[
{
"path": ".gitignore",
"content": ".nfs*\n*.pyc\n.dumbo.json\n.DS_Store\n.*.swp\n*.pth\n**/__pycache__/**\n.ipynb_checkpoints/\n*.tmp\n*.pkl\n**/.mypy_cache/*\n.mypy_cache/*\nnot_tracked_dir/\n.vscode\nlogs/\ndata\nevaluation/data\nexp\n"
},
{
"path": "LICENSE",
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},
{
"path": "README.md",
"content": "# LETR: Line Segment Detection Using Transformers without Edges\n\n## Introduction \nThis repository contains the official code and pretrained models for [Line Segment Detection Using Transformers without Edges](https://arxiv.org/abs/2101.01909). [Yifan Xu*](https://yfxu.com/), [Weijian Xu*](https://weijianxu.com/), [David Cheung](https://github.com/sawsa307), and [Zhuowen Tu](https://pages.ucsd.edu/~ztu/). CVPR2021 (**Oral**)\n\nIn this paper, we present a joint end-to-end line segment detection algorithm using Transformers that is post-processing and heuristics-guided intermediate processing (edge/junction/region detection) free. Our method, named LinE segment TRansformers (LETR), takes advantages of having integrated tokenized queries, a self-attention mechanism, and encoding-decoding strategy within Transformers by skipping standard heuristic designs for the edge element detection and perceptual grouping processes. We equip Transformers with a multi-scale encoder/decoder strategy to perform fine-grained line segment detection under a direct endpoint distance loss. This loss term is particularly suitable for detecting geometric structures such as line segments that are not conveniently represented by the standard bounding box representations. The Transformers learn to gradually refine line segments through layers of self-attention. \n\n![\"Model](\"figures/pipeline.svg\")
\n\n\n## Changelog\n05/07/2021: Code for LETR Basic Usage [Demo](https://github.com/mlpc-ucsd/LETR/blob/master/src/demo_letr.ipynb) are released. \n\n04/30/2021: Code and pre-trained checkpoint for LETR are released. \n\n## Results and Checkpoints\n\n\n| Name | sAP10 | sAP15 | sF10 | sF15 | URL|\n| --- | --- | --- | --- | --- |--- |\n| Wireframe | 65.6 | 68.0 | 66.1 | 67.4 | [LETR-R101](https://vcl.ucsd.edu/letr/checkpoints/res101/res101_stage2_focal.zip) |\n| YorkUrban | 29.6 | 32.0 | 40.5 | 42.1 | [LETR-R50](https://vcl.ucsd.edu/letr/checkpoints/res50/res50_stage2_focal.zip) |\n\n## Reproducing Results\n\n### Step1: Code Preparation\n```bash\ngit clone https://github.com/mlpc-ucsd/LETR.git\n```\n\n### Step2: Environment Installation\n\n```bash\nmkdir -p data\nmkdir -p evaluation/data\nmkdir -p exp\n\n\nconda create -n letr python anaconda\nconda activate letr\nconda install -c pytorch pytorch torchvision\nconda install cython scipy\npip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'\npip install docopt\n```\n\n### Step3: Data Preparation\nTo reproduce our results, you need to process two datasets, [ShanghaiTech](https://github.com/huangkuns/wireframe) and [YorkUrban](https://www.elderlab.yorku.ca/resources/york-urban-line-segment-database-information/). Files located at ./helper/wireframe.py and ./helper/york.py are both modified based on the code from [L-CNN](https://github.com/zhou13/lcnn), which process the raw data from download.\n\n- ShanghaiTech Train Data\n - To Download (modified based on from [L-CNN](https://github.com/zhou13/lcnn))\n ```bash\n cd data\n bash ../helper/gdrive-download.sh 1BRkqyi5CKPQF6IYzj_dQxZFQl0OwbzOf wireframe_raw.tar.xz\n tar xf wireframe_raw.tar.xz\n rm wireframe_raw.tar.xz\n python ../helper/wireframe.py ./wireframe_raw ./wireframe_processed\n\n ```\n- YorkUrban Train Data\n - To Download \n ```bash\n cd data\n wget https://www.dropbox.com/sh/qgsh2audfi8aajd/AAAQrKM0wLe_LepwlC1rzFMxa/YorkUrbanDB.zip\n unzip YorkUrbanDB.zip \n python ../helper/york.py ./YorkUrbanDB ./york_processed\n \n ```\n- Processed Evaluation Data\n ```bash\n bash ./helper/gdrive-download.sh 1T4_6Nb5r4yAXre3lf-zpmp3RbmyP1t9q ./evaluation/data/wireframe.tar.xz\n bash ./helper/gdrive-download.sh 1ijOXv0Xw1IaNDtp1uBJt5Xb3mMj99Iw2 ./evaluation/data/york.tar.xz\n tar -vxf ./evaluation/data/wireframe.tar.xz -C ./evaluation/data/.\n tar -vxf ./evaluation/data/york.tar.xz -C ./evaluation/data/.\n rm ./evaluation/data/wireframe.tar.xz\n rm ./evaluation/data/york.tar.xz\n ```\n\n### Step4: Train Script Examples\n1. Train a coarse-model (a.k.a. stage1 model).\n ```bash\n # Usage: bash script/*/*.sh [exp name]\n bash script/train/a0_train_stage1_res50.sh res50_stage1 # LETR-R50 \n bash script/train/a1_train_stage1_res101.sh res101_stage1 # LETR-R101 \n ```\n\n2. Train a fine-model (a.k.a. stage2 model).\n ```bash\n # Usage: bash script/*/*.sh [exp name]\n bash script/train/a2_train_stage2_res50.sh res50_stage2 # LETR-R50\n bash script/train/a3_train_stage2_res101.sh res101_stage2 # LETR-R101 \n ```\n\n3. Fine-tune the fine-model with focal loss (a.k.a. stage2_focal model).\n ```bash\n # Usage: bash script/*/*.sh [exp name]\n bash script/train/a4_train_stage2_focal_res50.sh res50_stage2_focal # LETR-R50\n bash script/train/a5_train_stage2_focal_res101.sh res101_stage2_focal # LETR-R101 \n ```\n\n### Step5: Evaluation\n\n1. Evaluate models.\n ```bash\n # Evaluate sAP^10, sAP^15, sF^10, sF^15 (both Wireframe and YorkUrban datasets).\n bash script/evaluation/eval_stage1.sh [exp name]\n bash script/evaluation/eval_stage2.sh [exp name]\n bash script/evaluation/eval_stage2_focal.sh [exp name]\n ```\n\n### Citation\n\nIf you use this code for your research, please cite our paper:\n```\n@InProceedings{Xu_2021_CVPR,\n author = {Xu, Yifan and Xu, Weijian and Cheung, David and Tu, Zhuowen},\n title = {Line Segment Detection Using Transformers Without Edges},\n booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},\n month = {June},\n year = {2021},\n pages = {4257-4266}\n}\n```\n### Acknowledgments\n\nThis code is based on the implementations of [**DETR: End-to-End Object Detection with Transformers**](https://github.com/facebookresearch/detr). \n"
},
{
"path": "evaluation/eval-aph-post-wireframe.py",
"content": "#!/usr/bin/env python3\n\"\"\"Post-processing the output of neural network\nUsage:\n post.py [options] \n post.py ( -h | --help )\n\nExamples:\n post.py logs/logname/npz/000336000 result/logname\n\nArguments:\n input-dir Directory that stores the npz\n output-dir Output directory\n\nOptions:\n -h --help Show this screen.\n --plot Generate images besides npz files\n --thresholds= A comma-separated list for thresholding\n [default: 0.006,0.010,0.015]\n\"\"\"\n\nimport glob\nimport math\nimport os\nimport os.path as osp\nimport sys\n\nimport cv2\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom docopt import docopt\n\nfrom lcnn.postprocess import postprocess\nfrom lcnn.utils import parmap\n\nPLTOPTS = {\"color\": \"#33FFFF\", \"s\": 1.2, \"edgecolors\": \"none\", \"zorder\": 5}\ncmap = plt.get_cmap(\"jet\")\nnorm = mpl.colors.Normalize(vmin=0.92, vmax=1.02)\nsm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\nsm.set_array([])\n\n\ndef c(x):\n return sm.to_rgba(x)\n\n\ndef imshow(im):\n plt.close()\n sizes = im.shape\n height = float(sizes[0])\n width = float(sizes[1])\n\n fig = plt.figure()\n fig.set_size_inches(width / height, 1, forward=False)\n ax = plt.Axes(fig, [0.0, 0.0, 1.0, 1.0])\n ax.set_axis_off()\n fig.add_axes(ax)\n plt.xlim([-0.5, sizes[1] - 0.5])\n plt.ylim([sizes[0] - 0.5, -0.5])\n plt.imshow(im)\n\n\ndef main():\n args = docopt(__doc__)\n\n files = sorted(glob.glob(osp.join(args[\"\"], \"*.npz\")))\n inames = sorted(glob.glob(\"data/wireframe/valid-images/*.jpg\"))\n gts = sorted(glob.glob(\"data/wireframe/valid/*.npz\"))\n prefix = args[\"\"]\n\n inputs = list(zip(files, inames, gts))\n thresholds = list(map(float, args[\"--thresholds\"].split(\",\")))\n\n\n def handle(allname):\n fname, iname, gtname = allname\n print(\"Processing\", fname)\n im = cv2.imread(iname)\n with np.load(fname) as f:\n lines = f[\"lines\"]\n scores = f[\"score\"]\n with np.load(gtname) as f:\n gtlines = f[\"lpos\"][:, :, :2]\n gtlines[:, :, 0] *= im.shape[0] / 128\n gtlines[:, :, 1] *= im.shape[1] / 128\n for i in range(1, len(lines)):\n if (lines[i] == lines[0]).all():\n lines = lines[:i]\n scores = scores[:i]\n break\n\n lines[:, :, 0] *= im.shape[0] / 128\n lines[:, :, 1] *= im.shape[1] / 128\n diag = (im.shape[0] ** 2 + im.shape[1] ** 2) ** 0.5\n\n for threshold in thresholds:\n nlines, nscores = postprocess(lines, scores, diag * threshold, 0, False)\n\n outdir = osp.join(prefix, f\"{threshold:.3f}\".replace(\".\", \"_\"))\n os.makedirs(outdir, exist_ok=True)\n npz_name = osp.join(outdir, osp.split(fname)[-1])\n\n if args[\"--plot\"]:\n # plot gt\n imshow(im[:, :, ::-1])\n for (a, b) in gtlines:\n plt.plot([a[1], b[1]], [a[0], b[0]], c=\"orange\", linewidth=0.5)\n plt.scatter(a[1], a[0], **PLTOPTS)\n plt.scatter(b[1], b[0], **PLTOPTS)\n plt.savefig(npz_name.replace(\".npz\", \".png\"), dpi=500, bbox_inches=0)\n\n thres = [0.96, 0.97, 0.98, 0.99]\n for i, t in enumerate(thres):\n imshow(im[:, :, ::-1])\n for (a, b), s in zip(nlines[nscores > t], nscores[nscores > t]):\n plt.plot([a[1], b[1]], [a[0], b[0]], c=c(s), linewidth=0.5)\n plt.scatter(a[1], a[0], **PLTOPTS)\n plt.scatter(b[1], b[0], **PLTOPTS)\n plt.savefig(\n npz_name.replace(\".npz\", f\"_{i}.png\"), dpi=500, bbox_inches=0\n )\n\n nlines[:, :, 0] *= 128 / im.shape[0]\n nlines[:, :, 1] *= 128 / im.shape[1]\n np.savez_compressed(npz_name, lines=nlines, score=nscores)\n\n parmap(handle, inputs, 12)\n\n\nif __name__ == \"__main__\":\n main()\n"
},
{
"path": "evaluation/eval-aph-post-york.py",
"content": "#!/usr/bin/env python3\n\"\"\"Post-processing the output of neural network\nUsage:\n post.py [options] \n post.py ( -h | --help )\n\nExamples:\n post.py logs/logname/npz/000336000 result/logname\n\nArguments:\n input-dir Directory that stores the npz\n output-dir Output directory\n\nOptions:\n -h --help Show this screen.\n --plot Generate images besides npz files\n --thresholds= A comma-separated list for thresholding\n [default: 0.006,0.010,0.015]\n\"\"\"\n\nimport glob\nimport math\nimport os\nimport os.path as osp\nimport sys\n\nimport cv2\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom docopt import docopt\n\nfrom lcnn.postprocess import postprocess\nfrom lcnn.utils import parmap\n\nPLTOPTS = {\"color\": \"#33FFFF\", \"s\": 1.2, \"edgecolors\": \"none\", \"zorder\": 5}\ncmap = plt.get_cmap(\"jet\")\nnorm = mpl.colors.Normalize(vmin=0.92, vmax=1.02)\nsm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\nsm.set_array([])\n\n\ndef c(x):\n return sm.to_rgba(x)\n\n\ndef imshow(im):\n plt.close()\n sizes = im.shape\n height = float(sizes[0])\n width = float(sizes[1])\n\n fig = plt.figure()\n fig.set_size_inches(width / height, 1, forward=False)\n ax = plt.Axes(fig, [0.0, 0.0, 1.0, 1.0])\n ax.set_axis_off()\n fig.add_axes(ax)\n plt.xlim([-0.5, sizes[1] - 0.5])\n plt.ylim([sizes[0] - 0.5, -0.5])\n plt.imshow(im)\n\n\ndef main():\n args = docopt(__doc__)\n\n files = sorted(glob.glob(osp.join(args[\"\"], \"*.npz\")))\n inames = sorted(glob.glob(\"data/york/valid_copy/*.jpg\"))\n gts = sorted(glob.glob(\"data/york/valid_copy/*.npz\"))\n print('len(files):{} len(inames):{} len(gts):{}'.format(len(files), len(inames), len(gts)))\n \n prefix = args[\"\"]\n\n inputs = list(zip(files, inames, gts))\n thresholds = list(map(float, args[\"--thresholds\"].split(\",\")))\n\n\n def handle(allname):\n fname, iname, gtname = allname\n print(\"Processing\", fname)\n im = cv2.imread(iname)\n with np.load(fname) as f:\n lines = f[\"lines\"]\n scores = f[\"score\"]\n with np.load(gtname) as f:\n gtlines = f[\"lpos\"][:, :, :2]\n gtlines[:, :, 0] *= im.shape[0] / 128\n gtlines[:, :, 1] *= im.shape[1] / 128\n for i in range(1, len(lines)):\n if (lines[i] == lines[0]).all():\n lines = lines[:i]\n scores = scores[:i]\n break\n\n lines[:, :, 0] *= im.shape[0] / 128\n lines[:, :, 1] *= im.shape[1] / 128\n diag = (im.shape[0] ** 2 + im.shape[1] ** 2) ** 0.5\n\n for threshold in thresholds:\n nlines, nscores = postprocess(lines, scores, diag * threshold, 0, False)\n\n outdir = osp.join(prefix, f\"{threshold:.3f}\".replace(\".\", \"_\"))\n os.makedirs(outdir, exist_ok=True)\n npz_name = osp.join(outdir, osp.split(fname)[-1])\n\n if args[\"--plot\"]:\n # plot gt\n imshow(im[:, :, ::-1])\n for (a, b) in gtlines:\n plt.plot([a[1], b[1]], [a[0], b[0]], c=\"orange\", linewidth=0.5)\n plt.scatter(a[1], a[0], **PLTOPTS)\n plt.scatter(b[1], b[0], **PLTOPTS)\n plt.savefig(npz_name.replace(\".npz\", \".png\"), dpi=500, bbox_inches=0)\n\n thres = [0.96, 0.97, 0.98, 0.99]\n for i, t in enumerate(thres):\n imshow(im[:, :, ::-1])\n for (a, b), s in zip(nlines[nscores > t], nscores[nscores > t]):\n plt.plot([a[1], b[1]], [a[0], b[0]], c=c(s), linewidth=0.5)\n plt.scatter(a[1], a[0], **PLTOPTS)\n plt.scatter(b[1], b[0], **PLTOPTS)\n plt.savefig(\n npz_name.replace(\".npz\", f\"_{i}.png\"), dpi=500, bbox_inches=0\n )\n\n nlines[:, :, 0] *= 128 / im.shape[0]\n nlines[:, :, 1] *= 128 / im.shape[1]\n np.savez_compressed(npz_name, lines=nlines, score=nscores)\n\n parmap(handle, inputs, 12)\n\n\nif __name__ == \"__main__\":\n main()"
},
{
"path": "evaluation/eval-aph-score-wireframe.py",
"content": "#!/usr/bin/env python3\n\"\"\"Evaluate APH for LCNN\nUsage:\n eval-APH.py \n eval-APH.py (-h | --help )\n\nExamples:\n ./eval-APH.py post/RUN-ITERATION/0_010 post/RUN-ITERATION/0_010-APH\n\nArguments:\n Source directory that stores preprocessed npz\n Temporary output directory\n\nOptions:\n -h --help Show this screen.\n\"\"\"\n\nimport os\nimport glob\nimport os.path as osp\nimport subprocess\n\nimport numpy as np\nimport scipy.io as sio\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nfrom scipy import interpolate\nfrom docopt import docopt\n\nmpl.rcParams.update({\"font.size\": 18})\nplt.rcParams[\"font.family\"] = \"Times New Roman\"\ndel mpl.font_manager.weight_dict[\"roman\"]\nmpl.font_manager._rebuild()\n\nimage_path = \"data/wireframe/valid-images/\"\nline_gt_path = \"data/wireframe/valid/\"\noutput_size = 128\n\n\ndef main():\n args = docopt(__doc__)\n src_dir = args[\"\"]\n tar_dir = args[\"\"]\n print(src_dir, tar_dir)\n output_file = osp.join(tar_dir, \"result.mat\")\n target_dir = osp.join(tar_dir, \"mat\")\n os.makedirs(target_dir, exist_ok=True)\n print(f\"intermediate matlab results will be saved at: {target_dir}\")\n\n file_list = glob.glob(osp.join(src_dir, \"*.npz\"))\n # Old threshold: thresh = [0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.97, 0.99, 0.995, 0.999, 0.9995, 0.9999]\n thresh = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.525, 0.55, 0.575, 0.6, 0.625, 0.65, 0.675, 0.7, 0.8, 0.9, 0.95, 0.97, 0.99, 0.995, 0.999, 0.9995, 0.9999]\n for t in thresh:\n for fname in file_list:\n name = fname.split(\"/\")[-1].split(\".\")[0]\n mat_name = name + \".mat\"\n npz = np.load(fname)\n lines = npz[\"lines\"].reshape(-1, 4)\n scores = npz[\"score\"]\n for j in range(len(scores) - 1):\n if scores[j + 1] == scores[0]: # Cyclic lines/scores. Choose the first cycle.\n lines = lines[: j + 1]\n scores = scores[: j + 1]\n break\n idx = np.where(scores > t)[0] # Only choose the lines which scores > specified threshold.\n os.makedirs(osp.join(target_dir, str(t)), exist_ok=True)\n sio.savemat(osp.join(target_dir, str(t), mat_name), {\"lines\": lines[idx]})\n\n cmd = \"matlab -nodisplay -nodesktop \"\n cmd += '-r \"dbstop if error; '\n cmd += \"eval_release('{:s}', '{:s}', '{:s}', '{:s}', {:d}); quit;\\\"\".format(\n image_path, line_gt_path, output_file, target_dir, output_size\n )\n print(\"Running:\\n{}\".format(cmd))\n os.environ[\"MATLABPATH\"] = \"matlab/\"\n subprocess.call(cmd, shell=True)\n\n mat = sio.loadmat(output_file)\n tps = mat[\"sumtp\"]\n fps = mat[\"sumfp\"]\n N = mat[\"sumgt\"]\n \n # Old way of F^H and AP^H:\n # --------------\n # rcs = sorted(list((tps / N)[:, 0]))\n # prs = sorted(list((tps / np.maximum(tps + fps, 1e-9))[:, 0]))[::-1] # FIXME: Why using sorted?\n\n # print(\n # \"f measure is: \",\n # (2 * np.array(prs) * np.array(rcs) / (np.array(prs) + np.array(rcs))).max(),\n # )\n\n # recall = np.concatenate(([0.0], rcs, [1.0]))\n # precision = np.concatenate(([0.0], prs, [0.0]))\n\n # for i in range(precision.size - 1, 0, -1):\n # precision[i - 1] = max(precision[i - 1], precision[i])\n # i = np.where(recall[1:] != recall[:-1])[0]\n # print(\"AP is: \", np.sum((recall[i + 1] - recall[i]) * precision[i + 1]))\n # --------------\n\n\n # Our way of F^H and AP^H:\n # --------------\n tps = mat[\"sumtp\"]\n fps = mat[\"sumfp\"]\n N = mat[\"sumgt\"]\n rcs = list((tps / N)[:, 0])\n prs = list((tps / np.maximum(tps + fps, 1e-9))[:, 0]) # No sorting.\n\n print(\n \"f measure is: \",\n (2 * np.array(prs) * np.array(rcs) / (np.array(prs) + np.array(rcs) + 1e-9)).max(),\n )\n\n recall = np.concatenate(([0.0], rcs[::-1], [1.0])) # Reverse order.\n precision = np.concatenate(([0.0], prs[::-1], [0.0]))\n\n for i in range(precision.size - 1, 0, -1):\n precision[i - 1] = max(precision[i - 1], precision[i])\n i = np.where(recall[1:] != recall[:-1])[0]\n print(\"AP is: \", np.sum((recall[i + 1] - recall[i]) * precision[i + 1]))\n # --------------\n\n\n\n\n # Old way of visualization:\n # --------------\n # f = interpolate.interp1d(rcs, prs, kind=\"cubic\", bounds_error=False) # FIXME: May still have issue.\n # x = np.arange(0, 1, 0.01) * rcs[-1]\n # y = f(x)\n # plt.plot(x, y, linewidth=3, label=\"L-CNN\")\n\n # f_scores = np.linspace(0.2, 0.8, num=8)\n # for f_score in f_scores:\n # x = np.linspace(0.01, 1)\n # y = f_score * x / (2 * x - f_score)\n # l, = plt.plot(x[y >= 0], y[y >= 0], color=\"green\", alpha=0.3)\n # plt.annotate(\"f={0:0.1}\".format(f_score), xy=(0.9, y[45] + 0.02), alpha=0.4)\n\n # plt.grid(True)\n # plt.axis([0.0, 1.0, 0.0, 1.0])\n # plt.xticks(np.arange(0, 1.0, step=0.1))\n # plt.xlabel(\"Recall\")\n # plt.ylabel(\"Precision\")\n # plt.yticks(np.arange(0, 1.0, step=0.1))\n # plt.legend(loc=3)\n # plt.title(\"PR Curve for APH\")\n # plt.savefig(\"apH.pdf\", format=\"pdf\", bbox_inches=\"tight\")\n # plt.savefig(\"apH.svg\", format=\"svg\", bbox_inches=\"tight\")\n # plt.show()\n # --------------\n\n\n # Our way of visualization:\n # --------------\n reversed_rcs = rcs[::-1]\n reversed_prs = prs[::-1]\n filtered_rcs = []\n filtered_prs = []\n for i in range(len(reversed_rcs)):\n if reversed_prs[i] == 0.0 and reversed_prs[i] == 0.0: # Ignore empty items.\n pass\n else:\n filtered_rcs.append(reversed_rcs[i])\n filtered_prs.append(reversed_prs[i])\n\n f = interpolate.interp1d(filtered_rcs, filtered_prs, kind=\"cubic\", bounds_error=False) # FIXME: May still have issue.\n x = np.arange(0, 1, 0.01) * filtered_rcs[-1]\n y = f(x)\n plt.plot(x, y, linewidth=3, label=\"Current\")\n\n f_scores = np.linspace(0.2, 0.8, num=8)\n for f_score in f_scores:\n x = np.linspace(0.01, 1)\n y = f_score * x / (2 * x - f_score)\n l, = plt.plot(x[y >= 0], y[y >= 0], color=\"green\", alpha=0.3)\n plt.annotate(\"f={0:0.1}\".format(f_score), xy=(0.9, y[45] + 0.02), alpha=0.4)\n\n plt.grid(True)\n plt.axis([0.0, 1.0, 0.0, 1.0])\n plt.xticks(np.arange(0, 1.0, step=0.1))\n plt.xlabel(\"Recall\")\n plt.ylabel(\"Precision\")\n plt.yticks(np.arange(0, 1.0, step=0.1))\n plt.legend(loc=3)\n plt.title(\"PR Curve for APH\")\n # plt.savefig(\"apH.pdf\", format=\"pdf\", bbox_inches=\"tight\")\n # plt.savefig(\"apH.svg\", format=\"svg\", bbox_inches=\"tight\")\n plt.show()\n # --------------\n\n\nif __name__ == \"__main__\":\n # import debugpy\n # print(\"Enabling attach starts.\")\n # debugpy.listen(address=('0.0.0.0', 9310))\n # debugpy.wait_for_client()\n # print(\"Enabling attach ends.\")\n \n plt.tight_layout()\n main()\n"
},
{
"path": "evaluation/eval-aph-score-york.py",
"content": "#!/usr/bin/env python3\n\"\"\"Evaluate APH for LCNN\nUsage:\n eval-APH.py \n eval-APH.py (-h | --help )\n\nExamples:\n ./eval-APH.py post/RUN-ITERATION/0_010 post/RUN-ITERATION/0_010-APH\n\nArguments:\n Source directory that stores preprocessed npz\n Temporary output directory\n\nOptions:\n -h --help Show this screen.\n\"\"\"\n\nimport os\nimport glob\nimport os.path as osp\nimport subprocess\n\nimport numpy as np\nimport scipy.io as sio\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nfrom scipy import interpolate\nfrom docopt import docopt\n\nmpl.rcParams.update({\"font.size\": 18})\nplt.rcParams[\"font.family\"] = \"Times New Roman\"\ndel mpl.font_manager.weight_dict[\"roman\"]\nmpl.font_manager._rebuild()\n\nimage_path = \"data/york/valid-images/\"\nline_gt_path = \"data/york/valid_copy/\"\noutput_size = 128\n\n\ndef main():\n args = docopt(__doc__)\n src_dir = args[\"\"]\n tar_dir = args[\"\"]\n\n output_file = osp.join(tar_dir, \"result.mat\")\n target_dir = osp.join(tar_dir, \"mat\")\n os.makedirs(target_dir, exist_ok=True)\n print(f\"intermediate matlab results will be saved at: {target_dir}\")\n\n file_list = glob.glob(osp.join(src_dir, \"*.npz\"))\n thresh = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.525, 0.55, 0.575, 0.6, 0.625, 0.65, 0.675, 0.7, 0.8, 0.9, 0.95, 0.97, 0.99, 0.995, 0.999, 0.9995, 0.9999]\n for t in thresh:\n for fname in file_list:\n name = fname.split(\"/\")[-1].split(\".\")[0]\n mat_name = name + \".mat\"\n npz = np.load(fname)\n lines = npz[\"lines\"].reshape(-1, 4)\n scores = npz[\"score\"]\n for j in range(len(scores) - 1):\n if scores[j + 1] == scores[0]: # Cyclic lines/scores. Choose the first cycle.\n lines = lines[: j + 1]\n scores = scores[: j + 1]\n break\n idx = np.where(scores > t)[0] # Only choose the lines which scores > specified threshold.\n os.makedirs(osp.join(target_dir, str(t)), exist_ok=True)\n sio.savemat(osp.join(target_dir, str(t), mat_name), {\"lines\": lines[idx]})\n\n cmd = \"matlab -nodisplay -nodesktop \"\n cmd += '-r \"dbstop if error; '\n cmd += \"eval_release('{:s}', '{:s}', '{:s}', '{:s}', {:d}); quit;\\\"\".format(\n image_path, line_gt_path, output_file, target_dir, output_size\n )\n print(\"Running:\\n{}\".format(cmd))\n os.environ[\"MATLABPATH\"] = \"matlab/\"\n subprocess.call(cmd, shell=True)\n\n mat = sio.loadmat(output_file)\n tps = mat[\"sumtp\"]\n fps = mat[\"sumfp\"]\n N = mat[\"sumgt\"]\n \n # Old way of F^H and AP^H:\n # --------------\n # rcs = sorted(list((tps / N)[:, 0]))\n # prs = sorted(list((tps / np.maximum(tps + fps, 1e-9))[:, 0]))[::-1] # FIXME: Why using sorted?\n\n # print(\n # \"f measure is: \",\n # (2 * np.array(prs) * np.array(rcs) / (np.array(prs) + np.array(rcs))).max(),\n # )\n\n # recall = np.concatenate(([0.0], rcs, [1.0]))\n # precision = np.concatenate(([0.0], prs, [0.0]))\n\n # for i in range(precision.size - 1, 0, -1):\n # precision[i - 1] = max(precision[i - 1], precision[i])\n # i = np.where(recall[1:] != recall[:-1])[0]\n # print(\"AP is: \", np.sum((recall[i + 1] - recall[i]) * precision[i + 1]))\n # --------------\n\n\n # Our way of F^H and AP^H:\n # --------------\n tps = mat[\"sumtp\"]\n fps = mat[\"sumfp\"]\n N = mat[\"sumgt\"]\n rcs = list((tps / N)[:, 0])\n prs = list((tps / np.maximum(tps + fps, 1e-9))[:, 0]) # No sorting.\n\n print(\n \"f measure is: \",\n (2 * np.array(prs) * np.array(rcs) / (np.array(prs) + np.array(rcs) + 1e-9)).max(),\n )\n\n recall = np.concatenate(([0.0], rcs[::-1], [1.0])) # Reverse order.\n precision = np.concatenate(([0.0], prs[::-1], [0.0]))\n\n for i in range(precision.size - 1, 0, -1):\n precision[i - 1] = max(precision[i - 1], precision[i])\n i = np.where(recall[1:] != recall[:-1])[0]\n print(\"AP is: \", np.sum((recall[i + 1] - recall[i]) * precision[i + 1]))\n # --------------\n\n\n\n\n # Old way of visualization:\n # --------------\n # f = interpolate.interp1d(rcs, prs, kind=\"cubic\", bounds_error=False) # FIXME: May still have issue.\n # x = np.arange(0, 1, 0.01) * rcs[-1]\n # y = f(x)\n # plt.plot(x, y, linewidth=3, label=\"L-CNN\")\n\n # f_scores = np.linspace(0.2, 0.8, num=8)\n # for f_score in f_scores:\n # x = np.linspace(0.01, 1)\n # y = f_score * x / (2 * x - f_score)\n # l, = plt.plot(x[y >= 0], y[y >= 0], color=\"green\", alpha=0.3)\n # plt.annotate(\"f={0:0.1}\".format(f_score), xy=(0.9, y[45] + 0.02), alpha=0.4)\n\n # plt.grid(True)\n # plt.axis([0.0, 1.0, 0.0, 1.0])\n # plt.xticks(np.arange(0, 1.0, step=0.1))\n # plt.xlabel(\"Recall\")\n # plt.ylabel(\"Precision\")\n # plt.yticks(np.arange(0, 1.0, step=0.1))\n # plt.legend(loc=3)\n # plt.title(\"PR Curve for APH\")\n # plt.savefig(\"apH.pdf\", format=\"pdf\", bbox_inches=\"tight\")\n # plt.savefig(\"apH.svg\", format=\"svg\", bbox_inches=\"tight\")\n # plt.show()\n # --------------\n\n\n # Our way of visualization:\n # --------------\n reversed_rcs = rcs[::-1]\n reversed_prs = prs[::-1]\n filtered_rcs = []\n filtered_prs = []\n for i in range(len(reversed_rcs)):\n if reversed_prs[i] == 0.0 and reversed_prs[i] == 0.0: # Ignore empty items.\n pass\n else:\n filtered_rcs.append(reversed_rcs[i])\n filtered_prs.append(reversed_prs[i])\n\n f = interpolate.interp1d(filtered_rcs, filtered_prs, kind=\"cubic\", bounds_error=False) # FIXME: May still have issue.\n x = np.arange(0, 1, 0.01) * filtered_rcs[-1]\n y = f(x)\n plt.plot(x, y, linewidth=3, label=\"Current\")\n\n f_scores = np.linspace(0.2, 0.8, num=8)\n for f_score in f_scores:\n x = np.linspace(0.01, 1)\n y = f_score * x / (2 * x - f_score)\n l, = plt.plot(x[y >= 0], y[y >= 0], color=\"green\", alpha=0.3)\n plt.annotate(\"f={0:0.1}\".format(f_score), xy=(0.9, y[45] + 0.02), alpha=0.4)\n\n plt.grid(True)\n plt.axis([0.0, 1.0, 0.0, 1.0])\n plt.xticks(np.arange(0, 1.0, step=0.1))\n plt.xlabel(\"Recall\")\n plt.ylabel(\"Precision\")\n plt.yticks(np.arange(0, 1.0, step=0.1))\n plt.legend(loc=3)\n plt.title(\"PR Curve for APH\")\n # plt.savefig(\"apH.pdf\", format=\"pdf\", bbox_inches=\"tight\")\n # plt.savefig(\"apH.svg\", format=\"svg\", bbox_inches=\"tight\")\n plt.show()\n # --------------\n\n\nif __name__ == \"__main__\":\n # import debugpy\n # print(\"Enabling attach starts.\")\n # debugpy.listen(address=('0.0.0.0', 9310))\n # debugpy.wait_for_client()\n # print(\"Enabling attach ends.\")\n \n plt.tight_layout()\n main()\n"
},
{
"path": "evaluation/eval-fscore-wireframe.py",
"content": "#!/usr/bin/env python3\n\"\"\"Evaluate sAP5, sAP10, sAP15 for LCNN\nUsage:\n eval-sAP.py ...\n eval-sAP.py (-h | --help )\n\nExamples:\n python eval-sAP.py logs/*/npz/000*\n\nArguments:\n One or more directories from train.py\n\nOptions:\n -h --help Show this screen.\n\"\"\"\n\nimport os\nimport sys\nimport glob\nimport os.path as osp\n\nimport numpy as np\nimport scipy.io\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nfrom docopt import docopt\n\nimport lcnn.utils\nimport lcnn.metric\n\nGT_val = \"evaluation/data/wireframe/valid/*.npz\"\nGT_train = \"evaluation/data/wireframe/train/*_0_label.npz\"\n\n\ndef f_score(tp, fp):\n recall = tp\n precision = tp / np.maximum(tp + fp, 1e-9)\n\n recall = np.concatenate(([0.0], recall, [1.0]))\n precision = np.concatenate(([0.0], precision, [0.0]))\n\n Fscore = (2*precision*recall/(precision+recall+0.0000000001)).max()\n return Fscore\n\ndef line_score(path, threshold=5):\n preds = sorted(glob.glob(path))\n \n if path.split('/')[-2].split('_')[1] == 'train':\n gts = sorted(glob.glob(GT_train))\n gts = gts[:501]\n else:\n gts = sorted(glob.glob(GT_val))\n n_gt = 0\n lcnn_tp, lcnn_fp, lcnn_scores = [], [], []\n for pred_name, gt_name in zip(preds, gts):\n with np.load(pred_name) as fpred:\n lcnn_line = fpred[\"lines\"][:, :, :2]\n lcnn_score = fpred[\"score\"]\n with np.load(gt_name) as fgt:\n gt_line = fgt[\"lpos\"][:, :, :2]\n n_gt += len(gt_line)\n\n for i in range(len(lcnn_line)):\n if i > 0 and (lcnn_line[i] == lcnn_line[0]).all():\n lcnn_line = lcnn_line[:i]\n lcnn_score = lcnn_score[:i]\n break\n\n tp, fp = lcnn.metric.msTPFP(lcnn_line, gt_line, threshold)\n lcnn_tp.append(tp)\n lcnn_fp.append(fp)\n lcnn_scores.append(lcnn_score)\n\n lcnn_tp = np.concatenate(lcnn_tp)\n lcnn_fp = np.concatenate(lcnn_fp)\n lcnn_scores = np.concatenate(lcnn_scores)\n lcnn_index = np.argsort(-lcnn_scores)\n lcnn_tp = np.cumsum(lcnn_tp[lcnn_index]) / n_gt\n lcnn_fp = np.cumsum(lcnn_fp[lcnn_index]) / n_gt\n\n return f_score(lcnn_tp, lcnn_fp)\n\n\nif __name__ == \"__main__\":\n args = docopt(__doc__)\n\n def work(path):\n print(f\"Working on {path}\")\n return [100 * line_score(f\"{path}/*.npz\", t) for t in [5, 10, 15]]\n\n dirs = sorted(sum([glob.glob(p) for p in args[\"\"]], []))\n results = lcnn.utils.parmap(work, dirs)\n\n for d, msAP in zip(dirs, results):\n print(f\"{d}: {msAP[0]:2.1f} {msAP[1]:2.1f} {msAP[2]:2.1f}\")\n"
},
{
"path": "evaluation/eval-fscore-york.py",
"content": "#!/usr/bin/env python3\n\"\"\"Evaluate sAP5, sAP10, sAP15 for LCNN\nUsage:\n eval-sAP.py ...\n eval-sAP.py (-h | --help )\n\nExamples:\n python eval-sAP.py logs/*/npz/000*\n\nArguments:\n One or more directories from train.py\n\nOptions:\n -h --help Show this screen.\n\"\"\"\n\nimport os\nimport sys\nimport glob\nimport os.path as osp\n\nimport numpy as np\nimport scipy.io\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nfrom docopt import docopt\nimport lcnn.utils\nimport lcnn.metric\n\nGT = \"evaluation/data/york/valid/*.npz\"\n\n\ndef f_score(tp, fp):\n recall = tp\n precision = tp / np.maximum(tp + fp, 1e-9)\n\n recall = np.concatenate(([0.0], recall, [1.0]))\n precision = np.concatenate(([0.0], precision, [0.0]))\n\n Fscore = (2*precision*recall/(precision+recall+0.0000000001)).max()\n return Fscore\n\ndef line_score(path, threshold=5):\n preds = sorted(glob.glob(path))\n gts = sorted(glob.glob(GT))\n\n n_gt = 0\n lcnn_tp, lcnn_fp, lcnn_scores = [], [], []\n for pred_name, gt_name in zip(preds, gts):\n with np.load(pred_name) as fpred:\n lcnn_line = fpred[\"lines\"][:, :, :2]\n lcnn_score = fpred[\"score\"]\n with np.load(gt_name) as fgt:\n gt_line = fgt[\"lpos\"][:, :, :2]\n n_gt += len(gt_line)\n\n # DETR NEEDS TO SORT CONF\n #score_idx = np.argsort(-lcnn_score)\n #lcnn_line = lcnn_line[score_idx]\n #lcnn_score = lcnn_score[score_idx]\n\n for i in range(len(lcnn_line)):\n if i > 0 and (lcnn_line[i] == lcnn_line[0]).all():\n lcnn_line = lcnn_line[:i]\n lcnn_score = lcnn_score[:i]\n break\n\n tp, fp = lcnn.metric.msTPFP(lcnn_line, gt_line, threshold)\n lcnn_tp.append(tp)\n lcnn_fp.append(fp)\n lcnn_scores.append(lcnn_score)\n\n lcnn_tp = np.concatenate(lcnn_tp)\n lcnn_fp = np.concatenate(lcnn_fp)\n lcnn_scores = np.concatenate(lcnn_scores)\n lcnn_index = np.argsort(-lcnn_scores)\n lcnn_tp = np.cumsum(lcnn_tp[lcnn_index]) / n_gt\n lcnn_fp = np.cumsum(lcnn_fp[lcnn_index]) / n_gt\n\n return f_score(lcnn_tp, lcnn_fp)\n\n\nif __name__ == \"__main__\":\n args = docopt(__doc__)\n\n def work(path):\n print(f\"Working on {path}\")\n return [100 * line_score(f\"{path}/*.npz\", t) for t in [5, 10, 15]]\n\n dirs = sorted(sum([glob.glob(p) for p in args[\"\"]], []))\n results = lcnn.utils.parmap(work, dirs)\n\n for d, msAP in zip(dirs, results):\n print(f\"{d}: {msAP[0]:2.1f} {msAP[1]:2.1f} {msAP[2]:2.1f}\")\n"
},
{
"path": "evaluation/eval-sAP-wireframe.py",
"content": "#!/usr/bin/env python3\n\"\"\"Evaluate sAP5, sAP10, sAP15 for LCNN\nUsage:\n eval-sAP.py ...\n eval-sAP.py (-h | --help )\n\nExamples:\n python eval-sAP.py logs/*/npz/000*\n\nArguments:\n One or more directories from train.py\n\nOptions:\n -h --help Show this screen.\n\"\"\"\n\nimport os\nimport sys\nimport glob\nimport os.path as osp\n\nimport numpy as np\nimport scipy.io\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nfrom docopt import docopt\n\nimport lcnn.utils\nimport lcnn.metric\n\nGT_val = \"evaluation/data/wireframe/valid/*.npz\"\nGT_train = \"evaluation/data/wireframe/train/*_0_label.npz\"\n\ndef line_score(path, threshold=5):\n preds = sorted(glob.glob(path))\n\n if path.split('/')[-2].split('_')[1] == 'train':\n gts = sorted(glob.glob(GT_train))\n gts = gts[:501]\n else:\n gts = sorted(glob.glob(GT_val))\n\n\n\n n_gt = 0\n lcnn_tp, lcnn_fp, lcnn_scores = [], [], []\n for pred_name, gt_name in zip(preds, gts):\n with np.load(pred_name) as fpred:\n lcnn_line = fpred[\"lines\"][:, :, :2]\n lcnn_score = fpred[\"score\"]\n with np.load(gt_name) as fgt:\n gt_line = fgt[\"lpos\"][:, :, :2]\n n_gt += len(gt_line)\n\n for i in range(len(lcnn_line)):\n if i > 0 and (lcnn_line[i] == lcnn_line[0]).all():\n lcnn_line = lcnn_line[:i]\n lcnn_score = lcnn_score[:i]\n break\n\n tp, fp = lcnn.metric.msTPFP(lcnn_line, gt_line, threshold)\n lcnn_tp.append(tp)\n lcnn_fp.append(fp)\n lcnn_scores.append(lcnn_score)\n\n lcnn_tp = np.concatenate(lcnn_tp)\n lcnn_fp = np.concatenate(lcnn_fp)\n lcnn_scores = np.concatenate(lcnn_scores)\n lcnn_index = np.argsort(-lcnn_scores)\n lcnn_tp = np.cumsum(lcnn_tp[lcnn_index]) / n_gt\n lcnn_fp = np.cumsum(lcnn_fp[lcnn_index]) / n_gt\n\n return lcnn.metric.ap(lcnn_tp, lcnn_fp)\n\n\nif __name__ == \"__main__\":\n args = docopt(__doc__)\n\n def work(path):\n print(f\"Working on {path}\")\n return [100 * line_score(f\"{path}/*.npz\", t) for t in [5, 10, 15]]\n\n dirs = sorted(sum([glob.glob(p) for p in args[\"\"]], []))\n results = lcnn.utils.parmap(work, dirs)\n\n for d, msAP in zip(dirs, results):\n print(f\"{d}: {msAP[0]:2.1f} {msAP[1]:2.1f} {msAP[2]:2.1f}\")\n"
},
{
"path": "evaluation/eval-sAP-york.py",
"content": "#!/usr/bin/env python3\n\"\"\"Evaluate sAP5, sAP10, sAP15 for LCNN\nUsage:\n eval-sAP.py ...\n eval-sAP.py (-h | --help )\n\nExamples:\n python eval-sAP.py logs/*/npz/000*\n\nArguments:\n One or more directories from train.py\n\nOptions:\n -h --help Show this screen.\n\"\"\"\n\nimport os\nimport sys\nimport glob\nimport os.path as osp\n\nimport numpy as np\nimport scipy.io\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nfrom docopt import docopt\n\nimport lcnn.utils\nimport lcnn.metric\n\nGT = \"evaluation/data/york/valid/*.npz\"\n\n\ndef line_score(path, threshold=5):\n preds = sorted(glob.glob(path))\n gts = sorted(glob.glob(GT))\n\n\n\n\n n_gt = 0\n lcnn_tp, lcnn_fp, lcnn_scores = [], [], []\n for pred_name, gt_name in zip(preds, gts):\n with np.load(pred_name) as fpred:\n lcnn_line = fpred[\"lines\"][:, :, :2]\n lcnn_score = fpred[\"score\"]\n with np.load(gt_name) as fgt:\n gt_line = fgt[\"lpos\"][:, :, :2]\n n_gt += len(gt_line)\n\n # DETR NEEDS TO SORT CONF\n #score_idx = np.argsort(-lcnn_score)\n #lcnn_line = lcnn_line[score_idx]\n #lcnn_score = lcnn_score[score_idx]\n\n for i in range(len(lcnn_line)):\n if i > 0 and (lcnn_line[i] == lcnn_line[0]).all():\n lcnn_line = lcnn_line[:i]\n lcnn_score = lcnn_score[:i]\n break\n\n tp, fp = lcnn.metric.msTPFP(lcnn_line, gt_line, threshold)\n lcnn_tp.append(tp)\n lcnn_fp.append(fp)\n lcnn_scores.append(lcnn_score)\n\n lcnn_tp = np.concatenate(lcnn_tp)\n lcnn_fp = np.concatenate(lcnn_fp)\n lcnn_scores = np.concatenate(lcnn_scores)\n lcnn_index = np.argsort(-lcnn_scores)\n lcnn_tp = np.cumsum(lcnn_tp[lcnn_index]) / n_gt\n lcnn_fp = np.cumsum(lcnn_fp[lcnn_index]) / n_gt\n\n return lcnn.metric.ap(lcnn_tp, lcnn_fp)\n\n\nif __name__ == \"__main__\":\n args = docopt(__doc__)\n\n def work(path):\n print(f\"Working on {path}\")\n return [100 * line_score(f\"{path}/*.npz\", t) for t in [5, 10, 15]]\n\n dirs = sorted(sum([glob.glob(p) for p in args[\"\"]], []))\n results = lcnn.utils.parmap(work, dirs)\n\n for d, msAP in zip(dirs, results):\n print(f\"{d}: {msAP[0]:2.1f} {msAP[1]:2.1f} {msAP[2]:2.1f}\")\n"
},
{
"path": "evaluation/lcnn/__init__.py",
"content": "import lcnn.models\nimport lcnn.trainer\nimport lcnn.datasets\nimport lcnn.config\n"
},
{
"path": "evaluation/lcnn/box.py",
"content": "#!/usr/bin/env python\n# -*- coding: UTF-8 -*-\n#\n# Copyright (c) 2017-2019 - Chris Griffith - MIT License\n\"\"\"\nImproved dictionary access through dot notation with additional tools.\n\"\"\"\nimport string\nimport sys\nimport json\nimport re\nimport copy\nfrom keyword import kwlist\nimport warnings\n\ntry:\n from collections.abc import Iterable, Mapping, Callable\nexcept ImportError:\n from collections import Iterable, Mapping, Callable\n\nyaml_support = True\n\ntry:\n import yaml\nexcept ImportError:\n try:\n import ruamel.yaml as yaml\n except ImportError:\n yaml = None\n yaml_support = False\n\nif sys.version_info >= (3, 0):\n basestring = str\nelse:\n from io import open\n\n__all__ = ['Box', 'ConfigBox', 'BoxList', 'SBox',\n 'BoxError', 'BoxKeyError']\n__author__ = 'Chris Griffith'\n__version__ = '3.2.4'\n\nBOX_PARAMETERS = ('default_box', 'default_box_attr', 'conversion_box',\n 'frozen_box', 'camel_killer_box', 'box_it_up',\n 'box_safe_prefix', 'box_duplicates', 'ordered_box')\n\n_first_cap_re = re.compile('(.)([A-Z][a-z]+)')\n_all_cap_re = re.compile('([a-z0-9])([A-Z])')\n\n\nclass BoxError(Exception):\n \"\"\"Non standard dictionary exceptions\"\"\"\n\n\nclass BoxKeyError(BoxError, KeyError, AttributeError):\n \"\"\"Key does not exist\"\"\"\n\n\n# Abstract converter functions for use in any Box class\n\n\ndef _to_json(obj, filename=None,\n encoding=\"utf-8\", errors=\"strict\", **json_kwargs):\n json_dump = json.dumps(obj,\n ensure_ascii=False, **json_kwargs)\n if filename:\n with open(filename, 'w', encoding=encoding, errors=errors) as f:\n f.write(json_dump if sys.version_info >= (3, 0) else\n json_dump.decode(\"utf-8\"))\n else:\n return json_dump\n\n\ndef _from_json(json_string=None, filename=None,\n encoding=\"utf-8\", errors=\"strict\", multiline=False, **kwargs):\n if filename:\n with open(filename, 'r', encoding=encoding, errors=errors) as f:\n if multiline:\n data = [json.loads(line.strip(), **kwargs) for line in f\n if line.strip() and not line.strip().startswith(\"#\")]\n else:\n data = json.load(f, **kwargs)\n elif json_string:\n data = json.loads(json_string, **kwargs)\n else:\n raise BoxError('from_json requires a string or filename')\n return data\n\n\ndef _to_yaml(obj, filename=None, default_flow_style=False,\n encoding=\"utf-8\", errors=\"strict\",\n **yaml_kwargs):\n if filename:\n with open(filename, 'w',\n encoding=encoding, errors=errors) as f:\n yaml.dump(obj, stream=f,\n default_flow_style=default_flow_style,\n **yaml_kwargs)\n else:\n return yaml.dump(obj,\n default_flow_style=default_flow_style,\n **yaml_kwargs)\n\n\ndef _from_yaml(yaml_string=None, filename=None,\n encoding=\"utf-8\", errors=\"strict\",\n **kwargs):\n if filename:\n with open(filename, 'r',\n encoding=encoding, errors=errors) as f:\n data = yaml.load(f, **kwargs)\n elif yaml_string:\n data = yaml.load(yaml_string, **kwargs)\n else:\n raise BoxError('from_yaml requires a string or filename')\n return data\n\n\n# Helper functions\n\n\ndef _safe_key(key):\n try:\n return str(key)\n except UnicodeEncodeError:\n return key.encode(\"utf-8\", \"ignore\")\n\n\ndef _safe_attr(attr, camel_killer=False, replacement_char='x'):\n \"\"\"Convert a key into something that is accessible as an attribute\"\"\"\n allowed = string.ascii_letters + string.digits + '_'\n\n attr = _safe_key(attr)\n\n if camel_killer:\n attr = _camel_killer(attr)\n\n attr = attr.replace(' ', '_')\n\n out = ''\n for character in attr:\n out += character if character in allowed else \"_\"\n out = out.strip(\"_\")\n\n try:\n int(out[0])\n except (ValueError, IndexError):\n pass\n else:\n out = '{0}{1}'.format(replacement_char, out)\n\n if out in kwlist:\n out = '{0}{1}'.format(replacement_char, out)\n\n return re.sub('_+', '_', out)\n\n\ndef _camel_killer(attr):\n \"\"\"\n CamelKiller, qu'est-ce que c'est?\n\n Taken from http://stackoverflow.com/a/1176023/3244542\n \"\"\"\n try:\n attr = str(attr)\n except UnicodeEncodeError:\n attr = attr.encode(\"utf-8\", \"ignore\")\n\n s1 = _first_cap_re.sub(r'\\1_\\2', attr)\n s2 = _all_cap_re.sub(r'\\1_\\2', s1)\n return re.sub('_+', '_', s2.casefold() if hasattr(s2, 'casefold') else\n s2.lower())\n\n\ndef _recursive_tuples(iterable, box_class, recreate_tuples=False, **kwargs):\n out_list = []\n for i in iterable:\n if isinstance(i, dict):\n out_list.append(box_class(i, **kwargs))\n elif isinstance(i, list) or (recreate_tuples and isinstance(i, tuple)):\n out_list.append(_recursive_tuples(i, box_class,\n recreate_tuples, **kwargs))\n else:\n out_list.append(i)\n return tuple(out_list)\n\n\ndef _conversion_checks(item, keys, box_config, check_only=False,\n pre_check=False):\n \"\"\"\n Internal use for checking if a duplicate safe attribute already exists\n\n :param item: Item to see if a dup exists\n :param keys: Keys to check against\n :param box_config: Easier to pass in than ask for specfic items\n :param check_only: Don't bother doing the conversion work\n :param pre_check: Need to add the item to the list of keys to check\n :return: the original unmodified key, if exists and not check_only\n \"\"\"\n if box_config['box_duplicates'] != 'ignore':\n if pre_check:\n keys = list(keys) + [item]\n\n key_list = [(k,\n _safe_attr(k, camel_killer=box_config['camel_killer_box'],\n replacement_char=box_config['box_safe_prefix']\n )) for k in keys]\n if len(key_list) > len(set(x[1] for x in key_list)):\n seen = set()\n dups = set()\n for x in key_list:\n if x[1] in seen:\n dups.add(\"{0}({1})\".format(x[0], x[1]))\n seen.add(x[1])\n if box_config['box_duplicates'].startswith(\"warn\"):\n warnings.warn('Duplicate conversion attributes exist: '\n '{0}'.format(dups))\n else:\n raise BoxError('Duplicate conversion attributes exist: '\n '{0}'.format(dups))\n if check_only:\n return\n # This way will be slower for warnings, as it will have double work\n # But faster for the default 'ignore'\n for k in keys:\n if item == _safe_attr(k, camel_killer=box_config['camel_killer_box'],\n replacement_char=box_config['box_safe_prefix']):\n return k\n\n\ndef _get_box_config(cls, kwargs):\n return {\n # Internal use only\n '__converted': set(),\n '__box_heritage': kwargs.pop('__box_heritage', None),\n '__created': False,\n '__ordered_box_values': [],\n # Can be changed by user after box creation\n 'default_box': kwargs.pop('default_box', False),\n 'default_box_attr': kwargs.pop('default_box_attr', cls),\n 'conversion_box': kwargs.pop('conversion_box', True),\n 'box_safe_prefix': kwargs.pop('box_safe_prefix', 'x'),\n 'frozen_box': kwargs.pop('frozen_box', False),\n 'camel_killer_box': kwargs.pop('camel_killer_box', False),\n 'modify_tuples_box': kwargs.pop('modify_tuples_box', False),\n 'box_duplicates': kwargs.pop('box_duplicates', 'ignore'),\n 'ordered_box': kwargs.pop('ordered_box', False)\n }\n\n\nclass Box(dict):\n \"\"\"\n Improved dictionary access through dot notation with additional tools.\n\n :param default_box: Similar to defaultdict, return a default value\n :param default_box_attr: Specify the default replacement.\n WARNING: If this is not the default 'Box', it will not be recursive\n :param frozen_box: After creation, the box cannot be modified\n :param camel_killer_box: Convert CamelCase to snake_case\n :param conversion_box: Check for near matching keys as attributes\n :param modify_tuples_box: Recreate incoming tuples with dicts into Boxes\n :param box_it_up: Recursively create all Boxes from the start\n :param box_safe_prefix: Conversion box prefix for unsafe attributes\n :param box_duplicates: \"ignore\", \"error\" or \"warn\" when duplicates exists\n in a conversion_box\n :param ordered_box: Preserve the order of keys entered into the box\n \"\"\"\n\n _protected_keys = dir({}) + ['to_dict', 'tree_view', 'to_json', 'to_yaml',\n 'from_yaml', 'from_json']\n\n def __new__(cls, *args, **kwargs):\n \"\"\"\n Due to the way pickling works in python 3, we need to make sure\n the box config is created as early as possible.\n \"\"\"\n obj = super(Box, cls).__new__(cls, *args, **kwargs)\n obj._box_config = _get_box_config(cls, kwargs)\n return obj\n\n def __init__(self, *args, **kwargs):\n self._box_config = _get_box_config(self.__class__, kwargs)\n if self._box_config['ordered_box']:\n self._box_config['__ordered_box_values'] = []\n if (not self._box_config['conversion_box'] and\n self._box_config['box_duplicates'] != \"ignore\"):\n raise BoxError('box_duplicates are only for conversion_boxes')\n if len(args) == 1:\n if isinstance(args[0], basestring):\n raise ValueError('Cannot extrapolate Box from string')\n if isinstance(args[0], Mapping):\n for k, v in args[0].items():\n if v is args[0]:\n v = self\n self[k] = v\n self.__add_ordered(k)\n elif isinstance(args[0], Iterable):\n for k, v in args[0]:\n self[k] = v\n self.__add_ordered(k)\n\n else:\n raise ValueError('First argument must be mapping or iterable')\n elif args:\n raise TypeError('Box expected at most 1 argument, '\n 'got {0}'.format(len(args)))\n\n box_it = kwargs.pop('box_it_up', False)\n for k, v in kwargs.items():\n if args and isinstance(args[0], Mapping) and v is args[0]:\n v = self\n self[k] = v\n self.__add_ordered(k)\n\n if (self._box_config['frozen_box'] or box_it or\n self._box_config['box_duplicates'] != 'ignore'):\n self.box_it_up()\n\n self._box_config['__created'] = True\n\n def __add_ordered(self, key):\n if (self._box_config['ordered_box'] and\n key not in self._box_config['__ordered_box_values']):\n self._box_config['__ordered_box_values'].append(key)\n\n def box_it_up(self):\n \"\"\"\n Perform value lookup for all items in current dictionary,\n generating all sub Box objects, while also running `box_it_up` on\n any of those sub box objects.\n \"\"\"\n for k in self:\n _conversion_checks(k, self.keys(), self._box_config,\n check_only=True)\n if self[k] is not self and hasattr(self[k], 'box_it_up'):\n self[k].box_it_up()\n\n def __hash__(self):\n if self._box_config['frozen_box']:\n hashing = 54321\n for item in self.items():\n hashing ^= hash(item)\n return hashing\n raise TypeError(\"unhashable type: 'Box'\")\n\n def __dir__(self):\n allowed = string.ascii_letters + string.digits + '_'\n kill_camel = self._box_config['camel_killer_box']\n items = set(dir(dict) + ['to_dict', 'to_json',\n 'from_json', 'box_it_up'])\n # Only show items accessible by dot notation\n for key in self.keys():\n key = _safe_key(key)\n if (' ' not in key and key[0] not in string.digits and\n key not in kwlist):\n for letter in key:\n if letter not in allowed:\n break\n else:\n items.add(key)\n\n for key in self.keys():\n key = _safe_key(key)\n if key not in items:\n if self._box_config['conversion_box']:\n key = _safe_attr(key, camel_killer=kill_camel,\n replacement_char=self._box_config[\n 'box_safe_prefix'])\n if key:\n items.add(key)\n if kill_camel:\n snake_key = _camel_killer(key)\n if snake_key:\n items.remove(key)\n items.add(snake_key)\n\n if yaml_support:\n items.add('to_yaml')\n items.add('from_yaml')\n\n return list(items)\n\n def get(self, key, default=None):\n try:\n return self[key]\n except KeyError:\n if isinstance(default, dict) and not isinstance(default, Box):\n return Box(default)\n if isinstance(default, list) and not isinstance(default, BoxList):\n return BoxList(default)\n return default\n\n def copy(self):\n return self.__class__(super(self.__class__, self).copy())\n\n def __copy__(self):\n return self.__class__(super(self.__class__, self).copy())\n\n def __deepcopy__(self, memodict=None):\n out = self.__class__()\n memodict = memodict or {}\n memodict[id(self)] = out\n for k, v in self.items():\n out[copy.deepcopy(k, memodict)] = copy.deepcopy(v, memodict)\n return out\n\n def __setstate__(self, state):\n self._box_config = state['_box_config']\n self.__dict__.update(state)\n\n def __getitem__(self, item, _ignore_default=False):\n try:\n value = super(Box, self).__getitem__(item)\n except KeyError as err:\n if item == '_box_config':\n raise BoxKeyError('_box_config should only exist as an '\n 'attribute and is never defaulted')\n if self._box_config['default_box'] and not _ignore_default:\n return self.__get_default(item)\n raise BoxKeyError(str(err))\n else:\n return self.__convert_and_store(item, value)\n\n def keys(self):\n if self._box_config['ordered_box']:\n return self._box_config['__ordered_box_values']\n return super(Box, self).keys()\n\n def values(self):\n return [self[x] for x in self.keys()]\n\n def items(self):\n return [(x, self[x]) for x in self.keys()]\n\n def __get_default(self, item):\n default_value = self._box_config['default_box_attr']\n if default_value is self.__class__:\n return self.__class__(__box_heritage=(self, item),\n **self.__box_config())\n elif isinstance(default_value, Callable):\n return default_value()\n elif hasattr(default_value, 'copy'):\n return default_value.copy()\n return default_value\n\n def __box_config(self):\n out = {}\n for k, v in self._box_config.copy().items():\n if not k.startswith(\"__\"):\n out[k] = v\n return out\n\n def __convert_and_store(self, item, value):\n if item in self._box_config['__converted']:\n return value\n if isinstance(value, dict) and not isinstance(value, Box):\n value = self.__class__(value, __box_heritage=(self, item),\n **self.__box_config())\n self[item] = value\n elif isinstance(value, list) and not isinstance(value, BoxList):\n if self._box_config['frozen_box']:\n value = _recursive_tuples(value, self.__class__,\n recreate_tuples=self._box_config[\n 'modify_tuples_box'],\n __box_heritage=(self, item),\n **self.__box_config())\n else:\n value = BoxList(value, __box_heritage=(self, item),\n box_class=self.__class__,\n **self.__box_config())\n self[item] = value\n elif (self._box_config['modify_tuples_box'] and\n isinstance(value, tuple)):\n value = _recursive_tuples(value, self.__class__,\n recreate_tuples=True,\n __box_heritage=(self, item),\n **self.__box_config())\n self[item] = value\n self._box_config['__converted'].add(item)\n return value\n\n def __create_lineage(self):\n if (self._box_config['__box_heritage'] and\n self._box_config['__created']):\n past, item = self._box_config['__box_heritage']\n if not past[item]:\n past[item] = self\n self._box_config['__box_heritage'] = None\n\n def __getattr__(self, item):\n try:\n try:\n value = self.__getitem__(item, _ignore_default=True)\n except KeyError:\n value = object.__getattribute__(self, item)\n except AttributeError as err:\n if item == \"__getstate__\":\n raise AttributeError(item)\n if item == '_box_config':\n raise BoxError('_box_config key must exist')\n kill_camel = self._box_config['camel_killer_box']\n if self._box_config['conversion_box'] and item:\n k = _conversion_checks(item, self.keys(), self._box_config)\n if k:\n return self.__getitem__(k)\n if kill_camel:\n for k in self.keys():\n if item == _camel_killer(k):\n return self.__getitem__(k)\n if self._box_config['default_box']:\n return self.__get_default(item)\n raise BoxKeyError(str(err))\n else:\n if item == '_box_config':\n return value\n return self.__convert_and_store(item, value)\n\n def __setitem__(self, key, value):\n if (key != '_box_config' and self._box_config['__created'] and\n self._box_config['frozen_box']):\n raise BoxError('Box is frozen')\n if self._box_config['conversion_box']:\n _conversion_checks(key, self.keys(), self._box_config,\n check_only=True, pre_check=True)\n super(Box, self).__setitem__(key, value)\n self.__add_ordered(key)\n self.__create_lineage()\n\n def __setattr__(self, key, value):\n if (key != '_box_config' and self._box_config['frozen_box'] and\n self._box_config['__created']):\n raise BoxError('Box is frozen')\n if key in self._protected_keys:\n raise AttributeError(\"Key name '{0}' is protected\".format(key))\n if key == '_box_config':\n return object.__setattr__(self, key, value)\n try:\n object.__getattribute__(self, key)\n except (AttributeError, UnicodeEncodeError):\n if (key not in self.keys() and\n (self._box_config['conversion_box'] or\n self._box_config['camel_killer_box'])):\n if self._box_config['conversion_box']:\n k = _conversion_checks(key, self.keys(),\n self._box_config)\n self[key if not k else k] = value\n elif self._box_config['camel_killer_box']:\n for each_key in self:\n if key == _camel_killer(each_key):\n self[each_key] = value\n break\n else:\n self[key] = value\n else:\n object.__setattr__(self, key, value)\n self.__add_ordered(key)\n self.__create_lineage()\n\n def __delitem__(self, key):\n if self._box_config['frozen_box']:\n raise BoxError('Box is frozen')\n super(Box, self).__delitem__(key)\n if (self._box_config['ordered_box'] and\n key in self._box_config['__ordered_box_values']):\n self._box_config['__ordered_box_values'].remove(key)\n\n def __delattr__(self, item):\n if self._box_config['frozen_box']:\n raise BoxError('Box is frozen')\n if item == '_box_config':\n raise BoxError('\"_box_config\" is protected')\n if item in self._protected_keys:\n raise AttributeError(\"Key name '{0}' is protected\".format(item))\n try:\n object.__getattribute__(self, item)\n except AttributeError:\n del self[item]\n else:\n object.__delattr__(self, item)\n if (self._box_config['ordered_box'] and\n item in self._box_config['__ordered_box_values']):\n self._box_config['__ordered_box_values'].remove(item)\n\n def pop(self, key, *args):\n if args:\n if len(args) != 1:\n raise BoxError('pop() takes only one optional'\n ' argument \"default\"')\n try:\n item = self[key]\n except KeyError:\n return args[0]\n else:\n del self[key]\n return item\n try:\n item = self[key]\n except KeyError:\n raise BoxKeyError('{0}'.format(key))\n else:\n del self[key]\n return item\n\n def clear(self):\n self._box_config['__ordered_box_values'] = []\n super(Box, self).clear()\n\n def popitem(self):\n try:\n key = next(self.__iter__())\n except StopIteration:\n raise BoxKeyError('Empty box')\n return key, self.pop(key)\n\n def __repr__(self):\n return ''.format(str(self.to_dict()))\n\n def __str__(self):\n return str(self.to_dict())\n\n def __iter__(self):\n for key in self.keys():\n yield key\n\n def __reversed__(self):\n for key in reversed(list(self.keys())):\n yield key\n\n def to_dict(self):\n \"\"\"\n Turn the Box and sub Boxes back into a native\n python dictionary.\n\n :return: python dictionary of this Box\n \"\"\"\n out_dict = dict(self)\n for k, v in out_dict.items():\n if v is self:\n out_dict[k] = out_dict\n elif hasattr(v, 'to_dict'):\n out_dict[k] = v.to_dict()\n elif hasattr(v, 'to_list'):\n out_dict[k] = v.to_list()\n return out_dict\n\n def update(self, item=None, **kwargs):\n if not item:\n item = kwargs\n iter_over = item.items() if hasattr(item, 'items') else item\n for k, v in iter_over:\n if isinstance(v, dict):\n # Box objects must be created in case they are already\n # in the `converted` box_config set\n v = self.__class__(v)\n if k in self and isinstance(self[k], dict):\n self[k].update(v)\n continue\n if isinstance(v, list):\n v = BoxList(v)\n try:\n self.__setattr__(k, v)\n except (AttributeError, TypeError):\n self.__setitem__(k, v)\n\n def setdefault(self, item, default=None):\n if item in self:\n return self[item]\n\n if isinstance(default, dict):\n default = self.__class__(default)\n if isinstance(default, list):\n default = BoxList(default)\n self[item] = default\n return default\n\n def to_json(self, filename=None,\n encoding=\"utf-8\", errors=\"strict\", **json_kwargs):\n \"\"\"\n Transform the Box object into a JSON string.\n\n :param filename: If provided will save to file\n :param encoding: File encoding\n :param errors: How to handle encoding errors\n :param json_kwargs: additional arguments to pass to json.dump(s)\n :return: string of JSON or return of `json.dump`\n \"\"\"\n return _to_json(self.to_dict(), filename=filename,\n encoding=encoding, errors=errors, **json_kwargs)\n\n @classmethod\n def from_json(cls, json_string=None, filename=None,\n encoding=\"utf-8\", errors=\"strict\", **kwargs):\n \"\"\"\n Transform a json object string into a Box object. If the incoming\n json is a list, you must use BoxList.from_json.\n\n :param json_string: string to pass to `json.loads`\n :param filename: filename to open and pass to `json.load`\n :param encoding: File encoding\n :param errors: How to handle encoding errors\n :param kwargs: parameters to pass to `Box()` or `json.loads`\n :return: Box object from json data\n \"\"\"\n bx_args = {}\n for arg in kwargs.copy():\n if arg in BOX_PARAMETERS:\n bx_args[arg] = kwargs.pop(arg)\n\n data = _from_json(json_string, filename=filename,\n encoding=encoding, errors=errors, **kwargs)\n\n if not isinstance(data, dict):\n raise BoxError('json data not returned as a dictionary, '\n 'but rather a {0}'.format(type(data).__name__))\n return cls(data, **bx_args)\n\n if yaml_support:\n def to_yaml(self, filename=None, default_flow_style=False,\n encoding=\"utf-8\", errors=\"strict\",\n **yaml_kwargs):\n \"\"\"\n Transform the Box object into a YAML string.\n\n :param filename: If provided will save to file\n :param default_flow_style: False will recursively dump dicts\n :param encoding: File encoding\n :param errors: How to handle encoding errors\n :param yaml_kwargs: additional arguments to pass to yaml.dump\n :return: string of YAML or return of `yaml.dump`\n \"\"\"\n return _to_yaml(self.to_dict(), filename=filename,\n default_flow_style=default_flow_style,\n encoding=encoding, errors=errors, **yaml_kwargs)\n\n @classmethod\n def from_yaml(cls, yaml_string=None, filename=None,\n encoding=\"utf-8\", errors=\"strict\",\n loader=yaml.SafeLoader, **kwargs):\n \"\"\"\n Transform a yaml object string into a Box object.\n\n :param yaml_string: string to pass to `yaml.load`\n :param filename: filename to open and pass to `yaml.load`\n :param encoding: File encoding\n :param errors: How to handle encoding errors\n :param loader: YAML Loader, defaults to SafeLoader\n :param kwargs: parameters to pass to `Box()` or `yaml.load`\n :return: Box object from yaml data\n \"\"\"\n bx_args = {}\n for arg in kwargs.copy():\n if arg in BOX_PARAMETERS:\n bx_args[arg] = kwargs.pop(arg)\n\n data = _from_yaml(yaml_string=yaml_string, filename=filename,\n encoding=encoding, errors=errors,\n Loader=loader, **kwargs)\n if not isinstance(data, dict):\n raise BoxError('yaml data not returned as a dictionary'\n 'but rather a {0}'.format(type(data).__name__))\n return cls(data, **bx_args)\n\n\nclass BoxList(list):\n \"\"\"\n Drop in replacement of list, that converts added objects to Box or BoxList\n objects as necessary.\n \"\"\"\n\n def __init__(self, iterable=None, box_class=Box, **box_options):\n self.box_class = box_class\n self.box_options = box_options\n self.box_org_ref = self.box_org_ref = id(iterable) if iterable else 0\n if iterable:\n for x in iterable:\n self.append(x)\n if box_options.get('frozen_box'):\n def frozen(*args, **kwargs):\n raise BoxError('BoxList is frozen')\n\n for method in ['append', 'extend', 'insert', 'pop',\n 'remove', 'reverse', 'sort']:\n self.__setattr__(method, frozen)\n\n def __delitem__(self, key):\n if self.box_options.get('frozen_box'):\n raise BoxError('BoxList is frozen')\n super(BoxList, self).__delitem__(key)\n\n def __setitem__(self, key, value):\n if self.box_options.get('frozen_box'):\n raise BoxError('BoxList is frozen')\n super(BoxList, self).__setitem__(key, value)\n\n def append(self, p_object):\n if isinstance(p_object, dict):\n try:\n p_object = self.box_class(p_object, **self.box_options)\n except AttributeError as err:\n if 'box_class' in self.__dict__:\n raise err\n elif isinstance(p_object, list):\n try:\n p_object = (self if id(p_object) == self.box_org_ref else\n BoxList(p_object))\n except AttributeError as err:\n if 'box_org_ref' in self.__dict__:\n raise err\n super(BoxList, self).append(p_object)\n\n def extend(self, iterable):\n for item in iterable:\n self.append(item)\n\n def insert(self, index, p_object):\n if isinstance(p_object, dict):\n p_object = self.box_class(p_object, **self.box_options)\n elif isinstance(p_object, list):\n p_object = (self if id(p_object) == self.box_org_ref else\n BoxList(p_object))\n super(BoxList, self).insert(index, p_object)\n\n def __repr__(self):\n return \"\".format(self.to_list())\n\n def __str__(self):\n return str(self.to_list())\n\n def __copy__(self):\n return BoxList((x for x in self),\n self.box_class,\n **self.box_options)\n\n def __deepcopy__(self, memodict=None):\n out = self.__class__()\n memodict = memodict or {}\n memodict[id(self)] = out\n for k in self:\n out.append(copy.deepcopy(k))\n return out\n\n def __hash__(self):\n if self.box_options.get('frozen_box'):\n hashing = 98765\n hashing ^= hash(tuple(self))\n return hashing\n raise TypeError(\"unhashable type: 'BoxList'\")\n\n def to_list(self):\n new_list = []\n for x in self:\n if x is self:\n new_list.append(new_list)\n elif isinstance(x, Box):\n new_list.append(x.to_dict())\n elif isinstance(x, BoxList):\n new_list.append(x.to_list())\n else:\n new_list.append(x)\n return new_list\n\n def to_json(self, filename=None,\n encoding=\"utf-8\", errors=\"strict\",\n multiline=False, **json_kwargs):\n \"\"\"\n Transform the BoxList object into a JSON string.\n\n :param filename: If provided will save to file\n :param encoding: File encoding\n :param errors: How to handle encoding errors\n :param multiline: Put each item in list onto it's own line\n :param json_kwargs: additional arguments to pass to json.dump(s)\n :return: string of JSON or return of `json.dump`\n \"\"\"\n if filename and multiline:\n lines = [_to_json(item, filename=False, encoding=encoding,\n errors=errors, **json_kwargs) for item in self]\n with open(filename, 'w', encoding=encoding, errors=errors) as f:\n f.write(\"\\n\".join(lines).decode('utf-8') if\n sys.version_info < (3, 0) else \"\\n\".join(lines))\n else:\n return _to_json(self.to_list(), filename=filename,\n encoding=encoding, errors=errors, **json_kwargs)\n\n @classmethod\n def from_json(cls, json_string=None, filename=None, encoding=\"utf-8\",\n errors=\"strict\", multiline=False, **kwargs):\n \"\"\"\n Transform a json object string into a BoxList object. If the incoming\n json is a dict, you must use Box.from_json.\n\n :param json_string: string to pass to `json.loads`\n :param filename: filename to open and pass to `json.load`\n :param encoding: File encoding\n :param errors: How to handle encoding errors\n :param multiline: One object per line\n :param kwargs: parameters to pass to `Box()` or `json.loads`\n :return: BoxList object from json data\n \"\"\"\n bx_args = {}\n for arg in kwargs.copy():\n if arg in BOX_PARAMETERS:\n bx_args[arg] = kwargs.pop(arg)\n\n data = _from_json(json_string, filename=filename, encoding=encoding,\n errors=errors, multiline=multiline, **kwargs)\n\n if not isinstance(data, list):\n raise BoxError('json data not returned as a list, '\n 'but rather a {0}'.format(type(data).__name__))\n return cls(data, **bx_args)\n\n if yaml_support:\n def to_yaml(self, filename=None, default_flow_style=False,\n encoding=\"utf-8\", errors=\"strict\",\n **yaml_kwargs):\n \"\"\"\n Transform the BoxList object into a YAML string.\n\n :param filename: If provided will save to file\n :param default_flow_style: False will recursively dump dicts\n :param encoding: File encoding\n :param errors: How to handle encoding errors\n :param yaml_kwargs: additional arguments to pass to yaml.dump\n :return: string of YAML or return of `yaml.dump`\n \"\"\"\n return _to_yaml(self.to_list(), filename=filename,\n default_flow_style=default_flow_style,\n encoding=encoding, errors=errors, **yaml_kwargs)\n\n @classmethod\n def from_yaml(cls, yaml_string=None, filename=None,\n encoding=\"utf-8\", errors=\"strict\",\n loader=yaml.SafeLoader,\n **kwargs):\n \"\"\"\n Transform a yaml object string into a BoxList object.\n\n :param yaml_string: string to pass to `yaml.load`\n :param filename: filename to open and pass to `yaml.load`\n :param encoding: File encoding\n :param errors: How to handle encoding errors\n :param loader: YAML Loader, defaults to SafeLoader\n :param kwargs: parameters to pass to `BoxList()` or `yaml.load`\n :return: BoxList object from yaml data\n \"\"\"\n bx_args = {}\n for arg in kwargs.copy():\n if arg in BOX_PARAMETERS:\n bx_args[arg] = kwargs.pop(arg)\n\n data = _from_yaml(yaml_string=yaml_string, filename=filename,\n encoding=encoding, errors=errors,\n Loader=loader, **kwargs)\n if not isinstance(data, list):\n raise BoxError('yaml data not returned as a list'\n 'but rather a {0}'.format(type(data).__name__))\n return cls(data, **bx_args)\n\n def box_it_up(self):\n for v in self:\n if hasattr(v, 'box_it_up') and v is not self:\n v.box_it_up()\n\n\nclass ConfigBox(Box):\n \"\"\"\n Modified box object to add object transforms.\n\n Allows for build in transforms like:\n\n cns = ConfigBox(my_bool='yes', my_int='5', my_list='5,4,3,3,2')\n\n cns.bool('my_bool') # True\n cns.int('my_int') # 5\n cns.list('my_list', mod=lambda x: int(x)) # [5, 4, 3, 3, 2]\n \"\"\"\n\n _protected_keys = dir({}) + ['to_dict', 'bool', 'int', 'float',\n 'list', 'getboolean', 'to_json', 'to_yaml',\n 'getfloat', 'getint',\n 'from_json', 'from_yaml']\n\n def __getattr__(self, item):\n \"\"\"Config file keys are stored in lower case, be a little more\n loosey goosey\"\"\"\n try:\n return super(ConfigBox, self).__getattr__(item)\n except AttributeError:\n return super(ConfigBox, self).__getattr__(item.lower())\n\n def __dir__(self):\n return super(ConfigBox, self).__dir__() + ['bool', 'int', 'float',\n 'list', 'getboolean',\n 'getfloat', 'getint']\n\n def bool(self, item, default=None):\n \"\"\" Return value of key as a boolean\n\n :param item: key of value to transform\n :param default: value to return if item does not exist\n :return: approximated bool of value\n \"\"\"\n try:\n item = self.__getattr__(item)\n except AttributeError as err:\n if default is not None:\n return default\n raise err\n\n if isinstance(item, (bool, int)):\n return bool(item)\n\n if (isinstance(item, str) and\n item.lower() in ('n', 'no', 'false', 'f', '0')):\n return False\n\n return True if item else False\n\n def int(self, item, default=None):\n \"\"\" Return value of key as an int\n\n :param item: key of value to transform\n :param default: value to return if item does not exist\n :return: int of value\n \"\"\"\n try:\n item = self.__getattr__(item)\n except AttributeError as err:\n if default is not None:\n return default\n raise err\n return int(item)\n\n def float(self, item, default=None):\n \"\"\" Return value of key as a float\n\n :param item: key of value to transform\n :param default: value to return if item does not exist\n :return: float of value\n \"\"\"\n try:\n item = self.__getattr__(item)\n except AttributeError as err:\n if default is not None:\n return default\n raise err\n return float(item)\n\n def list(self, item, default=None, spliter=\",\", strip=True, mod=None):\n \"\"\" Return value of key as a list\n\n :param item: key of value to transform\n :param mod: function to map against list\n :param default: value to return if item does not exist\n :param spliter: character to split str on\n :param strip: clean the list with the `strip`\n :return: list of items\n \"\"\"\n try:\n item = self.__getattr__(item)\n except AttributeError as err:\n if default is not None:\n return default\n raise err\n if strip:\n item = item.lstrip('[').rstrip(']')\n out = [x.strip() if strip else x for x in item.split(spliter)]\n if mod:\n return list(map(mod, out))\n return out\n\n # loose configparser compatibility\n\n def getboolean(self, item, default=None):\n return self.bool(item, default)\n\n def getint(self, item, default=None):\n return self.int(item, default)\n\n def getfloat(self, item, default=None):\n return self.float(item, default)\n\n def __repr__(self):\n return ''.format(str(self.to_dict()))\n\n\nclass SBox(Box):\n \"\"\"\n ShorthandBox (SBox) allows for\n property access of `dict` `json` and `yaml`\n \"\"\"\n _protected_keys = dir({}) + ['to_dict', 'tree_view', 'to_json', 'to_yaml',\n 'json', 'yaml', 'from_yaml', 'from_json',\n 'dict']\n\n @property\n def dict(self):\n return self.to_dict()\n\n @property\n def json(self):\n return self.to_json()\n\n if yaml_support:\n @property\n def yaml(self):\n return self.to_yaml()\n\n def __repr__(self):\n return ''.format(str(self.to_dict()))\n"
},
{
"path": "evaluation/lcnn/config.py",
"content": "import numpy as np\n\nfrom lcnn.box import Box\n\n# C is a dict storing all the configuration\nC = Box()\n\n# shortcut for C.model\nM = Box()\n"
},
{
"path": "evaluation/lcnn/datasets.py",
"content": "import glob\nimport json\nimport math\nimport os\nimport random\n\nimport numpy as np\nimport numpy.linalg as LA\nimport torch\nfrom skimage import io\nfrom torch.utils.data import Dataset\nfrom torch.utils.data.dataloader import default_collate\n\nfrom lcnn.config import M\n\n\nclass WireframeDataset(Dataset):\n def __init__(self, rootdir, split):\n self.rootdir = rootdir\n filelist = glob.glob(f\"{rootdir}/{split}/*_label.npz\")\n filelist.sort()\n\n print(f\"n{split}:\", len(filelist))\n self.split = split\n self.filelist = filelist\n\n def __len__(self):\n return len(self.filelist)\n\n def __getitem__(self, idx):\n iname = self.filelist[idx][:-10].replace(\"_a0\", \"\").replace(\"_a1\", \"\") + \".png\"\n image = io.imread(iname).astype(float)[:, :, :3]\n if \"a1\" in self.filelist[idx]:\n image = image[:, ::-1, :]\n image = (image - M.image.mean) / M.image.stddev\n image = np.rollaxis(image, 2).copy()\n\n # npz[\"jmap\"]: [J, H, W] Junction heat map\n # npz[\"joff\"]: [J, 2, H, W] Junction offset within each pixel\n # npz[\"lmap\"]: [H, W] Line heat map with anti-aliasing\n # npz[\"junc\"]: [Na, 3] Junction coordinates\n # npz[\"Lpos\"]: [M, 2] Positive lines represented with junction indices\n # npz[\"Lneg\"]: [M, 2] Negative lines represented with junction indices\n # npz[\"lpos\"]: [Np, 2, 3] Positive lines represented with junction coordinates\n # npz[\"lneg\"]: [Nn, 2, 3] Negative lines represented with junction coordinates\n #\n # For junc, lpos, and lneg that stores the junction coordinates, the last\n # dimension is (y, x, t), where t represents the type of that junction.\n with np.load(self.filelist[idx]) as npz:\n target = {\n name: torch.from_numpy(npz[name]).float()\n for name in [\"jmap\", \"joff\", \"lmap\"]\n }\n lpos = np.random.permutation(npz[\"lpos\"])[: M.n_stc_posl]\n lneg = np.random.permutation(npz[\"lneg\"])[: M.n_stc_negl]\n npos, nneg = len(lpos), len(lneg)\n lpre = np.concatenate([lpos, lneg], 0)\n for i in range(len(lpre)):\n if random.random() > 0.5:\n lpre[i] = lpre[i, ::-1]\n ldir = lpre[:, 0, :2] - lpre[:, 1, :2]\n ldir /= np.clip(LA.norm(ldir, axis=1, keepdims=True), 1e-6, None)\n feat = [\n lpre[:, :, :2].reshape(-1, 4) / 128 * M.use_cood,\n ldir * M.use_slop,\n lpre[:, :, 2],\n ]\n feat = np.concatenate(feat, 1)\n meta = {\n \"junc\": torch.from_numpy(npz[\"junc\"][:, :2]),\n \"jtyp\": torch.from_numpy(npz[\"junc\"][:, 2]).byte(),\n \"Lpos\": self.adjacency_matrix(len(npz[\"junc\"]), npz[\"Lpos\"]),\n \"Lneg\": self.adjacency_matrix(len(npz[\"junc\"]), npz[\"Lneg\"]),\n \"lpre\": torch.from_numpy(lpre[:, :, :2]),\n \"lpre_label\": torch.cat([torch.ones(npos), torch.zeros(nneg)]),\n \"lpre_feat\": torch.from_numpy(feat),\n }\n\n return torch.from_numpy(image).float(), meta, target\n\n def adjacency_matrix(self, n, link):\n mat = torch.zeros(n + 1, n + 1, dtype=torch.uint8)\n link = torch.from_numpy(link)\n if len(link) > 0:\n mat[link[:, 0], link[:, 1]] = 1\n mat[link[:, 1], link[:, 0]] = 1\n return mat\n\n\ndef collate(batch):\n return (\n default_collate([b[0] for b in batch]),\n [b[1] for b in batch],\n default_collate([b[2] for b in batch]),\n )\n"
},
{
"path": "evaluation/lcnn/metric.py",
"content": "import numpy as np\nimport numpy.linalg as LA\nimport matplotlib.pyplot as plt\n\nfrom lcnn.utils import argsort2d\n\nDX = [0, 0, 1, -1, 1, 1, -1, -1]\nDY = [1, -1, 0, 0, 1, -1, 1, -1]\n\n\ndef ap(tp, fp):\n recall = tp\n precision = tp / np.maximum(tp + fp, 1e-9)\n\n recall = np.concatenate(([0.0], recall, [1.0]))\n precision = np.concatenate(([0.0], precision, [0.0]))\n\n for i in range(precision.size - 1, 0, -1):\n precision[i - 1] = max(precision[i - 1], precision[i])\n i = np.where(recall[1:] != recall[:-1])[0]\n return np.sum((recall[i + 1] - recall[i]) * precision[i + 1])\n\ndef fscore(tp, fp):\n recall = tp\n precision = tp / np.maximum(tp + fp, 1e-9)\n\n recall = np.concatenate(([0.0], recall, [1.0]))\n precision = np.concatenate(([0.0], precision, [0.0]))\n\n return (2 * np.array(precision) * np.array(recall) / (1e-9 + np.array(precision) + np.array(recall))).max()\n\ndef APJ(vert_pred, vert_gt, max_distance, im_ids):\n if len(vert_pred) == 0:\n return 0\n\n vert_pred = np.array(vert_pred)\n vert_gt = np.array(vert_gt)\n\n confidence = vert_pred[:, -1]\n idx = np.argsort(-confidence)\n vert_pred = vert_pred[idx, :]\n im_ids = im_ids[idx]\n n_gt = sum(len(gt) for gt in vert_gt)\n\n nd = len(im_ids)\n tp, fp = np.zeros(nd, dtype=np.float), np.zeros(nd, dtype=np.float)\n hit = [[False for _ in j] for j in vert_gt]\n\n for i in range(nd):\n gt_juns = vert_gt[im_ids[i]]\n pred_juns = vert_pred[i][:-1]\n if len(gt_juns) == 0:\n continue\n dists = np.linalg.norm((pred_juns[None, :] - gt_juns), axis=1)\n choice = np.argmin(dists)\n dist = np.min(dists)\n if dist < max_distance and not hit[im_ids[i]][choice]:\n tp[i] = 1\n hit[im_ids[i]][choice] = True\n else:\n fp[i] = 1\n\n tp = np.cumsum(tp) / n_gt\n fp = np.cumsum(fp) / n_gt\n return ap(tp, fp)\n\n\ndef nms_j(heatmap, delta=1):\n heatmap = heatmap.copy()\n disable = np.zeros_like(heatmap, dtype=np.bool)\n for x, y in argsort2d(heatmap):\n for dx, dy in zip(DX, DY):\n xp, yp = x + dx, y + dy\n if not (0 <= xp < heatmap.shape[0] and 0 <= yp < heatmap.shape[1]):\n continue\n if heatmap[x, y] >= heatmap[xp, yp]:\n disable[xp, yp] = True\n heatmap[disable] *= 0.6\n return heatmap\n\n\ndef mAPJ(pred, truth, distances, im_ids):\n return sum(APJ(pred, truth, d, im_ids) for d in distances) / len(distances) * 100\n\n\ndef post_jheatmap(heatmap, offset=None, delta=1):\n heatmap = nms_j(heatmap, delta=delta)\n # only select the best 1000 junctions for efficiency\n v0 = argsort2d(-heatmap)[:1000]\n confidence = -np.sort(-heatmap.ravel())[:1000]\n keep_id = np.where(confidence >= 1e-2)[0]\n if len(keep_id) == 0:\n return np.zeros((0, 3))\n\n confidence = confidence[keep_id]\n if offset is not None:\n v0 = np.array([v + offset[:, v[0], v[1]] for v in v0])\n v0 = v0[keep_id] + 0.5\n v0 = np.hstack((v0, confidence[:, np.newaxis]))\n return v0\n\n\ndef vectorized_wireframe_2d_metric(\n vert_pred, dpth_pred, edge_pred, vert_gt, dpth_gt, edge_gt, threshold\n):\n # staging 1: matching\n nd = len(vert_pred)\n sorted_confidence = np.argsort(-vert_pred[:, -1])\n vert_pred = vert_pred[sorted_confidence, :-1]\n dpth_pred = dpth_pred[sorted_confidence]\n d = np.sqrt(\n np.sum(vert_pred ** 2, 1)[:, None]\n + np.sum(vert_gt ** 2, 1)[None, :]\n - 2 * vert_pred @ vert_gt.T\n )\n choice = np.argmin(d, 1)\n dist = np.min(d, 1)\n\n # staging 2: compute depth metric: SIL/L2\n loss_L1 = loss_L2 = 0\n hit = np.zeros_like(dpth_gt, np.bool)\n SIL = np.zeros(dpth_pred)\n for i in range(nd):\n if dist[i] < threshold and not hit[choice[i]]:\n hit[choice[i]] = True\n loss_L1 += abs(dpth_gt[choice[i]] - dpth_pred[i])\n loss_L2 += (dpth_gt[choice[i]] - dpth_pred[i]) ** 2\n a = np.maximum(-dpth_pred[i], 1e-10)\n b = -dpth_gt[choice[i]]\n SIL[i] = np.log(a) - np.log(b)\n else:\n choice[i] = -1\n\n n = max(np.sum(hit), 1)\n loss_L1 /= n\n loss_L2 /= n\n loss_SIL = np.sum(SIL ** 2) / n - np.sum(SIL) ** 2 / (n * n)\n\n # staging 3: compute mAP for edge matching\n edgeset = set([frozenset(e) for e in edge_gt])\n tp = np.zeros(len(edge_pred), dtype=np.float)\n fp = np.zeros(len(edge_pred), dtype=np.float)\n for i, (v0, v1, score) in enumerate(sorted(edge_pred, key=-edge_pred[2])):\n length = LA.norm(vert_gt[v0] - vert_gt[v1], axis=1)\n if frozenset([choice[v0], choice[v1]]) in edgeset:\n tp[i] = length\n else:\n fp[i] = length\n total_length = LA.norm(\n vert_gt[edge_gt[:, 0]] - vert_gt[edge_gt[:, 1]], axis=1\n ).sum()\n return ap(tp / total_length, fp / total_length), (loss_SIL, loss_L1, loss_L2)\n\n\ndef vectorized_wireframe_3d_metric(\n vert_pred, dpth_pred, edge_pred, vert_gt, dpth_gt, edge_gt, threshold\n):\n # staging 1: matching\n nd = len(vert_pred)\n sorted_confidence = np.argsort(-vert_pred[:, -1])\n vert_pred = np.hstack([vert_pred[:, :-1], dpth_pred[:, None]])[sorted_confidence]\n vert_gt = np.hstack([vert_gt[:, :-1], dpth_gt[:, None]])\n d = np.sqrt(\n np.sum(vert_pred ** 2, 1)[:, None]\n + np.sum(vert_gt ** 2, 1)[None, :]\n - 2 * vert_pred @ vert_gt.T\n )\n choice = np.argmin(d, 1)\n dist = np.min(d, 1)\n hit = np.zeros_like(dpth_gt, np.bool)\n for i in range(nd):\n if dist[i] < threshold and not hit[choice[i]]:\n hit[choice[i]] = True\n else:\n choice[i] = -1\n\n # staging 2: compute mAP for edge matching\n edgeset = set([frozenset(e) for e in edge_gt])\n tp = np.zeros(len(edge_pred), dtype=np.float)\n fp = np.zeros(len(edge_pred), dtype=np.float)\n for i, (v0, v1, score) in enumerate(sorted(edge_pred, key=-edge_pred[2])):\n length = LA.norm(vert_gt[v0] - vert_gt[v1], axis=1)\n if frozenset([choice[v0], choice[v1]]) in edgeset:\n tp[i] = length\n else:\n fp[i] = length\n total_length = LA.norm(\n vert_gt[edge_gt[:, 0]] - vert_gt[edge_gt[:, 1]], axis=1\n ).sum()\n\n return ap(tp / total_length, fp / total_length)\n\n\ndef msTPFP(line_pred, line_gt, threshold):\n diff = ((line_pred[:, None, :, None] - line_gt[:, None]) ** 2).sum(-1)\n diff = np.minimum(\n diff[:, :, 0, 0] + diff[:, :, 1, 1], diff[:, :, 0, 1] + diff[:, :, 1, 0]\n )\n choice = np.argmin(diff, 1)\n dist = np.min(diff, 1)\n hit = np.zeros(len(line_gt), np.bool)\n tp = np.zeros(len(line_pred), np.float)\n fp = np.zeros(len(line_pred), np.float)\n for i in range(len(line_pred)):\n if dist[i] < threshold and not hit[choice[i]]:\n hit[choice[i]] = True\n tp[i] = 1\n else:\n fp[i] = 1\n return tp, fp\n\n\ndef msAP(line_pred, line_gt, threshold):\n tp, fp = msTPFP(line_pred, line_gt, threshold)\n tp = np.cumsum(tp) / len(line_gt)\n fp = np.cumsum(fp) / len(line_gt)\n return ap(tp, fp)\n"
