmt) {\n\treturn options.ecmaVersion >= 5 && stmt.type === \"ExpressionStatement\" &&\n\t  stmt.expression.type === \"Literal\" && stmt.expression.value === \"use strict\";\n  }\n\n  function eat(type) {\n\tif (tokType === type) {\n\t  next();\n\t  return true;\n\t}\n  }\n\n  function canInsertSemicolon() {\n\treturn !options.strictSemicolons &&\n\t  (tokType === _eof || tokType === _braceR || newline.test(input.slice(lastEnd, tokStart)));\n  }\n\n  function semicolon() {\n\tif (!eat(_semi) && !canInsertSemicolon()) unexpected();\n  }\n\n  function expect(type) {\n\tif (tokType === type) next();\n\telse unexpected();\n  }\n\n  function unexpected() {\n\traise(tokStart, \"Unexpected token\");\n  }\n\n  function checkLVal(expr) {\n\tif (expr.type !== \"Identifier\" && expr.type !== \"MemberExpression\")\n\t  raise(expr.start, \"Assigning to rvalue\");\n\tif (strict && expr.type === \"Identifier\" && isStrictBadIdWord(expr.name))\n\t  raise(expr.start, \"Assigning to \" + expr.name + \" in strict mode\");\n  }\n\n  function parseTopLevel(program) {\n\tlastStart = lastEnd = tokPos;\n\tif (options.locations) lastEndLoc = new line_loc_t;\n\tinFunction = strict = null;\n\tlabels = [];\n\treadToken();\n\n\tvar node = program || startNode(), first = true;\n\tif (!program) node.body = [];\n\twhile (tokType !== _eof) {\n\t  var stmt = parseStatement();\n\t  node.body.push(stmt);\n\t  if (first && isUseStrict(stmt)) setStrict(true);\n\t  first = false;\n\t}\n\treturn finishNode(node, \"Program\");\n  }\n\n  var loopLabel = {kind: \"loop\"}, switchLabel = {kind: \"switch\"};\n\n  function parseStatement() {\n\tif (tokType === _slash || tokType === _assign && tokVal == \"/=\")\n\t  readToken(true);\n\n\tvar starttype = tokType, node = startNode();\n\n\tswitch (starttype) {\n\tcase _break: case _continue:\n\t  next();\n\t  var isBreak = starttype === _break;\n\t  if (eat(_semi) || canInsertSemicolon()) node.label = null;\n\t  else if (tokType !== _name) unexpected();\n\t  else {\n\t\tnode.label = parseIdent();\n\t\tsemicolon();\n\t  }\n\n\t  for (var i = 0; i < labels.length; ++i) {\n\t\tvar lab = labels[i];\n\t\tif (node.label == null || lab.name === node.label.name) {\n\t\t  if (lab.kind != null && (isBreak || lab.kind === \"loop\")) break;\n\t\t  if (node.label && isBreak) break;\n\t\t}\n\t  }\n\t  if (i === labels.length) raise(node.start, \"Unsyntactic \" + starttype.keyword);\n\t  return finishNode(node, isBreak ? \"BreakStatement\" : \"ContinueStatement\");\n\n\tcase _debugger:\n\t  next();\n\t  semicolon();\n\t  return finishNode(node, \"DebuggerStatement\");\n\n\tcase _do:\n\t  next();\n\t  labels.push(loopLabel);\n\t  node.body = parseStatement();\n\t  labels.pop();\n\t  expect(_while);\n\t  node.test = parseParenExpression();\n\t  semicolon();\n\t  return finishNode(node, \"DoWhileStatement\");\n\n\tcase _for:\n\t  next();\n\t  labels.push(loopLabel);\n\t  expect(_parenL);\n\t  if (tokType === _semi) return parseFor(node, null);\n\t  if (tokType === _var) {\n\t\tvar init = startNode();\n\t\tnext();\n\t\tparseVar(init, true);\n\t\tfinishNode(init, \"VariableDeclaration\");\n\t\tif (init.declarations.length === 1 && eat(_in))\n\t\t  return parseForIn(node, init);\n\t\treturn parseFor(node, init);\n\t  }\n\t  var init = parseExpression(false, true);\n\t  if (eat(_in)) {checkLVal(init); return parseForIn(node, init);}\n\t  return parseFor(node, init);\n\n\tcase _function:\n\t  next();\n\t  return parseFunction(node, true);\n\n\tcase _if:\n\t  next();\n\t  node.test = parseParenExpression();\n\t  node.consequent = parseStatement();\n\t  node.alternate = eat(_else) ? parseStatement() : null;\n\t  return finishNode(node, \"IfStatement\");\n\n\tcase _return:\n\t  if (!inFunction && !options.allowReturnOutsideFunction)\n\t\traise(tokStart, \"'return' outside of function\");\n\t  next();\n\n\t  if (eat(_semi) || canInsertSemicolon()) node.argument = null;\n\t  else { node.argument = parseExpression(); semicolon(); }\n\t  return finishNode(node, \"ReturnStatement\");\n\n\tcase _switch:\n\t  next();\n\t  node.discriminant = parseParenExpression();\n\t  node.cases = [];\n\t  expect(_braceL);\n\t  labels.push(switchLabel);\n\n\t  for (var cur, sawDefault; tokType != _braceR;) {\n\t\tif (tokType === _case || tokType === _default) {\n\t\t  var isCase = tokType === _case;\n\t\t  if (cur) finishNode(cur, \"SwitchCase\");\n\t\t  node.cases.push(cur = startNode());\n\t\t  cur.consequent = [];\n\t\t  next();\n\t\t  if (isCase) cur.test = parseExpression();\n\t\t  else {\n\t\t\tif (sawDefault) raise(lastStart, \"Multiple default clauses\"); sawDefault = true;\n\t\t\tcur.test = null;\n\t\t  }\n\t\t  expect(_colon);\n\t\t} else {\n\t\t  if (!cur) unexpected();\n\t\t  cur.consequent.push(parseStatement());\n\t\t}\n\t  }\n\t  if (cur) finishNode(cur, \"SwitchCase\");\n\t  next();\n\t  labels.pop();\n\t  return finishNode(node, \"SwitchStatement\");\n\n\tcase _throw:\n\t  next();\n\t  if (newline.test(input.slice(lastEnd, tokStart)))\n\t\traise(lastEnd, \"Illegal newline after throw\");\n\t  node.argument = parseExpression();\n\t  semicolon();\n\t  return finishNode(node, \"ThrowStatement\");\n\n\tcase _try:\n\t  next();\n\t  node.block = parseBlock();\n\t  node.handler = null;\n\t  if (tokType === _catch) {\n\t\tvar clause = startNode();\n\t\tnext();\n\t\texpect(_parenL);\n\t\tclause.param = parseIdent();\n\t\tif (strict && isStrictBadIdWord(clause.param.name))\n\t\t  raise(clause.param.start, \"Binding \" + clause.param.name + \" in strict mode\");\n\t\texpect(_parenR);\n\t\tclause.guard = null;\n\t\tclause.body = parseBlock();\n\t\tnode.handler = finishNode(clause, \"CatchClause\");\n\t  }\n\t  node.guardedHandlers = empty;\n\t  node.finalizer = eat(_finally) ? parseBlock() : null;\n\t  if (!node.handler && !node.finalizer)\n\t\traise(node.start, \"Missing catch or finally clause\");\n\t  return finishNode(node, \"TryStatement\");\n\n\tcase _var:\n\t  next();\n\t  parseVar(node);\n\t  semicolon();\n\t  return finishNode(node, \"VariableDeclaration\");\n\n\tcase _while:\n\t  next();\n\t  node.test = parseParenExpression();\n\t  labels.push(loopLabel);\n\t  node.body = parseStatement();\n\t  labels.pop();\n\t  return finishNode(node, \"WhileStatement\");\n\n\tcase _with:\n\t  if (strict) raise(tokStart, \"'with' in strict mode\");\n\t  next();\n\t  node.object = parseParenExpression();\n\t  node.body = parseStatement();\n\t  return finishNode(node, \"WithStatement\");\n\n\tcase _braceL:\n\t  return parseBlock();\n\n\tcase _semi:\n\t  next();\n\t  return finishNode(node, \"EmptyStatement\");\n\n\tdefault:\n\t  var maybeName = tokVal, expr = parseExpression();\n\t  if (starttype === _name && expr.type === \"Identifier\" && eat(_colon)) {\n\t\tfor (var i = 0; i < labels.length; ++i)\n\t\t  if (labels[i].name === maybeName) raise(expr.start, \"Label '\" + maybeName + \"' is already declared\");\n\t\tvar kind = tokType.isLoop ? \"loop\" : tokType === _switch ? \"switch\" : null;\n\t\tlabels.push({name: maybeName, kind: kind});\n\t\tnode.body = parseStatement();\n\t\tlabels.pop();\n\t\tnode.label = expr;\n\t\treturn finishNode(node, \"LabeledStatement\");\n\t  } else {\n\t\tnode.expression = expr;\n\t\tsemicolon();\n\t\treturn finishNode(node, \"ExpressionStatement\");\n\t  }\n\t}\n  }\n\n  function parseParenExpression() {\n\texpect(_parenL);\n\tvar val = parseExpression();\n\texpect(_parenR);\n\treturn val;\n  }\n\n  function parseBlock(allowStrict) {\n\tvar node = startNode(), first = true, strict = false, oldStrict;\n\tnode.body = [];\n\texpect(_braceL);\n\twhile (!eat(_braceR)) {\n\t  var stmt = parseStatement();\n\t  node.body.push(stmt);\n\t  if (first && allowStrict && isUseStrict(stmt)) {\n\t\toldStrict = strict;\n\t\tsetStrict(strict = true);\n\t  }\n\t  first = false;\n\t}\n\tif (strict && !oldStrict) setStrict(false);\n\treturn finishNode(node, \"BlockStatement\");\n  }\n\n  function parseFor(node, init) {\n\tnode.init = init;\n\texpect(_semi);\n\tnode.test = tokType === _semi ? null : parseExpression();\n\texpect(_semi);\n\tnode.update = tokType === _parenR ? null : parseExpression();\n\texpect(_parenR);\n\tnode.body = parseStatement();\n\tlabels.pop();\n\treturn finishNode(node, \"ForStatement\");\n  }\n\n  function parseForIn(node, init) {\n\tnode.left = init;\n\tnode.right = parseExpression();\n\texpect(_parenR);\n\tnode.body = parseStatement();\n\tlabels.pop();\n\treturn finishNode(node, \"ForInStatement\");\n  }\n\n  function parseVar(node, noIn) {\n\tnode.declarations = [];\n\tnode.kind = \"var\";\n\tfor (;;) {\n\t  var decl = startNode();\n\t  decl.id = parseIdent();\n\t  if (strict && isStrictBadIdWord(decl.id.name))\n\t\traise(decl.id.start, \"Binding \" + decl.id.name + \" in strict mode\");\n\t  decl.init = eat(_eq) ? parseExpression(true, noIn) : null;\n\t  node.declarations.push(finishNode(decl, \"VariableDeclarator\"));\n\t  if (!eat(_comma)) break;\n\t}\n\treturn node;\n  }\n\n  function parseExpression(noComma, noIn) {\n\tvar expr = parseMaybeAssign(noIn);\n\tif (!noComma && tokType === _comma) {\n\t  var node = startNodeFrom(expr);\n\t  node.expressions = [expr];\n\t  while (eat(_comma)) node.expressions.push(parseMaybeAssign(noIn));\n\t  return finishNode(node, \"SequenceExpression\");\n\t}\n\treturn expr;\n  }\n\n  function parseMaybeAssign(noIn) {\n\tvar left = parseMaybeConditional(noIn);\n\tif (tokType.isAssign) {\n\t  var node = startNodeFrom(left);\n\t  node.operator = tokVal;\n\t  node.left = left;\n\t  next();\n\t  node.right = parseMaybeAssign(noIn);\n\t  checkLVal(left);\n\t  return finishNode(node, \"AssignmentExpression\");\n\t}\n\treturn left;\n  }\n\n  function parseMaybeConditional(noIn) {\n\tvar expr = parseExprOps(noIn);\n\tif (eat(_question)) {\n\t  var node = startNodeFrom(expr);\n\t  node.test = expr;\n\t  node.consequent = parseExpression(true);\n\t  expect(_colon);\n\t  node.alternate = parseExpression(true, noIn);\n\t  return finishNode(node, \"ConditionalExpression\");\n\t}\n\treturn expr;\n  }\n\n  function parseExprOps(noIn) {\n\treturn parseExprOp(parseMaybeUnary(), -1, noIn);\n  }\n\n  function parseExprOp(left, minPrec, noIn) {\n\tvar prec = tokType.binop;\n\tif (prec != null && (!noIn || tokType !== _in)) {\n\t  if (prec > minPrec) {\n\t\tvar node = startNodeFrom(left);\n\t\tnode.left = left;\n\t\tnode.operator = tokVal;\n\t\tvar op = tokType;\n\t\tnext();\n\t\tnode.right = parseExprOp(parseMaybeUnary(), prec, noIn);\n\t\tvar exprNode = finishNode(node, (op === _logicalOR || op === _logicalAND) ? \"LogicalExpression\" : \"BinaryExpression\");\n\t\treturn parseExprOp(exprNode, minPrec, noIn);\n\t  }\n\t}\n\treturn left;\n  }\n\n  function parseMaybeUnary() {\n\tif (tokType.prefix) {\n\t  var node = startNode(), update = tokType.isUpdate;\n\t  node.operator = tokVal;\n\t  node.prefix = true;\n\t  tokRegexpAllowed = true;\n\t  next();\n\t  node.argument = parseMaybeUnary();\n\t  if (update) checkLVal(node.argument);\n\t  else if (strict && node.operator === \"delete\" &&\n\t\t\t   node.argument.type === \"Identifier\")\n\t\traise(node.start, \"Deleting local variable in strict mode\");\n\t  return finishNode(node, update ? \"UpdateExpression\" : \"UnaryExpression\");\n\t}\n\tvar expr = parseExprSubscripts();\n\twhile (tokType.postfix && !canInsertSemicolon()) {\n\t  var node = startNodeFrom(expr);\n\t  node.operator = tokVal;\n\t  node.prefix = false;\n\t  node.argument = expr;\n\t  checkLVal(expr);\n\t  next();\n\t  expr = finishNode(node, \"UpdateExpression\");\n\t}\n\treturn expr;\n  }\n\n  function parseExprSubscripts() {\n\treturn parseSubscripts(parseExprAtom());\n  }\n\n  function parseSubscripts(base, noCalls) {\n\tif (eat(_dot)) {\n\t  var node = startNodeFrom(base);\n\t  node.object = base;\n\t  node.property = parseIdent(true);\n\t  node.computed = false;\n\t  return parseSubscripts(finishNode(node, \"MemberExpression\"), noCalls);\n\t} else if (eat(_bracketL)) {\n\t  var node = startNodeFrom(base);\n\t  node.object = base;\n\t  node.property = parseExpression();\n\t  node.computed = true;\n\t  expect(_bracketR);\n\t  return parseSubscripts(finishNode(node, \"MemberExpression\"), noCalls);\n\t} else if (!noCalls && eat(_parenL)) {\n\t  var node = startNodeFrom(base);\n\t  node.callee = base;\n\t  node.arguments = parseExprList(_parenR, false);\n\t  return parseSubscripts(finishNode(node, \"CallExpression\"), noCalls);\n\t} else return base;\n  }\n\n  function parseExprAtom() {\n\tswitch (tokType) {\n\tcase _this:\n\t  var node = startNode();\n\t  next();\n\t  return finishNode(node, \"ThisExpression\");\n\tcase _name:\n\t  return parseIdent();\n\tcase _num: case _string: case _regexp:\n\t  var node = startNode();\n\t  node.value = tokVal;\n\t  node.raw = input.slice(tokStart, tokEnd);\n\t  next();\n\t  return finishNode(node, \"Literal\");\n\n\tcase _null: case _true: case _false:\n\t  var node = startNode();\n\t  node.value = tokType.atomValue;\n\t  node.raw = tokType.keyword;\n\t  next();\n\t  return finishNode(node, \"Literal\");\n\n\tcase _parenL:\n\t  var tokStartLoc1 = tokStartLoc, tokStart1 = tokStart;\n\t  next();\n\t  var val = parseExpression();\n\t  val.start = tokStart1;\n\t  val.end = tokEnd;\n\t  if (options.locations) {\n\t\tval.loc.start = tokStartLoc1;\n\t\tval.loc.end = tokEndLoc;\n\t  }\n\t  if (options.ranges)\n\t\tval.range = [tokStart1, tokEnd];\n\t  expect(_parenR);\n\t  return val;\n\n\tcase _bracketL:\n\t  var node = startNode();\n\t  next();\n\t  node.elements = parseExprList(_bracketR, true, true);\n\t  return finishNode(node, \"ArrayExpression\");\n\n\tcase _braceL:\n\t  return parseObj();\n\n\tcase _function:\n\t  var node = startNode();\n\t  next();\n\t  return parseFunction(node, false);\n\n\tcase _new:\n\t  return parseNew();\n\n\tdefault:\n\t  unexpected();\n\t}\n  }\n\n  function parseNew() {\n\tvar node = startNode();\n\tnext();\n\tnode.callee = parseSubscripts(parseExprAtom(), true);\n\tif (eat(_parenL)) node.arguments = parseExprList(_parenR, false);\n\telse node.arguments = empty;\n\treturn finishNode(node, \"NewExpression\");\n  }\n\n  function parseObj() {\n\tvar node = startNode(), first = true, sawGetSet = false;\n\tnode.properties = [];\n\tnext();\n\twhile (!eat(_braceR)) {\n\t  if (!first) {\n\t\texpect(_comma);\n\t\tif (options.allowTrailingCommas && eat(_braceR)) break;\n\t  } else first = false;\n\n\t  var prop = {key: parsePropertyName()}, isGetSet = false, kind;\n\t  if (eat(_colon)) {\n\t\tprop.value = parseExpression(true);\n\t\tkind = prop.kind = \"init\";\n\t  } else if (options.ecmaVersion >= 5 && prop.key.type === \"Identifier\" &&\n\t\t\t\t (prop.key.name === \"get\" || prop.key.name === \"set\")) {\n\t\tisGetSet = sawGetSet = true;\n\t\tkind = prop.kind = prop.key.name;\n\t\tprop.key = parsePropertyName();\n\t\tif (tokType !== _parenL) unexpected();\n\t\tprop.value = parseFunction(startNode(), false);\n\t  } else unexpected();\n\n\t  if (prop.key.type === \"Identifier\" && (strict || sawGetSet)) {\n\t\tfor (var i = 0; i < node.properties.length; ++i) {\n\t\t  var other = node.properties[i];\n\t\t  if (other.key.name === prop.key.name) {\n\t\t\tvar conflict = kind == other.kind || isGetSet && other.kind === \"init\" ||\n\t\t\t  kind === \"init\" && (other.kind === \"get\" || other.kind === \"set\");\n\t\t\tif (conflict && !strict && kind === \"init\" && other.kind === \"init\") conflict = false;\n\t\t\tif (conflict) raise(prop.key.start, \"Redefinition of property\");\n\t\t  }\n\t\t}\n\t  }\n\t  node.properties.push(prop);\n\t}\n\treturn finishNode(node, \"ObjectExpression\");\n  }\n\n  function parsePropertyName() {\n\tif (tokType === _num || tokType === _string) return parseExprAtom();\n\treturn parseIdent(true);\n  }\n\n  function parseFunction(node, isStatement) {\n\tif (tokType === _name) node.id = parseIdent();\n\telse if (isStatement) unexpected();\n\telse node.id = null;\n\tnode.params = [];\n\tvar first = true;\n\texpect(_parenL);\n\twhile (!eat(_parenR)) {\n\t  if (!first) expect(_comma); else first = false;\n\t  node.params.push(parseIdent());\n\t}\n\n\tvar oldInFunc = inFunction, oldLabels = labels;\n\tinFunction = true; labels = [];\n\tnode.body = parseBlock(true);\n\tinFunction = oldInFunc; labels = oldLabels;\n\n\tif (strict || node.body.body.length && isUseStrict(node.body.body[0])) {\n\t  for (var i = node.id ? -1 : 0; i < node.params.length; ++i) {\n\t\tvar id = i < 0 ? node.id : node.params[i];\n\t\tif (isStrictReservedWord(id.name) || isStrictBadIdWord(id.name))\n\t\t  raise(id.start, \"Defining '\" + id.name + \"' in strict mode\");\n\t\tif (i >= 0) for (var j = 0; j < i; ++j) if (id.name === node.params[j].name)\n\t\t  raise(id.start, \"Argument name clash in strict mode\");\n\t  }\n\t}\n\n\treturn finishNode(node, isStatement ? \"FunctionDeclaration\" : \"FunctionExpression\");\n  }\n\n  function parseExprList(close, allowTrailingComma, allowEmpty) {\n\tvar elts = [], first = true;\n\twhile (!eat(close)) {\n\t  if (!first) {\n\t\texpect(_comma);\n\t\tif (allowTrailingComma && options.allowTrailingCommas && eat(close)) break;\n\t  } else first = false;\n\n\t  if (allowEmpty && tokType === _comma) elts.push(null);\n\t  else elts.push(parseExpression(true));\n\t}\n\treturn elts;\n  }\n\n  function parseIdent(liberal) {\n\tvar node = startNode();\n\tif (liberal && options.forbidReserved == \"everywhere\") liberal = false;\n\tif (tokType === _name) {\n\t  if (!liberal &&\n\t\t  (options.forbidReserved &&\n\t\t   (options.ecmaVersion === 3 ? isReservedWord3 : isReservedWord5)(tokVal) ||\n\t\t   strict && isStrictReservedWord(tokVal)) &&\n\t\t  input.slice(tokStart, tokEnd).indexOf(\"\\\\\") == -1)\n\t\traise(tokStart, \"The keyword '\" + tokVal + \"' is reserved\");\n\t  node.name = tokVal;\n\t} else if (liberal && tokType.keyword) {\n\t  node.name = tokType.keyword;\n\t} else {\n\t  unexpected();\n\t}\n\ttokRegexpAllowed = false;\n\tnext();\n\treturn finishNode(node, \"Identifier\");\n  }\n\n});\n\n\t\tif (!acorn.version)\n\t\t\tacorn = null;\n\t}\n\n\tfunction parse(code, options) {\n\t\treturn (global.acorn || acorn).parse(code, options);\n\t}\n\n\tvar binaryOperators = {\n\t\t'+': '__add',\n\t\t'-': '__subtract',\n\t\t'*': '__multiply',\n\t\t'/': '__divide',\n\t\t'%': '__modulo',\n\t\t'==': '__equals',\n\t\t'!=': '__equals'\n\t};\n\n\tvar unaryOperators = {\n\t\t'-': '__negate',\n\t\t'+': '__self'\n\t};\n\n\tvar fields = Base.each(\n\t\t['add', 'subtract', 'multiply', 'divide', 'modulo', 'equals', 'negate'],\n\t\tfunction(name) {\n\t\t\tthis['__' + name] = '#' + name;\n\t\t},\n\t\t{\n\t\t\t__self: function() {\n\t\t\t\treturn this;\n\t\t\t}\n\t\t}\n\t);\n\tPoint.inject(fields);\n\tSize.inject(fields);\n\tColor.inject(fields);\n\n\tfunction __$__(left, operator, right) {\n\t\tvar handler = binaryOperators[operator];\n\t\tif (left && left[handler]) {\n\t\t\tvar res = left[handler](right);\n\t\t\treturn operator === '!=' ? !res : res;\n\t\t}\n\t\tswitch (operator) {\n\t\tcase '+': return left + right;\n\t\tcase '-': return left - right;\n\t\tcase '*': return left * right;\n\t\tcase '/': return left / right;\n\t\tcase '%': return left % right;\n\t\tcase '==': return left == right;\n\t\tcase '!=': return left != right;\n\t\t}\n\t}\n\n\tfunction $__(operator, value) {\n\t\tvar handler = unaryOperators[operator];\n\t\tif (value && value[handler])\n\t\t\treturn value[handler]();\n\t\tswitch (operator) {\n\t\tcase '+': return +value;\n\t\tcase '-': return -value;\n\t\t}\n\t}\n\n\tfunction compile(code, options) {\n\t\tif (!code)\n\t\t\treturn '';\n\t\toptions = options || {};\n\n\t\tvar insertions = [];\n\n\t\tfunction getOffset(offset) {\n\t\t\tfor (var i = 0, l = insertions.length; i < l; i++) {\n\t\t\t\tvar insertion = insertions[i];\n\t\t\t\tif (insertion[0] >= offset)\n\t\t\t\t\tbreak;\n\t\t\t\toffset += insertion[1];\n\t\t\t}\n\t\t\treturn offset;\n\t\t}\n\n\t\tfunction getCode(node) {\n\t\t\treturn code.substring(getOffset(node.range[0]),\n\t\t\t\t\tgetOffset(node.range[1]));\n\t\t}\n\n\t\tfunction getBetween(left, right) {\n\t\t\treturn code.substring(getOffset(left.range[1]),\n\t\t\t\t\tgetOffset(right.range[0]));\n\t\t}\n\n\t\tfunction replaceCode(node, str) {\n\t\t\tvar start = getOffset(node.range[0]),\n\t\t\t\tend = getOffset(node.range[1]),\n\t\t\t\tinsert = 0;\n\t\t\tfor (var i = insertions.length - 1; i >= 0; i--) {\n\t\t\t\tif (start > insertions[i][0]) {\n\t\t\t\t\tinsert = i + 1;\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t\tinsertions.splice(insert, 0, [start, str.length - end + start]);\n\t\t\tcode = code.substring(0, start) + str + code.substring(end);\n\t\t}\n\n\t\tfunction handleOverloading(node, parent) {\n\t\t\tswitch (node.type) {\n\t\t\tcase 'UnaryExpression':\n\t\t\t\tif (node.operator in unaryOperators\n\t\t\t\t\t\t&& node.argument.type !== 'Literal') {\n\t\t\t\t\tvar arg = getCode(node.argument);\n\t\t\t\t\treplaceCode(node, '$__(\"' + node.operator + '\", '\n\t\t\t\t\t\t\t+ arg + ')');\n\t\t\t\t}\n\t\t\t\tbreak;\n\t\t\tcase 'BinaryExpression':\n\t\t\t\tif (node.operator in binaryOperators\n\t\t\t\t\t\t&& node.left.type !== 'Literal') {\n\t\t\t\t\tvar left = getCode(node.left),\n\t\t\t\t\t\tright = getCode(node.right),\n\t\t\t\t\t\tbetween = getBetween(node.left, node.right),\n\t\t\t\t\t\toperator = node.operator;\n\t\t\t\t\treplaceCode(node, '__$__(' + left + ','\n\t\t\t\t\t\t\t+ between.replace(new RegExp('\\\\' + operator),\n\t\t\t\t\t\t\t\t'\"' + operator + '\"')\n\t\t\t\t\t\t\t+ ', ' + right + ')');\n\t\t\t\t}\n\t\t\t\tbreak;\n\t\t\tcase 'UpdateExpression':\n\t\t\tcase 'AssignmentExpression':\n\t\t\t\tvar parentType = parent && parent.type;\n\t\t\t\tif (!(\n\t\t\t\t\t\tparentType === 'ForStatement'\n\t\t\t\t\t\t|| parentType === 'BinaryExpression'\n\t\t\t\t\t\t\t&& /^[=!<>]/.test(parent.operator)\n\t\t\t\t\t\t|| parentType === 'MemberExpression' && parent.computed\n\t\t\t\t)) {\n\t\t\t\t\tif (node.type === 'UpdateExpression') {\n\t\t\t\t\t\tvar arg = getCode(node.argument),\n\t\t\t\t\t\t\texp = '__$__(' + arg + ', \"' + node.operator[0]\n\t\t\t\t\t\t\t\t\t+ '\", 1)',\n\t\t\t\t\t\t\tstr = arg + ' = ' + exp;\n\t\t\t\t\t\tif (node.prefix) {\n\t\t\t\t\t\t\tstr = '(' + str + ')';\n\t\t\t\t\t\t} else if (\n\t\t\t\t\t\t\tparentType === 'AssignmentExpression' ||\n\t\t\t\t\t\t\tparentType === 'VariableDeclarator' ||\n\t\t\t\t\t\t\tparentType === 'BinaryExpression'\n\t\t\t\t\t\t) {\n\t\t\t\t\t\t\tif (getCode(parent.left || parent.id) === arg)\n\t\t\t\t\t\t\t\tstr = exp;\n\t\t\t\t\t\t\tstr = arg + '; ' + str;\n\t\t\t\t\t\t}\n\t\t\t\t\t\treplaceCode(node, str);\n\t\t\t\t\t} else {\n\t\t\t\t\t\tif (/^.=$/.test(node.operator)\n\t\t\t\t\t\t\t\t&& node.left.type !== 'Literal') {\n\t\t\t\t\t\t\tvar left = getCode(node.left),\n\t\t\t\t\t\t\t\tright = getCode(node.right),\n\t\t\t\t\t\t\t\texp = left + ' = __$__(' + left + ', \"'\n\t\t\t\t\t\t\t\t\t+ node.operator[0] + '\", ' + right + ')';\n\t\t\t\t\t\t\treplaceCode(node, /^\\(.*\\)$/.test(getCode(node))\n\t\t\t\t\t\t\t\t\t? '(' + exp + ')' : exp);\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\n\t\tfunction handleExports(node) {\n\t\t\tswitch (node.type) {\n\t\t\tcase 'ExportDefaultDeclaration':\n\t\t\t\treplaceCode({\n\t\t\t\t\trange: [node.start, node.declaration.start]\n\t\t\t\t}, 'module.exports = ');\n\t\t\t\tbreak;\n\t\t\tcase 'ExportNamedDeclaration':\n\t\t\t\tvar declaration = node.declaration;\n\t\t\t\tvar specifiers = node.specifiers;\n\t\t\t\tif (declaration) {\n\t\t\t\t\tvar declarations = declaration.declarations;\n\t\t\t\t\tif (declarations) {\n\t\t\t\t\t\tdeclarations.forEach(function(dec) {\n\t\t\t\t\t\t\treplaceCode(dec, 'module.exports.' + getCode(dec));\n\t\t\t\t\t\t});\n\t\t\t\t\t\treplaceCode({\n\t\t\t\t\t\t\trange: [\n\t\t\t\t\t\t\t\tnode.start,\n\t\t\t\t\t\t\t\tdeclaration.start + declaration.kind.length\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} else if (specifiers) {\n\t\t\t\t\tvar exports = specifiers.map(function(specifier) {\n\t\t\t\t\t\tvar name = getCode(specifier);\n\t\t\t\t\t\treturn 'module.exports.' + name + ' = ' + name + '; ';\n\t\t\t\t\t}).join('');\n\t\t\t\t\tif (exports) {\n\t\t\t\t\t\treplaceCode(node, exports);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\n\t\tfunction walkAST(node, parent, paperFeatures) {\n\t\t\tif (node) {\n\t\t\t\tfor (var key in node) {\n\t\t\t\t\tif (key !== 'range' && key !== 'loc') {\n\t\t\t\t\t\tvar value = node[key];\n\t\t\t\t\t\tif (Array.isArray(value)) {\n\t\t\t\t\t\t\tfor (var i = 0, l = value.length; i < l; i++) {\n\t\t\t\t\t\t\t\twalkAST(value[i], node, paperFeatures);\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t} else if (value && typeof value === 'object') {\n\t\t\t\t\t\t\twalkAST(value, node, paperFeatures);\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tif (paperFeatures.operatorOverloading !== false) {\n\t\t\t\t\thandleOverloading(node, parent);\n\t\t\t\t}\n\t\t\t\tif (paperFeatures.moduleExports !== false) {\n\t\t\t\t\thandleExports(node);\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\tfunction encodeVLQ(value) {\n\t\t\tvar res = '',\n\t\t\t\tbase64 = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/';\n\t\t\tvalue = (Math.abs(value) << 1) + (value < 0 ? 1 : 0);\n\t\t\twhile (value || !res) {\n\t\t\t\tvar next = value & (32 - 1);\n\t\t\t\tvalue >>= 5;\n\t\t\t\tif (value)\n\t\t\t\t\tnext |= 32;\n\t\t\t\tres += base64[next];\n\t\t\t}\n\t\t\treturn res;\n\t\t}\n\n\t\tvar url = options.url || '',\n\t\t\tsourceMaps = options.sourceMaps,\n\t\t\tpaperFeatures = options.paperFeatures || {},\n\t\t\tsource = options.source || code,\n\t\t\toffset = options.offset || 0,\n\t\t\tagent = paper.agent,\n\t\t\tversion = agent.versionNumber,\n\t\t\toffsetCode = false,\n\t\t\tlineBreaks = /\\r\\n|\\n|\\r/mg,\n\t\t\tmap;\n\t\tif (sourceMaps && (agent.chrome && version >= 30\n\t\t\t\t|| agent.webkit && version >= 537.76\n\t\t\t\t|| agent.firefox && version >= 23\n\t\t\t\t|| agent.node)) {\n\t\t\tif (agent.node) {\n\t\t\t\toffset -= 2;\n\t\t\t} else if (window && url && !window.location.href.indexOf(url)) {\n\t\t\t\tvar html = document.getElementsByTagName('html')[0].innerHTML;\n\t\t\t\toffset = html.substr(0, html.indexOf(code) + 1).match(\n\t\t\t\t\t\tlineBreaks).length + 1;\n\t\t\t}\n\t\t\toffsetCode = offset > 0 && !(\n\t\t\t\t\tagent.chrome && version >= 36 ||\n\t\t\t\t\tagent.safari && version >= 600 ||\n\t\t\t\t\tagent.firefox && version >= 40 ||\n\t\t\t\t\tagent.node);\n\t\t\tvar mappings = ['AA' + encodeVLQ(offsetCode ? 0 : offset) + 'A'];\n\t\t\tmappings.length = (code.match(lineBreaks) || []).length + 1\n\t\t\t\t\t+ (offsetCode ? offset : 0);\n\t\t\tmap = {\n\t\t\t\tversion: 3,\n\t\t\t\tfile: url,\n\t\t\t\tnames:[],\n\t\t\t\tmappings: mappings.join(';AACA'),\n\t\t\t\tsourceRoot: '',\n\t\t\t\tsources: [url],\n\t\t\t\tsourcesContent: [source]\n\t\t\t};\n\t\t}\n\t\tif (\n\t\t\tpaperFeatures.operatorOverloading !== false ||\n\t\t\tpaperFeatures.moduleExports !== false\n\t\t) {\n\t\t\twalkAST(parse(code, {\n\t\t\t\tranges: true,\n\t\t\t\tpreserveParens: true,\n\t\t\t\tsourceType: 'module'\n\t\t\t}), null, paperFeatures);\n\t\t}\n\t\tif (map) {\n\t\t\tif (offsetCode) {\n\t\t\t\tcode = new Array(offset + 1).join('\\n') + code;\n\t\t\t}\n\t\t\tif (/^(inline|both)$/.test(sourceMaps)) {\n\t\t\t\tcode += \"\\n//# sourceMappingURL=data:application/json;base64,\"\n\t\t\t\t\t\t+ self.btoa(unescape(encodeURIComponent(\n\t\t\t\t\t\t\tJSON.stringify(map))));\n\t\t\t}\n\t\t\tcode += \"\\n//# sourceURL=\" + (url || 'paperscript');\n\t\t}\n\t\treturn {\n\t\t\turl: url,\n\t\t\tsource: source,\n\t\t\tcode: code,\n\t\t\tmap: map\n\t\t};\n\t}\n\n\tfunction execute(code, scope, options) {\n\t\tpaper = scope;\n\t\tvar view = scope.getView(),\n\t\t\ttool = /\\btool\\.\\w+|\\s+on(?:Key|Mouse)(?:Up|Down|Move|Drag)\\b/\n\t\t\t\t\t.test(code) && !/\\bnew\\s+Tool\\b/.test(code)\n\t\t\t\t\t\t? new Tool() : null,\n\t\t\ttoolHandlers = tool ? tool._events : [],\n\t\t\thandlers = ['onFrame', 'onResize'].concat(toolHandlers),\n\t\t\tparams = [],\n\t\t\targs = [],\n\t\t\tfunc,\n\t\t\tcompiled = typeof code === 'object' ? code : compile(code, options);\n\t\tcode = compiled.code;\n\t\tfunction expose(scope, hidden) {\n\t\t\tfor (var key in scope) {\n\t\t\t\tif ((hidden || !/^_/.test(key)) && new RegExp('([\\\\b\\\\s\\\\W]|^)'\n\t\t\t\t\t\t+ key.replace(/\\$/g, '\\\\$') + '\\\\b').test(code)) {\n\t\t\t\t\tparams.push(key);\n\t\t\t\t\targs.push(scope[key]);\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\texpose({ __$__: __$__, $__: $__, paper: scope, tool: tool },\n\t\t\t\ttrue);\n\t\texpose(scope);\n\t\tcode = 'var module = { exports: {} }; ' + code;\n\t\tvar exports = Base.each(handlers, function(key) {\n\t\t\tif (new RegExp('\\\\s+' + key + '\\\\b').test(code)) {\n\t\t\t\tparams.push(key);\n\t\t\t\tthis.push('module.exports.' + key + ' = ' + key + ';');\n\t\t\t}\n\t\t}, []).join('\\n');\n\t\tif (exports) {\n\t\t\tcode += '\\n' + exports;\n\t\t}\n\t\tcode += '\\nreturn module.exports;';\n\t\tvar agent = paper.agent;\n\t\tif (document && (agent.chrome\n\t\t\t\t|| agent.firefox && agent.versionNumber < 40)) {\n\t\t\tvar script = document.createElement('script'),\n\t\t\t\thead = document.head || document.getElementsByTagName('head')[0];\n\t\t\tif (agent.firefox)\n\t\t\t\tcode = '\\n' + code;\n\t\t\tscript.appendChild(document.createTextNode(\n\t\t\t\t'document.__paperscript__ = function(' + params + ') {' +\n\t\t\t\t\tcode +\n\t\t\t\t'\\n}'\n\t\t\t));\n\t\t\thead.appendChild(script);\n\t\t\tfunc = document.__paperscript__;\n\t\t\tdelete document.__paperscript__;\n\t\t\thead.removeChild(script);\n\t\t} else {\n\t\t\tfunc = Function(params, code);\n\t\t}\n\t\tvar exports = func && func.apply(scope, args);\n\t\tvar obj = exports || {};\n\t\tBase.each(toolHandlers, function(key) {\n\t\t\tvar value = obj[key];\n\t\t\tif (value)\n\t\t\t\ttool[key] = value;\n\t\t});\n\t\tif (view) {\n\t\t\tif (obj.onResize)\n\t\t\t\tview.setOnResize(obj.onResize);\n\t\t\tview.emit('resize', {\n\t\t\t\tsize: view.size,\n\t\t\t\tdelta: new Point()\n\t\t\t});\n\t\t\tif (obj.onFrame)\n\t\t\t\tview.setOnFrame(obj.onFrame);\n\t\t\tview.requestUpdate();\n\t\t}\n\t\treturn exports;\n\t}\n\n\tfunction loadScript(script) {\n\t\tif (/^text\\/(?:x-|)paperscript$/.test(script.type)\n\t\t\t\t&& PaperScope.getAttribute(script, 'ignore') !== 'true') {\n\t\t\tvar canvasId = PaperScope.getAttribute(script, 'canvas'),\n\t\t\t\tcanvas = document.getElementById(canvasId),\n\t\t\t\tsrc = script.src || script.getAttribute('data-src'),\n\t\t\t\tasync = PaperScope.hasAttribute(script, 'async'),\n\t\t\t\tscopeAttribute = 'data-paper-scope';\n\t\t\tif (!canvas)\n\t\t\t\tthrow new Error('Unable to find canvas with id \"'\n\t\t\t\t\t\t+ canvasId + '\"');\n\t\t\tvar scope = PaperScope.get(canvas.getAttribute(scopeAttribute))\n\t\t\t\t\t\t|| new PaperScope().setup(canvas);\n\t\t\tcanvas.setAttribute(scopeAttribute, scope._id);\n\t\t\tif (src) {\n\t\t\t\tHttp.request({\n\t\t\t\t\turl: src,\n\t\t\t\t\tasync: async,\n\t\t\t\t\tmimeType: 'text/plain',\n\t\t\t\t\tonLoad: function(code) {\n\t\t\t\t\t\texecute(code, scope, src);\n\t\t\t\t\t}\n\t\t\t\t});\n\t\t\t} else {\n\t\t\t\texecute(script.innerHTML, scope, script.baseURI);\n\t\t\t}\n\t\t\tscript.setAttribute('data-paper-ignore', 'true');\n\t\t\treturn scope;\n\t\t}\n\t}\n\n\tfunction loadAll() {\n\t\tBase.each(document && document.getElementsByTagName('script'),\n\t\t\t\tloadScript);\n\t}\n\n\tfunction load(script) {\n\t\treturn script ? loadScript(script) : loadAll();\n\t}\n\n\tif (window) {\n\t\tif (document.readyState === 'complete') {\n\t\t\tsetTimeout(loadAll);\n\t\t} else {\n\t\t\tDomEvent.add(window, { load: loadAll });\n\t\t}\n\t}\n\n\treturn {\n\t\tcompile: compile,\n\t\texecute: execute,\n\t\tload: load,\n\t\tparse: parse,\n\t\tcalculateBinary: __$__,\n\t\tcalculateUnary: $__\n\t};\n\n}.call(this);\n\nvar paper = new (PaperScope.inject(Base.exports, {\n\tBase: Base,\n\tNumerical: Numerical,\n\tKey: Key,\n\tDomEvent: DomEvent,\n\tDomElement: DomElement,\n\tdocument: document,\n\twindow: window,\n\tSymbol: SymbolDefinition,\n\tPlacedSymbol: SymbolItem\n}))();\n\nif (paper.agent.node) {\n\trequire('./node/extend.js')(paper);\n}\n\nif (typeof define === 'function' && define.amd) {\n\tdefine('paper', paper);\n} else if (typeof module === 'object' && module) {\n\tmodule.exports = paper;\n}\n\nreturn paper;\n}.call(this, typeof self === 'object' ? self : null);\n"
  },
  {
    "path": "calliar_server/server/static/paper.js",
    "content": "/*\nvariables\n*/\n\nvar canvas;\nvar mousePressed = false;\nvar curr_img;\nvar currStroke = \"\";\nvar currSketch = [];\nvar currStrokeSimplified = \"\";\n\nvar allPaths = [];\nconst colors = ['black', 'red', 'blue', 'green', 'orange', 'brown', 'purple']\nvar oldImageName;\nvar newImageName;\nvar numImages;\nvar procNumImages;\nvar readonlyInput;\nvar input;\nvar ctr = 0; \nvar paper; \nvar strokeWidth = 3;\nvar currImageId = 0;\nvar imageBlob;\nvar existOnServer = true;\nvar blob;\n\nfunction addRaster(url)\n{\n    if (url==undefined){\n        alert('no more images to draw or something went wrong, going to home page')\n        if (window.location.pathname !='/'){\n          //window.location='/'\n          //window.location.reload()\n        }\n\n    }\n    var raster;\n\n    if (existOnServer){\n        raster = new paper.Raster({source: \"/media/calliar_images/images/\"+url})\n    }else{\n        raster = new paper.Raster({source: url})\n    }\n    \n    var w, h;\n    raster.onLoad = function ()\n    {\n        raster.opacity = 0.3\n        h = raster.width;\n        w = raster.height;\n\n        const scale = 600;\n        if (w > h){\n            h = parseInt((h/w) * scale);\n            w = scale;\n        }else{\n            w = parseInt((w/h) * scale);\n            h = scale; \n        }\n        raster.fitBounds(paper.view.bounds)\n        curr_img = raster.image\n    };\n\n}\n\nfunction addImage(imageName)\n{\n    addRaster(imageName)\n    var text =((imageName).split('.')[0]).trim()\n    newImageName = text+'.jpg'\n    input.value  = text;\n    text = preprocess(text)[1]\n    readonlyInput.value  = text;\n}\n/*\nload the model\n*/\n\nfunction getImageUrl(){\n    var xmlHttp = new XMLHttpRequest();\n    xmlHttp.open( \"GET\", '/endpoint/next-image?id='+currImageId, false ); // false for synchronous request\n    xmlHttp.send( null );\n    response = JSON.parse(xmlHttp.response)\n    numImages = response.num_images\n    procNumImages = response.proc_num_images\n    document.getElementById(\"ctr\").innerHTML = 'You processed '+ctr+ ', remaining images '+numImages+', total processed images '+procNumImages;\n    currImageId = parseInt(response.id)\n    return response.image_path\n}\n\nasync function start() {\n\n    var slider = document.getElementById('myRange');\n    readonlyInput = document.getElementById(\"text-readonly\");\n    input = document.getElementById(\"text\");\n    paper.install(window);\n    paper.setup('canvas');\n\n    // Create a simple drawing tool:\n    var tool = new Tool();\n    var path;\n\n    // Define a mousedown and mousedrag handler\n    tool.onMouseDown = function(event) {\n        // If we produced a path before, deselect it:\n        if (path) {\n            path.selected = false;\n        }\n        \n        path = new Path({\n            segments: [event.point],\n            strokeColor: colors[currSketch.length % colors.length],\n            strokeWidth: strokeWidth,\n            fullySelected: true\n        });\n        \n    }\n\n    tool.onMouseDrag = function(event) {\n        path.add(event.point);\n    }\n\n\n    tool.onMouseUp = function(event) {            \n        currStroke = getPoints(path)\n        \n        if (path.segments.length > 5){ //only simplify if the length is greater than 5\n            path.simplify();\n        }\n        else{\n            //if the length is less add a single point\n            prev_point_index = path.segments.length - 1\n            prev_point = path.segments[prev_point_index].point;\n            path.add(prev_point.add(1))\n        }\n        currStroke = getPoints(path);\n            \n        path.fullySelected = true;\n        currStrokeSimplified = path.exportSVG().getAttribute(\"d\")\n\n        const currChar = readonlyInput.value.charAt(currSketch.length * 2)\n        if (currChar == \"\")\n        {\n            path.remove();\n            alert(\"cannot add empty characters!\")\n        }\n        else{\n            currSketch.push({[currChar]:[currStroke, currStrokeSimplified]})\n            allPaths.push(path)\n            readonlyInput.focus();\n            readonlyInput.setSelectionRange(0, currSketch.length * 2);\n\n        }\n\n    }\n\n    tool.onKeyDown = function(event) { \n        if(event.key == 'right'){\n            getNext()\n        }\n        if (event.key == 'left') {\n            getPrev()\n        }\n    }\n\n    next()\n\n    slider.onchange  = function() {\n        strokeWidth = parseInt(this.value);\n    };\n\n\n\n    $(input).change(function(e){\n        text = input.value.trim()\n        newImageName = text+'.jpg'\n        text = preprocess(text)[1]\n        readonlyInput.value  = text;\n    })\n\n    \n}\n\nfunction undo() {\n    const lastStrokeIdx = allPaths.length - 1;\n    if (allPaths.length >= 1){\n        allPaths[lastStrokeIdx].remove();\n        currSketch.splice(-1, 1)\n        currSketch.currStrokeSimplified.splice(-1, 1)\n        allPaths.splice(-1, 1)\n    }\n    // update selection\n    readonlyInput.focus();\n    readonlyInput.setSelectionRange(0, currSketch.length * 2);\n        \n}\n\nfunction save() {\n    if(currSketch.length != readonlyInput.value.split(\" \").length)\n    {\n        console.log(readonlyInput.value.split(\" \"))\n        console.log(currSketch)\n        alert('sketch size is not the same as the text chars!')\n        return \n    }\n    var xhr = new XMLHttpRequest();\n    xhr.open(\"POST\", '/endpoint/', false);\n    xhr.setRequestHeader('Content-Type', 'application/json');\n    xhr.send(JSON.stringify({\n        sketch: currSketch,\n        oldImageName:oldImageName,\n        newImageName:newImageName,\n        existOnServer:existOnServer,\n        imageBlob:imageBlob,\n    }));\n    const response = JSON.parse(xhr.response)\n\n    if (response.result == true)\n    {\n        next()\n        ctr += 1;\n        document.getElementById(\"ctr\").innerHTML = 'You processed '+ctr+ ', remaining images '+numImages+', total processed images '+procNumImages;\n    }\n    else{\n        alert('server refused to save image')\n    }\n    currStroke = []\n    currSketch = []\n    currStrokeSimplified = []\n}\n\nfunction clearCanvas()\n{\n    paper.project.activeLayer.removeChildren();\n    currStroke = []\n    currSketch = []\n    currStrokeSimplified = []\n    allPaths  = []\n}\nfunction next() {\n    clearCanvas();\n    existOnServer = true\n    oldImageName = getImageUrl()\n    console.log(oldImageName)\n    addImage(oldImageName)\n}\n\n/*\nclear the canvas \n*/\nfunction erase() {\n    clearCanvas();\n    if(existOnServer)\n        addRaster(oldImageName);\n    else\n        addRaster(blob)\n\n}\n\nfunction getNext(){\n    currImageId += 1\n    next()\n}\n\nfunction getPrev(){\n    currImageId -= 1\n    next()\n}\n\nfunction loadImage(event) {\n    imageName = event.target.files[0]['name']\n    blob = URL.createObjectURL(event.target.files[0]);\n    clearCanvas();\n    existOnServer = false\n    addRaster(blob)\n    var reader = new FileReader();\n    reader.readAsDataURL(event.target.files[0]); \n    reader.onloadend = function() {\n        var base64data = reader.result;\n        imageBlob = base64data\n    }\n\n    var text =((imageName).split('.')[0]).trim()\n    newImageName = text+'.jpg'\n    input.value  = text;\n    text = preprocess(text)[1]\n    readonlyInput.value  = text;\n}\nfunction clearImage() {\n    document.getElementById('formFile').value = null;\n    frame.src = \"\";\n}\n"
  },
  {
    "path": "calliar_server/server/static/unicode.js",
    "content": "//https://jrgraphix.net/r/Unicode/0600-06FF\nmap_chars = {\n    \"\\u0623\":[\"\\u0621\", \"\\u0627\"], // أ\n    \"\\u0622\":[\"\\u0605\", \"\\u0627\"], // آ\n    \"\\u0625\":[\"\\u0627\", \"\\u0621\"], // إ\n    \"\\u0628\":[\"\\u066E\", \".\"], // ب\n    \"\\u062A\":[\".\", \".\", \"\\u066E\"], // ت\n    \"\\u062B\":[\".\", \".\", \".\", \"\\u066E\"], // ث \n    \"\\u062C\":[\"\\u062D\", \".\"], // ج\n    \"\\u062E\":[\".\", \"\\u062D\"], // خ\n    \"\\u0630\":[\".\", \"\\u062F\"], // ذ\n    \"\\u0632\":[\".\", \"\\u0631\"], // ز\n    \"\\u0634\":[\".\", \".\", \".\", \"\\u0633\"], // ش\n    \"\\u0636\":[\".\", \"\\u0635\"], // ض\n    \"\\u0637\":[\"\\u0627\", \"\\uFEBB\"], // ط\n    \"\\u0638\":[\".\", \"\\u0627\", \"\\uFEBB\"], // ظ\n    \"\\u063A\":[\".\", \"\\u0639\"], // غ\n    \"\\u0641\":[\".\", \"\\u066F\"], // ف\n    \"\\u0642\":[\".\", \".\", \"\\u066F\"], // ق\n    \"\\u06A4\":[\".\", \".\", \".\", \"\\u066F\"], // ڤ\n    \"\\u0643\":[\"\\u0621\", \"\\u0644\"], // ك\n    \"\\u0646\":[\".\", \"\\u06BA\"], // ن\n    \"\\u0624\":[\"\\u0621\", \"\\u0648\"], // ؤ\n    \"\\u064A\":[\"\\u0649\", \".\", \".\"], //ي\n    \"\\u0626\":[\"\\u0621\", \"\\u0649\"], //ئ\n    \"\\u0629\":[\".\", \".\", \"\\u0647\"], //ه\n}"
  },
  {
    "path": "calliar_server/server/static/utils.js",
    "content": "function preprocess(name)\n{\n    var text = name;\n    const diacritics = \"[ًٌٍَُِّْ]\"\n    const numbers = '0123456789'\n    for (i = 0; i < diacritics.length; i++) \n    {\n        text = text.replace(diacritics[i], '')\n    }\n\n    for (i = 0; i < numbers.length; i++) \n    {\n        text = text.replaceAll(numbers[i], '')\n    }\n    var outText = \"\"\n    for (i = 0; i < text.length; i++) \n    {\n        if (text[i] == \" \")\n            continue\n\n            if (text[i] in map_chars)\n            if (i < text.length - 1 && text[i] == \"\\u0643\") // if we get kaf at the end \n            {\n                if (text[i+1] != ' ')\n                    outText += '\\uFEDB'\n                else\n                outText += map_chars[text[i]].join(\" \") \n            }\n            else\n                outText += map_chars[text[i]].join(\" \")\n        else{\n\n                outText += text[i]\n        }            \n        if (i != text.length - 1)\n            outText +=  \" \"\n            \n    }\n\n    return [text, outText]\n}\n\nfunction getPoints(path){\n    var points = []\n    path.segments.forEach(segment => points.push([segment.point.x,segment.point.y]))\n    return points\n}"
  },
  {
    "path": "calliar_server/server/templates/server/explore.html",
    "content": "{% load static %}\n<!DOCTYPE html>\n<html lang=\"\" xml:lang=\"\" xmlns=\"http://www.w3.org/1999/xhtml\">\n <head>\n    <meta charset=\"utf-8\"/>\n    <meta name=\"twitter:card\" content=\"summary\" />\n    <meta property=\"og:url\" content=\"https://arbml.github.io/Calliar/page/index.html\" />\n    <meta property=\"og:title\" content=\"Calliar: An Online Handwritten Dataset for Arabic Calligraphy\" />\n    <meta property=\"og:description\" content=\"Calligraphy is an essential part of the Arabic heritage and culture. It has been used in the past for the decoration of houses and mosques. Usually, such calligraphy is designed manually by experts with aesthetic insights. In the past few years, there has been a considerable effort to digitize such type of art by either taking a photo of decorated buildings or drawing them using digital devices. The latter is considered an online form where the drawing is tracked by recording the apparatus movement, an electronic pen for instance, on a screen. In the literature, there are many offline datasets collected with a diversity of Arabic styles for calligraphy. However, there is no available online dataset for Arabic calligraphy. In this paper, we illustrate our approach for the collection and annotation of an online dataset for Arabic calligraphy called Calliar that consists of 2,500 sentences. Calliar is annotated for stroke, character, word and sentence level prediction.\" />\n    <meta property=\"og:image\" content=\"https://raw.githubusercontent.com/ARBML/Calliar/main/media/sample_calliar_image_3.png\" />\n\n    <title>\n        Calliar\n    </title>\n\n  <!-- inline stylesheet files into the above <style> element -->\n  <link href=\"https://fonts.googleapis.com/css?family=Montserrat|Segoe+UI\" rel=\"stylesheet\"/>\n\n  <link href=\"https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/css/bootstrap.min.css\" rel=\"stylesheet\"  crossorigin=\"anonymous\">\n\t\n  <script src=\"https://ajax.googleapis.com/ajax/libs/jquery/3.3.1/jquery.min.js\"></script>\n \n  <script src=\"https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/js/bootstrap.bundle.min.js\" crossorigin=\"anonymous\"></script>\n</head>\n\n <body>\n    <nav class=\"navbar navbar-expand-lg navbar-light bg-light\">\n        <a class=\"navbar-brand\" href=\"#\">Calliar</a>\n        <button class=\"navbar-toggler\" type=\"button\" data-toggle=\"collapse\" data-target=\"#navbarNav\" aria-controls=\"navbarNav\" aria-expanded=\"false\" aria-label=\"Toggle navigation\">\n          <span class=\"navbar-toggler-icon\"></span>\n        </button>\n        <div class=\"collapse navbar-collapse\" id=\"navbarNav\">\n          <ul class=\"navbar-nav\">\n            <li class=\"nav-item\">\n              <a class=\"nav-link\" href=\"../endpoint\">Home </a>\n            </li>\n            <li class=\"nav-item\">\n              <a class=\"nav-link active\" href=\"#\">Explore </a>\n            </li>\n            <li class=\"nav-item\">\n              <a class=\"nav-link\" href=\"https://arbml.github.io/Calliar/page/index.html\" target=\"_blank\">Article </a>\n            </li>\n            <li class=\"nav-item\">\n              <a class=\"nav-link\" href=\"https://github.com/ARBML/Calliar\" target=\"_blank\">GitHub </a>\n            </li>\n          </ul>\n        </div>\n      </nav>\n    \n      <div class=\"container-sm\">\n\n        <p style = \"text-align: justify;\">\n            Explore saved calligraphy as animated strokes.\n        </p>\n        <div id = \"jsonList\">\n        </div>\n\n        <br>\n        <div style = \"text-align: center;\">\n            <input type=\"text\" class = \"form-control\" id=\"text-readonly\" value=\"\"  dir=\"rtl\" readonly> \n            <br>\n\n            <div id=\"canvas\" style = \"border-width: thin;border-color: black;border-style:solid; margin: 0 auto; width:600px;\"> \n            </div>\n            \n            <br>\n\n\n              <div class=\"btn-group\" role=\"group\" aria-label=\"Basic radio toggle button group\">\n                <input type=\"radio\" class=\"btn-check\" name=\"btnradio\" id=\"btnradio1\" autocomplete=\"off\">\n                <label class=\"btn btn-outline-secondary\" for=\"btnradio1\" onclick ='speedUp()' >2x</label>\n              \n                <input type=\"radio\" class=\"btn-check\" name=\"btnradio\" id=\"btnradio2\" autocomplete=\"off\" checked>\n                <label class=\"btn btn-outline-secondary\" for=\"btnradio2\" onclick ='speedReset()'>x</label>\n              \n                <input type=\"radio\" class=\"btn-check\" name=\"btnradio\" id=\"btnradio3\" autocomplete=\"off\">\n                <label class=\"btn btn-outline-secondary\" for=\"btnradio3\" onclick ='speedDown()'>0.5x</label>\n              </div>\n              <div class=\"btn-group\">\n                  <button type=\"button\" class=\"btn btn-outline-secondary\" onclick ='generatePrev()' >Prev</button>\n                  <button type=\"button\" class=\"btn btn-outline-secondary\" id = \"generate\" onclick ='generate()'>Animate</button>\n                  <button type=\"button\" class=\"btn btn-outline-secondary\" onclick ='generateNext()' >Next</button>\n              </div>\n            \n            <input type=\"range\" id = 'slider' min=\"1\" max=\"10\" value=\"3\">\n        </div> \n      </div>\n    </body>\n\n    \n\n    \n\n    <script src=\"https://cdnjs.cloudflare.com/ajax/libs/raphael/2.1.0/raphael-min.js\"></script>\n\n    <script src=\"{% static 'fabric.js' %}\"></script>\n\n    <script src=\"{% static 'unicode.js' %}\"></script>\n\n    <!-- main script -->\n    <script src=\"{% static 'explore.js' %}\" > </script>\n    <script src=\"{% static 'utils.js' %}\" > </script>\n    <script src=\"https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js\"></script>\n    <script src=\"https://cdnjs.cloudflare.com/ajax/libs/paper.js/0.11.8/paper-full.min.js\"></script>\n"
  },
  {
    "path": "calliar_server/server/templates/server/index.html",
    "content": "{% load static %}\n<html lang=\"en\">\n<head>\n  <meta charset=\"UTF-8\" />\n  <title>Calliar</title>\n\n  <link href=\"https://fonts.googleapis.com/css?family=Montserrat|Segoe+UI\" rel=\"stylesheet\"/>\n\n  <link href=\"https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/css/bootstrap.min.css\" rel=\"stylesheet\"  crossorigin=\"anonymous\">\n\t\n  <script src=\"https://ajax.googleapis.com/ajax/libs/jquery/3.3.1/jquery.min.js\"></script>\n \n  <script src=\"https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/js/bootstrap.bundle.min.js\" crossorigin=\"anonymous\"></script>\n\n\t\n  <!-- canvas script -->\n  <script src=\"{% static 'utils.js' %}\" > </script>\n\n  <script src=\"{% static 'fabric.js' %}\"></script>\n\n  <script src=\"{% static 'unicode.js' %}\"></script>\n\n  <!-- main script -->\n  <script src=\"{% static 'paper.js' %}\" > </script>\n\n\t\n</head>\n<body>\n  <nav class=\"navbar navbar-expand-lg navbar-light bg-light\">\n    <a class=\"navbar-brand\" href=\"#\">Calliar</a>\n    <button class=\"navbar-toggler\" type=\"button\" data-toggle=\"collapse\" data-target=\"#navbarNav\" aria-controls=\"navbarNav\" aria-expanded=\"false\" aria-label=\"Toggle navigation\">\n      <span class=\"navbar-toggler-icon\"></span>\n    </button>\n    <div class=\"collapse navbar-collapse\" id=\"navbarNav\">\n      <ul class=\"navbar-nav\">\n        <li class=\"nav-item\">\n          <a class=\"nav-link active\" href=\"#\">Home </a>\n        </li>\n        <li class=\"nav-item\">\n          <a class=\"nav-link\" href=\"../explore\">Explore </a>\n        </li>\n        <li class=\"nav-item\">\n          <a class=\"nav-link\" href=\"https://arbml.github.io/Calliar/page/index.html\" target=\"_blank\">Article </a>\n        </li>\n        <li class=\"nav-item\">\n          <a class=\"nav-link\" href=\"https://github.com/ARBML/Calliar\" target=\"_blank\">GitHub </a>\n        </li>\n      </ul>\n    </div>\n  </nav>\n\n  <div class=\"container-sm\">\n\n    <div class = \"row-10 justify-content-md-center\">\n        <span class=\"badge\" id = 'ctr'>0</span>\n        <input type=\"text\" class = \"form-control\" id=\"text-readonly\" value=\"\"  dir=\"rtl\" readonly> \n        <input type=\"text\" class = \"form-control\" id=\"text\" value=\"\"  dir=\"rtl\"> \n        <center>\n          <canvas id=\"canvas\" width=\"600px\" height=\"600px\" style=\"border:1px solid #b9bfc9;\"></canvas>\n        </center>\n    </div>\n            \n    <div class = \"row justify-content-center\">\n        <div class=\"col-3\">\n          <input class=\"form-control\" type=\"file\" id=\"formFile\" onchange=\"loadImage(event)\" accept=\"image/*\">\n        </div>\n        <div class=\"col-5\">\n          <div class=\"btn-group\">\n            <button type=\"button\" class=\"btn btn-outline-primary\" onclick ='getPrev()' >Prev</button>\n            <button type=\"button\" class=\"btn btn-outline-primary\" onclick ='save()' >Save</button>\n            <button type=\"button\" class=\"btn btn-outline-primary\" onclick ='getNext()' >Next</button>\n            <button type=\"button\" class=\"btn btn-outline-primary\" onclick ='undo()' >Undo</button>\n            <button type=\"button\" class=\"btn btn-outline-primary\" onclick ='erase()'>Clear</button>\n            <input type=\"range\" min=\"1\" max=\"10\" value=\"3\" class=\"form-range\" id=\"myRange\"> \n          </div>\n        </div>\n        \n    </div>\n          \n\n             \n  </div>\n</body>\n<script src=\"https://cdnjs.cloudflare.com/ajax/libs/paper.js/0.11.5/paper-core.js\"></script> \n\n<script>\n\t\n\tstart()\n</script>\n</html>\n"
  },
  {
    "path": "calliar_server/server/templates/server/index2.html",
    "content": "{% load static %}\n<html lang=\"en\">\n<head>\n<meta charset=\"UTF-8\" />\n<title>Calliar</title>\n\n<!-- bs4 css -->\n<link rel=\"stylesheet\" href=\"https://maxcdn.bootstrapcdn.com/bootstrap/4.1.0/css/bootstrap.min.css\"/>\n\t\n\n<!-- jQuery library -->\n<script src=\"https://ajax.googleapis.com/ajax/libs/jquery/3.3.1/jquery.min.js\"></script>\n\n<!-- Popper JS -->\n<script src=\"https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.0/umd/popper.min.js\"></script>\n\n<!-- Latest compiled JavaScript -->\n<script src=\"https://maxcdn.bootstrapcdn.com/bootstrap/4.1.0/js/bootstrap.min.js\"></script>\n\n\t\n<!-- canvas script -->\n<script src=\"{% static 'fabric.js' %}\"></script>\n\n<script src=\"{% static 'unicode.js' %}\"></script>\n\n<!-- main script -->\n<script src=\"{% static 'main2.js' %}\" > </script>\n\n\t\n</head>\n<body>\n<nav class=\"navbar navbar-expand-sm bg-light navbar-light\">\n  <!-- Brand/logo -->\n  <a class=\"navbar-brand\" href=\"#\"><h1>Calli<small>ar</small> Labelling</h1></a>\n</nav>\n\n  <div class=\"container-fluid\">\n        <div class = \"row justify-content-center\" style=\"margin-top:3em;\">\n          <div class = \"col-3\">\n            <input type=\"text\" class = \"form-control\" id=\"text\" value=\"\"  dir=\"rtl\"> \n          </div>\n\n          <div class=\"btn-group\">\n            <!-- <button type=\"button\" class=\"btn btn-outline-primary\" onclick ='erase()'>Clear</button> -->\n            <button type=\"button\" class=\"btn btn-outline-primary\" id =\"save\" onclick ='save()' >Save</button>\n            <button type=\"button\" class=\"btn btn-outline-primary\" id =\"skip\" onclick ='next()' >Skip</button>\n          </div>\n        \n        </div>\n\n        <div class = \"row justify-content-center\">\n          <canvas id=\"canvas\" width=\"500\" height=\"500\" class=\"canvas\" style=\"border:1px solid #b9bfc9;margin-top:25px;\"></canvas>\n        </div>\n\n\n  </div>\n</body>\n\n<script>\n\t\n\tstart()\n</script>\n</html>\n"
  },
  {
    "path": "calliar_server/server/tests.py",
    "content": "from django.test import TestCase\n\n# Create your tests here.\n"
  },
  {
    "path": "calliar_server/server/urls.py",
    "content": "from .views import EndpointView, NextImageView, ExploreView, NextJsonView, ListJsonView\nfrom django.urls import path\nfrom django.conf import settings\nfrom django.conf.urls.static import static\n\napp_name = 'server'\n\nurlpatterns = [\n    path('', EndpointView.as_view(),name=\"home\"),\n    path('endpoint/', EndpointView.as_view(),name=\"endpoint\"),\n    path('endpoint/next-image/', NextImageView.as_view(),name=\"next_image\"),\n    path('explore/', ExploreView.as_view()),\n    path('explore/next-json/', NextJsonView.as_view(),name=\"next_image\"),\n    path('explore/list-json/', ListJsonView.as_view(),name=\"list_jsons\"),\n]"
  },
  {
    "path": "calliar_server/server/views.py",
    "content": "from django.shortcuts import render\nfrom django.views.generic import View\n# Create your views here.\nfrom django.http import HttpResponse\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.utils.decorators import method_decorator\nimport os\nfrom django.http import JsonResponse\nimport json\nimport shutil\nfrom django.conf import settings\nimport base64\nimport io\nfrom PIL import Image\n\n@method_decorator(csrf_exempt, name='dispatch')\nclass EndpointView(View):\n    \n    template_name = 'server/index.html'\n    def render_to_template(self):\n        # context = {'next_image_name':self.get_next_image_name()}\n        context = {}\n        return render(\n            self.request,\n            self.template_name,\n            context,\n        )\n    \n    def get(self, request, *args, **kwargs):\n        return self.render_to_template()\n        \n    def post(self, request, *args, **kwargs):\n        data = json.loads(request.body)\n        file_name = data['newImageName'].split('.')[0]\n        exist_on_Server = bool(data['existOnServer'])\n        new_file_name = \"\"\n        counter = '' \n        while True:\n            new_file_name = counter+file_name\n            if new_file_name not in os.listdir(f'{settings.IMAGES_DIR}/annotated_images'):\n                if exist_on_Server:\n                    shutil.move(f\"{settings.IMAGES_DIR}/images/{data['oldImageName']}\",\n                                f\"{settings.IMAGES_DIR}/annotated_images/{new_file_name}.jpg\")\n                else:\n                    image_bin = data['imageBlob'].encode()\n                    save_path = f\"{settings.IMAGES_DIR}/annotated_images/{new_file_name}.jpg\"\n                    image_object = Image.open(io.BytesIO(base64.b64decode(image_bin[image_bin.find(b'/9'):])))\n                    image_object.save(save_path)\n                break \n            else:\n                if counter == '':\n                    counter = '1'\n                else:\n                    counter = str(int(counter)+1)\n\n        file_path = f\"{settings.IMAGES_DIR}/annotations/{new_file_name}.json\"\n        json.dump(data['sketch'],open(file_path, 'w'))\n        result = JsonResponse({'result':True})\n        return result\n\nclass NextImageView(View):\n    \n    def get_next_image_name(self, curr_id):\n        image_paths = os.listdir(f'{settings.IMAGES_DIR}/images/')\n        processed_image_paths = os.listdir(f'{settings.IMAGES_DIR}/annotated_images/')\n        \n        if not any ([\n                os.path.isfile(f'{settings.IMAGES_DIR}/images/{file}') \n                for file in os.listdir(f'{settings.IMAGES_DIR}/images/')\n            ]):\n            return JsonResponse({'result':False,'description':'No more images to return'},status=404)\n                \n\n        if curr_id >= len(image_paths):\n            curr_id = 0\n        elif curr_id == -1:\n            curr_id = len(image_paths) - 1\n        \n\n        return JsonResponse({'image_path':image_paths[curr_id], 'num_images':len(image_paths),\n            'proc_num_images':len(processed_image_paths), 'id':curr_id})\n\n\n    def get(self, request, *args, **kwargs):\n        return HttpResponse(self.get_next_image_name(int(request.GET['id'])))\n\nclass ExploreView(View):\n    \n    template_name = 'server/explore.html'\n    def render_to_template(self):\n        # context = {'next_image_name':self.get_next_image_name()}\n        context = {}\n        return render(\n            self.request,\n            self.template_name,\n            context,\n        )\n    \n    def get(self, request, *args, **kwargs):\n        return self.render_to_template()\n        \nclass NextJsonView(View):\n    \n    def get_next_json_name(self, curr_id):\n        json_paths = os.listdir(f'{settings.IMAGES_DIR}/annotations/')\n\n        if curr_id >= len(json_paths):\n            curr_id = 0\n        elif curr_id == -1:\n            curr_id = len(json_paths) - 1\n\n        return JsonResponse({'json_path':json_paths[curr_id], 'id':curr_id})\n\n\n    def get(self, request, *args, **kwargs):\n        return HttpResponse(self.get_next_json_name(int(request.GET['id'])))\n\n\nclass ListJsonView(View):\n    \n    def get_json_list(self):\n        json_paths = os.listdir(f'{settings.IMAGES_DIR}/annotations/')\n        json_names = [json_path.split('.')[0] for json_path in json_paths]\n        return JsonResponse({'json_names':json_names, 'size':len(json_paths)})\n\n\n    def get(self, request, *args, **kwargs):\n        return HttpResponse(self.get_json_list())\n\n\n\n"
  },
  {
    "path": "demo.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"!git clone https://github.com/ARBML/Calliar\\n\",\n    \"%cd Calliar\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%%capture\\n\",\n    \"!pip install -r requirements.txt \\n\",\n    \"!unzip dataset.zip \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/tmp/ipykernel_352743/198341245.py:8: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\\n\",\n      \"  from IPython.core.display import display, HTML, Video\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import io \\n\",\n    \"import glob\\n\",\n    \"import json \\n\",\n    \"import base64\\n\",\n    \"from scripts.vis import *\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt \\n\",\n    \"from IPython.core.display import display, HTML, Video\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"كن فيكون\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<PIL.PngImagePlugin.PngImageFile image mode=RGB size=563x302 at 0x7F321A189C70>\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"npy_files = glob.glob('dataset/train/**.json')\\n\",\n    \"json_path = np.random.choice(npy_files)\\n\",\n    \"drawing = json.load(open(json_path))\\n\",\n    \"print(get_annotation(json_path))\\n\",\n    \"data, _ = convert_3d(drawing, return_flag=True, threshold=50)\\n\",\n    \"draw_strokes(data, stroke_width = 2, crop = True)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%%capture \\n\",\n    \"create_animation(json_path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<video alt=\\\"video\\\" autoplay>\\n\",\n       \"                <source 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  },
  {
    "path": "notebooks/Check Wrong Annotations.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import math, json\\n\",\n    \"from rdp import rdp\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from matplotlib import animation\\n\",\n    \"from IPython.display import HTML\\n\",\n    \"import tqdm.notebook as tq\\n\",\n    \"import pickle\\n\",\n    \"from collections import defaultdict\\n\",\n    \"import cairosvg\\n\",\n    \"from PIL import Image,ImageDraw\\n\",\n    \"import glob\\n\",\n    \"import os \\n\",\n    \"import re\\n\",\n    \"import svgwrite\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def get_bounds(data):\\n\",\n    \"    minx, miny = 600, 600  \\n\",\n    \"    maxx, maxy = 0, 0\\n\",\n    \"    \\n\",\n    \"    for i, (x, y, z) in enumerate(data): \\n\",\n    \"        if minx > x:\\n\",\n    \"            minx = x\\n\",\n    \"        if miny > y:\\n\",\n    \"            miny = y \\n\",\n    \"\\n\",\n    \"        if maxx < x:\\n\",\n    \"            maxx = x\\n\",\n    \"        if maxy < y:\\n\",\n    \"            maxy = y \\n\",\n    \"    return minx, maxx, miny, maxy\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def convert_3d(drawing, return_flag = False, threshold=10):\\n\",\n    \"    out = []\\n\",\n    \"    corrupted = False\\n\",\n    \"    for item in drawing:\\n\",\n    \"        char = list(item.keys())[0]\\n\",\n    \"        stroke = item[char]\\n\",\n    \"        if len(stroke) == 1:\\n\",\n    \"            x, y = stroke[0]\\n\",\n    \"            out.append([x, y, 0])\\n\",\n    \"            out.append([x+5, y+5, 1])\\n\",\n    \"            continue\\n\",\n    \"        segment = []\\n\",\n    \"        for i, point in enumerate(stroke):\\n\",\n    \"            x, y = point \\n\",\n    \"            if i == len(stroke) - 1:\\n\",\n    \"                segment.append([x, y, 1])\\n\",\n    \"            else:\\n\",\n    \"                segment.append([x, y, 0])\\n\",\n    \"        \\n\",\n    \"        start = 0 \\n\",\n    \"        for i, point in enumerate(segment):\\n\",\n    \"            if i < len(segment) -1:\\n\",\n    \"                x, y, _ = point\\n\",\n    \"                next_x, next_y, _ = segment[i+1]\\n\",\n    \"                if any((\\n\",\n    \"                    abs(x-next_x)>threshold,\\n\",\n    \"                    abs(y-next_y>threshold),\\n\",\n    \"                )):\\n\",\n    \"                    corrupted=True\\n\",\n    \"                    start = i +1\\n\",\n    \"        \\n\",\n    \"        out += segment[start:]\\n\",\n    \"    if return_flag:\\n\",\n    \"        return out, corrupted\\n\",\n    \"    return out\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def make_square(im, min_size=256, fill_color=(255, 255, 255)):\\n\",\n    \"    x, y = im.size\\n\",\n    \"    size = max(min_size, x, y)\\n\",\n    \"    new_im = Image.new('RGBA', (size, size), fill_color)\\n\",\n    \"    new_im.paste(im, (int((size - x) / 2), int((size - y) / 2)))\\n\",\n    \"    return new_im\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def draw_strokes(data, factor=1, svg_filename = 'tmp/sample.svg', stroke_width = 3, square = False, return_res = False):\\n\",\n    \"    min_x, max_x, min_y, max_y = get_bounds(data)\\n\",\n    \"    dims = (50 + max_x - min_x, 50 + max_y - min_y)\\n\",\n    \"    dwg = svgwrite.Drawing(svg_filename, size = dims)\\n\",\n    \"    dwg.add(dwg.rect(insert=(0, 0), size=dims,fill='white'))\\n\",\n    \"    lift_pen = 1\\n\",\n    \"    abs_x = 25 - min_x \\n\",\n    \"    abs_y = 25 - min_y\\n\",\n    \"    p = \\\"M%s,%s \\\" % (abs_x, abs_y)\\n\",\n    \"    command = \\\"M\\\"\\n\",\n    \"    for i in range(len(data)):\\n\",\n    \"        if (lift_pen == 1):\\n\",\n    \"            command = \\\"M\\\"\\n\",\n    \"        elif (command != \\\"L\\\"):\\n\",\n    \"            command = \\\"L\\\"\\n\",\n    \"        else:\\n\",\n    \"            command = \\\"\\\"\\n\",\n    \"        x = float(data[i][0]) - min_x\\n\",\n    \"        y = float(data[i][1]) - min_y\\n\",\n    \"        lift_pen = data[i][2]\\n\",\n    \"        p += command+str(x)+\\\" \\\"+str(y)+\\\" \\\"\\n\",\n    \"    the_color = \\\"black\\\"\\n\",\n    \"    \\n\",\n    \"    dwg.add(dwg.path(p).stroke(the_color,stroke_width).fill(\\\"none\\\"))\\n\",\n    \"    dwg.save()\\n\",\n    \"    cairosvg.svg2png(url=\\\"tmp/sample.svg\\\", write_to=\\\"tmp/sample.png\\\")\\n\",\n    \"    img = Image.open('tmp/sample.png')\\n\",\n    \"    if square:\\n\",\n    \"        img = make_square(img)\\n\",\n    \"    if return_res:\\n\",\n    \"        return img, dims \\n\",\n    \"    else:\\n\",\n    \"        return img\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"npy_files = glob.glob('server/larger_data/*')\\n\",\n    \"file = np.random.choice(npy_files)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"file = 'server/larger_data/1فسبحان الله حين تمسون وحين تصبحون.json'\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"drawing = json.load(open(file))\\n\",\n    \"data1, flag = convert_3d(drawing, return_flag=True, threshold=50)\\n\",\n    \"plt.imshow(draw_strokes(data1, stroke_width = 5))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def apply_rdb(drawing, verbose = 0):\\n\",\n    \"    new_drawing = []\\n\",\n    \"    total_prev_strokes = 0\\n\",\n    \"    total_post_strokes = 0\\n\",\n    \"    for item in drawing:\\n\",\n    \"        char = list(item.keys())[0]\\n\",\n    \"        stroke = item[char]\\n\",\n    \"        processed_stroke = []\\n\",\n    \"        if len(stroke):\\n\",\n    \"            if verbose:\\n\",\n    \"                print('processing ', char)\\n\",\n    \"            post_stroke = rdp(stroke, epsilon = 2.0)\\n\",\n    \"            total_post_strokes += len(post_stroke)\\n\",\n    \"            total_prev_strokes += len(stroke)\\n\",\n    \"        new_drawing.append({char:post_stroke})\\n\",\n    \"    if verbose:\\n\",\n    \"        print('reduced from ', total_prev_strokes, ' to ', total_post_strokes)\\n\",\n    \"    return new_drawing\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#https:#jrgraphix.net/r/Unicode/0600-06FF\\n\",\n    \"map_chars = {\\n\",\n    \"    \\\"\\\\u0623\\\":[\\\"\\\\u0621\\\", \\\"\\\\u0627\\\"], # أ\\n\",\n    \"    \\\"\\\\u0622\\\":[\\\"\\\\u0605\\\", \\\"\\\\u0627\\\"], # آ\\n\",\n    \"    \\\"\\\\u0625\\\":[\\\"\\\\u0627\\\", \\\"\\\\u0621\\\"], # إ\\n\",\n    \"    \\\"\\\\u0628\\\":[\\\"\\\\u066E\\\", \\\".\\\"], # ب\\n\",\n    \"    \\\"\\\\u062A\\\":[\\\".\\\", \\\".\\\", \\\"\\\\u066E\\\"], # ت\\n\",\n    \"    \\\"\\\\u062B\\\":[\\\".\\\", \\\".\\\", \\\".\\\", \\\"\\\\u066E\\\"], # ث \\n\",\n    \"    \\\"\\\\u062C\\\":[\\\"\\\\u062D\\\", \\\".\\\"], # ج\\n\",\n    \"    \\\"\\\\u062E\\\":[\\\".\\\", \\\"\\\\u062D\\\"], # خ\\n\",\n    \"    \\\"\\\\u0630\\\":[\\\".\\\", \\\"\\\\u062F\\\"], # ذ\\n\",\n    \"    \\\"\\\\u0632\\\":[\\\".\\\", \\\"\\\\u0631\\\"], # ز\\n\",\n    \"    \\\"\\\\u0634\\\":[\\\".\\\", \\\".\\\", \\\".\\\", \\\"\\\\u0633\\\"], # ش\\n\",\n    \"    \\\"\\\\u0636\\\":[\\\".\\\", \\\"\\\\u0635\\\"], # ض\\n\",\n    \"    \\\"\\\\u0637\\\":[\\\"\\\\u0627\\\", \\\"\\\\uFEBB\\\"], # ط\\n\",\n    \"    \\\"\\\\u0638\\\":[\\\".\\\", \\\"\\\\u0627\\\", \\\"\\\\uFEBB\\\"], # ظ\\n\",\n    \"    \\\"\\\\u063A\\\":[\\\".\\\", \\\"\\\\u0639\\\"], # غ\\n\",\n    \"    \\\"\\\\u0641\\\":[\\\".\\\", \\\"\\\\u066F\\\"], # ف\\n\",\n    \"    \\\"\\\\u0642\\\":[\\\".\\\", \\\".\\\", \\\"\\\\u066F\\\"], # ق\\n\",\n    \"    \\\"\\\\u06A4\\\":[\\\".\\\", \\\".\\\", \\\".\\\", \\\"\\\\u066F\\\"], # ڤ\\n\",\n    \"    \\\"\\\\u0643\\\":[\\\"\\\\u0621\\\", \\\"\\\\u0644\\\"], # ك\\n\",\n    \"    \\\"\\\\u0646\\\":[\\\".\\\", \\\"\\\\u06BA\\\"], # ن\\n\",\n    \"    \\\"\\\\u0624\\\":[\\\"\\\\u0621\\\", \\\"\\\\u0648\\\"], # ؤ\\n\",\n    \"    \\\"\\\\u064A\\\":[\\\"\\\\u0649\\\", \\\".\\\", \\\".\\\"], #ي\\n\",\n    \"    \\\"\\\\u0626\\\":[\\\"\\\\u0621\\\", \\\"\\\\u0649\\\"], #ئ\\n\",\n    \"    \\\"\\\\u0629\\\":[\\\".\\\", \\\".\\\", \\\"\\\\u0647\\\"], #ه\\n\",\n    \"}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def preprocess(text):\\n\",\n    \"    char_comps = []\\n\",\n    \"    \\n\",\n    \"    diacritics = \\\"[ًٌٍَُِّْ]\\\"\\n\",\n    \"    numbers = '0123456789'\\n\",\n    \"    for diac in diacritics: \\n\",\n    \"        text = text.replace(diac, '')\\n\",\n    \"\\n\",\n    \"    for num in numbers: \\n\",\n    \"        text = text.replace(num, '')\\n\",\n    \"    \\n\",\n    \"    outText = \\\"\\\"\\n\",\n    \"    \\n\",\n    \"    for i in range(len(text)):\\n\",\n    \"    \\n\",\n    \"        if (text[i] == \\\" \\\"):\\n\",\n    \"            continue\\n\",\n    \"    \\n\",\n    \"        if text[i] in map_chars:\\n\",\n    \"            if (i < len(text) - 1 and text[i] == \\\"\\\\u0643\\\"):\\n\",\n    \"                if text[i+1] != ' ':\\n\",\n    \"                    char_comps.append({text[i] : '\\\\uFEDB'})\\n\",\n    \"                    outText += '\\\\uFEDB'\\n\",\n    \"                else:\\n\",\n    \"                    char_comps.append({text[i] : map_chars[text[i]]})\\n\",\n    \"                    outText += ''.join(map_chars[text[i]])\\n\",\n    \"            else:\\n\",\n    \"                char_comps.append({text[i] : map_chars[text[i]]})\\n\",\n    \"                outText += ''.join(map_chars[text[i]])\\n\",\n    \"        else:\\n\",\n    \"                char_comps.append({text[i] : text[i]})\\n\",\n    \"                outText += text[i]\\n\",\n    \"\\n\",\n    \"    return char_comps, outText\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def concatenate(images, mode='h', margin=10):\\n\",\n    \"    widths, heights = zip(*(i.size for i in images))\\n\",\n    \"    if mode =='h':\\n\",\n    \"        total_width = sum(widths)\\n\",\n    \"        max_height = max(heights)\\n\",\n    \"\\n\",\n    \"        new_im = Image.new('RGB', (total_width, max_height), (255, 255, 255))\\n\",\n    \"\\n\",\n    \"        x_offset = 0\\n\",\n    \"        for im in images[::-1]:\\n\",\n    \"            new_im.paste(im, (x_offset,0))\\n\",\n    \"            x_offset += im.size[0]\\n\",\n    \"    elif mode == 'v':    \\n\",\n    \"        total_height = sum(heights)\\n\",\n    \"        max_width = max(widths)\\n\",\n    \"\\n\",\n    \"        new_im = Image.new('RGB', (max_width, total_height+margin*(len(images)-1)), (255, 255, 255))\\n\",\n    \"        draw = ImageDraw.Draw(new_im)\\n\",\n    \"        y_offset = 0\\n\",\n    \"        for im in images:\\n\",\n    \"            new_im.paste(im, (0,y_offset+margin))\\n\",\n    \"            y_offset += im.size[1]\\n\",\n    \"            draw.line((0,y_offset+margin-5, max_width,y_offset+margin-5), fill=(0, 0, 0), width=3)\\n\",\n    \"    return new_im\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def generate_characters(file):\\n\",\n    \"    char_drawings = []\\n\",\n    \"    annot = file.split('/')[-1][:-5]\\n\",\n    \"    char_comps = preprocess(annot)\\n\",\n    \"    drawing = json.load(open(file))\\n\",\n    \"    new_drawing = apply_rdb(drawing, verbose = 0)\\n\",\n    \"    i = 0 \\n\",\n    \"    for comp in char_comps:\\n\",\n    \"        char = list(comp.keys())[0]\\n\",\n    \"        j = i + len(comp[char])\\n\",\n    \"        char_drawings.append({char:new_drawing[i:j]})\\n\",\n    \"        i = j \\n\",\n    \"    return char_drawings\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import glob\\n\",\n    \"npy_files = glob.glob('server/larger_data/*')\\n\",\n    \"file = np.random.choice(npy_files)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"1800\"\n      ]\n     },\n     \"execution_count\": 57,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"len(glob.glob('server/larger_data/*'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 91,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"wrong_annotations = []\\n\",\n    \"for file in glob.glob('server/data/*'):\\n\",\n    \"    drawing = json.load(open(file))\\n\",\n    \"    stroke_chars = ''\\n\",\n    \"    _, original_stroke_chars = preprocess(file.split(\\\"/\\\")[-1][:-5].split('_')[-1])\\n\",\n    \"    for comp in drawing:\\n\",\n    \"        stroke_chars += list(comp.keys())[0]\\n\",\n    \"    if stroke_chars != original_stroke_chars:\\n\",\n    \"        print(file)\\n\",\n    \"        print(stroke_chars)\\n\",\n    \"        print(original_stroke_chars)\\n\",\n    \"        wrong_annotations.append(file)\\n\",\n    \"        data1, flag = convert_3d(drawing, return_flag=True, threshold=50)\\n\",\n    \"        plt.imshow(draw_strokes(data1, stroke_width = 5))\\n\",\n    \"        plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 95,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2417\"\n      ]\n     },\n     \"execution_count\": 95,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"len(glob.glob('server/larger_data/*') + glob.glob('server/data/*'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 117,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2500\"\n      ]\n     },\n     \"execution_count\": 117,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"len(glob.glob('server/static/processed_larger_images/*') + glob.glob('server/static/images/*'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 118,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"wrong_annotations = []\\n\",\n    \"for file in glob.glob('server/larger_data/*'):\\n\",\n    \"    drawing = json.load(open(file))\\n\",\n    \"    stroke_chars = ''\\n\",\n    \"    _, original_stroke_chars = preprocess(file.split(\\\"/\\\")[-1][:-5])\\n\",\n    \"    for comp in drawing:\\n\",\n    \"        stroke_chars += list(comp.keys())[0]\\n\",\n    \"    if stroke_chars != original_stroke_chars:\\n\",\n    \"        print(file)\\n\",\n    \"        print(stroke_chars)\\n\",\n    \"        print(original_stroke_chars)\\n\",\n    \"        wrong_annotations.append(file)\\n\",\n    \"        data1, flag = convert_3d(drawing, return_flag=True, threshold=50)\\n\",\n    \"        plt.imshow(draw_strokes(data1, stroke_width = 5))\\n\",\n    \"        plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 119,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[]\"\n      ]\n     },\n     \"execution_count\": 119,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"wrong_annotations\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 71,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import shutil\\n\",\n    \"\\n\",\n    \"for file in wrong_annotations:\\n\",\n    \"    file_name = file.split('/')[-1][:-5]\\n\",\n    \"    os.remove(file)  \\n\",\n    \"    shutil.move(f'server/static/processed_larger_images/{file_name}.jpg', f'server/static/larger_images/{file_name}.jpg')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 90,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import shutil\\n\",\n    \"\\n\",\n    \"for file in wrong_annotations:\\n\",\n    \"    file_name = file.split(\\\"/\\\")[-1][:-5]\\n\",\n    \"    new_image_name = file.split(\\\"/\\\")[-1][:-5].split('_')[-1]\\n\",\n    \"    try:\\n\",\n    \"        os.remove(file)  \\n\",\n    \"        shutil.move(f'server/static/images/{file_name}.jpg', f'server/static/larger_images/{new_image_name}.jpg')\\n\",\n    \"    except:\\n\",\n    \"        continue\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 109,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1818\\r\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!ls server/larger_data/ | wc -l\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\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\": \"3.6.9\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "notebooks/Clustering Characters.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \\n\",\n      \"The text.latex.preview rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\\n\",\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \\n\",\n      \"The mathtext.fallback_to_cm rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\\n\",\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: Support for setting the 'mathtext.fallback_to_cm' rcParam is deprecated since 3.3 and will be removed two minor releases later; use 'mathtext.fallback : 'cm' instead.\\n\",\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \\n\",\n      \"The validate_bool_maybe_none function was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\\n\",\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \\n\",\n      \"The savefig.jpeg_quality rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\\n\",\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \\n\",\n      \"The keymap.all_axes rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\\n\",\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \\n\",\n      \"The animation.avconv_path rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\\n\",\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \\n\",\n      \"The animation.avconv_args rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import math, json\\n\",\n    \"from rdp import rdp\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from matplotlib import animation\\n\",\n    \"from IPython.display import HTML\\n\",\n    \"import tqdm.notebook as tq\\n\",\n    \"import pickle\\n\",\n    \"from collections import defaultdict\\n\",\n    \"import cairosvg\\n\",\n    \"from PIL import Image\\n\",\n    \"import glob\\n\",\n    \"import svgwrite\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"ء  أ  إ  ا  ة  ث  ح  د\\tر  س  ص  ط  ع  ف  ك  م\\tه  ى\\r\\n\",\n      \"آ  ؤ  ئ  ب  ت  ج  خ  ذ\\tز  ش  ض  ظ  غ  ق  ل  ن\\tو  ي\\r\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!ls characters\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def get_bounds(data):\\n\",\n    \"    minx, miny = 600, 600  \\n\",\n    \"    maxx, maxy = 0, 0\\n\",\n    \"    \\n\",\n    \"    for i, (x, y, z) in enumerate(data): \\n\",\n    \"        if minx > x:\\n\",\n    \"            minx = x\\n\",\n    \"        if miny > y:\\n\",\n    \"            miny = y \\n\",\n    \"\\n\",\n    \"        if maxx < x:\\n\",\n    \"            maxx = x\\n\",\n    \"        if maxy < y:\\n\",\n    \"            maxy = y \\n\",\n    \"    return minx, maxx, miny, maxy\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def convert_3d(drawing):\\n\",\n    \"    out = []\\n\",\n    \"    for item in drawing:\\n\",\n    \"        char = list(item.keys())[0]\\n\",\n    \"        stroke = item[char]\\n\",\n    \"        if len(stroke) == 1:\\n\",\n    \"            x, y = stroke[0]\\n\",\n    \"            out.append([x, y, 0])\\n\",\n    \"            out.append([x+5, y+5, 1])\\n\",\n    \"            \\n\",\n    \"        for i, point in enumerate(stroke):\\n\",\n    \"            x, y = point \\n\",\n    \"            if i == len(stroke) - 1:\\n\",\n    \"                out.append([x, y, 1])\\n\",\n    \"            else:\\n\",\n    \"                out.append([x, y, 0])\\n\",\n    \"    return out\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def make_square(im, min_size=256, fill_color=(255, 255, 255)):\\n\",\n    \"    x, y = im.size\\n\",\n    \"    size = max(min_size, x, y)\\n\",\n    \"    new_im = Image.new('RGBA', (size, size), fill_color)\\n\",\n    \"    new_im.paste(im, (int((size - x) / 2), int((size - y) / 2)))\\n\",\n    \"    return new_im\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def draw_strokes(data, factor=1, svg_filename = 'tmp/sample.svg', stroke_width = 3, square = False, return_res = False):\\n\",\n    \"    min_x, max_x, min_y, max_y = get_bounds(data)\\n\",\n    \"    dims = (50 + max_x - min_x, 50 + max_y - min_y)\\n\",\n    \"    dwg = svgwrite.Drawing(svg_filename, size = dims)\\n\",\n    \"    dwg.add(dwg.rect(insert=(0, 0), size=dims,fill='white'))\\n\",\n    \"    lift_pen = 1\\n\",\n    \"    abs_x = 25 - min_x \\n\",\n    \"    abs_y = 25 - min_y\\n\",\n    \"    p = \\\"M%s,%s \\\" % (abs_x, abs_y)\\n\",\n    \"    command = \\\"M\\\"\\n\",\n    \"    for i in range(len(data)):\\n\",\n    \"        if (lift_pen == 1):\\n\",\n    \"            command = \\\"M\\\"\\n\",\n    \"        elif (command != \\\"L\\\"):\\n\",\n    \"            command = \\\"L\\\"\\n\",\n    \"        else:\\n\",\n    \"            command = \\\"\\\"\\n\",\n    \"        x = float(data[i][0]) - min_x\\n\",\n    \"        y = float(data[i][1]) - min_y\\n\",\n    \"        lift_pen = data[i][2]\\n\",\n    \"        p += command+str(x)+\\\" \\\"+str(y)+\\\" \\\"\\n\",\n    \"    the_color = \\\"black\\\"\\n\",\n    \"    \\n\",\n    \"    dwg.add(dwg.path(p).stroke(the_color,stroke_width).fill(\\\"none\\\"))\\n\",\n    \"    dwg.save()\\n\",\n    \"    cairosvg.svg2png(url=\\\"tmp/sample.svg\\\", write_to=\\\"tmp/sample.png\\\")\\n\",\n    \"    img = Image.open('tmp/sample.png')\\n\",\n    \"    if square:\\n\",\n    \"        img = make_square(img)\\n\",\n    \"    if return_res:\\n\",\n    \"        return img, dims \\n\",\n    \"    else:\\n\",\n    \"        return img\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#https:#jrgraphix.net/r/Unicode/0600-06FF\\n\",\n    \"map_chars = {\\n\",\n    \"    \\\"\\\\u0623\\\":[\\\"\\\\u0621\\\", \\\"\\\\u0627\\\"], # أ\\n\",\n    \"    \\\"\\\\u0622\\\":[\\\"\\\\u0605\\\", \\\"\\\\u0627\\\"], # آ\\n\",\n    \"    \\\"\\\\u0625\\\":[\\\"\\\\u0627\\\", \\\"\\\\u0621\\\"], # إ\\n\",\n    \"    \\\"\\\\u0628\\\":[\\\"\\\\u066E\\\", \\\".\\\"], # ب\\n\",\n    \"    \\\"\\\\u062A\\\":[\\\".\\\", \\\".\\\", \\\"\\\\u066E\\\"], # ت\\n\",\n    \"    \\\"\\\\u062B\\\":[\\\".\\\", \\\".\\\", \\\".\\\", \\\"\\\\u066E\\\"], # ث \\n\",\n    \"    \\\"\\\\u062C\\\":[\\\"\\\\u062D\\\", \\\".\\\"], # ج\\n\",\n    \"    \\\"\\\\u062E\\\":[\\\".\\\", \\\"\\\\u062D\\\"], # خ\\n\",\n    \"    \\\"\\\\u0630\\\":[\\\".\\\", \\\"\\\\u062F\\\"], # ذ\\n\",\n    \"    \\\"\\\\u0632\\\":[\\\".\\\", \\\"\\\\u0631\\\"], # ز\\n\",\n    \"    \\\"\\\\u0634\\\":[\\\".\\\", \\\".\\\", \\\".\\\", \\\"\\\\u0633\\\"], # ش\\n\",\n    \"    \\\"\\\\u0636\\\":[\\\".\\\", \\\"\\\\u0635\\\"], # ض\\n\",\n    \"    \\\"\\\\u0637\\\":[\\\"\\\\u0627\\\", \\\"\\\\uFEBB\\\"], # ط\\n\",\n    \"    \\\"\\\\u0638\\\":[\\\".\\\", \\\"\\\\u0627\\\", \\\"\\\\uFEBB\\\"], # ظ\\n\",\n    \"    \\\"\\\\u063A\\\":[\\\".\\\", \\\"\\\\u0639\\\"], # غ\\n\",\n    \"    \\\"\\\\u0641\\\":[\\\".\\\", \\\"\\\\u066F\\\"], # ف\\n\",\n    \"    \\\"\\\\u0642\\\":[\\\".\\\", \\\".\\\", \\\"\\\\u066F\\\"], # ق\\n\",\n    \"    \\\"\\\\u06A4\\\":[\\\".\\\", \\\".\\\", \\\".\\\", \\\"\\\\u066F\\\"], # ڤ\\n\",\n    \"    \\\"\\\\u0643\\\":[\\\"\\\\u0621\\\", \\\"\\\\u0644\\\"], # ك\\n\",\n    \"    \\\"\\\\u0646\\\":[\\\".\\\", \\\"\\\\u06BA\\\"], # ن\\n\",\n    \"    \\\"\\\\u0624\\\":[\\\"\\\\u0621\\\", \\\"\\\\u0648\\\"], # ؤ\\n\",\n    \"    \\\"\\\\u064A\\\":[\\\"\\\\u0649\\\", \\\".\\\", \\\".\\\"], #ي\\n\",\n    \"    \\\"\\\\u0626\\\":[\\\"\\\\u0621\\\", \\\"\\\\u0649\\\"], #ئ\\n\",\n    \"    \\\"\\\\u0629\\\":[\\\".\\\", \\\".\\\", \\\"\\\\u0647\\\"], #ه\\n\",\n    \"}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def preprocess(text):\\n\",\n    \"    char_comps = []\\n\",\n    \"    \\n\",\n    \"    diacritics = \\\"[ًٌٍَُِّْ]\\\"\\n\",\n    \"    numbers = '0123456789'\\n\",\n    \"    for diac in diacritics: \\n\",\n    \"        text = text.replace(diac, '')\\n\",\n    \"\\n\",\n    \"    for num in numbers: \\n\",\n    \"        text = text.replace(num, '')\\n\",\n    \"    \\n\",\n    \"    outText = \\\"\\\"\\n\",\n    \"    \\n\",\n    \"    for i in range(len(text)):\\n\",\n    \"    \\n\",\n    \"        if (text[i] == \\\" \\\"):\\n\",\n    \"            continue\\n\",\n    \"    \\n\",\n    \"        if text[i] in map_chars:\\n\",\n    \"            if (i < len(text) - 1 and text[i] == \\\"\\\\u0643\\\"):\\n\",\n    \"                if text[i+1] != ' ':\\n\",\n    \"                    char_comps.append({text[i] : '\\\\uFEDB'})\\n\",\n    \"                else:\\n\",\n    \"                    char_comps.append({text[i] : map_chars[text[i]]})\\n\",\n    \"            else:\\n\",\n    \"                char_comps.append({text[i] : map_chars[text[i]]})\\n\",\n    \"        else:\\n\",\n    \"                char_comps.append({text[i] : text[i]})\\n\",\n    \"\\n\",\n    \"    return char_comps\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def concatenate(images):\\n\",\n    \"    widths, heights = zip(*(i.size for i in images))\\n\",\n    \"\\n\",\n    \"    total_width = sum(widths)\\n\",\n    \"    max_height = max(heights)\\n\",\n    \"\\n\",\n    \"    new_im = Image.new('RGB', (total_width, max_height), (255, 255, 255))\\n\",\n    \"\\n\",\n    \"    x_offset = 0\\n\",\n    \"    for im in images[::-1]:\\n\",\n    \"        new_im.paste(im, (x_offset,0))\\n\",\n    \"        x_offset += im.size[0]\\n\",\n    \"\\n\",\n    \"    return new_im\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import re\\n\",\n    \"def generate_characters(file):\\n\",\n    \"    char_drawings = []\\n\",\n    \"    annot = file.split('/')[-1][:-5]\\n\",\n    \"    annot = re.sub('[0-9]', '', annot)\\n\",\n    \"    annot = annot.replace('_', '')\\n\",\n    \"    char_comps = preprocess(annot)\\n\",\n    \"    drawing = json.load(open(file))\\n\",\n    \"    i = 0 \\n\",\n    \"    for comp in char_comps:\\n\",\n    \"        char = list(comp.keys())[0]\\n\",\n    \"        j = i + len(comp[char])\\n\",\n    \"        char_drawings.append({char:drawing[i:j]})\\n\",\n    \"        i = j \\n\",\n    \"    return char_drawings\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"test  train  valid\\r\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!ls dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"dataset/train/الا بذكر الله تطمئن القلوب_1.json\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"npy_files = glob.glob('dataset/**/*.json')\\n\",\n    \"file = np.random.choice(npy_files)\\n\",\n    \"print(file)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"ا\\n\",\n      \"ل\\n\",\n      \"ا\\n\",\n      \"ب\\n\",\n      \"ذ\\n\",\n      \"ك\\n\",\n      \"ر\\n\",\n      \"ا\\n\",\n      \"ل\\n\",\n      \"ل\\n\",\n      \"ه\\n\",\n      \"ت\\n\",\n      \"ط\\n\",\n      \"م\\n\",\n      \"ئ\\n\",\n      \"ن\\n\",\n      \"ا\\n\",\n      \"ل\\n\",\n      \"ق\\n\",\n      \"ل\\n\",\n      \"و\\n\",\n      \"ب\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<PIL.Image.Image image mode=RGB size=5632x256 at 0x7FC65C49CA20>\"\n      ]\n     },\n     \"execution_count\": 47,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"char_drawings = generate_characters(file)\\n\",\n    \"images = []\\n\",\n    \"for i, comp in enumerate(char_drawings):\\n\",\n    \"    char, drawing = list(comp.items())[0]\\n\",\n    \"    print(char)\\n\",\n    \"    img = draw_strokes(convert_3d(drawing), square = True, stroke_width = 3)\\n\",\n    \"    images.append(img)\\n\",\n    \"concatenate(images)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"os.makedirs(f'characters', exist_ok = True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"npy_files = glob.glob('dataset/**/**.json')\\n\",\n    \"\\n\",\n    \"char_drawings = defaultdict(lambda  :[]) \\n\",\n    \"\\n\",\n    \"for file in npy_files:\\n\",\n    \"    drawings = generate_characters(file)\\n\",\n    \"    for i, comp in enumerate(drawings):\\n\",\n    \"        char, drawing = list(comp.items())[0]\\n\",\n    \"        os.makedirs(f'characters/{char}', exist_ok = True)\\n\",\n    \"        img, res = draw_strokes(convert_3d(drawing), square = True, return_res = True, stroke_width = 5)\\n\",\n    \"        ctr = 0\\n\",\n    \"        img_path = f'characters/{char}/{ctr}.png'\\n\",\n    \"        while f'{ctr}.png' in os.listdir(f'characters/{char}/'):\\n\",\n    \"            ctr += 1\\n\",\n    \"            img_path = f'characters/{char}/{ctr}.png'\\n\",\n    \"        img.save(img_path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 95,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"classes = os.listdir('characters')\\n\",\n    \"images = defaultdict(lambda  :[]) \\n\",\n    \"for char in classes:\\n\",\n    \"    if(len(glob.glob(f'characters/{char}/*.png')) > 100):\\n\",\n    \"        for img_path in glob.glob(f'characters/{char}/*.png')[:100]:\\n\",\n    \"            images[char].append(Image.open(img_path).convert('L').resize((64, 64)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 96,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"dict_keys(['ن', 'ر', 'ك', 'م', 'ا', 'ف', 'ع', 'ه', 'ت', 'و', 'ق', 'ي', 'ح', 'س', 'أ', 'ب', 'ل', 'د'])\"\n      ]\n     },\n     \"execution_count\": 96,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"images.keys()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 97,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2\"\n      ]\n     },\n     \"execution_count\": 97,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.random.choice([2, 3, 4])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 129,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"x, y = [], []\\n\",\n    \"\\n\",\n    \"for i, char in enumerate(images):\\n\",\n    \"    for image in images[char]:\\n\",\n    \"        y.append(char)\\n\",\n    \"        x.append( 1- np.array(image).reshape(64**2)/255)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 130,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(1800, 4096)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(x)\\n\",\n    \"print(x.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 131,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0., 0., 0., ..., 0., 0., 0.])\"\n      ]\n     },\n     \"execution_count\": 131,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 132,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.preprocessing import StandardScaler\\n\",\n    \"from sklearn.manifold import TSNE\\n\",\n    \"import seaborn as sns\\n\",\n    \"# x = StandardScaler().fit_transform(x)\\n\",\n    \"tsne = TSNE(n_components=2).fit_transform(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 136,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(-80.0, 80.0)\"\n      ]\n     },\n     \"execution_count\": 136,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1080x864 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize = (15, 12))\\n\",\n    \"sns.scatterplot(x = tsne[:,0], y = tsne[:,1], hue = y, palette = sns.hls_palette(len(images),  h=.1), legend = 'full')\\n\",\n    \"plt.xlim(-80, 80)\\n\",\n    \"plt.ylim(-80, 80)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\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\": \"3.6.9\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "notebooks/Collect bism allah.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"graduate-blake\",\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"from shutil import copyfile\\n\",\n    \"data_path = 'server/larger_data'\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"induced-chicken\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"os.makedirs('bism_data', exist_ok = True)\\n\",\n    \"for file in os.listdir(data_path):\\n\",\n    \"    if 'بسم' in file:\\n\",\n    \"        copyfile(f'{data_path}/{file}', f'bism_data/{file}')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"arctic-blame\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"updating: bism_data/ (stored 0%)\\n\",\n      \"  adding: bism_data/106بسم الله الرحمن الرحيم.jpg (deflated 13%)\\n\",\n      \"  adding: bism_data/47بسم الله الرحمن الرحيم.jpg (deflated 2%)\\n\",\n      \"  adding: bism_data/31بسم الله الرحمن الرحيم.jpg (deflated 8%)\\n\",\n      \"  adding: bism_data/24بسم الله الرحمن الرحيم.jpg (stored 0%)\\n\",\n      \"  adding: bism_data/92بسم الله الرحمن الرحيم.jpg (deflated 2%)\\n\",\n      \"  adding: bism_data/109بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/4بسم الله.jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/37بسم الله الرحمن الرحيم.jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/76بسم الله الرحمن الرحيم.jpg (stored 0%)\\n\",\n      \"  adding: bism_data/22بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/104بسم الله الرحمن الرحيم.jpg (deflated 3%)\\n\",\n      \"  adding: bism_data/بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/81بسم الله الرحمن الرحيم.jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/15بسم الله الرحمن الرحيم.jpg (stored 0%)\\n\",\n      \"  adding: bism_data/162بسم الله الرحمن الرحيم.jpg (deflated 20%)\\n\",\n      \"  adding: bism_data/79بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/85بسم الله الرحمن الرحيم.jpg (deflated 10%)\\n\",\n      \"  adding: bism_data/113بسم الله الرحمن الرحيم.jpg (deflated 2%)\\n\",\n      \"  adding: bism_data/123بسم الله الرحمن الرحيم.jpg (deflated 53%)\\n\",\n      \"  adding: bism_data/بسم الله الرحمن الرجيم.jpg (deflated 13%)\\n\",\n      \"  adding: bism_data/127بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/161بسم الله الرحمن الرحيم.jpg (deflated 3%)\\n\",\n      \"  adding: bism_data/124بسم الله الرحمن الرحيم.jpg (deflated 7%)\\n\",\n      \"  adding: bism_data/99بسم الله الرحمن الرحيم.jpg (deflated 6%)\\n\",\n      \"  adding: bism_data/125بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/145بسم الله الرحمن الرحيم.jpg (deflated 18%)\\n\",\n      \"  adding: bism_data/39بسم الله الرحمن الرحيم.jpg (deflated 29%)\\n\",\n      \"  adding: bism_data/4بسم الله الرحمن الرحيم .jpg (deflated 4%)\\n\",\n      \"  adding: bism_data/67بسم الله الرحمن الرحيم.jpg (deflated 9%)\\n\",\n      \"  adding: bism_data/10بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/32بسم الله الرحمن الرحيم.jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/3بسم الله.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/73بسم الله الرحمن الرحيم.jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/44بسم الله الرحمن الرحيم.jpg (stored 0%)\\n\",\n      \"  adding: bism_data/137بسم الله الرحمن الرحيم.jpg (deflated 2%)\\n\",\n      \"  adding: bism_data/2بسم الله الرحمن الرحيم.jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/8بسم الله الرحمن الرحيم.jpg (stored 0%)\\n\",\n      \"  adding: bism_data/143بسم الله الرحمن الرحيم.jpg (deflated 8%)\\n\",\n      \"  adding: bism_data/بسم الله  الرحمن الرحيم.jpg (stored 0%)\\n\",\n      \"  adding: bism_data/26بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/80بسم الله الرحمن الرحيم.jpg (deflated 2%)\\n\",\n      \"  adding: bism_data/133بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/33بسم الله الرحمن الرحيم.jpg (deflated 47%)\\n\",\n      \"  adding: bism_data/53بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/46بسم الله الرحمن الرحيم.jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/129بسم الله الرحمن الرحيم.jpg (deflated 2%)\\n\",\n      \"  adding: bism_data/115بسم الله الرحمن الرحيم.jpg (deflated 2%)\\n\",\n      \"  adding: bism_data/111بسم الله الرحمن الرحيم.jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/151بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/156بسم الله الرحمن الرحيم.jpg (stored 0%)\\n\",\n      \"  adding: bism_data/17بسم الله الرحمن الرحيم.jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/3بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/38بسم الله الرحمن الرحيم.jpg (deflated 31%)\\n\",\n      \"  adding: bism_data/114بسم الله الرحمن الرحيم.jpg (deflated 32%)\\n\",\n      \"  adding: bism_data/40بسم الله الرحمن الرحيم.jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/108بسم الله الرحمن الرحيم.jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/71بسم الله الرحمن الرحيم.jpg (deflated 31%)\\n\",\n      \"  adding: bism_data/بسم الله الرحمن الحريم.jpg (deflated 14%)\\n\",\n      \"  adding: bism_data/97بسم الله الرحمن الرحيم.jpg (deflated 7%)\\n\",\n      \"  adding: bism_data/66بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/112بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/140بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/56بسم الله الرحمن الرحيم.jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/52بسم الله الرحمن الرحيم.jpg (deflated 50%)\\n\",\n      \"  adding: bism_data/163بسم الله الرحمن الرحيم.jpg (deflated 2%)\\n\",\n      \"  adding: bism_data/90بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/117بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/3بسم الله الرحمن الرحيم .jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/36بسم الله الرحمن الرحيم.jpg (deflated 29%)\\n\",\n      \"  adding: bism_data/27بسم الله الرحمن الرحيم.jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/51بسم الله الرحمن الرحيم.jpg (deflated 33%)\\n\",\n      \"  adding: bism_data/11بسم الله الرحمن الرحيم.jpg (deflated 6%)\\n\",\n      \"  adding: bism_data/105بسم الله الرحمن الرحيم.jpg (deflated 2%)\\n\",\n      \"  adding: bism_data/94بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/28بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/9بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/اقرأ بسم ربك.jpg (deflated 39%)\\n\",\n      \"  adding: bism_data/148بسم الله الرحمن الرحيم.jpg (deflated 25%)\\n\",\n      \"  adding: bism_data/1بسم الله.jpg (stored 0%)\\n\",\n      \"  adding: bism_data/82بسم الله الرحمن الرحيم.jpg (deflated 44%)\\n\",\n      \"  adding: bism_data/18بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/100بسم الله الرحمن الرحيم.jpg (deflated 7%)\\n\",\n      \"  adding: bism_data/41بسم الله الرحمن الرحيم.jpg (deflated 15%)\\n\",\n      \"  adding: bism_data/75بسم الله الرحمن الرحيم.jpg (deflated 39%)\\n\",\n      \"  adding: bism_data/2بسم الله الرحمن الرحيم .jpg (deflated 23%)\\n\",\n      \"  adding: bism_data/154بسم الله الرحمن الرحيم.jpg (deflated 4%)\\n\",\n      \"  adding: bism_data/بسم الله ماشاء الله.jpg (stored 0%)\\n\",\n      \"  adding: bism_data/87بسم الله الرحمن الرحيم.jpg (deflated 34%)\\n\",\n      \"  adding: bism_data/98بسم الله الرحمن الرحيم.jpg (deflated 3%)\\n\",\n      \"  adding: bism_data/157بسم الله الرحمن الرحيم.jpg (deflated 39%)\\n\",\n      \"  adding: bism_data/59بسم الله الرحمن الرحيم.jpg (deflated 6%)\\n\",\n      \"  adding: bism_data/65بسم الله الرحمن الرحيم.jpg (deflated 2%)\\n\",\n      \"  adding: bism_data/5بسم الله.jpg (deflated 46%)\\n\",\n      \"  adding: bism_data/58بسم الله الرحمن الرحيم.jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/69بسم الله الرحمن الرحيم.jpg (deflated 2%)\\n\",\n      \"  adding: bism_data/155بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/131بسم الله الرحمن الرحيم.jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/55بسم الله الرحمن الرحيم.jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/93بسم الله الرحمن الرحيم.jpg (deflated 24%)\\n\",\n      \"  adding: bism_data/126بسم الله الرحمن الرحيم.jpg (deflated 5%)\\n\",\n      \"  adding: bism_data/89بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/116بسم الله الرحمن الرحيم.jpg (stored 0%)\\n\",\n      \"  adding: bism_data/30بسم الله الرحمن الرحيم.jpg (deflated 30%)\\n\",\n      \"  adding: bism_data/142بسم الله الرحمن الرحيم.jpg (deflated 4%)\\n\",\n      \"  adding: bism_data/78بسم الله الرحمن الرحيم.jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/70بسم الله الرحمن الرحيم.jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/4بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/29بسم الله الرحمن الرحيم.jpg (deflated 45%)\\n\",\n      \"  adding: bism_data/158بسم الله الرحمن الرحيم.jpg (deflated 64%)\\n\",\n      \"  adding: bism_data/13بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/144بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/160بسم الله الرحمن الرحيم.jpg (deflated 3%)\\n\",\n      \"  adding: bism_data/14بسم الله الرحمن الرحيم.jpg (deflated 6%)\\n\",\n      \"  adding: bism_data/45بسم الله الرحمن الرحيم.jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/86بسم الله الرحمن الرحيم.jpg (deflated 55%)\\n\",\n      \"  adding: bism_data/42بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/7بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/136بسم الله الرحمن الرحيم.jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/بسم الله الرحمن الرحيم .jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/120بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/50بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/147بسم الله الرحمن الرحيم.jpg (deflated 7%)\\n\",\n      \"  adding: bism_data/118بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/بسم الله.jpg (deflated 18%)\\n\",\n      \"  adding: bism_data/60بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/110بسم الله الرحمن الرحيم.jpg (deflated 5%)\\n\",\n      \"  adding: bism_data/83بسم الله الرحمن الرحيم.jpg (deflated 34%)\\n\",\n      \"  adding: bism_data/150بسم الله الرحمن الرحيم.jpg (deflated 37%)\\n\",\n      \"  adding: bism_data/101بسم الله الرحمن الرحيم.jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/49بسم الله الرحمن الرحيم.jpg (deflated 27%)\\n\",\n      \"  adding: bism_data/77بسم الله الرحمن الرحيم.jpg (deflated 41%)\\n\",\n      \"  adding: bism_data/بسم الله الرحمن الرحيم و انك لعلى خلق عظيم.jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/95بسم الله الرحمن الرحيم.jpg (deflated 5%)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"  adding: bism_data/74بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/159بسم الله الرحمن الرحيم.jpg (deflated 5%)\\n\",\n      \"  adding: bism_data/138بسم الله الرحمن الرحيم.jpg (deflated 21%)\\n\",\n      \"  adding: bism_data/96بسم الله الرحمن الرحيم.jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/121بسم الله الرحمن الرحيم.jpg (deflated 22%)\\n\",\n      \"  adding: bism_data/16بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/88بسم الله الرحمن الرحيم.jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/34بسم الله الرحمن الرحيم.jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/153بسم الله الرحمن الرحيم.jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/141بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/102بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/91بسم الله الرحمن الرحيم.jpg (deflated 24%)\\n\",\n      \"  adding: bism_data/1بسم الله الرحمن الرحيم .jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/بسم الله ما شاء الله.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/152بسم الله الرحمن الرحيم.jpg (stored 0%)\\n\",\n      \"  adding: bism_data/54بسم الله الرحمن الرحيم.jpg (deflated 51%)\\n\",\n      \"  adding: bism_data/135بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/128بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/بسم اله الرحمن الرحيم.jpg (deflated 48%)\\n\",\n      \"  adding: bism_data/119بسم الله الرحمن الرحيم.jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/64بسم الله الرحمن الرحيم.jpg (deflated 19%)\\n\",\n      \"  adding: bism_data/25بسم الله الرحمن الرحيم.jpg (deflated 2%)\\n\",\n      \"  adding: bism_data/23بسم الله الرحمن الرحيم.jpg (stored 0%)\\n\",\n      \"  adding: bism_data/12بسم الله الرحمن الرحيم.jpg (deflated 30%)\\n\",\n      \"  adding: bism_data/68بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/146بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/130بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/84بسم الله الرحمن الرحيم.jpg (deflated 3%)\\n\",\n      \"  adding: bism_data/139بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/103بسم الله الرحمن الرحيم.jpg (deflated 5%)\\n\",\n      \"  adding: bism_data/43بسم الله الرحمن الرحيم.jpg (deflated 22%)\\n\",\n      \"  adding: bism_data/21بسم الله الرحمن الرحيم.jpg (deflated 10%)\\n\",\n      \"  adding: bism_data/134بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/20بسم الله الرحمن الرحيم.jpg (deflated 4%)\\n\",\n      \"  adding: bism_data/149بسم الله الرحمن الرحيم.jpg (deflated 4%)\\n\",\n      \"  adding: bism_data/107بسم الله الرحمن الرحيم.jpg (deflated 7%)\\n\",\n      \"  adding: bism_data/132بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/72بسم الله الرحمن الرحيم.jpg (deflated 17%)\\n\",\n      \"  adding: bism_data/1بسم الله الرحمن الحريم.jpg (deflated 42%)\\n\",\n      \"  adding: bism_data/57بسم الله الرحمن الرحيم.jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/61بسم الله الرحمن الرحيم.jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/48بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/122بسم الله الرحمن الرحيم.jpg (deflated 49%)\\n\",\n      \"  adding: bism_data/5بسم الله الرحمن الرحيم.jpg (deflated 1%)\\n\",\n      \"  adding: bism_data/62بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/1بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/35بسم الله الرحمن الرحيم.jpg (deflated 37%)\\n\",\n      \"  adding: bism_data/6بسم الله الرحمن الرحيم.jpg (deflated 35%)\\n\",\n      \"  adding: bism_data/19بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\",\n      \"  adding: bism_data/2بسم الله.jpg (deflated 17%)\\n\",\n      \"  adding: bism_data/63بسم الله الرحمن الرحيم.jpg (deflated 0%)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!zip -r bism_data.zip bism_data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"compatible-minimum\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\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\": \"3.6.9\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "notebooks/Colorize text and image.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"binding-membrane\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[31mhello\\u001b[0m \\u001b[32mworld\\u001b[0m\\n\",\n      \"\\u001b[31mhello red world\\u001b[0m\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from termcolor import colored\\n\",\n    \"print(colored('hello', 'red'), colored('world', 'green'))\\n\",\n    \"print(colored(\\\"hello red world\\\", 'red'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 198,\n   \"id\": \"sitting-blend\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"out = [{'.': [[332, 124]]},\\n\",\n    \"{'ح': [[331, 150], [336, 149], [354, 169], [318, 169]]},\\n\",\n    \"{'ا': [[321, 170], [315, 169], [308, 162], [303, 124]]},\\n\",\n    \"{'ل': [[286, 119], [286, 157], [282, 163], [268, 163]]},\\n\",\n    \"{'د': [[250, 144], [260, 156], [264, 172], [259, 176], [244, 176], [238, 171]]}]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 199,\n   \"id\": \"engaging-hopkins\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"colors = ['red', 'green', 'blue', 'magenta', 'grey']\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 200,\n   \"id\": \"convertible-bookmark\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[31m{'.': [[332, 124]]}\\u001b[0m\\n\",\n      \"\\u001b[32m{'ح': [[331, 150], [336, 149], [354, 169], [318, 169]]}\\u001b[0m\\n\",\n      \"\\u001b[34m{'ا': [[321, 170], [315, 169], [308, 162], [303, 124]]}\\u001b[0m\\n\",\n      \"\\u001b[35m{'ل': [[286, 119], [286, 157], [282, 163], [268, 163]]}\\u001b[0m\\n\",\n      \"\\u001b[30m{'د': [[250, 144], [260, 156], [264, 172], [259, 176], [244, 176], [238, 171]]}\\u001b[0m\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for i, ins in enumerate(out):\\n\",\n    \"    print(colored(ins, colors[i]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"id\": \"several-botswana\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from PIL import Image, ImageDraw\\n\",\n    \"import json\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"def img_from_stroke(drawing):\\n\",\n    \"    img_A = Image.new('RGB', (600, 600), (255, 255, 255)) \\n\",\n    \"    draw = ImageDraw.Draw(img_A) \\n\",\n    \"    for j, item in enumerate(drawing):\\n\",\n    \"        char = list(item.keys())[0]\\n\",\n    \"        stroke = item[char]\\n\",\n    \"        if len(stroke) == 1:\\n\",\n    \"            x, y = stroke[0]\\n\",\n    \"            stroke.append([x+5, y+5])\\n\",\n    \"        for i, point in enumerate(stroke):\\n\",\n    \"            if i == 0:\\n\",\n    \"                x_prev, y_prev = point\\n\",\n    \"                continue\\n\",\n    \"            x, y = point \\n\",\n    \"            draw.line([x_prev, y_prev, x, y], fill=colors[j], width = 3)\\n\",\n    \"            x_prev, y_prev = x, y\\n\",\n    \"    return img_A\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"id\": \"cordless-ticket\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<PIL.Image.Image image mode=RGB size=600x600 at 0x7F70B15B6DA0>\"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"img_from_stroke(out)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"id\": \"specified-comedy\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import math, json\\n\",\n    \"from rdp import rdp\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from IPython.display import HTML\\n\",\n    \"import tqdm.notebook as tq\\n\",\n    \"import pickle\\n\",\n    \"from collections import defaultdict\\n\",\n    \"import cairosvg\\n\",\n    \"from PIL import Image,ImageDraw\\n\",\n    \"import glob\\n\",\n    \"import os \\n\",\n    \"import re\\n\",\n    \"import svgwrite\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"id\": \"neither-pharmacy\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def convert_3d(drawing, return_flag = False, threshold=50):\\n\",\n    \"    out = []\\n\",\n    \"    corrupted = False\\n\",\n    \"    for item in drawing:\\n\",\n    \"        char = list(item.keys())[0]\\n\",\n    \"        stroke = item[char]\\n\",\n    \"        if len(stroke) == 1:\\n\",\n    \"            x, y = stroke[0]\\n\",\n    \"            out.append([x, y, 0])\\n\",\n    \"            out.append([x+5, y+5, 1])\\n\",\n    \"            continue\\n\",\n    \"        segment = []\\n\",\n    \"        for i, point in enumerate(stroke):\\n\",\n    \"            x, y = point \\n\",\n    \"            if i == len(stroke) - 1:\\n\",\n    \"                segment.append([x, y, 1])\\n\",\n    \"            else:\\n\",\n    \"                segment.append([x, y, 0])\\n\",\n    \"        \\n\",\n    \"        start = 0 \\n\",\n    \"        out += segment[start:]\\n\",\n    \"    if return_flag:\\n\",\n    \"        return out, corrupted\\n\",\n    \"    return out\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 190,\n   \"id\": \"severe-immigration\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def get_bounds(data):\\n\",\n    \"    minx, miny = 600, 600  \\n\",\n    \"    maxx, maxy = 0, 0\\n\",\n    \"    \\n\",\n    \"    for i, (x, y, z) in enumerate(data): \\n\",\n    \"        if minx > x:\\n\",\n    \"            minx = x\\n\",\n    \"        if miny > y:\\n\",\n    \"            miny = y \\n\",\n    \"\\n\",\n    \"        if maxx < x:\\n\",\n    \"            maxx = x\\n\",\n    \"        if maxy < y:\\n\",\n    \"            maxy = y \\n\",\n    \"    return minx, maxx, miny, maxy\\n\",\n    \"\\n\",\n    \"def draw_strokes(data, factor=1, svg_filename = 'tmp/sample.svg', stroke_width = 3, square = False, return_res = False):\\n\",\n    \"    min_x, max_x, min_y, max_y = get_bounds(data)\\n\",\n    \"    margin_x = (600 - max_x - min_x)/2\\n\",\n    \"    margin_y = (600 - max_y - min_y)/2\\n\",\n    \"    \\n\",\n    \"    data = [[x + margin_x, y + margin_y, z] for [x, y, z] in data]\\n\",\n    \"    \\n\",\n    \"    min_x, max_x, min_y, max_y = get_bounds(data)\\n\",\n    \"    dims = ( 5 + max_x - min_x,  5 +max_y - min_y)\\n\",\n    \"    dwg = svgwrite.Drawing(svg_filename, size = dims)\\n\",\n    \"    dwg.add(dwg.rect(insert=(0, 0), size=dims,fill='white'))\\n\",\n    \"    lift_pen = 0\\n\",\n    \"    abs_x = 25 - min_x \\n\",\n    \"    abs_y = 25 - min_y\\n\",\n    \"    p = \\\"M%s,%s \\\" % (min_x, min_y)\\n\",\n    \"    command = \\\"M\\\"\\n\",\n    \"    j = 0 \\n\",\n    \"    for i in range(0, len(data)):\\n\",\n    \"        x = float(data[i][0]) - min_x\\n\",\n    \"        y = float(data[i][1]) - min_y\\n\",\n    \"        lift_pen = data[i][2]\\n\",\n    \"        p += command+str(x)+\\\" \\\"+str(y)+\\\" \\\" \\n\",\n    \"        \\n\",\n    \"        if (lift_pen == 1):\\n\",\n    \"            command = \\\"M\\\"\\n\",\n    \"            dwg.add(dwg.path(p).stroke(colors[j],stroke_width).fill(\\\"none\\\"))\\n\",\n    \"            p = \\\"\\\"\\n\",\n    \"            j += 1\\n\",\n    \"        else:\\n\",\n    \"            command = \\\"\\\" \\n\",\n    \"    \\n\",\n    \"    dwg.save()\\n\",\n    \"    cairosvg.svg2png(url=\\\"tmp/sample.svg\\\", write_to=\\\"tmp/sample.png\\\")\\n\",\n    \"    img = Image.open('tmp/sample.png')\\n\",\n    \"    img = make_square(img)\\n\",\n    \"    return img\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 194,\n   \"id\": \"fresh-strategy\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import json \\n\",\n    \"file = 'dataset/train/'+'عمر_15.json'\\n\",\n    \"drawing = json.load(open(file, 'r'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 195,\n   \"id\": \"thick-portfolio\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"data = convert_3d(drawing)\\n\",\n    \"plt.imshow(draw_strokes(data, stroke_width = 5))\\n\",\n    \"plt.xticks([])\\n\",\n    \"plt.yticks([])\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"sweet-estate\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\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\": \"3.6.9\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "notebooks/Convert Strokes to Images.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"composed-partition\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os \\n\",\n    \"json_path = 'server/data/'\\n\",\n    \"image_path = 'server/static/images/'\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"differential-copyright\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<PIL.Image.Image image mode=RGB size=1000x500 at 0x7FBD7C0C9D30>\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from PIL import Image, ImageDraw\\n\",\n    \"import json\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"def img_from_stroke(json_file):\\n\",\n    \"    drawing = json.load(open(json_path+json_file))\\n\",\n    \"    img_A = Image.new('RGB', (500, 500), (255, 255, 255)) \\n\",\n    \"    draw = ImageDraw.Draw(img_A) \\n\",\n    \"    for item in drawing:\\n\",\n    \"        char = list(item.keys())[0]\\n\",\n    \"        stroke = item[char]\\n\",\n    \"        if len(stroke) == 1:\\n\",\n    \"            x, y = stroke[0]\\n\",\n    \"            stroke.append([x+5, y+5])\\n\",\n    \"        for i, point in enumerate(stroke):\\n\",\n    \"            if i == 0:\\n\",\n    \"                x_prev, y_prev = point\\n\",\n    \"                continue\\n\",\n    \"            x, y = point \\n\",\n    \"            draw.line([x_prev, y_prev, x, y], fill=(0, 0, 0), width = 3)\\n\",\n    \"            x_prev, y_prev = x, y\\n\",\n    \"    return img_A\\n\",\n    \"\\n\",\n    \"json_file = np.random.choice(os.listdir(json_path))\\n\",\n    \"A = img_from_stroke(json_file)\\n\",\n    \"B = Image.open(image_path+json_file[:-4]+'jpg') \\n\",\n    \"B = B.resize((500,500), Image.ANTIALIAS)\\n\",\n    \"C = Image.new('RGB', (1000, 500))\\n\",\n    \"C.paste(A, (0,0))\\n\",\n    \"C.paste(B, (500,0))\\n\",\n    \"C\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"caroline-vegetarian\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def create_dataset():\\n\",\n    \"    os.makedirs('pix2pix', exist_ok = True )\\n\",\n    \"    os.makedirs('pix2pix/train', exist_ok = True)\\n\",\n    \"    os.makedirs('pix2pix/val', exist_ok = True)\\n\",\n    \"    os.makedirs('pix2pix/test', exist_ok = True)\\n\",\n    \"    \\n\",\n    \"    for i, json_file in enumerate(os.listdir(json_path)):\\n\",\n    \"        A = img_from_stroke(json_file)\\n\",\n    \"        img_name = json_file[:-5]\\n\",\n    \"        B = Image.open(image_path+img_name+'.jpg') \\n\",\n    \"        B = B.resize((500,500), Image.ANTIALIAS)\\n\",\n    \"        C = Image.new('RGB', (1000, 500))   \\n\",\n    \"        C.paste(A, (0,0))\\n\",\n    \"        C.paste(B, (500,0))\\n\",\n    \"        if i < 400:\\n\",\n    \"            split = 'train'\\n\",\n    \"        elif i <500:\\n\",\n    \"            split = 'val'\\n\",\n    \"        else:\\n\",\n    \"            split = 'test'\\n\",\n    \"        C.save(f'pix2pix/{split}/{img_name}.jpg')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"loved-chuck\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"create_dataset()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"painful-julian\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"  adding: pix2pix/ (stored 0%)\\n\",\n      \"  adding: pix2pix/test/ (stored 0%)\\n\",\n      \"  adding: pix2pix/test/Amira_أميرة.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/test/Dalia_داليا.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/test/Amela_أميلا.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/test/Jehan_جيهان.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/test/Stephanie_ستيفاني.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/test/Elaph_إيلاف.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/test/Zaina_زينة.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/test/Aimee_إيمي.jpg (deflated 9%)\\n\",\n      \"  adding: pix2pix/test/Nouf_نوف.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/test/Anees_أنيس.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/test/Bebhinn_بيفين.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/test/Fadwa_فدوى.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/test/Zakaria_زكريا.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/test/Alden_ألدن.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/test/Nisha_نيشا.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/test/Adnan_عدنان.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/test/Jessica_جيسيكا.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/test/AbdulKareem_عبد الكريم.jpg (deflated 15%)\\n\",\n      \"  adding: pix2pix/test/Tahseen_تحسين.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/test/Zubaida_زبيدة.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/test/Pedro_بيدرو.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/test/Shane_شين.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/test/Ayah_آية.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/test/Fuad_فؤاد.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/test/Alia_علياء.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/test/Aruna_آرونا.