},
{
"path": "evaluation/lcnn/models/__init__.py",
"content": "# flake8: noqa\nfrom .hourglass_pose import hg\n# from .dla import dla169, dla102x, dla102x2\n"
},
{
"path": "evaluation/lcnn/models/hourglass_pose.py",
"content": "\"\"\"\nHourglass network inserted in the pre-activated Resnet\nUse lr=0.01 for current version\n(c) Yichao Zhou (LCNN)\n(c) YANG, Wei\n\"\"\"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n__all__ = [\"HourglassNet\", \"hg\"]\n\n\nclass Bottleneck2D(nn.Module):\n expansion = 2\n\n def __init__(self, inplanes, planes, stride=1, downsample=None):\n super(Bottleneck2D, self).__init__()\n\n self.bn1 = nn.BatchNorm2d(inplanes)\n self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1)\n self.bn2 = nn.BatchNorm2d(planes)\n self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1)\n self.bn3 = nn.BatchNorm2d(planes)\n self.conv3 = nn.Conv2d(planes, planes * 2, kernel_size=1)\n self.relu = nn.ReLU(inplace=True)\n self.downsample = downsample\n self.stride = stride\n\n def forward(self, x):\n residual = x\n\n out = self.bn1(x)\n out = self.relu(out)\n out = self.conv1(out)\n\n out = self.bn2(out)\n out = self.relu(out)\n out = self.conv2(out)\n\n out = self.bn3(out)\n out = self.relu(out)\n out = self.conv3(out)\n\n if self.downsample is not None:\n residual = self.downsample(x)\n\n out += residual\n\n return out\n\n\nclass Hourglass(nn.Module):\n def __init__(self, block, num_blocks, planes, depth):\n super(Hourglass, self).__init__()\n self.depth = depth\n self.block = block\n self.hg = self._make_hour_glass(block, num_blocks, planes, depth)\n\n def _make_residual(self, block, num_blocks, planes):\n layers = []\n for i in range(0, num_blocks):\n layers.append(block(planes * block.expansion, planes))\n return nn.Sequential(*layers)\n\n def _make_hour_glass(self, block, num_blocks, planes, depth):\n hg = []\n for i in range(depth):\n res = []\n for j in range(3):\n res.append(self._make_residual(block, num_blocks, planes))\n if i == 0:\n res.append(self._make_residual(block, num_blocks, planes))\n hg.append(nn.ModuleList(res))\n return nn.ModuleList(hg)\n\n def _hour_glass_forward(self, n, x):\n up1 = self.hg[n - 1][0](x)\n low1 = F.max_pool2d(x, 2, stride=2)\n low1 = self.hg[n - 1][1](low1)\n\n if n > 1:\n low2 = self._hour_glass_forward(n - 1, low1)\n else:\n low2 = self.hg[n - 1][3](low1)\n low3 = self.hg[n - 1][2](low2)\n up2 = F.interpolate(low3, scale_factor=2)\n out = up1 + up2\n return out\n\n def forward(self, x):\n return self._hour_glass_forward(self.depth, x)\n\n\nclass HourglassNet(nn.Module):\n \"\"\"Hourglass model from Newell et al ECCV 2016\"\"\"\n\n def __init__(self, block, head, depth, num_stacks, num_blocks, num_classes):\n super(HourglassNet, self).__init__()\n\n self.inplanes = 64\n self.num_feats = 128\n self.num_stacks = num_stacks\n self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3)\n self.bn1 = nn.BatchNorm2d(self.inplanes)\n self.relu = nn.ReLU(inplace=True)\n self.layer1 = self._make_residual(block, self.inplanes, 1)\n self.layer2 = self._make_residual(block, self.inplanes, 1)\n self.layer3 = self._make_residual(block, self.num_feats, 1)\n self.maxpool = nn.MaxPool2d(2, stride=2)\n\n # build hourglass modules\n ch = self.num_feats * block.expansion\n # vpts = []\n hg, res, fc, score, fc_, score_ = [], [], [], [], [], []\n for i in range(num_stacks):\n hg.append(Hourglass(block, num_blocks, self.num_feats, depth))\n res.append(self._make_residual(block, self.num_feats, num_blocks))\n fc.append(self._make_fc(ch, ch))\n score.append(head(ch, num_classes))\n # vpts.append(VptsHead(ch))\n # vpts.append(nn.Linear(ch, 9))\n # score.append(nn.Conv2d(ch, num_classes, kernel_size=1))\n # score[i].bias.data[0] += 4.6\n # score[i].bias.data[2] += 4.6\n if i < num_stacks - 1:\n fc_.append(nn.Conv2d(ch, ch, kernel_size=1))\n score_.append(nn.Conv2d(num_classes, ch, kernel_size=1))\n self.hg = nn.ModuleList(hg)\n self.res = nn.ModuleList(res)\n self.fc = nn.ModuleList(fc)\n self.score = nn.ModuleList(score)\n # self.vpts = nn.ModuleList(vpts)\n self.fc_ = nn.ModuleList(fc_)\n self.score_ = nn.ModuleList(score_)\n\n def _make_residual(self, block, planes, blocks, stride=1):\n downsample = None\n if stride != 1 or self.inplanes != planes * block.expansion:\n downsample = nn.Sequential(\n nn.Conv2d(\n self.inplanes,\n planes * block.expansion,\n kernel_size=1,\n stride=stride,\n )\n )\n\n layers = []\n layers.append(block(self.inplanes, planes, stride, downsample))\n self.inplanes = planes * block.expansion\n for i in range(1, blocks):\n layers.append(block(self.inplanes, planes))\n\n return nn.Sequential(*layers)\n\n def _make_fc(self, inplanes, outplanes):\n bn = nn.BatchNorm2d(inplanes)\n conv = nn.Conv2d(inplanes, outplanes, kernel_size=1)\n return nn.Sequential(conv, bn, self.relu)\n\n def forward(self, x):\n out = []\n # out_vps = []\n x = self.conv1(x)\n x = self.bn1(x)\n x = self.relu(x)\n\n x = self.layer1(x)\n x = self.maxpool(x)\n x = self.layer2(x)\n x = self.layer3(x)\n\n for i in range(self.num_stacks):\n y = self.hg[i](x)\n y = self.res[i](y)\n y = self.fc[i](y)\n score = self.score[i](y)\n # pre_vpts = F.adaptive_avg_pool2d(x, (1, 1))\n # pre_vpts = pre_vpts.reshape(-1, 256)\n # vpts = self.vpts[i](x)\n out.append(score)\n # out_vps.append(vpts)\n if i < self.num_stacks - 1:\n fc_ = self.fc_[i](y)\n score_ = self.score_[i](score)\n x = x + fc_ + score_\n\n return out[::-1], y # , out_vps[::-1]\n\n\ndef hg(**kwargs):\n model = HourglassNet(\n Bottleneck2D,\n head=kwargs.get(\"head\", lambda c_in, c_out: nn.Conv2D(c_in, c_out, 1)),\n depth=kwargs[\"depth\"],\n num_stacks=kwargs[\"num_stacks\"],\n num_blocks=kwargs[\"num_blocks\"],\n num_classes=kwargs[\"num_classes\"],\n )\n return model\n"
},
{
"path": "evaluation/lcnn/models/line_vectorizer.py",
"content": "import itertools\nimport random\nfrom collections import defaultdict\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom lcnn.config import M\n\nFEATURE_DIM = 8\n\n\nclass LineVectorizer(nn.Module):\n def __init__(self, backbone):\n super().__init__()\n self.backbone = backbone\n\n lambda_ = torch.linspace(0, 1, M.n_pts0)[:, None]\n self.register_buffer(\"lambda_\", lambda_)\n self.do_static_sampling = M.n_stc_posl + M.n_stc_negl > 0\n\n self.fc1 = nn.Conv2d(256, M.dim_loi, 1)\n scale_factor = M.n_pts0 // M.n_pts1\n if M.use_conv:\n self.pooling = nn.Sequential(\n nn.MaxPool1d(scale_factor, scale_factor),\n Bottleneck1D(M.dim_loi, M.dim_loi),\n )\n self.fc2 = nn.Sequential(\n nn.ReLU(inplace=True), nn.Linear(M.dim_loi * M.n_pts1 + FEATURE_DIM, 1)\n )\n else:\n self.pooling = nn.MaxPool1d(scale_factor, scale_factor)\n self.fc2 = nn.Sequential(\n nn.Linear(M.dim_loi * M.n_pts1 + FEATURE_DIM, M.dim_fc),\n nn.ReLU(inplace=True),\n nn.Linear(M.dim_fc, M.dim_fc),\n nn.ReLU(inplace=True),\n nn.Linear(M.dim_fc, 1),\n )\n self.loss = nn.BCEWithLogitsLoss(reduction=\"none\")\n\n def forward(self, input_dict):\n result = self.backbone(input_dict)\n h = result[\"preds\"]\n x = self.fc1(result[\"feature\"])\n n_batch, n_channel, row, col = x.shape\n\n xs, ys, fs, ps, idx, jcs = [], [], [], [], [0], []\n for i, meta in enumerate(input_dict[\"meta\"]):\n p, label, feat, jc = self.sample_lines(\n meta, h[\"jmap\"][i], h[\"joff\"][i], input_dict[\"mode\"]\n )\n # print(\"p.shape:\", p.shape)\n ys.append(label)\n if input_dict[\"mode\"] == \"training\" and self.do_static_sampling:\n p = torch.cat([p, meta[\"lpre\"]])\n feat = torch.cat([feat, meta[\"lpre_feat\"]])\n ys.append(meta[\"lpre_label\"])\n del jc\n else:\n jcs.append(jc)\n ps.append(p)\n fs.append(feat)\n\n p = p[:, 0:1, :] * self.lambda_ + p[:, 1:2, :] * (1 - self.lambda_) - 0.5\n p = p.reshape(-1, 2) # [N_LINE x N_POINT, 2_XY]\n px, py = p[:, 0].contiguous(), p[:, 1].contiguous()\n px0 = px.floor().clamp(min=0, max=127)\n py0 = py.floor().clamp(min=0, max=127)\n px1 = (px0 + 1).clamp(min=0, max=127)\n py1 = (py0 + 1).clamp(min=0, max=127)\n px0l, py0l, px1l, py1l = px0.long(), py0.long(), px1.long(), py1.long()\n\n # xp: [N_LINE, N_CHANNEL, N_POINT]\n xp = (\n (\n x[i, :, px0l, py0l] * (px1 - px) * (py1 - py)\n + x[i, :, px1l, py0l] * (px - px0) * (py1 - py)\n + x[i, :, px0l, py1l] * (px1 - px) * (py - py0)\n + x[i, :, px1l, py1l] * (px - px0) * (py - py0)\n )\n .reshape(n_channel, -1, M.n_pts0)\n .permute(1, 0, 2)\n )\n xp = self.pooling(xp)\n xs.append(xp)\n idx.append(idx[-1] + xp.shape[0])\n\n x, y = torch.cat(xs), torch.cat(ys)\n f = torch.cat(fs)\n x = x.reshape(-1, M.n_pts1 * M.dim_loi)\n x = torch.cat([x, f], 1)\n x = self.fc2(x).flatten()\n\n if input_dict[\"mode\"] != \"training\":\n p = torch.cat(ps)\n s = torch.sigmoid(x)\n b = s > 0.5\n lines = []\n score = []\n for i in range(n_batch):\n p0 = p[idx[i] : idx[i + 1]]\n s0 = s[idx[i] : idx[i + 1]]\n mask = b[idx[i] : idx[i + 1]]\n p0 = p0[mask]\n s0 = s0[mask]\n if len(p0) == 0:\n lines.append(torch.zeros([1, M.n_out_line, 2, 2], device=p.device))\n score.append(torch.zeros([1, M.n_out_line], device=p.device))\n else:\n arg = torch.argsort(s0, descending=True)\n p0, s0 = p0[arg], s0[arg]\n lines.append(p0[None, torch.arange(M.n_out_line) % len(p0)])\n score.append(s0[None, torch.arange(M.n_out_line) % len(s0)])\n for j in range(len(jcs[i])):\n if len(jcs[i][j]) == 0:\n jcs[i][j] = torch.zeros([M.n_out_junc, 2], device=p.device)\n jcs[i][j] = jcs[i][j][\n None, torch.arange(M.n_out_junc) % len(jcs[i][j])\n ]\n result[\"preds\"][\"lines\"] = torch.cat(lines)\n result[\"preds\"][\"score\"] = torch.cat(score)\n result[\"preds\"][\"juncs\"] = torch.cat([jcs[i][0] for i in range(n_batch)])\n if len(jcs[i]) > 1:\n result[\"preds\"][\"junts\"] = torch.cat(\n [jcs[i][1] for i in range(n_batch)]\n )\n\n if input_dict[\"mode\"] != \"testing\":\n y = torch.cat(ys)\n loss = self.loss(x, y)\n lpos_mask, lneg_mask = y, 1 - y\n loss_lpos, loss_lneg = loss * lpos_mask, loss * lneg_mask\n\n def sum_batch(x):\n xs = [x[idx[i] : idx[i + 1]].sum()[None] for i in range(n_batch)]\n return torch.cat(xs)\n\n lpos = sum_batch(loss_lpos) / sum_batch(lpos_mask).clamp(min=1)\n lneg = sum_batch(loss_lneg) / sum_batch(lneg_mask).clamp(min=1)\n result[\"losses\"][0][\"lpos\"] = lpos * M.loss_weight[\"lpos\"]\n result[\"losses\"][0][\"lneg\"] = lneg * M.loss_weight[\"lneg\"]\n\n if input_dict[\"mode\"] == \"training\":\n del result[\"preds\"]\n\n return result\n\n def sample_lines(self, meta, jmap, joff, mode):\n with torch.no_grad():\n junc = meta[\"junc\"] # [N, 2]\n jtyp = meta[\"jtyp\"] # [N]\n Lpos = meta[\"Lpos\"]\n Lneg = meta[\"Lneg\"]\n\n n_type = jmap.shape[0]\n jmap = non_maximum_suppression(jmap).reshape(n_type, -1)\n joff = joff.reshape(n_type, 2, -1)\n max_K = M.n_dyn_junc // n_type\n N = len(junc)\n if mode != \"training\":\n K = min(int((jmap > M.eval_junc_thres).float().sum().item()), max_K)\n else:\n K = min(int(N * 2 + 2), max_K)\n if K < 2:\n K = 2\n device = jmap.device\n\n # index: [N_TYPE, K]\n score, index = torch.topk(jmap, k=K)\n y = (index // 128).float() + torch.gather(joff[:, 0], 1, index) + 0.5\n x = (index % 128).float() + torch.gather(joff[:, 1], 1, index) + 0.5\n\n # xy: [N_TYPE, K, 2]\n xy = torch.cat([y[..., None], x[..., None]], dim=-1)\n xy_ = xy[..., None, :]\n del x, y, index\n\n # dist: [N_TYPE, K, N]\n dist = torch.sum((xy_ - junc) ** 2, -1)\n cost, match = torch.min(dist, -1)\n\n # xy: [N_TYPE * K, 2]\n # match: [N_TYPE, K]\n for t in range(n_type):\n match[t, jtyp[match[t]] != t] = N\n match[cost > 1.5 * 1.5] = N\n match = match.flatten()\n\n _ = torch.arange(n_type * K, device=device)\n u, v = torch.meshgrid(_, _)\n u, v = u.flatten(), v.flatten()\n up, vp = match[u], match[v]\n label = Lpos[up, vp]\n\n if mode == \"training\":\n c = torch.zeros_like(label, dtype=torch.bool)\n\n # sample positive lines\n cdx = label.nonzero().flatten()\n if len(cdx) > M.n_dyn_posl:\n # print(\"too many positive lines\")\n perm = torch.randperm(len(cdx), device=device)[: M.n_dyn_posl]\n cdx = cdx[perm]\n c[cdx] = 1\n\n # sample negative lines\n cdx = Lneg[up, vp].nonzero().flatten()\n if len(cdx) > M.n_dyn_negl:\n # print(\"too many negative lines\")\n perm = torch.randperm(len(cdx), device=device)[: M.n_dyn_negl]\n cdx = cdx[perm]\n c[cdx] = 1\n\n # sample other (unmatched) lines\n cdx = torch.randint(len(c), (M.n_dyn_othr,), device=device)\n c[cdx] = 1\n else:\n c = (u < v).flatten()\n\n # sample lines\n u, v, label = u[c], v[c], label[c]\n xy = xy.reshape(n_type * K, 2)\n xyu, xyv = xy[u], xy[v]\n\n u2v = xyu - xyv\n u2v /= torch.sqrt((u2v ** 2).sum(-1, keepdim=True)).clamp(min=1e-6)\n feat = torch.cat(\n [\n xyu / 128 * M.use_cood,\n xyv / 128 * M.use_cood,\n u2v * M.use_slop,\n (u[:, None] > K).float(),\n (v[:, None] > K).float(),\n ],\n 1,\n )\n line = torch.cat([xyu[:, None], xyv[:, None]], 1)\n\n xy = xy.reshape(n_type, K, 2)\n jcs = [xy[i, score[i] > 0.03] for i in range(n_type)]\n return line, label.float(), feat, jcs\n\n\ndef non_maximum_suppression(a):\n ap = F.max_pool2d(a, 3, stride=1, padding=1)\n mask = (a == ap).float().clamp(min=0.0)\n return a * mask\n\n\nclass Bottleneck1D(nn.Module):\n def __init__(self, inplanes, outplanes):\n super(Bottleneck1D, self).__init__()\n\n planes = outplanes // 2\n self.op = nn.Sequential(\n nn.BatchNorm1d(inplanes),\n nn.ReLU(inplace=True),\n nn.Conv1d(inplanes, planes, kernel_size=1),\n nn.BatchNorm1d(planes),\n nn.ReLU(inplace=True),\n nn.Conv1d(planes, planes, kernel_size=3, padding=1),\n nn.BatchNorm1d(planes),\n nn.ReLU(inplace=True),\n nn.Conv1d(planes, outplanes, kernel_size=1),\n )\n\n def forward(self, x):\n return x + self.op(x)\n"
},
{
"path": "evaluation/lcnn/models/multitask_learner.py",
"content": "from collections import OrderedDict, defaultdict\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom lcnn.config import M\n\n\nclass MultitaskHead(nn.Module):\n def __init__(self, input_channels, num_class):\n super(MultitaskHead, self).__init__()\n\n m = int(input_channels / 4)\n heads = []\n for output_channels in sum(M.head_size, []):\n heads.append(\n nn.Sequential(\n nn.Conv2d(input_channels, m, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.Conv2d(m, output_channels, kernel_size=1),\n )\n )\n self.heads = nn.ModuleList(heads)\n assert num_class == sum(sum(M.head_size, []))\n\n def forward(self, x):\n return torch.cat([head(x) for head in self.heads], dim=1)\n\n\nclass MultitaskLearner(nn.Module):\n def __init__(self, backbone):\n super(MultitaskLearner, self).__init__()\n self.backbone = backbone\n head_size = M.head_size\n self.num_class = sum(sum(head_size, []))\n self.head_off = np.cumsum([sum(h) for h in head_size])\n\n def forward(self, input_dict):\n image = input_dict[\"image\"]\n outputs, feature = self.backbone(image)\n result = {\"feature\": feature}\n batch, channel, row, col = outputs[0].shape\n\n T = input_dict[\"target\"].copy()\n n_jtyp = T[\"jmap\"].shape[1]\n\n # switch to CNHW\n for task in [\"jmap\"]:\n T[task] = T[task].permute(1, 0, 2, 3)\n for task in [\"joff\"]:\n T[task] = T[task].permute(1, 2, 0, 3, 4)\n\n offset = self.head_off\n loss_weight = M.loss_weight\n losses = []\n for stack, output in enumerate(outputs):\n output = output.transpose(0, 1).reshape([-1, batch, row, col]).contiguous()\n jmap = output[0 : offset[0]].reshape(n_jtyp, 2, batch, row, col)\n lmap = output[offset[0] : offset[1]].squeeze(0)\n joff = output[offset[1] : offset[2]].reshape(n_jtyp, 2, batch, row, col)\n if stack == 0:\n result[\"preds\"] = {\n \"jmap\": jmap.permute(2, 0, 1, 3, 4).softmax(2)[:, :, 1],\n \"lmap\": lmap.sigmoid(),\n \"joff\": joff.permute(2, 0, 1, 3, 4).sigmoid() - 0.5,\n }\n if input_dict[\"mode\"] == \"testing\":\n return result\n\n L = OrderedDict()\n L[\"jmap\"] = sum(\n cross_entropy_loss(jmap[i], T[\"jmap\"][i]) for i in range(n_jtyp)\n )\n L[\"lmap\"] = (\n F.binary_cross_entropy_with_logits(lmap, T[\"lmap\"], reduction=\"none\")\n .mean(2)\n .mean(1)\n )\n L[\"joff\"] = sum(\n sigmoid_l1_loss(joff[i, j], T[\"joff\"][i, j], -0.5, T[\"jmap\"][i])\n for i in range(n_jtyp)\n for j in range(2)\n )\n for loss_name in L:\n L[loss_name].mul_(loss_weight[loss_name])\n losses.append(L)\n result[\"losses\"] = losses\n return result\n\n\ndef l2loss(input, target):\n return ((target - input) ** 2).mean(2).mean(1)\n\n\ndef cross_entropy_loss(logits, positive):\n nlogp = -F.log_softmax(logits, dim=0)\n return (positive * nlogp[1] + (1 - positive) * nlogp[0]).mean(2).mean(1)\n\n\ndef sigmoid_l1_loss(logits, target, offset=0.0, mask=None):\n logp = torch.sigmoid(logits) + offset\n loss = torch.abs(logp - target)\n if mask is not None:\n w = mask.mean(2, True).mean(1, True)\n w[w == 0] = 1\n loss = loss * (mask / w)\n\n return loss.mean(2).mean(1)\n"
},
{
"path": "evaluation/lcnn/postprocess.py",
"content": "import numpy as np\n\n\ndef pline(x1, y1, x2, y2, x, y):\n px = x2 - x1\n py = y2 - y1\n dd = px * px + py * py\n u = ((x - x1) * px + (y - y1) * py) / max(1e-9, float(dd))\n dx = x1 + u * px - x\n dy = y1 + u * py - y\n return dx * dx + dy * dy\n\n\ndef psegment(x1, y1, x2, y2, x, y):\n px = x2 - x1\n py = y2 - y1\n dd = px * px + py * py\n u = max(min(((x - x1) * px + (y - y1) * py) / float(dd), 1), 0)\n dx = x1 + u * px - x\n dy = y1 + u * py - y\n return dx * dx + dy * dy\n\n\ndef plambda(x1, y1, x2, y2, x, y):\n px = x2 - x1\n py = y2 - y1\n dd = px * px + py * py\n return ((x - x1) * px + (y - y1) * py) / max(1e-9, float(dd))\n\n\ndef postprocess(lines, scores, threshold=0.01, tol=1e9, do_clip=False):\n nlines, nscores = [], []\n for (p, q), score in zip(lines, scores):\n start, end = 0, 1\n for a, b in nlines: # nlines: Selected lines.\n if (\n min(\n max(pline(*p, *q, *a), pline(*p, *q, *b)),\n max(pline(*a, *b, *p), pline(*a, *b, *q)),\n )\n > threshold ** 2\n ):\n continue\n lambda_a = plambda(*p, *q, *a)\n lambda_b = plambda(*p, *q, *b)\n if lambda_a > lambda_b:\n lambda_a, lambda_b = lambda_b, lambda_a\n lambda_a -= tol\n lambda_b += tol\n\n # case 1: skip (if not do_clip)\n if start < lambda_a and lambda_b < end:\n continue\n\n # not intersect\n if lambda_b < start or lambda_a > end:\n continue\n\n # cover\n if lambda_a <= start and end <= lambda_b:\n start = 10\n break\n\n # case 2 & 3:\n if lambda_a <= start and start <= lambda_b:\n start = lambda_b\n if lambda_a <= end and end <= lambda_b:\n end = lambda_a\n\n if start >= end:\n break\n\n if start >= end:\n continue\n nlines.append(np.array([p + (q - p) * start, p + (q - p) * end]))\n nscores.append(score)\n return np.array(nlines), np.array(nscores)\n"
},
{
"path": "evaluation/lcnn/trainer.py",
"content": "import atexit\nimport os\nimport os.path as osp\nimport shutil\nimport signal\nimport subprocess\nimport threading\nimport time\nfrom timeit import default_timer as timer\n\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom skimage import io\n#from tensorboardX import SummaryWriter\n\nfrom lcnn.config import C, M\nfrom lcnn.utils import recursive_to\n\n\nclass Trainer(object):\n def __init__(self, device, model, optimizer, train_loader, val_loader, out):\n self.device = device\n\n self.model = model\n self.optim = optimizer\n\n self.train_loader = train_loader\n self.val_loader = val_loader\n self.batch_size = C.model.batch_size\n\n self.validation_interval = C.io.validation_interval\n\n self.out = out\n if not osp.exists(self.out):\n os.makedirs(self.out)\n\n self.run_tensorboard()\n time.sleep(1)\n\n self.epoch = 0\n self.iteration = 0\n self.max_epoch = C.optim.max_epoch\n self.lr_decay_epoch = C.optim.lr_decay_epoch\n self.num_stacks = C.model.num_stacks\n self.mean_loss = self.best_mean_loss = 1e1000\n\n self.loss_labels = None\n self.avg_metrics = None\n self.metrics = np.zeros(0)\n\n def run_tensorboard(self):\n board_out = osp.join(self.out, \"tensorboard\")\n if not osp.exists(board_out):\n os.makedirs(board_out)\n self.writer = SummaryWriter(board_out)\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n p = subprocess.Popen(\n [\"tensorboard\", f\"--logdir={board_out}\", f\"--port={C.io.tensorboard_port}\"]\n )\n\n def killme():\n os.kill(p.pid, signal.SIGTERM)\n\n atexit.register(killme)\n\n def _loss(self, result):\n losses = result[\"losses\"]\n # Don't move loss label to other place.\n # If I want to change the loss, I just need to change this function.\n if self.loss_labels is None:\n self.loss_labels = [\"sum\"] + list(losses[0].keys())\n self.metrics = np.zeros([self.num_stacks, len(self.loss_labels)])\n print()\n print(\n \"| \".join(\n [\"progress \"]\n + list(map(\"{:7}\".format, self.loss_labels))\n + [\"speed\"]\n )\n )\n with open(f\"{self.out}/loss.csv\", \"a\") as fout:\n print(\",\".join([\"progress\"] + self.loss_labels), file=fout)\n\n total_loss = 0\n for i in range(self.num_stacks):\n for j, name in enumerate(self.loss_labels):\n if name == \"sum\":\n continue\n if name not in losses[i]:\n assert i != 0\n continue\n loss = losses[i][name].mean()\n self.metrics[i, 0] += loss.item()\n self.metrics[i, j] += loss.item()\n total_loss += loss\n return total_loss\n\n def validate(self):\n tprint(\"Running validation...\", \" \" * 75)\n training = self.model.training\n self.model.eval()\n\n viz = osp.join(self.out, \"viz\", f\"{self.iteration * M.batch_size_eval:09d}\")\n npz = osp.join(self.out, \"npz\", f\"{self.iteration * M.batch_size_eval:09d}\")\n osp.exists(viz) or os.makedirs(viz)\n osp.exists(npz) or os.makedirs(npz)\n\n total_loss = 0\n self.metrics[...] = 0\n with torch.no_grad():\n for batch_idx, (image, meta, target) in enumerate(self.val_loader):\n input_dict = {\n \"image\": recursive_to(image, self.device),\n \"meta\": recursive_to(meta, self.device),\n \"target\": recursive_to(target, self.device),\n \"mode\": \"validation\",\n }\n result = self.model(input_dict)\n\n total_loss += self._loss(result)\n\n H = result[\"preds\"]\n for i in range(H[\"jmap\"].shape[0]):\n index = batch_idx * M.batch_size_eval + i\n np.savez(\n f\"{npz}/{index:06}.npz\",\n **{k: v[i].cpu().numpy() for k, v in H.items()},\n )\n if index >= 20:\n continue\n self._plot_samples(i, index, H, meta, target, f\"{viz}/{index:06}\")\n\n self._write_metrics(len(self.val_loader), total_loss, \"validation\", True)\n self.mean_loss = total_loss / len(self.val_loader)\n\n torch.save(\n {\n \"iteration\": self.iteration,\n \"arch\": self.model.__class__.__name__,\n \"optim_state_dict\": self.optim.state_dict(),\n \"model_state_dict\": self.model.state_dict(),\n \"best_mean_loss\": self.best_mean_loss,\n },\n osp.join(self.out, \"checkpoint_latest.pth\"),\n )\n shutil.copy(\n osp.join(self.out, \"checkpoint_latest.pth\"),\n osp.join(npz, \"checkpoint.pth\"),\n )\n if self.mean_loss < self.best_mean_loss:\n self.best_mean_loss = self.mean_loss\n shutil.copy(\n osp.join(self.out, \"checkpoint_latest.pth\"),\n osp.join(self.out, \"checkpoint_best.pth\"),\n )\n\n if training:\n self.model.train()\n\n def train_epoch(self):\n self.model.train()\n\n time = timer()\n for batch_idx, (image, meta, target) in enumerate(self.train_loader):\n\n self.optim.zero_grad()\n self.metrics[...] = 0\n\n input_dict = {\n \"image\": recursive_to(image, self.device),\n \"meta\": recursive_to(meta, self.device),\n \"target\": recursive_to(target, self.device),\n \"mode\": \"training\",\n }\n result = self.model(input_dict)\n\n loss = self._loss(result)\n if np.isnan(loss.item()):\n raise ValueError(\"loss is nan while training\")\n loss.backward()\n self.optim.step()\n\n if self.avg_metrics is None:\n self.avg_metrics = self.metrics\n else:\n self.avg_metrics = self.avg_metrics * 0.9 + self.metrics * 0.1\n self.iteration += 1\n self._write_metrics(1, loss.item(), \"training\", do_print=False)\n\n if self.iteration % 4 == 0:\n tprint(\n f\"{self.epoch:03}/{self.iteration * self.batch_size // 1000:04}k| \"\n + \"| \".join(map(\"{:.5f}\".format, self.avg_metrics[0]))\n + f\"| {4 * self.batch_size / (timer() - time):04.1f} \"\n )\n time = timer()\n num_images = self.batch_size * self.iteration\n if num_images % self.validation_interval == 0 or num_images == 600:\n self.validate()\n time = timer()\n\n def _write_metrics(self, size, total_loss, prefix, do_print=False):\n for i, metrics in enumerate(self.metrics):\n for label, metric in zip(self.loss_labels, metrics):\n self.writer.add_scalar(\n f\"{prefix}/{i}/{label}\", metric / size, self.iteration\n )\n if i == 0 and do_print:\n csv_str = (\n f\"{self.epoch:03}/{self.iteration * self.batch_size:07},\"\n + \",\".join(map(\"{:.11f}\".format, metrics / size))\n )\n prt_str = (\n f\"{self.epoch:03}/{self.iteration * self.batch_size // 1000:04}k| \"\n + \"| \".join(map(\"{:.5f}\".format, metrics / size))\n )\n with open(f\"{self.out}/loss.csv\", \"a\") as fout:\n print(csv_str, file=fout)\n pprint(prt_str, \" \" * 7)\n self.writer.add_scalar(\n f\"{prefix}/total_loss\", total_loss / size, self.iteration\n )\n return total_loss\n\n def _plot_samples(self, i, index, result, meta, target, prefix):\n fn = self.val_loader.dataset.filelist[index][:-10].replace(\"_a0\", \"\") + \".png\"\n img = io.imread(fn)\n imshow(img), plt.savefig(f\"{prefix}_img.jpg\"), plt.close()\n\n mask_result = result[\"jmap\"][i].cpu().numpy()\n mask_target = target[\"jmap\"][i].cpu().numpy()\n for ch, (ia, ib) in enumerate(zip(mask_target, mask_result)):\n imshow(ia), plt.savefig(f\"{prefix}_mask_{ch}a.jpg\"), plt.close()\n imshow(ib), plt.savefig(f\"{prefix}_mask_{ch}b.jpg\"), plt.close()\n\n line_result = result[\"lmap\"][i].cpu().numpy()\n line_target = target[\"lmap\"][i].cpu().numpy()\n imshow(line_target), plt.savefig(f\"{prefix}_line_a.jpg\"), plt.close()\n imshow(line_result), plt.savefig(f\"{prefix}_line_b.jpg\"), plt.close()\n\n def draw_vecl(lines, sline, juncs, junts, fn):\n imshow(img)\n if len(lines) > 0 and not (lines[0] == 0).all():\n for i, ((a, b), s) in enumerate(zip(lines, sline)):\n if i > 0 and (lines[i] == lines[0]).all():\n break\n plt.plot([a[1], b[1]], [a[0], b[0]], c=c(s), linewidth=4)\n if not (juncs[0] == 0).all():\n for i, j in enumerate(juncs):\n if i > 0 and (i == juncs[0]).all():\n break\n plt.scatter(j[1], j[0], c=\"red\", s=64, zorder=100)\n if junts is not None and len(junts) > 0 and not (junts[0] == 0).all():\n for i, j in enumerate(junts):\n if i > 0 and (i == junts[0]).all():\n break\n plt.scatter(j[1], j[0], c=\"blue\", s=64, zorder=100)\n plt.savefig(fn), plt.close()\n\n junc = meta[i][\"junc\"].cpu().numpy() * 4\n jtyp = meta[i][\"jtyp\"].cpu().numpy()\n juncs = junc[jtyp == 0]\n junts = junc[jtyp == 1]\n rjuncs = result[\"juncs\"][i].cpu().numpy() * 4\n rjunts = None\n if \"junts\" in result:\n rjunts = result[\"junts\"][i].cpu().numpy() * 4\n\n lpre = meta[i][\"lpre\"].cpu().numpy() * 4\n vecl_target = meta[i][\"lpre_label\"].cpu().numpy()\n vecl_result = result[\"lines\"][i].cpu().numpy() * 4\n score = result[\"score\"][i].cpu().numpy()\n lpre = lpre[vecl_target == 1]\n\n draw_vecl(lpre, np.ones(lpre.shape[0]), juncs, junts, f\"{prefix}_vecl_a.jpg\")\n draw_vecl(vecl_result, score, rjuncs, rjunts, f\"{prefix}_vecl_b.jpg\")\n\n def train(self):\n plt.rcParams[\"figure.figsize\"] = (24, 24)\n # if self.iteration == 0:\n # self.validate()\n epoch_size = len(self.train_loader)\n start_epoch = self.iteration // epoch_size\n for self.epoch in range(start_epoch, self.max_epoch):\n if self.epoch == self.lr_decay_epoch:\n self.optim.param_groups[0][\"lr\"] /= 10\n self.train_epoch()\n\n\ncmap = plt.get_cmap(\"jet\")\nnorm = mpl.colors.Normalize(vmin=0.4, vmax=1.0)\nsm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\nsm.set_array([])\n\n\ndef c(x):\n return sm.to_rgba(x)\n\n\ndef imshow(im):\n plt.close()\n plt.tight_layout()\n plt.imshow(im)\n plt.colorbar(sm, fraction=0.046)\n plt.xlim([0, im.shape[0]])\n plt.ylim([im.shape[0], 0])\n\n\ndef tprint(*args):\n \"\"\"Temporarily prints things on the screen\"\"\"\n print(\"\\r\", end=\"\")\n print(*args, end=\"\")\n\n\ndef pprint(*args):\n \"\"\"Permanently prints things on the screen\"\"\"\n print(\"\\r\", end=\"\")\n print(*args)\n\n\ndef _launch_tensorboard(board_out, port, out):\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n p = subprocess.Popen([\"tensorboard\", f\"--logdir={board_out}\", f\"--port={port}\"])\n\n def kill():\n os.kill(p.pid, signal.SIGTERM)\n\n atexit.register(kill)\n"
},
{
"path": "evaluation/lcnn/utils.py",
"content": "import math\nimport os.path as osp\nimport multiprocessing\nfrom timeit import default_timer as timer\n\nimport numpy as np\nimport torch\nimport matplotlib.pyplot as plt\n\n\nclass benchmark(object):\n def __init__(self, msg, enable=True, fmt=\"%0.3g\"):\n self.msg = msg\n self.fmt = fmt\n self.enable = enable\n\n def __enter__(self):\n if self.enable:\n self.start = timer()\n return self\n\n def __exit__(self, *args):\n if self.enable:\n t = timer() - self.start\n print((\"%s : \" + self.fmt + \" seconds\") % (self.msg, t))\n self.time = t\n\n\ndef quiver(x, y, ax):\n ax.set_xlim(0, x.shape[1])\n ax.set_ylim(x.shape[0], 0)\n ax.quiver(\n x,\n y,\n units=\"xy\",\n angles=\"xy\",\n scale_units=\"xy\",\n scale=1,\n minlength=0.01,\n width=0.1,\n color=\"b\",\n )\n\n\ndef recursive_to(input, device):\n if isinstance(input, torch.Tensor):\n return input.to(device)\n if isinstance(input, dict):\n for name in input:\n if isinstance(input[name], torch.Tensor):\n input[name] = input[name].to(device)\n return input\n if isinstance(input, list):\n for i, item in enumerate(input):\n input[i] = recursive_to(item, device)\n return input\n assert False\n\n\ndef np_softmax(x, axis=0):\n \"\"\"Compute softmax values for each sets of scores in x.\"\"\"\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=axis, keepdims=True)\n\n\ndef argsort2d(arr):\n return np.dstack(np.unravel_index(np.argsort(arr.ravel()), arr.shape))[0]\n\n\ndef __parallel_handle(f, q_in, q_out):\n while True:\n i, x = q_in.get()\n if i is None:\n break\n q_out.put((i, f(x)))\n\n\ndef parmap(f, X, nprocs=multiprocessing.cpu_count(), progress_bar=lambda x: x):\n if nprocs == 0:\n nprocs = multiprocessing.cpu_count()\n q_in = multiprocessing.Queue(1)\n q_out = multiprocessing.Queue()\n\n proc = [\n multiprocessing.Process(target=__parallel_handle, args=(f, q_in, q_out))\n for _ in range(nprocs)\n ]\n for p in proc:\n p.daemon = True\n p.start()\n\n try:\n sent = [q_in.put((i, x)) for i, x in enumerate(X)]\n [q_in.put((None, None)) for _ in range(nprocs)]\n res = [q_out.get() for _ in progress_bar(range(len(sent)))]\n [p.join() for p in proc]\n except KeyboardInterrupt:\n q_in.close()\n q_out.close()\n raise\n return [x for i, x in sorted(res)]\n"
},
{
"path": "evaluation/matlab/eval_release.m",
"content": "function eval_release(image_path, line_gt_path, output_file, result_path, output_size, mode)\n\n% lineThresh = [0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.97, 0.99, 0.995, 0.999, 0.9995, 0.9999];\nlineThresh = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.525, 0.55, 0.575, 0.6, 0.625, 0.65, 0.675, 0.7, 0.8, 0.9, 0.95, 0.97, 0.99, 0.995, 0.999, 0.9995, 0.9999];\n\nnLineThresh = size(lineThresh, 2);\nsumtp = zeros(nLineThresh, 1);\nsumfp = zeros(nLineThresh, 1);\nsumgt = zeros(nLineThresh, 1);\n\nlisting = dir(image_path);\nnumResults = size(listing, 1);\n\nfor index=1:numResults\n filename = listing(index).name;\n if length(filename) == 1 || length(filename) == 2 % '.' and '..' folder.\n continue;\n end\n filename = filename(1:end-4);\n fprintf('processed %d/%d\\n', index - 2, numResults - 2)\n gtname = [line_gt_path, '/', filename, '_line.mat'];\n imgname = [image_path, filename, '.jpg'];\n \n I = imread(imgname);\n height = size(I,1);\n width = size(I,2);\n \n % convert GT lines to binary map\n gtlines = load(gtname);\n gtlines = gtlines.lines;\n \n ne = size(gtlines,1);\n edgemap0 = zeros(height, width);\n for k = 1:ne\n x1 = gtlines(k,1);\n x2 = gtlines(k,3);\n y1 = gtlines(k,2);\n y2 = gtlines(k,4);\n \n vn = ceil(sqrt((x1-x2)^2+(y1-y2)^2));\n cur_edge = [linspace(y1,y2,vn).', linspace(x1,x2,vn).'];\n for j = 1:size(cur_edge,1)\n yy = round(cur_edge(j,1));\n xx = round(cur_edge(j,2));\n if yy <= 0\n yy = 1;\n end\n if xx <= 0\n xx = 1;\n end\n edgemap0(yy,xx) = 1;\n end\n end\n \n parfor m=1:nLineThresh\n resultname = [result_path, '/', num2str(lineThresh(m)), '/', filename, '.mat']; % Support the data from DETR.\n % resultname = [result_path, '/', num2str(lineThresh(m)), '/', sprintf('%06d', index - 3), '.mat']; % Support the data from LCNN.\n resultlines = load(resultname);\n resultlines = resultlines.lines;\n ne = size(resultlines,1);\n edgemap1 = zeros(height, width);\n for k = 1:ne\n x1 = resultlines(k,2) * width / output_size;\n y1 = resultlines(k,1) * height / output_size;\n x2 = resultlines(k,4) * width / output_size;\n y2 = resultlines(k,3) * height / output_size;\n\n vn = ceil(sqrt((x1-x2)^2+(y1-y2)^2));\n cur_edge = [linspace(y1,y2,vn).', linspace(x1,x2,vn).'];\n for j = 1:size(cur_edge,1)\n yy = round(cur_edge(j,1) - 0.5);\n xx = round(cur_edge(j,2) - 0.5);\n if yy <= 0\n yy = 1;\n end\n if xx <= 0\n xx = 1;\n end\n if yy > height\n yy = height;\n end\n if xx > width\n xx = width;\n end\n edgemap1(yy,xx) = 1;\n end\n end\n \n [matchE1,matchG1] = correspondPixels(edgemap1,edgemap0,0.01);\n matchE = double(matchE1 > 0);\n\n sumtp(m, 1) = sumtp(m, 1) + sum(matchE(:));\n sumfp(m, 1) = sumfp(m, 1) + sum(edgemap1(:)) - sum(matchE(:));\n sumgt(m, 1) = sumgt(m, 1) + sum(edgemap0(:));\n end\nend\nsave(output_file, 'sumtp', 'sumfp', 'sumgt');\nend\n"
},
{
"path": "evaluation/process.py",
"content": "#!/usr/bin/env python3\n\"\"\"Process a dataset with the trained neural network\nUsage:\n process.py [options] \n process.py (-h | --help )\n\nArguments:\n Path to the yaml hyper-parameter file\n Path to the checkpoint\n Path to the directory containing processed images\n Path to the output directory\n\nOptions:\n -h --help Show this screen.\n -d --devices Comma seperated GPU devices [default: 0]\n --plot Plot the result\n\"\"\"\n\nimport os\nimport sys\nimport shlex\nimport pprint\nimport random\nimport os.path as osp\nimport threading\nimport subprocess\n\nimport yaml\nimport numpy as np\nimport torch\nimport matplotlib as mpl\nimport skimage.io\nimport matplotlib.pyplot as plt\nfrom docopt import docopt\n\nimport lcnn\nfrom lcnn.utils import recursive_to\nfrom lcnn.config import C, M\nfrom lcnn.datasets import WireframeDataset, collate\nfrom lcnn.models.line_vectorizer import LineVectorizer\nfrom lcnn.models.multitask_learner import MultitaskHead, MultitaskLearner\n\n\ndef main():\n args = docopt(__doc__)\n config_file = args[\"\"] or \"config/wireframe.yaml\"\n C.update(C.from_yaml(filename=config_file))\n M.update(C.model)\n pprint.pprint(C, indent=4)\n\n random.seed(0)\n np.random.seed(0)\n torch.manual_seed(0)\n\n device_name = \"cpu\"\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = args[\"--devices\"]\n if torch.cuda.is_available():\n device_name = \"cuda\"\n torch.backends.cudnn.deterministic = True\n torch.cuda.manual_seed(0)\n print(\"Let's use\", torch.cuda.device_count(), \"GPU(s)!\")\n else:\n print(\"CUDA is not available\")\n device = torch.device(device_name)\n\n if M.backbone == \"stacked_hourglass\":\n model = lcnn.models.hg(\n depth=M.depth,\n head=lambda c_in, c_out: MultitaskHead(c_in, c_out),\n num_stacks=M.num_stacks,\n num_blocks=M.num_blocks,\n num_classes=sum(sum(M.head_size, [])),\n )\n else:\n raise NotImplementedError\n\n checkpoint = torch.load(args[\"\"])\n model = MultitaskLearner(model)\n model = LineVectorizer(model)\n model.load_state_dict(checkpoint[\"model_state_dict\"])\n model = model.to(device)\n model.eval()\n\n loader = torch.utils.data.DataLoader(\n WireframeDataset(args[\"\"], split=\"valid\"),\n shuffle=False,\n batch_size=M.batch_size,\n collate_fn=collate,\n num_workers=C.io.num_workers if os.name != \"nt\" else 0,\n pin_memory=True,\n )\n os.makedirs(args[\"\"], exist_ok=True)\n\n for batch_idx, (image, meta, target) in enumerate(loader):\n with torch.no_grad():\n input_dict = {\n \"image\": recursive_to(image, device),\n \"meta\": recursive_to(meta, device),\n \"target\": recursive_to(target, device),\n \"mode\": \"validation\",\n }\n H = model(input_dict)[\"preds\"]\n for i in range(M.batch_size):\n index = batch_idx * M.batch_size + i\n np.savez(\n osp.join(args[\"\"], f\"{index:06}.npz\"),\n **{k: v[i].cpu().numpy() for k, v in H.items()},\n )\n if not args[\"--plot\"]:\n continue\n im = image[i].cpu().numpy().transpose(1, 2, 0)\n im = im * M.image.stddev + M.image.mean\n lines = H[\"lines\"][i].cpu().numpy() * 4\n scores = H[\"score\"][i].cpu().numpy()\n if len(lines) > 0 and not (lines[0] == 0).all():\n for i, ((a, b), s) in enumerate(zip(lines, scores)):\n if i > 0 and (lines[i] == lines[0]).all():\n break\n plt.plot([a[1], b[1]], [a[0], b[0]], c=c(s), linewidth=4)\n plt.show()\n\n\ncmap = plt.get_cmap(\"jet\")\nnorm = mpl.colors.Normalize(vmin=0.4, vmax=1.0)\nsm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\nsm.set_array([])\n\n\ndef c(x):\n return sm.to_rgba(x)\n\n\nif __name__ == \"__main__\":\n main()\n"
},
{
"path": "helper/gdrive-download.sh",
"content": "#!/bin/bash\nfileid=\"$1\"\nfilename=\"$2\"\ncurl -c ./cookie -s -L \"https://drive.google.com/uc?export=download&id=${fileid}\" > /dev/null\ncurl -Lb ./cookie \"https://drive.google.com/uc?export=download&confirm=`awk '/download/ {print $NF}' ./cookie`&id=${fileid}\" -o ${filename}\n\nrm ./cookie"
},
{
"path": "helper/wireframe.py",
"content": "#!/usr/bin/env python3\n\"\"\"Process data for LETR\nUsage:\n wireframe.py \n wireframe.py (-h | --help )\n\nExamples:\n python wireframe.py wireframe_raw wireframe_processed\n\nArguments:\n Source directory that stores preprocessed wireframe data\n Temporary output directory\n\nOptions:\n -h --help Show this screen.\n\"\"\"\n\nimport json\nimport cv2\nimport os\nimport numpy as np\nimport math\nfrom docopt import docopt\n\ndef main():\n args = docopt(__doc__)\n src_dir = args[\"\"]\n tar_dir = args[\"\"]\n\n image_id = 0\n anno_id = 0\n for batch in [\"train2017\", \"val2017\"]:\n if batch == \"train2017\":\n anno_file = os.path.join(src_dir, \"train.json\")\n else:\n anno_file = os.path.join(src_dir, \"valid.json\")\n\n with open(anno_file, \"r\") as f:\n dataset = json.load(f)\n\n def handle(data, image_id, anno_id):\n im = cv2.imread(os.path.join(src_dir, \"images\", data[\"filename\"]))\n anno['images'].append({'file_name': data['filename'], 'height': im.shape[0], 'width': im.shape[1], 'id': image_id})\n lines = np.array(data[\"lines\"]).reshape(-1, 2, 2)\n os.makedirs(os.path.join(tar_dir, batch), exist_ok=True)\n\n image_path = os.path.join(tar_dir, batch, data['filename'])\n line_set = save_and_process(f\"{image_path}\", data['filename'], im[::, ::], lines)\n for line in line_set:\n info = {}\n info['id'] = anno_id\n anno_id += 1\n info['image_id'] = image_id\n info['category_id'] = 0\n info['line'] = line\n info['area'] = 1\n anno['annotations'].append(info)\n\n image_id += 1\n print(\"Finishing\", image_path)\n return anno_id\n\n anno = {}\n anno['images'] = []\n anno['annotations'] = []\n anno['categories'] = [{'supercategory':\"line\", \"id\": \"0\", \"name\": \"line\"}]\n for img_dict in dataset:\n anno_id = handle(img_dict, image_id, anno_id)\n image_id += 1\n\n os.makedirs(os.path.join(tar_dir, \"annotations\"), exist_ok=True)\n anno_path = os.path.join(tar_dir, \"annotations\", f\"lines_{batch}.json\")\n with open(anno_path, 'w') as outfile:\n json.dump(anno, outfile)\n\n \ndef save_and_process(image_path, image_name, image, lines):\n# change the format from x,y,x,y to x,y,dx, dy\n# order: top point > bottom point\n# if same y coordinate, right point > left point\n \n new_lines_pairs = []\n for line in lines: # [ #lines, 2, 2 ]\n p1 = line[0] # xy\n p2 = line[1] # xy\n if p1[0] < p2[0]:\n new_lines_pairs.append( [p1[0], p1[1], p2[0]-p1[0], p2[1]-p1[1]] ) \n elif p1[0] > p2[0]:\n new_lines_pairs.append( [p2[0], p2[1], p1[0]-p2[0], p1[1]-p2[1]] )\n else:\n if p1[1] < p2[1]:\n new_lines_pairs.append( [p1[0], p1[1], p2[0]-p1[0], p2[1]-p1[1]] )\n else:\n new_lines_pairs.append( [p2[0], p2[1], p1[0]-p2[0], p1[1]-p2[1]] )\n\n cv2.imwrite(f\"{image_path}\", image)\n return new_lines_pairs\n\n\nif __name__ == \"__main__\":\n main()"
},
{
"path": "helper/wireframe_eval.py",
"content": "#!/usr/bin/env python\n\"\"\"Process Huang's wireframe dataset for L-CNN network\nUsage:\n dataset/wireframe.py \n dataset/wireframe.py (-h | --help )\nExamples:\n python dataset/wireframe.py /datadir/wireframe data/wireframe\nArguments:\n Original data directory of Huang's wireframe dataset\n Directory of the output\nOptions:\n -h --help Show this screen.\n\"\"\"\n\nimport os\nimport sys\nimport json\nfrom itertools import combinations\n\nimport cv2\nimport numpy as np\nimport skimage.draw\nimport matplotlib.pyplot as plt\nfrom docopt import docopt\nfrom scipy.ndimage import zoom\n\ntry:\n sys.path.append(\".\")\n sys.path.append(\"..\")\n from lcnn.utils import parmap\nexcept Exception:\n raise\n\n\ndef inrange(v, shape):\n return 0 <= v[0] < shape[0] and 0 <= v[1] < shape[1]\n\n\ndef to_int(x):\n return tuple(map(int, x))\n\n\ndef save_heatmap(prefix, image, lines):\n im_rescale = (512, 512)\n heatmap_scale = (128, 128)\n\n fy, fx = heatmap_scale[1] / \\\n image.shape[0], heatmap_scale[0] / image.shape[1]\n jmap = np.zeros((1,) + heatmap_scale, dtype=np.float32)\n joff = np.zeros((1, 2) + heatmap_scale, dtype=np.float32)\n lmap = np.zeros(heatmap_scale, dtype=np.float32)\n\n lines[:, :, 0] = np.clip(lines[:, :, 0] * fx, 0, heatmap_scale[0] - 1e-4)\n lines[:, :, 1] = np.clip(lines[:, :, 1] * fy, 0, heatmap_scale[1] - 1e-4)\n lines = lines[:, :, ::-1]\n\n junc = []\n jids = {}\n\n def jid(jun):\n jun = tuple(jun[:2])\n if jun in jids:\n return jids[jun]\n jids[jun] = len(junc)\n junc.append(np.array(jun + (0,)))\n return len(junc) - 1\n\n lnid = []\n lpos, lneg = [], []\n for v0, v1 in lines:\n lnid.append((jid(v0), jid(v1)))\n lpos.append([junc[jid(v0)], junc[jid(v1)]])\n\n vint0, vint1 = to_int(v0), to_int(v1)\n jmap[0][vint0] = 1\n jmap[0][vint1] = 1\n rr, cc, value = skimage.draw.line_aa(*to_int(v0), *to_int(v1))\n lmap[rr, cc] = np.maximum(lmap[rr, cc], value)\n\n for v in junc:\n vint = to_int(v[:2])\n joff[0, :, vint[0], vint[1]] = v[:2] - vint - 0.5\n\n llmap = zoom(lmap, [0.5, 0.5])\n lineset = set([frozenset(l) for l in lnid])\n for i0, i1 in combinations(range(len(junc)), 2):\n if frozenset([i0, i1]) not in lineset:\n v0, v1 = junc[i0], junc[i1]\n vint0, vint1 = to_int(v0[:2] / 2), to_int(v1[:2] / 2)\n rr, cc, value = skimage.draw.line_aa(*vint0, *vint1)\n lneg.append([v0, v1, i0, i1, np.average(\n np.minimum(value, llmap[rr, cc]))])\n\n assert len(lneg) != 0\n lneg.sort(key=lambda l: -l[-1])\n\n junc = np.array(junc, dtype=np.float32)\n Lpos = np.array(lnid, dtype=np.int)\n Lneg = np.array([l[2:4] for l in lneg][:4000], dtype=np.int)\n lpos = np.array(lpos, dtype=np.float32)\n lneg = np.array([l[:2] for l in lneg[:2000]], dtype=np.float32)\n\n image = cv2.resize(image, im_rescale)\n\n # plt.subplot(131), plt.imshow(lmap)\n # plt.subplot(132), plt.imshow(image)\n # for i0, i1 in Lpos:\n # plt.scatter(junc[i0][1] * 4, junc[i0][0] * 4)\n # plt.scatter(junc[i1][1] * 4, junc[i1][0] * 4)\n # plt.plot([junc[i0][1] * 4, junc[i1][1] * 4], [junc[i0][0] * 4, junc[i1][0] * 4])\n # plt.subplot(133), plt.imshow(lmap)\n # for i0, i1 in Lneg[:150]:\n # plt.plot([junc[i0][1], junc[i1][1]], [junc[i0][0], junc[i1][0]])\n # plt.show()\n\n # For junc, lpos, and lneg that stores the junction coordinates, the last\n # dimension is (y, x, t), where t represents the type of that junction. In\n # the wireframe dataset, t is always zero.\n np.savez_compressed(\n f\"{prefix}_label.npz\",\n aspect_ratio=image.shape[1] / image.shape[0],\n jmap=jmap, # [J, H, W] Junction heat map\n joff=joff, # [J, 2, H, W] Junction offset within each pixel\n lmap=lmap, # [H, W] Line heat map with anti-aliasing\n junc=junc, # [Na, 3] Junction coordinate\n # [M, 2] Positive lines represented with junction indices\n Lpos=Lpos,\n # [M, 2] Negative lines represented with junction indices\n Lneg=Lneg,\n # [Np, 2, 3] Positive lines represented with junction coordinates\n lpos=lpos,\n # [Nn, 2, 3] Negative lines represented with junction coordinates\n lneg=lneg,\n )\n cv2.imwrite(f\"{prefix}.png\", image)\n\n # plt.imshow(jmap[0])\n # plt.savefig(\"/tmp/1jmap0.jpg\")\n # plt.imshow(jmap[1])\n # plt.savefig(\"/tmp/2jmap1.jpg\")\n # plt.imshow(lmap)\n # plt.savefig(\"/tmp/3lmap.jpg\")\n # plt.imshow(Lmap[2])\n # plt.savefig(\"/tmp/4ymap.jpg\")\n # plt.imshow(jwgt[0])\n # plt.savefig(\"/tmp/5jwgt.jpg\")\n # plt.cla()\n # plt.imshow(jmap[0])\n # for i in range(8):\n # plt.quiver(\n # 8 * jmap[0] * cdir[i] * np.cos(2 * math.pi / 16 * i),\n # 8 * jmap[0] * cdir[i] * np.sin(2 * math.pi / 16 * i),\n # units=\"xy\",\n # angles=\"xy\",\n # scale_units=\"xy\",\n # scale=1,\n # minlength=0.01,\n # width=0.1,\n # zorder=10,\n # color=\"w\",\n # )\n # plt.savefig(\"/tmp/6cdir.jpg\")\n # plt.cla()\n # plt.imshow(lmap)\n # plt.quiver(\n # 2 * lmap * np.cos(ldir),\n # 2 * lmap * np.sin(ldir),\n # units=\"xy\",\n # angles=\"xy\",\n # scale_units=\"xy\",\n # scale=1,\n # minlength=0.01,\n # width=0.1,\n # zorder=10,\n # color=\"w\",\n # )\n # plt.savefig(\"/tmp/7ldir.jpg\")\n # plt.cla()\n # plt.imshow(jmap[1])\n # plt.quiver(\n # 8 * jmap[1] * np.cos(tdir),\n # 8 * jmap[1] * np.sin(tdir),\n # units=\"xy\",\n # angles=\"xy\",\n # scale_units=\"xy\",\n # scale=1,\n # minlength=0.01,\n # width=0.1,\n # zorder=10,\n # color=\"w\",\n # )\n # plt.savefig(\"/tmp/8tdir.jpg\")\n\n\ndef main():\n args = docopt(__doc__)\n data_root = args[\"\"]\n data_output = args[\"\"]\n\n os.makedirs(data_output, exist_ok=True)\n for batch in [\"train\", \"valid\"]:\n anno_file = os.path.join(data_root, f\"{batch}.json\")\n\n with open(anno_file, \"r\") as f:\n dataset = json.load(f)\n\n def handle(data):\n im = cv2.imread(os.path.join(\n data_root, \"images\", data[\"filename\"]))\n prefix = data[\"filename\"].split(\".\")[0]\n lines = np.array(data[\"lines\"]).reshape(-1, 2, 2)\n os.makedirs(os.path.join(data_output, batch), exist_ok=True)\n\n lines0 = lines.copy()\n lines1 = lines.copy()\n lines1[:, :, 0] = im.shape[1] - lines1[:, :, 0]\n lines2 = lines.copy()\n lines2[:, :, 1] = im.shape[0] - lines2[:, :, 1]\n lines3 = lines.copy()\n lines3[:, :, 0] = im.shape[1] - lines3[:, :, 0]\n lines3[:, :, 1] = im.shape[0] - lines3[:, :, 1]\n\n path = os.path.join(data_output, batch, prefix)\n save_heatmap(f\"{path}_0\", im[::, ::], lines0)\n if batch != \"valid\":\n save_heatmap(f\"{path}_1\", im[::, ::-1], lines1)\n save_heatmap(f\"{path}_2\", im[::-1, ::], lines2)\n save_heatmap(f\"{path}_3\", im[::-1, ::-1], lines3)\n print(\"Finishing\", os.path.join(data_output, batch, prefix))\n\n parmap(handle, dataset, 16)\n\n\nif __name__ == \"__main__\":\n main()\n"
},
{
"path": "helper/york.py",
"content": "#!/usr/bin/env python3\n\"\"\"Process YorkUrban dataset for L-CNN network\nUsage:\n york.py \n york.py (-h | --help )\nExamples:\n python dataset/york.py /datadir/york data/york\nArguments:\n Original data directory of YorkUrban\n Directory of the output\nOptions:\n -h --help Show this screen.\n\"\"\"\n\nimport os\nimport sys\nimport glob\nimport json\nimport os.path as osp\nfrom itertools import combinations\n\nimport cv2\nimport numpy as np\nimport skimage.draw\nimport matplotlib.pyplot as plt\nfrom docopt import docopt\nfrom scipy.io import loadmat\nfrom scipy.ndimage import zoom\n\ndef main():\n args = docopt(__doc__)\n src_dir = args[\"\"]\n tar_dir = args[\"\"]\n\n os.makedirs(tar_dir, exist_ok=True)\n dataset = sorted(glob.glob(osp.join(src_dir, \"*/*.jpg\")))\n image_id = 0\n anno_id = 0\n for mode in [\"train\", \"val\"]:\n batch = f\"{mode}2017\"\n os.makedirs(os.path.join(tar_dir, batch), exist_ok=True)\n\n anno = {}\n anno['images'] = []\n anno['annotations'] = []\n anno['categories'] = [{'supercategory': \"line\", \"id\": \"0\", \"name\": \"line\"}]\n\n def handle(iname, image_id, anno_id, batch):\n\n im = cv2.imread(iname)\n filename = iname.split(\"/\")[-1]\n\n anno['images'].append({'file_name': filename,\n 'height': im.shape[0], 'width': im.shape[1], 'id': image_id})\n mat = loadmat(iname.replace(\".jpg\", \"LinesAndVP.mat\"))\n lines = np.array(mat[\"lines\"]).reshape(-1, 2, 2)\n lines = lines.astype('float')\n os.makedirs(os.path.join(tar_dir, batch), exist_ok=True)\n\n image_path = os.path.join(tar_dir, batch, filename)\n line_set = save_and_process(f\"{image_path}\", filename, im[::, ::], lines)\n for line in line_set:\n info = {}\n info['id'] = anno_id\n anno_id += 1\n info['image_id'] = image_id\n info['category_id'] = 0\n info['line'] = line\n info['area'] = 1\n anno['annotations'].append(info)\n\n image_id += 1\n print(f\"Finishing {image_path}\")\n return anno_id\n\n if mode == \"val\":\n for img in dataset:\n anno_id = handle(img, image_id, anno_id, batch)\n image_id += 1\n\n os.makedirs(os.path.join(tar_dir, \"annotations\"), exist_ok=True)\n anno_path = os.path.join(tar_dir, \"annotations\", f\"lines_{batch}.json\")\n with open(anno_path, 'w') as outfile:\n json.dump(anno, outfile)\n\n\ndef save_and_process(image_path, image_name, image, lines):\n # change the format from x,y,x,y to x,y,dx, dy\n # order: top point > bottom point\n # if same y coordinate, right point > left point\n\n new_lines_pairs = []\n for line in lines: # [ #lines, 2, 2 ]\n p1 = line[0] # xy\n p2 = line[1] # xy\n if p1[0] < p2[0]:\n new_lines_pairs.append([p1[0], p1[1], p2[0]-p1[0], p2[1]-p1[1]])\n elif p1[0] > p2[0]:\n new_lines_pairs.append([p2[0], p2[1], p1[0]-p2[0], p1[1]-p2[1]])\n else:\n if p1[1] < p2[1]:\n new_lines_pairs.append(\n [p1[0], p1[1], p2[0]-p1[0], p2[1]-p1[1]])\n else:\n new_lines_pairs.append(\n [p2[0], p2[1], p1[0]-p2[0], p1[1]-p2[1]])\n\n cv2.imwrite(f\"{image_path}\", image)\n return new_lines_pairs\n\nif __name__ == \"__main__\":\n main()"
},
{
"path": "helper/york_eval.py",
"content": "#!/usr/bin/env python3\n\"\"\"Process YorkUrban dataset for L-CNN network\nUsage:\n dataset/york.py \n dataset/york.py (-h | --help )\nExamples:\n python dataset/york.py /datadir/york data/york\nArguments:\n Original data directory of YorkUrban\n Directory of the output\nOptions:\n -h --help Show this screen.\n\"\"\"\n\nimport os\nimport sys\nimport glob\nimport json\nimport os.path as osp\nfrom itertools import combinations\n\nimport cv2\nimport numpy as np\nimport skimage.draw\nimport matplotlib.pyplot as plt\nfrom docopt import docopt\nfrom scipy.io import loadmat\nfrom scipy.ndimage import zoom\n\ntry:\n sys.path.append(\".\")\n sys.path.append(\"..\")\n from lcnn.utils import parmap\nexcept Exception:\n raise\n\n\ndef inrange(v, shape):\n return 0 <= v[0] < shape[0] and 0 <= v[1] < shape[1]\n\n\ndef to_int(x):\n return tuple(map(int, x))\n\n\ndef save_heatmap(prefix, image, lines):\n im_rescale = (512, 512)\n heatmap_scale = (128, 128)\n\n fy, fx = heatmap_scale[1] / image.shape[0], heatmap_scale[0] / image.shape[1]\n jmap = np.zeros((1,) + heatmap_scale, dtype=np.float32)\n joff = np.zeros((1, 2) + heatmap_scale, dtype=np.float32)\n lmap = np.zeros(heatmap_scale, dtype=np.float32)\n\n lines[:, :, 0] = np.clip(lines[:, :, 0] * fx, 0, heatmap_scale[0] - 1e-4)\n lines[:, :, 1] = np.clip(lines[:, :, 1] * fy, 0, heatmap_scale[1] - 1e-4)\n lines = lines[:, :, ::-1]\n\n junc = []\n jids = {}\n\n def jid(jun):\n jun = tuple(jun[:2])\n if jun in jids:\n return jids[jun]\n jids[jun] = len(junc)\n junc.append(np.array(jun + (0,)))\n return len(junc) - 1\n\n lnid = []\n lpos, lneg = [], []\n for v0, v1 in lines:\n lnid.append((jid(v0), jid(v1)))\n lpos.append([junc[jid(v0)], junc[jid(v1)]])\n\n vint0, vint1 = to_int(v0), to_int(v1)\n jmap[0][vint0] = 1\n jmap[0][vint1] = 1\n rr, cc, value = skimage.draw.line_aa(*to_int(v0), *to_int(v1))\n lmap[rr, cc] = np.maximum(lmap[rr, cc], value)\n\n for v in junc:\n vint = to_int(v[:2])\n joff[0, :, vint[0], vint[1]] = v[:2] - vint - 0.5\n\n llmap = zoom(lmap, [0.5, 0.5])\n lineset = set([frozenset(l) for l in lnid])\n for i0, i1 in combinations(range(len(junc)), 2):\n if frozenset([i0, i1]) not in lineset:\n v0, v1 = junc[i0], junc[i1]\n vint0, vint1 = to_int(v0[:2] / 2), to_int(v1[:2] / 2)\n rr, cc, value = skimage.draw.line_aa(*vint0, *vint1)\n lneg.append([v0, v1, i0, i1, np.average(np.minimum(value, llmap[rr, cc]))])\n # assert np.sum((v0 - v1) ** 2) > 0.01\n\n assert len(lneg) != 0\n lneg.sort(key=lambda l: -l[-1])\n\n junc = np.array(junc, dtype=np.float32)\n Lpos = np.array(lnid, dtype=np.int)\n Lneg = np.array([l[2:4] for l in lneg][:4000], dtype=np.int)\n lpos = np.array(lpos, dtype=np.float32)\n lneg = np.array([l[:2] for l in lneg[:2000]], dtype=np.float32)\n\n image = cv2.resize(image, im_rescale)\n\n # plt.subplot(131), plt.imshow(lmap)\n # plt.subplot(132), plt.imshow(image)\n # for i0, i1 in Lpos:\n # plt.scatter(junc[i0][1] * 4, junc[i0][0] * 4)\n # plt.scatter(junc[i1][1] * 4, junc[i1][0] * 4)\n # plt.plot([junc[i0][1] * 4, junc[i1][1] * 4], [junc[i0][0] * 4, junc[i1][0] * 4])\n # plt.subplot(133), plt.imshow(lmap)\n # for i0, i1 in Lneg[:150]:\n # plt.plot([junc[i0][1], junc[i1][1]], [junc[i0][0], junc[i1][0]])\n # plt.show()\n\n np.savez_compressed(\n f\"{prefix}_label.npz\",\n aspect_ratio=image.shape[1] / image.shape[0],\n jmap=jmap, # [J, H, W]\n joff=joff, # [J, 2, H, W]\n lmap=lmap, # [H, W]\n junc=junc, # [Na, 3]\n Lpos=Lpos, # [M, 2]\n Lneg=Lneg, # [M, 2]\n lpos=lpos, # [Np, 2, 3] (y, x, t) for the last dim\n lneg=lneg, # [Nn, 2, 3]\n )\n cv2.imwrite(f\"{prefix}.png\", image)\n\n # plt.imshow(jmap[0])\n # plt.savefig(\"/tmp/1jmap0.jpg\")\n # plt.imshow(jmap[1])\n # plt.savefig(\"/tmp/2jmap1.jpg\")\n # plt.imshow(lmap)\n # plt.savefig(\"/tmp/3lmap.jpg\")\n # plt.imshow(Lmap[2])\n # plt.savefig(\"/tmp/4ymap.jpg\")\n # plt.imshow(jwgt[0])\n # plt.savefig(\"/tmp/5jwgt.jpg\")\n # plt.cla()\n # plt.imshow(jmap[0])\n # for i in range(8):\n # plt.quiver(\n # 8 * jmap[0] * cdir[i] * np.cos(2 * math.pi / 16 * i),\n # 8 * jmap[0] * cdir[i] * np.sin(2 * math.pi / 16 * i),\n # units=\"xy\",\n # angles=\"xy\",\n # scale_units=\"xy\",\n # scale=1,\n # minlength=0.01,\n # width=0.1,\n # zorder=10,\n # color=\"w\",\n # )\n # plt.savefig(\"/tmp/6cdir.jpg\")\n # plt.cla()\n # plt.imshow(lmap)\n # plt.quiver(\n # 2 * lmap * np.cos(ldir),\n # 2 * lmap * np.sin(ldir),\n # units=\"xy\",\n # angles=\"xy\",\n # scale_units=\"xy\",\n # scale=1,\n # minlength=0.01,\n # width=0.1,\n # zorder=10,\n # color=\"w\",\n # )\n # plt.savefig(\"/tmp/7ldir.jpg\")\n # plt.cla()\n # plt.imshow(jmap[1])\n # plt.quiver(\n # 8 * jmap[1] * np.cos(tdir),\n # 8 * jmap[1] * np.sin(tdir),\n # units=\"xy\",\n # angles=\"xy\",\n # scale_units=\"xy\",\n # scale=1,\n # minlength=0.01,\n # width=0.1,\n # zorder=10,\n # color=\"w\",\n # )\n # plt.savefig(\"/tmp/8tdir.jpg\")\n\n\ndef main():\n args = docopt(__doc__)\n data_root = args[\"\"]\n data_output = args[\"\"]\n os.makedirs(data_output, exist_ok=True)\n\n dataset = sorted(glob.glob(osp.join(data_root, \"*/*.jpg\")))\n\n def handle(iname):\n prefix = osp.split(iname)[1].replace(\".jpg\", \"\")\n im = cv2.imread(iname)\n mat = loadmat(iname.replace(\".jpg\", \"LinesAndVP.mat\"))\n lines = np.array(mat[\"lines\"]).reshape(-1, 2, 2)\n path = osp.join(data_output, prefix)\n save_heatmap(f\"{path}\", im[::, ::], lines)\n print(f\"Finishing {path}\")\n\n parmap(handle, dataset)\n\n\nif __name__ == \"__main__\":\n main()"
},
{
"path": "script/evaluation/eval_aph_wireframe.sh",
"content": "# Fail the script if there is any failure\nset -e\n\nif [[ $# -eq 0 ]] ; then\n echo 'Require Experiment Name and Epoch'\n exit 1\nfi\n\nname=$1\nepoch=$2\n\noutput=exp/$name\n\ncd evaluation\n\necho \"Post-processing\"\npython eval-aph-post-wireframe.py --plot --thresholds=\"0.010,0.015\" ../$output/benchmark/benchmark_val_$epoch ../$output/post/$epoch\n\necho \"Evaluation AP-H\"\nmkdir -p ../$output/score/eval-APH-0_010\npython eval-aph-score-wireframe.py ../$output/post/$epoch/0_010 ../$output/post/$epoch/0_010-APH | tee ../$output/score/eval-APH-0_010/$epoch.txt"
},
{
"path": "script/evaluation/eval_aph_york.sh",
"content": "# Fail the script if there is any failure\nset -e\n\nif [[ $# -eq 0 ]] ; then\n echo 'Require Experiment Name and Epoch'\n exit 1\nfi\n\nname=$1\nepoch=$2\n\noutput=exp/$name\n\ncd evaluation\n\necho \"Post-processing\"\npython eval-aph-post-york.py --plot --thresholds=\"0.010,0.015\" ../$output/benchmark/benchmark_york_$epoch ../$output/post_york/$epoch\n\necho \"Evaluation AP-H\"\nmkdir -p ../$output/score/eval-APH-0_010-york\npython eval-aph-score-york.py ../$output/post_york/$epoch/0_010 ../$output/post_york/$epoch/0_010-APH | tee ../$output/score/eval-APH-0_010-york/$epoch.txt"
},
{
"path": "script/evaluation/eval_stage1.sh",
"content": "# Fail the script if there is any failure\nset -e\n\nif [[ $# -eq 0 ]] ; then\n echo 'Require Experiment Name'\n exit 1\nfi\nname=$1\noutput=exp/$name\n\nepoch=(\"499\")\n\nfor ((i=0;i<${#epoch[@]};++i)); do\n mkdir -p $output/score\n mkdir -p $output/score/eval-sAP\n mkdir -p $output/score/eval-fscore\n mkdir -p $output/score/eval-sAP-york\n mkdir -p $output/score/eval-fscore-york\n\n PYTHONPATH=$PYTHONPATH:./src \\\n python ./src/main.py --coco_path data/wireframe_processed \\\n --output_dir $output --backbone resnet101 --resume $output/checkpoints/checkpoint0${epoch[i]}.pth \\\n --batch_size 1 ${@:2} --num_queries 1000 \\\n --eval --benchmark --dataset val --append_word ${epoch[i]} --no_aux_loss\n\n PYTHONPATH=$PYTHONPATH:./src \\\n python ./src/main.py --coco_path data/york_processed \\\n --output_dir $output --backbone resnet101 --resume $output/checkpoints/checkpoint0${epoch[i]}.pth \\\n --batch_size 1 ${@:2} --num_queries 1000 \\\n --eval --benchmark --dataset val --append_word ${epoch[i]} --no_aux_loss\n\n\n python evaluation/eval-sAP-wireframe.py $output/benchmark/benchmark_val_${epoch[i]} | tee $output/score/eval-sAP/${epoch[i]}.txt\n\n python evaluation/eval-fscore-wireframe.py $output/benchmark/benchmark_val_${epoch[i]} | tee $output/score/eval-fscore/${epoch[i]}.txt\n\n python evaluation/eval-sAP-york.py $output/benchmark/benchmark_york_${epoch[i]} | tee $output/score/eval-sAP-york/${epoch[i]}.txt\n\n python evaluation/eval-fscore-york.py $output/benchmark/benchmark_york_${epoch[i]} | tee $output/score/eval-fscore-york/${epoch[i]}.txt\n\n\ndone\n\n"
},
{
"path": "script/evaluation/eval_stage2.sh",
"content": "# Fail the script if there is any failure\nset -e\n\nif [[ $# -eq 0 ]] ; then\n echo 'Require Experiment Name'\n exit 1\nfi\nname=$1\noutput=exp/$name\n\nepoch=(\"299\")\n\nfor ((i=0;i<${#epoch[@]};++i)); do\n mkdir -p $output/score\n mkdir -p $output/score/eval-sAP\n mkdir -p $output/score/eval-fscore\n mkdir -p $output/score/eval-sAP-york\n mkdir -p $output/score/eval-fscore-york\n\n PYTHONPATH=$PYTHONPATH:./src \\\n python ./src/main.py --coco_path data/wireframe_processed \\\n --output_dir $output --LETRpost --backbone resnet101 --resume $output/checkpoints/checkpoint0${epoch[i]}.pth \\\n --batch_size 1 ${@:2} --num_queries 1000 \\\n --eval --benchmark --dataset val --append_word ${epoch[i]} --no_aux_loss \n\n PYTHONPATH=$PYTHONPATH:./src \n python ./src/main.py --coco_path data/york_processed \\\n --output_dir $output --LETRpost --backbone resnet101 --resume $output/checkpoints/checkpoint0${epoch[i]}.pth \\\n --batch_size 1 ${@:2} --num_queries 1000 \\\n --eval --benchmark --dataset val --append_word ${epoch[i]} --no_aux_loss \n\n python evaluation/eval-sAP-wireframe.py $output/benchmark/benchmark_val_${epoch[i]} | tee -a $output/score/eval-sAP/${epoch[i]}.txt\n\n python evaluation/eval-fscore-wireframe.py $output/benchmark/benchmark_val_${epoch[i]} | tee $output/score/eval-fscore/${epoch[i]}.txt\n\n python evaluation/eval-sAP-york.py $output/benchmark/benchmark_york_${epoch[i]} | tee $output/score/eval-sAP-york/${epoch[i]}.txt\n\n python evaluation/eval-fscore-york.py $output/benchmark/benchmark_york_${epoch[i]} | tee $output/score/eval-fscore-york/${epoch[i]}.txt\n\n\ndone\n"
},
{
"path": "script/evaluation/eval_stage2_focal.sh",
"content": "# Fail the script if there is any failure\nset -e\n\nif [[ $# -eq 0 ]] ; then\n echo 'Require Experiment Name'\n exit 1\nfi\nname=$1\noutput=exp/$name\n\nepoch=(\"024\")\n\nfor ((i=0;i<${#epoch[@]};++i)); do\n mkdir -p $output/score\n mkdir -p $output/score/eval-sAP\n mkdir -p $output/score/eval-fscore\n mkdir -p $output/score/eval-sAP-york\n mkdir -p $output/score/eval-fscore-york\n\n PYTHONPATH=$PYTHONPATH:./src \\\n python ./src/main.py --coco_path data/wireframe_processed \\\n --output_dir $output --LETRpost --backbone resnet50 --resume $output/checkpoints/checkpoint0${epoch[i]}.pth \\\n --batch_size 1 ${@:2} --num_queries 1000 \\\n --eval --benchmark --dataset val --append_word ${epoch[i]} --no_aux_loss \n\n PYTHONPATH=$PYTHONPATH:./src \\\n python ./src/main.py --coco_path data/york_processed \\\n --output_dir $output --LETRpost --backbone resnet50 --resume $output/checkpoints/checkpoint0${epoch[i]}.pth \\\n --batch_size 1 ${@:2} --num_queries 1000 \\\n --eval --benchmark --dataset val --append_word ${epoch[i]} --no_aux_loss \n\n python evaluation/eval-sAP-wireframe.py $output/benchmark/benchmark_val_${epoch[i]} | tee -a $output/score/eval-sAP/${epoch[i]}.txt\n\n python evaluation/eval-fscore-wireframe.py $output/benchmark/benchmark_val_${epoch[i]} | tee $output/score/eval-fscore/${epoch[i]}.txt\n\n python evaluation/eval-sAP-york.py $output/benchmark/benchmark_york_${epoch[i]} | tee $output/score/eval-sAP-york/${epoch[i]}.txt\n\n python evaluation/eval-fscore-york.py $output/benchmark/benchmark_york_${epoch[i]} | tee $output/score/eval-fscore-york/${epoch[i]}.txt\n\n\ndone\n"
},
{
"path": "script/train/a0_train_stage1_res50.sh",
"content": "# Fail the script if there is any failure\nset -e\n\nif [[ $# -eq 0 ]] ; then\n echo 'Require Experiment Name'\n exit 1\nfi\n\n# The name of this experiment.\nname=$1\n\n# Save logs and models under snap/gqa; make backup.\noutput=exp/$name\nif [ ! -d \"$output\" ]; then\n echo \"folder not exist\"\n mkdir -p $output/src\n cp -r src/* $output/src/\n cp $0 $output/run.bash\n\n PYTHONPATH=$PYTHONPATH:./src python -m torch.distributed.launch \\\n --master_port=$((1000 + RANDOM % 9999)) --nproc_per_node=8 --use_env src/main.py --coco_path data/wireframe_processed \\\n --output_dir $output --backbone resnet50 --resume https://dl.fbaipublicfiles.com/detr/detr-r50-e632da11.pth \\\n --batch_size 1 --epochs 500 --lr_drop 200 --num_queries 1000 --num_gpus 8 --layer1_num 3 | tee -a $output/history.txt\n\nelse\n echo \"folder already exist\"\nfi\n\n\n\n\n"
},
{
"path": "script/train/a1_train_stage1_res101.sh",
"content": "# Fail the script if there is any failure\nset -e\n\nif [[ $# -eq 0 ]] ; then\n echo 'Require Experiment Name'\n exit 1\nfi\n\n# The name of this experiment.\nname=$1\n\n# Save logs and models under snap/gqa; make backup.\noutput=exp/$name\nif [ ! -d \"$output\" ]; then\n echo \"folder not exist\"\n mkdir -p $output/src\n cp -r src/* $output/src/\n cp $0 $output/run.bash\n\n PYTHONPATH=$PYTHONPATH:./src python -m torch.distributed.launch \\\n --master_port=$((1000 + RANDOM % 9999)) --nproc_per_node=4 --use_env src/main.py --coco_path data/wireframe_processed \\\n --output_dir $output --backbone resnet101 --resume https://dl.fbaipublicfiles.com/detr/detr-r101-2c7b67e5.pth \\\n --batch_size 1 --epochs 500 --lr_drop 200 --num_queries 1000 --num_gpus 4 --layer1_num 3 | tee -a $output/history.txt\n\nelse\n echo \"folder already exist\"\nfi\n\n\n\n\n"
},
{
"path": "script/train/a2_train_stage2_res50.sh",
"content": "# Fail the script if there is any failure\nset -e\n\nif [[ $# -eq 0 ]] ; then\n echo 'Require Experiment Name'\n exit 1\nfi\n\n# The name of this experiment.\nname=$1\n\n# Save logs and models under snap/gqa; make backup.\noutput=exp/$name\nif [ ! -d \"$output\" ]; then\n echo \"folder not exist\"\n mkdir -p $output/src\n cp -r src/* $output/src/\n cp $0 $output/run.bash\n\n PYTHONPATH=$PYTHONPATH:./src python -m torch.distributed.launch \\\n --master_port=$((1000 + RANDOM % 9999)) --nproc_per_node=8 --use_env src/main.py --coco_path data/wireframe_processed \\\n --output_dir $output --LETRpost --backbone resnet50 --layer1_frozen --frozen_weights exp/res50_stage1/checkpoints/checkpoint.pth --no_opt \\\n --batch_size 1 ${@:2} --epochs 300 --lr_drop 120 --num_queries 1000 --num_gpus 8 | tee -a $output/history.txt \n\nelse\n echo \"folder already exist\"\nfi\n\n\n\n"
},
{
"path": "script/train/a3_train_stage2_res101.sh",
"content": "# Fail the script if there is any failure\nset -e\n\nif [[ $# -eq 0 ]] ; then\n echo 'Require Experiment Name'\n exit 1\nfi\n\n# The name of this experiment.\nname=$1\n\n# Save logs and models under snap/gqa; make backup.\noutput=exp/$name\nif [ ! -d \"$output\" ]; then\n echo \"folder not exist\"\n mkdir -p $output/src\n cp -r src/* $output/src/\n cp $0 $output/run.bash\n\n CUDA_VISIBLE_DEVICES=1,3,8,9 PYTHONPATH=$PYTHONPATH:./src python -m torch.distributed.launch \\\n --master_port=$((1000 + RANDOM % 9999)) --nproc_per_node=4 --use_env src/main.py --coco_path data/wireframe_processed \\\n --output_dir $output --LETRpost --backbone resnet101 --layer1_frozen --frozen_weights exp/res101_stage1/checkpoints/checkpoint.pth --no_opt \\\n --batch_size 1 --epochs 300 --lr_drop 120 --num_queries 1000 --num_gpus 4 | tee -a $output/history.txt \n\nelse\n echo \"folder already exist\"\nfi\n\n\n"
},
{
"path": "script/train/a4_train_stage2_focal_res50.sh",
"content": "# Fail the script if there is any failure\nset -e\n\nif [[ $# -eq 0 ]] ; then\n echo 'Require Experiment Name'\n exit 1\nfi\n\n# The name of this experiment.\nname=$1\n\n# Save logs and models under snap/gqa; make backup.\noutput=exp/$name\nif [ ! -d \"$output\" ]; then\n echo \"folder not exist\"\n mkdir -p $output/src\n cp -r src/* $output/src/\n cp $0 $output/run.bash\n\n PYTHONPATH=$PYTHONPATH:./src python -m torch.distributed.launch \\\n --master_port=$((1000 + RANDOM % 9999)) --nproc_per_node=8 --use_env src/main.py --coco_path data/wireframe_processed \\\n --output_dir $output --LETRpost --backbone resnet50 --layer1_frozen --resume exp/res50_stage2/checkpoints/checkpoint.pth \\\n --no_opt --batch_size 1 --epochs 25 --lr_drop 25 --num_queries 1000 --num_gpus 8 --lr 1e-5 --label_loss_func focal_loss \\\n --label_loss_params '{\"gamma\":2.0}' --save_freq 1 | tee -a $output/history.txt \n\nelse\n echo \"folder already exist\"\nfi\n\n\n\n"
},
{
"path": "script/train/a5_train_stage2_focal_res101.sh",
"content": "# Fail the script if there is any failure\nset -e\n\nif [[ $# -eq 0 ]] ; then\n echo 'Require Experiment Name'\n exit 1\nfi\n\n# The name of this experiment.\nname=$1\n\n# Save logs and models under snap/gqa; make backup.\noutput=exp/$name\nif [ ! -d \"$output\" ]; then\n echo \"folder not exist\"\n mkdir -p $output/src\n cp -r src/* $output/src/\n cp $0 $output/run.bash\n\n PYTHONPATH=$PYTHONPATH:./src python -m torch.distributed.launch \\\n --master_port=$((1000 + RANDOM % 9999)) --nproc_per_node=4 --use_env src/main.py --coco_path data/wireframe_processed \\\n --output_dir $output --LETRpost --backbone resnet101 --layer1_frozen --resume exp/res101_stage2/checkpoints/checkpoint.pth --no_opt \\\n --batch_size 1 --epochs 25 --lr_drop 25 --num_queries 1000 --num_gpus 4 --lr 1e-5 --label_loss_func focal_loss \\\n --label_loss_params '{\"gamma\":2.0}' --save_freq 1 | tee -a $output/history.txt \n\nelse\n echo \"folder already exist\"\nfi\n\n\n\n"
},
{
"path": "src/args.py",
"content": "import argparse\n\n\ndef get_args_parser():\n parser = argparse.ArgumentParser('Set transformer detector', add_help=False)\n parser.add_argument('--lr', default=1e-4, type=float)\n parser.add_argument('--lr_backbone', default=1e-5, type=float)\n parser.add_argument('--batch_size', default=2, type=int)\n parser.add_argument('--weight_decay', default=1e-4, type=float)\n parser.add_argument('--epochs', default=300, type=int)\n parser.add_argument('--lr_drop', default=200, type=int)\n parser.add_argument('--clip_max_norm', default=0.1, type=float,\n help='gradient clipping max norm')\n parser.add_argument('--save_freq', default=5, type=int)\n \n parser.add_argument('--benchmark', action='store_true',\n help=\"Train segmentation head if the flag is provided\")\n parser.add_argument('--append_word', default=None, type=str, help=\"Name of the convolutional backbone to use\")\n # Model parameters\n # * Backbone\n parser.add_argument('--backbone', default='resnet50', type=str,\n help=\"Name of the convolutional backbone to use\")\n parser.add_argument('--dilation', action='store_true',\n help=\"If true, we replace stride with dilation in the last convolutional block (DC5)\")\n parser.add_argument('--position_embedding', default='sine', type=str, choices=('sine', 'learned'),\n help=\"Type of positional embedding to use on top of the image features\")\n\n # Load\n parser.add_argument('--layer1_frozen', action='store_true')\n parser.add_argument('--layer2_frozen', action='store_true')\n\n parser.add_argument('--frozen_weights', default='', help='resume from checkpoint')\n parser.add_argument('--resume', default='', help='resume from checkpoint')\n parser.add_argument('--no_opt', action='store_true')\n\n # Transformer\n parser.add_argument('--LETRpost', action='store_true')\n parser.add_argument('--layer1_num', default=3, type=int)\n parser.add_argument('--layer2_num', default=2, type=int)\n\n # First Transformer\n parser.add_argument('--enc_layers', default=6, type=int,\n help=\"Number of encoding layers in the transformer\")\n parser.add_argument('--dec_layers', default=6, type=int,\n help=\"Number of decoding layers in the transformer\")\n parser.add_argument('--dim_feedforward', default=2048, type=int,\n help=\"Intermediate size of the feedforward layers in the transformer blocks\")\n parser.add_argument('--hidden_dim', default=256, type=int,\n help=\"Size of the embeddings (dimension of the transformer)\")\n parser.add_argument('--dropout', default=0.1, type=float,\n help=\"Dropout applied in the transformer\")\n parser.add_argument('--nheads', default=8, type=int,\n help=\"Number of attention heads inside the transformer's attentions\")\n parser.add_argument('--num_queries', default=1000, type=int,\n help=\"Number of query slots\")\n parser.add_argument('--pre_norm', action='store_true')\n\n\n # Second Transformer\n parser.add_argument('--second_enc_layers', default=6, type=int,\n help=\"Number of encoding layers in the transformer\")\n parser.add_argument('--second_dec_layers', default=6, type=int,\n help=\"Number of decoding layers in the transformer\")\n parser.add_argument('--second_dim_feedforward', default=2048, type=int,\n help=\"Intermediate size of the feedforward layers in the transformer blocks\")\n parser.add_argument('--second_hidden_dim', default=256, type=int,\n help=\"Size of the embeddings (dimension of the transformer)\")\n parser.add_argument('--second_dropout', default=0.1, type=float,\n help=\"Dropout applied in the transformer\")\n parser.add_argument('--second_nheads', default=8, type=int,\n help=\"Number of attention heads inside the transformer's attentions\")\n parser.add_argument('--second_pre_norm', action='store_true')\n # Loss\n parser.add_argument('--no_aux_loss', dest='aux_loss', action='store_false',\n help=\"Disables auxiliary decoding losses (loss at each layer)\")\n # * Matcher\n parser.add_argument('--set_cost_class', default=1, type=float,\n help=\"Class coefficient in the matching cost\")\n parser.add_argument('--set_cost_line', default=5, type=float,\n help=\"L1 box coefficient in the matching cost\")\n parser.add_argument('--set_cost_point', default=5, type=float,\n help=\"L1 box coefficient in the matching cost\")\n\n # * Loss coefficients\n parser.add_argument('--dice_loss_coef', default=1, type=float)\n parser.add_argument('--point_loss_coef', default=5, type=float)\n parser.add_argument('--line_loss_coef', default=5, type=float)\n parser.add_argument('--eos_coef', default=0.1, type=float)\n parser.add_argument('--label_loss_func', default='cross_entropy', type=str)\n parser.add_argument('--label_loss_params', default='{}', type=str)\n\n # dataset parameters\n parser.add_argument('--dataset_file', default='coco')\n parser.add_argument('--coco_path', type=str)\n parser.add_argument('--coco_panoptic_path', type=str)\n parser.add_argument('--remove_difficult', action='store_true')\n\n parser.add_argument('--output_dir', default='',\n help='path where to save, empty for no saving')\n parser.add_argument('--device', default='cuda',\n help='device to use for training / testing')\n parser.add_argument('--seed', default=42, type=int)\n \n\n parser.add_argument('--start_epoch', default=0, type=int, metavar='N',\n help='start epoch')\n parser.add_argument('--num_workers', default=2, type=int)\n parser.add_argument('--num_gpus', default=1, type=int)\n # distributed training parameters\n parser.add_argument('--world_size', default=1, type=int,\n help='number of distributed processes')\n parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')\n\n\n parser.add_argument('--eval', action='store_true')\n parser.add_argument('--dataset', default='train', type=str, choices=('train', 'val'))\n\n return parser"
},
{
"path": "src/datasets/__init__.py",
"content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\nimport torch.utils.data\nimport torchvision\n\nfrom .coco import build as build_coco\n\n\ndef get_coco_api_from_dataset(dataset):\n for _ in range(10):\n # if isinstance(dataset, torchvision.datasets.CocoDetection):\n # break\n if isinstance(dataset, torch.utils.data.Subset):\n dataset = dataset.dataset\n if isinstance(dataset, torchvision.datasets.CocoDetection):\n return dataset.coco\n\n\ndef build_dataset(image_set, args):\n\n return build_coco(image_set, args)\n"
},
{
"path": "src/datasets/coco.py",
"content": "\"\"\"\nModified based on Detr: https://github.com/facebookresearch/detr/blob/master/datasets/coco.py\n\"\"\"\nfrom pathlib import Path\n\nimport torch\nimport torch.utils.data\nimport torchvision\n\nimport datasets.transforms as T\nimport math\nimport numpy as np\n\nclass CocoDetection(torchvision.datasets.CocoDetection):\n def __init__(self, img_folder, ann_file, transforms, args):\n super(CocoDetection, self).__init__(img_folder, ann_file)\n self._transforms = transforms\n self.prepare = ConvertCocoPolysToMask() \n self.args = args\n\n def __getitem__(self, idx):\n img, target = super(CocoDetection, self).__getitem__(idx)\n image_id = self.ids[idx]\n target = {'image_id': image_id, 'annotations': target}\n img, target = self.prepare(img, target, self.args)\n if self._transforms is not None:\n img, target = self._transforms(img, target)\n return img, target\n\nclass ConvertCocoPolysToMask(object):\n\n def __call__(self, image, target, args):\n w, h = image.size\n\n image_id = target[\"image_id\"]\n image_id = torch.tensor([image_id])\n\n anno = target[\"annotations\"]\n\n anno = [obj for obj in anno]\n \n lines = [obj[\"line\"] for obj in anno]\n lines = torch.as_tensor(lines, dtype=torch.float32).reshape(-1, 4)\n\n lines[:, 2:] += lines[:, :2] #xyxy\n\n lines[:, 0::2].clamp_(min=0, max=w)\n lines[:, 1::2].clamp_(min=0, max=h)\n\n classes = [obj[\"category_id\"] for obj in anno]\n classes = torch.tensor(classes, dtype=torch.int64)\n\n target = {}\n target[\"lines\"] = lines\n \n\n target[\"labels\"] = classes\n \n target[\"image_id\"] = image_id\n\n # for conversion to coco api\n area = torch.tensor([obj[\"area\"] for obj in anno])\n iscrowd = torch.tensor([obj[\"iscrowd\"] if \"iscrowd\" in obj else 0 for obj in anno])\n target[\"area\"] = area\n target[\"iscrowd\"] = iscrowd\n\n target[\"orig_size\"] = torch.as_tensor([int(h), int(w)])\n target[\"size\"] = torch.as_tensor([int(h), int(w)])\n\n return image, target\n\n\ndef make_coco_transforms(image_set, args):\n\n normalize = T.Compose([\n T.ToTensor(),\n T.Normalize([0.538, 0.494, 0.453], [0.257, 0.263, 0.273])\n ])\n\n scales = [480, 512, 544, 576, 608, 640, 672, 680, 690, 704, 736, 768, 788, 800]\n test_size = 1100\n max = 1333 \n\n if args.eval:\n return T.Compose([\n T.RandomResize([test_size], max_size=max),\n normalize,\n ])\n else:\n if image_set == 'train':\n return T.Compose([\n T.RandomSelect(\n T.RandomHorizontalFlip(),\n T.RandomVerticalFlip(),\n ),\n T.RandomSelect(\n T.RandomResize(scales, max_size=max),\n T.Compose([\n T.RandomResize([400, 500, 600]),\n T.RandomSizeCrop(384, 600),\n T.RandomResize(scales, max_size=max),\n ])\n ),\n T.ColorJitter(),\n normalize,\n ])\n\n if image_set == 'val':\n return T.Compose([\n T.RandomResize([test_size], max_size=max),\n normalize,\n ])\n\n raise ValueError(f'unknown {image_set}')\n\ndef build(image_set, args):\n root = Path(args.coco_path)\n assert root.exists(), f'provided COCO path {root} does not exist'\n mode = 'lines'\n\n if args.eval:\n PATHS = {\n \"train\": (root / \"train2017\", root / \"annotations\" / f'{mode}_train2017.json'),\n \"val\": (root / \"val2017\", root / \"annotations\" / f'{mode}_val2017.json'),\n } \n else:\n PATHS = {\n \"train\": (root / \"train2017\", root / \"annotations\" / f'{mode}_train2017.json'),\n \"val\": (root / \"val2017\", root / \"annotations\" / f'{mode}_val2017.json'),\n }\n\n img_folder, ann_file = PATHS[image_set]\n dataset = CocoDetection(img_folder, ann_file, transforms=make_coco_transforms(image_set, args), args=args)\n return dataset\n"