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/test/Shafia_شفيعة.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/test/Hannah_حنَّه.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/test/Abdul Ati_عبد العاطي.jpg (deflated 6%)\\n\",\n      \"  adding: pix2pix/test/Marwah_مروة.jpg (deflated 17%)\\n\",\n      \"  adding: pix2pix/test/Ashwaq_أشواق.jpg (deflated 6%)\\n\",\n      \"  adding: pix2pix/test/Justin_جاستن.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/test/Gloria_جلوريا.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/test/Faheema_فهيمة.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/test/Aladdin_علاء الدين.jpg (deflated 6%)\\n\",\n      \"  adding: pix2pix/test/Abdussalam_عبد السلام.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/test/Maia_مايا.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/test/Rafed_رافد.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/test/Nena_نينا.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/test/Mervat_ميرفت.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/test/Alyssa_أليسا.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/test/Diar_ديار.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/test/Faisal_فيصل.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/test/Enas_ايناس.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/test/Reema_ريما.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/test/Francesco_فرانشيسكو.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/test/Tucker_تكر.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/test/Muhydeen_محي الدين.jpg (deflated 6%)\\n\",\n      \"  adding: pix2pix/test/Shaddaad_شداد.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/test/Muthanna_المثنى.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/test/Bayan_بيان.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/test/Azra_عذرا.jpg (deflated 8%)\\n\",\n      \"  adding: 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(deflated 6%)\\n\",\n      \"  adding: pix2pix/train/Badr_بدر.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/train/Elizah_العزة.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Ali_علي.jpg (deflated 9%)\\n\",\n      \"  adding: pix2pix/train/Yas_ياس.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Baraa_براءة.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/train/Adam_آدم.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/train/Hussein_حسين.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/train/Jonathan_جوناثان.jpg (deflated 14%)\\n\",\n      \"  adding: pix2pix/train/Rimas_ريماس.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/train/Ebaa_إباء.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/train/Jacobo_جاكوبو.jpg (deflated 7%)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"  adding: pix2pix/train/Raafi_رافع.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Jead_جيد.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Bedo_بيدو.jpg (deflated 9%)\\n\",\n      \"  adding: pix2pix/train/Cardillo_كارديلو.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Nataly_ناتالي.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/train/Bervian_بيريڤان.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Amaleia_أمالية.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Khaleda_خالدة.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Florencia_فلورنسيا.jpg (deflated 15%)\\n\",\n      \"  adding: pix2pix/train/Thaier_ثائر.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/train/Faee_فيء.jpg (deflated 9%)\\n\",\n      \"  adding: pix2pix/train/Zahira_زاهرة.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Chiara_كيارا.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Hashim_هاشم.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/train/Rawiya_راوية.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/train/farzana_فرزانة.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Hawa_حواء.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/train/Ghazal_غزل.jpg (deflated 16%)\\n\",\n      \"  adding: pix2pix/train/Tanzeela_تنزيله.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/train/Luisha_لويشا.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Jebran_جبران.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Shahd_شهد.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/train/Saleema_سليمة.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Amir_أمير.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Florence_فلورنس.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Labiba_لبيبة.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Hala_هلا.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/train/Victoria_فيكتوريا.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Bilal_بلال.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/train/Angelique_أنجليك.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Eman_إيمان.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/train/Ellen_إلين.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/AbuBaker_أبوبكر.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Sabreen_صابرين.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Alia_علية.jpg (deflated 9%)\\n\",\n      \"  adding: pix2pix/train/Henny_هني.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/train/Sumaya_سمية.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Athbi_عذبي.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Carla_كارلا.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Areen_عرين.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/train/Novie_نوفي.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/train/David_ديفد.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/train/Cheyma_شيماء.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Ihsan_إحسان.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/train/Dora_دورا.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Lia_ليا.jpg (deflated 9%)\\n\",\n      \"  adding: pix2pix/train/Elie_ايلي.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/train/Jasheem_جاشيم.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Musaida_مساعدة.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Aous_أوس.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/train/Kawthar_كوثر.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/train/Awatef_عواطف.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Delaram_دلارام.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Hilary_هيلاري.jpg (deflated 6%)\\n\",\n      \"  adding: pix2pix/train/Attalla_عطا الله.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Taymaa_تيماء.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Maram_مرام.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/train/Salam_سلام.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/train/Shaista_شائستة.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/ (stored 0%)\\n\",\n      \"  adding: pix2pix/val/Hazem_حازم.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Eva_ايفا.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Hanaa_هناء.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Anas_أنس.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Gemma_جمة.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Dana_دانة.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Samar_سمر.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Janice_جانيس.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Tamam_تمام.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Atif_عاطف.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Dylan_ديلان.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Mehdi_مهدي.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Bilind_بلند.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Ahdi_عهدي.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Najia_نجية.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Aron_هارون.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/FatimaElzahra_فاطمة الزهراء.jpg (deflated 6%)\\n\",\n      \"  adding: pix2pix/val/Yaroslav_ياروسلاڤ.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Federico_فيديريكو.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Talat zehra_طلعت زهرة.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Alaa_آلاء.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Abdulnoor_عبد النور.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Matthew_ماثيو.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Tareq_طارق.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/AbdulHamid_عبد الحميد.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Khadija_خديجة.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Tony_طوني.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Farida_فريدة.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Camila_كاميلا.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Kenza_كينزا.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Shahiyah_شهية.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Dawn_داون.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Sanya_سانيا.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Ismael_اسماعيل.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Damani_داماني.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Uzair_عزير.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Holly_هولي.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Suzan_سوزان.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Mahjoub_محجوب.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Ouais_أويس.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Zairah_زايره.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Abdulrahman_عبد الرحمن.jpg (deflated 6%)\\n\",\n      \"  adding: pix2pix/val/Gian_جيان.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Maria_ماريا.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Amatullah_أمة الله.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Aston_أستون.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Kinan_كنان.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Aysha_عائشة.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Salik_سالك.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Pearl_بيرل.jpg (deflated 13%)\\n\",\n      \"  adding: pix2pix/val/Fatma_فطمة.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Carolina_كارولينا.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Asem_عاصم.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Barrak_براك.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Fatima_فاطمة.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Emelia_إيميليا.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Raghad_رغد.jpg (deflated 9%)\\n\",\n      \"  adding: pix2pix/val/Alreem_الريم.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Aaria_آريا.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Tamara_تمارا.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Habib_حبيب.jpg (deflated 16%)\\n\",\n      \"  adding: pix2pix/val/Azoz_عزوز.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Amin_أمين.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Scarlett_سكارليت.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Aziz_عزيز.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Amanda_أماندا.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Basheera_بشيرة.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Bassant_بسنت.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Buffy_بفي.jpg (deflated 9%)\\n\",\n      \"  adding: pix2pix/val/Izabel_ايزابيل.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Fattoumah_فطومة.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/tahoura_طهورة.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Mirna_ميرنا.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Al Jude_الجود.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Ibtisam_ابتسام.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Azzam_عزام.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Anghel_أنجل.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Anayah_أناية.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Felipe_فيليبي.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Nadera_نادرة.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Zain_زين.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Samia_ساميا.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Firas_فراس.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Karam_كرم.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Ayoub_أيوب.jpg (deflated 9%)\\n\",\n      \"  adding: pix2pix/val/Harry_هاري.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Kaylee_كيلي.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Moayad_مؤيد.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Ziad_زياد.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Sherya_شريعة.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/AbdulRashid_عبد الرشيد.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Jens_ينس.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Austin_أوستن.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Batool_بتول.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Maanmohan_مانموهان.jpg (deflated 6%)\\n\",\n      \"  adding: pix2pix/val/Salah_صلاح.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Gianola_جانولا.jpg (deflated 7%)\\n\",\n      \"  adding: pix2pix/val/Bassam_بسام.jpg (deflated 8%)\\n\",\n      \"  adding: pix2pix/val/Samir_سمير.jpg (deflated 8%)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"  adding: pix2pix/val/Alim_عالم.jpg (deflated 8%)\\r\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!zip -r dataset.zip pix2pix\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"miniature-fellow\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\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\": \"3.6.9\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "notebooks/Fix alignments of strokes .ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import math, json\\n\",\n    \"from rdp import rdp\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from matplotlib import animation\\n\",\n    \"from IPython.display import HTML\\n\",\n    \"import tqdm.notebook as tq\\n\",\n    \"import pickle\\n\",\n    \"from collections import defaultdict\\n\",\n    \"import cairosvg\\n\",\n    \"from PIL import Image,ImageDraw\\n\",\n    \"import glob\\n\",\n    \"import os \\n\",\n    \"import re\\n\",\n    \"import svgwrite\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def get_bounds(data):\\n\",\n    \"    minx, miny = 600, 600  \\n\",\n    \"    maxx, maxy = 0, 0\\n\",\n    \"    \\n\",\n    \"    for i, (x, y, z) in enumerate(data): \\n\",\n    \"        if minx > x:\\n\",\n    \"            minx = x\\n\",\n    \"        if miny > y:\\n\",\n    \"            miny = y \\n\",\n    \"\\n\",\n    \"        if maxx < x:\\n\",\n    \"            maxx = x\\n\",\n    \"        if maxy < y:\\n\",\n    \"            maxy = y \\n\",\n    \"    return minx, maxx, miny, maxy\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def convert_3d(drawing, return_flag = False, threshold=10):\\n\",\n    \"    out = []\\n\",\n    \"    corrupted = False\\n\",\n    \"    for item in drawing:\\n\",\n    \"        char = list(item.keys())[0]\\n\",\n    \"        stroke = item[char]\\n\",\n    \"        if len(stroke) == 1:\\n\",\n    \"            x, y = stroke[0]\\n\",\n    \"            out.append([x, y, 0])\\n\",\n    \"            out.append([x+5, y+5, 1])\\n\",\n    \"            continue\\n\",\n    \"        segment = []\\n\",\n    \"        for i, point in enumerate(stroke):\\n\",\n    \"            x, y = point \\n\",\n    \"            if i == len(stroke) - 1:\\n\",\n    \"                segment.append([x, y, 1])\\n\",\n    \"            else:\\n\",\n    \"                segment.append([x, y, 0])\\n\",\n    \"        \\n\",\n    \"        start = 0 \\n\",\n    \"        for i, point in enumerate(segment):\\n\",\n    \"            if i < len(segment) -1:\\n\",\n    \"                x, y, _ = point\\n\",\n    \"                next_x, next_y, _ = segment[i+1]\\n\",\n    \"                if any((\\n\",\n    \"                    abs(x-next_x)>threshold,\\n\",\n    \"                    abs(y-next_y>threshold),\\n\",\n    \"                )):\\n\",\n    \"                    corrupted=True\\n\",\n    \"                    start = i +1\\n\",\n    \"        \\n\",\n    \"        out += segment[start:]\\n\",\n    \"    if return_flag:\\n\",\n    \"        return out, corrupted\\n\",\n    \"    return out\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def make_square(im, min_size=256, fill_color=(255, 255, 255)):\\n\",\n    \"    x, y = im.size\\n\",\n    \"    size = max(min_size, x, y)\\n\",\n    \"    new_im = Image.new('RGBA', (size, size), fill_color)\\n\",\n    \"    new_im.paste(im, (int((size - x) / 2), int((size - y) / 2)))\\n\",\n    \"    return new_im\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def draw_strokes(data, factor=1, svg_filename = 'tmp/sample.svg', stroke_width = 3, square = False, return_res = False):\\n\",\n    \"    min_x, max_x, min_y, max_y = get_bounds(data)\\n\",\n    \"    dims = (50 + max_x - min_x, 50 + max_y - min_y)\\n\",\n    \"    dwg = svgwrite.Drawing(svg_filename, size = dims)\\n\",\n    \"    dwg.add(dwg.rect(insert=(0, 0), size=dims,fill='white'))\\n\",\n    \"    lift_pen = 1\\n\",\n    \"    abs_x = 25 - min_x \\n\",\n    \"    abs_y = 25 - min_y\\n\",\n    \"    p = \\\"M%s,%s \\\" % (abs_x, abs_y)\\n\",\n    \"    command = \\\"M\\\"\\n\",\n    \"    for i in range(len(data)):\\n\",\n    \"        if (lift_pen == 1):\\n\",\n    \"            command = \\\"M\\\"\\n\",\n    \"        elif (command != \\\"L\\\"):\\n\",\n    \"            command = \\\"L\\\"\\n\",\n    \"        else:\\n\",\n    \"            command = \\\"\\\"\\n\",\n    \"        x = float(data[i][0]) - min_x\\n\",\n    \"        y = float(data[i][1]) - min_y\\n\",\n    \"        lift_pen = data[i][2]\\n\",\n    \"        p += command+str(x)+\\\" \\\"+str(y)+\\\" \\\"\\n\",\n    \"    the_color = \\\"black\\\"\\n\",\n    \"    \\n\",\n    \"    dwg.add(dwg.path(p).stroke(the_color,stroke_width).fill(\\\"none\\\"))\\n\",\n    \"    dwg.save()\\n\",\n    \"    cairosvg.svg2png(url=\\\"tmp/sample.svg\\\", write_to=\\\"tmp/sample.png\\\")\\n\",\n    \"    img = Image.open('tmp/sample.png')\\n\",\n    \"    if square:\\n\",\n    \"        img = make_square(img)\\n\",\n    \"    if return_res:\\n\",\n    \"        return img, dims \\n\",\n    \"    else:\\n\",\n    \"        return img\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"npy_files = glob.glob('server/larger_data/*')\\n\",\n    \"file = np.random.choice(npy_files)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"file = 'server/larger_data/1فسبحان الله حين تمسون وحين تصبحون.json'\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"drawing = json.load(open(file))\\n\",\n    \"data, flag = convert_3d(drawing, return_flag=True, threshold=10000)\\n\",\n    \"plt.imshow(draw_strokes(data, stroke_width = 5))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"server/larger_data/1فسبحان الله حين تمسون وحين تصبحون.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<PIL.PngImagePlugin.PngImageFile image mode=RGB size=305x293 at 0x7F1F5251B6D8>\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import glob\\n\",\n    \"from IPython.display import SVG, display\\n\",\n    \"import svgwrite\\n\",\n    \"print(file)\\n\",\n    \"num_sketches = 1 \\n\",\n    \"\\n\",\n    \"drawing = json.load(open(file))\\n\",\n    \"draw_strokes(convert_3d(drawing), stroke_width = 5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"ename\": \"NameError\",\n     \"evalue\": \"name 'generate_words' is not defined\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[0;31mNameError\\u001b[0m                                 Traceback (most recent call last)\",\n      \"\\u001b[0;32m<ipython-input-9-4c643fef5183>\\u001b[0m in \\u001b[0;36m<module>\\u001b[0;34m\\u001b[0m\\n\\u001b[1;32m      1\\u001b[0m \\u001b[0;32mimport\\u001b[0m \\u001b[0mre\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 2\\u001b[0;31m \\u001b[0mword_drawings\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mgenerate_words\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mfile\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m      3\\u001b[0m \\u001b[0mimages\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;34m[\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m      4\\u001b[0m \\u001b[0;32mfor\\u001b[0m \\u001b[0mi\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mcomp\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0menumerate\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mword_drawings\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m      5\\u001b[0m     \\u001b[0mword\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mdrawing\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mlist\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mcomp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mitems\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;36m0\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mNameError\\u001b[0m: name 'generate_words' is not defined\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import re\\n\",\n    \"word_drawings = generate_words(file)\\n\",\n    \"images = []\\n\",\n    \"for i, comp in enumerate(word_drawings):\\n\",\n    \"    word, drawing = list(comp.items())[0]\\n\",\n    \"    images.append(draw_strokes(convert_3d(drawing), square = True, stroke_width = 5))\\n\",\n    \"concatenate(images)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"ename\": \"NameError\",\n     \"evalue\": \"name 'generate_characters' is not defined\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[0;31mNameError\\u001b[0m                                 Traceback (most recent call last)\",\n      \"\\u001b[0;32m<ipython-input-10-3476723c4cf4>\\u001b[0m in \\u001b[0;36m<module>\\u001b[0;34m\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mchar_drawings\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mgenerate_characters\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mfile\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m      2\\u001b[0m \\u001b[0mimages\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;34m[\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m      3\\u001b[0m \\u001b[0;32mfor\\u001b[0m \\u001b[0mi\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mcomp\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0menumerate\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mchar_drawings\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m      4\\u001b[0m     \\u001b[0mchar\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mdrawing\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mlist\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mcomp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mitems\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;36m0\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m      5\\u001b[0m     \\u001b[0mimages\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mappend\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mdraw_strokes\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mconvert_3d\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mdrawing\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0msquare\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mstroke_width\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;36m7\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mNameError\\u001b[0m: name 'generate_characters' is not defined\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"char_drawings = generate_characters(file)\\n\",\n    \"images = []\\n\",\n    \"for i, comp in enumerate(char_drawings):\\n\",\n    \"    char, drawing = list(comp.items())[0]\\n\",\n    \"    images.append(draw_strokes(convert_3d(drawing), square = True, stroke_width = 7))\\n\",\n    \"concatenate(images)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"drawing = json.load(open(file))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"ename\": \"NameError\",\n     \"evalue\": \"name 'concatenate' is not defined\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[0;31mNameError\\u001b[0m                                 Traceback (most recent call last)\",\n      \"\\u001b[0;32m<ipython-input-12-7120ab870496>\\u001b[0m in \\u001b[0;36m<module>\\u001b[0;34m\\u001b[0m\\n\\u001b[1;32m      3\\u001b[0m \\u001b[0;32mfor\\u001b[0m \\u001b[0mi\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mcomp\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0menumerate\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mdrawing\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m      4\\u001b[0m     \\u001b[0mimages\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mappend\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mdraw_strokes\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mconvert_3d\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mdrawing\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mi\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0mi\\u001b[0m\\u001b[0;34m+\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0msquare\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mstroke_width\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;36m7\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 5\\u001b[0;31m \\u001b[0mconcatenate\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mimages\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n      \"\\u001b[0;31mNameError\\u001b[0m: name 'concatenate' is not defined\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"images = []\\n\",\n    \"drawing = json.load(open(file))\\n\",\n    \"for i, comp in enumerate(drawing):\\n\",\n    \"    images.append(draw_strokes(convert_3d(drawing[i:i+1]), square = True, stroke_width = 7))\\n\",\n    \"concatenate(images)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def apply_rdb(drawing, verbose = 0):\\n\",\n    \"    new_drawing = []\\n\",\n    \"    total_prev_strokes = 0\\n\",\n    \"    total_post_strokes = 0\\n\",\n    \"    for item in drawing:\\n\",\n    \"        char = list(item.keys())[0]\\n\",\n    \"        stroke = item[char]\\n\",\n    \"        processed_stroke = []\\n\",\n    \"        if len(stroke):\\n\",\n    \"            if verbose:\\n\",\n    \"                print('processing ', char)\\n\",\n    \"            post_stroke = rdp(stroke, epsilon = 2.0)\\n\",\n    \"            total_post_strokes += len(post_stroke)\\n\",\n    \"            total_prev_strokes += len(stroke)\\n\",\n    \"        new_drawing.append({char:post_stroke})\\n\",\n    \"    if verbose:\\n\",\n    \"        print('reduced from ', total_prev_strokes, ' to ', total_post_strokes)\\n\",\n    \"    return new_drawing\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#https:#jrgraphix.net/r/Unicode/0600-06FF\\n\",\n    \"map_chars = {\\n\",\n    \"    \\\"\\\\u0623\\\":[\\\"\\\\u0621\\\", \\\"\\\\u0627\\\"], # أ\\n\",\n    \"    \\\"\\\\u0622\\\":[\\\"\\\\u0605\\\", \\\"\\\\u0627\\\"], # آ\\n\",\n    \"    \\\"\\\\u0625\\\":[\\\"\\\\u0627\\\", \\\"\\\\u0621\\\"], # إ\\n\",\n    \"    \\\"\\\\u0628\\\":[\\\"\\\\u066E\\\", \\\".\\\"], # ب\\n\",\n    \"    \\\"\\\\u062A\\\":[\\\".\\\", \\\".\\\", \\\"\\\\u066E\\\"], # ت\\n\",\n    \"    \\\"\\\\u062B\\\":[\\\".\\\", \\\".\\\", \\\".\\\", \\\"\\\\u066E\\\"], # ث \\n\",\n    \"    \\\"\\\\u062C\\\":[\\\"\\\\u062D\\\", \\\".\\\"], # ج\\n\",\n    \"    \\\"\\\\u062E\\\":[\\\".\\\", \\\"\\\\u062D\\\"], # خ\\n\",\n    \"    \\\"\\\\u0630\\\":[\\\".\\\", \\\"\\\\u062F\\\"], # ذ\\n\",\n    \"    \\\"\\\\u0632\\\":[\\\".\\\", \\\"\\\\u0631\\\"], # ز\\n\",\n    \"    \\\"\\\\u0634\\\":[\\\".\\\", \\\".\\\", \\\".\\\", \\\"\\\\u0633\\\"], # ش\\n\",\n    \"    \\\"\\\\u0636\\\":[\\\".\\\", \\\"\\\\u0635\\\"], # ض\\n\",\n    \"    \\\"\\\\u0637\\\":[\\\"\\\\u0627\\\", \\\"\\\\uFEBB\\\"], # ط\\n\",\n    \"    \\\"\\\\u0638\\\":[\\\".\\\", \\\"\\\\u0627\\\", \\\"\\\\uFEBB\\\"], # ظ\\n\",\n    \"    \\\"\\\\u063A\\\":[\\\".\\\", \\\"\\\\u0639\\\"], # غ\\n\",\n    \"    \\\"\\\\u0641\\\":[\\\".\\\", \\\"\\\\u066F\\\"], # ف\\n\",\n    \"    \\\"\\\\u0642\\\":[\\\".\\\", \\\".\\\", \\\"\\\\u066F\\\"], # ق\\n\",\n    \"    \\\"\\\\u06A4\\\":[\\\".\\\", \\\".\\\", \\\".\\\", \\\"\\\\u066F\\\"], # ڤ\\n\",\n    \"    \\\"\\\\u0643\\\":[\\\"\\\\u0621\\\", \\\"\\\\u0644\\\"], # ك\\n\",\n    \"    \\\"\\\\u0646\\\":[\\\".\\\", \\\"\\\\u06BA\\\"], # ن\\n\",\n    \"    \\\"\\\\u0624\\\":[\\\"\\\\u0621\\\", \\\"\\\\u0648\\\"], # ؤ\\n\",\n    \"    \\\"\\\\u064A\\\":[\\\"\\\\u0649\\\", \\\".\\\", \\\".\\\"], #ي\\n\",\n    \"    \\\"\\\\u0626\\\":[\\\"\\\\u0621\\\", \\\"\\\\u0649\\\"], #ئ\\n\",\n    \"    \\\"\\\\u0629\\\":[\\\".\\\", \\\".\\\", \\\"\\\\u0647\\\"], #ه\\n\",\n    \"}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def preprocess(text):\\n\",\n    \"    char_comps = []\\n\",\n    \"    \\n\",\n    \"    diacritics = \\\"[ًٌٍَُِّْ]\\\"\\n\",\n    \"    numbers = '0123456789'\\n\",\n    \"    for diac in diacritics: \\n\",\n    \"        text = text.replace(diac, '')\\n\",\n    \"\\n\",\n    \"    for num in numbers: \\n\",\n    \"        text = text.replace(num, '')\\n\",\n    \"    \\n\",\n    \"    outText = \\\"\\\"\\n\",\n    \"    \\n\",\n    \"    for i in range(len(text)):\\n\",\n    \"    \\n\",\n    \"        if (text[i] == \\\" \\\"):\\n\",\n    \"            continue\\n\",\n    \"    \\n\",\n    \"        if text[i] in map_chars:\\n\",\n    \"            if (i < len(text) - 1 and text[i] == \\\"\\\\u0643\\\"):\\n\",\n    \"                if text[i+1] != ' ':\\n\",\n    \"                    char_comps.append({text[i] : '\\\\uFEDB'})\\n\",\n    \"                else:\\n\",\n    \"                    char_comps.append({text[i] : map_chars[text[i]]})\\n\",\n    \"            else:\\n\",\n    \"                char_comps.append({text[i] : map_chars[text[i]]})\\n\",\n    \"        else:\\n\",\n    \"                char_comps.append({text[i] : text[i]})\\n\",\n    \"\\n\",\n    \"    return char_comps\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 221,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def concatenate(images, mode='h', margin=10):\\n\",\n    \"    widths, heights = zip(*(i.size for i in images))\\n\",\n    \"    if mode =='h':\\n\",\n    \"        total_width = sum(widths)\\n\",\n    \"        max_height = max(heights)\\n\",\n    \"\\n\",\n    \"        new_im = Image.new('RGB', (total_width, max_height), (255, 255, 255))\\n\",\n    \"\\n\",\n    \"        x_offset = 0\\n\",\n    \"        for im in images[::-1]:\\n\",\n    \"            new_im.paste(im, (x_offset,0))\\n\",\n    \"            x_offset += im.size[0]\\n\",\n    \"    elif mode == 'v':    \\n\",\n    \"        total_height = sum(heights)\\n\",\n    \"        max_width = max(widths)\\n\",\n    \"\\n\",\n    \"        new_im = Image.new('RGB', (max_width, total_height+margin*(len(images)-1)), (255, 255, 255))\\n\",\n    \"        draw = ImageDraw.Draw(new_im)\\n\",\n    \"        y_offset = 0\\n\",\n    \"        for im in images:\\n\",\n    \"            new_im.paste(im, (0,y_offset+margin))\\n\",\n    \"            y_offset += im.size[1]\\n\",\n    \"            draw.line((0,y_offset+margin-5, max_width,y_offset+margin-5), fill=(0, 0, 0), width=3)\\n\",\n    \"    return new_im\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 198,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def generate_characters(file):\\n\",\n    \"    char_drawings = []\\n\",\n    \"    annot = file.split('/')[-1][:-5]\\n\",\n    \"    char_comps = preprocess(annot)\\n\",\n    \"    drawing = json.load(open(file))\\n\",\n    \"    new_drawing = apply_rdb(drawing, verbose = 0)\\n\",\n    \"    i = 0 \\n\",\n    \"    for comp in char_comps:\\n\",\n    \"        char = list(comp.keys())[0]\\n\",\n    \"        j = i + len(comp[char])\\n\",\n    \"        char_drawings.append({char:new_drawing[i:j]})\\n\",\n    \"        i = j \\n\",\n    \"    return char_drawings\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import glob\\n\",\n    \"npy_files = glob.glob('server/larger_data/*')\\n\",\n    \"file = np.random.choice(npy_files)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 163,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<PIL.Image.Image image mode=RGB size=256x768 at 0x7F1F523EEA58>\"\n      ]\n     },\n     \"execution_count\": 163,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"char_drawings = generate_characters(file)\\n\",\n    \"images = []\\n\",\n    \"for i, comp in enumerate(char_drawings):\\n\",\n    \"    char, drawing = list(comp.items())[0]\\n\",\n    \"    images.append(draw_strokes(convert_3d(drawing), square = True))\\n\",\n    \"concatenate(images, mode='v')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def generate_words(file):\\n\",\n    \"    word_drawings = []\\n\",\n    \"    annot = file.split('/')[-1][:-5]\\n\",\n    \"    annot = re.sub('[0-9]', '', annot)\\n\",\n    \"    char_comps = preprocess(annot)\\n\",\n    \"    indices = [m.start() - i - 1 for i, m in enumerate(re.finditer(' ', annot))]\\n\",\n    \"    indices = indices + [len(annot) - len(indices)- 1]\\n\",\n    \"    drawing = json.load(open(file))\\n\",\n    \"#     new_drawing = apply_rdb(drawing, verbose = 0)\\n\",\n    \"    i, j  = 0, 0\\n\",\n    \"    c = 0\\n\",\n    \"    word = \\\"\\\"\\n\",\n    \"    for cntr, comp in enumerate(char_comps):\\n\",\n    \"        char = list(comp.keys())[0]\\n\",\n    \"        j += len(comp[char])\\n\",\n    \"        word += char\\n\",\n    \"        if cntr == indices[c]:\\n\",\n    \"            word_drawings.append({word:drawing[i:i+j]})\\n\",\n    \"            i = i+j\\n\",\n    \"            j = 0 \\n\",\n    \"            c += 1\\n\",\n    \"            word = \\\"\\\"\\n\",\n    \"    return word_drawings\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"word_drawings = defaultdict(lambda  :[]) \\n\",\n    \"npy_files = glob.glob('server/larger_data/*')\\n\",\n    \"\\n\",\n    \"word_drawings = defaultdict(lambda  :[]) \\n\",\n    \"\\n\",\n    \"for file in npy_files:\\n\",\n    \"    drawings = generate_words(file)\\n\",\n    \"    for i, comp in enumerate(drawings):\\n\",\n    \"        word, drawing = list(comp.items())[0]\\n\",\n    \"        img, res = draw_strokes(convert_3d(drawing), square = True, return_res = True, stroke_width = 5)\\n\",\n    \"        if res[0] > 100 and res[1] > 100:\\n\",\n    \"            word_drawings[word].append(img)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1080x1080 with 1 Axes>\"\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 1080x1080 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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JpGmXKlGHGjBnUrVuXn3/+mZEjR5KZmfmvP6eU4vLly4wePZru3btz9uxZnnvuOWbOnElERIT83t/DihUrRrFixbBYLCxbtkwutlxDKYXNZmPnzp189dVX+Xb9RbvdzsaNGzEYDHTp0sV9gfPfaJpGaGgoAMnJyXe6RHEXSbASQojbLCAgAEC6/oi/0TSNkiVLMn36dKKiopg7dy5Lliy5YUuGw+EgISGB+fPn06xZM8aPH4/BYGDs2LFMnjxZFhLNB3x8fKhSpQoAa9euJSUlxbMF3UMsFgvvv/8+jz76KD179mTLli35skXP6XSSkJCAwWAgOjr6pn8nJVh5JwlWQngRVxc0mRXQs8LCwgBISkrycCXiXqRpGjVr1mTMmDEopZg5cyYZGRk4nU6OHDnC2rVrmThxIt27d+eBBx6gZ8+eHDx4kEceeYSlS5fy2muv4e/vL6EqH9DpdNx3331omkZcXBw7d+70dEke52qBHTp0KO+88w6pqakEBgZSrFixfPmddnXrs9vtnDlz5qbDYUhICJqm5SlYOZ1OUlJSpCX0HiJzOQrhRVzrXmRnZ3u4koItIiICgEuXLqGUypcnC+LO0jSNli1bEhMTw+7du9m+fTsPPPAAvXv3ZvPmze6LI6GhoTz44IN0796dp556ioCAAPk+5TOPPPIIwcHBpKSksGzZMh599NH/XCjaWzmdTv744w8GDx7Mb7/9htPpJDAwkNGjR1OxYkVPl3dLDAYDTzzxBFu2bOF///sfjRo1wsfH5z9/ztVildtWTFdwu3LlCosWLWLbtm3MmjXrpl5T3HnSYiWEFwkJCQH+7FqQH7tUeIuIiAjMZjMJCQkyzkr8o+DgYNq2bUtOTg5r1qxBp9NRr149Hn/8cQYNGsTcuXP59ddfWbVqFc8++yyBgYESqvIZ17i6ypUrA7Bq1SrOnz/v4aruPqUU2dnZzJw5k9atW7Np0yacTiehoaFMmjSJF198Md+u26TT6ejYsSOlSpXiu+++Y/HixTd1/L2VFiun08mZM2f44IMPaNKkCQMHDiQ+Pl72C/eQ/PktFkLcUFhYGDqdjqtXr0pLiYdomkaRIkXw9/fn3LlzWCwWuZIo/lHDhg3x8fFh+/btOBwO3nnnHQwGAwaDQX5/vYS/vz/Vq1dny5YtnD9/nmXLlvHqq68WmO2rlOLs2bO8+eabLFy40H2xqXjx4nz88ce0a9cu34Yql+joaEaPHk2fPn2YMmUKrVu3Jjg4+F9/JjAwEKPRSHp6Ojab7V8nvXA6nZw9e5b58+fzxRdfuNfB8/X1pVu3bjc1YYa4O6TFSggvEhAQgMlkIiMjw71Irbj7ihQpQkhICAkJCTKVrvhHmqZRsWJFQkNDOXHiBCkpKfj6+mI0GgvMSXdBoGkajRs3RtM0HA4HX3zxBVevXvV0WXeFw+Fgw4YNtG7dmrlz57pDVc2aNVm8eDHt27fP96EK/tzGbdu2pWrVquzfv59ff/31P1ut/Pz88PPzIyMjA4vFcsPHKKVITk5m0qRJPPTQQ4waNYq4uDiUUhgMBl599VW6d+9+J96SuEUSrITwInq9Hp1Oh9PpvOFOXSmF0+nEbrfjcDjcj5Nug7eXr68vZcqUISsri1OnTsnnK/5RcHAw5cqVIzk5mZMnT8p3xUvVqlWLQoUKAXDw4EE2bdrk1dtaKYXFYmHmzJk888wz7Nmzx92LokmTJixevJgGDRq4J1zyBoGBgXTr1g2r1cqHH37I5cuX/3UbBwQE4OfnR1pa2t/GRbuO1Tt27OCpp55i2LBh7lYq+LMb4ahRo3jjjTcwm81yIeYe4j3faCEERqMRvV6P1Wr928yANpuNrVu30q9fPxo3bky7du3o27cv7777LnPnzi2Q/f7vFJPJRExMDBaLhX379v3j45xOJ+np6SQnJ5OZmenVJ1rixvz8/IiNjSU7O5tDhw55uhxxh5QqVco9zsrhcPD999/f0dlbXS0dv/32G1988QVz5sxh//79d232uNTUVIYNG8agQYNITEwE/v9YpPnz51OuXDmvCwM6nY7OnTtTq1YtNm3aRPfu3dmxYwdWq/WG+/aQkBBCQkK4evXqdT0blFJkZGQwbdo0WrduzcaNG7Hb7e7/L1myJF988QUjR450L+0h7h35v/1VCOGm0+nQ6XTu1iiXnJwcJkyYwMSJE0lJSXHPSOV0OnE6nRiNRlatWkXx4sU9VbpX0TSN+vXrM2XKFDZv3vy38RSuk54ZM2awfPlyMjIyCAsLo1GjRjz22GPExsYSEhKCTqfzupMPcT1N06hatSoABw4c8HA14k4xmUw0aNCAX3/9FYDffvuNpKQk9wyit4tSirS0NL755humTJnCsWPHyM7Odo/9HDx4ML17975jJ+RKKS5dusSgQYNYvHixOzwajUZ69uzJu+++654NzxuFh4cza9YsevbsyU8//cTOnTt5+OGHadOmDQ0bNqR48eLo9Xo0TXP3bDh69ChHjhyhfPnyKKXYvXs3b731FmvXrr2uS39gYCAtW7bk9ddfJyYmxqta+7yJBCshvJxSimXLljF+/HjMZjNDhw6lRYsWpKWlceHCBS5cuMCVK1eIjo72dKleQ9M0qlevjr+/P4cPHyYzM5PAwED3/2dkZPDiiy/y7bffYjab8ff35+TJk/z66698+OGHREVF0aRJE0aPHk1kZKQH34m4G0qXLg3AhQsXPFyJuFM0TaNGjRro9XocDgfnzp1j9+7dNG3a9LZdPHF1HRsxYgSbNm1Cr9cTGxtLnTp1SE5OZtWqVQwfPtw9Nud2X7RRSnH69Gl69+7N+vXr3a00JpOJgQMH8uabb7qXBPFWmqZRrVo1li1bxieffMKCBQtYunQpS5cupWjRojz22GO0b9+emjVr4u/vT+3atVm5ciVr1qyhSJEi/Pzzz0yaNImLFy+6n7NQoUK0bt2aXr16UatWLUwmk1xwu5e5xlfczK127dpKCHHv2r17twoMDFT169dX6enpSimlUlJSVO3atZXRaFTTpk1TNptNOZ1O9884nU7lcDiuu0/k3eXLl1V0dLSKjIxUp06dct/vdDrVwoULldFoVBUqVFCrV69Wx44dU+vWrVMjRoxQ9evXV0FBQapChQrq8uXLHnwH4m7ZsGGD0jRNtWjRQn4PvdjOnTuVj4+PAhSghg4detu2t81mU1999ZUqWrSo0ul0qnbt2mrx4sUqNTVV2e12ZbFY1KxZs5TBYFD169dXFovltryui9PpVIcOHVL169dXmqa536PJZFKvv/66yszMLFDfbafTqWw2m4qLi1NffPGFatWqlSpcuLDS6XTKZDKpEiVKqJo1a6ry5cu7PydfX1+l0+mUXq9XoaGhqmHDhmrcuHFq7969ymq1FqjPLz8AdqobZCVpsRLCi2RmZmK1WvH398doNAJw4sQJDh48SExMDJ07d/7bDEyapsnVrzsgKCiIQoUKcfbs2ev6z9vtdr777jucTieDBg2iWbNmaJpGdHQ0TZo0ITMzk1OnTpGQkOAe7C68m4+PDwaDgezsbOx2u/t3V3iXwoULExAQ4J4BbvPmzf85zfbNsNvt/O9//2PQoEHk5OTQr18/hg8fTkREhHvfrtfrKVOmDDqdjoCAgNvajUwpxZ49e+jRowd79+513282m3nttdcK5AQLmqZhMBiIioqiR48edOvWjZMnT/L999/zww8/cOrUKeLi4txd/axWKwBFixZl+PDhNGvWjJIlS7pb+ArSZ5ffSbASwotcuHCBnJwcIiMj3QfrtLQ0bDYb4eHhMtD1LjIajQQFBWG328nMzHTfb7PZOHjwIH5+fjRs2NB9wHT9GRAQQLVq1ahWrZpH6hZ3n6+vL2azmezsbGw2mwQrL+Xr60tISAhJSUlomsapU6e4dOkSJUuWvOXnVEqxatUqBg8ejMPhYNy4cfTt2/dva+cppThy5Ah2u50qVaq4x9nmlVKK48eP8+yzz7J//373/WazmWHDhjF8+PACF6r+StM0jEYjFStWpGLFirz66qskJSWRlJTE3r17eeGFF8jOzqZ8+fLMmjWLBx98UMZP5WOy5YTwEjabjbVr1wIQExPjPpCVLFmS0NBQDh8+zNmzZz1ZYoHiumKplLpuRidXdwH4c7IR199FwWU0GjEYDDeczVN4D6PRiK+vL/7+/hQqVIgrV65w7ty5W34+pRTnzp1j5MiRZGVlMXLkSF599dUbLkiulOLw4cMAVK5c+bYEHaUUV69epX///uzfv9/9nK5Q9frrr+Pr61ugQ9W1XL1DfHx8KFq0KEoppk6disViwdfXl0mTJtGwYUMJVfmcbD0hvIDrquWSJUvQNI2kpCQuXLiAUorSpUvz8MMPc/HiRVasWCEn8ndJeno6ly5dci/2evHiRZRSmEwmYmNjycrKYu3atTe9PZRS2Gw2srKyyMnJke3oRUwmEwaDAYvFcl0IF97FaDS6Q0+ZMmWw2+0cPXo0T8+5YMECDh06RLNmzXjllVf+sVuhw+EgOzubyMjI2xassrOzGTlyJD/++CPw5z7q2lBlNpvz/BreyG63s2LFCtq2bcu2bdswGAzYbDaZBdZLSLASIp9TSvH777/Tv39/srKyKF26NDNmzGDEiBFYLBYMBoN7bNWSJUuu65b2b895bcuKyB2n08mSJUvYv38/hQsXZu7cuXTq1InLly9jMBjo0qULRqORDz/8kE2bNv3r2jJKKXJycti6dSsvvvgiLVu2ZNGiRXfx3Yg7zc/PD7PZTFZWlnushfA+BoMBHx8fbDYbJUqUcM+id6v72ezsbL777juMRiMvv/zyv3b1NhgMfPzxx/z222/UrVv3Vt+Cm91uZ9q0acyZM8e9/zIajQwePJjhw4fj4+MjIeEv1P8tmvzJJ5/w3HPPER8fT5cuXWjdujV2u52EhAQ57noBCVZC5GNKKbZt28Zzzz3HuXPn6NmzJ4sXL6Zw4cIsXLiQ0aNHk56eTsOGDalUqRI7d+5k+fLl/9jdyLXjP3LkCO+99x6rV6+WnXwuuQZyjxs3DrvdTtWqVTly5Ai//vorI0aMIDU1lccee4wePXqQkJDAs88+y+rVq69br8QVpk6fPs38+fNp3749jz/+OLNnz+bcuXM0atTIg+9Q3G5+fn74+PiQkZEhwcqLGY1G/Pz8sFqthIeHo9PpOHv27C0v2hsfH8/Ro0cpXbo0devW/dcgo2kaQUFBlC1bFj8/v1t9C8CfF46WLl3K2LFj3d9XTdPo2rUrI0eOvGFXRAFZWVm8/fbbjBw5EoCxY8cyffp0qlSpAsDGjRtZt26ddAfO52TyCiHyKdfVzj59+hAXF0f37t0ZP348wcHBvPXWWwwaNIiJEyei0+kYPXo0ffr0YeDAgQwYMIAjR47QrFkz/P39gT9nEzx79iwHDhxg586d7N69m8TERB588EEaNmx43RpM4p+5QlX37t2Ji4vj/vvv57PPPuPy5ct06NCB//3vf1y5coXXXnuNESNGYLPZ+PLLL+natSvt27enS5cumM1mtm3bxvr169m5cyeJiYnu7mGBgYGMGTOG0qVLy9VgL+Lj43Pd5BXCO2maRkhICE6nk6CgIHx8fDh16hQ5OTm3FHauXLlCWloaderUISws7A5U/HdKKbZu3cqgQYOum+30wQcfZNy4cXkObd4qLS2NN954g88++4zQ0FA++eQT2rdvj8FgcC8SPX/+fNauXcvcuXNp2rSphysWt+xGc7D/003WsRLi9snJyVHr1q1TFy5cUA6HI9c/n52drbp27ao0TVOtWrVSSUlJ7nUu7Ha7Wr16tYqIiFAmk0n17NlT/fzzz+qll15SZrNZAcpsNqvAwEAVEBCgTCaTe90RQEVGRqr27durFStWKKvVervfuldyOp1q165dqkqVKgpQMTEx6sCBA8rpdCqn06l+/fVXFRMTozRNU35+fqpSpUqqTp06ymg0uj93Hx8f5evre922AJSmaapcuXLqyy+/lO3hhbKzs1VMTIzy9/dXJ06c8HQ54g5xOp2qb9++ClBvv/22Kl68uCpSpIg6c+bMLT3f+vXrlU6nU82aNbulY0huOZ1OdfjwYRUbG6t0Op0qVKiQ0jRNFS9eXP3xxx+yztI/SEtLU71791Z6vV6VKFFCrVy58rrtNXXqVAW49/1DhgzxYLXiZiHrWAlxbzl06BDPPPMM1apVY+nSpQQFBeXq5/fu3cvy5cspUaIE77zzDgEBAdd1IXjkkUeYMmUKgwYNYvbs2SxcuJDIyEj3jEM5OTnk5OQAf15JDQgIoGrVqrRt25bWrVsTFRXlnnhB/Dv1fy1V3bp149ChQ4SFhTFt2jSqVKni/vweeOABVq9ezYwZM1i1ahUnTpzAZrOh1+vR6/XodDp8fX2xWCzu6bbNZjOVKlWiXbt2dOrUiVKlSsmMUULkY6VLlwb+bMGoWbMma9euZcuWLZQsWTLX+9pixYoRHBzMqVOnSExMdLd83AlKKfbu3Uvv3r05ePAglSpVIiEhAZPJxOjRo6levbocK24gOzub0aNHM3v2bEqWLMkXX3zBQw895N6PK6VIT08HIDIyktOnT3PlyhWUUvJ55lMSrITwkOPHj5OYmEhMTIy7S97NUv/XDTA9PR2lFP369SM8PBx/f380TXMvMhodHc20adP4+eef+emnn0hISMDHx4eAgAB8fX0JCgqiUqVKNGjQgMaNG1OhQgUCAgJkh55LSim++uorDh06hF6vp3///tx///3XfY6aplGyZEneeecdhgwZQkJCAsnJye7/DwgIICgoiKSkJM6cOYNSijJlylCmTBkCAwNlmwjhBcqUKYOmaZw/f57mzZuzcuVKFi1aRLt27XK9flnJkiWpUqUK27ZtY/78+fTv3/+2rU/lopTC6XSyfv16XnnlFU6cOMEDDzyAzWbjyJEjdOvWjS5dusgFnxtwTfAxdepUIiIimDVrFg8//LB7X66UIjExkW+//RaDwUCLFi2YNWsWFy9exGq1yqyK+dWNmrH+6SZdAYW4fRYtWqT8/PzUr7/+muufdTqd6syZM6pTp04qPDxc+fn5KZPJpAwGgzIYDMpoNCqDwaA0TVN16tRRZ8+eVWlpaer48ePq4MGD6vjx4yohIUFlZWUpm80mXTjywOl0qj179qjixYsrQDVr1kylpqZ6uiyRj0hXwIJjx44dKjAwUNWuXVsdOHBAlSpVSgUHB6stW7bkej/sdDrV999/rwIDA1VYWJiaM2fObe0q7HQ6VXJysnr33XdVWFiYMhgMqkuXLuqNN95QRqNRVa5cWcXFxcnx4wacTqdavny5CgkJUSEhIeqbb775W3dNh8OhPvzwQ6XX61XDhg3VL7/8okJCQlSNGjVUSkqKhyoXNwvpCijEvadatWrUrFkz1z+naRqlSpVi9uzZJCQkcP78eVJSUsjKyvrzF/v/1sSZPHkyO3bs4JdffqFLly4yCcVtppQiOTmZYcOGcf78eYKCghgxYgQBAQHSlUPcNNcBWb4v3q9MmTIULlyY06dPYzab6dy5MxMmTGDq1KnUqlXrH9ehuhFN02jatClDhgzh3Xff5dVXX+Xy5cu88sortzzdufq/WWCzsrLYsGGDe4p2f39/3njjDR599FGeeeYZTCYT77zzjkykcwNKKeLj4xkxYgQZGRmMGzeO1q1b/61VLyEhgRkzZmA2m3n99depVKkSoaGhxMfHEx8fT2xsrHy2+ZC03QrhQWXKlMnT1LQ+Pj5ERUXxwAMP0KJFCzp06EDHjh1p164d7dq1c6+VIlM4/zulFGfOnCEpKSnX08vPnTuXdevWAX+Oifrss88YOnQo2dnZOJ1OrFYrVqv1lqdUFt7P6XTidDrdY+2E9woICKBy5cqkp6dz5MgRevbsSYkSJVixYgXbtm3L9f7HZDIxdOhQJkyYgF6v54033uD5559nx44dZGRk3NTzuY4RSUlJ7Nmzh08//ZTHH3+cjh078uuvv1KjRg2+/vprBg4cyEcffcT58+fp1q0bLVu2lBP/G7BarYwbN45Dhw7RokULXn75ZQyG69sx7HY7X3zxBSdOnODRRx/l4YcfJiwsjGbNmpGcnMz8+fNlsfB8yuMtVnFxcbz33nvUrFmTPn36yEFFFCh3amrl7OxsPvroI1auXEmxYsV48MEH78jreIv9+/fz7LPPUqVKFT777LObbtlTSrFixQr3pCFXrlxhwYIFFClShOzsbFJTU0lLSwMgOjqa5557jtjYWNnPieu4gpVOp5MTVS9nMpmoXr06q1atYt++fTzxxBM8//zzjBkzhg8//JDatWvnespys9nMSy+9RJEiRRg2bBiLFi1i1apVxMTEULduXWJjYylduvR146+cTiepqalcunSJU6dOceLECY4dO8b58+fJzMxEr9dTsWJFevXqRZcuXShUqBArVqxg9erVVKhQgeHDh8sYoBtQSvHTTz/x9ddfuyeW+usYarvdzjfffMOkSZMIDg5mwIAB7s+ye/fuLFy4kOnTpxMaGsrAgQMxmUyyX8hHPB6sduzYwezZs+nYsSN9+vTxdDlC3FU5OTm3dQFepRSXL1/mnXfeYdasWQQEBPDBBx9Qrly52/Ya3mjmzJns3buXsmXL5qplSdM0Gjdu7L467JKQkMC0adPQNM09IP37779n0aJFLF68+G8TW4iCzeFw4HQ6MRgMErq9nKZp1K5dG51Ox65du9A0jV69erFw4UJ+/PFHVq1axVNPPZXr/YPBYKB9+/bUrl2bzz77jGXLlrFnzx62bNnift0bcR1/jEYjgYGBlC1bljp16tCyZUsaNWpEaGgoABkZGUyaNAm73U6/fv0oVapUHj4F75Wens6ECROwWCy89tprxMTEXDdZRXp6OtOmTWP8+PFYLBbGjBlDw4YN3Y+pV68eY8eO5Y033mDMmDEYDAa6dOmCXq9393xwOBykpaVx4cIFzp49S05ODpGRkVSrVo2oqCjMZrMcXzzIo8FKKcWhQ4dwOBzXffmEKCji4+OxWCwEBATk6XmUUtjtdn799VdGjhzJ9u3bKVKkCJMmTaJdu3ZysvYfsrOzKVWqFB9++GGup70fNmwYjz32GHv27CEpKcndpSskJIQiRYoQFBSEw+Fg8uTJrF69ml9++YX777//Dr0TkR/l5ORgs9kwm81/6zIkvE/lypXx9/fn6NGjZGRkUKxYMQYOHMgrr7zC+PHjady4MYULF87182qaRpkyZXjvvfcYOnQoBw8eZN++fRw6dIgLFy5cd9FI0zSCgoKIiIigdOnSREdHU6ZMGUqWLImvry96vf66QLBq1So2b95MTEwMHTp0kPO1G1BKsXLlSrZu3Ur16tWvmy1RKcXRo0cZNmwYq1evxt/fnzFjxtCvX7/rfuf1ej19+vQhMDCQV155hREjRjB58mT0ej1paWnu7oEOhwO73Y7NZnOPqw4MDKR+/fr06tWLxx57zD1LsLi7PLoHt9vtHD58GKPRSKVKlTxZihAeERcXx6lTp6hateotDzRW/zf1+uTJk5kzZw7p6ek0atSI999/nzp16kiougmaptG2bVtKlSqVq+2gaRo+Pj40aNCABg0a/OPjTp48yaVLl9Dr9URGRt6OkoUXsdls2O12CVYFREREBEWLFuXSpUtcuXKFoKAgnnnmGRYtWsTPP//M3LlzGTRo0C0dEzRNQ6/XEx4eTqNGjWjUqFGufvZGUlJSmDRpEk6nk1dffZXw8PBc11UQpKWlMWXKFABeffVVwsLCUEphsVhYtmwZo0aN4tSpU8TExDBhwgQee+yxG06PbzAY6Ny5M3q9nk8//ZSzZ8/icDgICgpyr3sYGBhI0aJFiYqKwtfXl7i4OHbu3MmaNWtYt24drVu3ZvLkyURGRkq4uss8uge3Wq2cOHECHx8foqOjZeOLAictLY25c+fy7rvv5mo2KFcL1YkTJ5g7dy5fffUV586dIyIigiFDhvDSSy8RGhoqv1O5ULRo0VteA+ZGn7NSiuzsbNatW8ebb77J/v37adasGe3atZPtIq5z7eQV8t3wfoGBgYSFhREXF0dqaqr7vqFDh7J161ZmzpzJ008/TYkSJfL0Orfju6SUYvny5ezatYu6deveUjfFgmLjxo3s2LGDmjVr0qpVK3Jycti8eTMfffQRGzZsQClF165deeedd/7zIp4rXLVt25bU1FSUUvj5+bnPE3Q6HTqdDoPB4F678sqVK3z//fdMnDiRb775BoPBwMyZM6Xl6i7zeFfA9PR0DAYDISEhnixFiLsuOjqaiIgIZsyYQUxMDF27dnXvJG/E1Rc+PT2d/fv387///Y8VK1Zw6dIlgoKC6NSpE4MHD6Z69eq3fZFIb6eU4uzZs+4JBPL6XHa7nZ07dzJhwgTWrl2L1Wrl8ccfZ9q0abKvE3/jGmMlswIWDEajkYiICPfJMPwZgho1akSTJk1YtWoVX331FUOGDPH49yE9PZ0ZM2YA0LdvX4KDgz1az73K6XSyYsUKbDYbDz30kLvlcf369WRmZlKhQgUGDx5Mly5dbnpyEk3T8PPzu6nHG41GihUrRu/evWnQoAEdO3bk22+/pWbNmgwaNEjOCe4ijwcru92OpmnS/UEUONWrV2f06NEMHjyYV199lZ07d/Lcc88RHR2NXq93r3hvsVg4f/48x44dY8uWLfz+++8cPXqU7OxsChcuTK9evejZsye1atXCaDTKlalbtHr1aoYMGULJkiVvuVumxWJh27ZtzJgxg9WrV5Oenk758uXp27cv3bp1Izg4WLaP+BtXV0Cj0SgnQAVE8eLFUUpx/vx5930+Pj7079+fjRs38vnnn9O5c2dKlizpsRqVUixdupRdu3ZRo0YNWrRoIfuvG1BKceXKFbZv346maXz22Wd89NFHAERFRdGzZ0+effZZihYtesc/P03TqFq1KhMmTKB79+689957NGzYkPr169/R1xX/n0fTjGvwnavPqBAFiV6v5/nnn0cpxbhx45g+fTrz588nMjISs9mM3W4nKysLi8VCdnY2WVlZOJ1OfHx8KF++PE8++SSdOnWiQoUK7pnnxK3x8/MjLi6OsWPHMnHiRPz8/G7qAKiUwmazcfLkSTZt2sTy5cv57bffyMjIoHjx4vTt25c+ffrcclgTBYMrWJlMJjkWFhDFihVDKUVCQoL7Pk3TePDBB3nsscdYvnw5c+bMYeTIkR5ptXKFvg8++ACdTkf//v2lteoGlFKcO3eOAQMGcPToUZRSZGVlUapUKTp27Ejfvn0pXrz4Xd2GmqbRvHlzevTowSeffMKKFSu477775Bh0l3g0WNlsNpxOJ0aj0ePN3UJ4gmv9kQceeIDZs2ezfv16Ll265O6SptfrMZvNREVFUb58eerUqUP9+vWpXLkygYGBaJomO8vboEePHvzwww98+eWXOBwORo8eTYkSJW64X1JKkZmZycmTJ1m/fj1r1qxh165dJCcno9frKV26NEOGDKFLly5ERUXJNhL/yW6343A4bqrFynXiduzYMXx9falQoYIcP/Mh1zTmKSkpKKXc+wiz2cyAAQPYsGEDX3zxBV26dKFMmTJ3fR/idDqZNm0ahw8fpmXLlrRp0+auvn5+oJTixIkT9OrVi02bNgFQp04dRo0aRb169YiIiPDY/l+v19OgQQOmTJnC+fPncTgc0jPsLvF4sHI4HPj4+MiBQRRIrp1ujRo1mDhxImlpaaSkpGCz2dDr9fj5+WE2mzGZTPj4+Mjg9jukRo0afPbZZ/Tp04c5c+awadMmnnzySR599FGKFi2KwWAgJyeH06dPs3XrVrZs2cLBgwdJT09Hp9NRvHhxmjdvzhNPPMFDDz1EkSJFZJ8mbtrNjrFynci99tpr/PzzzwQHB7No0SLq168v+4V8xrVobFZW1nX3a5pG/fr1ad68OYsXL2bevHmMGjXqrm5fpRT79u1j1qxZBAcH8/rrr990K35BoZQiLi6OHj16sHnzZvdn8/TTT/Pkk096/LNyzVir0+lu+3qZ4t95NFhZLBZ3sJLuD6Kg0+v1hIaGuq9kirtHp9PRtGlTli9fzptvvsnGjRuZOHEin3zyiXtNF5vNhsViQSmFr68vJUqU4Omnn6Z58+bUrVuXIkWKSOuUyLXMzEx+/vln93o0/3QCpJTi0qVL9OzZk99++43g4GDOnTvHvHnzqFevnhxD85Fr9xM3WpDcZDLRt29fVq5cyezZs+nevTtlypS5a/VZLBbGjx9PYmIi/fr1o27durJfu4ZSisuXL/PCCy+wefNmGjRoQEJCAhcuXLinPivXRZrcLHov8s6jwSozMxObzUZAQIAcFIQQHqVpGtWqVWPhwoXs27ePH3/8ke3bt5OYmOge/1K8eHHq1q1LvXr1iImJISQkRFoRRZ5cvXqVTz/9FJvNRmpqKhaLBV9f3789LjMzk9dff53ff/+dBx54gAEDBtCjRw927txJdnZ2nhcZF/cOTdOoV68eLVu2ZOHChcyYMYOxY8fela5crsWAv//+e8qVK0e/fv2k9f0vrFYrY8aM4eeff6Z+/fq89957dOrUifDwcMqXL+/p8txcF2nk+HR3eXwdK6fTidlslmAlhPA4TdPw9fWlXr161KtXD6fTSU5Ojrublo+Pz3WPFSKvihUrRtOmTZk5cybnzp0jISHhb63WNpuNiRMnMn/+fMqUKcOnn35KsWLFCAgI4Ny5c2RlZUmw8jJGo5EBAwawdu1aZs+eTYcOHahVq9Yd3e8opTh58iSjR4/GZrPx2muvuceJiv9vw4YNzJkzh2LFirkviiQlJVGvXr17pseJUsp9UTA4OFjC8V3k0U/a6XSilJJQJYS4p7i66rjGuQUEBODr6+u+X040xO1iMBjo2rUrfn5+pKen88svv1zXHdBms/H111/zwQcfEBwczKeffkr16tUxm834+/u7u6gK76JpGrVq1eK5554jMTGRd999l+zs7Dv2ekopLl68yIsvvsjhw4d58skn6dy5s5yQ38DGjRuxWq0MHjyYmjVrEh8fj8VioVSpUtddfPMkh8PB+vXrgT/HEMt2vHs8/klLU6UQQoiCrHbt2u4W0qlTp7Jt2zauXr3K8ePHeeONN+jXrx82m43Ro0fTtGlTNE1zr3MHyEmTlzIYDPTr14/y5cuzcuVKvvvuuzsyCYFrUpRnn32WDRs2cN999zFx4kRpBf0HDoeDChUq0KlTJ3Q6HadOnQKgXLly98S5rFKKAwcOsGbNGgoXLsxjjz12w7pcYzo9fXM6nX+73ehxefk88vocueHRroCuGZDsdrsMrhNCCFEg+fr60rt3b/bs2cOBAwd4/PHHiYyMJDU1lcuXLxMeHs7bb79N79693T087HY72dnZGI3Ge+Yqubj9SpUqxYgRI+jTpw+jRo2iWrVqVK1a9badwDudTn7//XdefvllDh06RL169Zg1axalSpW6J0LCvapZs2YULlwYgLNnzwJQunRpT5YE/BkisrOz+eCDD7h69SovvPACwcHBnD17loyMDNLT00lPTyczM5PMzEwyMjLIzMwkOzub7OxsrFare8buv9Lr9ZhMJoxGI2az+ZbWz3QtV5Kenk5WVhZZWVnk5ORgtVrdj/Hx8SEwMJDAwEACAgIICgoiICAAk8kE/HnBwWQyERAQQFhYGBEREYSGhmIwGNDpdDidTmw2G2lpaSQlJREfH8/27dupXLkyXbt2veO95DwarHx8fDAYDGRnZ99wIwohhBDeTtM02rdv7w5Qu3fv5uTJk4SGhvL000/Tv39/6tate90JwcWLF8nKyiIqKsp9wiHyh2uvnv9Xa6OmaXTo0IFffvmFOXPm8NJLLzF37lzKli2bp+CjlMJut7NkyRKGDh1KQkICrVq1YtKkSRKq/oOmaZQuXdq97VxdcfV6/XVrkt0Oru+Jq2UnJyfHHY7S09NJTk7m8uXLXLp0iStXrpCcnExCQgJr1qwBYNWqVaxevdp9nm23293r5rmWebj2feW2rlvhep1rX+/av1/7nm/0Wq7u+AaDAaPRiMlkIigoiODgYEwmEzk5OSQlJZGdne3uKm232ylevDhVq1alVq1at1z7zfBosAoMDMRoNJKWlobdbvdkKUIIIYTHGI1GHn30UerVq+e+ulyoUCGioqIwGAx/O/E4efIkmZmZREdH33AWQXFvc53z3MzVc19fX8aNG0d8fDwbNmygY8eOTJw4kfr162M0GnN9Iu+a2GDSpEl88sknOBwO+vXrx5tvvklwcLCEqv+glCI+Ph6Hw4Fer6d69epomsbixYtp167dLXehVEphs9nIzMwkLS2Ns2fPEhcXR3x8PKdPn+b06dMkJCS4W5wyMjKwWq037OamaRoBAQHuVu2goCD37drWoICAAPz9/fH19cXX19fdInWj76XD4XC3aLn+zG3A0ul0+Pn5ERgYiJ+fH76+vu61Ol1dnC0WC+np6WRkZJCRkUFaWhoZGRnYbDYA9+unpaWRmJjIxYsXSUlJIS0tDaUUOp0Ok8lE4cKFCQsLo0SJElSrVo3GjRtTqVKlW9o2ueHRYOXn54fRaCQzM1OClRBCiAJN0zSCg4MJDg7+z8fu3LkTu91O9erV78o03OL2urbF6r+CjKZpFClShFmzZvHiiy+ybt062rRpQ7NmzejcuTMNGjQgLCzsP1u/lFJYrVY2b97Mm2++yZYtWwgNDWX06NH06tVLupTmwrx58yhVqhQdO3bkySefZPr06fz0008sWLCA559/3r0trm11uXY8UUZGBqmpqSQlJZGQkEBcXBzHjx/n1KlTxMfHu1ukrVarO8DpdDrMZjPBwcFERERQvnx5wsLCiIyMJCIiAj8/P/fsok888QSjRo0iNDSU4OBg93qxrptOp3MvFZKfgrTr83R9jna7HavV6u7maLPZMJlMBAcH4+fn5+6++NeLU3eSR/fGJpMJs9lMZmbmdf0rhRBCCHFjNpuNLVu2YDAYqF+/fr46MRJ/coVhu91+U93HXN3Pvv76az7++GPmzJnDokWL+Pbbb4mOjuaZZ57h6aefply5cn8L2kopUlNT2bFjhzsAZGVl0aBBA9577z3uv/9+mQAlF6Kjo8nMzGTw4MF8/PHHlCtXzh1ax40bx6FDh9wh1TUWMjs7m9TUVJKTk0lMTCQtLY2srCwyMjLIzs52Bwaj0UhgYCDBwcHExsZStmxZypQpQ6lSpShdujRFixYlICAAPz8//Pz83N2AnU4nEyZMICEhgcqVKzNx4kTKly/vdfsG1/txBUSj0Yivr+9NXYy6WzwarHQ6Hf7+/iQmJpKVlfWfj1dK4XA4yMzM5OrVq6SlpWEwGPD397/hlMhCCCGEt4mLi2Pv3r0UL16cmJgYT5cjboGrC5+rK9fNnLNomkZYWBhjxoyhR48erFixgqVLl7Jr1y7eeustpk2bRosWLejWrRsRERGkpqZy9uxZNm7cyJYtWzh27Bg5OTmULl2a559/nj59+hAeHi7nS7nUo0cPSpQowfTp09mxYwdbtmxxr3V45swZJk2aBPz/sUDX3lytRQEBAYSGhlK+fHmKFClCVFQU5cuXp1y5chQrVowiRYrg5+eHwWD4z0XoHQ4Hixcv5oMPPsDf35+PPvrIK0NVfuHx/gM6ne66aWNvxNXv9Pfff2fOnDns2LGDxMREcnJy3H0pjUYjRYoUoWnTprRs2ZLY2FgCAwMBmcpdCCGEd1BKsXHjRpKTk92zB4r8xxWobmWMlF6vp1y5cgwYMIDevXszduxYxo8fz6VLl5g9ezYLFy7Ex8cHi8WCxWLB6XQSEBDgniK8U6dOlChRQlqpbpGPjw8tW7bk0Ucf5fLly5w5c4akpCSSk5P54IMPOHz4MA8//DCtW7d2z9rp4+NDcHAwISEhhIWFERgY6B7XdO0YydxOIOF0Ovn666/p378/2dnZjB49+h+nVxd3h0eDlc1mIzU11d30eSOuQZZjxoxh7ty5ZGZmEhAQQJEiRQgNDcVut7sHuB0+fJg//viDSZMmUblyZbp06UK/fv2k/7kQQgivYLfb2bhxIzqdjscff1xOjvMp1xTdRYoUueWTYE3T8Pf3p0yZMlSrVo3U1FSUUuj1eoKCgggNDaVw4cLcd999NG7cmIoVK+Lv7y/fmdtA0zR8fX0pXbq0e5p1pRRBQUF069aN06dP07JlS8qVK3fHanA4HMybN49BgwaRk5PDG2+8wcCBA+/4dOLi33k0cZw7d46UlBSKFy9+w1lUlFJcvXqVPn36sHz5ciIjIxk6dCitW7emWLFimEwm9+A1m83G0aNHWbVqFWvWrOHQoUMcPHhQUrsQQgivkZqayq5duwgMDKRevXpyjMuHXAu46nQ6KleunOfn69GjBx07dnSPVdc0zT0NtdlsvqkJMkTeaZpGixYtaN26NYsWLWLq1KlMmDDhtl/cd61V9eWXX/LGG2+Qk5PD6NGjGTBgAGaz+ba+lsg9jwUrpRR79+4lNTWVRx55BH9//789xuFwMH78eJYvX061atWYOXMmtWvX/serLZGRkTRs2JARI0awf/9+QkJC5MqMEEIIr6CU4uDBg1y4cIGaNWtSrFgxT5ckbkFqaioHDx4kMDDwtgQro9FISEhI3gsTeWY2mxkyZAg//fQT8+fPp02bNjRs2PC2Luh89uxZ3n33XebMmYPJZOKdd97h1VdflfXs7hEeSx1Op5PVq1fjdDo5ceLE3yavUEqxa9cuZs+eTaFChZg+fTp16tT516CkaRo6nY7g4GAefPBBYmNj5SqNEEIIr/HLL7+QnZ3NAw88cMMLkuLed/78eeLj4ylZsiQlSpTwdDniNtI0japVq9KjRw+uXr1Kjx492LJlS54W1IU/z4mvXLnCxIkTefTRR5k1axYRERF88cUXEqruMR4LVufPn2fTpk3An32N/xqsHA4Hn3/+OampqfTs2ZO6detKSBJCCFFgWSwWNmzYgNFo5JFHHpFjYj61e/dusrKyqF69+i0vJivuXQaDgREjRtC9e3fOnDlD7969+eOPP24pXCmluHTpEtOnT6dp06YMHTqUCxcu0LZtW7755hs6dOggoeoe45GugE6nk0WLFnH+/Hngz0kskpOTKVq0qPsxCQkJ/Pjjj4SFhdGtWzfp0ieEEKJAO378OHv27KFUqVLUqFHD0+WIW+B0Ot0n2XLB2DtpmkZISAgfffQRGRkZfPfdd3Ts2JEPP/yQFi1a/OdMkEop7HY7Z86cYeXKlcyaNYvDhw+j1+tp3LgxQ4YM4aGHHsLHx0e+P/egux6slFKcPXuWWbNmYTQaKVq0KOfOnWPPnj1UrlzZ/SU5fvw4ly5d4sEHH6RMmTLy5RFCCFFgKaVYuXIlaWlpPPPMM4SHh3u6JHELrFYrBw8exGw2Exsb6+lyxB2iaRqhoaFMmzaNwMBAvvrqK7p168aTTz5J586dqVOnDmFhYZhMJvcCzqdOneLw4cOcPn2aP/74g82bN3P58mXMZjNNmjTh5Zdf5pFHHiEgIEDOie9hdz1Y5eTkMHbsWI4fP07r1q158MEHGTp0KBs3buSZZ55xf1nOnTuH1WqlbNmy7hWshRBCiIIoPT2dpUuXYjKZaNOmjfTiyKesVitnzpzBx8eHqKgoOUH2YpqmER4ezpQpU6hevToffvghixcv5rvvvqNYsWKUKlWK0NBQbDYbZ8+e5eTJk+Tk5LgXGy5UqBBPP/00Xbp04aGHHsLf31++L/nAXQ1WTqeTJUuW8PXXX1OiRAnefvttrFYrZrOZ3bt3Y7FY8PPzAyAtLQ2A0NBQ+SIJIYQosJRSbNu2jX379hETE0P9+vXluJhPJSUlkZKSQlhYmMzkVwBomoafnx+vvvoqLVu2ZNmyZaxYsYKjR4+ybds2cnJy0DSNgIAAoqOjqVGjBuXKlaNSpUrUqVOHkiVLYjAY5Pc9H7lrwcrhcLB06VIGDx6M0+nkzTffJCYmhsTERHd3wEuXLlGmTBn34wG5KieEEKJAy8zM5NNPP8VqtfL0008THBzs6ZLELVBKkZCQQGZmJhUrVpTeOAWITqejbNmyDBo0iL59+3Lx4kUuXbpEWloaRqORQoUKERUVdd0CzhKm8qe7EqycTifffPMNr7zyCmlpaQwZMoSuXbui0+kIDQ2lePHibN26lYSEBHewioiIAODixYsopeQLJoQQosBRSrFgwQLWrl1L5cqV6dy5sxwP87Fjx46RlZVFmTJlJFgVMK7fWx8fH8qUKeM+3xXe5Y43B9lsNubPn0/fvn3JyMjgjTfeYOTIke7VoQ0GAxUqVMBut3PkyBH3z5UuXRo/P78brnElhBDCe9hsNlJSUvK81ou3UUpx5MgR3n33XfR6PW+99RbFixf3dFkiD7Zt2+aeEVB65Ajhfe7Yb7VSiuzsbD755BNeeeUVsrKyGDp0KEOGDMHX1/e6K26uaWO3bt3q7gJYtmxZIiMjOXLkCKdPn77tB1ylFE6nUw7kQgjhYbt376ZXr17k5OR4upR7hlKK9PR0RowYwZkzZ2jfvj1PPvmktFblYxaLhb1792I2m6lZs6ZsSyG80B0LVleuXOGll15i5MiR6HQ6PvzwQ0aMGOFuqbrWAw88QFBQEJs2bSIlJQWAQoUK8cgjj5CSksLChQtvewCy2+3MnTuXK1eu3NbnFUIIkTs5OTkcP34cm83m6VLuGQ6Hg8mTJ/PDDz8QGxvL22+/fcPjp8g/EhISOHbsGEWKFKFcuXKeLkcIcQfc9mDl6rrw3HPPMW/ePEqUKMHnn39O7969MZvNf7tCo2ka5cuXJyYmhtOnT7N9+3aUUuj1ep577jkCAgKYOXMm69atw+l03pb60tLSmDx5Mm+88QYWiyXPzymEECJv4uPjOXHihPQi4M9QtXjxYiZMmEBAQADvv/8+pUuXlhaOfEwpxc6dO0lOTqZ69eoUKlTI0yUJIe6A2xaslFI4HA42btxIu3btWLNmDTVq1GDJkiW0b98eg+Gf58nw9/enTZs25OTk8OWXX2K1WtE0jbp16/LSSy+RlJTkDmqZmZkopXJ18HU9Pisri59++om2bdvy+uuvk5OTI32chRDiHpCamsqGDRs8XYbHKaX4+eefGTRoEHa7nbfffpumTZtKqMrnnE6n+wJxkyZN/vWcSAiRf92WVOHqC/7+++/ToUMHjh8/TocOHfjuu+9uqh+xpml06NCBqKgofvjhB7755hucTidGo5GRI0fSr18/kpOT6dOnD82bN+eTTz5h//79ZGZm4nA4bhiylFLY7XaysrI4duwY06ZNo2XLlrRp04YNGzYQERHB+PHjiYyMvB0fgRBCiFtkMBjQ6XT8+OOPWK1WT5fjMUopDh48SN++fbl69Sr9+/end+/e6PV6T5cm8ig5OZlffvmFgIAAGjduLEFZCC+Vp0smTqeTjIwMjh07xpgxY1izZg3+/v6MHDmSgQMHEhQUdNM7j1KlSjFs2DAGDBjAkCFDKFKkCA8//DABAQGMGzeO2NhYJk2axO+//86vv/5KcHAwlSpVolq1apQsWZLAwEB8fX0xGAxkZGSQlJTEmTNnOHnyJEeOHCExMRGlFMHBwbRr146hQ4cSGxsrLVZCCOFhpUqVolixYvzxxx+cOXOGChUqeLqku04pxcWLF3n55Zc5fvw4HTt25PXXX5dxVV5i165dxMXFcd9991G+fHlPlyOEuENuOVhZrVa+/vprPv30U06fPk1KSgpVq1Zl/PjxPProo+j1+lxdkdE0jWeffZYTJ04wceJEunfvzvDhw+ncuTNhYWE899xzPPnkk/z+++/88MMP/PLLL+zfv5/t27fjdDrRNM39etd2FdTpdBQuXJgmTZrQuHFjWrRoQWxsLEajUa4YCSHEPSAyMpIHH3yQRYsW8csvv1C+fPkCtX9WSpGYmEjfvn357bffaNSoER999BGBgYGeLk3cBk6nkxUrVmC1WmnevDl+fn6eLkkIcYdouRmrVKdOHbVjxw4yMjJ4//33mTRpEpmZmYSHh9O9e3cGDhxI8eLF83RATEtLY9SoUcyaNQur1UrNmjXp1KkTderUwd/fH03T3DNIrVy5ktWrV5OWlvb/35CmYTQaKV68OLGxsTz++OM0a9aMYsWK4ePjU6AO1kIIkR8opfjiiy948cUX6dq1K7NmzSowY1CUUiQnJ/Pyyy+zZMkSqlSpwqJFi6hcubIcr7xEUlISDRs25Ny5c2zcuJFatWp5uiQhRB5pmrZLKVXnr/fn+sgVHx/Pa6+9xtKlSzGZTLRv357hw4dTo0YNdDpdng8EgYGBfPDBBzRp0oQPPviAHTt2sHPnTnQ6HQaDAU3TsNvt7vWu4M/JLwoXLkzNmjVp0KAB999/P9HR0YSHh9+WmoQQQtw5mqZx3333ERgYyC+//EJCQgIlSpTwdFl3nCtU9e/fnyVLllCpUiXmzZsnoSqPsrKyyMrKIjAwEJPJ5NHPUinFgQMHiIuLIzY2looVK3qsFiHEnZerYGWz2ejWrRu//fYb0dHRvP3227Rq1epvC/7mhaZpmEwmWrVqRePGjdm4cSM//fQTx48fJysrC6UURqOR0NBQoqOjqVy5MpUrVyY6OpqQkBDp4ieEEPlQ+fLladSokXsCo/79+3v1vlwpRUpKCv3792fBggVER0fz5ZdfUr16da9+33eaUooffviBESNG8OKLLzJ48GCP1/Pjjz9isVho0qQJvr6+Hq1HCHFn5SpYnTt3jv3791OxYkWWLFlCTEzMHTsAaJpGSEgIbdq0oU2bNu5Z/lxrXP11liQ5EAkhRP5lNpvp06cPP/30E1OnTqVly5aUK1fOK/ftrjFVAwcOZMGCBZQrV465c+dSt25dr3y/d1tmZiYnT57k9OnTni4Fi8XCjz/+iNlspkWLFrJ9hfByuZoSLzk5maJFizJjxow7Gqqu5ZqUQqfTYTKZMJvN7i6B196EEELkX5qm8fDDD9OqVStOnTrFgAEDuHz5stctGKyU4ty5czz//PMsWLCA8uXLM3fuXOrVqyfHstvEdeHVNbGVJ507d45Tp05RsmRJKlWq5PF6hBB3Vq6ClVKK5557jgceeEB2DkIIIW4rHx8fxo0bR/Xq1Vm1ahW9e/fm/PnzXhOulFLs2bOHTp06sXLlSqpVq8aCBQu477775Jh6GzmdTuDe6Mly6tQpkpOTcTgc7Nmzx12bEMI75SpY6XQ6OnbsKIsVCiGEuO00TaNs2bLMmTOHGjVq8MMPP9C+fXt+++236yYsyo+sVitfffUVrVu3ZsuWLTz00EMsWrSIGjVq3BMBwJtYLBbgz6DuafHx8TidTk6dOsW8efMkWAnh5XIVrPR6PeHh4XeqFiGEEAWcpmlUrVqVr7/+mkaNGrFjxw6eeuopJk+eTGZmZr5rvVJKkZaWxpgxY+jTpw+XL1+mV69efP311wVuva675V4KVn5+fu5lYNq0aSMXpoXwcrkKVg6Hg1OnTuW7A5sQQoj8Q9M0KlasyDfffMPw4cPJyclh2LBhPP/888TFxeWbY5BrPNULL7zAhAkTCAgIYPLkyXzyySdERkZKqLpDMjIygD+XYvG0okWLotPpKF68OA0aNJBtLoSXy9WsgE6nk1GjRjF9+nQqVqz4rxNHuA58f/3zn9yuA6XT6cRqtWKz2XA4HO41r1y33DxPbprsNU3DYDCg1+sxGo3uiTZcV6dc62ld+3m5/v7XP//6dyGEKGg0TaNQoUK89dZb1KlTh+HDh7NkyRIOHz7MJ598QuPGjdHpcnVt8K5RSuF0Ovn5558ZNmwYf/zxB+XLl2fKlCk0adJEWi3usMTERAAKFSrk0TqcTie//PILDoeDBg0aULhwYY/WI4S483IVrHx9fdm0aRNNmzala9euPPzww5QoUQIfHx+UUuTk5JCcnMyVK1e4fPkyV69eJTk5meTkZLKzs7HZbO4A5Qo9Lq7/s1qtt/RGHA4HWVlZ5OTkYLPZ/haoHA4HTqfzpgOc68B4szRNc08DbzAYMBqNmM1m94Ffp9O573OFLpPJhI+PDwEBAQQEBBAYGEhAQAD+/v74+/tjMpkwmUwEBgYSEhKCn58fRqPRHdz0ev11oexGJxnXzqrouv313ze6T4KdEOJe4OpCFRsby9ChQ/nhhx/o1KkTH3/8MR06dMBoNHq6xOsopbh48SJTp05l+vTppKWl8cQTT/DBBx/Iwr93yZUrV9A0zeNBJjs7m40bN+Lr60uvXr0kUAtRAOQqWEVHR1OrVi2+++473n//fSZOnIjRaHSf0DudTneIsdvt7qlO/+lE/Wbvu1nXTstuNBoxGAzum4+Pz3VB5L+4AsbNUkq5A53NZiMtLQ2LxeIOZw6HA6XUDVvy/npzvb4rFOn1+usCz42mmXe937++P1dL2rWfh+vvrpD2138bDAZ3qPP393cHv4CAAPz8/Nyh8Nqb62dc/+d6rb8GzRtNl3/t9+PfWvGEEAWTpmlER0czZ84c3n77baZOncpLL73E8ePH6d+/P8HBwR7fV7jGUi1btoyPPvqIgwcPEhISwhtvvMGAAQPuiRoLAqWUO1h5eky40+nEYrFgNpspXLiwbH8hCoBcBSuTycSMGTN44YUXWLFiBXv27CEhIQGr1YqmaZhMJkJDQylcuDBFihQhPDyc0NDQ61pbXDuWaxf5dZ38u1p1bmXno9Pp8PX1xcfHx31Sr9Pp3KHE9efN+qcWoH+ilLqudcxms13XQud0OrHZbOTk5GC1Wt237OxsMjMzyczMJD09nYyMDPe/rVYrOTk5pKWluVv9XD/nCnGu13a1sN2oRc5ut2O1Wt3dG6+9uVry/npfbrk+Y1cwu/Yz1+l014U6179dLXFBQUH4+/vj6+vrDsWu1jwfH5+/XZF2Pcd/XakuVKgQdevWpUSJErl+P0KIe4umaQQFBTFu3DhKlSrFmDFjeOedd/j9998ZO3YstWvX9kiLgFKK1NRU1q5dy5QpU9i6dSs6nY5mzZoxatQo6tWrJ70A7qK0tDQuXrxIYGCgx4OV0WikcOHC7Nu3j3PnzhEbGyvfAyG8XK6CFYDZbOb++++nQYMG7nFMLte2sLhah2Qnkjs3aslyBSZX69e1LVuuf7taCf8arJRS2O32f7y5Atpf/+0KgVlZWWRkZLhDX1ZW1nXB0BUUXeHNYrFc1x3TdXP9X1pamrs189rbjVrtXH/eqBXuZuj1eqZMmULv3r1vbWMIIe4pmqbh4+PDK6+8QkxMDK+//jrr169n9+7dPPvss7z88stERUXd8bFXrv3UpUuXWL58ObNnz2bv3r04nU5q1qzJwIEDefLJJ/H395dj4F2WmJjIuXPniIyMpGjRoh6txWw288ADD7B69Wq+//57mjVrJt8HIbyclptJI+rUqaN27tx5B8sR+ZHrJOPayUJu1Ap27f/Z7XbS09NJSUlxBzZXi5wriOXk5JCdnf23wGiz2dwtgP/E6XRy6dIlBg8ezH333Xc3PgYhxF2klCIhIYGPPvqIOXPmkJSURFRUFIMGDeL555/H19f3tp/Eui5UHT9+nAULFrBo0SJOnjyJXq+nVq1a9OrVi3bt2hEaGion0B6yevVqWrVqRdOmTVm+fDkGQ66vH99We/fupXHjxgQHB/Pjjz9SoUIF+W4I4QU0TdullKrz1/s9u8cRXuHasVK3cyD5P4X+m70Y4HQ679lZw4QQeaNpGkWKFGH8+PF06tSJDz/8kO+//55BgwaxceNGevfuTd26dQkKCsrV+Nq/cvUWSE5OZuvWrcyfP59169aRlJREQEAAjz/+OD179uSRRx4hKChITpo97ODBgzgcDqpXr35P7P8rVapEixYtWLBgAUOGDGHGjBkUKlQITdPcQT0nJ4fLly9z7tw5kpKSKFSoEPfdd597DSwhRP4hwUrcs/7pgJKbCUiEEN7LNT63Vq1afPnll6xatYrhw4ezdOlSVq1aRVRUFPfffz+tW7fmvvvuo1ChQteN7b0RVwu81Wrl/Pnz7N+/n/Xr17Nu3Tri4uKwWq0UKVKEXr160b17d2rXro2Pj4+cAN8jUlNT0el0VKlS5Z7YJiaTibFjx3LgwAFWrlzJo48+SsWKFTEYDNhsNpKTkzl//jxJSUnurvZms5k2bdowZcoUwsLCPP0WhBC5IMFKCCFEvuYae9W2bVtq167NggULWLZsGUeOHOHLL79k7ty5lCxZkgoVKlCrVi3q1KlD9erV3UHLFaTOnTvHgQMH2LlzJ9u2bePkyZNcvXoVpRSBgYHUrVuXtm3b0qZNG0qXLi2TUtyjfHx83GttepqmaURFRfHZZ5/x+uuvs3v3bk6cOOH+P9esxREREURFRREeHs7mzZtZtGgRNWrU4LXXXpOLhELkIzLGSgghhFdRSpGRkUFcXBwbNmxg2bJlHDx4kJSUFBwOBwaDgeDgYCIiIggICMBut5OcnExiYiJZWVk4nU5MJhPFixenSpUqNGrUiIcffpjKlStL96x73KhRo5gzZw47duygSJEini7HzfWdPHbsGJcuXcLpdGI0GgkMDKRo0aKEhoa6Z8Fds2YNHTp0ICYmhg0bNhAYGOjp8oUQfyFjrIQQQhQImqYRGBhI1apVqVq1Kn369OHixYscP36cXbt2sX37dvbt28elS5c4f/68e3xo2bJlqVq1KrVq1aJevXpER0cTHh7ubjGQQJU/REVF3XNhxPWdrF279n8+tkGDBhQrVoy4uDgSExPvufcihPhnEqyEEEJ4JVcQ8vX1pWzZspQtW5amTZu6F3FPSkrCYrGg1+sJCAigUKFCuV5MXtx7IiMj8fHx8XQZt8y1JmhiYiKZmZmeLkcIkQsSrIQQQhQYrsXsw8PDPb6ArLgzQkNDZVySEMIjZM8jhBBCCK/hms5cCCHuNglWQgghhPAa/v7+ni5BCFFASbASQgghhNdIT0/3dAlCiAJKgpUQQgghvEZSUhK5WUrmXqOUwmazude5EkLkHxKshBBCCOE1rl69isPh8HQZt8zpdJKZmYlOp8PPz8/T5QghckGClRBCCCG8xvnz58nKyvJ0GbfMYrGQmZmJj4+PBCsh8hkJVkIIIYTwGmfPniUjI8PTZdyylJQUcnJyCAsLw2g0erocIUQuSLASQgghhNdISUnh6NGj+XacVUJCAtnZ2RQpUgSTyeTpcoQQuSDBSgghhBBewcfHh5ycHDZt2uTpUm6JUoqzZ89isVgoVaoUZrPZ0yUJIXJBgpUQQgghvEK9evUwGo2sX78ei8Xi6XJuybFjx1BKER0dLQsdC5HPSLASQgghhFeoXr06pUuXZs+ePRw4cMDT5eSaUor9+/ej0+moXLmyBCsh8hkJVkIIIYTwCoULF6ZVq1akp6ezZMkSnE6np0vKleTkZA4fPkxwcDCVKlXydDlCiFySYCWEEEIIr9GhQweCgoJYunQpFy9ezFeTWBw9epT4+HiioqIoUaKEp8sRQuSSBCshhBBCeAVN06hWrRqNGzcmLi6Ob7/91tMl3TSn08maNWuwWCw8+uijsoaVEPmQBCshhBBCeA0fHx9eeOEFDAYDc+bMISUlxdMl3ZSMjAxWrVqFj48Pjz/+uIyvEiIfkmAlhBBCCK+haRoPP/wwdevWZf/+/axatSpfdAfctWsXBw8epEqVKtSqVUuClRD5kAQrIYQQQngVf39/evXqhaZpzJw5k/T0dE+X9K+cTifffPMNFouF1q1bExwc7OmShBC3QIKVEEIIIbyKpmm0bNmSqlWrsnXrVr7//vt7utXq0qVLrFmzhpCQEFq1aiWtVULkUxKshBBCCOF1wsLC6N+/P06nk4kTJ3LlyhVPl3RDSil+/PFH4uPjqV+/PhUrVvR0SUKIWyTBSgghhBBeR9M0WrduzYMPPsju3buZMGECVqvV02X9jcViYd68eQB07twZs9ns4YqEELdKgpUQQgghvFJQUBDjx48nMjKSzz77jMWLF99ziwZv3bqVLVu2EB0dTbNmzaQboBD5mAQrIYQQQnglTdOoW7cuo0aNwuFwMHToUH7++ed7ZryVxWJh6tSpZGdn07VrVwoXLuzpkoQQeSDBSgghhBBeS6fT8fzzz9OvXz+uXLnCCy+8wPbt2z0erpRSrFy5kjVr1lC2bFm6desmrVVC5HMSrIQQQgjh1cxmMyNHjqRLly6cPn2a559/nv3793ssXCmlOHXqFCNHjsRmszFgwABKlCjhkVqEELePBCshhBBCeL3AwEA+/vhj2rdvz+HDh+nTpw/nz5/3SLjKyspi1KhRHDt2jNatW9OjRw90OjklEyK/k99iIYQQQng9TdMIDQ1l8uTJNGnShG3btjFw4EBSU1PvarhyOp3873//49tvv6V8+fKMGzcOPz+/u/b6Qog7R4KVEEIIIQoETdOIiIhgypQpVKlShWXLlvHBBx9gt9vvyusrpfjtt994++23MRgMjB07lujoaBlbJYSXkGAlhBBCiAJD0zQqVqzIp59+6m7BWrt27R1vtVJKcfjwYV5++WWuXr3Kyy+/TOvWrSVUCeFFJFgJIYQQokDRNI1GjRoxdOhQLBYLI0eOJC4u7o6FK6UUJ0+epGfPnhw6dIhWrVoxcuRIjEbjHXk9IYRnSLASQgghRIGj0+l44YUXaNGiBfv376d///5cuXLltocrpRS7d++mU6dObNu2jUcffZTJkycTHBwsrVVCeBkJVkIIIYQocDRNIygoiI8++ojq1auzatUqXnjhhds6U6DT6WTNmjV06NCBP/74g6ZNmzJr1iyKFSsmoUoILyTBSgghhBAFkqZplC1bljlz5lC1alW+//57OnTowPbt23E6nbf8vEopLBYL//vf/+jevTtnz57l2WefZe7cuZQsWVJClRBeSoKVEEIIIQosTdOoVq0aCxcupEmTJmzfvp02bdrwwQcfcOHCBRwOx023YCmlsNlsHDx4kJdeeom+ffuSmZnJsGHDmDx5MoULF5ZQJYQX03LT3F2nTh21c+fOO1iOEEIIIcTdp5QiMTGRd999l1mzZpGVlUXZsmVp0aIFrVq1olq1aoSGhv7jQr4pKSns2rWLr7/+mh9++IErV65QunRp3nrrLTp16oTZbL7L70gIcadomrZLKVXnb/dLsBJCCCGE+P8tTps2bWLixIn89ttvpKenYzabKVWqFJUrVyY2NpaiRYtiNpvRNI3MzEz27dvH9u3bOX78OFarlfDwcNq1a8err75KpUqV/jGMCSHyJwlWQgghhBA3QSlFdnY2hw4d4vvvv2f16tWcOHGClJSUf+wW6O/vT6VKlWjbti3t2rWjfPnyGAyGu1y5EOJukGAlhBBCCJELrnOk7Oxs4uPjOX78OIcPH+bq1atYrVaUUphMJipVqkT16tWpWLEi/v7+Mo5KCC8nwUoIIYQQQggh8uifgpV0+hVCCCGEEEKIPJJgJYQQQgghhBB5JMFKCCGEEEIIIfJIgpUQQgghhBBC5JEEKyGEEEIIIYTIIwlWQgghhBBCCJFHEqyEEEIIIYQQIo8kWAkhhBBCCCFEHkmwEkIIIYQQQog8kmAlhBBCCCGEEHkkwUoIIYQQQggh8kiClRBCCCGEEELkkQQrIYQQQgghhMgjCVZCCCGEEEIIkUcSrIQQQgghhBAijyRYCSGEEEIIIUQeSbASQgghhBBCiDySYCWEEEIIIYQQeSTBSgghhBBCCCHySIKVEEIIIYQQQuSRBCshhBBCCCGEyCMJVkIIIYQQQgiRRxKshBBCCCGEECKPJFgJIYQQQgghRB5JsBJCCCGEEEKIPJJgJYQQQgghhBB5JMFKCCGEEEIIIfJIgpUQQgghhBBC5JEEKyGEEEIIIYTIIwlWQgghhBBCCJFHEqyEEEIIIYQQIo8kWAkhhBBCCCFEHkmwEkIIIYQQQog80pRSN/9gTbsCnLlz5QghhBBCCCHEPa20UqrwX+/MVbASQgghhBBCCPF30hVQCCGEEEIIIfJIgpUQQgghhBBC5JEEKyGEEEIIIYTIIwlWQgghhBBCCJFHEqyEEEIIIYQQIo8kWAkhhBBCCCFEHkmwEkIIIYQQQog8kmAlhBBCCCGEEHkkwUoIIYQQQggh8uj/AcfKGOs5FPhoAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 1080x1080 with 1 Axes>\"\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 1080x1080 with 1 Axes>\"\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 1080x1080 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"cnt = 0 \\n\",\n    \"for word in ['الله', 'نور', 'على', 'محمد', 'الرحمن']:\\n\",\n    \"    if len(word_drawings[word]) > 10:\\n\",\n    \"        plt.figure(figsize = (15,15))\\n\",\n    \"        plt.imshow(concatenate(word_drawings[word][:6]))\\n\",\n    \"        plt.xticks([])\\n\",\n    \"        plt.yticks([])\\n\",\n    \"        plt.show()\\n\",\n    \"        cnt += 1\\n\",\n    \"    if cnt >= 5:\\n\",\n    \"        break\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 224,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"npy_files = glob.glob('server/larger_data/*')\\n\",\n    \"\\n\",\n    \"char_drawings = defaultdict(lambda  :[]) \\n\",\n    \"\\n\",\n    \"for file in npy_files:\\n\",\n    \"    drawings = generate_characters(file)\\n\",\n    \"    images = []\\n\",\n    \"    for i, comp in enumerate(drawings):\\n\",\n    \"        char, drawing = list(comp.items())[0]\\n\",\n    \"        img, res = draw_strokes(convert_3d(drawing, threshold=50), square = True, return_res = True, stroke_width = 5)\\n\",\n    \"#         if res[0] > 100 and res[1] > 100:\\n\",\n    \"        char_drawings[char].append(img)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 225,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"imgs = []\\n\",\n    \"for char in char_drawings:\\n\",\n    \"    img = concatenate(char_drawings[char][:20],)\\n\",\n    \"    imgs.append(img)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 226,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.image.AxesImage at 0x7f1f399952e8>\"\n      ]\n     },\n     \"execution_count\": 226,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 2880x2160 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize = (40,30))\\n\",\n    \"plt.xticks([])\\n\",\n    \"plt.yticks([])\\n\",\n    \"plt.imshow(concatenate(imgs,'v',margin=5))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'ء'\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"list(char_drawings.keys())[-1]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"npy_files = glob.glob('server/larger_data/*')\\n\",\n    \"\\n\",\n    \"images = []\\n\",\n    \"for file in npy_files:\\n\",\n    \"    if 'بسم الله الرحمن الرحيم' in file:\\n\",\n    \"        drawing = json.load(open(file))\\n\",\n    \"        img, res = draw_strokes(convert_3d(drawing), square = True, return_res = True, stroke_width = 5)\\n\",\n    \"        images.append(img)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def map_axes(i, num_sketches = 16):\\n\",\n    \"    sqrt = int(math.sqrt(num_sketches))\\n\",\n    \"    x = np.array(list(range(num_sketches))).reshape((sqrt, sqrt)) \\n\",\n    \"    dim1, dim2 = np.where(x == i)\\n\",\n    \"    return (int(dim1), int(dim2))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1080x1080 with 16 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, axes = plt.subplots(4, 4, figsize=(15,15))\\n\",\n    \"indices = [0, 1, 17, 3, 4, 5, 6, 7, 8 , 9, 10, 11, 12 , 13, 14, 15]\\n\",\n    \"for i, j in enumerate(indices):\\n\",\n    \"    ax = axes[map_axes(i, num_sketches = 16)]\\n\",\n    \"    ax.imshow(images[j])\\n\",\n    \"    ax.set_xticks([])\\n\",\n    \"    ax.set_yticks([])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'.': [[332, 124]]}\\n\",\n      \"{'ح': [[331, 150], [336, 149], [354, 169], [318, 169]]}\\n\",\n      \"{'ا': [[321, 170], [315, 169], [308, 162], [303, 124]]}\\n\",\n      \"{'ل': [[286, 119], [286, 157], [282, 163], [268, 163]]}\\n\",\n      \"{'د': [[250, 144], [260, 156], [264, 172], [259, 176], [244, 176], [238, 171]]}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import pprint\\n\",\n    \"\\n\",\n    \"drawing = json.load(open('server/larger_data/22خالد.json'))\\n\",\n    \"drawing = apply_rdb(drawing)\\n\",\n    \"for stroke in drawing:\\n\",\n    \"    pprint.pprint(stroke)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<PIL.Image.Image image mode=RGBA size=256x256 at 0x7F5AC1F764A8>\"\n      ]\n     },\n     \"execution_count\": 38,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"draw_strokes(convert_3d(drawing), square = True, return_res = True, stroke_width = 5)[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 131,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"number of words  3026\\n\",\n      \"number of characters  16777\\n\",\n      \"number of strokes  24861\\n\",\n      \"number of sentences 1414\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"num_chars = 0 \\n\",\n    \"num_words = 0 \\n\",\n    \"num_strks = 0 \\n\",\n    \"num_sents = 0 \\n\",\n    \"\\n\",\n    \"for file in glob.glob('server/larger_data/*')+glob.glob('server/data/*'):\\n\",\n    \"    text = re.sub('[0-9]', '', file.split('/')[-1][:-5])\\n\",\n    \"    for char in text:\\n\",\n    \"        if char in map_chars:\\n\",\n    \"            num_strks += len(map_chars[char])\\n\",\n    \"        else:\\n\",\n    \"            num_strks += 1\\n\",\n    \"            \\n\",\n    \"    num_chars += len(text.replace(' ', ''))\\n\",\n    \"    num_words += len(text.split(' '))\\n\",\n    \"    num_sents += 1\\n\",\n    \"\\n\",\n    \"print('number of words ', num_words)\\n\",\n    \"print('number of characters ', num_chars)\\n\",\n    \"print('number of strokes ', num_strks)\\n\",\n    \"print('number of sentences', num_sents)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 309,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"already removed\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"for file in wrong_annotations:\\n\",\n    \"    file_name = file.split('/')[-1][:-5]\\n\",\n    \"    try:\\n\",\n    \"        os.remove(file)  \\n\",\n    \"        shutil.move(f'server/static/processed_larger_images/{file_name}.jpg', f'server/static/larger_images/{file_name}.jpg')\\n\",\n    \"    except:\\n\",\n    \"        print('already removed')\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\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\": \"3.6.9\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "notebooks/Generate Characters and words.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \\n\",\n      \"The text.latex.preview rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\\n\",\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \\n\",\n      \"The mathtext.fallback_to_cm rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\\n\",\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: Support for setting the 'mathtext.fallback_to_cm' rcParam is deprecated since 3.3 and will be removed two minor releases later; use 'mathtext.fallback : 'cm' instead.\\n\",\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \\n\",\n      \"The validate_bool_maybe_none function was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\\n\",\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \\n\",\n      \"The savefig.jpeg_quality rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\\n\",\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \\n\",\n      \"The keymap.all_axes rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\\n\",\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \\n\",\n      \"The animation.avconv_path rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\\n\",\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \\n\",\n      \"The animation.avconv_args rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import math, json\\n\",\n    \"from rdp import rdp\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from matplotlib import animation\\n\",\n    \"from IPython.display import HTML\\n\",\n    \"import tqdm.notebook as tq\\n\",\n    \"import pickle\\n\",\n    \"from collections import defaultdict\\n\",\n    \"import cairosvg\\n\",\n    \"from PIL import Image,ImageDraw\\n\",\n    \"import glob\\n\",\n    \"import os \\n\",\n    \"import re\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def get_bounds(data):\\n\",\n    \"    minx, miny = 600, 600  \\n\",\n    \"    maxx, maxy = 0, 0\\n\",\n    \"    \\n\",\n    \"    for i, (x, y, z) in enumerate(data): \\n\",\n    \"        if minx > x:\\n\",\n    \"            minx = x\\n\",\n    \"        if miny > y:\\n\",\n    \"            miny = y \\n\",\n    \"\\n\",\n    \"        if maxx < x:\\n\",\n    \"            maxx = x\\n\",\n    \"        if maxy < y:\\n\",\n    \"            maxy = y \\n\",\n    \"    return minx, maxx, miny, maxy\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def convert_3d(drawing, return_flag = False, threshold=50):\\n\",\n    \"    out = []\\n\",\n    \"    corrupted = False\\n\",\n    \"    for item in drawing:\\n\",\n    \"        char = list(item.keys())[0]\\n\",\n    \"        stroke = item[char]\\n\",\n    \"        if len(stroke) == 1:\\n\",\n    \"            x, y = stroke[0]\\n\",\n    \"            out.append([x, y, 0])\\n\",\n    \"            out.append([x+5, y+5, 1])\\n\",\n    \"            continue\\n\",\n    \"        segment = []\\n\",\n    \"        for i, point in enumerate(stroke):\\n\",\n    \"            x, y = point \\n\",\n    \"            if i == len(stroke) - 1:\\n\",\n    \"                segment.append([x, y, 1])\\n\",\n    \"            else:\\n\",\n    \"                segment.append([x, y, 0])\\n\",\n    \"        \\n\",\n    \"        start = 0 \\n\",\n    \"        for i, point in enumerate(segment):\\n\",\n    \"            if i < len(segment) -1:\\n\",\n    \"                x, y, _ = point\\n\",\n    \"                next_x, next_y, _ = segment[i+1]\\n\",\n    \"                if any((\\n\",\n    \"                    abs(x-next_x)>threshold,\\n\",\n    \"                    abs(y-next_y>threshold),\\n\",\n    \"                )):\\n\",\n    \"                    corrupted=True\\n\",\n    \"                    start = i +1\\n\",\n    \"        \\n\",\n    \"        out += segment[start:]\\n\",\n    \"    if return_flag:\\n\",\n    \"        return out, corrupted\\n\",\n    \"    return out\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def make_square(im, min_size=256, fill_color=(255, 255, 255)):\\n\",\n    \"    x, y = im.size\\n\",\n    \"    size = max(min_size, x, y)\\n\",\n    \"    new_im = Image.new('RGBA', (size, size), fill_color)\\n\",\n    \"    new_im.paste(im, (int((size - x) / 2), int((size - y) / 2)))\\n\",\n    \"    return new_im\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def draw_strokes(data, factor=1, svg_filename = 'tmp/sample.svg', stroke_width = 3, square = False, return_res = False):\\n\",\n    \"    min_x, max_x, min_y, max_y = get_bounds(data)\\n\",\n    \"    dims = (50 + max_x - min_x, 50 + max_y - min_y)\\n\",\n    \"    dwg = svgwrite.Drawing(svg_filename, size = dims)\\n\",\n    \"    dwg.add(dwg.rect(insert=(0, 0), size=dims,fill='white'))\\n\",\n    \"    lift_pen = 1\\n\",\n    \"    abs_x = 25 - min_x \\n\",\n    \"    abs_y = 25 - min_y\\n\",\n    \"    p = \\\"M%s,%s \\\" % (abs_x, abs_y)\\n\",\n    \"    command = \\\"M\\\"\\n\",\n    \"    for i in range(len(data)):\\n\",\n    \"        if (lift_pen == 1):\\n\",\n    \"            command = \\\"M\\\"\\n\",\n    \"        elif (command != \\\"L\\\"):\\n\",\n    \"            command = \\\"L\\\"\\n\",\n    \"        else:\\n\",\n    \"            command = \\\"\\\"\\n\",\n    \"        x = float(data[i][0]) - min_x\\n\",\n    \"        y = float(data[i][1]) - min_y\\n\",\n    \"        lift_pen = data[i][2]\\n\",\n    \"        p += command+str(x)+\\\" \\\"+str(y)+\\\" \\\"\\n\",\n    \"    the_color = \\\"black\\\"\\n\",\n    \"    \\n\",\n    \"    dwg.add(dwg.path(p).stroke(the_color,stroke_width).fill(\\\"none\\\"))\\n\",\n    \"    dwg.save()\\n\",\n    \"    cairosvg.svg2png(url=\\\"tmp/sample.svg\\\", write_to=\\\"tmp/sample.png\\\")\\n\",\n    \"    img = Image.open('tmp/sample.png')\\n\",\n    \"    if square:\\n\",\n    \"        img = make_square(img)\\n\",\n    \"    if return_res:\\n\",\n    \"        return img, dims \\n\",\n    \"    else:\\n\",\n    \"        return img\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"npy_files = glob.glob('server/larger_data/*')\\n\",\n    \"file = np.random.choice(npy_files)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"file = 'server/larger_data/1فسبحان الله حين تمسون وحين تصبحون.json'\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 150,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"server/larger_data/وكلهم اتيه يوم القيامة فردا.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/2محمد.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/2صلى الله عليه وسلم.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/قال إني اعلم مالا تعلمون.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/1رمضان كريم.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/2الله أكبر.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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CxoqL614274IILNFHZv3+/R9p2KgICArjuuuswGAxIKVm6dClms7lJ29CSaayonKpe8nLgZucqUD+gtM406ZzFZrPx1VdfUVBQgBCCG264QTMWNgar1crnn39OYWEhAFdddRUnjvbqCown78KtWrVCCIHdbqe4uLje54qPj9dSIGRlZXlU+E7GoEGDtP/Btm3b2Lt3rxqtuIn6LCl/AvwOdBRCZAshpnHqeskrgUPAAeA/wF0eaXUzo7KyUoscjoyM5Oqrr9YMqo2huLiYpUuXYrPZaN26NRMmTPjT8eqKiic9R0NCQrSo45KSknpnVwsODtZWgMrKyjSBbCratGnDiBEjEEKQm5urxUcpzp76rP5MklLGSCmNUso4KeW8U9VLdq763C2lPE9KeaGUsvEOGC2I7du3a8bITp060aNHj0YbTqWU/PLLL5qBtlevXic19oaHh2vTi6NHj3rkLiyEIDg4WJvGFRcX11tUDAaDtvxdUVFBbm7TDmgNBgOXXXYZgYGBmsFWue27B+VR62FcwX6u5d0xY8acVfyNxWJh+fLlWCwWhBBcd911WlrHukRHR2vnyc7O9ljGsxNFpb62CT8/Pzp06AA4vHEPHz7cpNMPIQT9+vXTVq/++OMPj6dhOFdQouJhqqur2bp1KzabjcDAQPr27XtWxzty5IhmoI2Li2PgwIEn3c9kMmlG1KKiogYZURtCSEiIJioNmf4YjUZSUlLQ6XTU1NRw5MgRj7TvdJhMJi644ALAMQVr6qXtlooSFQ9TXFzMzp2OCIf4+HgtsVFj2bFjB+np6YDDG/dUx9Pr9Zohsrq62mMrLEFBQY0aqQghMJlMmi2ovLy8yQ2ler2ejh07Ao7vqClyu5wLKFHxMIcPH9ZEoH379lqy6MYgpWTlypVaFrfLLrvspFMfcFwwF198MeAwFG/ZssUjF23d6U9paWmDlmaDgoI0UamoqPCKqLg8ey0WS5Mbi1sqSlQ8zC+//KItl15yySXaSkljyMnJ0aY+YWFhDB069JSjHiEEvXr1Ijg4GJvNxsaNGz0SDWw0GjW7hNlsblAcj5+fn+b8V1NT0+Si4jI0g2OFTNUFcg9KVDyI2Wzmt99+AxyrDYMGDWr0saSU/Pzzz1og4uDBg0876hFCcMEFFxAT4wi92rBhg0cC9+pGKgP1LtUBjimHa8UlKCioyWsbSyk1wRdCnNUyv+J/KFHxINnZ2ezbtw9wJCY6//zzG30sq9XKokWLsFgsBAQEcO21157RIzcuLo4uXboAjlGOp/KHuERFSlnvpNJ2u53s7GzMZjNCCKKjo5u8vrHdbtfaazAYGh3YqTgeJSoeQkrJgQMHyMzMBKBHjx5anExj2L9/P1u3bgUcBt/Ro0ef8c5uMBiYOHEiBoMBq9XKvHnzPGI3iIqK0tpS3+BQi8XCrl27kFISHBxMUFAQlZWVTToFMpvNWs6Z4OBgtyYFP5dRouJBfvnlF8xmMwaDgf79+zc6/YDdbuenn37SloVHjx6t2THOxPDhw7VM8hs2bGDBggVkZGS49eKNjIzUntdXtGpqatiyZQvgMJi+9dZb/O1vf2vSFZitW7eye/duwGFE91TOmXMNJSoewmKx8MMPPwCOAlpDhgxptM2gurqa1atXaxUHR4wYUe/5f5s2bZg2bRpGoxGr1cpjjz3GX/7yFw4fPtyotpyMugJXX7vN3r17tTaUlpaydetWFi1axH333ecxD+C6mM1mlixZQnl5OUIIxo8f77GcM+caSlQ8xN69e7Wl5PPOO0/zHm0M2dnZ/PLLL4Bj6tOnT596C5QQgiuvvJKePXsCDrHbvHmzVnTMHZhMJq09JSUlZxQEs9nMggULtFSOgwYNIjIyEikly5cv5+233/aoy7yUku3bt/Ppp58ipSQmJoaxY8c2uaG4paJExQNIKdm9e7c2XRk8eHCj74JSSr799lvNhXzIkCFaIF59iYiI4LbbbtN8Wmw2G59++qnbvGxDQkK0OKOKiorThgRIKdmxYwdLly5FSkl0dDQvv/wyr7/+urb8/d5777FhwwaPjVZKS0t5/vnnNSPtFVdcQZcuXZSouAklKh5ASkl6ejoWiwW9Xk+XLl0anSipqqqKH374AZvNhp+fH+PGjWvwMXQ6Hddccw19+vTRpirp6emaTeNsMRgMmv+NxWI5bXrGqqoq/v3vf2tJsq+//np69uzJ+PHjmTJlCjqdjry8PGbPnu2R8qR2u50FCxbwzTffAJCSksL999+vpWFQnD1KVDyA1WrV/EmCgoJISEho9F0wJydHu/jPP/98Onfu3KhjhYeH8+GHHzJz5kwAamtrWbFihVumQHq9XhsF1dbWnlJUpJQsW7aML7/8EnAkq7rtttswGo0EBgby4IMP0rVrV8CRlPrpp5+udza5+mC321mzZg0vvfQSFouFwMBAHnnkEbXq42aUqHgAq9WqGSGDgoKIi4tr9LF+//13LYdqr169Gl22VKfTkZyczJVXXklUVBRSSn799dcGOaud7tiukcqpREVKyaFDh3j++eepqKjAz8+P+++//7iC8YmJiTz77LNERkZis9mYO3cu9913H/v378dqtTZ6OiSlxGw2s2LFCu68805ycnLQ6XRcd911TJw4UTm9uRklKh7AZrNpUbeBgYHHeZw2hNraWq0+sl6v58orrzyrfLNCCJKTkzWj7Z49e9ixY8dZ2y5OHKmcbPRTVVXFU089pZUNGTJkCNOmTTuuP0IIxowZwyOPPEJAQABWq5WPP/6YUaNG8cwzz7Br164GiYurtGxqaioPPvggkydP1iKRBw4cyNNPP60c3jyBlNLrj169esmWRF5enmzVqpUEZNeuXaXFYmnwMex2u0xNTZVxcXESkO3bt5dZWVln3Ta73S5ff/11qdPpJCAnTJggKyoqzuqYubm5smvXrhKQXbp0kZmZmce9bzab5Zw5c2RAQIAEZHx8vPz999+l3W4/6fGqqqrkm2++KWNiYlxJ1aVOp5Nt27aVN9xwg1yxYoU8duzYKT9vt9tlaWmpXL9+vXzwwQdlu3btpBBCO86oUaPk7t27T/l5xalxXqunvZ49l2b9HKa4uFi7W0dGRjZqeC2l5LvvvuPIkSMIIRg5cuSf8tA2BtcS8+uvv86hQ4dYtWoVq1evZty4cY22++j1em11y2w2HxeYZ7Va+frrr/nnP/9JTU0NgYGBPPzww6ddFg8MDOSOO+6gd+/evPbaa6xZs4bCwkLy8/P573//y1dffUXnzp3p1asXKSkp2rltNhv5+fkcPHiQgwcPkp6ernn4CiGIjY1l4sSJzJw586yixRVn4Eyq0xSPljZSWb9+vQwJCZGAvPrqq6XNZmvwMcrKymTv3r0lIENDQ+X333/vtjur1WqVr7zyivTz85OA7Nevnzx69Gijj19UVCSHDBkiAZmQkCB37dolpZSytrZWLlmyRMbGxkpA6vV6+be//a3eIyO73S7NZrP87bff5IwZM2RCQoI0Go3a6KU+D51OJyMjI+Xf/vY3uW3bNmm1WtUI5Syoz0jljBc8MB9HCY6ddbY9iaP0xh/Ox+V13nsUR+LrfcCoMx1ftkBR+eGHH2RQUJAE5M0339xgUbHb7XLFihUyNDRUArJ///5nPUU5kaNHj8q+fftKQBqNRvnUU09Js9ncqGOVlpbKMWPGSEC2bdtWbtu2TdbW1spPP/1UtmnTRru4L7/8cpmXl9fg49vtdllbWyszMjLkv/71Lzl48GAZGBgodTqd1Ol0UgihPXQ6ndTr9TIsLEz26tVLPvzww3Ljxo3SYrEoMXED7pr+fAi8CSw4YftrUsqX624QQnQGJgJdgFjgeyFEBymlZxKk+iiyjiGxMVMKi8XCl19+SXl5OQaDgRtuuMHtLuSRkZE89thj3HzzzZSWlvLaa6+RlJTExIkTG5xD12AwaHlJzGYzOTk5rF69mjlz5lBQUKAZmf/97383ymgthMBgMJCQkMD06dOZPHkyubm57Ny5k9zcXC0xlE6no3Xr1iQmJpKYmIjJZMJkMnm0mJriz5zx25ZSrhVCJNXzeOOAxVJKM5AuhDgA9MFR4uOcQa/Xn1Uhr7y8PM05q127dowcOdLtaQGEEIwaNYqbb76Zt99+m5KSEmbMmMHBgwd54IEHCAsLq7cg+vn5aU515eXlzJgxg6ysLCwWCzqdjquuuop33nnnuGjmxqLT6QgPDyc8PFzLL6vwLc7ml3qPECJVCDFfCOGKKKt3LeWWjMlk0kSgoa7wdrudzz77TPMfGThw4FnlYTkdfn5+PP7440yYMAGDwUBJSQkvvPACs2bNorCw8IxLt1JKLBYLx44d04yzNpuNgwcPYrFYCA4O5pZbbuHNN990i6AomgeNHRe+AzyDwxj2DPAKcEtDDiCEuB24HSAhIaGRzfBN6q745ObmYrVa6z2lyMvLY9GiRdhsNoKCgvjrX//qsYtRCEFkZCRvv/02CQkJvP3221RVVfH++++ze/du7rzzTi6//HJCQ0O1Nkin78exY8dYt24dP/74I99///1xibV1Oh0XXnghjz76KFdddRUBAQHnrKDUtTXodLpz4ntolKhIKTU3TCHEf4AVzpf1rqUspZwLzAVwrnK0GEwmE/Hx8ZSUlFBQUEBWVla9ypxKKfn000/Zvn07AAMGDKBv374e/SEKIWjdujVPP/00cXFxPPnkk5SUlLB27Vo2bdpE586dGTVqFOeffz5+fn5kZmayefNmNm/eTGFhoRZpXJeRI0fy/vvvExMT0+TZ3LyN1WqlqKiI/Px8jh49Sm5uLocOHaKoqIgZM2aQnJzs7SZ6nEaJihAiRv6vRvLVwE7n8+XAf4UQr+Iw1KYAG8+6lc0MPz8/evTowY4dOygqKmLLli20b9/+tOIgpSQtLY3//Oc/2O12AgICuOuuuwgPD2+SNgcGBvK3v/2NuLg4XnzxRbZv3051dTVbtmw5beChXq+nbdu2REdHk5qaqo3KziVBsdlsrFq1iqVLl3Lo0CGOHTtGaWkppaWlWpUAk8nEFVdcoUQFtFrKQ4FIIUQ28AQwVAjRA8f05zBwB4CUcpcQYimwG7ACd59rKz/gEJVhw4axZMkSqqur+fbbbxk3btwpy2lIKSkvL+eJJ57QMpGNGDGC4cOHN+lw2c/Pj2uuuYaBAweycuVK5s+fz86dO6mqqqK2ttaxXGgwEBgYiMlkom/fvowaNYq+ffsSFhZGz549KSkpISsrSysj0tKRUvLbb79x5513aqlDT8RVNeDYsWNIKVv8FKg+qz+TTrJ53mn2fw547mwa1dwRQjB48GCio6PJzMzk22+/JTs7+5SjFbPZzD//+U+++OILpJTExsby2GOPERoa6pW2t23blilTpvCXv/yF1NRUdu3aRX5+PrW1tURGRpKSkkL37t1p1aoVBoMBIQRlZWW0atWKkpIS7S7tqpDYUpFScuTIER566CFNUAICAmjbti0JCQna0rbr4Yq5avGcyZGlKR4tzflNSofX6uTJkzXHr5kzZ8qamprj9rHb7dJiscj33ntP88ANDQ2V8+bNk1ar1UstbxyVlZVy8ODBEpAxMTFy9+7d3m6Sx6mtrZVPPfWU1Ov1EpC9evWSmzdvlnl5ebKkpERWV1c3ypval6mP81vLH596CZ1Ox6233kqbNm2w2+3MmzePr7/+WptG1NbWcvDgQf7+97/zyCOPaOkA7rvvPm688cZmF46v1+u1FA81NTVuSang69TU1LBixQpsNhsRERE899xzXHTRRbRt2xaTyURAQMA5MQU8EeVq6CGEEPTp04ebb76Z1157jZKSEu666y5+/vlnTCYTaWlpbNu2jQMHDiClRK/XM2nSJGbOnHlK24svo9PpiI6OBhzTOXcmV/JVCgsLtWRcCQkJDB48uMXbS+qDEhUP4ufnx0MPPcTOnTtZvXo1BQUFvPXWW8Dxrvzh4eFMnTqVRx99lLCwMG8196zQ6XSYTCbAkVOloqLCyy3yPBUVFZrHdHh4eL18kVxThKqqKnJycsjPz8dms2EymejYsaMW7tCcUaLiYaKiopg3bx5PPfUUixcvpry8HHBchO3ataN3797cfffdDBw4ED8/v2Z7p6srKlarVVtKba79qQ9t2rQhPDyciooKdu/ezTfffMPo0aNPOXWVUpKVlcXy5ctZsGABhw4d0pJOtW3bliVLlnDRRRc1cS/cjxIVD+PK4/Gvf/2LyZMnk5aWhl6vp127diQkJBATE+OVOsKeIDQ0FL1ej81mo7S0tMWLSkREBBMmTOCNN96goKCAW265hT59+tC9e3fi4uIwGo2au8CxY8coKCjgp59+Ij09/U8xYYGBgaetQtCcUKLSBAghCAoKYsCAAQwYMMDbzfEIQgitVIfNZqOsrKzF+6oYjUbuu+8+UlNT+fnnnykoKOD//u//+L//+78GHSc8PJyrr76a9u3be6ilTYsSFYXb8PPz04b+ZrP5jAGJzR0hBImJicybN4/XX3+d5cuXk5ubi81mw263n3TkYTQaMRgMhISE0KlTJy6++GKuuOIK+vfv3ywN9CdDiYrCbbTkqc6pEEKQlJTESy+9xKxZs9i3bx/p6ens3LmTDz/8UKtvJISgR48eTJ8+nT59+hAZGUlgYCCBgYGaA2FLQYmKQnGWCCHw8/MjOjqa6Ohoevfuze+//67VlQ4MDGTKlCk8+uijxMXFtSgBORlKVBRNgsvhLzc3l23btrF+/XqysrKorKzEaDTSunVrunfvzvjx42nbtm2ztcXYbDYWLlzIxx9/jM1mIzw8nH/84x/cfvvtLcYgfyaUqCjcxok2FCGEJiapqaksXLiQr776iqysrLo5kDV0Oh0ffvghDzzwAFdeeSWBgYHN6iKUUrJ161aeffZZqqqq8Pf35/HHH+eee+45p8qqKlFRuAUpJZWVlVp1QpcT186dO3nvvfdYvHgxRUVFpzXe2u12Nm7cyLRp0xg5ciQzZ86kb9++x6Xn9GUsFgtz584lJycHIQTXXHMNt99++zklKKBEReFGCgoKNFGpqqpi9uzZfPLJJ9rIBCA2NpYBAwYwfPhwLrzwQkJDQzGbzezdu5e5c+eyYcMGKisr+fLLL/n111+ZOHEid955J506dfL5KVFmZiZff/01APHx8cyePbtFeMg2FCUqCrfgKuTlYu7cucd51boKed1444107doVo9F43Oijd+/ejB49miVLlvDmm29y4MABCgoKeOONN9i2bRtLly4lJibGG12rF3a7naVLl1JYWAjAqFGj6NSpU7MYYbkb35Z+RbNASkcB9LpJisrLy5FSEhERwa233srKlSt56aWX6Nmz50nDEVz5cu+++25WrlzJ/fffT2RkJDqdjv79+9O2bdum7laDOHbsGF9//TU2m42wsDAmTZp0UkGx2Wzs2bOnZfvxnCk3QlM8WmI+lXMFu90u8/Ly5IMPPiiDg4O1yoAGg0GOGTNG/vzzz7KmpqZBhbxclQk3btwoH3jgAXngwAGfLwR2+PBh2alTJ6nX6+Ull1wii4qKTrrfkSNH5IUXXiinTp0qU1NTfb5fJ6JqKSs8ipSSDRs2MGvWLH777TctniUiIoIXX3yRCRMmEBER0eApgMvvo3fv3vTo0aNZGGpjY2P55ptvOHLkCGaz+ZS5hTMyMsjJyWHnzp2YzWY++OCDBhdv83WUqCgahdls5osvvmD27NkcOnTouPd69+7N1KlTz7oyoBCi2aycGI1GkpKSSEpKOu1+GRkZVFVVIaVk3759LSaIsC5KVBQNprKyknfeeYdnnnmGsrIyhBDodDrtAhk8eHCzy1zXFEgpyc7O1sq0tlS7yhkNtUKIeCHEj0KI3UKIXUKIGc7trYQQq4UQac6/Ec7tQgjxbyHEAWcFw+afIEIBOC6K6upqXnrpJWbPnk1ZWRl6vZ7LLruM7t27A47Ez3379vVyS30Tm81GQUGBJiRFRUWNKovr69Rn9ccKzJRSdgb6AXc7C7HPAtZIKVOANc7XAGNw1PtJwVGB8B23t1rhFaxWK2+99RYvv/wyZrNZq030wgsvaOkjExMTOf/8833eBuINXKLsorq6moKCAi+2yDOcUVSklLlSyq3O5+XAHhz1kccBHzl3+wgY73w+DljgNBavB8KFEL7rYKCoFzabjUWLFvHcc89RVVVFQEAAM2fO5Pnnnyc/P19bTu7YsSPt2p1z5bPrhXTWnnZhtVrJzc09zSeaJw3yUxFCJAE9gQ1AW/m/KoV5gMuRoF5F2oUQtwshNgshNrdEtW5J2O121qxZw6xZsygpKcFoNHLbbbfx6KOPEhwczK+//qqF748YMaLZGFe9QV3jtcViYf/+/S3OrlJvQ60QIgT4HLhPSllWd3grpZRCiAZ9M7IF11JuSUhnOdaZM2eSn5+vxbQ8/fTTWtTtHXfcQUpKCr/99hujR49WU59T4Foqd2E2m1m4cCEXX3wxF1xwQbNYOq8P9RIVIYQRh6AsklJ+4dyc76qp7JzeHHVur3eRdoVvI6WksLCQRx55hJ07HeWyhwwZwosvvojJZNIugPj4eG6++WYmTZp01svILRmdTvcnn5S1a9cyatQoLr30UiZOnEj//v0b5dvjS9Rn9UfgKHO6R0r5ap23lgOTnc8nA1/V2X6zcxWoH1BaZ5qkaEaYzWaeffZZLedqUlISL774IomJiSd1s3fVDFacHCHEcSkj/fz8sNvt5Obm8vHHH/OXv/yFyy+/nFdffZXDhw8322lRfW4rA4CbgB1CiD+c2x4DXgCWCiGmARnA9c73VgKXAweAKmCqOxusaBrsdjuffvop8+fPx2q1YjKZeOmll+jdu7e3m9ZsqSsqQghuv/12pJQsX76cvLw8Kisr2bBhAxs2bMBms/HQQw95ucWNoz4F2n8FTjUWu/Qk+0vg7rNsl8KLSCnZv38/Tz/9NBUVFfj7+/Pwww8zbtw4NRI5S1zTHykl3bt3Z/LkyTz00EMsW7aMzz//nI0bN2Iymbjsssu83NLGoybAij/hcnA7ePAgAJdddhn33HNPi4tRaWrqThHtdjtmsxmDwUBiYiLTp0/npptuYtu2bezdu7dZp01QoqL4E7/++iuffvopUkoSExP5xz/+QWhoqLeb1SLw9/fXRMUVA+QKc2jVqhWXXnopl176pwlAs0KNZRXHYTabWbBgARUVFQghmDZtGr169Wq2d01fw2QyaStkhYWF2O12L7fI/ShRURxHVlYW69evByAuLo6JEycqO4obad26tTaNzM/PV6KiaPmUlpZqBbAuvPBCn07h2ByJiorSVoAyMzOVqChaPlarVYtPMRgMapTiZuLi4ggKCgIgPT1dSxTeklC/GMVxREREaPlgMzIytCp7CvcQFhZG69atAUce39LSUi+3yP34rKhIKbHZbFRXVzdbz8L64Mrr6StER0eTnJwMwN69ezlw4ICXW9SyEEJoomKz2VqkaPvkkrKUkm3btrFw4UK2bt3K4sWLW9zcXkpJaWkp33//PSaTiR49ehAZGen1VZbQ0FCGDx/Ojz/+qAW89evXT/moNIBT3SRsNhulpaUEBAQAUFtby6ZNm6isrNT2EUIQHBxMVFQUkZGR2rbmhM+Jit1u5/fff2fatGns27cPf39/Vq9ezU033dTsvtxTIaUkNzeXO++8k9WrV2M0Gmnfvj0PPPAA119/vfaj8wZCCMaPH8+bb75JXl4ey5YtY/z48Vx++eUt5vt3J9JZ1rWiooLy8nLtb15eHtnZ2WRnZ3PkyBHy8vIoLi6murqarCxHZpCqqipmzpz5pyBMf39/TCYT/fr149prr2XAgAHNyk/Ip0TFbrezdetWbr/9dvbt24cQgtDQULKzs7HZbC0mArampoZnnnmGlStXYrPZqKmpYfv27dxzzz1UVFRwxx13eDXHa0pKClOmTGHOnDkUFhby4IMPYjKZ6N+//zmXe9YlGtXV1dTU1FBTU8PRo0fJzMwkPT2dw4cPk52dzbFjx7RHSUnJccmYTndsV8a8E8nOzmbXrl188sknXH755TzxxBN06dKlWQi78IX5fO/eveXmzZvJyclh3LhxbNmyBYCLL76Yt956ix49emhJgJo7Ukp++eUXxo4dS0VFBeHh4XTq1InNmzdjtVqJiopi6dKlDB482Gv9lVJy5MgRbrjhBtauXQs4SlDMmDGDm266iaioKHQ6XYv4f4Cjv3a7Hbvdjs1mo7KyksOHD3Pw4EHtcejQITIyMjh69Cg1NTWaLexUS8IuL1nXX51Oh9FoxGQyUVNTQ2FhITqdjh49emg2LHDcWI8dO8bu3bspLi7Wkon36NGD1157jYEDB3o170rv3r3ZvHnz6U9+psJATfHo1auXtNls8rnnnnMle5LdunWTW7dubXbFls6ExWKR99xzj9bPxx9/XObn58spU6ZIvV4vATl69GhZWVnp1Xba7Xb5xx9/yL59+2oFwoxGo+zUqZN87rnn5K5du6TVavX5/4/dbv/Tw2q1ysLCQrlr1y75ww8/yI8//lg+/fTT8qabbpJ9+/aVMTExMjw8XAYGBkqdTqf1/1QPo9Eoo6Oj5YUXXiiHDRsmr7vuOjl9+nT54osvyoULF8o1a9bIXbt2yZycHJmfny8ffPBBCUh/f3/5/vvvy8rKyuMepaWlMi0tTf7zn/+USUlJ2nkiIyPliy++KGtqarz2fTarYmL79+/nww8/REpJcHAw//znP+nRo0eLuRu6yMrKYuXKlUgpiYmJ4ZprrqFNmzY8/fTTbNy4kd27d7Nu3Tr++OMPLrnkEq+1UwhBt27dWLhwIU8++SRff/015eXl7N27l8cff5x3332XSy65hL/85S90796d2NhY/P39vf7/klJSU1NDcXExpaWllJWVkZeXR0ZGBpmZmWRmZpKdnU1xcbFmA6moqDht/Z2AgABatWpFREQEYWFhREVFkZiYSHJyMsnJycTHxxMaGkpwcDBBQUEEBwefcmRtt9sJDAzUXvv5+Wl+K3UJCwvjoYceYtiwYdx7771s3ryZwsJCnn32WSIjI7npppt8Nm2nz4jKBx98wIEDBxBCcOWVVzJkyBCv/0DdTW1tLfPnzyc9PR2AQYMGadGo7dq14/rrr+fJJ5+koqKCNWvW0L9/f69+B0IIUlJSeO+995g0aRLvvvsu69ato6SkhKysLJYsWcIXX3xBUlISnTp1ol+/fvTq1Yv27dsTGhpKYGAgAQEBJ62dfDZIp52jpqaGqqoqysrKOHToEHv37mXfvn2kp6eTn59PYWEhRUVFVFVVnfGYRqORwMBAAgMDCQ0NpX379nTq1ImOHTuSnJxMTEwMbdu2pVWrVm7vz6nQ6/X06dOHpUuX8sILL/D+++9TXl7Oo48+SlhYGFdffbVP2rh8QlRqamr45JNPkFLSqlUrZsyYcVL19hVcP+ry8nL8/PwICQk5449MSsm6deuYO3cuUkpCQkKYNm3acUl7Bg0aRHBwMJWVlfzxxx9UVVURHBzcFF06LSEhIYwdO5bLLruM9evX88knn7BixQry8/Opra0lLS2NtLQ0VqxYgU6nw9/fn7i4OBITE4mNjSU2Npb4+HhiYmJo3bq1dtcPCQk5rceuK5K3oqKCyspKjh07RmZmJhkZGWRlZZGVlUV6ejrZ2dlYLBbsdvspl3P1ej06nQ69Xo9er6d169YkJiaSkJCgjToSExNJTEykXbt2WjSx6//qThGpa4c5k8eyEIKEhASee+45SkpKWLp0KUePHuX++++nXbt29OvXz+duvj4hKlVVVRw96khxe9VVV9GzZ0+f+6LqsmXLFt5++21++uknZs2axW233XbKfV0/8vT0dGbPnk1BQQE6nY6pU6cyaNCg4360SUlJxMbGkpaWxtatWyktLfUJUYH/ZS0bPHgw/fr14+9//zvffPMNq1evZvv27aSnp2O1WrHZbFRVVbF//37279+vfdZgMGiPuhf4mXAZUF1G1NraWmpra08bMxMQEEBsbCwxMTFERUURGxtLYmIi8fHxxMXF0a5dO8LCwvDz88NoNGI0GpvU8OwqIOb6Xs6EEIKIiAheeeUVysvL+eabb8jOzubvf/87n332Ga1atfJ0kxuET4hKZWUlZrMZo9HI0KFDfd7RKi8vjyVLllBVVcWyZcsYNmwYZrNZu6DqxnNUVFTw+++/8/nnn7Nv3z4AunTpwsyZM4+bW4MjgXSnTp1IS0sjOzubP/74g9jY2Cbt25lwiUt8fDy33XYbN910EwUFBaSnp7NhwwZ27dpFeno6hYWFml2jurpaEwN34e/vT3h4OK1atSIyMpL27dtzwQUX0KlTJ5KTkzGZTISEhBAcHOwTtp66uOw3Qoh6T1+EEMTExPD666+TlZVFamoq69at4/PPP2fatGk+FaPlE6JSXl4OOOJOunbt6lM/gJPRt29foqKiOHz4MD/88ANjx46lsrISq9VKRUXFcReP1Wo9bkiemJjIq6++Snx8/J+OazQaGT16NCtWrMBqtfLll18ycuRIn/XPEUIQGBhIQkICCQkJDBkyBHBMZwsKCjSbRn5+PhkZGRw5coTCwkLNn6OsrOy0IQp6vZ6goCDCwsIICQkhIiKCxMREzjvvPNq1a0dkZCRRUVG0adPG54TjdJyuz6dDCEH79u2ZNWsWt956K1VVVXz00Udce+21REREuLmVjeeMv1YhRDywAEexMAnMlVK+LoR4ErgNcFUCe0xKudL5mUeBaYANuFdK+d3pzlFTUwNAeHg4HTt2bFxPmhCTycTgwYM5fPgwtbW1SCk5evToKevi6vV6AgICGDJkCI888shx054TGT58OFFRUeTn5/Pbb7+Rk5NDQkJCs7lgwDH9iI+P14TTdRGd6u/pqNvvE+0bzek7qYtr1cZlm2soY8aMoVu3bqxfv57Nmzezfft2hg4d6uZWNp763AJdtZS3CiFCgS1CiNXO916TUr5cd2dnneWJQBcgFvheCNFBSnnqNTsnLgehE39svvbjMRqNDBs2jE8++YTa2lpMJhPDhg1Dp9NhMpn+tGTYoUMHBg0aRJcuXQgODj5tf+Lj4+nfvz/Lli3jwIEDpKamkpCQ0BTd8hjNXQTcTVhYGOCwF7lG6fVFCIHJZGL8+PGsX78ei8XCt99+61OrpfXJpp8L5DqflwshXLWUT8U4YLGU0gykCyEOAH2A30/1AZ1Oh81mw2KxUFJSQkVFBd9++y3V1dXMnDmzQR1qCoQQ9OnTh6ioKHJycigrK+PTTz+ldevW+Pv7HzddEUJoj/oQFBTEyJEjWbFiBbW1tSxbtoyxY8f6zA9GcXYIITRRsdlsDRYV1zEGDx5MQEAANTU17N69m5qamj/Z6LzF2dRSBrhHCJEqhJgvhHBN6upVS7kurgC6I0eOaAWV7r33Xt5//30OHTrkk9mxUlJS6NChA+CI08jKyiIsLAx/f39t2dK1ytEQQRBCMGrUKM2iv3btWi0ATdEycAUHSinr5UNzMqKiorTI/dzcXMrKytzWvrOl3qJyYi1l4B3gPKAHjpHMKw05cd0C7a6lvZqaGn7++Wf279+P3W4nLS2N2bNn+2R2LIPBoNVmsVgsrF271m15UWJiYhgxYgTgSDn4ww8/+FTOFcXZ4fLBstvtVFZWNup/GxwcrN14XCtsvkK9ROVktZSllPlSSpuU0g78B8cUB+pZS1lKOVdK2VtK2Ts+Pp4pU6YQEhJy3D4RERFMmTLFZ5eY+/fvT0BAADabjU2bNjX6rnMiAQEBjBs3jsDAQCwWC4sXL9aM2YrmT3h4uCYsR44cwWw2N/gYrkBF4LROf96g0bWUnUXZXVwN7HQ+Xw5MFEL4CyGSgRRg4+nOYTQa+de//sW3335L165dAcdIYMaMGQwfPtwn7QlCCC32AyA1NZXCwkK3HfvSSy+lffv2AKxbt46vv/7aJ6eBioYhhCAqKkpbAs7IyGjUKCM/P5+8vDzA4fFct0azt6nPSMVVS3m4EOIP5+Ny4CUhxA4hRCowDLgfQEq5C1gK7Aa+Be6uz8pPYGAg6enppKWlAdC9e3emTZvmsz4a4JimnHfeeYAjUND1T3YHERERTJ06FaPRSEVFBc899xw5OX8a8CmaIa4YIoBDhw41aIRrt9vZvXs3jz/+uGZrS0lJwWQyeaStjeJMYcxN8ejVq5c8fPiw7N69uwSkwWCQ77zzTrMIq3eFsQPy448/duuxCwoK5PDhwyUghRDyySeflFar1W3nUHgHq9Uqx4wZo/3WN2zYcMbP1NbWyszMTPnyyy/L888/X/vNxcbGyvXr1zfZtVKf1Ac+49u7fPlyduzYAcCwYcO47rrrfHLaUxchBPHx8Vo73TmScCVIfuSRRwgJCUFKyQcffMCWLVt8av6saDh6vZ4LL7wQcIw8UlNT/7SPdCaAqqio4Oeff+Yf//gHQ4YMYdasWVoy8oSEBObMmeNzFSR9Ym5hs9n4+OOPsdvtBAUFcd999/lckNSpqDuXrU8KwYYghGDAgAFceeWVLF68mIyMDO69914WLFhASkqKT/2QFA2jZ8+egENUtm/fjpSOmso2m42cnBz27NnDjz/+yHfffaeVSnHdTIKDgxk5ciSzZ8+me/fuPpf+wCdEpbKyUssx0rNnTwYOHNgsLhgpJQcOHND+2Z4I/gsODuaxxx5j586d7Nixg40bN3Lvvfcyf/58YmJimsX3pPgznTp1IjAwkOrqanbu3Mm2bdvYtm0bq1evZseOHRw+fPg4W4sQgjZt2jBo0CCmTZvGgAEDfMuOUpczzY+a4hETEyOFEFKn08mXX35Z2mw2t88FPUFRUZEcMGCABGRgYKDctGmTR85jt9vl77//Ltu3by8BqdPp5PXXXy8LCgpkdXW1z9ueFP/DbrdLs9ksd+7cKZOTk7Xfjslk+lOaSoPBIE0mkxw4cKB8/vnn5fr166XFYvHq/7vZpJMsLS1FSklERAR9+/ZtFndfKSUbN27U7EA9evQgKSnJI+dyhQW8/vrrTJs2jaNHj/L5558TFBRETEwMDz30kE9FqSr+h+tCq62tZf/+/WzZsoUNGzawbt06MjMzAaiurtaWlXU6HdHR0fTp04dBgwYxfPhwOnTooHlqNwd8QlRcX2hMTAzdunVrFqJSXFzMCy+8QFlZGXq9ntGjR3vUDqTT6Rg9ejQvvfQS9913HyUlJXz88ceEhoYybNgwRowY0Sy+t3OJyspK9uzZw9q1a1m+fDl79uw5ZfmOLl26MHDgQEaOHEmvXr1o3br1GYNPfRWfEBXptEn06dOnWRRNslqtLFy4kN9/d8RIduvWjalTp3o8UY7BYGDSpEmUlpYye/ZsysvLKS4uZtmyZQwYMMCnU3C2VFyu9mVlZZSWlpKdnc3OnTvZtWuXVja2oKDguBU7Pz8/IiMjqayspLS0FJ1OxwsvvMDll1/uU8mWGotPiAo4hviuJD++jN1uZ9WqVTz//POYzWZCQkJ45JFHaNfutDGTbsPPz4877riDsrIynn/+eaqrq/nggw/w8/PjvvvuIz4+vkX8MH0J1/TFbDZjsVgoLCzkwIEDWsrMw4cPk5WVRXZ29kkLrgshCAkJITk5mWHDhtG/f3+6du3Kq6++ygcffIDdbsdoNDbLUcnJ8BlRiYyM1Fz0fRWXHWX69OkcPXoUIQTXXHMNV111VZNeyH5+fsycOZO0tDQWLVpEdXU1//73v/n+++959tlnGTt2rFcLTjVH6hoaXQm3MzMztaJiLgFxjTxc+XhPVtrDFZfj7+9Pp06dGD58OKNHj+biiy8mKChI+9+4amdLKX0qyvhs8RlRcWUy99ULQUrJvn37mD59OocOHQJgxIgRPPvss01e+1gIQUBAAK+88goxMTH85z//oaioiJ07d3LLLbdwyy23MH78eDp06OATRd99EVfunqKiIgoLC8nNzeXgwYOkpaWxf/9+srKyqKyspKamhurq6tPWBTKZTERHRxMbG0tCQgIXXHABXbp0oUOHDrRq1Yrw8PCThpu0bt1ayyVUUFBwkiM3T3xGVNq3b++zDm9SSjIyMrjvvvvYvHkzABdddNEpc802Ba473VNPPcWoUaN45pln+OWXXygqKuKVV15h/vz5xMXFMXToUMaOHUtiYiIhISG0bt3aqwXg3YHNZqO6uhqz2UxNTQ1ms1lLrG21WrXM+yUlJeTl5XHkyBEKCgq0Uh+lpaUUFhZqxcRcRcfOVFDMZDIRHh5OmzZtSElJ0eoCxcfH06pVK1q1anXGsiN1iYqK0gQ/Pz/fLd+NL+AzotKhQweftAXY7Xb27NnD3XffrdUVTkpK4s0336RLly5ebp3Do3fo0KF07dqVV155hXnz5nHs2DGKioooKioiNTWVN998kzZt2hAVFUVKSgqDBw/m0ksvJS4ujpCQEK8EbdYtveEK3XdFYdctx2GxWDh69KhWYTA7O5ucnByKioooKyvThMG1LFtTU6NNTRqKwWDA398ff39/QkJCSEpKIiUlhQsuuIDzzz+f+Ph4LeH2ib/VxowG6x5HjVQ8QOvWrb3dhONwza9/+uknpk+fzu7duwGIi4vjrbfe8il/Gteo5ZlnnmHs2LF89913bNy4kU2bNlFWVobdbic/P5/8/Hx27NjBV199RUBAAF27dmXw4MF07NiRtm3bEh0dTXx8POHh4VrGuhP76FrFOPGv67ndbj+ucmBJSYm2MuKaTrhWS8rKyrTpRU1NjZZEvLq6WpuWFBYWYjabtWM3NHeIqw+ui9fVHz8/P6KiooiOjqZdu3ZaQbGkpCSSk5OJi4vTfENO9V2cLSaTSTtmSUmJW4/tTXxGVHwNq9XK4sWLeeyxx8jOzgagY8eOvPHGGwwfPtznRlVCCIxGI4MGDeKSSy6hsrKS7OxsVq1axYYNG8jOziY3N5eMjAysViuVlZVs2LCBDRs24OfnpxXWCggIIDIyksTERKKjowkJCdESUblqGlksFq0GcWVlpVaL2GKxUFlZqRkwbTabNmpw/a07OmksOp2OsLAwwsLCCA0NJSQkRCuxGhAQoBUtCw0N1dIuRkVFERYWRlBQEKGhoURERGglWV2jk6YsKAbHi8rJVo2aKz4jKu5KcOQOSktLefvtt5kzZw7FxcUIIbjkkkt49dVX6d27t88Jyono9XrCwsLo3LkznTt3xmq1UlZWRnFxMWlpaXz33Xf89ttvHD58mGPHjmGxWI5zyMrNzdU8hT2FEAI/P7/jKhe6LmpXSZPAwECCgoK0guh1y5KaTCZtn4CAAIxGoyaM9a1+6G3CwsK035ISFQ9w8OBB7Ha7Vy9YKSWZmZnMnj2bJUuWUFtbi9FoZPz48bz00ks+vTp1OgwGg2ZIbN++PaNGjaKyspJdu3axb98+rdBXZmYmBw8e1Lw+XeVFXdMNnU6nXfiuC7fuNlcZz6CgIEJCQggLC9POGxERQVhYGAEBAYSGhmIymQgKCsLf318bKblsO35+foSGhhIWFobJZNLq5NSlOf4fTiQ4OFjrm6sY3cn62tzwGVHZs2cPeXl5Xou8lVKyZ88e7rjjDn777TeklAQFBXHHHXfwj3/847ih6tngGvZ7Yo5eH1znDAkJoU+fPvTp00ezV0gpsdlsFBUVacbQ6upqLBYLOp2OwMBAjEYjBoOB4OBggoKCCAwMJDg4WKvZFBQU9KeyJCd7XffvuYpOpyM4OJjS0lJqa2upqalRouJOXEXJx44d26TndRlkf/75Zx588EG2bduGlJK2bdvy5JNPMnnyZAICAtxyAVgsFhYtWoTNZmPKlCled1Cre3G7RohGo5HY2Fifq+HcEqn7vbt+hy0BnxGVqqoq3n//fYYNG0ZwcHCTnbeoqIh3332XN998U8sx26FDB80g667lVpvNxvvvv8/f//53dDodtbW1TJs2zWcrBSgUjcUnLI6uId+qVatYsGBBo3wMGorVamXXrl3cfvvtPPHEE+Tl5aHX6+nTpw8LFy5kxIgRbvXfsNvt7Ny5k4qKCoqKinj00Uf573//22LuToqGU9c3x1vTYU9QnxIdAUKIjUKI7UKIXUKIp5zbk4UQG4QQB4QQS4QQfs7t/s7XB5zvJ53pHNHR0QQFBVFdXc2zzz7Lhg0bPHax2e12jhw5wosvvsgVV1zBF198gc1mIygoiDvvvJMvvviCiy++2O0GY4PBwDPPPMPNN9+sGTcNBoMSlXMYV4wROH4fzd3TWeNMWZwAAYQ4nxtxlDzth6MMx0Tn9neBvzmf3wW863w+EVhypnP07NlT3njjjVrGq759+8q0tDS3Zbiy2+3SbrfL4uJiOW/ePNm1a1dpMBi08yUkJMiPPvpIVlZWuuV8p+Po0aNy+vTp8qOPPpIWi8Xj51P4LocPH5ZBQUESkP37928WGfzqk/mtQWkfgSBgK9AXKAQMzu39ge+cz78D+jufG5z7idMdt1evXnL//v2yd+/e2oU+YsQI+ccff5x1akmbzSazsrLk/Pnz5SWXXCIDAwO1c4SHh8uJEyfKTZs2NWnpi8rKSlVqQyG3b9+u/R5Hjx59bokKoAf+ACqAF4FI4ECd9+OBnc7nO4G4Ou8dBCJPcszbgc3A5oSEBCmllD/99JNMTEzULvqUlBT5yiuvyJycnAZdhK48oPv375dPPvmk7Natm9Tr9dpxAwMD5ciRI+W3334rq6urz+pLVigay3fffSf9/f0lIG+++eYWIyr1skRKR4XBHkKIcOBLoFN9PneGY84F5gI4RygMGjSId999l+nTp2th6A8//DAffvghN9xwA2PGjOH888/XHKVchi1XZ2w2G+Xl5WzcuJHly5fz9ddfk5OTo9kt/P39ueiii7jjjjuYMGFCs03Xp2gZZGZmaosSiYmJXm6N+2jQ8oaUskQI8SOO6U64EMIgpbRyfBF2V4H2bCGEATABx+pzfJ1Ox8iRI/nss8947rnnWLFiBdXV1ezYsYPHH3+cF198kfbt29O/f3969OhBSEiIFpVaUVHB7t27+fHHH8nNzaWqqkoTk+DgYIYOHcpNN93EyJEjMZlMPu9qr2jZSCnJysrSVn88lTTdG5xRVIQQbYBap6AEApfhmAL9CFwLLAYmA185P7Lc+fp35/s/SNfVXQ90Oh3dunVj3rx5/PDDD7z11lusX7+e8vJySkpK2Lp1K1u3btXcxK1W60lXUIQQxMXFMWzYMG699VZ69OhBaGioGpkofAK73U5mZqYWmuKtvDyeoD4jlRjgIyGEHscS9FIp5QohxG5gsRDiWWAbMM+5/zxgoRDiAFCEYwWoQQghCA0NZdy4cQwbNowNGzawatUqfvrpJ7KysigtLaWmpua4SFdX+r6IiAi6dOnC2LFjGT58OJ06dWoRrs+KlkVFRYVWYD0iIoI2bdq0mBveGUVFSpkK9DzJ9kNAn5NsrwGuc0vrcERyjhgxgksvvZTa2loOHDhAamoq6enpmM1mgoKCtPD1tm3b0q1bN5KTk7Uo1Zbyj1K0LEpLSzVRiYmJ8bl8QmeDz7jpnw6Xt6G/vz9dunShc+fOmnH2xOQ7CkVzoKSkRMvTEx0d7bOpVBtDsxCVE2lJLs2Kc5NDhw5p3rTJycktqmaTWgJRKJoYKSW//vor4LAFdu/evUXdJJWoKBRNTEVFhVbd0t/fn4EDB3q5Re5FiYpC0YRIKTlw4ADp6emAo4ZyU1W3bCqUqCgUTUxqaqpW56dfv36YTCYvt8i9KFFRKJoQm83G999/j91uJyAggAEDBrQ4PyolKgpFEyGl5NChQ/z444+Aw+ntkksu8XKr3I8SFYWiibDb7Xz++eda2tKxY8cSExPj5Va5HyUqCkUTUVhYqCU+b9WqFTfccINXSs56GiUqCkUTIKVk5cqV7N+/H4CLL76Y/v37tyj/FBdKVBSKJqCkpIRFixZpBepuueUW/P39vd0sj6BERaFoAux2O507dyY6OpqePXsyePBgbzfJY7S8CZ1C4YO0atWKOXPmcPfdd3Ps2DHatm3bIqc+oERFoWgSXFH2HTt29HZTPI6a/igUCreiREWhULgVJSoKhcKtKFFRKBRu5WxqKX8ohEgXQvzhfPRwbhdCiH87aymnCiEu8nAfFAqFD1Gf1R8zMFxKWSGEMAK/CiG+cb73kJTysxP2HwOkOB99gXecfxUKxTnAGUcqzmqHFc6XRufjdHV8xgELnJ9bj6PoWMuLmlIoFCelXjYVIYReCPEHcBRYLaXc4HzrOecU5zUhhMvnuB2QVefj2c5tJx7zdiHEZiHE5oKCgsb3QKFQ+BT1EhUppU1K2QNHedM+QoiuwKM4aipfDLQCHmnIiaWUc6WUvaWUvdu0adOwVisUCp+lQas/UsoSHOVOR0spc51THDPwAf8rLOaqpeyibp1lhULRwqnP6k8bIUS487mrlvJel51EOAIYxgM7nR9ZDtzsXAXqB5RKKXM90HaFQuGDnE0t5R+cxdsF8Adwp3P/lcDlwAGgCpjq9lYrFAqf5WxqKQ8/xf4SuPvsm6ZQKJojyqNWoVC4FSUqCoXCrShRUSgUbkWJikKhcCtKVBQKhVtRoqJQKNyKEhWFQuFWlKgoFAq3okRFoVC4FSUqCoXCrShRUSgUbkWJikKhcCtKVBQKhVsRjqBiLzdCiHJgn7fb4WEigUJvN8LDqD62DE7Xx0Qp5WlTNfpKLeV9Usre3m6EJxFCbFZ9bP6oPp4ZNf1RKBRuRYmKQqFwK74iKnO93YAmQPWxZaD6eAZ8wlCrUChaDr4yUlEoFC0Er4uKEGK0EGKfs6D7LG+3p7EIIeYLIY4KIXbW2dZKCLFaCJHm/Bvh3N7sitgLIeKFED8KIXYLIXYJIWY4t7ekPgYIITYKIbY7+/iUc3uyEGKDsy9LhBB+zu3+ztcHnO8nebUDDcBZdXSbEGKF87Xb+uhVUXGW/XgLR1H3zsAkIURnb7bpLPgQGH3CtlnAGillCrDG+RqOL2J/O44i9r6OFZgppewM9APudv6vWlIfzcBwKWV3oAcw2lm76kXgNSnl+UAxMM25/zSg2Ln9Ned+zYUZwJ46r93XRyml1x5Af+C7Oq8fBR71ZpvOsj9JwM46r/cBMc7nMTj8cQDeAyadbL/m8gC+wlFYrkX2EQgCtgJ9cTiCGZzbtd8s8B3Q3/nc4NxPeLvt9ehbHI4bwHBgBY7aXW7ro7enP/Uq5t6MaSv/V50xD2jrfN6s++0cAvcENtDC+uicFvwBHAVWAweBEiml1blL3X5ofXS+Xwq0btIGN45/AQ8Ddufr1rixj94WlXMG6ZD6Zr/UJoQIAT4H7pNSltV9ryX0UUppk1L2wHE37wN08m6L3IsQ4grgqJRyi6fO4W1RaenF3PPr1JyOwXH3g2babyGEEYegLJJSfuHc3KL66EJKWQL8iGMqEC6EcIW01O2H1kfn+ybgWNO2tMEMAK4SQhwGFuOYAr2OG/vobVHZBKQ4Lc9+wEQcBd5bCsuByc7nk3HYIVzbm1UReyGEAOYBe6SUr9Z5qyX1sY0QItz5PBCHzWgPDnG51rnbiX109f1a4AfnaM1nkVI+KqWMk1Im4bjefpBS3og7++gDRqPLgf045q5/93Z7zqIfnwC5QC2OOek0HHPPNUAa8D3QyrmvwLHqdRDYAfT2dvvr0b+BOKY2qcAfzsflLayP3YBtzj7uBP7h3N4e2AgcAD4F/J3bA5yvDzjfb+/tPjSwv0OBFe7uo/KoVSgUbsXb0x+FQtHCUKKiUCjcihIVhULhVpSoKBQKt6JERaFQuBUlKgqFwq0oUVEoFG5FiYpCoXAr/w8isNNPHEdY9AAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/ولعل ما ترجون سوف يكون.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/خيركم من تعلم القران و علمه.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/مسجد الفتاح العليم.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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8jNDQUkyZNwltvvQWtVouAgAC8+eabcvG6ubGG/+j1+mrDVJoT6yjen3/+ORYsWID/9//+nzyg6tXYjuxTXaux9frVFP/o5+eHmJgYmM1mpKam4tChQ5gwYUIz5qb+WIvmTb2HmRmHDh3C7Nmzce7cOTk9MTERV65cwbp16+wGsG1rNOnqEFEqEZ0mogQiOmZJCyCi3UR02fLpX9dx9Ho9vvvuOzAzevTogRdffNGur29rY1uv48wuv4uLizzjnkajQWFhYbW9IKzz7xYWFspjGAKV4xQuWrQIYWFhMBqNeO+99/Dggw8iJSUFCoUCs2fPxpgxY1rkGtj2LNFqta3iNVnF8Pvvv8c///nPGgec8Pb2louyVi/ZlqsbS65er1QqMXnyZLi4uECv12P79u3V9vxpDfLz83HkyJEmHcM6yO+cOXNw7tw5KBQK9O7dG127dgUzY8+ePfj8889bPcytOWkOV+dWZo5jZuvEE4sA7GXmWAB7Lb9rpbi4WJ7NbMaMGYiOjm72h886dpzRaITZbK7VlZckSf5TW6v+wyq+VhuvHi6/JsLDw+Hm5gZJknDlypUq64lIHsZfp9MhOTnZbp21YcTHxwd6vR6XL18GUDm0/4IFC1q0WsDPzw9EBJPJVG23vpaAiBAZGVnr/+ri4oIOHToAqBTpq6fp9PX1hVKphNlsrlHA4+Li5GqLX3/9tVmnUjWbzdDpdNDr9bXGMVZUVODNN9/E8ePHq6yzreerCWvf8sOHD2P+/Pk4ffo0FAoFxo8fjx07dmDz5s2IjIyEyWTC+vXrnXpGwrpoiTLfJAAbLN83AJhc1w4FBQUwm82IiorCxIkTm9UYa8+IP//8E4sWLcKTTz6Jl19+GUePHq1VbFrDI2RmmM1mFBYWYs+ePVixYgUWLlyIhQsXYunSpfjrr79qHRmGiBAUFCR7NtXFEgJA7969AVQ+1JcvX7Y7nlKpxNSpU/HSSy/ZxUtqtVr8/fffMBqNLSJQ1lFerOesrmeMI7HWq+p0Ortiu7Vu0zpMW02tz8HBwRg+fDiICMnJyTh69GizXEdJkrBt2zbcfffdeOihh/Dzzz9XK4bW7dauXVvFU2NmJCQkYNSoUVi2bFm1xX9mhlarxZNPPomxY8di//79AIBBgwZhxYoViIyMRHx8PO655x4AQFZWFv76668m589RNLWOkAH8QkQM4DNmXgUglJmtT2QOgNAa97ZgbZkbPXp0s9dHabVafPDBB3j//fdRUFAgp2/cuBEff/xxswtvXVgfBrVajT///BM//PAD9u/fj5ycHJSVldkJsL+/P0aOHInXX3+9xusSEhICDw8PlJWVITU1tdpzxsbGQqVSQavV4uLFizCbzXaip1AoUFBQYPdAnTlzBtOmTcPEiRMxb948xMXFyYLbXP+PSqWCm5sbjEYjSkpK5FnzWhpmRklJCc6ePQtJktC/f3+72EiFQiFXJ5SWlqKoqMhuThg/Pz94eXnJQerVCZyLiwsmTpyINWvWQK/XY/Pmzbj99turDFLbULtPnTqF559/HikpKQCA3377DevXr8fIkSPtBoxNSEjA4sWLq510vrS0FEuWLMHJkydx8eJFhIeHY8aMGVXOtWXLFmzatEk+RlhYGBYvXgwfHx8UFxdDp9PJ10mn0yEpKanReXM4V8c7NWQBEGH5DAFwEsA/AKiv2qa4hn0fBXDMsrC7uzvv37+fmxOz2cxr165lDw8PBsAuLi4cGBjISqWSAXDv3r358uXLLEmS3X4XLlzg4OBgBsDTp09nnU7XLPZIksTZ2dm8fPlyjo+PZ09PT0bly4QBMBGxr68v+/v7s0KhkNPGjRvHhYWF1R7zypUrHBERwQB47NixVfLCzJyYmMjR0dEMgCdOnMjl5eV2648cOcIhISEMgKOionjkyJF2tgUFBfHIkSN5xYoVvH//fj558iRfunSJk5OTOT09nTMzM6ss2dnZXFhYyEajscbr8fvvv7Ofnx8D4BdffJHNZnO9r2VhYSGXlJRUm9/aMBqNvGvXLr755pvZ39+f/fz8ePbs2axWq+VtTCYTP/nkkwyAg4OD+dixY3bHSElJ4ZiYGAbAd9xxBxsMhmrPVVRUxEOGDGEAHBAQwEePHm2QrVeTl5fHY8aMYYvjIS833ngjZ2dny9uVl5fzPffcI9/zH3/8sbzObDbzqlWr2M3NjQHwsGHDOC0tze7am0wm3rdvH3ft2tXuPCqViq+77jru2bMn9+zZk2NjY7lDhw4MgF1dXXnVqlVNyl9LA+AY16RlNa1o6AJgMYCFAC4CCLekhQO4WI99uVevXpyVldVsmZYkiQ8cOMCdOnViANyhQwd+9913+dixYzxz5kxWKBRMRHz//fdzaWmp3b6XL1/mjh07MgC+9957uaKiosn2GAwG/v7773nQoEGyEANgLy8v7t27N8+dO5e//vprPnz4MB89epTXrVvHffr0kW/m1atXV3vc/Px8joqKYgA8atSoaoUhLy+P+/fvzwC4f//+rNFo5HUajYYnT54sn2fp0qWs0Wh4y5YtPHDgQHZxcbF7GFxdXTk4OJijo6O5a9eudg+G7dKnTx8eMGAAjx07lhcvXsxHjx7l0tJSO/uOHTsmv3CeeOIJNplMdnabzWY+ceIEJyQkyPtJksSpqak8efJknj9/PqvV6nqLYXl5OS9fvlw+p22e3n33Xfk4JpOJn3nmGQbAgYGBfOTIEbvjlJaWct++fRkA9+nTp8YXpdls5k8++YRdXV0ZAM+bN69KHutLYWEhz5o1S753brnlFh47diwDYKVSyc899xyr1WrOzs7mF154gVUqlSxe33//vXztzp07x7GxsQyA/f39eceOHfz000/zJ598wsePH+cffviBn3vuOQ4PD7e7RrUt7u7uPHLkSM7IyGhU3lqLFhFCAF4AfGy+/wlgDIClABZZ0hcB+Hc9jsX3339/jW/WxpCfn8833XSTfKP/61//Yr1ez5IkcXp6OsfHx8t/4vLly+08l6SkJFlA77zzzioeVEPRaDS8bNky9vf3l728kJAQnjFjBm/fvp0LCwtZkiS7B1qSJN6/f7/8EM2aNavaB16tVsve3s0331ztNlqtlkeMGMEAOCIiQvYuJUniL774QvYObr75Zs7Pz5fX5ebm8qpVq3j06NEcHBws29KYpUOHDjx+/HjesWOHLBxnz56Vvdnp06fb/QeSJPHBgwc5NjaW+/Xrx3///TebzWbWarU8bdo0+X+dN29enWJozctTTz0llw7c3d35tttu46CgIAbAPXr04PT0dJYkqU4hlCRJvp6RkZE1euvMzDk5OXz99dfL3vbZs2cb5MWazWZOTU3lGTNmyCLYqVMn/uuvv/jUqVOyZ+rt7c233XYbx8XF2b28vLy8eOfOnVxWVsaXLl3ie++9VxbPZ555hs+cOcNhYWGsUCjk+8C2hGL9Hh8fz3fccQcPGzaMhwwZwkOGDOHhw4fznDlzeMuWLfI97My0lBB2QWVx+CSAswBetKQHorK1+DKAPQAC6nEsfuedd5rtQkqSxBs3bpT/2MmTJ3NRUZGdV/Hzzz9zQEAAA+Dw8HD+448/5PWnTp2SXf6HH36Y9Xp9o+0oLi7mxx57zE5EAgMD+eWXX+YTJ06wVqutMd8XLlyQH9zp06dXW3TUarXywxAfH1/tscxmM0+cOJEBsK+vL+fk5LAkSZyTkyO/EDp06MA7duyosr8kSazT6fjEiRO8fv16Xrx4MT/88MM8ZcoUnjBhAo8ePZpvu+02u2XEiBE8bNgw7tOnDwcEBNjl3dfXl99++22uqKjg9PR0ufg1ZswYOyE0Go08a9YsuYqgb9++smf43XffyR67t7c3b926tcZrKEkSJycn8/jx4+Vj+fr68pIlS1itVvMzzzzDRMRKpZJXrFjBZrO5XkJoLXqGhYVxampqjfeAyWTit956SxaxF154oV5VAJIksUaj4f/85z8cFxcni1JYWBj/+OOPbDab2Ww280cffSTfI1d7aVbBGzVqFE+ePJnDw8Pl4/Tr148zMjL4u+++46CgIDldoVCwh4cHd+zYkR988EH5OZg1axYbDAbW6/XyYjAY2GQyVXmJOystIoTNuQDgTz/9tNkybDAYeNy4cbL7/9tvv1X5owwGA7/yyiusUChYoVDwrFmzWKvVsslk4uXLl8s3xVtvvdXoP1mSJP7vf/9bpXipUCjY3d2dAwMD+YEHHuA9e/bI3qqtfcuWLZPte/XVV6u1Q6fTyUI4YMCAGm29//77GQD7+PhwUlISS5LEa9askW2bPn06a7XaOvNjNptZr9ezVqvliooKLi8vr3YpKyvjkpISTk5O5k8//ZRHjBghv5i8vLx47dq1rNFo5CJmr1697KogrEXg0aNHy3WnVsEzmUy8bds2joqK4vnz59daV1hcXMzjx4+XH/ROnTrxl19+KV/vEydOcFhYGAPg2267jfV6fb2E8OGHH2agsg7x7NmztV6z5ORk7tKlCwPgrl27cnJycq3CXVpayl999RX/4x//kEWOiPi6667jH3/80e6FUVZWxkuXLuXevXtzVFQU9+rVi6dOncrr1q2TSzVXe3ldu3blnTt3siRJrNfr+eDBg/zss8/y448/zm+++SZv3bqVk5KSOCMjQz7GiBEjGlSH64y0CSG0VrRa3y5NecNcunRJruMYPnx4jQ+4tfK5T58+vHnzZt6zZw+//vrrcsNBYGAgHzhwgDUaDRcXFzf4RpAkibdv3y6/nWta/Pz8+JFHHuFLly6xJElcVlbGS5YsYV9fXwYqK9r/+uuvas9RHyGUJEkuTvr4+HBiYiKXlZXx0KFD5eP/+eefLfZWt3rG7733Hnt5eTEA7ty5M585c4YnTZok23D48OEq1QMpKSl899138+rVq+08c5PJxMePH69S73g1+/btk6/jDTfcwIcPH7arp9Pr9fJ1iIqKYrVaXS8hfOyxxxiobEg6efJkrfk3m8388ssvy57nkiVLqtxLVoE/ePAgjxo1yq6xysvLi2fMmMHnzp2r9h40GAycn5/P2dnZnJ+fzxUVFWwymXjt2rXcrVs3dnFx4YCAAB46dCi/9NJLfOnSJbvjWF9wVu/O9rhxcXEMgHv27MklJSW15tPZqU0InaKLHQCUlJRAq9Xir7/+wrlz59CnTx8MHjy4UT1MkpKS5Li0f/zjHzUeIygoCAsWLMDUqVMxf/58mEwmeVRsNzc3TJ8+HStWrMCpU6dw3XXXYe3atQgMDGyQLbfeeis2btyICxcuwN3dHQEBAVAoFMjOzsaOHTvw999/Q61WY+3atfj999/x1FNP4eLFi1i5ciX0ej08PDzwxBNPoH///g2+DlY0Go0cbuHm5gY3NzccPnwYp0+fBgCMGDECAwYMaLHQFSKCn58f5s6diytXruD9999HWloaNm/ejAkTJmD79u0oLi7Ge++9h1WrVsnBzESE6OhorFmzBl5eXnYhP0qlEjfccEOd5y4rK5ODhsePH4+BAwfa5dPFxUW+P2y7zFlDuhQKRZODyokI999/PzZu3Ii0tDSsX78e06dPR1RUFIDKAOmjR49iw4YN+O677+TePyqVCrfffjvmzp2Lm2++GR4eHtX+R66urvKAGbZMnz4dI0eOhEajgbu7O4KCguDr61ulux0RVXtchUKBLl26ICEhAaWlpcjJyZH7h19z1KSQrbnAUjS65ZZb5Ld39+7d7UIaGsK6devkIt/mzZtr9RgOHjxYpZI4MjKSX331Vc7Ozubbb79dtictLa1R9lSHJEmcn5/PX3zxBfft21cuurm4uMh1WSEhIfzRRx9VadW2paysjDt37swAePDgwdXmdc+ePbIndvfdd3NZWRkvXLiQAbCHhwd//fXXrVbHk5KSIjeQDB48mE+fPs033ngjA2A3Nzd+7bXXGlQna/UaDx8+zJmZmVXycfbsWbnoe+ONN3JxcXGVfa31lHFxcazVatlgMMgt6dHR0Xz58uUq55wyZYr8H126dKlOO81mM//rX/+S/+P3339fruf74IMPZBttFw8PDx4zZgy/8MIL/OGHH/KGDRv4v//9L//111984cIFzszM5PLy8hr/O7PZzHl5efz777/zG2+8UWtdZk37P/vss3Kp5cCBAw3a39lAW/AIr57ku1evXo0KPmVmFBcXw2QyyUPR10ZERAQWLlwIg8EAd3d3xMbGYsCAAejZsyeMRiM6duwIoLL3S2FhITp16tRgm6rDatuYMWOgVqvxwgsvoLS0VPZIvL298fbbb2PGjBm1emq2+/j6+la7zcWLF+Hu7g5JknDvvfcCAHbv3g0A6NSpE4YMGdJqfapDQkIwaNAgfPfdd0hLS4O7uztef/11PPDAA8jPz8cff/wBrVZb74nnU1NTcc899yAhIQFDhw7F+vXr5fmcgcpg8pEjR2LTpk1ISEjAt99+i4ceeghEBK1Wi3fffRcpKSkgIowaNQqurq7Q6/W4dOkSgMoeJrbB1EBl33irh207HUJtEBGmTp2KDRs2IDc3F/v378eUKVNw5MgRrFixAjk5OVX20el0+Pnnn/Hzzz/bpbu5uSEwMBABAQEICwtDSEhItfPqlJeXIzExEWfPnoXBYMCYMWMQHR1d90W1sblbt24AKj3rnJwcMLdO0HurU5NCtubi7u5uVyfi6+vLP/zwQ6O8FGvYSceOHZmI+J///CeXlZXVWjltfTObzWa77cxmM7/99tuyh/b88883Og7M9nwmk4mzs7P5s88+4yFDhti1qlo9Q1dXV77vvvv4woULtZ7z8OHDcut3TSE2BoOB9+3bx4sWLWK1Ws2nT5+WY+mmTJnSqi1+JpOJFyxYILdsnjhxgo1GI3/88cfs5+fHmzZtqrc9FRUVPGvWLLswj6lTp7JGo7GLEPj11185MDBQbpRJT09ntVrNL7/8stwYERsbyxcuXGBJkvjUqVNyWM0999xTpd7yzz//lNePHTu23mFfOTk5cmxobGwsDx061O6/9/Dw4Pj4eJ4/fz6PHTuWe/TowQEBAezt7c0eHh7s5uZmF4PakMXb25tPnTrVoP9KkiT+6aef5BLT0qVL23SDCZzdI+zRowfi4uKwceNG+c15++23N/rN4+rqKg+s8Mknn4CZ8eqrr8LHx6fKMWuqH7Guu+eee7By5UqkpqZi/fr1mDx5cpV6pvrAXNmvOC8vD1988QU2btyIS5cuyd6ch4cHBgwYgIEDB2LTpk0oKCjAli1b8Pfff+Opp57C1KlTq9TvGAwG7N69W55yMj4+vtpzu7q6Yvjw4bj55puhVCqRlJQkz8kyYMCABuWjqRCR3TBWkiTBxcUFM2fOhJ+fH26++WZIkoS0tDRER0fXWD8nSRJ27twpj5uoUCggSRK++eYb9OzZE4sWLZIHWB06dCimTJmClStX4sKFC3j55ZeRl5eHvXv3wmAwIDAwEEuWLJEHSThz5oxcxzxkyBC78zIzfv75Z6jVari5ueHuu++u15w2zIzMzEzZ84uOjoaLiwuMRiNUKhWGDRuGhx56CGPHjoW3tzfMZjOMRiOysrKQkZGBoqIilJeXQ6PRICMjA1euXEFGRgZSUlLk7m6Vz/r/cHFxQVBQEAYMGID7779fzl9D/quQkBB06NABBQUFSE5OrnKOa4aaFLI1lz59+sgtnzExMfKbuTFYQwI2btwoewFubm782GOP2cUS1heTycRvv/22/Oa+6aab+Ny5c3Uex9rybTabWa1W8+7du/nZZ5/lHj162IXTdOjQge+9917+6aefuLi4mA0GA+/YsYNvuOEG2dNxd3fnIUOG8Lp16zg9PZ3Lyso4JyeH33jjDblONTQ0lE+cOFEvu1auXCl7udu2bWtVj9BgMMgtrld7Kdbr9eOPP/J1113HK1euZK1Wa+exm0wmLisr461bt8o9atzd3XnOnDkcGhoqt0B/++23svdi7VFhrUu19aoCAgJ49erVckiKwWDg+fPny8fdv39/FY8wNzeXly9fzlOnTuW8vLw68yxJEhuNRp49e7ZcR7h8+XL+8ccf+cEHH+QdO3bYebF1HctkMrFWq2WNRsOZmZn87rvvyl0VrccfMmQIf/TRR5ySksJlZWWN9uTS0tLk0J/Ro0c3a6eH1gbOHj4TExMjh5gsWrSoUWEqJpOJExMT+YcffuAlS5bwnDlzuHv37vLN4erqylOnTuVff/2V8/LyGvTwFxQU8G233SYXXXv37s1btmyRhdVW9AoLC/n8+fO8f/9+/vzzz3n27Nncu3dvOTDVuoSHh/Njjz3Ghw4dqlLhba3Enz9/vtwbxfpgRkVFcbdu3Tg6Olq+Zh4eHvzqq6/W2q/Xitls5iVLlsjX5Ndff21VISwrK+M77rhDDqFJTk62W3/lyhW5+Ojp6ck333wzP/744/zcc8/x008/zXfddRf36dOHfXx8ZFGbM2cOq9Vq/uijj+SuZd27d+cTJ05wRUUFp6Wl8e7du3nQoEF2/0H37t15+/btbDAY5P/w8OHDcuhVr169ODMzs9p8mM3mevV1th53y5Ytslj16dOHU1NTWavV1hpQX9dxc3JyeOHChfLLEJYi96effsr5+fnN8r9WVFTwDTfcIF+v5uhu6iicXgit9S0qlara4OeakCSJy8vLed++ffzggw9y586dq7QAX734+vryuHHjODc3t94XUJIkvnjxIo8YMUL20lQqFffr148nTpzId911F9955508fPhw7tWrF0dGRrK3t3eVc7u6unL37t15/vz5nJCQID+ANaHX6/nAgQN89913y/WAVy+BgYH8wQcf1LsboNFolFsCfXx8qsTItTQ5OTmy9z948GAuKCiwW19aWspLliyRha62RaVS8Zw5c+RjVFRU8OOPP84qlYqVSiVHR0fzDTfcwF27drU7nkql4rvvvps7derEQ4cO5SeffJLffvttfv755+V+uAqFgt94440m14lptVpev3693BPG09OT161bx6tXr+Z58+ZxSkpKgwRLkiQuKSnhb775hm+88UbZu/X09JRjUZuzHk+SJJ4wYQIDdfeicXacXgitnk3Pnj1rfAPbIkkSFxYW8rfffssTJkyo4m2pVCoODg7msLAwDg0N5YCAAPb09JRFbODAgbX2D63pnOnp6Txz5swq56tpUSqV7O/vz9dddx0/9NBD/M0331QZ6aM+aLVaPnjwID/55JM8atQojo+P56FDh/KsWbP4wIEDDSquGAwGfuKJJ2QRPXHiRINsaSo7d+6UX1aPP/54tddCr9fz1q1befz48RwdHc3BwcEcEBDAgYGBHBERwddffz3fe++9/O2331YJLcrNzeUPP/ywSm8eq0fdt29fXrNmDe/cubNGsVUoFDx+/HjOyclpdD6tHtuCBQvk0CU3Nzd++umn+fTp0xwbG8tExAMHDuR9+/bV6c1bSxu//PILT5w4UfZ8iYhjYmJ43bp1Te4TX1M+/vnPf8pOxO+//97s52gtahNCp2gssU6SHRUVVSVU4Wq0Wi327duH9957DwcPHpQDXz08PHD99ddjwoQJiI+PR8eOHeWRmysqKlBcXIzU1FQcPXoUCoWiwYHa1pGNP/30U9x3333YvHkzDh48KDc6AJWV0yEhIejatStiY2PRo0cP+dM6P4f1WA3Bw8MDQ4YMwZAhQyrfXlcdpyHHIyK5AcJkMsFkMoG5dUIitFotNm3aBIPBABcXF4wePbrauTRcXV1xzz33YPLkycjOzkZeXh4MBgMUCgUCAgIQHh4OLy+vam0ODg7GqFGj8MADD+D8+fPQaDQIDw9Hv379cNNNN+Ef//gHwsLCcOTIEQwbNgynT5+WQ5CswdOenp6IjY3FH3/8gTvvvLPB18ZsNsvjBu7evRuSJMHb2xtPPPEEFi1ahAsXLshTARw9ehT33XcfFi9ejBkzZtiNi2i9d7OysrBt2zbs2LEDx48fl8cH9PHxwfjx4/HCCy+gV69eLfYfWkNodDodMjMzW+QcDqcmhWzNBZY38fjx42vsImY2mzkpKYnnzJljV+z09PTkESNG8BdffMHFxcVVugnZHsNaaX11v17bbcrLy+2OodPp+NixY3bd9CRJYoPBwGq1mtPT0zktLY3T0tI4Pz+fdTqdXWd0Z0OSJH7vvffkYci+/PLLVrFTkiT+7bffZG96wIABDaqeaOi5jEYj63Q61mq1cv9h266b1j7TWVlZ/Ntvv/H27dt5165dvHv3bg4MDGSFQsEjR47ksrKyBp23pKSE16xZY9fPNzo6mjdt2iSPumM2m/nChQt85513yt6xp6cn33ffffz111/zgQMH+L///S+/9tprfMcdd3BwcLDcuAWLZ3vLLbfw999/zxUVFS36/0mSxDt37mQiYiLif//73055X9cHOHvR2Pon33rrrVXGdpMkiSsqKnjDhg183XXXyTeEm5ubPKxTYwbovBpr5/i77rqLv/rqKzabzazT6Xjp0qUcFhbGH3zwQaNHoXE29u/fL1ew33nnnc028GxtqNVquZHE1dWVV69e7ZQPVHl5uTzOX3BwsN1YiDVhFd5Dhw7xxIkT5dhEpVLJQ4cO5UOHDlXbt1itVvPixYvtqlo8PDzYy8uLPTw87MRPoVBwcHCwfH8WFxe32gvs2LFjckPP448/3mZbjp1eCK11hHFxcbKXYL25/vrrL77vvvvkgGvrKBwbNmxo0KCcdVFYWMi33347ExGHh4fz1q1b+fDhw3JIho+PD69YsaLN3gS2lJWV8bBhw+R6n+3bt7fogAtXD1B6yy23NLiOtrUwm828fPlyuY7xvffeqzUY32Aw8KFDh3j27NkcHBws10P7+PjwM888w9nZ2bXur9PpeOvWrTxw4MAq4z0SEQcHB/Mtt9zCr7/+Op8/f77WzgEtRVJSkhxCM27cuDbbcuz0Qmh924SEhHBCQgIXFBTw/v37edq0aXajCfv6+vK8efM4MTGx2SPcy8vL+ZlnnpGLKjExMXz06FFeunSpXBS/9dZb6xU35uxYwzmslfi33nqr3ajVzX2uffv2yf2LAwICeM+ePU7pDVo5f/68/AKMi4urdtgs6+g3M2fOtLtHXVxcePjw4bxr1646hzWzYo1N/Omnn/jVV1/lZ599lt98803etm0bnz9/vtW8v5ooKirigQMHyg2abXUUGqcXwoiICFYqlUxEHBcXx3FxcXbFBVdXVx4yZAj/+OOPLVqMKy0t5aeffpp9fX35xRdf5LKyMtbpdPzuu+/yLbfc0uotrC1JRUWFPLCAh4cHf/jhh03uPng1kiRxQkKCPOagm5sbv/LKK07vVRsMBp4xYwYTESsUCo6Pj+dly5bxt99+y9u2beOVK1fyzJkz5ZAY6z0aFxfHH3/8cbPF8NUH23rPlsJkMvH48ePlF1l9IjucEacXwp49e8qxZbaLu7s7x8fH82effdZqN1dpaSnv2LFDDnK2Fn9yc3Od2otpKNY+2dbeN+Hh4bx79+5m87QlSeLTp0/LI2ArFIoq/YCdFUmSODExkfv161dloqSrF1dXV+7fvz+vXLlSHvm7NUXw5MmTvGrVqhYJnbE9jzXkytvbm//8888WO1dL4vRCOGDAAN6xYwcPHTqUQ0NDuVu3bjx9+nTetGlTo7rFNZXWvJkdidFo5FdeeUUOyu3SpQv/9NNPrNPpmpR/k8nEv//+u9xDhIh49OjR1Q6T5axYvdn777+f/f392c3NjV1cXNjFxYXd3NzYx8eHe/XqxcuWLZNfkq2ZN0mS+OzZs9y/f39WqVT82muvtagYfvDBB7Jzsnnz5hY7T0vSJCEEsBZAHoAzNmkBAHajcl6S3QD8LekEYAWARACnAPSv6/hsEUKz2czl5eWs0Wi4tLS0xhAXQfNSWFjIDzzwgNxC6e/vz3PmzOGDBw82ODTD2sK/fv16OXyEiHj48OGcmJjY5v5Pa35OnTrFGzZs4A8//JA//vhj3rBhAx84cIALCgocFiYlSRJ//fXXcmuuSqXit956q8UiG7Zv3y579k2ZvsKRNFUI/wGg/1VC+G/Yz1T3juX7OAA7LYI4GMDhuo7PFiEUOI7s7GyeNm2a3GpJROzn58djxozhzz//nM+fP89Go7Far8e2n/WZM2d49uzZcgu/QqHgCRMmNLgbmaB+GAwG/uSTT9jX15dVKhUvW7asxepfExIS5LCguXPn1qtfu7NRmxDW2bOEmQ8QUeerkicBGG75vgHArwCes6RvtJz0EBH5EVE4M2fXdR6B4wgNDcVHH32EgQMH4vPPP8e5c+egVqvx888/Y8+ePQgNDUW/fv0waNAg9OnTB8HBwfKw8eXl5UhPT8e+ffuwd+9eZGZmgpnh7e2N2bNn47nnnkNISMi1OZing3F1dcUjjzwCNzc3qNVqzJ07V+6x0tx06NABISEhSE9PR0ZGBvR6fb2GH2sz1KSQbO8Vdoa9R6i2+U7W3wC2Axhms24vgPi6ji88QudAkiTOzMzkL774gkePHl3tQA8KhYJ9fHw4MDCQAwMD2cvLyy7wV6lUclxcHG/ZsqXe4SOCpmE2m1vcQ8vPz5dH7+nbt2+VwTLaAmjJvsbMbG1ZaxBE9CiARwE02/D3gqZBRIiIiMADDzyAu+66C6dOncLevXvx008/IS0tDWq1GhUVFfKgpbb7+fn5ITIyEtOmTcO0adPQsWNH4QW2EgqFoto+282Jt7c3wsLCAACZmZlyf+drhcYKYa61yEtE4ahsTAGAKwCibLaLtKRVgZlXAVgFAJYQC4GTQETw9PTEjTfeiBtvvBELFy5EWloaLl++jPT0dGRlZaG8vBxms1meN6Nnz57o06ePPG+KEMFrC3d3d3nWPev8JZGRkQ62qvlorBD+AGAGgLctn9/bpM8noq8A3AhAw6J+sM1iFTN3d3d0794dsbGxACpHRaksaVR6I40ZBUfQtiAidOnSRZ4SITk5ucapIdoidQohEX2JyoaRICLKBPAKKgVwKxE9AiANwL2WzXegsuU4EUAFgIdawGaBg7AKXVPn+RW0Ta4WwmuJ+rQaT61h1W3VbMsA5jXVKIFA4HxYhdBkMiElJQWSJLV43WRrcW3kQiAQtDjWuZSBygYTnU7nYIuaDyGEAoGgXqhUKrmBJDs7G2VlZQ62qPkQQigQCOqFh4eHLIQ5OTlCCAUCQfvDNoQmPz8fGo3GwRY1H0IIBQJBvVAoFIiOjoZSqYTZbEZqaqqjTWo2hBAKBIJ6QUTo3LkzXFxcwMxITEx0tEnNhhBCgUBQb2JiYuTBFpKSkhxsTfMhhFAgENSbiIgIqFQqAEBqaiqMRqODLWoehBAKBIJ6YxtCcy01mAghFAgE9cbV1RWdO3cGABQWFkKtVjvUnuZCCKFAIKg3tkJYUFAghFAgELQ/lEoloqOjoVAoUF5ejuzsbHkkoraMEEKBQFBviAhRUVFwd3cHcO20HAshFAgEDcJWCK+VWEIhhAKBoEFERUXBw8MDgBBCgUDQTvH390dQUBCAysEXrp7Dpi0ihFAgEDQIpVKJmJgYAIBGo0F+fr6DLWo6QggFAkGDUCgU6NKlCwCgpKQEBQUFDrao6dQphES0lojyiOiMTdpiIrpCRAmWZZzNuueJKJGILhLR7S1luEAgcAxXC2Fubm6bD6Gpj0e4HsCYatLfZ+Y4y7IDAIioJ4D7APSy7PMJEYmZfgSCawxry7HJZEJaWpqjzWkydQohMx8AUFTP400C8BUz65k5BZWz2Q1qgn0CgcDJICKEh4fDy8sLQGXLcXvwCGtiPhGdshSd/S1pEQAybLbJtKQJBIJriPDwcHh7ewOoDKpur0L4KYCuAOIAZAN4t6EHIKJHiegYER27FlqdBIL2REhICHx8fABcG8NxNUoImTmXmc3MLAFYjf8Vf68AiLLZNNKSVt0xVjFzPDPHBwcHN8YMgUDgIGznLykpKWnzITSNEkIiCrf5eScAa4vyDwDuIyJ3IooBEAvgSNNMFAgEzgYRoWvXrgCAiooKZGVlOdiipuFS1wZE9CWA4QCCiCgTwCsAhhNRHAAGkArgMQBg5rNEtBXAOQAmAPOY2dwilgsEAofSrVs3AJBHoWnL1CmEzDy1muQ1tWz/BoA3mmKUQCBwfrp06QKFQgGdToeMjAwwM4jI0WY1CtGzRCAQNBgiQkhICDp06AAASElJgdncdgt/QggFAkGjCAoKgp+fHwAgOTkZkiQ51qAmIIRQIBA0isDAQFkIhUcoEAjaJb6+vrCGvuXm5qKkpMTBFjUeIYQCgaBR2A6+oNfrkZGRUccezosQQoFA0GisITR6vR7p6ekOtqbxCCEUCASN5mohbKt9joUQCgSCRhMZGQmVSgVJkpCWltZmG0yEEAoEgkbj6+srz1+SlpbWZgdfEEIoEAgajW3LcXp6OgwGg4MtahxCCAUCQaOxFcKMjAzo9XoHW9Q4hBAKBIJG4+bmJg/HVVZW1mYHXxBCKBAIGg0RoVu3biAimM1mJCcnO9qkRiGEUCAQNAnruIQmkwkpKSkOtqZxCCEUCARNokuXLlAqlTCbzUhNTW2Tgy8IIRQIBE3C398fAQEBAIDMzEzodDoHW9RwhBAKBIIm4eXlhY4dOwIArly5Aq1W62CLGo4QQoFA0CQ8PT0RHl45jdE1K4REFEVE+4noHBGdJaIFlvQAItpNRJctn/6WdCKiFUSUaJn3uH9LZ0IgEDgOT09PREZGAgDy8/OhVqsda1AjqI9HaALwDDP3BDAYwDwi6glgEYC9zBwLYK/lNwCMReXsdbEAHkXlHMgCgeAaRaFQoHPnzlAoFDCbzW2y5bhOIWTmbGY+bvleCuA8gAgAkwBssGy2AcBky/dJADZyJYcA+F01/adAILjGsLYcS5KEpKQkR5vTYBpUR0hEnQHcAOAwgFBmtoaR5wAItXyPAGA7QmOmJU0gEFyjWGe0Y2YkJSW1ueG46i2EROQN4BsATzGz3ZjcXJnrBuWciB4lomNEdCw/P78huwoEAicjIiICnp6eACoHX2hro9DUSwiJyBWVIvgfZv7WkpxrLfJaPvMs6VcARNnsHmlJs4OZVzFzPDPHWzttCwSCtolKpUJERGXBLzc3F6WlpQ62qGHUp9WYUDmh+3lmfs9m1Q8AZli+zwDwvU36dEvr8WAAGpsitEAguAZxc3NDp06dAAB5eXltbiInl3psMxTAgwBOE1GCJe0FAG8D2EpEjwBIA3CvZd0OAOMAJAKoAPBQcxosEAicD3d3d1kI2+KMdnUKITMfBEA1rL6tmu0ZwLwm2iUQCNoQLi4uiI6OhkKhQEVFBbKystCvXz9Hm1VvRM8SgUDQZIgI0dHRcHV1BQAkJiY62KKGIYRQIBA0C506dYKbmxsAtLlYQiGEAoGgWYiOjrYTwrY0HJcQQoFA0Cz4+fnJM9rl5uairKzMwRbVHyGEAoGgWXBxcUFMTAwAoKioCIWFhQ62qP4IIRQIBM2CUqmUhbC4uBhFRUUOtqj+CCEUCATNgq0QajQa5Ofnt5k+x0IIBQJBs0BEiIqKgpubmzx/SVtBCKFAIGgWiMhu8IW2FEsohFAgEDQbERERUKlUANpWLKEQQoFA0GyEhYXBx8cHAJCWlgaDweBgi+qHEEKBQNBs2I5CY20waQsIIRQIBM0GEaFr164AgNLSUuTm5jrYovohhFAgEDQb1QlhWwihEUIoEAiaDSJC586doVQqodPpcOVKlcHpnRIhhAKBoNkgIoSFhcHb2xsAkJycLDxCgUDQ/ggJCUGHDh0AtJ1RaIQQCgSCZiU0NPTaE0IiiiKi/UR0jojOEtECS/piIrpCRAmWZZzNPs8TUSIRXSSi21syAwKBwLno0KEDQkJCAAD5+fnQaDQOtqhu6uMRmgA8w8w9AQwGMI+IelrWvc/McZZlBwBY1t0HoBeAMQA+ISJlC9guEAicENuW44qKCmRmZjrYorqpUwiZOZuZj1u+lwI4DyCill0mAfiKmfXMnILK2ewGNYexAoGgbWAVwrbSctygOkIi6gzgBgCHLUnziegUEa0lIn9LWgSADJvdMlG7cAoEgmuMbt26AQC0Wi0yMzOdvuW43kJIRN4AvgHwFDOXAPgUQFcAcQCyAbzbkBMT0aNEdIyIjrWVbjgCgaB+hIeHQ6VSQZIkpKWlOX2DSb2EkIhcUSmC/2HmbwGAmXOZ2czMEoDV+F/x9wqAKJvdIy1pdjDzKmaOZ+b44ODgpuRBIBA4EUSEgIAABAQEAABSUlJgMpkcbFXt1KfVmACsAXCemd+zSQ+32exOAGcs338AcB8RuRNRDIBYAEeaz2SBQODsBAQEIDAwEEDbEEKXemwzFMCDAE4TUYIl7QUAU4koDgADSAXwGAAw81ki2grgHCpbnOcxs7l5zRYIBM6Mv7+/LIQZGRnQ6/Xw8vJysFU1U6cQMvNBAFTNqh217PMGgDeaYJdAIGjDuLq6ysNxlZeXIysrSy4qOyOiZ4lAIGh2bGMJjUYj0tLSHGxR7QghFAgELYI1hMYqhM4cQiOEUCAQtAhdunSBUqmEyWRCamqqEEKBQND+8Pf3l+sFMzIynHr+EiGEAoGgRfDx8UFYWBgAID09HTqdzsEW1YwQQoFA0CL4+PggNDQUwP9CaJwVIYQCgaBF8PT0RHh4Zb+LgoICqNVqxxpUC0IIBQJBi6BQKNClSxcQEUwmE1JSUhxtUo0IIRQIBC2GVQglSUJycrKjzakRIYQCgaDF6NatGxQKhSyEzhpCI4RQIBC0GOHh4fDy8gIzIz09HUaj0dEmVYsQQoFA0GJ4enqiY8eOAICsrCyUlZU52KLqEUIoEAhaDJVKhYiIygHqs7OzUV5e7mCLqkcIoUAgaDFUKhUiIyMBADk5OcIjFAgE7Q8XFxd06tQJROTUEzkJIRQIBC0GESEmJgYuLi5gZiQlJTnapGoRQigQCFoUWyFMTEx0tDnVIoRQIBC0KJ07d4arqysAICkpySlntKvP5E0eRHSEiE4S0VkietWSHkNEh4kokYi2EJGbJd3d8jvRsr5zC+dBIBA4MR06dEBISAgAIDc3F6WlpQ62qCr18Qj1AEYwcz9UzmE8hogGA3gHwPvM3A1AMYBHLNs/AqDYkv6+ZTuBQNBOcXNzQ3R0NIDKwRc0Go2DLapKnULIlVjbvF0tCwMYAWCbJX0DgMmW75Msv2FZf5tlSlCBQNAOcXV1bftCCABEpLRM5ZkHYDeAJABqZrZOVpoJIMLyPQJABgBY1msABDajzQKBoA3h4uKCzp07AwDUajUKCgqcrs9xvYSQmc3MHAcgEsAgANc19cRE9CgRHSOiY/n5+U09nEAgcFIUCgU6deoENzc3px2FpkGtxsysBrAfwE0A/IjIOi9yJABrpOQVAFEAYFnvC6CwmmOtYuZ4Zo4PDg5unPUCgaBNEBUVBXd3dwBwyhCa+rQaBxORn+W7CsAoAOdRKYj3WDabAeB7y/cfLL9hWb+Pnc0PFggErUqnTp3athACCAewn4hOATgKYDczbwfwHICniSgRlXWAayzbrwEQaEl/GsCi5jdbIBC0JcLCwuDt7Q3AOecvcalrA2Y+BeCGatKTUVlfeHW6DsCUZrFOIBBcE1hbjlNTU6FWq1FYWCiPSuMMiJ4lAoGgxbHOXwL8r+XYmRBCKBAIWhylUikLoUajQX5+vlOF0AghFAgELQ4RoXPnzlAqldDpdMjIyHC0SXYIIRQIBC0OEaFjx47w8vICAKcbjksIoUAgaBWsEzkBlUIoisYCgaDdYSuEycnJTjUclxBCgUDQKvj4+CA0NBRA5eALarXasQbZIIRQIBC0CkSErl27AgDKy8uRnZ3tYIv+hxBCgUDQKhARunXrBqBSCLOyshxs0f8QQigQCFoNq0dYUVGBrKwsp2kwEUIoEAhajY4dO8LT0xOSJCE1NdVpGkyEEAoEglaBiBAUFAQ/Pz8AztVyLIRQIBC0GkFBQfD19QUghFAgELRTAgMD4e/vD6ByOC6dTudgiyoRQigQCFoNV1dXef4Sa4OJMyCEUCAQtCrWEBq9Xo/09HQHW1OJEEKBQNCqXC2EzhBCI4RQIBC0KtbhuIxGIzIyMoQQCgSC9kdAQAACAgIAAGlpaTAajQ62qH6z2HkQ0REiOklEZ4noVUv6eiJKIaIEyxJnSSciWkFEiUR0ioj6t3AeBAJBG8LX1xfWKXydRQjrnLwJgB7ACGYuIyJXAAeJaKdl3bPMvO2q7ccCiLUsNwL41PIpEAgE8PPzQ1BQEAAgNTUVBoPBwRbVwyPkSsosP10tS22F+kkANlr2O4TKieDDm26qQCC4FvD09ETHjh0BAEVFRSgqKnKwRfWsIyQiJRElAMhD5bzGhy2r3rAUf98nIndLWgQA2wkJMi1pVx/zUSI6RkTH8vPzG58DgUDQplAoFOjatSvc3Nzg6enpFMNx1adoDGY2A4gjIj8A3xFRbwDPA8gB4AZgFSonfH+tvidm5lWW/RAfH+/4ZiOBQNBqTJkyBT179kRERAR69+7taHPqJ4RWmFlNRPsBjGHmZZZkPRGtA7DQ8vsKgCib3SItaQKBQAAA6Nu3L/r27QsicrQpAOrXahxs8QRBRCoAowBcsNb7UWVOJgM4Y9nlBwDTLa3HgwFomNnxvq9AIHAaiMhpRBCon0cYDmADESlRKZxbmXk7Ee0jomAABCABwBzL9jsAjAOQCKACwEPNbrVAIBA0I3UKITOfAnBDNekjatieAcxrumkCgUDQOoieJQKBoN0jhFAgELR7hBAKBIJ2jxBCgUDQ7hFCKBAI2j1CCAUCQbtHCKFAIGj3CCEUCATtHiGEAoGg3SOEUCAQtHuEEAoEgnaPEEKBQNDuEUIoEAjaPUIIBQJBu0cIoUAgaPcIIRQIBO0eIYQCgaDdI4RQIBC0e+othJa5jU8Q0XbL7xgiOkxEiUS0hYjcLOnult+JlvWdW8h2gUAgaBYa4hEuAHDe5vc7AN5n5m4AigE8Ykl/BECxJf19y3YCgUDgtNRLCIkoEsB4AJ9bfhOAEQC2WTbZgMopPQFgkuU3LOtvI2eat08gEAiuor4e4QcA/gVAsvwOBKBmZpPldyaACMv3CAAZAGBZr7FsbwcRPUpEx4joWH5+fuOsFwgEgmagzuk8iegOAHnM/DcRDW+uEzPzKgCrLOcoJaKLzXVsJyMIQIGjjWgBRL7aFiJfQHRNK+ozwftQABOJaBwADwAdACwH4EdELhavLxLAFcv2VwBEAcgkIhcAvgAK6zjHRWaOr4ctbQ4iOnYt5k3kq20h8lU7dRaNmfl5Zo5k5s4A7gOwj5kfALAfwD2WzWYA+N7y/QfLb1jW77NM+i4QCAROSVPiCJ8D8DQRJaKyDnCNJX0NgEBL+tMAFjXNRIFAIGhZ6lM0lmHmXwH8avmeDGBQNdvoAExpoB2rGrh9W+JazZvIV9tC5KsWSJRaBQJBe0d0sRMIBO0ehwshEY0hoouWLnltqj6RiNYSUR4RnbFJCyCi3UR02fLpb0knIlphyecpIurvOMtrh4iiiGg/EZ0jorNEtMCS3qbzRkQeRHSEiE5a8vWqJf2a6C56LXaDJaJUIjpNRAlEdMyS1uz3oUOFkIiUAD4GMBZATwBTiainI21qIOsBjLkqbRGAvcwcC2Av/tdYNBZArGV5FMCnrWRjYzABeIaZewIYDGCe5X9p63nTAxjBzP0AxAEYQ0SDce10F71Wu8HeysxxNmEyzX8fMrPDFgA3Adhl8/t5AM870qZG5KEzgDM2vy8CCLd8D0dljCQAfAZganXbOfuCytCoUddS3gB4AjgO4EZUBuS6WNLlexLALgA3Wb67WLYjR9teQ34iLaIwAsB2AHSN5CsVQNBVac1+Hzq6aCx3x7Ng21WvrRLKzNmW7zkAQi3f22ReLcWmGwAcxjWQN0vxMQFAHoDdAJLQxO6iTsIHaOZusE4CA/iFiP4mokctac1+HzYofEbQMJiZiajNNssTkTeAbwA8xcwltmNntNW8MbMZQBwR+QH4DsB1jrWo6bRUN1gnYRgzXyGiEAC7ieiC7crmug8d7RFau+NZse2q11bJJaJwALB85lnS21ReicgVlSL4H2b+1pJ8TeQNAJhZjcreUTfB0l3Usqq67qJoQHdRR2DtBpsK4CtUFo/lbrCWbdpivsDMVyyfeah8cQ1CC9yHjhbCowBiLa1bbqjswveDg21qKrZdDK/uejjd0rI1GIDGxr13KqjS9VsD4Dwzv2ezqk3njYiCLZ4giEiFynrP82jj3UX5Gu0GS0ReRORj/Q5gNIAzaIn70AkqQ8cBuITKupoXHW1PA23/EkA2ACMq6yMeQWVdy14AlwHsARBg2ZZQ2UKeBOA0gHhH219Lvoahsm7mFIAEyzKurecNQF8AJyz5OgPg/yzpXQAcAZAI4GsA7pZ0D8vvRMv6Lo7OQz3yOBzA9mshXxb7T1qWs1Z9aIn7UPQsEQgE7R5HF40FAoHA4QghFAgE7R4hhAKBoN0jhFAgELR7hBAKBIJ2jxBCgUDQ7hFCKBAI2j1CCAUCQbvn/wMn0wBolsSPJwAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/الله نور السماوات والارض.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/أنا لست لي.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/خالد محمد العربي.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/ومايعلم جنود ربك إلا هو.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/1إن هذا القرآن يهدي للتي هي أقوم.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/11محمد رسول الله.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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+g1Woxa9YsBAQEQKlUYurUqS/FUYsaWq0W8+bNQ2BgIFQqFSZPnoy6detmeUyMMdSrVw/9+/cHYwynTp3CkSNHXup3xylccCHmZAkRQRTFQn0hExH+/PNP7Nq1C0SEd999F8OHDy+yGRKA6ZiOHTuGrVu3gojQvn17DB06NFupdzKZDDVq1IBSqYRer8eSJUsQFxdXAFZzcgsXYk6W6PV6LFy4EF988QW2bt0Kg8Fga5MyQER4/Pgx5syZg7S0NPj5+WHmzJmFssB7Tnj27BlmzJiB5ORk+Pj4YNq0aXBxcXmth09EiIyMxKJFi6RJHZcuXcKePXsK9Y9pSYcLMSdL9Ho9tm/fjvnz52Pfvn2FqroXESEuLg6TJ09GQEAAFAoFJk6cWCRrSKQnOTkZs2fPxo0bN6BUKjF+/Hj4+/tnS4SjoqLwv//9D8HBwdJyo9GI5cuXIzIyMr9N5+QSLsScLBFFEcnJyQBMkwYKy+0+ESE2NhZTpkzBwYMHAQA9e/bEoEGDiuzMOQAQBAGrV6/Gpk2bQERo164dPvvss2x97mlpafjmm2+kIvHpG4revn0b27dv58XjCylF94zlFAiiKCIpKQmAqatwYRBiIsLTp08xcuRI/PbbbxAEAa1atcKCBQvg5uZma/PyREBAgBRWqFmzJubOnQt3d/fXesMGgwHLly/Htm3bIAgC2rZti9WrV8Pd3R2A6XtcvXo1nj59WhCHwckhXIg5WaLRaKRYo7Ozs81v+UVRxI0bNzB48GCp7sLbb7+NNWvWoEKFCja3Ly/odDosWbIEYWFhcHR0xHfffffaLAnA9MP0zz//YO7cudBqtahZsyaWLFmCPn36oG3bttJ6QUFB2LBhA/eKCyFciDlZkpKSAkEQIJPJbCrERAS9Xo/9+/ejb9++OH36NGQyGXr06IHNmzejRo0aRVqEiQgXLlzA77//DgBo3749unfv/towCxEhODgYX375JeLi4uDu7o758+ejVq1aUCqVGYocERE2btyIBw8e5PvxcHIGF2JOliQnJ0MURTDGbJaJYIkHz5o1C5988gmCg4OhVqvx2WefYc2aNShfvnyRFmHA5A2vXLkSSUlJ8PT0xJQpU15bG0MQBJw7dw4DBw7EjRs3pMHKjh07QiaTgTGGcuXKZSgO9PTpU6xevZoXjy9kcCHmZIlFiGUyWYELMRFBp9Phn3/+Qd++fTFnzhw8f/4cXl5emDNnDubMmVPkWh29ivv37+P48eMAgG7duqF58+avPC4igkajwdq1a/Hhhx/i8uXLkMvl6Nu3L8aMGQOlUplhfct2LPUptm/fzstkFjK4EHOyJDo6GkajETKZDJ6engWyTyKC0WjErVu3MGrUKLz//vs4deoURFFEs2bNsH37dowePTpDVkBRhohw9OhRxMfHw9HREQMHDoRCoXjlunFxcZg6dSomT56M8PBwODk5YcqUKVi6dKk0OGdZNyEhAVqtFowxjBkzBk5OToiKisLy5ct58fhCRObfNocD04X85MkTGAwG2Nvbw8/Pr0D2GRkZiTVr1mDNmjUIDw8HALi7u+Pjjz/GlClTUKZMmWLhBVswGAz4999/AQAVK1ZEs2bNMj0+S4hm7Nix2L17N4xGIypXrozZs2ejV69eGUIQFpKSkiAIAlQqFXr37o179+5h79692LVrF4YOHYqWLVvm+/FxXg8XYs4rMRqNePToEURRhIeHB7y8vPJtX0QEg8GAo0ePYsaMGbh58yYEQYC9vT26du2KiRMnwt/fH3Z2dvlmg61ITExEaGgoAKBevXqZhoCICElJSZgyZQp27doFURTx5ptvYvHixWjUqNErB/UsE3BkMhns7e3xv//9D6dOncLz58+xZMkSNG7cOFMB5xQsPDTBeSU6nQ73798HAFSpUiXfCqsTEaKjozF9+nQMGjQI165dAwA0b94c27Ztw8aNG9GyZctiKcKAaSKGJVfb19c303V0Oh1+/PFHaVJG69atsXnzZjRu3DjLzApL+EEul0Mul6NFixbo0aMHAODQoUM4ffo0n/pcCOBCzHklaWlpUqpTjRo1rC7E6b3gXr16Yf78+UhISICXlxe+++477Nu3Dz179oSTk1OxCkW8iNFolATT0dHxpWM1Go1Yv349li1bBoPBgMaNG2P16tWoVKlSlp+LxYsGAHt7eygUCiiVSowbNw7e3t5ISUnB4sWLkZaWln8Hx8kWXIg5ryQkJARJSUmQy+WoVauWVWfVEREePnyISZMmoV+/frhw4QKICC1btsQff/yBqVOnwsfHp1gLsAVBEKR0she9fkEQsH//fkyfPh1paWmoVKkSli9fjmrVqmXrs4mPNzVgd3R0hJ2dHRhjaNiwIfr27QsAOHnyJP7++2/uFduY1woxY2w9YyyaMXYn3bKZjLEwxtgN86Nruve+YowFM8buM8Y65ZfhnPzn8ePHMBgMUCqVqFKlilVEkYiQnJyMFStWoEuXLli+fDkSEhLg6emJr7/+Grt370br1q1fmTVQHBFFUZrtlv64RVHEsWPHMG7cOMTFxcHDwwPz589H06ZNs/VdWEI+AODi4iLFguVyOUaNGgVfX19otVosXrwYiYmJ+XBknOySHY94I4DOmSxfSEQNzY/DAMAYqw2gH4A65v9ZwRizfXECTo4hIoSEhMBgMEChUFglY8IyPfmjjz7CpEmTEBwcDIVCgS5dumDPnj2YMWNGifGC06NQKCQBtvSYIyKcOXMGn332GcLDw+Hs7Iwff/wRPXr0yHZRI1EU8ejRIwCAl5eXlO7HGEOtWrUwePBgMMZw4cIFabo4xza89hslotMA4rO5vZ4AdhCRjogeAwgG0CwP9nFshCiKCA0NhSiKcHZ2RqlSpXK9LSJCWloaVq1ahZ49e2L//v3Q6/WoVasWVq1ahe3bt6NVq1YlygtOj4ODg9TcNDIyUpq2PHLkSISEhMDBwQEzZ87E0KFDs/0ZEREiIiKkIj9VqlSBg4OD9D5jDMOGDUOVKlVgMBiwdOlSxMTEWP/gONkiLzHisYyxW+bQhSWLvByAZ+nWCTUv4xQxjEaj1NXB09MTKpUqV9shIoSGhmLMmDGYNGkSnj17BgcHBwwfPhwHDx7E0KFD4erqWuK84PTY29vDyckJgKkgvCiK+P333xEUFASVSoUvv/wSo0ePzvF3cP78eURHR4MxhlatWmX4jBljqFSpEkaMGAGZTIYbN25gx44d3Cu2EbkV4pUAqgBoCFOH5wU53QBjbCRj7Apj7Ar/JS58CIIgDfR4eHjkaqBOFEWcO3cOH3zwATZt2gSdTofq1atj3bp1WLp06WtH/UsKLi4uqFWrFgDg7t27uHHjBvbu3QsiQt26dTF+/Pgc5/oKgoCDBw9Cp9OhXLlyaNWqVabrffTRR6hTpw4EQcCqVasQGhrKxdgG5EqIiSiKiAQiEgGsxf+HH8IAlE+3qq95WWbbWENE/kTk7+3tnRszOPmI0WhEbGwsAJNHnNOwgV6vx5YtW9CvXz9cunQJcrkc3bt3x+7du9GvXz+o1Wouwmbkcjm6du0KmUyG8PBwzJkzBzdv3oRMJkOfPn2kGhE5ISkpCZcuXQIANG3aNNP8ZMYYypQpgzFjxkChUOD+/fvYtGlTno+Hk3NyJcSMsTLpXvYCYMmo2A+gH2PMjjFWCUA1AJfyZiLHFuj1ekRERAAAypQp81IhmawwGo1Ys2YNxo4di7CwMDg5OeGLL77A5s2bUbt2bS7AmdCqVStUq1YNRqMRu3btgsFggLe3d4YyljkhMDBQCku0bNnylWENxhjef/99NGnSBKIoYv369QgODuZecQGTnfS17QAuAKjBGAtljA0DMJcxdpsxdgtAWwATAYCI7gL4HUAAgCMAxhBR4Wlyxsk2CQkJSExMlEopZjc0YTAYsH37dkybNg0pKSkoV64cVq5ciRkzZsDNzY2L8CsoU6YMatSoAQCSCL799tuoXr16jrdFRLhz5w6Sk5OhUCjQokWLLD93T09PTJgwASqVCk+ePMGaNWt48fgCJjtZE/2JqAwRKYnIl4h+JaJBRFSPiOoTUQ8iiki3/g9EVIWIahDRX/lrPie/CA8Ph9FohEKhQNmyZbPVJSI5ORnz58/HuHHjkJCQgFKlSmH16tUYOHAgVCoVF+EsYIyhbNmy0g+eWq1Gv379cpVJIggCzp8/D1EUUa5cOVSsWPG1++7atSveeustEBG2bt2KO3fucK+4ACmZ+UKc1xIREQFBEKBQKFCmTJks17V0D/7qq6+wdetWGAwGlCpVCr/88gu6dOnCBTgbMMYwe/ZsCIKAGzduwNvbG23atMnVZxcTE4MzZ84AABo1apStYk0uLi6YOHEizp8/j4iICCxfvhzLly/PUUiKk3v4FGdOpkRGRkpC7OPj88r1iAgPHjzAkCFDsHnzZhgMBtSrVw9bt25Fv379inRH5YKEMQYPDw8sWrQIf//9N37//fcMtYWzi6W2cVhYGGQyGbp165atYkmMMbRp0wadOpkmw+7evRtXrlzhXnEBwa8SzkuIooinT59CEASo1epXCjER4cqVKxg4cCCOHj0KAOjQoQN+//13vPPOO4Wi43NRgjEGBwcHuLu7Z1r8JzskJydj/fr10Ov1qFChAtq3b5/t7Tg4OGDChAlwcXFBXFwclixZwovHFxA8NMF5CZ1Oh8ePHwMAypcvn2knDFEUceLECYwZMwZBQUFQKpXo168f5s6dWyKnKRcGiAgHDhzApUuXwBhD//79X1lWMzMYY3jjjTfQo0cP/Pbbbzh8+DBOnz79kphbWjVFRkYiJiYG4eHhCA8PR1JSEhQKBby9vdG4cWNpNh8/F7IBEdn80aRJE+IUHuLj46lBgwYEgPr06UMajSbD+waDgf78808qW7YsASC1Wk1TpkyhxMREEkXRRlZzoqKiyN/fnwBQxYoV6cGDBznehiiKdOnSJfL09CQA1LVrV0pNTZXei4uLo927d1PPnj3Jy8uLFAoFyeVykslkxBgjxhjJZDJycXGhvn370s2bN0kQBGsfapEAwBXKpgbaXISJC3GhIzIykkqVKkUAaPLkyWQ0GqX39Ho9bdiwgby8vAgAOTs7088//0ypqalchG2IwWCg77//XhLGH374IcP3lhP0ej2NHj2aAJCDgwPt2rWLEhISaN26ddSiRQtSqVQEgACQXC4ne3t7cnd3J29vb/Ly8iJHR0fp/WrVqtGhQ4dIp9NZ+YgLP1yIOXkiMDCQnJycSCaT0dy5cyWBNRgMtHHjRnJ3dycA5OHhQWvWrCmRF1lhQhRFOn/+PPn4+BAAatKkCYWHh+dpm7dv35bueBo3bky9e/eWBFgmk1HZsmVpxIgRtH79ejpw4ABdvHiR7t69Szdv3qRDhw7RwIEDyd7engCQu7s7jR49mh48eFCivGMuxJw8cfr0abK3tyelUkkbNmwgIpOXtHXrVskT9vb2pu3bt5Ner7etsRxKTEykLl26EABycnKiffv25fnuxGAw0Oeffy55tpawQ5UqVWjmzJkUGBhIer0+0/2Iokipqak0Z84cKcTBGKOaNWvS8ePHc+2pFzVyIsQ8a4LzEqZzyDR4o1AooNVq8euvv2Ls2LGIjY2V0qw++OADnmdqY0RRxJ49e/DPP/+AMYaPPvoIHTt2zNMAGREhISEBOp0uw3a6deuGw4cP49tvv0WNGjWgVCoz3Q9jDPb29pg4cSJ27NiBLl26QKVS4d69exgyZAj2798vNTXlmMmuYufng3vEhYsLFy5IHvGyZcto7ty55OTkJHnCmzZt4p5wISEyMpIaNWpEAKhy5cr04MGDPHnDoihSSEgIvfvuuySXyyWPWKFQ0Pz583McWhBFkRITE+n7778nZ2dnAkBly5alkydPFvsxBfDQBCcvBAQEkJOTEzHGqEGDBtLgi6+vL+3Zs4cMBoOtTeSQKXzw008/kUKhIIVCQXPmzMnzbX9sbCz17t1byn6oX78++fn5EQCqXbs2PXv2LMcCKooi6XQ6WrZsGbm4uBAAqlmzJp08ebJYn0tciEsIWq2WYmNjKTk52aondGxsLNWsWVPyhmBOhzpy5EiJie8VdkRRpP/++4/KlStHAKhRo0Z5HqATBIF+/vlnkslk0qBfUFAQLVu2jORyOcnlcpo9e3aesjG+++47srOzIwDk4+NDK1asKLZ3V1yISwjnzp2j6tWr05tvvkmnTp2yyjZFUaSnT59S/fr1JRGuWrUq/f333yVqxLswI4oiPXv2jN5++20CQI6OjrRz58483+rfv39f8n5hTk3csWMHRUZGUtOmTQkAVapUiYKDg3O1L1EUKTk5mWbMmEGurq4EgFxdXWnbtm3F8tziQlwCEEWRtmzZQjKZjORyOZ0+fdoq24yPj6cBAwZI8UG1Wk1//vlnsbxQiiqJiYk0YMAA6bsfN24cpaWl5Xm7a9euzXAXVL16dSnmvHnzZlIqlSSTyWjq1Km5vgOzhCl27Nghpdv5+fnRvXv38mx/YSMnQsyzJoooRITAwECIoghPT0+ULl06z9tMTU3FtGnT8Pvvv0uj2oIg4Pnz53yaaiHBYDBg1apV2LVrF4gInTt3xrfffpvjVkqZkZiYKD1Xq9X44osvUKVKFTDG0L17d7Rq1QqiKGLLli24d++eyZPLIYwxqFQqfPDBB/juu+9gZ2eHJ0+e4O+//87V9ooLXIiLKESEgIAAAEC5cuVyVakrPTqdDgsXLsS6detgNBpRq1YtODs7w2Aw4Pbt2zzdqBBARDh58iTmzp0LnU6HWrVqYcGCBfD29s7zDyWRKWXNQqdOnfDhhx9K1fPc3NwwYcIE2NvbIyIiAitWrIDBYMj1/uRyOXr37o2qVasCAPbu3Yu0tLQ8HUNRhgtxEUWr1SIoKAiAqTCPpR17bhBFEbt378a8efOg0+lQp04drFmzBvXq1QMABAQEQKfTWcVuTu6JjIzEtGnTEBcXBw8PD8ydOxfVqlWzyt1KdHQ0Dh8+DAAoW7Yspk2blqHYE2MM7dq1Q/v27UFE+OOPP3D9+vU8ebFeXl7o2rUrAODKlSt53l5RJjutksozxv5hjAUwxu4yxiaYl3swxo4xxoLMf93NyxljbAljLJgxdosx1ji/D6IkEh4eLnVZrlmzZq4nVhARLl68iKlTpyI5ORllypTBkiVL0KxZM6mz8IMHD6DVaq1mOyfniKKI3377DdeuXYNCocD48ePRsWNHq9V7fvbsGe7evQsAGD58OBo2bPiSwDs5OWHChAlwdnZGbGwslixZkucf6Pfffx/u7u5ITk7G9u3bS2yLpux8i0YAk4moNoA3AIxhjNUG8CWAE0RUDcAJ82sA6AJT09BqAEYCWGl1q0s4RISQkBAkJSWBMYY6derkejvh4eGYMmUKnj17BkdHR8yePRutW7eGUqmUOnMkJiZyj9jGREVFYdOmTRAEAXXr1sVnn31m1VmNRqMRRqMRAFC3bt1MBd7SiPTdd98FABw6dAjnzp3LtRfLGEPDhg3RokULAMC+ffvw4MGDEukVZ6dnXQQRXTM/TwYQCKAcgJ4ALL23NwF4z/y8J4DN5oHD/wC4vdD1mWMFnj59irS0NMjlctSsWTNX29BqtZg9ezb+++8/KBQKjB49GgMGDIBcLgdjTCpOLopiiY7fpSf9SHdB7nPfvn24f/8+FAoFPvnkE3h7e+fLfgBk6WWr1WqMGzcOHh4eSExMxOLFi/N0bqhUKowYMQJqtRphYWHYuXNnrrdVlMnRfQ1jzA9AIwAXAfjQ/zcNjQRgaeNQDsCzdP8Wal724rZGMsauMMauxMTE5NTuEk9qaiqICGq1Gk5OTjn+fyLCP//8gy1btkAURbRp0wZffPFFhrY6jo6OkMlkIKISJcRZCa3RaMRvv/2GCxcuQBCEAhHkqKgorFmzBkajEVWqVMH7779vsxZUjDE0atQIffr0AWMMJ0+exPHjx/PkFbdr1w6NGjUCYGrRFBMTU+K84mx/m4wxJwC7APyPiJLSv2fOmcvRJ0dEa4jIn4j88+PXvSSRm8GahIQELFiwAKmpqfDx8cGPP/4IT0/PDNuys7MDYwxEVGJCE0SEv/76K9MfHiLChQsXMGnSJPTu3RtbtmzJd8EQBAGbNm3CrVu3JG84qx6CBYGdnR3GjBkDHx8fpKamYvHixUhKSnr9P74CZ2dn9O/fH4wx3Lt3D2fPnrWitUWDbAkxY0wJkwhvJaLd5sVRlpCD+W+0eXkYgPLp/t3XvIxjRhRFGI3GAr/FtSAIAjZu3IgzZ85AJpPho48+QqNGjV4SdEt1LSIqMb3LUlJSsGrVKvz1118ZvhsiQlxcHGbOnInY2FhotVqULl06X/OriQhBQUFYsWIFBEFAvXr1MGTIkHzrBWg5luyckzVr1sTgwYMhk8lw/vx57N+/P0/ncseOHVG2bFkYDAYcP34819spqmQna4IB+BVAIBH9ku6t/QCGmJ8PAbAv3fLB5uyJNwAkpgthcGC61ezTpw+mTZuGwMDAAo833r17F/Pnz4fBYECtWrUwduzYTC9ulUpV4oQ4NjYWly5dwrJly5CcnCwtFwQBa9askX68PvnkE7Rr1y5fhViv12PRokV49uwZ7O3tMXny5HyJDQOmvF6FwtTCMjsZMkqlEiNHjkSFChWg0+mwdOlSxMbG5mrfjDFUrFgRfn5+AIBbt27lKUe5KJIdj/hNAIMAtGOM3TA/ugL4GUAHxlgQgPbm1wBwGMAjAMEA1gIYbX2ziy5EhKtXr+LgwYOYM2cO/vvvvwLdv8FgwLJlyxAeHg5HR0dMnz4dFStWzFRQSqIQ3717F7Gxsbh69SouXrwIwPSdXb58GYsXL4bRaETDhg0xadIkqFSqfLODiHDp0iVs374dRIQOHTqgZ8+e+RYbtrOzk2bnZXfMxs/PD8OHD4dcLseNGzewc+fOXKefqdVq1KhRA4AppzkuLi5X2ymqZCdr4iwRMSKqT0QNzY/DRBRHRO8QUTUiak9E8eb1iYjGEFEVIqpHRFfy/zCKDqIo4q+//oLRaISHhwdatGhRYNOHiQjXr1/Hn3/+CcB0O/juu+++cv8lUYiDgoIgiiIMBgOSk5OlGWczZ85EdHQ0XFxcMHPmTJQr99L4s1XRarVYvnw5kpKS4O7ujilTpmTaTdtaeHl5SdPkL1++nK27NLlcjiFDhqB69eowGAxYuXIlwsPDc51ZUr16dQCm8YuSNoDPZ9YVMPHx8Th37hwAoF69eqhcuXKB7Vuv12PZsmV4/vw53NzcMG7cuCxrFKQPV5SUKc4pKSkZXsfExOCHH37AyZMnIZPJMGjQoDx3wMgO165dw19//QUAePfdd9G8efN83ae3tzdq164NADh79my2Q2Zly5bFyJEjoVAocP/+fXzzzTdYvXo1du3aBY1GkyMbLNOdExMTERcXV6IyJ7gQFzD379/HvXv3wBhDp06dCrTV0JUrV3DgwAEAQJcuXfDmm2/yYj7pICJER0eDiCAIAvbv34/evXtj4cKFMBqNqFu37kspfvmBwWDAxo0bJW945MiR+X6eKJVKDBgwAAqFAs+ePcMnn3yCgwcPSncFLyIIAgIDA/Hjjz9KM+IEQcDmzZsxatQojBkzJkPtiuxQpkwZqFQq6PV6RESUrGElha0NKEkQEY4cOQK9Xg83Nze8/fbbBZYPqtPpsGLFCiQmJsLd3R2jR4/m/eZeQKPR4MGDBwBM+cKbNpnmK8lkMrRo0QLz58+Hr69vvtvx+PFjyRt+++234e/vXyA/mB07dkS/fv2wbds2XLp0CR9++CGaNGmCrl27omXLlnB3d4coioiPj8e2bdtw5MgRhIVlnhBVunTpHJ9f7u7ucHd3R1RUlPQ9lBS4EBcgycnJOHv2LIgIlStXRt26dXO9LcugSHYv0ICAABw5cgSAyRtu1qxZifGGBUFAfHw8VCoVXFxcXnncERERuHPnjvRaJpPBz88Pffv2xfjx4/M9XQ0wfa9//PEHwsPDoVar8dFHH+XroGB6nJ2dsXDhQpQvXx4bN25EREQEzp49i7Nnz0KpVMLR0RGiKCI1NTXLUJVarcbs2bPh6emZo/2XKlUKPj4+iIqKwrVr10BEJeYc5UJcgKS/0Nu0aZPrGrLpJ1golcrX5pVaLu74+Hg4OTlh6NChhcIbNhqNSEhIABHB3d1dSp+yFpbc34ULF+LQoUNwcnLCiBEjMHDgwJf2ZTQasWXLFkRERECpVGLEiBFo06YN/P39paySghCFiIgIbNu2DUSEunXron379gUmRowxeHp64vvvv0f//v2xfft2HD58GE+ePEFiYmKGUINarYazszPs7OygVCrh5uaGKlWq4NChQ9BqtTh06BA6duyYozCOu7s7atWqhVu3biEwMBBRUVFSvZNiT3YryOfno6R06Ni6dSsxxsjOzo52796d69Y2giDQzJkzpb5fwcHBWa4fGRlJdevWJQDUqlUrSklJydZ+Dhw4QGq1muzs7GjPnj25sjUzjEYj7d+/n/r06UONGjWiBg0a0Keffkp37tyxWmdfURTp+fPn1L9/f6kHGwDy8PCgc+fOZdiPIAh08OBBcnd3JwDUvHlzioyMJFEUC7TTsCiKtGTJEpLJZKRQKGj58uU263RsOfbnz5/Tvn37qHz58gSAZDIZ+fv70x9//EE3b96koKAgCgkJoeTkZEpJSaEePXoQAHJ3d6cLFy7keL9Lly6VWij9999/+XBkBQd4q6TCh9FopE8++YQAULly5ejRo0e53pYgCPTll18SACpfvjw9fvw4y/WPHj1KKpWKGGO0ePHibF/chw8fJnt7e7Kzs6M//vgj1/a+yNmzZ6U2OekftWrVopMnT+a5LZOlJfygQYNIoVAQAKpRo4bUzn3IkCGk0WhIFEXSaDS0c+dOqlixIgEgLy8vOnLkiE0EMDIykho2bEgAqE6dOhQREWHzlvOpqak0atQokslkJJPJqFu3bvT06dNM7RJFkU6cOCH1o+vXrx+lpqbmaH/Hjh2TOkjv37/f5sefF3IixDxrooCIi4vD5cuXAZjS1vIy6ENEUmqQSqV67S3933//Db1eD29vb7Rp0ybb+3FxcYFMJoMoinmqJZCetLQ0zJs3D1FRUXB0dET37t3Rrl072NnZITAwEMOGDZPig7mBiBAZGYkRI0Zgy5YtMBqNaNasGfbs2YMOHToAAH7//XdMnjwZ27Ztw/Dhw/HJJ58gJCQEDg4O+Oqrrwo0HJCef/75B7dv34ZcLsewYcNQqlQpm8dIT548ifXr10MURbRq1QorV66Er69vpnYxxvDmm2+ie/fuAICDBw/muEymvb29VPHPUpazRJBdxc7PR0nwiM+dO0d2dnbEGKMFCxbk6ZfeaDTSiBEjCADVrl2bIiIisly3c+fOUsv1nDSZvHv3Lrm4uJBMJqM5c+ZYxTu5fPmy5DGNHDmSdDodpaSk0KxZs8je3p4A0Icffpjt8MmLpKSk0Mcff5zBg7OEPC5fvkyVK1eWPPD0IYty5crRsmXLKC0tzSZemFarpffff58AUMWKFenx48c29wbT0tIkm3x8fOjKlSuvtUkURbp48SJ5eXkRAOrWrRslJSVle58nT54kAMQYo0OHDtn8M8gL4B5x4YLI1GtMp9PB1dVVKoSdFyzVwVQqVZaDdUlJSdIspYoVK+ZogNDLywtqtRqiKCIiIiLXXmp6Tpw4gcTERDg7O2PAgAHSaPzkyZPx/vvvAzAVCN+0aVOOPSJBELB+/Xrs2LEDoiiiQ4cOWLduHWrXrg3GGJo0aYLNmzejXbt2cHV1hb29PXx8fNC3b1/s2bMHo0aNglqttokXGhwcjNOnTwMwDeSWL1/e5t7w7du3cezYMQBA165d0aBBg9faZCmT2bdvXzDGcOLEiRyVyUyfDSSTyWz+GRQUPGuiAEhLS8O///4LAKhQoQLq1q2bpxOM6P+nHL8ua0Kn00lhDC8vrxztR6VSwdXVFdHR0UhKSoIgCHnKexYEQarf4Ovri/r160ufg729Pb744gtcuHABDx8+xMyZM1GhQgV07do1W/skMrV8+umnn6DValG9enUsWLAAPj4+0j4sHSb27duHGzduICUlBRUqVECVKlWk6dy2gIhw9OhRxMTEwMHBAX369Mn2MaekpCAhIQEymQyurq5SMX9r2HTs2DEkJyfDxcUFgwYNynbVN4VCgVGjRmHfvn0ICwvDkiVL0Lp1a3h4eOTZruIK94gLgPDwcNy+fRuAKUHfGjUDsptHnF6IXVxccrwfa4pTamqqNAGgUqVKGRqeWlo+zZkzB+7u7oiJicH//vc/3Llz57XeFJnjwlOnTkVERAScnJwwe/ZsyRN+8XicnJzQqlUrdO7cGbVr15bqLtsKnU6Ho0ePAjAV0nnjjTdea48oijh37hz69u0Lf39/NG/eHB9++CGOHz9uldiqTqeT4rvly5dHkyZNsv0ZMcZQs2ZNDBo0CDKZDOfOncPBgwetckdVXOFCXADcvXsX0dHRUCqVaN26tVUuektC/etu35KSkqT8z5wOEBJRjieOZIVGo0FqaioAZPBULchkMvTo0QMzZsyAnZ0dHj16hF27dr22opdOp8Ps2bNx/vx5yOVyfPrpp+jRo0eRua0NCwuT8stbtWoFd3f3LNcnMhWo//jjj3HkyBFER0cjLCwMhw8fxkcffYRjx47lWfRiYmJw//59AEDTpk1z7DzI5XKMGDECfn5+MBgMWLp0KaKjo1//jyUULsT5DBHhzJkzICJ4enqiTp06BSoQ8fHxUpPRChUq5Pj/Ld6VQqHIs92CIEg/IK9K9FcqlRg0aBAaNWoEIsKJEyeyrI8rCAJ+++03bNq0CaIoonXr1gVSD8KahISEICIiAjKZLFs/1NHR0Zg6dSoePnwIe3t7DBo0CJ988glcXFwQHR2NH374Ide1gS2EhYXh2bNnYIyhefPmOQ5JWWoMjxgxAnK5HNevX8cff/zx2h9VSwaQpXZFSfGiuRC/BiKSBEQUxRyfGKmpqbhyxVQJtGLFiqhUqZJV7LK3twdgqqiW1XTTJ0+eQBAEODg45LjFjiiK0gw+Ozu7PNfFUCgU0oy+tLS0V36Wrq6uaNasGQDgwYMHr6xNK4oiTp8+jZkzZyI1NRV+fn6YO3duvhVPzw8ssW1BEODq6opq1aq9dv0dO3bg4sWLkMvlGDNmDFatWoXly5dj3LhxkMvluHjxIrZs2ZKnink3b96E0WiEk5MTatWqlattyGQyDB48GDVr1oQgCFi5cuUra1NYSD/mkZ0C9cUFLsSvQRRF7NixAxMmTMDcuXNfKpP4OkJDQ3Hv3j0AwJtvvmmVugGWOCdgOlmz6mYQHBwMwCRu3t7eOfJq9Xq9dDE4OTnl2SO2t7eXbnGjoqJeuZ5cLkf9+vWhUCiQlpaGoKCgl9axiPCIESMQFhYGR0dHzJ49O9OWT/kJESE5ORk6nS5X3ptWq8XJkycBmEJH1apVy9L+1NRUHD58GEajEdWqVcPEiRNhb28POzs7jBo1CnXr1oXRaMS6detyHQogIty6dQtEBDc3N1SuXDlXnyljDKVLl8aoUaOgVCpx7949bN68OUuvWKFQSF5xSamBDWSvVVJ5xtg/jLEAxthdxtgE8/KZjLGwF7p2WP7nK8ZYMGPsPmOsU34eQH5jST1bvnw5tm7dmuMaq7dv30ZMTAxUKhVatWplFZFgjMHNzQ2AKQaclefw7JmpobaLi0uOi7AYjUaIogjGmNS/Li84OjpKNkRGRmb5A1KvXj2oVCpoNBr8999/GS5eg8GAAwcO4OOPP5Zuz7/66iubdDdOTU3FyJEjMWnSJISEhORYjKOjo/Hw4UMAwBtvvPHaAdXHjx/j0qVLAEzV0iyTPhhjKFOmDIYPHw6FQoHg4OBsF3h/EY1GI8WHS5cunadmpYwxfPjhh2jYsCFEUcSvv/4qHW9W/wOUnBrYQPY8YiOAyURUG8AbAMYwxmqb31tI6bp2AID5vX4A6gDoDGAFYyx/uh0WAOlTxRQKRY4udMugChHBy8sr01H83MAYQ5UqVcAYy7KtDBFJOcT29vZwdnbO0X5kMhlkMhmIKNce34vbs3S2SEpKyjKOWbVqVdSoUQOiKOLw4cNITU0FESE2NhYLFizA8OHDpdlwX3/9NSZOnJjrIkq5xVJMad++fVi9ejXWrl2b41ZBUVFRUu3dxo0bZ3l+iKKI/fv3IykpCQ4ODujatWuGlDKZTIb27dvDy8sLBoMBv/32W65u76Ojo6W7kEaNGuWpGJOlkNDYsWOhVqvx5MkTrFu37pWZHQkJCdDpdJDJZK8dtCxOZKdVUgQRXTM/TwYQCCCrPjE9AewgIh0RPYapd10zaxhrK9JXOsuJEGu1Wty4cQOAqZNBxYoVrWZT5cqV4eDgAIPBkKF0Y3p0Op2UMeHu7p7jsIijo6MknIGBgVaJ2TVo0ACAySM+f/58lnFiSw7xzZs3cfLkSRw+fBi9e/fG9OnTERsbCw8PD8ydOxeTJ0+Gg4NDnm3LCUSEkJAQzJs3DxqNBhUrVpTStXKC5XN1cHCQera9isTERBw4cACiKKJmzZqZdu2oXLkyOnfuDAA4evSolDaZEx4+fIiwsDDIZDK0bNkyz84DYwzvvfce3njjDRARtmzZgsDAwEzXffr0KYgITk5O8PT0LDKZL3klR2cNY8wPQCMAF82LxjLGbjHG1jPGLD9f5QA8S/dvochEuBljIxljVxhjV6zRn0oURVy+fBm//PILzp07l+smhi/y4uSJnJwYkZGRePToEQDA39/fanVlGWOoXr06PDw8QESvPN7U1FQppp2b20t7e3s0bNgQgCnEktP4eGa0bt0aZcuWhUajwU8//YTg4GBpEJSIYDAYkJSUhFu3bgEweXmpqakYPnw4+vXrhzNnzkAURTRs2BDr16/HyJEjpYHLgsRgMGDhwoW4f/8+lEolJk2a9Nr4bmaEhIQAMP3wlClTJsv/f/DgAe7cuQPGGHr06JHpHY5SqcSQIUPg6uqKxMREbNiwIUcdkYkI//77LwRBgLu7O+rVq2cVMXR2dsaECRPg6OiIiIgIrFy5MlO7nj59Kq1fkiaAZFuIGWNOAHYB+B8RJQFYCaAKgIYAIgAsyMmOiWgNEfkTkX9eR7mJCAcOHEDv3r0xefJkDB06FA8fPrRa6kv60ER2ZxcBpossLCwMjLFsJennhLJly0oZGA8ePJCmPKcnOTlZKtaTm7quKpUKDRs2hEwmQ2RkpCQaeaFmzZoYM2aM1Pm3Z8+e+Omnn7BhwwYsX74cw4cPR/PmzfHOO+9g7ty50i1sbGwsUlJSUKpUKXz++ec4cOAAevToYZO6ykSEs2fPYsuWLRBFEW3atMGAAQNydG5YsORVq1SqLEMrlpluGo0GTk5OeOeddzLdn+Vca9WqFQDTdHHLgG12SE5OxqlTpwCYsnwsDT3zCmMMHTt2RNu2bQGYCi9dv379pfUsA4wODg65moBUVMmWEDPGlDCJ8FYi2g0ARBRFRAIRiQDW4v/DD2EAyqf7d1/zsnzBMsI7adIkhIaGAjDdWp09e9Zq+0jvEefk1vP69eswGo1wdXVFzZo1rWYPYPIULUIcFxeXqbcaHx8vxY9zkzbHGEOtWrVgZ2cHg8FglfY1crkco0aNkgbWAgMDMW3aNIwcORITJkzA5s2bce/ePSmkYvm87ezs0KdPH+zfvx+zZs16ZQWwgsASf01ISIC7uzu++eYbafA0p6SfMJPV8aSkpODff/+FKIqoVKkS6tWr98p17ezs8Mknn8De3h6RkZHYsmULtFptthyTGzdu4Nq1awCATp06WTXkY29vjwkTJsDV1RVxcXFYunRphnAXEUk/TAqFokjlgueV7GRNMAC/Aggkol/SLU/vYvUCYAlU7gfQjzFmxxirBKAagEvWM/n/ISIkJSVh+vTpePToEZRKJVQqFURRxJEjR6ziEac/Oezs7LLt9QiCgAsXLgAwdcitVKmSVYWDMSbVjkhLS8s01Sc0NBTJyclQKBRSh9yc4ufnB5VKBUEQpNvGvOLu7o4VK1ZgwYIFaNSoEVQqFWQyGRQKBcqXLy/Nrtu+fTveeOMNAECTJk2wYsUKNGvWzObdRRITEzMU6CmItlMRERGSQLZt2zbLmW6MMbRv3x4NGzYEEWHx4sWYMmXKa0MUWq0Wa9euRWpqKjw8PPDee+9Z/Zx966230LWrKcFq//790mA2YLpmLAOXTk5OVikFUFTIznDomwAGAbjNGLthXvY1gP6MsYYwlRF8AuBTACCiu4yx3wEEwJRxMYaI8iUPhYiwevVqqdHigAEDEBMTg8OHD+PGjRuIjY1FqVKl8rQPURQlr9LFxSXbcd6QkBBpIkf9+vVznDqWHSwXyat+cCwDIm5ubihbtmyuLipXV1epJrGlo681Lk5PT0+MGzcOAwYMwLNnzyCKImQyGUqVKgVPT0+o1Wo8f/4cM2fOBGDKsbVGLnNeISJcunQJ4eHhkMvlaNu2bZ6yNSyhF8bYK3/kLXHbxMREqNVqtG7d+rUOgbOzM4YNG4arV68iLS0NOp0uyx8wQRCwbds27N27F4ApNa5hw4ZW/7xVKhXGjRuHo0ePIi4uDosXL0azZs3g6OiIuLg4SYgrV65s8x/cguS1QkxEZwFk9m0czuJ/fgDwQx7sei1EhNOnT2PBggUwGAyoX78+pk+fjsOHD+Pw4cMICwvDvXv3cjyJ4UXi4+Ol2/5SpUplKzSh1+uxdOlSPHnyBEqlEt26dcuX/FZLWpFl1l96iAgBAQEATKKX21xQtVotXRCWFDJrXZwymQze3t6vnAn37NkzPHnyBIwx1K1bt8CaaGaFwWDAnj17oNFo4OHhgXbt2uXp87DE9hUKxSuPT6PR4ODBgxAEAeXLl8/WeANjDG3atIGnpyciIiLg7Oz8yu9OEATs27cPX375JVJSUlC6dGlMmDAhX4SQMYamTZvi/fffx5o1a3D06FGcPHkS3bt3R2RkpCTElkHikkKRnFlnySf99ttvER0dDVdXV8yePRuVKlVCs2bNYG9vj9TU1FemdeWEJ0+eIDU1VcqBfV02hiiK2LlzJ9atWwciwttvv4333nsvz3ZkhsUTMxgMLyW/6/V6KWPD29s71x65QqGQRq/j4uIKrGuCJQdbq9XC3t4+V/UO8oOwsDCpG3abNm1QuXLlPG3PEiOVy+WvFL4nT55IYx4dO3bMdjlTDw8PKaPkjz/+wMKFCxEVFZWhIHlqaipWrFiBTz/9FDExMXB3d8fChQvRtGnTfLv7sEzNLlOmDDQaDRYtWoSkpCQ8ePAACQkJUCgUUppjScH2Z3YOISIYjUYsWrQI//33HxhjGDVqFDp37izNLipbtiwA4Ny5c3manUNEuHfvHlJTUyGKIlatWoUnT55kuf6BAwfwxRdfICUlBWXLlsWsWbPg5uaWLyd1VhNN4uPjpQkTVatWzdWIPmAaoLTkEoeGhko51fmNTqfD6dOnYTQa4eHhUSguTCLCwYMHERUVBZVKhT59+uR5Eonlh80yeeZFRFHEvn37kJCQAAcHB7z77rvZnmDh4uKCgQMHQqVSITQ0FFOnTkWfPn1w9OhRXL9+HVu2bEHfvn3xxRdfIDY2Fu7u7vjhhx/wwQcf5Pp8yQ6WkqcDBw4EAJw9exaHDh2Sqsb5+PhIE5ZKDNlt5ZGfj+y2ShJFkQwGAy1fvlxqBNmiRYsMrYK0Wi317t2bAJCnpyedPXs21+1W9Hq91PAT5tY6a9euzXR7giDQ4cOHydfXlwCQi4sLbdq0Kc+NMF+FIAj02WefSY0xw8LCMrwfEBBApUuXJgD0/fff5/oz0Ol0NGzYMAJA1apVo5iYGGuY/1qCg4OpbNmyBID69+9PWq22QPabFUlJSfT2228TAKpZsyZFRUXleZv9+/eXWl4lJia+9H5cXBw1bdpUanX1/PnzHG0/OTmZVq1aRQ0bNpRaQzk7O5Onp6fUWBUAVa1alfbs2UMGgyHPx5QdRFGkBw8ekJ+fn3RulStXjgBQp06dSK/XF4gd+QmKY6ski8Hbt2/Ht99+i+TkZJQrVw4//fQTSpcuLa1nZ2eHcePGSSkyCxYsyHbqzov7e/r0qVSwGzB5J8uXL0dkZGQGmxISErBmzRqMGDECoaGhcHJyws8//4z+/fvn2+00EUnxRTs7u5e8JI1Gk+vOHOlRKBTSgGd8fHyOJgfkFkEQsH37dkRGRkKtVuO9996zeXyYzKGSq1evSjPF8mMA9sV9Xrx4Ebdu3YJMJsMHH3yQ49xaJycnjBw5EocPH8bEiRPh6uqK5ORkxMXFQRRFVK5cGZ999hn279+Pnj175mk6c06wTNMfNmwYZDIZgoKCEBYWBqVSif79+xeYHYWG7Cp2fj6y4xGLokgHDx6UvDxvb2/at29fph6nVqul4cOHEwBydHSkXbt25dgjNBqN9N1335FMJiOlUknt27cnhUJBMpmMZs2aRampqRQeHk4HDhygd955h5RKJQEgJycnWrhwIel0uhztL6cYjUb6+OOPCQDVrVuXIiMjM7x//vx5qRnn1q1bc+0Ri6JIM2bMIADk6ur6kudtbURRpGvXrknekb+/P8XFxeXrPrNDamoq9erViwCQl5cXXbt2zSrbHTRokORhv3icWq2WBg8eTACoVKlSdOfOnTztS6vV0rlz52jp0qW0cOFC2rt3Lz158oQMBoNNmnSKokhPnz6lWrVqSZ55mzZtcuz1F1aQA4/Y5iJM2RBiURTp1KlTVKlSJem2/7fffiOj0fjK/7l165Z0a9uyZcscXcyiKNKVK1ekMEOLFi3o7t271KBBAwJADg4O1KhRI6pWrRqp1Wqp62z16tVp06ZN+S7CRKbQxKRJk6Suv0+ePMnwvqVrNADauXNnri80QRBo6tSpkgBl1THaGkRHR1Pbtm2lz3nnzp35Ft7JLqIo0t9//01OTk7EGKMRI0ZYLVTy+eefEwCqUKECPXz4MMN79+/flxyPDz/8sEDOq4LGYDBQ9+7dJSH+4osvinTn5vQUKyEWRZFOnz5NVatWJQBkb29PixYtem0MyWg00vTp04kxRkqlklasWJGtL9hgMNC5c+ekuJyDgwPt2LGDBEGgI0eOSOKe/uHj40MTJ06kR48eFdhJJIoiLViwQPJUAwICMrx/7tw56Udi+/btubZLq9XSRx99JHltsbGx1jA/UwRBoIULF5JcLie5XE6jR4+mtLS0fNtfdomKiqJWrVpJ4w6XL1+22rZXrFghORfnzp2TlguCQD///DMxxsje3p52795dbATKgiiKdOvWLfLx8ckQq3748GGxONZiI8QWEa5evToBILVaTd9//322vZGQkBBq2LAhtW7dmk6dOpXllysIAj148IDGjh1LXl5eBIAUCgWNGzdOEgNBEOjcuXM0ZswYatOmDfXr14+WL19Od+7cKfDBBVEU6ddffyUApFKp6ObNmxnev3z5sjSg+aoBxuyQlJREjRs3JgDUtWtXSklJsYb5mfLw4UOqUqUKAaB69erlexgkO+j1evrmm2+kH4cvvvjCqt/16dOnSalUEmOMNm3aJH1P0dHR0ufeqFEjio+Pt9o+CwuCINC3336bwalhjNHXX39t87sga1AshNgSjrB4wmq1mmbMmEEajSbbH4QoihQQEED379+nBQsW0LFjxygsLIxSUlKkE14URYqNjaV58+ZR5cqViTEmeShjx4596QIQRfGlR3YJCQmh//77zyoj06Io0vr16wkAKZVKunHjRob379y5Q6VKlSIANGfOnFwL8c2bN8nNzY0YYzR16tR8u0CMRiPNmDGDGGNkZ2dH69evt/nFKAgC7d27l9zc3DLN0LEG9+/fpzJlyhAAmjZtmvQ97du3j5RKJclkMpo3b57NPwtrI4oi3b59mypUqCD92DRp0oQAkK+vL925c6fIe8VFXohFUaQzZ85I3pG9vT3NnDmT0tLScvzlhIaGUtu2bUkmk5GDgwPVqFGDWrRoQcOGDaNt27bR5s2bqU2bNiSXywkA2dnZUadOnejo0aOk0WisdjKkpaXRoEGDyNvbm6ZNm5ajH5TMEEWRli1bJg1IvjiQ8/jxYymmPnHixFwdhyiKtGjRIpLL5aRWq+nvv//Ot4vjyZMnVK1aNUnwEhISbHohiqJId+7cke7GSpUqRSdOnLC6TREREVS7dm0CQEOHDiVRFEmn00nhoDJlytC9e/eKvCi9iEajkY5RrVbTzp07adeuXVI4bezYsQWWSpdfFGkhFkWRLl68SDVr1pRE+Pvvv8+1KCYlJdGYMWPI09Pzpdhu+odMJqMGDRrQpk2bKDEx0aonviiKtHv3bnJwcCAA1KdPnzzHPtMPopUuXZqCg4MzvB8XF0f169cnANS3b99cHY9Wq6UBAwZIsTtr5M1mhsFgkLxhlUpF69ats7nwPH36lN555x1JKBYtWpTl4HBuSU5OphYtWhAA6tChAwmCQMHBwVLWSN++fYtFTm16DAYDrV69Wroe+vbtS6mpqZScnExdu3aVsqIuX75s8/MgLxRZIbbEYOvUqSNdANOnT8+TZyqKIun1erp06RLNmzePPvnkE2rTpg2VK1eO7O3tSa1WU4UKFWjKlCn07NmzHIcbskNERIQ0+FeuXDm6du1anveh0+modevWBICaN2/+UsqPXq+X3m/RokWuLubExETy9/cnANS9e3dKTU3Nk82ZYTQaad++fdIPZfPmzSk2NtamF2BkZCT16tWLGGMkk8lo2LBhGcJZ1kQQBClroF69epSSkkLr1q2TQjQ7duwo0mL0IoIg0LZt28jDw0PKFrl+/bp03R09epScnJwIAA0ePLhIZ4oUSSE2Go106NAh6Xbazs6OvvjiC0pNTbXKiZg+pqvX6+n+/ft04sQJOnbsGAUFBZHRaMyXE14URVq6dCnJZDJSKBQ0e/Zsq8T7Hj16JHlNn3766Uu2i6Io5RlXr179pTzj7BAdHS2NaI8fP97qHqHRaKRdu3ZJMVJPT086cOCATYUnLi6OBg0aJInwu+++S5GRkflmkyiK9PXXX0vHf+7cOerRowcBoMqVK1N4eHi+7NcWiKJIf/31l5SS5+XlRb///nuG60Gj0dCHH35IAMjNzY3++eefIvtDVOSEuHHjxrR161bpC3JwcKCvv/4637yQgiQ2NpaaNWtGAKhOnTpWuagtk1vUajUxxui3337LdL0ffvhBSq/LzWSAO3fukLOzM8lkMvr555+tHq45ceKEJMIuLi60ePHifLn9zy7x8fH06aefklwuJ8YYtWvXjp4+fZrv5+CJEydIpVIRY4yGDh1K3t7eBICGDx9u08/DmhgMBtq/f780pdnFxYU2btz40vGJokjnz58nd3d3AkA9e/YsFCmMuaHICXGFChWkWxVXV1dasGCBVQfKbMmePXuk0e8ff/zRKt6wJcfUMpPv1q1bma63detWKc5++vTpHH+eFrFXqVS0ffv2PNttQRAEOnv2rDQQ5urqSitXriS9Xm+zGV6hoaE0cOBASYRbtGhBQUFBBWJP+h9rywxNe3t72rdvX7G4BjQaDS1btky6xu3t7WnBggWvDJfpdDoaMWKEdH7b+i4ptxQ5IbakjHl5edGGDRuKzeCEKIo0ZswYafQ7MDDQKieUwWDIUCzmVSlVlhzV3ExzFkVRmlzh6OhotUkMOp2ODh48KGXEqNVqmj17ts1EWBAEun37Nr3zzjvEGCPGGDVr1qxA06dEUaTFixdLmTuAqZBTdHR0gew/vxBFkZKTk+nbb7+VBuY8PDxo0aJFr80aunHjhhQWe+eddyg5ObmArLYeVhViAGqYWh3dBHAXwHfm5ZVg6uYcDGAnAJV5uZ35dbD5fb/X7YMxRmXLlqU//vijyKespCclJYXeeustacDMWgMPMTExVKNGDWkQ7VUn9f3796WZgFOnTs2RsBgMBho7dqyUlZGbGHN6RFGkZ8+e0dSpU6W8XHt7e/r222+tNg6QU/R6Pe3du1dKm5PJZNS1a1cKDg4ucHvi4uKoU6dOkhCPHDmySIclRFGkhIQEGjt2LKlUKgJAfn5+2a7wZjAYpCn8arU6T7NDbYW1hZgBcDI/V5rF9Q0AvwPoZ16+CsAo8/PRAFaZn/cDsPN1+6hYsSIdPXq0SJ94mfH48WMpJjZmzBirhCVEUaQtW7aQnZ0dMcZoyZIlrzxBExISpCT5Tp065Wj/KSkpkjC0aNEi0xKN2bU3Li6ONm7cSA0aNJBKMXp7e9PcuXNtFoJKSkqin3/+WfpRUKvVNHz4cIqKirJZeMQy3RkAvf/++3nONbcVoihSZGQkDR06VLojq1evHp09ezZH5+C9e/eoYsWK0jlYGIo/5YR8C00AcABwDUBzALEAFOblLQD8bX7+N4AW5ucK83osq+02adKkyP3aZYfAwEBpunReZrdZEEWRQkJCJHH18/N7KX/4xfX79euXq8yJqKgoKl++PAGgYcOG5ThcJIoiJSUl0e+//06tWrWSLkilUklvvfUWnTx5Mt8yVV5n15MnT2jIkCGSp+bt7U0LFy60mWdusctSX9oSG927d2+Ruy4sE2E6dOgghVr8/f3pxo0bOT4WQRBo5syZUn756tWri9TnYXUhBiAHcANACoA5ALwABKd7vzyAO+bndwD4pnvvIQCvrLaf3cLwRY2bN29K9R6yW3ToVYiiSImJiTRo0CApFe77779/7V3E4sWLCTAVA8/JgN2///5LDg4OJJfLs/S6M7MzJSWFTpw4Qe+++640U0omk1GlSpVo3rx5FBMTY5MLSqvV0sGDB6lhw4ZSPLh27dp05MgRm/wopCcpKUkqLGR5dOjQIV9yt/MLo9FIJ06coLp160rfedu2bSkgICDXn+2TJ0+kMFz9+vUpIiKiyIhxfnrEbgD+AdAqr0IMYCSAKwCuVKhQoSA+lwLn8uXLkie4efPmPJ1AgiDQ+vXrpRrD3bp1y1YltP/++48cHR0JAM2aNStbNoiiSD/88APJZDJycnKiixcvZut/EhISaM+ePdS9e3cpKZ8xRr6+vvTtt9/Sw4cPC1zwRNHU1eXevXv02WefkYuLi+SZd+vWzWoDqHnl3r17Um0QV1dX6cfz6NGjhcK+16HX62n79u1SOqJKpaKPP/6YwsLC8nze//LLL5LzUZTqbuSbEJu2jekAPrd2aKI4cvr0aUmM9uzZk2ePOCoqivr06UP+/v7Z9jKeP38upUY1a9YsW7He5ORk6tChgxTbe1WhblEUSaPRUFBQEC1atIhatWqVoT6zh4cHffrpp3T37t0CLz5umbjz8OFDmj59OlWoUEEq6OTt7U2zZ8+m+Pj4QiFyoijS1q1bpZKt06ZNk1K9Pvzww1zVWCkoRFGk6Oho+vHHHyWbXVxc6Pvvv6ekpCSr2B0REUENGzYkwNRS6fHjx4X280iPVYUYgDcAN/NzewBnAHQH8AcyDtaNNj8fg4yDdb+/bh/FVYgPHTokeQdHjhzJ8/ZEUaSYmJgcnYiiKNJ3331HgKk4UHYK99y5c0dKqP/f//6XYZRbFEXSarX06NEj2r59Ow0YMIB8fHykeKBMJqPSpUvTqFGj6OLFiwU6RdXi/cbExNChQ4dozJgxVLFiRWmAUK1WU/fu3enMmTM260qRGfHx8dSmTRsCTEX+AwICpII4Dg4ONHv2bEpOTi409hL9/3nw119/0RtvvCH1v7OkoFqzx6AoirRu3TqpQ860adOKxMC+tYW4PoDrAG6Zww7Tzcsrw5TWFmwWZTvzcrX5dbD5/cqv20dxFeJt27ZJAy///vuvzey4efOmVMthxIgRWd7aWaZky+Vysre3p0OHDmUQAFEUadeuXVSqVCmpAwjMU9Jr1apF33zzDQUEBOS7AFtEV6vVUkpKCoWGhtLBgwdp/PjxVKtWLSmEY/khbNmyJW3dutVqXpq1EEWRNmzYINUk/vLLL8loNNKFCxekjAE7Ozvq27cv3b171+a35ZYxgNOnT9Nnn30mZZ3I5XJq0KAB7d27N19SUGNjY+nNN98kAFS+fPk8xZ0LinwNTeTHo7gK8cqVKwkAubu706VLl2xmh0ajkQrL+Pn5ZdlJJC0tjd5//32CudZBZhXXzp8/T25ubqRUKsnHx4f69+9Pf/zxB0VFRRVIHrggCHT06FGaOHEiffTRR/Tmm2+Sj49PBvGVy+Xk7u5OnTt3ps2bN1NcXFyhvHCDgoKkSoNVqlSRcpgFQaATJ05QjRo1pIHFatWq0datWws85c8S5omMjKQtW7ZQp06dyMXFRQr1eHp60jfffEOhoaH59kMhiiLt3LlTStucMGFCoZ/4xYW4kDB37lwCTLVsXzUNuaDYtm0byeVykslkWWZwhISESAMuH3/8caaebWJiIs2aNYu2bt1KwcHBBS4MgiDQ7NmzM2QYAKaOKm5ubtShQweaP38+3blzp1DXK9FoNDR8+HApPWvp0qUZhEwQBAoICKB+/fpJdx9OTk40YsQICgkJybciVUajkXQ6HSUmJtK1a9do7dq1NHToUKpWrZpkB2OMnJ2dqXv37vTPP/8UiCgmJCRIue0+Pj509erVQvvdEuVMiEtYz+qCRavVAjC1pLd1O/jWrVujevXqCAwMxIEDBzBixIiXWpYTEc6ePYvY2FioVCq88847UCqVL23L2dkZ33zzDWQyWUGZnwHGGMqUKYMKFSrA1dUVPj4+qFGjBurXr4+WLVvCz88Pjo6OYIzZxL7sQEQ4efIkduzYASJC586dMWTIkAw2y2Qy1KxZE2vWrMFbb72Fn376CaGhofj1119x48YNzJs3D2+++SbkcnmujpWIIIoi9Ho9wsPDERgYiIcPHyI8PByPHz/G3bt3ER4ejpSUFBiNRgCAXC5HhQoV8N5772HAgAGoU6dOgX3WLi4umDBhAs6cOYOoqCgsXboUq1evtvm1ZRWyq9j5+SiuHrGlH5evry8FBQXZ1BZBEGjYsGEEmGoiZ1bQRqPRSDUsypQp81Jn6MJESkoKRUREUGxsLCUlJRWJwZv0xMbG0ttvvy15d5cuXcrSuzMajXTlyhXq0qWLNDDm4+NDM2fOpKtXr1JCQgJpNJoMD51ORwaDgQRBkErACoJAer2e4uPj6dixY/TNN99Qq1atqFSpUuTg4CCFGywPi7fu6+tLPXr0oGXLltHjx49tVhskJSVFCp25u7vnqphVQQHuERcO5HI5gP//sbMlMpkMnTt3xoYNGxAVFYXz58+jSpUq0vtEhMDAQBw9ehQA0K5dO5QuXdpW5r4WR0dHODo62tqMXCGKItauXYtz586BMYZhw4ahcePGWXqVcrkcjRs3xpYtW/DLL79g2bJliIqKwqxZs7BkyRK4ubnByclJ2oZCoYCnpyfKlSsHX19fuLq6Qi6XQ6PR4N69e7h48SLCwsKQkpKSYR92dnZwc3ND6dKl4efnh4YNG6Jly5aoXbs23N3dYW9vb9M7DQcHB0yYMAHHjx/H8+fPsXjxYvj7+8Pe3t5mNlmF7Cp2fj6Kq0c8Z86cQhMjJjLNUqpcuTIBoC5dumSoZfD8+XN69913pTS3F7MlONbBUm/XUnu7UaNGUmeY7KLVamnXrl3UvHlzafAKeHUbsFc9LBN2WrZsSePHj6etW7fSzZs36enTpxQTE1No85c1Gg0NHTpUilMX1nMV3CMuHFg8So1Gg4SEBNsaA8DX1xddunTB8uXLce7cOZw7dw7t2rWDIAhYtWoVjhw5AplMhvfffx9t2rQp1DHWoggRISkpCbNmzUJkZCScnZ0xc+ZMlCtXLkeftZ2dHXr16oXWrVvj2rVruHDhAoKCgpCWlibdeQmCgISEBMTExCAuLg4ajQaiKEKhUMDLywt169ZFq1at0L59e1SsWBGurq42i/nnFLVajbFjx+LgwYOIiYnB4sWL8dZbb8HZ2dnWpuWe7Cp2fj6Kq0e8Z88egrnc4/Hjx21tDhGZpl1bZkD17duXtFotnTt3Tqr92qBBgyIzc6moIYqmztuWae9jxoyxSr51+kwHy0Oj0VBiYiJFRETQ48eP6cGDB3T//n0KDg6msLCwIt94QafT0YQJE4gxRvb29oWytx94+lrhoDAKsVarpcGDB0tTUdeuXSslyru4uBTJil9FAVEUKTg4WKp9XKtWrSzzuTmvJzAwkHx9fQkAtWzZkuLj421tUgZyIsRF416EYzXs7OwwevRoeHl5ISkpCZMmTcL58+chl8sxYsQIdO7cmYckrAwRwWAwYP78+QgODoZSqcTkyZPh5+fHP+s8ULVqVQwdOhQymQxXrlzBrl27IIqirc3KFVyISyBNmjTBuHHjwBhDcnIyiAjt2rXDl19+CTs7O1ubVyw5cOAAtm7dCiJChw4d0KdPHy7CeUShUOCTTz5BlSpVoNfrsWzZMsTExEhx8qIEF+J8xDJhQhRFCIJgY2v+H1EUMwzMqNVqDBs2DJ6enja0qnhCRLh//z6+/fZbJCcno3Tp0pgxYwZcXFxsbVqxoHz58vj0008hl8tx9+5d/Pbbb0XSK+ZCnI9YchsFQYBer7exNSZR0Gg0WLZsGX7++WfJc9BqtVi6dCnCwsKKpDdRWCEixMbGYtKkSbh37x5UKhW++uorNGnSxNamFRvkcjkGDhyIunXrwmg0YvXq1QgNDS1y5zEX4nzE0dERcrkcRqMRKSkpNj85oqKiMGXKFHz77bdITU1F+fLl0a1bN8hkMly4cAFjx45FRESEze0sLmi1Wnz77bc4evQoGGPo378/hg0bJk304ViHUqVKYezYsVAqlXj06BF+/fXXQnUHmi2yO6qXn4/imjVx+/ZtqdvCTz/9ZLMRcr1eT2fPnqXWrVtLyf9Vq1alw4cPU1RUFPXs2ZMYYySTyah3797Z6vzByRpBEGjFihVSofy2bdtSaGiorc0qtsTFxVHLli2lkgKBgYG2NomnrxUWoqOjqUGDBgSYutC+qtNFfmEwGOjixYs0aNAgqR6xTCajdu3a0fXr16UfhqdPn1K3bt2IMUZyuZwmTpxo1cLeJQ1RFOm///6jcuXKST96hWFmZXHGUiZTrVYTY4zGjx9foE0JMoMLcSFBEAT65ptvpBKN3333XYEU9hYEgR48eEATJkyQukjDnCc8YcKEl1rGWzobWzwKnk+cN54/fy6Va3R0dKSdO3fyz7IASEpKkj53b29vunLlik3t4UJciAgJCaFGjRpJhb/z8/bU0sBzyZIlUk0Ji7D269ePTp48SVqtNlNREEWRzp07JzWw9Pf3p/Dw8Hyztbii1Wrp66+/lmbPjR49mt9dFBCiKNJff/1Fzs7OxBijIUOGZKinUtBYVYhhan10CcBNAHcBfGdevhHAYwA3zI+G5uUMwBKYWiXdAtD4dfsozkIsiiL9+uuvJJPJSKlU0qZNm6zuHVna1xw9epS6dOkiiYCdnR117NiRjh49Sjqd7rX7NRgM9N1335FcLie5XE5TpkzhIpID9Ho9rVy5Uuqa3aRJE3ry5An3hguQtLQ0+uCDDwjmbtinTp2y2edvbSFmAJzMz5UALgJ4wyzEH2SyflcAf5n/7w0AF1+3j+IsxEQZq5517tyZ0tLSrLJdg8FADx8+pNWrV1PHjh3JwcFBqiFbq1Yt2rhxY456tFk6RVvq5Do7O9PmzZsLpP1RUUev19OGDRukHm7ly5ens2fPchEuYERRpDNnzkjNb3v16kUpKSk2sSXfQhMAHABcA9A8CyFeDaB/utf3AZTJarvFXYiNRiONHTtWanXz+++/5zpWbDQaKTIyko4fP06ffvpphjbxMJfcHD16ND18+DBXIiCKIp09e1Yq0+jl5UVLly6l1NTUXNlbEtDpdLR69WpJhEuVKkV79uyxeaPPkopWq6Vhw4YRY4wcHBzo4MGDNvlBtLoQA5Cbww8pAObQ/4cm7pvDDwvx/12cDwJole5/TwDwz2SbIwFcAXClQoUKBfTR2I4bN25I4la+fHlas2YNPX78+LVVsCyNG8PCwujAgQM0YsQIqlevHqlUKkl87ezsqFq1ajR+/Hi6evVqnlvFGwwG2rx5s1SlTa1W05QpUwpdURVbI4oiJSUl0U8//UTOzs7SINH27duLXMeQ4oQoinT9+nWpomCHDh0oISGhwO3IT4/YDcA/AOoCKGMOP9gB2ARgOuVAiNM/irtHTGQSt0WLFknhA5lMRr6+vtSrVy9au3YthYSEUGxsLEVFRdHTp0/p9u3bdPDgQZo3bx7169ePqlevLrXIsYQfvLy8qFu3brRt2zaKjIyUWuJYy95du3ZJIRW5XE5jx47lnrEZo9FIV69epQ8++ED6USxbtizt2rWLi3AhQK/X06RJk0gmk5FKpaJt27YVuFecr1kTAKYDmPLCsjYADhIPTWSJRqOhVatWUZUqVaQBNZhT2ypWrEhNmzalevXqka+vL6nVaqnrsmU9uVxOPj4+1KlTJ5o/fz5du3btlVkQ1kAQBLp48SI1btxYKue5aNGiEi3GoihScnIyrVixQirBaInJHzt2jItwIUEURbp//z5VqFBByuOPiYkpUBusPVjnDcDN/NwewBkA3S3iavaKFwH42fy62wuDdZdet4+SIsREJnF78uQJbd68mQYPHkx+fn4ZPN30Hq9arSZ3d3fy8/OjXr160fLlyykgIIC0Wq1Vvd+sEEWRrly5QhUrVpTEeOzYsRQeHl7iBqIEQaDHjx/TsGHDpLbyjo6ONGjQILp7926J+zwKO0ajkWbOnElyuZwUCgWtXr26QOP2ORFiZlr/1TDG6ptDD3KYalP8TkSzGGMnzSLNYIoff0ZEKcxU228ZgM4A0gAMJaIrWe3D39+frlzJcpViBxHBaDQiKioKhw4dwtWrV5GQkABHR0e4u7vDz88Pvr6+8PX1ReXKleHi4gKlUmmT0omCIODgwYOYOHEiHj9+DJlMhlatWmHhwoVo0KBBsa+dIIoi4uPjsWvXLixevBj379+HKIqoVq0aZs6ciV69ekGtVvOyloUMIsKzZ8/QoUMHPHjwAPXr18dff/2FsmXLFsj+GWNXicg/WytnV7Hz81GSPOLMEEWRDAYD6XQ60uv1ZDQaC513JQgCXblyhdq0aUNyuZwAkJ+fHy1ZsoSio6OLZYaAIAgUGRlJK1eupKZNm0rhJJVKRZ07d6Zbt24Vy+MuTgiCQL/88gspFAqSy+U0b968AvvOwGfWcfIDURQpMjKSxowZIw06KhQK8vf3p/Xr11N8fHyh+wHJKaIokk6noydPntCyZcuoUaNGkgDL5XKqXr06LVu2rFgca0nAcs42bNiQAFC1atXo0aNHBbJvLsScfEMURUpLS6P169dT7dq1Je9YpVJR69at6c8//8zRJJLCgmUQ7tSpUzRixAjy9fWVYvcKhYJq1KhBs2fPpsePH3MvuIghCAKtW7eO7OzsSCaT0bRp0wpkUJULMSffEQSBnj59Sj/++CNVqVJFyu5Qq9XUpUsX+ueff0iv19vazCwRRZG0Wi09ffqU1qxZQ+3btycnJydpwFSlUlGNGjVo7ty5FBoayjMiijDx8fFSk1xfX18KCAjI931yIeYUCJY27g8fPqQvv/ySypUrJ83y8/DwoJ9++okSEhIKlXcsCAKlpqbSo0ePaMuWLTR48GDy9fWVwg+MMXJzc6P33nuPtmzZQuHh4VyAiwGWMpkODg5Smcz8dhRyIsSvzZooCEpi1kRxgsiUARIcHIxly5Zh27ZtSEhIgEqlQocOHTBu3Di0aNECdnZ2kMvlkMlkYIwVSJYBEUEQBKSmpuLOnTs4c+YMTp8+jWvXruH58+dSCyulUony5cujZ8+e6NevH+rWrQt7e3ueCVGMSElJQe/evXH69Gn06tULK1euhJubW77tLydZE1yIOVaDyNQ2/uTJk5g8eTICAwNBRHB0dETt2rVRtWpVeHl5wcXFBd7e3ihdujRq166NypUrQ61WSwKdVxtEUYRer8fz589x9epVnDx5EidPnsTjx4+RmpoqNZdUKpXw9PTEW2+9hW7duqF9+/bw8fGBXC7nAlwMISJcuHABYWFh6NixI1xcXPL1e+ZCzLEpRITg4GAsWrQIO3fuRHx8PF48zxhjUKlUcHR0ROXKlfHBBx9gwIABKFu2bKaC/OKtnGWZpUP28+fP8fTpUwQFBeH27du4du0aAgMDkZCQAI1GAwCQyWRQq9WoUqUKWrRogbZt26JFixYoVaoUzwMuIVjOnYL4rrkQc2wOEUGv1+PevXvYsWMHrl+/jsjISCQmJiIlJQUpKSkQBAFGoxFEBLlcjkqVKuH9999H48aN4eLiArlcjtTUVCQmJiIxMVESVa1WC61WC41Gg6ioKDx9+hTx8fHQaDRIS0uDwWCQ7JDL5XB2dkaTJk3QunVrtG/fHtWrV4eLiwsUCgUXX06+wYWYU6gQBAEajQY6nQ4GgwFGoxFJSUl4+PAhzp8/j6NHj+LWrVswGo0AAJVKJc0iNBqN0uN1yGQyyOVyuLq6onr16qhbty6aNm2KN998E76+vnBycuLCyykwuBBzigyiKCI2Nhb79+/Hb7/9hkuXLsFgMGQIZTDGIJfL4ejoCLVaDaVSCTs7O6hUKri6usLHxwfly5dHlSpVUKNGDVSrVg3u7u5wdnYu9tOvOYUXLsScIgcRISkpCSEhIYiOjkZCQgIEQYCjoyO8vLzg7u6eIevC4v2qVCrY29tDpVJxb5dTqMiJECvy2xgOJzswxuDq6or69evb2hQOp8CR2doADofDKelwIeZwOBwbw4WYw+FwbAwXYg6Hw7Ex2RZixpicMXadMXbQ/LoSY+wiYyyYMbaTMaYyL7czvw42v++XT7ZzOBxOsSAnHvEEAIHpXs8BsJCIqgJ4DmCYefkwAM/Nyxea1+NwOBzOK8iWEDPGfGFqCrrO/JoBaAfgT/MqmwC8Z37e0/wa5vffYTzBk8PhcF5Jdj3iRQC+ACCaX3sCSCAiy7zTUADlzM/LAXgGAOb3E83rZ4AxNpIxdoUxdiUmJiZ31nM4HE4x4LVCzBjrDiCaiK5ac8dEtIaI/InI39vb25qb5nA4nCJFdmbWvQmgB2OsKwA1ABcAiwG4McYUZq/XF0CYef0wAOUBhDLGFABcAcRZ3XIOh8MpJrzWIyair4jIl4j8APQDcJKIBgL4B8AH5tWGANhnfr7f/Brm909SYShoweFwOIWUvOQRTwUwiTEWDFMM+Ffz8l8BeJqXTwLwZd5M5HA4nOJNjor+ENEpAKfMzx8BaJbJOloAfaxgG4fD4ZQI+Mw6DofDsTFciDkcDsfGcCHmcDgcG8OFmMPhcGwMF2IOh8OxMVyIORwOx8ZwIeZwOBwbw4WYw+FwbAwXYg6Hw7ExrDCUgWCMJQO4b2s7rIQXgFhbG2El+LEUPorLcQDF/1gqElG2SkvmaIpzPnKfiPxtbYQ1YIxd4cdS+Cgux1JcjgPgx5IeHprgcDgcG8OFmMPhcGxMYRHiNbY2wIrwYymcFJdjKS7HAfBjkSgUg3UcDodTkiksHjGHw+GUWGwuxIyxzoyx+4yxYMZYoe/mwRhbzxiLZozdSbfMgzF2jDEWZP7rbl7OGGNLzMd2izHW2HaWZ4QxVp4x9g9jLIAxdpcxNsG8vCgei5oxdokxdtN8LN+Zl1dijF0027yTMaYyL7czvw42v+9n0wN4AcaYnDF2nTF20Py6qB7HE8bYbcbYDcbYFfOyInd+AQBjzI0x9idj7B5jLJAx1sKax2JTIWaMyQEsB9AFQG0A/RljtW1pUzbYCKDzC8u+BHCCiKoBOIH/bw/VBUA182MkgJUFZGN2MAKYTES1AbwBYIz5sy+Kx6ID0I6IGgBoCKAzY+wNAHMALCSiqgCeAxhmXn8YgOfm5QvN6xUmJgAITPe6qB4HALQloobpUruK4vkFmBomHyGimgAawPT9WO9YiMhmDwAtAPyd7vVXAL6ypU3ZtNsPwJ10r+8DKGN+XgamvGgAWA2gf2brFbYHTM1fOxT1YwHgAOAagOYwJdgrXjzXAPwNoIX5ucK8HrO17WZ7fM0XdTsABwGwongcZpueAPB6YVmRO79g6kT/+MXP1prHYuvQRDkAz9K9DjUvK2r4EFGE+XkkAB/z8yJxfOZb2kYALqKIHov5dv4GgGgAxwA8BJBAREbzKuntlY7F/H4iTA1wCwOLAHwBQDS/9kTRPA4AIABHGWNXGWMjzcuK4vlVCUAMgA3mkNE6xpgjrHgsthbiYgeZfgKLTCoKY8wJwC4A/yOipPTvFaVjISKBiBrC5FE2A1DTthblHMZYdwDRRHTV1rZYiVZE1BimW/UxjLG3079ZhM4vBYDGAFYSUSMAqXihO31ej8XWQhwGoHy6177mZUWNKMZYGQAw/402Ly/Ux8cYU8IkwluJaLd5cZE8FgtElADgH5hu4d0YY5Zp/OntlY7F/L4rgLiCtTRT3gTQgzH2BMAOmMITi1H0jgMAQERh5r/RAPbA9ANZFM+vUAChRHTR/PpPmITZasdiayG+DKCaeVRYBaAfgP02tik37AcwxPx8CEzxVsvyweZR1DcAJKa7lbEpjDEG4FcAgUT0S7q3iuKxeDPG3MzP7WGKdQfCJMgfmFd78Vgsx/gBgJNmj8amENFXRORLRH4wXQsniWggithxAABjzJEx5mx5DqAjgDsogucXEUUCeMYYq2Fe9A6AAFjzWApBILwrgAcwxfS+sbU92bB3O4AIAAaYfimHwRSXOwEgCMBxAB7mdRlMWSEPAdwG4G9r+9MdRyuYbqVuAbhhfnQtosdSH8B187HcATDdvLwygEsAggH8AcDOvFxtfh1sfr+yrY8hk2NqA+BgUT0Os803zY+7lmu7KJ5fZvsaArhiPsf2AnC35rHwmXUcDodjY2wdmuBwOJwSDxdiDofDsTFciDkcDsfGcCHmcDgcG8OFmMPhcGwMF2IOh8OxMVyIORwOx8ZwIeZwOBwb838jaGz3/LElsQAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/وقل رب ارحمهما كما ربياني صغيرا.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/الدين النصيحة.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/لا حول ولا قوة إلا بالله العلي العظيم.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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Ua6SMWi1GpUiXBOfr374/58+d/tlwzApiy13zKLb8dqi9evKDKlSsLoivUarXZxyclJVGVKlXY8T179jR5PM/zlJGRQaGhoTR69GjmXReJRNSoUSOaOHEiTZo0idauXUv37t3LE99LbGwsS9QQi8W0YMECk218+PCB3N3d2b5z5szJF9ugvs3d3d2dZs+ezZy1EomEdu/ebfS4lJQUatKkCRvLxo0bm+TqyQ2//PILOTo6EgCytramW7dumXXcr7/+Ss+ePaMHDx5Q0aJFmX23Ro0atGzZMipWrBjzadjY2ND69euNtqNWq+nHH39k1+Lr61sgTsn8QHp6Ok2ZMoVkMhmbj506dTKwFfM8T2lpaXThwgVq2bIlOTo6GvhWxGIxubu704QJEwTJfPpQKpW0cOFCAwe+7ng/Pz86f/684Nz79u1jDksPDw86efIkBQcH0x9//EGZmZlERBQeHk6BgYHs/ojFYurXrx8lJycXuM2b53m6deuWINlx3bp1BXpOS4H/JZs7kKXpVq9enb1Z79+/j1evXpl9/Js3b5CYmAggKw4+ICCAac76SE5OxoEDBzB27Fh06dIF27dvZ3GwPM/jwoULWLJkCRYvXowffvgBTZs2xahRo/Dy5UuLrsfR0ZGtHLRaLWJjY6FWq43u6+rqyuJztVotwsPDjbJdfgxEIhEaNmyI4sWLA8jScHSaWXZYWVkJzGT379/PM++Hra0t0w55nkdycnKux/A8j0uXLuHMmTOwsrJiDJ/0t8ni0KFDmDBhAkqWLAm5XI4ePXqgW7duRtvSaDSCZDYPDw+DeZGYmJjnFVp+QZfotmXLFpZnUKpUKUyfPh2FChUSaJwxMTGYPn06unbtij/++APJyckGJjbdnFu+fDkWLVqEpKQkg3PKZDL07t0bjRo1MtBotVot7O3t4evrK/i+TJkyTCtOSUnBu3fvUKJECbRo0QJWVlb48OEDpk2bhitXroAoi/upYcOGmDNnDhwcHApcc9YlEer7VSx9dv9J/CeFu1wuR8uWLZlp5sOHD/jrr7/MfuhCQkLYBJZIJKhUqRL7jed5REdHY8+ePWjevDkGDx6MjRs34s6dO7kKm4SEBOzevRtDhw7Fw4cPze6PzkapQ2xsrEmBLRKJmMkEyMqsyy/nrj5KliyJtm3b4vvvv8fJkycxZswYow+bbvx0ppnk5GSWtWopbGxsBEt/c64rJiYGT548waVLl1CkSBGsW7cOgYGBEIlEkEgk8Pb2RpcuXbBlyxaMHTsWCxYsEGQE60Or1TIzF5BFB53dLHDkyBFcuXKlQLiGzIUuA1tntrKzs8OcOXNQsWJFdo+ICA8fPkTv3r2xdu1atq9IJIK7uzuqVauGdu3aoVatWgLb99q1a7FmzRqjyXCenp6YM2eOAUW2TCZDt27dUKRIEcEcKVGiBGs7MzMTwcHB7GWUlJSEefPm4eDBg0xpqFq1KhYvXvxJndguLi4oXLgw+5wXfqR/DKZU+k+5FUQSU2JiIluCA6ABAwZQWlparsdpNBqaN28eM684ODjQu3fviOd5iouLo127dtHXX3/NwvKyb9bW1uTm5iagELazszMI+2rRooVZ/dH1qVu3buzY5s2bU0xMjNF9eZ6n6dOns30rVKhAISEhFo2dMeibZTw8PCgmJoaUSqVZcc3379+n0qVLM3PIjz/+mKc+XL9+nfz8/AgASaVS2rZtW67HXL16lfz9/alcuXL0+PFj4nmeXrx4QU2bNqWuXbvS+/fv2TXkdi3JyclUq1YtNrb9+vWjjIwMwT6HDx+m0qVL08mTJ/Oda8gcxMfHU8+ePdn8k8vlNGHCBIN+xsXFGYSpOjs704ABA+jq1aukUqmI53lSKpW0Z88eZurTPRPXr183en6NRkPbtm0T7G9tbU0//vijwXxXqVTUvHlzwTMRFxdHKSkpNHXqVEECUbFixejq1aufPPxQo9HQgAEDWD/Kli3LTEafA/C/ZpYBsrSVihUrss+RkZEmQyL1kZGRgeDgYKYt+Pv7w9HREY8ePcJ3332HESNG4PLlyyyCQCKRoFChQujSpQvWr1+P8+fP48iRI4IlqJ2dHRYuXIgaNWowzeXSpUu4fv262dejrz0kJibmSCNQtGhR9n9MTAwyMjIKRNuQyWQ5hpPq4OPjAw8PDwBZysSjR4/ylMZtbW3NzCBElCsfCVFWmnpkZCQiIiKYY7F06dLYsmULli9fDk9PT3YNuk3Hrpld+9Y5pXVIT083uI4KFSogLCwMI0eOtJhq4WOh0Whw4MABHD9+nN3v+vXrY+TIkWwVC2SNy7Zt23DhwgUAWSvD0qVLY8eOHVi2bBnq1KnDil7IZDJ07twZM2fOFHAprV692ijVhlgsRteuXTFhwgQWHpyZmYmVK1dixYoVyMzMZH0TiUSoWrUqO/bly5eIjo7GmjVrBDQJPj4+WLBgAWrVqvXJnZhisRheXl6CgAZzHfn/OExJ/U+5FYTmrtFoaOLEieyNW7FiRXr79m2ux71//56qV68ucMYuWrRI4FTB3xpoQEAAzZo1i4KDg1nRDl0Bj7Vr1wqy2/r3708XLlwgf39/dvyYMWPMuhatVkuLFy9mbZUsWdKkNs7zPF24cEGQmn3p0qWP1niWLFnCsgILFSqUa6JK9v737t2baZNVq1Y1ufLICc+fP6eSJUsyJ+6SJUty3D8zM5MGDRrExm3MmDGkUCiIyHQCmy6bs2HDhrR06VIKDg5mGZOZmZnUv39/1l6NGjUMaBBiY2OpQoUKTNvMK+NhXnD37l3y8vJi/fPy8qK7d+8aXGdUVBR99dVXgtWdsf104HmeUlJSqE+fPuweent7Cyg5su+vUqlo7ty55OTkxM4jl8tpzJgxLIlOx3qqWxGKRCLq06ePwCnr6upKe/fuJY1G848lDW3atIn1ydPTk+7cufOP9MMY8L+ouXMcJ9Bg9cn3TYGIEB0dLXCaXLt2DXPmzGGMjxzHoXDhwpg8eTL27t2L6dOno2TJkhCJRALtr3379ggMDGTtHDt2DKmpqShWrBg714sXL8wiGuM4ThByl5iYaEDkpb+vi4sLXF1dAWTZSXUkaR8DiUTCkqaIyKRD1xhEIhHKlSvHjk9KSkJEREQuR+Xeh9zGTudQ1uHKlSuCBCRjWqBWq8W+fftw8eJFTJs2DX369EF4eDiICGKxWLCCio+PN4gB1w8TffPmDcaNG4cDBw5AqVQWmK2WKKt4yvz581mYrLW1NUaOHCmws+vw9OlTNp/FYjG6detmNDxSB47jYG9vj++++46tXKKjo3HlyhWjKzCO4yCRSDBq1ChMmjSJBQMolUps3LgRc+bMYc+ij48Pm9s8z2P37t3Mn+To6IipU6fi22+/hVgs/sdCD93d3ZlvRaVSCZKrPmf8p4W7PrNjQkICFApFrg/YyZMnBcv9Dx8+sIw4Ozs7dO7cGRcuXGAOKmNRNLrMxXHjxrHIlbi4OKxbt05w/tTUVLPZ8Ly9vZlgS0hIyNF56+TkJLj2x48fm3WOnKAzwQDmCdbsKF++vCAyIqdYfVPQF+4Acu2DTCZDjRo12Of79+/j559/NhrtocOzZ8+wb98+1r6XlxdL+OE4TkADq29i0MHKygrVqlVj1xoeHo6hQ4dixIgRePjwoUUvRXOhVCqxevVqnDp1ivWzffv2GDBggNH5mZGRwcZOJBKhYsWK0Gq1SEtLQ3R0NF69eoVnz57hxYsXCA4ORkhICMLCwuDr68ucmRqNBkFBQQK2VH1wHAc7OzuMHz8eP/30E3spZmRkYOPGjZgxYwaSkpLg6uoqSATTvSzs7e3x448/YtiwYQWWpGQuChUqJMj6zikP4nNCQRXI/sfBcRxsbGwgl8uhVCqh1WrNCsFLSkqCra2tgdB1d3fHjBkz0L17d7PS9zmOQ4MGDdCuXTts3boVAHD58mVBdIWVlZXAFppTW7os2YSEBPA8j4iICEGVJn3Y2dnB29sbd+7cAZBVKpD+DiXLK3QrEx0stZnrJ32oVKo88XebY9/Xh0QiQZUqVWBvb88SmXR23xkzZhgkpimVSmzbto0Jf2dnZ3Tp0gVOTk7s3Pr3y5iyIBaLERgYCH9/f7x58wZA1sts8+bNuHr1Kpo3b462bduiVq1aLCHrY+4LEeHChQtYu3atQBueMmUKW71lh4ODA7sOjUaDBQsWYO/evVAqlUhJSUFSUhKUSiWLKNL9BSCwN+v8WDrbenboNPjevXtDKpVi4sSJiI2NhUqlwubNm+Hs7Iz+/fvD3V1Y31kmk2H48OEYNGiQWc9HQcPJyYkpFebKkc8Cpuw1n3IrKMrfmzdvCqq0/Pbbbzna7XiepytXrlCZMmUEtvUGDRrQtWvXLCa70kVmlCpVyiCqhuM4GjFihNl2xOfPn7OIEwC0aNEik8cqFAoDFkVLkriMYd26dYym1c3NjV6+fGnR8a9fv2Z2S7lcTps2bbK4D6GhoVSuXDnmS5g5c2auxzx+/JgCAgIEYy+Xy+nIkSMGyTxXr15lRcCBLAZA/QgPrVZL69atY7/b2NhQdHS0wTnVajVNnz7daESVWCwmOzs7qlGjBu3du5cSEhLybEvmeZ4ePXpEZcuWZe07OjrStm3bcrTzx8bGUmBgoNFoL0u2wMBA+vDhg1l9VavV9Msvv5Cbm5sgiqZjx46CyBq5XE6TJk2ixMTEz4KYi4goIiKCXF1dCchiNV22bNk/3SUG/Fds7rpOp6en4+nTp4iIiGApzWTE3CKXywXL6Jw4ZogIwcHBmDp1qsDmXqdOHaxbt45FEFgCjuNQrFgxDBw4ENbW1oLfZDIZOnbsaLbWZmtrK9DEcrJZy2QyFC5cmK0SEhMTzUr4KUjY2tqya9VoNAVqg9ZHsWLFUKJECfZZJpOhTZs2zPehg1qtxqFDhxjthFQqxbBhwwTzJ7vmrlKpjJpZJBIJhg4dKogE0UFn/rh9+zYGDhyIUaNG4cmTJ3kai9jYWPz4448ICgpi5+3bty86duyYY1q+q6srxo4dK/Af5AWWpP5LJBJ07NgRM2fOZMVnMjMzceTIEcFqwMXFBX369GGrpc8BdnZ2As3dksIi/yT+VWaZpKQk7NmzB1u3bsWHDx8glUpRq1YtfPfdd2jatCmsrKwEE8LGxgbOzs5MEOrzxeiDiBAZGYmpU6fi2rVrAP4/C3PFihWMM8ZS6F5EuiWuPurXr4/q1aub3ZaNjY1AuOs7CrND5/SVyWTIzMyEWq1GZGSkyWW6OdCFxgGWO1SBLAef7nitVvvRBYzNhY2NDVq0aIE///wTKpUKWq0WXl5eKFmypGCuvHr1Cnv27GFC9ptvvkGdOnUEbekcizY2NsjIyADP84iKijLKk1O4cGHMnDkTQ4cONSi/yHEciAgZGRm4cOECBg0aZPF1qVQqrFq1ihWG5jgOgYGBmD59uskkLP3zt2zZEkePHsWBAwdw69YtEBFsbGxQqFAh+Pv7w9XVlb28NBoNFAoF1Go19u/fzzheXFxcBJW/coNMJsPAgQOhUCgwdepUo2Gin6PZw9raWiDcdYrJ5/LyMYV/jXCPjo7GnDlzsHHjRkGEwuHDh3H+/HnMmjULI0aMEAy4nZ2doFRWWFiY0bY1Gg2WLVuGY8eOMVtyrVq1sGbNmjwLdp7n8fLlSyxevJhFS+hDq9VaxBppieYOZDmB5HI5MjMzodFo8P79e1SoUMGyi9CD/gTXVZm3BGKxGDKZjB33qR4QjuNYBurdu3eh1Wqxa9cuNGnSBK1btwbHcUhOTsayZctYFISzszP69OljVEg6OzvDwcGBXce7d++MvqRFIhG++eYbzJo1C2PGjBH4GIoWLcripTt37mwxZa1Wq8Xx48exZs0a9pItU6YMFi9ebPYLXCKRoFatWgJqiNyQmJiIo0ePss+enp4W28RFIhFcXFwgkUiMCne1Wv3ZOSwlEonAMa0jUPvchfu/wiyjUqmwZcsWbNu2jQlE/dCoxMRELFiwACdOnBAITHOEO8/zOH78OLZv386OLVu2LJYtW4bSpUtb1E+dRhsZGYmFCxeiY8eOzFGVvc/Xrl3DgQMHzBbwVlZWcHNzY8fHx8fnuDz09vZmWpVarc5R0zcH+oySOkpYS6BzcOuQmZn5yepROjo6YuLEiXBxcQGQtQKcNWsW3rx5A7VajT179uDXX39lWnvt2rXRpEkTo0WWbWxsmImNiHI0d8lkMvTq1QsTJkwQCMGkpCS0aNEC3333HUaOHCkYl9xARLh69SqmT5/Ozu3i4oJp06ahUqVKBSZwdBw9OhOKjY0Nypcvb5HmrlAosGnTJkydOpWFO2bvr0qlQkxMzCcx2VkC/cLzGo3ms+ufMfwrhPuNGzewYsUKNiF8fHwwfvx4fPfdd8wmGhMTg1mzZiEoKIgNvI2NDXuggSztX/+mEBFev36NpUuXsggJNzc3LFiwQBBCZy5iYmKwZs0aNG3aFDNmzEBQUBAT3n5+fhg5ciSz/+qW1eYSEelMLTq7v0qlEpBYZYefnx8TQiqVCqGhoRZfjz7s7OwEwt1SvhqO4wQc4sYIqgoKOjOEPuf+48ePMXfuXBw/fhxz5sxh1+Ph4YFp06aZ5DuXyWQC30tuLzm5XI6RI0dixIgRTEAkJCTg4MGDcHd3t5jX/tWrVxg1apTAzj5kyBB069bNaNhjfoCI8ObNG/z000/sGSxUqBDq1Klj9stEpVJh27ZtAupeuVyOTp06CfwfmZmZOHbsGCIjIz9bAWrKx/fZwZSn9VNuOUXLREdHCzgwHBwcaO3ataRSqSg5OZlGjRolyMYcP348i2rheZ4mTpzIfvf19RWUhFMqlTRx4kSWIcdxHE2YMMFk3dXs0GXixcXF0Z49e6hJkyaCwr/42/vfsGFDunjxImVkZNCSJUtYFIVEIqGxY8ca8H6Ywq5du1i5PBcXF5MZgkRZGbr6RaX79etncbSPPm7evEmFChViUSKW1g7NzMyk+vXrs/707t3bZHk+U8hLtIw+IiMjBdwwdnZ2VLhwYUH0y6JFi3Icp0ePHrE+cBxHP/30k1nnjo+Pp9GjRwuyluVyOfXq1YvevHmTa2QIz/MUHR1NPXr0EMz3rl27FnjJt4yMDPrhhx9YhjLHcTRo0CCznhOe50mhUNDmzZvZ/AFAtra2NGnSJIqNjaXff/+dUTnj74iUPn36UEpKymcRMaPRaAQ8VWPGjPno6LP8Av6tNVS1Wi1t376d7O3tBZNK/6ZHRUUJwrocHR0FpEYLFy5kk9LT05PCw8OJKGvSnTt3jrUNgGrWrGlWWj3P85ScnEyXLl2iyZMnU8mSJQ3C3qysrKh27dq0ceNGxuHO8zwlJiZS3bp12X729vZG67Maw6lTp1g6t4ODA/3222859lGflKl9+/YmubjNQVBQEEttl8lktHz5couOVygUglqznTp1ouTkZIva+FjhzvM8Xb582YBKAn+nvg8dOjTXELwXL15QxYoV2XycM2eO2edOSUmhpUuXCkL/OI6jDh065ErHoFAoaNKkSYyfHQA1adKEQkNDC1QAajQa2rNnj+Cl9NVXX9G7d+/MOp7nedq7d6+gFqmNjQ0tXLiQ0tLSGJ+8p6en4H5IpVIaPHgwRUdH/+MCXqPRMNoLADRq1KiPUpTyEzkJ98/aLJOWlobff/+dec8LFSqEcePGwd7eni0H3d3dMXjwYBZelZKSglWrVrFkHwcHB4E5IS0tDUSEDx8+YPny5czR5erqipEjRzKCq+wgImg0GsTGxmLHjh3o06cPevXqhUWLFuHVq1fM/MJxHHx9fbFo0SLs27cPAwYMgKOjI0uCcXBwwMiRI5njKzU1FT/99JNZoYrOzs5s6a3RaHLMtAQgiOJISEjIU+KQDo6OjuzcWq0213NnB8dxgnBQhULxyWzu+n2oXr06evToYRDWWqdOHUyZMiXXEDyRSCQIATTXtKSLtPnhhx+wfPlyRixHRPj999+xbNkypKamGl3uExFOnz6NjRs3ssxSX19fzJs3z4BGNz9BlMV5v3DhQuY38vT0xJQpU8wKoyQiXLt2DbNmzWL1EeRyOYYPH47hw4ez8FgrKytByCmQ5SfavXs3oyowNi6fEvo2d5VK9cnnbl7wWUfLvH37FmfOnGGfe/bsKeCLAbKclO3bt8cff/zBQtlOnjwJZ2dnVK5cWWCD18UYa7VaHDp0CBcuXGC/denSBZ06dRI8uLqCEO/fv8ebN29w7NgxnDlzBjExMQap71ZWVihatChatmyJIUOGoGjRokbjgEUiEVq2bImOHTti69atzFG1Z88eDB48OEe7qS7KAMgSKrlFFeiKaQBZMdHJyckGBRPMhZOTE5vgWq2WFZO2NGNUh7w4pYyVhrMUIpEIzs7OEIvFLNLE1tYWEyZMEHDmm9sHS2FtbY3u3bvDx8cHw4YNw4sXL6DRaLBmzRpYW1tj4sSJgpcgEeH+/fuYOnWqgV/IklDavODly5cYMWIEnj9/DiDr/g0bNgytW7fONcZd1+/Ro0cjJCQEQJZgHzduHCZNmmSQP1CoUCG2nw4ZGRnYtGkTAGDGjBkGhUY+JfTviVqt/u8Ld47jwgCkAtAC0BBRdY7jXAAcAFAEQBiALkSUaGnbOm1FX7P+5ptvjAo/a2trjBkzBqdPn0ZsbCxSU1OxYcMGJnx0QiAjIwNHjhxBWFgY1q1bx5xDzs7OaNasGZ4/f47ExETExsYiOjoab9++xevXrxEUFISwsDCjBTKsra1Rr149fPvtt6hduzbKlSuX68S3s7PDqFGjcOLECURFRSEtLQ0bN25Eq1at4O/vb3ICG1uF5AR9fpnExMSP0txlMpnA+ZeamgqVSmVRtIQ+3rx5g+XLl8Pa2hqVKlVCs2bN2LWZun6xWGwRcVh2EBFu3LiBjRs3Cu5leno6Ll++jJYtW+bqlBSJRIIomrxwxYjFYnz99deYN28e+vXrh+TkZKSnp2P58uVwc3PDwIEDWV5BcnIy5s+fL6iD2q9fP3To0MGiJKKMjAw8evQI5cuXz9WJS0SIiIjAlClTcO/ePfZ98+bNMWTIkFyT+YgIsbGxmDZtmuD49u3bY9y4cUbrEetHtbm7uyMzMxNpaWlQq9XYunUr0tLSsHz5cjg7O/8jAl5/nqvVaovCmP8xmLLXmLMhS3i7ZftuCYDJf/8/GcDi3NoxZnNXKpXUoEEDZudq2LAhRUZGmrQ9KZVKmjVrlsAmaWyTyWTMKanbRCIROTs7k7OzMzk4OJCNjQ3J5XJBsQD9zc7OjsqWLUvDhw+n27dvU1JSksWFGTQaDS1evJj1V0cBnJMtLz09nRWr4DiOxo8fnyNN64ULFwR9vnjxokV9zN6evmO7U6dOBnS3OUGlUgnocjmOI4lEQlKplBwdHally5a0ZcsWevLkCb1//54yMzMNri06OprZu0UiEY0ePdqi/j969EhALaG/FSpUiF69epVrO+/fv6c6deqw44YMGWJ2H7JDqVTSmjVrBPZoR0dHmjBhAsXGxpJCoaBZs2YJHP7ffPMNxcfHW3Se9PR0WrRoEbm4uNCkSZNy9HXwPE9BQUHUvn175kfiOI6aNGlCL168MMvx++HDB+rTpw87XiQSUfPmzU36B3iep759+7IxaNCgAa1atUrg7JZIJNS2bVt6/vz5Jy+CotVqqXHjxqwvPXr0sNhfVFBAQTlUTQj3IACef//vCSAot3aMCffXr19T8eLF2eQYN25crjc1JCSEatSoQb6+vlSlShXBQ5Mfm5+fH/Xq1Yt27NhBoaGhHz3J3r59K3AGe3h40OXLl03ur9VqBY6doUOHmuQQ4Xme7t+/z/YVi8X0559/fhSPSdeuXVl7LVq0MMqrYgpqtZomTZqU6xh7enpSgwYN6IcffqCff/6Zzp07R+/fvyeNRkPx8fGs8DnHcTRs2DCzz//u3Ttq06aNoNByqVKl2IteLBbTuHHjco0ASUhIoEaNGglech+DtLQ0WrBggUApEYvFNHz4cNq2bZuAi6V06dJ048YNi+5hZmYmrVmzhlUCs7a2phkzZhh9MavVarpw4YJAqQJA5cqVo3v37pl1PoVCYRAVVKFCBXrw4EGOisioUaPY/nXq1KHXr1/TL7/8wuof6ORArVq16O7du2Zff35Aq9VSixYtWD86d+5MiYmJn7QPplCQwj0UwH0A9wAM+vu7JL3fOf3P2Y4dBOAugLt+fn4Gnb569SqLzpDL5WaVVNNqtXT9+nUKCgqiyMhICg4Oph07dpCDg4NRQSISicja2ppsbGzIxsaGbG1tydHRkdzc3MjLy4uKFClCdevWpYkTJ9Lx48cpNDQ0X8OztFot/frrrywMjOM46tSpE8XExJjUcGrXri3QIHIKJwwKChIUPti3b99HCXf9whcNGjSgiIgIs4/XaDS0dOlSi16mcrmcXF1dyd/fn9q3b09HjhwRhCGaK9yVSiVNmTKFRU0BoGbNmtHjx48FgqxEiRJ08+bNHMdIrVbTt99+y46pVauW2eUSTSEtLY0WLVokEOQymUwQHujg4EB79+41GoKXmJhIx48fZ+Ug9aGLdtEPQ7SxsaFZs2Yx5UQXxbV48WLy9PRkL0CO46h8+fJ08eLFXBUZXdTL4sWLycbGhp3Lx8eHLl68mCth35w5c9gxAQEBFBwcTCqVis6dO0clSpQQlK2sUqUKXb9+/ZMVQdFqtdSqVSt2/m+//fajIs/yEwUp3L3//lsIwCMAX2cX5gASc2vHmOZ+7tw58vDwYNrG4cOHzbrY7BV2goODWTvZt4YNG9L69etp06ZNtGnTJtqxYwf9+uuvdOnSJXr69CnFxsayCjAFFY6lUqmoZ8+erE9WVla0detWk8K9U6dObN+WLVvmGEIXFhYm0HwWLVqU5weC53kaP368QKi9efPG7ON1Ya264yUSiWAVYs5mZWUlMFEMHTrUrHNfuHBBICgDAgLo2bNnxPM8HTt2TBC/PWPGjBxNYzzP0+TJk5nJrkyZMhYzZBqDQqGg/fv3C0wRuk0qldK4ceOMCvbU1FSaMmUKyeVy6tmzp9HVlFarpY0bN7IQTEdHR5o/fz6b2/fu3aNu3bqxsdW/x5cvXzZr7uv6oa9IeXh40P79+3ONCed5nnbt2sWOc3d3p/v377Pfbt68SQ0aNBCYSQMCAuj48eOfJExSq9VS69at/7eEu6Ah4EcA45FPZpkbN26Qt7c3EwQzZ86kjIwMi2/m27dvBQ+MTgPw8PCgP/744x+PoeV5nm7fvi2gmi1XrpzReHue52nIkCFsv8DAwBy156ioKKpRowbbf9iwYXlOvuB5nmbMmMHaqlatGgUHB1t0fHZBOnv2bIGW5+bmRlOmTKFOnTqRn58fOTk5kZWVlUBr07+P/fv3z/Wc7969E8T7u7i40K5du9g4xMfHC5KratSoQSkpKTm2u2fPHnYd7u7udPLkSbPHISeo1WrauHGjYEwAUN26dSkkJMRgrioUClq6dCnL1ZBKpTRgwABW9Ft/HDIzM2n9+vVUrFgxWrhwIaWkpFBqairt27ePSpcuLcjTsLOzo++//56eP39ulo09JSWFJk6cKOi3jY0NrVmzhpU1zK2Nv/76S2Ay0y8NqdVq6dWrVwYC3s/Pj7Zu3Urp6ekF+hz/W4V7nuPcOY6z5TjOXvc/gG8APAVwHECfv3frA+BYXtovXrw4ow7QaDRYtWqVAWmYOZDL5Sy6QSQSoWbNmqhduzY6d+6MwMDAf5z8h+M4VKpUCYMHD2b8I8HBwfj555+NlgXUL4ygVCpzjLOWy+Xw8vJin9++fftRIVz6HCiWRgxkL/9HRPD29kbPnj1Z1EdycjKsrKywe/duPH/+HBcvXsTatWvRt29fwXXojs+tbKJWq8Uvv/zCCkEDQNeuXdGlSxcWFePo6IhWrVqxefD06dNcSdFq1qzJIkYSEhLw8uXLfAmN02q1eP36tUEUUFBQkCBsVwepVIoKFSqw3Ay1Wo1t27Zh9uzZgnnBcRzkcjkGDRqEw4cPY9y4cXj79i1++OEHDBgwgNFkcByHEiVKYNmyZVi/fj3KlCmT6/OhVCqxfPlyrF27lo2bu7s75s6diwEDBpgdTeXk5MSI2rRaLaKiothvIpEIxYsXx7Zt29C+fXt278LDwzFq1CjMnTv3H6e0/hzxMaGQHgCO/n3zJQD2EdEpjuPuADjIcVx/AG8BdMlL425ubqhQoQKeP3/OkmYWLlwIPz8/tGvXzuwwMP2wNZFIhBYtWqBXr16QSqUWETblBCKCVquFRqOBWq1GbGwsQkJC8O7dOxZfTpQVlunt7Y3atWujdOnSsLGxYRXme/fujcOHD+PevXvQaDTYv38/2rRpg7p16xowXeqQmZmZYzigXC4XJGVFREQYCAhLoE9+paOCtQQ2NjaC/ms0GowcORJXrlzBy5cvoVarsWXLFtSsWRNNmzZF5cqVUblyZXTp0gX37t3D1q1bceTIEcbnkhuzZEZGBg4ePMjGqHjx4hg9erRA4IhEInh4eEAikUCtVkOlUuXKF+Pl5QVPT0+8fv2aCWSlUmnA2W8JNBoN9uzZg40bNxqMa1xcHGbPno3ChQujZcuWrCqWSCRCkyZNsGzZMgwbNgzv37+Hk5MTAgICDEjPdGNUqlQp7N+/Hz///DPu37/P5oNEIkHDhg0xc+ZMwcsrJ+hI937++Wc2Zg4ODli4cCG6detmtmDXJTI5OzuzWH594a7bp0iRIli5ciWcnJywe/duqNVqpKWlYeXKlUhPT2fJVQWhsOkrMpZWBPvHYEql/5SbKfqBBw8e0MSJEwUUAf7+/nT06FGzbccxMTHMMSuRSGju3LlmHWcOMjMz6enTp3To0CEaP348NW3alAoVKkRyuZykUilJJBISi8UkEonYJhaLycbGhjp37kwvXrxgbfE8T7/99psgTLNVq1YChynP87RlyxaBs0pnmzQGjUZDs2fPFpgk8hrCxfM8bdiwgbVVvHhxevDggUVtPH36lCpUqMDamD17NvE8T3v27BEs6UuUKEG3bt0yMC2oVCpBdFHbtm1ztI8/fvyYmU8kEgktWbLEqFlq165dLLpDIpHkam5SKpXUsWNH1o8GDRpQbGysRWOhD61WS6dOnRL4R9zc3KhZs2aCcfHy8qINGzYYRPRoNBr6448/qHbt2rR161ZSKBQGY6dUKun69evUuXNngWMZyOJcmj17NqPJMAcajYbOnj0roHJwcXGhjRs35ik1/+3bt/TVV1+xtkaPHm3Uiauj/li8eLGAxkEkElGbNm0oJCTE4nPnBo1GIzDdfffdd7ma7j4V8G/lliHKigQYM2aMwNZWpkwZunv3rlmOzvwU7jzPk1arpYSEBDp48CD17duXKlSoIIhIMXfjOI46d+5MsbGx7BqSkpKoa9euzPZoZWVF+/fvZ7/zPE9//vkna8Pe3j5H8jAiovXr1zPBZWtrm2fnH8/ztG3bNnbuIkWK5PhiMYaIiAiBcO7WrRuz2X7//fcCu29gYKBRm2/Lli3ZPi1atMgxUuXQoUNs3nh4eNDp06eNXteWLVuYM1EqldLbt29zvI7sL013d3eLnMvZzx8bG0v16tVj7YnFYlqwYAFFRkbS4MGDBT4HNzc32rx5M6lUKsHYaDQaev78uUCw6p6P2NhYmjNnDhUvXlzQlkgkonr16tHZs2cpMzPToj6/ePGCvvrqK9aeTCajKVOm5Fl5iImJEUSkdOjQIUcFLiMjg7Zu3WrA09O6dWvmLM8vaDQaqlKlCjvPoEGDzCb7K2j8q4U7EVFcXBy1bt1aMDEDAgJo9+7d9OjRI4qOjjY5EfJDuOu0hWvXrtHUqVOpaNGiJh19OgHh6upKfn5+VLp0aapQoQJVrFiRypcvL3DuWltb04YNG1jfeZ6nGzdusPh+IIuk6eXLl+xBvXnzJvtNIpEIHE/GcPz4cVb/0crKik6cOGHx9ev6ph/tUqRIEbNjn3VQKBTUo0cP1kaVKlVIrVYTz/MUERFBHTt2ZGMqEomobdu2FBYWJmjj+++/Z8fXr18/x8S2tWvXMuFeokQJunPnjsE+GRkZNGzYMNZmoUKFcnWW8TxPJ06cEDCA/v777xaNhQ5paWk0evRoAVPo999/z+Ko4+PjadiwYYIVnYuLC82bNy9H7VHHIrl7926qWrWqQFvnOI6KFClCCxcutJhRkud5Cg8PpwYNGrB7JZFIqGfPnh8VEpqenk4DBgxgfaxQoUKuzn+1Wk0nT54UrAZFIhFVrFiRbt++nW8CXqPRCOogjxgx4l9BHPaPC3YyIdx1VKFnz56lSZMmUZUqVQwyRsViMRUtWpQ6d+5MoaGhRi/+Y4W7VqulK1euUL9+/YyGqekeFl9fX2rVqhVNnjyZ1q9fT7/99htdvXqVnj59SqGhofT27VsKCQmh48ePC9oZOHCgQAvQaDQ0c+ZM9uBIpVKaPn06KZVK4nmeHj58KNBwc4v4uX37NlvuS6VSWrVqlUXXr0N+CHciogkTJggEblRUFPstODhYEMfPcRx169aNwsLC2DVOmzaN/V65cuUcVyKbNm0SaO6//fYbabVa9qKMj4+nlStXCuLLmzdvbpaQevjwoaBg+bRp0yweC61WS4cOHRJon1WqVDFYscTFxdG0adMEoYrW1tb0448/MmZFov/X1JVKJR06dIjat29vQEEtk8no22+/pQsXLlichKd7YfTp00cQktqoUSN6/fp1rrHsOW1arZamTZvG5n2hQoXMyoDm+SymT30BD2RFPd25c8egT8bOnRs0Gg35+vqytseNG/fJYuxzQ07C/bMjDiPKqi155coVLF26FPfv30dqaqrRyAytVovQ0FAoFIqP4k0xBa1Wi1WrVmHhwoWIj48XRERYW1vD09MTtWrVQseOHVGrVi3Y2trCxsZGUG80O7y8vFC7dm1WriwsLAwqlYo548RiMYYOHYoTJ07g/v37UKvVWL9+Pdq1a4fq1atDLpfD0dGRlYTTLy5s6nw6J6ZWq8WbN29A9M+VCCtatCikUimUSiXS09MRGhrKGAZLlCiBJUuWYNiwYaxo9KFDhxAfH48VK1agXLly8PPzY3xBycnJbByMISAgADKZDAqFAjExMRg8eDA2b94Me3t7JCQk4PXr14iMjGRRNw4ODvjuu+/Mcox6e3ujWLFirGjGzZs3LRpXIsLz588xbdo0dg8dHBwwb948lC5dWtCOq6srJk2aBLFYjJ9//hnJycnIzMzE0qVLkZqaijFjxsDLywspKSm4desWVq1ahStXrghYJmUyGcqVK4exY8eiXbt2AmZVc5GRkYH58+fjl19+YU7fSpUq4eeffzbKTsn/XbErMTERiYmJCA0NRVxcHNLT05mT28rKCh4eHvDy8gLP85BKpVCpVFAqlXj27BnKly/PytzpuIX0z8NxHOrWrYsdO3Zg0qRJuHDhAniex507d9C3b1+sWbMGVatWhVKphEKhQEZGBkJDQ3HixAkAwLJly3J1/Gq1WkHggpWVldEqXZ8bPivhTkR49eoV1qxZg3379pldS9Ha2tok4ZNucutgyYR+//499uzZYyBArays0KJFC4wePRr16tWzqE0iEhQAlkgkBse7u7tjzJgxGDFiBJKSkhAfH48lS5Zg8+bNkEgkTDgBEETiGIO7uzsjauJ5HhEREdBoNGZFQxjr+8eiXLlyTLinpqbi1atXqF27NoCse1OnTh2sXLkSQ4cORXBwMLRaLc6ePYuBAwdixYoVAjK0pKSkHIV7xYoV0bZtWxw6dAhEhOjoaJw8edLovlKpFEOHDmXRKLnBxcUFRYsWZS+a8PBwJCcnm6zglB3v3r3DxIkTERwczM4/cOBANG7c2Oj57e3tMXHiRHh4eGDKlClISUlBeno6VqxYgefPn+Pbb7/FlStX8OuvvxoUmPb19UXfvn3Rr1+/HInpcoJSqcTWrVsFtMPFihXDihUrEBAQIGiTKItS++TJk7h8+TIePXqEV69e5Rq6KhKJmAKVnp6OSZMmwd/fH3Z2drC2toa1tTXkcjkkEgmkUqngfx2d840bN5Ceng4iwrNnz9C3b1/UqVMHiYmJiIqKwocPH1gZv4CAAISHh6NkyZI59ku/JKRYLIaVldWXaBlzt2rVqpFWq6ULFy5Q9erVBWYHsVhMbm5u1KhRI5o3bx799ttv9Mcff9Du3btp6NChZGNjQ0WLFjVpY3vz5g0zg8hkMlq2bJnZS55bt25RiRIlDMwwIpGI3N3dqXz58jRmzBi6fv06xcfHM/uxKajVajp06BCzn+qKj6Snpxvsm5iYKHCuOjk50a5duyg4OFhQFebnn3/OdXndpUsXtn/Dhg1zLQxhDDzP06ZNm1g7xYoVszhahiiL/EvnAxCJRDR37lyDMdNqtXT+/HmqUqWKIBW+TJkytGrVKkElom3btuXIWfLq1Svq1KmTAVmcbpPL5VSiRAlavny5RURoRETLli1jnDA+Pj705MkTs45LTEykIUOGCBK6WrZsSW/fvs3VTKBQKGj9+vUCOgGO48jW1lZgthSLxeTu7k6jRo2iBw8emJVMZAparZaOHz/OkgqBLA6g7NmnPM9TUlISbd26lapVq2ZyzD924ziOxGIxSaVSksvlZG1tzTZTZH/GtkKFCtEff/yR6/VHRkYy051UKqWlS5fmeSzzG/g32NwfPnwocFoAIGdnZ/rhhx/o5s2bAlspz/MUExNDXbt2ZTezbt26Rln97t+/z26MjY0Nbd261eyBUyqVNHXqVAEJkrFNJpNRYGAgLVq0iF68eGHUHqdWq+nIkSOM1REAeXt70/nz501SDTx48EDwEFeuXJkuXrxosXCfPHky279q1aoUFBRk9hjo92ft2rWsnZIlS9KjR48sbketVgvu8w8//GAgeHT3+NGjR/T1118Lxjq7EJs5c2audAGpqam0Y8cOGjhwILVt25ZatmxJHTt2pIEDB9LPP//MhKqlDrjDhw+zUEVXV1ej0TjZoVKpaOHChQL7efny5c1iXNRdj0qlMprJqi+0hg0bRnfu3GEUAx+DJ0+eUNmyZQX2/vXr1wvmne5F2qtXL4OqZLrNwcGBihQpQqVKlaKAgAAKCAigEiVKkLu7u0F4ZkFuYrGYPD096ZtvvqG//vor1+t/8+YNubi4MGUgr36rgkBOwv2zMMtoNBosWLCAkfWLRCLUqlULkyZNQsOGDQ34p3mex549e3Ds2DG2XFIqlUaTkpKTk5m9XiwWsyw4cyCTyTB27FjUrFkTt2/fxsOHD/H06VPEx8dDqVQyLm+VSoW//voL169fx969e9GjRw+MGDGCFSTQaDS4dOkSxo4di/DwcABZy/Du3bujdu3aRpd4HMchICAA/fv3x5IlS6DVavH48WNs3bpVkEGpXyHGFAoVKsT+z8zMzHV5bAqkZ5bJ67KU4zj4+Pgwc0R8fDwUCoXA7qlru0KFCli3bh2mT5+OkydPQq1WGyQYhYWF5Whm4jgOdnZ26N27N7p164bU1FRm27WxsYFMJsvztXh6ejJzoFqtztFEBGTZbo8dO4aVK1cym7WPjw/mzp2LUqVK5doPnueRkZGBc+fO4ejRoya55OvVq4cff/wR7u7uebiq/wcR4e3bt5gyZQor5C6RSNC7d2/07NlTwK3/4cMH/PDDD7h48SJ73qRSKTw8PNC8eXN8/fXXKFq0KOzt7Zn9HMgat8zMTGRkZCAhIQFz5szBo0ePAGQlMpYuXRoZGRlITU1FRkYGq8qk23TjqlAozMqYLlmyJCZPnoyKFSvC09PTZOU1fahUKnYukUhk1jP3WcCU1P+UW/HixQWJSm3atGH8GMa0jtDQUAEXS4kSJUxSoR49epSRGTk7O9PZs2ctejPqe/M1Gg1lZmbS/fv3afXq1dS+fXvy8fExWApKJBJasGABpaenU3JyMq1bt45F7ODvt//333+fa4Fonufp9evXghhokUgkCBc8ceJErprZ5s2b2THFixen27dvWzQGur6sWbOG9aNUqVJ50tw1Gg316dOHtdO4cWNBxIyx88bHx9PgwYONane1a9f+xxJKnjx5wmra2tjY0JYtW0zuq+NP0Q9ztbe3p23btuVqzuN5niIjI2n79u3UsGFDA4IvsVgsWF3qCq9bambKjujoaOrUqZNgfrdu3ZoiIiIE/Y2Pj6devXoJQoOLFi1KCxcuZHTNua2MdL/rh0PWrVuX3rx5Q2q1mtRqNalUKlKpVJSenk4JCQkUGRkpCE/OPjd0Y6HfLysrK1q8eLFFY/PixQtGH25tbU0bN278qHHNT+BzN8vosza6ubnlKHzUajXNmTOHPeg2Nja0fPlykzzcO3bsYMtXNzc3unXrlsUDaAqJiYl09epVWrhwIQUEBAiWo66urrRo0SIaMGCAgJFQLBZT9+7dDR4QU9BqtbRz506DkDYgizgpJ55sHfbt28cmuJ+fH129etXia80v4a7VagVmoipVqhjEshvDhw8fqG/fvgYC3tPTk+Li4izuR34gNDSULddlMhmtXLnS6H48z1NCQgI1bNhQMA/Gjh1rMhlGP2Fuw4YN1KRJEwMbNsdx5OPjQwsXLqRp06YJ5pmzs3Oes0V5Pou+d+zYsYIXSdmyZenhw4cG+/7xxx8C82GFChXo1KlTeSKpW7hwIWundOnSRs/H/02EduTIEWrQoIFR85S1tTU1aNCANmzYQO3btxfMGycnJ5oxY4bZhGNfhPtHbPo3p0OHDibfqro4b/1qOjVr1syxMs3y5cuZ06tw4cJmVduxBDzPk0ajobi4OFq5cqXgAZNIJAKtx9bWlr777jtKSEiwyA6qUCgE2Xs6rb1Pnz5maSC//vorE+7e3t506dKlPF1nfgn31atXC15Q5mTN6jIt+/XrJ3hQRSIRzZw50+IxzQ+kpaUxocZxHE2fPt2o/yM9PZ2mTp3K5qFYLKZevXpRdHS00T6rVCp69uwZ/fTTT1S2bFmDF5pYLKZixYrRqFGj6OXLl0yr3bFjB3NW64TYgQMHLBLwuoS9SZMmCYRm2bJl6aIRXnetVkuLFy9m89zKyoquXLmS50I2J06cYOd0dHQ08Emlp6fTuXPnqE2bNgZCneM48vPzo06dOtHp06cpPT2dtFotJSYm0qxZswTPplQqpREjRlBUVFSu8+aLcP+ITV/jnTx5skktnOd5QUo5x3G0Y8eOHKMlZsyYwTRqHx+fAtHy9NO8s1ex0dekFi1aZLIQh6l2NRoN3blzx8Cx6OjoSCdPnjSrrT179jDh7u/vnytlgam+5Idw53meDhw4wNpxcHCgx48fmz0mUVFRNHDgQAHlg52dHY0aNcoibpT8gEajESQymXIO//nnn+Tp6SnQSLPzCuk09adPn9L06dOpUqVKRh2Tnp6eNGXKFLpx44aB0FYqlfTjjz8KhF7RokXp+PHjZiXd6BKg5s+fzyo36ebu0aNHjWriWq2WFi1axPa1sbFh9MR5uRfPnj0TRBHt3buXjc2zZ89o5MiRBsmEHMdR0aJFaeLEiXT58mVKTU01OHdGRgYtXLhQUJ3NysqKunfvnquAf/nypUC4b9iwweLrKih89sJd/yatWLEiR2G9b98+gSacU+SHRqMRpJaXKFEi3zPL1Go1vX37ljZs2EBVq1Y1GVkzZMgQVl0mt0nP81l8Kw8fPqTRo0eTl5eXAdWBTCajWbNmmRXitmHDBnZ8yZIljabh54b8FO76tV05jjOaSZjT8Y8ePWIVmfQ1sS5dulBISMgnyx7UaDSC2pqdOnUyoC4IDQ2lSpUqsX3s7e3p4MGD7Hq1Wi3Fx8fT+fPnqX///uTu7m5gU7eysqIyZcrQ7NmzKTg4mGUrGxub5ORkGjdunKBsn4+PD506dSrXqKK4uDiaNGmSQLC7ubnRli1bTJpYeJ6n3bt3C44pW7YszZ8/n54/f25xDYaIiAgqVqwYa2vixIn08uVLmj9/Pnl5eQleeDpNfcyYMRQWFmZyXHT9VCgUtGPHDkHxHrFYTG3btqXw8HCTx4aFhbEVkVwup59//tns6ylo/GuEu7W1NW3fvt3khWQX7nZ2djmaWdLT06lXr14CE05+anaRkZG0fPlyqlOnTq7xtWKxmKpWrUoLFy40yYeie8COHj1Kffr0ETDuGduKFStmFoeGfnk7Pz8/WrlyJf366690+PBhunXrllkab34Jd6KsEor6Auzq1asW3ZfIyEgBTYH+GNeuXZuOHTv2SYooa7Va+uGHH9j5q1atKqDBiImJod69e7O5IZFIaNiwYUyzfPv2La1fv546duxotBSkSCSir776ipYvX05BQUFmj1FiYqKBf6JIkSK0a9cuk8pARkYGjRkzxmBFtGbNmlxJst6/fy8ghNNfoQwdOpR+/fVXio+PN6v/CQkJAhNkyZIljVKP2NnZUe/evY2uYHKCRqOhffv2CRzbEomE2rdvb7QgChFRbGwsC6fWBUt8LvjXCHc7Ozvau3evyQvheZ72798vYKJ7+PChyUkTHx9P7dq1YzexdevWHy3ctVotxcTE0K5du6hy5coGjJAymYz8/f1p+PDh1Lx5c4NJKZfLqWLFijR+/Hg6deoU3b59my5cuEAbN26kvn37UunSpVkEhv7m4OBAHTt2pIULF7LVAcdx1L17dwNGP57nKT09nd69e0eXLl0SFPcVi8Vkb29Pjo6O5OjoSB4eHlSuXDkaOXIk3bp1izIzM01qhStWrGDtlClTxuykney4fv26wEF87tw5i+5LWlqaQGPOvnl4eNDChQstMoHlBdkTu9zd3enJkyfMvLFgwQLBddapU4fu3btHf/31Fw0YMIBKlChh4CjnOI4cHR2pbt26tHPnToqIiLDYMamLrtHngAGynPy//PKLYGWj0/anTZsmMOe4urrSmjVrzGKL5PmsUn3Vq1c3eB5011OhQgWaNm0aBQUFGVAS60OlUtGMGTNMkvLZ2tpS48aN6dSpU5ScnJyn+6tSqejKlSuCBEWxWExff/21UQ1eqVQKot0mTpz4SZQHc/CfEu6XL18WOI1WrVpl8gZHRkZS06ZN2b59+vTJ88Ou07Q2b95MTZs2NerkqlChAi1YsIBevXpFWq2WQkJCqGvXrlSpUiWDpba5m52dHTVv3pz27dtHaWlpFB8fT99++y2b/DY2NvTrr78yu+Tbt29p165dNHjwYAoICLAoY8/d3Z2mTp1q1C/B8zwtXryY7Vu+fHl6/vx5nsby5s2bgtDXU6dOWXRf1Gq1oHSer68v1atXT7Bkl0ql1KFDB3r06FGBCXidiUmfROvi38WgL1y4ILDvWltbU79+/ahZs2YmTXdubm7Uu3dv+vXXXwWEYHmFbuWgL3C9vb3pzJkzTMCr1WpatGiRwKxSqFAh2rlzp0VZrTzPU1hYGK1cuZLq1q1r8hr9/f1p+vTpOUYJbdy40ehLr27durR58+Z8KVLP8zydO3eOAgICBM9wjx49DMoUajQagWltwIABRrPK/wn8Z4Q7URapv37cd7NmzUxqF+Hh4QJH5A8//GDxpFCr1fThwwfavHkzVa5c2SAcTSaTUcmSJWnFihUUFBRkkI4dGxtLr1+/pnXr1lGZMmUEtlBTm1wuJw8PD+rSpQudPn1aEFXB8zydP39ekAresGFDOnPmDA0ePJjKly8vEJzZHxCZTEZ2dnbk4OBAdnZ2Bv2xtramRYsWGWiLPM/T3Llz2X65MTLmhOzC/c8//7TovvA8L1iR1atXj16/fk3Dhw8X3B+O46hatWr0559/mnTSfywePHggsOFu3bqVIiIiqEmTJoI5Ym9vb5T338bGhooUKUITJkyga9eu5atTmOezMrnHjRvHzs1xHFWsWJFu3bpFWq2WTp48KWDFdHR0pK1bt+Z5vLRaLYWHhzNWSg8PD4M5ZmtrS0eOHDG4Tq1Wy1YA+kqJk5MTjR49mt68eZOv/hS1Wk23bt0SMErK5XJq3bq1QIPXarWCOgLt27fPMULvU+JfJdz37NmT48Wo1WoaPXo0u/n29va0Y8cOozc9NDSUatWqxW7KhAkTzH5wMjIy6OrVq7RgwQKTkQulS5dmRRXMSdKIioqiFStWUOvWralEiRLsgbe1tSU/Pz+qUaMG9erVi9asWUOvXr0ymTqePVZcJBKZTN8Wi8VUpEgRatKkCQ0cOJBWrFhBx48fp0uXLtGpU6do+fLl1KFDB0GYWEBAAD1+/Nig/9OnT2f7VK9ePc9hpR8r3ImIunfvzo6vXbs2paWlUWZmJu3cuVMQKqsTDosWLTIr7M1SvHr1iipWrMjONXnyZJo2bRoTaDr+IP2YcY7jyN/fn7p27Up79+6lmJgYRq+R39DFrPfv318wh2vWrEl//PEHVatWTSDYV69e/VE8NLpz6iK93rx5QytXrqSmTZsKzD7Nmzen6OhodoxKpaJTp04ZUJCIRCIaPny4WYEIee3r5cuXBUmRIpGIevXqxVawWq2WBg8ezH4PDAw0WsD+n8C/RrjLZDLauHFjrjfx+vXrAm1Dv6CFPkJCQqh69epsv6lTp5q0J2u1WlIoFPTu3TvauXMnde7cmfz9/Y1mA/r5+dHkyZPp7t27FieJ6LT5Bw8e0IULF+jMmTN07tw5un37NoWEhJi1HNdqtXTnzh3Bsl9/k0ql5OHhQd27d6edO3fS3bt3KTIy0ugDwvNZGaDLly9nph65XM5MPfr7TZw4USAcXr9+bdG169q5du2aQIu11OZORNS/f3/W3xo1arAoFbVaTdevX6dGjRoJhJmNjQ21aNGC7t69m2tGqCWIiIgQrA6/+uorwdwsUaIEhYSE0KNHj8jT05P8/PxoypQpdPv27TzbjC2FzqTYpk0bphSJRCLy9fVl81skEtGIESMKJNtXq9VSZGQk9e/fX/DCPXz4MHsJHD16VBAlo7/16NEjTwlR5kKpVNJvv/0mcLLa2dnRvHnzSKFQkFarpQkTJgjmfm7Vuj4VPnvhru88mTlzZq43UqvV0o8//mhQMCA0NFTwsDx79kygxf30008GAis5OZnu379PGzdupPbt25Ozs7NR+7hYLKbKlSvT/Pnz6cOHD/lCyGQpdP3du3evQXECnVBu3Lgx7dixgwlzczXC8PBwQSz23LlzDRxv48ePF2jLeSktx/M8HT9+XNDn+/fvWzyWI0eOZMK9SpUqgggknUNx5MiRBlEo7u7uNGPGjHx7OBMSEujbb79l7evPZScnJ9q9ezdptVrSarV0+vRpioqK+sfmTlBQENWpU8eoAK1bt65Aky4IvHjxQmDCatSoEb1//542bNjAMn2BrIgU/aCCGjVqmJXF/DHQarV048YNgblTLpfTypUrKTMzU2CSrFChQp4Um4LAZy/c9Z0n3377ba51GHmep9DQUEHRWrFYTM2aNaNr164xzezu3bsCgbVz505Sq9Usrnjx4sXUoUMHKlWqlEkmO6lUSnXq1KElS5bQs2fP/tEKLLGxsTRkyBCTGrudnR3t3LkzT578qKgoAfPf9OnTDfwH+SXc9Qtte3p65skxO336dKaFBgQEGDURpaSk0Lp166hYsWICoSuVSumbb76hCxcufPRyX6VS0YgRIwzuhUgkon79+rFyeZZAZ9bQ8RnpM6J+LC5duiRgJgWyImMOHDhQ4BEgGRkZNHz4cEG0W9u2bQXzWSqVUs+ePWnixIlsP19fX7p8+XKB9o0oS4PPnmXu7+9P586dEzCi+vn55TmYIL/x2Qt3fV4KX19fCg8Pz/WitFot3b9/X1C4Vhd21b17d9qwYQPNnj1bsPxv06YN1a9fnwoVKkS2trZG7dQcx5GTkxOVL1+eBg4cSBcuXKCUlJR8XcrnBRqNhlasWGGUY0Z/q1WrVo4JGaYQHBwsiEJatmxZgWjuWq1WIAwrVapkskRiTlizZg17IRcrVsxoYhbP86RWqyk4OJi6du0q0AZ193ns2LEUFhaWZ8HG8zytXLnSIDqkevXqFt8Hns/iTHn06BFt3ryZxowZQwMHDqRZs2bRoUOHWBjhx+DNmzcCO7tOUx4yZEiei1ubC543rBGs/9K1tbWlkSNHUnJyMt29e5fdX5lMRrt27Srw50+X6LR8+XLBc9a4cWNat26d4GWY3Sf1T+GzF+5FixYVOKDOnz9v9sVdvHiRypcvn6PAM2eTSCRUrlw5Gj16NB06dIiFM34uyMzMFDDmmdo4jqOOHTvS9u3bzY680Gq1NHv2bIH2ZMzmnh/CPS0tTbBC6NixY64FqY3h0KFD7OXs6emZI9snz2cVkdi1a5eBs1UsFlNgYCAdO3bMYnMJz2dxqy9evFigRPj7+9Pp06ctbistLY1mzZpFRYoUMYjz1oXaLl261GQuQm5QKBRGVxlAVhbs5s2bPypU2JxjlUol9e3b1+D6nJ2dad68eWzOxsTECGgGJk6cWGART9mRlpZG3333naAAuH5ItYuLS54T+PIbn71wL1u2LJUsWZINniXFhjUaDb148YK6d+9Ozs7OJpMfsgtAOzs78vLyoipVqtCQIUPo/Pnz9OHDhzw/OAUNXQKXv7+/SROS/mZjY0OBgYF04cKFHFPAVSoV/fLLLwJbaJ06dQzMHDzP08CBA9k+gYGBZq2wsuPPP/9kMdVWVla0cuXKPL1Er127xhQCR0dHOnjwYK7HaDQaCgoKosGDBwucnjrhMnToUHr69KnZ/cnIyKB169YJClxzHEdDhgyxSBDpIql++OEHo+GS2e/rxIkTLdaydZmZ+rZtb29vwTiUKVPGLJbR7GMQHBxMt2/fpqdPn1J0dLTJ8dNoNHThwgVBbLmOF+bYsWOC5KbU1FRq06YN269BgwafjNqZ53m6c+eOgOJCPzTTzs4uT1XICgKfvXCvVKkSNWvWjA1e06ZNLX7g09LS6MiRIzR48GCqWLGiwG4GZIVMli9fntq1a0cTJ06kHTt20I0bN/4xLvC8IDMzky5cuEDz58+nIUOGUM+ePalp06ZUvnx5wUOrvxUqVIgmT55M7969M2hPq9XS/v37BVzYNjY2jKxJHyqVSuCM69Chg8Uat0qlolGjRrEHxcfHx4DS1VyEhoYyU4hUKqUNGzaYLZSUSiUdPHjQIJ4ayErOOnLkSK6ZmampqbRs2TJB8o9u69Spk0V84TpNUT8eXC6XU+XKlenrr7+mYsWKCZz8jo6OtG3bNrP9PzzP05s3b6hGjRqCl9mJEydo2bJlrG2JREKTJk0yKyuViCguLo5mzpxJ3t7eJBKJyN7enpo2bUpHjx41+nI7c+aMQahj3bp16dKlSwbXolKpaNq0aYIXUV5WeHmFWq2mtWvXGs1LkUqleeJnKgh8lHAHsA1ADICnet+5ADgL4NXff53//p4DsApACIDHAKrm1j5RVpm9H374QUBuZUwYGYNWq6UnT57QpEmTKCoqitLS0ig0NJRmzJjBlu1SqZSmTp1KYWFhFB8fX2Axs58KWq2WMjIyKDk5maKjoyksLIweP35sMiVf5xQ+fvw447BOSUmh9evXCxzOUqmURo8ebZA9qLOV6nwjYrGYpk6darFzOTg4mL766it2vubNm+eJb5yIKDk5WcAhPmPGDIs5Rl6/fk0jRowgR0dHwYrPzc2NBg4cSK9evTIauZWRkUHz588XROLoH1+9enUKCQkxqx9arZY2b94ssNnrMkTfvn1LUVFRFBISQmvWrBGcr2LFimY/IxqNhsaMGSOoPTthwgRSqVSUmJgoSAr09fU1q+Rfeno6TZ8+3agPqHDhwrR8+XL2ktBoNPTnn38KTGISiYQaNmxIQUFBRhU5nudp8+bNArt7XsyAH4OUlBTq06eP0WI8/xXh/jWAqtmE+xIAk//+fzKAxX//3xLAn38L+VoAbuXWPv0t3Ldv386WpG5ubiYL12ZmZlJERAQTLA8fPqTatWuTWCymDh060Js3bygoKEggREqWLEnPnz//Vwv03KBLp9YX1tk3GxsbmjNnDoWEhNCwYcMED6ZMJqOBAwca5RhPS0ujESNGsAfNycmJrly5YlH/tFot7du3T2B22LVrV56vNyMjQ0Ae1rt371wrW2WHzmZ+8uRJatGihcFDXLJkSdq2bZvgZZecnExLly4VCGMHBwfBi9XV1ZWuX79uVh/evXsnWBH5+vrSr7/+ahCppFKpaPbs2QJN0hR/fHbcvHlTYDqqX78+CxvW3Rf9Fcj48eNzTci7ceOGyagt3Vz77bffWJKQvtlVV7Ame+hy9nP8/vvv7BwikYjOnDlj1pjmF3iepxcvXlDVqlX/m8I963gUySbcgwB4/v2/J4Cgv//fCKC7sf1y2qpVq0aPHz9mmolMJjNK/atSqWjnzp1Us2ZNOn/+PGVkZFDnzp2Z1iSVSqlly5aCkmwcx9HMmTNZSFliYiI9evTos3KW5hfS0tJo5MiRBrSouvHR0Ztm58aRy+U0YsQIo0RbarWadu3aJYikCQwMNHvproNCoaCuXbuyNvz9/fMUJWOqvaZNm+Z52c7zPEVERNCoUaMMzFsuLi70448/UlpaGqWlpdHEiRMFJj8nJydauHAhXblyRTDn9Gl9czrvqVOnmODlOI5mz55tdAXC8zyFhIQIXmhly5bNVWlJTk6m/v37sxeXo6MjHTp0SDD/IyMj6ZtvvmHtli5dOsdVAc/zAge8g4MD/fTTTzR48GAB/UOdOnXowoULgixxsVhMHTt2NOBvMYbr16+zzFGRSESbNm3Kcf+CgFqtpnXr1gmUkv+6cE/S+5/TfQZwAkA9vd/OA6huos1BAO4CuOvn50cKhUKQoTZkyBCBxqRQKGj37t3swSpSpAgdP36cLl68aBABob9ErlmzJkVFRdGHDx9o27ZtVLNmTfL29s73ikyfA3TkTfoFJOzt7embb75hJozs3DheXl60YsUKgwIHOtPNli1bBBqak5OT2UVC9JGYmEh169YlsVhMHMfRsGHDDMw/ljiz1Wq1gIKhZMmSOdZizQ067fj8+fPUpEkTg1XN999/T7NnzxbYvu3t7WnFihWkUqkoLCxMEOI3ffr0XM1WWq2Wfv75ZzZfraysGLVvTEwMy2K+ceMGPX/+nCIjI2nTpk1Me5fJZLRgwQKT5iie5+nChQsCp2nHjh0N9ud5nrZu3coEmIODAx04cMBkv5VKpSCcslWrVhQdHU3p6ek0adIkplxIpVJBkINOYzdHsBMRPX/+nCXrcRxHP/74Y67HFASSkpIE12tjY0P37t37LCwBBSrc//6cSBYKd/2tWrVqpNFoqG3btmwAW7RoQTExMewi0tPTadiwYQKttHjx4nT27Fk6ffq0gL5Tt7m7u9P+/ftJo9HQ6dOnWWiVtbU1/fLLLwU45P8ceJ6n1atXC4RQu3btBPwn+uN35MgRQey0LjY8JCSEBg8eLNDYraysaOLEiRabP4j+3zcyfPhw8vX1ZcRRmZmZFBcXRy9fvqSRI0fS2bNnzfKJ6GzVur5ZW1t/1EpAB50WP3PmTIGpQiqVCrQ3V1dXWrhwIWMHjI2NFYTLtW7dOlcfgEqlopkzZwpenAsWLKBRo0ZRo0aNqHTp0lS4cGEqUqQIVaxYkerXr0/169cXPAO1a9c2+VLLXqzGwcGBLly4YHRsnz9/zl7iVlZWtGrVKpP9fvbsGVttSCQStjImyqJj0Fcu9LdatWrR69evzRaKb9++ZeZVjuNo9OjRZh2X38geKiwWi2nPnj3/WeGe72YZrVZLU6dOZQNYrlw5gQOF57OKDHfr1o1pLtbW1jRz5kzSarV04sQJgT1SLBbTlClTmNf+0aNHLDMvp2LG/3bwPE8fPnygtm3bMo1JJBIZhIhaW1tT79696ejRo3T16lW6efMmXbt2jQ4ePEjff/89eXh4CGzQNjY2NGbMmI+iouX5LJ7zZ8+eMbPO9u3byc3NjaytrUkkEpGrqytNmTIlV2ImnufpxIkTAg07L+UDTbWtMwH6+/sbCCmZTEarVq0SvIQUCgWNGTOG7VO4cOFcI7E0Gg0tWbJEcG+y193NbROLxXT27Fmj9yQ0NFQQ4tqjRw+TuQ/h4eHsRS6Tyeinn34yOTZr1qxhLzpnZ2eBLVxXIze7abBy5co51l4whujoaIGzt3///mYfm5/QarU0btw4wbg3bNjwH81W1yEn4S5B3nAcQB8Ai/7+e0zv++Ecx+0HUBNAMhFFmdMgx3EoWbIk+/z+/XtkZGSAiMBxHDiOg5OTE5YsWQIiwokTJ9C7d2+MGjUKCoUCFy9ehEajYcd//fXXGDlyJGQyGQDAyckJUqkUAKDVahEbG8va/q8g615nwcPDA2KxGBqNBjzPG+ybmZmJXbt2Yd++fXB2doZMJoNGo0FCQgLUarVgXycnJ/zwww8YP348bG1t89w/juMgk8lQrlw59p2LiwuICJmZmQCA+Ph4LF26FC9fvsSUKVNQrVo1iEQio205OTnBxcUF79+/BwAEBQWhTp06ee6fftsSiQTffvst7t27h7Vr10Kr1bLfxWIxihYtColEwuaPTCZDsWLFIJPJoFKpkJaWhpiYGNjb25s8j0gkQtmyZeHs7IyEhAQAEMxhXV8A4b3Vh1arxenTp9G4cWPB90SE3377DYmJiQAAe3t7NG/eHA4ODgZznogQGhrK7rtYLDbZ7/T0dNy6dQsKhQIA4OrqimrVqgn6W6hQIUilUjZm7u7uWLBgAcqXL2/280ZEkMlk7JkFgLS0NKjVaiQkJCAmJgahoaGIiIhAcnIyGx/dcfb29vDy8kKFChXg7+/P5lBennciwp07dwTfvXjxAklJSXB1dbW4vU+FXIU7x3G/AGgAwI3juHcAZiFLqB/kOK4/gLcAuvy9+x/IipgJAZAB4HtLOlO0aFHI5XIolUokJyfj/fv3AkHAcRx8fHywdu1atGjRAh06dIBIJMLixYuxYcMGJsSqVq2KJUuWwMPDgx1bqFAhODk5Ach6IF69egWFQgFra2tLuvjZIjMzE69evcLJkyexdetWvHv3zkBQeHl5wd/fH48ePUJGRgaALGESGxtrtE0nJydUqlQJM2fORN26ddmLMj9RtGhR2NvbIz4+nn2n0Whw7Ngx3Lp1C3PmzEG3bt2MvlTc3NxQqFAhJtzfvHmTb/0iIvzxxx/Yv3+/QLADWWM9Z84cFCtWDGXLlmXKR+HChWFjYwOVSgWNRoNnz56hePHiJs/BcRyaNGmC6dOnY8uWLUhLS4NYLIaVlRUKFy6Mr7/+GmXLlkVqaioiIiIQFhaG9+/fIz4+Hm/fvmWC+/bt2wZtZ2Zm4vr161CpVAAAqVQKhUKB5ORkODo6Cl4aYWFhWLp0KVJTUwEADg4OCAgIMNrnmJgYBAUFsc+BgYFwdnZmn0NDQzF37lwm/AGgdu3aaNq0KUQiEdRqNcRiseCFTUTQarVQKpXIyMhAWloaUlJS8O7dO/bSA4AzZ86gXLlySEpKglarZeOc/f5wHAeRSASJRAJra2uUKFECzZo1Q7NmzVC2bFnB9ZuDqKgovHjxQvCdQqHAq1evPmvhbpZZpqC3atWqEVFWWKOvry9b+qxfvz7HZZxaraaffvpJYBt1cXGh48ePG42G6d27t8BWmR822n8aSqWSzpw5Q6NGjRIkIxnbihYtSlevXqXly5dT/fr1jRbe5jiOihcvTt26daMDBw5QXFxcgdoWExMTjfoDdFulSpVMMgJmL9wyfPjwfOvX7du3BX4cuVwucNyLxWIaOnSowCn8+PFjdg8kEgnNnTvXrHPpeM9v3bpF9+/fp7dv35rMcOV5nhITE2nAgAHs3pUoUYLS0tIE+0VHRwt8AMD/1x198OABMylERkbSt99+KzCjdOjQwWQ01P379wXEY/rPaFJSkiAyR7d99dVXtGnTJpo7dy6NHTuW9u7dS4cPH6bdu3fT6tWrad68eTRmzBjq06cPtWrViipXrkzu7u5mZZtbsjk5OVGPHj3o9u3bFkXL/frrrwaBCLa2tnTo0CGz2ygooADMMgUCOzs7FCpUCBEREQCytABjICJkZGRg8+bNWLBgAdLS0gBkLfHnz5+P5s2bG13K16pVC7t27QKQtYQPCgqCv7//v8o0Q0RQKBSIjo7GpUuXsGfPHjx8+BCJiYkC84u1tTWKFCkCPz8/XLlyBRkZGXj79i327duHhQsXolevXsjIyEBMTAzCw8OZ9lO4cGEULVoUjo6OsLe3t2hsiAhKpRKRkZGws7ODu7t7rsc7OjqiSpUqePz4MYCsZb6/vz/TlCZMmAAfHx+jx8rlclhZWbHPycnJZvc1p2t49OgRxo4di9evX7PzDB06FAMGDMDAgQNx48YNaLVa7Nq1C23atEHz5s3BcRz8/Pzg4OAAIGt1+Pr1a2i1WojFYpPnUigUUCqVcHNzg5+fn8l9ddCZo3x8fJjZTa1WIzU1VbC6sbGxQbdu3ZCUlIRHjx4xU9GePXtw+fJl9OvXDz4+Pti4cSPu3r0LrVbLTKMzZ86EXC43en6e5wWa8tGjR3Hv3j0oFApERUXh1q1bBmbABw8eYPTo0VAqleB5ns0rrVbL+p9d+84JEokEUqkUVlZWsLKyglQqFcwzIoJarUZmZiZSU1NZ20lJSfjll19w7do1jBw5EkOHDs115a5QKHDq1Cm20tVBJpPB39/f7D7/IzAl9T/lptPcP3z4IKiN2aVLF6NaY2pqKs2aNUuQsWdra0s///xzjpXanz9/LmAG7NmzZ66V3T8XaLVaioiIoAMHDtDw4cNNRiTIZDJq3LgxrVq1it69e0cxMTGCkm8eHh4mVzYfA6VSSTdu3KCxY8dSsWLFaPjw4WZr/Pv37xfcx507d9KiRYto5syZOdaqjIuLE8yXzp07f/R1RERECFYDunYTEhKY414/Uaxhw4b04cMHIsrSqvUjvho1amSUI13HJbNnzx4aOnQodezYkb777jv66aefzC5duGjRIhZX7+vra3R1w/M8hYeH04wZM3JMbtNtpUuXpnPnzuU4N168eJEvRH3mbPo5GkAWj/qgQYNo/vz5tH79evr999/p4cOH9PbtW4qIiGDbmzdv6MGDB3TkyBGaMWMGtWjRQhD1BWQFFGzevDlXp+jjx48NQq2BrByDz0F24HPnltEJ9+TkZOrWrZvg4cgeex0WFkbDhg0TLJNsbGxo3rx5uRatTUpKonbt2rEJY21tTUuXLs2RWOufhEajocTERLp9+zYNHz6cypcvL3g56TaxWExOTk7UunVrOnz4MEVGRgom7ZkzZwQlzho3bpwnnvGckJqaSoMGDWLL+6JFi5qdgh8WFiaI6hgyZAilpKTkGpmTlJQkIJdq1qzZR0XyJCYmCq5BJBJR7dq1BYlCGRkZNGLECGZ6sLGxoQ0bNjCBqM+HUrlyZQPeb11RiEaNGhkUEpHJZNSoUSOjVcWy93XWrFks3NXPz09QfOTDhw907949Nn6ZmZl0+fJlatWqlWAe6DaJREIBAQFGOV6yIzk5mX744QeTBbCNbfphuRKJhGxsbMjBwYGcnJzIxcWF3N3dWZnJjh070rhx42jr1q20atUqNi9EIhGtWrXKYmI/jUZD0dHRdOXKFWrbtq0guqpkyZJ09epVk+1pNBpauXKlYD7Y2dmRSCSiYcOGfRYy418j3DMyMuj7779ng1+tWjU2gGq1mq5evSrI0AOyQrGWLVtmVogez2cVl9a3GTo4ONDYsWPN5ukoaKhUKoqIiKCLFy/SsmXLqF69eiY53OVyOVWtWpUmTZpEN27cYJS12cchIyNDkCMgEolo9erVrJjx+fPn6eLFixQbG/tRwnH//v3spatLsDGnPJqOOEt3Xe7u7mbxiGg0GoEfpVKlSnkux6bRaGjLli0CpaFmzZr07NkzAwUjNDRUUHOzc+fOjKVx+/bt7HtfX1+6evWq4Ng7d+7k6BvhOI7at29PsbGxJvuanJwsqP5UqVIlpkXqkrt09uULFy4wtsX09HTasmULqwkslUqpQoUKNGvWrBypAPShoyZevHgxtW7dmr7++mvB9XAcZ0B01qFDB3JycqKvvvqKZs6cSfv376dLly7R7du3KTg4mGJiYkitVrOKVbo5fPjwYeZPE4vF9Ndff+Xp3ur3e9GiRYKQ6X79+plUCmNiYgTMkDVq1KDDhw/T6NGj81Q9rCDwrxHuarWahg4dKlgmarVaiomJocWLF1PZsmUNCJ42bdqUq8auD6VSSatXrxYkpOhMGdu3b2e0vwVZCk3H6aFWqykzM5NiY2Pp/PnzNH/+fOrQoQN99dVX5OPjY7Tcn1QqJVdXV+rQoQPt3buXgoKCck2W0QkV/eVlmTJlaNy4cVS7dm3y8vIiHx8f+uabb/JUz1SHxMREwZK9fv36ZhWs0Gq1tGfPHqbJisViWrZsmVn9GDFiBJsTZcuWzXPBiaioKIFj183Njc6fP2+0D0qlknr16sX2DQwMZML48uXLgoxTXZ1Qns+qVduzZ0+m9XMcR4ULF6bGjRsLXhZSqZQWLlxoklDr4cOHArPc4MGD2TmeP38uuI727dsLnK0ajYaePn1Ks2bNos2bN9OzZ8/yVAdYo9FQXFwcPXv2TGAas7e3px9++EFAUnb69Gk6efIkBQcHmx0brlaracGCBWws7ezs8kUBS0xMpNatW7P+Fi5c2Ch1tVqtpiVLlrBnUCaT0cKFC0mlUpFSqfwsYtyJ/kXCned5QbKAp6cn/fHHH4wYTF87qFu3Lp07dy5PmlpmZiYtWbJEwCqom4hubm7Upk0bmj9/Ph07doxu3bpF4eHhBun5uUGXCJOYmEjv3r2joKAgun//Pl27do1OnjxJ27Zto0mTJlHr1q3J3d2dpFJpjskrvr6+1KZNG9qyZQtFRERYzGyp1Wpp1apVgheGsWiE8ePHf1RVIv10eqlUapQ+2BiioqIEBFr169cXZCibwvjx49m4lS5dmlWst7Tfc+fOZXNMLpfTnDlzTEasqFQqgXBv3LgxO+/79+8F9l3d6kWXdKVvFmnRogW9efOGlEolXb9+XVDExNvbm65fv24wdjrTkX4hiVOnTrHMYn1bvFgsNjr+OuFsbn3dnHD27FmBotSuXTvBC75KlSoUFRVldEWZE5KTkykwMFAwH/KDnpvneYPol2PHjgn20Wq1dO7cOUECW9WqVenVq1efhbauj5yE+2cVLcNxHKytrSESicDzPOLi4jBo0CC8e/eO7SOVStG6dWvMmzePxRhbCisrKwwbNgx+fn6YNm0a3rx5A6KsWNu4uDj8/vvv+P3331m8sbe3N1xdXeHu7g4fHx/4+/vDzc1NEKmhQ2JiIiIjIxEaGoro6GikpqYiLS2NbZmZmUhISGAJWqYgEokgFotRoUIFtGnTBo0bN0bFihWNJqGYA5FIhO7du+OXX37BjRs3AMDo+dPT03PsV25o2rQpihcvjpCQEKjVauzYsQOdO3cWJKMYg4eHB5o2bYqbN2+C53kEBwfj3r17aNasWY7XK5PJ2O88zxskYJmDmJgYnD17lkVVBAQEoGfPnibj+rVarSCSy8XFhV2fXC6Hv78/i9t///49eJ6HWCzGr7/+yqIuPD09MWXKFBQpUgQcx6F69eoYP348hg8fjszMTHz48AF79+5FxYoVYWtrCyJCQkIClixZgl27drF79NVXX6FixYoAsqKF9u/fz8agbNmyaNq0qcH4cRyXa1SOOVCpVFi/fj2LaXdxcUFSUhKLdJJIJGjdujVcXFwsnrNBQUGCxKGGDRsafd4sBcdxKFasGHx9ffHy5UsAwPPnz9G2bVsAWc9EWloali9fjrdv3wLISuoaPnw4ihcv/q+KrPvHtXbS09yJiObPn2/UHIG/l8qzZ8+mhISEfHmDajQaCgkJocmTJ1P58uUNHFzGNrFYTHK5nKytrcnGxsZgs7KyylULN7bZ2tpSsWLFqHbt2tS7d29at24d3bp1K1/rt6rVapoxY4aBjVd/VTR06NCPWnIqFAqaMGECa9PBwYHOnTtn1rEvXrwge3t71pfp06fnajLQ11SLFi1qMSGczrarTy27YMGCHMf72bNnrJ8ikYhmzZrF+pmcnEzt2rVj19CqVSvKyMigjIwMgSbYp08fA44ehUIhiLbx8vKiR48eUWxsLF2/fp26desmYPP08fERRD7t2bOHzTsds2pefRDm4MyZM4LVr27+6z5XrlzZwGdhDjQajaAcoK+vL/3111/5pjWHhoYKfHc6zhodxcm4ceME/qlevXp9tkV98G/Q3IkoR82rVKlSmDJlCnr06JFvmZJisRjFixfH/Pnz0adPHzx8+BC3b9/G3bt38fz5c0HWpA5ardaimFxTkMlk8PPzQ7FixVChQgVUqlQJZcqUQYkSJeDk5FQgGkJCQgL27duX4z6WZu9lh1wuR9u2bbFr1y5ER0cjLS0Nhw4dQp06dXKNKfb19UW1atVw6dIlAMDVq1eRnp7OMouNQT8WX6PRID093aL+qlQqXL9+nWV7EhFiY2ORmppqNM5fq9Vi9+7d7DzOzs6oUqUK09ylUim8vLzY/u/fv4dWq4VarYZSqQTw//QF2bNuZTIZBgwYgHPnziEjIwORkZFYuHAh0tPTcfv2bURHR7N9XVxcMHv2bDRr1gwikQhEhD///JPFmJcsWRJNmjSBRFIwj7harcZff/0leEb0Y8FtbGwwYcIElClTxuJciVevXuH8+fPsu4oVK6JSpUr59kzoMlj1z0lESE9Px8KFC7Fhwwb2jJcoUQKjRo2CnZ1dvpz7U+KzEe5arRYHDhzAxo0bBWnzVlZWaNSoEX766SeULFmyQCarSCRCmTJlULp0aXTo0AHp6enIyMjA69evcf/+fTx9+hRhYWGIiYlBRkYGFAoFVCqVUfOFLn3c2toaDg4OKFy4MAoXLoxChQqhUKFCcHNzg4+PD3x8fGBrawu5XA5ra2uBeaEgQEQ4dOgQW2rqf6//svL09Pzoc9WsWRN16tTB0aNHwfM8bt68ibCwMJQtWzbH4+RyOZo2bYq//voLRITbt28jJiYmR+Gun8CS/VrMgUqlYslKujY2bNgAhUKBWbNmCSgsiAjXrl3D4cOHmRCtVKkSGjRoIOiPt7c3+xweHg6VSsWSbYCsuR4REYGUlBSW9MTzPBQKBVxdXVGkSBE8f/4cAHDw4EGDpKASJUpg3rx56NChA1N0Pnz4gKdPn7J9KleujNKlS1s0FpYgLi4OV69eNTreVlZWGDduHKMHsQRKpRLbt29HcHAwgKwXXqdOndg45QdUKhV70QKAra0t4uPjMXfuXGzcuJH95ubmhlmzZqFKlSr/LnPM3/hshPuZM2cwbtw4gXYik8kwcOBAzJgxA25ubgU+wBzHQS6XQy6Xw8XFBT4+PggMDAQA9maPjo5GTEwMkpOTDbhbgCyNxdXVFZ6ennB1dc0X22Z+IC4uDocPH2Z9lkqlICKDayhUqNBHj7NUKkXXrl1x9OhRAFmZxu/evctVixOLxahevTpcXFwQHx+PjIwMXLp0CaVKlfqo/uQEsViMihUrQqVS4dq1a0hJSUFmZiY2bNgApVKJuXPnMk08NDQU8+fPR0hICIAsm3K/fv3g6OgoaM/Hx4dxJKWmpuL9+/coX748SpcuzbKvDx8+DLVajfLly0OlUiE1NRWxsbG4e/cuE2wABIJdLBajatWqmD9/Pho3biwQnM+ePWPPjlQqRe3atXP1c+QVutWNLqtYHxzHoXHjxhgyZEieeJvevHmDffv2sXlZtWpVdOjQIV+f/czMTMEqIzU1FSNHjsSvv/7KuHg8PDwwe/ZsdOnS5bN5hi2GKXvNp9zKlStH1atXN7BDOzg40PHjx/PRQvW/CZ7n6Y8//mD2UY7jaMCAAUZrrp45cyZfbJtBQUEC+/mSJUvMsuVnL+TcpUuXHKN3tm3bxuy8Xl5edPPmTYv6qdVqKTk5mWJiYmjz5s3k7e3Nzi2VSqljx4709OlT2rdvn6CgNsdx1KFDB6PJYH/++Scba4lEQr///jvxPE9HjhwRjInu99z8M1KplEqXLk0bN26kt2/fCsaD53natGkTtWzZktnjbW1t81x43BzwPE8HDx402ldfX1+6e/dunuaQWq0W2NptbW1p7969+Z5NfePGDUGui5OTk8DP5+7uTtu2bbO42tg/AXzuoZCmajHa2dnRvn37CnBo/jegUCho/PjxbFzt7e3pxYsX9PvvvwtKxonFYrp8+XK+CPeYmBiqX78+a7tmzZpm5SOo1Wrq3bs3C/WrUKFCjiGRBw8eZEleHh4edPHixTz1VxceeOzYMUHMOZCVAZrdyV+lShW6c+eO0bG6desWqyomEolozZo1RJSVrLV06VKDVPicNmdnZ5o2bRqFhYUZDSd89+6dARWFt7e3RbkfeRmr7I55IKt04rFjx/IkjHk+qwawfq3X1q1b04cPH/I1/JDns2qzGsvUBUClSpUyKEP4OSMn4W6ZQayAkJSUBCBrSVeiRAlGWqTRaPKFDOp/HZmZmbh69Sr7XLduXfj6+qJJkyZo3bo1+57nebx48cKoL8FSODk5oVGjRmxJ++TJE9y5cyfXtiUSCSpXrsx8KwkJCQKKWWP760BGzEzmQhce2KpVK6xevRplypRhv4WHh7N2JRIJKlasiCVLlsDPzw+3bt1iobQ6uLu7Mxsx/e0gBLJsu0OHDsXu3bvx7bffwsfHB05OTnB2doaXlxfKli2LFi1aCPwe9erVw7Rp0xjBXXaCrAsXLjDKYx0UCkW+3MPsICKkpqZi586d2Llzp+A3T09P/PTTT8zBa2m77969w7JlyxAXFwcgi3Z4wIAB+WIm1IdGo0FwcLABERjHcShfvjw2b96Mdu3aWXwNnyVMSf1PuUFPQ9q3bx9bGkskEpozZ07+v+7+R6DVaikpKYmuXbsmoDBo06YN/fnnn8TzPJ05c0aguZQoUcIkxa4l4Hmebty4wcL/OI6jbt26mRVSduvWLaZZWVlZ0bp160xqb+fPn2cJKc7OznT06NGP7jfP83T79m2BeUh3DaVKlaI+ffpQo0aNWPLZvHnzBCan1NRUwaqlS5cuBufQaDSUmZlJUVFRlJycTGq1mtRqNZ0/f15QpPunn34yee0pKSkC2gbdZmNjk+901lqtlp4/f07fffedAa9MsWLF6PTp03nWdiMjIw2uo2/fvvkaxqnLzF22bJmgVjOQlbTWrl07evHixWeXpJQb8LmbZXQPztSpUyk5OZkti3V1E/9tA/4poBNCGo2GlEolExRXr16lrVu30rRp06hHjx7UpEkTVmRYf2vfvj3xPE+XL182sAFPmTJFUFc1r8jIyKDu3bsLzAUXL17M9X4mJyeTj48PO27QoEEmzQz37t1jdmw7Ozvavn270fb5v0v8PX78mJk4cgLP83Tz5k2qWbOmYHz0cwJ0W48ePQQvrczMTGrQoIHgZWoOVCqVgCvH3t6ebt68afJ6Xr58abR2sFwuZzVqPxZarZZSU1Np3bp1Ap+D/ta2bdscuXByajs4OJi6d+8uiN+vUKFCvnC36O75ixcvaPr06VShQgUBrwyQRR44efJks4t2f274Vwh3f39/ioyMJJ7nBc7VPn36fBbUmv80eJ6njIwMevfuHT1+/JguXrxIO3fupClTplDr1q2pVKlSZGtra3YSVdOmTYnneXr06JEBMZm3tzedPn06Xyb7jRs3BKne+gk/pqBWqwWaXLly5SgiIsLovjExMeTm5saUAV1NXX1oNBo6efIkdenShRwdHc1+eSmVSlq4cKEgMSf7Zm1tTc2bN2d+AV1inE7r5zjOrNqfKpWKtm/fzsaK4zjq2bOnSfZOnudp+/btAqoHfb9A48aNKTIyMtfz5oS4uDg6cOAA1a9fXyB8df3T/d+uXTuLaB94nqfU1FT6/fffqUyZMoK2dEXvP9bmnZqaSsePH6euXbuSo6Oj0ZeyXC6nRYsW/Sscp6aQk3D/bEIhS5Uqxeo26scWJycnIzMz819VDo/MtHdm348oK5ErNTUVMTExiI+PR3R0NOLi4vD69WuEh4fjw4cPbNP5KiyFRCJhhR6srKzg5ubGQvSArMSbNWvWoH79+h897uXKlUO1atVw+fJlAMBff/2FcePG5RimJxKJ0KhRI+zevRsAEBISgqSkJHh7exvYX52dneHp6Ym4uDgQEaKioqBQKGBjY8P20Wg0+Pnnn3HmzBkAwKFDhzBw4ECW+m8KiYmJOHfunKBknEgkQqFChVC1alVUrFgRFStWRIkSJeDo6AiFQoHDhw/j8uXLAj+Sr69vjmPE8zyuX7/OEpaArJDU77//XhBmqQ8iwo0bN9gcKlOmDPz8/HDy5EkAwM2bN7Fjxw6MGzfOrKQ/XTtEWXb1P//8EwcPHsT58+eRkpIiuP6aNWsiMzMTDx8+BJAVt589Fj+n80RFRWHx4sU4cOCAIPS5ePHiWLp0KRo1avTRNu/o6GhMnTpVEPufHR4eHqhXr16+0Bp8jvhshPudO3dw+/ZtBAYGCjL8kpKSWPHkggYRscowarWa1WfU33TQZdNqNBq26fZRKpVQKBTIzMwUCHCFQoHU1FSkpKQgLS0NycnJSE5ORlJSEhITExEXF4eEhASoVCqD86pUqlwfIJFIBFtbWzg4OMDOzg729vYoXLgwQkND8ezZMwBAlSpVsHHjRpQoUQISiQQODg4oUaKEQLgDWXkHBw4cQO/evT/qQbOxsUHTpk1x5coVEBFu3bqFyMjIHBNsOI5D0aJF4eDggJSUFKhUKkRERJis6+nj44MnT54AyBLIKpVKINylUil69eqFy5cvQ6FQ4PXr19i3bx+mTp2aY98TExMFtTO7d++OUaNGoUiRIrCxsYFcLodYLEZqaioePXqEn376CX/88QerFQpk3ZMqVaqYPIdGo8Ht27cxaNAgQeLOiBEjEBgYmOPLRz8hrWTJkpg6dSqePHmC8PBwpKenY/78+bC2tkaPHj3g5uZm9D5qtVrEx8cjKioKFy9exLlz5/Do0SMkJCQI5q9EIkGRIkUwdOhQ9OjRA9OmTWPCPSMjwyxOH5VKhfPnz2PWrFm4f/8+e54kEgkCAwOxYMECVKtWLV8cqIULF0a9evXw7NkzEBFcXV3h4eGBt2/fshdoqVKlUKlSpY8+1+eKz0a4JyUlYdSoUZg+fbqg8rpOczdXGzYGoiwyIH3BqiP0Sk1NRWpqKpKSkthv6enpSE9PR2ZmJlQqFdRqNVQqlSArVa1Ws2xV3abLfDNXi/lYWFlZwdfXFyVLlkSRIkVQrFgxeHp6wtfXF56enihcuDBsbW0xYsQIJtxtbGzg6enJiho7OjqidOnSuHjxoqBtpVKJdevWoW7duihZsmSe+ygWi1GtWjW4uroiLi4OSqUSV65cyVW4Ozg4wNXVlWmN+lmk2ffVL1KckpJiIGhEIhECAwPx1VdfsZfML7/8gr59+8Lb2xsajQbXr19nhcK9vb1Rq1Yt8DwvyGTURe68e/cOCoUCSUlJePfuHV6/fo2bN28avCB1bdWuXdto37VaLU6fPo1JkyYxwS4Wi9G+fXsMGTIk12zsqKgo9r+LiwtKliyJsWPHYubMmUhJSUF6ejqmTZuG06dPo1WrVihRogQ8PDwgFouRlpaGt2/fIigoCLdv38b9+/cFWrQ+3N3d8f3336NHjx6oUKECOI6Dm5sb+z01NVUwTtlBREhMTMSWLVuwcuVKQb8dHR3Rq1cvTJ482WQ5xbzA1tYWrVu3xpUrV1C3bl00b94cHz58wOTJkwFkzYkWLVr8K2kFzMVnI9yBrCy7QYMGCTSM8PBwrFu3Dn5+foJ9dena+oKUiJCZmYnExESmDev+6mvZ+hpxds37Y14i+QWxWAypVAqpVAqZTAaZTAYrKyu4uLigbNmyKFeuHAICApgpy8bGhtWTNKb16IcHikQiwfhaWVmhXr162Ldvn2D5DWTVvty6dStmz55tsqZmbuA4DqVLl4a/vz/i4uLA8zxu3bqFAQMG5Hico6MjPDw8GPtiTuGQ+oImOTmZZRnqw8fHB126dMHt27ehVCrx+vVrHD58GMOHDwcRYeXKlYzPpEmTJjh06BAKFy6MRo0a4fDhwyAinDlzBlevXoVYLAbP89BoNFAqlQZzpk2bNqhevTqOHDmCb775xsC0QpSV7Xzw4EFMmTIFMTExAP5f4CxatAguLi45jo9KpWJhg0CWcLezs8OAAQOQkZGB+fPnMxqNU6dO4dKlSwKaC61Wi8zMTGRmZhqlELC2toaHhwfatGmDvn37oly5csx8QUQCigUd+ykRGYRqKhQK3L59G/PmzcPVq1eZiUsqlaJixYqYP38+6tWrJ1hp5RcaNWqEU6dOwcXFBRkZGejSpQub44UKFcK3336b7+f8nPBZCHd7e3ukpaWBiAwETHx8PFasWPEP9SxvEIlEkMvlBnwxEokENjY2sLa2hpWVFWxtbWFjYwNbW1tmTnFwcICjoyOcnZ3h7OwMNzc3ODs7w9vbG+7u7hCLxVAoFIiIiEBsbCy8vLw+ineD4zg0atQIfn5+BvZJjUaDzZs3o2PHjqhRo0aez+Ht7S3wo4SEhIDn+RzNPU5OToJjTAl3juMEgiYyMtIghhnIuiddunTBmjVrEBQUBIVCgYMHD6JTp07w8fGBTCZjcy86OhoZGRlwdnbGkCFDcP36dbx//54JZVOQyWT4+uuvMXz4cDRt2hSjRo2CVqs18C+EhYVh6dKl2L17NyvuLpFI0KpVK6xZs8YsDVZnutNdm44q29bWFuPGjQPHcdi0aRN7OepWlznBzs4OVapUQeXKlREQEIDAwECULl3aqMIQEBDAqLkjIyPx/v17VKhQgf1ORPjw4QNWr16NjRs3IiEhgf1mZWWFAQMGYNy4cQVaoN7a2ho+Pj4gIuzYsQN//fUXgKzx6tmzJwoXLlwg5/1c8FkId39/fzRq1Aj79+9nWnZ+Q6exisVi9r/+Z7FYDLFYDDs7Ozg5OcHR0REODg5MK7a2toaNjQ2biHK5HA4ODrC3t4etrS3s7OyY4NbZYkUikWDiikQiSCQStkmlUvZXX0s3JfSIsoirxo8fj/j4eEgkEuzevRtVq1bN8QHRX94bY7UsXLgwOnToYNT5pOMQ3759u8BcZgl0Y6WDUqlkZFqmoCNd0yE8PBwKhcLAwavj59YhPj6eJfFkHxN3d3f06dMH06dPB8/zuHv3Li5fvozu3bvDy8sLHMcxBSM6OhrFixdHvXr1sGvXLixZsgQPHjxgmnpaWppg1ejg4IAxY8Zg4MCBrC19jV3X7okTJ7BixQo8fvyYzXMrKyv07t0b06dPF7yocoKVlRWbJzrzke6apVIpRo8ejdatW2PLli347bffmO9Cd+91+8lkMpQoUQJNmzZFs2bNUKRIEbi5ueW6UvP29oaXlxfevXsHjUYj4N7XarUIDw/HqFGjcObMGWayEYlEKF68OMaPH4+uXbuadBbnJ4gIDx48wNq1a9n9Klu2LHr06FFg3DufCz4L4S6Xy7FixQoMHDgQJ06cwJ07d3Dy5Ek2Kby8vAwmgm5yZn+ApVIpE7I2NjZMM9YJYp3Qzv7Z2dkZjo6OBpEFedEqCpLgTC6X4/79+1CpVOA4DqGhoahatWqOx+js6wCYo1cfHMehT58+2LZtm0G2IwCcPXsWBw4cQN++ffPMyqk/rjqTRk7Q0TFLpVKo1WqkpqaaZJZ0c3ODRCKBRqNBRkYG04aNoUOHDtixYweCg4OhUqlw8eJFdOvWDRUrVmQRRHFxcQgNDUXx4sUhk8nQsGFDBAYGIjo6GmFhYXj16hXGjh3LtFG5XI4xY8Zg2rRpkEgkBvefiPDmzRvMnDkTBw8eFFy7l5cXhg8fjjFjxkAul5s9dyQSiUAA60yUYrEYHMfBysoKAQEBWL58OWbPno03b97g9evXSExMBBGxoiJFihSBr68ve1GYc36O4+Di4oLy5cuzQjqXLl3C5MmTWcTQsmXL8OTJE2aycnFxQceOHTFhwoSP8uFYisjISMyYMYM5xqVSKYYOHYrKlSv/K5keLcFnIdyBrLd6+fLlERAQwJw8uonTp08f9OjRw+BmZBc0uhRynUlEx/CYkzb8bwLHcXB2doaPjw9LeX/+/Dk6duyY40TVp8w1tTz39PREq1atsGnTJoPfUlJSsGHDBjRv3jxPTi+VSiWgkdCtXHKCTiOXyWRQq9VQKBQIDw83EO46Jk9bW1t2Dn0TQPZ9HRwc4O3tzRyYOuGTXbg/ePAADRo0YMJaLBbDy8sLXl5eePLkCXtBSiQS9OzZEyNHjjTQBHV+oZMnT2LVqlW4ceOGIEKkbNmyWLJkCQIDA/MUjufu7o6wsDAAQGxsLNLT0wUrJB1dgaOjI6pUqZJj1I6lcHJyQkBAAE6fPg0iwuPHj3HhwgUcPHgQR48eFdyDYsWKYcGCBWjRooVRjvyCgM6ENmfOHNZHjuMQGBiIHj16/CfkQW74bIS7DrpSe46Ojky4W1tbo1y5cv8TNyQ36EIX37x5AwCMUzunsdE9UETEon6yw9raGm3atMGRI0eYo87a2po5ou/du4c1a9Zg/vz5FlGgEhGePXsmsJkXLVrULAeth4cHewnkxDNkZWUFR0dH9ntkZKTJNjMyMlhUDJAlIDmOQ8mSJVG2bFk8fPgQarUa+/fvx3fffWfULtu3b19kZmZi1apVKFasGGbNmiVYHQFZL8QLFy5g69atOHPmjGDMixYtin79+mHAgAHw8PDIs7CrWLEiK0UXEhKCxMTEfOU9zwlSqRR16tTB1q1bkZSUhLi4OLRv317AayOTyVCvXj0sW7YMFStW/GTPL1FWScI5c+Zg586d7IVasWJFLFq0KMf6AP8l5DraHMdt4zguhuO4p3rf/chx3HuO4x7+vbXU+20Kx3EhHMcFcRzXLC+dEovFAtuqztn6BVnCvVy5ckwg3L9/32SYoA76WqEujj87OI5DrVq1BEtmJycnQc7BL7/8grt371p0L9RqNX777Tf2MgKyHIphYWE5tkNEiI+PFzgNTb0QrK2tBQ+sfqhd9jZfvXolGC9d7LyNjQ26d+/OBNDjx4+xd+9eo2GtcrkcQ4cOxfbt2xmBmO7lqQv1HDFiBAYMGIATJ04wwS4SiVC1alVs2LABkydPRuHChfMs2DmOY7VTAeDFixcGBGYFBaKsoihOTk7snugi1XTnt7e3x/jx47F161ZUqlTpkwr2lJQUzJw5E1u2bGGm3UKFCmHhwoX/E+YYBlOpq7oNwNcAqgJ4qvfdjwDGG9m3HIBHAOQAigJ4DUCc2zn0a6gSEUVFRdHXX3/N0oQ/tq7nfwk6bnZdyr2OXC2ndO19+/axFG8/Pz+6evWqybaXL1/O9rWzs6PWrVsLUuK7dOlC8fHxZlMTvHjxgjw8PAz4WYYMGWJQQ1S/HzExMfTdd98xGgVvb296+fKl0f3fvXsn4HLp27evQf94njfYz8/Pj9X45Hmenjx5QpUqVWK/Fy5cmPbs2UMKhcIkvwvP86RWq+nt27d0+PBhatmyJdna2gpS6kUiEfn7+9OECRMoLCwsX+hkeZ6ne/fuUeHChdl5OnfuXOBUvwkJCXTlyhXq378/ubq6Cq4TyOJgb9euHV25coWUSmWB9cUYtFotXb9+nZo1ayagYihcuDAdOHDgPylD8LHcMgCKmCncpwCYovf5NIDaubWfXbjHxMRQs2bN2M357rvv/pM3Jq9ITEwUMBY2aNCA3r17Z1LgmivcibKKZegL4woVKpCXlxf7bGdnRwcOHDBLuGdkZNCgQYOMcrLY2dnRihUrmODUbVqtlh4+fEjdunUTcG63aNHCpLBISkqizp07s30DAwMFbarVajpx4gS1bNmSPfQikYgGDx4seMGoVCravHmzgEvG29ub5s+fT6GhoaTValmbCoWCgoKC6ODBgzRhwgSqX7++UY5wBwcHGjZsGF29ejXfi1UnJydTr1692LlsbGxo7dq1lJ6enu8c6GlpaXT8+HHq3LmzoCh29i0wMJCio6Pz7dzm9k+tVtPx48cpICBA0B93d3fauXPnv5o/JicUlHAPA/AYwDYAzn9/vwZAL739tgL41kSbgwDcBXDXz89P0OH4+Hhq3749u0EdOnQo0Cru/zbwPE+LFy9m42NlZUUrVqww+QLcvXs3E+5FihSha9eumWxbqVTShAkTGNESx3EGpEuVKlWiqKioHPuoVqtp165dgsIUfn5+gmIMdnZ21LFjR9q7dy+dPn2ajh8/TgMHDiRvb28B8ZmrqyudPXvWpMBSq9U0bNgwtr+npyetXbuWDh8+TJs3b6bWrVuTk5OTQMssW7YsPXnyxKDNjIwMGjFihIDWVi6Xk5eXF7Vr147Gjx9PvXv3pnLlypG7uzvZ2dkZkLSJRCIqXLgwdevWja5du5bvwlYHnufp4sWLAgZNe3t7Gj58OIWHh3/0CkGtVtO7d+/o0KFD9PXXX5ODg4OBMHd2dmarSN3nBw8efFKGxdjYWJo1a5ZgbonFYipTpgz9/vvvuRLV/ZtREMLdA4AYWTb7+QC2kYXCXX/LrrknJycLqGKbNGnyRbhnQ3BwMBUtWlQgOF+8eGF0323btjHBVqxYsVxL0T148IDKly9vUjuTyWT0448/5siseOPGDSpZsqTgoT99+jQtWLAgR5bF7Juvry9t27YtVxbHWbNmCV5IOZ2jXLlydPz4cZOmlqioKBo5cqRF/dTv7+jRo+n8+fOfxCyh1WppwYIFgpcRx3FUv359OnDggMWMqhqNht6+fUunTp2iWbNmUa1atQxocgGQo6Mjde3alQ4ePEibNm0iXTU1juOoTZs2FBQUVOACnud5CgoKot69ewsUEF0JxIcPH/5rKirlFfku3E39hnwyy6SmplLfvn3ZzapXr94nEe48z9PTp0/pyJEjlJmZ+VnzO6tUKlq5ciUzBXAcR7169aK4uDiDfi9btoyNZZkyZejBgwc5tq1Wq+nBgwf07bffkqkSiEWLFqXr168LzqVbHt+7d09gNpJKpTRp0iRSKpWUmppKK1asIE9PTwN7bXbh0axZM7p586ZZQnLbtm0mS6fptGlXV1caPHgwPX36NEczH8/zlJKSQqtWraLy5cuTvb290b5KJBJydHQkT09PatSoEa1evZqeP39OGRkZn3TuJCcn06JFiwT2d90LNTAwkBYtWkTXr1+nsLAw+vDhA8XHx1NiYiIlJCRQTEwMhYWF0enTp2ny5MkUGBhIxYoVIzc3N4PSgmKxmLy9valv375048YNSkpKIp7nKTk5mVq3bi0Yl3bt2jEK7/yGVqulxMREOnDgAJUvX17QTycnJxo1alSBnftzQ0Fo7p56/48BsP/v/wMgdKi+QR4cqikpKQJbYqNGjQpcuGu1Wrp79y5VqVKFnJ2dac2aNZ+1nZ/neYqNjaU2bdoIzAeDBw+mpKQkwb6jRo1i+1SvXp1CQkLMaj8zM5NOnjxJLVq0MOB8B0CdOnUS2DITExNp9erVAo1dJBJRt27dBA+bSqWiixcvUr9+/ah06dLk4uJCdnZ25ODgQH5+ftShQwc6cuQIpaWlmf2ARkRECJyhOmHk5uZGVatWpdGjR9Ply5dJrVab3abOgXjw4EEaNGgQNWzYkGrUqEENGjSg7777jubNm0fHjh2jkJAQ0mg0RmucfgroirYcP36cqlevbpS7nOM4cnBwoPLly1OTJk2oVatW9M0339BXX31FHh4eOb5oOY6jsmXL0pgxY5g2nP2lfvXqVSpTpozguPbt29O9e/fy7TnSzfndu3dTy5YtDVZWxYoVo127dn32ill+4qOEO4BfAEQBUAN4B6A/gN0AniDL5n48m7CfhqwomSAALXJrn4wI98TERPr222/ZTWvdunWehTvP86RSqUxGPOj2CQ0NpQYNGjD7aaFChZg54HOeKNeuXRPYGq2srGjgwIH05MkTSklJoaioKKpXrx77vVmzZgbCPyfozBQ7duyggIAAgX3Z1taWNm3aRFeuXKF169ZRy5Ytyc7OTiDYa9WqRW/evDEavZKamkrPnz//v/buPzaqMl3g+PeZttvWbrUtNoBSbtlawYHuBWwuJWvkh78KbtgVN4uLCCEmRiOJKAlpb+1NiAbuNcreS93gqqvLJrBVs0vW1E3QpcbEhApdtrACqS3pJW0tS7mw00qT/nCe+8ecGadMC/01PzjzfJKTnvOe0/Y9z5x55j3vnHNePXbsmB45ckQbGhr0xIkTI559jKWe7733nhYWFmpWVpampKToxo0b9dixY9ra2jqpL9SCA6V0dnZqW1ubdnR0aG9vb8IdF99++602Nzfrtm3bNCcnZ8QkP5bJ4/FoRkaG3nLLLbp06VJ99dVX9fTp09dMmkNDQ/rxxx+r1+sd9uE6d+5cfeONN/TSpUsRHwpjEXz/Xrx4Ud966y1dtmzZsEHdg2cK99xzj9bX1yd0gywaJt1yj/Z0dXK/ePHisNO8devWTTi5f/PNN7pr1y6trq5Wn883aj9rT0+P7tixY9ioQbfeeqvW1dUl3Js43NDQkL7//vtaUFAQcUp+77336sKFC0Oj6Hg8Hq2oqJjQG8zv9+vx48cjLmsUEfV4PBEtv/T0dF27dm1M+l7D6+jz+fTQoUO6fft2/fzzzxP6tYuGYCu+sbFRKysrdfny5VpQUDBiv/nVr9e8efO0vLxcn3/+ed23b5+eOHFCBwYGxpyU/X6/1tXVDUvwweS7dOlSffvtt7W1tXVM7+Xwyy5ffPFFnTt3bsQX1yKic+bM0aqqKu3u7k6611r12sk94e5QBUIDXgSFP7BrrFSVvr4+Xn/9dXbu3Ilq4PbjqqqqiGfSiAjZ2dls3bqVvr4+du/ezdDQEPPnz4941HCiSUlJYc2aNfT397Nly5bQXZqXL18OjX4UNGPGDJ544olxxzJ4g85HH3007O5O+K5xEL7tzJkzeeGFF1i/fv2kbtQZbx0hcJPXgw8+yP333588N6uECT4q4e6772bRokV0dXXR0dHBhQsXaG9vp62tjfPnzzMwMEBaWhq5ubnMnj2boqIiioqKmDVrFrm5uRO66UhEWLVqFbm5ubz00kscPnw4dIfzkSNHaGpqwuv1smTJEsrKyigrKws910ZE8Pv99PX10djYyGeffUZDQwMtLS20t7cPO8Y8Hg/5+fmsW7eODRs2UFJS4trRlCZltKwfy+nqlvvXX389rCthy5Yt4z7dCg5wPHv27NDfuemmm7Sqqir0RdBIfD6fVldX68MPP6xnz569YVoDwWu516xZozk5OREts6KiIq2trZ3waavf79eDBw/qAw88oMXFxZqdna1paWmamZmpGRkZmpOTo4sXL9ZnnnlGm5qaxtW3baIveA/B4OCg9vf3h6aBgYHQ9wVT+b98Pp++8sorWlJSEtE9JCKalpamWVlZWlpaqo899phu2LBB77vvPp0+fbqmp6ePOAZwenq6lpSUaHV1tZ49e1YHBgaS/hjjGi130bBPxHgpLS3VxsbG0HJnZyePPPJI6LkZ27dvZ+fOneN6pgkEniPy7rvvUllZSW9vLxB4VOkHH3xAWVnZqC274NB+sWp1TqVLly5RX19PQ0MDXV1deDweiouLWblyJUuWLJnUY05VAyPqtLW1ce7cOfr6+khLS6O/v5+vvvqKxx9/nDvvvHPcr1MiGBwcpL6+ngULFoQe2WsmZ2hoiObmZvbv38+BAwdob2+f0ChlGRkZlJaWsn79elasWEFxcfENeYxFg4j8VVVLR1w5WtaP5XR1y/3cuXPDrrPesWPHhK9XHRgY0L1792peXp7m5eVpTU1NzC9VizW/36/9/f165coVvXLlStRb0cEra27UmPb29uprr72mN998s65du1Y7OjriXSXXCB6LnZ2d+s477+hDDz2kd911l06bNm3E1jnOGXZhYaEuW7ZMKyoq9OjRo+rz+ZLuy9Kx4Ebqc1cNDM0V3rc70UEiIPD0uk2bNoVaYps3bx7TaPA3MhEJDfwRq/93o/Z5Dg4OcuDAAV5++WV6eno4ePAgWVlZ7Nq1a8wDZ5jRBY/F2267LTQOa0dHB6dOneLkyZO0tLRw/vx5/H4/2dnZ3HHHHXi9XhYsWIDX643K8HvJIuGSOwQeFbp8+XKampro6ekJDco7UZmZmWzevDk04pIxQcFBQWbMmBEayKK2tpaUlBRqampcPYByPKSnp4e+vF29enVoYHlVJTU1lczMzBEH4THjl3DJXUQoLCyktraW7u5umpubmT9//qRfbLe31s3EeDweVq5cSU1NDU8//TStra1kZmZSWFhoDYEoS01NtQ/PKEq45B4uPz+f/Pz8eFfDuJyIsGLFCvbs2UNlZSWPPvooW7dujRiv1ZgbSUInd2NiRUQoLy9n3rx5FBQUTHisWGMShR3BxvDdTVBz5syJc02MmRo2KKkxxriQJXdjjHEhS+7GGONCltyNMcaFLLkbY4wLWXI3xhgXsuRujDEuZMndGGNcyJK7Mca4kCV3Y4xxIUvuxhjjQpbcjTHGhSy5G2OMC1lyN8YYF7LkbowxLnTd5C4iBSLyqYicFpFTIvKcU54nIp+ISIvzM9cpFxHZIyKtInJSRBZHeyeMMcYMN5aW+xCwTVW9QBnwrIh4gQrgsKoWA4edZYBVQLEzPQXsnfJaG2OMuabrJndV7VLV4858L3AGuB34CbDP2Wwf8FNn/ifA7zSgAcgRkZlTXXFjjDGjG1efu4gUAouAL4DpqtrlrDoPTHfmbwfaw36twym7+m89JSKNItLY3d093nobY4y5hjEndxH5PvAHYKuq9oSvU1UFdDz/WFXfVNVSVS3Nz88fz68aY4y5jjEldxFJI5DY96vqH53ifwS7W5yfF5zyTqAg7NdnOWXGGGNiZCxXywjwG+CMqu4OW/UhsMmZ3wT8Kax8o3PVTBngC+u+McYYEwOpY9jmR8ATwN9FpMkp+3fgP4H3ReRJ4Bzwc2fdn4HVQCvQB2yeygobY4y5vusmd1X9HJBRVt83wvYKPDvJehljjJkEu0PVGGNcyJK7Mca4kCV3Y4xxIUvuxhjjQpbcjTHGhSy5G2OMC1lyN8YYF5LAZelxroRIN3AFuBjvuiSQW7F4XM1iEsliEimZYvIvqjriw7kSIrkDiEijqpbGux6JwuIRyWISyWISyWISYN0yxhjjQpbcjTHGhRIpub8Z7wokGItHJItJJItJJIsJCdTnbowxZuokUsvdGGPMFLHkbowxLhT35C4i5SLSLCKtIlIR7/rEioi8IyIXROTLsLI8EflERFqcn7lOuYjIHidGJ0VkcfxqHj0iUiAin4rIaRE5JSLPOeVJGxcRyRCRoyJywonJDqd8joh84ez7eyLyPac83VluddYXxnUHokREUkTkbyJS5ywndTxGEtfkLiIpwK+AVYAX+IWIeONZpxj6LVB+VVkFcFhVi4HDzjIE4lPsTE8Be2NUx1gbArapqhcoA551jodkjks/sFJV/xVYCJQ7w1f+F/BLVb0DuAw86Wz/JHDZKf+ls50bPQecCVtO9nhEUtW4TcBS4FDYciVQGc86xXj/C4Evw5abgZnO/Eyg2Zn/NfCLkbZz80RgXN4HLC6h/bsJOA4sIXAHZqpTHnofAYeApc58qrOdxLvuUxyHWQQ+5FcCdQRGikvaeIw2xbtb5nagPWy5wylLVtP1u8HEzwPTnfmki5Nz+rwI+IIkj4vTBdEEXAA+Ac4C/1TVIWeT8P0OxcRZ7wOmxbTC0fffwHbA7yxPI7njMaJ4J3czCg00NZLyOlUR+T7wB2CrqvaEr0vGuKjqt6q6kECL9d+AefGtUfyIyI+BC6r613jXJdHFO7l3AgVhy7OcsmT1DxGZCeD8vOCUJ02cRCSNQGLfr6p/dIqTPi4AqvpP4FMC3Q45IhIc4D58v0MxcdbfAvxfbGsaVT8C1ojI/wK1BLpm/ofkjceo4p3cjwHFzjfd3wMeAz6Mc53i6UNgkzO/iUCfc7B8o3N1SBngC+umcA0REeA3wBlV3R22KmnjIiL5IpLjzGcS+A7iDIEk/zNns6tjEozVz4B652zHFVS1UlVnqWohgXxRr6qPk6TxuKZ4d/oDq4GvCPQjVsW7PjHc798DXcAggT7CJwn0BR4GWoC/AHnOtkLgqqKzwN+B0njXP0oxuYdAl8tJoMmZVidzXIAfAn9zYvIl8B9O+Q+Ao0Ar8AGQ7pRnOMutzvofxHsfohib5UCdxWPkyR4/YIwxLhTvbhljjDFRYMndGGNcyJK7Mca4kCV3Y4xxIUvuxhjjQpbcjTHGhSy5G2OMC/0/toQeBscNHKkAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/إن الله مع الذين اتقوا والذين هم محسنون.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/28 بسم الله الرحمن الرحيم.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/وإن من شيء إلا يسبح بحمده.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/23عبد الله.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/1واعبد ربك حتى ياتيك اليقين.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/أللهم افتح لي أبواب رحمتك.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/ألا بذكر الله تطمئن القلوب.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/فتوكل على الله.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/الرحمن على العرش استوى.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/2أدعوني استجب لكم.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/115أنا اللى بكرا هبقى.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/23رمضان كريم.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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cnJ0ybNg0///wzypUrV+LRKr6+vmjYsCEAID4+HufOnSvR83M45goXd44IESE4OBjLli2DUqmEo6Mj5s6di2+++QZOTk5GKRNjDJ07dwagCY8MCgqCWq02Slk4HHOCiztHJDMzEytXrkRSkib9/sCBAzFy5EijT2tXt25dcURnaGioOC0dh8PJHy7uHJGoqCgcPXoURAQ/Pz+MHz++WGkD9AFjDD4+PqhcuTIA4O7du3j8+LFRy8ThmANc3DkiERERolXcuHFj1KhRwyRGhLq5uSEwMBCMMaSmpuLw4cM8JJLDeQtc3DkiycnJomi6urqazCQSusRbulbE/v3782Se5HA4r2IaTy/HJKhWrZoooPfu3UN6erqRS/Q/atWqhXr16gEAHjx4gFu3bnHrncN5A1zcOSKenp6ib/vatWs4d+6cyQioi4sLWrduDcYYkpKScPHiRZMpG4djinBx54h4eHigb9++kEqlSE1NxcqVK8XMj8ZGKpWiZcuWsLe3BxHh2LFjUKlUxi4Wh2OycHHniMhkMgwdOhQVKlQAAJw9exYnTpwwGQv5vffeQ+nSpQEAN2/eRExMzFv24HDeXbi4c/JQtmxZDBs2DDKZDKmpqVi1ahXS0tKMXSwAgJOTEwIDAwEAqampuHz5spFLxOGYLlzcOXmQyWT4+OOPRd/7yZMn8eeff5rEqFCpVIrmzZtDIpEgMzMT//33H3fNcDj5wMWd8wrlypXDxIkTxWyMixYtMonoFIlEgrp168LNzQ2AptNXN28rh8PJCxd3zmv56KOP8MEHHwAAIiMjMX36dLx48cLoAl+lShXR737r1i2kpqYatTwcjqnCxZ3zCowxuLi4YNasWShfvjwA4NixY1iwYAGys7ONWjZ3d3dUq1YNgGbQ1e3bt41aHg7HVHmruDPG1jPG4hhjt3Ktc2OMHWOM3df+ddWuZ4yxZYyxB4yxUMZYA0MWnmNY6tWrh/nz58PZ2RkqlQpr167Fhg0bjOrnZoyJnapqtZrHu3M4+VAQy30jgI4vrZsG4AQRVQFwQvsZADoBqKJdRgJYrZ9icoyBVCpFnz598Pnnn8Pa2hrp6en47rvvcOLECQiCYLRyNW7cGHK5HESEq1ev8tmZOJzX8FZxJ6IzABJfWt0dwCbt/5sA9Mi1fjNp+A+AC2PMS09l5RgBmUyGyZMno3fv3gCAFy9eYMqUKYiIiDCKxcwYg6+vL3x9fQFo0iTweHcO51WK6nP3ICLdExULwEP7vzeAp7m2i9KuewXG2EjGWDBjLPjFixdFLAbH0DDG4ODggB9++AFNmzYFoBlANGvWLKN1Znp6eqJWrVoAgEePHiE4OJi7Zjiclyh2hyppnqpCP1lEtIaIAogoQBf9wDFNGGOoUKECfvzxR3h6eoKIsHv3bqxdu9Yo/nc7Ozt06dIFUqkUarUaO3fuNKqbiMMxRYoq7s917hbt3zjt+mgAPrm2K6ddxzFzGGN4//338e2338LW1laMfzdWcrH27dujTJkyAIArV67g4cOHJV4GDseUKaq47wMwWPv/YAB7c63/VBs1EwggJZf7hmPmyGQyDBkyBH379gVjDHFxcZg6dSoeP35c4gLv4eGBVq1aAQBiYmJw48YN7prhcHJRkFDIrQAuAqjGGItijA0DsABAe8bYfQDttJ8B4CCAhwAeAFgLYKxBSs0xGra2tvjuu+/QoIEmyvXKlSuYNm2aOO9qSWFjY4NmzZrBysoK2dnZOH/+vEmkSOBwTIWCRMt8REReRGRFROWIaB0RJRBRWyKqQkTtiChRuy0R0TgiqkRE/kQUbPgqcEqS3P73smXLgoiwc+dOLFmyBEqlskTL0aRJEzg6OgIATp8+XaLn53BMHT5ClVMk2rZti++++w42NjZQqVRYtWpViacHrl69OsqVKwcAePz4MZ48eVJi5+ZwTB0u7pxCwxiDVCrFxx9/jOHDh0MqlSIxMRHff/89nj9/XmICb2VlhYYNGwIAsrKyEBISwv3uHI4WLu6cImNvb4+pU6eKc5sGBwfj559/LrH8M1KpFO+99x4YY1AoFAgNDeXizuFo4eLOKRbe3t6YNWsW3N3dxfwzhw4dKpG4c8YYqlatCgcHBxARIiIiTGZaQA7H2HBx5xQLxhg6d+6MoUOHgjGG1NRUzJo1C48ePSoRK9rDwwMuLi4ANH53Lu4cjgYu7mYKEeVZjIlUKsXnn3+OZs2aAQBu376NpUuXlsjoVQ8PDzg7OwMAnjx5wpOIcThauLibIUSEqKgoHDt2DAcOHMCjR4+MWh7GGMqWLYvZs2fD1dUVRIS//voLJ0+eNPiLx83NDaVKlQIAJCQkIDk52aDn43DMBS7uZsqePXvwwQcf4MMPP8TevXuNbr3r0hPoomcSEhKwdOlSg1vSUqlUnFBErVbj8ePHBj0fh2MumLS4ExGePXuGtLQ0o4tXUSEi5OTkIDU1FdHR0XrpaNSFIuowlZGZcrkcEydORKVKlQAA58+fL5HcMxUrVgSgudaRkZEGPReHYy6YrLgTEUJCQtC/f39MnjzZbOfKfPLkCQYPHoz69etjxIgRehumL5FIIJFofj5jzoyUG8YYvLy8MGjQIEgkEqSlpWH9+vXIzMw06Hn9/PwAaO4ZbrlzOBpMUtwFQcCpU6cwePBgnDt3Dhs3bsSaNWvM0nqXyWS4ffs2Hj58iLNnz+LGjRuv3U6lUiEhIaHAQ+ilUikYYwA0lrupXBuJRII+ffqgQoUKAIADBw7g/PnzBj2nj48PGGNiXwRP/8vhmKC45+TkYN++fRg6dChu3rwJQGOZ1a1b18glKxqenp5ikq309HTMnTsXDx48QGpqKp49e4aTJ09i8eLFGDFiBHr27FngiSdyi7upWO6AxnqvVq0aPvroIzDGkJ6ejmXLliElJcVgLyB3d3fY29sD0MwUxSNmOBxAZuwC5EYQBOzcuRPjx49HQkICGGNo2LAhVq1ahYYNG4piZk5IpVK0bdsWmzdvhiAICAoKQufOnVG6dGmEh4cjLS0NKpVK9Jv/999/aNKkSYGOa4qWO6AR+CFDhmDHjh0ICwvDyZMncfLkSfTo0cMg57Ozs4ObmxvS09ORkpKC9PR0Uew5nHcVk7Lco6Oj8eOPPyIhIQESiQTt27fHH3/8gYCAANG/bI4EBgbCzc0NgMYvfP/+fVy4cAHx8fHIzs6GWq0GYwylS5cusE/+ZXE3Nfz8/MTImaysLCxfvhxpaWkGOZetra04kCk1NdXgPn4OxxwwGctdEARs27YNt2/fBgC8//77WLt2rehPNWc8PDzQtGlT7Nu3D4DGD+/m5gZXV1dUqVIFDRo0QL169eDn5ydmOXwbL7tlTMlyBzTlGzhwILZs2YLQ0FBcunQJ+/btw8CBA/X+ora1tRUHMqWmpiIjI0Ovx+dwzBGTEffIyEisX78egiDA2toaX331lUUIOwA4ODigb9++OHr0KBQKBcqVK4ctW7agVq1acHZ2zlPHgtZXIpGYtOUOaPobxowZgy+++AKZmZlYvHgx2rdvDw8Pj7fvXAhsbW3h6uoKAEhOTkZqaiqIyCLuHQ6nqJiMr2Pr1q24d+8eAKBdu3Zo2bKlxTycjDF06tQJlStXBqCxLqVSKVxdXUWR1i0F5U2Wu6mkJZBIJOjfv7+YlvfWrVtYt26d3juAbWxsUL58eTDGkJWVxWPdORyYiLhnZ2dj06ZNICI4ODhg3LhxYjPbUnB2dkbXrl0BACkpKfjvv/+KFbKXX7SMIAi4cuUKfv75Z6Snpxev0HrAxcUFX3/9NZycnJCTk4PffvsN9+/f1+s5GGOoXr266O7RufY4nHcZkxD39PR0PH36FIDGam/RooXFWO06pFIpAgMDYWdnB7VajdOnTxcrZO910TJEhCtXrmDIkCGYNWsW5s6di/T0dKNa8IwxdOjQAR07dgQAPH36FKtXr9b7lHjVq1cXR+2Gh4fr9dgcjjlSkAmy1zPG4hhjt3Ktm80Yi2aMhWiXzrm+m84Ye8AYC2OMfVCQQqSlpSE7OxtyuRzdunWDnZ1d0WpjwujCOr29vQFo5vx8+PBhkY+XX7TMxYsXxbzmv/76K+bOnYucnJziFb6Y2Nra4vPPPxcjhrZv347r16/r9aXj5+cHmUzThfTkyRM+kInzzlMQy30jgI6vWb+EiOppl4MAwBirCWAAgFrafVYxxqSv2TcPutQCbm5u4sw6lkjZsmXRvn17AJo679mzp8gC9zqfO2MMw4cPx5QpU2BtbQ2VSoXnz58bfZATYwwNGjRA3759wRjD8+fPsWzZMr2+dBwdHcWXR0pKit7SPHA45spbxZ2IzgBILODxugP4m4iyiSgSwAMAjd62k+4hr1ixIqpWrVrAU5kfjDH06tULdnZ2EAQBJ0+eLLII5Rct4+DggKlTp2LKlCkYOnQoFi5cCFtbW72UvzjY2NhgwoQJKFu2LADg0KFDOH78uN6sdysrK7FVlJmZiRcvXujluByOuVIcn/t4xlio1m3jql3nDeBprm2itOsKRJs2bWBlZVWMIpk2jDHUqlULNWvWBABcu3atyJEdb4qWcXBwwLRp07B06VKUKVPGZFpCVatWxYgRIyCTyZCUlIRff/1VbwObrKys4OnpCUAzWXZCQoJejsvhmCtFFffVACoBqAcgBsAvhT0AY2wkYyyYMRYMaAb2BAYGFrE45kOZMmXQtGlTcUq6M2fOFOk4UqlUjA55XZy7nZ2dyfVdyGQyDB8+XGydnTlzBvv379eL9f6yuCcmFrSxyeFYJkUSdyJ6TkRqIhIArMX/XC/RAHxybVpOu+51x1hDRAFEFABoQgVLlSplMlamoZBIJOjUqZMY2XH06NEi+Z5NObdMfuhSAo8ePRpWVlZQKBRYs2aNXvzjUqlU9LkrlUqDpTrgcMyFIok7Y8wr18eeAHSRNPsADGCMWTPGKgCoAuByQY7p7OwsjjK0dOrUqQMfH807MCwsrEhRM6aaFfJtMMbQp08f+Pv7AwAuXbqEW7duvWWvgh3X2toagOZlZ+wIoZKCiMzm5c4pWQoSCrkVwEUA1RhjUYyxYQB+YozdZIyFAmgN4EsAIKLbALYDuAPgMIBxRFSgsfEuLi5i8idLx9XVVcz8+OzZM4SGhhb64TRHyx3QiLCnpyc+/PBDAIBCocDOnTuLXX7GmNhfo5v9ylyuSVEhIsTExOCHH37Ao0ePLL6+nMJRkGiZj4jIi4isiKgcEa0jokFE5E9EdYioGxHF5Np+HhFVIqJqRHSooAVxcXGxuFGp+WFjY4P3338fVlZWyM7Oxrlz5wodl507WsacLHdAI8QffvihmJb36tWrZjvTljF5/vw5Ro8ejblz52L06NF48uQJF3iOiEmMUAU04i6Xy41djBKBMYYmTZqI4nbx4sVCJ/8yV8tdh5eXlzjXanR0NGJiYt6yx5shInHUq0QigVwut/j+m4iICFy9ehVqtRrHjx/HqFGj8OzZM2MXyyJQKBRm79ozKXG39IcxN5UrVxbdUFFRUUhOTi7U/ubqc9fh4uIiTmz94sULxMfHF+t4L4u7JYfU6mjcuDGWL1+OsmXLQhAEpKSkmOW9YIrcuXMHK1asMHr6juJgMuL+rnSm6pDL5aLlqlAoxNw6BcXcLXcbGxt4eWn65TMzM5GQkFCsOhARsrOzAWiuzbsg7jKZDN27d8eKFSvQqVMn/P777/D19TV2sSwChUKBJUuW4McffzTbaRtNJp/7u+Jv1yGRSMQRlTk5OYiLixPFrSAtGHO33CUSCUqVKgWJRAJBEIo96EgQBFHcdW6ZdwGpVIru3bujffv2sLe3f6dav4YmKysLS5cuhbW1NaZMmQIbGxuzur4mI+66uO93BYlEgvLlywPQWAlhYWGoXbs2nj17hkaNGr31JjJ3yx2AmOgLKP6EI++i5a5DIpHAwcHB2MWwKHQBC1lZWVi0aBESExPRuXNn+Pv7w9PT0yym/TQZcS+uz9XckEgkqFevHmxsbMRwwO3btyMxMREbNmxAYGDgGwXenKNlgLxiDKDYYkxEUCgUADTi/q5Y7hzD4OrqKuZkysjIwLJly7Bu3TpUrFgRAQEB6Ny5M+rXrw8vLy/Y2tqapEVvMq+f3G6JdwHGGOrVqye6Zs6dO4eLFy8iLCwMEyZMQGho6BvDI83dcler1YiJiYEgCJBIJMWeek8QBNE3KpVKxQFNHE5R8PHxeWU+44yMDNy8eRMbNmxAv3790KZNG4waNcpkDVOTEffnz5+bfehRYfHz80OLFi1eWX/16lUMGjQIly5dyne6vLflljF1kpKScPfuXQCayJnipp5Qq9VirLxcLhfDTDmcomBra4sOHTqIn6tXr44OHTrA1dUVjDEQEZ48eYJbt26ZRNbV12Ey4v748eMCRYwIgoD4+Hizs1RfB2MMX331FerWrfvKdzdv3sTQoUNx/Pjx1+77pqyQ5sCdO3cQEhICAKhWrZoYFllU1Gq1mCxMLpfD0dGxuEXkvOO0bdtWNBJcXFywcuVKnD9/Hr/99ht69eqFypUro3PnzrCxsTFySV+PyYj706dPce/evTeKlEqlwq5du/Dxxx/j8OHDZh2DCmjEvWbNmli1ahVq1679iuV67949jB49GgcOHHjFr57fTEzmgFKpxPr160Ufefv27cWkX0UlOzsb0dGaHHW2trZwd3cvdjk57y6MMVStWlU0vEJCQhAWFobq1atjxIgR2Lp1Kw4ePIgJEyaYbDCIyYi7QqHAli1bkJ2d/Ypg6wao7N27FxMmTMDRo0fRt29f/PHHH2Yt7sD/Rqv+9ddfaNmyJdzd3dGmTRsxkuThw4cYMWIE1q9fj8TERAiCACJ6pUPVXK4DEeHs2bM4ePAgAMDT0xP9+vUrdodUQkKCOBCsVKlS70yeIo7hKF26NHr16gWJRAKFQoG1a9ciKysLjDHI5XJUqVIFnp6eJtmZCpiIuOvefAcOHMCSJUuQlpYm+ppzcnIQHByMyZMnY9SoUYiNjQWgeYCbNGlishe2IOjqKAgCXF1dMXLkSLRv3x6dOnVC6dKlxe1iY2MxceJEdOzYEatWrUJYWBgAmJ3lTkSIjo7GN998g/j4eEgkEvTr1w+VK1cu9rHj4uLEDuhy5cqZ9X3BMQ10M6fpBoadPHkShw4dMhtDyiRCIT08PBAfH4/09HTMmTMHe/bsQdOmTQEAt27dQmhoKF68eCFeVKlUinHjxsHf398sH2IiQnp6OsLDwxEUFISDBw8iMjISycnJyMzMxD///POKYGdnZ+PKlSu4fv06PD094evri+fPnwMwH8s9MTERU6dOxZUrVwAA/v7++Pzzz/US2ZKYmCheA+6SsWx0g950fSze3t4GG8Dl4+ODsWPHYubMmUhLS8Mvv/yCVq1amcc9prMejbnUrVuXPv30U7K1tSUAb11at25NsbGxJAgCmQuCIFB6ejrdvn2bfvnlF2rXrh2VLl26QPV921K2bFlKTEw0dhXfSHJyMk2cOJGkUikBIC8vLzp06BCp1Wq9HH/Dhg0kk8kIAM2YMcOs7g1OwRAEgaKjo2nBggUUGBhIFSpUoAoVKlCHDh1ozZo1FB8fb5DfPS4ujgICAggAyeVy+uWXXygnJ0fv5ykKAIIpH101CctdJpNh1apVaN68OVatWoV79+6JHYgSiSTPYJdy5cph/vz5JjU3aH7oLnJ6ejpOnjyJrVu34tixY0hOTs6TakAul6NMmTKoU6cOqlatCnt7e8jlcqjVamRnZyMzMxM3b97ExYsXxU7I3JhynDtpBxctWrQIq1evhlqthqOjI2bNmoUOHTrobaRfRkaGeA14GKTlQUSIiorCqFGjcPjw4Tz3e2RkJE6cOIGDBw9i6dKl8PX11as2lCpVCl999RWGDx+OjIwMLF26FK1bt0a9evVMW4PyU/2SXBo2bEhERCqVihITE+nUqVO0efNm2rx5My1evJgcHBwIANnb29OGDRtIpVLp+wWod3SW+rZt26hZs2ZkZ2eXx9qWyWTk7+9PY8eOpQMHDtDz588pPT2dlEolqVQqUqvVpFarKScnh5RKJSUnJ9O6deuoevXqr1ju7u7uFB8fb+wqv4LuGnz33Xdkb28vWj7z5s2jrKwsvVpZs2fPJolEQgBo1apV3HK3IARBoPj4eOrZs6f4G1tbW1O9evWoWrVqYotNIpFQly5d6NmzZ3r//dPS0qhXr17iM9ejRw9KSkrS6zmKAt5guRtd2CmXuL9McnIy9ejRgxhjxBijwYMHU2Zmpr6ui8HIysqiY8eOUadOncQXEwCSSqVUqVIlGj58OB04cICePn1aqOadWq2me/fu0ejRo8nDw4MYYwSAbG1t6dixYwasUeERBIGePn1Ko0ePJhsbG/GBnDp1KqWlpen9XKNGjRKv8549e7i4WxAZGRk0YcIEUcS9vLxo06ZNFB0dTY8ePaLVq1eTh4eH+IyNGjWKMjIy9F6Oa9euUYUKFcR7ecuWLUa/z8xS3NVqNW3cuJHkcjkBoMqVK9PNmzeNfjHfhFqtpvDwcBozZgy5uLiIYiOXy6levXq0ZMkSCg8PL7afOTs7my5evEg+Pj7ijfbnn3/qqRbFJycnh/777z9q1aqV+AKys7OjSZMmUWpqqt7Pp1AoRKvK2tqazp07V2L3SU5ODqWmplJsbCxFRUXRs2fPKCYmhmJjYyk+Pp6ys7NN+p59HYIgkFKppKysLMrIyKCcnByj1UEQBNq/f7/Y8nV1daWNGzfmeYZycnJo27Zt5O7uLho7//d//6f3Fr5KpaLly5eTlZUVAaDAwECjt5jfJO4m4XN/GSLN0N7FixdDqVRCKpXi888/R61atUzSx0WkicP/999/MWvWLHEwFmMMVapUwejRo/HJJ5/A3d0djLFi10Eul6NRo0ZwcXFBVFQUWrdujfbt2+upNkWHtP71LVu24IcffkBUVBQATTrnmTNnYvz48QYZzadUKpGRkQFA42+3s7Mz2H1CpJmQOjk5GUFBQbh06RLu3r2LsLAwpKamwsrKClZWVpDL5XB2dkaTJk3Qvn17tGzZEg4ODiZ1/xJpwnBzcnIQExODiIgIPHr0CNHR0YiLi0NaWhpycnLg6ekpDr8vV64cZDJZidSDiJCUlISlS5ciMzMTADB27FgMHDgwz/llMhl69+6N+/fv4/vvv0dWVhYWLlyIVq1aiXMm6AOpVIoBAwZg8+bNYuTarl27MGzYMNPMEpmf6pfk8rLlrlaradq0aaJ/rU2bNhQXF6e3t52+iYqKookTJ5KTk5NorZcqVYomTZpEDx8+JJVKpXfLR61WU/369alp06b04MEDo1uHgiDQ7du36dNPPxX96wCoWrVqtGvXLoNasAkJCdSiRQsxcujmzZt6P4cgCJSYmEhHjhyhL774gqpUqVKg6C7GGDk4OFDz5s1p27ZtlJ6erveyFQWFQkFnzpyhGTNmUGBgIJUuXZocHR1JLpeLra3ci5WVFZUtW5ZGjhxJFy9eJKVSafAyCoJAO3fuJGtrawJAtWvXpsjIyHy3TUxMpI4dO4rX/YsvvqDs7Gy9lknnUdD99vXq1aPnz5/r9RyFAcVxywDwAXAKwB0AtwF8rl3vBuAYgPvav67a9QzAMgAPAIQCaPC2c+QWd0EQ6NatW+Tr60sAyMnJiQ4dOmR08XodOTk5dPLkSWrWrJkY4mdlZUVt2rSh06dP6/3Gyo1arabPPvuMrly5YtRrIwgCvXjxgn799VeqUqWKKAzW1tbUq1cvunXrlt7CHfMjNjaW3nvvPQJAFStWpLCwML0dOzMzk4KDg2nWrFkUEBBAzs7OeURPIpGQq6srVapUierUqUO1a9emmjVrUrVq1cjDw0M0UACQg4MDffXVV5ScnKy38hWlPidPnqQ+ffpQqVKl8n0pWVtbk5OTEzk7O4v3tm7x9PSkCRMm0KNHjwz626pUKhowYIAYgLB06dK3nu/UqVPk6upKAMjPz4/u3Lmj9+cjOTmZmjdvLj7vS5YsMVqQR3HF3Usn0AAcAYQDqAngJwDTtOunAVio/b8zgENakQ8EcOlt58gt7iqViubMmSM+FIMHD6asrCzDX6VCIAgCpaSk0KJFi8SOHADk7OxM06dPp+fPnxtccAVBoCdPnhhV2HUdx23atBE7u3QP/8KFCyk5OblEyvf06VOqXbs2AaCaNWvSo0ePinU8QRAoLS2NTp8+TZ9++ukrIiiVSsnV1ZU6dOhAK1asoKCgIIqMjKSUlBRKTEykuLg4io6OpsuXL9OiRYvI399f9NNaWVnRxIkTSzwwQK1W0507d2jo0KF5+oMkEgk5OTlR9erVqXfv3jRt2jT67bffaNeuXXT06FE6fvw4bd68mQYOHEilS5cWn0vGGNWvX5/27dtHCoXCIGUODw8XjTxPT898rfbcZGZm0sCBA8UyrlixQu/3oCAItG3bNrGFWr16dXr8+LFez1FQiiXur+wA7AXQHkAYAC/63wsgTPv/7wA+yrW9uF1+S25xf/78OVWrVk0UyxMnTpiU1a4bSDFkyBDxgWWMUZ06dejff/81qLX+cjmMcV0EQSCVSkU3btygMWPG5HFFWVtbU7du3ejChQslOsjj4cOHVLlyZQJA9evXp+jo6EIfQxAEUqvVFBcXR5s3b6auXbuSvb292BJhjJGNjQ01adKEfvjhB7p27RplZWWRWq1+4++gVqvp+fPnNGPGDLFT0NnZme7evVucKheqXiqVinbv3k3VqlXLU5/y5cvT119/TWfPnqWUlBTKzs5+rQtREATKzs6mGzdu0Pjx4/O8HJydnem7776jpKQkvd6ParWafv/9d/EZGzRoUIGMPEEQaPfu3WIgRosWLfRuHOqMuw8++EB8Qf76669GeR71Ju4A/AA8AeAEIDnXeqb7DGA/gGa5vjsBIOA1xxoJIBhAsK+vLxFpLtratWvFH6Zdu3YmZbULgkA3btygVq1aiU1VGxsbGjx4MD148MDg7gdjk5OTQyEhIfTFF19Q2bJl87gc6tSpQxs3biwxaz03YWFhVLZsWQJATZo0oRcvXhRqf7VaTXfv3qVZs2ZRjRo1XvGl+/j40JgxY+jMmTOUkJBQ6Ca4IAiUlZVFn376qWj5L1u2rFDHKCoJCQk0Z86cPK0PLy8v+vrrryk8PLxQvnNBEEihUNCRI0eoadOm4otCLpdT3759KTIyUm+/fWZmJnXu3FmMtPr7778LfOy4uDiqWbOm2Pd18eJFvZTpZXbv3i2+fFq0aEEJCQkGOc+b0Iu4A3AAcBVAL+3n5Je+T6JCiHvuRWe5p6amUteuXUUr8I8//jAZq12tVlNwcDA1aNAgT6fp4sWLKTU11WTKaQiys7Pp+vXrNHHiRPLx8clj/Xl6etLkyZON2qkbEhIiNpHbt29f4BhnQRAoJiaGfvnlF6pUqVKejkS5XE7Vq1enb775hm7evFnsDkRBEGjXrl2i+6pHjx4G7VwVBIFu3rxJ3bt3FwVIKpVSq1at6Pz586RUKov8ewmCQI8fP6Zx48aRo6OjaL02adKEQkND9XIf3Lhxg9zc3AgAValSpVCdlkqlkr7++mtxfMz8+fMN4hOPiYkR0xI4OjpSUFBQiT8DxRZ3AFYAjgCYlGud3t0y9+7dE2NVa9asSY8fPzYJ0VSr1XTlyhXy9/cXH/7KlSvTv//+azI5JvSNril+8+ZNmjBhAnl6euaJAHF3d6ehQ4fSpUuXDBINVBiCgoLEsg0YMOCtZdGNnN2zZ0+eznBoO/DbtWtH69ato6dPn+rV/fXgwQOqUqUKASBvb2+9dvzmRhAECgkJoXr16uXpzB03bhzFxMTopT46K37t2rXk5eUlnqdJkyYUFhZWrHMIgkALFiwQf5cJEyYUqlWsi43XvXgGDBhgkH4BtVpNM2fOFFuwU6dONS9x17pcNgNY+tL6RcjbofqT9v8uyNuhevlt52jYsCEJgkC///67aD0NHjzYJNwcgiDQlStXxA473Yvn4sWLJlE+faLzOyclJdHBgwdp3LhxeUbCAiAXFxcaMmQIXbhwwWQG6GzdulV86XzxxRf5bicIAuXk5ND58+epb9++eVJCyOVy6tmzJ506dYoyMjIM8ttmZ2dT//79xXMaYuCZIAgUERFBTZs2zeNWWr9+vd5TPuj8+UeOHKGKFSuKv0GHDh0oKiqqyOfKzMykfv36iW7P/fv3F/pY4eHhokFSt25dg7WSLl++LLYa69evb5BBem+iuOLeTHuThAII0S6dAbhrXS73ARwH4Eb/exmsBBAB4ObbXDKkFXeFQkE9evQQm3j//PNPCV6i1yMIAl29epXq168vPii1a9em8+fPm4So6RNdaoNvvvmGqlWrJlo9uqV06dKipZ6ZmWlS9V+5cqUoLHPnzn3tNrr6jR8/nsqUKZMn8uW9996jrVu3lkh/wYYNG0RL76OPPtKru0AQBIqMjKR27dqJ56hcuTKdOXPGoC1MlUpFR48epXLlyonXtE+fPkUemxIbG0t169YlAFS+fHm6fft2oY+RmZkptpLc3d0L3Q9TUJKTk6lJkyYEgNzc3Oj06dMGOU9+FNstY+ilYcOG9ODBA6patSpBG5/68OFDA16St6PrPG3YsGEei/3ChQsWZbGr1WqKjIykGTNm5IlT171kPT09adCgQXTu3DlSKBQmJeo65s2bJ5Z3+fLlr3yfkJBAv/32G1WtWjVPf4Gfnx/NnTtXdL+UBA8ePBBfLv7+/hQVFaW3Y0dGRlLHjh1FYS9fvjwdPXq0RO5XlUpFO3fuFEODZTIZjR07llJSUgp9rLt374o5mRo3blykY+Tk5FDjxo3FiJ4HDx4U+hgFPc8333wj+vcXLVpUovrwJnE3mfQD8fHxiI+PB6CZadyYyfCJCGFhYRg2bBiuXr0qlkk31d2OHTvQpUsXg00QUBIQEdLS0rB792789NNPuHv3rq6lBjs7OwQEBKBr165o06YN6tatm2fOVlNDl3oAABwcHABo6qdWq3H16lXMmzcPhw4dgkqlAmMM7u7u+PjjjzFmzBhUrVpVLykhCoqHhwd69eoFhUKBDz74QC8TeRMRYmJi8MUXX+Dw4cMANBNY/Prrr2jbtm2JDI2XSqXo3r07UlNTMWnSJCQlJWHt2rWoUaMGxo4dW6gyPHz4UPxNK1euXORrpEt1IQgC0tPTi3SMtyGVShEYGAgHBwekpaXhwoULBkuzUWjyU/2SXBo2bEgHDx4UO1CGDBlitI5KQRDowYMH1Lx5c9HKq1q1Kp07d46eP39ODRo0IDs7O+rZs2eR4qmNjc5PevbsWerZs6eYsRFav3OHDh3owIEDlJKSYvSO0oIyffp00XLftGkTCYJACQkJ9OOPP5Knp6f4O8pkMurSpQudO3fOaP0FgiBQZmam3lpBgiBQUlISDRo0SLTYS5cuTTt27DDKqEmlUknz5s3Lk/Dv7t27harr8uXLxd9s9uzZRSpHSkoKVapUSbweMTExRTpOQXj48KEYilu7dm29Zz19EzAHy/3JkydQq9WQSCTw8fExyoziRISEhARMmjQJ586dAxGhQoUKWL16NRo1aoSlS5fi5s2byMnJQVxcHOzs7Eq8jMWBiPD48WOsWLECf/zxhzhNn1QqRYMGDTB+/Hh8+OGHcHFxMVkr/XXknqYvLS0Np0+fxvfff4/z589DqVSKCdy+/PJL9OvXD25ubkarH2MMtra2ejteRkYGZs+ejb///huCIMDNzQ2LFy9Gjx49jPIMyWQyjBkzBpcvX8bevXvx4MED/PXXX5gzZ06Bj/Hw4UOxFVmxYsVCl4GIEBoaKk7DV61aNTg5ORX6OAXF09NTnCAmJiYG2dnZYgvSqOSn+iW5NGzYULS+rKysaO3atYZ5zb2FjIwM+uKLL8S44LJly9LBgwdJrVbT/fv3xVzOjo6OtGPHDrOwaok01l1GRgbt2rWLGjZsKFpFEomEKlasSD/99FOJ+p31zZIlS8TWR7169fKkhHBwcKAhQ4bQnTt3LKqvhEiT/uHHH38UW1/Ozs60YsWKEknq9TaCgoLEKBJ/f/8Cx6mr1Wrq2bOn2C9y9uzZQp87IyODPvvsM9EPPnnyZINek5ycHHH8i7Ozc4mmIoA5dKiOGzdOHLy0c+dOA16O16NSqWj9+vVieJyTkxP9+eefpFKpKCcnh77++mux2dujRw+zmDSESFOv0NBQ+uyzz/JMHOLs7Exjx44V88ubq7ATEW3ZsiVPZI/uxVW9enX6888/TS66Rx8IgkBbt24Vf1MbGxuaM2dOsQYn6ZPk5GRq1aqVGK1y7ty5Au2XkpJC77//vhh2W9g0DYIg0KFDh8Tr4u3tTdevXzfoNVGpVGJWUicnpxJLLUFkJm4ZtVot/l/SzUkiQnh4OObNm4fMzEzI5XJ8+eWX6NOnD6RSKZ48eYIDBw5AEAS4uLhg4sSJptFh8hZycnKwe/duTJ06FY8fPwYRQSKRoHHjxpg1axZatWoFa2trs3LB5EZ3E7/cWWZnZ4ePP/4Y06ZNg5+fn2nm2i4GRIQ7d+5gzpw5SE9PB2MMw4cPx5dfflliudbfhp2dHfz9/REUFITU1FQ8e/asQPslJSUhKSkJgKbzuTDz4RIRIiIiMH36dPG6DB48GP7+/ga/JnK5XCyDLve8sTEZcRcEQfy/pB/G9PR0/Pjjj4iIiAAAtGvXDhMnToSVlRUA4MKFCwgLCwMANG3aFAEBASbxAOUHESE5ORnLly/H0qVLxYfFx8cHn376KcaNGwdPT0+TrkNBUCqV2LhxI3744QdxnaurK+bPn49BgwbB1tbW7Ov4OtLS0jB79myEhYWBMYZ27dphxowZeom80RcSiUQUZkEQ8hhvbyI5OblI4k5ESElJwaxZs3Djxg0AQGBgIEaMGGFwPcnMzBQnppHL5UaN9MuNSYp7ST6QRIR9+/Zh165dAICyZcvi22+/hZubGwCN9fv3339DrVZDKpWiV69eJvUQAZo6CIIAqVQKQRAQGRmJ6dOnY+/evVAqlZDJZGjXrh3mzJmDhg0bGqWjTd+kp6dj5cqV+PHHH5GamiquL1++PLp27Wp2nd0FRalUYtWqVfj3339BRChXrhzmzZsHT09PYxctD0qlEk+fPgUAWFlZFailS0R5LHdPT88C/Y5EmrDeb775Bjt37gQRwcPDAz/++CP8/PyKVY+CnPvq1auIjY0FoOm8LVWqlEHPWWDy89eU5NKwYUP68ssvxXC8kpoPVJcTXTcaTiaT0YIFC8QwTEEQ6Pr162KYk6+vr9HyNueHIAj033//0ZYtWygrK4uuXbtGAQEBeSbPHjt2bInkmC8pkpOTacqUKWK4nVQqFRNylSpViq5du2Yxdc2NIAi0b98+cTIKOzs7+u2334w2UUR+CIJA9+7dE0esli9fnu7du1eg/Xbs2CH2m0yYMKFAeYKSk5NpzJgx4v1gZ2dHq1atKpFw6qysLBoxYoTYAfztt9+WaBg3zKFDdeHChaLALl682ICX43/ossfp4utbtmyZZ5iyWq2mhQsXir3uo0ePNtjEBEVBN4q2Ro0a5OjoSOPGjRNTnQKa1K6//fYbZWRkWIzYJSYm0ujRo8WIJrlcTmPGjBFzkTDGaOPGjRZTXx2CINC1a9eoRo0aYofxiBEjTLJjPzMzk0aNGiUaGL169SqQ4AmCQEuXLhVf2D/++ONbt3/27FmeuRXs7e1p/vz5JXJddOlJdMkOvb299ZYVs6CYhbhv2rRJfDhLIruaIAh0+PBh8Ydxd3enw4cP5zlvenq6OH2bg4PDK98bE930dh9++OErkSIAqEaNGnT06FGLyVopCALFxcXRZ599ludB/uabbyg5OZk2b94sWu/du3c3qZdwcREEgcLDw8Wp3XSGiLFn4nodarWa1q1bJ4ZBlilThs6cOVOgcgqCQF9//bUYEr1x48Y3bnv37l3q3LmzaJw5ODjQggULSuyFlzvkEgCNHTu2xFtRZiHuhw8fFh/OQYMGGTxWNzo6mgIDA0UraOLEia8I4b1798RJDurUqVPiGd/eRE5ODs2ePVsUOt0ilUrp/fffp8uXL5vcg19UdHnXBwwYID7Izs7OtGDBAnEylydPnpCPjw8BIFdXVzp27JjR6q9zS8yfP5+2bt1KiYmJxTpWbGwsdenSRRSRKlWq0KVLl/RYYv2gVqvpxIkTojtGJpPRt99+W+DZydRqNQ0ePFgM7Tx06NBrt8vOzqajR4/mmVvBxcWFfv311xKb3EetVucJRfX29qarV6+aV8rfklgaNmxIwcHBVLp0aQJAbdu2paSkJINcDF0eal0yfwDUsGFDevTo0Ss/zJkzZ8QJkXv16lViU+i9DbVaTQcPHhRbHbrFxsaGxowZo7ec3aaATtj79u0rjjNwcXGh5cuX54npViqVNGPGDFH8u3TpYtDJMN5EYmIiffjhhySRSMja2pp+//33Ih8rNTWVPv30U7FeZcuWLbFkYIVBEAQKDg6m6tWriy3wbt26FWp2IrVaTb169RL7il6OjdelWpg3b544kQcAKleuHG3fvr3EBm+p1Wo6c+aMmOZYIpHQzJkzjTJ4zCzE/eHDh+KNUbVqVXry5IlBLoYgCLRz505xHkhHR0fas2fPa8Vwx44d4qCmcePGmYyLIyoqKs9EDADIw8ODli5dSunp6RYj7ESaKdP69u0ripurqyutWbPmlRetIAj08OFDMc2rvb29UUYRKxQKmjVrVp7O3t9++61Ix8rKyqLZs2eTtbW12Fm8fft2k+tAVavVdPr0aapVq5ZoMAUGBtL9+/cLdf3VajW1b99e7BQNCQnJ893Vq1fpww8/FK8toMkaGRQUVGLXRK1W09mzZ8UMtowxateuHcXGxpbI+V/GLMQ9LS2N2rRpI3aSnTlzRu8XQjeRQZ06dcQHb/Lkyfk25VauXCm6PebNm2cS1lJcXByNHz9eFDuJREKBgYEUFBRkEsPO9UlycjINGzZMdNe5u7vTunXr8m1BqdVq+uWXX8Tt27VrV6LzWqpUKtq8eXOeScNtbW1p+/btxT6WnZ0d/frrryZjYBD9b0arzZs3i1YsoJkc48qVK4V+sarVatFVam9vL6bpTUpKotWrV4vpP3T+9REjRlBERESJvcAFQaDLly+LndqMMXrvvffo1q1bRjOozELcBUGgWbNmiT/erFmz9H7BsrKyaMyYMWLzvmXLlhQbG/va87xcnnXr1hnVItZNxNCnTx+x/DKZjHr27KnXiYlNhfT0dJo+fbr4cnVzc6ONGze+UdwEQaCnT5+Koa1WVla0YcOGEstnfuDAATFsVrc4OzvTsWPHCnUsXWSMn5+faIR88cUXBZ4btiRQq9X08OFDGj58uNi6ZYxRw4YN6dKlS0W6H3NycqhWrVoEaIbxP3v2jG7cuEF9+/YV7wPGGFWoUMEgM0u9CUEQKDQ0NM/EPQEBAUYVdiIzEXciorNnz4o/YrNmzYqUpD8/Xp5XsVSpUm/sxc/OzqYxY8aIvuz8XDclgSAIFBYWRu3atcszmUaHDh0oLi7OooRdl5J49erVYkIse3t7Wrx4cYHD6VatWiW+ACtVqkTBwcEGzy1y6NAhsUNXJ0K6l1Jhk19lZmZS3759xeN06dLFYDMJFRZBO7funj17yN/fP4+h0aFDB7pz506Rr3VqaqroVnNzc6MffviBfH1986Rs7tWrF4WGhpaoa0r3ss09cU+NGjUMfl8VBLMRd12+9NwWj74uXmRkpDhTuVQqpWnTpr1RLDIzM2ngwIFiWU6cOKGXchQWhUJBf//9N9WoUUN8kABNgrW9e/capUyGRBAEOnjwoDjpskwmo6lTpxYq+dfz58/pgw8+EEWhVatWFB0dbZAHUalU0u7du8nX11cU48aNG4tzmLq6ulJQUFCBj6dSqWjTpk1iKKGvry+FhIQYXUSINNZ6WFgYjR8/XhxIpQt3/P777yk+Pr5Y5YyKiqLy5cuL7sbcvvXy5cvT0qVLixV5VBRUKhUFBQXl6U+oWLEiBQUFmYSb1mzEXaVS0ezZs0V/8siRI4vtY9Slux0zZkyezp63xQhnZmbSJ598Ior78ePHi1WOwiIIAqWkpNCcOXPy+HB1S8eOHQ0WUfS2cmVkZBjERaAblOXv7y8+4AMHDiy031wQBAoJCRE7vaRSKQ0bNkyvoayCIFBaWhqtXr1ajPJijFGrVq3ozp07NHLkyDw+94KKXkREhOjTtba2psWLFxu9A1WtVlN0dDQtXbo0z1SFMpmMmjVrRidOnCh2f49arabz58/nmd9Wd/169+5NV69eLXFrPSsri7Zs2SK2yBhjVKtWLTpz5ozRfxMdZiPugiDQ7du3xdhyT0/PYjd9lEol/fzzz6Jf0NXVlY4cOfLWY2ZlZYkxt46OjnT06NEil6Gw6Pzrn376qRgpkXuxs7Oj3bt3l7g1p+tAmzp1Kn355ZeUlJSktzIIgkBRUVHUsmVLsZ5NmjShx48fF+kcarWa/v33X/Feksvl9NNPPxU7nFX3crt48SINGDCAbG1txRdRu3btxBTKy5cvF1tan3/+eYHELzMzk8aOHSuKZ7t27fR6jQuLWq2mhIQEWrt2bZ6UFowxcnd3p6lTpxYr7Fbngnvx4gWtXbuW6tatm+ccFSpUoN9//53S0tJK9Bq8zrBijFFAQIDJpbYolrgD8AFwCsAdALcBfK5dPxtANIAQ7dI51z7TATwAEAbgg7edQyfuRBpf98iRI8Uh/x999FGR4pV1vsGVK1eKTUgrKyv6/vvvC/SAKxQKGjp0qNgzf/jw4UKXoSjoeuQbNGggioOVlRV5enqKote2bdsSncpLR1ZWFs2cOZNsbGzIysqKxowZQykpKXq52ZOTk2ngwIF5fOVFibjITU5ODi1atEj03bu4uIjx0IIgiMvbEASB1Go1JSYm0o4dO6hnz57k5OSUx4Lt37+/OOGJLuZbZ4VWqFDhrRNh69xROjFxd3enkydPGuUFrhtbsGrVKgoICMjjHrGysqJu3brRuXPnipw7Xifq4eHhtHDhQqpTp06ecwCgpk2b0q1bt0rcQtaF1H700UeiYcUYo65du1JYWJhJCTtR8cXdC0AD7f+OAMIB1NSK++TXbF8TwA0A1gAqAIgAIH3TOXKLu65prvO92dra0s8//1xoiysmJoamTp0qdqDKZDIaOXIkJScnF2j/7OxsMSGQvb09HThwoFDnLyrnz59/JT/MN998IzbV7ezsaOvWrUa5yZRKJS1ZskQclSeXy2nWrFnFtoaVSiUtXLhQ7EwvU6YM7dmzp9g+TUEQKDU1lfr06SNeT1dXV+rQoQP9+OOPtG3bNjp58iRdunSJQkJC6M6dOxQVFUUvXrygFy9e0NOnTyk4OJj+/vtvmjhxIvn7+4u+cN3i5+dHCxYsoISEhDy/iVKppK5du4r33sKFC98oVC9evBDTC+hScJT0oDmVSiWOrK1du3YewZXL5dS8eXNav379K3UtKDk5ORQZGUn79++nIUOGkI+Pjxi2+vIyZcqUEg/7VCqV4shXnZFhZ2dHEydONNmBgXp1ywDYC6D9G8R9OoDpuT4fAdDkTcfMLe5Emubg/PnzxTenq6sr/fTTTxQbG5uvtaDzkUVHR9OePXuoVatW4o0jl8tp4MCBFBcXV+CLplQq80TLGLrzUqVS0blz5/IMTqpTpw4dOXKENm7cKD5ogYGBBX5BGYKsrCxasGABOTk5kY+PD+3bt69Y1pUux4/OyrW2tqZly5bp9cFeunQpubu754k00i3W1tbk7u5OXl5eVL58eapTpw41atSIGjVqRHXq1KHSpUu/sp+VlRVVqlSJpkyZQiEhIa+tvyAItHv3bvFlUKlSJQoNDX1t+XRuHN1vXL9+/RLJPqp7+UVGRtLu3btp5MiRVKlSpTwd93Z2dhQYGEjr16+n69evU//+/en333+nyMhIio+Pp6SkpDxLfHw8xcXFUWxsLEVFRVFERARdvHiRfv31V+rfvz/VrFkzz6TsgCbssXnz5uLLEAAtXLiwxDosBUEzofq8efPyTNHo7e1Ny5cvN9pI54LwJnEvVD53xpgfgPoALgF4H8B4xtinAIIBfEVESQC8AfyXa7co7bqXjzUSwEgA8PX1ffk7jB07FhEREVi/fj2SkpIwc+ZMbNu2DfXq1UOZMmVgY2OTZxah7OxshIWFITg4GBEREVCpVAA0M8JMmDAB06ZNg7Ozc4HrKpVKxe2VSiVSU1NBRHrPNU9EyMrKwj///INvv/0WT548AQD4+/tjw4YNKF++PPr27SvmZR8+fLhBJ/t9G9bW1vjyyy/h6uoKb29vdOrUqciTIRARQkJCMHnyZMTFxUEikeDjjz/GZ599BplMf1MNjB49Gu+99x62b9+Of//9F9HR0VCpVBAEAdnZ2cjOzn7rMWQyGWxsbNCgQQP069cP3bp1g7e3Nxhj+d4Tbdq0QevWrbF//35ERERg5syZWLNmDTw8PMR9iAhRUVFYsWIFlEol7OzsMGnSJJQrV05v9dchCAJycnKQlpaGkJAQXLhwAcHBwWI+ciHXnAr29vZo0qQJhg4dis6dO0MikWD8+PHYsWMHduzYAS8vL5QvXx42NjZ56qK7nkqlEpmZmYiPj0daWhoEQdAZewA0z5ebmxs6deqE/v37o2XLlrhx4wZq1aqFxMRENGjQoETmdVCr1bh27Rq+/fZbnDhxAiqVChKJBPXr18fPP/+MFi1amO+EL/mp/ssLAAcAVwH00n72ACAFIAEwD8B67foVAD7Jtd86AH3edOyXLXcdMTEx9Mknn7zij2OMkUQiIalUKi65rQ3dNv7+/rR169YiZYkTBIF+/vlnMXJn8eLFerckBEGg58+f06hRo8QOX2gt9v/++4/UajXt3bs3j0UXFRVl9OahzmdaXIs9NDSUGjRoIFrG7733XpE7UAtyvuzsbIqNjaX9+/fTTz/9RBMnTqQBAwZQp06dqE2bNtSoUSOqVKkSeXt7k7e3N1WqVImaNWtGw4cPpxUrVlBISAilpqYW+D4QBIEuXbpE3t7eYtROly5d6Pr166RSqUgQBFIqlfTNN9+I91mHDh2K1Z+Suy9Bl0cpIiKCjhw5QkuXLqX+/ftT+fLlyd7e/pVnBtp+iX79+tHBgwfzdGTGxMRQ165d83WjFHTx9PSkzp0708KFC+nevXviQCTdkpOTQwqFwuAuGUEQKDMzk3777bc8A89sbGzos88+o8jISJMIdXwbKK7lzhizArATwJ9EtEv7Unie6/u1APZrP0ZD0wmro5x2XaHx8PDAypUr0aJFC/zf//0fnjx5ghcvXkCtVuexAnRYWVnB09MT1apVQ+fOndGzZ0/4+voWybJkjKFUqVKQyWRQq9WIi4t77TmLCpFmvscpU6bg33//hUqlgpWVFTp16oT58+ejRo0aUKlU2LRpE5RKJRhj+OSTT+Dl5WV0S4IxVuzZnO7evYtRo0bh+vXrICLUrl0bK1euhI+Pj0HqxxiDXC6Hh4cHunTpgi5duoCIkJOTg+zsbKjVaiiVSmRlZYlTwkmlUjg4OMDZ2RlSqbTQ5WKMoUGDBpg7dy6+/vprxMfH4+DBgwgJCUHXrl3Ro0cP2NjYYMOGDVCr1XB1dcVXX3311qnliAhqtRoKhQKZmZnIyMhAeno6MjIykJSUhCdPniAyMhL379/Hw4cPxdmN0tLSXrmH7ezs4OPjg5o1a6Jdu3Zo1qwZKlWq9EoZPDw8sHHjRuzfvx+7d+9GeHg4MjMzkZOTk2c7mUwmLlZWVrC3t4ebmxv8/f3RpEkT1KpVC15eXnBwcHjt9dTta0iICLGxsZg7dy42bdqEjIwMAED16tUxbdo09O7dG/b29kZ/zooLe5tgMU0NNwFIJKIvcq33IqIY7f9fAmhMRAMYY7UA/AWgEYCyAE4AqEJE+U6iGBAQQMHBwfmWgYiQkJCAx48f4+nTp0hLS4NCoYBCoRC3kUgkKFu2LKpUqYKKFSvqZZq1gwcPon///khPT8fAgQOxceNGcV7V4qBWqxEUFISvv/4aISEhICK4uLhg8uTJGD16NNzc3MAYw/3799GxY0c8fPgQPj4+CAoKQsWKFYt9fmNCRHj06BFGjRqFY8eOAdBMTbZmzRo0b97c7B+o16FSqbB9+3bMnDlTnKgcAGxsbODi4iJO0ebh4YHBgwfD1tY23+OkpKQgKSkJqampyMjIQFpaGlJTU8UlMzMzXyOEMQZbW1u4uLigZs2aaNmyJRo1aoQqVarAx8enQKJKRFCpVIiJiUFaWhpycnLE8zHGYGVlJS5yuRyOjo5wcHAwmUnK1Wo1rl+/jilTpuDMmTNQq9WwtrbGhx9+iFmzZqF27dpmdQ8yxq4SUcBrv8zPpNctAJpB02QJRa6wRwBbANzUrt8HwCvXPjOhiZIJA9DpbefIzy3zOl5udr686JPw8HBxgEqVKlWKPWhI1+zctm1bnqagp6cn/fHHH3k6iwVBoO3bt4sRJP379zep3CJFQRAEevz4cZ7c5D4+PnTixAmju5oMiS6U8urVqzR06FBycXER3TAvL7oQ4PyW1+3z8iKRSEgmk5FcLicHBweqXr069erVi7777jvauXMnPXr0iJRKJanVaou+7rnRueX+/PNPMRIPWjfU3LlzKTU11SyvBcxlEJOpoVAoqEWLFgRoBjIVJ+5YN6Jx2bJl4sAa4H8pS1/2MapUKjEU08rKilatWmWWN58OQdBMYNGmTRvR1+vp6Ul79+41mdF+hkbn57158ybNmjVLnCugKItMJiNnZ2fy8fEhf39/at68OXXr1o2GDBlCM2fOpDVr1tDx48cpMjKSEhISKCMjQ/Tzv2sIgkCJiYk0Y8aMPNe8WrVqtGvXLsrOzjbb6/ImcTesc8vMsbKyQrt27XDmzBlkZGTg1KlTaNGiRaH9zSqVClevXsX8+fNx7NgxZGZmQiKRoFOnTvjll19QtWrVV5qCKSkpOH36NADAyckJbdq0Mavm4svcvHkTY8aMwcWLF0FEKFeuHBYtWoQuXboU239vLujcIrVq1UJ4eLjo2/fx8cHPP/8MAGJkyeuQSCRwcXGBm5sbHB0dYW1tDRsbG9ja2sLW1hZ2dnawtrY2GReIKSAIAsLDwzF9+nQcOHAAOTk5kMlkaN++PebPnw9/f3/LvV75qX5JLqZquQuCQCdPnhQn9qhZsyZFR0cXav+0tDRavnz5Kz3yAwcOzHfUom60om4AVtu2bUts+jB9o0spoUukBWhGbOpjkJK5kpaWRm3bthVbZT/99JNJ5Wm3FHJycujYsWNUp04d0aXl5OREkydPLtSYF1MG3C1TdHI/iFKplFavXl3gIetRUVE0cuRIcdCGLl/G8uXL3zhsX61W05w5c0Q/a0kO6NAnuqHcuSd29vPzo+PHj79T/t7cCIJAu3btEkNfa9asme+cApyioQsBXb16dZ60HeXKlaMNGzYUOW2CKcLFvZhs3bpV7NisXbv2W3NMKBQK2r17NzVo0EDsOLOysqIBAwbQ7du33+pjVqlUeWaBN1a6geISGxtL3bp1E33sFSpUoGPHjr0zPvbXkZycTB07dhT95oYYP/EuIwia2daGDRsmjg7WzZh09uxZi2shcXEvJs+fPxenAGSM0YABA8QkUblRKpV0584dGj9+vOjKATS5UubNm1fgXNS5xV0ul9Pff/9tiGoZlMTERBo5cqT4cvP19aWDBw++80K2d+9esSXn7+9fImkG3hUUCgXt27cvz8A4a2trGjBgQIlOx1eSvEnceYdqAShdujTmzJmDW7duIS4uDtu3b0dERAQGDhyIWrVqQSKR4NmzZzh79ixOnDiBhw8fAtAMyGjYsCFmz56N9u3bW27HTS6ICImJiZg5cybWr18PtVoNFxcXzJs3Dx07djR28YxKWloa1qxZA4VCAcYYBg0aBB8fn7fvyHkjRIS4uDgsXboUv/32G5KTkwFontvPP/8c48ePh5OTk1kHJBSJ/FS/JBdTt9yJNFb5ypUr80ycIZVKSS6Xk1wuJ5lMlicO2cXFpcj5rs3VchcEgZ49e0YfffSROEzdxsaGFi1aZNbhZvoiLi6Oxo4dS87OzlSjRg2KjIw0dpHMGl3s+qFDhygwMDDPpPGNGjWi48ePU05OjkXfd+BuGf2ga/Y1adLklXw3usXDw4N69+5Np0+fJoVCUaTzqFQqmjJliuiX/fPPP03+BhUEgW7dukUdOnQQHzJnZ2datGiR2Q++0he6iT4uXbpU7Gya7zq6CW0mTZqUZ8o/JycnmjhxIkVFRb0TLsA3iTt3yxQCa2trdO3aFQEBAfjvv/9w6tQpJCUlQRAEuLi4oF69enjvvfdQtWpV2NraFrkZKJFIUKpUKUgkEqhUKjx58gRE+s9IqS/UajVOnz6NyZMn4/r16wA0Q+m///57DB48GNbW1kYuoWnAGIOdnR0aNWpk7KKYLaR1++3duxdLly7FnTt3oFarIZVKUb9+fcyYMQOdOnWCjY2NsYtqfPJT/ZJczMVyz40hUyAIgkD79+8X49z79etX5FaAIdHl0N+wYUOeOH4/Pz/auXPnOxvuyNE/unvt2LFj1LFjxzzZKV1cXGjSpEmvDXKwdMAtd/1jSCuaMYaaNWvC3t4eaWlpuHHjBpRKpUlZwESE5ORkLFy4ECtXrkR6ejoYY6hXrx6WLVuGpk2bvhMdyBzDQkQQBAGhoaFYtmwZdu/ejZSUFACarJ2tW7fG119/jVatWsHKyspkW7fGgIu7iVK2bFn4+PggNjYWz549w82bN9G0aVNjFwuA5oG7d+8eZsyYIQ7ptrKywocffoiFCxeiYsWKXNg5xYaIEBkZiTVr1mDz5s2IjY0FEUEikaB69eqYMGEC+vTpA3d3dy7qr4GLu4liZWWF9u3bIzg4GOnp6di+fTsCAgIgl8uNViYiQmZmJo4cOYIffvgBISEhAABHR0eMGTMGU6ZMEdMVczhFRdcq3LdvH5YsWYKbN29CEAQwxlCuXDl88sknGD58OCpUqMCNiDeRn7+mJBdz9LkbGkEQ6PLly+KcjuXLl6eIiAijlUWtVlN4eDh99tlneWaN8vb2pk2bNpFCoXjn/J0c/aLzqx8/fpw6d+4sjgqHNgpm2LBhdP36dd6XkwvwUEjzRKFQUI8ePcSRsSNGjHhjThpDoAvf27RpE1WrVk1MJSCRSKhZs2Z0/vx5HtLHKRa6aRuvXLlCgwcPzpOWVyaTUdu2benYsWPilHyc//EmceduGRPG2toaw4cPx6lTp5CSkoLNmzfDy8sLM2bMKJHO1bS0NAQFBWHlypU4d+6cOB2Zp6cnRo0ahZEjR5rEtH8c84SIoFAoEBwcjA0bNuDQoUN4/vy5GPZbrVo1jBs3DgMGDOB+9aKQn+qX5MIt9/xRKBT0008/ia4QBwcH+umnnww2MEg3ocTZs2epT58+YvIlaEfLtm3bls6cOWNxCZg4JYtCoaCQkBAaOnQoubu755mJytvbm6ZOnUrh4eHvxECk4gDuljFvMjMzadKkSeKoWBsbG5o9e7ZeXTSCIJBSqaSQkBAaNmxYnqaxVCqlatWq0ZIlSygpKYk3jTlFQq1WU3p6Op06dYrGjBlDZcqUyTM1YOnSpWnMmDEUEhLC/eoFhIu7mSMIAiUnJ9Po0aNFgZfL5WIK4aIOntLtp1Kp6Nq1a/TVV1+Rp6dnnhw5FStWpB9++IGePHnyzk7Txik6unvs6dOntHbtWmrRogU5Ojrmuce8vLxo0qRJdOvWLd4xX0jeJO5v9bkzxmwAnAFgDU3o5D9E9B1jrAKAvwG4A7gKYBARKRlj1gA2A2gIIAFAfyJ6pBcf0jsKYwxOTk6YP38+7O3tsXLlSigUCmzbtg1XrlzBgAED0KNHD1SsWBEuLi5vDA8jImRlZSEuLk6Mnw8KCsKZM2cQExOjeeMD8Pb2xoABAzBixAhUrlz5nZkKj6MfiAipqam4ceMG9uzZg0OHDuHhw4dQKpUANPe0l5cXOnfujLFjx8Lf3x9WVlZGLrVlwXQPc74baHox7IkonTFmBeAcgM8BTAKwi4j+Zoz9BuAGEa1mjI0FUIeIRjPGBgDoSUT933SOgIAACg4O1kuFLJ2MjAysW7cOP/30E549eyaKsbu7O2rUqIHatWujevXq8PPzg4ODA4gI2dnZyMzMRGxsLMLDwxEREYHIyEg8efIEmZmZ4rEZY3Bzc0O3bt0wduxY1KtXDzIZ73PnFBwiEuf//f3333H27Fmkp6eL39va2qJy5cro27cvOnXqhDp16hh17Ia5wxi7SkQBr/0yP5P+dQsAOwDXADQGEA9Apl3fBMAR7f9HADTR/i/TbsfedFzulikcKpWKgoODqXv37mRvb5+niQut/1IqlZJMJhMXqVRKEonklW0ZYySTyahMmTI0evRounjxokVNQ8YpGQRBoJSUFPrjjz+oXbt2ZG1tned+dHBwoC5dutCff/5JCQkJ3KeuJ1DcUEjGmBQa10tlACsBRABIJiKVdpMoAN7a/70BPNW+OFSMsRRoXDfxBTkX5+1IpVI0aNAAW7ZsQUhICP7++29cvnwZ4eHhSE1NhSAIb9zf2toaPj4+qFixImrVqoWAgAC899578PX1hVwu5yFnnCLx77//Yty4cWLuF8YYatSogZ49e6JXr16oUqUKHBwc+P1VQhRI3IlIDaAeY8wFwG4A1Yt7YsbYSAAjAcDX17e4h3vnYIzB0dERzZs3R5MmTRAfH4/o6GiEh4fj/v37ePz4sejftLa2hq2tLUqVKoUqVaqgYsWKKFOmDEqXLs0fNo5eYIyhVatWKFWqFLKyslC9enUMGjQIvXv3hq+vL++zMQKFcqgSUTJj7BQ0bhgXxphMa72XAxCt3SwagA+AKMaYDIAzNB2rLx9rDYA1gMbnXvQqcGQyGTw9PeHh4YEGDRoUaB8u6Bx94+npienTp0MQBHTt2hWenp78PjMiBYmWKQ0gRyvstgDaA1gI4BSAPtBEzAwGsFe7yz7t54va709qfUMcA8MfJI4xkUgkGDJkCBhjPKGXCVAQy90LwCat310CYDsR7WeM3QHwN2NsLoDrANZpt18HYAtj7AGARAADDFBuDodjYjDGuPvFhHiruBNRKID6r1n/EMAr84URkQJAX72UjsPhcDhFgredOBwOxwLh4s7hcDgWCBd3DofDsUC4uHM4HI4FwsWdw+FwLBAu7hwOh2OBcHHncDgcC4SLO4fD4VggXNw5HA7HAuHizuFwOBYIF3cOh8OxQLi4czgcjgXCxZ3D4XAsEC7uHA6HY4FwcedwOBwLhIs7h8PhWCBc3DkcDscC4eLO4XA4FggXdw6Hw7FAuLhzOByOBfJWcWeM2TDGLjPGbjDGbjPG5mjXb2SMRTLGQrRLPe16xhhbxhh7wBgLZYw1MHAdOBwOh/MSsgJskw2gDRGlM8asAJxjjB3Sfvc1Ef3z0vadAFTRLo0BrNb+5XA4HE4J8VbLnTSkaz9aaRd6wy7dAWzW7vcfABfGmFfxi8rhcDicglIgnztjTMoYCwEQB+AYEV3SfjVP63pZwhiz1q7zBvA01+5R2nUcDofDKSEK4pYBEakB1GOMuQDYzRirDWA6gFgAcgBrAEwF8H1BT8wYGwlgpPZjNmPsViHKba6UAhBv7EIYmHehjsC7Uc93oY6AedezfH5fFEjcdRBRMmPsFICORPSzdnU2Y2wDgMnaz9EAfHLtVk677uVjrYHmpQDGWDARBRSmLObIu1DPd6GOwLtRz3ehjoDl1rMg0TKltRY7GGO2ANoDuKfzozPGGIAeAHSW9z4An2qjZgIBpBBRjAHKzuFwOJx8KIjl7gVgE2NMCs3LYDsR7WeMnWSMlQbAAIQAGK3d/iCAzgAeAMgE8JneS83hcDicN/JWcSeiUAD1X7O+TT7bE4BxhSzHmkJub668C/V8F+oIvBv1fBfqCFhoPZlGizkcDodjSfD0AxwOh2OBGF3cGWMdGWNh2nQF04xdnuLAGFvPGIvLHdbJGHNjjB1jjN3X/nXVrjfLNA2MMR/G2CnG2B1tOorPtestpp5vSLlRgTF2SVuXbYwxuXa9tfbzA+33fkatQCHQjmG5zhjbr/1siXV8xBi7qU2TEqxdZzH3a34YVdy1nbQroUlZUBPAR4yxmsYsUzHZCKDjS+umAThBRFUAnNB+BvKmaRgJTZoGc0AF4CsiqgkgEMA47W9mSfXUpdyoC6AegI7ayK+FAJYQUWUASQCGabcfBiBJu36Jdjtz4XMAd3N9tsQ6AkBrIqqXK+TRku7X10NERlsANAFwJNfn6QCmG7NMeqiTH4BbuT6HAfDS/u8FIEz7/+8APnrddua0ANgLTXisRdYTgB2Aa9DkR4oHINOuF+9dAEcANNH+L9Nux4xd9gLUrRw0wtYGwH5oIt8sqo7a8j4CUOqldRZ5v+ZejO2WeRdSFXjQ/+L8YwF4aP83+7prm+b1AVyChdXz5ZQbACIAJBORSrtJ7nqIddR+nwLAvUQLXDSWApgCQNB+dofl1RHQ5MI6yhi7qh0ZD1jY/fo6CjVClVM8iIgYYxYRnsQYcwCwE8AXRJSqGcumwRLqSS+l3ABQ3bgl0i+Msa4A4ojoKmOslZGLY2iaEVE0Y6wMgGOMsXu5v7SE+/V1GNtyL1CqAjPnea7RvF7QWIKAGdedaVI/7wTwJxHt0q62uHoCmpQbAE5B46JwYYzpDKLc9RDrqP3eGUBCyZa00LwPoBtj7BGAv6FxzfwKy6ojAICIorV/46B5UTeChd6vuTG2uF8BUEXbQy8HMACa9AWWxD4Ag7X/D4bGR61bb3ZpGpjGRF8H4C4RLc71lcXUk70+5cZdaES+j3azl+uoq3sfACdJ67A1VYhoOhGVIyI/aJ67k0T0MSyojgDAGLNnjDnq/gfQAZpUKRZzv+aLsZ3+0KQqCIfGpznT2OUpZl22AogBkAONr24YNH7JEwDuAzgOwE27LYMmUigCwE0AAcYufwHr2AwaH2YoNGknQrS/ocXUE0AdANe1dbwFYJZ2fUUAl6FJrbEDgLV2vY328wPt9xWNXYdC1rcVgP2WWEdtfW5ol9s6jbGk+zW/hY9Q5XA4HAvE2G4ZDofD4RgALu4cDodjgXBx53A4HAuEizuHw+FYIFzcORwOxwLh4s7hcDgWCBd3DofDsUC4uHM4HI4F8v+h/mksyoF5dwAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/حسبنا الله ونعم الوكيل.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/فوربك لنسألنهم أجمعين.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/1لا اله الا الله.