},
{
"path": "src/datasets/transforms.py",
"content": "\"\"\"\nTransforms and data augmentation for both image + line.\nmodfied based on https://github.com/facebookresearch/detr/blob/master/datasets/transforms.py\n\"\"\"\nimport random\n\nimport PIL\nimport torch\nimport torchvision.transforms as T\nimport torchvision.transforms.functional as F\nimport numbers\nimport warnings\nfrom typing import Tuple, List, Optional\nfrom PIL import Image\nfrom torch import Tensor\nimport math\n\nfrom util.misc import interpolate\nimport numpy as np\n\ndef crop(image, target, region):\n cropped_image = F.crop(image, *region)\n\n target = target.copy()\n i, j, h, w = region\n\n # should we do something wrt the original size?\n target[\"size\"] = torch.tensor([h, w])\n\n fields = [\"labels\", \"area\", \"iscrowd\"]\n\n if \"lines\" in target:\n lines = target[\"lines\"]\n cropped_lines = lines - torch.as_tensor([j, i, j, i])\n \n eps = 1e-12\n\n # In dataset, we assume the left point has smaller x coord\n remove_x_min = cropped_lines[:, 2] < 0\n remove_x_max = cropped_lines[:, 0] > w\n remove_x = torch.logical_or(remove_x_min, remove_x_max)\n keep_x = ~remove_x\n\n # there is no assumption on y, so remove lines that have both y coord out of bound\n remove_y_min = torch.logical_and(cropped_lines[:, 1] < 0, cropped_lines[:, 3] < 0)\n remove_y_max = torch.logical_and(cropped_lines[:, 1] > h, cropped_lines[:, 3] > h)\n remove_y = torch.logical_or(remove_y_min, remove_y_max)\n keep_y = ~remove_y\n\n keep = torch.logical_and(keep_x, keep_y)\n cropped_lines = cropped_lines[keep]\n clamped_lines = torch.zeros_like(cropped_lines)\n\n for i,line in enumerate(cropped_lines):\n x1, y1, x2, y2 = line\n slope = (y2 - y1) / (x2 - x1 + eps)\n if x1 < 0:\n x1 = 0\n y1 = y2 + (x1 - x2) * slope\n if y1 < 0:\n y1 = 0\n x1 = x2 - (y2 - y1) / slope\n if x2 > w:\n x2 = w\n y2 = y1 + (x2 - x1) * slope\n if y2 > h:\n y2 = h\n x2 = x1 + (y2 - y1) / slope\n\n clamped_lines[i, :] = torch.tensor([x1, y1, x2, y2])\n\n target[\"lines\"] = clamped_lines\n \n for field in fields:\n target[field] = target[field][keep]\n return cropped_image, target\n\n\ndef hflip(image, target):\n flipped_image = F.hflip(image)\n\n w, h = image.size\n\n target = target.copy()\n \n if \"lines\" in target:\n lines = target[\"lines\"] \n lines = lines[:, [2, 3, 0, 1]] * torch.as_tensor([-1, 1, -1, 1]) + torch.as_tensor([w, 0, w, 0])\n target[\"lines\"] = lines\n\n\n return flipped_image, target\n\n\ndef vflip(image, target):\n flipped_image = F.vflip(image)\n\n w, h = image.size\n\n target = target.copy()\n\n if \"lines\" in target:\n lines = target[\"lines\"]\n\n # in dataset, we assume if two points with same x coord, we assume first point is the upper point\n lines = lines * torch.as_tensor([1, -1, 1, -1]) + torch.as_tensor([0, h, 0, h])\n vertical_line_idx = (lines[:, 0] == lines[:, 2])\n lines[vertical_line_idx] = torch.index_select(lines[vertical_line_idx], 1, torch.tensor([2,3,0,1]))\n target[\"lines\"] = lines\n\n return flipped_image, target\n\n\ndef ccw_rotation(image, target):\n rotateded_image = F.rotate(image, 90, expand=True)\n w, h = rotateded_image.size\n\n target = target.copy()\n\n target[\"size\"] = torch.tensor([h, w])\n\n if \"lines\" in target:\n lines = target[\"lines\"]\n lines = lines[:, [1, 0, 3, 2]] * torch.as_tensor([1, -1, 1, -1]) + torch.as_tensor([0, h, 0, h])\n # in dataset, we assume the first point is the left point\n x_switch_idx = lines[:, 0] > lines[:, 2]\n lines[x_switch_idx] = torch.index_select(lines[x_switch_idx], 1, torch.tensor([2,3,0,1]))\n\n # in dataset, if two points have same x coord, we assume the first point is the upper point\n y_switch_idx = torch.logical_and(lines[:, 0] == lines[:, 2], lines[:, 1] > lines[:, 3])\n lines[y_switch_idx] = torch.index_select(lines[y_switch_idx], 1, torch.tensor([2,3,0,1]))\n\n target[\"lines\"] = lines\n\n return rotateded_image, target\n\n\ndef cw_rotation(image, target):\n rotateded_image = F.rotate(image, -90, expand=True)\n w, h = rotateded_image.size\n\n target = target.copy()\n\n target[\"size\"] = torch.tensor([h, w])\n\n if \"lines\" in target:\n lines = target[\"lines\"]\n lines = lines[:, [1, 0, 3, 2]] * torch.as_tensor([-1, 1, -1, 1]) + torch.as_tensor([w, 0, w, 0])\n \n # in dataset, we assume the first point is the left point\n x_switch_idx = lines[:, 0] > lines[:, 2]\n lines[x_switch_idx] = torch.index_select(\n lines[x_switch_idx], 1, torch.tensor([2, 3, 0, 1]))\n\n # in dataset, if two points have same x coord, we assume the first point is the upper point\n y_switch_idx = torch.logical_and(\n lines[:, 0] == lines[:, 2], lines[:, 1] > lines[:, 3])\n lines[y_switch_idx] = torch.index_select(\n lines[y_switch_idx], 1, torch.tensor([2, 3, 0, 1]))\n\n target[\"lines\"] = lines\n\n return rotateded_image, target\n\ndef resize(image, target, size, max_size=None):\n # size can be min_size (scalar) or (w, h) tuple\n\n def get_size_with_aspect_ratio(image_size, size, max_size=None):\n w, h = image_size\n if max_size is not None:\n min_original_size = float(min((w, h)))\n max_original_size = float(max((w, h)))\n if max_original_size / min_original_size * size > max_size:\n size = int(round(max_size * min_original_size / max_original_size))\n\n if (w <= h and w == size) or (h <= w and h == size):\n return (h, w)\n\n if w < h:\n ow = size\n oh = int(size * h / w)\n else:\n oh = size\n ow = int(size * w / h)\n\n return (oh, ow)\n\n def get_size(image_size, size, max_size=None):\n if isinstance(size, (list, tuple)):\n return size[::-1]\n else:\n return get_size_with_aspect_ratio(image_size, size, max_size)\n\n size = get_size(image.size, size, max_size)\n rescaled_image = F.resize(image, size)\n\n if target is None:\n return rescaled_image, None\n\n ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size))\n ratio_width, ratio_height = ratios\n\n target = target.copy()\n \n if \"lines\" in target:\n lines = target[\"lines\"]\n scaled_lines = lines * torch.as_tensor([ratio_width, ratio_height, ratio_width, ratio_height])\n target[\"lines\"] = scaled_lines\n h, w = size\n target[\"size\"] = torch.tensor([h, w])\n\n return rescaled_image, target\n\n\ndef pad(image, target, padding):\n assert False\n # assumes that we only pad on the bottom right corners\n padded_image = F.pad(image, (0, 0, padding[0], padding[1]))\n if target is None:\n return padded_image, None\n target = target.copy()\n # should we do something wrt the original size?\n target[\"size\"] = torch.tensor(padded_image.size[::-1])\n if \"masks\" in target:\n target['masks'] = torch.nn.functional.pad(target['masks'], (0, padding[0], 0, padding[1]))\n return padded_image, target\n\n\nclass RandomCrop(object):\n def __init__(self, size):\n self.size = size\n\n def __call__(self, img, target):\n region = T.RandomCrop.get_params(img, self.size)\n return crop(img, target, region)\n\n\nclass RandomSizeCrop(object):\n def __init__(self, min_size: int, max_size: int):\n self.min_size = min_size\n self.max_size = max_size\n\n def __call__(self, img: PIL.Image.Image, target: dict):\n w = random.randint(self.min_size, min(img.width, self.max_size))\n h = random.randint(self.min_size, min(img.height, self.max_size))\n region = T.RandomCrop.get_params(img, [h, w])\n return crop(img, target, region)\n\n\nclass CenterCrop(object):\n def __init__(self, size):\n self.size = size\n\n def __call__(self, img, target):\n image_width, image_height = img.size\n crop_height, crop_width = self.size\n crop_top = int(round((image_height - crop_height) / 2.))\n crop_left = int(round((image_width - crop_width) / 2.))\n return crop(img, target, (crop_top, crop_left, crop_height, crop_width))\n\n\nclass RandomHorizontalFlip(object):\n def __init__(self, p=0.5):\n self.p = p\n\n def __call__(self, img, target):\n if random.random() < self.p:\n return hflip(img, target)\n return img, target\n\nclass RandomVerticalFlip(object):\n def __init__(self, p=0.5):\n self.p = p\n\n def __call__(self, img, target):\n if random.random() < self.p:\n return vflip(img, target)\n return img, target\n\nclass RandomCounterClockwiseRotation(object):\n def __init__(self, p=0.5):\n self.p = p\n\n def __call__(self, img, target):\n if random.random() < self.p:\n return ccw_rotation(img, target)\n return img, target\n\nclass RandomClockwiseRotation(object):\n def __init__(self, p=0.5):\n self.p = p\n\n def __call__(self, img, target):\n if random.random() < self.p:\n return cw_rotation(img, target)\n return img, target\n \nclass RandomResize(object):\n def __init__(self, sizes, max_size=None):\n assert isinstance(sizes, (list, tuple))\n self.sizes = sizes\n self.max_size = max_size\n\n def __call__(self, img, target=None):\n size = random.choice(self.sizes)\n return resize(img, target, size, self.max_size)\n\n\nclass RandomPad(object):\n def __init__(self, max_pad):\n self.max_pad = max_pad\n\n def __call__(self, img, target):\n pad_x = random.randint(0, self.max_pad)\n pad_y = random.randint(0, self.max_pad)\n return pad(img, target, (pad_x, pad_y))\n\nclass RandomErasing(object):\n def __init__(self, p=0.5, scale=(0.02, 0.33), ratio=(0.3, 3.3), value=0, fill=False):\n\n\n if not isinstance(value, (numbers.Number, str, tuple, list)):\n raise TypeError(\"Argument value should be either a number or str or a sequence\")\n if isinstance(value, str) and value != \"random\":\n raise ValueError(\"If value is str, it should be 'random'\")\n if not isinstance(scale, (tuple, list)):\n raise TypeError(\"Scale should be a sequence\")\n if not isinstance(ratio, (tuple, list)):\n raise TypeError(\"Ratio should be a sequence\")\n if (scale[0] > scale[1]) or (ratio[0] > ratio[1]):\n warnings.warn(\"Scale and ratio should be of kind (min, max)\")\n if scale[0] < 0 or scale[1] > 1:\n raise ValueError(\"Scale should be between 0 and 1\")\n if p < 0 or p > 1:\n raise ValueError(\"Random erasing probability should be between 0 and 1\")\n\n self.p = p\n self.scale = scale\n self.ratio = ratio\n self.value = value\n\n @staticmethod\n def get_params(img: Tensor, scale: Tuple[float, float], ratio: Tuple[float, float], value: Optional[List[float]] = None\n ) -> Tuple[int, int, int, int, Tensor]:\n\n if isinstance(img, Tensor):\n img_c, img_h, img_w = img.shape[-3], img.shape[-2], img.shape[-1]\n area = img_h * img_w\n elif isinstance(img, Image.Image):\n img_c = 3\n img_w, img_h = img.size\n area = img_h * img_w\n else:\n raise TypeError(\"img is not type Tensor or Image\")\n\n for _ in range(10):\n erase_area = area * torch.empty(1).uniform_(scale[0], scale[1]).item()\n aspect_ratio = torch.empty(1).uniform_(ratio[0], ratio[1]).item()\n\n h = int(round(math.sqrt(erase_area * aspect_ratio)))\n w = int(round(math.sqrt(erase_area / aspect_ratio)))\n if not (h < img_h and w < img_w):\n continue\n\n if value is None:\n v = torch.empty([img_c, h, w], dtype=torch.float32).normal_()\n else:\n v = torch.tensor(value)[:, None, None]\n\n i = torch.randint(0, img_h - h + 1, size=(1, )).item()\n j = torch.randint(0, img_w - w + 1, size=(1, )).item()\n return i, j, h, w, v\n\n # Return original image\n return 0, 0, img_h, img_w, img\n\n def __call__(self, img, target):\n i, j, h, w, v = RandomErasing.get_params(img, self.scale, self.ratio)\n img_tensor = torch.tensor(np.transpose(np.asarray(img), (2, 0, 1)))\n new_img = F.erase(img_tensor, i, j, h, w, v)\n new_img = new_img.numpy()\n new_img = Image.fromarray(new_img)\n return new_img, target\n\nclass ColorJitter(object):\n def __init__(self, brightness=0.4, contrast=0.4, saturation=0.4, hue=0.4):\n self.brightness = self._check_input(brightness, 'brightness')\n self.contrast = self._check_input(contrast, 'contrast')\n self.saturation = self._check_input(saturation, 'saturation')\n self.hue = self._check_input(hue, 'hue', center=0, bound=(-0.5, 0.5),\n clip_first_on_zero=False)\n\n def _check_input(self, value, name, center=1, bound=(0, float('inf')), clip_first_on_zero=True):\n if isinstance(value, numbers.Number):\n if value < 0:\n raise ValueError(\"If {} is a single number, it must be non negative.\".format(name))\n value = [center - float(value), center + float(value)]\n if clip_first_on_zero:\n value[0] = max(value[0], 0.0)\n elif isinstance(value, (tuple, list)) and len(value) == 2:\n if not bound[0] <= value[0] <= value[1] <= bound[1]:\n raise ValueError(\"{} values should be between {}\".format(name, bound))\n else:\n raise TypeError(\"{} should be a single number or a list/tuple with lenght 2.\".format(name))\n\n # if value is 0 or (1., 1.) for brightness/contrast/saturation\n # or (0., 0.) for hue, do nothing\n if value[0] == value[1] == center:\n value = None\n return value\n\n def __call__(self, img, target):\n fn_idx = torch.randperm(4)\n for fn_id in fn_idx:\n if fn_id == 0 and self.brightness is not None:\n brightness = self.brightness\n brightness_factor = torch.tensor(1.0).uniform_(brightness[0], brightness[1]).item()\n img = F.adjust_brightness(img, brightness_factor)\n\n if fn_id == 1 and self.contrast is not None:\n contrast = self.contrast\n contrast_factor = torch.tensor(1.0).uniform_(contrast[0], contrast[1]).item()\n img = F.adjust_contrast(img, contrast_factor)\n\n if fn_id == 2 and self.saturation is not None:\n saturation = self.saturation\n saturation_factor = torch.tensor(1.0).uniform_(saturation[0], saturation[1]).item()\n img = F.adjust_saturation(img, saturation_factor)\n\n if fn_id == 3 and self.hue is not None:\n hue = self.hue\n hue_factor = torch.tensor(1.0).uniform_(hue[0], hue[1]).item()\n img = F.adjust_hue(img, hue_factor)\n\n return img, target\n\nclass RandomSelect(object):\n def __init__(self, transforms1, transforms2, p=0.5):\n self.transforms1 = transforms1\n self.transforms2 = transforms2\n self.p = p\n\n def __call__(self, img, target):\n if random.random() < self.p:\n return self.transforms1(img, target)\n return self.transforms2(img, target)\n\n\nclass ToTensor(object):\n def __call__(self, img, target):\n return F.to_tensor(img), target\n\nclass Normalize(object):\n def __init__(self, mean, std):\n self.mean = mean\n self.std = std\n\n def __call__(self, image, target=None):\n image = F.normalize(image, mean=self.mean, std=self.std)\n if target is None:\n return image, None\n target = target.copy()\n h, w = image.shape[-2:]\n\n if \"lines\" in target:\n lines = target[\"lines\"]\n lines = lines / torch.tensor([w, h, w, h], dtype=torch.float32)\n target[\"lines\"] = lines\n\n return image, target\n\n\nclass Compose(object):\n def __init__(self, transforms):\n self.transforms = transforms\n\n def __call__(self, image, target):\n for t in self.transforms:\n image, target = t(image, target)\n return image, target\n\n def __repr__(self):\n format_string = self.__class__.__name__ + \"(\"\n for t in self.transforms:\n format_string += \"\\n\"\n format_string += \" {0}\".format(t)\n format_string += \"\\n)\"\n return format_string\n"
},
{
"path": "src/demo_letr.ipynb",
"content": "{\n \"metadata\": {\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\": \"3.7.9\"\n },\n \"orig_nbformat\": 2,\n \"kernelspec\": {\n \"name\": \"python3\",\n \"display_name\": \"Python 3.7.9 64-bit ('detr': conda)\",\n \"metadata\": {\n \"interpreter\": {\n \"hash\": \"410c54daf323f5212ce889dbd0c7b13970b5bad95aeecb5b05a0f5b22af8bc3f\"\n }\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2,\n \"cells\": [\n {\n \"source\": [\n \"# LETR Basic Usage Demo\"\n ],\n \"cell_type\": \"markdown\",\n \"metadata\": {}\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import torch\\n\",\n \"import cv2\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.rcParams['figure.dpi'] = 200\\n\",\n \"import torchvision.transforms.functional as functional\\n\",\n \"import torch.nn.functional as F\\n\",\n \"from models import build_model\\n\",\n \"from util.misc import nested_tensor_from_tensor_list\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"class Compose(object):\\n\",\n \" def __init__(self, transforms):\\n\",\n \" self.transforms = transforms\\n\",\n \"\\n\",\n \" def __call__(self, image):\\n\",\n \" for t in self.transforms:\\n\",\n \" image = t(image)\\n\",\n \" return image\\n\",\n \"\\n\",\n \" def __repr__(self):\\n\",\n \" format_string = self.__class__.__name__ + \\\"(\\\"\\n\",\n \" for t in self.transforms:\\n\",\n \" format_string += \\\"\\\\n\\\"\\n\",\n \" format_string += \\\" {0}\\\".format(t)\\n\",\n \" format_string += \\\"\\\\n)\\\"\\n\",\n \" return format_string\\n\",\n \"\\n\",\n \"class Normalize(object):\\n\",\n \" def __init__(self, mean, std):\\n\",\n \" self.mean = mean\\n\",\n \" self.std = std\\n\",\n \"\\n\",\n \" def __call__(self, image):\\n\",\n \" image = functional.normalize(image, mean=self.mean, std=self.std)\\n\",\n \" return image\\n\",\n \"\\n\",\n \"class ToTensor(object):\\n\",\n \" def __call__(self, img):\\n\",\n \" return functional.to_tensor(img)\\n\",\n \"\\n\",\n \"def resize(image, size, max_size=None):\\n\",\n \" # size can be min_size (scalar) or (w, h) tuple\\n\",\n \" def get_size_with_aspect_ratio(image_size, size, max_size=None):\\n\",\n \" w, h = image_size\\n\",\n \" if max_size is not None:\\n\",\n \" min_original_size = float(min((w, h)))\\n\",\n \" max_original_size = float(max((w, h)))\\n\",\n \" if max_original_size / min_original_size * size > max_size:\\n\",\n \" size = int(round(max_size * min_original_size / max_original_size))\\n\",\n \" if (w <= h and w == size) or (h <= w and h == size):\\n\",\n \" return (h, w)\\n\",\n \" if w < h:\\n\",\n \" ow = size\\n\",\n \" oh = int(size * h / w)\\n\",\n \" else:\\n\",\n \" oh = size\\n\",\n \" ow = int(size * w / h)\\n\",\n \" return (oh, ow)\\n\",\n \"\\n\",\n \" def get_size(image_size, size, max_size=None):\\n\",\n \" if isinstance(size, (list, tuple)):\\n\",\n \" return size[::-1]\\n\",\n \" else:\\n\",\n \" return get_size_with_aspect_ratio(image_size, size, max_size)\\n\",\n \"\\n\",\n \" size = get_size(image.size, size, max_size)\\n\",\n \" rescaled_image = functional.resize(image, size)\\n\",\n \"\\n\",\n \" return rescaled_image\\n\",\n \"\\n\",\n \"class Resize(object):\\n\",\n \" def __init__(self, sizes, max_size=None):\\n\",\n \" assert isinstance(sizes, (list, tuple))\\n\",\n \" self.sizes = sizes\\n\",\n \" self.max_size = max_size\\n\",\n \"\\n\",\n \" def __call__(self, img):\\n\",\n \" size = self.sizes\\n\",\n \" return resize(img, size, self.max_size)\\n\",\n \"\\n\"\n ]\n },\n {\n \"source\": [\n \"## Load Model Pre-trained Weights\"\n ],\n \"cell_type\": \"markdown\",\n \"metadata\": {}\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \"LETRstack(\\n\",\n \" (letr): LETR(\\n\",\n \" (transformer): Transformer(\\n\",\n \" (encoder): TransformerEncoder(\\n\",\n \" (layers): ModuleList(\\n\",\n \" (0): TransformerEncoderLayer(\\n\",\n \" (self_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (linear1): Linear(in_features=256, out_features=2048, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" (linear2): Linear(in_features=2048, out_features=256, bias=True)\\n\",\n \" (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (dropout1): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout2): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" (1): TransformerEncoderLayer(\\n\",\n \" (self_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (linear1): Linear(in_features=256, out_features=2048, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" (linear2): Linear(in_features=2048, out_features=256, bias=True)\\n\",\n \" (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (dropout1): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout2): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" (2): TransformerEncoderLayer(\\n\",\n \" (self_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (linear1): Linear(in_features=256, out_features=2048, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" (linear2): Linear(in_features=2048, out_features=256, bias=True)\\n\",\n \" (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (dropout1): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout2): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" (3): TransformerEncoderLayer(\\n\",\n \" (self_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (linear1): Linear(in_features=256, out_features=2048, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" (linear2): Linear(in_features=2048, out_features=256, bias=True)\\n\",\n \" (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (dropout1): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout2): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" (4): TransformerEncoderLayer(\\n\",\n \" (self_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (linear1): Linear(in_features=256, out_features=2048, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" (linear2): Linear(in_features=2048, out_features=256, bias=True)\\n\",\n \" (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (dropout1): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout2): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" (5): TransformerEncoderLayer(\\n\",\n \" (self_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (linear1): Linear(in_features=256, out_features=2048, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" (linear2): Linear(in_features=2048, out_features=256, bias=True)\\n\",\n \" (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (dropout1): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout2): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (decoder): TransformerDecoder(\\n\",\n \" (layers): ModuleList(\\n\",\n \" (0): TransformerDecoderLayer(\\n\",\n \" (self_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (multihead_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (linear1): Linear(in_features=256, out_features=2048, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" (linear2): Linear(in_features=2048, out_features=256, bias=True)\\n\",\n \" (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (dropout1): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout2): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout3): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" (1): TransformerDecoderLayer(\\n\",\n \" (self_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (multihead_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (linear1): Linear(in_features=256, out_features=2048, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" (linear2): Linear(in_features=2048, out_features=256, bias=True)\\n\",\n \" (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (dropout1): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout2): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout3): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" (2): TransformerDecoderLayer(\\n\",\n \" (self_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (multihead_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (linear1): Linear(in_features=256, out_features=2048, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" (linear2): Linear(in_features=2048, out_features=256, bias=True)\\n\",\n \" (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (dropout1): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout2): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout3): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" (3): TransformerDecoderLayer(\\n\",\n \" (self_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (multihead_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (linear1): Linear(in_features=256, out_features=2048, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" (linear2): Linear(in_features=2048, out_features=256, bias=True)\\n\",\n \" (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (dropout1): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout2): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout3): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" (4): TransformerDecoderLayer(\\n\",\n \" (self_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (multihead_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (linear1): Linear(in_features=256, out_features=2048, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" (linear2): Linear(in_features=2048, out_features=256, bias=True)\\n\",\n \" (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (dropout1): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout2): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout3): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" (5): TransformerDecoderLayer(\\n\",\n \" (self_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (multihead_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (linear1): Linear(in_features=256, out_features=2048, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" (linear2): Linear(in_features=2048, out_features=256, bias=True)\\n\",\n \" (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (dropout1): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout2): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout3): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (class_embed): Linear(in_features=256, out_features=2, bias=True)\\n\",\n \" (lines_embed): MLP(\\n\",\n \" (layers): ModuleList(\\n\",\n \" (0): Linear(in_features=256, out_features=256, bias=True)\\n\",\n \" (1): Linear(in_features=256, out_features=256, bias=True)\\n\",\n \" (2): Linear(in_features=256, out_features=4, bias=True)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (query_embed): Embedding(1000, 256)\\n\",\n \" (input_proj): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))\\n\",\n \" (backbone): Joiner(\\n\",\n \" (0): Backbone(\\n\",\n \" (body): IntermediateLayerGetter(\\n\",\n \" (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\\n\",\n \" (layer1): Sequential(\\n\",\n \" (0): Bottleneck(\\n\",\n \" (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" (downsample): Sequential(\\n\",\n \" (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (1): FrozenBatchNorm2d()\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (1): Bottleneck(\\n\",\n \" (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (2): Bottleneck(\\n\",\n \" (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (layer2): Sequential(\\n\",\n \" (0): Bottleneck(\\n\",\n \" (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" (downsample): Sequential(\\n\",\n \" (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\\n\",\n \" (1): FrozenBatchNorm2d()\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (1): Bottleneck(\\n\",\n \" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (2): Bottleneck(\\n\",\n \" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (3): Bottleneck(\\n\",\n \" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (layer3): Sequential(\\n\",\n \" (0): Bottleneck(\\n\",\n \" (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" (downsample): Sequential(\\n\",\n \" (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\\n\",\n \" (1): FrozenBatchNorm2d()\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (1): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (2): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (3): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (4): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (5): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (6): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (7): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (8): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (9): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (10): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (11): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (12): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (13): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (14): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (15): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (16): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (17): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (18): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (19): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (20): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (21): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (22): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (layer4): Sequential(\\n\",\n \" (0): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" (downsample): Sequential(\\n\",\n \" (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\\n\",\n \" (1): FrozenBatchNorm2d()\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (1): Bottleneck(\\n\",\n \" (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (2): Bottleneck(\\n\",\n \" (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (1): PositionEmbeddingSine()\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (backbone): Joiner(\\n\",\n \" (0): Backbone(\\n\",\n \" (body): IntermediateLayerGetter(\\n\",\n \" (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\\n\",\n \" (layer1): Sequential(\\n\",\n \" (0): Bottleneck(\\n\",\n \" (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" (downsample): Sequential(\\n\",\n \" (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (1): FrozenBatchNorm2d()\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (1): Bottleneck(\\n\",\n \" (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (2): Bottleneck(\\n\",\n \" (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (layer2): Sequential(\\n\",\n \" (0): Bottleneck(\\n\",\n \" (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" (downsample): Sequential(\\n\",\n \" (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\\n\",\n \" (1): FrozenBatchNorm2d()\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (1): Bottleneck(\\n\",\n \" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (2): Bottleneck(\\n\",\n \" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (3): Bottleneck(\\n\",\n \" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (layer3): Sequential(\\n\",\n \" (0): Bottleneck(\\n\",\n \" (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" (downsample): Sequential(\\n\",\n \" (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\\n\",\n \" (1): FrozenBatchNorm2d()\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (1): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (2): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (3): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (4): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (5): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (6): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (7): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (8): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (9): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (10): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (11): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (12): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (13): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (14): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (15): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (16): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (17): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (18): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (19): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (20): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (21): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (22): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (layer4): Sequential(\\n\",\n \" (0): Bottleneck(\\n\",\n \" (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" (downsample): Sequential(\\n\",\n \" (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\\n\",\n \" (1): FrozenBatchNorm2d()\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (1): Bottleneck(\\n\",\n \" (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" (2): Bottleneck(\\n\",\n \" (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn1): FrozenBatchNorm2d()\\n\",\n \" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\\n\",\n \" (bn2): FrozenBatchNorm2d()\\n\",\n \" (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\\n\",\n \" (bn3): FrozenBatchNorm2d()\\n\",\n \" (relu): ReLU(inplace=True)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (1): PositionEmbeddingSine()\\n\",\n \" )\\n\",\n \" (input_proj): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\\n\",\n \" (transformer): Transformer(\\n\",\n \" (encoder): TransformerEncoder(\\n\",\n \" (layers): ModuleList(\\n\",\n \" (0): TransformerEncoderLayer(\\n\",\n \" (self_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (linear1): Linear(in_features=256, out_features=2048, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" (linear2): Linear(in_features=2048, out_features=256, bias=True)\\n\",\n \" (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (dropout1): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout2): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" (1): TransformerEncoderLayer(\\n\",\n \" (self_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (linear1): Linear(in_features=256, out_features=2048, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" (linear2): Linear(in_features=2048, out_features=256, bias=True)\\n\",\n \" (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (dropout1): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout2): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" (2): TransformerEncoderLayer(\\n\",\n \" (self_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (linear1): Linear(in_features=256, out_features=2048, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" (linear2): Linear(in_features=2048, out_features=256, bias=True)\\n\",\n \" (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (dropout1): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout2): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" (3): TransformerEncoderLayer(\\n\",\n \" (self_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (linear1): Linear(in_features=256, out_features=2048, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" (linear2): Linear(in_features=2048, out_features=256, bias=True)\\n\",\n \" (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (dropout1): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout2): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" (4): TransformerEncoderLayer(\\n\",\n \" (self_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (linear1): Linear(in_features=256, out_features=2048, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" (linear2): Linear(in_features=2048, out_features=256, bias=True)\\n\",\n \" (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (dropout1): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout2): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" (5): TransformerEncoderLayer(\\n\",\n \" (self_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (linear1): Linear(in_features=256, out_features=2048, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" (linear2): Linear(in_features=2048, out_features=256, bias=True)\\n\",\n \" (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (dropout1): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout2): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (decoder): TransformerDecoder(\\n\",\n \" (layers): ModuleList(\\n\",\n \" (0): TransformerDecoderLayer(\\n\",\n \" (self_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (multihead_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (linear1): Linear(in_features=256, out_features=2048, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" (linear2): Linear(in_features=2048, out_features=256, bias=True)\\n\",\n \" (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (dropout1): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout2): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout3): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" (1): TransformerDecoderLayer(\\n\",\n \" (self_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (multihead_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (linear1): Linear(in_features=256, out_features=2048, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" (linear2): Linear(in_features=2048, out_features=256, bias=True)\\n\",\n \" (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (dropout1): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout2): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout3): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" (2): TransformerDecoderLayer(\\n\",\n \" (self_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (multihead_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (linear1): Linear(in_features=256, out_features=2048, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" (linear2): Linear(in_features=2048, out_features=256, bias=True)\\n\",\n \" (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (dropout1): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout2): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout3): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" (3): TransformerDecoderLayer(\\n\",\n \" (self_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (multihead_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (linear1): Linear(in_features=256, out_features=2048, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" (linear2): Linear(in_features=2048, out_features=256, bias=True)\\n\",\n \" (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (dropout1): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout2): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout3): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" (4): TransformerDecoderLayer(\\n\",\n \" (self_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (multihead_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (linear1): Linear(in_features=256, out_features=2048, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" (linear2): Linear(in_features=2048, out_features=256, bias=True)\\n\",\n \" (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (dropout1): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout2): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout3): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" (5): TransformerDecoderLayer(\\n\",\n \" (self_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (multihead_attn): MultiheadAttention(\\n\",\n \" (out_proj): _LinearWithBias(in_features=256, out_features=256, bias=True)\\n\",\n \" )\\n\",\n \" (linear1): Linear(in_features=256, out_features=2048, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" (linear2): Linear(in_features=2048, out_features=256, bias=True)\\n\",\n \" (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (dropout1): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout2): Dropout(p=0.1, inplace=False)\\n\",\n \" (dropout3): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (class_embed): Linear(in_features=256, out_features=2, bias=True)\\n\",\n \" (lines_embed): MLP(\\n\",\n \" (layers): ModuleList(\\n\",\n \" (0): Linear(in_features=256, out_features=256, bias=True)\\n\",\n \" (1): Linear(in_features=256, out_features=256, bias=True)\\n\",\n \" (2): Linear(in_features=256, out_features=4, bias=True)\\n\",\n \" )\\n\",\n \" )\\n\",\n \")\"\n ]\n },\n \"metadata\": {},\n \"execution_count\": 32\n }\n ],\n \"source\": [\n \"# obtain checkpoints\\n\",\n \"checkpoint = torch.load('../exp/res101_stage2_focal/checkpoints/checkpoint0024.pth', map_location='cpu')\\n\",\n \"\\n\",\n \"# load model\\n\",\n \"args = checkpoint['args']\\n\",\n \"model, _, postprocessors = build_model(args)\\n\",\n \"model.load_state_dict(checkpoint['model'])\\n\",\n \"model.eval()\\n\",\n \"\\n\"\n ]\n },\n {\n \"source\": [\n \"## Load Demo Image\"\n ],\n \"cell_type\": \"markdown\",\n \"metadata\": {}\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {},\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"execution_count\": 33\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": \"\",\n \"image/svg+xml\": \"\\n\\n\\n\\n\",\n \"image/png\": 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OTjIYVDtJe+Nc1TnRFdNl2GE+2PRIJgBr62l5pG63KI4XQi8704kgpURQ5hFbYXACiLGAc5S5+6utjO12X4ez77RqmyxmMIVpCOJYRuT3GAPojUH650zQOj2t8NlFaOFveo3G5PfzmAR81JpqiogLSfeksOlRlEeipqEaEBTD1J9rYtL32vvbskr110YG8TQIbFZVL2nF2eH2VcGpniQPtgWaDG/Jz87i4+oTbEG/luIK+C7dLbxedrrLuoitoIUvZCk92CcGuOg8VfF0C2FfGUCMkRI9PwdR5TRt2r9Bv42y3d+ivVbITllXP7N+9TShlFTIkpPsEtobnrUtqjA0EeMz5ZY87X0lCSEiq/irPXd0aJjlHkRcoyxJxFEEKrvnMGBv+ELjNH9V3JyARiuQMVUYni5icBBxFR9x3+hshWsavxkMP030C/IT4czpFtcwHd/xLvUYhRKJVTkh2VAsjoRdYkmbNzFgLFRPCaz8P6ZIQNLuWNAwYH1QhcFfe6Qq8JxT20LkV990Qy8h+5lpa7rMuRdb1vO+Za+l10RXypl2BNcQK9z1rCGZHaA6FIWn78PqE93JeULMdQgpfvbMv5ggPiaH18rVb/duk8ecdgh+kXjjm5X3Up+EFPQlbCVr1M4rdelqlafJ4pf6rUo3HQHQZlFK11atQCptRAs5LcM7BhVQL2GRTYZsyXG+4z4MOfe+C40aLmkQA9kLCnkQeIsLvjkvb8vUaYvR6PPzqPweqF/2nRg6hs82TDtUufmf2S+rEVRTIEO1B6rZd75jvI9/IH2g+brxro/B50ebkNokVRUYcOBWF7fOcfdDlkbrCwBXobmP4zrx2BXFIaYbo6jIifAe/2Hcbh0LVPrwhhThUgS+rHIfists/ZGz48HXR43vfnacWJk23rT1QXS8vNKBD7e2uFFc4BKRa8uz10r31ILaSrw/VUOWKqk5ch5oppepEssCefderbfCQFmpEK3Z3DrsSoJbcY5SBUgJQBhZFkFzxrlkJ7n6aa8dq3rfnFVuK+xj+hTs2fP05FI+dv/Fd9o2X476zwNLrfUJ7GWUYLKQFHmdCApK024OANLR5K1JipW/wn/oCe8eAUXfG0G165TCasKEYXQVuFCXRuCv6O8abT0d481h2vo+fmte1otGMFSVStq/6rMZe/VtKFb1atdI+1fuwfc/sOWXfaWF2WveZ3UHLCmP7mQ+3eRc6M7qiAXU/hrY8mXdDLdI+CA3qkyhqG98qvOxV0WTK6vPmzWtfn7p8clJabFAngPk9xEaZRAJEQEoWEN6KkwjRC2ooBXHotevjO1/erRkxbrps5ncTRhEDlerqTCqBkitDJc9zcMH1TV3qYKAIBIJICO1hq2kYQ0dtlLZ4EziGyn4Pgk9vLslyxxPq/YU0+sXmixX0TDXuQCCMIkYg5G6gwf6at4nDJ0rbQ9YDHBTqkpBh0Yj+c0BCOCx9HiTfHcGArJS8zfMEytsWx+rbMJyKwg5Zym4aF1ylwPS1f753Nhjh0keH2dvrCnQfvSHvP7Tn10ejL9xqpwvlCYGxWs3c9RAl6iqtISGkrr7z0d535vvQSEvIu/KBej/c4Ogy1lw6hoLhJ5dm+zshAuZmDjfC43rUpm8JIdX2MUopWBQBRHviztYw94QzQ31ofJkyeFmgzHPkeY4iz5CmKbIsAy/V0aNRpAwTLsvaY2hs66rHiFc4kqbwHqKUQoaQa7Aua4St2stpwMntUz9aUuk05/nJFpIC2tuu3zRwu2X14iJE42v2t0+eNnDov0JKtUfbyBI7kqX/mnPIQzQ0n9fYu3io+u4YwA2FG5LZdj3dv57IxCph5Qo7pgxc1ns1hRBgMQNjEbIsAwMQaS/Fp6xaTAO0Ft9U7/Qzrg998L3rUqher8VTjklHjcJ28BmGDJ0v7uKx84Q62KdYhw7WkMExBFqhr462WdYQ8IHdN/bArp8DtXJoRtDspugyCrvCZn11awmaIQNQEkhBQKhR0rrfhVRhSeJpI22Nm3rqhzpMXWKecVzYuIQsK8DAENEYjDIwJgFaX2up2lGAUQYhBKIoAiVUGQKMqXIEB4EEFxxZkYFSxbt5XqDIS/BSgHOBshTgZb3oDHqlvMujLv/anoivtUKGbLA5BwjrVUefhoBbb7OnekksrXGtfqt35revTJgkxH5eu/xdvHqc1vGNiaofG7QPO4XRvFP7sknDMHTL6ZryrH6b/2WHF+3KUClbnjUhat0Jl6LVp8T8rJqbgEBCCsX1UtahcH4KhuLq57ChG9pY3tTMq2nh74ZAEBai0IMfgcHnY/Qu8Ckfl/lCV1uaOgHwzr3YeZa1qvqiEXb7hITkUMHn1ttn0PTlP6kw7KLXjX7YAshK1aLd/PZ97yrfVRyhNvKB/bjRh5piKdWHuBxv+NYSdtX4lxKAiaAI5FmGw6NDzPMFLl95GLP5HIxRxHEExmKUxQKUamKknveTxkOzjEIYwWRObFPXbjJGleKPY5QlBy85OBdqsRlX330C2OZD932Xsnbb67S8X18/dtFx0rIUssDzTgjTeDza/JGVnzQEFWj1fztQ70ZufOO28d1EJQKGgddItBS2fln9rYwmacn9KpJgjQdNv7AiF8IzLlYBK1fYd3Z3ceH8uWqlHSEEUggQfWQiSH1OclgQNpuki219QtvFa6yz0DGEofIrCIRDXOiyKn1K2fYqhwxO+1QzF3eX1xEybHxGSRe9yxpIbv6hedoKXXFMV0RjCN4+Jd6Vxm9kBCIMqMWP7Rm4/SyFaBl+ysMoITgH5yX29x7g5q2bmC1m+PTPfhqL2SFICozTCZI0Rp7P1BnelFnUkIq2qkw97oxRSoQEiyLESQIICRJH4JxXir1SxFa4MqS0Gy1SGVnNcdv2hdr9expQ0blUEe3EobqGjM+uqMBKvX9XVLUfBWnqg75Ihk/GNb3XDmIcqJKdxHDpUNZVEmec25gbhjOUIg5IAws3GvV075JftcIepsGWgN//4hdB4wRRkmrvAiBC1vPMRJ3HbASJ+Zj5udDxjQC8aRljYIy18tq/3fx2Gvs70yFDaR2/2BLRJr+n7r5wsktXn3J209np7blrr4Vp4ejCa/K4+GzjwUdnSGiFFNJJoVmef3V5F23muU/hHJeOIVEIe0Db7VwDgbq6kit+k/YcsbqQI88WWMxnWCzm4MUCRBSY7+/h6utv4K033sDde3dAKJCkKQhhVZFVH0i7H2s+NkdYlIKDC1W+ECVAqb70gyJJY0RRhCiOEEW0Mb7cNvU3QqMJPNBt+DTDu+0xcSxoZWtHCOpP+7kPQsZ2lX4gux23Tj76lnFuup4fZ6y06CH9vEKkBNVeLoU6d5wR0vhQhOWiyxdtGV+r3UY7Sdla5KlJrsYktXHpd1QCRBIQR50bY9ge7/bR0auClSvsP/76d/Glr38d87zA2voGsnkGSil4XlgN2/bsXHAVqq9D3PSuMjdKOIqiasuVnaazfIvKZQVGn7d2EugdAEuWOcQKHOKZrhL66nCSOvqMm+NEClZFnzpRrf4IIQCpvOuiKECkQEwl7t66jqPdu3j7rddR8gzTtQm4FFgUebXwTHmSPuONApJWoT0pVMi7LArket81igzz+REAWStoYsbf4OoAqrQV+cun43WfPu5wEV7eOOWIdUhWhnn/ZAQt5Vkalq2S18qxUpLeZ/XHKHXqrQtp9UNXzxu6qfNpa60azKI58+FSncN/GrJx5SHx8SjGv/7Xv4uUjfCZT34c65tbyOczLIoM6WgESvQeTse6CTGVDaEQDQ0woP2sk4kqi5B4vfIWPeZZgDYf3XbozBcu67J+fc98RkEjrR2m8VmSPVK4YtwOw8YNUbfCTSfwHLry28972yGAvy991xSCXV9fu5qm94aQJQGcfJUVLtHaCqKuwRRYG0/wnW99C4cPdpEkEcbTMSRUeDtbZEDEwJgWbbL27BVqUocadciOUQrJIgjGwOIIEAJHR0dYLDJlKBCgKAoUJYcUpt4KV4iPQ89+0uCnNzThtlx4fhVG3HG3WdljbpVpm8HhNp4auvvf9z3kHPmeVPLFStMd2bHmo6VUPG/KlbLtnboh9Ko8LfvMYlGpplSaMTIF9ly1EAKltZPiPaGwR0mKg4MjfOP5byCNKf70z/0ciiJDTBIAaISbQ+BTvD7h6L7rCtkSQjyhSSetp+yqDCfdEHBDRCFmDilgk26IIm+lWUJXdgkTe8Vml+IPtWufB2vj7KtTqC1877sMqOOAWw9VVjtNKG/7vXIrZHV1kqxCybaBF8cJptMNHOzv48G9+7hx4yaOjmYQUiIvddQK9WIzhU5AiNB4MNd7MkiqppNipnY+FHnRMiZqWruNGt/vP2mgqmUfwuNN5ck3fD+v7SCsApaL1rXr1Uf3caJVzbz99fSlqIttG9/2dzu8HsJuV7FhKFttJ2HJKiEbKCtjAMaTNuPJiVaZMWqXLmW1K4rLpqft1mkVsPKQOBHAOElw7Z3r+P4PX8CbV69ivLYGAYBRqoSK8G3faVskLnO4H3dezcYTsnBCHrgbJtcJgMoz8s8dh8oJpfXRM7RTu9J10eKWP8RDciMTyzCerx+H5A8Lkq6AlL9MF3wK1/fpgyYPNp9b1MCM7JBAlNLQbB1LauqhPVkAIJSBRgkoiXCwf4iy5CA6ClSUHEIob7n2igSkNB9Dh92uVEe5tNFR0UHAyxKEEEQsaq0LcQVr39iqmsFf+9ALr4G3bB/1wXEMuC7DcxBdS5LdFaUL09KtrFdtTg3iAaCxS6LOo1XjwL4gOi0l1syxBKqTV6SlgAMOTvWBX5rY7VrNUGsvvDrvQOsC85FShb99q8JPw8teucIWBcckTVHkJV5940380XPPgcSxOjuC0noOwrMoDAhb7a7lWinaTgZuK6qQ4rdx+BhdOjh9+ENKYEjHDVVyIYOhz3A4iTcUUnT2e/dzHNw2fv12kAAfKjhcWo8Ldf5Qu5ilXd2g0hvhYBuwtIr2SACcA5RGyLIca9M1jEdjEErBhdonTXV6aZUr9dYw+1xRU2UCCsFFdc91WZaAJCoUDqJOQqPuGpImzVJK201qtA3QZ2KdPOJxPDhZuUONTm860k5zHIHem68LXUgOnFCn9I+p7qiY/TeIG1CHB5p/suax+kO806Ot9iaNzmh+rDIh9MI0U5a0s5kweB0O99Vt1Qp75SHxNI5RZAUiRnF4cICvP/8tPPPs0/hzP/c5XH/rbSQswjgdIZe8kc9VRC603ksJQikknANJIIPswawDW8LeXNMDbYdB9bxIh6ERWhkopWx58bbl3OfxhsrrA19ad795qP19nqn93TZ4XBiiGN2yzG91ihjzNbW3HF95bpu6de2iz1dn1+sP5VftAhVuQvu0PtsbUJhQHU0qhNAeNANBCUoYWBQjihPs7FzAnb09FCVHyTkoY0DJtcC1PZdK9lSNZ5S/FGp8CM4huVArxnMOCIHFIkNZFpBSgAugLNXlH+riD7tdK8cjCJXh4Ai5GkfYwHYxrQpCY8lXNnEWK4UUiysj1MMKyWC6fOP/WML+GDaJmU7xlSmPh7IFdv2G1tM3Bl0HKVROqD0lPF6q1V92f9q5fW2g8LXl5kkMsj5YucLmKAEGRCwGlTEWRwv8/b//j/GBp9+H9e1t5IsZZsUCSTJCnucYjUZY5DkoY2ohDFeKnBLTYAF2cb0+APWlCyYII8CFCllQShEP8KqElGp/LNDwMIyRIDhXYXJeGxxGadn02O/svz4IHcFqh/vduWQf9ClN1+J03/vAvb/ZVYgu3SFahhocxnMd4gH3tW2rjo70cYXtEOOiTuM36gghYGaxCjz0SQkijMGkDEhRCQiiVpwSnYcQECrBIokoJtjNZyiLAnEUI40TlLMcZBxhvrcHxmJQMJg5aiGF+h7FkJSCixKUMXBCEBEgigiYpBAiAUlHQBRjtlgoL5sCgktwYSvdOnqg4uiiOl7N7UtzvYkRZxT90z7h9wRDlLbPGOvDHYqoVcV6aBwkgE+o4QxPdY31VSsCwH9h0bCqNAdWF92tnE5/hRyHxtWzIfykzSkNQ8FHALEMM1vhApVmJ5b/ZbhRSgmunUNGCEqpTiVkcQQpdYicoDrqd1Ww8pA49J5rztXlAtPpFEII/NL/9I9wdDRHko4BQpBlC8RxjDzPwXT4gRfq/OQ0Tav5OcDv8dkdXM0fCHPHrxEZFITUofchA84cP+oNw1q/Q16d/c6X1gdDBqXPwgxB1+K6Za2+1tSFayh14DOGRvMM6raBYyDkzfe1Tx2e8q/QrOhEm9a+tvBNmXSlBVApXQkJ4hz2LQEIYh0lSgBCoU8dq8uiTO0hBXR/Atjc2sJ4PNYha7VlMc9zxCzS/G3V1VlYYxEJCAFeqm1jRZGjKNVBLVEUYTweYzQaI4oixBHT5xw42yulabdA25Hm167pgeNMTdhhep8RuYyxd5yyTwpdNAbH+XGcbUdW+ssdhtjF0cTlN8Z9ynNI+7keal9kxCohSDOgZbvlELTo8eE3Y0ooh01K1DKNcxS8RK7P/S/LEocHB8gWC3WvfF701nVZWLnCllJbHZQAUiAvckynY7z2+tv40pe/grv3dzGZrkMKgYipU2QF54AU1YEqeZ6r21o8ysH+XV1fZglsKdTNQkKgElomL7evDayEOKqPcJSyTwmY8lpGg7VZXjrvlwHXCx6qYLtCu8sIsd4w8RLK3jfwugZgyLoO0blq6IoSDBU0re9KPiiFSlDzNEH9u/pLAD0GTB5ACYiIMYzHYyRJAqLPBhd6Dtq+1tXQ2qqL8fYt+gQX4IU6qGWxWKAsS72lrDu0N5QFiP1vybbsApueoHF2DA90SKTG/G2PlX7DL2RcDGqPQJK2Odgu065Hu1366G8azS6OLtqH9LIrR5eNHrrlEZh92013m1TjrE2Xa9QYXMYANvpFChV9FUKAGy/amsMmhIDqRZ0xY4ijCPliMaAVhsPKQ+KUqqNIoQe+FBJJmuDwaIbnv/UdbO1sY2N9A+PxGJyrk5bMJQmAarDSusyjN5SGWkBJbQGRyrupcarnHoa2ccn2nHXrGZpCr0WP9M+fmGdd3vayoSSfAOhtr47BFlJYoTotAz4l7eKs28cNsaH1rA+G0Htc76wfPLQS/R+p61OvtnCkiKXAjdBI0xRclBiPx4jjGAQEggsQpuvp42WNjEAZC2qxjmwqUN1EWZYpQ1mo0858hmqXx9fgK+kX/53C/Vj8ZXilJ1WPIval7VIc/nf9CsXG22Uc+olVRfjHi5+GvgiVm84P4TqsAsxIWUbuAW152kCoElZK10CohYOOglSMLAEQZww0onbWGgD75kgpBBZZNqheQ2HlCptFDEVeQEiA6GMOORcYjxJcfecGvvu9H2FzfQOf+KkP4fadO6CEYDQaqZOX9Lwwse4c9oUw3UGjFifVDdpU1BIkcGITIaRx3aYPfJ6BBDr3k/vwuXT1Ca8u6CvXNTpOCst6l8HBFMBt563nqky50hp0qi+7aOxq2y5jpcsDHBqSa+Kw3whIMK00bVzE3sFlIZKW0lZpx+Mx8myOyWSCNE0rnmeUQhQ5wFgDkVHi0Pgbp6HB4n2mru9czOYoigIQ6g5vIXjDCHaFFfQdS952gFGlTcPF1/5DlUoDv29MDs4fNvxCytjnYbvvumgM4RuSr2ofSym4OFY13pcxVruMePu5HYHwkWYra58Mscvzle9738DTyOOvR2f+qv2beUwcSlrpTSSMc45cnyIoVrhoEjiN27r0yUpSClAQJFGEvfkBCCHYWJ/ihz/8ARbzQzz2yCVsbmxidnSIPM8RRQxxxJDlBUgUIY5jLTzaIRi385SQbwqUZjrt7cs2U7p3ZFdvnVDNsoNieUFyOtAlbLqgS6GFyuhL72tD22hyDS6f0rcXwbnKINTmIYXbp4iHKOph73Tbq3CQ0qPag4YtEEidj+q1F3GcYDKZQBYZyrLUYWvN79pLr5S/paMbl44AkIRUc8nCuv6WMYY0TXE4m6vQesQQRRQy5wAImOWm2NM9dvv42syudx+syqgcZiQeT5G6ZfWl9/NO0zvsKnuZNrHLOo7yduuzbLTDrau/7t3RkCHRsJO02ZB3biRJSn06mjZYIZt3d0vU07JUn8UPHZnKiwJFWeLyww931mtZWPkcdlbkiJIYaZoCEjg6PMJ0NEbCIn1dn8Rrr7+Fz//yr+LC5YfBRdOCJ1AT0GVRVAvXbPArh5pZfUwr5fBzXUOplvEEury140KXdxhK10XXsmWvCq+UvsswljcouvD/pEFFAZrCqjkbU78n2ou2lTUh+gSyKEI6SpBOxsjyHGVZYj6fI1sswAVHFEd6h4XZTdC8bKCeXoB+L6ECFAKcFyjKXEXDhMA8W0BIrlaPM32wypL1Pp22PynOnvzLoD8xKe+eAWPDqmRQHwyhW3b86ntzXLk3mBpLYddrkvQ6JqnnrYUAhzrdjAtR7YQoi6JSplwIJKMRtna2cX9/7xh0hWH1J53pU5S49nqTKKoPTxcS4zQBJQSvvPYW/vHnfxnnLlwCCEGWZeC8RBRFoISAF0UVdh5qSRFC1Hw4hXZn2paTi6/123gRznvX8gopnNB73zsf3lAaX119OILtEggZ9+X30erL06dwCSGt1eIhXH14XO+6D0LRgqHeSN8UhgtB/pT11ZWwoz01q4IQtS1MXb5BqvOMhRAQkIiTBHGSIkkSJEkCKWXLsLW9TXcKwXj35sQo5TNwLBYz5HrFOOdchcSrBZrNcTikPYiEY6QcF06q5HoMwTpiayFzkpj6mu5asi2qPIEFXEPGvVuOr9xQnj754JOJqhk65OwxjQDS8ct943N8fOUu60zU7d6DQ3vVrpwyylopcAnJBbI8B+ccRVkCVK0Z2Z8d4YVXXgrSdhxYucKmLGqFzsqyrPZYE6luA+IC+PLXvoE33noL6WiEJEnUHBoASlHfroV+C7FmOAl7F6gdgmqmq/PZz4co9C4a+hbqDFFKQz3pEA1ufXwh5S4P1/c9pOT76LWFWqiskMJ28bcFVjiqMgSWVdq+egTTARYLkpYyIFoQ2DmIJLUHRqB+a4FGqTJE5/M5ilJ5xFwIFGWpxonZdoamYVZtnTNheOhQOQgoY4ism+xcvmiccuZMPVRpnar1tkugr47L5+a773ctD5p93au4EVZUXeDyYh9PdhnhofRD5JCd9jiKWhkWmp6AQg0ZBkO7sUuWdBkqg6JwAVve7pOQ89FqK0JaQREzJ+0eRWrGGhcC6+sbSNIEewcHSpmtEE7Bw7bmGog+WlGIanJNvSOgLMaNm7fx3Je/hiwvEKdp5Z0bwVMLjCb+UIeZhRlVu5PmXs1Bylpl7B2yg63qDoXkU2hdTOkKgpCXa9fPLdN9F7LUfWWHIFSWW/eTCOuQ8Am1r6+duvq/r6xlBGYl6hy93OBlKRuKQepnzdUseiyAgoCqqzA5h5DmSNECEWN6aEmANOutlK7BazhbpZVcQkrlgVMWIc8ydYY4Zaq8hqAcWOtWQqfcAUrkpNCkod3//WUPpE22+X6wIoB/zJvvPkPJV0aQtKXq24ZlzCdfe4dosX/b7+2/IRhSF2fotPJ30RaUEVppqziU1GtBTN7aPpB6qzBitSaEUYq8LBClSSfNy8LqD06RoimYCQGLIggpQaMIIKQ6fzVJYvy7L/wBXn3jTZQCmKyto+QCEgScq7k4idqa6QN9K6oWcEbw1FW0Tw7zdY6a/yOqk6wB6Qp/lx3Me/se7i7l2fBSBirHkFUaeu+ms9P76HM/vvC1z1NZJiTYldZ93ndYScggCB2T6ua1/9r1CD0LtaU3L0EVFmsZZpRAEgIBZchKAj2lXSs3IxDUNiwKcApRSiCiYEkMFkVqURoIIkohS7UnW0KXC4BLARozgABCcnDODVmQXCJfFMgXhcIL4MHuA4zSFBFLAEn0WQaaByCgThFsGoxmMXu4oXUaIfW1nuGky/BGcywuh6sfBnqKjfD4kDUyPo+0h87jViFQbh8s2259Rq1PvrppmufV90TzHFo9BAGa1+ChxfCzGzFy05kP51wtiCUAh0QpBYSOKgm9dZnq6Nh8NoekBOUowmI+B88KJKMRMn2oyqpg9QrbAy0BDwkiS8RxjCRh+PV/9a/woxdfxnR9E/cfPECSJIhjtYDd15gnpSPk6RmhKwSvtrU008CaI6+fhzwyG7ePFqPcl7GG+8Juy3iLXda/rfh8SqyrDbtoG9pOXXfKdhk5obr5BIEPR1dkw5evS5kTX17tNcP2tmtsgNQXDlhtSCnRmtYcCVH/M0raPssAcOqulZAEUBYZGAFGaYrRaIQ4iiDLEvv7D3B0dIT5fI68KNp1ks36+sBtgxYvBMbM8uN6ufQhngwrp27juJ1/CQ1v4W1Cd318Y6vBWwPLrcv3PAtgGCKf+vqjRa8zTocYCnaKof3vTVf5kuGIHyUECYtQFIU67wBKJpb6hECix2SW5zg8PEScpnjy6afwwxdewJ3795AXxcr3YAOnsK1rCFACUB1iSEdjXL95D1987mtgUYyf+9nP4MUffA8XLpxreiYnhC5PzShqlUbCPwmiQ44AZMdqT185tmCyj0l1Pbo+OI6xMtRrOS6eVfaRwdMvIJXAIY4COC4dQ/Mcx3PzpjfeGQBqqmrX2am+iTAJqaJP0vyTUNsohVbodaFBehhjABdQhwipVa7Q5x6MxyNELAYAcFFq4xoNZWsL3GX514wuG45ngPswLQ99Zbfet0TDMWjwipeAzAk89kEwWUickbbSHlLUSRym5XAtUfmebF0GZvAdAAYCUZRaYdWLPw2UZQkOiXgywmQ6xbe+9z3MFgu1cFpv85qsry1fhw54VxR2m/HV9q2IReAgSMcTvPn2VXzxy1/Blccfw8WHHsLiYB80itWNRA64VlqfIB36zlaozUSGftL4356DtC1IV5jZysTeR+xerDGUZh8s6zH2leHzJLvSm/3sXd6oD78r9Id7ue2R6QtV+3CGREGX0g95Zp395DPe9DoL4/JKIturqS2Fbl4JqY7WZWA6LKf+CX37ls+DpXbwr2o7gbLMUZQFSl6qBXD6ik11Zjiq++pthS318WV9UYpuWI2ibUOzR11+8hkXx1I8pM05Q3i9mcGXL8BDgcfeso6p37pQDKlTS+l1VceRid1Gdnhsd0Xp6hWf3bLKh7MdHtc0m/eox0UpOMqyRJKOQOMYb7z9Nm7euw0aUYynU0SEQEIgZqtVsaey6KwfJKRQx5Ia4TWbL/Dmm2/jD/7gDzGaTMEiZen3hVHdsvvK7wrZBtNZ5ZqOC+Wx/3bR2sAn/XuTfXhDYaVQnq7vodDxEHoNDJkz9uFxv4eUre/dccpp4A6kHdoOfWV00mvLFmnC1W7ouOnREkKBSvHoVyatMaQccoUQ6qID/c5wsS14zJQMpMBisVCrz/W2rla72DL52F7W6pR1s3/6Dc8hytpnNDfHWitHK+2yUZiTBKbaispXQE1fLy3HJ8WL5Djt4cqY40XuuvMMmo7RMpnSeuxBSlDtaEkhEEcxJIC9wyO89c41EMZAWQQWMTX9VJZIkvgY9IfhVC7/qH2D0AcAgdo/CjVnTChweHiIr3z167h+/SbSyRQsquex3Q48aWjGx0y+jlQCrg4fta0wv/LpU6R91l0oX+h3F/iY0y2zz8tttIdnMPmMFRvPMt58iL6+Ng2V3fVsKAzlt3YUwE3Q+NOirabRqou5tUuZ/EpZo7b+7TytfoQxCupn5kY6RhmoDoeXZQkpVRnuvmvomvTxaBdUSt8M/2MIcxdWgWP10K1o3HEUak7feDsRVcfAYYxJm57GeydqFSq3S8asou+G4PB70N1tIqSwcKu0lBAIzkEkMJ1MwIXE7Xv38GD/AFGcgMVRdWww5xyT8ejY9fLBKSw6G8AYBCCU6QMfBCAFkihCHCd4sH+IX//N3wRLUsRJGvTgjFdqvtt/vVT1KAX7vVnFXie0YpSwBXIYT2iwLTMAvcbDgLw+79T+a8/DhMp1lXKXAgfqazi76PENUp9nYlZzhsrSOYMDPhQKd8v01XdZ6DQSvPraStMyfLSnTBTLVR9NIxeW52vRbocAfUZS5YBLdQYCtJegjjhVK8oBIElSRHE9DbUSRWgraWmZIQ3bfZVKV3p5yobQcxXB6Dcwu8EfzXLLWVYZ2+NxWWXXkkGDcqHX5bbRhqJW7vdllLYrh3xyxHyCVyJ7ZLJLh7dcqBM9uGeBo+AcaZLg3NYWpARu37mH0WSi5q+TBHESg6rtGdjc2AyWcxx4V1aJt4FAgIElCXhZII0o0ohiFFNMJim+8a0f4A/++CvYPzhEFEWtjnDvViZk2OKXYWlCLwjM/drECNZAGb3MELBWfRZrlwGwjHA5SYi3T2m/WzC0vGWjFzYcR1C7gqfySgbmr2gzytk8h8MHeqsjpArPqfd621W1EM3hD7s++phFc51mtlggzzO1MjzPQUh7iqOum9/QGQqkqqIElZVdUn9fGRt5VYc/ZaCv2wbPu+/B+3joOOPNl3pVtTlJswxt+8EwIFJhP+tFJyU4kdbd9rWzM05H2N7cwubaBkZpCjCKKBlhkRcQAphO1zCdThERijT6Ex4SHwISBIKoVXQRoRBlgTybo8gXiCjFxvoa/vm//A1cv3FDpdfhBRcIIWDWzV4mbQiGKjafF1gtqAIBIcxezhPMH/LkTMcbWqs7uu15bI9ytunwLfDqmgfvqr9tFLi0u3T5LOJQJCAUQvO1g/le39bV3Nceorvv46PTVw+fwg213ZDoii9/X0QglL7esiUbitMsDKfGaIWyKxueiKETNY/EcYzxaFTdr53nBQ4PDzGfL5DnBTgX7T4Sw+po/1WeirUoTr2t/q+97cD0QQD6+tlNW50H7WwVHKIA+6I1J4UuxWX6bwhvB/Fj2NgcAsdRfEDb+fDlrX/7rzj29Vvjg/aY9NEeaoc2vjY/c84hucDm+jq2NjbBCAWkOi54bW0Ns9kMk8kEjDG1/SvPwVZ80tnKV4lHUaTnw2oBTyl1lIkEk2Z1rGoMaq7A5BKUc9CywO/+3h9gtsjxyU98HPsP7kPwElQraRACIQmyUlRnlRuFY6Dvuz8sxnWAUe1/VQevNBlCYwnisN/bDGgrxLo8WSkkO50P7DY1f22mcxW4ncatu91Wbj47vU9ZuvQPVUK2MnbrYNNMrS0U4RCgTefywrSrzVy6zPcuxWrjreqmatHsJ6pUFaUUJKI69FbfFFctDtP4KCFItHE2XywwiSYoS4GSCwgBRCyGLEtISHWYCoEaJ5QiGk8gCYOQRGPkiPSaEUEAGkWgFBD7JSJCME5TCGm2qxheaLdbn6B233OttJtLkJ08nnwNXERHtYSbpp4O8EGof+33XbQPhVXlM+1rj7s+3u4u2/T9yejy0bEMHwyVaVK2xyEA7zSe2/NDjLfQs9Y7qYxjXipHUXCOiFJMNtfw8quv4uMfX4ekBFFMsD1NMVtEyCTBI5sbkLzEvCzARinG7D1wNGnX9h4pJaDDYIxSUEpAKVMforaVUEKQRhHefOsqfvDCi3jr6jVsbGyqDetQVqOUElII5X2g2fC+DnfBp+iUl2KsfTcw6T4PKXzAVuYhTz0UejwJBNt7QJ4h731t7POal6XXNRyGGgD1u773bYt9GXDT90UY6oT601hi3cBU53FIqttB1ldlEgrK1FghoEYNaoPAqPvauKARa8QtpVSrW8uy0Meclmq7GOegTN0QRnS6IdAnwIlWsn1njvvGQas/g907TFn7cLvGYK+3e0petg98suk4dPjwnAS6jNY+WXFcCCraAO6+aIKvHbwyQupzC6CciNFohHM7O5jNZ+osf14ijiNsb65jkkxQFBzr6QiiLFHyEnESI/FsSz4JrP4+bFnvZR5y/CbxeHCmkbKiwKuvvY71tTU89fh/BhrFkJSAV51AEFEjrLpDkfb7LgYywt+2+Jrv1NGnYRzaPyek+n5cGMJoy0Ao4mDK6vKKTR6f5x7yYIZ6Bl0DqDKkPLibXghgFGBfnYZCyKBSZXRHLVq4DJHE/CVN5oKEWdxo0hJCIKREqReb0YiB6cs6KFX8JaW0VoG7/Eaaf6W6v1cIoY8qpSAcyItCRayEuolI+IQgam+7y2P1ZPyJwUkVRQiWqn9HfhdC42GIt2slbhhpq4Iu4+yk46wLr1u+TwH78nTLeNKYHrFljIsriiJIqRZrTqZTbG1tIU1TFUnmHBFj2FzfwHxWgJclxmkKXnLwokQSxYhWrLBX7mGX+uxUW6C2GJHohvfNO+jnlFJESYLbd+/j+W99F89/+9t45MoVSElQliUIBdJRDK5v+LLxu8J1iJVXKQjYAwXQi9i1m6DPKe+weGtF3R8u6vs02qUjn/2ur8yuZ64Cdsv30WGDL7rivg8NvEaUY2C9mumW88qHQKjtQ2m85WlW8IV8CSHVyfe+NRFKmFAwxpDnuT4mkSMvC+RlASkFoigC5wVU6N0UqPEDKgRvoSaEII5jjEYjjEYjsCjC0XyGoihQloUOBXoUxgl0n3s7+BA4ibIdMub7vOkhcBL+GhyRGtAMDTo6PMeTQMjx6fOkfbxUp2uOrb7dK33g0mJwuvLSt5bBd1udeUcIQZqmWFtbQxRFiOMYGxsbyjkFME1TMClAQXB+5xwgBYqiQBLp1eIrhJUrbHdLjhv6Vff86vOzgXaDolZ3aZJgOh3j8HAf/+M/+DwePNhHlKZgUQIpgKIowGjdQUZh2ErD7qCuQVr9rRiMwL5bzVUuvtD2KhRE6Pegwb2ico8DbaNleNl9RklX3i5ajhORCJXbLssfarSFmtAX3OsHbkFLEEURpwkWiwUODg9RlmUlUBaLRbWmA4C6QyQk5fU929UBKaW687ooCrVYJo6rqzrfFSDer53p3ttwjHG2grov15/vZkjkmFEEL4SdERe3uu+91lEm4mR0BNPTQ3me62gWRRonSKIYWZZhOp5gbW0NREqMkwQXtncwSUYQvEQSR6AEmIxHOHfuHLIVnyd+KqvEq1tRrMrbz6WZxLag5Z0RQHBltURRjLwo8Lf/7v8ToBHiUQoBiaIoEEW0JeBDQncIA9SeNpT0CzBwA7eaXoS50ahvdXNf2V3g80yDdA3A06cw7TJ8YWufx7ysAeBavn2K1veu08sdkH958Le7XQ/zu9m2fk/Ax6uEEBBKISmQ5zlG6Qjr6+uI47i6LWg0SrXwsfIHbo8ytHH9EVIJqsPDQwCoTjk7qaczCFpd4I+uVMZzMJ8HdU84eRB5Awy+5XEvZ8gu8z4U9evj9fZ7/1g/jsNwWg6Ijzb37Ax3XLl/fW1jlDOglDrnHFtbWxinIzBKq22I586dgyjVOEmiGJNkhGyxwPpkiosXLmCRZZjNF0hGKRaLxbHqGIKVK2zf7VNexdUTGlXvOKTgoJRgOh3jjbev4Y+f+zKyvMTG5hYYiypvgzHWGPC++XNfeMRVTATG0wbs0HaXELCnJc2GUlvJdYWHuxafdYeT0HreZ7CEYKhgGuJ5+gaNuy3MV35X2csM7OO2gU2L2x6h6MGQ0H+VJ4A3hMdt8yiKVGhdCFBCEbEIhFIURW7nAiCa3rtejKZC8gofpRRRFKlrbwEcHR2hLEs1nSXb527X42EF0D9b1AKC9tRD+9PFx/4Q7HGhwVdL1qdLofbxU+u9Uy1X9nWtIeqiz4XBES8cb8y55bZiWVbZPnnYN+Z98svVEbYcNh739vYWJuMRJpMRkiQGIRKcFzi3swVKCRghSOII41GCiADj8RhxkkLd0kwwGo1RFO3tyCeBUztL3A4zALX14vPI1HGIzcYkhIARAFIAQiBNExBC8bVvfAtvvX0NeVEiTUcoOe8Mx/q8wBBDSWnm1R3vOeBJ2tEACwtApD7Mok1Hn/Xre2+XHYJlB0pXOY26OfiPaxjYgy1U9hDjJERfqMzjQqiPfDhD9Wh8d9Kbv77tOzYHEgBMH9YjpKz2h1JtXNaaGFVehzq0bpeTEuqEQZWvLArEcVxtrfS2B8JrDIaBZQB43nRiG2QsmET22PR46EvBgDqSjimIDlTHUdy+slvFLDk+u5RbsFjPmJAWD4bSBXnLGffooLuP70J19/12jRn7fRRFOLezDUYJRmmCNI31OeIca5MpKAGSOEKaJEjiBHEcI5vPce3aNWR5jjhJwaIIWcOgPjmc2m1dbuWNB2wr8SqtZnoJALK+YDyiRO05FVxtTp+O8frrb+Fb3/4uRmmCZ59+Alk2b3u8AcYwSqNLCBNif28KW1txNryQVnHWc+nib3tYLtP46Hbr0JW+C9z89rOQMvKBS8MQAeMrx23ToRDq81D7duH2ef9DeGoo33nLdPK1jDNSK2dGGQSvp1uUxykAoY8aLUp17KeU8B0ZRkjT6eZcoCw5KCGQUkBygTRJwBhT5yfXOSulp7Zyn8RDJUH9J/Vrby5ChilEu5yGuWOVI4EgEZ5y3fDw8gpE1jQsYTMMGeNd8ic0lrvKC7xpI18SfGPEdXqOY1DYv31Kucspc2mwzwkx0afRaISN9XVkiwWSJFHn7usrM9MkhhRqzIxHKSIWIYoYDvb38aMf/QiT0RTp2jpAqPqsEFausI1QMQ1gwg5xrI5osxsGUOe1EmsQCQBM41E3eglQohpaedURvvTcl8HLAo8+/BA2N7ewe/9BY97YdAiBGez1IDbjwFjgBqTe1Up02sagIU0cgH01pi3Emvil9DOYKwD8AxJO/m7G9RkifYZLFz4Xp+sh13UMDwyXBncg2fm7PIxuA6u+xcqlPQS+Mn14XbrrtjD9LNGuvgTgeqqaw81/slsMVl4XJRClWiCWJEm1nYQyvW1SCvBSYBzHKInNn3bJdZlSSkCo1eRM3yxUlCXm8zmKLIeIAUKoxXd2/fz9FgKfEUpqapoWhKUY3Fq0lLWsn3X130miK6vIr2CYEXucMruM66HjO4Szzru8sg6NrRANx6m/T276vg+JHqhLcKg6l6AskaYpxuMxtjY31XoOLpCm9Z0WaZpW6z7iOMZ4PIbgEotFhvWtLSyKHBvrm4jiBKAMV554clCdhsKpHU1aeQPaq86yDJxzMMYQx3FDmZu05uAGQAtJQkAiBsooJC8QARhFESgheP5b38H/5x98HqOtS3VYUUpQAjBKwMtSDW4uAQEQUL2aFgAR+qMlJ5FKpBKzT7WpLNXfZnpAfaWEgZEIBAxSAgWXyEuBgsvq6GcfCCkgZAkhS20qUEhJajlGAEIlKIP+1APAd1xo1WbWs1Corc8TtNO4q/670vaBTVvocJ3QQPdZzk2lKiCEvkwmsGK0yzofQnudXvOD/islh5TcegZAEkhBAEkhdd/qTgUhDIQy7wUnNhBCtAYTmI5S3Dl6gIPZIYqygCSAiCioBBYQEBQAU1cB5iUHS+JqmkkKfSMeJEAjUBohiiOwOIKgBAezIyTjEQjT5Wmvmkt1T3ahD1nhENXGCXvbi+vluO3W164QEoQLUNFUEdL6LAMnU7bd3tpw3MenwWd4h8bZyaYp/ODDMsR79aXvg+PMsxv8Q8s0usa8E0KPF0KAvATNOEopASqRxhHWRinG0zFEyZGmaZXfAOccVAhM0xSSU7x17Ta+8JXfx2c//Rl89mc+jenaGFQWuDhaw4W1naXq1Qencr3mYCFo/klrcRLQaFhCiFpwAwmuj1wcj8cghODq1av4/Oc/jyeffEoJJSlRcoH5fKHyEHP2MgCiO4pL8FKCc6FXxOqPDC9Is42LSik4z41Hz2DPS9aD3z0zW1XUhAkl2uKp3YaUDVO6XXUIKT4X71Al7DMWjuMpdHnibnkunb68XuU3sC5D6+Azjoj2dKUx8Bzc2gxCaCW3D7/U4RYBgAuhjToCSALaCLmFoxQaIzjnyPMceZajzAugUGMqSRIQwlByXh0tTIBG5IoqFMH26W0zUn9CBpS2m1vsv4xhOATCuPojM9aTDrx+PIQQ7557SDlobA/yXD3juQtaY2pguavok+PIHANDjAifQUmhjhwtNJ/nvIBkwObONra2tpAkCebzIwCoHEl7UTMAEKYWbd69exevvPYa4u0tcL3rYm0yxfp0DVHEqsjyquDU5rANuELQ3u4F3g55qcY1me0XtVfGKEEpCObzOZ5//pv45Ec/gieffAKz2SGyxRyRvhAkitW+ObXjijY7VkJ5tlKH+PTDfqahVViuCvFJWQ2SCrcOaUO2FVlz/ol4rCaziKMOIRIAlNHqKOUuheQrrwtCStPG3RVed3+HwnHLKkQ3z7Lg0hISDF3h+JOB8cSVEie2fVb98NPtEAgu1LhQe6cVb0ipjvEFSM1rurSqHk74OmYMIoqUty0lsizHfD4HYwxc1OcmlGUJAqJPVPMLxxAPmnbv4oVWPtMwktRbKqWe0lrScXSjNj44vkdnWhjwoejC2zmVsGSUyv3dUCZNMgcgrdOG+suVByfx5kM80I3TX6GQwvY9q7xsLgCqppxKIcAFRyYEaBwhHSVIKMOiyEGTWDlfVbFE3d4lpTZiGWaLBR7s7WNjYxN7BwcoBAchFExfEPUnfluXD3xennvASSO9zmMUohCqs8x+bimlXtFKcf/+ffz+f/gjzBcZoigBi+IqMKk6TEJAQErhlGWEhBWGRnNOlWhhaP4pYUJgrtgMeXJ11LytqFrKQPMhIdq1INXyO11XVHQqBPV7ezC5A2oIDGFyt25uuaE0oTJClnofnctEbpbBH/LUfVEHX7ownfpjvMraNbUKH0Q8CCF6n6lagFm1vzEwTb84eaoyajuyKpJSddNQWRQQXFRktaYoPGQO7YtuXvB4q9roqL4fQ1m75a/SKzdwHF48KR2uodQVRZKyGbk8jnHS572GZNoyhr0x6IZEyKT003Wcj+EtLgQKUYKlEe4+uIdSqjkZSghie1eTMXyNI0UsJ48QUEYQUYr9g31IAkRRjDhOELGocYf9KuBUtnWFPBi70Uw66klbGzR64ZZ1y5PZ0E4owWicIk1TPPfVr+G7P/ghSiEwXVtHoW9YyfNcz/0KCOlvuIomfQRpzRhGdqhtMfXlCtpfcpnYrRugjpw09bQMlHb56k5jNReq7zb2GA7VARmeAWQz+RAPt1X/wKAMecU+L37YwPMLjyFekY13GXBpHYojlK4rf0NZVyDqd2092oKGANQ8JKTUB57w6qzv3hpYER9NALhQ+63VvLZUtxDFEdIkrRaLmiiYGntmLDRjYSf1sAzO5gtHZ7vN6I3GncyYWx6WU7yDjQYPbw7J5xuTLh5vvsb3MJ/3Zm6lW10/+Mat+RuSLUM+jBIILlCUBUohQGKGN6++jazI1ZSTENV4kFoR2GUR7XULITCdTnDh4nnwvMBsdgTCGKbTCdbGUyRxohafrRBO3cO2FZb5LqU6pUyI2rKv0qovAADBJQSXgF4wVpalVloqxMh5CRCJUZrg85//J3jxpVdAWYxIXxqeZVnjxLGuO3Hdd+pmI30VoXln3ov2gDAdaypkyvUxl6uc1J513/wyqaw539j1K/8+ZRKG1gAYiDtoyQbwN9qitfXG064OdM2nhYwMX9kuH3TVp4sePy1OnUy0JUC/r25ufQz/HQdsXpNGIUOg5OosfqKP+G0e4+s/gOi0wN4uLonPqPFPaTRSePhgOe/2GPX0tk0XHs87T0h4aJv75Fkf2Pdkt0lpRp1aRgfxjyf1u03TENr7Pvauo6GyyIcfAPKiRCk4WBwhHY3w3e9+FweHh5BSIGIMUcRAIKp5a2HlJ4To5xRlWeLy5cv4zKc+jfObW2AAuODYWFvDdDxCWZRYX9/sbYNl4F0JifugbsCwcrE7C0Cl+Jmeo14sFijLEhvbW5jnHL/127+NP/rSH+Pxx59AVpaYTqfIFwUkBxhlAxjIDn+gQZthVynQmnf3EK+joP1KrCpGegYezMpjs0LRP5COMzf8bkGvsAy8PonhsSysvs0qlxYyENnx1buvrWpjVYEZvHQJ8hmhiCOGdJyAUoIsy7BYLJDnuVr9qscXoBU4ZLUg06+YTgqBPrY+7y70KXe/sl0Oz+rD9EB/9KpOeDIa3k0Z06d8+975npVC7VbiJcedO3cAQSC5mnsejUZIRyPkZVkbLrq6brRUCIGIUmxP13Dl0iXcv3cPUkqMohijSJ09Pl1fW2l7nOq2Ll8o3CuIA/1v5+ecI45j3WDqDm2zem82m2FjY4o7d+7j+ee/ja9+7at49OGHcXQ0Q8RiAAS8FN75SntO256v8A/Cml731DYXqrl663a1kKddTzK6c8RCr2AvIVHWDVWtfidtHB3taNPQN+gaUxM9OPvee610B44jBHz1Cv12y3LfLTvH2NWm6nuV0o/AIa2/PRX+SN8ZrzATSK4MOm9uT/05L5VyztW+U845RqMRptMp4phBStE63Mg3xbVMuNbH93W0zRinOg8BhM3Sg7rFrecSqn5pthu++hoYHrYPKZkQrwYjSYH3ThV6aQ3Rd5x8Q/L2vetqH5/X7R7Nar+LRwkKKfBgfx/7+/v4T3/xF7F/Zw8oVcRXSKhQtrQcRbOOSejdE7p9iyxDMZ/jyuVHIIoc58/tYJymSCN1ZOnh7CBY9+PAuzKH7b4jhFRes3rRSNT4aVsz1T2+hCKO1TVnnHMdThJgjOD2ndv44h99GUeHR1hfX0dRqkU1Q5QGoMKOVWdLUW/3gol4hxWDsb7Mc8AsrutuL8V0Cr+owu2iwqFLajJwQ9L0h8aXETJDYahnv8yAHtxPAaFm5vu75uuHwHGET12+rbR1fVpSsqmNwm2keUCHIQmtV1GbMHadziS38MimTrLtUWG2eOUZiqKoQuUAqi2WVTsSYu8y9/ZRSMAOhWqXo2meqpn6DOnutrXpbfHXwCERmoZ5d2FIqNvJIetQbi/2JcaHSb8qGIIrNEb6PGs3fZYXyPIc4/EYTz/5FAgIptMpilxdMVtFd4Ra2GSUMzF0SkvWS3X2x/b6OhghSEepCptTisl4vPQFUH1wKvuwXfB5tEZR1atMYOSSUtoeYVDPpakV5JTUYQnBBeKYYb5Y4MWXX8HXn/8m1tbWdahZtBa3VZ1SlV17KRVd7dpZnyH11YLaVmwmt5YpshLILm6pc6sbk802HmM0QCt1k4eQGmdjVTK6Gd1byx6PwI2chN4vCz7lvWyeppGzXLnHFVbddFoKldTPpJReZeFtTwC8uphdKzbDA05fq9lJtZNBfZc6n6xOSYxYBEoYBBfIsxyQ6rhShd7wK6noa85fOgZCD/jbpj2O3PqSSjZA1VP/a6byYPYI6+bH5Zm+erQNj+PwyzCwaZL+V/DXEWi3dXtcBEr11KUybDxGpQuhcn0lLktTVzt39bNZH2X3NxcC48kYB/sHyLIMGxsbeN/Tz4DnBdLpGBwSUqqF0FwKSL0TSUIdrEUJ1JZhSIBQgEWQVEW81scjsIiBUqbqTQmimCJlzEv7ceFdCYkDbYu7eidVDIJKfdG9lmO+W6yEUAsBRMlR5sojiCgDFRKM6JPCQDBfzPFPfuXXMM8KjMYjxBEDJLe8WdUFGjOIVvwUAlQSUElBQQGplKW5aEHJMXVKmm252gqsdUOYOWnNqotaEw5wycElB6gAYxKEStRHnVL1kRRSEEihvqsPFAbCAcJBmajyEiKVR6+3f9kHwnT1FRwl7POQ6jBmeyDViwo72cJbtm/qxIaQQPGlqT8clSHjyefyp+80t65yQ0raCIq6LcyBQGYtgjGybH70LOqi6vxuQtTJfbngIKXQfarXdZQCktXbEqsth4QiitVUkOYCAAKUqbP5mZ4mKvISRVZgMl5TU0eE6QWVBIqJqEUOASUKI4jadSHhF/LN7y2fz/o0gUiASTUnTwXAhATlEoQrGRE2pG3cwvnbKKErc42lR5Esj6NhSTcpqvjIGN4Ozxo0pPqvl5bhxvkAhQk/nr7x2Ka2mdfVA13j3l0o7KNDagdG6G2PZVk2aikgkZUFts5vY7a/D1mWWJ9OcWnnHLbXNzAXJZCoBcsU6kwCQSOllLmAFBwRVfdc69ELSROUhIITgbVxAhYTCDBIwiCIABcZktAalmPCqd3WZcOQzl26HAAMBJFRtkQJS0opRuMx5nmOf/iP/hEm0zWM19ZQCIEkVkefxnEEIut93pxzJEmCggsvQ9bPTMnt+oUsQsX0jcSA0EwofLhro0Lo09tqe9V44z5haBkJlQIYdq+x8daAWtDaZ7L7Bky4DcKCpE/ZhULhvgiNdOht5zPpPHXtqUPfc5/SHxLCd+vk0t+p8PRzdesda92sJR3cZoVrhUMCRJ+6U5RqgVleFDiaz6vTzUpegIvw/LVNn+IPieqIX02Fze0KjyuElUFgH8Nrh6erHATmZlBtydfcrvL5+lElJvr4V8BuQ+LwqKHP6jcv77Yjg2FQ1LdxNJW368jUdNCK1rrfPbUM8E6Lmh4P1a5blwEc2lkT+nTR4I7bEF4XR+hqZBunnT5JEsWj1PCDOjHz1q1beN+zz2JzYxNlXoAAGCcpyjzH/OgQZZEjohST0QhEFgAEwAhAKEouMZ/PkbIYMi8wpgQJIRASmE7XUJZ63RKh9QmEItT2x4Of2CpxG4KDQI9eu2N8W7MIhZqnFlJ56EQd0v61b3wH//b3/gD7RwvsnLuABw8eoCxz5FmGKFILbMqyRBzHKLkAZVElGDuoRXPw93dINQTNQIPyIpTDTpr7v4V09oNb6lraz61m0r99W3AI/IO5D4Z6En+S4CSG4NC5c6C/bZalo69cqXlaSqmUNdXTJA1l0zQMKSFaySktKKQEJEEUqSFPI1Z5IlEUWUeQkuqynVBd27RK1Af6+M9yXx4kzEFCtrFa75xYAX4XTuRHhCMHy4HfE1+ammOM3z5nq4tPh5S3DE3LyFcDjDAwwpBlGUp9XsFsscA8z1AIjtt37yCKYzz9xON44rFHsL42wfzoEEkSYXt7C0B9HG9Z5ogIQCERJwlokkLGCQSLsba+ifEoAeEZpChBaATBJXbv3EG5yKpFaZSYY7VXB6c6h9032FsM4CRXEUXZsnzdMKJCIWDmhMuyRJwkGE9G+He//4f4xre+h8OjOc6fPw8hBJIkgZSqcxij1apYvyL0WZV+q85X94YV7LaL+SPVaW61ooZjqKjW8F1sISVp0aMi3LpMbRmodlYCldA6jQtufYcqsS7o8kRPbBh0Za+Mm3qufwh0eS4heru8jC4IeQtNPCYSJK1f0gwNzUDQ876qj9ViFyX8hZQQXCilTfSCTwJwWSLLF8rglbyeFiD+OnV7Qnb71mF4QFQ2cHe7KGVsPqIK5quPZa1CLci0tzx2j8WKPP+PAdDHO833rbA2zHhTn2X4w51Hdss9yejx8ZpvGrOiY0mj9jhju8+rd9PZn5KXKMsCo5HaA30wP0KUxsiKHG+//TbW1ia4ePEiRnGM7c0tbG9uNhZaLhYZuOCgVK8ZyRfIs7mKvMYxChbhnXv38Htf+iLYOEYBjlJwJEmKyXQKSRhKvRuJUDX3TaPVzmGv/CzxEwlgb1RTKRoC0ui8WgHqM4utG7sIIWp+O4lxf3cX3/z2tzEZJ/izn/sU0tFYMQTn9bnmEUWW5QBVS7zc+rQCXAFFFlJyVegHbcYjhFQnV5nfdhnqlTmC0qbEWOLS+muEhWOlW3Ug+gYHdY56e5CGFJSpg52mb+C675dV/KYNOp+TjvTK4kObsXzPmnhsOEmEotWe6qVJ5M0jpWyRR6B4uuJ5k7/Cob8T9byxOrXiOVl/JwCX6vAhxmhl1BBCvbqpzxBv1lEaoivjszO9jZu01K8HQbcXasZMM0sLsRf8/GYf8OQbH+rTzNvkMZ+B76e9LQus4e1SG2yF7ojNcMO1K7/rlAzF3cs/A50hV2ELruawKVFrmeIkwWKxwOHRISQkHr58GevTKVCq/jJ3XzOq+J8QgpJzLIoCeVEgpQSSUiSTCe4dHOK1q1fx8kuvIM+O8Of+oz8LRgTyeYFFlmEjmSCKE5RFCQoKRoAFF+BktXPYp375B3CCUCVpKuuWAkA9D0IpU8fKAWCUgguhltknCd54802M0hjPPPEYHnn4Ydy9e6daNd5USEB99/VwGDIYXebyRhaIm0e9NHVU9oumz3hXIJa3LBv5GkgrJaHTtuRZWGkb+kPzy13PXIt9Gdy+d67RNiSP8xaVj+pJtyxen4B1jRwXXfXeytcy1CwFYHYLGA+56lJrPUM130uUuvSHrXXtteEn9TZJE7arPECnPqH6+n77wR5TvvSk8a2Voml7uo8qvCGlat77PP0h5Ks0zUNrDE7bUGryB/HyRrv8fkMo1Oa1sWAv9nSNTWPX2c995chWW/RFk4akC42XkFLuiuq4f1veuF4/kBcFJFHbEPcODpDnOXa2d/Dw5YcREwbOM0hwEMYAotZ7sCiCJEDBObI8R8k52GgNi5Lj5v37ePXqNfzw5Vfw2utvYH2a4o3r13H53A6iUQrkqn8ppciLXCl/erKoZAhOfR/2EGU9JMzihmMIIQA1F4JQFezVm5kjvZS+KApsbq6DEopXX3sDv/Xb/xbrm9vI8wJRpPZxU0qQZxnSNFGboAfQaASxO5/u0qieO3PuFqOaeY6gamlc+SkqhqxW8lafZjsLYdEmoG53Mjj17iBDZ+iMc1+7d0UVQrBsxGWZELw7qEPl2TGSIZ7AEA/AJ1T68PjShqIQzb9qDkMKva9fXz5TgxI8ahm3Uuh1flSRKqJdXrN/1ESZzMlmdRg9REe4PbpAqRUJEM9Hv1M6j9Z0kvpj/jHU/N4uoaalPsOg+brZ/kbZ2WF19+OG3M2zZs3qriDW3y5Ddvmpk1ZlzC89PWCuCTa/64+qg/vM/jTr31FyD6+HxmPXGG0o3MB46lLWjQ8ASQkKro4evX7zBg6ODjCeTHDlyhWc29gEzzLwUoWxo1h54IRSrG9sIC8KZEUBDgAsQry+iXtHc/yr3/4d/O4X/gBvvXMD081NHM0z/Nq/+A28fe0mRuMpzp3fAYspJDjmixmKQh33G8ex3q2xOli5wrZXp/oG+3EYtWEbWvmJJaTMjV4mHJ4whoQxZLMZJuMEjFF887s/wm/99r/Fk08/q88bl4jjCEkS4fDgAIxRmItCQkzjq4uPkZrUNxWGUdK2onef2YKlOQdbW5XVZSCDW0+BfdRrlwL2KeohfReyivvAp7xsHnLpHYSXaIGlhmGwX833Lg/AprHRTy0PBq28fV5Ia8yYsoxHqT04SiIQ0gx323RVtPoULCH6Dnggmy8wn82Q57klMNW1mrxst9XxlAvgKj5VR/utpTCkqbl6YxtadY21AeOGphvb5kT1tMpSgWgYv01lbAxq93n7a0VNq+/atBkiVBM25UGf0edCX7IhRmKXJzsUGuOgJ82QMrtkaddz80wIgaIoUOQFJtM17B8c4OUfv4KLly7h8ccfR77IEDEGwjnSdIQoShDHCcbjCe7evY9FVqDgArNFhnlWgBOKX/t3v4df/e1/g7du3wUdjZBORtjdfYC/8PN/Hh95+iP4J//jP8U//+V/jkUxw/mL57Gzs4Usy8B5iSiKwViMGzduLdWufXAqHrbrsS3DDI2U1ChkTzoLZ1mW1XwEJQSi5OrIRu1p5osMkgskaYLP/8qv4tr1G5CEoihLHB4eoixLrK1NqpvAhODgotDfaw+5udCtb2Vvvzfl1qOP8f3FGSFh0tnhRQa/8OgeQEMGjUt7V7360p4qeIr10TJEWdvgzpUP8USDJHrb2AhD81zNy1HqHGbhob0V0TF4oLzoiDJ1oh/ngJCIqTKyI70CXaI26jov/pA1vw03WFwvtq6J0ZUStWKq1EHLAXS9WGWUmU+7GxwEUmhjzqHbzUd834d7o+qD1nfj9Q7B0wV9EbDTgoZhf4w8offu35CytmVyJaN17x8cHeLm7Vv43M9+DtPJBIvFAqN0hMO9A4ySFCVXZwgssgzv3LiBQnBM1qcQACZrUwhIfPmrX8Nbt+7i3myOaDrBaDrFZDzFw+cughQS/7v/1X+Nv/EX/ypuXr2Fv/N3/19YLOa4fPkyhOBI0xTj0Qizoxm+8IUvHLuNfbDyOWwhhNomVepr/KSsbtpyhVpIMEqYsLN63mAIiequaftubcP4EkqOCOmEtyXUoSgC+Gf/7J/jb/7Nv47JeIK8KMCFBNF7sM02GFOw2U9naDN7vW3FbbwkA+732rIOKDtrMZl6bhSwPq1Kex5EMiv6pj1yYa5uNC1F4DacodGmzabDR7t5b18E4auf7SG6aYYqMR8/uDS7dPbhrfPS2nEizXxSGi+WNPqgy9O36QhFKGzaDb/Ud+tSSNRTKr52Mx6y4TOBEqUowfMCiCN9ZawEJ0BEKaQoIDkF0QtSOTervQm43s+vtkxLcF4ARB1LWuYFyiwHWQOgb4yDlIr7tEcv9bHAXmFLZM1rnX3QpQVrXpV2H7S+aX40V3pJNG73UpWXjimj/trjr0pOaGXsKPJqBmnU0+tV27RZkQDJ/BkcGmRFeCPU0NJ8Pg+0TqynEUg77XFBRfJIo34uv6tnNX9BytbYdA8h8vF3qG5+J0EdrJUVBaI0hRBc7XkuSzBtZJa8QEEkFlTi7t07KESOMp/h3Pp5jJMUMQUoo+BcIlmf4v7+A+zt7yEvchCZIhZAmZe4dvc+JL+P16/exO58gfXxFJRRrKcp1qdjsLUSkh/i7v2r+MznfhokLvHc17+Jv/P3fwmgI8y5xBt3buOde3dw6+ZN7BfZifvFhlNZJW6UcxRFleI2N2zZ4Jsna6ZR5jYBqQaFGiJ1WXZ+H1TKBBKQAutrU7zw4sv44Y9exIc++D6sbWzg6PAQKLkShNw392RKrsttM3KzDcwz4Wyc9ylKaf1f1dsIA2k9k0boB2vb0Ew+JTekDu1+QONdKHrSZQyEaPEprFB62zgYDt1K3jQXcd95jBwfLSG8/R6PWSzpD3LVIdtKPNdbUHSoXJFOIAj0QUDt+jba1qJbedhCjytjpGqMRCl4r+rp8ZJ86Zu/A+ks0olJZxnODeVs1bRS0cb4qpSv76+VrsrfxBmCcD9Ko7fqJ55Kmstb6jz2GALsxa4e3d0qs8blH1MhOnxpXZlrftZ/DV84ytZRujYu97e/rJCCblVXnYZJKQp91jeNVFQIXCAvChBCUfAc9w8PcO/BfWxMxpC8xChNsD6ZgkLdxJXNc+wdHWJvfw9HR4cAIcjKAuN0DAmK2/d3MZtlODxaAJLj8oULEFKAEYmUElw4dwGXd85hf38X6+Mx3ve+Z5CVwL/5gy9gEQEXL13A9uY6mBC4cfcOymi1EY9TOTglz3MAUEvmGauu7bPvh7bBDqM3hFyI34zQQtsKtXE2cUlwcCSjBFmW4+tfex5Xr15Hmo4RxQmKsgClytOS0oQQpZ4bs8qFOoTCptct09ASYsCQt+rW1x4wovKk/aHsLgXhayM3vx36tP+6h7G49eqLlnR9P01o0mh7RY1U1qeFwIsrVIYqpx2eDLUPIeHFhi4dVbqQF6asDY0YIJS0DQGdRoXV1S4KoVYfWjiJsQW6BWiQ1LantAyEypQSEGaxGQHMgjVZmzLVb8vf9cNq5SdqY0d5qM0xVPOW/bv26H2GhTq32ubJdpu05YybdnjbhzxdT8ogXr9sCCnjkAyzDRoXuxASTDuAgnN9Tay6vTHPcwgAiyzHg70HWCwWWF9fx3gywWg0wmQywfraOghR27Zu3bmN/cMDFJxDAihLjnE6BhcSd+/v4vadu5CMYG0yxkMXz+Pizja21tawMZ3i0vkLuHj+AhaLBR482MPGxjo++tEPYW19ilu3buL+7i5miwX2Zoe482AX0WQ8sB+Gwco9bONdc64WrjDGqhC5Ca+aMCHQbQ02Il6WQALQOSx9npmOH2P/cB+bm+t44eUfY2v7HHa2z+Opp67gjTdexWiaQNrreSDV8n6gWlnrgq24uhRTyEJXgtvOawvaprVqh5q6QvBwBoe3PQI0uXUwYVtfWV1es/tbWulD+bpw9JUZft4829t8t8vwhRWX8+KbNDSVdc3f5sASou9cdbmpoeAbvp/9rP3eVKL2HNuGQoVDmpOcimrth8IX9sx9holPUfi+LwuhvFVbkmbNWyKeeF52AFkyvQeDlVm2frfHmltIQElJwO9PLU9kd5v6cTYWNmp6wv3aEw/okJs+ZW6nkapgAM3p1jzLwCiFKEsQSjFbzHE0m4ELoZT1eIxHH3kUk/EYSZxgMp7g/oNd7D04wIODPVBGkUQxoigBJRGEBO7fe4C9wyOUQmI0XcOF9RHWRyNwSnH+kcu4cG4HPMtx784dbK2tY217C0eLGW7cvoVHHnsEP75+FfEoxd7sCLv37+LtOzfxvg9+uLNtloVTCYnHcYyiUAIhTVNMp1McHBxUB7K7Qt9VCk2EaPID1VK3k4Gc/ADUdoYSQARBgOl4gue/8Tx2d+/hv/tv/xa2drYxmx8goYm6+ANNZjJ3b3caGB6rd1nFE6xGh/J0y/IZD0PKChkfqwATfuyiZflwd0d5AQPKZ0wMxRcCt92aBikA2OF1iz605/9a5aLpqVNKKu9cecOyVtBVGlpHoKqrZgCiL9CgNMJ8USAvyppm2eZbZdxIL8/bSnsVvNIMT/vheAeS9mElzld5TMXdLM8XJg8qyND8g9773WzeZYkbnl71aWhsAsDycsEXeQvhsJ+3j5/WOxwgEVEKQhnKLIeUEpcfeRhvXruK2WKOoigwWVvD/t4epuMxKKHg+pjSrCiwe7iPZJwCUnnteVFiPRmBlwKH8zlKKUEYAxccm+MU59Ym+PjHPoejgwO8/eabODw8xOc++7PY3N7Cm2++iVffeh3X79zCeHOMjXObuHX3NmaLQxRFBjB2MlbywMoVtpm3NsyZ5zkYY5hMJjg8PAQhxLuQyYVK4A6Qp53CtLJyCSijEFyqo0h1ePDtt67hH/7SP8L/4f/4t/DmW28Asp6r6VJ8Xd7YUMVde8yymrdTc+1Wu9QSvlWuXV5VVk8Y1q6L7RGGaHTBJ7RPquCXKd+mwRc5sGnqMwCGtoGdrqts/+9aFdllmEjIkHpWINRJZI2SdZIqSuA+qKjQh3xKCcoiZFmGPM+d+9ub9YU1nzkElokwue0o1cPhhRk8jUJ8KYaYAk2a2ijsvgyBs13Ni6oO43uyO+2nHxMn0RL1qfOE07ueLQnUtVPROqm7FHKXkefSYqcrdfIoUlcpl0UOSSSuPHkF9/b3Mc8zcKku/VhfW0NccuSLDJP1DUBI7O3tYXN7C29fvwFCCSIagUYRCGW4t7uH6zduoShKrK+tIx2PIYoCH3z2GTz95BX86HvfwSQd46krT+DS5Ydw595dHGZzfP+lF/Dmjas4LBZg6RicAIWQoFGMlFLwkuPBvXvBtj8OnMq2LnfgZlkGQgjG47G6HlN7ip2ehav0DJ/L5vsQuIxBCAElEaSEnlMHklEMLjheevlVPPfcV7G1tYMkTQAiYK6qdD1rdy7ZfPfR7ypIt24GX0O4BwQ/8ZRjaGhsbfDMN/tocAdP6GPn6wpfDYEhCvSk0NUXqmyjiNr1DCltlxeXo7NmWrdMH4+02lbWAlFKqfbrV950feStSmrRRlB51tTKL6UEGEVWNEPigFCrtIlfubq0+a4jbdXcyeNrv3p8y3fvJqJVuD2upSDDdWymUx/pPQDGRl3zjeluQuqJi6pdT1KFjvFdH8bSvYZFP+xVxCEe6roJzM6vppNUm/FS3aJ1/twOJpMJ7u/uYlHkACGI4xg7O+r5ZDxW3rhuw0JwFGWBmEWQUuqTMQn2j44wzwtkWQbBC6ylCZ545DKiiOHevXt45OGH8fjjV5AkEV566UWQiOHtWzdw9c4t3D86QA4OSQkIoRilY5SlQMk54jhGEv8JP0vc3qpCiF7gwjmKokAcx+3bpDxKzechymqWzfGArbK7Pb2mwqVMbQUTBFjkGf7wi1/C448/gVFE1NxItWWMgOotIHAY3Hy3PdyQ0Pd5HPUzCTcUZbeF2y52uS4YOkOK2gdd7+2B1KXIfLR0KWi73bp4oCvvUHD5TeUHfF6Ej6bjQLNNlaFgvGojgEIXAzTz6oiLZaiqkLjHSzdKA7IO79revblTWq+25dxS2Dq9Ktv0uZ+2ZQ2rZdpz2XZvBJmHknWsrnU8zw4cIfnWJEBWNLspCGCtiu+OcrTyHjPa5XcGNIEdpfbJjmUM/16DmAB5kYMSielkip1z2ziaHSHnJUDU8bxmOoowhjRJ9Pn7BKPRCIdHRyB6txIhKqwuBHDn/n3kgiNNE0xGKdbHCTYnoyoyHMexPhCFIx2lmBcZXnnjVTyYHUBG6vjrNE6QxikoKPIsR5oyrG9uVNHkVcGp3NZlh9kMw2ZZVh2D6Nvi5eJoWfBdBqunk22vqlYs6sOYCl8JqbyKNI3x3e/9EN//3o+QLTJELFILwYRasWnmDK1ivfT6LEbz3vXkml6b3xv2elxOuZ4m6bRy6xPSmjjs8uw6DK1TqI5dIeYuRe/73lf3ULpQOxJS09nVznZ9XJp83oOHijot/PzqCnk7VNxSAIRUnnBTX2s6hKyZ3VTUvIcECFGLzjivQrE1TXYfttvRV68QDJmWaODSt1y5yf14nPHufHzoB5Ld0v61bSc96QIoPHzh2mHV0cL2kayoeUw5lMvxeZ03PO7cZz6wo1Hup2vs+5Sw+3eoEm989BgtywJpmmBrawNJHOHe/btgcQQwdTuWEAKLxULdSQ0CXqgdSuPxBHt7e0hGqVplTikojbAoCty6cwdcCKxNJ9jZWMfmZIIRIRhPJ9je3sbh4SFu376N2XyGRx57FG+98zZ+/NbrWMgCaZpikowwTSeYRCPwLEeR56CMYW1j/ZjGYRhWrrCp9k7tbVzGyjg4OAAAjMf1Unefh2ULc+NNUNI84UklRmMUeBWYxbxCSIxGI32MXQZCJCJ9U9d4nOLX/uVv4Padu4jjWIXupTpvuTqTN8DcLebyDAofk7qGRBfuIbiMMDeRjVBoU+hLH9wT3HwDxnfSVai+vuddinXonLkP38nC1Ab30HR+A6NPufsiEpXqbEnvNj7D+406WuFwNz9Muuq3xlOVKSrxzzlXi3EAcKt8IaU6R6aiPawE3OofJyphFJPR0tWJC5b2lda/uvBAWaE+7bK4vWk9sqYLZxc60i7c5hHV71pxSwJIWutHB0LywP49FNxxu0y+rrE+RHn3pTeyRwihQtdEHZyytb2Nzc0tlJzjtddeq3TM3v4+BIDt7W0sFgtsrK9jfjSrdJEQAkdHR+oOCZZACon5bIa79+7hcDaHkBKLxRyMEWxvruOhC+dx4aFL+NELLwAgeOLJJ3H54Ufw/Le/hd/99/8eNIlA4gicc5CcY2e6gUgCi3kGAoqyFNjb30eU/gm/Dxvwh4MppWCMYTabYT6fY2Njo3FimJ22jdD52GmFbAlvL02UgMQMAvpGLxKBSQYmKGJKQQiQF3P88r/4DXzj+y9g+9JldbcpJCgEJC9BAMRRCnWQVD3wjHFiK8nQMaZdRz3a9Nv7aM3zoihQFM0jU1ttJ2WjqQTn1eE1Nhh63fzunJV5bo5tLcuyoejNM9sAcPF1Kdy+flsG+nC0BZT9QdDQ8eH2Kdce6qoyKSQiRnT0pvYq1eJuBw8loBEDlwJclkqvSQkpOIQ0V2NyACXUPDSBYAlAInB9RzbV00lCADFLgKIAhVogGkVa6JhwvZCQ+ppCUV0Q4e+f4ygLPy41tcX1x+/XOTos1NzEwU1QH2/c6YJ3wfIZfUYbsb10h8z2eFblEYQv5jFwMqM17O2GymCRBGW+S0WaedzonM8BKEuuDb86WqSMSQEuBUopUAiOPM8xXVvDy6++hpd+/CpmswWefPwprE838M7NG5jxHMkowSRJIOZziDzD1uYmklEMSQUEOKgEIs4hwBGNUhSU4trd+zjIclAaQ0qCtfUN7FzcAR0RvPHSa/jo+z+MIi/x1vXr+MHrr+L5V16EXBtjOl7HudEGLm1s46HzO7g8XcdkY4r75RxklICCYO/2Lvb3F0v3SRec+vWa0qyaQc3EQgjM53Mwxirrh3PV4eZZURRNpjdMa3V2KETpe6aYRQJCqONGST1XRAlFotPfuHELX/vaNzAejfDZT/4M3nj1FYzTFOloBCEJ5os5KIuqBT3G6+oaVL45ebs9fO989ekLM9t1bexztwaLsejdsLjXG2xNKdT7wX3K3pRrLyr09ZGP7q5nboh8qGfeVe5x8/g8fNOmvjYxoUVvnzXGBuBTCPYhGuq8EGVE2knrfdfqYTUtTUzI1VY2AmVRYJEtqkVn1UJKoKqL+jT74qRGVR+PV4MyAMN6sskryndVIVVdQ+hZgU4+a9Z7CT6SdRu2XtnPPOh8nvhJoU+RLz/n3eYLX3ldfGPLKsaYvt1KbQfO5gtlpFJ1naxykiJwIfHlr3wZH/vYT+PJK49hYzpBPltgPp8jTVKMJEee5djN72M9TasrMycxQyk45vN5tY87iWIczBe4e/ce7t27hzhJAADl4gi3b9/C5lqKp688ivPn1/Hm1bexc/4cDhZzXLt6A6+/+QaefPYZbK9tYBzFmI5HOLe5gUcvPoaHb97Fm/fvgecFjo6OUCxmeOb9zyzRtv1wKvuw7b9Am9mFEMiyDEmSVPPZPsYSUnkf7mWCUspqCsid8PJ599U7nU9zXD1VpUOAagW7xBtvvo3oS1/Fow9fxrnzF3B0cIi8KHVCCWVZtorupKMrXRdD+9rTTucqT6+C19W093G7eF2l6BoidvjOVcL2c19djOdq5/FFH0Lt4+vTLkOgi/fcdDWu5ulgIbyuUO8yRNrt1EzjM3yMcq9os/JJzXStIgmq+U8C6Lt4KQBeyX2jsqVURwdniwxlWTYWglZ1IaQRgLYF9KqiIUHwBdhs42TZsLsZ5wGDqBIAVfIhdWvmaRe5fPu4vDJkFb6d3lf+qpR1Q/kCMJcHmscKj3prjmIOeegGDM+xiAEEEFw5bUyfwgcdFSISECVHlCQ4PDzEdG2KydoaiD6Tfz6fg0UMMY+xWMyRlyXWds4hiiK1F3ttqu65LkuMRmohWSYFHhwe4t7eA8yyBZLxFCUvEEUUeVHgaDZDKTjy+RHWtzaxe7CPN69dxdXr13D+wgWsjSeYRAk2xhNcOH8eW5sbeOfta3j5xRcxjhNEALY3NrDz6COYxklv+y4Dp+Jhh5jAFtYmxGpCkMbSt4WoERp901VDhYjxNmlFh8GiFZtU2wJmszleffU1fOm5L+Nv/NW/gqPZHCUv1f3VjAEQcE8CWqY9XGU1dNCEvHGfNetTLq4SGeLVA6imLtzQvr240HcmtkuzW6b7zn7e1ybH8aB9dLjGxlB8yytr58hStPuk5kcLiO8IU1+5pPpL7bIlMU5lhdrs2mgfWGTxi1UXu16rVtYNfmiUTJznJ/Q4g2QfBy8J62xjHbvFW23ni2Z1GeZDoG88dEUTfOm6IgT+NKQy7Hw84zof5lPqEzEJISiLAoxQSD1vDaJWfkNKbG9vYzQaYTSZqANQ9vaRzxd67nkBLtRUXUSA7c1NTKdT3L9/D6XgACHYPzrU3UaQlSUeHBxg7/BATZASAJCIYoYo1jfWSaAUAogobty5jXdu38TBfIbN7S1sTNdwaWMbYxYrTzud4JVbdyELFcpP0jU8fOESnnniCvbnh539tiyc+tbHUBiXEFLNfQKoFLcJj1NKQcx8ovQwvIVukLLW3odZPagfNlbhUgARY0jiCEeHh/h3//73cW93H1GS6plsgFBASN7Cf1JBFgp5uyFp377Frmcuje5qdvt316crrQ+v+wk9DwmIUHjSfuYKh5ZS9AilkKAaGmoPQV/oX6fqwdJugzqHfaa9pxyiDgKSBKA0qvNbvGMMK7WQh1e/63Fli4PVhGSHgokJ2Cul7bdUvx/yIVKCyNoFbNWkw/AYzAPvbvN4wTcGVpnXNz7bf+2PPzLYJa/UvRPKaC31QkgJiaIsUXJ122OSprh8+SGsb26iLDlu3b6D1958E9dv3ARhEfb397FYLCAFRxxH2Nrews72No6ODnF4cIDd3V3cvHkTizyHkEBWltg7PMTRfI44jkEpMEoTJHGE6XSCNe3Bj8Yj7O7t4drNGzhczBGnCYgEHjp3Hk8+egWXts9hHKcAl4hZjI984IM4fLCHtXSEKw8/jCcfeRTn1zeX7psuOLVFZ/W8WrsDCVE3eUkpqys47bBphacaFe3FUA0B6wiw0Hd7Vbnr+QB6hXtZgoIgSVPMFyX+4S/9Y2zvnEOUJijN4hzhVzo2hN77FFEojO+m7VJ6blnuSnADIaVrIh7u6nH3uY3X/msvSAstNAsp6aFWfai9h3onPkMopNz7DIqTgg+fOwWhvgCESMVzzuGczKg3Yu2gIASM0RY+VRdRTUER1AvszPqROp1rgATG08qg6WX7lPBxgDh4GzWxwrmtfAMMvyAMZRPZ+jIIXHpC42OIIg6l6Rtf5rX914fTh8c14OM4rvQAIQQsjiGgo6qUYDQZ46HLD6EoCswWc/z+f/gDfOcH3weJYjz6xBPI8hzT6VQtoGQRRmmKKGK4fOkhXLlyBc8++wwefuRhJGmKKIpACMHe4RyzrAD0PutsMcf29qbaLra5ia2tDeSFulBkb3aI27t3scgzjEYjTCcTfPDp9yGSBOe3drA2niJmET718Z/BRz/0YdCywNOPPYonH3kUTACTOO7r0qXg9DxsD4+7nWoUtBH4UsrGQjTDzF7hhpp5KfHc9OUBKtE9PoRUh6To8rZ3NvGd7/8Qv/mvfwdSUpw/fxGilHAOhxwkzIcwr53OZfyugRlS5pXydaspBIS+J9n34VxUHyFEFQnxKW5XybvK3FXyPsPiuF7CshAqwzd10GVYdOF1+8k3FymlXFJM63w905pSSqhFT/awFsrjFLXCzrJFdV+8a8yp30Ooq8fnyWG1RkDdn35/fTVFBuo+FC9pfTlpyauBSgl3lVLHQ6STvstI973LFxnKoqyeHxwcYJ4tUEqJJ555Gu//8Idw8+4d/ON/8k/wzvXreOKZZ/Chj34Ulx59BKWUOFoscDibg1KGza1NnL+wA8k5Hjy4ryO1DKN0jM3NbVy6eBnj8QTXbtzEwdEMgDJWj44OcGF7C6MoUguM4wR5nmNjYwNvvPEGKItw7tw5bG9sQixyzPYPcPniJUzXpkiSGJtra3j2mafxnW8+j52dbXz4Qx/ClUcfRkQknnniyZV2z6mvEg+BmT8zFr8RbI15TRFeVEE8g9E391l5q1DDV8IvhIms52UZJZCatsl0in/7776Ara1N/PSHP4hROsLh/gPELPKWY9ehgT/gwYXmfl1cIaVg0vjmZX3l1QqyLbB9v2vcAooUs8+7PR9uR0nsRWb2c9+8eKjtlgEbVyic3v3eH5L34XXpdr+HaGu8khLEc463v/rUmroxElIrcApAEkgmVUhcKhwmrZQSQgoIySv+ns/nKPUctrtzQOWzJ2Lr73U95Ym0RqiNfO+WmdZo4Ko86JpUnxIa6mU2yzRt5IdGO6EeI13l+cZsEHwOTEebduNyf/o95C5cIYPWPYDJp7iTJKkuipJSXaHJywLv+8D7sX+4j69/85v43vd/gD/10Y9ivL2JzZ1z4CC4eecOrr95FTeuX8fG+a1KxozSEabTKaSUGKUp5vM5DvV+7CiJcff+fewfHGC+WEBKdTb5pQsXsbWxjvu3bqDMc0BKpHFSrSxP4hhUApvTdXzisz+P82sbuHn7FkajEZI4xohGODrYw+c+97P41ud/gMVihjiKsb29jfRP+vWafYxn3tdCrBbmRoA09sKadNS/MMPMe4egVthmYZl+Rmrl3ZSjQp+eBiwWC+WRLDJ89StfBeUcn/qZjyFicQu/W6ZPQftWc7uCyqdU3PYz+Xxl2+3r/m5+hG/ct/DafeFGMeq/ADz7Rd06uQLAJ6RDz22cIeHna4/WNEtQsLUvDAmFyu0+8hkbvnaocVnh5RYlPrDGAZRhaS17rj0cUw9i01dRDaO8CSHIskW1n9/wpZnOaF4uQqz89liS1tMhoBRXqD8HKyqDbUB6ZWZIFVsl+u+KcHf2nLSVXph/hxoK3YZD2Aj15XPHleIXTfNSCjl8varveddUXpHn1fuyLEEjhq3tbbz08su4dv0dPDjYx9PvfxZPPv0Udn/4fVx95xqyLMditsBaMsIjjz6Kw3zWiNBGcaw85OkaoKdTIx25ffDgAfK8BGPqDIJRmmBnewOMUkSMImIMjLBqV81sNgMowXQyweb6OlBy3LpxE+d2dpCORogYQaSdx0cfuQwCUyfl3edZEWzX48BPzMMGbM+jPS/TXAzTZvIupRYoraGeq0FFjOeNessqUK1UjGOG8SjFq6++jslohIceuoQnn7iC3ft3EMcxjCBUAonWAlTqKABtC3dfPez6+AS/75AZnyJ08QNqe5w7UHzbL4wYlrbQMdYLAYg0fUWr+VTbA6e03jWs6kOr6yDtskIK1yfIQt5rn8fSB26+Wq8353BPgtNACxMhDVeaEL3mw1p9bCJCJpWQUh384CyaJABEld4YHnVdTE9W3E8oFrMFJBdqPsycL24ZyOpeCqPkW5UEJIE90dLHf+rnsPHqGkBdxtsQHBpTM2gwON+wd25RoVXiq4a2YdZRlueRT6aGIJSuz/CQUsL+B/1XSAEpJMpSH86jF5vNeYGDe3fxxhtvQfAS587v4H3vewYZJ9hb5Jg92ENEKbama3jooYuAFJjdmkFCggGIqDpDPD84QDpKsdBXcMZJCgHg3u4DFFzdoT1KYqxPx7iwvYlxEiOJI0SUqpXqlGCRZyAAJukIa6MJ0jgBLwrEcYTReAQJiSzPUQgBFhNsr6+DSIaszFHwAokkWBTvMYVtFI27YAxoKl2jSEIhYp+yawCpt8o0HzseKdHPLKFcOS3MHAmonkSUgkIgSVR45MVXX0M8GeO/+fh/g+zW24gTBujToNRIVXvuhBBqXStRit8MDHOilM9Asf/6vGR325uvfi4OQ4utsOs0tTA2z6sjKm18Qj013qGaH+UgQvUVJeZyFMvAkmrrHCMSgNoGR1XngFhH1oa8Vxdsuo3H3+d5uHlD4IY6jXKRknj7KuRF++pBSL3FCrJWjMQobFIbQIRQEHO2tNYvlKh1FwIEuRBgAkhJBEYpQCQkBVAKyJhWJ5pJohej1cWq8SfrKYvF0RwxoRA0AswJW1RdP6tOOZOWQ+qcpqc/wpmSWkYhdfWfTxl0hcnD8qD6r7OMPjq70q8yMhA09gLj20nVS0vfqokhkQ7jmPieu/2mPuq3qN5LxbdaH/CSg9MYIueAFACjuDvfx/Pf+g4evfgIfvpDH8FjD1/E7oP7+PoLr+P6g0MkEcFHn3kCP/XssxhRgu/86HsgVGJEGVJKEYFhMhpjtreHKE5QHM7AhcBoNEbBJe7uHyErC+1JA+tJhHNrU2xvrmGUxDqCRRBHMW7cvYnz585jOh5jOhojJhSbW1u48tgjuHHtHRwe7CPLM0ASLKY53rd9DqJMMSsz5DLHOltHMXA//VA4ldu67P259pGYruDzeYcGhgyGhle6JJ0NWoBKQEnheo6KjjSNUJY5XnvtNfyzX/5n+N/8l/8FXnjhBSRRhCRJwDlHli0wHk9VaMcTTh1St1CbLOvxuSEqV8lz7glTkVpZm6Ep6kwNBVbRQymkVAe+UkJAuDa6KAUgQETtDUooZW7TZuOzj0v1KUmTp2sKxGcE2O/6IjP1u7Ci9kVC3FC9bQxWEVnLkdVNUucPCMJQKLmLfkpNtEcbiIYGpb1Rcn28rFkUKCUkV0rdEGbS2nRYhahfHl4dwrtdHlooGtMGv/I4DSDGgoKfnwbjCcmDnnxDvNhw5ray7pIxvufN9O1299FhFHaSxChmavqFaIdIcAFI5cxls3ug6QgyGuHgKMd3nvsWnr3yMH7hF/8CDrIcz7/yKl748WsoGAHKOf7CZ/4Mnjx/EawscWf/PkbbOzjY28P6aAwumbpVSwikaar4WwplNDOGw/kca2trKG/dwCiJsTGd4PzWNi5s70AKDioJxqMxxuMxZkcz3Lt3D0899RQO9vZx5bHH8PhjV3Dw4AG++MUvAlzg8uVLOH/hIiaTKTZGY+RlgWiU4CjPkPES6cYYB3tZuG+OAafiYfssYJ9nZBjhOPNYbjnLQMvTN/8RqDPH0RQaRVEgiiKUnGN3dxdf+MLv4ac//CyefvppPLivDo9fW1vDJEqQZTnSNFUKUajrOftosQ2cPpoN3dXvDrlVRTYcHKbdW+f8WkLJ5LEjfEap2h6/cmT0VACp9/kCUMdBEoJIaIWljTmXJ+x58j6FClhXpDprAo7LD8cJt/Yp/5bCkdJozTrfwHLNlIOZnzZ9IqV1P7LaqAVCI0AviDRXtpqypPaki7JUhphzbjoh1lqRwKJEIBxdPs4YPj6cUFl36Xv3XbCo/jE4BAZE6xslLlXUMshDKBpRSkNFU5G7ssVQm+e5GqdcQBQ6WkfVGRyz2QypimPj2u3r+M63foDPfOJT+E9+8Rfwb/7oC3jtxk3sHS1Qcomf+5kP4+7NW9iaJNhaG2MSj4Akxve/+zzyXCDemGBzYwub6+vY39/HhEVgjOkV6MB4NMbunbsoyxzTNMHGZIzzW1u4cvkRbE/Xsbu/iySKMB6PweIYe0eHICDY2dlBmefY3d1FvsggyhKPPfYYzp8/jzLPkRc5Huzv4sG9u/ipD/yUmj8vS+SlQCmAd27eOFnjO3AqR5PaK09db8QW1vZvN/RowGdxL2fVVo5Cp9Vu+Nr2dky0QAgBQiniSDHBfD7HL//Kr+K//d//LbA4AeMCRV5WlynY4R/fiWjHFWxeT6VydP1nexPdALbSVofVeEJY9u9mwS3aq7/mf0KshU/NeXt35anNI3akpWVI9XpZ4bZZNr3r9bvpfIalTbtteLg4pFanxlFdhtLapDRtYXs6hj7VxoRQdTCQfcSq7bvrrFmeV7xjsJUmlkLMio5uT1g/DL/rgXaepioKRVhWBqRD9XUU1+QBEzZppzHQL7f89Q616XFaIjQF4YuODI1s+n6741gCyLMMozjRZ7lr+VMKzBcZWBQjGm3glTdew+7hA/zMpz+Gj33yZ/Arv/M7uHrvLmazI2ykKX76pz6A/+hnPot/8Tv/P+znc3zvzVfx4NYdPLj3AOcffQTrozVsb2zh4s42tjanyOcLbGxtAzDHnAIRo1gsZoDguLCzhXGSYmMywYWtLciyRASKNEqQJAmklNjb38PO9jZEWWJjfQOUECRRjHgywf3dXdy9exdJHCNJYiRJhLVkDCqBvMiQ5RmybIE8z5HE6TF6LAwrV9jGg7IFWYgJhwjkkBD3pvU+VfN5SuTJ1mU+KkX9Vwo1AA1D23PwhKgbx+I4xltXb+C5r3wVn/rkJ7GxvokHu/cxoQzS8iLd8G2fN9ZVt5AQ8KWxFYjQR/yhNai66TDKl5q8gaiJ8tooCNGL1RwPsM5To3DbptnGJLjAzv3ua09Xobrv+gxA9ds0WVs5H8eYqHE4dFpGAOlRII3yiPWC6OU8kurbGR3DV0qtU9TiJCEFFvpuekOHlGoRkBuyd8HnRRHnXbjufcdvhvs0QA2C7TUIjp+3y4irQKJlqK6aDpueTrloG+8Y0hdovLPluXpGG8/dNICZt65Xf0uh+EtIgbLk4JAYjUd48/Z9zEqJre0LOH/hIr7+/e/g6t07yLICVx66jPc9/iievfII5vszHB7N8IMfvYDNtXWc39jEs+fOQxB149e5tQm2JmMkUYwsz9U0peAwa5WElMizDJvra0glR0IZ1sdjjNIUR4sMMYthzFvOOQ4PD/HUpUvghTrfnFEGSgiKokDMIqxN1zAapYgjhogSpDRS63ZovShUCIn5fLW3da384BRzmkyXcm6F4QhpPTfvhoPsZ/2+MKRsexVGaQOqA6SUiKIIIBG+8tVv4Oq16+AA4jRFlucAIer+1o4B4auzqxx8ysaHyzdoQmW1KuvgCH2EtA9WsY8ildaCNs8AVz+89LbK6DnaNNQ+fbR3tZfd7s2//nymbB+EPMJWfwRo6fNrmuPHxmeFkCr33eYp2ztXhxTlWabnq33bc3yGdZtmaf12KO2kv0+xD+nDqq4/IXDHXPOl/kvaab24BpS3ughDmLdd/vbJb3uc93/U2ghz86Lg5gAldW0mSxNkguONO7fA4xTTjXPYO8jwre/9EAQSj+xs4SPPPIlnn3ocC1Hg+e9/H/f39rGYZ9ha28D7n34WP/XhD+PcuU1M0wjbkxGmSazpJ0iSFILrOx8IAedqLdX21iZ2Njaws7GB9ekEEaMAJKie8iw5R1EWyPMcmxsbqhuFMq6FEEphxwmma1Ok+lYwKYCyUFcYR5SBAOAlR5Zn2NvfW1HfKVi5hx3ro9jMRnigyRDujUjmY6+CNu9CjO5n4G6mVo7JsNCqHarlnFenr3F9YhcAbG5t4tr123juK19DHEf4xM98DK+8/BIm43P6fFw0Fp51eYWh93b79Q1aVwn58DUVnV+Y2EoaULwKaH+GGDzQC5sEpFAL02xbqFo8JmtPnZC24LenTmxa7XC5Wz/b0/Up8j7oS2MG/FB8Q/oGEmbxd+UXDvEPpZRVPiHUNAal1QoLTbDuSwLV3tLEkgB1q5z2rqQEI8p7yPO8iiIJu1OIijDZCrlBS0s5hT3lUH2Ggq+/nRSdZZ0W9NZhAEkNnu/BP6TNfAbocYmzle6QyGfQ+NZKmxBzHS9T54MLASQMo+kUL732YxzyBSbRJm7vz3Hj+g2sr53DVizxp//Uh7C1vY0bt+/gD771Ddy/sQtGgb/0i38NP/XM+5ACuHr9Ki5dvoSyyLA+HmGUxDgqOQSXiOMEZV4ohS0EBBeYTifgXGAsOSbpCJtr6yASiJMEBS+QlQUO5zOwJAYlFGuTKY74AUopITiHJARpnKAsS1y/fl0dpyolIkKwPp7g4YuPApQhL3PsHx3gPh9h72i/t82XgVOZw6aUIoqixvGUoTlDOwSqGFltkXLnB31M2acEjwO2hemGahmlkFA7kIUEtrY28N3v/RC8FLh4/jzOn7+A+dERRqOR2sjfoXh85Q6tx5DpgVA+SlVY3wjzxlwyamVNhFo0V3lSaA5ilY/pFcgSIAKMWUrU9pCIhNom1abJV4f24TkAiF+R97WRXY6r7EMhdWJ5qS6Ndr7Q90ZaEEgiG8aiel7rOyk71RwkuE6s5qlb5cC0sxX+1O9Ete0QIJRAcI6iLMGgtuOZi2wY1MUhZn9s0xPzG9GV8bEkH/rANuhC7e3kOHGZQyFUv8bzJeyHkFLuU7ydnvoART9U+fdFQ9z3bp5q/AMQeiurMgLVwtZSCOw+uI/X3n4LWw89hIPZAYpMIJmOsTVK8R//mc/g5jtX8aVvfBOvX7sGOhrhv/pf/pf47ve+B0Dg/v17yI+O8KWvPAc5ZvjgM89gfX0dUZRgdngEIdQRvLnIlLwjACMU25tbuHv/HtbHI2xM1zAdj1DKEtE4xez+AUjEMJvNkMQxzp/bgShz7O/vY3tzSzls2lmbz+cYjRJsbGwgTVNMxmNsrW3g2Q98AI88cQV0lEBGBNPtdVx67OHeNl8GVq6w5/N5FRZ3B5pvj3U73fIe5iAvx1OuD7gUIFLtn40is9rWZlyleBZFjjSOQBnwyo9fwj/71V/F//n/9N/jjVdf1SsjiZpTHOAF2gaIUqZo5fHNtbr5Q2Uo/eO2cwnOnSsyTRl6XlpAuc/2Jio9FkGpnoOW2qNmTY9X6v+oSU8kGGUVHTYt9uI+ANVxtXYFXIFgjA8fGNw2TpN3qNJWyjE8LTGU39S6iV5fGj6Jb8LflfqswuF+fJV3IwQ0G9W4ierfjHN1jWHEAFg7BXT51bQEkZ3K2EQJVgcK4yqU/08ETmg/9NX7ONGJvne+qInPGbC9Z50qmL/ysqWsDEkpJRZFjmQ0Ahccd+/dxQ9+/DImO9vgsxKMSsQjgvUpw/uffBpf+MrzuHrtGqaTMT732T+NX/zZz+LhnW18++tfxW/9+9/B7PAQO9N1/NwnPo2//Jf/Mm5eu4ZplOBofoSiyLA+narzBhYLRFGMiBAILVvjOMY4SZAwCkgBIQhkFOFgdoTJ+ho4JHJe4tKlh3Cwt4/N9Q0AQK4XaqZpWp0MuL+/D66EIVIa4bnnvorXr72N6Qwo54c4uLiH9fObg/ttCJzKti7OOaIoQhzHiKJI7Y1zDg0xHqy5StMIjiRJqssmbMHuzlm6z3rB48X4wu6M1Oc22wK9EVqWAOMcLIrUQfFFgTfefge/9i9+E3/xP/mPsXv/NohUR0sUpajOy40i1ogcCCEQxXGtBYFGNMKtsy98bHu8dr0aVXcUfcSYbndSeb7KERZQa+6oWtxAKHIITZ9qRAmhvV2iLWYBBgZCCfKcq/2PkkBwCVABxqgKxQquDk2x6u/blmWvqleKnFgd176FytvVur7GIu6LxLQVeJOvQtMVNj+H2lqRbXvhTcWs6kuqFRiqdLWHWkoJSSkko0DJISTAklTh5RKMqWkaJgmIICBM39dOGSQkKI0gyxJFKcG5uhlpcX8XkBzCLByCPoxGSpSFAJGabjsE0JiYJZpeQ/FyIds+6B/T6tIR2TCC2n0muTpiWEWOtDm0pFF/EuPhNA2Pd8uo8StkVJ6zPT6kVIvLeCW/OGRZgAsJSQhKQpEXJW7t7uLVN6+CRmPwkoAmDAICjEagNMWLr7+Gt99+DT/9gQ/j4x/6KC5ub+PFN17DP/7tl3Ht+lu4cG4Tf/7Tn8YnP/RhXNzexv7dW5gdHWBt6zySKEGitzQyxsClMjwBQAqBmBJsTaeI0ggxi9SRTkRiczrFoshQUI5IAhNKcGFzA/f39pEkBJPJBHmWY7GYoyxz8DLHeH0d29vb2BiNMYkSsHQMGaf40os/xu5sH69f28ONe68gmiT4v6ywT07lLHGgFpaUUqRpWilt+3AMoOk5cc6rOfBQSNP3zsbj0tLIN7QOqMWQ64URqIUXTMs04+kVJceXv/YNfPCD78Mjly6gyNV5zZFe1i+1NWcr3Wo1tCcU5lMEPmvYHTS9c7QqccOzFnoDNpFEryY2G3sICJHqfgld7wY9AjDbjIi+Itx4aBFjYCyCBBrbyFwaXSXpPq/UGzH09ytqt43svy38rb/whu77yuzz9nVqbRmFsJG6cEshyeo/6AtD6laQ0GslYHiGwrjlhJgi9VG0hCBfLKpwpfGrK4PRhM7hJ9H1sAxRQ8djHzTwuEW1XrQjEo0ojP1sYIf6DHgfbb4oyzL5li3bxtNVzpA2tx2GLhiWBrDHtYnUSCkhSg5eFpCEgUuAE+De3h7u3NvFoigxXl9HUXLEEwZGGaQkOJwtcLS/j4889Syeffpp5KLAD155ET9+9cc4KiXS8QjnL17EhQsPIYpS/OiFl0FiiosXLiAiEgUEKKM6UmkdwEQIwPX2rjgClwIRixAzhoXgSKIIlAB5WWCSMKRRjJiShizjTK/9lhLpSB1Leu/+PdwqS5ScQ7IIyfoWOJUYr22AJQlKEOw/OOjtk2Xg1BR2Vdkowmg0UmGRxaLypG1BapjIvobR3R5m4x46CFwvbij4cLrP1JWe6jljDIwxvHP9Bv7wi3+M//yv/hXQKIYsSucsbQHjCFd7drloyJ2Qwu1VxIQEB7XvuX3hSkvx6y1AEASUOIdTylogGidMSK5WfjKmz2DnYFR77kKqc3X1/ka3D0PTIV7h3UwySEg20pKmuvcbD+Z7jctOFwqJez3rjvRWTvi9VKV0jeoxCrlaCK6JrN4TwwMapzmkBbKB3iyIdNtRjUs03nV7uT6V6c83VBnpKrlEeAqxHwTGt0li8dmflHC7b3rLx4vmnf3bTq+iHKTVxy7Yhozvuf6FQS6NMZyJMualrG/lUhFTfe1uWYKX6oasUgjsz2e4s3sf+7MjREmiDhiZcQgpEdMIouQ4XMwxYjE++uyHkUPiOy+/gLffegtrYPj4pz6O16+/gyiKcf32Xezf28fNa1fxvvc/haenU1DJIUQBSQlYFAE6AkSoMmCllGqbF6MQZanC41GMLFf6JkkSzI5mWI9SxIna4kX1RTjVOf/alUuSFIt8gVu3b+PO3i5250fIuABYisNigbXNLYwna5ASyPJ5f5suAaeyD9sOz5pVqUmS1Ff76QYDag/VMKGtsENh8JAQ7BKqy1ighJCWYWFA4VAfSpMqLSEEk8kIf/hHX8EHPvB+/NRHPoTp+gbu3LqFhx66BF7mVr1U3XjJteKsfXpfPe3nbn36pghsg8gGSoh94wXUIrSmEpUEiEmE0sxrCgEOqaMMUnujRB3iL1S5KiQOQM+PcsHV9IATMQiHtWvBQYg9ULqgKWyCnrblefnaq8lbbaUdyjMEWrzk5rOq0KS/adhW4WpP/WojSOGrttxZSn+xWFhYtbLWH3M0LYwSCNSjr54+6Bp/rfpKQ6FqlFZTedAEjVT1I5h+2TD5aYBvnIfaq8GL1TRKuL+WKz/cP7ackaJJr301spGHpRBKZbIIRbnAy2+8DkEYEDHIgoMLjogx5AczIImBiIGlCZ586mm8eO1NvPDySxBE4MlHHsGf+fgncfmpK7j2u7fwyksv4cc5xzOPXsF/9pf+Eh569BKOdu+jFFzPmwNMT7UKXiKJUlDGkEECkiNikZo6okpuQQBFlmGUprh37x42khHiOEU6GmNtrQTTdTJOWVEUmC0W4FJiPF3DldEETwqCo7zAjfsPsMsf4OD+Ho7mGcgowUwv6lwVnNrRpObEr7IsMZ/PwRirvCwTHgeaIUU7TNsKRS85sE4yEEMDyMVf5Jm6aEFHAzY3N8F5iV/5lX8O+Tf/F/jsZz6F9Y0pdh/cxzgdVSFLAgJelFALthgkmlddmkEQUsB9tA4BQqyLKaxnZq+5MUwY04OQEHCioqbqhEtzhKWaL7LXKpirGqWQmIzUAFArK0fVgkRPqwNO2H04nKyvQx65iaCg2rUQbv9j8Zts/mjTQhtpjJdtxwmUWnP39TfiCDCHphBK1XWBhu6QAkXzXcszC1XnpB6s6117l7ZR69kKlO1Ax3LptCeElUUDunWxt9y+qKUQ7fUbRmmb9UeUMCCKMcty/PitqzjIcpAoxmg8xnQywf27u9je3ILkBaQEYhZhNB7jlTdex+39e/joM0/h5//Ux/HIhYv43isv4e/93/42kjHFZz72U/jUhz6IDzz2GMZpgtu33kE8moKwCQRnYEWh1sqAgIIgjWKAEiyyBSA4wAiSJEYcJyBc1fPcuXOYvfwjHBwcYOuD27h06SHs7e5CSgGaJBC8BKTEaDzGzvlziEYjZKIEiyNAEpQFR84FnopSXP2NXwdfLFAsMhRHc4wnybG7zgendvmHCW8DqG67WiwWahn8ZFJ52pzzyqsGmh5MX/jRMAy3tg6E0nYpPTd9F9MqT03tQy7LUglLqsLj+/v7WF/fwN17u/ij574Kwih+4c//abzwgx+AgCFNEwghwcsSknOMRmN9Epmfjjbd9fYoqb2QWqm0vbJO0EiM9VhHCmRDaZtrMxVOFSISAGy70Q6v2+sQSl5i7/AA65MJkiRp1cdu03AYW/l7uqSqj0OK1gddfGTj6jOMbN4caji18OtQNdHfpSRWPZs0GWOhwkXV0aOux0issurIVOUuV8+hI1zG+JB6v6ztmbvd0w6b2n/t0v2wjEdOGgpac1od//fk9pdNTIjB4pwgLKOAB6Xt1+o+XhkWfQgr1GAEo26KwUq7H4xx3TzwqBS8nmKjFLMsxw9ffAl0OkGUpABjKIRAmS2ws7OFxWwBMkmRxOrypL3bdzFZm+LjH/goHtk+j1ffeBN//NWv4PU7N/Ds+9+PWXaAK1cex6OPqrO8wUuMshwHR0dIJjESSpFTBirrtirLUp1yNl+AxhFSFmFRlihZCabH3tHhIc6dO4etjU2sT6cYjVIcUIpJqhyMmMWgVKBYLHD12jt488YNvHX9HZA4whGR2BMFqKSgjOFwdoSYqmng9ThBsfgTHhIH0AgnG0YyHVmW6sxtI8B9XrVPePqEpRFE5vLykAD3MX2XsPd5tk16oG6nihh4qbY2RZSCSxVN2NrcwK1bt/H889/Gw5fO47HHruD+3buQolZCLI6g5rQFwPxha//vOhStyHQPJfGfkmbXy/fMCHplOCmPX33a95ITQpSfI9V/XAjkZYnJZFKlM1MeQofRqbUI0cZlt2nz3FgBELPcjVTPlFKrvcg+pe1Txi70GQD1O+l5Fi7XLd8oSiPsGvPRPWC3fRXOBqoFeYYfzF/jQZupCwOLxcJPt+H5ymBDwMt221Erxl6PsKnIOpVLY17fTefytpWEtNOtVE8Ngh5+7EpxQg++wccSrTB5aApInRWgDLg+h8UQKqWR82afv9QzbGp878/nuHn/AQ4WC2yur6kFqJRCQMkLLjhYxCBLAS5ysDhCurOBIi8gjhZ4bf8N7O3vocwyPH7uEtYmMSgb49a9XXznhVfwQvkSrr7xGh576nF89IMfwJiUmPEcZcHBUgaWpFjf2UKSJOrGLr0ynDIGRjgSotYYgarbw2LKEFGGJE6QRLGa5uNq1wsIwXyxwL3dXdy9v4vp+gYuPgLsz46QLRYY662vi6McaZwgHY1UnjIHSVlHOy4Pp7LozD3gwnjAtnJNkqQ6QcwXbgbaws6862SmJZi+z/v2KTj9BlLqeeBKiKqDVQTUCvJ5luHa1at47rmv4j//G38NLEoqMUSIWhjBy1KpI0mbq1o95fvpsCiq2mx4OK3LCDKD1u7L9oUdRjGoKANjDNeuvQPOOdI0wWQywdraGtI0RV4USAgBswwzo7D0zqbacSS2f9QM7zZlc7+iGOKNd4XFAYCoy7xV/znCbvnyaoFImo8VLzh47f6s2szgls3WCJVjznSGFLXCrh1w4y9Z9XMjNl0wcDGXObKtK4mDp25C16Mn7cfQVqtTho/HjxNuHspnfdDZBCcMtzfKtywVH22NZwS9ho1Pkde/9ZQLCITgyPIc+4dH2D04RDweqWk/IZSypAwcHHlRqD3NpQQvOQQloHGKghc4ms0wKzIUUuChS5fwcx/+GL7+o29DEoK3rr2DO9dvYZ3GmMYRtra2kTKGSHJEBIjiGJJSHCzmOCwylIuZWgCX5xAlx/xohslDO1jbvoAkGoFli4rvKaVquxdVC9OScQoWRYhohDhNEScpSBRhNJ1iQgkKxnBUcIijBUbjMTgpEccJKGXgQoBK+d64/MN3E5NRzmZ+0/z2KWwb+kKg7nN7xWSXEB5iBIRCqYA+slOq1eIA9BGmFDFlyPMcESWYHR3hG9/4Fj760Y/gmaeeBi9yQEKH0Am4XpBALenZp5CH0DbEo4T0L1MJtYlrSJhVxVJKdeB9xHB0NMOrP34NQghMpmNsbW/hoUuXMB6PUQqOqDp61lIIWlE3q6FOuzMSTPVVVXqVd0hbDQlld/KVjmLYC8L6+DEEVYibWHvM67fwS+x2dKMbrD4zYUutrCWkOu8eTQFt7sU24fmOGgTqFEjtKpAlodnvTjkV8W4bWu9UOEb1l4N76HRKF4QN+tXCEGMgNFadVLDHVOtVMHV3uXZkNMsy7B0cYO/gALkoMV1fR6Q9ViIkooiB6UNNwCgiCUgpUPISeZYBUYSjMkccRdjZ3MQzjz2Op598Cr//7efVPdOHhxCjMR597Ar+7Gc+jUceu4z5gweAABhliJIYh9kc96+9hbsPdnFweAgKiUmSgmc57t+5i435BSQguLi5g9FkrBbGEoIoYogYAwVR+7bjGEVRoASHlECcpIjSFPO8wIPDIyxKjllR4nCRIZ2sgVCGKI7VVDAXSChDSlarYk/Fw46iqJrXAIA0TVGWZXU3KqUUWZZVe7TdkLcLrnAcYin7LGozcG0mtAeDLxTkllsJcQl1IbsQKMsCPC+xub6BxWIBSlWzCiEwXyzw//0Hv4S/+7f/7+ClOkkNpQQvc8Rx3Dhz3alBJeBNODYUiWg+sxyvDqiUtqfuoSiD8bzNyvb6ZLIIlEZ4+62ruHPnDp5537NYW1sDANy+fRtJkuDcuR0QoraASVBIiMrYsctqCx4T8jcKXP+G8cBNWj8OX7Qm1HZtZWzjJ1X4uSrdNRZ7oiNWSajcmuYTbx3s+rWBwBwmYkLiyr4gSkkrq0r1LxcozLauqlwJbqZmzBaphvHYwUyBVw3aXefYm9567blOz+7bJpixGCIEne8lmu86+8wTzFilku5SyiHvuMvJCJTiTRvMI5tRJZPWbCdz5WvJBQ4O9nD79h3MQTCargNSXQiVxjGKkkMyjtFkApbEOJrNUFAKlkRqTcdhhu1z23jAZ5hECR7duoBinuHv/NN/iJxMsbe3hw8+/jD+05//OfzZj38cjBfYOzwCixKUHJjnc9zfe4C3rl/DS2++jgdHB+BC4LFHHsUn/tRP45GdcyjnGX77i7+HN156BR95/wfx5/78/wxXX3sda2trmIxGiPThTkmcIC9y3LxxE7du3UaWleAgOOIlDkuBl6++CUEZkrUpxlsbyCKCPKIgVKLgHKDKSc2K98AcNlArN3vxWX1XNCpvm1JahceB9r5gAzaDGq/cXuzkg8ZzQiApAfT52JLWq21Doc1W+co9BgUBI4AsOYR+xuIUs7wAo1ElNOMoBY3GuLd7D/+P//ffw3/9v/1fY219E3u7u1C1pWBgygMicOpTuRAw3mZNpqEbsCWiOrmsX5Co96LykilT/VIIdUId1VYhL9U+ciGE3q4ltUFCQGNzCw8HiQjAKN64ehUPPXwJ6ZhgOk2QJmvgBcPhwQLb52aATCE5AYr6QhW7vW3vvkmvu7VHOn/9HnVd12Gh8XZ+K25cxewrLQA3HNwX0aGUgmhDhVA9b6it96rujdrotGAoFwBBgVGcgkoCyTkiKgE5AsiRzsVAZASzkpoRilIWAJUgguLoaIHs6AjJeIxclii5OpmKgQCEQgiAm+C4sNvYVdy18TJMZ1XhiZbSNlMqtQFjyhJWXgq14JApI831uolskNdjZtT5PYmCzkCjzHfHs14lmJHmSkqbfiFL641/nBCi+FbMc5A0RUQpZFngkHAkkoCMRtg+t4N4nmNRAue21/DjG7sgU0DsASmNcWFnHa9eu40EBHGcQEg1ry1jikVZYH26hoP5HN989SXwskQSR7gyAdY2LuPJRx/G9toYb+3exVef/w6uXr+B8+MUf+ZTn0A6HWNxT+DNmzfBJhNcPreD/OgITzz8KH7qmfdjvvsAbBzjv/+v/jt84Y/+EC+8+CLmv/Gv8df/2l/HN19/EROaYMRiSABzXuBymeJPfebnUTCG3fkcb925g++8+BKyoyM89PAVLIoccRIhjige3L+HnZ3zSqaWhVpJz0vwvFhlN56uwjZC6qRM7SpweyW6jX9QmMsIRn3v9VDK6lPJoAUPqRSzHS5tRuM4JOG4eOEcXn31LXzxj76ET37i47hw7hx2794FRQ5mHfzQbCclxIgWYt1NqF4q+8RvRdfVV+85l6CUgTGKLM+ryAjnHBwchFA1L00UDULwKow7Go30Ob2RUvCUIisyJFGMnZ0dJDHDeDLB4cEcz33pG3j8iUfx0JWfBuElIgZETBlbcRz3hrV1U1fGiTmsQSUywt5pjQ7FHAppdyt6Q8OwFeo+w8N+Rog5I7zHsELNn8L27HSkSBBZrYhVyAFQOwig62DC8NVujNpDqkgMene20eGmXSas3FFXYmraiH05eVQkwbFRm6/NV4LqFrL2yXja0Opp+7DHG6pzTf/QqZp2/iae5SCQz3osOmgz9KnXdUTPl17onSNlWUIUXIXB8yPEhQQnAkmSguUC3NxLzTkoF0hYhDRJVN68xObGRO/XVmcACCHU7iEd8UlHKZJoDdtbW/gv/uIv4I+/+jV8/cev4j9853tICHBpYw1/5Rf+57i8dQHjZIrXb7yDH7/+KvYPDjDe2kCeLVDmOXiRoyxySC4QxwmuvfEW/tynP4dnHnsC//K3fwu/+cdfwMc++QmUEIhGCc6dO4eH7tzFS/u38eu//k/x6ttXlTOWjjFe38C84AAoxtMpuOQoFgucO38R+3t7KMsSozRFRBnKvLAM39XAqe3D9oUgfaFt38eAvYLcDQO5ynoQiHqhj7mO0Bv+lU2B7Q40c8+reg/UGrsWwnV9BPI8QzpK8LWvfwtr0w1sfGoTk7U1zI72MU5TEOnzjGsvrx5I3WDP+Zn0XeF2804p6kL5aBRQqz4FGGXKCCAA56VaHCIlBC8hUSq6IQHJwAggigLr03Vsn9vAaDTBvbsHuHr1HewfHeBjn/owxjEDhdqnraZE1LyTal9SG0FAkG+M8lR9VNen1YeetrHB5VHf37pN24pd/e0Wrn2K3Sgfo0L8qFyP0zyV+qk+yMZRQQq3VHOHUGSWWmE7LWFSWwq5pr9tAPq/1+mDNdUeuW2G2MfBWlENawy1URkGsHG305FGeqBe9NYMC/giaV7ye3XocZSsm385Zd0cK7VB2Fo74IS2wyHwemNdV4ieEIIkSbCYzRERdWLY3et3cXlTXS08HY1BWY5SCMRpqmSLlBilCcajERiNUBQ51qcXcPfBA4AQxLG6y7rkHKLkoBHDeDTC2toa1jY28G+e+yPcuXUP+4cLXNzYwUeefAKf/MD78Myjj4IL4GCe4/7RAXYPDwBGkYxiZLM5IkaRxDFiylDo9hKjCIfzI6xNJvj5z/4s/vj5r+HnP/7/5+7Pgy1L8vs+7JOZZ73r22uv6up9nZ0zgxlsA0IASBCCSNMyRYmUKIUVUoSlCFsOK0IKm7bDoRAjJDtkKyTRlmVLFEkQpEiCIMEBKIADYDD7dM90T+9rVdf+9ne3s2Wm/8iz3fvue1Xd081QOLtvvXvPkiczT2b+fr/vb/s8m8NV3rt1k8PJhP1bdxkXGY9dfIgLZy9x694ON7d3mMwylJREcQjaIKRAeT6j0ciNsbGEXoDOc0yW8eQjjz7w+3yQ8sHjdt6nnARrnnRtBW0fl0CWENKy3I9LrK6Z0/NYe/yzpK6TWr2McB+/aPEet6kXRU4Q+mxv7/LKK6/x5pvvEHe6pTWhXlrXfD9O7O4HLJXFcKl2MM5PUQjhIFpjEBiwBmsKjM6xViMEzqAu8IhCHyEsvqdQUuApiackwhiyNEFYycpwlUF/gPI8xrMZewdH3Ns5oCi0k3iMrQOsWFMmF8HObSiL3+ePNUDHsfd8Wu+X1nXy3+P6dBauOV7nSfUvtmEetz0u79XXlOfB0anqU8EOtkRfWiSoVQclU+ouL/L8xLFqE9L7be5NHcvHau66ipWYI9Zzna/pb9O/RvKvDeFslVVs8Z2fsGJba7yB20863x6DJeWUrWxxX/g4il3ybe6ZH9nzS8Z58fkL4yKFdJn3rCUq00se7h/S6/XqKJZSShfNzA8pdI61gigKiOMQT3oYndPrdsteLTDIlMKaEGhjmCYzXnzzXW7cusO59XW+/OnP8HM/8WWuXLjCeJZxa3+Pt++8z+2DbQosXug7IaMoCH2fbhQR+L5Lj2zLJDizGUpKHn/8cawx3Lp5E20sd7a3uXbrJlG3y+MPP87l85fZXN2g3+0T+iGUML6Qqkwu4yJXpmmKEoLA8128fmNYX1lha3X1I3o3rnws+bAf5PxJOsuTN5NFjvLBn98+dr9nteNIzUksVRtcJfWxausRQtTQozvnapDC+SdaC6Ef8Oabb6Ok5NGHH6bXG3A02sOTwi0A5sflQQlRA2Et9mnev9v9qQLUlMFtjEFhEVKgdTE31sYalPLdNVLgl1HKsjR1bbQuMb3AkucpUiqKvEAgiTsdur0+XhAgpc9bb19jtdsj9oN6Ey6zc86P77E+LHL7bWz4gwdPWfz+IBL2sdGurzn9WYsoU/t8Bdm2vcyPv+/jBKlmaFtnm3bM6/orK3EpBVjI0rQhdrVx17L51SaIHxm3SB2DfG78238b6bqRJRbFftodPrY3LJZmaB7Q/exDlmWGYCcz4vdvxxw62Rxdem+1J1VM3rG5uwTdXESwFr9X1yxjYBWqzhPR63XRhWYyGbO5ucm1u7edEaqSaKvLxB4FWJ84CulEEVaAxNDvdlwsi1Z0RF3WK4UgyzLSNMUC3WiNo70jnnzoEp98+gk21tZ44QcvcjCdUqicm3duMEoSZOCjckuRFxR5StztucBNvufaYgxBaceklERGEZceeohvv/wi+4dHDFdWOLd1lmcfe4K1i5f4zvMv8OKbb3J7d5dxlqF9hYp8kiLHDwJAYHOXQMSXLmvh/s4um6urPPrQVdLJ9L7v+oOUj02H3S6LkwKWb2bLuPqTFuSDE7PT61m8xtQbyHzozmbxVPrHFqdf9bPcGB0xcpKpp1zoTmM0fhiQzhJef+1Nfu1v/Tr//r//v2b/5T0kpqVTPP7MkxZbq+Un9Og4TFb91brA93185TNLpkgrybWhKAriOGY4XMHzFEmS8dZbb5GmM4o8r6W0MAxL9zzpXDWUzxc+/yVAcP3997msFGtrqzx89SHeePNtvv2t73B2dYgEOnFM18Z1flqBhTJTGPfV11f9KgM+POB7Pe3Y4jxcNtdOhsWXv5dlY36s3uqfE9DQ+nz5a066Ls/ZFgGriF5zi8WWAWistcySaet4SzI/dcAXGYjj5SQE7MGYKdfxSrKa45aP3W5PlCZP2wuEu+AjYj1OlurvR6yr4x+GaJ8qpMzddP+6qt/z7Tnezvb3er8t256nGYNBn36/z+hoRJZnrK+vo5TnclB7QZnF0NmtSC3pRTHdKGA8mxEFitgPa4OzOrdE65naGPI8RwjBV575IsnlS9w72OZv/Nbf5ejoiNfeeJtnnnyGL3/xCyTjhFkyQ/S6ZEVGlhsoDP0ophuEkGvXdikhL8BXLnXwZMIXP/s5/ou/8dfwPI9f/Rf+BT7xzHP8/X/0D/kb/81fJc81XhDQ7fUJB13y6ZTIU4iiQBiNLzwCL6DjBygp2d3ewfckm5vrrK+t8jv/8B/e9z1/kPKxE+z2xraMQC+DxE9dfAvQ+aIe+0E27vtdc+pGU0s3bY6/PEUZY7u8zlpDnhukUAgpKXRG2AmxxvLd53/IP/jNf8RP/uSXmBwe1tJPpZt/8NKWghbbPe/P3NaDKgFFlmIAzwsYz6acOXeWKIq5fuMG3/jd3+WNN97i9VfeotsN+NxnPs3FixfxvIAoCMBagqAV+EaApyKGK6usyXU8pVhb7fLv/K/+LWZJxvX33yWdTBiPEnQBeabJ8xwpPKI4QErqMLVK+adIvE3Kgx9bbViN4H0I77LrKgn7QefesmdSaZ+tBWGXdsfNM42tUl+KJqJZ9e5rRk8IrKxIdinNGoOVDlqcTCZNG0vGsr3bu59tyXq59N2Uk1OKLl/Di4RqgfiJUy79CMpHU+UHZxgeqPwYty9DBE+9fpF4lx9zgrAw9zGGXBf0o5iV4ZAoCNhNE5IkodPplJm6nJQcRxHjowkrgy6zBAa9mG4YsL97wMZqH4FzM7RCEPg+nueRZRnWuihjfuDT7ffoRDHbeze4sbvD9VvXkTbnicvn+b/87/49PvuJ5/hHv/M1UnwKDyLPR0pFID3WBgMunTvHoNsjmc2QQqCCED/yya1F55pAwxee+hQvfuFNfvDyy/w3/91fIy1ydpIJF89eZTqdooIQJX1sZggzgzEzZKFRvmXQj1mJO+wdHWIBD0un02F/d48fTn7Il770pQ//YpeUjx4Sp5lAy6SQdlkGiVff24R4EbJp67k/yCZ5opRaBrAoDcfnJI5jjMTi6hDMbZjukECUBlvOGlsgPZej1emEJb1uzK//7d/gwvnLrK908TxVxlZvUtWd1O7TUIfjpRrDxeMGW2boEkJw/vwFXnz1Vf7om9/mvWs3OBpNyLIcYTWogINxgr55hzxL8Urdu+8pojii3+uxvr7OlYtn2dneJ+4Ket2YbifGWsXli4/w+c8+h0BhtCFJZuzsbHPv3m2SJMUYgx94eL4LWWtb7nhuyEUtSTab7slj0GxITro8bil8+n2nSdPHifZy6eS06+tj7fnE8U2yfmm2Ola3vulHdYmgtKtqwaGVHzaOcGezpHlWSZiNMWWE+ON9aEb6mDNQ+bedoKZ9vAUC2Ka9QlSJO9ouW+Wh2lJczp9zpISGgWgn/1hsb7vIE44/mCT8cZRaQF1WTuAm7ifAgHuXwi4P6eyee38BCKhDk5rW3yrPQzUfjNUcHR5y7uo6cRxhdEGazYg7YSklC2azBOEJer0Ok9GIjfVVbt7Yo9+JiTyPdDpja30Vk6VIz8HreZ43iUOqvV9QekRYDo/2GE2mPHT5Ej/x3LN8+ZmnORuFvPqd57mzvY2OQ5TxsdagtcH3AjbX1jm7sUm/00VXunVPITKNzQuCKCQedvnvv/obPP/6K+yMDrl8/hJnN88ynk54/c3367DRea4prEGGAX4YMkunZBKsMKiiID8a8dxnPsX+5gaHh0dIKRj2++Tmf+LZuhbn40kTbpFIn0a8F8uPs8jmJmjZ4PqYaGBCQeMys9g226qrvc6MaUHblAFFlEeSzPBCD1O9PAEWwXQ65atf/Sf8yp/8OTY3VnGxxfO5bDjz/TYLG3fVGtv6Xj6g1cal41/6pAsBfujz3vvX+drv/wFvvH2NJC1K/8iCMIxJsoJ33r2O77tE777nIaRLUbexvk7c6bG+ucVDDz3C+sYUrUdYm5KlCWlSYI1lb0dw9uxlBsMhnW6XTqdDGAbMZmPyPEMbTZYXGAuBp0rqM2+1SikBni71VoxVKX0uQTBP0uWdVE4i5qfd35ZKljKKthWoZMmzmgMV8W0IuGi6d4xxPElgNdaS59kxCbuCxhceSEnSWw9ooUp1O5t7TyaMAiEW/acXxuNEIubUJA3RX3jGkne77ORp8LR7zP2Z/geFp0+v40PddqwsdrttO3PadfXxhfaL1vF2Mo/2x5aR8ozRjEZH+Erh+x7TJCFNU9bX16lUZUWRI1VAFEfsH+xz7tw6t+0enTAgEIo8zdjob2EKXbdAlAFLKtRUVwyl1mhtyDLwjeXKxipPX7nI5bNn2L5+k7v7Y7b3DkhMjgbSXHN0OMLrRHhSEYcRvuej8xwvCNDWMrOaOIqwwHvXr/ONb32LaZoyHPTprAzJFNzZ3iaRzg1MKOkUcCVTWqQZJslYXV2l74eEFjZXVthcWeOtt9/maDzBD5xxro47P9a7XiwfOyR+0iQvl6L7XsHiUqKNcQv1QTjL1ua2uOiq+xfrmN8Qyyltq7/VMdfCZfW264dGwmmr16qNSoqWa5qpOAH3PIMlCDxefOklnnjiIYLoKXq9DkmaopQAq0FIsMIB20vbMb+hLtvUKii1PQxuo3bXCymxAl559VVeee1NpklOEMZ4vo+n3SKdJRnj8dRFpgucQUgYhi7XeWHxwojByjpKvoaxFk9ppNRY6+CxtCgYHe6xvXfE2to6g0Ef3/dRQUjHk6VuvCAv8vq774eO44fSaMo2r2thPObfcSP9uffUJorHN/DTiH/7mnnJ/cE2+WXtm5NIG86vNRUXAreUH2stVaIuUXazbR4p5t6v63fbBdQCWZaBEphyMxRQuua1Zs2SoVw81F7TFVBwfN6xeNeS7+2yfH7Pv1s3FvUzl0RFm5fM71/mahfNfYuQcfPbVgfmnvNBiPiPTfhb97ZX/EmEeOHOJaYBFSNYIhmWMqugpmLgtNYURqOtpbDaJcmQkoMiJyk0Z1fXmM0SBv0uo6MJUkAniriRJazGXTxp6cYRtrBMkoS1boft3QSFwAt8PN8nLwr3EqTAU8o9T2ussZxb2UAUHXwh2dk/4JX33uPNN97i1s4etw/2yYRB+R7SCoo8pxuvsLG6RhD4js7UCKizU/J9j3SWMto74LErD3Nvf5/9ImM0mXE4S9g9GiE8HytdDASXHMUFLDJ5jq8Nq3HMMO4QaYPwFUejAw6PjjgaT1jfWKPf77K/s/uh3vFJ5WOzEj95Q3V6Xikk2jYxx5XnIT1FXjhjqFqygKX+1ouw1qJEukiwT4SVqZdck4GqbOMDcd60t4jmemMN1risVmEYYrV1OWJt0xbP85jNZvzht7+DF8d8/rOfRmNRWCeBWYW1EmMF0vOQNRzYXvDLGAuz8JfWPS5ymUGAcCnhsjzjjbfeIkk1AumsPqczhLBkqTP6MJT2AlIxns2wyoWfvXnnLnd39nj9rWtk2W9Q6JzHH3uUyxcvsr62RqcTc+HCeQ4mM95477soT9Hv9+l0ely4cIGtjS3iMGLY6+B7AYcH+xzsvE/oRxTazQ9PyTpanjnh3VQQeMUTNcKc81QWp2zk95O4T5asT7AmV/eB4UUZdU9brGrNoEXM1AJIqJIJKFFbfGMlAg+EdMygrOafwFpRR72r6jRAmqbgKwpTAC5fsEVQGI0L/bqMCCx0wh6faxW/0VC/9vsRVGkyq6xOx8ek/anmt2G+JdTXNG1aZITKnXVJaaMD7SvswnDX763uVHWm3Y4y7ecJz2ras6QdJxDr+/0+Gd52rT6JdXGjL2rmTbTnLA3zboxBWIN0vXMZ27TFU+4d5HnBNCswQhD1Y+K4QyQlWV4w04YLgxW2t/dYWxlyOJ4gJPSiiKN8ykbQJfYsw16X0SRjbzJhqxfxzp0ZkfKIej2sUmzvHzhibS2dIMCkhjQr8JXmmScvkumCt66/x/vf/B7Wk9zZvkOapUSBj6c8OiKiG4YMel2eePgRnnr0MaR2jIfyvNLrxUNZFxraFDlnVlb5E7/0J/nrv/WP+a0//ANuH9wg7HQJuyvYIiEvCqxSGOGESWMMgYBuJ6IbBvS7XXwjGB3c5uU3fkTc6zNKc9bW13n6qSf4nd/8zRPnyIcpH0s+7GUQd3uiVcE3pHc8W5fv+y5saZrV9TQ6FOq6lknU1bllk/1B4PX76bseTGc8f77KTgbM9bWKqW6t5Z23r7My+CGb66s8/cTj3HjvbXqduJQinMtXlmaEgV/35eRn24W/7bY3z8bg0tyV+aunswSEpSgsEoO0UOQpAI8//jjb29tIKVhfX2N7e5ssy/jiF7/Im2++xXg85vz5s2xvb3P9/RvMpgnf+Oa3uXv3HlEQ8FM/9SXCMCRJZgwGfYIgpCiO2N3dZdAfEvg+n/3s5/jyl36S8XjM89/c4ehoRKcb19HXjDGowEcYUydPOWlTc91vJFm391ZuWG3qcn+Ju33sJJ314vWLhHfpfD2xlpPnVQ2zL54oVQdOiGivA4eQYC22KBwkvliXrSDnRpxetoaaVi8RweckzvZ1pURX7wXzDOdJxLdd/+Jwt3XrS4to6j7e9vl6KiF9DnArGQWx0Nc5dKVm8T9gad3wIPrlBystZu/Y8xbnSrM3NFPV1iGcc22wJXNnAel7FEWG1i4D12SakFnDubNDpJR04hitNePJmPNnNrl79y5rWwOMKTBa45fRvnzPw/NV6RaaY6ymPxgwm91k0OvhhxFJoVEWfM/HmhzyAmXAF45Zf/HGO4xGI2azGdY6G5+OH7MW9+h1O4S+TzqZkB9NeeziJR45d5HAOMFQeMrlgs9zp6Y0hjRPGQ5XuHL1Uf7a3/w1/uDFHxJ3YuJgSG5hOkvQuQsmFXs+xrrxARj2+nSkYDwaMQg6RN0eN27fZLi1RTdaRXgB48mE7z//PFcf+5944JSqnGroAAjZZLkyxmVrqaQoaAha27hMCJdnufp4nofv+zVBP2mj+SDw5Ye5/oPW2SYEnufR68b86Eev8rf/zj9gMpnQH65QGIuQEmM1WZ4Qd4KPoBWlFGqdBC+wSOGcyt5+930ndUtnBKKE5eKFMzz22GPs7e3VjNP+/j5Xr14lDEMODw9ZW1tldW2VO/fu8thjjyEQPPnUk2xunWFlZZXPf/HzJFnO66+/wXQ64d69e7z66qu8/OqrvPn220jf4/a9u/zdf/D3+U/+s/+UW3dusnnmHFEnxhhnCOgFjonL89yFpC39NufGt/V37u0dmxMWRNuobTmT9qDz6KR5Vxl1LTKEtT76hDrvR6xPKhUdE1Q2lG4jVgik1tgsI03SueubOh1xNxiWsAMfonzYOtrE2C75/Dh1H5eTT17nlYS/+Oz6TpbMtPuXpRD+j1dOq7FBSVwfxNwZg7W6/LQhb+MEqhL9zLKEwhQYICsKDvYP6XZjpBKowCMtcpIsY/3MJtu7u8SdDliDFBBFEUWREUcd+t0OYRAhrUAZzdbmGY4Oj9gYDumHEcpYlIFAeigDJnfGkIH0EBa2RyNyISisYDhc5dGrj/DY1YdZGQxZX12jEzs1XhTHPPTwVZ58+hkKA3leZocUgl6niykKrIHBcJWbd+/xX/7X/zWXHnmEJ55+hvFsxvbuDuPx2M0NKfB8n8nRiHQ8QeYFJDn5eMrezi6e5yE8SVJkrKyvk2QpBsvh6IjdvT2OxiMODg8/0vf9kRPsKrY0MJexaxGuVmUscK11HZO64mLbrk2LG+K8IYSdu6Zdar14Cxr/oHqm+92zyEycdq4tGVd9rOoOw5Ag8Njd3ua//+9/jV5/BeWFpFmBsZYw9Mmy9FibTmcs7NI+VL+FwKWSk5LJZEqaG4QMEEiiKGJldYUoirh+/Trdbpd+v8/a2jpra2u8/vobPP7447z//vuEYcj6+hoHBwekacozzzzFj370CkmScOHiBbS2PP/9H6KNRKmA7e19bt66x0MPPUQcx/zar/8dtnf3WFldpdAF/7f/+3/Gvb09kqIgyVLyIsdimaZTPM8BQtbY0iag9cGBvLV0VB4/PgaLBGF+/jwIwTxe14csD4rY2EoSahHYVvuFEPWRhnGx5cbpItjlWdpicpz0uPi4KgpeY/dwH0nfVkZri9dVbVuelGd+CBbXzmn3VeFgm08VTbBSUbY/zF25+Nzm+Uses/il1ccPSazre08vH2yfqsbKlExo6fmBwaLrcwKXH88pQOaZVVvPrTKmt3VweGEMWZGirctjHcURvu8zmkycxFwhX9bgBT4rwxXA4AeqTlPp+b7z/pABg2G/SfgkLCvDNY5Gh3Qit/c1yYgs1FbiAqkkSkrINaH0OLOxwZn1TbpRhyRJOJxOeffWDW4d7HOkM/aLGf/jt77OP/79f0rhS0TkY5UTStI0JfJDQi9kZ2ePWZpx5fHH+Kd/9Ee88NKLICX93oAoDCl0AdLRkMDzIC8QuebimTOsrazQ7/fJC83O7i53tu/hhwHnzl/gzp27jKdTrBIIJUn1R5v84yMn2L7vo5SqF0J7cTeLs5m4UjoCEYYhnufVDvRtorRIFNvEbhnBbD/rQaXl9rX3h5tbR5YSheMb0WJ75giAsQS+Dwheee0tXvjBi4RxB6E8ijKYiW0xKYtEe5mUthwha2/pLqGHNYZep8v6yrB26+jEMf3+gP39Qy5cuOBC+SUJSTJDSsnm5ibj8RilFHt7e8ymMy5duMjNmzeJooijoyMXFMXzuf7+DT756U8znSUcHh7R6/c5c2aLmzdv0ul2ieOQGzdv8IMf/oA33nyTcxfO8z/+7u8xSzOSPCdJM7Q29ZyqUBgpTooj305g4D71Hl8Z/tnyXS4Q/EUGcHGs24xWe7N7kA32x4U+FyVzYH462sUD7h6ndnFuM8boOjWqLa8/3s/T5/jxk63vS+nYIrO02OjTx2XZ+l8kysuKAewJF9by5kn3nwCrN0yKI4rt8Zs7f8pnrhEfVWnDS1XvbNNmatXCsnY2AhBWOGMzYxyTl+eIUsrsdDqEUUQymxGFAarM4meMRSpFp9sFgQvPGZRCm3GGZkVhGPR7WONsYqRSxFFMMp0ShT6eJ2tGsTAaKyWmymZXvj9PeZjCoHPNaDRie3uHg4NDsixjMptxcOS+R36ARJDOEvb2dp3dRlmH9BTGGoYrqxTGcGv7Hm+9fx2v28GqMmWwdZbwVhcIUTIYWAJP0QlDBt0uK/2e6481JFlKkqckWYaxhlkyQypH04IowIs+CmS0KR+LhF3Fkj2JANoSrqsgbt93RgMVzL1I6JbB49W5Ch5ffN5JUOePU07aoE+S0E6S2o4xM8alUVRKMUlSfv/r32Q8S5CeX08aWgvMLvl+vIgl30sCVj5fawd/deKYZ556HF+AsKZ+D9PZjDNnzpCmaR3UJE1Tut0Oh4eHDAYDptMpo9GI4XDI0dFRDVtDlWzCsrKygud7zGYJ3W6H4coK77zzLtoYVoZD9vb2eOvtd3j//RvEcYf3rt/grbffYTSeonH5mh0jt8j4HR/vhlkpqYddGI2aSC95uUve1eK7bD/npPP3o0P2lOcsLaKSqRfQkqbZlaxd20FV9FtItwkVRV5G3zuumjnW/g/SsLohrc+8WRfHL1osx6n+Scz2ScjV8traDPTypi8vlf76GEcEVOuuUh+UiMdpzNRJj/iwZe6dLwEE6r/L96I241mhNqZcN86Nqij3aI3vK4LAEew4ikizlF6ng0s5YF0uBCCMorJuTej7SCHIs4xBv8dsltEf9CgKTZJmeKGPp3zyNCOKQ5RyQaaQZXpXURJrSlohLFYKCmuYpgkHR0fsH+wznUwRxhJ5Ph6C9e6AJy8+xFOXr3J56ywyNy4rY6lylUoiPMkkmXE0mXA4nTLKMrRS+HGMgdIq3SFTwkLgeXhSEga+y6NgDb1elziOEAK01RRWYzCMJ+NGYJWOQfCCj9ZM7CMn2BVxbRNZWODqyuQP7fPaVFGuHAGXSi4l3NXfth572bMq3Xh170n6x3bbTiqnShj3Ke0Fsqy+qk6tXSbifr/HCz98mRd+8BJFoel0ukjplQtLl5uF0z0Zo2n0ULo+32xkCiFcMANb5RymglCFMwKyljyd8gt//CtsrQ7xPUmepSRpwmBlyHg8Zmdnh5WVFdbW1phMJrz33nsEQUC326Xb7VIUBbdu3WJ9fb0m6JPJmCxP+dSnPsFXv/pVzp49SxjHTKYJ+/sHzGYpN96/xf7+IQLJ6nAVJT3+zt/+H3j8qaf5zX/4W9y+e4cgDEFWFppOfSKkrInX4gZ0/J0uSjkVzW4Mnupt9wRG7CRm7MTrH0B8Wva8pWhQyyDK1b0oaJdMjBQ1nTEVpO3ech2YQhtTzoflff1wUt9yeHieoJYdaLV58bsTgtrXn/LEEwj58rZRv/e5UjIWx4l/SdwrZ3fR0mULs6CHrqTteabEUt57jJM57ePuX4Tzlw/AXO8aG/G2j2mFltSoSVtnbUp1pJOQtdYUJUNeFDlaFwhh8TxFEASEgU+3ExNFIbrQrA1XsLrRfedFjh/4zs0zy4mCEGlhOpuxtr7G0eGY4bDnYoNnKd2es08ptKbX6+ApZ6ujfK9M0OHAsHrkjSUpMrRyRBslUL4TZmI/YGuwyuWNM3z2saf4pc//FF9+9tN87olneOLyVVY6PazWJLMpxlr8MOT553/Aezfep7sy5JOf+xwvvvoKSe6CVkkEvucTBSEYw9rqKkEQ4HkShOHw6IAg9F0uACkcwTYF/WGfyXTK6soKRmtm02mZm+H+aqEPUj5ygl0RYt/3CYKAIAhqOLO9iVa/K8ktz/JakgMIgqCuryiK+nPcod82MOkC4V60Ln8Qon2/TXuxnvtJ8+22tIn2/MWmTgMnBKyuDfnv/vqv8+Zb7yKEIg47oJdJkvbYWJQt5BS8DxBIP0B6PrrIOdjd4aFzW/xr/8qf4+GL5zFFTp4XrKxtlIZlaxhjXOq8bpdLly4hhOC9995DSslgMGQ8nhKGIe+++y7DlSGDYZ/R6JAfvfwiP/XTP8He/j2SZMbu7i5ZlvHz/9w/xw9/+EOeeOIJhBCMRiOyNCXPMv763/g19g+PeOONN7l+/bqLfmYNaZoijK255pPe0fz7WKYyOF2ivK8EfcK1HwQeZWlbT7dLaN/HQh9kiTK1t//CFhjrVCpZlmHKdbK0PeVxY22VdfrkNiwtJ+l2K2l1/tAynXNDME9XZZ3UhproIpGlRcNxSn1KS9vEun5Y+Vs07+Y4yu4IYkXkRU3sT//Y+h7TMAOtqDjLnbWOjUbz/PpvA9m7zHsNM9uOXlbtvVmhG0m7hMOVEPQHXeIwJAwC4k5Mp9Mh8HxW+gNnKFxFZjQuN3YYxKSzhG7H5QmYzaZsnt1kf/+A4XBIlmvyQrO6OiRNE4wxbKysECiFzTWxH9KNOy5tpbbOR1tIpIWuF5KOJvjKI45jOr0O3WGXta118jzH9xR+HJJ5cHN3G9UNIJTk1hnSeUHAoNfnhe99n8tXLvNTP/sVgl6P//y//n/RWV9DKM8Z4BYanWVgNYHyONzf5+Bgj+lsgsHgRT7vXn+X929eZzweYTF4gUJIGAz6HOwf4CuP4WBAt9sl6oQP8A4fvHzkBLuCLD3PhZmsdNPzXGzj0mStS3fm+e6aijALRE3wq/sXpfcKeq0IfQWRV1L3g+qvT4Ku7/f7JMO39vcHgfWcz6whL5yRmRCCKPT4nd/5Hb797e+4hRIED9SfNsy1rM3VVpUXhsIYlKcY9Drcu/U+Tz32CP/Kn/9zfOHzf4ydnV3eeuNNVlZW6sWZZTlZmZReCMGFCxc4PDzk4OCARx9+hHt3tvmZn/kZ3njjbbS2XL58mdlsxv7+Plprur0BcafHZJrw2mtv8Gf+zJ/lj77+dS6cP8/FCxeYTqZcOn/B6c6UxyzLSTOnR/NLxq8KYXgSk3Q6CvJhjYWW1LTEbmCuHSfoN8sfD1y3WPKs+ee6v6oMPNTungs846LnFUV2MsNYXb8EG5gfz2Vz6QOUU4f/x30vVeXz6/5+JK+5i9IoC6R1v6U9rclt1uikix6U8TmNuX6g6u5b5gl2SayNdq5c5Z4hRRXjwOXI6MYdfN/D9z3CIHSEstNxSYM8jyIvkFIRRRFpVrC6uorOc7qdDkHgl8R5lfFkQrcXO+neWLrdHlnujOKG3R6+EFDkhMqjE8Uuh7uxeELgS4VnIJzkdLUkthKT5oynExJTcO3ebUY25dbRHv/kB9/mP/27f43/4h/9Ov/R//e/4puvv0QRe6yc2SSKIg539/jMs89y5aGH+K3f+Sp/7zd/gwuXL7O3v+86DEynU2bTKXEQOqhdKnrdHqtra/SGfSazMWme0e3FrK4NGQ4HBL7P0dER/V7fuaVpg688+r0uhwd7H/6lLSkfuR9220K8KhXRLoqiNH4pjYgAXXH0ouVfbCyJThwhb1mdt6HleYInnQRhjJtMvl9LFW3CXj13NpvN6b3ryUy5RE4htsus3tulYljcD/dHlUFi2j7qVlbQnyAtcjyl8L0mY02vE3NvZ5/vvfgKneEKP/HFL3Dv3ddRng+iTO9gLUWhCaMQXeSokhnSZRslrQ2/em6pg3ORFCxO7+khvICd3Xtsrvf5xZ/9Ek8/eoXXXn+dH756zUFJniQIfISKSLKMrMgRhWJlYx1TFOzs3mXrzDqvvPwS585uMhmNmYwnPHz1Kjdv3GR1dZW9vXvO4nzQYWf3Hg8/epXuYMB0NkNJwXDYJwg8gjDi6GhMFMZ0ewOsEeSZRklc8DdjKAPizb2L5Xpt6nFetD6e14GWFVoXTqyaE4t1z8cZOA61CiFQlaV62ZZ2fGRrS398ZA2LW4uDtCuZykId1kR4COlh0CAKXFYz6ZpKhhASi6IoJUoPoFSLSOPhBQKtrUt+oJYYzVXzuoKmy+cvZ4gWf5++FsqrWtdYaiK0sEYq2Pw0XqbeH+bqbukM5v6644sSiTnBtcqthmpASum3qsm2Au+I+bFph2edq802kLWtO1gFs3EVHY/RvqRdc5W32lRyEqaORdNW8bixrvdVA0KDkYIcS5K7CGNoiy0KF5QnCnjv3h0CIVnr91nt9xC4vWXYH7g5bzRbZzfw8FBKkGpLJ/LZ7HcQxnLx/DrXb1/joQuXCIOQIj/icmfAu57mYneD95M7ZGbKhfU1poczCCwrYYTWkFjDxtBHqQ6Fzgk8v5z3lrSYIbwAE0gSXeB7AZ7yydIMm1qSWeb6axVW+Jxbe5hIDXj7neuYrODS2XNsrawTiIBe2OU3vvZ1buyNCHurzHRBvxMzmxyyEnc409+gH0X0OjE39nZQSlLkBoVG6MIZ1AmBH7goaDZQSBTFnX3CR2K3TrVTQc2KnMFw7b7v+IOUjyXS2eICbhPNNoRdSSJmgUDKFlxurbN0DYLAWfyVpSZ85b1V/VXdnufNwT/tjXeZLvJUuax1zyI0fhLMXhHHKsTQnC4f5rllUVkntrJpSYWSmu1793jhBy9y6cpDDOKYJE2xQiCV58L3FRqjXQxzN14aUebWrnN1WYGQzrmj2nekdBahlaVnGMWkaYopCga9Dk88epW1YZ/OYJO79+6xu7fLZDpGG4MfBPiBcwGTQjrjuMCijSWKu1gr6HR6IODwYESvN2AyTRgMBu69aE0cx+zvHfL4Y09yeLCP1gVBEDGZJuRpyvlzZ3j46lXWVldIkqRF2CRG6HLje1CRw21gp0nFzTZnj5GAZaVBUBaZguXXzUvbJfJZh021LdpTMldtQ8G63abscvNbCEvlsDOvGrYlkiudm06Rl21toxDz0O8yIj2vynqwcjIBbyMHYvFQea87uGwfOa2+hQXVei/N26zf7UnVlu/gmPBbE/H5G+fffaua+WFd2u772zlUnEzDRNTtWajLPbOZW9XVtnWLRZCkKYXERf3KCqx2SJUVzjL8cDJhJY7xPUXg+8hAEgahC4BSwuQrKwOElCglydOCwPfpdzskacZg0CW5lpVGxC6Z0Wqnj1CCftxDG41B04tjJrMUPEXkB2AFVgg6nZAiccalUsiyxxbPd5y6pyTaaERR4Amnw/b8AN/z6He7rA8HbAwGDKKYR86fRxYFYeARiwBrwQSK1268zbV7dxllKVo4PbqQ4EuJErDS67I5HKKsZTtweSCwFmucMZ7vecSdCIQgyVKK0sjW60RMD46w2qAC5Vx1hcfeeP8+7/mDlY8FEm/DwW1ip5QzYvB9/9hGNkcIS/+3SsdSEe1KKq7uaz+z0pNXBjYVLA/z+uMTDeEWsK+TdaLLr1kslS6oItCLeu82nO4kLlH3typRGJDMZrz55pt89zvfJYhjrFS1cOyIusRqlwFMWxe9rG43btGaEvIyrexNiwZ6lSohy1LSJEEpxZUrl/nKz36Rz3/uOR65epH1lR6BtAij8aRCasimGTozhFGHNM1ZW9sgzzW9fp/hcIX9gyP6gxWMEQwHq/heiM4Nw/4qB3uHnDtzjvW1TVaGa0RRl53dA1b6XT7/mU/z2MNX6HVisnTmtjhbaeZELZ0ee48LHxauW17a5+cZuJPm6fzvkw3gTlKrWGxJsEUp6bbWjJQ1IlKRtup7q7kn9qRkg0uJ0WK0odDzmumKAaJCEpa18T6EepFhPen3g6qmltV9v8+y3jdS/TwxbRPr+zdJLhBHUVfQfvaJfROOEM5j6g/O9JSctrtHlD7SnDbPHXLmfs/PP4NDMtMsQxeFy85WMuoGyI1hNJlSFLmDv6PQ5Q2IIsIoLLtj8T1Fr9N1iTqUIi9ylOfR6XSYTKYEYegyZfkBUrnMfN2ScQ/DuHa1C6OQo9kMUcLtCCdA9OIYawxCyDJ+gIPNe50Y3/NRQpZBtpxeGiUQgaIz6LK6tc6lK1s88cgmV88P+YlPPM2XPvFJHr/wECvxAJ0bjrIZP3zvTY7yKdM8YZbO0DoHYxl0upi8wFeKMAicXtz3SNPUhc1WCoSjYXGn44yepcJmBWaWEqz0SA/HBEJipSAIA9a7A7LR9MHf+QOUjy35R5tYt4lE5WddEVBdNAZotYTZ4vYr/WlRFMRxzHQ6nSPMvV4PIUQNO4K7N03T2vCtMlZbXKXzG1IZK7vU6bQZjcVNqLq3+n2SNGBLzliU6TtlGSym/fw569SKO9YG6Xv1mBweHPH7v//7fPGPPUsUx+Rphik0hckctEUF93ugrAssUrPXLWCulQ2ierZSLrLZbDYr1Q8CY5yVY5YmeJHic59+mk8+9wR37+3w1pvv8Nprb7G7f+SsII0lIyPNEqcnOjxkOBzWUvGlS1c4Ohrx+ONP8P577yKATqeL0ZYsTXn/+vs8/fQzhGHAtWvX+cEPXuTP/9k/yRc+/8dQEtLpmMCTjiHRGijqPokl7+Gkubj4TuffZXuoKtXB8TraY7f8GS4DV/Xejq+BChZ16RCbCgVlbsyGGFSHpKjnj7uuIUjLiyPSDrlyMcKdKqrtGthI2McYiw9AUxbH5aTjH4Zgf5jnzyMH7QxrDQMrWucXqOmSeeIia7v5UGICLQh88foT2Pol3yp8QbRg+Pkrjv8+KTpfk0r2OCFv5rcu1YHGWkZHRwTKQypJkhccjMZsH+6DgG63S6/bwy99r4MgwCtcesnA8+jEsWMmpSTJcoRUhFHAeDJmsLZGmhcucVC5r0RBhyxPCcMIbUFIRa/XY+dgRBhFdaRKaWHQ6bF9d4TwBEoJtHYpMTeGq4ymBbv7+xQ6J/R9lOdyIFiryYqMnYMdtvdu8Z18Rk8qVtbXOL+yhlAWfEORaq7dvMVoari3t8ckSdCF279Wun1We31uH1wnnSVMJlNu376NGbj2ra+vIwRkeVoHjCmKgl63S2ZnTMdjvKDL0IsZjTS3bu5R6DUunz9Ptvc/cR02zHPai9JNpXOurMBNybXM6ZPL66vByfPc+ShPJsRxzGw2AxpL8mqDrOowxtQEIwwdt5gXBbkumrRtxsy1t2njconoJDh1Tkpe3JhEe5NoPau1uKt2eNJFCNJaY7QmlIrJbIrveURRxL3dA/7a3/p1/sKf+/MoodDaRQDzlMJShngtn+mJk4GTRampkrDBoLWo0Qi3EXnoacKtg32EUHSjDl/+wh/jF37+55hNc66/f4O7d++xu7/HzsEB165dI45j3n3zdbrdHlEUsXPnNufOneP573yTbqdL4Puks2n9Lu/evsnBwR5hGBOEEX/uX/qX+JM/+1lefe1lup0u/W4Hg7N/UH5IEIbkeeFcT+T9AaI2UzT3auYYsvvXUb+7+zzHucw0Kpr2fcekIlPiqQvITlVcBjsxFzv9JMIgSui8jQxU7zLPc9I0A6EW2lDdW/61x0nG//+URVXHgzIRsrFt+Yj4jnbK17kxnxv/MsHIKW+kEgiOH59/x4UxFNaiPEU+S4j8gFmSoAErFTsH+9zevcfDVx9iOBjS6/foxRHgMryFfhdjnFWFr0qhSAiKQkOJZOpZ7p5VWJQX4oUh0hMIK5klE4TnkRUGJRVrKyu89v67zqXLU2X0M8HGcMCr2S6eL/GVQmcFgac4v7nB1779IsPVFQJTuH0114TKQ0qP2eiI3bsTpjnMtKKYjfjD7/1lfvIzn+BP/fGf57knn2ZgPW588/vcfOcWqUkp8oJuFLO5vs7B9g42DBn0exxNR8zyGTJWdLtd9g92McK5kyo8Cq3xcu3CnBpDp9el242YjA4ZD3xmecJKr0eWZrx7633+jT/353/M2TJfPvp82C1peRmRWyRwnlFz7liypb8rimJOf+15HuPxmKjkzCpDHq0tcTzvBlYR++l0Wjuz25I5WCqNVUS/pT1cbPdp8N6ypCdtlGBR4pCl7r6G6hEumGD5O8+dIZpLlKI5d3aTH/7oHc783tf46S/9BGvDIYd7uzX0r6SDkGwpXbVRikUI0eKsiqu2ubFXOB9NavWD7/sI46Pw0caQZzn7u9vsbN/FYhn0Is5sPoLynkIbJ60f7O8znkwo8qJUBUhmsxlSCJI8Iwh8At8nz3POnTvH9u4ef/BH32BlbY3zFy7ya7/2t/jM4+fAwHQyRljNoN+vkZn9/X1nxFhmC2sTw9Mk6WNjsICKVP7r1fW0v7eulwtMQptxa84187/NiB6Dzm2BQOHyZpbvowLHRcXcKpSSLlyjEI3qpl4n5S5fSumVFIi1YDXGuBjsWZYt4UxOIwZ26fcHKW29cUWZ3LFF9vXkZ56EaCwKAq27F977PDzevqdheeYl7NP62TZIW1rmuYHywALzXzINomX4ZhdubX9vM1/tNi5K2O3jc/PLNAx5oY2zeyn3UG0NszznYDxmmiWsr6+zvjIk9BVKCuI45s7BNuPDERc3z+GrYE4oCYOwVDk4PTKprg2JPc/H9z2kEggrGAy6SOWRZDlgGXZidiZT1koLa4RLNbwxXMXoNwmjgBBFXkAY+Jxb38CLQ4xwOmcpQBhLOs2wRrO1scYTVx7i3NoZzq+dodvtc3hwwFuvvsb3fu9F3n7hOhtnN8mUIQtT7GHGWqfH+uoqg06P3fQGWRYR9SKSPEH4ljNnz7C/t8+ZM2eI4xigjN7oMZtOGR+NsFBK4Kv89Bc/z3/7D/8BuVAoIJlMuHvvHkc65aMsHxskvjj5l+nD2tBjfb5akC2/QCGci1claVcxkaMyuo6UonbrAuqgKnVikaJAWotQCiEcvHps0hucxKOWh/ps/6024mXSat2vhXvafXZPFAjbwO/ampqplspZvcvSatyUfez3unz/+y9x4ex5us88SXcwYHS4T8f3WoFoJFgwugqUUMGDzrK4EhVMS2wQ0j3Dgsspi5O0j46OCFRQx3p3wW1yBC6oArZgNhthrcCaMpgNmrVhz7W7fIfhGRdQhTLgCcZgrMHkM65cPEsv9vGkJYoCVtdX+cFLL/HE448hsC7hfJLQ6/XK8db1u5ZCHBvz+8Gvy66xtv2eGvlr2dZ8klTj+mtahKm63hmKCaFK5oh6MxUaUGCNI9hWOItei4t8Z2uIXNFszCVWThMNqqTfNT2uVAW2bLAuNHleIJFlRKtG+mr35MNI16ejD4vH7gefiyXXLCdjxwn3PIM1f26+vuPEd/l6X/x94tyyDjyvrE+a9S9qIr2MwWj3aXFezUPlJ7fppLab0pPCWgfmKwSz6RQlJJ7vcefuHY5mU2QQEPe6mEKzNhgQeh6mVCGORmMOj0as91fxegHKVxQ6x1gXSrnae33Po8AZ+ga+74QeAUpJJIqtsxsY45gGJSSRrziYTlgbDvBKJiDwA1a6PZc9sBsgMwsYPCXoRzFJlhDFIZ6nah976flcOHeRJx57nG43JpaCwGRMR3f5uS9/iZ/73Kc42N3n9t1t3rl7m7sHR9wcT8gORzx04SLDwYBbN2+wujp0iU+K3BmMdWPwJJ4s0QPt0tFaa8kLFxtcSYHWhvFoRJGnjB6+zC/91Ff4e//4nyKUYn3QZxB1+K0/+n1+8Wd/Zfm8+RDlY8nWtYxYLerKln2vj9l5iamCaNvxpJ3Tvi6lc1HrqSu9bFsiqoj2SRx82Qj3fHPcaGjZxxHBUjgSUBmINMkTjnP3i4Fc3EJvNh5T5hZGCox1UYesNUhZBa4PODo85NXXXuPda9dcbFwgK/JSz+8sxrHVRl87ds1v0rYxRLOlOCdE6zpXQZlDtglbKIREKmeAYa2t3fSKPEMXKcls7KQHqxFYlBRuYhcusIGnJArceVNgixRhNZcvXsBTkrv37vLJT32C7z7/IlYokIpCW7LcuehhjVvg1kU9a8P37b+nvb/lc/X4+UVGshm75cZli/U1G/Hxa924G5qgFqbUN+vm3jqIBk6abunbbXtq1eqP4+qYqlRpVKv46+05TEXYq3tO4ncekJovIkknqSOqTxNyWCKlav1uGd3NEfTFCIinqyrmH+w+9dU1kVw+Bz5QWRDoG8b82ONpza4W6LH8ufcn1I2+ujpVretmP3UeA1a7+ZUVOaPZhFEypcAQRhECGPZ7xGFQMnkuKAoC0jwDKOMgOBeqKrCekhLfU/X3OIoR1iIlRHGANZZev8NkMsEYQ+gHhEoxmk5Y6/dR5R7leT6d0KXrVL5XjotjCCLfwxrj0nO23SOVItOae/t7vHvjBq9ee5cfvvc233n9Jb7x8ne4M76L1/fwez7jZMLhOCEnxFOKbifG8wSHRwd0ux2MdbQk8H0C30MKCMKAThw6hAu35xQlwcbCmc0tVgdDpqMxr7/xBp956lnOrK1TpBnDXp/nnnqad95794Gmz4OWj1XCbuvdHsR/uf60jlc66aIoiKKIPM/r+qpMX9X97dKGqNsR0U6S/OehfOY2mmWL2NhWXGbHQi+B2hoiuNiuqkgh0E0GWrTVeID0qsDzzoUrSRMCv0sYBLz19luEoWJlbUAnjsjTzC1KbbHSzgWaaff/GGERAimc1FY1SWsXfN9ZQ8akSUZhnP+vLNEJhCXPHbwlpYPQhACTOZ1WnhdUUj0IxuMJvV6vZJoMWI0pMsIwYHRwwMNXH2by2hu8fe06P/fzv8Dv/fbvsHc4YtCLkTgdXAWrK+kS0VttHVFvzalFlUT72CLxOE5IKsm7lOiWzMM24uMyRB0nRMvmSyNZV2NtSyIN0pSqDOFcW5CtRA010Xbis61Eaiglt+MUtgRNEJiaultjMLrMgKfbxkutdbdIvBf78QA0cdnYL/u7/FiLnLX2gpOQEyEqtON4sa2LGum2xazToq31OblQwwMyAXO1tu631bbQRgcW235c6m76dJIRW1NndW0j+FAT7vnIZY5J9JREepJkkpFbgxFghZMiPaHoxR0iB7dhjQtoFXc6ZFnu0m2WyJ8SAmEsSkhECbNL4Vw8e90uYFFK0unGFFmGUnBwcABAJ4oIPY/RZMpK/4xrdykcBL4Lbxp6IYK8ZgICT6FEKcnnri2UaYFv7e5ya3eXJJ1hy/jdST7lxRvv8OxDV3nyyiMILdkdj0izHF8GdPs9EIYkdalDpaeQCjpxhPIVnnDoZxyHZQCYogys2KgvZ0nC1uYmW+vrKK155Uc/4l/61X+RZx96lJtvXSPwA5595lm++Z1vPPgUeoDysfhhLzu2DFY6jTNu68GqzTXLspoQVW5IAHlu6HQdl1gR8krnWVuPl4RLmONSvVsobhkv6h4X+1VtIEXhCHuV/ajNodfEoZQI6gUFiJJQilYfDc6PmtJAKA5DgiBsmIwsIwxDslnOYDBgMjni5VdeIytS/tV/9S+we3cbYS0KB4fnhUsaL5XjFKVx/oumjLIghHD5tisEAgvlmFXjUcHOFoO2peQOCGTph+nXhEBrR5CE8hiNR3heABa0yTDacfBJmjiiASjpsm3pTLN3uM+5K1fp9/rO/SjTXLl6me8//wO+8LnPsLG2QjIbI7FMJhMXvEVIhFQI6dfvpCEA1bjOE2ZZBiap9MyLBLuCqiuUpFLXLOrDm/m5BJwq3+3xw20pvQr6IlC4DdYYAaL0qZYt4yirsSXqUu/7FTFvt4X5PktE2ZRyYzcWjKWxU2jN++pZx2p98LJsHbfHt9HtiwWJWLSGq5EQ22UZsa5QoZNbbWuS2xC1+/Wi1hHVdVTtOq1Uo76s+trr41jr3PPqXaFG56h4xQVVxaIg0BYOaBitBSSpEZa08w8Wbu3G/Q7+QYBEowKHWva7XRfdSyqsMXieot9z1uLJ0YQ0z7FCkmtnH2QL7dRiviPYfuD22+Gg52DywKc36DOdTUmSKXv7eyVBd9HTJrMJvW4MpcrAU6q22VG+U98pJfF8hS89pLXkSYLRGuV5KE8xTVOsgLjfw0Y+WVqgC0FnsMLu4T5/8KPrfOfF62z1B5zd2MDanPToLhtb6xyMDtFa0+l3maYzeoMe/W7XxVLPC3SauoyFBwcUhRMMfd93NKbQ3Ltzm5175/jJn/gyn//Ep/g//OX/gK9/4xt85pEnef57LzDLMiZpwnMXrp46fz5o+dgk7MVS6XzBTaQ6o1Oa1TFjFS4UXZ7nLsBGGagerDMnBzRl5iYB2hYEcQAKCqORJeva3mTnIq9JiRcGFKMSYi8JpyM4qrxGuGyyRhP6npMkTcMxi4oTFw6a93zXlqLQrb6WEqc1ro2+j4Eall9kCGzpkkG5EU2SHJHkJRTqoKwkm+GFAXsH+0gJ+azgjTfe5rf+0W/zcz/3FaazMYU1eAoiJdG6QJOXTIUjDqrW/0FI4DRbFXMh/bJNDc0RQjDwwoZTbzMj7U3Ugilam68U9YbZTvPXROiyFJlDSoZnXBzfTzzzGOurPX7nq3+XX/3VX+X//Vf/Kk88doWrV7bodXt4EkLfp9cbgHXSppRqrq3L4FdQ7p2JJpNV3eyKCUNQgvXH6phDWoTbZrXWLhRoW+o2DWXQLTTJ89VcrPtSkHUE23c2FZU1OFLieR5hGCJKWwolLB6aQAnyxHNJCJTLQ4yJUJ4bz0o1RMkm6iwn8hRFOmM2GzHNJ6hgnsFx/TcteVJQefweI1MnCJ3Lx3zxXCtBD4A1ZbCkqr3l8TIgTvWeDBZb+SCXjIoDHdo2Is2zasZqjpYtJaVIV1spM50ecWy+f/PkuWYZRI0P1OdMC7CpDpvyjkW527Yucu58rXgTprnKjUVDlBXSZaKSJVNeFE4yVZKiMCWqKDBWMJodcnQ34+zDW2gryTODJ6DrR2yuhgzjiGmeM05TOtZyYbDObnLE9jRBKo8CyTg3WDKk16WQEl8aIs8yVj5aFFzodpGZoKu6nF3d4K3xLYZ+DEFE2PHxOx1S1SXbvcEj5/80SZLQkSGbYQeZj7k1G/Pz8XnuqJxQFfS8gEQUjCcH/MyXP8/3vvcjyHOG66tIr0N6uI3JLI+ev8xnH32I5y6dY7J7xKeeeo6D6YzvvPIK3/jBC7zw+ut0ugP6YsCgN0RIt447cUwym2B0wMpg0HhVzDKmt24TP9IjnkT4wiPTGTdHd/nik1/g8UsP8/vf+gadIORf+9U/zV/9y3+F//C/+s9Z/+cvMFnxSPMR77zxFnate+rc+qDlY5OwFyWTNrEG5mJ/V8fb3GGe57V0IWjBu7ooN0CLtYv5s5dLNwDWmHojrSH6cgMxApfgXTh/S0eYqKGl8iaczFwSb0m9uWEdcTbuMmRLerEWcumkmlzrRggzbhN3G1WlOig38/JYvYTLcyQFnlJIJWrf6d/5J1/nm9/6Hnlm8Txb5ql156t2ygUxQIgmZnAlndUQr3s4lcDWMFptSG/eWlpKZz1Zv/e25Np6B0r5KOWiJCkhkaqM2BbEKM9nMk1447336X/7e8yynK9/8zvcuHWDlUGXJJmWcYZBeQFB4BGGpbRf6j6VkrVVdcMQVRmQnD+zg/Ub4l0RlV43KvvSoCNVAJ/qtx8ExJ1uGarVqz0VlOfa4XmeM+rxYoR0mYc838PZIcgaOkTIMitRgCqDUEjh3Lcq9EgIgbGGMAjr+dvr9WriX6Ub1do46UY0wK5traeiKCh0UVO2k3S0AuECfTD3qpdfe0zibRjwRf20m0slI1giSULKMoxuW+1kqMMwlC9HCodQGWspjFvzbR+OtvTdhs9df05sfVv7WzJ8p0nRDUxddfvYEJZ+Wm2Jvl5vrXlmyz3E1vfQUHtsvQ9UD6nW/nzrF16PBSGcblkI4cIy64LDoyM8P3RwLpJcG27cu8sjDz+N8px+xvd9+oMB2SwjDAf4nocyBs/zCcMQVQYRqYSPwrgEQFmWEYnOHNoZRRGzZFar44wx5EXOJNcMO05HLMrxNiWC1Y1ilPIIwgC/CNDGUmQpklKtZkEpDykUnheQZRl+4DPsdlhbWWX3zh66yBB5xtHRPt9+bcK3X3+J7GDMO6MRZ86e516eoFb6XHr0Ktk0IzkaM+jFZFlOnqYuMIpyoaPTMmx2mufk04xf/BM/y/e+9jp7cocnHnuEi1tPEr76Lr/3zX/Cv/sX/i1u3LpNoTXv3bzBU2eu8ss//0u8+/JrBJmh043ZubPNdrJ/ytz64OVjC016srGEK9VLbW/+1SZTRTlzB5mfyK2ipCQMq0g81cU097WeW52pNlldBkipVmHbTKyqyuKMsyqMqpJCKk5f2ypBgONgDZXlbWsxCgf1uShkTdvae14Vj7j6VBeV9KbFhVusNi42MOVCLQw7O4cY46BmhEQbhcXUz5nbX8tNRNQ9XoriNscqzmXueCNNV1BsWwqlHqcKwa2eIxvCWdUhBVJ6jrAB01nGvXt7XLn6CEeTI1548VUkkOcFge9hXPSFcqNoIFZBiwBUxACajZHmuvb+XP32aoaj1XYx/1cqiecpx5QJgec5/b1jElRpfCNKuF4gJXWwCiFwEaDKqHaqJOguWp1ClkxGxci4YbO1VLqzt8twMHSGY3UUPWoPCVVHsXPvw5RzOysKCq2bOTQHz1fEqDVvT8KOT6Fp7XFqG4016XCVYwjL8ZaiVFG0mFJjRP2QOsxquXYQFg/bEPR63jYw+smqi9MC6hw/dh9e5aQRaP1tk9R58tq011IFyjnpifV2Zo9fUk1pg2PInHrQp8gzsizHC3yCICRJc6RSzNKMO3t7bO/v83QcM57sU2Q5nlCEnk+iZ/QHfQc1FwWB5xGFEUpKoigiDAJ85bm8z7o07BUCXymkcHM2CiMODu8yDEMKo0mylCRJGU1nnN8aokvPniqCmRKSfq+LweL7HkHgU2hDkedEoY814CuPTsk4CKUYH41IkoSNlSG9bhert4njEOsrZpMxu4cZ4zyjIz1+43d/jygIOXPxAir0UaFHcnhIpx9zcHBQMhSuTb4fUOgCYyx5XpCmOVmW88Z713n0/DrvHSS88/4tpjPNVz77GXbuvMe3Xn2RTz75JFky5eU3Xufp84/w7KNPYA8y5FuvsrK2ztNPPMlvf+drH2pGnVQ+Fkh8kfNt/12mF26Mo5pr3EfWE7bNOVdMaSXVlJW1JNUWpGdtM9+FqJOJOGJcSp/VBkeLaIpSEq8opq2IZKkfszVSfuzTXqiC+TU3BwQs8Bdt3VVF7qxo96FM+mGc9aRX6uadMad07bElSoCoOf/5R1YPUc2TT9qky+tFGaax2l4rzl+UUrqltHqGOem67kclYZdZyRpdcaOLk54jegbB7t4ezz7zFNevp+zt7pHMZuSFIYp88tyFYXUPMzUacbzhZb1ClYTh+Lybb+gCg9lmusoiS+JcMXulZqYmTp5U7r6KSSqZicpFMQwDKpdCKRVSOKt3WYaYrT6er2oEhJI5GKwO+MQnP+nGrMn4UA5DM3GquVZlfStOINj1OLTXR+udLUPHYJ4ALlMdzBNqWaJojcQsSwm7QXhKJEpKdMXt2aqdVY6aivOULGvtSQR5WZvn72zPCvfg+ZGt7m9dcdqz7DwTMSdity6aYxorhgNqdM22jp9cnKxuaO231u2hhdb4fkhhYJZm7B4ecmdv1xmThgGT/SnWOkIphVuXqytDh/aUe6pfokWhL+jEMXEYom3mVGtKIYxwSFmJWgWBcp4sa+scjQuSNCUvcqbTGb3+xTregxQCUxR4UtDvdtC5KUNW+y4HttF0OhHWQuB7dKPISdxFQZHnZKW6tBvHWJ0TxD5eEFDkKeuDIQ8NVvCMJZ8m3Lx5i93tHWTgMZ5NXM7tQHFwcEgUhS4fgrUo3yfNc6gCUllBGMS8/84twjNrnN+6wPV7d7hz+zbpY4/xCz/98/zO977OZx96hCL0uXXnNpPJlLX+kCuXLqG+DTYv6iicH2X5Z+KHvfi9Tcwb/+F532VnKGbrza7Wn5bmtm6vMQ1MtficcsY30qkrUqnSLanBrNproy3ltglodWVlmVvFCS4FAaffLK+vmIDq7mpRWU5awvN/gVo0bhZwxdw0jI2xYI3GIByxsE5DJlVFFI8Hr6mlXXyoN47F3cFW/yPqcRfN7RVE7qhVzchUv4+VciB9P8BY66KUUYXOLK8xFm2d4dv+7g5hGNDv9Rh3uuRFTmEywijC2BQlVZm1q6gJZjXijZTmKvZk4GDoOXykYcRqhkE2Ngiuycd3TKU8PC906hpT5i+2rbcqHPGUKCon6YoRNMY6f2sE2lg0BcbmNZGWJUzuK0mgfQJfldC4a7NXushZU9TMEqVk7FwL2/12Kh9fVO6O+kQivIwuLDLZi+fuR6ir7xXML4QzMqwRhPo6p4qq1pGpVkbpWllojTDG6axcJbW3SVtttUiQlxHoZYLCsiKrtiy55L4S/MJaOqYWXPK8Wia3zd9qT6nuccS4dYNrDNYKhBLoQpfMgkQpr5SyI/ww5Nbde9y6e5f92ZTLZzdQfsAsTZBlYCRrDYGSrAwHdVtc5EkXACRQHr1Oj27coShcREalFGhbWoY7xMQvLby7vS7b+zskWYoUgkIXrAyGjKcu5aSSEl3mr+7FXSbJtM4xYYRj6HvdDgYXNKUXRVgNWZYS+wEWF2Sl34kxeY7qBXTDkMITfOqJJ/mJT32Wu7fu0O32eeXNN3jhlR9x69ZttICo28FKy2Q6pdPp0Ol0ybRGA4VxC8kxuorhYAiThO++8wq/8lO/wqMy4J1rb/G73/0j/s//m/8jL/3gecaHR3SHfc6fOc/u3j69tQ22zp3F8xSHR0ds7+/RCTsnzrUPUz52o7OTFkdbl11NkKq4wOu+gz8pjXUqaQXIdQ7WBYRw7lX1/lgTpPZTRbmyK0itYRgqHXUpKdabb7loHWbrjDxqWVM0z5PCfaDWX1vbSDeAc5GoOQDq5zZQ3nK0cSnDI5xFdrVRtQ3dauMca+txc4+spKHmWW5TyGoJrpGd50eupkdlh5s9z9RSgNto2nAm8x2y1OEXNWnt515tblI16Io1xmUByhK279xm2OtjNjeZjsfkNqNIC4o0R8vCNcmYmkeoofEKHrfuWM4EsHP9X1YK6zf9bkG8NZkXZb9lgi1KN7oqP0T1bARSWqR0M6VmBsqHdjouAEpRlHNODUp4UdY+676SBJ5HGLqcw4GvCH2fqNsBYxxCVMLsWZajPEEU+01iHNu480ipyngFLo7+Ihy+rNTGjw8gGBwz9Cvvk4iaMEspkb7Ekx6e8koVQJmvvkw4Xc29hmiZ2sc/LwpsGTVPQo3gVPEVFglxrSs+hUAv08E33+/f72Xj0NzX3ksWrmM50V4stR2Cq638XTFRjWDg5p6TPiskxgUvsuS64M72Djdu3WL38BATBqyurrpgUzjBxVOqZJgEceCDLqMctvYDYV1e7E4UMZtlpWBgy4hjwiFCyumYfd+n0+sxzW6Qa01/0CM8HLGxvs4s2SGOInzPI0tS4jAiDkIO9MQRbOmjAYmmH8eYwhL5IcNuF1MYgsDjyvnzvPLuNaIwYNjrobBIT9API9ICLq6v8OlL5/n+vbu89vJL/Nk//S8QhT5vXHsPEYb84OWX2FxdA6GQfogMAorJhGmSUVgYTxPSJCNLMvJOwV/8l/80/6f/+L/i97/9B3zpuU/w2Sef4f/z93+D3//+1/jf/qV/k7/52/+IrDB88VOfYXR7h/xgn8zzyT1JmiXc2d1ma3Pzg0+oU8o/Myvxdlm0Nq4Mhny/MSKrAmDURkzVRlTKR0pK/MCn2+1S+ftWKuJqs24vIikE0muMepSaP98Q7+pImdd4Dg8TLUl+uava4l5XM8ZCLF2xFdr3IEUp0EVVH6VVMCX02RixqTmXo5bRVZUdSlicy1ClOig3zsXdpEQNhLXI8mS9uYq2QG2xZn4M3Dg0koewIIxx70GVObsFpShjmhghpsBXcP7sFn/qV36F/f0D/vbf/jt8//kXEMIy6HeYzVJnbKWc/YKsGSGLqDHNcourDJ5Eozed72I1Anrp8blOaYkgAl1OHlMxkqWxWmWgpzWUOltjDJQR66T13ZzTjqAq4d6NVM6WXVU6flHnI6vnaOgrsixxUaiKAmkhikKEhCLXtESvel4q5cKSusiAJxPpY+UB5+OyokR7jblPGAX4XoDvhyjpI4Vq1pGo2N1qHQkwmsLLHMKQZuXaNDVBr9o3TyiXS9YfrAgWHKY+ZLl/Dc0+4YJyHGvJafuCGxCwllw7pMdTskwoI4jCkMPJlLfffYeo02XTD9gfj5iMR+wf7Jbr2nnEdDodrMkJ/QCDdlmpqmeXr8dXCiUcGVdClHPXBTsJfInveVjhEUQhBst4NsVg6Xd7RH7AsD/genGPfrdHHEbMJlPWVlcJQp80yx3SJixprjGmcFmyspxO3GF9uIouBKu9Hk89/DC/983vEPs+64MBfom0rXS73N4dMZmOOJjucuvgNj/3K/8c8bDLvb0d3nn3XcJuj44fk6cF2sI0SdBCMJ7N0BaC0ONoPEJat24OD4/4f/61v86f/9Vf5nsvfZfvv/wqF8+e49/9i3+R3/sH/4TH/u1HePbppyjSjGQ65Ymnn+Zv/v2/z9oTV5mJnISc3WRC9GOspWXln4mV+DIOt4K9pZSkaUpRFHPH29c6oy7noiBUpf8SNaRuFzYji9vEKwIvcBKGglL/R8vwzN3hOHbrLFNria2JRGSqnJY4aXMRgnM+zEUtdUJlZDOvozfGzMFtAmoLa5des/Ehlc3O5PpalBKbVCWxthTF8WAwRUMnsbW02XD+1tqaWMwN2tIXWv4pk51VkjUWaq2CG6H6lpZh/Vw4CmPd+FabtZAOHq6wFQkoK5hpyze/9S2uPPww5y9c4ulnnuP5H7wIymM8nYE1SGkxuAhMNfNQdbp6ONAKRVezJ23GpGJDlAya39a2+tmaWxKEp5GlHl6KZjDcOJfSnQxqtxyBSxfo3rtPxUgIAdK6ja+Gi5WDxD3l9IOep/A8iSzdeDypCKOodHl0BkdSCfzQb9PrEh5ymcOKLEPnuXs9DYhVl7lXWKIlH4ZkCVu6DyqXS9n3/TqkbafTwVMBfhDj+yGeFyClVy4l57olESgkxmiyfEaWzEjSGYgmV7yzX8Dp7KvnPgCRXrSnmS/NsSrSAEtG4P77roA5DfhiDXbu2tPrq2Tr5l3UyFgpyDTBkByjabTG9wKkkoxGE4qiYHNri/5wSGFhYzImDiXvv3cNv+sRdSL80McLfDa2NuiHMVOTIHDupMrzUNogA5+iHHflOeg61wXdICDwvNI11q1pKSWj6ZRca6IwpBvG+FIRhyFpktKNIqIgYDqbsL6+hlKSpMgodA7Ckhm3Qn0lKLQhDkOG/R67ezNCX3JuY5049Fnp99lYXcVkGVlmWV/b4N7+DllekBYWLSQvvPoK333hb3E4nrJ25gxJnpONx0zzlLg3ABWQaUsBZd98pLF4QL/T4czWGcY7I377u7/FH//cLzEdT7izfYN3d97h85/6In/wB1/nyUcexotD7uztEvgRg5UhN+/cIYpjZORzMD4i+AjYv3b52HXYi/DbsgU2m83IsqzOB125ErWl8Op35Z+ttWY2nXFwcFBCvPN1WiGcEUNFJKzFFNoxB3nudOOmuavRq1ULpJT+hcsl26q5hIkryUrOfRodm/soaLnsVBLEvKuIC31XbSq6lvRrPR31Nl+ODWjtji5a2Vccg5DVQqo2kbLBpuwDzTNPeIPUVvQlE2OpbAcaw7uK+LAMBhTzukBVUdXSol6WUdMaaaPUYwoYTWfkhSXu9ljf2ipDpDbdaEh0Y1RX0SpwqEDVhsp9p2JQpDi+YRryppL2SUmdXckKQWEKtHX+9FaU4GU5TEa48bamQOKYKissUro47Fa6PO2ijJLnSc/BivVHlkyiLImfwPdUHZ9ZSllv2NSMrfP7ruYzrQhsldqkYmqXQrKtg+WraX4vIXQnGdHUSJmUqJJQ+yXh7na7BH5MFHUJww5R1CWIIjw/BOUYY4zBZAVpNmMyOWLiKZBlLANtyrwALhFOW2Vh7fE95SQCvRwmn5dw2/tIxbTNM4LMz4/7kN5aW/Ag+7Zl+Tuq1BS23Ddaag0hnDADLh1lnjt1wtraGvcODgg7Meu9Ppe98xiT8K3vvMCVJy4SddzGkqYp/ZU+peVqzbA6aF0QhCHpNEUIiac8wiB0lumhorGjaIS0JE2QylmXx2HomEw/QBcFYaeP7/vMDg+btMhlFDbhBPvSkE2ija1jk2s9oRvH9OMIMHVebonTnW+ub/D6uxJbWDytMIll48om4/GMo9GEzFpyY1BxhJYW6flo6wKgaFNmPLQgpHCpMgWsrgxJZzPym4Kj6Qgp3Zjs3Dzg87/4Bb75zW+QX7mCkZKD6Qh94zpbW1u89KPvk+cpVsIkmdFZXXmAF//g5SMn2JU1IDTEum0NXn8XTQ7ronA6KuW59GVVZLJ6YlKGdiyJqG3SZpNnGd1OlzRL0YXbmCopTkjRROS3jniEYUi328VaSNIMXegWNN8WIV0JPJ9cO6jGmEYqVbKxLK/ud7mly1jRtDdAUUrvlVO024Sqa6p63aKA3Bb1RjRPhqoxbo61YfGakFZ7SCWgV5JlbSjXgOFtMftEgbskVvV2IlpSanV/231q4d75r42Rn4AyJnHtnVr2QaILzcpwwLmtLcZHh3VyEqFk7UqHlXMoQfu59UZZ+sA1KgHbpLVsddSgqax358i5aLmwyaYeKcs2t5hKV70s/Uxt6Q/u1UiH50mcNb1GqYYIS9ky0irnQRXqsY7VrGTJIIk6vjsIjLC1xG+pdLgSdIFWqmZOnXW6qdGN+j3X8701D04ggPNj2xyTYt7gTEnp4NIwotPpMFzbpNsd0Il7RFGMH0YEQYgXhOWY2jrKXZ7PiKdd/IMASpe1PC/wPBcrX7Ti8VeqjvtJ2A8Ok9vWv3Vva6ayJmjtGWyPkfjlz24zRRVjPf+U8rJmLbT/mDLYjK3/ujFTVf89xTSZkSYpvW6PM5vrvH3tPYb9LpsrQxcOtAg5nB5hjCEKQ6w2mFwThwrtNszS990xvBrL/uGhW3O+c6PseIoi1xiha+ZQlFEnTe72yMD3CALn+dCNAoRU5EVOXCJGmSlYiSOwkOcaqSTKF2jrpGujFWgX0lT6HoWxdOOIQDkUTClB5Pt0gphA+az2egRKoYQkUD5KSgbDoTOsC0NMUZAkM4QS9R6ibRWXQ9QImBaC3Fqmec7OwT7TfMb5tYts727TiXxC5ZNNNYP1IcJKkqQgkh4guLO/z5OPrGGzhMJoCivJc4PoRA849x6sfAwEu9GZAm6CtYh4VaRw+rUgcvmqwygk7naZTqcUpXEN2jgraOncmEwtMRiUp+jEMf1uh/W1dfb395jombOIrYmYRMpmYUgp2djcxA8CENsYO8aYFG2aNtoKJreiluiF56NNgrVFzQErITG6ZfRlbWl1aclzXbPUbrFZdOHcF1zcbUOhSzgXQZYWtTuF5wUUhXGSsQBBGYQaJzUbM69rbevd67YIylCUlQ5ezMG3bnQaw7HGwnihiIrporS0pmwTNRFs+n+y9NVUJ2roQQkQprILbjYwKwQWB7WdWR1ycWuV3btxCQ46eNll6xFU+mlomBH3vUV8rKwREeV5ZbQ53eqzLRlDgVSqPl7Hvi9FemudIZmvvJqzdz6wsra7qJ5duf65ZALOqtyTml7UI0kS0jxzVrEuRqmzpJYNI6Ck0xlKFMK6DVz4Ems0UnmlC5TFao0JnDWvEaKO6a4RUGRkMiDPEmec5/kURVLbGjTMdDUPbNXb42BuizGtJ0F5vApEU/mLu/4rlOcTx13WNrbYPH+V1bU1Op0OynNGS8aY2t3LMRsCryuJ6TEwa8S9jlsbWU6SpPi+JsvS1pyGJk73gxHu48Uu/SVozWfRjEj76orRadwk62GZG0tap2pivaydoq2KkJXxCOCM5AtrXPCYMsys1QZTaKQWyEBSYDlKZ6TpjCtXLrCx2ieShgsbq6x2uxzt7jGyBhNCMpkxHHRd3GxbEAUWLRRWge/52BKVya3mrWvvceb8ebpCI6Rm4Ct0YchFipAGoQKU7xPHAXmSItd8olARKBcXYqPfI7eSQueEnsBTggzN2U4EBrIsJwgVXuiRF5aOr5gmAmkmBD6oICItCjpxiNQK6XkIKfClYKWzghYpwzigEwROFRN6RP2IaTYhKWasbqxRGEt68zazNMEjAI866Y7TzTuJPtMFfqeLloIfvvE6PRHyxU9+knevvwOpx8ZgQG+tjydDQhUzGifEYcRmb8DO4YRResRaJ8J4PlkBAp8s+GhJ7EdOsD3PhfOsJNaKEBVFwWw2czBZ+bvKWQ0Q+AGDQZ/hcMj169fJsozYD1GeB7jJmiQZnbhbR3my1jAYDLjz/Ve5fPkyB4f7HB4e1Hm0sywnCLx6g5ClhLSzs0NeFPT7XbrdmN3dPbKsTJvWhoCtdUkoBn16vS5pmjKZzFBSkWsNkjqyT5UtTClnUFMUJbRtqRee1qVRh2gyijnYmVofD42fI7jjxpoaondSvDl9c6o487J+WaoHEHZur7gPfV2QxOaTuTR1PPhGWbmn1JB/I+fXzRZY4jjkk08/zs0b7/M9DPd2d+p3V1aAMZbKGL6W1MtdsZKGwIVurDsrdItQHZeL7tePKk59lYBG6wJj1FxSGSklUjlGS2Mocl2rSlZWVsiybC4RTmWPIQQIaZE0wUaqtRMGIcLzyohulXrF+W9bKZ3rnawImSnHx4CxFHmBbjF5ywly88UKjo1Pc92SuN7YGmmqLNVFGdBodXWVS5cusXXp8hwjVAdFQlLz8SUqZrTGV5LhcEg2m5ImE8ajI4o8rb0CHrS0VWnL3+3JtYmawZ1HYtw4NHdWeuZ5oELMnRdlfUvHb6FdjUTeuraMuORJRZ6lbvxKkGhmCkIvYnv7LlEU8tCjj3F26yw6Lxj0+wwGfQSC0WzKWBsuXbpElmUEQYhEkRxNWV/bwGpcONwSvfKUQgrFAI1q6wABAABJREFU2TNn+epXf5tPPPscTzz2OP2NTXRe0O/22fMOSLOc0XhMVhq9amsI44jVtTXObG2RTGf04h6duEs36rg1X1jWh+uQa6ICIiSFMUzTKd3NVe6N9zFKMlgZ0O/ETHb2iWPH+Eop8H0XqyAIPXSgWR0O6Pd7WGuYzCYM+n3eeOtN9g8PuLu3T2GcR0Xc6SGlZJqmRFFEoBRZ5tRbnvLqeR8GIesrqxzd2eGNOze4sLZBWIagFkIwvrfLzcNtjCe5cHaNKxcv8Na1m1y7fZug33eBghQEvV4TJ+QjKh95es1er0cQBDWUBY3Fd7fbRUpZQlwNYfc8j4PDI66/f4vZbMZTTz1Fv99HG1eHLo21wlZCDFlG4ZnNZszSlFt3bhOGIWfOnKHb65Fm2r3kwmWQcqkiHdEMw5DRaMJoNAFgc3Oj0XGWeeMqIzffl7WO3fd9VoaDOcOxSo9WFKaB9pVyUbBUsziNLch1GSaSJmd3WYmLWqQ1hXGGdV7gIz1Vu6sZGom50pf/OOV+xLrq20dZRC2NiVrksC3L7epx0+mYM1vrmCIhnY0JAwcr51mCtOWmbxq9Y2VRDdQSfFVqg8TWsSqoVlsXev+2izmi007nWeVdr34XhYv1bYxxecCBJEno9/sMBoOaUXWBKiof6yZ6W+C5+eP01x5BEDTvvFQXCSkQqlQ9CcdoCtvELTCmAGvIstTFw19CLOaKPU7I71eqEKqVZC2VqvsQxxGra6tsXTiH1s52xBmXahe21fdACHJtyArtQkUmGabQgCntDCwYjdE5GIOv1Ny8XTRqnXMx+xDlVKuO+1RbEWXRurT9fe7aB2ijLSt0iF3uiLZ2ca7zLKfIc4cMhorbO/cA2Frf4MKZs0hr6XU6dfjnXGsOxhNmSUqvN2B9dZ3ID/GVR6/bJQxD51JZRtEzWhNIhbLwiWee48mnnub5l37EX/8f/h4mikiTnNFoTK/f59KVy1y+cgUjBSLwUFFI0Oswo+Da3ja3jw545fo7TIVmKgyJBDXocObqJcK1FTbOnqE/GKBKAUsiONrfZ2WlT7fjCLz0BBvr6+R5Vu6bXh3l0g8UK6srrK6sEMUdkB5Rr8/rb7yF53msrjmm8erDDwO4VKICptOJEyADtw9nSRUTQZElGXfv3WNa5HQHQ2ZFwc7+PgdHRwyGQ55/8Yecv3yR3GqmWULUCbmwusYLr79MuLpCHPaQMqDwFOPZ+L7v+oOUj1zCPjpyBgWVRHp4eIi1lm63W7qXUIZ3dMnAgygsLcSd69DR0aELP7exznQ8ZXx0xCxNCEMPKbwaqtLaMJ2ldUyF2WxKUWT0ul021tddAI79Q5dRhjJ2ufRqKNJaS5IkZWSdmI2NDUajkZs0ZbQpK1ysZlWmlbPW4nvOiCZJp2hdbdyu706yLloQoUDjfldhSZ2KoGj0fUrUNMYyH0hmcQM6ydp+WVl0Nauh7LquhkAuc097kNJuo2kR0Ae7maYBFQFtnUqSqYOUA59Uaxeq0PehaLR8otQvLo6TqM7VgHtpoNNisqpzbaK9bMNvn1dlsIksy2qDr0WjyPp6ZB2LXCnl7lEK1Qr80YhkdWw6RzRKeLut2zYlY+t5Xh23wIX8rCzNK3VDO32mcdmHyhj2jfDWELpGh00tJX4QaLmBjVsEs9K1Gqdz9nwH9c/ZZRgHS0oc4+EIf4UQFBwd7HOwt8vo8IA8SUpju2Kh7fM55j8oJH5MV9/CHyxNW6tLKml60ajt2Jypa1h4Xms+zo3fQtttiXRU1vFau1CeugxJWyXpyXXBvemU8eiQS1tniYKQPMnQGPrDYRPfPgwIex22r9+k1wnZXF1lMkmZJSmDOGBlsMpod+qCokhFgYPEO90ON95/n7NnznPj7g6vvPkmv/P1P+Tf+9f/FQbr69y6c4d7N2+yf3TEO9ev8f6dW0Tvvc00Tdy+7Emm4wk/eu9tDkYjem+9grGGydGI9dUhb965ycH2jKAr6cQhfqa4vLbFpZV1trtDulGEH3gE3ZC1aBUpLL4SJSpjHGMbRkSex6DTJY5CkqLg/Zs32D/cZ+vsGWZpRprOkNJj68wmd+/eQWc53V4PhGR0eOQSCgFpkhNGAVEUEsUxd7bvEUiPsU2YTqcMw4i1zS2+/s1v8swjj3L7YJf3bl3jysVNHr38EPbVb3PnYJ847jHWY24f7PDs2csfaE7er3zkBFsIQZIkNTw2HA5JEtfhavOy5QbvlTpft0G5pA221M0dHRziez69fo8gDEizmQuo4gVUxmeijFhmLfi+h9Ga6XSKEII4itADw2Q6LVNDOoZAa+2IaimVFUXOZAK9niKOYyfBl5uA8lQZbMVxqg7OtMRRjLEBWZbXi1qUquai0LUhWBXIoFpgttwMK+JWjYfWjTuItU184MUUn/fLKd4ux4j7IsVmmSrt9A3vNML+YMS+2QxbYGOtNXCbpBMidZl1yFoH6yrhIMFUp0ioDQvrmppdtdW3hY3ctpNHtC31TybU7VJB1ItEfvGeKnRqReSb8801VTztdoARVbpFKeViizubB1XbD/i+jx/4KE/VkLjLJVJK3RU4K5xRJJjGSnxZOtD2ONRM54MQvebtOd1+o9JR0kWLStOU8eiIvZ1thlvnqbOStd5PZVsqSoZKG02RpUyPDtm5d5ednW0mk0mtciiKvExsMx8AZpHYLft+Utvb783OzY+mtN0AK6JuLfNzqa61IvoLxLmuY7mqYVkrjdFoXdTVmcqDRgi00YxnE967+T6DOKbT6dDtdF3ITU8xnoyxwGQ6ZTgcsrGxQfbGO8xMzoUnn+YHP3yN/cMjth67ih94Tnq1LkSoEo6J0lqzfW8bpGJjc4uV3X1eef01jqZT9t94jTfffIu9w0Nmec5oMiGzhmTmniu8EkIOQxKrSa1GahcM53A2QYU+7958n93dhLAjGHRjOrbLoNfl4tZZrq9cJ5CSPHFePYONHrKG6t28ieMIYonA0I1Cl3IzmbE/GhF0Ihc50XNhqPMsZzoakcxmrK6uueBDwOpwSJJk+EHAaDRCCPA8H8+XhJ2YyXTK0WzKZJagkKTacJRMyfOcza0NMl3wxrvv8uVnv0yn1yMDhOfQozTPj8e2+DHLR06wK2lCCEEQBMRxXBM7EI0+uQyjV1k2Vxyt5zn4c3R0RL8/II4jgsCHiSHPpiUULeoNwBVnci9wEu5ReW+/10VrBwvqEoqu9GeVYGBxkdVmsxlxHLu801mGNhpPeFShQEUVl9s6psHz/FpnbUrdmsVtRO56gyiloCqEX+UeVkk2QlSJTubfapswtyW4B3GvOa3U0k01ag84mU571oeVzufqr/+dl3YavbAoGSJb59RuX39so21/XWyXaH2puISTrm2VirFcjBEwV/UCxG6tnXdRpBXrXlZugwuW4iWTVvlmCynqa/3ARZNSntdIpSXhnhsS64w0C1tJpQaEbJCcYzrqNvt0/8PVQVOOuxWNbtp6CmMcBH5wcMCdWzcxKiAo2y5VGeVMOHcql0+9oMgLsixlNp1wsLPN3u4Oo9ERyWzqGGhsaRxaBTkyc/04jVDP/17s0Py8a65YsPeYq2+xlhbpPpVhsDWSdtK1NRMCFFo7N7byGmOcgaA2mlmacjAaMZqM6QUBYRAQRZGLjy0se3u7GCyzWcLKcMjq6iqDbpdeoHjsykO8/NIb5GlKpxO7aIOerG1shBAYAaYUcBCSQb/PysoKSZZzc/se+4eHvPPee4yTBKEUwlNE3Q65KUCUftyeh/Ut3bhDluZ0og6Zypj5E4IgZJakTJMZ4zxjOhvTUynjZEphDYNBnygIXbAlY+nEsYPt67Ui6HRiREdhdUHk+2gso9mMw9GIbq/H4WzqIrohKDJLoCTrq2t4SpGXNlRxr0c63XGJTKSkKDRJkhLiPIr2xyOmaeL04EKwOxoRBD47R/s89+wzHBzu8dZ71/ncJ3+K1ZU1iihCiKMS1RTLY9z+GOUjJ9hau2Tf1lqyLCNNUzqdDltbZzg8PCwJt0X6Zf5pJFmekxfOkMf3fSZHoxJ6TLHWEkUBKysraA1ppmuJos4zLKDI81p3rLVme2ePs2c2WF8dMp5MOBqNa1ja932MlYDEk8L5zE1dkIZOyWAkaeJQAM+Z57tNF7CW8XhMr9+pIcp2ZrFqD9fagHVZmUAihcJQzEGTDm5vSc0tKaeO9EZDsNvlw8CWbQnvge5ZKCcZIz1oe0QZy1NYi8JFsBOiCuxSi7uYwiKVhzYuSUav18MCWZE3zAsWaLt1lZummEcS2v2oQtzaUh8gSqfjZfYAx+BS0dgP1ES3hkznx6W6pvI8qNZE+3jtL73Y/oU2txBn/HK+VQgOwvmtN3BypS4wUKIULl77KURkTvJrpMJ5RGaecjfEpfRSrOezLpkqTZomHB4dYDEczWb0On06nQ5hFBL4QW2MM0sSZrMZ6WxKkkyZzWaMDg+ZTafkeeaSlxSFS00rVenFcTIEftI6WXbtMji6/XvJClhgco9L6/d7bm1TccJ11VzSJWKBNnWGNmMMBZZJOmP/6JCdw33Obm5h0xRhXfZCbQ27uzscTsfoytNEQCcKeOzhh3n47BmefvRxnj//I5IsI+7EoATdTpej6QihhEvfiyaOu6yvrzPJco6SFCUFURBw/fZtlOcRrwxQeRcD5NYQRBHK5KRpiskKZGYR1uJ7lsAqVAG+VfSCDpEM8IygG8Zo5cLUTvKE16+/yw83X8EPAjrdDt0oYrXXIw4D8tSFRvWkc+HqxR1ErLBFQegrplpzOJ2ys79HsNonEhFZmqGLgijwePqxx5HS41vf+haF0cSdHjYriIMAayz9Xp9ZmpDlWYlwCfaSKTLThHGM7He5sbfHxsoa79y6zi//0i8QBh6vvfUOr965ydbmGfZM5tIGC+dZ4/0Ygsyy8rFEOqssoCvd5mQyIctyOp0OQRAwm80Yj8esra2VsY5L2M5YTF4QxzEAWaZJ0wnT6Zhev8v65gazacre3n5ZX1gSNvCkpCgtvcMwIFAee9vbRFFMt9djZWWFg4Mjup0OaZJgjW7gOWOIg4DpZArWEkUR/V6fyWREnmuiKCJNM4w2NXc8mzW5X+dyL5fj4AzcDDl5A2+b44Q3z/PSfWfJYBpHmKrobO0xXibNnVRESaxPkzMWrz/teHtz+SCStSj93OdyadcBuUuiLQSxH5KmmjQpkMKn14nQGiIvREiLSVPntqZkQ+gX2l2Tv1bzpJROOm8JpKLVnmVEt6q3zmfdurYOhLPw/Daxrwh9pXuu3oXvB66NpVV1O/jOnC5cupzauWh0l9ba2hfXWhfNz2CdT66oEBpLVhI8Y40zYqqSnJ0AJwOllfgyiXEJwSv/adrUBD4yaVqmLDSMx2NnleuH9ThUqoIiz+ugSQbnqpRlWa17z4ucNMvIy3gJdgmDtDjmdV9OIdbu+LJjyw6epCpYPvc/qNTffhcVumasduu+ZE7zLENIidE5o+mEncN9xlnCJx9/gv3bd0imM4cKBj739nbprg4RnktbK7GQ5ax3ezxz9REO7+1wdmMDoySdXoyQ8Po7b5DlOecunmdr8wxB6LO3u8twbcj1H71KFMY8fuUqb731Ftu7O5w9d4EszUiznCCK2VrfYH9/H11kBFIRxRGRF6CzHF94bK6uczgeYXRBvzdwGbv8kGRySLwS4PmCw8MJK4NV0izj2q0bDLoK/cSTbF6+gOpa0v0E3/PxlEJgiaMAEbgIeaEfkMmcFDBKMstmZEVehre26CznjVdfQwjF/+LP/Bl+8MMf8u7194k8RbS+zvu3b5EZS9iJ8YIuWZ4RqAAjIegELsuZznnr/fe43O2TJSl7d+8Rx13OPvQoX3vpB3zhqcd5+42XkL7z5jja32fY/8TSOfJhy8cS6awyvGpLJXmZHDwMQwaDAb1ej+3tbaIoqi2+g8AnSRJ833cQoO873bQpmE4SxtPbDIdrrK2toXWB8kqJXjidYBg6gzJdZBS5e5bWBUeHB3i+z+bGKru7u+R5jpLUQU5cUBNL6PukSVq6PQT0+31m2S7TaYInJUFpwGatoMgLhGmIqYPGi3oMVAnhVi41jTFSWcrfFdxe/beoQ2tz+x8WXHGTttGhWmuPVXYS3V0kyB/UsGe+IfN1tI21qgYJa5lME0ZHY3w/oD8YOJ0QEAQBo/1DfCmcP68p4cl5ulm1vKwP2gFD5lQM5WXLDMcWoe9F/fVppbLObRNhz3MGj3meEYQ+cRwTeH6dR9svkzF4niQOgzr5Rxh4+L4is0WdKOXkFriBqODiPM9KOPmUcsrEeqB3bSuPfufXmueOaRZSYKznEDWbk2LQeVYyTcIxBbJhcCvbEmstaZKQlPYi2lhcZLwKzr9/kx68VBLyfVbXj7P4Pkhrys5V6Fo1QStCHnU7FDk1gxZ1OvSjDp/46Z/mndfeYDwec+b8Wbw4JMfghS7H9Hg8xjOaMxvrHO7ucubSZd5/7z3uTY54/JmHORgdMpmOOXPuAnfubfPS668wy2ac3dzk5Rde5Jf/xK/QHa7x7o1bnNvYYm9vmysPPczW5lkXwQ+wxpJPZpw9s4kxhrjbJYw7XL92jUcef4JX33oTFYfkORxOxpw9s0WSZogooLAGPckJcsEv/8wf51/8E7/M3/zNv8tLr3yfH77xMs9+6if4+c9/imx7z6lWyv1AeR4oje95REHA7sGEe6MDRrMRnWDAxQsX2Lm3i/UMa1srBCrgxed/yH/yV/5jnnnmWT7x1FN4cczv/uEfsXHuLLuHRzXDrLXmKBkxHA440gUiMYTKWc5/781X+N//pX+TvZ27iG6fc1eu8tIL32P9tTdZ31jh9p33mM0mxL7HvVu3P9I58rGGJp3fAN2idNbcPnHcYWNjvXaBcVx1QRAE9X0uTq4BYVAeWCOYTsd4yhnfxHHoNmvpaIGoCZFEeU0kNWudJDsaHXH+/LlGsq7bB7q03JbKuXRVDIasdJ04mMoRiDJkqXGBPIRoJSeo664gw4owm5JTp7kAQIpa0nNPKblzQFunv1pUhTjCa+eec7/SEOuSMJV6uHZ7G0n3OJGej163QOAX4WPmN/uqPhdXyPXQaFO3qfKr19pB4M89fp4nHr/M9s5dvvf883T6A3r9noNHrZMklXQbfpYV9Dqx2+zL8JzGWqLQJ8uzWh1QvYcKsq0lSHAJFKQoCUkTda4q2hriuEMUR/S6XW7dukkQBLVkCdSEyFQIE/MSk4uD73qfZRkSQSomKKnwlEuP6nsefuBRZCF55sJ66lBB4VF4LnajUrJMWWlBuoxcwqpyZAVYizIaoSKK9JC0sBgr8IRA56dTHltH07a1frr1kuvxq9+pdXPeCGr1RmE1RoOyyiXqmYHOPTJVeT/IJhJhq3bTUgM598cqFKlBV2GEy2AXjYT94NB41ebj0u+yDNgn13HaNRo7l4hm8W/72namMVvC35VKQZQcqNYuKU4U+GSzBCkU62sbbB8ekhWGqNulF3qsDgb0Bz1macK9nR3iTtelEC5cvnVPuUx0gTBMTE4a+wzWV7CBJLKKG9fu8PgzTxJEq1zbfpGX3rnJ+zv36K7eIZ0l7P7u77HS79OPYi49dIn9H+xyYesiBwf7aGMoTM57167x6CNXSSYJqysDppMp+3t3+dyzn+Tt69eYzSZEcUw3ivCkIJlMWBuuYNOCwPNZ2zjDpU9dII4i7ty4xpMXLvHUpUvc297hn/zeP0WYGV/53FeIfuQCrgir2Ox1SOKAO7MDxkVOYgpmuVOj9vs9DvYOMYVGaMNsPKY3WOPP/qlfJo7/5/zht7/FS6+8zPrWGT791FO8fe0aka8YzSbYmWV1dY2ZzrEWvMJA4NSwKtOc29ziS899mn/8td9mPJ0wWFllPZLcUwlxERKg8KzEIHjo3EP3nUMfpPwzS6/pCKMtOf8cmBIEQRk7VtWTtILRy7trAlbpOvM8x2hnlW2Mh9ZFbbQ2Z5DpdtOmHmNqv+/FsghhWtNEFy5Nhcq+NPq9KhiGixPegjDF6UR02ZZZZ2Zacm6OsjxAWQYNLnbZ2nmf0zmofAFqd9cvQvDH0xhWdSyO7rL6qm9VAhYhzNx1n/7UswReyN072wjp0R2scOXKZd546y0efeQqh3t7TMYjpJJIJchylyNaCoFQArRzKWosyRfKAsdRMSFtot6eAbqMsyxK7tslXDHle27NGxp5DUTt1tX0vXJ3avKVG2MxOItVbQSedtHsjHHGWy4ymQFbMkymik7m/mIF2hTIiuOyIIxGCEWeJk1Ql/J8m3BUSMsc3NsmLKfMPcf01auTSidvrIWKGBlAOyRKSn0M7p+rr2QSTBkyUmtT+5S7fjZXtj8PAo0vWvLPnz+dsJ9WFolytbYX1Qn3g/DbBnRt98iaWTeGJE2ITBdrTYk+unzxjoGD4XBImufsHxwQdiIKo+tAM6ZMVxpY44wRtWF1dYg1mtlkStjpkRjL8y+8wOvvvsfuaEIuPKYajPK5ted8kNe7Pc5tbLC6skoymdEJI0bjEZPxmOFwiOf5rK3E5LPEJe4YDhkdHYG1dOMYL3DRAW1esLGxwbA/IDk44KELF7h04YKDtSczQt8nEJJ+f8CwO2DvuV2u37zJD3uvARavDBfte4qRznjjvXdIUss0mTFLZigh8QwEQYjf6VHkBZPJlJv72+QeXBpucvnyZeKVAQejMXHgc+HMWa7v3mPQ6xLFMUoqZkcJM6vxpQuZXWCIpUJpQ24NK+vrzI6OODw6ZH1the30CDNLHBPu+4x1yv7ewX3n0QcpH3ngFJjXySzqOyuCPJvNnHFCaZijlKpDis5dX3P25UKwLsJUnuXkmYsF3t444TixgXJ519xvc11bB93eWAXHiYxo/agZjPJfJedTIQnRXF/XXa7AYxLskjGsIOzTyklWyieV9ji1t735zlXXLtcVVs+ZGy8hjlUhWsfa55qxp44KV7npVJb/ly5dwvedZXG322VtbY3HHnmUIst59umnuHrlCr1uD20MUrmocw5Yce4o0lPuXNvI7ITxqz9z49c63mq8tbaMaGfnNtl6zCr0YMHKH47ryWUZpMGTlRuXV7txtVNTyvJY1cZ2kJaqLse0ttpREswsTR3Rt6aMJz4vYTq1wHz7l5fl82qZXrZNeIxxVuBFUZDX+a1z9zfPyVqf+lzhYoYb3c5K1a5/+fMX5+tpa2HuvS9jHk4l1sf3tfpv+TmpXcvGa5m1u9bauTUK5yGhrcEqySSZMktctrpuFLGxskKl+lhdXcHzFEdHh06lWBlHYsmNJilylO8Reh4myxj2+sRxxMHRETKM+eGrr/N73/omb964TiYsveGATtzBk76zns4KJpMZ0+mMXrdXBrPRpLMZWZJw7sxZfOUTRhFFoYniDhubW9y+c4cwDBkOBi6DYmHoxR0unDlHxw9ZGwy5evkyly9cwBMCT0m6nRhrNEWWMez3+akvf5lBp8cL3/seRV7gh4ELfuQpDqcTrt++xc7eHgdHI6azmVM9ZTmh57O+usalCxe5+tBDrK6usn9wwCuvvkoURzz+2GOc2dwgTxI219fwpaAbhKz0+nS9AKapc5u0FpEX2DRHY9la2+Sl6+8w3Ngg6nS4de+uM7jzPKbJrA58ZbBMk+SUufTBy0dOsJdN1sVJWS2SKkm9Lo1TlpYWlaxgR4zLiOSi87S55+US3Xz7FqpfuL4tXbSNgKqmyJLyt/tUovJuMO08UTpGVBfbZuqkj/Pb5bzQc6zNbYm+uW45gV0m8c/JKa3zyzaVk47Xz1tSeb25mhJ+txV7s4C6tDrrNijFweERm1tbPP3MM1y9epVup8OFCxeIgoCzZ87yxz73OZ595tkyxKmg2+04Im0thTGgJBaLZh52XJS05jfttvS5fMNu1DxLmJa64hLetU3kM+dt0NJ/V0QbRR39rWYeHbztjldJQFRdeRs2lULgK9W8Q0pJtVxLSTIrYWSclNrOSnUKUVrsV9Psk7wEFpiAVr8LXZAVOVmelX9z97c6Vn7SFuEu8qJmTBo7E1O/m2OM0gNIw8v6NdeDUxjU+Wvmfy/+PelTna/c32rGS5vaDqZWHZVooLXWmeFLOHfxPPf2dkjzDCUF/Tji3OZmqWacsba+RhiGHI1GWGsJgqCG0bQ1FBZWV1YIPInNM/q9DnGnw8FozOvXr/N3fuu32ctTZL+L6nQorIvzrYsCYdx89PyATn/AdDrhkatXOdw/oMgyzm1uMej2OH/uHPfubLO2toGQijffehs/iChyQ+RHjA9HxJ7PFz71GdZ7A/Q05dOf+ARba+uYvCBQHqvDFQ729+hEMXHkIrDZQvOVL/0kRZKQZglWgt+N8Xsd7u7vYZXkYHzE9v4eR8mMYNBlYjJu7W0zSWZcOnOOP/uVX+Q/+Jf/Df7D/9lf4slHH+Wdd9/m1VdfYX11BQUc7u7S73Q42tvj7rXrrHY6xFJxfmPLGTPnGt86d+M/8Yu/yFef/xYyDPGDkGu3bnBvb4dOp4MuNFK5RD9hGPLss8880Nx80PKxSNj3fWgZVq6Cx6s43FWe2zkYcYH4eVXQCOGkcqfzbi5cRiTd8UUI9ISFWRKXKuLUUnjMNn+qvMdt1y5FRdiP32Y5vsEI4fxt24JMBbJVeZKba5kjGIt9XN73U6T1U/a6RdRisf5GqFjoDw0DMy9lVhtSJRnqMuezV9c3HA44ODjgjTfeQBeGu3fu8ttf/W32d/f4ua/8DDffv04ynXLhwjmeeeZZprOMNGuMq6o5pUuN7KKU27b+bvfxPmBGPQbOgEzVBLhdfzWvqoho7Wd5nldHI3O+xzngUIUKR6rYl2aIG59jrW353R2XQlLavLuAGlhnDYwjzBZNlmZgNVI4Pe2DErbFPp9UGoLUjGObIBW5RheGotRDF9rMfXRpVKaNiyhotMXoBQm9/LQJ+GlS6wkt/cD9vl/f22PQ/ts+vkwSr/vSgvxNKVVXe2FhDV7gMU2mHE1GeFHI2UsXOBqPKHRBHIasD1c4s7JKoTNms5nLijbs4ynJ9t17DAYDkMJB40IiPI+j0QiT5nSDgCgIMBb2xhOu39vFX1mnu7pG0OkiPM+lNC0KlPIIohjhB4yyjGt37vD4ow/z8isv40nJQ5cv8dCVK1hd8ML3n+fChQsYY9jb32c8ndDr9VBCsH37Nj/7E1/ml372jyNyzdHOHn/6T/0KAQplBf24S7/bIyiT6viBIs8zpuMxwhoGQcS/82/92xht+P2v/yH/9Otfw/qKUTZjb3zItMjJioJ8miIyi5cZNvpDXnnlZf7K/+P/yq/+L/8i/9Gv/ze8Oyx46KlHeerJJ/Gl5PnvfIdPf+I5/vQ//6fIkykXz2zymWefoYPkV/7En8QfJZzZWif0FdnhEUM/4m/9nb/NgTTc2N0FL+CRx59ACMG9m7e5cOYM6yur+EpxeLjPd7/5zQ81904qH7sOe1mpNtVK8mjcZZpJXkk9CKe7pk5tKWpoHGNRCxGcFhfJnOQsxMImc/KCg5MXbCUlK+l8Y6sNRgpnHFYFU6nS4dX12+OwoyMCVdjGeWFVVpGvpMRThiTN59rcQPoV4XCxlpuMXs2zm3FY7Ekpmi3p6kkSaXPOHLtGLqmruVeW97gxKgpHsKt37QcBFy5c4MqVh/ibf+NvYY3kk5/4FE889TTj2RRjDW+99hrpdIoVgsuXL/HGG2+Q59mc25Qt51VRFE2WMaiPLaaAbd5DE5KlHBkqeBwh0MbFBp+TkFpjWxkSaa1rV76KgLsY49WzRcvFyxHfKl96W9qu9OLamDK9aYuZpJHWijJsLti51KHG5HVM6qp9i+92jinjOGlz81YcP1bWUX+3TdjOKs+8EG58KnuP0yTcZfW774vojzl2zWn3Nz1bvtaXI1LH1QfL6j+tvsV9qP278s2HJmdBZSWvcePlBwG379xhmiZ0hgPubN+jPxiQpSlSCHq9LlEY1mNrtGY4HLK1tcVb777Hs5/+JJ4XkOUF0svc/gJYrRmPp4Rxh0IIXnnnHSbKJ+j2KLKcPEkBga9cPvM8z8lLS/A0z9i+vU9HZzz75FNcuLBFnmXcuXGTVBesr6xiipw8S/F9n/6gz2Q6wmYFP/vln8IXkr279yAv+IWvfIXt27foRBHCujDRWOdSC9TGx86IFO7t3EEPPJ5+6kmuvfsW3/jGN1lZu0awGeMry2w6AyUIeh3yWcKw2+XxSw/xS1/+WTZX11AIbly7zj/8L/9bLj56FTzFE489ys/95E8z3jvg2jvvsNLrIaSgFwY88+Rz/L1//FV+4ef+OP/gm19jq9/nyWc/yZknHub3v/0NXvzW8zwyPMvFS5dYXVtl771tIukx3j1gvD1iNpqwMVzll3/xl06cpx+mfCz5sI/paBcktWNQeevatlQranrifkvb2hBEo99atge4Z7a1p6frtObb6IilFI1uc/4aiyktgispbr5Cju1+lT4Ve/x5FrcnOqJhqfZV527WbNZStjew9mZAuTGK+ntVc/OYeUi4BBcR7YbWTINtjenxjb2uo7UBPVix5djKmmBXRMRiXf7cbg+HBEriKOLRxx/n6eee4+/9/b/L008/jS0KXnv5ZaZJwsb6Og9ducy169fndOWy1P86I8Pm6VV8gPu20paS78Jr9ZSi0+nUSTza7luLz6iybVlhsZUeNy9qRrXsNFhDZd2NWYRPdem6puq546T08r1jsLaJW191TdAwxTVDUUKjp2MJon7/bUZkfh4tHy9bZqmx9TxrbmgT62WMAixnIBafsYgSfBhY/EHvvd+xRen+JCJurcsrXbkWCqhjLlTqjXoPlNIhJUqxPzriaDYlyRK2d3fwAp9+v+/uywtsaAmDMoKcUmRpRuiHrK2sURRvobV2oZbLMY+DAN/zkcJCEJBkGQdJwsFsSm/rHCYrCKxXugLqmiEsigLlSZTn4WOdmkVJjsYjzm1tEoQhYRAw3h+zsbXF3v4BayurDJTiYHTEvaMjPvupT+MJONjdod/t8cjjj7O/s4vve0jZcX7VonFf8zyP0XjsQoUiOHvuHN1ul93tHQLP47lnnkHPMr72h9/lscufZnK4Q2Z0GRnO4McR+7Mx/+P3von33W+xPhjy0PmLPHrpCp/6zKfIMYSdDlEQYdKUXtTh3t4uxmi63Q5FlvHaj17iX//z/zK/+btfReQZP/PFn+RLn/s83797jfMPXWYl7vOjN9/k7njExQvnXbRK7byZzm2dIdEFb999n9s3b3ygeXm/8rEETnmQ4xWXOQdvMa/HqYixs9N20FGVAMu2Nq0GBj++CSxCuLCEwC7cA9XmLyrKBq1niFpP6aQfJYXToRYuD7YtXbiqFgnpMhkpLetUhO3xMOVm1EhVtpV1qZEoKmLQPtYQ6Hnr+nafG8n9uPhrsXOEqbqiTeibuk42nmmOiZpgHB/r+braxIHy7/+Puj8Nsi3Z7vuwX2bu8Yw11527b8/dbx7Ah/cAkAAIkZQ4CLSDCiuCUtghhR3hCMuSaH1RhIcIhRWWP8kKOyRTJO0QJXFQ2BwkghRJgARIAO89AG/uuW/3nevWXHWmPWf6Q+beZ59Tp27ffuhHk3lj36o6Zw+5M/fOtdZ/rfVfWZa6lC+JUj5xZFOptNYkqfXV5WVBlqVsb27R63QxpWO/kzTPUVmfk7nwsbzti/XEV91HjeDUFnbdba31kn9VL1iP9Tks5K/rhxchRGMN23mUKCktvm3mWpi9/hyZMUYuzFlNTVkvpJZsSNhIYTG/B21sxHya2VxmA81z1xZ6lwmlC9+b1e/LKku7bvaz+bzXc/A0H/LTm5nrlc8oqC9TCj6utY0KaCrHN/1ohsX+5frV6p/D0ebr2rwE6Ty+o2W0OE3Z1ru3EeEHJyfkpsJIyenpKTdv3GAwGJAlKUXpIsVdUGYYhDZVMQjoxB16vR7j8ZgoiuyzV1WgbC1tP4jw4g6nZ+ccjkZklSbWGl9KRF7iS4mnJCiJUAJPW+VaCpsLrxCEnQ6jyRQ/CG1deyFYG66RJjN7rDBUVYHEcGV7m2GvS5GkbKytMej38aQkF6bJ5ABHcysF0vM4G404H41IXADZIE3xPM/yfY9GXHvpBa6/fIUfvfUhZycnmCKhrGQTy5FnGYNOl+vbuygEgVSUVcl7dz8kjiLW+0Mi3ycKfJQWVEXBjRs3+M67bwKGwaDP81tXePzoIdu7W2xurLGzsY4Rhu2NDYq3Um5dvcF7P/xd8sMDBsOBQ8w8dnd2mJ1MbbEoZ4R8mu0nFiXe/n3RapjDZE10dr2fvnicPbROfNJzwdks9q1AmqXrLbe5YF/0Bbf/XryPj0/vqFyEsuf7NpdX1D7aliAUwlLdtWDbeZ/mRBcwL83ZXoCrqrKKgVLNuK1qZmkxaNaIZzQ+zNLPxXO3Lfrl87c/qwXO4rYc7VtVF2Hpqio5Ojq2QVbu7cuznImr4HZweIAf+lbjV4ooDJlOJpSFDUBEG1sLuizJ03Sh3GQ91stjd8HqqxW0hbGwArUsK9IkIU3TC1uSJCTuuyzL3N8z0jRzgZVVI9itm2MVAD2/1oWOuICzOiApzzJbZrEsG9933V+treWfpJnl3zauproQC8/I/HJzwbI83wtPxMdY2e3f6/cWxIVnYXlrH7fsu55HiptGsfm4dtkasHzNp60VC8fVm5hHCGjMQulb3dqvvY9uK0iYpuxq3bRxpXodPJTlOaenpxydn4KnCKKI8XhCHMVsbWwQBQG+b4WX71mru67XYF1Biu2tLY6Pj10pSSuwTVWSZxm+H1EJxb0nT3hweAC+R5ZlKOdK8zxFFAUuaMonjkICV2XOM4Z+FJEVBVqAHwQEQWijwNeGTMYjer1OwwnvScHt525R5Tm+lFzb3WVt0Of8/JQojigriwCVVWWteqWoBDw5POB8MqI0FUYIJsmMvCroDwacn52ji5LrV67wxquvsfd4z2UKGRASqWzBkLWoy+deeIU/9NWv8Qe/9tO8+uJL7B/u8+GD+2AMnhCEymPofOw3b9zADwI832NnZ5svfOEL/Nbv/DY/97Wv8TNf/DJZlvKjD9/l9pWrpKfnFNIQhBFplrF/fOiixBXDtQFZkTFLp0il6PS6H/t8fZL2qQvsNny86sWoX8o6J7ouClALrtrPd8nZG8nagHVtrXuFBj7v11wgX9T0l/tnrefLFhdtcH5GqPMiwzC05/Vs1C7C+mwNc8WgLvAwF7wuzc3UjGcuqKmup93K775ssazPXSsxtc9VuP4t3/uzLFDP0trnb39mVl201erxLVt+3rplWc6jRw8JghDP8xHYOs9VVfGd734HKW1ls9def53nbz/HBx98wLvvvmcrDLWqYi1WOpsriWKpT22YdnFz/ceNqftc1Yxlnr9AsdlWpIQQFurzlGPnagmF1uVrhUUIYctjStlU6FqkKZVziLssKMuCNEuYJVOrPCSJ4wxvBthVf7M1uHVlLK+AXrSaVwnpWnivboaaQKie3kv3a42v/eTjLfmLAn+ehdEmxLlM2F92/lXnbv+87NiF32vTrVb8Lt5qY2BfuNeFc837Vccq2KA7C+fmRcH5+Tl7T56QlxVBFBPFMZUxSATXr17D9zziTofh2hCkYH1tjW63S+D7TSDblStXODo4wpM2vsZXin6nQ5IkCM/n/uMnfPN3v8v7H96jN1zD9xWmLOj3rHUeBAEYTRyHhJ5HmaXoLGM97vHZV17jt771LW4+9xyjyYQ4jrn9wm2ePHnM7pUrFj1S1se+s71NN+4Q+h5h4HN2dsro/JwwDMjylDCO8AJL6oIQllb1YJ/zsc0S2dzaotPtEEYRx2dnFEWO7/ucnZ2x93iP3d0ddFnSCSNXM96+R5s723y495D//L/+f/Gf/9d/id/87u8w2Fzj577xM/zMV77K9d2rbK6t0+92Cf2Qa9eucnp6SpZnXL95g26vx6/8yq/wp/61P82LV26y5oUcT864f37A1d6Qzzz3Iu8dPWZtOIRS895HHxL3uyRpxltvvU2312NrZ4dJMuPBw3uXPmc/TvuJpHWtsqgve8FmsxllXiA0TYpWmqYNb3JjBZua4jSn0qXTcAVCBigsVNOGYqFeVCqMqEBaGP58dE6aZQ6Ckk64ysYiaC/aQgjWNtZRnqLUhgpAKSrs2iUNBMpHIiiKgt3dXRssUQsu6bIyBMymY8oiJ/AV/V6H4aCHwEb41oK5KKqG2jEKQ8emJRufdpYV6NLgKY8wCFzBddkscI1u34x1bTnSnOPpHszL5nRxbOohvrRylZ2uxtqY72OaOVVynqJUHy+loChy/trf+pucjxOM8Ig6IZ1YcPfDt3jrzbfZ2r3F7ZffIC00f+/v/V2Gw34ToDKPImYhPcpaeZbq1oCF+xYITVp9lKKJ/Lc3b58pKSVe4OGFCi0srN5WEtoR6EVRYKoKBQjXL6UURV7MubadJW20pjKmiYeAuuakTfnCCLSWIEtKneIrH1kKTJJDMQNypB/ZkohGII20hVUqTTKdoISxcKcWVMXT38VnarXSIXClPee/z+nz7MxrU2AowMXs15tuCWM7Z5XbdKtfl0DnP16vV97zx0H0zbNZ35KhSU+ssyCkWwdE3WPrJ2vSGWW9rwGp5zXZAZsb766TlSUnkynv7T3C39igNBpZaXxPQT/EE4brvR79zT4ylKArtjpdrqztYAoDxl4pUAGbg3WqPKfT71AJTaYLhKfob6wxKnP+h1/9NTIh2bl+g7zSaAx+J6AXSrpCEmpJiKHnQ5Xk7PQHbA8idtZCXr/9PGfJhH7c5eVrN4gRnO7vM9xcQ/tQ6opuFNOPOygpmExHlEbTWRsQ9XtoKZlMZwR+SJVXVGXVvHNZlnF8dMytK9dZi7rsDDa4urlLN4qpDKTZiDxPmeUlh8mMD84ecXNrm3FWOpIiyIsCk5T863/kT/Pn/+P/G//rf+N/hUk1//f/7P/BycExrz//Ir3eEGMkRV6S5imPJ6d8/rnnubm7TT4Z4ZuKP/On/gQ3e+v8yrd/g95z1wk3Nvno4RO+88473Lp1i04kubYec3U4QOSCb3/nB3zx1S/y5RuvUJ6eMz085Eq/z5de/uecS/yTLAT1QlgLyDrdq+Yeby+C7YW9vRljQEmysmgsnzrq0vM8tLH1ZHVlqAxEYUSn0+H0bOTqy7YiXVG2uIQLBAqikEjGICXT2dTCm2VpIWunUQsl0cYSwQBEUYTn+SRJQp4XCzzSeW4LHXieR6/XoyhsZZv2kGmtKcqy5XNcHC+NxmjhijQ8e/ENISwqUGEuRAMb0xjnF9DPudK0ev7m57/gT2gM1It9aUHQS59rY/jww3uoykLk0lMYz6MoCp48ecI//tV/xO9+55u8/+7bzXFtopLl/rUtyfqn1hqNhdvqPrSFb+OHxyxEbC/WN5/HDNTnqD+v52WhPwZXcamlWIg5sYt2wqs0Gt/5uwXgIdFyKXfXwTY1YiDERUFmebn1HLo1xmqO1fw9mit1i0dfeIcXTMpVk2pY/tDQpjZ92jNq5v9/KujPs53jWd8baI2QuThOlyo/7XeDmpB3ruzkRYYSEl1V+HFIlpSMxiN2dncZT2cLqKNUijTL6A+HKKUYTycoLfCCwNE5uxx/44I6PY84jnn8+DGlLon6fXwVoJG89f77TPOUaZaSGo2nFFVR0F9fx9cagUJWGlOW+MpDIcBoojCk3+vawMsw4vq1q8RxRD7ThEFIWEUU0uAJD08oC8Nj2LlyhfH5iA8/uEMYBvS6PfrdXlNvoqoqwiikqCoOj48oy5Jup0On23Wpbjl+FLK2tsb0fITyA4yA6XTK8fGxHZ/A+qgxEHk+lSn5L//yX+La1g4/+41v8Is/+3P8sV/4RYzWTLIMKsBoJNLy+Ve2qmQdnQ8wGY/pB10OJuc83t/n2voWL968xV//p/+A/8XP/3F6awPICoaba1wTgnffepNvfed3+Ze/9HVeffV1TvOUvTe/h/yUJew/s7SuRYFrf2aZFV42IEdTFCVZljV+vloDv2yBN8blL5aGTidCUEfC0lg0UtioYSkMpbbXjeMYKRWTyYQkSZtFWFcGXdogtiDwCYKAh48eEXViR7034+T0zL6kDoI2LgilquYpP557YXzfJ0nSptyocXBlnudMp9Mml9fyqFfunmiipz0nRNy72Nyz1rpZ6C+0S1yjgro0pG7OZ5YOE43UXhjlBcWg9k8+u2Im3D2ssGZXCDcBaCoEi4tQEAR8+OEdPvrwPsenB2RZhi/nz8iyQteGHmthuwA1S8dp7Y6rI77BuTsQTaxVXdpwlRK1fD/2Hty/9sS1wNLa9TGPRBeNA3TBjysE2thQHyvUa3+ubsalkQDuCnWAVO6i0Y2p4z8u6W8ttN2zfHFH+71p3Ud77ub3PY9pWPy+nn93tDFcCGKz0NhTx/biAa3zfWwzn+z0y0evgNTr35ch9mdFL5RSZElK3IlJC+vqiOOQs9kMpSSeHzXupW4UczYekZclcRThexYp8gKfWZLg+741Iio718r32d7e5r2P7rC2tU6/PyAIQk5HYx7tH3A2nZIZDUohsHTPkVIESoHxKEtbdzpQHnEYoaSm3+2w1h9iqorYDxj2+lRakxcFpa4aFMIGUxrHKyHY39+nG1viI8m8iI5Siqosm3fQ+r1nDAYDBoMBSsqG878qKnylmiC0IAwpdcXx6QleaQi6EXmSkmQpSMlLV2/wlZdft7zk+0/YP9znxs0bvPr8S5STGcL3EEIhnUrpKc8FaWYUaLKqIO51ydKKz91+mR+89zbrG5tc27nCdz96n9/78F1CFKfpiEIb+p2I65ubRGFINBxwtHfA+OQcD8Fw2PvxH7wV7Sfiw4bVPqpFjdSGaay0mJfaKn9UbSkVReEc+5YkoGqVwJQ1fOtgLCXtA12n+3Q6HQYDy4Fb1aUsm/uwP7M8t75ArYmikMGgbzVDl+9a+6LBWnpZltn8X2l9mZ6r8GXhY9FAprUFXvvw5wFYNYRdR4O3LBD3U7fGYR40d1EgLkB6zGFnK7Dq3+fb/Pv6bz723B/bxPx46SD8WmjOfcCWHMZTsgl6QTgaTqeAaV1xdHTE471HFHmO73vPvDg2QnvltS0Jiu/7ze9KeShPtf52VjWtSkorzt8anQWfZyPCxVxY1+f2Fvzh858ry20aXACZaU4/d4c45dE+7La0pp5zcJulvjb9bZTOi2NZK2ft25p/vjzuF9GN5Vb7gxeRj6VTfNz2DK25dnPypx+4en169vaJ9m+vdy5uwbh10PM8W9Yx8AmiwNVVtilZZ5MRuS7pRjYNqqwqlNtfOAVUuPfaGMPmxgbTyQRPecRRDEIxms7YOzokMxojpVMIDJ60bhTfxc0oIWztdSHodWI8Iel1uvS6XdIkYXNtjTi2xFdlHUyJI/Np82IYW+gGmHNFLAyFaAy2NM2s8hEEeL7fKJz1+iqlxPetv7soSpI0I3G0n9IFcCoh8aRkPJ1QKfBCnygKCX0fWUGR5hgEutJIrFvOOAGhfB/le2igrGwq7aRIefnKTSbJjKPxOUYKrm/t8M7RI0xZUWrLarbW7/DclSvc//BDpmVBt9OlF8XoqiLyPl2b+Cfuw15+ARrfVWUtyHoSagFX80uXrZSveoFcTGeaC+z1jTX3oNrFp3LCrs2pizEo9wJMJhPOz0coz2N9fY0ojqlfas/ljRWljcwOwoDZbMb5+QitNRvra3Q7nQUYf1Fom4ZyVWtNEPgYY5rocPu99VXXvnrf91F16c5mnJYEM6vXrMss1+b3pd8+bnG5oBg9wwK2CopetY8Ulj9bCTnfZL2JJmAlikIqU1FWpYty1qRpgta6iVydE9YsWtbtPs+VGRYC/uZKwlxoB0FA6OIG6i0MQ1fi1W+UqnYq4iplc0HINcrifA7qZ6atKHi+++lIhDxvfs02wUp9ztrabgqJVG1yHmvVZlnmSh86q9lc5Me/bP6fpX1SobbiDK3tx29P80M/i4C/7D7a78Engc+f1uaCWmCMJu7EDcmO5xRQbSqXwuQjlbVGO1HIaDpllmd04y6+tAKtrPScC8Dx8AusO2R9fd2WERYKJX3yUnM2nnBwfobxPUTgg7CcFp0wQDh4XGCFQhyGCAzDfp9AefTimE4cMRmPeO7qVULfImC1suc5Xvz2u6CNIQxDtNacnpwyGp2TJElDElMrqsaYhmM+CANKXTVopVQS37fpmN1ulyzPmUwnJFlia8B7HsJYhTj2A2Llc+/hA777zpvceXCPq7tX+MpnP8/LN26RpxlaScrSwue1clHpCqQg6nQsT7kxzLKUaZmzFnXY3dpiWmTsnRzx8rVbnFEwSxIQVsnpRgEv3rjJo/fucHd/jyu7V7h+5SpZniMvi5/+Mds/E2rSVQtbWVYozwqz2WxGmqYOEs/JS+u7bAf/rPQJOg0uyzKuXr3KxsYGvh80wr6oSjzfs4XfnWVUL7xFUbD3ZJ/H+/tsbG5w88Z1wApcy0kNSOEsZs10OuP4+JizszNu3brF5uZGs4DXsL2udLNGJEnKdDoDrCVvSSzmdamNMfi+x3Q6bSLD64OlbEGI0Hz+4yxvLVWp8Z0WNWWk29q/l6V1T9Rb25Kr52F5bj+u1fuUZUme503xB7vl5Hlry3KmkwTjXnZfSqo0BW24cf0aAEWZU1VFC4FYhuwX614vBjPVsPNiQORin5Y3q4CVrSC5Z23t8TGt6wlsgE6prWJYGReMtUy9CRg5Rwdq+LitGNTHWM52e+9pWlfqmrttDCsE1IrbuVRJWzHVzyrM5sjY6ra8Rly2tTvd8gYw78bqK6zu55JmtaxEtA6Z94FLFYEaQbGxBav7ZDAYF8HmeRLPWXZGCPKytJTN0vm8hSCOI65u75DrkvuPHjHo9tja3EIqxf2HD+n3+xaKjULiOCaKIiSCra0tunHHPmeVZpaVfPTgEV6nQ+YKJkkhkMbQDUN8TxEGPhJLELQ+GOAJydZwnW4Y0u/2iMOI6WjCay++iC8tX7at0e4yHIREuxSr+nnNc1sDvdvt4geBNcZc3fPSBWB6vk/c7bK5vc2LL79MqSuCMGyEudGGw/0DZrOUyWRCZQxBFGFrqiuU55OnGU8OD7m795g4CPlTv/gv8TNf+SpZmfHgaJ9UGuJOjJdUhC4FV2Mrf0nP5+j4lNF4Ql6UVMaW3I3jmIdHB3z5s1+g3+ny9rvvcHB4yE5/jdNkQpImnOwfcrK3zxc+8wb/h3/3P+DN99/h8eM98rykUgpffLoW9k/Uh32Z71l5HkHoc342xvd9bt16jp3dK4wmY/b39zk9PSUIeghjqFoBRe3ztIXAyeERZZazsb5Or9tlNBpxcnjkjimsFeV7lC6Yx1r1FvrRRcbD+w/Y2dnhueeeY/9wn8l0hjEW3u50OgiROKIKzfn5mLfffpsXXniBqqroRDFFntvIUNc9XZRWW5VWm0uShMhVsbHlJC2akGUF3a5DAWgTTcyFZDOGtL8zFxYxY+am3NOEygW0kNpL+fFz+fu1NGoCBmsVYCOLqf30cz8sQuBLn6qsqIxBBUHjbgh8z9bgLZ5NdW2P00JNb61t7ImZ1/qeN4PVZU1r8bVpXTUKVAcELV8LaPzl7c+ryqbuFK5yla3frRs3hHLuAK0rhKkwJqBSCqE1SgRosUiWA3bOGt+8sFabVQY0eZpYy1tpaqZAWnDlSkj6J9AWLNRP42LCnUfYp1ZAyyVu5g/zs15ryZMx/9zMr7MkeIXBMRNyQYsWAhf9ffE8teuiqirOxyN2O7t04ojp6QlZnuGHAWQzC207BMhXHj6eLWijJL3IEqVIzyMMQzxfkidWqZRSIozNPIn8kM31Dcq8sBW5ZEQhBGeTKYPNLWazhCpN6ShJL7LpW90oJpuWeEJaf7XO6cUxabdHv9Ml9gOkruiFEVWWUxS5TTXUVaNINmRClabCEIQhaW65CDpRRBREDXJk0VTBP/3N3+L7b/4QLeHJwT6/9PO/QDKaUKQZlRPug+6A/toQ5XtkRUFWlHihdSfmWU4n7vLSzRfY3trhO299n//0v/qL/MJPf52f/swXuDrcpEoKTkdj+jubSJxbMs05Pjvl4dkReusKMvTx4xijFNM05Xw2pRd1CaKAL91+hSDV/MN3vsPW7g59fKo44KVXX+eNm7c5e3LM0aNHfOdb3+Zz6zcoMQRRzO/87vf50hf/wDM+jB/fPnWB3Q7eWbAuWkKkKiuqEnq9HlWlebT3GKkUnvLY3blKvz/k0eNHBA6iqY9tc0DX/mPrP4FkNmM/z4miiG63y41bNzk4PHRR2I51ygiiKLJkG67whHIQyd7eHltbW1y5coWz83POzkaOp7mgzEubXqEEVVmhS83ew0f0ej26nQ6JsMLW99RCapHRddEGQ5ZlgHH0ooY8t+lbSiknRVetMO1xXFRWLlodl5yiPk/LQjCmrf3Pr7RKHuuasGvV9VhakJ/SREug2ONoLMX5PTnkIQxIU8fSVWmMs3TjOGY6nVlqRqed1znHc6vromI3DzRroTzM1+GLiqAd7zm0vQi7XzYWy4Kpvo4BF6ldB6/ZxczmX8/h+mYMxHwqtXElPVVr7KjHzVWKcz48U5c8EZDnOZZT3iyM80U31e9Pitbzf5nPuv5uYS1oSTnxsarixXPWx61UN0Qjy4HV/VrYXcyV2IvC+5JjRSOf3DnEhXudz5G93yaQEENW5Bwdn/LcrecZTSZ2LfM8stkUFfhoIPA8lPDwpEc3jAnjiKKwkc3dqEOn08MYy/ldyqrJ5ccPCP0STypuXLtOWloe8fPZjKOzM3r9AUVZIozBF9LWUTeGKA6J4hBPTdFCEAUBnlKYsqTf6dCJQsLQZzgckozO8Ny6WacsCinRZUkYBEhAC5f2JiV5lhE4NNWmR1o30Gw649vf/h16awP+xJ/6kxSm4i//5b/MC7ee49rOFTzHZS6VssVMsOvveDIhODujqEoQgqPjIzpewL2HD3n05JCbN25wenjEwaM97g7WqYqSbhTTXeuRTaZMi4zZbEaWppRFiVCSKO4AkqwoKbUm6nQ5LTMUgsePHrMe9/jCy6+xX0x4eHJINZqSBobvfed7vPfN7/PylRv88p/6V9kvc969+yFnacL2+i6/88M3+V9+3EP9CdqnLrDb6VhtWHK5GbcQeUGAkJJZkpCmOZPpjPWNDW7deo7zk2OS2XRBYM8XxfnCU/tDjdakSUJRFPR6PTa3tphOJ0ymCUWeE/k+OK5ar6yQgmbhA8HZ2TmVsYJ8a2vDPmTaFvUAmsXRGBvZPRqP6fXqFAX7cBoHixsXyV3D8a7LLWE5j6J1ssteQywuBDU/uM1FmH9+mdBY2VYYOM+C7F526vkUXEQ8Pr4jlwmKuQgtywqJzY03pcY4paeODZgjDPY4O6YtT/GSotgIKHN5P9tj6U7bCATTOle98LYt9va16gO01vNc73q8Wr8LIagLqi5ct/7UGCfgK7Reosd06AuNIBRzX7U7j9Zla5LrcMqL8QmtCy+MxfI72/7sMqXtsmOEEMy7t+qaLSXuGdvHK4pzZXeh3+4FNM2LePE8l61Xq1wF7XtchSguH5fn1v8ZRRGHx8fE3Q6msDEJSinKrMIPbOyEwq6jnlTEcZcwjihdwKqNUdAL15buOTBYV+FgMEDOpkzzktFkQpJleEEIxhAoD+EZlLaWuR969LpdJp0EiaQTR8SRRzaDXq9Pt9uh1+2wvbnFO0/uN9UWtbHc5yBsMSa33tXfFXlB4PtEnRgNFFVBZCIM8N4H7/PiSy8xWF8j6ncoTMXXv/51vvntb/En/+i/gq8UVWp9+lmW0en38H2fWZZydnbWUPXmec4rz7/A1nALXcFocgZCkOYZ/V6ftf6AKi/QxjBJplRSkBa55eMwBi2t1S6UQnoWwRtPJk1K6Zr2EBqMp/jS7Vd5+Pgx0Vqf3bU+28NNrg+3uNXfoMTwc1/4Mvf3Dzjc36OjQu6M82d/qJ+hfeo+7K3tbeJOB4SgdDDgKsFtzFwAS2FTAfIs4/z8nPPzEZ5S9Pt9+oMBURwvshxhFz+DaGj9bLEO+7AkScJ0OgEgjmP6/T6dbhcEjd+kLllZ90lJyPOM8WhMmqQ2BWFh4bd1a4WZ85knSUqSpA3ZSS2Y6zKK9mVquwaWfGH207m1JGg2mC/g7eWkPra2A0VrYbIEBKsjvedw+bPbVPWa1tg07c4tNb3ERNVulUvzqPtjqOHMurRkfTeuo8ZZjkKBkJS6QhtJWRa03NYrF8b238ufX6bgLCNCy+NTf65dGUjBnE52WQA2Vdrc37aQgrvv+vmwMEsD/zd9bvpLs+BpbctQSkeTK7SzmKlVOIFQdfEP01gxaZYhHAS+qKjV79Citb5KGK8aw8vax36/0JPfj1UvWtuFCyx9MH+vGi2sef4WD1qIG1i6l1V/r9zXzBnl3I4NS5sRgvFsxvl4TJJmKM/n8OgY5fkIqZp3zWY/lHgurqWOW9joDen2etx78oS9w2Om05SzszGesFkFRgq0Y2mSUiK0QSHwpKIoK0bJDJSypSulspwVUiKMJg4D+nGHbhjRiyJ6cUQ3ilnrD/AV9LsxvU6PTtSlE8ZMkxme71Pn48iay97oJvhNCmnHw63/ZVkifQ8VBqS65M077/Ho+IBbt26yub6O0oJA+PzCN/4QT/aPODg7pSxLQs/D4OqGG4MfBBRVZfvgOMLXen10WRAowY2tTZ7b3uG1l18mrUpmWYoBoiBkPBoRBLZ8Z6U1TYV4AxkalHTR4vbe0IYkT5HaZqloadjoD7gyWOMLr73Bl158nS+//AZfeO0zPHf9Osl0wotXr7O7toESkvPZjPPyn3OB/dzt2+xcuUKn10MoRdnikIb5w24tFGUnoqrwpCQOA3xfcXh4wL37d/GjkM3tHQZrawhlmZ+MoVkQEdJGhLuAHYR9eKQUJEnC8dEhutJsbayzs7NNJQUFNtih3RcphMt7FpRFwfh8xMnxiUvPcmU/tQZTIYxGOq28DdfXfkojQChLcWag0UAXhfJi6b52sZB6k84KRzvBWU+YSw+zkyfwpLS55tJFYLuI9boghHQMQA0C0Jqr+jpyhRxu0IC69KdLG6kPNCwKwEZY1+cWdd63oagsT7DyFUiYB4FZtMJu9npSSjwhqUyB8gOE51HoglJ7GGGQwnOKmnF0rpcwli1ZO8+KRljLVTdKYHu+qsoG6ynlN8U3qlWBaIKmvnnpck2VtM+W0a7cpqmojHblL1tjrudjX7OgVdrga2mVU21sCVMEpRRIJNIPmsIR1m2kmE5m1Gxv9fmQi5Hs9YJbb6sE6dME2aoxr/PPL7rFagXFPsyL8+FUzxUuh+VNinn6kGC+0Sh+9ZXq69Xju/i7QS+8g5/0XleNky41unbRGJsJY8rKIkQIDk5POTo7pzKgEZxPJlZIeB4IgdYVvTimylOU1vhKIZQkr0pubGyDUHzz7Tf54NEjZmnJ0cmIQIYo5aGloFJglCTwfWLlU85ydK5J8oLzPCPodCjygtgPiXzfGRaatV6P3cEakfTphSGDTkzsh/Q7fUIfBt2QfqdLpGKqoiSrKrRwnBcCPCEx2iKWQlpGQU/ZbBDfEVmdjUZI30NEAXvnJ/y93/x1xqpCi4oynZGcj8jHCa9ee5HNnau89/ABk+mMtU4HKOn4MXle4nk+WgoybSlJszTh1tWr7O895sP336YYn/CN11/nT/7iLyJ9n7fv3OH+4z2iTpeT4xN2NzYpkswqEkqihUAZKEOFcWl2fhjQGfQxRcV0NOFsMkIbjScEJ+enfPbFl/kjX/oaX7n1Ctt+j+RkxPHhASJP6QYBL21d5craJokHYq3ziZ6jj2ufOiT+rW99i89+9rN85jOfIUkS7t27x6MHj/E9SRxHzX5KKbIsIwwtebyNEs7wlGLY7zCdzXj77XdZGw7Z3d3mlVdeYf/xHk+e7FOZyhlixqYGIGxsR13CTszznUejkQ1WCkN2d3eba81z/Ky10wiRyyBTWduCxvmkqmZ5q8kA2sUK6qY1liDACdJ6Mah3mcOqptnfdcnxiVtqUkGdZ16BEkhhgzYGg4HVIs3cwhViEWIUSwukaCPTl7TlYWj7gusKNO1zSkcUVlOl1t9ZFMUF+lWVU7Aa4HcuUGlZe/WImNqqrRrlaR5AplCh+tjFtY5UFQ7xqaPUfeU13ODKWR5Pa7XlWqd71XV762vUygnQ0I/WMKVSCu38fasi1i3qYVrPhqauad72uyvHK44QCIkT3DWl76JlXGcltJ/ptvLShtAbm3P+38q2Sglq/7R/tPe9CB9/Os3Qvt/L+tnsveL5+PT6Mm/aVHi+XQfKmm1RWSswLzRVUdDtRDx34zpSSrI0QRrrl610hRGSaZIQRDFCCuI4tJkks4QXXnyFf/Ktb/HhR3cx2vDCrSGf/8JnyEubHhrEEVLjKHEto2E06HPw5DHnsxmVQ/eUkPhS4UlQUYhQsDYYMBz0KZOCQb+Lrmxutu8pBr0u62tDAs+nKuz91NHbDTWyNpZeWtiaCqVjaiy1VTZNVbC2sc7ewT53H9znzr2PWN/ZZm9/nwdPHnNtbYt+v0dpBI8fP+brX/0D/OZ3v8nhcI3rO1tUs4qsNPjdrl3jtMHzFXEYkRcTqrLk3/6f/1ukZ+f84Hd+l2/94Hu8+sbr/LGf/Xl+/Tf/KW9+8C4vvfgCV7a30Wjr+0bYCHH3ErVr3Y9G5xwfHyOE4NbNmySzGUjJ+fk5P/j+93nttde4+9FdQmVTMJXyMECpoULw/AsvcP3gCT988pBuFHyqz9hPhDjlO9/9Pr/129/kwYMHXLt2jT/wtZ+iNxwwm9kUKSntAlmWlsBkOp0wS1KMEXQ6HTDg+z7dTkiazrhz5w4//N73WVtb4zOfeYPtzU2UkE0OI7iXUlvIUGK1PoAkSTg/P+fs7Iw8SdFLCz+0oGwpW4uSxpgKY6o5rC2wFjTOKoVGmNSLceUiRJWwifm1FV778WocxpNWMOd57nJw/QYW0xo8z0LAliDBUrZ6Lgm/DZLXNXLnLFhWGWisNOOqOJrlxXOVUJ5vNaS+APG51hYstaJgSz1qN69l42+uo+vruQo8ha9EE2nd5LELgUJYXmZRW1xO+JSaorDH1+xP2uiF69Rb/Vl7Xpr7c89nm160tpyf1lb5LdtxGsvfKRdA6SuvYbMTBkylm8pCovXqtas62bKtLWi9DVfLuSUscDzVpiVI3TkqrRultB2YZVpj8ez3+/uFrz+N9vvpwz/DUzuUI89zG3wrJVoKHu0/4ej0mE6vw/bGBv0oJpASU5V0ux17nIEwDEnzHCk9qqqgrAqElERxjySr+PV//E+5snuV1197jY2NIUcnBwy2NkizzLKS+T6hVMRRxGA4ZFpmjIqc0+mU46NTJuOZC3Y1eEISeR7dMKAfRQS+R+gp1gd91ocDlIBOGBIFIVEYIoymyK1h0IkicMG+7SyFIAjoRLFTXnEKbshwY5OzyZQP7t7jdDJh9+YNxsmMEsObdz9gmqd04g66qpjMxnz+pZcJNTw5OuTRyRFGeUyTGY8e71EWmsAPEcDx/iHr/XV6UYf/91/8S7z37rv88i//MmvXd5mdT/mpl95ge7jOo4M9vv2j77K5s8nR/j7TZDaPbJcC5Xt4yiOKInr9Hv3+gDAMGY/HfHT/HscnJ+Qu6PX69ev8oT/0h7h67QprwwFRHCB9kL5iuLnOoyf7HJ2eU5YVvTBgZ/DPebUuYwz9XhclBUdHx7z55ts83nvM888/z0uvvER30CMrcsqycKlOJb7vE7uSbrPZjLIoLFm+cKk0znq5e/cux8fH9PsDruzu0ut0mc1mLC8MNSznOeYbhaDMC45PzijyohEWRuvGx7IsxK2gW7XA2Te8zXzVJtOwVKhzf7LnWVL7Ki+s/1tZ35StoDRf/IPAJ4psxZkw9KmqOpBHU5YFCG1Z0byapMU4haIuPbg8Dx83Txe3tqDXetFyrf3mNMLaNMJOL7kYFreLY7cQQbvqGGFFkpSedSdgKbCF8jFGMOfjbispF/9uYGw3P0KIhpykeQbMnJinrbAI4eBb5opNTdRTKwJ1Dn59jfrvehCXBb3Wel4GtB4Rbbc6N8m0jzXG0aKWrfFytKS2Z1Yw1Ghvg29boTH3V9O4MZ61zfu+woputWe1VC8LCny2dtGCb56lZpzNgnKy3Jah7qYP7VOvQgxW9eYy94qYuyWshVkxTmY8PthnOpuws7PF7edusb2+RjKdcPPmTYqqJIxDvMDnbDyiPxzaFKg4AiGokET9Ad/70Tvs7F5nY33T+rc9yYsv3iYpc4yAPE1R2tCNrcB8cHDAw8MjHh0fc+pyl5VTIC1K5BP4niNL0RRZhhCGMPTpdCJ6nQ6DXh+0psoLjIsGj6KITid2/mnrylPCkj/5cm481e+xCnxOzk5594P3SavC+rDzgt5giPR8Hhzuczw6JytydFXghz6yLLl96xbTNOHNDz+wedFGU+oSbSryNMEzgi9+9nN8+P4H3Lp2nT/yh3+Jrc0tfu9730MKzzKsFRVf+eznubq9w/d++AO8gYWn/SDAC0KkI9YSQpBX1UK53DzP2dzc5Or1a9y8/TxJnvL+B++T5xl33n2Xe/fucXR6TFbkSCnIiozSwqJ857vf480f/Yg4DLh1bfepz9InbZ86JK6EpHKRvABIGI9GFEVJHMdsb2+zubHBydER4/EYoLGIMNbfa4W456BFhe9g7MwFpdkHLrA1YZtbWIQE6ybdCmwX5kXBgrkY3Vn74Rb2a7V6TRTtBRSxIPSlbNW4VhYmDsPQpZjNFwzhfOG1QKmFiaXzMw4etz7ILLMsQMrzkDX8bwxFkTcrcb2GLAtrUfvDf4wm3ULfLoLRvlZzL+Li+NewaLs/cyG+OtAJIRrO7poetJ6Phdzm1nENgrGiL+3+zK9vF3e5tEBfuL/WuM3HdjUEv/BZS9kzWjtUYLHkLLVi5KSsEMZV23LKhxJNpHzbUrbP21yRuKBUOmUqz/NF37QQVlGwsEUDrT+Lr7Y9Pp/Ut/us5362Nn+WHHZV+1IuP+JZ5utZrvwM+1emskFgUlLmBbMiYzybkhYZCMOg12U46OMbux7oqsIIQ5plZEVuESjnWxZOy/KCAD+K+N4PfkDgB/T7fRQVRufcf3CPXtxpxkEoG6wySqa8df8Bp5MJh+fnjGcZUvqWFawsEUbb2BhXJa2qKtIkpRtGDT2uqawmGfgeStVsjna+wiBYUN6RAk95Nl2xqpr1ThlDkmXce/SQQlfkuqJCY6RAGevfnqQJh+enXFnfIg4jqkqTJFOuXdnl0dkhD5/s8cKNW5yen9HbWENg8JXH2nDIc7duUU4SHj54wIu3X0B5HlmasBHGTKZnTJIpz1+/yf7JEe/f/Ygfffg+m9JHKoV2rwPSBpp5nmoYq+q1PE0Szs7O2NndZTKbUumKl158kcC31/eVB8JQlgVKWsOs2+3hewqjK5RUPH/9+id6zj6ufeoWtpLS1uetqw1JSZHlHB4ccn52htGabqfL2vq6vTnfbywhY+YUngZnjbSEqnSDMhqNGI/H5FnWKpAhECuIIervpBOkc+sFakF7sc0h35VNzF/gZZ/1PAK8ZtOykY3dbreBtJsXzF3awrdWmZiTCizC81apKZv7qAVonrcE9lPm5YLQXrAmFyHy+d/ziPblQKL29uzNRvavXPqcu6GuLGUwjcCuey5ku//PLjzagr1tfdffzaFtB8W3fs5vTyzt+3Sr0TjruL7m6mGak+XUyEb9u24hH5bnnpbW0PRuAV1p4hica8I44VyP3/K1P27MVgWPPct8r3ITtM/547e5dG6CylpqyXK77PlYRnQuO27VPk/rvzEWJlZC2Qp+acr5ZIz0bX37wFMIbHyBckyOszS11qXRKCWJwpBep4OpDL4fOMt7zNHZCZ1eD1mTMc1mHB8fsfdkn0prvMBHeIpJkfLR4wfcefSAs2RKkudNkK2QwgrpqqTStpqbEFjo3tg1yfOVq2/gUZYlUWj96HXwKeDW3PlzJaCJawHw/IAoilCex97eHtM0Iep2MBiyoqCq7JqphKKsNEejM86mYzpxhEEzno3puns9m0w4PLW1sOtMoMD36UQx3bjDyy++SJ6kjEcjsjxDKIkHSE+RFDmRF7Cztsn6YI3vvPlDJkWGqimrmQc/W0t7UW4UeUFeFCR5Zs8tBVsbNq9b4OqYlyW6LPGVonTIyPVrV9lcX0eXJWuD/qXPy4/TPnWBLaUgCHw8JdG6IstSwBAFHsl0wkd37nDv3l3ibpfbt2+zublFEISN38MYQxAEjXVdM1xZ+k6vsbKyLLc0dS24syaggOUXy77UdTS4ZZiiEYrLfsyVr3nbMnb4sMGF++uqEQRVVS5ArLUSEscxnmchXoTzWzo5X5WGyvGLZ1lGr2cVmSDwkVLYvGTn77b3a/tfv7z1nbb7vbyu1LBh+ybN0ta+1Xou2l+sghWXhWFtQdfHN4sfLCgJRtRRyu4YXacw6bnAVqopjmCVpArQrprWXFlqX7sNja9KvZqPxUXLe7FJ6uj12jqGeYDZ04TQ3KfcOpuUi2NUT5IQziPdGph6sGgJ4QZFEI0VIAQNj3h9PeOIWeoCNKaem6X+LPS3UU5WK2Sr7u+yz5aVmMsE/Y8vvFcI6QYNcXtcIog/9sxPfR64MIDL/VeIpmykxJL6JGlGp9txQakV08mE49MTkiwjiEJOTk9ACsIopCpL+t0OvTgGJL1eH4Pgh2+/xdbuNlev7pLMpuw9fsjJ6TGbW5tMxhOyPCeIIwpheHh8wO+8/SMSoTmdTFAqIPAjt0ZpyjKnrAowGulJW0K418GPQ1ACL/CI44hOHKO1Jowil+UClTHWCsUqJ0pJWwmxDkZVHr4fEvghUnkkScK7H7zP1tYWcSfGDwOLKJal5c4wdgxPxiNOxiMC30dXFaezMUVZ4gchSMXe4RFra0MrtIVNVaPSJOMJs8mY1199DYlgMp2QFzmT0cjmt3uKs9MzBkGHn/rcF/nggw+5f3JMEEW2jKaSDU9GURSURUGeZeQu+CyKIl565WXCOEJj+c6Pj455cP8+d+/e5eHDB5yPRgghHSpiCKTklRde4ObVq6TTKbNZ8omfw6e1T584JU+pSwd6zv/Y+DpdZPb5+YjDkx9x4/oVttY3rDWApKrmBT3iOF4gYcnznMAL8JVnYwWUwveDpg41XHxR2z7KShuqikYwt+HVRWi2nRdcl6mf77e8xtgIcUUch5YHvbBCRSmJ8mx+7Gw2s5awknT7PYwxjEa2mIjn+3jCoyoLyqwAT1OozNIEZhnGVPjuoSqNrVQjfYWvXApXvSDihOTHzE8jMFetR+bi355w53bh67VArsehzqVvL141dCZqBQnTmotaUXH8606REK2LWhi8bJU9tWxNRVGgS+s6qYVWGw5f1VYJ7LbAbVvbi0rN5YrKqs/a55OyVVu7GRvRjJ2p/dX1XTcKjnD+eo3StqKSBqgqR5zSStFygt4s9V87voA66t0GmkmMMPUZFsbHolkXLciLY2qPXm5Ps7x/f9b001rd32c7/6cG5TcI1FyZqn8KYdGxLLd0xp7vk+mKJ2fH9Nd6hF5g0xXLiiTL8YOI8+mE4fo642QKxvqfyzRlY32dk2mGQDFLUu7c+5DhcI27D+7wtS99iZu3bqIkBEFEr9djMpsxyVMe7u/xnbd+yFTa9NOismmQSlVUGDwBOvIJAo8wDlAYQiUJwtCmhRltKwh6lS0+IgRZlpDlJSa0Fey6vW5zz57n43sVYI+NwpggDDk7O+ODux/x8MkeP/sHf467D+4zSqbMssz5uz08XHoekrPZlP3zUybjCVprDscjojyn0+mxNtzgyeERX3jpFSZ5ymw6QXmKYb9HoCSJe7+8wBYBwWieHB2w613jyu41TiYHRGHEF158nd/70Q/53Tvv8qeu3iISEvLMrvJ6HiDnKYXv+QSez9nZGenjkh++/Sa+53Pj6jWCIOCF52/T6XYQnp3PMslIZgko8ExFqGx+++jsnI8+uvf7f+5a7dP3YdeLrK5ZmgzKs7Wla+GpEHi+4tHDPSaTCeuDIYGvSFyObhBYQVwL61oo1Na0xjTBAlEUY+R5Iwxq+sd29Hi9SWnrKlsFoAQMUoqmXrW16j0uLgQ2F9uez36i3fniOHIUqxWTyYSjo2MLaWqD5YOxAqUoCqbThMGgz3A4oN/vc3B4SJIkdMLYWTiSstRMJhOklA2Zf54XpFmGlDhhJ2xpPpeWZK3tWiBetLRra7xdpYqW0LX7LS5C9e/CBZcpMc//br53P7U2teK9sDjOrUMn2P0A6StkJRFCETgXgZR1XWjn/vAU2SyziISUNuXFQJFmLrOgQgqzss+rnsca8WgHhplL8rfn5xNz18cKwXyZwLb3XDVW8vwaiz5sAQ1PvgGkw8CFVkgF0tGiKCFQyqEJAle9zMwVNLOIJDSBbfVcLMzU4q+XtZXjUhN2r3hm/kVoq4X2aiXkQnuG3YyxiF0chmxublAJSSEl/pOHDAfr9JRk0OsReCFprplOxhyfnbJ+dYfj81MXz2PRo821IVWiLdf1aGSFYSfiK298nls3bjA+PWE6GTGdzgg8j2vXrvL9H/2Qj/Yecp4nrF+5wsHZhDCKOD84JysLRCgRLh2r1CVJaqjyHFPk3Ll/F50VfOn11zk4POCj8V2UkNy4ehXPU3S7IX4QMpvlnI1Gjduy9m1XlVUQw06Xsqp4sr+PUpJXX32Z6WTCydkpwvfoRJaprchzdtc3UVKS7CVMssTC4udnGGEYlymFruh1OmwM13jr0R5JOmVtfYPnbt4kyVKUE9Tbm5scHx9aljLsmhYNe85tMKU0Bg9B14/5k7/4x/hP/u5fYZal+NKz1cGcS1YIKwcw1pLOXVnl28/dZPBoyO7uLq+++BI6ychnCWmW2pgF5buc9S6ZqTg/PubGtas8/9wtvG8rPm0Q+1MX2AZB5YQKQtpUFLOcOmPAlASeRGItitIYNCAcW5lSqiEfwcz5eZvDa2EgBbrQGFlbv6LJE2xTSFqQ1UKONYGIZeURzqrX+L7ElTzCGE2eZy5CV9t7ESwsuAiaiMKiKBgO19jY3mZ0fu58zlYqhp5PmiYIY5iOx+iyoNvvMej3LSMVAu3+CWiUlDRNGyvV3rewZAqOISwIQ4bDIQ/29m0hE0N9l2Bq8hWHFQjR5EjX/OumdhoDTbWCZk0z1OHHZaUpdNX0RbTXbmH9iiuFZsuUF0LgSY+SAmHAV6rJURZ1dbIaVvUVSoaAQOsSbSxbUJIVVLrEkzVLWtV0eVlw1j9rIW37XhHHsU2lk6p1P0twrrH3XQv7oKroxDFKSdI8px3ZUBPrgG40IBvbJZtgOZumVVpUCGuJCSEIfH+pPrgjwpHgSesOwCknoYjw8cmrjEoUBCpElD5JllHKEh8XqKQNVVGSpFOUryxJhzFgNEp4GLEqhe2iItaoGQuTvfTZhbPUkP3TtYJV/v6nnbe9z6exAM4fy4uW8uprCkeI4grF2AeuecHq+Jm337nDl197gW5oyAX4gYdSAeudDpu9CIFGCsOg02Hv9IhrN28yORtDBVGnQ65LtICuUaytb/Gjux/yeH+fW5tXuN7fojgbkw7HCF+iYp/x0REv7j6P9hQH6ZhMGfr9PmaaEJYVZ5OZfY59nwqLUG2u9Qk9hecU1qjT4Y//0h9B5Dm/+d23+OYPfsDB6QnXd3f5o3GXz968hqgKjPbseiwUIorQyrIQ+p6PLw1llnPj2nX+/q/9KkYJorU+aVXwg3ffZjAcoHyfwmUuKKU4OTtlZ2sbpWICI5nMEr537w4vvnDbPn+ZZtANKKMupS8ZlRnTj+7xwf2PGKyvcXvtebbCHk9GJ3Q7PZI8syxvQjE6GzO81gNTIkVFhSCTFYONDa7LNX7r7R/wU6+9xs5wnePjMV6k6CDJs4rCr8hVRSAk8fqAg6NDRqdnRJ7PQXefdDIlUh79ft9a5sKA1iRZge/55KZiPQ64trHBC5s7VMU/50xnbehv0cJYag5GEg3MN1/059zbZqHcILAA3xnnp5Sq9l1fZjHVfaEp9yZrqdbsYxb6IYT1xXc6HStAnQWPmOfx1gG7VaWZThPG4wm+79MfDKyPBBBm7tsWwqCriiRJmEzGjYYnZF0sYPEWFi245lNqZmkhRFPLe9F3OL/v9hpUW/7t8plNSlLptvpvbb/XxhKd1KlU8/FszfilC3gLqq8Pqs/l8qSLomgyAPIsI01TsjyzLGDGNL43IeYMSo3luHx/2DT3dvc8zwb8WOFs76EsS1tru7a2jVnI3S6rxVzuqpXbXfvTFwS8mPuVpVu853EOTgHENM+rdmPbPm/lrllX8irKkrIoXcUyCzsK41wMwjREPsbYQgt2MOw7URYFZaltP2rCAKd/NZALTsCKWsjOt2VhvcqnvdrPPT/vaj/3U56VS8952b6rP18+1+r28f7zi/74+WdKyeY9rY2RNE3IigLlWVdY4HzBvbjLWq/HWrdLFIQEvg3oGo3HVpGEhipTudK/vhdgEEymM5IkYdjrs7OxRZVZPysCy9JVFU5hf8w0TzHKIm+iMhRJ3ij/nufhex5FniMEcwZHIZhOp9z96C6HBwe8+8EdjkdjZlqzd3rGvUePmc0SjNGuUJKiyAtKUzml08bwCCHodDvs7T22PvB+l2ky48GjR2xubdLpdplOp2hjbJAxMBqNWFtfs3nbCGZpwsH5KVme20ImgC8U/ajD9voGR6cn9HpdkjxzueqSKAgI/ADp3K/CkdBIBJ5yle+ERTorYxkuX775HEenJ5a9TGs85VO5+thK+S5l1sGUSnJ4dEQYBnTjDoGrkKYc8qek43OQAuW5SoSu4E4Y+HSjmMPj46c+Z5+0/UTysOufT9va++KElVk6j14W1is0YK016hICi9aejd+zrOriEbWm3a6drOfV9BxE0ul0iGMbeKGpiUTmL7N0zD9aGyaTCVmWNfmKYWh9IvVLbdO4agE/YzabtaBUp7hfshDVS6HRdc63FU2BUwzalv/T1qJaeNbRxHV62OKmG9/oPAJ+9RzUY7ecx97aYfH61IqDXhDYtdCut3nQmCu2gXAvi3KCycw1guZadqvjtmqlpk1fWjWKQrlAtPK0eth1/6qyxYt/4aZEIwClbMHmzBf7OqrUBiZWK+7fFiTI85y8qLfClmVtZyJcGP95R2riDqPnNLjCQRim/ZK1BXfbem7NaX3+Ve2CcG0LabF6v0agP/VdXd0uCuHL+/Wxzazed5Wgb9YF0SrPypyKtyxLZrMZk8mEjZ0NjNAoX9lFHZumt9br0wk7xFGXIIiRjsGwyHK7rmjLZaCUtd79MOT07Iy8KCwTpG/TWJWShEFgYVtnUfaGAz66f4+iLFFCYbRNjc3zAukpuz45RjKbx2TfbyFt/MhkNuXR48e89fY7PHnyBM/zGA6GVFXFoydPOD47tTncnmUG1FUFurLUsGIeOR6EIQ8eP2Rja4NKl4zGY/Is58rOLkLYOB5jbGllIQR5UeAHAcNuD09KsjxnliZkaUogvabWdi/u8PyVazx+/Jit3R08zydLrWLv+T5xGGGc4BXCro/dTgcppM28EcJxORim0ymvvPIyCsHx6Qln0zFRp4MuK7QwNlhaSERlq6oJITg9OWEwGNLr9VCOlCao09rcOqMdilFzLOjKKcue4uj8/OOfx0/QfiIC++MCPNqCYCFIrPVC25fGtBbd9iJSH9DieG4Jn/ocbTYrg7ZQo+e1Kj7NfeS+b4fCU/XDrcld3vRwOGQw7BNEIZURZK40plR1yhP4vr3W8eExh4eH+J7P1tZmk85VM4bZfoGpIE3zVl/mC3st1Bqj1Fm5QtawN25sDGHUEtgOyp3nTNqtYRNz/OLNmvq0ta0xHhcXyLmCcXnKy+JCbgurND50Fyi3WNGtZb3Un1ELWBuEqI1ufNraaOvXbj9TYm7lCiGaFLHcCd26etacOEUu3Eu9GC+zl11m8TU83AsKJVZwi4vnsL541+8FeHcejFe/F1rPlapKa4qinJetbSkpxgXuzSfUYFxmhr2G49puFL6lOW+Pu9vk0v0+3aJefa628L7suE9iTV/WnlXQX/zQjV/r2V1lUNSft2MEagWrfm9Ho3MODp6Q5Smf+czrFFWB8m3BivOzUw729tjoD4jCGKUCtJDg+bz88sv0+13KqsALLMuWQpI7voU7H35EVWm2NrZQQjCejOkNemxvbwLa+nulJOx3ODg6RGAtc4mzNj1lhZVnY3J0WRIFAZ1OxwZMKUllNGEU87Wvf4Mky+j1ezaFyxgCP2A0m3J4fk5WVujKMjgGvkfoBxRZYvOtoxDleyRpStyJ8cKADz78kKqq+OIXvsjZ2RnHx8cEQYTGEiD5QYQfhTx4/IittTV6nQ6V0UzzjFmS0ItiwihCeZJ+J+bV67e4f/8hKoqJwoh0lnB6cmr3AdDGps35AWEQsLY2wBhjrXWlCMIQhGA8GvHiCy+ys77J0fEJjw4PiHs9JAIcoZWsDFTalnv2PMbjMb1uF60rDg8OGJ+P5op8UZCWOVlVUGEostzSZGvrspPKJ/8YWfhJ208AEv9krV5wljXz+jspPx6+0m5xbpsK7Revhsvzwi6W165do9vtYgw2AtIYisLCPGVZumIk9rr7+wfs7+/T6/W4ffs5nnv+JqWBLK8oSyv02wFuYeATKI+T42MO9w/xfd++jEo1AWOeUgS+9d/PZrN5nmQtsFsIJQKMFM7ynlcDq8fE8/zm2vX4lWW96M9h8BqGtX+3EFBZb27hbH5nYezbCpC93hwFqfMq55tprtnMgVgNY9pzy7lAry1od0/WCs1BWGXLFjhRC2lS7bb8t5TKBdupJqLe9utyoXGZALkMKVreZznyvC6NWVvcNkKvTo9ZHEsbFGTnT2soS2OfNW0cclAjPDSQp0CBS/UzxpClmT2329xFF4Xqx8i6y8bgcuv7GSzbH7P9foX209plz1B7DsMwwDj2OCElSZYxS1Mm0zGPHz/k7OyEr371y8S+hZ6NMaSzhDLLuH39OlSa/nCN77/3Ad/8/g85OD0hyVIe3H/gcoIFAkMchmxtbfHk+AQtrPDWRcnu7i4/8wd/hq3tNQJP4UlJp9NhbWudDx/fRwS2JGTNPGZKQ38wZDydOd5sjTSaMPBRCPI8Q3mKuNtFS8Wv/fo/5t6jh2xvb7K1toaPIpmlHJydcZJMOT47YzpLMNrgezZLp6bf9ZSi0hVHJ8d8/otf5K/+d3+dV994jVdff407H3zAdDql0+nghwGFc/VI3wPl8fDJPlvDddb6A5SUpEXG+XjE1toG3V4XL1B0Qp/bWzv04h5PTk+4du0aG2trFEVO3OsiEYR+QC+2AWo7W9sM+wMbW5RleH5I6Hj/k2SGJ+Fzr7wBRrB3eEhhNFEUURlbEU9VoCqB8H2Ojo9tmm2/x9raGv1Bj7gTkaYJk8nErfe2toAptUVBhB0fYSx/wuHJ6e/r+Vxun37Q2TNoFPVL3wT8zA9ehGHb0OLSIilcIYTGepyHKWOqeV50u2CFNIbZZML5eMz6+jrdbs5oNGY6TQgjn1maE7pqX3X/fN+jKEoePHhEEAb0el0+97nX+eiDjxp+bCu06n5rt4gaqrJgPB4TBIHzey0FOWELOBiXdmYFqWmUGKWUQxH0wgJbfz/3jc8FKkBV1qlrS9ZMczxLga9LfvKW+f0s8+mmbun3FYJ0wd/cWihd1SohBMIIjGdRhlqIF1o4f60lK9ArFtmn9Gzh2al99LLx3877Vy/+dh5NK9uhpp+dz52Usim+0W7t57pu2kFlTUqhEI1boA0VW0i7RhbsM1yZCkOFrkAKW5dXGDOHrl2krjAagUWZsixzfbGFGep+2V/m/Vw1gu1xWDhu6bsauWq+b59MCObP1LMJ1FWulmed40+y72XXa3+27IYbjaZWWfSs/9XzfLa3t9nbe0QQhqytDRgOeuw/PiSQPmVREYYRuzs7ZEnK1tYGqfL53fff5/GTfT732kt89YtfYGc25Xx0RlbkFGVBcT7i5pXr/N6P3mSaJlzZ3eXq1haeNrz11o9QQhMFPqFDibr9Ib/zw+8RdTsUZYHnBXQ7MXlaEMcBpS4R0grWUHr4nqATh8wSjR/4GAydXswv/OIvEvzhX+Cv/3//e0RZstbt4isPXWUcjse8LG1FMVMYpuMJVBrPKcJJlSCU5KVXX+Ev/KW/yJ/9s3+WD+/d5b33PyDLC9a3Njk9O7PQPBaZyoqcKIo4OzujLHNCzyP0fKbZjKPTEwa9HqlS1h8tBLHn8aXPfYn3PrrD7s4OHd/HN9g0KiAKQ6I4JvQDlLQ8F3leEgaRrf8tFGWZNilnV7d2uLqzy9HZGe9/eIcXrm9RVjlREFBmlllReR7HJ0fsXNmlqirOT0/JkoROGDEcDOl0Oja4WRtMXuD7Adk0JYw6FJVmkmbM8pxyRS2G30/7/4uF3X5ZFsktLu63HHRmm2gW8yAIXHGIogneMEvWYK0A1OlM08mMk+MTtNZsbGyws7tNUZR4nq3GJJVl4Kmr7bjOoMuSdJawv/eEa9euOYIXu6B7nkJKbGS4sWxAUknyomx4nZtlWdcR7E7hELIpcWmMAT0nEsEYF/1rj5W1YHPjY7nUW+MiJIHn2QAJP8B3vNk2UE4gxbwoydJoN/8Wxn/JxVCnhtlgLs/mVNabC/Cab95Fhaq5Es15G4TFtZqOtI7uL8oCjA2WMS1hdaH3KxdtsdCH9j3Mn5FF4bJsdc+rcdEKErNumGWiljlpTgv2NxaaNg4Kahf+mF9H0obSjREYbWMmSldrvL5Pwbzogud5LdYmu0ee5yjluXKoViEQUsyDZBxhRHtM2ve66vdVfy/MwZL1/jSYe1mZWzXm7fm47Pvl41f9vvLqTxHuy8LaGNOsCZXWZEVBkqbsPXmCVIqoExN1Irq9DlWpycqKqNcn7vXx/MChaT73HjxglBVMK83B6RkHh8eMRxOU71mfkZIEYYSQPg8PDi3Tl9EkoxGmyhkMe2xtbRB6PgoJGpIsZ+/kED/0CfyAbhSz3hvQiW1ak7WGBb1Oh6tb2witQVcEoY9QdoyyLOVXf+3XeO+997i6u8Pu5iaDKKYTRJyMRySmYjSbUuoKz1c2n9+hRkVVEkYRgR/w1ttv89WvfpXD/QP2Hj8mDANeeuUl0nTmjBYfqWSz3ne7XVsCeXRO6Aes94cIBOezKWAt1yAIQAkKXfLK7Rf46P59lBF0/NC6zpRAFzlRFOC7gL12sosfRm4tr8jygl63a9EFI7m6fYVu1OGdD96jEIZOEBIq67ostC2he3JyQqfbochtrMHa2jrra+t4nkeWZcxmM6qiwDOuUp+vyD3Bnb2H3NvfQytFvz986rP4SdunbmEvtEUzbqEts1DVBtgFeKrGR2n7T+3fTQSk77s8ZGthuC/nQSKN1e4WHWw6lg0esZDX+voa0+mMSmvKah6F3Sy8QiCMsUw4aUbgh44Iv2MrI1U2h9LWe7Z5yTXfeF0sooYxm7GhhjXn1nGNYZrm25b9ZWr+63lu82yWIIRTdrTByJYWVi8+Boy4WP93VVtc+JxbXMz72Lbk6/FpztY67yrLrKpzhlv3376/xWYaa7XIi0YgSxuIgDBmQUjXxCKN4HBnUVI15U3r82lHSjKHhucWmmiN73KT0pE+eB6B519QHJYFRW35yTaWoeuXwj5PaEOdQDq/plPYhI3SrzBNKpkxc3WnrRSApUsE49i2JFIoywUvHCJl6vFZJTIvto+DohetcXvSVa6tut+to1ee62nXeZbP5+uDeeo5V/XpMneHXacqqyZJG0xqEHS7MdOJFURhZEtKno7HxJHkyfEJYdghCEM2hkN8pbh//yF5abkIkrzg9HxEHMdoX0CRoZRPHMaUlWGWFQyFXT98JVjr9fCVLRDkK0Xg+UgU5+MJSZESiS6B7xP5AaEXUASG2eHMWYAQBQFr/R7Hh3s2KKtrrcMM69JL0xlPnjxhMk4YxF06YZd8/4ii0uyfHDPLMlfuM6Lf77F/bN03SinyrCBJbX1pT3kc7B9w9cpVhO8xnk5Is8zVYW9x4GPTFqMw5OT8jO2NDbbW1jk4PXJ+7JSOHzbGUloWXNncYZYkpNMpJi+sjuMpTFkRhaH11bs1UEnLzOb5IULZmhRFVRGElgvElJqN/pCjbo8P7j3iZDri1tYu0lg++NIxBSZpymAwAAG6sulhiU6cBW6VXx8JQlNpQdDrsD+e8OH+Y56MzyAKmGWfLtPZT9bCvuR9XxYc9ud8EbpMk14IIjN1+pVkfX3dBUz4gLDBPeZiYY/KQZDKpVHNZjPOzs5JkpR+v08UxQipGouvXuCVrHnIsRGFlebg4AApXdlLVRcqkXjK+VYrax03dKjGzK1jd1v1Qj63dC4qJLV17CknJOUcTtWVIZklbkxcic1WqlZZllSFjUA1Va0gzf2mzTStsGwaBLX1e3v+6q0m6mj8s63v5/s7pckYqvrYSyS2gUaoCyEw2gb61PNurURhC82reeUtX1n/oe/59qcrb1kjDLVi0a6ytSikn96scJxX+/IdgqCUapRGSycbIOW8fKdANPthFoVBPRGm5e/XeulvY4V1Y2G7qMN6vlSdr+3eodrvb/urEFI54T23qsUKS3mVtX3Zu7gKiagt/vqeLzvHZedddc7l662ak2dul8juVYK6/Ww3tJVO6bZ+2Ir1zU00VmhFYchsNmWUJpzNZvzeD37I+3fvIqRia30DX0pGoxHGCKQXUAKTacJgMEQLiTaglEcQhJyenWOkhza2sl+v02FrYx2jrUKmgED5SCE5Px+jJWhckQ4pwWh8z2c2m1GzMEZhQDeO8BzCFkVOGAobsxKE1pqdjkbEns/22jq9OCbudjk4OWaazsjdO9jr9zDGxoZ4nkdRFqRpQr/f5/Gjx4RhyM1r1wmDgEePH9lgUd+j1JVFzZybRgrB+nDI8fkpnuexOVjHlz5JkXN2PkIXlnJZC0irgmG3TxSGjE/PSSYT+4woa1HXSJ49vy1EYozN4BFSYRBoYwPJBAJdVnTDmLX+AOl7PNp/wrDTw5QVlTFUwpAkCdoYur0uUkrKoiBLrO86S21Qp6gNFwR+GDCtCt55cJd3H93nYDqi8CXj9J9zatK6Pasve+Hv+r+WRe3A3wXhrp0/sHR+xdu3bzObTTg+Pub87JwkSTCmBBcQ1l6QqqqyLGcSfN8GiBwdnXB+PuK5W7eQSjBy9bPr9CKgEcCes+pzbTg4OLSBKMI+iGAFgq+sBdXus7U8JDZNaW61Nv/qhQrTFLnwPNVAPb7vM5pOnC9Xo41sUnikVGgnkDVOCNgeWF+wO7+y4Um1MXRhri5bABuB6xawsqwa63tx8tw5WspSHfVcliVhEDgecOEWl7kvtUFGqOe/5hKvi3/UGQElWZZjXHU3m5e8+Ey170NKy7euHDk/zNn4atKcGn1Z1doCxJLZJGRZgkTjIh2b56sRokq5QbZzUSMS2ikKtTBuhJS750aJMQLmnhgM7j5xKXfLFrZQjVJV++mtMJdIqTEOhjdQx579WG1BiHLRkl5wtLT60z62vS6sQmGWr/e045f3eXrnWSm025Z0/XejiNY/Ad/zGU2mvPnuOyRpRpLNKPKUKLCCNstzuhsbfPe73+YHP/ghr7/6BhuDDXaiGKnhxtVrvL1/zjjLqIBpkjCZzphVCUVZEPghSMFbb7+D8H163S6x81d3OjGFNkRhiCNFQJcV0zJHOUHtex5oQ5EVeEFk1yHPIwgiup0O3ajD+nCNuOO+C3yUFIxGY7773e/y5/43/w4bg3WScUaaZqwNB1y7fp2DwwccnZ5y/+FDqqSg1BZtiKKI0WhkU6u6HfYO9hmPJ/zLf+KP8yv/49/j0eEe3Y0BZamZnZ0xXFsnCH1SnVp+9U6HMk+5v/cYjWHQifGEJM0Ljk6PWQ9jItWhklDpilB57O7scHp4QuhJ1taHlEWO73nossIIG6fk+z6eH9r5bK37AsulUSvtofBYGwzZvXqFD+/dJfQDyjK3yq+UnJ6d0u1GYGwJZl9JOkFEFEaWy1wKhLS52GEUsbG9zd/5W3+Dv//NbzKTilx5ZJWmOxx8/LP5CdpPzMKuBWt7awSYNhR55QIXZGNJGKc11cfXQhJo6i4rIV2akFv0K81v//Y3OTo8Ymd7m9dff40XXriNEJKk0BQVaBRIZVnRBFRgtSOtkcbQDX1EVfLRnQ85fHKIp3yuX7/O1atXrYXNHMpUSlIUBZ4StevJwTPzxaYGfww07FMAeVk5zbnO1TXo0vJk68oRmjDXGNM0bfK6r9+4Qb/ft3CN0VTG7hv3+jaYSTjfqVntiWi7HeQzL9lzi7+Otq4XTmv9S+cLdhaWEBfOLITAE+BLhapJ8t14NkpAZdA2vRNTAUZiqDDKw0hFZUqkMfS8HlWRI4ywRQaCgMroxrptosiFQmI35QrG1P2uWfSsAK6h77kwsM+o9SnXcqBBdbRBaIGvfKrSLFjSBix0LaxyJ7RufM3aKRfLwqeh3sWlibl9SqMptKF0ipIoK8rKQ/gewhQEQuGpkEII/F5goe7cICsBlWGWzOx7VE+4e6+MW7gENVyumr/bm1UALJZe1+tuH2O/XzzmWdWAy9CzZ22XWeCXIQDtn8utjg+wUftuFnSFqUpMVaLLgnQ2RYUDHh2dcXfvCRtXr/Av/fK/gjeMOEtHCE+wPlzDqxQlHd558IAXX3uZW1evMDs9J8XwUM/YuLLO57c3ecGPYabxOx1OyilhqPCo8NDE3Rjt+XTiPjor2Ap73N65xtr2NkUF636PKIiJog5hHFLKAh0Knu+voUyF8cDvxojcsN7bQOYFXpmhigSvyrm6eR3PC9jYGNLtDUD6VMJw67UX+W//9t8liHy++JXP88WvfI61bkDP88hVj4mn0B2B9iuenJxRSUk36tJRMUlacP/kiIdnJ/z7/7s/x3/x3/wlDkZHbO5s0Yk6kBdsbG2TZzmx8omDgKTKGVUJrw6vkBvJ/skDsvSEW9dvwyTlwegBp0qQFRoxzfGNJNUptweb3D/dY6+awDAiPR9TKIh6XTwDValJjaEwGiUk3ShGCUGR56RZyqA3oMoKjNRU0roKbnZ7nNzfZxwqssAgEBRpwcPTJ1wZbOAbYVFTYZhkM/YODzgfj2wGEAqvN+St0Yh/6z/6P/MX/t6vMg16FNqDTBNrgdSrmAV//PYTiRKvfcv277qqUtVYalJIwsAjyQqCSNtCH1VFksxIE+tb7oRhk+vYhtrm0Ot8AY5Cj8PDQ05PTxgMBuzu7vK1r32Njz66y/7BIdpVLkILB1ta80WXBqM1vi8ctCmYJVPyMiOKLEe4MBaGblzMZl55qZ32VFs1tYXVpFEhUL5EaUXgz/31VanxPIEGgtDHC2xt1TzPSfPCMv04ntvz83NOz86Ju7ElD1FzbnObYzwnyag/r1OYrOWyCG1bV+bHWSaLJsmie+Ei7H0pbHnh+ZiftT239d8IO0dpapmaaldG48MWAq1EU4YzdlWFmjgF93wJUZfmXLRGl+HY2rJrB4s1fbl0ZC4KnIXREgs/njpWFca5tNt+ATOHw7UBLZo889ZJMabCON5ygxX6lYP86/fP3mcd0IaVvg3ys9DLxfsRAqGwgXJLn6+yei+qahfvdVV7Grqzyuq+7Lv2320f9tMscCEFykDhePqbYkNKYYQgSzP6g3U+2n/CvYcPyfOMrWCbv/bX/hq3btxA5ylX1zeI4gg/8Pjgzh02h2t89fNfZHe4iVcJ/CDgzjvvMeiv8ZnXXyPqDvnuB+/x8O49Xv/8q2hAqoCyNBwfnnB6dMDm9lU++/prXOsP6Hdi8jyzJS89jzAI8AMfIW1gLJXBaOh1uijPcjKUxpCbytZ59n3ibpfB+gaZGZPnGWEYkxXgK4/14RqvvvoK2+s3uPP93yNNCtY2tuj2YgA6cYc4jjHGRncLKTCepJoknFQpT8andITHv/9n/23+vf/4/8jWrZu8snONNEv5cO8heVkQ92JG5wVCCkJHKJXOEtZvv0D0ns9smlIUFVc3t1HK5+x8RJ4l5IElavEqSeAHRGFIt9tDSsVsljJLUsv0KCCMY2SlyR2KZQNX3fNlaBBQz/OodEVVlkRRxI2bNxEI3nnvXQpXMKdIU2ZlyfWbz5OmOb7yQWuEEgzW+yjp8cqrr3FyfMrf/gf/I7/2e7/LCy+8yC+98DI/ev99pmWBDHx6gx7+j6mYXtY+dYFdCzObltLytalWLWjAYAVIZTR+ELC5aUlGyqLg8PCQNC8Rzo9Tn1M3i+88oKz2LVkBCePxmCzL6fd7bG9vsb6+ztHhEaenZ3O/bmWraYmG+GTuF68qTVnlYAT9bhfVrsFcQ/NiMWhuuc3963Y5rH2KVWUhTSlt8ZOqrPA8Sw8onL+62+2SpqkNtDIareewbeEgXc9Wg6B0xeftmFic29RCu+53Ne+fdlt9R89i5cwJX5aF9rP7f+tW+4FtVxcFWNuvq7WlbJR17AC4oB+BkcJRqGrKMiMIfIrC8vjWlrMwNZOaQXqrBUFV6QvjsCzEP+5elj6Yj1FTjWvpukufOaLTRdgduwDX/dTaULigmTrnvY4IVxI8zwriCmM5+XVFVpQI5dl4NiGbZ0I5WLyt8M57dlFBMwaaEvNm3qePGwvT+n95n6cqQkvC9VmO+f00KXCInvV/amXz80ut0Qb8KOLo/Jy3PviAvCzp9ToIIfmFn/9FdjbXufv+e3Q7XTzPpzSag6MDttc2uHn1OhuDIWcn59y59xE3rm8zjDtEwwFxb4AJPX7jm/+U0WjEcHOIVAFJmnB6fEAvinjp5g2eu3aV9TBEVpokSQiVj+8FaCHIypIky6iKimGnD9owGA7Ji4p0lhPEPaZZQm84wFPCkqhIAUIShjH9fh8vqwjDACEhTaccHh3wS7/0S5yfjdl7csh0MsWTiuOjY9KrQ4wALwyIuj3KqmLqGfb39tjd2GJne5u/8nf/FtuDNV578WUOHu/ZOuCeRYXibscZPQrfU8RByOT8jLAXM+x0SbOStNC8sLtFt9Pl9HzMbDajp3yLvBaG2WTKld1d4jAiLQuKvERKj26/T4Xg8ePHKOWzvrWJEIKgLguKXWsaI0vZ2tdlUeIJn7XhkI2NDe7cvYsKPPLznCTNuXHlKuvr60xGY/K8wJMeUkjG+YRrN27xD379N3j73XfIheRLn/8iqdC8/+QRiS7xfZ9Q+QSVIC3+hQk6WwV9WW3HuIVHuOjYJElJZglaW1axa9eusbm1iRfYCSsr7SKvJXXpQq01ZVU1WlHNF12WJZOJ9WefnY0oy4Jer8vW1hbD4RBP+Q08L4RoeLzrnFjloshtsNm8QER9T/WKJ9zPOuBqVUAcWC2+tnasJTW3UOtjavakOq87jmPCKLQwpjtXzeOrq2qeW2udv4u+ZDNfLhc2d22Wdq23xc8X4Vs7hRctrWV48mOX1baF3rrOxYwBjcAhGNhgOoR1Y9Q0g0JJom6n+ay2Sps0wNb12j1bFVx02Xcft/9FK7s2rcX82hZZXqBLreejQRqarZ4703zWuFdc6U1dQ/bODaGEAVNZ9rdKk5eVLSgjZBOoRjPH9iGo08fmc7e0iVpI2k20n53a0JcX538+DvVYXFR6LoOzl5Wmp+2zcuyfAomvum49LgJjUzAdMmawhDXaQIXk8f4+kzRDu+yE3L1/Oi/xEPjKlp0sMRyfn9CLYoa9PkIpjkZnvHf3Q3pxTL8TE8cBmxtr3L55jU4YcH4+IssrDIqy1CSzhK3hkEEc0o9DOg5NK8uC0MWyIASVsUpcWZRsDtZQ0qMTxiihKIoSEfgkRUF/YNkZpRPYXuATdzqEQYiv/Gaty/Oc/f09tDD0Bn263Y71DyuFNMZS8kqJ5wcYKRhnCSeTMdvrm/i+z8OjfR4e7fOV1z/P6dmIk/MRaZEjPcu45vs+yvPQQqM8Rb/bIZlMqaRhc7iG1oI0K1nv9dlcW2M8TSzaWubkxlH45jkbwzW21jfxlEeapoRRhDbw/ocfkmTOn+8Y3nzfVmWs/dZSyaZyogAwVmEP/YAru7s8fPSQIAxcla8ZA1dGVElbAhSHrEZRh3fv3OH9+/dIDKhODEpycn7OwfGhvWeXZmkpYD9dEfupC2wLX4pW7u88GEtX1oGvK0vRiBNYo9GYJ/sH7O8fkjtmn5s3b7C+sUHg0ijKqq6YZYViqatmk7IuyCHc7za47N69+9y//4BKV2xubbC7u0scdzCIRmjXL0A9mb5jElIOmhVCOB7nOjzMCkoprOfO2HJIFwR2szgYiKLIQvEuQMRgiVaUN184bfpSTpHn+L5PHEWWrEDMIb6qrKgKW4yiJtZYSBlasFtt09CwwNXC92mFUtpNCOEUoTnrWRtan193Ufg0x7uxWujf0uK8LAClU1JwI651ZXmTcX5z38MIwfrGBl/6ypfZ3t1xqRfCFc2wEb0IFtjNVll87ajotoCe/1whtFsQ+8L5YO7JFViLxiXXN2dakItzJcclqDkIXDfX0RiQIJWHEMpGsNYR5HUBlSInT2eUZUVR2SDEpKny5gS/mSPhoimTOhfWNWRuffIKsWJZEMJS8SplKW7bz8PF8ahFtmi9NcvnWx0tvrzP0z5bJbRXCf6nfdaGSxu3iQs8miYZhyenxP0+KooojCHXJe+//z4ffnCHMi9s9TxpaT7Px+cEvnVtHZ+dcuf+XR7uP6HSJVHkU5kSJQ1bawOubG+SJKnjibdIigR2NtaJlMADi4cIi5qFYWhxACmRvmctRQPDTp8oiPCEhyc8lPQohCE3hk6nQxiGzf1EUWw5sYWqixIi3To3nU743e/+HpPZhPWNNeIwoBOFbAwHCKNtgQ3PJysKjmZjjg+PuXb9OufTCb/3g+/z6gsvsbW5xVsffkCpDVEUUwnTxGYEvmdZ1yQMBwOqPGecp1zZ3EIKnyQr6YQBV3d2SNKCWTIjzTMKR0EsDYRByO7uFaKww2yWoo3g8OiYu/fu0xsO2dzedlHhZp4Zoq2yrzyFcam1BuNqcYPQhhvXb3B8fAxC2PWjKlkfDkmSBK8u+KFs3EYQx3znBz/A+AG333gNr9/jzQ8/4MGjx4iiIvQDjDCkuiCjxI/CC8/w76d9+j5sIZy2Pye4WPBT6vpzl1tqbGSfygvGkwnvvPs+nTjgpRdf5I033iBJEh49esSjR49IXbUZYyBQ81SaGoJfUA60JoospeD9Bw+J4w63bt1aKIkHrQVaOBL35k5EC94H6bn841q4My+tWMuc2hKvj8OlbEgp2d3d5ejoiOl0ai3mwGrDStnKPmhDVpQk1RQlrK/FlwItrJ+7qkqUFE2EfL1gzu957kc0rfsSYlE42QVXNdzpzd2uXHjn0P9l8GSDPqxYXBfOb+w9BYFPFbkXwMyv3e5jmieUbp6yLGc8Gdv9lCQMA5Ik56e+9jX+w//9f8hf/At/nu/9zu/xzttvk6e2EIyvbCnAPM8sN3JLaFslxGvKa9bfrfJhK1eYoY7Sl8oKzachCXXMw6qxvfC3FA28YYMu58JbAMo+ZIAtcZpmBdPJjMlkhpQhs3RCUirkeUG/36coCpIssxBq4C8gDpo6Mr+tkNTag1Mb6hsTLAjZ9ty0uQ3KsnJFRsRTn5G2vL5MCC8rTE9r7eu0z7fSt/6U69m0INGgXWiJF0pQHnml+fD+Q7qDIUjJaDomK3I243W+/JUvE5UlHV8x6HfBGGZpQlGkDIZ9Dk+OuPvoMfce7xF0O9zb2+Pa7hWyPLPlNnt9Xnj+Ntmj+8Rx17rx0hxfeWyuDfj8G6/RiyNkZTNGvMAn9gMktnhGEIZ0u13W8wwKTb+3htGGXqeHF/S4c3yECmzql68URlek2YwgGNIbhBhRWvQQhRCKzc1NxqOc/aMDiqJia22bmzeuoQ9PuX96TjKaYIqKMAzJy4rReMYszfg7v/0b+FLy1dc+y8s3n+ff/c/+L/zMz/0sa16H0eSc05MRac32GIUkRUZsOlzZ2KIbxuyfn3JrZ5e7e4+YZjlIzYu3nuMffeebzPKUJEtt3nncR0rJLMkIg4goisnTnHfffZ87777HL//r/zMkNriscJXqauSuds028SzCWtvCYNO4spxXXniZb33/99h/ss9sNqXfidjc3OT06JRcQzfuYIzg+PSMOz/4Ia9/4Us8PD7in3z725xNJ3hhhPQV/cGAqrIV23RRMZ1MMFH8sc/zJ2mfusDO8xKlxIJ1XecAQ+uFNZAVFZ24hkjdIuXYcH705tv86M23WV9fY3d3hy99+ctMxmPu3bvHbJZikARhSKfTpcy1NWY8HAuXR1lW6MLC5coY0tmU999/n6tXryLl3KfRQOPOyrUE9bZubVOlxwm9GhHXWiPNYrBU7W+sm10OLSn9eDzm+PiYjY0NBoOBg6D2Xd1w3QTXWTRHMJlMEcKW96wt3LIs8Ty/gc61pqn7bfswF84fZzzXlbJYnpPW7/XPOhVuXlNaXzimvt+Pa0WWk6e2Gpfv+Q2MXZ+vVijAVufCQJamnJ+dN32pudOTLOXw9Jh79+8zy1K0MY1Ase6Ni9XDFmBunKjSBpjzwbcFUp36VTPqSaUoqvLSimrt61ymALX7UQfz1P2prZG6H0opirIkLwqM8wEK5RNEHbr9AV7koyKJMRFCSKqyJE9TkiQhcCmLGFtl3T63BrFQzdveexsjEcI95O1nAsfGJ+fxJHa+dBNXsoDxt+9TCJ7t6fjkbRVC8yw+9vqz5r0vC6rKCqRZmoDWxN0evWEPPw4523uC0RXSk4wmY9754D3M6Zif//pPsz7cQJuK0eljdod9rt+6xgf3PiTLNS+99BKP9x5z/8ljvlh9Fj/w8ZQl70jTmWUuiwIOD2b0+x2+8pk/wNX1DUanZ2yGXQI/IDeaPJmCgX6ny2Q64cmTx+RZwh/48lf44dtv4m9cQUpFrz+kQPHdRw/RRuBpUEKhDOiyxPPBQ6A8Hx0r4jBEu5K6h8dH/MGf/irT8ynT2ZiNXpft4RrdIGQjClgbWF/5/t4TRuMZv/Gj7/Hi9Zt84fXXCZTi//Sf/l/5N/61P4M2cOejjzgdnyGlDRbL85Jut8fxyQl5XhIFIVubm7z/5AGfuf48nbjHJJlxPhvx8nPPEQd9RlnCZpHhiwFhp4PnSXRScOu5W3zw8AEPHnyE/4U/wL/zv/33ePOj95mOJ4ReQK/TsatHG1nB1Zd3a4MX2bryJi+JhOL5557nxpUbvPXBO4SBx+2bN/FDa0Qp5ZFkKbM0o1SKVz//ef7a3/7bPDk7pbexTr+3RlmU5J5gJjRlkeIVhkCDQtlMp0+xfeoC+/U3XuPs7IzzszOSxMIWnhBEob/grxRCEAZ2USlKJ7AcGYUxFVKAF4ZMZgmjOx/RjUNef/11vvDFL3J0dMT5aERelZxPx0iBpb6T8+pdVvDPfdPSLVqWxMJHGxu4ZLTB8+YBQ1C/zDb4BGiishcEci2kMXi+T9frUhQFsyRpSiEaoCwrPN/24ejomDAKiToxO1euMJtMmE5t1RswVA7C0RqkbF8Lygo8aZ2/bVjawuI15Cq4bHlsB9UJZ3m5Q57ql62VA2MWi4Es7/9MgUEtxaauDd0+VghBWQmE0AhtQJdk6YzJaNSgNQhB6YIHjRBUBmZpYgP7jEUZDPb8qwRrrXVTIxJuJ6nmSkPdrHDS7tn0FoXYM0K4y5Znfa9aa6aTydzP7Pap64B7TkCW2kLdaZpSFjaWw/N9ok4XTweEPY+qCqmylKRVY7u5Tm25S2lv1Szrc/MwxGXouP7dkq3YXO72w7U8XkbP7/eC9bvsklgYpPqTjw/2u2xsl//+OOu6vqyt4qbQrjDQyfExKMWt9XW21jf46OF9enFEEIeUVBSuPK8fBtZthSLPcqSRvHrrFkoJqqriyu5Vtrau8sE775FsxBiliFWE0YJZkbO7s8Pxo7sU44q1QZedzU02N9c5Ozlld3MLsJagdQEZAuUTeSHHkxlHJ8ekWcIbr7zCo/09giikE3UJ/JgsyRHSQ2vNMOqQlyn9bsDu9iZxuE6ajdFCILVAoVDKPktS+bz34fu8eOs2veE6+3tPuPn8y6x1uuyuR/SjDt0g5rkbz3H/N/4OfzQa8sf+4B/mh3fe4Z/8zm/xP/3lP83trWv8F//gb9BXIbEfkGlJFAsqhx4YKcjLgjIv2N7Z5s6D9zDK0B/0GWcp9/bv87kX/zCbm1tM8gTP81kfrDXuy0prDg8O8XyP115/jc9+9nP81b/614jWB1zZ3SUMg0bRrWMSZGkJWIxwyrg25JWNdg/8kMgLkGXF9vqmjUSPQhDwcO8hm70Nzs9HXLt1k8PTU97+3g/4R7/1m3z5699gyzzHg8ePGSUzjJREwqeLh4h7nJdjEkp8z6eYpc/8PD9L+9QF9ng8JgxDdq9coao0WZJxfn5OmhcIDL4rhF7VdVXVYqBXWVro1wZblM5vDdMk5f07HxHHtkTcxuYGldacn50hBC5tbP6y2ujpxVQkIURDuCKcRV8Hz9QL+QLk56ypZctSSg8hDEVRWnjeVWyp/YdNFLqQTYpBVVXkZUWVpFSuQkzc7dHvDzk9OSZNM+t7kQLfs+QoNZxjjMZzC4HABUJIsXB/9QpoYF4r2rW5W8KV12zt/yxNiDk1aXt8FmR063yGVQsyCwLvAuLS2tfemY0WT9PUVsZhzoCmXR66xuAFQUMqUjsEamEtXMGVVX5SY+r968/n1veiddwW0rQUpqfnEl8mPNpWvjAuJ17Mx8Y+M4uKYaVteUPtopeN4yZHShAKI6Q9jwFT2ftv+m/0PG5AiAU3xPy+Fp8ltyswZxec7z8fr/bn1pp+ipXL4ufPYglfJnRXfbd6vB3OdZnwRmAchXAQBhhtbDBTHDPs99g72Ac0ke+DsZaowUbk37p2la2tbTphRFGWxFHM68+/gHCuuI3hkM3+EJ1X7F69bnmtM+HgWlhbXyO7k0CVE0ehfb/LoiFDqifCBqF26Ha7lkAkdGuNKeh1O1y9ep3YC4iCGF0KsjQHhwr14y5JpvEcStjv9RAUGM8aEsK9a54XoDyfvCyYTCb01mJuXLtGnqZsDofcuLqBwhZV+uLnvkCoAq499xzf+uH3SWYTvvjSa9y+dYu/+Q/+Ll0vJA4jdFVZ5BBbg6FeP6uqosgK1odrJB8VVLqg2+mgPMWjg8cMe3021zc5TvcJwoC1wZDTyYTh2oC9owMboT6dMj4b8dFHH/Hyyy/j9Tv40qJAjRHQchHW/zfPsmcLQQltMGWFKDTrgwFrwyFZnnBydsqTg4grt7fZ3d3lgzsf8P233ubx0THf+IN/iAzDo7sPmM0SlFR4vq2PnVUFptJIBLEX4EmPvPp0BfanHnR2enLKdGqDYKIwZGdnhxs3b9LpdUFKKoMN8nIWa00zaQW2XaQxNpjKaI0wGmXNQI6Pjth/ss/p6RlJamtV11F4NWVjg9AZoI5wbcF9c2Yr41INVlhF2MpZzQIJ80VV1PxhjvoOWx2ryAuUUgzX1oi7Xctha0zjw8YdXlUVWZoym84oy5Iojiy7jmvLi5Ax81rYczvFwqM2p9BFUNf3ubQQ1vsjhEMgFgXO8rbcVlldphnjxUCsZpi4qA/MiUFcr8Q8+lg0m3FzZslEEHZhmc0KwFCUiZtjmyJX5AbflxaNMPOAPzsOoonuXOiZs8JrAVY/I5eNwSrhvQpVaB+/LJiWA9uaY1wRjjlVaF0IZj4wAlDGEgDl2ndMdRotTYNI2FrvAl3ZUqR12qMQVqhbOFtciClp7zd3SajGovY8G9wkXdlT5Xk24MztXwfi1M9jm2O+iVsRq5+3ZUVAiIvKwcU5WT3ey3/PP2/PZc2bMBfoolZghGCWJlSmIo5jut3OvOiPkMSBtcSUUFRGkBYVSZaT5TlGl3hK0Iljbl69SpVaGs1hr0c3Crh5ZZvndq/hC5+8KKiqEikEsyylLLWNwBYSy8GQ4nuSUpfOiDF4SrLW7RP4PllVEoYR/W6XKIyplODq7nU8POIwxvc98jJnNBvT7XbwpSMQEhJPQKAsF34oLZmQkQKkpOP5CAFK+YwmE8bTCf21AXEUst3vsNkfEgUxQijCQLIWd0jLjCdHT1jvdXn9udu8/+Ed7p4ccGvnKt0woiqrJp21wFCV9p4qozlPZmwMNhAFTNOMYTeiG/vcPzlCIHjuyg10aRCmpBN5RFGXh4fH3H9ywOnZiDQrKLRmMp1C4NnAOaVQwgWZmbnL067hpjEKlZR40kfign9lRVGUbPZ6rHUHCKQtnmMkG70hk+mUt9//gJPJhMHGJv21dY5PTzk+PbbGpRB4TlnWNY0y4AnVyK1Ps33qAns6mXFwcMTDB484OjzG832ee/45rl+/zmBjHRH4FM5SalJddOUi92wlqTrk3pduWKsSiSHwbNDM4cERD+4/4ODgwL6YGpeCNa8xXL+wdVh//c4bYxp6zPp1tsQjzj/n+uRslAb2NkI0tGal07Y9J7CT6YzDg0OSJGFze4v1zQ2COLY8waK+hsaTAk8KC8ukGWenJ4wmI4QQ+L5yOd9zLVxJhVBWeJXaIFQrtUfIRiu29+cEeq02CxtRWscHQG39fLL5rI9pM9VBvbg6QcuikJZWHs8jxIWLVjd1Jribj7oMmTAgHDkKlmawMqB8SxiSJNYazfMxwtiZq0pDlhg8r8L3hRXY2to/UgmEUaDrfs8tP1NZBVC5/jvV8Wkj0PzWjnlY5iK/IIxbgn8RlWgdUyuArfE1Tueyipr1OftU5EaSmBhfGoQq0UojlaDMBVIYR/yRk6Ypwj0bwqVBKscyt4wO2Dmcs9fVn9VR4EEQID0rrL0gIIgCPBdXoZQLFpU2h7ldo3xRaJqF6122tZWW1QLdLJxr+T6WSXEWyY3qeBSoiwS5CbHKdlly9/EDpC/ZvbLN5vo6RZoReAEGa6l2ww6eiii0JC0M9x885MmTx0ymI6TQdOLQWmjjlLXukE4cEHian/rCZ3h+YweyinEyJa0yBBX39h6D9BFG4fk+Ugl0mRH7EjybXy+BQCjWuz3QmkwYgiBk0OnTjbrMqordzauI3NANI6JORKkq9k8OWFsboIuKqgJf+fTCEGmsq9Azbnx8DxF6DIIAo0uk8siKgvPJiEmecO3aDld7MV3p0esM8YKIvUcf0Yt8Hj76iBdvXuWV529SFBl/99f+IZ974/Nc2dzCA6qiwLiguSKQVGVBFPhUGA7GZ2z2N+hUPkfnEza6ATvDmPvjcybjMZ95/iW8SpFnU6TIiYIef+fXf5sffHCXBw/3qYxgc/cKG1ubfO+dN3n//fdJk9QF0lnkREppyxa7WJ26UqOSikArpLHoShVoZkXOdr/LWjQgUAFxGHJr9wZrnT4/+uFbnE0TXnz9M7zxpS/zzd/9Pe4/3kN5ksB3ZZSrAqErAiwRj9GaQpfkuqS1DHwq7SdCnBI4mPL07Iyj73wHIeCnfuqneOmVl9k/OODB/fucHB1S5BWTycQGiRVlo7VXDrJ+Om7bsmhE7Yqc23r10cZ9agCqqvm2tu7ra9ZRr8Lxe/vevH51fT7nXLYlDStNmuUWtnKk+qenZ8yShMH6Bld2d8nWUh4/eoi9tL12nW5gOc0rTk9PCT0fYwRFoZFSW+pVB6mjdQORauaKR1PAomU1t+eg/rtdf7sZOW3mkvaZ57W9SLKwKMISnCkuwpvCtAQSzjo17X2FW5RFY0g152j8vjRWm9EGXZQNtaklyXZ82Q0EML9+gwaYeVxDfY32eD0NZVg4T30PLFpsbR9u/V0dsFcf26TV6UXP7gXI2MxjPjqdjvXdV3OiIF1pjLCEDvV1qqoEWnzl1FMtXILEYtphfWu1wmqfTxBogtDH1/MAOHCMfp5o0hMzmbtASKtk2hiUOe/8nDBpsa2EqU1r7mBpPGXz3C2P1fJ5wb5nuUuRtGtKSZZlzp0lmSW2aI6Sir2Dfd5++21+6Rd+ntHJqYU6lavMZQzdOEIriUpnSATr6+t87nOf4YWXXiRSktlkjDYVXhhQmJLSVCRJgjSwtrbG+uYmd+89YJalGFwOtbDV2KIgQgmJZyShFxDIgDSvmFG5yHBJmeX4MsDvxahOzHB9k7LSlHtn7Lz4Eof9DpWuMFIQxDFSSoadHsKXdPyI9fUem5tbKK+HikPKJCGoUluC1wtQCDpRwPn5Obdv3mRjsM7+/gGvPv8y125cx1eWnMhDcP36NY4Pj/iFr3+Dz770Ev/kd77JP/rWb/GzX/8GX3/ti/zFf/g3MWg02q2pJbEfUJYaPwwosVao8hRrwzX2jvZ5ZfcqOxs7kJU82rvPla0dMBLtxZxkJf/4N36dX/n1X+UbP/PTAEzHCUWaMj06ZXuwzqzIGVMRKIhUQKU1SZaiMQRhiHJr75wzQxAoHy3BSFsNsBN3uHHzBifJKXHs8cKLt/nv/vE/RGcl/+a/+mf41rtv8t//vV9hqgTr60NmJ+fEYYQRUJQVUqq5K8NY1LUy/wIIbINGKkvYLqVpCE2+/Tu/SxwFbGxtcPXqFb70hc/b4LHzc0ajEaPRqLUYWWty1b021lwLaoMlH1Zr50agQFNIAlhIvWlIUxxEWVaasjKEYcjVa9eYjMeMxlPyorQCVXl4ShJ6Ck8pS2ZiDHEQUJQlx/tPmI5GdLs9bl2/gZSSLMtI0xzjtFwh7ALqCasAaK2RRR0lD1VpEC6a3fc9dGULbmhjYWJbg7uudS2aBXWVpWev9XSf66Xz2Zyvpm+1NbDDMFzpm1UtX2W94FaVhRodMO4mZk6v2p5dO+tzawtEE8SnqwopgiZv1mA5xZEC5XtILfA99wR4XjN+ZSkWhM5ye5ZxuWAltlwd0HZ7iEYpWxy/y9vyPhbVqeO3BVoopOfjeQFG14x5EAifHEupqRwTXlYvGiuaFMsukcV7s/Mr8ZTC82zZSJANb7sdSxfUVmmmsxG4OfA86/81xvLOC2EQYo6o/LNsxpgm88IK6dqS9+yiKgSebxGI8XTCd7//Pd547RXW14f4QhBHCVEconXJ2qBHGPoUUhCHIeb0jPPzc76595D1yOe5q1foxDGdQY9xkjXpVFVRUqmCTqfD2eicWT7DKElWlJxOpmglOR+P6HdiB+UaKDVe6NGNY25tX2PQ7VMZwzhLSHRFN+ywJkNu71ylM+hxNvCo7n9I0IuIIlvp6/HhEWmScfLgEXx+g0BArxOxNlynyDw86ZEWFUrkDtEEXRUEfoDWBZPxhF4Qc2vnKqPRiO3tbdJpiuf7FLri7r0HfOm1N/jZr36d/+d/9V9yNjrlF37qp/nqZ77In/8r/zXhoINBk8xmVokxltQozzJCT7k6EgWjLOH5m9d5ePyQWZ4RBzF9r8sHjz/i5752m52tK9x5dMTe4bf58M5jfuobP01VVWxtbTGuMh7uP+Jv/PY/wu91+J/8wh/lZjxEVJJEVPi+T9VS3GoFEsBXiiJNERo8X+KHPqqSeMLgC0Un7lDmU978/g/Z6Q555ed+nr/w3/43PDw8JO50iD2P88kMGSjSsmgUTLtulwhq926tPH+67SdSrcvShRoHNRtXjtDyZO8/OeT46JQPwg/Z3dmi1+sxmUwcu5D1RQghra9HXvTttlDWC5ZjA7EbC6EtWy82SGx+BiHnSkEdQe0OxlOWiGM0GhFHERueR5blJGnmKnTZClkqdhWlhCDJbFEQT0iKJGVcllRV1wmrJqW2uZ6UdqEvioqisFVwlPQaF4Hn8ol1aWy6nCdadJRy4Vy1ojOHAVvWaQuKlbhULaOfCR63Qm4ezV3Tu9awcFswSQOl+7wNA9sqVylh6LOohrX6uCQzhXC+XwSmtGQ8RVniKztvGI2pNHEYYmuQSVvzW1hN2hJamCaVoy1QLxPQF1CB5t7qv+cWuWKOMLSFeT1mC+OyBNe6m76AjCxefP6r1pBlFUVlGhhdYP3MVVEhpMHDVjIri9KSz7QDOtz17KHzcajvqf5bSvu98nzCTo/19XW6nR5Rp0sUxbaMbFmRZSlpkjIaHTGZjEmSpLFmayvDnk+gtVjoxyrl5OLv7piWK2PZ7bDKDdFuujK2OIxTpptn1SluNjZiysnojLgbc+vWLfduCJSv0ICnJFEQ0ut2qJIU31P4ni3K89kXX+DWCy+wd/8udz94n+dvP8/N2y/gKUG/06ETRggDSZI0fVAuNc8IyIucfrfHzavXyPOEqijQRrC+ucVZpfmnb/2Qk/E54+kUpQ0/84WvEu9cIenGTKqcfDxjDcVZNiPsdfGDgKOzU96/e4dOEPL8zi6l0IjKcvL7XoTOrQPIlx6BVPhCNOQhvvLQXmDXHGPX6iAMOT4+JkkSer0+VWU4ODjg6tWr/A9//1fwpOQbX/gKN3au8t/8rf8Px4HmZhCSpjNbhtgYQs+HsiCviycJZdPDTg64sbvLm/feIisLrgx2uLlxjTsP7/In/2ifG9dv86vf/E2Ojve5tXOTs7Mz9g6fkL/3FioOeOOll/kP/sy/SdTrMhqPbNZQliOlpCclYRwhlKLIc8rSyhJPKqQvbO59YQs/GW2Yjif0tgecHB2TzhL6vZiru1d5+ZXP8ef+k/+I4ywl6HbwvIDJaGpdl56HJyW6KCnzAi0kgec3pE2fRGH/JO1TF9iWZcxCD3WkdJaXFmILfOeYzynyDIFmNpuRZRl1UEgtOC+zGGG+yNRtXhqdeYDYhb+F9TkvwZvawbRS2HKbFThyEtNQ4mlXTCEIAvwgIEtTJtMZQrSsISGIQ0sSX7ho7qqsGE+mzULesG410CwtASubwIVaG1RK4TlWI60NVVk0dYdBNPnRtc9zDvmaSwWTaH4uLqTP0kyr37qsGqi+FmRmYR+9AAM387iMxNcBBjWKLWqyDys8jIFSV+CgNN+3GItl+iroxBFC2KAWWwzDVlwSIrABi0YtpI8tQ99PE+AX7381grH8+6LvdVGYt8eyPufFC4nmp9ZQaUPc7TkWQTsuQkBZlOgKpD93s5RlQU3o42Ls6v9W9m3+GYAVsnG3x+61m1y5coV+f4jnBXie7/yCVjEoipJkvMGjRw94/PgxWZbZIEjnqrn03p65zZ/PttBeHr9VCJvboxnzOgOkpv4VwnJPz/IUvxuxubVJv9cnSzOKIgdj8Dzr2vOVJPA9ZGrRsG6nQ14UzNKMvYN99g8PmSQzgiCgzDL6cRdPWEIWhCDNCxvY1e1T6QpVZf8/7v48xrYkz+/DPhFx1rvmni/fXq/2rqreZ0azkDNDkRyKEklJJiUZEiRYhmBI8CLDEGTDMCAbFuB/bAuGYcgLLMk0QVM0tyGH5HCmR7P1LN3svWuvevt7uWfe/awR4T8izrk3873qLg57xIFPISvz3XvuuefEiRO/7fv7fgmDAGEsylriIAATuChbSY6nU97d32c0PmdzbZ2dzV3WBgNsN+G9Jw95ufMSFZZawkK66wrDkGk243R8xjxfsDkccm1nh1xniNobbBWiA9dhIcOQThKTRLFrHxSCKAwRRtNLOsRBRJ7lxHHM6eExZV5Qa0MUxVy/fo3/5ltfZzS/xRdeewNb13zjne+yPzvn+tVr2LKm0jXaGieuohRrScrhPCMKI4IgoFgsOB6f8pk7L1OWFbNsgRwK9ta3mVVHLKqC+48ecTyakGvLrMgYbg740vUvsjboczo6QwInpyfsf/Q+m2trrHf7hFIyzRcs8sUS6LlCZiUQBFJR1R4YphRBoJhVOf1ej9OTM6yx7F3Z4+q1a/y//v7focgrXrh2g1ldcTqdQjfG5AWiqrGBI2NxlUvD2mDA+fk5WB8YCUGlf7RqXT9y0FljTJq2o2bxbh4oJ4vpHsbZbMZ0OnVeiQedYXG9wj9kEV1dbCzLNe7ZHVd+C0fx1+l0CUNHwKH10sCB17W2S4avJjrM84K6rgiUpNfr0u12iaLQg4a8g+IX5gb0AFD5OuuF9L0/pcYANAQdrra+Kiri6y1hQKeTtAv16pg+D6m8uvkA272/soD6xPMP/fmkIRWssG/6L1otP1z+ZFunfs7xmnNc/fEfwoLTPUe4DIk/gjEaqyt6vY5LR/k2OO055mv/t/6EcVr9/U+yXT5Wc/6rI9M4TW6/5rsuU4I+fxO0DYe4coDFINm7dp0wilsnyVovKuP+hRCCWtdUPpq4GLkaVvXGn+/1NxE4JGnK1vYOu3tX2d7ZZW1jk/5wSLc/oD8cMlzfYHNri63tLbrd7oUsxg9ytJ97vc9xeJrzWY1Ufphj9UnZkedhO7TW7O/vc3J26gQt0pR+r0tdltRVhRBOKS9Qkli5sleoFFGgiIPQl+wCJosFiyJHBoET38gLukmHMIjACoqyYjSfo6UraYRhRBhEDhxlrSMxoXFNLEVdc29/n+99/BGjPGOhNbmAOgoZVQXTsiAvSqqqRguLTGOSJEUCZ5MR48UEhGG912W9PyCfz9H1EhsUKoWSkjgM6CQJaZy4TJR1nOhrvT5pGBOIgDj02tt+Pc7zAmMMm1ub7B8ccO3qNXpph8cHT/nexx+wvb3NXm+dUteUvh02CF20vrO2gbGG0H9voBSj2YRBp0sUJMzzgkprrmzsMNeCr33vu9x/9AgLbGxuMRwOWO8N2BgM2RgOicKQ6WzK3YcPeHqwj601EZLECwAtfFbDT4Bny2HeFimlCGTgunWSlKIoSOKUTqfL/sEhv/J7X+XOiy8x7A0QBsq6ohKuM8lojal9YOUR6INeH1tr0Nqx0okfuXn90UfYxrhI2nn7LhUUBi7KybKiba3AOn7XKIoupLx8J4onnH/Wo17dngtaubz517R1/OU9rx89n889TWjh6m1KURSVRxM27WC+JiklhW/jSMuStfUN9vb2ODs7ce0LHnSzqHOiMHDXay1YQxi4GrVciTYB39h/mVximXJ16lwlCEsYhnQ6KaaqqKqqjaobyszGiDfO0mqq+sJ4rYzlpzFWq8CqZiykkCixrA01961Jky8v5NKxPm24ZV0NvzFsTRrepcR1e3jXm1wyHPZI4tC5nmZpdGCF25vVuu2nN9aX065LQwxWXETcr/7tjGrjoLrPXCyfP8+laTYJ0rQa6o6ITfHyq6+yWORo26hJeVBXpKjritCLRNR16TMTqydHC8f8oRkC71Q3nPpOa/0iN741hqoqWCxmFEXuORCa4686pxZrf3hngrj0bHya7Yc69H6tbBzbBgw7nc/RWpPnGcQhUZowPjlhc30N6b1Ft2+I8CndOAzppClpnKDrc6yF7d0rdPsDzsMQoRS9/oDz43PCMKbT6bEoCk4mE54cn9Df3qKYTdkYDIiSGKZOF6CTJCRhyHwxpaxrVJDx4OkxT89H/Nid2zyaj/nO9x8x6Hb54s2X+PE7r6OLitl0iq5KtpIeJuwzPT/ldHTOolwQRZJBFBIpST6dEfRSdz/AY2YsURDQiZ2EcSgDbG0JZMDe9hbZbI6oNXvbO2SzBZ00JcsK39KnmGcL6rLii2+8xS/+w7/Lex9/QJKkfPmlz3BwckLuWeMC5boTTFWxs7HZciiEQUgURsyyBb04YXtth6wqyOuCm3s3+cp73+C/+Gt/nekChsMeO9tbvHLtOno05e3vfJeTxZhpnbG9sUESx/zMZz7DWqfH+OycCsdvMZ1OKTLHfhgoBR57k5eZWyPbbJej0W5KuJ1OB9kJOT4+4Wu/9lVu7d3gxTde5ze++lXOxhOEkCzOx66HvMjdXEEilMLWTrjKaOP4FUIvzvNPguz9FNuP3GBrbTCmoKGZbB5CB5CyreejAkVR1OR57owOtMAoKZfyA5/0CFtWjJ91P41QxoXIxu8vrGujOT0fURUZGxsbbGxsMJ/POT4+JssLAiWRcolGDoPAp1UsieekNtrw5PFTBmtD4igm7cSUSlF76UuD9TVuFx1qbai9kYWlQW5qwI1sZl07oySEA0MYA7WuKefuuFEasz4cslgsKMvKI9ql57t2I2LMkmP8h22fanF8joFzZQQ8qMft5rImLrMghMDKZUpeY1babp5/DheyJf7WCQtKuBikoStvaUGFoK5KssWYbicijBzISSonVmK89yyFbT3rVarcT4rGPum9yz/QJGwufu7y9Xza77jwur30nYCQkjTtonFqUo1EbNBKBVZYEVJVdQs6a7M6iGccr4tjD+4pk67FzRomozPef/d7jM6O6PeGxGlCFEXuflY188WUyWTC+PyM6WRCUeRICUWRtYh4fsB3/qC51w7NM2P0T2bMpXRkMsbiGPCUQCnBdDplb2+Pz3/+80zKjKKuwNMZr3V7FHlB6RfwIAgIlKLbSSmtS6EqII5jvvaNr/NdDL1A8sqtm2xtbZNPCyfAYiyn8zkPTk94cHbM6O2ajW6XGztXGPT7BGnC6XjEdn/AcDjgfDpCBYqtrS3OpgVJd8p3Pv6QL33mLf7sz/zz7G1u8/v/+Pf49e9/g72NDToqpBvHHJ+d8carb1GeTzg4PWaSz+h1Y9a7MUIZXrl9h/PpmKqqqXVJWTULpTPaaRwThxFVURGpiL2tK0zkGWVRYuul/GzzoE9mMx49ecprr73O3/ulX+L3P/gur7/yCj/95hd59P49vvvgLoEUhFa2qbcyy+mGLqtZ5gWJClgfDHh4dx9lDFv9HQ6P97m5ucPP/ewv8B/+X/6PdCNLWRrk5Jzzp4+5+61vc/W1F/mFX/jneeOl13j45CGz6YR/7S/8K/z6d76OziHsxCTKMQNGQcismrpS5iWgqVKKSCWOkhUnRrW1tcVkMmF9Y5Nf/d1fJ1uM+Zf/xJ/h5/78f4d/7X/y79Pf3XR97aVGqZg0iJgZg7QCJXCpf6kYdHugnUKX9JDRVqnxR7T9yA12o098GSnceDGNQTHGEChIkwQhHENP1aQY2kTf87eVktyn3vxa6NJhR8ecnJzS63Xp9/vcunWL+XzO0dERZVUjsESRR/uaZYtKk7pO44DZZEqmFnQ7Sbt4N4uVUm7CmibtpRqZztX6rmzJWXZ3d9DaMJlMyLMFVeHGJvTtcVVVUyxyjouSwWBArxeT5wWz+YzZbOZTeO5WXhbreO5Y2B9QQnhmoC8bMPdK09e4GrUvMWSXo9KmzejT3Ccffbb9wK69TnoXTvm2nLLMOT87Ie6V1HWB1rXru1SAL1EYLHKlnn65jPDDDOsnvdbc71UjvXrNl8GSn1S2+MSskXW8AAiJlYqiqvn+u++wvr6JurGO8FkUC5iqdu1HWrdKZc/rK798WZfPY/U+5fkCXZeYcsGRClwvd+BJgozGmBqta+aLgrrS7ecapsCGsdAYTRiFF57VTzbW9vJUe/Z9d+Y/aKflvbWQl4VH72ryRe7S4Ccn/NiP/Rg3b97k4fEB7z2+R6fTYW2wThy4jJXxnRn9Xo9yUbTHjcKIwWCd0dE+e7tbdALBIA7Y3N5GKQd0+vCd9yCMOBif8+joiJmuOXhwnyvDNRazOcN+H6KApNdBKMlkMsbqmm6nS6/T4fDwEITg9t4NplnGP/jd36LMFvxLP/cn+NnPfYm9tQ0ePbjP/v5T1voDECERklJXWAlxGGKKgq3tTTZffIm3P3yPKAyxukZXmiiVCK0JVEwSx45P3BiSuEMsQ/a2rzCdTjh8esDGq0PCIOLqtWvcvX+P8WTKSy+9xN/66q9DR/Fv/ak/R7/f54MnD/ml7/wub774KuV8TmYz8rokL0p0WVCXFVd2dzk/O8Nol1Y//cYp+SLn1t4NHgUZj/Yf83f+5t/m5HzO9S+9wsvXb1AvZnSDkJ/8wo9jkdx/dJ+/9dHf5eGRw0ycPX6KiQNuXb3KIO0w6A/pdnv0ej0m40k7a5p2LiceFCOsJq81YaAYrA85K3O2d3d4/4MPuH79Ol/+0p/lcy+/zr/+P/4f8NK122RZTp7PqaXAJgH56JxEKAfUM4aq1sSRIo2TpbPohZo+qTPlD7r9oaTELwNswKeWVZtnBlyLiAoiZxiEX5hVSFWWBMpxijd1rOa4FpfRsNaiK7dAGfD9yBeBNXWtkdLVEqRwyldKguNfcbrZi0VGGCq63Q5Xruw6etEsA59yrHRNAwyDZW3S2WTTGrBllIibJcalRowfCpfuX9aFGwMmhODk5JgwjEg7CYO1IWdn5xRZ5lDruAjWWTvJZDJbjoP33lbXwCamEq78hBICYS14ilSlHJmGuYSi/6RNAqFsqFJdPdmRvlpvQoUDinlyikAErpQhmk76pYNmjUXYZYuZ1YaGfQ3hVao8sElY41CdCHSpCYIIY2E8nlBXNbo2zOcZW3trjMdzrHXIaWwTXbl7JMTSgNZeO70RNGnmSlPzbX6ejZw9uY51HOeX1bjcKOBS8lLgMO3LvucminWpY28ghWPMa9LQAoG1jhtda/9IWJBWEUvBwdEImSQIMSQUAdIqalNilSHQEVIbj4itiKMYbAPelG1+2BnUZj4ugWbN5RotqK2jQbUaRnrqMhOe3QzRZFjc2BVF5SRzvcFunNblcyjR9ZIz/odu4uKMfD5W4KJgTwMka+5tc50CQaAC6rLCCkFt4L27d3n11ZfZ2Oij84LAalezjkPiMCHUliTpYeuKutKEBARpShxHJEVBGii6oSIA8rJiOp6Sbm8y7HTpoCinBSXwZHLKk9EZp2XO2nAdKVMKCw/Gc4Y1rHW7FJMF8XZMJwrZ1wZZC2LV5dF4wrW9G1R5zlq/x4vXrrO5tka2WPBXf/FvkyhFnRd0o5jPhRF7wymnYYVR0JUh/TDh5Wu3eXHjGgiIghArLMbWdJOISV6S2IAwdvK9mdVUCq70QoSp6fYHRIHClJXr7okixtMZYRixtrFBt9/j6f2H/E//h/8BO70h3/nOt/jmO9/lcy+/DFpzPDtHaEsgJWkQM04EZ6On3Nx6gdPjMbbKuTnsI9QW987v8+rVtzjYf8w33n+H36nvc+fmLhGKh4eHLOZzYiEx4tskMkbEmiDp0lvbIK0ytjc3iXpDZKhZ6Jqjhw8oi4w333iTTjdxZDDSrRtaa/r9Pufn58wWGbu7u0gleP/De9x98IC3H77D4fSQ7bXr3Ds+4Stf+yts7GwzK3PyInOsm1KgKsUiWyDTFISlAmoBaaS5M9hkMq9Ih5KNSNApDMf6nzCy/CHbH0pb1w9KOfpX2gUdLgXLdslR3cR3F+txDapbEAYhnU4Hg2eKwl6wXo1GdtNiJpV7X9DUXo1P4VdYa6jrhqvXpf8Sr0ld65JVz77hB7fW81bDSvTm1p1lDc9SlKVHo3uaUdGA0mxb2y7L0tU+hSTtpIR+Eapqx1ceBoFHrus28mwid+EXUm8NWiPZ+A5SiFZtqaWebDyNH7Zpg5WOKla0oCkHWHsmamsOuJI2buryAIEKMKGLxIIgwAgPkBJgPVWpkI7ZzdS0Bs8Y3bJ2VXWF8Yt0nKREYeJqu9opthl/f41ximt4Gc3mmhuw4aoUakPWs9qCBRej6OZ1JzIiWuN12bg7w9tMl2fLCc28tixlWptxauhV3XkJ8JSi1kJROp1erHEIVAS1N/wYVzt2WvMaGYZtwNqeigWEwRp/37wP2BjtZX1et45L7cd5dUzcdbg5rz0fwKqzczmD8enr0j88fd783XznKpnL6vdaa5eUvb4ebSQgBJtbmxRFBlWN0RosbK2tu1KDtQQqJLBOdzxMYoKFIi8ysNZpRMexS3Zal+50kbFLhcZphydHxxyWC3JdE8UxVVER+CxRUVXM8oJQOPBXv9MhDiNPw+zWDq0k3U6Xqc7IasPJbM44yxmGEVe3rnB1fZN8MUfXFWm3RyeOmOQz8jwjEpLNwZDdnV1CoYiSyLdwWjQGXeeoKEbVirIoOD8/4/D4CPvqK3SjkEgFSNy6OhwMKIuSJIkRQtDtdpnO53z/+9+j2+2y3V/jm9/5NgeHB+xt7zDsDbj79BGRUBRUSCWJgoDD2Ywsm/Py9R0EklrXDLo91vub7J8e8sYL/xxlUXM6miDShG4YYuYFW1d3SUSALUp0VjCzNeVsBiJhvJgQyNrhOCxEScxoNGNWZARSkFclCD8vlMMABWHIZDLhfDRibXubpydHnJ6dcDY6pw7gH7/zPeJBl9F8xuFHU47PxsS9HuUi92uTq0eLZs33GUWnZGdRwrC7tk6oAjcv0pQkiHhyfPIp5/+n2/5QDPYP2y5wT4uGOWv53sXIhAv7LiNTl77qdjpsb29jTEWe597wuUVN+RDLGUq7rIvbJvJapt6LomCxKFhb63tykLBlSarbvZYLsBQSbepWZGS1br56vrA08FXlkMsO1BNgbN0ak7KsKYoFWmuSbockSdprccFgE2Wzcg4+mmy+tkEmC54xOs8Arj6NsXY7Aw2+wP9eudKm7mpZku1fTqVfyHxIR04jG55r2UToK2OrBKbG9zA4g22wSOXRmZ5Bq9PtI4Qiz3O0MZ66tXH4Vq9/OQ5SLolqVl9bNczNOa+O1/OyRs/c69Ux/oT9Vs3S0pA3+y87KaR09WmrhWNy8+1qTROjsD6L0ji3xhmpNpptzt9Hpaz8bZt7eOkknSEW7TxqxmR17jRj8zwDffnfy+tc/Y6Lo3BhPPjBhrr53Rjo1eer+d2m94GiLIjCEBUGJIFgbW3I5uYGk8mEwAOOAqXYSHsIJal8Sj8UEi0EQS9FnnpCGFw6NYmjluc7DEPiOGmdQBuFHJ6cMhMamSZ0gohsmhHGMeD0zXVds8gWBELS7/UJpCIJY4RU5HnhQGmRwlSKeVVSTccoBN3NHTY2N7iye4Xx2Snj8YiyKhACsvmcuihJwpC1/oBet4e2GhV43IYwTtfAaEQUE6qQ87MzRmfnlHlBqELHNR66tlSspdvpousabUKSJGGR52SLOQcH++xe2eXx/Yd8ePdj+t0O165d4+D0iFxXRCqg1I4LII4cde1sNmNrbUgUKGpdIFXA9toa44krG5xOJtTGMkw76KxAlyXKWkLACkEUBaRRl0mWU1uB9DYiDALXQitDprMZCMFgY53ZYo7CEwzhWCO1sRyfnFDUNaNizjsfvcfB4SGdXpdOv8u0yIl6fQ5PzzifzUm7/RVmNOU4MxCts2qsU7lT+PWvrsnmM8ceFwakUUxX0Uo8/6i2fyYGG+sUiJSAKHKnEATKtQlJgVVO0OAH1fqaB1MJyZe//CXG4xFPnz7h9PSMLC+QEkqvphUEkjB0DRRtitMuiUvkyoM/nc6Q0kXXa8Oh6yu1LpXbLPxSSkdkX4PRpl2RlJIoIVvUrMUxKiVJx0cwU0cOLwRCKqq6JAwFwjiwj7WGoiwodU0axV4FLEJrp4kcyKCJcdtxUPgWLLvklHrewtnUGKuqutCX/MO2JpOgtUahVgxFYxB8ROjJ9H34SOuWrRjrvMg941tBpSRWG4TLYrcLt5QSqyJq7R8UJR3bt61BhA5wZ5y0plQhldYssswxRXkQlkAgg4AwbHDiyzrrEmV/MbW6mhZvxmbVYH9SWveCayKWvxon9HnRuvPYV8lvxIV72uhzC+UMdl0ZsDXG1CvG3d8/Y0E5Njhd67aMgrHIQPpIUyJVc7b+t2iUzJp7ZTHNjWgj+4s0rqx87/MAZZfH78LWHuL5n20Bhyuf/UHHa84NaIkxml5rABmFTLI5URVglSCIIzY31tne3mZyfuZ1oRXr/T6y1HQ6Hc7riS8BKUeekoTYskQFEqUCajRhFNBNE2pjWOQZi/mCqq4JwoiD8xEuGa9AW6ypiZCkKmSR50SBK/cs5guU0Aw6fay2rPeHVFZxPhoRRhELXVLFFiVqdvrrfP6V15jNp3zlO7+NsTX1ZE5aW964fodb/U3qqiKSikHSod/pInEg37woHDBROZnYbrfD4XjC+lqHxXyGsIbbN66zNhhQz+aEDUBKO4xNnCQYY+gPhtx7cJ/x+Jyf+LEv89HBIb/ym7/B59/4DJ1eysn5KR/dvcu1Wzd4enTkardWEAUR167scfjwI4adhG6aMJnOXUp6bY3J4oy/86u/zNFoQpAmlEYTxCFSCN7+6H3yImPQ67FxdZM3X7hJmFwniPvcP3zCbH7O7Rdu8XS0YDobk2UZ3V6fJO0wHk/ZGqxhABUE5GXFyfkZo/mc3vqQv/PLf5/pYkav1ydQXe59/CHXX3yJ9z66S4WgOxiiwphisfBz001gC75k5bkGrF90taXKS777nW9jtXYlWL8iRsEf8Rr2p9lcO1JEluXUdd0i6YyxVB7l+klEE0optHEPZpZlnJ6e8rVvfJtXX32ZF156ic+8GTMZjXn77bc5O594reuLvdpKCZ89XqLWpVIkSeTb0QxF6RDs4Dy5pte6WSSKukAISRTFbU2zLEuafleHkHepTCEE165dI89zDg8POT09o6waGVHHJObAMY4f2tSGuqhI04QmwmyyAavRcXP+DtxwkXmr3XdlLJ+3EP6wzdT6QuR2wXD5Q4iVcE0sX15+vy8DqEAia4dlUIHEqouGoIlQM4+4D8MQFbhMRl01eQ7tCC2CiKq0LVWnlM4h07WT3lQipkITBOpCZLa6qDfn2BrSlfu7ev6r/24Q74CXXm2u2o+XzzRcMKo8x4C1//TOojUr+zpglzSBk3i12kXX1tXR/eTFagNKoIQkr2vKurpwbUpITCtjs3JfnnN+F07tQkT8gw3x8yLhC38vv+hTfe5533s5zd78bsBt4LoWms/VWmOF5WwyYjoecf3aNV588Q4b60NPAwpGCW5cvc5Ld17h27/7NVdiKAqyIscaS6hiUinJiwWddIAII7StCFVAHEcoIIyc45XnJaPxhA/39ykBDIRW0okTButd5pMpNZY4iamNZVZXYA2765voquTG1TWy0nD/8ITBWp9Cl0RZiOp2eHS24L1f/y3W45i/9NN/gj/+2c+TT8acHB+RdFPW14dk8zmmrugmMdsb66yvDcnOF+xc2UYIMFWFrAy1LkjiiMV8xsbakJ2NTR6f7NNLu5RWgHb9yDoImc7nHI/HzLIF6+szaqO5cf06X/7CF/iP/zf/Kf/Bf/g/4vr6Ou+9/y4fPPiY1z/7Jvv3HlFaA3WN1ZpAwjDq8PD8HFvmXL9yhUe2Yv/olM++/hr/t7/6q+hgDZFE2DpkPJ1wY32bjTjlf/bv/nsEUlBkC3a2NhmNRtx/9CGT0ZzTowOKOqOqa+JOxN33nzDordPrdCnmGUkQMjo744XP3SQrch4dPuX+00d0hkP+xt/4a+zcusqwv0lV1Rycn7J38ybffvttTs4nJN0eURwxmUwRRrt5YRxiXrIs8UQqAOvWw1Apbt++yX/8H/9H/KN/+7/n56WmrFzd/Ee5/TMx2FobqsoZw1YGzVrv1Tlt0cUib9PWlyNtL5rVkppEgeDexx9z/+49up0OV67s8OM//uPM5wsePnzI8ckpiyxDBcq3UHlZRtEg2DW6dvUspSRW4WkEm1qvi2tXF44GhGOsAU2LjG/4yl0rsSCOAhaLBe+++y6DwYDBYMBwOGQ+n3N+fu4X2JXjStn2vzbGWBuIQonVzXdfTONaLi56l8Utmt+rBryta/6QrT2uZVkbF1wiVWnrGc/PBYvmvJYsZqsRbnsa1rbazRavwS0ch7jFtvXmQEniOMIiSNIUKQJ3v5pUcTM2HljXXHtTj72obX4R0X25dHBhzFb2b+fFaj2HVSP/CUlx4VntEFyG6jsj1NRgLQbd8tQbn4UR7VgaR99rZSt0UZUVxjgub6NXHQ/aMV2N+i9vl2vyl2vGn7R9khG31rbB/Op7Pyjdffm1y/s3/f4N4KyqqpbLoZkfxlrOp1PuPbzHrZs36A66lGXOxtoQ5Ye9MgZbG7oi4MaLLzCdTlnvDrl39x6VrvnCZz9HOZ8RdhJ6gy5YB6ALlaSXpJxMJ1RF4Vo+O102dnZ5uL/PZDYjjkLWh3221tfRZUkvTbl+/RpHpyecjMeUuiYJJUmUMikKOkFMZTWVNXQ7HU5nM6LNGFPPWQ8T/rk7r/K5Oy+Sjcb86m/9EvPJGCUEL77wAlEkeXKwTzdN2VhbI00S6rqmqEoODg7I8oxYSig0IgrQdYUVkl4npd/tIIXnUPDKg1VVI6IY0enxd37x71JUJTubG3zp85/j+o3r/O7v/A5WCt649SK/+Mt/jyAQfPFzX+B4fMpUGdZIGZcztHDRbWgkNgyxVPTiBCECTqczbr7Y5cGjE166vYXNDakKeeXOK7z5xmf43d/6Tf6ff/m/4OzslNF0TNyJ+dzNl3nx5evUJqQqClCGIJKMj8aMz0fcuX6HXtpjMZ444ZZej/2TI7753e9wcHaCjQI++s7X2bi+w3g6JggCBoMhVzY3uXvvIYeHR7z2+luMZ3PGkzlr/TVOjw/odDptVlQGynE7BE4YpvK8GIXVjCbnnJwdI2NF0usio5CqKBmuDT7xufmDbP+MImyBUgFlWbeEBq6ma8jy0pGNhIGnmHwWxevqtEsGG6MtYeD6fMsi58njxxyfnNHvdej3+6yvr5PlBafnZ4RJ7FIb2iB8jUep5YJYVqbtwWzTbFXt64pLUhMVOoac2qe/a9Ow3vjUnjXe2FoSn1oaj8csFguSJKHX67k03Pk5eZ659L6SWOsjB2OJVYBSxkVc2oBdGh7le8SDIHhmEb68+K1Gj8Y27z+fdezy9rzF/ZnXvNNgrEEKdfH7oQXcNR7Cs4u/aNP5wh/frhh500RX1jlGRtceyIdn/zLUGpQwrrbogWa2QWYbe8EwrzoLjfFusjyrzuHlyLs9xsr5LdPhvobNRaO/fFuu1I6bOc0Fo+2Q227OtN/j6V+sEc3otAPrkOtOFKaqSt/L79P8SmJZdlk04ydWcBuXo97lOdv2xyfLec7uvuhhV19o8Sl2tVZtLzrcn5h1uPTe84z4Kr1oc48aTAI4xypJYqbnhww21rFKEKUxG1sbzM5HJKGryc4WC3RdEyCorCUrKzb2tigFzKsCGQaoIKAz7BEohRQBcagR2kVUURgQqg5RmpKVFe/fvcuDxw/pD3pOdzpQGF0xn0/Z2txgOOxzcnqMxBLHKUU5RwB5pdmMY4x0Wax+t8skz4jKAFBkWvDOyTG//cF7DHopt67sIusKM884efd9fipIiZPYSZ5KhUCwWCw4OztzXOqVQQeCvDJ0hz06yhDkDRmOJZYBvaRDXVcoKwiTlHFR8K0P3uebH75Pp9sl6fc5Oj1DWct8NOHNN9/kF//+3+PGzWuEccBkPuXo9NSXHR24tKxrZlnG61euMy1yHjx96ASMrOT+k32Oz79Cmg7JQxeAFHXJxweP2B+fEgnLz//UT9FLErIio9AVd/ZuEKiao+MJ5/kULUqSNOWdd3+Xq7u7dOOEwCzXiM29Xb769d/nyeE+pdWEKmX32hXiTorRFYnHHozHEx49esRLL73EYj6nyDLQmul41II7lZQth/8y+HDSncr38J9PRvxXf/m/ZJHnkAWIRYaczIj2Np+Z4/802z8Tg+0WNekjQ+j3B1gLVaVZLBa+HUteQBivLqQra797YKUkUK4+ZOqaujLkRUWZO5KRJEkQUtLr9YjjGNcl5etzzyzk7r4sObs98thlIdvFuDZOaEIFrk8V4drUtGlYyIRjbfNRuwDqyrXdOBUXpwbW7/fQuqIo9NI4WVeodOIkK4Qjzcmt1F2baNJdlJtPjcRhs6Ctik8s78Ef8N4J35Yll4Vnv8S2UefSsPlfRqzameVmL/7b9U2LNjJrInKMbc9X15qqLCmKklpr3/7nImt3QOtLBbUzdW27nxtbY61PZbsMSeO2rCKaf1AE6v9w4KxL4Enrr0E9h/G3NUTt8QWr6mXNebjb78fLNga7eVZWJ74zpE3U7Mgx9PJt6UoJn0hB5A3qhVdXnjFjlziHyw7Ycjo+W9f/QanuTzLCn/TZ5+17Odu2mvFwMp/aPedJTNpN0TgwYq/fcwIeytVIEQ4AGXhw6ThfUB3uo5VES8F79z5C1ZrB5jpBbZEo8sA5i500ZVLmLLKKoqqYzOeUiwV5vuD61V1sXSEx1HXhsBfSBRJKOmBXZWCWLdDWUpgaFUUoaoq6JI6delfiAZZUFWQFL25fYW9zg+3hEF3klJ2MTpIQpYnj7RYO3+JYhpw6YBi6ay214STLmJ0cc1bN2Qm6bK+vkyYJ3bRDFATosmppmY/Pz/nad79LsDbERCEHkzHm/ffJ967ypTffxHz1q5hA0k07ZMWcs/GIIA6p8pJcumdOW02hNTv9dWoheHz0lK3N6wy6A755+g7Z0316/U1SodjddEbt5OyMtz77FsdPnmJqTT5fkOUZuak4OT2j1/Ha7jKgNhUqCHj69AlfePNFQiHBKyzWwnJ4esyjo31qYTAK5vmcjc1NhHSaEmEQkmU5h0fH7O5eQamA6ewUrV3b8CLLiaT07Vy+BNYCWn2LpJA+GDF0uyk3bl5n8P7HGCHRRYkpayL97DPyT7P9M0KJ41tRXKQbBCHdbo/NTU2326EoCrIso6pKZ4QFvk4rfdR7kQBDCdHWGqyxKCmRgaLIc/KsQEiXOl3b3GxTuk1adjXd54yRP8kVR6FBSMNyoai1qysHQUCcJMSei3Y+nzr+Xuujd49obPtfjaEonMhAEARcuXKFQClKITDWCc4HyqFQndhC026k/PK8XMyaFqYWK9Sc70pG4pk0L0uD8WmNdlOmFY0mtpQu9X8hEvQgQE/t6q2hz4aAUKup45VosbUAz0ZW7v5Y19MO7b0D4csqVZsJCQOBkq51r+kyaFPgwgEDDdaplOGu5UK70iV79rwoefX8rLwIFLtYhmiOu5xbygu4ILzE5YUSi4+iAdu8LgRY6bMqEosn3vERvPMqHYANAVVde851928pJGXtKFGXmYJnr7O5BRYQ1np+JmeMm888YzgvGc3njVEzqKuf/zR16x/29yoyvJlvDQgoz3OMMQwGA9JOh0WxwLVwOqe52+kQR5E/fwFKIaOAJIz56PFDxosZvU4XKwXffe8dgkDymf7rrKmEJIjIggCja7rdDlG+YDyZkFcV08Wck/GUThLTSROq3GDRDjipBFVdMp2OUVIShSGTRU5WOAyMDRQqDBHa6WQ7wBJIaR0ntdb0BfzYiy+zniSURc7MOlKl3qBHqTV5UdJP4pZtUPoxCYKAQCrOsznn+0+ZTCc8GR/x2b0X+MnPfZ40TekkCQrpas5xzDwreLR/wHv377H50osutX50xPT0lPU0ZXd3l6PDQ37m5/8Yi8Wc0/NT8rKgtzbgdLagEIZIuFbN2lq6KoYo4mw+4fpeTBzEnIzGBKmi292iLwxXOn2EEFSTGW++9hpfPx/x6MFDdFGQVwWlsMzHc67urtHtbGCMk02typIsW7C5to4sHIdAEIcYa3jvo3fIbEV30KPWmsV0jKkqMIYwCKnqmslkxnQ647Off4V33//QlQOk8m6sQanQ9enL5VpmTSNqZEjimEVRUpYFw96QP/bHfpq//bVvsygLgtqiwphBp/vcZ+QPuv2hGOzLD+NyUVz11J0BrqqKp0+fIoSg0+nw+uuvI6Xk3Xff5eDgkMKDQLTnY277n+1S6rGqtTNwPhUIeNDDMnWWLxY8yXN2d3faCFi2C+yKUROCunaggaqqaOvaK4ZECOG5m+s2VdqkuYNAMp5MqMoaW2vCQLYPT1nWDp0u8CT1rs6EdfGakgo8F7PEGUUr3fc1WYcG7W0BpCDyfea26UMHrF7+uzHgz9YhLZ+w3l7YViOYtgTh29SEXIl4/IRu7k/zGWONA+EEIRfcBOsMGngAIPiHwquaeVRzIxvanEsSJ0QqJApC8rxsxx4VEiiBVK71SYmUJG2Y4jy+oMFLGPe9YHgedadoavzeWbtQb79sfCxtn+bqfG+64Jt0ssMk1CC8w6kEUkmfuWmcFx9ZK/95Id0C4sl7jPEG3afkhR8wi6XyzGIX7rA1+IFc3nPvNIjmFtjVd1shVTeHrPXn1hzPemd3xWAvfbOL26Wo3J3n6iYuxvfPieQvHGN1TjXMeThnvSgKtDFMp64LY2d3h9oagigkiSKElCzmC0Ip6ff7JOdnnM9mFKamklBWFb/4K/+Quan58pufZXdtnVmRM88KnvzaV/i5z/04N6/eII4ipBAkUUS/1yPPMtI0pdKG+48fc2V3B1NXSOHzL9ZghKWoXV9wAxytq4rIi44MxTphHCNKB6ZECSIlmeY5CAiigGMFf+Vrv8FkPMKaGltVSG3oJx1+8uaLFFVBGG2QpClJHJOVbq4ZHwU+PT7mWx98yOMnjzgcn3D2ylu8eOsm/a4zJspCZAWdTpcHDx/y+Ok+VgWMsoWTAgWubm3S7Q945933KK0m1vDh08dYYVkfDpjnBTYNUeMFMpJY6drkZuMxKgyRYcDp6SkPHjzk5GzEH/9TP8Pd98+RseX+/iNG5yOenhzz4X++z1qnyxs3bnP9+jXSToe5LXnt9qtU5ZiqcI73PFuwf3DAYDCgm3awumZeGxZ1wdH4nAcHT0m6HcI0RmlNr+5QLBbUi4yNvavce/CIqq75zJtvcnB0xOHRCTt7exRFQZHlTifbCBZlSRCHzmH2gM8wCB0Vab8HIiMbLZjNZhztH1LmGWEUkaaSjgz57FtvXn4y/qm2P7QI+7npxeb5E2ADSZlVyNDVn8aTCQ8ePuLDDz/k6tWrvPjii7z11lsUec7R4RF3797lfDzG4kRBojhAxQlBp9MurqvfveqJAx6RLQnEEiHeAJxW6Twjr20dSEk3SRF2WUPF4li4lABtiYIQtGF0NmK2yOh1Oly7cZ2t3StMJhNGo3MW8zkIQVlVbRsZQFVWSIGnyhNkeUFVFQTScSBrozEeYBMEEcaUhIHwKRsLaNAgjOM3FtrXWIXwXiA0RByX6UodqcjlVqIfvDWI3LIsqaqqVaJ5nnPWto35ew0grStJqKAGkYNYAUVZzwDnGFQw2ncKKGdcNA5JX5cwX8wJVIQ2UCwWIAxhIjk8PUMIiAKnwJNEAYts1jpiQRCQJEnrvAghl+lia12TLLT94BZ54Rqb/n9rnOLbZS1sBBjRRNmuz1wAUqwKT8wQUhGFrhsBK/1c9HOrreW7F6zVaDRGCUIkVCUyEBjhCVJq1wJJFFPXBYESpGnsBA08U5XAOhYa0Sb/l88movURXFJCt/PcPOfeNkO1nBRea2o1T97uePlPsfK3XXmteWWZpVl+14pz5A2+xLW2GQtV7cpPZV1xeHiIEJb19TVEpCjDAKqKzX6PjoQsW2CjPkUh6CU9hDkiUQH9ZI1/fP+3EL1NdtaH3D84o5prvvzaGzwePWY2G/N3f+u/4Z/7wo9x88Zt5EYXO8n47N4O7+oMoWvGkyknwrKnLNicZJiiAkVdacrxAltaqrpESMNmN2Y9SXl0qHn7w3t84bOfhzDmrDxDa0tPBwRWYUOJrWrsPKMuK65vbHB17xYv3b5FFAhEVdETAbNa8tXf/2WS3k06aYqsJMVihuloglBw9/ic7z5+ysFsxBfuvEYZSU4fPSXLazodSV7V1Gj6ccy940eEUcCV/ibV0ZQwCYjimG5/nbv7+5ydHbG9M+AXfvZP8Mu/8g/54z/7M3QGXY7PTpmenVHVFWEQoKwhynLCquAsnLDVWePx0Zzf/se/wcHZGb3NNe598wNOyyknTwp+4ed+jjs/93OM8jn/5p/5c0QG3n/wMVWes8gz7p8eEpQ501Jjq5J+EjILY7714Xu8+rlX0POKfpIwyhe89/QRHzx6QMcIOklMtsgQtaEXxIxsASpkkZV0Ol3wGKX33nuPV157nacHR4Ck2+mjdU2xmJOkfddXLgyhEpjaYq2iWMyQ6woVlFg0lYn529/6bYyYUczhrHDtwJ39R888Q/802x+awW6ikSUHt3Q9t8Z6KcQahYPHG2PaKp4xhidPnnJ0eIRSgu3tLW7cuMHP/twfYz6f8/4HH3BwdOJTgA6gtvKlLlp9bioTR88ppUNct0Z4uXSohhXIGjCGebYA2bQNuc464YUtamvIywoZRgShotuJqeqCjz76iDRNSdOUYX/AYNAnlMrX5q1Po7vxKKvaR/HOyCqfWq6Nvtg+5Fd0rTW1hsCn2FfbjIy1GF27lNhqDdlezCAI4Z2GTxFdN5tTFlPtT4PUXXXKmn+7yHvVM3MecRN5uzo9ba1e0NyLZcpcelpabQzC62MLgSezCYjjmDAK25aeKIoIwtBnJ5a9zatSj8vIGocL+ARnRQi1kgVoXltqkMOzrUbtHPMfWDKXLp0lJ9ihkXbFQWwT0KK1e813Nk6iNY7BrMkmWRdmt+fVVPfrqvbvW6RwWQeXwarbNJ5SAdooX+9vT/GCsXWZmeZWWHiO0W42Q0Pk0py3uWhk2+0iTuR5f7NyGo2B9v+48JojN1neo16vy4NHD5lOx7z55md45ZWX+Pjux2ijiZIEGYZIFRJHCetr60gBdVmhpCIvK+7v7/P+3Y/pD1wqXChFGIXEccSgv0YUSmQQ8eTokCeHR4go4oXNXdIkphPFvPjCy3S7a7z/N+9jegmbGxsYL6IiEVRCsrO9yXQ6pd/vooTCVvDy66/RUzFRGFJr4wg2rKUoM9I4Ic/nlEWBFIK0lxJGIX0ZMbQBsqihtmwPe/R6fSdEJCO8605dGaIg5Wg65eNHj5icnXN12OeLb73Jk+NDXt/eY3x+zvHxEZWuidMUIwqmxxM21na5ce0aL96+xSKuODsfE3cEnaTD+noPqyRf+cpX+Lf/nX+TKFHsHz7l6PiITjelHtcIAf1en3kmmC3mTMcLXnvxFb7yG1+l0JZur8dup8O1/gY/+9mXePzeR9y4eZOqrvna136fh995m1Qowl7C5vo6SZoyNiWday8yrwpsGJKXFQcnp2STGT//+mdcKSAKOZ85AFl/0IfpnCzPCKIIKS11UVLVFYkvE/R7PQ6PjnnvvQ/48pe/zHvvf4wMAmptHPXoSmYxUAG1dqp0nSRl5vvbu70eRpbEM0MUJaytrRGGIRZHZlVVlcug/gi3Pzxq0ibt5h8009YhnWcvbCOF6BmmhOt3jqLA9f1qV889PT1jPl+QJA9Jkohet8tLL/bRxlDWFYvZ/MKCs4oehWW03RiL5W+zbNXBpwNbvm3XOmQBoRS6cNSlyKblyLVvhTjkuDHGSXMKiJRDh06nNUEY0Ol1CERz85uU4sWFqgGJ1R4Vb43xjGhu4W1aC0ajs/aatHaGT8q6Pe+L+VA/HpcM0ycBqj5pu2yYLtd2V50DIRp4lFimuH3sdOEcaOqyxhkt4WuzPg2s6xrr9a1p6s7+EEVRImVAXVWUnlO8vS67BNe5tLQCcdGIWC/d2Xzmk4xGM4SNO9egui8bpNVodfW63Xu0qV67khJ+BgDoa6oNVkIb46QewbcJ2gsR/fIkfSRuDEVeYHQzfwMHbrROhrNxBHRdg1hhXlqxifbSvHRc9D+gX39p1d0/PHn95cQ3uKS8becj7X5i5XXaTJZdptpXt4Z1SkWOktJnlILAdZMMBl163ZQ4DhkMeujTI2xVI1VI2ukSpynT2ZQ7ezdQQtJJO+RVxcHj+5zNxtx64Q6lNYzHE7K5A752Oh06sSLXhkk+pcwKQmvZ3d1lfn7OsNMjEoo6zwksZFXhzl27nFCAwgQhW2sbTCdjwJLEEUknZWtzg+nhGf1+j9P5jLIsiEMHYO11O5zkUwIVEEqFLitOJiPG5QkfjvbRZY7SmltrW+xu71HkJcO0SyeMqeqKvCwY9AZ884OPODw/IwkUr1+7zkvXr7LZ69JTMaP5jLPRmetIMILjxQwUJHHExtqA29f22F+cUs0yyrJCV5Vrk33yhOvXrwOWJ08eM8vmxHFMUddOOKUsXRkJgUbS7w+ZzuecnI/orq278lWheXL+iFkKx48eERjL9vYWr778MlFe89rtO4yyGUkUgRQU8ykCCK1ARjEoSaFrhIm4trZFL0w4nU84HJ+RFQW76RZFXlKbmqosnYCT1o54ygiubO9w7/59yrzgxrXrjE5HLci5waJEUURZla290sZ4bnIXlTsSnRCRVWSzOfNEYrSl3+9zfDzBGkmvN6DbSZ///PwBtx+9vKavDwpsC1IC2pYi2S6mvrRvtPtp8oJY10/b1HvKkkleMGFMksT0B0M63ZQgioiCkEL9YMXRZk1xzsLSiDeL72p0uPoeQBCGDAYDVBA6cXtdeyapJWrZB/XuvIUDixlt0NohlS0g/E1rqVBXjGCzAC/XLdtGwE0aWkpJmqZYO2Q+z/yY2QvRtG0iaVbX84vL52pkKJ6zJn7SZi99tgEeidXBZXn/VpZxmijN1VOba3YfasFQK/uvGndjfJ8G1jsEAq1rn2nQ1FVNVdVtnbtxBORKKrsxhm56NaPzvCiQdr+LTtWydHDZyD7v803a+cLgieW7S4dCtvvbNrvQzqpnbwJeZ9s2Q+rH1OMXdG1Q0vWHpmlKWSpqUzvD336fxZhy5d4sv8qu/KOdI9Cm7C/7ecvx9J/z4DexHLblvno52drvbT5r7crrF+9P+wnLitvnetWzvGA+n9Hrdlgb9qmqkChS1FXpMnXakXfESQoqYDKdMRvN+PE3Pk8cRSRxwmg85vHJkRNviBOErpysuq6xxulV59MSaRy625SGalHQTTsUkwlb6+tEYcB4NkeXFUbE6FoTCUknioiCiIUIGfZ7HhDm1AnXe+tgjTv/ToejyYiqdERJVaVRQnhAp+sDqOoaZQKG3T476+voqkDqmq3hBkkYIIUiMAplBVY22SvFvcNDJlnGdi/l5tYWg05KaGEQpFgBJ7MxVTYniRMm2YKyKphOx+g64NbVPerDCllqzmY5RZ6R5wVZWXHz1i0+/vhjDBVBHBIGAeP53GkFGO26Y6TCoshqzYMHjxCyQXgrAgXzsiTAyeROJhO6nZS0m1Drgs6ghw4c7qU2GqZjQBAhsUI5mlUBG2sbbPWH5LM5j44POZmMkIHj11BRgC0rx69hXPdJHEboonQKflWNtO6+H5ycsb6+ztlo7NYP6RD6zn5JsLqZ8O0zX2svbywlWEtdlZhaE0cJmAlVVRFGGqX+iMtrhmFEVbsUnmhSpKv15XbRdoujA0c4wIwjQnAtOspHFFI4ViwhBFVZcLB/4OgBe10GwyGdNPXHWy60zd+wNKaIiyQsy/eXi7cDSXmAm6XVAE6ShCzL/E+O1rXTo8YtotK3CghcGkT6Grg2lvlsvqSn89bd2mVd3NVULxpDh+4UVFXVamsPh0MGgwHG0LavWPB92EtaR1YCsCYbsHqdy/vwKTfrYFPGOqnKpcG+uE+7Uvv1tjHyzXc1qGNW7r8xzburqWZAur7j5tY1h72cym+AgQ0AUXmQofDpa2McdW1TG2+chOdtDrXtnafGYLUOHq2BWUbH3tS2NWzRZoygsV8Cg/SZlaZm3hhsCw2dq49IXJagGVI/dl7etXFuV1w7f97NGPuOaa1dd0RZOKU549vYvGOnPWHRMhPw7Hi09+5SdsUPQGtkjWkQ7zxr0duD2VZkY3nWqwbcrkTrlxxa75g0rTPGGLR147CYzxiNznjlpdtc3dthNnM8zmWekWVzx1IVKJJul6woOTk6hso40h1P+1vUNUfTCVGSkAShM9JRRBKHCAGdOGV0cEhgYNDp0lEJi9ncRWtBwNagjxaS+UlGUVV0VUhV1PQ7Keu9Ab1Ol5GctGA1JSW9TpfNjXVOphOyxZw4jqirEl1XdDpdzs9G1FYjjPZRqqGyhkEQcmf3Oq9cuw66Bl2zu7ZGJQ1Pj58iKktd1YRphAoDZouCx6cnlLqmlyas97quDisF3Tjmys4Ox4sJTw73XZvadIapC87PTohUj5tX98jNgkEYIw5OGU+nTv0tiklFh++//S1efOm24xkvC4qiQKaOllkKQRTGSFHz0ZOnfPvtd+h1eigkuqxdD3Qn5urGNtP+EYN+jySO0XnF6dkpp+enIJqSlkV40GmkAkpjKSsnJXrr+k3SMOLe0T0eHDxhnM2J4hhdlRjh1ouqKJFCEkUJMgwYzzPGZ2eEUhKHEdOxU6Tr9wccn5wjlXQqZlnm8Uw4g49AiGUW0xi3/isVEIURSkjqsnJlUyGpqpLZdEG380fcYL/00ouMRiNPEpJRVg7BHYbK1WibBUBKGo5nIYQDhUmfPvPHah7cpj3HIZSd9nFRluR5TmzjC2vF5cjnQlTUslVdiEMvkC407ThKuQnz+PFjOp0ua2trbGxsMJ/POTk5YZ5nYPC0nZa6roiCsAUqCZy4edBQhnra0dVztdYZwjiKsdZ6YJr0f9eEofVOgeHg4JAwDNjY2GA4HJIXOafn54xnU0AQhaFvb9EXjVIT3V2oY19C6P6ATSqF8HqPq3Xg9vDNBPbfY8D3hFva1qX2vqykQFdPrvlbgPHI7aU3a7xD05QUBFEYEQaufaPBSFxwCtvU98VMxmoqevX11fr08zjDnUOwym3fHE8tjylcPNSMiVgdd7yxXvlugyVs6vqreQWxZP9rv99H1sYYpFLOoQsCJ68aBYRhgEVzeHjAO++8w/n5GYs8QwYKXbvSUVVVGGvpxmvL47ISJNtlnblNNV8y2JfHLozEpflsn/t3lMQ/8P3GUWu6AVYxB83vJRd84dpo+l2++IXP8sILtzg9OyEIlMtCISgrTSeJSdI+YZxwsH/IvQeP+MkvfoksyxBKEsUJNTBaTImikH6SYsuC7bUh650OSvhnqqjYW9skCGM6nR67G1s83d9HVwVd1WOaZZwtJqgkIitrFouM9SSmm3bYGK6xmGXM53PmsynDYY9eJ2XQ77HQ5co1OVrVTifl/PQc47tP1vtDYhVyMDpjXOb8/Q++wV/55q9h6opBHPP6zlX+0k/8cb781hfoFJayqKjEgigKeHx4yjwrwGoahVUZSkQASS9FxSHykWR/f5/5bM7jR4947eVrdIMuoUhRaYoMLfOzCYt5zcHpCXOdMZlMePejd3jt9RfZ3t0lKxbM5/PlumsdRqnf6VJWil//5j+mxhJYAUXJoqpZLKbk2YKjjx8gjOXKlV0++8YbbA3X+Uf/6JfZ2tnh/PSUWtcEYciVzW2MtaRhTK2dc2CN5eUX7nB6dsbvf/ebjKXG+GewKkvG0ylbm+sUi4wwDtje3WF8ckaRFYwY0e10KSrNwfEjfvJnf57f/OrvkZWFAwEj3LMpHD9/GzhK5dTV7Nwp4nnnVQJRGHF6eoq1riwVhiHGwP37959ZT/5pth+5wRaB4pXXX2vrrsfHx9y9e5ezk5F7H7eeC6mRAscCpjV5nvvasWoXzTAM2+iprmu/UAc+2nM1uXIlsvlh9dkmxVzVbmI5o7xsVWrS0Y0cZRLHaK3ZPzji6OiINE0Y9PvceeEFTscj8rkjh6+qCiGdelgthBeY8MhctVpXXZ6LM9YO4Xr95k2shdPTU05OTrDakiYJYRh4+ruaIFCO+/bwCBWolhAkTVOyLFsqGD0nYloFpzV/f5padrOv9ipGl41+eyFtdGQuRNbQZnAxQiK99FwYho6FaUWTGgTGn3tZllgpiOIYISwqCFaY0Nx1a+PpZIE06RKoaetUaGMIhG3rvxfKD1w0PJ+2pr/MxFx09i5usn2v0Zu+4Ei4DjdqbX1NrPQOjCei8WRCsMQGSCURWrbo9uY+SulaZzSOy2DY7/LSnVsM+90Wa6Eb+Vh/j5QMKPMl13gTZbf3yzpHodY1xQo3dzuvLhnnui6XHQH2YpZodcyKqnzuWF4c14vO3MXy1dJo12XJ+fkx2zub/MIv/GkEmg/ff58333qTtNtjMpshZEg+OeHqzZeZLnImi4y03+flV14hKzOSTorO58yLgkVeEAUBwyhGJQk2DBh2O3Q6jp3wyuYWW2vrpL0+nW6XbpBwcnzIorRs72wxPzpkUeYknZhSG1fQM4IkdiCk4/MRtXVGedDtM+gPCVRAv9/32SBBVddkeY7G4XhEXTGMUkIhKa0ms5okr/hTL7zJ67dfoC4Kxufn2Czn6OiAOzu3CIOApJugOhHZ2Yi3P/qAMsvpJxFKWUazKVVdQxxzOB/z/jv3effex0SDlN/8x7+DRXNl5wr9qM9iXnI6mbA+6HKlO0SGHQpr+P69D3j08CFhFNMbrHF0coLBECYxoV8jsqIgCAKiIEZXBSdZxs2dPerRjH7aZXNnk/7WOvcePODKYI3Dk32ePn5MN4yYD9YxldOWCKVCSAcQM6Ym7MQwmlPmGbauWev2ef3VV/ilf/QPeTI9gzgkTBKCMGR0fEpnc4AuSuLASYaWecGd67d55533ePmzN5jM5kgpee211/n2t77jnGBjfQochIGaCqNByaDVw+73OhzaYzpJ6uxV4EhYyrJkfWPI5OETJ1aEpdQV8o+6+Mfbb7+HUh/S6XRYWxuyvb3FT/3UzxBIxeHBAQcHB5ydnTKfz3xK3InI17VGa+taFsTFVqvG25fCt71cSh9/UvS0ujWRXhzHpGlE7VOpq3VsYwxWO4CTDhwRQxi4FiGwlEXJcX7C6ek5KgjY2dmm2+2wmM958uQJk3lGpAQqdE5FrTW6vhgFri5Q4Bamx48f0+8P2N7e5saNG+zv73N0cERVVz7t4hdv2UhALnuHXV+2aVHjqyVAe/n6V9O8n5AOvTBm0Gr/PmOooWUgc3Gwj6Tx4yhWDPky0/tMHXj1XBpjDK5PPe12kBJPvUg7BljQlfYENP4+NrXzNq3cmj5//cIb0WcxC6tjtFpfXkU3r6bB/SsrvxtQ4bNjuhzzVXCeACspyxJY4av3zHbLMXFGvmnPa5TWGkeqMdiVrojjiK2tDZLEIeZ1rdFYpFDtuVsLUl3KIIgmI73ibDT5lwsR9tLhXDpAgbsu6z/TprI9RuRC+etSSaZ1EtxejqVwxYETF7/PzXVLGEU8fPSA8fiMx4/uc2V3m7feeoNOr8ciL5kvSuK4RxSEXL12le997z0Oj0+JopD3PvqI4auvsX1lj8X+E+ZZRhxFdKOYnlIk3R4WzbDfZ2N9nZnJicOEfrdHEMdgoS4qrl7Z47tvfwuqijJ39d1emnBWWoq8dM4Y7pnI8pzcGqI4ZtDr0+92ieKY0DpsShi7Nrwsy9CNg5O7+9kJImqt6QiJUZJv7N/jG4d3yfMMpS0vbV3h3/iJn+VqsM3R+SnTbEEkSuI44Gw6RmnN7sY613Z22djYxuaGPJH89u/+FufTCeN8wazO+MZH32d7bQNda3JTUFWaOIkoy4xESD7z8kvcffqYDx98RC9NuHLlJr/127/NS6/cZrA+aOd47ddyxzFRsH90ggpCQhWQ6Zq8LpjNpmhdki2mhNtXubF3le5gwPpwjc+89CpHZ0dMp1OiIILABVNxt8t4PmUziAjDkPXeAKsEo/NzvvrdbyL7KUkQEoURQinqKEBbQxyn6KJkURRMJhO+/OpbdNMOH3/8Ea++/gY7SYfvv/8BZ+MRvbVNlBdesgiiOKJYLOhEPU8L7WiXkzgBSyvx6ZYb7Z9l6Pf77B+ck+ULVBCyub3Nj3L70UfYQlDXFdPphDzPGI8nPH16SCeN6SQpOzs7XLt2DSvg+PCIJEmYjM9bY+OK/heP58BGrmYrlVpJsTapz2fPodlWFwopJYPhkKqqmM3nLBYLyqLE0ihIBSjrJDOFMSxmc3TlwCuNKIfxiMPSGM7PR5RFSVW6SCmKHGK11nrFwNnnLFjCXaPnuNbaoVPn8wWdTkq32+XmrZvkWcZsOnO9x97ga2svtOW0oD4a1ukmF7v8tstRSls//jTBpVnW2C8f78LHvaF0aVZvIJr9fX22qp1MaF1rhFiRuWwM1mrf+EpNW/uOAa01+JKGtYbS187KsvSoYeFr3f77jUHIi61dzjCZZ4xBG9FKRwV6cQhX69bL11Z/NwQsQogLKXFWjJZrbVQu04AhjuM2wm72tN7hafBX1iiajgYpBUEYESUxnU4HowSld2iSJCKKAte1oF1Xg0vFmzZ6bcZT+PO5AHYTq+fsnaxLUe/y78YxrhE05CqNARbtjsJ5cMhV8pXV+vWFkNq6fsWVPZt9nLyqQVqDkq7GXCUxAssHH7zPK6++ymKR8/DJPuejGVeuXkMISxgEjCYTZBBy/dYtgjDECsHp6Jy79+/x1LfcDLo9rm7tkOVzauuMJcLxLFTWsWoRR4S4rILFMOgP2H/yhNH5GUkUsj4cMDmbk5cZ4DoKFlnG6ficrNZ0O10G/QFx7BT4giBw0rHG6anJICCKEurSPROdKCEKAiwu3TqvasIgZr03oIo6SGPYXdvk9PSUa+ubaA1E0jO/VZxPJlzZ3OLazibDXh+MJJYRv/GNr3M0GZF2u3SSgMl5weH0nF63Q14UjhpVBcgGXGsNoQroJCnDXp9eL+FsNKIoK4IgBBzHhBWutTCOY06OT5jONePZjE6U0FEx9aBPkS3Is4xhmjJIUqI44krnCioO6SYpw27PB0yWQCm0R8amUUwZx4QmZLzvFNiyMmP/6T46UtQKVF0jKAmSmEGvx1E2JegMSJOUoqop8pxep8vu7i4nB/tc2dtDhBEHv/VVOp1Oy5ehAufgmtqB/7q9rrMDQrQZYIevcs+k00SPsEZT1iVCCaI0QUwWLLKMNf6Io8SVf/6N0RRZTb7IOD91Hu7G+jpra+v0+n3iNGYwHBAnMXm2aFHhQjj5O2e/V6I6AYgmupRtKq9ZbC9GTn7B4GI01axIUil6vR5hGDlmm7KiqpyYRLP0WHzatWm18gaq0eyurSVbzKFJRVtHKWpwPbcI14uHNy4X8uHNWCnR8veWRcliPmc+nwEuExCEISrwwIsLa9nSIrSpdtGAtFZqx37tlEK01+R2sW1U5AfqEzcjlgv96rYKEHaiFsKv0EvucwHLdiHvCDX1aEd84XiZxQrNp8BLkkoYnZ6Qdlw/5rDXw9Q1kRJIanS9oKpmbG9u0+/1CcORk5u0rrZsWbYNLjMDrrwiljaFJWBveUGrxtnSGPFVA33h4mmckwY0Zv0+jWFEeAEUnEPa8phL1V61xQ1f02PRgAOEkCA94511LVuL2YQTYzFFhlUB2WJKkc2wukJY79gK0XZetHGzNe6GNs9Ek/4T/kpXDWtz33j23i930i14ctVgX9jdidmtfOa5HsDypdbru5hibx23qiaUkl43JUxTvv6bv8ntF14gKzWHJ2ccnY3YvX4NqySjmQsa0qTLznCdQdqhNIYHD+7x6PSYab5ACskgSRh0e8RKYD33ONo4aVbhKI9j68avKAq0rYmihEdHB5xnczpRxEba53hcMrFzZnXNoqpJqorJYo4Rgt3BBr1unziOHR7CWLrdHmVZISykYUQax2TzOQpDP4kdV7UxxGHg69GGbhxjhSAWkltXdllb2yTXBUY4sJc0gqqqiQLBi1f3WFvr0fPZgXEx5+79u2xsbrG9uc00X3ByesqizFnYkpPTM9KdDlGUYCpNJAOwgrLM2dve5OXbN3m0/5h5rSi1xfisZF05NLauLWHS4/BswmRWkmuLrDV1tsBqS7ebcufqVT5352XePzxkbb3P2sa2I8TS2oHthHCZu0CghAPvhmFIoELXzlU55xxjOZuNqY2mF8ZU2RxTG1IlSaTCVpaSin6nQ15UHM+mqHLBlc1dsnnGaDZlUdVM8oIrmwPG5ZhYxgh/nzUSaS3XN9a4Oy/ILAgFgQRtwCpXwAs96Gy+mGMsTuwpjgmVZF7kJGH4CQ/PH2z70bd1+XqV8vVb8F5yXXF0dMTh4RFSSeI0Zm9vj73uHjJwi1hjh1xkZF00Il07iguA/CJ2oQa7oqLEMmBwRlQuuZF9xDabzRiNRqyvr7O+vo7RhvliwWgydvVirZ2oh1TIIEBbe0GZSyI8Clz43nLjs5lOeEQp5WBTzQKtHKVdy0K2sggq5UgsrHExnRSCuq7ZPzhAeuo7h7p0S3obvfn/GqBXS55imsS0X2yFIy0R3nEwdklV2kh1wkpU/pxtVRzFja83aNJH++2AC7/4N8aqifzxGs7uc044RPrIV3DlyhU6qRNkEdYSKMlkukAFsP/4AZubm3zm5ZcxRcV4PKYTBYAhUgVKzLiyucNrr7zG+GzO+ekYU4MKXN3LaYS7dHTj2Gi91F/3o4dziJbtcc2YNslhyRJ17oZ1Sc7SZhIMHldAWxpYki/gWlG88TRItAFT+QWqSUe3RpLWeForMVZRYYi1Qw8d7T8gW8zRZU4cdAi7CWU+pSoyAmmRQYiQji+5cYYcgrx29Lfts/N83IdL5z4/S3VhLviat13Z73l7Nmnyi5+9uI/Bts4hxiDtRTpdKR1hTyAUvSSmTiQ6Svjuh/f5C2WBtYppVnAynVHYHOKYR/tPEdbQD0N6SLoqYpLnfOvjjzipS4gjVFmTKIlU0I9igtClUxdl4fSPpQLpAKTCGLIiJxABRgTsZ3PyImOz2+f25jb3H59xogRH2YzzLKM/6FHoim63y+ZgSDdOicOYOtCI2ZzNjQ2yPEdZQTeMiQIHdAqwDOPE4R3qihCBMpqFzjnPJ5DlDIOIMFHcuv0io/uPkaFFITCFpipqblzZ4tUbV6mNQUlBoAT3xgdUVcYLO3tsb+5yeHLCw0oyRbKwNQfHR2yurxN1InSdE8sIVEBRLrhxdZva3uHuvY9IhldZVDWlNsTWOMGlokbIBIKU0+kZs6JGqACmGXQSlFZcv7bNT37uTf7kF36CX/neh1hzyGfuvML3336HbDZnUmRESYxoMExSEavQAS1rqCNLGEf0u32oXJp9Ohpza/c6+2ZOXudEkULFKTEhk2rKtd46RW64e3rG/PABt3euczKZ863vfZ/RYkEyWEPUGjoKcoHVFY6FMYaq5vUrWzx8eEplLFYZImkoS41IBMJYAgKCIGSxyFAqoq7mJGFEGgRMdMVG9484l/jzFoB2kfcPn7GWxSLn44/vMZstEMYznyln0GpdE6uYsqyQXr4M68BPKqLlBzbQUmXGcdwuxLrWjjhixbd3HroDnY1HU6YTp5saxzH9wYCb12+QJAlnZ2eMx+NWj7qqKmQUOepMH5FgnZRkU18Ht8DXtUO9W+OBUgKqypAkqVOY8ejPNuoyrl8bu1zUmtSl1rWXknTSkVjtU8vQhPqSZa2/XdxWImfhI5WGf9zU2veRe5PqU5Y//J4+J0HgucobQFK7VDfXsTLw7j+NlA4LoHWNANIk5ktf+hJ37tymk0RgDZ0kRRtBXsyZTMZMJ2Nu3LzGKy+/ytHxMZ1uymh0ynwxR8iQ/+t//p/zr/6Ff5lQhHz1d77Gwf4RadpB1zmg/D2pPfI6aR0Wf2WwmoJ2A0sb8TaXKlbjbdNGgUIYt6D7DIMPnFeAWE1EvUyJN8Q81gispwJ1sf/FCLd9bqRESos1htqWniXPtQipOCAQCie/6mREAc+9X/l/izZabrMArDyTiGeMrICWwGzllWc2qS6+3mR1Vi7An48bume/aeWzQIOyb0RllgBJh6bPsgxrLFEUUuYFh4cH/PRP/SR7V/Y4Hc8RwvVQB0HA2nCDwydHrK2tc+PadTY21ynyjOkoJ0oSzGSC1oYoUIymEwgVw0HfqUPlmUfGGmpdM51P6Q16RFFEbTSb25ssYku0H5HlC8CyubnOzsYm90b7LCZTFosZut5A4tpCB2tr/nkxpGmKUhN3vLp0Wu5SUBYFAHEYolRAHISOOnReYEuN6ClGRcZkPmY8OuWrH32XP/eN7/Df/Rf+vHOg6xq0K429dOcOw/6AWZYhhCUIJGZeM+gP2FhfZ3drG4tgbW0NiinVIie92nFsX9ZQYzg6eMpLt+7QGfT5ztvf59HTx/zZf+Ff4GsfPWXY69FNEqQQXsApoD9cZzydU9UVaEMSB3S31vmL/8q/ynRccvfxB9x9cJ9viYiqUBydH/DqrTddj7oArV39O1SBE3ASy86KqqqI0w5RFLl22zpnOp+yKHKEgOHakMlsRlVbXn3rJc6/8Q6j2QK7aRl0Uroq4ev33uXf/7f/l3zld3+Hp+dHbF+9xo/deYv/71//q/Reu0U404RJDKGgmpaUdcXO1jbGvoexhjD0eghBQBC4YEYpSRBK6rryJY6aJHVKccJrs/8ot/9W1bpasQ3fcF6W5coiFmApqP3CUtc1oVdiwjpdUqkCTK2Z5lOkUnS6HdY2NpitGNgWjCYdtN4ZXcfu1EkTNxEkHnbvouvJdMHR4SH9fp/NrW12d3fdJBgOODo8dOQlK3FoVWtU4ERCmj5pByxzBlwLp5pkfA8sUrKzd4WdPccxfn4+YjKZIyUEUkDtqAzdgroMRlZrx23FWAo/UZa12cYAuYidNrpXSiE8jegqvYwQArlkuPzEzeKyHQ2VaNOH3X5ve8DlsT4pGsMfSwhXOlBKsMhy3n//XZ4+echiPmMyHtHpdJhOFuTFHGsdXWOvO+Dnf/5P0El7jM9HgKWbpGgr0FXBe+++zSKb4xmwnYEOFAZNGERYG14w1I26Eyx55cPI1eOas3f2+2KK2AiwXrnHVx6RTRuaS2NgrfVa5aszxmkPO8jkkkBmdaTFJ5gzKVxq0FgNSKLIKUYV2QIpnSiIrjxtKcbTwmon3oFAWE/j2dwv454F49vMnhth2+UZ/iAU/XM64FY/6Cey/cETrfGMjBNWUEphxAo+wjpnzxhLVhYoQjrdHiYQTJ8esLWxzuMnT9nZu8qLd26Rm5rHT5/S3drm6aOnvHTzNnEUoZQg7cQ8evjQ3W8VEAUhsYTBoEfa6xInKTWaQJfUvrQwHPQIAkdFPM8WLmL1QhbSuN5gYaEqS27sXeGdhx9SFguEsago9HS00O126Xa6VFXFZD4jjmN2d3exxlIWBWVRIJRCIQikIul0yMqCzNQEwy7zwymyqLi9dYUbV1/i5mCdl/eus31th+P7D6mwpHFMHEXEdUQvTrmytc3T4yNHJmMtB/v7vPLiS9y6dp1+d8i9Bw957/vvkq532doYcv/+fTbXN+j2hsRpl71rrnf53r17JGnC57/weZJBj//Ff/p/4M/9+T9NJw7I8hkIg1TOGX/65CmLbM6VzW2ubu/y+9/7Fr/0d/8WiekwkQWvv/QCa9vbBFnI07OPXB04CB2Na1ESB6FDbKvApcOFQiHp9Xscjk+ZTWcIKUi7HTTQ6XY5Ox/RG/ZRQjEdj5nMxrz24h1+7bvf5GRyyt5wlzdvvMhvfO8f8b/e2mA0Ouf1F19mc+8q7374AWEQ0A8TpnZCbJ1oxzSy6MywtbVFEIXYuZuLYRBSGUsgQ7RxQUgSBSgFVVX4unZMnCTIQLEoix8w+f/Jtz9Ug726eLcPvn+Qm1ahOI5b4EVdGwJlUCrA6GWP9moduulBFdLpXxeLrPXwZOupaWqjqbO6jXCwgkVW0KCsHcbFGQ4CQW00k8mMxcK1l3W6HZI48m1ghmaTj1cAAQAASURBVFA6zW3pta0dXL/2KHXpGbe8prVxC7QDs0FRFDx8+JAwDBkMhty6dRtjDKenp8xmM08M4datQEiMdNkApSRWClcysFBbs6xP2yXRy2rUJIVA2qUhX23lasbHOUHmGbaz1b+bQN45Vy6iC4LgQvvbys115BxmSaG5uhljiKKIqqxapLP04+lAG8ky29HrsrGuEdIwn0+oqpI0STHGMBqNQBiiyCFClVQEgbtHdVk6DWwpSNKIPF94HNyyDto4dc2cklItU+Esx7C5nia1LQQt8EnriqpWF2rg7fgr6fu1Xd+1Y11zkXcYOtCdUKFLfUicTjhwAY2+HHl3r41rETTGIAInz2iqkqLICQILeHIH69L6yjsPxmjvnPrUvnWgmTa3ILxwB88E9ssz+SHZF2NbPPnlT7ZB+Sry/9n9Vo5lXN04DEN3LUp6EiNH0ysC56zowqLCkIAYbRwd5m//9lf5c3/+L3D96lXGizkPnz4iGW5htWDQG7A2HJCkEdPZmMliwnDzCk/OxuiyRsbKtZ+eniDX1ohVSBp3qKsaFQTMJ1NkGDFZzCmzgnqRE4Yhs9mMs3OHzel2Ophac2PvCtfW1zkfuRp1ludgBMP+kG63g1SSoiiZzqYMB2tIBEWRob1oRtLtsqhLjNbU8xxqTVRLOoTEKLKy4v7ZIQ+On2LLkl4Q8pe+9NO8fusFKmmpy5LZfIrA0ut03dwzljzLsFZzbe8q6+vrFFnOen+drc1Nrl+9xsn4mJdu3Obm9i7D/tDRQlc1pqwIewFxFNFPE+Zlzq/+w1/mxZvXeen2bQ5PHlPVJVI5cZv79+/x5MkjojQhwhLrmr29Xf7Ez/0xPn/tM/zGe98k7URsX9mlc1ozmS+w1hAGbl3Kcze2gXTgrkC6HyUFyjvAzfqyyHOOj4+5ffs2k8MzVBSgBMRBwMP9I37q85+n++0uR2cj1rsD7ly5yW9/vMF/+V/939ne2mZ6PmH/4ISDsxnXruwxncwpdU0n7jNMU8bzBSjodTqEcehKSxYwnkJah1SlJgyMo8Pt95jNJs7xt269EMqJq/wotz8Eg70Mt1YRofi04PI9POGI8fVpF+0YY5zBgbYnGmgNlLXWS1S6RUGFAbVvCVHK+PSKi6K10Vi7jIAaPW1rPbUcbkEJAk8xZ7SnFTUImbsoPwxB6Da61LqhqXPnr43TYHbgMlfblbZBGbv0sK6d1F1WltSVZjFbEEUR3bRDGiecn56S5bqtATsc85JAQwjhKV/x3+vT2vKi7GeT9mzG0THqXTTozX6r24V/ufzsxZT2c9bbFognLkaGq87Vhb9xPaZ2JYVfG8vZaOxQuJ4EpShLqkKzs7vFT/7UTxMnEefn5wy6QxbzAmM1nU7qFLNQbGxtoCLFYG1AmqYEwdSjrzXWakew4ssADQFPc6HOsfLj6tufmiwHLYBsicCnrb+LNv3c1CgsIDxaugF7SR+FC7MEna06ARci+PaPi1ZSSrHCFuiOXVcVWZYhhUbZGkIcoMzjBKxwzi9yOd+thUApL/3Y1Mx93fyC6fzk3vRnXr90Dc/bt4nuf9hmjet+EL5NLxANZ7im1r4eCgSBojYGbQWb6+vUdUlVa05OThlub7G3s82DJw853D/gytY2e9s7rA+GBEpRlCW1MXS6HRQuc9FLU9I44cO7dzmMYt54+RUG3S7z6dTJcXY7PD47pNKaJIzZWl+jqmsvZ2pavoZBv4+IY25ducKw5xDQ56MRAujGKZ1O142ydRSyVVliTI2pNVEQEEchvtZGEqdM8szNl0ASpSlxHJFjCETgUq79Idv9Pov53IlVdGJUFBAoQZ7ndOLIs3M5R7GuC7r9DkkQEamAUAZEKmDQ63Pr9g2ub11hrdsnVqGL8oWk1K7vfXNtnQdPHnE8OuMzr7zK0STn6HCfSpeAwGjQtaHIc+68cBupJF947Q0+9/KrnP3GrzGZjdlZGxIKRV6UlLomkg4nZK11QZAUlKUv+SjRMsO5ue+Jp4xmbX2du8dPOTg65NrODpWoCX2JpJemrHc6fPDoCX/6p77EizvX+fD0Pc6mY16/mtIJNnh89JSf/ek/xj/4ra8wmU758be+yHt336GqK5RxGR4VKgLv2vY7HQDCQNFNEkcbC06pEc89LmEw6LPI5gz6m1SVl0AWEhH8EWc6c3XeZx/Q9iVfK5RCUnsDKVsQT0N8sVxCzEpdVknl+5uNS5dEEWvrG2R5RZYvKIuiFYxwQbW3Pj5qEr6W12hSW19X09p9m/JgqMpHYk0WIFCuhmh9fVo2IJ6VRRghHMp81UAiUFjPcyWoak1VzsgWGWEQMByukabJs8Cu5vfKYuj0wJcGtxVTYWmAGw1sYxsSe1pyErv60xrUS/eo/eKLN874nyba0b7m61S0mhQxbbvZ5RYwY91nlFii+ZuxOz07ZySF44/HOSF1ZbHS8gs3f4EkiXj85Cmj8zl5XmKNJk5i4iimm3b58k98kd/66lc5Px+1PdlVVSKE4wNedgjgDa7wjk77IhbvAHoj1tLPiHZW0rRACSURKkCuoLib1Lf0xtwVs93rwrrWPindtTUGfjXlvqS78U5fM2dxx5KefKcpkUipUEHojq8D1wqttc8mXLqntmm7cqVhhzlYEaFpnpGLn3pu2Vqs/N9d8LOfvTinvGNifrjBbo6tG/78IGh9f2MsvgLkarHGAVK3NzeZz0ZsbG6xyDI2BGxurBGEEaOzU956+RV2NzfpJAkC7Ti5Axe9hUqRBCFpFBMnMWfjEcfzjNvXbrAxXEMiSaKYoiw5Pj2mNpYr2zusbaxT1pVTTcNl66IwZNDrsTCC69s7rK8PGRULTqcjgjAklIo0jlFCoqQiDAPKonDGCOta8oqArCyxWpPGjvmw9mtJGAYEYYisSgIPntvs9Hhhe4/N3hChFKPZhCgKGCQpcRKRZ8YD+0TLCdENfOay1k4UA8HG+jqvvfwqsdF04sQJFWnrENoq8GuKZj6bIazljTfe4Le/9X0ODw/orXVdaa6smM/nhGHAFz73FsdHR1y/ssNnXnmZjW9/m+OTI/Dc7mVdkVUFXROSRGmbrRMCyqp0FM+CpcGWLqPp1jFB2kmpdM35aMTO5iZZlTOIE2aLOVGvx+baOuWDp5yfn/LarVt8dPoRo9kMTUGXLvGwSxJF1MYQhCHXr+zxe9/9feJ+ByPcPbVYL8Ns6KUpGEOoFINul17SQWtNL+0QBoHvuTf0eh2qqnRdTMY4URWcNPKPcvtDMNgs9QUvLx7NLiv1xGVLlvtsQ4IhgVrXDQc+UoIKhQOUGU0gnAD89vYOG5tbnJwec3JywnQydUotuvbrr+urdUZOU1bumEFTA/YLBODUV1ajHtuwrGkfjHijL1yrh8G1iIVhgNaaoihdRN+AZaR0aZ0gIM9zF+UEzmMsioKDw0PSNPHpzGUP8ieNXYMEXzlB5AoznOPrXqbMsRdbYwy+1etyzfTC7RLLF7BY4aBA2lowvtTQiLU0zdyCFtgUyov0nhbQ1iCrCiM8MM9ahAxQGKaTmWuds86IOBZOyXg+ZjybcXSy4O/90j9gMinaVL7FCQPcvHGdP/Mv/Vn+xt/8RZQSVJWmrg1nZ+fEUYwU7v4ZvyiEUeTGUQXeWXMkKavkILa9/ovGqHHKIHBjLpp0uPQofrfAGBOgwpCGiMR1KxiksigZIEXgDXpjLKWPvHwKXC7pXK13MpzRcq0mIOn2Bq6zQFiUjRGRoMhzH7VpkCttZF4/DSv8/KlpkfwrmZfmSn8gBuHSv42vO/+gzXUU/ICMzsp+yq8bZV25TJWSKAHK6DbLIASgJIEKGAx7BLbixi3HaZ0mEXEgCMKQSC548fYthr0eoZAu22asy75UNZ0wwiaJW1ADRXdtyP75iKyu0BaEVKSdHvsnx4xnU6xwzHFJmlDNPa2oNo6WMghIgohZXrK7tk4RQH74hMVxTqfTQVgXkQV+rkUmZDaZsj5cQwhLHEeEgWI0mWJqTdgJGRKyyHNmpsaEHrNSQ5HnTLMcMV2wLgJ+8vW3SALJ977zIQLLrStXubK5xdls3iSJAAcQDAJJVRRMyylhmCCk4NrVq3TiGLnI6aUdBIKyKKlrTRBFBGHAx3c/ptvvcWvvDjKQvPv+e1y9uev7x0OEKjk7PGZ9sM5br7/G756fMh+dsxiP2Rysu3KDMHTiBG0EpanpCEk36TiRDT8X67q8QITVGmzjME0qUGR5SVlVPnvqAqm0m7KYOwEWJRWvvfQK3/j29/mL//y/wc69bSbZKU9OnrIZdxjsbPErX/k1dFmxtb7Bx/fuMh6PeHF7i9PF1PHeVzVI6wxxnCKwhFIy7PTY2dikyAs2+gM6ccI0y9GmJukk1LqkaUH0Cs2uHe1HuP3o27oacMuFiPrSPl5pqalJaF07TVi7sojgDGNTh9O6JvMoSiEFtak5Ox/xzrvvklclN2/e5LOf/zxJHLOYzbh79y4PHjygKGuwGoSrO4dJQhhI5xn5dHkYu0GttXb1beHSp0maYoGy0oSBbMlLjJd7DENFt9clSRLCKKKqa4qiAqyPrDXa1GidEAQBReFSaVIIosiB4uq6IfF4dhlT0lGQhlGENjkN25NL7YrWOwWWoLLWsXARoZTSpesbo2ybzMPqrZHLTMHK5oxQgBSO5UfJAIFGeN3dluFGgGtvE6w6Y+77fGOUlKRJh0KW6LwRoBBtjbzJ2wqrETKmMgUiCOn0h6xtbTLNnrK2ts7ofITAUhnDeJ4z2Nhkd2+HR4/2HVJ10CMQIbqu6XRSFw3Vta9XK6zXo/YBEtY64FYYxst69CXcRLM1+AStnSm03pF0HXLWE7M6ytxl5C0AQ+D5Axo1MStwdWQ30Z2jRRMBixWUtli2BCKojCHpDAmCGExNRIpIFJPRGTM7Q3tJQKkasiHvTQkBQuF4ZDzgDFqnwV+hj7x5fmH70ngs16JlZH/h2eeZfyzn1qpjLJrPWsd5XamVySlcfsqfpwoki6qiKAyR1BTzOdev3SQKJZ00Zpo7IY4f++Jb7GwMSaSkE8WgUqqyptcdOFWzKELKPv2NAQSK3etXuP/gIeMiZ1YV1BJyXTHLc4br65ycnXI2Okd7GuXFIiNNE0elXNUoKSmqnFQKFnVFXpZUVrM1XHPRWBhC5XAFcRxTliVlWaKUIgoCup2UebYgr0ussVS9kELUVLkTcElQ1FFIbSoWSlJYzcnDD7j///iIn/yxL/HRwX0mo3M+fP9dvvjmZ7l++wWSMPGlJ8dBnyQJZb1AGE0nTSitEycq+n1ev3kTgPPJmNli7lrb5hnz4QDVidm9todR8Hf+wS8xmk15beMzLIopYH00CUkUo8sCXeSYqkABW/11Prg3RgSSJIyZLUpmiznbacqg06OuKywOsKu1kzRtUvnSG2xtHIOawbH9IRxtcRhHqDpnoz9AaMNsOubo+JA/9wv/Iv/J//Y/5d/712M+88Ir/N53v8HX3/4+/9a/+N/nf/Wf/Sfc3n6JL3/+C5zOpvztv/erbF/bIpaKhXFEQImQTnfdWtJAEQhJICT9OOH6zi5FnpPIkDiMmOWF04NIE6Rn4G06ckxtyLL8ufP/D7r9oYDOGhKt51e3nMENlIuWtXGcxX6ddKllrT0fsuc2btuhnDrKMoJzBuvw8JijoxMHOEoi1taGXL9+nZ9/+RXy+YLRaMTJ6Smno3PquiavDJGSSKnALsn3G+S1btK/Wnu+WN/Da10aWvk0ZZY7jVghXOvGjRs3kFIynU6ZzWbkWebXKxe3xZHCWl9Dr2rqqiaKXDTWRA9SStcyJRwbUhg5kvlABUwm03YMl/2p0oG9AtVG1c0mpWwpQ1ej94tYgk+6S+49bU37I4xu2dIu8JFb2lSukeLCGq19GtZ6QoeyLKnqmiCMMKbyYDiFNjVGGwIVUNU1UZo4ZSZrQCrqGuZZ7jXAVdvucz6a8MKLL3J8ekZZVJRFhRaGfq9HlmWoQF1wIJqxW465vGyHPnFzbVaO4xi8Y8SyTEEz/MLdv6YDWojG2BtPxmPQGoKwoSX1NWUL0jQtg815NtG9RFhBrQ1KhQ6BXBeEMiXqJJSeAKjWTpJUCOHvvaAla3GzwmfA5EoqvjGgjnCGT3Ag23x3W/pgZR65/+lLH3HUtpfpUC+Jz1hHtGStY39zz6XAaCdVa8FdswcmPn5yyJOjIzqh4cbuOkhFkiZMxiMeHx44fexeTBxJbKGpyxoZhigZsj7ocDadEQeSza1dNq/v8rVvfIOrt27T2dgg6vUIOl0SazFScXh+SlbkKA+QLKqa45NDprMxV/Z2WRsOWOsNyIuM/YMn3Fm7QpFnVHWJxQGpoiB0WAJ8iU04BTeEIYoDdObkPKUQJFHM2tY2fRVx9/we09mC9atXeXhyyPnZGb0kYXdzk1dv3eanvvhFXn35Ff5P/9n/njIwDLY22O706PZ7vPvuu/zJn/p5ZtmCyWzCxkaPNE0ppk6oo1l/VKBIwgipLceTc6aLOUVdobHUWB4fHbBzZZcP7t/l8cFTDk+P+dwXvsiT/adc2dtxkpyjMXEcg9UcPHlC5Etck+mUQXfI2XSMlQ6ZLxCUVQldSzdOvJJbI+VqVzIpTbnNzTkVBMzHZ2RFTrfXZXf3CsLCdDqlXC/4zCuv8u5HH3D37sdsxopbdz7Pb/7+r3Bt+/O8fPs1fu97v86VnS6jScH//H/3H/Gr3/ht3v7m19npDcnRDLc36Z9NHQe4dF0o47kgFAqBcWIwgTPSKhBk0wndXtcxC+IyN0ZXKKGoq9yBkGvL6Gz8KVaWT7/JH77LP9kmrH2GFOGZzQIGlFCuZ9T3kLqI21E3Gr1c5BrjtKotKhAo4VLRCkscSCIlMFXF6dEJ73zvXX7/d36HDz/8kOlsxtrGGp/77FvsbG541TBnfJcc335hEQ2rmGgjrnZR8+feEJiEEtCaxWzGwf4+T5884fjoCCEU6+sb7F7ZY3Nzk8Fg4Gpz2jGnWZyii1Qusq21prambfrRPg09Xzj6VIC1tSE3b95gbX3o1Jl8tB2GIdpairp2adoVOVNnKJ08nTBOMIWmzu5jaie54X7kyo8SFoUz0sqv8e4hWvKKrwLeLvT0PgNEamqoLg0shQCjCZREWJfpkNLRAjpPr8ZWESEBaSRJY+XHWzm2L6mQEqzVJNGQK1c2XYnDSoQSiLAmLzKkCtu6rRuPBuBo/HW4G9qcbpsh8GUUIZR3DkIaVLUUDv9gPFaikX1s5nXj0KkVY+hEZ0q0rhBYlBRESoJVWCOds2NX55vwZQioLWgcyE2pAqFdfS8II8J4SBAqhLQE3rHTxs0tV0YXLd+xu04PqhFLAZFlXX05LtKn+6XPBjQ/SkhPHCTb81w+jSvn32Qq/AxDKPcjm4nkP2WXPxKJEsrJEzYZHBwWAKncTDWADdDUWFXS6yaEnQ6hqVhkGWfnc87P55jQogunXR9JQdSox2nBdneTUobU3Q6d/pDtcABKsDscMOimzIqMRVmx1t9ibsEkCYWwmEBSmZrDo32yfMHVrS22O13W4w5REDPKc/I8Z0RFpksCA2tBwqDXI17rtw6JMAZlLVESMy8KpJHEMiZSCVYE5EVFVyVoBEZKgiAkDgKUdoxnBJKzfM53HnzMX//KL/Od738P1U8I0tApZ+UZt2+9QGwCjs/PQQoG/T6DTh+bVSQqYlFkTPIJZb0gVJAVGWNdcTodMS0zqqYbJZAE3ZTJfM7dhw84m474iR//cWbTiQPQFjXTyZSsWPDa7TvMizn9/oAKyVlWMM7nbPUMHdtlPx/T76XspB3MbIEwAb0kACXRwmK9RLGuapIoohOnro5c1whrCHopX/vu9xlnCwSWK70ef/Fn/iQyl3xw/x61nfHirT3u3LrD/+dXvsIf/8JLfPO9J6Sh5M3re/RFl//z//sv8+e/9DP8zne+we+8/X32xxPCJEFrwYbSoHNKW6KlJRQJloQgkoRhQBwp+mnMta09op4g7Kf0VIewUlR5SSDhVmeXaZGRVQUgHCthdv6JZvAPsv3oDfZKIvCTNw/Y8qL3rdHEyRg6UImrYclLetp1bVz63Ni2pan5PtkYV2so8ozJZMzZ2ZnTWPVKWLlPxzYeXSPpt5TctBeCCxdRXvRAnKhH7Z0MoK111+R5znw+dRN5kbVjsspw1vzdfFYFygl8+AUX4Y0XjjBgPpszHk8oityhy7suDQ+NUljDr+1VoCweeOa/j4Y3uokqXVtS4wwtAXXuh+Y1a7yI+7LFZzUyel69s7mm59711Xp68z1Nwccua6lSepY60XC8u9eVT2u77gF3n42GXrfr3vPGRCnR4iHa7/DGdUkyYxwK2WofAfp7IsD3b7goiNWfZ6bxMhW8Mg+bVPOq8ZK+e6Fle8OlrZXvSQ8C4X9L17UQKIJQecUx4b/Df2HjNErn9DWKcw29r/SkE24f0WavhE/FIy8a1dVSACuv8cwP7e/Ln32esX7mGM3otOl4Ljy7sgHqtfuunJv/nPVOURLHrG9sEIiI0+kEjQAVOCMnJdI7CGnHIbaFhDiKuXrlGkEYI0LX75sopxwXhwFhoJjPF8znC8IoojaWII0RYYBQkkrXTKYT18YUesBa4NDdiyIjikKKqiCKY7pJSqwCrF4KFDU8EUEQkKQdiqL0c0H4e+rT5VlOWTngXRDFGON4JeLAtTJqY1kUBcejEf/g177C2WxGXZYoA4kM6AQxG/01zkcjbOC4rV2GIGjLK8a4+Y9x7WB5kTsmR2MQ1lJajULQTVMe7j8BLNvrm06DYT5vWzwBgiBECRc5CyGJIkefmpUlUawIw4RJnmGEQCpFUdXUQjjhk7pGGEusQjpJ2t5rbQ21NWgMRrp5cHp2zuODfQ6PD6nLnBu7V3jjpdco8pKn+/usdbp8+TNv8f7H97iyt0Y2tVRVxvqwy529Ozzef8xP/MzP8Hvf/AbHxyfEcUJJjakMwzhBe3BoqBwZUu1TxVJK4jAkjWOEcU67itz5Rh6Yp5Ri2Ok7G+ZthgC6neS5a+EfdPuRG+xPtzVI4WVPr5CNwdYtCvnCA+4X86Z+bP3T3qQ7V6M9p+frFqyiKJhMphwdHfPo4eM2hd30p66qH7njOKPUnMsqwYbLWTvAWbOgNGjjhsGqrmsW8znTyZjpdEpRFA7UZpsUKX6hX7KErRYQlm1Yon3IsyxjMpkyGo2pvWZ2swis9qo3D75f01tD7WrFCoRLPzcLvfRtTu2Pv4blv6Vn2lqiuz+hafdiFuI5r8PFxX31e5VUbcud8ijRpXFYMQCykT5t5o9lPluQpp3lOcrmnJeCMUJc9CEaR8366N7dC42xNUb7H1M7/IGpMLrC6LpN7V6oPKzY89XXl6nmZeq9yeQYY6jqGq1LtHGRd12X1Lqk1pU7D1N7Z8Jln1aR/a39gpVxVLScA833egdm6SwsWdxW78nyZFfbvVasqf9pnrkL769sF1yb5+7TzMfV9y86Cc8a/qXDKKVEY4lUSD/t0R2sYSrBxweHzMsaEwTIOCIJQoIgojIGEYaoKEIGId1ej53dXd/jK32Pr0ODV6VDKJdFzmKx8Frr2tFihhFCBhgDVVWTdjresfRYEqMpy4IwDKjrijSO2drcZGtji/l0RhLFLsXvF3IpFEmS+hHx0b+AKAxI4thpftcVMggJoshxg0cxgfJgRp+RCMOIJwcHjCcTZtMZURCysbZGXVZsrK2TLxZgXEAglVu7hApQKkKIAGucyIWua+qyaJ8taV3AlEYR2XzBbD6jk3bod/s8ePwIbQxhGGO0JlCKNEkpSxdVGqOJwohAOQR16O/FYpG7Z1EFaKDGgpToqkYhSEJnAJV3KLQ1GOFKbDWgcPXsJ/tPOTg5JCszBIYf/9znGXYHHBwcIyrLG7deQEnFOBtxY+cao9E5s9mU21dvkUQBcqPP3YcPKBaZq7kLiy5K0ijBCkEgA9IgRCh/Dp6vIg4j0ihGlxWycRqThCiKQDhFxV6n57UhHJujNYbN9XV+lNt/q0xnzeZq2JI8d1Fps1BHYYjEUlaGRjayWeiaTSmXQgx8q4Npn/eGsGQFsGAdMtxoja3BBG4hCwOFEBajbbsQANS19jUJ2p5gIYSb7I0xEAKhNWmauAfbaAymJeSoa8fd2xiePCuJ4+pC6ripd7pNUhR164gIH72UZU0Y6tZIBIEjXSiKkjAM2ppaHMctsCEOo9ZpEN5SrRrHxhgrpRChar/rmfsjls5IXVft51cdi2a/9qcBG14+lv/d1KdCT4a/yn/epthd2oNqUbTGrQVM+XmzupDXtebg4JB+f+gzMZ6T3jTpalojJYRtz8OfUXt2zjkrEcYZ1laX2vdoS9sYNoVU8QXL1DhJFxyZhlLRitaAu44DQ1XW5HlJWWp0Vl0YKFcHdscxjXGzNRZBVNfY0C2kjXNjkO11Le/FUsZ1tee8cXiXoW1TYffj0NrW5xGdNIazAZc1Xsklg/zczIq4+Ge7i78fggtzdfVaVlvyhI+yDIZukjhlsiBiPD3n2/fvEvUGrA/7ECp6SYKQIcejMZGI2RhGhHHKxsYW/f6AuqoIm/VGSpIw4uzsjDiMMBryImeRZ1hdU2pHdiODGIHrqx0M1zl4eujT3O4c67Lya5mr096+cYurV6/z1/7r/5q14Rp17ZjqrLEUZUEYhg417UsrcRTQ7STM84wkCphojQoDQmvIisLRGy9maF2jrUEJRSdKefOtm7z74duMxmN2tzbZun6VaZmRDnoEsykirwgSp91Q5AVBECOkRhsoS01RVKRJTV1XBFJSaI1Sgs20S5ikfP3b3+Ta9etoAYdnZ4zOxsSp60euy8oxTqaOVjhJuiv4IkfglAQhHRWQjacM0w2CTgdjS2xtMEUJcdwGO1EUobXrS29KMq4Xv6YTxmxvb/Pwvacs9ByCdcbnp7z50st8/4WXePfjt5mcTxmGCT/21uf4rd/9Xf6dP/vv8tWv/zc8ePCI1178Anu7e/y9X/9VRxmqneBTHMccn56iVICwAiUVcRiSRDVGQq1L0JokCemmKQGCThhTVRVJFLnavXDrzNpwiHzqnKC6qjBGs7nx/wcG23jucHAUoXt7e0SeOMAay9PHjzk8PHSGzCylEKV0PaW11tiyRIUBQeTqlGrl2KuL1f+Puv+KtWXL0jOxb5qwy257/D3X5b15MytdVTGryKITSYFkG1JN6UUNQQ4gGnrSg14kQNCrBLX0LAh6ZgOEWqbRrUaT3RKTZJEsVnpzM/P64822ay8Xds6phzkj1tr7nHMrs+oWix3AOXvvZSJmzIiYY4x//OMfWvs8pM+F+UWgrhufRw342iYyF/0NJ5UkSmIG4xHz8xl13dI448vLlGS+WvmFM9JBAMaglCSNtJdXtAG6dxbTXhY32Wx+jLduXscJWC5XzGazvnY6jr18qu/7bNBa0raWpmlpW39uXfcprIfPjfCOg3EOEQhiCGhMi2z9otJF5i9VotvahPBwdMeqli/5jD+pP/qaCyFomqbXft/uD75NYOvG1NWSmwDrb8Ow3QGbxvDLX3zM1751I5RPCLDQ2haE8g4WXFKD644hu+5weCMXp7n/S9Abvs6k99GdcbSN7YTKEFsGeXsOre26s3V54ggpffvEKEqI4xScQUZpfy59KReBIO68o9G0NVUgRTpHHzH7FIpvn7ntlPk59Zaxc7I2Ywxz3DkUQvR598+5cpvvud53urRd5jG85L1tm93BA9tIRAc78WrRFv9hi9KKSMdIp4lVyh/+7OecLUu+94tfcOfGIYc7Y8ZpznK15ifv/4xpNuTLb77De+++x3Rvn7quWa0WDIdDpoMBaRIjW0tZ+s5KrTOYtmG5XLJermiMv5eUjr3YSJIx3dlDRhFSaCIVoaSgDOhGHPvaaiUlAx0xnUwpVmsSIZHOt6KsVyv0IPPPubFEWqGNwrmWLFZYW2Ft1xBJUJUVg8EAe/yMpvHID8b3Y/j9n/6Ir7zzBqf3PyO3igkJmdXUjeHajes8X84QOEZZzmJW0DqDFZonx+c8O3rG42ePuW72GO97PkQcK2IdM4wz7h89RQjB23df5we/fJ/7jx7y1S+/x6PTH3BxMefwzg2SLKOxFkQLwvdLSOIM07YczU7Y258wHA1o6pJ4rBCRZlWUSGsZZxkmihBRjBQiOEwG0ZiNE+ccurXQtuyNpxxeO+S8UKSDjEEWc3TvIX/tz/8VHj19zP3Hz3hycsT/8N//W/z9/+U/4t23bvDDn4wobMnbb17H7v5N/hf/6/8V/+7f+Ds8r+YcH5/AckWDJR8OSa32axSGSZZRVRU6iUkjjXAtWMsgS3jt8DCkMrpmPoK2adgdT6DxFVDdGp59wZD4n4nBVoFMUVUVZVly7949L+zvLNeuHXL79m2++c1vMp8vOT8/5/TU56C9RGPjDXRYpJIkCbW7IRfuNn2fuwXGWRd6LsuguUyvlOawXIaVO7jas8av7R9w4/Aai8WCoiioqoqLi7mHNhvr2+OFtU8pFRpsbPXD3oK63eXVvYfGF8sl+WDA3t4eh4eHNE3D48ePqes6NOsQDAYxTdWSaN89yEFvzKXQoDaLfueMiP4cN5vFYdyL9bPOvbhYOudoWodpfM0pcmtx9jvr4cqri/JL89tC9EZFiKCR/bIbZCuf7baaS0sp/d8OcI7WtXz22Wf83b/3ez5v5oLESfd5Iftrae1GmtRvpo9GhROYdksetNNrFyE9EozvS5uvv2QTcjMhPXwt/bnr0FPXaEEURf2Yu9my/fe8sY2iiCSzxGmGlF5i0lqDlBvGv5JRyO9vuBJbo/G3Wki/2BcYoVsXrzOc3st51dld2f8fMRevuBcuR+Ybx+lleXCxhV6IkLKyxlIVFR+dPmeSDKmc4+nxMa4qePfOLdJkwPFyiZMR87Zl3RgGTvD8+ITZxQU74xGjLGWQ5Vzf3WW5XLI3GCEyiW0t9x8+4PqNQy8HLD2aJZ2gqhqSPCNJUqY7OwyyzHfUita4xqN0ZVFSlyVJmpPGCeOxLzuyYa2oS8MHH33I7Zs3kJEGZ1FKkCYJFxcXQec9om1r6rpECkee5r6KIjiTOO9kJIOc3//nv8/f/kt/kd/6ja9zY/+A6qJE6oRGCIqyopmvGcURk/Ee//Jf/4CHz094470v44Y5R0XB+aNH7N6+wXq1YjcbkieW49mMZrbkv//v/13+y3/2/6Oua95760usTYvFcfT8hL/w9fdI8yEnszmKFvANmrIko00a2gSMdqTDhNnijPFgilSaZbFkXSxR2tdYSwPOWdqmJHKeYKqDcI71kQJFueD1O7f52f1foLVmOBp5RLMsuPPaG7z7xts8PX7CTx59wjtfeZN3X/8S//xf/lOuHRyio4T7Tx/xne/+hH/vL/91EJKLxYKmrZkOB5izY0bDCcJ5DUAtwWGoVktErH2TpaCWOBqOSJQm0pphlntRHgF1VTEdTHw/b+u5PzpW8PIV7o+9/ZkYbGMttrZ98wDPojXUVcXRs+ecn5yhdYzWHkKN45jbt28TRYr1es2qKELNtKIoyx7K2946Q+IXbN/YsMvRKunl7zrIdTsykkr5aN1aitWas9PTPmc8HA65desWUkpOT09ZBS3cuqq8vCb0xt/fcILW+NrSq2PbLKySsiip6prZbIZSisFgwGQyIU1TiqLg/HxGWXgBf68otiEabR+zIzRtzt8/3Jdy0HTw7uVo/1WRjXOd2MqVc3Cyh4y9Vbps8LtzvPq9beIZnWPRvdd9l85gBYGRTmRHdZF4IIdZy+nZCdeuXyOOdZ+PjrRGCV9psLEvYaHze6LzZJwTQZyvczy8Y7epkaZvqeqlv7fV07Zg9TCn3Zx3htDXQfuubE3bIkRD07Q0jSEURffn7rp9CkXfrkWokLPfzGGnFdDZ3s7Z2oi1bCJhP8Wd19D9uJwf7gbQQebdabkrfIVu7umb1r7c0bt6vT83au7SQHRzp8K/DsZ3WNs5vzKsGYKqbbk4PeF4PeN37r7BcbHANgWmaUiSlLJqKREkOzuoQU7RNtRNC1qgJCRKMUhSxvkA2fjSzkk+JI0zFss1R0fHTJaDILhifb7beU5Mp5aYZjkq0r6JkdLUrWGYa5LIp03apmEyGlGsliwuFuyMR0RxRLs2fPTxR7z5xuv+nrUmrBmK8/NzvnTnLmXVYNoGXMtokHtiKr7e1zgT5I5bdsdTJm+/y/n5OcdnZ9y5dgsTSVZlwa3XX2dnNOHJsyUPnx5zdL5ibS3Dw30enZ1S25Zsb5dnTx9x7+iExcUFZ/mAUZKiBHzry1/h/sMHaKnIdqagFE8e3EPHmrZuSJOYNE5QeM5Ilgxo6grhFLSWZlFgViUHgymTRLEznBDFCYkWZEmGMo6isnRq/s5ZhFIYLK1wGBxGehXCuvViJbHTVMuSxWKJkxBHinI557233qam4uHRU+pFxTd/48vcf/Scb33jt6jqln/xB9/l2eOH/NW/8tf4L/7pP6dar4mVosKSBmcsGg6wpsZWNfl4CnXtq1mEoDKGqqkomoK6LInSiDxJiCPf10I4GCRpX4kjlUIFoaYvcvszMdiwRc7ZepittdRVTVmUWOd8i7kk8Q3Bo5go1K2maRLkPL1ObqcI5fd7Oad3yThAMN6Xc3GXFpTtCNI5qrL0pWPaw9MuyCKmeU6cJBhrWMznLJdLL2Wq/ULrJeu6Pb3CMHYRXFBbs8HCdPXfPifuyU52K0e8WYw3pLMuUsN547KJ6K9E9njEAfeSc78yxk4RbXu8PaQZXuhRCTaL8/bxwgg2xuBlEdSVMXRa11eQXH/NO2ZXQEcuLs5JkgitOzU723/eOUJ0tm2g5OWfvWHdZLW728n1s+CN1+aecf3rG4vjf3bNRjrjv72f7XmUwkts9vdld579c+GdAGu9EINsLU5vOBpCCl+v35/zq+b1MvwhNsnqzl+6NMeXr+1myO7FD/bH4er5cfX5fnHZ2iA12/vbHnunQBecgzD/nteiqJqaj54+Is5iEq3YzQcslw3GWgb5gMXC0QqwcUTpHIt1wcHA0LS+W5xrGoQxJDoiUcprUac5o+HEa2Mbg2stTdtg2waVKN/KtG6pKs+xkFoDXjJWys5Bt8hE9nyN4XBAWZc0pkKoEVIrjLPMlwvmiwumBxlSamzZsFqvcc6RZzlRu0A4QyQl0/GQRVkhcQzylNYaimJN09ScLue8df0aN4cDUhmxPJuRxxkmkpzMznh0/JxFWXJj5wY6y5lVDygFzNYrKmNwUnCxLrn/5BlVWbJuGvZGI/aTnAfPn2GbmhvXrnE0n3F0euybz2BIs5RIe9GXSGnqqkRHE6qyIE8mCJGghEWLmN3pPqJZUVSGwjYsmpbzssJKi4o1kfY19iGComoqjDWe1wM4AaUpwWZcm+5ztDhhuVjjAll5dTFjfzJmZzjk2dOHnM8uuH3rFj87n5ElKVW5ZHax4GvvfIWjxZxlWWJag4gVVjqSNCFSGhPWYokjSSNGSeaRkkjTIFjXBbPFha9cibRvi4rPtcdRRBKnKKE2z7iSXzit+8+GdNbBocHYdK91i4uUCuE8katYrymLAmMtUvg2ddlwQJalRFHQUfahyCYP2OekZb/vrh7XBolCJ8WVpSeUdvULzEZlrWkamqKgKksuZjOa1nH77h2yLEFHEVr7GlhrLSqKw9i9hrZQW1est7T0cPgmidiVmDlWqxXge313vayV8lJ3QtATx7bnkzBqJ/rfNnYkvOs6KNxtWO6fe53Y5HQRna3c5JQvQ/yXo+ee5NSPZhONdka+M+79PdB93nXjDR2St526EN0IfDR9cTGjNU3ISXew94Yg5ZzYmusucu8MxUa4xHVoQR89dlKpbnPOW60xNxGx/0uGn52BcvTBekiz9Ber73FNH7VvT3pnKP18Wee8wJBx0BlsednJ2UTYW4a5//2ywQa2gYEXtw6NCH90PsqLT8smxHfuxR32gfsLEH337Y3XIPqb60VncNtYd8Q9IQRFVfLRo3vs70yom4r90RhbrjDWMBqNKcoCpyQNUDQN69IrBVZVRZamxFohnb/ag8R3g0vimDxNWSUJWmti5fupY1ukgEhJhPIon9baO8YASLSKUFpTty2t9cqKcaQZDAaUVYmxDYvVgoEAnUQYZzk9O+VLd+4iLMzXK5bLFVmWEccJkVyicKhIszMZc7F8jHCWLE2obUtVFRRNzXzdcqMac+Ott9gdjLFFjdQ5SZLwdHbO07NjGufYj2KsEJyWS2bFmtIa3zTJgpOa45MZDZZKCs+olzE//NlP+dp7X2Y0GnHv+ROOz065cf06ZV15mD/c95HWLC5WRNkBdV2zO45Z1y3n6zmzVUmJ4vxiwXH1iFLAvLrgYbLAKsN0MGCQZZ7ApTSJVMRKIXFe1xwfYNS0NEXBG7fv8uTiiEV54Tu6YbBlwWSSsZMNyHXC87NjBuMd9g/3aBvHclGQpBG/963f5T/+z/+Bd4SFr1JK4hQdaaIkRjpBY1pa15IlMbeuXWcxnxNpTSQiWkxojRoH9rgPoqSUpHFKrGO01ODCOoX7b4fBvhrRXSUUdfKMhk0k0NdQh00JhVCbKLATc1guV1wsVsRJxGg0YDAa0pXRdrnDzmhv8pWbJaOLfiLt+7e2bdt37uoIas555mZXN7ndNUtKSRw5Pv3sPmkSMRoO6PTDlVIeHgPiKMIhqYOWdR+tCb9YOdg0C+nOUW5y3p6s1rIJhv3iZS0o5WuSdaSp64bWeWU4pZRvatIaNA6UxNoud+r/BRJ0MGK/AmS5PYFiE9HZ4CB5X+nF779sme5LqKyHvjavv0gK6+rsXUdQoCMU2nBcv5/5fMZisUCGWnRrLG1jQWikVBtlNif73zuoVQRHb6O75Q29kK43tKLP+XakMIfvhu1CXbT/3uYc3BaSsDFo3f1oQvcpY0DFun+eewQijMMFRRHvoPmyNxFe68RanPODlKHueCPhCdsrhQhhvu/TZTfX58oF6v2ErVcuGe5LV5cekrj6fL+4dY7a1mcEeP30Kwd1l+8DOqSoc7pCp7R1VXG6uuCNu69TFGteTw6ZCcm6rRgPR5zPDDpOaIWjBVAaFUcsFwtuXLvGGzdvsbszxRrDaDhiOhz5vtaVTz1NxmOSJCB54boopdCRZLXyhlUqhXCCKPJlWyiNwPcJKNZr4ihiOBpwdn7GYj5jdn7OG2+8xdvvfoUoilitVuSDAaZp+mNMpzv+OEoSKYmOYsbDEU1oDBJHEbb1wUBrG24d3OHnP32f61HK7d/5HV67+waPHzzF4rh14ya3r93kl/fv8S9+8H1Ozk5JhgPmdUGS5x7CRZHsJlwcn1K6ina+xJY15XBFfXzMX/uLf5Ff3vuU1WrFznDMsvYM+slwSlWVJFHuZZebmsneAGcvfKvN4xP+5S9+zJOLc07XludHD2lExMI0nBVnRJVmWSyYSMFAR4ySlL3BmNev3+B3vvp1bh0cMspytBC+qcYwZX225MtvfYn7p0/45f05TvhGTaM0RVrL4XjK27de5/HZYw7lmK987W0e/uKUp0+OuHF9n8xlfPfjn/ONG+9gDTgF10ZTHjw7JspTDsdTHj094vnZCV/98m/wG+++y+PHT2iamnyYMxyPEZFvN7pczFFShja/kixOvPZ5CEJaY3z5r/wV1tZfY/s3H2E7b1K79J2UciM9Cr3hayovzNpHdxav5a1920wNCOvQToQUp+kXgKta1n4TYCTGeAO2KqruVVCCOI5pDYhA8lLKq4F3i7xC+Gi19bWHoyTC4YiVlxtdE/prRxrbGt89KeSlOkPfNLaPohE+XlPKazvZIG0lVfDara+11MrrPpkgXWjCwl/ZiqpqfK4cr66FI8Csm4XbRy8C5yQm5P9a4/qGFb/K9ZJ4FaIoinw7wdReKsnqa9it7eH4bjPO949N05S2bjYoyEujwXBPKHA0gFe905HPJyunGA69brvWGtM2XMwWnJ8Y7rx2i/PzM5YXvnWpMxkyKgB9iSvWG2zhDW5Xl96FxF7RTPUeR2evhQRrWqqmwgk/z0Vhe4QeNtK2bVv5SN552FsqgVD4cp448oZXg2sdYks61XaIACCQ/vpLBbGjpQBXIIJYDAFlUDrCOo0ULQLfcaBru7o9t56n5CVZX2VWX7ldQb3d1devbFfjbZ+R2hjtS/vtofPgagu79b0NWtPdV6pdkU72ODR3OUh+yc7OgMaccb4843y99imJ2QqEZCefUlnHvC1ZiwYjDbPjY75++y0mwzFKRLSiZTwa8Xa+j1OK02pJZQqGg5hH7TmuKolUg3A1rtVIFzPJJ1TLktPTY4ajEekwx64FawejRDOWiqHSJCqj0UP+L//5f0YtHG1T8sbTI3734oJv/+7v8K/+yXeItWb/YJc8zzk82GV5dM4AGKUJF3mGUimDaIQgoikrduKUuZScyAhhU44eP+ff/e/9PX75w+/zr37yY5IkZZynjEZT/skffp+nRxfUNvJNfLSmSSRRNMACta1RCKp6STTSHJAyX604rUouTI2Q8F989w+pFwvyJGYwysE5SttyMEqoQ25/lOUsdcZXdw/44bNn/OCf/mNuTnb5e7/3F5lJzX/6n/xD7rzxBuMo4p10h9cm7/D2tVt886vfYJqNSKIIa1rm8xn3HtxjWSz55ME98iRhMhiyMxoxaadccAquIpZe77uYl+jxDufrM+ZnJVonvPH6XT679yltdc5h/hv860c/IB0M+St/9e/yv/s//ce8M75Ba2rqdcEkj7k7iPiDWULLmrfyA56Je8xFw8Fezs+Wlv/mR5/Quoq7u9e5vbNPLWEnG7MrIgbjEeliTiL9Gh1J4csAtSJOU2zT0NZXxXr/ZNsXbrDbTgyFjUG4mre1Igik48u6fDeryneEanzpkdYaZy4z7LrmZ91+Np79VkT3Ci/fP/iOg4N9rl07wBjDcrlkNptxdn5GEQy46oIk6euZuxrBTmDAtKEcKpRzddFg21qsrXuYTSmFAerG6yPnoa9q0zSUZUlRFKzKOoh7bBTXhPDGUdCidWcAWoxxXnbzSkRjrSfMbOeyu7ag3d9KeYMnVZe//Tw89MX3jDUhl+56IZhtg709x0r63rU9kY+uL7nXEu5K3i4v6JsIe5vlvf0+0J+rc14a1FrfSS3Pc27euskHv/iQ4+KMKLS08waKfl4750IKfflcnYMefvNjsGxgfRk+opQmyzKSJOvzq9tj7Qx2l4MVCIxxOOEj66VYEukEqbTPb0h6foKP0rs9bZqq2KCg51rQqSaOE+I4AaExHnn1ilX9+YiA4mw2H9mGNMEV6/vCvdChQZdeDpD1FcP9qu1FlP8VVhouj/VqxN/l5fuhOSrTItrWCxRVJXVZMRkM0VHM3s6ENE4YjEbI2qeVOiEmYwzn52eAZTQaYIwnuUaR5OjshIPdHSbZgKKpOVmseHZ6SpFBZBxNWWNkikoUaZwT5xkX6yXv3HwLgaMqCkbDIQo4Pjkj3jvARRErU/PDn/2UOEnY3Z3gGf4N73/yEYIvMdrf4+HREz548AlJmvL67df45ntfxRqJWfpcfBznuLZFSUjjCGFN/68uV9x47Tb/9J98hz//9d/gxu4+s+Nzbr75Jb77vR/w6NlTVk3FytTMSq8hzroligRCK+qmZrUuGMQZdbEmn04Zj8fEdcm68PP37OiI2weHYFtOTs5YlWuEgf3pTl95IYSmXFfESvL+D37MX/9zv8ebN29TVjXHj55zY7RH3AiaumJlHcdJxLNPf8H3nt9nKASTOGV/NObG7h43bx3ylYN3uVheMJufs1guOL9Y8G42ZjAZ01oTUEzBeDzm6MlzHjx8QCIzppMJ+TDzaYVI8fP3f8LNmzfIhhMePHjAyekp733ra3z40T1s64ijmHwwJE0lZVUwGKZICavlmsViyZ3bN2mqgp3JhJ3JlCROEEIyHo0YDiRpFBEpjZYBwtfaNysJz6w1LU1Tff7D8mtuX7jBnk6nPufbNL4erTVY572MTqFqQ8ShJ3B0i38U6b6v8fbDvCFVXbbJbvOBS+O4BNMFSF1KyXLpG3OkaUKSJFy/fo3Dw/0gJFCzmM8piqJfLOqm2SwoAkRQDGra1huOYGiVCiVLIYLekGpsXw4mQ3mI7yo1JoorEA7bOqpqk692zsvbXTIysqvtDeVrXVS+PT9uc9zutd6QX1LLEpcWx8ts8ct/b9f3bm8vENm642/Vd29H+QF93nz2c2BU72hcuax9DtNs8NwA0TdNw3QyJU3TkHP2hs73rG6DsQpIQH9TbRjV1oKKVXeJ+4t9SbSna3tpu/dEgKC7z3jRhf7bnfJYaF8lZeVlRkWok7YCJ7eae7gObBchN+jvd58L2+ib+/FaHD6v3SKxwnNCxEY4vJ/bF9ngrzDUv8b2Kgj8Za+/7DgdR8G/Hwyze2lmxaMeEHwGB0pjnKWqCtbVGlcZbt29y6r2xNBJNkRFmiRPe1lXZw1lXXAxvyCRvl66bCqUEGRpTlFXpNNDhkmOqVtovMLhcDThQip0kvhyMidIspz3P/yQjz/9lJs3rzPKB2it0UIxSnNOz+YkwxHzpubBo8e8/8lH5MMBZVGQpV7u9GS+4N6Tp2RSopIU21TMixWfPHnIqiz4S9/+XYajEaW5wJoWYxoiJb0MqnNESjHMUppBjlSKP/87v8vy2VPU9IA3X3uDqqw5uHadzxZzitkpVVP5fHCcYKVDCYitIEETy5j1fEWcpazLAhMc1UE+4PnjJ+RSM18skViqsqQoSvbGU9qipNEEwavUd6ZaL9kfTXjjxk2mwwkfnNzn8eMnjIcjDILaGBZlhV0sWKyXmLPQ0KcxRAhSrRlEEQeTCeMk5WA44mA85XCyA7FGWO31JQKHpyxLHj56hBNQmYZlsQbpyPMc07RESrN3+5CTiwXf/cF3ifOE4/MzX45qLMJCkg24cbhLVVXs7o8Zj4bM24ZExRzu5nz44GPGgwGx1pimwbnaayE437wKY/yzB+hgZzq1RIH4lXrB/zrbF26w9/f3+4XVGm+ATNDYrkN01obo2j+smxKRgIm/KkgOxvcKLneJXPPidon8FCLEi4sZRRGRphlJEqOU9vWucYQIuSvnHFmW9kQXGWQxRahF9kIaXS+szbFEaDTQkQ46A9hFwU3TorQOrEhLFMW0GGRzeaHtnAEhLufwegi2W+iEuBRNb792OXK9Mi+vmKuXziFXx7bJ629/r3OOutRGOPnN71xhkL/CeGxnMcQ2dB8MdSf60l3bk5MTkiQhimN/jA6xsJ2UqAjchG0EQVz6t5062bZtfl59CaJ1LvzczEvXGRRAhCjWWtfLO3YOKkLRNbMQoVuTl4vtTm2TR++V0ggEtaDAROgs5qzDYoP+gHcQe+Rlyym6NKd+okN8+8dfRH6Vb16tFOhe68a1cci2PiOu/Nk9txuIBAArFXXTUJYFrW2xjeFg/5D6+IgoS9kfjmisASlJ45h53VAUBWWSUqoIEfgqVV0RaY1OE7J8gJSKKPYkUiGkR9Rq33d5kKUgNA6BkYL3P/qA9fyC1bpglA9J4sT3ZdYJcZJilObx2Rnvf/oJF8Wa0WCAayy2bT0a5wRniwW3d3dJ8wFWwapYU7QVD54/5heffcztnRs+7WMcwnm1xjzNyNMM05RoIciTmLKqaNqWLM5ompbZ7IJr+wfMy4LjxTkFvp+2ax1t2ZCnCaauSbIYZwVVu2aSD3HAsm1Dms5LrmZpRlXXFJVnqFvryAdDWiEgtG7VUUScevGq07MTJumAcerRxNmy4PRiDvnAc3kEfj0oayIVM0gTbowPyHTs2eZCUK6W3qGQEZHy5VZlWfLo+VMEJaPRAc75krk2oLFREoGRVG2DLGFvd5fV+QXTvQPiJOHs4gEffPIx6TDn+OwU0UikdbR1w2JVkKeKsqq80x/HzGvI4pR8OkQ/FIzyAWmchApThRIilI56YnIkA4ExOM7OenGlSKsvXPv7CzfYccgvdv+EENimZbVa+ZaTZUndNDTGy24OBnnfkAN8C8urW1cqE/7wxoyri8fn4HUdzBe8R2sN67VXM+qY13meMxjk5PmQNBTDZ1kacs8GIT1s6gBrLFGssXYjLdkFfQQD07GZCcZTKRV66VY4qpBrh5GUl/J4nfG1QV7TGyD/pu0NkKXrobxpR7dZELcj4h5WfEVAddVYvkj+sr2RfhWp6BJL+aURlevCyMvjujS+LaNv8baNjXY4dOpi3SF8hAtw794D4ij2kNXmkl8x0FfPO+wjHNsaE0oFN2kOKd2L87