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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aqkq1Gp9Nh2rRpaNKkCX788ccqVblmsxl79+5VvHdvt1wkcfLkSezfvx+NGjVCnz59AACHDx9GVlYWOnTooAwKF8bV1RWurq5ISUkpMoQhSRLc3d1hNpuRnp5ulbX29vZGQEAAEhMTERsbW6H7k2UZe/bsgSRJuPvuu+uMJ7c1wylrAOwF0EIIcV0IMQbAhwDuF0JcANCn4H8A2IT8HBcXASwF8FJlCiWEQJMmTfDee+9BrVZjxowZOHr0aKWq3KSkJJw/fx7e3t4ICwsr9j5JbNy4ERkZGRg0aBD8/PxAEkeOHAEAdOrUqUQr6eHhAXd3dyQlJRUbO7M08K0dU3NxcUFISAgyMzMr7JcYGxuL48ePw9vbu1hb1J6xplf7OMlAkhqSwSSXk0wkeR/JZiT7kEwq+CxJvkyyCck2JCudJ0oIgcGDB2P06NG4ceMGpkyZgtTU1AqfJzs7G6mpqfDw8CgxLkteXh52794NrVaLvn37QggBWZZx6dIlqFQqNGvWrEQr4uLiokz1JScnFym3ZZ727NmzVi3G0Wg0cHd3R15eXoU8dEhi//79uHHjhjILUlewi5mL0tBqtXjzzTfRvn177NixA//3f/9X4YHlvLw8JadZSe2fhIQEnDlzBp6enspiGyEEXF1dQbLUKShJklCvXj2YzeYiPVvLmBqQH/MvJsa6Jq4lmV9FrHpeXp4yxfjwww/XmZV6gJ0LTwiBgIAAfPTRR/Dy8sLcuXOxbt26CrX3LNE7SxPeyZMnER8fj9atW8PSuxZCoEGDBpBlGVFRUSWKQZIkNG/eHLIs4+zZs8pnzGYzrl69CgCIj4/Hn3/+Wa6YzGYzcnJyIEmS1WE8SOLcuXPYuXMnAgMDMXDgwDrTvgPsXHhAvgh69uyJt99+G0ajEZMmTcKePXsq5CwpSRLMZnOxas8y1JKXl4cuXbpAp9MpxzRo0AAkER0dXeq5LdXwlStXlB9DTk6OMqgsyzI+//xzxMXFlVpekkhISMCJEyfg7u4Oa9OnksTXX3+NxMREPPTQQyX2vO0ZuxGe0WhEeno6cnJyij0klUqFMWPG4MUXX0R8fDzGjx+P6Ohoq8RnMBjg4eGB9PT0Ym3EvLw8HDhwAGq1ukiP17JoW61W4+jRozh16lSxawkhUK9ePWi1WkRHRyvTa9HR0bh69Sr8/f3RqlUrHDhwABMnTkR8fHyJ5TWZTFi6dCmio6PRqVMnq4UXHR2NtWvXws3NDaNGjaoTAY+KUFqIgZrcOnTowOXLlzM0NJSvv/56qRkSExMTOWDAAAohOGzYMKakpJQbPyQ5OZlt2rShp6dnsfgsCQkJbN68OX18fHj+/Pki7x04cIDBwcFUqVR8+OGHmZOTU+zcZ8+epbu7O8PCwpiWlkaTycSpU6dSCMGRI0fy4MGDbNasGSVJYqdOnbhkyRKeOnWKUVFRjIqK4rFjxzhlyhQaDAZ6eHjwt99+syoeitls5nvvvUdJkvjoo4+WWDZ7wO5jp3To0IHz5s2jEIIRERGl5o+QZZlnzpxhWFgYVSoVJ0yYwKysrDJvPi8vjwMHDqQkSVyzZk2RB3v+/Hm6u7uzRYsWTE1NLXJcTk4ODx8+zIYNG9LV1ZVbtmwpJoqEhAQ2btyYfn5+vHz5Mnft2kUfHx96enpy7969lGWZR44cYa9evajRaChJEl1dXRkQEMCAgADq9XoKIejl5cUFCxZYnZL08uXLbNiwIQ0GA3fu3Gm3wXvqhPB27dql/PI3bdrE3NzcEr9QWZa5c+dOBgUFUafTcc6cOczLyyv15mVZ5ueff05Jkjh06FDm5uYq723fvp06nY4PPPBAkf0WTCYT3377bapUKjZs2JBLly5lQkKCElQnPT2dXbt2pcFg4MKFC9muXTuqVCq+8cYbSplkWWZycjLXrFnD4cOH86677mKzZs3YrFkzhoeH8+mnn+aePXvKvIfbyzR58mQKITh06FBmZ2dbdVxtUCeEl52dzQEDBhAAXV1d+a9//Yvbtm0rMSu22Wzmt99+S1dXV3p4ePDrr78u1VpYkioHBQXRy8uL+/fvVxIoL1myhJIk8cUXXyzxeFmWmZqayjFjxlCn01GlUrFFixacMGECv/jiC3711VdKFiInJydKksRBgwbx1q1bxX40sizTbDYzPT2dCQkJTEhIYEZGRrHIUGUhyzJ37NhBb29venl5cd++fXZr7cg6IjxZljl37lwCoCRJSor3WbNmlWiNjEYjP/roIzo5OdHLy4urV68uVXx5eXmcNGkShRAcPHiw0h57+eWXKYTgnDlzynyAmZmZ/P7779mrVy8aDAYKIahSqahWqymEIAD6+flx0qRJjIuLqzYx3Lx5kxEREVSpVJw2bZrVVrK2qBPCI8kdO3ZQrVazXbt2nD59Ot3d3eni4sL169eXaEFyc3P5/vvv08nJid7e3ly3bl2J2bNlWeaVK1fYokULqlQqjhs3jpcuXWJYWBj1ej337dtX7pcoyzIzMzN58OBBzpo1iy+++CKffvpphoWFEQAXLlxIk8lUbaIzmUycNm0ahRC87777HIEZbbFZhHflyhUGBAQwJCSE0dHRnDt3LjUaDe+++26mpKQUuzFZlpmTk8MZM2ZQp9PRz8+P69evL7Xa/P3331m/fn2q1Wo2b96ckiSxV69eFQ5wKMsyTSaTYjUBcNmyZdUmBEsnJSAggJ6envzrr7/sXnRkHRJednY277nnHup0Om7ZsoVJSUls164d9Xo9//rrr1JvMDs7m1OmTKFOp6OPjw/XrFlTovjMZjO3b9/O1q1bU6VSMSgoqMq9wjfeeIMAyq2uq0JGRgYfeeQRCiE4duzYEtu99ojdRwS1oNVqcd9998FoNGL79u0wGAxo27YtcnJyyoy+qdPp8NZbb2Hq1KnIyMjASy+9hNWrVxebqZAkCffddx927NiB3377Db///jt69OhRpakmS6bu21eb2QpZlvH1119j48aNCA0NxWuvvVb3BotLwK6EZxGGk5MTfvvtNyUcBMkyV8hbQoe98cYbmDZtGnJycjBhwgSsWrWqmPiEEPD19UXPnj0RGhpa5fRVlsXXJXmVWH7dsizDbDYr03aWTZbl/GqnFMj8heUzZ86ERqPB9OnTUb9+/To1J1sadvfTadOmDcLCwnD69Gns2bMHZ86cgbOzMxo1alTusVqtFq+99hp0Oh3eeecdvPrqq8jKysLzzz9fbZ65rVq1wsiRI9G2bVsA+VmKbty4gTNnziAmJgZJSUlISUnBrVu3kJKSoni7qFQqhISEICIiAnfffTcaN25cLNBPUlISXnvtNVy/fh0vvPBCnch8ZDWl1cE1uVnaeGR+Q/rNN98kAHbr1o16vZ5hYWGMj4+3um2Rm5vLuXPn0s3NjS4uLnz//feZlZVVLW0ws9nM1NRU/vHHH5w0aRIjIiLo4eFBjUZDIQSFEJQkiWq1mhqNhlqtllqtVpnJsMxmRERE8O233+aBAweYmZnJ7OxsTpgwgZIksWPHjoyJibF52aubstp4dmfxAGDgwIGYN28e/v77bwghMGLECHh7e1t9vEajwUsvvQRXV1dMmjQJ7733HlJSUvD2228rK/2rAguqx4SEBGzduhXLly/HwYMHkZWVBb1ej3r16uGuu+5C48aN4e3tDXd3d3h7e8PDwwMuLi5K3JWLFy9iz5492LdvH06dOoVDhw5h9uzZaNeuHUJDQ/HVV18hMDAQ8+bNQ2Cg1atE6walKbImt8IWj8wfsO3VqxcBsEWLFrx+/XqlfnEmk4nr169nYGAg1Wo1n3nmGSYkJFTJ8pnNZkZFRXHmzJkMDQ1VLFnr1q351ltvcc+ePUxOTqbRaFRmSErDMiyTnJzMP/74g6+88ooyD438lXt87rnnmJ6eXieGT26nzgynWDCbzfzmm2/o7OzMZs2aMTY2ttI3bzabuXXrVjZq1IiSJPGRRx7hjRs3KvwgLRl4Zs2axSZNmlAIQRcXF/bu3ZtfffVVlQVtKev+/fsZGBhIAMpU3PDhw3n58uU6J746JzySzMrKYt++fSlJEhcvXlylL12WZR44cIBt27alEIJ9+/bl1atXrTqnLMuMi4vjwoUL2apVK0qSRIPBwCFDhnDnzp02bTsmJydz4MCBFEJw4MCBXLBgAZs1a0YhBNu0acOdO3eWODNjr9RJ4cmyzO+++45arZbh4eGMj4+vsvhOnjzJTp06UQjBe++9lxcuXCjznCaTidu3b2d4eDhVKhV1Oh379evHbdu22byzYjKZ+N///leZMrRYuHPnzvHhhx+mSqViYGAgf/rpp2qdmrMldVJ4JJmamspevXpRpVLZZGZAlmVeuHCBPXr0oBCCHTt25IkTJ0o8b1ZWFmfPnk0vLy+q1Wr27t2bP/74IzMyMmz+0GVZ5p49e+jj40N3d/civn+yLDMlJYWvvvqqMjOzcuVKu3cQIOuw8GRZ5i+//KK09aKioqr8ZciyzKtXr7Jfv35KFWZxlbKQm5urzP+6urry7bffZnJycrVYGVmWmZCQwJ49e1KSJL722mvFRGVxUHjnnXfo7OxMd3d3Llu2zGrH0dqizgqPzJ+HHTx4MIUQ/M9//mOTX7qlo/Cvf/2LkiSxYcOG3LZtG81mM00mE5cvX04XFxd6eXlx7dq11WpdjEYj33jjDapUKnbp0qVUtyqLN87s2bPp4uJCDw8P/vjjj3Zd5dZp4Vk6Br6+vvT29uaePXts8mXLsszExESOGTOGarWaAQEB/Pbbb7lhwwb6+vrSxcWFixcvrlarYjabuWbNGhoMBvr4+FjldZKbm8sPPviAWq2WDRs25KFDh+xWfHVaeGS+I6fF3btPnz429UVLS0vj66+/Tp1ORzc3N3p7e1OtVvOtt96yahGNZayuMsMzf/75J4OCgujk5MS5c+da3WPNzMzkM888Q0mS2KVLl0oND9UEdV54JHnjxg3F+3bKlCkleiVXBksS4g8//JB6vZ4A2LFjRyYkJFh1/ObNm/niiy9WyA3d4o7fpk0bqlQq/uc//2F2dnaFjo+Li2OvXr0ohOC4cePs0lXqjhCeZZGPr68v9Xo9v/76a5uOaeXm5vLjjz+mWq2mTqfjuHHjyu1QyLLMCRMmEAA7d+7MxMREq+4jJSWFQ4YMoRCCDz30EJOTkytcXlmWeejQIfr5+dHHx4fHjh2zO6t3RwiPzB/r+vzzz+nk5MR69eopSwhtRVJSEiMiIgiAKpWKI0aMKHNGwmw2c9iwYQRAtVrNBQsWlPtjyMnJ4dSpU6lWq9mmTRtevHix0veQl5enrBt5/fXX7W5w+Y4RHpnfyx07dqyySLqy87glIcsyv/jiC2o0GmXR0cMPP8zr16+X2tMcMWKEMq8aFhZWpheJ0WjkJ598QmdnZ3p7e3Pbtm1V/uHs3buXrq6ubNSoEaOjo6t0LltTJeEhP8LnTgCnAZwCMKFgvxeA7QAuFPz1LNgvAMxDfoy8SADty7uGZZVZQkICr1y5wszMzFJvxvK5Pn36UAjBUaNGlfn5iiDLMpOSknj33XcrYhJCsF+/fiW6ZcmyzP/85z/K54QQfO+990psb1mGaQwGA93d3fnVV1/ZxELl5OQow02ffvqpXVW3VRVeoEU8AFwBnAfQEsDHACYX7J8M4KOC1wMAbC4QYGcA+8u7RocOHZiXl8cnn3yS7u7u/Omnn8ptW0VGRrJRo0bUarX85JNPbDbsIcsyv/rqK2o0Gnp5ebFJkyZKqIiSJuoXLVpEAGzevDkNBgP1ej1nzpxZJMKB2WzmDz/8QG9vb7q4uHDhwoU2GxuUZZk///wzdTodu3TpwrS0NJuc1xbYtKpFftjZ+wGcAxDI/4nzXMHrxQAeL/R55XOlbR06dKDZbObw4cMJgK+++mq5v1xZlvnDDz/Qzc2Nnp6e3Lx5s81+7SkpKezcuTPVajXHjx/PFi1aUAjBli1b8vfffy9ynS1btlAIwQceeICff/45PT09qdVq+corryixXf766y8GBgZSq9Xy3XfftVmP3EJiYiLbtGmjLIqyF6tnM+EBaAggGoAbgJRC+4XlfwC/Auhe6L3fAUSUcK7nABwCcCgkJISyLHP8+PEEwPbt25e4nPF28vLy+P7771Oj0TA0NJQnT5602eDy6tWrqdVq2aFDB/7999/817/+RbVazZCQEP7xxx/KdY4dO0ZXV1e2b9+eycnJ/OGHHxgcHEwnJyeuXLmS169fZ8eOHalSqfjKK6+UG+ulMpjNZk6ePJkAOHny5DtLeAAMAA4DGFLwf8pt7yezAsIrvFk6FzNmzCAA6vX6YvOnJWEJMTFs2DAKIdilSxeb+a2lpKSwW7duVKvVXL58OVNTU/n8889TpVKxadOmygxKbGwsQ0JCGBAQwGvXrtFsNvPPP//kpEmTGBsby+eff56SJPH+++9nYmJitYli165ddHJyYkRERIXXCVcXVRYeAA2ArQAmFtpn06qWJJcuXao01GfOnGm1v9yNGzeUzkbv3r0ZHR1tE0+Wb7/9lhqNhu3bt+etW7eYkpLCMWPGKDFUjh49ypycHHbt2pU6nY5///23cqzRaOTnn39OnU7H+vXrV/s4W1JSEsPCwujq6srjx49X23UqQlU7FwLAlwDm3LZ/1m2di48LXg+8rXNxoLxrWIS3bds2xe27R48eVkdCsswEdO3alUIIPvjgg1X23yPz3bLuueceqtVqxRk1KSmJw4YNoyRJbN++PU+ePMnnnnuuiMOqLMvcunWrMtj95ZdfVvsYm9ls5siRIylJEpcuXWoX1W1Vhde9YGghEsCxgm0AAO+CavQCgN8AePF/Ql0A4BKAE+VVsywkvDNnztDLy4sA6O3tzXPnzll9k7Is8+zZs2zXrh0lSeJTTz1VLOZdRbE4o+p0OrZt21YZTI6Li+ODDz5IIQQ7d+7MKVOmUJIkPvvsszSZTLx69SpbtWpFtVrNadOmlRpyzZbIsszFixdTkiQ+88wzduEyVWcGkFNTUxkaGqpUtz/99FOFblSWZR4+fJjNmjWjWq3m1KlTmZOTU6WHnpaWxnvvvZcqlYqff/65YtGuX7/O3r17K71dtVrNu+66i/Hx8Xz66acVy1tV8VeEvXv3Uq/Xs1OnTqUGt6xJ6ozw8vLy+NhjjymDt7Nnz67wzcqyzG3bttHHx4dOTk6cPHky09LSqiS+devWKVbP4i8nyzJPnTrFZs2aKeV1d3fnf/7zHzo5ObFhw4alejdXF7Gxsaxfvz79/f1t4jRbVeqM8GRZ5jfffEO1Wk0AXLBgQaVu2GQycfXq1fT396darebo0aOr5EqVlpbG3r17U61W87PPPivilr5582YGBQUp4lOr1XRycuKqVatqfO40MzOTPXv2pLOzM//8889ab+fVGeGRZExMDBs2bEgA/PLLL1lZLJGhGjduTJVKxeHDh5c651oesizzxx9/pE6nY+vWrRkXF1fkOlu2bKGvr6/SRHjuuedqJUSs2Wzmc889RwB20cGoM9GiAChh+gEgMTEx/9dRCSwBgFavXo1mzZph7dq1ePTRR3Hy5MkKn1MIgfvvvx/33HMPTp8+jbVr1yrnsFzn4YcfBpAfE6V58+a1EtFJkiS0bNkSQH46K3vG7oRnSekEAL/88kuVUoYKIXD33Xdj3bp16NWrF/bv348nn3yyUgn5XFxcMG7cOGi1WixevFhJmkIShw4dwqZNmwDk5614//338dNPP5UZ4aq6sOQzq2gyvhqnNFNYk1vhqtayDiEoKIgeHh6MjIysssm3LO555JFHKEkSmzRpoizuqQgZGRl84IEHqFKp+H//93/K4HXnzp2Vhd4oaOv5+vqWGZe5uti2bZvSo7bnqrbWRUcW98fLy8vj6NGjKUkSP/roI5vNv8bHx3PUqFHK4p41a9ZUyGVclmVu3LiRTk5ODA0N5eXLl/n8889TCMFevXrxtddeoxCCPj4+1Ol09Pb2tqnzgjXs2rVLmaKrbcfQOic8S2/RycmJYWFhRRrzVSUtLY2vvfaasmZ2+vTpTE1NtVocWVlZ7NevHyVJUsLmhoSE8MiRI9y8eTM1Gg179erFKVOmUKPRsHHjxty9e3eNiW/Pnj1KGWp7HUadEx6ZPzTQp08fSpLEhQsX2uzBWRb3zJ07V1lR9sgjj/DcuXNWzw1v2rSJLi4uSk6OtWvX0mw288yZM3RxcVEGkp955hnFqaCmxFdYeLUdbaBOCk+WZWXgtn379jaJxlQYS1yUVq1aUQjBZs2acd26deU+LFmWefr0aQYEBBAAH330UcWyREVF0d/fn40bN2ZCQgKTkpL473//WxHf4cOHq11827dvpxCCgwYNcrTxytvKip3So0cPqtVqLlq0qFpilly6dIkjRoygVqulm5sbP/roozJnOnJzc/nUU08pnYimTZsq6z5iY2PZuHFjBgUFMSoqirKcn05q1KhRlCSJERERVVrcYw2rVq0iAD7zzDMO4ZW3lRU75fvvv6dOp2PTpk2rJUacJS7JrFmz6O7uTq1Wy6FDh5YYSUqW83NluLq6MigoiBEREZQkiR9++CHNZjOTkpLYqlWrYtkg4+Li+MADDyidkCtXrlRbHJa3336bAKx2K6tO6qzwyPzG/NChQ5UZgepIkSnLMvPy8vjLL78wNDSUQgg2b96cP//8szIcYukVWxwGZs6cya1bt9JgMLBJkyaMiopiamoq27VrRw8PD546darI+a9cucLu3btTCMGePXvaxGfwdsxmM5988kkKIfjtt986hFfeVl7slMjISNavX58Gg6HE9J22Qpbzw5gNHz6cGo2G3t7eXLBgAbOzs2kymfj6668rYSNiY2OZlZWlrPB6//33mZqayvbt29Pd3Z0nTpwodu5Lly6xe/fulCSJ/fr1Y0xMjE3vJT09nV26dKFer+fevXttdt7KUqeFR+b/kmfNmkVJkti7d+9qde22VL0zZsygXq+nTqfjpEmT+Ndff9HX15deXl78+++/FQ+VHTt2UK/Xs1GjRjx16hQ7dOhQovAs5z537hwjIiIohOCQIUNsGv4sOjqa/v7+DA4O5s2bN21yzqpQ54VHkvHx8Wzbti21Wi2//PLLaq1GLK7r33zzDQMDA6nRaBgUFERJkjhhwoQiPd/s7GwOGTJEiW0XHh5eqvAs5z516pQSN+XVV1+1SfNBlmVu2bKFOp2Offr0sYs8tneE8GRZ5sqVK6nVatmyZUul11idmM1mbtq0SQmG7eXlxaNHjxa5rizL3LlzJ11dXenn58eAgAC6ubmVOdUny/mRogICAujs7MyVK1dWeZZBlmW+++67FEJw8uTJtT5rQd4hwiPz2zCWZHIjR46sES/b1NRUJbKAEIL/+te/iq3nyMnJKeLAqtfreeTIkTLPazabuXz5cjo7OzM4OJgHDhyo0g8pNzeX9913H9VqNTdu3Fjp89iSO0Z4JHnixAklgsBnn31Wrb9sy/CJs7MzQ0JC2KxZMyWqwO1ts127dtHV1ZUAqNForGrcZ2dn86WXXqIkSezWrRtv3rxZafFdvXqVQUFBrFevnt3EULmjhGeZ0dDr9QwMDLR5xKjCmEwmvvTSSxRCcMaMGTx8+LCyiOf2YOA5OTlKJAQhBLdv327VvcTFxfHee++lEIITJ06sdJSBH374gSqViv369av1OVoLdUZ4ZrOZcXFx5S7Qyc3N5fjx45UlhtU1IBsfH8/mzZvTzc1Nme6yxClp164dk5KSlM/Ksszdu3fTzc2NQgj++OOPVl1DlmXu27eP/v7+NBgM/OGHHyp8LyaTiS+88AKFEIq7lj1QZ4QXFRXFdu3aceDAgeVG5Lx16xYHDBhAIQSHDh1aIQ8Ta/ntt9+o1WrZrVs3pT2ZlpbGrl27Uq1Wc+XKlUWumZubywcffJAA+MYbb1jti2cymTh37lwlZl5Fq9zExESGhYXRzc2Nhw4dqthNViN1xvX91q1buHjxIo4cOYLc3NwyP+vl5YVPP/0ULVq0wA8//IDJkycjIyMj/9dkI44ePQqj0YiuXbvCxcUFAGAwGPDCCy9ACIE1a9YgJydH+bxGo0H9+vUBAKtXr8alS5esKo9KpcLo0aPRp08fnDp1CsuXL7f6Pkji8OHDuHLlClq2bInmzZtX4k5rHrsSntlshizLUKvV5SYwFkKgWbNmWLRoEerVq4dly5bhgw8+gNFotElZSOLatWsAgCZNmhS5bq9eveDj44MTJ04Uy8xtEWhMTAyWLVtmtYAMBgMmTZoEFxcXrFixwmrXdZLYsGEDcnNz0b9/fxgMBquOq23sSnienp7Q6/XIycmxaq2FEAI9evTA0qVL4eXlhTlz5uCLL76wyVoHWZZx8+ZNSJKEgICAIu+5ubnBz88P2dnZxYTn7OwMIF8QX3/9NS5evGjV9YQQ6NatG3r37o3Lly9j/fr1VonWkrrU1dUVgwYNqjOJlO1KeP7+/vD09ERiYiIWL15cbnUL5K+s6tOnDz7++GOo1WpMnz4d27dvr3KVm52djZiYGDg7O8Pf37/IAyXzU9ULIYpZZicnJwD52cJjY2OxdOnSYunpS8PJyQnPPPMMNBoNvvrqKyQnJ5f5eZL466+/cOXKFbRv3x5hYWEVvMvaw66Ep9PpEBQUBFmWMXfuXEydOtWqJY6SJOHxxx/HpEmTkJKSgnHjxuHUqVMlHmcymZCSkoLk5GTk5OSUeu6MjAxcuXIF7u7uaNCgQZH30tPTcevWLTg7O8Pd3b3Ie5bz9e/fH56enli9ejUuXLhg9Q/h3nvvRUREBCIjI/HDDz+UeVx2djZWrlwJkhg2bJgi+rqAXQlPkiSlcZ6Xl4e5c+dixIgRuHLlSpEHQBKnT5/G+fPnkZeXByC/YT9x4kQMGzYMly9fxrhx45QliABgNBqxfft2PP300+jRowd69OiBYcOG4dixYyU+3KSkJGRkZMDb2xtubm7KflmWsXHjRiQmJqJ169bFhBcfHw8A6N27NwYPHozY2FgsWbIEsixb9R24urpi/PjxEEJg+fLlyMzMLPFzFmu3c+dONGnSBA8++GCdqWYB2NdwSmFHxgEDBrB9+/ZKRKbCc7O5ubns168f/f39uXv3bqX7LssyY2Ji2K1bNwohOHz4cCYlJdFoNHLmzJnU6/UUQtDDw4NeXl6Ky/uRI0eKDV/s2rWLzs7OvO+++5QJd7PZzM2bN9Pf3586na6Yz5vJZOLAgQMpSRK3bNnCI0eO0MvLi/7+/jx16pTVQySW0LLOzs5Foo8WJj09nffffz8lSeKsWbPsYm72durMOJ4s52dr1Gq17Nu3L8+dO8f77ruPQgg+//zzyoh8dHQ0g4KCGBQUxKtXrxa5WVmWefz4cYaGhlKlUvGpp57i/Pnz6erqSi8vL86cOZORkZE8efIkn3jiCUqSxHvvvZe3bt0qco6ffvqJOp2OPXr04KVLl5iQkMB169YxODiYGo2Gr732WjEPkKSkJLZu3ZoeHh48efIk8/Ly+Oyzz1IIwfHjx1u9+KbwD/CFF14oNh5YuHxt2rSpUgbz6qSq8fGcABwAcBz56QbeLdjfCMB+5KcVWAtAW7BfV/D/xYL3G5Z3jcJTZlevXmVAQIAy53j8+HEGBATQ09OTx48fpyzL/PXXX6nVatmnT58SXYpkOT//q2VuVavV0snJiZ999lmRh3jz5k126tSJKpWKq1atUixL4ZjCGo2Gfn5+DAoKoouLCzUaDcePH1+iT+Dx48fp6urKsLAwZS73yJEj9PHxoZ+fn9XRo2RZ5tGjR+nh4cGmTZsWE1ZWVhYHDhxIlUrFefPm2c1Mxe1UdQA5F0Bvkm0BtAPQTwjRGcBHAD4l2RRAMoAxBZ8fg/x4yE0BfFrwOasJCAhA27ZtERsbiwMHDqBVq1bo168fUlJSsHPnTpDE3r17kZeXh+7du0Or1RY7hxACnTt3xqxZs6DVamE0GhEQEID+/fsX6YX6+/tj7NixAIBvv/22SC86ICAAnTt3RlhYGFxdXSHLMry9vfHhhx/iv//9L/R6fZFrksTWrVuRmZmJrl27wtXVFUIItG7dGkOHDkV8fDw+//xzq9p6Qgi0aNEC7du3R1RUFHbt2lWkHbpnzx7s3LkTjRs3xpAhQ+pW285CaYosaQPgAuAIgLsB3AKgLtjfBcDWgtdbAXQpeK0u+Jwo67yFLZ4sy5w3bx6FEHzqqadoNBr54osvUpIkLl++nCaTSYlD/NVXX5X6azObzcrKfkvb7qGHHiqWD/bq1asMDAykr6+vkoJTlvNzw6anpzMlJYWJiYmMj49nXFxcqdNgKSkp7NChA3U6HX/99dci1zh27Bh9fHzo6+urWO3ykGWZ8+fPpyRJfOKJJ5TrZmRkcNCgQRWKE11b2CL4tgr5IWgzkG/BfABcLPR+fQAnC16fBBBc6L1LAHxKOGeRdAMWZFnmmTNn6Ovry+DgYJ44cYKdO3emRqPhtm3bFOGp1Wp+/fXXpd50XFycMsH/1VdfsX379pQkiU8++WSRJCRGo5Hjxo2jJEkcMGBApRKUyLLM9evXU6vVslOnTsVSJeTl5SleLs8//7zVbb1z587Rx8eHISEhyvqMNWvW0MnJiS1btiwzfZU9YMs8Fx7ITy/VvarCK7zd7hZlSZOkUqn44IMPUqvVsl27dkxMTLTK4lni2Wm1Wvbo0YOZmZk8evSoEqJ24sSJzMzMVKzbtWvX2KZNG6rVas6bN69CPUSLa1O3bt2oUqlKjHpgWbDk5+dHb29vHj161Kpz5+TkKAEhN2/ezBs3bvCuu+6iRqOxi/h35WEzJwGSKQXC6wLAQwhhCQIXDCCm4HVMgRBR8L47gMSKXEer1eLf//43VCoVfvnlF5jNZowZMwaenp4QQsDb21uZ0iKLj8HJsoxNmzbBZDLh4YcfhrOzM9q2bYtFixbBz88P8+fPx3vvvadM8AcFBWHAgAEwmUz46KOPcOLEiRLPW8L3gdjYWLzyyivYu3cvOnfujMcee6xYm0sIgZYtW2LYsGFITEzEokWLlPHH8r6He++9FyaTCb/99htmz56NEydO4L777sOwYcPqZtvOQmmK5P8sli8Aj4LXzgD+AjAIwPcAhhfs/xzASwWvXwbwecHr4QC+K+8aJTmCJicnKyk8mzdvrlQrspyfa0ytVnPEiBElVluWnA8Gg6GIC7rZbOaGDRvo5+dHrVbLl156iUlJSUXShYaGhpabk0KWZaalpfHbb79VqvAWLVqUm6b95MmT9Pf3p7e3t1Up3WVZ5m+//UZnZ2f6+fnRxcWFfn5+1er8akuqOpxyF4CjyE83cBLA9IL9jZE/zHKxQIS6gv1OBf9fLHi/cXnXKC12yvz586lSqdihQwfeunVL+bKPHTtGg8HApk2b8saNG8WOPXDgAPV6PcPDw4tFXTebzfz5559Zv359SpLEESNG8NatW1y5ciV1Oh2bNGnCPXv2MCsri2azWamOzWYz09PTefnyZa5atYr33nsvdTod1Wo177//fqsSqOTl5fGVV16hEIJPP/20VZ7CN27cYL169YiCHLofffSRXaQSsIY6M4BcGFmWeevWLcXpcv78+cqDzczM5IABAyhJEj/++ONibTJLRsWnn366RDGYzWbu3r2bLVq0oCRJfPzxx3n16lU+9dRTlCSJ3t7e7NChA3v27MlHH32UTz31FB966CG2a9eOvr6+VKlU1Gg0DA8P57Jly6yOKm/pOAUGBtLT05MHDx4s95j09HRlsVHPnj2tyvFmL9RJ4ZH/y9Co0+mUJMQWC7R582a6uLgwKCiIO3fuLDKdNnjwYEqSVGRQuKRz79+/XxHfkCFDeOLECT777LMMCQmhs7MzVSoVJUlSNoPBwMaNG3PQoEFctWpVpSJYmUwmTpw4kUIIjh49usw1FpYF456engTAjz/+uE5UsRbqrPDIfOs2cOBACiH4xhtvKNVTdnY2X3nlFarVajZu3Jg7duxgTk4Ot27dSjc3N4aEhPDKlStlfjGyLPPQoUO86667KEkSBw8ezBs3bjAmJoaRkZHct28ff/31V65fv55btmzhiRMnGBcXx7y8vEoLQJbzMxDVq1ePnp6e3LdvX6nnio2NZadOnZRlk88++6xDeLbcyoudsmfPHvr4+NDT05Pbt29XvvzU1FSOHj2aarWavr6+7Nu3L4OCgqhSqfjuu+9a1RayzO22bt1aSQVflWWG1mCJw1J4kPx2cnNzOWHCBEqSpMTi69u3r106A5RGnRYemf+g3nvvPapUKoaHhyurymQ5P23opEmT6OPjQ7VaTQ8PDz7zzDMVStFpmRtt164dhRB87LHHiqwgszWW4ED16tWju7t7sTW4spyfOdKyhNOSBbJz5842T7JcndR54ZH5wyv9+/enJEnFVqEZjUaePXuW27dvZ2RkZKXyl93u1TJy5MhqzS9rcUQQQvDJJ59UnB0sVXGTJk2o1Wq5cOFCnjp1ih4eHmzTpk2N5karKneE8CxWwjJuNnbsWGZnZ9tUGJZqvWnTplSpVHzyySerTXyW+6lfvz7d3d2VGMkZGRlKPMARI0YwMzNTmU9u0qSJTQORVzd3hPDI//VE69WrR51OxxkzZtg8KpIsy9y7dy+bN2+uDLXYOv6yBZPJxGnTplEIwWHDhjE7O5ufffYZNRoNW7RooYStjYuLY+PGjRkYGGgXyfGs5Y4RHplfRa1du5ZeXl7U6XScNm0aMzIybG759u/fz9DQUEV8tki8XBKXLl1i/fr16erqyk8//ZSBgYHU6/X8/vvvleulpKQwLCyMXl5evHjxos3LUF3cUcIj8y3Fd999Rz8/P2o0Go4dO7ZSXiVlIcsyDx48yLCwMEqSxD59+lidkqAimM1mTp8+XXFYlSSJr776apFORFpaGlu1akUPD48isZXtnTtOeGS++H799VcGBwdTrVZz/PjxVc5LezsWD+JOnTpRCMGIiAiePHnS5tc4f/68MmTSrVs3xsfHF/nMnWjx7GqVWUVQqVQYMGAAVqxYgYCAACxcuBCTJ09Genq6za4hhEC7du3w/fffo3///jh8+DBGjRpldWgKazl69CjS0tIAoNiqNiB/GWN2djacnZ2h0+lsdt1apTRF1uRWGYtnwWw2c9u2baxfvz7VajWfffbZKiVFLgnL6rW+ffsqq95Onz5d5WvIcn5M5KZNm1KtVtPZ2ZkGg4E7duwocu5Lly7Rz8+PzZs3L7Ioyd65I6vawljmNBs1akSVSsVhw4bxxo0bNhff1atXef/99yvV7tmzZ6s0dZaamsohQ4YovdqpU6cqU3eFe+vHjh2ji4sLIyIimJWVZatbqnbueOGR+Q/y77//VjoD99xzj9XrGypyjZiYGCWJXtu2bSudJiovL4/vvPOOEprs0qVLjIqKYtOmTanX64sEdty4cSMlSeKDDz7omDKz5WYL4ZH5wjhx4gTvueceCiHYtGlTbtq0yab+axZX+YcffpiSJLFVq1YVFp/FNd/NzY2enp7cunWr4vP3wQcfUAjBhx9+mFlZWZRlmXPmzKEQgq+++qrDScCWm62ER+Y/1Bs3bvDf//43NRoNvby8uHDhQptmBJJlmQkJCRw6dKjifWzJfWHNsSdPnmTjxo2p0+n4ySefFPlhXLt2jc2aNaNer+fWrVtpMpmUReF1YZ1FYf5RwiPzH256ejrfffdd6vV6uri4cNq0aUxPT7fZg5Pl/BRTTzzxhJKd8fZUBCUdk5KSoixPLClyvdls5ocffkhJkjho0CDGxcWxQ4cONBgM3LNnj03KXlP844RnwWg0cuXKlfT19aVarebo0aNtOgMhy/nZGf/9738rkdstmRxLIicnh6+//jpVKhXvuuuuEnN1WNqRLVq0oLOzMz/++GN6eHgwNDSUiYmJNil3TfGPFR75v0A7TZo0UazItWvXbCq+mzdvKpHbhw4dWqJjgdFo5OzZs+ns7Ex/f/9iQya3n9OSQsvd3V1ZG1LbiY8ryj9aeGT+gzx8+DDDw8MphGDv3r156dIlm7aXTp48yZYtW1KlUvHll18uMhySm5vLOXPm0GAw0M3NjatWrSq3dxoTE8PQ0FACoCRJda59RzqER/J/fm6W1J1du3a16dyrxaUqODiYOp2OS5YsodlsZk5ODmfOnElnZ2e6ublx0aJFVlkus9nM//73vxRC0NfXt07N0VpwCK8AWZZ5+fJl9urVS5mBOHPmjM3EZzab+dVXX9HFxYX169fn0aNHOXfuXLq4uNDLy4srVqywuro0Go184YUXCIBOTk785ZdfHBbP1ltNCY/MF190dLQy/dW2bVvu37/fJgOzsiwzJydHGf4IDQ2lXq+nq6srV61aRZPJZPWQy44dO+ju7k61Wk0hBPv06VOt6VKrA4fwbsMyCPzQQw9RkiQGBwdz5cqVzM3NtYlVuXTpEkNCQgiArq6unD9/foU6BsnJyezVq5fiad2yZUs6OTnxxx9/rFNWzyG8ErAsGB87diydnZ3p4uLC999/v8pzobIsc+PGjfTw8CAA3nfffUqAIGswmUz88MMPqVKp2LFjR8bGxioRFXr16lWnrJ5DeGWQk5PDJUuW0NPTk1qtls8991ylx/pkWeaxY8eKRCL19PS0Kk6K5fgDBw7Q39+fbm5u3Lx5szJQ3aZNG+p0Oq5fv77OWD2H8MrBZDJx/fr1bNCggbK21hK1wFpkWebFixfZoUMHCiH4xBNPKG29iRMnltuGtAwcd+3alUIITpgwQVlvK8syFy5cSLVazZ49e9rc27q6cAjPCsxmMw8dOsS2bdtSCMFu3bpVyLslMTFRifjev39/xsbG8uDBg3Rzc2Pjxo1LDC5UmPT0dI4YMYKSJLFXr17FVpPFx8ezXbt21Ol0/O677+qE1XMIz0pkWeapU6fYs2dPCiHYunVrqzxPjEYjJ0+eTEmSGB4ezsuXLys93AceeIAqlYpffvllieeRZZnZ2dmcPn061Wo1mzVrVqJ7vSzLXLJkCVUqFe+55546YfUcwqsAsiwzNjaWjz76KCVJYuPGjfnTTz+V2is1mUxcunQpDQYDfX19uWvXLkU0sixz+fLlVKlUHDx4cIkeMjk5OXznnXfo5OREX19fbtq0qVSh37p1i+Hh4dRqtcVybNgjNhEe8uMgHwXwa8H/1ZJuwB6wNOhHjRpFjUZDd3d3Tp8+vVhOXMt4m7e3N11cXLho0aJivn9XrlxhcHAwfXx8iiRZsXggz5gxg05OTvTy8uK3335bpu+gLMtcunQp1Wo1u3fvbvchy2wlvIkAvikkvO9QNCLoiwWvX0LRiKBryzu3vQmPzH/ImZmZnDVrFr29valSqYqsr7WMBUZERFClUnHatGklBt/Jy8vjqFGjKITg7NmzaTabef36dX7++edKUHFvb2+uWbPGqkHsxMRERkREUKPRcPXq1XZt9WwR9T0YwO8AegP4FYBANaUbsDdMJhO3bdumxNF79tlnmZGRwczMTI4ePZqSJLFv375lhrrYsGEDNRoNu3Xrxl27drF58+ZUqVTUarUMDw/nunXrrPaSlmWZK1asoFqtZpcuXZicnGzDu7UtthDeOgAdAPQsEF61pRuwRyzjaw0aNKBarebUqVM5ZcoUajQaNmzYkJGRkWVansTERLZs2ZJ6vZ6//PILO3fuzEGDBnH9+vWVWhGXlJTEjh07UqPRlNppsQeqGgN5EICFBa9tJrzCmz1bPAuyLHPDhg2KU6lKpaKfnx9//vnnch+82WzmuHHjKITgRx99xOvXr1cp4JAsy1y1ahU1Gg3vvvtuu7V6VV3Q3Q3AQ0KIqwC+Lahu56Ia0w3YI0IIDBgwAC+88IKSwv7ZZ5/FwIEDyw37L4TAPffcA7Vajb1798LPzw9OTk6VThcghMBDDz2EDh064PDhw9iwYYPlR15nKFd4JKeQDCbZEPmdhR0kRyA/38WjBR8bCeDngtcbCv5Hwfs7WNe+lRIgiejoaGzYsEHZt2bNGpw9e7bchy6EQKtWraDT6XDhwgUYjcYql8fd3R3jxo2DEALz588vN5u3vVGVEBZvAJgohLgIwBvA8oL9ywF4F+yfCGBy1YpY+5BESkoK3nrrLURGRqJv37549NFHceXKFcyYMcOqFPZeXl7w9PRESkoKUlNTq1wmiwXu2LEjjh07hp9++qluWb3S6uCa3Oy9jZeens5Ro0YpA8qnT5/m5cuX2aRJEzo7OyuT+WWRkpLC8PBwenh48MSJEzYplyzn5zbTarVs37693S0GuiOD9tQUJpMJ8+bNw+rVq9GoUSN8+eWXCA0NRYMGDfDCCy/AaDRiyZIl5Vo9jUYDg8EAo9GIrKwsm5TNYvU6deqE48eP44cffqgzVs8hvDIgiW3btmHWrFnQ6/WYN28eunbtCiEEhBB47LHHEBISgh07duDkyZNlPnSSMJvNkCSpSM7cquLq6opx48ZBrVZj4cKFSEysG/04h/DK4OLFi5g4cSIyMzPx+uuvo2/fvkpPVAiB4OBgDBgwAKmpqdi5c2eZ58rJyUFycjKcnJzg7u5uszIKIdCvXz907twZJ06cwPr16+uE1XMIrxRyc3Mxa9YsnDt3DoMHD8bYsWOhVquLfEaSJLRr1w4AEBUVVeb5MjMzkZCQAIPBAC8vL5uW1WL1JEnCokWL6oTVcwivBEhix44d+OabbxAcHIy33noLBoOhxM9axCiXk/L9ypUryMjIQHBwcKnnqixCCDzwwAPo1q0bTp48ie+++87urZ5DeCWQl5eHJUuWIDs7G+PGjUPLli1LHOwliWvXrgHIj+RZGiTx999/Izc3FxEREdBoNDYvs16vx9ixY6HRaLB48WIkJCTYtfgcwiuByMhI7NixAyEhIXj88cdL7QwkJSXhp59+gk6nQ+fOnUs9X05ODrZs2QKNRoM+ffpUS4JjIQT69u2L7t2749SpU1i7dq3Nr2FLHMK7DZLYtGkT0tLS8Mgjj6BevXolfk6WZXz99deIjIxEp06d0L1791IFdeXKFZw8eRL169dHREREtWXWtlg9tVqNxYsXIz4+vlquYwscwrsNk8mEyMhISJKE0NBQyLJcrMqSZRlbt27FBx98AK1Wi0mTJhULmG3BUs2mpKSgU6dO8PHxqbayCyHQp08f9OjRA2fPnsW3335bbtuztnAIrwRCQkKgUqkwdepUTJgwAWfOnIHZbAZJJCUlYc6cORg1ahSSk5MxduxY9OvXr1QrlpeXh61bt0IIgf79+9t0DK8kXFxcMH78eGi1WixevBhxcXHVer1KU9qURk1u9jZllpSUxNdff51+fn4EwICAAE6aNInz589nx44dqVarqdfr+eabb5YZ7NES1T0gIIABAQG8cOFCjZQ/IyODDzzwACVJ4uzZs2vNX8+x2KeCyLJMk8nEEydO8Pnnn6e3tzeFEBRCUKvVskePHvzll19KdHW//Twff/wxJUkqNS9tdZV/06ZNdHZ2ZmhoKGNiYmrkurfjEF4VMBqNPHDgAMeMGcORI0dyzZo1TElJscqKpKWlsVOnTtRqtdy4cWONWp7MzEwOGDCAKpWKH3/8ca1Ei3cIr4pYIrKbzWarxWOxOjqdjuHh4TXuJSzLMrds2UIXFxc2a9aszBC51YXDO6WKCCGUyX1rh0JycnKwbNky5OXl4YknnrDp/Kw1CCHQo0cP9O7dGxcvXsTXX39tVz1ch/CqAZLYsGEDNm/ejCZNmmDo0KG1Ug4nJyeMHz8eLi4uWLZsGWJiYso/qIZwCK8auHnzJmbOnAmTyYTXX38d9evXr7ZB47IQQqB79+64//77cfnyZXz55Zd2Y/UcwrMxJpMJc+bMwalTp9C7d28MHz682sfuysLJyQnjxo2Ds7MzvvjiC1y/fr3WylIYh/BsCEls2bIFS5Ysgbe3N9555x3o9fpaLZMQAt26dcMDDzyAK1euYNWqVXZh9RzCsxEkcerUKcVx9NVXX0XHjh1rpYq9Ha1Wi7Fjx8JgMGDFihWIjo7OH9KoRRzCswEkcenSJTz99NO4dOkShg8fjvHjx0OlUtV20QDkW70uXbqgf//+iIqKwooVK2pdeLU+hsc6MI5XFpZIoD169KAQgn379rV5rlxbIMsy//zzTxoMBjZo0ICXL1+u9ms6xvGqCZKIjIzEsGHD8Ndff6FHjx5YsmQJAgIC7KKKLYwQAp07d8aAAQMQHR2NFStWwGw2116BSlNkTW51zeLJssysrCx+8803SqDt/v378+rVq3Zn6QojyzL/+usvuru7MyQkhBcvXqzW8josng0gieTkZFy6dAl//PEHRo0ahdGjR+Pq1at4/PHHsWLFCoSEhNidpSuMEAIdO3bEgAEDcO3aNXzxxRe119YrTZE1udUFiyfLMl966SV6eHhQo9FQCMEWLVpw+fLlzMjIsGtLVxhLzjV3d3cGBwdXa440h8WzEX5+fggMDETXrl3x2muvYfPmzRg9ejT0er1dW7rCCCHQoUMHDBo0CDExMVi+fHnttPVKU2RNbnXF4qWlpTElJYV5eXkV8lSxN2RZ5r59++jp6cmgoCCePXu2Wq7jsHg2QAgBV1dXuLu7Q61WV8hTxd4QQiA8PBwPPfQQbty4gWXLltW41bNKeEKIq0KIE0KIY0KIQwX7vIQQ24UQFwr+ehbsF0KIeUKIi0KISCFE++q8AQeVQ6PR4OWXX4anpye++eYbXLhwoUavXxGL14tkO5IRBf9PBvA7yWbID8xtiYPXH0Czgu05AItsVVgHtkMIgXbt2uGRRx5BbGwslixZUqNWrypV7cMAVhW8XgXgkUL7vyyo5vchP2RtYBWu46CaUKvVePnll+Hh4YFvvvkG58+fr7FrWys8AtgmhDgshHiuYJ8/yZsFr2MB+Be8rgfgWqFjrxfsc2BnCCHQtm1bDBkyBHFxcVi8eDFMJlONXNta4XUn2R751ejLQogehd8kSeSL02qEEM8JIQ4JIQ4lJCRU5FAHNkSlUuHFF1+Et7c31q5da1VMZ1tglfBIxhT8jQfwI4BOAOIsVWjBX0u8BCXqewGFI8IXPucSkhEkI3x9fSt/Bw6qhBACbdq0waOPPoq4uDgsWrSoRvz1yhWeEEIvhHC1vAbQF/m5LApHd7896vu/C3q3nQGkFqqSHdgharVasXrff/89zpw5U+3XtMbi+QPYLYQ4DuAAgI0ktwD4EMD9QogLAPoU/A8AmwBcRn4SvaXIz23mwI4RQqB169YYNmwY3N3da2RRkKiJ+rw8IiIieOjQodouxj8akoiPj4fJZEJgYKBN1olERETg0KFDJY6yq0va6eCfhxAC/v7+5X/QRjimzBzUCg7hOagVHMJzUCs4hOegVnAIz0Gt4BCeg1rBITwHtYJDeA5qBYfwHNQKDuE5qBUcwnNQKziE56BWcAjPQa3gEJ6DWsEhPAe1gkN4DmoFh/Ac1Ap24fouhEgHcK62y1EH8QFwq7YLUQYNSJa4hNBeXN/PFQqN4cBKhBCH6ur35qhqHdQKDuE5qBXsRXhLarsAdZQ6+73ZRefCwT8Pe7F4Dv5h1LrwhBD9hBDnCiKITi7/iH8GQoj6QoidQojTQohTQogJBfvvjEispQVHrokNgArAJQCNAWgBHAfQsjbLZC8bgEAA7QteuwI4D6AlgI8BTC7YPxnARwWvBwDYDEAA6Axgf23fQ1lbbVu8TgAukrxM0gjgW+RHFP3HQ/ImySMFr9MBnEF+gMs7IhJrbQvPET3UCoQQDQGEA9iPOyQSa20Lz0E5CCEMANYDeIVkWuH3mF/H1slhidoWnlXRQ/+pCCE0yBfdapI/FOyuUiRWe6G2hXcQQDMhRCMhhBbAcORHFP3HI/KztywHcIbk7EJv3RGRWGt9AFkIMQDAHOT3cL8gObNWC2QnCCG6A/gLwAkAlqDEU5HfzvsOQAiAKACPkUwqEOp8AP0AZAEYTdJuo13WuvAc/DOp7arWwT8Uh/Ac1AoO4TmoFRzCc1ArOITnoFZwCM9BreAQnoNawSE8B7XC/wOm5V4oVk/dIgAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/اقرأ.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/1القرءان الكريم.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/الحب من شيم الكرام.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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hoKOLj4xEYGIgRI0bk8YO3ZqxS3J2cnODl5YWYmBg8ffoUer3eIsRdEARER0dj27Zt2LBhA27dusUHFp2cnNC+fXt8+OGH6Ny5M+zs7CxKcO7fv48//vgDAFCtWjWMHj260GIsxgJRq9VITEzEu+++iypVqmDChAlIT0/HkCFDzBop0s3NDS4uLkhOTsaTJ09gMBhgZ2eXx1dawrpQKpW8hS3GjjLV8yK+JIxGIypUqIAOHTpg9+7dePToEWJjYyVxL0vs7Oy4zS0uLq7MJ6sIgoCEhARs374dq1atwu3bt7m/tbu7O3r27Inhw4ejVatWFifqQHb5N27ciHv37kEul2PEiBHw8fEpdDl9fHwAZD+EcXFxkMvlaNu2LXbs2IGYmBi0aNHCrHV2cnKCh4cHkpOT8fjxYxgMhhcGOZOwHp4Vd1MOzIs2d6PRCEEQ8NFHH6F169bo0qULatWqZbLjlDVWL+4JCQllJu6iTX3Xrl349ddfcevWLej1ejDGUKlSJQwYMAAjRoxAgwYNzDrpqCQQESIiIrBx40YA2aF9hw8fXqSWdvXq1QFkz/aLj48HEfHwrb6+vmavt5OTEzw9PREREYFHjx6V+cteouQolUreG09LSzOpa63oViwIAjIzM9GxY8dyOeBeInFnjD0AkA7ACMBARAGMMXcA2wHUAPAAwLtEZNLA2gqFgnet1Gp1qc8uE71fAgMDsXjxYly/fp3b1L28vPDee+/hww8/RO3atcvM86WwEBHWrl2LBw8eQC6XY+TIkahevXqRyuzq6gqVSgWtVovExEQu7qVVb9FMB2QnDMnKypJS7Fk54oAqAKSmppo0pIWNjQ1UKhU0Gg0yMjIszhHDVJii5d6RiHIHzv4KwAkimscY+yrn+1QTHIfDGMvTsixNcRcEASEhIZg9ezaOHz/OfagrVqyIAQMGYMyYMahfv75VmAWICJcvX8bGjRtBRKhbty6GDBlSpBudMcZjqmu1Wt5yL00UCgVq1qwJILv38Pjx4+cmqkhYF0qlkot7RkaGSeNGPSvu5TVJtjnMMn0BdMj5/AeAUzCxuAN5MwCVRqAqMUnIxo0bMXfuXJ7Bxc3NDW+99RbGjRuHxo0bW4Woi2i1Wvz444+Ijo6GjY0Nxo8fj6pVqxa5FePg4AAHBwckJycjISEBgiCUaqo9AHyMwGAwlLsIof9FZDJZHhfXxMREPnmxpIjiDmTnSChvM1NFSiruBOAoY4wA/EZEqwB4EZH4dMUAeGETijH2EYCPgP+32RbpwLkuiLm7VIIgICwsDN999x327NkDrVYLGxsbtG/fHlOmTEG7du3yuFdZA0SE48eP48CBAwCAVq1aYeDAgcU6l6K4A9ljIGXxsIgBzMQAbBLWjxi3yGg0IjY2FvXq1TPJfhUKBX9e1Wq1JO750IaIohhjFQEcY4zliZ1JRJQj/M+R8yJYBQABAQFFOrtExLtpjDGztZbFFH579uzBd999h/DwcADZdvVJkyZh7NixcHZ2tkp7XUZGBn755Rekp6fDwcEBkydPLvasP3t7+zziXhaJUzw9PbkQSLlTywfi5DOj0WjSmOsKhYJrhkajkcT9RRBRVM7fOMbYHgCvAYhljHkTUTRjzBuAySPh547VLZfLzWIvEwQBkZGRmD17NrZt2wa1Wg25XI7XX38ds2fPRps2bYqccNpSICIcO3aMh/Xt0qULOnbsWOy65M5jWlYtd/HFJAhCngTqEtZLxYoV+QvblL0xmUzGzYbl2bOq2KrIGHNgjDmJnwF0BXATQCCA4TmbDQewr6SFfBaj0ci9U2xtbU0usHq9HocOHUL//v3x+++/Q61Ww9nZGZMmTcKuXbvQrl07yOVyqxR2IDuzzfLly5GVlQUHBwd88sknfDZgcbCxseEzbTMyMkyS77KoODg48CQdpvaLligbxLAWgiCYNDevTCbjDUKDwVBu75WStNy9AOzJETgFgC1EdJgxFgRgB2NsFICHAN4teTHzotfreeRHe3t7k7XcxXyly5Ytw4IFC5CcnAzGGJo2bYrvvvuOJ8mwdiIiInDp0iUA2dOvW7ZsWaIXFWOMD37p9XokJyfzBBqlhVKphI2NDdRqNdRqdZkM6kqYFjc3N95yN6W4554glZGRIYn7sxDRfQBNXrA+EcAbJSlUQRgMBh6e01TiTkSIjo7GrFmzsHHjRmi1Wtja2mLIkCGYMWMGqlWrVi7cpYgIwcHB0Gq1kMlk6NmzZ4kDMQH/P6BpMBjKxOatUChgZ2cHtVqNzMxMSdzLAbkbUmJP3VT7FXuqKSkpZTJGVBpY5QxVMYMRAJMEhRIEAadOncKMGTN4ACxvb2/873//w9ChQ00ifpaC0WjE5cuXYTAYYGtri4CAAJPsN3eM/bLwVhH9opOSkpCeni6FIJDIFzs7O57gJT4+vtza3a1S3E2JVqvFxo0bMXPmTMTExIAxhkaNGmHhwoXo3LlzuWv9paen4+bNmwCyQw3kThZcEkRx1+v1ZSbu4gNr6unqEuULlUrFPXFSU1ORlJRk9pwDZYH12xlKgFqtxo8//oiJEyciJiaGm2H27NmDrl27ljthB4Dk5GSe7b1BgwYmi4AnzggVW+6lbcdUKpU85EBqamq5bY1JlBzGGJ/RrNPp8PDhwzIukXn4z4q7TqfD6tWr8cMPPyAzMxMuLi749ttv8euvv6JWrVpW6wlTELdv30ZKSgpkMhmaNGlislDJHh4ekMvlMBqNPL5MaaJSqXjrKykpSRJ3iZfSsGFDMMag1Wpx69atcjmo+p8UdzHj+axZs5CVlQUXFxcsXrwYEydOhL29fbkVdiLCv//+C0EQoFQq4e/vb9IY2WLLOT4+vtTNIrnFPTk52aSxSCTKHw0aNIC9vT0MBgNu3rxZLhsD/zlxJyLcvn0bU6dORUpKCmxtbfHtt99i6NChVhdCoKjodDrcvHkTjDE4OTmhYcOGJtu3nZ0dN/EkJCSUurgrlUruF52enl4mvvYS1oO7uztP/RgREVHukmMD/8EBVY1Gg3nz5uH+/fuQy+UYN24cPvzww3Iv7ED2bN7PPvsMTZo0gVarNakvur29PVxdXfHo0SPExMSUurgzxlChQgUepzs+Pt4qEy+I5oHy2nu0FJydnVGtWjWEhYXh/v370Gg05S5M9H9O3K9fv47AwEAAwGuvvYapU6fyqfPlHYVCgXbt2qFt27YwGo0mHTB2cHDgIQBiY2PLpJsr5lIVxd3aEOda3L59G+3btzdbzllrRcwTYAocHBx4wMLY2FgkJibyuRrlhf+cWSYoKAhpaWlQqVQYM2aMycKIWhOMMZMnjbazs+Nmkbi4OD6DuDQRY5EQEU+UbS0QER4/foxRo0bhvffew9atW8ulHbioiPeoqQc8ZTIZd5wwGAw8hHd54j8n7o8ePQKQbUaoW7eu1P01EQqFApUrVwaQPXegLMS1UqVKvOUeExNT6scvKWfPnsXZs2cRHx+PiRMnYvv27eV29mRhYIzxWeFGo9HkAl+5cmV+v5THHABWKe5GozHPDNWi4OHhASA76W54eHi5dIEqK8T0fAaDAU+fPi314+cW9+joaKu7tm+99RamTJkCBwcHZGZm4uHDh1ZXB1OSO3qjucRdjF1THsXdKo16er2ee0M4OjoWyXbcsWNHuLi4IDU1FevWrUOfPn3K5ey0skA0cQmCUCbxZZydneHg4ACNRoPExESrCkHAGINKpcKUKVP4C2rixInlciJdYZHJZFCpVFCr1dBoNCaPF1S1alXI5XJuljGlTd8SsMqWu8FgyCPuRQno1ahRI3Tt2hUAcOHCBezates/3fU1JWLiEkEQkJGRUerHl8vlPA6QRqOxOps1Yww2NjaYMmUKpk2b9p8Z6M8PuVzOg4eJ4m5KXFxc8szNKG9zI6xS3I1GI78QKpWqSG9bOzs7fPbZZ3Bzc4NWq8Xq1auRkpJippL+txDFSIypXtrIZDLeUtfr9Vb50hYzi0meMv8f6RPIznVqavdahULBxT0zM9PqGgMFYbXiLl4IGxubInelAgIC0Lt3bwDA1atXcfz48f+0bdNUiF1mIiqT8yl6AQHmsdGWV8Qoq5Z2vhQKBW8wmEPc5XI5339WVpYk7paCeCMWJ9WdSqXCqFGj4OjoyFvvUmq2kmMJ4lCebKalgSAIOHnyJCZOnGjSPKWmQC6X89hH5jDLCILALQCmdg22BKxW3EuKv78/unXrBgA4c+YMjh07ZhHiZM2ILSvG2H96INCauHTpEkaPHo2VK1fy6KiWgkwm4z0xc6TD0+l0SExMBJAdjqA8ZFnLjVWKO2OMv2WL2520t7fHmDFj4OzsDK1Wi/nz5yMqKsrURf1PIWbLEe3GEpaPUqmEnZ0djEYjduzYge+++84i4/KYo+GVmZnJB/49PDzK3T1boLgzxtYxxuIYYzdzrXNnjB1jjIXn/HXLWc8YY78wxiIYY9cZY83NUWiFQsG7a1lZWcXqrjHG0LZtW7zzzjtgjCEkJAQzZ85EQkKC1IIvJrlnpZa3VlB5xd/fH+vWrUO9evVQqVIldO/e3WKunSAI3A6uVCpNbjZJTEyEIAiQyWTw8PD4T5plfgfQ/Zl1XwE4QUS+AE7kfAeAHgB8c5aPAKw0TTHzIrY2gOwEt8W1xalUKnz55ZeoU6cOiAgbN27E5MmTER8fLwl8McjMzAQRQSaT8etT2uSerl4eryER4f79+zh9+rRJBhgZY2jRogU2bdqEDRs2oFevXhaTKzi3TdzGxsbk5crKyuK+7eUplaZIgWeLiE4DeDZvWl8Af+R8/gNAv1zrN1A2FwG4MsZMk8ctFzY2NvxiJCcnF/smZ4zB19cXv/32G2rXrg2j0YhNmzZh1KhRuHfvXrkUB3MidnHL6mERJ70A2b0Ia3SFfBlEhAcPHmD06NF47733EBgYaJI6MsbQrFkzdOrUySxjJUQEvV4PrVYLnU5nMc9V7qTb5TEqbHFfhV5EJM7XjQHglfO5CoDHubZ7krPuORhjHzHGghljwUWN4GdnZ8dnlSYkJJTIhUkmk6Ft27a8a2o0GvHXX39h4MCBCAoKsliBICIIgsBDMeReBEEok5araKtljJksw1NRkMvlvMegVqvLnWsbEeHo0aO4cOECnj59ivHjx+PAgQMmuc65x7FMBREhISEB27Ztw8cff4w+ffpg6NCh+Pvvvwv9XJmzJ5Z7f5bSWzElJZ4pQUTEGCvyWSeiVQBWAUBAQECRfm9jY8OTIaenp5e4eyqTydCmTRts2bIFEydOxD///IPLly9jyJAhWLFiBTp27GgR3h/izZiamorjx4/j2LFjiIyM5OYQIHs8ok6dOvD394efnx98fHxQrVq1UnX1ModQFAaZTMZfKjqdzmJfzCLiC7qw7rwymQyDBw/GvXv38MsvvyA5Odli838SES5evIgvvvgCISEheVrJ586dw88//4z+/fu/VFTlcjkf5DRHiz+3A0B5nDRW3BrFMsa8iSg6x+wiOshGAaiWa7uqOetMikwm45MPDAaDSbKoMMbQtGlTbNiwAVOmTMGuXbsQHh6OESNG4JdffkHv3r3L/AaIj4/HsmXLcODAAYSGhuYbVvfMmTNYu3YtHBwcUKFCBbz66qsYOXIkWrduDUdHR7MJb+5WVlkI67NmmdJOGFIUiAjXr1/H6tWrMWXKFFSrVq1Q18XR0RGzZs2CjY0NnJ2d8eGHH1rcQCAR4dixY/jkk08QEREBIHuqf8WKFfHw4UNERUVhypQpqFevHurVq5dv+ZVKJW/ERUVFQa1WmzQOVEpKCh8jKpfxpcTuzssWADUA3Mz1fSGAr3I+fwVgQc7nXgAOAWAAXgfwb2H27+/vT0Xls88+IwBkb29Ply9fLvLvX0Z8fDyNGTOGbGxsCABVrFiRtm7dSnq93qTHKSyCIFBsbCwNGDCAZDIZASAAxBgjV1dXql69OtWtW5fq1atHNWrUIA8PD1IoFHw78Tx16dKF9uzZQ6mpqSQIgsnLuWrVKmKMkY2NDW3evNnk+y+IrKwseueddwgAVatWjR4+fFjqZSgMgiBQREQEvfrqq8QYo65du9KDBw+KdE20Wi3pdDozlrJ4CIJAZ8+epVq1ahEAUigU9M4779DZs2fpwYMH9MUXX5BCoSDGGH355ZdkNBrz3ZfBYKCJEyfye+rYsWMmLeuSJUtIJpORjY0N7d+/36T7Li0ABFN+up3fP+j/hXwrgGgAemTb0EcB8EC2l0w4gOMA3HO2ZQCWA7gH4AaAgIL2T8UU959++olkMhmpVCr6888/S3B6nkcQBEpLS6PJkyeTra0tASAPDw/6/fffy+SBEgSBvv32W5LL5VysbWxsaMaMGXT27FkKDw+nuLg4SkxMpPv379OlS5do8+bNNGzYMKpatapoNiMApFKpqG/fvhQcHEwGg8Gk5Tx69CjJZDKSyWQ0d+5cs7xAXoZer6cxY8YQAHJycqKwsLBSPX5hEQSB7t27R61ateIv6f79+1N6enpZF61ECIJAjx8/ptdee40AkFKppI8//piSkpJIEAQSBIGio6OpYcOGBIBq1qxJ9+/ff+n+Dhw4QDY2NsQYo6+++sqk99TcuXO5hhw/ftxk+y1NSiTupbEUR9wPHDhAcrmc5HI5LVq0qMi/LwhBECgzM5O+/vprsre3JwDk6upKy5cvJ41GU6rC9fTpU/Lz88vTEvfw8KCoqKh8yyEIAhkMBgoNDaVhw4bxOohL1apVaeXKlZSWlmayct6+fZscHBwIAI0aNarUX4SCIND333/PXzB///13qR6/KAiCQLdv36bXX3+d3N3d6ddffzX5y7a0MRgMNGXKFJLJZMQYo/fee49SUlLy3KOCINCcOXOIMUZyuZyWLl360tb7kydPqEqVKgSAevToQRkZGSYr7/fff0+MMVKpVHTq1CmT7bc0eZm4W+0QsY+PD+RyOYxGIx49emRyGy9jDHZ2dpgxYwamTZsGR0dHpKSkYOrUqfjhhx+QkZFRKt4oRISgoCDcv38/z/pKlSq91H4uhgDw8/PDr7/+ikWLFsHLy4v//8mTJ5g4cSKmTp2K1NRUk5TV1dUVNWvWBACEhYUhMzPTJPstLIwx1KpVCwqFAkSE8PDwUj1+UWCMoW7duli/fj1Wr16NUaNGWb3HxqNHj7Bt2zYIgoC6devi+++/52Ggc9OnTx9UrlwZRqMRe/fufWmoXUdHR35PRUdHl/o9Zc1Y7d3k4uLCsypFR0ebJWen6NL35Zdf4rvvvoOLiwsyMjKwcOFCTJgwAbGxsaUi8ImJiXm8DQDA1ta2UANp4kvqww8/xObNm9GkSRP+O61WizVr1mDFihUmcRt0dnZGrVq1AAARERFlEva3evXqfOD78ePHBWxdtogC379/f8jlcosbGC0KgiAgMDCQZ+D64IMPeI7S3IhzS5o3z568fu3aNYSFheX7HNnY2PD0jfHx8RYZGsFSsVpxt7W15Zl/4uPjnxM/UyEmUPjkk0+wYsUKVK5cGVqtFhs3bsSwYcNw+fJl7ldemhTVNUyhUKBjx47YsWMHWrduzdfr9XosWbIE58+fL3Ed7OzsUKNGDQDZk8vKIspgpUqVeMv96dOnFjNhJj9Et1FrFnYgewLbli1bYDAYUKVKFfTu3TvfOtnY2KBfv35gjCE5ORmHDx9+qbhXqFABQPY9ZQrPuP8KVivuNjY2cHNzAwAkJSWZTdyB/xf4gQMHYuPGjahfvz6MRiOOHTuGfv36YdmyZWZtpVaoUOG5GXSpqalFdvWTyWTw9fXFypUr0bhxY74+Pj4ev/zyS4kz0YitMtFcdufOnRLtrzi4ublxd8j4+HiLdocsTwQFBSE0NBQA8MYbb8DX1/el27dt2xZVq1aF0WjE4cOH8xXt3HFfNBqNSUNz584FYU79KCusWtzd3d0BZM9SLY2LI5fL0bFjR2zbtg3du3eHUqnEkydPMGXKFHz66ad49OiRyY/JGEPNmjW5CUokNTW1WHlKGWNo0KABVq1axVvZAHD27FmTiHHDhg25uF+/fr3UW84KhYLfF2lpaVJLrxQQBAHnzp1DZmYmZDIZ3n333QIjLFavXh1NmjQBANy8eRMJCQkv3I4xBjc3Nz4eIYboNQViFiYiKpf5HKxW3FUqFR8gTEpKKrWcnYwxNGzYEJs3b8b3338PLy8vaLVa/PHHHxg+fDhCQ0NNLmi1a9dGs2bN8qzT6/XFnp3IGMOrr76KL7/8krdyExIS8OjRoxKX/ZVXXoGjoyOICPfu3Sv1EAAKhQJVqmRHvIiNjTXZYLFE/mi1Wty4cQNEhIoVK8LPz6/A36hUKm53z8zMfOngt4uLC+RyOYjIpInX3dzceM7f8phq02rFnTGGqlWrQiaTwWAwlOrgmdiamDx5MrZs2YKmTZsCAE6dOoVhw4bh0qVLJvXesbGxwdChQ/O0hvR6Pe7du1fsfTLG8N5776FOnToAsuPim8ITwc7Ojo+FJCQklPoAmI2NDReXJ0+e5NsilDAdaWlpCA4OBgDUqVMHnp6ehfpdjRo1wBiD0WhEbGxsvtu5urrylrspxd3d3R2MMRARkpKejY1o/VituANAzZo1IZfLIQhCiYSuOIiuhh07dsTOnTvRuXNnyGQyhISE4P3338f+/ftNmk29e/fu6NKlC/+u1+sRHh5e7JcIYwyOjo7cuyW3KJcEGxsbVKpUCUDZeDcoFAo0aNAAcrkc6enpCAsLK9Xj/xe5efMmYmJiwBiDv78/nJ2dC/U7MbSAIAgvvU/c3d15y72oQQZfhqenJ2+5l8cw31Yp7nq9Hrt374ZOp4NMJgMRlZnbG2MMr7zyCtauXYsBAwZALpcjMjISo0aNwq+//gq1Wl3im4YxBhcXF8yaNQve3v8fQTkiIqJYNmVBEJCRkYG9e/ciJCQEQLYNtE6dOiX22lCpVHnMImXh616vXj0ee+jWrVulenxLgIgQHR2NCxcu4PLly2a1JxMRzp07B51OB7lcjnbt2hX6HhJNZoyxlyYI8fLy4oH7oqKiTCbCbm5uUCqVvOUuiXsZQ0QIDg7GuHHjMGnSJO4SmJycXGYXRzQRrVy5EuPHj4dKpUJiYiK++uorTJkyBYmJiSYReH9/f3zxxRfcj/vWrVuFtikTEQwGA+7fv4+VK1eiV69eGDFiBJ48eQIA6NWrV54XR3FRKpV8LCQ5OblM/JJ9fHx4dMiIiIhy99AWhsDAQPTo0QN9+vThJhNzkJWVxUNju7u7cxNlQRgMBpw7dw5EBJVK9VLvmgoVKvD8AE+ePDFZj9jGxoYPqqanp5u0p20JWJ24Z2ZmYsGCBYiPj88j6GKEt7KCMQYPDw/88MMPmDNnDtzc3JCVlYVff/0VQ4cOxa1bt0pcPrlcjhEjRqBjx44AgPv372P37t0vNM2IU5CNRiMiIyOxc+dOjBo1Ch06dMDEiRNx+vRp3qpu2rQpPv30U5PMkBTHI0RbalkMaFasWJG33J8+ffqfdIfMyspCamoq0tLSzPpcJCUl4erVqwCAJk2aPOfVlR+PHj3CP//8AwDw9fVF3bp1891WpVKhevXqALJNfabqiSgUCi7uGRkZ5S7+v1WJOxEhMDAQhw8fBpCd/1H0dc/IyLCIN6+DgwPGjx+P9evXw9fXFwaDAYcPH8Y777yDgwcPllho3Nzc0K1bN8hkMuj1evz66698VqCITqdDaGgoVq1ahQEDBqBTp04YMmQINmzYgMePH+e5ib29vTFv3rw8bpElQTQhiTbSsvBCUCgUfPwgPT291DypzIVWqy2yQIvPgjg2ZC7u3r3L7e0BAQGFysBFRDh48CAePHgAmUyGAQMGcJF9EQqFAtWqZUcST0hIQHp6uknKnlvcpZZ7GSLOOFy0aBE0Gg3c3d0xfvx4PniXnp5ulhAExUGpVKJ3797YuXMnunTpArlcjjt37mDkyJH4/fffS+STzxhDkyZN8phm1q9fj/T0dNy5cwfr16/H22+/ja5du2LcuHHYt28fHjx48NyN6+DggPbt22PDhg3o3LmzSWdIuri48F5AWXghMMb4oJ5Wqy31+0KtVptsApdWq8WcOXNw/vz5Ig2ei9dbJpMVStwNBgOuXr2KkydP4tKlS4VuhFy8eBFGoxE2NjZ47bXXCuz9EREiIiKwYsUKEBEqVaqEfv36vbSMSqWSD/zHxsaazNe9vIu71aQfISKsWbMG165dA2MMbdu2xc6dO7lrVGpqqsWIO5D9UDVu3BibN2/GDz/8gNWrVyMuLg6TJk1CSkoKxo0bx00HRYGIULVqVdjZ2fHxhp9//hknT55EWFgYoqOjX/g7caafn58fOnbsiLZt26JFixZmSd5RoUIFyOVy6PV6xMTEmHTfhSG3uGs0mlKdyKTX67Fy5UpoNBp8/fXXJd7fjRs3sGzZMuzcuRPbtm3LM7O4oHIA2de9MElmMjMzMWjQIISFhaFTp07Yv39/gfenwWDAzZs3QUSwtbXlk5Lyg4iQmZmJ2bNn4/bt25DL5Rg2bBh3x80PmUyGunXrQqlUQq1WIywsDAEBAQXWqSCUSiWf8GbuWe5lgdWIe1xcHLZu3QpBEFC5cmVoNBocPXqUd1eTkpIsLqgQYwyenp6YN28eqlSpgjlz5iA1NRUzZsxAdHQ0Zs6c+cKoebkRB0Kjo6MRHByMf//9FyEhIXkEKzExEadOncrzO7lcDnt7ezg7O6Nhw4bo3r07OnTogHr16kGpVJo1nknu+C5RUSZPxFUgMpmMB5tKS0srNbu/GOVw9uzZ+Pjjj0u8PyLCtm3bkJSUhPT0dFy6dKnI4l5Ys0zu7GZarbZQrdiMjAzupVa1alW4urrmuy0RITU1Fd988w22bt0KAGjevDnGjx9fYPnEAGv29vZITU3FtWvXMHjw4ALLVxA2NjbcJ98S9aOkWIW4ExFOnDiByMhIANk+2WfOnMljhywrz4yCECNLTpo0Ca6urvj666+RmJiIpUuXQqPR4IcffoCLi0seoRUHQjMyMhASEoI9e/bgwIEDePr0KQwGQ772V1dXV7Rs2RI1a9ZE3bp10ahRI9SvXx+urq5QKpWlFlK2UqVKfMLVkydPeJ7Q0kImk3EbbUpKCh94N2dwLiJCSEgIJk+ebLKJNhqNhruqymSyQtmzRUSzSmHFnTFWZHE3GAy8t+zq6prvcQRBQEREBKZPn459+/ZBr9ejcuXKmDt3Lry9vQt1XUQPqNTUVERGRprkesrlcj4RUq1WIyoqCrVr1y7RPi0JqxB3nU6H7du3825TZGQkBEHgs8uAbJuZ6HJoaRH2GGNQKpUYOXIk3N3dMXHiRERFRWHVqlVIT0/H4sWL4eHhASKCWq1GcHAwDh48iMOHD+PevXvIysp6TtAVCgW8vLzQtGlTnDt3DikpKXBxccG8efNQr169Uk2I/Sz29vZwcXFBUlISUlNTodPpuGtiYRBfbpmZmXj8+DHi4+Oh1Wrh7OyM2rVr80ktL4tlX716dSiVSuh0Ojx48ADt2rUzVfVeiNFoxMaNG0063yIhIYGb2WQyGezs7Ar9W/F8C4JQKHND7peHVqstlOcIY4y/tPPz2MrKysKePXswZ84c3LlzB0SEatWqYenSpejQoUOh71F3d3c4OTkhNjYWMTEx0Ov1zwXTKyq54/8bDAaEh4ejffv2JdqnJVGguDPG1gF4E0AcETXMWfcNgA8BiNPFviaigzn/m4bsVHxGABOI6EhJCkhEuHnzJi5cuMDXCYIAuVyO999/H+Hh4bh48SIEQcDcuXOxZs0aVKxY0eIEHsgW5P79+8PW1hbjxo3DkydPsHnzZgDA1KlTcfr0aWzatAk3btxAenr6c4Iul8tRq1YttGnTBt27d0fLli3h5uaGOXPmYP78+UhOTsatW7fQqFGjMq2/GEMeyH4xF9bFTDRBXbhwAVu3bsWJEye4LVQQBB4U7K233sKkSZNQuXLlF9aTMYYaNWrA0dERycnJuHr1KoYNG2bSOj5LTEwMDh06ZNLGRXJyMg+foFAouGdYYRBnf+r1+kL1aIvTcs9P3IkIGo0G//zzD5YuXYpTp04hKysLjDG0aNECP/74I1q0aFEkLx65XA5vb29EREQgLS0NaWlphQ5z8DJq164NhUIBnU7H48pbonYUh8K03H8HsAzAhmfWLyGiRblXMMbqAxgEoAGAygCOM8bqEFGx/f9EO+azXhfNmzfH/PnzcerUKVy9ehUajQYHDx7E559/jmXLlsHV1dUiL5JcLkePHj2wYcMGjBkzBuHh4diyZQuOHTuG+Pj4Fwqhp6cnWrdujYEDB6Jt27bw8vLiLXMiwqefforY2Fj0798fnTt3LoNa5SX3jEODwVAozwtxItr333+PDRs25Otlk5qaiiVLluDu3btYu3Ytn0L+LLVr14arqyuSk5Nx7do13iAwB0TEvZJMuc/ExETuSiqTybhgFwZRqI1GY6EcDWQyGVxcXABkj1MUJoR1bnE3Go0gIuj1epw6dQo///wzzpw5w33SXV1dMWzYMEyePBlVq1Yt8rMpk8m4Z1xGRgbS09NNIu41atSAg4MDsrKyEBoaCo1GU6QekiVToLgT0WnGWI1C7q8vgG1EpAUQyRiLAPAagAsv/1n+JCcnPzdRx9nZGTNnzkSlSpVQrVo1KJVKaDQaCIKAHTt2wNHREYsWLXqp72xZIk7VrlKlCsLDw/mAaW4cHR3RoEED9OnTBz179kTdunVfeNMxxuDt7Y3ly5dDpVJZxAstt7jr9fpCtdy1Wi1mz56NpUuXwmg0QqVSoX79+mjWrBlq1qwJe3t7PH78GH/99RfCw8Px119/Yfbs2fjhhx9eKHpubm7w8fFBZGQknj59isTERJPEznkR8fHx2LhxY56X2Mum0xeWyMjIPLbzwni9iIjbiiauglAqlahduzYYY4iPj8e5c+dgMBhQvXr1fD2qcot7RkYG9uzZg4MHD+LYsWP8peTg4ICOHTvis88+Q9u2bYt9XsRJgkD2WISpxtecnJzQtGlTHDt2DLdu3UJMTAxP62ftlMTm/iljbBiAYABfEFEygCoALuba5knOumIhDqTmDgrGGMOgQYPQrVs3Pu0/d7REg8GA9evXw8nJCd9++22RBqHMjUajQXBwMJYuXYojR4680ItDqVTi/fffxwcffIBmzZoV6E0D/P+graXwbBe/MOJ++vRprF69GkajETVq1MB3332Hnj178sh9QPb9MHToULz77ru4d+8eVqxYAW9v7zwhGURkMhnq1KmDU6dOIT09HQkJCWYRd61WixUrVuSZ4s8Yy5OvtricO3cuz/eivLhzi3th/ONFd0OVSgWNRoOPP/4Yrq6uaNSoEUaOHImOHTtyc6cgCEhNTcWDBw/4LOe7d+9i1KhR/FiiqI8dOxYdOnQo8XMoBroDshtHpnJ7trOzw2uvvYZjx44hKioKsbGx/3lxXwngewCU83cxgJFF2QFj7CMAHwHgU4ufRaPR4M8//8xzIevWrYsvv/ySC7q7uzsfvKtQoQIUCgWio6OxbNkyODo6YurUqWXazRIHSS9fvoxVq1YhMDDwOVGXy+VwdnZGSkoKHwCLioqCr68vnJycLKI1XhRyu9UVxuau0+mwdu1apKenw97eHnPnzsW77777nIcNYwxNmzbFTz/9hA8//BAxMTFYsmQJ3njjDfj7+z93nkR3yKysLJPPlBXtyitWrMCiRYueax2XVNwTExMRFBSUZ52pk8A/i6+vL+zt7aHRaKDVahEbG4vY2Fj8/fffaNiwIRo2bAhHR0ckJSXh8ePHiIiI4GMCooODm5sbGjdujE8//RS9evUyWaND3DeQfT1NlflMJpOhVq1aUCqV0Ov1iIyMxOuvv26SfZc5YgySly0AagC4WdD/AEwDMC3X/44AaFnQ/v39/elFhIaGkoeHByH7JUJyuZxWrlxJgiDwbdLT06lx48YEgFq1akUbN24kLy8vAkB2dna0aNEi0mg0L9y/OREEgdRqNZ09e5YGDRpETk5OvB7iIpPJqHr16jRr1iw6evQo1a5dmwAQY4zkcjk1aNCAjh8/TkajsdTLXxLUajW9++67BIBq1apFkZGRL90+PDycatSoQQCobdu2lJ6e/tLtDQYD/fzzz6RSqQgADRs2jLRabZ5tBEGgJUuWEABycHCgEydOlLRaefadlZVF06ZNIzs7OwJALi4u/PopFAoKCgoq0f537tzJ6weAnJ2d6fz584X+/dq1awkA2dra0r59+wr1u+TkZKpZsyYBoEqVKlGPHj3I1dWVGGPP3bu5n0kXFxdq2rQpTZ06lc6cOUNZWVl5nlFTIAgCLV++nB/z+PHjJtv3/v37yd7engDQzz//bLL9lgYAgikfXS1Wy50x5k1EopG4P4CbOZ8DAWxhjP2I7AFVXwD/FucYlDNIldtn2N/fH/3798/TQsttkjAYDOjRowcAYPz48UhJScG3334LR0dHjBw5ssDUX6bCYDAgNDQUv/zyC/7888/nWo2MMVSoUAGDBw/GmDFj8Morr0Amk2HmzJn49NNPkZ6eDqPRiFu3bmHcuHHYtWtXmXvAFAWZTMbHO9Rq9Uu70ESEW7du4fHjx2CMoVu3bgXOjJTJZBg6dCi2bduGCxcuYP/+/Th37txzrnVij81gMJhs9iHlDPzOmzcPy5cvh1qthru7O2bNmoULFy4gIiICLi4uhQ6g9aL9JyUlYeXKlXnOm9FoLFJMFXGSm0wmK/R9b29vDz8/P0RGRsLe3h6LFy9GSkoKNm3ahJMnT/I5C0qlEtWqVUODBg3w6quvol27dvDz84Odnd1LXVRLimivNxqNfADXFMdycHDgg+2miltjCRTGFXIrgA4APBljTwDMAtCBMdYU2W/vBwDGAAAR3WKM7QAQCsAA4BMqpqdMcnIyAgMDeVdULpdj3Lhxz9lNc7vd6fV6CIKAgQMHIj09HVOmTEF6ejqmTZsGJycnDBw40KxBlCgnjvaqVauwevVqREdHP+fO6OHhgbfffhvjxo3LM1sUAAYNGoQbN25g0aL/d0IKDw/H1KlT8fvvv1usi+ezyGSyPAHdCpr+LwZUc3Z2RuvWrQs1xuDq6oqJEyfi2rVrSE5Oxo8//ohmzZrlmRCW2x1QDL5V3PNHObbrO3fuYNq0aTh8+DD0ej08PT2xbNkydOvWDUeOZHv9uru7F8scQUTQ6XRYtGgRTp8+DSDbyyQlJQVGo7FIpiXRc0elUhU6eYZcLoe/vz8OHTqEmJgYJCQkoE2bNvD390dGRgYP8eHo6AgHBwfY2trC1ta21O5JlUoFmUxWYHKPouLs7Mx1oVyl28uvSV+ay7NmGUEQ6PDhw3m6pW3atKH4+PjnuiVqtZp69epFAKhevXr0+PFjIiLSaDQ0f/583m2uWLEi7d271ywmDkEQKDMzk7Zs2UJNmzYluVz+XPfVxcWFRowYQUFBQaTRaPLttt64cSOPKQo55pvBgwdTUlKSybu75kAQBJo3bx4v+5kzZ/LdLioqivz8/AgANWrUiFJSUgp9nPT0dOrWrRsBIKVSSTNnziSdTkeCIJAgCLRnzx6SyWQEgDZv3lyscyfuKzIykr799lvy8fHhZooaNWrQtm3bSK/XU0ZGBr8Pa9euTVFRUUU+jlqtpvnz55OjoyMBoJo1a9KMGTMIANnY2NC6desKVdbg4GCqVasW30dhyyIIAu3YsYNsbGwIwHMm0LJm165dvGwbNmwwWdkiIiL4Mzd8+HCLqnNB4CVmmTIXdnqBuGdmZtLbb7/Nxc3V1ZUOHTr0wpOu1Wrpgw8+IABUpUoVunPnTp79zJo1i78kqlevTkeOHDHpxTMYDHTx4kV66623uN0u92JnZ0d9+vShU6dOkVqtLnB/Go2GBg8ezG3vrq6u3I47adKkQu3DElizZg0pFAoCQHv27HnhNqJtWXxgv/766yK9fAVBoEuXLpGPjw+3S8+bN4+Sk5OJiOjgwYN836tXry7ydTcYDHT37l2aO3cu1alTh78oZDIZtW/fnoKCgnh5tVotjRgxggCQvb09hYSEFKkeT548oY8//pgcHBwIALm5udHWrVvp+PHjxBgjmUxG8+fPz7cOGo2Gbty4QYsXL6Y6depw2/S0adPIYDAUuizXrl2jihUrWqTQHT58mJ+fn3/+2WRlS05OpmrVqhEA6ty5M+l0OpPstzSwOnE/d+4cOTs78xt06tSpzw2YiRgMBvrmm2946+bs2bN5LnpGRgZ9+eWX/CH39fV9bpviIAgCxcXF0axZs6hSpUrPibpSqaQ2bdrQrl27ChwgfJZvvvmGGGNkY2ND8+fP5wN19vb2tHz5cqsYYN2wYQM/59u3b3/hNgaDgQYNGkQAyN3dnc6fP1/k62I0GmnDhg38oVcqldS7d286ceIE7dq1i7eC586dW6jzJvbCzpw5Q6NHjyYfHx8u6owxql27Ns2ePZuio6PzlFUQBFq/fj0plUpijNHUqVMLJaoGg4EuXLhAbdu25cdxc3OjNWvWkE6no9DQUP6C79mzJ129epXCw8Ppzp07dOXKFTpy5AjNnj2bunbtSt7e3rxXIZfL6Z133qGEhIQindOMjAxq1KgRAaCmTZsW+d41J6dPn+bn4ocffjCZuGdlZVGrVq0IADVu3JhiY2NNst/SwKrEXafT0ahRo7hItmzZ8rkHKTeCINCmTZu4kPz+++/PPXRpaWk0btw43pJs2LAhXb58udg3h8FgoODgYOrSpQt/IHObUHx9fennn38u8oMl8ssvv5BcLielUkl79uyhwMBAqlChAu99hIaGWlSL6kWsXr2an++9e/e+cJvHjx9z74zWrVtTVlZWsY6lVqtp2bJlVLlyZX4d7O3tqVatWrwMHTp0oN9//51OnDhBt27dek60dDodPXnyhDZv3kw9e/bkjQvxmtasWZNmzpxJ9+7dI6PR+MLzHxUVRb6+vvw6Xb9+/YXlFb1tbt26RTNmzODlZoxR/fr1ad++fbz1mJqaSl26dOHlqFChAlWrVo28vb35iyv3IpfLqVq1ajRx4kSKjY0t1styyJAhBIAqV65Mt27dKtLvzcnFixe5+WT69OkmewZ0Oh3vdVWqVMmi6lwQViPugiDQtWvXyNvbm3ez//rrrwIv4pkzZ/gbfdq0ac9tLwgCJSUl0ZAhQ7g9/NVXXy2WSBoMBjp48CB33cu9uLu70/jx4ykiIiJfASgMS5cu5eK+e/duMhgMNGfOHJLJZMQYowkTJpSJe2dhEQSBFixYwAXrRTZ3cVxFbHHPmjWrRA+r2AJ+4403XmgeE8sik8nI0dGR/vzzT4qJiaHQ0FBav349DRs2jPz8/PKMl9jZ2VFAQAAtXLiQwsPDC7ym4nUSXyht2rShjRs3UlBQEN2+fZuuXLlChw4dogULFlD//v25+UPsdfbv35/u3LnzXONkz5493GzwojrZ2dmRt7c3de/enZYtW0Z3794lg8FQ7DGGuXPnEgBydHSkkydPFut6mIPg4GDy9PQkADRlyhSTibvRaKT58+dz8+fff/9t8Y0nkZeJu0VFhSTKjl8dHR0Nxhj69u2LN954o8DReDE+REpKCm7cuAEies5d0tXVFYsWLUJ6ejoCAwMRFBSEsWPH4vfff0eNGjUKNeJvNBqxb98+fPrpp3nCBSiVSrRq1QrTp09Hhw4dShSRkYhw//59HihLDEk6bNgw7NixA1evXsUff/yBZs2a4b333oONjY3FedAIgsBjw9jb2+c7OzEsLAyZmZlQKBR49dVXS3RMuVyOFi1aYPfu3Thx4gQOHTqE4OBghIaGQqvV8un7YijliRMnQqVSITo6GhqNJs80fxcXF7Ru3RpDhw5F9+7d+Szhgs6zTCbDyJEjcfjwYZw5cwZnz57FxYsXIZPJ+G8FQYDRaOReYAqFAj4+Pvjwww8xZsyY58I/M8bQu3dv1KhRA7t370ZsbCzUajVUKhVcXFxQpUoV1KxZE02bNoW3t7dJQjuLbpyFDTpWFmTrmmlgjMHHxwc2NjbQ6XR4+PChyfZdpuSn+qW5+Pv7kyAI9ODBA25frly5Ml25cqVQb1CtVkstWrQgANSgQQOKi4t74XaCINDDhw+pU6dOxBgjxhh169aNnjx5UuBxjEYj7d+/n/cqxKVChQo0f/78YptgXlQXcTDZzc2Nnjx5wsu+fv167v3j5ORE06ZNs0gPGp1OR2PHjiXkeCmFhoY+t43BYKAvv/ySAJCHhwcFBweb7PiCIJBWq6W0tDTq1KkTASBPT0/66aefaOrUqWRra/ucKcPe3p6aN29OkyZNovPnz1NmZmaxW743b96kHj16kL29Pcnlcm4HF3sOSqWSXFxcqGPHjrRkyRJ6+PAh6fX6lx5PEAQyGAyk1WpJo9GQVqst8DfFQRAE2rZtGy/vzp07Leb+yt1y//LLL01arkOHDvFe5C+//GIxdS4IWEvLff/+/YiMjARjDMOHDy/0xB25XI5mzZrh0qVLiI2NxePHj1GhQoXntmOMoVq1alixYgWGDh2KoKAgHDt2DBMnTsSKFSvyjTBIRDh//jwmTJjAW+ziVPgFCxagffv2JpsglZ6ejkePHgHITlAg+vAzxjBw4EA8evQI8+fPR3p6OhYtWoTw8HAsWbIEVapUsZgWvCAIfHq4jY3NC88N0f8nz7azszNpDCDGGD/uK6+8gpMnT0Iul6Nz586oUKEC4uLiEB0dDUEQ4O3tjSZNmqBt27aoVasW93ku7vR2xhjq16+Pbdu24ebNm7h06RJiYmKQmZkJlUoFDw8P+Pr6okmTJqhUqRIcHR0L1dIWk26Yc56GiJOTE49xXlpZrMqa3D0zc4d5KC0sRtyzsrKwefNmGI1GeHp6on///oXuXspkMjRq1AhA9uSnuLi450wzIowx1KlTB6tXr8bQoUNx48YN7NmzBw4ODvjpp59emBXp+vXrGDt2LM8EpVQq8c477+CHH34otEmnsKSnp+PJkycAssPW5o6LY2dnhylTpqBWrVqYPn06Hj16hN27d0Or1WL9+vXFnhVpanInlnhZbHBRQBUKhdlmD4v5OdPS0hAeHo769evjt99+45Oa7Ozs8gQdIyKcOXMGP/zwAyZMmICuXbsWOSkEy8nh2qpVK7Rq1cqk9TE3jDE4ODhApVLBYDCUSYLz0oaI8iTIFkMfWzull/usAEJCQnjG+Pr166Nhw4ZFEk0xtZvRaERcXNxLt2WMoXHjxlizZg38/PxgNBqxefNmzJgxAxkZGdyeRzn277Fjx+LWrVsAsmezzZgxAytWrDC5sAPZQbTEGNgVK1Z8TvRsbW3x/vvvY/v27WjWrBkEQcChQ4ewa9euQmesNzdiYgUge8afWJ9nEc9zYezZxSUgIAB2dnZQq9U4e/Ysnz7v6OjIW6i50Wq1+Omnn3Ds2DGMGjUqT7TH/wpKpZKfF41GY1L7dkkwGAy8VW3qtI3x8fHQ6XRgjPGAc9aOxYj7oUOHeDe9qNHkxJaS2MIqTGuDMYaAgAD89ttvqFmzJgwGA1avXo05c+bwG/rRo0f4+OOPcenSJQBAlSpVsHz5cnz11VfPtfBNhWgvA5BnIC43MpkMLVq0wNKlS+Hp6QmDwYCZM2ciMDDQIh5EMfa2TCaDXq8vs3gdLCexcq1atQAAx48fL9DckpmZyQXd19cXDRo0MHs5LQ1LNFEQESIiIvi95OPjY7J9C4LAQ4U4ODjAzc3NYkycJcEixN1oNOLAgQMAsrvo/v7+Rd6Hra0tb+VmZGQU6jcymQxt2rTBypUrUbVqVeh0Ovz000/46aefcOfOHYwePRrHjh0DEaFevXr4/fff8f7775c4d+PL0Ov1vHv4Mk8YMWXZ+PHjYWdnh/j4eEydOhVXrlyxCIEXBIGX40V2YsYYXn31VbRt2xZdu3YtUpahouDl5YUWLVoAAO7fv4+goKCXnp9Hjx5xO3OTJk0KHZelvEBESEtL414y5mrEFBWDwYCDBw9Cr9fDzc2tWBqRH4Ig8N6+k5OTReWAKAkWIe5qtZonFnZwcChUgopnedbdrCi/69KlC37++Wd4eXlBo9Fg9uzZ6N27N06cOAEiQtOmTbFp0ya88cYbJu8OPouY/FdMK/ay8yCXyzFlyhSMGzcOcrkc4eHh+PDDDxESElKmAk+UHTmRiKBQKJ57WIgIDx8+xP379/Ho0SP06NHjhQPgpkAmk+Gtt96CQqFAeno6/vrrr5ear6Kjo3n8+Vq1almEsJU2d+/ehVarhY2NDWrXrl3WxQEAPH78GP/88w8AoEGDBqhXr57Jrg0R5XEAMGfjrTSxCHEXcyIC2eJurlZcfshkMvTt2xdz586Fk5MTMjMzce/ePRARateujZUrV6JZs2al8qDHxsYCyO7BFBQFUkxn99VXX6FXr15gjOHy5csYOXJkmbbg9Xo97t+/DyA7/+uLWr+PHj3Cxo0b8fDhQ6xdu7bAyJEloVmzZqhbty4A4OjRoy81E6nVat44+K+12oFsoRPNUu7u7vDx8SnzF5wgCNi/fz9/Nnr37m1yjRB7KkqlstRCg5sbixD3tLS0PKF9i+PuldtWXZybUSaTISAgIE/SXblcjsGDB78wy4+5EIVHJpMVqnvIGONhZ/v16wfGGG7cuIFRo0bh5s2bZSLwer0e4eHhALIHusXwv7nL3KJFC57x5vTp07h06ZLZyurl5cW9ViIjI3Hjxo18t83d6ytrUSsLMjMzcfnyZQDZ184SUs6p1WoEBgbCYDDA29sbb775pkmvDRHxxkXuwWRrxyLEPfcglziDr6jkHkkvareKKDsO+2effcbdHYHssYDVq1fj+PHjpTKwRESIjIwEEUEulxc656fov7906VL06dMHjDFcvXoVEyZMQExMTKkLfFJSEhf3atWqPSfuQPYYyYcffgh7e3ukpqZi7dq1ZvP2kcvl6Nq1K2QyGTQazUtfJHZ2dlw4TJXKzVogIly9epW74r722mtlbn8mIty+fRshISEAgBYtWvABclMeI3fLXRJ3E5L7oRazrBQVjUbDbaVFvSFTU1MxefJkbtNzc3NDjx49oFQq8fTpU3z88cc4ffq02UVSq9Xi2rVrALJNAr6+vkX6fZUqVbB06VJ07doVjDGcPn0a8+fPN1ky4cISFBTEB7VfffXVF75sGWPo0qULHxg7cuSIWU1JjRo1gqOjI4xGI8LCwvLN65o7eYqpsjdZC4Ig4OTJk0hNTYVSqUT37t3LukjQ6/XYsGED0tPT+fiJqZPB5265m3PORWljEeKem+KIuziAJ4pYbtNKQeh0OixcuBC7du2CIAiwt7fHN998g02bNmHMmDFQKBR48OABxo4di3///desAh8XF8dt1XXr1i2Wzbdq1apYsmQJ6tatC0EQsHbtWhw5cqTUWu86nQ6HDx+GRqOBjY0N2rdvn++2jo6O+Oijj2BjY4P4+HisW7euwGTaxcXR0ZHfF/Hx8flOrFKpVFzczTkOYImkpqZi7969ICLUrFmzVM2R+XHt2jVs3rwZgiCgQYMGeOONN8xyHFFz8nM/tkbKjbhHRETAaDTC3t4elSpVKtTvBEFAYGAgli1bBr1eD4VCgXHjxuGjjz6Cu7s7vv/+ewwbNgxyuRxhYWEYO3YsD0xmDk6fPs3DL7Ru3bpYLRTRt/vbb7+Fk5MTMjIy8N1333FvJHPz8OFDHDx4EEC2K2H9+vVfWtbu3bujSZMmAIA///wTwcHBZjm/crmc5+DUarX5mtnc3Nz4mE9Bk+HKE4Ig4M8//8Tt27cBAJ07d0aVKlXKtEyZmZlYunQpkpOToVQqMX78+EI/20VF9ILLPXZn7RQo7oyxaoyxvxljoYyxW4yxz3LWuzPGjjHGwnP+uuWsZ4yxXxhjEYyx64yx5kUpkCAIRbZvExFUKhV8fHxQvXp1VK1atcC3r2jL+/rrr/kMyj59+mD69OlcVF1cXDB//ny8/fbbkMlkuHr1KsaOHYuIiAiT3wCZmZlYu3YtDAYD3N3d8eabbxbb7VImk6FPnz4YOHAgZDIZrly5gh9//BFZWVlmvXG1Wi1WrlyJmJgYyGQyDBgwAO7u7i/9jYeHB8aNGwelUon4+HgsXrzYLLZujUbDr7Ojo2O+g/ZeXl7cjBQVFVVuHvSXQUQIDQ3FggULoNVq4enpieHDh5dKHJsXlUWr1fJnc9euXSAiNG/evEghSYqC6HUGZJuBzNV7LHXyiyiW6w3mDaB5zmcnAHcB1AewAMBXOeu/AjA/53NPAIcAMACvA7hUiGPwCH1OTk508eLFQkdFI8qOZKfX6ykqKor++eeffLM25SY5OZn69+/PI/bVq1fvuVja4r6jo6OpV69efNvOnTvTo0ePTBY5ThAE2rt3L0++MGDAgELVoaB93rt3j+cntbW1pRkzZpBWqzVLxDudTkc7d+7kcfX9/PwoMjKyUL9NSkqiN954g8dQ37x5s0mzTQmCQFu2bOERGqdOnZrv/tPS0njCjXbt2lFGRobJymGJiHlsu3TpwqNWTp48ucj3n/gMxsbGUkhICP37778UFBRE169fp7t371JMTAwlJSVRRkbGS6NZZmZm0qRJk6hixYr8eXNxcaE9e/aYLVKjRqOh3r178/tWzMNsDcCUyToA7APQBUAYAG/6/xdAWM7n3wC8l2t7vt1L9pkng865c+fMekK0Wi3NmTOHlEploW4eMUFy+/bteUacvn37UkxMjMnS9b3++utFSlDy7D7ETEK5RctoNNKuXbt4FidHR0datmwZ6fX6EpX52WNnZGTQokWLyN3dnb9I1q1bV2iBFgSBjhw5wrMfNWrUiCIiIkz2MGs0Gho4cCApFApydnamc+fOvVRcOnTowMsRHR1tkjJYIoIg0KNHj6hPnz48o1iHDh2KVGcxsffly5dp+vTpVK9ePXJwcOCLo6MjOTk5kbu7O/n6+lLnzp3piy++oA0bNtCtW7coMzMzj9gbDAaeeUoul5Ofnx+tWbPGrMlptFotz1tco0YNioiIMNuxTI3JxB1ADQCPADgDSMm1nonfARwA0CbX/04ACHjBvj4CEJyzcHFXKpVmzYRiMBho3bp15OTkxDOv/PDDDwW2VARBoNDQUPL39+c33vvvv0+JiYnFLqv4YMyYMYO/aIYPH16kG1mM87169Wpq0qQJHT16NI+o6vV62rJlC29Ru7m50YoVK0rcghcEgYxGI4WGhtJ7773HY6SrVCqaMGFCkXNvqtVqGjduHI+z/+6771JKSopJ7gOj0UiRkZG0YsUK+uabbygzMzPfbXMnuvbw8KArV66U+PiWiCAIdP/+feratSsX9vr169PVq1cLdc4FQaD4+HjaunUr9e3blzw8PPKknBSvY+5nO/eiUqnIw8OD/P39afz48bRp0ya6fv06ZWVl0ezZs6lly5b0448/0sOHD82eM1in09Hw4cMJAPn4+FB4eLhZj2dKTCLuABwBhAB4K+d7yjP/T6YiiPszv81z4c2VIMBoNNL27dt5S1Ymk9H7779PycnJhfq9IAgUHBxM9erV4y+Gjz76iFJTU4tcXvHhmDx5Mk8LV7169UI/XOI+jEYj/fHHHzyJQYMGDZ4zh+h0OlqyZAk/jpOTE02ZMqXYiT7EJNIbNmygOnXq8Ie4QoUKtGzZMkpPTy9yz0PsHYkvT6VSSePGjeNlFJfioNfr+cusoAQXRqORVqxYwV+28+fPt5rEDYVFEAS6ffs2tW/fnl+7Zs2aUVBQUKGFNDU1lfr27Zsn8QljjHx9fWnUqFE0ZcoU+vLLL2n8+PE0atQo6t+/P3Xo0IFq165NKpXqObFXKpXk5eVFzZo1o8GDB9OPP/5I58+fp9jYWJ7e0FzXQavV0vvvv08AqGbNmnTv3j2zHMcclFjcASgBHAHwea51JjPLPJtkesmSJSa/kKKwV6pUiR+nXbt2RbadC4JAZ86coVq1avGbctKkSUWyzer1ejpy5Ai1adOG59v09PSkjRs3FqmVYjQaKTAwkLy8vAgAOTg40MKFC1/Y8ler1bR48WJycXHhL6b+/fvTP//8Q/Hx8YU+rlqtprNnz9Lbb7/NXxZyuZzatGlDx48fL5LJRxzPOHLkCC1atIiGDx/OM3GJZWzcuDH17duXZs6cSSdPnizyC0kQBNq3bx/17t2b/vrrr5e22kXu379PVapU4eMrJR3/sCSMRiP9/fff1Lx5cy7IrVq1ops3bxbpvBqNRpoyZQoBIFdXV+rSpQutW7eOIiMjyWAw5NlW7F2mp6fTw4cP6cKFC/TLL7/Qu+++Sw0bNuS96NwLY4zc3NzIz8+PBg4cSH/88QeFhobyxOGmRKPR0FtvvUUAyNfXlx4+fGjyY5iLEol7jsllA4Cfnlm/EHkHVBfkfO6FvAOq/xZ0jGcTGn/xxRcmFXej0Ui7d+/OI+xNmjSh69evF+s4RqORjh07xpMWq1QqmjlzJmVlZRX42/T0dJo3bx63TwOgWrVq0Z49e4osjP/++y9/ydjZ2dHs2bNfatLR6XS0Y8eOPMm9nZycqEmTJjRkyBD6/fffKSoq6rkEzVlZWRQaGkq//vor9ejRg9zc3Pjv3d3dadq0aUUafzAajXT//n2aP38+NWnShLeSC1rs7e2pbdu2tHv37kKZrgRBoLCwMKpfvz4BIG9v70Kl89PpdNStWzcCQK+88go9ePCgUPWydLKysmjVqlVUuXJl3nPt2rVrscc3bt68SVOnTqXz589TRkZGkfdhNBopJiaGLly4QMuXL6f33nuPGjZs+JyJRxT7KlWq0Ntvv007d+6kx48fP/cSKS4ajYb69etHAKhu3br06NEjk+y3NCipuLfJOcHXAVzNWXoC8MgxuYQDOA7Anf7/ZbAcwD0ANwoyyRARRJOCuAwcONCkmc2PHDlCVatW5fuvX78+BQUFlegYBoOBAgMD+QvDzs6OFixY8NJWXnx8PE2YMIFsbGy4WA0aNIiuXr1aZLtiRkYG9e3bl7dwP//880K9XIxGI128eJHatGnzXC5RmUxGzZo1o7lz59LevXtpy5YtNGvWLOrZs2eeFyNyBk07duxIx44dI51OV2g7bXR0NC1evDiPOUc8F15eXlSzZk2qV68eNWnShHx8fF5ot3VycqKvv/6a4uLiCsw7OmfOHN67+Pjjjwv1UjAajTR9+nTeGzLnGFBpIAgCPXnyhCZOnMh7WyqVioYPH05Pnz4t0ZiRKcwlufeTmJhI//77L23cuJEmTJhAr776Knl6euYRe3GgdezYsfTXX3/Rw4cPi53zlijbLPPuu+/yl/n9+/dLVJ/SpETiXhpLzZo189jhWrdubZK3siAIdOXKFapTpw7fd6NGjejixYsmeVgNBgNt3bqV27udnJzo119/fa4Frtfr6erVq3m8EipWrEgrVqwo1k0pCAIFBgbyB7Vz586UmJhYpN8nJSXRzp07aezYsdS4cWNydXV9LpGzTCbLI65yuZw8PT2pW7dutHHjRkpOTi502TMyMmjnzp0UEBDAz4FcLqeqVavSZ599RoGBgXTz5k1KTEzkyZ/v3btHAwcOfO4lJL6IunTpQmFhYfkeU6/Xc8+LqlWrFrqFKggC7d69m9+TkyZNMos5oDQwGAx07NixPOfd1dWV5syZU+SxkdJEHE/KysqioKAgWrJkCbVr145cXFyeuyerV69Offv2pXnz5tG5c+coLi6O1Gp1oeum0+noww8/JABUuXJlun37tplrZzosXtwbNWqUx1RQv359SkpKKlGlBUGg8PBwatu2Ld9vQEAAXbt2zaQ3tF6vp1WrVnFbtru7O23ZsoUMBkMeN0Fvb29+U9aqVYsOHDhQbJfE5ORk6ty5M+8xFNV1UkQcYExPT6dLly7R2LFjycvLi2xsbEihUJBSqSQ7Ozsu6D/++CNdv3690A+OaGu9du0avf/++2RnZ8dfHpUqVaKvvvqK7t69m+8gpyAIlJaWRitWrKAaNWo811UHQKNGjcr3+I8ePaJXXnmFANAbb7xRpPMdHR1NDRo04GK4a9cus3ttmBLx3lu9ejXvdYkDnrt27bKql5Uo9JmZmXT69GmaMmUKNWzYkOzs7PIIvUwmI1tbW6pTpw4NHjyYlixZQv/88w/FxcVRVlZWvg1Gg8HAxw/c3Nzo8uXLpVzD4mPx4u7v70/ff/89yeVy3sq6fft2sUXYYDDQmTNn6LXXXuMX/5VXXimxKSY/dDodLVy4kBwcHAgAVapUiQIDAyk6Opo++eQTLmpyuZzat29fJK+EZxEEgbZt28Zblf379y+y22F+aDQaunv3Lm3dupWWLVtGq1evpsOHD9ODBw8oIyOjSGUW3TzXrFlD1atX59fB0dGRRowYQVevXi20wBgMBrp9+zZNmTKF6tevTyqViuRyOclkMurdu3e+xz9z5gz3nf/666+LXP61a9fyiWW1a9c2ecPAHIgv1KtXr9LgwYP5vadUKun999+nO3fuWNVL6lnEBklCQgIdOXKEpk2bRh07diR3d3funJC7Ve/o6EjVqlWjN998k7755hvatWsXBQUFUUxMDGm1WjIYDGQwGOiHH37g5sGiTqIsS6xC3J8+fUoBAQEEgGxsbOjHH3/krd/CIg7+rVq1ins7iD2BY8eOme2mFo/7xRdfcBHz9vam1157jd9wFSpUoIULFxZoJy6IzMxMbmt3cnKySHuwOOtx7Nix/IUnl8upVatWdODAgWLbR8UZkBcuXKBVq1bRd999R1u2bMm3DJs3b+Ympi1bthT5mBqNhj7//HNunurcubNJJq6ZA1HUb968SZ9//nmenqKrqyt9++23xXLZtXSMRiOlpKRQeHg4bdmyhcaNG0evv/76c+YbcbGzsyN3d3eqWrUqtW3bliZOnEjr1q2jMWPG8Pv0xIkTZDQa8yy5xwWeXZ7dNvdvzM3LxJ1l/79sCQgIoKCgIOzZswfDhg1DZmYmPDw8MGfOHIwYMQIKhaJQsWJiYmLw7bffYuPGjcjKyuIBuH777Tf4+fmZPC4FESEzMxPXrl3DwYMHsXv3bty5c+e57Xx9fbFkyRJ069atxLGib9++jbZt2yIxMRHdunXDzp074eTkVKJ9mhJBEBASEoJJkybhwoULEAQBbm5umDhxIsaOHYsKFSqUStQ9IsK8efPw9ddfw8nJCfv3739phMr8iIuLw/Dhw3H48GHI5XIMHToUixcvtpgkykQEnU6Hq1evYsuWLdi9ezePiSOXy9GyZUvMnDkTHTp0KDfp416G0WhESkoKYmNjcePGDVy8eBHXrl3DvXv3EBUV9cKghDKZDAqFgod4rlWrFnx8fODo6AhbW1u4u7ujQoUKqFixIjw8PEBESEpKQmJiItLS0pCRkcEXMTJtjRo1sGzZMrOHD2aMhRBRwAv/mZ/ql+bi7+9PRERxcXHUtGnTPF4R48ePp+vXr+drLxUnA+3evZtatWrF7bIymYw6depE4eHhZnmDqtVq2r9/P3Xs2JHP/nx2sbOzo759+9L169dN0msQBIGWLFlCjDFSKBS0dOlSi2qJ6fV6CgwM5HZu5Axg79+/v9RtvEajkSZMmEAAyMvLi65evVrsfd24cYPb3xUKBfXq1YuCgoJM5opXHHLf93369KHcHmdyuZwaN25My5Yto9jYWIu6R0obcewhPDycjh8/TkuXLqUJEyZQ165dyc/PL48jgSmXXr16mTTMR37AGswyRNkXIvfsQHGpUqUKjRkzhv788086f/48nTt3jg4ePEirVq2ijz/+mAICAvJ421SqVIkmT55Mjx8/NvmNbTAYKDQ0lEaNGpVn8oVKpaIqVaqQUqkkNzc3at++PW3atInS0tJMduz09HTq2rUrAdmTnixpmrRGo6GVK1dykVEoFPTWW29RWFhYmYiLwWDg7m01atQokXubIAh06tQpqlmzZp57csGCBfTkyZNSq584jnH37l368ccfqUWLFtytVrwHmzRpQj///PNz8xUk8mIwGCgqKoouXrxIW7ZsodGjR7/QK6uwC2OM5HI52djYkIODA/3000+lMrbxMnG3qHxSgiAgMjISSqUyTzKFqKgo/Pbbb/jtt9/ydIXpGZMSYwxt27bFggULEBAQYNLA+0TZGdK3bt2KOXPm8HR8jo6O6Nq1KwYNGoQ6deogMDAQHTp0wKuvvsrDiJqKBw8e8PyWrVq1KvN428D/n5clS5Zg3rx5yMzMhL29PUaPHo1Zs2aVqfkiOjoaAGBvbw9XV9di70e8r7Zs2YJZs2bh1KlTiIqKwldffYUtW7bgyy+/5EmbTV1Xo9GI9PR0xMTE4PTp0zh69CguXryIp0+f8vvf1dUVr732GoYNG4YuXbqgQoUKvNwSL0Ymk8HJyQkqlQr3799HWFjYC002crkczs7OcHNzg5+fH5o3b86ThhMRGGNgjMHOzg4KhQI2Njawt7e3iEQnZd5qp5yWuyAIdOPGDT57LveiUCjI3t6eh2x99v/IMcP06tWLHjx4YPIWiyAIFBsbS2PGjOFvd4VCQf7+/rRz507KysrKM7hirhbT8uXL+bGXL19e5i0zQRAoNTWVPv/8c95zcnV1pQULFvBzUlbo9Xo+M9Xf399k8yaSkpJo3rx5VLVqVW4CtLW1pT59+tD+/fspOjqaUlJSKCMjo8imKEEQSKPRUEJCAl2+fJl+//13mjBhArVr145cXV2fm8jj7e1NH374If39999F8uv+ryIOOkdHR9O6deuoe/fu5OTk9JymKJVKqlGjBr333nu0bNkyOnfuHCUlJZFOp3vp4GpZAGswyxiNRpoxYwa3J+d2n3N1daUPPviARowYQfXq1XvO39nBwYFGjx5N0dHRZhP2AQMGcFdNNzc3mj59Oj19+rTU3Mq0Wi316tWLe96EhoaWynHzQxAESk5Opo8++oib0SpWrEi///672WLGFwW9Xs9j2QcEBJjsOomueLdu3aJx48bx+Q3IMYt4eXlR8+bNqVevXjRx4kTat28fRUREUGpqKmVlZXH3O6PRSHq9njQaDSUlJdH58+dp2bJlNHToUKpTpw45ODg8Z56Uy+Xk7OxM7du3p7lz51JYWJhFnGtLR7xmkZGRNGfOHKpXr16ec6tQKPK4Unbt2pXi4uJIo9FY/Lm1CnGPj4/nD2PdunXp3LlzeWZ02tnZkaurax7bukKhoHbt2pXIve5liJNoPvjgAy7sfn5+dOjQoVJ9qARBoLt375KPjw8BoC5dupBarS6VY+dXnmeFvVq1arR3795SGUQqDHq9nlq0aEFAdqTMwkb+LCxiK/vo0aPUrVs3UqlUL+xV2tvbk4eHB/n4+FDLli1p+PDhNHPmTPr+++/piy++oL59+1LNmjXJ2dn5OT9tmUxGNjY29Morr9Bbb71FS5YsoWvXrlFqamqp+6rndvmzFnJPovviiy/Ix8eHP8eMMXJxcaE+ffrQ6tWr6dKlS9x9ul+/fhYv6iJWIe779+/n0+nFwGGxsbE0YcIEPhFDXBwdHalTp060bt06SkhIMNuF0Ol09O233/IXSv369enff/8t9QsvCALt37+fP/zz5s0rs4dMEARKSUnJI+w1atQw6zyC4mA0GmnkyJF8gP3atWtmOY54Po4cOUKff/459e7dm15//XWqXbt2oQOi5V6cnZ2pRYsW9OGHH9KKFSvowoULFBUVVaZmLkHITgbz22+/0fLly82aOMNUiHFqFi1aRFWrVs3z4q1evTp9/vnnFBISwgOexcfH88bTm2++WS7E3SIGVIkIBw8eRFZWFuzt7dGzZ08AQMWKFTF37ly0b98e58+fR3p6OurVq4eWLVvCz88Pzs7OZhu0MBqN2LFjB3788UdotVp4e3tj2bJlCAgIKJOBkjNnzsBgMMDJyanMygAA6enpmDFjBtavXw+9Xg8fHx+sWrUKnTp1Mkt+y+LCGEPjxo0BAMnJyYiKikKjRo1Mft4YY3BxcUHXrl3RpUsX6HQ6ZGZmIiMjA2FhYTh//jzCwsIQHR2NpKQkxMfHIy0tDYIgQKVSwc3NDZUrV0ajRo3QqlUrNG3aFJUrV4abm5vFnE+9Xo9ff/0Vs2bN4vlFx4wZUyY5VguCiJCcnIxDhw5h+fLlCAkJgU6ng1wuR7169fDuu+/i3XffxSuvvJJnzknu+0IoYg5niyU/1S/NpXHjxjx0bUBAACUkJJjtTVcY9Ho9HThwgHfTHB0daf369WXm16zRaKh169YEZMeliYmJKZNypKen0+TJk7n7XfXq1Z/L/GQpCIJAf/31Fy+rOXIEFKUsWq2WYmNj6datW3Tx4kU6f/48hYSEUEREhEUH8FKr1bRw4ULu9uvk5ETLly8vUx///FCr1bR7925q06ZNHhfRKlWq0Lx5817qtpqQkMBb7j179rTY6/EssHSzTK1atbgXyvTp08v0xOp0Olq9ejXP1mRjY0MzZ84s067o7du3+Yumf//+ZfJgpaWl0dSpU/lDU6VKFTp06JBFCrtI7vPWt29fixkPsBYyMjJo9uzZ3Fzq4uJCy5Yts7jkJQaDgW7dukWjR4/m4S6QMxdk+PDhFBISUqAXmyTuZlo8PDz44NP58+fNeCpejsFgoA0bNvAZp7a2tvTxxx+XaUwOsQUq2v0XL15cqmUR3R2/+OILLuxeXl60Z88ei2y95Uaj0VC7du34uEBUVFRZF8kqyD3eJTa67O3taenSpRYl7IKQnVz+xx9/zBNV1tnZmYYNG0bnzp0rdK6BuLg4ql69OgGg3r17S+JuqkUUjWbNmlFcXJwZT0X+GI1G2r9/f56Udd9//32xMsyYEkEQaNGiRfxlc+jQoVL10snIyKDPPvuMC3ulSpVo586dVtEKFgSB/ve///Fzt2nTJqt5aMsKo9FI169fp27dunFPNTc3NwoICKCPPvqILl++XOYvdXEQe+fOndS6dWvuaCCXy6lZs2Y8U1dRrvXjx495Qp933nnHau4Tixd38Y07ePDgMrlxBEGgy5cv85gotra2NGvWrDJ1NxQxGo30ySefmN3r41lEO/H//vc/3muoUqUK7du3zyqEnSi7DufPn+dpAVu2bEnx8fFlXSyLRHTt3Lx5M9WuXZt7l9SuXZv++OMPCggIIMYYNWnSpMx6QKLXzqFDh+iNN97IEy7Ay8uLZs6cSVFRUcUyFd68eZMqVqxIAGjs2LGSuJtqQY7f6Zw5c8x4Gl6M6MueO2XdhAkTzOI3XxwMBgMNGDCAD6aWVvJevV5Pv/zyCx9Iq1SpEh04cKDMW21FRa1W05AhQ/jMwyVLllhdHcyNIAj08OFDGj16NI9fL2a6unLlCsXExPB8wd27dy9SMnhTljEqKoomTpyYJ1Cfg4MDDRo0iIKDg0sUnO7EiRM89v+cOXMs4tkvDCUSdwDVAPwNIBTALQCf5az/BkAUcuVVzfWbaQAiAIQB6FaIY5BMJqM///yztM4Jx2Aw0OLFi3nrVExZZykXV6fTkb+/PwGgxo0bmzQQWX6ICcXFsRBXV1favHmz1YrilStXeFgLPz8/io6OLusiWQxGo5EuXLhAr7/+OjfDODs705dffskjSiYlJfForXXq1CnVBNLi5KmgoCB6/fXX+SQkOzs7euutt+jkyZMlNp0KgkCbNm3i8xI2b95sMc9/QZRU3L0BNM/57ATgLoD6OeI++QXb1wdwDYAKQE1kJ8qWF3AMsrGxKVFY1uIgCAKdPHmS29m9vb3p7NmzFnVh4+Pj+WBR165dzV42QRDo4sWLPAKinZ0d/fzzz1aVlu1Z9Ho9ff7558QYIxsbG1q7dq1FXeOyIiMjg1atWsVb5QCoadOmtG/fvjzeYXq9nr766iueV/f3338vlfIJgkBPnz6lOXPm8MFOANSwYUPaunWrycbDBEGgpUuX8pmrJ0+etJr7w6RmGQD7AHR5ibhPAzAt1/cjAFoWsE+qUaNGqbYIiLJzbL766qvc5fGnn36yuNbpgwcP+EDPoEGDzHrTCYJAN2/epGbNmnET1RdffEFZWVlmO2ZxMRqNdPjwYTp16lShbKzBwcE8Doyfnx+dPHnS4q51aSCOpQQFBdF7773H7dZKpZLeeeedfJOIh4SEkLu7OwGgNm3amN3xITU1lXbv3k0tWrTgPQqFQkH9+vWju3fvmvQ5MBqNtGDBAq4DZ8+eNdm+zY3JxB1ADQCPADjniPsDANcBrAPglrPNMgBDcv1mLYC3X7CvjwAE5yzUpUuXUjE5iGRkZNBHH33EU6gNGTKkVI9fWMLDw7lJ4YMPPjCruN+/f59atWrFba5vv/22RZmoRIxGI59kVr16dTpy5EiBAq9Wq2n06NFcKHx8fMoklERZIQjZqSCDg4NpzJgxeZJ7eHp60jfffEMpKSn5ng+NRkODBw/mccunT59uck8y0QVz9erV1KVLF/7iYYyRj48PzZs3zyzhRoxGI3333Xfchv+fy6EKwBFACIC3cr57AZADkAGYDWAdFUHcn9k3ffnll6X2oBmNRlq7di2/eRo2bGi2jE0l5f79+3wizpAhQ8xWxvT0dBo6dCh/mLp06WKWZCemQK/X07Rp07h7po+PT4HmNHHQ8P333+euc61ataLIyEiLrKOpMBqNlJCQQHv37qVBgwZxzyGxldq6dWs6evQo6fX6As/f1atXuQlHpVLR5MmTTTK7Vq/X0+PHj+mnn36igICAPAHUnJ2dafjw4XTz5s0i51QuLAaDgaZNm8YnaoWEhJj8GOaixOIOQJljXvk8n//XAHCTSmCWWbVqVWmcCxIEgSIiIngEShcXFwoMDLTYB/zp06fc3tinTx+zlNNoNNKKFSv4oLK/v7/FvuyI/t/PeezYsTz29pkzZwosryAIlJCQQH369CHGGDHGqEePHmWWLcpcCEJ2xqawsDCaN28e+fv754mmqlQqqVmzZrRq1aoi9czESX6iy6BKpaLPP/+8yC140aUxLS2NgoKCaMaMGVSnTh0u6owxcnV1pX79+tHBgwfNHqter9fTp59+yl0qb9y4YbZjmZoSiTsABmADgJ+eWe+d6/MkANtyPjdA3gHV+yjEgOqGDRtK5WSkpKTQkCFDiDFGMpmMJk2aZNFR7tRqNTVq1IhP8srMzDTp/gVBoKNHj5K3tzfvoh87dszixU4MOzx9+vQixbcRBIHCw8MpICCAC0nDhg1p7969VhsbXfQo0Wg0dPPmTVq5ciX17duXKlasyL1LkDN/o23btrR69WqKjY0tlj+4Xq+nPXv2cFOhSqWi0aNHvzBui5CTxMJgMJBer+cvnB07dtAnn3xC/v7+5OLiwk1ljDGqWLEijR49mkJCQkrNHVmr1dLgwYNJHPuLiIgw+zFNRUnFvU3OzXEdudweAWwEcCNnfeAzYj8d2V4yYQB6FOIYZm+5i/7s48eP5y5PzZo1o8jISLMet6QYDAZ65513CABVrVqV7ty5Y7J9C4JAt27dooYNG/IHdd68eVbjGSO2AIs6MCoIAl25coXatWvHJ+u4uLjQsGHD6Nq1a2bNpmUKRNEU3RRPnTpFc+bMoY4dO5KXl1ceswZjjKpUqUIjR46k48ePU1JSUonrZjAYaM+ePVSpUiVCrpmhq1evppMnT9KxY8do//79tHXrVlqyZAlNnDiR+vbtS40bN6ZKlSo9l6uUMUa1atWiyZMn040bN0p98qBGo6E+ffrwwfbHjx+X6vFLgkls7uZcRJu7OYNQibHZRTttjRo16O+//7boh5go+0FeuHAht5Hu27fPZO5fT548oY4dO/JBso8++qhMJqiUBWL9x44dy4NNMcaoatWqNHv27FJNfF1YxFmkd+7coZ07d9JHH31EDRo0yJMNSlwqVKhAXbp0oZ9++onu3r1r8pgwBoOBjhw5Qg0aNODHVCgU5OTkRA4ODvkmLxEXe3t7atSoEb333nu0YcMGioyMLDPvpczMTD6XpHnz5pSYmFgm5SgOViHu7dq1M3m2HBGj0UgbN27kD0HVqlUtPqKhiOiLL84c/Pzzz0tcbtF/uH///rxL3KNHD4qJibE4QTMnogfJ7t27qVWrVnlilDRp0oSWLl1KDx48KHCw0ZxkZWXRvXv36PDhwzRjxgzq1KkTVatWLY+5RbSj16hRg958801asWIFXbt2zexmDUEQKCwsjMaMGcPt8M8uKpWKPD096ZVXXqHmzZvTgAEDaP78+XT69GmKjo62CHfU+Ph4at++Pbm5uVG/fv0sokyF5WXizrL/X7YwxsjBwQFHjhxB69atTbpvg8GA/fv345NPPkF0dDScnZ2xfPlyDB48uOyzkxeS+Ph4dOjQAaGhoWjcuDHOnDkDZ2fnYu2LiBAdHY1PP/0U+/btgyAI8Pf3x+bNm1GnTh2rOSemhIiQkJCAP/74A8uWLcOjR49ARJDJZKhVqxYCAgLQoUMHvPbaa/Dy8oKnpydsbGxMXg6DwYCUlBQkJCQgLCwM//77L0JCQhAWFobHjx/DaDTybcUkIdWqVUO7du3wxhtvoHHjxvDx8eFJNErjWhIRjEYjrl27hqCgIKSlpcHGxgYODg5wdXWFm5sb3Nzc4OnpCU9PT9jb2+epgyVAREhKSkJERARUKhWaNGliMWUrCMZYCBEFvPCf+al+aS7IecuPHDnSpN3HZyM92tjY0DfffFPoMKCWgiAI9OGHH3LXsBMnThSr/IIg0KNHj6hfv368xd6oUSMKCgqyqvNhDsRByVu3btFnn31G3t7eecwKjDGytbUlPz8/GjZsGG3cuJHu3r1LSUlJRR6IFc0rCQkJFBkZSUePHqWFCxfS0KFD6dVXXyVXV9fnTBo5DSCqWrUq9ezZkxYtWkRnzpzh+VTL+vrlHgd4drEWrK28RFbQcrezsyONRgM3Nzfs2bMH7dq1K/Gbk4jw4MEDDBgwAFeuXIGNjQ0+++wz/O9//4Ojo6OJSl46EBEOHDiAt99+GzqdDuPGjcNPP/1UpNajIAi4desWxo8fj9OnT4OI0LBhQ6xfvx7+/v5W01IxN5TTEr19+zZ27dqFQ4cO4e7du8jKyoJer+fbKRQKODg4wM/PD3Xr1kWlSpXg7u4Od3d3eHp6onLlynB3d4etrS0YY8jMzERycjKePHmCe/fu4ebNmwgNDcX9+/eRmZkJo9GYJ72bXC6HSqWCk5MTmjZtipYtWyIgIADNmjWDp6cnFAqFxaThkyg7XtZytwhxr1GjBsXGxkKj0aBTp07Yvn07PDw8SiQ4mZmZ+OSTT7Bx40YQEYYNG4alS5fCycnJhCUvHSjHbNCzZ08EBwfD09MTe/bsQevWrQs8R+Jb/J9//sGnn36K0NBQnl90zZo1krDnAxFBEARkZWUhMjISly5dwtWrV/Hvv/8iNDQUGo3muVybMpkMcrmcLzKZjJ9bcX8GgwF6vT7Pb8XfqVQq1KxZE40bN0aTJk3QpEkTNGzYEK6urlCpVHn2JyEBWIFZplmzZtS/f38+4r5gwYISdY+MRiOtX7+eu1w1bdqU7t27V+z9WQKCINDq1au5G2fHjh0pPj6+wFmFarWa1q9fz2cWymQy6tq1K4WGhlpdF7QsEQSB9Ho9JSYm0sWLF2nhwoU0cOBAatKkCbm4uJBcLueBtV7mJSLOr3B2diZ/f38aOHAg/e9//6Ndu3bR7du3KSEhweyTdiTKD7B0s0xAQACtWbMGffr0wePHj1GrVi0cOHAAfn5+RW6pCIKAEydOYMSIEYiKioKLiws2b96Mnj17Wn2rJzU1FcOHD8e+ffugUCgwefJkfPPNN1CpVHwb8XqKg1xLly7Frl27kJmZCaVSicGDB2Pu3Lnw8vKy+vNRlhARdDodUlNTkZ6ejoyMDCQmJiIxMRHR0dF48uQJkpOTodFoAAD29vZwc3NDlSpVULt2bdSqVQvu7u5wdnaGjY2NdC0kioXFt9z9/f3JaDTS7NmzSS6XE2OM3n33XUpJSSnSW0wQBDp8+DCfrq9UKmnWrFkWlfexpFy9epWHAHZ0dKRVq1bxAb3k5GS6efMmbd++nQYNGsQHkoHsVGnffPNNmeaDlZCQMC2wdD93f39/IiKKjY2lNm3acGEeOXIk3b59u1Bp3bRaLe3YsYMLn42NDY0dO5ZSU1OLfeIsEYPBQFu2bOFZY1xcXKhz587Us2dPCggIIG9vb+4JI57Hli1b0sGDB61m5qmEhEThsBpxFwSBzp07xxNFiKE+J0yYQFeuXHlhgCK9Xk+RkZE0c+ZMLnhKpZI+/vhjSktLK5etVK1WS7Nnz+YzK59dGGPk5uZGbdq0odWrV5slTKolIAgChYaG0vHjx60mr6uEhCl5mbhbjM09ODgYQPbL5tixY5g8eTJCQ0NhNBrBGIObmxvatWuHYcOGoXXr1iAi3LhxA/v27cPBgwfx4MEDCIIAJycnjB07FtOnT4ezs3O5tGUSETQaDf7880+sXr0a4eHhICJ4eHigSZMmCAgIwGuvvYamTZvC3t6+3J6D8PBwjBgxAhEREVi2bBn69+8PhUJR1kWTkCg1LN4VMre4A9mDotHR0fjjjz+wefNmhIeHcx9jlUqFatWqQa/XIyYmBjqdDkTZswkrVaqE7777DkOGDPlPDFIJggCdToesrCwIggB7e3soFAooFAowxsp1/YkIa9aswWeffQa1Wo2KFSti1apV6N27t+T/XQzEBoPocilhHbxM3C3yKspkMlSpUgVTp07FiRMn8PPPP6Nr166wt7eHVqtFREQEHj58CK1WC4VCgXr16uGrr77C33//jeHDh0OlUpVrYRORyWSwtbXlE2fs7e1hY2Pzn/GHHjx4MCZOnAiVSoWsrCxER0eXdZGsEiLC7du3MWTIEJw6deo5/30J68QiW+7PQkRIT09HcHAwtm/fjvDwcCiVStSpUwfdu3dHQEAAPDw8pC75fwwiQlZWFhYtWgQXFxeMGzcuj1uoROGIiorCO++8g4sXL6JmzZpYvXo1Onbs+J9oIFg7VmeWyQ+i7Fl+GRkZkMvlsLe3l7qQEtDpdABglmBe/wVSUlLwxRdf4I8//oDRaESTJk2wf/9+VKtWrayLJlEALxN3q2rqMsYgl8vh4uJS1kWRsCAkUS8Zrq6uWLBgAWQyGf7880988MEHqFixYlkXS6KEWFXLXUJCwnykpKQgODgYbdu2lcxbVkKJBlQZY7aMsX8ZY9cYY7cYY9/mrK/JGLvEGItgjG1njNnkrFflfI/I+X8Nk9ZGQkLCLLi4uOCNN96QhL2cUBiDtRZAJyJqAqApgO6MsdcBzAewhIhqA0gGMCpn+1EAknPWL8nZTkJCwsIp7+6z/zUKFPeciVAZOV+VOQsB6ARgV876PwD0y/ncN+c7cv7/BpPuGAkJCYlSpVCuJowxOWPsKoA4AMcA3AOQQkSGnE2eAKiS87kKgMcAkPP/VAAeJiyzhISEhEQBFErcichIRE0BVAXwGgC/kh6YMfYRYyyYMRYcHx9f0t1JSEhISOSiSE7iRJQC4G8ALQG4MsZEV8qqAKJyPkcBqAYAOf93AZD4gn2tIqIAIgqoUKFC8UovISEhIfFCCuMtU4Ex5prz2Q5AFwC3kS3yb+dsNhzAvpzPgTnfkfP/k2QJ/pYSEhIS/yEKM4nJG8AfjDE5sl8GO4joAGMsFMA2xtgPAK4AWJuz/VoAGxljEQCSAAwyQ7klJCQkJF5CgeJORNcBNHvB+vvItr8/u14D4B2TlE5CQkJColhIgVkkJCQkyiGSuEtISEiUQyRxl5CQkCiHSOIuISEhUQ6RxF1CQkKiHCKJu4SEhEQ5RBJ3CQkJiXKIJO4SEhIS5RBJ3CUkJCTKIZK4S0hISJRDJHGXkJCQKIdI4i4hISFRDpHEXUJCQqIcIom7hISERDlEEncJCQmJcogk7hISEhLlEGYJGfAYY+kAwsq6HGbAE0BCWRfCxJTHOgHls15SnayH4tbLh4hemIS6MGn2SoMwIgoo60KYGsZYcHmrV3msE1A+6yXVyXowR70ks4yEhIREOUQSdwkJCYlyiKWI+6qyLoCZKI/1Ko91AspnvaQ6WQ8mr5dFDKhKSEhISJgWS2m5S0hISEiYkDIXd8ZYd8ZYGGMsgjH2VVmXpygwxtYxxuIYYzdzrXNnjB1jjIXn/HXLWc8YY7/k1PM6Y6x52ZU8fxhj1RhjfzPGQhljtxhjn+Wst9p6McZsGWP/Msau5dTp25z1NRljl3LKvp0xZpOzXpXzPSLn/zXKtAIvgTEmZ4xdYYwdyPleHur0gDF2gzF2lTEWnLPOau8/AGCMuTLGdjHG7jDGbjPGWpq7TmUq7owxOYDlAHoAqA/gPcZY/bIsUxH5HUD3Z9Z9BeAEEfkCOJHzHciuo2/O8hGAlaVUxqJiAPAFEdUH8DqAT3KuiTXXSwugExE1AdAUQHfG2OsA5gNYQkS1ASQDGJWz/SgAyTnrl+RsZ6l8BuB2ru/loU4A0JGImuZyD7Tm+w8AfgZwmIj8ADRB9jUzb52IqMwWAC0BHMn1fRqAaWVZpmLUoQaAm7m+hwHwzvnsjWwffgD4DcB7L9rOkhcA+wB0KS/1AmAP4DKAFsieNKLIWc/vRQBHALTM+azI2Y6VddlfUJeqOaLQCcABAMza65RTvgcAPJ9ZZ7X3HwAXAJHPnm9z16mszTJVADzO9f1JzjprxouIonM+xwDwyvlsdXXN6bo3A3AJVl6vHPPFVQBxAI4BuAcghYgMOZvkLjevU87/UwF4lGqBC8dPAKYAEHK+e8D66wQABOAoYyyEMfZRzjprvv9qAogHsD7HhLaGMeYAM9eprMW9XEPZr12rdEdijDkC+BPARCJKy/0/a6wXERmJqCmyW7uvAfAr2xKVDMbYmwDiiCikrMtiBtoQUXNkmyc+YYy1y/1PK7z/FACaA1hJRM0AZOL/TTAAzFOnshb3KADVcn2vmrPOmolljHkDQM7fuJz1VlNXxpgS2cK+mYh256y2+noBABGlAPgb2SYLV8aYGIIjd7l5nXL+7wIgsXRLWiCtAfRhjD0AsA3ZppmfYd11AgAQUVTO3zgAe5D9Mrbm++8JgCdEdCnn+y5ki71Z61TW4h4EwDdnhN8GwCAAgWVcppISCGB4zufhyLZZi+uH5YyEvw4gNVeXzGJgjDEAawHcJqIfc/3LauvFGKvAGHPN+WyH7DGE28gW+bdzNnu2TmJd3wZwMqdlZTEQ0TQiqkpENZD93JwkosGw4joBAGPMgTHmJH4G0BXATVjx/UdEMQAeM8bq5qx6A0AozF0nCxhs6AngLrJtoNPLujxFLPtWANEA9Mh+O49Cth3zBIBwAMcBuOdsy5DtGXQPwA0AAWVd/nzq1AbZ3cPrAK7mLD2tuV4AGgO4klOnmwD+l7O+FoB/AUQA2AlAlbPeNud7RM7/a5V1HQqoXwcAB8pDnXLKfy1nuSVqgjXffznlbAogOOce3AvAzdx1kmaoSkhISJRDytosIyEhISFhBiRxl5CQkCiHSOIuISEhUQ6RxF1CQkKiHCKJu4SEhEQ5RBJ3CQkJiXKIJO4SEhIS5RBJ3CUkJCTKIf8HHrj01q02OVkAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/ولا تنسوا الفضل بينكم.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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uDMYacnBykp6eXeBzHcXjrrbfQrVs3/P777/jnn3+g0+lARNBqtdDr9SAi5OXl4fr169BqtYiLiyvXPVQVzs7O4DgOWq0WiYmJAApaNY1Gg8zMTEgkEri7uz/1/RX+X6vVIjU1FT4+PpVaF0ylvCJLFLqBjDFfAEmG/fEA/AsdV8uw7ymIaBOATQAQHBxcoVlkhUIhPq1yc3Mxbdo0LFiwACNGjIBSqayUL1ilUolPSEFwpp736tWrePz4Mezt7TF+/HjIZLJSjxfKzvN8qa0nYwwymQyBgYEIDAws8bjs7Gy0bdsW9+/fN2mcU9UwxtC4cWM4OjoiNzcXu3btwunTpxEZGYn4+HikpqaiX79+WLVq1VOf7dixIzp06ICzZ88iIyMDH374IdauXSs+sCxBeUV2CMB4ACsMfw8W2j+dMfYDgPYAMqtiPObg4AAfHx/x/8jISLz11ls4ceIEFi5ciICAgAp/wSqVSmzJSmtVSkKv1+P27dsgInh4eKBWrVplfiY3NxdEBKlUWqYgARh9j4yxYi2Q1kSzZs3g7e2N8PBw7N69+6n3fX19i33Q1a1bF9OnT8fFixeRn5+PQ4cOITExERs2bEBgYKBFhFamyBhj36PAyFGDMRYHYBEKxLWXMTYRQDSA4YbDjwDoDyACQB6A181Q5qeQSqWoU6cOAMDFxQU+Pj64f/8+du3ahUuXLmHp0qUYPHhwEQueqRQeQCcnJ5tsXeR5XnT9ysjIQHR0NOrWrVvij05EuHHjBnieh5OTEzw8PMpdduF8ycnJyM3NBWOsxPGdtWBvb4969eohPDwcCoUC9erVg6enJ3x9fVG3bl0EBQUV+xswxuDs7AyJRAKg4L6vXbuG69evl9rKmxNjrIsjS3jrhWKOJQDTKlooU2GMoX79+mCMwcXFBVu3bsX+/fuxdetWhIeH49q1axg8eHCFriH8aECByExFJpPhtddew65du5CdnY1Vq1YhKCgITk5OxR6flZWFX3/9FTzPo3HjxvD39y/2OFO4dOkSkpOTIZPJ0KZNlUxhlhuJRIKAgAD8+eefaNCgAX799Vf4+PjAzs6uzNbI0dERHMdBoVBgwIABaNmyJYYNG2ax7qJ19xlMwM/PD4wxZGVlwdHREStXrsSuXbswbtw4TJ06tUKtGAB4enqKQktJSSmXhbFFixYYMmQIOI7DX3/9hf379xf7NCYihIaGIjQ0FBKJBCNHjhStbeWBiJCdnY2NGzdCo9GgadOmaN68ebnPVxVIJBI0btwYjDGkpKRAJpPB3t7eKKHY29tDIpGAMYYxY8Zg3rx5UCgUVVDq4nlmRFazZk3REpeZmQm5XI6XXnoJX3/9dZHxWnnx8/MTRZaTk1MuC6ODgwOWL1+OoKAgaDQarFq1Cnfv3i0y0UxEePToEVauXIm8vDzUqVMHvXv3rtBTmIiwY8cOnDt3DlKpFJMnT7b67qIwhaFUKqFSqRATE2P0Z+VyORhjICLo9XooFIpqaV20OlxcXKBUKpGbmysaJhhjsLOzq5Tz+/v7w8HBASqVCmq1GpmZmXBzczP5PDVq1EDt2rVx6dIl3L17F0OHDsW4cePQvn17uLq64vr169i4cSMuXrwImUyGiRMnVqirSEQICwvDp59+Kk6ADxs2zOoNH0CBcUMul0OtVotmfGMQJuB5nrcKt6pnRmQymQwKhQK5ubnlnscqDUdHR9SqVQspKSlQq9XIysoq13kuXLiAv/76C0CBwebu3bv48MMPYWdnB6lUiry8PGi1WiiVSowdOxbTpk2r0FM4JiYGM2bMQGxsLDw8PPDRRx9V2IhSVXh5eUEulyM7O1s0NhnzXQjzgoyxImNpS2H9jzMjkUqlYr/bHCJTKpWi2V2r1ZZr8aNarcb69euRmZmJ2rVr44svvkC/fv3g4eEBIoJarYZCoUDbtm3x1VdfYc2aNXB2di6XyIRu58yZM/Hvv/9CoVDg/fffL3MC3JpwcHCAVCoFz/PIyckx+nMajabA+53jKjSWrSyemZZMIpGIc0mm+LoZi1wuR82aNQEUPCnL0w25ceMGjh07BgAYP348pkyZgtdffx2PHz9GREQEsrOz4enpicDAQDg5OZW7S0dEePjwIaZOnYq//voLHMdh6tSplWIAqkoKi0Sj0Rj9OUFkjDGbyCqTwhOspXmslxeO40RjgV6vN+lHBwrm2Xbu3ImMjAz4+vpi5MiR4DgOdnZ2qFevHurVq1cp5dRoNDh+/Dg+/PBDXL16FVKpFBMnTsTChQthb29fKdeoSoQHp1arNbq7KPgvCmZ8S/PMiMzcMMbg6uoKjuPK1ZIlJyfj999/BwD07dsXjRo1qtTyERESEhKwdu1abNy4EZmZmbCzsxPXZJU0H2ftFF7GYyyCyAoPISyJTWQmIEyECmMyU/wX8/LyRH/B7t27V6p1Lz8/H8eOHcOSJUsQGhoKnufRoEEDLFiwAK+++qpVVLSqpHBLVtwyoqrGJrJywPO8Sat1gYLuomAsqaylF0SEiIgIfPHFF9i5cyeys7OhVCoxePBgLFq0CI0bNwZgvE+jtVF4CGDK5H9+fr5o+LCGB4xNZCZQuLJmZmaW+/PlNf8LEBFSU1Oxc+dOfPnll+JizEaNGmH+/PkYNmyYUe5H1k5hw4VgzDCG/Px8qxqTPTMmfOB//XZzVa7CP3JGRoZJn3V3d0eDBg0AAGfPni339XNzc7F//36EhITgnXfeQWRkJJydnTFp0iT89ttvGDdu3DMhMIHChg9jKWxdNGb1grl5ZloyrVaL/Px8AAXzK+agcMVNSkoq5cincXd3R6tWrXD9+nWcOXMGiYmJRrt7CQsuT58+jfXr1+PEiRNQq9WQyWTo0aMH3n33XXTr1s0qzNWVSXktxoIlkuM4qxDZM9OS5efnixOWVeGXFxkZaZLFSyKR4OWXX4ZcLsf9+/dx5MiRUj9PROB5HsnJydizZw9CQkLw8ssv48iRI9BoNAgMDMSmTZuwf/9+9OrVq4jAsrOzkZmZ+b+4f/8xBI8PADaPj8pE8CkUTO3mJjY21uS5si5duqBNmzbQ6/VYuXIl7ty5U0QEgijy8/Nx/fp1fPTRR+jRowdef/11nDx5Evn5+WjUqBGWL1+O33//HePHj4eLi8tTXcOTJ08iJCQEP/zwg9i6V1eEe+N53uQHhhBAyNI8M93FzMxM8DwPhUJRJZOuGRkZSE1NhZ+fn9GfcXNzw/vvv4+xY8fi/v37GDNmDObOnYtmzZqBiJCUlIQ7d+7g6NGjCA0NRWpqKogIcrkcwcHBeO211zBo0CDUrFmz1CmAmJgYnDt3DlFRUQgODkbDhg0r45arHMYYHB0dARSNsVLdeKZEBhT4GFbF3EhWVhZSUlJMEhljDP369cOsWbOwZs0aXL16Fa+//rpYkdRqtRjmgOM4uLm5oWfPnhg1ahS6du1abOCY4khKShKDzpja2loTHMeJoe2E7q8xCEGEAOvoLj5TIiMiKBQKs4mssN+fEEHK1IA6crkcH3zwARo0aIBly5YhPj4eOTk54iDfw8MDLVq0QN++fTFgwAA0aNDA5GBAglDz8/PN4sdZVXAcJ84pCj2VshAcrYkIEonEKoxBz4zIBKOHQqEw29xIYUtVfn5+ucIQAAWt7bhx4zBgwABEREQgOjoacrkcHh4eqF+/Ptzc3GBvb19urxBhHKZWq8VgPNYwNjEVjuPEVl64j7IQLLFAwfdsa8kqEeEpZ87BrrAYUCA+vthod0bBcRw8PT3h6emJjh07VkbxABRUMuGBk5+fX67IWtYCx3Hi+DonJ8dokQneOOVdJlTZPDPWxarAzc2tyI9mjbELhSX3AtYexLQ0Cq9sF7qAZcHzvDg+d3FxsYoV4JYvQSVRFU8sHx+fIj+aORaHVpTCFjkAePDggQVLU3FM9cIvLDIhCrGlsXwJKgnB2KHVas0W16FGjRpFBtJC37+q0Wg0JabvZYyhRo0aYuW8f/++2UzfGo3GLGv3KgIRFWnJbN3FSkR4emu1WrOZre3s7Ip4k1gi/7JGo8H69evRv39/XLhwoVgBNW3aVHyCx8XFVdghuTjS0tIwb948MTakuTDVC5/nefF+bS1ZJSPE5BPycpkDpVIJT09P8f+qnhwlIjH1UlhYGF577TVcvHjxqXI0bdpUtLAmJyeb7GdZ+HqF55wENBoNli9fjrVr12LKlCkmCY3neSQmJhotGqHnoNfrjUqDxPO86LwtLLK1NJYvQSUhVCpjf4zyXqOwyKoaxhiaNm2KRYsWiVls7t2799Rxvr6+osd/WloaUlJSTL4WESExMRFvv/02QkNDiwhNKpWie/fu8Pb2xuPHj/HJJ58YdY38/Hx8//33CAkJwYkTJ8p8SAlLXUwJ76bRaMR5xycNVZbimRFZ4SeeubIrKhSKIt1FiyQvkEoxYcIErFmzBp9++ilGjBjx1DFubm5o1aoVgIKKHR0dbfJ1VCoV5syZg/Xr12P8+PG4evWqKAqO49CvXz9s3LgR7dq1w8cff2zUwyclJQWffPIJLl++jHnz5iE+Pr5MoZkqsqysLGi1WkgkEtuYrLKpCsMHx3FFYmVYqisilUrx+uuv48033xQr4ZPvd+jQQdx//fp1k7u2MpkMnTt3hoODA27duoWPP/64iDVVENrhw4fRp08foyqzr68v3nvvPdjZ2SE0NBQff/yxuIq5tHII0YCN+V1zcnKg0+me+q0syTMjMkdHR8hkMuh0OpNDAxiLkDFEwJLeBBzHlSry4OBgsXW/evWqya27TCbD5MmTsWzZMgQFBeH9999/ap2eRCKBp6en0a0Fx3EYNmwYxowZAwDYtWsXfvzxx1I/Y2pLlpOTA71eD4lEYhNZZaNUKsVKYM6k44W9CKxhUF0SNWvWFONEPnjwwKQw1wJyuRxTpkzBb7/9hnbt2lVK18ve3h4ffvghWrdujby8PCxevBg3btwosTUzNeR2bm6uKDJzLd41FeutJSYik8lE7wBzmKwFHB0dxcpmDf39knB3dxdjOcbExCAiIqJc55HJZPD29q7Ue/X398fy5cvh5uaGqKgofPXVVyUeWzh5hDEGrfz8fOj1+krNg1BRnhmRSSQS0UvenAsVreXpWBZ2dnbo3LkzgAKL29GjR81mEDIVxhh69OiBl156CQBw8eLFEh+MhX1RjRlXWltgU+AZEhnHcaLIzLmGysHBwapbMAGhIgsrB7Zv344rV65YzcJHmUyGTp06gTGGhIQEJCRUTtZjYQU1Y8xqQpLbRGYi1SnUdcOGDVG7dm0AwOPHjzFz5kyEh4dbjdB8fX3BGENeXl6JLmqFw3Nbw7KV8vDMiKyquotSqdTiLZmwhKUsLwsPDw/4+vqK/1+8eBGTJk3Cw4cPrUJoaWlpICLY2dmV+PAqbC2sLl31J3mmRCZ0jcoSmeBEGhkZibt37yIuLk5cTVwWhS2KxY1xSnJFKqs8xh5PRDhy5Aj69u2Lbdu2leo/qVAoxJXFCoUCMpkMZ8+exRtvvIHo6GiLCk2tVuPMmTMgIjg7O5eaO1uIa194dUF14pkRmVQqFeeFSluCotVq8csvvyAkJAStW7dG69at0aFDB4wePRq//PJLmcv1C4vsyW4pEeHmzZt4++238euvvxptaIiPj8eCBQvw5Zdflrk0JSMjA2vWrMHFixexfv36Ui2phZe9tGnTBjNmzIBcLsfJkyfx5ptvIi4uziJCy8vLw8aNG8U5sl69esHb2/up4wqHErCW5BHlQniKWnILCgqiipKTk0Ndu3YlADRu3Lhij+F5no4ePUqurq4EgAAQY0x8bWdnR9OmTaPs7OwSr/PXX3+RVColAPTqq68Sz/PE8zxpNBoKCwujNm3aEADy8/Ojf//9l3ieL7XcPM/T7t27SSaTkUKhoF27dpV67MaNG0kmk5FUKqUPPviAHjx4QHq9/qlj9Xo93bp1i5o1a0YAaOjQoZSdnU1z5swhqVRKjDEKCQmhW7dukU6nK7WMlYVKpaLz58/T6NGjSalUEgB67rnnKDw8vNjvied5WrBgAQGggIAAysnJKfMa+/fvJ4VCQS4uLnT9+nVz3EaxALhMJdRv6zC/VAJCOlsAJWZlVKlUWLt2LTIyMuDv74+5c+fC398fV69exf79+3H37l1s3rwZAQEBmDFjRrEDbScnJ3FMlpmZiezsbFy6dAnbt2/Hn3/+KU76xsfHY+3atfjuu+9KDezD8zzOnj0LrVYLuVxeasTbhIQEfPnll9BqtejQoQNu3bqFvn37Yu7cuejZs6foV5mcnIxjx45h7dq1iIiIgEKhwJAhQ+Dg4ID58+cjIiICv/32Gw4fPoywsDCEhIQgJCQETZo0QY0aNSqUgPBJ9Ho9Hj9+jH///Rd79uzBiRMnkJmZCcYYgoKCsHHjRjRo0KDYcS4VcqV6MvRDSdjZ2UEikYDneYut93uSZ0ZkUqlUrMxCPIgnf5TY2FhcunQJHMdh+vTpmD59OhhjGDx4MCZMmICxY8fi7NmzWLZsGZ577jl07979qXMUroBXr15FSEgILl26JI6NGGPw8vJCcnIy/vjjD4SGhqJjx44lVqK4uDgx+ybP86Ix4Mnj1Wo1NmzYgFu3bkGpVGLEiBFi93LKlCnw8fERu1yPHj0Scyw7OTlh1qxZePnll8EYg4eHB7Zs2YLly5dj27ZtiIuLw4YNG7Bp0yZ4eXnB398fPj4+qF27Nry8vODn54eAgAB4enrCxcXlqS6bVquFSqUSlxhpNBrk5uYiKSkJ4eHhCA0NRVhYGCIjI8Xuc82aNTFq1CjMnj0bNWvWLFE8VGgC2lhzvIODgygyU1LgmpWSmjhhA+AP4G8AtwHcAjDLsN8dwDEA4Ya/bob9DMA6ABEArgNoU9Y1KqO7yPM8jRw5kgDQ888/TxqN5qljfv75Z1IqleTs7Ez//PNPkS4Kz/N0/vx58vPzE7sxDx8+FLuDer2eYmJiaMuWLWJXp7gtODiY/vjjD/E8gwYNoqysrKe6QzzPU2ZmJk2aNKlIl3X48OGkUqmKHBcTE0OTJk0ie3t7AkADBw6k9PR0+vXXX6ljx47Flsfe3p66d+9Ohw8fJo1G89S9ajQa+ueff2jSpEnUsGFDkslkJd6TRCIhd3d3CggIoGbNmlHz5s2pefPm1KxZM2rYsCH5+fmRp6cnubi4kJ2dXZH7ETaZTEaBgYG0ePFiunPnDul0ujK70jqdjubMmUMAqHXr1pSXl1dmPbh+/Tq5u7uTXC6nPXv2lHmNygKldBeNEZmvIBQATgDuA2gG4FMA7xv2vw9gpeF1fwBHDWLrAOBCWdeoDJEREU2ePJkAULt27YodV33zzTckkUjI29uboqKinnpfr9fTt99+S/b29sQYo4kTJ1JYWBjt2bOHxo0bR/Xr1yeJRFKk8jk4OIive/XqRdevXyetVkuLFy8miURCcrmcPv/88yKViud5evToEY0ePVocH3l7exNjjJydnWnPnj2k0+koLS2Ndu7cSa1atRIrbuPGjenff/8Vz5OWlkanTp2i1atX06xZs2jWrFm0evVqOnfuHGVnZ5dZyfR6PcXFxdHJkyfp888/pwkTJlCvXr2oTZs21KhRI6pRo0aRey5tY4wRY4w4jiMnJyeqU6cOde7cmWbNmkW//fYbJSUlmVTpC4usVatWRoksOTmZatWqRYwxWrFihVWIrMw2mIgSACQYXmczxu4A8AMwCEB3w2HbAZwE8J5h/w7Dhc8zxlwZY76G85gVwQycn59f7IR0YWsf0f+iOul0OuTk5CAnJweNGjVCy5YtceHCBezcuRMHDx5EWlqaOCcllUohkUig1+vh5OSECRMmQKPRoF27dhgwYIA4Lpo6dSpOnTqFv//+G8uXL4eLiwvatm2LjIwMhIaG4rvvvsP169cBFKS3ffvttzFjxgzcvXsX06dPx44dO8RFmRqNBkqlEmPGjMH8+fNRt25dABAXJnbt2hVdu3Yt13fGcRz8/Pzg5+eHbt26ASjoAubk5ECtViM7OxvJycmIjIxEdHT0U+McOzs7eHh4wMnJCfb29nBwcICrqyscHR3h5OQEDw8PcdW6qZQnP5m9vT18fX0RFxeH6Oho6PV6y3t+lKS+4jYAdQHEAHAGkFFoPxP+B3AYQJdC7x0HEFzMuSYDuAzgcu3atSvlabJ48WLxaZ+QkPDU+4cPHyY7OzuSSCTUqlUrev7556lz587UsmVLCggIIH9/f3Jzc3vqya1UKikwMJCmTZtGP//8MwUHB4v7z507V2xZeJ6nf//9l/z9/QkAKRQK8vLyIkdHR7FVUigUNGnSJEpMTCSe5+mPP/6g2rVrF7m2VCql4OBg2rVrF+Xl5VXZk9ka4Hle/E3r1atXqtVXID8/n4YNG0YAqH///kW63uYElWFdZIw5AjgAYDYRZRV+MhGRUHFMEfcmAJsAwFBpK4zQiuTl5RU739W+fXt07NgRJ0+eRFhYWJH3BEdUjuMgl8vh7OyM5ORkSKVSrF69GsOHD4e7uzt0Oh22bNkCoODpKiw6fPJJzRhDu3btsHXrVsydOxd37txBWlqaOHfVtm1bvP7666LVDyiYLzpw4AA2bdqExMREuLu7o0+fPujduzc8PDws7mliCYQ4HUKrWtaEtEQiQZ06dQAA0dHRZgtFYQpGiYwxJkOBwHYT0U+G3YlCN5Ax5gtAiNYSjwJjiUAtwz6zU6NGDQAFk9HFiczDwwNbt27F7t27ERERAZ7nwRgTuzWurq6oVasWAgICQEQYOnQooqKicOfOHbi5uUEikYjx6gGUaSbmOA69evXCkSNHEBYWhrS0NNSoUQP169dHzZo1iyybEY4PCgrC119/DZ1OJ3ZN/4viAgoeVO7u7pBIJNBqtcjIyCjiJlYcHMehdu3a4DgOqampyMrKsrinSJkiYwW/8FYAd4hoTaG3DgEYD2CF4e/BQvunM8Z+ANAeQCaZMB4jg9m2PD6CQl4ytVpdrGsVYwx169bF/Pnzi4zPiqvIOp0OvXv3xubNm3HixAnk5uaKMSOExZBA2bEXGWPimMcYBO9xi48jrASFQgGO44qY80uDMYZatWpBLpdDrVbj0aNHRX4vS2DMjGNnAGMB9GSMhRm2/igQV2/GWDiAXob/AeAIgIcoMOFvBjDVlAJlZWVh/vz5OHHihMmxOoTsJzqdrtQfpHBFLknMEolEDEaTnJxcRLSF53b+q62MNePj4wOZTAaNRlOuSF2VjTHWxbMoMGwUxwvFHE8AppWnMESEv/76C+vWrcP27duxf/9+PP/880ZXZMFbQq/XV0rATaHL+aQQhZXCRFRtl188yzg6OkIikYgT45bGqvokubm52Lp1KzQaDXx9fdGkSROTWorKyuxCREhNTcXBgwU94Fq1ahVxjSrcx7eG/FeVgdAdy8rKQlpaGhITEyGTyVC3bl14eXlVqxbbwcEBHMdBr9fbRPYkly5dwunTp8FxHMaNG2dyIFGhi1hWJKeyyM/Px6pVq/Dvv/9CoVBgwoQJRdYyCcKSSqVFfBmrE0QFmThTU1MRGRmJy5cv49KlS7hx4wYiIyPFecZGjRrhxx9/RNOmTS1SzsK5oo39TQXXKr1ej7y8vGKtv1WJ1YhMq9Vi9+7dyM3NRf369fHyyy+bfI6KioyoILfXZ599hvXr10Ov12PQoEEYPXp0kfMJYaCJyOoSLpQGUUGCvKioKJw8eRJ///03QkNDER8fD51OV6QnoFQqodFocOPGDSxYsADbtm0zykpX2RVaWOcnkUiMzqAql8shl8tBRLaWrDBarVZMqteqVSvUrl3b5B9LWCxZHrM3ESE8PByLFi3CgQMHoNVqERQUhOXLlz+1oPDx48eiwDIyMiz+pCwNooIFqhERETh+/DiOHTuG0NBQsdzA/yJS+fn5oV27dmjXrh38/f1x8OBBrF+/Hr/99hsuXbqEHj16lHqt3Nxc7Ny5Ey+++KIYKauiqFQqkwPjFM7QmZWVZfHfx2pEVpjyjnOESgOYZvXLzc3FTz/9hKVLl+L+/ftgjKF3795Yt24dAgICnjpX4cWO5U3mYG70ej2ioqJw6NAh7N27F9evXy8y3eDu7o5GjRqJLlkNGzaEv78/FAqFeL9+fn7Yt28fHj9+jLCwsFJFlp+fj48//hiff/45OnfujK1bt1aK0Aq3rsb2TiQSiTidI7jEWTJGplWKrLBYzHkNvV6PK1euYNWqVTh8+DDy8/Ph6OiIN954A++9916xA36e5/Ho0SPxf2sRmfCd5eXl4erVq9izZw9+++03xMbGiu95eXmhRYsW6N+/P3r27InGjRuLsQmfvE8iQlpaGvLy8sAYMzq+BhHh77//xty5c7F9+3aLTARLJBK4ubkB+F8cEUtiNSKTSqXi0yc2NhZardYsy82Fbt6tW7fwzTffYO/evaK7U5s2bbBw4UL069evxEWCGo2mSEJ2S8/DCPfz8OFD/PHHH9i3bx9CQ0PFtVQODg7o0KEDRo4ciU6dOqF+/fpiT6G0dVzJycn4+OOPkZmZCU9PT3Tv3r3UcsjlcixcuBA6nQ67d+/GsGHDLBZclOM4MZy60F20JFYjMplMhjZt2mDv3r2IiopCTExMsV218iJUxjt37mDTpk1iNwgomLycOHEipk2bBh8fn1KvqVKpiuSKtpTIyLBqOCwsDNu2bcOvv/4qZkkRvB769euHMWPGICgoyKSWKDs7G++++y7++OMPSKVSTJw4UfT8LwnGGOzt7bFkyRKMHj0agYGBFptDLDwmMzahuzmxGpExxtCxY0fIZDIkJSXhxIkTCAgIqPB5hfmfmzdvYtu2bdi3b58YSNPDwwPDhg3D1KlT0axZM6MqhVqtFq2LQEF3saoG1kJlyc7OxunTp7F9+3acOHFCjP0vOB4PGTIE/fv3R506dUxyzxKsq/PmzcPu3btBRBg1ahTef/99o8fJDg4OaN26tek3VwKF4y4aey8cx4kPFVtL9gRNmzZFq1atcOHCBaxevRrdunVD48aNy1WBeZ5HamoqTp06hR9//BF///232M3z8PDAkCFDMGXKFLRs2dLo+BFAgUGhcBi2xMREZGVliV1dcyCMH2NiYnD48GHs2bMH169fh0qlAmMMPj4+GDRoEMaPH48WLVo85Xhs7DVycnLw4YcfYvPmzdDr9RgwYABWrlwphpWrSPmF6YPk5GQ4OjrC3d0dHMeVWU4hL7WwOsIYOI4T51jT0tKgVquNNv+bhZLWwFTlJqyM5nmejhw5Qi4uLgSA+vXrR/Hx8UavoTpy5Agxxkgul9Po0aOpWbNmpFAoxFW7np6eNGnSJLp06RLl5+eXa21WVFQUeXl5ieu9fH19KTw83OTzGAPP85Sbm0snTpygSZMmUa1atYjjOHGdWZMmTWjBggV0+/btYsMtmEJOTg7NmTOHZDIZMcaoX79+FBsbWyn3kJycTCtWrKAWLVqQr68vBQQE0Lx58ygjI6PM3+CTTz4hAOTv709ZWVlGX3fr1q0klUrJy8uL7t27V9HbKBNUJPxAVWyFww/k5+fTe++9Jy7Lf/HFF+nq1auk1WqLvTm9Xk95eXl07949eu+9955aEq9QKKh58+Y0f/58unHjRrnFJfDw4UPy8PAosqjy5MmT5T5fcej1esrIyKBffvmFBgwYQE5OTuKDwtHRkV544QX69ttv6dGjR5USzk2lUtHixYtJoVCI33lMTEyFF4gK4RGEMAswhGkQvrf33nuvxN9VoLwiO3r0KNnZ2ZGzszOdOXOmQvdhDNVKZEREGRkZNHXqVPGH8fX1pXfffZeuXLlCKSkplJ6eTnFxcXTixAlavnw59enThzw9PYusaHZ3d6cRI0bQ/v37KTExsdJiCz4pMgC0evXqCp+X53lSqVR08+ZNWrZsGQUHB5OdnZ1YMevUqUPTp0+nM2fOGBW7w1g0Gg19+eWXYqySdu3a0Y0bNyg3N7fYeI6moNPpaPny5SSVSkkikVCfPn3o888/pxdeeIEYY+Th4UGnTp0q9V7KK7Lr16+Tk5MTyWQy2rdvn9lXlFcLkaWnp9Pp06dJrVYTz/OUkZFBCxYsIE9PT/Ep7uTkRD4+PlS3bl2qUaOGGPAGAHEcRw4ODmJ3au3ateK5KpOHDx+Su7t7EZG98sor5RaxXq+n1NRU+uWXX+jVV1+lGjVqiPcgl8upWbNmtGLFCoqMjCStVlup96PT6eiHH34oEuzV29ubAgMDqXfv3rR48WK6evUqqdVqk8/N8zzdvXuX6tatK3b9hTALN2/eFMMsvPjii6WGFSivyB4/fkw+Pj5iXbCJLCiIvv76a3J3d6c5c+aIX6ZWq6WLFy/SmDFjyMvLS2zZBNHZ2dmRj48P9enTh1auXEkHDx6kevXqEQD65JNPzPJlPnjwQBwzCmOzli1bUlJSktHnEMZa165do8WLF1Pr1q3FsG7CE37gwIG0Y8cOevz4Men1+kqtJHq9nh49ekRr1qwRY5AUt3EcRx4eHjRhwgS6cOGCSWLTarU0c+ZMYoyRq6srnT59WrwHnU5Hy5YtI8YY1ahRo9RIv+UVWXp6Oj333HMEgObOnVvhVrksShOZVVgX9Xo9fvzxR6SlpSE0NFTcL5VKERwcjK1bt+LevXu4fv06kpKSoFKp4OLignr16qFZs2bw8fGBXC5HdnY2PDw8EBkZibi4OLOUVafTiYtJu3Tpgp9++glRUVF49OhRmasGhOClJ0+exK5du3Dq1CkxoYJcLkfTpk3xyiuvYOjQoWjSpEkRF6fKgIiQn5+PAwcO4NNPP8Xt27eh0+kgl8tRv359tGvXDjVr1oRer8ft27dx8eJFpKSk4LvvvsPPP/+M4cOH491330W9evVKLRcR4fbt2/jhhx8AAMOGDSuSDlcikaBr166QSCTi0prKRiaTwcfHB9euXUN8fLxFXausQmQqlQrh4eGQSCQYNWpUEVccISxYy5Yt0aJFCwD/8/R+8oe2t7cXP5uSkiIeV5lkZmZCq9VCJpOhS5cuYtjpixcv4rnnniv2MzzPIy4uDvv378fu3btx48YNaLVaMMbg6emJTp06Yfjw4ejVq5fRpm1TISKkpKRg2bJl2Lx5s+idLpFIMH36dMyfPx8uLi7iUn+9Xo979+7hu+++w969e/Ho0SNs2rQJ//zzD1asWIE+ffqUGlL8woULSE5OhrOzM956662nzO/C/QlP+5LKXN41goLTMwBxkt5ilNTEVeUmmKZ9fX0pIiKi3F0jnudp8ODBBIB69epVYbN2cRw9epSkUik5OzvTsWPHqG3btgSARo4c+ZSlTKfTUXh4OC1evJgCAgKKjLUCAwNp2bJllWLxLAue5ykiIoIGDBgglkHYunTpIo6VivucTqej27dv05QpU8jR0VE0Ku3cubPELpher6cJEyaIhpS0tLSnjtmwYQNxHFdqd5HneVq0aJEYEs6YhBOFP/vOO+8QAGrSpAnl5uYa/dnyAGvvLgp5fn18fODn51fupzhjTMwsmZKSguzsbDFMXGUhtJBKpRK+vr5o27YtLl26hFu3biE9PR2enp7QarW4d+8eduzYgR9//FF00nVwcECnTp0wYcIE9O7dWyybOb1FiAhXr17Fm2++icuXL0MqlcLT0xOJiYlQKBSYMWMGPD09iy0DM2S3bNKkCdauXYvu3bvj/fffR1RUFObPn4+mTZuiTZs2xX42JiYGAFCnTp2nXLpUKhV+//138DyPOnXqlOqyRWTags3CeHt7g+M45OXlISMjw2JZUq0iP5kQS6Nx48YVjtIkLK9IS0tDdnZ2hctWGCLCnTt3wPM8nJyc4O7ujk6dOgEAHj58iMjISJw8eRKTJk3CCy+8gFWrViEmJgZOTk4YMmQIDhw4gF9++QWvvvqqGEfR3AK7dOkSxowZg8uXL8POzg4zZsxAYGAggIJ0tz169CizDIwxyGQyDBs2DFu2bIGXlxdiY2Px0UcfFRuti4jEvGmOjo5FflMiwqlTp3D8+HEwxjBkyBCzeOozxuDt7Q2JRAK1Wo309PRKv4axWE1LBhS4VVV0cOrl5QUAYpjpyiQtLQ3Hjx8HEaFx48bw8vJCs2bN4OLigszMTEyYMAFRUVHieMfd3R19+/bFm2++ifbt25fp/c7zPK5cuYJz587h1VdfFQP2lAciwsWLF/Haa6/h7t27cHR0xMcff4yQkBBRWIMHDza6pRceCN26dcO0adPw0Ucf4cyZM7h16xbatm1bpJw8z4vGISGCmFCmuLg4LFiwANnZ2WjQoAGGDx9ervszBmdnZzF6WVnJHc2JVbRker0eMpkMjRo1qvCTXfAhzMvLq9QE7Xq9Hjt37sSVK1cgk8kwZMgQ8DyPpKQkcUnOrVu3kJeXhzp16mDWrFn4888/8d133+H5558XLYWl3V90dDRGjx6Nt99+G6+99hpSU1PLNWAnIoSFhWHChAm4e/cunJycsHLlSkybNg3h4eFITU2FQqFAt27dTPaUl0qleOWVV+Dg4IDMzEzcvXv3qWMK36dguCAq8F1csmQJrly5AqVSifnz55e50kL4fHkevoVDBFZmXTAVq2jJgALv7bJMw8Yg9Lu1Wq3JcRtLgud5HDlyBMuWLYNOp0P37t3RsGFDTJo0Cb/++qvYFXFwcMDs2bMxYcIE1K1b16SKQUS4fPkyYmJiwPM8jh8/jgMHDmDy5MkmlZUM5vNJkybh9u3bcHR0xKeffoqJEydCKpWKjsV+fn5o1qyZSecWcHV1hUQiARGJVtLCcBwnildIR6vRaLBy5Urs2rULjDGMHTsWI0eOLFNgQrezPIkJ7ezsxKhVZeURNydW0ZIBBeIQjBYVQSaTgeM48Dxf4TjoZJhX2rlzJ6ZMmYLk5GQolUpIpVKMGDECO3bsKDKglkqlGDlyJOrXr29yheB5HpcvXxYrg06nw549e0zKFklEuH//Pt544w2EhobC3t4eH3/8MSZOnAiZTAYiErNcenp6lmtRpdBKqtVqSKVScXHkk9SvXx9AQeucnJyMZcuWYfXq1cjPz0e3bt2waNGiMj3j9Xq9uHbPw8OjXCJjjFlcZBY33xu6Q+Tn50eJiYkVNqWePXuWJBIJMcbo/Pnz5T4Pz/OUnp5Oc+fOFU3XwP9ycAGgGjVq0Ntvv0379u0jd3d34jiOli1bZrJ3gZDor3HjxgSA3NzcRPN2WFiY0ee4e/cude7cmYCC/NdLly4t4qXB8zydOHGCli1bRps2bTI54wnP85SUlEQvvvgiAaBGjRpRfHx8scetWrWKAJCnpye9+uqrovNxp06d6P79+0ZNWeTm5lKPHj0IKMgDbuqUzI0bN8jBwYFkMhkdPHjQpM+aCqzdrQoGn7kHDx5U+GYrQ2RCpR8xYkQRVy5hs7Ozo1deeYUuXrxIGo2GVCoVhYSEEABq2rQpRUdHGz3vxfM85eTk0KRJk0T/ywULFpC9vT0plUo6fPiwUcnd79+/T126dBHLt3DhQlKpVEUSD8bExNCaNWsoNTXV5Hk5nucpOzub3nzzTZJIJCSTyWjZsmXF+mzyPE+///67uMwIBhetHj160L1794y+dnJysujjuGTJEpMfXuHh4aI/6969e806F1ktRObs7FzEv628nD17ljiOK5fIeJ6n/Px8Onz4MAUFBRHHcSSRSERveABUp04d2rNnD+Xk5BSpwIcOHRJ/0EmTJhlVkXmep7y8PPrwww9JoVAQx3E0YcIEun//Pnl7exMA+vzzz0s9D8/zFB0dTT179hT9ORctWvRULrOkpCTq168fyeVyeu2110wSmrBC4IMPPhDLOXr0aEpPTy82TW9GRgatWLFC9MfkOI6GDx9u0sOHiOjff/8lZ2dnUigUtH//fpPrRkxMjLhMaNeuXf9tkQldsJUrV1bYkfPkyZPEGCOJREKXL182+nPCk37WrFmiA7CdnR2NHj2aGjVqJHodnDx5stgyqlQqeu2114gxRlKplIYMGUK3b98u0ZuD53lKSEigqVOnklKpJMYY9erVi+Lj4ykzM1Ps9o0fP75ED3/hHC+99BIxxkipVNLChQuLTRaYnp5OI0aMII7jSCqV0uLFi8tcyyVcIy8vj1asWEEODg7ierNHjx4VuYZer6eUlBTau3cv9ezZk+RyufhgcnR0pEuXLplUyXmep6+++ookEgl5eHjQnTt3jP6sQFxcnPhb7tix478tMmHMExQUZJI3e3EcPnxYFEhp3t0CQndt79699Nxzz4mtYKNGjWj79u107NgxcV3Spk2bSnwI8DxPcXFx9Morr4gLTuvUqUOjR4+m9evX0+XLlyk1NZU0Gg3p9Xq6dOkSde/enTiOI47jqEuXLuJYRavV0htvvEFAQa7kktyJcnJy6PXXXyeO40gmk9GcOXOKtLBPli8xMZFeffVVCgoKorCwMKNa2pSUFJo5c6bYmrdr167ImEqv11NycjJt2rSJ2rRpI3YRZTKZ2IpwHEcLFy40StQCKpVKdJHr2LGjUVk2n8QmskJbvXr1SKlUklQqpXXr1lXoy/jhhx8IALm6upb59FOpVHTmzBkaMmSIWIns7e1p/PjxdP/+fdLr9bRv3z5SKBTk5uZGV69eLfV8PM9TamoqzZs3T/xxgf8lcG/evDlNmjSJVq5cSbVq1RJXbo8dO7bISmSe52nDhg3iUzw0NPSp70Sn09EXX3xBSqVS7GZmZmaW2bVMTk6myMjIMr9jnU5HoaGh1LdvX3GM26lTJ7p27RrxPE9ZWVl07tw5+vDDDyk4OJhkMpl4P4GBgbR27VpauXKlaCTy9/enO3fuGPXb8jxPZ86cEQ1AS5cuLVcPxyayQlurVq2oV69eouEgNja23F/Itm3bRKvW/fv3n3pfp9NRUlISHT9+nEaPHi3+CBKJhAIDA+mHH34o0t366aefSKlUkqurq1HdT2Fc9+eff9Ls2bOpbdu2T63aFl57enrSZ599VuxK55s3b5Krqysxxp6yWApWQmE9W9euXZ/qvpUXIWTA119/LT4IZDIZDR06lEJDQ+natWv02WefUY8ePcTuIwxOz126dKE9e/ZQSkoK6fV6unv3Li1YsIDc3NyIMUZjx46l5OTkMsupVqtp1KhR4hjYFGNJYWwiK7QFBQXRr7/+Svb29sRxHM2cOZPy8vLKdbNbtmwhAOTj40MRERFE9L8u4YULF+j//u//KDAwsEiAndq1a9PixYuLrajnzp0jJycnUiqVJlmoBA/2vLw82r17d5GQBYwxatWqFR0/frzE8VZOTg699NJLBIAaNGhAd+/eJZ7nxW5fx44dxRbiwoULlRKPIyMjg/bt20fPP/+82DIBICcnJ3rhhReodevW5OzsLHrycxxHNWvWpEGDBtHOnTufMoQID5zJkyeL4+QBAwaU2qIJRiQXFxfiOI4++OADk7qZhbGJ7AmR5eTk0IgRI8QB/CeffFKuZe/ffvutuGr5/Pnz9O+//9KyZcvohRdeEMcIQEEgl7p169I777xDd+/eLbE7kpycTAEBAQSAevToUeKykNK4cOECubm5ieKfPXs2RUVFldm1+/PPP8Uyv/jiixQZGUk6nY4WL15MEomEFAoFbdq0qULxSwTjybZt26hbt25FzO4lbTVq1KBu3brRl19+Sffv3xfDIpQ0FoyNjaURI0aIwm3UqBFt2rSpiCgFC+aBAwfEkAUBAQEUHh5ebnHYRPaEyHiep8jISOrUqZNouFixYgXl5+cbdZPCAHzNmjWiiBo0aCCakYUWpGbNmjRq1Cj64YcfKCoqqsyl/TqdjtasWUNyuZw4jqPx48cbFcqsMGq1mt577z1auHAh3bhxg3Q6nVGfV6vVNH/+fHGu7rnnnqOPPvpI7CYOHjzYZIOA0LrExsbS77//TrNmzaLmzZuLXViO4ygwMJCGDRtGLVu2pCZNmlBgYCD16NGDpk6dSlu3bqWwsDBxDs7YMVZGRgZ9+OGH5OzsLHZBO3bsSO+++y7t2LGDNm/eTMOGDRMD+ri7u9O+ffsqZG2Oi4sTr2cTWaG4izdu3KA2bdoQAHJwcKAvvvhCHCM9uWVlZdG1a9fo22+/pbFjx1LLli3J3t6+yFOXMUY+Pj7Us2dP+uyzz+jOnTtiJTe2gmRmZtL48eNF8/fYsWMpLi7OpK5jaU/7sq49a9asInN1wrhuwYIFdOnSJYqNjRWDBhXe8vPzKTk5mSIiIujChQu0Y8cOmj17Nr3wwgvUoEGDIl1CuVxO7dq1o6+//lqMK5KRkUHJycmUnZ0tPoxMvYfC96LRaOjXX3+lTp06FRmjPrk1btyYfv7553J3EwWuXr1K9vb2JJVK6cCBAxYTGSt437IEBwfT5cuXARSI/saNGxg9ejRu3rwJhUKBPn36YOjQoaLH9uPHj/HPP//g7NmziIiIKHGt0NChQ/Hyyy8jODgYderUKXOpSUkQEZKSkjBlyhQxxW2XLl2wceNGk1PumgpRgXPt3r17sWjRIkRGRhZ538HBAd7e3vDx8YG/vz/s7e0hkUiQnJyMpKQkZGZmIjMzE2lpaU8t95BIJKhTpw7atWuHV199Fd27d4eLi4vZ7yctLQ0//PADfvvtN4SHhyM9PR0cx8HHxwf9+vXDm2++WWFncSLCrl27MHHiRDg7O+PYsWOVGj78SRhjV4gouMTCWHp7Mu4iz/N08eJFatu2rWi9AkBKpbJI9w/4X6i4Nm3a0KRJk+i9994jxhhxHEcXL16stCcVUUGYsTfffFOcaG3VqhVdvHjRrE9IgdTUVGrXrp043mzevDk5OTkV+X5K2xhj5OzsTM2bN6eQkBCaN28eHT58mGJiYiotJqWpaDQaiouLo9u3b9Pdu3eNsjwai16vp//7v/8Tx4Dp6emVct6SgLWHH3gSxhjatm2LAwcOYMuWLTh8+DDCw8NFj3Q7OzvUqlULrVu3Ro8ePdCxY0f4+/vDzc0NFy5cwOeffw6tVlvpC/W8vb3x2WefoWbNmvj0008RFhaGUaNG4ZtvvkHPnj3NGg3pzJkzCA0NhVQqxdKlSzFo0CAkJSXh/v37uHHjBu7cuYPk5GRotVro9Xo4OjrC09MTtWrVgr+/P+rWrYt69eqhRo0acHNzKzUITlUhk8ng5+cHPz+/Sj+3Wq1GdHQ0JBIJAgMDLZInTaQk9VXl9mRLVhjB6+D27dt04sQJOnHiBN24cYPi4+NJo9E89eS7cOGC6PH9999/l/fBVCpqtZrWr18vBgWtVasW/fbbb2aL7afT6Wj8+PHiPGJCQkKxx+Tm5lJWVhZlZmYWcQ7+LyLElty+fTv9/fffZv8uUBHDBwAlgIsArgG4BWCJYX89ABcARAD4EYDcsF9h+D/C8H7dsq7xpMgEbwNTglkKVIXIiAqCd+7Zs0e09NWsWZP27dtnlghZMTExVL9+fQJA06ZNM3ugzmeJ8hpqTKU0kRnTv8kH0JOIngPQCkBfxlgHACsBfE5EAQDSAUw0HD8RQLph/+eG44yG53n8/PPPeOmll7BgwQIxY6S1IZVKMXz4cGzcuBE+Pj549OgRJk+ejC+//NKkhZbGcP78eURHR0OhUGDQoEEWzX9c3TB3sCKjKEl9xW0A7AGEAmgPIAWA1LC/I4A/DK//ANDR8FpqOI6Vdt7CLdmdO3fEUNv16tWj27dvm/REqaqWTECn09GRI0fECWulUknvv/8+ZWVlVcoTVKPRiC5GLVu2pOTk5EootY3KBhVsycAYkzDGwgAkATgG4AGADCIS1vfHARBGr34AYg0C1gHIBOBRzDknM8YuM8YuC8n58vPzsWbNGkRFRcHNzQ1r165F48aNjXtaGCi436pDIpHgxRdfxN69e9G5c2fxHt59910kJydXuDzx8fE4e/YsAKB3797w8Hjqq7Rh5RglMiLSE1ErALUAtAPQpKIXJqJNRBRMRMGenp4gIjErJgCMGjUKffv2NblrRP9rdassZzHHcWjVqhV27tyJF198ETqdDps3b8a4ceNw69atCgnt6tWrSEpKgr29PUJCQiqx1DaqCpNqMBFlAPgbBd1DV8aYMAVQC0C84XU8AH8AMLzvAiAVZZCRkYEVK1YgKysLDRo0wJw5c8oV6FSn00Gn04kx9KsKxhjq1q2LrVu3YsSIEZBIJPjjjz8wfPhwnDlzRgxtZiodO3bEN998g2nTpqF169aWH1/YMJ2S+pGFWgRPAK6G13YAzgAIAbAPwAjD/g0AphpeTwOwwfB6BIC9ZV0jKCiIvv/+e5LL5SSTyWj9+vXlHs8cOXJEHBtdu3atXOeoCIIr1EcffSQ6p9atW5d+/fXXcrsJFXbLsmGdoIIm/EAAVwFcB3ATwELD/vooMO1HGASnoP+Z/PcZ9l8EUL+sa7Ru3VpcT9amTRtKSUkpd4U6cOCAuDzj1q1b5fzKKobgp7dt2zYxiaGHhwd9+eWXxYYGsFH9KU1kZfbHiOg6gKecvojoIQrGZ0/uVwMYVtZ5C5OTk4N79+5BIpHgtddeg7u7e4W7RZbsVgmx48eMGQMXFxfMnj0b0dHReOedd/D48WPMnz9fjAlYHSDDmLK6lNfasIoJl+TkZOTl5aF+/foYOnRohc4lBDTlOM7i80kSiQQDBw7E3r170a5dO6jVaqxatQpLliyxaGx2Y0lKSsLs2bMxdOhQHDp0yNLFqbZYhciEUMzDhg2Dr69vhZ6YhUVmDU9ejuPQtm1b7N69G127doVGo8EXX3yBJUuWVPqkdWWTmZmJ/fv34+DBgwgNDa3y6ZFnBasQGc/zsLe3R7du3SosjMJdG2sQGVBQloCAADH5hCC05cuXWzQRgrEQkZhjzYbpWIXIgIJY5y1btqywMISWTCKRWLy7+CReXl5iXmlBaD/99FO5zftVSXR0tJgSyoZpWE0tbNy4caVkxdTr9QCsp7tYGIlEgoCAADHRQk5ODt5//31cuHDB6luJyMjISk+q+F/BakRW2RPH1lhp7ezssHjxYnz88cdiNpTo6GhMnToVDx48sMoyCzx+/BiPHz+2dDGqJVYjsuzs7EqpZEJCPp1OJ7Zq1oSdnR1mzZqFr776Ct7e3gCAa9euWfWKA6CgexsWFmbpYlRLrEZkDx48wKNHjyosNGEFrFqtrrQkgJWNTCbDyJEjsWPHDtSuXRtEhJ9//hm7d++22vGZXq/HtWvXrLZ81ozViCwxMRGbN29Gfn6+KDQigkqlQlhYGDZu3Ii1a9fit99+KzW5n7u7OziOg0qlsuqWQSKRoFevXpgzZw4kEgny8/OxatUq3Lt3z2q7jbGxsdXCGmptWEWMD2dnZ2RnZ2PdunXQarUYM2YM5HI5rly5ggMHDuD06dPIysqCQqHA5s2bS/Wul8vlkMlk0Gg0lZ6YvbJhjGH48OHYtGkT7ty5g8jISKxZswZffvllmVkoq6p8hUlMTER+fr5VlK009Ho90tLScPfuXQQEBMDX19eyBSrJ36oqt+bNm1O7du3EKFMODg5ihkQUirb08ssvlxmS4MKFC2IqohMnTpjogVb16PV6Wr58uRj62sXFhf7880+r8G+MjIwUk/ABoPr161dKNlRzExsbS8HBweTk5ESbNm2qFuEHzI5SqcTu3bsxbNgwuLi4QKPRID8/v0i3sHv37li9enWZUYdkMpm4RKY6uC4xxjBo0CBx/iwzMxOffvqp6AVjSZycnMR82ACQmppq9V4qAODi4gKJRILs7GwcP368wrnDK4pViAwAGjRogO3bt+OPP/7A999/j/nz54vdkhYtWmD9+vWoU6dOmXNfEolEFFl1GD8wxtCgQQMEBQWJ+06fPo2dO3dWyMhARLh06RL+/PPPcltZnZyciiRez8/PrxZmfHt7e3Tv3h2MMVy4cAEpKSkWLY/ViIwxBqVSibZt2+L555/HuXPnoFKp4ObmhtWrV6Np06ZGTS7zPC9WzqpaGV1RZDIZXnjhBfH+NBoNVq1ahRs3bpTbCHLlyhWMGTMG48aNw6FDh8olNJlMhnr16on/6/V6PHz4sFzlqUo4jkP37t2hVCqRmJiIK1euWNSYZDUiE1Cr1Vi6dCmOHz8OqVSK//u//ytSAcsiJSUFubm5YIxVigdJVdGqVSs4ODiI/8fExOCjjz4qd/csOzsbOTk5SExMxNSpUyGEQTeVRo0aia91Oh3Cw8Ot1vopwBhD69at4evrC5VKhZMnT1p06sGqREZEOHLkCLZs2QIiwiuvvIKZM2eaFIbg77//BsdxaNq0qVki0xaGiHD69GlERkZWqOIxxlC/fn04ODiIYQwkEgmOHDmCgwcPluvc3bp1w6ZNm+Dn54cOHTqgbt265Sqbv7+/+JqIcOfOnWrRDXdzc0OnTp0AFNQJS47PrUpkiYmJWL58OVQqFZo1a4Zly5aZHF6ZMYZatWrh22+/Ra1atcpdlpIsRYUrvE6nw+rVqzF+/PgKd6OcnJzEh8mwYcPQqlUrqNVqrF69GklJSSYLjeM49OvXDwcPHsSGDRvg5eVVrnJ5e3sX6UVcu3atWjgKy2QydOrUCRKJBI8ePcKjR48sVharmCcDCvr73333HUJDQ6FQKPD++++jbt26Jjv5hoSE4LnnnkOLFi1MDsQjiCglJQXh4eF48OAB4uLikJeXBw8PD9SpUwf169dHo0aNYGdnh9jYWFy9ehVxcXH4/vvv8cEHH5TbKVkmk4ExBiKCu7s75s6di9dffx3Xr1/Hli1bMG/ePJPPzXFcEYOKqTDG4OjoCIVCIc45xsbG4vbt2+jcubPVOWAXhjGGGjVqQCKRQKvVWtQqajUii4uLw9atW8HzvJgqqTw/YocOHdChQwejjyci6HQ6REVF4dy5czh69ChCQ0ORkJDwlMcIx3FwdXVFkyZN8PLLL6NGjRqite2vv/7CzJkzi1jjTCEzMxM6nQ4cx8He3h4DBgxAnz59cOjQIWzcuBGDBw9Gs2bNKqViC62iMeeyt7eHXC4XRZabm4tdu3ahXbt2VRoNzFSICAkJCdDr9ZDJZEXGuxYpjKW3oKAgWrZsGXEcR05OTmafjOV5ntRqNd26dYs2btxIAwcOpJo1az6VPN3FxYUCAgKoefPm5O/vT0qlUpw0FlIRCcc7ODjQqVOnyp0g76+//iJHR0eys7Ojo0ePEs/zdOTIETH538SJE43OOlrWte7evUuHDx82Km7/lStXyN3dnTiOE+Pxe3t709WrV61iwrwk1Go1jR49mgBQ27Zty52D3Fhg7Zk2W7VqRW3btiUA9NJLL5FKpTLLFyFk+vj222+pf//+5OXlVUQ09vb2FBwcTLNnz6YDBw7Q/fv3KSEhgZKSkiguLo7CwsLoq6++oj59+hTJPy1sffv2LVeOLb1eT0uXLiXGGHl7e1NUVBQlJSVRp06dxPLVr1+/QvmTiQoEFh4eTh07diR3d3faunVrmbnJrl69Sh4eHiSVSmnBggXk4+NDjDGaMGFCuXJ6VwU8z9PDhw+pTp06BIDmzp1r9hxsVi+yJk2akIODA0mlUtq2bVulPyH1ej0lJCTQmjVrqFmzZqK7ltByduzYkRYuXEjnz5+ntLS0UrOm8DxP2dnZdOrUKWrYsOFTKWYnTpxocvL2tLQ0Cg4OJgA0aNAgysvLo7y8PBo2bBgBIEdHRzFjTHm/G57nKSoqinr06EGMMbKzs6M1a9aUGguS53k6f/48ubi4kEwmo59//pnefvtt4jiO3Nzc6OTJk1bXmvE8Tzk5OTRjxgziOI6cnZ3p+PHj1p06qSq2mjVrEsdx5OvrS+Hh4ZV240Je6e3bt1OrVq3EBOd2dnbUvn17WrZsGV25coUyMzNNTkf08OHDIn59hYXWo0cPOnfunFHBTPV6PW3atIkUCgXJ5XLauXOnmO7n2LFjYjbNSZMmlTtmI8/zFBcXR/369SPGGMlkMho4cCAdPnyYIiIiSjwnz/O0fft20Z/02rVrdP/+fTEhyIABA0xOUm9OeJ6n5ORkeuutt0T/1XHjxpmtZ1QYqxeZkEyvW7duldYF0el0dP78eQoJCRFT4NrZ2VHfvn3p0KFDlJGRUaE8Xz/++CNxHEcKhYL69esnprgVNm9vb1q0aBHFxsaWeB2e5+n06dPk7+9PAKhDhw5FsraoVCp66623iDFGCoWC5syZQ6mpqSYnd4+Pj6dBgwaJAps6dSoFBASQg4MDLVy4sNjy8TxPqamp1KNHDwJAwcHBlJqaSjqdjpYvX05SqZTkcjmtWLGiwgnUKwO9Xk8PHjygQYMGkUQiIY7jqGfPnhXuYhuL1YtMoVAQAJo+fXqF+848z1N+fj5t3ryZvL29CQBJpVJq27Yt/fDDD5WS0kir1dKYMWMIAAUGBlJ0dDRNmzatiOFEaNWaN29OK1eupPDwcMrNzRVbqezsbPrll1/ElEteXl70119/PVW2qKgo6ty5MzHGSCKRUP/+/enKlStGfU9CFzEkJIQYYySVSumtt96ipKQk6t27txix+eHDh6TX68XIxxkZGRQaGkpjxowhqVRKUqmU1q1bJ5YtOTlZ/LynpyedOHHCYq2ZkIl1y5YtFBgYKD5IRo4cSbGxsVVWLqsXmTA+2rBhQ4W/lLy8PFqyZAk5OjoSAPL19aVPPvnE5HFSaTx48EBsfWbPni3+0ELFc3BwoK5du5KDg4M49vP29qZ+/frR9OnTadasWdSrVy/xfVdXV9q2bVuxLYIglCFDhojd3Zo1a9KSJUsoOjq6xFYkPz+fjh07Rq1btxYFNmXKFEpPTyee52nXrl1il+q5556jSZMm0bRp02jMmDHUvn17cnZ2FpceDRw4kNLS0oqU6fLly2J3uW3btlVaoYkKWq7Hjx/T5s2bqX379uI428HBgebOnSveZ1VRLUTm6OhIx48fr9CNqtVqWrJkidg9bNWqFZ09e7ZSLUs8z9P69euJ4zhydHQUzfY8z9PBgwfJ3t6eZDIZbdy4kQ4ePEj9+/cXc0s/uXEcR02bNqUff/yxVHM6z/OUnp5On3zyiZg+VzCpT506lX7//XeKjIyk+Ph4evDgAe3du5dGjRpFbm5uYjd55syZlJmZKVa8zMxMmj59ujhF8OQmWDqnTZtGCQkJT1VYnU5HmzdvJrlcTowxmjx5slnHPjzPk0qloqioKDp48CDNnj2bWrRoIT545HI5de7cmfbt22eRfNnVQmS+vr4UHR1d7pvUarX05Zdfkr29PQGgjh070p07dyr9y87MzKRu3boRAOratSvl5OQUea9169YEgEaPHk1qtZpUKhWdO3eOli5dSn379qWWLVtSs2bNqFevXrRmzRqKjIw0uow6nY7++ecfGjx4sNgKAiCZTEY+Pj5Ur1498vLyKtJtrVWrFm3duvWpeSKe5ykvL4+2bNlCQ4cOpVatWlGLFi2oU6dO9Nprr9E333xDN2/eLDGbjFarpc8++0wUqaenJ509e7ZS8lkL3dbU1FR6+PAhXbhwgdasWUMvvfQS+fn5Fbk/hUJBnTp1ou3bt1d561WY0kTGCt63LIwxatu2LU6dOgU7OzuTP09EOHbsGEaMGIH09HS0bt0ae/bsQePGjSvd9ef+/fto3749MjIy8OWXX2LatGniNfR6PebOnYsvvvgCLVq0wJkzZ+Dq6io8SMDzPHQ6HXieh1wuL1dsSCKCWq3GP//8gx9++AEnT558KvaGUqlEQEAA+vXrhzFjxqBFixbFBnotXC7htRB5uawIzESEv/76C1OmTBH9NgMCAjBixAiEhITA09MTTk5OUCqV4rX1ej10Oh3y8/OhVquRl5eHnJwcZGZmIjMzExkZGcjIyEBMTAwePnyIhIQEJCQkIDU1FTqdTiyjnZ0d/Pz80KZNGwwfPhy9e/eGk5OTpZOMXCGi4OLesxq3qqZNm5Yr6R9QkPL1gw8+QHp6OmrXro0NGzaYRWBAQVStnJwcODg4oE2bNkXeY4yJK5zVanWRigsUrG+r6Bo3xhjs7OzwwgsvoGfPnkhISMCtW7dw//59qNVq2NnZoWnTpnjuuefEpT4lfQ+Fy1Wecrzwwgs4dOgQli9fjl9++QURERFYunQpPv30U9SoUQMeHh5wcHAQzy8ITKVSITc3F5mZmcjNzRW/p5Ie+BzHwdPTE40aNULPnj3RrVs3NGvWDF5eXlYVjr0krEZkjRo1KtePnZeXhyVLluDKlStQKpVYtGgR2rZta7YvXvCId3BwgKur61PXEXz8BIdfcyFULj8/P9SsWRO9e/d+6n1zw3EcmjVrhi1btmDMmDHYtGkTzpw5g5SUFKM934XFuoU3Z2dn+Pj4ICAgAI0bN0bDhg1Rp04d1KtXT4yrae3CKoxViExYT2Vq7Hqe5/H9999j165dAICxY8di5MiRZv0BPD09wRhDdnY2YmJiiqzY5nle7Dp5e3uXu2U2FUvnYlMqlXjxxRfRs2dPPHjwAFFRUXj48CFiYmKQnp4uxtiQy+Wwt7eHq6srPDw84OPjAy8vL9jb20OhUECpVEKhUIhhDwrfV3US1VOUNFiryo3jODpy5IhJA02e5+natWtUt25d0YwcHx9v9oFvdHS06PHQtm1bOnr0qOgxcuPGDapVqxYBoJkzZ1rFJK2NqgHWbl3kOI6OHTtm9A3xPE9paWkUEhJCAMjd3b3KwqjpdDpasWIFCRPo9vb21KpVKxo2bBg1adKEGGPk7u5OZ8+eNXtZbFgPpYnMKrqLgPGDbyISA838/vvvkEqlmDlzJnr06FElXQqJRIIZM2aAMYZNmzYhKioKYWFhYpx4uVyOKVOmoF27pzL92viPYjUiMyY2HhEhIyMDX3zxBdatWwe9Xo+QkBCT44BUFHt7e/zf//0fRo4ciRMnTuD48eN49OgRvL29MXDgQPTv3x8ymazKymPDyimpiavKjTFG33//fane4Hq9nm7evEkvvfSS6ELTpk0bunfvnkW9wIWJ0+zsbMrPz7caj3QbVQusPYIwEWHPnj2IjY2FVqsVYyfq9XqoVCpER0fjs88+w6BBg3D48GEAwIABA7Bz5040bNjQ4tY1mUwGR0dHyOXy6m0Fs2EWrMbjg+M4NGzYEJ07d4arqyuAgjDbMTExCA0NRVJSEvR6Pdzc3DBz5kzMmjWr2HkqG5UHEUGv10Mikdi+5zIozePD6C4dAAmAqwAOG/6vB+ACgAgAPwKQG/YrDP9HGN6vW9a5PTw8ivjiFbc5OzvTgAED6I8//qiUWBc2Skew4C5YsIAuXLhQ5d1gvV5P0dHRVbLgsjJAZZjwAbwNYE8hke0FMMLwegOAtwyvpwLYYHg9AsCPZZ27devWdPDgQRo/fjy1b9+emjdvTs2bN6egoCDq378/LV68mE6dOiWux7JhftRqNU2bNo2kUik1bdqULl68WGXfvV6vp+PHj1Pz5s1p1apV1UJoFRYZgFoAjgPoCeAwAAYgBYDU8H5HAH8YXv8BoKPhtdRwHCvt/EFBQcTzPGm1WsrPzye1Wk1qtZry8/NJo9GICwptVB06nY6++eYbMSJXr169KCMjo0qunZeXRwMHDiTGGDk4ONDq1auNiqxlSUoTmbF27y8AvAvAyfC/B4AMIhLs7nEAhJjYfgBiDV1RHWMs03B8kdQajLHJACYDQO3atcEYq1IzvI3S4TgOEydOBM/z+PrrrzF37lw4OTmV/cFKQKlU4pNPPkF8fDyuXbuGO3fuiPETqyUlqY/+14qFAPja8Lo7ClqyGgAiCh3jD+Cm4fVNALUKvfcAQI3SrhEUFFQlTxsbpiH0LqKjoytlnZip175x4wYtWLCAUlJSrL4ngwq2ZJ0BDGSM9QegBOAMYC0AV8aYlApas1oA4g3HxxtEF8cYkwJwAZBasUeBDUsg9C5q165tkWs3b94cS5YsEf+vrpQ5T0ZE84ioFhHVRYEh4wQRjQbwN4BXDIeNB3DQ8PqQ4X8Y3j9hULoNGyZhzOLR6kBFJqPfA/A2YywCBWOurYb9WwF4GPa/DeD9ihXRho3qjUmWBiI6CeCk4fVDAE95wRKRGsCwSiibDRvPBFbhVmXDxrOMTWQ2bJgZm8hs2DAzNpHZsGFmbCKzYcPM2ERmw4aZsYnMhg0zYxOZDRtmxiYyGzbMjE1kNmyYGZvIbNgwMzaR2bBhZmwis2HDzNhEZsOGmbGJzIYNM2MTmQ0bZsYmMhs2zIxNZDZsmBmbyGzYMDPWknAiG8A9S5ejgtTAEwFcqxm28leMOkTkWdwb1hKy9x6VlBGjmsAYu1yd78FWfvNh6y7asGFmbCKzYcPMWIvINlm6AJVAdb8HW/nNhFUYPmzYeJaxlpbMho1nFouLjDHWlzF2jzEWwRizyrj5jLFvGWNJjLGbhfa5M8aOMcbCDX/dDPsZY2yd4X6uM8baWK7kYln9GWN/M8ZuM8ZuMcZmGfZXi3tgjCkZYxcZY9cM5V9i2F+PMXbBUM4fGWNyw36F4f8Iw/t1LVl+o9PZmmNDQR7qBwDqA5ADuAagmSXLVEI5uwJoA0MONsO+TwG8b3j9PoCVhtf9ARxFQTbSDgAuWEH5fQG0Mbx2AnAfQLPqcg+GcjgaXstQkIu8AyoxpbJZy2/hL09Mg2v4fx6AeZYsUyllrfuEyO4B8DW89kXBXB8AbAQwsrjjrGVDQZqr3tXxHgDYAwgF0B6VmFLZnJulu4ti6lsDhdPiWjveRJRgeP0YgLfhtVXfk6Hr1BoFrUG1uQfGmIQxFgYgCcAxFPSAMsiIlMoAhJTKFsHSInsmoIJHptWbaRljjgAOAJhNRFmF37P2eyAiPRG1QkFW13YAmli2RMZjaZEJqW8FCqfFtXYSGWO+AGD4m2TYb5X3xBiToUBgu4noJ8PuanUPAEBEGSjI8toRhpTKhreKS6kMa0ipbGmRXQLQ0GAlkqNgkHrIwmUylsJpe59M5zvOYKHrACCzUJfMIrCCfLBbAdwhojWF3qoW98AY82SMuRpe26FgPHkH1SWlshUMZPujwNr1AMAHli5PCWX8HkACAC0K+v4TUdDHPw4gHMBfANwNxzIAXxnu5waAYCsofxcUdAWvAwgzbP2ryz0ACARw1VD+mwAWGvbXB3ARQASAfQAUhv1Kw/8RhvfrW7L8No8PGzbMjKW7izZsPPPYRGbDhpmxicyGDTNjE5kNG2bGJjIbNsyMTWQ2bJgZm8hs2DAzNpHZsGFm/h9mvpmjumeUQAAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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MYYyAi7Nu3D4MHD9Zrb01E4tyP4zgolcpyXWPfvn149uwZrKys8Mknn8DKyqrYkVcikYhEiImJgUqlgqmpaZHHWlpaYvPmzQgJCcGBAwcQGhoKoKBTyM/PBxGJauWDBw+QlZWFlJQUgyaatbU1gALiJCcnw8nJCUQEpVKJ9PR0mJmZwcbG5qVztd9nbm4u0tLS4OLiolcNpywoL9HiBZWQMeYGIEGzPxqAp9ZxHpp9L4GINgDYAACtW7fWy7guvEhhLvXee+/h888/f2nuU14IPzBQ0HhzcnLKfA2lUolz586B4zi0atUKHTt2LFE2xphIrNLmKIwxWFtbo3v37ujevXuxx/3999/o2bMncnNzDdpoxBhDs2bNIJPJkJ6ejnXr1sHExATh4eGIjY1Famoqpk2bhvfff/+lc/v06YMdO3bg3r17iImJwYIFC/Dtt9+KnXFVo7yeIQcBvKP5/A6AA1r7x2qsjwEA0qtqfgag0JwsLy8P27dvR//+/bFjxw4olcoK6+lEJJJLrVYjPT29zNfIzMxEZGQkAKBBgwYwMTEp9Z7Z2dkAAIVCoVMjEayWxW3axxWnshoCGGMICAiAQqGAUqnEmjVrsGLFCuzbtw+XLl3Cw4cPcffu3SJ/15YtW2LcuHGQSCRQq9XYsmULxo4di6dPn1bLfK3UEY0x9gcKDB+OjLEoAF8A+BbAbsbYBACRAIZpDj8KoC+AMAA5AN6tBJmLhbu7O8zNzZGZmYnGjRsjMjISjx8/xqRJkxAcHIyvvvoKbm5u5b4+EYkTbJVKJRoVyoLs7GykpaUBAEJDQ5GbmwsLC4tiCZSfn48HDx4AADw8PCCTVUzbJyJERkaCiKBQKGBpaVmh61U2HB0d4erqirCwMFhaWqJ27dpwcnKCm5sb6tSpg06dOhV5HmMMNjY24nvleR6XLl3CkydPUKdOnap8BAC6WR1HFvNVjyKOJQDTKypUeWFpaQknJydkZmZi6NChaNmyJRYuXIg7d+7g7Nmz5VL1tMEYEw0RarW6XETz9PTEG2+8gdWrV+Off/7B3r17MXbs2CKPFdba7t69C6lUig4dOpQ6ApYGlUqF06dPg+M4uLi4GOz8TICZmRm8vLwQFhaGzp0749dff4WNjQ1MTU1LHd2trKwgkUhgbm6OwMBAdOjQAd27d69RqqNBwtzcHA4ODgCAxMREDBw4EIcOHcKMGTPw5ZdfVrgnk0gkcHJyAlDQYFNSUsqshshkMowePRre3t7Iy8vD8uXLERUVVeR1OI5DUFAQMjIy4Orqin79+lWokRARbt++jSNHjgAAevbsWaQhwZBgZmaGevXqASgwBllbW+usQltaWoqd48yZMzFt2rQKawTlxStFNDMzM9jb2wMAoqKiABSMICtWrMCIESMqPB+RSqXw8PAAUKCKJCcnl+s6LVu2xPLly2FmZoYHDx5g1apVyMnJEckmWAfPnj2LnTt3AgACAwPh7u5eIfmzsrLwzTffIDk5Ga6urpg4cWK1WeF0hVQqRf369SGRSJCSkoKEhITST9JAGPU4jgMR1WzzviHB1NQUdnZ2AAo8L4TF3YqqWwKkUqnYuwIFniglLSAXB8YY6tevD3Nzc+Tm5mLdunV48uQJhg4dCj8/P9EjYvPmzUhISICbmxsmT55coYaiVquxbds2HDlyBFKpFBMnTkTjxo0NnmhAQWcpkUiQnZ1dpuUI7UX6srhvVQZeKaIxxmBhYQGgwIigUqnKTILSru/j4wMTExPk5+cjMTGxXERTqVTYuHEjUlJSIJVKoVKpcPDgQRw7dgxmZmaipZHnebi6umLZsmVo1apVuUnB8zyOHz+OxYsXIy8vD506dcL7779v0BZHbbi5uUEikSAnJ6dM82KBXIZgXX2liAb8v3eI4OOnq8uOLmCMwdPTUyRaVlZWuUzFDx48wM6dO0FEGDZsGBo1aoSdO3ciKipK9H6wt7dHjx49MHPmTLRr167cHQbP8/jnn3/wwQcfICEhAV5eXli6dCmcnJxqxGgGALa2tmCMQaVSlcmglZeXV+A5r+VhUl145Ygm+B4KRNM33N3dRVW0LC5BAtRqNX7//XckJSXB2dkZn376KZo2bYr3338f4eHhiIqKAsdxqF+/vjh6lpcQarUaJ0+exPTp0/H06VM4ODjgu+++Q0BAQI0hGQDxffM8D7VaDSLSSX6BaPqcPpQXrxzRBBVB2yFVn7CxsRF7x/IsgsfGxiIoKAhEhN69e6Nx48aQSqWwsbFBy5Yt0bJlywrLSETIyMjApk2bsHTpUiQkJMDR0RHLly/HoEGDql2NKitkMhkkEgmIqEydpxBdYSRaJaCye2qZTAYrKyvEx8eXi2jBwcGIioqCpaUlxo4dq3dzM8dxuHnzJr744gucOHECKpUKHh4eWL16NQYOHFht5u2K4EWPFl0hOBYbiVYDIZFIRPW0rImAiAhxcXFQq9VwcXGpkIGjqGunpKTgl19+wffff4+4uDjIZDL06NEDX331Fdq2bVvjRrKKQugIpVJpsY7YVQUj0SqAjIyMMo9oggHFxMSkXLFsRSE/Px/nz5/HkiVLcOHCBXAcBycnJ3GR1s7OrkbNyYqCdlCrrjCOaK8I8vLykJubq3MojmABAwoMFTk5ORUK4+F5Hvfv38d3332HoKAgZGZmQiaToXfv3li0aJE4ir0KJBPmxWUxQGmPaEai6RnCCFNevb4sUKvVyMrKEt2+SoMQ9mFqaorU1FTcu3cPXbp0KZOcwvNFRUXhl19+waZNmxAdXRCJVLt2bXz44YcYN24crK2tazzBBEgkEnFumZ+fr/N5wrFG834lICsrC0CBV0Blv1zBg19XTwXGGFq1agVra2skJSXh5MmT6Ny5s06EEKyosbGx2Lt3L9atW4dHjx6BiGBnZ4dRo0Zh5syZeou9MzQIz1QWVV2lUolaRHUbgV6p2bFgEAAK1tP0NQcqDrm5uYiNLVu4nZubG7p37w4iwu7duxETE1Ni4xFCcx4+fIjFixfjtddeEzMRKxQKvPnmmzh8+DC+++47MYOWcF5CQoIYWf1fhBDSpB11UV14pYimVqvFiGEbG5tKf7lKpRLR0dFlasimpqYYO3YszM3N8eTJEyxduvSlZQIiAs/zSEtLw19//YXx48ejR48eWLx4MR4/fgxTU1P06tULu3fvxvbt29G+ffuXRm+e57F06VKMHTsWV69erfZ0axWBtgtVdfsslhevlOoouEUBEJ2LKxNEhIiICJ09FYD/TyE3dOhQbN++Hb/88gtyc3MxevRoODg4ICcnB8+fP8fVq1fx119/ISwsDDk5OWKagl69emHixIlo3759iblGeJ7Ho0ePcOTIEeTl5WHHjh2VPsJXFqRSqSh7RkaGTucY2ij+ShFN2xeuMuOstBt3ZGQkeJ4v0xqVhYUFvvjiC4SGhuLy5cvYvHkzdu3aBVNTU9EaKXhAyOVy1K1bF4MHD8bw4cPRpEkTneKxBNURAHJycmr0iCaXy8VIcCE6XRcIqqNUKq32eauRaOWA9sS6rKqjgDp16mDXrl1YvHgx9u3bh5ycHOTn54vJeGrVqoUOHTqgf//+6Nq1K1xcXMo8oRdckDIzM2s00WQyWZmJxvO8mEjJzMzMSDR9Qq1Wiy/XysqqUu7BGCvU4GNjY6FWq8ts4RQiAX744QfMnz8f9+/fR2ZmJszNzeHp6QkPDw9YW1uX26lYSDEHFDTOmjq3AQqIJqw36pq1S5tohpAg9pUimrYjcWUZQhhjhRY/s7KykJqaWu75j0KhQJ06dfSeMCYvL09UP1NSUsq0/mRokMlk4vvNzMzUaU7McZxISkNYU3ylrI5VAYlEUkgtValUBpkbUci1DxSokJVRi6CqIJFIRF9FXesdcBwnGk6EeLbqxCtLtMqyOslkMri4uIj/a6urhgQzMzNx5FWpVKWmBzdklMfLR3tEs7W1rQSpyoZXimjaPm0VqfRS2j20iVbWnPD6RGpqKtLS0orsVKRSqbjEkZ+fX2mJQ4lIDLCsCuh6nxeJZhzR9AiZTCamLhCy++objDG4uLiI5nye5yslkrs0REdHY/z48Zg+fXqRae8kEgkaNmwIoKDRhYWFVYrlMTQ0FNOmTcPt27crlWxlXbDWJpp2ItXqwitlDJHJZKIuX5lEc3V1hUwmEw0MVb04yvM8/vjjDxw6dEg0AK1du7ZQOIxUKkXTpk3Fc0JDQ8udrEjwVHkxEiAlJQXTpk3D6dOnce3aNWzbtg1NmzbVqVGr1WokJyfD2dm51OPL472fn58vEq0qnBdKwys1omk7j1amOicQrbrAGMM777yDt956CwBw+/Zt0cdTGy1atBCzgj19+rRclkciwpUrV/Dxxx8jMTGxUKdiaWmJ3r17w8zMDLdu3cJ3332n03tPT0/HsmXLMHjwYDx+/LjUjkqbaLo+Q0ZGBlQqFWQymUEkiX2lRrTyxi2VFc7OztVONEdHR6xZswbu7u4YNmzYS6V0GWNo2rQpHB0dkZ2djbi4OGRkZIhlkHRFeHg4xo8fj4cPHyI6Oho//vijmEHLxMQEH374IRhjOHr0KObOnatTJPP9+/exfPlypKWl4bPPPsPmzZtLXfcU5t5qtRocx5XqiZOWlgae5yGXy0t0VasqvFIjmlQqLZQ4p7Ig5HQHqi9nIGMMTk5OWLp0Kdq0aVNkQ7K1tUWzZs0AFBiHhHppZYG9vT3atm0Lxhj279+PLVu2FBqBTExM8MEHHyAoKAgNGjTQqUH7+/uLxRcPHDiADRs2lDj30i5dxXGc6FpVEjIyMkSiGUIhj1eKaNovtTwFKHSFQqEQf/jqTs5Zkh+fTCZD69atARQQrbgSRyXBzs4OK1aswNixYzFgwACMGTPmpfuZmJjoHPwqHD979mx07twZKpUKS5cuxcWLF0uUTXjfQsq50iAQTdt9qzrxShFNIpGIayZZWVmVpj5KJJJCP56hJr1hjKFJkyYwNTUFz/O4fv26To30RTg4OGD16tXYtGkTXF1d9aKGOTs7Y9myZXBzc0NiYiLmzp2L+Pj4Yo8XXNF0HdGE3Cza7lvVCcNsIeWEEEoCFMzRKotoUqlUNDIYMhhjaNCggSjr5cuXy22NtbKy0nuSn9atW2PBggUwNTXF5cuXsXv37mJHNe1a1roQLTc3V0zXXt0ZsIBXjGgAyqzLlwdCza2aAG9vb/j5+QEoKFIfEhJiMLFagvW0efPm4DgOFy5cKHZNsqxagxBMq+2+VZ145YgmWKc4jqs0j/WaRDSFQoHOnTsDKFCnli1bVqw3SVVDKErStm1bAEBYWJje/Ea1C1xUdxoDwEi0coExptfiGZUJiUSC9u3bi736mTNnsHDhwnIX6KgMCOWO09PTi10nE0Y6iURisHPiklDzJC4F2sXnKkt1fDEmrbqQkZFRqIBhUWCMwdfXV+wY1Go1NmzYgC+++KLUc6sKwmK7EH9XFIRktfpMPFuVeCWJBvz/wmZJyM/PR2xsLB4/foywsDCkpKTo7A+onW2qqHO0C8vrirIU5lAqlfjkk08wevRoXLlypcTz7OzsxI7B0tISKpUKP/74I7788ksxm291gIiQnp6OK1euACiwbhY1n+J5Hunp6QAKL63UJFR/t6xnCBY2lUpVrBpCRIiOjsbSpUtx+PBhMU+9n58fAgMDMW7cuJc8LbShvXZWFKGUSiWCgoJw+fJlzJkzRyzHWxoOHz6My5cvo0mTJhg4cGCx80AiwqVLl7Bz505kZmbCz89PnOcUBYVCIVrtJkyYgDt37iA4OBhr1qyBiYkJFixYoHNdaH0iLS0NixYtwqVLlyCVSot9Zp7nRQuyubl5tXt5lAtCL1qdm7+/P+kLO3fuJMYY2dvb0507d4o8JjMzk958801ijBEA8a/w2c/Pj86cOUM8zxd5vlKppLfeeosAkKWlJQUHBxMREc/zlJKSQmvXriVzc3NijNHw4cMpMzOzVLmVSiUFBgaK94+Oji722IyMDOrXrx8BIC8vL9q5cyelpaUVKW9+fj7t2rWLzM3NSSqV0q+//kpPnjyhDh06EABSKBT0+eefU1xcXLHPq0/wPE/p6el09OhR6tGjB0mlUmKMUb9+/Sg5ObnIc/Ly8mjIkCEEgHr16kUcx5V6n5UrV5JEIiEPDw+KjY3V92MUCU07LrKNVzvJSM9EO3z4MDHGyMrKiq5du/bS9zzP0+HDh8WGN2LECNqzZw9t3ryZhgwZQpaWlgSAmjdvTuHh4UXeIz8/n8aNG0cAyNTUlA4ePEhPnz6lH374gQICAsjU1FQkrkKhoIMHD5baiCMiIsjb25sAUIMGDYptHDzP0+7du0mhUJBUKqXJkydTvXr1KDAwkE6cOEFRUVGUmppKCQkJdPXqVfrggw/IxsaGAJCPjw89fvyYeJ6nixcvUt26dQkASaVSat68OX3xxRd0/vx5evbsGWVnZ+uVeEqlkkJDQ2ndunXUtWtXUigUBIBkMhn17duXIiMjS+zYBg0aRACoX79+OhFt3bp1JJVKydXVtdjfUd8oiWivnOooOJAWt2DNcRxOnDiBnJwc+Pj4YOXKlaLVa9SoUdiyZQs+/PBD3Lp1C5999hl+/vnnlxxeJRKJuC8/Px/ffvstYmJiEBkZKc53LC0tIZVKkZ6ejvXr16N79+7FugIREU6cOIHnz58DKDDDF+WrSUSIiorCd999B6VSiaZNm8LX1xebN29GWFgYTp8+DU9PT9jY2CAvLw+RkZFinksfHx+sWrVKVIkDAgKwZ88ezJs3D8HBwbh16xZu3bqFb775BrVq1YK7uztq1aoFLy8vODo6wtvbG3Xq1IG9vf1LyWlJE/ypVCrFSqt5eXnIzMxEXFwcHjx4gKtXr+LOnTuIjY0V17d8fX3x3nvvYfz48SXGjPE8L863dU2CZGlpCYlEIqbvq3YUx0BhA+AJ4AyA+wDuAfhAs98ewEkAoZq/dpr9DMAaAGEAbgNoVdo99DmiXbt2TVRHBJVOG7m5udS1a1cCQG+//Tbl5eUV+l6pVNKMGTOIMUZyuZz+97//kUqlIp7nied5ys7OppCQEBo4cKA4ar24yeVyWrRoES1ZsoQkEgmZmZnRb7/9VmRPzPM8hYSEiKMLNCPMnj17xB6e53lSq9UUHBxM7dq1I8YYmZiY0C+//EJJSUn01VdfUZ06dUgikbwki7OzM73zzjt0//79l0YMQY3bvXs39e/fn9zc3Iq8hrCZmJiQi4sL1a9fnxo3bixujRo1orp165Kbmxs5OjqStbV1oVFdezM3N6dOnTrRTz/9RM+fP9dp1MzOzqbAwEACQEOGDNHpnL/++osUCgVZWVnR2bNnSz1eH6iQ6gjATSALACsAjwE0ArAMwFzN/rkAlmo+9wVwTEO4AACXS7uHPol2584dMjMzIwB06NChl77PysqievXqEQCaP39+kY0/Pj6eunfvTgDIwcGBDh8+TOfPn6dvv/2WunTpQra2ti81HplMRgDI3t6elixZQllZWRQdHU0tWrQgAFSvXr1CjV0gz/Hjx8nX15cAkIWFhajmtWnThsLCwkitVtPjx49p3rx55OjoKDb44cOHU1paGhERqdVqioyMpL1799KiRYtoxowZNHv2bPr111/p/v37lJ+fX+I743me8vLyKDQ0lA4dOkSLFy+mESNGUJcuXahZs2bk4+ND1tbWheayJW2MMWKMkUwmI1tbW/L19aUePXrQF198QRcuXKCMjIwyqaU5OTnUt2/fMhHt/v37ZGVlRVKplH7//fcqmX9WSHUkolgAsZrPmYyxBwDcAQwC0E1z2FYAZwHM0ezfRkQE4F/GmC1jzE1znUqHXC6HQqFAbm5ukV4GRFTI7M9xnPgycnNzkZ2djdTUVAwYMACXLl1CcnIyxowZg/z8fNFPUIjFEqyaAQEBaNq0KSwtLTFw4EC0atVKdGZdsmQJRo8ejSdPnmDmzJlYtGgRLC0tERMTg8OHD2PXrl1ITk6GtbW1aP2bM2cOrl69ioEDB8LDwwP3798XE7V6eHjg888/x8iRI0VVVCqVwsvLC15eXnjzzTfL/M6E56lXrx7q1auH/v37i+pgdnY2cnNzkZ6ejtjYWISHhyMmJqaQqxRjDJaWlnBwcICFhQXMzc1haWkJOzs7mJubw8bGBvb29uIaZ1nxYpwhUenp5hwdHWFra4vMzExERkaW+Z76RpnmaIyx2gBaArgMwEWLPHEAhIw17gCea50WpdlXiGiMsckAJgOAl5dXWeUuFiYmJuLibFF52mUyGerVq4enT5/il19+wd9//w3GGJRKJTIyMpCfn4+cnBxkZGSI86TU1FQwxuDs7IwWLVqgb9++kEgkmD17NvLz8yGXy7FixYqXFrEZY3j99dcxffp0LF++HKdOncLVq1chl8uRmZkpziE9PDywbNkyDB06FGq1GtHR0Vi9ejXu37+P+/fvAyiYe/br1w+ffPIJWrRoUeneEYL3i/AuPTw80Lhx40q9Z3GQSCTiQrauTtGmpqbw8PDA8+fP8fTpU9HBuLqgM9EYY5YA9gL4kIgytHsUIhLUCp1BRBsAbACA1q1b623F1NTUVOzpi8plaGJigjFjxuDy5ctISEgQ89MLEPJiSCQSODg4iLknmjdvju3bt6NOnTowNzfHlStXxB+uJP88mUyGuXPnQiKR4McffxSPlUgk8PDwQGBgIN577z2RPDKZDIsWLUKTJk1w8OBBqNVq1K1bF2+++SZatmxZLetd1Q2ZTCaGP6WlpUGtVpdawdPU1BSenp64dOkSwsPDRSNVdUEnojHG5Cgg2e9EtE+zO15QCRljbgCEFhuNAgOKAA/NvipBaUSTSCQYPnw4rKyscPr0aXHUk8vlcHBwgJ2dHRwdHVGnTh34+Pjgt99+wxdffIHw8HCo1WpxQdzJyUkcwUryG2SMwcrKCp9//jmGDBmCx48fg+M4eHt7w8vLS8ypL5CHMQYzMzOMHj0aw4YNE2Wrif59+oKQuoExhtzcXGRlZcHe3r7Ec+RyuegoEB0djby8vGp1myv1zqygBWwC8ICIvtP66iCAdwB8q/l7QGv/DMbYTgDtAKSXZ34mzKPKWoPZxMRE9C4Q3HaKOmbw4MEYOHCg6D4ljGIvjNR44403sHbtWiQlJSE4OBgtWrQAUGA+trGxQWZmJvLz88FxXImmZxMTE7Ro0UI8vzRoh+8bAVGF1dVZXCKRwMvLC1KpFJmZmUhKSqrWGEJdusmOAMYAeI0xdlOz9UUBwXoxxkIB9NT8DwBHAYSjwLy/EcC0sgpFRNi3bx+WLVtWZM7CkiCTyXTOGyKoajKZrMiUAIwxce2I53nExMSI35mYmMDJyakMT2VEVcPd3R0SiQS5ublFZgmrSuhidbyAAlN9UehRxPEEYHpFhEpKSsLSpUtx69YtPH/+HGvWrNF52JdIJOLcSR+JTVUqldiDassgl8vh6OgIoPrzhhhRNIQkSmq1utIyV+sKg/MMISKcPHkSt27dAmMMnTp1KpO1SDDVAxXP5UFE+OuvvxAdHQ2pVAofHx/xO+3gT7lc/soYKASzfnp6OhITE5GcnAw7OzvUrl3bINK2lQWWlpZgjBmJVhQyMzOxadMmqNVq+Pv74/XXXy/Tj0v0/2ErFZn8EhHu3r0rxm01b94cgYGB4veMMVFF1XcujaoEESE7OxuJiYl4+PAhrl69imvXruHevXuIiYkBx3GQSqUYNGgQfv7552pLRqodMa3ruxaIplKpjER7EdevX8elS5cgkUjwzjvvlGpdehHafnHlJRrP87hx4wamTZuGx48fw9raGosWLSoU7qKd272mFfnjeR6pqal48OABTp48iX/++Qe3b99GamqquIAPQExsk5ubi3379qFNmzb44IMPStUwhPP12fkIQaoymaxU076AF0c0XRa6KwsGR7SYmBjk5eXBzs4OXbp0KfP5PM9XaETLzc1FUFAQFi5ciIiICCgUCnzyySfo27dvoR9JpVKJywdCRU1DiLouDjzPIy4uDnfu3MFff/2Fc+fO4cGDB4UqwZiZmcHd3R0+Pj5o3749WrZsCWtra3zzzTc4e/Ys1q1bh9GjR8PZ2bnY+xARYmNjsX//frz99tt6K5kkOAbL5XKdiSYEiebk5FR7DTuDbRmCRbA8PVB55mg8zyM0NBTffvstdu7cCaVSCVtbW3zxxRd47733Xvpx8/PzxTyEwg9paOZ4Yb51+/Zt7NixA8eOHRPXA4GC9+Pu7o4mTZrgtddeQ9u2beHj4wM3N7dCo1ZiYiL++ecfJCYmIjw8vESipaSkYOrUqTh27Bhu376Nb7/9Vi9ke3EZRhcIefdTU1ORnJxcYRkqAoMlGlD5VVqE6ycnJ2Pnzp347rvv8PTpUwBA8+bN8b///Q+9e/cucqTKzMwU1+ny8vKQkZEhWiGrE8IzJSYm4ty5c9i2bRvOnz8vyiqTyeDj44PWrVtj4MCBCAgIgLe3t0isFzs2nucRGxsrZv0tLSmRcH+1Wo1NmzahVq1amD9/frWM9nK5XJxTJicnG1VHbTg4OIAxhszMTLF6ib5fjtAY0tPTcejQIfzwww+4fv06OI6DtbU1xo4di08//RQeHh7F3lsoEg9AjL2qThARlEolbt68iT///BNHjx7Fo0ePRBldXV3Ru3dvvPXWW2jRogXc3NxKdQYgIly/fh3r1q0Dx3Fo0qQJ6tevX6IcDg4O+Pnnn8HzPJ4+fYrAwMBq8zHUTgdenPNClclSrXcvAn5+fnByckJcXByuXr2Kbt266e3aAsEyMjJw5MgR/PjjjwgJCREdgzt16oT58+ejW7dupc4D4uLiRCOIMKJVB4gIGRkZOHXqFDZu3IhLly6JsshkMjRv3hxDhw7FkCFD4Ovrq/PIQkQICwvDe++9h/DwcNjZ2eHDDz8sdUQT6sdt2rQJqamp8PPzq7ZRRCqViksw1d0RGhzRnJyc0LBhQ8TFxeHIkSOYMmVKhU3KwtpaSkoKjh07hvXr1+Pq1avIz88XC0G8//77GDhwoM7VIRMSEkSiKZVKpKenV5lqIixhREVF4dChQ9i+fTtu3bqFvLw8saF37doVw4cPR6dOnUQ/wbJc/+nTp5g4cSKuX78Oc3NzLF68GP3799fpOkKkQ0lzubKAiMSQJG2HhNKgnbpd+H2qCwZHNAsLC/Tv3x9///03Ll68iI0bN2LWrFnlWnwWfqAHDx7g4MGDCAoKwsOHDwsRbMqUKRg0aFCZG6N2TkSVSoWIiIgyy1dWCOrhjRs3sHv3bhw4cADPnz8XLZ5NmzbF6NGj8cYbb8Db21tn69yL94iMjMSECRNw/vx5sfLLxIkTKzzPEjqIjIwMpKSkwMHBAdbW1qX+tkQkevkIxS50geAoDhRoINUZKmNwRBPysR89ehSnT5/G0qVL4ePjg8GDB+tENu0FzZs3b2LIkCH4999/xcmwiYkJ2rZti0mTJpWLYAJezIf44MGDSptPEhGSkpJw9OhR/P777wgJCUFaWhqAgvRrAQEBGDt2LPr06QMnJ6dye8QQEZ4/f46JEyfi3LlzkMvlmDVrFj799NMKZ2YmIoSHh2P16tU4evQosrOz4eTkhClTpmDixIklWmy1g3XLQnbBqsoYQ1paGtLT06vPYFVc6HVVbi+mMuB5ni5dukTu7u4EgFxcXGjjxo3FhsDzPE/5+fmUlJREBw4cEFMVCJtEIiF7e3saPHgw7dmzh5KSkioc2r5w4cJC9+jSpUupKQPKAuGZIiMjaeXKldS0aVOSy+ViThE3NzeaMGECBQcHlzk1QHH3i4+Pp4EDB4o5ST799FOdUuXpcu3Hjx9TQECAmA5BKpWKaSA2bdpUovxqtZrefvttAkCdOnXSKQuWgI0bN4rZsB48eFDhZykJNTLdHMdxdPjwYZFsCoWC+vTpQ3v37hVTqiUlJdHt27dp27ZtNGnSJKpfvz6Zm5sXSnLTqlUrWrJkCd25c4dycnL0ljviRaJ5eXlRZGRkha/LcRylpaXR8ePHacqUKeTt7S02SoVCQe3ataMVK1ZQaGgo5eXl6eV5eJ6n1NRUGjt2LEkkEmKM0YQJEygmJoZyc3MrfI+srCwaPnw4McbIzMyMxo4dS6tWraLGjRsTAPL19aWwsLBiz68I0f766y+Sy+VkaWlJ58+fr9BzlIYaQzSO4+jixYsUGxtLPM8Tx3F08uRJatu2rZj8RqFQkIODA3l7e5O7uztZW1uLPT00eRaFnIHNmjWjqKioMv0wuuLzzz8vRDSFQkGnT58uV6MUkuOEhYXRypUrqV27doU6DCsrK+rTpw8FBQVRamqq3p8nNzeXPvroI/EdM8aodu3a5O/vT4MHD6Z169ZRREQEqdXqcj3boUOHyNzcnCQSCc2ePVvs8A4fPkyWlpbEGKP333+/WI2gIkS7d+8emZmZkUQiob1791Zqkp4aQ7SIiAjy9fWlgIAAunLlipjiLSEhgVasWEFNmjQhMzOzQtmY5HI52draUpMmTWjatGkUFBREM2fOJADk5+dHMTExen+hPM/TvHnzxIxUQuaqFStWlOmH5DiOEhMT6dChQ/TOO++Qm5ubOHrJ5XLy8fGhGTNm0JkzZygrK0vvjUSlUtGdO3do2rRphYj94iaTycjHx4cWLVpET548KRPhUlJSqFu3bgSAGjVqVCjFXF5eHo0aNYoAUJMmTSg+Pr7Ia1SEaM+fP6datWoRAFq9enW1Ec1gjCFEhKNHjyI8PByxsbFiDWohjP2jjz7C2LFjcfnyZURFRSE1NRUymQweHh7w9fWFr68vLCwsIJVKxXPT09ORmZkpJkjVp6xCYh0nJyd4eHjg8uXL+Pfff0FUukEkPz8fERER2LNnD4KCgkSfQwCwsbFBmzZt8Pbbb+P111+Hs7NziXWqyyt/YmIiVq9ejc2bNyM+Ph5EBEtLSzRs2BCtW7eGnZ0dsrKyEBISgjt37iA8PByLFy/Gli1bMH36dEycOLHUpRAiwrFjx3Dp0iXI5XLMmDEDtWrVEs+Ry+Xo3Lkzfv/9dyQkJFRKolOFQgEnJyfExMSICWqrAwZDtOzsbAQFBYHjOLRo0QIdO3YslEeDMQYnJycxFZo2XvyxXVxcxGtWxo/HcZxo9fPy8kKLFi1w+fJl3L59G4mJieL9tSGQ88aNG9i8eTMOHz6MhIQE8DwPExMT1K9fH/3796/0JDw8z+P27duYPXs2zpw5I/oQWllZYf369Rg0aJCYFo6oYDnh0qVL+Pnnn3Hq1ClERERg3rx5OHPmDJYuXYrGjRsXK6darcaZM2eQl5eHJk2aYNiwYS8dq10s5MXfVQDR/4c+ldWiqlAoREtjVFRUmc7VJwyGaKmpqbhz5w4AYNCgQSVW1CytAdrb20MikVSa1zbHcWJovLOzM1577TVs2LABz549w8OHD+Hs7FyorFNOTg4uX76M9evX4/jx46I7kJ2dHTp37oxRo0ahW7du4lJDZS16cxyH48ePY+bMmXjy5Im4nzGGKVOmYOjQoS/lPbGwsECPHj3QuXNnXLx4Ed9++y2Cg4Nx9OhRPHnyBL///jtatWpVpMxKpRLXr18HALRv377IEfDmzZsACjrH4nJ6EFGZU4IL0CZaTEyMThpHZcBgiPb8+XNkZGTA1NS0wvkD7ezsYG1tjbS0NDx79kxPEv4/1Gq1qJ7a29ujefPmhdzGunTpAqKC2l/nz5/Hhg0bEBwcLI6uHh4eGDx4MEaPHo2WLVuKjacyG4BKpcJvv/2GOXPmIDExUYyWzsjIgKenJ6ZOnVpsIxYSBXXr1g0tWrTA6tWr8d133+HRo0eYNWsW9uzZU6QXiEqlQmxsQV6m+vXrv7RYLDg+A0DTpk1LjD0URruyjmhSqVSULTU1Fbm5udWSpMdgEl08fPhQdOr19PSsUKOztrYWPQIqI0ttdna26OXv5eUFZ2dn+Pr6AgAuXbqE6OhobNiwAf369cPQoUNx+PBh5ObmwsfHBwsWLMCpU6ewevVqtG3bVvR0qGySbdq0CR999BESExPh6emJzz//XHS4DQwMRO3atUu9DmMMtra2WLBgARYuXAi5XI4LFy5g3bp1RRZjzM3NFd3CrK2tC33H8zz++OMPPHz4EHK5HG+99ValeG0wxuDm5gbGGLKzs6vNJ9VgRjTB09zW1lbnwn3FwdzcXKz2UlRux4qAiHDu3DnEx8dDoVDA398f1tbWaNq0Kc6fP4/g4GC89tprePLkCXieh0QiQcOGDTF27FgMHz4c3t7epRIrNzcXu3fvhrW1Nfr27VvuODdB5dq4cSPmzZuHjIwM1K9fHxs2bEBcXBzi4uJgbm6OESNG6Ex0IYXDlClTcPbsWRw5cgRBQUGYNm3aS1nB8vPzQVRQOUY7HTgRISQkBEuXLgXHcejYsSO6detWaZ2NoLKqVKpSM6NVFgxiRCMihIaGAgDq1KlTbHkjXWFqairWORaMFvoAESEuLg4rV65EXl4e6tWrhzZt2ojuXcL9QkNDxWgAwYgwZ84c1KlTR6fQlB07dmDKlCkYPXo0fvjhh3LV4hYMCFu3bsX8+fORkZGBBg0aYMuWLejUqRMuXLgAnufh5eVVokGjOFhZWWH48OEACrSGuLi4l47RVvOEEU94h7Nnz0ZsbCwcHBywZMkS2NnZlfosQPnUa6EtlFQFtrJhECMaz/MICwsDADRq1KjC2au080roy+oozLnmzp2LkJAQmJqaYvLkyTh27Bh++OEH0ZADFASNLl68GF27doW1tXWZPecPHz4smvu///57vPHGG6hbt26Z5d29ezfmzJmD9PR0NGjQAFu3bkWbNm2QnZ2Nu3fvAih434KaXVYI5xVXL1yIkBdK4xIRkpOT8cEHH+DChQuQy+WYN28eOnbsWOJ91Gq1mHNf1+gKbQjleNVqdbURzSBGNCHkAyiYNFdUhZBKpaLzqT5eLBHh2bNnmD59Onbs2AGe5+Hh4YFdu3Zh6tSpuHHjhliRBQAaNGiAfv36latRJCUl4cGDB+L/Qv6N4kzfRYHneezfvx8fffQRkpOT4ePjg40bN6JNmzaiCpWdnQ3GWLnnw0SEf//9F0CBZVIYNbRhYWEBFxcXEBEePXqE2NhYzJgxA3v37gVjDGPGjMGUKVNKnZvl5+eLmkl5ktYKsqnVar3k+iwPDIJo2tBHyPuLab0rAp7ncenSJQwZMgR//PGHqMY9ffoU//zzDziOQ7t27bB161aMHTsWAHDhwgWEh4eX+V5EBXkkw8PDIZFIYGdnB57nERwcrHO6NIFk06dPR3x8PDw9PbF+/Xp06NBBfC8KhQLTpk3DkiVL0Ldv33LJeevWLWzfvh0A0LlzZ3h7e790nJmZGZo2bQoAOHXqFMaNG4c9e/YAAEaMGIFly5bpNE3Izc0Vi5G4u7uXWV5tolXXiFbt7ldEhObNm5Onp2e53JiKglqtph49ehAAGjx4cLmuIXjPBwUFibWltTfGGHl7e9OKFSsoISGBeJ6n8+fPk5WVFTHGaMGCBWVyVeJ5nm7duiUWJfT396d3332XAFDjxo2LLaSuDY7jaO/eveTq6koAyMPDg44cOVLIZYnjODpz5gz99ttvYiXTsr6X8PBwat++PQEga2tr+uuvv4o9dsmSJS+5zI0fP75MERS3bt0SfSV37txZZpnPnz9PMpmMFAoF/f3332U6tywweF/HFi1aiJUxp06dWi7nVW2oVKoKEY3neYqJiaHZs2eL1T2FHxoa37+33nqL7t+/X0jWnJwcGjJkCAEgR0dH+vPPP0mlUul0PyGMBADZ2NhQUFAQ/fLLL8QYIxsbGwoNDS3xGhzH0ZEjR8jNza1Ykgmdgbe3N9na2tK6devKRDae5ykuLo769u1LjDFSKBS0bNmyIp2BOY6j58+f07hx48T3ZmZmRvPnz6f09PQykWXr1q0klUrJ3t6eQkJCdD5PwNWrV0kmk5FMJqPTp0+X+XxdYfBEa9Wqldh7N2vWjNLT0yv0wHl5eWKd6mHDhpXpXKVSSYcOHSJ/f38xZKRevXr0zjvvkImJCUmlUpowYQIlJycX2VhCQkLE0B5HR0f6/vvvKSUlpdjOQ61W07lz56hVq1Zi4126dCnl5+fTuXPnyMrKikxMTOjAgQPFyszzPJ09e5a8vLwIANWqVeslkgnHXbt2jRo2bEhAQdngM2fO6NToeZ6n2NhYGjlyJEkkEpLL5aInvvYxarWanj9/TmvXrqWmTZuKTtLQOHknJiaWiWRqtZqmTZtGAKhBgwaUkpKi87kCrl+/TjKZjKRSKZ08ebLM5+sKgyeav78/bd26leRyOZmZmdHu3bsrpD5mZmaKo8OECRN0OofjOAoLC6Pp06eTtbW1GPoycuRIunPnDo0dO1b0Mo+JiSlWPo7jaN++faK6aWJiQu3bt6dZs2bRnj17KDw8nDIzM4njOMrKyqK1a9eKqp6ZmRktWLBAbLwxMTHk5eVFjDH68ssvi73nvXv3qEGDBiK5g4KCivVwF4JqmzRpQuPGjaOUlJRS3zXP83Tv3j3q1asXSaVSkkgkNH78eHFk4nmeVCoVRURE0KJFi8jHx0cMuTEzMxNripuZmdG+ffvK9NsmJCRQ06ZNCQCNGTOmXNqOkWhaRIuNjaVmzZoRAOrYsWO5ei4BKSkp1KpVKwJAM2fOLPFYjuMoJiaGVq5cST4+PmKhcz8/P9q6dStlZWVRfn6+GOoxatQoysvLK/Waly9fpi5duhTq0U1MTMjR0ZG6detGX375Jb3zzjtkamoqjkIbNmyg3Nxc8Tp5eXnUs2dPAkCvv/46ZWdnv3Sv5ORk6tevnzhf2rJlS6mNked5CgsLo9TU1FIbvVKppL1794qjoImJCb377ruUmJgohvkcOXKEpk+fTvXq1RPVREtLS+rWrRvt3LlTDIWBJhJdKHJfGniep19//ZXkcjmZmpqWuwPWJtqpU6fKfL6uqBFE43meNmzYQHK5nORyOW3cuLHco1piYqLYC86ZM+el73mep5ycHHr8+DF9//33hVQcGxsbmjhxIoWFhYmjQn5+vtjgR44cSUqlslQZhNQAP/30Ew0bNozq1atHVlZWhdIrMMZIIpFQhw4d6J9//iGO4wo9M8dxtHTpUmKMkYODA924caPQ93l5eTR//nySSqUklUpp4cKFekunwHEcRURE0AcffECWlpYikefNm0ehoaF0/vx5mjdvHjVv3lzsLASCDRkyhE6cOCHG0AUHB9N7771HMpmMTExM6Ouvv6bs7OxSf9+kpCRq27YtAaCAgACdDEJFwTiiaRGNqODFCtas+vXr0927d8tFtvj4eDFMfsGCBeJ+Yf6wZcsW6t+/Pzk5ORWaqPfq1YtOnjz5UooAtVpN48aNIwDUrl07Sk1N1UkObbUqLi6OxowZI95PuOfkyZOLVUV5nqcbN26Qs7MzMcbo3XffpaysLPG7o0ePimruwIEDdZarNJmjo6NpxYoV5OfnV0heDw8P6tWrF/n5+ZFCoRCtiXK5nOrWrUvjxo2jv/7666WUETzPU2JiInXs2FFUyadNmyZG0hcFlUpF3333HcnlcjIxMaFt27aVu+M1Eu0FovE8T0FBQWIP2qlTpxJzSRSHhIQEatKkCQGgWbNmUWhoKO3cuZPGjx9P9erVK6TOWVtbU/fu3SkoKIgyMzOLbfA//fQTSSQSUigUtH79+jLPFVQqFX3yySfEGCO5XE6tWrWibdu2lZrHRKVS0cyZM0VDyf/+9z/KysqiuLg4sVOqW7cuPXjwoNwNUegMHj16RF999RU1atSoEMGK2qRSKXl4eNAbb7xBf/zxB8XExJBarS5WBp7nKSQkhNq3by+O5AEBAXTgwIGXDCrp6em0Zs0asrOzIwDUrVu3Ck0ljER7gWhEBerQV199RSYmJqJOHx4ervPD5ubm0s2bN8U5hYuLC7m6uhZqOAqFgtq0aUNLliyhixcvlpomQDD3t2nThgCQs7MzHTx4sEwh9TzP06NHj2j48OH022+/idY3XQwRz549E0llYmJCI0aMoMmTJ5NEIiETExPasGFDudbDsrKy6NGjR/Tbb7/R22+/LYb8C+/otddeo8DAQGrYsCE1aNCAWrVqRX379qU5c+bQnj176MmTJ2VeHnj27Bm9/fbbYp4Xc3Nz6tWrFy1evJh+++03WrNmDfXq1Uv8/evWrUuXL1+ukHHMSLQiiEZElJ2dTZ9++qn4sl9//XUKCwsr1DC11bLIyEg6fvw4zZ8/n7p160Zubm6FFkkFNa1x48Y0ceJEOnbsGKWlpenU0AXwPE9///03eXh4iMaL7du3l2lOJMhblvsK5926dYs6duz40nO5u7vTnj176N69e5SYmCiOKtpbdnY2xcTE0IMHD+jMmTO0evVqGjduHLVv355cXV0LXdPa2pr69+9P+/fvp6ysLFKpVJScnExJSUliRiztrazgeZ4yMzNp/fr1VL9+/WJHTIlEQh07dqR///23wsmIjh49SlKplMzNzenChQsVulZJKIlojKhiLkr6QOvWrSkkJET8n4iQmpqKqVOnYu/eveA4DvXr18eIESPQoUMHWFtbIysrC3fv3sXp06dx9+5dREVFFenl7uXlhUmTJqF79+6oX7++6AhbHv8+nudx8OBBTJ06FXFxcbC0tMT8+fPFnPSVGVNGVFB3bMWKFfj5558LuWQxxmBvbw8XFxfUqlULrq6uMDU1BcdxiI2NRXJyMtLT05Geno6UlJSX3pOZmRn8/PzQtWtXjBgxAq1atSpTRuDyPs+zZ8+wdetWnD17Fk+ePEF2djZkMhlq166NoUOHYuzYsXBycqqQHESEb775Bp999hk8PT3x999/F+kupg+0bt0aISEhRQtbHAOrcntxROM4jn7++eeXVD5o5gbm5uaF5lnQeGt4e3tTYGAgLVmyRJyjvfnmm3rNfKRWq+n48ePUqFEjAgrS282ePbvM3g7lAc/zdPLkSbKwsCDGGDVs2JC8vb1JLpe/NNIVtTHGSCqVkouLC7Vp04ZGjhxJy5Yto3/++UcvSWXL+0y5ubn09OlTun//Pj169EjUNvSBvLw8GjZsGAGg9u3bl7o0UxHUiCxY2njy5Am+/vrrImOcOI6DUqmERCIRC2J06NABr732GurXrw9XV1cx8vfu3bt6D/STSqXo1asXdu3ahffeew8XL17E999/j+TkZCxfvhz29vaVNhKoVCps374d2dnZ8PHxQVBQECwtLREbG4t79+7h7t27CAsLQ2ZmJlQqlRgR7eLiAk9PT3h6eqJu3brw8PDQOe99ZYMxBoVCoVOEd3mQlZWF2NhYSCQStGnTpsw5R/QFgyMaUUEEc1RUlFhGVbughIeHB0aOHInAwEAxjYBQq1gbQgMqKsS+omCMoXHjxti+fTumT5+O48ePY9u2bcjOzsYPP/xQYXWnOMTHx+PMmTMAgMGDB4t5OLy8vNCuXTsQkeihznGc2IjLWzn1VYCtrS22b9+Ov/76C23atKk+QYob6oQNgALAFQC3ANwD8KVmfx0AlwGEAdgFwESz31Tzf5jm+9ql3UNbdczPz6eRI0eKBofNmzfT+++/T7Vq1SrkddCzZ0/auXNnsd4NgYGBBID69OlTaSqR4P/39ttvk0QiIYlEQoMHD6bIyEi935Pnedq+fbuY3vrUqVPVourVVJTXeFMWVMjqCIABsNR8lmvIEwBgN4ARmv0/A5iq+TwNwM+azyMA7CrtHtpEi4iIEJ1jGWPUtGlTunz5Mj169Ii++OIL8vX1fSkX/fr16ykuLq6QZ0VVEE1AUlISvfvuuySVSokxRgEBAXT16lW9pu5WKpU0fPhwAkDNmzfX2Y3JiKqD3sz7AMwBXAfQDkASAJlmf3sAxzWfjwNor/ks0xzHSrqu9oL1tm3bChk65HI5/e9//yOO40TPjlWrVlGLFi1E879cLqfGjRvT0qVLKSIiglQqVZUSjajAv3LmzJli3n9fX186ffq03sj29OlTcZ1r3rx5FQ4lMkL/qDDRAEgB3ASQBWApAEcAYVrfewK4q/l8F4CH1ndPADgWcc3JAEIAhHh5eRFRwWLzG2+8UchSNm7cuJfCZgRn1i1btlDnzp1F73CpVEq1a9emOXPmiE7FVUU0ooL1v1WrVokeDZ6enrRnz54K+x/yPE+bNm0iuVxOVlZWdO7cOaPaaIDQ54hmC+AMgE4VJZr2JoxoDx48ICcnJ5FkderUoUePHhX7YIK7zp9//kn9+vUTnXYFFx8A1LNnT72WayoNKpWKfvvtN3JxcSEAZGtrS19//XWFYuzy8vJo7NixxBij1q1bVzhez4jKgV49QwAsBPCJvlVHnudp+fLlhaKYv//+e52DErOzsyk4OJhGjRpFDg4OIlnNzc3prbfeohMnTuilYJ8usgi13YS0BEL4vpDyoKzgOI6uX79OX331FW3atKlSylAZUXFU1BjiBMBW89kMwHkA/QHsecEYMk3zefoLxpDdpd3D39+f0tLSRO9uANS5c2edghK1wfMFdcYuXLhAdevWLaSCmpub0+uvv0579uwpNjpan+A4jm7fvk09e/YkiURCUqmUhgwZUm6LpEDgkhx3jaheVJRozQDcAHBboxYu1Oz3QYHZP0xDOlPNfoXm/zDN9z6l3cPf35+Cg4PFuZapqSnt2bOn3A0qLS2NWrduLTqlagckyuVyat++PW3cuLHEMA19QMixMWrUKNEi2b59e71bJI0wDBi8U3GrVq1o8uTJ4ugTEBBQITccbaJNmzaNnj17RitXrqRmzZqJIfZSqZSaNWtGK1asoIiIiEpr+DzPU0pKCk2fPl20SNarV4+Cg4Nr3MhUFWtRNRkGT7QmTZqIXvEymaxC0dVEhVMZfPDBB2IDSUhIoM2bN1OHDh3EpQHGGNWtW5cWLFhADx48qBSzuRDRrW2RrF27Np08ebJGNNzLly/T2LFjafjw4fTw4cPqFsdgYfBE8/DwENfOfH19KS4urkINMDk5WUxfN2vWLHG/QLi0tDQKCgqi3r17FyopW6tWLZo+fTqFhIRQfn6+XknA8wUZorZv3y4aa7y9venUqVN6K/peWfjjjz/IxMSELCwsKjWeq6ajJKIZRKbipKQkMXd7u3btxIJ85YXwcEDhcBihiouNjQ3efPNN7Nu3D/v378fQoUNhY2ODmJgY/Pjjj+jduzfGjRuHc+fOQalUiteqCBhjkEqlGDlyJFavXg07OztERkZizJgxGDduHNLS0vRyn8qEUqlEbGyswctpiDAIogke9hKJBJ06daqwRznP86IzcUnF9SwsLNCrVy/89ttvOHbsGCZMmAAnJyekpKRgx44dGDhwIIYOHYpDhw4hKytLLw1MKpWiXr16olyxsbEICgrCkSNHKnztygbHcQgLCzMSrRwwCKIJsLS0RJMmTSrsaU70/2V+dCluZ2pqivbt22PdunU4ffo0Zs+eDS8vL2RlZeHIkSMYMWIE+vbti+3bt4slmirS2KysrFC7dm3xOVUqFdatW6fXElOVhbt371ZKRMSrDoMimkQi0Xu8UFkIIZfL0bRpU3z77bc4c+YMvv76azRq1Aj5+fk4f/48Jk2ahF69emHt2rWIiYkBz/PlIlzDhg2xY8cOBAYGiqP35cuX8fXXX4vlmgwVDx8+rLZifjUZBkU0oQ5WRVWTipZtkkql8PHxwZw5c3Dq1CmsXbsWbdu2BQDcuHEDs2bNwmuvvYavv/4aT548gVqtLpPMjDH4+Phg06ZNGDNmDGQyGTiOw88//4w///zToFWzhIQEREREVLcYNQ4GRbTs7GzcuHGjwtcxMTERy9FWpGaxRCKBm5sbpkyZgmPHjmHr1q3o2bMnTExM8PjxYyxatAivvfYaPv30U9y+fVssJasLGGNwdXXF6tWrMXPmTEgkEmRnZ2PhwoVi9VNDRHp6unGeVg4YFNGICNu2bcOTJ0/EH5KIxCQzQUFBWLNmDdatW1dkmgMBCoVCrLuVmJhY4UYhJL8ZPnw49u3bh6CgILz55puwsrLC8+fP8f3336NXr15477338M8//yA3N1fne9rY2ODjjz9Gw4YNAQChoaH49ttvDVY9U6lUePbsWXWLUfNQnN2/Kje8kECmffv2dOzYMXr06BEFBwfT7NmzqV69emRqakoSiYQCAwNLDHzkeV6MRwsMDKyUaOecnBy6cOECjR8/npycnMTkONbW1jRo0CA6dOiQzgl7eJ6nxYsXi+/AwsKCDh06ZDBra8I6miDfxx9/XCNcyPLz8ykmJobOnDlToQSsusLgF6y1f0RhUygUZGlpWSj1NFBQLUWXeKy+ffuK8WiV6V6Vl5dHN2/epFmzZpGnp2ehFOPdu3en7du361Sq6Pbt24WiDjp16kSJiYmVIndZ8eeffxbKr//WW2/ViMDTmzdvkp+fH9na2tKRI0eqNZWBQaiOnp6e8PHxKWTWVyqVyMrKKrRgbGtri1WrVhUqE1sczM3NARQYQ4rK96gPCHWrmzVrhqVLl+LMmTNYvHgxGjRoAJVKhTNnzmD8+PHo06cPfvzxR0RHRxdrGvf19S2UPObSpUv4448/DMKU7uDgUGiZJCoqqkbM0ZydnaFSqZCWlobg4ODqlbk4Blbl5u/vL1Z2GTlyJHXs2JECAgJEJ1xoPPpXrVqlc7Ty22+/TUBBSnHtUkiVDaEM1I8//kj+/v6iyiWVSql+/fq0ZMkSCgsLe2lE4HmeVq1aVWhUr127Nt29e7dC8uTl5dGePXsoNDS03D36o0ePxGIaQEFBwZqQsyQvL09M9NSqVSvKzMys1PsZvOooBH4K/oBKpZKWLVsmetozxmj8+PFF1gcrDkLSzG7duulUZkmfEJ4lJSWFduzYQd26dRN9Khlj5OnpSR9//DHdunWrUMdx6dIlMVRI2IYNG1buBqJWq2ndunVkbW1NHTp0oEePHpWLbElJSVSnTh1RJldXV3rw4EG5ZKpK8DxPGzduJKlUSra2tnTlypVKVR9rBNG0ce7cOXJ0dBR/WH9//zIFTObm5oqldQcOHFhtE3eBcJmZmXT48GEaOHBgoZHBycmJJkyYQBcvXiSlUkmRkZFUu3btQkQzNTUtd1S1SqWiJUuWiJpBjx49ymUUyMzMFAsxQjP/PH36tMEYa0rCw4cPxYiJVatWGYkmIDk5mbp37y7+qG5ubnT+/PkyvaDHjx+Ti4sL2dra0jfffFOWd1UupKam0t69e0scOXmeJ6VSSefOnaPRo0cXMnxYW1vTkCFD6ODBg2Icnbm5uZj1qn79+uWKzBaso59//jnZ29vTt99+W66U2EqlkkaMGFGoA/jpp59qBNEyMjLE9tSnTx9SqVSVdq8aQzSO42jVqlWiyiiXy2ndunVl7s1DQ0PJ1dWVvvjiiwrNJV6snFJcJZXLly+Tg4MDffPNN6XOIXmep/z8fLp27RpNnz69UKkkMzMzsrCwIGiS+qxcuZIsLCxIIpHQ3Llzy9VIhHwq58+fJ6VSWe6cJTNmzChEtNGjR9cIEz/P8zR37lxijJGvry/Fx8dX2r1qRO59IkJoaChWr14tWgl79uyJUaNGldmb38nJCd988w0CAgJgY2NTZjkAIC8vD2FhYbh37x5u3bqF9PR0cBwHa2tr+Pn5oWPHjvD19YVUKsWpU6eQkpKCtWvXYujQoahXr16x12eMQS6Xo1WrVmjevDlmzJiB33//HX/88QfCw8PF+2dlZSErKwsBAQE4ffo0Nm/ejCFDhsDf379MTteMMZibm6NTp05leg8vXsPOzq7QvpCQECQlJVVa+nN9gTEGZ2dnAAUW6GpzBCiOgVW5+fv7U15eHr333nvimpm9vT1duHChStQTIfFNWloanTlzhj7//HNq3759odK72htjjNzc3GjUqFG0f/9+GjBggGhZ3LJlS5ll5jiOIiMj6bvvviMfHx/xPlKptNCcbtiwYYWqY+rjuXWVVTtDGTSR8Fu3bjV49ZHneZo9e7ZoLU1ISKi0e9UI1fHSpUvipBUoSJyqr8LnRUGwcCYlJdGJEyfok08+oebNm4tlfQVCmZqakpubGzVq1Ij8/PzIwcFBVG2hWVjXXobo06dPucnA8zy98847LxFb2CwsLGj37t1lqrJZHITyU7du3dLpWj/88ANJpVKysLAQ1d2KFHCvKmjP0fr371+pC+0GT7RWrVrRqFGjxAZlZ2dHly5dqpTeUjBK/PPPPzR79mxq2LBhoXQGwkhSp04d+vTTT+nixYv07Nkzio+Pp9jYWAoPD6cTJ07Q2LFjxfmU9mZmZka//vprueYvKSkpYgoGANSkSRPq1q1boZHEycmJpk2bRleuXCl3CgSO42jfvn3k7OxMTZo0oWvXrpV6nZ9++omkUim5ubnRZ599RjKZjGQyGa1fv95gRzWe5+nq1atkY2NDjDGd84SWFwZPtIYNGxYazbp27ar3RWbBXercuXM0bNiwQvcTNnNzc+rRowdt2LCBnj17VmzvJ1jzdu7cWUjVEzZXV9cypwLneZ6OHDlSaHQcN24c3blzh9zc3F66h4ODA40cOZJOnz5N2dnZOjcgjuPo6NGj4qjk4eFBFy9eLPF8nudpxYoVJJFIyMPDg27dukWdOnUiANS4cWOKiorS+TmrCjzPU3JyMg0dOlT8TW7fvv3fJpq2Uy4AWrZsmd5eiKAi3rlzhyZMmEC2trYvNVorKysaOnQoHT9+vEzZjFUqlWjRevGaNjY2NH/+fJ0TDaWmpoqO0MKoumnTJlKpVPTBBx+I96hbty45OzuLo5yVlRUFBgbS3r17S03Rx3EcBQcHk7e3t3juggUL6MSJE5Samlrsefn5+TRu3DgCQM2aNaPs7Gzat28fmZmZEWOMPvvss0o1m5cVPM9TREQEDRkyhGQyWYWstmWBwRNNe85jaWlJZ8+e1QvReJ6npKQkWrZsGXl6er5ECLlcTr169aITJ06UOT9/SkoKLVq0iNzd3YudU0mlUgoICKADBw5QVlZWsdfPycmhBQsWFJr7denSRUwh/uTJE2rYsKHYMy9evJhmz55NderUKVTCqmPHjrRp0yaKj49/SXXlOI7OnTtH9erVE1XQOXPmkKWlJVlZWdHZs2eLfYfXr18XR9Xp06eTWq2mrKwsevPNN0VV/+jRowahQqrVarpx4wZ16tSJGGMkk8lo6NChlZ4sl6gGEE27cXp4eFBkZGSFH5rneXr06BEFBgYWasDaI86SJUvKlR6c4zhauXIlyeXyYkn2ohFj0KBB9Oeff1JCQoKYyk6tVlNUVBR98sknosrIGKPmzZtTSEiIKBfHcRQUFETOzs7iGtu8efPoypUrtHTpUmrcuLEoi1wuFxPDRkZGklqtJpVKRadOnRLTpNvb29P27dvp/v37ogfOlClTxLAeYR6bmJhIx44do3bt2onk/Pfff0W5bt26JXqyNGvWjMLDwyv8u5UXgo/pihUrRHXezMyMZs6cWSUp4IlqGNHatm1bYd9EnucpJCSEWrRoIRZI15772NnZ0caNG8ttTMjIyBDnKAI5ilIf69evT3Xr1hW/MzU1pUaNGtHbb79Ns2bNogkTJlCDBg3EUcnCwoImTpxIT548eUkulUpFR48eJT8/P3G0bN++Pe3evZseP35MGzZsKOSILZVKycfHh+bPn09fffWVWN3GwcGBtm7dSiqVqpBKaGJiQr169aKpU6fS1KlTaciQIdS4cWPxemZmZrR48eJC6hfPF1QhNTc3J8YYjRkzptIdd7UhdFaRkZG0cuVKatKkifgu7ezsaPny5VVaSahGEW3ChAkVMsEKqk7jxo3Fxu3r6ytaCB0cHGjLli0VWjqIj48vVF6qY8eONHHiRLKxsXnJYOHv71/kiKq9yWQyatasGe3cuZNyc3OLbRgcx9Hdu3fpzTffFOPDTE1NqUOHDvT1119TcHAwff/999SsWTOR3NolrCwsLMR5n4CHDx9Sly5dilwvFGTz8/OjNWvWFNlolUolTZ06VSTr2rVrK9WEzvM8ZWVlUWhoKP3xxx80ZcoUqlu3rii/ubk5BQYG0okTJ/SeBLc01CiirV69ukIvJywsrJC/YOfOnUWSubq60s6dOys8KY6OjhaNKnK5nLZu3UpKpZLOnDlDQ4YMKTR6lkSuWrVqUa9evWjjxo06G00EJ+VffvmFmjVrJvbgjDEyMzMjT09PcnV1LZI45ubm9Pbbb9PZs2dFSyXP8xQTE0MLFy6k3r17U9OmTalp06bUs2dPmjlzJv3xxx8UFRVVqGyxNjIyMuidd94Rid2iRQuKiIjQ2xxbqVRSQkIChYWF0blz52jx4sXUs2dPcnJyKlQZ1sLCgnr37k1//vlnifPhykRJRGNU0NCrFZofCebm5ti3bx969+5dLree9PR0jBs3Dvv374dCoUCvXr3w77//IjExEbVr18b69evRo0cPnXI9loSEhAQ0btwYSUlJqF27Ni5dugQXFxcABW4+77zzDnbt2gUAqFu3LmrXrg1TU1PY2dnBxcUFtWrVgq+vLxo1agQvLy8xxV5ZnpmIEB8fjyNHjmD37t24efPmS/lR7Ozs0KRJE7i5ueHvv/8W86xYWFige/fumDx5Mrp27QorK6sXOz4xq7OwFQe1Wo2ffvoJixYtQmpqKiQSCfz9/TFmzBh07twZdnZ2sLS0hKmpqXgdjuOQn5+P/Px85ObmIicnB5mZmUhPT0d6ejrS0tKQmpqK8PBwREZGIi4uDrGxsUhNTS2U4s/S0hKenp5o164dRowYgc6dO8PMzKzaXMJat26NkJCQom9eHAOrcoOmV3JxcSmxwmdJUKlUtGLFCpLJZCSVSqlPnz7iRN3Z2ZmOHTumt14uOTlZnHAHBgYWUkN5nqdvvvlG7GkXLFhAHMeJW1FOyRWBsD549+5d2r17N61cuZKWL19Ov/76K4WEhFBOTo64vPHJJ5+Qt7d3oTlj165ddU63UBxUKhWdPn2aunfvLhplGGNkbm5O3t7e1KJFC+rQoQN17NiROnToQG3btqVmzZqRr68vubm5kYWFhTjPLWquK2xSqZTc3d2pV69etHTpUrpw4QIlJycXO9pWNWqM6ujn51euyTTP83T69GnRgtaqVSsKCAggoGCtaNu2bXqdN6Snp4uWuKFDh75kIPj+++/FxrZ48WK93bc0lBRhIPhzPnnyhL7++mtq0KCBqF5KpVJq2bIlrVmzhqKiosrVEfA8T6mpqbR161bq1q1bIVc2XTaJRELm5uZkb29P7u7uVLduXfL396cBAwbQxx9/TOvXr6dTp05RWFiYOPcyBHJpo0Z47wOAj4+PmI9RVxARoqOjMWfOHCQlJaFWrVrw8/NDUFAQJBIJPv74Y4wYMaLC6qI25HI5XF1dAQBXrlxBZGRkoZwn2qm9a9Wqpbf7loaSVCZBBfTx8cG8efMwbtw4HDhwAJs3b8bNmzdx48YN3Lp1C2vXrsWoUaMwcuRI1K1bt1TVUfv6tra2GDNmDIYMGYLHjx8jIiIC4eHhiI6ORkZGhpj/RKFQwMLCAnZ2dnBwcICrqyscHR1hZmYGhUIBU1NTKBQKWFtbi2kDdXlGg0ZxDKzKDfj/GKeyGiqUSiXNmDGDJBIJKRQKGjFiBNnb2xNQ4MqVlJRUtm5JB3AcR999951YxXPAgAF079490WQuBEmam5vT1atX9X5/fUFY0N+xYwd1795dtGQyxsjLy4s++ugjunnzpl6cmP8LqDGq4/jx48tENI7jaMeOHWRpaUmMMerSpYu4zuTu7k7//vtvOV6Xbnj+/Dm1bdtWbJhubm7Uu3dv6t+/v6g29e/fv0rXlcoLnucpPT2djhw5QoMHDy6k9rm4uND48ePpwoUL5Q4c/a+gxhBt8uTJOhON53m6cuWKaPDw8vKigIAAcRK+ZcuWSo0A5nme7ty5Q4MGDSqU81DYbGxs6MSJE5V2/8qAdjT2uHHjyMHBQTRO2Nra0ptvvknHjh2rNvO5oaPGEG3cuHGlEo3neVKpVBQcHEzNmzcXvRYaNGggOpBOnz69SjJf8TxPGRkZtGXLFho2bBj5+/tTgwYNqEuXLrRjx45KjaerTAjrVzdu3KAPPviA3N3dCy169+zZk3bu3Empqak1Ip1BVaHGEK1fv34lJo8RGvbKlStFvz+pVCoGYzLGaODAgaIzblVBaJgpKSmUmJiocyrwmgCVSkWPHz+mRYsWka+vr+jlYmpqSm3atKF169ZVatRyTUKNIZqjoyMdO3ZMXPsR1p7y8vIoNTWVjh49SgMGDBBVNRsbG/Lw8CCJREKMMeratavevBKMKAy1Wk3Pnz+n77//nlq0aCEmhpXL5fTVV19Vt3gGAYP3DJFKpSSYfm1tbdG1a1fUrl0bMpkMarUaycnJuHXrFsLCwpCbmysmXFEoFIiOjgbHcWjXrh22bNkCPz+/mmsCNlAQFVT0kUqlICKkpKTg6NGj2LRpE54+fYoDBw6gZcuW1S1mtUMvniEApABuADis+b8OgMsAwgDsAmCi2W+q+T9M833t0q4tmONL2xhjZG1tTc7OzqL/olQqpV69epU7C68RJYPnebp37x7NnTuXYmJixHcsqPHXrl2r1IBKlUpFERERNaKohl5URwCzAOzQItpuACM0n38GMFXzeRqAnzWfRwDYVdq1GzRoIEb9lkQyhUJBNjY24sTc3t6e5s6dW+Vzsv8SoqOjqUOHDiSVSumNN96okgBKASqVin799Vfy8/Oj3bt3GzzZKkw0AB4ATgN4DcBhAAxAEgCZ5vv2AI5rPh8H0F7zWaY5jpV0fX9/fzp69Cg1bty4kEd2cZtCoaCePXtScHBwlYdC/NeQnp5OEydOFH1IBd/NqkB0dDQ1a9aMgIKg0z179hj0b60PF6zvAXwKwErzvwOANCIS6iFFAXDXfHYH8BwAiEjNGEvXHJ+kfUHG2GQAkwHAy8sLffr0QbNmzbBv3z4cPHgQ4eHhyMnJAc/zkEqlkMvlsLOzQ5s2bdCvXz/07NkTFhYWxvlYJcPKygrLly8HYwxPnjzBu+++W2Xv3NXVFcuXL8fEiRORmJiI0NDQAsNCDfzNSzWGMMb6A+hLRNMYY90AzAYwDsC/RFRPc4wngGNE1IQxdhdAHyKK0nz3BEA7Ikoq6voA0Lp1awoJCRFGT9EAkpaWBo7jIJfLYWZmBnt7ezEMoia+7JoKIkJ2djays7Ph7Oxcpe+e53kEBwfjxo0bmDFjBhQKhcH+9iUZQ3QZ0ToCGMgY6wtAAcAawGoAtowxmWZU8wAQrTk+GoAngCjGmAyADYBkXQQVXqDgtCs47hpRvWCMwdLS8iUH36qARCJBjx490KNHD1GWmohSk9oT0Twi8iCi2igwbgQT0SgAZwAM1Rz2DoADms8HNf9D830wlTZsGmFECdAlANXQUZHSunMAzGKMhaFgDrZJs38TAAfN/lkA5lZMRCOMqPkoUzwaEZ0FcFbzORxA2yKOUQJ4Sw+yGWHEKwODKBZvhBGvOoxEM8KIKoCRaEYYUQUwEs0II6oARqIZYUQVwEg0I4yoAhiJZoQRVQAj0YwwogpgJJoRRlQBjEQzwogqgJFoRhhRBTASzQgjqgBGohlhRBXASDQjjKgCGIlmhBFVACPRjDCiCmAkmhFGVAGMRDPCiCqAkWhGGFEFMIgiF4yxTACPqluOCsIRLySJrWEwyl9xeBORU1FfGEqx+EdE1Lq6hagIGGMhNfkZjPJXLoyqoxFGVAGMRDPCiCqAoRBtQ3ULoAfU9Gcwyl+JMAhjiBFGvOowlBHNCCNeaVQ70RhjfRhjjxhjYYwxg8zTzxjbzBhL0JSkEvbZM8ZOMsZCNX/tNPsZY2yN5nluM8ZaVZ/koqyejLEzjLH7jLF7jLEPNPtrxDMwxhSMsSuMsVsa+b/U7K/DGLuskXMXY8xEs99U83+Y5vva1Sk/AN1L61bGhoK62E8A+AAwAXALQKPqlKkYObsAaAXgrta+ZQDmaj7PBbBU87kvgGMoqIoaAOCyAcjvBqCV5rMVgMcAGtWUZ9DIYan5LEdBbfQA6LG8c6U/QzW/QLEkr+b/eQDmVfdLKUbW2i8Q7REAN81nNxSsBQLAegAjizrOUDYUlNjqVROfAYA5gOsA2kGP5Z0re6tu1VEsw6uBdoleQ4cLEcVqPscBcNF8Nuhn0qhRLVEwKtSYZ2CMSRljNwEkADiJAk0ojXQo7wxAKO9cbahuor0SoIKu0+DNt4wxSwB7AXxIRBna3xn6MxARR0QtUFBdti2ABtUrUdlQ3UQTyvAK0C7Ra+iIZ4y5AYDmb4Jmv0E+E2NMjgKS/U5E+zS7a9QzAAARpaGg2mx7aMo7a74qqrwzylreubJQ3US7CsBXYz0yQcHE9WA1y6QrtEsIv1haeKzGchcAIF1LPasWsIKatJsAPCCi77S+qhHPwBhzYozZaj6boWB++QA1qbyzAUxu+6LACvYEwILqlqcYGf8AEAtAhYK5wAQU6PynAYQCOAXAXnMsA/Cj5nnuAGhtAPJ3QoFaeBvATc3Wt6Y8A4BmAG5o5L8LYKFmvw+AKwDCAOwBYKrZr9D8H6b53qe6fwOjZ4gRRlQBqlt1NMKI/wSMRDPCiCqAkWhGGFEFMBLNCCOqAEaiGWFEFcBINCOMqAIYiWaEEVUAI9GMMKIK8H92pEJY5gVUWwAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/7عبد الله.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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GHH354wl9ERMjMzMSiRYswd+5c3L9//4njKRQKvPjiixgxYgTvgasu+gx+pS0ANgJIBnCz2DZnAMcAROr+Oum2MwDfA4gCEALAr7zjExG6detGHONw5MgRCgwMpFdeeYVef/11evjwoUns0Gg0tG7dOpLJZASAZDIZnThxggRBKPe3giDQ5cuXydfXlwCIS6dOnWj79u1UWFhYYTvUajVt3bqVHBwcxOO0adOG/v33X9EWQRAoPj6eAgMDSalUSs6pXxhj9Mwzz1BKSkqVy6Q+AuAKlaQzJW0kqRANAOD3mBAtA7BA93kBgK90n0cDOKwTpF4ALpZ3fOJCZFSKioooLy+P8vLyqLCwsEIPvrH4888/ydnZWXyYV69eXSF78vPz6YUXXhBFjDFG/fv3p6tXr1JRUVGl7cjPz6e5c+cSY0wUxdmzZ1N+fj4JgkAZGRk0ffp0sra2LlGEFAoFjRgxgm7evGnS8qyNVFmItL9Fs8eEKByAh+6zB4Bw3ed1ACaXtF9ZCxei+sHVq1epXbt24gMdGBhIGo2m3N+dOnWKLCwsRBHq3r07hYaGVlkEBEGg6Oho6tWrl2iLk5MTRUREUFxcHE2dOlUUvccXBwcHevfddyk1NZWLUBUoTYiq6up3J6IE3edEAO66z54Aime+itNtS8BjMMYCAQQCEJNnceo2bm5u4lAPALh9+3a5vykqKsLPP/8s+nDs7Owwe/ZstGvXrsp+GcYYvL298fzzz+PGjRvIz89Heno6Vq9ejYyMDOzevbtEp76DgwPeeecdvP/++zU+eLiuU+0+RyLSV3Er+7ufAPwEAP7+/jxTVjUgIgiCgIKCAuTn56OgoEAc2iGTyaBUKqFQKKBUKmFhYQELCwsolcoad7A2bNgQ7u7u4npkZCQKCgrKTOgfGRmJy5cvi07t9u3b47nnnqv2/GwKhQLPPvssfvrpJ0RERAAA1q1bB41GU2Kgo4ODA95//328/fbbNTIBQX2jqkKUxBjzIKIExpgHtM5sAIgH4F1sPy/dNo4REAQBCQkJuHXrlpj9MCIiAtHR0cjIyEBubi5sbW3h6uoKV1dXuLi4oHHjxvDy8oKnpyeaNGmCFi1aoEWLFlAqlUa318LCAi1atIBcLodGo0FeXh6ioqLQuXPnUn8TGhqKxMREcX3SpEmwt7c3iD2tW7fGkCFDRCEqrRu/cePGeP/99zF79uwn4rI4hqGqQnQAwHQAX+r+7i+2fQ5jbAeAngAyizXhONVE354uLCxEaGgo9uzZg1OnTiEuLg6JiYklds3n5OQgJycHMTExku1KpRLOzs5wd3dHp06dMGnSJAwYMAB2dnZPxB4ZCv00QwqFQqx53L59u1QhUqvViIyMRGZmJgBtcObAgQMNZhsRSZK2lYS3tzeWLVuGsWPH8qBFY1KS46j4AmA7tD4eFbQ+n1cBuAA4AW33/XEAzvS/7vvVAO4CCAXgX97xiTurK0RBQQGFhobSqlWrqEePHmRtbV2qQ7WyC2OMlEol+fj40JIlSygsLIzUarVRruPq1atkb29PAEgul9OCBQtKdfrm5eXRnDlzRDu9vLwMFn5QUFBAe/bsIW9v71J7xvz9/enixYsVcqhzKgaq6qwmosmlfDW0hH0JwOzyjsmpOBqNBrdv38bWrVtx8OBBhIeHlzhMQyaTwcPDA+3bt0erVq3g5uYGW1tb5OTkID09HampqUhJSUF8fDwePnyIrKws8bdEBJVKhYiICCxatAh79+7FggULMG7cOIPXAlq3bg07OztkZ2dDo9EgPDwcubm5YmqO4qjVauTm5orrNjY2BhnhTkT4448/8OGHH5Y4q4hcLseYMWOwaNEidOzYkY+qrwF4XdMMIV3zKz4+Hj/88AP27duHuLg4iQAxxmBnZwcPDw8MHToU48aNg4+PD+zs7GBjYwMLCwvIZDKxCaRSqVBUVITCwkIUFBQgNjYWp06dQlBQEO7cuYOsrCxoNBpx2p8333wTwcHBePvtt+Hq6mqw5pCtrS18fX2RkKBtsT948ABxcXFo27btE/sqlUrJWLSUlBRkZmZKHN6VRa1W4/Dhw3jvvfckvqfi9OvXD+vWrYOLiwv3B9UUJVWTanrhTbP/IQgChYeH0/vvv09eXl4lNqPatWtHr7zyCu3cuZPS0tJIEARxqeg59EthYSH9/fff9O6770pifKBrOs2cOZMePXpk0Ov77LPPxHO4u7tTUFBQiftqNBpauXKlJLr59ddfp+zs7CqdOy4ujlavXk2tWrUqs6narFkzyszMrM5lckoB1QloNPbChUiLIAgUGhpKgwcPJrlcLnk4ZDIZtWzZkj755BO6fv065eTkGDSgrrCwkK5cuUJjx46VPPgWFhY0b948evTokUHOJwgCHTp0SIxqVigUtGHDhlL9MCEhIRKBdHBwoEWLFlFycjKp1epSBVgQBNJoNKRSqSg1NZU2btxIgwcPJltb2yeEp2HDhtS+fXvR52ZjY0NHjx7lAYtGgAuRmaPRaOjvv/+mLl26iA8pALK0tKSOHTvSF198QQkJCaRSqYz2gAiCQI8ePaJly5aRo6OjxIZZs2ZRYmKiQc5x8+ZNaty4sXj8d999lwoKCkrcX6PR0Nq1a8nGxkbiSG7Tpg3Nnz+f/vjjDzp//jxdu3aNbty4QcHBwXT58mX6888/aenSpTRmzBhydXV9YswYY4waNWpEAQEBFBISQsePHyc7OztR9GfNmlWpMWycisGFyMwpaVCnt7c3ffnll3T79m2j9WKVREFBAX3++eeS2oNCoaC3336b8vLyqn38Bw8eUJ8+fcRjjx49uszmVk5ODs2ZM4csLS1LbErZ2tqSp6cnNW3alJo2bUqurq7l9ii2atWK9uzZQ7m5uSQIAuXm5krKv0uXLnTz5s1qXytHChciM0VfQ+jbt6/kbd25c2c6evRolQZ1GsKmrKwsWrJkiaRmZG9vTytXrqx2TSE9PZ0mT54sEYW0tLQy7UlKSqKPPvqI3N3dqx22YGdnR3v27JGIuyAI9MUXX4j7WFtb0y+//MK77g0MFyIzRP+AjR8/XtIc8/Pzo4sXL1bKAW0M2woKCmjFihWSZlGLFi3o7Nmz1bJLpVLRggULxGtWKpX04MGDcu0RBIGuXLlCc+bMoQEDBlCjRo2e8KUV96m5ublJbNeL0Oeff/6EwAuCQDdu3KBGjRqJ+z733HNVdoxzSqY0IeLd9yaCSNtFv2zZMvz111/atwKAFi1aYPny5ZLZUU0BYwyWlpYIDAxEVFQUfvzxRwiCgHv37mHp0qXYunVrpdO86lEoFGjUqBEsLCxQWFgItVqNuLg4eHt7l/obfVl069YNnTp1QkJCAmJiYpCSkiIu+shyKysrNG3aFI8ePcLXX3+NBw8eANDGB40fPx6zZs16Ij6KMQZPT0/07dsXe/bsAQD8888/SEpKKjHGiWNgSlKnml7qW41IEARKSEig2bNnk5WVlfgG9vDwoN9//71G/UHlIQgCRUZG0qBBgyQ9XfPnz6+Wv2jnzp3k5OQkXvuWLVuqbJ++d6yoqIiKiopIpVJRfHw8jRkzRtKMGzhwICUmJpZamxMEgdatWyfxjX3xxRdVvkbOk6CUGhEPGTUB2dnZWLhwITZs2CAOtLS0tMS7776LZ555ptojyw0JYwwtWrTA3LlzxRQearUa27Ztw9mzZ8WaXGVxdXWVzCsWH1+1sdGMMchkMjG7gFKphEqlwvLly3Hw4EExnUe7du2wbNkyNGzYsNSaJmMMAwcOROPGjcVtBw4ckEShc4wDF6IahIiQl5eHJUuW4LfffhNFyNbWFgsWLMAbb7xRpUn/jI1MJsOIESMwb948USQfPnyIP/74QzIEozI4OztLrlUfaV1dioqKsGHDBvz888+iSDZu3BiLFy+Gn59fuc3dli1bok+fPrCysoKbmxt8fX1LHZXPMSAlVZNqeqkvTbPc3Fz67LPPxHgVAOTs7EwrV640eICiMUhOTqaBAwdKmpJ37typkt0xMTHUpk0b8VgBAQHVtk+j0dAff/xBHh4ekt6v77//vlLN3YsXL9KPP/5I9+7dI41GY/b/l9oEeNPMtBARdu7cie+++06cRkcul2P+/PmYNWsWbG1tzX5ck5OTE8aMGQMLCwsA2lrMyZMnq3Qsa2tricO4rBlaKwIR4e7du/j444/F2pVMJsNLL72El19+uVLN3R49euD1119Hs2bNjJYSpSqU9ADXFXivWQ1QVFSEgwcPYv78+UhLSwOgfRBnz56N2bNn15qMf3K5HIMHD0bz5s0RHh4OANi9ezdmzpxZab+WUqmUjGqvTvOHiPDw4UO8++67CAsLA6AVoeeeew4fffRRrU7rSkTIyclBcnIyUlNTkZmZicLCQtjY2MDJyQmOjo5o2LBhrXiRlQUXIiMjCALOnj2LBQsWiCIkl8sxZcoUzJ0712DZBmsCxhjatm2LNm3aiEIUFhaGhIQEeHl5VepYjwtRUVFRle0qLCzE+vXrceLECbGW0L59eyxatKjMkABzJy8vD+fPn8fOnTtx/vx5REZGStLY2traokWLFujbty+GDBmC4cOHVzmkwuSUVN2r6aUu+4iSk5MlUdMymYxGjhxJCQkJpjatynzxxRdiIGGDBg3or7/+qvQxsrOzJUMqBg0aVCVbBEGg8+fPU8OGDcVjubi40J9//llrfTv6QNfZs2eTh4eHJNi1pIUxRs7OzjR69Gi6cOECFRUVme21g/uIap68vDx88cUXOH/+PABtc2Hw4MFYvXp1tXLqmJrevXuLTbG8vDwEBwdX2l/xeDOisr/Xk5aWhgULFiA5WZs23cbGBnPnzsVTTz1VK5sqgiDg1q1bCAgIwJo1a5CQkCApG0tLS9ja2kpyjBMRHj16hEOHDmHs2LFYtmwZUlNTTWF+leFNMyNBRDh+/Di2bdsm3kgeHh746KOP0KxZs1r5kOjp2LEjrKysUFRUBJVKhXv37kGlUolO7IrwuBO4pKyT5aFSqbB582ZcvnxZ3NazZ0+89NJLtXIueiLC/fv3MXfuXJw+fVq8b5RKJbp3745nn30WzZs3h1KpRH5+PqKjo/HPP//gzJkzYvklJyfjs88+Q1RUFP7v//4PzZs3rx33WknVpJpe6lrTTBAEiomJoUGDBonVZwsLi0p3I5sr+fn51LJlS0nXe1ZWVqWOoVKpqFu3buIxunXrVqnmhCAIdO7cOUmSMycnJzp37pzZNkvKIzU1lUaPHi2JBvfw8KA1a9ZQenq6JAWMIAhUVFREGRkZdOjQIRo8eLAkO4FcLqfnnnvOZFOMlwZ406xm+e2333DmzBlxfdSoUZg+fbpZRU1XFcaYpKdPpVKVOINIWSgUCkn3vX4etoqiVquxfv16REVFicd77bXX0KNHj9pRA3iMrKwsfPnllwgKChLLoVWrVli9ejUCAwPh6OgIhUIhXhtjTEylO2rUKGzfvh3vvfceXFxcAGhzne/btw8bN26sFd38XIiMQHR0NNauXSs+nM2bN8dbb71VpwZPFn/Y9W+1ylJclImoUkJ08+ZN7N27V1zv0aMHZsyYUSuFXqPR4M8//8Qvv/wiNrEcHR3x1Vdf4emnn67QNTVs2BAfffQRvvzySzRq1AiAtkw3b96MuLg4o9pvCLgQGZiCggL88MMPYlCdQqHA9OnTMWDAAD4bhIEoKCjAypUrxTFgNjY2mDFjBnx8fGplbejBgwf47LPPxPAOOzs7LF68GOPGjauw340xBmtra7z88suYMWOGeK/FxsZi//79Zl8r4k+GASEi3Lx5E4cPHxZrQ15eXnjllVdqZCbVmqR47YUxVmMCQEQICwvDhQsXxIfLx8cH48aNq3UiRERQq9VYt24d7ty5A0DrxB87dixeeumlSl8PYwxyuRwzZswQm875+fm4cOECsrOzDW6/IeFCZEBUKhX++OMPREZGAtA2PWbPng1PT08TW2ZYiEgSgKhQKGqstqdWqxEUFCTOXMsYQ2BgoJgZoDahVquxefNmrF+/Xtzm4+ODuXPnwsnJqcrC2qxZM/Tv319cv3XrVpWzG9QUXIgMSHJyMrZv3y7WFjp16oTx48fXujd1RSjunJbJZDUiRESE5ORk7NmzR4wwbtq0KUaNGmX0cxsaIkJUVBS+++47PHr0CMD/HO5dunSp1j2jUCgwYMAAcf3BgwdmH1fEhchAEBH279+P+/fvA9DeDM8++yy8vLzqpBBVF0EQJLFDFRWzo0eP4saNGwC0taExY8aUmWPIXBEEAVu3bhXHxjHGMGnSJEybNq3a1yKTydC2bVvRyZ2env5EYKS5Ue5/njG2kTGWzBi7WWzbYsZYPGMsWLeMLvbdB4yxKMZYOGNshLEMNzdSUlKwYcMG8Z/dtGlTjB8/vs75hvQUrxFVxUeUn5+PwsJCcd3e3r5cIUpMTMSKFStEAWvWrBkmTpwIa2vrSp3bHAgNDcWPP/4olmPnzp0xf/58ODk5GeT47u7ucHNzA6B9SUZERFSqV7KmqUiN6FcAI0vYvoKIuuiWQwDAGGsPIABAB91v1jDGal9/aiUhIhw7dgzR0dHitgEDBqBDhw617k1dEUg3IlyPhYXFEzmgyyM3N1dSI3J0dCyzrFQqFX799VfR/8YYw6BBg9CtW7daVcb65uVXX32FjIwMANrymzhxItq3b2+Qa2GMwdbWFo6OjuK29PT02l0jIqLTAB5V8HhjAewgokIiugcgCkCPathXK3j06BEOHDiAzMxMANoUH6+//nqdrQ3FxsaKQiSTyeDq6lqp4R2AdoxacSEqa9Q4EeHWrVvYtm2bWItycXHBe++9V+tqQ2q1Glu2bMGBAwfEGkrbtm0RGBho0PtFLpdLXg4qlap2C1EZzGGMheiabvr6pCeA2GL7xOm21Vn01d5//vlH3DZ8+HB06NDBhFYZl4sXL4oiYmVlhbZt21apaVZciMpKhyIIAo4fP45bt24B0Irfm2++iXbt2lXBetNy584dfPfdd2IiODc3N3z++ecG7/XT5/LWU9nI95qmqkL0I4CWALoASADwbWUPwBgLZIxdYYxdSUlJqaIZpoeI8Ndff0F/DdbW1hg/fnyte1NXFEEQcPr0afHGtrGxQefOnSt9nMebZmUJUWFhIXbt2iXWIFq3bo3nnnvOJAGiRISsrCwcOnQIycnJlaplqNVqfP/994iN1b6rFQoFXnzxRQwePLhWNS+NQZX+k0SUREQaIhIArMf/ml/xAIpnovLSbSvpGD8RkT8R+eudarWR/Px87N69W1zv0qUL+vbtW2dvrIiICFy6dEl8AL28vKokRElJScjPzxfXy0pgFhoaiuvXrwPQ1oaGDRuGNm3amKyM9+7di+effx7PPvssjh07JvGXlQYRISgoCPv27RO3de3atVZl6DQmVRIixphHsdXnAOh71A4ACGCMWTLGmgNoDeBS9Uw0b4KDg8U3nEwmg7+/P5o0aVJrhKgy48RUKhX++usv0WEMAKNHj660fwjQ9jIWF6KmTZuWuu+ff/4p1p7s7e3Rv39/k9U4o6OjsWzZMuTn5+PSpUt47733xKjoskhPT8fWrVvFYRwymQyvvfYaWrRoUWvuFWNSblcHY2w7gEEAXBljcQAWARjEGOsCbcqBGACvAQARhTHGdgG4BUANYDYRmXfjtBoQEQ4dOiQ6UG1sbDBs2DCzd1JrNBpMnToVV69eBRGhQ4cO2L9/f5m/IV2unHXr1okC0qhRI0yePLnS5xcEAbGxseJURDKZDC1atChx36ysLJw8eVIUSzc3NwwcOLDS5zQE2dnZ+Pbbb8U0uTKZDL169UKbNm3K/B0R4c6dO5JUtgMGDMDzzz9vNBF6/AVj7mJXrhARUUl32oYy9v8cwOfVMaq2kJOTg+vXr4v+EmdnZ0lovbkik8kQExMjptCwtbUFEZV5sxYWFuK7774Ta0NKpRIzZsxAy5YtK32T5+XlISYmRvT5NGzYUBwx/jghISFijRPQZods2LBhpc5nCARBwMGDB7Fz507x/92yZUt8+OGH5WZV0AcvJiUlAdC+sAIDA42ar1yj0UjyW1taWpq1GPHI6mqQkJAgpigFtJkLa0Pycv3gSD2CIJQZ7KbRaLBr1y789ttv4raWLVti0qRJVZoQMjs7W4xAB7Tjq0qqRRIRQkNDxQcYAEaMGGGSByo1NRVff/21OBzD2toa8+bNKzfbpn6Q7u+//y5u69y5M/z9/Y3qbFer1RIhMvfOEy5E1SA2NlYyQ2nPnj1NaE3lKB5jQkSldu8KgoC///4bn3zyiRiAp58KqXPnzlUShYyMDImfqWPHjiX6mXJycnDp0iVxqiEXFxeT1TjXrFmDkJAQANoYnYkTJ2LChAnlXn9KSgoWL14sjvWysrJCQEAAWrZsaVR7CwsLJXPFNWjQwKxrRDxndRUhIiQlJYlvSEDbY1ZbqKgQxcfH4+OPPxajxvVjol5++eUqvdH15fbw4UPxeO3bty+xRpSdnS36YwDA398fDg4OlT5ndSkoKMCZM2fEWmOjRo3w1ltvidkQS6OoqAhbtmzBsWPHRH9N+/btMXnyZKOHHhQWFkp685ydnbkQ1UU0Gg3i4+PFt7W1tXWZPT/mRnlCRERITEzE/PnzcemStuOTMYYhQ4bgww8/rFaX8/Xr18VeMGdnZzRv3vyJB5OIkJeXJ2nCtWvXrkpNweoSFRUlyXLYvXt3dO3atczfaDQanDlzBt98840oCA0aNMDixYth7HAVIkJaWpok+t3Nzc2shYg3zaqISqUSc+IAQOPGjWFnZ2fW/+zilCVERITo6Gi8++67+OOPPyQ5lJcsWYJWrVpV+TqJCP/++6+43rhx41IzFCQmJiIxMRHA/3rWqhIqUF1u3rwp8QUOHTq0zPSthYWFOHToEF599VXRfisrK8yePRtDhw41+j0iCAKioqLEWpiLi0u5tTdTw2tEVaQkITJ3h6AeIpLUQPRCpM8bHRISgv/+97/4+++/RYFycHDAJ598Uu3k9BqNBteuXRPXPTw80Lhx4xL3DQ4OFkXQ0dER3t7eNR5NrVarcefOHTEtrUwmQ9++fUvcl4iQn5+P1atXY82aNZLaXI8ePTBr1qwaCV4UBEFybjc3Nzg4OJj1S5ILURUpKirC3bt3xfUmTZrUmjnWk5OT8eDBA3Fd/3Ds3r0bV69exbZt2xAXFye+Ud3d3bF48WKMHz++2kJw9+5dsXYhk8ng4+MDZ2fnEve9cuWK+LlRo0YmafpmZWXh5s2boiD6+Pg8Mb22vhkZEhKCr7/+Gn/99ZeYwVIul6N79+7YuHFjjdkvCILk/+vi4mL2U5tzIaoieXl5kvgWT0/PWlMjunjxoqS3r1+/frhz5w7mz58veZMC2pre0qVLMXHiRINMWnjjxg3xIVUqlejcuXOp4qZPGgZoH6bSak7GJCsrC7dv3xbXO3XqBAsLCwiCgPz8fERFRSE0NBRnz57F4cOHERsbKwq4XC7HiBEj8OWXX9ZoBDURSVLDOjk5mf0MMlyIqsjdu3fF7lFLS0t4e3ubLKL61KlTOHbsGNq1awdfX1/4+PiU6kspKCjAqVOnxKEGcrkcQ4cOxdq1ayXCqlQq4e/vj88++wwDBgwwyLUREa5fvy7Gt1hYWJTq9M3KypI8TO7u7jXu5yAiBAcHIyIiAoDWWe/r64u//voLS5cuhUajQU5ODjIzM5GdnS0KEGMMDg4OmDVrFt577z24u7vXaLNI35Gix9nZ2exr61yIqog+XSmg9Z+YchrpY8eO4csvv4RGo4GTkxOCgoLg7+//xH76N2XxSfy6du0KDw8PhIeHixHLbm5uGDNmDGbPnl1qxHNV0Ddz9H4nR0dHtG/fvsR97927J/ZIyuVytGrVqsbnLCssLMTKlSvFHr7GjRujV69e2Lt3L27evFnib5RKJXr37o333nsPo0ePlkyKWFOkpKSIubH0+aJM0dtYGcxGiARBqNFpaaqDftogPQ4ODk/4DWoKtVqNiIgI8eFWKpWljtsCtM0y/SBNxhiefvppdOjQAT///DNSU1PBGIOzszPatm1r8B6qBw8eSLrBu3btWuoDEh0dLdacZDJZlYaSVAciwqVLlxAcHAxAW1bdu3eHn58fPv30U8m++llX/fz8EBAQgKeffhotWrQw2Tx2oaGh4ovG0tKyVgzCNgshCg8Px9tvv41PP/3UYDl7jYlarZbUiBwdHU0WQ5ScnCzx9zRr1kySIrQ42dnZWLVqlSTn81NPPQUbGxt06tTJqHYSESIjIyU9jWXl4QkPDxcHE8vl8hpPgpafn4/NmzeL84FZWVnhlVdegZ2dHTw9PdGjRw8oFAq0bdsWXbp0wZAhQ9C0aVNYWVlBLpeb9MG/fPmyKEQ18b81BGYhRDk5Obh27ZpkbIw5ExsbK5mepXnz5qU+/MYmISFBYktpwy40Gg22bNki5vVhjKFPnz7w9fWtkYdGpVLh4sWLYpPB1tYW3bt3L3FfjUaDmJgYUTCVSiV8fHwqfC69eFQ1rouIcO3aNZw4cUKSznXw4MFQKpXYtm1bpY9ZUwiCgFu3bol2W1tbl5sdwBwwm4BGa2vrWjMlc0xMjOioZoxVex6qqkJEJQpRSftduXIFa9euFWsZDg4OmDlzZo31puTm5uL48eMSO0trMmRlZSEpKUl0/np4eFRoMLFarUZISAhmzZqFRYsWVTk9qlqtxpEjR8QucLlcjtdee61WJDBLT09/wslvimwFlcUsakSANi7HnJN7FycjI0OsvTHGTNYs0+cISk9PB6D1pTwuRPoR7B9++KHo15LJZJgxYwYGDBhQYwIaEhIiNmf1QYGlDXXIyMiQiGvr1q3LfEnpx6/t2bMHn3/+ORISEuDg4IB+/fph3LhxlX7BRUdHY9OmTWKtolu3bhg+fLjZ+1kAIC4uTrwfAJQZHmFOmI0QZWVlmX2Cbz35+fkSW/XBYqUJqbFu4IKCAkk+JDc3N7GXi4hQWFiIw4cP4+uvv8aFCxfE3/n5+eH111+vsQdLEAT8+uuvkt6ynj17lhqXlJWVJTbhAJRac9KX95UrV/D555/jxIkT4viq7OxsrF+/Hv7+/iAihIeHo2fPnmVGGOun0l65cqXoVFcoFBg0aBA8PDxqhRAlJiaKUeAAas0EA2YjRJmZmZJk6uZMXl7eE0KkVquRnJyMyMhIcTCsvb09fHx84OnpCUdHx0rP/VUeKSkpOHLkiLjepUsXyOVy3Lt3Dzdv3sTatWtx+vRpMRMioPVnffLJJ/Dx8amxB+vGjRuSWU7atm2LIUOGlHr+7OxsMeUIoE0j+/i++kG5W7ZswYoVKyRNOaVSiU6dOqFNmzZ4+umn8fDhQxQVFWHXrl0YObKkKfr+J9xbtmyR5A5q164dAgMDDRLMaWweryEDJTfVzREuRFVArVZLaj/p6elYt24ddu/ejXPnzkmc7q6urujTpw/69u2LZ555Bm3btjVYVfnQoUMSf0B8fDw++OAD3Lp1C1FRUWIEs5527drhk08+wciRI2usuq73t+h79hhjCAgIKHVYB6D1J+kdzoB2eMfjQpSamoo5c+bg4MGDkvJ2c3PDSy+9hNmzZyM8PBw7duwQU7X88MMP6Ny5Mzw9n5zhKjMzEz/99BO+++47MdjTwsIC7777rtFzBxmKwsJChIeHi/FXDg4OtcZ2MbetKRcAJJfLKSIiggRBIHNnzZo1ZGNjQ9Dm7KbmzZuTra2tuF7SYmFhQe3ataOvv/6aMjIyqnWdgiBQQkICdevWrcxz6hcbGxuaMGECXb58mdRqtQFLonw74+PjadiwYaItjRo1otjY2DJ/d+DAAbKyshJ/s3fvXkl55eXl0dtvv00KhULcR6FQULdu3SgoKIiys7NJEASKjo6mvn37Svbp1asXffbZZ/TXX3/RkSNHaPv27fTxxx/ToEGDJP9DmUxGkydPpoyMDGMXk8FISUmhESNGiNfQo0cPSkpKMrVZEgBcoZI0oKSNNb3oC+7ixYu1Qoh+/PFHiRAVXxhjZG9vT25ubpKHSb8olUp6/fXX6eHDh1U+f2ZmJv3nP/8hpVJZpgB5eXnR2LFjadeuXVRQUFDjZSsIAgUFBZGdnZ1o05tvvkkFBQVl/m7v3r0SkTl8+LBou1qtpi1btpCLi4v4vaurKy1atIgSExMl1ygIAh05ckSyr/5/JJPJxIUx9sT/aPLkyRQVFVUr7kci7bXeunWLnJ2dxet44403KDc319SmSShNiMymaQZAku3QnCktbN/BwQEvvvgiBg0aBDs7OyQnJ+P06dM4dOiQmJdGpVLh559/Rm5uLhYvXozmzZtXylcjCAKCgoKwbds2sUni7OwMNzc3FBYWwtbWFq1atYK/vz/69u0LX19fODk5mcTRKggCdu3aJTqQHR0d8dRTT5UZsU26lCTFfXDF909LS8OGDRvE5pNCocDChQvx5ptvPnFcxhiGDh2K999/H8uXLxdzX9P/XoBPYGdnh5kzZ2LevHklNuHMmevXr4vPkJWVFbp06WL2QztESlKnml6gU/Cff/65VryBNm/eTPb29pI3bKdOnejkyZOUl5cnXoNGo6Hc3FyKjIykuXPnUoMGDcTfyOVy6tOnD12/fp00Gk2FzqvRaOj8+fPk5eUlHsfW1pZWrVpFycnJlJKSQqmpqZSVlUVFRUUmL8vIyEhJbWTYsGEUFxdX5m8EQaDt27dLmkinTp0iQRBIEATatm0byeVysdwDAgIoLS2tzOMVFhbS5cuXafr06dSsWTNydnYmOzs7srOzI0dHR2rYsCE1b96cXnjhBfr7778pNzfX5GVXWQRBoIkTJ4rl1rJlS7pw4YKpzXoC1IamWWBgYK24AbZv3y4RFS8vLwoKCipTUAoLC+nbb78ld3d3STOgc+fOFBQUREVFRWWeU6PR0MWLFyV+IblcTtOmTaOUlBRDX2K1UalUtHDhQlE0LC0taeXKleX+fwVBoM2bN0uaSWfOnCEiooKCAho0aJD4naenJwUFBVX4ntFoNBQZGUn79++njRs30saNG2nnzp104sQJio6OrlH/maGJj48nb29vsWyeeuops/Rv1Qoh6tKlC6lUKmOWQ7UpKCigDz74QPRhMMZozpw5VFhYWObvBEGg/Px8+u2336hp06aS2lTLli1p3759VFhYWOJDpVarKSwsjHr06CE+2ADI39/fLP0YgiBQaGgode3aVSLW5TmpibRi8csvv4i/s7KyorNnzxIR0blz5yQ1rPHjx1N2draxL8fsEQSBNm7cKPot5XI5LVq0yOzuC6Ja4iNKSEhAREREqakhTA0R4datW9i9e7ck1MDV1bXckeqMMVhZWWHy5Mlo1qwZZs+eLY6Svnv3LqZOnYrAwECMHz8e3t7eYsxRRkYG/v33X3z11VfiFDyMMXTq1AkbNmwwyymLVSoV9uzZg9DQUABae2fNmlUhn8vjGRhI58vRaDQ4fPiwGOioVCoxbdo0s8+zUxNkZmbi0KFD4rAjOzs7TJo0yezui7IwKyHKy8tDaGio2QqR3vmqnyFVT/EJAMtDJpOhd+/eWLduHT744AOcOnUKgiAgNzcX3333HXbv3o3mzZuLichSU1MREREhmSe+devW+Prrr9G+fXuzvNliYmLwyy+/SEb5jx8/vsK/Lx48qFKpxGDRK1euiMds2LAh+vfvb5bXX5MQEWJiYiRpaXr16lV74od0mJ0QXb9+HRMnTjS78TGCIOCff/7B2rVrJT0uRISHDx+CqOwpm4sjk8nQvXt3fPbZZ5g/fz5u3LiBvLw8cU744pkSiyOXy9GyZUts3LgRvXr1qvFEYRWhoKAAy5YtE1POKpVKvPjii5Wa+cPa2hoWFhYoKiqCIAjIyMhARESEmBsIAEaPHm2SOc7MDUEQcPr0afHlKJPJ8Nxzzxk8it/YlPu0M8a8GWP/MMZuMcbCGGNv67Y7M8aOMcYidX+ddNsZY+x7xlgUYyyEMeZX3jn0habRaBAeHi4J7zcXYmJi8Omnn5ZoW2JioqTGUhFkMhn69OmDAwcO4PPPP0fPnj3LfFA9PDzw2muvYffu3ejTp49ZipBKpcLu3buxZ88ecVunTp0wbdq0CncjM8Zga2srSfYeFxeHf//9VwyBsLS0xOjRo82yDGqae/fuYcWKFWJN0dvbu9ozrZiCisimGsBcIrrGGLMHcJUxdgzADAAniOhLxtgCAAsA/BfAKACtdUtPAD/q/paKra2t2Pa/d+8eEhMTyxwCUNMUFhZi27Zt4kSDgFZI9KOzs7KykJiYWGZmxJJgjMHV1RWzZ8/GM888g9OnT+Pq1auIjo4Wj21nZwc/Pz8MGTIEHTt2NNu504gIISEhWLp0qSjWCoUCr7/+eqXLxc7ODg4ODmKs0P379yVl7+PjgzZt2phlOdQk+gG6+tqnQqHA2LFj0bp169pXNiV5sMtaAOwHMAxAOAAP3TYPAOG6z+sATC62v7hfaYunp6fYE2Jvb09HjhwxK4//5cuXyc3NTbSxSZMmNHbsWHG9cePGdPz48WqdQxAE0mg0pFKpqKioSLKo1WqzKo+SSE9Pp+HDh0uGU0ydOrVKkb1hYWHk5+cnHqt///5iFDljjF599dV631umVqtpz549knCQjh07mmUvanFQSq9ZpRwxjLFmALoCuAjAnYj0OUoTAbjrPnsCKO7kiNNtKxUbGxsxQVd2djZu3Lgh1ghMTUFBAb755hukpKQA0DYLXn75ZUyePFnc59GjR4iMjJT4jioLYwwymQwKhQJKpVKymDr1aHloNBps2rRJMsLe398fH3zwQZWmWHJ0dJTUiC9duiRGkVtZWaFr1671ureMSJsQ77vvvhM7SmQyGd56660az+1tKCosRIwxOwB7ALxDRFnFv9MpXaWeQsZYIGPsCmPsSm5uLlq3bi1+988//5jFSHxBELBv3z4cPXpU3Obn54dXXnkFvXr1Ev0YBQUFCA0NrbSfqC4gCAL+/vtvrFq1ShQLV1dX/N///R/atWtXpYfi8TnM9FklAW0zvm3btrXyYTMUgiBg1apVOHfuHADtS2z06NGYMGGCiS2rOhUSIsaYEloR2kpEf+g2JzHGPHTfewDQTw4eD8C72M+9dNskENFPRORPRP4NGzaUdNkXz21sSpKSkrBp0ybR52FlZYX58+ejSZMmaNCggWROrsuXL4s+jfrEgwcPsHjxYnHWW5lMhpkzZ2LUqFFV7vm0tLRE+/btS4zNcnR0rBXJ4I1JWFgYNm3aJI7Ha9WqFRYsWGBWftXKUpFeMwZgA4DbRLS82FcHAEzXfZ4Ore9Iv32arvesF4DMYk24EpHL5eIMmoC2eXb16tXKXYmBISJcuHABZ8+eFbeNHDkSw4YNA6DtYu7Tp4/4XVhYGO7evVut5lltQ6VS4auvvpJkfxw4cCBef/31ah/b39+/xGadn59fqSlm6zpEhOzsbKxdu1ZMpatQKDBt2jR07969dtcSS3IckdQ53Q/aZlcIgGDdMhqAC4ATACIBHAfgrNufAVgN4C6AUAD+5Z2jW7du9M8//5Crq6vokHz33XdN6nTLycmRjGtyc3Ojo0ePijYJgkAHDhyQDDmYNGlSuWPG6goqlYp+/fVXyZi7Nm3a0JkzZwzyf8vJyaG2bdtKxuXJZDLat2+fAayvnWg0Gtq+fbsk1Uf79u0pNTXV1KZVGJjzWLNu3brRo0ePqHXr1mIBDxw4kDIzM41ZJmVy9OhRMZ8QY4wmTpz4xD/84cOHNGTIENFmJycn+ueff0xjcA0iCALdvn2b/P39Jb1kP//8s8EGjgqCQPPnz5cIUevWrSk+Pt4gx68J9BkDDPVCvX//Pvn6+orlYWNjQxs3bqxw9gZzoDQhMpvwZUdHR8k8V/fv3y91Wl9jk5ubi59++kky9c6UKVOeaIO7u7sjICBADNbLyMjAt99+K0Za11Wys7PxySefiHOkyWQyvPDCC5g0aZJBgwzbtm0rWff19a2x6Y+qC5E2f/S8efNw+fLlat0PRIT09HQsXLgQISEhALRlPnbsWDz77LO1u0mmpyR1qumlW7duRES0Y8cOUe0tLCxoxYoVNZ6aQZ9VsHhs05AhQyg/P7/E/XNycmjw4MHivpaWlrRo0aJyR+PXVrKzs+mjjz6SZAHo3LkzBQcHG7QpnZubS/369XuiRpSQkGCwcxgLQRAoMjKSRo4cSQDI19eXLl++XKXyEQSBMjMzad68eWRtbS1pkl2/ft2sY4ZKAubeNCMiunv3riRAKyAgoMZzquTm5tI777xDMplMbJZt27at1H+4IAh06NAhScBjw4YN6fz587XuJimPoqIi2rhxo+jLA0AODg60efNmg/rGBEGgw4cPk4ODg0SI5HI5HTlyxGDnMRYZGRk0Z84cSbrb/v3705UrVyp9T6hUKlqyZInEF2dtbU1btmypVU0yPbVCiDIzMykgIEASsRweHl5jD7QgCBQcHCwRld69e1NCQkKZQpSfn0+ffvqpJEd1v379KpR/p7YgCAL9+eefkrKxtramJUuWGLzWmpmZSVOnTn0ilzQAeumll8xe4AVBoLNnz1KnTp0keadGjBhBycnJFT5OTk4Offvtt5Kk/vb29vTFF1+UWkM3d2qFEKnValq1ahVZWFiIBV9WbcTQCIJA77zzjuRBW758eYXe9o87rq2srOjDDz+knJycGrDcuAiCQJcvX6b27duL16dUKmnGjBn06NEjg/5/9A9x8eRxxZdWrVrRgwcPDHY+Y3LixAmx58/NzY3WrFlTYQEpKaOnQqGg+fPn16pessepFUJEpM3C16JFC7Hwx4wZU2N+ogcPHlDz5s3Fc/v5+VFUVFSFfisIAp0/f17Sne/i4kJbtmyp1f4iQRDo2rVr1L9/f0lzdeTIkRQTE2Pwl0RRURF9+OGH4rkUCgX5+vqKtSNra2uT+A6rglqtpqCgIOrevTtt2LChQiIkCAI9evSIFi5cKMmLrlQqKTAw0CzTv1aGWiNEmZmZ9Oyzz0oe5qtXrxqlUIqjVqtpxYoVokNQJpPRp59+WqkHraioiL755hvJ9Dn29vb03XffGbzmUFOEhYVRz549JbUSPz8/ioyMNMr13L17lxo3biyeq0ePHnT48GGJwPv6+hrcOW4sBEGgpKSkCvlzBEGgGzdu0AsvvCBpFVhaWtIrr7xCycnJteKay6LWCBER0U8//SSOtlYqlfTxxx8b9Q0oCALFxMRIer/s7e3pzp07lT5WQkICjR8/XnyjAyBHR0cKDAysVTeSvufn6aeflvhq2rRpU+5EAVVFo9HQggULxPMplUr66KOPKDs7m9544w2J0/qll16SzJhSm9HPNHLgwAHy9/eX9EjKZDIKDAyk2NjYOnGttUqI0tLSJD6CXr16UUREhMELRY9arably5dLUk0EBgZWqUmlfwM+//zzZGlpKXl4Bg0aRNeuXTP7CQL0s6Q+88wzEkFt06YNHT9+3CgvBUEQ6OTJk5L/e7t27cTZf4ODgyXJ+JVKJc2ZM4fi4+Nr9QOqL+v//Oc/5OjoKKl5uru705IlS8SZa+sCtUqI1Gq1JKrWxsaGNmzYYLRa0b179yR+KW9v72oNVdBPCf3OO+88Mf9Zly5d6MCBA2Z9Y8XGxlJAQICkJtSkSRM6ePCg0c6ZlpZGkyZNkgjfsmXLxHJSqVT022+/ScrT0tKSnn/+eYqLizPr8iyN3Nxc2rt3Lw0ZMuSJHsI2bdrQzp07KS8vz9RmGpRaJUSCINCZM2ckPQbdu3c3iqNOpVLRvHnzJM7Rd955p9pT9eoD0TZu3Ehubm6SG83T01N0YpvTA6TRaCgsLIzGjh0riYHx8vKirVu3Gm0cnUqlom+++UYS/tC9e/cnpuXOz8+nzz//XLKfXC6nZ599lsLCwsxiUsnyEASBioqKKDIykqZNm0YuLi6Se8PGxobGjx9P165dqxUO+cpSq4SISOu0njVrligQMpmM1q1bZ9AbTaPRUFBQkGRiujZt2hjUEatWq+nYsWPUs2dPydve1dWVli5datLxdMUpLCykoKAgSfNH3+3822+/kUajMcpDrtFo6MiRI5Jmibu7O+3Zs6dEP1Rubi4tX76cPDw8JHY2adKEli1bVqk4HVMQExNDX3zxRYnhCS1atKBVq1bVqabY49Q6IRIEgY4fPy4JoOvZs6fBUmEKgkDJyck0evToUpsDhkKtVlNwcDANHTpUcuPZ29vTW2+9RSkpKSa78QRBoJycHPr+++8loQsAyMPDg9atW2e0mpAgCHT9+nXq3bu3WCvQx8pkZWWV+ru8vDz69ddfJb2T+q79SZMm0dWrV80mva5+0GtmZiZt2rSJBg8eLPEd6v1d48aNoxMnTpi9/7C61DohItJW2WfMmCH+wxQKBf3nP/8xyBtDrVbTxx9/LJlHfeDAgZSYmFit45aGIAgUGxtLU6dOldyISqWSJkyYUKMR5HrUajVFRUU9YROgHct08OBBozV3VCoVXbx4URIjxBij4cOHV6iWWFRURLt376aePXuKnQz6mnPDhg3pvffeo6tXr1JqaqrJBKmwsJDu3r1Lv/76K3Xt2lXSpAS04ym7dOlCmzZtotzc3Fo5ZKOy1EohIiK6ceMGNWvWTPznOTk5VdvZq/dBNWrUSOKgPnz4sNFvhvj4eJo3b57kpmSM0ejRoykuLs6o5y5OUVER7dix44kmo1KppGHDhtH58+eNeu59+/Y9kW+oY8eOlRocqu9xmj9/viTOSL80atSIJkyYQCtWrKCTJ09Senq60UVJEATKzc2ls2fP0ocffki+vr4SoSxu28KFCykiIqJeCJCeWitERUVFtHLlSsl4mx49etCjR4+qVBCCINCdO3do6NChkubAokWLqKCgoErHrCyZmZm0cuVKatiwocTpOmnSJIqKijLajSkIAqnVarp//z698847ksGrAMjOzo7ee+89un//vtFsUKlUdPTo0Seaga1bt6Zjx45VqWmSk5NDe/fupe7du5f40FtaWlLjxo3Jz8+P5syZQ3v27KG4uDjKysqiwsLCKl+rvjzz8vIoJSWFTp48SYsXL6Z+/fqRl5eXxOGvr605OjrSSy+9RGfOnKlzPWIVodYKkSAIlJqaKulOtrCwoP/7v/+rUs9WbGysJHIbAPXt25fS09Mrfayqou852bVrF3l5eUlqRt27d6cdO3ZQYmKiQcVAH1Kwbt06ateunaSnRiaTUceOHWnz5s1G860IgkBpaWm0fv16id9PLpdTr1696NKlS9UKl9APjViyZAn17NlTkjLj8YUxRra2ttSvXz+aP38+rV+/nvbu3UvHjx+na9euUWRkJMXGxlJiYiKlpqZSWlqauCQnJ9P9+/cpODiYDh8+TMuXL6cpU6aQt7d3iYN09edr0aIFzZgxg06fPm00x39toNYKkZ6LFy+Sk5OT+M9t3Lgx7dq1q0JdnPobNTIykqZMmSJpirRq1YqOHTtmkhtDrVbTL7/8Ikn9CWgjsUePHk3Lly+n8PBwURwqY2Px7IDp6em0bds2evbZZyU1S/1DMmHCBLp06ZJRnNJ6G0JDQ2nq1KmS/yFjjAYNGkTXr183mOiqVCqKjIykX3/9lcaNG0eurq6lCkRxO6ytralRo0bUvn176tWrFw0aNIiGDRtGo0ePliwjR46kAQMGUMeOHUtsDj5eA/Lx8aEPPviAzp49W+/nYiOqA0KkUqlo8eLFkjE4Hh4eFBQUVO4DlJ+fT8ePH6euXbtKwucbN25MO3fuNFlPhT60f9u2beTj4/PEjWxhYUEuLi40ZMgQWrduHd28eZMePnxY6tAGtVpNOTk5FBcXRyEhIfTjjz/SuHHjyNPTk2xsbCQPpFwup3bt2tGqVauM5jtRq9UUERFBn3zyCXl6ej7hi+rfvz9FRkYabbhIdnY2PXz4kH7//XcKDAwkX19fatSoUZm1peosCoWCXF1dqW3btvT888/T1q1bKTk52ezixUxJaULEtN+ZFn9/f7py5UqZ+xARkpKS8M4772DPnj3ivGfNmzfHp59+igkTJjwx60N+fj7Onz+PEydOYNOmTXj48KH4nYuLC7788ku88sorVZ72xlAIgoBz585h1apVOHr0KLKyskrcz9XVFZ06dULHjh3h6ekJGxsbMU2oWq1Geno6EhISEBYWhtu3byM9Pb3U44wfPx5z5sxBhw4dDH79RITk5GTs27cP69atQ3BwMIrfZ/b29njppZfw/vvvo0mTJjWW6jQ9PR3BwcG4ffs2bty4gZs3byImJgZJSUni1DyVxcLCAk2bNkWnTp3QtWtX+Pr6omvXrmjcuLHJ7ytzhDF2lYj8n9heW4QI0N7gd+/exfTp03HhwgXx5nZxccGwYcMwbNgwWFtbIzs7Gw8ePMDFixdx69YtJCcni8LFGEPr1q2xePFijB8/HpaWlka9topCRHj06BGuXr2KjRs3IigoCLm5uSgqKipxf7lcDoVCIa4LgiBOcFgSCoUCFhYWeOqpp/DWW2+hR48esLe3N5gI6N9seXl5CAoKwvfff49r164hOztbYoO+7IcPH44GDRqYJN+y3s7U1FRkZWUhIyMDKSkpuH//Pu7evYvk5GRkZ2cjNzf3iUkz5XI5nJyc4OHhgQ4dOqBbt25o1KgRXF1d4eTkZNCc3XWROiFEgPYmio6OxsyZM3H27FnJjLCMMTDGRIF6/NqUSiUGDhyIFStWoEOHDuJvzAkigkajQVxcHE6cOIFDhw7h1q1biImJQUFBQaWOZW1tjWbNmqFFixbw9/fHwIED0bdvXyiVSoNetyAIePjwIU6cOIENGzbg4sWLEgGVyWTw8fHBiy++iFdeeQWNGjUyq3J//H6pyDOht19/z3EqRp0RIkB749+5cwdLly7Fjh07KjQ9dYcOHRAQEIApU6agefPmteLmISLk5ubizp07CAsLw6VLlxAcHIzQ0FBJTUOPUqmEq6sr2rRpg65du6Jbt25o27YtWrdubdDaj942AEhLS8P27dtx8OBBnDt3Dnl5eZL9GjRogClTpoiTAPIaQ/2mTgkRoH0QcnJysGXLFmzevBkPHjwQawwymQxKpRK2trbo0KEDpkyZgsGDB8PR0dHgtYGagohQUFCAgoIC5OXlITs7G3l5eaIgKBQKODs7w9LSEhYWFrCysoKlpaVRrrWoqAgPHjzAn3/+ibVr1+L+/fuS2hpjDC4uLujZsycWLlyILl26wMrKqlaWO8ew1DkhKk5ubi5CQ0ORkJAAIoKlpSUcHBzQokULNG7cmD8ABkKj0SAsLAwHDx7E1q1bcefOnSeaMW5ubhgxYgRefPFF9O/fHzY2NiaylmOOlCZEipJ2rm3Y2tqiV69epjajzkFEEAQB+fn5uHnzJrZv346jR4/i3r17T/iA7OzsMH78eEybNg1du3Y1mSOaUzupE0LEMSz6Zm94eDjOnTuHrVu3IiQkBEVFRZIakFwuR6tWrTBq1Ci8/PLLaN++PeRyORcgTqUpV4gYY94ANgNwhzZw6yci+o4xthjALAApul0/JKJDut98AOBVABoA/yGio0awnWNA9AKTlZWF06dPY/fu3Th37hxiYmJKjLHx9vZGQEAAJkyYAD8/PyiVypo2mVOHqEiNSA1gLhFdY4zZA7jKGDum+24FEX1TfGfGWHsAAQA6AGgM4DhjzIeIqhYxxjEqgiAgKysLMTEx2LdvH3bs2IGEhARkZ2dLaj+MMTg4OKB58+ZiE8zd3d1oDnFO/aJcISKiBAAJus/ZjLHbADzL+MlYADuIqBDAPcZYFIAeAC4YwF6OgdBoNAgPD8f58+dx9OhRnDx5EqmpqU/sxxhD+/bt0bt3bwwfPhxDhgyBi4uLCSzm1GUq5SNijDUD0BXARQB9AcxhjE0DcAXaWlM6tCL1b7GfxaFs4eIYGSKCWq2GRqNBbGwsLly4gNOnT+Pff/9FTEwMcnNzJfvLZDJYWVmhe/fuCAgIQP/+/dGsWTPJkBIOx5BUWIgYY3YA9gB4h4iyGGM/AvgMWr/RZwC+BfBKJY4XCCAQAJo0aVIZmzmVJC8vD//9739x6tQpREVFQaVSQRCEJ7re7ezs0KZNG/Tt2xdTpkxB165doVAoePQwx+hUSIgYY0poRWgrEf0BAESUVOz79QD+1K3GA/Au9nMv3TYJRPQTgJ8AbRxRVYznVAy1Wo0LFy7g5s2bJX7v6OiI4cOHY8SIERg4cCCaNWvGI6A5NUpFes0YgA0AbhPR8mLbPXT+IwB4DoD+Lj8AYBtjbDm0zurWAC4Z1GpOpbC0tETPnj1x7do1MMZgbW0NW1tbNG7cGKNGjcKLL74ILy8v2Nvb8xHjHJNQkRpRXwAvAQhljAXrtn0IYDJjrAu0TbMYAK8BABGFMcZ2AbgFbY/bbN5jZlosLCwwbNgwREdHw8fHBx07doSvry98fX3F7AO86cUxJXViiAenfLKzs5GTkwM3NzdJ+hAOpyap00M8OOVjb28Pe3t7U5vB4ZQIdwhwOByTw4WIw+GYHC5EHA7H5HAh4nA4JocLEYfDMTlciDgcjsnhQsThcEwOFyIOh2NyuBBxOByTw4WIw+GYHC5EHA7H5HAh4nA4JocLEYfDMTlciDgcjsnhQsThcEwOFyIOh2NyuBBxOByTw4WIw+GYHC5EHA7H5HAh4nA4JocLEYfDMTlciDgcjsnhQsThcEwOFyIOh2NyuBBxOByTw4WIw+GYnHKFiDFmxRi7xBi7wRgLY4x9otvenDF2kTEWxRjbyRiz0G231K1H6b5vZuRr4HA4tZyK1IgKAQwhIl8AXQCMZIz1AvAVgBVE1ApAOoBXdfu/CiBdt32Fbj8Oh8MplXKFiLTk6FaVuoUADAGwW7d9E4Bxus9jdevQfT+UMcYMZTCHw6l7VMhHxBiTM8aCASQDOAbgLoAMIlLrdokD4Kn77AkgFgB032cCcCnhmIGMsSuMsSspKSnVuggOh1O7qZAQEZGGiLoA8ALQA0Db6p6YiH4iIn8i8ndzc6vu4TgcTi2mUr1mRJQB4B8AvQE4MsYUuq+8AMTrPscD8AYA3fcNAKQZwlgOh1M3UZS3A2PMDYCKiDIYY9YAhkHrgP4HwPMAdgCYDmC/7icHdOsXdN//TURU1jmuXr2ayhjLBZBa1QsxMK4wH1sAbk95mJM95mQLYH72NC1pIytHI8AY6wyt81kObQ1qFxF9yhhrAa0IOQO4DuBFIipkjFkB2AKgK4BHAAKIKLo86xhjV4jIvxIXZDTMyRaA21Me5mSPOdkCmJ89pVFujYiIQqAVlce3R0PrL3p8ewGAiQaxjsPh1At4ZDWHwzE55iREP5nagGKYky0At6c8zMkec7IFMD97SqRcHxGHw+EYG3OqEXE4nHqKyYWIMTaSMRauGyS7wEQ2xDDGQhljwYyxK7ptzoyxY4yxSN1fJyOefyNjLJkxdrPYthLPz7R8ryuvEMaYXw3YspgxFq8rn2DG2Ohi332gsyWcMTbCkLboju/NGPuHMXZLN+j6bd12U5VPafbUeBnVqQHpRGSyBdqQgLsAWgCwAHADQHsT2BEDwPWxbcsALNB9XgDgKyOefwAAPwA3yzs/gNEADgNgAHoBuFgDtiwGMK+Efdvr/meWAJrr/pdyA9vjAcBP99keQITuvKYqn9LsqfEy0l2jne6zEsBF3TXvgjZsBgDWAnhD9/lNAGt1nwMA7DTWPV3ZxdQ1oh4AoogomoiKoI1LGmtim/QUH7xbfFCvwSGi09DGXFXk/GMBbCYt/0Ib4e5hZFtKYyyAHURUSET3AEShhJCOatqTQETXdJ+zAdyGdjyjqcqnNHtKw2hlpLvGOjEg3dRCJA6Q1VF88GxNQgCCGGNXGWOBum3uRJSg+5wIwL2GbSrt/KYqszm6ps7GYs3UGrVF15ToCu2b3+Tl85g9gAnKyBgD0k2BqYXIXOhHRH4ARgGYzRgbUPxL0tZlTda9aOrzA/gRQEto81ElAPi2pg1gjNkB2APgHSLKKv6dKcqnBHtMUkZkhAHppsDUQiQOkNVRfPBsjUFE8bq/yQD2QvsPTdJX6XV/k2vYrNLOX+NlRkRJuhteALAe/2ta1IgtjDEltA/9ViL6Q7fZZOVTkj2mLiOq5QPSTS1ElwG01nn5LaB1oB2oSQMYY7aMMXv9ZwDDAdzE/wbvAtJBvTVFaec/AGCarneoF4DMYk0Uo/CYj+U5aMtHb0uArjemOYDWAC4Z+NwMwAYAt4loebGvTFI+pdljijJijLkxxhx1n/UD0m/jfwPSgZIHpAMVHJBeY5jaWw5tL0cEtG3bhSY4fwtoezVuAAjT2wBt2/kEgEgAxwE4G9GG7dBW51XQtulfLe380PaUrNaVVygA/xqwZYvuXCHQ3swexfZfqLMlHMAoI5RNP2ibXSEAgnXLaBOWT2n21HgZAegM7YDzEGiF7+Ni9/QlaB3jvwOw1G230q1H6b5vYax7urILj6zmcDgmx9RNMw6Hw+FCxOFwTA8XIg6HY3K4EHE4HJPDhYjD4ZgcLkQcDsfkcCHicDgmhwsRh8MxOf8PKeSuOSwoGZgAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/على مع الحق و الحق مع العلى.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/ان الله جميل يحب الجمال.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/4الله أكبر.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 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      \"server/larger_data/ايحسب الانسان أن يترك سدى.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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q1+Prrr1GvXr08e+O0hBvJTx+sss5jK58kYTQasWbNGkyYMAExMTF49913MW3aNERFRWHFihVYvnw5Zs2ahZUrV6J9+/bo2rUr6tevDzc3N9jZ2UGv1xcq1vK/0Wg0IFkiD2NB0Ov1aNGiBU6dOoXz58+jZs2ajzx36/W9cOECNm3aBLPZjN9//x2///47gP8X2nfffRdjx461jaAuLiRJQr9+/XDz5k3MmDEDwcHB+P7779GhQ4c89UuyipTBYMCdO3dw69YtREREIC0tDTqdDv7+/vDz84Ovr2++PCqdTgdnZ2ckJycjJSXloe4EJHHz5k28//77iIqKwtSpU9G1a9cnRkAKzKO6NJfkJ/OQibwgyzJv3brFDz74gPb29nR1deXs2bNtQx5kWabZbOalS5c4evRoVqpUiSqViiqViu7u7gwMDGTt2rXZqVMnzpo1i1euXClwd/fMNk2bNo2SJHHZsmWFSqu4kGWZ33zzDYUQnD179iOHdaSnp/PQoUPs3bs3XVxcKIQgADo5OXHo0KGcPn06Bw8eTD8/P2o0Gn7yySc0Go0lYn9qaio/+eQT6vV6enl5cfXq1bnGOjYYDDx+/DinTp3KVq1a0cvLi2q1mpIk2T4ajYblypXjyJEj8zykhCTj4+NZv359+vr68tatWw/9HxMTw+7du1OSJAYHBzM5OTnfw2nu37/PypUr86mnnmJMTEy+9i1NchoyUeqiw3wIj9lsZkREBL/55htWr16dkiSxevXq3LJlyyMzvslkYnh4ONevX89BgwaxefPmrFatGn18fGhvb09Jkujn58f//e9/hXp4HgfhIcnVq1dTpVJxwoQJDz0Asizz5s2bHDp0KF1dXalWq1m3bl1OnDiRAQEBrFChAsPDw0lmXNfDhw+zYsWKdHV15cGDB0tkfJosy0xLS+P8+fPp7OxMNzc3rlmz5qEXh3X83YkTJ/j666/TxcWFkiTRycmJ9evXZ9++fTl+/Hh++umnHDt2LHv16kV/f39qNBqOHTuWKSkpebInLS2N7dq1o7OzMy9cuJDlP4PBwDFjxlClUrFt27a8e/duga7RgwcPWLNmTVauXJkRERH53r+0eKyFR5ZlpqSk8OTJk5w6dSpr1apFlUpFFxcXvvPOO3me1cHqCSUlJTE6Opp3797lkSNHOHLkSLq4uNDZ2Znr168v8MMjyzKnT59OIQR/+OGHYnkIrfanp6cXOI0tW7ZQrVZz6NChWWw0Go387bffWLduXUqSxKCgIC5YsIBRUVFMTU1l8+bN6e7uztDQ0Cz2fPvtt1SpVBw4cGCxzLLwKIxGI5ctW0ZnZ2d6e3tz9+7dtvORZZl3797lmDFj6OnpSUmSWKtWLU6fPp0nT55kfHy8LT9k9pD/+usvVq1alVqtlvPmzcuTF2w0GtmlSxfq9XqePXvWtt5sNnPFihV0cHBgYGAgz58/X+A8ER8fz7p167JChQq8e/dugdIoDR474bFmhNu3b/OHH35gmzZt6OTkRCEE3dzc2KdPHx4+fJjp6emFesCtb8UVK1bQ3t6ejRs3LvDoX1mWuXDhQgLIsRhTGO7fv89XXnmFX3zxBdPS0gqUxrFjx6jT6fjiiy/SbDbbxOzzzz+ni4sL7ezsOGDAAN64ccN2DiaTiR07dqRer+e5c+eypHf9+nW6ubmxUaNGTE1NzZctsiwzOjqap0+fLtD1MhqNnDt3Lu3s7Fi1alWeP3+eZrOZBw8eZOPGjSlJEitWrMhZs2bx/v37NpHJyZ6DBw+yQ4cOPH78eJ5sMplM7Nq1K/V6Pc+cOWNL58iRI/T19aWzszN/+eWXQuWH+Ph41qlTRxGeov5Yhcdahj9z5gwnT57MypUrU6VSUa/Xs1mzZpw6dSpPnTpFg8FQpA92UlISGzRoQC8vryxv9PxiLcZMmjSpWITn1KlT9Pf3p16v57Jlywp0jJs3b7JcuXJs0KABExMTef/+fQYHB1Oj0dDPz49Lly5lampqlrRlWWavXr2o1Wp59OjRLOk9ePCA1atXZ0BAAO/fv58vWx48eMAOHTqwZs2aDAsLK9D5pKWlcfTo0VSpVGzXrh2//vprent7U6vV8rXXXuPly5fzVX9nNpvzFdbk38IjyzJDQ0PZoEEDarVafvHFF4Wu/4qOjmb16tVZpUoVRkZGFiqtkiQn4SkzrVpmsxknT57E7NmzsW/fPsTFxcHLywvBwcHo27cvmjRpAkdHx2JpDVCr1XB2dobJZHrkQMi84OzsDJVKhbi4OJB8yNaUlBScOHEC3t7etrmS8kPdunWxZMkSfPPNN2jWrFmBbNRqtbC3t4fBYMC1a9cwfvx47NmzB/Xq1cO3336Lpk2bZttK9Kjrnp6ejvT09P9/k+UDJycnVK1aFfv27cO3336LGTNm5Pua6HQ6TJw4ERcvXsTOnTvxxx9/wNHREdOnT8e7776b78BukiTla9ZU6xQ31r428fHxGDVqFE6fPo033ngDw4YNK3Tfm9TUVCQmJqJcuXJlqg9TYSgTwmONITt79mzExMSgVq1aeP/999GnTx9Ur1692PsvCCFgb28Ps9lsi3NbEHLrTHb16lV0794dLVq0wKZNm/L9kEmShA4dOqBly5YFjh1k3Sc6OhoDBgzAmTNn0LFjRyxatCjH6XENBoNtyhsrJLFv3z7cvn0bPXv2zDYmc06o1WqMHDkSO3bswPLly9G7d280atQo3+dlZ2eHgIAAkITZbEbXrl0xdOjQEnlIrTGLZFnGnTt3sHnzZmzbtg2tWrXCjBkziiQ8aWJiIhISElCtWrUnJs5RmehAeOfOHUycOBGSJGHGjBnYt28fpkyZgqeeeqpI+9nkhDW0ZGH64FgH+hmNxofe/iRx5MgRJCQkoE6dOgV+KKwiWdBrYp0mJSIiAmfOnMFLL72E//3vf7nOyW3tAZ5ZLA0GA1atWgWVSoX+/fvn+81u7e39wQcfIC4uDjNmzMh3UP+UlBRMmTIFS5Ysgb+/P8qVK4cdO3Zg//79+fbACoIQAr6+vpBlGQsXLsS8efPg7++PefPmoVy5ckWSd2/evAmDwYCAgACl53JREhkZiUqVKuHnn3/GyJEjbT1lS4r4+HhcvXoVjo6O8Pb2LnA61gBY2fVgNRgM2LZtG7RaLTp16lQqHcjIjAkNU1NTbTGuFy5caIsymNN+JpPJFqnRSlxcHFJTU9GwYcMCj4uTJAnBwcFo2LAhduzYka9pW9LT0zFnzhzbMIS1a9dixowZMBgMGD9+PO7ceWjm7GLBzc0NJLF7925oNBrMmTMn24BeBYGkrYd4zZo1i8DaMsKjKn9K8iOE4Jo1a0olTrE1bq5arWavXr1oMBgKnNbRo0dpb2/PHj16ZGlalmWZ165dY7ly5VizZs1S6QRm7aPTpk0bAqCdnR1PnjyZp2uemprKFi1a0M3NjdevX8+SZlxcHENDQwvdurhp0yba2dmxcePGjIqKyrX1KTU1lbNnz6a9vT0rVqzIP//807Z+wIABFEJw6NChD1WUFzXW/AOAWq2W33zzTZF2K5BlmWPHjqUQgmvXri1zsbxzoszHXFar1WjVqlWJewEkER4ejjlz5kCj0WDgwIH5mnfq3+Tk8ezduxfR0dFo164dnJ2dC2N2viEzuu0PGDAABw8ehJ2dHcxmM6KiovI9SNF6fUgiJiYGJpMJAQEBhbp3Qgh07twZ3bp1w99//43ly5c/0ushiaSkJEyZMgWTJ0+Gi4sLFi5ciGeeeQZCCFtlc7Vq1bBkyRLMmDGjQGFJ80paWpptGEmrVq3w5ptvFunsD2RGBEaVSlXsw1JKkjIhPNZ6h5KEJBISEjBq1CicOXMGvXr1Qps2bQp1Y/V6PSRJemj2U4PBgJ9//hlarRY9evQo0WIkSVy4cAF9+/bFgQMH0LVrV7z++uuQZTlf4Tv+LQSxsbF455138NZbbyEqKqrQdtrZ2WHcuHFwc3PDt99+i+vXr2dbTxYTE4Phw4djzpw5qFChAlauXIkuXbrY7psQAlWqVMEPP/yA8uXLY/r06Xj//ffx4MGDIq/zMZlMWLRoEVatWgUgI/5ycURhpKWFtCTzTXFTJs7EaDSWWGUg8P+iM2HCBPzyyy9o3rw5pk+fXuhWEGdnZ2i1WsTGxtoqqUni7NmzOHr0KGrVqoWGDRuW2FuLJE6ePIk+ffrg+PHjeOWVV7BkyRJUrVrV5jnkNR2z2ZwlEt53332HrVu3Ftn8YUIINGjQAG+99ZbNC81c0U8SERERePfdd/Hjjz+idu3aWLduHdq1a/fQAymEQKtWrbB69WrUqlULK1aswNChQxEZGVlkeUyWZfz888/49NNPAcAW8L2oIwQyU/3akyQ8uda/AFgGIBLA+Uzr3AHsAXDV8u1mWS8AzAdwDcBZAA1yS9+yH6tXr57n4Q+FQZZlRkVFsW/fvlSpVKxduzbPnj1bJMeNiopi1apVGRgYyKioKJIZHdImT55MSZI4a9asEiujm81m7t69m4GBgVSr1XzvvfcYGxtLWZa5aNEiAuDMmTPzZE9UVBSrVavGqlWrMjIykpGRkQwKCqKnpydDQkKK7JxkWWZYWBirVatGZ2dn/v7771nWd+7cmZIksVWrVrx8+XKux5VlmZcvX2bz5s0pSRJffPFFRkREFNpeaw9nHx8fOjk5ceLEiXRxceFzzz2X797buR0nPT2dAwYMoFar5S+//MIHDx4wIiKC9+7d4927dxkeHp7ncWUlTWE7EP4PwLcAVmRaNx7APpIzhBDjLb/HAegCIMjyaQpgkeU7R7y8vHD16lUMHToUCxYssAVOz6tnIMsyzGZztlH4rbMwWpfj4+MxfPhwbNiwAfXq1cPixYsRFBRk679jfbP8ez9a3pQkH/rfip2dHTw9PREaGoqEhAR4enrCZDLh8OHDsLe3L5Gg80BGEWD9+vUYPXo04uLi8OGHH2LSpElwdHQESXh7e0OtVttmbsjOpsznm5aWhpSUFLi5uUGlUuHOnTuIiIhA3bp1s1y7zPtZl60fazwk6zQ7//5k5rXXXsPUqVPx+eefw9PTE/Hx8RgzZgz++usvVK9eHRMmTABJXL16NUsx3VrHZp3yV6PRwNfXF0uWLMGgQYOwfft2DBw4EHPnzoW/v7/tHmb+AI/uLGk9p0uXLmHIkCGIjo7GJ598gm7duuG7776zXSfrOaanp8NgMMBgMCA9PR1GoxEmk8nWUTUlJQUpKSlISkpCUlISEhMTER8fb/tt/f/vv/+G0WjEhx9+CI1Gg6SkJBiNRsiyDCEE1q1bhzZt2hQ4v5QGuQoPyYNCiIB/re4OoI1l+UcAvyNDeLoDWMGM3HdUCOEqhPAheS+nY/j6+qJhw4bYvXs32rVrh1atWqF58+Zo3rw5ateubSs3WzNxeno6YmNjcffuXZw7dw5nz57FrVu3cPfu3SyuvxACer3etr9KpUJCQgLOnj0LMiO40scff4y0tDRb5tdqtXBwcICjo6NtDiqVSmXLSCRhZ2cHHx8fVKlSBTVq1ICfnx88PT2h1Wrh7u6OM2fO2OxISEjArVu3UK5cOVSsWDG3y10oSMJgMGDJkiWYNGkSSGLatGkYMmQIhBB48OABUlJSYDKZoNFocOrUKWzfvh1JSUm2eDKpqam2b+vDEhcXhwcPHiApKQlvv/024uLikJKSggsXLqB79+5ZHlSr+JvNZhiNRhgMBqSlpWXp3ZzdJzPWflD79u1D69atYTQabdczLCwM/fr1y1YwrEURlUplm4tKr9dDp9MhLi4Osixj+/btuHz5MmrXrg2dTmcTKLVabfu2TgVkLVoC/x/U3Ww2Y9++fTh//jwcHR1x9OhR7NmzB4mJiTh9+jTatWsHo9GI1NRUm8hkfilaPyaTKcs8WdmJnlVUrdcoJiYGrq6ucHJygkqlgiRJsLOzK9C026WN+PdNz3ajDOHZTrK25XccSVfLsgAQS9JVCLEdwAyShyz/7QMwjmRITuk3atSI27dvxxdffIEtW7bg3r17MJlMcHR0RLt27dC0aVPIsoyoqCiEhoYiPDwc0dHRiIqKsmVovV4Pe3v7h1qlzGYzzGaz7YECMuIjW9+SmT0cALZMYc0o1pueeTtrZpIkCRqNBl5eXqhYsSKefvppHDp0CP/88w8WLlyI5s2bIzk5GT179oS7uzt++eUXW4xd6wNjTUOj0di8POvH6iFYM65VCAwGAxITE5GYmIjk5OQswnHkyBGsX78eaWlp8Pf3x9NPP217kyYnJ9u2T0hIsB0/88OfOT/8+5ytQm61xRpMLTPWB9W6n1UE1Gq1bdkq5tbv7BoW0tPTcenSJRiNRmg0GjRo0CDLUIbMHpT14bUKllXsDAaDzfvI/J2YmGiL6KfRaGxiYH0Z5YXM56VWq231ZR4eHrZ11uuj1+thZ2cHrVZrmwbI3t4ejo6OcHJygoODAxwcHODk5ARnZ+cs63Q6Hf73v//hm2++wZdffon+/fvbAthZhTG/kzSWFI0aNUJISEi27mOhu0GStAaKyhdCiEEABgFAxYoV4e3tjTlz5mDcuHE4d+4cjh49is2bN2Pnzp3YunUrgIybrdVqodfrUb58edSuXRtPP/006tevj4oVK8LT0zPLeC5ZlmEwGHDjxg2MHDkSZ8+eRYsWLfD5559nadLWarW2G2d9W6WmpmYpFlhvsBDCFmr1ypUr+OeffxAaGorz58/jxIkTtqb04cOH27ZPTk5GdHQ0nn322SxvZWvG1+v1tkxmPY7l2iI9PR1paWlITU21ica/bcv8sRYFNRoNIiMjsX//fqhUKuh0Ori4uKB8+fKQZRnnzp2Dr68vnn/+ebi6usLR0RH29va2j9VTVKvViI+Px9ChQ+Hh4YHvvvsOGzduxHfffYcxY8bgtddey3JfMz8Q1vuVOVbwv9/s2RVr4uPjMXfuXNusrO3bt8fq1auziNy/i3T//raKjMFggNFotH3S0tIwZ84crFq1Cq6urvjss88QFBRkE3frdc3soQBAeHg4pk+fjtjYWDz//PN49913bdcJAPr374+EhATs3LkTfn5+0Ol0thdJ5qJ55hfOo4rsmSGJbdu2AcgIQevl5fVENKkXVHgirEUoIYQPMiqfAeAOAP9M21WwrHsIkj8A+AHI8HiAjDK6Nfxkp06d8MEHH+DYsWO4ffs21Go1XFxc4O/vDx8fHzg5OdmKQ0D2GZgkQkNDMW3aNJw7dw7PPfccfvjhh0L3O7GmDWR4VAkJCbh//z4uXbqEpUuXYseOHahduzY8PDwQExODs2fPAsgImm59u2d2uWNiYnD37l3bGztzgHprfYVOp4OdnR28vb1hb28PZ2dnuLq6Qq/X48yZMzh9+jSEEGjbti369+9vc8mdnZ3h4uICR0dHW9EiJCQEL7zwApo3b4758+fnOJEcAFy7dg1GoxHe3t5o2bIlfv31V6hUKjz99NNF2puWzJh6Z9y4cdizZw88PDxgMBjw999/4/bt26hTp06R3Lc5c+aAJNasWYPFixfju+++Q+PGjbOknVnYoqKiMHDgQMTFxeGFF17A999/j/Lly9v+txbj7OzsUKlSpXyPWcsNa0/zJ2WcFoC89VwGEICsrVqzAYy3LI8HMMuy3A3ATmS0bjUDcDwv6fv5+XHLli1F1uNTlmWaTCYeOHCATz/9NCVJYvfu3QscAS4/zJgxg0IILliwgCaTiTExMaxVqxY9PDx48uRJJiQkMCEhgXFxcYyNjWVUVBTDw8N57do1Xrp0iRcuXOD58+dtn8uXLzM0NJTh4eGMjo5mUlISjUajLbLiyJEj6eTkRK1WywEDBtjizuTE3r17aWdnx7feeitP1/zkyZO0s7PjCy+8QLPZzKFDh1Kj0XDr1q1Fcs2scZE2bNjAgIAAqlQqPvfcczxx4gSnTJlCSZL4+uuvFzgGUXbHi4mJ4YABA6hWq1mxYkVu27Yt2yiGMTExfPnllylJEp977rls89CFCxfo5ubGFi1aFHkLk9ls5rBhw6hWq7l58+YiTbu4KVQ8HgA/AbgHwAggHMBAAB4A9iGjOX0vAHf+f3P6AgDXAZwD0Ci39C37sW3btoUarmDF2o3/iy++oKenJ3U6HQcOHMjIyMgSacpesmQJAfCzzz4jmZFxPvzwQ0qSxClTphRJbOe0tDT+8ssvNlGtUqUKly1blud4vjt37qROp+OgQYPyJDwHDhygVqvl66+/TrPZzHHjxlGlUnHNmjWFOhcriYmJnDFjBp2dnW3N0zExMZRlmffu3WPdunXp4ODAHTt2FGnTfWJiIqdMmUIHBwd6enpy5cqVWWLnpKamcsSIEVSpVHz22WezBEfLzOHDh6nX69m7d+9CRYfMDpPJxEGDBlGr1XLHjh1FmnZxU+YDgQFgnz59Ch0wSZZl3rhxgz169KBarWb58uU5f/58JicnFyrd/LB+/XoC4NixY20R786ePUtPT08GBgby9u3bhQqvGhYWxvfee48ODg7U6/Xs379/nvq0ZObnn3+mWq3myJEj8ySE+/bto1arZXBwsC3cqRCCn332WaGEwGw288KFC3zllVeo0Wjo4+PDVatWZXl4rWOhdDodW7duzbi4uCxpyLJMg8FAk8lUIFsMBgMXLFhgC387ffp0xsfH02g08ssvv6SdnR2rVavGf/7555Hpnzhxgo6OjuzWrVuReWVWTCYT33zzTep0Ou7Zs6dI0y5uHgvhGTBgQIGLWtaOVtu2bbPFDG7ZsiWPHz9uC+9ZUmzfvp1CCL7//vu24xqNRr799tuUJImjR4/Ot2dndfkXLVrEoKAgCiFYvXp1rlu3rkCDINeuXUtJkjh+/Pg87ftv4fnzzz/p4ODAhg0b8t69e/k6vlWMIyIiOGfOHPr7+1OSJDZt2pRHjhzJVgiTkpLYpUsXqtVqLly4MMvxIiIi2KlTJ7733nu5Di59FEajkatWraK3tzfVajV79+7NhQsX0snJieXLl+e+fftyTPfevXv08/NjUFAQY2Nj8338nMgsPLt37y7StIubx0J4CurxWN+ab7/9Nh0dHanX6zlw4MB8PxBFxa5du6hSqTho0CDbQyTLMi9evMiqVavS3t6ey5Yty1P4VlmWGR8fz9WrV7Np06ZUq9V0cXHh0KFDH+n254X8Cs/+/fup1Wr5xhtvUJZlJicn89VXX6UkSezRowfDwsLy5DmZTCaGhYVx9uzZfOqpp6hSqejp6clJkyblWDdl7Sns6urKqlWr8saNG7b/oqKiGBgYSEmSOGnSpAK/vMxmM48dO8YWLVrYprtxcXHhTz/9lOu5JScns0mTJvTy8uK1a9cKdPyc7Bo9ejQlSSrUZASlwWMR+vTGjRswGo15DnREZow1WrFiBWbNmoXw8HBUq1YNkydPRs+ePW3RAEsaaxNp5v4gQghUr14ds2fPxptvvolhw4Zh79696NWrFypVqpTlnK1Nz5GRkTh06BC2bduGY8eOQaVSoWvXrvjwww/RrFmzQo2izy/WAGfp6ekAMvpBTZ8+HXfv3sXmzZtx/vx5W3+rp556Ch4eHtBqtdBqtVCpVEhLS8OFCxewbds2bN26FeHh4XB2dsarr76KYcOGoWHDhjnedyEEmjVrhr59+2LRokW2MKlqtRr29vbw9vbG9evX8eOPP+LNN99E1apV832OkiShcePGmDZtGl544QWkpKQgMDAQ7du3zzUfqVQqeHh44OLFi4iPj8/3sXNCCIFy5cqBZJEMxi0zPEqRSvKj0+no5ub20AwG2WFtsTp27Bi7du1KjUZDd3d3TpgwgeHh4cX2RrAWERISEnIsx1u9g+Dg4IfevkajkWvWrLFN0SNJEh0cHOjk5EQnJyc6OzvT3d2dXl5etLOzoxCCdnZ27NChA3fs2GGbsLCwrFu3Ll8ez+HDh6nT6fjqq69mmUImPDycb7/9Nr28vChJEoUQton2fHx86O/vz0qVKtHX15cajYZCCPr6+vK9997j8ePH8zVLiCzLvHr1KitVqkQPDw8eO3bMVsTu3LkzARAAJ02aVKAKfOsYvi5dulCSJLq5uVGlUvGdd97JtdLebDZz0KBBlCSJ27dvz/exc+O7776jJEmcPn264vEUJW5uboiIiMCaNWswbdq0R/YrMZvNuHHjBn744QcsX74ccXFxeOaZZzBt2jQ0b9682MOkbtq0CTNmzMC4cePQq1evbLdxcXGBTqdDdHQ0TCZTlnNRq9Xo06cP2rVrh/379+PAgQO4d++erd8OSaSkpCA9PR1eXl5o1qwZWrdujXr16sHBwaHIzk2j0UCSJBgMBpDZj9XKTEpKCkhmiR9sDfm5cOFCjBkzBufOncOJEydw4cIFREdH23oPm0wmODk5oXnz5ujYsSM6dOiAihUr5juOtjXcxfDhwzFmzBjMmDEDq1evhp2dHVxcXAAAlStXtoUhzW9P3pSUFIwZMwa7d+9G+/btMW7cOAwfPhw//vgjPDw8MHny5EcOTbB6JbIsIzY21raeGdUIhb5v1qgJhZmIoMzxKEUqyU+tWrXo5+dHT09PHjhwIMtbNS0tjffv3+fu3btt0+Za50uaPXs2Hzx4UGJvgU2bNtkqHx/VbBoWFmabPia3qXDNZjMNBgPT0tJsn+TkZCYmJhb5FD6Z2b59O7VaLYcMGZIn78B63sOHD881MqDBYGBSUlKWvkpxcXGFngPNSnR0NJs2bUq9Xs+NGzdSlmVbHcjSpUsL5O2YTCZ+/fXX1Gq1bNCggS1KwqFDh1i5cmVqNBoOGTLENhFgdkybNo0AuHjxYpIZ9/b48ePcsGFDoc87p9lfyzJl3uOxs7PD+PHjMXr0aIwYMQILFixAeno6/vrrL/z++++4fPky7t+/D5PJBD8/P4wcORLvvvsuAgMDc+1yXpS0aNECAQEBOHjwIG7fvo3KlSs/dGwXFxfY2dnZovPlhCRJxRI4KjesNz+vpKamPuTxZIcQwla3U1y4u7tj/Pjx6Nu3L6ZPn45nn30WVapUgSzLiIiIyHdeIIljx45h2rRpcHFxwddff23LVy1atMDq1avxzjvv4IcffoC3tzfGjRsHrVb7UC9na52e1dOKjIxEcHAw0tPT0axZM/j5+RU4n1rv1ZMwVMJKmRlZNnDgQPTv3x/nz59H+/bt0alTJ0yePBlHjhyBWq1G165d8d133+HPP//EzJkzERQUlKO7LssyQkJCsGHDhiILVuXp6YmOHTsiKioKf/zxR7bbWMcnWQealjVoqZQ3m815nj/KWqlcGiIJIMuYKSEEOnXqhG7duuHUqVNYtmwZAgMDodFocO7cuSzDTfKC0WjEvHnzEB8fj0mTJtlCqFqP1axZMyxduhTe3t6YPn06vvzyy4cGkpLE/fv3IUkSvLy8AGTklRYtWtjGCV6+fLnAQcKSkpJAMl/zfZV1yozw2NnZYdasWZgyZQp8fHxgNBrxxhtvYO/evTh+/DjWrVuHt99+G5UqVco1TCpJ/P333+jRowf69euHBQsW5HrTjUYjduzYgV27dj2yLC1JEjp37gxJkvDHH39k6zWUpAdWUKxxePL6FrZ6SKUxAtpoNOKHH37AsmXLbANw9Xo9xo0bB3d3d3z77bfQarXw8fHB4cOHERERkee0SeLEiRPYtWsXnnrqKfTr1++hvCWEQOPGjbFkyRK4uLjgu+++e2j2CqPRiBs3bsDe3h6+vr4AMl5AAwcOhIODAzZu3IjOnTtj3bp12cbjzg2r8JR0rO7ipMwIjxACbm5uGDduHBo2bAidTofg4GA0bdrU1jyb1wc6MTERkyZNQnh4OIxGI7Zt25brRH13797FwIEDMXz4cCQnJz/Sxjp16sDV1RVnzpx55HZlnQcPHkCSJHh6eubpmlqvfWEmOywoYWFhmD59OkaNGoWVK1faxL5evXoYMGAA7ty5g9WrV+PZZ5/FnTt3sG3btjwXI81mMxYvXoykpCQMGjTokYM7JUlC+/bt0a5dO9y9excXLlzI8r/RaMS9e/fg6OiIcuXKAcjIK/Xr10fXrl0BZMyNNWjQIHz++ef5Dj5vbQR4UmYRBcqQ8AAZN8tsNiMiIgIODg7w9fUtUJl9/fr12L9/P5o2bYoqVarg+vXrub4Jr127hujoaAQFBcHV1fWR2zk7O8PDwwPx8fE5ZqD81KGUNPkd7Wyd3SCvMZqLkipVqmD27Nnw9vbOUtRTqVR4//33ERQUhI0bNyIoKAh6vR7ffvutzaPLjbt372LPnj0ICAhAjx49csxrKpUK3t7eIPmQR2w0GhEbGwu9Xp/FK7HOeOHn5wcASE5Oxv79+/PVOkXSJviK8BQj5P8HFi/IrIlxcXFYtGgR9Ho9pk2bhmbNmiEmJgbXr1/P8ZiXL1+G2WxGjRo1cixSWINqOTo6ws7OLtttrFHj8lvfUFKkpKTkS3gcHR0hSRISEhKK2bKHUalU6NWrF/bs2YM+ffpkqX+pUKECRo0ahZSUFOzfvx/dunXDxYsXMWfOnDxd+9DQUERGRqJJkyZZwlw8ivj4eAghHqprSU5OhsFggLOzc5Y8K4RA7dq18fHHH0Oj0cDHxwdfffUV3Nzc8nUNrNNH58frL+uUSeFJTU21VdLmd9+tW7fi3LlzaNOmDVq2bIm6devCZDLhzJkzj3wLpqamYvv27Tm6s2TGLJxfffUVIiMj8fTTT2fbr0OIjLmdrKFSs0vn7t27iIuLe8geMmMmheKulM5uVtCccHNzgyRJiIuLK1a7HoUkSdlO3yuEwKuvvopnnnkGhw8fRlBQECpWrIgVK1bgwoULuXo9YWFhkGUZVatWzfWBTk5OxqVLl6BSqWxRL61Y+yvZ29s/9NKSJAmvvfYann32WURFReHQoUP59obze78eB8qk8BgMhnw3NZMZU71+/vnn0Ol0eP/996HT6dCsWTNotVrs3LnzoXm5rc2ga9euxf79+wFklMXNZnOWPgdJSUnYsmULunXrhoULF6JKlSoYPXp0tpXcVuGxxhz+N7dv30avXr0wePBgxMTE2DKhtaKzc+fOWLx4sS20Z+bIgkVFfj0yOzs7CCFszeplCWdnZ0yYMAE6nQ6bNm1C165dERsbi/nz5+dakRsdHQ2SefJ21Go1PDw8YDQasXHjxiyNFdb4yo/q5Ono6IhPP/0Ubm5umDVrFi5evJiv62jNZ9Z8+SRQ5oQHgC0OcF6bH63eyLBhwxAaGoqBAweijWVyvgYNGqBBgwY4cuQIdu/eneVBT0lJwdKlSzFu3Dio1Wo4Oztj8+bN6NWrF4YNG4bhw4fjjTfeQLt27dC3b1+cPXsWL7zwAjZu3Iinn34620xmdYllWc4241vDXv78889YtmxZlowUHR2NO3fuYOLEiTh8+DDi4+MxYsQIjBgxokjrV6yzTeQ1TWto2LS0tDKX8YUQePbZZ9GrVy9cunQJaWlpCAoKwrp167Bz584c7f13IPfskGUZt27dwpw5c3Dy5ElotVo0a9Ysi2djnfHBzs4u22K6EAJNmzbF4MGDERERgSVLluSrad3V1RWyLJeax1ksPKpnYUl+LD0cSWYEX2rTpg1dXV159uzZ7LtEZkKWZd69e5d9+vShSqVi586dGR0dneX/devWUa/XMyAggDNmzODWrVu5ePFiduzYkdZxYt999x3XrFnDqlWrUqvVUq1WU61WU6PR0NXVlS1btuSGDRtyHS+VmprK1q1b093dnRcvXszW3qtXr3LMmDEPRbMzm83cuHEjR48ezbi4OM6fP58qlYoajYbr1q0rkl6rsizzs88+oyRJ/OGHH/KUZmhoKN3c3NisWbMijzdTVJw/f54+Pj709vbmhAkTqNfrWadOnRyjTlrjCk2dOpUpKSlMT0+n0Wik0WhkfHw8jx49yvHjx7Ny5cqUJImenp785JNPHorvdOTIEdrb2/OVV17JMRDY9evX6efnR39//3zNN/8kjtUqddHhv4THGnFNpVJx06ZNOYZKkGWZp0+f5rPPPktJktisWbNsg2KlpaVx/PjxdHR0pBDC9tFqtWzZsiV37txpCyR17949Hjp0iFu2bOGWLVt44MABXr58mUlJSXm66bkJj9X2R8UJsoYBjY+PpyUWNQHw1VdfzTVsiPWa5DasYcmSJbYHLi/ndOPGjTIvPGazmVOnTqVKpWKvXr1sL6J58+Y98hx37txJrVZLd3d31qlThx07duSrr77Knj172qIeCiFYrlw5Dh48mKdOnco24NiePXuo0WhyjSllMpk4dOhQ2+SOeR3esWbNGqpUKo4bN04RnqL8ZBYeWZa5YsUKqlQqdu/enYmJiQ+dkDWQ1DfffEN/f3+q1Wq+9NJLDAsLe+TDbDAY+Ndff3HmzJkcOnQoJ06cyO3bt+c4/qYgpKSksHnz5vT09CxwbBZZlnn48GHa29uzVq1a9PHxYZUqVRgREZHjPvfv3+f06dO5f//+HM9p165d1Ol0fP311/MUv+ZxEB7rS+Ppp5+mvb09P/roIzo7O7N+/fqMiYnJdp/ExEQOHz6c1atXp5eXF52dnWlvb097e3t6eXmxbdu2nDVrFi9evEij0fjIa/rrr79SkiQOHTo0RzGRZZnHjx+nu7s7K1SowDNnzuQp7+3YsYNqtZqDBg1ShKcoP5mFh8wYCNisWTNqNBoOHTrUNhDUaDTyzp07/Oabb1i3bl2q1Wq6ublx8uTJtul5S5vExESbWNy9e7dAaZjNZk6aNImSJPHzzz9nhw4d6OTkxL///vuR+8iyzM2bN9POzo4dO3bMcYBqaGgovby8+PTTT+c6kJUkb968SS8vLzZq1KhYpsu1hjrJy/1LTEzkuXPnsh10KssyV61aRZ1OxyZNmrBFixbU6XT89ddfH/lCSk9PZ0xMDMPDw3n69Gn++eefPHz4MK9fv57nMCQ7duygSqXiwIEDcxVyk8nETz75hCqVii+88AITEhJyTf/w4cPUarV85ZVXykQezyuPnfDIssxjx46xZs2alCSJrVu35ocffsj+/fvbos05OTnxlVde4Z9//pnj26ikiY6Opr+/PwMDAxkfH1/gNJ566il6eHjw7NmzfOONN6jT6bhz584czzM+Pp4tW7akTqfj5s2bH7ltXFwc69atSz8/P966dStXe+7evUt/f3/Wrl37oZjHmbFGWly2bBkfPHiQ+4ny/wVzyJAhvHTpUq7FxP/973/08vLi3Llzs902MTGRXbt2pUqlYpMmTShJEocMGVJkM5hkR3h4OPv3788JEybkWnySZZnR0dFs3bo1NRoNlyxZkmvevXjxIh0cHNi2bdtCxyUvSR474SEzbtCFCxfYqVMn6vV6SpJErVZLHx8fvvXWWzx8+HCxho4oKNevX6enpyebNm1aoCDzsixz48aNVKvV7NGjBw0GA+fOnWvzfnJ7MDds2ECtVsuOHTs+0jtJTU1l+/bt6eLiwvPnz+dqU2RkJAMDA1m9evUsFff/JjY2lu3ataNarebSpUvzdK4XL15k5cqVCYBDhw7NUSCioqJYv359AuCnn376SC/m0KFDdHV1pVqtphCClSpV4s2bN3O1p6BYi/IJCQl5yo+yLHPv3r20t7dn27Ztc/Uib9++TV9fX9arV6/AL7PSoMyHxcgOIQRq1KiB9evX4++//0Z0dDScnZ1RrVo1+Pn5FTr0J1k8oQasc5M7OzvbmlbJjInq4uPj8cwzz+TYM9pkMuHnn38GAPTp0wdarRaNGjWCVqvF0aNHIctyjhPwdejQAbVq1cKJEydw48YNPPXUUw9tp9Fo4O7ujpSUlDyP3LdOqfwoZFnG0qVLcfDgQZhMJltflZyub3p6Oj7//HPcuHEDQMZsnWazOdvzI4kNGzbYJke8evVqttdCCIEmTZqgX79+WLhwIUgiPDwcN27cKLa56/MbDkQIgbp168LNzQ03b96E0WjMsRe5dfbc1NTUAg0yLYuUiX48JHHo0CHcuXPnoVjFzs7OaNOmDXr16oWOHTsiICCgwKJDZoyz+eeff7Bo0SKcPn06x4epIMTHxyMtLQ1ubm62h8JkMmHixIkIDg7G8uXLbQ9Pdty4cQP79+9HpUqV0KpVK5AZsXbt7Ozg5eWVa8ZzcnJC69atERcXh9OnT2e7jTV8g8lkQnR0dK7nZB2+Yp3i99+QREhISJaQEVYReRQk8ddff2Hz5s22Hr/WKZ+zIyUlBT/99BMkSYJer8eePXtw+fLlbO+fWq3GyJEjUalSJdvvsjTOiSSOHDmCBw8ewM/PL9ceydbZZNPT08vsMJz8UiaEx2AwoFevXnj77beLJbwjSURGRmLOnDno3LkzWrVqhQ8++ADr168vcuG5d+8eDAYDfH19bRlKpVKhevXqCAsLw6BBg7Bu3bps9zWbzVi6dCkiIyPRs2dPeHl5gSR2796NlJQUdO/ePdcHSJIkNGzYEEIInDlzJtttrKE6gYyAVbmh0+ng4OCA5OTkbO/P3bt38f777yM6Oho9e/a0bZtTJzmDwYBvv/0WaWlp6NevH/R6/SPvBUn88ccfOHnyJBo1aoTBgwcjKioK06dPz3agrhAClStXxosvvggAKF++PCpXrpzreZYE1hfJF198AZIYNmxYrmPm1Go1tFotDAZDmYzxVBDKhPCkpKQgMjISgYGBjxx4WVDIjKEUr7/+OsaOHYuQkBBUrlwZH374IXr27FnkRa3Q0FAAsEWxAzIehBEjRqBevXq2AGUXL17M8mDKsoydO3di6dKl8PPzw8CBA20xpAMCAmA0Gm1p50a1atWg0Whw+fLlR2ZUa8CqvMxcoNFooNfrYTAYsnhc1mLMBx98gJMnT+KVV17BqFGjoNFochQeq5j+9ttvqFOnDl577TWo1WrEx8c/9EYnM8avTZs2DWazGe+//z5GjRqFevXqYd26dRg+fDhu3LhhOxaZMdZv69at2LRpEwDg/v37OHjwYJG/ZPKC2WxGcnIyrl27hj/++AOLFy9GcHAwTp48iRdffBFdu3bNNQ9mLmplN/7vcaRM1PEkJCRACIG2bdsWqRDIsoxjx45h0KBBuHjxIjp37oxJkyahdu3acHJyAlD0dTxRUVEQQsDb29u2zjqSunPnzjh16hR2796Njh07Yvjw4Rg4cCB0Oh1+/fVXDB8+HKmpqfj888+zTNFSt25dqNVqW9EwN5vLly8Pd3d33Lp1CykpKdkGkPL09ASZMdQktzRVKhVUKhVMJpNt3JjZbMaxY8cwatQohISEoGXLlpgxYwY0Gg0cHBwQHh6OtLS0h14kZEYkgPHjx8NsNmPMmDGoVasWXFxccOPGDSQnJ9v2IYmbN29i2LBhOH78OHr27ImXXnoJer0e3333HQYOHIjly5dj//796NmzJypWrIiIiAj89ddfOHr0KACgR48e2LFjB2bOnIk2bdrkOQZRbvz55584efIkgP8fxFmjRg1UrlwZZrMZKSkpuHjxIo4fP44TJ04gLCwMCQkJMBqNUKlUaNKkCaZNm5anCAHWOrnU1FTExsYiICCg0PaXOo+qdS7Jj52dHT08PHj58uUC16D/G7PZzK1bt9Lf358ajYaDBw9mdHR0sbaCybLMAQMGUKVSZTvd7NKlS20tLXq9niqVik8//TSbNGlCe3t7Ojg4cNasWQ/NNHrhwgVbC0hemoWtfYnKly/Pe/fuZbvNgQMHqFar2aVLl1z78siyzC5dulCn0/H06dO8du0ahw4dSnd3d2o0Gr722mu2qYVSU1P53HPP0cHBgYcOHcpyvWVZ5pkzZ9i4cWOqVCoOHTqUqampTEtLY7du3ajT6bhixQo+ePCAYWFhXLlypW1++LZt22YZZiDLMm/evMlhw4bRy8vL1hsdAO3t7dm4cWOuXLmS8fHxfOWVV6hSqThjxoxCz11vZfTo0Vl6wQshqFKpbJ0QNRoNJUkiAAohWKVKFfbu3ZsfffQRt2/fnq9JCmRZ5rBhw6hWq7lu3boisb8kKPPN6UIIBgUF5dhHJD/IssyjR4+yfPny1Gg0HDt2bJ46ahUWo9HIbt26Ua/X89SpUw/9Hx8fz86dO1OSJL700ku2Jm1nZ2c2a9aMa9asybaLQHh4OH18fFirVq08TZGbnp7Ohg0b0tXVlbdv3852mzt37rB+/frUarWcOnVqjmOMZFlmr169bMMRrGOXKleuzLlz52ZpRpZlmXPmzKEkSXz99deZnJxMs9nMuLg4bty4kUFBQVSr1ezfv7/t4ZNlmb/88gvt7e3p4uLCGjVqsFy5clSr1dTr9QwODs52zJW1U+n58+e5ePFizp49m8uXL+eRI0dsPdJlWWZISAi9vLzo7++fa1+hvPLHH39w7ty5nDdvHr/99lvOmTOHwcHBbNasGX19feng4ECVSmUTnkqVKrFbt2786KOPuHbtWh49epR37txhUlJSrjNwyHLGfPUAOHv27DLXheRRFKo5XQjhD2AFAG9kjBv6geQ8IYQ7gHUAAgCEAehNMlZk+LHzAHQFkALgTZJ/5+Z1OTg4FGn9zo4dO3D//n2oVCr89ttviI6ORqNGjdC6dWtUr169WGKbJCcnIzw8HK6urtkGe3JycsLUqVNx9uxZ7N+/H/Pnz8fChQthNpvh5+cHR0fHbIsBHh4eqF69Oo4dO4aTJ0+iXbt2ORYX+P+C/shtfHx8sHDhQvTu3RuzZs1Ceno6KlasCIPBAAcHB7i4uCAlJQWxsbG2FjKz2YyNGzfC29sbQ4cOxejRo+Hv75+le4AQAn369MGPP/6I9evXIy0tDZ6enjh+/DguXLhgq++aPHkynJ2dbTZ27doV06dPx7Jly5CQkAAfHx907twZvXv3xnPPPZdt3rDGqKlVqxZq1ar1yHO1hkmdPXs25syZg2+//bbQ3TFat26N1q1bZ1knyzJSU1ORkJCAe/fu4cqVKwgJCUFISAiuX7+OvXv32kbM29vbw8nJCX5+fqhSpQqqV6+OBg0aoEaNGvD19YWDg0OWyQw8PT2hUqkQGRmZp+J2medRimT9APAB0MCy7ATgCoCnAMwCMN6yfjyAmZblrgB2AhAAmgE4lodjsFGjRkU2Dsj6lmvbti2rVKlCT09PqlQqCiHo6enJL7/8Msc3fGGO6eTkxBYtWjyyU5jZbObq1avp6OjIwMDALPOA55T2kiVLqFar+eqrrz5UFPv3tjdu3GCFChVYuXJlRkZG5rjtzz//TB8fH3p6emYpNlhnBrUWXwBQkiS+8847vHDhQo5DHKxjzWrVqmVLx83Nje3ateO6deuYlpaWrfciyxlzxd+/f5+JiYmPHEibX2RZ5q1bt1i9enU6OTnxQKa524oT6zkZDAaGh4fzyJEjXLJkCUeNGsXnn3+eNWvWpJeXF3U6ne36urq6smbNmuzWrRs//PBDfv/99/ztt9+4cOFC6nQ6vvLKK4yLi2NKSgpTU1OZmprK5OTkIptltijJyeMRzGdNvxBiC4BvLZ82JO8JIXwA/E6yuhDie8vyT5btL1u3yyFNNmrUCIcOHSqy/hYkba0wERERuHTpEv78809bDJzff/8dtWvXLpJjARkVjKNGjcKCBQswbdo0TJgw4ZFvJaPRiJEjR2LhwoWYNm0aJk6cmOsbLDIyEu3bt0dYWBh+/vnnh+b0pqXC99y5c5g8eTJ+++03vPPOO/j2229z9O5MJhOuXr2KS5cu4eLFi7Czs0NCQgISExNhZ2cHT09PODo64n//+x9CQkLw+++/o1mzZrnaSxJ37tzBqVOnbM3bVapUsQUVK2lIYsmSJRgyZAjatWuHjRs32hoYStoOkjCZTDAYDLYYTJcuXUJISAj++ecfhIaG2vqDkbTdv/T0dDg7O9taf62epslkQu3atfH999/nOgNLSdKoUSOEhIRkf7MfpUjZfZBRrLoFwBlAXKb1wvobwHYALTP9tw9Ao1zSZcOGDYt15LMsy4yJiWGbNm2o1+t58ODBIkvbZDJx48aNdHZ2ZpUqVXj9+vVc97F6R61atcrT0ApZlrl48WJqtVoGBQVxzZo1jImJYXJyMkNDQ7l9+3bbPOYqlYpt2rTJkzeVF6x1PFqtlkePHi2SNEuDuLg4tm3bllqtlsuXL89X5e6ZM2c4f/78Ag/8zetxkpKSePfuXR49epRr1qzh1KlT+eqrr9LX19fmeSKTB2qNJ/XCCy8U63i0glAklcsAHAGcBNDD8jvuX//HMh/CA2AQgBDLh/Xq1SsW4bG6unv27GH79u2pUqnYoEGDIslA1srNtWvX0tPTkw4ODlyxYkWeMnRsbCyrVq1Kf3//HMNdZCY5OZmjRo2iXq+nRqNhjRo1WKdOHXp7e1OlUtkqfD///HNGRUUVmettMpnYsWNH6vV6njt3rkjSLA1kWebu3bvp6OjIWrVq8c6dO7leI1nOCDRXv359SpLE+fPnF3vLqFWAQkJC+Pnnn7Np06a0t7cnAKrVatatW5fvvvsuly1bxmPHjvHatWu8d+/eY1XUyqvoaADsAjAq07rLAHz4//VAly3L3wN4LbvtHvURQrBGjRp5CtGQV8xmM2/fvs3du3ezX79+dHJyolarZdu2bXnq1KlC3yTrKOOpU6fSxcWFTk5OXLBgQZ7rjgwGA9u3b09nZ+dsW8Aedcy0tDRu27aNzz//PL29vVmuXDnWr1+f/fv357Jly3jr1q0iazK2kpCQwAYNGtDb27tYB1vmFVmWmZKSwtjY2Hyfq8Fg4JtvvklJktivX79cWwlNJhM//vhjW9P4G2+8UWyehclk4pUrVzh9+nQ2a9aMjo6OlCSJzs7ObNKkCZ2cnBgQEMDbt2/nOYxIaVIo4bEUo1YAmPuv9bORtXJ5lmW5G7JWLh/P7RgajYYVKlTgnTt3iuykExIS2K5dO9rZ2VGlUrFOnTpcvnw54+LiCnXDZFlmQkICf/rpJzZp0oRqtZp+fn5csWJFvkIWGI1G9u3bl3q9nr///nu+bUhNTWV4eDjDw8MZHx9frBkxLCyM3t7erFu3boFHR1s9z6IQ/L/++otdunRh7dq1eezYsXzv/+OPP1KlUlGlUnHv3r052nT58mX6+fnZmsa7d+9e5KEpTCYTL1y4wGHDhtHX19cW9uWZZ57htGnTeOLECd6+fZvVqlVjlSpVGBUVVaTHLy4KKzwtLWXKswBOWz5dAXhYilFXAewF4M7/F6oFAK4DOJdb/Q5JODs7087OLtfIefnBbDZz8+bNDA4O5nfffcf79+/nGhY0J6zu79atW9m2bVtqNBra2dmxR48ePHHiRL7fvEajkX369LGdd0E4d+4cT58+neewrAVBlmVu376dGo2G/fr1K5A3ZfUO33zzTW7evLnAD64sywwNDWX16tVt/WO+/vrrfKWRlpbGF154wVZP0rdvX6ampma7rdls5oQJEyiEYIcOHWhvb89OnToVWYuotRj36aef0sfHh0IIVqxYkaNHj2ZISAiTk5NteTY2Npa1a9dmxYoVi7WeqSgp8x0IK1asSEmSOGDAgCJ9m+Qnst2jMBqNDAsL448//sjnnnuOdnZ21Ol0fO6557h9+/YCNWPKssyTJ0/S19eXPj4+eaqMzi6NSZMm0dnZOcfY1IXFbDZzzJgxlCSJ33//fYGOYzQaOXnyZKpUKvr7+zMsLKxAtphMJo4dO5aSJNkqW2fMmJHn/WVZ5oEDB+jo6GgTHnt7e27fvj3b5v3Q0FAGBgbSy8uLn3/+OdVqNbt27Vpo4bF2G1i2bJmty0H58uU5efJkhoaGZptnExIS2LhxY/r4+BT4+pU0ZV546tSpw8qVK9Pb25v//PNPqZddrYJ19uxZvvfee6xQoQLVajV1Oh1btWrFtWvX5jnoU3ZpX7lyhY0bN6ZarebHH39cILE1Go3s378/dTod9+zZU2zXLDk5mY0bN6arq2uBKpatFbpubm4EwHbt2hW4Lu/atWv08/Ojr68vp0yZQgCcMGFCnvdPTU1lz549baJjHfLRsmXLh+p60tLS+Pbbb9siGG7dupVarZb9+/cvcB2PtWV1zZo1bN26NbVaLZ2cnPjGG2/wzJkzOaabkpLCNm3a0M3NjRcuXCjQ8UuaMi88DRs25OTJkylJEt97770cO8gVJ7KcMfvDtWvXOHr0aHp7e9vc37fffpvbt28vsOBY0z99+jQbN25MSZLYv3//AqeXlJTERo0a0d3dnZcuXSqQPXkhNjaWQ4cO5bvvvptt4P2csDZDW4tGADhlypQCna+1klcIwZEjR3LNmjUEwLFjx+bZll27dtHBwYF6vZ5CCHbv3p2dO3emWq3mt99+a7PLbDZz+fLl1Ov1rFWrFm/evMmTJ0/SycmJXbp0ybfHI8syo6KiuGjRIjZu3NhWTG/fvj337NmTp7ovs9nMvn37Uq1W88CBA/k6fmnxWAjPzZs3Wb16ddrb23PRokUlGlvWWln7999/c8yYMfT396ckSaxYsSKnTJnCmzdvFrrIZjab+dtvvzEwMJBqtZqvv/56oQatXrp0ie7u7mzcuHG+BSE/WL2//J6/LMs8ceIEn376aapUKrZu3Zo6nY7t27fPd0hYWc6IwV2uXDmWL1+e586d46JFiyiE4BdffJGnNKKiotiiRQtqtVqOHj3aVnm7Z88eurm52XqRWz00b29vuri42IphoaGhLFeuHKtWrcrw8PA82x0XF8eVK1eyfv36VKlUdHBw4PPPP89ff/01X3Vz1vFatWvX5vr16/O0T2nzWAiPLMvcsmULPT096eTkxK+++qpAMYvzivWBCg0N5dy5c9m+fXu6urraytujRo3i1atXC900Lcsyk5OTOW/ePHp5edHOzo7jxo3LdVqd3OqnDh48SI1Gw759+5apjmPWpu5Vq1axcuXK1Gg0fOedd3jr1i22aNGCer0+2zqVnIiJiWGHDh2oVqs5a9YsmkwmTpo0iUII/u9//8t1f5PJZJtzq3v37rx37x6bN29Oe3t7btu2je+//z4lSeKIESO4e/duVqpUiXZ2dvzqq69sL0CDwcC33nrLNsD3+PHjj/RUrC2f69evZ/PmzanVaung4MBXXnmFf/zxR4GHN1jjOpdWiSC/PBbCQ2ZkkHXr1tHb25s6nY4DBw4s0lAW1qLUgwcPeODAAQ4dOpQVKlSgJEm0s7Nj/fr1+dlnn/Hy5ctF0jwtyzKvXbvGfv36UavV0tPTk9988022Y5X+vV9MTAw/+eQTXrhwIdtt//zzT1tIirIiPGazmRcvXmTfvn2p0+no6OjIiRMnMjExkbIsc82aNbZJFPPaLG80Gjlx4kSqVCp26tTJ1h1i7NixFELwp59+ytWmX3/9lW5ubvT19bX14Vq5ciU1Gg3btGnDw4cPs2LFirS3t6enpyd1Oh2nTJmSpbXLei+bN29OSZLo5ubGHj16cOHChdy3bx9PnjzJ48eP85dffuGECRPYtGlT2tnZUa/Xs0uXLty5cydTU1NLvf6yJHlshIfMyCgHDhxg3bp1qVKp+Oqrr9qawnPD2vRo9RZSU1MZHx/P27dv8+jRo1y+fDnfffdd1qxZkzqdjpIk0d/fnx988AH//PNPW6YuCsFJSEjg4sWLbdPxNGrUiH/88UeeREKWZf7www/UarVs2LAhr169+tA2Fy5coJubG1u0aFGsRa28YG2l+fbbb1mpUiVKksSGDRvy119/zRLyISkpydYymFv/GfL/RcPFxYUBAQFZJsCbMWMGhRBcuHDhI21KS0vjhg0b6OfnR71ez8WLF9s82Pj4eHbq1ImSJLF+/fr09vYmAGo0Gk6dOjVbkZDljIkkZ8+ezVq1alGj0dji8Gg0GqrVatugWGdnZ7Zt25abNm0qkwM4S4LHapYJSZLw7LPPYsOGDXjrrbewYcMG3Lx5E8HBwahUqRJcXV3h5eUFFxcXqNVqpKSkIDo6GhEREYiIiMDdu3dx6dIl3L9/H8nJyUhKSsLdu3eRkJCA9PR0CCHg7u6O9u3bo2vXrujatasttENRDF40mUw4fvw4pk6digMHDkCv12PYsGEYM2YMfHx88nyMV155BUeOHMHff/+dbYD3ChUqIDAwEKdPn8bu3bvx8ssvl/jgSzJjsOPhw4cxbdo0/Pnnn3BycsKYMWMwYsQIeHt7Z7HJ3t4eL7/8Mg4cOIADBw6gXbt2OaZ98OBBDB48GAaDAWPHjkXt2rVt6VmjKsbExID8/zARZMbg4NOnT2POnDm2MBQfffQR+vfvbxtY6eTkhIULF2LQoEE4duwYVCoV3NzckJycjKCgIOh0uoeupzVW9ahRo/Dmm2/ixIkTOHHihG1QpyRJKF++PJ5++mk0adIE1apVg16vf/xDWBQHj1Kkkvw8al6t0NBQ9uzZ0xY2QK1W097enh4eHvTx8WGFChVYrlw5Ojs7U6PR2DqVWQNIWd3rOnXq8IUXXuC4ceO4fv16Xrt2rUjn5LKO2Tpz5gw/+OADuru7U61Ws23btty3b1+B+n1YvQhrhWd2/69bt4729vYMCAjg0aNHS+ytaj3fkJAQDhgwgC4uLtRoNOzYsaNtgsVHcfz4cer1ej733HPZdtyzeirr1q1jpUqVqNPpOG3atIe23bt3L3U6HQMDA/nhhx9y3rx5/OabbzhhwgR26NCBLi4uVKlUrFevHteuXZttvYi18vf06dO8dOkS169fT3t7ezZo0CDHcCLZpVNUITyeJB6rolZmrBWzu3bt4kcffcTBgwezZ8+ebNasma37eJ06ddihQwcOGDCAkydP5oIFC7hz506ePXuWV65c4a1bt5iQkGCrsynKzGFtDTt8+DAHDBhADw8PW7S5efPmFXp4Rm4YDAZOnjyZGo2GderUyfNc3AXFKgqnT5/m+++/T3d3d0qSxOrVq/P777/PU9eA2NhYNmrUiA4ODvzll19sD6xVBI4fP84333yTer2eDg4O/Pjjj7Mt9iQnJ3PQoEF0dHTMEkcIlk6BderU4ddff52vwbKpqans3bt3kYdJ/a9SpPF4ioNGjRoxJCQkx22sBpvNZlvQcbPZDLVaDZVKBbVaXWTFpdzskGUZkZGR2Lt3L1atWoW//voLKSkpqFKlCt544w3069cPlSpVynHivqKyJSUlBaNGjcKyZcsQGBiI2bNno3PnzjnOUZXfY5jNZkRHR+OPP/7AypUrceTIEcTHxyMgIACDBw9G37594ePjk6fzJYmffvoJb7/9NpycnNChQwdotVrExcXh+vXruH79OlJTU1G7dm1MmTIF3bp1g0ajeehcyIzZJP755x9cvXoV9+/fhyzL8PT0RM2aNVGtWjW4uLjk6x6QxKlTp9C5c2fodDrs2bMHNWrUyPc1U8igyOLxFNfnUR5PWcHqSsfGxnL37t187733WKVKFVtv5iZNmnD+/PkMDw8vlbdkfHw8R4wYQb1eTycnJw4cOJC///47IyIiClSktFbOR0ZGcuvWrQwODrY1jWu1WtarV49Tp05laGhogc43LS2NX331Ff38/KjRaGzpenl5sU2bNvz88895+/btUim6mEwmjh8/nkIIvvPOO2Wy6bqoPffi4rEtapU2siwzPT2d586d48cff8y6devaWsN8fX35+uuvc+fOnVkCixf18fOSrrUItGrVKlarVo1CCNrZ2TEgIIBdunThwoULefv27WzrITIfw2g08v79+9yzZw+HDBnCoKAgajQa2xirPn36cMuWLYyNjS30+ZrNZt64cYO7d+/m3r17eezYMd64ccPW1aA0H6zbt2+zevXqdHZ2LtKBy4/CWp+Xl06zRqORmzdv5q1bt8q8+DwRRa2ShCRiY2Pxxx9/YPXq1Thw4ADi4uLg6uqKpk2bokePHmjfvj0qVKhgm3SvOGwICwvDmjVr8N5778HNzS1P4UbDw8OxdetW7Ny5ExcvXrTNbFqpUiV06dIFjRo1gre3t62YZjQaERkZievXr+Py5cu4du0a7ty5A7PZjHLlyuGZZ55Bjx490KpVK3h7exdZEa4sQxJLly7FkCFD8Oyzz2LTpk3FFiaVzJg7bNCgQejSpQuGDh36yDnYSWLnzp3o27cvGjZsiOXLl8Pf37/M3g+lqJUHrN7NpUuX+Nlnn7Fu3brUarXUaDSsVasWP/nkE549e7bEOoEZjUa+9957tqlg8hMHx+oBRUVFcf/+/XznnXdYvnx5W8B7rVZLrVZr+21tCbS3t2flypXZvXt3/vDDDwwNDS1065/ZbH4sK2nj4+PZrl07arVaLlu2rNjuuSzL/PPPP1m+fHk6ODjw559/znHbe/fu8YUXXqC7uzt37NhRpr0exePJAZIwGo04efIkvv/+e+zcuRNRUVFwcXFB69at0a9fP7Rr1w4eHh4Ain7m0Zzsunz5MoKDg+Hq6orVq1fD09OzQOnIsowbN27gxIkTuHDhgm22UycnJ2g0Gnh6eqJixYoIDAyEj48PPDw8isSTMxqNWLp0KTQaDYKDg4tlSqHigiT27duHHj16oEKFCti7dy98fX2L5ViyLGP79u1Ys2YNvvzyS1SoUCFHuyIiInDu3Dm0a9euTAV3/zeKx5MN1qb6ffv28ZVXXqGzszNVKhWDgoI4btw4njp1KtehDSVh482bN7OdzK6g6Vkrjq2eSHHWp1y5coUBAQF0cnLi0qVLHzvPx2AwcMCAAZQkiZMnTy7WoSlmsznP3nRp14HlFaVyORPWcVAbNmxgp06d6ODgYAugPX/+fN65c+exe0DKKtaxd4GBgVy1atVjeV3/+ecf+vr60tvbu0hidf+XUIQnE3fu3OEzzzxja8Zt1qwZFy9ebOtopmSsosVkMjEsLKxEw5wUJWazmZ999hklSeJrr71WrFMwPWnkJDzF28OtDOLm5gYvLy906tQJa9euxa5duzBw4EB4enpCCFFmWwgeV1QqFSpVqvRY1e9kRpIkvPPOO6hbty62bNmCPXv2ZLyxFQrF45kbCoGdnR2WLFkCBweHbAcCKij8Gy8vL4wbNw5vvfUWpk+fjmeeeQZubm6lbdZjzX/O4xFCwMPDo9Sm0lV4/BBC4IUXXkCHDh1w7NgxrFq1CrIsl7ZZjzX/OeFRUCgI9vb2GD9+PJycnDB37lyEhYUpRa5CoAiPgkIeEEKgcePGeOONNxAWFob58+fDbDaXtlmPLYrwKCjkEY1Gg+HDh6NSpUpYuXIlQkJCFK+ngCjCo6CQDypXrozhw4cjISEB06dPR2pqammb9FiiCI+CQj4QQqB///5o0qQJdu3ahW3btileTwFQhEdBIZ+4ublh/PjxkCQJM2fORFRUVGmb9NihCI+CQj4RQqBDhw548cUXcebMGSxdulRpXs8nivAoKBQAnU6HcePGwcPDAwsXLsSVK1eUIlc+yFV4hBB2QojjQogzQoh/hBCfWtZXFkIcE0JcE0KsE0JoLet1lt/XLP8HFPM5KCiUOEII1K1bF2+//Tbu3buHr776KttpiBSyJy8ejwFAO5JPA6gHoLMQohmAmQC+JlkVQCyAgZbtBwKItaz/2rKdgsIThyRJGDJkCKpVq4Z169bh0KFDiteTR3IVHstA0yTLT43lQwDtAGy0rP8RwEuW5e6W37D8/5xQxiYoPIEIIeDn54cPP/wQaWlp+OKLL5CYmFjaZj0W5KmORwihEkKcBhAJYA+A6wDiSJosm4QD8LMs+wG4DQCW/+MBeGST5iAhRIgQIkRpFVB4XBFCoFevXmjdujUOHjyIDRs2KF5PHsiT8JA0k6wHoAKAJgAKPdkQyR9INiLZyMvLq7DJKSiUGk5OTpgwYQLs7Ozw1Vdf4e7du4r45EK+WrVIxgE4AKA5AFchhDWsRgUAdyzLdwD4A4DlfxcAD4rCWAWFsogQAq1atULv3r1x+fJlLFy4UGlez4W8tGp5CSFcLct6AB0AXESGAPWybBYMYItleavlNyz/76ci/wpPOBqNBqNHj4avry+WLFmCc+fOKV5PDuTF4/EBcEAIcRbACQB7SG4HMA7AKCHENWTU4Sy1bL8UgIdl/SgA44vebAWFsoUQAtWrV8eQIUPw4MEDzJw5EwaDobTNKrPkGoGQ5FkA9bNZH4qM+p5/r08D8EqRWKeg8BghhMDAgQOxYcMGW5jU559/Xgk4lw1Kz2UFhSJCCGELkyrLMmbMmIHY2NjSNqtMogiPgkIRYg2T2qlTJxw/fhyrVq1S6nqyQREeBYUiRq/X28Kkzps3TwmTmg2K8CgoFDHWMKn9+/dHWFgY5s6dq4RJ/ReK8CgoFAMqlQrDhg1DQEAAVqxYgePHjyteTyYU4VFQKAaEEKhcuTJGjBiBxMREzJgxAykpKYr4WFCER0GhmJAkCa+//jqaNm2K3bt3Y9u2baVtUplBER4FhWLE1dUVEyZMgEqlwqxZs5QwqRYU4VFQKEYyh0k9e/asEibVgiI8CgrFjFarxdixY+Hp6YkFCxbg8uXLpW1SqaMIj4JCMfPvMKlffvkl0tPTS9usUkURHgWFEkClUuG9995DtWrVsGHDBhw8ePA/3cKlCI+CQgnh6+uLsWPHIi0tDdOnT0dSUlLuOz2hKMKjoFBCWMOkPvvss/jzzz+xfv36/6zXowiPgkIJ4ujoiAkTJkCv1+PLL7/E3bt3S9ukUkERHgWFEkQIgZYtW6J37964cuUKFixY8J8cx6UIj4JCCaPVajFq1Cj4+vpi6dKlOHPmzH+uyKUIj4JCKWANkxodHY1Zs2b958KkKsKjoFAKSJKEt99+G/Xq1cO2bduwe/fu/5TXowiPgkIp4enp+Z8Nk6oIj4JCKSGEwPPPP28Lk7py5cr/zDguRXgUFEoRa5hUZ2dnW5jU/wKK8CgolCJCCDRq1AjBwcEICwvDvHnzYDQaS9usYkcRHgWFUkatVmPYsGGoXLkyVq5c+Z8Ik6oIj4JCGSAgIAAjR460hUlNTU0tbZOKFUV4FBTKAEIIW5jUPXv2YMuWLU+016MIj4JCGcHFxQXjx4+HWq3GrFmzEBkZWdomFRuK8CgolBGEEGjfvv1/IkxqnoVHCKESQpwSQmy3/K4shDgmhLgmhFgnhNBa1ussv69Z/g8oJtsVFJ44dDodxo0bB09PTyxcuBCXLl16Iotc+fF4hgO4mOn3TABfk6wKIBbAQMv6gQBiLeu/tmynoKCQB4QQqF27NgYNGmQLk/okNq/nSXiEEBUAdAOwxPJbAGgHYKNlkx8BvGRZ7m75Dcv/z1m2V1BQyAPWMKk1atR4YsOk5tXjmQtgLABrgdMDQBxJk+V3OAA/y7IfgNsAYPk/3rJ9FoQQg4QQIUKIEGWuIQWFrPj4+ODDDz9Eeno6pk+fjsTExNI2qUjJVXiEEM8DiCR5sigPTPIHko1INvLy8irKpBUUHnuEEOjZs+cTGyY1Lx7PMwBeFEKEAViLjCLWPACuQgi1ZZsKAO5Ylu8A8AcAy/8uAB4Uoc0KCv8JnJycMGHCBNjZ2eGrr75CeHj4EyM+uQoPyQkkK5AMANAHwH6S/QAcANDLslkwgC2W5a2W37D8v59PytVSUChBhBB45pln8Oqrr+LKlStYuHDhE9O8Xph+POMAjBJCXENGHc5Sy/qlADws60cBGF84ExUU/rtoNBqMHj36iQuTmi/hIfk7yecty6Ekm5CsSvIVkgbL+jTL76qW/0OLw3AFhf8CQghUq1YNQ4cORUxMDGbOnPlEhElVei4rKJRxhBAYOHCgLUzqrl27HnuvRxEeBYUyjhDioTCpMTExpW1WoVCER0HhMUAIgW7duqFz584ICQnBqlWrHmuvRxEeBYXHBL1ejwkTJtjCpIaGhj624qMIj4LCY4IQAg0bNsQbb7yBmzdv4uuvv35sZyFVhEdB4TFCpVJh2LBhqFKlClavXo1jx449ll6PIjwKCo8RQggEBARg+PDhSEpKwvTp05GSklLaZuUbRXgUFB4zhBDo16+fLUzq1q1bHzuvRxEeBYXHEFdXV0ycOPGxDZOqCI+CwmOIEALPPfccXnrpJZw7dw6LFy9+rMZxKcKjoPCYotVqMXbsWHh6emLRokWPVZhURXgUFB5TrGFS33nnHdy/fx9ffvkl0tPTS9usPKEIj4LCY4wkSRg8eDBq1qz5WIVJVYRHQeExRggBX19fjBkzBgaDwRYmtayLjyI8CgqPOUII9OjRA23atMGhQ4ewdu3a0jYpVxThUVB4AnB0dMSECROg1+sxZ84chIeHl7ZJOaIIj4LCE4AQAi1btsSrr76Kq1evYsGCBWV6HJciPAoKTwhqtRqjRo2Cn58fli5ditOnT5e2SY9EER4FhScEa5jU999/H7GxsZg5cyZSU1NL26xsUYRHQeEJQpIkDBgwAPXr18f27dvLbJhURXgUFJ4wPDw8MG7cOJDEjBkzEBsbW9omPYQiPAoKTxiZw6SePHkSK1asKHPjuBThUVB4ArGzs8P48ePh4uJSJsOkKsKjoPAEYg2TGhwcjJs3b2Lu3LkwmUylbZYNRXgUFJ5Q1Go1PvjgA1uY1KNHj5YZr0cRHgWFJ5iKFSti1KhRSEpKwowZM8pMmFRFeBQUnmAkSULfvn3RrFkz7Nu3D1u2bCkTXo8iPAoKTzguLi6YMGEC1Go1Zs6ciYiIiNI2KW/CI4QIE0KcE0KcFkKEWNa5CyH2CCGuWr7dLOuFEGK+EOKaEOKsEKJBcZ6AgoJCzljDpL788ss4f/48lixZUurjuPLj8bQlWY9kI8vv8QD2kQwCsM/yGwC6AAiyfAYBWFRUxiooKBQMrVaLMWPGwMvLCwsXLiz1MKmFKWp1B/CjZflHAC9lWr+CGRwF4CqE8CnEcRQUFAqJEAK1atXCu+++i4iICMyePRtGo7HU7Mmr8BDAbiHESSHEIMs6b5L3LMv3AXhblv0A3M60b7hlXRaEEIOEECFCiJCoqKgCmK6goJAfVCoVBg8ejKeeegqbNm3C77//XmpeT16FpyXJBsgoRg0VQrTO/CczrM/XGZD8gWQjko28vLzys6uCgkIBKV++vC1M6hdffIGEhIRSsSNPwkPyjuU7EsAvAJoAiLAWoSzf1hnF7gDwz7R7Bcs6BQWFUsYaJrVt27Y4fPgw1q1bVypeT67CI4RwEEI4WZcBdARwHsBWAMGWzYIBbLEsbwXwhqV1qxmA+ExFMgUFhVLGwcEB48ePh729PebMmYPbt2/nvlMRkxePxxvAISHEGQDHAfxK8jcAMwB0EEJcBdDe8hsAdgAIBXANwGIAQ4rcagUFhQIjhECLFi3Qp0+fUguTKspCL8ZGjRoxJCSktM1QUPjPQBJXr15F+/btkZaWhh07dqBhw4YQQhTZMRo1aoSQkJBsE1R6Liso/AcRQqBq1ar44IMPEBMTg5kzZ8JgMJTY8RXhUVD4jyJJEt566y3Ur18fv/76a4mGSVWER0HhP0zmMKnTp09HTExMiRxXER4Fhf8wQgh07doVXbp0QUhISImFSVWER0HhP45er7eFSZ0/fz6uX79e7EUuRXgUFP7jCCHQoEEDvPnmm7h16xa+/vrrYm9eV4RHQUEBKpUKH3zwAQIDA/HTTz/h5MmTxXo8dbGmrqCg8FgghEClSpUwduxYHDx4EMU9flIRHgUFBRvBwcEIDg6GWl280qAIj4KCAoAMr0ej0ZTIsZQ6HgUFhRJHER4FBYUSRxEeBQWFEkcRHgUFhRJHER4FBYUSRxEeBQWFEkcRHgUFhRJHER4FBYUSRxEeBQWFEkcRHgUFhRJHER4FBYUSRxEeBQWFEkcRHgUFhRJHER4FBYUSRxEeBQWFEkcRHgUFhRKnTExhLIRIBHC5tO34F54AokvbiH9RFm0CyqZdik15ozhtqkQy2xiqZSUC4WWSjUrbiMwIIUIUm/JGWbRLsSlvlJZNSlFLQUGhxFGER0FBocQpK8LzQ2kbkA2KTXmnLNql2JQ3SsWmMlG5rKCg8N+irHg8CgoK/yFKXXiEEJ2FEJeFENeEEONL8LjLhBCRQojzmda5CyH2CCGuWr7dLOuFEGK+xcazQogGxWSTvxDigBDighDiHyHE8NK2SwhhJ4Q4LoQ4Y7HpU8v6ykKIY5ZjrxNCaC3rdZbf1yz/BxS1TZlsUwkhTgkhtpcFm4QQYUKIc0KI00KIEMu60s5TrkKIjUKIS0KIi0KI5qVtEwCAZKl9AKgAXAdQBYAWwBkAT5XQsVsDaADgfKZ1swCMtyyPBzDTstwVwE4AAkAzAMeKySYfAA0sy04ArgB4qjTtsqTtaFnWADhmOdZ6AH0s678D8J5leQiA7yzLfQCsK8Z7OArAGgDbLb9L1SYAYQA8/7WutPPUjwDetixrAbiWtk0kS114mgPYlen3BAATSvD4Af8SnssAfCzLPsjoXwQA3wN4Lbvtitm+LQA6lBW7ANgD+BtAU2R0OlP/+z4C2AWguWVZbdlOFIMtFQDsA9AOwHbLw1LaNmUnPKV27wC4ALjx73MtC/mptItafgBuZ/odbllXWniTvGdZvg/A27Jc4nZaigP1keFhlKpdliLNaQCRAPYgw0uNI2nK5rg2myz/xwPwKGqbAMwFMBaAbPntUQZsIoDdQoiTQohBlnWlee8qA4gCsNxSJF0ihHAoZZsAlIE6nrIKMyS/VJr8hBCOAH4GMIJkQmnbRdJMsh4yvIwmAGqU5PH/jRDieQCRJE+Wph3Z0JJkAwBdAAwVQrTO/Gcp3Ds1MqoTFpGsDyAZGUWr0rQJQOkLzx0A/pl+V7CsKy0ihBA+AGD5jrSsLzE7hRAaZIjOapKbyopdAEAyDsABZBRjXIUQ1iE3mY9rs8nyvwuAB0VsyjMAXhRChAFYi4zi1rxStgkk71i+IwH8ggyRLs17Fw4gnOQxy++NyBCiUs9PpS08JwAEWVojtMio+NtaivZsBRBsWQ5GRh2Ldf0bllr/ZgDiM7mqRYYQQgBYCuAiyTllwS4hhJcQwtWyrEdGndNFZAhQr0fYZLW1F4D9lrdqkUFyAskKJAOQkWf2k+xXmjYJIRyEEE7WZQAdAZxHKd47kvcB3BZCVLeseg7AhdK0KbNxpfpBRk36FWTUG0wqweP+BOAeACMy3gwDkVHu3wfgKoC9ANwt2woACyw2ngPQqJhsaokMt/csgNOWT9fStAtAXQCnLDadB/CxZX0VAMcBXAOwAYDOst7O8vua5f8qxXwf2+D/W7VKzSbLsc9YPv9Y83IZyFP1AIRY7t9mAG6lbRNJpeeygoJCyVPaRS0FBYX/IIrwKCgolDiK8CgoKJQ4ivAoKCiUOIrwKCgolDiK8CgoKJQ4ivAoKCiUOIrwKCgolDj/B2bmPHFOgWF0AAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for f in npy_files:\\n\",\n    \"    drawing = json.load(open(f))\\n\",\n    \"    data1, flag = convert_3d(drawing, return_flag=True, threshold=50)\\n\",\n    \"    if flag:\\n\",\n    \"        print(f)\\n\",\n    \"        data2, flag = convert_3d(drawing, return_flag=True, threshold=10000)\\n\",\n    \"        plt.imshow(draw_strokes(data1, stroke_width = 5))\\n\",\n    \"        plt.show()\\n\",\n    \"        \\n\",\n    \"        plt.imshow(draw_strokes(data2, stroke_width = 5))\\n\",\n    \"        plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"server/larger_data/1فسبحان الله حين تمسون وحين تصبحون.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<PIL.PngImagePlugin.PngImageFile image mode=RGB size=305x293 at 0x7FAD53C9A278>\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import glob\\n\",\n    \"from IPython.display import SVG, display\\n\",\n    \"import svgwrite\\n\",\n    \"print(file)\\n\",\n    \"num_sketches = 1 \\n\",\n    \"\\n\",\n    \"drawing = json.load(open(file))\\n\",\n    \"draw_strokes(convert_3d(drawing), stroke_width = 5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def apply_rdb(drawing, verbose = 0):\\n\",\n    \"    new_drawing = []\\n\",\n    \"    total_prev_strokes = 0\\n\",\n    \"    total_post_strokes = 0\\n\",\n    \"    for item in drawing:\\n\",\n    \"        char = list(item.keys())[0]\\n\",\n    \"        stroke = item[char]\\n\",\n    \"        processed_stroke = []\\n\",\n    \"        if len(stroke):\\n\",\n    \"            if verbose:\\n\",\n    \"                print('processing ', char)\\n\",\n    \"            post_stroke = rdp(stroke, epsilon = 2.0)\\n\",\n    \"            total_post_strokes += len(post_stroke)\\n\",\n    \"            total_prev_strokes += len(stroke)\\n\",\n    \"        new_drawing.append({char:post_stroke})\\n\",\n    \"    if verbose:\\n\",\n    \"        print('reduced from ', total_prev_strokes, ' to ', total_post_strokes)\\n\",\n    \"    return new_drawing\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#https:#jrgraphix.net/r/Unicode/0600-06FF\\n\",\n    \"map_chars = {\\n\",\n    \"    \\\"\\\\u0623\\\":[\\\"\\\\u0621\\\", \\\"\\\\u0627\\\"], # أ\\n\",\n    \"    \\\"\\\\u0622\\\":[\\\"\\\\u0605\\\", \\\"\\\\u0627\\\"], # آ\\n\",\n    \"    \\\"\\\\u0625\\\":[\\\"\\\\u0627\\\", \\\"\\\\u0621\\\"], # إ\\n\",\n    \"    \\\"\\\\u0628\\\":[\\\"\\\\u066E\\\", \\\".\\\"], # ب\\n\",\n    \"    \\\"\\\\u062A\\\":[\\\".\\\", \\\".\\\", \\\"\\\\u066E\\\"], # ت\\n\",\n    \"    \\\"\\\\u062B\\\":[\\\".\\\", \\\".\\\", \\\".\\\", \\\"\\\\u066E\\\"], # ث \\n\",\n    \"    \\\"\\\\u062C\\\":[\\\"\\\\u062D\\\", \\\".\\\"], # ج\\n\",\n    \"    \\\"\\\\u062E\\\":[\\\".\\\", \\\"\\\\u062D\\\"], # خ\\n\",\n    \"    \\\"\\\\u0630\\\":[\\\".\\\", \\\"\\\\u062F\\\"], # ذ\\n\",\n    \"    \\\"\\\\u0632\\\":[\\\".\\\", \\\"\\\\u0631\\\"], # ز\\n\",\n    \"    \\\"\\\\u0634\\\":[\\\".\\\", \\\".\\\", \\\".\\\", \\\"\\\\u0633\\\"], # ش\\n\",\n    \"    \\\"\\\\u0636\\\":[\\\".\\\", \\\"\\\\u0635\\\"], # ض\\n\",\n    \"    \\\"\\\\u0637\\\":[\\\"\\\\u0627\\\", \\\"\\\\uFEBB\\\"], # ط\\n\",\n    \"    \\\"\\\\u0638\\\":[\\\".\\\", \\\"\\\\u0627\\\", \\\"\\\\uFEBB\\\"], # ظ\\n\",\n    \"    \\\"\\\\u063A\\\":[\\\".\\\", \\\"\\\\u0639\\\"], # غ\\n\",\n    \"    \\\"\\\\u0641\\\":[\\\".\\\", \\\"\\\\u066F\\\"], # ف\\n\",\n    \"    \\\"\\\\u0642\\\":[\\\".\\\", \\\".\\\", \\\"\\\\u066F\\\"], # ق\\n\",\n    \"    \\\"\\\\u06A4\\\":[\\\".\\\", \\\".\\\", \\\".\\\", \\\"\\\\u066F\\\"], # ڤ\\n\",\n    \"    \\\"\\\\u0643\\\":[\\\"\\\\u0621\\\", \\\"\\\\u0644\\\"], # ك\\n\",\n    \"    \\\"\\\\u0646\\\":[\\\".\\\", \\\"\\\\u06BA\\\"], # ن\\n\",\n    \"    \\\"\\\\u0624\\\":[\\\"\\\\u0621\\\", \\\"\\\\u0648\\\"], # ؤ\\n\",\n    \"    \\\"\\\\u064A\\\":[\\\"\\\\u0649\\\", \\\".\\\", \\\".\\\"], #ي\\n\",\n    \"    \\\"\\\\u0626\\\":[\\\"\\\\u0621\\\", \\\"\\\\u0649\\\"], #ئ\\n\",\n    \"    \\\"\\\\u0629\\\":[\\\".\\\", \\\".\\\", \\\"\\\\u0647\\\"], #ه\\n\",\n    \"}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def preprocess(text):\\n\",\n    \"    char_comps = []\\n\",\n    \"    \\n\",\n    \"    diacritics = \\\"[ًٌٍَُِّْ]\\\"\\n\",\n    \"    numbers = '0123456789'\\n\",\n    \"    for diac in diacritics: \\n\",\n    \"        text = text.replace(diac, '')\\n\",\n    \"\\n\",\n    \"    for num in numbers: \\n\",\n    \"        text = text.replace(num, '')\\n\",\n    \"    \\n\",\n    \"    outText = \\\"\\\"\\n\",\n    \"    \\n\",\n    \"    for i in range(len(text)):\\n\",\n    \"    \\n\",\n    \"        if (text[i] == \\\" \\\"):\\n\",\n    \"            continue\\n\",\n    \"    \\n\",\n    \"        if text[i] in map_chars:\\n\",\n    \"            if (i < len(text) - 1 and text[i] == \\\"\\\\u0643\\\"):\\n\",\n    \"                if text[i+1] != ' ':\\n\",\n    \"                    char_comps.append({text[i] : '\\\\uFEDB'})\\n\",\n    \"                else:\\n\",\n    \"                    char_comps.append({text[i] : map_chars[text[i]]})\\n\",\n    \"            else:\\n\",\n    \"                char_comps.append({text[i] : map_chars[text[i]]})\\n\",\n    \"        else:\\n\",\n    \"                char_comps.append({text[i] : text[i]})\\n\",\n    \"\\n\",\n    \"    return char_comps\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def concatenate(images, mode='h', margin=10):\\n\",\n    \"    widths, heights = zip(*(i.size for i in images))\\n\",\n    \"    if mode =='h':\\n\",\n    \"        total_width = sum(widths)\\n\",\n    \"        max_height = max(heights)\\n\",\n    \"\\n\",\n    \"        new_im = Image.new('RGB', (total_width, max_height), (255, 255, 255))\\n\",\n    \"\\n\",\n    \"        x_offset = 0\\n\",\n    \"        for im in images[::-1]:\\n\",\n    \"            new_im.paste(im, (x_offset,0))\\n\",\n    \"            x_offset += im.size[0]\\n\",\n    \"    elif mode == 'v':    \\n\",\n    \"        total_height = sum(heights)\\n\",\n    \"        max_width = max(widths)\\n\",\n    \"\\n\",\n    \"        new_im = Image.new('RGB', (max_width, total_height+margin*(len(images)-1)), (255, 255, 255))\\n\",\n    \"        draw = ImageDraw.Draw(new_im)\\n\",\n    \"        y_offset = 0\\n\",\n    \"        for im in images:\\n\",\n    \"            new_im.paste(im, (0,y_offset+margin))\\n\",\n    \"            y_offset += im.size[1]\\n\",\n    \"            draw.line((0,y_offset+margin-5, max_width,y_offset+margin-5), fill=(0, 0, 0), width=3)\\n\",\n    \"    return new_im\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def generate_words(file):\\n\",\n    \"    word_drawings = []\\n\",\n    \"    annot = file.split('/')[-1][:-5]\\n\",\n    \"    annot = re.sub('[0-9]', '', annot)\\n\",\n    \"    char_comps = preprocess(annot)\\n\",\n    \"    indices = [m.start() - i - 1 for i, m in enumerate(re.finditer(' ', annot))]\\n\",\n    \"    indices = indices + [len(annot) - len(indices)- 1]\\n\",\n    \"    drawing = json.load(open(file))\\n\",\n    \"#     new_drawing = apply_rdb(drawing, verbose = 0)\\n\",\n    \"    i, j  = 0, 0\\n\",\n    \"    c = 0\\n\",\n    \"    word = \\\"\\\"\\n\",\n    \"    for cntr, comp in enumerate(char_comps):\\n\",\n    \"        char = list(comp.keys())[0]\\n\",\n    \"        j += len(comp[char])\\n\",\n    \"        word += char\\n\",\n    \"        if cntr == indices[c]:\\n\",\n    \"            word_drawings.append({word:drawing[i:i+j]})\\n\",\n    \"            i = i+j\\n\",\n    \"            j = 0 \\n\",\n    \"            c += 1\\n\",\n    \"            word = \\\"\\\"\\n\",\n    \"    return word_drawings\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<PIL.Image.Image image mode=RGB size=1536x256 at 0x7FAD53D1EFD0>\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import re\\n\",\n    \"word_drawings = generate_words(file)\\n\",\n    \"images = []\\n\",\n    \"for i, comp in enumerate(word_drawings):\\n\",\n    \"    word, drawing = list(comp.items())[0]\\n\",\n    \"    images.append(draw_strokes(convert_3d(drawing), square = True, stroke_width = 2))\\n\",\n    \"concatenate(images)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def generate_characters(file):\\n\",\n    \"    char_drawings = []\\n\",\n    \"    annot = file.split('/')[-1][:-5]\\n\",\n    \"    char_comps = preprocess(annot)\\n\",\n    \"    drawing = json.load(open(file))\\n\",\n    \"    new_drawing = apply_rdb(drawing, verbose = 0)\\n\",\n    \"    i = 0 \\n\",\n    \"    for comp in char_comps:\\n\",\n    \"        char = list(comp.keys())[0]\\n\",\n    \"        j = i + len(comp[char])\\n\",\n    \"        char_drawings.append({char:new_drawing[i:j]})\\n\",\n    \"        i = j \\n\",\n    \"    return char_drawings\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import glob\\n\",\n    \"npy_files = glob.glob('server/larger_data/*')\\n\",\n    \"file = np.random.choice(npy_files)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"server/larger_data/الحب من شيم الكرام.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<PIL.Image.Image image mode=RGB size=3902x306 at 0x7FAD53B2A9B0>\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"print(file)\\n\",\n    \"char_drawings = generate_characters(file)\\n\",\n    \"images = []\\n\",\n    \"for i, comp in enumerate(char_drawings):\\n\",\n    \"    char, drawing = list(comp.items())[0]\\n\",\n    \"    images.append(draw_strokes(convert_3d(drawing, threshold=10000), square = True))\\n\",\n    \"concatenate(images, mode='h')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"word_drawings = defaultdict(lambda  :[]) \\n\",\n    \"npy_files = glob.glob('server/larger_data/*')\\n\",\n    \"\\n\",\n    \"word_drawings = defaultdict(lambda  :[]) \\n\",\n    \"\\n\",\n    \"for file in npy_files:\\n\",\n    \"    drawings = generate_words(file)\\n\",\n    \"    for i, comp in enumerate(drawings):\\n\",\n    \"        word, drawing = list(comp.items())[0]\\n\",\n    \"        img, res = draw_strokes(convert_3d(drawing), square = True, return_res = True, stroke_width = 5)\\n\",\n    \"        if res[0] > 100 and res[1] > 100:\\n\",\n    \"            word_drawings[word].append(img)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 140,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x720 with 1 Axes>\"\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 720x720 with 1 Axes>\"\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 720x720 with 1 Axes>\"\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 720x720 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import random \\n\",\n    \"cnt = 0 \\n\",\n    \"for word in ['من', 'نور', 'هو', 'خالد']:\\n\",\n    \"    if len(word_drawings[word]) > 6:\\n\",\n    \"        plt.figure(figsize = (10,10))\\n\",\n    \"        plt.imshow(concatenate(random.sample(word_drawings[word],  6)))\\n\",\n    \"        plt.xticks([])\\n\",\n    \"        plt.yticks([])\\n\",\n    \"        plt.show()\\n\",\n    \"        cnt += 1\\n\",\n    \"    if cnt >= 5:\\n\",\n    \"        break\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 79,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"ename\": \"KeyboardInterrupt\",\n     \"evalue\": \"\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[0;31mKeyboardInterrupt\\u001b[0m                         Traceback (most recent call last)\",\n      \"\\u001b[0;32m<ipython-input-79-79ffb3cf2f85>\\u001b[0m in \\u001b[0;36m<module>\\u001b[0;34m\\u001b[0m\\n\\u001b[1;32m      4\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m      5\\u001b[0m \\u001b[0;32mfor\\u001b[0m \\u001b[0mfile\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0mnpy_files\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 6\\u001b[0;31m     \\u001b[0mdrawings\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mgenerate_characters\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mfile\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m      7\\u001b[0m     \\u001b[0mimages\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;34m[\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m      8\\u001b[0m     \\u001b[0;32mfor\\u001b[0m \\u001b[0mi\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mcomp\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0menumerate\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mdrawings\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m<ipython-input-17-206de63ad3e4>\\u001b[0m in \\u001b[0;36mgenerate_characters\\u001b[0;34m(file)\\u001b[0m\\n\\u001b[1;32m      4\\u001b[0m     \\u001b[0mchar_comps\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mpreprocess\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mannot\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m      5\\u001b[0m     \\u001b[0mdrawing\\u001b[0m \\u001b[0;34m=\\u001b[0m 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\\u001b[0;36mmoveaxis\\u001b[0;34m(a, source, destination)\\u001b[0m\\n\\u001b[1;32m   1426\\u001b[0m         \\u001b[0mtranspose\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0ma\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mtranspose\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m   1427\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 1428\\u001b[0;31m     \\u001b[0msource\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mnormalize_axis_tuple\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0msource\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0ma\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mndim\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'source'\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m   1429\\u001b[0m     \\u001b[0mdestination\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mnormalize_axis_tuple\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mdestination\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0ma\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mndim\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'destination'\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m   1430\\u001b[0m     \\u001b[0;32mif\\u001b[0m \\u001b[0mlen\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0msource\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;34m!=\\u001b[0m \\u001b[0mlen\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mdestination\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m~/.local/lib/python3.6/site-packages/numpy/core/numeric.py\\u001b[0m in \\u001b[0;36mnormalize_axis_tuple\\u001b[0;34m(axis, ndim, argname, allow_duplicate)\\u001b[0m\\n\\u001b[1;32m   1356\\u001b[0m             \\u001b[0;32mpass\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m   1357\\u001b[0m     \\u001b[0;31m# Going via an iterator directly is slower than via list comprehension.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 1358\\u001b[0;31m     \\u001b[0maxis\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mtuple\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mnormalize_axis_index\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0max\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mndim\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0margname\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;32mfor\\u001b[0m \\u001b[0max\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0maxis\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m   1359\\u001b[0m     \\u001b[0;32mif\\u001b[0m \\u001b[0;32mnot\\u001b[0m \\u001b[0mallow_duplicate\\u001b[0m \\u001b[0;32mand\\u001b[0m \\u001b[0mlen\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mset\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0maxis\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;34m!=\\u001b[0m \\u001b[0mlen\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0maxis\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m   1360\\u001b[0m         \\u001b[0;32mif\\u001b[0m \\u001b[0margname\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m~/.local/lib/python3.6/site-packages/numpy/core/numeric.py\\u001b[0m in \\u001b[0;36m<listcomp>\\u001b[0;34m(.0)\\u001b[0m\\n\\u001b[1;32m   1356\\u001b[0m             \\u001b[0;32mpass\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m   1357\\u001b[0m     \\u001b[0;31m# Going via an iterator directly is slower than via list comprehension.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 1358\\u001b[0;31m     \\u001b[0maxis\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mtuple\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mnormalize_axis_index\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0max\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mndim\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0margname\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;32mfor\\u001b[0m \\u001b[0max\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0maxis\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m   1359\\u001b[0m     \\u001b[0;32mif\\u001b[0m \\u001b[0;32mnot\\u001b[0m \\u001b[0mallow_duplicate\\u001b[0m \\u001b[0;32mand\\u001b[0m \\u001b[0mlen\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mset\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0maxis\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;34m!=\\u001b[0m \\u001b[0mlen\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0maxis\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m   1360\\u001b[0m         \\u001b[0;32mif\\u001b[0m \\u001b[0margname\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mKeyboardInterrupt\\u001b[0m: \"\n     ]\n    }\n   ],\n   \"source\": [\n    \"npy_files = glob.glob('server/larger_data/*')\\n\",\n    \"\\n\",\n    \"char_drawings = defaultdict(lambda  :[]) \\n\",\n    \"\\n\",\n    \"for file in npy_files:\\n\",\n    \"    drawings = generate_characters(file)\\n\",\n    \"    images = []\\n\",\n    \"    for i, comp in enumerate(drawings):\\n\",\n    \"        char, drawing = list(comp.items())[0]\\n\",\n    \"        img, res = draw_strokes(convert_3d(drawing, threshold=1000), square = True, return_res = True, stroke_width = 5)\\n\",\n    \"        if res[0] > 100 and res[1] > 100:\\n\",\n    \"            char_drawings[char].append(img)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 61,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x720 with 1 Axes>\"\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 720x720 with 1 Axes>\"\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 720x720 with 1 Axes>\"\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 720x720 with 1 Axes>\"\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 720x720 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import random \\n\",\n    \"cnt = 0 \\n\",\n    \"for char in ['س', 'م', 'ك', 'ح', 'ر']:\\n\",\n    \"    if len(char_drawings[char]) >= 6:\\n\",\n    \"        plt.figure(figsize = (10,10))\\n\",\n    \"        plt.imshow(concatenate(random.sample(char_drawings[char],  6)))\\n\",\n    \"        plt.xticks([])\\n\",\n    \"        plt.yticks([])\\n\",\n    \"        plt.show()\\n\",\n    \"        cnt += 1\\n\",\n    \"    if cnt >= 5:\\n\",\n    \"        break\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 225,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"imgs = []\\n\",\n    \"for char in char_drawings:\\n\",\n    \"    img = concatenate(char_drawings[char][:20],)\\n\",\n    \"    imgs.append(img)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 226,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.image.AxesImage at 0x7f1f399952e8>\"\n      ]\n     },\n     \"execution_count\": 226,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 2880x2160 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize = (40,30))\\n\",\n    \"plt.xticks([])\\n\",\n    \"plt.yticks([])\\n\",\n    \"plt.imshow(concatenate(imgs,'v',margin=5))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'ء'\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"list(char_drawings.keys())[-1]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 147,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"npy_files = glob.glob('server/larger_data/*')\\n\",\n    \"\\n\",\n    \"images = []\\n\",\n    \"for file in npy_files:\\n\",\n    \"    if 'بسم الله الرحمن الرحيم' in file:\\n\",\n    \"        drawing = json.load(open(file))\\n\",\n    \"        img, res = draw_strokes(convert_3d(drawing), square = True, return_res = True, stroke_width = 5)\\n\",\n    \"        images.append(img)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 148,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def map_axes(i, num_sketches = 16):\\n\",\n    \"    sqrt = int(math.sqrt(num_sketches))\\n\",\n    \"    x = np.array(list(range(num_sketches))).reshape((sqrt, sqrt)) \\n\",\n    \"    dim1, dim2 = np.where(x == i)\\n\",\n    \"    return (int(dim1), int(dim2))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 149,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1080x1080 with 16 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, axes = plt.subplots(4, 4, figsize=(15,15))\\n\",\n    \"indices = [0, 1, 17, 3, 4, 5, 6, 7, 8 , 9, 10, 11, 12 , 13, 14, 15]\\n\",\n    \"for i, j in enumerate(indices):\\n\",\n    \"    ax = axes[map_axes(i, num_sketches = 16)]\\n\",\n    \"    ax.imshow(images[j])\\n\",\n    \"    ax.set_xticks([])\\n\",\n    \"    ax.set_yticks([])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'.': [[332, 124]]}\\n\",\n      \"{'ح': [[331, 150], [336, 149], [354, 169], [318, 169]]}\\n\",\n      \"{'ا': [[321, 170], [315, 169], [308, 162], [303, 124]]}\\n\",\n      \"{'ل': [[286, 119], [286, 157], [282, 163], [268, 163]]}\\n\",\n      \"{'د': [[250, 144], [260, 156], [264, 172], [259, 176], [244, 176], [238, 171]]}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import pprint\\n\",\n    \"\\n\",\n    \"drawing = json.load(open('server/larger_data/22خالد.json'))\\n\",\n    \"drawing = apply_rdb(drawing)\\n\",\n    \"for stroke in drawing:\\n\",\n    \"    pprint.pprint(stroke)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<PIL.Image.Image image mode=RGBA size=256x256 at 0x7F5AC1F764A8>\"\n      ]\n     },\n     \"execution_count\": 38,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"draw_strokes(convert_3d(drawing), square = True, return_res = True, stroke_width = 5)[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 131,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"number of words  3026\\n\",\n      \"number of characters  16777\\n\",\n      \"number of strokes  24861\\n\",\n      \"number of sentences 1414\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"num_chars = 0 \\n\",\n    \"num_words = 0 \\n\",\n    \"num_strks = 0 \\n\",\n    \"num_sents = 0 \\n\",\n    \"\\n\",\n    \"for file in glob.glob('server/larger_data/*')+glob.glob('server/data/*'):\\n\",\n    \"    text = re.sub('[0-9]', '', file.split('/')[-1][:-5])\\n\",\n    \"    for char in text:\\n\",\n    \"        if char in map_chars:\\n\",\n    \"            num_strks += len(map_chars[char])\\n\",\n    \"        else:\\n\",\n    \"            num_strks += 1\\n\",\n    \"            \\n\",\n    \"    num_chars += len(text.replace(' ', ''))\\n\",\n    \"    num_words += len(text.split(' '))\\n\",\n    \"    num_sents += 1\\n\",\n    \"\\n\",\n    \"print('number of words ', num_words)\\n\",\n    \"print('number of characters ', num_chars)\\n\",\n    \"print('number of strokes ', num_strks)\\n\",\n    \"print('number of sentences', num_sents)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 309,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"already removed\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"for file in wrong_annotations:\\n\",\n    \"    file_name = file.split('/')[-1][:-5]\\n\",\n    \"    try:\\n\",\n    \"        os.remove(file)  \\n\",\n    \"        shutil.move(f'server/static/processed_larger_images/{file_name}.jpg', f'server/static/larger_images/{file_name}.jpg')\\n\",\n    \"    except:\\n\",\n    \"        print('already removed')\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\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\": \"3.6.9\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "notebooks/Generate as minified json.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"alternative-quality\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"test  train  valid\\r\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!ls dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"tested-boxing\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import json \\n\",\n    \"import glob\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"sacred-closing\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2000\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"json_files = glob.glob('dataset/train/**.json')\\n\",\n    \"print(len(json_files))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"id\": \"listed-wilson\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"out = \\\"\\\"\\n\",\n    \"threshold = 20 \\n\",\n    \"\\n\",\n    \"for i in range(100):\\n\",\n    \"    file = json_files[i]\\n\",\n    \"    drawing = json.load(open(file))\\n\",\n    \"    drawing_name = f\\\"stroke_{i}\\\"\\n\",\n    \"    curr_drawing = \\\"[\\\"\\n\",\n    \"    for d, item in enumerate(drawing):\\n\",\n    \"        char = list(item.keys())[0]\\n\",\n    \"        stroke = item[char]\\n\",\n    \"        new_stroke = \\\"\\\"\\n\",\n    \"        start = 0 \\n\",\n    \"        for i, point in enumerate(stroke):\\n\",\n    \"            if i < len(stroke) -1:\\n\",\n    \"                x, y = point\\n\",\n    \"                next_x, next_y = stroke[i+1]\\n\",\n    \"                if any((\\n\",\n    \"                    abs(x-next_x)>threshold,\\n\",\n    \"                    abs(y-next_y>threshold),\\n\",\n    \"                )):\\n\",\n    \"                    corrupted=True\\n\",\n    \"                    start = i +1\\n\",\n    \"        \\n\",\n    \"        for j, (x, y) in enumerate(stroke[start:]):\\n\",\n    \"            new_stroke += f\\\"[{int(x)}, {int(y)}]\\\"\\n\",\n    \"            if j != len(stroke) - 1:\\n\",\n    \"                new_stroke += \\\",\\\"\\n\",\n    \"        curr_drawing += f\\\"[{new_stroke}]\\\"\\n\",\n    \"        if d != len(drawing) - 1:\\n\",\n    \"                curr_drawing += \\\",\\\"\\n\",\n    \"    \\n\",\n    \"    out += f\\\"{drawing_name} = {curr_drawing}] \\\\n\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"id\": \"guided-thread\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"794698\"\n      ]\n     },\n     \"execution_count\": 31,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"open(\\\"json_sm.js\\\", 'w').write(out)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"id\": \"challenging-milan\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"-rw-rw-r-- 1 esp1 esp1 777K Jun 24 20:23 json_sm.js\\r\\n\",\n      \"-rw-rw-r-- 1 esp1 esp1  17M Jun 24 08:35 new_js.js\\r\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!ls -lh *.js\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"protected-canal\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\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\": \"3.6.9\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "notebooks/Split dataset .ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \\n\",\n      \"The text.latex.preview rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\\n\",\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \\n\",\n      \"The mathtext.fallback_to_cm rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\\n\",\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: Support for setting the 'mathtext.fallback_to_cm' rcParam is deprecated since 3.3 and will be removed two minor releases later; use 'mathtext.fallback : 'cm' instead.\\n\",\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \\n\",\n      \"The validate_bool_maybe_none function was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\\n\",\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \\n\",\n      \"The savefig.jpeg_quality rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\\n\",\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \\n\",\n      \"The keymap.all_axes rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\\n\",\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \\n\",\n      \"The animation.avconv_path rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\\n\",\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \\n\",\n      \"The animation.avconv_args rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import math, json\\n\",\n    \"from rdp import rdp\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from matplotlib import animation\\n\",\n    \"from IPython.display import HTML\\n\",\n    \"import tqdm.notebook as tq\\n\",\n    \"import pickle\\n\",\n    \"from collections import defaultdict\\n\",\n    \"import cairosvg\\n\",\n    \"from PIL import Image,ImageDraw\\n\",\n    \"import glob\\n\",\n    \"import os \\n\",\n    \"import re\\n\",\n    \"import svgwrite\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def get_bounds(data):\\n\",\n    \"    minx, miny = 600, 600  \\n\",\n    \"    maxx, maxy = 0, 0\\n\",\n    \"    \\n\",\n    \"    for i, (x, y, z) in enumerate(data): \\n\",\n    \"        if minx > x:\\n\",\n    \"            minx = x\\n\",\n    \"        if miny > y:\\n\",\n    \"            miny = y \\n\",\n    \"\\n\",\n    \"        if maxx < x:\\n\",\n    \"            maxx = x\\n\",\n    \"        if maxy < y:\\n\",\n    \"            maxy = y \\n\",\n    \"    return minx, maxx, miny, maxy\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def convert_3d(drawing, return_flag = False, threshold=10):\\n\",\n    \"    out = []\\n\",\n    \"    corrupted = False\\n\",\n    \"    for item in drawing:\\n\",\n    \"        char = list(item.keys())[0]\\n\",\n    \"        stroke = item[char]\\n\",\n    \"        if len(stroke) == 1:\\n\",\n    \"            x, y = stroke[0]\\n\",\n    \"            out.append([x, y, 0])\\n\",\n    \"            out.append([x+5, y+5, 1])\\n\",\n    \"            continue\\n\",\n    \"        segment = []\\n\",\n    \"        for i, point in enumerate(stroke):\\n\",\n    \"            x, y = point \\n\",\n    \"            if i == len(stroke) - 1:\\n\",\n    \"                segment.append([x, y, 1])\\n\",\n    \"            else:\\n\",\n    \"                segment.append([x, y, 0])\\n\",\n    \"        \\n\",\n    \"        start = 0 \\n\",\n    \"        for i, point in enumerate(segment):\\n\",\n    \"            if i < len(segment) -1:\\n\",\n    \"                x, y, _ = point\\n\",\n    \"                next_x, next_y, _ = segment[i+1]\\n\",\n    \"                if any((\\n\",\n    \"                    abs(x-next_x)>threshold,\\n\",\n    \"                    abs(y-next_y>threshold),\\n\",\n    \"                )):\\n\",\n    \"                    corrupted=True\\n\",\n    \"                    start = i +1\\n\",\n    \"        \\n\",\n    \"        out += segment[start:]\\n\",\n    \"    if return_flag:\\n\",\n    \"        return out, corrupted\\n\",\n    \"    return out\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def make_square(im, min_size=256, fill_color=(255, 255, 255)):\\n\",\n    \"    x, y = im.size\\n\",\n    \"    size = max(min_size, x, y)\\n\",\n    \"    new_im = Image.new('RGBA', (size, size), fill_color)\\n\",\n    \"    new_im.paste(im, (int((size - x) / 2), int((size - y) / 2)))\\n\",\n    \"    return new_im\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def draw_strokes(data, factor=1, svg_filename = 'tmp/sample.svg', stroke_width = 3, square = False, return_res = False):\\n\",\n    \"    min_x, max_x, min_y, max_y = get_bounds(data)\\n\",\n    \"    dims = (50 + max_x - min_x, 50 + max_y - min_y)\\n\",\n    \"    dwg = svgwrite.Drawing(svg_filename, size = dims)\\n\",\n    \"    dwg.add(dwg.rect(insert=(0, 0), size=dims,fill='white'))\\n\",\n    \"    lift_pen = 1\\n\",\n    \"    abs_x = 25 - min_x \\n\",\n    \"    abs_y = 25 - min_y\\n\",\n    \"    p = \\\"M%s,%s \\\" % (abs_x, abs_y)\\n\",\n    \"    command = \\\"M\\\"\\n\",\n    \"    for i in range(len(data)):\\n\",\n    \"        if (lift_pen == 1):\\n\",\n    \"            command = \\\"M\\\"\\n\",\n    \"        elif (command != \\\"L\\\"):\\n\",\n    \"            command = \\\"L\\\"\\n\",\n    \"        else:\\n\",\n    \"            command = \\\"\\\"\\n\",\n    \"        x = float(data[i][0]) - min_x\\n\",\n    \"        y = float(data[i][1]) - min_y\\n\",\n    \"        lift_pen = data[i][2]\\n\",\n    \"        p += command+str(x)+\\\" \\\"+str(y)+\\\" \\\"\\n\",\n    \"    the_color = \\\"black\\\"\\n\",\n    \"    \\n\",\n    \"    dwg.add(dwg.path(p).stroke(the_color,stroke_width).fill(\\\"none\\\"))\\n\",\n    \"    dwg.save()\\n\",\n    \"    cairosvg.svg2png(url=\\\"tmp/sample.svg\\\", write_to=\\\"tmp/sample.png\\\")\\n\",\n    \"    img = Image.open('tmp/sample.png')\\n\",\n    \"    if square:\\n\",\n    \"        img = make_square(img)\\n\",\n    \"    if return_res:\\n\",\n    \"        return img, dims \\n\",\n    \"    else:\\n\",\n    \"        return img\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"npy_files = glob.glob('server/data/*')\\n\",\n    \"file = np.random.choice(npy_files)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"False\"\n      ]\n     },\n     \"execution_count\": 43,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"target_file_name + '_'+ ctr +'.json' in [f.split('/')[-1] for f in glob.glob('dataset/**/**.json')]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"files = glob.glob('server/data/*') + glob.glob('server/larger_data/*')\\n\",\n    \"shuffle(files)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 113,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"!rm -r dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 114,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def preprocess(text):\\n\",\n    \"    char_comps = []\\n\",\n    \"    \\n\",\n    \"    diacritics = \\\"[ًٌٍَُِّْ]\\\"\\n\",\n    \"    numbers = '0123456789'\\n\",\n    \"    for diac in diacritics: \\n\",\n    \"        text = text.replace(diac, '')\\n\",\n    \"\\n\",\n    \"    text = re.sub('[0-9]', '', text)\\n\",\n    \"    text = re.sub('[a-zA-Zö\\\\xa0]', '', text)\\n\",\n    \"    return text.replace('_', '')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 115,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import shutil \\n\",\n    \"import glob \\n\",\n    \"from random import  shuffle\\n\",\n    \"os.makedirs('dataset', exist_ok = True)\\n\",\n    \"os.makedirs('dataset/train', exist_ok = True)\\n\",\n    \"os.makedirs('dataset/valid', exist_ok = True)\\n\",\n    \"os.makedirs('dataset/test', exist_ok = True)\\n\",\n    \"\\n\",\n    \"for i, file in enumerate(files):\\n\",\n    \"    file_name = file.split('/')[-1][:-5]\\n\",\n    \"    \\n\",\n    \"    target_file_name = preprocess(file_name) \\n\",\n    \"    ctr = str(0)\\n\",\n    \"        \\n\",\n    \"    while target_file_name + '_'+ ctr +'.json' in [f.split('/')[-1] for f in glob.glob('dataset/**/**.json')]:\\n\",\n    \"        ctr = str(int(ctr) + 1) \\n\",\n    \"\\n\",\n    \"    target_file_name = target_file_name + '_'+ ctr \\n\",\n    \"\\n\",\n    \"    if i < 2000:\\n\",\n    \"        shutil.copyfile(file, f'dataset/train/{target_file_name}.json')\\n\",\n    \"    elif i < 2250:\\n\",\n    \"        shutil.copyfile(file, f'dataset/valid/{target_file_name}.json')\\n\",\n    \"    elif i < 2500:\\n\",\n    \"        shutil.copyfile(file, f'dataset/test/{target_file_name}.json')\\n\",\n    \"    else:\\n\",\n    \"        break \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 116,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2500\"\n      ]\n     },\n     \"execution_count\": 116,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"len(glob.glob('dataset/**/**.json'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 117,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'server/data/Benyaagoub_بن يعقوب.json'\"\n      ]\n     },\n     \"execution_count\": 117,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"file\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 118,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"زنيرة\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"file = np.random.choice(glob.glob('dataset/**/**.json'))\\n\",\n    \"print(file.split('_')[0].split('/')[-1])\\n\",\n    \"drawing = json.load(open(file))\\n\",\n    \"data1, flag = convert_3d(drawing, return_flag=True, threshold=50)\\n\",\n    \"plt.imshow(draw_strokes(data1, stroke_width = 5))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 119,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#https:#jrgraphix.net/r/Unicode/0600-06FF\\n\",\n    \"map_chars = {\\n\",\n    \"    \\\"\\\\u0623\\\":[\\\"\\\\u0621\\\", \\\"\\\\u0627\\\"], # أ\\n\",\n    \"    \\\"\\\\u0622\\\":[\\\"\\\\u0605\\\", \\\"\\\\u0627\\\"], # آ\\n\",\n    \"    \\\"\\\\u0625\\\":[\\\"\\\\u0627\\\", \\\"\\\\u0621\\\"], # إ\\n\",\n    \"    \\\"\\\\u0628\\\":[\\\"\\\\u066E\\\", \\\".\\\"], # ب\\n\",\n    \"    \\\"\\\\u062A\\\":[\\\".\\\", \\\".\\\", \\\"\\\\u066E\\\"], # ت\\n\",\n    \"    \\\"\\\\u062B\\\":[\\\".\\\", \\\".\\\", \\\".\\\", \\\"\\\\u066E\\\"], # ث \\n\",\n    \"    \\\"\\\\u062C\\\":[\\\"\\\\u062D\\\", \\\".\\\"], # ج\\n\",\n    \"    \\\"\\\\u062E\\\":[\\\".\\\", \\\"\\\\u062D\\\"], # خ\\n\",\n    \"    \\\"\\\\u0630\\\":[\\\".\\\", \\\"\\\\u062F\\\"], # ذ\\n\",\n    \"    \\\"\\\\u0632\\\":[\\\".\\\", \\\"\\\\u0631\\\"], # ز\\n\",\n    \"    \\\"\\\\u0634\\\":[\\\".\\\", \\\".\\\", \\\".\\\", \\\"\\\\u0633\\\"], # ش\\n\",\n    \"    \\\"\\\\u0636\\\":[\\\".\\\", \\\"\\\\u0635\\\"], # ض\\n\",\n    \"    \\\"\\\\u0637\\\":[\\\"\\\\u0627\\\", \\\"\\\\uFEBB\\\"], # ط\\n\",\n    \"    \\\"\\\\u0638\\\":[\\\".\\\", \\\"\\\\u0627\\\", \\\"\\\\uFEBB\\\"], # ظ\\n\",\n    \"    \\\"\\\\u063A\\\":[\\\".\\\", \\\"\\\\u0639\\\"], # غ\\n\",\n    \"    \\\"\\\\u0641\\\":[\\\".\\\", \\\"\\\\u066F\\\"], # ف\\n\",\n    \"    \\\"\\\\u0642\\\":[\\\".\\\", \\\".\\\", \\\"\\\\u066F\\\"], # ق\\n\",\n    \"    \\\"\\\\u06A4\\\":[\\\".\\\", \\\".\\\", \\\".\\\", \\\"\\\\u066F\\\"], # ڤ\\n\",\n    \"    \\\"\\\\u0643\\\":[\\\"\\\\u0621\\\", \\\"\\\\u0644\\\"], # ك\\n\",\n    \"    \\\"\\\\u0646\\\":[\\\".\\\", \\\"\\\\u06BA\\\"], # ن\\n\",\n    \"    \\\"\\\\u0624\\\":[\\\"\\\\u0621\\\", \\\"\\\\u0648\\\"], # ؤ\\n\",\n    \"    \\\"\\\\u064A\\\":[\\\"\\\\u0649\\\", \\\".\\\", \\\".\\\"], #ي\\n\",\n    \"    \\\"\\\\u0626\\\":[\\\"\\\\u0621\\\", \\\"\\\\u0649\\\"], #ئ\\n\",\n    \"    \\\"\\\\u0629\\\":[\\\".\\\", \\\".\\\", \\\"\\\\u0647\\\"], #ه\\n\",\n    \"}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 120,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'زنيرة'\"\n      ]\n     },\n     \"execution_count\": 120,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"file.split('_')[0].split('/')[-1]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 121,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"train\\n\",\n      \"number of words  6065\\n\",\n      \"number of characters  24722\\n\",\n      \"number of strokes  36561\\n\",\n      \"number of sentences 2000\\n\",\n      \"valid\\n\",\n      \"number of words  738\\n\",\n      \"number of characters  2946\\n\",\n      \"number of strokes  4410\\n\",\n      \"number of sentences 250\\n\",\n      \"test\\n\",\n      \"number of words  753\\n\",\n      \"number of characters  3052\\n\",\n      \"number of strokes  4601\\n\",\n      \"number of sentences 250\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from collections import Counter \\n\",\n    \"char_counts = Counter()\\n\",\n    \"\\n\",\n    \"for split in ['train', 'valid', 'test']:\\n\",\n    \"    num_chars = 0 \\n\",\n    \"    num_words = 0 \\n\",\n    \"    num_strks = 0 \\n\",\n    \"    num_sents = 0 \\n\",\n    \"    for file in glob.glob(f'dataset/{split}/**.json'):\\n\",\n    \"        text = file.split('_')[0].split('/')[-1]\\n\",\n    \"        for char in text:\\n\",\n    \"            if char == ' ':\\n\",\n    \"                continue\\n\",\n    \"            char_counts[char] += 1\\n\",\n    \"            if char in map_chars:\\n\",\n    \"                num_strks += len(map_chars[char])\\n\",\n    \"            else:\\n\",\n    \"                num_strks += 1\\n\",\n    \"\\n\",\n    \"        num_chars += len(text.replace(' ', ''))\\n\",\n    \"        num_words += len(text.split(' '))\\n\",\n    \"        num_sents += 1\\n\",\n    \"    \\n\",\n    \"    print(split)\\n\",\n    \"    print('number of words ', num_words)\\n\",\n    \"    print('number of characters ', num_chars)\\n\",\n    \"    print('number of strokes ', num_strks)\\n\",\n    \"    print('number of sentences', num_sents)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 122,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Counter({'أ': 426,\\n\",\n       \"         'ح': 944,\\n\",\n       \"         'م': 2436,\\n\",\n       \"         'د': 875,\\n\",\n       \"         'ي': 2089,\\n\",\n       \"         'س': 758,\\n\",\n       \"         'ر': 1704,\\n\",\n       \"         'ى': 272,\\n\",\n       \"         'ف': 609,\\n\",\n       \"         'ا': 4530,\\n\",\n       \"         'ئ': 56,\\n\",\n       \"         'ز': 199,\\n\",\n       \"         'ل': 4382,\\n\",\n       \"         'ه': 1332,\\n\",\n       \"         'ص': 182,\\n\",\n       \"         'خ': 245,\\n\",\n       \"         'ش': 236,\\n\",\n       \"         'ء': 128,\\n\",\n       \"         'ذ': 190,\\n\",\n       \"         'ك': 832,\\n\",\n       \"         'و': 1626,\\n\",\n       \"         'ن': 1879,\\n\",\n       \"         'ق': 504,\\n\",\n       \"         'ة': 344,\\n\",\n       \"         'ب': 1137,\\n\",\n       \"         'ع': 1006,\\n\",\n       \"         'ظ': 62,\\n\",\n       \"         'ؤ': 31,\\n\",\n       \"         'آ': 58,\\n\",\n       \"         'إ': 242,\\n\",\n       \"         'ج': 292,\\n\",\n       \"         'غ': 95,\\n\",\n       \"         'ت': 682,\\n\",\n       \"         'ض': 131,\\n\",\n       \"         'ط': 129,\\n\",\n       \"         'ث': 72,\\n\",\n       \"         'ڤ': 5})\"\n      ]\n     },\n     \"execution_count\": 122,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"char_counts\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 139,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"df = pd.DataFrame(char_counts.items(), columns=['Char', 'Count'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 155,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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4b9Y/TXtf4a7f6Zfr7A9gpMDDgZw/82hbEo6Ojde7cORUWFiooKEiFhYVKSUlRdHS0jDGWxuwUER6qIKfD63mFeflKy8gpgY4AAABQltkWxCMjIxUTE6PVq1era9euWr16tWJiYoqXl1gds0uQ06Hzc2Z5PS+q/3OSCOIAAACVTakE8VdffVXr1q3T999/r2eeeUYul0uffvqpxo0bpxEjRmjWrFmqUaOGpkyZUjzH6hh+EBHuVJAzxOt5hXm5SsvIK4GOAAAA8GOlEsRHjx6t0aNHX/V448aNtXTp0mvOsTqGHwQ5Q3Rixh+8nnfz4A8lEcQBAABKWqU+WRMAUDLCXFUU6rD2IyYnv0CZ7mw/dwQAZQ9BHADgd6GOYHX++H8szV39eHduvQ6gUiCIo1TUDHcq2MKa9YK8XKWzZh0AAFRABHGUimBniLbN6+z1vPv7rRZr1gEAQEVUKre4BwAAAHAljogDAABUYhHhVRXkDPJ6XmFeodIyLpVAR5UHQRwAAKASC3IG6ezfjnk9r+6wxiXQTeVCEAd84HI55HCEWpqbn58jtzvfzx0BAIDygiAO+MDhCNXidztYmhv/9GeSCOIAAFRWnKwJAAAA2IAgDgAAANiApSkAAHghzFVFoQ7vf3zm5Bco051dAh0BKK8I4gAAeCHUEaxuy7Z4Pe8fv2ulzBLoB0D5xdIUAAAAwAaV/oh4RHiogpwOr+cV5uUrLSOnBDoCAABAZVDpg3iQ06Hzsxd6PS9qQE9JBHEAAABYw9IUAAAAwAaV/og4AAB2CHNVVagjyOt5OfmFynRfKoGOAJQ2gjgAADYIdQTp8WW7vZ738e/u4eor5VjN8GoKdlpbkFCQV6T0jCw/dwQ7EcRRKbnCnXI4QyzNzc/LlTsjz88dAQAqg2BnoPbPPWdp7h1/quPnbmA3gjgqJYczRCv//rCluV37rJFEEK/swlwhCnU4vZ6Xk5+nTHfuj+qEKtTh/ZWbfqiVr0w3J40DpaGmq5qCHRaPZOcXKd3NkWxcjSAO4JrCXQ45HaFez8vLz1GGO78EOipbQh1OPfKPSV7P+99uI5WpfwfxUIdDnZa/aamHTx8bokyu3gSUimBHoLa9d97S3Pt7R/m5G1QUBHEA1+R0hGrC/3Twet6Y7p9JKrtB3F9HsisqjtADQOkhiAOoVEIdTj2y4s9ez/vfR6decSS7ogp1ONR52fuW5q7+XS+O0AOAFwjiAFABcCQbgN0iwqsqyOn9JTkL8wqVlvHvS3JGhFdRkNP7iFqYV6C0jGyv59mpXAfx48ePa8SIEXK73XK5XJoyZYoaNmxod1sAUOpCHQ51WjbP0txPf9ePI9lAORQRXk1BFi6FWJhXpLQSuAxikDNI56Z/7fW8Oi/8+id1gnXujQTv6zzfwus5divXQXzs2LGKj49X165dtXLlSo0ZM0bvv2/tT6pARWD1BEup8pxkCaBkhbuqyWnx6iJ5+UXK8PPVRVyuanJY7Cc/v0juMny1kyBnoE68ftbreTe/WLcEuil7ysOR9XIbxFNTU5WUlKQFCxZIkjp37qyJEycqLS1NERERHtUIDAz44b9h1Sz1cHl+8b/DwvxSJyjM5ac6tfxSxxFW2y91Qqr7p06oxTo/rVXFT3WqVbd+Xdcf16lezfc6Tkeo/r6yl6Uafbq+r8DAgiseC69qraefvmYRfqpTu4q1Kw9cVadqTT/VCfdTHWufHT+tVbtqdT/VsfaZeHWdqmWqzg+1qvilTlRVa/chuLqO9ycOX7uOtR/nP63jqur9soKf1nE6ArVwrbWri/TsGFVcq0aNqj4F6AsXfljq4HAEassnaZbqtOoSccW2hVSz1o905ffIUd0/dSQpqIbvr9kPdfyzDwXWsLZE7upM5ft7LMgZrO/f/cLrGrWebnlVP1bdqE6AMcb45ZlK2f79+zV8+HB9+umnxY898sgjmjZtmm6//XYbOwMAAABuzPqvZAAAAAAsK7dBPDo6WufOnVNhYaEkqbCwUCkpKYqOjra5MwAAAODGym0Qj4yMVExMjFavXi1JWr16tWJiYjxeHw4AAADYqdyuEZekY8eOacSIEbpw4YJq1KihKVOmqFGjRna3BQAAANxQuQ7iAAAAQHlVbpemAAAAAOUZQRwAAACwAUEcAABUOhcvXtT777+vzMxMHTx4UDt37rS7JVRCBPHr2LNnj+Li4vTNN9/Y3QoAAPCzdevWKSwsTJ07d9a0adPUuHFju1tCJcTJmtexYsUKRUZG6rbbblNkZKTd7QAAAD8qKipSYCDHI2Ev9sDrqFevnrZt26bMzEy7W0Eld9ttt9ndAmw0adIk7du3z+42SszZs2fVpk0bu9vwm6VLl2r+/Pnq37+/3a2UKTt27NDTTz/tl1qff/654uLidOLECZ/qXA7hFfEztkePHna34Hf+3IfKEoL4dTRv3lzvvvuuGjZsaHcrqCB69+6tvLy8Un/e9PR0tW7dWgsXLiz15/ZGRfph6M8fgiNHjtSdd97pt3q+2Llzp/7rv/7L5zopKSnq3LmzDh486IeuypZbb71VS5cu1enTp5Wammp3O36Rnp6ul156Sffff7927dpldzvau3evVq9erTp16tjdyhXKymdYenq66tevX2Zer8rEys95gjhQSt577z05nc5Sf96cnBy1b99eMTExfq07aNAgnTt3zq81K4qPPvrIL3V27dql6dOn68knn/RLPV/de++9eumll3yuk5aWpsGDB6tp06Z+6KpseeONNzRkyBDdd999eueddyzV+Mtf/qL58+dr1qxZfu7OmrFjx6p+/fpas2aNYmNj7W5HkZGRevrpp3XkyBG7W/GbtLQ0PfXUU3rggQf061//WnFxcerRo4fy8/O9rlXWXq/L0tPTy9RfimbOnKnPP//cb/UWLlyoVq1a6cUXX/RqHkG8hKSlpenVV1/V9OnTdf/996tt27YaOXKkCgsLLdXLyspS9+7d1aFDB3311VeW+8rMzNTvfvc7JSQkWK7xY5MnT1arVq3Upk0bLV++3C81y4Jp06bpwQcfVNu2bbVy5Uqf661du1atWrVS3759lZ2d7YcOPedwOBQXF6df/vKXft2umTNnlrkjUv6SmJio+Ph4NW/eXP369VN6evoN5/j7PV9YWKhmzZrphRde0MKFC4uPsmzevFlz5871qtZ7772nuLg4n/+sm5mZqV69emnv3r0+1WnSpIlSUlJ05swZn+r4k7/+ipGVlaWgoCDVqFFDhw8ftlRj8uTJ6tu3r5577jm/9OSr48eP695779WRI0c0depUbdy40ad6ffr08eg9dT0PP/ywVq1apTvuuMOnPsoSp9OpQYMGaejQoXK5XIqPj9fAgQMVHBzsdS1/vV4HDx7U6NGjr3hvuN1uy69dzZo1NWfOHEufr/528OBBnT17VrNnzy5+zJdtk6SePXuqb9++mjlzpncTDa4rJibGp/kZGRmmU6dOZu7cuSY3N9enWq+//rpZsGCB+frrr0379u19qnX8+HHzm9/8xuTl5flUJzU11dx+++3m4MGD5syZM+bvf/+71zX2799vNm/ebIwx5tSpU2bZsmXGGGOWLVtmRo4c6XW9TZs2mb1793o978cub9eRI0fMsWPHzIwZM3yqZ4wxLVu2NIcOHTLPPPOMWbx4sVdzfd0PL/P3di1btsxs2rTJL70ZY207i4qKzEsvvWTuv/9+M2rUKON2uy0//+HDh82ePXvM5s2bTcuWLc3GjRtNbm6uGTp0qFm0aJFHNfz5nt+wYYMZPHiwMcaYrVu3mu7duxtjjPn444/NqFGjPK6TlZVl7rnnHvPdd9+ZCxcu+NSTMT98n9q1a2cKCgos13j22WfNE088YS5dumTOnDlj2rRpY6nOa6+9Zu655x7zpz/9yeTk5Fju58dOnTplXnzxRY+/vrCw0MyaNcsUFRUZY3543eLi4kz79u3Ns88+6/Xz+7pP5+fnm7lz5xb381O7d+82W7Zs8armmjVrTNOmTc2DDz5oXnvtNXPo0CGv5htjzPbt203v3r2NMcasW7fOPPbYY6Z58+amX79+Ji0tzata/n7d/fUZ62udffv2mTZt2piTJ0/6VMeX1ysjI8P07dv3uvvPBx98YMaPH+91T/74fPV1HyqpbbtszZo1pmXLlqZPnz7m0qVLHs/jiHgJmj17tjp06KB+/fr5vCRh3759ioiIUEhIiM6fP+9TrYYNGyoiIkJHjx71em5hYaEWLVokY4wiIiL0l7/8RQMHDtSECRPUsWNHr+sZYzRv3jxJ0pEjR7Ro0SJJ1s9mP3PmjD755BOv50lSQkJC8Xb17NlTvXr10ocffuiXk0MuXbqkkJAQValSRRcuXPC5njdKarvy8vK0ZcsW/zRp0eHDh/X1119r3bp1atiwoQYPHuzx3CeffPKKk72OHTumoUOHavz48frggw8UFxenjRs3avfu3brllls8qunLe75///46efJk8b9jY2O1ZcsWHT9+XDt27NChQ4f0r3/9S0eOHPHqLxFFRUUKCAjQ0aNHde7cOX3//fde9fVTTZo0UVhYmNfLAvLz84vXTM+bN09LlixRlSpVlJycrIiICK/7yMjI0LvvvqvFixfL4XBo3bp1XtcoKCjQ2rVrr3isfv362r17t5KTkz2q8c033+irr75SQECAJKlt27bavn27evXqpSZNmnjdky/7tCQlJSVp48aNxf38VEJCglfv27y8PL311lvq16+fNmzYoOHDh+uXv/ylVz392Ny5czVp0iQNGzZMX3zxhUJCQrR69WqP5/vjdS+r3n77bQ0ZMkQ33XST5Rq+vl7ffvutsrKyrrv/7NmzR7Vq1bphnZL4fL3M6j7kr227nkmTJumdd96RMUYrVqzweB5BvAR98cUXeuSRR3yqkZ+fr759+6pOnTp666231KtXLw0bNsxSrYsXLyo7O1u7d+/WqVOnVLduXa9rJCUlafny5cU78pNPPqn169fL5XLpvffe+9m5xhi1a9dO3333XfFjjRs31t69e5WUlKStW7fq0KFD+vbbb3Xo0CFFR0d73d+lS5cs/dJz+PBhvfbaa8XbNWLECK1Zs0Znz571eBnAqVOn1LlzZ5lrXBF00KBBevzxx7V7926fPmS95Y/tuh63263q1av7o03LjDFyOBySpLvvvtvjcHjy5EklJyfr5ptvLn6sevXqysnJUXp6ujp16qSHHnpIixYt0pgxY/TAAw94VNfqe/706dM6dOiQGjRoUPxYcHCwAgIC1KNHD61fv15Dhw7V448/rl27dukPf/iDx7WrVq2qgQMHatCgQXryySeVkZHhdX/Svz8/EhMTdfr0adWuXdur+QcPHtTvf/97HT16VPn5+SosLNSuXbs0YcIEPfjgg173Ex4erqeeeko9e/bUhg0bLP2CceTIkSv+NH1ZZGSkx/VOnTp11SVu3W63Fi9ebGltv9V9+rLo6Gh99913Wr9+ffFypvT0dO3cuVNTp07Vhx9+qG7dunlcLyEhQS6XSy+88IJCQkK86uWnDhw4oOXLl2vp0qWKiYnRmjVrlJiY6NUvLP543cuqw4cP66677pL0w1IpK8vafH29GjZsqOPHj2vFihXKzs6WMUbnzp3T+vXr9dxzz+nYsWM3XMZVUp+vkm/7kD+27edYPeDm/eIjeKxq1apyu90+1Thw4ICOHTum+fPn+9zPN998oyFDhsjlcmns2LGqWbOm1zUaNGggt9uthIQENWnSRMnJydq+fbsSExM1ffr0n5174sQJBQYGXvHbb15enpxOp5555hlFRkbq5ZdfVvfu3dWoUaNr/oD8ORkZGVq6dKlGjx7t9XZVqVJFaWlpSk5OVnR0tC5evKidO3dq//796tSpk0c1CgoKlJaWpoSEBMXGxiokJESFhYXKzMzUr3/9a8XHx2vbtm1q3bq11/1Z5Y/tupa0tDQtWbJEkyZNsjR/w4YNatu2bfEvCFlZWapSpYrXdZo0aaKYmBi1bNlSN910kyZOnOjRvO+///6qIHn8+HE99NBDmjp1qtd9XGb1PZ+SknLVUe5t27bpgQceuGK9Ye/evb2unZycrC1btmjChAnq1KmTpe+z9O/Pj/DwcI0ePdrro9h33HGHunTpon79+iklJUUOh0N169ZVly5d9Oyzz1rqaeTIkRo2bJi6du1q6QpXubm5qlat2hWPZWZm6sSJEx7/wnzLLbdo165dunjxoqpXr65du3Zp/PjxGjx4sOrVq+d1T1b36cuioqI0ffp0zZw5U3/+859VUFCg8PBw/eIXv1CLFi20fPlyr/6ikpeX59O9NL7++mv94Q9/kDFGRUVFunDhglq2bKmwsDA1bdpUEydO1H333edVTV9f97IqKipKp06d0s0332z5hG9fXy+Xy6U5c+Zo+vTpmjBhggoKClSzZk3deuutat++vTp16qSqVav+bA1/f776ax/yx7adOnVK/fv316pVq646sn75gFtQUJB3B2QsL4apBHxd77Vo0SLTtWtXk5iYaPLz843b7TaffPKJV+vhtm/fblq3bu1TH/526tQpM336dPPKK6+YN9980/zzn//0aD3snj17ite5XrZs2TIzcOBAy70UFRWZlJQUs2LFCvOf//mfZsSIEZZrLV261LRu3dq0aNHCtG3b1rz44otm27ZtXtVYu3at6dGjh2nevLm58847zV133WXatWtnBgwYYBYuXGiysrIs92eVP7bLmB/Wwp47d8784x//MO3atTPjxo2z1E9SUpLp1q3bFY8lJCSYLl26WKpnxdmzZ829995r9u7da3JyckxCQoJp1aqVpe/Lj1l9z/+4n9zcXLN9+3bTrl07s3//fp/6qchyc3PN/v37zdNPP226d+9uac16ZmamadGihdmyZYvJzc01ycnJ5rnnnjNjxozxqs60adNMbGysiY2NNb///e/NV1995XUvZVVqaqq57777zOLFi825c+dMenq62bx5s0lISLClH3+87iXF18zwySefmI4dO5qdO3eavLw843a7zYoVK7zKDGXh9Sqpz9ey4Pjx4+b+++83X375ZfH5CQUFBSY9Pd3s2bPHvP766+aJJ57w6mc9QbyEffDBB6ZLly6mWbNmpnnz5mbAgAEmMzPT4/llMYgbY8y2bdvM9OnTzeOPP+7xnJSUFBMbG2sSExNNTk6O+fzzz02rVq18ChszZ840bdq0MUOHDjVffPGF5Tq4sZkzZxYHeV8+1E+cOGGaN29ukpKSTFZWlvnnP/9pWrduXXyibmlZsmSJ6dixo2nWrJnp2rWrWb9+vV/qWn3PL1682LRv3940a9bMPPbYY2bHjh1+6acimjdvnomLizPdunUzM2bMMNnZ2ZZrJSYmml69epnmzZub1q1bmxkzZlg6kT0rK8vnE+BLwowZM4pPiLcqKSnJDBo0yPzmN78xLVq0MD179jTffPONnzr0nD9fd39LTEw0cXFxZt++fT7VWbp0qenWrZvlzGBM2Xi9SurztSzw9wE3bnGPUvXxxx9r/vz5OnfunH7xi19o+PDhat68ud1toZQtXrxYb7/9ttxut26//Xb17t1b7du3t7stALBk+fLluvnmm9W4cWNLyz5ReRHEAQAAfLBt2zatWbNGvXv3VuPGje1uB+UIQRwAAACwAZcvBAAAAGxAEAcAAABsQBAHAFxlxowZlm8eBgDwDDf0AYBKbNWqVVqwYIGOHz+uatWqqWnTpurfv7/dbQFApUAQB4BKasGCBZo3b57Gjx+vBx98UA6HQ1988YU2btx4wzvMeaOgoEDBwfy4AYCf4qopAFAJZWZmqlWrVpo0aZIefvjhq8ZnzJiho0ePKiQkROvXr1e9evX02muv6Ve/+pUkad68eVqyZIlSU1MVHR2tF154ofha8MuXL9eSJUt05513auXKlerRo4deeOGFUt0+ACgPWCMOAJVQYmKicnNzf/ZGSps2bVKnTp20a9cutWnTRhMnTiwea9CggRYtWqTdu3dr0KBBevnll5WSklI8vm/fPjVo0EBbt27VgAEDSnRbAKC8IogDQCXkdrtVs2bNn10ycs899+ihhx5SUFCQunbtqoMHDxaPPfzww6pTp44CAwP1yCOP6JZbbtG+ffuKx2vXrq2nnnpKwcHBCg0NLdFtAYDyikV7AFAJuVwupaen/+z67Vq1ahX/f2hoqHJzc4u/fsWKFVqwYIFOnz4tSbp06ZLS09OLv75u3boluwEAUAFwRBwAKqG77rpLTqdTGzZs8Hru6dOnNXr0aL3yyivasWOHdu3apSZNmlzxNQEBAf5qFQAqLI6IA0AlFBYWpiFDhmjChAkKDg7WAw88oODgYCUkJGjHjh2qUqXKdedmZ2crICBAERERkqRly5bpyJEjpdU6AFQYBHEAqKT69OmjWrVqadasWRo2bJiqVaum22+/Xf3799fWrVuvO+8//uM/1KdPH/Xo0UMBAQF69NFHdffdd5di5wBQMXD5QgAAAMAGrBEHAAAAbEAQBwAAAGxAEAcAAABsQBAHAAAAbEAQBwAAAGxAEAcAAABsQBAHAAAAbEAQBwAAAGzw/w0mQhrTNCwgAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x216 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import seaborn as sns\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.figure(figsize=(12, 3))\\n\",\n    \"ax = sns.barplot(y = 'Count', x ='Char', data = df)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 73,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def preprocess(text):\\n\",\n    \"    char_comps = []\\n\",\n    \"    \\n\",\n    \"    diacritics = \\\"[ًٌٍَُِّْ]\\\"\\n\",\n    \"    numbers = '0123456789'\\n\",\n    \"    for diac in diacritics: \\n\",\n    \"        text = text.replace(diac, '')\\n\",\n    \"\\n\",\n    \"    for num in numbers: \\n\",\n    \"        text = text.replace(num, '')\\n\",\n    \"    \\n\",\n    \"    outText = \\\"\\\"\\n\",\n    \"    \\n\",\n    \"    for i in range(len(text)):\\n\",\n    \"    \\n\",\n    \"        if (text[i] == \\\" \\\"):\\n\",\n    \"            continue\\n\",\n    \"    \\n\",\n    \"        if text[i] in map_chars:\\n\",\n    \"            if (i < len(text) - 1 and text[i] == \\\"\\\\u0643\\\"):\\n\",\n    \"                if text[i+1] != ' ':\\n\",\n    \"                    char_comps.append({text[i] : '\\\\uFEDB'})\\n\",\n    \"                    outText += '\\\\uFEDB'\\n\",\n    \"                else:\\n\",\n    \"                    char_comps.append({text[i] : map_chars[text[i]]})\\n\",\n    \"                    outText += ''.join(map_chars[text[i]])\\n\",\n    \"            else:\\n\",\n    \"                char_comps.append({text[i] : map_chars[text[i]]})\\n\",\n    \"                outText += ''.join(map_chars[text[i]])\\n\",\n    \"        else:\\n\",\n    \"                char_comps.append({text[i] : text[i]})\\n\",\n    \"                outText += text[i]\\n\",\n    \"\\n\",\n    \"    return char_comps, outText\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def concatenate(images, mode='h', margin=10):\\n\",\n    \"    widths, heights = zip(*(i.size for i in images))\\n\",\n    \"    if mode =='h':\\n\",\n    \"        total_width = sum(widths)\\n\",\n    \"        max_height = max(heights)\\n\",\n    \"\\n\",\n    \"        new_im = Image.new('RGB', (total_width, max_height), (255, 255, 255))\\n\",\n    \"\\n\",\n    \"        x_offset = 0\\n\",\n    \"        for im in images[::-1]:\\n\",\n    \"            new_im.paste(im, (x_offset,0))\\n\",\n    \"            x_offset += im.size[0]\\n\",\n    \"    elif mode == 'v':    \\n\",\n    \"        total_height = sum(heights)\\n\",\n    \"        max_width = max(widths)\\n\",\n    \"\\n\",\n    \"        new_im = Image.new('RGB', (max_width, total_height+margin*(len(images)-1)), (255, 255, 255))\\n\",\n    \"        draw = ImageDraw.Draw(new_im)\\n\",\n    \"        y_offset = 0\\n\",\n    \"        for im in images:\\n\",\n    \"            new_im.paste(im, (0,y_offset+margin))\\n\",\n    \"            y_offset += im.size[1]\\n\",\n    \"            draw.line((0,y_offset+margin-5, max_width,y_offset+margin-5), fill=(0, 0, 0), width=3)\\n\",\n    \"    return new_im\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def generate_characters(file):\\n\",\n    \"    char_drawings = []\\n\",\n    \"    annot = file.split('/')[-1][:-5]\\n\",\n    \"    char_comps = preprocess(annot)\\n\",\n    \"    drawing = json.load(open(file))\\n\",\n    \"    new_drawing = apply_rdb(drawing, verbose = 0)\\n\",\n    \"    i = 0 \\n\",\n    \"    for comp in char_comps:\\n\",\n    \"        char = list(comp.keys())[0]\\n\",\n    \"        j = i + len(comp[char])\\n\",\n    \"        char_drawings.append({char:new_drawing[i:j]})\\n\",\n    \"        i = j \\n\",\n    \"    return char_drawings\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import glob\\n\",\n    \"npy_files = glob.glob('server/larger_data/*')\\n\",\n    \"file = np.random.choice(npy_files)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"1800\"\n      ]\n     },\n     \"execution_count\": 57,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"len(glob.glob('server/larger_data/*'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 91,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"wrong_annotations = []\\n\",\n    \"for file in glob.glob('server/data/*'):\\n\",\n    \"    drawing = json.load(open(file))\\n\",\n    \"    stroke_chars = ''\\n\",\n    \"    _, original_stroke_chars = preprocess(file.split(\\\"/\\\")[-1][:-5].split('_')[-1])\\n\",\n    \"    for comp in drawing:\\n\",\n    \"        stroke_chars += list(comp.keys())[0]\\n\",\n    \"    if stroke_chars != original_stroke_chars:\\n\",\n    \"        print(file)\\n\",\n    \"        print(stroke_chars)\\n\",\n    \"        print(original_stroke_chars)\\n\",\n    \"        wrong_annotations.append(file)\\n\",\n    \"        data1, flag = convert_3d(drawing, return_flag=True, threshold=50)\\n\",\n    \"        plt.imshow(draw_strokes(data1, stroke_width = 5))\\n\",\n    \"        plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 95,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2417\"\n      ]\n     },\n     \"execution_count\": 95,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"len(glob.glob('server/larger_data/*') + glob.glob('server/data/*'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 117,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2500\"\n      ]\n     },\n     \"execution_count\": 117,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"len(glob.glob('server/static/processed_larger_images/*') + glob.glob('server/static/images/*'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 118,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"wrong_annotations = []\\n\",\n    \"for file in glob.glob('server/larger_data/*'):\\n\",\n    \"    drawing = json.load(open(file))\\n\",\n    \"    stroke_chars = ''\\n\",\n    \"    _, original_stroke_chars = preprocess(file.split(\\\"/\\\")[-1][:-5])\\n\",\n    \"    for comp in drawing:\\n\",\n    \"        stroke_chars += list(comp.keys())[0]\\n\",\n    \"    if stroke_chars != original_stroke_chars:\\n\",\n    \"        print(file)\\n\",\n    \"        print(stroke_chars)\\n\",\n    \"        print(original_stroke_chars)\\n\",\n    \"        wrong_annotations.append(file)\\n\",\n    \"        data1, flag = convert_3d(drawing, return_flag=True, threshold=50)\\n\",\n    \"        plt.imshow(draw_strokes(data1, stroke_width = 5))\\n\",\n    \"        plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 119,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[]\"\n      ]\n     },\n     \"execution_count\": 119,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"wrong_annotations\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 71,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import shutil\\n\",\n    \"\\n\",\n    \"for file in wrong_annotations:\\n\",\n    \"    file_name = file.split('/')[-1][:-5]\\n\",\n    \"    os.remove(file)  \\n\",\n    \"    shutil.move(f'server/static/processed_larger_images/{file_name}.jpg', f'server/static/larger_images/{file_name}.jpg')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 90,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import shutil\\n\",\n    \"\\n\",\n    \"for file in wrong_annotations:\\n\",\n    \"    file_name = file.split(\\\"/\\\")[-1][:-5]\\n\",\n    \"    new_image_name = file.split(\\\"/\\\")[-1][:-5].split('_')[-1]\\n\",\n    \"    try:\\n\",\n    \"        os.remove(file)  \\n\",\n    \"        shutil.move(f'server/static/images/{file_name}.jpg', f'server/static/larger_images/{new_image_name}.jpg')\\n\",\n    \"    except:\\n\",\n    \"        continue\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 109,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1818\\r\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!ls server/larger_data/ | wc -l\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\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\": \"3.6.9\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "notebooks/data npz generator.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \\n\",\n      \"The text.latex.preview rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\\n\",\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \\n\",\n      \"The mathtext.fallback_to_cm rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\\n\",\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: Support for setting the 'mathtext.fallback_to_cm' rcParam is deprecated since 3.3 and will be removed two minor releases later; use 'mathtext.fallback : 'cm' instead.\\n\",\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \\n\",\n      \"The validate_bool_maybe_none function was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\\n\",\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \\n\",\n      \"The savefig.jpeg_quality rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\\n\",\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \\n\",\n      \"The keymap.all_axes rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\\n\",\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \\n\",\n      \"The animation.avconv_path rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\\n\",\n      \"In /home/esp1/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \\n\",\n      \"The animation.avconv_args rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\\n\"\n     ]\n    },\n    {\n     \"ename\": \"ModuleNotFoundError\",\n     \"evalue\": \"No module named 'rdb'\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[0;31mModuleNotFoundError\\u001b[0m                       Traceback (most recent call last)\",\n      \"\\u001b[0;32m<ipython-input-6-c9625b751940>\\u001b[0m in \\u001b[0;36m<module>\\u001b[0;34m\\u001b[0m\\n\\u001b[1;32m      8\\u001b[0m \\u001b[0;32mimport\\u001b[0m \\u001b[0mtqdm\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mnotebook\\u001b[0m \\u001b[0;32mas\\u001b[0m \\u001b[0mtq\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m      9\\u001b[0m \\u001b[0;32mimport\\u001b[0m \\u001b[0mpickle\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 10\\u001b[0;31m \\u001b[0;32mimport\\u001b[0m \\u001b[0mrdb\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n      \"\\u001b[0;31mModuleNotFoundError\\u001b[0m: No module named 'rdb'\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import math, json\\n\",\n    \"from rdp import rdp\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from matplotlib import animation\\n\",\n    \"from IPython.display import HTML\\n\",\n    \"import tqdm.notebook as tq\\n\",\n    \"import pickle\\n\",\n    \"import rdb\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from PIL import Image, ImageDraw\\n\",\n    \"import json\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"def img_from_stroke(drawing):\\n\",\n    \"    img_A = Image.new('RGB', (600, 600), (255, 255, 255)) \\n\",\n    \"    draw = ImageDraw.Draw(img_A) \\n\",\n    \"    for item in drawing:\\n\",\n    \"        char = list(item.keys())[0]\\n\",\n    \"        stroke = item[char]\\n\",\n    \"        if len(stroke) == 1:\\n\",\n    \"            x, y = stroke[0]\\n\",\n    \"            stroke.append([x+5, y+5])\\n\",\n    \"        for i, point in enumerate(stroke):\\n\",\n    \"            if i == 0:\\n\",\n    \"                x_prev, y_prev = point\\n\",\n    \"                continue\\n\",\n    \"            x, y = point \\n\",\n    \"            draw.line([x_prev, y_prev, x, y], fill=(0, 0, 0), width = 3)\\n\",\n    \"            x_prev, y_prev = x, y\\n\",\n    \"    return img_A\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def apply_rdb(drawing, verbose = 0):\\n\",\n    \"    new_drawing = []\\n\",\n    \"    total_prev_strokes = 0\\n\",\n    \"    total_post_strokes = 0\\n\",\n    \"    for item in drawing:\\n\",\n    \"        char = list(item.keys())[0]\\n\",\n    \"        stroke = item[char]\\n\",\n    \"        processed_stroke = []\\n\",\n    \"        if len(stroke):\\n\",\n    \"            if verbose:\\n\",\n    \"                print('processing ', char)\\n\",\n    \"            post_stroke = rdp(stroke, epsilon = 2.0)\\n\",\n    \"            total_post_strokes += len(post_stroke)\\n\",\n    \"            total_prev_strokes += len(stroke)\\n\",\n    \"        new_drawing.append({char:post_stroke})\\n\",\n    \"    if verbose:\\n\",\n    \"        print('reduced from ', total_prev_strokes, ' to ', total_post_strokes)\\n\",\n    \"    return new_drawing\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import glob\\n\",\n    \"npy_files = glob.glob('dataset/**/*.json')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<PIL.Image.Image image mode=RGB size=600x600 at 0x7FF414232EF0>\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"file = np.random.choice(npy_files)\\n\",\n    \"drawing = json.load(open(file))\\n\",\n    \"new_drawing = apply_rdb(drawing, verbose = 0)\\n\",\n    \"img_from_stroke(drawing)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def add_z(drawing):\\n\",\n    \"    new_data = []\\n\",\n    \"    x_prev, y_prev = None, None\\n\",\n    \"    for item in drawing:\\n\",\n    \"        char = list(item.keys())[0]\\n\",\n    \"        if x_prev is None:\\n\",\n    \"            x_prev, y_prev = item[char][0]\\n\",\n    \"        stroke = item[char]\\n\",\n    \"        x_data = []\\n\",\n    \"        y_data = []\\n\",\n    \"        segments = []\\n\",\n    \"        \\n\",\n    \"        if len(stroke) == 1:\\n\",\n    \"            x, y = stroke[0]\\n\",\n    \"            stroke.append([x+1, y+1])\\n\",\n    \"            \\n\",\n    \"        for i, point in enumerate(stroke):\\n\",\n    \"            x, y = point\\n\",\n    \"            if i == len(stroke) - 1:\\n\",\n    \"                z = 1\\n\",\n    \"            else:\\n\",\n    \"                z = 0\\n\",\n    \"            if i >=0:\\n\",\n    \"                segments.append([x-x_prev, y-y_prev, z])\\n\",\n    \"            x_prev, y_prev = [x, y]\\n\",\n    \"        new_data += segments\\n\",\n    \"    return np.array(new_data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def create_dataset():\\n\",\n    \"    train_data = []\\n\",\n    \"    valid_data = []\\n\",\n    \"    test_data = []\\n\",\n    \"    count = 0 \\n\",\n    \"    valid_size = 100 \\n\",\n    \"    test_size = 100\\n\",\n    \"    pbar = tq.tqdm(total = 2500)\\n\",\n    \"    for file in glob.glob('dataset/**/*.json'):\\n\",\n    \"        drawing = json.load(open(file))\\n\",\n    \"        new_drawing = apply_rdb(drawing)\\n\",\n    \"        strokes = add_z(new_drawing)\\n\",\n    \"        \\n\",\n    \"        count += 1\\n\",\n    \"        pbar.update(1)\\n\",\n    \"        if 'train' in file:\\n\",\n    \"            train_data.append(strokes)\\n\",\n    \"        elif 'valid' in file:\\n\",\n    \"            valid_data.append(strokes)\\n\",\n    \"        elif 'test' in file:\\n\",\n    \"            test_data.append(strokes)\\n\",\n    \"        else:\\n\",\n    \"            pbar.close()\\n\",\n    \"            break\\n\",\n    \"  \\n\",\n    \"    print(\\\"save dataset\\\")\\n\",\n    \"    with open('dataset.npz', 'wb') as f:\\n\",\n    \"        pickle.dump({'train':train_data, 'valid':valid_data, 'test':test_data}, f, protocol=2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"5cc19408197a40028ee948c1a5c6ac32\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"HBox(children=(IntProgress(value=0, max=2500), HTML(value='')))\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"save dataset\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"create_dataset()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\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\": \"3.6.9\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "notebooks/data word npz generator.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import math, json\\n\",\n    \"from rdp import rdp\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from matplotlib import animation\\n\",\n    \"from IPython.display import HTML\\n\",\n    \"import tqdm.notebook as tq\\n\",\n    \"import pickle\\n\",\n    \"import re\\n\",\n    \"from collections import Counter \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#https:#jrgraphix.net/r/Unicode/0600-06FF\\n\",\n    \"map_chars = {\\n\",\n    \"    \\\"\\\\u0623\\\":[\\\"\\\\u0621\\\", \\\"\\\\u0627\\\"], # أ\\n\",\n    \"    \\\"\\\\u0622\\\":[\\\"\\\\u0605\\\", \\\"\\\\u0627\\\"], # آ\\n\",\n    \"    \\\"\\\\u0625\\\":[\\\"\\\\u0627\\\", \\\"\\\\u0621\\\"], # إ\\n\",\n    \"    \\\"\\\\u0628\\\":[\\\"\\\\u066E\\\", \\\".\\\"], # ب\\n\",\n    \"    \\\"\\\\u062A\\\":[\\\".\\\", \\\".\\\", \\\"\\\\u066E\\\"], # ت\\n\",\n    \"    \\\"\\\\u062B\\\":[\\\".\\\", \\\".\\\", \\\".\\\", \\\"\\\\u066E\\\"], # ث \\n\",\n    \"    \\\"\\\\u062C\\\":[\\\"\\\\u062D\\\", \\\".\\\"], # ج\\n\",\n    \"    \\\"\\\\u062E\\\":[\\\".\\\", \\\"\\\\u062D\\\"], # خ\\n\",\n    \"    \\\"\\\\u0630\\\":[\\\".\\\", \\\"\\\\u062F\\\"], # ذ\\n\",\n    \"    \\\"\\\\u0632\\\":[\\\".\\\", \\\"\\\\u0631\\\"], # ز\\n\",\n    \"    \\\"\\\\u0634\\\":[\\\".\\\", \\\".\\\", \\\".\\\", \\\"\\\\u0633\\\"], # ش\\n\",\n    \"    \\\"\\\\u0636\\\":[\\\".\\\", \\\"\\\\u0635\\\"], # ض\\n\",\n    \"    \\\"\\\\u0637\\\":[\\\"\\\\u0627\\\", \\\"\\\\uFEBB\\\"], # ط\\n\",\n    \"    \\\"\\\\u0638\\\":[\\\".\\\", \\\"\\\\u0627\\\", \\\"\\\\uFEBB\\\"], # ظ\\n\",\n    \"    \\\"\\\\u063A\\\":[\\\".\\\", \\\"\\\\u0639\\\"], # غ\\n\",\n    \"    \\\"\\\\u0641\\\":[\\\".\\\", \\\"\\\\u066F\\\"], # ف\\n\",\n    \"    \\\"\\\\u0642\\\":[\\\".\\\", \\\".\\\", \\\"\\\\u066F\\\"], # ق\\n\",\n    \"    \\\"\\\\u06A4\\\":[\\\".\\\", \\\".\\\", \\\".\\\", \\\"\\\\u066F\\\"], # ڤ\\n\",\n    \"    \\\"\\\\u0643\\\":[\\\"\\\\u0621\\\", \\\"\\\\u0644\\\"], # ك\\n\",\n    \"    \\\"\\\\u0646\\\":[\\\".\\\", \\\"\\\\u06BA\\\"], # ن\\n\",\n    \"    \\\"\\\\u0624\\\":[\\\"\\\\u0621\\\", \\\"\\\\u0648\\\"], # ؤ\\n\",\n    \"    \\\"\\\\u064A\\\":[\\\"\\\\u0649\\\", \\\".\\\", \\\".\\\"], #ي\\n\",\n    \"    \\\"\\\\u0626\\\":[\\\"\\\\u0621\\\", \\\"\\\\u0649\\\"], #ئ\\n\",\n    \"    \\\"\\\\u0629\\\":[\\\".\\\", \\\".\\\", \\\"\\\\u0647\\\"], #ه\\n\",\n    \"}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def preprocess(text):\\n\",\n    \"    char_comps = []\\n\",\n    \"    \\n\",\n    \"    diacritics = \\\"[ًٌٍَُِّْ]\\\"\\n\",\n    \"    numbers = '0123456789'\\n\",\n    \"    for diac in diacritics: \\n\",\n    \"        text = text.replace(diac, '')\\n\",\n    \"\\n\",\n    \"    for num in numbers: \\n\",\n    \"        text = text.replace(num, '')\\n\",\n    \"    \\n\",\n    \"    outText = \\\"\\\"\\n\",\n    \"    \\n\",\n    \"    for i in range(len(text)):\\n\",\n    \"    \\n\",\n    \"        if (text[i] == \\\" \\\"):\\n\",\n    \"            continue\\n\",\n    \"    \\n\",\n    \"        if text[i] in map_chars:\\n\",\n    \"            if (i < len(text) - 1 and text[i] == \\\"\\\\u0643\\\"):\\n\",\n    \"                if text[i+1] != ' ':\\n\",\n    \"                    char_comps.append({text[i] : '\\\\uFEDB'})\\n\",\n    \"                else:\\n\",\n    \"                    char_comps.append({text[i] : map_chars[text[i]]})\\n\",\n    \"            else:\\n\",\n    \"                char_comps.append({text[i] : map_chars[text[i]]})\\n\",\n    \"        else:\\n\",\n    \"                char_comps.append({text[i] : text[i]})\\n\",\n    \"\\n\",\n    \"    return char_comps\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def generate_words(file):\\n\",\n    \"    word_drawings = []\\n\",\n    \"    annot = file.split('/')[-1][:-5]\\n\",\n    \"    annot = re.sub('[0-9]', '', annot)\\n\",\n    \"    annot = re.sub('_', '', annot)\\n\",\n    \"    char_comps = preprocess(annot)\\n\",\n    \"    indices = [m.start() - i - 1 for i, m in enumerate(re.finditer(' ', annot))]\\n\",\n    \"    indices = indices + [len(annot) - len(indices)- 1]\\n\",\n    \"    drawing = json.load(open(file))\\n\",\n    \"    i, j  = 0, 0\\n\",\n    \"    c = 0\\n\",\n    \"    word = \\\"\\\"\\n\",\n    \"    for cntr, comp in enumerate(char_comps):\\n\",\n    \"        char = list(comp.keys())[0]\\n\",\n    \"        j += len(comp[char])\\n\",\n    \"        word += char\\n\",\n    \"        if cntr == indices[c]:\\n\",\n    \"            word_drawings.append({word:drawing[i:i+j]})\\n\",\n    \"            i = i+j\\n\",\n    \"            j = 0 \\n\",\n    \"            c += 1\\n\",\n    \"            word = \\\"\\\"\\n\",\n    \"    return word_drawings\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def apply_rdb(drawing, verbose = 0):\\n\",\n    \"    new_drawing = []\\n\",\n    \"    total_prev_strokes = 0\\n\",\n    \"    total_post_strokes = 0\\n\",\n    \"    for item in drawing:\\n\",\n    \"        char = list(item.keys())[0]\\n\",\n    \"        stroke = item[char]\\n\",\n    \"        processed_stroke = []\\n\",\n    \"        if len(stroke):\\n\",\n    \"            if verbose:\\n\",\n    \"                print('processing ', char)\\n\",\n    \"            post_stroke = rdp(stroke, epsilon = 2.0)\\n\",\n    \"            total_post_strokes += len(post_stroke)\\n\",\n    \"            total_prev_strokes += len(stroke)\\n\",\n    \"        new_drawing.append({char:post_stroke})\\n\",\n    \"    if verbose:\\n\",\n    \"        print('reduced from ', total_prev_strokes, ' to ', total_post_strokes)\\n\",\n    \"    return new_drawing\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import glob\\n\",\n    \"npy_files = glob.glob('dataset/**/*.json')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[{'و': [[386.76275634765625, 430.04132080078125], [375.49652099609375, 428.93536376953125], [373.561279296875, 428.93536376953125], [371.76416015625, 428.79718017578125], [368.446533203125, 427.48388671875], [367.4097900390625, 426.37799072265625], [366.64947509765625, 423.75140380859375], [366.51123046875, 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460.1082763671875], [92.182861328125, 459.693603515625], [90.800537109375, 459.2789306640625], [89.41815185546875, 458.72589111328125], [88.03579711914062, 458.10382080078125], [86.86080932617188, 457.34356689453125], [85.82403564453125, 456.3758544921875], [84.85638427734375, 455.20086669921875], [83.81964111328125, 453.88751220703125], [82.99017333984375, 452.43597412109375], [82.22991943359375, 451.19195556640625], [81.26226806640625, 449.878662109375], [80.501953125, 448.2197265625], [79.94903564453125, 446.6300048828125], [79.32696533203125, 444.90203857421875], [78.77401733398438, 442.96661376953125], [78.22109985351562, 440.89306640625], [77.73724365234375, 439.16510009765625], [77.25344848632812, 437.43707275390625], [77.184326171875, 435.22528076171875], [77.184326171875, 433.42816162109375], [77.59902954101562, 431.3546142578125], [78.1519775390625, 429.35009765625], [78.84317016601562, 427.552978515625], [79.11959838867188, 426.17059326171875], [79.39608764648438, 424.7882080078125], [79.53433227539062, 423.8896484375], [79.810791015625, 423.06024169921875], [80.08724975585938, 422.2999267578125], [80.64022827148438, 421.74688720703125], [81.19314575195312, 421.6778564453125], [81.46963500976562, 421.81610107421875]]}]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for item in generate_words(npy_files[0]):\\n\",\n    \"    word = list(item.keys())[0]\\n\",\n    \"    print(item[word])\\n\",\n    \"    break\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def add_z(drawing):\\n\",\n    \"    new_data = []\\n\",\n    \"    x_prev, y_prev = None, None\\n\",\n    \"    for item in drawing:\\n\",\n    \"        char = list(item.keys())[0]\\n\",\n    \"        if x_prev is None:\\n\",\n    \"            x_prev, y_prev = item[char][0]\\n\",\n    \"        stroke = item[char]\\n\",\n    \"        x_data = []\\n\",\n    \"        y_data = []\\n\",\n    \"        segments = []\\n\",\n    \"        \\n\",\n    \"        if len(stroke) == 1:\\n\",\n    \"            x, y = stroke[0]\\n\",\n    \"            stroke.append([x+1, y+1])\\n\",\n    \"            \\n\",\n    \"        for i, point in enumerate(stroke):\\n\",\n    \"            x, y = point\\n\",\n    \"            if i == len(stroke) - 1:\\n\",\n    \"                z = 1\\n\",\n    \"            else:\\n\",\n    \"                z = 0\\n\",\n    \"            if i >=0:\\n\",\n    \"                segments.append([x-x_prev, y-y_prev, z])\\n\",\n    \"            x_prev, y_prev = [x, y]\\n\",\n    \"        new_data += segments\\n\",\n    \"    return np.array(new_data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"cnts = Counter()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"save dataset\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"train_data = []\\n\",\n    \"valid_data = []\\n\",\n    \"test_data = []\\n\",\n    \"count = 0 \\n\",\n    \"cnts = Counter()\\n\",\n    \"for file in glob.glob('dataset/**/*.json'):\\n\",\n    \"    for item in generate_words(file):\\n\",\n    \"        word = list(item.keys())[0]\\n\",\n    \"        if cnts[word] < 5:\\n\",\n    \"            new_drawing = apply_rdb(item[word])\\n\",\n    \"            strokes = add_z(new_drawing)\\n\",\n    \"            cnts[word] += 1\\n\",\n    \"            if 'train' in file:\\n\",\n    \"                train_data.append(strokes)\\n\",\n    \"            elif 'valid' in file:\\n\",\n    \"                valid_data.append(strokes)\\n\",\n    \"            elif 'test' in file:\\n\",\n    \"                test_data.append(strokes)\\n\",\n    \"\\n\",\n    \"print(\\\"save dataset\\\")\\n\",\n    \"with open('dataset_words_str.npz', 'wb') as f:\\n\",\n    \"    pickle.dump({'train':train_data, 'valid':valid_data, 'test':test_data}, f, protocol=2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2430\"\n      ]\n     },\n     \"execution_count\": 43,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"len(cnts)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\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\": \"3.6.9\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "notebooks/data_visualizer.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import math, json\\n\",\n    \"from rdp import rdp\\n\",\n    \"import glob\\n\",\n    \"import io\\n\",\n    \"import base64\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from matplotlib import animation\\n\",\n    \"from IPython.display import HTML\\n\",\n    \"import time\\n\",\n    \"import matplotlib\\n\",\n    \"import random \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"30720\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from IPython.display import clear_output\\n\",\n    \"\\n\",\n    \"while(True):\\n\",\n    \"    clear_output(wait=True)\\n\",\n    \"    print(len(glob.glob('characters/**/**')))\\n\",\n    \"    time.sleep(1) \\n\",\n    \"    break\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def create_data(drawing, center_data = True):\\n\",\n    \"    data = []\\n\",\n    \"    new_data = []\\n\",\n    \"    total_len_strokes = 0\\n\",\n    \"    xs = []\\n\",\n    \"    ys = []\\n\",\n    \"    for item in drawing:\\n\",\n    \"        char = list(item.keys())[0]\\n\",\n    \"        stroke = item[char]\\n\",\n    \"        if len(stroke) > 1:\\n\",\n    \"            for j, (x, y) in enumerate(stroke):\\n\",\n    \"                z = 0 \\n\",\n    \"                if j == len(stroke) - 1:\\n\",\n    \"                    z = 1\\n\",\n    \"                data.append([x, y, z])\\n\",\n    \"        elif len(stroke) == 1:\\n\",\n    \"            x, y = stroke[0]\\n\",\n    \"            data.append([x, y, 0])\\n\",\n    \"            data.append([x+1, y+1, 1])\\n\",\n    \"            \\n\",\n    \"    min_x = min([point[0] for point in data])\\n\",\n    \"    max_x = max([point[0] for point in data])\\n\",\n    \"    min_y = min([point[1] for point in data])\\n\",\n    \"    max_y = max([point[1] for point in data])\\n\",\n    \"    \\n\",\n    \"    margin_x = (600 - max_x - min_x)/2\\n\",\n    \"    margin_y = (600 - max_y - min_y)/2\\n\",\n    \"    \\n\",\n    \"    new_data = [[x + margin_x, y + margin_y, z] for [x, y, z] in data]\\n\",\n    \"    \\n\",\n    \"    if center_data:\\n\",\n    \"        return new_data\\n\",\n    \"    else:\\n\",\n    \"        return data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(0.9411764705882353, 0.00784313725490196, 0.4980392156862745, 1.0)\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"cmap = matplotlib.cm.get_cmap('Accent')\\n\",\n    \"cmap(random.random())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def map_axes(i, num_sketches = 4):\\n\",\n    \"    sqrt = int(math.sqrt(num_sketches))\\n\",\n    \"    x = np.array(list(range(num_sketches))).reshape((sqrt, sqrt)) \\n\",\n    \"    dim1, dim2 = np.where(x == i)\\n\",\n    \"    return (int(dim1), int(dim2))\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def get_color():\\n\",\n    \"    bl = (0.0, 142/255, 204/255, 1.0)\\n\",\n    \"    cmap = matplotlib.cm.get_cmap('Accent')\\n\",\n    \"    rgba = cmap(random.random())\\n\",\n    \"    if rgba[0] == 1.0 and rgba[1] == 1.0 and rgba[2] == 0.6:\\n\",\n    \"        return bl\\n\",\n    \"    return rgba \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(0.7490196078431373, 0.3568627450980392, 0.09019607843137253, 1.0)\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"get_color()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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1j3BJxolHSFqHaq1Grt0Lq/DHPJKYkTgdTBOq+l6GlGHXnf2HWdSgm65i2S4y9Nbtr+ekTW/n2NbO5dGnWWDenX02eJgJqgARLAnfMumPAbc3x6QB4W+uOR9OOiQkbCYRHa0OEo5HR76NKB65AiHMyI/jzivR+A6kj6RSF/GgzqeHSWyTGN11OZzBVJsGUmLi6pva1+drGtiHixJMwBUwR4G4Bx/ioyadEZ6DMvxksMdBRj7rnOdSAZLM82VU2OvEFQvy/53ayr3z8fqc3e7RC1HHmuEG3NUbEAuB3jN/fZzATNpjqyujnaDv6YMqg03qhwk36Me+REmK06bK0h9BQhfToi+FTQyH8zjZCXRmXxkh0WDQgI1Ni9CmKDmK1AufYq8e2MUdQLLEoc286HFDtfRk1FBzrZokxdMNZ+XzxzDxCKvz+2Z0EguOz5Embtw2AaFP04Bt3rQlUx+fvMhRjGky5nT4cIxxZsthM6A06fJ4APu/RfcnPjtcCsy11Tkm7K044ugwtmFJlzZQYhvbCLez/+9dZ97UprP/aVNbems3m7y3n0OsPEPQe/zT7XcFUq3fi9l6K8UuxJQBaQopxxWhBmXIx6E3QXoFa+I48p5zkvnrRVFLjrBTX2Hl9/aGxbk6fWrwtwNBGpkJ+bcRVZxrf68AGMmZrprweP7vWH8LnDTB3WXb3SNNQKYqCxWaio92Dx+U/qox+0+PMpNiMVHX4eamojWsmx4z4WEKMN0qiDQC10YUaCqH0sQBUiC7OqgMUPfUj2go+635Nbw4n6OnAVVNE2Yu/p/qDJ5l0829IWHTRcWtXuDEco86IK+DCHXBjMUzcL14xDtnitf+OUjClhoLasTrqUd2t4HdBwA2hQOefoPZHDWo98zoj6A2AAkEfBLzgd4LP2fPA9XtQjVbIPUNm0pykzCYD91w+gx/+awv/fKuAM+amEBtpHnzH48jp167bCNPgOQ1Cfi8AOuPEXTYzJsGU3xdg1/pDuJ0+wqPNWEe47kgNab0zR3s/0SkKPzklma9/VMkt75URVFWumxI7vLaoKp/VOHmpqJUZcRa+Miv+6BolxChRjHqwGcHphw4fjLObrhgfVFWl5oMnKPnfrwj5PRisUaSedTMpZ9yIJTGLoNdF24GNlL34ezrKdx8utHic6BQdMWExNLgbaPO2STAlRpel8zvfPfKRTzUUhOZi1Mb90FysBU2jwWjVAqyu41Vt0kaqspeNzvHFhLNyTgpLpieyYX8DT60u4r6rZ411k3pw+LRSLDaDbdBt/Q6tA8Ngiz6WTTqmxiSYKtlbj9PuxRpuYs7SLAxG/bCP0dHuwenwougULKOQBOLO2QnsaHDz+N4mvriqjJeLWvnj8nSyo8IG3M8fVHmpqJWvfXgIh0+b72nQIcGUGHX/2d9MhcPHDxYld6/zGyrFZkJ1+lE7fCgSTInPCbjsHHjsPpq2vA1A8orryLvhlxhth+s56cOsxM05k9hZp9O86wPi5p5z3NtpM9rAfbjXU4hRE9bZg+7rQFXVYY36qH431O7UsgH6Og6/YY6G8GSwxqGYbGC0aCNQOn3nHwMoem10KhiAkB9UFQwmLVgyhYPJhqLotKl97hZoLkIt/QT10FrtvdR5o/v/QUwIiqJw5yXT2bC/gVWbK7jtwilEWvt+FlZD2ujn8RzJ7PBrn4OhjEy568sBsCRlH8MWHVvHPZgK+IPUV2kLiGeekjmi6XmBQJB9W6oAsLr20VHcTvTUJUPat9kd4Jcba/j9snSsxsPTnRRF4dGzM1mQaOW7a6t4qaiNV4vbOC87kvOzopifaCU9wkikSY9Jr1DS5uWXG2t5pbit1zn+uDx92L+TEANxB0Lc9n45gZD299+cNrwq4Uq8FbXBidrshtTIYZ//6f3N/GBdNUk2AwZFodzuo9EdYFGSldcvzSclfPAMmGJ8ctWWsOfPN+OuK0FviWDKV/5M4imX9ru9otMRP+/cIR//YOtBPqr+iEAogM1oI9wQzpV5V2LQDf/eH24MBw5/UQsxWhRFh6ozdE7B82vBzCDUoB+14jOo2qLtA2CNR0meDQlTUcyjV1xaURSwxml/jFbUg2+jFq+G8ESUyOF9H4gTw6T0KBZMimdbURNPrS7instn9NrG3VhBwd/vIvXMm0le/oXj1jZ3UMuHMJQZBO76Mm3bxOxj2aRj6rgHU/WV7YSCKtHxVmyRA4/69CUUDLFvcxUuhxd9oBXnu9/FlfybIQdTX1xVStHBvczY9QRf/cZPUHSHR8UUReGOOQlckBPJT9bX8OzBFlaV2VlVNrSF+zdOjeWnp6YwOab/nn+n00lHRwcxMTGYTBN3fqg4vhpdAQKdiW7+tK2e22fGDzpqeiQlTruhqc0jSxxw83vlANQ4/T1e31Lvosbpk2BqgmrZu4b9D95OwNWOLWMaM775b6zJOaN2/E+rP+Xj6o+7f272NGPQGdArw5+NABCm1655X8g3Ku0TogdDGPgC2nqlQYIp1dmIuu8VbbQIIDobJWMxxOQe8xEAJXk2akedVtS34HVYcBuKYfjPU2Liu/vyGXz5j5/y4qelXLksm7T4ntPq2gs2YC/airOygMhJC7Em5x7zNvmCPgKhAHpFj0k3+HOut6UWOFxvaiI67ivRWxu16RnJGdHD3jcUCrF3cxUt9R0YTXpiHR+gBD3aIs6hnHvvWn684Xae2H8XU7Y+QsXu9X1ulxUZxtMX5FD71dn886xM4i39x5xxZj2Pn5NF211zefqCnAEDKYB//OMf/OxnP6OsrGxIbRYCwO47fI37girfWVM1rP2VBO0GG6obfo9+TUffD66z4i28e0U+C5IGnxMtxp+q1f9i9/+7joCrnbgF5zPv52+NaiDV4mnpEUh1pcg16Uw4AyObptcVhAVGay2KEEfqDNYJeAfcTG0qRN3+lBZIWeNR5t6Ebs4XUWLzjttUKiX3LAhPAk87aulHx+WcYvyZmhnN+YvS8QdC/PWVvb3eT1r+BRJOvYygx0nBw3cel9T6Dr+2XircGD6kz0PQrW1vsI7eSO7xdtxHprrSmJttwxuVCYVU9m2uornOgcGoZ+6ybFrWxNEEdFTsG3T/xq3vsP+hr6EGfLiNEXwYtRTHPi9/n62i/9z6k5CqsrnOyXMHW3mhsJUmt9ZmBTg9I4Kbp8VyZX4MkWHD7121WLQRApfr+KcWFhOXu3NYKtFqoMMX4uXiNtZUOViRPvh8ZABdVjQAoUNtwz53vUu7/mfHW9h10/Rh7y/Gn9o1z1H8nx8DkHnZveRc9b1Rz/IY+lzNkK5iu66Ai4+qPuLSnP6nEvanK5j6/LGFGBX6zhH2AYJ1tWE/asEbgAqJM1Amn48yhCmBo03R6WHqJajbntDWayXPlul+J6k7L5nOmj11rN1TR0FFK9MyD2ekVhSFKbf+EXvRVhxlu2jY9CZJSy4/pu0ZznopAH+HlvTFYJNgasj8ncGUaRiBSDAQYt+WSprrOjAYdcw9LYvwKDPBzql9TdtXk3/zb9Dp+/51Wvevo+CRu1ADPtLOuZXQBd/n2hdLcTSE2PX8Qa6eFE2ESU+DK8DuJhdrqjtocB2+mU6KDuNLM+K4cWocmZFHd9OUYEoMRlVV1FoHarMb/awkAOpd2vS6eQlWTk2x8cuNtfx+S93Qg6nsaO3Yh4Zf8LRrVCxqBJ0HYvxp3buWg49/G4D8m39D+rm3HZPzxFviuX7y9ZS2lxJQA9Q566hyaiOqOmVkgZtRpz3sDjbNz/foVjw//QjTbfMx//bsEZ1LnIS6pp/2E0ypTYWHA6nMpSjZK0ZlJKrF7sEcZsA6zDXkii0BNX0RVG5Erd4mwdRJKjHGwqVLsnju4xLe3FDRI5gCMFgjybrsXgr//V3KX/4DCYsv7vd5eTR0FVaPNA6+PltVVdx1pcDEnuY3ZnWmhlpzzuv2s3dTJfZWNwajnjmnZRHRWZMqMn8BluRc3HWl1H78X9LO/lKv/Y8ckUpeeT35N/8GRVF449J8rltVyqY6J5vqek85yYgwcvWkGK6bEsuiJOuoDd1HRGgPvw6HY1SOJ04cgXWH8P52LcEdtagNTnTzkonYegcAh+zaw2NmhIl75ibyuy11vFtup7rDR9oA2Sw/OGTngR0NnG0y8WVAdQw8faUvzZ0jszESTE14nqYq9j/8NQgFybj47mMWSHkCHv5d8G9UVNwBd4+EEflR+ZyZduaIjtu1mNkT8Ay6rdrkItQknVZiGLqSovQxFUptr9LWJ6FC1mnoslcc9el2l7bwpxd2UVRtR69TuP7MPO68dPqwnjeU5NmolRu1lOyhYI914OLkcdEpGTz3cQkfbq/mvqtmYTT07LBKXnEdlW//HXddKfXrXiRl5RePWVtqXdoaqERr4qDbeltq8TtaMITHEBY3cTsDjnswFWY14urw4XH7CY8aeH1RU52DA9ur8XuDhFmMzFma1SNphaIo5Fz9PfY/dAfFz/ycsLjU7ixToWCAyrcepuyl/wdqiNSzv8Skm3/bfZM6PSOCA7fM4NWSNnY0uPAEVGLNeqbEmlmSEs6UmLBjMvc5OjoagNbWkdeyECcm99ffJrS3QfshKgxdvK07RW91hzYylRlpIs5i4MLsKF4raePt0na+Ojuh32MWtXl5u6ydZGvnjdU//OlRRW1aAJYzjIQXYvxRVZUDj34Tv6OFmFlnkPuFHx6zc4UI0eBu6P7ZqDOSG5nLwsSF5Eflj/jeauqcTjVoAopo7btFbXWP6DyfFwiG2F3agtMTYH5+HDaLJFw5maiuFtS9L2kjVslzULKWH9XxQiGVR98u4D/vF6GqYDbp8fiCPP1BMTkpkVywOGPIx1KscajmKPC0g6dNy/YnTjr5aVHkJEdQVudgR3ETi6f2DGR0BiNZV3ybA/+4m/JX/kTS0ivRGY/Nd3qFowKAdNvgI03Oyv0A2NKmTOgi1Mc9mLLYTLTixNHqJj657ylKPk+Akn311FW0ARCTYGP6ovQ+06gnnno57YVbqF79L/befwvxCy7AHJ9B884PcNeVAJB91XfJuvxbvf6hos0Gvjwjni/3ziZ5zMTHa/WnGhoaBtlSnGy61jPp8mLQn5IOBh2+v23C9NUFdPi1ntKIznT+Z2dG8FpJG+tqOvoNplRVZfUhLRNlsl7penHY7drSOXI7J6F3itOQGkJBmdA3wZNF09Z3aNu/HmNELNPu/Nsx7cEO04dx58w7AS0AijJFjXhq35EUtOtMZeDrWBdv1barP/oU6hUNHXz/sU2UdyZviYkI48GvLyE/beLO7xdDp4YCqPtfgYAbYvO0NVJHcb8LhVR+/9xO3txQgV6ncOM5+Xz5/Cm8sracB1/dy4Z99cMKpgAwWIB2rbCvOGmtnJNCWZ2Dj3bU9AqmAJKWXkHlWw/hrDpA7Sf/I+2cL496G9wBNzXOGnSKjoyIwa/jtgMbAYjMnz/qbTmejns2v4TOGje1h9oIBnv2krs6vJTsrWPj6iLqKtpQdAp5M5OYc1rWgPWo8m/6NdlXfx9Fp6dp6yqq3v0n7roSzIlZzP7es2Rf8e1x87CXlqYNY1ZVDS8bmzjxmX91JigQKmnF/789+P+zC8997+K67iW8Qe3hMUyvfWRnxWuBzcHWvqc7VXf4uPbtMl4raSNMr3BLuPbgrAaHH0xtrteCqcVHZO1rcjfx7/3/5v+2/B+/3/573ip/C29w+FMIxfFT9d6jAGRd8S1Mkce2qLhe0ZNkTSLJmkRMWMyoBFLDoZuudTAEd9Wj+keevepQvYM7/rKW8roOUmKt5KVG0urw8sN/bcEfkCQYJwO1YgM4G8ESgzL9cpSjvJZfXFPKmxsqCDPq+dMdp3LHJdMJM+pJ6ly+4A2M5HrtvK8fh0xtYvw6e772fPnRzhrc3t7r/hSdnuwrvwPAodcfIOgbnZH7I5XZy1BRyQjP6C5lMZDWfWsAiJm5ctTbcjwd95GpmAQblnAT7g4fezdWkpwZhdPhpbmug472ww+GcUnh5M9Kxhox+D+GoihkX34fycuvpXnn+wTdDqxpk4mdfeYxXWQ3EnFxcZjNZux2O62trcTExAy+0wiEGp0Ed9ZpPbPeICigRJtRMqLQT4tHCZcpW+NN2DdOxXjxZILbalHdfrwPbiS0o47A2kP479KSrZg6R5i6UvAXtnq7pwL6gyobajt4fG8Tzx1sxR9SCTfqeO7CXLJ3VuOCYY9M1Xb4qXT4iQrTMy1OO2e7t50nDjyB068FWd6gl60NW6l0VHLz1JuxGXunSg+EAvhCPqwG6wj/74ijoYaCtBduAUUhefl1Iz5OyO/F01SFt6UWX3sDAWcbAXcHAZe98087AWc7QbeDoMdJ0Och6Okg6HESCvh6POwpBhMGWxTmuDSiJp9CwimXEJm/YFQ6vnTJEeimxBE62ExwczWG0zKHfQx/IMQPH99Ca4ePU6Ym8LuvLEZRFG75fx9T0eBkY0EDy2clH3VbxfilulqgYgMAyuQLjzprnz8Q4qnVRQD84ub5nDrt8OhBSY02i2DN7jp+/9xOpmfGcMmSzEE/D6rPqQV7ALZj20kixs7ByjZ+8Nhmvn75jO6g6fPyUiOZmR3D3vJW3t5UwdUreteUil94IeHZs+ko303tJ8+Qfu5Xeryvqio7ipuZmhk97IQoAIcchwDIiRy8zIbf0UJH+R4UYxhRkxcN+1zjyXGPNBRFYebiDLavKaOloYOWhsPTMPQGHfEpEaTlxhIVO7yHrrJaOzkpqaSddctoN3nYVFWlptnVq3gagE6nIy8vj3379lFaWsqCBQtG77xuP75/bMH39C5Cu+oH3FaXG4NuZiLW569BMY2vgPNko6oqakU7wb0NqC1uQgca8b9xkNB+7Qsy7N5Tu1OjWzoXlSZZDUSadLR5g/xjdxMbazt4vbSddq/2sKpT4JpJMfxuWRp50WH4d1SPqG1rq7VEKfMTreg6v9Q/qPoAp99JdkQ2X5j0BRw+By8Uv0C9u543yt7gi5N7Lmwtt5fz5IEnMegM3D3rbqLDokfUFnF0FJ0eNRDEVVNIZF7fUypCAR/uhkO468vwNFTgba3D11aPp6kKT2MF3paaEU0V7Ysa8OFvb8Tf3oijdCdV7/6TyEkLyb/x/4jMm9f3Pp098F3T/QZiOCcP38FmAquLRxRMvfHZIcrqHGQk2PjtVxZj6XywWD4rhWc+LKa4ul2CqROcemgdqEFImoUSPfxr6PP2lrfQ6vASbjEwOT2KrQcb2XeolR3FzWwqODz1//X1h3h9/SH0OoWLTh3kvLU7QA1BXD6KYeB16GLi+s/7RdS1uvnpE1v7DaYArj0jj71PbOXVdeVctTynVzCu6HRkXX4f+x74MpVvP0LqWV/qMeiwsaCBbz2iTb3b8LfLht3Ockc5AJkRg39emnd9BKpKVP4C9GETu6N1TJ6iw6PMLDozj8riZnweP2ariegEGzEJNvT64Q+h7y1r4fb713LOgjR+ecvo9GyOVCAY4obffkxVYwerfncBUX3U00pPT2ffvn1UV1ePWjClOrx0rPg3od2dQZTViH5+CrqMKAjTQ0jVHtQPtRE60ESotBXVE5BAahwIbqjEufzfvV5XUsIx/+FcjF+chf31YgD0ipahb011B3afFmDd9VFF9z5TY81clhvF12Yn9EgY0ZXFTxlmfbcXCrVEKRflaOtDfEEf+1u0BaOX516O1WDFarBy85Sb+dvuv3Gw7SBt3rbugKnGWcOTB54EtNGpoTwEi9Gn6PQknXY1dZ/+jx2/uoyYmSsIi0tDURSCPje+tkYtgGqs0B7M+j8QYfFpmOPTMUUlYgyPxmCNQm8Ox2CNxGCLxmCLxGCJRB9mRWcyo7dEoDfb0BmMKHojiqKgqiohv5dARyuuuhKad35A3ZoXsBdtZcevLiHz0m+Qddl96Aw9Ez101ZcayrRBw7n5+B7aTGB1Cfxy+NkD39lSCcDtF03t0UMb6gwmDQN8VwUDIVqbnNhbXLgcXryeAH5fEFRQFK3jMDLWSkJqBFFxNnQ6+VyMC13XvqKgBjzQdFD7MXtZz81UFbW8jcC6CkIVbRBUMZyZg2FZ1oCHj4vUgp0Od4CrfvlBn9ucNS+VDrefTQca2VveOmAwpXodqFVbtDamTeyefTGwjITDnfNub6C7c+fzVs5OITrcRGmtgx3Fzcyf1Hu0Mn7+ed2ZsFt2fkD8gvO734u0Hr7nHqxsY0pG9JDb2O5tp95Vj1FnJCN88PVSNR8+BWi5Dya6MXuStthMTJ6TMirH+ttrWtHe1LjRS2E+Uga9jqQYCxUNHazfW8eFp/S+EWZnZwNQVlY2auf1PrCB0O56dPmxmP9wLobz81H6+bCp/iChwmbUxt4p4cXxp5+VhJJoQzcrEV2CDV12NPrTMjGcnYtiMtDiCbCqTJsCcu2qvq+Z356WylWTYrqn/31eqHMhvpIcPuR21Tv9vF7ahk6Baydr01E7/B0E1SCRpsgeI0xRYVFMip5EQWsBJe0lLEhcQJO7iUf3Pdq9zQ2TbyAqTBbtj5XJX/49ik5P7Sf/pWXXh31vpOgwJ2RiScrBkpRFWGwapqgEwuLTMcdnYI5PQ2c4+gKliqKgN5nRx6YQFptCzPRlZF/xHcpf/gNV7z7KoVfvp/3ARmbe9xQG6+FERV3B+FCK9hqWa/fe4M46VN/wOo7aOrzsP9SKyaBj2cyeo09d07GSY3snZHG0uik/2EhzfQdqaOARPEebh+rSFsxWI6eeO2nMv7sE4O9MpW+0QEOBlr0vOgvFHI2qqgS31uB/Zjf+1w+gVvSs2ef95SeE/Wg5Yb84A6WfQDszMZxf3rKAZz8qprXDR0KUmckZUTS1e1izu46ZOTH85MZ5fOXPawHIGeB+raoq6oG3IOCBmFyIHjiQExNblO1w5+iHO6q5+NS+/72NBh1XL8/h8XcO8tTqwj6DKUWnI+X0Gyh97v+oW/N8j2DqyBpVP/73Vl76+dDr9O1u3g1o5S+6agL2p+3ARuxFW9BbI0k87aohn2O8mvDDEg2tbnaXtmAN03PTOZOO67lVVeWvr+zl/W3VfO/aOazsDA6Xz0pmy8FGPttX32cwlZ6upYusqakZtbb4X9JGC8wPXYTxnLwBt1WMevQzBs//L44PJSKMyNrv9nq9sNXD7z+p4pkDLT1en5do4Yz0CHY1uvmw0sHXZsXzw8WDdEw4/Z3n6vtBOBgMoShKjx7yR3Y3EgjBZXlRpHfuF2GKQKfosPvstHpbiQk7fOPNjMikoLWAGmcNKc6UHoHUjVNuJD8qf+A2imNKZzAx5bY/kn3Ft2gv2oLf0QKqis4UhjEyHktiFpbE7GOWLncwBks4+Tf+ivgF57P/4TtpK/iMvQ98mTk/eL4782BXnamyxmYqwzvISOz/YVOJNKObHEeosJngrnoMiw5PjdlZ3Mzjqw50JgE4pVcgU17XgarCpPSoHj3Au0tb2HygEZNBx8LJh7Noelw+inbX0VR7uH5gZIyF6Hgb4VFhhFmMGE16FJ2CGlLx+4I013fQWGMnItosgdR44evsYDTZUFu1QqLETsX3xA5tDevuw9PnlVgL+tMy0c9IIFTvxP/UTry/XUtgUxXWZ65Gl9B7mj/AuQvTOXdhz5TRP/qXNrq0fGYyP31iGyU1djISbFy8ZIAAqWoLtJWD0YIy9SK5hk5woSOmVz/7UQkXndL/erprTs/lvx8Ws/lAI5UNfd8nk5ZdQ+kLv6V55/v4HM2YIrSU+jqdwinTEtlU0EB1k5N95a3MyB58bb8v6GNjvTY9cH7CwJn5gj43B/+lJcJIP/c2DOa+PysTyYQPprYUautK5k2Kx2Ye/dof5XUO/vTCbhQFfnHLgu5heoAPtlfz/CfaDfep1YWsnJOCqqrUtmi9Wx1uf5/HjI+Px2g00tbWhtvtxmLp3cM5HKovoK2v0SndvbFi4gqEVH6xoYbfbakjpNJjYtzOG6cxJ0GbW/xqcSsfVjqocfZ9nfXQdZA+OsvdTh9bPy7FYNSx6Kw8DAY99U4/f9muPTh8a35S97ZGnZGZsTPZ3bybt8rf4ouTvoihs9BlvFnrAdvWuI1tjdu697ll6i1DWowqjo+w2BQST7l0rJvRr+hpS5n38zfZ/vMLadu/jvJX/kTO1d8HIMqkjWzWOBrYVdY8YDAFoF+WqQVTn5R1B1Pbi5r426t7OVCpjSwcqGzr0RsL0NiuZblKjD58b25oc/Pzp7YC8MUz84jpTI7UUN3Oge01BAMhdDqFtNxYMibFETbI91F0vI3c6YmERpBhU4w+NeiHoBcUvZZqvEO7/zmv/4TQZ9popJJgxXj9bIzXzkS/KBVFd3gEKnDDbFzXv0TwwzL8z+0h7J5Th3zuzETtYfJ/H5XQ7vQRYTHy/25f3G8CALW5GLX0I61Nky9EMQ19xoGYmEJHjHSX1jpYva2a8xb2Xccp0mri9DkpvLulio931nDzuZN7bRMWnUjMjBW07vmYpq3vkHrGjd3vXbksu3sN32NvF/DA15cO2r6N9Rtx+p2k2lIH7Tgtf+VPuGuLsaZOIvPSbw567InguKdGH20NnUUZc/qpWXU09h9q5fb717CtqImthU08+1FJ93tub4C/v76/++eCijZ+/cwO7v7bZ93bXbo0u8/j6nQ6EhK0Xs36+oETRQyJOwAhFWxGlGMQUIrjp8rh48yXCvnN5jpUFW6bGUfhl2f0yuIHEGfWvmhbPUNIh9tVZLSPAL/2UCsBfxCPy09jtZ2KQ6288V4R8arKhTmRrEjv+dk6K/0sLHoLJe0lPL7/cdbXrmdz/WY+qvqo17HvmHmHBFJi2CwJmUy/6xFQdBx6/a/YS7YDkGxLRgmasEV6qXcOXqvPcLp27QU+KcflDXD/S7v5+oPruwMpgKb23uUF7J0dFNHh2ojsloON3PanNdS1uJmRHcOt508BwOnwsn9LFcFAiITUCE49dxL5s5IHDaS6KIqC3jDhv4bHNd9TO/E+sgV1sJTjAe06UA1h+J/fi9qmXSOhXXZ0U+OxPHE5EYe+heX+8zGckt4jkAIwnJFD+NavEfaTFZjuPmVIbVNVlb1lLdQ0aR2w7U4fafFW/n7vMnJSIvvex1GLuv81QIXMpSjxvR+UxYmrax3+X1/eQ6uj/3IkZ85LBeDNjRU9ArEjJSy6EIDmnT3X7y2dkYSp87606UAju0qbB2yT3WdnbY02NfXsjLMHHCVt2buGyrf/DorClK8+gN7Ue2lCbYefb31aiX8CdTRN+JGprpGiFvvo1bhpd/r434fF/O+jYgJBFZvZgNMTYPOBBkCr8PvEuwepa3UzKT2Ks+el8sibBby9UUsEYA3T89Mb53P63NR+z5GSkkJNTQ3V1dXda6hGzGYEswEcPkKNzn6nF4jxraDZzTmvFFHd4SfFZuS5C3NYkR6BOxDCF1Qx6ZXubH4AkSZt6pPdN3gwpURqvehqH3WpmusOZ9Q8sF2bepqvwBXRRu4+o/dIZ1RYFDdNvYnnip6jzlVHnauuz3N+d953+0yTLsSRVFUl4GrH395EwG1HDYVAUTCERxMzcyWtez7m0BsPMuu+J9EreuJ0WTRRREFrATDwonvDCm2a1M7iJv7y+4+pbnKh1ynccu5kalqcvLu5ik931bJ8Vs9psuYw7bNVXufgN8/s4K3Oe/vcvDh+f/tiTEbt/bqKNlQVkjOjmTo/VaZajTNqqxvPd99DbXajy47GeMEASwECHkItKp7fOwlufRnbCyYUs4Ll4XMwXr+k33VQR9KlRWIeJNmJ0+1nR4mWvW/Dfm0qFWhrXb6wMpfbLpjSb3IB1d2KuvdFCPkhaSZK9opB2yRODF0Jb85dmEZxtZ0dxc38+cXd/PrWvu+BS6YnkRxroarRydbCxj6L+MbM0mo7tRds6C6x0nWur1w4lb+/oQ0YPPb2AR6657R+27a6YjX+kJ+pMVPJjeydjr2Lq66U/Q/eDqpK1hXfIiq/dwI2VVW58q0SNtY6qXT4eOGi3AlxX53wwVRW5wLNdXvrsLt8RFpHtjja4wuydk8tH2yvZsP+hu6CjFcuy+bOS6dz8Y/fo6jazgfbqtla2Mjrnx1CUeDbV89iTl4cM3NiOVDRRmxkGEumJ/WZxa9Hu7Oy2LZtGyUlJZx2Wv8X6VAoBj2G5VkE3i/B/7/dhH1zyVEdTxx/9U4/53YGUsvTwnn54lwSOrPqeDuvRbO+5w3FZtRurk7/4IvxdWlaL2eotLXH626nr7u+m96gIxgIUeILsUvVcdc5uWRH9b1+JtWWytdnfZ2ClgJqXDV4/D52t+zsfv97876H1TixU52K0RcKBugo20174WYc5btxVh3EXVdKaJDikb6W2u6/n5d/Gr943k9VmZUPc6s5a17/aYJrLQb+eXEe72dGQpOLSWmR/PiGeUzJiKaysYMPtlXz9qZKVsxOYcXswwGVsfPBZUdxMzuKmzEadHzpvMncfM6kHln8/J2FMWXd0/jk+fWnqM1u9KdnYzh/4KlHgQ9Kcd/hQ20CJc4ClmigBeP5cUMKpPrTVbdnw/56dhY3U1DRRvCIkYLocBOXnJrJFctzSBmgJIzaXIxa+I62ris6W5veJ9fcSaNrpCgQVPnxDfO46Xcf8+GOGs7Y0fc90KDXccHiDJ54t5CPdtT0GUyZ4zMwRsbjtzfhaTiEJSm7+72rVuTwz7cKCIZUthU2sbu0hdm5sb2OcbD1IHtb9mLQGTg349x+2x/0uSl4+E4Crnbi5p9H9hXf7nM7RVH42+kZnPdqERfnRE+Ya3xCBFN2nx2bwYa+cxHykWbnxDJ/Ujzbi5r4+ZPb+O1ti/rt1fm8dqePLQcaWb+vjvV763F0ToFSFDhlWiK3XziFGdnaxbNidjLvb6vmp09qc+b1OoXvXTeHOXnaor35k+L7zJrSn6lTpwJw8ODBIe8zENOdiwi8X4LnF59gODcf/bSEwXcS48adH1VQ1eFnaYqNd6+YhNV4+Mu7a6Rb/7mbSljnF7wvNHgwpV+s3WwDn5YT3FaDfoE2alpZrA3fJ2VEkTcjiTq7l+wwA7dFhQ16EwvThzE3YS45zunccv87JE6OITVHC9YkkBIAQZ8HR+lO7MVbaS/aSlvBZwRd9l7b6c3hmKISMFgjUfQG1FCQUMCHGgxgScwm6/L7uredFJfNZdOX85eCvfzyP9upaXJx3qJ0EqK0gMbp8bOtsIlX15WzsaABMiMxBENcF23lq99ZibHzoSQjIZzbLpjCP986wI/+tYUl05MwGhQKq+zdowWgfRfcd9VMspJ6TyWPiLFQe6iNxho7abmxE+aL/2QQLGrG99BmUMDy5/P6/bdRQyG8v16D95efAKCfa8H69l0ovr2oZZ+gNhagJEwdcTv++0Fxdw8/aM8OM3NiWDQlgSXTk5ieFYN+gNT4qs+FWvI+NHQeIyoDZeZV3UlZPi908G1w1EHADX43hALaCFbmUrk+JzBj5zOBLxAiLd7G3ZfP4I8v7OaBl/awZHpSn+vrzpyXxhPvFvLhjmruvWom5s9lNFUUhci8eTTveJ/2oq09gilrmIHbL5rKP94sAOCNz8p7BVOt3lbeKHtDO1famcSaewdbAAGPk31/vQ1H2S7MCRlM/dqD/V6/AAuTbZR8eSbR5gkRogDjOJjy+YOYjHpaPC08eeBJ0m3pXJV3Va+ASlEUvnPNLO7863o2FjRw7a8/5OrlOZw2M5mMBBsmo55QSKXN6aOysYOiqnYKDrVRUNFGeb2jR/3JqRlRXLA4gzPmpZIQ1TMpxLevmY3VbOBQfQeZCVZiA2XMSh78IbY/6enpGAwGmpqa8Hq9hIUdXQYtw6VTMFw1ncDL+3Ge9SS2d25CP2foBSVVVcX/7B58T+zAcGYO5h/K9IHj5dMqB68WtxFh0vHcRbk9AinQElIAGD73hdu1jso7hHnFupQIHFdNI+LlAjqufYGIrV+jvt1LdamWKTBzUjxhFiNZls/V9QmplNY5sJj0fRahtrt8fOWBD6hvAKc3meTMNnR6FW/QS5h+bLLCibET8DixF22hrWAD7YWbsJfsQPX3nIJtScohetpSIvLmEZ4xHWtqPgZr3+tD+nPNylxqW9w893EJf39jP39/Yz9Ggw4F7WGji8mo48wYG1+4fyMZ81K6A6kut5w7Ga8/xH9WF7Ju7+HpqhEWI06Pn7svn8k1K3P6rSmVmBZFyd562ppcNNU6SEgd3u8hjh3fo1shEMJ4y1z0c/vOdqp6A7i/9Cr+F/aBTsF0iw7TnRnokiNQPdOhfA00HkR1NaNY40bUjrMXpPHy2jLOWZDG/Px4ZufGYrMMvqZODQWhdgdq+TotMNIZULJXQvpClIHqrLlb4XPrCdXyNSjRWRDVd8ICMf51jUx1zZq6/LRs3tpYQUFFG0+/X8TXLp7Wa5/81EhmZsewt7yVV9cd4otn9s70HDlpEc073sdevJXkZVf3eO/a03O7g6m3N1Xy3WvnENY5xdkb9PJc4XM4A05yI3M5NbnvhCsdFfsp+MfdOCv2Y4yMY9Z3/ovR1rNEiqqqtO1bR8zM5d2vTaRACsZpMPXCJyW8uaGCv92zFDduvEEvB9oOUOOq6bMQWE5KJI9+azk/+fcWiqrtPPJmAY90XgBGg45AMNQjaOpi0CvMzo1jyfREls1MJnuAJBZRNhM/uG4uADt37uSRR1bTWFHAT37ykxH9juoRDVKHMLIwGEVRsD55Oc42N8EPy+g4/Qmsz16N8fyB08WroRCB90pwXfzM4ReDIZBg6rj59SZtCtN3FiSR0Ufq8q4r5fOdisbO4Kq/RZqqqtKy60MqV/2D6fc8Rvl1M8jbVou1rI2qS/9Hyb2ngKKQMy2B8Kjei0ADwRDf+eem7qw+Z85L5ac3zsfcuVar1eHl6w99QlWdH2uEhx/dmssOezV2nx1XwCXB1DFU/9krqAE/iUuv7FXY9nhSQ0HsJTtoK/iM1j2f0F64GTUY6LGNLX0qUVMWE5m3gOjpSzHHD17McTCKovDNK2eyeGoCL68tY1dJMx1u7bxGg4781EjOmp/GRadkEBUIYf/9ZwQ/PUSosl0rZH7Ecb528TQuW5rFnrIWFBRS4qxMyYgasChvF6NJT+70RIp213FwRw3hkWYs4Udfh0scHTUUwv/8XgBMX+k7TbPqC+C68jkC7xZDZBiWR5dhSFzX/VSkmKNQk2ZB3S7U0o9QZl4zorakxFp5+RfnDDj61LPtQWjYh3poHXg6E6VEZ6FMvgDFMniKaiX3TNSC18HTpr2gM0LKXIgYnbqeYmx0Zat2uHyAlsL8vqtn8dX71/L8xyVce3ou0eG9v3NvPncy33t0E899XMxVy7O713t2iZq0EAB70dZe+5pNBv769SV88+ENADz/cQk3nzsZX9DHc0XPUe+uJzYslmvyr+lVSD3gslO1+l8ceu0vqAEfluQ8Zn37P1hTegd0pc//msq3Hib76u+TfcQshIlk3AVTHl+QV9aVc6i+g7++spef37yAm6bcRIe/Y8CKypmJ4Tz5vdP5bH89H26vZmdJMw2t7u4oPtJqJDXOSm5KJFMzo5meFUN+WmR3lD0c27ZpaZ8XLRp5xfENGzYQCASIoYONX81lxb/Lh1zfRQ0EcT+8FndlKXF/urX7dcVqwvbG9bhveRX/S/txXfQMhnPzMN2xqFcRX9Xjx/fvHXjuWdXr+JZHLhnx7yWGp7bDzwcVDiwGhXvm9l37q2ttlGsIa6O6tBV8RumLv8deuFk7z0dPsfiiuykNgem21+nIjsZo1JM7M4nUnL6H5ldtqmRTQQMWkx5FgY921NDi8HLr+VMorm7nfx8W02T3Ygn38vUbYzkjdxEH9mzFjh1PwAMSSx0ToYCPkmf/D19rLWUv/4GMi+4iZcV16I9DrY5QwE/HoT1awcXibbQVfEag44h1eDo9EXnziJ66hKgppxI1aSHGiL6vr9GwZHoSS6YnoaoqvoDWaWYy6HrUSwMwXDCJwKsF+N8uJOyO3vft5FgryQOsVxlIWm4szXUdtDR0sH1NGXOXZWGL7Lt4tjg+1PI21GoHSpIN/ZLezw1qKIT79jcIvFuMEm/FtvpmdNkB1J3rIHg446mSswK1sQCai1GbCkecOW8ogZQaCkLdbtTKjYcDIWs8Ss5KiBtaUWdVVVErNmj768NQpl4Ccfkyve8EEN+5frnpiGRrs3JiOXVaIhsLGnj+k9I+R6dOm5FEbkoEpbUOnnivsNc2ETlzQKeno7KAgMvea5bAkWutHnmzgNMXRbCq+nXqXHXYjDZumHJDd/0/AGdNEbUf/ZfaT/9H0K3V3Us54ybyvvizHgXYj2RLnwaKQvvBTYSCAXT6cReaDGrctdhs0vPQPafx4Ct7ufeqWQCkhw9taFqnU1g2M7m7Yn0opH3BGg26IfcKDUVxcTEAM2fOHNH+1dXVvPjiiwBMpwpUFX9HG2ExSYPsqbGX7WD/x18jqPOzqPI8wjIOLz5UzEYsz16Nbt56vL9dQ2B1CYHVJWAzol+YimI1EXinqM/jmr57GuZfnYFiGneXxQlrY2cmvdNSw4npZ1jbZtShV6DDH8IbCBHWOdzfVcTv85d2864P2fPHGwAwhMeSdek9pJ79JfQmA1OvnolvRQ62cBPTLIYBv2RfW18OwPeum8OU9CjueegzdhY3842HPuveZmZ2DLdeHcupmVqWy66bqjswcEIBcRQUHblf+CEVb/4NV00Rxf/5MWUv/T+Sl19L6pk3Yk2dfNQPT6qq4murx1VdhLPqAI5De+g4tBdXdZFWj+cI5sQsYmetJHraUmJmndFrCsfRUgMebepSMACGMLDG9ZpvryjKgB1jhnPyCLxaQODD0j6DqaOhKAozFqezZ1MlbY1OdqwtZ/qidGIHqYHVn1BIZe/GCtwuP1PmphDdx/RaMbBQ531Vl9V7Abuqqni++Q7+/+4GmxHbqhvRz0lGdXQmOVEPZ0dVTOGQvQK15APUwnchIgUlbHTLsKgBL9Tv0YIob2fRZ0ssSuZSSJox8JS+z6vcCM2FYDCjzL4ORUajThjJMVpnT0V9B25voDs3wC3nTmZjQQOvry/n1vOn9JrKrNMpfPua2dz9t/U8tbqQ+ZPiWDTlcICkN9uIzF+AvXAzbQc3Ej+vdxKJJ767klv//DG5M+r518FHUXQqMWExXJd5OWEN9TTUrKW9aButez7BVXP4+TJq6hKyL/9Wj+l7ff5uy67GFBVP1JRTJmQgBeMwmAKIjzLzqy8vPOrj6HRK95Sk0dLW1kZLSwtms5mUlOHfqMrLy3n44Yfxer0sXryYGRVVOErAWVUwaDClqioVbzxI2ct/gPAg5rZYXA99gun31/f4wlB0Osw/WI7p9gX4n9yB7/HtWuHKTw/1eVzT1xcT9p2l6DKjh/37iKOzr1nLpDc3of9ecZ2ikGwzUt3hp9bp786w152Y4nPRVOWqfwCQdNrVTPrS7zFYej7UmRL7fzh77mALl+ZG09TqoqCiDYtJz+lzUjGb9Dz5vdN58r1CiqvbiYsyc97CdJbNTO4xCmA2aD3y7qAEU8eKTm8gefkXSDrtapq2vUPlqkewF22l+r3HqH7vMUwxKcRMP43wnNnYUicRFpeKwRqNztg5/UxVCQX8BN0O/B0t+Nqb8LbW4mmswF1fjre5Gnd9OUFPR5/ntyTnEj1tKZH584meurTHouXRoKohaC1HbSmB1nJwNfXcQB+GmjwLJes0lCEmOjGcrrUxuKFyVNvafXyjntlLMtm3qZLm+g52rT9EUnoU6flxQ870FwqGOLizlrqKtu7XnA6vBFMj0XlPUgM9R/PVQBD3XW/j/9d2MOmxvnRtdzIeOouPE/pcqYm0hdBUCO0VqHtfgrk3oOiPbiqnGgpCaxlq/V5oLoJQ59RYazxK1mmQMLVHENVi9/CnF3eztbCJL583pc+1L2pLGWrZJwAoUy6SQOoEExMRxqycWPaUtbB2Tx3ndhbsnZMXS15qJCU1dj7dXcvZ83tn9ps/KZ7rzsrg2Q8q+dETG3j+x+cSG3F4NClq8iLshZuxF27pEUypqkqTp4lKZScrr9xHmEW7Tr2Ffmat/wcFjt/1OpfeEkHC4ktIO/sWbdRriGJnnT7kbcejcRlMjWdNTdoXe2pqKjrd0HuMQqEQH330Ea+++iqBQIApU6Zw0003UfVaA46SbdStfXHAi0lVVUqe/SVVq/4BikLqtOtI+EUEuneKcK59HNNXF6JfnoUuyYba4dOCpy3VBD49RKiqd/Ys49XTMd2xCP3SjB7T/8TxdbBFC6amxAw8LSgmTE91h79HTSl3d8r0w9ehu+EQbfvWojNZyL/5N70CqYE8tLOBez6u5MyMCJZ1PkifNT+tu0MiPsrMd74we8BjnJNxDmemnUlU2OiOTojeFJ2OhEUXkbDoIhxlu6n58Cmatr2Dr7WW+vUvUb/+paM6viE8BmtKPrb0KYRnzsCWmkt4Wh6G6GOziF0NBaB6G2r1lsM99KA95FpiQB8Gfqc2SlW9VXsQnXsTim3wLKq6SbFg0KHWdqB6A8fknqfX65h5aiaHChs5dKCJ+qp26qvaCTMbiEmwEZ1gIzzKjNlqQq9XCKkqPk8AZ7uX8oON3SUKuiRnRpPWzxRcMTBdnvb/LXSgCdXlQ7GaUDu8uL74EoFVRWA2YH3lOoznHpkuvTPgVXsGYIqiwIwrULc/BR11qDv+A1nLIDYPRT/09YpqKADtVajNRVpmPr/r8JtRGShpiyC+96hyRUMH9/19AzXN2vYvry3rFUypfjfqAS2rGlnLpJDvCeqcBWnsKWth9baq7mBKURQuW5rF/S/t4fX15X0GUwDnr4hg9V47cakOniz+BwsS55MZkUm4MRx9lpatsqV0O2ZnDXafnTJ7GYVthbR6tWncYRZwtJgp2JpORuUBVGc7OqOZsLg0LMk5RGTPImbGciInLRrTdbxjRZ6ih6mjQ3vIDA8f+kNqXV0dTz/9dPf0wJUrV/KFL3wBg8FAyunXU/HWQzRseJWMC+8gIntWr/39jhYKn/g+jZvfRNEbmX73P0hYdBG+/L14vrmK4KZq3JuqB2yDfmEqxmtnYrx+FroBEm2I4+tga1cwNfACI2Nn5j7/EfVJGjsX3MdZDo++Nmx4DYD4hRcOe7rV2ZmRJFsNNHX4eGdHFWF6hZvPGTiByefFmUeW8UocnYic2Uz5yp+ZfOsfcVYfpK3gM1zVB3HVFONtayDgbOuRGEIxGDGYwzFGxGKMiMMUk4w5Ph1LUjbmhEzMCRmYInr+W4YOvg27/os6+QKUlKH3OA6F2nZIm0bl1rJLYomBhGkoMTkQmdZjWp/aUY9a9B7Yq1Frd6Lknz348ZvdEAiBSQ/6kU2BVFUV/5M7MV47A6WfeoY6nULO1ERSMqOpKmmhrrINrydAXWU7dZXtQzqPxWZiwem5GEd5VsXJRJdgQ39KmvbdeM8q9LOT8P5tE2pZG0qsBeur12FYltVzp85RdfyuHgVMAW0EdNYXUPc8D85G1P2vgs6IGpunBS5hEdr0wFBIC8aCHvC5Uf0uLWjyOqC9Uiu228Uah5I4U5vKZ+77Xr2nrIVv/2MjDpefKJuJdqePUKh3wiG19GPtPFEZ2siWOCGdNS+Vf797kMRoS49r9PxFGTz46l62FzXR7vT1Wed0cswk/t8dfj6oep9WbzsfV3/c/Z7F3sYpQFPFLt7a92iP/Sx6C1NjpuJvTufPb1eSGG3hob9eCcHvYrBEoAxjUOFEJsHUMLnd2vQls3nwBcY+n4933nmH1atXEwgEiIyM5IYbbmDu3Lnd21gSs0g76xaq3/83e/58E9Pv+jtRU5eAGsJZdZDmHaupfOdRAh0t6MKszLj7n8TNOwcA07UzMV48Gd9/dxN48yDB3XWorR4UqxFddjS6uckYTsvEcGYOunQZKRiPap3al2tfWfyO1FVj6sjv0cLOQCwn8nAg1rr3UwASFl3Y53E+/5BwpKmxZj66ehI/++dGqkIq152dT8YI132IsaHodIRnTCM8o/dC5KOhuluhbo/2Q3Tm6B036Ect/QhqtmsvWGJR8s7Sev37uU6V8CTIWqY92NqrhnQe34MbAdAvzUBt96LEDS/ZRKjJifvW1wm8XUhgYyXWf1464PZmq4n8WcnkzUzCaffS0tCBvcWN0+7B6wkQDPROJhObFM70hekSRI0S82/Oxnnuf/A/uZOuEEY3NR7rq9ehn9zHaKbJpo1+BjxafSZTz2tEscbBoq9CzXbUhn1aLaemA6hNB4beKFuCdm0nTIXw5AGnf+4obuI7/9iIyxtk2cxkvvOF2Vz+s9W0OLwEgqHubJNq1Wao2wWKHmXS+cNbYyUmlNhIM2/++rxemUYjrEbm5MaxraiJLQcb+x2dmhE3nWmxUyluL6awrZA6Zx3uoJtA5/0wrMNJkiURmzGctPA0JkVNIi08Db2ipznew5+pwu7yg8GM0TyyhD1tzS6MRt0Jl6RHgqlh0uu1Lzq1r1zrR6ioqOCxxx6joUFLK33aaadx1VVXYbP1nv+e+8Wf0lG5n/YDG9n5myu1HNifO370tKVM+cr9vdYnKDYTYV9bSNjXjn6NmTj+2jun7UWFDfwA1bUs6chgal21Nko6P/HwTc1ZXQhARO7cHvuroSDqgTeh8QBqbA7KjKtQdL0//h+tK6Oq1kFKrJXbLxx5oUpxYlGrNgMqJM8eUnrmIR2zo15L4exqBkWHknkaZJ7a53XZS2QaoIDfjaqGBnyADHxajvf/rQMg+Ek5/uf2EPb1U4bczo5Nlfgufw59gxOizRjPyx98p06KohAeZe4uPWBvcXGosImmWkfn+5CaE0vO1ASMMt16VBnOyMH24S34ntgBKhgvmozhymko/aS8VxQF1RoHjhpw1oMpp/c2OgOkL0ZJX4zqaYemQm1tXygAiq7zjx4MJjBatRGtrj+RKShhQ6tDtnZPLT99Yhtef5DzFqbzkxvnYdDrSIk1U9vioby4mLz8HC19eqXWUaBMOm9IU17FxNZfyYZTpyeyraiJjQUN/QZTADpFx+ToyUyO7jkVdJ31XwRc7dyWfW2vWQkAcZFmMhNtVDQ4KatzMHkEHfSONje71x9Cb9CdcFlP5e49TF3FdbtGqD7P5XLxzjvv8MEHHxAKhUhNTeWGG24gP7//L2C9ycLs7z1HxZsPUvvJ//C1asUjw+LSiJm+jIRTLyV29pmS3vQE1DXiNFjZXV3ndsHOINvpD/J8oTaX+YJs7aYW8Djx25u0ecwxn1t8XL0FGrXaa7SUopavQ8k9vccmz35Uwn8/KEZR4Oe3zO9Vj0KcnLS6N/sBtHUdo3HMhv2oB9/WHkKtcShTL0WJSCa4oxa1xY1uViK6AUZFFUMYLLlby7Y2gODOWpxXPgdBFf3yTIJrKwgdaBpwnx77l7QQuOxZ9I0uPDMTSHjjBnRZ0UPeHyDgD9JQ1U7toTbsrdr3hk6nkJwZTebkeCx9TMkRo8OwIhvDiuyh7xCRrAVT9lqI6R1MHUkxR0H6IpT00csO6fMHefiN/bzwSSkAlyzJ5PvXze1OMjQlLkhtCxzc9AG59Y7u9V3K5AtHfeqtmFhOnZbIw6/vZ9P++gFnoPRHZzKDqx3V7+t3m5Q4LZhqaHOPKJiyRoQRGWvB0ebpc7rqRCbB1DDFx2s9P1VVVQSDwe6Rqvb2dt5//33WrFmD1+tFURTOPPNMrrzySozGwRfj6U1mcq76HjlXfY9QMICiKL3S/4oTT6RJT5s3SKsnSOwAFb/1nxuZ+v2WOtq8QU5NsTE/SRuZ8tu1h0RjZFyPecxqKIhatUX7IW2xFlhVbkC1xKCkzMHp9vPImwW8vLYMgO9dO4c5ubL2SXRqr9SmPtkSUML7roU2VKoaQi1bA5VaEUiSZ6Pkn9u9kN/7p/X4n9uL5akrMN048MPhoIHU9hqc5z0NbR4Ml0/F9O2luJb/G//zezH/9myUiIHXKYYaOnBd8gxKowv7rETKfrIcS2QYQxlbUFWVjnYPNeWt1Fe2d0/rMxh1pGbHkJ4fR5j55FukPd4pkWmoNdtR2w6hZC09rucuqbHzq6e3U1jVjkGvFZO+4ayeNaKmTs5hbfFBGt0GLZCKSEXJPRMl+ugLYYuJLS81kphwE012L3UtblKGOZW5K9trKNB/MJUYrY0kNbZ5+t1mIHq9jllLMvE4/dgiT6xClBJMDVNKSgqJiYk0NDRw//33Ex8fT0tLC6WlpQQC2gLvKVOmcOWVV5KdnT2ic0zUPPti+HKjTFQ4fOxvdpMX3f/N5eWL8/CHVBIsBlYfsvPbzdro5Z+WH86s1nUT1Bk/N3TeXAi+DrDG89DuRHKYykVxBQQL3+HD3a08tMZDk92LXqdo8/JPyx7131NMYB312n+jji6LnxrwoBa8CS3FgKKtjUpb2ONhUe38klaij276R2DdIZyX/g/avRgumoz1f1eDSY9+STrBDVV4fv4xlvvP73f/UFkrzkueIXSwGd3sJDoeOI9gi5sda8uYMi+VpPSoXj2/Pk+AtmYnrY1O2hqduDoOP5REx1tJzowmMS0KvUHWtIxbsXnaNL22clRHHUpE8jE/ZXmdg3++VcCnu2tRVUiLt/LLWxYwI7t3JserV+Zz7ZmTCTOooKrDyiYoxh+nx0+HO0BMuOmoZ4IoisLkjGg2FTRQWNU+7GAq4HYCoDNZ+t2mK7GF3dV/wDUYvV53wgVSIMHUsOl0Om688UYeeughiouLuzP0KYrC3LlzufDCC8nKyhrkKEJoVqRF8ElVBy8VtXJJXnS/2yXZtC/Nd8raueqtEkIq/GhxMqelHe6d7xqe764n1PV640EAnNHTeeXZQ3j9Co0z4vnStCYOlRTTZE9gZnYM3712zqBD96GAj+L//oyMi+7CkjB6iQjE+KU6tXWfim1oRcX7PkYT6r6XtWx9BjPK9CtQYrJ7b9hVPO0oHix8/9mJ+443wRvEcNV0rP+9srsQufn+83Eu+xe+v25EbXET9r3T0E2NR9HpUH0BQkUt+F/Yi/cvG8DpRzcjAdu7NzElwQrba6irbKdgazWl+xqw2IyAQjAYwuPy4ff2rE9kNOlJyogiJTuG8BNobcCJTDFaUNMWQNVmvLteJGzedSi2hFE/j6qqbD7QyLMfl7CpQPt8mQw6LlmSxZ2XTMNm6R0kqara5+tiYvEHQry1sYLXPyvnYGeGT6NBx+T0KGZkxTAjW/uTGmcd9lS9rMRwNhU0UNPsHNZ+vvZGAh0t6M02TJH9r7sLdt6fjf2s2zqZSTA1AlOmTOFXv/oVRUVFBAIBbDYbeXl5w0qXLgTAjdNi+c3mWv57oIW75iRySkrfBTp9wRD/t6mW326uI6TCrTPi+L+lqT22Cfq1Xn2d8XO9Pp0jC+Ep+TzxXQsvrS2joDWJf1W14UxK4w/LEzhtRlKP4rtd3AE3r5a+yjkZ55BgSaD81fup+eBJmnd+wIJfvoMpavQfNMQ44+ss3jvExfOfp9btRi1araWFtiVoyU/6S2LRlYjFOfyeT7XNjfvuVfif1bIOmu5YiPnBC3skHDAsTsfy78tx3/4G/qd34X96l5Yu3agDl7/H4kXj1dOx/PMSlGitp3bqgjSi4qyUH2jE6/bjdft7nF9v0BERYyE2wUZUnJXIWGufnykxvilZp2GvKSIi2ErrhqcxnnonEeH999YPR4vDyzubK1m1qYLSziQkYUY9F56SwZfPn0xCVN/nUev3oNbthZlXHXXBYDF2yuscfP+xTVQ0aMGOyaAjwmqk2e5lX3kr+8pbQUvIS0xEGGfMSeG8RRnMzI4Z0r0kvDPYdnoCg2zZU1vBegAi8xcMmOrc1zld2Sij671IMDVCMTExLF68eKybISa4STFm7pmbyAM7Grjg1SL+flYm10yK6V5wHFJV3i2384N11expcqMAvzg1hZ+dmtKr1yrg0Gr0GGyHH1RVNQSezvo25mhywsP47heGvlD5k+pPKGwrpMZZw9dnfZ3Mi+6ibd9awrNmYOwj4484Aek7g/Pg8AIcNRRELX4fandoLyROR5l8wYAPg7pMbWQ0VNo6rHMFtlTjvv4lbT+rEcv952P8yvw+e3ZNN85Bf0o63t+uIfBhKWq1A3xBUEDJjsawJAPT1xZiWN5zhoGiKKTmxJKSHYPL4cXnDaCqoNPrMFuMhFkMkiRonPP5g6hoD7H9pt43mFmjnE103Xu8XBRN1fr1ndPuhp/FUlVVqptcbC9q4qOdNWw50NC97jUm3MR1Z+Rx2WnZfdYF6j5G0Ida9il4HajFH6BM6V32Qm2vAkctpM4bWjZMcdy1dXi59+8bqG91k5lo47YLprJyTgphRj0Ol5+CCi2Y2neolf3lrbQ6vLyyrpxX1pWTGG0mzKjnnitmsHxWSr/nCLdo//Ydn+voGUzTtvcAiJm5YsDtGtu0BDrR4RLQf5586sZQq8NLi8NLXurIenzFieEPy9OpcPh4pbiNL64q49u2Kk5JsREMqWyuc1Ln0nqZcqNMPHFuNivS+y667GnSau6ExR5xs3W3asUkwyK1DGjDdFb6WTS4G5gcPRmLwQIGC3N+8AI6k0WK9Z0sOkeR1I46lMSh1a9SAx7U/a9Ba5lW/2by+ZA0a9BgQ78oDdiC/90iwr4zePFR1e7B8/OP8T20GUIqunnJWJ+7Bn3+wIG+flIc1ieu0I7h9msFfc0GlCFML1QUBVukmb7HkMV44/MHeeK9Qt7ccIhmuxfQAplTpiVy6dIs5ubF9bouL1mWT1VjCq3lW6iub+crf17DqdMSOX9xBrOyY0juZ9TR4wtQ1eRi/6FWthc2sbWwsfucAAa9wqlTE7lsaRZLpicNqYdf0Ztg9hdRC99FyV7e4z1VVaHiM9TytYCqrUeccbWspRpnVFXl/57eQX2rm5nZMfztnqWYTYcfvyOsRhZPTWTx1MTu7Yur7byzuZKPdtZQ35kF9HuPbuY/3z+dSf1Mxw/rvH/5+qhj15+g10XzjvcBiF940YDblnSOpuamyDPr50kwdZytW7eOlpYWVpx5Hvc89BlN7R7+dvfSfj8c4sRn1Cu8eHEuj+5u4g9b6yiz+3i1uK37/exIE3fNSeDuuYlYBvjytZdsA8CWNunwi13JA2wjy8Jm0pu4ccqN6JXDD5l6szxGnkyU6CzUyo3QXIyac/qgAZFqr0bd/zp428FoQZl5DUpk/3VPjmS4eDJYjQQ/Lsf3/F5M187sc7tQnQPfv3fg+8sG1BY36BRM9y3B/OszUYaZJU+RdSgnLI8vyH1/38DOkmYA9DoFRYHWDh/vbqni3S1VpMRaue3CKVy4OKPHtZ2eYOOf9y3n3+8c4MU1ZWwsaGBj5/omi0lPXGftMH8gRCAYwu0L4upjelV0uIk5uXGcOj2RM+amDjgK1R/FGocy94beb9RsRy1fc/jn1nKtmPXMa3p0nqntVaDTo0T0P6ohjp1NBxr5bH89ERYjv7l1UY9A6kiqqkJLCXjayDdHcc+5Cdx96WR2lzu486/rWDojidQBEksEgp1ZQ/VDHyFv3b+OoKeDiJw5WJP7LwfQYvdQ3eTEZNSROUDZipOVBFPHUXNzM//73/8IBoOYLTaSYmyU1Ng5UNkmwdRJTqco3DEnga/Ojmd3o5vCVg86RWFqrJkZceZBH2CDPjfN27Xepbh553a/rjZ3JkiJGtrDbF+ODKTESSg6C0w2cDVpPd9xk/rcTFVVqNmGWvJhZ9rmZC3RhDl6yKfSxVox/+EcPHevwn3ra+DwYvzyPBS9jlBDB4F3ivH/bzeBj8q66wTol2VieeAC9PPkQVH09PiqA+wsaSYh2sz/fWkhs3O1DHmH6jt4b2sVb2+qoLbFxa//u4O9Za189wuze4w4mU167rpsBjecPYl3Nley+UADByvbaXF4qWrsvcjfoFdIibWSnxbJnLw4Fk5OIDcl4phM/1RVVevkAJQpF0NkKuquZ6G9CuzVEJsLQGjvS9BcBCYbypJvjHo7xOAefUur8XjLeZNJjBlg/V3VZtTSj3q8pMy4irn5k9nwt8sGPY+/M0GEYRizRurXvgBA3IL+s5sCbCnUSq/My4uTNVN9kGDqOIqLi+PWW2/lnXfe4dRTFnH66Ta2HGzktJnHPv2qmBh0isLcRCtzE4eX1rR+7YsEXO2E58zGmpIHgBoKaF+iAAlDm54lxOcpOj2kn4pa+qGWSCIyHcXY84FAdTWjFr0HbYe0F1IXoOSdNaJaeaY7FhEqaML38GbcX3sT97feBb0OjpguhVGH4aLJhN29GP1ZubJWSfRid/l4cY1W/PZ3ty1me1ET//ff7dx9+QxOn5PK1y6exu0XTmXV5gr++MJuXltfTnxkGLddOLXXsaJs2vqm687Q7q3tTh9tHVo9SaNBh0GvYDEZsIYZRj3piM/no6KigvDwcJKTj3hWcLeA1w6mcEiaqX0G5t4IjlqUrkDqwFuHvwMMklFyLFQ2dFBQ0Ua4xcBVy/sf+VG9Dm1tHECM9u+H1w7D6IyydybuCbcObbTd3XCIxi2rUPRGUlZcN+C2q7dqywiWzBh5VtcTmQRTx9nChQuZN29ed7FfCaTE0Qq47JS/dj8AmRd9/fAbLWVa0oDwpP6zpwkxFOkLobEAHDWo+1+FmVeDzggd9ai1O6ButzYaZbSgTDofJaH3A+lQKYqC+a8XaEkifvUJoWItsQo2I/pT0zFeNQPjNdPRxQ7e4aC2e/D8bi3mX5w+7Ol/YmLbXtSEzx9iXn4cM7JjWLe3juomF/vKWzl9jpYJVadTuPjULGIjzHznnxt5/J2DLJmRxPSsge+XUTbTiKbrDYfb7eaTTz7hww8/xOHQ1qp8+9vfZvLkydoGXQlhDGHdnQmKJRos0dqoVfFqqN/TfTxl4e3HtL2ib5/urgVg2cxkzKYBOpdaSrX1zXGT0M28ekTnqml2AZA80OjXESre/BuoIZKWXtNzrfXnNLS52bi/HoNe4dwFR1dv8EQlwdQY6AqkhDhaaihE4ZM/wNdaR0TefBIWX3L4vSatvpQSP/IHWyEAFEUH0y9H3f4UtB1C/exB0Okh4OnaApLnoOSejmIc3qhq3+dTMN0wG+P1s6CzkC9RYcNOeuK6+RUCbxUS2lOP9Y3re6RJH0xIDeHwOYgK06Zgq61loDcNef2XGFutDi3YSE/Q1nh2ZePbV947U+TSGUl8YWUuz39SyntbqgYNpo61LVu28Pzzz3cHUV1KSkoOB1PWeNCbwNWM2laBEp2pzUZoKUMt+QA8bd37Kcu+LaO3Y2RPmdYZdMq0QdYtd91Lw7T1SGrAC+2V4HeBLXFIBaT3lmvnyk8bfNmIt7WeunUvgqKQeck9A267alMlIRVWzkomJuLEK7g7GiSYEmKCCAV81H7yP1JOvwGdwUjQ56boqR/R8Nkr6MKsTL39/u6HTTXghc5gioQpY9hqcaJQzFEw9wbUA2+Bo0arG2UKh/gpKGkLUKyjnypfURQYYi9rX8y/Pgvnxir0K7KGHUi9VvoaJfYSvjT1S8QHgqh7XwJFD7OvQ4lMHfwgYkzFdj70VXbW9DF0/vuX1Tn63H5efjzPf1LKpxt38aWz0omJOf4BlaqqvPLKK6xevRqAvLw8Lr74Yt5++22Ki4tJSjo8xUrRG1FT5kHVJtRdz6AardoDudozk5uy9F6pTTWGnG4tKUncYIW7uzppanYQaqvozMR7xL9l1rJe2RyPVFZrp67FTbjFMKQM0eWv/BHV7yV+0UVYU/P73c7rD/La+nIALjk1q9/tTnYSTAkxQRx6/QEOvXo/1av/hS1jGm0HNuBvb0RnNDPz3iewpR8egVLL12jTQKIyj8lDrjg5KdY4lPm3oPpcQAiMtnHd462flUT4vq+jix9eBsqgGsThd+D0O9nasJXzM8+DuHxoq9RGA8S4N39SPGaTnp0lzdzw2484VK8Vnz6tnzUftS3aFCmvy86///1vvv3tbx+3tgL4/X6effZZ1q9fj06n47rrrmPFihV4vV5KSkrQ6/VMm9Zz7auSswI16IW6PdoIxpEsMSjzbum1vlEcX11r6Loy7fVHiUpHTZ0PNdvBpWWfJDJNK5beeAD10HpIW9DnyL+qqvz1lb0AnD0/vbtOZX/spTup/eQZFL2BnGt+MOC2L3xSSn2rm7zUSBYPNrp2EpNgSogJInrKEuri03HVFOGq0RYV2zJnMOW2PxKZN797O7V2J1RvBUWHknfmGLVWnMgU09FP5TtehhtIARh1Rr446YtsbtjM0uSl2jTHaZeB1z6s7IRi7ERYjdx31Sz+/OJuSmsd6HUKVy7L5ptX9k6373T7ef6TEgAi1XoaGjzY7XYiI49PPZ22tjYee+wxiouLMRqNfPWrX2X27NkAeDweVFXFZrNhsfQMjBSdAWXyBah5Z2vrF/e9rAVVEckos7+IIkknxlx8Zwr9xq7pygPQTToPNWuZ9m9oCkcxWrT1b+1V4HOAzwV9BFMvrSlj04FGomwmbr9w4JkoQZ+bg4/eC6pK+nm3Y0vtOzsraLVQn1pdCMA9l88YNEg7mUkwJcQEETNzOYt//ymt+9cTdDuwpk4iPPtwIVRVVaF6q5aaGlDyz5W6IkKMkElvYlnKsu6fFUU3rMxaYuxdujSLlXNSKKmxk50UTmwfU63anT6+9+gm6lrcTE6PZBo2ykrrePzxx/nGN76BwXDsHpNUVWXLli0899xzOJ1OoqOjueuuu8jKOjydymrVHp5dLhehUAhdX+sGnQ2o+1/pDKRSUWZfK4HUOJGZqHXmHKhs41IGnyanmGxaKYouzUVaIGW0dhdQP9KO4qbuUanvXju7z2v8SKXP/xZn1QEsyXlkX/mdAbf91zsHcXoCnDotcfA1Xyc5SRYvxASiC7qJ1TeTmD+F8MyphwMpdxvqvpe1hceoKNkrUFLnjW1jhRBijEXZTMyfFN/nQ2ZDq5s7H1jH7tIWEqPN/Pa2xXztq7cTERHBwYMHeeSRR7Db7aPeJlVVKSgo4MEHH+Rf//oXTqeT6dOn86Mf/ahHIAVgMpmIjY0lEAiwb9++nsfxuwmVfYq68xnwOSE6C2X2dRJIjSOnTtOmla7dXUuoszbeUKk+F2rRuwAomUt7lZpod/r4yb+3EgypfPHMPM6aN3BynNZ966h+7zEUvYFpdz2M3tz/qP2hegevrS9Hp8Ddl88YVrtPRjIyJcQEotbvgbZy1LZy0BlQrXHaIlVnE6Bq2camXHRUqamFEOJEt6+8hR/+awuNbR5ykiP4y11LSIqxADa+8Y1v8Je//IW9e/fyq1/9ijvvvJO8vLyjPmdLSwu7du3i008/pbZWS5ltNpu5+uqrWbZsWb/rD09fMIVX3t/Amy/8hxm5P0AhBO2V2tpYv1vbKHU+St7ZI6rtJo6dKRlRJMdYqGt1s2F//ZDL4ajuVtR9r2hBcmQapC3s8b7D5edbj2ygxeFlTm4sd106fcDjOcr3sO+vtwGQddm9RObOHXD7h1/fTzCkctnSrCEltDjZSTAlxASiZC4BcxRq3R4to1pHfdc7kDhDS00dJjc+IYToi8Pl56nVhTz3cQnBkMrcvDh+f/viHnWjMjMz+clPfsKTTz5JYWEhDzzwADfffDMLFy4cdsKVpqYmduzYwdatWykvL+9+PSoqihUrVrBy5UoiIiIGPMbpi6bzwdpNHGqws+u1B5iTfsS6magMlJzTUaKk/s94pCgKV6/M5aHX9nH/S3uYNykea1jvR2+1owHczeBxoDqqoalIqztliUWZcWWP667V4eW+RzZwsLKdlFgrv7hlQXe2yr44qw+y6/fXEnC1E7/oIjIvu3fANm8vamLtnjqsYXpuv0g6ZodCgikhJhDFYNZ6IFPno/qc4HUAqpa5SaZ2CCFEnxwuPy98Wsrzn5TgcPlRFLjujDzuunQ6RkPvB9G4uDjuvfdennnmGdavX8/jjz/O6tWrWbhwITk5OURGRhIeHo7RaMTv99Pe3k5jYyNtbW20tbXR2tpKaWkpDQ0N3ccMCwtj2rRpLFy4kHnz5g15PZYpcyHnnbaDd9fvxqezgjUOTBEoKXMhYeq4zqgp4NrTc3lvSyVF1Xa+/+gm/nTHqYQZe44gqjXboXZHzx0TZ6Dkn90jg9+O4iZ+9uQ2mto9pMXbePgbp3WOqPbN21rP7j9cT6Cjhdi5ZzP964+g0/d/3YVCKg++qq3BuvHsSYOndBeABFNCTFi9FqoKIYToweMLsmpTBY+/c5BWhxfQ0qZ//bLpgxbn1ev13HTTTWRlZfHGG29QUVFBRUXFsM5vNpuZMWMGCxYsYNasWZhMw0+tryg6Vl5xK8svCxEWJkVTJxqDXsdvbl3EHQ+sY2thEz97Yiu/uW1Rj9EkJSIF1e8CkxUlPFlb/3ZEwommdg9Pv1/ES2tKCakwOzeWX9+6kISo/gOpUMDPvr/djre5msj8Bcz4xmPoDANff+9uqeRgZTsJ0Wa+eObRT209WUgwJYQQQogTRjCksq+8lQ+2V/HelirsLj8Ac3Jj+erF05iXHzfk0RxFUVi5ciVLlixh9+7d7N+/n5qaGpxOJx0dHfj9foxGI5GRkcTHxxMdHd39Jz09nczMTPT6o1/HZDQaj/oYYuxkJIbz168v4a4H17NmTx33PryBe66YweT0KBRFQUmZg5Iyp8c+qqpS0+zipTVlvLKuDJ9fq1V18zmTuP2iqQNO7VNDQQ4+/i3shZsxxSQz874n0ZsGrjm2r7yFP72wG4CvXTwNs0lChKGS/1NCCCGEmPDanT7+33O72FbY2B1AAUzNiOKmcyZzxtyUEU+JM5lMLFy4kIULFw6+8VFyewPsKWth8VRJR30iyU+L4i93LuG7j25iW1ETX/rDpyREm1k8JYH8tCisZgMGnUJts4uyOgf7ylupa3V3779ydgq3XTCFSelR/Z7DVVuCp7GCQ2/8lfYDG9GFWZj5zX9jikoYsG0lNXbue2Qjbl+QCxZncMGijFH7vU8GEkwJIYQQYsILtxi7A6nUOCsrZqdw/qJ0pmREj3XTBtXobmRtzVrOz7iI7/xjC7tLW/jFzQs4e8HA6a7FxDIjO4anf3A6T75XyCe7amls8/D2pkqgss/tI6xGTpmayE3nTGLyAEFUl6L//JjWPZ8AYIxKYMY3Hicyf/6A+xRUtHLf3zficPlZPiuZH10/F50U6B0WCaaEEEIIMeHpdQq/+tJC0uJtpMVbJ0xihpAa4qWSl6h31eMJeJmbP5cdxc2U1zvGumniGIiLNPPta2bzratnUVjVzs6SZqoanXh8QXyBEEkxFrKTwpmUFkVeauSwApuI7FmoAR/W9ClkX/FtTJHxA26/rbCR7z26CZc3yNIZSfzqSwsHnD4o+ibBlBBCCCFOCKdMm3hT43SKjqtyr+KZwmdYnLyIvMl5zM+PZ8HkgR+ExcSmKApTMqJHdeQ099ofD2k7VVV5d0sVv392J75AiHMXpvPTG+dJIDVCEkwJIYQQQoyhRGsi98y+B4NOeyxbOGXgNS5CjJTd5eOPz+/mg+3VAFy9Iof7rpolU/uOggRTQgghhBBjrCuQEuJY2VbYyK+e3k5DmweLSc99V8/i4lMzJ8yU2PFKPrlCCCGEEEKcoOpb3Ty+6gBvb6pAVWFmdgw/v3kB6QlSq3I0SDAlhBBCCCHECaah1c0zHxXz2rpyfIEQep3Cl86fzJfOmyzro0aRBFNCCCGEEEKcAFRVZf+hVlZtquTdLZW4vEEAzpmfxu0XTSUjMXyMW3jikWBKCCGEEEKIUVJaayfCaiQhynLczlnX4uLdLVW8s7mSioaO7tdXzE7mKxdMHbDYrzg6EkwJIYQQQggxCjbsr+eHj29hSkYU3712DnkpEcckwUNdi4udJc3sKGpmZ0kTFQ3O7vdiI8I4b1E6F56SSX5q5KifW/QkwZQQQgghhBCjYEp6FOEWA7tLW7jpdx+THGNh6YwkTp2exILJ8VjDhvforaoqrQ4v5fUd7D/Uyv5Drewtb6WxzdNjO4tJz9IZSVx4SiaLpybImqjjSIIpIYQQQgghRkFspJm/f3MZT79fxGf76qlrdfPKunJeWVeOXqeQHGslJdZCUqyVSKuRcIsRg07BH1QJBEP4AiFaHV4a29w02T00tnlwegK9zhNhMTI7L5Z5+fHMzYtjSkaUBFBjRIIpIYQQQgghRklmYjg/vmEeoZDKwao21u+tZ1NBA/sPtVLd5KS6yTn4QY5gMxvISgpncno0M7NjmJEdQ2ZiuBTaHSckmBJCCCGEEGKU6XQK0zJjmJYZw1cunIrHF6CuxU1ti4v6Vjcdbj9OTwB/IITRoNP+6HVEh5uIjzKTEG0hIcpMpNUohXXHMQmmhBBCCCGEOMbMJgPZyRFkJ0eMdVPEKJLJlUIIIYQQQggxAhJMCSGEEEIIIcQISDAlhBBCCCGEECMgwZQQQgghhBBCjIAEU0IIIYQQQggxAhJMCSGEEEIIIcQISDAlhBBCCCGEECMgwZQQQgghhBBCjIAEU0IIIYQQQggxAhJMCSGEEEIIIcQISDAlhBBCCCGEECMgwZQQQgghhBBCjIAEU0IIIYQQQggxAhJMCSGEEEIIIcQIKKqqDn1jRWkEDh275ogTTJaqqglj2QC5ZsUwyTUrJqIxvW7lmhUjINesmGj6vWaHFUwJIYQQQgghhNDIND8hhBBCCCGEGAEJpoQQQgghhBBiBCSYEkIIIYQQQogRkGBKCCGEEEIIIUZAgikhhBBCCCGEGAEJpoQQQgghhBBiBCSYEkIIIYQQQogRkGBKCCGEEEIIIUZAgikhhBBCCCGEGAEJpoQQQgghhBBiBCSYEkIIIYQQQogRkGBKCCGEEEIIIUZAgikhhBBCCCGEGAEJpoQQQgghhBBiBCSYEkIIIYQQQogRkGBKCCGEEEIIIUZAgikhhBBCCCGEGAEJpoQQQgghhBBiBCSYEkIIIYQQQogRkGBKCCGEEEIIIUZAgikhhBBCCCGEGAEJpoQQQgghhBBiBCSYEkIIIYQQQogRkGBKCCGEEEIIIUZAgikhhBBCCCGEGAEJpoQQQgghhBBiBCSYEkIIIYQQQogRMAxn4/j4eDU7O/sYNUWcaLZt29akqmrCWLZBrlkxHHLNiolorK9buWbFcMk1Kyaaga7ZYQVT2dnZbN26dXRaJU54iqIcGus2yDUrhkOuWTERjfV1K9esGC65ZsVEM9A1K9P8hBBCCCGEEGIEJJgSQgghhBBCiBGQYEoIIYQQQgghRkCCKSGEEEIIIYQYAQmmhBBCCCEmkGAgRGtjB6FQaKybIsRJb1jZ/IQQQgghxNgq3V9PVUkLAAmpkUxflI5Op4xxq4Q4OcnIlBBCCCHEBJKQGtn998YaO5++vp+2ZtcYtkiIk5cEU0IIIYQQE0h0vI0Vl04jPMrc/dqONWXsWFtGKChT/4Q4niSYEkIIIYSYYPR6HYvOzGPB6bndr7U1ufj0jQLcTt8YtkyIk4sEU0IIIYQQE1RkjIWVl00nKs7a/drG1UW0NjrHsFVCnDwkmBJCCCGEmMB0OoX5K3KYPCel+7Wd68opP9Awhq0S4uQgwZQQQgghxAkgLTeWucuzu38uK2hky8clqKo6do0S4gQnwZQQQgghxAkiJt7GaRdM6f65o83DJ6/tx+v2j2GrhDhxSTA1BE5/kGBIenWEEEIIMf6ZzAZOv3w6kbGW7tc+e7eQdkmfLsSok2BqEIGQypVvlnL5GyU4fMGxbo4QQgghxKAURWHBylyypyZ0v7Z9TRm15a1j2CohTjwSTA2iuM3L1nonb5W1c9rzBzlk9451k4QQQgghhiRnWiKT5x5OTHFgRw2Fu2oJyYwbIUaFBFODmBprZtMXpzIlJow9TW4WP3uA4jbPWDdLCCGEEGJI0nJimTo/rfvn6tIWdq0/hN8bGMNWCXFikGBqCPKjzWy8bipnZkTQ4ArwxVVl+KTCuBBCCCEmiJSsaKYvPBxQtTU52fpJKR3t0kEsxNGQYGqIos0GXr44l6xIE1vrXfx4fc1YN0kIIYQQYsiSMnoGVB6Xnx1ry3DKEgYhRkyCqWGINht49oIc9Ar8aVs9n9V0jHWThBBCCCGGLCkjmvS82O6fA/4QuzccwuuR1OlCjIQEU8O0JDWc7y9KBuA7a6qkEJ4QQgghJpS8GUlYw03dP3tcfnZvqCAQkKzFQgyXBFMj8P2FySRYDGyodfJqcdtYN0cIIYQQYsh0eh1TjkhIYTDq6WjzsH9zlWT5E2KYJJgagcgwPb9YoqUZ/e3mujFujRBCCCHE8ETHWYlJtAGQmB6Jwainub6Dol21MutGiGGQYOpzXH4Xtc7aQbe7dUY8Zr3CtgYX9U6ZZyyEEEKIiSUtR1s71dbkYtaSTHQ6hZryVqpLW8a4ZUJMHBJMdfIGvXxa/Sl/3f1XXix+kaA68Lxhs0HHyvQIAD6osB+PJgoxbB12D+3NrrFuhhBCiHEoLjkCg1GPy+ElzGxg6gJt6l/p/gZJSCHEEEkwBdS76nlw94N8XP0x3qCXGHMM7oB70P3OzYoE4P0Kx7FuohDDFgqF2LGmnO1rymiuk2tUCCFOZk67h1CoZ41MnU4hJsEKQGujk6T0KOKSwwkGQpTtbxiLZgox4RjGugFjzR/y83zR8zj9TlJtqZybcS7ZkdlD2ve01HAAttU7j2ELhehbKBjAb29C0ekxRSX0et/d4SPg10ZYq0pbiEuOON5NFEIIMQ6oqsquzw4RCqosOD0Xi+1wJj9rhBlw4HVrI1H5s5JpriumrqKN7KkJmK2mfo4qhAAZmWJn005avC0kWBL48rQvDzmQApgVb0GnQEGLB3cgNPgOQowib1MVG+6Zy/ZfXtzn+x734SkarY1OfJ7A8WqaEEKIcaS5vgOvO4DBqMdsNfZ4z2TWA3R/R1jDw0hMj0RVoaKw6bi3VYiJ5qQPporbigFYmrwUo844yNY9WY06psSYCaqwr2nwaYFCHBP9ZF3yug4HU2pIZffGCjwumQMvhBAnm7pDrQCkZMegKEqP90xh2iQln/dwh1vWZG22Q3VZK26n7zi1UoiJ6aQPptp97QAkWhJHtP/MOAugjU4JcVzptI+vGuo7WYrPp70enxJBmMWAo9XN1o9LaJOEFEIIcVLxuLVAKTrO2us9Y2cw5fcd/i4JjzKTmB4FQH1V+3FooRAT10kfTBl0nTeR0Mh67KfGmgEJpsTxpzNo89jVQN/Xbqhz6mlEtIWFZ+R11xMxW4Y3AjtcqsdPqNGJ2uGVWiVCCDEOdI0+ddh7P6uoQe0+rdP1HLFKyugMpiolmBJiICd9AorYsFiqOqqoc9UNa71Ul7yoMAAqHDIMLkbHW6Vt/GtvM4+dk0W8pf+PqM6kBfJBf9+BfHcYo2hfpHOWZuF2+nrNlz8aoUYngfdLCK6vILi7nuDeBrB7u99XYszoF6SiX56F8Qsz0E+OH7VzCyGEGJqk9Eia6xyUFzSSlB6Fwajvfs/Vod2zwz7X0RabGI5er8Pl8OLzBroDMiFETyf9JyM3KpfdzbvZWLeRmXEzCTeGD2k/T9BDub0ce6CIG6ZXkBHh5cFdBuIt8SxKXMSk6EnHuOXiRPXI7kZWldlZkR7OffOT+t1OZ9QC+ZDf2+f73X2MnVGVoihYw8NGpY2hRiee763G/7898PnkKyY9SrgJ1eVHbfUQ+KCUwAeleH/+Mbr5KZiunYnxxtnoJLugEEIcF4npUVSVtmBvcVOyt54p81IBLelEVYlWoDfqc1MAdToFa4QJR5sHd4dPgikh+nHSfzJmxs5kU90mal21PLbvMc7LPI+pMVPRKT1nQIbUEPWuekrsJRS1FVHhqEDtfErNj9G2afFCi7eFwrZCLsi8gFOSTznev444Adw+M4FVZXYe39PEvfMSey0W7qIzaiNTql+bTvf57ZTOKRujPdUusLkK19XPo1Y7QKdgOCsXw9m56OenoJudhJJgQ1EUVFVFrbIT3FyN/62D+F87QGh7LZ7ttegXpEowJYQQx4miKEyek8K2T8uoKW/F5w1gthqpr2zH7wtiiwwjOTOq1376zhGsYFAyFgvRn5M+mDLoDNww5QaeLXyWamc1LxS/gM1gIzMiE6vBiopKq7eVOmcd7uDhjH0KChnhGbj8yTy8ExYlJnP/6Znsa9nHJ9Wf8H7l+0yLnUakKXLsfjkxIV2UE0WS1cD+Fg/b6l0sTLb1uZ2iKCh6I2rQjxrwoRh7jjp1BVOh0OgFU4FNVTjPeQqcfvSnZWB54gr0ebH9ty8jCl1GFMarpqM+4ifwbjH+VUXoV2SNWpuEEEIMLiLawvSFaezfUk1T7eFC7tHxVqYvTEen672MvquLTpa/CtG/kz6YAgg3hnPr9FvZ2rCVTXWbaPG2UNBa0Gu7KFMUOZE5TIqeRG5kLhaDhSf2NXGw5RBLkuNIsCRwetrp1DprOdh2kIKWAhmdEsNm1CtcMzmGh3Y28nJxW7/BFIBi0IKpkN/XPe3vWPLc9w44/RhvmI3lX5ehHDHvfjCK2Yjx8mkYL592DFsohBCiP4lpUUTGWKivshMKhYiOtxEdZ+13BkRX4Xej8aTPVyZEvySY6qRX9JySdAqLExfT6Gmk3lWPO+BGURSiTFEkWhKJMkX1uuG0eLQbTaTp8ENlXlQeB9sOUueqO66/gzhxXJYbzUM7G1l9yM7vlqX1u50+zELI6yLk9wDHdtpcqLKd4KZqiAzD8sjFwwqkhBBCjA9mq4msISQDCoVUXB2+7n2EEH2TYOpzFEUh0ZI45LpTpe3a4v+cqMM3GptRG0nwBCVduhiZU1Ns6BXY1eiiwxck3NR34KIzaXXOgt5jXzsquLcBAP2CFBSbfLEKIcSJrKq4mWAghC0iDJNZHheF6I+M2x6lg531pbpSpAO0e7WaDFZD7+J4QgxFuEnPnAQrQRW2NfQfKBms2oLhgLN3HRBD57SMrmkaR62zh1KJsYzO8YQQQowLn09U1NLQQcm+egByZwytc1mIk9VJH0w12z28v62KD7ZXExzmQv1ASGVTnROARZ3rWkJqiF3NuwDIjMgc3caKk8rcBC1o2d3o7ncbo00LpvzOtt7vGbWexIBvlIKp8M7RKHvfqdiFEEJMPKqq8q9//Yt3332X1qYOinbXsmdjBQBZU+KJT5FEWkIM5KQet/10Vy0/e3Irvs46OV88I49vXDlzyPvvbHDR4Q+RG2Ui2aYVu9vWuI16Vz3hxnCmx04/Ju0WJ4dZ8Vowta95gGAqQsukF+ho6fWewaT1lfhHaWRKl6+dK7ijFjUQRDEc/ZopVVXxfGMVoRoHlvvPR5cVfdTHFEIIMXTl5eVs2bKFLVu2sOajz1g86xyiwmNJzogiZ5qMSgkxmJN2ZMrh8vOr/27HFwiRm6It3H91fTl2l2/Ix1hdYQfgrAyt16bGWcN7h94D4PzM8zHqjP3ueyQ1FEQNDe+BN6SGKG4vZl3tOtbXrqfcXj7q9YTE2JoR1xVM9b/2zmDTipz5+5rm1xnsBP2jUx9Elx+LbkocarMb/yu9s10OV3BPPXbDL/H9fQuB1w4Q3CUJW4QQ4njLycnhG9/4BjZLBM3t9by7/hkq23cwaW5yv1n+hBCHnbTB1M6SZlyeAMmxFv5291Lm5Mbi8QV5a0PFkI+xqkx7gD0/O5J2bzvPFj5LQA0wP2E+M2JnDOkYav1e1PV/Qd3wN9T2qiHtU+mo5O97/s5/D/6XDyo/4P3K93nywJM8UfAEDp9j8AOICWFarFaU90Br/8FUVzp01d+7E0A3ynWmFEXBdI+W6t/z9bcJ7msY0XHUQBD3PW/TMfeRw8fOjMJwyZRRaacQQojhmTFjBj/92c9Yvnw5iqKwfsNaHnvsMQKBwFg3TYhx76QNpuI7E0bUtbi56MfvsatUmyY1bYjTjMravayvcWIxKCxKCvLUgadw+B1kRWRxYdaFQ+rNUe01qAffhpAfAm7Usk8G3Wd3026eOPAETZ4mokxR3encbQYbFR0VPHngSVz+Y5/ZTRx7aeFGbEYdTe4ALZ6+v9B0Bm30U0uN/jnHoEPR9LWFGC6YhNripuPUx/D+bSOqc+ijuaFqO/aw/8P39y3dr1kevZTIsvukB1QIIcZQTGwkN954I9///vexWCzs3LmT559/vtd2GzZsoLa2dgxaKMT4dNIGU9MyY/jutbPJS40kOtxEZyc+n+wc2g3i6YJmAG6Y5uK54ido8baQbE3m2knXYtANvhRN9btRC14DNQQJnUVMHQNPc9rfsp9XSl8hpIY4JekU7pl9DxdkXcCF2Rdy56w7SbQk0uxp5s3yN4f0O4jxTVEUcjuD/vL2vgMWnVEbvQoFhh7QHFWbdDqsz1+D8cbZ4PLjufdd7Kl/wnn183h+twb/K/sJbK4iVNpCqMZOqNpOcFcd/lWFOC95Bkfm/T2OF15wN6bb5h+XtgshhBhcdnY23/zmNzEYDKxZs4aqqsOzZnbt2sWTTz7J/fffT12dTM0WAk7yBBRXLsvhymU5ADz7UTEPvrqP/RWtg+6nqipPFzSzMuMgaZGFOAOQG5nLFyZ9AbPePKT91cJV4GmHiGSU9EWojQVg7j9jTqO7kVdKXwHg9LTTOT3t9B7vhxvDuX7y9Ty05yEKWguocdaQakvtdRy7z45e0XfXwhLjW6rNyJ4mN7VOf5/v68L6rzPVNdKjjtI0v+7j2kxYn7oS/xXT8P75M4KfVRJ4tYDAq0NfR2X+wzmYvrVURqOEEGIcysnJYdmyZXzyySd8/PHH3HTTTQBMmzaNqVOnYrfbsVql/IsQcBIHU6GQSlmdg71lLWw60MCnu7QRqYWTEwbd99OqRuYlrWd6fB0KCmekn8GylGXolCEO9FVtgqZC0IehTLscHJ2jYda4PjdXVZU3yt4gEAowO242K1NX9rlddFg0CxIWsKl+E7uadvUKpqo7qnls/2NEmiK5d869Q2+vGDJVVUc1QEi0ah/RBnffwZTerH2ZBT3O3u8ZOutMBUYnAcXn/X/27js8jupc/Ph3tq96782W3HvFmGp6r6YmQCgh1JDc3FSS/JKQdhNyk5sEAqEk9BIIvYPBYOOCjbtcVKzeu3a1fc7vj5FkK+qyZMn2+3kePZZ2z5w5K4925z3lPdZLZmC9ZAahwkZCn5cT2lKNvr8FvaIV1eIFbxB0hao90DYtOZzIom+hOYeWnEUIIcT46Aqm9uzZ0/2YzWbjzjvvxOfzERkZOY6tE2LiOGaDqd8+v5U3Dko2YTZpXHtaLjefO/Ai+PymfD6sfJ2ZCV6UsnLttCuYGjN1yOdV7dWo/asB0KZfiOaMRbVVgjMWwhL6PmdzPuWucsKt4Zybfe6AN+sz42ayoXYDpe2lPR73hXw8kv8IYIxOaWOxoOYYp5TiO59WkBxm5ftLUkalzrjOXeebvH1nezTbjGBK9/deM9UVTIXGKJjqPk9ePOa8eLh+fq/nfH/dgPdb74AC2+1LcPzfuWhmCeJF/xobGykoKCAhIYG8vLzxbo4Qx6yEBOOepK2trfux+vp6YmNjJZA6DNwBN03eJjIjM8e7KWIQx2wwNS0jhi9i65k9KY7Zk2JZMT+NpBhnv+XrPfW8X/Y+Ba0FWExQ1hrHdxdezdSYoe/BoII+1O7XjHVS6YvREqYAoCXPRkue3W9q8w01GwA4Ne1UnJb+2wiQ6DRG1pq8TT1GSd4re6+7zBkZZ8j0qjHwyw01/PHLOqwmjZVTYsmNsR9ynYlO40+0viNAq9tPdLitx/Oaxfi5rzVTloOCqZGMmAWCOrpS2K3D309K6TreH36I//7PAbD/+nTs3ztRrjsxqP379/OPf/yDRYsWSTAlxDiqra0FIDzcWBZQWVnJH//4R3Jzc/n617+OxXLM3kKOKV3p+EI+ntv3HBXuCs7LPo+lyUvHu1liAMfsX8IlJ+Zw+cmTBi3nCrj4pPITNtdtRqFQysrbxdOZGrOAGfHDCKSUQhW8C55mCE9Em7yiV5m+bjRdARdlrjLMmpm5CXMHPY/T7MRqsuLX/fhCPhwWB/lN+XxZ/2V3meNTjh9yu8XQvLO/lf+3rgoNePH8yaMSSAEEO9c7vb2xnPLP9vH0D1f0E5D0fsxkNqFpmnHtjSCYemdjOQ+9uZvbL5zBhcdnD/k45QviuelVAs/vBIsJ5yMXYetj1EqIvui6MZJqMskIphDj6eOPPwZg4UIjSVAwGCQUCrFr1y6qqqrIysoaz+YdlfwhP4/kP0K9p777sVUVqySYmuCO2WDKbBr4xtIb8rKhZgNrq9fi1/1oaEyPWcCdHyXS5LXz8GnDnMZVvRXq8sFkRZtxCdoQMv4BlLUbUxGzIrOwmwe/Qdc0jXBrOC2+FtxBNz7dx4uFL3Y/f/OMmzGbhj/SIPrX7g9xywelKOC+5WlckhczKvX6gjpPdmaNtLm9FNe3s31/E/MmH7S2rnOzZ62f69lk0giFFHpIMZx7U6UUL3xSRHO7D6tl6Acqf5COK18k+OY+iLAR9q8rsZ4lowuiN9VUjGopRcs+Ec18YA1dKGRc0xJMCTF+CgsL2bhxI5qmsWKF0fmbnZ3Nt771LbxerwRSY2RP854egRQY96NiYjtmg6n+uANu1tesZ2PdRnwhHwBTY6ZyZuaZfPdTN03eRlZOiWFh8tCz2Ki2SlTRhwBoU89BC+97bVRfWv3GxsBJzqGPgoVbjGDKFXDx+v7Xux8/Nf1UmXs7Bn61oZoqd4ClKWH8aOnorJUC+J9NNRS3+pkR5+CqDAv/rm9j7c7ansFUt36CKbNGKAR6SMEwcj58sa2a4up24hwWTl+QPqRjVDBEx7UvE3xzH1qck/D3rsO8sHdGSSGUHkIVvAfeFlRjAcy/Ds1qTGHuGkHtGqESQowdT9DD5vrNlNWUcfnMy9F1nfXr1/Paa6+h6zqnn346iYkHEnNlZw99loIYvvZAOwDRtuju+7+hdKSL8SXBVKcmbxPra9ezpX4LAd3InJYdmc2K9BXkROXwYWkbT+SXYTNr/PqEod1cAih3PWrHv0APQuoCtOTZI2qfroZ+YxFtj6bSXckrRa/Q4m8BjGDs5LSTR3Ru0b92f4gHthm9SH9ZkYVplNYEbanr4JcbjD08Hjwtiyivj39/WsK7X5Rz0znTcNg6Rxe7ztfPejuTqevGdOjp0ZVSPPLUFgAuKmsf0siU0nU8X3/dSI8e45BASgxIM5lhxkWoPW9CRyOqdC1a3hkAOBzG9hJer/TGCjHWdKXzYdmHKKX4/o+/j9/t7x4dXrhwIStXrhznFh5bIqwRAGREZHBV6lX8fdff8YV8tPpbibZFj3PrRH+O6WAqpELsa9nHF7VfUNxW3P34lOgpnJR2ElmRxjB2qy/EzR8Y2fH+37JUpsQOvpcUgHLVobY/B0EPxOehTTlr2G2MdxijEFXuqiEfE2k1sux0BVIAN824SVKhj4Hn9zbhCuiclB7B0pTR2burrM3PJa8XEdAVt89N5NTMSHQ9gry0KAqr2njhkyJuOMvIIGmydiagCPj6rMtsNYM3SDAQYqhDU+9vrmSnP0i0N8iFr+0j8HI+1stn9lteKYX3v94j8OQ2CLcS/tZXJJASg9Ki0mH6hagtT0DDXugMppxOY4RKgikhxl64NZx0RzqVvkpIBr1QZ8qUKZx22mksWLBAkgYdZl2zkMray1iZu5Ip0VMoaC1gf+t+5ifOH1ZdBS0F2M12MiIy5P5vjB2TwVSLr4Uv679kS/2W7iFVs2ZmdvxslqcsJzksuUf5ez4pp6zdz+LkML67aGjTuFRTMSr/VQj5IHYy2sxL0UZwMedE5mAxWah0V1LpqiQ9YuBRMV/Ix4baDT0e++6C7+KwDC0AFMOzqty4fr4yPW5U6tvf6uP0l/ZR1u7n+NRw/nBKBmCMMN196SzueWAdT76/jwuWZREf5cBkNf5f+wumbHYLHe0+/N4gDKFTa295C797YRsAX8+KJTyg0/HVl7Hn12G7cymmuN7TW32/+hT/XzaAzUz4v6/GskymkoohCu+cPuR3dSdJ6QqmPB7PODZMiGPHgtQFVJZUkn1aNl+99askRg2+36YYGylhKUTZomjzt1HYWsiUGCOYym/OH3Yw9V7ZezR4G7hh+g1Miho84ZoYuWMmmFJKUdJewsbajexp3oPCmPYUZ49jSfIS5iXMI8zS+0bxxb1NPJHfiMOs8dQ5k7CaB+6lUUpB6VpU6WfGAwnT0GZcNOSEE//JZraxJGkJ62rW8XzB81yQcwFTYqb06mWo89Sxo3FHdxr1Lt9f+P1B06mLkdtYY2xIe3zqoY9KfVbRzuVvFlPvCbIkOYy3L8nDedAUu6XTkzhxdgprdtbwvy/t4Jc3LsZkNeZS9xdMOTpTq3v72fT3YJv31fODRzfS4Q1yxsJ0Lr1hIb4WP/4/rcf3s0/w/epTzEvTMS9IxTQjEVN6FIGX8wk8ZQRfjl+fjmlOMqrdB04LmkUSnYhBdE1f1szdPeA2mzHa6vf3TvcvhBh9c+LnsLF2I3WeOp4qfoprplxDanjqeDfrmKRpGsclH8cH5R/wTuk7XDz5YjQ0ilqL8AQ9Q76fa/Q20uBtwGF2kBUhyULG2lEfTAX0ANsbtrO+dn13hhSTZmJW7CwWJy0mOzK732Hs4hYf3/jIyKb3v6dkMj1u4NEdFegw1gA0FQEaWs5JkHX8iEakDrYifQWV7krK2st4ruA5YuwxZEZkYjfb8QQ9VLorafG19DrulLRTJJAaY42dm+mmR9gGKdk/XSn+sLmWH66pJKTgjKxIXrogl2h772DkW5fP5suCelZtqeKZrELOSx44uLeHGVP7fAMEU+0dAR5/dy8vflKEruCUuan89LqFmEwmnH84B+uF0/D9dg3Bj4oJrS0ntLa8z3q8//0+3v9+/8ADFpMRVIVZIcyKKT4MLTEcLTEMLSkcU04Mprx4zDMSMGXIXPBjUrBz9Ml64L1VphUJcXjZzXaun349zxc8T4WrgkfzH+W87PNYlLRovJt2TFqStIRtDduo89Tx5J4nUShCKsTelr3MT5g/pDq6tsPJi86TDM6HwVEbTPlDfjbWbmRd7TrcAWP0IMIaweKkxSxKXESkbeDduzsCOivfKqLFF+Li3GhumztwBj7VXIra8wb428HiQJt+IVr86KSEtpltXD/tejbWbmR97XpafC29gieH2dErfaZk7ht7CQ4Lrb4QdR0B4p3D/3Pa3+rjpvdL+KTCBcB3FyXz6xPTsfST6jw9IZx7v7KQex//ggdey0ef3UgWoPWTRtrmMNrk9wV7Pdfq9vPa2hKe+aiQto4AJg1uOGsKXz9/Ro+tAyynTsJy6iT0pg5CGyvRt9fie3Ajqrytu4xpWjyqIwCeIMpj/EtQh3Y/qt0YYQjtb+n396BlRWM5IQvr5TOxnD8FzXbUvjWJg3mNabIc9H7s8xmjrFbrMNJPCiEOSYQ1gq9N/xpvl77Nl/Vf8kbJGzT7mjkt4zRZb3OY2cw2bpxxI++Wvcu2hm3dj2+s3dgjmPIGdexmrVcHVGFLIetq1gGwLGXZYWnzse6ou2PRlc72xu18VP5R93qolLAUlqcsZ1bcrCFF6Eopbny/hC11HnKj7fzzrJx+e0uVHkTtXw0VG40HotLRZlyM5hjdnnaLycLy1OUsS1lGlbuKOk8dQT2IzWQjJTyFsvYy3i59u8cxCY6hp2AXIzMr3kFRq4+PytuZET/0UcCOgM7/fFHD7zfX4AkqksIsPHZmNhdMjhn02NMWpPHDa+bzuxe2sfrLMq4DWr0QDOlYzD0/9KydQUnAFyIQ1CmsauXLgkY+31XLtqJGQp1Z/hbkxXPPZbOZltn/+U1xYZjOmYI+NR7vrz8FwPatZTjuP7vX34dSCvwh8ARQniDK5Uc1dqDqO1B1bvQ6F3pRM3pRE6HttaiyVgJlOwg8twMt3ont7uOw37MMLUrW+h3VOju6sEV0P9SVeKIrq58Q4vCwmCxcNOkiMiIyeGP/G6ypXkOVu4qVuSsJsw59Oxhx6JwWJ5dOvpRFiYt4Ys8ThFSIKncVtR21JIcl825JK3d8VMZ9y9P4ygwjUVmDp4E11WvY2rAVMEa4MiIyxvFVHDuOqmCqwlXBO6XvUOmuBCA1LJXTM08nNyp3WFNHfvx5FS/uaybSZuK1i3KJcfT9a1ItZaiCd6GjEdAg+wS07BMOeVrfQEyaiYyIjB5/IDsad/BO6TsAnJN1Du+WvYuGRpQtaszaIQxXTYvj9eJWHt3ZwJ3zEod0nb1b0spdq8opajV64K+eFsufT80kMWzoPfEXLc8mLsrOvx7ZBm7Ytr+Fn33/bfLSo0mNC8NhN6OhEQHMtFvYVtDAHc9vJRA8kGLfbNJYNiOJq1fksnT60NquQjod1/8b2v1YLp2B4/dn9Xmcpmlgt4DdghbT+eCUvvbHMtKq67vqCb5fiP+pbeg76vD97BP8f92I4y/nYbtyZNsJiCNAoGua34GOiGDQGEXtWjslhDi8FiYuJNIaySvFr1DcVszDux7mqilXkRYuWVoPt+SwZL42/Ws8tvsxAP6282/cMfsOytpbafU38bvNjaREmClu29N976uhsSJjBSemnjieTT+mHBXBlCfo4YPyD7rniEZYIzgj8wzmxs8d9vD0g9vq+PXGGswavHDeZGYl9B5tUO56VMmn0LDPeCAsHm3a+Uaq38NsZ+NO/l30bxSKFekrmBY7jXfL3iXcGi5D84fBZXkxpIRZ2Fbv4ZXCFi6bEttnOaUUqytc3LehujsD4Ox4Bw+elsVJGQNPOe3PibNTyFw5h/0Pg9NuocMXYntxE9uLm7rLpEXYmbk0E00pAkGdrKRw5kyOZ9n0JI6bkURkPwGcCnrR+sgAGXhiK6F1FWhpkTj/fmG/0wuHQzOZMM9JxjwnGdt/LSf0aSnen3xEaG05nmteIvRpKY6/nCdraY5Gwc6pyQdda12b9cr/txDjZ0rMFL4x+xu8WPAile5K/rH7H1yVdxV5MaOzfEH0ppSi0l3JnuY9lLSV0ORroiPY0avcgzsfBODuziVtn1Ub/1pNVubEz+GE1BO6t9URh8cRH0wVthTy6v5XcQVcmDUzx6ccz0lpJ41ox+i3ilu5+2Njcf0jZ2Zz7qSeU/WUqxZV8QXU7gQUmCxomcuMJBMjzNZ3KDbXbebNkjdRKE5OO5lT0k+hoKUAkCl+h4vDYuLe41K5++NybvuojGmxju4A3BfU2d7gYW2ViyfyG9lab/TCR9vN/HhpCt9ckITN3Hcwois1pA2AwxxGMHTc9EQuvelsiqrbqW/x4A0YiTFMOtDYQVqUgw/+51wiwgbv7VeNRag9r8OMS9DiDqRTVS4f3p+sMl73/5zZZ5r0Q6VpGpZTcghffRP+hzfh/c57+P/2BUTYcP72zFE/nxhfKmiMzmqWA+/XXcGU6T8CdaUUjbUuWurdxCaGE58ysk4IIcTQRNuiuXHGjby+/3W2N27n2YJnuXTypcyJnzPeTTvq1HnqeKvkLUrbS3s8btEshFvDsZgsNHobux+Pd8TjCYYoaQ1R6YrluwsXsTRlBjazjOiPhyM2mAqpEKsqVrG2ei0AmRGZXDTpIhKdI9sfYXOtm6veLkZX8NPjUrlxVgIqFABXDaq5BBoLwFXbWVqDtAVoWSei2SMGqnZMBPQA75a+y+b6zYCRte/U9FMB4w8SDmz8Jsbe7XMTeaO4lfdL25j3dD65MXYCIUVZu5+QOlAuwWnh7vmJfHN+Uq+po0opPqlw8db+Vt7e38rXZsbzvSWD72nWFcSrUIC4KAdxfawxWvP2HgK+0JD/2FVzEQS9xhTWJbeida4z9P1uLarGhfm4dKzXjO2HqaZp2G9bgiknho6Ln8P/+7VYL5uBZanM/z6qhDrTnx90A+ByGclYwsIOBOvBQIjdmytpqDZGdav2N7Ps7CnY7EfsR5gQRwSLycIlky8h0hbJ2uq1/Lvo30RYI2TfolGilGJdzTo+rPgQXek4zA7mJsxlavRUksKSiLRGdo/SP5r/KBWuCgDumH0HZpOZC18t5M39rZyTlcaJ6RJIjZcj8pPIHXDzctHLFLcV95gbOtJpbaUtHXz77S2cF97KNZkhLo4uRd/UZKyFUgfWmGBxQNIstIzFaM7R2aS1PwF/CD2ko+uKUEhHDym08CBFrUWsqV5Dg7cBs2bm/JzzWZi4sPs4h9lBWnia7BFxGJlNGq9cmMtdH5fxRH4j+5o7e9uBabF2lqVGcEZWJFdMicVu6fsaveeTcv6ytb77508q2ocUTJlsRvAU8vW/wWlYhJ1WXwcedwDHEEamtNwzUSYbWsqc7kBKeQL4/mLsYdZXwomxYj1nCrZvLcN//+f4/7gOy3NXHJbzisOke5rfgZGpggJjdD011XgPCwV1tn1eSluTB4vVRDCgEwrplBc0kDu777+RVrefveUtzMqOJdzZeyprwNVM+TsP07RtFZawKKKnLiF+/plE5i6Q6YVC/AeTZuLMzDMJ6SHW167nuX3PcfPMm0kOSx7vph3RlFJ8VPERa6rXALAocRFnZJ7R75Y2J6SewAsFLwDgCriItkdz+ZQY3tzfyhvFLdw+zxhMaOvwsz6/jrMWj13noyfoYX3NeubEzyHBKTOhjrhgqrajluf2PUeLv4VwSzhX5F1BTlTOkI5VLWXgrkP53eB3g99FqKOJVE8Ln0w+aAih4aCDwhMhOhMtbjLEThrz6XzN9S72bq3G4+q9YeXqtH8Zd+gY0/guz728V9C0KGmR7A0xDsKsJh4/K4f/PTmDmo4gZg0yI204+gmeDvZFjZu/bK3HYoJvzk/iotwYlqcObcSzK5jSA95+y1isRhtCwdCQ6tQ0DW3yqT0eC7yxF9p8mJekYVl+eDcAtN99HP7/XUfg37tR3gCaQ1JmH2kat31E2Rt/ob14Gxnn3MrkK39oPNG1z1TnzUMwGGTr1q0AzJ8/H6UU+ZsqaGvyYHdamX9iNsGAzuZPiqkubWHSjCRM/zFVtrHNy3W/+Zhml5+4SDv33biYhVMOfNi7yvLZ/vtr8TfXdD/Wkr+G0lf/SMKS85l19yOjshZQiKPNWVln0RZoI78pn3/s/ge3zLxFbqQPwab6TaypXoNJM/U5fVLX9R7TnfOiD6xXq+6oJtoezdnZxnKUzypdBHVFMKjzvYc3sK24ifaOAJefPPojiEE9yP1b7iekQrT6W7lk8iWjfo4jzREVTJW0lfBcwXP4Qj7SwtO4aspVRNuGnoJc1WyH2h09HjMDOlARCCM5MRVrZBJaeAKExYMzrsdc/pHQQ0FM5qH9mn2eADvWlRMK6ZjNJrx4CBJE14KEtBB2k4OMyHRmxs1kbsJcrCa5qZxoYhyWfrM/9ueZPUbCiG/OT+IPpwxvbzCzzbgJ1f39B1MHBleH1uPu8/koLS1l6tSp3Y8FX9sDgPWqw59Zz5QRjWlaPPruBkI76rAsOfyJXsTI7X/595S+8ofun5u2f3wgmOrqBOjM5rdr1y46OjpIS0sjLS2NiqJGGqrbsVhNzD8hm7AI4/142oI0ElIjewVSAJ/vqqW5szOqqd3Hdx5azws/OZ2kGCf+1nq2/8/V+FvriJqymJxLv0PI76Ulfw01nz5PwxdvUbf+VZKXXzaGvxEhjkxdN/2BUICC1gL+Vfgvbpl1i9yLjIAr4OKDsg8AuHjSxT0CqVAoxIYNG3jnnXe44YYbyMszgqiDf8+FrYVMj51OaoSVydE2ilv9bK1z8683drGtuInEGAcnzhl8dstQNXmb2NG4gz3Ne6j11KJ33lhIogvDERNMFbcV8+y+ZwnqQWbGzuTS3EuH/QesxeagzFY0azjYwvn7ng4e2BegzRTF6qtnYY86tMDpYK37NrLlFxcBkPuVn5N57jcGPaa53k0opBOTEM68E7LZ2riFkK6RGp5FjD2GiywnyhSUo9C+ZuOGcl5iGBXtfjxBncnR9h4b5/bHZDOuWT3g6/N5pRSuNqN+Z8TgU/wCgQB/+ctfKC4u5hvf+Abz5s1DhXSC7xUCYDl/6iA1jA1Tdgz67gZUnXtczi/6pus6X3zxBV988QUNDQ1kZ2ezcuVKIiON5BDN+WuMQMpkJjpvMa37NmCLPqgnuyvS75xO+umnxv5ly5Ytw+8Lsn+3sQZ0+sJ0wiKNa72svIXKNi9pOX1nzgx2LlQ8d2kmdc0eNhc0sD6/jouWZ1P03C/wt9YRPf145n3/edr3b8NVlo8zZRLR04+naeuHNO9aI8GUEP2wmqyszFvJwzsfptZTy6dVn3J6xunj3awjzo7GHfh1P3nRecxLmNf9+JYtW3jppZdoaDCmSK1Zs6Y7mFLqwAyqBs+BKVQnpEVQ3NrEE6uK2bS9hkinlT/dcTzJsUPf+3IgT+19iqLWoj6fO5T06yrQgSr+GDqajPWzIR/a5NPQEqcfKNPRiCr8ADQT2uwrJuw98BERTFW6Knlu33ME9SALEhdwYc6FI1ofpSXPRks2etYf2lbP7dvKsJmdrL5iKtnDDKTK3noQX2MlzuRJOJKyMJmteOrL6KjcR936Vwm0Hci6YrIMLejrWkztcfvRQ3qPtVDi6BXWORXwhvdKuh/Ljbbz4eVTyIke+Lo0WQYOptpbvPi9QWwOC2FDCKYsFgvZ2dkUFBRQXl7OvHnz0PPrUc1etJwYzFPHZ0qH8gQA0JxHxFvWMcHlcvHwww+zb9++7seqq6sJBALceuutAFS88zAAaSu+ijUqgdZ9G7BG9XENKUVJSQk7d+7EarWyfPly9ufXEQzoxCVHkJBqBGcVte18ubYUp8VEdVIEqX1k9JucZjy2dmcNNqsRpMVG2tFDQeq/eAuA6bf+ifJ3Hmb/i7/udXzN6mcJS5mEyR5G8vGXYo0c2/WxQkw0wUAIk9mEqZ8OPbvZziWTL+Hx3Y+zrmYdy1OW97vOR/StK5HEzLiZ3Y+5XC7+/ve/d2c0veaaazjppJO6nz84TXqD90AwdXpmFE/tbmLrbiNJ2j2Xz2Zy6ujtMxpli8JmsmE1WXEHjQ5NDY3rp19/SMGN2v0GNBf3fDBgvEbld6G2Pdu5j2vXASHQJuY9wMRs1UFafa08W/AsAT3A3Pi5Iw6kDvbcnibu/LgMgEfOyGbZENenHKx+4xu0F20ZtNyMOx8i+fhLhlRnbFI4EdEOXK1e9u+uZ8rc0RuiFRPXL09Ip9zlZ2eDl0ibCU9Qp6jVx/2ba/nraQOvT9IsRoDUXzBVtd+YQpiYFjWkNz1N01i5ciUzZ85k5kzjTV7fZ7yZmWeNX4ZI1Tl6p41ST5s4dE888QT79u0jKiqKCy+8kKSkJP70pz+xdetWfD4fdru9OzFK1UdPdB8X8rgOVNK5BtXv9fD440aZU089FYvJTnVpGZoGeXNSuq/dQJOHaLuFynYv//fGbn5185JeI7izso0Rq7aOABBg9qRYls9Kxt9Uge7rwBaTjGYys/9fvwUgetoyWveu71FH8Qu/AiB29skSTImjVl1FK421LsIi7YRH2tFMUFbQSEu9m/jkCOYuz+732KzILCZFTWJ/2362N27nuOTjDmPLj3xdgdHBS1XMZjM2mw2v1/i8e/755ykqKuKmm25C0zSafc3dZd0BN7rSMWkmjk8Lx6TrBFo6MGtwytzRTUC2In0FAFvqt3S3+YKcCw4po6Ny1RmBlNmGNv1CcEQbWV1t4aia7ai9b/Uor828ZFy2IBqqidsyIKSHeKHwBdwBN5OiJnHxpIsPOZB6vaiF697dj67gvuVpXD9zZPM9J13+PdwVe+io2Y+3vhylB7HHJlO75iUAwjOmM+9HL2Hrqxe2H5qmMWVeKls+3U9jbTtTkGDqSFJf1UZteQsZufHEJIQP+bjpcQ42XDOj++dfb6zm3rVVtPsHTxhhshrBlAoFej3XUN1GdWkLmgYZk4d+Q6hpGrNmzer+WS8yAjLTlPG5qVS6jl5sfIho6bK30ESwd+9etm/fjtPp5Ic//CFxcca1kZKSQnV1NXV1dWRmZpJ77f9j32P/jae2BLMjHF9TFQ2b3kYP+DBZ7WBxoJTi+Vfeora2ltTUVC666CJKdjeiFCRnRhPeOb3P7wtSVmD0xq4qbWZ3nZvvP7KBe69dQGxnmfI6F89/3HM6yq87Ay5/Z2+v0oMEPe3dUwy7AimzI5zM825HKYWnuoiGL99DhYJj/8sUYpw017upKWvp8zmfd/Brf0HCAva37WdP8x4JpkZIO2gts9Pp5Dvf+Q6ffPIJmzdvxuv1snHjRi699FLi4uIoaS/pLqtQeIIewq3hxNrNhPsDaEBuejQRfWQw7YsKhMCkofWz3yWAL+TjndJ32N28G7NmZkX6Co5LOe7Q18n5jW0uiExDSzCWD6igF7XlSXAfyGxM4gy0KWejWSd2R+qEDqY+r/mcKncV0bZorsi7AnPnvPqR+qyinSvfKiak4EdLU/jxcSOP3uPmriBu7ooej5W9+QAAtphk5v3wX8MKpLq4WoyeXMsQssCJiaWqpJmmWhcxCeHDCqYOVtbm5/ebjKH6lVP6XhNyMJO1MzW6v2dq9IaadnZtNKYR5MxI6l5vMhKqrTPVe9z4vJnp+1vA5UdLicCUdPj3dRO9rV69GoDTTz+duLg4PB4Pjz32GNXV1QBERBj/T5E5c1h033t4G6vY++h38DVVEZY2xQikABxRvJvfyufbSrFardxyyy34OkJU7TeC56yDsvCVFzaihxRxyRHcM3cB//3wetburOWKX3zIjKwYXJ4Ae8pbe7QzPSGc+Ejjb8Qen449Ph1fYyW1n71I5gV3Uf3Js5isduLmnELWRfegB7yUvvIH6je+CUDZ639m5p1/G7tfpBDjKH1yHJExDtztPtxtPvy+IO7O9/uFQ8gC15VJucZdM3BB0UvXwEBQHQha29rayM/PZ/fu3d1T/bKysoiJiTEymzbl96ijKwlEpM1MuM9IujM9c/CkbCoYwvujj/A/sBEtzIrz7xdhvXRGr3Jt/jae2fsMtZ5a7GY710y5ZsjZswdl72xnezV6yafgaYa6nq9PW3o7mjNmdM43xiZsMNXub+fTKmMx8kWTLiLMEjbIEQPbWtfBha8X4QspvjEngV8uTxuNZnYLdrRR+vr/ATDt6/+LLXp4mwcH/EEqi5u7F1wffBMhJj6/N0hTrQtN00jKGHqGyYMVtfi46LVCWnwhLpwczQWTB6/HbDf+LnSfB6WHQDNRXthI0U4jIEubFEv2Ia5zUsHOJAED9F6NpeAHxkiDefHo/s2KketaJ3Xcccd1/7xjh5EpddmyZcTGGh0BFe8+Qt36V2nfvw0VCmKJiGX6bX8BjMXUL31eykfbWgC44StXk5aaxhcfF6OUIn1SLBHRRiDUXO+mbJ8xKpUzLRGHqZ2fT17HPyumsqM1mk2dz9mtZs5YlM61p+Xi8YWYkRXTve7DZLYw7ZY/sP33X6H87b8RN+805v3gBexxqbTuWc/G/z6+1+uMmbF8LH59QkwIEdGO7r8xgNbGDr78dD8RMQ7MQ+jQjbBGoKHhCXkI6aFD7vA+loRbjA5Xd+BAUqVQKMQrr7zS/fOSJUu49tprMZlM7GvZR5W7CqfFiadzSwm72d75r0Z0wAjKkhIH73D0fvMd/A9vAkB5g3Rc/S/C37sOy6kHAug6Tx1P73mKtkA78Y54rp5yNYnO4d3XHiwUCmEymQ4sNwiLh8hUaK+G0rU9C9si0JbdNWGTTfRlwgZTa6rXENADTIuZRm507iHVtbPBwxkv76PVF+LyvBgeOC1r1P+Tqj95hlBHG9HTlxE/r//MNu31jXjb3fhUOMGAjtcTwN3mpb3Z252pZdKMxBHfkIvx0dY5ohgdH9adSGQo/t/nVUTZzext8vLU7ka8IcXMOAf/OCtnaGucTCYsYdEEO1rxtjRTVOChvqoNgOxpiUyakXjI17opyXjTV7WuQUqOPqVU95u+deWsQUqLwyEQCNDe3o7ZbCYx0fhwzc3NZfny5SQkJHD22Wd3l/XU7qetcDOYzCQuu5jca/4fjvg0fD4fTz75JJs2bcakwfXLEljkKKZkbx4d7T7CImzkdqb1bW/xsHODscY1a2oCToubL39+Mf66Eq4FIi77NcEZF+C0m8lLjepzk94ucXNOZfa3/8Gev91N07ZVNG1b1XdBzcSsu/9O4tILRueXJsQRINA5tXyon2EmzYTNbMMX8uHX/ThNE3sq1kQSaTOmrLf4Wrofi42N5eabb6aiooK5c+d2Z/HTlc4H5UYa9Xnx81hfux672d491U7TNBKDAYLAvt4z/nvwP7XN+Ey1mwl/+6sE3tiL/0/rcV/yHOGvXoN5XgqNz6yn+bHPyLhQp/3qTK6Zcg1h1pENaNTU1PDCCy+we/duFi5c2J2cSNM0mHct1O5C+drBVQtNhWC2oy288YgKpGCCBlPugJvN9ZsBWJGxYpDSAytr83PWvwto9IY4f1I0z5w7aUgpp4dDKUXVqqcByDz39v7LNezDtutNQiEnO9tOBHr2/MQmhZM1JYE4mcp0xOloN6ZGhEcOnjGvy44GD/dtqOag7aK5dnocfz41k/hhZK2zRiUQiJrO5s9rCQTAbDExY2E6iemjk83HlB0DgL63ceCCYyD4biH69lq01AisV0owNRGEQsYNl8Vi6f7Ai4iI4IYbbuhVNv3sW0hcegFhGdOwRRrrUwsKCnjmmWeorq7G4XBw8vzTmZdVhLe5kfJ24xqbtiANPaSoKKyndF8DoaBOQmok2ZPD2fbbK/DWlXSfw1rwPosvu6n7547qIoIdrUTl9p0NNWHBWSz5n08pffUPVH30ZI/nUldcR9LxFxMz/Xg06WUXx5quWyM1YKkezJrxdxJSQ9sUXhjSwo2ZFsVtxazgwH3u0qVLWbp0aY+yWxu2Uu+pJ8YW031cRnhG9/tve0eAYIcfXYNH9ru4pdnL1FgH/ym0uQrPXUZiB+f/nYfl1EmYT8pGVbUTeHEX7tONJEBWIAUwRTjJ+dn1fa6Patr5KXv+dhdx889g+tf/t8/X2NzczP333097u7E+avPmzTQ3N3fPXNDMNkhbAHoItelR47HsE9HsR9498IQMpr6s/5KgHmRK9BRSwkaehMET1LngtUKq3QFOyYjgpQsmYx+DtUhtBZvw1BRhjU4kbn7vUSmlFKrgPajeglUDvy2GjOxwTPZw7A4LYZF2IqMdWIcxoiEmFl03Pn3MlqHfgE2NsfOXFZl8UdtBbrSdy/JimJUwvJ69gD9EcNbtELOAQACiYp3MXJwxpD2lhsq8xHjzDm2sQIX0ARerjjbf/3wGgP3bx6PJ38eEYLPZsFqt+Hw+3G434eH9rw8MS5lMWMpkAJqamnj99ddZt24dAMnJydx+++10NJrYVRRHACe6Do4wK0W7anuM1idlRDNjURqFT/yA9qItOBIzmfXNR9n8k7NxlWzvPp+3oYKtv7qMkK+Ded9/gai8vgMqe2wy6WfeRPXq51FBP9O+/r+knnLtaP2KhDgiOcONz432Vg9KqSGNDvhDxlodm2n0PnOOBXnReVhNVspd5TR4Gkhw9j0d3x/y83HFxwCclnEaFW5jLXRGREZ3maLO2Si2CActfp1THt7Gh3mRzFppZORVviCBF3biuecdcPmxXjsH6y3Ge6NmNuF86jJMuXH4/rIB3H7qjndQcVEUJ9x2Q5+BVLCjnfy/3kbQ1UTN6mfZ6Y9n5Z339ir34osv0t7ezrRp0wgGgxQVFfHl+k85/dyLexYsXweeJnDGQvqiYf4mJ4YJd3eilOLL+i8BWJK85JDq+unnVexo8DAt1s6rF+biGKOkDmVvGGsAUk++BpO5j19pw16o3gKaGW3yqUSkL2HKKAxhBjdUYMqJwZR85EXxRxuz2fj/DIX0IR9jt5i4c/7I0o0rpagtb6VoVy3+mAUQ8pEYVsesU84e9eFxLTMaU14cemETwbf2Yb1o+uAHjQL/P7cQ+qwMou3Yvn5kvsEejUwmE5MnT2bv3r28/fbbrFy5csBrrq2tjdWrV/P+++/j9/uxWCycc845nH322dhsNoIJIarLmgn4jJ5tb0cAb4cxVyU2MZysqQnEJobjLt9N1UdPopmtzP6vJyEhF2W2EWhvIuh1Y3GEY4tNIXrqUhq+fA+/q6nfNildp+DJe1FBP6mnfkUCKSGAsAgbznAbHrefpjojPfpA3AE3QRXEarIeena3Y4zdbGdW3Cy2Nmxlbc1aLp50cZ/lajpq8IV8pIalMjV2Ku+WvQvA1Jip3WXyS42EPWfMSMTcrFhtt3Dxl/Vs/PVDWE2ds0o631OtV87C+djFPd6zNYsZxy9Px37vyQSbm+l45SfEBvzYIu/os021a18ieND7q9rwDKHbfoDZfKAzuaysjC1btmA2m7nxxhsp2LuboqIiNq5ZxWnLF6FFZxidZTXbUSVrjHZMOeeInREw4YKpMlcZzb5mIq2R5EXnjbieXQ0e/vhlLSYNnjpnEjGOkb1UVfEFyl2PlrYALbJ39r+m7R/TuOV9TFYHGed8ve86yjcAoOWejjZKUXdwYwXus57ElBVN+Ic3SEA1zuyd6zS8Hf4xP1dzg5vC7TW4Wo29KOy04PvwHhwnnY+mnTPq59M0Ddtti/H+9/v4/rz+sARToS+r8NxhZFRz/v5stKjeUxbE+Dn33HPZt28fH374Ifn5+cyaNYusrCzCwox59S6Xi/LycoqKiigpKekeYVq4cCGXXnopSUkHOhFK99YT8IUwmTQyp8TjCLPhcFqJjHVitRkfrEqp7r2f0k6/gYjMGfgCIRqJIYE6dm3fzbyliymqdvPX4Feod84m9TMn305sJjvRgdl2YMRXDwYoevZntOSvxRoZx6SrfnS4fm1CTGiappGSHcP+/DrKCxsGDaZK20sBY5TkSFvjMhGckHoCOxp3sKV+C3Pi5jA5enKvMlmRWdwz7x5cARdvlbxFR7CD9PD07ul+AF8WGgl4Fk9N4DsL0rj0r19y9xv5mPNr6ereNc1PwX7XcVi/Nr/f/yvNacVsjaF53WugmVC6jmbqPQjRuOV9AKbc8Bt2PfUzYvRWCr9cw7QlpwBGB9pjjz2GUopTTz2V2NhY5s1fgN36NGWNXlrW/YPo+GTQQ+BtMc6dcxJabM4If5Pjb8IFU3ua9wAwJ37OIe0p9e3V5YQU3DY3gSUpI0tTrdwNqKIPje9rtkPOSZB1PFpnu7yNVex5+B4Aci7/7z4z+ClvK7RXGZuRpcwZ4avpzZQTgyk7GlXVjqpxgQRT4yqi82a/a2rSaH+wKKVobeygbF8DjZ2JIOxOC5NmJGOu2U++q4KO6qJBahk5240L8P5iNaGPSwi8nI/18pmDHzRCeq0L9xUvgi+E9eaF2G7ue6qWGD8zZszgtttu45lnnqGqqoqqqqp+y5rNZmbPns0ZZ5zB1KlTezxXXthAWUEjmgazl2X1e/NW/ckzNG37CLMjguyLjfdcu9WMP3Uhe2oreeefX+B4r4WKeje6Aiw5VO5r4LrffMx3l7q45PrrAfDUlbLn79+idc86NLOF6bf9pXstlxAC0ifFUravgeY6N831bmIT+79/2tawDeCQNm89liU6Ezk57WQ+rvyYfxf/m9tm30aEtfd7oDfk5c39b1HuLiMUNHFG+rk91ktt2msEU0umJRJhs/D+t5fAN+YT2lkHJs24Xxzg//FgmrlzhFHp0M99jKt8N2BsEeRZXsOqL/eQ8O6n3JSWR1FREW+99RYtLS2kpKRwySWXAGB3OPnG7beToSqJbN4BHZ1rsK1haJNXoKXMHeqvbUKacMFUuascoM8IfajWVbn4oKydaLuZ+5anj7wxnRGzQaFKPkVVb0fLPh5Xh07+A7fjb60jZsZyMs/rJ/FEc4nxb2yOsdhuhEJ6iA21G1iavBSLyYIpKYLwD29AVbswz5PNfcebM8KG3WnF5wnQ0tAx4AfQcOi6TkNVO+WFjbQ1GxkDzWYTWVPjyZySgNlswm02RnA9YxhMaTFOHPedhveed+i49XUiF6d1J6YYTXqDG/dZT6JKWjAvTsP553NH/RxidMyfP59Zs2ZRUFBAQUEBNTU1eL3GaKnT6SQ1NZVJkyaRl5eHw9FzZFHXFUU7a6jo3BB62oL0fgOpxm2r2PeP7wMw5Wu/6dFpdeFPH+GB13fh2lxJXZ0bs0njipNzuPrUXB765xts2O8l/NPfsa3qLYKedtqLt4LSscUkM/Ouh4mZvmwMfjNCHJmUUlhtFjLz4inZU09VSXO/n2V1njr2tuzFrJlZmCgdXiN1YtqJFLcVU9peyhN7nuC09NNIcCYQ1IO0+lvZ1bSLnY07UShCfhubVmWT0+rmpnOM0f7H3tmDLxBi0dQEkmKMEXhN0yDchuW4jIFO3bfOvavQtD47hQPuFvzNNZhsThyJmZx1w7fYVPlrKioq+MUvftFdLjc3l9tuuw2b7cB976xZc4A5KP0McDeAyQxh8d0DFEeyCRdMNXmND9eR5rNXSnHv50Yv6Z3zEkkYRla0XoLGjQGJM9BS5qD2vYvma4F97+ApLsBbV0rkpHnMuuexfud5qnZjE0stagQX9UHeLH2TLfVbKHOVcUWusYGxKSkCJPPfhKBpGilZMZTuradgezULTswZcUKRYDBEa6OHptp2astbu9PVWqxm0ifHkjE5HttB01YdSTkAeOrL0EPBvtftjQLbnUsJflBE8M19dFzzL8I/uAEtfPQWHYeKmui45Dn0/HpMMxMJe/MraA6Zhz+RWa1WZs6cycyZQxupVErRVOemaEcN7nYfmqYxbUEqqf0E5g1fvs+u/7sZ9BCZF9xFyolX9Hg+MszKD66ez7cvn0NxdRtJMU7iO0eJf3L7uWz41TUEy5po3mlsMqyZrSQdfxmTr/oJ9tjkkb9wIY4yKuhDbXsWMpYSGW3cr/g8/efZruuow2ayMTdhbp+jKWJozJqZlbkreWLPE9R76nmh8IVeZTQ0FiQuIKJ9Dh/W7+DRt/fw3hfl6EpR2dCBSYNvXNB7093BBEM6lmEmlOqaAROWNgXNZMbpdHLXXXfx5JNP0tzcTGpqKkuXLmXJkiWY+pgiCKCZLBB5dA0CTKhgSlc6HcEOACKtkSOq4+39bXxc3k6s3cx/LzrED0tf5746tnC0uFw6Mk6j4qnvkDZ9BnX7i8g47zYmrfx+j/n4vbiMzVOJOLS2LElawu6m3YRZwmRu8gSVNSWeuopW3G0+Nq4qIjU7huj4MMIj7dgc1u7NQ7sopQj6Q517jflob/HQ1uzpkcUMIDzKTtqkWFKzYvvcSNFsc2CLTsLfWoe/uQZHQs/AvauuQ71uNE3D+djFuBY/TGhDJR2XPk/YK1ePSkAVeGMvnhtfQTV7Mc1MJPz964c8LUFMfN4OP811bipLmmnvHGF1hFuZuSiD6Pi+9y+p+fQF9j72HVQoSNqZNzL5yh/2W7/VbGJqWjRmiwldVzS1+0iIjuaEX76Bq3w3/pY6zDYH4VmzsIbLHn5C9FK7A1w1qD2vowfSsGhziE3sv1N7dvxscqNzJSX6KIi0RXLrrFv5ou4L9rXswxVwYdbMRFgjyIjIYH7CfOIccQC4rjbxwOv5lNcbm/3GRNj4zsq5zJkUN6xzvrm+lOc/LubPdy0nLtJ+4AnNZHwpvc/O2WC7MeBhizowPTo5OZnvfve7I3npR40JFUwFdWMHZ4tmGfF6qZ+vN0alfnxcKrEjTDrRRbUaiyu1cGOxdHjGDPK+/S88NfuZed59mO0Db2KmlAJ3vfFDxMiytnVJC0/j1lm3EmOPOaS1ZGLsWKxmFpyUw84N5bQ1eyjtnMfcxeawYDab0HUdPaQIBnWU3veGHpGxTmLiw0jKiCYyxjFoIGSPT8PfWoevqRpHQgZK12nc8j5Vq56kZc960HUcSVmknHgFGefdPuLRK1NCOOHvX4/71H8Q/KgY19K/E/b8FZjnjKyzQC9rwfuTVQSeNtJbWy6YSthTl0nCiSOQ1xNg18ZyzBYTZrOJYFAn4Avi8wQIBg5kubTazGROSSAjNw5zH72iwY42ip79OdWfPANA5gV3MvmqH/f7N6DrivxNFfi9QeYsy+IPL+/g8/xaHvjmCWQmRhCZPRuyx+Y1C3HUSFuEZrIQ2PsBCdYq7DFBwqfMG/AQp0U26R0tNrONE1JP4ITUEwYsd/EJOZy9JJOyOhcakJUcgd06vAx4gaDOc6uKKK5u5/4Xt/Hrmw/sa6VpGtaoeAKt9fibq3EkZPY4NuQzBjxMg9z/9sUdCBE+zLYeKSZUMHXw5m+60ocdNHxZ28EXtR3E2s3cNnfo0wSV34Uq+gisTkx5ZxmPeZqhqRg0M8RPOdBGRzgRObOHVnHID3oATFa0Ee4efbCungkxcdmdVhaeMonmOjeNtS7aWzx43H783iB+b7BXeYvVhN1hJSzSRkSMk8gYJ1EHZTEb8nnj0mgv3oqvqZKWPSEKn/oJrtIdPcp0VO6j+IVf4SrLZ8YdD454pMo8NYHwVV+j44oX0fPrcS17BMevTsd251K0IbxRKqUIbajA/6f1BP6dDyEFTotRx93H9Zk9SEx8QX+ItiZPn89ZrCai4sJITIsiOSO6zxFWpRQNX7xFwZP34m+pxWR1kPuVn5F+xtcGPK/fG6S1sQO/N0hJYQOltS7qW7w8/3ER371y4JtBIYRB0zRInU/InoY5/2kiQ3Vo3oZDnlUjRp/DZmZqxshH2K0WE3+5azm/f3E7/7Wyd+KHyJw5NG1bRfOuz3ptGxHo3FjdGh4zpHO5Wr143H5C0U5WvLSP62fE8cOlvTNjH+kmVjBlMhNmCaMj2IEr4CLKFjWs45/eY/wnf3VGHGHWYdyQhYJQlw/2aOgKpsrXG88lzUSzjTAQ6lpzZZFe9mOJpmnEJUcQd9CCel3X8XuD6CGFyaxhMhu9933dVI6EI9HoPSp97c+4y/MBsMWmkHn+HSQffykmq4OW3WvZ/dDd1K17hejpy0g//YYRn888PZGI9V/Hc887BP6xBe933sP/8CZstyzEctF0TLmxPYIi5QsSWl9B4J0Cgq/sRi/s3KPCYsJ61SzsP1+BeYpkVTuSOcNtLDgph1BIRw8qzFYTVpsZu9OK1WbuN3hXStG6Zx37X/4drXuM992ovEVM+dpvicwZPAOqI8zKgpNyqCppJndGEn/Ijee5jwq54eypgx4rhOjJGZeEnjwTqrag6najSTB1VIqLcvCbW5b2+Vzi0gto2raKyvcfJ+Xka3q8d7cVbQEgLGPaoOfweQJsXVNCMBCiLTOWvU1e7l1bxQWTY5iTcHSNak6oYAog3hFPh6uDek/9sIOp14paALhq6jBHcLqSR3TO/VXueqjeBmhoWccPr66DBX3Gvxb7wOXEUc9kMuEIG7sd4vWAca25y/MxWR1kXnAnWRfe1WM9X8Kic5h28/3k//UbFD37c+Jmn4IzOWfE59TCbYQ9ejGBi6fj/e576Psa8X7vA/jeBxBuxTQpFs1hQflDRvDUcWAxs5YYhu2mhdjuWILpEHrYxMRhtpiISRj6OreQ103dhtep+vCftO83UixbIuKYdPl3STv9+mFt3hgWYSdvtrGgOcxu4ebzDs/G0kIcjbS4PFTVFmir7H5s9+7d5OXlYbVKUqCjXdLxl1H84m9wle6kaeuHxC84E4CQ30vTto8AiJt98qD12BwWUrNjqChu4qTsaP5xlh0dddQFUjABg6nU8FTKXeXsb9tPbnTukI+rcvkpbvUTZTOxLHWYC9e7PrT1IErpqL1vAwrSFqKFHUJveahzA9dDSIkuxECCXjcF//w+tWteAoxsZYt/u5qw5L4XiSQtu5iGTe9Qt/5Vyt9+kKk3/u6Q22C9cBqWs3MJvrGPwEu7CK4uQdW60XfW9Shnmp2E5fTJWC+ejvnELLRhZhESR76Qr4OW/LXUb3qH+g2vE/IaSX4sEbFknHUz6Wd/XRJECDEGlFJ0XPo8lhU52O4YZEq2o/Nv0G/8fW7cuJHHH3+cOXPmcOutt0pAdZQz2xxknX8nRc/+jIKnfowzZTJhqblUvPMwgfYmIrJnE5Y++MiUpmlMnpVMWk4czggbNwyjs+1IM+GCqRmxM9hYu5FtDdtYkb4C8xB7JwtbjJ75WfFOzKZhrgXpWpulFGr/p8Ymu/ZItEmnDK+e/9SVr38YPaxCDJW7Yg87/+8WPNWFmGxOUAo94MVsG3ha6ZQbfk1E9iwyzr1t1Nqi2SxYL5/ZvZmv3tiBqmhD+UNoZg0tKxrTUfxGKvrnb61ny30Xo/u9+Jp6bu4bNWUxqSu+StKyiwfOiiqEOCSBF3YSfGMvoXXl2K6fD7ED/L3ZOqeodwZTqamphIWFUVtbi8fjkWDqGJB+5o3UrPkX7rJdbPzuCdhiU/A31wAw6cofDXnNtaZpOCOO/gGFCRdMZUdmk+BIoMHbwPbG7SxIXDCk44KdWdEsww2kDqYHoXwdoKFNvwhN1jqJUaIHfGz99eVompkFP33tkOtr3LaKXX++Bd3XQVjGNGbd/Qj7/vE9Wvesx1W2a8A9dKyRcWRdePcht2Egpvgw6CfltTi2KD2Ep6YYAM1sITxzJgmLziZx6QWED6F3UwhxaJTbj/d77wPg+NXpaAMFUmCs89ZMEPKjQgEyMzP5r//6LyIiIoiKGt7yC3FkMlntzPvhixQ/dx/1G9/E31yDZrYw+ZqfEj/vtPFu3oQz4YIpk2bipLSTeKX4FT6u/JhpsdMIswx+U5YdZaxLWl/tYkeDZ1hzMlVnSvauNVPa1HPRYrKG33gh+qOZaCvYhDYKG+o271rDzj/diAr4SFp+GdNuvh+zPYzwjOm07lmPu2KPvNmJCcMalcDS369FM1uwx6VhskivthCHU2hDBaqyHdPUeKw3Dd5BrWkaqnOvITA6qjMyMgY+SBx1bJHxTL/1T0y96ff4mqqwRsRhCRvZHrBHuwm5aGFO/BzSw9Np87fxctHLQ9oULjfGzg2ZfgI6XPbqblp9gx+jlELV74YtT3c/pk07Hy11lNLpdrVb9oU65nUtplf6oW1w2FGzvzuQSjvja8y4/YHu/c4iMo0pdu6y/ENrrBCjyGS2EJaaizMpWwIpIcaIUgrVWIhqKeux6TsAnTN3tNSIIW09oQIdxkwdsw1M8jd7rDNZrDiTsiWQGsCEvMs3aSZW5q0kzBJGUWsRrxa/OmhApZTir7Pbmedso7Bd8b23v+j3xlV1NKFK16C++Dsq/1XwNh94MnmIe0gB3g4/O9aXEfD13j8IgK7za7Jm6linmUyY7Ma6pmBH+4jq0AM+8v/6DUKedhIWncuU63/dY95yRLZx7bpKd45Km4UQQhwhGgtRO/+F2vYMatuzqJay7qe0nBgA9IKm3oFWX1rLjX/Dk0a8H6EQx5IJN82vS6w9lmumXsNTe55iR+MOgnqQy3Mvx2Lqu8maphE+7Uxe0jfwgw213BeVj9qwCRWfa/Su6CHwu8FdB56mAwfao9Cylhub9uoBCAWGnMp879ZqmmpdbPOUsujUyfKmIwZkj0nBU7sfX3P1iHp4Cp/+Ka6S7TgSs5h265969TCGZ81AM1twV+4j5HVjdoxvwofWws1UffAPAq5mMs+7ndhZJ45re4QQ4qhlskBMDrSUQGuZEVRFZ6FlLkWblIuWEIaqaie0rhzL8oGXMai63QBocZPHvt1CHAUm5MhUl8yITK6bdh12s53dzbt5fPfjNHob+y2vaRp5M5fxr0tnkxAVBf52qN4KFRuhajM07DECKbMdkmejzb4CbeltaGkLwN6ZvcbbMuT2TV+QRliknaypCX0HUl0JLLo27xXHNFussQ/Of2Y0G4raz1+h6qMn0Cw2Zt799z7TR5ttTsLSpoDScVfuPeT2HorS1//Clp9fQO3al2ja9hE7/nAd7sp949omIYQ4WmlxkzDNuwZt+T2QdYJxn9Nahtr5Emz5B9ZrjQDK97NPBhydUjXboX63sTwhefBNs4UQEzyYAsiMzOTGGTcSbYumyl3F33b8jdWVq/F37eHUBy0mC23x19HmXYuWdxbapBVouWcYGfoW3IB2wrcwTb8QLT7vwMaQkanGv21Dv9G1O60sOS2XpPR+9kWxdibBCHqGXKc4ekVkzQKgZc/6YR3na6qm4J8/ACDvuvuImjy/37JhqXkAdFQXj6yRo6Dq46fZ/+KvAI3MC+4kYdG56H4Pe/5+D0rXx61dQghxtNOsYZgmnYy27A60ySvAFgnuOmwriiECgh8VE3j5c/Sgv0dQpYI+9OJPUHvfMuqZfBqaQzL3CTEUE3aa38FSwlK4bfZtvFP6Dtsbt/Nx5cdsrt/MmZlnMjtudp+jQpqmQUy28TUEWmQaqi4f1VpmjFQNkWmgVOxd7RrKHGVx1ItfcAaV7z9K45YPmHzFD4Z0jNJD7H74mwQ7WombfwZpp10/YHlncg4A3rqSEbVR1e5CFbwHkaloc6480NkwRK0Fm7oDv2m33E/qKdcS7Ghn4/dPor1oC7Wfv0zKiVeMqG1CCCGGRrM4IHMZpC+Gmu1Qsw37VyrwPRzCd++HmBM+BQ2U2W5MEQx0YGTu09ByT0fLWDLeL0GII8aEH5nq4rQ4uSz3Mm6YfgMpYSndmf4ey3+MkraSQz9BvNGjT/1eI5PNaOgKomQtlQBiph+P2RGOu2wXbUVbhnRMySt/oGXXZ1ijEph28x8GXZfnTM0FoKO6aNjtU03FqD2vQ8hnzLuv2T6s431N1Wz5+QWoUJCMc75B6inXAmAJi2TyFT80Xs+/7x/aAmghhBCHTDNZ0NIWYlp4I7Zf3IyWaEUvVIR2db4Ph3wQcAMKojPR5l4tgZQQw3TEBFNdJkVN4tZZt3JhzoWEW8KpcFfwzz3/5Om9T1Ptrh5xvZozFuImgwqhKjeNTmO9rca/XbuJi2OayWon7YyvAVD8/H2DBhX1X7xF6Sv/C5rGzDv+NuBGvF26p/lVFQ6rbSrQgdr7pvGD3UiOoRoLhny8r6WOdd88MKKbfem3ezyffOJKLGHReOtK8bfWD6ttQghxrFPN+9EL30d1NA1euB+mhDSs1xuBUqhwHsy5GmZeirbsTrSTvodp/lfRYnNGqcVCHDuOuGAKjNTpi5IW8c153+TU9FOxmWwUthby8K6HeanwpQGTVAxEy1pufFO+EeUbWfrqg6mmztGByLRDrkscHbIu/CaWiFhadn9O1aon+y3XsncDux+8E4DJV/6I2NknDan+8PSpALir9qEH+19X+J9UwftGtsvoTLR5XwUgWJOPr3FoawhLX/lDj5+3/XoljdtWdbdBKQVdUwYPca8tIYQ4liilULtfg8rNqM2Poaq2jHiE33rRNACCH+zHFDcJU+J0NHvUsKd0CyEOOCKDqS52s51T00/lnnn3cHzK8Zg1MzubdvLAjgd4q+Qt3AH3sOrTojMhYRroAVThB4c0HUmFAlC3y6g3cdqI6xFHF2t4NFOu/zVgpDqv3/ROrzIte9ax4/fXoge8pK74KpkX3DXk+i1hUThTclEBH22Fm4d0jKraYmRvMlnRpl2A5ozB19GBxWYnUD/46FTzzs+oWvUUaBp51/8KR1I2rtKd7Pj9tay9bQZf/ux8vvjeSQRdTdjj07HFDD7CJoQQolPID4HORFZ6EFXwrrGnlM817KrMS9LBakLf04BqlUzDQoyGIzqY6hJuDefsrLO5e+7dLEhYgFKKL+q+4K/b/8rW+q3DCoq03NONfaka9kLtIWx+WrPNePOLTJWRKdFD8vJLST/766iAj13/dzMl/74fPeBDD/gof/shtv3mSkJeN0nLL2fK1347rP3LlB4kIctIgVv98dODl28uRRW+D4A25Ww0ZwwA9c0hNjz7OKXvP4Ue6mdTaqB51xp2/O/1oHSyLvwmGWfdzOJffsikK35AWMY0Ql43bYWb8dTuxxabwozbH+i1P5YQQogBmG2gGSNH2pRzjG1XmopQmx5B1e4c3j2O3YJ5YSooCG6sHKsWC3FMOaruamLsMVw8+WJum30bk6Mm4wl5eHX/q/xzzz+p66gbUh2aIxot9wwAo/enbfhvNsrnQpV8ZtSXebxs5it6yfvqL8hZ+X1AUfLv+1lz6zQ+uyWXomd/hgoFSD/768y47c+YzMNLuKmZLKTMWQZA/cY3CLha+i2r6vegdr4ISof0JWgpB/YUSVhxMwF/gPqNb7L5J2dR+/kr6MFAj+NrP/83239/LbrfQ8pJVzFp5fcAI+FE9sXfYulvV3P8X7cz/8evsvDnb7PsjxuJmb5sWK9HCCGOdZqmQUSS8YMzBm3xzRA7GYJe1J43UPveGVZAZV6WCUBofflYNFeIY84RkRp9uJLDkrlu2nVsa9zG+2XvU9peykO7HuL4lOM5Je0UbGbbwBWkzIX2Kqjeitr1Miz42pD3W1BBn3FM0AuxkyBh6ii8InG00TSNnEu+TXTeYgqf+SnucmPH+Yjs2eRc9l0SFp094rrD5l9EUt4rOKKi0Tc9ip4+Fy0yxdiEUengd6EaC6GroyBlHlruaT3rSM1l7vefZ/eDd+Auy2f3g7dT9NzPSVvxVSxh0TR8+R4t+WsASD/rZvK+el+fI072mCTsMUkjfi1CCCGAqHRor0a1VmCKnQRzroTaHcZWFjXbULZwtEmnDKkq07QEAPSy1rFssRDHjKMymALjZnV+wnymxUzjo4qP2FS3ibXVa9nTvIdLJ19KRkTGgMeSdxbK0wwtpajtz8KcK9GccQOeU7nqjNTS7nqwR6FNv0BGpcSAYmefxOJfryLY0YbJasNscx5ynVrsJGbc9Qiq8APwtULVZvrsszTb0XJOgvTFfV6nMdOXsfT+tdSueYmKd/9OR1UBJf++v/t5kz2M3Gt+StrpN8h1LoQQY0iLyTEyDTcWQs5JxntuylywhqN2/gvKPkfFTkKLyRq0LlO88TmjGj1j3WwhjglHbTDVxWlxckHOBcxPmM9r+1+j3lPPY/mPcVrGaZyQegImre+ZjprJDDMvRW1/Dly1qM3/gJyTIW0Bmsn4tSmlwO+C1jJUbT40FQEKnHHGhqeSEl0MgaZpWMOjR7fOhCnG3mltldBSYqTTVbqRUc/iRItMhYQpaIOM0pptTtJOu47UFV+lafsqmnesRg/4iMiZQ+LSC0e93UIIIfoQmwMmK7hqUN627tkyWnwuKns5lK5FFX2EtujGwetydN76+SWzqhCj4agPprpkRGRw66xbWVWxinU16/io4iNK2kq4PO9ywixhfR6jWZ0w7yuovW9Bw15U0YdQ8hkqPBH0AHhajA3vug8wQepCtEmnoFnsh+eFCdEPTdMgOgOiMzjUcSNN04ifdzrx804flbYJIYQYOs1sRcVNNpJjNeyFgzbW1bKWoyo2GYGWp6U7kZAQ4vA4qhJQDMZqsnJ21tl8depXCbOEUdRWxCO7HqHe0/8moprFjjbzUrRZl0NEshE8tVWAq9b43uKAmBy03NPRlt2FacpZEkgJIYQQYlRpiTMAUJWbUAft16eZLNA1va+tYtB6lLszmZDzmOlPF2JMHZN/SXkxedw661ZeKHiB6o5qHst/jGumXkN2ZHaf5TVNg4SpaAlTUd4W8LaB2Qr2aLA6Zb2IEEIIIUaN/4mt6PsasP/oZFAKwm2QOA1K4sDTBDXbIW3BgQPsnUmyAh2D1h3aZCQfMucNvA5cCDE0x9TI1MFi7DHcOONGpsVMwxvy8tTepyhpKxn0OM0RgxaThRaZimYLk0BKCCGEEKNGb+rA+9338P12Dd6frKIt/n/w3PAKmmYykgYBquxzlNIPHDTEexHlCRB43thD03LBtFFvuxDHomM2mAKwmW1cOeVK5ifMJ6gHeWbfM5S3y74L4tijlEK1V493M4QQ4pjn++nHqEYP5hU50BGAoE7gme3Gk4kzjAyss1eiHZxAy+8y/rU4Bqzb+/0PUFXtmOYlYz6+/6zGQoihO6aDKQCzZuaiSRcxN34uAT3AM/ueGXANlRBHG6UUat/bqC1Pohr2jndzhBDiqKQCHajyjeg7/4W+62VUyWeoPqblBf6dD4DjRyfjf2Rzj+c0TcOUdyZaRPKBepUOLZ0dwZGpfZ87pOP94Qf4H9gINjPOhy7sc29AIcTwyV8SYNJMXDz54u4pf0/vfZp2f/t4N0uIw8caBkpH1UswJYQQo0kp3UgasfFhVPFHxl5RDftQpWtQm/9p7Gl5EPOiNADcFz3b/Zj1unn9n6CxCAJucMZCWELPc+s6vt+twbXkYXy/WwsWE85/XIJlqYxKCTFaJJjqZNbMXJ57ORnhGbT6W3mp6CVCSvZgEEc/TdPQJp2KNv0itOkXjHdzhBDiqKHcDaitTxubqAe9Rvbf6ReizbjIGEXytaL2vdvjGMefzsU0MxE8we7HzIvT+q5f6ajSzwDQUhf0WsetmUz4/rQOfVstWmYUYa9fi+3qOaP8KoU4tkkwdRCb2cbVU68mwhpBaXspH5V/NN5NEuKw0DQNLXlWzzn4QgghRkTpIVTp56jNjxubp9si0GZdjjb3arTk2WhJs9DmXAVmm7Gxuqel+1hzbhwR2+8gcv+3sN97MgDe//cxelVb7xPV7DC2arFHQdrCPtti/+FJOB+5iMjdd2M9O28sXq4QxzS5c/oPEdYIrsi7Ag2Nz2s+p9JVOd5NEkIIIY4aqnwD+t63e+yVdDRR7TWoLU+gSlaDCkHKXLQlX0dLmNpj5EizOo39KwG8LT3q0DQNU1YM1qtng1mDFi/K5e95npDfOAcYswvM1j7bY797GbabFqI5+35eCHFojsl9pgaTHZnN8SnH83nN56yqWMV1068b7yYJIYQQRzyldFTxKuP7mm2QcwqkzkWzRYxzyw6NUgqai1EVX0DzfuNBRzTalHPR4ib1f4yvc312P1n4zDOTiNh+B/quekypkT2PL1oFfrcxXTBp5qi9FiHE8Egw1Y8TU09kc/1mitqKKGkrIScqZ7ybJIQQQhzRNM2EyjweytcBGCMrJatRMdloiTMgcRqaNWycWzl0KuSH2p2oyk3Q0Wg8aLJA6gK0SSejmW39H9ywzxiRsoVDRFK/xczTEzFPT+x53podUL0FNDPalHNkz0shxpEEU/0Is4ZxXPJxfFr1KTsad0gwJYQQQowC0+RTUUkzUZsfO/BgSymqpRQK30clzkDLPK5H+u+JRAW90FyCaiqGhr1GYgkAeyRa2iJInW9M4RuojpC/e4ROyzphWOtVVWMhat/bxrF5Z6JFpozshQghRoUEUwOYGjOVT6s+pbiteLybIoQQQhw1tIgkWP4t1N43jVThXZQOdbtQdbtQsZPQMo+HmKxhj7yoUKDfNUTDpZQykjw0FaOai6G1AlAHCkSlo6UvhoRpaCbz4PUFfaj8V41RqfBESFsw9LZUb0MVvGv8njKWQur84b4cIcQok2BqAGnhaTjMDpp9zTR5m4hzxI13k4QQQoijgmZ1wqyVRvDUlTr8YM37Uc37ISIFkmdD4gw0++Brq1TtTlTJpzDjYrSo9GG1SSkFgQ5w10NHA6q9Gpr2G/s4HWg5RGeixU2GuNxhjaApdwMq/xXoaACLA23GJUMalTLWmn0CFRuMBzKOQ5u8Qqb3CTEBSDA1AJNmIjsym70te6l0V0owJYQQQowiTdOMQCl2EqrkM6jeSveojzUM9BC4alCuGij6CBWTZQQxMdkQkdwrEFFKoaq3gbcVteVJVNxktKSZRkBmMhv1hQKgB42vQAf4XChvixHguBsg6OndUHskxE5Gi8uF2Gy0fhJG9Ed5mlGla6B2l/H6wuLRZq9Ecw5+X6FCftTu16GxADQT2pSz0WRESogJQ4KpQURYjV4wT19vrkIIIYQ4ZJotHG3qOai0BcYoVWu5EejYIyFushEENRUdWFsFYI9ExU9Bi86CmEw0W4QRnM29ygjMKjcbU/OahjlV32yH8AQIS0ALT4TYHOP74U41VLoxulazw1hbpXTQTJAy3xhVstgHr8Pbhtr1kjHN0OJAm3kpWmzO8F6PEGJMSTA1iOzIbBSKBEfCeDdFCCGEOKppEckw7yvQWIAq/hg8TVC/G6Iy0GavhEAHqqXMSD/ua4OqL1FVXwKgnHHG9LuYTLS0hZBxnDGFsLkYvK1GQGay9PyyOo3EEfZICEsw1jB1BWUjoPQQtFWiGguhbhf4XV2vDJLnoGWfiOaMGVpdDQXGmrKgF5yxaLOvQAuLH1G7hBBjR4KpQcxNmMvchLnj3QwhhBDimKBpGiRMhbhcqN6KKv0M2ipQO16A+CloGUth6rnQVmmMVLWWG0khPE3gaTL2rwKwRxsjVgnTIDoTnHGjusZIKR08LZ3TA+tRrjpoKem59ssZi5Y8B5Jnozmih1ZvwIPav9pIfQ7GuqzpFxxRKeOFOJZIMCWEEEKICUczmSF9ESTPQpWth8ovjBGrxgIIT0LLWAKZx2HKPsEYEXLVQms5qrXMCK58rVDbiqrdaVRodaKc8eCMQXPGgjMWHLHgjAGLs1egpZSCkN8YXfK1g6cZ5Wk2svB5mo3gTQ/2brgzDuLz0BKmGiNqQwzglNKhepsRSAU9xvqoSadCxlJJNCHEBCbBlBBCCCEmLM3iQJt8KipjsTGlr2oLuOtQe9+C4o9RqfPR0haiRaVBVBpa5nFGIOSuh9YyVEs5tJYZa7ACFcYoVx/nUWY7WGzGmikVAp8L9MDAjbNHQlgihHeur4rONAK1YVKtFaiiD6C9xnggJhst7yy0cFliIMREJ8GUEEIIISY8zRaBlnMyKms51OWjKjcZo1Fln6PK16MSp6OlLoToztGgiCSISEJLX2wEV52jS3g7R5i6vrytEPId+KL9wElNFrBFGEGT4+ARrRgIixt2Vr+DKT0IDXtRFZugvcp40BaJlnuakQZeRqOEOCJIMCWEEEKII4ZmskDKXEieA60VRlDVsNcIsOrywRGNSpqFljTLSEGuaUZg4ogyvsjmP8MUpXRjSl/QZ3yZzEYQZbaN7jorPWSMjDXsM9Kkd2UKtjggbSFa1vFoZtuonU8IMfYkmBJCCCHEEUfTNCPBREwmytuKqtpiZNDzthqjVWWfgzMWFZeHFpliZOoLizeCsV51mYyA5hBGmvqjvK0HUrS3lBhBW5fwJCPzYPIsCaKEOEJJMCWEEEKII5rmiDbWVU06BVrKUHU7oWGfMY2v8ouD1khpKGfsgX2knLFgcRop0ru+zDZjPyi0AUellFLG3lEq1Dmi5TUCuY4mlKcROjq/Ah09DwxLgIM2E5bpfEIc2SSYEkIIIcRRQdM0iM1Gi81GTT0XWitRzfuhox7cDQey8HmagH19JqI4mEIDTesOrowHQ0YQNVRmu5FQIm6yEUQNMUW6EOLIIMGUEEIIIY46mmbqngbYRelB6GiCjnqUu8HY+DfgMb6Cnf+G/AcFSwq6RqD6PInJ+LLYjREuW4SRmCIsHsLijVGoQ9gEWAgx8UkwJYQQQohjgmayHMjyN0hZpRRGMKUfCKi6Rqk0kxGsCSGOeRJMCSGEEEL8B2M0qWuKnxBC9E3eIYQQQgghhBBiBCSYEkIIIYQQQogRkGBKCCGEEEIIIUZAgikhhBBCCCGEGAEJpoQQQgghhBBiBDQj9ecQC2taPVA6ds0RR5lspVTieDZArlkxTHLNiiPRuF63cs2KEZBrVhxp+r1mhxVMCSGEEEIIIYQwyDQ/IYQQQgghhBgBCaaEEEIIIYQQYgQkmBJCCCGEEEKIEZBgSgghhBBCCCFGQIIpIYQQQgghhBgBCaaEEEIIIYQQYgQkmBJCCCGEEEKIEZBgSgghhBBCCCFGQIIpIYQQQgghhBgBCaaEEEIIIYQQYgQkmBJCCCGEEEKIEZBgSgghhBBCCCFGQIIpIYQQQgghhBgBCaaEEEIIIYQQYgQkmBJCCCGEEEKIEZBgSgghhBBCCCFGQIIpIYQQQgghhBgBCaaEEEIIIYQQYgQkmBJCCCGEEEKIEZBgSgghhBBCCCFGQIIpIYQQQgghhBgBCaaEEEIIIYQQYgQkmBJCCCGEEEKIEZBgSgghhBBCCCFGQIIpIYQQQgghhBgBCaaEEEIIIYQQYgQswymckJCgcnJyxqgp4mizefPmBqVU4ni2Qa5ZMRxyzYoj0Xhft3LNiuGSa1YcaQa6ZocVTOXk5LBp06bRaZU46mmaVjrebZBrVgyHXLPiSDTe161cs2K45JoVR5qBrlmZ5ieEEEIIIYQQIyDBlBBCCCGEEEKMgARTQgghhBBCCDECEkwJIYQQQgghxAhIMCWEEEIIIYQQIyDBlBBCCCGEEEKMgARTQgghhBBCCDECEkwJIYQQQgghxAhIMCWEEEIIIYQQIyDBlBBCCCGEEEKMgARTQgghhBBCCDECEkwJIYQQQgghxAhIMCWEEEIIIYQQIyDBlBBCCCGEEEKMgARTQgghhBBCCDECEkwJIYQQQghxFPM2VuFtqBjvZhyVJJiawLxeL7quj3czhBBCCCHEEezLn53H+m8tRg/4xrspRx0Jpiao2tpavve97/H000+Pd1OEEEIIIcQRSimFv7kGgNZ9X4xza44+EkxNUK+99ho+n4+1a9eOd1OEEEIIIcRRoOrjp8a7CUcdCaYmqJKSku7vlVLj1xAhhBBCCHFUqF//GkGPa7ybcVSRYGoCamtro7Gxsftnr9c7jq0RQgghhBATjdJ1QlurUaGB19drmoY1KqH75/aiLWPdtGOKBFMT0J49e3r83NLSMj4NEUIIIYQQE5L3W+/iWvQwged2DFrWFpPU/X1b0eaxbNYxR4KpCSg/P7/Hz21tbePUEiGEEEIIMRH5H9gIgPc77w1a1h6b0v193YbXx6xNxyIJpiYYXdfZuXMnAImJiQB4PJ7xbJIQQgghhJhgHL85AwDr9fMGLXvwND93WT7uyn1j1q5jjQRTE8yuXbtob28nKSmJyZMnAxJMCSGEEEIcKxo8Dexs3EldR92A5UK7jOdNk2IHrdMWGd/9vcnuxF2xZ4DSYjgs490A0dNnn30GwAknnEBzczMgwZQQQgghxLFiS8MW1lav5bSM00gKS+q3XGhLNQDmJemD1mmLSQYgcemFTL/1T5gd4aPTWCEjUxNJS0sLO3bswGQysXz5cpxOJyDBlBBCCCHEsSLSGglAu7994ILmztt4T2DQOu3xaQCoUFACqVEmwdQEsnbtWnRdZ/78+URFReFwOABJjS6EEEIIcayIskUB0OYfOAGZ5XRjOYjvD5+j9IHTo9uijREuf1vDKLTw0IRCIZqammhoaMDv9493cw6ZTPObIPx+P6tWrQLg5JNPBugemZJgSgghhBDi2DDUYMp+93H4/7GF4Jv78P/hc+zfPbHfspbwaACCHa2j19Ah0nWd4uJi9u7dS0FBAUVFRT2CqLi4OJKTk0lPT2fRokXdOQOOFBJMTRBr1qzB5XKRk5PD9OnTAbpHpmSanxBCCCHEsSHGHgNAg7eBoB7EYur7dt2UHUPY45fQcdnzeH/0EZZLZmCeEt9nWWuEkaQi0N40Jm3uSyAQYOPGjXzwwQdUV1f3eC46Ohqz2UxLSwtNTU00NTWxe/duPvzwQ5YuXcrKlSuJjo4+bG09FBJMTQBtbW288cYbAJx77rlomgYg0/yEEEIIIY4xEdYIkp3J1HpqKWkvIS86r9+y1ounY7lwGsE39hLaWNl/MNWZzS/Q3oTSQ2gm85i0HYwgatWqVaxatYqWlhYAYmNjmTdvHlOnTmXKlClERRmjb6FQiMbGRmpra9mzZw+rV69m48aNbN++nUsuuYRTTz21+754opJgagJ46aWX6OjoYObMmcybd2CvgPBwY4Fga+vhH5IVQgghhBDjY2rsVGo9text3jtgMAVAyFgvpUXY+i1islixxSTjb6nF21iJMzFrNJvbrba2lr///e9UVFQAkJaWxtlnn82SJUswm3sHcGazmaSkJJKSkpgzZw4rVqzg+eefZ8eOHRQXF7NixYoxaedokmBqnBUVFbFhwwYsFgvXXnttj+g7Pd1IdVlVVUUoFOrzIhRCCCGEEEeX6THT+azqM3Y07uC0jNNwWpz9llVNxnIQLb7/MgDOlMn4W2rxVBeNSTDV2trK/fffT1tbG4mJiVx11VXMnj17WCNLCQkJ3HnnnWzbto1JkyaNehvHgmTzG0ehUIjnn38egDPPPJPExMQezzudTpKSkggGg1RVVY1HE4UQQgghxGGWFp5GTmQO3pCXNdVrBiyrFxrroEzZMQOWC0sxEjt4aktGo4m9vPrqq7S1tTF16lTuvfde5syZM6IpepqmMX/+/CNmzZQEU+Po3XffpaysjNjYWM4999w+y2RnZwP0WrgnhBBCCCGOTpqmcUbmGQCsq1lHXUddn+WUP4hq6ACzhpYRNWCdjqQcADx1JaPZVADq6+tZt24dJpOJ6667rjsj9bFAgqlxUlJSwptvvgnADTfcgN1u77PcZZddxh//+EeWLl16OJsnhBBCCCHGUUZEBosSF6Erndf2v0ZIhXoXsprBZoaQAm9wwPqcyUYHvaeudNTbunPnTpRSLFq0iKSkpFGvfyKTYGoc+Hw+Hn/8cXRd5/TTT2fGjBn9lo2LiyMsLOwwtk4IIYQQQkwEZ2SeQZQtikp3JRtrN/Z6XtM0tCQjYZle1T5gXY6ETAB8DRWj3s7m5mbASDhxrJFgahy8/PLL1NbWkpaWxqWXXjrezRFCCCGEEBOQ0+LkvOzzAFhfsx5d6b3KmGcYa+5DmwdeX9+111TQM3DQNRIWi5HT7uDNeA+Huro6Pv30U+rq+p4GeThINr/DbO3ataxevRqz2cxNN92E1Wod7yYJIYQQQogJamrMVKJt0bT6W6lyV5ERkdHjecvpkwh+UETwtT3Yrpzdbz1mhzGCFRqDYCo1NRWA4uLiUa+7L01NTbz22musX78egNzcXL73ve8dlnP/JxmZOox2797N008/DcBVV11FZmbmOLdICCGEEEJMZCbNxKQoI014uau81/OWc6YAg49MmezGspGQ3zPKLYRZs2ZhsVjYt28f9fX1o15/l5aWFp5//nnuvffe7kAK4NRTTx2zcw5GgqnDpLKykoceeghd11m0aBEnnXTSeDdJCCGEEGPE99cNeG57A9Uy+jeu4tiTHmHsPVrt7p3dWQs3Zjkpfx8JKg5itjlBM6H7POihgZNVDFdYWBhLly5FKdWdYG00VVZW8thjj3Hvvffy8ccfo+sHpjvedddd45qoTYKpw6ClpYW//OUveL1eADZv3sw777wzzq0SQgghxFjxP/Yl/kc2E+rcA0iIQ5EaZkyjq+mo6fWcChiBhWYzD1iHZjJhjYgBINDWMLoNBM477zwsFgvr169n8+bNo1Jnc3MzTzzxBL/4xS/YuHEjweCBIDAmJoYf/OAHzJkzZ1TONVISTI0xr9fLAw880J3lpMvatWt7RNVCCCGEOHqYJhuL/fWi5kFKCjG4JGcSGhr1nnoCeqDnk1374qrB63F2btzbUVkwug0EEhMTuxOrPf744xQVFY24Ll3XWb16NT/+8Y/5/PPPezwXGRnJeeedx09/+lMmTZp0SG0eDRJMjaFQKMSjjz5KWVlZ92NdO0E3Njby2muvjVfThBBCCDGGTLlxAOhFMjIlDp3NbCPeEY9CUdtR2+M5LdoBgGoafEppRI4xitNWvGX0GwmcfvrpnHzyyQSDQf72t7+NaP1UXV0df/jDH3j22Wd7jERlZWXxta99jd/85jdcfPHFhIeHj2bTR0yy+Y0RXdd56qmn2LFjR/djdrudO++8k0AgwAMPPMC7775Lamoqy5YtG8eWCiGEEGK0dQdTBY3j3BJxtMiIyKDB20Clu7JHRj8tKRzCrKgmD6rV2x1c9SU6bzFVH/yDtsLRmYb3nzRN4+qrr6ahoYH8/Hz++te/8r3vfW9IgU8oFOLJJ5/skVgCICUlhauuuooZM2Z0D0pMJBJMjQGlFM899xzr1q3rfszhcPDNb36T3NxcAK6++mqeffZZnn/+efLy8khISBiv5ooJqrCwkM8//5ycnBxOPvnk8W6OEKNO1xXuNi+RMc7xbooQhyzY0Y63sYKIzBkAmKfFA6DvGf21KeLYlBqeytaGrVS5qiD5wOOapmFKiUAvbkavc2MeIJiKnDwfgPb928esnWazmVtvvZXf//73VFZWct9993Heeecxc+ZM4uLi0DQNr9eL2+2murqa8vJyNm7cSHV1z+Qadrudq6++muOOOw6zeeD1YONJgqlRppTipZde4tNPP+1+LCEhgdtuu61HKvSTTz6ZXbt2sW3bNp588km+/e1vT8hoW4yfpqYm1q5di8/nk2BKHHWCwRDr3ysgFNQ54bxpWKwT94NSiKHY9/h3qVv/KpOvupesC+/GPD8FgNDWGlQghCbXuDhEKWHGNdXg7R2ga8nhUNyMqnHBlPh+63AkZYOm4W+pQQ8FMZnHJhRwOp3cfffdPPTQQ5SUlPDMM88Y7dQ0NE0bMG9AZGQk1113XXe69YlO1kyNstdff50PP/yw++fZs2fzox/9qNeeUpqmcf311xMeHs7evXvZtGnT4W6qmOAiIiIAcLlc49wSIUafxWImLNKOrisaqkdvA8ngxgq8931C4P1CVOeHtTfoHbX6heiPu8pY0F/8wq+o/+IttBinkYTCH0LfJ1P9xKGzm+0AvRNQAFqiMY1O1Q58z2AyW7BGxIJSBNpHtp7P//iXuM95Cr3BPWC52NhYvv/973PTTTcxc+ZMIiMjUUqh6zp2u71X+alTp/KnP/2J+++/n3nz5h0RgRTIyNSoeuGFF1i1alX3z+effz4XXHABJlPfMWtERASXXnopTz/9NC+//DJz587t8+ISxyYJpsTRLjkjmtbGDuoqWknJijmkuvQGN9573iHw/M7ux7RJMez+cQrvzNzDpZMvZV7CvENssRD9s8el4i7bBcCu/7uZrIu/RdLsTChuJrS9FvOspHFuoTjStfpaAQizhPV6zpQZDYBe3jpoPZawaALtTYQ87RAz/OvS8/XXAQg8vR37t44fsKzJZOK4447juOOOA4x1UUop9u3bx2OPPYbL5SI2NpYbb7yRadOmDbstE8ERNzKlK0UgNITcj4dRR0cH3/jGN3oEUnfccQcXXXRRv4FUlxNOOIGsrCyam5v56KOPxrqp4gjSFUy1tLSg1MS65oUYDYnpUWgaNNW5CPhGvoGk8gRwn/2UEUg5LVgum0Eo14ra38InqXsAeKX4ldFqthB96tq/JzxzBmgmyl77E02TjdGq0LbeewMJMVxbtq8BIFtL7fWcaYax9j60vbbXc//J0rXX1AhGplS7r/v70O7hZ+ozmUy8++67/PnPf8blcjFz5kzuvffeIzaQgiMomCpv9/OTzytJ//t2wv+6hZNf3MsXNQMPLx4OO3bs4Nvf/naPx373u98xb97QekBNJlN3Tv5PP/1UbppFt9jYWCIjI3G5XNTWDv7mOFwuT4DvP7KBDzZXjHrdQgyFzW4hNjECpaC+qm3E9fj+93P0rTWYcmOJ3H4HzTe3kX/qk+hPLWFOe1Z3uZAKjUazheiTJdzYVyrlpKuY9/0XSFp+OclzLwRAVYz8+hYCoK6jjj32ckwBmF+Z1ut583wjwBpKMGWPMdZe+Zqqht2O4PqD7hl8w3tP7cp0/cYbbwBwwQUXcPfddxMZGTnsdkwkR0Qwta/ZS85jO/jlhhpqOoIEdMVnlS7Oe7WQklbf4BWMkbq6Ov761792/3zxxRfz0EMPER0dPax6pk+fTkREBM3NzTQ2yrxqYdA0jalTpwJGZr/RtnFPPZ9ur+Gn/9yMrksQL8ZHUkYUAHUjDKZUIIT/rxsBcD50IeU7nqLw6Z8Q7GjFk9JE8skHOrYqXZWH3mAh+mE9qLc/dvZJzLzjAUzJxvWt6se/81eMrh2NO9jRuOOwdIKHVIi3S98GE8x5DRzvV/cqY8ozUvGrssGn+TlTjY173ZV7h92W4JsHHTOMGQXBYJBHH32UtWvXYrVaufPOO7nwwgsHncF1JDgiXsGUGDtLksO5elosq6+YStPt8zg7O4oGT5A7VpX1e1zI1zGm7fJ4DmyO9tvf/pbzzjtvRBn5TCYTWVlG72l5efmotU8cedra2li7dm33JnXZ2dnA2FwX9a0Hrt+QBFNinCSkRqJp0FLvJhTsP7tTf0KrS1B1bkzTE2iO3M3+f/2m+7nISXN7lC1qLSK0tZrAG3tlFoAYdY5E43O8ozMRBYAWZayDPnhqlDg6vFz0Mi8XvYxibN9LlFK8XfI2Je0lhCk7yx6H4IfFvcppsZ0b97YMnnCnKz16y+7Ph9UWvbED/z+3HmhbR+9EGH3x+/08+OCDbN68GYfDwbe+9S3mzJkzrHNPZEdEMKVpGmuvmsZz503m5IxIYh0WnjonhwiriXdK2vi8qvcC/bbCL1l3z2IqP3pizNqVnZ3Ngw8+yMMPP0xsbOwh1dWV7U+CqWPbL3/5S5588km2bzf2f8jIMDblq6wc/R71/NKW7u+tliPirUAchaw2C2ERdpQC9whuOENbjB5ay5m5Roaqg3gbev7dxNy9Edeih+m45Dn0L3v37ApxKKLyFgHQVvTlgQe70qGPoKNACKUUn1R+wub6zVg0C1dNvpKwdhP69lpUW8+gSbOYwaSBrlDBgaffxc46GTQTbQWbCHqHPmrq+5814PJDjBG46fn1qNDA13ZXILVr1y4iIiL4zne+Q15e3pDPeSQ4LHdQvpY6OqoKD6kn0GzqOeKTGGbl7vlGBpK/beu5AE4pReEzPyXoasLXMLbTOkZrE7HExETASDYgjl2trcbw/ObNxs7kqanGHOjq6upR70n3+g8Mz0svvRhP4dFG7317i2eQkn3onA2gOgLEzFje3ePqTMklbt5phNzGDcecV2Hy60b9pjlJmGZLZjUxPB0BnfvWV3PWy/v4/mcVvFvSist/4KbVkWB0ivpbD7on6XreJntMieH7tOpTVletBuDy3MvJTszFvDQddEXg7YLeB4RbjX9d/gHrtYZHE5E9GxUK4irdOWDZLnp1O/4HjSnV4W99BS0zCr24mcA/tvR7TCAQ4G9/+xu7d+8mMjKS73znO90zsY4mYx5MeRsq2PDtpWz83onsfuB29ODA/8HDcctsI3PJvwtbaD/oDa3yvUdpK9iENSqBrAvvHrXzjaWuXPpd07vEsW3Tpk0opYiNjcXhcOByuWhvH729eADiIg/skL5+d92o1i3EcMQkGPujVBQ1DRrYK6V4/fXX2bp1KwDmk4ypsMHX9uB/ehspqRcw+ZwfsOCHrxJ6uQD/tz8AQO/cCMRy+UwivrwNzS47g4ih8wZ1Tn5xLz9dV8UHZe38blMt575SyDc+OrDUQDNbjOBe6eihzs/yQOe9iWzYe9QxacYttK7GZtRxQ+0GPq78GA2Ny3MvZ0bcDACs1xrT43z3r+3eS6+L5jSCKeUd/F7SmTIJAG99/8tluihdx3PHm+AJYrlkOpZlmTh+eyYAnh9/hGrtPbUwFArx0EMPkZ+fT2RkJP/1X/9FWlrvxBlHgzEPpvytdegB45dct/5Vdv/trl7/+SM1OcbO8anhdAR1VpW3d57jNQqf+X8ATLn+V1jCjowMIV6v8TtyOByDlBRHs65pfQC7d+9G0zQSEoxOg6amkW2u1x+r5cBo73Orika1biGGIzUrBrvTSke7j9qK/hdPK6V49fmXeOutt3j0kUdpa2vDvDgN0/wUVEMHnhteIeKuBqJvaMGT9ACer75MVGdiq8YccD51GeEvXolmMslorBiWv+9oYHNdB5OibPzttCy+vziZJclhNHqCPLPbSBxlrJnuOYtGrzLuTbSE3vsCiSObRTM6ZEY7S6hSijVVa3in9B0Azs85nznxB9YX2b62AC09En1LDcFX9/Q8eBjL9m3Rxuh8oK1h4PboOt473iL4+l6ItuP8w9kAWK+aTcu0MKjvYNvt/+z1nvrqq6+yc+dOIiIi+Pa3v33UBlJwGIKpiJy53f9hAPUbXmf/y78btfrPzjYy5Xywv5n9L/0P+X/9Biid7Eu/Q9Kyi0ftPGPN7TbmrDqdzjE/l/K1o8rXo+94EX3LU+i7XkZVbEQFRjDFRoyq8PDw7u/fffddAGJiYoADUwBHS2zEgQ2iv9hbT8EAN7FCDFUoqOPtGN4MBJPZREHZVgBqyvvvNHj11Vd595MPMelw9e40IiMj0cwmIj66AcdvzsBy2Yxex6TuBE2HutkmuGo6AIEPinCf9Bh649gmKRJHj88qjaDo3uNSuW1eIj9cmkq1O8B7pW189d0Sytv9xs1k5yiFZjJGolRnMGXKGl6WX3HkGM2OGX/IzyvFr/BhxYcAnJ99PouTFvcoozmt2H9wEgDeX67uef6uJTFD2I/VFmV01PoHCKaU24/nmpfwP7IZnBbCX7wSU46xNlXTNL68Moq3l7TxiHMLf/rTn9i8eTNer5ft27fz/vvvYzKZuP3220lPTx/y7+BINObBlMlsIWHxuQBETVlibGT3+v/RsmfdqNS/IjOS2EATc9+4k9JX/wiaicnX/JScy/57VOo/XGpqjA39kpLGdh6/qt2J2vgwqvhjaCqCtgpo2Icq+gi14W+ohj7m4IrDpmvtnM1m4+abbwYOBFgdHYd24+cN6qytdLGuyoUvqJOTcmDU1mzS2FnSfEj1CwHGBrzr3itg1xfDS6bT0mpcfwUFBbhcPZMKhUIhXnjhBd59911MJhNfWZ/InNV+gi/lA6Da/aCBvufATYFpbjIRu+8isf1nxIXFo6PT7GtGhXS833+f0LoKPNf9+xBfrThWZEbaAPjBmkoWP7ObmAe3UuEyMpnNT3SSFm7tDqTQtO7Mvl3Tn7RomXVyNFFKEVTGVDqLaXSmDDd5m3g0/1G2N27HarKyMnclS5KX9FnWdvNCtMQw9G21BJ7a1v24Fmd0yKu6wZNK2GKTAfrNLRB4rxDXoocIvJQPUXbCX7sWyxm5Pcpc+rPbmPPoTZjD7ezZs4e///3v3HPPPTzwwAMALF++nNzc3L6qP6oclknj8fPPoOqjJ1ChANkX3UPpa39k99/uYvGvV2ENP7TemuzSj3ks/7vEBluxRMYz886/ETf75FFq+eHTFUx1JRwYC6pmB2rvm8YPcbloybPAFgneVlTtDmgpRe16GeZejRabM3h97npoLIS0hWgW+6DlxeC6FmbOmzeve7+y0VhPt6Wug4tfL6S83fjwnxHn4NWzjHNpGrz8szNJjh37UVFx9GtpMIL+sIihvycopVg6/2S8Lp26hhp++9vfcsstt2C1WiktLeXjjz+mrKwMs9nMTTfdxKzoZvz5a+m4+l/wtVfgoPUBWlok9h+fgu2mBWid61ScFuPa9oa8aGYT4W9+hY5rX8Jx/1mj+MrF0exHS1PZWONmbZWbBs+B6+3SvBiePDsHs0lD78yg1jUqBaCXG/unmdKOjCUHYmhcARe60nGYHaMSTJW2l/JCwQt0BDuId8RzZd6VJIcl91tes1tw3Hc6ntvewPP11zHlxmE5IQvzjET0XfWEtlRjXjDw/WRE9mwA2oo2dz+mlCK0tgzfrz4l+L4x/d80O4mwZ1dintV3Z//s2bP59a9/zfr169m0aRP79+/vfm7NmjXs2bOHmTNnkpiYSHR0NNHR0URFRREREUEoFMLtduN2u3G5XPj9fkKhEKFQCL/fj8/nw+fzEQwGux83m81YLBYsFgtWqxWn00lSUhKpqakkJCSMaIuiQ3VYgqmYmSegWe2079/G7G8/QdOOj2kv3sq+x7/LzLseHtEL97c1UPDEj6jf8DqxwJeR81jxnYeJy8sZ9faPNb/fT1VVFZqmjVkwpVx1qH3G/Ftt8mlomcf1LJA8G1W8Cio2GuWWfB1tgDcI1VyC2v6cUV9YPCRMHZN2H2umTJkCwJ49e9B1HZPJ1J12v7Z28F3N++Lyh1j5ZhHl7QGmxtqp7wiyu8nL500+bBYT/qBOVJh11F6DOLa5242e+MiYoQXnSin259fhdemYLRquQB319fX85je/6VEuNjaWW265hby8PNRCHVNyON6ffwLtfgi3Yjk7D9u1c7GcPwXN1vO9K8pqTAdv9rSQSAqmGCdhq752VGwWKQ6PBKeFz66cxqVvFPFakTElev9Ns8mJPqjToGu6lXbgulJNxvR5LdGYYdDQ6iU20t4rQ7E4sjT5jOnIcY64Q6pHKcW6mnV8UP4BCkVudC5X5F2Bwzz4SKb1loWE8uvw/3kDHde+RNg/L8W8JJ3AS/ndW0YMJCJzBmZnJN76cjyfbUf7sIHACzvRCzqnWkfbcfzoZGx3LUVzDHyPEBERwRlnnEF4eHh3MJWXl0d1dTUNDQ18+umng/8yRkFYWBh5eXnMmzePpUuXYrPZDst5D0swZbaHET11KS27PqNl9+fMuONBNv/4TOo3vE717JNJW/HVYdVXt+F1Cv75AwLtTZjsYXw8+3Z+rk7lMV8EC8boNYyl4uJiQqEQmZmZY7ZmSlV+ASoEKXN7B1J0LpyddCqqeT+466F6G6Qv6ruugwIpAGInjUmbj0WpqanExcXR1NREZWUlmZmZ3XuQVVcPf18cdyDEha8VUtzqZ16ik43XTOf+zbXcu7aK/EYv4U4r/nYfHb4gTsluJkaBpzMlb1jE4B9ioZDO7k2V1FcZvfczFmWw9My7eemll9i+fTsOh4PU1FSmTZuGlqdRZiqjra6NSncl53/rfGx3HWeknnZa0AYIjJKtqYSawmmqNrFBLwQgPMpO9tREkjKixqUnUxx5ilv9vL2/DQ345IqpPQMp6A6iVCiIUsq4rroCLLPGw2/u5ukPC7jva4s5df7Ruxj/WFDnMTLgJjlHvjSjzd/GmyVvsq9lHwAnpJ7AaRmnYdaGlvlR0zQc/3MmoS+rCa0pw33GE2gzjXVQekETKhDqHp3vopRClbcS2lmHXtCI3RNNB+20XPYQYU3Ga9HSI7FdNw/bt47HlBje4/j9bftJCUvpHu0/WEtLCy+88AIAN954I8uWLUPXdUpLSykoKKClpYWWlhZaW1tpb2/H7XZjMpmIiIggPDyc8PBw7HY7FosFs9mM1WrFbrf3eMxsNhMKhQgGg91fLpeLmpoaKisraW9vZ/v27Wzfvp033niD66+/nlmzZg3vP2YEDtvdU/y802nZ9Rn1G98gefmlTL3xd+z+250UPvljoiYvICJ78Bfrb280RqPWvwZAzMwTmXbLH9hYZkd9VsnnVW5unJUw1i9l1O3bZ/whTZ06NqM7SiloMM6hZfQOpLpoJjNknYDa/SqqcpMxfe8/bjJUUxFqx4sHjjnudjSzjGoMR7W7GpvZRrwjvtdzmqaRlZVFU1MTtbW1ZGZmdk/z0wfIglle2EhkjKM7xXSXb39SwScVxvqTCycb0wY7AkY9YdYDGc3kZlKMllDn5qSWQfbV8fuC7NxQTmtjB2aLiRmL0klMM0aQvvrVnh1s9Z56HtzxIAqFWTMTUiEirBGsyFgxaMrp2opW2BFHStDoQXaEWQkGdNxtPvI3VeD1JJE9NXGkL1ccQ3KibDx4WhYFLV5Ozug9bc9k6fws7CdVdlyknWBI8fRHhZwyL1Xed49gLr/xuRplixr2sSEVYmPtRj6u+Bi/7sdutnPJpEu6U58Ph2azEP7edfj+8Dm+369F5RvrRoMfFNEW+Su09Ci0SBtahA3VEUAvbAJ34MDxpwchCfRkG7aVi7BcMgPL6ZOMDYD/Q2FLIc8WPEtKWArXTbuuV0D1ySef4PF4mDNnDscdZ9xrmkwmJk2axKRJY9/prpSiubmZXbt2sXr1asrLy/nLX/7CLbfcwuLFiwev4BActmAq6fhLKHruFzRu+QB/eyPJJ1xOc/5aalY/y87/u5nFv3wfS1jfF6UeClLzybMU/+s3BF3NmOxh5F77/0g77Xo0TeMU3Vhot7pidPfhOVz27t0LwLRp08bmBEEPBL1gtqOFDxJsJk6D4kjwNEFbJUQfSNWtKjaiij7q/lk77nY0R8zYtPkoVOWuYnXlava27GVm3EyuzLuyz3Jd6fEDAeMNr63VeNO29BO01pS1ULijBl2DE8+Zir1zON4b1Hlk54EF+b/cUMPjOxuxmo0P8IwIG+t0I5iSKSditAyW2EopRWVxE8W76giFdHAGSJjrIBTTgTtgJswS1usmM9GZyOW5l7OxdiPLU5ezunJ1rwxXfZ2naFct5QVG2upWZx17ojbxlblXkx6WTlVJMwXbaijd20DWlPGZZy+OLGaTxi1z+v8M1YOdN6ma6cD11PVvUOeCZVk8+vYedpU0s62oifl5vTvUxNGtwlXBG/vfoNZjTNufFjON83LOI9rWO3+A6vCjhQ0+wq85rDjuPQX7nUvx3rca/5/Wg90MvhCqpIX/fEvWEsIwzU3GnBeH1bwRWmqwP3kezoUDryFNcCYQZYui3d+OJ+jpEUzpus66dUZiubPPPntc3k81TSMuLo6TTjqJE044gddee413332Xxx57jNzc3O4lE2PhsAVT9tgU4uaeStO2VdSt/TcZ53ydKTf8ClfJDlylOyh86idM/8b/9ThGKWWkUv/Xb/HUGnMwY2efzNSbfo8zKbu7YVuaigABAABJREFU3PzEMGxmjYIWH62+ENH2I2dzPL/fT0lJCZqmkZeX9//Zu+8wqcrrgePfe6fPbO+VXXrvoIACKmJDEewaW9QklkSTaEw3Mb8UY6JpJmpi771XEFBAkN572V229zp95t7fH3d3Yd3eaJ7P8/Cwu3PLOzA7c899z3tO/5xEa+yB0IUZJEVR0RNGQOE69KoDKNEZ6EEP+paXwX24sasy7fsoNllQ2xW6rvN+7vtsLN8IGL0poq3Rh9NAOrF+3XpjP1ytHqsud7Nro1GJ5z+VQaaZVGzAlnIPtzU2k0x2mvnJlBSe2VHB9srDjfWSnObmICoU7p+mg+Kbx2xRCQXDhAJhrEekjuq6TlVpA7l7yqmr8uIx1VOecpB88wHCBWEoMLazm+wkO5OJtERiUS3NfVNMiokh0UOo8dcwMGogT+16inRXOmekn0GCo/UFbs6uMvL3VaIoMHRcKjtMhfhK3Wwu30zmwEwyBsVzaG8lfm8QT30AV5QU0RG9owWM91fVeni9i5Jo9JfSy9w4bGYumzWIpz7Zw0tL90swdQJrmpGq8HXco6mJN+Rlcf7i5uuAGFsM5w84n+Gxbd9ED7yyDd/dn+L67AZMI7s2c67EOLDffyaBR9dBIEzEvjtB06EhgN4QALsZdWAsavzhnmfW/3wMqzYS8nTeGiXGFsONI24krIdbrRXbuXMnNTU1JCUl9d+1bDeoqsqCBQs4ePAge/fu5dChQydHMAWQMvtqqrYspWTFq2Sc9x1MVgej7niU9b88m5IVr5J02qXNlfjchXvY98zPqdm1CgBHyiAGXv4zEk+5qNUFqMWkMCbewcYyD9sqvJyeHnE0n1angsEg5eXlpKSktFrwfODAAUKhEJmZmS16DPWppiAq6EUPB1BMHd/pUGIGGGus6grRC9ej71/c8vHT7+70GOKwEk9J8xtotDWaSwZfQlZkVid7He5dceCgUa5+yICWqbA+T4Ada/NBh9drQ7xVF2bg6mK2VXhZcqiOsA4pTjOLLh3G2AQHP5qUxL0rCvn7xlKGx9o5IyOSFxwWqhsC1HuCxEdJ6V7Re64oOz5PkOpyNzanhYA3RGVpPYUHq/A0rqfKj9nNQdc2YwcdUp2phPQQdYE6fGEfefV5XTpXtb+aFFcKpztOJxQMU13uJi45gtpKD3l7jIucMdMGkJASid4wilWlq8hvOFyy3RVpw+8N4nVLMCVa0kLBw2l7XRTy1AC0yLJRGquk6nV+AC6dNZDnP9vHyu0l5JXWk5UsNyVPRAOjjLS1/TX78Yf92Extv38EwgHWl61nZfFKPCEPqqIyI2UGs9JmYW3jOkrXNPy/+wL//30BQPD1HZjuO6PL41IibJjnDib0wV5Cn+zHdvspHW5vshmBVdjb0OF2TWJsMW3+fONG4xpn+vTpx80sf1NRt71791JZWdmv5zqqwVTCxHNQLDYa8rYT8tRjdkbiTBtC1sIfk/PaH9n37C+YfP/HHPrgEfI//Dd6OIQlMo6Bl/+clNlXo5raH+7YBCOY2n6cBVMbN27k8ccfB2D+/PnMmzevxeM7duwAYMSIEf02BsVsR49KN4KjA0tg6Hkdv9hdjQsqa/LQa464qHEmoEy+qUXZV9GaruusXLmSadOmYbFYjLvrjWoDtTy962lOST6F8wec3+b/Q1NQXVNTA8B111/HznUFELRScKCSyBgHtVUe8vdVEgyEiUuOoN4WhupqHtpgpA6YFPjBhER+Nz2NaE8+eq0ZNTqTv87K4P7pqdhNKiZVISbCRn65m+oGP9nIh7rovcS0KCqLqtn73rPsTZsBlsPvxzaHmfRB8aTFm8k7uJNxCeM4LeW05pklXdepD9azv3Y/2yu3c7DuYIfnGp8wnlOSjIuFQ/sqydtTTmpWTHN59oEjE0lo7KfWtEax2l+NpmsEfWHqa41Ka9YTKJtB9L9AbTmb/3gp2Zf+hKRTLurGfkYAb40+XJRAaXxt6QEjQyQu0sb5UzN5b3Ueb6/M5YeXju3DkYujJc4eR2ZEJvkN+XyY+yHzB85vUSK9ylfF+rL1bK7YjCdkvB8NiBjARQMvItHR9kyTHgrjveFtgq9sB1XB/vC5RpGdbrIsGGkEU8tyOg2mbPFGM11fRff6An5dU4Gs462nVHy88b5fUdG1GcSeOqrBlGqxYXZGEawtJ+xrwOw0PuQyz/8eJctfxVu8n5XfNUpDoyikzbmegVf8AosrptNjD40x7gocrPX31/C7xe128+tf/xq3+3DjNJOpdVWVLVuMZmvjx4/v1/Eog89G3/IiFG9GDwcheSzYIsFkBhQIuMFXjV5XDJVtNO6NH4oyaqEEUl3w2Wef8cYbb7B+/Xp++MMfkuBI4OqhV7OnZg9F7iJKPCWsLV3LhIQJpLlaV3RKSUkBoKzMSKucMmUKcc5M8vZUsG9rSYtto+OdjJqSwdNhNxcNiianPkhmpJWzB0SS5LSgN5Si73gDUIz+YdEZuI5YsB/feDe+8jj5vREnvuTMaMrW76V6/Z8hZgiWeU8SFesgOTOahNQoVFVB1+P5YeQPibQanwG6rpNXn8f2qu0crD3YXHa4iUkx4fRHERGMZeywoQyNG0KCveU6p7JCI02lOK8GMKoJDjiisITD7CDSEonb72H/3kLKDrgJ+sNExTqIlB5r4gh7nrwHT+Fe8t9/hMQpF3T5cy/krgFazkxhbsxGOSKVesHpWby3Oo8lGwslmDqBXZB1AU/uepKtlVvZV7OPtIg0FBTqA/XNa6IA0l3pzE6fzdDooe3eyNZ1He+tHxiBVKQV5yuXYzlvaI/GZRpv9KfS9nQeQDhTjBk2b2luj84FxtibeqU2Xb8cLxISjBt1J9XMlK6FCdYZT8gSdTjHXQsFW5S1NbtiGHvPC0QP7Xr1jewoY7o0p+7oXhRu27aN4uJiFEVBVY3qaLt27WL79u0ttps/fz7nnNNycV9paSllZWW4XC4GDRrUr+NUotJgxHz0Xe9C2Q70sh1d3zkmC2XUAgmkuiAvL4+33noLgDPOOANFUdB0jbz6PGoDtc3lVJ1mZ7vT5U19EY5s0jtwZBI2h4XyojpCgTCuSBsJqVEkpEWCuwx9y0tcljYJZfislm/WrkRIGI57/1pqPn+NoM+HJTqBlJlXYnFFk9R4EVlS7WneJRQMU5pfS9rA2ONmul6cOFRVwVqxAoDsOQvJPr/1mgBFUZoDqUP1h/gw98MWFx82k42syCyGxgxlUNQgrH4H65fm4HBZmZbe+gLD7wsSarzz33wMp4WALwiKQjgYxusOMqhmHK7aeAqLjVLsMQkuRp+SIa9z0SxYX0XlpsUoJjNjfvRMp597uq7z6foCThmRRMOhnQA4Uzv+PB+RGYNJVaio8xMIhrF2UpFSHJ9SXancMOIGPsj5gFJvKQdqDzQ/ZlbMjI4bzdTkqaS70jt9jwm+sJXg05vAYcb1yXWYp2X2eFzqQGNtkJZb0+nabEeqsb6pIW97u9t0xufz4fF4sNlsREV1v7phfzopZ6YCdZWga1gi41DNFnRNo/TLNzj42p8IVB/uoRM/4exuBVIA2Y132HPrAt0eV+irfPT8OswLRrSqyd+Z9evX89VXX7X7+IwZM7jkkkuIjGydQrV9+3YUXWfs2LGtZq2OpIWCVG1bRmTWWGxxPW/qqyQOB9fN6EUbob4Egh7QGi/YzXZwxBipfNGZ6NtePbzf6Es7bOArDlu8eDGapjF79mwmTjS6nqmKyrqydQS1IAoKY+LGcHbm2TjNzjaPcfCgkdp05GtGURTSB8aRPrB1g0DdWwMhPxSsg5Txxv9jIy3gY8+yTyhb9VaLfQo+eoxJv/2QzMYeErklhythblqRS0OtD4vNRFJ66wpDQnREC/qpWG80CE869eIOtz1Yd5AX9ryApmtEWCKYkDCBEbEjSHWmYjriIjaohlEUY52g1x3A4TJuOOi6Tml+Lfu3lRD8WjBVXeZm9actZ9ljMGaC7VFmBo9IITFNekyJlmp2rwZdI3rEaV36vN1XWMf9z20kIdrOA0mbAYgcNOHwBk2VUkOHZ6a8gTCarqMqxs2HrtB1ncqNi6jduwZbXBppc65HNcva5WMtMyKTW8fcSqWvkip/Fbqu47Q4SXYkt7kmqi16KIzvF58B4PjXvF4FUmCs01Ni7ejVPvSiepT09gMcV8YIVIsdX/mh5uU3nakPhImwHK5Y6fMZhVfsdvtx937atGyiaYz95egGU7XGXXlrdBK1+9az/7lfUp9jpLlFDppA5gW3suvR71O66i0GzL8TV3rX+y4Namyel9ONdCU9rOG78yMCjxnV0pTsGGz3no71xgkoXWxgOnr0aCIiItB1nUAgwIoVK5of++tf/9pmENWkevtyzvcuJSJuTrvbeMsPseHX5xJqqCZu/BzG/eTFLj67tinOeJQhc9t9XNd19D0fHN5+6LkoZlmY3VVNU93Tp09v8fO5mXOxm+wMjh6My9J2oZGGhgbeeustvvzySwAmTJjQ/Fipp5R9NfuItEaS6kwl3hHf3NhPSRwOoxaAxYlyRCDlqyhg+99upCFvO6rNQdK0BdgTB1C+9gPch3aw79lfMHzBQwDsOlTTvF9adix7txRzaF+lXGyKbqs7uJmwtx5n+jCcaYerOunhIJTvguSxKIpCUAvyfs77aLrGlKQpnDfgvBZrDo5ksZpIyoimNL+WTStySBsYhxbWKSuoxes2bqDFJLhwRdkoPGikCEbHO5sfM5lVbA4Lu/xbKbbl8J2p3ybKdnzdQRXHh2BDNQD2xrUknflii3Ej+LTRidR+uRaAqMGTmh9XGm9Y6aWHU/4/WnMIXYdxg+Iwm9pvNt1E1zT2Pf9LihY/3fyzhkM7GfGdh7s0RtG/FEUhwZHQZlXRrtAOVKMXNd7QVBW0CjdqQu8KkqnjUwh/nkt4cwlqB8GUajJjjoglUF1MyFPbaTBV4g5y1ht7uXRoDL+bnoaiKAQCxvuszXb8XSuGw8ZNto4mLPrCUQ2mnCmDGPfTV9j//K/ZdP+FAFhjUxh0+c9JPv1yFFWleucqipc+R8HHjzP8loe6fOwkpxlVgUpfmJCmY+7C3R7fzz9rDqRwWtBza/Dd/gGhJQdxvdZ2D6CvO+WUUzjlFGOBX1N6V3x8PD/72c86DKR0XSc2fyVmvQHfR38kdM48zI6WhTMqNi5i+8PXN38fOXBcl8bUU7quGwUqSo+Y7nX0XynJk1FTap7F0rIK1CnJLReB1gfqcVlcqIpKQ0MDS5YsYenSpfh8PsxmM9dddx3Dhw+nLlDHR3kfsbt6d4v9zYqZszLOYkbqDACUxJYFTDwlB9nyx8vwVxVhT8pm7N3P4ko30q1SZ1/DmrunUbHhY0bNvQWTqpBX2oDXH8JhM5MyIIacXWXUV3upqfAQm9j9N/XQ+kLCX+QS3lGOXudHMSmok1Kx3TYVRaoGntRq9xgXlDEjpjX/TNd1Y81mfTGKokLyGNYt+Tfm0vWkZwzlvMxz2g2kmgwZm4K3IUBdtZecnYfbNDhcVrKGJ5IyIBpPQwCTWSU+OaJVA2uA1ds+ot5bTX2wXoIp0SZbjLHexFuW26XtV+0wbqCdHlNMoKYUe2ImzvTDqa1qivG5rhUbF8uVdT6e/NjoLXnFGV1brJ//0aMULX4axWIjfe63Kfz0CUqWv0L2Jfdgj2+97lYcG11td/J16qBY1DFJaNvL8N70DiigTkzFfEY2lgUjMZ82oNvHNI1IIPx5LlpOdYfbhTx1BGpKUUxmLJGtM1++bn2pm73VPv6wpoSFg2OZlOxsDqaaligcT7xeo8jQ16/J+tpRDabqDmxiz/9+jL+qCMVsJfP8Wxlw8V2Y7Yc/9DLP/x7Fy56nZOXrZF/6E2yxXVvMZlIVYmwmqnxhavxhEhwdP7Xw1hICD68Ck4JryY2YZmQSfGMn/j8ux/qdyd1+bocOHWLx4sUoisLNN9/cad6ooihMuPtpVv9sDpHuUtb//lIGzvseZkckvqoi9j390xbbJ824hKyFd3d7XN1SuM74o6iHO7ir/fsCPNmkpqZSXFzMqlWruOKK9gPyNw68QWlJKfH58WxZu6W5Qe+oUaO4/PLLSUtLY0fVDt7PeR9f2IdZNTMydiQhLUSxu5iaQA0Wr5ddj36fuAlnkzx9QfOxA7XlbP3zVfiriogedipj7n62RREXW2wymfNuJ+/thyh6/+9kp9zOgaI6DhbXMzo7FpNZJX1wHLm7yik4UNmtYEr3BfHe/K6xiLbVk95J4C9fYv3xDGzfP0WCqpNU1VajsXfMyNOaf6YoCqRORK8vRt/3KUSl41n0IqOL84HPWPX8M0QMGEPUkEnEjplN7JhZraq3Wm1mJs0eSEVxPdVlDZjMJqITnMQnRzRfwLgibQwendzu2FwWF3jBG/b2/RMXJ4WooVNQzFZq96zBX13S4TWIPxhmX2EdNgLYVj1KCEiatqDFBbU6xLhADW8vIxjS+NVT66lpCDB5WAJnTug8jbBk5RscfOX/ABj9/f+SMPlc/BWFlK99n/J1H5B53nd794RFn9n+txtRFJXB1/wGR3J2l/dTLCYivryZwPNbCb2zi9CKPLSNxQQ2FhN4eDUR67+HaWI3l3g4Gq/d/KEON8t775+ga7gGjGkuk96RCwfF8NL5A3GHNCYlG9s33UQ2m4+/5SD79+8HICur83Y0vXFUnrmuaeS98zC5bz8Euk7k4EmM/N4/W6SANHGmDiZx6jzK135A/kePMuRb93f5PLE2M1W+MFW+UKfBlO+XS0AH621TMc80/pGtV47Bcvmowx3LuygYDPLUU0+haRpnnXVWl0tDJqRl4T79B9hXPgx5W9j1n9vb3G7Mj54hYfJ53RpTd+nle4xZKUAZcRH6oVXgLu9So19x2Pnnn8+mTZv4/PPPGTp0KBMmTGjxwep2u/ly45dsWr4J/yE/ORjNqMeMGcMFF1zQ/NpZVbyKRfmLABgaPZSLBl7U3CQQwBNws/3BayjdvYayr94lasgkHIkDCPs9bHv4enzlh4gcOJ6x977U4mZFE2eKsUDaV5bLwHGRHCiqI7fUCKYA0rPjyNtdQUVxfYs1Kp3x/fhTI5CKsGK9Zizq+BTURBe6J0jgqY2El+fh//VSgq9tJ2LTbZJCeJIJ+dzU7l0Hqom4cWe2fDBlHFQdgIo96DvfIX7yXGr3b8ZcWkioupS6feuo27eOgo8fJyJ7HOPufQlrVMu0GUVRSEh0EfnERoJv7gRPkPpA2GhQueU21KSO22J4gkahlfZ6wghhiYglfsLZVKz/iKIlzzLwsp+2u21ZtRdLqIGbAk/gztmJLT6dzHktP8fVkYloNhN6Xg3ff2g5WwtqSYiycf8Nk5vf//LLG1i9o5T5M7KxW49YK+iuYfdj32/+PmHyuQBEZI+lfO37BGrKEMeHoLuWqi1L0HWd4T1Iv1QibNhum4rttqno3iChZTl4LnoJAK2grvvBlK8xiOpgyUr19hXkf/gfUFQGX3Vflw99xfDOZ7COF3v37gVg6NCeVUbsqn4PpsJ+DzsfuZXKTYtAUci6+EdkXXJ3hz2jBlz0A8rXfkDJitcYePnPMVm7dgc7zm7iQC1UekPQQXZacPEBQh/tgygbtl/MavGYonaev/x1H330EcXFxSQnJ7Nw4cJu7XvJjbcRmn8RpSteJ+/dv7V4bOAVvyBz3u0d/lv1Bb2htHmdlDLwDJSkUc2BFWYpGdwdAwYM4Mwzz2Tp0qU89thjREVFkZycTDAYpKGhgcrKyuZmvKpJZcb0GcyZM4e0tMOpGmtL1zYHUudknsP0lNZN8CoWP4tn9xrAWIty6N1/MOzmv7LrsR9Qf2AT9sRMxt7zQqtAKhzwUfDx4+S+9RcAkmdcRlrj3aiSysMV/ax2M8kZUZTk11J4sIohYzufIdbKGgg8sQFMChHLbsQ0qWX6ieXacYQ/z8V3/zIsl4+WQOok1JC7FbQwEVljW5aHpnF2atj56PUl0FDC+Kw0lPNvR4nOIFBfSUPOVmr2rKFk+Ss05G6l4NMnGHT5z1ocQw9reK5+g9Dbu1qf3B9u/bMj99V1KnxGRackR1KH24pvtozzv0fF+o/I//gx0ubciC227dlOX1ku3214mNRwAZaoeMb99BUsES0vPhSLiZJBsbya7GBrQS1RTgt/vXVac5P0shovV/9+KWFNJxwKcsWs7OZrntIv3zjiODaC7hosrpjmMtYmm3w+Hy+qti5DD4eIGTmj1Wug28wqwRe2AqAMiMY8Z2C3D9HU10yxtr1WqGzt++z6zx2ga2ReeAexo0/v8XCbPss1Tetky6MrEAg0B1PDh7euKtuX+vUqPeRtYOtfrqFu71rMEbGMuuMx4sbO7nS/yIHjicgeR0PuVirWf0zyjK4FKMlOYxal1NP+tKau6/h/uwwA+89noib3rsFvSUkJn3zyCYqicP3113c7Z9RqtaJGJeIpPlx1Kv3c7zD4ql+hWnp/93Rn1U6WFy3niiFXEGdvqxJctVG5LxyAxJGQOQ1d14xKfwDWzqd9RUuXX3458fHxfPbZZ1RXV1NXV9f8mMlkwpZswzzAzC3n3cLw1Ja/4AdqD/BR3kcAXJh9IVOSWla11LUwee/+ndw3jWBo0JW/5OCrf6B09Vs4M4ZTse5DTM4oxt7zAtboxOZ96g9uoWLTIkq+eJlAjVGCOuOCW8m+9CdkrDGa9eWWtuyAnj44npL8WgoOVJGYFkV0fMevhfDyPAjrmOcObhVIgfGGaz5zIK4zsqExoBQnl/qDm4GvVTM7gmJxwMTr0Xe+DXUF6FtfgYnXYY1MJm7cmUSPmE7trtUEqkva3N//22VGIBVtw/nsJagjE4yLBZsZJaHj12exp5iwHibSEikzU6JDMcNPJWHy+VRs+Jgd//oO2Qt/jCMpGy0UIOz34CnaT82uLylb/Q6pYS8VpmTm/uo9XGmtU4l8gRD/PiebDcEw5rDGgtOyGJgSSSissSOvmr+8upWwZrwfPv3OWk71fUHqqedTtvpd8t4+vG5cD/rZdP9FmOwu4/dMUUmYfP7R+icRnajauhSA+IntF/jqCq2sAc81bxBelgsRVpyvXYHi7MFaJH/7M1MVGz5h57++B7pG2txvM+iKX/RqzGrjJIR+nH2ub9u2DZ/PR1ZWFnFx/Tub1m/BVDjgY/vD11O3dy22+HTG/fQVXGldn2ZLnX0V+3K3UrLi1a4HUy7j6ZS4g+2Pa3ke4a8KUOIdWG+f2uXxtEXXdd5++200TeP0009nyJDWaYudqd65kj1P3IOvLBeTPYJh3/4zyadd2qtxHem1/a8BsLF8I2dnnt3iMd1Xh775RaNhb0wWyogLURQFvaHcWDNli5aS6D2gqipnn302c+bMoaysjJqaGsxmMxEREcTFxfHglgcJakGyklp+8HpDXt45+A4As9JmtQqk6g5sYv/zv6Ju/wYAht7wR9Ln3kTFpsXU7V3LgReMafrUM67BX1VC9Y4V1O5ZS/X25c3NJAEissYw6Or7iBtjzMqOyIwBYEduy4WqUbEOMgbHUXCgip3rCzj17CGoHVSe0iqMAFxJ77gakKIozam0eo2X4Ht7CH2ei2lyKrY7ut/tXRw/PEVGfnrEgFHtbqPYImD8Neh7PjR63m1/A0YtwF1bx54nfkz9gU1YouJJO+v6Fvvpmkbgv8Zr3/nK5VjOaf/9VncH8P1mGaGP9uJ8+2pMwxPIqTNSaofG9G+6hzhxaKUNhJYcxHLVmFZZKYOvvZ+6g5uo27uWrX++qt1j5MZM51n9Cqx5MP9r95Cq6v3c9/R6NgTDOAJhbl5dyCMmlRc+M35PGmMoMhNdNNQ3UO2L5vW33ufUV37TfIy0OdeTcd732PLAFXiKjJuuqsXO0G8/0OHvmTh6dC1M9dbPAYgd0/mEQav9dR1tVznhTSX47v4EvdyDkhKB8+2rME/tWlXJDg7e4tvi5a+w98l7QNcYMP8uBl7+s15niTTtf7wFU03VtZuKxPWnfrlS1jWN3Y/fSc2uVVhjkpnwize7tRgPIGn6Qva/cB/VO1YQqK/EGhnf6T5NM1Nl3vaDKf+/jNQo6x2noET07u7kpk2b2Lx5MzabjXnz5nVrX13TyH37IfLeeRh0HdeAUYy+88nmbtR9wR8+XCY+3t7y30/3VKJvfx0C9RCV0bKXVF2B8Xd0L3+Jv+EURSE5OZnk5JYpIhGWCKr91VT5qkhzGZ++gXCA1/a/Rn2wnnRXOmeknwEYr5PKzZ9RuOhJqrd/AYA1Jpnh33mY+PFGSf2h1/2eDb8+3BC64KPHKPjosRbntCdlETd2NomnXkzMyBkt3jwHpUXhtJsprvJQWedrTj/RNY3kP62k6JIR+DxB6qq9bVZIa3oDNQ031rcE39xJcP4IzPOHt/kmres64S9yCTy+nuBrh5tHh97bjfX2UyT97wTWNJvuryrucDtFNcGw89B9NVBXSHjjs+x573Xqy4qxxacz9u7nW1Up0yu96BUeiLRintv+utTw3goaRj7S/L22rxLT8ASK3EUApLvkfU0YvD/4iNCbOwl9novzv/NbPOZIHMCU3y8m/+PHqd2zhkBNCYrJisnmwJ44gMiB441iEMUufM9t5ImPdnPWhDQiGhf+r9lVxv3Pb6S63k9cpI2/3z6d/UV1DF26n32FRrZCeoKLMyekcv3cYazZVcLDL36Fw+nA6kgictAE0s+5ibixZwBw6l9XUbNnDQoKkYPGt0qjFcdO1bYvCNSW4UgeiCtzZLf31/NraRj7n+bvTWdmGzPvHZQ074wSabwX63XGdaCuhcl58y8cevfvAGRe+P0+CaTg+Ezz27VrF7t27cJutzNt2rTOd+ilfgmmct/+K+Vr3sNkj2DcvS+3CqS6Uj7SEhFLzKjTqd72OZWbFpM6q/07Q03i7MbTqWknd17LryX03m4wqz2q2HekYDDIm2++CcDChQu7NYXory5lzxM/omrLUlBUshb+iKyLf4Rq7ttiD0eW03YcsfappHQTMfuWYg0HICIFZcxlLXpJ6XWFAChRGX06HmHIjsym2l/Nvpp9pLnS8If9vLjnRQ41HCLCEsFlQy4j98sVlHz1PuHK/YTK94KvCpPFTvq5t5A1/64WvSCsMcnGTI+uY0/MxOSIwhIR0/yBHztmdodBuklVGJYRzeb9lewrrGsOpjw7y9k1OQXNrGICnJFt33zw3vg2wbd24fryZixXjyX48jY8l7yCkhmFaVQSSnokaqILJclF+KsCgq/vaPM49j/NlUDqBJc47WIKFz9F0bIXyFrwww6rQ3lK8yjZk0eEP5/E7IFMuOgy6t0hXGfchSUiptX2SpwDrCaoD0BDAL72etR1Hd/dnxL4x+Em6kp6JObzh+IJethbY+TOZ0dl98lzFSc+vXE2PvjkRvhaMAVgjU5k8FW/6vAYZ6fpvL48hx251dz3zHrmTs5gxbYSlm02gvdJQxO477pJJMc6GJoRzfmnZBIKGxedR/aYOmtiBjPHXYLNcnmb51EttuZsAnF8KV9rrDlPPu3SHn2GaaVulKxoFKsJ2z2nYblpYo/W7x9JSTWWsOjF9XiKD7Dnfz+mdu8aUFQjq+XsG3t1/BbnOs5mpqqqqnj22WcBOPfcc4mI6N1ynq7o82CqYsMn5L39MCgqo37weItp6LI175Hz2p/wlh8iadoCRt3+7w6PFT9xbreCKYfZ+A/1htqOjgOPrYOwjuXK0ahpvburs2TJEioqKkhJSWHWrK6/wVVs+ITd//sxoYYqzK4YRt7+H+LHn9WrsbRF13XWl61v/r7SV8nawhVsKllDcbiBizAzMX6kkdr39aa83hrj7yi5g9sfRsWNYlPFJtaWrSXGFsPqktWUeEqIskZx3fDriLXFsrvSin/wt6HxBrxCCGe0k0C0k8J8Hw6XhjPKhjPChi02mRmPbKU+ZwvxE87u+ORAfY2XyJiWC5fjGi9M6z1Gvwi/L8i2ojq8g+OwljYwfFMp1oWjWx0rvKu8eaGstrYQx7MLMU1Nw//XVej5dYTy61rt83WmmQOw/+4szLOyO91WHN+ih51C5KAJ1B/cTN47f2fA5GmYLHZIHY9idRF011D+1XuUrHi1OV1VUU3ocy8maUAmUZFWlFAdENPq2IpJNXqxbCzG9+ulmMYmowdC6GVuwtvLCL3VsiiF5eZJOP4zD8Wksix/GUEtyKCoQSw6tIjMiExOST4Fq+n464sijh7HoxfRcMp/AdAKalEzort9DJOq8KtvTeSWh5azemcZqxt7oNksJr593jCuPXsopq/1vTwyiNLrfChRdlRVwab2b2NR0T+askZ6uobNPDWdqIM/6sshoaZFoZmC5BU8T8nPlqGHg1ijkxhx2yN9HpQfTzNTbrebf/7zn1RXVzN48GDOPrvza6K+0KfBlLcsj12P3wkYC+Ob0pAAChY9wf7nDt/hKVv9NiNv/VeH0XfCpHPZ/9wvqdqylLDPjamNEs9HirAYb0T1gdb/oXowTOB/xoe39fu9y5+sqKjgww8/BODKK6/sUmdlLRTgwEu/o3DREwDEjj2DEd/9e5f7aDWp2bWK3Hf+RqihmsHf+i2xo9quwLKrehf5DfnN3y/OX9z8tV2HQGwWyqiFRgPNr1EmXAv+OrB1vPZF9MyQ6CFkRmSS35DP2wffBiDWFst1w69rLhKSNCiduvJagiEz/qBCKAD1tQHqawMtjqUoCq4oG85IKzb7GGp3lqIoClpYIxgIk5AaSXxKZPObXU2Fm00rcklIjWTMqZnNP7eajddBMKTh8wTYvDIPrzuAy25m8E8/wxLbumqUruv4frXk8Pe1PhSTiu2u6Vi/fyra7gpCy/Pw/2k5emF9i30t356IaVQi5nnDmtMDxYlPURQyL7yDnf/8Dvkf/puMuACq1ca+D56irjAHT+Ee9LCxMNpkjyBp2sWkzL6ayEHj4cu/gR4Ge/sXtLbbpuL9znsEGtO12+S0ELHpVkxDjNRmT9DD5orNAExImMBbB9+iwF3A9NTpffa8xYlJnXS43LTvd1+0SvXrquyUSJ6+dzZvrcilss5HeoKLBadlk9zG++aRtPxa3HOewXLteOz3ndGlc+neaqjYhx7yGp/ftkgjwySye9cSom/ouo6/sWCOM/34WI+paxrlvlXkznuVoN4AmkLK7KsZfPV9va802IaYmBgURaGyspJAIHDMmvd6vV4eeeQRiouLSUtL44477jhqY+nTYGr/i78h7KkjYfL5LXotBGrLOfCy0XQu+9J7yX3zQVSrvdNpTHtCBpEDx1Ofs4WaPV+1CM7akuw0nk5RGwUowl/kold6UUcmYJqe2d2n1kzXdV566SUCgQBTp05l1KjOF4AGG6rZ/vebqN29GsVkYdBVvyLj3O90exq3du86Nv/pctCMNMbdT97DtL+sanUcd9DdXBHutNTT2FaylnotSLamMtGZwYiss7HGtN9RW1GUDi9oRO8oisIVQ65gScES3EE3WZFZTE6a3CIVc/DYlv8/AX8Id70fT50frzuApyGAu96Hzx2kodZHQ62vzXMV59Uw+pQMktKjCQXD7NpgpHBGRNtbpCM0fe11B9i4PAe/N0REtJ1xU9PxNQTRG4LoYQ3FdLhqj/+BFYTeOZxKGvxgL+YLh6MdqiX02QFCH+5F21XR/Lj5ouHY/zxXgqeTXNIpF6Hf9m/qNr2L2WqjobKc4tVGGgyqidgxs0g+/XISp87DZHOi6zrB/HWY9TC4ElE6eO+x3jQJJTmC0Id70QNhwstz0Q4cLpwSsfa71Iy2sa0hn9FaFBbVwq7qXQQ14zOh0G28/sfEjcGkyCzAN92R74Htpfp1VWZiBHddMqZb+4TXF6Hl1OD/vy+wXDIS05j2G07ruo6euxwOrQaMdKojk6r01Akog+egyGzr0aVrxh8Ajn2aeuWWpRx89fe4D+0EFzh8KYx84Cmihkzqt3M6nU4yMjLIz8/nwIEDjBzZ/XVjvVVUVMSjjz5KWVkZcXFx3HnnnbhcHU/A9KU+C6Zq962ncsMnqDYHQ7/95xZvUhXrP0YP+okbfxaps68m980HMTu6NusRO2YW9TlbqN6+vNNgamiMsdZjR6W31bqs4OIDAFguantRfFfous6NL6/iUH6ACU4nV1xxRaf7uIv2sf2h6/GW5mCNSWbMD5/u0Ys6HPCx+/E7QQuTPPNKijd+hL80l62rn2fsjOtQG2eY/GE/r+1/jYZgAwMiBzAnYw5nRgwm3FCGNXksivXovbhE+yKtkSwYtKDL21ttZqw2M7EJLkKhMNVlbipL6inz1xFuJ60VIC4pgqhYY91K7u5yfJ4gEdF2soZ9PaAxPpZz91QQlxJJdLyTsdMGYLGa8A+MQc+pIfDwKqx3z0DbVobvl0sIfWxUllKyY9Bzawgvz6Nh1CMtD+u0YJ47GNudp2I+o++Kq4jjW/Jpl5KYEgMFazGljWf0XWdji03BmT4Ms+Nw/rqm69z38Rruc34OCijZM5sf0+v9VDyzmaXeMFfeO4OQFiKnLgffND9jzjgH372LmgMp83lDcPx3PpVxfv699V+Nx9aYlDiJhuDhkv9rSo0ZrbHxY/v/H0GcEKw/nk7g4dWAUQVS6WKD8r5gWTgSx7MLQVU6DKQAOLTK+IMCSaNQHPHoehh8tVC+G4o3o9ccgpEXyyzVUaSoJqwxKQSqi/FVFPRpEbHu8JTkcOCl31K58VMArBHJpHw2ksS42UT2YyDVZNSoUeTn57Np06ajGkzpus6XX37Ja6+9ht/vJyMjg9tuu43Y2L6fgetInwVTOa//CYCM876HLaZlQ0R3gXH3Onb0LHQtTOSgCV2eaowdO5tD7/+L6h0rOt12YLSVJKeZMk+IPdV+RsQdbvYbXmaUxTXNzu7Seb9O03Wue3MzL5XaUVNm8ZPpJqKiOl53Vb72A3b/9y7CPjeuAaMZe/dz2ON7tg6p4OPH8Jbm4MwYzsDr/4/d5lpil33C/o//zRJXJcNjhhNhiWBj+UZqA7VEWiK5ZNAlqIqKGjsIc+ygHp1XHB8CvhAVJfWUF9VRXeZusdDTYjURHe8kMsZBRIwdV6QNu9PSutFvsZFqN3RcSnOJc13Xqa304KnyAuALhkkfGMuQsYe3cTwwF8+Vr+P72Wf4fr0Ugo3BW4wdx38uRE2NwPeH5UaltXo/SnIEplPSscwfgWl6BopVyut/I/mN9XLOgVNwJbdebwewISef261fYlZ0ahMmEZtwuO/aOwdr+H5FkJIoO4OKGhibbObF3S8yfIVK1n+j0Q9Wg0XF9stZ2H45i6Ae4p3drzbvX+uvpdpfzdbKrS3Ome5Kl4p+opllwcjmYCq8vghzD68Resp6zbhOt9F9teh5XwIYlXcTjHSypnd4PXMa+q53wVMBvhqQYOqoisgaTVV1MfU5W456MOUpOUj+h49SsvwV9HAQkz2CrIU/JiViLt7/vYiSenRuDkydOpVPP/2UDRs2dHn5S2/V1dXxwgsvsGXLFsAogX7ttddisx39PoJ9cpVTu3ctNTu/xOSMIvOC21o93rTWqXLzZ9gTBzDgwu/jryom/6NHSZg6D0di+ylnUUMmg2rCnb+bsN/TYXUoRVE4MyOSV/dW80lubXMwpeVWE95QDC5Lp2+Uuq5zy+I8it1B3r5oMDazSljTufil9XxYrqJqYe5JqeaCGee2fwwtTM7rD3DofeMOaeK0ixlxy8Odrvlqj7csj9x3/gbA0Gt/j90RxfmX/YWv1q5Ci02gzl/LurJ1zdunOlO5bMhlxNhienQ+cXzQdZ3qMjf5+yupKmvZUDcq1kF8SgQJqVG4omzdmm3VNJ1wWKOqpIH8/ZXUVnkI+ox1LEmZ0Qyb0LIkteWy0dgf8eD/8wr0/DqURCeWy0dj+8Us1FRjhjlCikeIr2tMR8bUukqprutQuZ8JxR9hsgT4rC6OweNmEguUeYLcuuQQb++vgRgHkw5Vw2+WoltUbvlAIbJQQ6cadWwSzucuwTQuBV/Ix0v7XmpO4wPYXLGZNaVr8IV92E12Tks9jUhLJCPiRkjVSNHMNOXw+11oWc5RD6a6pGSbsZ4wcWRzIHUkJSIJJt0IFXtQEkcc/fF9w0UNmUzV5s+o2bWK5OkL+v18vspCKjcuomLDx1RvX278UFFImXklA6/4ObbYFEJLDho/tx2ddOaMjAySk5MpLS1l06ZNTJkypfOdemHr1q0899xz1NfX43A4uOaaa5g6deoxe2/vk2CqcNFTAGTMvRmLq3W+e8rMKylY9AQ1O1dSs3Nli8cOffAIpz68FnM7gYbJ6sCZMghP0T68ZXlEdFLDf8GQGF7dW82zOyu5c2ISqqI0l2K2XDgcxdFx+fFP8+p4akclABvKPExPdTH/udV8VG3DrAX5WXIFv7um/Z5S4YCXnf++jcoNn4BqYvDV95Fx3nd79R984KX70YN+kk+7jNgxRhqMPTqRWf/eDiYz0xoKyanPwRfykR6RzojYEbIe4AQVCobJ319JRIydgv2V1DQ1wlUVYhNdJKZGkpAWhbWNruadSc6MJnd3OVtXHwJdb+7lZ7aYcMXYoaCWqHYWTNtum4r11ingCUJjP7fQh3tR5g2jPqBxz/ICHpiZ3tyeQIjmICrUcj2f7qtF3/cJVB3EBGwPp7DgwGim+QsYn+Dg5T1VlHpCRFhUfmeHG/61EpMOASASqE2Bku+kMfWXNxNUw2wt38QXRV9Q468h0hLJxQMv5rX9r1EbqAVgaPRQFgxagMsiKc6iNeWI91L/n1di/+2Zx3A0bdN9NQAosdntbqOYLJDcvTVbom/ET5xL7ht/pnzNewy64ud9XuRB1zSjdsCuL6nc/Bm1uw+3f1AtdpJmXELmvNtwpR0OtPWgcTNLMR+da0FFUZgzZw4vvfQSb775JmPHju2XGaL6+npef/111qwxUraHDx/OjTfe2K32RP2h11c+uhamatvnACTPbHsNkTNtCJPv/5jCJc/irypGQcEak0Tx5y8RrKvEU7Cnw3VEtvh0PEX78FcVdR5MDY4h2Wlmc7mXhzeUcs+UFIKNi+Qtl3VeLOJvG8uav/7rhlKqy8v4vNaGSQvx4NAAP5p/Ybv7akE/O/75Hao2f4bZGc3oO59oDn56qmrb51Ss/wjV5mDQ1/pdqGZj+jYzMpPMyJ4X1RDHj5L8GnJ3lzd/b7aYGDA0nrTsWCw9CKCOlDE4nrLCOjz1RhO/iBg7KZnRpGbF8t5TRhn9pJj2q08pigKN6wl89y3F/4flWH9wKnecM5wX9lSzvszNqitHYDf3rj+GODkorkR0QC/fjZIyDj3oRS9YC/lrjLvsZjtK1ulolpH4t+5hWX49y/KNVNTZGRE8d+5ABkRZCU1IIvTZQRSLSsPEKJ51vkVYLeLzbQ/hC/vQGhd/JzuSuWrYVcTaYrlt7G0crD1IoiORzIhMmYkSHTJfNJzQ+3sgEEYrd6MmGoG31x9iy8EqxmTHNjfjPSYa1zrrnsoulTjQw0FQTW1W6xV9L2LAaGJGzqBm1yo2//EysubfScLk85qbmHeXFvTTcGgHdfs3UrdvPdU7VxCsq2x+XLXYiZswh/gJc0iYcgEWV0z7BzuKb30zZ85kxYoV5Ofn89FHH7Fw4cI+O7au63z11Ve8/vrruN1uLBYLF198MXPmzEHtZU+uvtDrYMpdsIeQuwZ7YmaHuaKujBEMu+FPzd9XbllC0ZJnUUyWVk19v061NqbrBfydjsduVnlibhYXvXuAn39ZyFk2lcFrCsBqwnzO4A73XVfiZlHe4b44b++vAayYtSB/GBLgR/Pbr82v6zq7Hv2+EUhFxDLhl291Gvh1JuSpZ+/TPwUge8GPu11GXZx4inIOVybLHBJP1vBELNa+ubNksZqYetZgAr4gFpsZ0xG9TkqrjRmwzkr5NjGdmgFWE+GdZTzw69msKfVw+dBYCaTEYSnjjcpjVQfQVj8CQY8RRIGRrjTkbBRrBBOA5ZcP5+PcWiIsJqYkOzlrQCRqYwBknpKOeYqxxskGzK/Q+CD3Azwh4zWb4cpgStIUxiaMbZ6Rj7XFMjmpd43ZxcnD/5+16FVebPfMQLG3DorUAYczakLv7sZ6y2RCYY0fPLKKHbnVDE6L4umfzMZyjN7flIRh6PlfQeF6dHs0JI4Ei7PVTQLdV4O+9VXwVoEtCk69TQKqo0BRFIbd/BBb/3wF7kM72PnI97DGJJNx7i2kzLoKa3Riu/vqmoa/qoiG/J3UH9hEza7V1B3YiB5q2QbFlpBB3JjZxIw6nfgJZ2N29qx1jV7tJfDKdkJLDqLtrwRfCCUjCstFw7F+eyJKlL3zg7RDVVWuvvpq/vKXv7Bo0SKGDBnC2LG9L/ZTXl7Oiy++yK5dRi/BESNGcO2115KY2P6/69HW62DKV34IAGfasC7v4y3LY/djPwCMUumWyI6n58IeI8AxObrWxfjCQTHcNTGJf2wq45W/rOaXOpjPHoQS0f5dgmBY58dfFABw75RkzsuO5n/bykl0WLhmoI1Ts5Pa3Rcg752/Ub72fUz2CMb//PVeB1IAB16+H19ZHhFZY8g4/3u9Pp44vjXU+nDX+VFUhYxBcQwZ2/fBs6oq2J0tF6SGNZ28xjVZmYld+x2zzBuG67MbME1MIcJpZeO3RhLRR0EfGDcnvA0BnJFHfyGp6BuK1QXD56Hvfh8CjX3GYrNRsmaiRGe02Pa09AhOS4/AF/YR1sKoikLVNqMRZtzY2S22HZ8wnpGxI/GH/djNdizqMZwxEMc9raQe3y8+g/oAplMzsMxteVNVr/YSfH9P8/f+x9ZhuXkSX2wtZkeucXPrQFEd/3l3K3ddOuFoDr2ZEpWOnjYRijah718M+xcDCrrFAaoF/LVt7yeB1FHjTBnIlD8uo3TlaxQtfR53/i4OvvoHDr72R5ypQzA5ItFDAcJ+D7qmgR5GC4UI1le2CpxQFJxpQ4kcPJHoIVOIHjENZ9rQ7s2w6y2/1Qpq8T+0isCTG+Hr7YP2VRFelkvok/24Pr6uZ/8AjQYPHsz555/PRx99xOOPP863vvUtpk2b1qPsAI/Hw6JFi/jss88IBoO4XC4uu+wypk+fftxlG/Q6mArUGX1krDEdBxtHbr/1wWsI1lcRN+5MBlx4R4fb61qYhnwjTc+e0PVUtj+fns4X+XVc+LlREr1q/gjay5jXdZ27l+ezsqiBZKeZe6ekEO8wc2Zm1yL/yi1LyX3zQVBURt7+HyKzep+3XLTsRYqXvYBitjLi1n81p/SJk1c4bKQr6ZqOpukU5VZjMqsoioKiGHe/VJOCqiooqmLcuVea9tUJBcIEAyFCQY1QKEw4qBEKhgmFNMJBjWAwTCgQJhzW0MK60bdE10GH+2cPRtd1Ni3ZbxxfUYxzq8b5VJOCalIxm1RMFhWzxYQSaUU5UNXhc9I0vcVz0nW9efxNb4aqapxLVRV0HXyeADUVHnyeADPOG45V1mGdsJTEERA7EIJesDhQzO0Hx76wjxf3vIg/7OfyqBnsevgGAMbe/XyrdGmryYpV+umILgg8shbqA5jnDWsdSHmDuK98Df1QLeqYJIKFdbCphNDSHD7YUwrAmRPSWL61iFc+zyMrWmfB2ROPxdNAHXoeekwWetEmqMkDdGO2ty1Jo1CGnX9UxyfA7Iggfe5NpJ39baq2LqXos2eo2r4cT9G+DvezRCfiSh9GRPZYYoZPI3r4qb1fd+U3ikrpJgXvTz41fg8CRmaAac5ArFeMwTQxFSKshDcV4/3e+4QWHSC8uxzTiN7N+MyfPx+3280XX3zBM888w9q1a7ngggsYMmRIl4KgiooKVq1axbJly/B4jNf41KlTueKKKzqton2s9PoqpSknVAsGOtkSgu4atv75arwlB3ANGM2o7/+308a11duXE2qowp6U3Wk64JFsZpV7TDrjC+uodFoYV6dx+/ICfnVqKlFHVDfRdJ1fflnEvzaXY1bhzQsHE+/o+j9LyFPP7v/eBcDAy35KwqRzurxve8rXf8zep+4BYOh1v++TWS5x/IuKdZCWHUtRbjWFBzsOUvpLOKQRbvqm86zafmW1mXE3+CWYOsEpZht0EEQ1CWkhvCEvFb4K8pJCJM+8gooNH2ON7dqNOiHaEmpsi2K9bWqLn+vuAO6FLxNekoOS5EJ57XJeufltvrW6iPV3fsBXZ2XhsJr48WVjOTW+ggeWBPjre3lkpsQweczR7yWkeyqNnlJaGOMu2temHtKnomRM7bDptTg6FEUhfvwc4sfPIRzw4i0+SDjgRbXYMFkdjevZFBSTGUtEbI8rPXdEb2x3Ev6qgPDH+0Ex6gbYfj4T04TUFtuahicQWpZD8ImNBJ7YiOOv7Ver7gpFUbj66qsZOHAgr776Kjt37mTnzp1kZ2dz6qmnkpKSQlxcHLGxsZjNZoLBILm5uezevZvdu3eTk5PTfKxhw4axYMECBg/ueJnOsdbrq5SmFL1ATWmH2+lamK0PXk1D3jbsSdmMu/flLuV8HvrwPwCkzr6629N685fsRwNem5yBW1X595Zy7pqU1BxM1fhC3LQ4j7f316AAL58/iNPSu5bm1KRg0RMEa8uJGjqFARf9oFv7tqV6+wp2/uu7oOtkX3IPaXOu7/UxxYlBURSGT0wjJSuGypIG/N4g4bCGrhmV93RdN2Z4GmeVtMZZJQDVpGC2mLBaTZitJkxmFbPZhNmiYrKYMJtVzFYTFosJtXEWqGkG6nCzEppnj3RNJxzS0Jq+DutoYY1wyJjtCoe05lmnzp6TotA8wwWKMfbw4UbDWlgnFAobTeQVsNnNRMQ4iIp1NO4jvgkiLBHcMOIG9tTsYUrSFPQbJ5F18Q+xx6d1vrMQbdB1nfA249rEdMrh3mJ6IITnmjeMQCrZZaQsD08k7kczeMv9Be+OS8RqUbnv+kkkRNuZP/8cCsrfYndJgCEV76PXXtIqVbXPx66Foa4AvfIAVO4z1kE1UVSIyUaJHwrxQySAOo6ZrA4istruswfGa1QrqUdN6dkaqPaEt5YYX1T7UIfG4Xj+UsxT2++vZ71lMsEnNhJ8bjP2P8xpUeWyJxRFYfr06YwePZrPP/+czz//nNzcXHJzczvd12KxMHHiRGbOnMnQod1MbzxGeh1MOVOMaNFTuBdd19t90opqIn3uzeS5/8b4n7/WqrFvW6p3rqRmxwpMzijSzr6hW+MK76lAe3ErugL/m5GNRYVzBkSyt9rPlnIvKwsbeGJ7BeXeENE2Ey+dP5ALBnbvDUnXwhQteQ6AgZf+tNNZts5U71zJtr/dgB4Okn7OLWQtvLtXxxMnpug4J9Fx7fdT609SUF8cS5HWSKYkGf1JFFWVQEr0jjtgrA9xmFHjjfdUvd6P56rXCX2yH2LsuJbe2JzWdNWlY9hzSgbDan2MyooltnHNpqKauPWGiwjvfAdzrRt9y0swcDakjEOxdK1oT2d0XTcCpuoc9KocI5VPO2Jti9luBE7xQyF2YIcps+LEoNf68Fz7JuEdZURuvb3Ddf3dEfrsAIHHNwCgZEUT8dV3UDqo1AtGvzXLLZOMPmt9GLtERUUxf/58zjvvPDZs2MDevXupqKigurqa6upqNE1DVVVSU1MZPnw4I0aMYOjQodjtPS+EcSz0OpiyJw7AGp1EoLaM+pwtRA2a0O62KadfRtKpF3WpXKSuhTnw4m8BGDDv9o5LP359X13He8cHENSwfnsC58zJ5rGtFbxzsJZ3DrZcqHlamounz8lmaGz3/+Pq9m8kUF2MPXEAMaNP7/b+Rypd9Ra7//tD9FCA5NMvZ8i1vzshonEhhBDiuGQzg6qAL0Tww73oDQF8P1+MnleLEu/A9cl1rdaHDM+MYfjXlmfrmkb4ld2Yv3U5HPwMijaiH1wKOV+gJw5HSRgG0Vko1q7fBNN1DbzVUF+MXnMIqnNbF5JwJRqBU/xQiM6QghInGe8dHxL6aB/E2AlvK8M8vfctbkKfHcB98cvN66Ost07tNJACYybJ+fj8Xp+/PVarlenTpzN9+vR+O8ex1OtgSlFVEk+5kMLFT1HyxUsdBlNAl+vuF3z6BA1527HFp5Nx3ne7Nabg05sIL8tFSXBif2Aujya4uGJYHC/trmJ3lQ+LSWFCooOLBsVwRkZEj4OW2n3rAIgde0aPjxEO+Mh57Y8UfPJfANLn3sSQ637f61murwu+vwd1WDym4Ql9elwhhBDieKRYTJjPGkjos4N45r/U/HN1QgrOly/DNKz9z8P6QJjvfpbHH2akk3rfEgL/Xovli1wc/5sPsQPRizYYAVDZTvSynQDoriSITDHS7swOo3G1ooCmGbNMQS+6vx7cZeAuBy3U8qQWhxE8xQ40/rb1beqXOH7owTDBd4xS3xFf3tzrog8AoTUFuBe+Ar4Q6rB4tL2VqHF9M3MqOtYnK7vTzr6Bws+epviLV8icdweOpKxeHc9bmkvO6w8AMPSGP2Gydf1uj1Zcj+/eRQDYHzoXNcFY2HdmZmSXq/N1Z5wAroyul4U/Ut3Bzex+/E48hXtRTGYGf+u3pM+9uc9npEKf5+C57FWUBKeR0iABlRBCiG8AxwuX4v/zSsKbiiGsYVk4Eusdp6CYO05q/tWXRbyyp5pVRW62XDgM5amN6PUBCOsoCcOM3k++WijbgV6dB3UFjUFS2ddLQ7TPFgWuJJSYTIjJgogUyUj5htD2VIA3hDootk8CKS232rhh4AliuXacsb56byX0YcsS0b4+CaZc6cNJmr6QslVvsf3vNzHx1+9i7mJPqK8LB7zs/M9taAEvSdMXdqs6nh7W8N74Nnq1D/O5Q7B8a1yPxtBVYZ/Rm8fsjOnWfoH6SnJee4Diz18EXcOROoSRt/6TqMGTejWeUChM7u5yBo5IwnREc0HTKemYZg5AO1CNIr9YQgghviHURFePqpP934w01pe6OScripjpaYS/vAV1VCKK5fBnqGKPhgEzUAbMQNdCUFcEnkojyAr5jJknXQPVBKoZzA6j/5orEVyJfbbeSpx4tP1GQRG1D25u6w1+3AteRq/wYJ47GMcTF+O9+d1eH1d0XZ/VHB56w5+oP7AJ96EdbP7DJYy9+zlssd1rOqqFAuz8923UH9iELSGDIdf/vsv76pqG9zvvEfrsIEqiE8f/5vf7HR6TzZj1CrlrurS9v6aMoiXPUvjpE4Q8taCayDj3ewy8/Kfdmn1rz56NRZQV1lFf7WP8aQNQG1MFFacV13vXoFd4UAfE9Po8QgghxMksymZi6WXDsDXemDSN7/h6RlHNEDMAYgb05fp9cZLSio1G5mpG7/om6bqO9+Z30baVoQ6Px/nKZUbA31QJN9zledJjZm9BLVsPVjIiM4YxA+OO9XB6pM+CKYsrmnE/fYUtD1xJQ+5W1v10NkOu+z+ST7+8S0FNfd529j55D/UHN2N2xTDunhewRsZ36dy6ruP72WcEn90MTgvOt65CTe//xl5N5S6rd6wg49xb2t3OW5bHoff/RcmK15o7XceOmcWQ6/+AK21on41n4Mgkaio9xCQ4mwOpJorTijJAmlwKIYQQXWEzS8EH0T/0SqMPlJLQuxvpgX+vJfjGToi04nzrquZiE0qMUVRNr/H2bqD97PUvDvK3N7ehN8Z8Z05I4zfXT8JmObGyqPq0G6YjKYtJv3mf3f/9IVVblrD78Tsp++pdsi+5u80UNl3TqNn5JYVLnqFi3YcA2OLTGfPDp3BljOjyef0PrCDw0CowqzjfuBLzjAF99pw6kjDlAvY990uqtizBV5GPPeFwJRYtFKRy46cUL3+Fqi1LQNdBUYiffB6Z532XmJEz+nw8zkgbU88ajLWX/QGEEEIIIUT/0EuMZSJKcs8b9ob3VOD76WIAnE9c3GLtlZodY2yzu6Lng+xnq3eWNgdSs8amsH5fBcs2F+G0mfjVtb1b9nK09flVtzU6kbH3vEDpitfY98KvqdqyhKotS4jIGkP0iOlYIuPQQwG8ZXlU71hBsLYcAMViI33ODWQtvBuLq2v9nnRdx//LJfj/vBIUcDy7EMu5Q/r6KbXLFpNE0qnzKVv9Ngde/j8GXflLanavpmrzEqp3rGhO/1PMVpKmLyDrojtxpvXv+CSQEkIIIYQ4fmmHjDL4Pc2i0nUd7+0fgC+E5frxWC5r2RjYNCnVOM+m4t4NtJ8UV3n47bMb0HX4zrwR3HTecPYW1PLdh1fw4Zp85kxKZ/qo5GM9zC7rlytvRVFImXUlsWPPoODT/1K87EUa8rbTkLe91bb2xEyST7+CtLOu69YaK90fwnvbB0Zqn1nF8cTFWK8a24fPomNhvwd3wR6cGcNBNVG+5j3K17zXYhtn+jBSz7yW5OkLsUb3vlqLEEIIIYQ4sekVbgCUlJ4Vaws8vp7w540tgB5qXWBFHWysPdIK63s+yK/RwxrawWpMQ7u2BKc9obDGfU+vp84TZMaoZG48x6iIPSwjmlsuGM6/393J/z7czbSRSSdMdct+ncawxSYz+KpfM/DSe6na/gWeov2EGmpQzGbsCRlEZo/HNWBUt/+xQivz8N71MdrmEmON1CuXY5nXs/Lk7anavpz6A5vQAj7CfjchbwNhbx3+mjIC1SX4KvJpTvI8QkT2WFJmXUn8+LNxJGf36ZiEEEIIIcSJTStrDKaSup/mpx2qwfeTxhZA/7wANa71uislwlgjr3uCvRjlYdX1fip+9zmJj6zF+e95WG/qeRreK8sOsD23msQYO/ddPwlVPRwDXDZrEC8tPcCuQzWs3V3OqSOT+mL4/e6o5ISpFhsJE8+BiV0rc64Hw+A2CjXomg4NAbRSN+ENRQRf3kZ45SHjuINjcb5yOaZJad0eU6C2HNXqaLeEe+XGTylc9GT7B1BNONOHEDFgDJEDx9NwaDulK16jIXcbnsETie/icxVCCCGEEN8gIQ2gR+1qvD/6BDxBzJeOwnrlmLY3ajquP9T2492094M9DP77VyiajpLa856tuSX1/PfD3QD8/OoJRLtaFkazW01cdcYgHn1/F88u2ivBVG+EVx7Cffaz7W8QZcN256nY7jkNJdLW7eMHasvZ/IdLMEfEMu4nL7UZUMWOmY3J5kK1WDHZIzDZXZgckVijE7HFJGNPzES1HD63rutEDhzHgRd/S9GS5yha9iLxE+eSMPEcokdMw5E88ISZrhRCCCGEEP2k6XpQ617p8uD7ewi9sxsirDge7qB/mt0MFhX8YfxPbsR2c89nknRPgMH3LcOi6Xx+ahoXn9+zKtS6rvPgq1sIhjTGZMe2uybq0pkDefKTPWzaX0lVvZ+4HlznH23HZTCFWYXow/94SoQVJc6BaWwyptOzsF4ztkdBVJOw30PY78FTvJ+anV+SMLn1CzJh0jndahisKAoZ59xC9PBp5H/4KGVfvUPlhk+o3PAJAPakLJKnX0LiKRfiyhyBop5YZR+FEEIIIUTvKdE2dEAr96Bmx3ZpH73ai/eODwCw338makb7xdoURUGJsKJX+whvLIJeBFO+Xy/FcrCavDg7f5+QyHnBcI9Kly/ZVMSm/ZUAbM+t5pdPrePb5w1nSFrLIhwuh4WspAj2FdZRXuOVYKqnzDOziK76eb8d35GUxYRfvEndgY1tBlK9EZk1hlG3/5vBV99HxfoPqd75JbW7V+MryyPv3b+R9+7fsEQlkDR9Ielzbuj36n5CCCGEEOL4YZqYiratjPCyHMxT07u0j/cni9AL6zFNy8D6g1M73V5JjjCCqdX5PR6nlltN4D/rQIGnLxmOz6RSUuUhK7l7qX6+QIh/vGUUoTOpCqqqsHRTEUs3FTHv1Ex+cuX4FgGaPxgGwGw6MXq9nRij7AeO5GySZ1zSb8e3xSaTPvcmxtz1JDP+vY3xv3iTlJlXYotPJ1hXQeGn/2Ptvaez+U+XU73zy34bhxBCCCGEOH5YFowEwP/31WgFtZ1uH1pykODTm8Ci4nhqAUoXggzzvGFgUtC2lBJakdftMeq6jve770MgjOWqsTQMN6r4VdcHun2sZxfto6LWx/DMaJb/7SLeuO9sLps1EKtZ5cM1+fzwP6up8wQaj+8nv9yNxaySkdjzPlxH0zc2mDqaFNVE7KjTGPG9fzDt7+uZ9LtPSJl9DarNQc2OFWz546Vs/O08Chc/RcjbcKyHK4QQQggh+on5wmGYZmWhl7pxL3wFvYNCEaG1BXi+9QYAtl/MwjQ8oUvncDx4DrZfzALAd+8idE3r1hgD/1lLaMlBlHgH9ofOJcphFIuorPd16zi7D9Xw/OJ9KArcdckYVFUhKdbB3ZeP48l7ZpEQbWfz/kp+/OhX5Jc18H8vbETXYerwxB6lEx4LEkwdZYqiEDVoAiO+8zDT/7mZ7EvvxeSMom7/BvY9+wtW3zmR3LcfJuxzH+uhCiGEEEKIPqaYVJxvXok6KBZtYzH+h1a12kYPa/j/vhr3rKfQyz2YzxmM7Rczu3Ue290zUFIjCK8tJPTuni7vF1p1CN89Rvl1x38uRE2OYHxj76pP1xV0+TjFlR7u/d8awprOpTMHMnFIy0BwSHo0//vxTFLjnOzIreaK/1vC6p1lRDot/GDB6HaOevyRYOoYsriiyV74Y6b/YyMjb/8P0SOmEfbWk/vmg6z7+RnU7Gr9yyWEEEIIIU5sapwTx2MXAeC//3MCz28BQNc0QksP0jDtf/ju/hSCGtbvn4Lz3atRzN2bqVEibdjuOc04x0Nfooc7n50Kby7Gc/HLEAhj/f4pWC4zgpoLp2dhNil8ub2Eshpvp8fZmVfNdx9eTnmNjwmD49sNjlLinPzz+zMYkx2L1aIyY1QyT90zm+yUnpdgP9okmDoOmB0RJM+4hIm/eofxv3gT14DR+Mrz2fyHSyj49IljPTwhhBBCCNHHzHMGYfv5TAhpeG98m/opj1EX9wDuuc+hbSxGyYjC+fZVOP5xAYq1ZzXjrDdMMGanVhfg+9nidrfTwxqB/66nYdZT6FVezBcMxf7Xw0Xa4iJtzBqXiqbDI+/sQNfbL+u++1AN9zy+hoo6P5OGJvDgd0/F2kHKXkaii//dPYsvHr6Ih26bdsKslWoiwdRxJnbUaUz+3SdkLfgRAPuf/xVFS58/xqMSQgghhBB9zf77OdgfmQcWFW1TCdQHULKisd1/JpE77sAyf0Svjq/EOnC+cCmYVQIPrybwvw0tHtf9IQKvbKNh4qN4b/sA3EEs3xqH8/UrUL4WAH133gjsVhOLNxTy/OJ9rc5VUevj8Q928b2/raC63s+pI5P4xx3TiXRaevUcjnfHZWn0bzrVbGHgZT/FEhXP/ud+xd6nf4o9cQBxY2cf66EJIYQQQog+ZLttKpaLh6MdqEYdEoea2rcpbuYzBuJ49EK833kP723vE/xoL0qUDS2vhvC2MqgxikooWdHY/3g2livHoDQ1Fj5CVnIkv752Er98ah2Pvr+L3fm1pMQ5qK73U1TpYXtOVXMf4otPy+Luy8adMOXNe0OCqeNYxjm3EKytJO/dv7Hzke8x+Xef4EjOPtbDEkIIIYQQfUhNi0L9WgPbvmS9aRJauRv/L5cQeq9lMQp1fDLW707B+u2JKLaOQ4OzJqbxq2sn8sDLm1m2uajFY2aTwumjk/nWnKGMGxTX58/heCXB1HEu+9Kf0HBoO5WbFrP/hV8z9m5J+RNCCCGEEN1j/+lMLJeOIvxVAQQ11Mwo1GHxKJnRbc5EtWfeqQMYNzCOL3eUEgprxERYSYh2MCorhiintR+fwfFJgqnjnKKqDL/lYb768SlUblpM3YFNRA2eeKyHJYQQQgghTjCmIfGYhsT3+jiZSRFclRTRByM68Z38iYwnAWt0IulzbwIg752Hj/FohBBCCCGEECDB1Akj8/xbAajavrzbXayFEEIIIYToCU1rvwy6kGDqhGGNTsQSFY8e9BOoKT3WwxFCCCGEECe5umovaz7bR30XGvV+U8maqRPIkG/9DtXmxOTsv2ovQgghhBBCABQcqMTnDrJ9TT6nzh2Kqna9UMU3hQRTJ5Dk0y491kMQQgghhBDfECMmpQGQPihOAql2SDAlhBBCCCGEaEVVVUZNyTjWwziuyZopIYQQQgghhOgBCaaEEEIIIYQQogckmBJCCCGEEEKIHpBgSgghhBBCCCF6QIIpIYQQQgghhOgBCaaEEEIIIYQQogckmBJCCCGEEEKIHpBgSgghhBBCCCF6QIIpIYQQQgghhOgBCaaEEEIIIYQQogckmBJCCCGEEEKIHpBgSgghhBBCCCF6QIIpIYQQQgghhOgBCaaEEEIIIYQQogckmBJCCCGEEEKIHpBgSgghhBBCCCF6QNF1vesbK0o5kNd/wxEnmSxd1xOP5QDkNSu6SV6z4kR0TF+38poVPSCvWXGiafc1261gSgghhBBCCCGEQdL8hBBCCCGEEKIHJJgSQgghhBBCiB6QYEoIIYQQQgghekCCKSGEEEIIIYToAQmmhBBCCCGEEKIHJJgSQgghhBBCiB6QYEoIIYQQQgghekCCKSGEEEIIIYToAQmmhBBCCCGEEKIHJJgSQgghhBBCiB6QYEoIIYQQQgghekCCKSGEEEIIIYToAQmmhBBCCCGEEKIHJJgSQgghhBBCiB6QYEoIIYQQQgghekCCKSGEEEIIIYToAQmmhBBCCCGEEKIHJJgSQgghhBBCiB6QYEoIIYQQQgghekCCKSGEEEIIIYToAQmmhBBCCCGEEKIHJJgSQgghhBBCiB6QYEoIIYQQQgghekCCKSGEEEIIIYToAQmmhBBCCCGEEKIHJJgSQgghhBBCiB4wd2fjhIQEPTs7u5+GIk42GzZsqNB1PfFYjkFes6I75DUrTkTH+nUrr1nRXfKaFSeajl6z3QqmsrOzWb9+fd+MSpz0FEXJO9ZjkNes6A55zYoT0bF+3cprVnSXvGbFiaaj16yk+QkhhBBCCCFED0gwJYQQQgghhBA9IMGUEEIIIYQQQvSABFNCCCGEEEII0QMSTAkhhBBCCCFED0gwJYQQQgghhBA9IMGUEEIIIYQQQvSABFNCCCGEEEII0QMSTAkhhBBCCCFED0gwJYQQQgghhBA9IMGUEEIIIYQQQvSABFNCCCGEEEII0QMSTAkhhBBCCCFED0gwJYQQQgghhBA9IMGUEEIIIYQQQvSABFNCCCGEEEII0QMSTAkhhBBCCCFED0gw1UXBQJjqcje6rh/roQghhBBCCCGOAxJMddHujYVsXplLVWnDsR6KEEKIfhLeVorvl5+h1/uP9VCEEEKcAMzHegAnioriegAqSxuIT4k8xqMRQgjRHzxXvY62uwIlwYntRzOO9XCEEEIc52Rmqpt8nsCxHoIQQoh+EN5djra7wvh6TeExHo0QQogTgQRT3VRZ0sD2NfkSVAkhxEkm+Or25q+1QzXHbiBCCCFOGBJMdVFUrKP56/KiOtYs3k+JfNgKIcRJQQ9rBJ7cePj7Su8xHI0QQogTxTc+mHpj+UFu+8dKPtvYcUqH1W4sLxsyNpmkjGh0XSci2n40hihEj+iahue77+H92eJjPRQhjnuhzw6iF9ZDhBUAvcZ3jEckhBDiRPCNL0BxoKiezfsrmTMxrcPtbA5L41cKo6dm4B2VhMNl7ffxhTz1+CryCXnqAKMsuzU6CVt8OiarBHOifXq5h2DjnXbLxSMwT888xiMS4vgV+PtqAGx3nor/jyukmp8QQogu+cYHU9WNH5hxkR0HJk0zUwFfEKDfAild06jeuZLSL9+gassSgnWV7Y8pOglH6mDSz76BxFMuQlFN/TImcYI6oiea9wcfErH8JhRn/98AEOJEE1pfSGjRAYiwYv3hdPx/WgH+MHpYQzF94xM4hBBCdOCkDKZ0TUNRu/YBWN1gBFOxkR1fZNoagym/L9S7wXXAXbSPvU/cQ+3eNc0/Uy127ImZmF0xKKoJXQsTqCnBX1VMoLaMQG0ZtbtX40h5kAEXfZ/k0y5DNcsF8zeFruug622+3pVIW/PX2qYS3HOexfnO1ajJEUdziEIc9wJ/XQWA9XtTUOOd4LCAJwi+EByFDAQhhBAnrpMumHJf8ALhHWVErLoFNT2q0+2bZqZiI2wdbme2GLM+oWC494Nsg7csj02/m0+ooRpLdCJpZ11H8vRLcKQORlGUVtvrWhh/dQlVm5dw6INH8JYcZO9T9xI7ehb2hIx+GaM4vux7/teUrniNEd/7JwmTz231uOKyQqQV6gMQYye8tpCGGU/g+vhaTMMSjsGIhTj+aAW1BN/aCWYV213TjB9aTeAJovtDxu+REEII0Y6TLpgKfbofAN8vl+B8ZmHn24cb1yGZO57Jslgbg6mA1ssRti33zb8QaqgmduwZjPr+Y1hcMR1ur6gm7PHppM25npQzrqFs9Tv4yvMkkPom0XVCnlo8xfuA1sEUgJoWibanEtdrV+D71RK0cjdKjKy1E6JJ4L8bIKxjuWJU8w04xawaK1TDeof7CiGEECddMIVFhaBG8Pkt0IVgqsuHbQymgoG+T/PTQgEqNnwCwLAbH+g0kPo61WQm5fTL+nxc4tjQdZ0PDtZydlYUjg6CfGfaEAA8Rfvb3UbNikHbU4nuDeJacgN6qRs1SdL8hADQ/SEC/9sAgPW2qYcfsDWuP+3HtG4hhBAnh5MumLL9bCb+//sCoEuLh5sy6MJ6x3cgLVbjnyoY6Ps0P3f+bsK+BhypQ3AkZ/f58cWJ5Y6l+Ty6tZw/nJbGL05JbXc7R8ogALxlue1uo2bHAKDl1aA4rSgD+zZlSdN1dlT62Fvto6ghSH0wjDekEQzrOC0qCQ4zWZE2sqOsDIqxdRgcCnG0BZ7ehF7mRh2fjGlmVvPPlXgnemE9erkbMqOP4QiFEEIc7066YMp6w4TmYCr8VQHm0wZ0uL3TZvwTeDq5A2m2GheBoWAYXdfbXMfUU4G6cgBJ0RMAXDY0hke3lvOntSXcPDqBZJelze2aAm9P0b52X5NKhpG2pBfV9+kYVxU18N9tFbx7oIYaf9duMFhUhWmpLs7KjOTMzEimpbiwSXAljhE9EDKq9gH2n89q8fujpkWibS1FK6jDNKnjthlCCCG+2U66YEodGNv8tefK14gquKfD7WMaGzRW1XXcU0RVVRRVQdd0NE3HZOrDYKrWCKas0VIUQMBZA6KYNzCaD3Nq+dWqIv43N6vN7ewJmZicUQTrKgnUlmOLSWq1jRLnBECv9PbJ2NzBMN9fms8zOw+X7B8QaWV8ooP0CAvRVhMOs4rFpOAJapR5QuTW+cmtC7C/xs+KwgZWFDZw/1fFvHXRIBYOie3gbEL0n+DL29EL6lBHJ2K+dGSLx9TG2SitoO5YDE0IIcQJ5KQLpgAsV4wm+NoO9OKGTrdNi3cBFRRVejrd1mRSCGk6WljH1IctnTS/cW6TzdV3BxUntAdnprMor44ntldw3cg4ZmVEttpGURQiMkZSu3cN7vxdbQdT8Q4AtC68vrviWx/n8O6BWuwmhbsmJnHTmASGxXatoEW1L8TywgaWHqrn84J6ZrfxnIQ4GnRdx//gSgBsP57RqrWAkm68NvVCCaaEEN9s4f2VhNcVoURYMZ2aLuuu23BSBlP2h88j+NoOALT82ua7jG1JjTfu3BdXdX6x2ZQGomt9W+FJtRgXo1oo0KfHFSeuUfEOfnFKCvd/VcyvVxXxxRXD29zOnphJ7d41BKpL2nxcaSycQrD3VSgbAmHePVALwLprRjImwdGt/WPtZi4eHMPFg2N6PRYhekPbXIK2uwIl2YXlW+NaPa6mGemxWmHfpscKIcSJQg9r+H6yiMA/vjr8Q7OK5dpx2P/vrOb3SQEn5YIFNfXwHW//w6s63DY51rggLK/1dXpck+Xwuqm+ZImMA8BXUdCnxxUntrsnJxNhUVle2EBObdtpqCaHcYco5GtnFlZtTEftgxsAdrOKs3GNU4LjpLwPI74hAs9tBsCycCSKpXWaQdNaQ61IZqaEEN88uq7jvf0DI5Ayq5jnD8d01kDQdILPbKZh3H8ILc891sM8bpyUwdSRAv9c0+HjkU5jcX+9p/NZIWtjRb9AH1f0ixw0wRjDgU1o4f4pxRt015Lz5l8oWPQE4UDfrJ8R/SvSauLcLOOi7rNDbV/UmayNaXz+dmZWm6pZ9kEwZVYV5gwwblS8vKeq18cT4ljQg2GCL28DwHrTpDa3MZ+SjmvpjTj/d/HRHJoQQhwXAv/8iuATG8FuxrX4elxvX03E4huI2P0DzOcOQa/24V74CuH9lZ0f7BvgpA2mrLcf7hmih9oPfkxNqXtduNY0NzXu7eOZKVtsCo6UQYR9DdTu+arzHbpI1zQqNnzC2ntn8uX3hpP39kPsf+5X1O7pOMAUx4+5jcHUory2gynV2pgiGmyngEofzkwB3DgqHoB/bSrDG+qfBtZC9KfQp/vRyz2oIxNQJ7XdekCJdWCenY2aFXN0ByeEEMdY8IM9+O7+FADHExdjnpXd/JhpcBzO96/BPH841PjwXPk6ul/68Z20wZTl0lHNX4cWH+ybYzamg4T6oddU0jTjDuih9/6J3pXIrgNa0E/Rshf54vo0tv/tRjxF+5ofix0zi9jRM3t1fHH0zEo30vi+Kna3+bhqsQGgBduZWe3jYOqiQTGMibeTUxfgD2uK++SYQhxN/r+tBsB648Q+bXEhhBDHC90bJPjpfvyPrSPwxAZCq/PRtc5vgIa3luC59k3Qwfa7M7FePbbVNopJxfnsQpTsGLTNJQTf2tUfT+GEctIGU+rQ+Oavg89sane77lxiWmxGMBXopCdVT6TPvQmzK4bq7csp+uyZHh1D13WKv3iZ5d/OYu+Td7d4LG78HKb9fR3jf/YaitqHpQhFvxoeZ8dlUSloCFLpbf26a14z5W1nbYfaVDSlb2aRLCaFx882SrX/eX0Jm8r6pkrgV8UNzHptD3cty+/1zQQh2hP68hDhz3Mhxo71O5OP9XCEEKJPacX1eH/yKXXpf8VzwQv47vgQ7/fex336kzSM/jfBt3e1+xmrldTjnv8S1AewXDEa2y9mtXseJcqO7Y5TAAh9ntMvz+VEctIGU0ra4SIUwTd2orvbvnMfbrxjr6qd36G0OYz1VX5fsA9G2JI1OpFhNz0IwP4Xfk3R0ufRta7NgGmhIAWLnuCL61LZ878ftXhswPy7mP6vLYz7yYvYEzL7fNyif6mKwrBYY/ZpX03rIilN5fTDvrZnrpSmprh9UM2vyYy0CG4bl0hIgwvf2U9+fc+qUOq6zpJDdWQ9sY3pr+xhRWED/9la1uUmwEJ0V+BfRoqz7fapKNFdK+kvhBDHOz2s4f/LSuqH/IPAw6uh1o86KRXLzZOwXDceJTMKbW8lnstepWHSYwQ/2NMiqAp+up+GGU+g59dhmpaB4+kFnc7cq6MSAdByqvv1uZ0ITtqSXIqiYJqdRfiLPABCK/KwnDe01XbNMVQXboY7XEaDX3d9xw1+eyrp1PnU52wl/4NH2PvUT8h98y9EDZtK5MDxmOxOVJMVxWJFD4cJ1lfiry6mbu86GvK2tzrW0BsfIHnGpZid0svnRDcg0sqmMi9FDa2DeLPTWFMV9rZdzU+JMgIxvZOm1N318OwMdlR6WV7YwLlv7eOjBUPIjrZ1aV9N13n/YC03fJpL7dcCpyfnZhNrP2nflsQxpFW4Cb6zC1QF63enHOvhCCFEn9BK6vFc9TrhFYcAMC8cif3nMzFNTmveRg+FCTy+Hv+fVqBtLcVz8cuoY5MwjUtB21tBeF0RAKYpaTjfvgrFbun0vM03pHp4Q/VkclJftZhPzWgOpsJf5LYZTFkb10H5u1BUIjLGqJxWX+1F1/V+ybcfdOUvicgcycHX/4S/ooCKdR9Sse7DLu8/8b73iB52Sp+PSxw70Y2FT2rbWKtnshl90tqt0BhjvNnpXSj93x12s8o78wcz87U97Kj0MfLZHfxwUjI3jIpnRFzbd/zDms6/Npfxoy9atwBYOCSGR88aQLKr8zdwIXoi+PQmCGqYzx/aYe9BIYQ4UWgl9TSc9iR6bg1KagSOJy5u81pXMZuw3XEq1lsmE3h0nRFUbStD21ZmbBBlw/6LmVjvmoZi7WJo0DQbIan5J3cwZZp2OK0ttL6ozW2iGkuj17STBngku9OCzWHB7w3SUOtrDq76kqIoJJ92KUkzLsFbfIDafetwF+5BDwbQQgG0oJ+G3G24C3Y372OLT2fkrf8iZuSMPh+POPZcjQG/u41UvcMFKNoOlpSm2aI2UgR7K9ZuZsUVw7ltySFe3VvNA+tKeGBdCVlRVsbGO8iOtmJWFAKaztv7ayh2t55Zu25kHL+ZlsbgmK7NagnRE7qm4X98PQDW26Z2srUQQpwYvHd+jJ5bY8wovXcNanJEh9srNjO2H07HeusUwisPoRXWoyQ4MZ+RjdKYfdVVetN1RUT39jsZHRfBlFbuRk109flxTZMPl73VdpS1uU1KnHFnv6SqawvpYxOclOTXUlPh6ZdgqomiKDjThuBMG9L8s5DPTe4bf8ZduBcAa0wyAy//GSkzr5CiEicxh9m4+9NWKXKlOZhqO42vaRper/X3y2xqrN3MK/MGcdfEBh7dWs4HB2vJqwuQV9fxzYnH5gzg8mGxxElKnzgKwl/koefUoGRFYz5vSOc7CCHEcU7LqyH01k6wmXC+eWWngdSRFLsF89mDe3f+LSUAmIbEd7Llye+YX8mEPs/BfdFLOB4+r8+rKynpUSgJTvQKD3qpG63CjZrQMmiLclqwWUy4fSHcviCuTvJEoxNclOTXUlflAY7eC6ju4GZ2/PMW/BUFoKhkzrud7IV3Y7L3fRAqji+JjYVP2prZaQqi9VDbFSYViwmcFvAEoSEAkf0zAzQ9LYLpaRGENJ291T52VPrYUOrmz+tLm7c5PS2Cd+YPJt5xzN92RDcEF+0n+NwWbD+a3iIH/0QSeHYzANZrx6OYTtq6S0KIb5DQkoOgg3neMNSMo5u6rIc1As9tBsA8t3dB2cngmH6qaPm1eK56HTzBfqkGoigK6rjk5u/DG1v3xVEUhbjGC8yqLhSWiGxcg1Lfx2tQOlK+9gM2/34h/ooCIrLGMPl3nzD46vskkPqGaKrmt6uq9WtONRmBlh5uv8Kk0k/rptpiVhVGxTtwWVSe3GF0Rk90mPlwwRBWXDlcAqkTUOijfQRf3kbg1daFbk4Euq4T+siYzbd8a9wxHo0QQvQNrcBoiWIannDUz+2/bynargpjtn/+8KN+/uPNMQum9FAYzxWvoZd7MJ89CNv/ndUv51ETnM1fh9cVtrlNVOOi97o27vx/nTPCuLD1uYNo3WiEWu8J8sg7O/j9CxvZcrCyy/vlf/QYO/55C1rAS8qsq5h0/0dEDpQLgm+ScYlGOunWcg/BrxWhUMzGa1cLt9/7rGndlN4P66a+Ttd1fvdVEfPe2U+FN8TZAyLZcu0oLhgoC/5PVE0N0IOv7+izfmVHVZ0fvdILFhUlUnL7hRAnB73CWJ6iJB29G+u6ruO7byn+B1aCquB4fL6RAfMNd8yCqcD/NhBeW4iSGYXjpcv6LfVCP+LiM7y5pM1t7I0vhECo84p+JrOKzW5G13X83q71m/IFQvzgkS95ccl+PlyTzx3/+JLckvqOx63r5Lz+Zw689FvAqPI3/Dt/QzXLxcA3zcAoGyagyB2iqLBlc16lOzNT/RxMabrODz8v4Deri1EVeOD0dD69ZCipEVKh70RmOi0TJSMK/VAt4bVt35A6rkXZUCelQlCjfsg/cC98mcCzm9Eq2u7NJoQQJwLdY3zuK86j8xmraxq+nyzC/4flYFJwPHcJFknxA45RMKVXe/H/ZhkAjofOQ413drJHD8/T4Ce88lDz99qu8ja3szeWnva1UXq6LY7GyiXehq7V1v/X2zvYk19LeoKTCYPjCWs6K7e3HdiB8YLd/9wvyXv3b6CaGHHrvxhw0Q/6pRS76Bubyzz8YmUhWj+UCM2tCxAG7Ao0VLa8AFQ7KUBhbNRUvrR75/WGNFbtq6SkovPiLCFN59uf5vLPzWVYTQqvzxvET6emoMpr9oSnqCqWecMACC/PO8aj6T5FUXC9fw3mBSMgECb03h68N71Dfdpf8XzrDbRObmwJIcRxqel64yh8zmpVHjwXv0zgb6vBrOJ88TKsV4/t9/OeKI5JMOX73efolV5Ms7MwXzKyX86h6zreuz9Fr/CgjkkCQNtfhd5GPylnY9GJhi7ONNmdjcGUp/NgatnmIt5amYvZpPDHm0/h/FONcu1782vb3Wf/i7+hcPFTKGYro+98gpTTL+/SuMSxUeoOcv7b+/jTuhIe3dJ2wN5T7mCYmxblAjDTqVJVUk84fDjVymRv7DPlbz/gab571c0eTr95fy/124rZvjafUAd92Pwhjcs/OMhzu6pwWVQ+uHgIlwyN7da5xPHNNMN43wp9eaiTLQ3+6lJ0rWs3p44GNSUS15tXEXnox9j/dQHmc4y7qcFXtuOe+RR6PzViF0KI/tJUylxv6N/3r9DaAhpO+S+hj/ahxNpxffgtLJeP7tdznmiOejCl13gJPLYeFHA8fF6/zbYE/r6a4BMbjZKRzyxEHRoHQQ1tW2mrbZt6TdV3MZjq6sxUfnkDf3p5MwA/WDCaYRnRDE2PAmBfUdvBVM2uVRR++j8Uk4Wx97xA4pTzuzQmcezcuuQQJZ4QM9MjuGVM3y0ErfKFOOuNvSwvbCDZaebugZGEgholh2qatzHZI0BRCHvr21831dSdvIs9JHRdZ8/mIs4njFVRKFZVVFPbv6e6rnPL4jzeOVBDjM3EZ5cOZW5WVHeepjgBmE8fAED4y0OdrpuqO7iZDb+ay/4XfnM0htYtaloUtttPwfXxdUTuuwt1XDLawWp8//fFsR6aEEJ0izrAWIsc3t5265/e0nUd/0NfGjeccoxeVhEbbu11SfWT0VEPpoLv7YFAGNPsbEwTUjvfoZt0TcN3/zJ89ywCwPHkAkwTUzGdZlwMBN/Z3Wofp82oMOb1d+1Oqqux+l9DB9XRtudUcdvfV1LvCXLamGQunz0IgMGpUVjNKrklDZTVeFvsEw542fPkPQBkXXwXcWNmdWk84thZcqiOdw7UEGlVefH8gdjMffMrdaguwNw397G2xEN2lJXPLx/OhJGJxmN7K5qLn6gmM2ZHFOg6YU9dm8fSKxsXqcZ33hdN13UObC+lKKcaXYE/VwR5zq2jqm0/r1f2VPPC7ioiLCpLLh3GtNSu97kQ/ads7fus+/mZ5H/8OHofpJ4qWTHGuqlqH9quina3K1/3IZt/v5BAbRnugl0dp58eY2pWDI7/zgcFAv/4ql8qygohRH8xzzGuK0Mf7+vz4kC6P4T3mjfw3bsYQhrWH07D9cW3UbNi+vQ8J4ujH0y9vgOgX6YItYJa3Oc+j/93X4CqYH9kXnNOp/XGiQC4/76aVctzWuxna1wzFegglelIUXHGRWldlbdVRT9d1/lwzSHu+NeXVNb5mTwsgftvmNw8A2e1mDh1pJF2eP1v3uHBvzzJv5/9lEffWM+Df3+JmpJCnBnDGTD/zh7+K4ij6fdrjHL7P52SQmYfVArTdZ0Xd1Uy/oWdbCzzMDDKyoorhjMizk5SehQOlxWfJ0hpfk3zPqrNeD2GA97WxwuF0at9oIAS23EwFQyE2LmugPz9lSgKZE9MY1FDmOWFDdS3sZ6wuCHIHUuNtK+HZ2cwKbl/1j6K7itb/Q7u/F0cePE3bP/7twn5eldsQVEUzE2pfitbp/rpmkbOm39hxz9uNiqPzr6acfe+3Lym73hlnpqO5ZpxENLwP7ruWA9HnARqfCGu+ziHRXnGza1AoGtrq4XoLnVCCsqAaPTiBoKv7eiz4+reIJ6LXzaOGWXD+dZVOB46D6WTPqzfZEe16YvuDxFafAAUsPThWild1wm+sh3fDz5Er/ahJLlwPLMQy7mHO92bZ2bhn5WFbXkeOfd+SvYH15HW2MDX0jibEAh1LbK32S04I6x4GgLUVXmIaTzOnvwa/v3uTtbtMdbNXHxaFvdcPg7z1yoV/uTKcZSXlbO71Mnbh5xwyAcUAgmMUhx4Cvaw4VfnYHJEYnZGHfEnGrMz0vjbFYXJEYXZFY3ZYfzMEhWPapI+PkfL3mofnxc0EGlV+f6EpF4fr8Qd5DuL8/ggx0gBvWhQNE+fk93cm0lRFLJHJLJrQyGH9laQnBmDqiqYrI3BVBvrppoq+Ckx9g4rZlaU1LNnYxEBfwiTWWX01AziUyKZmR7B8sIGntpewV2TDvds03Wd73yWR7U/zHnZUX2a3ih6z5lyOA2jcsMnbP7DQsbf+wqWyLgeH9N0+gCCr+0gvOoQfG9K889DPje7H/sBFes/AkVl0FW/IvOC206YgjnW26YSfHErwbd24njwnGM9HHGCe3pDLi/sruWF3VW8mn6ApUuXcuONNzJt2rRjPTRxklEUBdvPZuK7/QO8t32AaWQipvEpvTqmHtbwfOtNQosPoCS5cH1yXa+P+U1wdIOpMjcENZTUCNSkvkkH0io9eG/7gNCbOwEwnz8Ux5MXoya3Pn7Un+fin/4EczeWcuvvlnDlJaOZOjyxuTS6199+r56vi0uOwNNQRVVZA9tL6nljeQ5rdhl5q5FOCz9YMJqLpme1uW9itIPHfziTjz9dyY69BQSriwhWFWIijE03Ln7dBa3TETujmMzY4jNwJGVhT8rCkTTA+DvR+N7ikl4/fendAzUAXDwohmhb7/osrCio58qPcih2B4m2mfjrzAxuHhPf6oI0KSOanF1leBoCHNxRypCxKVgi4/CW5hBqaCNNqbH4RHvrpfzeIPu3lVDWWHI9Ot7JiIlpOBtTWX88OZnlhQ3c/1Ux52ZHMyLOKLP+4u4qPsypJcZm4om5WSfMhfM3RdSQyQBYohMx2Zw05Gxly4NXMeGXb2PuYbNv09R0AMKbDjc/91Xks+2h63Hn78LkjGL09x8nbtyZvX8CR5FpahrYTOg5Neh1PpQo+7EekjiBlW34ApgAwHsNMbh0na1bt0owJfqF9buTCS/LIfj6DtznPofr0+t7HPzouo73u+8Renc3xNhxLbkB06je3yj+Jji6wVRTg7GE3qcD6bpO8KVt+O751AjSIqw4/noullsmtXthZz8lA+9vzuB5n5/ieh9/f3M7ADGNF5rhLjbh9QVClDdepH70ZR4vbjUuLuxWEwtPy+b6c4YSE9Fxeos1IpqLL53HxYCvsoivfjgFdI2Jv/kAa3QiYZ+bkLeekKeWkLuOsLeOkKeOkLvW+Lm71vjeW0fIXUfIU0OwrhJfWS6+stw2z2mJTiRiwBiihkwk8ZSLiMjsn0qK3xTLCxoAOL8XDWlrfCF+/mUhj2+tQAdmpUfw0gUDSY9oO/hRVYURk9PZsjKX/P2VWO1mzI1BctBd02p7vXEdoPK1YC8UDFNwsIpDeysIhzRUk8LAkUlkDmkZwM0fFM1Fg6J5/2AtZ7y+h2fOzWZ2RiQ/X2n0G3p4dka7YxXHTvSIaaCohBpqmPjrd9n64DU05Gxl179vY8yPnkZRux/8m8Ykgaqg7a5A9wWpzdvIjn/cRLCuEkfqEMb++FmcqSfewmTFbEIdEoe2oxztQDWmiX2/lld8cwyJMkPjfa0Xa+M5PWYkE8LHT2VLcXJRFAXHMwvQa32EFh2g4Yyncb52RY/6PwX+8RXBZzaDy4LrnaslkOqGoxpMKYlGEKXl1KAHQijWnp0+vK8S310fE/p0PwCmWVk4n1qAOrDzcsyx953Bj3WdSVuKWbS+gK0Hq6hqLIu7bHMRigKVdX58gTAxLisWi5EapWvgC4bJL2tgb0EtA6Pt3DQxHbtJYUCSi4umZ3HR9Cyiu1gx7UjFn78AukbiqfOJHjql8x3aEQ548ZXn4yvLw1ueh6/sEN6yvObvg7XlVG9bRvW2ZeS9/TCujBFkLfgRiafOl5mFHthQZtwcmNqDtUK6rvPW/hq+v9SoBGhW4d4pKfx2WhqWdirnNYlNcDFicjq71hdyYHspjqCR5hesbaMwQNNsq81oNO2u81NyqIai3GrCjWmtCamRDB2X0lzy/0iKovDKBYNY8N5+Fh+q5/y39zc/Nj7RwQ2j4rv93EX/s7iiicweS33OFnxleYz7yYts/O08Kjct4uCrf2Dw1fd1+5iK04o6PB5tVwWFr/yXA8seQA8HiR17BqO+//gJPfOtJEfAjnK0cje9m2NuX8Aforrcjd8bxGRWiY5zEhEts2Anm+FDBuNY7cFrNj4Xyu2JBINVx3hU4mSm2C0437ka7/VvEXxjJ54LXsD2uzOx/fR0lHaKR31deF8lvp8tBsD57CWYZ7adWSXadlSDKTUj2ihFu7UU//2fY//D2d3aP7y3Av9vlhlFLHQgxo7jL+dg+fbEbgUDiqJw5oQ0zpyQRiiscde/V7NxXwWVdX5e/yKn0/1VBeIbF/MPS43i5m9P6XEwous6JV+8AkDanOt7dIwmJqsDV/owXOnDWp9H0/BV5NOQt52qrcsoX/ch7oLd7HzkeySt/5hhtzzU4/SfbyJPUKPYHcSiKgyK7t4i+zJPkFuXHOLt/TUAzEh18d+zsxid0Hm1vSYpmTFoYZ29W4rxhiJQgOK9B7EMr8fmMKMoClpYI1BYjwXwhTW2fboP/xHl/2MSnGQNTySuk5Rbp0XlwwVD+fumUn67uhhPYxC2pdzLjZ/mMiHRyfBYO0NjbSQ5zETbTBKcHwdix8yiPmcLVdu/YMg1v2X0nU+w9cGryP/wP1ijE8m84LZuH1Mdn8ghxzuUf2bM6qefcwuDv/XbE36tptJY0ZUuNm7vrsKcKvZvLWlVsCgxLZKRkzMw9VEVUHHsBLUg3pCXhIQErFohXpycVrKSUTU7qbcOONbDEyc5xWbG8fJlqCM+x//75fh/tZTwmkKcL17a3I+qI767P4WghuXGCVgWStZSdx31T0DHQ+fiPuc5/A+sBEXB9n9ndXrhpZU24P/jcgKProOwDlYTlmvHYf/9nDbXRnXHkcUhrj9nKLERNqJdViIcZmoaAoTChz/87FYTSTF2RgyIwYLClx/vQdX0TscfWnIQom2Yp6S3eqwhbzv+qiKssSnEjDytV8+lI4qq4kjKwpGUReLUeQy94U8Uf/ESB1/+HWVfvYO7aB8Tf/0uZoeUtu6KQ429mzIjLZjUrgcOKwsbuPLDgxS5g0RaVR44PZ1bxyWi9iD4SMuOJTrOwbbcaHxAbVk1275qWWnNtbuC4UBA1/F7g1hsJhJSo0jLjiWqk+p+R7KYFH4yJYUx8Q4ueOfw7NTzu6p4flfLu66qAlZVwWpSsKiNf0wKVlXF0vgzh1kh3m42/jiMvxMcZhIdZobE2BgWa8dpkQvM3ogdPYtD7/+Lmh0rG78/nWE3/5U9//0hB166n7DfS/bCH3f5eGGfm33Rz1IzfDsKJoZ95y+kzr6mv4Z/VOlu4/dZcfZ9taqywlr2bjZSwWOTXLgibYQCYcqL6ykvqgcKGX1KhtyAOIG5g25e3vsyvrCP82POR2lsR5DpL0MBysrK0PXOrxWE6A1FVbHffxamUzPwXv8Woff34D7zaZzvXYOaEtnuftrBKkIf7gWnBfsfuzfJIQxHPZgynzUIxzML8d70Dv4/rUCv8WH/5/mtpiJ1XUfbXkbg+S0E/rMWvCFQFSw3T8J+32zUjL5LKSmtNtK15p06gAFdLIzRdIcxFAx3+CYZ3lSMe/5LYDPhWnR9q4CqcpMxrRo/Ye5RfaNVzRbS59xAzIjpbH/4BtyHdrD7v3cx+s4n5A2/C5rWzKV08eKr2hfit6uL+dfmMnTg9LQIXjx/IAOierfeyBVlJ31YOgfWQWQEmBJdBBtT+1RVwdVYadJhMzPlzEFERNu79P/rC2nY27hb/lWxUWL7jvGJfG9cIssL6tlV5WNPtY8DNX4qfCHqAxq+sI4v3PP+RgpwaqqLG0bGc9OYeKwdVCIUbYsaOhnFZKHh0A6C7losrmhSZ10FusaeJ+4h980HscWmkHpG5wFRoK6CbQ9dR33dJkx+O0Pc3z5pAikAvcAowKKktX/B0aPj6joHdxiFiQaPSWbA0MNVLwfU+dnwxUHKi+ooyq0mfWDrSou6rlNwoIrivGqyhieS3Iefe6JvVPureW73c1T7q4m2RmN2mtEx3mN/9tN7efYv9+N2u6mrqyM6Wv7/RP+zXDAM9ctb8Fz4IuENxbhnPYXr0+vbXQoT/Gifsd+8Yb2eoPimOia5GdZrx6NE2vBc8waBR9cR3lOB5bLRKGYVrdyNXlhHaGkO2u7Da0DMFw7D/n9nYRrXfpWSQF0FxcteJHPebajmrl+kVtQaa6bio7qerqWqCoqioOs6uqajtLPORR2ThPm8IYRX5aM4Wl94V24ymgvHT5zb5XP3JVf6MMb+5EU2/PpcKtZ9SOXGRSRMPveYjOVE4mgMNOqDHZfTdwfD/HFtCf/cVEZDUMOkwE+ndm1tVFeZbEZufmQEDD89u8Vj4YRSGgBLWCcypmszUSsK6rnqoxzeumgwp6a2TP38tLF3ynnZUYxNcDC2jdTEYFgnoGkEwzpB7Wt/Gn/mDmpU+kJUesPG374QFd4QJe4ge2v87K/x8VWxm6+K3Ty2rZyllw0jzn5ip5IdbSabk8jBE6nbu5ba3atJmHweAKmzr0HXNPY+eQ97n/kZrgGjiBo0od3juAv3sO2hG/CV5WKLSWfQCzNxxJ88F4VapQftYDVYTV1ad9sdtZUevO4ANruZzCEt1xe6omwMn5DKzvWF7N9WQmyCq7mKJkAwEGb72nxqyo0bGJUl9RJMHYfWla6j2m9UnDApJhr0BoKq8VkfF+HA5XLhdrvxer1ERUWiKHJjSPQ/0/AEXF/e3BxQNZz2BM73rmkzQyq0+AAA5nNOvAJCx4tjdnViuXgErnevxnPV64SX5hBe2nqtkhLvwLxwJNabJ2E+JaPdY/kqi8j/6FGKl72AFvBijUkidfbVXRpHWNPxB8OoCri62ZBMNSmEQzqaptNecSzFYsL5yuXo+bWog1reedRCQepztoKiEDuq/1L8OuNMGcTAy37K/ud/xYGX7ydu3BnHfbPNY21EnB2HWWFbhZfX91Zz2dCY5hkfXdfZUOrhmZ2VPLuzkobGgOvsAZE8ODODiUl929y2KZhqq8+UEm8EO3pl68fa8/TOSorcQa7+6CB7bhzTHPTV+cOsK3VjURXOyGj/Dr7FpGAxmaAXGVPuYJh3D9Ty61WFbCn38t3Febx+4SCZNe2m2NGnU7d3LdU7VjQHUwBpZ15LQ952ij57hl3/uYOpDyxr8wZU5abF7Pz3bYR9DURkj2PsD57G9+j/0D216KEwirm/yjUcPeFlxmeP6bTMw2un+khFST0ASZnRbb52kzNjqCxtoDS/ln3bShg/w1j07a7zsXllHoEj2nUcOasljh/jEsaxo2oHtYFaqvxVvJHzDl6z0a8sxWnBYjHeCHV3Bfqql9FjB6KOWnAMRyy+KdSkCFyf3YD7slcJL8nBfcbTOF+/Esv5Q5u30YNhQl/kAmDuQQVAYTimt3rNZw8mYs+dBF/YQnhrKehGxT81NRLT2GRMs7JQLB1/WB/64BFyXv8zethIu4qfOBdXN0p+BxsX03+9sW5XNH04fn1RcavtLCaUQa1TOAI1JaBrWGNTMR3j4g9pc26gaMmzeIr2kf/x42TNv/OYjud4F2E1cffkZH6/poQrPjzI2AQHk5KceIIaXxY1UOQ+XOhhRqqLv87KYHpa+9PnuSX1vLBkP2XVXirrfFw3dyjnTc3s0liaXjthn7vVY01tCPRyD7qmdamyz3/PzsKiKtw6LrHF7NnGMg+aDpOSHERY+/ci2mUxcc2IOKanuhj/wk7e3F/DczuruGG0VA/sjtjRs8h7+2Gqd6xo9djga377/+zdd3gc1fXw8e9sL+q9WZZsWe69F8A2pvfeWyDAmwRIzy89IQmBkIRQEkLvJZhejHED44J775as3nvZvjvz/rGSbKEuy5Zkn8/z8Dz27szsFV7tzrn33HOo2bsWV0kWhUufI/Xi77c8pwb85L73KPkfPw5A7MxLGXX3v9CbbXiSQtGKG9AK6lH6eCWnP/hXHgHAsCC9z6/tatpb2dn+xIzxCVQU11Nd1oir0YvL6WXnurxWx0w6I00q/w0gWbVuhoSYMBt0qJqKXzsa9PoDVjRFhyXgwagPZq8A6FU3+N1QsR8tLwZS58rkkDjhlDAL9k9vwnXvp/he2YHzsjex/PN8zD+YCQRbFhlmpKBWO9ENkZXv3ur3vBldtA3zA7N7da6j6CBH/vcQoBE76zKGXnI/IUPH9ugaJoMOvU7B61fx+gKYugjejqVrKjygdbM/1be5K4O9eiwxbZddTzadwUjGLX9i1yPXU7T8RVIv/kG3S2qerh6cnUSMxcCfN5Wyu9LF7kpXy3NJdiNXjojg7vGx7abCHcvrC/CT/26g+JjVo+IerCQphuDMp+r3tX3OZACTPlilzBsAS9f/pgadwjOL2pZFbS4FP7WPV9Y6kx5u5on5Q7hjWR73fZXPwtRQhoRKX6vuCsuYgt4SgrPoEO7KQiwxR1f49SYLGTc/yO5HbyT3vUeJnX4R1vg03JUF7P/vfdQd2ACKjvSrf0Hqpfe33PjphkcSKG5Aza7u87S4k03TNHxLgwVVTsSsrNp0I63r5LPUZDYQkxhKeWE9B7YXUVt59HffajcxYXZqq/Q/0X80TePPS7bwl8Nwx8gwnr5gJCHGENx+NwBDQ4eSpE0DvJgIVoZsaAiuTpqi06G46Tq5a8DnQsnon/R+cXpRTAasL1yGLikUz1/X4H7gc7QGD+ZfzEOXGIp92a1oaudbFkTn+j2YOh6laxaDppK44GZG3vn3Xl1Dp1OIDDVTWeemqsFDYlT3bxSbJ5WaZ5664vSpLZXNADw1pQCYI3vXrbqvRY47C2NYDN6aUtwV+Vjj0/p7SAOaoig8MCWeeybEsq64kdx6Lxa9wrgYKxNirN2edVy6uZDiKieRoWZ+deMk4iOsJPTgfQhdvE7z6lIPCkKo1U503xrD1rLgytfUXvTVOh63jYnmw+xaPsqu496VeXx6WYbM6HaTzmAiYuw8qrYupWbv122KRkRPXEjcnKsoX/8eh1/5JbGzLiPr1d8QcDdiCo9j9PefbpOCrEuPJLAmP7jPaAAKqBpLNuazM7uKq88cxqjUiI6PXV+All+HkhyKflpSn4/F0rRPtq7KSUxi+6mxmqahb0qXPDaQSh4WxfBx8eil+MqAoKoqVz+/nA8cwXTLFRu38+i+Tzlj7ly+P/77KAaFSHMkn2/dD3gx64Kft15vcHXSbLWBLQacwb3gSuKk/vgxxGlKURQsfz4bZUg47u99iuc3q/CvysH28hXoksNk8vw4DepgqjF3FwDRk889rutEhwWDqer67gdTqqrh8wVnnvTd2Dfg8AW45KNsYiwG3rggHaNeIeAKzljprWG9H3wfUhQFc1QSvvpKfI01Ekx1k8Wg4+zU3v8bfrguF4AHrhjLvHF9G1hrLl+wEqZBB90s4OD/KgfHFW9jfeYSTNeOa3m8eeVtcuzJDaYUReE/C1P5smAvS3Lq2VLmZHqC9ETrrsimYKp69+p2K/Bl3PQHyte/R/WuL6ne9SUAMdMuJPM7f8MU1nafjm5YcDVKza09oePujfIaF5f9blnL3zUNfnvLlA6P9/0v2C/LeMP4E3IzET8knOLcGvIPV1JT6SAqzo7VbkZRgk18G+vc1FQ48Lr9rc6bclY64T2aUBEn2sOfb+YDRwyKpnKpepiUyvVklfjIOnQIq9XKjBkzmDdvHkeaqgOH6YL3BzabDY/HQ2NjI2ZzGDgrUUZfjmKPbXV9ze3D+5/NoFcwfX/GKbEfUQw85numoUsOxXXXRwRW5dA46WksT1yI6Ybx/T20QW1ABFN+f4DKkgYShkT06DxvXQUAdQe+we+sx1NVhN9Riz1lFPHzrun2l2N4U0Ozeqe3269dV+VADWhY7SaM3dg/kl3rYWuZg3qvypUjIrh+ZBQBdyPAgOrtpKnBL4Dm1DFx4tU2Bt93G/aXs25PGZsPVRAXYeVvd88kvge9oNqj7gv+juiGRaJ0c4bbvyYP6j24f7QU40WZLQ3/yl3BG77kkJOfZpcUYuLqEZG8uLeKjaUOCaZ6IGrcfABq9qxps29O0zQqty1rdfzIu/9FwhnXdbj615zap2ZXt/v8yaRpGtuzqiiucrBuTxlf7Sxp9fw1Zw3r9FzfpwcBMF455oSMLyLGzoiJCWTvKaOhxkVDjavLc2adOwJrN5psipPH4QvwyEE/KAYeSPXx2NU34nJdwebNm1m3bh25ubmsXr2a1atXszF2BkRPIk0f/LeOjY2lpqaGhuJDRNc17YULiWvzGu4ff4H3mS3Bv/hVzD/pv6JU4tRmvHgk+p3fw3XHh/i/yMJ183uo2dWYf32mZH30Ur8HU6qqsndjIdXljXjd/h5VLAofNQtHwX4Kljzd5jm/u5GUc+/s1nV6+ubRNI3cg8Gl+vhubtibEGtj+VWZLMur57rM4M2I3xUMpvq7+MSxNDV4w6x0VJ5QtKF5/fje2YuaX4flV2f2+PwLZw7hhc8PsnRzYctjtY1elm8t5OZFIzo5s2v+tcEvb/3s7hWzADD/5qxgY+wLM1t1Tq9xBwPtSEv/vDdGRAQ34OfWd3/SQ4A1cTimyAS8NaW4yvOwJQQLLXhqyzn4/I+p3rGi1fF6i73Tz0TdyOBndOBgZYfHdEXTNDaWbWRT2SZsRhuXpl1KnK3tDWZn5286UMFzSw6wN7dtuuHVZ6Zz14WjWibK2r1GXi1aXh1KlBX99LYpft0t2NKVlGHRJKZGUlPhoK7aiccV3NtoMOqxh5qpq3ZSVlDXcrzPG8A6cL4SBLAyv4F6xUK0u5I/Xxj8jLdarZx55pmceeaZ/Pa/y2ko2Y/DWcbB8JEA2LI38sILJVRXVxNtNxBXvR50AUiaTLUSzo8XH+AfF2cQYzUE+2k2B1KA5+G1mB6YJatT4oTRxYdg++wmvE9vxn3/Ejy//xKtsB7LUxfK+64X+jWY0jSN/VuLqS5vxGjSE9NJh+b2ZNz0R2yJw6nP2g5qAFNUAs7iLKp3rKDu4MZuB1M9VVFcT22FA4NRT0o7Vfo6MiPBzoxjZtTVplLWAyqYaipioOj7Pc4ePPQ6XN/9GLwBzPfPRAnp2WbxOy8YyYRhURzIr8VqNvDC5wepc3gx96AYSkf8X+YCYJiX2u1zFEXB8oszOn7+eAfVS1ZD8JW9Adko2xOKomBPGYW3phRn0SEssUMoWfUaRxb/lYCzHoM9ghG3/RW/o5bDr/yS/I+fJHb6xR0GVPpRwWBKPVjV6/Lo35R+w7KC4IpYtaea5QXLuWnkTd0+/4kP9vL2l8HeKJEhJmoajwbYyx65kNBuNNNWs4NBmG5cXJugSS1pwHnze5junobpunHtnd4jeoOOmMTQNvumGmpdHN7VejXN7w0c9+uJvrW3KrjKFO8qw/it97s/oLJir5MaayblQ2bhDuhJchSR4Cpl06ZShsWY+e45Cdh0fghPZV/4XC59Yis5JgOO53fyZowJ1+0ftLqmVu1Cq3GjxA6cewNx6lEUBfP3ZqBLCMF5y/t4n9uKmleL7c2rUY4zK+Z00693zEf2lVNeWIfeoGPinKE9rlikM5hIOfcuaNoypfp97Hz4GgBsScc3o98Rt9PLoR3BL79hY+MwHkdfkua+QM19gvqbz1GLqywXRW/EHNX3m7FPWaoGfjUYZfTi/aAoCjNGxTFxeDR/fXMHdQ4v0WFmzp3WcW+1b58PgNb6Jkw9Uo1/ySHQKxjOy+jxuL7NrFfwqRouv4rZcPI3q/qb6mcYdZKG0FP25Exqdn9F4RfPcWTxQzgLg+ltURPPZuSdf8cclUjA6yLvw8dozN1F9c6VRE9a1O61lFAzumGRqEdqUPdXoh8f36OxlDvLWVEYXA2bFjeNLeVbqPXW9ugaZ05IYMnGfG46O4P0xDB+8dxGAJ78wZxuBVIAWlMPp283U/ctz8Z16/to5Q48RfUYrxp9QmZqA36VfZsL0bRgsQmPy0dlSQN+v0wWDDSjo4I3lkdCh/H+lkNcPycYYNe6/XyeW8f+xEjqrHYIQIKzhGfmRDB7zG9p2P0JqaZaAPaXe3nMOZ03vj6M22RgYmEtf3xpM646d+sX0ysYrxnb0tZCiBPNeOUY7ImhOK94C/+ybBrnPo996S3oOineI1rrt/IdzgYPBYeDaSLjZg4h9DijYE0NcPD5H1F3YAOm8DiSF93eB6NszecNsOubfHzeAJFxdpLSjq8scHMwpRsgwVTt3rWgqYSNmIZhAK2WDXRahQNUDSXO3mVftPaU1bj4cF0u33l0NV9sKcRi0vOHW6d2mqJ0LJ0p+LsT8B79Uta8fpx3fgQBDeONE9AlH3+Rk+aS5AWNbUuwnwyVTXu27H2wYne6MYQEP6tq963FWXgQS2wqYx94gfE/fR1zVCIAepOVlAvvBYL9+zqjmxw8J7C9pNPj2rO8YDmqpjI1dioLkhcA0OBt6NE1JmfE8OGD53LVGen8Y/EuNA3uOG8kUzNjuz65SXMKa2BXKf4vc9AcXlw//QLnBa+hlTvQn52O/as7TljKy5F9ZTgbvdhCzQwfG3/MSmDvWm2IE+fS4eFMsblxG6zcsNHDyKc3MPw/3xD99A5u/DyXOqsdU8DDrIrNvDs/kotG2ojMfpdUUy2aouPl0jjOzp7JCwcacAc0bh8dxaokC0Ns35p8CzejP2c4hktHyd4VcVIZZg8hZOPd6MbHoR6sovGslwgcqOjvYQ0a/bYylb2nDE2DxKERRMUdXwEGT3UJh176BVXbl6EzWxn3o5cxhXf/S1XfNNPt62RG0O30sXtDPo56D7YQE2OnDznuDzvVG0wd0JsGxnJq9e7VAESNn9+/AxlktJpgENObZfEP1ubyt//tbPl7UrSNR747g4zkHjTP+9b7UFNVXHd+RODrPJR4O5ZHj6/aZbOhYSb2V7vJrfN02TvrRFhfHNxjOP0kl2YfzFS/l6JlL5L77iMtj6Vf+yuGXHAPOmPbTICkBbeQ98E/qTuwgcb8fYSktl+YQT8+Dv97+wjsKe/ReMpd5RyuO4xBZ2BhykJsBhtjosYQYgxB1VR0Svfn96xmA0+8v4eyGhejUyO44/zMHo1FPzEe7Ea0kkYcNyxGF2YOpv7pFMy/Pwvzr87sdtGWnqqpdFCYXY2iwJhpyegNOgzG4Gv5JM1vwNEpCqtunsxF/17CBv0QDrmDq5mKppLgKiW9IY+pBhfTUkOYrduGlhX8rCJiKLoR53GFLhz1cC2apnLBM5tJmBSFmhyGq6QRTHp0wyLRHF60gnoCS7NQp/d/70kxeGhqoE/2ueuGRhDy5R04LnmDwDeFOOa/hH3FbejHHc0+UP0+qneuInzULIx2afLbrF+CKa/HT2VpAzq9QvqY7m86/raA10XxilfIff9RAm4Hemso43/6OmEZHZfCbU9E0wxlbWPbje2aplFRVM/BHSX4fQGsdhMT5w7tVgW/rjQ3SRsIxR40TaN6d7AscuT4s/p5NIOL1pSmoYT3vLHmmKER2Mx6poyIZf7ERM6ZmtyjxtFtxtLowXn7h/g/2A8hJuyf3ISuj/Lu08KCvyfZdZ4+uV5PFDV6WV/SiF6BOUkDp/rlQKVpGlU7lpP9xh9xlQb3FkVPPoeMm//UacsDgy2U+HlXU7ziZUq+eoMRt/6l3eP0E4Il/AM7erYytbE0mI43KWYSdmPwfXltxrU9ukazbYcreWf1ERQFfnH9RAw9DHyUCCu2z27C/aOlqDvLUCuc6CbEY33uUgzTTtzNrNfjZ/+WYMP2oSNjCY0ITkw0T851t2+hOHmcHj8frC1k6fcvYs2mzWwqrsRi0DE5IZTMoeNwF2hkqnuAGvAC9liUtDMhegSKohAJ3DU+Bu+bu3D9dzOOV7ZjWBgsBGN5eBHmB2YDoBbU4V+Th37SwOg9KQY+v6uR3X+/mYSzrifxzOuP+3pKpBX7sltxXvk//MuzgwHVV3e0BFRFy14g+80/EDFmHpN+9e5xv96pol+Cqcamm8+QcAtmS89LcNdnb6fw82eo2rWKgLMegOip55Nx84NYY7u/0b5ZXNOKQtm3ytbWVTnJOVBOTXmwWWlUfAhjpiYf1z6pVpq/PAdAWoez+BCeykIMIVGEpkm/gZ5o3nOhNfS8ytyI5HC+eOTCHt8ItsdYaaJx3guou8sh3Iz93evQT+27vW/N/aXWFjXywyk92ydzvJ7ZVYlfhatHRBBtleIonWnI3U32Ww9Su3cNANaE4WTc/McO90B9W8IZ11G84mWqti/rOJia2pTmt7UETdO6tUrv8DnYWRlchZ0ZP7NbY+lIZZ2b37y4mYCqcfOiDEb2sK0GgFpcj/fR9ajbg83TTfdOw/KP81B68Z10LN9nh/A+txXD+RmY753e6rmAX2X3hnw8Lh+hkVaGjjxavdbjDqbP9uY7UZxYj727m0835JNb2sAfbjuTC771fIMVyrcdYE1xKEkjpzBn6vR2fyeM14/DvyoHrdKJ0vR52pzZAKAbEo7pxgmo5Y2ohXXoUmTmX3Sucuvn1B3cQN2hjYSmTegwm6AnFJsJ2wfX47xuMf7PDuG8+n+EbL0XxW6iYssSIJgyLo7qn5WppgaFFmvvvjR8jTWUb/gQgNBhk0i74idETz6n1+Np7uVTVhsMpuprXOQeKKeqtKkPlFHHsLHxJKVF9mkec3PFPNXTde+RE63wixcAiJ12wYBYKRtMdCOjwWpA3VeBml/bo02bOp2C7jjr4ykuSNo+i7jDY1AD5ehGRmP74Ab0I7vfZqA7FjU1Jl5V0EBA1VrSY0+0arefJ3cE08num9T7lexTnas8j5x3/try2WiwhTP0yp+QvOh2dIbu9y0KTZ+A3hqKu6IAd1Uxlui2AbmSHIYSZ0crd6Dl1qKkd71/dFPZJvyan8yITGKt3U/D/jZ/QOV3L2+hptHL1MwY7r2k5zcPvsV7cX3vU7RqF4SbsT59SZ9U7QNQC+vxf3IQJb71inCwem0h9dUuzFYj42cNQXdMFUG3MxhM9fZ7UZwYmw6U8+mGfEwGHbec035hq4AtgSuWjAAUovdl8cHMyRiNbf8dFZ0O7V+L2PzlWsbpEzG+uB3PP9ajn5qE8ZJgSXXXjz7H+8RGlIQQwop+eiJ/NHEKSJh3DZ7qUjTV3yeBVDPFasT2v2tonP0c6u5y3L9ZifWxC4iasID6Q5sAghlhsr8e6KdgytJUbcnl6F2/mMgxcxl+84PETD6305SV7ooOs6BTwOzxs2NtLjUVwZUovV5HSkYUQ4ZH991q1DFCUsdSseEj6rO2kHBG71Jd+oK7qpiyNe8AkHLBPf02jsFKsZkwXjwS3+K9eJ/ejOWvvQ/se0Lz+vG+sB1+v4r4qkkAGG+diPWx81Ei+n5P07AIMyMjzRys8fDxkVquyDi+Aizd9eCGEmo9ARalhnJmSs/aJ5wOVL+X/I+fIO+TJ9F8HhSjmeSzb2fo5T/EGNLzfyNFpycsYyo1u7+iPntr+8GUoqCfmIB/eTaBnaUtjXw7EtACbK3YCsCchDk9HtOxXvj8INuzqogJM/PH26b2KKjXNA3Pg1/heTC4P9RwXgbW5y7tkwItzZSmfmjUHlMQRtM4vKuUiuIGDEYdE+ektlmB0tRghoK5m9UIxcnxwufBypffuWAkw5Pavk8q6lz89sUtNDeNqPLYKKlsJDWx9e9EUVERKz9fxuZNm/AqKpdcdDEL75iM76XtOC9/q811tabJXCG6MvTS+07IdRWrEdsLl9M453m8T2xEPy2Z5Etva9mDW/jFcwy97Icn5LUHm34JpuxhFnQ6hYZaN65GL9aQnnV71xnNDDn/7j4Zi9vlw9jg4edz0ggzG6ipcKDX60hKjyQ1MwbTCQiimkWOnUcOULn1CzJu+Qs6w8n/ElUDfg4+90NUn5vYGRdjT+7ZJm4RZPrxbHyL9+J5fAPG70xBPyL6hL2W5vTifWEbnr+vRysMprk2xpZQcfYRJr30hxP2ugDfmxjHA18V8M+t5Vw+POKEV5z6sqCBJ7aXo1PgkXndKxV/OmnI2cmBZ3+Io2A/AHFzrmLYdb/CEt16z49f9bO/Zj/josZ1698sNH0iNbu/ojF3D3EzLmn3GN34OFieTWBPOcbLR3d6vYKGAhp9jUSZoxgaOrR7P1w7DuTX8uqyQ+gU+OPt04gOs3T7XE3T8PxuFZ6H1oBOwfLY+Zi+P6PP38NK0/eZ1rQHV1VVDu0ooSSvFkVRGDtjCPZ2xj3znBGoARVFSv8PGKqqsT+/FoCrzkhv9ZzHF2Dx6iO8/MUhHG4/MeEWwnwFaHU5rF7pIGP4MNxuNyUlJRw6dIiSkqb9hQpkFJmIfzEb/bzptFsb1W4krPIXJ/RnE6I79FOTsDx6Lu4fLcV154fYv7qj5bmcxQ9LMNWkX4Ipo0lPXEo4pfm1ZO0pZdzM46+M1xMBv0plaQNlBbVUlzWiaQQDKbefGTNSiB8S0ScFJroSOmwytqQROIsPU7ZuMYln3XjCX/Pbjrz1IDV7vsYYFk3GLX8+6a9/qjDMSMF48wR8r+/CsegV7EtuRj+2b1PStHo33v9uwfPYN2hN+/h04+LQ/XA8h1dcg7mdFYS+dvuYaP7wTTFrixtZfKiGa0d2v2l1TxU2eLnp8xw04HczE5kiVfxaaKpKwZL/cOSdv4IawBKXxsi7/kHkmLmtj9M09tfsZ3nBcmo8NRh1RkZFjury+iGpweDIUXigw2Oa39/q/q7L5x6pPwJAZkRmrz/rnR4/v39lC6oG1y8YzpQR3U9j1XwB3PcvwfvsVtArWF+/CtO13U/rc2eVcaQii1HDJqKL77wASvPvptdqwFvRSNaeMhpr3ej0CuNmDOm0eq3uBFUPFL2jKGAzG6jzezmQX8u0kbEEVI3lWwt55pP9lDbts547Lp7/u34S+dn7eOaZzaxd8zVr13zd6lp2u51p06ZxVuxYQq7/HGprcX+yvNUxxuvHYfnrIunvI7plxdYi3voyC51OYXhiGJNHxDA9M4aobk4yaZrG+2tyKa91ccnsoaR0UKzKfP8s1OxqvE9twnn9YkY/9QT7374fAEfRQezJI/vsZxqs+m0nd/roWCqK66ksaSBnfwXpo2NPaEDl9fipLGmgsqSemnIHalNKhaJAWJydfyw9RL2qcuUNE0/YGL5NURRSL32AA//9AUfe/jORY8/EEnPyZt+LV75K4dJnUfRGxj7wIuZIqSB0PKyPX4CaW0tgbT6Ns57D8uACTPfNPO4+NWphHd4XtuF9cmPLZmX9tCTMvzoTwyWZ+F0NsBL8zrq++DE6FWbW8/C8ZO5Zmc/9XxUwPcFOei+qGHalwRvgko+yKHH4OCslhN/MTOzz1xisNDXAwRd+SunqYGpQ8nnfZdi1v2zT/FvVVN469BaH6w4DEGOJwazv3r+VNTHY5NlVltPhMbqmPXmBA5VdXi+nPnid9LD0Lo7s2D/f3U1+uYNhiaHcc3HnK2HH0urdOK9djH95Npj12N68usuVtGMF1ADv7HiDI6n1nPPqPub89K5Ov6ucHx9AAYrDzJSvzQOCqe1jZwwh7Dj7KYqTS1EULp87lFeWHeZnz25kzth4dudUU9GUwpmRFMb3LhvD7DHBgjwxU6bws5/9jK1bt+JwODCbzcTExJCens7w4cPR64PfBerONLyv7ECrdqHE2jHMHoJ+7pAT1tNMnFr8AZWnPtzL/7460vLYnpwaPlof/LxJjrGTmRJOfKQVi0mP2ajHYtJjNemxW400OH18sC6Xw4VH7xk04HuXdrznyvL38whsKiKwqQj7fzwQCSiQ+96jjL3/+T79+VS/D29dOVogWF9Bb7ZhDI0a0Pv5+y2YsthMjJ6azJ6NBeQdrMDj9JIxIbFPV4SaO8qXF9VRW+ls9VxYlJX4lHDiksPxK5Dz9k7M/dAMNH7uVZStXUzNntXs+tsNTPjF221SdPqapqrkvvcoeR89BkDmHY8QMfL4qmuJYKll+9JbcN39Mb43d+P+6TK8L2zD/NO5GG8Yj9LNlFG12om6oxT/6lz8K44Q2FDY8pz+jNRgEHXO8JYbOoMtDMVgIuB2EPA429xU97W7xsfwv0M1rCpoYOG7h/j62pEtDX37gtOncvGHWeyocJERYea9i4djkNSnFtlvPUjp6rfQmayM+cEzxExpv4/YwdqDwZ5OioFzU89lauxU9N38Mmrea+V3dtxMVz82WERC3V+B5g90eCPoCXgobCxEQel1it/qnSV8tiEfk1HHn+6YhqWb3xNqSQOOi15H3VmGEmvD9sENGGYP6fbraprGx7kfcyS1HkstJD1dhLtqBdaH298XGThQgfLZITS9Qu2sZMxWI4lpEQwZHo1Bmk0PSnddOIrKeg+fbchn1fZiINgP8I7zM7lgRmqbPXsZGRlkZGS0ekzVNIpq3Tz/8T6+d+kY4lPCsfxaWpCInqt3evm/5zaxPasKvU7h7otGMS49in15NWw5WMHOI9UUVTooqnR0+5opsXaumz+s02MUox7b+9dTO+GfFJW9TmhoBg3GLCo2fdrtiq6dCbgdlHz9FuUbPqbhyA40f+uaCjqjBVtyJvaUkdhTRhE361IsMd3/LD/R+rXGcGxSGGNnpLB/axGlBXVUlTUyJCOapPRIjKaeD83vC1BX5aSmwkF1eSOO+qP9cBRFITLOTmxiKNGJoa02/+qbVqm8/pPfLFFRFMbc9ww7/nQ5jsIDbPvdBYy690miTlCvJ5+jjgPP3E/Vti9A0ZFx84Mkzj/56YWnKsVqxPbaVfhuGI/rviWo+ytx3fkRru9/hn5KIrpRMehSw1GirKBq4Amg1bjQatyoebUEdpehFX3rBtZiwHDhCMz3zcRwZlrb11QUTOGxeKqK8NZVYI3r/Z6U7tApCh9cMpxz3j/EplInC989xIqrRjA07PhXqHZVOLljWR7byp0k2Y0svWKElEI/RtGKlyn8/BkUvZHxP3mNyLHzOjz2UO0hAM5KPosZ8TN69kJacw+8jtPOlDALSnoEWk4t6t4K9BPbX9kubCxEQyPZnozF0HH6ic/nQ6fTtczeN8svb+ShN7cDwZnTYYndKxYROFiJ44LX0PLq0GVGY19yc5eFMr5ta8VWdlbuxKgzcp1zHlEFX+H953rMP56N7lvpemp5I85r3oGAhumuKcz5bvvlscXgYtDr+M1Nk7nqjDSOlDSQFh/CmKHdr+yrahp3Lc9j+zc5WCrr2Ztbwyu/OAu7lMAXvWAzG1AUhZgwM3+5cwYThgVT7aeMiOHmRSPwB1SOlDRwpKSeqnoPbm8Aj9ePx6ey5VAFR0qO3l9MGRHDn+6YRlRo19/dmqZRVbiGwxe8jVetwW5Mp3nDX+3etUSOO6PXP5O3vpLtD17W0hMRwBQRj85oQtM0Am4H/sYaGnN30Zi7C4CwjCkSTB0rLjkce6iZgztKqKtycmRfObkHKgiLshIaYcUeZsZkMQRzybXgP6imagQCGj6PH5fTi6vBS2ODG7ej9VZOnV4hMtZOXHI40QmhHa569ffXndEewaTffsjef32H2v3r2fXIdcTPvZqEs64nYvTcPvlCVgN+Krcs4cjbf8ZdkY/eFsaY7/+X6IkL++AnEN9mvDATwznD8b29B8/j36BuLyWwvoDA+oKuT7Ya0I+NQz83FcOCdAzz01C6+LAzhkbjqSrC11B9woMpCKb7Lb1iBAvfPcSOChcTX9/PM2encm1m79oHVLv9/HljCU9sLyegBRsEf37FCIZH9H0K4WDlqSkl+80/ApB556OdBlIA1e5qAJLsPd9LF2hq16Azdp57b5iTii+nNthotINgKrsu+AWZGtp5D8DVq1ezbNkyrrzySmbNmgVAbmkD9z25jnqnjzlj47nmzM5nT1vGv7sMxzmvoFU40c9MxvbxjehielbCt95bz/KC4J6WS9MvJX3aeBznF+JfchjvC9uw/PLMlmP9K7JxfudDtKIGdGNisf7tXAmkTjGjUyMZndrz6pgNXpXt5U5220OYH/Bz8awhEkiJXjPog6vzqqoRE97289mg15GZEk7mMT3K/AGVF5ceJLc0GEilxoXw+1unMGZo++9nTQ1QsORpyta9h6s8D5oaiave4PeCrSqWYQvvZ/fOHwGw8+FrmP96aa9/prwPH8NVmo0tOZO0K35C5Pj5GO2te6z5nfU4Cg/gKDhAY+F+7Cld7/09mfo9mIJgdb/JZ6RRU+6gIKuK6vJGaiudbVLzuqLoFELCLUTF2omMsxMeZevWht7m7zxNC1bv0bWTUqRpGoUVDo6UNKDTKWQkh5EY1XfpVEZ7BBN/uZj8T54k571HKVv3LmXr3sWWMpIh599D3Jwr0Zu6X7mqma+xhpKv3qBo+Ut4qooACEmbwNj7nzspN92nM8Wox3TLREy3TEStdhLYXIx6pAatsA6t1g16HZj0KJEWlEgruoQQdOPj0Q2LROnhRnSDLVgy3O9qvaqlNXrwL81CPVIDOgX9jGT081I7XXHorkiLgZVXZ3L7F7l8cqSO65fk8NSOCn40JY6L0sMxG7p+jT2VLp7cUc5r+6tw+TV0Cnx/YiwPzU0mzCxpUccqWvYiqtdFzLQLu9Xp3qAEP94bfY34VT87K3eyv2Y/keZIzk09F6Ou4xs6b00ZAKawzos8GOan4XtjF/6VRzD/oG2qsKqp7K3eC9Bp4QtN01i3bh11dXWYTMGU0ayiOu7/9zfUNHhaZlDb+2z+tsC+chznvYpW4cRw7nBs716HYu9ZGqqqqXx45EM8AQ8jwkcwLipYrMJ030z8Sw4HS6troEuLwPf6LvxfZAGgn5WC7b3rUNq5yRGnp3CznhVXZfLPbWX8atpk7CewQrAY2Krq3T2qQArg8quszK+noMFHSoiReckh3VpJapZX1sCf39jOnpwaFAVuPDuD7144qtNU6fxPniJn8V/bPG6whpN4YCYxhRMJe/sKhq+vJ/uN3wPBe83etOLQVJWyde8BMOZ7TxMydGy7xxlsYYRnziA8s4dZFifJgPmtVhSFqPgQouJD8Hr81Fc7g6XTHV68bv/RghE6JdjoVKdgMhswW43YQk3YQs3YQszd+rJt77WtJj0ubwCX199q1sjt9fPx+jw+WJdL7rf6Plw8K5WfXDMeSy9SEtsdh07P0Mt+SNysyyn9+n+UrH4TZ+FBDj7/Y4688xfi515NeOYMQodNxhyV2GrmU1MDeOsq8FSX4CrLpT57Gw3Z22jI3d2Se2qNTyfl/LtJnH8jOqPM+J9MuigbuvMyuj6wl4xNN73e2mBzW83pxfO3dXge3wDHpLsC6KcmYnvnWnRpx98nKspi4KNLh/PMrkp+vb6ItcWNrC1uJMKs55Jh4cxPCSUz0kK4WY83oFLnCZBT72V3pYt1xY1sKTs6YXLe0DAempssVfs6ULH5MwBSzv9ut44fFTmK7PpsPs39lCV5S/AEjr4PYq2xnab+NTSlUti6aJVgOHc4AP7l2WgOb5ugJb8hnzpvHeGmcIaEdJySkZOTQ3FxMaGhoYwdN54P1uby5Ad7cHkDzBwVy8PfndGtz1n/liKcF76OVuXCcPYwbB/e0O29isfaVLaJI/VHsBlsXJp+actnrfHcDEw/mYP3H+vx/HbV0RNCTJj/bx7mn82VIgKijWirgb/MPbF7ocXA9vH6PP7x7i4evXsmM0Z1XenXG1B5Yns5f91cSrX76BYUvQIXpYfz6JkpZEa2H5j5Ahq7yxrZuKWAN1ZmEVA1YiMs/OHWqd2qglq9+0sAMm4N3nfq9AbUgB+9xY7S6EctqkcJt5By/t0twdTBF37KuAde6M7/ilY8NaX4HbUYw6I7DKQGgwETTB3LZDYQkxhGTDdz4/vkNY3BYMrjU7FbwOHy8f7aXN5alUVNU7+QcLuJ0akRaJrG1sOVfLohn315NTx053SGxvddM1FrfBrp1/yCoVf8iPINH1P4+TM05u2m8PNnKPz8GQB0Zht6ix3N50X1e1F97pal2G+LHD+flPPuImrCwj5ZkRADjyk02NfK31iDf20erjs/Qs0KpnnpZ6egnz0EPAF87+8jsLUE563vE/L1nX3y2oqicO/EWG4cFcVzeyp5fX8VOypcvLa/mtf2V3d6rs2g47Yx0dw3KZbR0VLprCOaquIqC1ZuChs+pVvnTIyZyPbK7RQ7gpvmE2wJmPVm8hryqHJXdXpu1Y4VAESOndvpcbqUcPSzUghsCKa/Ga9p/WXYvCo1LnocOqXjz56vvw6WkU4aNp7v/H1NS17/udNS+NWNk7pVHMi/Ph/Hha9DgxfDBSOwLb62V4FUjaeGlYUrAbgk/RJCTa0/261/OxfjucPxvrwDzeHFMDcV4+2TepxGKIQ4Pby1KpsnPtgDwJ7cmi6DqU2lDu5YmkNOWSMuk5EpcTamxNk4UO1mQ2kjHx+pY0luHf9vQiy3j4kh2qqn0uXn68IG3ttfxeZKN5o/wJS8EnQKXDp7KN+/fAxhtq5X6F1ludQd2ABA1qu/pmzdu8TPvpK42Zej04dDuAF908q7oigYw2Px1VVQ2TTZ11PN/VVVnxdNVQftPeqADKb6g9Wkp84BLref93cW89+P99PgCu7BGp0awa3njGDe+AQMTelXWUV1/PrFzRwpaeA7j67mxZ+d1acBFYDOYCJh3tXEz72KuoMbqdm9mvrsrTTk7MLvqEX1tE6DNIbFYI5MxByTTGj6RMKGTyZ02CSM9og+HZfoW4H9FejSIlCsvc+jN0XEBdOOXinGsfil4J/HxWH990UY5h1N5zT/fj4NGY8TWFdAYHcZ+vHxffEjAMF9VD+ZGs9Ppsazr8rF8rx61hY3Utzoo9YTwKRXCDfrSbIbmRBjY3KclTOSQ7EZB+eH50mlKKDoQAugdTBp8m0mvYm7xtxFtbsak95EmCmMD458QF5DHiHGjnsdeRuqqDu0CUVvIHL8gi5fx3jlaAIbCvF9fKBVMOVX/eyr3gfA2KiOZxxramrYvHkzAB/tM+JVGkiIsvL9y8Zy9uSkbu098n+di+OSN6HRi/HasVhfvRKlF9XzNE3j45yP8ak+xkSNYXRk+yXUDYuGY1g0vMfXF0KcXj75Jq8lkPrZtRO48lvNn79td6WLi17ZSWJ5LeMDAX5+92yuHB3d8jlY5vDxy3VFvLKviid3VPDkjvb7/FkDKqMzYvjRJaNbilR0xO9qpHjVq5Sufgtn8eFWzzVkb6cheztZr/+WKX/8nLDhk1s9P+yaX3Lw+R8D9KqqnzEsBnNUEp7qYuqztxE+YlqPzgfw1JRRd3gTUePmt2x5ONkkmGpitxqhxsXvXtnCvrxaACZlRHPbuZnMHNW2B1ZGcjgv/uwsHnlrJ16/SmonjRiPl6IoRIyaRcSoWS2P+Ry1qF4POqMJncGEYjSj08s/52Cj+QI4L38LzeXDvuxW9KNie3UdU1g8qRvnY8/RQAHzL8/A/Nuz2szM66JtmG6agPfpzXif34r18Qv74sdoY0y0lTHRVh6Y0nfB2ulMURSssam4ynJwlmQROrR7DWd1io4YazCto9pdze6q3SgoHQYJAGVr3wU1QOTEhW02AbfHcOko+PlyfB8fRKt3ozTtCThQcwCH30GsNZZEW8d9wt5//338fj8JQ0dR5YvjprMzuGjmEEzdDIb8a/OCK1IuP8YbxmN9+fJep9p9U/oNOfU52Aw2Lhx6Yn43hBCnh22HK3nk7Z0A/OSa8V0GUg6Xj58+t4mM4loA4iOtjA0xtLr/jLcbefHcNB6YHMdj28rZXOqg3hvArleoKqxhmEnhqtHRXDcrs8sJfk3TqNj4MVmv/RZvXXCLgN4WRvSkcwgbPpn8T/+Nt6Yk+LglBL2l7ep7xDGFkFSfG72pZxkmiqIQN/tyCj77D0fe+hMTf/lOt7ehaKpK8ZevcfilXwCQfO6djLj1Lz16/b4id99Nmt+r+/JqiQgx8ZNrJnQ5K2q3GPnj7VPx+dWTXrnJaI8AySoZ9LwvbEPNqkaXGY0uo/PZo45obh/W31diyxmFagwQ8s5NGC/teLO/6btTUfNqMVwworfDFv0gdPhkXGU51B3c2O1gqpk34OXd7HdRNZWJMRNbAqxv01SV4hUvA5C44JZuXVs/Ihr9/DQCX+Xi+femlip32yuD5cynxU7r8PNx2bJlbNq0CYPBwP+780ZiY+Pa9O3pjFpYh/PKt4OB1G2TsD53aY+LtzTLqs1qqd53Sfolna7eCSFEZ+ocXv7w6lYCqsYtizK4uotKpJqm8Zc3t1NfXEtAUTh/bhq/umJsh4UiJsbaePm8tFaP5ZY2MDQ+pFv3o56aMvY//T1q960DICxjKqmX3E/UpLNbJuZTzrury+v0RV/LIRd/n7K171J3aCPb/3w5mXf8jdC08Z2e4yzJ5tBLP28ZP4A5sv2KsifDoMuvWbGtiA/X5VLv9HZ9cDet3llCdnE9AJkp4bz+ywUsmpLcrTekoijdnkEV4liapuF97BsALA8u7NVsuuYL4LzhXZR1VfjMLvKv2NRpIAWgn5iA/ZObMJ4vwdRgEjkuGKRUbPi4W8fXempx+V04fA7eOvwWxY5iIkwRnJd6Xofn1Oxbi6ssB3N0MtGT229M2x7Lr4Jj8z72DZrTS42nhuy6bPSKnvHRbb8UXS4XL730Eu+9F6zidOONN5IQH9+jQErTNFx3fxIsNnHOcKzPXtLrQCq7Lpu3s95GQ2Ne4rxOV+6EEKIrLy09SEWtm/HpUdx9cdefJ19sKeTLHSXoDDo8Y1O45dzMbjcnb5aWENqt+9bKbcvY+ptzqN23DmNoFJl3/I2Jv1yMo/AAms/T5fnHKl75asufXWW5HR5X5fLz0KYS1hY1tnnOFBrNhF+8jTk6mYbs7Wz9zTnsf+Z+6g5vQVPVVscGvC5yP3yMTT+b2yqQipp4Nsnnda8404kw6FamXvniEFnF9USGmDlrYsepI921dHMBf359e0vthhsXZvS4dKUQvRH4Og81qxolORTDlT2/eVOrnTiveYfAV7kQaSFr1v/wWt19P1AxIMROv5isV39N3aGNNOTt6XJ16ov8LzhYexAdOvyaH5vBxk0jb8Jm6HgmsXDJ0wDBip89SBvWL0xHPzOZwMYiGn7+BRt/Ftz/NyZqDDbj0derqalh/fr1rFixAqfTidFo5JZbbmHmzLZl1bvie2s3/i+yUCItWF/qfWrfvup9vJf9HgEtwJTYKSxMkd57Qojec3v9fLAuFwjukzJ0MclTXuvin4t3A/DL6yZw8ayet61RA372/PM24ucEi0Uourafh5qmUfj5f1v6FYaPms3Y+57FFB5LznuPkvfBP6g9sIGJv3irW6/ZmLeXvI8fb/l77f71hAxp/17mxs9zWJZXj1GnsPWm0YyPaZ0OGJI6hmkPrSLvw39StOxFyta8Q9madzCERBKaNgFTRGwwBb0dQ6/4MWlX/LRfi1cMqmDqSEk9WcX1hNqMzB7TdWnJrqzdU8qfXtuGqsHIIeEcLKij1tGzqFyI3vK+HEyDMt02qccz6mqlA8fZr6DuKUdJCMH2/vW4n3oCvCpqwC/7505BBmsICWfdSNEXz1G49FlG3/NEh8eqmopf9aNqKioqw8OHc0naJUSYIzo8p+7wFqp3fYnObCN50R09GpuiKFgev5DGeS/Q+O5WNt2ogAESXYmsWbOGwsJCsrOzKSg42rR6xIgR3HzzzSQk9Dw1Q2v04P7ZMgAsj5yLLrHnm45VTeXr4q/5qugrAGbEzeD8oed3WnVQCCG6sj+/Fq9PJTMlnBEpne879QdU/vTaNhpcPuaMieeimZ03N+9IxcZPqN65EmdJNrEzL20TTPkcdWS9/lvK1rwDQPq1vyL14h+g6HQEPE4KPv03AEMvu79br9eQs4vd/7gFzedBZ7ahepwULn2OxPk3temJuqPcybK8YPaXT9V4aFMJb13YNu3RaA8n46Y/EnHmLaxa8SnFB7cyqXA5NXtWtzuGhLNuIOX8uzsM4E6mQXXHtSenBoBZo+OOO7WusMLBH17ZiqrBHednYjHqOVhQR1m1qy+GKkSnNI8f34cHADDeMrFn59a5cVzwOuqecnSjY7AvvSVYovqFEALOegLOenShvdt/dSKomka500+EWY+lG418RceSz/kORV88R8WGjxl27a86zBHXKTpuGnkT9d56NDTCTZ1/oWtqgKzXfwdAyvl3Y+zF+8cwPZmiO9LYnXgE1aDhynHxyqpXWh1jNBoZM2YMZ599NiNHjuzxazTz/HsTWmkj+ulJGO+Y1OPzG32NfJzzMYdqDwGwKGURcxPnnvS9r0KIU4/PH0xNC7N1XaH3X+/tYcuhSiJDTPzfjRN7/RlUvSvY9y753O+0lBtv5ig6xO5/3Iq7PBfFaGb0PY8TN+vyludrD3yD6nMTmj6RiFGzO30dTdMo+epNsl77DarXRcSYuYz70Sts+8OFOIsOkfvuIwy/8fetznk/qxaAC9LCWFnQwNsHa8iMKGbBkFAMOoUGb4Aj1Y18c6SELZVeDrtNqJxBWnQmLxUub3WtqAkLSD73TsIzZ2Cwnbz2SV0ZVMFUXnkw1zI94fhKHza6fPz82Y043H7OmpDIdy8cxaodwV4shZWO4x6nEF3xL8uGeg+6SQnoM7tuotdMq3PjuPB11G0l6IZHYl9+W8usvN5kJeCsR+1hzvOJ4gto/HFDMf/eWUGtJ0CIUccjZyTzvYnHv6p8urIlpBMz/SIqN3/Gkf/9hdH3Ptnp8WGm7n3Z5H/6bxqyt2GKiCf14h/0enzWh+dy4NARFFUhpjKGuMw4IiMjSUpKIj09nfT0dEymrnuddEbzB/D+K9gHxfxgz3vnZddl8172ezj9Tix6C1cNv4oREX27fzAQCKAoCrpB2jNFCNF7zXs//WrnbSzeWpXFe2tyMBp0/O3umcSGt18Jr9Eb4MbPc/jVjARmJbZfGCfgDt67Hvt5qPq9lH79P7Le+D2qx0nI0PGM/t6/sX+rGbuzJBuA0GGdT+w6S7I59PL/Ubt3DRBcGcq8/WF0RjMjv/sY2/94CQVLnsZdVUTaFT/GnjKKGrefF/dWAvCjKfGcnRrGT78u5MGNJTy4seRbr6AAZnRagGGuPEY7DqGYrcROOZ/EBTcRMWrOgO1DNaiCqYKmYCo1vvdVlry+AP/33CZyShtITwjlNzdPRlEUhjaVNs8tbeiTsQrRGd/iYDNT49Vjun2OVuPCccFrBDYXo6SGY192a6v0JqW5+Z2/74qz9FadJ8BFHx5mXXHwAz7SrKfGE+D7qwrYVOrk2UWpmHpZLOB0N+y631C1YwVlaxcTNWEB8XOuPK7rNeTuJve9vwEw6u5/YbD27vPVp/r4LH8JAAtSF3DmrDOPa1wdCWwtQSt3oBseieGc7vd6UjWVtSVr+bLwSzQ00sPSuSz9sk5TH7vj271VAoEAzz77LFarlVtvvVUCKiFOM8377kurnR0eE+w/FbwP+OUNkxiXfjQbILC1GP+XOZh/Gmya/vj2cj45UsdXhQ3svXUsQ0LbTkhFT15E5ZYlZL/xRxqO7EDRm6jeuRJvbRkAcbOvYOSdf2+3vLkpNBoAX0NNu2MNeF3kf/Ik+R8/iRbwYQiJYsStfyZu9hUtn33hGVMZcdtDZL3+Oyo2fkz5xk/IHX4BP4u/jyqPyuQ4K2enhnLO0DAmxVp5dV8VOfVe/KqG3agjuj6XxMJ1zIxUGB9lIDI+FfuQcwhN/2m3S6X3p0EVTJXXBlPwEiJ7V4oxoGr84dVtbD1cSXSYmX/cO4uQpkapQ+ND0esUCisdOD1+bOZB9b9GDCKa24fv46YUv2s6bmba6pymFanA5mKU9AhCVtyGLi2y1TE6Q/ADVvP7jmt8efUefv9NMb+cnsDIqJ71jADwBlSu+CSbdcUOUkKMvHXhMOYlh/DG/iruXpHPK/uqCDHqeGph73LDT3e2hHQybvw9h1/5FQee/SF6awgxk8/t1bV8jjr2P/19tICf5HPvJGpC1016O7KyYCVV7ipiLDHMSZjT6+t0JbA+HwD9WWndTokpd5bzad6n5DcEzz0j6QwWJC847v1Rxbk1lBfWMX5WKvqmFNaSkhL27duH1+tl5MiRzJ7dedqMEOLUkhJrx24xUFrtIre0gbRjsqk0TeONlVn8+6NgQ/P7Lh/LBTOGHH2+zo3jgtfQqlygaph/Po9fTE9gV6WL0VGWdgMpgIQzrqchZxfFK15uVajBnjKK1Mt+SNysyzr8vAwZGrwPqd79FX5nfUv6nLeugrJvPqDg03+3BGUJZ17PsBt+2xKAHSt50e3si5vDpy/9jYsql7K9XkdVhMqcRDtvXTgMXdPrn50axtmp386ayAR69z02EAyqiEF3nPnszy85wJc7irFbDPzz/80mMfpoUGY06BieFMahwjoOFdQxKaPtG2WgCAQCrF69mtWrV1NdXU1sbCwXX3wxU6ZM6e+hiW4IbCyCBi+6CfHou/E+0xo9wRWpTUUoaRGEfHkHuiFt98A0fwD6GquPa3x3LstjZUEDy/MaKLp7Qo/P/936Yr4saCDBZmDNtSNJCw/OKt00OpqMCAtnLj7Iv3dW8L2JsYyJ7nmwJiBp0R04S45QtOx59vzzdoZe/iOGXvbDNrnynQl43ex57DacRYewJY0g/dpf9Xo8uyp3saFsAzpFx2XDLsOgO3FfLYGtwdQQw4yULo/1Brx8VfQV35R+g4aG3WDn8mGX90lan98fIGd/OV63n6zdpYycnARASkoK9913H9u3b+9VlUIhxOBm0OtYNDWZj9bl8a/3dvP3e2dh0OsoKG/kkbd3svVwMO3t/ivGcsPCjFbnKuEWLI9dgOv+JejnBSccDTqFty5M7/QeWNHpyLz9YZLOvo26A8GWKyFpEwjLmNrlpJM9ZRTho2ZRd2ADO/5yFZHjz6I+ayt1BzfQXOo6ZOh4Mm55sMs9VTNHDufaET+gdPodTEuKYMvoNKbE2U75/aiDKpjS65vyUANqF0e2telAOS9/cQidAg/dOZ3MdiqsjBoSwaHCOg4U1A7YYKqgoICXXnqJoqKilseKiop45plneOCBBxgzpvtpY6J/BHaWAqCfkdzlsZrTi/Oq/xHYWIQyNJyQlbe1G0gBWBOG03BkB46iQ4RnzujV2Fbm17OyIJjqOi2+5yvAlS4//9wW7KT+/iXDWwKpZjMT7dw6Oprn91TyQVatBFO9pCgKGbf8CYM9nLwP/0neB/+gatsXZNzyZyJGzeryfG9dBfv+fS91BzZgikxgws/fxNBO+kd35Dfk81HORwCcl3oeQ0KGdHHG8QkcDN6I6MZ1vPfOr/rZXrmdNcVrqPfWo6AwPW46C1IWdFoavicMBj2T56VxaGcJ6aNbjyUzM5PMzMwOzhRCnOq+e+EovtxezMYDFdzwl1WEWo0cKKhF0yAy1MzPr5vA/IlJ7Z5rumkCxgtHoEQe/X7s7mJCyJDRvapuN+q7/2LHX66kMW83jXnBMu2KwUTk2DNIXHAzMVPP71ZAFGbWU3HvRAw96Bl4KhhUwZSpqY+Ixxfo0XnV9W7++Oo2AO68cBQzRrX/JTwyNRy+gYMFtcc1zhNly5YtvPTSS/j9fmJiYrjmmmsYOXIkS5YsYdmyZSxevJjf/e53p/wMwGCnlQSDFd3QiE6PU3NqcFz9P9QdpSixtuAeqW+l9h0rZOhYyte/R/WuL0lacHOPx1Xu9HH7F7ktfz9Y426zH6Qz9Z4Asf/d2fL32Ult9940eAPkNwT3dDl8PZ8UEUcpikL6VT8jYvQcDj73Qxrz9rDjz5cTNWkRiWdeT+iwSZijklo27GqahrP4EKVrFlO84iUCbgfG8Fgm/PwtLDG9C4DKnGW8dfgtAlqAaXHTmBHXuyC+J1p+f4a0La7hU31sr9jOupJ11HnrAEiwJXBR2kUnJMizhZqZNC+tz68rhBjcosMs/PP/zeI3L22hsCK4d9hk0HHe9BTuvmg0MeGd9zM9NpA6GazxaUz76yrK1r2L31GHLTGDqAkLelUx73QLpGCQBVPNb77mvVPd4fUF+OULm6lu8DA5I5rbzu14tnDUkAgADgzAYKqgoIAXX3yRQCDAGWecwbXXXttSFeuyyy5j48aNFBcXk52dTUZGRhdXE/0qpGlvU33HVfcCO0pwXPQGWmkjuowobB/d0GVKYNysy8h59xEqN3/GH9/8kN/feHm3h+T2q1zz6REKG33MTLBzpM7NwRoP64sdzE3uXkGCb1fmmfP2AS4dFkFyiJFGn8rGUgcfZddS6wkQatJx25iBufo72ESOmcv0v35F/mf/oeCz/1C9YwXVO1YAoOiNWGJSMNjCcFcX46uraDkvasICRt71T8xRvWt+nteQx/8O/w+X38WI8BFcMPSCkzKRozUF40pYcNVT0zTKXGXsqtzF9srtuPzB74dYayzzk+czOnK09I4SQpx0Y9OiWPy7RezLqyWgqoxIDm/Zpz8QGe0RpJx7V38PY1AaVMFUUtMep+KqjiukHEtVNR56cwe7jlQTF2HhT3dMaylZ2Z7hSWHodQr5ZY24vX4spt7/7wkEVErza3HUewgNt5AwNOK4bjRWrFhBIBBg3rx53Hxz61UHg8HA7NmzWbp0KatWrZJgaoDTj48HILAmr93nfZ8fxnn9Ymj0ol+Yjn3xtSgRXc9SWaKTCcy6Bd2a55m88teo117YUpSiMx5/sGDE10WNJNgMfHjpcJ7cUc5Dm0r5zfoiVl6d2WWKwbK8ev61razl72EmHd+UOPimpG2rgTmJdl44N42RUZ3PzInu01vspF/1M5LPuYPSr9+meucqHMWH8dVV4CrLaTnOGBZD1IT5JC/6DmEZvdtjqWkam8s3szR/KaqmMiJ8BNeOuBa9cny9/7rNGSywkucr5nBBNgdqDlDlrmp5OsmexNzEuRJECSH6nUGvY8KwgdP38UTTfC60rGUow85GMYfgD6gsXp3D1WemYzyF+0wOqmAqtal8eXZRfZfH+gMqf/vfTr7YUojVpOdvd89sKVfZEbNRT2pcCDmlDeSUNjA6teOUqs6oAZVd6/OpPaZnlaPBQ8b49htsdqW2tpYNG4J9VYYNa9s1GmD+/PksX76cbdu2UVdXR3h45006Rf8xLEwHu5HAhkL8W4owTAvundLcPjx/Wo3nkbWggfGG8VhfuAylB5UlR17zc57Yn8de+2jSawOM76KFVYM3wNWfHmFZXj0xVgNfXDmCBLuRH0+J55ldFXxV2Mi/tpXz46nxHV7j/cM13Lw0h4AG/zc9gb/OS6bRG+CznDrWFjdS7Q5gM+gYE23hvKFhsk/qBDKFxZB68Q9aekUFvC7cFfkEXA6MYdFYYlOPa1Kn0dfIZ7mfsb9mPwCz4mdxTuo5JzSQKnGUUO4qp85bR5W7ioVNvVtezno12JYEsBlsjI4czcSYiQwJGSKpzkII0Q+07BVQvg+tsQym3MHfF+/lo3V57Miq5M/fmX7KBlSDKpgalx4MbnbnVKOqGroOVpm8vgC/e3krq3eVYDLqePSemYxsSuHryvCkMHJKG8guru91MHVkXzm1lQ5MZgNJ6ZHkHaygIKuKxKER2LsI6Nrz1FNPtfz5tddeo7q6mosuuqhV/5LIyEjGjRvHzp072bx5M4sWLerV2MWJp4SYMf2/6Xj/vh7ndYsx/3AWan4dvnf3oeXXgU7B/PuzMP/mrB7fFCZHhVF38Z/5bHclBz7OZvW1mSSHtL86tavCyTWfHeFQjYdYq4EVV41gQmxw9TfaauDFc9O47ONsfrG2kEmxVhYeU8rU5VdZW9TIkzuC/S8A7hoXw1/mBjfUhpj0XDcyiutGnj4zcgOR3mTFnjzyuK+jaio7K3eyrGAZLr8Lk87EpemXMi56XI+uo2kaXxV9xYSYCURbupfmub50Pburdrf8/QwLGN2Q4o9hyJARZEZkkhqaevJWxoQQQrRLGX42WmM5SsJ4FL2RK+amsWp7MaF2U6eZYYPdoAqmUmLsJERaKa1xsX5fGfPGtV3pOVJSz0Nv7mBvbg2hViOP3jOTicO7vzcjvakJam5pY6/GWFPpoCCrCkWBcbOGEB5lw+30UppfR1lhHcPG9CyY0jSNgoKCVo99+umn7Nu3j8svv5wRI0a0BFVTp05l586dHDhwQIKpAc7yu/kEvsolsKUY9w+XtjyuGxOL9emLMcwb2utr//OsIWwuc7Kt3Mn0Nw/wf9MTuC4zkni7EU3T2F3p4vHt5by6vwq/CuOiLXxw6XAyIlq/Ny8dHsGPpsTx2LZyFr13mLlJIYSZdZQ4fOypdONrWiEIMer405wkHpgcJysCpxhN08iqy+LLoi8pdhQDkB6WzqXplxJp7vlk06byTawuXs22im3cOebObjXMHRo6FFVTCTeFE2mORBm/CTZXcFv52Rjn9rxqlRBCiBNDMdpgym0oTe0xRg6J4KWfnUVilK3DBZBTwaAKphRF4eqzhvHUh3t5/P091DV6MegVNKDe4WV7VhVf7ypB1SA+0sqjd89kRDsl0DszND6YSpjTVDGqJ1RVI2tXsOz10JGxhEcFZ/kPZO8iwjiUw/tyGTam43Sp9qxZs6blz4899hj5+fm88MILHDlyhH/+85/Y7XYSExPR6/WUlAQLAOj1MkM70Cl2E/ZVt+N9ZgtqVjVKQgiGeano56e1VF/rLZtRxxdXjuDKT7JZU9TIA18V8MBXBSTajTT6AjR4g1X0dAr8vwmx/OOsFKwdLL0/ekYKJp3CP7eVs7b46ASDAkyIsXLZ8AjuHh9DSgeNBMXgo2oqFa4KDtcdZlflLspdwVL3ocZQFg1ZxIToCb0OmifHTGZf9T7MejMhxu4VNpkWN41pcdNa/u65xot783I8//oGw2Ujj/v3RQghRN9RvtVnMDmmd203BpNBFUwBXHVGOh+vzyW/3MGf39je5nm9TuHKuUO555LRhNl6foM3dmhwtnVbViWVVU5iorvfkyT3QDmNdW4sNiNDRhxdDRs5Np2Sg34MipW1a9cxb97cLq+lqiorV67k/fffB+DOO+/EZrMxatQoHnzwQVauXMnatWupqakhKyur5byoqCguvvjibo9Z9B/FbsL84zkn5NoxVgNfXp3JR9m1PLenkq8KGihxBDfux9sMXJkRyY+nxrVZjfo2vU7h4TNS+Pn0BDaXOvAENOJtRkZFWQg3S9B+Kvo091O2VWxr+bvdYGdO4hymxU3DrDd3cmbXTHoTN2XehE7R9bqxr+k7U/D8fT2Btfl4/vw1lt/NP64xCSGEEMdj0AVTFpOeZ398Ju+uPkJBhQOt6XGrWc+IpHDOGJ9A3HHU50+IsjFhaAS78mp55t1d/OrumV3OwmqaRtGRavKamkmOmpqMwXD0RnPM2NGUZu9A8+lY8sky8vPzmDdvHsnJyW1WkSorK9m7dy9r1qxpSe+74IILmDHjaP8Wq9XKxRdfzEUXXURVVRVVVVWoqkpoaChJSUmt9lKJ05dep3DliEiuHBGJL6BR6vRhM+iItvb81z7KYuC8NClqcjpIsieRXZdNamgqY6LGMCJ8RK8Dn/aY9Me3iqlEWrG+fAXOi9/A88ev0I+JxXj12D4anRBCCNEzgy6YAgi3m7jzwlEn7Pp3nDeSHz27kRUHKpi+9BDzzkjDFtJ2RlZVNapKG8g7VElDTbC3Scb4BCLbWdKctWA032xcR3VdGatXl7J69Wp0Oh2RkZEYDAY0TcPhcOBwHK0AGBERwY033sjEiRPbHaeiKMTExBAT00XJNnHaM+oVhkgqnuiGKbFTWqXVDUTG8zIw/3khnl+txHnDu5i3l2D+vzNQQo9v5UwIIYToqUEZTJ1oM8fFM3tkLN8crOBfq7Lw1ntITgojNikUg1FPwK/SWO+msrgBr8cPgMlsYPj4eBI6qBpoCzVz9qKFjBo9ki+//JL9+/dTWVlJVVVVq+MsFgtjxoxhwoQJTJs2DaNx4DZ4E0KcegZLbybzz+eBN4DnD1/heXgt3me2YHpgFuafzEHpRYq3EEII0RsSTLVDURT+dNd0vv/4Og4W1rGusJZzTfpWfaOa2UJMJKZFkpQe2Sq1ryPJycktTXd9Ph81NTWoarAggM1mIyQkRNL0hBCiC4qiYPntfAwLh+H+5XIC6wrw/OErfK/txPynszFeNRqlG5/JQgghxPGQYKoDdouRR++dyQdrc7n17AyqSxupr3aiqhp6vQ6zzUhUXAihEZZeV7YyGo3ExcX18ciF6Dmv14vRaJTS5mLQMcxNxb76OwRW5+J64HPUPeW4bnwXd6wNw7kZ6GeloM+MRhkSji4pVFIBhRBC9CkJpjoRG27l7ouCfUyS0iJJSutdE18hBjKXy8Xjjz9OWloa1113nQRUYtBRFAXD/HRCttyD76XteB7fgHqgEt8bu/C9sav1wSEmFJsRTHrQKShGHVgMKOEWlFAzSpgZJc6OLi0i+F9GFLqRMShm+boUQgjRlnw7CHGaKygooKCggJycHCZMmMCYMWP6e0hC9Ipi1GO6exrG705F3VuOf3Uuge2lqNnVaEX1qEUN0OhFa/S2nKN1cr0WBh268XHoJySgHx2DblQs+vFxKEMjZPJBCCFOcxJMCXGay8zM5N5776W4uFgCKXFKUBQF/bh49ONaN0nXNA3q3GguP3gDoGloPhU8frQaN1qDB63eg1bSgJpbi3qkBjWrGvVwFer2UtTtpfiOfZ14O4ZFwzFclInx3OEox9GWQwghxOAkwZQQgvHjxzN+/Pj+HoYQJ5SiKBBhRYno2Xlao4fA9lICe8pRD1ai7qsgsKMErczRkkroMugwLBqG+Sdz0C9IlxUrIYQ4TUgwJYQQQnRCCTFjOGMohjOGtjymaRrq/gr8nx/Gt+QwgTV5+Jdm4V+ahW5CPOb7ZmK8fhyKzYRa6UDdXY5hQXo//hRCCCFOBAmm+pimaby3Jofzpw8hxCo9ooQQ4lSkKAr6MXHox8Rh/slc1EoH3me24H1qE+quMlzf/RjXDz9HlxaBeqAS7CbCSn6KYpHvBSGEOJVIQ6M+9vqKLP6xeDc//M83NLp8XZ8ghBBi0NPF2LH8+ixCc3+E9eUr0M9MBocPdW8FAIbZQ9AqnP08SiGEEH1NVqb62KIpyXywNhe7xYBBL7GqEEKcThSzAdMtEzHdMhG1tAGttDHY4yra1t9DE0IIcQJIMNXHEqNt/PeH8wizm7CY9P09HCGEEP1ElxAKCaH9PQwhhBAnkARTJ0CclMcVQgghhBDilCd5aEIIIYQQQgjRCxJMCSGEEEIIIUQvSDAlhBBCCCGEEL0gwZQQQgghhBBC9IIEU0IIIYQQQgjRCxJMCSGEEEIIIUQvSDAlhBBCCCGEEL0gwZQQQgghhBBC9IIEU0IIIYQQQgjRCxJMCSGEEEIIIUQvSDAlhBBCCCGEEL0gwZQQQgghhBBC9IIEU0IIIYQQQgjRCxJMCSGEEEIIIUQvSDAlhBBCCCGEEL0gwZQQQgghhBBC9IIEU0IIIYQQQgjRCxJMCSGEEEIIIUQvKJqmdf9gRakA8k7ccMQpZqimabH9OQB5z4oekvesGIz69X0r71nRC/KeFYNNh+/ZHgVTQgghhBBCCCGCJM1PCCGEEEIIIXpBgikhhBBCCCGE6AUJpoQQQgghhBCiFySYEkIIIYQQQohekGBKCCGEEEIIIXpBgikhhBBCCCGE6AUJpoQQQgghhBCiFySYEkIIIYQQQohekGBKCCGEEEIIIXpBgikhhBBCCCGE6AUJpoQQQgghhBCiFySYEkIIIYQQQohekGBKCCGEEEIIIXpBgikhhBBCCCGE6AUJpoQQQgghhBCiFySYEkIIIYQQQohekGBKCCGEEEIIIXpBgikhhBBCCCGE6AUJpoQQQgghhBCiFySYEkIIIYQQQohekGBKCCGEEEIIIXpBgikhhBBCCCGE6AUJpoQQQgghhBCiFySYEkIIIYQQQohekGBKCCGEEEIIIXpBgikhhBBCCCGE6AVDTw6OiYnR0tLSTtBQxKlm69atlZqmxfbnGOQ9K3pC3rNiMOrv9628Z0VPyXtWDDadvWd7FEylpaWxZcuWvhmVOOUpipLX32OQ96zoCXnPisGov9+38p4VPSXvWTHYdPaelTQ/IYQQQgghhOgFCaaEEEIIIYQQohckmBJCCCGEEEKIXpBgSgghhBBCCCF6QYIpIYQQQgghhOgFCaaEEEIIIYQQohckmBJCCCGEEEKIXpBgSgghhBBCCCF6QYIpIYQQQgghhOgFCaaEEEIIIYQQohckmBJCCCGEEEKIXpBgSgghhBBCCCF6QYIpIYQQQgghhOgFCaaEEEIIIYQQohckmBJCCCGEEEKIXpBgSgghhBBCCCF6QYIpIYQQQgghhOgFCaYGoMLGQuo8df09DCGEEEIIIUQnJJgaYKrcVbyw7wXePPxmfw9FCCGEEEII0QkJpgaY/TX70dAoc5b191CEEEIIIYQQnZBgaoApbCzs7yEIIYQQQgghukGCqQEmuy67v4cghBBCCCGE6AYJpgaQgBbAp/oASLQl9vNohBBCCCHEqSKgavxmXREv7Kns76GcUgz9PQBx1LH7pGKtsf04EiGEEEIIcSrZWu7kL5tKAVg4JJT0cHM/j+jUICtTA0hRY1HLn816eYMLIYQQg40voPX3EIRol/eY9+aK/Pp+HMmpRYKpAaTYUdzfQxBCCCFEL/16XRFRT+9gV4Wzv4ciTkNqTg1qeSOaqrb7vE45+ucX91ahaRL49wUJpgaQQsfRSn4a8gYXQgghBpOHNpXS6FP51bruTY4WlDdSUec6waMSp4vGmc/SkPh3tKr231OmY6KpDSUOPsyuPUkjO7VJMDVA1HvrqXBVtPxd1dqfVRBCCCHEwPZZTh1qJ7P+mqbxyTd53PbIVzz46jZUVSZQxfHRVBWtxg2AEmFp9xidorT6+y1Lc/mqoOGEj+1UJ8HUAHGg5kCrvzdX9RNCCCHE4HD/pLiWP49+ZS+Pbyuj1u1vdYzL4+c3L23hoTd34PIGiAwx4/UHTvZQxSDg+/wwzlvew7+pGz1IG7ygahBiQjHq2z3EpA8GUyMjzdw6OgqHT+XCDw+zPE/2Tx0PCaYGiL3VewHIjMgEJJgSQgghBptFqaEtfz5U4+GHqwu5fklOq2M+31TAqu3BNMB7Lh7Nb26ejMUkxZVFW/5lWfje3I3/owNdHqvVBFP7lMj2V6UAmtel9IrCS+elcde4GFx+jV+vK+p0JVV0ToKpAaDWU0teQx56Rc/0uOlAMO1PCCGEEINHgt0IwIQYK+9ePIwFQ0K5a1xMq2PSEo4GXM98up+zfvwps+/7iMt++wV7c6tP6njFwGa4sGmC/bNDXR6r+YPbQxRT+6tSAIGmgElRgil/zyxK5S9zk/j08ow2KYCi+2QqZADYXrEdgNGRo0mwJQBQ46npzyEJIYQQoodCjME5ak9A5aoRkVw1IrLNMVNGxPC3u2fwvy+PkFfWQGW9B4DyWjcHC+oYmxZ1UscsBi7DmUPBbkTdXY6aX4suNaLrkzpZYKrzBNNJw5sCLp2i8KsZiX0w0tb8AZW9uTXszqnmYEEdBwtqqXf60Cmg0ynYLQZiw63ERliIDbcQG2FlWGIoU0bEoAzCoE6CqX7mDXjZXL4ZgKlxUwkxhmBQDLj8Ltx+NxZDx8u1QgghhBg4wszBm9R6b+dFpM4Yn8i8cQk8+9kBXv4iuOqwYFISF81KPeFjFIOHYjZgODcD/wf78b29B/PP53V6LIDm8Xd4THObKaP+xAQs9U4vry8/zEfr86h3drxdpareQ365o83jY9Miuffi0UwbGXtCxneiSDDVz7aUb8Hpd5JkTyItNA1FUVg0ZBEWvQWdIlmYQgghxGAR0RRM1bg7vqFt9sWWwpZA6rsXjeKO8zIH5ay8OLFM35mM/4P9eJ/fiulnczt+jzSlmGqN3i6veSK2R23cX86Dr22juiG40poaF8LUzBhGpUYwakgEseEWNE0joGk0On1U1LmpqHVTUeeirMbN6l0l7M2t4b6n1jNzVCx/v3cWBv3guA+WYKof+VQf60vXAzA/eX7LL8ishFn9OSwhhBBC9ILNoMOoU3AHNDx+FbOh45vBMJuJMJuRKSNi+M75I0/iKMVgYjgvAyUpFDW7hsD6Agxz21+9bNkr5et4VTTWGrztL+1k1ag39ubW8LNnN+Lzq0waHs0PLh/TabpqbLiV9MSwVo/dd8VY3vnqCG+sPEx4iHnQBFIgwVS/2ly2mUZfIwm2BEaEj+jv4QghhBDiOCiKQozVQInDR6XbT3KIqcNj54yN54tHLjyJoxODkaLXYbxxPN6/r8f78vYOgym6saqZEhp8PxY2+NA0rc9WQv+xeBc+v8plc4fy82snotP1/Lo2s4Hbz8vkinlp+PyDq9fq4An7TjGegIe1JWsBWJiyUJb2hRBCiFNATNPsf7mz61Q/IbrDdMdkAHzv7UPz9v59FWbSEWbS4fSrVLn7preZP6CyP78WRYEfXjm+V4HUscLtJmLCB1e9AAmm+snGso04/U5SQlJkVUoIIYQ4RUQ27Zuq9UgjXtE39KNi0Y2OgToPgQ3daODbAUVRGNK0OlXUjb1V3b9ucw+r07NXlQRT/cAb8PJN6TeArEoJIYQQpxJr0z4p9yBLVRIDm2HRcAD8q3LaP6D5VlLtPKBJbCpUUeLom31TBr2OkSnhqBps3F/RJ9ccbCSY6gc7q3bi8rtItieTHpre38MRQgghRB8xNZWd9nVxUytETxjOSgPAvyav/QOaC1B4O18RDWs6rrGL8v09cc7UFABeX3GYwGn4vj8lgqlNpQ4e31aGdiJqPZ4Ah2sPAzAtbpqsSgkhhBCnEHNTMCUrU6Iv6c8cCjqFwNp8tFpXm+cVgx50Cqgamq/jgMrSvHIa6Lv352VzhhIZamZPbg2vLDvUZ9cdLAZkMLWrwklhQ/dyOes9AW5YcoQfri7kud2VJ3hkfaPOWwdAvC2+n0ciTlcOn4OdlTv7exhCCHHKsTXdrDokmBJ9SBdtQz93CPhV/F93sDrVlMKHo+N76OZ+vX25gGS3GvnDrVNQFHhhyQG2HT7++3FN01izu4TzfrGEC375eZ9c80QZcMHU37eUMvH1/TyypbTLYzVN4/ur8jlS52VSrJXbxkSfhBEeP7vBDkCJo6SfRyJON96AlzXFa3hi1xN8cOSDNu/BgwcP8tBDD/Hwww/T2NjYT6MUQojBK8IcrOZXJwUoRB8zzB4CQGBTUbvPK03vPa2LVD/o+8a9M0bFcdu5maga/P7lLS3Ne3uqOYia/+NP+fmzm6h3+qht9OJw921vrL404IKp89LCAXhhTyWVrs7LP768r4rXD1RjM+h468JhnTbHG0gmxkwE4JvSbwZNaqIY/Oq99Ty791lWFq7EE/CQEZ6BUWdsef7AgQM88cQT5OXlkZOTw+bNm/txtEIIMTiFmYP3Ig19uCdFCAD9zODeJP/GDir6NS87BTq+t7Q0NcN1nYCV0zsvGMmk4dFU1nv40X++oaaHAdXe3Bqu/dNKfv7sJrzHjO/eS0ZzxvjEvh5unxlw0cf4GCsXpofh8ms8taO8w+P2Vrr4/qp8AJ4+O5VRUYOnJv3YqLGEGEOodFdypP5Ifw9HnAY8AQ9vHHyDSnclMZYYbh15KzePvJkYawwAeXl5/Oc//8Hv9xMdHc0ll1zCiBFSsl8IIXrKpAveWnn6cE+KEAD65pWpjYXt7otSmppEa50EMeFNpfvrurF61VMGvY4H75jKkFg7hwrruO5PK/n3R3vZn1+Dv4PfB03T2Jtbzez7PuKuf3xNYYWj5bkLZgzhvd8v4rZzM/t8rH3J0N8DaM/PpiawJKee/+6q4FczEjDpW8d89Z4AV36ajcuvcduYaG4dJOl9zQw6A9PjpvNl0ZdsLNvI8PDh/T0kcYr7PO9zylxlRFui+c7o72Az2lqeq62t5amnnsLj8TBz5kxuv/12dLoBN88ihBCDQnNdKck7EX1NFx+CbkQU6uFqAjtKMUxPbn1AmBkArb7jYCrUFPx+b+ykSMXxiA238vQP5/Hbl7awPauK11dk8fqKLGxmPXGRViJDzOh0CgFVw+n2c6iwrs01pmbG8IPLxjIqNeKEjLGvDchg6qyUEMZFW9hT5ea9w7XcMCqq5TlV07hjWS6HajyMj7Hyn4Wp/TjS3psaN5U1JWs4VHuIMmeZFKMQJ0xeQx47KndgUAxcP+L6VoEUwGuvvUZ9fT2ZmZnceuutEkgJIcRxMDRFU75OUq2E6C39tGTUw9Woe8vhW8GUEmUFQKtqW+2vWUuBFN+JWzmNDrPwnwfmsTe3mk++yWfr4UoKKxzkljaSS8f7sa+dP4zr5w8nMdrW4TED0YAMphRF4fsT4/h/q/L5987ylmBK0zR++nUh72fVEm7W8/4lw7AZB+eNX4gxhCkxU9hUvolvSr/h8mGX9/eQxCnqy8IvAZiTOIdYa2yr57KystizZw9Wq5W77roLg2FAfiQIIcSg0bx9W5L8xImgxAQDDa3O3eY5XYydAKBVOjs8P8oS/J6vdp/4Ailj06IYmxa8h69t9FBZ76GmwcObK7PYsD+4lcdmMfDXO6czY1TcCR/PiTJgI5GbR0cRatKxrtjB3spghP3I5jIe21aOUafwzkXDyIgYPPuk2jMrYRYKCruqdlHrqe3v4YhTUG59LrkNuVj0FuYkzGn1nKZpfPzxxwAsXLiQ8PDw/hiiEEIIcUrxf5WD7929aOoJCKmb25O2U9tcCe86zS/WGgymKlwntzpeRIiZ5Ggb76/JaQmkbjo7gy8evmBQB1IwgIOpEJOem5pWpJ7ZXcG/tpXxy3VFKMBr56dx7tCw/h1gH4iyRDE2aiyqpvJl0Zf9PRxxitE0jVVFqwCYGT8Ti6H15MOWLVs4ePAgNpuNs88+uz+GKES3BAIBfL6BWxZXiGPpmtL8An3ZyEcMGpqm4f75MpzXLcb38o4+v7Z/TbDHlBKvoQW+9bnYvGGvEy0FKE5y6f7yWhf/7/G1fLWzhBCrgb/dPYMfXD4Wg37AhiLdNqB/gnvGB1OSntxRwY9WB8tAPn12KteNjOrstEFlYcpCdIqOXZW7Wpr5CtEXDtcdJr8hH5vBxuyE2a2e83g8vPPOOwBcfvnl2O32/hiiEN3y6KOPct999+F2t01rEWKgMeiCN7R+aX1yWgqsySOwtQQl1obxhvF9em3/p4dQt5eiROjQx66GxrIeXyOiKZiqPY5gSnNUoBV2v33KkZJ67vrH1xwsqCM5xs7zPzlzQJc676kBHUxNjLW2+vvTC1O5Z0JsB0cPTlGWKEZFjkJDY3fl7v4ejjiFfFP6DQDzEue1WZXatm0b9fX1DB06lDPPPLPT6zQ4fWw+WHHCxilEV3JyctA0jdWrV/f3UIToUqApiNJ3Y5VAnHo8T2wEwHTvdBSrsYuju08tacB132fBa99qQTEpYA7t8XVim8ZU7uy8l2tHGktz8G55FS17BVrFwS6Pzy9v5L4n11NR62bisCie/8kZDI3v+bgHsgEbTHn8Krd9kdvqsXsnnlqBVLNxUeMAOFR3qJ9HIgYyv+qnqLGIAzUHyKnPIaB1PKvU4G0gpz4HvaJncuzkNs+vX78egHnz5qF08oWfW9rAnX9fzU+f2cCB/Nrj/hmEOB6ffPIJDoej6wOF6EeyIHX60ho9+JccAgVMd0/tu+s6vDgvexOtoB797BSM10YGn/A0tD6wZT9Vx3u1Ypr2TFW5/T1ORdWqsjAdWIwRL1meeIjuvLVPdYOHB55aT3WDh+kjY3n8B3OICDH36DUHgwFZuqvG7eeyj7NZU3S0fKJJr9DgDRBq0vfjyE6MlJBgR+sKl8z+i7aq3dV8U/oNu6p24Qkc3VQab4vn+hHXE2mObHPO4brDAAwLG4bV0HqFNz8/n0OHDmE2m5k+fXqHr3uwoJbvPbEOp9vPiOQwwu2mPvqJhOi+xsaj3wM+n4+XXnqJ733ve1LCXwxY8bbgrVVxo+zzO934lx8BTwD9rBR0SX2zt1/zB3De8j6BrSXohkVie/96qFsHxZVolQdRwlNaju1OaXSjXiHKoqfaHaDK7SfO1r3VM63yMNq+9zEoKp/kRPBKTjLvndPxPbnPr/LL5zdRWuNiXFokj3x3BmbjqXcPDwNwZarC6WPBu4dYU9RIcoiRbTeNZk6iHW9AY3lefX8P74SwGYJlLo+9URYioAZYWbiSp3Y/xebyzXgCHqIt0WRGZBJmCqPMWcabh95sd4Uquy4bgBERI9o8t3z5ciC4KmW1Wts8D+APqDz42jacbj9nTUjkmR+d0WnfB0fRIco3fdKbH1OITuXn5wMQHh6OzWZj9+7dfP755/08KiE6NiIymFZ9sOb49/j5nfVUbltG/qf/Ju+jx8n/9Ckqty7FU1t+3NcWfc+/Lvh5ZTgvo0+up3n9uG56D/9HByDCgu2jG9DFhaDEN+3FKt2FFvC2HK8khATPK2lo73ItkkOCk6OFDd0L+LWSHWj73gdNheQZPH84jZJqN7tzato/XtN49J2d7DpSTVyEhYe/OwOreUCu3/SJAfWTVbr8LHz3EHuq3GRGmllxVSZDQk1cPCyc9SUOPs2p48oRbWfhTxWapqFpWqdpV+L04PA5WJy1mNyGXAAmRE9gTuIcEmwJALgDbv67579UuCrIrssmMyKz5dyAGiCrLguAjPDWH+j19fVs2bIFRVFYuHBhh6+/clsRR0oaSI6x8YfbpmAxdfxR4a2rYMefL8fnqEO530DstAt6+2ML0cbBg8Gc/OnTpzN69GieeuopPvnkE4YOHcq4ceP6eXRCtJUWFrxRzW/wdnFkxzRNo/Dz/5Lz3t9QPe2vMphjUhh23W+In315r19H9K3AzlIA9FOOv7iCWu3Eef1iAitzIMyM/bOb0I8JlhBXwpLQwpKhvggtdy3K8OD3uS41InhuTm2n1x4aamJ3pYu8Bg9T4jueKNVUP1r2SijeFnwgZSbKsAVcMGMfr63I4t8f7eWp++ZiNLRem/lsYz6ffJOPyajjke/OJDpscLcy6sqAWZmq9wQ49/1gIDUqysLqa0YyJDT4gXTxsAgAPsupOyVLjboDwdkrs97cp4FUjaeGlYUreXbvszy24zGe2fMMS/OW0uDtfMZC9C+nz8nLB14mtyEXu9HOHaPv4MrhV7YEUgAWvYWxUWMBKHGUtDq/oLEAT8BDjCWGKEvrypfr1q1DVVXGjh1LTExMh2P4dENwdu3GszM6DaQATOGxJC64BdQA+566h5o9a3r08wrRmT179gAwZswYxo0bx8UXX4ymaTz33HOUlfW8kpUQJ1qM1UCIUUetJ0C5s3epfmVr3iH7zT+ielyEjZhO8nnfJfWS+0g5/x4ixp6B3hKCp7IQvaXjG2Fx8qmHqgDQjzm+Pf5qTg2OOc8TWJmDEmcnZMVtGGYNaXWMMnwRoEDhJrSaXAB0I6MBCBys7PT6w5r6UR2u6TgjSnNWo219KRhIKTqUzAvRDV+IoijctGgEUaFmdh2p5k+vbcPjO5ohU9Pg4ckP9gLw8+smMqopwDuVDYiVKW9A5YpPstle7iIjwsyqqzJJsB/N4RwXbSEtzERuvZctZU5mJp5aZZz3PXYHM/J2UnHZdX1yPZ/qY1n+MraUb0HjaPBZ562jxFnC3uq9fH/C97Hoj84U1Hhq2FC6gZnxM9vcgIuTR9VU/pf1PypcFcRaY7ll5C2EmdrPu9Yr+pZzjlXQWAAE90sdy+fz8eWXwX5mnfWVqqxzs/VwJSaDjnOmpHR43LHSr/k/Am4HRcue58BzP2TGo+vQm07tmShx4pWXl1NYWIjFYiEzM7j6euGFF1JYWMj27dt58skn+cUvfkFo6KlVGUoMbD7VR4O3ocPvSp2iMCHGyvoSB3sqXSxM7VlFN9XvI+fdRwDI/M6jJC28pc0xmhrAUXQIS2xqz38AcUJoARWtKLgdRRkS3uvrBLaX4LjkDbSSRnSTErB/eAO6dq6nhCWhpc6G/PVo+z6AMVegSx8COgWtoA7N7UOxtP/ey4wMBlPZdW2DKU1ToWgrWu7XEPCCNQpl9KUooUdX28LtJv5+70y+//g6lm8r4mBhLfdcPIYFkxL590d7qXf6mD4ylgtnDGlz/VNRv69MqZrGd5blsaqggQSbgWVXjiAxpPU/vqIonJ8WvKFcnn/q7ZtyleViq6snMjSh64O7UO+t58V9L7K5PFj/f3z0eG7OvJn7J9zP7aNuJ84aR4OvgT1Ve1rO8Qa8PL7zcTaWbWR3lZRn7087KneQ15BHiDGk3UCqORUUwOl3Akf33DUrcQZXqhLtrdMMdu/eTV1dHUlJSYwePbrDMWzcX46mwbTMWEK72Jga8LopWPosux+9EUdRMB3LU1VE8YqXuvHTCtG55lWp8ePHYzQG34s6nY7bb7+d1NRUKioqePrpp9s09HW5XC17rYToa+8cfocndz3JgZoDHR6T3jTzn9eLVL+avV/jqS7GmphB4vyb2j1G0ekJGTIag+XUmlwe1HRKt5rmdkQLqHie3kTjGS+glTSin59GyJe3txtINVPSzoCYkeB3o+16Cy1vFbrMaNBA3ddxUbPm9+eRY4IpLeBDK92NtvlZtOwVwUAqdjTKlNtbBVLNRqdG8u8H5pIaF0J+uYNfv7iZOfd/zGcbgxO6P712wmmzbaXfV6Z+u76YNw5UYzfq+OzyES3/wN+2KDWM/+6qZGV+Pb+Zeeo0+gJQ6mvRgNGpc47rOnXeOl7e/zI1nhoizZFcm3FtqxvqKEsUE2MmsrxgOeWuo5tXXz3wasuf08PSj2sMove8AS+rClcBcF7qea0CKa3yEFr+N9BQAjo9Wvx4piZPZUjIEJLtya2uU9RYBECSPeno+ZrGsmXLgK7LoW9q6ik1c3TnaQqqz8Puv99M7b61bZ7L+/hJks/7Ljp9v3/EiEGivf2ihYXBZu3Dh7cuv2uxWPjBD37AX//6V7Kzs/nggw+49tprW57/7LPPWLFiBVdffTWLFi068YMXp5XCxkI0NN4+/DZ/mPGHdo+JsgQzB+p70Ri1MW9f8BoTFqBI1cpBQ1EUlKHhaDm1BHaWYpiW3PVJTQJ7y3Hd+SGBzcUAGG+fhPU/F6N0UbRBUXQw5nK0vHWQvx6KtqCEBrNVAvnl6KcktXteStOiRYnDh1aTi1a+FyoOBAMoAEsEyvCzUWIy2z2/2ejUSF7/5QI++SaPl5YepLL+aHB219+/JnNIOMMTw0iIshEbbsFs0mE26jEZ9JiMOuwWA6FWI3arcVBX+uvXO533Dtfw0KZS9Aq8e/GwTjfBLRgSigKsK3bg8AWwD+L/6cfyVJeguhrQma1kJE3q9XUCaoD/Hf4fNZ4aEm2J3DzyZuzGtjNWZn0wWPWrwWZtG0o3UOgI3rAk25NJDZWUgf6ypXwLjb5GEm2JLb3HALSCTWhHVh49UPVDyXYSkiaTGDOx1TUavA3Ueesw6UzEWo8GQwcOHCAnJwebzca8efM6HENA1di4PxhozxgV1+l48z/9N7X71mIKj2P4zQ+iN1mp3Po5pV+/jTEkUpqtiG45dOgQn3zyCcOHD+fyyy9v9ZzTGVx9DQtrm+oaHh7OvffeyyOPPMKqVauYNWsWqamp7Nixg5Urg78vzamBQhxL0zSozobwISiGnve8WZCygCV5S4Bgyp9R13YF36QPBkHeXuzzNkcEP3udRdJ7crAxnjMc77Nb8X90oFvBlFrWiOdva/E+uRECGkpKGNZ/nIfx6rHdfk1F0aGknYEWNRzt8FJ0sYUEAPXrT1GTN4MtBswhoDcFq/EFPMQ0eoAUyurq0XZ9fPRioYkoiZMhYXwwUPsWb10FpvDWE61Gg44rz0hnckY0Nz70ZcvjDS4fWw9VsvVQ5/u3mpkMOuxWI6FWA3aLkVCbEbvFSFSombgIC3GRVoYlhpEWH4JpgMUA/RZMVbv93LMiD4B/nJnC+Wmd55dGWQxMi7exuczJ6sJGLkzvfT7qQJKz+GEAoiYsPK7l0E3lmyh2FBNuCueWkbdgM7YfmBqU4D+5X/WT15DH0vylLc/dPvr2Xr++OH47K3cCcFbyWS3vBc1RiXYkuFqlDDsbkiYF/168Ha1gA8roy1pdo3m/VHJIMrpjPgibV6XOOecczOaObx52ZldR5/CSHGNjaHxIh8cFvC4Klz4LwOjv/YfIscEALWbqeWTe8QiK3oCiG1gfdmJgCgQCHDp0CLfb3SaYau4lpXbQgDItLY0FCxawcuVK/vKXv5CZmcmhQ8Eb0AULFpCaKpNDoi3tyJdQuBFl6DxIO6PH50+Lm9YSTC3JXcJlwy5rc0yCzcDYaEtLg9SeiJ58DjqTlZo9q6nZs4bIcT0fo+gfxmvH4X12K97ntmK6fxa62PbTMAO7SvE+swXvC9vApwab/N4zDctfF6GE926/sRKWBFPuQDflY1iyHbVEAWdl8L9viVQVIIW6gAHNGoMSm4kSPw7FFt3h9RtydrLzr9cy5OIfMPTS+9o8//aXRwC4aFYqv75xEhV1bg4W1JJf7qC02klVvQevP4DXp+LxBfD4AjjcfhwuHw0uH16/irfBQ01D522C9DqFUakRnDcthQtnDsHewb6wk6nfgql/76igyh3grJQQ7p/c+Qx4s3OHhrG5zMnK/PpTIpgqW/8BpWv+h2I0k371L47rWjsqdgBwfur5HQZSAAZd8J98V9UudlXtann8F1N+0e7smjg5HD4HZa4yDDpD63LmDcXQVEREy/0a8tZAU+EJAj40TW01e1TkCKb4NTeCBqisrGTfvn0YjUbOOuusTsfx1c5gisH8iUmdBveVm5fgd9QSkj6hJZBqpjOeet3NxYmTkBDcK1pbW9vmuebAf9myZdTU1OByuXC73Xg8HhoaGqipqaGgoKDl+OZm1Jdeemmnpf/F6U0JS0QDtIYSejOFqVN02Aw2nH4n2yu3c1HaRS3frc1+Oi2Bn07r3T5oY2gUqZf8gNz3HmXvk99lws/eJCxjSq+uJU4u/fw09GekEliTj/OyN7E+cyn68fEAqIV1+D45iO/1XQQ2BDOCUMBwyUgsvzurw5S8nlAUBd2oYcB2NO8wlMlngrsWPA5QfcE9XXoTZoMF2x4PTr8Ox8TvEGbuevLTUXQYv6ue/I8fJ+GMazBHHn1/l9e6WLIpH50Ct54zAkVRiIuwEhfRfi/Lb9M0DY9PpdHlo9Hlw+H20eDy0+j0UdXgprzWTUmVk+ziOgoqHOzNrWFvbg1vrcrmD7dNZcKw/i2c1m/B1JsHqwH49YzEdm/aNE2jcsvnxEy7oOX5hUNC+cumUlYVDP7S3rUHNnDw+R8DkHHTH7En9z4dxRPwUOYqQ6/o223SeqxqT3Wbx+6bcB9WQ/fe8OLEaA6CkuxJ6D0NaPWFYImE6BEQmhjcK6W23mSvJE1uswyfU58DwJCQoxV0Nm7cCMCkSZOw2zverNzo8rF0c/ADftGUztMTyjcG0wISzuibCpTi9NX8nmxO6QMoKipi6dKlbNq0CQg27u1OQQmLxcLDDz/cYTNqcXrzq/5g0GOJCD7g6f29xI2ZN/L8vucB2Fi2kbmJc/tghEelXvoADbm7qdq6lO1/uoyks28jedHt2JLaNoNV/T7yP3mC6t2rGXbN/xEx+vj2X4veUxQF26tX0jjneQIbi2ic9DS6YZFoTh9aaePRA8PMmG6agOneaejHxffpGPRN5dHVA1UoYckQ1vb7XAES7XvIrvNQ4vB1K5hKmHc1oGGNS2sVSAF8taMYf0DjrAmJpMZ1nNXSEUVRsJj0WEx6YrpYmXN6/KzfU8ZrKw5zqLCO//evNfzg8nHcsHB4p+edSP0STJU6fByodmM36pif0rakraZpZL/xewqXPkvKBfeQcdMfAZiVGIJRp7CzwkWDN0CoaXCmEdUe3MiuR29E9bpIXHAzSWffdlzX8zZtGDTqjC3lsr+twlXB18Vft6nW990x3yXa0vGyrjg5ypzBfjmJtkSoK0Q7+CnEjUE3+jKUKbejBXyABpqKlrUCynaj1eSiRB398Kjz1FHsKMagM5AWmtbyePMN6ezZszsdw+LVR2hw+pg4LKqlL0Rubi52u53Y2KM50n63g5rdqwGInX5xH/z04nRmNBrR6XT4/X78fj+VlZU88sgjeDytUz0WLFhASEgIFosFk1GPvqGcUKuZ9BlnU1JSwj/+8Q8iIyMlkBLt8ga8PL3naUZFjmJh7HT0AD5nV6d16NjCP8sLlvd5MKXTGxh737Nkv/kgRcueb/nPHJWENSEdgz0CRVHw1JZTf2hTy3mlaxdLMNXPdKkRhOz4f3j+tBrvC9tQj9QEn7AbMZw9DONVYzBeMRrFbur0OpqmEdhchO/dfegnJWC6cUL3Xn9MLBh1qAcr0ercHaYNJoUYya7zUNToZWRU91ILE+Zd0+7j6/YG72HmTzrxBeJsZgOLpiZz1sREnv1sP6+vyOKJD/agaho3nd12suFk6Jdg6puSYHQ+O9GOUd92VSr7rT9SuPRZFL2RiFFHPxRsRh3DI8wcqHaTU+dhQuzga1bXkLub3Y/ehOpxEj/36uD+kuMsHWk32gkxhtDoa2RH5Q4mxUxCURS8AS9H6o+wvWI7B2sPtjnv0vRLSQ7pfrUZceI0rxjGWGPAHBF8sKGkJY1P0RvR1ACU7gpW3IE2uc3bKoIdykdGjMSkD35Il5WVUVpais1mY9SoUR2+fl5ZA68uPwzAXRcGjysvyObJx/6FisLPfvF/JCUFUxAqt3yO6nMTljkDc2TfzqiJ04+iKNhsNhobG3E6nSxZsgSPx8P48eO57rrreOKJJygvLyc2NpZ5U8aS9dpvqdq5Ap/HhX/ENELPvhyr1YrZbKakpITa2loiIiL6+8cSA8ym8k3UeGrIrc9FnzQ/+KDP1W4Vye5QFIWLhl7EZ3mfAfDUrqeYED2BWQmzMOlN+Lx+Co9Uk5ASgTWk85vmjugMJkbc+mcS599I4efPULl1KZ7qYjzVxe2PSW8g845HevVaom/pYuxYH78Qy8PnoObXodiNKAkhKIauFwE0TcP7j/V4nt6MllsLgP7Mod0OphSLEf3kRAKbighsLcawcFi7xyU3VfQrdvSusXQzh9vHtsOV6BSYNbp723b6gtGg4/uXjSUtIZQ/v76dZz/dz8LJSSRGnfzYoF9qbmbVBmccx0W3nUH01JRRuOS/wed/+CIxU89r9fzQ0OCHUl59z3s3HFsOvD/4nQ3sffxOAu5GYmddxqh7Hu+TTfo6RccZScENqh/lfMSj2x/lsR2P8fC2h3n78NscrD2IXtEzJmpMq/OmxEoO9kDh9ruBpp5RYclgDgVXDdqe99Dy1qHu/xhtw1NoWcuD6X5xYyHh6Aer0+9kY1kwnW963PSWxw8fDgZIo0ePRq9v/73m8vj5zYtbcHsDnDcthWkjY/H5fPz32edpdHuJ0BqIjz8aNFVs/AiA+NlX9O3/BHHaak71q66uZuvWrSiKwvXXX09sbCxXX301AB9/9BHfPHQjFZs+QfW4AKg/vIW8j59Ar9MxbFjwhiE3N7dffgYxcLn8LtYWB1s4LBqyCJ3BHNx7qgWC1VF7aVrctJY/V7orWVW0iid3PUlAC1BWUEfu/gq2fJXd5jyf6uONg2/w9+1/b5kE60xI6hhG3fM4c57ey6TffkTE2G8VpFAUxv34Vc56pRCdoXeBmzgxFKsR/cgYdCnh3QqkAHyv7MD9i+VoubUoiSGY7puJ5c9n9+h19ZOCaXiBXWUdHhPaVBHP4Wu/wE93bT5QgT+gMS49ioiQk79n+qKZqZwzJRmvX2Xx6iMn/fWhn1amypzBD69Ee9uCB976o03G8j/7N1lv/pHkc+4g5dw7ARgaFvygyO9hI7y91XtZnLWY+cnzmZ88v5cjPz7Zb/8Jd0U+IUPHM/qeJ/q02tmMuBn4VT/rSta1NHNVUEiyJzEmcgwTYyby4ZEPW46XQGpg8QSCEwxmnTm4D2rkxWh7FkN1Flp11tEDbdEo6fPb9H5YXrAcd8DNsLBhDA0d2vJ4RUXw96l5VenbKuvc/PalLWQV1zMk1s7Prg0GaO+88w5F5dXYVQcXDjG2BGI1e9dStWMFit5IzPSL+urHF6e55oa8e/cfxO/3Y41I5M2vS9HpyrhuQSYTJkxg165dbGk0MA3QW+wE3A4Act55iIhRs4iLi2P//v1UV7fdFypObzsrd+IOuEkLTWNYWNMsvcECPgf43aDvXfElV8DV5rFQUygKCm5XcLbf386N6tritRyuC050fZzzMfXees5IOqNVmr6madR4aihsLGRsaAZVO1dS/s2HVG37Ai0QvIeypYwk9eIfEDvjYvQmSW89FWgOL+6fB6vvWv5+LqYHZvWq15huQnACNLC742BK17Qg25MuJlrTwceu5m4+FLzPmD2m/zJVrj5rGMu3FbFqezH3XT72pDcL7pdgKtDUd8Gga/vDhgwZTejwKTRkb6PuwAYACj77T0swFWcLDrnS1f3ZpPyGfN7Pfh+g3yrW1R3aRMmqV1H0Bkb/vyf7vOKZoijMTZzL7ITZNPoaCWgBbAZbS1+pFQUryK4/OkPW/LgYmJTINJh2F1QcQPN7UMyhED4E7LFtPiQO1Bxge8V29IqeC4Ze0Op5rzc46fDtfSQ+v8qH63L576f7cbr9xEZYePSemditRnbs2MHXX3+NQa9nnmszrgM+Ste8g7M4i4Klz4CmMeSi77X0QhGiJ0qqnJTXupg4/GiaqqIo1CnxvLp0LxFAXr2VjV8GP6/Om5bCddddh81ZQsrOYEpVcyAFoBhMWOPT0euDxVc6KqMuTl9bK7YCMCN+xtHPR6M1GEz5XMFMgF7YX70fgGFhwxgRMYIv8r+g2FHMawdf4wz70WbRakBF19R3Kq8hj3Ul6wAYEzWGfdX7+KroK/ZW72V42HCMOiO17mpy647QoAaDtZI33sVc1VTeWqcneur5JC24OdhSRZr6nlL8Sw6jVbnQT0vC9MPZvQ4K9COailA079dqh9Mf/Kxsb7tNezRN44dfFWLQwd/PTGkZW15ZcOvOyCH9V2V7XFokESEmympclNW4SDjJqX79Ekw1F46oa6czuKLTM/m3H1J/eAuFXzxP5ZYlmKOP7usJaVqWbOzmsmS9t563Dr9FQAswLW4acxKOf2NmVb2b/fm1jBkaSVRo94KSI/97CIDUi+/DntLx3pXjpVN0hJlaN7jcXbWbtSVrUVAw6824A25CjD2vtiJOHIshuPnT4T/mJtEaiZYyE/XrPAL7K9FnNqKfHwPHfPCVOcv46Egw7W7RkEWtGvUC+HzB2VGXK/ilXN3gYfnWQhavPkJRZXAFc+64eAAO8coAAEArSURBVH5+7UTiIq1UVlby6quvAnDFlVeSuM9HxaZPOPDM/S3XTDr7NtKv/nlf/y8QpwFN0/jT69vYkV3F72+dynnTUtA0jQP1seQZE4jzBWfrp2bGkj5hLKqqER1uwe5zknH4HQK0nUTLvOMRTOGxLdX+IiMjg7OnPieKqePqleL04Ff9VLgqUFAYGTHy6BPGpgkmX9vVpe6oclexonAFABOiJzApdhLRlmg+PPIhOfU51Hg/YCILACitraBeV8P+6v3sqtqFhsaU2Clcmn4p2XXZfJzzMRWuCipcFa1ew+ByE1FUjF/1EzN8MnEzLyV25mVYoo+/hLY4sVx+laxaD+NjerZiGMiqAkCJDwFvAMwG1PxavK/vwr88G+PVYzB/f2aX11FSgveBWnH7FStVTWNdcTAIGtPN4hN7qtw8vasC3/9v776j46quxY9/7/QmadRHvVty793GgE01pucRHDoBfhBCCHkh5OWFJCSkPiD9JfBooYeEjgvNFFsGF9wl2ZKt3kZ9pOnl/P64smwh2SousuF81mJhae69OqM1mrn7nH32jggmxpu5aVICALVO9ToZiWN3X6nRKOSlRLO1vJX9Da6vRjCV3rvprco1eGMujc5AVM5UXBXqbFLG+bf2PXbwPjIyzHXJzc7NeENecqJzBszaj8amMif3ProJfzBMrM3AMz88i/joo78Q3fX76Nr7KVqTjYyLvnVMP3+kqlxVfel9s5Jmsdm5GY2iYVL8pJM6Dunoks3JlFBCg7uBqQlTAYi0uPFc/iLh4kN9dDSTkjD/3yXoZqfR6m3lmb3P4A17GWcfx9zkgW+wbW1thNHx8gelfNKYSPGeZkJh9W8n22Hj/100gTOmOFAUhWAwyGOPPYbb7WbSpEksXbqUyBmLsGVNpPvADvRRcTjOuIqYcXMIra/G96P3CW9vQpMbi/mh8464yVWSDtq6r5VtFW3E2gzMLVID/+fer2C/1wEiwtyJ6VTvLMcYautXlcnT4MacnE1P5aHeeJbUAnK//mMSZpzLunXrqKiowGg0Mn5cLmLni9BZhVA0KOMuRHFMPunPVTo1CMSg/+4LpkIjD6a6/F08u/dZvCEv+TH5TElQ06PH2cdxx+Q7WFuzljLnoaJPL5b8E5exre/r+Y75LMtQV67yYvK4c8qdHOg6QKuvlVAkRKB8G0rxO6Qnjcc+bjmxP/sNxrgTXyVNOn4e/KyRX29u4pElGXx7mL1UAbTj1ffF0Nv7cCX8Bk1qFJGKQ6nLorF7eMFUkjqRFGnuGfTx50rbOdAVICNKzxzH8CadJieY+ddFuTxV0sY3itS+Tr5ACGenD51WwRE3tqmmiXb1XryzZ+Q1FY7VmARTk3oj9e0tg7+JBd1d7H/2fgKdzUTlTO23N+PgipRFP/TSthCCna3qh++S1CVHLBs+XKFwhF+/sAN/MIyiQEdPgPc+r+eqM49e277t87UAJMy+EJ35+EXuwt2i7qFRBv9d1HTX8Py+5/tW5Q5WeJuWMI0Yw+nf9PjLJDs6G+phd/tuzko/C2NAi2fFc4Q3N6A4bOjOzyf0QSWR3U7cZz6J54VlPJ+xgZ5gDznROXj2T+P7H2zCYtQRbVEnKzp6AmypSqFbnwVBhX07m9AosGBiMsvnZnLGFAe63tSTUCjEk08+SVVVFXFxcdx0000oioLWYCLrkrv7jTX41l48V7wEvSkCkZ3NuK98iej6/0Qxy8bP0pFt6511vWBuJnabkVfXV/GX10sAyAjvZNnsC3hu30YqKirYuHEj8+bNU6v9peYz84G1+NvqCXQ50UfFY0rMxO/388wzz7B+vVpc4KqrrsJsi0F0Vqk/UETUNgOWOLXfivSVo9foSTIn4fQ6qeiqoCi2NzNEN7qVqfLOcl6vfJ2eYA8plhS+lv81NId9Btv0Nq7IuwJnipM9TepKkyOSQVxUFNnR2UyOnzygHYleo6cwtpBC1JUzkXoGypl3j+r5SmOvsSfIQ1ubCQuYmTyyFRLdJUWYH7sY/182Edne1C+QAtAuyR7WdZRYs7r60B1ABMMo+kP3v2XtPu7+SJ2kfWB+KtpBttwcycV5dlbkxvQtTBzMcEmNt/TdT4y1k7xdChijYGpaogWDVmFXq5c2b4h4c/9h7Hv8e7Rsekst83nTb/utJu3vrQSYGTV0xRqn10lXoAub3kZmVOYxj/vdrfU0tnvITLJxwZwM/v5WKS2dviHPc9epM1T2onnHPIaDRDiA+Pxp0Blg9m0ouv7phjXdNTy791kCkQCT4iZxYdaFgNrHKNUqUwRONZm2TNKsadS763mp/CUu/4eDyOYGlGw7tvU3o0mJQviCeO9azc66z/nAtoZAELKisri64Gp+uO5ziksG22hqRCFCeqzCVcumsHiyg6TY/rNHra2tPPHEE+zfvx+TycTtt99+xOa+osOL94ZXIRTBcOccjP+9hO6iP0Gnj0htF9pxCSfgtyN92XS5A/z9rVKeWrsPgGX5IZwlDTidTlasWMHLL7/MU089xdq1aznrrLMoLCwkKioKrS0BDNHUNDZSsuE1iouLcblc6LQazs7QsWDBAuioHPgDXQ2DNq6UvhqmJ05nbc1a1tWto8BeoE6s6ntvcoPuo5/cq8HdwMcNH1PWobamyI7K5qqCq464/zjJksQe1GAqxz+B+ePHDXrcYE725nnp+Hr482Z8YcFl+XYWpI5sAl1RFAw3zcBw0wwiLW4iFe34vrtanVjNiMb0wNnDvg5mPfQEwBuE3mCq2R3k3Ff20e4Lc1FODNdNGHmf0cNfn8HeSVWTYUzCiUGNxd/PmDx7i17DolQbH9R28+/yDm6d0n+fR1TeDHwttWRdeg9ROVP7vh+KCN6tcQEwM2noZcn9XeoG5ryYvL6Zo9WVXUQELM8d+crM1t6KJZcvzsbjU3P3hxPQH6za52uto+rVh+mu2knBtb/AlJA+4jH0aStXS2SbkgYEUiXtJbyy/xVCIsTk+MlclntZ3/OX6X2nJkVRuCLvCh4vfZxKVyX/zKhizkwo+vMVaFLUzdEhA6z7Ly2beyv8F3yscOXXLsKgNXDL8iIuXZSFxxfC5VH3SUVbDXQ1H+DdN16gMDqPyxdf3O9Nxu12s379elatWoXP58Nut3PHHXeQmXnkiYfAi7sQHT60S7Iw/f4CwhtrodOH4rChyY3td6zo9uO+/EUArO9cJ28QJKbnqx/cb3+q7m9SFLjrsklkm1t4sgTq6+u59VY1rfudd96hsbGR559//qjXTLbAjNb3sO/ppnXL+cRTrz5gjgONDqyJkDzxxD0p6ZQ3K2kWnzV9RrO3mU8aPuHMtDNRjFEIQPhcDPbO5Av7aHQ3UttTy/6u/VR3VwPqKtKZaWcy3zG/34rUYGx2Ez2dPnwj6OMjOrwE11Zg+Pqh1FR30I1FZ5HvoacBbyjC47vVYiE/nO0Y0bmBF3ehyYxBOysV/GHCH1bh+8kHRPa2oaTYsL5zHZrk4QdnilmH6AkgvCGUaHB6glzwWjm13UHmp1h5cXkOmmN8TR08XYykJOAJ0tyhrjJHWU5+hsyYhZI3T0rgg9puHvq8mRsnJvSrJpK5/A4yl98x4Jx/l3fQ7AkxLtbI9KShczP7gqloNQ2vwxfixneqcHpCbLq6iFnDzBM9qLSmE4CJWbE89Y46o5qWMPQ14qYtpemTl6h+9eG+7yUvuHzYwZS/o4lAVwtR2YfeXIVTTY1Rkg71jgpGgrxf+z6fNqtVEKcnTuei7IuGfMOXTg1xpjiuLbyWp3c8Qe3MALUzwW78N+kV6QgEla5KPCEPWkXL0lWJTHigidC6tRjfvoaiTDtF2Adc0+2OZeN7r1JeXs5LL73E3Llz8Xg87N69m/Xr1/dV+5s+fTrXXHMNNtvR36jDH6k3FIaVU9TG0E9uA0B/7dR+PTREIITL/qtDJ4YifTNj0lfXrMJEvnlhEa8XV5GRaOPmCwqZUZDAgQPq+39LSwuKorBs2TLOOusstm7dypYtW6ivr8ftdhMOh9Hr9SQmJpLhSMC245/EtOxCo9GQPnU2dtEE3ja8uhgss25G0Zw6s6XS2NFr9KzIWcEze5/hw/oPafY0M00bQwHQ46ql3LmV7mA33YFuOgOddPg6+hqpH2TQGJiROIMFKQsGFHk6koy8eEq31g/4vhCCqrIW4pJsxMQfSgMTwTDuC54hvLkB0ebB+K25tPvaearsKSbETuC8zPNkQHWK21DfQ4c/zNREM7NHcI8p/CF8d76N6BiY7aSZlIT19avRZMcOcuZRHEy79wYpa/dxwavlVLkC5NuNvLoiD+tx+Ew2G9X32IOTuGMlFI70u0c/2cbsk+ZrBbHcX9zAvg4/v9nSxH/PPfrmymBY8NNPGwG4Z0bykG8o4UiYmh519jMnOgeAaIOWM9KieLm8g3drXCMOpg5GvS5PkOI9zeh1GhZPGXpTaOKcFcRNOYv2nesAMMQ6SJh5wbB+ZtjvYfdD1+CuL2fSzb8gbtG1BLydaNv2AwokjAMh2N+1nzU1a2j1taJRNJyTcQ7zkufJN97TjMPi4KbXJ7KlaxslVxvppJNOf2e/x5dnLSctO47u3z1CaE0FkcoONDmDv3lYrVauv/56/va3v7Fu3TrWrVvX7/EJEyZw9tlnM2nSpGG9ViL16sqwprfsqunHS9Bk29F/7dDMvxACl/kXfV8bbp/dL19b+mq7+YJCbr6gsN/3EhLU9FBnfQ1eZzXmpCy0Wi1z5sxhzpw5/Y4VQuD89HXKn7qPkLuTzHnLcExfjCnUDl4nPqHj3D3juDG6k29OlmmnkiovJo+Lsi9iVfUqSjtKaY4oFGAg6G3jzao3BxyvUTQkm5NJt6WTFZVFXkweZp0ZEQkjupvA3w1hv7r6aYkfdP9yYlp0XzDV4/Jh6y1W5ax3UVXWQm1FGzPOyMEWo35f0WvRXzeN8OYGtZIbatXAnmAPm5ybmJ44nWTL2PXykYa2o1W9T1w0wvQ+vEH0N80g9HoZkf3tYNCinepA/40pGG6ajmIZeTNmJcaIAFZXdHJ9eTVtvjBzHBbeuDif5EH6vI5GWoIVs0FLc4eXNpdvyIJsJ8rn5a34AmEyk2zEDrPK9vE0ZsGUXqvw16WZnPdKOfcXN5AbY2Rlb3WQwfx2SxNl7T7y7UZuGEaOZ1V3FcFIkERzIlEGNU1Kq1FYkGrl5fIOGkaw7H6Qpjen76dPb0UIuPqsvGGVRvfU76W7cgcA9gmLmPy9f6DRDe+FvPf/vkd31W6MtmgsoWZEOECwdgM6BPs0YVaXPo5eo6fVpy4rx5viuSz3MtJtx5BCKI0pa12IBS/AWfPPp6HuAM0bdqE7N5eMW84l2dw7kRAFuvPyCb1WRqi4FsMRgimAadOmce+99/Lee+/R0tKCyWQiMzOTuXPnHjWlbzCabDvh4loCj21FOz8dTaYd04+W9D0e3tdKz/g/932tXZyJ6U8XjvyXIH1p7d69m5deegm3201GRgazZ89m7ty56EQIf0SHxx/iSHkHPbWlVP7rN7RtXQNAxhmXklWYhSJ6wDGVd+oD3FkWT0PYyg+tOtZUdbE4zXZcZmCl09+spFkU2AsoaS+htacR6vcSLTRMj5+G1WAjyhCF3WDHbrQTb4pHd9jKpuisIdKwFdoPQHiQamFaIyJpPEryZIhOUwv4HLYhv/5AO4XT1P3KianRJKXHEPSHMNv63yQb75iDbkEqinUPorWcgoQCrsq/CoHoF0gJTzu0lCK6G9TATtFAdDpK8kSwOeRE6hg52AM11TayYEWxmzH/9lz47bmDNsYdjVBqFA9kxPH7Xeoq6/KcGP65PHdYBdyGS6tRmJoXz6elTor3NLNiftZxu/ZIvLVRXTw5d9bY3PuOaQ7EuVnR/GphGj/cUM81qyupcQW4d3bygBzOd6td/PTTBgD+9+xMjLqhXwj7OtU0vL7KPb0SeotdtI2g6W/fudEmuj1Bur1BCtKiuekLs6uDCbq72PXwDQS724mbejYTv/MEWsPwIveemhKcG19FY7Qw6ZLrMChBxKZHMQfUvgGfm810BboAMOvMLExZyLzkef0+AKTTjyZDTSEJv7aX+A8rie8C64/ORGfpn3+t6d03KFxDF0HJzc3t24tyLIzfX0jw1VKCL+wi9FEV+qsno52YhHD2EPqgktA7hxpDaxdnYvvwpmP+mdKXR09PD48++ih+v1pIqKysjLKyMoqLiymwBQh21rLvHz9iyo0/x5paAICIhHFVfE7D+0/TXPxvEAKtyUbeN36KY8nViNI3weOkLn4uF6yqAuD6CXGseF19Lf50Xgo/mS+L7kiqGEMM8x3zAYg0PYQ2HODirPNRdIN/LgshoGYjouqjQ980x6p78nRGCAehxwn+LmjcjmjcDoYoRPJElNQZfac0Vnf2BVMajcL4mWkIIfoFXAdpHB2IvZ8jGj6HM+6jMPbQvYYIuBH734PeVP9+uhsR9ZshvgAKl6Pox7ZU9VeRsXfLijc0+ubhxyMQLmnz8o3FBWw36dEi+PnCNH4w23HMe6QGs2RqCp+WOnm9uJqL5mWe9EC+yx3go52NKApcODfjpP7sg8b8rvsHs9WZlh9uqOeHG+r5d0UH98xIZnlODFoNPL67jXs/qSMUgftmO1iWNXSushCCiq4K4NB+qYMyeqsAHug6ch36il1NhEMRsscnYjQdml2obDrU/OyBG2ZhHGK2U0Qi7H30bnzOKmxZk5n4nceHHUgBtGxSUw+SF1yBbcntiJ0v9M1AKbln8fW0WTS4GxBCkGJNkUHUl4T+xun4//ApodfVqlFKRjTaeQNnW8L7e3P62z9EuHNQrIkDjjnetFMcWN+9Hu83XydS1krgoeJBj1Oy7Vg/uOGEj0c6vezatasvkDrIZrOxf/9+vn3NSjqf+x7+0p1svncxhpgkFJ2eYE8HEb9aflfR6kk5+xqyVtyFMS6Fni4fu+qKyMqdTUJMLInmOlq8IZ4uOdTT5+yMqJP6HKVTS6Sq48h7TQxW8AYg4IZBgikRCSPK10KTmllCxnyUlGkoZvvAY92tiKad0FIKfhfUfoqo20SUaSndPjMiIvB2ezBHqXukFMIovk4izj3qZ3rWor6bUOE+rHlvVy3Y1QwC0VmDKHkVgh5QtJA0ASUuVw3uQgFE2z5o2gVt5Yhd/4TpsvDPyZbWuyJV7Tr5vY4APMEID25q5Hdbmgma9GS2e3hCG2bpnBPXp+y8Wen8/a1S9lR18FlZC/PGD7+v1vHw8kcHCIQizClKJOUkN+s9aMzvvhVF4b45DiYlmLj1vRq2NHtYuXpgadtvTU3kwYXDm11scDfQ5mvDorOQYesfpU6MV2dq9rR5iQgxaJTurO/C7w2RVXgo3/61DVX9jsl2DP0BXf/uE7RuXY3WEs2Ebz+K1jCyWaK2be8CkDDrAhRLPMy+DXqawWxHMdhQQKbzfQlpxyVgfec6/A8VQ1hg/K/FKJr+s5fhijbCH1aBFrSTQ4j6rSjjzh/2z6jq8mMzaPtWagcjQv4BlSIBdPMzsO3+FuHiWoJv7kU0dKPYTShRBvy/Xg8aBeu/rxowZklyuVz9vv7ud7/Lo397DABjcgGzHnyPqlf+h9bNbxPocvYdZ0rMIGH2ctKW3Yg5SU0jaW3sZvfmWkRYUFfnJyVfYePXiyh8aje9fal54+I8FqfLYOqrSIQj+O57l8AfP8P60Y3o5g0yY623gLfjyL2mmnergZRGhzL+YpSEI2ejKNYElLyzEblngaseUb8FWkoxhjvo7k1cdW54iwxL1cEB9r9A6gw1uAPoqjn0PKrXo9hXItrKEXteAREBe5bajPoLQZ0Sm4VIn43Y9gx0N6hVfxOGX5JdOnYTehvX7mwdeTPoYxGKCJ4va+fHxQ3UdKuB3I1awc8e+oiE66YOcfaxMRt1rDw7n7++UcLjq8uYW5R40oL4+lY3z7xbDsCN5w2dLXaijHkwddBFuXbKb4ziqT1tvLC3nU8b3QhgdrKVH8xO5rL84Vfn+LzlcwCmxE9Bq+m/epRg1pFi1dPoDnKgy0++feBsVCigLs/q9Fo8/hB/fb2Ef3+iBnhLp6fysxtmDTkGT+N+Drz0IABFt/4BiyNn2OMH8Dqr6anejcZowT5+AaDOyhIjg6evAt2iLHSLBs89FkLgvfUNCAv0KwvRxFSBswSRv2xY1csqu/yc+fI+Yk1a3rti3ICASogIonw9tH4OM25EMQ1sI6AoCrqFmegWHtpz5b7yJQD0N0xDO+3EzYJJp6/CwkIURUEIwYwZMygvL8ft7SHGFkewy4ghP4WiWx5G3Pw7/O2NCBFBZ4lGb7X3u059ZTv7tqsFid7rCbNoShyKotDlD6PTKITDgpeX57Iizz5wENJXgu/u1QT+uhl0GiK7nDBoMNU7wRk4Qq8pxxToaUZJnoQSPbzJXEVRICYdJSYd4V+K5rNK6F0obQpkkm7aP6CpqJJzFkpvICXaD0B348FHoLMa4SxB7FutBlJpM1Hylg0odtF3LZMdkTwJaj9V0w9lMHVSTUsyY9Qq7GzxUuMKkBk98sIRI+EPRXhhbzu/3NREeW8f1qmJZv56diaz3qvA6w8hTkKlvSvOyOG59yvYXdnBmxtruHjBid875Q+G+cnTWwmEIpw/O51p+SPvmXW8nDLBFIBVr+Vb05L41rQkIkIgBCPqzAzgC/nY2bYTgJlJMwc9ZmaShbcqu9jS7BkQTEUignBYDaaefrecV9ZX0d7tR6tR+N7XpnDZouwhxyAiYcoevZtIwEvywitJnDW8yn2Ha/pEvTFNmHnBiFIDpS+/wKNbCX9UjZJgwfT7S6DyBXA71Y3Rw/jgNGk1mHQKe9q8fO70cO5hqbPC34MofU1NLQHCW7egW7h0yGuG1lUSerUULHpMPztr1M9N+nLLzs7mvvvuo7KyEq1W29dDatbEM2muc+H1VFI0Iw1rlHHQ1hHhUIT9e5qpP6CmuFbZTPyyqpMpxQ1safHyh23N+MOCWycncOW4k18eVzo1BP6xXQ2kDFqsq69Bd+YRJjMNvRXXAj2DPqwoCkrBuaMeh2KMQrHEQpu6IusJR1HqnkmmaR9GjQ9ttANN4XkotkR1b1ZLKWKfWlxFyVmCiISgegOi9HX1gvEFKHnnHHXWX4iI2qQawDA2KU9fZVa9lkvz7Ly0r4PHdrfw8wUnpln47lYvT5e08XRJGy29NQDyYozcPy+FbxTFodUoBK1qICd6TnzKocWo47tXTuanT2/l9//excTsWPJSh9dCYDSEEPz2xR3sqerAEWvmrsvGtofqKRVMHU6jKAzaSW8InzV/RjASJDsqm0Tz4HtIZjvUYGpTk5uvF/avINjhVN9U2zwB/u8Ddal9fKadH3x9KoUZ9mGNof7dJ3GVb8YQk0T+db8Y+oQvEJEwTR+rwVTKGV8f8fnSl1fow0p8d68GwPTI+WjiLQjPJMSBDxCN21CGEUyl2PR8eGUh21u+EEi1H0CUvQlBD5F2ge/XQSxvzB3yesITwHvXKgCM9y1CcwLfQKXTW3NzMwBtbW289957CCG4+OKLWTRvMbs31eJq97L5/QoSUqKIjrNg6O1h4vcFcXf5aWvuJhSMoGgUCqelMC3Jxk+qetjZ6u1Lq7koJ4Y/nTU2m5ClsRc50I73jrcAMP/pwiMHUtAXTIlAz2huN9QAKOgGbycEusHbhQh61D1NQTf4XCgt6UAmiYY62gIOWgJptAR6b7A7wdDkZMGkckRrmZrGD5BQBBnzUMJBRPWGvp+n5J199EAq5FNXsLpq1D1giUVHPFY6cb49LYmX9nXwh21ObpqYQE7M8SnV3RMI83xZO0+VtLGx8dBq6tREM/fMSGZlURy6wxYglEQ1mBbOwScLjrfzZqXzaUkzazbX8YPHPuPx/1xCjPX4r8wJIXjo5V2s2lSLUa/l17fMGZNy6Ic7ZYOp0Wj3tbO+cT0AS9KWHPG4Ob39pT5r7L+0L4Sgeq9aYnx3q5vl8zJZNiNtRPmfnoYKDvxTTe8bd9NvB6SnDEfb9vfxt9VjSszEPmHhiM+XTn+RNg+aw5s5CkHwmR14b38LAmEMd87BsHKK+qBjMlR9DO0HED1OFNvQmz9TbHpSbGr6nggHEAfWQYOaHktUBp4rKhB+/ZDd1oUQeG9/i0hJC5qiBIzfnT+6Jyx9JTz22GPU1qqrnoqicNlll3HeeWoj0jlL89m/p5mm6g5aGrppaege9BrRsWYKCw1Y4vQEfvYR27r8/PWa6bT7wyxKs3FZvv2EVKySTg/ee9aCN4T+65MwfHPw7JSDFIMVAUdO8zuMEAK6GxBtFerKj7tFDZiGoKCmB9rjLeQ4tFQ7LXQ6ewiHQoSFFk3Id6hSoMGKkrUYUqaqaXw6I8JkB1+nOoY9r0D6HEgo7LefVXg71SqCDVvVsu1aA8qkK1H0cmVqLCxMs3FlgZ1/lXdyyRv7WXNZPqm20QUVVV1+1lS5WFvt4oNaF67ebSjRBg1XF8Zxw8R45jqsg96jKgcr/radvP1bP/j6NCqbutlb28V/P7GZR+6Yj26QipWjFQpH+O1LO3hzYw16nYZffXP2sBc6TqQvTTAVjAR5ueJlgpEgk+Im9TXqHcy83mBqi9ODPxTpK7VeWeqkq92DVq/h3lvmYBlhkzQRCVP697uI+L0kL7qShJnDLwjQdw0hqH71IQDSzrlJbuL/Cgptqcd99lMYbpuFdoqDSIOL0Jt7CW+sA8Bw60xMDx96bSl6CyJlGtRvQZSvgWnXHDGf/nBCRMC5B1H5iVrWV9GgZC1EJM9FdDwIujAiFEbRHblqZeDhYoLP7gSrHsuLXxtVY0HpqyMpSQ30ExMTOeecc8jNze17TG/QUjQ9lZyiRFoaXXi6AwR7G5cajFosUUbs8VYsWhdix3OE1xvwP9KGLhDm3klJGG+fPSbPSTp1hEtbCL25F8w6TA+dN/QJvT0o8Q8euANEQmG8lz2Jbr4P/cJBjtOZwGxH6KMpCcUidBYmJ5jV4hbGKJSKIFS5IHEC1ux4JmSDcJYimvZAJIzQGsA8CyU6DRLG9dv3KoToX6jC3YLY+zbsXYWwxINWpwaCh4/fnqXuqRrGpJp04jy6LIudrV52tXqZ9mwpP5mXwsqiOGJNg992B8OCup4AB7r8VHYF2Nzs5oPabio6+1c/XZhq5ZZJiVw5zj5k/zxNivr6jtR2IYRAURTcbjclJSW0t7cTCoUwGo3MmTOH6Ojjk1FiMmj5zS1zuel3H7FlXys/fnIL91097bisULm9QX70xGY+K2vBqNfyy5tnM3/CqdHE+ksRTIVFmFf3v0qjpxG70c7y7OVHPd5u0jEhzkRJu48tzR7mp1io2HUoD3/i7PQRB1IAjeueo3v/5xhiHRRc98tRPZeG95+mu3IHhpgkUs++dlTXkE5vofcOgDtI4OGN/b6vxJow/eZc9DdNHzALpWQtQrSUqVWk9q6CcecfsRiFCPRA0y61h4m/t7qaLRml8CIUWxIKoCmII1LeTvjjanRn5w68RiSC/1ef4L9/HQDmxy5BO/nUeFOTTl3D6XVmNOtJzz3yRmIRsIDeitbRifkfSwk+XYnh+hNbrUo6PQT/tQcA/dWT0Qyj4i4Hq+F52494iP+uxwmtaiBcDLpZMSi5k1DsWWBLBmMUa6q6eaa0jQ9qu2n2hLgi386/Jh9qyaJoGgdcU0kaj5I0fujx+bvUQElnQpl3p1poqHkXdNWBp/XQcVojxOWipM9WgzJpzMWadKz/j0KuXlXJ+7Xd3Lmulrs+rKXAbiSuN6DyhiIEwoKeYIS6ngARMfA6MUYtSzOiuDAnhqUZUWSPIGVQiTGBUQveEL7OHt56bw0fffQRwWD/ghSvvfYaCxcu5NxzzyU+/tiLOCTHmvnNLXO4+68b+XBHI9v3t/HtSydywZyMUVX5E0JQvKeZP7++h6qmHmJtBn5321wmZscNffJJctoHU6FIiNcOvEZJRwkGjYGrC67GrBu6BPnZmVGUtPt4cZcT4z5Bd6cPRaNQNCOV+OSRl9INuruo/NevAci/5gF0lpFH+S2bV1H+jx+p17j2AbQm64ivIZ3+TPctRjcvneDrZYgWD0qiBe30FPSXj0exDf5GqujNMOEytRdZ8y6Eqx7ylkJsDiga9UO5swbRWq6Wy6X3XdtkR8laCMmT+q1m6b8+Gf/PP8L/P8Voz8rp9wYYaXHjve3Nvj5Ypj8vx3DV2G7+lL46FIMVpq4EbweGxenor5wve+lIAIQ31QOgOydvwGM9Lh9arQbz4TPk5jj1/dHXiQh6BzS5Db5RRuDvajEH05+no1l6oVpVt9ejO1u47f1DZcxTrHpSbPp+1wgF1ZUlrW4UWSa9hYCISVd/bspUlJSpiHAAPO3qqpXerL6PDyMbQTq5Ei163rmigFcrOvnrjhY+ru9mb4cf8A84VkHtUZUbYyQzysDURDOL02zMSrb22wc1UsKoZUdaD2//8ud0uboAtapqZmYmOp2Ouro6du3axYcffsjHH3/MRRddxPLlR1+QGI5JOXE8/p9n8JsXd7Ctoo2fP7uNf31cyTXLClg82YF+GH8PTe0e1m1vZPWmGsrr1YnfrGQbD/2/eaQlnFr3x6d1MOUL+fhnxT854DqAXqPnmsJrSLYMb3b8G0Vx/Hl7C8/t7eDiDCM2q4EJs9OJGWXDr5o3/0iwu52Yonkkzlkx4vPr33uK8qf/C0SEzBV3kTTv0lGNQ/py0J2Zc/SN04NQYtJh6kpE2VvgbUfsfhlQQFHUkrp9B2ogLh8lZar6/0FuRA3fmoP/9xsJra0g8MdPMdw1D9HqIfB/Wwk8vBHR7oUYI5ZnrkC/XJbelU4uxWDt68kjAympj18NXJRBWp5U7Gyio8XNpLkZJPYWyVE0WkR0mhq0dFYPKNgQfG5n37+9d5YQfK4H8/9djMYRhScY4fufqKnXP5zt4LoJ8RTGGge8Hn29ZamN5v5B1nCIzt79hTH9C6ooWgNEOUZ8Penk0ygKVxTEckVBLJ5ghANdfrp6X6cmnYJRq8Gs05Bm02MaTcB9FJFIhFfmtPNZdje41GqqK1euJCurf9ny+vp61qxZw+bNm3E4jt/rKis5ir/ctZDVm2r58+sllNZ08qMnNmM2aJmen0B2ShSOWDM2sx6tRsEfDNPtCbK/wcW2ijYa2z1914qNMnLtsgIuW5SNyXD09MaxcNoGU63eVl4of4E2XxtWnZWV41aSZhv+8vZch5WCKD0V3UE6HTGcNTsV3RD5p0fiaaqkbo3aeDLv6p+M6MPd39FExTP/TcsmtfpQ1mX3kH3590c1DklSotNg1s1Qt0VNB/G0ghBq/n5UCkpsDiSNRzEcvbCEJtGK+U/L8d7wKr571uL7yTroPlReVXt2Dpa/r0CTe+oss0uS9NWmOHqr81V39fu+2+Wno8WNRqtg/8KMthKXh+iqRbSVo3whmDL+9CwizT2Ei2uh009odTneb76B9a1vUNHpwxWIkGTR8eDC1L7PfSEEkcPauni61VUIyyAFCJz1XTjrXKTlxhGbOHCmXck9E+ILwDp4ZWLp9GLRa5iUMHTm1PEghOD555/ns+xudCG4avllLLr0XDSD7MNPS0vj5ptv5pJLLiEu7vh+piuKwoVzMzl7eipvbqzhtQ1VHGjsprikmeKS5qOeazPrmFmQyHmz01kwMRnjKO/RT4bTLpgSQrCtdRtrqtcQiARIMidx9biriTWOrKeIoig8dUEODrOO3LjRv7iFEOx7/HuIUADH4quIzps+rPPCPjf17z1J9WuPEPa50RgtjLvxNzgWfW3UY5EkQN0rlTkPJXMeIhIGxLCa+X6R4dqpEI7g+9H7iKYeMOnQLcnGeM98tEtz5YqAJEmnFO10B8HndhLaVIfhlkOV/JpqOgBIzrCj/+KsdsI4qPwQ2ioQkTCK5tDj2vGJ2D68CREKE9nfQc+EPxN6bz8A2dFGLDoNTk+IuS+UcUZ6FN2BME+VtGE3atn2jfHEaSAYCKPTawZdmWqu7aK1sRt7omXwYEpvgYSC4/Cbkb5q1q5dyyeffII+rHDT2lim/tecQQOpwyUkJJyw8ZgMOr62JJevLcnF2ellx/426ls9NHV48AfChMICg05DtNWAI87MtLx48tNiRtxrdqycNsFURESo6KqguLGYqu4qACbGTeTinIsxakdXX35B2sj3Rn1Ry2dv0FlajD46nryVPznqsUIIXPs/x7nxNZzF/ybYrW56jZ95PgXX/mLQJpUjUVlZSVpaGgaDrKgmqQ6/MTios7MTu90+rPMNN0xHf91URKcPJdp41Mp+kiRJY+lganTozb2IQAjFoN7itDaq1e4cGTEDzlEs8Qhrktr4vHUfDFIYQtFp4WAKVu/seLRRy4sX5nD92io2N3vY3HwoJcnpCeEPC9pb1NLp9viBpatDwTDtzWr/n8QU2ZtPgq6uLrZt28acOXOwWEZf1n779u28+uqrKIrC1VuTyWtUUI5QRXAsJNnNnDPz2O53TzWnzm93EO6gm/WN62nxtlDvrscbUmvlG7VGLsy6kCnxU8Z0djwSDlH5r98CkHPFD9BHHXl51F2/j92P3IC36UDf96LyppN9+feJn3r2qMfwWaObOQ4LpaWl/OUvf2HcuHHcfvvtMqCS+ghPG4pFrdDz5ptv8sEHH/Cd73yH7OzsYZ2vaDQoo9xLKEmSdLJopjnQTEoisttJ8OUSDN9Qe/Ed3LcUZR88C0VJmYqoeBfRtH3QKnuR2i48X3sJAP2lh1IBV+TZqfnmZN6pdvGv8g5e2KuugD1zfjY5MUZ29qYxxTsGplV3tnmIRAS2GNOo9lMdTaTNo7bT2NoAwQi6FYVyb+tpoLi4mNdee419+/YNq/LpYKqrq3nyyScBuHTFxUx6bBPoNH0psNKJcUoHUwLBxqZD5aHtBjtzkucwPXH6sCr2nWhtn6/F27QfU2ImjiVXH/VYU2IGga4WDDFJJC24jKR5lxCVO7DE9Ug8uaeVm9+p5rszkrgnPwaz2UxdXR1dXV0kJsocawlE0061VHr+OYiU6dTV1eHxeFi1ahV33HHHWA9PkiTpuFEUBeO35+K97U38D3yoVkA163s/ZwWRiGDQtfWkibD/A+ioQvi7UYxq1orwBQk+uxPffe8iOnxoxsVjeqR//0ibQUtRnIn1Deoq062TE7hmfDzubj/tzd0oCsSnDMyCMfauFPh9QcLhCNrj0NhU+IL4fvohgT9+2leMAyDw2FaMPzoD0wOjn7iVTqxIJMKGDRsAWLBgwaiu0dTUxB/+8Ad8Ph+zZs3inFmL6WETSowR5Tg2zpUGOqWDKYvOwrKMZcQaY0mzphFjiDml9mk0rnsWgPTzbkGjO/rMktZgZsZP3sScko9Ge3x+7fEmHTqNwuoqF/fPK+Kee+5Bo9HIQEo6JBwABKJhK0rKVG699VZWrVrFOeecM9YjkyRJOu70103F/8dPiexpwX3J85gfOh+TVY/b5cfT4x+0Yq+iNyPi86F1L6K5hHBdCsFXSwk+vR3RpmbE6C4swPzEpWi+UMDizf2dXLOmElcgwoIUK48sySAYCLNnUy1CQEp2LEbTwPsDW4wJW4yJni4f9QfaySw4tv0qwhfEff4zhD9RS7XrluaiOy8P4Q2pbS4e/Bjdkmx0Swf2DZTGXnl5OS0tLcTGxjJhwoQRnx8IBPjb3/6G2+1m8uTJ3HjjjSh1vc2cLcd35fN4EJ52RE0x+LogyoGSOhPlYN+309ApHUxpFA2LUhaN9TAGFfJ007HnE9BoSVp4+bDOsaYXDX3QCFycZ+etS/OZkmAmxqglJjX1uF5fOv0pabN6GzrmoGh0aIEVK0Zeul+SJOl0oBh0WJ6/EveZTxJ+v5Keaf+L6YeLcM9MofX1MsxWA8rBtLpgGOENITq8EAqinw7hdz7Ac9uhPkCaaQ6M9yxAv3Jyv8lcIQRTnilhd5sPgCvy7fzj/Bz0QrD7sxrcLj+WKCN5Ewdv16IoCjkTkti1sYa6/W2k58WjOYbN9oG/byH8SQ1KWhSWl69CN/ewPSnBMP5ffEzgmR0ymDpFFRcXA+qq1FCFIgbz0ksv0djYSEpKCrfccgs6ne5QE+BTaBECQLQfQJS82jvZC3TVIOq3QuZ8yFp4WvZMO6WDqVNZV/lmRDhEdMEsDFHH3jF6tM7NkhtXpaNTHJPHegiSJEknjXZSMrbtt+P/zXoCz+0k5v0DtM1MoaXdQ8I33xj8JAPoXjGgLVDQLo5BO70I/VWT0M5NHzQjRlEUUm0GanuC/HC2g/+cmURbYzflO5sI+EIYjDqmLsgcWD3wMPHJNixRRjzdftqdPSQ4RlcUSwTD+H//qfo0rpuGdkZK/9/HLLVtTKSmc1TXl068qqoqAKZNmzbiczds2MD69evR6XTcfPPNGI1qUTYlxYbQKIg6FyF3AJ117PfSC1c9Ys+/IRKChEIUx1SEcw849yCq14O7FSZcekploQ2HDKZGydNQDoAtc+IYj0SSJEmSpMNp0mMw/2k5pofOw7y1geoGF97cWDz/bybRtb3pT1pF3VMVa0JJjULQgEIVlr+PQ1N4wZA/47kLcrApAmdtF5vf24/Xrc60R8eZmTArHZPl6DeviqIQl2TF0+3H7fIfMZjqqS2lbvXfSTv3ZqKyB06OBf+xA1Gj9tby/+oTAk9uw3DHbDRJNsJ7nASf3aH+TgrGbuJXOrJQKITT6URRlBE3zW1oaOCFF14AYOXKlWRkHGrwrJj07L9vIf5kK4VdPuKPQzDl7vZjsRlGFeyIkE9dkYqEwDEFZdyFKIqCEp+HcExB7HkFWsvAWQLJp9e9tQymRsnXqnY+NyVmjvFIJEmSJEkajGLQYZifSfqeZqr3tVJxQT4TZqUTP0jgIrztiE2PQvNuRNZiFNPAzA8hBJ7uAN2dXtqdPeypdxHpzacyWfVk5MWTlhs37JvNcCiijvMoh2/72UWEfW4UrY7Cm/9nwOPeWw+ttmkK4oiUt+O/f12/Y7RLczD9ctmwxiSdXK2trUQiERISEkZUidnr9fLYY48RDAaZN28eCxcuHHCMYfk4XI3deDxBjjWUbmvqZtentWTkx5E7MXnEAZWo+gT83RCVilJwfr/zldhsyFuK2LcKUfspigymTk+VrkoybZloB+nLM5iQuxPgqOXQJUmSJEkae9njk/D0BGhpcLFzYw1JadGk58cTHWvuu6lTzHGIxCJoKUXUfUY462z83iBed4Ael5/uDi9dbR6CgXC/a8c7bKRkxZKQEjWiG8zWRhdNval3sQkDm/YChLw9hH1qvyprxsB91+GyFlCLFWL95Ca08zMIrS4ntKoc4Q2iybKjOy8f7Zy00y516qvC51P33Vmtg78GBhMOh3nsscdoaGjA4XBw9dWDV5ROcETR2thNZ6ubjPzRh1MdLT3s/qwWIUTf5MFIiNZ9UL8FAGXc+YP2wCRpAuxbBW7ngAbapzoZTAE7W3fyyoFXGB87nivzrhxmQNX7YpJvTpIkSZJ0StNoFCbOSad6XytVpU6c9S6c9S6MZh1mqwEUBREWiPB4Qj3JBDpMhHaVDXotg0lHdKyZmHgLiSnRmG0jS58SQlCzr5UDJU4AMgsSiIodvN1LxbM/7vt32rIb+18nFFZXpQTob56BboGaKaO/cBz6C2VfqdNFMKj2QdPphndLLoTg2WefZc+ePdhsNu68805MJtOgx9oT1QCto8VNOBRBqxt5cYfuDi87i6uJRCDVoSF/smNEgbmIhNV9Uge170cYo1D0hyprioAbUaOWhsdkP60CKZDBFADxpniMWiNOrxNv2ItNM3RzM71NXZEKutpO9PAkSZIkSTpGiqKQXZiII8NOfWU7jVUd+L0h/N7QF45U0/s0SgSjxYTJqscaZcIWY8SeYMVk0Y96lSfgD1GypY4Op7ralF2USHbR4O1MOnZ/QtNHL/R9HQkF0fYWtBBC4Lv3XcIbalFSozD9cumoxiONPZtNvefs6uoa1vFr1qyhuLgYg8HAHXfccdR2OGargehYM64OL61N3SSnx4xobJFwhJItdUQikGyoJT8mgKIMbGx9VK17+30pKj+Cyo8RtmTQGcHvAm9H76MKSt7p91qWwRSQZkvj+qLrselt2PTD6xJtTs4GoKdmzwkcmSRJkiRJx5PJoidvYjK5E5Lw9gTw+4IIoa5eoShoQy70pS+gx4dm+rUoMelDX3QIkYigsaqDylInwUAYvUFL0cy0Ixad6CjZwO7f91+Jat2ymuQFlxFp7sF37zsEn90Jeg2WZy4f0P9KOn0kJiai1+tpbW2lpaXliMFRJBLhrbfe4u2330ZRFK6//nry8vKGvH5yRgyuDi/O2q4RB1OVZS14egKYrTrGGXagtCoI72IUc+ywzheRMOLAof17yqT/QNRvgY5K6Gk6dKCihdgclKwFKNFpIxrjqUAGU71SrSPr0RQ7aQkAbdvfIxL0o9EbT8SwJEmSJEk6ARRFwRJlxBL1xc9vCxHPDKgpRux9G2bcgKIb3We8EIKWhm6qypy4XWr/qthEK0Uz0zCZBzZTFULQ8P7TVDzzY0Q4SOLECzE+5aJu9nrqfvNrLCU1RGq6IBQBsw7Lc1eiOzNnVGOTTg06nY5Zs2axceNGnnvuOe68884BKX9NTU0899xz7Nu3ry+QmjVr1rCun5gaTfnOJtqdPQQDIfSG4d36N9V2UrOvFYCiGeloWsarJczL3oKpK4dMxRMigti3Wl15MseizLwZRatXq/eF/OBugUgQ9FawxJ92qX2Hk8HUKFlS8rBlTaanehfOja/hOOOqsR6SJEmSJEnHgZK5ANFWDu4WROWHKAXnjej8SETQ0uCiZl8rPV1qgQGTVU/eRAeJqYMXqgj2dLDvyR/Q8planS/9/NvIW3k/bvEuLZ/twlqdQPhAO4pGg+7iQkwPLkU7IenYn6w05i699FJ27dpFaWkpDz/8MJdccgkWi4X6+nq2bdvGjh07EEJgs9m44YYbmDx5+P0jjWY9sUlWOpxuasrbjthI+nDOehdlW+sByJ/swJ5gRUQvQ3RWg6sOUfIaFF6Ioh98r58IuBGlb0BnFWh0KEUXo2gPTR4oOiMchxXfU4UMpo5B2nk3s/fRu6l+4w8kLbgcjW7gLJMkSZIkSacXRauHoosRW5+Axu2IlOkotqEDl1AoTFN1JzXlbfi9amEBg0lHVmECKVmxaLWDFwDoKNlA6f/eSaCjEY3RQtEtD5M071IAbP91PnNdOxBVnWDUocmIRhmih5V0erHb7XznO9/hL3/5C/v37+fhhx/u97hGo2Hx4sVccsklfXusRiKnKIkOZyW15a0kpkYTfYSCJwFfiMpSJw1V6h6mzHEJfVUAFYMFJl2J2PECtO1DbKmH1JlqUGSwQSQMQTei/QA07oCwH/RmlAlXoESPLPvrdCODqWOQvOAKat74I96mA+x97LsU3fZHFM3IK6VIkiRJknRqUWxJiNRp0LBNbTY680YU7cAgJhyO0NbUQ0uDi7am7r7eURabgfT8eBwZ9iNWUeupLaX6tUf6VqOiC2ZRdNsfsThy+x2niTbBlJE1dJVOL5mZmdx///2sXr2asrIyIpEI8fHxFBYWMnv2bGJiRrbf6XAx8RbS8+Ko29/OzuJqCqen9ivl73UHqD/QTn1lO5GwQFEgZ3wSmeMS+l1HiUqBGdcj9q5SV6iqPj7yD43NRSm8EMU4+L7ALxMZTB0DjU7P+Nv/wvZfXkHzhn8R9rkZf/uf0ZrkRlBJkiRJOt0puWcjuurUdL+S12H8xSg6I5GIoL25h9ZGFy0NLkLBSN85MXEW0vPjj5jOJ4TAVb6Zmjf/TNu2d9SfozOQueLbZF36XTRaeWv2VWW1WrnyyitPyLXzJibj6QnQ3qz2jLJGG7HFmHC7/H2pqKD2psqdmIQ1evBy64olHqZdo5Y4byuHHieEvGoRCZ0JohwoSRO/9KtRh5N/sccoOm86U/7zWXY/ciOtW1fz+QMrmHjX41gcckOoJEmSJJ3OFK0Bxl9KZNszeJzNdDS9RYcmjy6PgXDoUPPSKLuJpLQYElKjsNj6F6sQQuBzVtO591O6D2ynfec6fM5qADR6E44zrybzojsxxR+qYhap6sBzyxtYHr8ETab9pDxX6ctNo9UwZX4m9QfaqSprwe3y9xVF0WgVElOjSc+NIzrOMsSV1OItxOejxOef6GGfFmQwdRzYxy9g+k/fYvfD1+OuKWHr/ecx/rY/kTBzZBtWJUmSJEk6/hqrOrBEGzGa9Gh1GjQahXAoQigUJhjo/c8fIuALEQyECQXV/wL+MKFAGJ/nPMJhcdgVBRZtD0nxgsTJ07D1zuJHwiE8DRX01JbgKt+C68B23HVlhD2ufuMxxDpwLPoP0i+4FUN0/1QqAO+97xL+oBL3ec9g23E7yjArsEnS0SiKQnpePCnZsXS0uAn6QxjNemLiLUfczycNTf51HifW1AJmPrCGsr9/h9atq9n9yPVkXnI3OVf+YNTN/SRJkiRJOjYBf4iybQ3HfB2jSYvS8jmx1OKIDxFt09Bcto9dL+0ARSHscxPyuEBEBpyrs8URUziHmPyZRBfMJrpg1lHT+SyPrsDd2I3x7vkykJKOO61Wc8QeZ9LIyb/Q40hniWbi3U9Q+/ZfOPDSL6l5/fcEu9sZd8OvZWEKSZIkSRoDkbDAkWmnp8tH0B8iFIogIgKtToNWr0Gv16I36tAbtBiMur5/6/Qa9Ibe75t0GIw6IuF8nBv+TeXHLxJqqyQS8OHv6uz384zxaVjTxhGdP5OovBlEZU9GH50woolVxW7G+tGN8t5Bkk4DMpg6zhRFIfOiO7FmjGfP72+m8YN/oGi1jLv+V2M9NEmSJEn6yjFZ9IyfmTb0gcOg0epwnHEVjjOuQghByN1J2OcGBBqjBZ0l5rgVkJCBlHQqEO4AnhtexfTfS9BOlRUlByP/Uk+Q+KlLmfz9Z1H0RhrefZKm9S+P9ZAkSZIkSTpOFEVBb4vFlJCOKSEDQ1S8rMQnfen4f7eB0CuluJc9TaSxe6yHc0qSwdQJFDthEQXX/gKAfU/ei7elZoxHJEmSJEmSJEnDY/zhYnTLx2G4fTaKY+QNg78KZDB1gqWcdQ2Jc1YQ8Xup/KdM9ZMkSZIkSZJOD4pRh+WVqzD+7CxZUO0IZDB1gimKQt7K+1H0RpwbX8W1//OxHpIkSZIkSZIkDYui08pA6ihkMHUSmBIySD/vFgAOvPQgQoghzpAkSZIkSZIk6VQng6mTJHPFt9FZ7XSWbJCrU5IkSZIkSZL0JSDLzpwkemsMBdc9iD46gei8GWM9HEmSJEmSJEmSjpEMpk6i5IVXjPUQJEmSJEmSJEk6TmSanyRJkiRJkiRJ0ijIYEqSJEmSJEmSJGkUZDAlSZIkSZIkSZI0CjKYkiRJkiRJkiRJGgUZTEmSJEmSJEmSJI2CDKYkSZIkSZIkSZJGQQZTkiRJkiRJkiRJoyCDKUmSJEmSJEmSpFGQwZQkSZIkSZIkSdIoyGBKkiRJkiRJkiRpFGQwJUmSJEmSJEmSNAoymJIkSZIkSZIkSRoFGUxJkiRJkiRJkiSNggymJEmSJEmSJEmSRkEGU5IkSZIkSZIkSaMggylJkiRJkiRJkqRRkMGUJEmSJEmSJEnSKMhgSpIkSZIkSZIkaRQUIcTwD1aUFqD6xA1H+pLJEkIkjuUA5GtWGiH5mpVOR2P6upWvWWkU5GtWOt0c8TU7omBKkiRJkiRJkiRJUsk0P0mSJEmSJEmSpFGQwZQkSZIkSZIkSdIoyGBKkiRJkiRJkiRpFGQwJUmSJEmSJEmSNAoymJIkSZIkSZIkSRoFGUxJkiRJkiRJkiSNggymJEmSJEmSJEmSRkEGU5IkSZIkSZIkSaMggylJkiRJkiRJkqRR+P+uf9+tU0wHEgAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 1080x1080 with 25 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import matplotlib.animation as animation\\n\",\n    \"from random import shuffle\\n\",\n    \"num_sketches = 25\\n\",\n    \"npy_files = glob.glob('server/larger_data/*')\\n\",\n    \"max_count = 1000  \\n\",\n    \"min_count = 0\\n\",\n    \"c = 0 \\n\",\n    \"# cool_files = ['من كان في حاجة أخيه كان الله في حاجته',\\n\",\n    \"#               '1ان الله مع الذين اتقوا والذين هم محسنون',\\n\",\n    \"#               '1إن هذا القران يهدي للتي هي أقوم',\\n\",\n    \"#               'ومن يوق شح نفسه فأؤلئك هم المفلحون']\\n\",\n    \"# cool_files = ['server/larger_data/'+file+'.json' for file in cool_files]\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# for file in cool_files:\\n\",\n    \"#     drawing = create_data(json.load(open(file))) \\n\",\n    \"#     max_count = max(len(drawing), max_count)\\n\",\n    \"\\n\",\n    \"shuffle(npy_files)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def data_gen():\\n\",\n    \"    drawings = []\\n\",\n    \"    \\n\",\n    \"    cnt = 1\\n\",\n    \"    files = []\\n\",\n    \"    for file in npy_files:\\n\",\n    \"        if ('عيد' in file) and ('فطر' not in file) and ('رمضان' not in file) and ('سنة' not in file) and ('يعيد' not in file):\\n\",\n    \"            files.append(file)\\n\",\n    \"            drawing = create_data(json.load(open(file))) \\n\",\n    \"            if len(drawing) < max_count and len(drawing) > min_count:\\n\",\n    \"                drawings.append(drawing)    \\n\",\n    \"                cnt += 1\\n\",\n    \"            if cnt > num_sketches:\\n\",\n    \"                break\\n\",\n    \"#     print(len(files))\\n\",\n    \"#     print(files)\\n\",\n    \"#     raise('error')\\n\",\n    \"    for drawing in drawings:\\n\",\n    \"        if len(drawing) < max_count:\\n\",\n    \"            while len(drawing) < max_count:\\n\",\n    \"                drawing.append(drawing[-1])\\n\",\n    \"\\n\",\n    \"    for i in range(max_count):\\n\",\n    \"        points = []\\n\",\n    \"        for j in range(num_sketches):\\n\",\n    \"            points.append(drawings[j][i])\\n\",\n    \"        yield points\\n\",\n    \"\\n\",\n    \"sqrt = int(math.sqrt(num_sketches))\\n\",\n    \"fig, axes = plt.subplots(sqrt, sqrt, figsize=(15,15))\\n\",\n    \"\\n\",\n    \"lines = []\\n\",\n    \"\\n\",\n    \"for i in range(num_sketches):\\n\",\n    \"    ax = axes[map_axes(i, num_sketches = num_sketches)]\\n\",\n    \"    ax.set_ylim(600, 0)\\n\",\n    \"    ax.set_xlim(0, 600)\\n\",\n    \"    ax.set_xticks([])\\n\",\n    \"    ax.set_yticks([])\\n\",\n    \"    ax.set_aspect('equal', adjustable='box')\\n\",\n    \"    line, = ax.plot([], [], lw=2, color = get_color())\\n\",\n    \"    lines.append(line) \\n\",\n    \"    \\n\",\n    \"# initialize the data arrays \\n\",\n    \"line_data = [([], []) for _ in range(num_sketches)]\\n\",\n    \"\\n\",\n    \"def run(data):\\n\",\n    \"    global line_data, num_sketches, c\\n\",\n    \"    for i in range(num_sketches):\\n\",\n    \"        x, y, z = data[i]\\n\",\n    \"        line_data[i][0].append(x)\\n\",\n    \"        line_data[i][1].append(y)\\n\",\n    \"        lines[i].set_data(line_data[i][0], line_data[i][1])\\n\",\n    \"        if z == 1:\\n\",\n    \"            color = plt.cm.hsv(c)\\n\",\n    \"            line, = axes[map_axes(i, num_sketches = num_sketches)].plot([], [], lw=2, color = get_color())\\n\",\n    \"            lines[i] = line\\n\",\n    \"            line_data[i] = [[], []]\\n\",\n    \"            \\n\",\n    \"\\n\",\n    \"    return lines\\n\",\n    \"\\n\",\n    \"ani = animation.FuncAnimation(fig, run, data_gen, interval=10, repeat=False, save_count=max_count)\\n\",\n    \"d = ani.save(f'video.mp4', extra_args=['-vcodec', 'libx264'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<video alt=\\\"video\\\" autoplay>\\n\",\n       \"                <source 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type=\\\"video/mp4\\\" />\\n\",\n       \"             </video>\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"execution_count\": 34,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"video = io.open('video.mp4', 'r+b').read()\\n\",\n    \"\\n\",\n    \"encoded = base64.b64encode(video)\\n\",\n    \"HTML(data='''<video alt=\\\"video\\\" autoplay>\\n\",\n    \"                <source src=\\\"data:video/mp4;base64,{0}\\\" type=\\\"video/mp4\\\" />\\n\",\n    \"             </video>'''.format(encoded.decode('ascii')))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%%capture \\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import matplotlib.animation as animation\\n\",\n    \"from random import shuffle\\n\",\n    \"num_sketches = 1\\n\",\n    \"\\n\",\n    \"        \\n\",\n    \"def save_video(drawing, mx):\\n\",\n    \"    max_count = 1000  \\n\",\n    \"    min_count = 100\\n\",\n    \"\\n\",\n    \"    def data_gen():\\n\",\n    \"        for point in drawing:\\n\",\n    \"            yield point\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"    # initialize the data arrays \\n\",\n    \"    def run(point):\\n\",\n    \"        global line_data, line\\n\",\n    \"        x, y, z = point\\n\",\n    \"        line_data[0].append(x)\\n\",\n    \"        line_data[1].append(y)\\n\",\n    \"        line.set_data(line_data[0], line_data[1])\\n\",\n    \"        if z == 1:\\n\",\n    \"            line, = ax.plot([], [], lw=2, color = 'k')\\n\",\n    \"            line_data = ([], [])\\n\",\n    \"\\n\",\n    \"        return line\\n\",\n    \"\\n\",\n    \"    ani = animation.FuncAnimation(fig, run, data_gen, interval=10, repeat=False, save_count=mx)\\n\",\n    \"    d = ani.save(f'video.mp4', extra_args=['-vcodec', 'libx264'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%%capture \\n\",\n    \"cntr = 0\\n\",\n    \"line_data = ([], [])\\n\",\n    \"sqrt = int(math.sqrt(num_sketches))\\n\",\n    \"fig, ax = plt.subplots(sqrt, sqrt, figsize=(5,5))\\n\",\n    \"ax.set_ylim(600, 0)\\n\",\n    \"ax.set_xlim(0, 600)\\n\",\n    \"ax.set_xticks([])\\n\",\n    \"ax.set_yticks([])\\n\",\n    \"ax.set_aspect('equal', adjustable='box')\\n\",\n    \"line, = ax.plot([], [], lw=2, color = 'k')\\n\",\n    \"file = 'server/larger_data/ختم الله على قلوبهم وعلى سمعهم وعلى ابصارهم غشاوة.json'\\n\",\n    \"drawing = create_data(json.load(open(file)))\\n\",\n    \"save_video(drawing, len(drawing))\\n\",\n    \"video = io.open('video.mp4', 'r+b').read()\\n\",\n    \"encoded = base64.b64encode(video)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<video alt=\\\"video\\\" autoplay>\\n\",\n       \"                <source 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57dEX/AAADAfMAAAALAZ59akX/AAADAfMAAAA+QZpiSahBaJlMCE///fEAA7PW3QAbdp4bSHwlQjyDD3dgFXeZnxvztxWXOya/f4Q5dTCHkVaEXNpoE/YwH5AAAAAcQZ6ARREsbwA+wcFMyABNBHicVs8wnSU2hyh0wQAAAAsBnr90Rf8AAAMB8wAAABABnqFqRf8AUfPSqdjXeQ9JAAAALkGapkmoQWyZTAhP//3xAAILsgqfSkdgcAVLlUNr9O3ijfXmGajcsHVohfVkCbgAAAAPQZ7ERRUsbwAjka9EUAIvAAAADQGe43RF/wAs3eaICTkAAAAOAZ7lakX/AC1MmvrgyoEAAAAjQZrqSahBbJlMCE///fEAAgr6yNQAjTKo3NNJ0Q09/9mgJuEAAAAPQZ8IRRUsbwAjM6T5xAi4AAAADAGfJ3RF/wAtXAfBZQAAAAsBnylqRf8AAAMB8wAAACVBmy5JqEFsmUwIT//98QACCiDIABW4uo9jsvtIYkongl+S4GBAAAAADEGfTEUVLG8AAAMBlQAAAAsBn2t0Rf8AAAMB8wAAAAsBn21qRf8AAAMB8wAAADZBm3JJqEFsmUwIT//98QACGhStAE7kQhFfk7/xbdI0pLHr0dHCtGDOQNZjZAo1DAufgkRuCkkAAAAXQZ+QRRUsbwAkvsbqsAECZrfx317gJuAAAAARAZ+vdEX/AFMLtIGIlyNnYb0AAAAOAZ+xakX/AFMLtMD/gtsAAAAyQZu2SahBbJlMCEf//eEAA14iYjOJAFA0g33Zyp6Zfz0ZHcX/8ceIDhfg/E7Yi81oIGAAAAAXQZ/URRUsbwAkmAGytKQALYv+ymiRBvQAAAAQAZ/zdEX/AFMLtKgOg4iCbwAAABABn/VqRf8AUwu0h/3n0M+AAAAAJEGb90moQWyZTAhP//3xAAIaFK0ALBHyg3QQlSLjBuxfzVkCXwAAACdBmhpJ4QpSZTAhH/3hAANgBughSkgC/02VoL9U6Huk1RJ0i7rQQMEAAAAYQZ44RTRMXwAt9kewPMALdkPXe2vbeDFgAAAADQGeWWpF/wBTC7TwCgkAAAAYQZpbSahBaJlMCE///fEAAhxUhlHhcC7gAAAAI0Gaf0nhClJlMCE//fEAAhyrvAMf0AKNQILDI2sbeYC9WQJfAAAAEUGenUU0TG8AJJX/zM9KTCDhAAAADgGevHRF/wAuazLU7wHTAAAADAGevmpF/wAvus+CpgAAADRBmqNJqEFomUwIR//94QADciFGOHoAVyL5frUKAJFkrEQCWpZ2IhczdfTjwHLTkYrSxUXnAAAAEkGewUURLG8AJaiRfJvrWaiTgAAAABYBnuB0Rf8AL/TRAJqoU7Yu0ExsJUSdAAAADgGe4mpF/wAvs7QH4KmAAAAAOEGa5kmoQWyZTAhH//3hAAo0oMZdAzbLgKAsAbwEsBtvo07sBOMi4/UdzVX0MCaZuiVjaapA3gZVAAAAE0GfBEUVLF8Aiq8/aeV/l/gsK2EAAAATAZ8lakX/AC/00QAmYazt+w/BUwAAACZBmydJqEFsmUwIT//98QACKygxAXAC/MICifaEa+RXvfDnVkCTgQAAADlBm0pJ4QpSZTAhP/3xAAIrJPQXjXAFb8t+s5DfkSfVGHgCUuS+fm2NN6sFInZEkcx0HRfu3Ju8FtAAAAARQZ9oRTRMXwAvmAaMWMMaLaAAAAATAZ+JakX/AC/T/RACDOqsR50BxwAAAC9Bm45JqEFomUwIR//94QADcqGuAByui+RYKni6wsY2refoOwZvnWGzBDP/BLQPyAAAABdBn6xFESxvACWXXb0gAIZocjzfaoAg4AAAABYBn8t0Rf8AL5SMJVlAAh9HaBQro6LbAAAADgGfzWpF/wBWR8JbkwqZAAAAQUGbz0moQWyZTAhP//3xABUd1cbc5bIANeqdXrrYP1q/OGDA2N5mkQ/PRX0H6F8Xgn/1GuEbAavhDlV8bYfx+AbVAAAAOEGb80nhClJlMCE//fEAC67KV4nIepuMAQTOaKtkePO05mlzSHNKRsGojgIpLF85YF2UUE8lwCHgAAAAEkGeEUU0TG8AyHtFbE+BGAC7gAAAAA4BnjB0Rf8A+MSCkSAK2QAAAA8BnjJqRf8A+KebQzGYAXcAAABHQZo2SahBaJlMCE///fEAC5kQc50sqAIIBroIkSHbdi2oxgBiOos1+U5RcZIXahpH2NweF0Se7kf+dQouP0IfAL/+WbvAQ8AAAAASQZ5URREsXwD3RV2MLuYz2BnxAAAAEQGedWpF/wD3RWFbVuX8IFbAAAAAVkGaekmoQWyZTAhP//3xAAudq9gAFAW11Fnm3Rks2kYGPBf2UhamD3Dc95iBzxmHcOiuTeLgKhEGf41LWwTG1ftdLvE/ojZda9rlR9PDis9/XzlNzAFBAAAAEUGemEUVLG8AyUibpd+/QEXBAAAAEQGet3RF/wDylsjgtAINsBNwAAAADQGeuWpF/wD5CF4AakEAAAA9QZq+SahBbJlMCE///fEAC5lJo0pl3TgBGBW9Pqh4lIcgbBj0c9idMiTviSeWHob7tyCdqBg1YF3u+W8BDwAAABNBntxFFSxvAM5FXrWatCTNAgPnAAAADQGe+3RF/wCK01kAm4EAAAALAZ79akX/AAADAfMAAAAsQZrhSahBbJlMCE///fEAC6oEv6AEZHUe1dhTqVeFDSTZbErZ/tVBJp7vAQ8AAAAXQZ8fRRUsXwD+iB5+AAh8VooGiHlwK+EAAAAWAZ8gakX/APtVhVKYAOzQdx6Eb3gV8AAAACFBmyVJqEFsmUwIT//98QAGayfYgql0AI0VeDzSUdkAyoEAAAAQQZ9DRRUsbwBtLZQoZkAtoAAAAAsBn2J0Rf8AAAMB8wAAAAsBn2RqRf8AAAMB8wAAAC1Bm2lJqEFsmUwIT//98QAGbZWpgCtTo7kR9IX/8fjC/o0igeuRiW4e4fgAUkEAAAAYQZ+HRRUsbwBu/ywAD9qyINA1z2M2IBbRAAAACwGfpnRF/wAAAwHzAAAACwGfqGpF/wAAAwHzAAAAOkGbrUmoQWyZTAhP//3xAAaVZ5wBW/LfiTnmqpP0nIo/yQwE1/mdESBO+5pJaes5qH27FsVX0iNwGpEAAAAPQZ/LRRUsbwBxXXp+AMqAAAAACwGf6nRF/wAAAwHzAAAADgGf7GpF/wCORwfPXAfNAAAANkGb8UmoQWyZTAhP//3xAAaTicfNqfACpSOgi7VPlM0ab8GWaMguUThRYw+l0o/BW+XUAlwDuwAAABxBng9FFSxvAMvVe/VTABaYp2WpBxbnzWxpOBvRAAAAFQGeLnRF/wCOmyOACdEMnlu6y/4DegAAABMBnjBqRf8Ajr8uABMG4P0mugEvAAAALUGaM0moQWyZTBRMJ//98QAL/qanIM6zAFtW7L7NCwz5C4OjcsHD2WQZWQBswQAAAA0BnlJqRf8A+1mJ+AekAAAAK0GaV0nhClJlMCE//fEAC/iyMf4OAEdRWZdMcFPwWj1XohhD13R8mQlwCDgAAAAVQZ51RTRMbwDNH/VQCgAVOfgzIAGLAAAADgGelHRF/wD8xYeguAi4AAAADwGelmpF/wD8g1Xgr4AZ8QAAAC9BmptJqEFomUwIT//98QAL6pT4A5RW/HZWJ3YUjfQG4V+hv4S8/aP7UubR93gIOQAAABZBnrlFESxvAM3w/h4AA4ecERHZYEzAAAAADQGe2HRF/wD9llDhgUUAAAAMAZ7aakX/AI6u+ALuAAAAK0Ga30moQWyZTAhH//3hAAp899IA5hszqZMzVn6taKUPJL/UbisDirhhAScAAAAWQZ79RRUsbwBxsuQAISXKF5kAvogR8QAAAAsBnxx0Rf8AAAMB8wAAAAsBnx5qRf8AAAMB8wAAAChBmwBJqEFsmUwIT//98QAGmD3GaOgBHvIb6dpuXmz3tbV8+OzfAAoJAAAAJkGbI0nhClJlMCE//fEABpWUVQAceHMb40OOPCZWkeDx4VT5LgHdAAAAEUGfQUU0TF8AjL40k2DfAF3BAAAACwGfYmpF/wAAAwHzAAAAKUGbZ0moQWiZTAhP//3xAAaTMuY3QAh2X9Y+HLE6WGYAwGa3m2dWQDFhAAAAF0GfhUURLG8AcazLAB5WGE/avqBIALKBAAAACwGfpHRF/wAAAwHzAAAACwGfpmpF/wAAAwHzAAAAN0Gbq0moQWyZTAhH//3hAArV/w7h+YoArfebGFxKGAcM3Qwpyx0uLL3o9EkfF8H7MSJLPr4wEPAAAAARQZ/JRRUsbwByf3iwhFYAxYAAAAALAZ/odEX/AAADAfMAAAAWAZ/qakX/AJJFmu0aAFuMvpbPhaQHTAAAACZBm+xJqEFsmUwIT//98QAGxJGW3AFWyqi6IG0+9HzruLn5JZAMCAAAADVBmg9J4QpSZTAhP/3xAAw5BGB3wBWwHzR1nDcfGon0xmh3lXSwGLbiqzSmF6MMdwB70ID1gQAAABFBni1FNExfAQYXByJLBfQKuQAAAA4Bnk5qRf8BBktfQHAQ8QAAACxBmlJJqEFomUwIT//98QAMOgeIl8AU5N8aKwerTQg//YbVDZsKjZojhG4BxwAAAA9BnnBFESxfAQTxbP4AZUAAAAAPAZ6RakX/AQTQZAsyAKSBAAAAL0GalkmoQWyZTAhP//3xAAxIHkNp+8AHHgCqXdlxXR4ibNd7GA0Quqnnfzu5gEvAAAAAEUGetEUVLG8A0m4Jfw/lABZQAAAACwGe03RF/wAAAwHzAAAACwGe1WpF/wAAAwHzAAAALEGa2EmoQWyZTBRMJ//98QAMSDWzPACOBxzqp4clxu4VsmUS2iO0K/WS4A/xAAAACwGe92pF/wAAAwHzAAAANEGa/EnhClJlMCEf/eEAE2XlRwAtiItDoNm9oYWbn42FicJj7BEF/yYIl4YPsDvYo6tALiAAAAAeQZ8aRTRMbwDTTNoAEsHGP1fUJQ0asJfSf0ExAGBBAAAACwGfOXRF/wAAAwHzAAAAFQGfO2pF/wEFcAgAQ7d01dUXboApIQAAAC9Bmz9JqEFomUwIR//94QATT9rwCaSmRb1upWlEkgmFQ63ABWGf9UoDVkRNbPgDLwAAAA5Bn11FESxfAQVdtjsBxwAAABQBn35qRf8Ak3BOADwfEBQay6AScAAAACtBm2BJqEFsmUwIT//98QAGvgWRAAHG8rtdT19yUNXip7CWASM12QsxkAwJAAAAMUGbgknhClJlMFFSwn/98QAGu+/3JqwAAWHy3YW36AALqhb4Z4lbxotR73BALVkAwIAAAAAOAZ+hakX/AJJoX298Ak8AAAAoQZumSeEOiZTAhP/98QAGuzhftaJsqgA49dUYGIIsjmZmc5IjvgATcAAAABJBn8RFFTxvAHJ0ZZc5c2gIEXEAAAAQAZ/jdEX/AJC+DI7c1kAk4QAAAAsBn+VqRf8AAAMB8wAAADZBm+pJqEFomUwIT//98QAGuzcMldOAFgnTrEsjzji4Vg/vYHcV0OdcdLtBNdQispWe/qyAYEEAAAAYQZ4IRREsbwB0bMsAGphP+zCzwJEIAK2AAAAACwGeJ3RF/wAAAwHzAAAAFQGeKWpF/wCWrfn4ACBHkfbwDYAm4QAAAEJBmi5JqEFsmUwIT//98QAG71Npjq/3gDXqmp3IBdPx9zY2uD81r8sh8V8usiDn6EcCcOSHMJQqkRFPA3z7fEbgMqAAAAAfQZ5MRRUsbwB2X21yFoAHFNIBpj+uNWiemJO0JgBNwAAAABYBnmt0Rf8Ali29oFvb4AHXVmYOlgJvAAAAFAGebWpF/wCXcE4ASYM+b5tzgO6BAAAAXUGacUmoQWyZTAhP//3xAAyJDFmhFasMkE3boNDa2UDW4I+c/te8ER1KrdT8MBHVyXbuIibiO1z/UBzeL+WVKHewrsBNqBu34ImHHCz0EdtPO5sU1w1iRE6Oui4DuwAAABJBno9FFSxfAQ1XxNRpl05YgsoAAAAOAZ6wakX/AJr6JaTAxYAAAAAnQZq0SahBbJlMCE///fEABw5+XQGNk1O7wOiCctniAjJslXlDeA3pAAAADEGe0kUVLF8AAAMB8wAAAA4BnvNqRf8Amj0B48BnwAAAADdBmvhJqEFsmUwIR//94QALWepXAKeUTnRDzwZ2af9/xsgkrCwHWMvnKI5xl+dC4ou/BmP4+AxZAAAAGEGfFkUVLG8AeWaxCrABMJhAkAlSXWWB+QAAAAwBnzV0Rf8AmpJfAZ8AAAALAZ83akX/AAADAfMAAAAbQZs5SahBbJlMCL/6WAAwBVcwAgKC9hjn8A44AAAFK2WIggAL//72rvzLK0cLlS4dWXuzUfLoSXL9iDB9aAAABIwcuNOg+dc3TQAGcGr9wgS+ICDnwE7be+9Qrfcqro9SeIQnRFFadPLekMOsj9J3SB0mivTWTC0N78b/EYuYYnsmGDxfAJlQAAEpifuxI+NwbOJBn2/oNOGbSibjtVg7f9J7AnQOaIJRy5JbBIgukZa52QRJlhp7TEkxmTp03tbAfx8Yo5ct0e9XekzrrnnvxKJEWgDR3T35kDH1iCr7ubWlaST6/nWHFNpYXvcPXCqZ5VTeWRqoSrEoh7ToLjnEF+o59UwIvmZt6H6vrxkgWldjejSIm4YsMowHxBLpoDK0GawGeCVSpDF3w6QfZxAKkwQlUSLV4DQjL6sqjo0V/nwVdS6Qc5yc2suVRreCNjUUVluJJ+TGH+k1L3vGvBTE5Y4Ml6Hjm55ukcJRRctCL8RG7Uy/mfFN9Hl8C4DK6hDwf/IAp6/AKvs8NmDQDCRrUgOK3I1D72X1OCpsbI7ikUhS7vbjS/CyxU21IGMB8SjxMOzo8ajhv3dnYjhup2jnRcVT5Uzfecz6JZ1BO0HMKlxQUdnXkYppjAJXj792/Fw9dx7+CIje18H1T8/oN3YwP5NuRzJz+a7+Z0stB3Db5AOye9RhcfSpGo1/AzOsvDIybM2qJbbCRoUjfa92DLvvHM7taTkyn25MC6p9tp8i1D5SNtBqHXqSTaTCOeVDB84BV6vusN/pcrSLjDKTGTKvN9qu6KxjU5/9nI5nNSkxaCY5SN8TNMh2e54/ml1jQiLPuc3AcFrdsjpNn7UDIq8F8NlcMG9vK+KvRYc2uWOKwP+knNgbnxEeR6/3LZm3Jl4DcDHkB3AnT8zSQFmYQ9iYOuj+SHe/6v9Ug8F89t69xNxGd5Spe7QZKa40GWyXxfi7gu3fu9FG/ltXo0Z1h+DLSV6s/Wtvx9OFz5QpSMbghka/1KO0WHvSrM5/Ewq+Od/+7H6EEJnzNgkR0ezQZfHSD2kjn9r6SjrxGQ2j16VDxEpfNAJYYNX0FKwiND/d7mgx3CZeiASiTpwHVsjw9fifJdj5Fo1bWRUJqTt7tHeNyUHcezq83usV2GZsABuLGHUy/R11VGNeEzcRZ/TpGgPHQcTVuL/cEggD9Q5D7gwxgxml9fRSK5BgM49s9dgFol3nW9GvchjlUIbEpGUtpPfY51AKX0k4JWCsTXSIYz8E7kR5xjYQAzHMegYLLNuXvQ1Kdhj22JuVqklg5CFiQBzEaRMM9+MNFXr1yDw8GCyy8NtVoDNUdMRkO5gi3XS/wZzda+SRFXY9f8bXeP7zXtqxIIjn6e3y3MVyjK2dsNiPuRDhI4TYl2HDJXu8YZZuhMf+AqLPCdnFZnONbbOGtmvKXL3ROCzSRyY+t5UUq7bSgi4fWQq9jgLJK58BSX5P/UYQgN7v0QPlxGcX6EuPSB3XxH5T3RwSNsfzLHi0RNFUEpQVs5IxbAzckRk5Nkz4THwnbOIG/impU/W1TeoyEp2l9kzha0eGsLXPxZWXAs2bhqYKCr7CcIRoeH+1AyFElTmdZtdyFDi4ur1mM1LxIvsBkPZkd961LplGGEI/ZGzeF2S6vXwkXENvgx0FG8vWT0eiV3s1369Bmoc7htE/Htc8ZxuQyoStV1ozG4b9R/NIjOGnrRCN6mFBHVx377V9KxdXddMBIJvGBAAAAwAAAwAAAwAAAwAAAwAvHNehJzfDs6wbjt4DWq51Eoo7CIRBdtj97c7ofb7wAABgQQAAAEhBmiRsQj/94QAR73ViXrkcAnanVZeNrDRg1BV7cjzaqHQCmHaOsHJsy5t/OJmT98UHne2JiD6IHi+cC3jNPd0zocZW97EI2YAAAAAXQZ5CeI3/AMQV1WoHUbf9YfZFa1Bxgl8AAAAWAZ5hdEX/APMB31yHobPrhf1BKRgFBQAAABEBnmNqRf8An5PlQjNNvTCggAAAADNBmmVJqEFomUwIR//94QAGbYLigJ3cJVnxkXU4M+tZQ3/7EnOuE+AnHfuC6vEuZXwAGVAAAAAuQZqGSeEKUmUwIR/94QAGbYLigJ3XGCmBOvZU6at7Cf7pf/uN66pYPbkQ1Zg/wQAAAC9BmqdJ4Q6JlMCE//3xAAP5+6dT4A16QzndF4ULjDi4szsOAyF5jMAe0H+SMzUFDAAAADJBmstJ4Q8mUwIT//3xAAP52hjdeAZiYP4AY2GzQIIFKpmvnAkV1LKe01NO23/IBS1BQwAAABFBnulFETxvAEUs6eC/W0WBiwAAAA4Bnwh0Rf8AnRdpB/4GBQAAAA0BnwpqRf8AnRdp4AqZAAAAMEGbD0moQWiZTAhP//3xAAPj1171GABoK50+jRMmZx1OnLC4LEm/xN8qNrLV0uAyoQAAABhBny1FESxvAEOyMgATPXRunfq7tI5EDukAAAALAZ9MdEX/AAADAfMAAAALAZ9OakX/AAADAfMAAAAmQZtSSahBbJlMCE///fEAA9/SfgCJjqeJtnJXdskJlTwp6AfABB0AAAAVQZ9wRRUsXwBWsrYAE6qQzolOoB8wAAAACwGfkWpF/wAAAwHzAAAAKkGblkmoQWyZTAhP//3xAAPehA6wSAAi1tdxD/DUkEvVWBVVObIMS4DKgQAAABFBn7RFFSxvAEJqljkz3ysDKwAAAA4Bn9N0Rf8AVS9aCNwD5wAAAA8Bn9VqRf8AVS9vGCPgakAAAAA+QZvaSahBbJlMCE///fEAA9/MPgCHvdrkI8gCpGmdC0wXhjta12R6712Gz63fC+Ylk4Wa5rPbQtdMyuJgMqAAAAAWQZ/4RRUsbwBDYJoADlDnYRAhX6AIeQAAAA0Bnhd0Rf8AUwu08AoIAAAADwGeGWpF/wBWRWNu14sCHgAAACVBmh5JqEFsmUwIT//98QADyIPNs+9AGZZUCWhNuNaw8t2fABDxAAAADUGePEUVLG8AQVziwZ8AAAAMAZ5bdEX/AFP4D4GBAAAACwGeXWpF/wAAAwHzAAAAKkGaQkmoQWyZTAhP//3xAAPfzD4AiY2pgFek53V2qRuZHYFXikKmI3AsoAAAAA9BnmBFFSxvAEN9N+LAyoEAAAAMAZ6fdEX/AFP4D4GBAAAACwGegWpF/wAAAwHzAAAAK0GahkmoQWyZTAhP//3xAAPfzD4AiQqgeXe9vJQjeiMtrjXstDAjvTzAeMEAAAAOQZ6kRRUsbwAlvtmwtoAAAAALAZ7DdEX/AAADAfMAAAAOAZ7FakX/AFZIKCTWBvUAAAAvQZrKSahBbJlMCE///fEAA9/W3QAcbxu/JpqjmAfmaJGZqPw1bLAVkKYYYN3eBlQAAAAYQZ7oRRUsbwAmvrJJWACGhzbEqnm9wEnBAAAACwGfB3RF/wAAAwHzAAAADAGfCWpF/wAxEjfBSQAAAC1Bmw1JqEFsmUwIT//98QAD39bdACL5rno3A5Pql+WdH9oExo38sA0z/qFQYsEAAAAXQZ8rRRUsXwAxImnt8AErCyjErR3VAk8AAAAMAZ9MakX/ADESN8FJAAAALkGbUUmoQWyZTAhP//3xAAP1zD4Arflv1SH1l7Vc6tcDUrY1DNmuI7ar5ZC+CNgAAAAXQZ9vRRUsbwBFf1+ACWlNBPEjkbWzQvIAAAAQAZ+OdEX/AJ0Xat8dAnCHgQAAAA0Bn5BqRf8AnRdp4AqYAAAAKkGblUmoQWyZTAhP//3xAAP1zD4AiQrdhcp2vFapKZgy4bE6yENzAVkBQQAAABlBn7NFFSxvAEWsfAAueamn/PE8DZjeQEfBAAAACwGf0nRF/wAAAwHzAAAACwGf1GpF/wAAAwHzAAAAQ0Gb2UmoQWyZTAhH//3hABT5QuF1G1OW2/H4Hi0DWXsh9JCagNd9sWsbyoPmAB9To2Sy6Dsk32kbIbdCjI0075swOmAAAAAQQZ/3RRUsbwDivCftlMcBSQAAAAwBnhZ0Rf8AWHgPgW0AAAAXAZ4YakX/AR1dkzIAA/m9mH2dCyggLaAAAAAYQZoaSahBbJlMCE///fEAB0Ourb1rgGzAAAAAMUGaPUnhClJlMCE//fEAB0Tiinf/MADiGbv/z0N2nFq6/RSiHLnjevW8tUhDe2XwJ2EAAAAbQZ5bRTRMXwCfLi1ACajWQvJm66XwN15EzgOOAAAAEAGefGpF/wCeEroWSOO6BSUAAAAnQZp/SahBaJlMFPCf/fEAA/T00BYBqADeMT0gXX8FGCSE4H+zQCghAAAAFQGenmpF/wBXZbw1wAH/iA3fecDPgAAAAChBmoJJ4QpSZTAhP/3xAAP12s4ApkIGeOyprzjB44OmnK0gpr26sgKDAAAAFUGeoEU0TF8AWPbnAB+Z6OGJV0A9IAAAABMBnsFqRf8AWPbnABsngicwzgZ9AAAAKkGaxkmoQWiZTAhP//3xAAP1zD4ApZKeoWXfXHcrXAmJAbBN63lFkuAxYQAAABlBnuRFESxvAEV/X4AHvXtJkR1388uYgI+AAAAAEwGfA3RF/wBY7KwAIAPCO2PwLaAAAAALAZ8FakX/AAADAfMAAAAvQZsKSahBbJlMCEf//eEABmz8vAHKHM4J1rR3iH5oVe6Nw0CXVRgohv9sz7qAzIAAAAAPQZ8oRRUsbwBFXNnG4IuBAAAACwGfR3RF/wAAAwHzAAAAFAGfSWpF/wAxOqaAFY0Omk810BvRAAAAOUGbTEmoQWyZTBRMI//94QADh+vJAF5gVhE1pO2LPX9nL+PoD2+SwYkdSxoaVcr6zmohtfiZXWge0QAAABQBn2tqRf8AMRRo4AJlPYrP66A3oQAAADRBm25J4QpSZTBSwj/94QADh+vJAF/sivRylH7UOFobeu/+a0nZr8l8BoRsdgDXCxVZgIOBAAAADwGfjWpF/wAw0PsodkBvQAAAACZBm49J4Q6JlMCE//3xAAI8rdHNGuAK35b6NlL8yv26Q6cefVkCPgAAAD9Bm7NJ4Q8mUwIR//3hAAOdvKRF95vbpKgg0zqehpcU+zvXYj06GV6hNT5OmzJcQ6xMORHmMpRrdMg//MEgekEAAAAYQZ/RRRE8bwAn2Hq/UrseAAWvvlD/EHBAAAAAEAGf8HRF/wAwXXc55sIdQg4AAAAPAZ/yakX/ADJiYR0K0hSRAAAAMEGb9EmoQWiZTAhH//3hAAOgY2nV4K95jX8GbtL0dA5ienIphUg6hO18Qz+TdEBGwQAAADJBmhVJ4QpSZTAhP/3xAAJLSS7DXgGr5avKLOmufF+yrso9/9vxwx0oUKkxtTY1WhCmgQAAACVBmjlJ4Q6JlMCE//3xAAFHNLKtoAcpCRH63iJ+ewWSl8n3MFtAAAAADkGeV0URPG8AFigxNkLuAAAADgGednRF/wAbsekkPYNTAAAADQGeeGpF/wAbo14kDFgAAAA9QZp9SahBaJlMCE///fEAAUevgcvgCdq+hYl3uDsK0/fFGgyTlzDjnwV6uXPe0FeZwnc+OiY3nnZRncwW0AAAABlBnptFESxvABYi8tVgCJovpecruEnyYBqRAAAADQGeunRF/wAbsmA+FJAAAAANAZ68akX/ABr6PzPhUwAAAClBmqFJqEFsmUwIT//98QABPbVqYAbmoQMV2zOQsEeGW8QucgvDmfgBqQAAABlBnt9FFSxvABWJnp8BUgAOITWmgPxZEMCAAAAACwGe/nRF/wAAAwHzAAAAFQGe4GpF/wAbH9FgBLAnptdbi7oGVAAAACxBmuNJqEFsmUwUTCf//fEAAT2zuoAX+zZGIjTyYr5bGx8N32FAfIbbbJcFBQAAAA4BnwJqRf8AGu+gqqgPSQAAAClBmwdJ4QpSZTAhP/3xAAE9Mm0z/q7UAXLC+Ji7bz8W7JNWgSQ87zQH+AAAABFBnyVFNExvABVNIpm6sjwIOAAAAA4Bn0R0Rf8AGuzEYlwqYQAAABABn0ZqRf8AGtPAcRhvBIUEAAAAI0GbS0moQWiZTAhP//3xAAE+atkPgCe2EC9uRqAr/NkDuYLvAAAAD0GfaUURLG8AFZLJMqVhQQAAAA4Bn4h0Rf8AGvd2gu4DZwAAAA0Bn4pqRf8AGljJzAfNAAAAK0Gbj0moQWyZTAhP//3xAAE2Rra4kDgCITu7WR1OegOBIHhQOrl+utGQQcEAAAARQZ+tRRUsbwAU81XMlwiwpIEAAAANAZ/MdEX/ABoTKGA+YAAAAA4Bn85qRf8AGkwoiQgPmAAAAClBm9NJqEFsmUwIT//98QABNZir3fgCgv99QkKl0sMyBIT98+lTncwYEQAAABBBn/FFFSxvABUCfdqu+WFJAAAADwGeEHRF/wAaYnTCv2DegAAAAA4BnhJqRf8AGaXSHIuFlQAAAClBmhdJqEFsmUwIR//94QAB2TFRhEMvuAHukAVe72KKsaWmZccW74QZ8QAAABBBnjVFFSxvABQvubqKGgHHAAAADwGeVHRF/wAZr4nl7LhZQAAAABABnlZqRf8AGa93TDRIRIVMAAAAIkGaWEmoQWyZTAhP//3xAAEto6sbQA5pyk1paF/AWaQYKmEAAAAmQZp8SeEKUmUwIT/98QABLaRZD4Ajc4Mbr+U3sLanGRKru8RuEnAAAAAQQZ6aRTRMbwAUcsk4I2BFwAAAAA4Bnrl0Rf8AGcJ76CUhUwAAAA0BnrtqRf8AGSb9+8KCAAAAKUGaoEmoQWiZTAhP//3xAAElRT48feoARS1Gb3U9PnfnD0Era6VvVwVtAAAAF0Ge3kURLG8AE5/CGAQAE2gv66JmkM+BAAAADwGe/XRF/wAY0UkXj9BHwAAAABABnv9qRf8AGQ+H09D+iQrZAAAAMEGa5EmoQWyZTAhP//3xAAElyrDXV51qAQSMX4jxfYggJpH9OqD0QL51f0+6puYMqAAAABBBnwJFFSxvABPiocRbwI+BAAAADwGfIXRF/wAZF3aQRpCtgQAAAA0BnyNqRf8AGHjZI+F3AAAAG0GbJkmoQWyZTBRMJ//98QABHgNcSTEbs0CPgAAAAA0Bn0VqRf8AGDMoYEPBAAAAF0GbSEnhClJlMFLCf/3xAAEdouD3iAHTAAAADgGfZ2pF/wAYMUkjwIeBAAAAJkGbbEnhDomUwIT//fEAAR1+GG5yAPam1SDkNGhGibbtQyPo3CbgAAAAEEGfikUVPG8AE1bk6VbAk4EAAAAPAZ+pdEX/ABh3dFwJsHdBAAAADQGfq2pF/wAX2Nkj4YEAAAAqQZuwSahBaJlMCE///fEAARVFP0R/eoARYvKfBbA4tI9Bcr2gkWrs0CThAAAAEEGfzkURLG8AEpwKAUR3wtoAAAAOAZ/tdEX/ABfIG3w0DjgAAAAQAZ/vakX/ABfPd0w0SESFtQAAACdBm/RJqEFsmUwIT//98QABFYbz6DgEhMB/Mln3gS2bBadsKg3MGpAAAAAQQZ4SRRUsbwAS1uWHZbAl4AAAAA8BnjF0Rf8AF9d0XAmwekEAAAANAZ4zakX/ABc8kZnDFwAAADFBmjhJqEFsmUwIT//98QABDaNpUP71ADqkL0IZ/YpsRcM6e/RYydejqC8kUF87mDZhAAAAEUGeVkUVLG8AEjjG8ij0X8LuAAAADgGedXRF/wAW/WG9eFbAAAAADwGed2pF/wAW/WG04+wfMQAAAB5BmnxJqEFsmUwIT//98QABBN++IpgA+6ARi31ZBNwAAAAPQZ6aRRUsbwAR3fasWMCAAAAADQGeuXRF/wAWvJ/ZA7sAAAANAZ67akX/ABaty9uGVAAAAClBmqBJqEFsmUwIT//98QABBqxIyWw36gCxJPnzhiZ8Jf5HodMWyDBgQQAAABJBnt5FFSxvAB8Xlw+qIqACiykAAAAOAZ79dEX/ABagXRmfCygAAAAQAZ7/akX/ABamdSEuZBgP8QAAADhBmuRJqEFsmUwIT//98QABzuk/AUM5MxRkSEKzT4diwFgRzAAv3Gde6YMn1M0CS+1bysOwgx+E7AAAABFBnwJFFSxvAB8Xjr2scITCbwAAAA4BnyF0Rf8AFhrckPhnwQAAABIBnyNqRf8AJ67NlJ/k2H/QpIAAAAAwQZsoSahBbJlMCE///fEAAc+Tt/S8BQShTU52bu8UCsK9/Iz1fBmU1aeh/cHmMEDAAAAAEUGfRkUVLG8AHvZf38WeJ6I3AAAAEQGfZXRF/wAnOtivQAPRybHTAAAADwGfZ2pF/wAo4xo7Cc7EvQAAADRBm2pJqEFsmUwUTCf//fEAAdzU3F61FwAUH4vIi+XBCeOXP+0Rs3exsQroYOZmJ0wrsLaAAAAADwGfiWpF/wAo5MR2ZTw44QAAAFlBm45J4QpSZTAhP/3xAAHagGAAWkq/bUmqQ80oln5axuOkilEZdFSNMZGrek4fHrmhY2Brx3afgssiw123LH3CGTlloSus4JT9UAjbwB2Hmp1zdydf8swb0QAAABNBn6xFNExvAB+5O7GHziNPCYQtAAAADQGfy3RF/wAo7jeApIEAAAAYAZ/NakX/AAyeqaAFTpuuB5FU4AS0HqEfAAAAKEGb0EmoQWiZTBTwn/3xAAD5fK2FG8vIAqUIGjYnoi5/yeLbjcaw+YAAAAAQAZ/vakX/ABWSC3BzeMeUMQAAADRBm/RJ4QpSZTAhP/3xAAEFBW4ALZ294Wex+lRox+H+XzgOAPWaooFM28cbnxUmQMCePxEwAAAAEUGeEkU0TG8AEcW8OgY2+lxAAAAADAGeMXRF/wAWv6yB3QAAAAsBnjNqRf8AAAMB8wAAACxBmjhJqEFomUwIT//98QABBlML+4AOPXLULBY3brx2ELp+XYotWUoDG+AHzQAAABFBnlZFESxvABGaffKiQyCLgAAAAAsBnnV0Rf8AAAMB8wAAAAsBnndqRf8AAAMB8wAAADNBmnxJqEFsmUwIT//98QABBW5pIAfRSWiuXGq2+UOUdpEQLg3SBRxW1cNq6rRicI1ZBNwAAAAVQZ6aRRUsbwAR6x8ACDD/Is5TpEOOAAAACwGeuXRF/wAAAwHzAAAACwGeu2pF/wAAAwHzAAAAKUGavkmoQWyZTBRMJ//98QABBQJ/AFVhU7VmAVa73heUg/3HKpik+AHzAAAACwGe3WpF/wAAAwHzAAAAL0GawknhClJlMCE//fEAAQUCfwBzDqdYU7hPdpU+tWhm704MVxpldipWrpnsgwYEAAAAGEGe4EU0TG8AEesfAAiBKqx5UHaxlIYB/wAAAAwBnx90Rf8AFi+sgekAAAALAZ8BakX/AAADAfMAAAA7QZsGSahBaJlMCE///fEAAP55u/oAoAjYIbl/7fQJZMary4Lq/XltTSaHNIDMfbyU0liffhuV/3HIooEAAAAPQZ8kRREsbwARVzWEhMRcAAAAFgGfQ3RF/wAWJw4RjbYAQkcDWZ+YR6QAAAAPAZ9FakX/ABWSCiM/eLuBAAAAL0GbSUmoQWyZTAhP//3xAACShStACMjgLYINP74l7FjIr83tCLwJ2KB/Sb7xMK2AAAAAFkGfZ0UVLF8ADJ6poAW4+DDjr/uYHTEAAAALAZ+IakX/AAADAfMAAAAxQZuNSahBbJlMCEf//eEAAOh0Z7RPQAOkbFyAXD6bUftHKap/K5EPl00QNboxxhDUgQAAABBBn6tFFSxvAAnmnUKU5GfBAAAACwGfynRF/wAAAwHzAAAADgGfzGpF/wAMhYLoD8UFAAAAJEGbz0moQWyZTBRMJ//98QAAkoE/gCmN6O+2vshrD76L/JcK2AAAAAsBn+5qRf8AAAMB8wAAACdBm/JJ4QpSZTAhP/3xAACSgrcAHBgmCBFB/KdRNGd999jKgabNBF0AAAAQQZ4QRTRMXwAMZ1PAU14oIAAAAAsBnjFqRf8AAAMB8wAAADRBmjVJqEFomUwIT//98QAAkrplOAEX+rQ3sXNfqxEstjw4T+AEhE991zp51vda9YU++AOPAAAAF0GeU0URLF8ADHXYKLAB+zZd/MYIXgg5AAAAEwGedGpF/wAMn+iwAXrfS3rvBB0AAAA7QZp5SahBbJlMCEf//eEABWJQuACgDG6cfn6f6eBv+Ayu13+qhapCxCQS33tKfyWyMZct5I4BolgjHzAAAAARQZ6XRRUsbwA6DxyVhz7CIQcAAAAMAZ62dEX/AAyL1kGzAAAAEQGeuGpF/wBJVxdvyxU9/fGzAAAAK0GaukmoQWyZTAhH//3hAAL5xmpKrNwAKJ9RWJGJdgkPfl4o15Y7/vh8PmAAAAArQZrbSeEKUmUwIT/98QAB2UOLJua4An3wFoR4syEEOXuZmY4/atOibAgD5wAAAC1Bmv9J4Q6JlMCE//3xAAHZQs8+6AG9RB9W0YSpsHhetXCUL+DhkSwNMfmgK2AAAAAZQZ8dRRE8bwAfvDpAAnRFTGMwUgsSaGwj2wAAABYBnzx0Rf8AKZTqqhgcAH0wROaV0gz5AAAAFgGfPmpF/wAplOqqGBwAg+zEGKSFgi4AAAApQZsiSahBaJlMCE///fEAAQUR10AIitrs+1Fu7nCG1NqmHFkscBJLgwMAAAAXQZ9ARREsXwAWvjIsAHvMoe9kHf4/CygAAAATAZ9hakX/ABbP2UAB2KoZts+FlQAAADtBm2ZJqEFsmUwIT//98QABBRGJQAjqKzMSR+6Tcxgjsz+gnT85/FwA5fdFzApWL+gsqT1412YnFNBzQQAAABxBn4RFFSxvABHYrLABct3UR0Hc4JSwz0vNDeLiAAAAFwGfo3RF/wAWzK2AA4emUG+62a4IsNSAAAAAFwGfpWpF/wAWs70QAcyKJNc+dbLcEWGpAAAAKkGbqkmoQWyZTAhH//3hAADnmjrgAVB1OboIJy719lWnGjEwRKACxhDUgAAAABhBn8hFFSxvAAoch3HZIACDci3M/eWysVMAAAATAZ/ndEX/AAyf6LABa/lQAB8EHAAAAAsBn+lqRf8AAAMB8wAAAC5Bm+tJqEFsmUwIT//98QAAlp0HoCp8t+slYy+apt0LPJJQQJiKxu8iNaOI3EnBAAAANEGaD0nhClJlMCE//fEAAJcq2f7KAAI949gnOi3+zDTomF7t+TdNRUlaMyBLW5uEGuI3EnEAAAARQZ4tRTRMbwAKMr/0Dv78VMEAAAAPAZ5MdEX/AAzawikWhIqYAAAADAGeTmpF/wAM3rPibgAAADpBmlNJqEFomUwIT//98QAAlywOMAAOO16MIQsoznnGb53DM0+a/AfGgSWXCASDFvz/lOYau//VBhUxAAAAFEGecUURLG8ACi7dv0AK2gjhCrFTAAAADgGekHRF/wAMll9c8WMCAAAADAGekmpF/wAHEgtygwAAACZBmpVJqEFsmUwUTCf//fEAAJaCtwAjSPw81+UW3q9MwGDu/fAG9QAAABMBnrRqRf8ADO00QAlfiwTtHwP9AAAAIkGauUnhClJlMCE//fEAAJbGwLaXQArg3aJJ9YBZrW1ZCHgAAAAQQZ7XRTRMbwAKhh/Uv6+KSAAAAA4BnvZ0Rf8ADNY2Kmg1IQAAAAsBnvhqRf8AAAMB8wAACFdliIQAM//+9uy+BTX9n9CXESzF2kpwPiqkgIB3NMAAAAMCXKe3py1upYQMAAJpKr7hBz8wC8JTtJmqtRCtfcqro/1+ARJBpLXkbvMR9KEz8h3xZVzI/lZFgL4mPwSwAYsyRKFDzXH8kDkAAAMB34CZg5E7cGziXVzv6DThm0om5Xwp6z/fvF8k/OcCUcuSWwQoKUAxVqFPgPhzu0e4SfcuRH9deWeocvKAt4m9rYD5P086v9oEl3sOGD4qdwGX2DwAFGndVlOM4PqRqDOS8NnVIhcfSuxQBVIhfX8TClo1VN5ZGhurtItlFyLPGUt2DQzOUBF8zoHjnK6859Qi9z6fhXbjqp//GHEs3tAZo4jz0Z4MVKkMXfDVFO2IBUmCEqiJdn8+dZfVls1iiv8+CrtGeK+I/ZGhw5XW+w2ZdXZxQ1S/q8P9JqOrnteCmJ8WjfN+uObwtOpwlFLW5l80LmMMPpGUU2sKtYKMnuf2rpRkAB255dtVEXbJZtsx5/L7aPp+KC74iEDcNCwlW4zj/jHZixysl1TXnP6jNIvyAclgV7ZQJsIJ4kXslwYuX/Z9oZtqbkN+boaf4X9GZfkfwTEKzyzWULq/ArcrtSy2+/HiYrfHseQu8JdXzNFcH7MD5tjPKGnbNmY6xLidw591iJEOhsIJpLBVNnRCqs3BJ+YVmoZNH91bQdEm1d2boDNZWpLm3I5k5/gh/oju8lagdVzn5x0L0Mac/8rPVM6NZMleti5NqiW2w0UI5vBdKd36Pw0FbL/ZT7cmBgvman3UXgfKRsdZ7jZLsEArSp3UMIWwFXq+9M6vMtr4Qwti/SXAoayQ1GAyYpnNeeiv7R0y80bcZ/D+QAvt1Qzj2I2po1m54Bon0dLZiWWstFs8YUngsNNFY3nhpb8B+1x1DBP8J4UC4GYhEYAe2mCdX+hcA9FwMecMdtMpd5lkyjPdhQ3DWDQuhf9X+nwwppmt69xNxGd8co7AgkCRMh6Hzk64Y3hQZ7dpBUc3PCZsVBDgO+TsMqrfin6X3AmG+MofGjAb9bggTeya/ZCkNQmcN7KVThLTmtx39bbvflwssoX/hT14kR7DFtTqiRTYF0YHjYJ2dNa3vyQUdycEKlYwN8hPrX0ilspFiET/GrqlivDg9bibw+EyBNaTy0+2JX/00ESVHfLv/5JnP58uiuGikyOOpDkW0OKEaF+u8VbjQAU+7uQNxohfdVOeK5YFVMjzAIK4bBjXnb+Il3Qvbuff//igJfur2Qmm0UslCoAeXCViXWn/sRuiW6uKEFs+pOk6R6nZDbqJMli0mAJ0NvpfNGY11ArPJO3ziN+zlA7iZUHXXMe9W1itkZ2vHwnJP5UzCj20GjJYHaBICkfHImG319GwddyaasA5dUAh7k+XyqYLiWLPMUCK0l3FXk56Edh1YT446gdKgALhQiCA1G6wQoUmYwIIulvkV0x53eY/fMenH83nYN2NMFhuXp8k0O2DWVjt1Xx1TVWhfo6UVMeRlXgr5FOAXyKPJM66BaxTmpZe+uUSsQ3FwPgh4zO2NEvn3BHY8hElQvrdF36DLy5pb80xbbGrHY7hyl74bNScT+rcZbjT5N4qT5565NyamJvjC9NH0EnYDKXj+oeh99WWiVwTQeXWttJj63lSS1AqOPL/fyfTIl8Zff1BW+hr5a8h1nYv44AMkJMet5OEHsTvktZvrJVBE3Nu7RbgFgFaFtqga9ys/dWLJOwFiQ0a0CK/A5Wkhmwg2b1PXw5+392sruzdMbUyN9g8uOgWPAmznK9RfkB/Ul1SsSVpXDYC6IhSR6Ir9/SD3vKXLg5DJhsqT6vtQuxpcPbV/D/BWplBTGFmFVXCpD7gY8bPdkQp+iBswEGSNZPdD0GEecSJtG7u2NZCLJHR/96N7CE5cpRWGRo2koWWdXvuO/riU6NamUFSbIz0Y/F2xE4PTJuvsGoMR3Ml2wFiWStYlK61aaUNHWJakEDmVwSAh0XDclkdevX7dhloHhroe5j9lsgu7aXdkD5hwuOwgW430gpyyWXgHVeixvXF72l9Ed1+ZfwXtN/36No2Fj3uypVCLmrKrjEaYO9uMwBUeoVYt4GBvgk/hf9+FIDgJPHIJQj2MIJL5AI3oG71+8tV+tMGe8FeAIDmb+5tkvu3uMNsBh5INTlst79RURNm8OIWNF/taqcipYQz2fHuOnVgp4aR7I5dsALp8/l9+MW33sS6bB8+68ayrVeQv0rNaiCbn1jdzz5QRT+DR7O1EfqvBLVuE50jTgVMssNJbihvdPVILxgevOEst8Nqh96m7GwTlgxOuwLcN7NO/iRA4AFZ8hP6KHwNMBtA/Y7RXoOCWHFpjnIf++yrpKww9LbUML//bUAt+T5k4fiqoTLilQkad2TJ80rxaM7kBXsvNH4UOxbHL4Y9hSh5IwTLGqodryJT747PeE/+wPzeYdbGPlsOOjcMYRx/VTVrzp4R06pB0gc4wUqedDj5J4n3qYVfmcj9/iK7ZPQ+Zo69wao0DpmJYU/BuI1goJLZ3+usdjPMjbNm/M2T9MRZa4BRLbxkc4DIM3M7VwJ9VuSV83htrCN9b6RkoWWO0+DUFpRIcg3sddxTLAvamjm0aqcC4z9wyXyNYxXQV5FUyI9IbHeQQOWsQKP2Vuu/mu0eFNTdrZJa8X8tCaLPq524p7Jbe844mTSfS+NkO8ziEOZom8/+0gLJZyHEVPxQj00edB4yAGfV7EsOokXJD/+ugB4ThA1kv0C3vy9EiPIlekm2gwH0m40aB27+JdUXUzbtR3Y2dymAi012DdukKsNGLJRbBLUiNzOsB07R4KMkhgvI8Xn8j1lcI2HqAAAKyAAAAD9BmiRsQj/94QAR73ViXrkcAnanVZeNo4qFXkIF6dGj9B/YCSJpQuX4d6L/8jhHj9eSMmeydfg3uAf7ugVumfEAAAARQZ5CeIv/APLwysGriaO0yggAAAARAZ5hdEX/APMB1ZrkhVoHCVkAAAALAZ5jakX/AAADAfMAAAAhQZplSahBaJlMCE///fEAAJaI66ADgrNbFyxcXpk+rIQ8AAAAKkGaiUnhClJlMCE//fEAAJaIMgAEVMlbti47vctmpT2lOmP47+mX2aCHgQAAABZBnqdFNExvAAopGPzAA8Yxz/lXZEZUAAAACwGexnRF/wAAAwHzAAAACwGeyGpF/wAAAwHzAAAAMkGazUmoQWiZTAhP//3xAACWhStACHZuljhPb9WQ1BLYsdMEwK8IJCJOJ0t/MjFXNBDwAAAAGUGe60URLG8ACkaYwANTt6vHiYHIv7nbZUwAAAALAZ8KdEX/AAADAfMAAAALAZ8MakX/AAADAfMAAAAoQZsRSahBbJlMCE///fEAAJaEVwAcYTrPUr1I7Vbx5U3sO9pdhLhUwQAAAA1Bny9FFSxvAAo5hRGVAAAACwGfTnRF/wAAAwHzAAAACwGfUGpF/wAAAwHzAAAALkGbVUmoQWyZTAhP//3xAACWgY1ADTcy3VGeWCEf7pNCK6ckPmz+nheSAqS4VMEAAAAXQZ9zRRUsbwAKPnmgAQcN+nh8evUxqhkAAAALAZ+SdEX/AAADAfMAAAAMAZ+UakX/AAcVvblBAAAAMEGbl0moQWyZTBRMJ//98QAAloE/gCJCtVq2XlfoiNpSlOcImS+quAI1q3TnQ7YZkQAAAA0Bn7ZqRf8ABxhC8HTAAAAANEGbu0nhClJlMCEf/eEAAOzvKQBHaQOxcb36yjKh6K+HXf9GyWnjtxs2W0WEaBUoBfj4k4AAAAAQQZ/ZRTRMbwAKOYIBawL0XQAAAA8Bn/h0Rf8ADNvUxsNBqpkAAAAQAZ/6akX/AAcMa5zMrlQ+YQAAACZBm/1JqEFomUwU8I/94QAAgsa/a3y4AHK3xiC8O+sH4jlpyFQi7gAAAA4BnhxqRf8ABxU3al4OmAAAADBBmgBJ4QpSZTAhH/3hAACCypZfy4ANdwl2OUKy21hFD9wVsbn3omSRuNWZVmlasUEAAAAVQZ4+RTRMXwAHG/RYAStBSl2ZDQwIAAAAFQGeX2pF/wAHQbi4SACHKbWDMF2DKgAAACRBmkJJqEFomUwU8I/94QAAhmeExhfDYBqUizhw6uXyn8wSNSEAAAAQAZ5hakX/AAc80ndgZ+xlQAAAADdBmmVJ4QpSZTAhH/3hAACGlrUtAgADoDWBXUAmnViS476Y51CVCmwyD0M1qYVoXSZYFcCXWjRhAAAAEUGeg0U0TF8ABzBVpc7n7k3BAAAAEAGepGpF/wAHLgpMExE8LuAAAAArQZqmSahBaJlMCEf//eEAAIayL8gAc97Ti9OBaSQCE7Dcn1D+1WSvnSsTcQAAAC1BmsdJ4QpSZTAhP/3xAABWVRAIPAEHjvJOBvDKWhs0E3XVcP9x0JzHoLfAMWAAAAA0QZrrSeEOiZTAhH/94QAAhxQ89eRSQCBQXRZCgBwrB8oVVvYUq71+zNPJWbAOYgOo/GEW0QAAABBBnwlFETxvAAXL+51qltJxAAAADwGfKHRF/wAHNzF0kPBxwAAAABQBnypqRf8AB0f0WADXC0Yz1fwccAAAACZBmyxJqEFomUwIT//98QAAmo4Mt3AHMMNVIy7SZLFJWh1mv9jDHwAAAD9Bm1BJ4QpSZTAhP/3xAAEVEPbAETHJnpdFymVM5nR5oj2aXLkZmUhmv6GRt79MnX1AU6uWRA4B9pwfBQqowbcAAAAPQZ9uRTRMbwAS32qZ9K+BAAAADwGfjXRF/wANKAVM4G4rYAAAAA0Bn49qRf8AF/E7IHHBAAAAKUGblEmoQWiZTAhP//3xAAEVEYlABx7QhCWSW8hQTgzNI+iJCQn+S4LbAAAADkGfskURLG8AEsMxBBBxAAAADAGf0XRF/wAX16yBxwAAAAsBn9NqRf8AAAMB8wAAADBBm9hJqEFsmUwIT//98QABFQVuAEaqndknaUOtb5HjZGWKEZiNmS+2uCvcRStWQScAAAAXQZ/2RRUsbwAS2KywAbTr/3UIZtL0Q2cAAAAPAZ4VdEX/ABeOvQzZQOOAAAAACwGeF2pF/wAAAwHzAAAANkGaHEmoQWyZTAhP//3xAAEVBW4AR7yv0aJEpR7K+Xo6mSTuy6mOF7RS7umq33zLUDqIjk0G3QAAABBBnjpFFSxvABLXMa4RSHJfAAAAEAGeWXRF/wAX4ok/4+2vFBEAAAAPAZ5bakX/AA0WDhCCyDPgAAAAK0GaX0moQWyZTAhP//3xAACaiDIABGR1NgeapCtLoEDUt1UsH9E06o2S4UkAAAAQQZ59RRUsXwANFg4Lu7wPmQAAAAsBnp5qRf8AAAMB8wAAADBBmoFJqEFsmUwUTCf//fEAAJpKzzSOTxwBW/K/sOYbGAAbnoG90Sgc5PsYdQclwpIAAAALAZ6gakX/AAADAfMAAAA6QZqlSeEKUmUwIR/94QAA8nMRABvQEGHtgquxEsPjXZWxDLRmbI0K32ZrOAo1xKD20pFA3ayEBgwjYAAAABhBnsNFNExvAArOMcWYAH31jShgvgj3BB0AAAALAZ7idEX/AAADAfMAAAAVAZ7kakX/AA00/0QAg32zU0RnwPmAAAAAJkGa5kmoQWiZTAhP//3xAACag57gCkaGvu0SV4YY7RhZoYNdmgg5AAAAM0GbCknhClJlMCE//fEAAJ/urgZlagBEfYi6Sfbv/W4IfMLJ0wtsVQGN7FC8WpwUOI3EXAAAABBBnyhFNExvAArHTL6uPBBxAAAADwGfR3RF/wANhKINdbDZgQAAAA0Bn0lqRf8ADXzbkgyoAAAAKUGbTkmoQWiZTAhP//3xAACagT+AK35MtPOk8PmjTGg0vTqCKXpsgwpIAAAAEEGfbEURLG8ACsVkGP5WG9EAAAALAZ+LdEX/AAADAfMAAAANAZ+NakX/AA0ufpcDewAAADlBm5FJqEFsmUwIR//94QAA+AuisAcTpLpHc6c5RnIF/juNMHfdSkX8xGFwp8PfxeFePibOyU1aHBAAAAAYQZ+vRRUsXwANf5aIAWsNQjUia64roMqBAAAAFAGf0GpF/wANjgbAAhR6zO0XAZhRAAAAJkGb0kmoQWyZTAhP//3xAACfkGPRaAFcyUsxvHcVIs8jXdvVkH+BAAAAMEGb9knhClJlMCE//fEAAJ+UhGTIAACMjJtiwRfo9Ckb0NB0vJTCcF2kLMe1bNA/wQAAABFBnhRFNExvAAqlUVSziqIOmAAAAA8BnjN0Rf8ADXArIL1IMqAAAAATAZ41akX/AA2OqaAD8H9NS10GVQAAADRBmjpJqEFomUwIT//98QABHVQPgAvxJStUKfRgia969W3773Oq7S8VS9H3TQXxnob00GtAAAAADkGeWEURLG8AE1BKCGpAAAAACwGed3RF/wAAAwHzAAAACwGeeWpF/wAAAwHzAAAALEGafkmoQWyZTAhP//3xAACe1i3ADXFkNVXE/o5VArVc3HJy1a8dyG3ilwoJAAAAGEGenEUVLG8ACs6zkACDh6XwKwruz+yggAAAAA0Bnrt0Rf8AB1bMT8g4AAAADQGevWpF/wAHVsxPyDkAAAAsQZqgSahBbJlMFEwn//3xAACe1POAIkF57tysrcF6p7AHlakKgVFotu+ANSAAAAALAZ7fakX/AAADAfMAAAAxQZrESeEKUmUwIT/98QAAntFPgBrlG3vXBFY6wrhLjFSBvXAk4k/3sDeuzbWRdyXCgwAAABZBnuJFNExvAArVF4AGptrna41piDpgAAAAFAGfAXRF/wANjVogAvFwQcTjzA9JAAAADAGfA2pF/wANfrPiTwAAAB9BmwhJqEFomUwIT//98QAAntLfgBumfmgMitcusg/wAAAAD0GfJkURLG8ACsl8YKVjjwAAAAsBn0V0Rf8AAAMB8wAAAAwBn0dqRf8AB229uS8AAAAjQZtMSahBbJlMCE///fEAAJ7RT4AbmoORqnpFur32wwXoYUEAAAAPQZ9qRRUsbwAKzj3l0ZcQAAAADAGfiXRF/wAHaLMhbQAAAAwBn4tqRf8AB229uS8AAAAzQZuQSahBbJlMCE///fEAAJ7RT4AbmoQcz3q/V1LBQQS2jLeZVUPqLvjg78+WY969jDCgAAAAEkGfrkUVLG8ACsl8wGN8TM1MwQAAABQBn810Rf8AB2+GOATGi6oIm9k1fAAAAA8Bn89qRf8AB2kK5WzDV8EAAAAxQZvUSahBbJlMCE///fEAAFh3D1QONwAtZhpeCj0h2RPisMMllHhosggGL3bFURuPmQAAABJBn/JFFSxvAAXkZdn/e4Tst60AAAALAZ4RdEX/AAADAfMAAAALAZ4TakX/AAADAfMAAAAlQZoYSahBbJlMCE///fEAAFhrFuAGgm1zEenlc5Ytd6lynpLiPgAAABVBnjZFFSxvAAX/LkAC3POWBH77tH0AAAALAZ5VdEX/AAADAfMAAAALAZ5XakX/AAADAfMAAAAyQZpcSahBbJlMCE///fEAAFhrFuAKZI6ubXV4P+9jBcC+ch6Xt5mCUjCUhByS2HGXEfEAAAAYQZ56RRUsbwAF+oPIAE0LdfQ5pgpMkBqRAAAAFAGemXRF/wAHc1TQAmUJQMfE/kHBAAAACwGem2pF/wAAAwHzAAAAPkGagEmoQWyZTAhP//3xAACe0t+AG67ew8qG83dpm+51n0GTPbhivc9YTmDOCyGj1llMUO7BdwYTMgbTGGFAAAAAD0GevkUVLG8ACs49DPqZgQAAAAsBnt10Rf8AAAMB8wAAAAwBnt9qRf8ADX6z4k4AAAAlQZrESahBbJlMCE///fEAAJ7S34AbYT++a0N8JMhRZ/zUD3wBqQAAAA1BnuJFFSxvAArJhRGBAAAADAGfAXRF/wANhleB6QAAAAsBnwNqRf8AAAMB8wAAAClBmwhJqEFsmUwIT//98QAAntP3QAcHO8woQBC0A2lFvjtG3/42VNZB/gAAAA5BnyZFFSxvAArOPiyggQAAAAsBn0V0Rf8AAAMB8wAAAAsBn0dqRf8AAAMB8wAAACtBm0xJqEFsmUwIT//98QAAntP3QAcWdBaco+/fwYdl7La4+dmhv8HwS4UFAAAAF0GfakUVLG8ACspcWADZ6sGNt2zSRGBAAAAADAGfiXRF/wAN1leB3QAAAAwBn4tqRf8ADc6z4j8AAAAvQZuQSahBbJlMCE///fEAAKM7BygAIw1vsdwulfizC/EMHRXgTzXZsBXQb+8TCbgAAAARQZ+uRRUsbwALFUM0TdsLQsEAAAAOAZ/NdEX/AA3Urx0/A1IAAAAOAZ/PakX/AA3HyQzpBi0AAAA0QZvUSahBbJlMCE///fEAAKM+ZNwAEOy5AJ7PYx4i9BiAhcITvz6ylyFdXjE9Ij5M7c0D5wAAABBBn/JFFSxvAAsZEcwZW6lZAAAADAGeEXRF/wAHlLMhZQAAABYBnhNqRf8ADc9MQAJlhmwLN023FtHTAAAAKEGaGEmoQWyZTAhP//3xAACjUt+AIewcX4lG/gqJOMAeSNsI6+S4TcAAAAAOQZ42RRUsbwAGI/mbRcEAAAAMAZ5VdEX/AAeUsyFlAAAADQGeV2pF/wANzrIR2Q8AAAA1QZpcSahBbJlMCEf//eEAAP1xdgBeVI1zrEDbHxR0dC3LCmUgh+GsQtxaUjgCQNC5ov7rQ28AAAARQZ56RRUsbwALCeSA16MUmWkAAAAMAZ6ZdEX/AAeUsyFlAAAAFAGem2pF/wAN3gbAAh7jYqUGHIeAAAAAKkGankmoQWyZTBRMI//94QAA/XMRAB0i45WuEiCw/+D3+EDxwekPzKHxDwAAABQBnr1qRf8ADd4GwAIe4EuQ6JYy9wAAABZBmr9J4QpSZTAhP/3xAABa09q9mg44AAAAK0Gaw0nhDomUwIT//fEAAFrpo3RMswAAHJQtpWmRXl56j50cje2CcgtzQccAAAAPQZ7hRRE8bwAGGwN1hklTAAAADgGfAHRF/wAHiSsK1BswAAAACwGfAmpF/wAAAwHzAAAAOEGbB0moQWiZTAhP//3xAACjUt+AIl6DNWac856lvcilfCRvBqJtZlsKUu2BSqtBA+ljNrF2ML2BAAAAEEGfJUURLG8ABhWX64VyVMAAAAATAZ9EdEX/AAefVNACq2Kjuc/H+QAAAAsBn0ZqRf8AAAMB8wAAAC9Bm0tJqEFsmUwIT//98QAAo1FPgCEY0hdL0QpTvblU1QzQzV2XYF/+xAJ674Az4QAAABVBn2lFFSxvAAsdF4AH7THpf/cf2TcAAAALAZ+IdEX/AAADAfMAAAALAZ+KakX/AAADAfMAAAAkQZuNSahBbJlMFEwn//3xAACjUt+AIeC1oBrak/U8A/++AM+BAAAADQGfrGpF/wANwfkMDugAAAAsQZuxSeEKUmUwIT/98QAAo1P3QAh2bsNJQQS/wlXZZshkn8jqyMDFvO+AM+EAAAAWQZ/PRTRMbwALFpssAGwGF0qAclIBxwAAAAsBn+50Rf8AAAMB8wAAAAsBn/BqRf8AAAMB8wAAADNBm/RJqEFomUwIT//98QAAp9FS98eIir2npf65M4W6AUZ4Ho8gJq3YxQyiBggOuERG4g8AAAAYQZ4SRREsXwAOMIHn4ACBH/7kONeDYGfBAAAADQGeM2pF/wAOMIXgdMAAAAAyQZo4SahBbJlMCE///fEAAKe7G43nwDRjVtg3EH58zQmR00f0XzSl3ZSjQ+hZu7XcwsoAAAASQZ5WRRUsbwALXX8LPN7dPnHBAAAADwGedXRF/wAOICHt//sM+AAAAA4BnndqRf8ADiHZ5fBgQAAAADlBmnxJqEFsmUwIT//98QAAp7l/N58A0YY4OE56aQ6bW8CEilQqL0QZjiVNvw74lQUPomd97C9NC4kAAAAYQZ6aRRUsbwALWhVAAnPGAbBWq504jUjBAAAACwGeuXRF/wAAAwHzAAAAFQGeu2pF/wAOJBa4FAG0HNnbjW7LiAAAACpBmqBJqEFsmUwIT//98QAAXRR/buffSIABV+6Li9gmXi1G2HwTmXezQb0AAAAQQZ7eRRUsbwAGQugUKXyUkQAAAA4Bnv10Rf8AB8YbPKQrYAAAAA8Bnv9qRf8AB7xrnM94NSAAAAAnQZriSahBbJlMFEwn//3xAABcxqhYn1S6AEaX+0Nc1gTD+Jo3ZDehAAAADgGfAWpF/wAHwOy/3x8wAAAAQEGbBknhClJlMCE//fEAAF/1NwaUTgCo8t2W6Zl1ML8+z1UzUhNmUxPtsdDQhOsDqrRrKjeMKtuYmHKQf7IMQcEAAAAZQZ8kRTRMbwAGc+2wTrVgAnJ/9H20cjAtoQAAAAsBn0N0Rf8AAAMB8wAAAA0Bn0VqRf8AB/RC8GfBAAAAM0GbSkmoQWiZTAhP//3xAABf9TZZdHMSAL70IuBUbD1KSUZME8vAWZq74EaCmJ3Yb4BZQAAAAA9Bn2hFESxvAAZqPSZS2g8AAAANAZ+HdEX/AAfsCy/HpQAAABEBn4lqRf8ADnwKjbRvuwy0gAAAADtBm41JqEFsmUwIT//98QAArFFPts6eIgfW9obXI8m5vZqKaphExssf0VK06BmAerT/mwqUFd/GT3eJOAAAABhBn6tFFSxfAA6DeWA4AFpqKeLiikE0Eb0AAAAVAZ/MakX/AA6IhddXAB4NF6I7FdlpAAAASkGb0UmoQWyZTAhP//3xAAEk1FagAtnlvzr/TjoO0QP5d3/kzyPGa3Wxu84Fva/mUKcBzwmpP+zdWo1VrF7DA8fl4rqs+sK/UcIXAAAAD0Gf70UVLG8AE+yBzRpRswAAAA4Bng50Rf8ADoU16PA44QAAAAwBnhBqRf8AGSkb4UEAAAAhQZoVSahBbJlMCE///fEAASWN/DboALT5Akm+TuZLIIuBAAAADUGeM0UVLG8AE+MKIZ8AAAALAZ5SdEX/AAADAfMAAAALAZ5UakX/AAADAfMAAAAvQZpZSahBbJlMCE///fEAAgrgEwAX+0IQlkzTONa2cOCf4SdRHeGWQDZdgxdL4RMAAAAXQZ53RRUsbwAjvtmtVgAhZlrOw592i4gAAAALAZ6WdEX/AAADAfMAAAANAZ6YakX/AC2HW3BlQQAAACVBmptJqEFsmUwUTCf//fEAAgyud0DdACw9ykDSlAf8ZEBkvhExAAAACwGeumpF/wAAAwHzAAAAKUGav0nhClJlMCE//fEAAgkouFqUAI6RXMJWQUtLLpuRLplQ+aFvsYHNAAAADkGe3UU0TG8AE+x8WK2AAAAACwGe/HRF/wAAAwHzAAAADAGe/mpF/wAtY++CygAAACxBmuNJqEFomUwIT//98QABJQpWgBDsvtAB1vHuGygdOkFNTtReIVMXFcuCtgAAABRBnwFFESxvABPlI0gAtWwn8JA+YQAAAAsBnyB0Rf8AAAMB8wAAAAwBnyJqRf8AGR1nwoMAAAApQZsnSahBbJlMCE///fEAASUIrgA49Vzm65C6moUcbOutIsFpne74AccAAAAOQZ9FRRUsbwATzbEED5gAAAALAZ9kdEX/AAADAfMAAAALAZ9makX/AAADAfMAAAA2QZtrSahBbJlMCEf//eEAAc4nO4PiAIIC6vAoVBn3JcD5y2M3PmDMFzq0bnhboE6BPaFHWg8ZAAAAGEGfiUUVLG8AE/ovAA2HzQn0rqcahiB8wQAAABUBn6h0Rf8AGS3QgAOsGtv81ka6BswAAAAVAZ+qakX/ABkemIADsXU8jGXQ4EHAAAAALEGbrEmoQWyZTAhP//3xAAElAn8ARIVvitBLZ6f6ZgMHLuKAbDD0gg//3eFbAAAAOkGb0EnhClJlMCEf/eEAAc4hmu/gCtP+4XamCiNLll1PDGIu64nmzGfrAlALaQx0Ho+jtreTLnE/ghYAAAAaQZ/uRTRMbwAT4wDB6rABDNzM5jeTkt57SmkAAAAXAZ4NdEX/ABkXpnG+AB1gWKr45p+5pWwAAAAPAZ4PakX/AA3C7F8U8DuhAAAAK0GaEUmoQWiZTAhP//3xAACn1i3AETHUeF7mo+kanWkmCyWYge6MhZ3MLKEAAAA5QZo0SeEKUmUwIT/98QAAp9TzgDlFYIV7XQp2C7pcx7dYh/eqqd1VWnyereT+qUPy3f5dzXh6sg9JAAAAGEGeUkU0TF8ADhtkrCTAQAtRNGDeb9dBgQAAABUBnnNqRf8ADiTjYANOw2XyMmyfA6YAAABAQZp4SahBaJlMCE///fEAATUCfwByZBKkJy0oB9b82wlJ97ZFKavwztqFhdA1it31F+oJF9IUI19rvp/UPn2IuAAAAA9BnpZFESxvABUMgPgeeUkAAAAMAZ61dEX/AA4hZkGBAAAAFQGet2pF/wAaSxOaIAQkOk5Cs04S8AAAABVBmrlJqEFsmUwIv/pYAA6V8qmkE3EAAAt7ZYiCAAz//vbsvgU1/Z/QlxEsxdpKcD4qpICAdzTAAAADAlynt6ctbqWEDAACaSq+4Qc/MAvCU7SZqrUQrX3Kq6P9fgESQaS15G7zEfShM/Id8WVcyP5WRYC+Jj8EsAGLMkShQ81x/JA5AAADAd+AmYORO3Bs4l1c7+g04ZtKJuV8Kes/37xfJPznAlHLklsEKClAMVahT4D4c7tHuEn3LkR/XXlnqHLygLeJva2A+T9POr/aBJd7Dhg+KncBl9g8ABRp3VZTjOD6kagzkvDZ1SIXH0rsUAVSIX1/EwpaNVTeWRobq7SLZRcizxlLdg0MzlARfM6B45yuvOfUIvc+n4V246qf/xhxLN7QGaOI89GeDFSpDF3w1RTtiAVJghKoiXZ/PnWX1ZbNYor/Pgq7RniviP2RocOV1vsNmXV2cUNUv6vD/Sajq57XgpifFo3zfrjm8LTqcJRS1uZfNC5jDD6RlFNrCrWCjJ7n9q6UZAAdueXbVRF2yWbbMefy+2j6figu+IhA3DQsJVuM4/4x2YscrJdU15z+ozSL8gHJYFe2UCbCCeJF7JcGLl/2faGbam5Dfm6Gn+F/RmX5H8ExCs8s1lC6vwK3K7Ustvvx4mK3x7HkLvCXV8zRXB+zA+bYzyhp2zZmOsS4ncOfdYiRDobCCaSwVTZ0QqrNwSfmFZqGTR/dW0HRJtXdm6AzWVqS5tyOZOf4If6I7vJWoHVc5+cdC9DGnP/Kz1TOjWTJXrYuTaoltsNFCObwXSnd+j8NBWy/2U+3JgYL5mp91F4HykbHWe42S7BAK0qd1DCFsBV6vvTOrzLa+EMLYv0lwKGskNRgMmKZzXnor+0dMvNG3Gfw/kAL7dUM49iNqaNZueAaJ9HS2YllrLRbPGFJ4LDTRWN54aW/AftcdQwT/CeFAuBmIRGAHtpgnV/oXAPRcDHnDHbTKXeZZMoz3YUNw1g0LoX/V/p8MKaZrevcTcRnfHKOwIJAkTIeh85OuGN4UGe3aQVHNzwmbFQQ4Dvk7DKq34p+l9wJhvjKHxowG/W4IE3smv2QpDUJnDeylU4S05rcd/W2735cLLKF/4U9eJEewxbU6okU2BdGB42CdnTWt78kFHcnBCpWMDfIT619IpbKRYhE/xq6pYrw4PW4m8PhMgTWk8tPtiV/9NBElR3y7/+SZz+fLorhopMjjqQ5FtDihGhfrvFW40AFPu7kDcaIX3VTniuWBVTI8wCCuGwY152/iJd0L27n3//4oCX7q9kJptFLJQqAHlwlYl1p/7EbolurihBbPqTpOkep2Q26iTJYtJgCdDb6XzRmNdQKzyTt84jfs5QO4mVB11zHvVtYrZGdrx8JyT+VMwo9tBoyWB2gSApHxyJht9fRsHXcmmrAOXVAIe5Pl8qmC4lizzFAitJdxV5OehHYdWE+OOoHSoAC4UIggNRusEKFJmMCCLpb5FdMed3mP3zHpx/N52DdjTBYbl6fJNDtg1lY7dV8dU1VoX6OlFTHkZV4K+RTgF8ijyTOugWsU5qWXvrlErENxcD4IeMztjRL59wR2PIRJUL63Rd+gy8uaW/NMW2xqx2O4cpe+GzUnE/q3GW40+TeKk+eeuTcmpib4wvTR9BJ2Ayl4/qHoffVlolcE0Hl1rbSY+t5UktQKjjy/38n0yJfGX39QVvoa+WvIdZ2L+OADJCTHreThB7E75LWb6yVQRNzbu0W4BYBWhbaoGvcrP3ViyTsBYkNGtAivwOVpIZsINm9T18Oft/drK7s3TG1MjfYPLjoFjwJs5yvUX5Af1JdUrElaVw2AuiIUkeiK/f0g97yly4OQyYbKk+r7ULsaXD21fw/wVqZQUxhZhVVwqQ+4GPGz3ZEKfogbMBBkjWT3Q9BhHnEibRu7tjWQiyR0f/ejewhOXKUVhkaNpKFlnV77jv64lOjWplBUmyM9GPxdsROD0ybr7BqDEdzJdsBYlkrWJSutWmlDR1iWpBA5lcEgY/KfWHDDNrJG986W246MLTYQehTzeC4huZB+wMPdfkaZ6sQhGZ/6tFUfIFLlNyG3/AAEe2XQtODonw4+Ep1sP6qfDWyq8FkrMcA6q8tlbBgt8m5KYXyfM6zkm/GPhlU8zZ74uXqheQaM7QcM/dGBKKY3D5AI3oHQrOw7U19MGe8EqBVuRMVqTvhuwwjw3mw8k8CMH7IPYAIKTVsYMtGq5fRTipLCGDancFmnW3mEeHiEbZykt3z+WD3tH/exLpbWQvWXSHS3L4trK6C5yufwyJOjDFtAuiM453mqZLTOcqV3OT/UwtMUd31VIksDI/EYoqy2NeWIAkZkSWkK4uZPG/RPxqRZLuwonHR888CZ4NBhmE/tAiXu9OjwSbCvrqhvb2o3dpdakCCGHrtg5F6qyWXOxwuTrQAnPEpfR+UvQTqqOLke/4bHK1c3WFaDwlOu80RLH+G2N0Kae7hKbq5jCPdaLvYCH5lQ4ESGCh2QUPHAEXL3qREH1Zcz3CIybbMswbWh15U+jWiN263Axsiis04TVv6Vl5X4Wft5jqFwZiqm0FN46tY5d0D7ZIJ5V0FTUjfhmdbrD4ddhjkvcu2bM4RpBPXg2qKNiecu8khIr1tn1H94CBtrSxvM+AJRj0/knsWvS6Q52rATEw8nkwit09GrTYPejqyH5A1+YgRx8UNO6qLmkv1vWk6mTNg2273JoC9FSe5zbvBUCUBLCQ4RjUKaRzh6x8Zk/oviRzl1a1N1FVQtrTNvsgyDlyxDT78weppCWoiMet141YSHWPeLCrGai2Sib5yDixGXbN1vj/0uR0E512L4csW/tthXFcw/THh6Rgm+RTTDSTA3KKAcQk07IIfZj0BkD9Qs/Xw2GkRpNoFgz5AypMiOpmD82Gu3O2jRwBiGmZLWUrWFhVEYYSwdx3ktzucp8mj2A61kbV1fqb9N2grJEDitotXWcXGAqFSKJh4aJte76dKN21jWdaYmGIuUCL9JR3JNgLeU/G+PgGhb650WdbumuznWIqnbPSDqv3pQ8SyaL/4Z3IPwlLseZd+S1eKTj74XPk10rogejfUiTdSAjfNA9kxg2mh9cnLn5or9Nn5jrdVNQtuDCJZh/4sI9LWfbCWI6uwJxkkcyVMaEfpWElp0GgLPL+HItw69mPDJB4Yg/cls+GiOg7dCDNDNpc7Ni19XUzNpjPs2fiVdMODvBzhRFOXPQSkWbjjA7iuydW+4CLHvhvX2F4KK2VVNdbk4n9JyC+oUUHmHB8pUpwZrbHc2GTqEgdq0mE0+kUxOcS/pMqdo6efvaBb87/ip/Ea9auclZiwG9pvQjeBtyYBlygXE+xY5c3P7aRkYbco4X45xOVZpTQyAJ7NaSiohWPqQiaUH7YH3KaKTUPI4l176k9BxkDYQDQ48XZtPerpCXJHBaH8tOLI6LvLP6KBWzw5Y3Ice4ynMfkubNfJ6zAM/zYh3NNP4xDCbXz9cTz0BI7z22/BNXwVK732NwS85Qk2YHLR8wflBAq9eCroiPCYgGdB1qHH6cZZbBSXMv3tyB6lPTDtWzx7TXfp7ZHRFMbo/J2cKaEzajiqZHiqU0PscofBTjU65E/sDKrjxEuNM59WB9CLPzfLnX23xD4bOZvk/FO9z2++oO2BhdWu4kZJXIMhWN3tjTAE0Fe5h9RSe2EQLQEzK7iTF9bUM/iaGebuLej+Y5UBxgH8C7ZBOWFDsV1ZRGPfPe/Eg5p+UEFwPCY+anVTfB0Ncx6PJifaWROGl/cjT5CJt3RjkfWePXLtwDciLASnnc4c/KHAo4GNvLLlV36C9mNXG3ZhwlleEZV60hrTd6a+TqIRId7WWEcWx50Jh2A2Mz/Q4xXXGL/JfblSFVAEU5JkmYAAAAMAAAMAdP9kVW8VE97u7q7rRgiQ7MOxD9ENgyBQI97vF4RJhAAADlkAAABIQZokbEJ//fEAC2exGDfatADlYONNmbyNOxwAQJEzgBeGS2aZeJLOp6VSFtH2/h1N4AforriLlMFh96YYIvcOxckJgx9/IY6ZAAAAHUGeQniN/wDEFa/thEmyXL4AEPLTC3RBOFxJKUP8AAAADwGeYXRF/wDzAdWKLwoj4AAAAAsBnmNqRf8AAAMB8wAAACpBmmhJqEFomUwIT//98QAAsPS7dppjt+PmwJZiIpAe/3SvYvBXYN2QYR8AAAAWQZ6GRREsbwAeb7fTuBda6lydXVDjgAAAAA4BnqV0Rf8AJqSj81njewAAAAwBnqdqRf8ADtt7cS8AAAAsQZqsSahBbJlMCE///fEAALDn/kB4AG1V+yopMhqkjSJ3qpYoo2nhZiNw+YEAAAAYQZ7KRRUsbwAeb7fK/VYALnE2FmiVEPlBAAAADAGe6XRF/wAmpJfBnwAAAAsBnutqRf8AAAMB8wAAAC1Bmu5JqEFsmUwUTCf//fEAAKxaiqAEdqEBXVEnCNXQrkglLZ2VQSynk/s0DugAAAANAZ8NakX/ACbK2fBnwQAAACtBmxJJ4QpSZTAhP/3xAACsJRpSMfdOAEYvPPIxZnHMLoYqBWT/W64rIO6BAAAAGkGfMEU0TG8AHmv/5GezAA/BPV/tlSnPi+UEAAAADAGfT3RF/wAmpJfBnwAAAAsBn1FqRf8AAAMB8wAAAC5Bm1ZJqEFomUwIR//94QABDQCrAFWNM6jf08XG4PVy1ah+cyPvlPSYjvUuwYPuAAAAGkGfdEURLG8AHm+3w9Z2AAe8tpzdb1WXF8oJAAAAFQGfk3RF/wAmpKO2BwAD9y0/hVcccAAAABUBn5VqRf8ADoPhsAGpnTpUBVt5xN0AAAAkQZuXSahBbJlMCE///fEAAKxU84AiX65d4Lf0bxjOTpnVkHdBAAAAPEGbuUnhClJlMFFSwn/98QAArFTzgCJjpuPr1VwMcMyLE5K+WAk20nSoicRNOQd6RIA4/q+TSrCsEiNw/wAAAA0Bn9hqRf8ADjCF4HTBAAAAJkGb3UnhDomUwIT//fEAAKxWLcARMdTu9Bvw5iwty/eL2Cb2QYScAAAAD0Gf+0UVPG8ACzVmsgEW0AAAAA0Bnhp0Rf8ADieM+IuBAAAACwGeHGpF/wAAAwHzAAAAK0GaAUmoQWiZTAhP//3xAACsFZeDG8AHGnWynB8jrb6ozX3+AX1A9++AMWAAAAAMQZ4/RREsbwAAAwGVAAAACwGeXnRF/wAAAwHzAAAACwGeQGpF/wAAAwHzAAAAIEGaQ0moQWyZTBRMJ//98QAArFFPgCENAN6ELJafkuEnAAAACwGeYmpF/wAAAwHzAAAAMkGaZ0nhClJlMCE//fEAAKxUCtACo3zq8rBnCDXFh1Zzjy2ISkvMLIuTVjGaqAaYzQO7AAAAGkGehUU0TG8AC63yQAIQIl32wRK01FkuQDZgAAAACwGepHRF/wAAAwHzAAAACwGepmpF/wAAAwHzAAAAOEGaq0moQWiZTAhP//3xAACw0U+AG2qbi4TUnHX7h4J8sPKkqrGUtM7Tm8BTXV9no55QbyZ3MKmAAAAAEUGeyUURLG8AC+cfUB0nxlnrAAAADQGe6HRF/wAO4DTgb0EAAAAOAZ7qakX/AA7EhTPgb0AAAAApQZrvSahBbJlMCE///fEAALDRT4Aa0RuG77BhXBogg8ZD+WkJSfZoHTEAAAAZQZ8NRRUsbwAL/I0ACx6+ZJPPrMA77IitgAAAAA4Bnyx0Rf8ADsvl4Igb0QAAAAsBny5qRf8AAAMB8wAAAC1BmzNJqEFsmUwIR//94QABFlHmAAIwHN7dMZiYLlUk38/VQHKeYyCVadKwl4AAAAAOQZ9RRRUsbwAL52cIA1IAAAALAZ9wdEX/AAADAfMAAAAMAZ9yakX/AA7UFuJeAAAALUGbdEmoQWyZTAhP//3xAAE9rFuAKZI8QsrWLPrbPsffyJQp6b+sI6hGXwA1IQAAADRBm5hJ4QpSZTAhH/3hAAHv6MgAdAcbFVRrnPiWGlVXE7iTq4/0XKEcPw3LHst1ie9lWoZ8AAAAGEGftkU0TG8AFZQqgAQ8uppp3YPH7P/IOQAAAA4Bn9V0Rf8AGuoq/AYD0wAAABQBn9dqRf8AGxpogA5btNavULtDuwAAAClBm9tJqEFomUwIR//94QABFmQm5AFABvPxF6SLh5EgDMt4JfZFfjCFlQAAABRBn/lFESxfAA7mqaAEy9mLuH4g4AAAAAsBnhpqRf8AAAMB8wAAACxBmhxJqEFsmUwIT//98QAAsNFPgCmR2K60e3wWM7JQlN4BZlL0aQ3Wasg6YQAAADhBmj9J4QpSZTAhP/3xAACw0U+AN8dLQfXg1wuHXnRwgzL115eMu73PzBlloXP0ObUb+YRzH26IuAAAABdBnl1FNExfAA7lNEAJmmbsfpxBOJcesAAAAA0Bnn5qRf8ACBCVfQpJAAAAMkGaY0moQWiZTAhP//3xAACw0U+AOUVcqQkQz9cSz0wXrXNtdBvvee0tJXXNl9gP2MK/AAAAEEGegUURLG8ABpLlm4V0hgUAAAAOAZ6gdEX/AAglVLlLwZUAAAAPAZ6iakX/AAgjzIjz/jugAAAAM0GapUmoQWyZTBRMJ//98QAAYgB6z326AF5iYZ9GjMHpKOHHvZGH9VapL5GT05+T9hB/gQAAAA8BnsRqRf8ACCPMjizch4AAAAAuQZrJSeEKUmUwIT/98QAAYgB6z3unADld7qDX6QxTXcjXmxiTAdhUDY2rR1ZDUgAAABdBnudFNExvAAaMxp1gAH7VyADG2WRJuQAAABQBnwZ0Rf8ACDfvmAEzUALuVUXx3QAAAAwBnwhqRf8ACCrvgysAAABCQZsNSahBaJlMCE///fEAAGSIKDllHACPduPsI3nLlaMPn2DkHJcE7uppl1b1j+TUu/++nXuS8xv7e3NFjTL7IMPnAAAAG0GfK0URLG8ABrnP96joAEu7ZKHV/p/vTdZ1NAAAAA0Bn0p0Rf8ACHDl74K3AAAACwGfTGpF/wAAAwHzAAAAM0GbUUmoQWyZTAhP//3xAABkVKfAHKK36Pm2EYMD8tqVmV6i5S6CHf6hu6lrGvhpW6aJmQAAABhBn29FFSxvAAbGRoAE0w1i32voNCq1OOEAAAALAZ+OdEX/AAADAfMAAAANAZ+QakX/AAS5Wz5qQAAAADdBm5VJqEFsmUwIT//98QAAZFSnwBzDqeHtGPrUIUh4DyQXCrNa4vTtA5kYfGrPa81nXyJzehPDAAAAEUGfs0UVLG8ABr+GgSn+KRiwAAAADwGf0nRF/wAIaSSWzXuHgQAAAA0Bn9RqRf8ABJSxVvnpAAAAKkGb2EmoQWyZTAhP//3xAAA2N8WxViEm0AUj5O/SzzhBt+p1nLUElkuOmQAAAA9Bn/ZFFSxfAASUYPaSJOEAAAAOAZ4XakX/AASHGdYiFtEAAAAmQZocSahBbJlMCE///fEAADYEHDvq9ACr6WXq7ES9WCmcqWB8BN0AAAARQZ46RRUsbwADnND+cHbLOmAAAAAOAZ5ZdEX/AASSqVP9C2gAAAAOAZ5bakX/AASR8zI5EnEAAAAZQZpfSahBbJlMCEf//eEAAFZOTwf4ZJfmfAAAAA5Bnn1FFSxfAAR3Uo2hdwAAAA4Bnp5qRf8ABHRTj7C7gQAAADZBmoFJqEFsmUwUTCP//eEAAFZY7mF/A9sIAZjI+mLgDsBqH+KZdjaB/MDI8VurvZYPD+93xcUAAAAPAZ6gakX/AARx9Eugq8KTAAAAIUGapUnhClJlMCEf/eEAAFGZRWo6QAzyGv8LyO9310rH+AAAABZBnsNFNExvAAN06DysUALWPwzF+KTgAAAADQGe4nRF/wAEV6v3Im8AAAAOAZ7kakX/AARR82N5E3AAAAAfQZrmSahBaJlMCEf//eEAAFHaQNP0gCn68Mv+9dKx/gAAAC1BmwhJ4QpSZTBREsI//eEAAFHOvo3KAUt/td2GNIFjPSrOrKT1BlssQ+C/NmEAAAAPAZ8nakX/AARTbxcMaT0gAAAAHEGbKUnhDomUwIR//eEAAE+Us9ICgAqLm/WlZB0AAAAiQZtLSeEPJlMFFTwn//3xAAAyRBkpx9ABoGaWAZRxb4CpgQAAAA4Bn2pqRf8ABDHztJN0HAAAADdBm25J4Q8mUwIR//3hAACKgFWATSRHCQLrsOh+BYzwqT1sho8BMYIFp1dbok852ZsqEJKeH2MHAAAAGUGfjEURPF8AB23AWqSn9UAAbMZCE3mOplQAAAAOAZ+takX/AAQXWh4EUkEAAAAwQZuwSahBaJlMFPCf/fEAAFhMot5a5RIAzuA8OLYg9fidmW/JFNdEKvIM1bKdjFfBAAAAEQGfz2pF/wAHZQy31bhzJrHwAAAAO0Gb00nhClJlMCE//fEAAFhIfK5aE2EFgMnACLTsBhXxMP5+nHEGa6SDxLx3q2L9V8fOqCzu2Rv9JpgQAAAAFkGf8UU0TF8AB2mgzZxKmxgIoUf3m4EAAAARAZ4SakX/AAcoB8q5bujIVk4AAAAvQZoVSahBaJlMFPCf/fEAAFYop8AVqdOrQQxGsVFIghRG+zaICMBDZ1pJ4G9zFpEAAAAYAZ40akX/AAc+cbABfQLSGcfbHq3+fdBAAAAANkGaOEnhClJlMCE//fEAAFYop8AVvtx2z09d0xVgYnlQ1xiBzR5YyuFcM0/clMV+0RvWgDU8IQAAABNBnlZFNExfAAc915Jsk6aNPsKBAAAADwGed2pF/wAHDQxVY200HQAAADFBmntJqEFomUwIT//98QAAU8h+KRRohABxYjz9LFp8r65IUDIKi0lNkcozZmxncxZRAAAAEUGemUURLF8ABvwPI6pQOkl4AAAAEQGeumpF/wAG/B9fwFDIjvseAAAAGUGavUmoQWyZTBRMJ//98QAAUjDcVrcaD5kAAAAPAZ7cakX/AAblzHBaup0wAAAAJEGawUnhClJlMCE//fEAAFIN1oHK/gAcy6i+98qE/rIclFwDPgAAABJBnv9FNExvAAWE//uYhNBS4WEAAAAPAZ8edEX/AAbUOE0fHa9hAAAAEAGfAGpF/wAG4jDiqeU8JQUAAAApQZsFSahBaJlMCE///fEAAFHa7sYAQVlGp4ZPvC/HB+6E6HLbujuYtoAAAAARQZ8jRREsbwAFiKiNtMVb7pgAAAAQAZ9CdEX/AAbontFX2o26YQAAAA8Bn0RqRf8ABsG/g4quek4AAAAlQZtJSahBbJlMCE///fEAAE+aUaG3gEic3Lxfp9x8F9EXqAaD/AAAABNBn2dFFSxvAAVhAuWxEBQrOS8hAAAAEAGfhnRF/wAGuzCB6Bj3pOAAAAAQAZ+IakX/AAa/TMhpxDpcQQAAAEtBm41JqEFsmUwIT//98QAATb4BwtHj8JrTUARkdTxBySXjmlCNabLTnwKqU5/HDDxCF12WdZq7I3NriXYdPU28tvvWa+V6DKDsLyEAAAATQZ+rRRUsbwAFQMXRViAg43zbgAAAABEBn8p0Rf8ABpXueAE8eFS8gQAAAA4Bn8xqRf8AA8YN/0miygAAAD5Bm9FJqEFsmUwIT//98QAALYgNrwBEhSqs7qhSv4hiaOGRLpiOlFZWi2vyxCuCzN7L3pBHDvyurjgKf5fZcQAAAB9Bn+9FFSxvAAMTI0AC27Jw9JNyJ6efrMaBC9Nfo7KhAAAADgGeDnRF/wACHL/RPtWwAAAAFgGeEGpF/wACHcE4AgGSFa2srCuYrYAAAAAtQZoVSahBbJlMCE///fEAAC1UU+AIkK3mGp6bUkaXBw8b+ipl5131IQuyU0q5AAAAF0GeM0UVLG8AAxMjQAJmAj2G/2cJv+mAAAAADAGeUnRF/wACGkl90wAAAAsBnlRqRf8AAAMB8wAAACpBmllJqEFsmUwIT//98QAALVRT4ApJChfdSnU/fu/a29dBVt0QExVkccEAAAAWQZ53RRUsbwADD+nQAJkF12/fj2YjPwAAAAsBnpZ0Rf8AAAMB8wAAAAsBnphqRf8AAAMB8wAAAB9Bmp1JqEFsmUwIT//98QAALVRT4ApkK7fm6c39WRxwAAAADEGeu0UVLG8AAAMBlQAAAAsBntp0Rf8AAAMB8wAAAAsBntxqRf8AAAMB8wAAACpBmt9JqEFsmUwUTCf//fEAAC1npLwAjyI4QgBfp89+IHjcKzXMNP3wF3AAAAALAZ7+akX/AAADAfMAAAAlQZrjSeEKUmUwIT/98QAALUObszLL24ArfMZaMoQSCfsnEsjjgQAAABBBnwFFNExvAAMNdAxzfKphAAAADgGfIHRF/wADwI/xY/P8AAAACwGfImpF/wAAAwHzAAAAPkGbJ0moQWiZTAhP//3xAAAtVP3QAsE6eHYfR99p55D6soPYYUx69H48/yN7p661iMtGr1T1B07bByQPsZVxAAAAH0GfRUURLG8AAw/9vAA2cGrdv+yw9YUu4ZOWeA8o93AAAAAMAZ9kdEX/AAIsPt3+AAAADwGfZmpF/wACLKm2wP/tUwAAADhBm2pJqEFsmUwIT//98QAALmyjoBCrQhCF+PQN4OTNEPMYWQxQ/xv8StDFN9UJXh23G8npI9Jq2gAAAA9Bn4hFFSxfAAPi3qm+3dEAAAAOAZ+pakX/AAPi4BPrSxcAAAAsQZuuSahBbJlMCE///fEAAC6NSXNP4AHAPnfVmmILYlodOunAuRGtPBvEyHgAAAAPQZ/MRRUsbwADGjdgdOVJAAAADgGf63RF/wAD3vp+D8+YAAAACwGf7WpF/wAAAwHzAAAAJUGb8kmoQWyZTAhP//3xAAAudFPgCrZtGRIT2c5DLkTjhDnYC2kAAAAVQZ4QRRUsbwADJyNAAtY48PamU/cPAAAACwGeL3RF/wAAAwHzAAAACwGeMWpF/wAAAwHzAAAAMUGaNkmoQWyZTAhP//3xAAAujIL4ACgMUzWl4b1KJv66zQ7OzsxVmTIv8g5OlFAlyHgAAAAWQZ5URRUsbwADJyNAAqd1T5w8p/AyoQAAAAsBnnN0Rf8AAAMB8wAAAAsBnnVqRf8AAAMB8wAAACNBmnpJqEFsmUwIR//94QAAST9rwCaSmE/7DRGL5GH83YMdUQAAABRBnphFFSxvAAMnI0ACZ5ab+T/cPQAAAAsBnrd0Rf8AAAMB8wAAAAwBnrlqRf8AA+EFuj4AAAA3QZq7SahBbJlMCE///fEAAC50U+ATSPk8RJV3r3z9NQzb5KIzgQjueAHNyp2qnBw6Uu2aofd6HgAAADNBmt9J4QpSZTAhH/3hAAApG0lbCZ31uACNNO/fH9ba53/qHt5HPdJ6j3HBywu/jksrP8EAAAAPQZ79RTRMbwABucusYGTgAAAADQGfHHRF/wACKe//Lv8AAAAOAZ8eakX/AAIpoX6su/0AAAApQZsBSahBaJlMFPCP/eEAACjvqLpAFb4o9jizG8+fOJN0cgnh5ObSs/0AAAAOAZ8gakX/AAInJLQFRgUAAAAjQZsiSeEKUmUwIT/98QAAGazWUVuAKJ/AmS8pxO4YF51WTKgAAAAmQZtGSeEOiZTAhP/98QAAGdsenNanAEQl3gSmg03/691vGn5LnpAAAAAQQZ9kRRE8bwABuORFHr36QQAAAAsBn4N0Rf8AAAMB8wAAAAsBn4VqRf8AAAMB8wAAAC5Bm4pJqEFomUwIT//98QAAGdFvFGvADW7DqiEkp1vXKyI50gpV1hDGYnf2Jc9JAAAAGEGfqEURLG8AAbuRoAEstJkp8hge6xAE+AAAAAsBn8d0Rf8AAAMB8wAAAAsBn8lqRf8AAAMB8wAAADZBm85JqEFsmUwIT//98QAAGbUp8AQ9eH3gnCWSvD9hYv+pZlRzNoB2dJJ7QQjaWpZFf7IM9IAAAAAXQZ/sRRUsbwABufToAE4EfN9j+6vQqYEAAAALAZ4LdEX/AAADAfMAAAALAZ4NakX/AAADAfMAAAAvQZoQSahBbJlMFEwn//3xAAAZtSnwBD2EUtc1KL2gdd5tBfDwGsajbwxVn/iN2zEAAAALAZ4vakX/AAADAfMAAAAwQZo0SeEKUmUwIT/98QAAGbXFuAImOp4hOP0jo7FLPBT4zJpzF6Yug9SbkxBDO5o+AAAAF0GeUkU0TG8AAbvLkACalQ8f4CRe63pBAAAADAGecXRF/wACGkl90wAAABMBnnNqRf8AAi3BOADaNH1TPRGBAAAAK0GaeEmoQWiZTAhP//3xAAAZtcW4AiQrVkwW3q7yn7J0vKpfgPTTyndmjKgAAAAWQZ6WRREsbwABugYkACHVLFHH/MjJwQAAAAsBnrV0Rf8AAAMB8wAAAAwBnrdqRf8AAiq74wMAAAArQZq7SahBbJlMCE///fEAABm2UVQAcez5ycwOhNG3ilGsFlYOWi7X5eS56QAAABVBntlFFSxfAAIsEUWAD+b/Wbasu/wAAAALAZ76akX/AAADAfMAAAAsQZr/SahBbJlMCE///fEAABm1fugBDgQtWbSiHmE+zqpadzBubNV1DzyXPSEAAAAXQZ8dRRUsbwABuf7eABqbtiYR3H+YltAAAAAMAZ88dEX/AAI8Pt3zAAAADAGfPmpF/wACOrvi7wAAADBBmyNJqEFsmUwIR//94QAAKibYy+kgC5aCpi06Gu6qT7JlXm7GaCIPmNL4b8MwSrcAAAATQZ9BRRUsbwABw9RdSU7r48AbQQAAAA0Bn2B0Rf8AAjw0dfFJAAAADwGfYmpF/wACOZg+b4/6kAAAACxBm2ZJqEFsmUwI//yEAACkbZQSE7PgC1gR1VAmdmzezN/LXkVx91XyIBK2gQAAABBBn4RFFSxfAAI7v6/UN9SAAAAADwGfpWpF/wACOZeoTTgkIwAAACNBm6dJqEFsmUwIR//94QAAKfHl4A24Knt39lh4FrbS/pWfMQAAACZBm8hJ4QpSZTAhH/3hAAAqJTT2/EADnvI0nDAdJBptDEW//2DNCAAAACBBm+lJ4Q6JlMCEf/3hAAAp8wRQBydi2qFTwqCzhOwZoQAAAB9BmgpJ4Q8mUwIT//3xAAAacHKCRfACwTRqYqoIlzuhAAAANEGaLknhDyZTAhP//fEAABpWVqYAhGXoIzz6nFljSmWV2JNRLKkIIjPeKiYnDfyj1+fAoIAAAAARQZ5MRRE8bwABw2Am1sdRj4EAAAAMAZ5rdEX/AAI6SX29AAAAEwGebWpF/wACOxdzAB/SACUvnfMAAAArQZpySahBaJlMCE///fEAABryMAYnwF37iGNo8Q+Ou1pi9Plw8u35/ZBnTQAAABBBnpBFESxvAAHPgE3DGFBAAAAADAGer3RF/wACTD7d6QAAAA0BnrFqRf8AAks+hJOBAAAAKkGatkmoQWyZTAhP//3xAAAvouXcAVof5WW5V/PjM8PtTzFq3TcAzfd6DgAAAA9BntRFFSxvAAM1xiNmVBEAAAALAZ7zdEX/AAADAfMAAAAOAZ71akX/AAP0iSJf56UAAAAgQZr5SahBbJlMCL/6WAACQvpXDsACHXoRdu/Lz9O0j5kAAAAQQZ8XRRUsXwAD9Rq0T3z0gQAAAA4BnzhqRf8AA/Ub4veekQAADr5liIQAM//+9uy+BTX9n9CXESzF2kpwPiqkgIB3NMAAAAMCXKe3py1upYQMAAJpKr7hBz8wC8JTtJmqtRCtfcqro/1+ARJBpLXkbvMR9KEz8h3xZVzI/lZFgL4mPwSwAYsyRKFDzXH8kDkAAAMB34CZg5E7cGziXVzv6DThm0om5Xwp6z/fvF8k/OcCUcuSWwQoKUAxVqFPgPhzu0e4SfcuRH9deWeocvKAt4m9rYD5P086v9oEl3sOGD4qdwGX2DwAFGndVlOM4PqRqDOS8NnVIhcfSuxQBVIhfX8TClo1VN5ZGhurtItlFyLPGUt2DQzOUBF8zoHjnK6859Qi9z6fhXbjqp//GHEs3tAZo4jz0Z4MVKkMXfDVFO2IBUmCEqiJdn8+dZfVls1iiv8+CrtGeK+I/ZGhw5XW+w2ZdXZxQ1S/q8P9JqOrnteCmJ8WjfN+uObwtOpwlFLW5l80LmMMPpGUU2sKtYKMnuf2rpRkAB255dtVEXbJZtsx5/L7aPp+KC74iEDcNCwlW4zj/jHZixysl1TXnP6jNIvyAclgV7ZQJsIJ4kXslwYuX/Z9oZtqbkN+boaf4X9GZfkfwTEKzyzWULq/ArcrtSy2+/HiYrfHseQu8JdXzNFcH7MD5tjPKGnbNmY6xLidw591iJEOhsIJpLBVNnRCqs3BJ+YVmoZNH91bQdEm1d2boDNZWpLm3I5k5/gh/oju8lagdVzn5x0L0Mac/8rPVM6NZMleti5NqiW2w0UI5vBdKd36Pw0FbL/ZT7cmBgvman3UXgfKRsdZ7jZLsEArSp3UMIWwFXq+9M6vMtr4Qwti/SXAoayQ1GAyYpnNeeiv7R0y80bcZ/D+QAvt1Qzj2I2po1m54Bon0dLZiWWstFs8YUngsNNFY3nhpb8B+1x1DBP8J4UC4GYhEYAe2mCdX+hcA9FwMecMdtMpd5lkyjPdhQ3DWDQuhf9X+nwwppmt69xNxGd8co7AgkCRMh6Hzk64Y3hQZ7dpBUc3PCZsVBDgO+TsMqrfin6X3AmG+MofGjAb9bggTeya/ZCkNQmcN7KVThLTmtx39bbvflwssoX/hT14kR7DFtTqiRTYF0YHjYJ2dNa3vyQUdycEKlYwN8hPrX0ilspFiET/GrqlivDg9bibw+EyBNaTy0+2JX/00ESVHfLv/5JnP58uiuGikyOOpDkW0OKEaF+u8VbjQAU+7uQNxohfdVOeK5YFVMjzAIK4bBjXnb+Il3Qvbuff//igJfur2Qmm0UslCoAeXCViXWn/sRuiW6uKEFs+pOk6R6nZDbqJMli0mAJ0NvpfNGY11ArPJO3ziN+zlA7iZUHXXMe9W1itkZ2vHwnJP5UzCj20GjJYHaBICkfHImG319GwddyaasA5dUAh7k+XyqYLiWLPMUCK0l3FXk56Edh1YT446gdKgALhQiCA1G6wQoUmYwIIulvkV0x53eY/fMenH83nYN2NMFhuXp8k0O2DWVjt1Xx1TVWhfo6UVMeRlXgr5FOAXyKPJM66BaxTmpZe+uUSsQ3FwPgh4zO2NEvn3BHY8hElQvrdF36DLy5pb80xbbGrHY7hyl74bNScT+rcZbjT5N4qT5565NyamJvjC9NH0EnYDKXj+oeh99WWiVwTQeXWttJj63lSS1AqOPL/fyfTIl8Zff1BW+hr5a8h1nYv44AMkJMet5OEHsTvktZvrJVBE3Nu7RbgFgFaFtqga9ys/dWLJOwFiQ0a0CK/A5Wkhmwg2b1PXw5+392sruzdMbUyN9g8uOgWPAmznK9RfkB/Ul1SsSVpXDYC6IhSR6Ir9/SD3vKXLg5DJhsqT6vtQuxpcPbV/D/BWplBTGFmFVXCpD7gY8bPdkQp+iBswEGSNZPdD0GEecSJtG7u2NZCLJHR/96N7CE5cpRWGRo2koWWdXvuO/riU6NamUFSbIz0Y/F2xE4PTJuvsGoMR3Ml2wFiWStYlK61aaUNHWJakEDmVwSBj8p9YcMM2skb3zpbbjowtNhB6FPN4LiG5kH7Aw91+RpnqxCEZn/q0VR8gUuU3Ibf8AAR7ZdC04OifDj4SnWw/qp8NbKrwWSsxwDqry2VsGC3ybkphfJ8zrOSb8Y+GVTzNnvi5eqF5BoztBwz90YEopjcPkAjegdCs7DtTX0wZ7wSoFW5ExWpO+G7DCPDebDyTwIwfsg9gAgpNWxgy0arl9FOKksIYNqdwWadbeeDzE76wD2oiU7IARCkQ4HiUV7ojM/hrLkSRCmPuvY36nbN5y+72WTQFcOMlp09AYO9sdR6FGIljCTaw/zGzYWiJhyxDX29JnRng8oI8i2PWxQFuUJtiN6euTkrWmFEKzN7bgpoAXwN3gZc2ZP28xJ1jpT1dGrut+eVanEiquGs+PFZdgEsTMoJ1mjA4V251C8BP/Uz4zhmywlD6T0stjFHKEwDMUWXOd8X63Bsuo2UKSY1atxq1BJd+buwm1o56GINjUtAPQ3KZhJphTQpQP8NsboGB4BeTurmMI6AEV5jxysqGfSa1WBiSD4ISsBzw052/XQmxY8/J1yyF3rtcLtd6isRu3q41EJ0qnLn17B2XprkGY667G63WEAnXgHbHmFtFT0C10uY5lHzbTAPxAGJfO47MZ//qEhviLPg0geOUyAEdwRS3YWiF+kgiohM5BQxPrgXDh43yf9LAWKlzxdFFCUg/wjPFiYc/tKcs1EyHQx+gUpp44+Q22138xUdRGFIjNpnVkH1kxhuqipBxe+ZrRJGOn9QvU3zNBFqslaKTNAp/zbEjhbOX6eD7TiKa6cEgsjRnLENAjRUXSQPCeax63W9tFA+iqE8YyBdq7e0eDK6PFBQ1qLduWVdWY+Kd2v3LJaHQLWQZ/uLmI7KSa5wzbH88kpz3qwKyNMgISDGhFPoSR1WJraPaPn5yuFji8Tjw8Mj0nz11OYLWoeMHip2eHVeAOqrJsqJ0Tfohv33WPyf30uP7E/tvCcA2GpN5km7UPfA2OSWxaImwPQQpOv6YPZ8HKeEQfHhGlIozn0zlTcdNM72li+0nC8bFi9SFPWa8xtujg3UEJxy4+AuAIvjJ7ybXR+KEmfb0Ip4JJ9OfpxiNKsVNQYA0cDS7r6cMCHykCPT+L0MyRVezFobdHRF+PtSLTGYgdSDudscc7nKjsPmgtlnw/Dy0Pt9pzb97A4y/mA2BD9ImA+qXSOYW4p0+DW0Lw71rwkQKNloMwvHwC3zXHfTg5vm6ZWVYKGTlCN/MnpCYtlVHt6S9HdWjtk5KNU66u77wHqbciTIDm7VWAWToskMULSCkthZJ/WpqSP/abzJjC93Q+5JXmD5eG3Zg0MjMQYhdvW1E35Y4KC+cynm9RFrymeoVf2il7jNw70Y/xvTU1tw3giajvdAjCnyY4KoRGeXMMEQd1nebviMqlVukOaVMWMRnxwjlqsOBSOCxH8WaCWrTUWrNfr21doe4CLHLSjfU5e7+HIv6+yWWgSR75f/hC784+sTgs2IJFkhBJ95E47VpMKEp0TN5xL+kyqhhRYrrjGFEPyaHsbO+AdPno5XYhE5sG8Dbn6Y0xFpKAE23F7fse0faMwZ+rHOJykmBmFLOjftAyrSSZCjgvk/btg3wgVX1+5msutI5K1EQCkJkcJH0qiNqPoqFGkLDgHSZylKflX0NADBzi13kUPwa4FlKRavhezs5n/9OGQWHDprHH0s2kjjHiC0Rq7lYe2/1v2CsvaiLt8tOVYJlxmMfMH5RIcru0w/CJbK2rBDUqPQG5CUuT64cJ8cHCguZPgRJ5bX0Ss6Nl8jpShW0ZEfdfen/Ft08dkhTr/LSeeRFTns2BmE3d9adDktZ+K32/gxB321dEXYDHIDx6YkpFb11n6CYBCN5Xw9EJJrlwTDXWdSz02ld5sfi7UUmgVZR28lwmUeuIQdXmcXXG654Vi1d0syR1U1+J91g0mzSzmz8ix/7r269L7x7c8EAveDIx1KhNbCxFfILtW9YNru+bSC8FLllPPChW5bhUv87SCkeSyERBkrjnKloapFQfvyodUl5dYMJgcNPdmAQvrhi4h/YbLIAhHFFCc6n6iRZT6gLye24EdSRpa9yHtlxqPPmKVY/eWMrUnHbR+vhbsBXq88KVfZhpqj+xJiwUf4SvS71HMDtMYh1utD94/s+DoBdOX/l3MSd3wCypxF0ckRMwtp7Pz3zlSNw5zX9riHvyREqwWZSnEoouj9LZTrqkyMTDOuOoMZEd0sCySgd+T8/IBGdHQ+q8V6sh66jY8OWb5DI+hHmKomhxw3/6jHe9Xp/epm6WzcFzbf0h9AiADVnakC0pwTELIw8C7R/9huI+sz/bCfBODmIY1Yz8MrEDEL5pnDrsRB3pLYY/DtqSkTcY5vblrhjGSEeOD6F2AWciEP/6xFQNXL9c5cXWGw+6Y/QVcc9tv9RrHLvQWt9vzFXfbapO5YpltutYq2iFNhCJi6JbVrEFk0+NUBA6JOybrK8rLtgnhWSDLwKZ/Wn7NMfaIbvZlSGJNw9kWlLoDIrx9iG050sZR4kAmczRrce0a4Fdij2kPXrJGHoR460bCN3wGyBnc2yUEn5AYu8wmcATEN+xRPjyVpvnWhvYS7L5zM2TZBBP8c/9906Zz31yI9WuctVvQreUjYpZfGkInCuSk7JSNJ+V/YX8tvsnI8cqpVglAwqJvie9QKkuZzYpvlXsEc3xnMpmvPfv8ng4mw/KFhmNLlknqhvcUG4eYtvRWdYGS9uuWDImlZezxZhsdVdOki1hUxi24q9QYp/R+fchIkuBp2Ho3Q2J9u9SHDgR6ZtxLtzC3BduwUrNdBYXz/bXabPwbVpErb/8hdGLsYK1U5/fBZ7D6ekL4Un2dGbkFVpCzxg8EFJlNUM5bBKaQXVOup9oX7bmW/b65M675Kn47JOwtNskZH2ZoX5B9Jf4tnnPZtFuypKh3dKFw97TdBd5dxjs8GPIj3KjF4AihLeT2anRPj5vCPgdhAqAAAAwLxqg5Z1b6BRCpQ+QLTLsk6tqEPm1/T1Z6IJuOr4AAAKyAAAABFQZokbEJ//fEAC2exGDfatADlYONNmbyNOxwAQJEza+OOI/j3e4XgixCXdRoxAd27FANxsobXalCqekaJrjl5jL7YqqYEAAAAF0GeQniN/wDEFa/thEmDljf7HMkJIw8JAAAAEwGeYXRF/wDzAdWKL1LXJKlljlgAAAAMAZ5jakX/AAE19GUXAAAAKkGaaEmoQWiZTAhP//3xAAAvqlPgCt+W/PAb+dTSIEftQQTpQdbjjuZNwQAAABxBnoZFESxvAB5vtp2psAA/m/Gqa4O3T6ZTt8GBAAAADwGepXRF/wAmpJsIb7b1LwAAAAwBnqdqRf8AA+Le3R8AAAA2QZqsSahBbJlMCE///fEAAC+qU+AK35X5qcb3jAvycvKr7QZPYCujQ6xkBlIOs/vhKhuf3oX3AAAAEkGeykUVLG8AHm+2mmwjP/xJwQAAAAwBnul0Rf8AJqSXwZ8AAAAMAZ7rakX/AAP3BbouAAAAI0Ga8EmoQWyZTAhP//3xAAAaVcW4AbapSYouzyHlkHCx74FBAAAAGEGfDkUVLG8AHm+2alrZAA1x3AIDH+//gAAAAAwBny10Rf8AJqSXwZ8AAAALAZ8vakX/AAADAfMAAAAwQZs0SahBbJlMCE///fEAABpOJ5qzbcANciBpKvUQsr9ppFWu+gzI4WQF3LbTs0YsAAAAGEGfUkUVLG8AHm+2ahECQAOw1tQLvUNe1QAAAAwBn3F0Rf8AJqSXwZ8AAAAMAZ9zakX/AAI6u+LvAAAAJEGbd0moQWyZTAhP//3xAAAaVlamAImOHQ/obN3BYRD39bJiwQAAABdBn5VFFSxfACbK2mQj2+ACIDQ+VUXu4AAAAAsBn7ZqRf8AAAMB8wAAADdBm7lJqEFsmUwUTCf//fEAABpWUVQAcYm/c/f5Uqt/UvLwcVf0TZ3EPJa2nXQ5uwKZzu+WzRixAAAADQGf2GpF/wAmytnwZ8AAAAAwQZvdSeEKUmUwIT/98QAAGvUp8AQ97r4bkZXAXLreQNf/1wcA9Rit5zQ4s3/nc0PBAAAAGEGf+0U0TG8AHmv/Nc+7IkQAXTqKDBsFgAAAABYBnhp0Rf8AJqSZkcy4ADiykYdUahQhAAAADQGeHGpF/wACOrsOb1MAAAAoQZoBSahBaJlMCEf//eEAACtG1HaSUAKk5CqYm4/LJI+o+UqEZrrVNQAAABJBnj9FESxvAB5vtmswff+S/5kAAAAQAZ5edEX/ACakmZlHdEG96AAAAA8BnkBqRf8AAkdnzy1kk4AAAAAmQZpCSahBbJlMCEf//eEAACsSRWAOJYiY7sX8++QJ8VqAhvjCj4EAAAAWQZpjSeEKUmUwIT/98QAAGwCgfNc6YAAAACtBmodJ4Q6JlMCE//3xAAAa9lFUAHHtCEM7cEaJ9xGOHYj6QNTOxppJLJgQAAAAEkGepUURPG8AHmv/NZt8mA03UwAAAAwBnsR0Rf8AJqSXwZ8AAAAMAZ7GakX/AAJL6MknAAAAOkGay0moQWiZTAhP//3xAAAa76oMlkkgAEVfFfAhl0G1x/tGeRm0QHAWZtSj8pHLl1WUHL+SQdizRsAAAAAbQZ7pRREsbwAeb7Zqwm1WACcCPokGHqKq1RugAAAAEgGfCHRF/wAmpJmUvNC0YVewlwAAAAsBnwpqRf8AAAMB8wAAAC9Bmw9JqEFsmUwIT//98QAAMNkx8wzVngAjke0Rq68QnsAmUspjrKXbwlZivd5/gAAAABNBny1FFSxvAB5vtp9h7NN9lgA5AAAAEQGfTHRF/wAmpJsdBymNt7jMAAAACwGfTmpF/wAAAwHzAAAALEGbU0moQWyZTAhP//3xAAAxA6CgAOgVsem+3D9oFlCm0n5GY3C2ZAXp+ArZAAAADkGfcUUVLG8AHm+2LDegAAAADgGfkHRF/wAmpJshuhanAAAACwGfkmpF/wAAAwHzAAAAJkGbl0moQWyZTAhP//3xAAAa9cW4AavrIJ+6HzVLEUbmvbbD5LnTAAAAF0GftUUVLG8AHm+2a7rZAA1pzY9Ii/8xAAAADAGf1HRF/wAmpJfBnwAAAAsBn9ZqRf8AAAMB8wAAADJBm9tJqEFsmUwIR//94QAAKwCx9CKAEA0ePrsmYXeUld9Ytg9VlPrjNfhJ1Od+fkcKPwAAABxBn/lFFSxvAB5vtmtxAkAE6Lh7r/FCUq5669WAAAAADAGeGHRF/wAmpJfBnwAAABQBnhpqRf8AAk3BOAC9qWvgyINJOAAAADZBmhxJqEFsmUwIT//98QAAGvYA4ARkdTxGiXOiHDj3myfDNUXy3I3yq+v+voCp5HNww5grJgUAAAAlQZogSeEKUmUwIT/98QAAGuzNGSqADjrc4XYkghvHaY4P0tWTAwAAAA5Bnl5FNExvAB5r/xYb0QAAAA4Bnn10Rf8AJqSZmgB+2wAAAAsBnn9qRf8AAAMB8wAAACVBmmRJqEFomUwIT//98QAAGuzSpw6AEVYQBABY/0kUibQpZkwIAAAADkGegkURLG8AHm+2LDehAAAADAGeoXRF/wAmpJfBnwAAAAsBnqNqRf8AAAMB8wAAACFBmqdJqEFsmUwIT//98QAAGvV+6ADjePalv9fUw3fNGBEAAAAOQZ7FRRUsXwAmytnwZ8EAAAALAZ7makX/AAADAfMAAAAnQZrrSahBbJlMCE///fEAABr1KfAESFby88r2+w9RTVeFUjnsw9RsAAAAGEGfCUUVLG8AHm+2a7TYABsvGwHctnmvgAAAABUBnyh0Rf8AJqSZmhi4ADsRazFRvbUAAAALAZ8qakX/AAADAfMAAAA7QZsvSahBbJlMCE///fEAABr1M6gBnkF005vQ3fy81JqXZRqV8L/KMeKkg3w8wU1q3pyasK5JwRxl+qgAAAAYQZ9NRRUsbwAeb7ZraW7aY6LXjAJMjm6XAAAAEAGfbHRF/wAmpJjGICR1T0wAAAAPAZ9uakX/AAFHfs3rH32DAAAAKkGbc0moQWyZTAhP//3xAAAO0EU3AG3CYG6+TKnP+3M1LNstiaP/a+5vSQAAABtBn5FFFSxvAB5vtk/cPAALD90aKY8byFW9uMAAAAAPAZ+wdEX/ACakmMZqYAkhAAAACwGfsmpF/wAAAwHzAAAALEGbtkmoQWyZTAhP//3xAAAO1Y+Z5+oAgb/mKExQ6vT5JB5ct/CUQ5VffBFxAAAAEkGf1EUVLF8AJsraMQYLIoDnwQAAABABn/VqRf8AAUYJ6ZzMfcfAAAAANEGb+kmoQWyZTAhP//3xAAAa9X7oARVfoJpw8ueq25yBpZaffdkfT/yOUxNcss1iSdzd7pgAAAAaQZ4YRRUsbwAeb7ZruCwAB2XS6NXuDykXlrcAAAAOAZ43dEX/ACakmMUVd7oAAAALAZ45akX/AAADAfMAAAAhQZo+SahBbJlMCE///fEAABr1fugBDoAv/UeEtO/2aMCAAAAADkGeXEUVLG8AHm+2LDehAAAADAGee3RF/wAmpJfBnwAAAAsBnn1qRf8AAAMB8wAAACFBmmJJqEFsmUwIT//98QAAGvV+6ADa5YGe1NE5rt/kudMAAAAOQZ6ARRUsbwAeb7YsN6AAAAAMAZ6/dEX/ACakl8GfAAAACwGeoWpF/wAAAwHzAAAAL0GapUmoQWyZTAhP//3xAAAbk5lEEKVPD1EkNXTmSQX0I8nqKQPiDrVfYg2fnc0HAAAAEEGew0UVLF8AJsraaZodf4AAAAAMAZ7kakX/AAJb6MkfAAAAJkGa6UmoQWyZTAhP//3xAAAbklLAvABeYJWrEgZQbyvVB8F03Al5AAAAEUGfB0UVLG8AHm+2bXH5E/6RAAAADwGfJnRF/wAmpJmmjnEMTgAAAA0BnyhqRf8AAlphl+1IAAAAI0GbLUmoQWyZTAhP//3xAAAbm41+AIRm/WgwqKUr+cGPyXOPAAAAEUGfS0UVLG8AHm+2bUzvygQWAAAAEQGfanRF/wAmpJmjPpUOK9mAAAAACwGfbGpF/wAAAwHzAAAAOUGbcUmoQWyZTAhP//3xAAAbpyxxABYfK+75vRCswRRkjqe/aEjyXMggEMr7ZY+t1bkTKcWblC+BLwAAABBBn49FFSxvAB5vtm1o8jFxAAAADAGfrnRF/wAmpJfBnwAAAA4Bn7BqRf8AAlvor9byoAAAABtBm7VJqEFsmUwIT//98QAAG4yswCNSysesaJkAAAATQZ/TRRUsbwAeb7ZsWEssrZ/mjQAAAA4Bn/J0Rf8AJqSYzbdDhwAAAAwBn/RqRf8AAlq74ssAAAAiQZv3SahBbJlMFEwn//3xAAAbjOHAN0AI95MoPfwAzkuccAAAAA0BnhZqRf8AJsrZ8GfAAAAAJkGaG0nhClJlMCE//fEAABuWUVQAccmOoDSwirJTP+5aQpEMrJdxAAAAF0GeOUU0TG8AHmv/NtppgAfN1DE7zr0IAAAADAGeWHRF/wAmpJfBnwAAAAsBnlpqRf8AAAMB8wAAACxBml9JqEFomUwIT//98QAAG5V+6AEVeAGaCwACgey/OixIqEpBgUYZDKyXcQAAABlBnn1FESxvAB5vtm1aJIAH8skbv6KCShMXAAAADgGenHRF/wAmpJjOJHenAAAADAGenmpF/wABUB9/AwAAACpBmoNJqEFsmUwIT//98QAAG5Vb8ARMRgjeuGwuJHJHes+w3kKX7nJc44AAAAARQZ6hRRUsbwAeb7ZsaTKH3SQAAAAMAZ7AdEX/ACakl8GfAAAADQGewmpF/wABUTrb7MAAAAAlQZrHSahBbJlMCE///fEAABuVKfAEIzdtouRjxYb17zor2S5xwAAAABFBnuVFFSxvAB5vtm1TPj90kQAAAAwBnwR0Rf8AJqSXwZ8AAAANAZ8GakX/AAFQi9/gQQAAACNBmwpJqEFsmUwIT//98QAAG4zYJyzwBDs9Vilv3lcOP6sl3AAAABFBnyhFFSxfACbK2mmZsx7jSQAAAA8Bn0lqRf8AAljfc6/YNbEAAAAmQZtOSahBbJlMCE///fEAABuMj6Mq7dABaXoAeSWlswJpWF5c44AAAAATQZ9sRRUsbwAeb7ZsWEmJpO7dGgAAAA8Bn4t0Rf8AJqSZpP9w3i0AAAAMAZ+NakX/AAFQzuT/AAAAMUGbkkmoQWyZTAhP//3xAAAcPv25VnU8RKEjYjj+WQTzAMxih00ocWnllVSA2gXcz/EAAAAaQZ+wRRUsbwAeb7ZveIfGgAQhh1E9CSn7rFkAAAAYAZ/PdEX/ACakmaf9vgAdegjlxp78rXiwAAAADwGf0WpF/wACa+mBqwYIOQAAAClBm9ZJqEFsmUwIT//98QAAHF1rMEe5RAAcrs3Ip6gA2ndqesMQeI3YsAAAABRBn/RFFSxvAB5vtm9x/iuimAOppwAAABMBnhN0Rf8AJqSZrvas8t/v0kyhAAAADgGeFWpF/wACanPzPumAAAAAMUGaGkmoQWyZTAhP//3xAAAcXoz1GHgAr3uo7T5z8x1FlYa616nAqafV6qI5er/vgScAAAAVQZ44RRUsbwAeb7ZvBxdcUVqu/XpJAAAAEQGeV3RF/wAmpJmv05Ef09T+AAAADQGeWWpF/wABWjrb6kEAAAArQZpcSahBbJlMFEwn//3xAAAcS4dVdrwByitWNEYaHaFk2SPclY7H7u9vQAAAABABnntqRf8AJsraNVMAZrNBAAAAMEGaYEnhClJlMCE//fEAABxLxWwv6AHSOGcXVkidQsipkONaLyIo9bbM9PvBl8jMnQAAAB1Bnp5FNExvAB5r/zepzjjzpIAQ7SGMMJMagFBGvwAAABIBnr10Rf8AJqSY3htyDDoqEJ0AAAAPAZ6/akX/AAFY8239/cPAAAAAJEGaokmoQWiZTBTwn/3xAAAPhcOq31TAJDjpBl3YStUJGAZpSQAAABABnsFqRf8AJsraNUaEAGQJAAAAPEGaxknhClJlMCE//fEAAA/ml9jWRfACKvc2KFWos4wxzx0P8kjKr9wRkbZ/E/bemTsPDXEYLgI/9UGxYAAAABxBnuRFNExvAB5r/zi8WyWh2ABy89ZDtZrPz1oIAAAAEwGfA3RF/wAmpJjaNqlwLmog4EEAAAANAZ8FakX/AAFh6XWyXwAAADlBmwpJqEFomUwIR//94QAAGd04MfQq+twAgdjavDiKukBaNnVDRGEbTR0nDJNDo+HHklhsWSpC2JkAAAAfQZ8oRREsbwAeb7ZSumxwAC56/gasdL49bDl3t+KIwAAAABYBn0d0Rf8AJqSY3PCKMABdCict4cF4AAAAFgGfSWpF/wABXYhGlwAJlO1fXDM09IEAAAAuQZtLSahBbJlMCE///fEAAA/d4oEX6gCB0KF0wrgwuSZmUEkgZwMh86pjRubjgAAAADJBm21J4QpSZTBRUsJ//fEAABz7xJi3NuAaviqDiaEPbKIMONS91LLmv7ZhTURzf+X52AAAAA8Bn4xqRf8AAnoNnLjrdpMAAABGQZuRSeEOiZTAhP/98QAAMkSffUAI1F4L4ZirEX94oYuD5/nlJqOnqAOC/a1NYPX8DM5mKPhPSS0gIbqpIUHMRAH0XnnI+AAAABBBn69FFTxvAANhH8yhaSwfAAAADAGfznRF/wACe8B9lQAAAAwBn9BqRf8ABDV3wxYAAAAvQZvVSahBaJlMCE///fEAAFqp+6AHSOp4hiYfn4rwVK9IfSu6x+Rx6XO6tgWXzLkAAAAPQZ/zRREsbwAGIkjH41FAAAAACwGeEnRF/wAAAwHzAAAADQGeFGpF/wAHnELwbMEAAAAoQZoYSahBbJlMCE///fEAAFqpb8Acw6nBN+og+2Bv7ekTgvASc8lxFwAAABZBnjZFFSxfAAefA2ABdHmcWBk8V4/wAAAACwGeV2pF/wAAAwHzAAAANUGaXEmoQWyZTAhP//3xAABaqW/AFbeOipEeO7ef1Ngyf3E0VoNf4nzU1psTchNMOZKzAK2AAAAAEEGeekUVLG8ABgvm2gSCGfEAAAALAZ6ZdEX/AAADAfMAAAALAZ6bakX/AAADAfMAAAArQZqASahBbJlMCE///fEAAFqop8AVqc2LMr+7odBp4/EfR2uNMxGAtJcRcQAAABZBnr5FFSxvAAYiZtAAmhal7dzcH+0XAAAACwGe3XRF/wAAAwHzAAAAEwGe32pF/wAHlnGwAbTqMYRkck4AAAAqQZrESahBbJlMCEf//eEAAE9mCKAKtY1Dmdwqj8cEhPtrnbMyifmbcrIOAAAAFUGe4kUVLG8AA2FB5AAg2OiK9bIpeQAAAAsBnwF0Rf8AAAMB8wAAAAsBnwNqRf8AAAMB8wAAACRBmwZJqEFsmUwUTCP//eEAAE9mCKAK33mwh+C7yhY27GSsg4EAAAALAZ8lakX/AAADAfMAAAAfQZsnSeEKUmUwIT/98QAAMmIYavQAcey8/2GA/JcfMQAAADBBm0tJ4Q6JlMCEf/3hAABPzhYDh6AFSb0T/Le5fQ+qDrch4osjDs18XIIef5nYMbUAAAAQQZ9pRRE8bwADXcehcXZS8AAAAAsBn4h0Rf8AAAMB8wAAAAsBn4pqRf8AAAMB8wAAACtBm4xJqEFomUwIT//98QAAMiq34AiY6nhJjf/CBSjDUYmdlEEF2ArTqyM+AAAAM0GbsEnhClJlMCE//fEAADIZm8Ryy9uAIlYE3goGyqr8dqe3x+XD1UyizK+SUia+R3efMQAAABdBn85FNExvAANfw6VlYAHVHk8DDWXIwAAAABEBn+10Rf8ABCZpdr1JBocnqQAAABcBn+9qRf8AAlsXcwAcKqeR4M/eIhexwQAAAD9Bm/RJqEFomUwIR//94QAALUCPd/2+IAFfkvvJqX8pOJxhQURxcyq0AL/sv+aMij/o2odKSJ4PeArt559vGb0AAAAdQZ4SRREsbwAB5osSABDQ9SKIgbTEqv85OzM+3oEAAAAVAZ4xdEX/AAJtwTgA2l0QMZL72YEYAAAAGAGeM2pF/wACbcE4ANnFSdSMXJG9HhZGaQAAADxBmjVJqEFsmUwIT//98QAAHD844AWHyvzaTOycpLJ8JExYMqZc07ZfHUz2qFlvSWYvOVmf4Pk5CaA3YsEAAAAvQZpZSeEKUmUwIT/98QAAHD626AFSka7pdebzOY9LOFsaeBK1W7Gktdt0VGl9cCAAAAANQZ53RTRMbwAB5Zs3vQAAABUBnpZ0Rf8AAmxk7AAdYHt4pME58VsAAAALAZ6YakX/AAADAfMAAABHQZqbSahBaJlMFPCf/fEAADT4LfzaPOsAP3cumqz/ojvk0mwpu1d0WBGMa92Tmi4DNAlSnAVGDAzqhT9bRdTDfyX/HR2zQxcAAAANAZ66akX/AARxGKvN6AAAACRBmr9J4QpSZTAhP/3xAAA0mWlxedrF0AIquE/HLuTyJfqyMWEAAAARQZ7dRTRMbwADfaQLrLuyj4EAAAALAZ78dEX/AAADAfMAAAALAZ7+akX/AAADAfMAAAA0QZrjSahBaJlMCE///fEAADSbIMe1U6AEOzQu1AYRj53s2vBb2v809QXFp8/S7YlbuzQxYAAAABdBnwFFESxvAAONZlgA1MYcw8bhNPbugAAAAAsBnyB0Rf8AAAMB8wAAAAsBnyJqRf8AAAMB8wAAADNBmydJqEFsmUwIT//98QAANKyiqAEd4VPJj2cIAErIdl4l06yzLjBIEbPnYfOrsz6aSMAAAAAQQZ9FRRUsbwADiTjzjug9BQAAAAsBn2R0Rf8AAAMB8wAAAA4Bn2ZqRf8AAnpjl9kh4QAAAC1Bm2tJqEFsmUwIT//98QAAHRtxW9ugA0y5Lm2Su1x3ZvYVH4iJwmliLA+rJZQAAAAaQZ+JRRUsbwAB/Pra5WAC06u2aaHJWG3uLKAAAAAOAZ+odEX/AAJ6xMu8VMEAAAAPAZ+qakX/AAJ2w/ykskPAAAAAK0Gbr0moQWyZTAhH//3hAAAudBrgAWCUrxX0WPwXLEK0DgCQH8RNVOqBamYAAAAXQZ/NRRUsbwAB8OMkABOwSCymOPUxf4EAAAANAZ/sdEX/AAKGXaeKSAAAAA0Bn+5qRf8AAoZdp4pJAAAAKEGb8EmoQWyZTAhP//3xAAAc73OoAR7y3ly7QGXXRxruGp9ZIW6sllEAAAArQZoUSeEKUmUwIT/98QAAHOQSXu1PgBFrkHnHCUdcwlr47dQuhngaf8CPgAAAABBBnjJFNExvAAHrAZcLEWX/AAAACwGeUXRF/wAAAwHzAAAADwGeU2pF/wACc1/zak9lQQAAADpBmlhJqEFomUwIT//98QAAHO79uAIe92urb3u76sYUkd2VhWtIfW1egf9wBfQJB1rdQbgzZjI1iN2BAAAAD0GedkURLG8AAfCWCfi2gAAAAA0BnpV0Rf8AAoZdp4pIAAAAEQGel2pF/wAChl3SuBNEGY3zAAAAEkGamUmoQWyZTAi/+lgAAAMBGwAAEWlliIIADP/+9uy+BTX9n9CXESzF2kpwPiqkgIB3NMAAAAMCXKe3py1upYQMAAJpKr7hBz8wC8JTtJmqtRCtfcqro/1+ARJBpLXkbvMR9KEz8h3xZVzI/lZFgL4mPwSwAYsyRKFDzXH8kDkAAAMB34CZg5E7cGziXVzv6DThm0om5Xwp6z/fvF8k/OcCUcuSWwQoKUAxVqFPgPhzu0e4SfcuRH9deWeocvKAt4m9rYD5P086v9oEl3sOGD4qdwGX2DwAFGndVlOM4PqRqDOS8NnVIhcfSuxQBVIhfX8TClo1VN5ZGhurtItlFyLPGUt2DQzOUBF8zoHjnK6859Qi9z6fhXbjqp//GHEs3tAZo4jz0Z4MVKkMXfDVFO2IBUmCEqiJdn8+dZfVls1iiv8+CrtGeK+I/ZGhw5XW+w2ZdXZxQ1S/q8P9JqOrnteCmJ8WjfN+uObwtOpwlFLW5l80LmMMPpGUU2sKtYKMnuf2rpRkAB255dtVEXbJZtsx5/L7aPp+KC74iEDcNCwlW4zj/jHZixysl1TXnP6jNIvyAclgV7ZQJsIJ4kXslwYuX/Z9oZtqbkN+boaf4X9GZfkfwTEKzyzWULq/ArcrtSy2+/HiYrfHseQu8JdXzNFcH7MD5tjPKGnbNmY6xLidw591iJEOhsIJpLBVNnRCqs3BJ+YVmoZNH91bQdEm1d2boDNZWpLm3I5k5/gh/oju8lagdVzn5x0L0Mac/8rPVM6NZMleti5NqiW2w0UI5vBdKd36Pw0FbL/ZT7cmBgvman3UXgfKRsdZ7jZLsEArSp3UMIWwFXq+9M6vMtr4Qwti/SXAoayQ1GAyYpnNeeiv7R0y80bcZ/D+QAvt1Qzj2I2po1m54Bon0dLZiWWstFs8YUngsNNFY3nhpb8B+1x1DBP8J4UC4GYhEYAe2mCdX+hcA9FwMecMdtMpd5lkyjPdhQ3DWDQuhf9X+nwwppmt69xNxGd8co7AgkCRMh6Hzk64Y3hQZ7dpBUc3PCZsVBDgO+TsMqrfin6X3AmG+MofGjAb9bggTeya/ZCkNQmcN7KVThLTmtx39bbvflwssoX/hT14kR7DFtTqiRTYF0YHjYJ2dNa3vyQUdycEKlYwN8hPrX0ilspFiET/GrqlivDg9bibw+EyBNaTy0+2JX/00ESVHfLv/5JnP58uiuGikyOOpDkW0OKEaF+u8VbjQAU+7uQNxohfdVOeK5YFVMjzAIK4bBjXnb+Il3Qvbuff//igJfur2Qmm0UslCoAeXCViXWn/sRuiW6uKEFs+pOk6R6nZDbqJMli0mAJ0NvpfNGY11ArPJO3ziN+zlA7iZUHXXMe9W1itkZ2vHwnJP5UzCj20GjJYHaBICkfHImG319GwddyaasA5dUAh7k+XyqYLiWLPMUCK0l3FXk56Edh1YT446gdKgALhQiCA1G6wQoUmYwIIulvkV0x53eY/fMenH83nYN2NMFhuXp8k0O2DWVjt1Xx1TVWhfo6UVMeRlXgr5FOAXyKPJM66BaxTmpZe+uUSsQ3FwPgh4zO2NEvn3BHY8hElQvrdF36DLy5pb80xbbGrHY7hyl74bNScT+rcZbjT5N4qT5565NyamJvjC9NH0EnYDKXj+oeh99WWiVwTQeXWttJj63lSS1AqOPL/fyfTIl8Zff1BW+hr5a8h1nYv44AMkJMet5OEHsTvktZvrJVBE3Nu7RbgFgFaFtqga9ys/dWLJOwFiQ0a0CK/A5Wkhmwg2b1PXw5+392sruzdMbUyN9g8uOgWPAmznK9RfkB/Ul1SsSVpXDYC6IhSR6Ir9/SD3vKXLg5DJhsqT6vtQuxpcPbV/D/BWplBTGFmFVXCpD7gY8bPdkQp+iBswEGSNZPdD0GEecSJtG7u2NZCLJHR/96N7CE5cpRWGRo2koWWdXvuO/riU6NamUFSbIz0Y/F2xE4PTJuvsGoMR3Ml2wFiWStYlK61aaUNHWJakEDmVwSBj8p9YcMM2skb3zpbbjowtNhB6FPN4LiG5kH7Aw91+RpnqxCEZn/q0VR8gUuU3Ibf8AAR7ZdC04OifDj4SnWw/qp8NbKrwWSsxwDqry2VsGC3ybkphfJ8zrOSb8Y+GVTzNnvi5eqF5BoztBwz90YEopjcPkAjegdCs7DtTX0wZ7wSoFW5ExWpO+G7DCPDebDyTwIwfsg9gAgpNWxgy0arl9FOKksIYNqdwWadbeeDzE76wD2oiU7IARCkQ4HiUV7ojM/hrLkSRCmPuvY36nbN5y+72WTQFcOMlp09AYO9sdR6FGIljCTaw/zGzYWiJhyxDX29JnRng8oI8i2PWxQFuUJtiN6euTkrWmFEKzN7bgpoAXwN3gZc2ZP28xJ1jpT1dGrut+eVanEiquGs+PFZdgEsTMoJ1mjA4V251C8BP/Uz4zhmywlD6T0stjFHKEwDMUWXOd8X63Bsuo2UKSY1atxq1BJd+buwm1o56GINjUtAPQ3KZhJphTQpQP8NsboGB4BeTurmMI6AEV5jxysqGfSa1WBiSD4ISsBzw052/XQmxY8/J1yyF3rtcLtd6isRu3q41EJ0qnLn17B2XprkGY667G63WEAnXgHbHmFtFT0C10uY5lHzbTAPxAGJfO47MZ//qEhviLPg0geOUyAEdwRS3YWiF+kgiohM5BQxPrgXDh43yf9LAWKlzxdFFCUg/wjPFiYc/tKcs1EyHQx+gUpp44+Q22138xUdRGFIjNpnVkH1kxhuqipBxe+ZrRJGOn9QvU3zNBFqslaKTNAp/zbEjhbOX6eD7TiKa6cEgsjRnLENAjRUXSQPCeax63W9tFA+iqE8YyBdq7e0eDK6PFBQ1qLduWVdWY+Kd2v3LJaHQLWQZ/uLmI7KSa5wzbH88kpz3qwKyNMgISDGhFPoSR1WJraPaPn5yuFji8Tjw8Mj0nz11OYLWoeMHip2eHVeAOqrJsqJ0Tfohv33WPyf30uP7E/tvCcA2GpN5km7UPfA2OSWxaImwPQQpOv6YPZ8HKeEQfHhGlIozn0zlTcdNM72li+0nC8bFi9SFPWa8xtujg3UEJxy4+AuAIvjJ7ybXR+KEmfb0Ip4JJ9OfpxiNKsVNQYA0cDS7r6cMCHykCPT+L0MyRVezFobdHRF+PtSLTGYgdSDudscc7nKjsPmgtlnw/Dy0Pt9pzb97A4y/mA2BD9ImA+qXSOYW4p0+DW0Lw71rwkQKNloMwvHwC3zXHfTg5vm6ZWVYKGTlCN/MnpCYtlVHt6S9HdWjtk5KNU66u77wHqbciTIDm7VWAWToskMULSCkthZJ/WpqSP/abzJjC93Q+5JXmD5eG3Zg0MjMQYhdvW1E35Y4KC+cynm9RFrymeoVf2il7jNw70Y/xvTU1tw3giajvdAjCnyY4KoRGeXMMEQd1nebviMqlVukOaVMWMRnxwjlqsOBSOCxH8WaCWrTUWrNfr21doe4CLHLSjfU5e7+HIv6+yWWgSR75f/hC784+sTgs2IJFkhBJ95E47VpMKEp0TN5xL+kyqhhRYrrjGFEPyaHsbO+AdPno5XYhE5sG8Dbn6Y0xFpKAE23F7fse0faMwZ+rHOJykmBmFLOjftAyrSSZCjgvk/btg3wgVX1+5msutI5K1EQCkJkcJH0qiNqPoqFGkLDgHSZylKflX0NADBzi13kUPwa4FlKRavhezs5n/9OGQWHDprHH0s2kjjHiC0Rq7lYe2/1v2CsvaiLt8tOVYJlxmMfMH5RIcru0w/CJbK2rBDUqPQG5CUuT64cJ8cHCguZPgRJ5bX0Ss6Nl8jpShW0ZEfdfen/Ft08dkhTr/LSeeRFTns2BmE3d9adDktZ+K32/gxB321dEXYDHIDx6YkpFb11n6CYBCN5Xw9EJJrlwTDXWdSz02ld5sfi7UUmgVZR28lwmUeuIQdXmcXXG654Vi1d0syR1U1+J91g0mzSzmz8ix/7r269L7x7c8EAvewO/0ejdCozFgzZZXhZofa3jeEkWM8WaJ/votJ+9R1jLPnWZyuaZ4xz3lDX+Aw7X8pFC2qT4W3tUisYts9HWPo9jW0K+EdXzfdap8wnW9dMZJVHAKCTLyXkOM0SvjKTY4zhcTaopi41CDzN+fMdZYiT5ul6SCIt0DBTrQaMd1d9ZkD6lvxrnCE2gV8QrcAG9a3Zs1cQAg6UOljT+63gPyujJBWWFI03QOZRqwdin6ZZntdwBgXABw9i2rv5/oyMMloiUOgPtDiikzj6nbBTYJEnsngrMpU6FRDjEQ31Ztc7MQ4p11SN1kwzrj2Q1Rh6/yB7YgHuEMbGD8uAJEUjNXPiHS32dKNTj4Y9TwZWNUsqMMSqKdzlMB9aa+K/PQDw7HxBIU944fGJIwZkqq4Xdm9pSH3L/enSQymWO74soGoD560vNEBT7UO+223OW4bHpZMRml/RsdcazAc91pcbvDP/Pm6xjsAluPQx3M/GcSGeMCTjc2venKl+cbhBx1U2QegBodCMyrPxvZzKhAM4cy3DCCYzno8EJbHrDaKy0hMyMmzavBzjK2wwXNVMmApH85V8Mp2OMX4VIJgUBITx5ihpTM7ot+oJDVFAPoHrNSsjg5ouVJtlKAwFrQZMC37ARoFINAbQwui6a+pVY+F3lWjdnJfnjRXf6nchDJwM7uzrJ4m6UmYsVOEiYZLW/7n2JL9HqqqAiG25sCJn7EGeT/KVL70UrCvRaN0GeM4dnzBJXk/7vHU3ygraaKDhhZ8YQGyBSRr2DTUOEJ7lx3p8nQjklWQoXmjh1RX/oPwCF3QFUd5HTsqUBdZfvAOycDy6pPGF2S+apei/OLX6q26dhSrNzyF5J0bftt4u2adQZTxdy1FmUjE3NlBdBNJ4aGl7Mv+5OJUsqFWI3EfKvxY63djvXBJZAjhQTTozOZaStXtr10F35a5sAN+J7t4L0dxVbpQyUA3a/rj9cbop4TzeIxIvQe37UmHEwUsZhAFSFoWFfOGxgkLX0RHRQGltxpASsIez0HB+gxX2Z+Uewiwltfcq4s6hBsAd78WXZISSKl//Oj8jxgN+No7BV01YD0dJC+JZHOBfGVKOsDV2d9G7SQ1BfgmL4tPXpo55YSsxDZanxitB3SAWEaQHmW+D98aVWYYh6JIXmk7+3BDgrLgfknmIW4gBvoMGqpNO0coUJdfoSt87ROHCXbmMs0XtzYa7TO9dZuw68Wtmne4biQt3Gz3ik0nF1d+hDKnFFv1347L+88Yo5kVvHFj0uDq4MafPfO0arjq61kuIpWP1Lmp7X6kXdnUlIi/4iEbYK2qhvl5XugT01odP66XgypcupaRqstLnukzCfZf5Cad5aeB4td7QeFy43p5VP9AUv0IS65vI1gNGShjItMVfNGPzJqSTZcIw/cc3nvTspbNkXcuAio79bvBVAkRX8thmMiWrvlEIdF/0jOn0qNxzYs9DMEsL3MsKQIqrCGHuTI8RESa5TXJaRoc9wYKn6WteRE97At/giDURGDmQ9+MokXoCskT4Rp7Qjf9YYlyri8/CaoLxduI0tHynDB1gM5tPnS4QIBEloe9MEBWEQlcx3rsfxKicqQjUJ0GonEk5mYmYbHzemK9aTSZNo2bJvGjH5D4AUCwCk5uDZIIunU8OCL2vVrJ+ncTJvoADcnTkqNxMEKDdAVua8ealATi+ZmjDKGAIUfNBPzGaNY6rt71rEzhGfIoQxiTQ/nPKwviRFkTPhmoQN8MLW8/rRAZLKYOVTri+ONSk2xQ+f5iNgiT7ZNz9bkCeOrabJTIzhtuRLW5GGW31Dh78nGmVHwj0BKFlvWV0ZGK3y18JjpA+GQuBmAyu21WxIwXxomKaHBFfvxV5PqQsILb+R+CKA3CY4x5GjPWZxptjwrOivE7qxbmSkV9YnJsYpgBQCpIErTDx+xz/llV4h/KrXywtug/96UKV1AYWVyCViCpitqCVgA4gABkQQAAAERBmiRsQj/94QAR73ViXrkcAnanVZeNo4qFXkIF6dF0hGwi5GYw4ymGxelvStvExKw/fTeyXpbVGaqMyOnyVbLiZbjJgAAAABJBnkJ4jf8AxBWv7YRJglvEHF0AAAAPAZ5hdEX/APMB1YovCiPhAAAADAGeY2pF/wACj53JBwAAACdBmmZJqEFomUwU8I/94QAALnK/IAiQZKVGewahQY9g38bDFzPutTMAAAAPAZ6FakX/ACbK2nMdDrugAAAAK0Gah0nhClJlMCE//fEAABzvfxABGRzCXJqOOTBDmncwNTpQUK5mv/s0WUAAAAA4QZqrSeEOiZTAhP/98QAAHZ5izYTc40DbQA5zmPt84l1tk9iURtmIsAUTI2VL4g3rf4E517xG67kAAAAfQZ7JRRE8bwAea/85QZ84nYAGzhlWiQl8kLkYR3XxgAAAABcBnuh0Rf8AJqSZyiat2ABd+HYxvaCpgAAAABUBnupqRf8AAo+cDLgAOLKV+cXS1IEAAAAxQZrtSahBaJlMFPCf/fEAAB3DM7iAAI95b8aUkEeeShYUP9C1h6ExBtJw4Y8rYEbruQAAABEBnwxqRf8AJsracjCj3UvKgQAAADZBmxFJ4QpSZTAhP/3xAAAdzUwkdRzEgCJQQhEI/LF6brtmCyHVp+qDdOLFZczB7YjvnepkuakAAAAYQZ8vRTRMbwAea/85eNnXcAG6oJmdfczsAAAADgGfTnRF/wAmpJnKKuu7AAAAGAGfUGpF/wACjoUcAEyldJCNdqMMbXSDgAAAAEFBm1VJqEFomUwIT//98QAANdpBrRwioAaAucNCylPbAF5VDKiG2CCTLH1jsdscepn0Ii9PDleDqwPxFNb1qHedMAAAABVBn3NFESxvAB5vtql2eeMxot6/90EAAAARAZ+SdEX/ACakm3KIgeluwmAAAAAPAZ+UakX/AASRgEYrSJOBAAAAMEGbmUmoQWyZTAhP//3xAAA2I7hAAESdqPT3/7zolwGm7GQhfAALZfQ950cv/vgJuQAAABtBn7dFFSxvAB5vtqriBIAHOycfcu+ZVZJXlXwAAAAWAZ/WdEX/ACakm3m6uAC9p55WKC62gQAAAAwBn9hqRf8ABJV3wtoAAAA9QZvdSahBbJlMCE///fEAAGHVb8AVvt571tMtiyTeesGcEu+Wd3C3vWrR5wSTij1lRgZuaz4VUv1DGMYnYQAAABpBn/tFFSxvAB5vtxRbVABD47ERvEtv3Ot1vwAAAA4Bnhp0Rf8AJqSbdAD62gAAAAsBnhxqRf8AAAMB8wAAACxBmgFJqEFsmUwIT//98QAAYbNFyOABXM37akbQFmwuGtP5uOcyWm5MmrIakQAAAA5Bnj9FFSxvAB5vtiw3oAAAAAwBnl50Rf8AJqSXwZ8AAAALAZ5AakX/AAADAfMAAAAuQZpFSahBbJlMCEf//eEAAJp+14A5Q5fBe9fvGOwMtEj7ou29kvzzQDL046iFgQAAABFBnmNFFSxvAB5vtxMmpy5dwQAAAA4BnoJ0Rf8AJqSefazzKwAAAAsBnoRqRf8AAAMB8wAAACBBmoZJqEFsmUwIT//98QAANeuLcARIToScxwRZ98BNwAAAACdBmqpJ4QpSZTAhP/3xAAA16lPgCELTP044wPtjVNQ3ZSXXfo2aGBAAAAAYQZ7IRTRMbwAea/9VtNgAGmeRg1MbmVfBAAAADAGe53RF/wAmpJfBnwAAAAsBnulqRf8AAAMB8wAAADVBmu5JqEFomUwIR//94QAAViPLwBWhK6yDG4/7qtu90ZzlP+HCfbqN2+pudSxjxfTz/dR4wQAAABtBnwxFESxvAB5vtqrTeXsAA/GiZrWGPAsfDE0AAAAPAZ8rdEX/ACakm3QAFiu7AAAAFQGfLWpF/wACkbc4ANTGrexxLYDikgAAACFBmy9JqEFsmUwIT//98QAAHZ79uAG5g2tdNZYUHLJc1IAAAAAzQZtTSeEKUmUwIT/98QAAHZ7WcAVqc2mKpmTiIXRkHW2/kr0Lmt3+d9WiBZQv8tWF8CLhAAAAGkGfcUU0TG8AHmv/OTAa2wABCBGV+rMm9L/AAAAAEAGfkHRF/wAmpJnH+sZv3aAAAAAVAZ+SakX/AAKRZWAA/xBfcL+ql20FAAAAOUGbl0moQWiZTAhP//3xAAAdq8R8YHgBHvJp2u4wXYEOCKnKV4KMGMsYqid20DC08TYZRnUkF/L8zAAAABpBn7VFESxvAB5vtnNaJIAF9KZhbofyz2RZgQAAAA8Bn9R0Rf8AJqSY4jzqY2kAAAAWAZ/WakX/AAKOPvdvgBx5JVXkI+V3+QAAAD9Bm9tJqEFsmUwIT//98QAAEE3CAS1TAGxE7kDx1v2dvFl392/gyu8XjgOKJuvtClNVnMTK7WQGKgrhWlJEqYEAAAARQZ/5RRUsbwAeb7ZTuGyvYhsAAAAQAZ4YdEX/ACakmOYDYZDG0QAAAA4BnhpqRf8AAWrqD/L5UQAAAERBmh5JqEFsmUwIT//98QAAEO2DXD1vhIAv9n39Y/2ow+PTANUSgjnepUorO0kp1xZvCgOFLsjZa8syYjmiVenJ1Qa7gAAAABpBnjxFFSxfACbK2jt8mbbABwo89RbTFpCTgAAAAA4Bnl1qRf8AAXU6Su4+YQAAAClBmkJJqEFsmUwIT//98QAAENAn8AcT48O5jYRGFxzUpe2ccxyCh6MpeAAAABNBnmBFFSxvAB5vtlRTM4bUlxVBAAAADAGen3RF/wAmpJfBnwAAAAsBnoFqRf8AAAMB8wAAAC9BmoZJqEFsmUwIT//98QAAENAn8Acw6nUxjwniKEPQfQvwVmkRst9lsgJHlnc2zQAAABtBnqRFFSxvAB5vtlUWlQAXTlCAtY59+0UpkEEAAAAOAZ7DdEX/ACakmGQAiHsAAAAOAZ7FakX/AAFrztfrimAAAAAwQZrKSahBbJlMCE///fEAABBngij+AK0ST2FhpwQiI5KABU5WbVLBklJA03pF8D5gAAAAHEGe6EUVLG8AHm+2U9hSYAJYVxW5S31CKJhy2CEAAAAMAZ8HdEX/ACakl8GfAAAADgGfCWpF/wABaiefwT5UAAAAK0GbDkmoQWyZTAhH//3hAAAaS1GvGE5EgA4pABf4SsAerdMvH/ck1D3jYEEAAAAUQZ8sRRUsbwAeb7ZTosxCjoUTAk0AAAARAZ9LdEX/ACakmOHLW1UymrcAAAAPAZ9NakX/AAFg/nKcuJi2AAAAJEGbT0moQWyZTAhH//3hAAAZ0T7VlYArCg22ZbaOCGtLoRhd0AAAAC5Bm3FJ4QpSZTBRUsJ//fEAAA/XMPgCJCt+uWmv9zYOpT3+il8kzBpzau4YmaUFAAAAEQGfkGpF/wABXb23BLIQvzXAAAAALEGblEnhDomUwIT//fEAAA/Sc3oEFQBWCCHWM4asNeCRW167bM4H69W5O5nHAAAAEkGfskUVPF8AAWIULOWWqwE5UQAAAA4Bn9NqRf8AAVgnVdek3AAAACxBm9dJqEFomUwIR//94QAAGTFL7SgXgDiYDnf9xwzvnVsDWC3dGRQX8F/ZQQAAABVBn/VFESxfAAFYDgpGCHzSfFYNeTkAAAAQAZ4WakX/AAFUvVAmobtw8AAAACdBmhlJqEFsmUwUTCf//fEAAA83SryUfoAJW23k9IyGH3A45tkEz4EAAAAQAZ44akX/AAFQrUTYR5wxYAAAADtBmj1J4QpSZTAhH/3hAAAYbAETtBwMGAHUB0cEbxT0bU9fE/4/8wUcPxdRQnOzUIGn8kO/8srN7wX9tQAAABxBnltFNExvAAEORgqk/1dM/AA+TJkHUdsI2CRZAAAADgGeenRF/wABTwW3qelBAAAADwGefGpF/wABT3vkmvCc+AAAABhBmn5JqEFomUwIR//94QAAF8wTDX+etlcAAAAsQZqBSeEKUmUwI//8hAAAXUnUeEzvAEU87cyymR8XhlWbEuvA8DslS17IBvQAAAAVQZ6/RTRMXwABWgTvLAYEGE3TTXIbAAAAEAGewGpF/wABQr1Qs68ZVZQAAAAVQZrCSahBaJlMCP/8hAAAWpFVYJpJAAAAIEGa40nhClJlMCEf/eEAABcwTeX5sVACFMrqooNe62ZhAAAAF0GbBEnhDomUwIR//eEAABdGD/5+1uOBAAAAN0GbJknhDyZTBRE8I//94QAAF0peuvlEgEIi5lbOfeCSLqjPD/dB11zqNTHmv6HOGeViv9PG+YEAAAATAZ9FakX/AAFPgMk7hcMpLB8pTAAAACdBm0hJ4Q8mUwU8I//94QAAFqY+iJhIAjoeddd+fxGsA5Ae51xGGHgAAAARAZ9nakX/AAFPgJuuE6OruMEAAAAmQZtpSeEPJlMCEf/94QAAFqYN4i4AFfd8MnbxyGMitGk09LpW6YAAAAA0QZuLSeEPJlMFETwj//3hAAAWtpyZq3gAahnBuRHyJ5LmrMCitu4Kj8wBuEWCz2ShNfG/wAAAABQBn6pqRf8AAU+Ah0NrwskMW3+q8QAAACNBm61J4Q8mUwU8I//94QAAFhZCyfQUAHDZ6VQ6Cs/wXK2igQAAABIBn8xqRf8AAU+AbuKA0tjpjGEAAAAhQZvOSeEPJlMCEf/94QAAFitqArQ4xJABGIEeZ3XGoNjQAAAAIEGb70nhDyZTAhP//fEAAA3G4LIrz6EGgA4bJZHyKyu4AAAALUGaEUnhDyZTBRE8J//98QAADcbuJPxSLSDQAr0SVJdOT73i1YHvyrG9XxNxwQAAABIBnjBqRf8AAU+Ac59cS4U688AAAAAqQZo1SeEPJlMCE//98QAADYAwredDDkpAJjAMf691/dapJvD3DUOmlpgQAAAAEkGeU0URPG8AAQUFbrPLmQNmMQAAAA4BnnJ0Rf8AASS30Cof0gAAAAwBnnRqRf8AASVd8tsAAAA6QZp5SahBaJlMCE///fEAABkVKfAFLHxDoaKB0IJw0/kKT8JAxRKFGYsh9wcz1ml1o+auCQaEue73zQAAABxBnpdFESxvAAGv9OgATgSOcUx/Rl9MEI/BCFlwAAAAGAGetnRF/wACGlyIAExdKrZEArAvnvXIIQAAAA0BnrhqRf8AAhqvhcVsAAAALkGavUmoQWyZTAhP//3xAAAZDSC328AUoR4QG0I91LDhsgfVBHtN23aMMzXwKmEAAAASQZ7bRRUsbwABsJCqRE/GdEpxAAAACwGe+nRF/wAAAwHzAAAAFAGe/GpF/wACHXrgAQ+VfHmE1TUgAAAAJkGa4EmoQWyZTAhH//3hAAAmmt6XX+dQAinqqgpGyaWsKZgFjNi5AAAAGkGfHkUVLF8AAgyuAtXdRSACaveOXgkf/ITgAAAAFgGfP2pF/wABLuCcAGv/DEyRfPmbAdcAAAA0QZsjSahBbJlMCEf//eEAABYZX5AHMNmZogiXSTtc0gjP6YUxR7OjYcTuKlrP3Y2PQurE4AAAABtBn0FFFSxfAAFPgHSQkAIUUMVX5EuHJ7dr1qEAAAAVAZ9iakX/AAEuvXAAdlJAeayj0GjvAAAAGEGbZUmoQWyZTBRMI//94QAADElNgdA7OQAAABEBn4RqRf8AAU9+uwY2SWUTQQAAABhBm4dJ4QpSZTBSwj/94QAADEnfaFb0h+YAAAARAZ+makX/AAFPfrsF2N26tiwAAAAlQZupSeEOiZTBRMI//eEAAAxOoN9vyoAhC1pbMp0Xu90DT5uIwQAAABIBn8hqRf8AAU9+uwXY3bA3QPwAAAAgQZvMSeEPJlMCEf/94QAADDrjqOAagg6sANlG+17VrKAAAAATQZ/qRRE8XwABT367BLLr1wZIXQAAABABngtqRf8AAKOQUOC/9gUhAAAAGEGaDkmoQWiZTBTx//yEAAAad8nOWhCy4QAAABEBni1qRf8AAU9+zr6rEzIygAAAABZBmi9J4QpSZTAj//yEAAAacR6wcc+AAAAAFkGaUEnhDomUwI///IQAABpxskCuOfEAAAAdQZpxSeEPJlMCEf/94QAABr2YDiABEGkRnRitd4MAAAAnQZqTSeEPJlMFETx//IQAAC+tQ7eBYRAD4/N9KWetGyXnUj1rM9HcAAAAEwGesmpF/wABT3668MKOckCtvzEAAAAWQZq0SeEPJlMCP//8hAAADnhECIFy8AAAAB5BmtVJ4Q8mUwIR//3hAAADA7eczwXFAES+hK2TIfEAAAAYQZr2SeEPJlMCEf/94QAAAwO4AiLJSQ+BAAAAHkGbF0nhDyZTAhH//eEAAAMDt+uG8zgA4FvBK4jIfAAAACZBmzhJ4Q8mUwIT//3xAAADAly9ieXgCpGrDijMNqFe6KxqFhAMgQAAADlBm1xJ4Q8mUwIR//3hAAADA7JwcBhskA5KtWher53h39mvWbADgTs9JcSefET+bjJSP/ppChUtRvMAAAAUQZ96RRE8bwABBQSPpCoKGN0MrsEAAAAQAZ+ZdEX/AAAzb1HrSiBHQQAAAA4Bn5tqRf8AABxH7LEngAAAADxBm55JqEFomUwU8J/98QAABDRU50AOV8XidUFhxBqeD2hIDBKmXF9zj9WRInEfpEEihMH2fS+aJFMJncAAAAATAZ+9akX/AAFPfs6+qLfK3xvffQAAAClBm6BJ4QpSZTBSwn/98QAAAwJbGFrCAAAiozCkL9XtY3F59t912ZJd+AAAABUBn99qRf8AAU9+uuyFdiKGth3gkuEAAAAqQZvESeEOiZTAhP/98QAAAwJbGEhK3QAbizgOV6/aclZXtFqLtupRqPvwAAAAE0Gf4kUVPG8AAQUEj6QoYMi5ihEAAAANAZ4BdEX/AAAzX/4p7wAAAAsBngNqRf8AAAMB8wAAADRBmghJqEFomUwIR//94QAAAwOz8ASAL8K87PIjRKhLTqj17GmWbtF3voHdSwhj7rP+7CuBAAAAG0GeJkURLG8AAQUEj6Q9dlEoABCimZAug6isCAAAAAsBnkV0Rf8AAAMB8wAAAAsBnkdqRf8AAAMB8wAAACtBmklJqEFsmUwIT//98QAAAwJt0TV3MMquNwAWCbgc0twO3X1Z2sFbvFZIAAAAMkGabUnhClJlMCE//fEAAAMCbJLDJpgAFptH7dqT8DgqcRVe17I5zPHpnJBMuaauAHerAAAAFkGei0U0TG8AAQUEj6Q5wkmVfXq1AMEAAAARAZ6qdEX/AAA00/tyruVN10EAAAAPAZ6sakX/AAA0uu/G2E9xAAAAZUGasUmoQWiZTAhH//3hAAADA+WoZlUzcATuBagGcUkLpr2yOaSqhuSqLX+0kUjawjcWjjlXDq1FECFqxVIPK5ZqW8c9YVFoqlo06+GKHEd6TitAXIRv35frn0vrVRua2tNLnDFKAAAAFUGez0URLG8AAQUEj6RPenqLEaBnCwAAAA0Bnu50Rf8AABxCb9ifAAAAGAGe8GpF/wAANgRkQAJvnT9mhGTFveM6OgAAADFBmvJJqEFsmUwIR//94QAAAwPf/pyAavci6p6O+G+vRdkWHn7rxBzDXYJVvzufbywQAAAAKUGbFEnhClJlMFFSwn/98QAAAwJ+VHhF9XpO1QAXtmIUjagU/5GHnde5AAAAFAGfM2pF/wABT3667NNxRDSMkf1wAAAAK0GbN0nhDomUwIR//eEAAAMDyD0YGeZZVwARiW0O+V34VTjxFPY97Wd7CaEAAAAUQZ9VRRU8XwABT3667KK5Wr5zLMEAAAAUAZ92akX/AAA0/6LAB4PpxWiwh3gAAAArQZt4SahBaJlMCE///fEAAAMCaiOugBbVunnRA+DBzGZuKSbisPoE/yZkgQAAADFBm5xJ4QpSZTAhP/3xAAAEU3jnuYeAOYAtk4SYMR4Lc/asPyFnAz8CEM0rcqNG3jtAAAAAFUGfukU0TG8AAQUEj6Zr+ZCojvf2wQAAABUBn9l0Rf8AAF/wNgA6OZT8qiuMOLkAAAAOAZ/bakX/AABfIrmkTxwAAAAoQZvASahBaJlMCE///fEAAARWib8eXgCsj9eItckcCKfXpN+J84mG0AAAABNBn/5FESxvAAEFBI+mO55cAmJhAAAADgGeHXRF/wAAXw+E0dAQAAAAFAGeH2pF/wAAX+miAE0DpdAWpIgJAAAALkGaBEmoQWyZTAhP//3xAAAEVAn8Amvlt/3DvD2exJscF/Nsr/bWHziJwwc7pUgAAAAbQZ4iRRUsbwABBQSPpk8l0ALd2KslRNtRD1D5AAAACwGeQXRF/wAAAwHzAAAADQGeQ2pF/wAAXTO56YEAAAAwQZpISahBbJlMCE///fEAAAQ0HPcAcorfrKSb/DvZ0A9MDnrwaxi0kbm6AmhCH2TBAAAAGkGeZkUVLG8AAQUEj6Ypb+8oAA/uTjTlIeCAAAAACwGehXRF/wAAAwHzAAAACwGeh2pF/wAAAwHzAAAAO0GajEmoQWyZTAhH//3hAAADA8nXsACNTM7cXIFn/qeqgdnR2Mtlq17kVN2ONP9Cx4z22AR+3qI9fMBAAAAAGkGeqkUVLG8AAQUEj6Q9dFFQABzM/PKoPEX8AAAACwGeyXRF/wAAAwHzAAAADAGey2pF/wAAM3rR7wAAADFBms9JqEFsmUwIR//94QAAAwPJ0ZAAsEpj1UloSIYYHMlW+aZ9nP2qswVDv6tfGQqBAAAAGUGe7UUVLF8AAU9+uuy3iWYATTmyPJNaf2kAAAAVAZ8OakX/AAA01GjgAiBQAVNLpO7wAAAAKUGbEEmoQWyZTAhH//3hAAADA8nRkACL3iw6t1K0bkvdDwU4BKhHxkKhAAAAM0GbMUnhClJlMCE//fEAAAMCahqSAImOmbyucNxn+RyFZopvh4uw5/bplZv5WpOHwd8bMAAAAC1Bm1VJ4Q6JlMCE//3xAAADAmk8vvPmCLoAQ61xzYDRZVN95nHpDm6BGI8mZIAAAAAdQZ9zRRE8bwABBQSPpD+ouABqdgO/lEa+rHampcEAAAAUAZ+SdEX/AAA0+BsACFcWpLPH0+AAAAATAZ+UakX/AAA0+BsABxZqKwyHeQAAADdBm5lJqEFomUwIT//98QAAAwJ7U84AiX6U7V6r2AwLArQ5ClbU1J00PxQ4wZ3bKUrlBLV7xWCBAAAAE0Gft0URLG8AAQUEj6RPc6UQDZgAAAAMAZ/WdEX/AAA0r1qfAAAACwGf2GpF/wAAAwHzAAAAKUGb3UmoQWyZTAhP//3xAAADAntYtwBEgvPZQNeNp1tGJ7FZii2qUb/hAAAAGUGf+0UVLG8AAQUEj6RRxpAAnYP76mtDqPEAAAALAZ4adEX/AAADAfMAAAALAZ4cakX/AAADAfMAAAAtQZoBSahBbJlMCE///fEAAAMCf3mNEtoAZxdwMANtYn/svDtKBPr5U+VjPRr/AAAAD0GeP0UVLG8AAQUEj6HA0AAAAAsBnl50Rf8AAAMB8wAAAA0BnkBqRf8AAGJE7O9AAAAAKEGaRUmoQWyZTAhP//3xAAADAntYtwA/XLmxlMn9J9HaGMNcqZDVr+EAAAASQZ5jRRUsbwABBQSPpl2TgwVBAAAADQGegnRF/wAAYd6ocm0AAAALAZ6EakX/AAADAfMAAAAlQZqHSahBbJlMFEwn//3xAAADAntYtwBWp0dxrQcrN7Jt/DVr+AAAAA4BnqZqRf8AAU9+zrfG4AAAADBBmqtJ4QpSZTAhP/3xAAADAntkV0AIyOcGkch+AiCCeWy6HRZusWldMWPUXR0OTMEAAAATQZ7JRTRMbwABBQSPpEukk9kGpAAAAAsBnuh0Rf8AAAMB8wAAAAsBnupqRf8AAAMB8wAAAC9Bmu9JqEFomUwIT//98QAAAwJ6UMXestcARIT4M5WwPAbpGJLdYFSMV/aQDUPjUwAAABRBnw1FESxvAAEFBI+kYWe+TsxN0wAAAAsBnyx0Rf8AAAMB8wAAAA0Bny5qRf8AADdidqLAAAAAL0GbM0moQWyZTAhP//3xAAADAozsLaABQR920fl7521d/yE5Q+jRWntQ24H17xVvAAAAE0GfUUUVLG8AAQUEj6RhOlfKekAAAAANAZ9wdEX/AAA3VAMN8AAAAA0Bn3JqRf8AADdO8XhJAAAAJEGbd0moQWyZTAhP//3xAAADApDbs22QAF/o1yDhU9btfrWvMAAAABFBn5VFFSxvAAEFBI+kXvBjUQAAAAsBn7R0Rf8AAAMB8wAAAAwBn7ZqRf8AADc60b4AAAAoQZu5SahBbJlMFExf+lgAAB6f4uskAHxZhe2n7364M3IqUOiyJSRUgQAAABgBn9hqRf8AAU9+zrvVtEgAmWV06j4P+qgAABUbZYiEAC///vau/MsrRwuVLh1Ze7NR8uhJcv2IMH1oAAAEjBy406D51zdNAAZwav3CBL4gIOfATtt771Ct9yquj1J4hCdEUVp08t6Qw6yP0ndIHSaK9NZMLQ3vxv8Ri5hieyYYPF8AmVAAASmJ+7Ej43Bs4kGfb+g04ZtKJuO1WDt/0nsCdA5oglHLklsEiC6Sl/LmBF2PsNmBOhv6rjVSQo6PB3zozK9yva2A+TZTBH/1Dpd7Dhg+KhWhl9g8ABRp3VAyaiH1I1BTSGGzqkQuPpXVowQGtTYGuFz2O+Fqcv0CiTSMXP8gVqTWcn01ypgRfMzuL4C7vOu7Avc+n31RyQKQgbeRiEl00BlggW9CV+nGnff5BiU/T0dWe6XSgEzzRDQjL6ssis6NwuJswkuP1phXdocOV1vO9uXV2cUH/5kxh/pNSZrxrwUxOlszJeh45uhF1HCUUXLSfPERu1P45nxTfTSfAuBBt/auofgAFxgl1VURdgk3ARUX8wKf3nYf1D5m8RrwkLsvzUIyY7MWOVktOa85/UZjXuQDksCvbKAQhBPEi9kuDFzDT45NNtTchnsaNoOsU/Oj8jeFFUKvr5CVowhUz2WBZbffiRMVvj2PGWyEir5md5DpiZePxvKGnbNk3R5jE7hze7ESIdDYQTOWCqcSiFVZuCT7yLNQyaP7iiy08p1d2boDbYwYU7gcGk5/aT/rucYHmEoNdDRx179EYPsJvuFC1TsmmFWxcm1RLbYaI0c3gulO79K9iCtl/sp9uTAwZYkT7qLwPlI2Npqxsl2CAVpVF0u5mDQf8KpyGhHBfYXlVMlY6BfOqQrnBzU+SoCanXjxUy82ZzgVr7nWjU/Bh8Dss7CsvCW4nZ5B+S7HcPbxo8Ydl3sAvxm0IBuQs1/jtc0tVj/xrVSYAMexagCKTczGo5AmmnqockKYnq6u6xvo5zmgUSISgiQ73/V/p8NCeq7evcTcRnfHKUFoJAiucfwf5OgPXnUNOeC+BuejjE2D4izYK4bh8nTHZSo/Xwo0LShZ/O+P8+yx5WabPJ0o5K5rPzg62zt8PiXzLKxrCfx7L5s+Pr14kR7DFtT0SX64QD1tXYJ2Y/d/y5+0eRFnJ/rfhcItnJzl9fMCoEXP5v4Fr3HNhbibyM/iRNaWTE8n7poKIrSmP59XhP9nSZ8uiuQ6kyOOpDkW0ODtuPs/O54iIQAU+7uQNxoiPcJseK4QFVMjzAIK4cf7Xnb+Il3Qvbuq+nQ9/QfbhQhNNopZKFU5enMWJdah53cjtD3eHOvng95xOld2EM5dEyWLSYAnQ29w80ZkDIwp5J2+cVfmoUDuJxLdvpAeqPUy2Rna8fCzk/lTMKPZJLwzY3qj0nx/HhmG4dVx193E1U4xy6oBD3+y4LCZXEsWhJ4EVpLH0sl+xkdh8g1PB2IdK7Pb8oJm41lxYaPlabwGePTbed+ucDTDQh2/Tj+bz5G7GmCw3L092aHbBrKyqYr46pqrQv0e7FmleyrwV9eAVZIcMI9IUAAGm+zFMNuUSsQ3Fu+U2b4M5uiYWbgjseQiVud+dF36DLy6sJE0xbbJY/A7hyl74bPhVT+WQ4TLj5N4qT6ma5NyamJvjC9NHDAnYDKXj+oeifMWWiVwTN+XWttJj63lSVmSaOPL/fyfTIlsZfz0om+hr5a8h1nYv44AMkJMet5OHQQGTktZvrJVBE3Nu7RbgFghY9EQDl8OWJne8Zat1wF98m7pLxTkvKLGSXb9LXw5+4Ig4F4+5vI8jn7B5cdAr+BNnOV6i/ED+pLqlYkrSqGwF0RCkj0RX7+kHvekuXByGTDZUn1fahdjS4e2r+H+CtTKCmMLMKquFSHzAx42e7Ii29EDZgIMkaOe6HoMI84gTaN3dsax7WSOj/70b2EBy5SisMjRtJQsssvfcd/XEp0a1MoKk2Rnox+LtiJwemTdf4NQYjuZLtgLEslaxJ91q00oaOny1IIHNLgkDH5T9w4YZtZI3vnS23HRhaa41+GJC/eVXhxEdPh7r8jTPViEIzP/Voqj1gpcpuQ2/4AAj2y6FpwdE+HHwhOtXQ7TodUFvJqhV5SnJoZ0dJgW+TclML5PmdZyTfjH8yqeZs98XL1QvINGdoOGgujAlE4bh8gEb0DoWnYdqa+mDPeCVAq3ImK1J3w3YYR4bzYeSeAkD9kHsAEFJq2MGWjVc4wcpUlhDJtTuCzTbbzweYn/WAe1ESnZABPkiHA8SivdEZn8MtciSIUx917G/U7bvOX3eyyaArhxktOnoDB3tjqPQoxEsYSbWHsY2bC0RMOWIa+3pM6M8HlBHkWx62KAtyhNsRvT2ycla0wohWZvbcFNADABu8DLmzJ/vmJOsdKero1d1vzyrU4kVVw1nl4rLsAliZlBes0YHCu3OoXgJ/6mfGcM2WEofS+llsYo5QmAgCiy5zvi/W4Nl1GyhSTGrVuNWoJLvzd2E5NHPQxBsaloB6G5TMJNMKaFKB/htjdAwPASid1bBhHQAivMeOVlQz6TWqwMSQfBCVgOeGnO366E2LHn5OuWQu9drhdrvUViN287GohOlU5c+vYOy9NcgzHXXY3W6wgE68A7Y8wtoqegWulzHMo+baYB+IAxL53HZjP/9QkN8RZ8GkDxymQAjuCKW7C0Qv0kEVEJnIKGJ9cC5bcBKMehYCxUueLoooSkH+EZ4sTDn9pTlmomQ6GP0ClNPHHyG22u/mKjqIwpEZtM6sg+smMN1UVIOL3zNaJIx0/qF6m+ZoItVkrQyZoFP+bYkcLZy/TwfacRTXTgkFkaM5YhoEaKi6SB4TzWPW63jooH0VQnjGQLtXb2jwZXR4oKGtRbtyyrqzHxTu1+5ZLQ6BayDP9xcxHZSTXOGbWfnklOe9WBWRpkBCQY0Ip9CSOqxNbR7R8/OVwscXiceHhkek+eupzBa1Dxg8VOzw6rwB1VZNlROib9EN++6x+T++lx/Yn9t4TgGw1JvMk3ah74GxyS2LRE2B6CFJ1/TB7Pg5TwiD48A0pFGc+mcqbjppne0sX2k4XjYsXqQp6zXmNt0cG6ghOOXHwFwBF8ZPeTa6PxQkz7ehFPBJPpz9OMRpVipqDAGjgaXdfThgQ+UgR6XxehmSKr2YtDbo6Ivx9qRaYzEDqQdztjjnc5Udh80Fss+H4eWh9vtObfvYHGX8wGwIfpEwH1S6RzC3FOnwa2heHeteEiBRstBmF4+AW+a476cHN83TKyrBQycoRv5k9ITFsoo9vSXo7q0dsnJRqnXV3feA9TbkSZAc3aqwCydFkhihaQUlsLJP61NSR/7TeZMYXu6H3JK8wfLw27MGhkZiDELt62om/LHBQXzmU83qIteUz1Cr+0UvcZuHejH+N6amtuG8ETUR7oEYU+THBVCIzy5hgiDus7zd8RlUqt0hzSpixiM+OEctVhwKRwWI/izQS1aai1Zr9e2rtD3ARY5aUb6nL3fw5F/X2Sy0CSPe//8IXfnH1icFmxBIskIJPvInHatJhQlOiZvOJf0mVUMKLFdcYwoh+TQ9jZ3wDp89HK7EInNg3gbc/TGmItJQAm24vb9j2j7RmDP1Y5xOUkwMwpZ0b9oGVaSTIUb58n7dsG+ECq+v3M1l1pHJWoiAUhMjhI+lURtR9FQo0hYcA6TOUpT8q+hoAYOcWu8ih+DXAspSLV8L2dnM//pwyCw4dNY4+lm0kcY8QWiNXcrD24D9+Cqb2oi7fLTlWCZcZjHzB+USHK7tMPwiWytqwQ1Kj0BuQlLk+uHCfHBwoLmT4ESeW19ErOjZfI6UoVtGRH3X3p/xZdPHZISgZCoDKtyIqc9mwMwm7vrToclrPxW+38GIO+2roi7AY5AePTElIreusx5ph3KGVffE43GHjuo1T/L/6ZGSDpRfcAuoByrqdoZfRKEFDaGw7Uy1Pgmsyq3KK4S4FcGHDq+woB6YpYQKO2rmErn1dbAgJUkjqpr8VVF0aqTSzmz8ivI9erdM8oBKnWXUM3NS+jmKb1FkLP6aXFKIUHvDeN4SRYzxZ5lO+i0nwP/nw4dhP2IEjk0L1rF4EZTyPLtD7uZUnwtvapFDsluTmcyn/weQsouBsjCl4ohmdlrXWSkPFpRI/oahGMmiV8ZSbHpuECbVFMXHOgeZvz5jrLCRDmplqL75skJPemMhSbhFeVzzLF+V69TBwMf5+MYc47WPb+riADbp3Dsup7rdnDK6MkFZYUjTRdJlGrB2Ke5lmY17AGBYAH9Ani+Y0wwjr10GbDyC8v/KKS2PqdsFkgzAOyeCsvqzUDQ4lhDfjm2DsxDinXvpiOkwdchqB6MVb+QPa2NCHh4SY/JiCRFI0ltsj0t9dMHU482vU7nf2S3YdQM2u78+R7haIU+EremYCv2KxEuMePjB6YMyVVcLuVe1RLJy/vYQb7qRVX9iL5LvLJ0LG/uWICsFfzBZmsJpXxhvOmckNWMnmrzBq5SAa6KoAD4D/RS+vmiWFLu+dK5PVb0qYEvui0o+quGevGCsaPvJavJvdMCvfYjNISKOzsHgnugSGD+uOlr78v6djLuoKJldTGlW53PvWvS/a4cYH4f5GOyVAmsxF4I7AX3Jg93dwSO9UktPpTt2TGP+G+RIl1OeK20zSVEXY7uAhygXWo4CeFQVaAuNDfc0B84OhZAo2JTHiV9iktr13UCLrLsy7dbXCyYPpUAGVw7eqSiAHGzUp0XAW1yXv5e1hEi2ExqcMMxLWJwaGORfQl2GbAmCHzYtChQVRJDGCoZB2PC7iBmMIb6+m9pmTkI2tb9b+MwkKaSnDFBxeS+DR+Tdm+J+qC1z6ImTlVPdEqIFMKUpNZCx+WmCmYGIaWE2OLUkZJm+W79WsjU/CtlypiKVglxhYozB4JGxG5SZcFm9wn6lMp7wQ6XcmW2mEhT3HElQKS1zc3om2nWvYGd+dzCYkPo1yVTZ54+R59LtmygNS8HQZEsdzjLpZi/VjPtL046XXP17LNq25/0x4SWF05lYdhnl11WPqAMv+XKsm+VEW6RfpkCWj5IQCmakz01LULq49Wn8Tq4r+ivdUxXp1C55MAz1uu732QVKwNnZKRxHEf/4py9oNoJ36qKrVXTO9GJqg0a0/adi5HC6PdPg1S20TtLq7DZ2VgYn3h+sN+mKP9diLO0ulSjSZC/RAbqNHH0bijWYt8r7/BRhXz8JvGbYfRGE5mjZS4lZpMCMCBMb8W1dFcMkgCwL1CpWUMhF8wf1Z1CI59uaBMfNI5O5AcRvF1AulSSzdgPmkvjowL6dB7F4uEXrySrXcyWIrfQJbz7bwUBm5R7Z6aW9E1Hf6F4FhGTL+nz52ruGuOhkbkg7LmE3H2QQfipBTk+t1Wj1NiS2tPa5tUUU5rz1M29M+avE/g924yu4WB2aOBDwBOpJGfeZqfmWQj7dgVhZENHBmw73VCxzp6/72cvOImdhUbJht304qAjIxsEQXXR7Lg0W4QbdJlIgyB/V3ehLiPLK33PMatNx6p8Sc30sGnOLW6sQQahN+Sd4iEYSN7ovuy8zvzv1/kYn6/ceGJyCSC4b2BVUSYWU4bAtQxNnV/ZCwwe8aJRbtg3SnN/iZ47kMIgIH6oAxZKIpj2oiZvrBAWhMv+uGKN2XMwU96ew6z1SnZNaUDMM9T2y03mIII6kwhwXrbXqrVJSHCmhZ+tNjxG58KyNUuAwmrBQq5Qm0Un8eJ9qyvjV1mOsubXP08LMRVgjgUpgXK9fTZbgRILF1EctI3//1aX0oM1aQ/Td9hZ+xQGN3/SPOHct9+FuxqEnqGs9HqGp90VY6Sq8SvcBCO3iBWKLV4GaQS/q4cX2uXgst5/RAnLmpPJpfP9LEQardUPWrLXTTqhVMISNiNeU6f+kr0iABTEiqtv7gvoUA/EqaC6CIqDem6cLS9IeaZL+Jv1opp4LhFtJQVLaon4V2EdqzUKxY92jHu30g1558CrBVAhlFaj115K6bo/+aKlOHij0eyCQM5pvDmnc5OgDbw0yO3ro0ffLgmM2FPzf+Y/wFlg41bqTXtySuyYUBqOH8d4s+mo46pPqQbPbRcKhdygFlzxoKDSkAImAWsn8c0kB+no1asYgD7qZu1Q1AHdyccKR53SvYoBWbP3TExGC8lWDLVuKrLNdj2qI9iNkQPVDsiKDw0gzEl/scFSet0N3JJhRGK5SRFgRXi2Qi7a4RT/RDF9FrOVgFqYHgtqpYcpeujiUxKhskRwOXNLyPNlm0KnKxsOxA4yBIv1+nANSlHImGZSslTrlwfcfQhqoYvx5bkKAlVcVvN3Bh8pqlz8Hq4TopppnV+Mydav+x1R/8uZ0x2J7hhlv5mnflhchhUIHtkLY6TtTEGh0VTGUHUCNqzii1R/t1iMawW7pnRkSarUHHV98LWvldH93hx6rPf0rDQZcYbMFTyqLie+jzefFtaVw605gOY0mVdlxmpK0EkDw8X/xWGzyXcfKePoBvmjpIVo3hs67bXNdtIj6GKqHv5HiztRr3bfBEmPEB3dQwN6a7xoWVEqZ8SV51/0gndGV5ketSj+R6RvwUgTjL4iDKnt5hH5LZWP2xpqdj5GWIpFUI2RUuFhev78jto7RCtF0U9qJw29CRhygnFq7t/KJh1OtuGbW5Mc2BiW1Ggg+GoaMQ71GU7zKAM/QFqQVXzWyCaQKmKHYnRKRy9Fofy07vgvmKWGcXNOyHNrDJXlmN7m3ZEpkDA5hBg1qYgjtKz2zEULs9D/Uf5fE4SLitZkH0H5EGi9EhiVrbtlihk5LxIpcmyzSlUA+uK+yX0S+1V40yj809xR3GdpyVo3kEHJGikBWa3k+ngWP5rbY17xo10+H9vUhk/k6rgOwUtAcQHjHnpbMVTaPMF8Mtyxu2RDrUk8ayhN/P00+l1VGFb9J/sHYADq3R4pbWcGR5akH9Myk27htSUwgI2AY099sThNiR8bIPTksIcaUF58O3qcfjqx2KlwihcLMpEAB9RXx3KQQMLEh8sSqyLKGq+Ex3sMw1P/zMnml2ld51Eq1JAznbpG6j0kcKAkaujQ0XsLtWYBpBbZNt4GhDR+TQJZd5DhI5XsnpGX/8fmgvzOtXgbEYPvljXQUlcbQhRZ9JhOI40AYKwtO8ci6KDOGfKUD/o+a0vsslVbln3BCd0eeQIt23m0eXSY4ufyHuqD3jueG49sDLw/Mb4t0n5sjFTYqDImGjMvigq21A2MAIZkmQFAAAAPEGaImxCf/3xAAtnsRg32rQA5WDjTZm8jTscAECRM2vjjiP493uF4IsQl3UR1BXuZiWyv8bM1/4rmZwieQAAABIBnkF5F/8A8vDHsjonTPo+mR8AAAAiQZpFPCGTKYQj//3hAAANPsXw7DBLEgCtLIlvdkn5R31t0gAAABFBnmNqU8X/AAC/B26LHS9ELQAAABEBnoRqRf8AAL8G0cjIzx5w4AAAAChBmolJqEFomUwIR//94QAABziFgb6E9AAbPnxG+AOfhhjKpNZeMVSBAAAAGEGep0URLG8AAJbQsUkrFAKQATiElSM+4AAAAA4BnsZ0Rf8AAGQpLv74OQAAABABnshqRf8AAL8G0cP4KfhxAAAAIkGaykmoQWyZTAhH//3hAAAHOILmrPigEYNBusYnNq7sVSEAAAAlQZrsSeEKUmUwUVLCP/3hAAANMSLRbffeUqAM5GChVeaQIJ+VgQAAABEBnwtqRf8AAL8HZIAN0hBBoAAAACJBmw5J4Q6JlMFEx//8hAAAM3Mra5sIAaVuyL9mJQa8AUPgAAAADwGfLWpF/wAAvwdtfgG//wAAACFBmy9J4Q8mUwIR//3hAAANJ7T6rAHMC5W/LpdJVYxh6YAAAAAcQZtQSeEPJlMCEf/94QAADTlVXIPQBHf6Y9cPTAAAAB5Bm3FJ4Q8mUwIR//3hAAANOVPgA2gCwe3zAf18e9EAAAAqQZuTSeEPJlMFETwn//3xAAAId83x+xoP6gBdBfjSyfBrwDt42mu4EcJJAAAAEAGfsmpF/wAAvwXot1Xt2pcAAAA1QZu1SeEPJlMFPCf//fEAAA7PSfgCJjqd7yu9K4a+G7YSrVKaipW8dAoXszwI5gmcbTJrv1IAAAAXAZ/UakX/AAFIwRAAdk9NiisC8YyYOaAAAAArQZvYSeEPJlMCE//98QAADs9J+AIe+O8OSh4vGrbKYdrWZoMEJSq2Pku1IQAAABZBn/ZFETxfAAFHO9EAINNRXLCnHBzQAAAADQGeF2pF/wAAvwa2i4AAAAA+QZocSahBaJlMCE///fEAAA7Px1MARMdNrUtd/tOB0vIQ+kv/i49/iO/sujDuPo3VswSWLIG0yS8N0hrjAm8AAAASQZ46RREsbwAA/c5KK6j6s9uAAAAADQGeWXRF/wABR3G8pIEAAAAMAZ5bakX/AACoZ3L/AAAAOEGaQEmoQWyZTAhP//3xAAAHv5h8o8IEdVQEPJsaBFmmSFYr8LQ5Zk1JsHEeVmo4ywUEfeMk7n0wAAAAGkGefkUVLG8AAJbRAesBfAAg/JC3TCf8yEynAAAADgGenXRF/wAArP/TYBD4AAAADgGen2pF/wAArR3ro4Q/AAAASUGahEmoQWyZTAhH//3hAAAMnqGaIVvALH0mCU4nZIQU55wEDcNNLC0C7iq//VEXRa1RT8G0mzTT31oUyNtGXNoqO3UFcR9WrYEAAAAUQZ6iRRUsbwAAltD/mQH4ILhX5qUAAAAPAZ7BdEX/AACs5/UvdcDwAAAADgGew2pF/wAArHDj/gdxAAAAJ0GaxkmoQWyZTBRMI//94QAADIn5eAaiUBiQHNPgx4LV55D0y++dgAAAABIBnuVqRf8AAL8HTcdRP0qfz4EAAAAcQZrnSeEKUmUwIT/98QAABFslZCvxUACIn2w2gAAAADRBmwtJ4Q6JlMCE//3xAAAEWRJfYVAAARVWqg9nJk5Tp/2i1KcqGuFnyA3W9W3EhmeEfZsnAAAAFEGfKUURPG8AAJbQsKSxBLwfFNshAAAADAGfSHRF/wAAYd6zvQAAAAwBn0pqRf8AAGIkcMkAAAAsQZtNSahBaJlMFPCf/fEAAARURiUAI7UG7pRP/kAWyL+BeLBXIDLLqb5MNoAAAAAPAZ9sakX/AAC/BtExdrPAAAAAMUGbcUnhClJlMCE//fEAAAR5V7EOiAArjQZ94tfyd+5nNiZpVLMFsCBV6p2n2T3ijKEAAAATQZ+PRTRMbwAAltCwwfGWOSc7YAAAAA8Bn650Rf8AALFGuvBPwGkAAAAQAZ+wakX/AACwv0/x/D22wQAAADFBm7RJqEFomUwIT//98QAABHlbmhQAJ/BU7V6r2Ax3CtRNnXr69Z9xrx3t15bEpMMpAAAAEUGf0kURLF8AAL8G0QPES6AVAAAADQGf82pF/wAAsL9sT4AAAAAzQZv4SahBbJlMCE///fEAAAR0QZAAOVhM2qnA4V5oXaNJZhH7542wmg6Dhq7pKaxS9VofAAAAG0GeFkUVLG8AAJbRDA5ptOeyAB+y5e5Q3FRS4QAAABMBnjV0Rf8AAGJ1TQAmW6o1JcGSAAAAFQGeN2pF/wAAsZCZXVwAZ7XDKE6ZYAAAAEVBmjxJqEFsmUwIT//98QAADncw+AKtndluVZuBHg7H88ed1CwlDG8q85o8d+qXMAulP81cSSLlWm0R1/WG6COFe9+nXYEAAAASQZ5aRRUsbwAA+LzGUNU98Z+qAAAADQGeeXRF/wAAsO/86tsAAAAVAZ57akX/AAE+MogARAoXA6IWAUPBAAAAPEGaYEmoQWyZTAhP//3xAAAOdzD4A5h1PEmsFvdWQ12QjChiRDsXcjOgJ51Q1aqrXd65+QapX9CR/wPM4AAAABVBnp5FFSxvAAD4SQvkg0hT6M7Us+EAAAAVAZ69dEX/AACx7c4AcC91DlfZuaDgAAAADQGev2pF/wABNPtvz4EAAAA0QZqkSahBbJlMCE///fEAAAfHX/h/ioAFVeLW82NZ4666GNyIlfkKIa56ghxTx3RSDHJfKwAAABNBnsJFFSxvAADyjnOFfKHBmnwIAAAADQGe4XRF/wABNdZCi4AAAAANAZ7jakX/AAE0+2/PgQAAACdBmudJqEFsmUwIT//98QAAB7/jqYArUOhLvr4EsGqCX8ThTOhL5UAAAAAQQZ8FRRUsXwABNPvMHbkqgQAAAA0BnyZqRf8AATT7b8+AAAAAJ0GbK0moQWyZTAhP//3xAAAHvQdi0/hdACpB9mX+Qb4WZwabSWs1SQAAABNBn0lFFSxvAADyjnOFRC6Bo0+BAAAADQGfaHRF/wABNdZCi4EAAAANAZ9qakX/AAE0+2/PgQAAAC1Bm29JqEFsmUwIT//98QAAB+uYfAFWp2FUxi/ItFBbxlwLt7YYIaGBkfnc+OAAAAAbQZ+NRRUsbwAA8o5zhhhQnwAAPuf7TMchvYzKAAAADQGfrHRF/wABNdZCi4EAAAAPAZ+uakX/AAE0+7j5Qf5+AAAALUGbsUmoQWyZTBRMJ//98QAAB+u1nAFb8t+qTBfL4Q9d8gkyvMYitGoVJ6s0EAAAABoBn9BqRf8AATT7zB6LXXAC3MDEZtbbAEWXZwAAACpBm9VJ4QpSZTAhH/3hAAAM2kEUAVqUzTejTvN6VsXXj0oct9ap4hW/rhMAAAAcQZ/zRTRMbwAA8o5zhhbhnABc7hXMyryRRhH0gQAAABYBnhJ0Rf8AATXWdfGYACdFwPZYsW/SAAAADQGeFGpF/wABNPtvz4AAAAAmQZoWSahBaJlMCE///fEAAAfr3OoAR7iJHzBrhJ3lKtq5br6s0EEAAAA0QZo6SeEKUmUwIT/98QAAB+7xhTuAAIq92VHkU3aIkg2kQ2A4VhP1GDHm7ox7O2XwHLXxYAAAABVBnlhFNExvAADyjnOGCzhwxypqPpAAAAANAZ53dEX/AAE11kKLgQAAAA0BnnlqRf8AATT7b8+AAAAAN0GafEmoQWiZTBTwn/3xAAAOd0n4ArfldtIcDevJZk8m46ky4eFqRi7wohyMBbBNxSLNd7pRjgcAAAAQAZ6bakX/AAE/OXRLZ8Ax4QAAAChBmoBJ4QpSZTAhP/3xAAAOdzD4ArU6eIgHTL+/YX5zWCJGC1oBS7ZgAAAAGUGevkU0TG8AAPlI0ACElYsKu2LTGTNXkqEAAAAVAZ7ddEX/AAE/srAAdfskf5wuOH4VAAAADgGe32pF/wABPiDU158nAAAAJEGaxEmoQWiZTAhP//3xAAAOdzD4A5hQME9Ou4pOI/fcOvwR8QAAABBBnuJFESxvAADzbsAwhqDcAAAADQGfAXRF/wABPvTD7k4AAAAMAZ8DakX/AAE19GUXAAAAKEGbCEmoQWyZTAhH//3hAAAM2kEUAQ9qfYFq7SDF0QJfE5md/dneuEwAAAAbQZ8mRRUsbwAA833LEHF8sgAQ0LWzbE8f0I+lAAAADgGfRXRF/wABNSVRpxfpAAAADAGfR2pF/wABNfRlFwAAAC1Bm0tJqEFsmUwIR//94QAADNZULpJFAFagl9uqg4kRm3tQcNIUT3wgOB764TEAAAAYQZ9pRRUsXwABNlb+BZLJaACdkXgtROt/AAAADQGfimpF/wABNM0iSi8AAAArQZuMSahBbJlMCEf//eEAAAzaL8gCtSmdvQVbKBIXZ7sWbCVV/3ip/YOqgAAAACVBm61J4QpSZTAhP/3xAAAH66T8AQ9gG8Ju2sisuaeY64l9WaCAAAAANkGb0UnhDomUwIT//fEAAAf01V4AEOzcDOVSVzNPTl7DEWuOmCTM+TTZW8aDbupRVPc7jrkviwAAAB5Bn+9FETxvAADzYAWINitLECyLgAbEOOd8Gt/oR9IAAAASAZ4OdEX/AAE0ye8LArohtYF4AAAAGAGeEGpF/wABNM0jF6pwAHFlNDN/NC79IQAAADdBmhVJqEFomUwIR//94QAADSn5eAKtYbjFkgnPRsBqNeEX3Q08aXiH8AcUKvdvd7+4k4rH+PehAAAAE0GeM0URLG8AAPN9yxB4ZPPC+YEAAAAPAZ5SdEX/AAE0ye8T8JfpAAAADQGeVGpF/wABNM0iSi4AAAA0QZpWSahBbJlMCE///fEAAAg0cDRIAfZMhJef5h7eObmULAxfiuHR/TmlD8Mef2qmxP8HzQAAADJBmnpJ4QpSZTAhP/3xAAAIKDr4ACgMonjJRK2Oc9qNXoUiz70QDaSCwroDPUxk87B8wAAAABtBnphFNExvAADzYAWIOjFykofAAhlVkktvQ8AAAAAYAZ63dEX/AAE0ye8dBB+7fAAOq9rWZGjBAAAAFQGeuWpF/wABNM0jKapwAHXsKVfRowAAADVBmrxJqEFomUwU8J/98QAACGbWoBIAE2uRYYhWhQ+FgNJtdf0DK+7yl9YnyUmnXT5x52Qd3QAAABIBnttqRf8AATZW/gQ5aiGW3cEAAAAuQZrASeEKUmUwIT/98QAACGbdlVLwA1m958YGQj8EvT1AqvJ35hAd++2Yaj4RYAAAABVBnv5FNExvAADzYAWH0EEMVUV0wMEAAAARAZ8ddEX/AAE0ye89I311ZYIAAAASAZ8fakX/AAE0zSM82Zm5hiHBAAAAKkGbBEmoQWiZTAhP//3xAAAIaEVwAcWFlMF1QaF0C7qJ3OzVQYF8LH3PgwAAABxBnyJFESxvAADzfcsPs+TUQACRlD5GwP585dgYAAAAEQGfQXRF/wABNMnvVbdW+/huAAAADwGfQ2pF/wABNM0jU6PXFQAAACtBm0hJqEFsmUwIT//98QAACHGwloAOCoYAeWalK1L+yDJdGXaV2Lf9mpeAAAAAHEGfZkUVLG8AAPN9yw/nWMAFsxxzENoEO3/5ch8AAAANAZ+FdEX/AAE0ye5KLgAAAA0Bn4dqRf8AATTNIkovAAAAPkGbjEmoQWyZTAhP//3xAAAIbKkLwB4ALRVFVkgbVgIl5iUhEHtQ+ikV6D3Kp++fyMeKz9Nyaph7zeqzsfBBAAAAIkGfqkUVLG8AAPN9yzN/t4AE1HKPAzXWACPu2X+GNVoeQMEAAAARAZ/JdEX/AAE0ye9Vt1bQyN0AAAASAZ/LakX/AAE0zSNWACEcL4bgAAAAMUGb0EmoQWyZTAhP//3xAAAIt83MqhADizHREUtIj7oFYxjOYO41gqGOquXf8f/eW6AAAAAhQZ/uRRUsbwAA833JcCAAJ0TXUtuUdqGzNnpgZ3gFYHC3AAAAEQGeDXRF/wABNMnuuTk8JdfgAAAADQGeD2pF/wABNM0iSi4AAAA0QZoUSahBbJlMCE///fEAAAhoE/gCJjqdB18y8HltKwKSsx/EeDS1p+/yDQ03j7Rn3ux8EQAAABBBnjJFFSxvAADzfbzAPYfBAAAADQGeUXRF/wABNMnuSi8AAAANAZ5TakX/AAE0zSJKLgAAADJBmlhJqEFsmUwIT//98QAACGm+XgCEYUUSnAJPlK64j3GaGDGFdEuWrQrvWTErT/B6QAAAABtBnnZFFSxvAADzfck7L4AJoJtJDyz9KvLoVgkAAAAPAZ6VdEX/AAE0ye9UYlcVAAAAEAGel2pF/wABNM0jVbwtxUAAAAAoQZqaSahBbJlMFEwn//3xAAAIZJosTwBCM3w9GhUIeXiFhbrz/o+D0wAAAA4BnrlqRf8AATZW+nfG3wAAAC5Bmr5J4QpSZTAhP/3xAAAIaDnuAIRl3O5YtxsCOUdT03memUq3jGUwed5e+D0hAAAAGkGe3EU0TG8AAPNgBZX6qwADYDwlizVZZch9AAAADQGe+3RF/wABNMnuSi4AAAAPAZ79akX/AAE0zSNV6xJVAAAAQUGa4EmoQWiZTBTwn/3xAAAItrP+CLrbcRCEL8dyXcCFG4gZoV/tdeMm31BLVnkilXvhLELkQIJ5yiW+tP4e7nqQAAAADwGfH2pF/wABNlb8RbzOKwAAAEpBmwRJ4QpSZTAhP/3xAAAItFYpBHvACvX2gwl9I8agG01UlziKicPdkfNguHbS8P7OrMm1gty5DGuEYvjA/YyWYutBE7FGDtIm4QAAABRBnyJFNExvAADzYAVXcZJ1MCpWgAAAABIBn0F0Rf8AATTJ727vfK+PB4AAAAATAZ9DakX/AAE0zSNsBeZXxDoTcQAAAC9Bm0hJqEFomUwIT//98QAACKgT+AaYAzLZnNVSAztOEO2ENw/SKF9sxlBuyhN24AAAABJBn2ZFESxvAADzfb0BNDmEl+sAAAARAZ+FdEX/AAE0ye68Y4ioVzAAAAARAZ+HakX/AAE0zSK7v8KuD/cAAAAuQZuMSahBbJlMCE///fEAAATWPoK4nboARnF62k7vfHsdyLcjT5eu1wjJsqsYSQAAABRBn6pFFSxvAADzfb1a7+89ojg9YwAAABEBn8l0Rf8AATTJ7rrB4Cw1zQAAABYBn8tqRf8AATTNIry/b4AJyQEb5m3SAAAAMEGb0EmoQWyZTAhP//3xAAAE/JEO5tegBHn/bFZyQwcaYRav3N9Tw18jqtu5X4jkuAAAABJBn+5FFSxvAADzfb1SzdWtvMEAAAAQAZ4NdEX/AAE0ye6/MQePYgAAABABng9qRf8AATTNIr7WRf7QAAAAM0GaE0moQWyZTAhP//3xAAAE9hTY3AG3Q0oYfs8mDVrxy9oVWtTL50iSWe795DRBvfDUgQAAABxBnjFFFSxfAAE2VuWowlcALWjoo1wvK8uOpW+BAAAAGgGeUmpF/wABNM0jb9+TwzACWazW85+y4LeZAAAASEGaV0moQWyZTAhP//3xAAAI9rP+D3EkABnwQhC8Udyhm15ZdbdLZZrlwrQCkE8c1Xfzi7mn+FRmXTSpXICDDKzMdRZnr+x60AAAABVBnnVFFSxvAADzfczBwFLzcZFXNoAAAAARAZ6UdEX/AAE0ye9v32SI4aMAAAAPAZ6WakX/AAE0zSNsJ2w4AAAAOEGamUmoQWyZTBRMJ//98QAACOgT+AatbKCobo66YkJ143Snj+WjgZ+Y+WCK+dzK0a5c1IfK+t/mAAAADgGeuGpF/wABNlbkfri/AAAARUGavUnhClJlMCEf/eEAAAdneUgC2c3t5J450guJArmul9zpNK4PKIr5s2xTPauBLnbrB5+vB7k8CuSViyrzH6Ca6zBW6AAAABJBnttFNExvAADzX/5cYVeV/YsAAAASAZ76dEX/AAE0ye649TUi6/FBAAAAEQGe/GpF/wABNM0iuSMYOACAAAAAJUGa/0moQWiZTBTwn/3xAAAEtAn8AUvpNKuT+0Nr+s1fRELfNh8AAAAWAZ8eakX/AAE2VuScDgAICY7LuDBgCAAAAD1BmwNJ4QpSZTAhH/3hAAAHZ4uwAvMC1pNwzvezIeb7Q6thFZBP1rYsjSlystq/fIQFJXKUQaOptoT8xinxAAAAHEGfIUU0TG8AAPNf/mCgmAB/LcJGWn/yKpbGow0AAAAPAZ9AdEX/AAE0ye6GGUMmAAAAEwGfQmpF/wABNM0it/CaSXqUkAwAAAAmQZtESahBaJlMCE///fEAAAS0HPcAVvs/JBWUuBerDKomtIAXFhEAAAAwQZtnSeEKUmUwIT/98QAABLQc9wBW/K/Qf1/mXf9FAj41NKuClDXmt1iHLekL7JMgAAAAEUGfhUU0TF8AATYrZyprM+IDAAAAEAGfpmpF/wABNM0iheWCBFYAAAAzQZurSahBaJlMCE///fEAAAMCo7NFcf8/zaAIObj72Jjo+G7j9e67tBeI+eMbuXw5UtdJAAAAHEGfyUURLG8AAPN9t87tpkuncaABuqoa2xpafkEAAAASAZ/odEX/AAE0ye6FpR++q5FZAAAADwGf6mpF/wABNM0iasArawAAADJBm+1JqEFsmUwUTCf//fEAAAMCtfA2xHUAI95X2YmTtHcdtE2rBOUi5XF0y/+cIDMFgAAAAA4BngxqRf8AATZW1e6E1wAAADJBmhFJ4QpSZTAhP/3xAAADArXwM/PwAcWDVBLodjBn2vxKB7dYAMLzJwLALUZJP9kKnQAAABJBni9FNExvAADzX/vPFLaOnMAAAAASAZ5OdEX/AAE0ye6FNr2J53ZMAAAADQGeUGpF/wABNM0iSi8AAAAyQZpTSahBaJlMFPCf/fEAAAMCoM5LwAv9l3SYm3BhyuoESXSkZcq8FVMzf6iAafbO65UAAAAPAZ5yakX/AAE2VtrfzzOhAAAAKkGad0nhClJlMCE//fEAAAMCkANsWX1TAERkAtyOg5f+6HP5wUl6yC+b8wAAABpBnpVFNExvAADzX/uy/NjolGwATPRT61wAaAAAABIBnrR0Rf8AATTJ7oPQ+DlSUoEAAAAXAZ62akX/AAE0zSKE37fABD7xkAlGqUAAAAA0QZq5SahBaJlMFPCf/fEAAAMCjWelIAf09t921U7W3UeAJvJkjrlTyKTiH6jiPAJjurUa8wAAAA0BnthqRf8AATZWz+fBAAAAKEGa3UnhClJlMCE//fEAAAMCjWd1ACNQdfwqn7aiLvXGjHJoyL74z4AAAAAOQZ77RTRMbwAA81/4y9EAAAANAZ8adEX/AAE0ye5KLwAAAA8BnxxqRf8AATTNIoSVWpQAAAAwQZsBSahBaJlMCE///fEAAAMCjnvfUAIzi2ThI8WcaBes+EZG4Pe983pKtPFL+zfnAAAADkGfP0URLG8AAPN9sZegAAAADQGfXnRF/wABNMnuSi8AAAAPAZ9AakX/AAE0zSKElVqVAAAAJEGbQ0moQWyZTBRMJ//98QAAAwKNatTAER/I4m8Y9HnTG2b8wAAAAA0Bn2JqRf8AATZWz+fAAAAANEGbZ0nhClJlMCE//fEAAAMCoMghgAcGPei7K/i4IgtNmxQ9b+o98fXBh5m8cgS4LP7IVeEAAAAaQZ+FRTRMbwAA81/71U2AAbDu4CmcROHapNEAAAASAZ+kdEX/AAE0ye6GGINenqVBAAAAGAGfpmpF/wABNM0ihjbjABsQ6Z4WD4TOYAAAADdBm6tJqEFomUwIT//98QAAAwKfRT4AoDoSgKq4k4tSB7G+dExoDChfjFBLBfDrWC8EvFdqD2XjAAAAG0GfyUURLG8AAPN9t6MYAAEO4thj+lkmZLPCsQAAABoBn+h0Rf8AATTJ7oXswwAIeSwiyfBX9BGcwQAAABoBn+pqRf8AATTNIoX1VCWgCWmI3o2VDHV8wQAAAC5Bm+9JqEFsmUwIR//94QAAAwJJoDRuTpAHC9qwAf7qmTlU07jE/9YR9pEf+7MUAAAAFEGeDUUVLG8AAPN9tNCKEfKGbDeYAAAAEgGeLHRF/wABNMnuaoVk7Qk2oQAAABYBni5qRf8AATTNImrf0gAD9/tzrx0SAAAAJEGaMEmoQWyZTAhP//3xAAADAXO1amAK35X4Hsq2ro0FWJnXoAAAAChBmlRJ4QpSZTAhP/3xAAADAXOn7oAVwF5X/C19Q9VGfatkliuCLq49AAAAEkGeckU0TG8AAPNf+milhXfcgQAAABEBnpF0Rf8AATTJ7mqKFns2oQAAAA0BnpNqRf8AATTNIkouAAAAJUGal0moQWiZTAhP//3xAAADAXOn7oAdI6nAq70fccq0sxwZ16AAAAARQZ61RREsXwABNlbV6AMkheEAAAANAZ7WakX/AAE0zSJKLgAAAC9BmtlJqEFsmUwUTCf//fEAAAMBcx056rgAdE5lAzdb2s+OMpVepTaTX06KaH+a2gAAABEBnvhqRf8AATZW1e59DUcytQAAGPFliIIADP/+9uy+BTX9n9CXESzF2kpwPiqkgIB3NMAAAAMCXKe3py1upYQMAAJpKr7hBz8wC8JTtJmqtRCtfcqro/1+ARJBpLXkbvMR9KEz8h3xZVzI/lZFgL4mPwSwAYsyRKFDzXH8kDkAAAMB34CZg5E7cGziXVzv6DThm0om5Xwp6z/fvF8k/OcCUcuSWwQoKUAxVqFPgPhzu0e4SfcuRH9deWeocvKAt4m9rYD5P086v9oEl3sOGD4qdwGX2DwAFGndVlOM4PqRqDOS8NnVIhcfSuxQBVIhfX8TClo1VN5ZGhurtItlFyLPGUt2DQzOUBF8zoHjnK6859Qi9z6fhXbjqp//GHEs3tAZo4jz0Z4MVKkMXfDVFO2IBUmCEqiJdn8+dZfVls1iiv8+CrtGeK+I/ZGhw5XW+w2ZdXZxQ1S/q8P9JqOrnteCmJ8WjfN+uObwtOpwlFLW5l80LmMMPpGUU2sKtYKMnuf2rpRkAB255dtVEXbJZtsx5/L7aPp+KC74iEDcNCwlW4zj/jHZixysl1TXnP6jNIvyAclgV7ZQJsIJ4kXslwYuX/Z9oZtqbkN+boaf4X9GZfkfwTEKzyzWULq/ArcrtSy2+/HiYrfHseQu8JdXzNFcH7MD5tjPKGnbNmY6xLidw591iJEOhsIJpLBVNnRCqs3BJ+YVmoZNH91bQdEm1d2boDNZWpLm3I5k5/gh/oju8lagdVzn5x0L0Mac/8rPVM6NZMleti5NqiW2w0UI5vBdKd36Pw0FbL/ZT7cmBgvman3UXgfKRsdZ7jZLsEArSp3UMIWwFXq+9M6vMtr4Qwti/SXAoayQ1GAyYpnNeeiv7R0y80bcZ/D+QAvt1Qzj2I2po1m54Bon0dLZiWWstFs8YUngsNNFY3nhpb8B+1x1DBP8J4UC4GYhEYAe2mCdX+hcA9FwMecMdtMpd5lkyjPdhQ3DWDQuhf9X+nwwppmt69xNxGd8co7AgkCRMh6Hzk64Y3hQZ7dpBUc3PCZsVBDgO+TsMqrfin6X3AmG+MofGjAb9bggTeya/ZCkNQmcN7KVThLTmtx39bbvflwssoX/hT14kR7DFtTqiRTYF0YHjYJ2dNa3vyQUdycEKlYwN8hPrX0ilspFiET/GrqlivDg9bibw+EyBNaTy0+2JX/00ESVHfLv/5JnP58uiuGikyOOpDkW0OKEaF+u8VbjQAU+7uQNxohfdVOeK5YFVMjzAIK4bBjXnb+Il3Qvbuff//igJfur2Qmm0UslCoAeXCViXWn/sRuiW6uKEFs+pOk6R6nZDbqJMli0mAJ0NvpfNGY11ArPJO3ziN+zlA7iZUHXXMe9W1itkZ2vHwnJP5UzCj20GjJYHaBICkfHImG319GwddyaasA5dUAh7k+XyqYLiWLPMUCK0l3FXk56Edh1YT446gdKgALhQiCA1G6wQoUmYwIIulvkV0x53eY/fMenH83nYN2NMFhuXp8k0O2DWVjt1Xx1TVWhfo6UVMeRlXgr5FOAXyKPJM66BaxTmpZe+uUSsQ3FwPgh4zO2NEvn3BHY8hElQvrdF36DLy5pb80xbbGrHY7hyl74bNScT+rcZbjT5N4qT5565NyamJvjC9NH0EnYDKXj+oeh99WWiVwTQeXWttJj63lSS1AqOPL/fyfTIl8Zff1BW+hr5a8h1nYv44AMkJMet5OEHsTvktZvrJVBE3Nu7RbgFgFaFtqga9ys/dWLJOwFiQ0a0CK/A5Wkhmwg2b1PXw5+392sruzdMbUyN9g8uOgWPAmznK9RfkB/Ul1SsSVpXDYC6IhSR6Ir9/SD3vKXLg5DJhsqT6vtQuxpcPbV/D/BWplBTGFmFVXCpD7gY8bPdkQp+iBswEGSNZPdD0GEecSJtG7u2NZCLJHR/96N7CE5cpRWGRo2koWWdXvuO/riU6NamUFSbIz0Y/F2xE4PTJuvsGoMR3Ml2wFiWStYlK61aaUNHWJakEDmVwSBj8p9YcMM2skb3zpbbjowtNhB6FPN4LiG5kH7Aw91+RpnqxCEZn/q0VR8gUuU3Ibf8AAR7ZdC04OifDj4SnWw/qp8NbKrwWSsxwDqry2VsGC3ybkphfJ8zrOSb8Y+GVTzNnvi5eqF5BoztBwz90YEopjcPkAjegdCs7DtTX0wZ7wSoFW5ExWpO+G7DCPDebDyTwIwfsg9gAgpNWxgy0arl9FOKksIYNqdwWadbeeDzE76wD2oiU7IARCkQ4HiUV7ojM/hrLkSRCmPuvY36nbN5y+72WTQFcOMlp09AYO9sdR6FGIljCTaw/zGzYWiJhyxDX29JnRng8oI8i2PWxQFuUJtiN6euTkrWmFEKzN7bgpoAXwN3gZc2ZP28xJ1jpT1dGrut+eVanEiquGs+PFZdgEsTMoJ1mjA4V251C8BP/Uz4zhmywlD6T0stjFHKEwDMUWXOd8X63Bsuo2UKSY1atxq1BJd+buwm1o56GINjUtAPQ3KZhJphTQpQP8NsboGB4BeTurmMI6AEV5jxysqGfSa1WBiSD4ISsBzw052/XQmxY8/J1yyF3rtcLtd6isRu3q41EJ0qnLn17B2XprkGY667G63WEAnXgHbHmFtFT0C10uY5lHzbTAPxAGJfO47MZ//qEhviLPg0geOUyAEdwRS3YWiF+kgiohM5BQxPrgXDh43yf9LAWKlzxdFFCUg/wjPFiYc/tKcs1EyHQx+gUpp44+Q22138xUdRGFIjNpnVkH1kxhuqipBxe+ZrRJGOn9QvU3zNBFqslaKTNAp/zbEjhbOX6eD7TiKa6cEgsjRnLENAjRUXSQPCeax63W9tFA+iqE8YyBdq7e0eDK6PFBQ1qLduWVdWY+Kd2v3LJaHQLWQZ/uLmI7KSa5wzbH88kpz3qwKyNMgISDGhFPoSR1WJraPaPn5yuFji8Tjw8Mj0nz11OYLWoeMHip2eHVeAOqrJsqJ0Tfohv33WPyf30uP7E/tvCcA2GpN5km7UPfA2OSWxaImwPQQpOv6YPZ8HKeEQfHhGlIozn0zlTcdNM72li+0nC8bFi9SFPWa8xtujg3UEJxy4+AuAIvjJ7ybXR+KEmfb0Ip4JJ9OfpxiNKsVNQYA0cDS7r6cMCHykCPT+L0MyRVezFobdHRF+PtSLTGYgdSDudscc7nKjsPmgtlnw/Dy0Pt9pzb97A4y/mA2BD9ImA+qXSOYW4p0+DW0Lw71rwkQKNloMwvHwC3zXHfTg5vm6ZWVYKGTlCN/MnpCYtlVHt6S9HdWjtk5KNU66u77wHqbciTIDm7VWAWToskMULSCkthZJ/WpqSP/abzJjC93Q+5JXmD5eG3Zg0MjMQYhdvW1E35Y4KC+cynm9RFrymeoVf2il7jNw70Y/xvTU1tw3giajvdAjCnyY4KoRGeXMMEQd1nebviMqlVukOaVMWMRnxwjlqsOBSOCxH8WaCWrTUWrNfr21doe4CLHLSjfU5e7+HIv6+yWWgSR75f/hC784+sTgs2IJFkhBJ95E47VpMKEp0TN5xL+kyqhhRYrrjGFEPyaHsbO+AdPno5XYhE5sG8Dbn6Y0xFpKAE23F7fse0faMwZ+rHOJykmBmFLOjftAyrSSZCjgvk/btg3wgVX1+5msutI5K1EQCkJkcJH0qiNqPoqFGkLDgHSZylKflX0NADBzi13kUPwa4FlKRavhezs5n/9OGQWHDprHH0s2kjjHiC0Rq7lYe2/1v2CsvaiLt8tOVYJlxmMfMH5RIcru0w/CJbK2rBDUqPQG5CUuT64cJ8cHCguZPgRJ5bX0Ss6Nl8jpShW0ZEfdfen/Ft08dkhKBkKgMq3Iipz2bAzCbu+tOhyWs/Fb7fwYg77auiLsBjkB49MSUit66zHmmHcoZV98TjcYeO6jVP8v/pkZIOlF9v7il+X4jcyW8W4QUNobDtTLU+CazKrcorhLgVwYcOr7CgH5ilhAo7auYSufV1sCAlSSOqmvxVUXRqpNLObPyK8j16t0zygEqdZdQzc9L6OYpvUWQs/ppcUohQe8N43hJFjPFnmU76LSfA/+fDh2E/YgSOTQvWsXgRlPI8u0Pu5lSfC29qkUOyW5OZzKf/N5Cyi4GyMKXiiGZ2WtdZKQ8WlEj+hqEYyaJXxlJsem4QJtUUxcc6B5m/PmOssJEOamWovvmyQk96YyFJuEV5fPMsX5Xr1MHAx/n4xhzjtY9v6uIANuncOy6nut2cMroyQVlhSNNF0mUasHYp7mWZjXsAYFgAf0CeL5jTDCOvXQZsPILy/8opM4+p2wWSDMA7J4Ky+rNQNDiWEN+ObYOzEOKde+mI6TB1yGoHoxVv5A9rY0IeHhJj8mIJEUjNiq09KI9hMWa5kEPwEhsungTZSuDLD7fP7YnrFEE9B/PUylDWlE9a9wJQY/avj+Ykq3IjB9PPm5XcqnFHEJ532Lfh4HAJVztjVT484/4jTzSiFfO3WD/AJSbBs4iFZ2EkJYW4AEPAf73OA8lD5/dejYtyHnvZENj2GZZPb7t6vvktxOV+/kAeUi4T8hbE4lgj+Uqe6ONv6nkDx+MX+cuB6bvlPzDLX/f1aZ3Pvf8UnfHdG5xRLQNIX2e1ojwWkf59yYPd3kElhetpuvVeLbynvGNgI/wEmRUVdfqzSyvi7F6fp4b9E3kj0FBUi/qnMw/MMF1+GdeLfpaKIbBPrxF+HPlBB16KRhIL/6y76/v9/NWCGi5joAdd5GZZL+H/LGjBgPxW7rz83uM4UFNwswi9av9jfU2acuUemMttZqnv/G2KwHiTVDM3vv3G9iZhbPkN5+dtwo11HZ7dWoDLSkHlGaaDmiHvOx/C7ee/iU0HuyV8oVFnUNWDTDUwq8jJWIGTKxzf9/sV9MSEVcPWEYIAmKtAfOa54o638ccEPz3ZmYcdkpA6v5ILhJltpgqFWTu+6WFV83N6Jtp1sVaMW+WYTEh9GfwKbPPHyPPpdp7p+O1L7DtJW3duI05/1Yz7S9Vlbrn69lm1bc/r3lZcoecysOv2ONgvhKHbwG4QTVCX8mLpGIyBDoeHD2MIz0GXlxLtVSNhdt+Vqn3S/yU2/34Fz1opm63YVMYBuxcYjYJTNPeqCfrOOz9+E8VQbTFP47caJqg0qBiNw0HQaP8A2KCKWPAaXV2GztUl+9mQTLgw2E8hYiwgKFkswxOCLP+AIN4o0G/GtpuNSp5UA8EtbFn/cyBA7xB+tfAZAk1b59vZnF8QWwCZ6WCx4f1iSlqz5yCPnZwMr4yy1qBNo5zRpM6st7BW75/fM1KmvbwAwmdzyn4akK8QVxcxKhM1rhqQVimIw7hRCUDDMKwmQDc83guShSHuiJMH9AMun/QuzvqjBObQIUPFYNYsMCGYNOAqM5D2EWnJuJM/mrLfnEiY+DERTZlGNGCmT6uVWaOBDwBOpHbLeZqxPHK6gNBAWVmn5NHRYsOFjnT1/3svI1zje5RVuB9AhGQrAtiVId9cT1wyMzCsCYun8tbkj8zFObEQkJtcKC1tA0QhWLZ4RhLZpxM5ZVgo8Uq3DPPM0uXS6G0gPJwv7eV4igeWDBLZfdy99zC9sRQoww+MCgC2rEaan/g4DAvpFP7G4HiIH6jDPH+slEtA/VAjwsxkaOZGf7uZqmh5saAQJl8m3AjaOy1pPPmsvv8F6n6ZlEFVw5RBBHG8/EQrNusqnh3VbnW5sXaXeFkiZ4hBC1AruMj6fqpKXFcTNM/4zMI6PZjqW2knbpsXL8DyOIh3+Jg5hsuQbA14WI1WcDlNuu9HpM9kh3sCW/QJddnvTzNF6uGoOmb4zULk0NUQ0gjHRwUVpI0tm5q6/wmSorADC6brcFHkyeK0b46vsKEbUKAjeogLDuvXyKviG09PnjoxvjnnqhZE73853NDIm9IgAsjxarb+4JWjgU5h4yOd8wrwYSxV4peiG1N8mF65/+Ed8X+GstQNyx+CLs+09XeYUVEXCeb4/KBUMbSmgB0vk9e/dzyP7o/+a27YXwv2x14Yt72sIPbqajco0aUKb2S2xFymdgQ3UPzf+Y/v5EJ41bqTXtySux87tSBkkbO5ww32WY+nXOjT4iFP3mMIdQ40FBXeAFpA0as7HJID9KFU7/MA+nhEUUhO1H4iqe5ZIg7MI1Y2Pfug3fmej08EMtWCC4ukNdQ2NM8TK8knwrGOyjH2CxwlwFJxCZDjIFfVxOYqnkkm9e3jgqST8RI9gI+MgCiTCDt28OobvABfbEYdDtQ2cvtANlcWnzZZtDpaJYydfVBgSL9npwTcpRyJE4sT/pLgYQu9nRIqSfeZt+qxP20Aw+PsHyOB1v2b1YXv4Hi/W2LZ1Lje4EJVvqHEV+XqBua0CiRlHqsA9jBfq/wrehpReuR0VMdO4ZtXcGczVVnlXnbeCHgayUfVTtD0lIxb77aNIN16d55mwgqwo0cC3CAKuZ/KlEPrinbxDV0djuOgdVJG0zJHY913oBfRMtE3ebn7N1z+IzomucgLVmrb0o1nOc1Lkmed5r1pq4IfJEwA7szh2nxd5YWW320mDCGqldBIa+Xp7LAf1zUgrQ75iCS8k6WAGD7UJ9bo0n/rJBGXAoIyqbdwbOt3EuX7yRqh796+ho2+u/+NERoQ44OJVZNVQTYg9h4Ijyts6kSPHaML/u0q15Vekuq2CUhLF2pRZu1wrDH6L5DYZgzbHRLhOdUjj46vmpwJ8eYl2WdzQlIwo5ZGySGHI7xXYtNL4OFmt5zBPVQC8JVYbEaRT9p1SewCYtaUn6XBqdbw0bI76EyR1VcCxg1bcwj0Nmr0IslNodQnkFSqwcp1immdYKf3F6fN8HZFxrAuq5mHQzJxpgCU8a/Q3frfD4/pgyqSOiu1p1ob5zUaaTFST3uqNoVqu+fH/dQGcjfC+UvkXm7IMaPF/BfGzOgGYslXXoCzV+t2c3cPicD0MCpvD/9nlJZTkTL/5sTgwjJXM6m2UWeXhGFaQ9odSdZo9o8jH24C4MiWxYzCIEOof0cD/7itJieuPYbuEUyBZksV3vTf2bbprMaH/HETwLNdKioojUBdUNriB3MMnVdoUynUC9McN9RBRJOncj6dW22PqO7uYKPY0m/+CtQMaK/zks5f3f4i4I2+FI6FLkDmjKvKFFxPNhXMUnhoGxZnQtQ1Xqw46CEH82lxAXJHNC6CHawLYvRve9vOq/rkI4OvL74Z3M+34BOnP401tqS+yNa1+Xzifeo7OmxlxDHc6ZZ1LaMlH+WfhpJOqT8P//377KwKF71EAqW3pBi1mU2soPEP16lMVyBf0RfFX4PlX87L8WfcY/8t9AVfF86Z+Ha5x1UrbQjE5ccu2iISfLpaMo+2Je/eJU8+J8732fOvyeyXXpR7XWFki6lpFGZVjZ631ac+CAuN7HOSOjInkyFjuTd1dS6zVFjXrGY2MpI73uWN3MR+Q+/TNIU1zZhS5INSvN0TGED0XGuuD0kfhqnpAsP1uk3dfDo+ahiUr2dprHdzDjHWOlGj/TV8aOj84Xvv2X22SIxKEKAS3Q8Jy5RtP+tbSTwbp0U3zcJdgA1km388fD1zuehpK5cCmAxNCHDNCBaq+/tolj9hhQ9jUzmz2QfoXMKaAGRbf08mkiLnO/d7p/zCpZF+I+wuAhmCbDWgdHN/rDOku3UqfhLkQchpY6R3y0PndT6vVDG86Mh28Flb9fXuRRQz7+15LGfzdD1B6iO95o3dTaEuIcSVwT257b2WErWAeZg3Socbds4zfIMyRwmbklBqS4XrfO3Qh1Hod+g3dNJ/vLQ8FHblBtQf1rAoxGnSIL7vZtsnGfBrocaFvIWmXtI0akc+qpJ0eqgdeeU6AVSGh+1P8XqA1JQADbonw5REk19t6JhaMfrIZtKj20pbltD67fNKEQqxx38f834uiaGLVb8u88Q7w9zUa91l9G/8tLwy204XIh4pEfS4ZhlpPo3hDCWYVKM0YlktnGq1aq7+1XvOnphs3aqYis/oshiZWCpXMvbEQidgky+Aw5xeA2LIZpvULk1KbYiuOKPVjuhe4wGenkb02mgbfImUEN1JwXHHNIT3Sw9LBbIrf/1zyvLJuZ/RCLfK1FsiK1494PDl5DIvnrjMaf0X4dDWNAAF0S/kHrVyVld3/lV3ckDAqKMoJLVP8UyNaiLyoE6gatakXnMGmzIk5slV35+wS3eCTR9+WqSWgoILqON498WxU6D3l3Rlj2+yTcLhX1OqVaYvjFIYbgg0Bs3PQHwfXGzsXq92n8wYhoBgGo+dCH9y1OXdAnOzAMDWSJ03qhhRDsHqkGYKWjbvaoWnqAp99Xj9MncBvxYqAHFCFQ8FIf0gn/CWQmwUoOZnVWNgXkgntopQm0NWEzi7cP2NcxHGq/NI3MxPiFEm0vanJ09+U6xgrFiHf2RGGE4K6c8sIs6icIRDFrrsc4nuz9M8DTbuCBWSFO4tcg3SKiJSv1K/0rDIuggCv/ChLm/h+aDxjV1evF3Anu19wf8DQ39bQ9PVHqe9rJBmjFtR4a/3KYXpOa9digayvUOtJ6va7wAA98/dzAAAAVEGaJGxCf/3xAAtnsRg32rQA5WDjTZm8jTscAECRM2vjjiP493uF4IsQl3UR1BWDqUOIXkw5aub0qwzyoPL1UWPOhVy5Arv1cwWS+glqIpeFD4FqLQAAABNBnkJ4i/8A8vDH2INLTYyTUFBZAAAAEQGeYXRF/wDzAdWa45Dl5negAAAACwGeY2pF/wAAAwHzAAAAKkGaaEmoQWiZTAhP//3xAAADAM2uLcAcTzDc+89lHJV6UyZ9AgFtOcaPUgAAABZBnoZFESxvAAAN3lyABNMR2ew2CMbhAAAACwGepXRF/wAAAwHzAAAACwGep2pF/wAAAwHzAAAAJEGarEmoQWyZTAhP//3xAAADAMl/I2D9ugAbONsO4bgfn7HswAAAABBBnspFFSxvAAAYiSDNZxt1AAAADgGe6XRF/wAAHmvjrMEkAAAADQGe62pF/wAAENLtzZkAAAApQZrvSahBbJlMCE///fEAAAMAyYGal9QAPzMy/6oFJv3vSpYHNUWH9UwAAAAQQZ8NRRUsXwAAELLuJQOQIQAAAA0Bny5qRf8AABDS7c2ZAAAAOEGbM0moQWyZTAhP//3xAAADAMhxNk4DLpuAIA5yaLfstDruw4eVsFwwLxdLIIK5Kb5zG40NfL4dAAAAEkGfUUUVLG8AABg8ILqikzTqGQAAAA8Bn3B0Rf8AAB5Sw655KSAAAAAPAZ9yakX/AAAeZvAvt+NJAAAALkGbdkmoQWyZTAhP//3xAAADAMiuLcAcT48SAhqqKL8welKTV05C8S/rb6bxrMEAAAAYQZ+URRUsXwAAHnELrq4ANb900mErdHAgAAAADwGftWpF/wAAHgwxgtILtgAAAClBm7lJqEFsmUwIT//98QAAAwFra/CU3VV4esAVnySqKxp0Pex7nW8FwQAAABJBn9dFFSxfAAAeV0UqCV1Fy4EAAAAOAZ/4akX/AAAdlEjlML0AAAA+QZv9SahBbJlMCEf//eEAAAMCLKsiB03ABLubWc+xda0ZjSpqbLSDHE7jLqOdziLfT2Wi6syrYpznCfleSzAAAAAQQZ4bRRUsbwAAF8b4u1TZ4AAAAA4Bnjp0Rf8AAB1UhcphewAAAAsBnjxqRf8AAAMB8wAAACxBmj5JqEFsmUwIT//98QAAAwFhG655wv0ANFY1cNlEySt7hUMpqch6O/ybfAAAAClBmkJJ4QpSZTAhP/3xAAADAWEZ9BAqXQAjkZJIv8ezXYhLvq25ruA2+QAAABBBnmBFNExvAAAXjG370hzwAAAACwGen3RF/wAAAwHzAAAACwGegWpF/wAAAwHzAAAANkGahkmoQWiZTAhP//3xAAADAWqsW4Ah42AIqt16O2smcO+LhWnqsUQva8056/4lgL7CAm3gkAAAABpBnqRFESxvAAAYiRrvAAEyMTXKcQn0a28aYQAAAAsBnsN0Rf8AAAMB8wAAABUBnsVqRf8AAB5xBLb4AIEFTOMl2KcAAAAvQZrKSahBbJlMCE///fEAAAMCjUU+AImOp03UUr/13OKOL9+jfJYTd5cfmb/9W/cAAAAOQZ7oRRUsbwAAGH3DANMAAAAQAZ8HdEX/AAA3WUmBcdM7YAAAAAsBnwlqRf8AAAMB8wAAADZBmw5JqEFsmUwIT//98QAAAwK1urgcTdwBD3x4iJe1S+b8hcWdLQxUSmDuYf6Xrheulq6cq+AAAAARQZ8sRRUsbwAALhMh3JseS/kAAAAMAZ9LdEX/AAA55Zp3AAAADgGfTWpF/wAAOg3pBH6BAAAATEGbUkmoQWyZTAhP//3xAAADArFTzgGr5b9eaq9bNcvcuFWeMGr6kOnKTciiBfE0zQ9cLzBM3p+Xyk6XYAgzJoe/cbYcE3Ux+IfqhU8AAAAZQZ9wRRUsbwAALpiEhDJAA1OtOn7IhxQ43wAAAA8Bn490Rf8AADhTat+DQTEAAAAOAZ+RakX/AAA6If+d8qAAAAA2QZuWSahBbJlMCE///fEAAAMCjUU+AImOpcgl2bhW+GtFooaasFgMxt4pbThHtCcIhcHS30/2AAAAG0GftEUVLG8AACxGE9mAAWnXnu6emrjA9+o+gQAAAA0Bn9N0Rf8AADdZV9jeAAAACwGf1WpF/wAAAwHzAAAAL0Gb2kmoQWyZTAhP//3xAAADAWqzuoARqqeIjWUBfgLNSFaUDg9Vw3VYCPKH3y7hAAAAF0Gf+EUVLG8AABhnV4kAC0xw66o46WfBAAAACwGeF3RF/wAAAwHzAAAACwGeGWpF/wAAAwHzAAAAMEGaHkmoQWyZTAhP//3xAAADAo1LfgCJjI21wuVHQtb0M8EEr1Syoc68Eg4VR9ZfYAAAABVBnjxFFSxvAAAsd8kAB2SDdDDd94EAAAAMAZ5bdEX/AAAeUs3lAAAACwGeXWpF/wAAAwHzAAAAKkGaQkmoQWyZTAhP//3xAAADAo1P3QAjI6Sj4JkY6J2I9byLFgYLgmZfwQAAAA5BnmBFFSxvAAAYiSTjgAAAAAsBnp90Rf8AAAMB8wAAAAwBnoFqRf8AADc60b8AAAAtQZqGSahBbJlMCE///fEAAAMBarVqYApkjTr6bd+Kn6RBxFQ8zJk6OXQnmzscAAAAGEGepEUVLG8AABiKcWAAffFZN76vdlSBjwAAAAsBnsN0Rf8AAAMB8wAAAAsBnsVqRf8AAAMB8wAAADJBmspJqEFsmUwIT//98QAAAwFzrFuAImM83Nsfz0CgkmT5ZNJeO/VbW0MlJMwWo5xc8QAAABdBnuhFFSxvAAAZKRrvAAE5QcRZ/eTN4AAAAAsBnwd0Rf8AAAMB8wAAAAsBnwlqRf8AAAMB8wAAACxBmw5JqEFsmUwIT//98QAAAwFzrFuAIe90mBIPDWRJTqDZ5xMMsJklcwGuPQAAABdBnyxFFSxvAAAZPLkACYp1v3yS4johUQAAAAsBn0t0Rf8AAAMB8wAAAAsBn01qRf8AAAMB8wAAAC1Bm1JJqEFsmUwIT//98QAAAwKgT38ARMdTxFOeIGZ1y3QwQ7yE4z988QvMuoEAAAAWQZ9wRRUsbwAALXj4hsgAa+CUCV9V+QAAAAsBn490Rf8AAAMB8wAAAAsBn5FqRf8AAAMB8wAAADBBm5ZJqEFsmUwIT//98QAAAwKfRT4AiYzabeQ0co2ENOj0doqjHYLXCPB0zef3neAAAAAPQZ+0RRUsbwAALWYOlHmBAAAACwGf03RF/wAAAwHzAAAACwGf1WpF/wAAAwHzAAAAJkGb2kmoQWyZTAhP//3xAAADAXOsW4Agzv6ynmP/GWVytN7v1cehAAAAFUGf+EUVLG8AABk8uQAIDBWZkxc0KwAAAAsBnhd0Rf8AAAMB8wAAAAwBnhlqRf8AAB8IL/4AAAAtQZoeSahBbJlMCE///fEAAAMBc7VqYAh73aS9xYW3T0uCKMvKJR0in6Ryvy2gAAAADkGePEUVLG8AABkpJN+BAAAACwGeW3RF/wAAAwHzAAAACwGeXWpF/wAAAwHzAAAAMEGaQkmoQWyZTAhP//3xAAADAXO1ewACAnoXpMUdktStH9jwIFCg0M6LkniSeqOPQQAAABhBnmBFFSxvAAAZIUkwAP9NGbwWyjhJG0gAAAANAZ6fdEX/AAAfazFGpQAAAA0BnoFqRf8AAB9rMUalAAAALUGahkmoQWyZTAhP//3xAAADAX0mNKbgCtf6sZvKWOgdiIX+45SmOkFXu1kOOAAAABBBnqRFFSxvAAAZulWYa7aBAAAADQGew3RF/wAAH2sxRqQAAAANAZ7FakX/AAAftEsakQAAAC1BmspJqEFsmUwIT//98QAAAwF9JAPXgAIhOcy6TMBUtx72fQsi/vJh4NM6Os0AAAARQZ7oRRUsbwAAGZTuOP7VjFgAAAANAZ8HdEX/AAAfyFD/uAAAAA4BnwlqRf8AAB+IDm2CfQAAACFBmw5JqEFsmUwIT//98QAAAwF9NKdqAFQNcOLtOqGLjMAAAAANQZ8sRRUsbwAAGb4hiwAAAAsBn0t0Rf8AAA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type=\\\"video/mp4\\\" />\\n\",\n       \"             </video>\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from IPython.core.display import display, HTML, Video\\n\",\n    \"display(HTML(data='''<video alt=\\\"video\\\" autoplay>\\n\",\n    \"                <source src=\\\"data:video/mp4;base64,{0}\\\" type=\\\"video/mp4\\\" />\\n\",\n    \"             </video>'''.format(encoded.decode('ascii'))))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"cool_files = ['1اللهم صل على محمد وآل محمد',\\n\",\n    \"              '1ان الله مع الذين اتقوا والذين هم محسنون',\\n\",\n    \"              'رب اغفر لي ولوالدي ولمن دخل بيتي مؤمنا وللمؤمنين والمؤمنات',\\n\",\n    \"              'ختم الله على قلوبهم وعلى سمعهم وعلى ابصارهم غشاوة']\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\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\": \"3.6.9\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "page/index.html",
    "content": "\n<!DOCTYPE html>\n<html lang=\"\" xml:lang=\"\" xmlns=\"http://www.w3.org/1999/xhtml\">\n <head>\n    <meta charset=\"utf-8\"/>\n    <meta name=\"twitter:card\" content=\"summary\" />\n    <meta property=\"og:url\" content=\"https://arbml.github.io/Calliar/page/index.html\" />\n    <meta property=\"og:title\" content=\"Calliar: An Online Handwritten Dataset for Arabic Calligraphy\" />\n    <meta property=\"og:description\" content=\"Calligraphy is an essential part of the Arabic heritage and culture. It has been used in the past for the decoration of houses and mosques. Usually, such calligraphy is designed manually by experts with aesthetic insights. In the past few years, there has been a considerable effort to digitize such type of art by either taking a photo of decorated buildings or drawing them using digital devices. The latter is considered an online form where the drawing is tracked by recording the apparatus movement, an electronic pen for instance, on a screen. In the literature, there are many offline datasets collected with a diversity of Arabic styles for calligraphy. However, there is no available online dataset for Arabic calligraphy. In this paper, we illustrate our approach for the collection and annotation of an online dataset for Arabic calligraphy called Calliar that consists of 2,500 sentences. Calliar is annotated for stroke, character, word and sentence level prediction.\" />\n    <meta property=\"og:image\" content=\"https://raw.githubusercontent.com/ARBML/Calliar/main/media/sample_calliar_image_3.png\" />\n\n    <title>\n        Calliar\n    </title>\n\n  <style type=\"text/css\">\n   :root {\n    --small-thumb-border-radius: 2px;\n    --larger-thumb-border-radius: 3px;\n}\n\nhtml {\n  font-size: 14px;\n  line-height: 1.6;\n  font-family: Inter, system-ui, -apple-system, BlinkMacSystemFont, \"Segoe UI\", Roboto, \"Helvetica Neue\", Arial, \"Noto Sans\", sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\", \"Noto Color Emoji\";\n  text-size-adjust: 100%;\n  -ms-text-size-adjust: 100%;\n  -webkit-text-size-adjust: 100%;\n}\n\n@media(min-width: 768px) {\n  html {\n    font-size: 16px;\n  }\n}\n\nbody {\n    margin: 0px;\n    padding: 0px;\n}\n.center {\n    justify-content: center;\n    margin:0px auto;\n    display: 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Al-shaibani\n      <sup>\n       1\n      </sup>\n     </li>\n\n     <li>\n        Mustafa Ghaleb\n      <sup>\n       1\n      </sup>\n     </li>\n\n     <li>\n        Yousif Ahmed Al-Wajih\n      <sup>\n       1\n      </sup>\n     </li>\n\n    </ul>\n    <ul class=\"authors affiliations\">\n     <li>\n      <sup>\n       1\n      </sup>\n      KFUPM\n     </li>\n    </ul>\n    <ul class=\"resources\">\n        <li>\n         <a href = \"https://arxiv.org/abs/2106.10745\">arXiv</a> \n        </li>\n        <li>\n            <a href = \"https://github.com/ARBML/Calliar\">GitHub</a>\n        </li>\n       </ul>\n\n   </div>\n  </div>\n  <div class=\"n-article\">\n   <div class=\"l-article\">\n    <img src = \"https://raw.githubusercontent.com/ARBML/Calliar/main/media/sample_calliar_image_3.png\" width=\"100%\"> </img>\n    </div>\n   <h2 id=\"abstract\">\n    Abstract\n   </h2>\n   <p style = \"text-align: justify;\">\n    Calligraphy is an essential part of the Arabic heritage and culture. It has been used in the past for the decoration of houses and mosques. Usually, such calligraphy is designed manually by experts with aesthetic insights. In the past few years, there has been a considerable effort to digitize such type of art by either taking a photo of decorated buildings or drawing them using digital devices. The latter is considered an online form where the drawing is tracked by recording the apparatus movement, an electronic pen for instance, on a screen. In the literature, there are many offline datasets collected with a diversity of Arabic styles for calligraphy. However, there is no available online dataset for Arabic calligraphy. In this paper, we illustrate our approach for the collection and annotation of an online dataset for Arabic calligraphy called Calliar that consists of 2,500 sentences. Calliar is annotated for stroke, character, word and sentence level prediction.   </p>\n    \n    <h2>\n        <a name=\"exactline\">Demo</a>\n    </h2>\n    <p style = \"text-align: justify;\">\n        You can generate an animation of a subset of the data set by clicking the button below. Note that this runs directly in the browser hence we only used 100 samples for demonstration.  \n        The 100 json files where converted to a minified version where all points were converted to integers with no stroke annotations. At each press of generate a new \n        sketch is rendered where each stroke is drawn in different color. The annimation speed is propotional to the length of the stroke. You will see that some \n        strokes might have fast animations and some slow. \n    </p>\n\n    <div style = \"text-align: center; margin: 0 auto;\">\n        <div id=\"canvas\" style = \"border-width: thin;border-color: black;border-style:solid\"> \n        </div>\n\n        <canvas id=\"myCanvas\" width = \"600px\" height = \"600px\"  style=\"border:1px solid #000000;\" resize hidden></canvas>\n\n        <button class = \"button button2\" id = \"generate\">\n            Generate\n        </button> <input type=\"range\" id = 'slider' min=\"1\" max=\"10\" value=\"3\">\n    </div> \n\n    <h2 id=\"videos\">\n        Collection & Annotation\n    </h2>\n    <p style = \"text-align: justify;\">\n    The collection procedure was done during a span of months. We used some of the images that we collected to generate some calligraphy as shown in the image below. One of the main websites \n    that we used initially is  <a href= \"https://www.namearabic.com/\">namearabic</a>. Luckily this dataset had some text annotation and we had only to spend \n    a few hours to annotate with drawing a few hundred images. For other datasets we had to annotate the images with text then with sketch drawing.\n    \n    The annotation procedure took a few weeks. We used two Samsung Galaxy Tab S6 tablets as the main devices for the annotation procedure. It took two main phases: the first one \n    annotates each image with a text. In the second phase we used the pen of the tablet to draw the annotation. We repeated these steps multiple times to increase the size of the dataset. Because \n    the intial dataset was 600x600 we decided to fix the max dimension to 600 and rescale the other dimension accordingly to preserve the spect ratio. During the annotation process \n    we faced many problems like empty annotations, repeated strokes and problems with the touch screen which created wrong annotations. To deal with these problems, \n    we made a lot of back and forth verification steps from directly annotating and then animating the results immediately using Python. We would annotate some images then remove them because they had some problems. \n    One sanity check that used is to compare the text annoation with the json stroke annotation. This gives us some quick discovery of empty annotations. The other problems are difficult to deal with especially the problem \n    of having wrong drawings by the tablet. We relied on post processing to get rid of such annotations by comparing the variance of the points in one stroke drawing. \n\n    </p>\n    <img src = \"https://raw.githubusercontent.com/zaidalyafeai/sgan/master/calligraphyv2.PNG\" width=\"100%\"> </img>\n    \n       \n\n    <h2 id=\"videos\">\n        Creative Applications\n    </h2>\n    <p style = \"text-align: justify;\">\n        There is a lot of controversy in the field of AI whether we can use it to create some intelligent and artistic programs.\n        In the last few years there has been a lot of research in applying AI creatively like Style Transfer<sup>[1]</sup>,\n        Deep Dream<sup>[2]</sup>, GauGAN<sup>[3]</sup>, etc. Most of these technologies apply for images. These applications apply mostly for computer vision, on the other\n        hand it is much more difficult in natural language processing (NLP). The intrinsic difficulty lies in the complexity \n        of modelling language, let alone creating some creative applications. One of the most interesting applications are sketch generation. \n        One of the most interesting papers is the one Alex Graves <a href =\"https://arxiv.org/abs/1308.0850\">Generating Sequences With Recurrent Neural Networks</a>. \n        The paper assumes the existance of an online corpus for generation stroke sequences for Engish. Building upon that, there has been many papers in that field like\n        sketchRNN<sup>[4]</sup>, GANwriting<sup>[5]</sup>, Scrabble-GANs<sup>[6]</sup>, DF-GANs<sup>[7]</sup>, BézierSketch<sup>[8]</sup> and DoodlerGAN<sup>[9]</sup>. Most of these papers work on English and it is needless to mention how simple English is \n        compared to other languages like Arabic. The complexity of Arabic raises from the cursive nature of connecting letters together. Not to mention the long history \n        of Arabic calligraphy which is used extensively nowadays. The different styles of Arabic calligraphy joint with the freedom of drawing some letters makes the problem much harder. \n\n        Being the only dataset that collect online stroke information for different calligraphic styles, this opens the door for many applications. \n        The difficulty still lies in modelling natural language from the strokes. There is a trade-off between creating nice strokes and generating sensible language. In the literature, \n        most of the generated strokes are usually words not sentences. In Arabic calligraphy, creating words is not an interesting problem. Much of the beauty is from following style in addition to \n        conditioning the words to specific text view which is usually a circle like in Diwani and square like in Kufi.  A proper contribution that we want to achieve in the future is to generate calligraphy \n        conditioned on style and text. \n    </p>\n    <h2 id=\"videos\">\n        Tweet\n       </h2>\n\n        <blockquote class=\"twitter-tweet tw-align-center\"><p lang=\"en\" dir=\"ltr\">Pleased to announce Calliar, the first online dataset for Arabic Calligraphy. Joint work with <a href=\"https://twitter.com/_MagedSaeed_?ref_src=twsrc%5Etfw\">@_MagedSaeed_</a> <a href=\"https://twitter.com/alwaridi?ref_src=twsrc%5Etfw\">@alwaridi</a> and Yousif Al-Wajih. <br>Paper: <a href=\"https://t.co/q5mwcP6roa\">https://t.co/q5mwcP6roa</a><br>Code &amp; data: <a href=\"https://t.co/tDCPJRYUcl\">https://t.co/tDCPJRYUcl</a><br>Colab: <a href=\"https://t.co/46yD0V3wht\">https://t.co/46yD0V3wht</a> <a href=\"https://t.co/qbbb4tZJ6l\">pic.twitter.com/qbbb4tZJ6l</a></p>&mdash; Zaid زيد (@zaidalyafeai) <a href=\"https://twitter.com/zaidalyafeai/status/1407383686786527234?ref_src=twsrc%5Etfw\">June 22, 2021</a></blockquote> <script async src=\"https://platform.twitter.com/widgets.js\" charset=\"utf-8\"></script>\n\n   <h2 id=\"videos\">\n    Videos\n   </h2>\n  \n   <div class=\"l-article\">\n    <video controls=\"\" loop=\"\" width=\"100%\">\n     <!-- t=0.001 is a hack to make iPhone show video thumbnail -->\n     <source src=\"https://user-images.githubusercontent.com/15667714/122690255-1acff980-d231-11eb-9752-730d8024041e.mp4\" type=\"video/mp4\"/>\n    </video>\n    <div class=\"videocaption\">\n     <div>\n      <strong>\n       Video 1a:\n      </strong>\n      Animating various calligraphy styles. \n     </div>\n    </div>\n   </div>\n\n   <div class=\"l-article\">\n    <video controls=\"\" loop=\"\" width=\"100%\">\n     <!-- t=0.001 is a hack to make iPhone show video thumbnail -->\n     <source src=\"https://user-images.githubusercontent.com/15667714/122690261-1d325380-d231-11eb-99ab-158455483561.mp4\" type=\"video/mp4\"/>\n    </video>\n    <div class=\"videocaption\">\n     <div>\n      <strong>\n       Video 1b:\n      </strong>\n      Animating various calligraphy styles. \n     </div>\n    </div>\n   </div>\n\n   <div class=\"l-article\">\n    <video controls=\"\" loop=\"\" width=\"100%\">\n     <!-- t=0.001 is a hack to make iPhone show video thumbnail -->\n     <source src=\"https://user-images.githubusercontent.com/15667714/122690258-1c012680-d231-11eb-98d0-4c89afd8aba3.mp4\" type=\"video/mp4\"/>\n    </video>\n    <div class=\"videocaption\">\n     <div>\n      <strong>\n       Video 3:\n      </strong>\n      Animating the same phrase using multiple styles. \n     </div>\n    </div>\n   </div>\n\n  \n\n   <div class=\"l-article\">\n    <video controls=\"\" loop=\"\" width=\"100%\">\n     <!-- t=0.001 is a hack to make iPhone show video thumbnail -->\n     <source src=\"https://user-images.githubusercontent.com/15667714/122690265-1efc1700-d231-11eb-9830-328725a82c11.mp4\" type=\"video/mp4\"/>\n    </video>\n    <div class=\"videocaption\">\n     <div>\n      <strong>\n       Video 3:\n      </strong>\n      Animating complicated calligraphic styles. \n     </div>\n    </div>\n   </div>\n\n   <pre><code>\n    @misc{alyafeai2021calliar,\n        title={Calliar: An Online Handwritten Dataset for Arabic Calligraphy}, \n        author={Zaid Alyafeai and Maged S. Al-shaibani and Mustafa Ghaleb and Yousif Ahmed Al-Wajih},\n        year={2021},\n        eprint={2106.10745},\n        archivePrefix={arXiv},\n        primaryClass={cs.CL}\n  }</code></pre>\n\n   <h2>\n    References\n   </h2>\n   <ol>\n    <li>Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. \"Perceptual losses for real-time style transfer and super-resolution.\" European conference on computer vision. Springer, Cham, 2016.</li>\n    <li>Mordvintsev, Alexander, Christopher Olah, and Mike Tyka. \"Inceptionism: Going deeper into neural networks.\" (2015).</li>\n    <li>Park, Taesung, et al. \"GauGAN: semantic image synthesis with spatially adaptive normalization.\" ACM SIGGRAPH 2019 Real-Time Live!. 2019. 1-1.</li>\n    <li>Ha, David, and Douglas Eck. \"A neural representation of sketch drawings.\" arXiv preprint arXiv:1704.03477 (2017).</li>\n    <li>Kang, Lei, et al. \"GANwriting: Content-conditioned generation of styled handwritten word images.\" European Conference on Computer Vision. Springer, Cham, 2020.</li>\n    <li>Fogel, Sharon, et al. \"ScrabbleGAN: semi-supervised varying length handwritten text generation.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.</li>\n    <li>Tao, Ming, et al. \"Df-gan: Deep fusion generative adversarial networks for text-to-image synthesis.\" arXiv preprint arXiv:2008.05865 (2020).</li>\n    <li>Das, Ayan, et al. \"BézierSketch: A generative model for scalable vector sketches.\" arXiv preprint arXiv:2007.02190 (2020).</li>\n    <li>Ge, Songwei, et al. \"Creative Sketch Generation.\" arXiv preprint arXiv:2011.10039 (2020).</li>\n   </ol>  \n  \n  \n\n  <div>\n    This website uses the template from <a href=\"https://nvlabs.github.io/alias-free-gan/\">alias-free-gan</a>. \n </div>\n\n\n<script src=\"https://cdnjs.cloudflare.com/ajax/libs/raphael/2.1.0/raphael-min.js\"></script>\n<script src=\"https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js\"></script>\n<script src=\"https://cdnjs.cloudflare.com/ajax/libs/paper.js/0.11.8/paper-full.min.js\"></script>\n<script src = \"json_sm.js\"></script>\n<script src = \"index.js\"></script>\n  "
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    "content": "\n<!DOCTYPE html>\n<html lang=\"ar\" xml:lang=\"\" xmlns=\"http://www.w3.org/1999/xhtml\">\n <head>\n    <meta charset=\"utf-8\"/>\n    <meta name=\"twitter:card\" content=\"summary\" />\n    <meta property=\"og:url\" content=\"https://arbml.github.io/Calliar/page/index_ar.html\" />\n    <meta property=\"og:title\" content=\"كاليار: مجموعة بيانات موسمة للخطوط العربية\" />\n    <meta property=\"og:description\" content=\"يعد الخط جزءًا أساسيًا من التراث والثقافة العربية. وقد استخدم قديماً لتزيين المنازل والمساجد. عادةً ما يتم تصميم هذا الخط يدويًا بواسطة خبراء يتمتعون برؤى جمالية. في السنوات القليلة الماضية ، كان هناك جهد كبير لرقمنة هذا النوع من الفن إما عن طريق التقاط صورة للمباني المزخرفة أو رسمها باستخدام الأجهزة الرقمية. يعتبر هذا الأخير نموذجًا عبر الإنترنت حيث يتم تتبع الرسم عن طريق تسجيل حركة الجهاز ، مثل القلم الإلكتروني ، على الشاشة. في البحوث المنشورة ، هناك العديد من مجموعات البيانات لرقمنة الخطوط العربية التي تم جمعها مع مجموعة متنوعة من أنماط الخط العربي. ومع ذلك ، لا توجد مجموعة بيانات متاحة لتتبع الرسم للخط العربي. في هذه الورقة، نوضح منهجنا في جمع مجموعة بيانات عبر الإنترنت للخط العربي تسمى كاليار . تم جمع 2500 صورة وتوسيمها بالرسم ليمكن اتمتة العملية بسهولة تامة في المستقبل.\" />\n    <meta property=\"og:image\" content=\"https://raw.githubusercontent.com/ARBML/Calliar/main/media/sample_calliar_image_3.png\" />\n\n    <title>\n        كاليار\n    </title>\n\n    \n  <style type=\"text/css\">\n   :root {\n    --small-thumb-border-radius: 2px;\n    --larger-thumb-border-radius: 3px;\n}\n\nhtml {\n  font-size: 14px;\n  line-height: 1.6;\n  font-family: Inter, system-ui, -apple-system, BlinkMacSystemFont, \"Segoe UI\", Roboto, \"Helvetica Neue\", Arial, \"Noto Sans\", sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\", \"Noto Color Emoji\";\n  text-size-adjust: 100%;\n  -ms-text-size-adjust: 100%;\n  -webkit-text-size-adjust: 100%;\n}\n\n@media(min-width: 768px) {\n  html {\n    font-size: 16px;\n  }\n}\n\nbody 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/* Green */\n  border: none;\n  color: white;\n  padding: 10px 15px;\n  text-align: center;\n  text-decoration: none;\n  display: inline-block;\n  font-size: 12px;\n  margin: 4px 2px;\n  transition-duration: 0.4s;\n  cursor: pointer;\n}\n\n.button2 {\n  background-color: white; \n  color: black; \n  border: 1px solid #008CBA;\n  margin-top: 15px;\n}\n\n.button2:hover {\n  background-color: #008CBA;\n  color: white;\n  \n}\n\n.button2:disabled {\n  border: 1px solid #999999;\n  background-color: #cccccc;\n  color: #666666;\n}\n\n\n\n/*---------------------------------------------------------------------*/\n  </style>\n  <!-- inline stylesheet files into the above <style> element -->\n  <link href=\"https://fonts.googleapis.com/css?family=Montserrat|Segoe+UI\" rel=\"stylesheet\"/>\n </head>\n <body>\n    <select onchange=\"if (this.value) window.location.href=this.value\">\n        <option value=\"index_ar.html\">عربي</option>\n        <option value=\"index.html\">انجليزي</option>\n    </select>\n\n  <div class=\"n-header\">\n  </div>\n  <div class=\"n-title\" dir = 'rtl'>\n   <h1>\n    كاليار: مجموعة بيانات موسمة للخطوط العربية\n   </h1>\n  </div>\n  <div class=\"n-byline\" dir = 'rtl'>\n   <div class=\"byline\">\n    <ul class=\"authors\">\n     <li>\n        زيد اليافعي\n     </li>\n\n     <li>\n        ماجد الشيباني\n\n     </li>\n\n     <li>\n        مصطفى غالب\n\n     </li>\n\n     <li>\n        يوسف الوجيه\n\n     </li>\n\n    </ul>\n    <ul class=\"authors affiliations\">\n     <li>\n\n      جامعة الملك فهد للبترول والمعادن\n     </li>\n    </ul>\n    <ul class=\"resources\">\n        <li>\n         <a href = \"https://arxiv.org/abs/2106.10745\">الورقة البحثية</a> \n        </li>\n        <li>\n            <a href = \"https://github.com/ARBML/Calliar\">الأكواد</a>\n        </li>\n       </ul>\n\n   </div>\n  </div>\n  <div class=\"n-article\">\n   <div class=\"l-article\">\n    <img src = \"https://raw.githubusercontent.com/ARBML/Calliar/main/media/sample_calliar_image_3.png\" width=\"100%\"> </img>\n    </div>\n   <h2 id=\"abstract\" dir = 'rtl'>\n    ملخص\n   </h2>\n   <p style = \"text-align: justify;\" dir = 'rtl'>\n    يعد الخط جزءًا أساسيًا من التراث والثقافة العربية. وقد استخدم قديماً لتزيين المنازل والمساجد. عادةً ما يتم تصميم هذا الخط يدويًا بواسطة خبراء يتمتعون برؤى جمالية. في السنوات القليلة الماضية ، كان هناك جهد كبير لرقمنة هذا النوع من الفن إما عن طريق التقاط صورة للمباني المزخرفة أو رسمها باستخدام الأجهزة الرقمية. يعتبر هذا الأخير نموذجًا عبر الإنترنت حيث يتم تتبع الرسم عن طريق تسجيل حركة الجهاز ، مثل القلم الإلكتروني ، على الشاشة. في البحوث المنشورة ، هناك العديد من مجموعات البيانات لرقمنة الخطوط العربية التي تم جمعها مع مجموعة متنوعة من أنماط الخط العربي. ومع ذلك ، لا توجد مجموعة بيانات متاحة لتتبع الرسم للخط العربي. في هذه الورقة، نوضح منهجنا في جمع مجموعة بيانات عبر الإنترنت للخط العربي تسمى كاليار\n    . تم جمع 2500 صورة وتوسيمها بالرسم ليمكن اتمتة العملية بسهولة تامة في المستقبل.   \n        \n    </p>\n    \n    <h2 dir = 'rtl'>\n        تجربة\n    </h2>\n    <p style = \"text-align: justify;\" dir = 'rtl'>\n        يمكنك إنشاء رسم متحرك لمجموعة  صغيرة من الملفات من مجموعة البيانات بالنقر فوق الزر أدناه. لاحظ أن هذا يعمل مباشرة في المتصفح ومن ثم استخدمنا 100 عينة فقط للتوضيح. تم تحويل 100 ملف json إلى إصدار مصغر حيث تم تحويل جميع النقاط إلى أعداد صحيحة . عند كل ضغطة على إنشاء ، يتم تقديم رسم جديد حيث يتم رسم كل حد بلون مختلف.\n    </p>\n\n    <div style = \"text-align: center; margin: 0 auto;\">\n        <div id=\"canvas\" style = \"border-width: thin;border-color: black;border-style:solid\"> \n        </div>\n        <canvas id=\"myCanvas\" width = \"600px\" height = \"600px\"  style=\"border:1px solid #000000;\" resize hidden></canvas>\n\n        <button class = \"button button2\" id = \"generate\">\n            إنشاء \n        </button> <input type=\"range\" id = 'slider' min=\"1\" max=\"10\" value=\"3\">\n    </div> \n\n    <h2 id=\"videos\" dir = 'rtl'>\n        توسيم البيانات\n    </h2>\n    <p style = \"text-align: justify;\" dir = 'rtl'>\n    \nتم إجراء عملية جمع البيانات خلال عدة أشهر. استخدمنا بعض الصور التي جمعناها لإنشاء الخط بشكل اتوماتيكي  كما هو موضح في الصورة أدناه. أحد المواقع الرئيسية التي استخدمناها في البداية هو موقع الأسماء العربية. لحسن الحظ ، كانت مجموعة البيانات هذه تحتوي على بعض التعليقات التوضيحية النصية وكان علينا فقط قضاء بضع ساعات لتوسميها برسم بضع مئات من الصور. بالنسبة لمجموعات البيانات الأخرى ، كان علينا أن نوسم الصور بالنص ثم بالرسم التخطيطي. استغرق إجراء التعليق التوضيحي بضعة أسابيع. استخدمنا جهازي تابليت لوحي Samsung Galaxy Tab S6 كأجهزة رئيسية لإجراء التعليقات التوضيحية. استغرق الأمر مرحلتين رئيسيتين: الأولى تعلق على كل صورة بنص. في المرحلة الثانية ، استخدمنا قلم الكمبيوتر اللوحي لرسم التعليق التوضيحي. كررنا هذه الخطوات عدة مرات لزيادة حجم مجموعة البيانات. نظرًا لأن مجموعة البيانات الأولية كانت 600 × 600 ، فقد قررنا تحويل البعد الأقصى إلى 600 وإعادة قياس البعد الآخر وفقًا لذلك للحفاظ على نسبة أبعاد الصورة. أثناء عملية التعليقات التوضيحية ، واجهنا العديد من المشكلات مثل التعليقات التوضيحية الفارغة ، الرسوم متكررة ومشاكل في شاشة اللمس أدت إلى إنشاء تعليقات توضيحية خاطئة. للتعامل مع هذه المشاكل ، اتخذنا الكثير من خطوات التحقق بشكل دوري  عن طريق رسمها بواسطة بايثون.  في بعض الأوقات نضع رسومات على بعض الصور ثم نزيلها لأن لديهم بعض المشاكل. أحد عمليات التحقق من الصحة المستخدمة هو مقارنة نص الصورة مع وسم الحروف المختلفة . هذا يعطينا فكرة سريعة عن الأخطاء الشائعة. من الصعب التعامل مع المشاكل الأخرى خاصة مشكلة وجود رسومات خاطئة على الجهاز اللوحي. لقد اعتمدنا على معالجة الملفات المحفوظة على شكل json لتتخلص من هذه المشاكل. \n    </p>\n    <img src = \"https://raw.githubusercontent.com/zaidalyafeai/sgan/master/calligraphyv2.PNG\" width=\"100%\"> </img>\n    \n       \n\n    <h2 id=\"videos\" dir = 'rtl'>\n        التطبيقات الذكية\n    </h2>\n    <p style = \"text-align: justify;\" dir = 'rtl'>\n        هناك الكثير من الجدل في مجال الذكاء الاصطناعي حول ما إذا كان بإمكاننا استخدامه لإنشاء بعض البرامج الذكية والفنية. في السنوات القليلة الماضية ، كان هناك الكثير من الأبحاث في تطبيق الذكاء الاصطناعي بشكل إبداعي مثل Style Transfer [1] و Deep Dream [2] و GauGAN [3] وما إلى ذلك. تنطبق معظم هذه التقنيات على الصور.  من ناحية أخرى ، فهي أكثر صعوبة في معالجة اللغة الطبيعية (NLP). تكمن الصعوبة الجوهرية في تعقيد لغة النمذجة ، ناهيك عن إنشاء بعض التطبيقات الإبداعية. يعد إنشاء الرسومات أحد أكثر التطبيقات إثارة للاهتمام. واحدة من أكثر الأوراق إثارة للاهتمام هي ورقة  Alex Gravesتوليد تسلسلات مع الشبكات العصبية المتكررة . تفترض الورقة وجود مجموعة بيانات موسمة بالرسم لتوليد الخطوط باللغة الإنجليزية. بناءً على ذلك ، كان هناك العديد من الأوراق في هذا المجال مثل sketchRNN [4] و GANwriting [5] و Scrabble-GANs [6] و DF-GANs [7] و BézierSketch [8] و DoodlerGAN [9]. معظم هذه الأوراق مخصصة للبيانات باللغة الإنجليزية ولا داعي لذكر مدى بساطة اللغة الإنجليزية مقارنة باللغات الأخرى مثل العربية. ينشأ تعقيد اللغة العربية من الطبيعة المتصلة للحروف. ناهيك عن التاريخ الطويل للخط العربي الذي يستخدم على نطاق واسع في الوقت الحاضر. تترافق الأنماط المختلفة للخط العربي مع حرية رسم بعض الحروف مما يجعل المشكلة أكثر صعوبة. نظرًا لكون كاليار مجموعة البيانات الوحيدة التي تجمع معلومات الرسوم للخطوط العربية لأنماط الخط المختلفة ، فإن هذا يفتح الباب للعديد من التطبيقات. لا تزال الصعوبة تكمن في نمذجة اللغة الطبيعية من وإنشاء خطوط يمكن فهمها . هناك مفاضلة بين إنشاء خطوط جميلة  وتوليد لغة منطقية.\n    </p>\n    <h2 id=\"videos\" dir = 'rtl'>\n        منشور التويتر\n       </h2>\n\n        <blockquote class=\"twitter-tweet tw-align-center\"><p lang=\"en\" dir=\"ltr\">Pleased to announce Calliar, the first online dataset for Arabic Calligraphy. Joint work with <a href=\"https://twitter.com/_MagedSaeed_?ref_src=twsrc%5Etfw\">@_MagedSaeed_</a> <a href=\"https://twitter.com/alwaridi?ref_src=twsrc%5Etfw\">@alwaridi</a> and Yousif Al-Wajih. <br>Paper: <a href=\"https://t.co/q5mwcP6roa\">https://t.co/q5mwcP6roa</a><br>Code &amp; data: <a href=\"https://t.co/tDCPJRYUcl\">https://t.co/tDCPJRYUcl</a><br>Colab: <a href=\"https://t.co/46yD0V3wht\">https://t.co/46yD0V3wht</a> <a href=\"https://t.co/qbbb4tZJ6l\">pic.twitter.com/qbbb4tZJ6l</a></p>&mdash; Zaid زيد (@zaidalyafeai) <a href=\"https://twitter.com/zaidalyafeai/status/1407383686786527234?ref_src=twsrc%5Etfw\">June 22, 2021</a></blockquote> <script async src=\"https://platform.twitter.com/widgets.js\" charset=\"utf-8\"></script>\n\n   <h2 id=\"videos\" dir = 'rtl'>\n    فيديو\n   </h2>\n  \n   <div class=\"l-article\">\n    <video controls=\"\" loop=\"\" width=\"100%\">\n     <!-- t=0.001 is a hack to make iPhone show video thumbnail -->\n     <source src=\"https://user-images.githubusercontent.com/15667714/122690255-1acff980-d231-11eb-9752-730d8024041e.mp4\" type=\"video/mp4\"/>\n    </video>\n    <div class=\"videocaption\">\n     <div dir = 'rtl'>\n      <strong>\n        الفيديو 1 أ: \n      </strong>\n      تحريك أنماط الخط المختلفة\n     </div>\n    </div>\n   </div>\n\n   <div class=\"l-article\">\n    <video controls=\"\" loop=\"\" width=\"100%\">\n     <!-- t=0.001 is a hack to make iPhone show video thumbnail -->\n     <source src=\"https://user-images.githubusercontent.com/15667714/122690261-1d325380-d231-11eb-99ab-158455483561.mp4\" type=\"video/mp4\"/>\n    </video>\n    <div class=\"videocaption\">\n     <div>\n      <strong>\n        الفيديو 1 ب: \n      </strong>\n      تحريك أنماط الخط المختلفة \n     </div>\n    </div>\n   </div>\n\n   <div class=\"l-article\">\n    <video controls=\"\" loop=\"\" width=\"100%\">\n     <!-- t=0.001 is a hack to make iPhone show video thumbnail -->\n     <source src=\"https://user-images.githubusercontent.com/15667714/122690258-1c012680-d231-11eb-98d0-4c89afd8aba3.mp4\" type=\"video/mp4\"/>\n    </video>\n    <div class=\"videocaption\">\n     <div>\n      <strong>\n        فيديو 3:\n      </strong>\n      تحريك نفس العبارة باستخدام أنماط متعددة\n     </div>\n    </div>\n   </div>\n\n  \n\n   <div class=\"l-article\">\n    <video controls=\"\" loop=\"\" width=\"100%\">\n     <!-- t=0.001 is a hack to make iPhone show video thumbnail -->\n     <source src=\"https://user-images.githubusercontent.com/15667714/122690265-1efc1700-d231-11eb-9830-328725a82c11.mp4\" type=\"video/mp4\"/>\n    </video>\n    <div class=\"videocaption\">\n     <div>\n      <strong>\n        الفيديو 4:\n      </strong>\n      تحريك الأنماط الخطية المعقدة\n     </div>\n    </div>\n   </div>\n\n   <pre><code>\n    @misc{alyafeai2021calliar,\n        title={Calliar: An Online Handwritten Dataset for Arabic Calligraphy}, \n        author={Zaid Alyafeai and Maged S. Al-shaibani and Mustafa Ghaleb and Yousif Ahmed Al-Wajih},\n        year={2021},\n        eprint={2106.10745},\n        archivePrefix={arXiv},\n        primaryClass={cs.CL}\n  }</code></pre>\n\n   <h2 dir = 'rtl'>\n    المصادر\n   </h2>\n   <ol>\n    <li>Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. \"Perceptual losses for real-time style transfer and super-resolution.\" European conference on computer vision. Springer, Cham, 2016.</li>\n    <li>Mordvintsev, Alexander, Christopher Olah, and Mike Tyka. \"Inceptionism: Going deeper into neural networks.\" (2015).</li>\n    <li>Park, Taesung, et al. \"GauGAN: semantic image synthesis with spatially adaptive normalization.\" ACM SIGGRAPH 2019 Real-Time Live!. 2019. 1-1.</li>\n    <li>Ha, David, and Douglas Eck. \"A neural representation of sketch drawings.\" arXiv preprint arXiv:1704.03477 (2017).</li>\n    <li>Kang, Lei, et al. \"GANwriting: Content-conditioned generation of styled handwritten word images.\" European Conference on Computer Vision. Springer, Cham, 2020.</li>\n    <li>Fogel, Sharon, et al. \"ScrabbleGAN: semi-supervised varying length handwritten text generation.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.</li>\n    <li>Tao, Ming, et al. \"Df-gan: Deep fusion generative adversarial networks for text-to-image synthesis.\" arXiv preprint arXiv:2008.05865 (2020).</li>\n    <li>Das, Ayan, et al. \"BézierSketch: A generative model for scalable vector sketches.\" arXiv preprint arXiv:2007.02190 (2020).</li>\n    <li>Ge, Songwei, et al. \"Creative Sketch Generation.\" arXiv preprint arXiv:2011.10039 (2020).</li>\n   </ol>  \n  \n  \n\n  <div>\n    This website uses the template from <a href=\"https://nvlabs.github.io/alias-free-gan/\">alias-free-gan</a>. \n </div>\n\n\n<script src=\"https://cdnjs.cloudflare.com/ajax/libs/raphael/2.1.0/raphael-min.js\"></script>\n<script src=\"https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js\"></script>\n<script src=\"https://cdnjs.cloudflare.com/ajax/libs/paper.js/0.11.8/paper-full.min.js\"></script>\n<script src = \"json_sm.js\"></script>\n<script src = \"index.js\"></script>\n  "
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    "content": "cairosvg\nsvgwrite\nrdp\nmatplotlib\nnumpy\ntqdm\ndjango~=3.2\nbackcall==0.2.0"
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  {
    "path": "scripts/chars.py",
    "content": "#https:#jrgraphix.net/r/Unicode/0600-06FF\nmap_chars = {\n    \"\\u0623\":[\"\\u0621\", \"\\u0627\"], # أ\n    \"\\u0622\":[\"\\u0605\", \"\\u0627\"], # آ\n    \"\\u0625\":[\"\\u0627\", \"\\u0621\"], # إ\n    \"\\u0628\":[\"\\u066E\", \".\"], # ب\n    \"\\u062A\":[\".\", \".\", \"\\u066E\"], # ت\n    \"\\u062B\":[\".\", \".\", \".\", \"\\u066E\"], # ث \n    \"\\u062C\":[\"\\u062D\", \".\"], # ج\n    \"\\u062E\":[\".\", \"\\u062D\"], # خ\n    \"\\u0630\":[\".\", \"\\u062F\"], # ذ\n    \"\\u0632\":[\".\", \"\\u0631\"], # ز\n    \"\\u0634\":[\".\", \".\", \".\", \"\\u0633\"], # ش\n    \"\\u0636\":[\".\", \"\\u0635\"], # ض\n    \"\\u0637\":[\"\\u0627\", \"\\uFEBB\"], # ط\n    \"\\u0638\":[\".\", \"\\u0627\", \"\\uFEBB\"], # ظ\n    \"\\u063A\":[\".\", \"\\u0639\"], # غ\n    \"\\u0641\":[\".\", \"\\u066F\"], # ف\n    \"\\u0642\":[\".\", \".\", \"\\u066F\"], # ق\n    \"\\u06A4\":[\".\", \".\", \".\", \"\\u066F\"], # ڤ\n    \"\\u0643\":[\"\\u0621\", \"\\u0644\"], # ك\n    \"\\u0646\":[\".\", \"\\u06BA\"], # ن\n    \"\\u0624\":[\"\\u0621\", \"\\u0648\"], # ؤ\n    \"\\u064A\":[\"\\u0649\", \".\", \".\"], #ي\n    \"\\u0626\":[\"\\u0621\", \"\\u0649\"], #ئ\n    \"\\u0629\":[\".\", \".\", \"\\u0647\"], #ه\n}"
  },
  {
    "path": "scripts/vis.py",
    "content": "import glob\nimport json\nimport math\nimport os\nimport pickle\nimport re\nfrom collections import defaultdict\n\nimport cairosvg\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport svgwrite\nimport tqdm.notebook as tq\nfrom IPython.display import HTML\nfrom matplotlib import animation\nfrom PIL import Image, ImageDraw\nfrom rdp import rdp\n\nfrom scripts.chars import map_chars\n\n\ndef get_bounds(data):\n    minx, miny = 600, 600  \n    maxx, maxy = 0, 0\n    \n    for i, (x, y, z) in enumerate(data): \n        if minx > x:\n            minx = x\n        if miny > y:\n            miny = y \n\n        if maxx < x:\n            maxx = x\n        if maxy < y:\n            maxy = y \n    return minx, maxx, miny, maxy\n\ndef convert_3d(drawing, return_flag = False, threshold=10):\n    out = []\n    corrupted = False\n    for item in drawing:\n        char = list(item.keys())[0]\n        stroke = item[char]\n        if len(stroke) == 1:\n            x, y = stroke[0]\n            out.append([x, y, 0])\n            out.append([x+1, y+1, 1])\n            continue\n        segment = []\n        for i, point in enumerate(stroke):\n            x, y = point \n            if i == len(stroke) - 1:\n                segment.append([x, y, 1])\n            else:\n                segment.append([x, y, 0])\n        \n        start = 0 \n        for i, point in enumerate(segment):\n            if i < len(segment) -1:\n                x, y, _ = point\n                next_x, next_y, _ = segment[i+1]\n                if any((\n                    abs(x-next_x)>threshold,\n                    abs(y-next_y>threshold),\n                )):\n                    corrupted=True\n                    start = i +1\n        start = 0 \n        out += segment[start:]\n    if return_flag:\n        return out, corrupted\n    return out\n\ndef make_square(im, min_size=256, fill_color=(255, 255, 255)):\n    x, y = im.size\n    size = max(min_size, x, y)\n    new_im = Image.new('RGBA', (size, size), fill_color)\n    new_im.paste(im, (int((size - x) / 2), int((size - y) / 2)))\n    return new_im\n\ndef draw_strokes(data, factor=1, svg_filename = 'tmp/sample.svg', stroke_width = 3, \n                square = False, return_res = False, crop = True):\n\n    os.makedirs('tmp', exist_ok=True)\n    if crop:\n        min_x, max_x, min_y, max_y = get_bounds(data)\n    else:\n        min_x, max_x, min_y, max_y = 0, 600, 0, 600 \n\n    dims = (50 + max_x - min_x, 50 + max_y - min_y)\n    dwg = svgwrite.Drawing(svg_filename, size = dims)\n    dwg.add(dwg.rect(insert=(0, 0), size=dims,fill='white'))\n    lift_pen = 1\n    abs_x = 25 - min_x \n    abs_y = 25 - min_y\n    p = \"M%s,%s \" % (abs_x, abs_y)\n    command = \"M\"\n    for i in range(len(data)):\n        if (lift_pen == 1):\n            command = \"M\"\n        elif (command != \"L\"):\n            command = \"L\"\n        else:\n            command = \"\"\n        x = float(data[i][0]) - min_x\n        y = float(data[i][1]) - min_y\n        lift_pen = data[i][2]\n        p += command+str(x)+\" \"+str(y)+\" \"\n    the_color = \"black\"\n    \n    dwg.add(dwg.path(p).stroke(the_color,stroke_width).fill(\"none\"))\n    dwg.save()\n    cairosvg.svg2png(url=\"tmp/sample.svg\", write_to=\"tmp/sample.png\")\n    img = Image.open('tmp/sample.png')\n    if square:\n        img = make_square(img)\n    if return_res:\n        return img, dims \n    else:\n        return img\n\ndef apply_rdb(drawing, verbose = 0):\n    new_drawing = []\n    total_prev_strokes = 0\n    total_post_strokes = 0\n    for item in drawing:\n        char = list(item.keys())[0]\n        stroke = item[char]\n        processed_stroke = []\n        if len(stroke):\n            if verbose:\n                print('processing ', char)\n            post_stroke = rdp(stroke, epsilon = 2.0)\n            total_post_strokes += len(post_stroke)\n            total_prev_strokes += len(stroke)\n        new_drawing.append({char:post_stroke})\n    if verbose:\n        print('reduced from ', total_prev_strokes, ' to ', total_post_strokes)\n    return new_drawing\n\ndef preprocess(text):\n    char_comps = []\n    \n    diacritics = \"[ًٌٍَُِّْ]\"\n    numbers = '0123456789'\n    for diac in diacritics: \n        text = text.replace(diac, '')\n\n    for num in numbers: \n        text = text.replace(num, '')\n    \n    outText = \"\"\n    \n    for i in range(len(text)):\n    \n        if (text[i] == \" \"):\n            continue\n    \n        if text[i] in map_chars:\n            if (i < len(text) - 1 and text[i] == \"\\u0643\"):\n                if text[i+1] != ' ':\n                    char_comps.append({text[i] : '\\uFEDB'})\n                else:\n                    char_comps.append({text[i] : map_chars[text[i]]})\n            else:\n                char_comps.append({text[i] : map_chars[text[i]]})\n        else:\n                char_comps.append({text[i] : text[i]})\n\n    return char_comps\n\ndef concatenate(images, mode='h', margin=10):\n    widths, heights = zip(*(i.size for i in images))\n    if mode =='h':\n        total_width = sum(widths)\n        max_height = max(heights)\n\n        new_im = Image.new('RGB', (total_width, max_height), (255, 255, 255))\n\n        x_offset = 0\n        for im in images[::-1]:\n            new_im.paste(im, (x_offset,0))\n            x_offset += im.size[0]\n    elif mode == 'v':    \n        total_height = sum(heights)\n        max_width = max(widths)\n\n        new_im = Image.new('RGB', (max_width, total_height+margin*(len(images)-1)), (255, 255, 255))\n        draw = ImageDraw.Draw(new_im)\n        y_offset = 0\n        for im in images:\n            new_im.paste(im, (0,y_offset+margin))\n            y_offset += im.size[1]\n            draw.line((0,y_offset+margin-5, max_width,y_offset+margin-5), fill=(0, 0, 0), width=3)\n    return new_im\n\ndef generate_characters(file):\n    char_drawings = []\n    annot = file.split('/')[-1][:-5]\n    char_comps = preprocess(annot)\n    drawing = json.load(open(file))\n    new_drawing = apply_rdb(drawing, verbose = 0)\n    i = 0 \n    for comp in char_comps:\n        char = list(comp.keys())[0]\n        j = i + len(comp[char])\n        char_drawings.append({char:new_drawing[i:j]})\n        i = j \n    return char_drawings\n\ndef generate_words(file):\n    word_drawings = []\n    annot = file.split('/')[-1][:-5]\n    annot = re.sub('[0-9]', '', annot)\n    char_comps = preprocess(annot)\n    indices = [m.start() - i - 1 for i, m in enumerate(re.finditer(' ', annot))]\n    indices = indices + [len(annot) - len(indices)- 1]\n    drawing = json.load(open(file))\n#     new_drawing = apply_rdb(drawing, verbose = 0)\n    i, j  = 0, 0\n    c = 0\n    word = \"\"\n    for cntr, comp in enumerate(char_comps):\n        char = list(comp.keys())[0]\n        j += len(comp[char])\n        word += char\n        if cntr == indices[c]:\n            word_drawings.append({word:drawing[i:i+j]})\n            i = i+j\n            j = 0 \n            c += 1\n            word = \"\"\n    return word_drawings\n\ndef get_annotation(json_path):\n    return json_path.split('_')[0].split('/')[-1]\n\ndef clean_text(text):\n    char_comps = []\n    \n    diacritics = \"[ًٌٍَُِّْ]\"\n\n    text = re.sub(\"[ًٌٍَُِّْ]\", \"\", text)\n    text = re.sub('[0-9]', '', text)\n    text = re.sub('[a-zA-Zö\\xa0]', '', text)\n    return text.replace('_', '')\n\ndef concatenate(images, mode='h', margin=10):\n    widths, heights = zip(*(i.size for i in images))\n    if mode =='h':\n        total_width = sum(widths)\n        max_height = max(heights)\n\n        new_im = Image.new('RGB', (total_width, max_height), (255, 255, 255))\n\n        x_offset = 0\n        for im in images[::-1]:\n            new_im.paste(im, (x_offset,0))\n            x_offset += im.size[0]\n    elif mode == 'v':    \n        total_height = sum(heights)\n        max_width = max(widths)\n\n        new_im = Image.new('RGB', (max_width, total_height+margin*(len(images)-1)), (255, 255, 255))\n        draw = ImageDraw.Draw(new_im)\n        y_offset = 0\n        for im in images:\n            new_im.paste(im, (0,y_offset+margin))\n            y_offset += im.size[1]\n            draw.line((0,y_offset+margin-5, max_width,y_offset+margin-5), fill=(0, 0, 0), width=3)\n    return new_im\n\ndef save_video(drawing, mx):\n    num_sketches = 1\n    global line_data, line\n    line_data = ([], [])\n    sqrt = int(math.sqrt(num_sketches))\n    fig, ax = plt.subplots(sqrt, sqrt, figsize=(5,5))\n    ax.set_ylim(600, 0)\n    ax.set_xlim(0, 600)\n    ax.set_xticks([])\n    ax.set_yticks([])\n    ax.set_aspect('equal', adjustable='box')\n    line, = ax.plot([], [], lw=2, color = 'k')\n\n    def data_gen():\n        for point in drawing:\n            yield point\n\n\n    # initialize the data arrays \n    def run(point):\n        global line_data, line\n        x, y, z = point\n        line_data[0].append(x)\n        line_data[1].append(y)\n        line.set_data(line_data[0], line_data[1])\n        if z == 1:\n            line, = ax.plot([], [], lw=2, color = 'k')\n            line_data = ([], [])\n\n        return line\n\n    ani = animation.FuncAnimation(fig, run, data_gen, interval=10, repeat=False, save_count=mx)\n    d = ani.save(f'tmp/video.mp4', extra_args=['-vcodec', 'libx264'])\n\ndef create_animation(json_path):\n    max_count = 1000  \n    min_count = 100\n    \n    drawing = convert_3d(json.load(open(json_path)))\n    save_video(drawing, len(drawing))\n    return 'tmp/video.mp4'\n"
  }
]