AFXfuSvLA/BEL4kjIXxi/D8bsSRP+mP2cfbW/06zcRp/SRuwvsQBGaociNEXcBJejrqTtDvTWP/dxfPuHeRXk1QezqF+mdw45k+Ksa7W7uup9XmeyXrbSH8p3YlNh1+xFbjqcVkqapvXJWpLGtZTQaY589IZOaUZZS1p7/kEUJ5/MVpZLUwwYnHFVRoqSkbmvOli0qi9nb2SOWMcu6Zm0NRnpZzdV8QWlb9qIRCIVFMFsuuf/4Edd2pt6hxutPW2NIVUQ+GLCoKj579oxPnjxBZQnGetYxAR2SOqZqWpI0YzgcoWKF0Ip1VVCtV/zgJz9GvguD0Zg0S1HS4WxLEseM8gHrixpbt6RxxLwq+fCDD/mtL71L0xg++ewe+9eu88sHn/LJ44eYLIFE+zKppibWMW3ryHVMI3wZ6fX9A1azOVVTYARgLW1VsTudcnp0RJW1PpKUkt3JDmfzCx/tS42MNHEao3TEw6cPyeIECVR1w0VRMa9q9CCnsi2RDohH4zjY3eNwd4d3Dm9ybXeHneGIPE6Yzc5Yl2vfXlhJsIZiveLo9AStWrLhHsYa6qYB5xv/GFlCK2mbhqquSdOUhTknHww4Oj/n/uNHHM9Oya9fZ3VyTOpyYimxbcvT50coGk7PZty6dY1YK1zjiHTMaDBiZzRmOh6TxQmRVMRp5uvSk9SvOVFMFsW+kyDON7BxXtgriqJOQuIL275wg/3Tn74PeDJVHEfkec7OZMre7i43btwgyzIQviyjEwp5/Phxr0wGnq3da9JegW+3t0u6DuLlS8k2M11r3RN8NsQ0Hz0tl2vW6zXO+b7aeZ714+ijl63jtm3r89aug/qVZ33jH2K3FXl1OQ0BRFr2x65r78hEUdxHz90mpejh805jd1UWPWTuQrT3sqh4O7fcG1rHZaMbpusqfPmqhfzFxXZrfxvM8qX56Q4qx21yvHTR+JX99u/17S07BOHSaC7t+8MPP+Qv/MX3mEwmdHKOQnQOzSZvv+0sdJF0DwQEL0HKDqGgN9h9BC7wbGyxKTPqb8JebYTgqHpI3G2Gu+X8hLK6rsqOK/ZR+Cy6kN5bb41B97rVvsLCIcM95rDG9zXenJfskZ1Qg/ACGfDzthdc396hvGK0O7h661r/kfvur8HnfH7L8ROXvuPPzZiaSEtuHBxgG0vTWop1wXgwZpwPaCyMhjnaQaYj0sjX+eo4Js8gSRKMgB/+9Mc8e/aM//Dv/g+4vX+b/+wP/gmlbRgoze54ynG7wKa+ciROcuZFzYeffYDQEQfXrzPZmSKkpKor8jRjPBhytl7zfHbB0/MZq7ZlPBnjTMveZIdqXbIsS5q2obWWndGUvcmUR48XtEVFnqVoJ2irmt//V/+Sr33963zlvfeIZESWJRSrFdIqTG0wdc3Bzg6z9QKtNY+fPmXy+tvs37rNP//+97n3/BmtgKIuUE4ysJLR7h7HJ8957foNxklGguRGOmTVNkyv77E8OqIofMRuWkOUezToYrHCWUMSRYzKiqJpmE7GFE1FZQzTNGV3Z58PfvRj/tw3vkUURyzWNavWM6VTJVACUh2jrMPVNbJ1mLLlnz34Beazhsg6MhSxg8PJlFsHB7x+8yZ3Dq+xMxpzVrV89tnPfboUQmmer+EWrqYND5J1lvl8TpKlFHXF9378Q97/6ENkEjOfz31ntlayu7dDhOHpsxP2r+3y45/8nN966z0m0zHPZ6c0dUM+jrl54zrDwcB3c5MRk8EIiSJPfSlaFsX+9yQBuwmWlPZscnVVNetPuP0pRNh+l9Za6rqmrmvOz8/57NPP+gUgTmLGkzFaa15//fXQ9SSiWBe0des9q7D5BeLlUdIlA9QbjldtLiiqtVS18exr6SEdpWRPIKubto+ohBBe3J3K55fDTbHpLGXCWP1C1rQebotj7Us+AjFLBTnK1m3Ke7pxb3LEdrPIuk0jk20nI8kzrxduDE3dUNft516L7ch4+58NkA28GFFffc0n/1/8J5QMcPCLEZRgozsuRei2o1R/bC83GHS+r2iad/uRQnphgq088svy8wD379/nf/Q/+ffY39/vO6xJKQPqsTE/vqWg1wTuan57NEL5tnm9zHYXFLMJYH0Z+6Yuu4uMrzo6xnkYs5MllEqDsCAsLrTz9KWN/p71UWUXuYccdugEZURgtEv6kpFOlMc6cDL0GSdE5lvXzooNbyRcTHx3bdH5V5fvF7YfoVejE7zinVdB4S9PfWxq5V+2n6vXvEvzRFFEaxqyPOXNGze4f+8Rj49PkRYm4wnjvV2OKq8eFjmHqBuINVhHUZRk1rfZfHZ8woMnjymXC1azGeO3vsKPPvollXTc3Tvg9WxMtV6iIk2eDUAolqszHjx6xFtvvMlkMGQyHBEhWNcNtTXkaUKcZszOZ6y8QgvrxZyDnR1iwEnJME3RVnP+6CmPP7uPWVyQDjJuHVzHaR8ILOcLpIFPH39GZSt+9xu/iYoUcZQwnUy9XKZS5HHMveWCN157DVkZFmXBw5MjXKR4dPScaJijtHciZWuRVcVvv/4O84tzkqGixfF8doq2cF3vE9ctWimSdEAUJyzrkjLzsLhF0DrBfFVRlDXTyZSLp08xrZdLnc+WvPnOW9jWMhyOOGsWHF8scK3hejJkVrTIxtFax7q13J8d89nFU+aLNUVZYkLtOsqzrSlKBlIxjTP2siHXJ7sMhppvfOO3aQMT2wnB22++xcef/gwpwNiWqmkwrWNdlDz+8CM+enCfs9UckoTFxYrpZMqzT+/z5/7ytxkNhvzhd9/nL3z7N/m//cP/O9OdMcPhgKYxzOcXsGvZnYxRaIRtaevGt9d0vsObbS3OOGKtyZIkkHG7MlpNEid9Fc8XtX3xLPH2shHpF3EtUCGaaJuGk5MzhIDhcEhd+xIuqQQ6KBz5vHEH+W0Cmf4n7sri6XDI4P1vC7JAF/daa73UaaQReHGTumlQQUCrq+Xt9te2bW8g3ZZoSRRFFKVn/+V5TpbnxFlGXdcURUFb+QYXxlq0Emwby+05cc56VvVWpNsZEGO9olJd154lbh07ezubEiDtUMZ/p9Muvzrvl4yo3LQA1VqHiPfy51/2exfFQde0ZdNh7dLi7Lbzk5te5bAxrl2KpFt81RW0o58f1GWL6S6z4zvY2n9WcHR0RJqmnnsQDLPfpaVTKNs2HP6nDfdKAImNv6dMd6HpotVAkuwcnG1i2Va6wX+lI5HZkOfu3rchLwhgfEvN1msBeGCoOx7BTkqcbXHOa0Z36qxKda1FNyptWkhQV0mBXVuCF51dEZColxrKrWvZEQU7hKBD7V9OIruM0rzs/Rcj65cZ8Zd/v5tnpRQ66CREGm7v7PPwwTOenZ4wjRPGozFxPoC2RLqWREp2R2OMFhTrFYVU3HrtS1Rlw7PjY64dXuN3/jt/HazjYj738qLVmrKuEFNNe9FiqoLR7RHPnp/w5OkzilDh8WS55NPxlMO9PfI8R8cRVVEgI8VssWBVFP7ZritiaxglKa723b+MdZi2QeK4c+MWs/k5y4s5OotpW8vtG7d5LJ9xtjjnyclT/uD7f8jdN99h/q9+wOnROelowP50n2q9Yj8f8eTeQ24dXMM4y9H5GWfLOVJrbu4fsFosyeOYndGIh0+ecF5esHuwgxYC6ob9LEfHMY9PnpJKRZpkDPIBSZbz8PlzX0KofP6+FdA4gxAKbRy7oynT4ZDYQTlfs3NryrW9Q1rTcnp+xuxizmAwINLQ1CVCGZzSiEjjtGU03eXvfPWb3N7ZZ3c8ZjoYoKRgb3+XOI2xrqUuS1aLJcerludHDxAOFvMF52dnPHz0EBfl3Nzfpy0c89WaWblmsVpzsZjz4PlTVqamVV7S9s033+ToyTF70wE3D6dINKt1xf5kwtOnJ9x78BmtaRkMhhRVySDNKNuSSCcI6zu8OQfj0ZjJeOK1RIzBOYij2IsbhadISBDqssDVF7F94Qb785iitlsBhM8RWWP70ikpZChM9t9VUvbkmEswLi9HyLto52XtmkVnhLv0uO0+58JCILfyw1cj38Au7hWrPIFMa0XTGE8iq71R73TPBb4JQFPXtE1FkiSUgXl+2Wh7I6jly6OK7XOzxnBxsQBc71RckiW94hRcvSZdHj2KIt/i0rpLC+nVa9VvUoTz1Zc+/0IkJUVwarx1e9nCu132pEJP4m0d9G6/VdnghLxkajqD7SP4rvbb/z0PzH5nw30ThEWECnluEfr8Sg95e5j8qvPkUGprHGy6fHWdvaztovrtcrHuHqWHuoV0iC0oTAhw1tC2vve1kIKmsUhs37K1v86hjl4KXyJiraOxkCQ5Ey1DxYUPrUXnqIaceThaDylv+nx0zof/3XUH+5ytf1Tpsxe90b4aXr/sWnfb5dz15v686rz2r11Rcds+hhDeQRHSd7Ib52PG4ylF3XBjPCTLMnSakDSCxdkxgyTBGMmirSiKEpMP2d3ZpW0t8+WaQZrw+vVbnBydUFQlu5MJ54+DzPChpTENmVSYtuXs/JzVasWN6ze4cf0Gedti25aiLIjSmEGkGY2GnN2/R902Qc1OhUg4IhKeRayUIo01r732GrPZjDiK0IF1HKuY8Tjn4uKCVbVGRL5X/Kpc8fT5M8aTHZIkRwrte2pHCa6s+fJbXwoaB1457/TsHCWlJ3+tSrT1nfuyWKO0xJmWVtD3nK6rgut7uyQq9wJDzlEWFaZuWC4WDPd2MJXnHhkHsY5J45jcCYQB17TcuX6LwxtDYhPRGsNqXbAuCq4d7tBISyla4ihGSElb1VC2TKOEB+s5J01BfKaJpUAjuLG/xzjLmGY502zAJMsZ7+9yuD9iXcO948cU9ZK93R0ePD8n0xF5lGLzAYWzrJYLVBTx/PgEpxXpYMCyqHHGMTs55c1buwzTmPWqwVmPXL12901OT0+oKq+VsFwuvEiNMehEefQNaKqGLM1I4qQPYCTCn1vPKfFrh7Hmpevxn2T74oVTPmfB9u/7H1JITIjyeoWvDmcMr3Vyot3mIc7NjpRSZFnGdDrBhcjP9yENsqBy60t9xEOAJDfDEUKExXFbsvPywiG3IhNrLDqOcBi/oDYN69U6RN8arXzkpCOFEHFfzrO9XcpZB5jZx32euNBFNR458DW7dV31Y+3mSymNNSFH/DkLJw5f2tVB0q8w2HBlAd76XrdWq8CYt66r6u0m89XKZ52zsw3Lm/Ca7M/Tbx4yv2pQtwy23LCddaRYLM9Yzit0pMkGMWVRk6c2dHV7GXy7XZkQWNs4fJPWjSPQGXkRrJYQG1W6DU9gM87uegXVUY8M4B0Zf2tppNIonWGtQYQ89baTsNE+7xjlXpLXGN+hyrf0DPeEkFi6a7NBLbz2vde29rn3bmYvR+FwxbEO/3VG+qXXsBtoN40vw9YvfWm7LOvy3q7eZ33530v2gfP5QS0lGkUkNcNBzpfevMOD42ek1w4RKqJpLYMkZWYaBnFM2wiWTeVJXjpmdzThebHAtA2xSkm04mRxxmg55Y3Daxw/f0ZdlZQYGtOSJDHnizWz+RLbGg72hwy0YjrMIXANlPRIyOlqzvnigqIqcTiiSNOWMMwHSByp1hggUoKd/QlPPvgQpCTJMjLnBZmc8Y6+l0L18qdN6zg6OeHmzWtMpkOMtbStokaglOBgPCZLUs6WCx6cHGGEYxinCNOiY59iaU1DGmvfotcanFQYCaVtMXXNZDpGWUmqYkxjmS+XFFXRd6NyUuCkxBmLjmOEkIyyjEQpXGvJkpRrk0Nc1VCVlvliTVWvmIxvsqoajxqJACZJiagb6qLhk6KkMW3QuGhw1jBOUnKpmKY5e4MRB5MpB9evk0WONBrSNoY0ThhmGeOkIJKAUCSRZBSnzOpT5k3FsqlonBdzSiONqQvyPILY57q1EAyHCSsnuLYzobEC43zJZGnqYJe8LVLCo5JVXTAZpWgR+V7dQVQrS3LKtsYKh1TaE0WdCMTTL2774g32rzTAkLPDryU+lRcWe7e1rGzWwy329OYQSilGI8/grKuK1WpFUZQ0IQ/thQpCjtQ5dKh7tcJ55bIAzxljvOfufGRk6doxbqIBF8xVB16GQMuXC1lHsVoFcRGf444iTRR5OUQTLqztBr41R1JKpNYI53r4uaunNUGEpg0Qr3SbHLEIkXakFW3beI3ll9RL9+iGtbShH7Vpfz3PzznfAUsGRrsMoiAdxb5DCvprduW7kpDLbttAaGk3EGkHo4dL3SnfSW368Qsh+7Kqbus0vuNYsVqfs145BqMhO3sDPvv0mNHY0ZQgVbfnrgc44EINe1fbLBRKgDWB0S281GcH2TvrXSlvtDuD7VCqiyC5FFG7wOD3TpX0MohGEUU5STpgOJzSNL6l4mW4OJxrd4Vd58JBJTMEtVc+A0yIzje66gKpBEor6qL25Ulb89QF3n6AV5Cwq6mRcJO/FK3ZvrYvv1s2nxKie8q3yHdbnjJbz32//w3i0UffzjsrWvprkjgNTiNjzde+/CaP7n1EPhhipGa9KhiOpkhhGcYJrRW+D7W1ZEnOOM346PQxWRYxyGLm63N+/uADsjzjqzfv8PjBfZ7MT1mYCoMlT4c8ObtgUTQkOmakNHa9QOUxToDUvkRvPjvjp59+zPlqyXy5wAhJEsXU1ud0m/WaQazRQlC3sJMPeegcKo4Y6TFCCcqqYnZyRpylDJKU1lla62hCq8c33r7DdJphGwdNzfFywWCSQ1Hwja/+Br98fJ+fP/gYmWn2GNBUa0iCmp9zpGkEwpM/RRTTVj733jpD4WraYsX14S6RlpR1SWlqDvf3eD6/wAmIUl++Bp5MtjseMkxTqqpmsViyl+zSDOcsFoaz8zWtXTEdRDw7q4hFjGgNRktUHCHbluPZklZLWmd9B0fjU2nz9QrXNtAeIa0jUoLd8YCDSHH34HXO5ido1bCeL9kfjUC1LCsf6EzimHut4cHRc2yiWc8u/OtZTlEueeOt25zMjynqllGSce1wzMNlQWTWtGgaa7CuwsaeOxIpjRIgpcZgadqSvXGKslEAi513HpMxq6rECEMSp8S1V0tUL7Sz/ZNt/0akSV9GROnfw/ULqbvynW5xBP8gSwQon8MSzmFbQ1mWSCHY398PLTh9j+G6rjk9PeU8QFlt06AEIScNOpL4ShlvTBvnjXYTIsnoyjg3AhkOI+yWJrNfkLrcNjiMaWmbmrZtcFXZK3QZ051TRwbyBipLQ/lYXVPXvue3CyucZytvjIS1DpQMRjM4EwKEkn18cpWcdRUuvwxTfv4mhLiUI9/ex8sW9G3E9Or+uwjQhXRDF1m/cLzAJRABSehi79YaGmsQ1qKUd5a6LY5jdnd22d3d5d5nRzRN0xPduv1u0hy++YYLVqzzpF84l35sts/LOyewVmyMsujCBk2Xf/bj2jh3YWe41mDahtbUNI1jS+p+63qI3mD71/GKaNKweYENcrTVyeOyEXabz77kWmy//qq67EvP6SsdvKuvb0fNV6L3rfeujmZ7eFfz8dsohgXSLEXHMmjE+/VjNBwyHA7BSZq6QDiYjEZULHBzQ1WXtNYwX8zBWN57+x1sU/KTX/ycDz/5lLduvMU33/kab7x2l9lHa46ePGcYZwySlF9+9CnOWa7fuMFrd26jpQ8G0uGAyhk+eniPz+7dY4XkydEpq/WKJM3QccQgSRgNRyyb1pcOqoiBTrh98zqPPhlz+9ZN5vMLimKNNcY3CylL6nXJ/rVD0jzn6OiEer7myYOHvHv3bQajEc45jk6P2R/uMEkHNEXF7Ws3+a1vfIv/93/1X/Le179FtVqgtCROEoZxTtMUKCWorcMJhVAlRZHgYq/WleSahpbVsmC+XHD7xk3uPX3OalmgsgSdRjTGsjw7JY4jRtmQ1sLJ4oKyWLGzs8Oiqni6mDNfe0g5SwdU5ZFPLTgvKOKXME3bNtweJFyb7HI42eHaZJc7+we8dfMOo2HupYBr33q4KQXf+/4fcF5ZnpiW09UZ33n/h9y69ho3himxzNE6ppASJwWtbciyjOVi6YVSEFxcXPDeV79CdAQKQaw0Nw4P+f/+o3+MPJ/xo+99n4VriQcxeZ73JbPWuL45kFKKOE6DYqMj1hHjwRAVEF8sDAdDVsWcs/Mz6iZ/xXPzx9v+TLp1dcYY6KNK1y0wXdtAsYXkQU8yE3TwtI+Yqqriww8/DaQc74FrqUizhMl4zK1btxiPxygpWS8XOOd4+OgpjWm9NnnIKxljaLe6WG0bNM8gN31065wD7SMR07ZegrSuwwXumjGETkuB6i8jjVfd8idlQqecumkYQJC8TIjjGKVUqBH3TeqbpqVtPVO4M+RdFN5Y46Nr5KWFdztq6/LXSilEd2PxeYswl74LlzufXc0/bhuKjYPFpc93kdLnOm/hPWtbREBGjPWiEd31jSKNL7/yvAcZCCXL5ZIsy9nd3Q8oTCduY3rDsU2euzTIzWgujeVlDocM8qpYh5J605ylPy3fEc5Hqhsnyue1tzXcbai1v3QET+YTqt9h56RZ6QIp0vU27DLc/eL2edf3ZVyEMEsvANOvNNVdVuGVR3nFsT9nTJs5v1wOs3G2IE1TqgYuFudkLsc5R5ompGlMXfsKikQIGglpkpBEMRbHol6DVhzu7pKmCc9OnnMyO+Vr3/wWRyfHSCU5mO6yPxxzurpAZhnrdcV8cYGWmqrNWRcFb71+h4/ufUy2M+Z0MefpyXOqSFBWlqKtfVMIrcmjGDUYcm1nhzSsX23rI0lXFVw/3CeSElNVuKZlkKQYaxnmA77ypXe5mC9YrFa8tn+Dv/EX/gqzkzMuZudoqRiPhyRphDMwHY6gbZkvFyxn59w6vM7HH3+MFi17tw8ZDHOyJCUqHZH2IiGNFT6nLiQq8hULjalpTEOrHLt7O8RxymRnyqpt0TpCCcVFtebi5Iy6qdgdjng2O+H+0ROmkxFZnJAku/zh6imLYsU084Iji9mMwcEObetwxqKEIlIxZeO4kIrWtpwsz/lgOSN68DG7v/gx+/mQg8mU67u73Lp2jde+/Cb7t/epTEzyL/8JHz74gL/51/8mTx4+ZVGckaeaGB9wRUnMvYcPMFIyHo5IdIR08LUvf4Xf/84/42tf/zLSwigf8Fvf+Bbf+/lH/P3/6f+Mew8e8MmzxyzqJednF15qtG/z6npbkaYpAs9hSuKEa3v7jLMRVVWxMxyBGDBfNayPy0sNqL6I7U+NdNZtL180Qg4ubPbqQr6d02Nj0DflOiFShfBaaLIBOGMp64ayrJjPlzx5etTnlgd5ys7ODn5RhTrUWGu5ZcSk8jkiZ6lNS+vAOJ9SldLrcWvp8+8++t0YMh+JbUmw+gnoDZ+Toqv0CUQmfz4X8wuMsbSt1wDP8wwhNnXYSulAVpIIZwNxSm1Fv757V49WXMlP9+SIMHfbWuOft3Xpgu77Xe62y9/218OFVnJ2Yxi3u6+11tJeIch1EeBVNKCfS+kjv9ZYX1ZiPTrStgaEQ1qQwjefB3jy5AmDkQvXF4RQNE1NHJjp3YJ/+VgBbnUWKVTfprUbub0C1/qa6AodYPv+Du3n0SuXaa2QQvX793yMLh3S3c8bHsXmtu9crg2LzDmLMS1EHSpyuSa9g7tfdu3cK65vxwnY/L41tsuI9eb3K9fqpdvnBNsv7HMbn39ht6K/PqKDGGBzzymBcS1FWRIlCbu7u0Ra9e0OnXBkcUy7roiVItbekS5pqW1LqiJWrWWYZNy6dYez+Yx79z9mvSqZjqcM8yEfPPiM3cM9To6PsdaSJpo8SUhC/2dh/RwuijWz9YJ8OKSuSi/o5BzCORSOLNJkUQSjsX/0nQPrVc9u37jmBUnwalpxklA1NXt7e6zOFtzau870S1PSPKMoSqY373Dv/n3atgkIFOwPp0wmE8bTMSfPlyzmcy4WMwZxyt7+vofaywKNQ+LAGtqmQaoYjUA5RxqixLmZsShLyrKB2pGnAxaLBQqBqxqaxvh2wjLGWsMwy6lOGk4Xc+5ev8VquWAw1Dw9PaFoSq7v7WGswNaVd65RSAwKQEQoYSjXjlWxprYtZVtRNiVtXdGWBdIYYinI44jJMOXNvX3eufMbHJ2fEkvFSMbc2r/O2WmNTFKMESyKFfcePuD69eucLeeMx0OW53OOHz/jb/3tv82zJ0+4tXeNRGm0kOzv7nN7/4A7N24wzHKMlHz48BOOjo/7QOfqOulVKv16pIRkmGYI4OT8jEGc4NCk8Rm1bWDLzn0R259JhO23jWyn2Hpit41MeMVHkyFqsiGffBXqtdbSAaie7esfcmMaTAttU2HahvF44qHrzhiFhcFZfH1rqGtVyi+QaZoipcK2dWC1e2alcZcX8459vK1Gtm2APJvXbS1Em3VeBBnNToe7XK9xeF11F97brk3fjmz7Ui2lPMn+Jeu3ECIY/k3nMD/GPzq/0h2nFwS4sg/RnUhoQNEZcLl1LeXWfvr9fs7xPUIhe7i2NW1/LH98FaBp01uYx48f86V3b7Kzs9OfrzFeZKEn2uHLDnVXBiVEYPN2c9s5it5wyt4YB4U8Y7G2xboGa31DFgLa4x3QNtynvqtZNzap/H3YmhZlTX+du/u/27wPY3ByE5l7YksgqAXRlq2Cq1deL7fZ4QuOQXeu/f3XGePP8d9+HWPdj+5lX/kVwvEraD4bww3OGWzocd5xDG7evOUbvrhAHhWWSCviKKK2rX8mnMMowZOTI1Ktcc6S6IhWOHbzCbPhmLoqSeOY0WiE1Iq1bXl+ckIkBDvDIQfjKZN8gDOG6XjiW9s2bXCiJGVV04ZmI84aJI5YSySOYZZinF8XhHVgLLOq8s6z8HoDztmg5ufYneyQ5TmJjlFIcp2QpSnDPKdpG5q2JhtmjPMRUeJ7XRd1ybpco5OY2jmM9vLHom4onSNPEn9NjCVJIkziiJUmjWJ2RmOOj55hcQxGA+I8YnmxYpTnDLMBy/mSVVGAUKRRitaCKIm8/+EcN68dBCKr4Gw5R0jBzb1DqqIiUj6BKKREWe/ItNZ5HQzbkijFbpIy2tljOhowSBJiJRDW0NYV63LNShra8zMePX7Ek7NjmnbN/+e//sfcuXmX3bEkEYqT5YKnz4/QSUx5XiCE72G+O52Qy4hYSG7sH/DatevsJgnTwZg8Tbm5d4CpG24dXufR8yOUUDRuK3W45ZRvejP4Z9iz/hMwllWxJtMxUqUkUYxxljSJ/ugb/tfY/gwN9rZmuLj0IG8rdPt1VYIMcpzBI9/kFDfRAV1/4q3dKY+hB4+oDfBx+JgQCOcgwB7W+si/M+JRFDEcDj1hq658MxPjL2ITIiylFcYERjKbyNob7G4BlX3+zZc+eXPg8De7VgJpRQ8C+oYpm6py3GVJSOcuL6Cd8elIu9uQZgcN92SiX/cqbRnsbY+z22e3X2F9DkMG3F6wMQRK+A5k26SxbfTkVUQ570h1EDNI5RdhpEL4+r8eGXny5Clvv3OD6XSKFJIsyzx8bn2Euj0Pm3FvzxOXiIXbc9WzwQUeahHQMYQ3ae4rLHe3MbpKC2/oTefEuf6YlzfXG9q+pDH0BVeiexa68brNOP9Ix2sDf3fppBD8X5nvfhi//iYuo/Mvu9V6I/659+ErAfOwjzC3UhDpCGMMBweHKOWXsg7Z8cpmELeOCF/i1mB58OwxB4MRo/HYw8HGsTcYw2t3+65nWZoyHI9Z1TVn52fcmB5w++CQ63t7ZFGMQpDnOc8vzijWBUoIsM6jPy44ePgGLsp3gyBJMy+8ZB1aCLSUnBwfhc52HjWxxvjyv6ZiMvRtJ4Vz2MZ4pMjBznTK8cU56/WKJE3JkoQoiSnqksV6RVGVICUWyaKuUcIRS9DWMUgyr3+NJI8ThJMkShMpxSDNiJTG4kjimEznSGJm9TGDwYgUgXSWhW0QOsbhU4iNMUgHe7tTZA1F3XKxWqCk5HC6yydPj8izJJBilc8kWoO1np/jREOkYJRm3JyOuLO7z7XxhP3xmGGaoKWgrCsupOD+x7+grDRFW/P0dMGz0yOEismzAxqzZja74PxijhWwLtckeUZVlmRSc7C7wyjL2R9POdzZZ5LGZDpBScUbt+9QlSW3r91kmGYoISndduqst0b9utv91J00NFA1DaMsJ2pjotAhcZAkn//c/JrbF26wf1X28aU14kqXoV50ols5gtHuFnhjjTd2IdJWodyjK3exrYe0hegEJ7zqmBS+ZWZdN3SrUr9YiVAutFVPHEURWZZ58pKx2JCP0FqzrkqapiFOU9brgtbYsBiGMg+6yMznW4UIzRuECWcmcD0jmC2HwQWFL0Ece++sLH3TFKG6z1rfWjEYHp+n3UTNV2HmrqWldrpHI35V0tnLr5032NsSqCp0s5LSs+27z7kAASop/YLUISLBkG+Ps7/+1iKFZ2U7a/scthQKpTVCKiIdE+kIa330/fz5EUJIptNpn2eK4zFNXdLUdZ9L8kZL9TB+5yl3kqOb+20LsqWL+rwGuFQSYXxNt6MzfKERiOg6o4WabEChthyCbbThxWdl82onCGTDvjaj2TLnL0TGvUMCPSR+6TL3Du3nOXAvwtQvy3lfHvflSP5VDPOrr162396ZeNl9ud0VTUqJQqN1zLoo2d09wKO9BoFv76ojjWgtsdIkSiOBVV3xcPaE4Wt3yd3IP2vGMc0GHLy7CwiauibSiunOlKP1HNe0vHbtGm+/9hrT4YjFbE4SxQghOD05ZrleIrRCGIt0fh2ySvn+6RgMEmM9qqODrGwS9BB8p0LZrzubK2+p24YoToi1RknfM71tGvb29lhWJUezc0QckUSafJjx5PgZy9USYwxVXREPx5yv1kTCMYw0uVAoNLFSpKplmOQoamKpiPBjunFwyMl8Qd06RCx57Y273H/8mNFOxGBnQhZLzlcrzgtJ29QsqjVVVaOtYDTIaeqa4+WKxXLF7nDIdDDi5PiXTEYDlm1LmiVEgKlqIAEMFS0OhzQlen1BayrOzo65Md3lzuEhN/f2ub13na9M93hvuk+U73LtZ9/lp5/9jG//5m/x+PExy6ZhsVgxW6wwzvoUo7WMRkPOnx+xqAzZ4S1uHBxwfnTiUQnpmenKOb78pbdpq1ngHkRoqWgaGxjxW2mn7TRSuC+VUiit0ErQYDjY2eFsVqOc10IfxP+WG+yXbX+UYZDS37TITlxls1h2edRL5Cb8YiSkRGqFaQ1S+3IqIRQo2ecJCRGNsxYnOgKWDJCpCWpmbTDu0tevhnzy+fk5z58/74vjdXjQkiTh+q2bvlNMFNE0LYjQuMS5EEFvSCaXlMiE8Aup9BJ3SqkQeW1y0/57pjcyQniVKxd+twE+F46+o48xNeIVi10nj9ob119RfcePo8U6S9M2SCGp67o3zN15Xe3cFWK4fh/g8z3bLTU7o331eNtkOUK+va49s78oCupmDc73mW7ahjTxva/LomSQ50SJ71r06NEj73TpnqnXkwu1CkhAx0YXaoNKiI0R2Y5moZNVdZjWQ3+IQHyT/n4iGH0vBxk0zZ0D4WiaCl362mjvaL4shy3oG4JsZqX/y4VoeuOUXTHc3fx3cD/dSWxKpcIpvphu3n7/JffQy97b8m8+38BvWeWrSM9VcZft7784DhkccNk760VRoHXkpZCrGuf8vTYaJxSzOZlMyaOESAhmiwsGBUx3d3Ha379JklCvS4g05+cnjAYDlJCcn5xyUSz4vd/8NgfDUYj2JFVdkwxz1sdPaa0hjiLSLCVNEqTzhkJIicVSNiWxlLRt7aPsOEarThAHn16zDmO97p6OFLgWrZWPXp1B2hblLJlKfUMlpcmynCRJOV/Mee3dr6K1YrlcIAQc7O/TJBlHiwVYR5LnREqDk746hogszhmkQyQlWZSQZylpHHNt9xqmdJTWMphO+Qf/8D/hb/2lv05TN5wX54zyhN/6+td4/+dPyLOMlfHlgzem+4yHGU2h+PHT+7RY8jwnVRFHRye8/vYNTp6ekQ0GaGlpihYZj7DNCmUjGiM4risezdcUpmJdrDF1TSo14yzn2u4u/913vs5XX7vF22+MMaZG1hU3JxOuTQ8RseODz55gRIGKNDLy61tb1wzTnPEk58a165wfn5AnKdJthLmapmGYDygoKMsC21qSJCNPh32A6DU0Lyscdrdq9xRa54mhhzu7LGfPsXWFcJY8+m+hwf6jtg3EKkOk6A2V7aMMgE1JFOClNrU3oFJ0C45/AAj5wdB/MODQEhl5w9Iai3NNbyCU6vLdDtu0WOdIEh+lRVEUIM6u4UdLVZQcn8+Ioojd3d0QOXrI1gXmr0cA/NbJgXqDYT2Ebm0gUrXkic95Gtq+btwb6VBza4zPZXeNKboD4J0MLQIxTspLC+/2/MJm0X0ZDP2q66KUQthNr+erC/u2gXXO9UjH9j56khpcil6dEJfgftc5M/09oXw+t0NKEIG24oldSZKQJBFlWXJxcUFRFKgo7iPSKIo8YSbotMdx7IkismPJd3PRWRSvinbZWGxDYAIpI4TQ4bVNu0tnN6Uf2nSseF+MqLQ30gaH7lM6VyLf7mjOG2YXSh2vOjXW+WP1sLlwbD0kv/LWZcLtJafkj7+9AjC4FMRvR9OfdzTxwjXYvN797IidUZSQZRnOQlXVlGVJ27bsHoyo64ZcJ0RCoVAUZUVRw3Ay9pUdVYU0Bts6LtZzVnXJ/OKCJ0fPaMuC24eHfOvd91DCEmtNVVcIBckgx2pJYwzGGnSkmEyn1MUjqnVFNIh9+an0zluSp8RxzCAbIEQgLDpf2SGURkdx0B9ovCpiU5NmGh26YGEtURrz5OFj3Jkkm064++ablJ98gqtbztcLFhcXtFVNJHzaaLFYMR1PqIqasnUMpaYualyakcYJgyxHq4jpeMLO7oSD3T3mF48YxBmybTk7n3Htzi1++xvf4P33f0pVaUaTEW/cuMbsGKw558GzJ7R1w+Fkh7JYM0yGfPzwEUpHTMcjlBSslmtGoxHt4+P+wrdVA4mkriqcjsiTnHEU+7RSm5NNblLWJYv1knVV8vzolP/9J/8p0WrGV197h8Eg4u6tPbJhSiKHPH5wj9vZDtFU8tF6znpdkOc5k8mEpTknTRMOD/bYm+4SiwgtFCLoG1hjcaYhimKkkJ6zpCRPnx2FADCkXkVIbXIZyXQSnPCBxXgyYZgPGKQZWkhM27IzGv76D9PnbP9G67D7Bw6BEoo2GObJZMJkMubuXcdgMGS9XLJarVDSQ7h1XXMxn6O15vxiRtM0dApSTdVincDgxVDoFjnniU+hjsZPrjG9GHu3IHRlM7521oD1xA+JN5TGGL8mBnika2qRaA9RubZGArH2JAnRnWfnLADCOtI4IUvSrcQh1G0LQlCVBVVVe25wiPCMDc0bQiTYOq/DZYPh7nKXrbNYXiSS+ag3fB6JkAqhVMiubbouvWrbXiw3i3kQHNli7W68TW+UDcbXPYpNVLchb1h/9PD3lt5YiNIcWF9vHUvPIjWu8UQuC0ZWeGqh349n2zZEsWRZHHMxL0nznPGuoFxaIi0ReqO001rf+UpITZfH9dNo8IIlBhEi40s5bzor6nwu2npd8I4l7+8vg3MGqVQYY9eMw3fW8i1QOm+8awLyaqa+6FprOok1Pvo0FqzzC0eg8fgcvfSOrZIRWkVBZMbvQwbHwVmQKEQf0oY6dF68Vj0f5CX3g+u8jc07GwxA9HcGHawupMQae+We2oLPhdp6JpxH2WyL1vHmngghvBAEQp5E44iFwrSWaDjm//pf/b+IjeObt17nzes3WGKZiIhhFDEapIzynLpcMhrtMh5NKZZLsDUgeTY75mA8Jo81Z8s5ZVMglGAlai4uZkyzAY2OaCKJjGNGxNSLgrJqiGJFLCOadUlZ1kzyIVoLYq2RSjCbLYnzCf/N+z/hm3ff4e7+IXEsWdYFO7sjTFPRNCVCQBZnOGdRTiLqFlkbIp2gk4TZxZyyaVFxhLAWbVtUUfDRo4/RecrclLQaYqmZ1ILDOMLYllop1hGstCW1HqpNk9h3I0x8vfH50THXd6ZYHdEmgtYaolry1Vs3GU9idnd2GA5j4kRxenKMUC0ffHbC6Jrj7njC3emUtnDEI8UHJxW7A81OHjGrDBU1qUxx0qEwWAtzJ5i0BUb4sjIai3Yt0zjhG+99mbdv3+Jwd4zAUpZrFqsFz9Zrnp4ec7Za88nDB/zgRx/zf5j9Q776+pf4vW/+FouTGZ89fsD7Dz5ERArT1CRCEeVDru3scbi3TyQkt29d970j0DjhvBvtfF26NZZYCYZZSpYNqOrCE1S7+5dNnwSlBEjPvHfWUtaWfBgRW19qi7DsxTFfvfbmS5/vP+72p1LW9Spv/VJkF55DKQR17T3jqipZLhY0oXnGcDgiTTPSNN14TMWaqqqCqlhgJdPv8vLx6Dz6jUiJH+MmwvHj3Y4WJV3T5d6L2s53B4jRR4qhNlZ4dmrbmh6i7D6vQoTZjXcT1SuQns1cb8Ggoiu23q7RFSFf6jaIgwiYbV+exIuOURdBbobkx345b/jq7SoTX/QLp7i8Zm8tqs55Myxesp9NM5fL0Pj21jMXRMgFb+VTXUBcBIQ8uSekKSWpKu/0gGA0zFjPt3K/212htuCsS05JH0l7EtB2LOh6k/TySPYy6sCla9SZue7sfNTd7XpTOOYdh0CqlFfjUv8JF8bXOVuC0DZTyZAuEVtjF1t/b5+v2Nz/W6/BJkXRlbdtz9E2nN7ddz1XYxu5IaQBtjHD7dMRV/7uJi0sCN0lkcKB3aSBeoO9NS6tY/LBkE8fP+bRyQmJdezHOamQjBLL/mhIomKS2LfYNCvDaDQmjRLmzYxlUVC1DZ8ePSWJY9I89VUigSwW43XEXWt88CCDE+QEzliiSJPGCYmOEc47+KPBAOFar8qmFTLNeH50wh/88AfIwjL4asRrNw9BuND1T9DfbQGt0ZHun9ROHreua5I0Jc1SIqUwbUMWx5zMz6FQLMoVTggirUmVZm84Ytb6NJSxhqKqcFFC0zTsjqfEOgIkWZJSIEmTlHVZUJmG1raslxWjPKZpKvIsI45Aa4dzCiktpvFiUFGkGAwGGOMv3vHFBW/u50RKcHx+zuHeLlVrSZSX6WxDyWysI4QUGOkohUc1W+v4+dkTHi2P2ckzxlnKME3IooRruZcobaRgrGM+dZ9xY2eX5eKc7/7kuwghKKkYTyeczWd0BMskSUniGJwjjiO0VpimxSkVomavJV47S922GOvRD58ObPr7jvDZnvhMtwYGtMuBaxpcBvFwgM5SpBDs7B/yRW5/KhH25xntbuveV0JSFSVn52dczGY9pKq1oq5b8tw3z4hCvW2aeJWZtm17BbFfcVAggv618OUy251CrOuUywC8jnnfv9i/dHkJd2KzsIVVJiDneIQ99CQW9AIvbeuZwgife1bR5Wj/6rx1hj2OfdlY2waW+xUY82UlOZ1x3/7c9n6xG0fkhfeu7HtjzPpY6tJxrsKh3adE950r79ne4Pj3t0zKZkwBHfDPRGfUJK6n6YXjOE+qa5qGpmmQ0svVPrFnvam1W8ffGK3LCIKULjyM3Z63f26MRldSeJU0tg3X2jADXf56e1bEltH3tdrQR5zCox+e1NL10nYoafu0z8Zp8P8uO2T9QC/Nd//K1nuXP9ad95bzsPlSf+9vPuOCs7ClRHcl6t6Kxy/NzatWBZ8C2Xxze87o54RNwxMEMtJkcsBPPvoQGktjLc/PzxlLzVyUXHvjPRShp3xg7ToHWmrqtuVoMed0Oefe+SlZPuSt9BZOKayWCCkZ263vCE82jKTEWoNUkiROSOM4GFCLI5TxtMGhkJJ8OGI2n3N8fMqng4e8ceMmr9++Hmp5XV+SqYQnpCED+qS9KBDCp+KqqmI4HHn4H6+1EMcxdWVYLdcUtV8ntdLkccI4G1AuF0FHwVHjsA6a1qKkJgoOiFKKKE2R2jcZqU1N0zas1yuuH1ynrmqSJEGKFoFBRwnr1REaAcYjSlGa4RpBYyyL+Rn7b91FK82TkxNev3mT2bpgGGkUisZahLBIGfn7KqQxa+GN+ezoMeVqjbKWaZ6zOx6zN57w+njCZGeHm9evsz8e0+7v8613vsTJ2THPj5+SjocQCeLEIzNd6W4URWilL/F4HEFjQQikVkhfy9t3NET6VGVXheQQ4THerI0iNKroSLVSRmgraITgrCyYrdeUTctpuX7FHf/H2/5Mc9g9Scon67DGURvTR96nZ2ecnJzSIcyCDiYWDAYDptMpef6rSb8551snttagoggTSmy6KLQTIHHBy1WhdrdfEPtIxH/ea41DYywy1HAL0Ymm+FyoZ5ZvomvPRfM5yMY1oW2m7dXNvHzpZlzgDXuSJEgpg+pZ00P1XWs3T4qQoSvZ9pLYFcht8s9daVZrDdvs4/6auC3PhJBHN7xg4F7MMV5+f2PaA2rhZICTN3Pqtj7ff5cAN9E1uXDBu5WbewU8YUz4WuwoiSiKuu+YNt2ZYu2xh+i7/tNb43s5DN3B9t2pbMRLNlHyVrS/HUVeOY8OJO7e9fvsNL/DvDuHVhmdsRbb95fo4iuBoQIsUkmUJOjFdw5Zi8VhjcFuIUI2DNKfQTfOjSDMVry/MaJuM/JtM+/6OelMdnjPe1tbUQY9ZLg10ZcD6q357Y14Pxi7DX7Qv+k2BhtB6PLnz1FHEdZUvP/pJ6QyonE1s3INkebTTz7mrdt3Och30FoRSemJWmczlIhonODJ7JwPnj5ibRt+cO9jDnb3aAPLG2BkBNaZIPrhORVZnNKaBqEEidbESiMdlHUN0hJpTRQpjGtw1hKnGflwyGt37rIyLSfzi/4+BY/AKamIo5g4jv1zbVtkpJGJxgjLarlkPp+zu7sb0KSGJpA/k0HORV14XXMpiaOIZBBRFg1pUbIsSp8yiTWVMRgBTelz3UZa6qZBaMXFekljWlpjqNsG6xpee+01nIUsjhGm8iWtCB4/fsgwUdiyRgiFznPa85KLomS5POfW/m8jneR49oivv/k63/nlp9zIcoyKKVtDJAVNbXA48sY7sSry2uuFacnHe1R1xdJaLs7nfHR0yk+kX2e/9Npr2HVBBug45q3XX+fN12+zrgp++ukn3H/4kOFoSJYMfLWAliRRRKwjJIIojn30LAVIiSRI3EpFlucMhkPSJPWBYOdEB/LNVUTQVyR4YalYacZZyqpp+Bc//AE//sUvaaqG7/zhH/J7v/u3+KK2PxWD/asQWGTwzKuqQWtfshUFElld17SNhzpFtGHnSSlprKFtvdcYRRHj8fhXGpN1DttaptMp+/v7gGeTFkXBfD7vtaeLouhlRq/mhW230LjO+HlYSymFimKk2JQOdbXUVxemq1HGtuKYcxvdcGMcVVX3/cWF8E7KdilVD1VvwYQdyav/O0TAUm3YzFprouhlRuvlxqytfe5/I2azPX5/Ha/a/t5kbawBOopASrS2aKWvnHv3MEhM0YZ58l5aXwMu/cIputeExFj6PtvOec96b3cP+JBOanw7FfMqYp5n5Bt6zFZ4zfDwzHqn0b7cSF9yOAIUJ8SWIQ5hvrU2OHGevHhR3rt03RCBmOf6WBxhBVIkqGSIaRXCdkrjojd83blbXL9wCxW6jsntcjXhVVy2/w5OkAC/b4t3KOTWBd1AOpfO3TZmE5N3l3r7eoY7YZPm4AUj3iEXbP24dFzRtbcNLVlRVG2N0IrxeMrjTz7l2cU57915g3VT4jCMplMOXcF3f/ojfu+bf843RTGWQZoyjAeYxjBbrDhaLJk1NTvjCZ/d/4yPnz0ljTR1a1E6ZjQYImKFTFOiNENGChd7rsmz50+ZTifkcYq00ESOsikYZhGREEBM2VQ8fPiQr33lG7x+53Xe/+hDns3OvLOhPQnMNJ4jo6Rf+5wAZ6B2LefLC+qy4eL8gtPjE27euEGe51RVhbHGw72uZblYkOW5F4epK6b5FH0YMVutSVXEwlQsqpLdnSmN8C0454sFi3LN6WLGbL0knaSoRGJriRSQJJr9vT1W5xdEMoIoweqIOElxzpBlOVkSkaYJOsuJl457Z2e4dsGN6R7zdcVsveRwNObRySnf/sbbPF06DAVJpJlfzFEjzVqY0HxJ4CKJVf5ZrpyhrDeo5CogKp/+7GfEUnAwGjH//X/GX/vN3+YwH/H84RnLWUGWZx6NyDMGWY4wDtdatJTEcczp2Sl13VCaFgtEUlFVFUVTs57OmC3nrFYryqL2ErIhmu7u2j5t1AcOHimTWpMIxXd+8D1+9LOfgRN85e0v8wf/+g/5Irc/kwi78+z9ZjHG0RpDG8qYbGjAgXN0HRK2o0Nj/OJX1zWr1epXOqYKENXs7JzzszMALzyQpuRJishyBLC/s0trvVrRcDjk9PSUsiz7yLcDbdVW3XOXozaNH6v3siOMdTQmQPempWtpCfQ9G7TwPW+3c43+fPFNRNoOmoeqqnoIuJs/pbyn3M1bN1eXjVIwn1vQ5DbI+ae1OUJK4GXvvdxf6Ld+jAG12Da6vRGV4ExITzhJXTVY45hOd8LnuhzoRvxle462c/6d0d/MTWd0N59pW0Ndm34sXYpg22D3RLQeFrd9j3efM7FeQtM5VBy/gFR0ZY1dM1Pb+gUcNveasdaLcAif/7T4mk+lNVEcEcWeXezklsCNPwmc2pxnx9nozkUYg3Qd7L+5aleduH7elHvhtau8B+9JhB7e3Wcv7SvE9b5l3yVEq0NVpFK+uY1zCBmqQmKNzBLOiyXWtgx0jBKwKJc8PXrGN997m3/+kx9wOp+R64yD3V2ePL6PjAVFXfHk+IiL9ZLhYEDewN7BPp88f8JOFBNJSNIEk2rO5hc4m1Apye5kzOHBAU1TkWUZh3v7ZDJmtVzx+OyMui6JhOBwZ4e6rXl+WrCu1vzyg5+TOu90Pj8/5YOPP+Kdt79EPsiJ47h3ypMkwWlJtVrx0f1POD4+YZgN+d3f/jZf+fJ7fPjLD0mzDIQPQIqqZF5ckEUJrmlAKLI0ZpRnpInkxvXrlM5SriyFszw5PWUUadTRc0aTCUbAum1Y24qnJ89xzpDGKdk4YTcbIa1glI/8fWt9dUusY+7efYPzi+ccphmRkNR1Q5amPPj0lGmuGaVDZoua5XpFmiWsiiW7kwkP5yesq5I4klTWkQqFbRxOSAQKU9VYa7h7/Tpf+/pvcn0wIXeSdlVwVFXcO3lGGTkeHT/lwdNH/OiXP+N73/su3/ryV3nnjbdJdvfZjeHB/fvMBdzdPySRPsLW0qdRLy4uuHv3DZ6fnXJ8dkJTVx42T2KccCityfKc4XDIqliHdFkU1uiAUoU0n7MWGfme59W6ZLx/g5OjI9Io5mCacWvvkL/0N37v85a5X3v7MxFO8QGHX4VU6B3d5cO29Z67fG23YLRtGxLEG1nHuq5/pXF1C7YUgqIoMNayXhdcqNC9KQQbURT7yDjkjDtj3Y2jN6zuMvFKSolVFownQhlr+txpBwN7gslGe7vLwauQn3f9510fRfrFVAanoDM29tI8hbX78+fceonLtm19DXXb9Mz3zSR1kc9mZ1LJIMlpLxlNf1zXR53bMOvWUfsXrQDbttRBD1zW9SVkoIOYpfS10iL2NdIusKX98YOYCtb3mA69o22IOufzBcvlmp3dnTA+2+ecnXAvGJPt86nrur++2/B0Z7Q9crHJ1frve6fnhWjd2Y7w7JvYyA2E3olkSCWJ4r3+GJtNbrLdDpzyuUORxCjFFiGty3PbPu8tlfTCLpHyYh4QuriFaFsI39e4v1BbzkIXfQcju31trm59wC035y230kV+Ttgy2PXmvZAWost7hyoGnzIQfrzKN1npUAehZEAffLWEk4qiKnn06TGfPX9CqmMvRiK0N+7OMM6HjAdD7t37jGk+Jk9yXj+4hlk2PD095vj8lKooGO6M2VU5BTUXiznOFUzTlMl4wNoZyrrm/R/9iNFgwN3r13nn5i1eu32DYZ6yN54gjGC9LGgC2pBqTRpHKAlJpIOSoeJgtMO90RilFGXTEKeJT0sBcZx4ZUOtmc/Oefj0MS72db1VW3E2O+PGOzcwpvVEv4Bm1E0dODzWlzkGw2Trhslkl/jUt42Vzju2a1FR46hMS922FLZlUa2pTYsTUCzWlOuaREXcPtzHVoZEaxpToKRECY3Skiwf8b1f/oB3bl8nU4qmKhnFEx4cPedwd4QSEcZI399Ba2IcUkeUdUldlwzTmCoCU9Rgha+aCE5dFEU8evyY1ekph6MxN3b2uHN4jb/01ru8t74BieaH7/+Yj1zLO1/+MqvVmgcPH/GTn/8QoQXZICVJU8p1gQAGWeYbgCQJ5+cz9vf3acI6ZHG+bjukCNfrlbcnzoV+Ab5rolJBU4GuvbBfmxC+lNj3gNAsq5K3X3+Th/Oa57MLpIQv3X39hefnT7L9G42wX8bK9b9tSkO6z3TQw9XIaCPvuImaftWti9B7vW98K0zw65cnh/nFRWtNO2wCTL1hlwvhb/6uRlwpXw88Go28gQWcsTSN7/3cWuPFWEJ0/II7I4JhcRsjvS2qsTEu9PXinVF5meHx52URbBv07nPe6BnTbulZby3IrjOxm1F2vbOvkuE2+6WPjvoTugTVhsbhV87cbZHeNmNwwTlxGy5AqM/vDQGBpOZcSDlID10Bs9kFi8WCvd39MD7b5yI6A9JFqZdzURAUL8MYxKX57wPml0AFLzNoveOFN6u2ryzbnG+fuhD0vAEPc/ez0r+mhMSK4BxAEA0CoVQw/l6QpYOkLxO3xAbNEH7ee3BFeJZkVw/f6Qz1JLIu8t7M0qXNXkFshNh8SnTzsGWkvcHe9KfHOYTsbrqgsyCD06GVv4cFvZNhnMNgMc4wXy756N4nPDl5ziRJGeQpkYkYSdiZjmnrhjxOWV0s0SgOswG39w44aU/57MkDatsyyjMmScwkHzCvV6ylZL0qGAjJOD2kNg1RknK0XrKyLXmWsptmHB7ssjOZeuUyKYmimMZ44yasN9AISxQpdKRJI82N8ZRca+qqYLa4oDZesMkbA2jqmuV6zXJdoJOYtS1AK4yznJyfEccRSZrQtC0q0kRxTFlVFE2FoSM4en/ONC3CWHZHEwbxMZmuaJ1hVs9ZVQVjl3jH0UFV1zjlsG3LMB/QFJZIal8GpRQK369aCYnUEVpLpE54vrhgNMgZpQlYg9KSR8dH3Dncp2kcTWMZZCll2zKINI2F2jZYjCecyYpEKBotPABjHa41xElG2TYcL+acrRfcmx3zy9MnFOslSZrwxpuvM8lSDvMBX711h6quORgO+ej+PU4WF9TGkMYJTghfb54PSIOGel3XHFy/xmKxomkbPKlX06OPXXDooG1a2g6Nc9YjZf26F55h6Z9XnG/0c1EsePeNN/nZZ094cnzMxWpOJF98bv4k2596hH11Qev/Dh8z1oIUPT3K0cF+oRXhVsQjgnHz+wkkizj+tcanA6FEKx9td4uTEL7JvevgeDyDXfXyk2Hhcq5XTQtnRNcLuyOPYR113VA3tW/4oBRivQ6OwhWTLURoQCGuOBSbXHQXyWqtggPxMhWoEKl2xnGrjra7GTuj3bV/lC/Zx6V9XTHU3fm+AKe7jcEOcdKV72wgeY8a2P7YV52GLh9LaJ3pulril2xdNYFf+wXnsxnz+YKbt/evjHcDXW8clw2aIEXIHYf2MZeMngjysgis8Ezgl4358rkItjF/77O4PrDsttZUParRR5MdbyIwyAUO63zuzDobRBzwIjpSemG0LeUvj0CE8eC2ZZB7ydjOQCIC6z7MuQi15j1xLTjG/hzpf9/M/+WXxNYz0Z2XEw6JDvu1HpVwwcAEzQMXdiaE87Kv0sOMPXIT7l3jnG+8YwzLYs2zk2Nm6wW3h/tMxkMGxkEdM9qbUlc1gzijVJ6QpZRkdzxFtvD05DkykuzEQ4aRRkSwk2ZUbc3RfElrDJNkwMniHD1OqCJBHGkaIZgvlywWC4aDAaZuUCohimLKuiZPUw/buyCXHCl0rIi1Zm8wYJymHJULTmfnvoTIeVU0hE8Jlm1N1dRESUq9WuCkF3Oaz+c4Z8kHOWVVkkcDoiSiqitqWhrhu815YSW88asbbu0f8vzomFXVYNqa89Iym19wbbjv1xsLzhocLaJtGU33qBaGPI453N9HtQollF8DQ8pFRwIpYxZtzWgwYJgkFK1P+zw9PeHPf/UGZdVQVDWDPGW2XDJJYyrraIXxYAqK1tZMx3uc1r5Jh3Ab5zFNB9TWcwBmyxX3Tk548Pgxh9Mpf65Zszo9pTGWQZIxTFIGgwEgUM8e8WR2hjHGSxNHMcM8J4ljmqb2HJ44ogiy0uARRNP6dMLmOSQ0cfH6DAi8YywE2C1iqrNb0bZkURe8ff0uO8MB1jacLArquuCL3P6McthdmYbxN60NkKggyIcGEROga+dorC/49zDkpkb5kszmtm1xBH1ewn4cbd0Jrrig17CBen0vbQ97bEf6hOYLfdQYIl2pJE3tIfNOaUtKQRzHJEnSG6cojv1F7eRSQxjb1RRb5/qSta7tZV13NeaiN+RCWJpmW6pU9ZGa3PbithZRESD4bai3m/vPaaO8uU5b6EZvSNlEi72BChFaF6q6LlrcmkchPOO2j26FuPTeZk7cZuxSeuMj2NpXuE/8CHwO33lIfDFfsF6v2du7dSV6v3z+m2hQ9HrmIkCN22mPzfgI3ZQ2UG8/lquTTmeowlC7SPUFp2ET+Xbf7h0TEbTnEeDazTPgOmfSl+hI66sejHMkautR7iLY7WsURqAvkQc6tGIzkO6MNgpr3fXtRrp1f4T//UK3TTzsUA3/03Y8NyHDnPiSyk5zwO/HX0PZ9W0P91UX8XdtK60Q1EFSWEQaESnyOGZnMmYHBcUal8TExOyNd2jqliRJyPOcaZYzfXOX5z//MdpoEgmxsDybHfPO/m2INWdnZzghGMYpJ2VLEQlWbcsoiSGKmM3nnJye4mIJTUOc+xrs2XzOcDgk0pq6rtCJIkvSXkI4UxF3rt+gMiUXywUq0uhYB6MAURxhtBctOT46otFtb8SqtmZZrMiynLIs0XHsX29q1DilaSoGaUIUpE8zlZDpiOvXr3N0csJ8XbK+OGMQpxw/P+J3br2JbQ1VWRJpyXq1JpvuUK4KTGtI8oRBNmB5UjAYZkidUVUW5ySRVkgVQaRI4ggFmKbCOsPZbMbrt77Jcl6yWpWkUcLTk1N2Rim18wiWkl58qW5qbl27zvGDj7HSIrSitY6LumKU5+xPDhilOcrCxWzGHzz/mE/u3+dH9+4zFJJ3bt1ioST7oyGfffQJx2czVkWJCdymW7dvY40hDwa7XJUMh0PquqZYF4EF7p1Ei68rLxqHcd4wJ3FMWVboKEIqr1uvtPL3rQOc51wJnNdnjxLmbk3b1Ny+cYO9x3t89PxTrPi3vL1mGoTxW+tPyNpNidIL0SVQloVnRzqvXmWbzhh7Og34hTMK2r9SCFpLkPj0bQ49TKrAdYQg3wBASt+3mrAnKxxREiOkoG7anpXuc8ltyC8SOkRZGtugYkVbNVxFNqw1/dn4aF+E86lomqZXUBMCsiz2nqUWmNY7AFJ6t8MYX6altWY4HJKmKWmaUoeGFev1mvl8Qdv6EoW29cIeHUzvCXo2KFyFhdIKmlB36NO83qCapvWLonWv7JV8desg5EttQ8Piv53X3jR6D5G83BCzTMid+7IzNo5L2Efgfgdnwr+uhEI6QWNbmqoEvAJaa73AgpQO6aBYKZIMoKUoZpi25ca1d0E41kWD1l56NlJB0KPrliZ8ra2TktZaQsnrVrS3FSE7AhTry7KsabEGD9nKDdoilKSpfXUDwSgjO41xQuWBQjjvgHlS3AZF8QzzDUfCM/bkJg1gfNcuHRwA0zYkWnr5VRO05aWPFAjqTNK2l9j4Hawntn7fvtbgc566D8btBjURhMR8d+fLrWhe9K9dcsTCpLrtUF8qoJNeDfONRCMQUiO1RSgdyhaDTK/wSR4lFOu6paoaIqmJrKOiJDUtk9EAkWcQR1TVBXd2b/ONu9/ARrA2S8zFmuu7h6TDCW5+xDjRDCZjnj6bk6aK6miFwyBjDXHMtTu3+cWDz8iIqC/WnDUOE0ecrFdMm5h6XZMPJphBwtPFOV+7+TpJLBjGGqVSShwxOQ8unnMnfoNqVVJdlKxTx/Onz4hTRR5FICJO5kf84uGn7I1ybtze497TZ+QqJtKwMmtWi3Ou7+/w2fPnGNfisJwUF9Srgp3JFLuqcDJntDPlzuEtEp3ggFgpRpFmR2lsmvPRvfu888bb2KriYnFBnKY0M8POcI+Pnj0lTjX7O2NiEyHdilZaLClC1SRIxnrEik94TwwYp1NElmGbmroVHJ8d882bX+L/+fB9Wrfmd19/je98dJ87r+2wvqhoihaMwbUNSZSykye00j8BiYqIdNBgr0rOm5KDO3f46utv8Obht/k/Hv6H/PT+c77z8w/5wb1P+d6j+3znf/O/5T/49rf4H/8Hfw9VFZTVklmxYHc64WA4JleKWEps21IUBTd3D2lrx+nFnGyYohPFqlgSCciE7xieDkcM6wpzUfHRp484uZjx5OSIyc6U27dvs7+7y2/cvctEp8Q4CuNYtQZrCiYyoTAlO5Mxo8GEZQVbEuRfyPaFG+w7d26hVNR34Npm4IKHuOM4ZjBIefbsGev1mqIILGyEV6IxPrfcGcJteNaX+NBLQ5ZluYnmQqconxvcRENdRKeUp/BrLZFSY1pDWdYIAXEcBZEBH82zpTfd5SDl1rLkOvgpxEfbrON+kVHB2Yiivsa6M6r+3Ly+ta+vbqnrGhnUz7rGItZ2zUO4bDTdpt1lFEVEOgn7FX2TCyElrWmRQva9gyG4QV0k+zlw8za7uot6Oti/h9+76MttR48bWc/O4Od5TlXXECQ6Lx1jK+rb5jAY1/U/93OVJAkuj1BK+J7nztFUCin9NazrmqoqEUAcw7VrBxRF4esxlSRSCme7ZiVwFeK9asCuvrxdFuc7iqm+5Mw5hwj90DuxCmcFRgqEaTGmpqoqEJLY1DgrcfLyefup7KyY8UiTpFf88pO25fT0897dh/2HNjb1kuPhNrDfi2d/9QbYGpvr0wQblMXRJ/UdgQwn8VKPV0raulz51r4vH/tqEsVdeZY2IhVNXfXO7SDLqIoSueMFTtJ8QKw1s/UiIBQWhPVKV2SsFgX5eMjTo2dkkUFFGRrBOM5YLhakcUKkFFpKBoMhmRpQfvhznPP33ng65dpoxGi6Q3V+wc54wroo+OzpI46PjuDG6yghyLMMFWesWksc+77sJ6fHGNMgBH0N9cVyCVIxm11QVSUHB4csT4+YzeZe50FYOsLjxXJBqgYsl6veqXM4tJCkcYpxDUkU+QYjWrFeL9kbXiPPMw72dknSlNP7H3Cwu4dUinVTsyoKirL0qcUkoSoLDq7dYn9vvyen5nlO3dYoBJGQCCVYLBccHByQJinCeTJm3bSYumAynrBerXxTlsMJy3XB1790i88+W6BwZEmMUBGHOwll0/reDdbhmgpaR9027BzusiyXfO/nP+UPf/J9JI6vf/O3+PN3Xuc/+pt/me//YMi/zgW/9R/9z/lH3/ln/J//wf+D6XSEEw1Y12uC53nukVXnCX1121A3Bq19qlMh0EiauiZOY7J8xNOzcz787DNOlwu+9+A+jbPMypKHj5/w44cPWS8W3NjZ4Y0bN3ntxg3eeuMN7t68hVl6pTlnLJFQ5Dohj2Lq6t9y4ZSnT5/3RlZIiQiNL7pFQgpJmiSsR7nXC1cKrSO/6FkXREY6MtnL4YRu0eiMk8PngFTw/k2ImMy2vGcwqjIstr4l5ab9JmyV5Ejhm6tLjZQKpdrtvSCgF6cw1oayI3pj3I0NRK9T7lzbG/4uH+jFV1xfE+2c/9u5BmMcSoUoaStn3eXw/Wd9LrptW5y9vPhba3sDopQCHfWQa2dg/ihG/zZJq4PFtyVhYQP94i4vst1nunEaY/qo+oXjbJDuS+fQ2Zq+EUanPOfEZvUXfmxaS9bFivPzGXXTkGWp75LkOsazALcpcXIhz9+1bd1c31dvnfMVRRGmtRtHRm4Masfql9IT6JRUeAzNBFWrYPCFpLItbN0P3dzhNjKwL7sezkHH5ev+lt17l2B6Ll9n60BtSuQu5dz7sYN/7jyk7o20nzHb/x3e7w2yRwM6uL573V0R4aFLI2yPs7t3YKORH97ox+02N4dSksj5OZRCEEnl5X+FR9Cc6ErWFOv1msO9Q9IkwbYGpBciqaoCISUNDo1iHKWYqiGWCtEYGkoPa+cp66r2zTRWSx7XNaJpMHXJREe8fvsOz05OOTp6zv50ShJrsjRlNBxi0eAWfSnnarVgMMjYmUxw6xnO+CZEIkgrr4o1Z+s5e+Mxi9kqNK0hyI1GnJ2fM813Wa+9wZZaIoxHma7t7jE7ndFWDdW6YDoe82xVUNYVKtIMs5xUxfzmV36DFsMvPvglw0EOkaJqan/tjKOpavI8YzQe0bSGKI5oTcNqtfLQb+pbDddlybXDg61SSMmqKEgjSZoPqQoPresk5+j0nOngPc4W90i0QhNTVg23ru1TNg3COExdU9EirfCCSFKSphk61n7ddIY/+MlP+eCH7/PDn31AtV4QR5K3ru/x9//Ov8MvPviYjx4+5Gw1gyiiKnzXrUE+7NeurlVxsV57XMv4ft6maUl0QtM6jp484+HxMUdnM4gS7p2esyzWm/XLGoQUzJ884/lixc8ePuKd58/53a9+hW+/+y6zizl5q9mfTNgZDKGuSXT0OSvKr7994Qa7WK/pUT2frQ/sZv/EKSFYas3FwndOGo2GlwzdVSOyzYK+Gg36C5t6T0/6fsQ2QC5SihdkPK21nrjTl01tFitjOk5vWMz9L+HLIizu2+VLHr7tmN+bKMwbYtX1roZQouLZ55vj+q5ktj+/zT4u50e78fu9ddHVdj2xLzfaiLZ0C7e0ss9F+iYVl4VgXhVdb5OotueuZzazMZ7bEOvlqEj0n+kh9a1r69iszf2a30Vhoit/kp7fEAyJMYamNVgrcNYDysZohDREkaYsCy7mF1hjmEzHzM4WKBF5p876XJWzFtdFxFuO39Vxbc/RtoGz1mJas6VI50sA7dY5b/bix+27/mzm1Fl3xZl88Tp7tnhXP/Hih4Jdv4widRO4Pbn9WfmfLzfW284edHrnm3vdbdlM1zuNvZ/hurx9x6rdur9ctw4IxFYO/oXT8qH/S842GPPOsBtf/tPVw6ZRTBInPHn6lExF3Ng/JFKa1vocpe+k1GJrQ5ZlVKZCCMe6rqjCYr2bjTGuomlbRklGoiOsNaEiBd9kxhga1dJYg8WvF2mW0ZiGslhzsLOD17VX5FlGa31vbKV896yqLJimUwZ5xnw9xzeG0SxWa1ZlgdaKvb092tUy3PcBXVKSOIo5v7igudZgGuMJVCgirZhkA7IoQY6n2CbI9oZJ1JHGOksSaQ5GE3ayfQrX8NPv/xCdaKwIjn/r1R+TKCZPEuI4om0qtFaenzO/INbaK54pL+O5szvt12SlNLP5it3xACE0tvXCU1GSM1ssmAzGzNYrbu4lOGGZzddcn0w4nq8RzpImMUpp6rrFOEfV+nbFeZwyHg7Y25lydHzG0dMTfnD/IU1TsDtK+f5Pf8C/8+0/T/Lma7i64tG5YF6vWM1mmKYlzwfY2lfEJEmCVJLlehUibL8GSBRxlHG2WPDs9Iyj8xmz1ZpVWVPYwq9h1t9vWkVEOqJGsigbzpdrjGnZHWb81d/5NiezGThLFsUMk5QsitG/YurxV92+cIOtlOiNiHPek2GrjMYIgatrzGoVFjO1YUYLepLTNhO3+32jPOb/dZ7Yzs40kJQEpvU5Tw8rG1/L261jgXhkHERB+YsQAfr9dYzwAMmajiQVTm4rMujVb3qDvSGIdZBotyD6KExuW+Twz/9prxjgLrrcTgV0Rn1z/leN7maV7hfKre969CCokl1xiq4a7u33tx2l7X1tX5er+9iIh2xIg70YTr/jrWF3P0KUBFuM8sCgB1/S1zYtRngikxTOR804dKxZFz5XJYRgf3+PRw8+CC0NPS5isZfmpL+RPmfrYGSH6/un+7EEJzI4o9316ciCnZa8QHqtYjaOT2samsYh46vet7/utjuiePn4tm7Hy6fgtoxlv0e3NcebSgfExqF42ZqyTQS8dLk649z9wSY1JLfHKsSGlU6o796uXNg+H/Gquu/Nsfvr0D0neMdnECcMs4x7D+4TS8XOeEKepKzqkizRWIyHoBtLkqacLWakccxpUVDWLVmcMogyFmJJXay4trdLohOU9n3ppdBgLUpJ4jQlzjPywYDmYoWTMkjfGnZGQ2zTIJzX+NZWEWlfOZKmCVVZEg80gzQl1RFJlFDWLcuipKxrVKQZTEfcO3pGx44x1qKdJNIxs/kiIIeiNyJZmnDr2nWUlRzsHtDWDdL6vvHW+TG3bc3/n7k/jbUly+47sd/eO+Y4053vGzNfzjVkZQ1JVpHFIlXi0OIgiUK30ZYg2W3YULvbnxpt2IBtGbAbaNhAf7AN2LDa7VZbgmVJlEmKLImaSJGsKrKqsuasnIc3v/veHc98Tgx7b3/YEXHi3PcymVXKghWJm+/ec07EidixY6+1/uu//stTkp3BgJlvOMmmSE+yNAVZxYKWwiX7BmnX9Yuun3fhxJrmszk68MnT1DUskoJ+v4MxGqyPUj6j6Yj97T55YZDWoPwQFYTkRUG/02e8mPF05FQpy2LMle1tbhy/hUIQdzqoIOB0PKIsNbM8I5CCWHl0/IjLGzs8vXeRg/0R79y7z60Hd7l1esrv/uG/YTdJ2Em6XNzZwIs9DoYniKxwzlwYMc+nWAue5yOUZDqfknRCEI5PFHghQgWcjCccjcacTueM507Sta8ienGMznKiwCeOIqSUThUtL5iZOSfDETfv3cGPokryWuMJSIKAbpy4fOqHuP0YDHbFXpYWtK5yMKsFqyaiKAlZplsM5Ira0hiHR0ea7c0ZFsuly5fwfQeTCXCNw5PEif5XOVcLZEXJ6elpZWhpoj6XZ3da3n4QIPTqnIyuFjq7MuYV5QqgWcBreHoNysUthqrlhNSOjJQrg+sgF9cfW0rbOAltlnZtJJVaQeQ1P8B9vsUAr45Zk/+EeP8Srh9le7/690Zdq/XjUh4r1bpV1rJ2ROpIvR3tuS5oUjnSVl1C0fZ3XMmOX0m6aucJ+z4XLlzg+/LNNWu0dk618/VnoA2rrUIqhERJz9VmC6i7Y1lrGsfCzZ06+lANL8GJNLi68hXpzK5FxEJ4jmAmVvP74TK5c2f1UET9Ppvru9lEyw2sTfsc2sdcl3F1Zr7tbdXeQH1D6r+r7xHCoT8VSrK61atjyPe9wrVTI41iCqMdeq41m50OaZwwFDBdzpku5mx1+5zNnICMlZYgDAi8gKPhKW8c3GR/a4vxwYKiNHhewH53wLtZhpcJrl2+TBp3XQeqqqSnl3YYTscsihwVhIzGU8qzIYs8ozAlwmpSPyKWTqZUVQx4X3n4ysMTksVijPIk25ubKCnp9we8+da7/Oov/gWCKOCVN1/nje98nWevXWM5mSKVdGk+4UiAyyzHSsc818Kl/XppwkZnAEKwu7XNZDxhdDZ2zUKKnIPD+8yXC1IVoMuC8WTMwuZMF1Om5bJxDHa3d7l3cJ/N3sDJeeIc0SzLUJ7Crzg1i2zpau+tIU2jSsNCoKTPcDrn8u4W03mBrxzXw+DRTRK6nQ0WyymdKEIJhTCWZ64+zj/55reJgoDI88jKkvl8TpR0KIuSMAqZL+a89c4Zr7/+Ks88+ySffeIZfuXTv8S3Xn6Zf/W1P+FsWfCf/5/+Nj/9/Mf55Z//Iv3tLZZlwSCKnfqbdlLACBCeQlvNfDFne2eD2WxGUZSEUcp4vuDd2wccjIccjUaM5ws6acpWL3HkTV+y3euyu7NNHMd86/sv048j0jjk7PQBt+/e5eDwkN7GJtP79+hu7RGHEcLoh4Kjf9vtQzfYZVnrQLtNCLUWKYJ7lmvVrprE4xTL2pEm1JFWwzauyp7qSCbPc05OTnjzzXepUs+NE18bcM9zmtWe7xOnHQaDQaNq5s5lBRO6Bhs51kIcRwwGA6I0oVLUaNqALpdL/EA1+eE8LyrymoXqMXBGsh4DZ3g8JdG4aN5od6LZMiMMgyo6X+XTrdUtwZZHR8DtiNfV9cu114VwKkKyju6lM5QfNIcNLldvrFn7zjbB7lHn1z7P9n7NAg4tA3MekhXN5xqWcWsTVW28ks5pspomkrDGUuQFJ8fH7Ozs4Pn1WFY1zGb9YBUKy3lZ2EdvVZRv20hBe7zd30Ks0iI1jI8QaF066FFWjhsVgdS6Jh/WuH+FWoce6vz9n4UE/LBbQ0DDgDhPZXVzxZo/4ztrWKT5fwN4r93VtlPxZ7pFrTlTowEN/wUYn5zS3dogTVOm4wmX9/cZj13N8qIsmM5meMojTbqUZsGDswcMkgEb6QbdjU1OXn2JJ689xcHZKUWpybRms9fn22++RmkKdgebJFHKIsuJuz3KQmNKR2qczme89vZbfP6jz/PYpSsslktOT08QGJ69do3N3i7lfOSeMSPwhIctNUVWUBYFURjQTXokYcBsPGG6zPjq179BHPps7Wzx77/4PO+89gaLw/sMel200mhw6RzhSt66nS5n0yHz+dTB1FHKydkpeZ5VjUM0k/mM0mhOj45YLBaEiaLEEPg+J3fuknY6TJdztDZ0opQkjFhqzV6SEHs+npBY32c0GrG5uUmn02E8HjmFSGuwpSZOQnc/KyRvOJlyaW+D8WhOEjv293B0xt5WwtloibFzoihAaAE6Y2dji3tHRzx3+SKbm5tM5nPO7h8S18+BsRRKorsx1k/43v0HfPlPvsnjO1sE0rI16PLXfuHn+cKnP8d/87f/Dt/9xvfpdCMu7G3y3NPPMh9PGJ6dYY3r8ChwaIGoWqQqFSCVYLJc8vtf/Sp3RiOsHyD9gCg0LBcLBAVp5NMNuwQSlFly9cJlbt5MuPTEkxwcn5D4Al9n/Nf/9/+av/W3/hZyPsNKwXy5YDqfPfxo/VtuP572mhWX/dEkI2cwAs8ZOt3oh9smEqmhaFgZiHberTbmWuuKWY2TwavwQWOsy4dozXKZVV8rUJMpm5ubCIljC8pKPk+IxtHwlEBXZV3L5ZLpYo6nXG11HMdNd7BS5w30O51Omc2mLceEZlGuI9sgCKrytfNlUvXfThFLiDoSZw19qPPu9T71WHp1/U0NITefr5qEmJUWd53bbjfx+LMiSyGclvN7OQ/te3T+/fZ9k1I6fsEaxO8cirVjNj2sxVokbi3VGLnI1OVVtUtplLZqzKIYjSe88sorPPXUU01qw1iLMAYrqjI/UeWc7UobXqwupnG4zjsn7liteW0r0lrLMQOqjlq26SViresUZ6qSOBeZr0Brl890PysW38q6WeOkG939e1h8p05ztA3dw09eK7duz91Hq6klUesj1tfz/k7duejcrn6t75loLHfFGWi+167963ySVqVBhX7Z1n0S1jG+6yYwkR+AsSRxRNq/gi8ky3yJsBZPKG7evkNna4NuOkD5IePxKdP5lMf293nn7j3OphMm2ZKi0JyOT9m5sOs6beU5i0VGECfEYcQkX1IWBWEa8dRTzwDw3e9+l5/66c/i+T6h53Flb49OssVRNkdZgfIDet0uadLB98FUXb06UcxGt8+1q08Q+CEf+/gnmIxPuHnnOi+/+TJ7g03Sbtc16Kn6cstqzTTWsNHrM11MWS4zIs/1or5/+IDJbIGSijhNGI5HRFGEH3hYNFmZs9QFnu+zWCyI4pST0Zg8z+n3enjSYz6b00tSVw5WTRUpFFK4LlpSKMpSs8wy17jJug5XnhdglwUnoxFPX9vj8PiUnc0Bw2XG8ckpT125wLs37rHRC/CkxGhBP43xvZjj8ZBO9ymsKZhNJxggjiLm8wVFliPjAM/3KKUhzUvywRbXcwOUdI6PKP7Z73DN8/hP/4O/wsx43Hxwj8PTAzpxQjGdM5vOSZPU5fJNyXK5IIxc5Y3yfWxpOBuOWVjIBZwOTwmUj4fA05bNTsLT166RKEm/kzLodvG9kGeuXGGxyGC65LGdCzz1+GXUcsHvfOl3+YXPfRbhe5TGkJUFp+PR+zw/P/z2oRts0Y46WGcL04K8nUFZ5bDrvPeq9vLhxWJlAFYBh1KqSo9V+TnhSn7q6LcsWzCslA2E7Sm30J43XrURMcaSZRmTyQRramhT1hdZHV+QJEnFnHaRnjPitoEVjaXp3d1u2iGEK5FyDNLZmhEHx16vGclhGK6Y7dXY1U7N+fxy9cfaGLZHsVkYz0Ox7xH7NM7H+zSDqL//POegff/WFuOWQ+E+Y5oTWSs5g0ZAxZXytaM595uSHmWZowIntjOejLl58ya/+pd/odqvgtFb9cHN+RgXoVthHFQnRGMw165B2Hrgmn3dOKzGo0YG3Bjb1Xxndbw692hr/fXGwFWfa6VdkCu02jTG67zMq6hv0Mq42xWasboXKydSYFv3nFXeBruCtR/azhvZ9p/iUe80e1lra4CqcbQf+pJm2rbGt57PzlNrHB9MJbAinC5dGkZMizlhmlAuM/Isx1ceWjupT9kJ0biKidFkgud7JMoj8SOGzJjM55yenjKaTXiy9xShCijmBUJYh34FAWEQ4Jceyyzj+o0bmKTH1d1djk+OWS4X9Pt9Br0OeWYpi4KyKPF8QaACAs8nChQizFBKEccR3bTLbL7ADwPeeOstkkgx2BjQ3+lzdv/IzRVtmgBGVnPDGEOvMyAZx+4+Gu0QTOm5xhx+gB8EHD14wJa32aTL8jInN5rYD12UHDglOc/zkUjK3JVwbQw2iYKQsuomF0dR1frTlYtpDMssIwg8jHaaDk6GVTCeTNjZvsrh9QmbgwH5cMTp8ZRrj13m5P6YrY1OFdl6XNjdJgoT18PbD9DWkFlNriyTcom2jjHua4tXOPLcz37qeTY29vjyt7/HjZMjMmU5DhP+D//ot/mFT32Gzzz/CXq9BOXvO8JeVQXkAiUnUFMURWOLjIHxZMqN27fJtOZ4NEL6viOQ5iWmKNjp9tnpdMmmYzrKY3+wQRKlXPiZfW7dfcAgugcKBmmXT37mRf6r/8v/mZ/+1At0Ew8vDBBKMV38O17WtZbzclhetdDUixI40QRXm+lgydWCVxs2XeeRcZGNFKJl+F0usTZojQFvrTeuQsSuw9JCUORFA4W6U3QLgxKV2EVrATLGUBaFE/YXq9cdUusgb08pdAV/1jCoEG2W7aqbV1nqCoJ1pRDWOmSgHrdmBFvqYqs8qGz+rZuGrBqfiGZxXI376m+Jg+SFlK6sqCqFW89NvkekLZwBkhUxsG3+xWoQV/HSe0RktQH0lHJjV+W51o2kO2Yxy5CV2IgT+Xbi+s7ouknTkNp0iRC2qjMvWS4WHB2fcOXy426MDU6Lur4OrxIrEXWkW0sG1A5fazRWQXB1vdU1N5bW0L5vWOvKdIxtjK+xTuxE4FSewGBMiS4t1uYIpBNTqar8a0SA2kGpeqoLnWOtUwpbIRSqerbqaoBV9O7mj2xsMfV5vy+g0vKEH/qcbeZzawKwzus+50xUjs6Ka9ZgI2tHrjvXCSuch2udU9OeS3WTGSNcc5Usz5jNp3QHTzE/XWKLgnyxZKEFpRJkZUYpBEXhyoKEZ1kWc3zhoYTHZqfH2WTMcDxitLUEK+iEHUJc9zspHe8lDkPUXIEFXWjyZUYZlJyNhxivxGDoJCmhCpiSg3BkMYvFCxWdJCJUkiTpE3g+GOs0F8wCX0l63YTAlyzLBctiiVYCu8gocR2ifFE3bpHMFnPEYJPA9/GVj1TClXgJiS5KVKLwQo/5fMFg0zg4ncpBNAYv9LESlosFgVAoJSiykigJwZYkUer0KbIlxtimd7TyPCcBXFYsfSWaNVFKiTBgyoyLWzu8+r1bXHqsj5otmC+O+eTeJX7v1XfYGnTJcvCN4epGjAaKIqefJJxNZ+RliQwkeVWBEUpAW2xhyW3OfDHn8gWfQRqyuYwhCnni6mMMb93j4GzIG9ff5cqFfbY2NsC6Vr55UVZ95BVGWxaLJWHg4PFlnnE8GnHz/gMW1lKWmm4U4wuBthblSYSCThoTY0mCEFEaiuWS7mCbjV6H8sIuRVnQTxOuXbuKr+CNt9+l++l9wiREGsnZInu/B+6H3j78CLuOILErNa3ajtQLgnWG1FhXhlXnpWvoto6MRW1EWxGDMa49oVISzwuQ0l2CW7ZoPi9F3eO4FXoYi640ZNsGsT5vozWmMqR1VrPudVQn5QSVgIaQKz3aighVl5HV/ZDbcL4zsCVgq8jeHT9JojXYe8Wyh6Kqea7L3qRShJ7noKVWeZVAspaGFesG3OXT3cQNaxJea0Gu81Dt+9e8J93y6vu+QyfOv09tD1bj+F5GW0mJFD6ecv2AoyiqEJJ1mH4yHyGFVxmbAqNzlAiwViKsrlIurtZWmwVBLf8qoCwKRsMpvXQLTynKzLgIq1JRUl5FyPNqrXhZGUsXvbabpghBJW6yYvwL4xxQZ7gdd6BROhMGJf3K5rnXtNEVpOkqKKQ0GJOji7JR48OueB66NJRV6ZmUEmEd30GVHbABkgoZMBbhOfa1oCUlWjl4ZVZWfebbjvKqyqK+HrfZ6tpX9rqJ+wVOQ72OelmZZCns2r1fN9i2CcCFXR2PGtk5t9UOMMa47lItQqgQshJSkhjPiWtMJxOGwzO6231Opmdk0xnLyYygK5nZkqVeQhBgS7BaY2RBUS7xtETIgP2NbU6HJxycPWCmLJ0gJVVJ1eRCI0XponHfxxpXnSARDLo9Op0O1995gyeiqwRKEQYhQsNczxuJWyMteAbPl/T9lDDqkgQhWZYzXxT4gw7dJOQTH/sIxyfHvPr2G9w8vMPOzhZMCrQvCVEEVlboneJsPCTfv4DyFJEfYINKJ8IKdFagEAShi5ANlqJC5JQFT1ukJ0n7CSc3zkiiFINgMp+hOhJlqfQiXDOOWszINS9ygYJLS1hKNGW2alwjNITScmmww/H0u1wOtrHKY7pYcqnf5+2jY77wwgazsSXVGU9f8BjNF9gyZ68/YDpdUGQFYejDUlBogaF0z4IwjO2cf/ntH/DmcEKWLxC+4ULi84vXrvHsz/083379Ne7cvceR9Nnq9fF8nyCOmMxnzlkTDombzxdsbG7iSY/R9JR7JyfcH4/RXkAaRCRSuXknBSoOOZmcEqQBmxsb+BryRcYoHzPNMnJt6HZDhIjod2KwOT/705/lay99m8ef/ShREhGagKPp8pFr4Y+6/Ri1xOsyF1Y5rMrquY48Eq1zlOehjZMGLbVxkp1aO9Z26XxEVS1eNSxrgFKvyrEadvSjoj/3l9uvgp2dOImLqmTdzUlSS5M1so6wgvSa0qLKgNbkMXCQq1LykUavhvl1VT9aw7RK0dIGbzGfRT1KoMvSlakVhRNdAafS1hh406ih1cdZlb61oxPTpBpczr/OV74PA9lWsL6s0AnlrUHw9Xc0dd6PgMnPG+8syxrhFSEEy0plqd09SwhRtc406+t//Z3Uxt1dj+f51bUVCCsoi5IHD+4zGp/R7yWcHLt+6Q6dcF491jHLXQRuKXThWN9IsCu2vxu7FZRrz59PE+0aJyoiFMaWazrvzjmrSwQ1tjLOSimiJHUSrG0VOoNT2qvQFF2W5FkOwmvG3FacB2krpnmNnT90C1vPQ5tpCSvUq72vEIimG8ej0ZK2mX/U96y/W5fEre5dk+c+N18+KAkSY1nOFyxmMwJfMej26CYpxXxJEPhEUcB0MqnmnruXeV4wPBtycnpCr9cjTRLSJKHX6zMvF4xHo4qfEmO1cfnjKhpWlcyxpxRlWXBycsKWH/IXfvlX+OY3/xRPWHauDtCl4d71W/iFYG97HwGcHZ/w6vdf5q/85V9HlAVhFOEpH50X3Ll1m09/+tN85zvf4Wx4iooDXvjkC7z+xmt4SuF5LlUoKxliz/NYLpdVoOJaQprq/tWptbIskYUkDEOyLCNMElfzj5OJnkwm7G3v8PYP3mKrs0HS6SKU4t7BAR+79gTSczdfeQKLE55J0tix3T2PMs8oqhy2NoWb98KtncpXro+CH5BGKYJTsmxJp9Pl+PSQS7s/xTujOQjB3u4+h6dnWGPZ3t7m4GwEuF4PtoAoShB6jq6CsyhKEP4+PzidEJdz1HxIOTni1fvbvPDxp/ns08/wmSefZV5kzMslQmuWi2XlZDZTG3CBh6cCDg7uc+fuXaSUPDg6ot9J8T3lqoK04zG9e+Md5svPsd/fohtE7trSFCsko/GUw+MjTKkxy5w3X3uDX//Vv8Qf/uH/hoP791BeyKVLlxgOh3/2nP4htv+/NP9wtaiOTS2VQuJY3Vo7OU6dF24xVrgcjZDN54MwgLKkliZtNwKpt0fYDiyWsppg7QUVqDTJDWVpXSlINQmFkqBkQ8dp59jqtdtW8N2KRFZ9WzVRnAyqrOqzV61B62Mp5eEJgZF6TdHMyW+2FNisprSOXa8qB6HuEKZL17qz/p52tNpcv12R3dYISq3IuL6AOjq3VL2dhTNwdW26qBpNCB698L7XVjsU9fnVjsx67r5mWleRirVV/9qcsvTQtmqKUnUnWunNO6cqL3JOz46x1nLl6iXOzt6i1AW+ceecZ2VlICthDF07Pl7rWC20QNbKXrVEbY0grUEa1ExzTymK0pW7yKrZBVXtrDE4Nn/VljFbGsC4siYpEcJJbBpAGItUzqEKAp+yKRmrc/EWYYSD5e2jOR/1TbLioZd+qE3AijH+Q+78/kVpj/guCyuFQ7dn+7o6acJoMiFfZgTKqXjZyrFNoohOnHJ0eEjc6SKEJM9ypqMJkfKJ/ID9jV3yPKfX7fLY5ct4keLo7gEb3R6hH5CbHF8p0ihikud4SqAkJGGI8gNXfikk3/j2t7h66RLkBaYokZ6ChSXe7OOlCYU25IuMjV6f3BZsbvYRuUHnOYEX8PTTT/Hf/nd/h1//S3+ZJ568xv3TI77/xqtITzVBBFTz2rpc7HA4pCjKCokpWeSuP/bm5ibGGKIoQipJv9dnNB5xZTBwJaXaoIVGaoMqLX/ll3+Vs+GMg+NjyqJgvlywc2HPOZ0Wl5KrnktPKkyp8apcttUlkec7oSkhKrl7SxAqjA3wpcAXPkIbbJkx6GxxfHafK3uXeOvt1xFKcPXiVb52+wBfuP4R8+WC+WKOHwoyDF7oYTMndiSlpJNEdBaQzC2H0wyTSxbTjL/3pd+nUDH/0c/8PCfH95kvcgIvBiHI8gxfeU2ghXXPlO/7HB2dEKcd9vb3Obt9B6NddD86O8VTgk6nw8bGDvOl4uDggH6YMBGScpkjhaLf77O3e4EXPvpxpDXMRmOOjh8gjeTPf+GLfP1PX0JGHZ79yHO8ffOrP9wD82dsPx6Dfb61Y7U1UBoSqQRGl0RRhLWW5XJJWWpX7mUtXt1y01SRCW7yLpdZE6EVRd7kVa2166WgLSScNjyPM0JWm2Ypsdbi+wo/EM6oG9ecw1ITxmrjRgN5SQR1m8r2ilSLp0DNal6NQ73Q1obbGUKXg5Kl00F3BowqMnMEm7rj04pMt8pnW0sj+9kQm87lsNe/f/2+tF+T9UU2N2yl2CaEeKjDV5sF3k4xnCebnf98m4tw/lyklNiyQk2kdIpnYlVzXkcXILHaJUhr2NTzK7V3K1gu5zzzzDO8/dYdppNVizul/DVHSAC+H/CoENVWY1Bh8+46NejSNESq6sSbnHLNwXC5xaqVqy8xpkCIzOWptaUsXZ4MXOmcZIWwSCERyhlwad29LY3BmFpIxqEFLsI3rc5a6/e5jjDWnKpz0fi64+YenDrqdgTMhyAOd560VAHfcxPNOL6XY7dKoeG6U7UdoaqrF9X9EkK4KKws6Hc7fOLjz5MEIZ044cQcs9Hts7u1zdHhMUEauzUiLyiKAiEkm4NNtja2mU9nWM8jiWIG3R43pm9xdf+iE8YpCrAQhCEsMyfOJD3KUEBZcDIbczId8fy1xzg7OWKQxGwMBszmC9Jun9tHR6RbW+xtbnH5wkVubm7S7/YIvADfGqIkQkvF17/1LT772c+SxDEvvfQSx5MzevtbnAxPiFFufkmXp7ZAmqY8OLjvSGNao41hNp+jlCIMHZksz127Vk8pzk5OuXjpUsNlQCmW2QKzzNnY77K3dQHPD3nrxg3m8zm9fp+yKJueA0VZ4Ac+UimyxQywjoTmKaQIyMsSrGuIU1pLGChyI/CEQGcaYSAJAzwZYSjoBykWjfQDBv0t7nzv+/TTlKIoWCwWSCHY29rmbFySmwKhCxw/2CKXUHoFX/z0J9nevcjdB0e89vZb3L1/j//2H/023/nGd/jrv/prPL53gSIrHE9GCMIwwPOciasDg3YlzWw24/69e4S+x8XdbU6sqyHf2NxgZ3eHg3tjkiQhTRM6YYLQbp4mYcTo+ITjuwdEYchg0OWxq1c5Ojrii3/+5zn4rX/E3eMTZJhSvIei4Y+6/Rhy2CsM4mGWN9TEF2udQSvLkjRNkVISxwkONlZONUlSMRYNvh80bGnH9hMEgdewvj/YuVWMY6Wc6hnufKxdSWiKFiO5vrlKrdjWLjqFVfuumoFb/WVrkt3qtXZkW0fRLlqzzedtHYGJ1Vg10buuGfesLcBN+QuOhId4pJDlQ8b6PaNraofnEdFUKyq3WCdKcO6457/vvAFvs8Tb5/Go7bwhcBSEVWMNJZzj55CZAq0LlHKd2Iqy4PXX3+DCxUsEYYAdz6hZDueNGazqphtjxap0cJWfrnPQldRuy6g1hk1UULO1GGTVoUogpFk3ktX/DLpCNOrvr3TnEU4d0JEyXIc6rR1U3jqIFas5+ShU6fw9af3R3M/zzP72561d5bXbNMp2zNz+/f2N9/tvq+K6ysl4j0O5lIklCkI2NwZkiyXdtEMcBPR7PTYGA+7dPWhK6FQFJ/ueh6d8oiBA5yVZpV9tSk2ZF3TStFmPah6NNQYlJWkcYYqSRV6wmM+JH7vC8ekZfS8g8kMwLtdrwpBXr7/LGzfe5TMf/Rgfu/YkvSSlE0ZYA4EX4ikPIxwU/Mrrr2HKkm63S9CNGZVzpJSURQlaNg4c1hLHMUZrV0ssBEEQYqbj5r6550LgBz7dbreR3nSEXYM2JfNsSSdJyBcZadKnk6bEUcxkPObC3h7zk1GTOrNVYGCrRUlK1zwnCEK0tk6D3Dg2u7aaNPYpS1wNdqFRQL8bkeeWOBQEMkBYjee5ftd3Dw/Z2ejhK4+iLBEItvsb5HrGYjIiEAKEK4fURcl4mfPdO9f5iBTM8wW9bshTe5/kuUt/kW+/8xo/ePVdlsMl165eAhw/xQ8ClFQVIqHX1qLxeMRsMqGXpmgMvjVs93t005TeoI/v+witubS3j85LFuUcT3gIBApJEPh0kgQhBSWWwpbcvn+fjW2JF/qcjUbcOPg2jz2T/sjPxKO2Dz/CXlsXHoa02u9bC0VREAQBnU6niXiUUuRZjlRu8dVaE8cJadqpaq8zaus1Go0fdej33NI0ZW9vz03ysqSsPHDfdzWFSEFeOIg8DMPmPNvber6tzmGv2N7VM9ZAu+uqYHWUSZVLt1VOX+H5XsUQtw2UrkuNxZUo0ET6rVx1lRdviGR2dcL1eba9yvP3pn1/HvoM4qFyrvq714wH9VLu/m+sbeIk27z6wfKU6++3UIMWiYnm3yq6t7UynCOBaat58813ePYjTxFFEXUNco1aNNdfR4qNsVrVmteBqPua9ZnV5NdxNbJUpC5jNU5YVFSOq1OgQwh0S/WoiWZF3cGjfVEShKHpZ1Vdp9baMcvXUhqVBv17DWb7GtZHdXXo9TceMvxryMrKx6nGbm3XdfD7z3IgGgisOpatzsuuOnnZyglqRqdCWRx5UeD7HmVe4AlJJ0mJwwjP8wiDgNLUyoAKTymUdP9KKSnLjMJqcuOY5VhLmqRIsZJUri9BIpzBljnThYcSktJoTkYTdvcv0IkSAt9HYxlmS+4eHTI8PiL1fS5tbdPv9pBGIJTnurcJUYl3wHA45OknnyTtJIyXM47nI7f+Vez/dhDgeR4IQVGWRL5PHMcI4dYNVb1Xz4cgCJqOf+BQQY1hsViQ9jYoc1d6JqUi7SQIK+imHRYnI1YSwsJJAVddu4R1OXUvCNDWusjRikYjoBsHFAV0uwlFWaIkbPZSpvOczTRC4eEJix8oRBBx78ED9ne2iKLIwevW0k87HI2XlFoTK4WyYGTV5MREvHt0zFIbRFkSGMvTmzv82ue/wNZGnxvvXicviyY4ciRU9+zZisPjeR51rh9r6CQxl/b3GY7PUFoz6PXY3toiThKGkzFKCDb7A+ZnYwoMKnQcp9IYgjDACwMKo5nmS0bHY24e3OF0UeDHAUbC9Zu3uPbcx9//Qfghtx9zDnsVqa2AL/evkApZrLzZIAgaI6QQrnezlHiee+iiKMT3vcrTW7WLXM4d3LlagFn93ZyDaGC5wWBAr9dDSclyuWQ2m5ItFnQ6Hfr9PrnWTKczFvM5nu9jtKnVHJuaYGt1Y8iUkijPI4rCxji3DV/Nhp5MJpzPt7vI3JVPKM/DC/ymNWZNJsuyzC32VQ5fsHICVnlwhVfpUtu6LKZltM9/vt4eginX36yOt7p2D5c3bzgA1U7WWkrTYuSLyga17FD99/s5DuuR/qouv64/9zzPifhXXdSshjxfYNEux47rlY0Q3Lp1l1/7i79GmsTNAlmjFlq7Jg2qQlkcbKjWjJVLq4iVEami57JcdSqrc9x1/lpXxtT3PISo0BFr0VpiTNkgLw36ICubvYZIVJE6q+oIgYP0Sl0jNU6ERVClZ1qEzPbx28b6fAT9npt96Je1e1ajUuc30Xr4agRhzec4twKs71vvY5ud7cpaNY4qQOgHBH7AYjFnOpmytTEgzzJ63R5KKrJFxmDQZ6kLRx9QrqLAWhrhorzIwXPRY5G7uu04DCtVRNGKMN3ZJmGIFZJ5kpOmMcenp3SNQQLdOGXQ7VFgeefeXYy1dDodsiLj/tEDLm/tki9z+ts97h8du1KuJObgwQE7O1t8+tOf4tXXXuXg4ACtXM/6ftJlUWYN8tfMWem5cis/IIlTBKtufO75dp/XFfRb6+orT2FwhFOda2TsyrKKUqM8j53tbcgLpHXOtnMaBFleuCYeSjmkSTgRJY1EU3X9q0rGenFIUWgG/Q7zRYaUsL3R4/RszKWtAcJKQiWJwoDS87n/4AGf+9QVkihBV6hlN4od4qE1XuBjPIlBII1kI+5jihk3jk7RsxlJaWCR8RPPf4zPP/kUz13ZYzSdUiyKxlFZVTbYJgAzxqCLgt2tTfw45nQ6wxOaNPDZ3Riwt72NEIrT42PiMCLwPBbWOYpxFDtUUQnG+ZKT2ZhFWTLLl0xGRxwdHhHP5iT9Dv3NAdJTjKerdNyHsX3oBvvhCKq9ULSe4mpiCCE4OjpiOp0ynU4xZtXzu9FmEuB7Aoyl0kGh2+2wu7NFEATtr3roPGyzAECeF1y/fp379+8jGxjcNhGtEALpOSOtpGR7exthwfdVawFx5CNVGQBPefhB4JSFqrrwdo7URe9+tcivyrdW0ZbED901qMqbzbKsMdyNoEqzcNumRr3+jrqDVM3CrZ2Z98olAytI/xFjRnWX3Li7elRPee5ctXaa2JXaVJPDb8HrtXFo57c7nQ7Ds2FluNaNQQMr1/n3ZlW3TWQtEMRxTBj6eJ6Pp3wEguHoBHDdfrLMMeqtFbz11jukaY8kiQgChVSuxbnvqyqX5cRppHCRRH397fNfn7KOsNLpuAYAqrp2oVxeud5feo6kU8+rsnCdh2wpAYM1peseZSyqFFWbVlfSZKVFm8rpEq6cSljH93DSpu0a5iqVgis1E9ZxOB6FYDTX8z4w87kdOB9Cr6Vhmjli1z+7FoGvH/NR0XzzhrWI+mkXVY38Iz4shEO/6o5Y2pRkywxTGgb9AUJK5vMFcdJhNDtxveC90BnpLCf0Q4IgYG6cslsQODWybrdbpb5U1e7Szdk4jhEW0jDEAmWec3R0xE/83Oc5efsdkighjWJ8pVjonO98/3tc2t7mY088QeJ75EVOGEdEQYCXpPzzr/8uw8MHfPa55/jEp1/gB6+9ypd+93eJkthF+CanLHM2B33un56itUFrg1Q+i4XLpztkIWhUy7773e86uWUpSdOUIAwpioLexsCtI1JS6pKz8RkGQ7ffYzkt2IgiirMTbty4ydNPPEm5XCIseMLV9NumGx2Mp1OUlCiVoLFYLwQvw5cKjGvK04l8loucfj/h8OwYpQQX+lscHBzxkccvM1/M6aUh2/0UU5RMp1OuPf4Y1kBZGEI/ZGewSbZ8HWNLrFFOdMpY8kXOkTJckl22khjt9ZnNprx09xb/7H//n/O3/8Z/wk88/zEGccxI0zgsbi1wz4i1liiKKPKcrY0eaS/FPznlbHTG5d0dLmxtcmVvjyiKGc9mRMoj6cQUuiQIA6SRzOdzJvmCQsJwPubuwV1Op1Nya4l8Q5EvMaMh+1zE64TsXbrEO+/eevRD9yNuP9ayrvd7r37w4yCsmMgCJRWRr6jLMWRV8qV1iSekKx1ovE23eNQGuyadPcr7B2fEAs/Hl4oyr3o6i7of9sq45FnmjF4YEoahEwwwjvRTQ9mecjChNpZlnrHMM4bDYeurVw6KUpLBYFDpoNMytHV0LDk7OyPPc4rCfabUliBw8I2pHpy6RePKiK/OWWvtCHo1BM56FOv7Lvp+CAavjex73D1rnSFwQhVOq3u5XCI91Ui5yirna7Frhve8TKr799EEjPO5dMFKAU+KFfQ/nU6Zz6c00DEg0BhTIqWHXy3IeaF55513WSxyx1ewhjzPKoES53wVRYEWtd67dYpN50fiHJ6c5zmLxZJAeeuGXYhm/JXvVaI9zkFzEZFqHJXGTklcVI+lroRwTp2sunU5p0Bi0Kao7rHXpCRczSxgLUbX9fHnjOSfEVFX7Xbae6xdevs4DzkCFYQtmj9aQPsHieTrY685aO03VmmH9iaVq9lQShFGAV/60pf4uZ/7Obq9LkEQslxm3Lx9hzJyc9Xr9JFKkec5ZVGQXrpMnsw5Gp0RdRIuX7zEyeERZbmKZo11ncfiOHbs69AnKzShch2bXn/9dR7vD/jIc8+RWMtoeIqJPM7OzvjolSs8/fgTRIHH8PSY+ydHPP/xT/Ebf/CveePgDirPmM3nbG5u8vWXvsHf+A//KsPhiLuHB8wWM/KyLjt0d19UAlFlWeJ5fuP8G21ZZjlXHrtKr9NluVwihENipvMZvu8zmU6rMVtp6kvpcru5KUFJdvZ22d/dxbcCaV0r4NJosjzDqzQbDg+PSNOEOEkoTcXhr1iSRmt0URAqSVGUpJ2YoiyIAslmv8tb14959vHHGY0mdOKEQbfLcpmhhOXC3i6Hhw84Gw6RUrE52HJVKDV3SCgUFqyhUIoDJvzsMx/DlBnHZ8d8wttnf3Ofv/UP/wGfeelj/MpPfpbPv/BRFlXA4wz2agr5vs88W7LR7TA+OkJgePbJJ5lMpkzPThk8+RS+H1AWmq3NTU4enDj2v6gMhZTkZclXvvtNFrLkbDIE5RPECceTITubPXRpuHH3FpOxIStKlBd+4Gfhg2w/FoNdR0QWnDiEWil3Ne9WTrlLNQlXgG9r5u56CRLWgnI559oAOLjUklXqYfW2llerFpla2KS96HhKVAInOOjT1n2lq+NI6eBO5Wph201HrLVQwUHrWtKsqbTVZ7MyXrRg0bpphRNkUIi6bUhVD6ib7mO60G5srKmM/ioX69TCXI5bVnnY2sAb4xjEVrjrqcfGEWrKJppdZQ0fLsERuL6+UkhM1e/YaoOnvAoqrqNL4xSFrKlU49xMkJXHLoVqcrXnCWnrrUXduSGKSmzGYLRACCdAglEI6eRfsc7I1lCmo9cXBL5iOp8SBgEXLuzz7jtvMxyO6XYDlgsXaenqJjg42TZOo6gMzjrJqvYDq3MWgFRurKr73OIgYm2r+Q2SojBYLZEECFu1kqWOKgUu1w3GCFcKjgWhK5lWd1fKxRRvd4BR0n1OOyjK+KBMjKZcwehYtC1RVjknpZ77gpaJrud+fQ1UanDutYeCXFGpzLEiOtbXKVo/zSDU71Rzvh7J5llZmfrm3jdtHU3dZlcjcCkzXZYoXB9xT3koJcmE5jtvvMGnX/g0Fy9cIrOG0WJBFEZoWWB8EMqitCEoIUw7LErIhMUq7VpvGkEYJiih0KUm8EOnmZ1nxJ4i9CQRHrkXEISBa4OZRnTDkHs3b7G/sUkQdzlbTCinM5QqkCxJgwGkfU6GQ5aLGX0/puv3eLA45VuHB8ivfY2f+NSLvP32Wyx1RmYKPF9QFhYfRYByAklolssZSZzw1OOP8fLrr3E8GjPNc4QXgtbM5lMQEol03am0pZ/2ODw5RYW+u7G5JpQKY3OWpiQ2BZHvs5v2WY6nZNZw++gWofLpp10GSYfRbIHshaTbmxw+uM88W/BTn/0sgQWx0CyVxZcGTyiSZI9ickZ3b4+seJXEDxjEfY4W7/LFy9vcuL4kDWMGUcp0kjHOljzTSfjKnSFLKdjrhjydRgwXFi905Zum0E4MJg44OVsQpV3+9HtvkmVTQk/z4tNP8jf/2n/Iz73wad69foez4Qlf+cbX+cQLLxDFEUXFmhdVakuiiFTEQmussYRSoZKYFIFIY6JOjCnAGonBc6lYIyktCCnIdMlb9+9wZzlmIwjY7g8q5UiPoNchL3MKW7Ix2CANC4rskMPjnA9z+yBtin6orWaW1jlOu/5mtfKtXl3pK5tmsWkfq16wGu/XmEq9cL2LlLA0zQXWI8lV04hVPpdWPnNVZlUbVqgXaZcP0hWDd3UOqyWp3taZ0dD+RDu3fT5iaOqi6z3s6ruqIzeOTy19Wp/HClZ3n2qXqdXntAYvNxGEbkGrazdo9V+DoNq1pbU9PrReb8aB1vjbeiE/t5zbcw5Ze28BGKd/3ey1VkjsTI5ztuTqFOpI3tiGuDeZTNna3qLb7VJzJWyb/FWdi61IbaZu0GFNM0bWmsZBXI1py0Orfuo2m6srFquxqOalfOj+t3Hh1r5yNWpuvNz9Fw0rnCbCtbURbY+QePTv7e9sv75KPLA2X9bvy/tFzqLl9NX3rHWiD13vuUuu3mqnsUT9C9WYV8+Ii3gDPKU4OjqmM+ixtbXF2XDIjVs3eXB8RLffYzKd0YkTh1V4kjhNicKQPMtdftQYdFm6HgQGJ5Ri3Xf5vkcUhu5yrfu+wFP4viJOQpaLBdlyiR/4hGGEkII8y7iyf4FOmuB5CmM0WZGT6ZLZfMpmr0snDDGlYbJYkpWa2XxOp9shSiIsDhEIfB/f91aSy6Ul8AJ63QF3jw556Qcvc/3gHsuiIM+yau1a+V31zAmD0AU11iGUuigIg4Aiz12VgzGuLrmqsz4bDZkXOePlnPF8jsYSBj6LyZSLu3tEymN4fMpkOCaJYucwSjAYh5wJibUlnvLxfEWSxHSTDqPJiO3BgPFkiqcUURCQ5QXGaDY7XWbLDCMscRgQS0VeuABMVyklqnWePEMqQ6E0uQdjbXj57n1+4/f/kKiT8ImPXONjT19ja3MLWzeFqgh5ouobEXgewliWWUYcx/S6XSTQ66RsbgwqG+BaLecVe1+XzhEWUqKN5vj0BKTF95xD1Y1j9geb9OMOvSglUj5pmJBEMUpJ8uKDVzF9kO3HY7BbRrN+8M4vubAyUG3WK+cf/IdtXGOE62YIjYOwtlC161Ero91aqFeL4coItqFZc84otn9vG/b6+O3rWM8dtw1o/fOw0TYtuLQ+p/pcqkDlIbS/OY6wrUV3fZFsBVA0S7NT72gN8CM2u/rsyjGyD13D6vXWytsaYFsdB8FaTfX5a6jRlxqBqBWeagTjUU5AG/GofUHntjjjeXBwwObGJv1+v/kOWxHZ2uPzkGGxjZt0flAePVYPXUvbgVvdd1pzdIW4CLBOHrXZh5akKGL9/CrkoiHUnJtv58/lvU6/dsre8/aLtvu2dmAQFYJQ38vVrXnkKLWd2UedS30+sJov7W9tP4PGGJTn0hynpydcuXyJ3b09huMRt27f5vDwiCCOmS/mpCrAkwqrJIUHk9kUTzg9e1sbbG1IomhlGKq5rCpEKvADwijAqxboyPcpi4K8KJwTplxeGWN4+vFrDPp9SqM5Ojvl7oMDZtmCs/EZnTRiM02IlaRY5ti6jtp3HBghoMgLfD9wEsRR6OrxEcRhQqYtL736KveGZ+TVM7IYjRgNR/heQN2bANHuPSArpcQcYzVBEFDmBYrKYFvHPi+1a8mZpF2QHouipDQa3/eweU6ifNc4ZLnklTfeJIpCyqJwzrFdrcNSufkthSAMQ6IoZjoeMugNWC7m+J7C932WWY7CkqZdZktHyooCdw1lWaDEetmrNgbfAy0yoo5H2okRyufO8Zi/+09/j3/6p3/M2WLI7v4WTz7+hFM8LDWz5cKx2aVzgq01eFJQ5jlB4JPEMVJAEsckceykpoVoyIlBELhnrJrjZVkynUxIoogkivGlohun7G5skvohW90+vbhD5AVEXkAaxQ0/5sPaPnSDDeuLsNOgXZUhucXUeStuW2fO1sCtMY+OARtDgfMesa6ESIrVElfDl7UMaB3k1FFdA8MLl7/2qvxw08vbVqzcsqwm/2rBkVISBJXSVxVKNFG3Xe/+BavXyrJsKZ2tG7y69WL7dUSdx3UPnlCr+vDzi5+tFvHaotcOxbrBqIxmywsSNX363NaYB1uNm7VY1xkbhEWsnUe9R32DLCu6YPu12nmoa5lVs7C05VTPy6zK1lxq0BtLFflqB88KqHPAsmpTKRC8/vqb7Oxss7W1VUWpK5RmlQt2pTH1GNULTq0hL5sxfHg2tp2o9px/qLypcTjWnR5bwxi29UGoEIWq6UOFADintx3lVqQz2zZutQFdLyNcWeWq97ZoGdc1Y29pO1jVo7r+UzlebSO9dvpNxP/+zkT7/TVjbav5c26/RvusejbzssBazcW9C0hfYazjOJwOzyiMRvgSVRjiIGKJ4frRfV5/6002ul2MKd090AZbluzv7zFbzJwGtzXkec50OsUaS7/fxw99lKccp8BAHEZIT3F4fMh0NsbzJFHg89Tlx9ja2OTk7IzX3nqD77/xKkejE+4dHxB6kqs72+x2u2SLBUdHRww2Nrhz7x7D0RBtnPH0lGsvHEcRYRji+xHKD/nu62/y0g9epX/hIkm/z2w6ZT4ccfv2XRfli5XEb016rSVK6/y2JxVllmNLgy1c6ipJExZFRm4tF7Yv0E8HrpWm1mhb0u90OHxwHy0suS/5va99BWsN8/mUsuoT7nkenu/jV8hFnmcuDeb5LGYjNjobGF2SxiGh7zOZz/GUoZN0GE4mSAGdJEYqSZFnBLLirgiwwkXaUSdkupzhSUHPD9jxI3bDBK0L/vbf/7v8w3/+z3j79m06na4rc1OSG3fv8uD0hEWZI3zFYjknjkIEFlO6NGIYBPiVDGyeO1Eu3/cbKVg/dC0+87JkNp9jtaaXpGykXfpph820x05ng42ow4XBFhc2tkj9kG6UsL2x9XCU9W+5fegGW1VdnZr2j7j8sDVOVN5at+b4nmogSSd84bq01ItpUDU2aHvXQGOE69fOm/Ua/nbnsDKQUkmiOKrKx3hoQRHVsWsVoWYJFOvKZWVpWC5z6jzxqoUjDTFshQCsmnnUxq0xElWZldaVOH3lGECtV04zHlqbihRT61KbxqGohQ6gWqeFy/NpU2Bsia4/b1ZG9HyUb8/9NJugyn+vG3VnFCxCtiNusGtla5bzidCicCxud94l2hQUZUZe5E29fW2wm7w/ztDWjlw7FdIY8CoqEo1Vcdsrr7zCxYuX2Nt32s6u//mKpNguGWuOZw3WltW1GFz/zYfTNee3NkKzZowehkWaX5USKE/iVT++L10U4gk8BV7lbDY9wlnvid5GYpoxp0YfFI2Fbb57tW/739U1vNcl2rXDuMsQ675A6wz+rCVq/XudPKvlXCTSdgTbAUD1exRFXLl0iV4a87u/9yVU4BEnCdPZjMlySRiHhEi63R73T0/5o299ndPhCcoafKUcZwaB7/kEUcgsWxJ1UqaLObP5nG63W9VslwgJYeiTxjGxFzA8HnL/8AFJv4sXBeTlkm4acXVvnzSKuXHrJiejISoIeO2dNymlpZuGPP/ENZ67cpXEC6DQvP3O2+xc2CdMEkrtoNcwDJku5pi8JFAB1vO5dXbG7/7+v+aZjz9PUZaURc7WZp/nP/FRPvaxj7FcLh0p1EKttieEIE1T5vM5s9m0EpoKMIVBUZUZYlC+ZJZnfPPlH/Cn3/4ex9M5w6Lgpdd/gEoigjjibDQE3yPdHHC6mDGpnBRpDLLSW5dSEsYhSEvguy5j0gh6SUQ33UBKiAJHmpwtcnb6KUmUMhyP8KVgo9fDCyOybEE3Cgkrp8hWdRTlwpBNDdu9PT71zMf4uU88z4sXN/h//5f/W/6L/+x/wWRi+Y3f+uf849/8TYzy+Ddf+1NOFhP+4Gtf4V9/9Y84mQy5cPUS48UYAfQ6XbqdDsZohsMhcZw05Z5BEFIWJWHVpyDpJNw7uMs7b7/N5178SYrZAh+BbyWhUGx3+vzMiz9J30/YTLpcu3CZrV6f+WSKKf4dVzpDiFVTDuFERaiiI+W7GyakwOByICrwUcbiZS63IqStItwqcmoduvEetVMG00W5YgLiDIsnVxO2zrsJ4cTc5/MleTWxnRFus5eVWwyUQgG+50qXlktHGqgbfNRM7bJ0C0xR5SiMcUTCagjWFtIVwUo0+7ljrhyc85F57bnWMqR1HeH5ntg1Ga4en/q4bWZvDfEppfB9r4HBaqGah25h619PuhrOtgb4eUcHqHKp64aLluESwhIEPtaumlgY68ZOKYXvub7fQRBwePygugaLNbp1nWJtHW/XltdjKIRFGQ9f+dy5cwclJWEQYIxhPp8349HOn3ueqmrtXWu9GgmoP1vDv+uj8/DWzsE2pqvJv5sqgq/KlqwgLxYI2o1CZFNu1zh80kmUOoDEkRPrbnDN97qRXp3Zo+DwtU9/ONsj2eMfeHtv0y5bSbPzLrlCkuUFJSWdTspynvCtb32TT734IpcvX2ZRFJwOzxidDfnsi1/gzVu3eOv2TUpjufzYFfIix/d8dGEwoqS0htki5+17d9i7dpXMaqaLGWEQuLxqHDJfzDHaVFBniC89rj31NCfTMZ6AjW5KGEcM4pS7yylSKp649gS9wRb/9Pf/BbnO8QOPi1ubfPyppzhbTHn5+9/lr/31v8o8mzIajVgslg1kvFgs6HgdMuD6g/v86fe/z6VnnqITRGykCRvdFGkNNx8c8Mkn5+zs7WJnzqkKPA8lE5ZZxqVLl3j9nbcpypIwDJxCpB+zyHOS7ZDIU5TjE7731hs887GP80//+A/Q0tLpxCS+x5e/8xL/y7/5P2N3e5f5MqNYFDy3f5WXvvMt/twLV5zamnE9wFUo8AKfbLmgGzt0ICsK4tQnX1gmkyHFYsp8NuNkPGV3kKKQDCcTAl8y6KUI5TFfzOgnCVNjWeYFxpQoG5ISEXgRN965y2uvv460Cz525QIxgs8/9jS/+J+8yJ2TI7756vf4b/4/f4+L1x7jrZvvEIYBs+MH3Pk3B8zyJZ/95Kd467W3mM9miArh63Q6hKHTia/n5nK5ZJSPCOKI2wcHjCZj4jTm6uXL3Hxwh61On2c/8iyJF1GO55zeP2S7v4GvAgZbu3T728yWhm+9/NqP+Hw8evvw67Axjo9QraylLtHGwYF1K8UactPGlcpIi4M37Yp1XEd1tlqkrVnPg9cGC20quLtsFscaTrdWNOullJIwChFKUueVG2JcBTtauWo4UVbRVxB4K2JYtTj5vjMuQkiSJG76UrvPUUHt9fdAHMfEcVwxdutSrLIxwoeHh8xmc5yGuaiifdF07KkXZ9dOtGyiw5oo197a0L3WGimqZhly1fWnhqPbC+F7LfGmcMQcKUQj02mtdWIWtRqTqFMd1X2q7925YynPWxkWITDGMepllTfyAgertfPdLoelsbWhaxkjCVjp2Msr2pMrc1Ke5MHBPbIsJ4xC0k5MqS1hGK3VyYN1ilmmTqs8PJb1d5YVqlGNDKskcIN7V1F/ZewRjzRqAufgOGEOsZ6/rj9flS0KrBtTY1FrTkOVA6//tjSmrT366w5Ua2sHzbZOl4hWKmR1znXuf+0ahKhQm+p6eO859Mgvb6Lnh98VD3125ShKTzI8O2M0HRKFHtsbm+xfuECRZ2wOBjx+5THuHR2RRAGzMuOdW7cYT6bs7e2z0R9QlDlSCDw/QHg+pfR4/eab/P7X/oST+YR+FJMoH18K9nae4vbdWyzzHG1c2WknTilLzYPTUz7z0Y9w7/q7fPe7t/jZn/0CW1GH6emUQb/Ppf1LdDoDkjDh+u3bvHDtWUITstXv89wTT3Pr+nVeeeUVdi9u43mea+JT9bgXCOJOn9ffeI3vv/MWIgoJwoDx8TH+PCWxll6/y2PXHueJJ64xHI9ZLBcEvo+RsioRLYhV4soKPY9uN2U6XdDrbBL3Btw9PeXOySFv37lFHvi8fvMmJBHWGjKcnv3xbM7f/70v8RMf/QSb3R79QY/nnnyC/+73/oAvvvi4g8Azl6JQVbnkYj6jE8cEfshkmaFtzsnxiNKUBIGHtoaT0Zhu5COsYJEt2UxCenGMLjVlnrGZpmTLDEcEdYjnHEsSeJSUqDQmJ+Cbd475v/6Df8J/72c+z/L0DkfHh4zLBWEv4eU3XyO3JTGmQms1v/ml3+H49IiPP/ERYs9VA4QixJaOgBf6Pr5y1Sy+50EO3W6P29/6JlbAtccfJwlCelFKqDxmownxwGdzawtp3TobJx1KITk9PuLBvftc2r/0gZ+KD7J96Abb1fxqp0zmeRUc6zSS0yShKBzElHY7TCZTpwq0WOIWoCqqWGGs1OSiZmlqJc2kcCU93U7aiGAIsWqQgBCIyjjV8HXa6VQEhHq1OwcLVuQ0IQRJkrC/v99E1XV0W0e6QtSt7ValY8bUqlirJcxaWyEBK9GU2uCWZekakTwCrixL3eS/hYUi11hrEHKV710j+ImVw1KXCllWhDtRHav+zFpU9h6bbJKdrJj49Qs1c5oVAWyFkrpl3ArbGK6yclLqc63vdw3W60popjmmO0Lj3NhKH7pOB9e/15+V0uVGXaMNwXB0Sp7nxFFEr9fl6PAUT+pmnNw9cGNv7Mow19uaEQXH1AYEhqb7xzk0pfZg1jkG6xB5DZk7bfPWbtW42taY2/U3V7GntSuhlNZY1Maw/bu19Z5rJ+qeD869VN+61teed0Ieem7E+p/nt4fTAue1zxvP49w51u/WGIKr78+KnNF0Qp4pLmxv0+/3WC6WbAwSBr0+b965xcWdfW7cP+B4MibwI3b7m2TLJVZabGkI45iFNtw9OeGNmzfRgc+7B3e5srPLhd4AAyRJzGw2RQQOffOEIg5iBIrJbM5bN66zPD0m1yVn4yEXL11hsVgQhzGDTpcojJFGcDYeOx2HsiAKQi5s77gOY1qjlMciW2CMwfN8ijynl/Y4GJ5x9+SE2XJJd2uTbhyz2QnpeB46Lzg7PSVOI6azcYXc2GYOeJ7HMssJg5AkTinyKUVZkva6nI2m3Dg85nA24v7whLsnR0x0ScfziMOomleWQluiwYAf3L5JaeHjTz7J/sYm2XJBYUrnxGiNEj7K86HqmS6AbuJ6Q4znM4LYYziaEEQB3V6K53vM85zNQQ9Tuk58cdQljSKyosDqkn6acnhWVnPXpfNMAMYzmFIj8fBkSGZL/uSVN1Cm5PELm+B7CF8yzRZoq+n1+ihPESiFxDIZjbh1/4B+5PLOcZIQxTHleOralWqXs61lbJWSzOZThsMh/e6Afr9PtljQS1Mubu8RpzHGaMazKR6CXqdDr9fj9v0HXH/nHV5/7XVkGL/3g/EjbB+6wd7ccsXvvW4X3w9YLjOKPEdIRb8/YLGYI4Rgd3+Xw8NDJylZlC3v3hlLUy9oVexmm+hlFbnUkqa7u9tri4KUXqOtrSrvVQhBnufV7zQLK6wWphoqBQdFhmHYRLGrfLHT6p3Npmu56TYBq46y61WoKArG47GTGW3l1cEdSwnhmJFiZchcDrukLF2fWAHoCi5QVZmCZUUwqbc6z10b0BpNcCVPpuk+pmsi0/tY7BoSB7cIYO1DC3zzwdXq3rxUr+T1PXO5+GINqm8bz/q1+tY4lMSRy5qe0q1zrg2Jxa4cJuuaaijpM53NKbVrLtPvD7h37wES2UDsxuiqSYRBoM4Jx7bv6+qqRcvI2eZer+bm6j6s+sG378N6bltCXcssVsdaHaSFaLTHuj3+rbdW8+qcYW5d0/lDrH+shWC4wW/OwxmD9v7rKIrgPU7xPLrQPBerUWxdAcI+Gma3OIet3qPQGmVKdJEzGPSQSrq0SuBUzfpJh29ev4GWkkFvQD/uUGQZyyLDEx5RHDMaTbhx+IB7Jyd0d7eZTidMlkv6UUaWZ2hTUuQFaSdC565yIfIdsqYRvPnuO3QkbKUJWZFjhety1YljAuGhtCBSAaU2FNZgSscC3+j2icPI3TsBRUVIjWPHVvd9n2++9Q7H4wlBGJMEId0wZG9ji0gIyqJgNp8zPD7l/v177O1fWI2rcN3nhFjieT6dTpfxaMliuWR39wpvXn+dd+/dZWIyzpZTDidDlkA/TQkqmdHSGrKyJO32OD054vvvvEU3jdnqpARS0N0YcDYaslgu6EURcZxQ4NjevvIIPZ8sz5lkOd1Bl+l0ThgHJHGIFYrSwN7WJkVW9YkIA+IoqEqgLN04RTKmjtuMNoTCkFWoqsoFvifohiHD5YLf/drX+dizT/LYxT18BZPphH6/T5x20MaJbHlS0tvcwAtCZvM5ZlHQ6w/Y71+lZIaSqloTndWxxpAmMXfv3aHUJVEcEYUhk8mEfqfL1mADFUjyQpNnJdpCluUknS6L+ZzlIkMJyXAyfdTT9iNvH7rBfvqZZ9Ba0+l0sBZOT08ZDUfk1YR2nllJOpsxm81dgr90OssS4SLjNpxX/dcsBpVhVFI00PTW1hZZlpEtM7I8pyw1RVGQlS5a8yqlryLPmyjX9yp4uiJ11RGokgKqnsPz+ZxbN2+S50ULKlxtQeC5Wr2KMOcadqwW3RqOLIqCLMsoy5pg1pKylKvyHdeLtjYQVfTcRD2rOl4pVUUEXu+2dD6HrZQAYxqYvXYEqKLK82SyenzdhbreuGVZRaCt/PWKyd6c+WqBr9dfUZ23rY22aHLx1hqkkJS6QAhVwfoV075m22McQ9TW8rE0qIQVzoGT1slI2grtcKVgAmEFfhQ4VqeFpNNhY2MDgRPuF1ojZVWjSd3LtD0Gqyzq+ri4rTE3tT1jZZyrYanGamWg6nkn6lSOhbozl9u35qXXY9siCVb1qGLNyK3/9kHQkvoztr5n1NFr9ZUNXCDW7elDgHfbqbAPvbt667wDIR75nqi+7r06dLXtd2kNQkmiKCSWcHx0zOVLlxj0twi9mGV2ViEhgtuH97m0f5WtjU18BL1elweHhzz12JPMi5LF0TEPTk8pq1Rdbq1r+pDlFMMxu1ub9HopQZwwK5YIBGkcY40liCPC0GMxPmUyt2xv7zCZTqrozCNfZBgsVy9dYSFnLPOCUPsEvqCbxHzyhRf46ne+TrKRgHD92JWUJGnKdD7nu2+8QdLpsrexgVcYfK1ZLCaoKOaJJx4nCWNe/t73WS7nzJcz8qIkwBk3VTmZddOkPIsxxpIVJcfDISqJCWWAFJpyNiZOExZ5jrUaq0ssAuUHjGdLOhsbzB88YDqeEFrFiz/5U+z/8de4e++A07Mhnf0+cRIjde00G/Is43QyZ5Rl7OxsMV/mKM9xYApdYLBc2NtmMpmjrSGOQuLQEb2EFHSTpJkzUkp0YUjzkjNbUliILQRW41Oyf3WHd25Zvvz9G7x5/R7X9hKkFGwM+hyfDCl14XhAQjAejvnJT3+Gn3juk3znay9x4+ZNBvu7eL4zg4Hn4/sBeW5ZzObsPb3PG2+8Sb8/YGtry3V9Ux6bvS6z6RThCXqDDS5c2AFtOH5wyGKxIAxCfvqnfopPfu7z/K//y//joyf1j7h96Ab79//VHza/P/IhrjDTu7fuYCxcvnSxYQGrahGuyUiuu7Fulhmn3uXYu57vIZXk8OSY73zn+1TctrWHu7YfAveeDBRxHFLZS0yleKMEKAlSulhLW1ejacsCYQ2+qqUg12t4y7ysVJdWjSPq/HHbgK4xiO25qKyGsasfhTOSQeBhihoKrjgBssr1A6JSXK8j5rppwUPjbVdRnutY87DO+CM34bLCSq3O31jHnVZy1U5ytba7uySFU1vygxCEqmQCY7QOyPMJcezz2JWrfPqTn+Yf/Mbfp6REKgnGsUqV8dD4aDtCihJP+ijhI0WB0RLlu+9CgJSawmj8NHY9qq2ECmZDgrGSN167y+ZOyIXLO2Cd1CxyjjER0iqkKDG2AKFaRnmFDYvaF2mNpS/dzDSYNeNc339EnecW7pwAIQxCOChdWJcnc/PA9SuuYc3VU+MmtLAGpK6O4+MY1RpbzRZrQMiicsgcGlE7emVZYmWldiYFWNOqvZZrhruZp83FrK69OpWHNterm3Uvtpo7q60VQa9dX/Ph5lNGCCclXhmbWgfOyQa4FFehfGIvYS/dpLuRspiPiReG7nZEFviUoc/ljX1eORpi5yX+cskgVuztbiBKizI+cdwhXmZktuROPiSn4EkTM+p2ORmeURYF/U6H3/g3/5S//MUvIMqUTC4JvJJ+ErARW2bjISRd/MKSphGP9/e5eXJKJAMCL0QrTRAZPvvCxxBARybgS5TvY3HI32w+w1oN1pUnagmFCvnnf/wVLm7vsLezQyeKKOZzstEY4fmknYByNmO2WLDRS9m8eIHvv/IK1x57nH6vR6gUelkQAKGV9IOYaLCLUpbf+O3fgUGP2ydnjGZzkIKNtE9R5gRkLEyBLnSlvCjxEa5px8YGL928wfXjY744G/KJ3T1u3nqbb737Di+98So2z/n1v/Br7G3t8Sdf/gpFmRMHCulFMPQwqsuFQUSvs8vwrMAbnvL0xy5weyqYmZxL/Zgr3S5370MewWM7+/xx8T0mxYTSGjq6w1koiAkJ5hlCa6wfECQ9PnPtozy/NeTd2ze4fnDId98ccmk74epjguF8Rq4t3TimF0omR/eJLPTKgC+8+NN87/Uf8Jv/6B/wH/9P/ybzskBYS6kLtLB0ejFZEHBwdMQv/fSfI01SJsMRKvAxWcFclBgLx7dv8c71d9nf3eeJK48xHY24tLeDjGLujVzK98PcPvSyrjgISMKQJHReUxKFxJFPEgckSUgahyRhQBD4CFivxRUCvDqaEbR7Up/fapi3LEtCTxL6HqHnEfntH0XkSSJfEfgenpAVwc0tBLW4gFASK6CsojcpBb7vGJtu8XDf0+6V696rz6WKCo1B61VZGawg1fZ11KVZNTFcVvmS9mUaY1xut1osV8xmquOuoj4h6mtR7z1erEOy7781q/pqfyGcAEHre+uPuKM5ZSblCaRnKcolpZ6TpJbcTpks75N0B1y6+jSf++nP8x//p/8TCm0RSHThYbRyXonvDJInVQOB1wiJlHLVlMCALhVKxsxnGbrUlV645dKFHXqdECk1t27dIAojnnnmWUpTICV4foCSDrd5WG2tNQrrl1ldqamIMEXrPtc10RprdcN3cOmP+mc9DSDqfuzn7lINAzZntPah6jgO919Hk8/Xvj9iW3380X3Tz3/6UcB0zXQXwnULO49EvOfWzJVHfbO7YHGO2HauKM05FLYgiX0G3Q6h8pB+gA0DTs5OmY+GBMKQDmLefv11NtIuF7Z32RpsEkUhi2xJ3E159e03eOvWdY6Hp2AsaRCwvb1N7AcEUhGHERsbGwTK4/qNW645R8flZcuioBMn9PtdyszxI/b29uikKeiSXtoh9H2EsZRZQZ4t2N7YJPA9pITpbMRodMqf+9kvkKQpZVmVbOJInUdnZ4zGE/JsyenRMZOR61X905/7KZ58/HGWiwXLZUYURWxubjKbTYnjGBAUpcZYiJIU5XtkRc4iWzCajFkUJZ/7whfwghCkJKqIsNa652o0mRIon0GvSxyFzCZjJG4dmi+WKN9nXuT8wZe/yjPPPENeFCzyJVpYhrMJv/Gl3+Gr3/oGxB5BP6EQlnm25OrVq5wdHdLtdBCeIrcaG0rSjQ0Ojo4JpSKNEnwvIC9cS+XN3gAhFL70iIMQz1dEns9yPAPtHPLRbMrx8Iw/+NpXeff+AfHmgMefusTVKxucTA1/9KevMUg69HwDekqJ5uoTz7IV9ZhNxywWcy5e3OeLP/dz/NZv/RaDwYAkSZq1zVce3/zmN+n1+sRRhBSu1XFRtV6uCcFbW1tcuXKF6XTKt7/9bRewoCmKDFPkROrDNbEfeoRtrEbaamGvoVIsVtaQXl2PXEOBq3aBjcBGBcvWyjnWrZMNy1xVOcsal3S2RKwZ0KZNXh3NUqOzVdjd9MysINtK2aiWIdXa3ZyaXFb/rJVg2fWcpoN8V6SmhlTV1BaLVqp3RRB5VE2trSLYen3GOE322mFos8ObnrVilduvI/iyIrYo1y3lfQ3UozYLIJ2iE9CwzNf6SlvhOqlVsqCh77PMS/JSo3PFzvYlfv4XfpG0v83h4T0m8zlno2N++S/8eYajjNdefYvxZIKVlkLnSOETRRF5XlLkJb4foNSCIPApywLPd7Cj1ZL5csHe3gUuXdojCjz2d7b5iRdf5OrjV3j1jdc4OloQRhGbwbYzU8JS5BpF6fTNhXDH+rOGpI4+RZv5Xav61drXbkx834Nmriu0dg95Iypjqz7ZVapg/X64TlKrE2qJidRzigY2wUXc68VP68dr8IGHIur6s2LNQ3jfQfhAc+eHLfVq2PD2vfe11jqdbFOQZzP0MieRKUfDMw6l4NqFy6jlkuHZGUfDQ/YHAx67eImrFy/RSztgLH7kM5yPuXXvHtPlnKPpFJ0XKG3whCDyPNCaPFtSFiW727u8e+Mm+ztPEIcBvX6P0eyENI4p0Bit2ehvcunCxYYxr5TCl64MT+cFZV4gpWCxmGOtxfMCPOHx2quv0E07rvwpX1CUBjPLuHH3kM2dPbphQOT7CF1yeP8+fllyaX+Px69edQzmakzOTk9I04Sy4tcoP2CZFRghSboJQRwSkmA8j6984xsczmdk2lXEuGXFUOQFURSRBgGU7hneHPSZLTOstMRRhCkKlxrUBaPxGVJVKm9KUEq4Pzrhj176Gvu723z0I8+RBgGLokAJRagEtmpcYpVgaUvwfebLJb0kpJem+J5PnudEUrDZ6yOsQAknUqVyxbIo8RBuHfRUpeuuKKzl+uF91Mijk/hsbgzY3H6cN157gzu373Bhr0/aSbBWIYRmo9ODirgYhiGPP/44L33zJW5cv07g+S5w0ppuGnPjxg0+/4vPE4WOUW4xWCPw6sDLGibTKUsl2dndIZAeBw/uEwYBvY1t+mnKY5c+XJb4h690Zk3zY+vfWwtMXX6zMq6V9CWrBRBR5XKrQ7bJP2tyldQ2e/0hb8PTzX5NvtUJUpw38K4+XDZscqiZ4ecjoxW7uX59JZ4iecTpNEa7KSVrDPpqgXIKagFxHJOmCWmnQ5IkBGGIlKJpCVzv03YG6tdqZ6Is9VprTmtNo9XeZqp/gJvp7otYlYVJWbPw22plonEktHYF6VJ5KC8iDPtcuvgkg/5l+v1tur0eUikePHhAFPp88vmPc/HiPkkSIaVLR4ArsxOWKifoiIJlmSHQGF1SFgW6LNC65JlnnyL0fQb9Hhf399ncGLC/t8tf+rVfYTDoudajBsLIQcqu3tKu5uW5e9G+b819ruaV8mTVA12ufj/343lepZbkO0nLup6+GT/R8AoaB7J1T2upxzWyWTPXaRTbmtebR+zR9/Vhg766vvWDt807rQes/ux7IREfMMp+n+1R0/FRZD1PCbTR5NrB/bceHPL9mzeY5DlaW4psyWQ55cnLl3j68cfZ29omCUKkECSdhOF0yKxcMs4WzIsMYQ2BEHhKEPs+kVSgNVm2ZGdriywvuX3vDnmZN6xg3/OwpcaW7vcwCCiKsum3XN/7+hryMmcyHTObTciWc4zO8T3FxYsXMRak9EEoptM5o9HERcFG42PxBU1qLvB9gsAnDAM8z2OxXLJczOl2ezginHP3RrMZudFkZUFhCk4nY24/OMSLInJtmqZGQjhH21rXKztSHoGSRL7HoNtxc7LUzqlEYoRES8nJ8TFKOP6PH/ik3S5+HDErl9w5vM87t29yOj4jiAPSOMXH0EvSFbFLwcbmNodHh3SjkG6cIKVHURbEvqSbpriPCSLlcvu6KOnFKWEQYrGul7f08IOIzFrGsxnD8YLZ0pCGio1+4KoJRnNsLtjvbbIRRnR6HYQvHQfIuuZEH3n2WV7+3vcbdBVj6CQp2XLB/v5eFSi68tN6na6fQypHc1GJ1ySdjguU8pzQUzx19eq/9bPR3j50g90QRywr4wxIK1zkbZv0oPtYvUC18mZO+3UdwltbFETrtTrasWaVKqNl4FvRvLCghMRTKxnL+hzqyKkuCVMtKONRpVP15m7iqqypOV5rP5c/Ng8ZyhpG9zyPKIpIkphOp0Onk5KmKUmSEIaBa2ZBbZRtC3KtDbMrAStLTZ4XLJdOjjDLMvK8aBjuq3ryD3YvHRBhH3rtvMPQ5NmldIS/0vEOPM8nDGPCMObWrTvcuXODIl8S+AGH948oFhkb/ZT9vU02N7oEvlOgkhJCPyAIQtKky/bWNvv7+0SRT5pGBL7nSIeeoNuNuXBhl/H4jPl0yng04pWXf8DXv/Z1osj1TF4uM3TpBCG0LsHWTp+7l221uNW9eZ+IVdTQdZ13prE49TywjaNU3fN64jfOZ30sWkZ79TzYGpmiFp6p5mDz/srZaJh673cnbevnoWuqbmx7aj9kgx9Op5xn0D+0R21weS9T//CrDx/v3H0Qrv1oaQWFEdy4f8zrdw+4PxyTG+PkMHXB5Z1tLm5t040SfOW7jkphwHA6QkU+pXIENl9IOn5AHEXEyqMXOTWzIi/op116/T537t1lPJsgK21uryXeVBYlWZ5jjCEIQubzBUYIrJQU1pLrkkWWMZlOmM5nTKcTssWcK5cv0e12KUuNVB7GWM6Go6pBjSEQkkBIQuXRi2M2+n1C33MiJUrhBwGT2QSJoNPpoIKARZ5zPDzjcHjC0dkJd+4fcDw64/7JCQcnJ2xfuIAXBAipULX8Z6VUVuQ5kecRez6x7xMHAb4USGMxuXYTQilybVnOl8RBiC8VgedajoZxRNLrMM8zXn/rTd69cZ2iLOh1e/jSMuh0kVZQ5DmekuzvX+LuvXv045BOFCOQ5LqkE3hEvhMxkdYSSIlUkOcZW4MNukkKCLIsA20JlE8UJwQqIFsajk9nTIaHbPY94iRmPteUC8t21OXK1hZhElWCXR7GGhbTGZ/7yc9y8+Ytt05USEkShYSBz872NuDSmEq5cYuiyMWhxjH6gyBgNBpxeHxMv98njFw9uzCWqxf23/P5+FG2Dx0SbyDClpFtIuX6f8IJpAjZeuhb3rzbTzqY3K63wdTWVNrbNdwtnTSeWBefaOdZhaj6FYv1GmCkYxbXEbYxhqJwrOrai/U8pzS10iGvI2J3+Lq0B2jVRdNA016lmLYylKtotYayaxnEGoJfLpcOym69thoX29RZr16DtiFeRyRW6MGqa9gPB1k2125dPbmSK91iACU9Ai9kmc2QnqDQJa71Ys54OuOb377Hs899lJ1slygIKP2QO0ry83/uF/nS7/0OUkku7PahzBCFKyf51Asv0E17BEHEF7/450nTmB98//ssZnPyqtJAWEOQxNy6+Q5BoJhOx7z62ogyL+kN+hw8OOKt6zf5/M/8LNvbe3S6MScnp0RhpxnL2oY197PlbLU3ay1Ga4pFvpbGWM012eznctkCrHL1nRiE0BRlgecFCLyKRFVlk5vvsjRKP6JOu7RkO4UA6nr0iqJoXRlbTTSrpvyaYRYV/O5+qiqM1vWtUARx7hlsPsFDhvN9t7V4fnVOj9x/nZC5GkPb7C5b783nC0oDmbbcuv+A48kc04l56/ZdOp4rMZovM7LZlDIv0FhUpXV9OjljPJuQbm2T65K8zPE8xVbSZXdzg3w+Y7c/QGuDQmALw+Urj/Haa28wHJ8RBq6NayeKiRYL5iJjMZsznUyIwojZ8Rn3jk5QcUoQBcxnUxbzKb2tDQprkNoRDD1TEHmC73zvuzz53LMYYxieDbl/9x6XHnuCAsXHrj2OzTLyPKOTpGx0+/ie50RQOh3myyXj8ZhrVy4hpMQPfW7dvsO9ewekaYdbt26xsbfN2WREOtjksa09/vBPvsLSWoTy3FTTTuYUYGdzi24YU4i8qofWxL6HZ2GRF06CVQtOjs/YeOIjBIHk4vYe0/mYw4P7DKdTvChkZ3eH+WTMvYN7zIYTPv3MsyS9mDQJEFpTLpZYXdLrbDCaTNjd6JCGEVqDtpZ+HCG0JStLsBAoSSYs0/mUC9u7HBwfMi6WlMJCViCDiFB5pEmPoihZZhmHh4d0ewGdJOXS1YukQnJ8cJ8vfO4nKXPXuCXwfCQeWbFkZ2Oba9eucfDgAVJ5pEmCxZDGCd20w2I5AmvxlKI0hjRJnFKalJSFBgMbW1vY0vLmO29zcW+fXq9HUVg64b/j/bAbwyFwrevOLQzC1eRg1cNQMy5LUPXbraDVqsTGClvloKucaUVdlecW1vfahBBI38MIyMqSUq8vKZ6xlLUcqhROsnC5YFlUBBtRH8Oxs3XVhzsIAqI4RkrZNJevxeNr4+t5Hnmes1gsqNnadYnVcrnk3p27rnSJOkJzBiSO48roipZxWIf8HTRt1pjpa2NeaauvcvIrh+OHgTJd1Oh+DGZtQdVlyaIskQKU5/Mrv/xrDDYGFFXEHwQBURRxcnIbtEVazfW3b3JyeIKxiueefpo8K9jeeMDWYJv/6H/0P+Db332ZL/3Ol+j0uvzkZ38CXS546/Uf8KnnXySJ3AOl9RItJGEcgxGOkLIsKUvDz/7cF/mN3/5NvvOdb/KpT7/Ik088xd7eFtffvYunQspyAdgKTVHve+3t7XyzklVbTZq0gAN8nBGXUjgVqEq/3jV4UIDC2LrioB5LwfnItoGz65RLq/FGbaEdEQ03MUwL1XKisueiYvvI73n/7RHRODTOcHMOax9oew/ul/NB/Pnt/Frx0Fy2Fik9ojjh8HjIH33t6wyzCZcvPsHdBwcMoohLF/bY2tjCCwRZviAKusRxihd4PDh9gBKCKAjxVYA1YMqMTn+LfrfDst8n9gMWWUaRl4QoJJLORo/TyRm6LOn1+vSWY8oHB6RRRDftuC5YWqPiiFJ53B+PKcaa8WTEfDJh98oVup0OPu5ZeXB6zFdf/h67Fy9grWE6mhEqj48/+zTawKLQkOc8fe0aG4MBZZaRpgnj8RihFFY4zYasKNjc3MVKxRvvvsbRyTG5KZhO5qTbPWQU4puUm/cOuP/KG8SdFIf4Gqd9oTWhcqWNUlswJcq6qpnSGhQwGPQZTqYstUYjGWxs8NIr32MjCtg73GBrs8dnnn+BeNDn1v173L19x8HWHctoPOXv/sPf4DPPv8jjGzt4wicNQy5sbbJcFozyOR/d3iQJQ/LSUGLY3eiSz5fkucZTPnEQMhZzgsDjiceucOf+PcqyQErJfDShFyfkZYGvAvqRRycoeOduxiz3ubYpeXp/g2tXr9LvDtjsbGCnM4QSleiLK9O7fesWv/ALv8Rv/+7voHyfJ689gaqqgnw/ILMCX7rKjtliRl3FIDzpSsKs5cGDIy5dusQzzz3H4b17ZMuMQX+LyP9wTeyPKcJei6lpvOwqkrZUdb12ZXScV+0eSm2NIxcgMNI9/EZbFBXpqzbswkG2uorE6/+wKxJQuySpqAKVtJNirSUIIqIowvM84jgmLwo2tzfxPMdQzvOcw8PDpg7YRbcrwpkFFoslRVkymUyaSLhWE6uj7CRJmM/naF0R5GRNDHPSpFa7MjJtDGV1fGNqxbLVQtjOV8PqX63beU93ck2DlOo+1OcCP5wY/XmHqn5NSukacjS5TYPnhcwzTRT12Nm8Qlka7t+/z8n9ExaL+5wNbzKfjZB47OxeIu73efFzf54nnrjGzRvvcPjgjO2dTYanp/z7v/5XuHXjLkjBc888x61bb/HJT36CB3cPuH76LkWeoXxY5gVBHHFycspnPvUZPv7R54nCiO6gw7s332E2X/Dg/jGHh6c8/cxTfOMb30dKVdWj29W4iqoK+lGRZ2s+r/c1r52o9VI5pTxoyY6aqvypTiHUAg3tplpNHXVt1WzrWbH23HPV3By3p7HryS1Tk7gssiXbaqhLyajm76PQhLqYqo5yz2ncU8+DdfIdwq5+X3343L6P3hzprL6k5qKr66rGVAgKoFzm7v52EoQPFzd2uNjrMS4LDscnGKXZ39hGi7rKwpXglVXDHE8qkjgi9AJ8qSiVdr0DlOTizg7j0ZTxbEapNTu9TW6dHCA9xXQ+Ip/N2ey6CpEwCJgVRfU8S+bZglv3H3Dn8Ijs+IhSOVlmU+a8efsWL1x7gn7a4fj4iIOjI579+Me5/42vsyxdA5ytfpePPPW0a45jJFtRl26SEkiPOA0Yj4euTjgOCQKfReYCgYsXH+f1629weHKIlob+bp8sK/CjhOk8597xMYtCM9jc5PjsFG0dN2Sz30OYgMnZEKEEaRiCdupuntGcDIeURcYgTRmdDcnmc7SnSNOYO/fucunCM1x57CrKlBwe3ufB8Qkn04lrFWo1XuAR9GLwQv7oe19j/9qAx7oJnhDYQoMKmNmSzX6P0A/IC01WarYHfWbzGcuiIEl9kjikMBMWsxndKMITThLXSqc4OZ7NMInPosxBl0Sh4CPPPcf9Ow84PjxiZ+sLPH75CotZRrZYEisP33PtRake+lv9AAEAAElEQVTSlEGcYLBs7uwyXyw5OjqmPxggEAS+7+Bt4cip9Rw1xiBRTboxiEKOjk+ZDEcMOh2SMELrksD/4MHAB9k+fJa4aJVjVOv5mkiDFY1cJawaODRwbb0QSJzxqlsialAVWUhbi9bux9jV4vEoD90YF30aACVIuh22rIVKLrX2lvI8ZzabscwWqIoo5HlepRomkXI99+t5HkVZdZ/SJYvFAq0tniebNp31sZMkcUphlKu0Yysfbev8sjFInEmtDXsN2RoDdSC4TlyrF11oOACcgxV/xO1R41k7DHVJbTuv6XL0cHx8hJISazWTyQmIBaU+o9dJ6CQhpdEs8xlvXr/O7pVnuHNvxIPDKYUWREmILguKLOeXfumXuH94yPe++11efe27fO4nP8Xo+JR+rwu2g5RglcRI4QQi8ozbd24yGAwQocSKkp/87GdI04SDgwOeeuoJAPK8REmB8hSOxV117BItaZJz0V1rVJoxcPN2NeZ1bXXdKrW+Hw6TeDQ7/5HfcS4UXaU6av37ldl0H7fIGkq3q53WZGSpY1zVeu38d5vWe+e6ZzWHsRWUbyqPozLS5411s4Plg1Jl6oqL5qzaefp60xpfCJIwIO1EzKQhMJrAk8yzOacjw1YSM5nPeKLXJVQpnh9i0ZSFJgpCAuVjCo3RBulJdJmhlCAMfHQUunXI8xAWPCFrjINllnF/9oDNC9tcvXKFV197h8lkwmK5JE07TKYLRosFczQi8lGBByheefMNtuMEubWN7wfs7O9x8OAB08WCWOBYx9Ywn47pd3ss85IkDAk8H4FFFzlKyEbZ0aFCHr3+BvNlxo2bN0FJvFBS2BwtDflyxmiywI9iCgpmi2UVxFiw2h1P2qq8URB6PoEf4Hk+Rrue4aZ0UWg3icmKglkFoas05t7RIQeHD9hMErCC6XjEZDZFBQqMpjAlhXBpyInN+Jdf/jf87LMv4omQi/t7FFoz1wVRFKCUosw0pS5db4YspywNSjnypjYGgaUTxxRFTlkWEAVoa7BGkxtRdTA06AI2wzm//qs/y9e+/Kd89+U3iGXCT3z849y9c4uy28EYl/JQ1frvKcV8vuTylSvcu3ePk+EZO7s7+J6H8lyPbmMsynfNiepyX4e0SdBuffaUszOT2RQlBL1unyDwP9Dc/6Dbh9+tizpaWC0p7XVD1Mb6nDEwjQFzIYDvBxjjcq++5xL7ZVEQRSHGgl91djo7O62il9Uh1whDUPVzdlC3Ma4TS027rrvNGOteL02JVIokidnc3MIY8H15TjrTNl6JsQZ0TVwCrR/OK6/XRruFcp0BW7XUpG5Asb5IrSJqmn+FWBmVGkZ/OACrj3VeEvNcTrH1+TpH395WoiytyF6Iqs1lva+sSsgkR4cHYN3DCAVR5KNkilIhnp8ghccim/LGjRNGM4sMYLQomOZz8tJdz3gy4vHHr6Kt5tXXXubuvQOWy48TxV1CP3ULjQTpeZjKkGmtmc0X5IWmMIZOkvDCJ19A2JTh6SlPPPkUGDAmA0qE8FFSYCkQ+NXCLFoT1pkPY+2qGYk5D/8+bNxXYyVW3bf0KnJ1D4Jp7FEzXxpT3DK+FlzsX+AqKlzEaIXFWkcGcuVpboe62mI109bvMRVMvjrbVbrFOdfn7v+jfD7ndzfPkHNMnPFerzmvjg+PnHeP+htrcX3X28NQVytYl2ar1omFNqQdRVnmbPX6nI7HzBc5ypeUheSdB/d5Yjegm3YwFgprGYRdQuOBcYzzKAopjOMchFFCWQIqd46gtoTKYzvuMpOSs2LI4ekJTz/1OL7yeNeDQpdMFznDWcb1+/dYWIsWAs9KJB5IOF3OePPeXdIoYavfJQh88tEQiUAXJVFQdwZcsj3YYmnKqmJFNUROT3lIoQgqaVRjXTruzuEdjkbHBN0QKaHMHLpnS/eMWgG51mS5416Uua6gXo9ASBbKQ5cFke9X0SSYUuNblzpIhI9CEgUB1iiWwiILSyFLTo5PkBsuOOl2enQ6fWykKLMls8WCSb7EIBCe4Gg04nvvvsl2b5vOxiZW+Sid0YkTPOmTa40RBZ0wJhcKLZy8cKB8pHDnlgaWwoK2khDXqKlo0B5nLBel4ej0hKsXd4l/6gu8/sprvPXuOzxxZZ9OmpJZizDCCQkph4AJISimMy5u7XB6eMRwOmFhNHEYQzXXhBRIz0NU5XBCSryqzFgLga+EK+nFB2nIi6Kql/93XEscqnWmzpVVW/P81TloUUeOpoKYIudVSSfvmSYJpXY5v06aMtjoMxoO6XS7boGRktJojo4euMiuFQE3Bq6CzRtimIblfMm9u/crFbX19cjaCiFQZdVBJ6qO6Vpjtg3Wqmf2KsKS0kElSq3nk2tj0oayaznOOvqvy66QAlFB8G34u4ZhXYendbaulCtD+8jI2zpeQE2KqyHIxjVorZqPIlu1X2t+laJpRyeqUq+yKAkCydHRAdbkDPoDl4e3Pmm0xcLOCcIOG709wrjk1oPvkZuAUroHaZpNWSwEQRiyXM4I45gsX3BydoK2gh+8+iYeHgLhImQlsCJHKldqIaREG0OWL9HmlMv7l3ni6hVuXL/PaDTm6qVPVwhO4cRPtOceWFkgUevG067urTYun4YAdD328mEjXaeGq7ltsYgK0jVVfatrLVs5f3UDkbZ1qu15/Y51qQch5xUqozC4qM9QglFI6Tvts4qZvp6RWpnwuqXsQ99Z32fWt/eCsN0OK8130Yq01/tar7QLmt1wTukjHQFR8SPqAagMjqn+E9Vrxlhmi5zD4YxPPrbDcq55vD8gW5YcTSZ4SYi/jPjyy98l/lTEdrfnau2VYjvoYbQzhHEcMuiE5NMcoyHsppRWkFtDXuZoT5L6AdudHsN8QVZY5scn7KU9oryknwaUCGaZ4fUb93jtxnVUkqCkwscj0BIjfUS3y1tH97m4f5EwCjDFkl6a4kmFEhLfD/A9H4FEqQDQGCGQnoukdWmQwjimu+e71Ea1Xrx79x0ycgIRYjWYwhBIH18GLJXgKJ+yyOYY65qLkLvgwvd9Is93JWpFThIEBL7PMs8p8xJVWtczWgaYoiQJQqLA48FsRF/4bA0GzGZTkjhgc3uL7bRL34uZ+ZZsNmM0HhNMx2SmYLlwHczePrzH3bNTXkhfoBSKji3oxCm+CNB6ihUZ/TAlEx5alPjSEuKB8Qj8kNjPKb0A8ImsQEvBQlhCFL60SF+SlwUPTkbcuHGHv/T5fw89nfPg+C6vXH+dn/nk58mOT1EVF8QI5776GESesZXsk4YRh2en3D87oxt3kNKDKu0lq3LiwrpeBKHyKLQGLL4nKTONNJYgDDEGRuMpcfzhKp39WEhnLmfcZMKo870SgesFXHeQckID165dQ0pVtam0TCYTet0uZ8MxZZHj+x7WGGazGadnZ8znc8Ioor8xoNvtshhOXfRh3BJTGyXTrApixVWrNikVco1W67bSGqRSeF5bycxUJC3ZQPirRcitWrWYiBCrXLM7l/Z3yjUjLuX6a7UD0yigSQEogsCR2cKKcairNo+18toH3RpjLVzf8Fpdzqmprdjrbci9/g7f9ytW++qCarKWtZYid559nhfcPzjg4N5BM/pRGLDRHzBejLh44XGeefKjbG51eO21l3jhxV/l5PA2o7MjYj+ikwZsbW9hrWP237l7ly9/5auknZg//P1/7bTNW5csWGXld3e3+OhHnuOzP/U5FlnOz/65L/Jbv/0bvPqDt7ly+Ql8/1eJEh8hJL7yUSJGCh9DjFNqW7UrrX8XQqCMQfkuPaKLJVixdl/X2M2AFArHFF/1LFdSItbaq2oCL2ylMlZzt35yagdXVuizkKKlT7CKn00t3deKrt3fhhUcfQ4Cr52Ch6zy+rX9UNt5D5j3SS2Ic39Y+8i5LKjS2PX7nkRjyIuMYrnA5CWhihmNRkwnE6wpCbyQOE44PTvj5u3b7HU22N/dpROn9NIeZ0uX9orCiDiImJTzCuGyjb6AzQ3CU/R7A4LQx7cppZKOsGoMURKzs7FJri2Umtdef41yscCP/KaHu8USJRGLXKCN4Huvv075+FWef+ZJ8vGMg4MDnn/+44TKUR6kEA7+thYUeHGAr3zmizl5WbCzMaAoS4IoIo0TFILRZEyapM2cEtY1/4iTPq/e+D5GevheQFFmCOG6C+aLuSuvqgKCJIrpxgmJH7pui8ZyNl8SSOVg6yAg6KTMy5yzdx7w13/1P+DNg3cY37rJx598kp944TO89uab/Ok3X+LdkwOkgs3egN3tHTpxB7Ph5sDGxganhyf84Zf/mI9+/AXCTtzoY5hSs5gv2NnYZDIaUmYZsb9JHAQMz4bI0KCk58p0qyAkWxZ4/QitNVlREipJGPh0ux3+9j/4XeLQ58VPPweLZzm4P+Te6SEd6SStrXDSwkZr8HzCIMRYS5IkSCl588032ej12Lqwx+13b1BMZgRI+mFKKUCGPpEXEJBTWsPZqODJx/ZY5lMmk4I07LA16HD38MaP+DA9evvxRNith7S9TJga9BMOxpPCaWWfnJwwm80ZDkeUZc5w6Jqa10xqYx18VZSWMFAUpQGhKLLcaWkLp0Ps+mFUee2W0lmFxaO8Sga1CS5aObjq/8bgPP2qKcjKALvPlKVeg5/bn6m7ZMGqH7YQtBbpVX1tvS2Xy8ZoV6df7e8erjpvpZRqWnTW9ddtQ1HnN9/3vtA6l4r8VBvrtsE4/1M7KO2ytnZZmayIPGWV09fUELJzzBZ5TnF2wuc//3k+8uzH2N3eQ5uMv/JXfonlZMR4fIdiMSJRIYNOxL/4V/+S//5f++t85zsv8/IPfoDyFHfu3MEYQ7/ryjeKoqiiLkESBRRFgRCSyWzGO9dv0O12+c/+8/85Dx4co0tIO5sknQ4XLu5wcPCAmj1dao3yXF1rfT3tOVxff836z6uRdKVVq1Ku81laIWSLy+HawCrpUdd/yzXrdj5B0X52XHTZ3Jv6HWub3W2lt7+Co91P+6jCrh+7JoCunXP7nM7Z9w+22Wa8ftTtvfZtnDSpmGcZy+WCNPAol4bHLm1j5ks8YQiVIAoCEpFgDTw4OeHu4QM63S4X9y/Q8yJu33BRVqcyVGpgeeW11xh0+jzxxDXSbpfZYoFSPpPJhEG4wWZ3wPbGLlcuPsbJ7TvOMEQxUZhireLl177JXjdFxhHzPCP0JLHvMT45xY99/LTL6WzGjeNjep2Es8mUNE3odhKUKZG6BGvwlCKJ3Z3I8wzhW4I4IC8KCl2y0d1w3pueEfkhR9MhXuzSOZ5SxFFIv9PjtTffpdvtMsxzhIUgDKte21DmBWEY0U1SiumcUArmkzFR1Gd7c4tIhRw8OOLq/kWWpqQTp2hrocy50Bvw6ec+xv/u//Ff8ezeHju3NhBSMrean//FX+AvhgFKG0bTKXdPj7lzeB9feExmExbzGRbwooDf+J3/L2E35uKFC3SiDg+OTimWczq7e8zn82osBJ4n8QPF7vYmUnhVnwmDlIEj9ViDqtDPTFl6Gx1+5flP8kff/h7/xf/z/8b/6n/4P+ZXPv2z7Pcu8Mbd19m58hhF6URlTDVmFgiikNlixt6FfebC8vt/+hWCzZT/19/5O3zq2rNc2rvAfLngzXff4rPXfobT+/d59fVXuLi1y5PXnuBgeMxv/skf8qmPfIKnN3eQhWE4GvGRFz73Iz8Lj9p+LAa73mzLENY20hHItBOwwEVpx8fHjMYT5rMFAovvuZXCayT4qIRMcqRSCG3r1GBF8KL5nroNZ01Wa55/CcLaJsq21i1zLme2MmSqou46g101IZGygX9dR642A90dr2ZRO7Uv3Sz4TZTWaKYrfN8jDEOUUnQ6HY6Pj5nN5tV3iYqV7mqe605fNazuzm0VjdQCL2vwLOtIh7RVHn/lQVQqPavPPrxQiuZ8muO2PtNemE3l4bZzv7VhEtK6iMMYysJwdHjKfLpkczvg8ceu8i/++TeJU0EgLb5SDEen/NKv/hKnZ2f85m//Nn/85a8wn8/JiyXK9xjPpogaErZO03c8W6CkYDgeM35jzitvvIXnSabTOb7nIaWHtpbFMuOxa49x69YD0JbS5hgtiGPXmW3N0Tz3u+d51b2uZrNcjVF7jsO6EIuQYCqUorBlg9IATX9thKhqpd29EfWOVU7b4nqCW+u62jXhsRCsR9H18NfCLutETKfd4jSrq7tU38wVBN967c/aXF/w+ncamLwx/O1cfvOd9eHXo/1aIGbt+GupmMrJ8T10nuMpwcXdLRbTjCs7e5ydnhIcH5EtF2TTBcHWDirwKaVgtFxyOp6wu7dH4IUUWmNKQxyHbPU2yCcLlPC4cec2e/v7dDodPClZLBZ4YUBeavLFkigQdLyQYxwSN0g6bG7sUlqBVYIk8AjjgDBwUHbix+z0+xyPR0xLDUHAyXzBW7fv8tjjj2FsSTdxzT107sbStfTMUTqlF0UEQcjxdI6wrqIE6bgiTopUIH23FoWh00H3pYcuNN1OhzIvUKXB4lABz/PI5gs6aUqRZyylIAh9Qmu5sL1NImIioZhri+95bHT6vHrvOk/sX+LG3RvMp2N+/qd/mtH9I0yp+cSTz/JLL/401y5e5quvv8yffPMbPDg9pigLRGEIrKK7NSDqp1y9fJXpdMp4MmE6nzOcjBgtJpwMz9ja7eOFTtymmyTMphnWWrppSr/TpSxzLm9fxlMhRZETRR6bmwMWi4JSL5HGKcstdcnx0Smv6pv8zb/6a/y9f6z5vT9+ibKQ/I1f/gtcGu+wyEuEcd3RrKwIx0VO5PnM5nNU2qHb67O5ucnbb/2AfHvMwdEhT128yjNXrvHiR17gn/z9f8wnPvsi/UsXeP32Xb79ztv8e1/4Kb7w/2Puz4NvSa77PvCTmbXf7be//b3ufr1jI1auMHdKoqhdIiXbMZY0GlmSx56wR2NFWGM5FGHLEfZMjGY8YVmLV8mSPNRYpESJokSQBAkQIABiaQDd6PXt7/325W61Zub8kVV16/5+v9doQA2Ns+P2775761ZlZWXmOed7zvme93+UT3zyU2QvPMNTV64iheQTP/uLPPUfPvtN19I7bd9Rgd1t7dJt3Ff1em59XNYghEUKseQDhlPCqHbuWlgEAtmOndzxEzfXpb2ebQlXzneiOWEocZtbU4Wria5s+tSwi1lLR4ia9tgmAK3xNfu+T6/XO1OcI89zptNpCzsvNtnlcpJlqdt7kh2a6UYh6EL0jfLRHN/4Upv7b9spA+uMdV7LBNv4NtrrLbsdqJ8pwo2XtgsFoFEShBBI5fH6G2+SZ5bnnr3J+mafn/0H/4y7d4+JIp/RMOHq5Qv0Rwlf/PKX+M1Pf47Pfe4LHB0f1YqHC+6rjMVr6FHbp2ixQlLUuaWe7zOfO+5x3w8pCs1sNuPe/Uc888zTfPo3fru2SBtLOcCRyTRit2v5NkKxG3jXhGa5Mo62nthN+kdXUXNzoC4LigXrXB5CCOdLa64kGxi8UcBke01tGorfhWCzNUx8jplcA0odAdl53wpx0UDfC+HdTd17J+a1tV0SnlrBE02QWdPbc+YWZy1p8ZjjltAeXJ6/lIow9Lm4vsK97UesDQak8xn9fg/P9/CEh8SlchXWkOmSeZaRzeasriZYJRAYYt9n1B9wO8vor22SFnkdgObmvacUQRCCEBhtMKWLfVhdW2V3Z5te0nMCpqjw4oDAs0hrQZeOAVEYdJGxkvQYHx1ihKEATrKc/cNDMK6cb2lr96DnEwQRZanxhYTSCVvPCmI/IlC+g72dL43j6Ri/FzKbTxn0egTSGTNKSIaDIXmW8WgyISsL546xEAc+phR40lU+LPKUXhiRBCEbgxWqvEJoQ+QF+NKjKEp6SUTk+/TDiKevXSPfSekNeqxsbXCSznj1rTeQgcfG6hrrgxG9MHTjJhW5KZnrgnk6R5cVEkEYBGRVRWUrPvfbn2f0PQNEGKCNphcnHI8rlCeJoxAlBePZlI3BAIFPWRaEvseo3+dOtQOeBl2BClgNewjgpftvcePrfX76h3+SX/3iZ/nKa9/g4kafH3rqo+weH+LJOgYAF9SsixI/6fPozj2+9san2ZtN6W+toZOQH/uhH2O8e8Du9g6/vv05Ll+5wvf/4A/x+S99CZEExKMBfj/hf/jlT/DT3//D/PjHPsZvv/YN9o7GvP+Z5+hdHnzTdfSttHefS/y8tnDLue2go/kvims0lmgjgBb82w3xR/d8jb8QqIWEe99YjK0UqX+wlAollvuwvFEsLOOGlGWRD91EshukVHieK10Iog0Sa7tYn9Kciiq21lJVFXmeM5/PmUwmbd72ee3t0MUzMvZxx9pTB9luLPF5523Gi8fu20voBcsQ65mIdOGQhb29fQ4Oj9HGMhiFfOXLX2d8ssPe7iN2d7aZzaasba7yi//sn/OLv/RLbO/unoFupZR1hHQdXW9qX3PnfpQUrX+dWrGYzqbcuX2PZ5991vko6yhTF629wIAWc9H1ecFdL2nyrWmHZaGwCBZzpFG82mdiG9/oIkZAdgRRN27g9KtZNK0raTHILSzeKKqNUBOcOk9zd831mus3x50+/lt6LWheu+u46Xsb936OQrj0glPCfdm6bsZJCYUSgtD3WBv2MUbiSYUxlkE/4eLWBv2o16ohudFkuiIvS/K5cz+VpkJJSRwERF7gBInvoa2hqCqquoCP7/uA4y4XOG5rjGHQ75PnGXEU4fteXQrUUpgKJSD0XHxMVeWUVU7ke/jG4luJsIK01BwenRB4CmktSrrAM+UFzLKCuDcgihIkEqstoe/Tj5IFu62UGGGZZWlbL82Xys0NY110d9InzR1rmbHUJWydMeKpZu9yAZVGa4S1DHoxvhL4nmRzdZWqLEjCkDAI6ccxq/0ewyh2BXgQrA2HeLWr7tKFi6wORwz7fXpRTByGBIGHripUXenK1YG3KOFSqXzf587DB7xx77YTpGFIFIakRYbyFb7naFrT+YwLKyOMFlRVgbUVUgjKUuMpgS8ERmuGccLNS1cZjQZ86rdfAmn4yIsvcnl9i6+/+ib7xZQg8BD1nu32bVdS2QsC4qRHWWn2Dva5//AhJ2VBEMZcu3KdK1dvEAyH3Dk84M7+LgUwnmfMixKjFONS8+mvfZmV4ZDN0Qazec5Lb76ON4zO2z6/7fYdIU453U5DqdjFRuesS1FblBZPLfzEfp103vhOlZJIpZBt5SO3yGta13Yza9PKOtZGu+CVqgN3BMs9FW2fGmFttYMb3bW92gpy3sGkztcMgoA8z5nNaPt6+t6LomA6nXaKTrimtSUMxRmB3vRDCIlSto4MpyZeaaw2WmWmKzyX1BTh4NRG5i3dr7VLAvs0RNlKAtFBKzpCuIkvcFa/G9/KdIulOMGJcM+wAXh1Vbkyep6oP3XukePjisPjMauba/zaJz+F1paoLWvn4HYXgyBccROzUODCMEBrtwkjIK/5ytMsR6CwpiJNZ9x66w4/9fs+jucpiqq5P1cus4V2z5m/izFq3hlX01oAYsEstlBCa6Y+GuTF1hzOoq2C2SJLLMPvi/nRSDG3yZ4uelPbrghbBxK2rgs3rl0h3DTZsawbK9u2834x/xfneft2WtF1lchqRaipLtYqGLQrriVsOXPCxYdNH7trtyHrkUDgSUa9GKkCp/jOp8RxyNb6OqNk0PI8F0YzL0tX9KLUSE8xmU3xfY84jJzAVArpKawUZEVOWTp2Pk85ToWSEhH28AKfrCpQOLdeFIegBGmeMZ/PyDzN6uoqvbBHVmTMs9QxqlUlozAiVorUarKiJFU5SRiDNoRBSCUNhbY83Nnn+eefQSUJIghQwqUwBcqjrLSrYmcNRVWCJzBVRRJGLqe4MggrSOIeqcnY3tmlNA5xUlJiKSmynLjeu6QSeL6k0CXWlChlkcoJ7uFoxP37j7h+4RK+7zHo9UgCSVWkVFojJikX4yHPXL6OLwTxhQ0e3nvI7b2HTKZjdF5gSxfzf/HaFRAShXRplZVBWEsvjlEefP2t14nCAUESEYQh49kU6bno7Koqsbri2tYmZaExpkIXJbPphLIyjMKQWMUcZlPwJJeuXuT3XN7k7/3iL/N3P/Fz/Ed/4v/Ax8MP8Inf+Ayfu/UyP/H8dzFPS5AKg6UqCzzPJy8KnnjyKXobG6y+/DV+9p/+Y1KR809+5Vf5ng9+hGfe8yLPfNd38Vtf/BL/xV/7b/hzf/bPcrC3x+17DxECfuf3/gD/+d/9G6RC8Ds+8v0MHtzn53/tX7AU6fwutO9IlDh0LQx75vt2I1FgpXCcv2YB+3pezXdcn6dh6GqEX6ktgW1YxPrA4SJbzAqEUJ3rOYEmRReO7g5iw/5Ub7ZOc2ijsFdWhkRR5Ejdw9ClEIU+2w932rrY0+nU0QYKsSTQYOFj7lb+cvfl/nqewhjRCkCg/at1VRf2WCgBTdBXY2A1kGR3g+uOv5QLxKCLUrydYAJbB/Mvp8otnsVCWWjea+0Ui0a5aFASaysXYV/nOQsJcS9ic+uyy6MWEYYcXRdzUDGEoSTPNXnmIrJVTcdZVVVNIOO4oannQwPVNxHZcRRhjCEOA0c2Y6HIUt58/TZXr/4beIFkPqsQGISsqKxECnVGgJxWVBqCHTd+izm2bBG759cImBZ219ql3dTBe1J6tV+8gwLRjUeQzceLNVVbo7Z5+J1fujSzxWdKKAwOgrSi0b3qgg9CNhOGBlXotiXr/uzU6CApiw/dXHTsfaZJOef8oiTCXWRpjKkV7G7uxemxlVK2lmSgFOurI5LBiIePHrB/dMiFrTX6cUw+zwmCkDiKKbFkVUFZlQTSoyxL9o8PWB+u0A8j0IY4iti8eIH+vXucTMYcj/us9/vMJxP6/T6HJwdUuiSvCo6nYyhLZ0UOehgrmBYz5umMlcub+FHMMIlJkgimgkePdghWPJ5/4gaZttze3+OtoxN847MyiFyUshUcHJ2wc3DEM888x6995rO88J4Xee6pm2wMBthphtEGTyo8qTgcnzA+OWFjYx0jXABmqHy3zpSLuL5z5w5+GGJz6xADa1AGAikxZVFbqoJSl6z2BmxtbTDPJ/iR5EK8ijQ+j3Z2+a73vI9Xb79OHPoMV/rkWUpmcq5fuMja5hplJLi3s83hrW9weO8RL9x8Ah+oMEzKnIfbOxwfHzMZT5sHSmk0hS4pi4JkY4Osgu2dR2TpjN/18e9mXhVIz2UNzZgjk4gbly8xP7KEniQtUqaTCWWpWV9bQ01D9mYnfOWNV9g+2uFP/ejv5a/8xb/Ev/+X/m3+13/08/wbP/EH+HP/+h/nr//c32Lv+BBpQ/zAx49CRv0EU2k+9eu/SWktN9/7Xj744Y9gooDXfvWzbNuCv/B3/jpXkxV+8n3fwx/+iZ/iif/yBf6d/+j/wsc+8EGeuHQNneX83C/8f/kr/+f/hD/9V/9Tvr5/nz/yoR/gP/zpP8m/+Vf+Mn/h3/vzZ9fRt9m+Az7s0wv0HODVmbFUhUYIhbZuI1cKKq0dyXrVVIBawOTLG4uzymezWZsetbhk7WE0ZlGWUmuKsqw3/Trq9pTd2cKDiDaN6oknniDPc6qq4tH2I/b29x2UVFqSJK5J3qua5WxZWDZCq/msYUtrrPiuIF7qg2iEvULKJuDItuNx+vjzgnW6QW9dyLUrhN5eaDfjY1t4sClm0pxnQcEKSKhKR7Fqa6ulex1fCUoEoKl0wXRWOOIVHaC8EozC4lHWmbja1PePC5Bzc0C7Td1arNYILEHgiDMcA5GzWvM8rwt0GHRl0JVmNpnx6quv46sAVQf2NSkiQpgWCXAAWdfSdD5oY5wlPxpu1jN6EYvQFdYuZ1o5q6ZO9asql81QFEVduMQRzCzVFO8gPg56ry30mlLT0q22ZpeWlRQuTcW2z6s5r3JW+XnPtb7m4z0uj5kb537srPbGteDmx9vNrbPfdaeiESyl7sEi/dEXPkJD4HlsXlxjdf2IyXSCDDx6gz5JHDEb5/h+QFkW2MDHAJWuKMuC2WRKWZZ4ShJHAUns6ImrquLSlcuUs5TDg0P6fkC/3ycvM3pJj/5ggOf56OkJgzh2aWVWM57MmcxmjFZWmFvD0fExIZYgCegNeozKFebjKf0wZL53SDGdo4Eiz1GrMWtrqxyfzAjjHk89fYHv+tBH+Zt/66/z+v4Omy99hQ899x5+6od+lPJkQjqZEiUR+V7O/sEBmoqLFzc5ODigFyd4QpKO59x64xbra5s8THOUdlappyQryYDp4RHDQQ+lBNpqVOCTplk9h53C6wmBjyRJIq5tXuRrr79M4Hmsra6SJBEHJ/f5wQ99jLUgIUA6KDwecLG3xp3pIQ8e3MNaw2h1xOVrlxE7u1y+dIWyKChLTakrdg4PsEKSVgXCWDJTcTKfcufuXYajIVGSUFYVMz1HKEnsB+TSJ4pjgjhgEA/I8m0uXrjA3skhAsGoP0LIgP/s5/42/8ef/N38v//cf8p/83f/B/7GL/wsf/HGGj/9wR/kQT5hc2XEPM/Z299nMptw+eIlnnvxBSZZxud++wu8fPstxlXJndkBf+rZP8IP3HgvL731Br/w0hf4h1/5HD/+0e/nz//pf49/+qu/zFe/9lWevHAJsXGV//Jv/lf84fd/mK/fu8f/69d+iRdv3uS/+st/8W3Wwbfe3n1I3EiEWgRCGduUoFQtFObYohy5vCtKbxyftl7Avw30u1jILk3K1BChMNrlLNabu6idyI56z/1ifX2VJOk5q9j3GdQc4g0cLmUdgVtvnLKO6DJYSl0xmc3YvrXr/Ow4K9lUtq2buhCKrpOOTm8RNObugzYdywUbLSwoZ+gopBKgNY410LQR8M3m3AiEquqmVS2Ed5NO1j1+8ZkTAF195p0020DvDXW6dVHr3TS00/1zKW6NQrAI1LLWBYVZIfC8yJXCS4+dYFTu+RnrxI0yHlQSJSzCNoEhCytUNrIGR9JhcFK3qhGaBppuhYeU+EGA1oKT6Q5JtMFzzz3FV2avMZvPERaqUuAHLjWqQyrqxtEaZ+xKKHRJMSvaByuQ9V8H7zgRuKinbqzBmApoioI086J2EJwr0ywWvRDeSqCEhylzlKcWQl42+fNuHGxb0MOR5AilELah7m0UxvpGFppBe59tkTDE44Mb2p+dwSLcnbfzQrpnZ11/LefHaDRzp1VCBLhKZhmeTNDkSGFRIsCQomSApSLpRZRaUhaS73v+Sd56/VVGVrEajkjCFU7UNsIKtnojDoqULMtc7esk4Gh+wkbcYxCFRFGPfm+NOIyZHB0gqhyM4wcPg6j271ru7DzkjYd38T3nS75y9UnGJxPu3b3PuEhJybi0MeSN+wfMViJKPJSusNUMrI/XNxzMD9kvJlTSMKg5sFMZshb22cuOSWcnbEWGBw8P2VeapzavsL62xt50wt/9+X/ID3/4u7kwGGKMxvMVcRIyn6Ssx5tkco6oavQqjLlw8SKv7O2TURLoisRCLwiIwx6ZGqNkiSddIF1pBf3VNXa2d1hf7TPo+ShfgS+5sLXOpJoyjEOGccTmYIVASC4MRpwcbmOqqq3KeDIZowwoKXny+g2qqmKaZew82mFvf5+0n5KXBdpapKfwI5/1eBVfCbR0886rDL/x+d/GW7nMljIoI5kWljibMex5PNrRCFPw3iee4ukLz/BLv/Y5nl/pMRlaONwBa/CTmC11kb/9qU/zx3404qd+6nexc/cef+Nv/G3+8r//77Jzq6AsUqSuEFXJ8e4en/rNT/Pg5ITe2jqj9Q1uvvgeylKz9/Uv8U8++5v0kh5FVTEarSKV4nNfe4mv3XqDSToHLK8c7bpiUEbxpW+8gddP8H3F6/fu8zdOfonf8YM/8fYL6lto3wEf9qJ6UfevsYtCHE6S1NazFPieC4KoKolg4ZdtBaG2NZdrvQlpg643IxeBnZAksbOyPFcpq/ncGEOWZeiqIktTV1GLZWHXxJQ1QkbWUsFYQ5qmGGPxlPMftnWy2z42FILL1nUX0ofGQtDLvmdqc9sKlPKII1drNQyDlpUsyzKKolhCFhsr3fkgO/9eeg5dwbXAMZvznLbUu812/u+s88V1XS75QlC7z5fdAI2iAN1UHXds4Pv4fkCWpc4etG4chHUCUAqPeu9ue7MkgDuWb/tHdDZ/WKrgZowjXFBSoKucO7fvcv36NV77xh3m6cxBxFayrBzW52wd24trLMcoNGNQP0MEYNC6eS6OeUzW7H1d94JTqDpwtIUl/u7m8zbswZ59xuf8wwnfBYzcdVGdhrltZ+KIdoqcVSQeh8QsPhbnHNsd00U8f/faS+hQvS80iIAULjDQKRRy6Tx+4GNLS5bmDKKQsixd/fgwJvAd3GlxwVqqylrSGS0cWjVIHMuYU9o9lPIoysL5R6vKBWHVcylNU9I8YzKfEUcxW2vrCBzaUhwUzNI5RZkTBx6+VGR5DjhubikCYs8iqJjOxlDz15t5Wu8xdWCrsQSeYthPmM5mFFpzNJ+SxAmDIGZl2OeNW2/hP/kkG+E6URDSi2PG8zGX1re48+gOVoMvXCnXkywDT+EbQa4tvqfohVGtuOr6ngWeUEgqdnZ2+djNm2TzCaEKiQNHB92PI0xVsDockoQhnucjLfTipC1W1AQEV6V2zH225qBQCk959JM+WT8HJaHEcYHnmrKq8IOAtCgpZUWa5symKeurPe7evsXWxhoIxXg+4fLWJnEcMpsdYHXFStJjc3UNIeHCaIgVM4SAvNLkpeZSz+eVezN+6fOf4U/+xE+yFT/Hb37mmJ9/6Tf5oasf4WB+iBf4bF24wGh1lctP3eTu+Ig7u7scT1OO9w/qLJOQwhiq+cwhZdpg6/Kcej4njGOKqmSa58R+QFVqskoTFC7QzmC5vb1z7tr5dtt3pPhHA7QJKR1rj3CRls6X3NnkhXDWZQvn4QSlNu3mYml4tl10oUsXcsEsyvNJkggvjOogkYYYxRFVYB0xyWw2cxCUgJWVFZpdfiF4qPvEkiXatfIXljFAI+S71ksXbl6c7/Qmv7iWaaNigyBojwlrcnkXeGeWziNO7dDnweFNf5YD2c6LJTgr5M9ry8csC+bmWt1jmzFbcna2v7VEUUCcRMzneY2Y6FYgYqEs9cLi6py/OevyCJxSDZd1g7qvC8XIGMM3Xv0GTz35NEnvSxwcHLoUmXPqXDyuLd97/VyWRqdzTD0eokZymtzx5v1S2OOpZ3Gey6KNuK5frfIlamTgMfPhzD1AOwGW7uexVv/57bRCcOY6zT3UCtFpnHtxjwLqlM420rye3w4FoIXxLTUSJpww7ft9sixjfX2dIHQ821EYIWw348RQ6coFgUm3B4Shj+/5SOXK4WZ5hkC0AsjNVst05jbronRul0F/4KKk67KJuqrAGPpxQuB7jozIGCI/ohcp0qzAUlFmOb7fqyOfK6d6aItQHmEY0Bd9hoMRj7aPW8E/S+ekSUIlEsazibNQtcbzFFEQIBGsjlYIwtDtecpDW83xeMLK1iZ37t+v90LHlOh5jjHSV67Eq8GhkcfHx/T7/boQggfWpV5Fno8tS5em5fvUvkf8KKSoahIna9xe1cwgIVrGtV4QQQCeBTzl3BJ1Dnle5FRaM01nZNYV+xAyJx702H3jAStrN5DGQ3qKrYtbTsmfZwTSoxcnJHGEFIJRv+/45XEGkS4q4p7iQ088z2fe+Cyv33uLDz75Ah/48Ef51Fc+xw9f/TCVlJTa4KMI4x4XBivo4YC9NGP7ZMLO8REnkwkyDgiCkKoq21LB2mjiOMb3fbdPG0drpLwAXWpX6lfWpFlSktcxL+9We9cFdlO+zW1UnVSYmvDB5U5LKmNcdLFSlHXaRakNge+iuJVSGN3kcS7O30SQe55HkiSsra05Nq10zng8ZjIet34qVRccN9YRAQz7SZs/3RUKqraeF4Qki02o8Rsba9toMFOjpE3q2eIFp4PAnFXaMJNRR1QvoHcpJUnSwxhNlqbMZjPyPHcpC17tBjDOVXBeYY7z2oIadbkvsIDyG594M7aPs6JOs711/ejn/ea8wDv3hQUMvX7CaDhgOt6rrdAmwtkpddNJeibSHruwmvWy+d1+39zn6dakfLmccM2bb77Bhz7yAYbDfw7cb63X5fF6Z1JrYdU35SsbI7XW8GoB7UrxdepSCxzX/ukx6rTlOASn2DpltImUPu1/XrZyH38PFjrw+enBfJwy9ngl7fw5sPxdV7VyEH4XAWgzOoSliQ3oCutTt9f+LssywjgkSzOSpIdfs9GFQUSpHUJnqoosSxnPphyOj+j7ARcubhFFIYNkgOe58rfH00PC0CcXCyGvjWE6m6GxLv6lrAiDgHyW14aEe7aB57M5WuXV23tUCoqiwFdDNlZGjMfHeKHgJHcWdqAkvlLkWY4uCoIkYdAfEFYBcTJgd+cVhoMRgyBAKMn9o33evPMWv//7f5hBmKCL0q1B30NXGi/0uby6wXQ2Q/kBQllkBWtraxyMj9lcXSP2A4oiJx44St4oCFBCUOgKrCWJY6bTKZuXrjGfzpjlJYOh52o5FyWi0lgpsQJUXY44LfJ2wVW6ahWswPcxNRFJ6IXuvL0hqxsb9Po94jgmCAO0Njx49JCjbMZEp+wcHHLXf4gVAhUG7Bzuc3XjKlsXL1A8SpFKYUvDan/I+miFOIzQVckgTvCFQkhJBPQkHKWa//1PfZyD47v8/K/9KjmCP/UH/k3e/Bvf4At3XmXr4iUODg452Dvg5GTMrYcPOchTMk9BGKL6MYGwddUwR1AjhEQqVwsgjCKUlM7StgY/CDBYgjjCCwKnnFhNluXveC95p+3djxIXbhswRmOqRVSyUgI/CAjDkF6vx8rKkDiOWV9f56233iJNc8qyan1pRVG1mrTBCa2y0C7vWTgLJU1T7t+/z92791sayGZbaRDfwPOcVq4WEeKNZSuEQFiLbqqELe4CWy9GWysfpmalqum3kVY4i7+mMDWtr9f9FUIQhkFLmDKZTGtFYSHUG7/w7s5OHQy3ELCwCLRp0qceZzw9bk48/vhOas83aU35yCaAqst/voB2TwuJ0zZn0wyjlT6rqyO+/o1vtELDWnf+MAzwvWjZT8/iWTb/7qLhp4X3eRHPCIPRLtr+rbde5w/+oZ/s8C8brKlQnmpP9rYBeUsyrmEZE0smr12IbhpkwQle1Y6TlBJTViwiKs5epsHDTSPshTn3fhvh3czr86ze5fmzsGwXAWiPb49TKh6H7px9f/r8y8c0cRKnCV5lx+J3fwxS+Qs3inLFHpSn6PV6SKmoigLleZzMxkgLSRgRRBF+HOKFAbPpnBtBROApMC5Wpd/vMbs1Y3XlKraweMIjK3JHNqdA6DqPGUte5EQCZumcyXzmrPwkYWttjUubG3zj3h3ymue7H8SsRiUbVy7wevom1o8okcSBTzqxrnSn8pFCEvohYTxg/2CH1Y019vSUDWF5/uI1XvzuJ/mtz3+WBzsP+N4PfJDN0QoXhmscDQ6xwhIKD384ZJpmZEXFRz70Uf77f/rzDNZXSbwQYVwgpy8Vge+jLJjSWfn9JGEynmOM4fX7D/nGq69RZnM++N4X+ch73osoCuaTMaHnoTyf8XyKwZJlOdJ3FnAzD4xxwssCWVEwzjMGwwFJkjCvCnbuH7C3t8f29jZ57tjMrLZMdMq0KCgyy8FuSRIk3L99ix4Rly5cJo5CEAq0ZiVJSHwPdIknBBfX1xn2BuRFwdZKj2evXuZXXnqN/+kf/4/82T/yR/mv/9E/5Od+5VcxVcX/7S/8Jf74X/w/8aMf+36kUORCMA98il7M0XzCvMwQZYEVgjRNicOoZZlEOtS2KAq3Nmvr2QpHayqkpCpKLIYyK9psFU8p3s32rgvshm4ziqK2dmgQBC0ftpSSNJ2zu7tDkiQkiatj3KQcSSFqXLqJsO6ky+BYfBoLr2iCoFiQZYjaolqwhJW1Ra7QwYJcop1ktcWmlKj5ois8b1Fwo231Rt0IO1O6c1dVhcVRlkZRRJIkhGFEEPgtPGSMo9Bs4N4uyUoTgS0EnajrukB6y7Cm31b4nm4NdWoTfNbeQgeyb8ZwGTpfhvS7712+fJOytAwLv5PWnLvf6xHFEXfvPnAoqXKBdNITDqaU4fK5zxFQrUACaqf1Y6/rIEQHj+Z5yW9/8fP4fkQc+3i+xGiodEnIcprWmWvV82Y5k77pgKWpM71wBtjOv5ox0G3goLWWKAx5W+4iKbGmofLVrc9wgQ51LdmzFvtZgXrWon63W1fZWcQ4LHP2n24u+NIpDwqBoV7HpyPNRRM458rvhqGDhS9dukQYBDUhjERo6A0cgUdfl5RY0jwDX1HkOZHnu/UpPUDQj3soIYmDmGg1Ik8ztrd3GK4MMFJQmArpCZQnyLKUzZVNjtOZg4UxhL7HIIx48eZNXnrrVXKtsUgiP+LSRkJenRDHMfPKEPkeF9fWSI+P8QzM0xyMwZOSshSMZ8dcvnqV1eOUuZ3z+Xtv8OW7b/CDz72fS17Ezp2H+E8o4kGfOI5IVIAOPYrJjM3+CnIt5tNvvMykyFjze3ihq50gtcVqw40rVynmB8RRiC8FJq0Yrq1SGM2/+I1PsXd8TKULXn14B6Ms3//C+xj2B1TWUuU5wlUpQRvdSVmt3J5Qubrs2loCIQmlgllOnlX4vs+lwSpXVzYwN59zUe1KYRDOwj48ZPvhHk88e4Vf/sxLbCYBnlHkx2MurW0gg5BsnjHsDwh8n6rIScKAjZUVIs/DaItUHqMkZrOa8Y3DOX/v1/4x/7sf/13cunWfv/dz/4jrV7b4mR/+fXzppS+RC8v+fMbLd28jRj0IA4pMU6UpwkDk+eR5ThRFdTlmh6RlWcZwOCTLMpcJU2d7FEWBMYZeGCCMBaORyluKp3k32rsusG/evOmKjmtNlmVMJpM2LarxFWrjUqzG4zErKysta1ieOwHhgj4WgSpC1KUUcZa71hAEBqWcZj0bTxzEbOp81xreXEBrzvJfsKotCE5cBSRZW1suV1pK6vznRoC6HFd3TA0l4/KBkyRpU3iaAhGz2ZTxWLdCMwzDpRQeKUTt63fCtSxKRwbSko50iFBqA8hF21P7Td9eUL4dzFof0WFvW1ynOWbZ+hY0wXjLZCDLucfnCQen7ywgRmshCFzAy/b2gRtD6R6O0RUH+7t88pOfXLLYBac3/7PQrBNX5w/IIvBL4amABw8e4qmQ9fV1er2E8cmMIGiKhyyu5ebcIiob+CaLb2H9irZvYum5N/7ZRmAXhQb02yIdUjpSD1lHn3fTqBuXgrGmRba60funx6tx93SDEd18Ot8V8K1+fvr7pi0UisZ9cA6MDijRgP0OvaJ2NzQJ6I0iq5R0dcyNK1S6tbHplD/A93zS0qUpBQgGUcysKtHG4kUxSeKKguRpgQogjAKEEAz7QwLPpzfoccwJO7u3CXsRVgnK2qdtrEP19MighbPOhZSEYYCHZOAHxFFEXhaUWuP7Ib4nOJxnoCTpeEoQxFzd2mJ+cEgShIynM8LAx5M+k3mO8t1iP5EVkfS5vrrJ01dvsP3wAbtFwcc//FHiJCabzijzkvF0TCAlQdIjimMybTg6PObytavsPXrAcBDjC0mhNaHnE/oeRg0pbO7GxPeYVxO0rbh+/Royjtg7PmCSzfjcy19nq7/KZtLHUx5IifTrgLWattntxwbfD6hKZ3EaW6dmeu4Z9ZIY6/tkwhGmKE9BHDDNMnoywLOCQRgTXrzCM1eu88bWLv2nrmELw8HBCTvHO3i9BKUU66srRKFPMc/wfYlvgcqgPI+k32M06OPpnP/4z/wH/Of/j7/KlWSL733fB/mP/8y/w//z7/zP/Jk//IeRQcB8PmNWlQQrA8amIptMoTIEQhBIibCQpzlJ1EMg68BfQ1zXtzZVhVTKyR1t0GVFGEeEfoDUlrIomE+nNavau9fedYF9dHRMUxe6KAqyNKMsCho+66YZDGlKbX16eJ5CCLf5aL1sxTV+Z6kawWrq/FYX+GDPET4NRWLDZ92lOnXHyIUwZ+GrXfy2myfb+H7d+V10OIB7WBVl7Xd2EYRFUWJqC79hAVv0q7OxYk9t6IvNrYHPbWfY/CbQRZ9mU1sI3IV/vjuGCxi26f95m2pzTFcQN8qXE2oVQiz7ZZajwBdCoruXN2MfhgFh5Czow/3j+hhdL/yKo6N9PvUbn1r0iQ6wbBvw+DSHuRvQ07KjHfMWQ7co5ZNmKdPJnM3NLdZW1zg5nhGGPlYv5oOoLTVZa88C0eZ1F3mxMFTFws/8zYK9FtkEqn0e1pwHHy83a+uKYlaeQRxsPUGMNZglQ93dr63xgIYVzVqWfd81zN5Q9X6zvjyunS+gT/enc9w5p2/mf/MsW7/2klVuMUa7iPv6fGVRODi8jpWR0s0H3/eIPB9Cnyqbk1cleVHQj2IwlqzM8C34QUxRFLUP3CeJE9JZ5pA5XAqSI2USaG0dq9psysnUcXQDLj6h0vR9R+OZVyXzoiCvKuaZQQsHEWtdEfo+G6MRxZXL+CpiPJ1zdZjgewFHs4LBqEee5Xg498m8yNk5PmRlbY2R8jk4PEIBgecjpcfR5AhZaeJej0JrDsZjBlGP2eE2IvZREjwE1vMQ1hLHEZP0AD9QGAVlNmf/8AAVBjx58TJFUZEWGdaT3N3ZZffwmK3+iiMmMtYhmThftXILF2sMXhBSiaLzLEUby1SkGaasCIMQ3/MQBsq8wGpDXhUYq/GVwg89RF6yORgShjBYHxF5ES+/9lWO51P8ICDpJSglyXThagUISeQHCARxGLI66JMWFT/w7E1+x3f/ILce3KbfC/mRj3wf77/2fn7hs5/g+SvPczCbsDc5oVRQFiXFPCeUyjESCkmpS6RwMDfW8SGYOuCsyIsF4lYZrNGEvgscLvOcqiiwZYUwrsDQu9nedYG9s73T+j2tpS7W0QR2LSxeB1sv8p+XLKjW2muXOm00rlhs2o6ftqzh4+VAm/Z8dT9av+eSdVAXDrFOQ+z1Yqx1lZnCOuIU6jznzjmlxNWLsoaqLDAFTKdThBCUTflNAAENi1mzITXWf9OMMQgpaiWHzjH1xJcCH1eW0W/9RZXLWxZdesuOQvD2suOxG/J5QrshSnGpSWfThRbXXgjs081ah26MRgPiOMJow/hkgpJOQ2+iiLN0zmuvvrrYz+ux6qoE7QlrIdS9k/a4pXkgQTTzxwm9R4922NhYZ31jnVu37uN5ajmuob1fiRGmHYcuU5yt+9EGTr3NmJ6Oem982dLrLr/lO2nOp7Wm0lUtghcFXhZCtxHJneexJKyX09Da31gLLMqrtsRq36KwfmfNdv4uK4uiWSjNv5e+t+1kds+voxxZF7BZpCmj0chVZRN1WpGUWKkIlI8XB0yrknGWMplN2eivoqRcENJYx5YXR7FLCa0jqK11rIcWt4cY3L+zLGMym3F0ckJlDYHnORRESNaHQwb9PvvHJ0yzlFmaMZ6VpF7OPEsBCH2fUa+HvHiJMjfM0ox4aw1fhYy3d+gNEk7mBaGUGAvjdI4+3GP16pOsjlbJJmPG07mjF/U88ppKdeAp5mXGLE2J4oi9g302L6wSeB6eEQjPrZUwCDguS+IkwQhBWZXM0zmV1Wz1BuxFMXtBQKkEx/v7HI4naCsIhMLaEmudAhsFju603uSRStSpqsIxGC7YeanyAulbkB4Ix49eCkduVBmNVY7fnAqK2YxBGFGZlFHSw6xYqrLkzbt3UH7g6IqV4/TwfA8FDHo9EBB7Huv9HnllSazhD/xrP8J/9wv/gFfu3GJjdZWPf/hj/Gc/+2sM+1uczGecpDNK6YSuKjVh6OEJgRWW3JR40idLU5Tn4UkPUwd9ZllGEIVYU9fTtq4gUVrlpGVFKD18z6cqy//tR4kHNd831ExjuG1D14JbCBfxKpRjc2qg56pylrWDSZsSlrpmoxLoWjg11rYSAt9XeJ5cglCt7VxLNhaTg16dlWTdpGkFnMDzQjY217hx44aDvOqFnKYpQjju6sYIaX5jDSA7PN62I1gECwWiVgDceCzDuY3lpmukABbWsRDg+V7t33Z+7izNXDCfk4Ct0Oi2RnAu77sLmPTtfNauqIlYElzON2OJorBGNs7PB368uF7A6GurayRxTJHnpPOcMFRkha4VJucWSbOic/6Oa8CClW48uxa8rQfrFGjbKhHNcQ65qZAKvvHqa6ysrLC5sUGDkhR1cMlpgd0I4y460z1/Y2yfbs15urEUTTxFO2qtj/Z0jvRiJB0MLih1RanrAMcanaHjNurOP1vDMo+3+rtzzSkkDUObFLUSe6oK19JPW4X6LNLytgL/1O0tqFQtIDtISZ2KV9+Tc8m48feV15LBBIFPdexcGWHoeLcxBt/3yY3BEwLphUjhaC7H0wm9zcvEUUxalVCfSwpJEiWEfojVlqpywa1ZljKfz1vBra2hKEvysiLNMuLYFbmIa6j9+qVrXL50mZ3DQ8bTKdN0TloY7u9to3VJqAKiIKAXRfgjODqcMjOawWCIqWDv4ADP8zDWUDmLhmHc49rWZb780ktsj0b8zO/9fVTzjId37wGGlX6f3cM94iwl8UJW+wO+8uAek3TGk94WURxhC02VViRBSOgpF6ntB5TGuR4HoyEPdx7x/PAGsZAESExVYaxge/+QNMuJh32UUJSlW5+h78oDt7AIlkqXGO05HKwW5royRL2YJIrRWreKy8BXBH6I9D2sNJjSQOXoVwPlE4UuBDEKAl54/nk+87nPc4XnieKYKAyZe0VdVMSwMhq5WuC+x2a/R+x53N0/YEMJ/uiP/C7++Zc/z3/7i/+Ef+v3/BE+8tzHeOPBfZLBgCCJOdzdIfH6rAQxcRRRmJKsKhGeB4UlzzMSmdDvJUhPkaUZVVWhSlXPHTcE6WxCWe8L165dR1nB9vYOolPz4N1o34FqXQajy9ryrX0/4BywEoywVMbl7gkBQRjhBxFSefWisJTGoGubQWtbw9WNddtYqha0Ae38WI680WJEfS0llvx6DkKvEEKwtrHGletXePq5p3nfB97Lhz78XVy/fp2iKLh16xavvPIKt27dqv0WFt9XNYOZu8OmlrSwslYwLUbbNpLcdUG0VnlTT3sh8OvNylqqMsdUlSs51mFHq4EIqrJkOpmwt7vL8cmY8WRGnrsAh66wftze3N0/HxeQJMTib5vSojVlWVGWVZ3aUKfkPWbGSBYbthNmrtqVta4IvdaW61eu4SvLzt49R2VqfZDO1aG1oswFoQeecLCbxeX1WynAk1jpZJxQEjp579SCB2sQthmXRhhBWQqKEoLEoA289uor3Lj2HDduPIu1UKSOZa+qjJub1r1KoymNJisr8ko7vi5foYUTKeaMilIziXXm3CI1y0HiTQqh83lrhNRIZRDSIKSuXxapwLm/LKY0SOOKJ3jSc8qLdnzvwroqUh4Srw7aUnW89cLHL5DW+YY1rnCOlC5Q0vcknmqqdxmsdSvplB3fmVCLt8suprOpZAvlx8PRpCoQypV6bIhDkCiUW0vGrW9PxUjfYqXEIDBUIBzph64EFonwJMqDIhuzFoXYeYbJCsAxZ0mtGfSHGCnITUWFIQk81uM+XhCxnWb8iy99nv/x5/8XVpMBo8GInheST6YEUvKB972HG+sXKZQkFB4DPyD0PeZVgfZgGEY8t3aJ6701YhTzsiStNDeTFdaQMCuYjjPmsSNeynTFxdU1Lg1X8AyEic/XXn+VfqCJ/B6FhrTY51rvBhM9ZRAE9P2APEvZ2dnmj/3+P8gH3/8B/ru//bd5684dXnjhBdJpiu+HeEKSeAGDOMGLQnanxwx6Iy4PLxKICKUtCRVxXnGpH2KjGCU8AuNT5T4vffVljvKKV7fvMqNgOOrRszDWc+6c7JCXGQYDVhAZhcUgtSUrc0rjarxjBPg+oXQVzoRUhL57ZvOZcyMYLP1Bn/W1daqiZHYygUojChesJkPJSVaxOooYRD2qPCeUln/tAx+mGlfcPXidQdwnjEfk2jKgoGTEKIZeDL1+wnC4ie+HiERw++SYJ9dH/I5nn+PF0UX+k7/+17i4dZGpUOxOM+bznL6nKA4nPP/EFu97+ll6UqLmGe+/8TQROR9931NQzMgn+6hyhs01FzZGjI+nFFlRc4EkBLZgf/+Y91y5zLUgYX5wzJ2dh3z8+efP3zC/zfbup3W93Zen8Fq3mZZtUJqUTlPWVeVqwionILR1MbjNBmDq3OrSaM6WbHjcpQWe7zNaW0X5PkVZcHx8zL3795nPs/osbpNvfL0LWNK0kLwQtS9dL+BEIWwHvne32PCEN/Byt2yos5id9TwajRgfH9eBdu4CDYrQUJo2EdrdcTtj5rbXXvi/W8TKNp+/zdM553QOKVj48q2pqTXlsovAYinblDR3/W79cCmdOL9+/Sq+7/Pg7gOMcc9UCuc2sbZC2/MpVJuIaFFfq7GnrbEgl+/pLHzu2NVAUVU5SsGdO3cwRhOGPkIYCl3gSQGd/OD2JJb2+QI1BMYiSEp2/eqN+Xl2MBdpcAsh3n2E5z3O1m3z9qvKnb+Flm377/ZebM1+5iCZrkOmvoaz1Btkh6U4gZr2tBP8907TuU4ft3wfp4R7c4y1576apq2hSDMqk+N7IUHSw4QRk9kxiTB4nqCYzVkZrDJPC5LAo98bEKdTyrJCSlew5+TomNl4QuT5FGlGMEjwlE8s+kwPMu7cvesCkIKA1Og2Al0iuHL5MruPtukPBkShszSlUkgFN65fo/9yj+FgyOrqCts7dwk8HyshiRKiyDEyWuWU4MD3Ub6Pxa13AB9BUZWOayKKCKKQ/+Lv/Xf8wM0X+NN/6I9x+/5d/uE//0VuXL/mhEUQID2P3eNDXrv9FpPpGFOVKCXRWUYUB6xurTHyB0wmU+IwpBd6FOM5ZVnygfe/F6ML1lY32D08QhrD5c0L7ORTTsZjHuzt0IsjhoMeaEtlLNKTWOkiILvBmta6OuLOiDFEYYi2i8yGLM/J5imj4QhpWVpvtkYyHMmLoCxKsjwnLwt+3+/5Pfyt//p/Ik1vUmqNFpb+aFhXCNSEvk8vjgnDmtRlmrI2GPH63TvkVcnv/R2/k+jyVQpp2VxdY293j0k6JxCaspjzPd/zffydv/P3CZOY69evce/ubdZHK3zjq2/ygfc/z+HxhO3tA7Y2+xyNSyLf0E9iytmc46OUazefIQwe8uHn388//Cf/FK0L/vAP/ABffXT42DX77bTvQPGPb94WcKHbwJr6vNY6f0Vj1VJrdU3WTmNpN5CorBllulvl6dZ8Z4WjGj05OeHRo0cUtfXoCmsYmqj0JoCt65dUyvm8GmHoiDCAxiJpYPElCxoaqNP3faIocmlnHdi1UVaMcVuZaf/XjlQH+j01hp1x7FZpcuO7GGewj/U9nz7feW2hANglhaT5rmldqlOlliP8hZSA5vKVS2ijuX//vhMtZhGX4CROE4FfbwD1s20h2PpYJz4kVgqE7JDrNLBEZxOoqgql3LPM8xIp4a3bb1GZkl4/IukFzCYl0lN1DEEH5m37tihRWkM2zvViNejm8/p+kc6KZbGBtf7wjkUqZZeu83SOfVeY12b0uUJ7Me9Oi8MzGkAtjc8IUWvr+bMcpGk7/W9iORbzZ8G43roGOuvlfCSnW4ero7Sfd1ctQmI776nHvCIIPHwryMoMqwI++7Wv8drrb/LkEzd4//PP41nnTgqjEOspPKFcWUcNKElRFoxPThglfZ5/6gZaVwyTBCkluanAk4RJjGc06fEUozWBH6BwlQWDIKhLvtpWIddVRZZleFLw3ueeAwvzPHNBS8YwiCKG/R5RFCKAKIiIopA4imqXSYWpNNZAHEb4gU9hDXmRoj3BH/vh38nd+3f5uS9+mvc9+TQ/8NHv5qtf+ypFVjDoDzmZT7m3/ZAHu4/QVUmVZ0SBR5o5dSwIPEfAIy1lVeLFjjzFVCWj4YAkCgmDgNXBEE/5FNriaYmQkuPpjEma0uvFxIFPoSuE57frzCFpFtX8rUsYV2iyIm/nRpZlRGFIEsXM53OGvX47BxoktMnkca4g2X652h+wsbrOzqNtBoMBybCHUSAC6VwSYUQShiRJzPraOoGBvZMxWsKFK5e5dOE6R6Xgv/nZv8NwdZ31fo9CCqbHR3z3d38Xt966w9HxHlvRZSyWk/ERa8llBr0+F7e2mBzPmE/GPPnkBV7+6kt83w99N2++cYuL/QFX1m/yya9+nZ/86PP8i3/+SeLQZ21ti/Es44vf+PI5M/zbb98BSNy11j966vOujxActV8bQXtqwRsD2nSivG3DcBbT6yWEYbhUF/ntmgCEdOH5k8mU2Wze5s41fNkLv2dT7tIsn6C9ueYea+jTLm+Wi/3KvWnKcDYBXA08XxRlXa7R7bqtdbgk9E+P39nxPH3dxnrrBpB1A/veaVtYys25Fq/m/Kdbk9duLe24NMGFKysD8izl4YOHtcDWLeGMlLW13bkHOLuhO2G9mCemRQ/O+0sbnCeFq/rleR77+/tUVclgkLCxsUpDmbl8P838te21GqtaCtGyjTXKppIK3/MJw6DlHXDZD+71rY3/6VVzvsDuiEWsWQjahWK1uIdlhcue+WutQ626msPCwu3+7vE8ro9Hcc5a2m1PO/OoK/BPBzU2nwlZF2QBkqiHF/Z49d597h8fcmdnm7vbD933SqKkxBPSoXCGuvCER1VpMJZBknB5cwvP8wg8j93DA8bpnLQqmM2mCE+C1VjjCvJIKbH12o3CqF3L4FC38fiEosh54vo11tdXmaUz8ixFSUkofXpxQtDhOfc8H1+5WsxpnrXBcFHtHmwisIuqYn9ywuVLlxj1BxyfnPBwd4eV0QhrLL04Ic0zTuYT5kWK70mGvYQ8S/GlIPR9ZM1TEScRAlfCOPAUChfwFQe+i4L2A9Z6A0Zxj0h4FHnB4fiEw/EJaZ7j+T55VaF8r92kZM3/7tUMjkqqJapmqOmljaHS2o3/qXnDqWNtPaZBEBIlEfPxlPc89zyz6ZTxZILwFKZG4sPAJ6ifc+D7XNjYwFSaKAix4CL7JxMGSUKV5VCU+BhCTxL7IVubI27duo1UhiBQ+J5Hvx+T5zmXL23x4N5DqqrgyuUtdAVRJDk+PCIKfeJejBaWopwynRXsH+3x4jNPc3F9g1u37/FjH/7QY9fLt9PedYFd68Pt38eZbo1QWfj4xJKV1mwUDsJy0YG9Xo+V1RXW1lYZjUZEUURZnl8J6JwLLlmYzfWlXMB9jeXeCKMmSKzxF7dCVCy20JbqtO23e2lNh6HM0gQeZVnOfD53/OZ1qcW6e21/Tm9c9vHD2P72cWPc/fvtNgfpezV/rhNADQnOIl1p4btddMq9tDaEQUCcBMxmM3Ye7SKo2fA6fnvp4hDPbc1EPRNe1hEmLZrS2eC76EITeDhP56TpnF4v4dLlS+ecz7ZBT11ZI6XbEALPd399n9APCIOwnZ+DwYDRaMRwOGQwGNDr9Vru4dNpdYt96vTzPkvP8nbN1nmvzX+GhcBrNsPGgln85nyl4PFXfZyV/4562P49cwV7Xl9OX6qjNMmFGw2hEEHIo+NjbBRykqfc39mmshpbo2LSCnyhUMJxWQvPKU6DJGHU6xMHAX7go6Ti9Tff4tH+HvM8o6wKNC4iuqFic3uUJEszBv0+vue7/teK4HhyQprOWBkOieOINE8p8gxPORKRKAhrQiZBFLj0xkD5pHlOVhQEtYAJlFMMrLYu2tpavnz7dfwg4D3XnsTkBW/cvUWYRFgDURhRmIp5lVPZikhJLm9ukGcpgaeII1dnIQwDNxeVU1B8T7k1IQUSS2UrPCnohRGjXp/EDyiKgqP5hEk6oyhLLFDoCum5wOLW+LK2jhpv0KMmZ161LjGEqxOR5ZkLrqv31a5x1JTl1cZBV37oE/cSjg4OeeG551BKOgrq2RQV+mhr3D16Hr4QeFKytbZBXrp0Pw/B4cEBL7/+Krcf3uPJC5eJPUefiq7qtQk7O7usb6wSJSFCCjY21xhPp6xtjnjlG69R6Yonb17n6GjClWsbvP7aG4xGA7QveHP3ATduXuArdx9x7YktrmxtoUvN4dEBf/AHf/TbXDPnt3ddYLtgHOrcxfOPaaAPz1NLEdSOBWrhRwwCj9Goz5Url3juuWd58cXneeqpp7hw4QL9/qAuWfjOKjcYYxY+It9ro5KBlo1smWfbnlIgGgFcC2QLlYHKdgBCcVrpAHDR5icnY8bjKWmatYxn7bUB5LJP0wn9BQf5ac/o222dXei+6zdtyEtOKbSPbUI4eDsMHaQfR1FdTSxsrcjm5QcBqhbkjt2ueYVIBBcvXCSKAubzlOPjaZ2P3O2vxlqN89I8XsFYoBBiyYL1PK9OyTn1Wf2MjTX4vtcKzf39fZRSXL9+w0HhLJS45kLdMRdC4Hs+vV6PXq9Hv99v33f/3fDbr6+vs7a2xsrKCv1+30Xm1mx/p6Hj8wRWQ/iy9J2oc2VaLWUZyfrWxPypVmuM5yt3dR2AJZh+2f/YjNHS788I3Y7Qr98+VkVo3D3Nr+v7rOp669P5nJdff5Wj6ZRxmiKCAKsU87JgVmSgoMxzPCAJIpIoJi8LKikIwoArly5yYXMDoSSVcMjdq2++wZt3bmEEPP/8C44Pui7+gQVPKJI4oqwZsMIgRAoXUKq1YZ7NKYuccj4nzzJyU7UxNqM4wVcKpRRR7JS7LC/pxX20FZRAr9cjCANC5RNZVwdhXpWoyvKxF97HL/zyL/HGnbf42Ac/xE/+0I/x65/5NNNZiuf5FEaTlilZPsOmc564fJFeEhFGAVEU0osTVkZDMBCGkVMghUQCvSRkNp0Q9CKkEgRCsDFaJfFDgjgiExYvckrpeDKh0qaGq5fR0oa336GZAlnP9S5i0hzfyIAl9ES4OCNZ83RnRUFRVSAEVVEQRxGXL1xgOp3w+uuvsbI2wmKIooDNtTXn1rCG1dGItCwZT6esJgNWkj4PDnb57//Xv88zl68wTBKXI13zZST9PmWZ88KLLxCEPoeH+/STmOPpCZN0Rmk1XuiBhAcP73Hh0gWkEqysDJnOZzzafsQf+h0/zss7u/ze3/nD/ObnvsBnv/Jl3vP+Z/jyV1/9ZivvW2r/SnzY5/lOm2IZk8mk3SijKGA4HDIajRgMBoRhADj/znyesr29zXg8RmtDHEeMRgMGgwHjyfyb9kEpl84AkOdlDZUurNpFvxYWtoOw3edduNrzPKIowhhDFIUtI1rTpGzu2Z2voSZ1fvsF97gQEMdxndbWWJuLfjkt1KKr5Vy+VqngcZv94rju8d9qs9YFz1VVge+7MZ7N5zQ1x5dgeyFapramPKGzhw3GGm7efBpjC9I8dUGEDWQvPKCqLUFR+0sd6tDm0J/ulGhS/OqYBoBu4F83J18YrCnAuqCgZhzeePMWHxy+h2efeW7p9LKxdpv+NyiQhTzLmIzHCwUIavh/YSFIKamqhoPendPzZKuYuqFaRlEej4BYzgi6+nOnqDpLxlrr8mA7519uy8LvfMEslq742NYV2kv9PM9/0z3snHn6mDMs9a3ta22JaUOhNTtHh3z2S18k2ljHV5IkCBEWjo7HvPSNV9jcuoDylOPPVh6+51GYgsl8iio1W1sbzPM5dx/c5fabd/kDV69z5dpVdg/3uHXrLebDETYS9PoJZSZad10YhmRZxnQ6ZX11jSAIqKqKdDZHW92yLc7SGUfTMWG9T1xY3STwfEqtUZ4iCSNyU7G+tsZJZpnmOaWuwFMkQUjql/SMJjISHUiOdvb483/83+YXPv0JHv7qP+OFS9f5t37yD7K9s0cQeUxmE0pT0u/FXBxGbPX7HBwfMZkco+izsbrK5so6jx6doCuDsC6n2NIgmYKHx/tc7a3TD3oUZcUTl65yQsWbD+5ycdDn5uYmF69cQeu77tlZFwCqhGiNNKlcXIknFWEQkGYuTRbr4pNC31U8m89mBImrqNikKQrcekiLnOPxSc3bbfEDR0Fa5DnPPfcc+Suv8Madu+08MWXJSpLQCyMwlrXhAC/0MUYQhxE29ullM2wcEEjB4fER8zxFSUEYCH75V3+dCtjdPSDLizrJSHH12iZ7uwc8/cwVlAp4tL3P2kbIvbv7fOTD7+H45IiVwOf68y/yyX/2BX7kA5f5+X/065yUU25+100u3LzJ3/qff5a/8hf+wtutqG+pfcd82KfbaU1cKbeBeb7PhYsXefLJJ3niySdZWVlhMplw69YtvvqVl/jSb3+Zr3zla7z22hscHZ3UEYR1ekwdBHLa+ly6bvOdMVT5AoJ28Khq/crNhubVtbmd9d5MZumYk6KAJEno9XoMh8OaNzxsYRxTFxExhpbCtLHwqsqlSTkhs9jMu0xn4CzaOI5aH73veXUE86mbfBsh/HYI+Nv5x887tq6od8YqPH2d1sfbFGezgG2qMUne//4PsL+/x/7eLiCwwhVxQdS5nML5o0C2KVMdtHgRsnRKW2/80w303fUVL3z5tP+WCJT0uH/fRarffOoZl79bawe2hsG7cXxtzJeQoDxQEuEp/MDx5EdRRBzHjj8/Cun1YgaDhH4/IUkWiEQ3juH0WHbv53G+3G/+vMRCgVn6YvH90kMTomY5a1lTloT56f40pzo7fx4/mYSoT3/Oudw9LmD7M7/rKhnQIkRIiQp8wn7INB3jYRiEAf0wdGOd9Lnz4B6DgSt56UmnIBZVxaPdbR7tPEJYg6ckk3TGcTrh1ltvsra+hjFQFpr19XUm6YzKVA4ZqbMIfOW5DBFr8f2gdYH4gU9RlUgliIMApSSlcYpoFEUMez0nsMuSk/EJ0+kUP3YprdM05WgyJstLZuncMafNcua6ZGY15SzjQTbmv/2Ff8BPfuQH+OiNZ3jw4B6/eetl3vv+93P//gOm0ylRGLE2GiHzgtBYer0YI5wlGwhFL07oJ33iICQKIgI/QkkfawT94ZA7u/dRUcDG1gZ5nqKEICtz/H4CnnIIpbVYXTkeCFuT2XTmilf734uiIM9TwhrNBIvnSaqq4Oj4gF4vRnkuA8K51dzvZmnKeDxmPp+TF7mztPOch7vbzNMUheDSxgY3rl11fuooJApCNtfW6cUxZVmSJAmx8LBGkEvBq3du8au/+qt87wc/yKv33mKapfhRzMrmJuuXNtjZH7O+tcL+4Ql5XqCEYHI85sXnn+boYJeLW+tsrq+QhB5bmxsoLCujIcf7B5xMTkg9zRfv3+Z7PvpevvjGGzz37FNcWV/ni5/5Ik9dWHvs2vh22r8ygQ2nITNnERljmE6n7O7t8+DBQx5tb3N0dMxkMiXLMnRVgtW1FdzwfRsXYWwfxyB97tXbDbvdcKx1vqL6BbSWm7W2DXBrfJJxHKOUm7jHx2Mmkxnp3EHcgIOYpFwOijKL6PIGMhcdy7ER9r7v0e/3WF9fY3V1lTAMW9ioZVprRq7jWn2cAKVz7Onx//aCz8wSVWuzobbXsI1wl+1ni4hoxRPXr7O7s83+wb7rQ011qI1uPZvdcelu3+11Tt2zOCcpXNBUeaqFjl2k4zUbvrGG3d09siynPxi4wDVtMZo2eAvr5lddfL31NTuFoAPnU9N8CNoiJKeRj9MKRhdx+naQj8e1rnVsoSUYWQxOAymJjiYrscKlfbUKUufZLn5aW7mdIINvRaEQZ7TqhW+mG9jW+cXyvTWwqRWUVUVWlRglmM9neNYySnpEfoDVrizmeDrGa+IMaoUpzVKOjo+ZzeeUpbPewl7CpetXSNOMra0LRFGEriqsMSjf1Zf2fR9PKRfEVpMvJXHcdlHWRVpox7xWhJSk1+tRVRVxGKE8R9RSVCVZmjEcjtx+oisMljiOKStXfEgr0TIgVgIi5TNMevzT3/gVjqqUF9/7HtLjCTu7ewyHIyyWPJtjq4oLa2tsrK4QBD5ZllIWBYEfIJEEKmTQGxAGIViHirlYoBLrS7b3t3n46CF+4JOmKb1+j/FsymQ2RVcVXp3t4FC2ep00U0pQE2It5kwT1NsYLkq5Ikla63av9DwXH6OkIi9y0jxDSEmcJIxWV1lZWyUvHaNlkeeMRiOeuH6DJI6xlUYiiKKw9YsHYUgkPdL5nPuHe9zafsD+7h4b/SG9zTXifg+NYJY69rtKSzYvbOB5Yc1CCGXuKEk3NgYkUQ+jS4psQqR6XNoakU1zNlbXiKOELCt47pkNPvHrX+fFZzbJS83du4/IJkd88IP/Gw86E3QsErdzLS3s5r2pHYdGa6bTKeOTEybjCbPp1BGD1LWw29K9LCBPB6GI1o/yuD3jjGASp/i06QiGJauG9n2rJFRVHdldUBQFaTqnLAoqXS1Fk7vfLm/W3Q27u3M1QiAKQ6IwqvOFG+3U1WJ1RPq0JDBNV08Z22f80o1APQOmfgubbHPOrlvA2oWVVV+p/n8jiGCRm9n80HDx4jq7e8fOf61EywktZadICwIrdHvx9gz1hRtBfK7C0fpyT/8nagFkWwVIScHu7g6TyZg4jhiOYipj2riLmldrSQN6PDJhFwNjO+9ZFsYNfH/mt4uRbX/TfQFYYZHWRajbGomwUCuZFtvw3JvlibBsUbtriPq1uLxtR62Bnd92fjQdaKe8/SYvFgqPFQgrEC3BzMKib10PYrEGm/VhO4xwBoO0EEqf2E/ItYsIX0liVqOISCkq46LAj+cTUBB5HpFw6V37k3FtdVZIBL2oz2i4xv7khNWVNQa9PhbLNEudwoYg9EN85buSugZGgwH9wYCqqigrt7FL0bDaGbTVhMpjNUyIvQBlwI8jpFAoJL6QZEXBWpKgpEJYQ6g8Br1BjWopTJ0jr6QCKRF5xbXNS6S64t7BHrsnh6yMBuwd7jIr5hhdIY0lVj4Xty6zPlrn8upFLq5eoJ/00dY4ihpP0Y96eMpVilPCEgQSW0LiR0zTOfvTI1DWIRMaTKFJi5JJkTGdT+knPYRxypNs/hOyJaZSysHhSeQYzpR01MraGPKyZJaljGcTcl2hfUirnFk2J9MlhD5lzS3eC2NGcY9Br0+v10cYl/rm+76jkdaauw+3iYYRcRDgSZcb7itBpi2zcsb2wS7H4zFeoCizjOPpBG1r2WMteamdC035REmC8rx6D4N0nrK1uUlZlmhd4QceuqpYX1uhzHN6NdOdkoKrWxtsP9zl6uULHBwccHx0zOWtTQZJ7/Fr6dto7355TYTb7DpW4GkzeGEpuUEri6J+2M5KdUxMzr9irCtu0Dh9m9QKIQSihqfcuZaF5Hmbo/M7tmKvlUaWuhLTqXNorWvhmbcd7u5lzi/dgbcxGHt2U2+JR5px6VrFyJoRyOVxTvPMEcloW/vwcQxxnf2vK+CaD7vdP239di3ybhrVO23t9Wprs/s8nfLgcE9rHW1qA29ZbJ32ZFlb67O3P+VknDoynKLC8xpyF9EuEiNKFK6i1VLkuXVBgEZ04yE6D8M2I9zgv83HrrwgwoB2QUu+F3C4v8vJ8RFB6HHl2gYvf+0OofIcm1p9FilqwW0Njjyl8R0vhO8CTVmMRxdWbvraRMw27xe0p83UPi+P2fEaN/wwvnDRtqbul9QGKaRzJbRo04IuVXatZhbw5alUf5p5VKvBdMzv0zOB5oQOmm76fFbAL8P63ZESi76KhpGtHsA2Or5BOWoNimaMJNqWBFIy8hMG4ZDcPiKJPFaSGM8PUVVJZg2DpMe9/W2GK6sEniK0gl7c43A+w4siRD2msR+TSc3t43u8L+6xMhhwfLjHOJvXYyaJgpiqyjCmIFQ+cRSSlSVZmaE8D196teXpKjiVpmIQxtxY2WQ6TxEyQEURorQEKKQfkJclq2GAUAplLLFQRPGALDsAITFWo5RAKR9rLGWaEwURLz7/Infv3eLLLz/kgy++Fy0KDk+OqcqCxAtY6Y3Y3LrIsLfKoLdOj4jj2RFZkbt0KyVIPAeFC6EIFChfE+QxsSmwSjCnpKdT1kZ9qvv38I2iQnCUzzk4OmBlsAIaFBIlFBKFEQJjMrw6pcuhWQKDYJqmzLM5ldEt0UlQV+8Kk5hqnqHzCuX7iCTE+opYhQy8kNALMFKyvrKGKis3N2pEVFjBV77xGh/52EeJPIXVmrQqCTw4KTVTPeP4+AhrDetbG0xmx+xsPwK/R5wkeL5HZQwq9JjMKvzIp8q1s9o9QZrlLsbg5ATP9xitrHJwcEAYJTCZIoUiCB2LXyBCttZipBdzfHyXXn/As08/w/bDR+eso2+/fceDzs6LFO8u8TP+q9ZEr9dvYyl2LVXpoJXGz9z1UbaBCMa0bGOAI9kAVz9bLkonVrVPu6pMmwd8Gg2w9iz03lgC7T2wvMW1Vqntft8QUyxuNQgCprMpeeY4ak3H0mhSzk4H7VlrqSr3b1n74mHByNbtU/c33Y++mZHdKCJN/88PbusqHg5u9nwHD9omME3CxsYITwmODg84OTlp++58/dXSNZs351nRS+lK9U0sgstqS7r+dtG1U3BE/X1VK0jWWJ6++Qyvfu2Oy4O1pvXJNZO0dcOc8xzOGxPViY5tPlu4NxZFJ7qK5eMhaCccdb3RNUJE1vngIDplY88Xnmf6K6AhSnHSvQ746SixzZAuRqwW5qJWPM8FORaW/bKrZmHlN6dvo8MXGkXn5i2nY94bciApHfVhg/QUacraICaJIvo9V2npsMq4evUqX3np67xw8wX8IICmlkBVsrG6hicVptR4CDwsJi2Q1jDsDxj2B0gh6A+GbO9tkwQh06xAJQkb167xha99jcODI55/5mlULNBl2ZbXxbqskLXVVdZW1/niSy+hpMvat7bC83186TObzR1DmZBoXaKrAqFcAKuSgtIYRsMRQz8mPxiTr0T8s5d+i5/+7h/ixz/4vTzYecDP//on+L//B/9X7t67zaPte/h+wMrqKlEUAa7S3nA4IjMpWTXDWueC8/xFidKG/a6fJDw43ibwBghgOp1w8eIF9EtfdftSnbEyTzNKLShtvVcJRzctPZ/RygpGCY4mR9zZfsitR4+YZynH4yMKrfFC30WBK+n2wbuvUY3n3Ni4wDCMOdw74M7uDj/zMz/DIB4Q+y4Kf55lSCEIQ5/ZfIouCqIo5P3vfz9v3b7F1pVL5FXJ7GCf6XxGfzhEA9N0DtIyHA0Jen0mWcZTTz3J7tEcbSx5XlDqiv5wwHSeUhUZUeATRjFYS5wkHJ0cM51OWVtdYX19leFwyHg8ptfvUxZl7QWz7O3v8t73vYdXX32dtbVVLl2+ghXw1a9+9bHr8dtp/39hOus2S5e7uGOZoN1GIhoubpcCcDp3r0vb2U1bAvB9RRS5FIZ+r8f66hr7R4c1P7nLKRTC0YS2kd6mC8stC4iOPDnHGqoVBhZwnvPXuLQncEUEhDNG3QZpXE6prvSCsrIJmrKLykRdYRv4i5znqqrq4LY6qrqr9yxZwW6zkp2NtBEa7XPoIANdxWcRDKdoSmGeEaa1taIteFKSV66ogLuu5Omnnyabz0nTAmMFQeihpGzH/DSpiLV2OdK77pyUEn0aMvgmzR2+yLmX0sf3QspZxcHBEft7hzz//Av8ws//MqouqdoEwggcGYT0fVeERcpWwTs9Tl1h1UCDzefN+EfRgmxDKVeQ4u0EdgNpWwvztClC4vgBjLHtPFruw3kKynI7R39pf9YqPEvsec3s/yYn/qatEfSL59cqXlgnqEWXrGj530JYtHX3jXLBT54AW5agNf1+TOD5iOmEULpa9ePJMUJ6BJFPejLnwmiNrbUNqllKlqcEUrE5WuXFm8+g04xQSAKpOD48osLVYEYbBsMRJ1nKr37hC3zy136dF556hhefDeglAzwFk9m4fg6SLM0RK5Jev4etqWz7cY/MzKmweL5imqbkaYawBlOVZNkcYSr6/T6F0az5IatBjPYUd5hxIRX8nvd8N7/8hd/kvS++wIeefYE/rH+Uv/k3/1u+5/s+ipSKQa/H2uqa85unFt8H5SlHpVwuxtD3ffwgcGmOSmC0pjdIyIqCvjFIz0MYQZkXPPPkU0xee5myyMnzjCAM2Lm7Q1kWSE8gfYWo/dr3t7d5tLfDwfE+URTx7KUrBEGACDxmecZkOmEymzFNZ5SFK9pyEmuuPXGD737vd3Fl8wK//pnP8Iu/+Iv8wd/z+xxTXZbjI5GeIlQRZjqhKgpyq6mqks3NTT7z2d/i+rVrWCz72w9ZLTJW11fYPdin0BXWUyAt0+mYKEnwpyX5bI6QkpXVVYqyoCg1ZWGYzSZYYwgDn4uX1tnd3SeKQmazOdbs0us1ELelN+hTlgV5lrKxsUEcx3iBx+bFTbSt+Marr7C+tfovuWaW23eMS7yD4J7bloWRO1qcQ8F4nqXV2qu1vwQcPN0IPd/3uHLlMjdv3iTLMh49esR0MuXw8JCiqWErm5QG24kcl2irlzbith9tf+o+aIvwRG1NOUHcpDXFUUgUuehxl2/utR23LAS+FbWwFbhiCC3854pULFNZCuI6sKKhuXRjWLSCuoG5TwtuKWWt5S98qUq5kpLLVjKtddvcv66+OTFN17BrFCqlZBuE99xzz/Hw4UOyuiJW0/+q0m26WyPI3i7NyXYeQqMYNUKga51bbBvlLRAIUwsDa5CeQzVkKjk4OGR7e4ebN5/B91x0fmhdzm2jRDRBRk1AUJpn5/ZvKZL6McK3mfPNMwiCsBn1uv+L990YgEo7BajG6+s1Y7D27dnTTiMABhct3cDmZ/pdy+Vmjp46GxbdxirQQSC+eVsu8blAHvTCwv6meobbG6QAL/DxAh+s4cLqOko4ru8kjlkZBIRJj0B6hEHA7v4evf6Q4aDPYJa4criVxlMeUrnyjtIKbly6QiAkG6trjKfHHJzsoavQ0YVWlsl8yjfu3eXzX3+ZZ97/flSmSdOMLE0JQ49SV2RFSaBcqlJVVujKuOtIiUVTlDlVVYBq9oYKYzWeUnhKobEEgU+eVvSNQngSI6Gfa3JZ8OrRI3704z/I3vY2n/qNT/HRZ9/DT/7kT/KZ3/oUQShZGQ7b/TQMQ/wgRM+mGGvbdWaMwRM+oecTBaFzx5WObTKOEiazKYnn89Sla4z3J1zYWCe+7SOMwVQVFosvBVmWtq4+Yx3HxdrqKk89exPhScbTE3YP9pjOUvb2dphlGaWuKKqSXFfOT+355Lbi9oN7DFSAX2je9/zzvH73Nq/fehP5xFNc3dhCzzNmlQYF0nOFmKp5xr27d/l3//U/wV/963+NoBcTRSFZmVKNDzCBQPmKoswo0hwjHBlKVlRcvrTF3v4R09mMNM3QpiLLcqwxDAdDfN9jPpsSJ32KqkLmEl956MA4Jdsa4l6fyXRKWZUICZWtOJ4cE8UBeZFRFCXalDz11BPvdJG8o/ads7DfoTLeGkytZbyAEZdhxc5GVr9XStUUkJKwTunops7cvXu3pqWbUhYFRZYR9RLXvY7gaCxzsbSxLMPIpy1LV8yjmcTOSg/DqD13Y/1rrWuhfnZcRC2oRX1/DTXkQgCrFvqXUhIGrvpNc94l6tTOGHXzymmoXe2iClf98dJ4dx9csx83ln4jZBpWt+7zaa+LQKkFDWljkEklefLJJ7h//wFFHQugtam52WnPbW0Do1uEkq2ft3stI8HYRfGNs/2u/bdNpKJ14+vO7ag3TQfNODkZc3B4xIsvfj/aWObzOd1UIgfxe9gGxj5zzeVx6H7Wfd9FgxoCHze33966bgSjqeG7ZkxcZa3GFqa2UDlPyp4eovb5Ll+vy2suWiO4vYf2tywMbWE51991pnVh+MU/lwmPuvgVnTXfdUc55Etbi6zJeZSU+MIV6KhMhfQ8+v1BjfS4msRFXhAGBSqOHLtXVSNb2lBZi5FAqRHGuKpXUYSnPOazOSsbo7p4BTzY3uX+o22COCSIQiJhWphYSmexer5PVWpkTXijpKNNGY1GDgnExeHYWmEOAh9TaQLfY9jvUQrB8XxGkeeoKCQRPpEQnPT7THVFJQy3793lUm/IxtYlvvbW6/zMe34/o+GQ6ewEY4yjIcXVKfCDEFUzEZZ6GfFRSroaB0EAZoZE4HteHexaYUxFHAVYfPpBhLKg6wqL1royp55Srhym5xGFETv7R7zy8iscHB9wkk6YVTkWmBUZGoGqaVkzq8mrgshTeFHANJtzOD5xRT2mMz7yXR/it774BY7GJ1xaX3f01cqlhvm+DxiqoiSdzrh86RKXr1xx7HRoQt8jL0t2DvYpTEFeZFipiHt9Km2YTcdoGxB6Eh0GzNIMLwjIqgKEJC0KCu3IWrb3DyiNxbeglE8SJSRxzKOdR+1+qpQz/ObzeVuIpShyrIXhoE/g+e9gjbzz9h2xsLt28mIbeLsfvL2qfjotpgkcayygwWDQUmQK4chJsmxCmqYt/acUEr8W8M05BeKUtWrPXLMLFS9ZLLVAkMKR1Ad+QBg6EgVXkrJqYecgqK3UjinaKAMOgmXh06vPLcXCTy87boGyLNuNvxVkbaWss+PWWNG2Vkxawdt9OKdbA4+2Vp89A6GfeUY4BaqBuZvNVknJlSuX+Y1PfoI8L1wVn1o4L8bVtIqFZJmhrTmXlHUN8iUXxbJZ1ip53fuoFbzGajR1xL8T2Cfs7+2ztbWFUoo8L/A81boOnILi0oSsWGQYnDtknflzBt7vvF/2YXeF5/LDW1DUCowVlGXV3l8b/W8b3/PiDEtPqLWEl/vlZr5oyWGaX7d9edyiPVchsOcc8/h5sjw2y5A+LAvr7nma8fVUx7VjDbHn0R/0OJ6MOZlNGPZHxEEMVjAcDEmnc6qiwEiXH42uEEpR5gVZVWF88AWUVYVfFG16lq7jW5IkYefwiO2DQ8pKs7Wxiakq+klCEkd4noujkULgByHj2RGRFy65OkajUT3/pHNjSOlQHimpyoLA9+n3exTGcjSdUlUV/cGAXphQGk0QRdh0hq1cRk0Z90iiBGMMOzs7DIZD5tkETymiMHL0qWXBPJ2767a83rViVCt8nnJ0qYVIHWGQcQGjQkCWpqwmI3wR0AtDpDSoOnbIqzk0jHYW5zxNEUI5yHs6ZjqbkJc5QjmF17cetjKIBk011uWylxrfC8jzkkk6J6sKAiO5cuEiSkpm8xnTeUqvdudpY/ADH10VbfGVqii48cQNXn7zdeZ5jhd4aGs4PNhz2QICojCg3+sxnc3RZUmel2hjQbtyvFY7a7+yhsporLD4SrF/fExRGeJAUJUVVaHpr/XIZnPiKHKEMRYspo7bMcRRRJpmIBzSWxT5Y9fCt9PefYHdIGX27LJdEuIdgdhYWt12emEv3rsfNAxixhiGwxFp6vi5syxvrTyXsA/UgtkPAsIwdBt3LSgbS7Ld2Dsw/flBRe5vZbSbvDUdZ1EUlGXJfD6v60e74xZ0pw1k3Ugpd62yLDspRGetn4V1a1xeem2ZLnOvL8ZyeaSXx7LrD6/pfh/bFsF7jWJxKj2tc1yzFzQCu1EiLM5Xe+HCBe7fv09eB3khTYtwCCFcDWusYz/jcXTiLq2r8SA7XWJhmXURmdMwb7NZKiXQlalz+y3Hx8c8erRNEicMhgknxxMXkChkHe1ep9hYV3QCKVx1rs54nunlqes3fOuNQtkVuue5Xk6PPyxyfN3n1K4cg0VA7ceuGVBqYd4JZgNXWrN5VovRq49fVkIaC8pNUNF+1sDoLRzejv9phOCdCWsX9FbX9HYDx9kJuRD+zVj5nqu3XJUlunBVtlZWVtnZ26UXxqwMVljtrZJVJZsbWxxxwHw2I5/NWRsNOZjsofyQTM85yTO0Maz4MV5lmEynFOg6B7hHmuWsDHt86c6rTOcpo9EKK2ur6DQl9jxGo0GtpBdo49KNZrM5vfWkdYeFYUCSxK0C3PCRh4kLMCvyHN/3SOIYUacsCSFYjYeoKKIoUxfdLQRmlrJx9RqHxZyjdMJzl6/x8isvEye+q4IVx4RR2CJ8x8dHyNBfGAb1vtG4izzl4iqs8Nza0hqvjpGZzWZcXbtI7MX0owipLL0ocjnTQUyWFaRpDtpwdHJMVbmUrSvXrnD96RuUunKVCLHsHR2wf3BMVuQUZYU1CqyHyjUon6zSnKQzDqcTbqxewBQuMLAsKw6Ojli/+gRFNqesKlTkIYzLk4/8gIf3H7K1tcWrd28xnaZ4hY/nSfYPD4lqMqNer+fy8YOgdgHkLsi3rJBWMk9LesMVTOlKknq+h1KStCjRxinM83mGZwWXNjYxpWHYG5LmGVmRY4whDkPKvHBsmtqSl04eGDN97Hr4dtq/MuKUM8J7CU5baPr2FMzmNnZHIuC+d9ahMbV2N59z585ddnf3mc9TwLrye76q4eMGxjHMplOm02ktaJXT0CrTCqMuz3Nzrcc1KZyGmaYpk+mU8XjCdDJtSVSUEnhK4ikfJf1F31uh1x2LhYLQBBRpbV092DRjOp0znc5bykvPUzVPd1DD5d3NYAFTL8H5tdBw4/kOnlcDR34T1LPZ3A0uCK6+WHufcRwzGIy4fe+ui8qmQ/SPQBhdk5UsxsRDoOqXJySecH+FEKgGEu4KjkVn2rddn3GTr+x5zsIxxvlixycT7t9/SFGWXL5yBSEUujJOGNS888ZotDUudaqDtJyeG91n2CgjsqMVnRbSp5WexwlvKUXNgU5dStSVdDVNda32HB052rHuWmO5PXcDp5/t2zJc0UG/21+0A3DGan9cWwrcXBqzZQVViLr+2duMixCCIs3wa5phYwz9Xo9KF2RVwSRPmeaZiy+3gjiK2VhbZ2UwpB/F3Lz+BCdHY4qyZJqlPDo+5PVH97i984hKQloUICXD1RWuXL8KUrC7u8tBlhKvrrK5sYnIcj74zDPsPbrPPJuSFillVZFEPTSW8WSC7wdIKdC6ZHNzg7IqGM/GFEVOpSu0hjCMW6TM+bCd7ZQkCXEYkWcV83mGsYLV9Q2i3FAoOKlSNnoDLkQDPv/WK/zIj/0Ir7/5JkEY0O/3sdrBxb1+TFmVpKkjThGtci/bOeF7PoP+EN/30VVFL44xlSGdp2AtXh2AN0z6XNzYZHN90xFdZRmTvCDThqDf5+qTT/Kx7/9enn/fezCeYPdoj1sP7/La/Tu89eg+kzzDBgrjeRhZswRGIUYIxy+gFLmu2D7cxw8CpvtHPPvkTTyleLSzgxeHjgfeVi7oUwjCKKIX9zg5PGQ2mVBVJXmeMZlO0QaKsmxZKKeTKdsPHiKNc1M888Q1PvLB9/HkjetMT46xVclkfIKkYaF0bhaNYuPiJdK0YGW0ysWtC3zus7/FhfUtVgcjhkmP0A8QCHRpsBqKLCcMAgZJj1B6FGn2tuvjW23fMR/224Pc76y5XNw6AKgW1G4BLwRJU9CjS5+pO5G87t8W4UnCKCLLMqpK176HxYboSFFcLrhS3Y2M9n17Zx1B2ObTAkoINAuLVAjALAgQDLrNufWUJAw9VlZWODk5ae9jcb2mrrRLferC3tYaiuJsZHhzzGmB3CAYC6XgW8vDPt0apWLRamu8rAPohHtW0vNI+n28wGd7+wRj63EwGhf9K11fxOL5aWOX5Ya1bf8dstJ5tucwnclTcK4Q0gk5bVpKSSEdM1JeuvKm1lqeefYmt9+6RVWWzgLRC4FnWQTRNSlUTd/arrSIhHHQf9tv8DzRBh4uftMVYh3looanmsOcUJZUVT2vxWJeOvI/u+QeEDjDV3f7ZbuDIjrvu89vcYxAtOhp98h/mTX9uGBCa0/3RC0JaU4JbWsMRV5wcHDAG6+/zo//7h9nZ7yPVZLJPOV4POW5J4aMHz5CVCGXLl7i0uYWk+mUeVGxNlrheDzm3vY2t3bvc1Qes2M8nly/RNxLmOZz9g8P2DvYpzdIeOWrX0XGCYWx6KrixoWLTA/2uXnjGulsQhj49JI+RV6wurraWtZZluH7PkmSsLu/x2wy5dLFS/h+QJZlBL7PYDCo/e+mdYHkeUkYhKg4YjabkZc5NlQcVRmXvQH50Zjbkxm+8ljp9/nyl7/MRz78YcYn+1hrSJIYk1m2t3fpDwaMp1OMsQReuIjZsW6cAz8kiRMmVUBe5KytrKEmEoVmMOyRzudcvnaJp25cB2FI4pjBYMj9nW8wns0YpzNmd6e88fprnEwn7B0e4EU+wgctoKgcKpj0BgRx1JJBSSmJ/BCrNWEYUpqKSZ5yb3sb85xBaMOVy5e5/+ABO8e7TPOUMAwZBDG3dx9R6BJdVQRKEXg+xyfHVFVFGEX4Qcibt2+xsb5JHMecnJzQS3o8cf0JHu3ssjocce/WbQYrK4wGI77ve76bw0nKOMuYpqkLijMabTSl1gzChOPjE1548imuXrnOrdfe4KMf/BCf+fxnuXb9Osr3SXd30brg+vXr3L17l/X1Eb7vObrYLPmXWDVn27sPiVsHI4oahnGBVLQbkaw3y9HKkJXRCltbW7zx5huYSYcYpd5gGsFp7SJNaJG6tQjkeVwTHX+axeXxxmHoqP8aoW6hLEoQsLW1wcrKCrBQBKxdCM6FpKYN+qmqqq3f2livXZhfeJYg8tHHrt+B79HrJQyHQ3ypyIuCqqbda6BHcEKxrIkCmujzZg+THWu5m9rWVVoaTdETLphESuGCbMziPF0L/Lzn2DSlGkpVibWuPKClY4UjkcJDG03U9xDWI09L+nHC0zeucnT0iMo4qLbB44yxaGlBOpi7CbhrIplrULbj43S0H0r5nTHuWnm2I3MWCpe2GuHVTEzapWsZWwCqLtQhePW1Bzz//LP88i/9GhKNJ+s4icqlEEkrEAb8wGPUH7hnVT8Huteu568fRXie387l2WxGmbu651VVuaA4cJD2KctWCOcvXxLEtSXqgAmJrZnCpFRYbUGqhQXVwJ31eAkLpg3batKnXIGVJSW06445Mx86/bMsUr46FvyZ+dP5qerqKEsSWrTnsIAULhL9vNbS4noBaZEznU+QgcVPPKLM58LKOpGKoKyo8pIw8iArqPIc3w8Ig4jp8TFPPfUUrz28zevbd5mVOYPhKrmwfOnua3zsiefIszlVVZD0Evb29jjKM7Z6PZQu8coMPxDE/SFVVlAZSzpL8fAZDoaMZ67mshKGMPTpDUbsHY3JSnj25hMEOL+4J50LTXqKeZlRScBTaG1IZ3PCJGbUUxR5RWE0yXAElWQ2L3j6ylVKmzPLJmhbEZVu/g3DEbHfx1RuT5JSYLSjdXZ0rZqiOkELEAqQkjwvOTkeE/W2GB+PWd/s4emCPM3RpWAmS+J+yIVhH2sMPRmSZ4bYj7g1PeGLX3+Fj774Hn7s4z9ML4lQpWMLrIqKkzLjIJ9xMp1w+/5dJumMo8IVXVLCpWlNDqdcHg4xvs+xnnOQnXA4PUFECpkWDAcDjsuUO/fu8vzVJ0nnc6I4RpSK4+MTDk/GzDBsHx3QT2ISIZiXJUiBH4WUpcb3Q+azlLdef5Nnn36WW7fv8p4PfJBHj3Z48GCbotKkWcXBZIbxFEEvwQ9jEJK1wOP2gwdcvHSRWV7y0suv8mY15ZWXXmZc5Agl8StBzwrWLl3l5Tde56M3nuW18R7JYMjNa8/wic986jEr5Ntr77rANjUlV2NlArUfp9dy+goBWpcUdW5fWRZtUfpuW/ZtmzZHWYpT1vTboHILy6URbE0wEXXhhpAgCFFKMhgMWl+0Mab1J4GgW+rQMfm4gCohROtX0HaRB97sw26jdaUdXRShjxSCNE2Z1bW8Tcf6cL7fxQbbuLcbv/DCx9iFMs+25WAnlwJ0Nk7g8ePVNcy60GRDOuP611xAIIXCVO7ZC+EWZS/pceHiFrPZpL1HWdNoOj3OLizARsg24XeNRUVt8wmx8L2f4+tvfLeNtdp+3tTDxjHRtc+vHvfZPOWtt25z48YN54Ov4X1jmvQ+D3CR5Ua7lDWttbNJO37ztimF0QYjFwFzTVBSg8Q0KWkGW5OfdIheGlSnHhfnY65JLhDtmmjmsVIdVKbjz28gbff5aet4wVDWiYlnaSGdNzeao5o+nz3k7O/t4jkufd0878UkWhrT7mm6/1aeIp2nlEVOEockddlXaRXSOD9sUeZ4nt+mDvq+wPddRPbqygrb925jrEYIiymdX/TRwR7p1SfRGMqyYDqbIoTk2ZvPEGSWQCmiKEAKQRTH5MZSlRWlqtDaVdsLlU/oh0jP42Q2Y38642A852uvvo7v+zx17RK9OMHmmkePHtEEzWJpa83nWUYQxvhS4QceXh285CkJ2rA/H7M1HND3V9k+3EEAceTqrcdhgq6aOeP2RuV7CM+jqDR+GBAVIeN5BrHlZDLmldde4+KNS1zuD+jHPXReoDR4UtWcA5aV0QhTVXjKoywrnn/heaIv/BpH0wmTLCXHsLvzED1LmRY5VlvysmRe5jURlAvKcmmewrmo6jXhex4hAarKSMuc6XzK+to6WmuSKCaOIrZ3d3nfjafJ6/02CFwRJqkkhamYTKdYq5mXBdMsY2PFIR3TdIo0lkGvz6A34I1bb/Hh93+Yr77ydeIw4uLmJvv7R7z3xffxuS+/RDxaYZJlzPKCMIqdoqMko8EIoTxKXXL1ylV2Dg/YurjBfDojUB6b65uUxhKi2Nrc4q3jfWxREQUBQz9+u1XyLbfviIWtlNemIrmylpEbYCkRQlJVJVmWMZvNCMOwJaNohJGLCpYd2HWxETnfZTeK9lvtn63rNQdtRSxwFnVRFC087ft+K5jawKpOb0xHmC3DlwsB24XTg8AnCgPnhysK5rMZunSVgJxQFou0qbe5reZay8Fxy/e3OG6RUtT97vSwdf3Hj/Pbd9OTmntaiFdnEru4AoMSFmMEYRiysbnJ8fHxGQThvP62guAU1Gyx7femFXrLm3sXal6cq75a+6xki9ZInEDMsjkPHtznfe/7GJ6nyKyt/cMGKVQ7z6xdcMp3S2k2/Wgem1SKQi+USyGgKEpnEzebVz3gjxFlS/feKk6NEHejUY9713T95muhe8TjIOql489MFE6j52fO2x5mOZv1JU4d1Hxsz/n+Mc3zPWzNRrc2WqEXRow9D61ckRxtNUZYPOWT26I+7ULhHPR6WF0R+Z4DGqxBGJhMpxymU7y6a/P5nCgKec+VZ5jtnxD6Pn6gKMsCX3mUUpJXuVPepEQqia8lYRBSVIbjw0P2Tybsjafc2nmElpaCiqdvPEE/SNjZ2Wa0soJA4EmJLxVKeiipyLKcIAiJo4SCFIyh33cFM7IiJS9D/MAniXpYoJf0CMMA3/PR2nGtI1wWgMYyKQoOZ2N2j45Y7fUZT10sSakrZumMqszp++vEfohOeigknvAc7a02jsDEBAhryYuSC1tbrrZ9lrJ/dEAUe9zdvk86mTIuczAgtAFt8QIfGbqiHqJW9l3xJkPgeRRV6dLzlMd8PudkMubSpcvkmSaOIwZxjzuPdmq3lMLkBuV5BEGIFZAVrixpXmSkeUZlDKMLF5jN5pR5gVfLoCgKUTjZM0x6aO2C1kIVIIzBF4J+HFNUmnmWY41GW0HU7+FZwTxLqWzFar/H4f5Dnl55hu1HD1ldWeHK5au8eu8WFwerFBi2BiMqLJPJmGcvXf3mk/pbaO+6wI6iiMGgTxzHdY60V6fM5BweHjKZTKl0ReAriqJibW2tpvWTta/WtAKoS80JLtWiKSbvvm8Ce755E8JFdAdBwOrqCqPRiDB0VWr29nbZ2zugqaTVKBuw2NiElO1mq42tCSiaSPXG8pYdobjAnYM6Z3Q2m1EUBVVZOS5jed5WR+trorWmlgVbV3i+XSR7Y8k6y/y8QKnlYztuw/Zv1/2wgOmXz+EsZ4U2mspaFBqMxA8Um5vr3H9wF8fGtmzrndv3rsDqjIqFjtBf0Icun6Oxx1tYYvG9kA6KbMZeSJQ0VGXBzvYjrly5gucr6LDNd6OtwSlk3fz3bjpgI4SlNRRZ1o5ng7AEnqv41DwzKQRKngMp21OWa2fsrXGFR4Wgza91fn/jqiSdEsISx+XcnvGMXBfAWfKVcwV5/dtGAbKiax0vN8Hy9+a04W4X70Xnfff6C3fWYpIKIfB8x7EQBgFXL16mF0Yo4wh+tDYYDF7ogVHYqqzzd10OceD5DHsJCkvoK4LAsXRZ4KiquH+4y8WVNVTgg7X4nuLK2jozP3Ec2cKSZSm6LLCNK0qAF/okg4Ri74gwjDgcT9idjHlweMj9vQP8KOIbD+5zkE6ohOQHvutDTOYTvMCjKgo8P6wLaQhGwxGP9vaJ+0OGBtKsIC0yNjbWuLfziKtexNH0hAMJH7r8FJ4XECc9fCkROD97pQ1SKUTgcbx/zO3dbW7tPsQcz/ndP/jDhFGMkIreoMcTT95gY22I1CWBVIwGIyI/osgKF4ymLXnhUs+8IKAwmnw6pZjNKfyAk6MjQl9SVDnSV/SSAXleQKmR2pBVJbI0zPMMr+bIsLasg+08TqYTZzgpj/08Y+/ggPdIybSaE0Uxq8MRr5ZuDJp9URqDMZpZmjKdT1G+Rzmv8DyfQb8PoU9+kCK1RYUeJ7MJ6XTOxz/6ffx//tE/5E//iT/FV770ZXZ393jy2nX+p7/7d1FxDy0kKowYRDHTPKWSHuF6QjqeMZ6dMNcZT66sMSkyQulzdHjMoDdkdWWN4vZrPLu6xZ2H93n+xlNMplNeu3OLn/jQ956/SL7N9q4L7B/7sR9lMpmwt7fH3t4eh4eHC07v2jLGQpa5jT8MQ8I8ZC5TjHZ+LEdK0giozgZPvXbtIs3onVrZps5h3traoigKdnd3OTkZu8nFwl/uYEbRCndw1ndbPq62hJsdxtqalAQXdAbU92vr80rSNGU6nS4JSIU7l+/76Do1oOFBadGJmkRfa9P6bWHZVXCe0OsOyemvO3rEmdYV2t3PFild3fS0xfmc9Vk5P6pwzEfCOuXtwsWL/Monv3hKuDW/PT93eXHuLorS/G0KqXBKQC0Gp6UVsRZb91XW/m+bppSAUNb5DNMZb7zxGlEUudQZU8PcDSTd5tO7ILkmArzbx/bqNdQeRWGbigdOAfOUWspCkFK2d9IdA9ucrIPOSNUpglL/tuWuhjrK3tQIwjnzoZmuollDj183b7eeGjQBREtO87ijGyXrcdwqstNN2Z5dnuumoXUdQFmnUPWShAvr6wyjhF4Yc3R8QpoVzPOUTBcMo1WOxmOKqkIp5woLAg8BzGdTDBY/9Ik8j2HcZz854OHxvgvashZdaYaDIeV4Sj/qE0WRi0afzYiiiKMjl7Pshz5GWHYP9tjor6D8gLTSbB8fc//wEDHsE49G9Mwad+7c4Qsvv8yltTWu3HABTB//3o9Tak1elPTiiAsbm6RZQTHPmacpeekCGOPAx7OWR9mY5288xcAL+fSXv8Rf+TN/nv2DXfwgQBiLLgtAEsUx948PefXObV57cIf7R3vsvHaL3YM9/uhP/zH8OCTwPNZGQ4eABgFxnCClolA5Uzt3+dw1U6TyFKPRiDiJUZ7PyeSYH/7QB/ndP/QjvPfZZzk+OUTmFbM8YzaecZjP2Mmn7O3vs3t0wOHJcZ0qqVyqZOUYIKf5nI2NdUKZsH14wM7RgQvcNBWxkgyimCAMeev+XZ64fB3fc/EaWmvyImeapQS9CH2sUX6IFwbsv3mbwcUNzMjiF5ZQ+ijf5xNf+BR/9Pf+Ab74W7/Fs888x9M3nuS3v/hF/vSf+JPc391jXJTceviQo/0DKms5qub4PUUYrRCRgPW4dOESW3ILhMCXHkWaM51OefLqk/RyzeTRNtcvXmZnd4dPbT8kisLHrqdvp73rAvtXfuVX6mIJC0vE91VtnZg28jsIZJ1S1TCCLTa3hiQEFlagEI41q6nWpdQi8OqdtGaTe/DgAfv7B4vUIiXa/Ng2RanebJs+OHYp6G6uxjrGLl/Q1kF2/e3CuU5BKYoCY3AbRi0ITKWptGmv29yj56k2elQIS1oztTXjI1oT5Z3dd3d4ugJzkf5ll8b4/LFbBAM2ik1zPlgIdCkVQlW4eh4OEl9dXeGtt95yVdiwZ9wGp891vgJSw9sWGitdcFq4iDphZdlV0ghhY42jKMXxgyMkFZYsy3jrzTfxlc+FCxuMxyeURVFHsztr2ylPrh6yeVw/O3pFpfW5GtGCuKYW1bVC1tzjwte+SA0TQoF1OeTdSHStDVo4Fj2lVMcXfbZbjzGE628MTn18p22hID3umt0jhV22sE+3M/jYY+lWnW9WConElRqNggBdlAwHA+TurktUs5Yiz0i9nL3DfcbTMZcuXGRrfYMsn5MXKbPxGH/UIw4DQiGZHx+zsbbO7d1tHvp7jPyArfV1PGOpihzpBVgbYCtDPk9ZG13iobVsXbzALE156ZWvc3B8zIefey/bRwe8evcuB1mGPxiglXKR2lXF6oUtplXBJz//WX7nj/wYb/zzT5DluTNAKo2yzsk0WhlQVQVKSULfZ5bPGa4PHWc3luOTY8LBKu9/73v5e//L3+dn/shPMzk+wlYVvpJoa/CThK/85qe5vfuQeZYxiHuMhqv8ypc/zxzLH/qdv5eVwYiVMCEvC7JeyAqKqnCZLL1ej6OjI8IwZH1jgzSdY7GsrKxgihhZGZTy+NLLX+WTn/0N9mcnTNMZeSARUhFaRYJCeorKunVUVCUeltALXN1vKZmUBShJHEZEns+jo31O5lMqLGmaUeYFo5URbz24yxPXr7eoq5QSz/eZpHMmsynS94l8D2UNRT/gwe273Lx2AxtKKgTKWm6Otri7+4hhr4+sY5SuXrzA5z79KdLKMCkrVNLjytYW4yxle/+AvgzYOdxH+oqknzDOU1bCiIdHB1y6dBlTVtx56zYf/tB38Y2vfpXN/ojJdMpgNOT7v+d7+eIbL/PxH/upd7a03kF71/OwdVFiSlfdZSn1p7UO6hqvNNWaThehWECA0LB9yTZSu6p0K/i/FQvbYltaz0aQNgKsqnR73iYwqulL09qCC8KlgSX9hF4/aQPWYJFOJpWoXwvCDICy1JRlRVnpts63s6BckZKVlVFLIp9lGeOTMfP5HGMaLuBvco9dv6BYBGqdf+xZ//fiXhevLpKwCNhrcsVdRTRrHKmJEyRgtHNB+H5A4Ac8fPjQKXB24e7osqounrk7l1l6uc+F6SpC4ox2IaxtzO7l+dRxGzSxEU2KmKf+f9T9eawtWXbeB/72jjnOeOd735TvZWZlVmVl1sBKzmQNlNimKFGyKVlWq2VbsAVIgCHLsNBoowFbaKDbsLuBBhptq1tSWxPVVpukLVEiKVaRRbKKLNY8ZBZzzjdPdz5zzLF3/7Ej4sS5773Ml2SWRAdw37v3DBE7dkTstda31vo+iWNJssyQKFy8eIkg6JhzqJCT5h6r4J2ziECdv9ZiWe2sVB2Rr1b0N0QxrSi7/dMu7qvnpCwMAUVRVI6WsBrnp3aKz/IHPGwTmqVO/cobYJKO9QfU8ufM/dR8vP6titzP/jTHq36XevnTfn1lCGeOYyaXM1qgJhXg2AZaLfKS+SLG8wOklIRBwGAwQClFlCREienLTZIYhMmnTqZTbM+pHCWNHziUukAUJaHrEUUx80WE7/h0HA9tVyiSqnUKTG+v5wfkZcnR6IQ7h/vMi5SX3nyVk8Uc7bgIxzHPRl4QWDa+ZWMpSPOSSZYyyVKcIDCRvu0Qej6eY6Q7fdclLTPKIsdSCikEtuOC57LuhcRFzp3ZCSLOefZDH+Te/fum7sa2sS2bUmmOTk+Y54kxiMBWp8flCxf42Cc+xnffeIWDg/sIbfK4RZ6zs7trnlfb5M5d1yfLCvI8r3QaSmbTKXfv3eVzX/tdhhd3oONSCrBtm621NZ7cOc+VtV127C496WDZhhwHFN1eh05gxFm01qSVrkCSZcziCKVK1nsDpmnE/tEhSZowmUyYzWb0uj32Tw+ZJzHT+ZwkSczz6zhMZlP2Dw+wLQvp2MyTGDWNGKwNuXV4n2Qe4SlT3FZ4FvPjEX4QMJlOUWXJiy9+HxfO7fHHPvMprly6iOdYJIsF0XSG3w2Jx1NwLBzPJUsy3rpxg6eeeJKvv/wSrm9Y5e7fu8fQ67B36SKDoMPx8TFaaZ69/DQ3D/6Iy2s2EQNGBxmWi32djmrnqU1+uCbCaH2W2gAtK3FLtVws6xxXUeTveXx1JftyOwutmv23ZRIFumE2cxyHbrdLze0cx8vx1pXdJjKr4eK6qG4ZzTZQoZR0u90KalekaUpexCRJgkA3C4t+SBX9ci5W4XLz+sM/W593G55/8HPvPo/NAi6oIGjTNlZ/1bYc009qOYzHc6jGKeWytexRBW6yte/2Zoynbi5d+9t1n/7yb7ODOg9dn7vJg5XmOlUSlUoVjEZjtrd38P0ApUfV/h6syn5HKOIhL9fXX+rl383rVQFb21BD1W2gQEiBlLZp39JUnRSmva5t1GXrYi6vy4ppfeetQm0eCZO39vE42E47qn9UnrveHvX2w4y/ECYXbUkLx3HRQpAUBZ1OgG27dHyfYb9PPF/g9z0s25DDmMy/4agfn07wgoBJGpEWGbbbJdclg3BIUiqm0ykLmbM2WEOUCtHxEBVFMJXDZ9s2QRBw7+SA09kEZUmcwGUaJUySObO0IK+cWFEq7LLEsy2KQpGVBeNUc/P+fYJej6wo8CzXGBxVossCxxIIWREkeT4dx8bCIk9yJk7ORhDQc1wm8QzLcfACH6t1//uez2x8zMl0QqpLQtdlu9Plqb1zbG4MufXmVSbjEYuNbbq9LhEJgeVR5hrLthFCUqqyKci1bafKZxfkScqlwRZ74YDnLz7JhbUNijhGuBKykqPphDzNmaQLjpMJi8WCxSxq8mi1HaiZJ13PJS1Mu9fu+iY37t3j8PSYPWlTFAZJcj2HSbLgZDwyXBmeURir041paoQ7bGmDLdDCpDE3hmvYlo2wJL7vk0ap4R2PY0LPJ05j7t67y/bWJrPpBNe1jYCK5TCPEwppkS9irF7HtKupEmFZeK5HlGcGm7IdHM8lcD1KrQg8w3TmaMGw08d3/8hXiesG2gOxwsZULwxKYxakFVh0CXOezc/WhtvArA6ObROGAbZtN/3S7761ySmWsGzzbvW78TdWj++6Lp7rGJ7puiAMzkRH9fm3jKfSKKHOVEi3oj+Mgbdtm1oUIooisootzXOdJdmBWs5FfazVuXp3Q9uuXG9HuPV1WP6/Gj02Ud8Dc1ZfZZPTNMVU5j3btnEdQ5MYLXKE8LBtUUWfZ6Ld1v7q11e2s7fQQ7aHGRJx5stam3EVBaaXuIpqS604OTllfX3dUNdKgbRtE9EiKgfj3eFf3fp96UhVxYFiibbURrrQSz74dpRdagXaFPJZloMQFkkSN8iAcX5MkVF9ndpzU6ddBA+7487OYvu15e81EUvrrwe+8bjbuxn4s9dOCNEY6fq9mmWtLEwawPM88jwnzjKC6roO+wPWegMODw+xwi62YxnpWlNliBaCRRThhwGnyYKiLAxkS0kvCJnHGSfZCUIKXMdBFhm51PhSYlXMiEIIbNvBdmxORiNTTBW4SNciSWEUzZmlCqRZpygK7LKgF/aIdU5R5sSq5PqdO/Q7AUcnJ/jbxiCSJWRpgpSGlQ8sSu2g84o4J1PM85wNDYG0mMuS0XjMk088gc6MxKjWxuhmRc5kPiWTMLQtho7DxbV1NsWQi9u7FEVBlMQEnQ5hFuGVkqQq1qzvQyMlaaJT13VNxG9ZPP/EUwykz7nhFntr6yysCblU5DoFBK7v44sSN18QS4kuVRWM1bVMVTubEARBQJpnZHnG+cEmGSWnkwnbw00oqYrTXEopOJmMCNwAVRYmzagN26VX1RoVeU6pNXbgo5KM0PWRtmXkQhH0vAA/DAwyhWIynZClKYPhgNffukYQhNhBB2U5HI1GpIBj20Z/QBvGzO2tLUaTCf21IWmW40rB2vo6QkoOqvy1JyUuFrYWPHnhiff4tLzz9v4znVUX3Ag8qCZFJqXJPVVLDAjdwNJ1DttEpXK5yBmssVmgfM9mbW2NIAjwPQ+tDQRrjrscQuM0tAzacnirPby6WhGWi90yKqr1ijc21hkOBliWJIoiDg8PGY8nhGFAGIaYyM2Mc2mcl/s0hRaGxcust0YnuobhR6enFWRc54IryNRaVprVBnlp+B/c2p+pI/az594wszU80EspyTa0ulJMVZ0DtfZ4hSTU9ccS04ddR2kCI9Jg2RZFWZAm4PgWtiPRakkC8rDro7VeZQWrotrV+HkZZT8I1Z75qxqrrvBb1/bxPNORrYWgKBVxknF8fMLa2tD05TsuQSdEqRIqvXHP80z6JM9XoAndNjdteEMszWVdXBiGIbZj+vClZTX16O0iMint5jkwjoskzwvieGbQgPq5YnnddOUANC5KC+mgGcXKLD98rs6cFc3V1Y/4xur1e8CpqadDL19YwbZaz12dEW9c/QccJBOxIo0sqRSSNMs5PD7GC0Ms26bf79Prdrlx9RrucEFe5qRJTFkWWLZNqRR5WRhu6cUcaRlCISEl5CUUhiZX2Bah7+OkgoPZlM6w16AtddFUUZYUZVkhZZp5HJEUKYXQFKrEFjY2hvPc9xw2uz0mIqa0BXNRsn9yTG9zm99/7VUGnQ47m1umtTRPkJ6DsASiAJXlzBYxqgyw/IC+5TGeTcmyBR/c3OPk5ITxZEzX9XAsGwksFhFU2u5IgaZEqJy+47DV6/HMpcv0O90qlSOxhUUgHLQLcWpa1XzPI/RDUIbC1BISgTG0mSuYipy3Du9w4+AWt27f5DSek+cZkzzDchxCaRFWBCmeZ1qwClmaDIcWoASz2QzPc5nHETPbxVvbJS0KJpUkqGsbWU8pBMO1NeaLBZ0gpNSKebQgSRKCMGRzfQMLwXg8ZhZFXNzbAy/k2s0bPH35KRzXYjGf85EPv8DXvvw1fvJ/85O8+uorjE9Peeapp3Ech929Haygy/F0zsl0WhVHa/rDAZPJmMLSrK0POX/uPK+89QYXLlxiMpvRDwPO7+0QJzFvvfEmH3v+BTpIZKlJFzHf/+EXHvJ8/cG374H4h24iBwTmxmtFbPX/tS2R0sYSNhJpeje1bqrKbcuQjQyHQ9bX1yl1wWg04v69+0gp6XY7eJ5DmuUtQ6+rSGrVYAuxlOM0RW1LY6Y1FFrjOAKVU1Wr22xt7uD5PpZlcf/+PebzOUWhGgjatm0TsZWKotQ4jpGs01VRkInmFVmWmPxXpdmtlCbLS6Q0MKdWxjAJbaphKxAcMPZKVTbBcexm/LDsHW/bvDoIa9jAMO1wNeFJWdUWyDOFcg9rE1up0nacZm5N0c/yM2YrEVojlAOYCttBPyBO9wFDnpOlMUWRPzSqbv+d58ZTr52bGrER1DllOBuXnREabSqwZQ3YWxLLs8nyDN9yTYpWCCMxbUmuX93ngx/eotv1QVvYuJQiwnLNQu/7AWWpWERzM1et8Z51IBQsxykwcqGWYJ7G5PMZRV5iVe1EZx2OZj6r/6Ww0Eowm88A069KRZNpOQ5N+0LbWNeGHVYMpEBU5WWNV3fGMK7SqNQzq9rjbH1eq9Vr0C6Jafajdbuuf+UaNR8UhgzJQiCRWEKB9oG0mlsbRAJYWMIi08qwUjkOf+ef/EP+y7/+N9naMTnY+XSO3+/hOSGz/B6+K7AsRV5mpFJxPDnkxY++yGQUkSYpohR8fOsSN0+PyYuISEW4skvHDzmaTRidnjLs77HWCQlcRXI6puM44NqksiBTOR1hs9Edcm0+Ic4XaKWxRImtC6Qr6YQBlgJpSygFZVxQ5AVJGhEFkm/ceJm9aIe9jR18T9J1PJTd5TSekxYGyte6ZC3wiZOYbjfEweG1G7f4q3/6P2B6egiOi0abdczzSCcLztsh0rexPUjcgswxBY0/8PzHSbOc6XTCeDFlwwtJAkk6MzoLQkjyLEUkOcOga1SsnA6JzLh7f5/X377F7cObfOWl32MYBIZ335FsDjrIuTLRvi7JbRcKxSxaoJSi2+1iSUGaZmxsrnPt2jVeWLvEm3HMPM9wgoCn13Y4mJ5ymM857/bwtCAqMi5ba7w9Oma7v0EZQhrYXNjZ5WA8wspspO9iOQ7rQQeVFGw6IZuXn+XVm9fYWF/nR1/4Pl7/8jf58Z/8NJ/99V9lZ2uLc+d3GY1OeeKJJxAavvGVrxAXJf21dX7wIx/hcy99E1kUeL0uTlmiE83tw1OuHe/zx6/8AN/57kv8wAc/xA99+KNc+853CfY22NneolgsSBcLyjyhmL23lO27bd8DLvF3B8ykkFiORZ7npsG9YhYDsyBsbW2wubGN7/tkWcZ0OuHqtbdZRDE1a06321mBp5scI6by23Ec8jw3POJyCfOkaVq1SS0L2Yx0IRUFqGjaquI45t7de9iOTZrU31tCx2VppBfrczbCH2VFkAG2LQkCr1LzWkpiVoFCM+42GqCpmc9odL3tKofmOA5pmlaqXWUzX5ZV51rVivGut7pI62wq4FE55OZKakOLasbSjvDr95eGSbe+gwbP9/B8v1Ijo3Fg2vt+VIR99nPNeZ65t2RrMKvOWcuoFKY3V2lNJiVJHJPL1DiMlkRJQVlqTk9PsexthJTkecZsNqNQUUVpqkiTqiVFWE203obu63ELIZCV81OqZUGlcA2Xsy0lSpQr8/VQuL2CS+p7wpzXw6/XO/VE/+vb3g34fudNAyVLzWukRplAzPxQPXN5hu06lEnOvTt3ePrKk8TRgicvX8S1bKLpHEearg/X9RBlYYia4sQUKyHwXJ/A88lVTlEqhus73J3NCLTCtT1cx8MPAvJScf7CRT73m5/nEx9+ge979jnWtjbRAk6PjohmC1zfpRd0KNKU+XRqZGxLhbQEru0YwpQkxVv3IE3wLIedYYdkOsbzPJ44f5E7t66TTGasdQZs7WxjJYrIkhRZRpFlRhGMgk4YEiURRVEQDgdcPn+e//F//Cf8h//+XySwLco8qxS3bK5cvsTh6IDB5hqanCJd4GjBbDpjrdvHywpm0YLx6YjNbZ+OGyI9iKPY7MNz6e5uc390QjcIyYUmzlImsxkf+fAP0f3iF/hzn/63eeHKk5Dn5BjlwYNkxng24+7BXW7dv01eFLh5ZpS7mrXaVGhLDUE3pBOHZKWB9/fWt/j9m1eRWuO4DlYJqkgZDAbE1+9QKkXoOEjgzu072IHPcG2NSTKn64fs7GySZzl920c5FhcvXcKxLEbjEef29nj5m9/iMz/6KVCKaL4g1hGL8ZSPP/ccn/i+Fzk8OeWtq9f55svfpYwiDrIphSXxghDhC45GJwx7Pa5ev06epqz1h4jA5+e//Nv87J/509w5OmR2fEzH9dnd2mZ/f/8P+2CtbN8j8Y+HedOtRRRt8hrVX71ej16vtwLZzmYzDg4ODFVokVMUNV2oXQkwmG+3IWjTv20WwTTNAdW0lAloCkZqDVsTbVYTUQmRt7eiKEiznLwoGtixlss0EbpuQlopaRZ3IWhY1AzdtAQWrFRlrxjNJQDZNlY1dCow6ECSJFWLmCk+qlMKptL+zIy3j8XSoJylNzXvvcOlPPN+Y+yp0GqqCuD6GkiJtqDT7RiN30bUZDWir/9/dG5YNwhBFYRhOqHrgbzTeJf7lRWhhKgU2ur7Sytlih2r3Pt4PCYMQlzHkGaUZdlA6UJUHNwtFKLdklUfs+18tfuk222Ohh5XYVuycvagaRlo5rpK22gqyFU2RYpC6kf0dpyNdh+yvUse/lHX42HRdb07XR3avFW3FbTj9Ach87PPmQFMzsDlDwzCvGcJgec4ULXOXdo7x8bGBrY0mvQi7DKazknS1DhUhSbLcuIoJk5SJIZ203M9nCxBaIvhxib9yZh4ZorEiiwHJJ7fQUiLqMi4euc2w7DHsxcugSUpioKOZ3KkFsY56HZ6HI1SHMcQQQmh8T2X9W6fju/jxXOKzCBsp2nKdBFBVtB1Q5Is5erNG3z0wx+BJCH0AyTasLJ5XQplKDkFglKVJFlCkqd88lOf5Or1azx56Ty+66BLRbRY4FiS3e0dgm5IEs+IkwSrImexLIkd+gSBjyUloedDUbKYR3iWR7/fIZOaf/E7n6dQBT/44efp+T4bW+sI36LfHWD7PnePjymLgvl0zCKNSeOEhTLoQVYYA51XeW9hSQPPK41WVaJFa0opTMFWkjCez7i4s8vX336VxSJC9zZwPBc7ytg9fx77+mtEcUxn0KfjB3S7XWzP40NPPcN333gFWWp8y8G2Bd3egHmRMhmPGQ4G7O7uMts/5tzmNrYWDHtr2MNN0iRmdDpCZTnT6SHJPGIQBHz0Qx/k9jcnXHjmWZQWjGZzJtECBISWw9Vbt/jAxUuE3R5X9+/zjTvX+K8//HH+5W/8GmvdLoEXcjwac3hy+o7P3HvdvmdqXe+4tVJ9rusaGFlrsiyr2MBy0iSr5OhK2oCnqq0ErZxsBTPXIhUgyPOyiYTRdRtSuVLopKvVpjbERdkCApt+2ZoOdZn7rqHaldYoDQhNtxMa7t9qsc7zzBB26NUU5wMuTfPmcn5qw1K3vmVZXrWe0UDcbcKVet9tg1zPQZ3Tbo/hsbY2RLuCr5qKhDb0CVRwMLieqQBN4ti8Lyrz927eQWtmHvikoFUMpR/gn19xLKrrpuvviVY3QmVhRPVebbC9Su+3RjmkZeB0q7rPTDtVA143vy+dI0GTM28hCO3Iv3EYdDvP3FjmatdtchndEtvQq4dfyTX/4SLch23vVmiHwLTTrXzujDF+yC5E88+Dx3jg/hCrX9BoyiJH65JOr8P53R16gz5lUZKolDwvTTdJkVdpMBuBRBVGezv0QxzLphuELOKIJMkoAbvSSraFBYWiLEo2trbZPz6kEIJ5lnAynzJLYsLuGlmS0vEDSm1y373eAMeZmTUGw4qIVtiWw9qgT+g6eNIiMzgCQsJ4PsOWNpvDDUbzCaP5lNF8xhODDebapAkc28L3PPJC4GQW0hJoFHEac3hyxPozLzKdHHP/8D69MKDX6eK4NmWWs7G2huO5WKpAxQtzL9sWJZog8HBDH6yq26BUCNtBSJvxZMort67xjTdfRziSjcGAD1++TOAHTO/d5kZ6g3k25407b7N/7LNYTCmEgqwgF0YKVwpBKZTpMxdLhFOjG6Ef27KJ8wzXcXHzkvFiynMXr6DRLOZzlFZ4noedxHS7XQLHI04TlCrxXQ/btgk9jws7O+zv3yNZxIhC4zse88WCYNDDc1wWsxl3793jyd1zWK6DLI2yoJQ2gecjBkMKpbGFoON7SCHIiwJZlsznMzzpmvodTLeBVopCKbY2t5guIu4c7NPb3GQWRYxOR+xtbuI4LqPRmOSxi6Ifb/vXaLBboaVofgOW2taz2ZzR6LTimgXLllUutookK6IV81AvIxlj1M/kPEVNvGIIF2zbxvO8FnOYbHLR4sx46nxuHckbVirV9GLWBT7tqKom7+90jHh9nht91jRN8f3lLLTbsJb5xtWZqt+vI7M8LzDFeTWRy/IBqPdZz2/jpFRzXmtkS1Gxd/HgIvqodbma5ea6GQSgdUxRlwoto2FdGVKnouKMomh5jMeCbVvn8sDrNMYXeNBgP+IclmM3IzTc0rp1KxpEx634483nzD2CNkZYClFpilum7qA1phYwbyRWtenDVo2xFkhv2RpkaGdbkWc1wvrMH3b29dnolbceDwdfNYpn0a7W51p/v6uxXvnSGdiIdqR+9thnCwhX0xntHZtq8bppsF7wYRFHpFmC1/HZc3fIVAGpIo5S4iTDsh0KZVJQnu1gWw5CmPz/oN/HsSz63S6T2YzFImKRJKRFjmvZ9HzTlpMmKZvn97h68xbYFrmAWRJxMhuzt7NFmRf0wg5pliC0xnfcBkXRWoOtsISp9eh3Qjquh2fZ5FaJsB0832UxnRijEXrEZc5oMebm/bu88P1PMzs+xaoIRULPIxO64sU2SGCW5aRRxP7xIXu767zxykv4ts3li5fY3d4jzQv6nZ6plM9iEsdDA3GekmUFA1uQlDlJkRHnGQiJtm3mUcLdu/f4+ksvcX8+oUDxytW3WQ87bPV7HO0fcmJHROmcaTRGaJ8sj7F8B9trnjS0BlWYdd12nWaRadfLhH7IPI4JXA/fKTmZjOkEAbbjMI8WFKURLHEqaudhp0uWZ8RJAraBmUI/oOt67G1sMhITRKlxXJt5PGcr3GFva5s7d+7w2huv8+EPfIDQ8cjinDzL0Urh2DbdTod5FNPv90izHKWmZEkERcFsOkbLAC0MiliUBWUu6fZ7DPp97h8f8/at63zi+Y/x5W9/E89xWev1sW2LebQA6/01sf8aDXYbEqXiRYbRaMR8PidNsyZK9jyXOE5pax+bnPMyqqyNFpiKY89tw9y6MrQGAvV8n7XhkN29PQ4PDhiPpywNnMlhal1WQu6quaFq2UpD6LEanRryFmMwfN/D9z36/T5HR0ecVDCI+ZzG92u4fhmR1/l2pZckG+35gWXet+bvrlOmy/M0ML1XEUGolkOhK5jeqtIAy+rwB4+zcpXOrK+1UazbsdpM23Wu3RI1smHyvGVuclSWbVV5w6rK/AFH4WHw6zuM7YHPrhY0PuiItIqq9BI1EaxooIKGOI5N6w4VtSomuq3vh6JU2MLcd6gH0yeyMjB1N4QQdnPssiyxEFAqVFGii9JwXItVAHn5d814Vg38rGFuR+0PTtMD598YwzaK09pXe+LasPi7x+16OWjOMp89+gKeZUh7oJND1gV7Dwr8OL7H0f4JJydHuIHL+b1zvPTad7nyxJPcunOfk9GEH/+xT3J0ckhWpnQCp6o9EDi2w7C/htSCfqdHGEyYzyfEWcZoMmYQdjm/tUOWJMSLCCEsJpM5juuR5DnH4zG7gyFpnhH4PkHoU5QBaZoSRxHz6QLHciizAltahIGPbQsCz2HY7TIIQ6SwEK5LJ/fJogXzOCZRmjjPENLmzetXKX74k5SqwHUdhJSEnkdcFobeVthkyghm+GGH3/3ql/j0p3+M08WUeDZlnizY2NpCC43nOIZ0ShrFrkII3rz+NvEsoj8cMo8j9o+PuDjYxOt2OLp/l9ODE2bTBZt7u3SlEfe4fvcOfl5wZXuH9f4a3bUd+l7IJ576EOfX14nnUzKVEyUxJ3lqmN1KVXWOVM8dmqIsK8U7w+y2ubHByXzCWrePUHB1chMhJL2gQ5ympBWcHnguRVFwceccdw/3ub+/b+4Nx2atP2A+mrA5WMMTDovJHJ0VXLlyGdt3eebKk7hC8urrr/H622/xqY+/SCzd6hkzPB6zKENpcKVDEidMxhPSOKHXCXnhxRdhXnD35IRsdEyULsB2Ga6vkZeKg/GIWZrymY/+IH/3H/1d/vq/+xfph12msylFmWMHf8T1sB++rT50Ukos2ybNUhzHwfM8ylJVkK8mjlMsaaGreumGrrSKepcsW2UlJ2cKx0yFo4lgw9DnR37kRxgOh9zf32d/f5+7d++ymM+rBUJW7GGmrazug9ZKNAbOLHaGG9y26mh/uVjatkOn00FrU7R0cHCAUlRFYBqtBa5riFaiKGqq35dRDASBT6pTc+zWHL17XtmMo60oVr/+MJjxweKux7hqAuzKMfJ9r0ISrGW/cF34BghlioQs20UVxmCbXssU25Y4tiQVolE7fqcIru65VRUKX19TA0jXUe2SQKQexAMLvxA4nr2cV2nRRG5CGuEHabSh87KWJTQm00gBmj56MNX7YRjS7YUGfdEmUpRQIexLgx2Gpn7BCB3ohrFpNpu17h27yZM3J3DmN1mNpan20NoUDvxh+Qn140HoZz/x4Lce9olHGPo2Dv5Ox2yhRnXE3763i6qwslCaLIlZzOa8/OornD9/EYUmylJyVVKonDzLUJ5q0krCtgyMnRcErocjJIvJjChNODw+on/O44ndXVPXkBhUy3U804II5GXJ6WhCFEWs9wd4gcdoMiLPc7wgoMjKSu5XUaqCJImxpMKzJA7Qq1oDS2lxfmOb7eEaWV6yd+4ce77DvfERWZbxO1//Ch997mNVJfccz/PpKI0YnWAhULnRye4MQ5756FP8yuc/yzNPXmJ9b5s0y/nKN7/GJ3/o04xPRxUBj0R6Ln4n5MatG2Rpgjs5Ii1KkjRD2RKn1+GV136fjz7/As8+9yz/8jd/i3u3bvLhT3yc4zevMi9LvMGA5595hp6zRdfqc37rMs89+SRlkXI6m3B6ekoQzzhSR0wWU+blAo0hAqL+XxjWxjiKuLB3jutHd7HdC3i2IWZJiozN3oBRNGc8n1IWGb1ej2Se8OyTT3Ht/m36Q5PDfvlXXuYHXvgYUmsCxyVzHcrA4+L5C8zSmJ21dYTS5FsRi8WM/vrArPUllZoeiIqNUgpIs4L19Q12987zdJbw1dtvEp2OOLp9wO39Q47TGHdjwCRe0JHwxd/9XcK1PhevPMG1V16nt97HluA6EqUKFvMZm2tr73rPv5ftX7PBNrk6pTRFkQJGICKKoha0bFVRZYltm97eGsZWGlzXwbY0ruviVjSBpS5QpWkH6/W6XLhwgWeffYbFYsHXvvY1jk9OKUrDiOR6HrYtqwh6GWXXP+amWlUCq3PGyzzwMspcLBbkRcF8EVc9ncvKahNhl9VP1apmW7iug2XbIBRhGFIkOQ/LdLTTx7AkO6lh7jp6OtvatRTaMFG6kGWDGry7I7Aa5Rp60NL0HwNZljU5qYaK1cSslYGVhmigLBr1tbI0OcGHVYk/rN9WVCctmwBOLGFsHr7sP8xYQ0V9W09iheqYHLNCKsOKhDYtcnlZLIl+dE01W3cFaKJowWh8YuZyJQe9eg+ZFM6Sr10p1dQ01NvS2WkZas0D+Yk6Aq9ttNZLSFnrOg8v6yD38aHsen9mR8Y4irOx79kPm5ujbZCXAMby9UcZelEZ7YfXMZwF55c7X6aeNLa0EUojSo0fBqyFXT783AfReYElBd1uyHQyMp0ino3lSKQlTB+xNMVgusyRlmRt0OfCxQukWYYXuASeQy/00YUmTgyNcRLFOI4hNkqzjKPTE+7du0ev00VYgkGvh6Lk4OiYIAgIc4Xr2HgCdCUa47segeOwtb6GNVswT1K6vR6vvfkmR1HJ5mBAV3QQScnOYI1rN6/zgSsfQiOxLNdobMcxRZxiCwvf9SlUwejklNHGNsKxmSYRwg7wXZukzBkvZti2Y1AdZRycyWSCRnPh/Hlcz+dkOuFOdJ+3b1xn+NK38W3wLPAcybntTZgnXH3tLdY7HY7iOW/ev8uHP/IRQJJS8tvf+irffv1l5tGMWTrHKhTYNg4WCEVGjinalA3CZ0mJbUnm8zmB75NVa2Po+gy7fU4XMy5u7XL3rd/ndDxiMV+wtrmB0LDeG6KALM/p+CFBr0eucvrdHkeTEVqXdDsBaRITBB6nBwesdwdsD9eILpznzRvX+NEPfZST01mTgiqVuadtaRmUQ+Rkac7t+/c5Hh3zgy98gh/9+A8wX6TcPj7m9uyUL7/yEuuOi7ezhd8JSaKIz772Hf79f+/PcuveHTZ3NpCeRUYBuuT93P71Fp218p+O5ZAX+QNCIUBj3NqtXlJKdFk20GyRFxXhSslwOGTQH2DbRmt1Npvy9a9/oypim5OkWXXDGI5k1RQJ1eu4MWRG2EI08GmplgIkNZ3p0tBU3OTVDdfOqwtZ73/5mikSswkC31D+CcEimpnIWy3bfNr56PrY7bGe3a+Zp+X7ZyFvXTk873UhX+5PNOddOwAakLLFvsaSgbp2DrQysHJDmPMunsIqxKtpzKFuMrfUxrweSzv6au+D1utKmwhVWBLLqd+TSwuiTYRd1yWYMZiJLMuqj7uO3rVitXK8goOrMS6lIqt7qKw+b2AXE+nUtQR6NbY+24/dXAAe4tScQclrtav3dInbNxWPA3/XX2tF0a2oWTxkD2dz0+94jAo4qAU8DLV5/X81R9Xvnu0x7PXprPdJ44het4dn2wy6HQpgMh7THYRNW2R9rwDoUpHrjKIwOtIXz1/g9p2beL5nlKGEJM4N41i/RkGUQBXKiFQEhmrSsmz8wCXNE4SQ7Ozu8o037uF2XHzXwUEjixShC+LFHK+3QRdJqU29i9/rcM8L2N3dxXM9dKEIpE1nuEZWZty8eRPfDemEHYPQLSKKvMSxbLwwQEuYjEfsHx7SXRsQ5QlqmtELQvbWdprzNWubQRiiKGJ9bchaf4AfBgjbYjqbE0URb7z5Jpe3N8miOYdZzlq/xwcuPsH9eIH0HMazGW/cuskXvvxltrrrHM+PibMpHVfhkLM17GIJwUmyQDoSlWtUVPHcW6YhsyzLSkTHYpYkOLaDtC1yZUijQj9gMp+xt7lF+ZYmSmKSODYEVpg06mAwRApBmiSsb64zGo1wLz1t1NS0oXbN84REZQzDHqIo6YY+5y9c4Evf/jqlqttuqwDRsknTFCkEnu0iMc/p5toGnX6PdD7nlVdf4WD/mP3jU2Y2pEXOdDw2bceTKUWRkfU8ntjY5dXjQybxDKUVbugxHY8f46l6/O17aLAftpC2/W/z2qqGtHnHEHvUvGi1ccLoB1eiEAiT697a2mR9c508ywxVYZwwn8+YzRZVlCMNwUOrH2jVdiydiKYoSRrxDq0UUix1sNsGsVkc6++cWYlEFRlKIRoqxVofXGlNlmZkaYFl2Wciz2osjZFc4dJ66KJcG/KHvldPta7nuzXsd9jn2b0IIasxmZ3qulLOgCaVIa3P26AMZQmu56H1at95vY821LkyYiEaI1h/1qpgbK1K2kt/XW191h2oX68JRCSy6dvWWjWym0qZwkLXUqAsQFKLYNjCNuNQqnEhpKgJQZf3TQ3H13zh1fCXY3nEJIuW0WyjNg3EXn9Nm2MqURuuOp6tPlDq2s98YA7OHNFwpLcnTLTfPTN/j+EBLB3VR733mJtoypUw10BVhlogpUYoCcLQxLqOSy/s0glC5pMRrrQQGrpBh1LajGdzeiLAkw62sKvFvkDkmiDoUeQ5OQXCsXFcySyeGUfa9QzTnJBoS2IBWmqkVpRoSmEiVcNYWOCWNo60CRyfSJUcRzPOD3fMMUUJjoVMLfIiowA816WnNZYtCcMOlobd7W16YUiWxKAVlhSEQZejySFrvXX6vS6B5xlec11i2eAGLlpKyhEoSmzLIYtKclEiPVjrD7GlII8r1TkUeZGgSp+94RbrgwGdIMQTHtPegtu3b8PoHh+5fA4bSZZmdDyXK7u7lAf7ZEqTqhKVpxweHPDUpUusO12u7J7j3OY6RZqhpCAtY5LjEiUEaWnmzKnImnzHY6FylC7BsqtAosCzHIo0RVsu/e4a9yfHPHfhMoHwyPKcWRYzKEz7F1qzGfaZphELVXBha4e79++iPv4D2NLCFhaFNPYhTRLs3pCiKPEsi40wxEWyPz6lZ4fLNVGYYlJVcfcLNK6UDDoh3TBka72Pb/fY8vts94fci+cQdxiNjsiUpsxiHFHyqY99H2We4QU+4/kcx7bpdPrc2n9/xT/ed7UukNRV3WeLgtq+dqmMatYyX7yEnevf27Dv2ZYsy5KEnYCd3W36/T6j0Yg7d+6yv79PFEWNcW0FTtX24GoqkBVMro0SVxjQ6YQobRipZAU5rrblVM4A9eK2OgtNTpPKo/Z9w8erFIvFgul0SpYVOJbTsLzB6kKpq+E+EDXrVkQq6nlaNcKrDsZqa5H5/8GI/OymtYGMjH9UtzUZx0mIFskWNBZZVo5KnhZkucYPQxSichT0KmLAmai4gtZrXWelTZGb0hotDLe2iZbqY9VQuXGMzv7U6IAUplrXqk6qrAy2IVQpsQSELojCASQahdYltnSQWlBXitfG9GE/jRKW0lUhYd3HTXOP1M9A7W6I5hyaGa8i+WVkKRVIVbGnyaqPFozgRyWEslQk080+2te7QQ/akWoz56w8p49jpJfQf/uee/T3mnda43rYZs5RgbDRwogNGMQKqFAR025nWM5sYaFcG8+ykQi6QYdhr0+iCqRQBJaHjVPduxq7VHS8DkpItNDkRcIinpGojMBycVwfaXsI20W6LirLwRHY0izmOZoozYjSlKTIWUQLHOnQ83uMJ3NOspi+7RBKB9sSYGsojcFMMBwSoecSei6dIETlJRuDIb1eB8e30VJRlCWhF5LqhCifUaiMju/jWzaOVFhSIW2BEhClGUHHJ08zVFriKIueE7LRHeJKiyRNqnnTpHmERLA33GazP2QrHLDX22Snv8NJPGeej9HSphv0GHS6BLbNpa0tznf7lFFMoUosz8ERghdf/BiXu+d5YmuXi3vn2NneI7D7ALhYiMwUVyqpsKWAvKDn+thVukxpjbQlWZ7QdXzKNKHMc9aGm9weH7HdHzC0u2RFwShboLSgcEyQsOV1AclU5TyxucPh8RGFAckQSiCEgxI2KCNzm+kqepcOlzZ3efv+HYQtTRtvUVRSutKQKBk3AikUvhR4lsXFS7v88Ec+xp/48U/yU5/8MT7y1JNcXj+HFoq8NMV0W4Muf/6Hf5zj8QmdTo/xdEa8SBj2huzPJ+/6PL2X7X2PsGtFqrMqRHWrjJCGLQohG/k2WBohQ+9pk2VFw0S2XBhKpBRNT/VisSCOY0bjMVmaNdFpfbw6wm7D7UuYHaoYzkDKpVnUtrd32NraxLIsbt68SVEWuK5bRdk13WhlzConoobUm6in2rTWFJWil1KK+XxuCuuUUZqpW8dk4wiohpjFtg0tqxlvXCk7vffrUc9rnXsvyyWa8V4gVAP/6ooIZomS1OtvXZmfZRlCS9I0I88yuuEeYCRMRcuIno3mDIWobqCztnGrz6GdO247Hu+0tbnplxroVmMA6krwwWCwhCqqc8uLvCXrWY+mNScr97ZoLJPWqomQEUthj/o79d7qqLL5bJO7pXFEzDUrDGRcV/zXKnhKoS3Tz1qzr6HPnMZylo3BWx7l3+DWxoyWr7Q32Ux3VcCpTD+6raHMC/I0ww8CfNcj3OzQdzvYng+LBUWcUBQFg8EacZQibIvh5ga60KSlSaUZ9jQjX5rnOZ7nEQYdcq0QiwVZnBDNFkghcC2buMjRWPhBwMnxiL3dNaJFjPCEIVIpFT0/xJEWvX6fWTHjeDKhiHPSrMDxfSzb0Id6GnqdLoN+H9/1cBwb3/Pp9wc4vod0fFIVkcQp03iO61q4jgWWRJcCUQKlIkkS0rxAlyVZnLARdlgfruNIiWc75h6yJEhZiS1ZdHuBSdkJSRB22N7dAQxvRZQm7K5t4TouerHg6Scuc/PWXeajCYUNousyiRYUWcoknvL5r34JyxdESY7GRYsZ/XAA2lCk1oiWJSWO5+K5LjqvrrZQJFnKWn9AHkfEWcrG+ibzOMKyLfrdDuNoxGgyqVJMisVizqA/QEyOmIxO2dreZXN93RQSuglZ7qAoSdMI13VJkgTXdpq178knn+S1q1f5yKUPsJhMmU6muK5Hp9el31sjjTPKPKfUCtf1iMqCk2jGK5/9FU4OjjgcHfPa3TtMrICZmhGEQ87vbPL05SsEfkAcxWxvb7J//z46LNha38CR70Vr/t2374lalzHYQJXrs6xl0UFZlpSV4QUag22qjhVJkiIETfGZiWRFky81EaVZFOM45vj4mLxQWNJwbdfGUUqzaCq1zDsqpRCWMvlHsdyn5zmcP3+e7e1tDg4OuHPnDkBV2OaSZVkT8ZtzNNByo7BUtSqcXQlFBeWmacp8Pm9ITsxRl8YjF8bglUpjWXDlyhWUUpycnrJYRBSFybvUNkFUcPKyp9HscVnktIz4a6dlaTDK6jOPb/211iafr2vRjhoOqA1m1YgkVCPwMp2Omc4m9HtrK5F025i0I0EDOS8n0ETNy4XctObI6v/l9x9r/FT61hUXeqk0Shn+eSElJYL19XXmsylJanpSLVuCMmQrllWpNdVezpnjLlMYVNdINvNCy2Gq78dalrU+27rQrZ3Hr6k4bWG6DEir/VSIg1IlqjDselRSjM2XdWtc7wWWfo/bw/f/kAfhwW8+4lX58Hcqr804xaabI0ky/DJnkSZYwmZvbQfHcVkkKR0vYDqds7V3gX4QIhyX/dGI8eEpz3/oQziuiy4yUytTgtSSbtDFsmyyNKXITcHZYrHArlqjpICiyJjMZ5zfO8/odML2YIjjuMRFie2GJPMZQmi6YYClNHmxIMlzNvd2uH7rNh/+4LP0ex3iyZxBr8/6cFiJGJlWvzxNmc5nJKU5zyiJOZqcMF1MCQOPqDAKVT3Xx7JcbnW6zJKIrrDY3NrmyUtXePqJp+jZXpXOMetAHCdMJjPWP7DJeDoldB3CXo/xNOLGrTscHp/yzAc/wu3De3T8kJ31LTxp8cT2Lld297g7O2UhTTT5+p3b/NNf+KfM5lOe611kd2cD23IRls/p4i7S8Tkejckio4YmpIVWGtt1CYMArTV5WWLbNqfTCZvDTW5HM+I8ZWfQZzyfooVgd23IaHHC8XhMUeZkZca81Kyvb+EduKRRjOO5rK+t4TluJblqVLkmeY4tLfKiwJI2aVbgZgXPfuBD/N5Xv87d/ftsrm9wYW2IJSRvXr3K/ukxg8GArh/i+C6WZSNzxVZ/jR/9me/H1pIoixgXGb/55e+wvtvj6ps3maULHOCtt95id3ebdBFhSYnSiulsSuC57/WxesftfTfYZQmOIyv4UjeVwkujoZtI1XXtFYISQ+dnvL06t1jD4u2WqrNtSrVxL0vzoMEykmmKpIQw0Ad16kKwtjZgbW2NXq9Hmqa8+uorpKnh43Vdl06n2xyjHntZalzXqVrQSpbFbK3oSdfFaeacs8wsDrZtYVnGYNSFdTVRh+e5rHU79Ho9JpMJ48m0ObbApADyoj6X1TlfGsClQ1OPw9hl1YypbLG5PU6o5VSN/1LISgaClSr4NtwKYDkOuVJMZxNm0ylh0DeGt9AIasdttXKygWjPOjx19E6tKa4a56W+L95tq42hqHJVTRArBNKyEZaFKHPOnd/l5PSYNEkqLnoblRZoFFmhKRJl1NNakfQDcaEp826QIVVFhrWRrjnqa0315l5lOaZmbFWBmi2dCpGCOEnJsgzfsZGWheU4SMcxOsBVBN08Fy3DfXZrQ/J1DcLZM2rPbNswn53zlaI/eMDpelyHYfm9uo/9bA+5GadJWzmUvkdalFy/e4/LFy9xOp2wNljDc13SJGK4sUmcJjhewNUbb7J/5z4f+/BHcVyX6XRs9JSFxPdCeh0T6epSVdrKmm6nh0AQej6+41LMJhSlwu0btazNXgfXcfB8n3gRczQ6Za0X4rsOUmssrXFtGx1ajKdTfumf/Rr3P/XjfP/zL3BpbdvUQlR9+VJqPNdl2B/gpDF6kRq00XVIipQ7925xYXMPvxPgKAhtjyw3lKCDrQssZgvO725iC4t4umDvwgZxnNDtdMjzHIFkOFivomxBkmZM53O0sNk7f45+f0jodzgej5jvRGwJDUozn0544ZlnSS3Fd25c5dr+fQpV8vHvf5GTw4wPfuAp1gdd8rRkOk85iFMWs5g4N46FUBphS1RRGHY136csCtLFnG7YYTafcWnzPLpKN3Q9jzIvWGQJ22vrXD+8wzyJWcSmFbcUEsuxCRwXSwsOTk+wtEBosC0LXVGhBq5HVhZY0mqKmtHQ7/TpdQdMF3O2trewPJfZZALS8Ncfj06ZWlOG3T69Xo98ETMfz5hYY3SWkauM/vYWH7xwgc2tkGcG2/zuS9/kay9/hyEu3/8jP8DN4yOUUCzSiPvHBwzXB491/z/u9j2AxJetQLXBqJ/ppbE1r9UGzxhDUdFu6kbM4mEQpNnP8m/btkmz3MCpejmGOmKvjwVGS9d1DMFJGIYV9J5x586dSsIwQimaBbWs4DNLWg3laQ3vQn2eaiX6X54naBSlEniNo6BbEaNovj/oD9DCqGodHZ2QpqYv2+T3Bdhy1dBWie0lGYqqUI1V2Uwwhs22raZWwLxXca4/xvXUVQW849gUZe0Mtd5vokOqCNw4FVE8ZxHNcZ2ATtemyIwogbRkA3k3qQu9KuRhevArHKLKOQshTA9q5cg9Np5f3QCWZTX83nXvupG4FFi25MKF8xweHpKkUVUk6KItG0fKqr/cMhzOcdwyimeUxqp7dtVpXC06rLf27+1NSoklZWPcUZCmBW4yWeEHqCP3sizRpW44BcyBefeL20LxRX0vvMOUvpNz9NAK98f43spwWpmHhzWX1amUvCjIdckiTRiNjnj9xnWUtPCvBARFTpJnCAnScUBKfv/VVxmPJoReQJqlpve+QjxcxzYc4GHHVJRX8Lvt2PiOR7RY4NqOQXakhdImL2oHDrP5nCvnz6O14Hh0ys27d9jsD0CXdHwPF4iSKafRFC3h0pUr3DnYZ2s45Ont8whoulxqYibHsnEsG0GKROK5LplS7B8fsLu+SVkW2NLFlRaWLQiDgDhOsLVJ6xgmSFCleaZcxyWN48rxCLEth3rtyPICz/fZXN8gT1Lu3b7LIAiIs5SsKPAtB11GdHyfSzu73D465EZ5n0xpjo8OuXH3NlYnpTfooLApS4uUglxrVFmCMi1c0raxpCTLcwZeSOnknJZTBmt9bh/dx7UNdWyhNAhNzws5mk5Y63XxPY/xNCJKYwLHpVAFpSpxbBtbCO4c3Gc76JgaFWny43ma0u10mM7nFGWJVSGttm2TZxnbm5scnZ5y8dIlkILxdEpeFliuUfBT2kD1eg7CdVGOoLA00pXkacm9w3380OXu3VtcOf8ku9tbeNd8XnnrdV785A8xnkywfZe4yNgfHeEN+491/z/u9r4XnS1JQ5aave08IoDjOAwGA3Z2dhkOh000vozYHjQKbSNtXlv+XS/+7UK19lavBVJaOI5bQeeiUgKbcnp6ynw+W6WSbHM+C1Odqlr5TM9zTU94yxCuthVVudcGXVga6GVrmHFahDQV6UmSsFjMTa8zyyiyzuHWxqaOvpY1AtUxm5Nt4icetnKL5oPv/tNUzjcRddsBMz91JTb1+SDJi5w4iVFKsnd+k7LVv76sA6gdG70CCz9y4W+/8bhQrxArBqWeM4NsGCdOCMGlSxc4ONgnjmNMHUZ93svPW9Jamc56rKI9GdVPXYTWHnIdaRu62fzMj3EOjcJcaYpzKmaopu7izL2tq+OejYZbI2zG/9CpqS/zQ6ftMR2iP+DnH7GTNljT7Lf9U6rCqHUJuH77NiezGdfv32WRp+QolDD92UiLg6Njjo9PqnaggSGrqdizahZFzzEc4rSKFx3bxvc84xxqmvqKUqmGp0FpjWXbZHnOyfiUw9NjBmFg0EU0nrQIXBelFJPZlCtXnmSRJBycHJOXRfMgmfoFkwywLQvf9Qw9p7DxLBdLSKJkwSxZGOESIZGViIfvOiRxjO96hnhJUxW2lhW6Z9ZVKQwBTL2GKFWiygJLSjpBSGC79PyQslLVyosSUZMMWbCzvs7ucJ2+36FQmnv37xMlMeP5jHE0Z5ZEzJIY4VjYjmt8ZK2r3neJ53gklVKZ73iosqRbsZkZQiqLQilSlbHdW+fe6QmDMCRwfdKiIM4StDbMk6VShnvcdjgan+I6btXJA2iwpU0YdLAtm1p3QlYFs9FiwfmdPWbzGVESkRd5VZi3XFuMVLem0CXSscmKnFyVBs2ybU7HJ7i+w8HhfZQuWFsbsrm5yTiaczqbUCpFVhZkuqS0JFnxR5xLfAl51sU2tQazYQbzPJdut8PGxkbzEN25cw+tVZPrzrK82tdqxFrvvzZSy9zg8vW2Y2BZsiFcAYFtGwNb55RrhjPjgUny0gis19B6O2KuDa2UAt/36HSW8p5K1cVSGinPRNutuVm2NBmnoDZas9mcLM8oKsQBzDjM/itifmHY2xqIp7VPczwaKLHGzA3lKhTFkuN4JSJ+jM2SNkJoSmWfcarqCJjmfIXUFIXGtWxKXRDFCbPpgueef5Zrb3+ZLM+rB8/00kvZcryg6tcGJUsUS/EOhWF003Kpxiblu5B8sBxXPU+mp1ZRU5YURUmW5fhhyMWLF7h79zZRNEepkjRN0IWiEJISjae8VaNUIze1UW7Np9ViYFMsufKzLKMojGFeKYZsoR11+sOpaHUty0EImyxp6YjrJcuaZVk4loN+wPJWA2tB2Q+Dp0X7o+KR9vuhc/petsf+jtArEH3zXVEpPKmSbrfLLI64d7iPFXgcjkbMkphclUhH0umE5Erx+htv0O30WR8MGfb69DtdyiLHcRzmc/OMmw4QUeV7K8dMKYQ0wj26LA0/fGng1kKbSu5e6IOAKIkYz6ekRUbo2HTCgDJNsR3oeKYz5O7dezz3gY+jEEwWC6bRgv7GlkGcbKtSrjKFg7brYDkBMz2lyE0XSpnnHE9ODS86pjc8K3KkMn3H3aCDKkzCynY9w8KnVcPmZVVKdapUeK5HPJ/i2hZSKxxpc35nj+/78Au8/NI3KfLcOM/VOlKiWO8PuLS9x+7aPtdOT5nOp+zt7HBxb4vhsIfWFuNxjLBjMgRzKUALhDZBTzfschgtsDcsw7muFEEQEKcZQkgcaZNnOdN0wROb57h1cMAPXXyS0A8oSk2UJgzd0DhMyhDRdIOQeWJ4yCWQFzm2ZRP4Aa7r4zguSqXIyqagFIv5jIvnzvHdN19hPJkQeD5IaWoaBNSU10IakRQLTRpF5J0Eq+viWTZFkuJYNnG0IM8zOkHAztY2s3jB29evsdHrcfV0n0xqhmsD5pX40fu1ve8Gu16Mta6hYwNhDocDzp07x872Lq7ncXp6zLe+9S3Onz8PmPxeUZTkeYnr2g1/NqwaiGX7itnqCu1aYnJp2HUFgVoUpdGorXmtF4vFygKilKao+obRNBCm53nNQug4Bka1bZudnZ0G9syLwuRGWcalUEdzZpEx0JepsK6tTA3XA6RpRlEWVW1NaVTHxLLS3nFNAYnrupW8ZlV01CKWEZUnrVk14mYu2iQrqyjAO21a08Dgdl4AJsVROyW62odGYHpv6vFYCF0ynk557c23+dSnPsm/+uWvV46cbirh2+hAtWw2LUqGp1w33m8DEdvlEjt4jMK5pjivim6VMnnDUhp6UWlZbKyt0e12eePNN5mOp6BMe4eZryqq0g9Cv/XwzGutyLeFzNRRvV1TyFIb5aX85sp3qv0awhnj1NUSpXbllCqtURUygLRAGcjejOWstauulH7w5frnMX23R26PY4wfK5f90N1UBaPVKKXWSKWwpaTb6ZKELnmScnxyQj8I8F2XKIqYL6bkeUG/12N9uE7f7+JLm2G3z0F0SJpnCGE1jqwlKgeqOpc8z6FUIAxTn2U5aDJOxyM+sLVDspjiODZplrBI5mzubeDaFr7r4GKqupVVUhYFiyxG5aY+P04zTidjnv5giLRtk2MWiqLIQSksz2E4GLA12OZkdsr90QEHo0Ou3r6JLY1xKlVJViSUSczG9jZWVRsymy84ODlie2OdSTSm3+/j2JZhF5MWnuNS5KaCWmrI0xQtBR977jm2B+tc2NohcF0jjmQ7SM9hkc3ohx2ePHeRuyczvvjqqwzW1uh6e1y5uM7WsAeF4Gpyh3uTMbPERKyWbYNSFGXB+toO164eYUsL6QZIIfEqw53mGb7tEYuM+9MTnt29xK989yv4rmvIsFyPWbSA/jpe4JOVOYHvs7WxWXn8AlXRWnuux/raGoskJgxD02VTGLa5sixIo4i9vT2Ga0MOjo4o8oK1/sAEQdqwEWqlSdOULM+xSs1a0MXXFrIosZTGtx0mp6dmHdEmleHbLufOX+DrX/8m//Gf+lluTI+Zzcf4vodjv78m9ntCnKKrxS0IPIbDIXt75+h2O4zHU954401G4xFJEqOUptPpcHBw0AhtGLYxmpxsLfLRbs3RVcQsq1xfUdFPtgVBzkai0qph0AqGVZWNMbuqFlUq6Fs1+SWrgpWGw3V2drYJw5Dr169zenpqbnzLapyLumjOUHNWi28r6VAv4LWhK0sDs9UUrKVWVcuacRNcd5nL9FyXyXTazJMUYiVvqcqy4rc251OjE3UBU12hvLw+734dlxC4qNrQlovuA/GcNpGRbdtkZQlaMRqNeek73+Wv/if/NtKyKPKcuqpdsyyeE7RERbThL7cwEV/j+bSP8wjY/GFbu1iqzmOXRYF0LCxhCCyeeeZZ0Ir7d09IkhTLNlXhEokuSnReNse0EM1YzxpwXQ+XB6Hd2oDX3zPFldnKa02kS82SJpDSxnZsKCqnBYEtjKazZdsgZUXYsqIW/uDWruw/+xZ/eKP9fmxt+N7Q3D5YYEqpmmuiSk3o+WRCMh6NSdbX6YY+oJjOI+yqQjfwfNb6AxaTGdPxmCAMsMYWaIXE6EwHVQVz40Vrje/7+EGAFKYKPwhCPMvixq3rXN7eJE4i8jLH8Ry6VoedzU2yLOb85ha+Z6MWBZ7nsbm2RuAHDAYDVJZw9/49dBXFCcvCtR0sAWkSkyUpfqiZTKdkaYalzRqQFhE9twMawjCkM+xyYXLCKDf1O91eF9f3yMuSJM/xAt8UYPk+fuIRpQme5zFejAy8nxcIBzzX5/TohMFzT/LcMx+kEBbSkmRFwTRaIH1JtIjoh30+cPkKl85f4Oqbb3F6qnjzpqA77OAHXfLcojdwyZMEoQ0VLGjKQrO5tk5eGs4J2xYmDWEbrv0ojvBtD8dOOJic8LGnnuFgdEomJWF/QNjpMl8s8NyA9Y0N5nFqlLuGQ3IN0+nUyCJLadIAleLA2mCI47kcHx6ZKvWqKK1IUz7w7DO89uqrHBwcsLW+sYK0UqG1lmNTehb3xid03XW0IxE2DM7v8Or123jb67xy9yah12N7fZPD2xMWiwXHB4d4lkM37FDkJbPJH3E97LKECxfOsb2zQxAEpGnOwcEBr73+BmVRhbBaV5DgUnGqNrT1//VW58DTNG/ywkK0SVZWF822UWog9SqX0TZaRhHIRIYSY/BMblivtJxtbW9w6dIlZrMZR0dHTCYziiJb6TM3jkINuxq2IlnRkyqxXNibpbGG3yQUeW4q6Vs93rIqSPM8rzr3lNPRxOTUoOrbXkK9gNG1VcsecVNCIEAZViwhBEVVZd5Eg2dW6YdGQVIghIVmCamqqpm3gbOF+Qxao0Vhzl3ByfGIL33pS/ytv/U3uXCuy61bOXkhKieoRErI8xLLqudIGEYprZZORp1HVyWObaEqeFIjWvfKoyPtmrxEaNM4llT9/UoVCGDQ3+AnfuJH+NLXvk2WZziWhS0kolJwE4BjWaA0uS6NM/KIuVrJL+uK9KVqsbNUpZhmPlgZf9lYefGQqVdohFbmepXV/SxNGkIJ3ZCuaExttbnPK2einU6qL/Uqor8cd/1TfbBxlM6c0yPn+BFQe+MjnDnuo/a4NM4G7pdCnnl+LPBckjRlNDrleHHKxe0+O5sbHM5GHKVz/LzLeDrn+z/4IVShoRR0hINUijD0yXSOb7uVCluB1pq94abZvRQ40qLUOVlR4AYB/tTBth02wy7TOOJwcsoTzz7L3eu3uXLhErN4QZYuuNDtUsSaXGr8XkDg+Nhxhuc55Kqk37G5NNjg8PSYg+kYRwqQCqvMAZPDLW0bXwuyZMHacI1td4O02GFw1Oe1+RuMTo557rkP43guizjm3OYu89GEt2/dofPBLgNHonSJVIoyyZG+z/7xCTf37zGNJuxsbTPsD5hNoZAluS4pswmeLOkEPaJZiiMkgfCwUYS2jcRC2w651BTpHHl4j/s4FHj49galcNGU9Hs2WVwY9IqCwrIQjkWRxxRCYGUKLTKk28F31kiyBRvdPQ5nE7Z6W5SW5urRVbY3tyDLSbII37UZdEI0JRf2LnB6cITV8bEdm67j8cH1PSau5nR2SigUlrTIlFFKK+IMKU0RX1GWjGdTw2hWljy5cY5r3nWOZ2NiVRAKm5Mkwu2Ydjy0NqIoQrDW3cSxBNF8irAk5y9c5NXvvMLOuR3eunuHzb7m3OYmaRqTWQ5fvXmN9Y0BjrQ4Hp00ZFPv1/a+G+wPfvAZHMchS1Om0xmz2ZzpdEqSJHXq1lQEN9BwWUlV6irqVFVxhGy9XsPSNkLIKg9YVoUWernotfKVdV7b/K2a188uLk10pOtcta4ifYvhcEgURZyenjKZTFgsIvI8ow3P1xFV7Ui4jgOYfKnj2PR6vQciq2U+2Yyx4MwCWsG4hmykbKL9djvTMpffzk0aSM9UQdfC9CWyVjFTlSGviTfe5VpqKmenotsQ0rTG6aoIrShbaYpKiUcLA5nXVbeLxZwb127zYz/2o/zyL/82x8cjQJi2D23EXpaoiUa08tpNhNU2RMKMA1WPjYdbu9Y51K1UospXmpaq3OTSwg4/8AMv8vO/8M9I0oyKO804X0WJbFAMM69+xSX9TlvjzFUGukZDyqJsIP9SKSO5WQ/yjBXTrf9l64Wq43ulBqExiK3XHmlo38H+rjhydQqgGluT6nns/HVNClMdsrpPH/btJm2ha/eq/V49aOOaaKHRyojLKBS9bgerknadLeacTsbYtk3X9wj9LioH1zHprTSOWFTRmmMZYhGqSLfUirwsQYimiMl2bLRSuLZNUdrYCENghCYMQ05PR8wWRsTi0u4uJD7H8xjPcwm8EN+LcCyLJI5xHYtBt8PpZMI0ismLsmrzNAWp1EI0FU9DWRZ4wiPwfLbXNuEDgrevX8dzHaSUOFLiWo6hUxWSRRQRex7aD6mvVtDtk9y9x2y2IMoSZos52vJRSlBqKLTG0oqizLFcB9cNTEAgZYMu2rZDUZYIVdINAz789NMkVkSWO/T763S6Lr4PtqUZxSZSxRFV7t2ggb7nIoWgUAWhYzPsDRhPTtkarDFZHLPdW8d3LdI8xXNdfCTjxYzQddnpDji8f4PA8yiyHCvwjDyt0mz2h1yfHDJLIjYdH8tylh0l0mhPmA41010gLYs0Tel1AzzPQywkSZKyHvYRkUFGJSZSRyvixHSMqLJo0NvFfM56f2ACSCkpdEmaJ2ysreHvH3Ln+BC/H+C6Dr7rEvh/xOU1fd9lPo+YTqfM5wviuMppyFq72mD/y57o5Wq1zE9XZrBVRFb3tgphICzXdRvjV+e4dRW5t42i2S/Nd5f/t4zuQxYhKSWu6zIejxmPx+Y8MNBY3dbVjvBrgy8ETXFb3R5WF8Ytz1W0/q62Vt653qfJb9WVzK18qVga7eW8LV+v25a0UlXv5eNB4A+spq15M46CjfacJaRbKXBpLTCUtGbBtS2raSHJ85yvfOWrfObTf5wv/d53OD45oYZKlNJGUYilIyWlNLnD1qBq56uWq7SqiFc3YjIP4rzN1NYQV5UOqfPhZVkS+B6bm1tcOH+Br33162R5ZuhL62ta3yMYfvl2R8A7bUVRNPB7+57LdYYqS0RZrgx3ef8tW92W763+f/bynN3PAw5pDUu9h01UO6+Z+3TbcLefl3fadwMqnUGCHrIt2+SqAy/fWflfVM6+EuaecC0bx7bQucKtRBym8xlhJ4SyoBuGCG0jpY1AE5WFYeITAtdx0EiUFGSqMDCuZ1XOSXUf28alcm3XLPgVopHnGcNOyGw+M2x+nQ4729uQ+sTlBMd2cF0jGyyFoCxMYNHv9Qh8n/FiQppm2LaDJWtRmLJ9phSFERZypBEcuXjhAnlemLoLDMwfRQlCK3zXhbJEFUUlJaqwHJvTyYQ4SSmV0XOfL2LiIjJV5apE2xLftclKs1+rOl9dpfWEVcHxaYrIc8Ig5MMfeo6rd99gNCnQuap6oG3ABBZKg+O4UGrDaSE0geviei5JnjEQsL025N7hfc5vPs23376GJTWh45KXOVpqhm7IaDZlONhiqzvg6nSB47h4no8lbdMyJizW1tZ49fAmQbeDY/moUpvzAKQljf52HRwJ8Hyv6dLwXA9bSsaTMRcH600RqK4DAmECPaVNwGMLC4VmEc3Z3d3h96+/jSNNOvZ0NqUbhmxvbnDz9m0WcYRlhyZn/z7nmt53g/3qK69QKloLsCGokKLuoV0aF6h7ni3SVFQFTcbgZFm+YgxNX7SJxPr9Hp2O8VxOTk4MqUhe71s1BqvWn67HUS/aZv0xxTtUEPQy50sV6ZdMp1OOj4/JsrxxBrReqonVbROGUhTMg6bodEx+pigKxuNxlR9bfs8YdQsq1qYW/dkyIsEIU6CriI16bXyQPKa+KeuiGZ3n1ed1NQeiKfSCB9fYRwZjok4tmAfXrhifamNkHlBVGU5BXgiUKrCkg23ZlIVmES343Od+g7/3//k77O7ucOv2TYrc8MgXBRS5akVy2vS7tlntBE1U7DgmuqB2fFrqb6s9SqJKCVTphaoyvZYhratBL1zY47nnnmU+j/jud1/BsgTCthvIWjRFXsv+aPUYxq+N9Kw4ItV7TQFdyzFZSe+0Lkjd9nN209Sow4MMcc1nWo7cYxV9tc+h5UgLVce3q/B2uxjinSLnx9/0gza7tS8w60VhGYcocFyyNCGULh0/AKVI04T1jW3KLMO1bCzbQytIs5SsyLDtAY5t49qmxzkucibzOZaU9Ho9yjSnUAqFwvEcbMu0Z2mlmDsmJ14UBYKqFkII/DBgOOgT2htEyQjbspDSwnM9hBbYUhIlMYPeGr1Oh/2TA0PqUqlDSUyft9QmFaOgQXbqq+DZDhcvXODWzVs4tiQMPOazOXkS41oWHd/HrRzRrCjod/p85ctfQ1kSbVkm3zuPyJOU0PNBaJQtcAKXQimyLKFQZcWHUHFq2xVZsFIUVfCwubHB129E3BlNELMxm3mfHdnHsyxmUQRC4lguQmhKUTIrMmwBYdhlFi/YKFJ2N9a4difh0s42X3pphuta+GFInmWkKmdvuMF4EbOxZrPWGRClOVgOtueTa/OoO7bHWn+IEBabOzvoRUq+MHVRNa9DWbWB1U5mp9uFoiTJUnzXxUKyf/8+H3riitHFbqFrSilTdKw0juuC0hRZhi0F29tbxN99GXybJE2YLzJs1+fyxYtcv32DyWJu0oJJRjKL3sP9/+7b+0+cosFCmxYczKKRV33FQAMBlWVptGm1Js8rsXi3alnI8oZqsSxVZahzPvKR59na2uL+/fuMRiPiqmR+2W5kjrFKZrJ8XWPISVbWgyqibUBlZRjKhBDMZjNDgeibnswahq+r2NM0wff9puK33+8z6PeZTqdMJlPS1FCaOo6DEDS6umWpyHPjUfu+Q640omo2UlWkbklDBmCM7nK4DXQsVg137QxoZWDws0a4rsheztG7XMdW8GSoIGMENBGKgc2MR10vKrUt1brEsat+7Lzg2y99l9OTMT/+4z/EZDrm5ZdexXXtqtJeVPeFMbpplmOLOj1Rrd0aYh037Xa6bqVT7fG2UZPWiUhzz1mWjeO49Lo2i8UCpTQf/8T382/91J/g537u51AaQs/0qqqqGKl2/Ope7aIomM3n1TEetCq1Ual75JUyLUBCCALfr+bdVJaKFsfwEiF62IVo779qM9CqZdQe/JJ+YBLqN5YG9p02ceb/ep/i7DHrm+QR+3wvxrqBwnXVnsjSSTD4jfm9KAqkJeiEAWu9LlKDY5sFf1yl4C5s7RoO6bIAbSJYyzKIme04JFmK5/uMRye8cvsax8fHDPsDds+fQzoW0pE42iB4SZLQDUIc22EWG46ENM8Iuuu4WrOI52RxwrDbBe0SeC5lUaAReH6IKx0s3+f49ARH+mhdUlacC0a5b3mvudLH0gVFaVCkPM8pCqNlIEroegG+7RAGIa7ncS/P6fo+Oo6YnY5ws4KB45OVCun7dNbXeP2ttzmNJuBL7k3HDDpdJpMRYeihY4ukzCqtBE2SpeRZUkmYOlhCUFAQhiFRHHP3zl2+/Z2XEGsBdpgyi2JiVaAsC2WaFfB9nySO8KRDP+yxiBVJHDEYDpgnEfNozsbGBocnIxylIDdylbZS+CLgOI25fPkpculSOD5OT1PYPl957ff52he/hHBsLuydY73X5/rtm9y8cYtf+MX/hUvbO+xu7rCxvonlWHgVQ2OUp6Rxhs4w16VULKKIjh/QC0Lu37nNNFo0BZ1CGxuQ5zlRtMDvBtzbv2dayXo9tDRU05cuXuTte7eYFQnYgnI8YmP3HGE3YB4vGHS7XL74BEWaPPYz8Djb94DpzOgIqyafKpuCrHpbGhlTiel5hqjd5IdNC1WeG77xzc0Nzp8/z87ODqenp3zzm98kiiI8zyMIAgM15sb4y8bAmRYv17WrhbakrC6C6QnXFVy5uqDUEXkdBYVhiBA0xtoY8yWZSW2ow06HIAgo8oIbN25VEL5xQCzLquB7mn50EHiegcuDIGBe5TYFFbNXuWpN28azNrpno7FlNGz2vzTMyzG3I57HQkoFhjWK2gDV1KYay6oM6nKdxbKlUdYpStIsxZIWrmOziGL+z//1f8t/9bf+96RZwUvfeYU0Leh2Q9LEXBONaNjOHMdpYCxD76lW4GWtq4Ir22qNtR7IqvEwFf9GHc6qWNZKpXn++Q/x4ovfRxh2+Nv/779PGJgFWleQmaquLxj1yjwvGsa4d9tka67rcdeLYr2Yn40+H2n3tLkI9TVvIvEHUgDvLodpjO7jGe1339G7f6S9PdYRV6rTanC4hRoIQ5WbFwWu6/DkhUtcPbjNMx+9zP7+IckiQtgOjrTRCGzHIa80pMPQoyhybt++jZT32draJi5z7o9PODg+4P7JEZtbm2z0BnjSMmxiFZLkex5FXqBLjef65KU25CJKVRXmLmVaIh1FmeVG7MJ1cAqwTQ6GvMxIs5g8z8jLksl0irCtiqzJQLiOtBAllEXKPIrod7sEftDUcNiWzdbGJlmek0Yx3bCDPtin5wVsD4fYCObzmNPpjG+++hrTJMEZdHBcxb3JMaPDhNByCFyHIHeJioyO5zGbR0ync5JoTlkWaFUSKUXoB/Q7XTQlnu/y7LPP8IkXvw/pwT/8J7/I5377C8RlSXhhF9eS2MKkrUrp4NkOHdslKELu7d/B0oLpPGIazNBrBcfTGd1Bj9AdkGmHYbjG0xef44svfZdoOuNbX/0aef4bpEXBrcMD7hz8AzY7PZKi4O7olIsbW4S9kE987BN85MMf4ebbb3P7xm0c22Z9uMb5nV3OX7iArqSaPcc4a57ncDga0e92uLizy/7BPvdOj7m4tWNkPqFC2ATDXh+tFNs7O1CWxGlKOpuxs77JlYuXePv2DeI4Jljrs7be5daN61y++AS3797h9OSUjaCLrcqzd/kfanvfDbaqYV+o8pR1P2srD1XlI7OsII7jikhCYArKzOK8u7tHp9PB9z3KsuQb3/gGcZySZQllqXAcd0nTSBtuFg1hSp1r1lrjOQ6dMGQRRcuwTZt+36WqzKoRqwvDaqNn1m9BlhVIWRPBeJRFwcHBQcVQtqxSL6v2rjBcMnuZ3LhDp9MBTPTa9H8KYbR3y5qIRTWRh3zI2JoWGKXOLPjLFbU+bkPw0oqIH7Zun31N6yqHSdtZqIlq6ohWN8a8ApORljHqlgWWknzp977Myy+/xgsvfJS//Jf/A/7BP/jHlGWOENq0EitDq1ijLLXBXo61BRlLWV2++p560Dws4X9z7R3XQaOa/vm/+Bf/t6ytDfmFX/gFiqLAc70V5TTVqlOwLWHyWWoVnF7NNS/fOatUZ5zYKp9m8kUr9+7D5/2sg9uc2rsaXOP4LT9Wz997hcXP7rOJsmvEqv5dV5X4fyhH4GFx/Zl3hcBzPPI0Q2tB6HjY0qIfhMzcACmmICW93oCD02MuXH4apyo0LHLT5nk8PkFYNgkl4zRlUeQ43Q4Uipv372Eh2ewNyFWJW7EUZnGChaggUiOPmzimQMuxLEI/wJIWsyTCt23DiCcFWgocy8b1A9IiNdzjnk+pFZPJpEr5iCYNoDF0nkIYtjWDxJn+YtNrnOF5HlR5dyklg24PUSgG3T5FmhEnCXePDjhaTJmmCXGasChzMjRpnjOeTOh3uwhVMo5CNgcDJlnM3aMjBp5n1hlE1Z2ScJTEbG1tY9seSZKwf3CfuVpw53CfUoATmPGcnJ5WhZ0CVZQUWlCKAh2nzOIJ6/2LjGanpEWJcD16g03wA9xgwKtXr/P7b9/gzu0jDiZHBLZPmoOSCuFJzl2+zPG92+ztncd1LRzpMM9SRvsz3rx/nT/1I5/kJz79GXRZkiYplIo8Ne1feZ6bFFwgyPMCyxIkeUqPLr2wy/raGrf273Jl7zxlklNjPXlu0pm2tJhWimGu77G+sc7t27dZ39riib0L5IeCo9mUhAVSCkLfI4tTFDl5oVhfX/tDPBMPbu87NWmtbV0/drVhaWs2q0r8ol74l4VlFuvra+zs7BCGAVG04PDwkPv371eFX1H1PVFFvhXpgSWb3GRDWFENwEBhRo/a9/2m2K1OAWrVHuPqdCzXt3Z+VLeiU02WZURRzGIRGeEAzYosaJ07llXxSBgGhj6wLCtUIW+KXNoVsrYlW605y/HUBrj+qY31MgJfXfDORuTt1x62LY9hTrlODwhhVb+3nYClUtiSG33JOmeqM02B1Ww653/+xV8inmf89E//KT7ywvMUeYFl1jZT4KRB6EdTaTbnreqKYlX9tNIDtGVQa1pZ1cDaoPnJn/wJnn32Gfb3D/jCF76I6y4JNEyxVRXt1+fWup9W53a1tfBR79djr1+rawLe7fvtc272+S6x6jL6bn3vIft/mOPR/M/qHd/+jqjGIHQN4z+InFV/nLmZzozxYft+1DxW78lquSqVQgoTAVlC0Am79DpdkzMWRr43KUuOT8doYSJtrcyzWqLAlZzMxxxNTknKAlwH2/c4Pj3hZHzKbDEnzUxRWCfs4DkeAlGxiZnirChOTIGV65l1RQhOR6Oqc6HiGbAknSDA9Qz5R6lKU4zme8QVJ33dtiqFgWOFlEYSs0oL1S2pSplCXcdxkLYpgMqKgn7YNX2/vpHW7Pb7HI5OmGUJJ4spo2ROUqSgDQGQ3+0QlzmzIuMkXnBvfEoiBXdOj9HCcELIquq+KHPSPGMRR4wnY/YP9rl+4wb3xifcG5/grXXprg8QrmmnsmxJmWfookCpAo2mH3RZ5IaWVJeghEUwGNDtb/Drv/d73Do+5cbdfW7cu88ozcjyglwrLNdDWIKiyEnTlAt75xmNxsRpSoEmV4rZfE6n0+Xw4IDbN28xnUwN4ZXrLotMNTi2TegHTV67qNJSrmW6eMYzwyduFPRExRNhAjlVKhzHFBEKIYjj2DgmCjbX1+l3e0b9T0KaJ3jC4vL5i/S7fe4fHJIX72+E/b4b7BVjUD31Jvpb8l7XhkYI8DwP3w8Iww7dbo/BoI/neY105sHBAScnJ0vauGr/SumKRKRu4TJLSVN8BRUNapder0cYBo1z0B7nw6Dh5T6WC1l73TFG2ETAWWb4d2uSltpIN/nMqh3AOA1eA/fGcdLk4BEVBWftcEiB67lNVfLZxbM2mO2/qx0t/9bLv5tlVqwqdq8E5K2fB9bZaqIMheODTkP9kbqBt6Z7NAxyJRqN67j87he/wssvv0q/O+TP/uzP0u10KuOosQRVP7xaHrM+H1aPp88O+OzWirjryLIoC5TSbGxu8Bf+wp9nPB7zjW98k6tXr2FXULmxcyYCNt9TTapCtPb3qO1sVE1r3G0ouz6fhnmtQj9W/1ZLNqV2hN3aakflIW+84xhXPnrGWJ/dzcPoSpv5aHzY1U+8UyTfTuU84Fw+RpStK7TL83zWB0PqNrzQD4yOc/Vc2n7A1Rs3idMMx3ErRC9FobF9l1kaMY7nZKqk0ArLdYiSlOlszmyxIMszbMvGcV3CIDSKaJqGrCZNTe43DEJ81wdlCizrwkaNMb6dTgdpSdI8oyyLBl1Ls6xSKayV2YzRlkJUzInVcySWa5aRB5ZIQ1xAoUpCL8CzXbp+yGBgiEaORqfMsoRJMidWRt+ZUqGKwrQlWhbasUlQnCQRkdDcPDkizTKErqV5dVNMEMUR88WMLE9xPZfNrU0sKTh//hzrG+tmbFohhckTo0yLp7Cg54dEecKg28eWNrP5grtHh0xmCb/zjW9ydzLmdDZjGscUnkev08MLfIQt6HVChmFAFkdsDdboeD5xFDObLR0q3wtI4oST4xMO9g84OjokWiyq9UchMKkEz3UN6VE151prHMui1+mQlSVxkjTirqaGyGrW6ppi2yBlCsd2SNOUYX9AN+yQZzm5MmRMOi/YWduk1+kyns3w/HdvA30v2/eAOGVZCV1vtXGuX68f1qIw3svOznZzU85mM/b390mSdIUcpd27XENFtmUMQ16UOI7VfK4ojKe4ubnZVGgXRUFSyRPWAzFQ+JKCst4M/FkrSjWv0hi96nWlKl3v2vgDQtI8iLVz4jgGOgdI05QkSRqJSquqJjcIgZkbx7UJgqDVmlaxm8na+XkwYoal04JWTSvOSqCzUsYDS6BzdROtWF9XrQ662kmL7GyJnFSfk63+77pXW0rQpeF/ns9jfvVXPgvAf/lf/R/50pd+m9/93a+BKJEVw12hSnRZjVWcGW11zXQLRnzglKrXhZDVdTEFRxpTlP3jP/7jfOYzn+Fv/I3/jM9//jfRaqm+JoQw5D7aQJO1wWwbpEcZuPb1aLfbteU9wcj41eIe9YVrPytn9yuhEv04e5U0Rja1lqyt7lXdepsz88LyNa1X31oxnmL5RTO1ywledQF5LKWv9uDfwZSDbuEHZ2oRGkdYCwIvIPB8bMswvo1OT5EY+k0UUGqGm5t84fNf4NzOebbXtwiCkDTNSLOEQAxIVU5a5pTKMPANwi6O51KUijTL0B1Ti5BnGYFj6mx836fT6RJFMcryWRuu0Q08HMvU0Vi2vXTCMWtKGASMJ6fYlXqY57kMBj3Kii4TTCQuqwWgrCmHrWU3hsasIaenp4TdLo5t43keShtZTifsmAiyqq+YzhdEDqR5jut75hHJcrIoZm47uJ0AKUxxZVmWTKIF86P7HO9sYw+GOJUGuJA2uSjJ8wzP87m4cZ6NjS02dzf4zhe/w/reLtKH2fwUneaUjmOeF0sibGnYJQuYxxF7O7t4bwe8+vYbvP7GTV56+xbnLu4gfB+dRhRA6Vl4kUbpnCjOubz3BBvDLt9+7SqjwxM+/clP8eWXv8FkNEb6HS6c2+WtV77DpT/zZ9nsDzg+OmT//n12N7fZWFsz902pUKKa01JTqhLLMQpijm3T63YJgoDZYk6ns2aeWVTTkWJZFvPZHCkkYa/L5uYWp/ePsCyLjXN7eI5LsliwiBVPXblMNkkQokTlJd1OlyeevPIOD8Z7395/atI6goCmtPNsGF8XnElJE03v7+9zenpKlpmb2HXN0OpIo/5ebbA816XX6RJHMZ5rRMrBvBcEHk8//TTnzp3j7bffZn//kDRNDbzlh0RxWrWBUZGRGNpNR0py3XY4lrKVS5i7jpZ0A7uoUjVG46zKV93fN5vNViIlIR+MMqSUdDoBvV6P2WxGmmUN7elS7KKGHEEoXRFx1PNqDKRliwccEKipU62V92onZOUSnoFxHcfBtmXVgkV1Xcx7jTY2AoSidoBkFSGrqhUvK+aEXY/9w7v881/6JcJOh3/12d/mk5/8JN99+TsUKiXoueRFBqmL57lYtmkdS9MUx7FwbEmRZliVQVGVFGpRFhSUjXiMLo39MCxDJXmWE3a7fP/3fx//3f/zv+ev/JW/wu/93pfJsxzfd3EcB4rSsLpVxWFKKRxpBD9URSKhtBEfaN3qK/NVb0tugOX7eVHQ6XQIKqfSsiyK2qOiZV+FcTxt28aStonsCsVpNsXGLOyivgelJC8LXNtBUL3egNarWw0rl4DUSwBj5dP1mHXLxp4x/G0fSdY7qEy6OnPQFT/qUchE7Xw13pkAS7YU4Iw3bFonHfKK5U5IKFBc3NllFI1J04yOa7HT36UXOCjpsujY3I9GXExmDDyfw/kJQSckVJIyKckzQ14SYnGpv8bV2Zz9+QnaE+yc28G2BV3PNR0M0ixkfiUeJHzJ5nDA0AvJioJZrgnLgrkboJSFzDUBgsGgR5QOOT48IRdG0nLP6yFRpHEErkS4FkUcMx2dcjw+4XQ+xbU9dnfPcf78BTbXt5iPJiAEx0dH9NeHOI7NZDLiyStbKK2wi4Rud4D/xAVmuuB/+bVfZWttjcv9baI85sb0Ho5jY7u2kQ11XBxLYmmJPVzn8PiAlw8PSXK4vLbJ7mDIoohJsxmiVOg4I9ML5pZLXCTMywIWY8LShlxA2SVxIBUxrnYRiUBa8MTTF7n9zRF/75d+ias377BIU7r9LnsXtnHr1rWgY8iFyoyDMmHH7rMWCm7cvsn9A48re7tcW5zw//rv/x/8tb/2V3G7IV/7zrf4H375F/mTf/zf4trN+3z19BUun9/lE594kdl4QpyleJ6H7Rn6aCEl2tGMZnMcyyJDIdOMMNZc2tzlxukRG90hlutgZZoiicmUIotSBmEXJSXzLGf/7bf4gaeeo7M24B/+y/8ZJRQ/+8mfYBot+Fe/8Tn+nZ/9c2zt7PLyy9/l85//Lb75lW/y7376zzz83v8DbN8bLvF3ed+0Otmkac7R0RGHh4dMp7OKcKJimaqquevP14aw7oU2cpWKPC8qdi3o9Xqsra3R6XQ4PDzktdfeqD5XYlkGXjYFK3Xeu87DVsQmspKt8z3CMKwK4FYLmAzZh0Spup9XPhAhPSyv91DIsUkNCHq9blOIdnx8TJ4XTe7EtDOVzcRalsS2rKbtS5dFtf9lXnkZiZue75pophYqaRvts8alvcVxvCyQOrPmNmhJddE1Swj6wWsuSZMEKSTT8Sl/9+/8bVxL8gs//wv8o3/8D/jFX/xFvvWtb2FZ0Ot52I7A813Aw01dsjwjy3LyUlekNBbSKlkkKUHg4ui6AlwZ7m2ohFLgySuX+RM//VP8jb/xn/E3/+bf5PO/+QVc12I4HAAV0YlvPzA/jcNVlk3feVoJcTTn/ZA5a8+lrC6CyY+1rrmGhlVhJUQtQNqGL9syaJIqlm2Kpp3tQYj8nQLqlTH9Ad975NayymfxmncGuB9y/BpxeIcFxHVsSl2S5ylRsuDixYvc2r+LZdlsbm2ys7FNz+9y93RKkedGSjGKGDoevuvhdwIc6WHZTtXKo/EqOlrPccjLjMV8xuHRPk/t7pEWCb7wTS2MYyO0Joli6HZRWlOokqLIyVSJ0+sxPjxhPS/p2y4SzenpCNd1GQ4GOLZFNwjo7IUE3Q5Hx6ckeUFZaKIoIssydvZ2GZabTCYz7t6/w517d9hY32QY9tlZ3zIpxDAgzlPW19fxPRfXGVZ60YqTkwk3rl1nOByws7tFnMwpyoKt9XXisiBNYqP7rFJKaRlUTMLaYJ1rR/ucjk6Znr9Ef3eTLJb0NtYpioLR6Zhrd64zeuUlhKW4e3Sfbu6zsdanG4b4gU1EikRTZjmFY1PaLpMk4/e++k2csIft+4ROSJal9DoB8+mESxcuczoZMY8jROCz54b4wz6vvvE6z+xeYG9jm6tH93jKG/C/+7/9X/jbf//vci4c8qd/+NP8Rz/17/Af/Z/+D7i55uOXn0EmGW+8+hq7u7sNomlatXRFQmXWpk6nQ5HlxGWOVwmJ3Pjud1hsnqfX62E7DikxKsuwHJv5fI7tuQwGfc7v7vHG1WuUN0tAcWF3lw9cfhINDMMuv/qrv8bOhYtcefpp/tJf+kv82mc/99iP0uNs73+VeP3Luzyl9aJeFAVxbIqvYBl11hBfvWguC6zMYlWUCi0gCHzCboednR3iOGY6nXF0dEJRZGitEMKqjJfE932klIairzLa0jEczHlu6D/DMGRtbY1er8fp6biJrk3fsclrm4rxVc3sNlT9zjm81dSfUoqw08GSkiRJiKKoqmY0cExZQfxam8Wq1tZGa4oK0qorrOv5M+OqoX6z6Kep4T9vRvaIIZ61PRrdQPyPcjqgWmQfkrdt71MrA+0jLZQS/Nw/+TmEFPyxn/zjvPD8R/iN3/gN/v7f/x+I04h5lGDN5xWyIJDCsKc5VcVsWWosW+K6kqLMcVwPKm7yJCvwXInreXzm05/mJ3/yJ9k7t8d/89/8t/zaZ38d2xakSUrKMu2SNmmMpeJZ83cVES+j4Iff3PVcNAQMrddVWRG3KGWqVgFVQU8re6uK/OpCJEs6CGTFWFfB363IvF0oaUDy9l/igev8BzLK7e8/5Nybe6DGgB52kEelEFoGX7Lst27yOZVj1KTRygJZoV1RFNHbGJDEEb3+kMD3cSvaWV2WSARJmjGPIrJOl2HPFEiVmSlCVHmOZTv0ex0C18V3HJLU8AvM53Pu7d9DCcUijiqKUgfHNnnNvMgpqpYd27KRnsP1kym/9+1vc3805sd++Ee5cP4c12/dZGd3i73tHWQN1w/WsX2PGzdvk+U50pXYnotPl/3DI0aLMb4fUGACAsu2cX2P+WLOxvq6iRALSb/Xx3VtfCHxbIey0sBO0wRLCoRQKFkg0YSOR8/rc/32LSzbEIFYtoVtSyanp/TWh6R+wCTNuHZ6zPDWdZ7a3kXkmpdfe5XRZERRlFjS5okLl3Dfvmash12CLClVhu1IilyyvbFDnJa8cfMOX/7u6wSDAdL1yAuDljqOzXDQp+M53L59A1vBhe1tzl++xOe/+iV+IHyCn/6RT/HW3dt85+bbXOitc19FvPmFr/Kf/MxfYP/4iC9++2v88u/8Jn/5T/9ZfvSDH+Fr3/kW0XzEc089ZRAzWjVOyGrNtkjjhM5wQJ5m5HlOGHqsDQakScIsXuB3QpyK6CtwXEqhsF2jqjY5HZGlKR94/oP8k3/6/+WJDzzFkxeewFEwHk94Yuccf/U//I+5dusm946Oufb2dcbj8R/0UXvo9j2BxN9taxeGWZbdRIdLQ/0g61M7aqw/43oeG+vr9IYD8jxnPl+YloskWRqJ1kLRFAS1igtUxc7hODabmxtVNCmJ46Qy+FXusapON7SWqjG2Z4+xPMfV863H3kyRWPInq9LI8OV5QZIsSWaWRUXGafAq9aElArFUL2v23SR3V0aDib5bdK0P+djD7JDG5OTPVtA/aNh5IJfcnpcaiVCqrBwfyb179/jn//yfozV89KMf5Y995o+zvrbBl7/+W3zrWy8zOh2TZ4Wp7NeS0qoKwERVoJerqlhPmz7ZysnzPIfLly7ygz/4g7z4iU8A8MUv/A6f/dyvEy0WOK5terzLEq3NNVWVyIaoyMyV1lURypL3vOk+ELSkNR9hvFmNglfSDNq0rIkz+W2thRH7qP8GlBagjdPZTsvopqbizIFFfe+984P4hzXcD2zVtX/o7ffQY7cfELFMmxtvqfXeqtFWpcKyRVUvYcD4JIrZ2TmH7/loBVlegDa65EVVUJRmGYNen6TMWKSpSdUUJu8ceh6h6xK4HlOoWIU0+4f3CTshKjNs/1KKRoazKE3FuLAktpQoy+LWwQGn0YL4rbfY2tmlN+gxGA5xbZt+p4sQisD16XV7pvBtNmcRxeR5QVHk5HluKss7PbIyQxsFFBSK4fqQycGJqeHQhl2xEwbkRU4v7OC4LlmaEcWRyY1rTV5kWEKbLgylcIXAr+bNdRy8Sm3QERJRKBzLYaFTRlHE/skJe34Pw08XRgABAABJREFUlSRYCtaH67i+iy0sNjY36fZ65ESUqqRUhtq00OD7HTQW49mM20fHFEogXQNNl9roMDiWhSoKkijiI89+kPsHB8wWcybHJ3zw/BOcjMfMpjM2On3Wwz7j0YgoibGFZHJ0yiAIeeFDH+atW9d58/rb/LGPfYKPv/A8cWRkk/M8r3jaK4rmVgCR53lT3FcAJRrf8whcj0USM8gzPNvkr4Uy9QWiVMvODuDl11/B7QSc2ztH6PnMJ3NCz8MSFkGnyxO75+h1egxHI27fvv0Hf6Yesn1PIPF328yaVedVrSoyNQQrtfhH27DWhqaGxmso2nVd3CDEsm3u3LlbMVipppWqrurTVeFQbeTqRa4szSLtODadTsjm5iazmYHQlFJNX6EZs1lEatpUMy69YihF8zA9+tzbi7GZB02SJCu5+hrurzW+jT6323BgLw112xkBWo5IGxKvHZW6ut14nTx01W5HUMahOQufP/gZrZfsVI865xpCXl5LC0vavP7664xG/4hPffJT/MzP/Ax//s/9eS5d3mJzbZOr125ycnzCbDZhPllUfM5g5EeWMLHnuLieh+d79Hpdtre3+cgLL/CZT3+GRRTx2c9+ll//jd9gPJnR73XIqr5/tDHOQhg96foeA4GopF2rBH0T1YmlZPID0Wa7jUprqsI7c951G5cUEmoSGNHmFBfL0LS+9yujXlb3rGXXLXZLx8xUEVfiLI9hhmtnsfn9EdfrvWzNp/WZv99pW07Vypcag9/aSW2sgaYgRmB6lfMsI0tTel1TeIXSTR+/JSSlKomzlDQ3ho1YMKdqvxTgWgJHCDqeR8cPsLRxAhzHYjwZ0xl2cT2bIjH1AbaQqFKhNBRqKbajtObGvXs4QcB0OuPazRvsbG3y5FNPshiP6QQhQmhCP8D3PMJulzTLGU+nDHp9tFYkWYrlOAx6HQ5HR2gJpVYs4gWD4YDp4WnjrKM0vutxOhnR8118yyfOEkbTSeUsKpI0wndr9TmFKAtC11TSe66D69hoIfEsmyxKsDwHiUVeKsbTOaNgjF8oLp+7hNcL0EIzn8xACQLXJ08iilKZtiYhUEiwJAdHp9w7PGWeFHS7XZI0x7NsLKtA6BJ0SRrHTMdjrvzQj5GXJddu3uD+wT5PbO1xfzHmcDTigxevsLuxzTUpyPcTtGPxzd9/iec/+CE+8dGPsbG+zv/97/133PihH+XFFz5KmWUcHNynKPIGoaxvtrYtUaUy94aQZGWB47psDNaYRxFJmjLo9AyiVpYm9y0UtmXheR5O4PO7n/8cLzz/AsN+Hyr97G4npCwKxkfH9LpddrZ3uFIW3L977z09S++2/Rsx2LWBhrp9BTgTMSilkTU4Vj+rVc+iqETl09TAmvfevlfJNJrvmypx07NYR6xKq6b9qm45QhgGqrW1AZ1Oh6OjI8bjMUIIwjBs+Ms9zzHwklJnoPslBC1E2cp101pUWXnt7KarBblsmNTAdZ0Gwq4FRIIgYDKZGKYsvSxos21Dk2qAgsr4tHjD20hDjQwsr8HDx1OfX72dJflow93NMXT9XfHAZ1e/ZzjYHcelzBWWdDg42Ofnf+Hn+a3f+i1++qf/JH/tr/5lfvLTP8XR0REvvfwSX/rdL/L6q68xGp0yHp2aoh9kk6daX1/nwhPnuXjxIk8//QGe//ALhGHIr/7qv+L/9/P/E3fv30cIieeuVvHWOeuyVIhGh1033pTWovq/dQ5KN8bkLLLSOHWOXXljVVRY5cNX2uBgNRJd+b3Vk187DLI2+vW1WdZuK0rkYzzK73tU3d73ezTyzaaX9887Ref1y7ZlV2mi3MgppjkoTTfsmNxkWqIKk7O0EGRZTpwZdrHQCumFcDSaYFs2vhfguzYqzwk9n2Gnyz1pUZQ5tmWRWYIsT9norlGWC2wh6QQdVFEaisospSgLIwKR5+zv7zPs9biwt4uU8PbVt/jMj/0YN8Zj1ofrFHmGwEIVpWFH7IQcHx9z+coVNjc3uH//Lq+/9TpKQFGJgeR5znF0AoDjOpSqRGoLu4r2ZrMpUhmy98lizHQxxgtc1KxgEZUgPLqOT+DaJo2nNd1uhzhKyPIC1/PJ4og4y3G7PYLARwjN8WzGgTfixWc+xBNPXeJocsKbb7/J26+/SW9tk2Semvohz0UKD0SJFwbs37vLtVdvoKXHcHOD+/v7BEGAJY2jneeZgeMdF9/1+We/+Vk+sHmOFz/wISYi5+f+p3/K//U//S8I1wb8i8/9Gq+9/hr/xV/76/zyF36d3/ndL/L893+Cw9mYX/ncr9Hvdvn3/uTPcHJ4wGd/+3NcuXiJZ688TRzHDSGVad8yaEJeFti2Q5kVpo3OlqRlga8ctjc2eevWdbaSDRzLMuIw0qQ+PASlLkmLDFXYuJ5H2AmYjcdsDtZZ39rk5s0bPHnpCRzbJk1SptkJtufzU5/6iT/Yc/GI7d+Iwa63ByPRB59Ws6iKhrlMykqesCxYLBaMRiPK0hSCmX3q5n9j8Orcs2iM33wxp9MN2NjYIAgCptMpV69eb44ZBAGO7eF7IbZliuNq6lMhjLHa2NhAa00cxyRJilJUDoNZQutItr1I6xYOfTa/CctzqJ2MXq+P57nmoT0+BpYc4La15LqWUlDU1drCyF4KVp2EBs7l4Yb6UVuNhC97hVevlagg2GaXVfSxdBRAiKrQSgooMUIceYxr20gBri1RGo6PD/nH//gf8Gu//C948cUX+ZEf+WE+8pHn+c//0/+cTidgPp8xm46JIqMCl2clfuCblhkgyVJu377Nz/3cz/FLv/RLpFmJwqQ7nIqm1iAkZn5ratlSFSb3W2uIn920QGMk985WgD/wUa1J08TMlV7KhTZIh66gcMsG0aZWfXjELqSsJP+WjlNzLbVmhXrsEZuAZfvV/wo2U0egW78vtxohMy14ORrF7vYejuOZ4rxSYUkbpQr8apFOyoxcF5RJRnfYI0lvYUsDhbu2NGxlrk8nCNnZ3GYRL0jiBM9x2D884PwzOyROiiUk3bBT8Xv7YFsI28Kxbew8J5vPKCT84Cd/lGg2597NW8wmIyNcY7uoUpOnOfP5HH8yQQpNrnJeevnbDXL49NNPsojn3Dq8g+t6BL5PkWaMR2M81yNJ0qqv3GUxj+j3h+zfvwO2Is5T3EAyFB2u3csQtofjdfB8H09YBGHAzHcQZU4/CLBsB4RkbTBkr9NlFC3QvkOmCw5mE6Kbb7G+t8XhK1OuXX2bewf3WN9coxv6zOdztCPQ2PD/p+7Pg33Lsvwu7LP3PvNvvPMb82VWzjVkV3d19UCPas1CtIxCk7GMDLZDIRsbQmFsE2CHhU0AsjAW4MC2wAaZQUAgLMkyEqJRV3dXdVdX11yVlVNlvpf5xjv/pjPvvf3HPuf8zu+++zKzqrNVeGfcvO/+hjPsc85ea33Xd32X9BgmI7577zanp0fcvHmDk5M5Z48e8kf+wO/j85//PKvzU/Z2dlGjIdlyxUvPv8AXfvXX+CO/5/fzxbde5fDBHf7Ez/we/tm/8zk+9bt+ij/8kz/HL/6u30MQBPzl/+u/xXOvvMjf+Ov/Of+bf/EvkBclz9y8xatvfAc5CPjLf+Ff4ku//uucPjri7t27DIfDriSureSpq4qsKJhOpw5Va+4tbTWeFUyHI5arFVnhSLZRGJKbGowlUD7RMGaepfzqr36OOI75hZ/+WV79+jd567tvkgxHvPLxT/DenXcZxa5Ll7WWusgJ/fAjfTb+gRvslgHqBCFcDtvzJL3+IO5zbT4L2+ULWwPveYooclrii8UCKU2nH+55iiTxqarK9SzdiDA1QRBw4/oNpBIslgsePHjQ1IM3ddzaIps81XK5dH2tPd9p7GIIAp+dnR3CMOwaYVzM1W4a6Uvy22w6Ky4Kaztr0fWIzvOMPM86OU1r15GWpzxXKylcPi9kjUAAnRBImz6wdk3CuHhIT4q0XX54HVH3o/L+d4QQXemeewhsUzssGq3kJiIXHgiLUobQ88jygiRyDpVSCr+JgI9Oj/ilX/4lfu3Xf43hcMjOzjZP37zJrVu3GA1dv18hJNJTHB0d8e7d9zg+Pubo6Iiz8xlZURJEEUaWeI1TY0zT7xiafth1k4us0FWNDAKMsOvGE22yGjCNsEsUREwGw8euJT2nyFrLsGEQV7Xz8ouicEhIUboewU1EdPH+2CC6ta836Ybuzw4iweHz34f20fuZ98ucEXHZRX/CZy/bx2WOwuPfbZ8Z0e2q47H0P2UtQRCgpGCBwgs8rl69SqA8l5c2FuU5Df8wCDASaqupdU2pc7bUNnlRYK3Fk450hbUEnscgiNjf3eN87pMtF2yPRhzeP6TWdXOPCycta6AyGs8POgdNSckPv/gCs7NTRoHP7sE+AyGp85KbN55yJLLaopVzYJfLObPlghc//jK3nrpFHIXMZ+fcuXObsixcdFdV6NrgITk5PeFjV5/i7PTMqfYpqOqand1dTk8PmS9nZHVOWWeAZTBICOIByosRQuEpGA4T4iREWEfOdIi+jy4L5GjM7OgEGQeoYUw4GpCVOb/y9S/zqWu3ePqpW3zykx+nVlCmC4KhzzxbkFcllTaYuuTdt9/mpedu8WOf+iwnJwu++JWv8ZVf/wLXtrdRnuD48JjVcoXn+dx/7y7PP/s8X/jaVxirgJEN+M/+q/8vn/vNX+f/9Rf/z3zttVf5r7/wOa4fHPCX/uV/mX/8f/nn+Df+lX+F/9Ef+1O8895dfvkLnyczhuzsnP/0P/yP+aFPfoIrL22TLVNHLJ7PSZJkAzH0pcKotYS1IxJbhLFMhiPCJKasKrI0YxBGFFYzCBPqRcqy1pylCx6dHvOn/+gf5xu/+RWuXr3K3vYuZ2dnfPOb3+Tll15ktVqBUE5VTWuydPnEZ+T7Gf9ADXa/XKP1KLWum45cF/PBvaycXbNh225XWq9zvnVtmvdd7qZoSrdcxy8HBUshiIKA4XjMcrlkNV+S5Y7wIaXs5A51kwPTWjvt4GYhCYKQ4WjIZDKirmsODw+7Fpout+7ylnWtN26SfkR02frWJ9cJQWe4pVxHtXXTD7t1SFyNrmzOvXZqYj0SVLudNpff6hO3Od92jj/wevWOsW0JabtStjXEK3ACFu3QrA1PKxwShCFFXjXHJxtiiKsjttbVkxsr8TyFFQItNMt8xSpPOT475e7d+3z1G98i8AJ3TYTrWFYUBVmROTJbS2hTktpYVBCC0Sgp8XwfpXUjH+vujXa/nu9v1BBfOjUCtNGUhd4wshsku+4GpoFtnaNVVRVRFHWt/sDdz9qaDedNdDe++7EtdOF4amveQgO3tymGDw12W5zwAOv77rKPPGmLHxaY6Rtae+H3xX31EiuX7+0xJ0E2KSTX6U0pH9mQB91zIdBVc883E9uVf6pW2tRpxis/IAgkq7MVWEEYhIRF4PoYC+misfs43W/rkSQJg6okjpPGOTzHz0sSqTg4OOC5W9c5DH2kqRlGQ8KDPRYLp7pV1QbpBUTSlZKVZUkUhyAst++8A8YQ+D67O9scHx9jpXN8sU7vIV2uXL9qsU6FiKbrobGW88WM2lYITzCIY4bDIWlRc3Y+x8Yhw0nUdMraJk8zFvOVS+8Jy2A05OzshI9/7DmOZ2ecrBaogU8pBSen5xy88qPcunqDssr55ndfxwYVyXTIWTHHDwN8z+fr3/wOLz//PIEsCKjZGka89OwzXL9xnc9/4dfQteAnPvujLJcrvvLlr2JqQ20NN7b2XWCha2Ix4Pq1a/yXv/xL/Kk//ie4dvWAr3/ta/yVf+/f5R/9hd+LyjSf++LnuXLlGj/7k/8Qv/Qrn2M5n1EUBa+/+QYHO3tc2d0jz52K5GQyabbteD+e75NXJRJQQlELp6tgtWYYJ4zGY2ptSFcrxsnA1V7PZlwdb/Pg9Jh7Dx+wd+WAQDlN+KPDQ5I4Zns8IQoC3nvvPSbTCcJzvBJrLXGSfMin5sONj9xgt+vJOr/bGgq7ySTufabL6oleDtFtrYFXRdNPe91IpI1G3ILXMsxbKHwND7s1r4mUrKEsC5arJXmWUhvdfa6FMG1vxXIscRiNhsRJ3EVmjpiWNfmedUTQkrSgrTV3GuaygT5bmcFu0RY44ZP+BIo1kavPnu8mpY3cYd1ZSusLC7+LjBsfxgW28pJI7OJi+Ni17C/f63xuez3bqEggqJv2oIImwhaim39oBV763zNNf+m217e7cdp6ZYeeu/x+rTXLqmaxmDnYWoBtep/bZiJbkomQTlzEaI3EEUcwBtk4ORvVB23Ovftvcw7aGVMWV5JjjSMadfPTXolmjhpjmGeORGhb+NxadFV37ONubpt7U1zccbt/0Zc4dWhU+5w42AksBscH6R/TE4Y1CNSl3tr3nYO+bDeNE2d72/wwDkC3Hti2Ic/6nmu3K5FYbdB1hZQWXbkmD0oqRFtQJ+lSD63YjdaOIKaVYpGl+FIyiCK8QLAQGcZTRETEecbAi1CRIQoTQi/maHbKzmib0SAmWixQFipteHB8zEpKpnHI7tU9ntq5gi5r0IZQeoyGMSfHZwilkEYQWIXwPIQHJi/YmmxxdfeAuq5ZpSlFXVBWNVrKzmFrEbRVtkL5fvecCynxlE9RpGg0eV2CsoR+hJKKEMFJXlApD8+XLApFoDXXDw6grqmjECErSm0QBsZJgpDgNTXpZe4ajRRS8XA5Rz68T5WnLM7PUEOJrktCP6KuDOdnM6wpeermVY7u38FUJdIIxknAC0/fID/9JL/5na9DXjDyI67fuMELT93inXfea9Ajl/pJwoD9nR2+/e1vcPfuu0SBz62nbnB6fsbqdM7LL7zIvcNj5k2b0Gs3rvFw9Yha15zOzvA9xXQwbHQaPPLctbaUTRWIc34k0gq0dSqGgXB57GEcsZOMmK+WnKzm3Di4gqcFpTXYwGOerljM5zz73LOUZYkyBikkdVWT5TlhELpSv0ojhXP4Ol34j3D8zginNDihW7QdSaxFu1oDa62TD/V8J6oumii1bW0oBTgl2GYhb77X1RiLtciFg7Afj9C7+mFcm8aiKsjLnOVy4aLiXqJ1DYM6OFcKgbGa0WjI7t4OnuexWCw5OTmmKIruPNs63n7UpZTsxEriOG76NgsQa89LAFIJjHElKGvHozd/zTHZJjpuIW6j9dpwt8YRulIiV7fcfJ61j9RPK3xw5rM/1trprfLaY5/obcysm491zltVVQgMomnOoZpactkjGjpnQONb6fqq24Z4KAW+v0YU2tI7a0WndW1xXjr12uEzRjuRCOhaIbbdkVyThvW1k/Zxqynaq2BAaXdf2Atgb99JaiM62zQ8ad0AKQToRiyl55j20YoNUlq7UDc196ZhXfq+1zkAQrQ8BYfGuGv/uGpd/1hpj7538Te4Df1490nwtxDvmzZ/UkT9xIi+e27WREb32rojW/vcg7tfjNYOOVGWNF8Rj7fwldP6NlrjKx/RtKSstespjRVI6VEIOFnMuD7eZitKMMqQhQGLKmcYxAz8CBsPqYMIhc8gHnO8mDEejZmGCb7WiFrjj4c8PD/lpC7ZHSU8XT3DwWiHo9XK3f8GJ8RS1yjPJxCOMGalxCqJsjWRF7E9mBLFEfMi5f7JIeePHpELZ0Q7RSglyMrMNf1oqyyEwlcBy/SEippaOJTLEx6+EcRIal1ThT6ZFMyLkqgAv2kdmiQRMvCwRcEiXbK/tce942MqY/H9AFXk+KFHFcV8/d13eMdTjKViEvgoIymWKxSK1TwnJ+PmzSvEiQ9CUdUaXWpMmeFT8rs+85N89Z3vsDg+IY5G7B0c8OM/9AlsXvH6nffwheu1UNqa0WDItZ09fvO3vsjNq1d59tbTfPqHP8Vf+b/9P3nllVd46sZTnDx6wO3jhwx29hhsxS6N5SvyquDs7Iy9/X0GwyHzxQLleURhAEI4Q9v0vc6LGm0NsR+Q1SUTLPvJiJP5OYfLGdZYvNKgkohZXbie2RaeuXLNiUkZy854itaa+WrJkAE7267KyGqDUK5ZUttj4qMaH3nzD3Cyog5yhLKsG+GPVnDERUJSuLIMr/m3bSISJxbhFp7Ad56SMxbuxNtFTNDUFffFrd9vdNHhmil92WgjZYvLq7700ktMJhMePnzIvXv3yLICIdrSK93V5/XHaOQajiilOD8/p9bVGj4Wro2ng7wtUnpdVN52HYP14twqsfU7gF1kcrdR7xrZWBvQ9wuc1vCq7ebnIlFuc3Lo4P+LP334X0p6r9Hl/2TveHWnyLZGE3pob+8gN4+33Ud3/M3C3Bmi1lFs4i3pKWRTk9lpfPc2ezFnLHqTZ2zTj7sN+b+HILR/nO2xWi6/8R4vk+tdA9tzslq04pLvtr7A+xwRrRH87QxxYT+PGfYnvA6Pn9ul91j/+40PtXG9lWi4gaIRyMBFlUJhdJOGEI6nUDX5yLzMscIQxiEnx0ekywW+J4maUkmlFF/72lfIy5wwDtna2mJ/bw9d12xvb2PqmrIonCALmvF4ROD7XeqsKAru37+PF0eMx2NqDMeLcx7MjyH0KXUNvqJQlqUuWaQp58sF77z3Lr/2a7/G3/uv/x5f+cpXKMqCnZ0dtz6oVgWgRYOgKDKM1dSmRhuN50uWacoyTbG41FmRF8RRzI1r19mdbBEp3wU7UlBWNWVZteAMSkoCz8cIy5t33iZSCpPniLLkxv4B5yenCAuzxQKUYrg9hSgEz2MwGnL37l2qquK5Z5/jxRde4u579zFak6UZZVU5aeGqZr6Yc+3qFX7xF/8RPvGJl/nKl36Loij4E3/0jzEZDRjEIcNBTBwFfOqTH+fee+/y0z/108RhxLe++Q2yLONP/Mk/yS/90t/j5OSYmzev89TNG3zx13+D3ekWT924wXQ8xgLnywWPjo8YjEcUdcUqS6nq2vWxNxa0QVqBwrU+DcOwUUi0jKdTPM8jTVPOF3Nqo5lOpzx8+JC6rtnf30dYVx0wGA45PT/DWsu1a9fIi4KTkxNGoxFCumYvRVV+pMgV/A5E2EHgk+dVA+FJoijoWLltaVEcJ1y9eoDWujNsl51YmwNsGdF1XXeNMmgMgJSC9/VhemvCRV3yy/bp8r5ui5PJhNu3bzObzVxUqIRT2FKSqnIC8W1+rDW6165dAehgc2uhURxthkBIV57jKcVwOGQ2nzdKa+sIuygqfN9rjkd0hLRuKxfgUvca3bl1p283v9NOyIfxc/rffb/F9eJx9X/bnrVpSUVSerSaXBcXeIFA4aCkFlZeO1qOOLd2DB7vPtb+3XQI7HLo6+5XFtGWWTXfkb1jlnItnEK7PSG6SLjd/4eZgw8aUrbCDv3vNqhRyxOghSnWcPHaiF92TZ4c/n7Ui8eHHR92v2untr0e0Dpg7ftlVWCxSM8j8mOWqyXj8cihXULiK488zRBKEgYBkXViGafzGc8fXOPh3XeIY4849gnjAKyHUJLj41MeHj7i2t4BQeiTrlLAIKxG64qyKlwPaqUYJAnndUZpNbaqKBVUeQFS4gc+9x6d81t3bnN6dsb1p5/m537653j39j3+o//P3yTXJT//Uz/J7/vZ38X99Jwff+WHUFJy//iQ1++8xeliCQOnb1+3SFyTZprP52itKcsCIRwvY1VnRImrkc7TFWVRMkxGxNGEt+49Iq81utLkaMZRQFEUmLLENo2LAt/D8xWlMNi84Mb2Doss4/VvfosrT91kWeauC5nRVFKSa83AegynWwhPMhwmHOztomtXDz8dTgiCAKygMjXv3r3Dz3z6d3H+y8ecn51wdW+X3/3TP8XXvv4VpuGUl59/hq3JhLOzMz7/pS/y7u23+EN/8Pfz7u232RlN2X3uBV77zmv4QcQv/uIv8uq33+T+3XtMpyM+/vLLvH3vO8RBSF5l1KImR1POz3n+5RfZ3d/j7OS0SY81KJXn0kot8dP3PazWjXGeEIcRy8WSo8U54/EYay337t9lGCXcuHadWjsRnaoqGY5GpFnG7PZtbt26xezsnDRNm2ZP637mH+X4yA12nldNk4kWwlwTora3tzk4OCAMQ05PT0nTlMVi0ZGqXETZKqCpDU+8D8Wu4VbbGfUnjmbh/iBz018UrHWkkJOTE46PTxs5PdlIlNoN4+kibInve1y7dpXFYsFqtXLHa23HUnbM7yaq9iTj0ZAkcXWYpteas7+2tYgECLSuOmPeX9j6i+FjRrLZpksXrF9z+e1LVLIuGbXub8t2ZLb+a/19XzqaSLquNW2Hs3YbLTTcbkMKKHWFh6u3NS1UKp0hb+ccu9aAb88Teg5AAz23UbWUri1h6xz2Uwntsfcduv7GLC6f/iQ4qoOV27TPJc5gd62acxFCgLaP7bPP9O9y/s2z1N4L7bPSXQMunPyH8a2+t5zI9z02EIB21737szXKF19HPD6H1lrKhkSofAl4rPKSVV7g+4IoiFzrQ+NSYZPBCGMyNIZ5tmBV5mSrBfvbO8RRiOcptBWsVguGwyEPDx+xPd3CU4r5csbu7i4n5ycIK1imKZ4MEJ4Ca6mKAk2N1TVShGxPt8jLouMvjLemEHo8ODrk3t27lGXFaHfK8vSQ1975Lk9du8Fv/NZvsDw+YX9vl3AUs7e3QxGGHKdzPCFBOjlcpQRG0HA4HLRrrMV6imWeuvagXlO2qiRpmjFIxhxsb1MenzArV+QYRn6I9HymgyHz2Tl5VWKVbJwDw9Z0RLpY4gnJz/zET/D5r3yV688+jcIyny2oVilJELL97DPcffvrJIOE0XCAsBZPuPInrY0TJkJ0iAhao7BYW7vct5S88hOfZej7rBYznrt1k53pmNv3bvP8M8/wxc9/nr0bV1DKoa0vvfgi33r1OxyfHHHr5g2UqVmt5piqYn93lyLLMbXGD3xUEDI/n/Of/62/ybO3niZfpUyHo64ngKlqVCNd2gZ+QjiycRBFbI/GpMsVJ6sVWzs73Lt/jyLLXcvM0Yj5bOb0HwScn58RhRHb29scHR0xnU47XpHnSeI43tB9+CjG7wgk3kZyLcs5SWJeeeUVnn32WQAePnzIyckJs9mcPHcSoC7y2hToWLOR279FY4BomOK6gaM//Mqz3v6TcmouyhFCkKZpIwnqY61jo/eZ1nXtGnRMJhN2d3eYz+ekadopLbXrrRNgcfMRRSHD4RCE4OT0jLrJ2btjWxueqFlQ+izxdrQw+cVyq/eNgu0afnZ/rhuFvN9P34CsN/U4nPlEmLOZgzWjec2Eb5n+LRTu+mcbXM8v2zH0W0KgbuqgW7W3jX1cGO33TQNb1s3vvkpc++8W2mwRnPan/Xz7ntHmsZ1t3E/NP/v3cTsnxhjXKKLZVlVVVFpTN06Jbs6vNppK11Radyxy1yPZReQXDZzLyX+whd5w7D7w09/b2KyGeFxW+P33ecFYr3MbG6OdQ08piiLj8OiQ9+7fIxqN+G9+9Vd5eHxMbVy/aIHL7Q+TqOlg5vgH8+UcYQyTgWtR6XseSRSjtSaOY9IsdxUljVMnhCUKXc/rsiiZL5cIT5IEPqHnYbQm8AO2p9vsTbadIc9zYi/g2v4Bz9x6htD3+c6rrxJHIaPBAG0MR+fnqCjk5tPPMNneoqxKzs/OmJ/PXCORqqAlvLbNY5SSpFmKkE4qNy8LtNEs0xVhFLjAxUDgh/jKIw5jdkYTplFMKF3Nvx9FzJdLiqJAIgm9gEB5aF0zmYyJRjHJOAFpOTs95hd+7mdZnJ6zmq/I0wJdQxjG3L3/kIcPDxkkMVHgo6ucQTKgrlxXuxYBGA7GLJduPfSl0/zHahLP53x2zvZkSuipppJDMIxjyiLn5o3rrrNdFBH4HlWRE8cRZZ45GN/3CX0fXynSNMMPfIIootaGo+Mj3njrDYQURLHrfBhGUWNEvSa1KlBSoYTT9/Ck19AFJDvjLSaDEYfnJ0RRyDvvvMN0MmEyGmG1RjVoW5plTHe2iAcJaepKycqyxOg1L8U1HzKP3cu/nfGRR9hCrFsvep5PkiSMx6OujebJyQlnZ2dNOZXYkP/sj746VGvIWkeglSi9aODf97joQYq8f25XSnEhL73WOW8jTAeNq46RWJZl03Gn6hGJ2mjQbSUKI8LQNSDJ84w0zbo2ov3j9AOPMAzQWjdwe0MssuvjeVIOcDN6cYuOI/VJ2jNyBLY2Q/bkYQWPLcL9bV82LhLw+jh12+O7NYDuWsqN3La1ruRKeV5HsDPW9pyGpqxKOKiwZSLbD0BS1iSuTc6BaPYrpezYue3r/VJCz/MQgK50g1Y8TjbsO0MX50lI6YRjevPZ3tP92v21PK07VolqdOzb4xENPN7b/odMV/xOjcvuwz6K8aG3Q+PUXUztCGeArbUIK6mqkvP5nJPZGbtXrvDGndtMdvYZDIaE/k7j2BuSMELNHdoilCQvMqSFrfGY0Pfd4h+EeMpnOByyWjyiamqug8Cn1jWB57E1HnN6fk6apQyUxzBOiIuUdDUjGo/Z3d1jlIxYpTWrxYow9JhubYPvcf/+A+7eu8eP/siPsZUMUUjmWYoGtnd22NvbR5cly2zBoli5fuyCxjlsetxbd+3LomQyVA0vSCOMoapKp2xYG4T0CLwAJT2iMGJve5vT0zNOVpJlXVPVNanWmMGQwPcdD0AKfCGJgtDp+ChLEPkkgwllljddRUXDelZoA++9955b26MIJSymqoiCkKqoGOwP0JXGaEMYeqzSyjlHyqOoXLntOEk4nZ0xSBLCwMfoGiUFgySmyDK2t7aYFSmmIelprUniyOnsW9OhZVIplqsVYZxwcrrg8PiI2XxOFEdMJmNXFeJ7CCkoqwrfD6iMS6koqbDGcaNUU70jkUwHI4bxgO/cu4MIJaeLFR/7xCsMBwN0Wbl2t006SluLJx1C0abLgigG6ByDi8HOb3d85AbbHZ/T5x4Oh+zu7hJFEXfu3OHk5LSDsIUQhGFEkiTNAroZNbbGcW0sWidANuVSYeMxff8ezEWyT//11hCfnp4jpct1gGzU1gR1bbsLlaYp5+fnHUHOzYPLsXueE7oPfI/hcICxkOUZq1UKrKH/VkNdearrKlZV1RMXw/aYn0Si66+TnQPS1WObptTsfRZTt05s5Izbz19mwJ9oKGlTGKITMijLsqmHNk7tTPVr7DVhFDUKZK7eVmsHgxqXU2iIhmvyVfdbdM9S93rX9apRuQM649s/h1an/SLCA3TeudGa7EPkpFoD23cSpO91BKfWodPaOQKtA2GMc9CsXTuDVltWq7xxINZulmguUBuQiv66ICyXt8x6/9HO40cZgV+WMrnUiIvNe3bDIbVgcWU4aHflK11zeH7Kma6ofMXbd99jazJlezQilE6VLg5cBFkLx3PRzaK8t7VDXZYoJIFUxH7IZDLhwb0HHdwcBE5hUEnJztY2WV5wcnaOWSwYDQaEi3N0rYmiiK2tLQLPZ2YKVlnKMNlmmgxRUcB4OOLo/CFRFLI3mjAKI85nJxyfnZLnOUp5jKcDgiigOK3JiwV+EDQlgU2ZmnUOX1VXBEGAMZCXTpRH4koGhWmqU6Tr6Ocrj52tLYZxjI9EV5rFMiUeJni+TyiEI+pJwTCIyVZLtKkoqpwkGfH8c8/y1/7T/4KrTz+DpmnaY+F0PuP46ISPvfwsQhfYukQYTSAVuqoZj8csTk+d9oBx92kYhoR+QF7kaKOZjsYcnp3hCUd6s7VBeoLxcESRuwqcPEuxnocXD/Ajn9APELpESenKYRtEqqhKgjjm/uEjbr/3LnEQ8mM/9mPosiLPM6R1+u9VXZGEMbppNqWUxAooTN2tLdIKxtGAQRhztpyjSskkiF0b0yCiSFN836EZfhhyen7GeDBid7JFlmUIC0EQNGsb+H7w336DLYRgd3eb0WgCwMnJCYeHTlYzCDx83+sMc7uAAQ3hat1Ss833tcYMC1EUkCQJURQRhiHGQlXrjdKg9xsfljjVel5ttOOMnO2Qg1arvKoq8jzvSGcONXALtpQCK5xa0WQywQgoipwsKyib3qyt3KkTkXGRe2us8zxvvDTbMMqb9GejdNaP/r730QiaPGk+ekFbHz7uQ+ob89VLvl8kgXVNLITozqmqKuqqAusiVtXoeJsG8i503SxMjnSGsQ1ZpNmXaYx1U87UUpdtu3+x3n+X729gb631Bsmsn79elw+tnaH2PSFExzXYmKpLbjwn2NPMDZbKaGxe915zByfs4znrNp3Svu4pH2FVb2Ib5T/rCHWi+du2OekeotFMEY58d+G4L7POF777YcaTnqkn5fGf9P2L76y36yo22tekdU0vwjimMIaT8zMGuzucpzlHszMWqWuRKAQMopA4CFhUhTOOOwEKwZW9fc7Pzh2hsdbURcl4NAIEVVlSVSVCOG5KWQrGwzHTScb5fMnDh4946qlbnVKeMRarravT9xXj7alD0oTC1oblco6NfSqtubV/laf2rvLdo4d861vfptaaLx+eE4c+0SDGG0bUjS6DNobQ9/CUB43jr7UmCiO0AZtl1KYGq1EW4ijC1pYiL4l3E7IiRwjJ1njE1nLCyVmFlB5CKFZZTjQYkkQxBhh4ITObOph5a8pqkfGVL32Jn/npn+Ybb7xJWuZoY6jqmuVyzjPXbvLUjZt8981vUwChpxjFUVM/KcjLnGSYsLu1w+nZI7JlytZoTJG7HP8wHpAdFui6xpN+1+MgiQc8fPiwsQsecRQTRiGLMsdrap6jICDyPdI85fx8jhcGFKbm1TffJPQVn/30jzAIIh4cn2JDze72DoMkASFc86BmHlupXm2tcya0UyCMlM8wivHDiMXilJc/+TFGoyGi1hirUZ6gLqEsSsbjMVVWcPfuXZ579jnS1Yo0TZ0Sn/IwjQjXRzk+coP9Ez/xE9y9e5d79+6xWmUNvOR244zgOjIqypK8KBzkqZqa0wYCBZq8axudRdy4cQOtNcvlkqIsG3hT9JsAP3FYLs+9Pj7WMqFtM5H+4t2uOc5oOzW0thSgtYG+7yJ/awxeKF0D+LMz6tpsQKBVpZHStdUMw5DBwKninJ2edttZdydz224jfyk3a/ye5LC43HvjfHSwa3umlyyg7o3e9+1jzkE/arosl33ZoQghWK1WXRQrG0Puour19loVtG7bbf15awB6hqa/58ezy3T53+7vJmI3ejMHLu3amMgGFm/lbFoj6jcQ6ocZ72usRA8mb9COuq66fUtvTYrTjqyBJ1y5n7SbBrIvB+sscGu1L0xC8+pG/fT7OLj24md/QMNxWxo0pX12tQbhhDaE7/Po6BHRcMIwCjidnXPn3TscfPzjeFIRBCF1VVEUOXUcN12uBIEfkcQDl48UgnSxxNaa4XCAFA56rsuSMPIpioLBIGFvd4/ZcsWd2++yt7WNj2Q8HGNrw2K2IHjKx5Ahw8Ap4uW56wK1s8NrX/sqeZ1zMJ1yZWsH33d9Cq7duMozOwdIa5inCw7nJx1HR1rXHrQNaNoUiu/7eNW6W+CNa9cJVMBkPCVPC8qswvd9zs7OGAwGjIZDRsMR9viY87MZO4OESms85TFKEjw/4GA35b1HjxgkEWWWUUqJiCLevf0OdV0zmy8QoY8XBXh2QBjGnJ2eIo3BlxIlJHEYdbyPql3DLaxWK6w2bG1tUdUlxhqGwyHaWsqyZjKZNtrvgiQZUq9SvKY6xqXOKqRyz/d4NMQa22kxDIYD9sYH/NKv/DJpVfDUU89y5eCAR48eEXo+SkmKssD3PAbJwKWjcM92e2tLTxLJgDxd4miMkmE44MqVq5zPjvnYx551wZQ2BMqjSDO8KKYqnRM4GY1JtkPu3L7N9vY2YRgS+L4z1lX9337hlK997RtOA1f3IqcNFuxakcxTykE/OKaw7uX0qsqJT4zHI7a3t/B9n+PjY3cDWEsSxyRxDLoVVaFZUAW1Nc1q3bCyhSCOQ7a3t7uG4sZY172mMXwuJ+1qiL1AEQQ+nRCJoIuCnQFb54bLygkzdE2cBI76b5tzFZKiqtC6z652+6pqd2zb21sATmYzy7vmGbJlyjfntzXZarTFXRMU2eRPqqqCVjSjxUjb+RZuXhyM70riGg2KHqi8Hi2E3YZbbt1210iKNXGsdYDcQ7Ae7bWQNIuMWBsZJdtDs3RlXaJV+lqjJJHxkL0abQFOCEPiDl66eLpvGF3Lzcdh14770P407/Xz2NZabHsejVEWdh2Bt13R2s/2FbzXc7XeFmy2WZXSOUfdqXfBo3Gn1BadOJe/u24Sp8rknCyBbPKOVluEdeIZxuDEOPoReI9L2heW6eeG2znZcCyaY79wShvje81LP2kbj7/W5JqF6FIQbXpe0LSw1BYrLR6SRIYkfoJCsR3FJGFIaUrOsjnxcIh3fo7QOLU7JLmpmaUzrm1NqMuSqjJo5YESeFFAVi4pqhVpvWQiRoRxgilLIhlR5RWT0ZBnb9xkdXxKIgU3ru6zvJt2ogpFrZECEu3ITqWUpLoiiQdUswUPH95ncutjPH3jGp95+CxvvvEdpKxYHj4gCgNk6KM9Qe1BjI8WhhrwhSBQPnEYcXZ4RmUtcTIgWaY8ePSI3WQf31MkUYwoLaXJWC1n3Lpxnaqs8ZFMo5itOORQp+jYwygPKXwCERF5EYMoxg8CdqNdHs0fkC9TrCeZ7MTcP3rIi7dukTWlcUVRsRRLkDVJOCYOY2xtsGlKnmji2KcSFq1LYlOCH1LpOYka8mj+kFWUIXwPZUEXKcMgotAVxtQMo5CT1QIZOGKZ65fghGCqgWC7Dh1ahMAID19Y3nr3LoG2vHjzFh+7dg1d1CjjAiKhJGVVUOkA5Ut01bbWFA3nxaIM5MbZKy8KSPMCbWteGu3yFj6LswVJpYg8H6lChDIUdU0cJZhaM5vNWfiK7YNd0izH8z2qEqeuiOuZ8FGOj5wlnmVZ12TisrKj/iLYGnb3t/N+2od5OEy4despDg4OADg9PWWxWHZQtW0iRiXXa0s/gnY5P8lwOGA8GZMkCcaYpmHH+vMXuy+ZJnfeLv+tAIzW6x7crUhJHyoWgkatrS9O0aixbdiLdbQgBQ7ab46rnY/+aPPpvu83UZcrjermoTEM7XFf/Lm4NnZ/947p4o/b72W/N1/sM8O7uW+8i4usccd4bk1H/6DsBv2t3Y1tPiaa/dm26sAapxneMxzdFe9vvvl57BjXE7txL4r+yffm/uLfxtpmrp1i22Pn380Lm3PSTn7/hzWKLURfHtV25y76F607nGabTzCcreF77Jwunk/vvFpf73s1xd+r8f6waalm6xe278qblHIdsiQgjcVHMIwihMAJieQpURSipCKOYpTnOcUxpYjjiDxr0k1CIJVHnCSUVYk2deN8W/zAiapgLXVVgxFMhmOevfUxdK3Z3dlGSUldVa7JC05XAW3w1JpIuzPd4mM3n0bgnoHhcMiNK1epigLf99jd3WE0HqE8SVWWaON4HQBtlUTrbemG4wBuTTC1JgocWhB4IWHT3QtgkCTEUcR4NGZrMmGUJBhdk5clyzzHCKcHYY1lkAwZDofEXsg4HjFMEozWpKsVL7zwHKvFgtVyiZAC5StSXWCFJAgjgjBCKCeQ4gmF0s1d5CkIfYQRrGYzSmMwjSBMrHx8bak9R7BtkSuBcPKeTdqqfS49KfF8jygIoW0a5flEYUCeFwhgb2uLrdEY0RC94tilRcqipCpLpBWYqsYX0hHohHNrVRPoSSEoi5yiLPGDgJtXr0OouH33XebpAo3u+pOj3HFiDYGnSFQAacHYCwmk5xAWW1NLsOqjhal+R8q6PszoG812/RLC6SUPBgPG4zFxEmOt65p1fn5OWa4Vw9alUOtFybIWvJBKcuXKFba2thqjuIZSNmqAO3i3XXya95qSn5aR3s9hd5+x68UWLl+8LrLS1+t1E3UqRVkUFEXR1Zr356aNBoPApyyLLsfZP/7O4ZFrWLktTWvP6/uNijbX17ZUrT2JDdvTfmLj3DfsZ+9zrXnajPDaLVj65r0/KWvE4XLVLPf++vuXnlPvxN5vXi5je3fvbfz5+LY2GM5rq/w9WMTvxbB9b+O3GyF/v+N7M9aPjxZBEkqiPOXSRHVFpBSDKEJJQZannM1nXa/0MAwdqajWDJqe2XmRU9UVrnTKkaKqsuq6cRndUzAUuLadtZM83d3edazywQhPSLSuG4PtEKMszxymbSxCW8Zxwgsfe5bACyiLEt/z2NvZdf22i9JB3L6HwCEL1ric6rpygs5BxEJZljg5WtU5m67yRDVptSFSKDzPRaiDwYDJaMwkGeIhybOctMgp6pqqcfjj0HGCAi9gOhoxHo6geb0NooqypKpqgihiWRcI5VAnz/cRnkdR1wy9EKkFCoVVHpXnIRr0J4xDaltT1yUe4FlBrp0amus66FIgLazeimJJ6XTifaGQynPE2eb6W0AJQeKF7E62SaK4aSrkEccJGBe0edJDIgkatrrnuX7innQ/gVCEyqMuKkytCX2f7e0tvMjnbHFOpSuEEijPiWq1TrWSglD5xNLD1k6z3lgL7fprwdT/fyBN+kHD5QVtR7BqYUTP8xmPR+zv7xNFEQ8ePOC9997j/Py8gaLX3bpa+M7Nj7u5TRN1KN8jGQ548cUXmUwmFEXJfL5wUHqSrMtjmtVzzep1Aih+k0dsI17PU11Ope0yBWv5zS6aM2bDKlm7rvF9PNfrfuq6pixLyrLqyBft6DsCDn4vNtIL7QPbapc7bXZFEHgdwa99INra0v6+P9y1YiOSvxiw9Xwc91ovumsdhnV5GN1cOSi4Z7i7OWy20sLpve935RwtUc9eYpTFOp9taHKdF0oANxyJXqT7OBzxuNHumN69frtP+pFKucYPzW+hmoYAH+hArZ2PDz0+hBF+//1+n8bUcuF6X14G+H7ktItO7fvurgfl+56iLnPGccwojvGkoChyTmfntPwAT0pEQ/4ZjcaMRiPKuqaoK0cGtNZ13asqQt/HGkPdVGdIJVG+h0VgagPa4CuPQZIQhQGh5yONe4Yr3HN+Pps5o1prbF2jrODp6zfxpEeW5mAFO9vbxFHMydERjx4+5PzsvNGjcM530PAlZIO4WGu7FE1ZFhhjO2Jjm1oEXEfBwcilUpqypcDzGSYDpsMxw2hAnhWU2jDLU1ZFjpQNsc1afKWcU5Mk+FJx4/o1Xv3WtznY3yeJY9J0RRSFzKumagGJVB5WKbKqYicZI43TKsxLzcliQWEMKg7Z399FlwWL2Rm1dvoVq9Wqadwim3Vk3SnRU+64WsfJQ2GsQDZOAkKQ5hkegnGQcLC1S+SHLJdL4tiVVtW1ZjAYESeu/j2O4mbNENBE2lIIl2LxQzwEigbB8X2CwGOYxEShT+R5BEq5VJ2uCTyFJwRoja4NKo44K3NSa/D8gMSLiYzCZh8g7PU9jn/g/bBhvdC3KjBKSba2Juzu7rK1vc3Dhw959933aKG/Nm8M63+rps0kQK2NYwxbiOKIg4N9nnvuOV599Ts8fHSI1jVJEqOUYj6fU1WaIHBqN205le97DQlizHQ6wfd97t+/3+SenUEPApdTyTLXBabrQmbX0HN7bu3f1tqGUQpSPr4wZVnWwOOiR7ZbB2Otck5dVV0JW3sTrwlpm12opFo/zGi9NtZCgGjz0h9utKz0rr2mNRv53I3r2sue9o2jaaD/Ks+afDjdEbSSgfZ9IuJ2e0JKVFO33xEBe7B4fwsbqRjLBsTWjj5EfvHzjnS1mVLwfb9bDAAUfSO4Pt+2VK8Ve7EN+72ua0ytm17wj5csrq9L7xi/B0PawuqXvd4e2/t8+0Pv57Kvfah7qvehD3JGLjP6QgisFNA5YOALGAU+4zjizPdZGcNstWA4O8UXHsK6SoSyKtFliTFNP20EURQT+K6dpi4rlwe2lqoou7IM5XvoRryoFhpPKtI0JRqG7IzGHB4eMp/PEZ4iX5WcrxZkVUGUxEgk5SpnMp3i+y71Ffg+B8MxN65eY7o34aXnnmOxmHN4ekyR5w4NrN1+VJPD9YRCKZ8gCCjLCgT4YeA61mmDEq42PQgC4iQmW2WY2mmc+56HLxWTaMg0TLifzbGBz3mWcrZasjueYo0hz3PydIW1GoVgMh7zza9/nc/+6I/y1rv3qcuKQZSQLldY0bTT1S1XSLKqa65M91hmOfNVxhtvvs3nT3+FFZq9SPJjP/xT7MWu/r32BaOtKYERaOWt+QlN6YK0oHwfTylUU68YCIEMPCcPDei6xgrB4f1D/vCP/gyDwOXSkyjm6tWr/MoXPo/0FMlkzHm25PaDu0zHU4o0oy4rlx4JQgZRzDgZOD4VjbaDMRRpRn6+5MWPf4an9q/ja0gXK0yusYHijdvfJfZDBjtbDHe2Geuazz77CVbLFSZQ5FazKCS8v3D29zx+IAYbWrUuV+P79NNPY4xhtVrxpS99qTGSXEp+aaO8DW9eubKwp566yWQyJk1TPve5z1FWdSdF2SqjgZMJbTVenXiLuymef/5ZlFJkWcbZ2VlTZ513RtFpw+Z4nmwY3y4qb6HyNay+CWe37/fsSzfWCMPjZVNt2UhV6d7n19GqtW0+uw+RO8ShPQ4nq+r39vO9jw8LoXaZ/x7c3BL3PrD8zPYutXg83usbBMsmJP1BZ2UFGEGP9Pf4aM1m2xjB9jbcGs2yLFksFt13WoPdIQIXUhUI0bX+7Oox7WXG6KKZ/t7HZYb6fT9/GTwvLpn4D73/j+JD645l/Xt149+1wYt8/DDAaM3WdEyVZ2wNBixGY1aLJXVVsVzOefraM/jzmduyBaMBJUmLnDv377O7s8+Lz72A5wUIWRMNRpjaOEKfEpga0izDC3zCKCDyA9CG3f097p0+cjDzvmCYJNx5eJ+TRcqiqLh7ckIBDOMEtGFLOmi6LEuwljLNWC0W3L77Nj6u/4K1ljiOSUKnzuZ7gUNxxNo5D0MH+bpmQK7Zie/7nYKXteB5QecctLlhXykGYUgoPZQQlNqQVhWrIndSqsKgpSEZDsizFVrXCAn7+/t89rM/ynsP/ksw1mmz+x66SS1Sawcn+yG1rtid7PDlt97g3Qf3efGZZ/i5P/ET3Ds94f6br/LG2+8QxAlxFPPw+IRBEKGLEhH46zXT9NjbQnS5ZSEEvvKRwsOiEQgHZyvF1mTKz/z4T7Gaz8myjGQ0JC0Lvvqtb3Dl5nUOV3MW6YqTk2N8P2Bra5uf/ZHP4gvXetf3ffAkBtE1jpFSkOY59+89JPAjhPAYb02Z7hxwspzjjYbUyZCj42O+/eabvPr33uJwdsZ4MuETTz/PK089y/P717kynJIvP1pp0h+IwW4hWd933VIePHjAYrGgamob3VrXSgyuCTzr6NVSlq4GWhvDaDJmOByS5xnn52dUVUVZ1oRhQNmJj7hFYDQacXR0vGE4Pc/jmWeeoa5rTk5ONqLWdq1oyWlSSWpt2dqaEIYhdV139cWb8Kk7zqqqCAIHi7SlWf1o3KUH1t8Vou0EtpZ27X/XzZ/ZOK7WFnbva3e+baRvzbqWFeB97NZjQzc16W0035aI9SHyxmQ12xYuf9dcLNOQxcqwbIzYBeNre80e6BDzx9b2dp6MMQ3znK6dpbkkausTytz8OS1hjUVdYjkkbUrlsR1vbM/rsT67xMrFvEDvmK1c8wja+7B/rL8T+eTHkI8n7kM8/tdHfDwXp/S3m8e22mnKSyExGAaDgdOn9jy2xhNWaQaNGloUOcEOT7bdAX2SeMjDo+/w1W98nf3dA6bDMVIKBvGAJElI07yBnktsE60X85KPXX+KcZBwfHgIoY8nJePBkHEywEp47bvf5WiRc1rkLB7c4zhdsb+1xTQesF2XhGWBrjVK1igv4uWXXqZ+o8YPAoQnqbOCZbZCWri2v4ttqkGM0SD8TnCqKArqusbzfKq6IgzD7h5vJW+VVBR52ZV/KqUYxDFXtvc4M3nToEJTxmPAUpQ5eV1Q2YpCl2RVSVXX7O3t8YVf+zzZckHkKWohWGUZwljm86W7ty0MgpB5WjIaDTicHVNWJVEUMIxDqpM5P/JDn+Hv/uoXCRPFx59/gWf2rpAvz1mZEp81Qa9dY9t1Hxo0EYESHqYJ4jxPoiSsVkv2dl0p1Z3ZOUenJ1jhehFkumCWrSiWM0pdoz1JoSu8uuDrr3+HQRQxnUy4dnCFaBDiCdepTFp3TjbV4HtsXdnnW6+/zunhCefzBfdOjyh8wXB7m+3dXW5dvcmPP/MJ5umKo3TOGw/u8re//EUq4ZpcPXVwlR//w3/ot3XP98cPFBIHS5qmLJdLsixz+QVLQ0IQYNeGoYVm181EHFnk6pWrSN/BVKtV2um3tnln6C+qbKiHhaHrVx3HMWmaMp8vSNMUz3MEjj4xrK0hl8p1qImiiCAImtyyg6mMWRsRYMNgXLYOOsTadGzu9by0ueP1At8u+K3z0odU28Ye4hJIYmOBtPZDYpeb16p/8JvHsP7c2gnoL8jOO+lJmlwaxW0Y2ovHvLHfxrlpPih7pLUnfacvcXuZqVg7C67wSvRef5Lhs3b9ufczPxZLWwO2joA/nMjPP4jxURjoJzkeG1Exl8/nB22z/fdGntu6sraqqsjLkmQwAOsY2uPBiO1JQeD55EXupCKDAE951JXudMJn8zmV0aR5xtHREUEQOo3/wRCsoCxrFosFyvco64rzdM6Do4fUeY41mrrU1FVNEkSEUUhWlzx8803uz1YIX1KlBbmwVMJSVBV7+a6TAtUGJxDsApVVmjb53KBr3+qqZ0o8pdC93gUtecml19xi0W/E0yJorpzWI8+d/rZbtzSB73Fld4f72TmHR/cZjhS+7xH4PufLM9IyY5GnZFlKURco36lP3n7nbYrCsH/lKsL3+fqr38IGHmfLJTIMUFIxDGMW6ZIkCEmLjOloSKQU2XzG3mSLeZ7x7skp+rxG+wFJNOXaZEplLcpaRJNPxrqWqS07XwrZ5fKlkEjPQxoQxjqOgDUURcFsPuO7d9/l4fEhXuAkUP1hQiUMNRble8RB2KUnH52dILAEx4e8d/iAyAu4vn+FYRgxTUbEQesEWQLfY3dnh2EYs5sVDLYmHBcpJ4sFZ6dnrOYr3hTKOYODhN29K4wa9GKZpbxx+8738VQ9efzAIPE26ijLcq0qJtqbr1lge9Fl8y1AdMa7NZirPGc2m3ewUGvIjDGPwZtF4fpZ+35AkgyI4wgQnJycsFqlzffDDQi3tXNSSaIoYjweURaFg8mNeSLcK6X7fL+m9OIcXBRkaQ1371M8ySxcluO7bJ5/O6OffkCsjU2be2094rYBhcApvNF+rzsLu3Emtnt98/htb5+PORv9z9I7posb4vK56H2581vWbO73J4JddAycUtL7G6AWFXJG2z5+rv0b8wc42nn/7R7K+/mCH3bTH2TUpRUY7Rbq1WrF/o0rMFuhpGIQB4xHY4SxLJogYA0ZN7wBT7HKMgajEVESs0pTdnamBIEkCkPqqqYsKubzOfFwgLGWrMp5dHqILSt2p1sYIai0U90aDIaIIqcympP5gvHulByLqUrEaklVFtzKr5IEoetqJT2CIGY8HqNrTVbkWOUIkrIhO5VVgR8PXYOc2kHA645+68iz6yGL7WSRHR/HJ89zwiigrErqqkIqwXg4ZDwYsHp7gbe1zXAwJPBc84+yLpmvllRl7kqXGvioyFOuXrnF1s4uWVUxjGKWGJZVwTBQ+EHAMEqQOOi9NjU3d3YZBwHFYs7u7hV+5bWvsaw1aZ5i7t1DEPCTzz/NrZ19F9TQapW7slnf99G17silwtLB+0pKrK6xWuP5HgbL0ekJ7z16wKPzE5KhI5bJKMAqJ7/qKY8oDAmUx+npGZnSlGVJrTUPTo9Au+uwMxijd/fZm2zj+x5x4BMqxfbVK/jWBYsHp6ecFilv3rlDVpYcrpY8XC5QQjAdT7i+f5XxeIuB1gSzGaensw9553+48QMz2C6SVsRxvM6vNgucM+Du4XdiJoJau77TrZynkw0sOT45YdWQwFxbT9F5UtA3hOsbXQjJ1taUIAjIspyjo2OnxyFbr1V2DT2kbCRHlSRJBmxNpwgpePjgAYOBU89p687dNmTTa1Xi+4HrzOWOZAP27ue729Gv7+5D4+17HcT9WI6v+9el87yOitlYUZ+0NHaGVQgHp1vboB8XYPDOYNrNAHojyF4buJ71dg7QJQdwmXvSoQkAG01Z1t8xF79p17D8RfayFaKT5uyXXz223b7T0Bx/VxUg+qfj9nMx2hS9D11+vS47+Yte3YW52DzL93Ea+i6R7f516TabfdqNzz6WG3hse/2NrbfxpMNp379cCe/xC3DZ304kSdeaLM1ZLOa8MHmR2bnjFfiBzyAZILQlzVxvYi8ICfzAbUE57Wyta3Z2tpkMJ0RRxGQyoS7S7rk31rJYLPBjVy6lrXawcZmB3GI8mXB0dEIchSRRjFWC0WRCffcQ6/kID7QSrKqSbD5ntlyyk4yxGAJTEwQ+Lzz/PFu/sc0qTamsBuXIotJTrkQrCNG1Kw9r1wHV6mBLiadcVOd0LGiaBblo1VMeq8XSpbDKiqKuus/tjCeIumY6GrE1HbtnsUHRZssZoef6g6fpkunOLuPJhN/7u3+B7779Dvdee41PvvgiX719m7mpkL4ijEKSOMZrgg9PKg6m2+wNRgTWIkKP1++9hwpDgkCw0oZvvneHR/fe5H/xp/4M0iqMsihr8KwjjYVBQGELxyBvGnP4nkdWFARRgLWOcBhHIft7+5ycnrIsMgpb4wmLH/hkaUoQR3hNrqsuSqLIwxYVYhQRDGLCZn0ospRllZE+SjGVQVnJIEnYnUxQ2qKLEl3UpKuM00eHqOGAl289Q6kNubbMipJ33nuX1+7e5sHpGSp2ZXKhFzDa2XnSE/F9jR9MDpv1g93p8Zo2fgZPSTTGic43H27JZVXtelMboLKGVZ4/UVGtv8cWLkqSpCOAzOdziqLsPuUIaq6Ct26gV2shDH329/fxPI/VasXh4bHrUWsMQRBgrXUF/M2Cq5RiOp26vrpnp3hh1DgC7nwsrla61prtrUkn2amUbCDy1vC5k2+N4Nr5EFgrmpad69cvj7A3oUnaOX2/ITaXy343KZepsJ3Rbp2s/lBqrY1thVv8NY4xTWd8Re98NuPudkkXUj5uNuym8Wmjw1Zh7cKHEZ7sPq8QrvtQDymw/W32Uhj9dIi0TlnMarsBh3fGigs7t4+btRb5aT/Q1Yr39i2QvWvUOGpWgzAgLIIaa0ssNUa4lpEWEFb25gP32S5kdt6FaLd5idV+/A4Bp5zHY+8IscHFfx/voz/6bPzH3IfmE43uAUFHstK1C/OU8rGlc8DrUFBTUlYpiTbsBSHzQPLV17/DC8+/zFM3nuHs8JSVfMhxseIgHhErn0W+4lvvvo6Oan782RdY6BoVxUwHY3RWMggHVGlJvipYrVIW6Yobo2c5Oj8nMmAp0YFhsD1F5gGDOmQ6jokGMYvTnJMHM168uu+qS9IUYxQEATMV8Nq9Q37kyrOc2yXzYo491KwelTy4e4dw8CyJP3DNLYzF5hVZpBjritBo0DWmSMnLmslkCyEcXGy1ZjIcOpRh94BAhRSFa7kZJzGldaVrSggqXbHMVkyiEX/kp3+B5w6uMZ+fQLpkhWFV53jWp5QWz2jQlkoL3nztLWblCnNywrPjCeL6NV6//Q4VFbLUTIzFQ1BKj60w4DuPTvjZT30GrSW59ZkOJpi65uzwHOMH+FbieSHeOOGwWvG//Xf+H/zzf/Z/ynA4pJ6dI9OMvWhIoTWFtdRS4XshUQ00Ot6+FeRWkmmLPskYxwOO0jlWrsW4JIIoCB3bHFf+WdUVc1LCyZDKt0SRY+PPzs4plwULUXBl9xrEO7z1KOPb3/kKbx8u+Kf+tX+d43RJ7XnIKEJIj63xFqJydfajKGA6iPF9n+nODlWlKfKC5SJldqGZ0EcxfjA5bNYRpssx4kQ/YB35sF4I11Hn2ptf/2xuux9trj/PhoynEylpVcVaqVFn8Gtt8FnnUuI44ubNm8xmM05OTiiKkiSJSdPMiTIoRd3AVS0paX9/nzR1Ag6mrvHC6LE58DyPvb09zs9OqSrdnN+aWNfOTXs+Sq0X+1ZQYP0Z281Lfx42I3KHoK0dpSdfH/fddZTf5eG7I+D98c+NjbnlWlrAbEa0muZ6t/nl3s39pBt9A8IGPshcdMpxFoxcNzIRzeS091EFCLN5j/XJMFY4B6I79R4k/tix9v7sl9u56H79EdEId7ifnpPTzIcQAiU9rBUIucTzIqQIsNZ1LLPGJcilpGsN2h0/lqaA3c2TACtNFzkLsdlKdGOOWT83F09p83n7MDKlm89o7/JtTplo1Q5bkmPt+icD4LuyvEbKuKorBILxYNiV/qxWC4osxRrXgCeIQrKqpChyPCVJ4ojD4yOmo4TPXHsOygIrJZ4vqWrNcDzGGMMyc92ifN/n9PQY5Tl1q7qunWpWVRE2TkVe5BSy5nw1o9AFoYoRFmI/oBauimCSxByeH/Pe2RFbexN86aGNx/Vr19ne32O6tYWSgjxdYqqaWzdukKYrsnJEoS1a+QwGY3b3YqbTLfI0QwQBKJ/RaAqm6DrJYUHXthMfKfKcwA8adMxgMSwWCyzGsc1r6ypmdI3yXEVLuszAws1r13j1tdf5+LMvkIyGlGnGIIm4desGd9+9zdUrNymPHlJrR65SfsCbt7/NJ5+6SmUlSRwQeB6n8wWzdMVwO8EXHnleMFvN2TrYZufqPv/qv/tv88d/7vfy6Wdf4MhLCacjyvmcwEaMvBhfBZzWKVPhOphZAdpoPN/n+s0bvHnyANOUpVlrCYOQqiwZxgnpKiWOkwb1FK4Dm9aszs7xtzyiKGAUJ2TWIKOK2w+/w1vvfYuqqinSgs88/TQ//ZM/xc7WLkVZ8eDwkG+/9SazqmYlNZWw1GVNnVWuA2FZYliTXdu+Dx/l+IGyxFshkrYsCdHWIjfwNWv4tR/FXIwo+4vr4/taQ3lSukYcWZY18BGdUQK6Ughr3Q0dhiF7e3scHx+TZVmnMlRVpROWLwon/tJEbK0RnjXiCW2XMViXY0mcrN5oNGI+n+MMcFPSaF3063kSv+kHXWsXeWjzeGTXd0j6oz8/UrZd0eQ6ghSitw23vYsM9z5JT7aRrrUdW9xFyL15781z70gQvegqCILuE85gNpUAmO5rQgjqSzxT1QilyN5xthyFvlF0d08TVQoQ1jb12y4/2Pa2lr3o3Tbn5aL/teHsIwBe893VanVhrh+/73TPgWtL+1QrnNKD6Z3SVqu8ty4Na/kc7pgkZWlcB7kmpymbfuaelI2Gve2cHt3NbTMnFqzYJCy2/IP189QyDNb3RDsP6/O8zDVaf+f9hhCXe4dPemYFFk8KtOujiLAG04icKM9rSFmSvd09p9IVhMwXKWVRYJtWrEp5zM7OGEdjgtAJYKSrFadHx4TPfBJKF40KBUKJppmQh5CKJBmiwoDVao7wBMr30dbp8FttUFIglGC2mlHlhuP5jLTKsbViECV4tbvzPSFcS8oi5fXD+3x2Z8woisgqR4Z66RMfp64rlssZ0himwzF57nQZCCNqK5ktFzx4cN/1JChew5QVu9NtdqfbBEohdEFZVk6PXrSthyOiRqLU8xSqlhjj+j4XWeqeMWOodIkqFZXVTYpKYBq5Ul3WzFdLfvzTn6EyJQjLZDTEhorn6qu8dPVZPn/4gLxyamVFbbn38B4//6mPsyhzBnFMHA24e+ddZOi7Rl5WuGDICpQVbF29gk4L/v43vsxpseQf/vGf4d67d1DDCH+Zk+VO5GUSD7B1hRWC2rhKD+EpojhiNp8RRRFFXWEF3Rq/Jig3kYuEWmsqXTOMYmxVk9e1K40LQ4IkRPo+suFGKenx3VffQVcVVZphi4q4hpvjHcIi5e7ijMLU6Kqm1rpJiwqCMOha5v5OVID8YHLYgk6552Lbxg7EuwCdtQuzW1pEb5FuodnHd7OOPN0H2n7L/cj1IrnJNpB22+t6uVyxXC43Sq822266f4dhyHA4JMsy8jynrl307onN0iLPd63XyrIkz3MCv5UYbI63hf+bGkRpHezdj0z6BrV/U1y8P9bzuind6f7RdN665KbqkFOxOfftkBcg7fazlxOGOrB2Aw2QdJ1A6K5681HTGtDeWOfQnxBTb3h2G1vtnI3+HLTGvtt+e027evjNaFs25MJ+rraVbVwfgvt8/9jb+0ubi/3E1g5OR+rrH0tz30jhYW0b2TdnZJu2mk26BGGcQW3mQHRzKbo8ukBgrFhDWD2n5mIKxL27aWT7KZn+ix9U/925a5d87DFHu0FbXMc+g22MdMdvkU3ZoLX4ymN/d5dBnBCHIWladN3YlBAk8ZDq4RFVVQAGTwrmZdG0c3UM6OPjQx4+vMczzzxLZTXKKnSTxvF8j1Ew4PjsEI2TQm4FjAhBKsEqT0kpSIsVKpCkZcH+cExsfGpjkAKMkNTCcufsiJeyp5FG8uD4mNPlKSdHR0SDuHt+aqORXoAXKu48fMh8lbHIViyzDG0teZYhtGE2n7NcLdkeTfAxLFcpgeeIWqLHGi/LkqipdrFmLejjnEZLqWvIU2prqKsaJRQqiqirmrqsMFh2xxN3XZQgCkPGoeJKmXNzexvleaAUSnossxJtNNuTCfXcNUKpSs2jszNqnOPgI6GWCG2oipLT+Zy9G9eZzc954/5dJl/7Cj/24sc5nZ/h+UFncJWF0hiEdITDFoGqjeH49IQoip3AjBKdLKzWTa26MQinWkVWusZKie83mvHGqdmpAM8boI0mzQvmixwrJA/Kgi+89QaBChDGVfSUVU0OpNqS1xpdadCtYp7fRdWiSSN+f+2Pnzx+cJC4FL0baL0YtFFAu4j0X+8+13/Gm7XJvv+64aLXHqN7c59rMpU2FilV08C+5uTktGmz2YviWohZrvtZtwzPo8PDdSSDe7DbBdz3fdec3sJyuWyORdEunH3D3j+u9njlhRXzorG+rMSpPW9oI781/H9xO5cZH3CoR6dg5rbw2Pxe5kC0Rlg0lNMWqhNCNga7D3H3cuGXGP/NNMgFKLq9J5rI8eLxtd/p1MYuGGvbROE0i9llBrvtH95HbJ5E+FrX8K/fr43eSAn0W5V2TPtmf7qF7IXAUz5S+mBbKVwLwmA7lofoGWzbn4b13DTXQog1DwHacsB2ttZac7Z3XdrPumNdn2PnH31QToKmTv4JKJBtvPP2P9uoihmj0cZ1vTMN98E5MCCswfc8dra2SeKYMAyJwtAR0qoKJSVxMiAMAsqqQFclClcSNUqG0Nz/p2cnHB094tbTz7DKUvJ85hp9SIk1NVEcUhQptbHoRt60qmpsCMKTZFXOSmdUuiIZRDyaL5G+YqgiikZqWHkK5XkcpnNO5nNkpMnSBWm15NHD+1y5frUjxeW1JkGRrnLeuP0eyzxHY6msoTYaT0qk1qzKlPPFDFNXbCfDJsJ2xkxY0TVUKooCnQya+9c0IlGeuxhSUNmaIi2xnqCqXIOMIIopZEk2WzAejRkGEWldYqTA9z0GymOoPEa+56JJ30cAi1XOZDJmGMXMMx9rICtKTmZzKmMIAx/fSIwQrlNXWfHw+IjRx55h9+CA/HzOr3ztt3j5uedJVEguKlo97rqs3BrkK6x2fbmrusICp2dnbE23iGLX/MXUToe8KAqCIHDPO5YgDEmzFKU8EIEL3LD4YUhZ1cxmGVlec3a2YLHIqI1lFsLX3nu3swme8vGUT+CFSCHQpYt7WuSyDfK6Vsb0UnIf0fiBscSlkE25U5O76izx2njaxuNGtIanx3bujwZqvhjDbC6a60W+hQQvEozce+tIvCyLblvWrhfUPpu7LdtKs4xHjx6hpOwemFbhql3A4zimKCuyLO/2qZSiqt3n+32u2xx7e/iqedi6Bf4SI9s/7/5MtEZba7vxet9puWx036s1Vl4WTT55tb6MXe3gX9vGfbTR5fpDjTGzvTzzk7bbf61/wBfGYwzxXnTd376LQsyl89q+t4apNwPGi8e52ae8gcQv5pit3XQgWdeVt965tU63WNetJG1T4iicuZZWY4UilNCK6QghoOvStul0YG3TC7ghuLWwuRAbDq87hr4D9oR7zFyYiCcMKZ+EvvS2b4EWSbD9pjsGcI0VVHNObcQ8HAyaZg5e16iiKkuSMEKpgCt7+5zNZxRlSRKGGOCZ609htEHrijAMGE0mnJ6dMZ9nvPadN/nEJz7B/sEeZ7NjHj584CQ4Td2sQRatHfnNKEFWFaRlirYwHg65d3aOlTAdDElXK+arlEEUMDIxizzj/vEJ+89M+NEfeYXcVCyLuYNVrcL3I5AB7x2e8IUvfZnxdEwQRfhhQBgGZPmK/e1dRK2JVYCPZL6c89SV6wyGQwLlNd2+NNa49Jy21vVXNxJjnYJb6PuuL7WwVEaTpSnJdExVl1RFwWg8QlvLfL7g5eeeJxCKVfNcqKY/rl5lmKLADyPnuOQpZ/MVzz3zNEYbQt8DArJCcrpaoqQkiWJMViKkZJAMMFJRK8nf/8pv8oc/+9P8yMdf4fa9d/mL//5f4d/88/88d978LlIJvDikzPImUHMVHst0xflqzmg8JC9LKl0zTsZoo8lXK0cOXi64cnCV89kMYQxxkqC1JggCtDWOkKcUYRxxdHKPt9++i1IBQgQI6aNUwIE/RPg+WoBxERW+8jB5jdKWuIJaKarABZ5SSorSkZilUp2M8kc5fjAGuxftVtW66YXoR1nQyUT2h4s0LyyYze9eRrS/K2cOmhq/1WqFMZuGp100+ouT1k6LN4pDirxY14ZbR/pyOXC3qGR5zvHxMeAE5z1P0bZflNLlM3Z2dpqOY7XLF7XRdNOJq1UkstZuGOt2nTPWdgIusBk1rj+3iVa089U6RVI+Ds9sRuliI7IUFxZxbDPHXTQvuqj0suGOqXd1WsO58aHmf73oWjbz0t/3RYi8nYMPGp3xVB5ariFWdQHNcJ+5PO/UpkmstU4Z+P19lUuJJsLSqb61x906rBjbORAtz8EYRxLqyGECbJNrREqE8kG6Vq9ZVSOEq/Vvo1nRkb2cG+vuFUvbikAI07vOHUThzhcL/Zx1027ssrn5oOVoIwnwPtfLNWKwaOWa7phGSrhPUlRKNXMikFJ1jjXW3Vt15cg/o3jAYjbnxrXrPHj0gKqqGMcJVVVzbf8aRVFSFRV7u/tcvXmT1157i0//0Gf4zd/6Cvce3qPQGWm64MqVPQ6PMpQQCM+JeFRVRWFKClOSVgVZniGUYpwk6NqyKDK24pDIVxS+pDIV+8mQoqy4e3rM1jAmieBbr7/G3eP7TPf28YKYrKw4nZ1z+94huzdvIXVOHIV4vkdZ11BpVmczRskAz3eRZ1m5gELb9b1jjaWuyiaHW2OMg34NrkrD6Y7X1EZT1jVpkTOSU3ypHOnSWPI85/jsjN/3C78HW4GpNL5S+MpDZzlJNKCyNVlW8OjwkO8O3+bBo0f83M/+MPN5SpIkFEbw4OyIt+/eZbK3w954ymF+SC1cM5B0sSDYGvH87nX+zq99jjdv3uJ//o/9DxgGEf/c/+lf5c//6X8CARyfnzEaD6nzgrJ2/IST01PePX7Ij/3IZ4gHCbIhAOratUcNm8g6jAKiwqEXgZIMwxhjYTQYIhBoU2PqCs8TfOqHnmdv/wq11pRljVAeR8czTk7PCXwPhKLWFYvVGdrAeDphQY60kkiGG8TjlnCmlHJytB/h+IFF2ACRH6y7NG1Eb+vf7ULWwuTQRgByY/Hvf+ayRaSdSPc591obybfvA50Xaa0TIJjPFvi+6vph+/76IiilmM1m5EWBMY4sJoVo8teAgKrSTCYT3nzzu43MIJ3D4j4iujxT6yTUte5IYn0IuFugWD+gLSrQhxhNY8Av2rg+4rCe7k2De+mifNmEtpfjYpTaO15rHUCNfRzObue40+5usdwGSuon7tvr3M5R//z70fVFBKBFLYQQrp1e8xBtnEZ3rG7iVH/S2uMxBt2UMXSphA14eHOCWg2A9r0WZWn/bvAEp9bUGHCBcCmPDgVoiEASEC6qV16AHyaEJEgZEo4GIBQ+CtOCygKwTZu/tjkNLaxvGtla0cErogd5u/NqnIb+NAmBsI87TB82eti4H+wmytMO2dQWu2i6xg8DiqpE15paW+raYLQF6XKF1rhnJTGjZmGsHEu3YRCjYZgMGI9HLFdLtHFaCaZ0zS1838MLPGQQ8ujRIZPJCGNKptMhN29e4/z8lMOHD11KVNdO4UyXLNMFUbJiVeRoWyMFxF7IKNnG8zxOVzP2Qp9BEDAYxJysUibRNgke54VT/NoZjHjupU9y/zfPCeMBi3TF0ckpRyfnePEIoytsWSP8gCgKGIcJMp6gK40nJAGuu1ZVamaLOYvlEn84RiCRzboRhiGyEWRpf8AhFp7ywa6hW2MMiR+ys7VNkeWkqxX7Vw7YSUacnJwwHU6oMeiqJvJC9navUCs4nZ0TRj6L5S6rLOOZvSvce/eQkTckKypOVytUFDCOEwZBSF7knC5nJIMRYRiTr3J+5KWPM5mMee32W/wz//t/gf/iL/5bmCzjc69+lR++9RxP7R1w++wRA+FTm5pkMEQIwWKx4N337jLZ3mI0HnO2PEdrTeg5XfYgCNBVzXgwdB3AyorpZMTDhw+ZXLlOPnOs/Mn2gOBawOHJKadn5yzSjLPZktUqJfAjdFnjK582PRkLxSxbknmeW6OtE/+q65og7Kd63FrVNrj6qMYPxGD7vscwGTCZTIjC0LEum/faEzbu2VxD481Yr/cNLP1hd3qJU9Bfb1pIw+h1FFqWZbP/thbaUNduG1KKRsbUaxbk5rgv5IalFBwfHzelKqLpyrX+XFvj7HSA6056tXUmWidDKdWhERdfV+oCvItYH7OxnfG/SPD7MOtt/wZse4Rba5tIu43oN6MvB79uMrbbGmDnhDWRXQdx0oPJHfz0QU5Eb2dgaBakx0ebnhCAVeuUwsUhm5PtQ+edcW2iXiEk+L77/PsYbCnket42DtXlwmUjRtPC0GsSX7udNu1Bo5ktUAokitUq53Qxd4iO76ORRF7o8rzt8dgm72ss3X9NKZjo2pe2v3vecXPAjtSzOZ9iTRTonfeHe/r6TtGTomyXcrDESYiuC27cuNJp+ru8o3AQr3WtJa3xKLMCIQR+Q1Cqa5fzdk6XxGrL1atXmc/OOTtfsL29gy5rwiR0LTWR1LWlqmvG0wmDYcKjw4cIaZsKkQNu376NCiVCeUigKHOyIiMrc7TRYC2+UAzChPFoRFYuQFj8wCOwAlV6iKIiFB4rU5JZ0DLA+hEGxXyxpKwqBknCeLLF8XyBkZY4SVBCYStXuZItVkwGI7I0J4hDkmFCkZYNL6TtVSBRjWPVloG25VzttTbGNMZcOslcJbHaEHgBnlKs0gphYWdvj4EfcmQA7dj5Vgl05dj0uXYtgf3AZzwd4Uch28Mxj/wZSnqs0jn3jw7Jspz97V1C5VHVNVYK4tEQk5bk84zvvPMWz916moNXPsurb7zGn/0//Av8S//0P8u3v/Ql7h0+RAvLOBpQ50XniAxHQ6SUvPXWW0wmE6ZbE5bFCiEEURRRla7PeLpaMZ1MEQiWyyVbW1vUZck4ipgrgSktgQooasvbb90j1RYrA4QXoPwxVklULDDSoyoq6rJEINHaR9kIWr0MZTrxLKMtQmxG2x/l+MgN9pqQ5W6YfstIKQXD4dCpmwlJpTXRRtTcRhfOyFhtmlvNLfaOsm+6hc+2i5QU7brdGfE2B+e27f5ujWNrZNrj3Tx2Z2AE7WK/eW7NqSBY94luiLu0bMz2M+1ir3sRVwsUtI5I27S9quq1TnazBdE0LYB1CsEdu9yI2tr56+firXFEKsNmLriHMG6qjz3pejbbU0qBVE3ZW0vsWiMaa8dGrOewMXjGuA5puhZdnWLrkXTn2xmQx/dvrev5q41BiTUk3xaNtf/qjw5ebn4r2YO7L3gs7f83nBN7If8rXOOXNgK9LF3TjvW+rGMddzlag9CPG8P+X33EaJ12ECglCPyA46NDHtx+m7t3XblMbSEJIqxYC9LI5hpordEdBN/cl6ZHyhPt/DbX0q7P3eKINq0Ijmi8LcEagWqTT25+3ed0i4y0kbu1HSLVEp6M3mwJ2527FIShz3Q04qkb+81na1ev3j5RVrpaXCGorCWvS6IwwFOONKitxkjwpCDLS0ajLZQKyEtQ9X20NFjhJEKFEGhj0FWFNJat8QRjNGVeECcR2laUpiCQAdI6oqSjT2pHRmsUGaUUjAYh18ZT3nh4jhUeQngIYxCeYLlaMvJjFoVmnq24c/IAL3JqjYt8RSAUW6MpO9t7mPoeWVbiK4fWKWuRBsaDEWhL1LQGxViGg4GTO1WqcctcqsA9dHbdBtcYV21iNUWdk8QRpjZUpcbzQ+rKoIxA+CF5WSMM3Ny/QlmVzbqrQCgHq0uPAM2qlk3zI8tyVTKahvgyRHpuvcmLipPFkpUteGH/Og9PHlLaGun5BEaSodHCcJanPDg74WC6zSefeZ6//9Xf5G//+q/w2WtPo8uCs9MzJgfXqH0PkWVIY1zEHsd8/a3XCScjnrt6k9nJGSubEkU+9XLFaDwgzXJGUURZVhwWOZPBkKqswHPNXpbLnLfu3EXU7j5KBiOicIgnQ4wWrITra17WlUNZmiXD8z2s1s757YJNQWWcjKyVCmEFnoHqEmXG3874HTHYbrSLtcv5tgL70+kEPwiYz+dkq5xkkDR5QdERrqx1i55ujHc7UW3E6nl+k1Ns9onTr14vSz1Wd3NMUjgxlDWkejHi7jWIaBYtKQRWyh6Ut44wjHUN3y00i2KzDeGOqDXiIJyKEc3fzeLZqp61BLOuqYlcG/tOHEVr1wcXtzgoJTtot3UaLjogDm5uI7l+1NicbwtF2zYKbhfli9cTaFv8CQG90hDbi+ro/btFK6R0yl1aawJfUeaicar6XkMvsrvkPuojEUbQwcaitTHN5zYRXNH9duVxjYPTcxT6+xBSuuXObs7fRjTYQjKdQbtcWhVcdNu6ELppYCMbVbK25297P6+dwLWjR+MQtPebbQhiURiQLmecHj/g/OQBg+mAsq7Ad72RpVTNj4M+q1anv3EqhRDUrCsFwJF4XDBuoeNDuOjf9z08z++i/T6S4lAhd08q5WR4hRCdcW4dpVpr0tWSVldf4Oph2/aIprsGThilrhWxZ1AYsA4Gd+VdjglvjcAqiUawKHPq0xOSMCQOfZRsZI2FxZNQlCVxOCIZ+ERJhllm1FJT6tLVsnsST1gSP6BKc3Yn2yzTJZ5ShGHA0dlDrFejhYdCIYWHrxSRL6mqrCFxuudxNAp4arLNW/duo63CoJBGIDxY1CsOohFJ5bFIl3z3Ucb+IMaTUFYlg3jINE7YjQZk011OyxNq1TrsEoxhZ2eXk6NjhsMhQjiN78BXBJ4ijkKHMFnj1ixjsdql9LQ1rjbdWrSpKHWBLyVCW4SWqMCnqi1SCwhCKgOB8Li5s88iz1BSoALH+pZW4gUBvslYlYrJeESea+4/OGVvf0hVWzxPgzVkRc0sLdA+vLh7nW+9/nVyKjw/QqSaWlR4UYAfBrzz6D7nizmfeeZF/sBP/Rz/wd/4z3jpn/rz7A1HFGcz5ssF/nSItwJdlgyCgOl4xHG5Ij6v+djeNd6N71DXJVJaBlLihT5ZWTCKItLGmAa+a1aS1ZooGmA554133+GF7ats70y5crBPIAPKtOZ8tqTWmizPKCuna648D6ksgZRo7XpJiNo58dYV+6CksxnSSPzakHofLm30YcfvgMGm689qjG285pBnP/Yxtra2ODw85P79ByyWK6bTMVEUOVGMhqTVLghlWTWsWPe3k+HUJEnEwcE+QRh2IhZtJNwWq7vFxEHQSsrGmKyjjm5RaoyqUi0j1Trii3Zt6jzPayCndmV2hjYMfCdp2sDE7kEBv/Fo21xz6yysI9/Hw7IPzAO2TotdOxkt2QzooJj+/Le/2wD8Mlimg0Tdr0veXxsTozVVY9CMblXZBLT0vgvGrX1fCNEtyG1N5PsF9RKcYbnQ/3PNqNZdxN++ftnYmFPjHK+2A1D/2PqGvTXC4sJ21p9fExG7998nynYCNTQIES6v2n/PbaFDfS6er7W4vsxSNUQh12nqY888za2DHYpixWR3SlbkeAZWaYqQzikFyPO8M56uVMoRPK20KKlc/alUSOVtpjRsE3Xqqnut5QG0z09ba5plKwQCz/fw/QDfDyiapjhtD/bVakVtqo5UKRsugNaa0A8oi7JpWVuTVyVJNGB2fMq9e/cYjUYMhuMe6iCpdcUgGVLbmvfefY+yzHnlU59ounL53XWx1uArhTQaUWt8BOMoocwLlA+LNMULfZLxiFc+8QmM1mxvb3N0ekxW54x3J9S1dqzgWuMFAaEXgnXkVSUUnvRcl6ZkQOBH7E63CKOQQtcIKRkNBswWGbnvI5QgDnxqbfA8Z/iqyhD4IZPhGE8qZrM5zz3zDHdQlGiKsqQsSkxdka2WTMbDpnTIcWPSbMXTV55jPBpRlxW60i6V6KtOGCjPs6YnQqPJ7fvce3TI9nQb6Qc8Oj7C8z1CP8STitFghG8hjgfURd4FBh3KZgxCemhKppMJs0XGMk359HM3OZ+dEQcxVeVy68t0yc5oQhBGnGcpVhs8X1IL14RpOJoyKGqmwykPiwX/zq//Xf6l3/2n+Gf+0f8e//pf+/f47//8H+QPfPrH+OWvfpEXh8+zPZlQacfu3tna5trBPrOzObqueerGdWbvLDg8PubHX/oUv/rNr3Lr6nUKbahqzVY85OHZGVf2rpCdZXhY4qHP00/v82d+8U/y1//OL3EyW3BvcZ+j/JyVqngquo6lqWyRrcPunFuBQCiJrmvKvEQOQwaDAXVeUhnt+CnaYp4gGvT9jo/cYCsl1403JERRyGc+8xmmkwm/8Ru/wdnZObU2XeSY5/mGEIm7QTZr11xuwmc8HnPlyhWKoiBdrUgbgx0EPmVZUVWt4ZYEgd9Frr7vkyROkL07cc8H1obPWkvdqBi1HX7yPMdqSxz4iEaCtCyrTn+8iyItWKub5vKt1GkLxzbG7AIM367RWlcbkeoarV1H8huRLJsGqb+tC0hvZ+TbXGCfHd+ugk8IErvROk3COmhNa930u15bq8vyzZ1R7Laz1smm93qbx+5tYeN7tjdnGNvlqi/mzftjAznpf7aFx5tFaBPCf9xYb5xHC7E3C2ELpDxptN+XLTys1/dZF1VuoDxi41g6CNtqdNM8SQk4PztjdXbEannO+eKMtEgplrlzfJVaH6NYs6jXDo9Bly4XKJRqiJs9cqMRneNs7LoHerttoCuN6ZANpdBlTW6zjXr/umHsIgSj6aRX+WC76pDQ8ymbfCNSYATUuXOWV4tjtw1tCMKCIIqJQh/hO0fh8PABy8U5YLHasD3dwpMBURhSVRWHZ0c8PR6QhCMCpUmDgIOtHbKswCLJrCWyAs8qdic7UFmuX7vBuw/us8pXSNkIYDT9kRXrntNJNCBUPld2r4CuCYRPXZRMBwMQMEuXHIzHHIy3yXRBtsxJhjFX7ADm55wt5izKitliwTMvfoytZIgsNWWRMxkOmE5GlFVNMHX64A/vP2SUxAxHA87PzxmORwThiAf3S8AhfUbUXfpDCA8pJGEYkeYrx2BWEul75HlJvkq5evMm2zu7VEVJWZZMRhPOzk4RAkbDEQqB036SHTrjeQpPCoo8R/o+cRhzdDIjz5e8eP0ZTh/N2B0OOZ2lHM1nzLIFL7/8MYwSPFzM8KOY2A8pa9dtbWsn4mj5iFeuHPDJybPcnj7gf/If/5v8J3/+L/BP/Ojv5le//mW+9M6r/K//6D/Oq++8RbK160h1COIg4qmDq3zl4SF37rzLy5/8BMsy45337jDeduS5rcGERZairODm7hV+67tv8Hs//eP85te+zs//1E9yS93gr/2tv85f/Wv/IV947Q2M9IkHMfEwYpwM0BVOnZBGp0HKLu3apgq0rTFVjbSuAmkQhGirGQ+HXBlOKc4ePHmR+D7GR26w2xKtyWTM3t4u2zs7PHr0iK98+SuOJY1bv5rUWKfnDQ4+N8YZ+yDwu+jW9z12d3d55ZVX+NrXvsZsNiNJEiaTKSen501Dd88tNI3KTbt4BIFPHMddy7m21Z4xutnfWjN7vU8nKSot+J5y/a6rCqkUURSQ52V37F1r0GasEQHTtEK26E4T2Y3+wty2/esmhrXhXbM4H7cMF6PDfq2rI5dsvnZxtJHfB+WwW2jT5UUFSkiM1E+I2h833P2/LzXsWESvCLhLPLQGT4guPaC17srKEI8f+GPENxwZTVhHVrqITjwW2QrRRfl9Y66tcX17+8f9flDBxWHX59/Pj7dkocab6xqltDlzB7G5ek6brmVioyhCVyFYi++FxOMQr5F9bVM3LaLRGkpHzvKRgQ9tHho2lO6MBtuojCE9hsNhl3JpDbAQTk/Aa6RzV6sVRVEwGAwIG2MZBFHH5VCeclGRkASBt+YkNOzdwWDg9m0ttdGdgxB4IXmes1qlICWjyZTFco6Ph/IVnrBMRxN2d7fxhMfu1g5VbciLgqOjIzKd8d7999BG4wsXJe/t7JLlOe8ePmBZFGyPxkSex2Q0BusQpCiKqKVBW8v52ZzYV04hzgqMtmhciZ+wgmv7V0lXS4rVCl3BzniK73tkVUFlNHEYcSB2eHB47CpIrCKUCqPh/tEJlTYkQUxdVti8wpc+ebZiMkioa0jiBGMtoVCMxgPKqmIySohjHz/0Odjf5vzkmNVi6Z6LBjK02lCZCqnWPbIRID3Fw4ePGA1GJEHEQCmevn6Td27fIY5jTo9P2N3aZmu65RwWFeB5tWu+0qTygjAiL1ZMthLSNCcvcvztKRN/wCEnBCIgLeecpQsqk/FDz73I4fk5y6rk2mhK7AWcFplLCVYVMvR59d5tyqzgZ178JF4Y8Of+0r/I/+6f/HP87PizvHn7Lf7tv/ZX+e/8I7/IapYRxKF7LizoVQ7acOVgj8T32RmOOdje4ztvfZdPPfcy2hiyomR7NOFgssPpV77I1vY2mVlwvDwD45HNNfsvPcUvvvgsj06O8WVAKEKyRcmrd991qFCT+mwDr+55wVVeGOsc0VGcoPMSXZdYv0TYGvGBnZa+t/GRG+wg8NnfP2AwSNDGcOfOHZbLVRN9ms5TaxfNto64zZGBg79dtCwYj4eMRiMAvvnNb3J+ft4pZhnTh53b+kw6CHt7e7uB7nIWi+VGhGtMW0ftFkZtLEIakjhmNBoRBD7pculY2ThoV2vXyavtqrVmAQr6da0djEpT+1rXHfzeGtE2JdqaKOG6STwWtNnOePRfu1jC1P5+XGb1Yhq2+Vf36+L++tFtuw2sI7TYNhq+xFBtEN56RukyUp/oH0x3zuuTN647aXdt+8eGXZcpda9dOIb3G31Ivfu7v32aF2xjzASgf/t5qP41axe/PjTeGmyXZqEj0VmHwXXb8XyfIE4QugJfII3Gt1DWLjfdV1kTQnQ949v9F3neHMP62iulGuJSWyVh1jnlhvPheZ6TosTloFdp6rThheMJ5EWBpzWe51PVtZPwBBKVODatdblyrTVGa0TgcIoWxq7qGowm8AJ8z8P6tsu/C+H6mFV5Rl5U5FWJkfDUzRsEYUCSJERRTL1MyfMcpTWEkgcnDxmOhoziEcpTTLbGPJyf89o771ALyPKMoR/w9PWnXPmO56OrmjzLqBrnJPQDtLFOZUy6NILWBmEEgQqwQYzUljBK2JvusLe1zdl8jjbgewFbvkfs+VhTU+Y5piwRVvDo5JjRcEisfKQ0aN+ihKDMc+q6ZhCOGcSJu0BbmiBULFc1cZQ4Bw4ngmJ91yTFU54zyo1zDRAozzmAxmKlm8/5csH+3gFWu/TfzmSbB8EjTs7OmS3mHOzukSRJd91NbahERdHUOQ8HA8dHGg1ZpSnG1IwHCcpIjNUo6bPIcubpEikszx5c41tf/S6+HzpxGwFFXZDEMXXpWlYmfsiDxTl/91u/xc99/NNEP/f7+Ou/+kv8xMuf5LOf+CFef/01Xn/jDT423sOLQ4SUJGHIrf1r/NY3v+5y7FXN/mSL5596hi9+62v8sd/9B/nGO28iLAR+gBcG7I4mLJYrBlFMWhQMBzGf/qEfRqL44Y+9xN+/9znO0lNO8ZlnrkwwThL3/Bizvhf7glui0WtXiiIviZRC5xVKCibjMfXDu7/ttaM/PnKDvbOzQxgGVFXFYrnk+PgErQ1xFGwQvtrR5mD7RlspgVIek8mYKI7AWhaLJVmWUlU1vu8OuyVitaNdED1PufzXYLCRyxMbn2uMNaKR7ZMMh0MGSUIYhrQdmiyuNlfgykraeuwwDIkitxi6KKZuoibTGWPR7KM9N9iErx8jEAo67lYXkdGy7uFD2KONeWidhNbod7sRF6D07vXLJEHpauza/y7u53sdfYO7Jryt33VNPtadsdbHfblkaf/9i0M0Rndzn6ydhubirH2ZzQjXGbbH89vvNy5G0/3j7Dsza5/Fbb29Tq7NZHPPGAO2jcSdzrbVGm1Nh0y4dWMN+TuEpSW7reu/od+2szk22dRYN8x219FNrJENz1UG9HPYla6pdU0gQ8Iowm+g9zAK8b2AoihRvodqc9kN7N6WXWFxTRcCH095G9e464BX1072srlX61qTZQUegqrIsZ4kjiKS4RBP+ijlkLMiL1HGQihZZEsMBuUrBBIVBhQnR5zNz7GBj1KSge9z5eAqPgVBkjRIHU7lUEAYBFR121BEopRPXTvHQloIvQAvdqVlw+GQ/a1dZvOl02KQkmEUMx2MXI9pXSGxxEHIMl+xPdxG6FZzvK14cA6x1zhRUgqSKHJdw8IA2ehU17oGaxmOh1S6ahxhgRIKoQTKNkqSzX1ljEMfLBCGEVZrLIIkjBgMhswWc0yt3ZzGseN+SOmMfdPwqCxzPE+SJANC3ydr+Apbo0Eju+u00GerjLTISUKfK6Mpf+v+PabJ2HXSqjW1rhiOB2RpjbYWXypqa3h3dsLr997lx174JGfzM+4dHzKOIn7k46/wnXu3mdmA4c4EYQWRH/LiM8+CscxXS3aTEdujCU9dvc5/88XPszueAgJfKjCWrC752LWbPHj0kP3xHnVWYsOKT7zwPN947Ttk85zZ6Yrj5YLS91x7z57mQ/egsfl8OfgMBI585lj6Ft+TDAfxhv7QRzE+2iIxYDqdcn5+zr179zg+Om4M3DpKdHm1TUZxa1TaqDcMA0bjIfsH+wRBwPn5jNls5gQT7Lp2uSwLpOwvkCCEIkkSdnZ2sNY2Pa9dM/TA8zur18qGItyamCQx165dYzweA460A2v9bqHkRkQ0mYw6GLCfE+3/ODTBR0p1IUJuD2OTcNRHetvcYn+0Ds1F0lR/3+stXYoab4zmdntsG5tGcv2e8yYuN5gX4e6LP/088UYE3/40b/Qh2ovG+vsZbc64Y2H3zrEPkZvmc60hMxdf7ymUdSfyhPHYubsXu5/2da3ds6CN+2nzzNY2vbKbWbA41rbWLveX5Tl5XlCUVSc560hfrtvQOo8tOv30VkO9hfja362SWncdmp9WbCOKIsI4xgsChFLd9UkGQ8IoIhkkjMYjBqMh060tBsMhg9GQ7e1tdvf2iAcDojhiMBi4LlJxTJIkeL5HEIYo3+v+nTQG0+XibeO4t00UNEWR4XuKOHFywHmaNiVHAiskxro5rWvtyrsaEZUgCPADj7DpYKU8R7pblRnvnR4yy5YUVYXvKUbDoZMCzTKwjmDnN88w0nV9q+uaOE5c9Ob5xPEAK5xB3x1vIY2gKCsqY4ijmP3pNoM4QnmOWzOMY2oaAmxVY2uHOmhjCZoGEi0KCOAHjvQXBiG+5xH4Pr7nY41lOBmR5ilpvqIoC7TVIISTflYul02zryzPGA4HzgnSBqsNEsVoMubo9JQwDBkNhkRhBKylkqUUCOmqH/IyByHwrKXUFVEUsDsZO4TSaqQfcLJcUNQFO+MR0yDknXv3uDLdwfd9iroEoxklMVprojhGSknsBexu7fA3vvEFDk+O+eM/9/tRVvD5r3+Zne0drlx1MqNau2cyUB7P3rxF4Pucnp+hq5pRlLA/3WY6GJFnGcMoIfZDirJgtpzz4tPP8O7997ixfQORG+pVyvWdKe8d3+fzX/s6d89T5pWlMoZAbZZ2XlyHWk4NDecDnNOGdU6X7ymSOMJXPh/l+Mgj7FdffW0dDQqn/gXraNjVdboHKwgC9zD5iqoSnTFOBgPG4wn3799ntVqh6xopBatVihAulwc0N5SrFS3LmjiOGI2GDAYDTk5OOD8/bybYds1GJGtN3LamOwh8rly5wtWrV/nWN79JlmWOCCMEdXMBqE1zfIrpdMx4PKZqpBBdDbXF85yCkm0icYQkCBzRrSXw9Bf9tk7SEc/6xtqhBEIItNgkpH0Yu9UGju2/+4a+PZbLHL8No9v8dgpUDT+gi9TXZT7rY9s0iP0cdxc1iobs1BxUB4/3jwF3U16MFPvH395blzks/dw+9FpdAkpsOkiPpxbWUbG1Ful53UR2hrcZLQfgssvR5qZbwyeE7Bw+wVozvBtyve32eFpCo+/5CCs4yxfkRY7yfUJviO9JZKAwAiLP71r8tUSvsIHCW46Fcx5d3+T22rQcDjoYVTQCLM5sl2WJRFA3JEslBH4YYa1lMBi4LnZNvlyXFcvZnCCIKIuCwrpcr+d5rNKUOI6xgq79YF92dN2URpJlaRNhV1hcJYaUkiSJiaKQYuXej4KALM/50pe+xM/+zM/j+wFRHDMajxG+YrGaI6CTK7W1xRMe1tRMxmNyXVMJTSpr3n7wLgcv/JDju4Q+AsNiOSPwfdIiI4kG2KZxi8HB96Hn46um2YM11HVFVuTsDMaE0iMvCs6zjBtSMo4SfGGYZzlpnWJTjZWQFynDMALp2MTGrlsAa2UoqwohA5LBgNVyjrUWT7kI2zUoiihNxcnpCUmYMIqdoEhVVVRVhfIlyllaisL1zd7b3nNEP6uQSLTRjMdjHhwf8vyzTxPFMUYb6qrC90NXFYBzEqpKOZRzbLBFiQYm4xFXdnZIi5zY96kRPDw9xQDP3rxOjOS9wwf8xCuf5EF2Sl7nBFKQ+I5wuDMYM69ypNU8H+8Svvwp/lf/0f+Fv/Rn/mf8k3/oj/L6O2/xp/+5f5q/99f/Jr9x8l8xn8+JwtD1HC9LtnZ3GEzHVHVNkWXEns+PPP8y337zdZ59+mnuHj3icHZGUFe8cut5zuczpqMJhXZ909Nlzue+8mUGw32GO3uuoYwU5MenBJ5ECYestgGnEm3psXtOpKfwrCszXixTxrFPURb4vmJ3Z/sjj4h/R5TOnGFrILiGoex5bX9SaPsot9rYLZN7OBww3doiyzLeeec2Li+93m5L+PI8jyiK8H2fxWJFVWsODvYIAldWcvfePYqiQqmmFtw5Qr3js42BVYzGI27cuIExhl/+5V9B65rxeEQYho1MaE3dNF+IIpcvQwjeuX2b4XDoPPDAp6xKtDFUtSEMfQZx7ERGTC+KM40KlhR4nlNTclChfqyrSxehXZhXh0JsGvGLhrz1zNvX25KaPtMZHs/ndvveOA6L8hxRxph1Httt+3LvYSPCpJ+q8NZwc+91KzaPpfuuBdkY7PZesbLnjfSuZ//f/eNqI03P87t+3FEUPQZZm8YBaEln7Xcd6cwShAFGa9LF8tJzvuz8XRS/voe7fdnN/L7RTRzddxaaa1aWNbV2hkL6nmPqIhCE+HHAqshJVzmIRnq02WZbXrWGsmuKIkNphR8G7n7oEZWQjbJaQ3ozTUesYZNWaolrjhOSkWcZSRy656MqOnZ2njq2eJuGmk6njEajjfluI/20aNjtge8iPmMZj8eO3OY13aa0RqFZriqU8giDAKSkEmA1PHjwgLrWvP7a63h+SBTHnC9nLo9oBaaqEcaphaXLBXe++zZWQhT5WAUpNcfzc4ywzM5OOTk6ZjGbE05ipHQRdxQm5FlOVTWNP7Qj2yWxixLLqiIeuXzz3njKdDghrQrmqxWVsRTLFDsM0dRoWwPGdVprc+O+pDA1Re0Y+Kos0dbiha5OOcsyPN8nLzPKVcZoNGYQR8zmS3RZMFsuiMIYPww6Rn9RFPjCI4xCxFyQZSlRGBHHMfkqwwuSbh3ww4DFcsHu3h6jwdDlweUapVFKEccR2lSs0pUjf1qDlTAej9jb2uLsZEESxyyzjEcnxyhP8eytp/CtZbZcsD+Y8Cg/o7KGKAjxEOR5xq7cIjCwKlLuBmf8wqc+w1aU8K/9v/8D3jq5xz/2e/4Q/8m/8X/nf/g//rP81f/jX+Y3vvpb6Fqzf2ObUHlO0awoUBPVRL2aT7/wcf79v/3X+QM/8/OcpUuqsyNCfKSCKI45rc75+r23ef2t24hUc+3W8/x3f98/zL1HD3njndvce3CKwCce+DgZxTXvRkrhZHJrhyZUtaYoSvyRIo4DoHZCNUISRyHT6fQD14vvZXzkBnsdWG3mJt0i1BrxRp9KCPI8ZzBImEwmJEnC8fExx8cnjTLWugZZytb4b+bnpISbN5+iKAoW8zl5njvvsk9aamyEEa50RGtLHIdsbW2RJAmnJyecnp4ihW2aJ6xLT+raoJRgOBrh+z5VVbOYnYN2C1pHSNANUUfatWa1MeRVTpIMkZnsIh3f94niGK1rsnTlhDaaifKUa9UZRxFFUWwKhxjTBD/S5UxaIfYnjNaOtcQ+xzIWG/bORXy9ccFJMNYSRSE0BqVVjBPuzY61bcXaWPaNeotuCAlFUbp56hnk9t5wu26iYprWpFJ2cLSU0vUlNuax6Pr94PLOaGGbhgcVerV2jqxtBGAaGLEjnLWpE+maJYwaOcQmZdWb5DYfvr4SVoiuAY0QbectVzPQQt+yd/6PDWsb7WqNFB7SSoQG6hpPSaosZTE7g6XASkHiRU2Zlt/NRRCEXeqpLbPyfUue5UhhOi1+Jd0xCdUgMwakUATKiQwtVyuqqm4EWZxwhZCSNMvICwcDS+FIZw75cgt8EAVIIRxDHO9Sp0ohqHJX194+057nEQQBUkl86eDEjtRTlE4MSGsCYGcw4qf/oZ/i7vExb915h8l4xPUrB4ShQJWKZb0i1xohnDrY+fIuKz9DegOGShBKD98ozo7OWeiaeDLm4OZVtK/JiiWR9Fmdr/BGAR6OdBd4AckgwfNclN+mTKqiZpaumMQDdqZbpEcPOTk/xwCLuiBWMTe39hn6GXfO5nB6RDyZoAmJVUCB5mR+zDfffotPPv0Cq2WKsobI81AmICsKED7xKER4PtpYxoMhv/Xt3+Sl514kVMrJtkpJ6PmkxjUiGgxGIB9yOj/hmetPUa9KlOdjJBhRo6zl9PQMP455au8q4yhxTTSsIasKpCexwjWh8f2QwA8o0zNWyZSt8dAxo60ko2AoxpS5ZZaeM5l6vLh7wJe+/F2G/z/m/jvaliy/78M+O1Q84eaXO8dBT57BDIABBoMwg8gggAQlUiAsmFy2KWqZy6Ilyl6WlizblL0oB1KJokRyAYzQshkAEoEgCGAwA0wO3dM5vZxuPLHS3tt/7F116t53X/cAbJCsXqffPaHSrqr9S9/f9/vQgLWNAcUVQ7Vo2NnMaOYNtbCUqcKVEuW8k/lPPvPr/PHv/yFi6/jKy9/g2u0b/Gd/8s/wn/z5v8B//bd/hh/5/k+yXCx48fU3+Mj7P0AxW2IDe5lVAmMUmxtbDFRMYy2x0mzEAxIZ8fybl/nYB76NZ197nvlkl+94/7v4oe/5FDev3+Qbr73BwdGUxmoG+TqpjCnFDImm0RaqBoylSCwpgsIYXNMQCYnUKRGOptyntjEySllUBVcvv8J87/C+c9PvZXnHa9htCrb/nmOpR29knHO+b20w6Lzqu3fvcnQ06SKbrn4SAoFW0NzfPBFJkpBlGU3jUYxtRIw7OaH7/ZpAvBFFmp0dH5HPZrOgolWtZBBZpY61VgwGA+LIC8T72lZgupJtnbmtn4vOw62riqIsAdml/5TyNTAlFdZ4IMfJKFpKD6apqqoz8OAnOdUxWYm3qaGefH/aj8N14ASj14k0sbNu9epFgKf+tvuz97vgU7SBXL92zH1efd1vQa+1zMeEiPa/+/sq3T7a1ruWptNYi3G+bccDsEJ+PRjp9jzbbVjnyXFcq6jCyjB3zmh416Xtew/BKpV/HHfQd7V86rzT1VoZf8exfRrT+NR+B4BSKKWJIt0rt/h7sapCKrh3fZrGkOW5r7URDLlpaJo61Lp9tFCWlUd6GxOMfhttecY1pVQHvIzjBB1FRFHEYDBgMMiJIt3d61F0nJilLylqjOm21X4HfuIui7K7/9tnXylFbQyN8xV+Zy1YyygfdngUJ6AxjS9BKBkyX55E6Wh6hNLKt3rFmjxLSaOI+WzGdDbj+s0bTOezjjipqCp0mhJnOTrNibKcbDQkGw6QWiO1JorjgGPRTKY+YBjnA9aGQ5SSFGXBZDFnupgym8+YzWaUizkb+YCDwwkGh1QK0xgOjw4plwVl3RBHMZGnbsNUDVpIIuUVtSKpSOME6QSTowkb6xskSYppDMVyiWmaEEx4MtW6aZgXhVe2EmGswxhK4SVJ1zY2ybMcLZTvOw/Oq5DtvevvNd/O11AbSxxFpFGMFp4FUeuI2WJO3VQMspT10Ro3dvfY2djAOBfAe4pRnlNUtZ8La4swXsM6iRMW2vGPPv9pHrv4EN/7zLcydjH/77/1P3Hx4kUee/JJXnjxJa7fuEE6GGCMIVJR127YLlmes7WxwcHREZHSDLIMaxx13bC9ucWymPPYAw/y9IOPMdmbcHP3iN/+xgvcONhnVsyp7QKZtPgQ0zFZEuaCEDGABCclRgmWpqaovLaEVprzZ87ygWeeYfQO17B/3wz2ScN97+9c98A655jNZuzvH7BcFj6KNrZLk/ZTim26XSlFFCaK+XxOEdohTvYsn1w/iiLW1sYopSiKgslkynJZhL7VVVQIdKmgNE0Dy5WP3kWv5thuvz0mHeqevo7UHGOyapGfAHVVUVd1ry3Mozp1FGHsiky+vTdafvWVsT5haGnP8XjGuD3347Xse4FR93u1JDCnGf1OYcvv4dj+O5ve/XhV8/VOymlGLaSlxUrh6th3vf8fs3bfhOE+5li0mxEEStv+wR1/iRPv+7rRLoxB9+ofjjvtPhTH1u0ffre+ECuymN7vWnAlYWyUUh6wpSOkUKFcYrtsSpvK7I+jlIGlq8dudrxM0r5fHbMKacf21bKctdtpqVCV0iRJSpKkgTpYh+ch6jIk7X7abcCKnbBlR2vng/7La9N7oQvr2oxNqM3Ol2yujcmTJCDbfXq/bmqiJKJqSsq6pLYN88WcPDjDQslQT1dIJTg42Pfayk3jnQLrcEoh0oSDxYLbh4dcu3uXyzeuc+PObawAFXmDHUW+T7ksfUfK9uYG58+cYX00pjENZe2PYbqcU1QliVJs5kMOZ5NQHtM01jBbLDi7ucXRbOp1rIVvyVJSYpoaHbISWiki6fmsnXGMhmO09vNGXTfdeGNBCUUUxcRpQpJlgQ+/zfisykfjtTXiKGqnFlQn19a5x0jhr7MJ2cdIahKtiaRCOV9iPJgdYTEMsgFZNuD6/h0urG8zKwrKuiLR2sufloVXwapqhPGZOqMESZryxu0b3Ni7y6Uz53n3I0/y5o3r/Mqnf50Pv/cDuMawd3TIpFxSlSWxW7EUWuc6kpiL5y9we3+XNI4ZDYZUxuMOkiQhT1LObJ2haSz//Dd+i99+7hvcOppgGoNwNU5UNHG9ApsG3XchZRckiJAJlZHCaknZlKR5GsqglkgqNsdrPHDm7P0np9/D8q9NXrONRpfLJfv7+5RlScvoVdd1QB2uopjjUfsq6jQ9trTWWPd/exJUNBoOyfOc27dvd+s5RzDgZZj4/DpRQJi2BBF1XXfKSsCxiUhHujPsJpCviJ7h0VqjZNSltlvlLR35SVlr5VV0pOLw8IgsSzoHRUjhe5OFxCsyt8h6d2ziP81wnbTp94CzTgzYKuL3ueGTxhT6bU+iM8And32PgRenG/52XdH7XWfwepFuW6ZoEZntEQqCEXXHz6PdZqc7LuWx+6b9TVczFj5+P3Z8rWMU4t9WBa1dt3+ex9vdVvuQvX12NVznU/6W1TF3TkS7jdZoEohfJERx3PXxW+s6FrUmtBo6Vtz9eZ5322qZB1uGMn9fqs5Y1nVNkiQBbe7T6lq3IMmVkYW2rOLPp+VK8AZ7lWVqjXR7v1hru37wtg4OdHXutqa9Il6Ju8/7yPmyLInSxFOOArb2aclhkngQU2OoqpLReMD01ow0TWhMzXw593KcdcF4POL6rQNfBpACLX3Hx929uzzy8EPU1RKHQEYxUZ4zKUuuXL7B0dGExXxBXZY8+dBDPP3wY0ilibSmrCrqqgJncc5w4dwZ8lHOwdEBzhlPT6kkpTMkWcL65ojJ3hEvNiVRHKPjqLvD3v3k03z6C19ikOXkgf86z3MODvbIhwPvNEmJs36eyZOcPMtZTDzro9TekauNwdaWKIsZj9fY2t7xylzO9rI3/v6IQ3YkUrpDj6su0PCEScFioXREVS8oqwotfelECoEwljjS3D7cRUjBeDgiijJevXGVh997kaP5jKouSeOI0WDI/vx14lhTNHUgF3Ec1QVrVvG+Bx/nV7/4O5Tv/yDf/i3v5ceHOX/xv/8rfOqvfYz3PfNePv/S13n52pu86+xFZGNBevBcZRxFWbCoSx5/8GGee+1lnn7iCUBw+doNhkmGc46d9TPMFiUv3LjCr372M4zPnOGB7TOc29xmd7LHXtFQxwFr4npzpBRoJK7lKZC+NKmNpapLLj14jtvXp1RVya3bt3j11Ve5dOki7+Ty+8Il/ra/ga7fuCgKisJ7z17YQ3a/cqFkK+UKAESoYzZNQ1EUzGbzY1Gk//e4UWjnfiUlm5ubvPjiiwE9638bRYrlskQpEZCzHlk7GAyYhLr4ivzEoXWLdm/Xj3B4o7+Yz7tebB+B+Kimrmus8SQqxhps6K0VEKIXH7G07WR1bTreYGtNIPJvVsalZ0Rd6yl3k3x73scvRmtn+1kEP2j+wRTdBRTd96sUZosn8PtqU8X+QW7bodpr4Dqj3h5DY02Xfu/AXb1jbLMp3mNauSGesKCHrg/eLv2+dAec4B/vj8FJgFk7buIUY3psG0L4urlzPi3GcfnUbj8n7rd7lKiCc3MyGdCeuWtxAP3j675zWDw9qR+H4Khag6ktyEDCE4hOhPAGcjabAYIsS7t7sDXcbVnJA4tEADxKhLBdGQo81sOY5lgvty8veMOdprmvN8sVbWf/HNpSRJqmHYizXVpp2rbjA+i6LtpyV5uS7DAMxtA4F2S+LU1TYcqSo709siiinBccHuyhYlgWBcPRGgjHvJxT1QVlVXD2/Dne0DdpjKFuarSALOgS3Lp9kxvXr7MsF2yd2ebOwQFfe/5FtM6IowgZx0RSsjeZsH90xP7hAXmS4qxlNMjZ25+waJZc2FwjizQHTcPdgymzYknqHIuqZH2Y8cCZs9xa1DSmIQrOSZTEnDl7ls3BOs+98jLLuuJ9z7yb9c1NTFl3Y59HvgRSliVlWXLp4iVPuSwEUkuiNAIt0HGMMaXnF2+8Eb5z8xajZI0kToh1RCQVwjjWhiNGaYbCZza7UkXPg3QIhFREOqEK1yqSIU3vgACYu7l/m2GesbG+Se0Ur929wfdf+jjfeO0yTgrWhyPWhyOmZcPOaEgtoaCBxpDNFTZXPHP2AZqi5J999Qu8dOc6/8Of+o9ZS3J+9M/8Sf67v/iX+Oh7P8AbL7zIlRvXUGfXkc4xPTjCNoaDxZJFU/HEhUt85mtfREtNGkPjDHkSc/vWLayL+ZXf+A0OFhO+74e/lz/8PZ/iC7/x2zz41JP81te+yo2XDklaci9hwHlcjZOSzCmWdUllK2wU4WpLMy/IUk0uJNJ4J2tZVbx8+XVefuM27+TyryXC9hxg/gFvvWnn/AShVBAPCQCzk8bXl61sNxn46LjHPHNiaY11O0G/8sor3Ta1bgkdPAgpijwneRLQlHt7e8xmC5JkpVjUThzW0qX2WlSsj/iPI9v7MpgtIt5ag5BedCSKIu+1VjWTycQTUITJqg+u8w/sKmrv1wTb/fix5dh3bWrUr9eEf+miPGjN9bE4txvPLoUtCJ72qj2p3zOtwv7a6Km7XsEQKqVOMMTfu5xEeEOrFb0aQ9u2hcExDejTln40d9KI9oFurUG/3/G0yGYdaZI8u68Wtg1/WrtClrYGVB07bteB9ODkqId9h5eSEi01du5Be0opoizHNCVGOowT5FHWpYqBnnNpu/pe+7xEoZ3GG88Vfa1zorvnVs+W68awc7yaJmSJQsbH0eNVaJ9XL1qiQ09x09Q0TdOl6IXwDGzD4ZCiKLqUclVVXZaqf99XQXVsOByyrCpPdITHouT5kHo5RxiLbRqapW9tS9MU5TzbVxxFWGOYHB3yLYMhF8+co1EGKSCLE24f7TOZLfjOM99BY2pu3b7F/v4+zz7/PGubGwwGG36MrEErRYqgLErG4zFpFDOfzahr371SVCW2MVAbTFUxKeYIrRgkGfMkJ09ztsZrDC5IHr5zjelsQsyQ8XDEA3HErTt3OawKnr/yJsl4RJznnFtbR0Qx2WCAcdbrxeuIBo+qv7N7lzevXebo8AgEZHHO008+zc7GGW+woohBlJHplFE+xAko6xrTWPI4JtURtqy9nKaU2E4dzV/j9qWC6uIsPHu+fBUcv5CVuXmwx9ntLc6ONphP5uw3cx7ZvsCvf/FrRNrLkPoygTfwdVUgLcQyYi3J2Evgn379C/yJj3+KR85f5De/+gV+4M/9NJ//B7/EK8tD/vbf/3t853s/yHd/5KNcvvImuwf7fr7OMxbzBfNiwWK34IkLl9je3urYMM9u75CnGZdv3qQyJT/w8Y+zs7bOw+cv8tjGDv+snPOP/+7fohGaUTomK2P2mDNIUios87pCSMGZ8TqXl3NceKYDSpmyWJDFmvFwwN7+gs3tLT744Q/xyuV/+pZz1O92+VdisO+NeP1k1hprIIBoZDB+zT2KTcfXX0VF9353fCL26HIfHdZ1HdCmNVqrLqXuHGRZSlmVOOdoTNMp3ADUddP7vQ31btOlcxvjwTohkKIV2vDBoo+qq7LuaRMLWhBaFEVUdU1dV1hrUMpPUHmedhGrEF7K0Gtc+wE4pvLUprldK+F4HKx26jX5Jq+bRKKloo/w8i1BK4egu6ZdLbTbAKI1dkJSs/rtWyVi+ga0n0nob19JiYrjLjLvUsonUvh9w3OyN7xvkE+r0Z9cjLF+UqMnrSnEsbp2d6yrd9BmKYSgCaA3cSx6abET/WVVDvCTA8Gw2RUJSqRxiMAK5vtzwbOF+RS36bJR/n4TYP32tGpLBeE4nEWFyVdJiQv16rau3OcmX/VRE7Yhu/2079vr1eczT9O0c37afuF2nf416XScw/s2na6UCuMesjpAkkQ0tSFSkvFwiI2gKEsvpyv8vRtFEUQe1Z3pmDyKmdoSUzeAI09j9mdTvvb8N3B1TVksMcbw0Q9+iOdffRWaitFw6OeOZcHGcIi0Hq2O9oNQNUEsRTjqpqaqS6qqYFEV6CRGWcEwSlnLh4yGOXJR8/6nnyGKNMVyiVSKzeE6i7pG5hlLa7hy5xbDwdCvszZeqQcG8G0Ux+wd3eXlV19mf3JAZT2ItViW3Ny/RRwlbK5tgQPRWB689CB37+xzuJizubHBxvoagzjlzv4heZyxLBa4KMJYA7Xo7rf2HgQ6iVorPBDQWAfCS49aHPuTA555/EEGOuH27V2iPGEQpeweHjIcp6Sx4nByiBB+TrHLkoGToBV3XMGYIXMt+bnf+TU+9sz7+aOf/BF+4dO/xg//r/4kf/3/8v/it9N1Xr15hZ//8mf5Ix/9OOmsZlmXJMMBQimSwwOMs9w92OXSufPUdYUDHjh/3peKkpgPPfMUd2/cYTYtuHNjj7//y/+Cy7MjGhl7ZjQaClcjJAwHOZVpKK0HyT145izX926jqBlkKWSSpTXs7x6xtrnG3kFFY2qmhxOK2ZLv/ui3ve288rtZ/rXVsNvFA1kUVWU6IQxvpERHiwerCVuEVqb7Gez7LTKAO06mR9uJ2nMHS5wwIeXoJ9ko0l0vdqtF7UJNsY9CbiOCe9PQQTjBmK7QLMSqfgqBCMY5ovBAOueNYRxFNI3//riiVx+FHSb7FsWNN/B9Q3TaMYWT9//0zMfJOm47Dk6sFJ/871YG+7SUc385yRT2zSAduwnfuU5Du78P0zuWzmiLXmq9/deGMT+RrekDrk51/LjXqWmvN3TCov5X/R+KNrW/2o9HM7fnY485C30sQVeL74+dtdThmVBK0hiDq0vKqiLSAoPAlDWWVTthG6220bA/Dr9tIQVKKuJIH3OKW2GR9jwtrouI2/Nuo+92H9audM9b49xG5P1tKZV3xB3tPdQan9U1EbQa9/0Ue5tVAsFisfTgJylwWCyOoqmIVcLOzjYuksybkiu3riOcQCE9IEoKkIrN9Q1SGZHKiEm9RCnNOMtZVDWjtTXKpkaYBiEgjSIunjnL4eER89mcVPjujcI6RlmGKA1lURIp7ZnjtKYqrAcNttfNGIrG4wNcbRgmOYM0CwbPsT0eM8wGzA4OiC2sbQ3ZO5pQOItWgrJpODg64sbNmzz1wEPMp9MVBEz4udPguLt7h0b43nUpJU1tuHn3Jq7xZTnnBc+5cuUqN+/uM3cNNw522R77CHO8vkHTGObLOVLkPmtSW5Q+DgAUruUql0itaKyhtsZT7Qt/vabLBdtr68RScf3uXcYbaygpmS2XnDu3QZ7GHBwcksYJVgKNwUpFg6EoKjbSARvDMa/fucZXr7zCtz7xDD/9B/8o/9XP/jV+9uf+Dp/4yLdhUs2Vq5e5cu0aDzz2MPt7e1Rl6VvtMl962T3aY5gP/D0jFaPBkGVRsbW+Ti5ihvmIG3sTvvrKl3ltekSUewU27W8VrAY7bUij2D+b1sso76xtdC3GkdZYJz3PB77rRwqfhVgGPpA0W7tnbvmXWf6VGOxTJ/IwsfXJHboJvZ1IwiSzijRPrzm+leFujXw/NQwrwFgbDfs+VR3Svn4xxnTH0kbGJ+u79KNB+oZ45Vi0Bqt90GiFPpyvEVqzSn27UKK1xuK0d1isOU6+0UYp/XNvsw69Ue8ioP44nRpJtsPdO/f+NfHn6ToqzTZDctLotLXudqyCMx7GZGXoxT2msH/U9yTmu9q3bQ1mG8G259U7j3bk22OSSD/JOImTPTMr7jXg/bF6u3jb9f/qRdt+II47hO2xy96Y3gPAE75Q1G2q/y48A9Yed4panECbSein/ZvGtya1OIj+OAKh3u2300a7UglwLaf1KkPRUgafvKf6z+9JZ7XvoLUtaKc9s/22rpaqt3VYW/BnPzWeRikGS4NFKIk1DhUp8nwIkcbMJ5TFkmQw8GnzQM/pcKyvrxFLzSDJYTlBK83GaMztwyMGwwHGGYQzxEoySjOGccxD585z/doNEiFIpSIfjUiURiW6u6dbxkVrHU6Irsfd4aidJY9zqC3D0ZhBmvtnXWuSxqearXXoSDFKc16+dp0aL6JkhWO+mLN7d5enLj6MdP66aakwzjs9TsFsOSPKPTLfSj+PzBZTrpeG9dE6zhkiHTFfLtmfTThqSsqqZPdgn+VyyeOPPcmiWLIolqSJ5373XTpZeNY98BHniXUirSGKPVrcelyBw8+XReVV0DSSu3t7jNfHOGtZlgWDLCVNYmaLGVm4jhaLEQKHRBnf+312tMH5rR1u3r3DszLiO7/lA/zo9/0A//Dn/xFPPPYYZ7Z2cEXJcy+8AGmMaYw32FlGmqW4pmG2mDOMEz9OSDQS4yzbG5sczkquH814afcOX776KuPz55HCokUL8NTEWcr0YEKsFMYqrDFYJdkYjUPSzPPLSyu9qqOQuBaULCWVMRwtZkzKVQbpnVje+bYu7sHgnBJ9rR52H736qKVtW2pTMFLcW68NQWUXTfa3ejJqOm6cwjY7z78XxYV6eduD2xpyYy1l2dKgrrx+gUdtr/a9Itjv9hkOzp+2bM13NwmLcJ5V5WuJvhRQ99LqnlSmTbVzYlyPTfwnTGAXs4iVo9O9wji1G+sbiGPXLHwj++sesy/3tn+1YiqrAfBvhcTr2L6dFRTHr2c7efcdB0+RagMYUR5/CekfohPtYC6kUSWiS2W32+vLo/aNSf9+Fb0/Tp43J8ZHnPjdybHqH++JU/cRMK3jFwya9sxkxniaTR1psjzv+qnjKGI4HHbo7vbY2xYsa01XQwYCM1dJVZUdcGlVNmjbEwN5iVTd+PvI2UvVpmnqFZt6Otk+kvZ/L5dLJpMJR0dHTKdTjo6OKIqC5XLp2yLFCi3er2v7jFYUWig9QC1JPHtZFMcMhgOv+BRKSnGaMhiOEFKxLAqmkwmzyRTpYDwYIPAtUC14ajgYEQnN1sYWUii0UKyP1lDhOu0fHrAoll7ZazjA1TUXz5zhkYsXGMQR2ll2NjZRCLI4Js8ykgC6c8HBEEKgQ0sbIdqO4hQMjPMh43xIpCMGa2NEbdjf20NKydp4jSxK2ZtOPOd6mmKMZbksKKuS27dvkUSx73uW0htOpb3whwIZeSNem5oki4nzhHkx587BXQ6nE3SS8OhTT5GtjZk3NSpPWNqGr7zwDf7pP/9VJsWc6XLBsq6orcfZtPeu76DwiwcwJoFyOYB0cVjpsUDW1KwNR+Dg7v4+a8Pcz2NNTRrHaKWYLpcdr0WhPAAyFxFDHbO7mFFZw8eeeA+Prp3hlZdf4i/93N/g3/vJ/wXf+uEP849+4R9z5cY13vO+93Jnd5cvf+UrnDt31rc2WsjTjDiKcTiqZYmOFDrWWFMTxxFra2u8uj/hF7/6VX7rlReRw4z1VDO5fZ3aFVTUCCc4k6xjnS8j6RBI4WA8yBDOn7eSkGrFQMWkKDIdo6VivljQYMk2xhwW07eZ9H53yzsfYVvQsaZuPDDLt3v49Fkcey+7MQZnw7/OhQh4NWG2NbHWQMNKqxp87UYqteInR/jWlkieAIUJMBZhvWeopaBaLpB4T8VHieFlHCoC5zxJgJTaH7/2D6MJzf1RAFz5yTmkpK0NKWKIorB/R1Ar8lSOAFKtegb9xC4wNmhnn+gfN8Z0fX+mMV20KnuZiVVquzWurEJMR0BSt86PRAWLqIOD0aLLO2PdRajuuOUMixQC1evL7hs4D94+LWL1xyKF5y5eRY0nItM2ZRtqk60FWwHLPOhHdl7IvSl41/2Prq7cj5cj5VOIWHcs23Iyc9N+1kaYQiucbKlGT8sWHdvZ8WO6T9R5zCE4MZb9dRtnaWiIJeRKgfOpYCe8IlOSZCAVg9Sr2rWOiDfgq3R2a1AXiwVxkgXj6r+bL3xngjIu0CuKLp2epmlPBEIhhQodDzXOmWOUt62z2xr1tv6ZZfkxQ91mu9qWzCRJwvj4GmweHBIC2AkpsI0nQCHJGaVDnDMY6+lWhRAUVUGSpJzbOss4H7E3uY1KRyzqChVVrGVj1kYDGhFz/uFN9OWXoZYM801GWcaynLF/Z5dzZ86S5gN0qphO9njkwiMk5y4xmU09R7SEJPXHbhUeSOkEWZKxrx1uXoDwx2arhk1ydCVpJOTjIWmaYSpDnKVkKuYrL36DRy49zGAwRCQRl3dv8kQ8ZNc1zGvP1503S4aHd9na3ESa9p5w1FJw5dYNtjbWaGovqiEEmEWB0hF6LeH1/cvs7085OJjxpctvsHt4yJnzF9idzzwP/faIl+/sQgq/fvl1zu3v8/j2Od51/iJuWaAxoBzGgbOgpSZLN6hxrKcZA62wGIo052hWot0CPcjZm5ZcLw75+Hsf5o1rB8QbI8/oNnfcnDuGw4r1AmqR4hBUlaFZFgyHKTcXh+xM9nn3U0+yuTnm7/zqP+Yv/z/+r/yl/+Q/5//4V/5v/JPP/Ao3brzOf/Dv/xl+6z/4U1hbkcYxsYpZuhKpIjKZUAPDaIBFUFp4z3vfz+e/8CX+27//3yDWh3z4/R/ie7/l27jz8g3+4Yu3sds5JTVKGR6Qmq/pMZNySorANA4ZRZzbzKDRVFYiogaso8Jgk4xC1eRDzdAKzGzJwfyIrXF276TwL7H8/nCJG4Oi1U0Wndxmy8WLCAjpXq2r1bBuW7iUkN4Ih6i7KIIAgWox5j4iF8L3oeZZStU0VJXfRxT5h0pCh2LsT5jWgQt0oi1a3O8/tI80NWmaMJvO0FoSKV9/a+om1LH9PL1cFlSB7F1KSVnWRJECbBct+bpPL/YM6zraCDYMS7BFXXrb2i7tCytjfWys3b2AJSEJmsrhN3gwY2cUeOsywrHth+u12pe718DcEzqfEmlz3Ii2keg9+wo14ZUxPb7N1ii/XbDerSVWKdvTkOL3pJhP+e5kCeb3stwPT3Byv6d9JoKH3zS+fCKFO35fnFi3dXZbciGgM9qDwaBDXTtnuxaqNgJvgZmtZn2bWu+wC8HYSiW68kg/a+CN+/ExbvurbS/DNZ/Pu0xAXXs4opKyYxlr2+jafbav+fyIlu1POkesJZPZDGMctvF8/dtb28SjhOs3rpNmAxyC+XLJMAm96dahpUIJn6rf2tri1pVDhnlOGiUIJzpWxbIyDMdrKK2ZL+fUdekZEiezbryqoiIKIj9pmlIslsRRwsWLl7iztwcI0sy3UknhiXGklCRZSp4PGQyG6CimrGqK5ZJkc41qdhBwN4rFcsm8WLIoSwaBpa6qKiaTSXfdhPDYBCE9SNY6ST4cM5Caza3zyCgjHw5448oVbt7d5c7urlcoS5LOyatqw97N2+zfvEkWSZ68eAltPDPjYjanLEpGwxFpkiAay3K5oCwLwJHoiIP9Ay6ePUeqY6rlEdY0PPHQo3z5s2+ynmUkUURZ1pRlydYg9fSnymCNZRDHrA+2uHX3Fvlwm6++8gKLi5d418OP8p//6T/H/+H//l/yyLse4t//Az/BP/+13+A3v/BZPv2N3+YHvuM7+coXXuKpB58gHiTUByUPXrrEa2++xnK5JMsGxInPVrz+0kv89b/6V/mxH/gRPvkd3804GfNbX/4qP/fi50jOrJEBi9mUWe6otnLUZUlja+J0SBxppPLcIRvrY26WRyGr58eH4BB7JH1MFMWnZtL+ZZd33GArJWiMQyuJElAbS20r4jQJvc7eyJrQ79fWbcFPzgrP5mOcRYU0tZdTUzjpkYk+7eTlAXGeQaydEJQUns0H7wSIjtnHhc9s17LrcL36XECTOy+NlqYJ85lPZzjnH0ytPDlK2ysNHvjhtXoLhLBdRsGv53oTHd22VmneXs1UHLd7Llim+xmJlRHpOQB92ym4r4G4Z1vc3wAK2R5YyG33m7NcSPu6Xr21ze3Se9sraTShzeLeg1hF2X6VVdTbphaPTeDH9nL8XPq/tT1RlRYgeDKibo3QN7Oc5p+81RjfDzfwTV8b59HIvp0x5D96qXxn8RSZrSRjMKJojbOWcll0mZRIRx0yu8VZtBF425bYIsBhRWiiQr3b9a5NS3TS/q5PN9o09rhzHLJHHjzpek66WWWKnPMCM84T1LTlhta5bFPnSkWhX7tGAq5y5HmOUtoTuUiJkhqcQkcZWT4miRKobejwcIxGY/J8gC0qJrMJw9GA0WjEnb0pEq9KhnWMRiOc8II9cRxTNzV1XZJEMXWS0tSN730OvdECr+a3WMwZjsacP3veG8G9Pda2tomVDsAtX75J8wytI/I8I4ojZmVBU9U+uzDd9eMYx6hIs3d46JXD4gSpBI1pmE6P2NjYQErJovBSw2mSoKII62D37j4Hh0fMixorI9Y3Nygaz6cfpykySTyjWrhGsYqRznFQLPitr3+F0dqI86MNBJIsSXHWsX90xKXzZ6FppWEN0kEWZ+xPrnJ+5yyR9Mh32zQ8fP5B/t71X+Ps+jqxjpjMFjgM62nOzM7BWrI4QjvBwewQpz1+INWKN69fp5oX/OHv/ST/4U//L/nLP/u32Eo2+M4PvJ9Lmxv87f/fL5BGER9893s4OpgQOcWZnR0W8wV5nrMolhjX+CDPKu7cuMn3fufH+eFP/Vsc7B/y65//DL/4xc9RphBbQVGWzKslUSIZxYnHdhjj2fBC+jKNI9byIS2QTytFHEe+3CYVSq+yn3Vds6ze2Rr2Ox9hO2+su9pXMDy28a0XDnCBjanjDBegurofXSrteB3SeQBRWH8FGvOgMmOCMIUIyGLrqfIg9L3ioy1roe3vbon7u9jOZ1p9bbNHAiHCedmQclLac/+2zoOP3Px5rozwKqW6oos8Nkz+2E4Y9N4vQnTYAoD6695rBNrtndzPsZqrksf4t4/VYrk3TdseX3udxLHIefWvaNMFpy29g9F61Xr0thFysBBtH/fJyPiUAVt93x+r/pGKfk/6CsV8mvE8Wau+N+K//9Lf5mnbbf99K++7u/eCZrXWng1Pa42w4VpYL8sonOu4t1uDqBt9LDpWSuESR236rVd0BrvvyKwiZHksSu8flzGma786mcFYlbNst/2+pGa7zTZN7u8tf52apvG606EG3G6rjcCt8WpXrS70crkIDnk3wgAMh2P2Dico5XkOBI4ICQFQlcYxi7JkMp2iR5JBnmGqGuEg0tq3vQkB0s9ZvgXT16bjWJOmlvl8hhyMyJLMSwALGRjPBGosGQ2HjEcjrl6+yigfEet4BSY0Fh0nQakwRmrN/PDAU49qRVM3uFDSKuuKXKfsHx0yTHMGcY6QMJtPydMMKfxcp6TPWlokURxjRYx1AisXTIqKRVGybGpQCqRC4jxQzTp05Jn+oiylcIbX79ziqy+/TPT405wZr5MmKY013NnbpWzWUSpCKNGVz2Ll77e1wRgMmKomkoKt0SaX717jI499FCkk83KJUoKtwZj92RGpFZxbW0NYeP3wCDfwjlAiNbWO2Z1N+PTXvsyPf/Tb+crl1/nF3/k0fOijvPuRx3nfE+/mF37rV/nERz/K+mAN7RTloiCOIwZ5zuHREdYYmrpBi4idzS0evPAAiZF88RvP8unnv8aRqYlNQlVXRJF/vlIkQyc8uM8aIinwHZHW61xHMc4EMNuxDJ7HFXjbEsi96rdjn/jdLb8PXOIikF2EHQTj6G9o35crEaSJF2MXLRgopI290bMdOGqVJl7Vitvte5BXWIcVA9hq4vJekZQSpfUxdrIVYKg3GGESs9YcY2ASrJjWWgIIv9xb8/QGe3Xc/cUdM5bt9+LY9/0oZrV3aAFsp9U62+2dtp/+dfHsVqvUZQv6aV/t+/7nxw1WqHc7uuj6nujxPoZbCIEK+9RaH/u7fd++oqCTrns9wCe7Cb7Z9PRJoFd7fidf7Xf9/fU/u4e97G2W+6XXTx7b/V4d+jpcs3Dr0mIOhPB8z+35GLuSaD3NAWkNZ59Mp+UBb9/3+cJ91BgdG5vVNfAtLGVZBj34Vcq8f2+1rz4QLg7XNoqiTiL32O97/d+RXu3f4ye8mId1BhUiWyklVVl6ngLnwrE1ZGmOs4Jbt26zt7ePEIIsHyCl9NKYaUaWZizKJUWx9FwDxnrEr9QoqZnO57jAqe3byfy5eQphGZTMaqT0GT6lFcvl0gckQqKVbyeaTaYM8oHnznZ+4rd1DdrTISupEUIxWyxI4hiLL4e1gUljLUIp9g8PmS+XILxhrqrSj5HSxDoijhK0jhFCkiYZaZIxHIwYj9cYjEZIHVHVDc4JjPVkOR6s5Y2uMdZHTklEqQTPvf4qL19+kzr0/AshaEzN0WxCEmlUpDzuJWBkjPWta6ZqwBiGaUKqU24e3eaBnbM465guFkRacWZtnVmxZKRizuVjNrIBTjiaSFIWBbJxDIdDaiX4zLNf5mj/iB///h/kyu4tfu2rX+Da/l3+0Pf/AFJK7uztsj5eY5CkTCcT4iQmTzPSOMbUhmKxpKkbdnZ2OHfuPF977lk+9/yzXDnaZ7yxQdJ4KVedRGRRTIZAlw2RUhhniIRAC4+vibQi9kaCVGsiKbE9ZzeOViUhB90z+U4t77jB9shv3w4Vh4e+fdB9DdvTJW5tbJIlaXcAreFdbaevqe18P7TxMm/DwZA88/UoY3y0IYOh7bOf1bXv40zTlOFw0KX2/DZbXuqTrSU+tdFONCLckO3E7Zyjrjw4JstyhJChlrcaA1/DDkjCbr1VGtw77m30EnrDj41D39CsnArXlbX7kXB/nXsj8ePLvUbkd/NayXl+84arv7TUk/e86jpgHFZo5tNqyN/s0s8iyBMG8DS0djsRnxS5OPn+m02bn+rInHJOp45F7+XHq6QoS8rKMZvNWS6Xq/R15FPhSRSRxAmDgU/tjoIUbJZlbGxssLOzw+bmJlmWkaUD8jwPSltR97vxeMxwOCSOk86Qt+DPsiy9BnaQrm1r2/P5jOl0ynzuj6tFna+O3b9aLv420gdPSdyORz8TYHtjZB1Y51t+ViBA/9vlcsnh4SEAVVV6QYxI46zzdMLTBVVR8C9+7V/wuc99jsVySTbIiaKIydER21tbnD1/Ficd+4f71FVFGnniDIkv89y6dQeUr3U76zpp2CTJmM/nK0W9cJ9Fccx8vkRrD/irqposzZjNZgyznFh54QBjG+qqphGB5EUqnBBMFwtG+TAA8WKP8Dc1g+GA2lrmyyWL5QKHJ5OJpPZc3k4grEChiFVMrCIUitu3bvPSiy9z5eo1H10XS8qqpq5XnRHtPFdVFY01LKsSESnOP/QgB0XBb3/1yyydobaWoiyIIs3tu3fI0gwZaT+vGQPOUZqGUZZTLgsElp2NNZrScLg84uEzZynLmv2jCanWnN3Y4u7BPtvjNWIdYZwlSlMvt2o8P7jFC6xk+YC/+Lf/Jk+Nx/zIxz7BjYMj/sd//A85e3aNH/3O7+bTv/1VjpYzkmHinabFEq11UHI0TGdzDidHHM3mvPTqa/ztX/9F9ssll85dYKxTpJLUpkEbSKxAGMusKoiVxvWyF0I4tJZovH0ZDwbESlGGEmnL29F2CURhDnknl3c8Jd40hjj2vZ+NsdS1f28DiGRnZ4fRaMjR0VE3GbReSKfNizc8vsaliCLvwWZpwkMPPYQQgqPJhNlsFgBj3rg3xltD3TX8w3AwDECJrIvIuygcOvKIOqQuxuMRW1ubxHHMnTt3fMQNNM6nhlUcUZc1Fy+dZTwe4ZwHnrV1OqXa9HLbAhF359fanPZzIXxrS1XXxyLT1qD/biK6t/upB7G5Ll3cB6uclhJvv2s/9+lNj1BWhLqy/eaNaFvP78hW6J2yW/UfO+d6rHFtpOgxCzh3rM3M3seIt0PhWLXa+fbBpsuc9FPifUN6rAQjV7KPbb31m11WBsYci3iPEcj0tne/CFwqjZCKou+0OS+FKamYVwsa645FyCpgOtqljcKdc74rIXCoriJuurawpjGhxr1qUexftyiKiSJNmnokeHtu/d+c5Dto7/82Wm6j7TYtnqapN8JF4c9ZaxT3OloudG90+uVadrgSYw1aS+LQoXLr6lUeffABJh/4AIeHh7zx+us8eP5iOBbBxsYmi2LGZLrH0fSI9Y0thoFPu6lqaiFBSawS6Cj2cqONQQmfCdjb2+PBBx5C65jGOAaDETMzYVkV6NhrCyyXBUJFWCdCa5ddjatxGGdJshSdZdTgDfZgyLIsUEKSpylSKg4ODnns/INMdvc4ODzkzM42SRozzHMaA+vjDSKhITAczmYL0mTAztYmo/GY3dmcmwcTyrq5555cLhbUhZ+D83FOXRtq0yBQ5OMxr7/0Cp979qt84MmnGCUxtiy5tXuXZ54Bi/R952Fb82LJxUvbLMsFWsLFMztcv3GXKJWcXRsxXSyYLhZsb68zyIccTCdcfOBBDiaHXDncRSjBg9kWs8mEa03JxUsXeGrtPOxO+aXDL/Bf/s2/zp/9iZ9i46MDfuNLv8P/+b/9r/nJH/0xvvaN5/mtL3+eb33P+3jkwYd47dVXuHDpInmaMZnMWJYF87LkxgvP8eobb/Lq7g3e//R7Ob9xljev3eSwmLEe5zRFwZKG0doaW5fOoy7fxVjn++5DQKgExPg23631dWbT0Koo8WnyKGSVpJ+3+mye78TyjhvsOI6oqgbjHFpp8jxjvliSpQkPP/ww1loODg44ODhgOBwC9NLfIqgb+cnLM8ms6mUf/vCHKcuSN954g8VyyWAw4OjoiMY4olijlGfAaoL3uLW1wSAfMZ/POTioOLOzFSLcUD8MhsJ7Rort7e2OPvHo6KibfPwE6HWDtY64eOkS47VB0N+uAd+v5x8GizENg0HWbatdWseirUV2xqITOfGG6bQadRs9nzSwbRq9H5m3351MIR8DWYlTHIJTrH5jm9CT2Yty+9kQ5zMcrv2su3whkvQQImw/7O0Z6GPHEGqZUaS73ZxmSMOG70kPue7YVodiEaeuf3I8jhkGt+rHb/99qzr/Wy33i7T7x/BWi7EG4RxKeoRqnmZIGqSzpHFCVTU01oMrpfACIHFQgKprj8h1FpTwRtbT8moQDqX6LH0qZBVaA+2NrO/F7hGuhMxQn/K0NdjGGCIddyneto7Ttnq10bSPUpNuDDpsgxC+DKKjrn4t26jbeIdgNFrHYRHhmSuKgsFo7KNd5xkIpRAM85hBmnDuzLbP9iWhrrm3h2saqrJkPl/4tDcwHo85s7NDGmco6aP1Cxcf4NXX3uDs+z5MHCXEukLHqmubG+SDUJtvsDZCqKirR0utQQjKsmBrawuhFKZqcAiiOEI7WFY129s7ZGlKVdVMFwsSHbEoFh5NblxoXVXc3d9jXSdMF3P29vZYXxuzubFJ4Qyb62sUswWmsahYkiYxCMd8NuP6rVssjEMmA0y9JEoSD8QUvv7c9PABnq5WU1UNxWSGlJIHnnyMr7z8AjubG3zr0+9ia32NZ994g+tXr1OZBhEpzylhDPNqyebWJteu3EZrwZmtTZ5/9TUefPASkYPD2RQr4KFz55hOpzipePqBR3j29Ze9M4EiQzFxjsFwwN6du3A051uffIaPZ4Zf/p0v8zP/5B/yQ9/27fzbP/jD/JW/83f4y3/rr/P9n/oOyqOGG9dv0ixLHnv0cZq6RDhPSlM2NaVzVErwm1/+PO/76IeZzo64evMGtdAMBwPmb1znyfc8TXHUUGGJ0zRwZVjyoBBnliWuacgiH2wN8wGuEehI0jQ+AnfOIZWgMjWLxYzBcO1t54jfzfKOG+zWY0+0xglB3RgunD/HxYsXuX37NkdHk64vWYV0U6nkMfKGthWnCf3JaZrx5JNPAPD662+wKBZdfdMaRxx5Jpo2Yau1ZjweMxqNmE5mzBdz0sRLE1rXp9RcRVLb29u8733v49lnn2OxmHeTkk+v+zReFMVd2vDq1Wtsbm72IgnfT66Un5za9jBjre83J7SXBRpD2SLbm6brNRZhwqU3obec5C1Ir/3dyrAQfruyt5JV9NomsKXr0WmGqPU0YN/bLSsD5LfR1lPfak3X+7ZvvvrGrB9x+wl5tR/n2n8tnNzWiQy9cL6FrYcM6M71tHPpovVwT5yGEbjfuNxjvMOx3G9fb7X+/ZY2uyFCiciYhroqWMwXOGMBj8+gV3NvU9ltOq51PK2zPcax49KVrfFtsz6+FdNnYdqI2YUHzAVnKE7aMoE/dxuc9LbH2qfHa4ajPNCMrmrsRVF0jnhRFP6qKh+xtXrw1rjQ+ti2x1jKUI6SwmfFyrIiimLSNA0p9ABuLCumhweeX9z6jERZlf56y5YEqaasim6cLly4gGsEcSTJBjGyqfnNz36R9z/+LrSQ4CCJExbFgu2tHdI0I0kStNTegVSKOKijaa2QOmJeFGxubiKlonJ0GcCocRTFnNF4hJKSRVGwWC5JAlhBK0UWRdTWsCwL3Nj3p0shAy+DYHNjg6u3bzMcDEmThFqKkI2sSJOEBx94gNF4zI39Q67tT7uyTlX6FtlW19wZ25U8BIJYKrJBymQ2Q+UD5tMjXnnjVbaSmIe2z1CWJVeuXqVuaqTyfPbW+Q6QOE0oat8NtDYa8qVXrnHp0kVs3TBbLJFKs7O5ze7BPlGcsBUPyIxE1galJHcO94mGGZETxFKzqAo++9pz/NB3fA97h1O+ceMa+rmv8vH3fpCf/mN/hL/4V/8yH3zf+3j/k09QL0v29g58hoK2Q0dQVJXXM9/bY+fBSyhjmZuSiazDfGuRSUymNFtxTppqn1lRikY40iT10qPO4nCkUexR/klCnfh7WHaqdsHRb3z2WK19c2W0b3b5fWE6c45QZ4nY2t7k/IXzTOct49ESrwbTMlX5h10Kj8rzBkAG1LdkMB6yub2JsYar165xcHjoa2Ghtizw9QQTBD6UUgzyjLXxiLIomM/nNE3dGWdogXCA857lcJBz9uwZDg8PmM1mVOWqxcUG4EeapoxHI/IsZzI5YjabH+tNbSe0NE3J0gzn8KnudlxCmjsJoBvTNFRV7Y23EMHD9S0WfaCCEF6KUgXgXOsZtyl91WMQ6zIULphS0WN6CpFwC2Si1wAVuIqOfda+usie1ij1UsguqPiGvz14pWUNCxzixt8VztgOrEaLCe5CYl8z7FDoFoQNm3KtQXV4xjivz9sZHTzzmXW2Ox4R/iV8JnBI4bCmCb3sLnzWqnJ66tWV7rVFCId1BofFz++r7097ufagW4tmXZcxOPlqQZbtf6tvjhvxVvXLOV/KEEphGovE3y+Ns8g4SLP2AHKtAW4NoufQb3uxLVVVUlcVdVV3YFApPDB0Va/2QjhKagSyHUrvRDrj9YerutuGNQZrjBdbsMZHwFISRxEOiZQahEaEl3UCKTVKRUgVEUUJWTIgUt4Aqla4Q2q09M+ANQZjGn8g4XlTKsLLgK4oc8ERxRGmqUmkZJwNGOdDmqr26GkXlL1sgxGORKfkOmNjbZ3xcEASJUirQUTcOtrj1u5tGlOTpSkaFQhrEmbLGXcPdtmfHID2bXPCrZ4/BUjnWB+PQTisMP4+9dTyxMYxTnMipXDOoJxjOByhlUYL5RnNpPSOVqRprMEKQW287kKWeja3NErI4sTTsEqF1jF5OqC2gsOiZFJUGGuJpEa6IKFpDUIKssSXOMqmIgK0tcQIRknOoiwRpcFozWt3bvHsG68hdMTWaJ3be7dxZQ0GqnCvOyAXCWXTeDKfOOfKnWs8dPYMZeUoqpJhlvLQmfO8evs6j5/ZZqgUTVNRuYYoiYlNmCIqX7qohWNvNuG161f5wfe8jwtbZ3j11k0+8/Jz7GwPefr8Qzz/xg0SHXFuc4NhnlNXJUVRMJ3PqJ3BKUHlDJP5lIsXzzOZzSkWJcL4588ZQ2EbdByT65jEKSxwJsk5XCx96SXygZQzjiTTWGcZJxnraUYqBUI05NGQuq4YDCKyLKIpa2T8zsbE7zzoLEQzWkc+zXR2BxVpXn/jdaazGQJLpIM8o/Myk22s1hKmAEilyUcDNjY3yAYZ165f48rlKzjnjbUfvKanq+wjgTRJGOY5Ejjc3++oP4UIoBFaRjBfD03ihLXRiEhrnv/Gc4Fgn2Ds/GArpRmPRwxHQ8Cxv78fItpV+1orLuJTfR5g19QrcQMPwvMoWSWlB3nUTUceo5QMaXIfOXSp2vDSUuFEMIJulXX2++2DqHpsUkFpq3UmBD4CFZ0FDhtp8+onDbYI6jz0UvFtpIU3fKGosFrHsdLrDsZZyRCF9Ywzzhve9u+eKUPY3m/C58YRIiPZ7dHgMG71sr0j8YfjOkCQQGADW50I3+ECR3KvF78zl8Eh7GrZPafo1Ff7nVwZYtn7r53IW4US4cJxhIvZOkbHMwn+JYWgEUBwaNM4Ic1S0BIZR8Q9pHULcPSlJOslLpXu7lOBo6mrzsB65S4PkME5mqpiuVgwm80CzkHh9a9tUGdq27W8+IU37kuKcsliMWM+m1KUS7yONkE1DKSOiaIUHaVIFaN0jIqSLo0cRQmRjFHI8FLEWhNr5VkJncNUTWhF8ulbpbSvITeeQtg0/nzqqsYFgiVhLKnSjELrlY4jz4FdNxhrQUkSnbKWrZPFSUf7Kayiqiy1FtzYvUVtawZZjrSen7ysS27s3eT1669z7fY1DA2J9sePA9sYXGPJoojRcEDTVEFFD6+hjCO3gqGOPaAJRywEo7UxkYr8PGicv2eVVwErmoraGcqmoaoMQmgkPhpP4tRfZ4vXrK4tV2/v8tr129w6OPI4B6mQ1gtVWOHLB4mO0EJQNhWZ1mRSEwtJIhV102BnJcQJd4o5r9y5yf5ixiPnLnE0P4CqxhlHaSwSi1KagU28NLJSpCrl2u4NHj9zjlnRUDclG4OUi5tnePHWFT700EPEEubNkoWr0IlmOx34a1M3NM5CFDFMc37ji5/lwY1tPv7Ue0njmM+/8hzPvfYSn3jfR3jl8mVu3b6DFpIzW1uYumZ/f5+j2ZRlU1I5gxWOJI1YH+bsH02xlSMXCboRUBuKskDFmjiKkU5QVhUPjMYczEusWGGTTGOIcq/WOI5zNtKcXEuEaBin69RVxWiUMBok2Noio3fWYP8+gM4atJY8+ugjjMYjbty6zeuvP8sgjdCJRrQsTFpQFgXZ+jpae/GB2jhPOVrWPPjgedY21jk6mnD5zctUy4osjYLHGSQbtcbh5S2VEozHI7IswxjD1atXqWtDmiYeMFJbGtMEYhfPiDQaDRiPh0gp+dpXvhIm3hAVWT+xKSU4c2aHJE6Yzqbs7u6iI/8wR1HEYrHoaBzX19cpy5LJYtKlJNtoJ0kSxuMxy+WSxWKBEII0jUMv6gpg1dYOYSXTaXEhxei/S+LIp0drryKWZymEsoI1XorROd8DLzrjKjD4z1vQydvWYB3+QXeu4+ju0qysesjhuGFva78t4jdOEqIkpi7rt08DOzAYOliaAyf8JIpwCNkqdEmMiI+l2+2Jc3LOoWzrQChv6J3w0YVYtR9a13pAq3Po18otEik8ml/J6C3HTQiBUQaEd0LbcoelHRMbaFwB4cshAuENuCBE6gIRfAQpJEJ6j79satI8IxaOsi5wxrFYLMgGaygZWoaMAKdIM48PiXWENb6VR0qJVqJ3r60Y0VpgWlseaNmFqmqJb1NsnUoJwnPvR5HPCHltd8VsNvMcBdbiaPy1NA6tvZMa5/Hq/mtgsVj47VnX8RoAXc92mwmzzrcNWdcQqbSHN5Asl55xLMsyhJBByrbEGd+REKfCGzYpKcsSoVWAn1uEdSihiHTsiTaWFQdHRyipuHRxmzdeu0WWZxRVQW2M718W3gm/s3eHg/kRzlqaYc3dvbucP3OW9bU1pFKUTQ1CsL65yWy6YH40Ic8yZOIplhtrvIPpHHXTUFYVZVX7FjXjW0eboqSxDXESczQ5ZGv7PMvlkmVaUNUVciFp5gVOKeJhTlQWyKIkjVI+99w3uL2YsZaPieOMvdkMKwTWGU9ZHJ4bCQhjEaEkNBjmCCcoyiXn1jZZHs1Qg4QsH1AD33j1Vd732LdgvvHlDiukhO9yWcsHiMhLGkeh5XC+XPDkg49y8/m72KYij2K0E+zv7fLMpz4FUrIsK8qiwjWG0XgI8z2PSxKCLMsYphnF9dv8V//8F/lPf/QniLXl7/7Wr/E//8ZX+JPf811sywlf/NpzaJnx9GMP88brr/Lmm28y3vFjf/P2bW7u3SUbDDk8PKRYFpy5eIk4z7l+6zZ1UZJHEc40DLIM5wz7hwesr6+Rxt6e+GysYll51Tmvxx4h0pxhPkAs9slSL4kslUIrz5fQnyPfieUdN9j5aMhDDz/M0dERly9fpigK1kc5y8WCNI5xFmzjFVqycda1Rvj2L8VsseShBy+xffYMV958k8P9Q6x1jEdeoq8oatI07iIKFMhIs7O5BfhJ4PDwCK0kkZbUoW6ltcRJ1fF0XbxwntFoRFGWvPHGm57aNLTL+Ppeg5KK8WhMnqTs7u76CUZKqqZBKahrnzZMkqRLRba1uW6AlSLPMqIoYjqd9iggm64ntS9CsepnXaU3rZVYHJEKKVRrEUiSKCaKHItlEUQTfGpYtdFgOIZ+r+3v9gZyQuCExArpo0e3SstI1aZwV45GFxeKIHfpfFBprcViQlr5rRcp0q4g7+2oAOGjdBHUO5QAdWJb94LowATcQGukjgPMoFVOW22jLQH0VNgkIF3QNjddRI24X2181ZfZGu5WKUxI54lPBEgxQPSuh7/3/ORgLDhnkUqgpY+GBoMB89kcmWjAZ1OybBCcXZ+VUjpCBcNb1zXLpgqZBp/GxTXdPbd6OWRQefK0oxJ5rBVRhJ5vidIrkqC6CoIXbXYsAN68XnqbffLZovlicQwVPxwOESJoBFRVIFNS4fnwmY0oilDa0/xCYEoTmlbWM8t8K1tbovHOryTLBpTLaSfdq5RGKQ3OIGXkxzTS5EnGeub1prPEI66rqiaOBIPhgIOjfUbDIXf3DzicTjmzdQapFc44mqZiMMxojGFaTPniVz/PR973EVQcc/PObYyD0WjEJSGZF0t21jdwywqjBCJSaKmpA6dDVVaURRkckNKPd8C6uLKmXC7ZOrvOfD5jMx54GcvZDB3HMEjYSDJmDTRphskzJpMJH33Xe1i7dJF/+plPc/X6NVz7DOBlM2kMKtSrSwcR0hus1GdE7h7e4VsefpLPf+3LJDYmShIK53j+6hU++qGPMjk44vZ0n0cuXUJbR0VDIgTzsiDRmkGUIAwsqoL3P/w0n/mlnyOKNGc2NrFVw/7BHT763vdz4/WbLIolgyzn/PYOaQnF3nUyGaGspSoKltbxJ//Qj/Hn/+7P8k++/jl++Fvez5M7D/Jnf/Zn+M/+7l/jv/i3f4KrV5a8fuUaWazY2j7DtRvX2Ts4oBGW4XjIpnBYIfjyl7/Mk+96L/lwjaPplGo+QdaWJx68xNHdOyRpwmA0oLGGs1vrTHb3mC6mxFoxSjIOZ3MiIcnS2Gc+tSKJY8plgW778EOAI4yheofVut5xg3323DmuXr3KbLYIUbCiqT05u62b0NIUYZ2fuLJh7r3GoiJOFI8/8RjD4YAXX3yRelmEHj+oipJYayphiOPYa8w6MAY2NjYo64piWVAsi5CuFmghMQQ9YmOpmwZjHRcvnEcnMbfv3vWN9pEG64iUphFeYARgMMhJ04Rr165izIr5LNGKxhhM3ZBohZYJzlqmkyNs3ZAnMVVtQtuTYzQacHBwwHLp25WUEkSRpCiWDAY5y+VKZ/k0A+BCfUgE6lQHoAVKC6qiJEkTrAUde8Smr1CvENAdOCuk8dt6E3hjwj12bvVZKlWYNL1RttbShGvXB7r1DWJjbbf/tscT2v2vdna/SFWIEqTvOce1GfPWgPsjtxZcfdrKx0/Hao1FdVgJRBMidbqWPmHt6kTCNqDFOTgQEodH1krVKw90o3icz91asxprPE5eihXZjpQtz/4CJ1bZDu8/2BBZtlKTFmMkxnjEeJwlOGepmxqDY7koGKqUwSBHKOWFQUKpJI9T4iCDaJoGJWA+L3y6v8MhQN3UwanyrWvWOETXfubxA2VAjrfAUM9y5nmUpZQY60tCdV2jpM989TM5kVCIoOdtjAl920skuccZSF9iARvoTRtPkGJWIje+Vc3LZaIUtYOyXPr6bAACqVCT1FqjVQRCHQOagT/3NE0RSlBWC9aHG34uyjKyPPfng6MsS5I0paprDqcTJvMZ2+N17HLKsijQUiOVJEo0Rlou37xO42C6XLAoSq4f7HJ9d4+1OOYh9QCmbnyaVEGWecObDYYenFfXni7VetyAigRuOccaS5KnzOdzzm6fx1QeP2BxlFVJMy/YW0yJh0NkUVBO5lzaOsfnn/sGn/vlX0YPcpJYs8DgAorZD7MFa1FCEEtBqjXKBsYy4XCu4cxgSNnU6MrTmVZ1ibFLdosZ58+d48Z0DyQMBhnFfEKmFWWzZJAmxFqzrCpUqhinYy7fvs721ibnt89wdHSEiiWXds7y3NdfYbJYkEYxF7bOsH/1JsY2NEKgGoldenrdF/Zv8H/6w3+cn/mlf0B1tODf+Z5P8l/85B/jz/21v8yvPPcmf+S7PsFy75DPfu63+eT3fT9PfcszfPbLn8dKcEpSNw3LpuL8+fOcO3+OV197nYP9A85vbXHryjV+6Hu/l1/4+Z/H1DXZIOPg8IBzj7+fgU5ZliWbayPybMThdMG5nTMc3L1N0zTkSUaeJjhTe/pWgi69VEht4JT5/F9meccN9t7eHsvlEoHzVG9K0VSl5/SmnewkzpmOw7iqDVvbW2yf2cE5y2uvX6YsS6R1pLGPXj2CMRjLuqYsCpIs49Kl89imYTad04T0cpZ65KNzDiUEDaJr+ThzZhtw7O3t++gbX9tGyQ6IFGlNGtiMJkeHLdqmi1IFUFUNcRR1vb3eKHmPSwiJVl59wxlDuVxiG0Os2zHw42Bx1GWJbUyImv2iBAhncMa3s/gCLgglPCo+9pGDsdbXb+u6m6RFG9WGdHK7zc72dHVSet+csFd9i+dA1CfITNp6OCH6JBjXdhWxWtcBprG+ZtqcQtXZ+2O1mmsT4lhvM7q2MNs7p/4+xT1/hD9t4yNefOjcoqLblHN7XiEfvTqO1kFwrouYW7BbC94LpxjG88R5CYHAdr3iUq4csvb8uvTzPefeFrRDS5mSHheHB1aakL7MswytIkRpWBZzkCJoMcvwjFlKJUOngSXWUSA18kAmIVbZl1VHQuAbUKs+aM+P7x2H9vs4jhGs5DGl9EpsVVkGukvbGWufdg/rChDaP/8S4Wu7zmeMpDyu8+7JdBxd65cQoWXMO0JFsQxtlWBEgwt4DX+fGR+hGt+WaEJ7mLQevR7ryH9eW7Ik85mCyDPttdkVLx4kcBIWZcGyWBJtbTOfzynLJS5O0SpCRgqtI27t7pKtjZjUJdPSE8nsTmaMdMSli5fYHIyQyONsc0KElKsjTRJMwJcI4ecsYw0ioOaNsyg8J0TV1JR1RRxrppMppm48Wn4txjSWo8mEj338O7l25w43DnZZll5dDWuxpkFYi3QOiSXWiiRSxDIwTypBnqTUywVxpMmSFGNKrIXCWb743Nc5e+4c6o2XPA4AsE1NmnhAX54mIGBWLBhtjJFWsDc/4slHnmRtPObOwT7b22tkScqiqpgtlxjhGEUpV+Zz4jgKTGwQqwgRaT797LP89Ac3+cgT7+bVg31+9rf/Bf/7H/nD/NiHv4tfef45PvD4Yzy0tsHO2bN89etfY3NnGycFi2Lha9nlksFwxCgbMJ1OqKuSLInZHI+YpQnnNjdYG+SkoyHjwYij+ZTz585gygYjLNtb2+BijpYLLm6ukca+zBLpiDxJ0VKRpakXlBGBI0Bqmvq0qOL3vrzjBns2nYLwMoxSiCBj6ZHJrvNyQyTWNFBVbGxusLGxgdaaO7fvsJzPETKkhWRP6SoAxcBPGmkSY5zjaDLpkKknwUCeNq9FWSvSLGF2OPH0d0IQRZraeURtHGlMqI0nScJyuaRpvGHujGmvtSoKBrtpGkzTEEe6mwg9gMqnr8vK09wJsRIzcM4Rxb4dTYkAqBI+6nMhZdqim9thc+F72hSzdYzXhkRR7NtIVEhdah0mbQ9uatOybWtYJ1HZM9bHU7itvRJooToxlRXKXHTb8H2v8piVd32L3zk6ogOX3RPMIrr9+rujlVtdlZbbGnlrMH1ufhXiHwt42036W4VV61n7I9f9vnVkvBQp94xH2zrXrt+B97rv2yPuLdZvRBCIUvBUla2xXhn3tkQRavW0mt/QEtUgBFVT88LLr0IwqgSn11qLk47GNFgR+vmFj+yjqK0X++jaWg8Mao25oKeMF9oWV62CFp/w8cfatnV5o+tr2UpppPDOabsNj+VQx0o70OPuN174RSqfsUniCOtW5C1CKISUSEGogwcAnrOIUCJS0hNRGNvgsERx5J8H06L4FW0nQUuX6TsIGn+tGkNT10FhCYqlp/d0CB/9W+uf8fCMCutwSlHUFcuyxOH1viPlWeaU8uUJZx2TckG5kMyrhllVsSgKJB6r8/KVN3nPk0+xlg8xZYUNwhEdV34oKzgJAn8h23usrhuSNKYsKxKdYK2jqCp0FPn0etvqhUDFXmf6sYcfRW2s8frNGxRlgcahnFcnpDadfoN0zjNyaU2iIh/oKMEwy1nMZgyy1JcXrfKsakLxleef41Pv/yjgVcPKqkRZz7xnjCGLE5raMiuWbG6uYRvLoi7ZWB+TpglXD65z8dwZpBAcLReUTUOcpYzSnOl81s2rQgiUUCgd4RrB1179Bk899Qyzq47nr17l5Tcv8+1PPM2vPf81nnvpJQbveoaLDz7IG6+9ytUb15jMJ8yNPz4XeMC31jd59qVXiLRiczwmT2M210ZkScQwTxmORyRpyq07t0nTiAjJsizIkpgsGrE3mfDA5gWSkM1USpFGMbFWREr5cqT1ZYdYKerQQvdOLe+4wXbhBnDG+FfI5yspIUj2OXzLljGGPI45f+EcdWO4c/sOt2/dZjwaUFeVb+VwlqZqEMIDNIQArXyfaBRHHNy5y3K58N6p9JOCabyBVEpRVSZ4Qr7nu1oWLGZzlFZoFZDViWC5rNCpJw1o68dVVXkErgyiI24VGcWRCuQG/jwFdIxu1tpV244QiAC8CiOEtR5o0tYS4yTyLWpCIAJ4rIvyBIC/OSpjiWPf46rihLM7Wzz66CWSJAnUqxKtPdWkb/eSq1RraBtr28hk932bAm8Nnzj2WRoQyK0DJIX0zoeU3Xb9fnuEL+1x9wBvMtwDIoxL60T4H/T+BlrGptbA+VS06hwwb2wcUq2iONrdtXXn1mHrAF5+n4429bpyAq11XsigXbd3fF1WQbRrrI6/3ekJc401q4JDv4Vx1fvsQobFX0sCY5wLPfu+9GBoTIMxcHC44GDys/5es9ZzOCNYFgW1NujKEOWJr7GbgIhNBMoLvHteamO8kdBR16cthNdoF2LFftU3tMasDO9KptSPgXNBVMcZpPTXoSzLY9tos2ktVe9qEg6dJLGmrgOZivRc863YjGblFBljOopjnOyyEEIp4sQ/c6WtvbkO97ZUPd4CZzEutLW1qHw8K2NZVJ4cBq/W5HXvZQAihpfEE2GUBWWgzx0Px4hEBbCnZVmVVM4xm04xQrJsGgpjyJKUqjF849VXOHv+XGjbiqjrBi2VV4NSvmRjbYNUHtBkrUMJL99YNbbjtEB7bEDdND7lrByjfIApKg+udZZ0OOBdTz3NL/3OZ7hy/RqLpmRtNCBCoJKYRbnwzHjBcfKiF5JBlGAag0EyHgyZHk5YG+TM6grlYBhnWGd59c3X+d73fytCCIqqZLKYkwuF0BLT1ORJwrQpWZQFZ7c2acoag2FjOCKSijv7+zx26SKiseweHWIErI1GbAxGzBZz5EaGFl7v3QXxjnddeITffvHzPPDup3nygQegbPiZX/5n/KHv/DY++sgDXL9xi9dGGzz8wMM8+OjDfO25r3MwneB0EOXQGtvU5FnCwe4+Dz34IGe2t5gdHbK1uYazvmMniWMkgvlshmkKhknGbDlHYhlEMddu36AsCuJYY0LbZxz59VQIVG3dgPK0pItqeaqd/L0u77jBzgLlZt00fmIKXohX4XGoKNxwtSHJMt7/gQ/wwssvcevWHcqiZJAlvt7WzvjBiLVeehJ0Z+uy4qA6YHI4ZZAmgbkLX7MI6aw49oQKLaBrOZtzsLtHnue0vOPtZDAee+ayOFLYumY5mzPMPIBDBoPVTuhKKTLwF8Y6onDzayE9aUfrmISMQD4a+Sb6EMEL4YjjjLquybPsGIFFS7jSnzhdFGQPnSWJYpbLBR94/3v5qZ/8d3ns0Ue4c/c2O9tbCIKEpOsZLHO8V3wVRR03bP2l+8w56qbqUN9tTN5Oyi2Pcluv7K9/cjmGvL7Pb9rFmt5xAsge3aUJGQwrcM3x42/LAP1zaLqPApgwRM+yA6ytJFbbcTk5VqsLAa4X+bSAu/5PhBC4eCVG3gK3jhOWBIKSaGUoCWPr26ZMB0TM8wEIxa//xj/zWSHnaALvtr+PLaJxVHUDwqPZldJIqT3q2lpM41MIURQhxfFHXuCzH767Q4dj1SRxgrFNZ0DbY7TWdf3xvhRqaWl5jTFdtN52FGgdIZVDq7i7b5yzLIsFspLhvnG9Fq0GIdwxDYLW0PtOiSo4nH5cJ5NJ6DeP0DrunLYszwP/gr/WHlsoUFGMcTKUViR5lqOjiKo2Xh86zYiUolgWXjPbSZzwBnu+mDGdTEjjhO3NLQ7mhzgg0Qki1ZjDBaUxFMZQW4dOUtJ8AFXFwf4ur1y7zCDNeGzrArPZzGcBIETqEmNqtBJEsaZBhpKPIM8z9vf3eeLsJcp5QZr7vvXlsmAjESyomDQFmYjYztcZrq/z//w7f4Mrh/s4AeuDAXmckDpJnudMljMiHRFp7zDqyBufzeE6R6bECsv6cMTr12+xtjbk2t3bDNIheTrg5sEe0XDArCo8Naq17B0dsLN9ARFHWGMZDXPmi4qyrLi4s8NsMkdrxeZggLKW3YM9PvaeJ2A259bdO8goYntrh7VsQFnXYBOE1rjaUS09N/3jZ87zng9+hJ/79V/iD374u/l3PvEp/tP//q9gf7vkBz/0YZ67eYcbN27y8//0F/h3//if4Glbo998jclyxnSxQNReMfL6lauM8pyLFy4QaclLzz/HEw89wp07tzHGt+JC6XvlXc0gyxCRINKSWEmOFnOuXLnM+XM7TOcztJFkUcwg8/30SZJAXXvhl+GQ+ujf8JS4tZ4gI45WD6gxBqkVCN/W4pxnDUuShM985jOUdeXFC9bGLBZztFRe2gwb9GgFIiBDy9ILyLtg0MbjAaoVxHB0nMot+tw7Cp5sxNU144EHe0RxRBSt0tPGNOR5hm0MSexTzNPptGt58VGzjxJNiFZayT1rj8sPrpihVv29cRwHQI5EBuPrkd1edhS5AtdYu0pXdqCdKGKQDrh8+Sp/7I/+ON//vZ/g3Nkt3nj5ZfI04tpr+7RF2DYaao8HCKlLb8D98bfR5kkDRPh9SGOKlbxm+5JSInp/h3zmPXXc/pjA6c7B/dbx906bvpYrqVTr2YsQDivvz9TWfq5cAH452zsO6Kfm29Rn30lqswInjopOiac3Hif3aai7qLv9/uQY+kxE1HFbt8dircWZpnMmzcYO6WCd/f19cA8RxRHDJEYuJYWpidOERCcgoy5tTBDLcMqLRPgUdxOwIIvumQQP2lRadIYZfGtmsSx9pBv1W/l8d7mvgCjf+hW2XTY1TgqWlX8+EVBbgyBiMZvhnKMs+jgP0WVumtDWVDVHAMcY2qy1VIHdzHdkZD770DREkSRJhx3bWlNbrAEdxYBgPp+HLIJEKF9nHgwGSOUxLnEcc+7CBaI4ZV5McQjPXugc0+mEJI6JqpqDxZQsSmisYTKdAjDMRyyqZcDmKA4nE9/FUlbIJCHREcb5EuHm2pg4SXj51VfYiAc8c/Fxjo6mVFVFNhh0WTw/ljVZEjNZVt39VJYlkY5ZFhWDQONqnM/GLB2Mk4w6zlAq4rBa8Ku//Fm28hHjMzu8fPsa1jQMkoTYCIZ5TqJU0GDwaHelBFVZsjHewDZLFtSMRyMWpuTc+CJxFHNua4dhOuDlN14n2Rlwe/cuUkiOJhNev/wmTwy2QEsEtiPhMWXN2fObTA6PSKKYcZJjCst0OufxC5eYHU5YllWXrZtPp12GrDY10giyKCZdG/Arb3yB//Cjf5TFnSO+8MrzLOyS/89/9Of5wT/97/GeB5/mD37f9/H155/lV3/tX/Clr3+VN15/jcP5ESiBiiLWxmPO7ZzhM7/+m1x6+CnGgwHL5dwTVmnJaDRiY32dbDCksg7XNCwWM86ePUuSOrIsZW04Ih74QOvixfNeNKSuEMKShAyjlrqTaB0OBxTlv+ERdieqQIh4BF4kXfj4TIc+amctEkijyM8axqdzoigCZ3EKpJNdIbM1Ln0JRGcsrm5wSiOURIXJtO2LbsExHny2Uh9qU9GIwK4WPnfNSoQkCpJ1PtIM6VE8Yb8RECnV1eRW7Vcr6kc4Pum3Rrcb+JZuUXhAT8C14ZwjFppFsSBK0jB2kiwa8Nrla3z/9303H/uu72Bre5Nbt255oRWliZMBps0A9HnCe6lbBDjlUPQMDvh6XS86Xa0nuhaxtlZ7v6Wt855cjn92byR/2jZ96VYg9Wr99t++YlZo9FqBxU7sqyUKEUKCWK3Xj4zb83ZCIPXxdHd7fKJnlN1q8/Qz4/3vhZNdBv1Y9B8Y1UT4vFV9EsIDy4QQYA1F05CkOalWDMYjhJbeANSOZWnR0qdtN/Ihh7MZpBm2rlHSoFxgNiMYu8VK5EUai9ZJkKKkO57WUamqKkTniiSVSOnbs5rahH5xn0L3wEuHMTpwl/tpxFpDkqTHrrMxhjTJKYoCHekAAPOMa32jLaRE6ah7hrzT6p+RNE07rIiSAYBG7I9PqK6copXB0NCYGoRnB/Oc6V5K1DrH4f4+cZohMCRKIuMEZyqSRLNYzj3Ri7MU5ZIkz2B2SCS8ypazPv2pZIZMvJhFbRuEhLXREI4OvQNSVUjtHfpW1SwaDjg6PODq4V1ev3OFgdQs6wUxEa5WjJKUS5ce5IXrV4lkRN1M0cKwMUiI8pyr129w5/A2l86coxQGYWryQc7e/DaDfJuZKqnrAqcM58+e4T2PPcVf/Xs/g3M1m4MBqVPMFnO2z52HxiEj0JF34i0NWZRQmhkbw5yxi2G6ZHNjhxfffIPz22fIlWR+tIfOBOvJmMPFkqquyLRg/+AuP//FX+PjH/849XSJtYKFrZjJgkezh3nl9m221lJyGXHkloi84eG1M7wxLXhpeodHN9d479YWkcq4JQ2XYoEqI0xTsaAgKyQPrz3E3//cL/FDH/s+vvTSC/zmF77Kp97zEf7sT/0Uv/HVL/Mt73qC9z3xLlxp+Oe/+s/47k99D9y8xtHkiMbWOBPjasv5nQt86KHHGI3HHEUx9oknWV8bUc/nnBmvYZW3RzvnzvE7L73EmaFgOW0wBQy2Ey7kA64f7vHMgw+TJyk0DhVpZq4hQXN2sMb16R5zV6EizabK7pnf/mWW3xc9bCF6M1kvvdga7fYBE9ABmNp0q5RtDVR0E9vJV/sgt6xS/Qn2nt/0PuuDa2AlvdgeR4sAPwkOEt0htajZFll7r7npR5AnI8qT59GOgbPu2PH56DsGIVChFrJ/eMDZM9v88A/9EOvrYz8JRCuNb6QkjhOSJD2mLd32oMowaclQf245zX3f7eo72X8vw6Td6Wir7r3/TJ34/rSX6r2Of9fWOE++pFJvu25/Gx5Id3I7Ky3p0+6h/m+8wVBtWsN7/MGZEkIc+6z/ettzFqux8pHp8XMC0V0bIUTAaEjSNCfNMi/T2CH/nW9TQtDv0W+CHrPDdRzcrUypte5YVN8vy57MDvgx9Mes2mNnlSVqtbdbp7duKlqxg1ZkpJWVbEtSbYpcKI2KNFGcEMWJ10KP4+DU+1S1tSs61b6yWiu72WauCFF3Y0yQz/XZrraM5No5BE7cxy0I0GCbhrqsKIuCsihorJdFxNluTmiM7WRVfUuYCAS0/nOBTy+ncUqkIvI8pwlZwLZGroTHvuAcsfblgdliybXbt7AClsXSA0GFF+OJtL++Tbh+kY7IOupSS5LETGdTyrryLXJNQ5KkzI+mhD0hBDz00AN84UtfQIT5zIbavccXRZ2Gg3dHbde14JwhEZDig4gL65vcPtzn4vYZXG24u7fLztY288WCyXTqM19xhFWCeV3xxmtvhOvtywhKSTbHaxzOpoyHg8BC55Ht66Mx86KkqivWhwM2hiOKssBKPE+Bdbj2ujYNY6kodcyLd65zYXODjzzyFD/7S7/I+TPnOTq6wwsvvMh8uuCJxx/n4Yce4tXXXiNJPWGTfxYl1ljOnT2HQiAtZFHE5toaF8+dw1QVg9z39Hs664YGGA4ymiDTHEcRwzTD4dgYrpNE4V4OlNFSeG4MT3bknexRmn9TdvObXX7fDPbv5tVOuK3xXhnwt16v7wy0+27/7U/UcBwN269Ttu+hJ3JAb0ITfQdCdNGA3xHd+idrnydTv9/MuHXZCecnhpax01hLURV88vs+weOPPowEymLZ0Zp2QJ2gArYyePKecTh5je5vdN7K2N17/e7rVP0e7gch6AxV9/6030kRUPPH3/df3G+9cB27f+Xqu/41v2f9btv3Md5h7E8f8+Pj0V43qdpMi5/w83xAmqZ0THfGYo1lMBjicB0rmWe1U0ilO+lKIVaiMH1HCoLojZQgThx7j3BFdfePV9zyz82qfavdjjE+6m5781vaV9uYDvjZSpLWTeNbqsKxSuW7METIbvl/W+dGdUbf78er5LXEPN344ctrJpQPnDNdd8FqDNqyBB1IrnUMyrJksVgwXyxownEKcVxyta3DG2s8M1loqTPWC1WkceIBttbre9eN6a5jF4yAT5FKj7IulgU37tzBSpgt5ojQ2oVzvp6tJM76DEocJ8RR0mEkhPRI9sY2GOdBiVpK6rL02UUJtWko6pJXXn8VoTydsbE+fadUS/caeTpU5zqJXBt6shMhSaVCRxGp8OJK42wAFsqqYX20TllWNLUhimOEjmgEWCW5evWav+8if7+kKmJ9NGYyn7G+to6xlrIqyZKUwWDIYj7HmYbNtXXG4xHT+QylJElgNmyffWstwjRk2YiXb1xBWMOTZ85z9e4uz776Mk8//CB37tzlyrUbRDri8cef4M6du14gx65YGqWQrI3HnbRrHEUkOuLM5jaxitjZ9spuk6MjFsuFf/YDMNDiiJOYtfE4SJ9mnu9eeH2IFlQbte2H+J72PEl/V3bg7ZZ3PCX+Vobq5Hf9yR5Ce5D/ZsV5Dacaw25ClKcbo3798bTP+pNqv87YTXBh2ydznl1MIr+58zw5cZ+s2Z48Nyk9uURRVYzTlCaILJw/d5af+hN/nMPDPZqqRIe+1nbbcey9837dvNtf+76bwO6V73yr5eT2Tp7fyd/cuz7dsb3Vto8d8+rdfTbKMTKM+x/DvcxqXeTYvYPTfFe/PdvlhByroTztXu4f8T01bkGvnW61rdbxpGcgvLEOzF5tlGEMGxsbPmrGG2NjLGmaIZxXkervTwY6TugZWGs9Etu5MCwrffiWmlLI0P3Q4eZCHBaQ69aZzpj5CFp7rXIhsY031O2otp0gRVEAEEU+UvUa92lIeys8kYzPPnS4CKpOrGRlrFcZFZSPyk0TWAm16FouvcStF6PxbHseGOfxNd5Rquuasqq8oa1roiTzTpDzbHbO+XHTOqJuGurG0DhHYw2LqmZ7OESH8kFdN9y+vUttGqLIt13hnAetCYEKBjtVEWWx5Ob+XYwUHM4mIOh4C5SQxFqDMMRxgow0TgqK0gOXpvM557Z2cALmywVr2YC6rIgjTQNgYDKd8ObVq0RpQl3MEbiujBFpTay1Z5wUDYLj+IpYa7IATBRSc/PGLc6sbdJUHtQ3Gvo+ci0UOvLMcVYIDJ5JcnfvgKPZlOF4zZNb6YhBmrE/OeLM2W2M8/PaKB+SphmTwyNcY9jZ3GRjvMY3XrqOFIJxknJUGYxSHbBxWVcM4wFHsznX79xmfSfi29/9Pv6b//lv8D/87/4Cz73wClduXifOIi9ZmuXcvn2XONZetEUpkihCWcl4fY3haMhsPkNaWMvGuFHF5s5Zbu7tsVjMscYwm899V4CzWEBHEZvrGyQqIosTbN0gHGSJ56BX0mOaWn0CJSVp1HYHvTPL75vBvh8iuDWQJ41U3xCcFp30/z4tkr2fkTxpJLrvTqx/8vt+ZHJs+25FXNFu/63Qz/2o+bTvAa/1GyIipTSz5ZLhcESapsxmC5Ik43/zZ/7XpFox2T8gzVKSJMVaj57VkY9YqtoLioie8TmeEnWd0f69cNyeZnDf6rxWS3uN7vNt7/PTMWiu20Z/m6ft897PTpe3W13ODlbu04fH9uDR1SL4beJE0fp+zpiTohP1uGenwp/PCrnuW5TaCHmlE+07+ZUQILza1Wg0IklSirKiKReYpkLpmLqoGQwGwWB5VrWOuhe6lLI33G262vd/ty30LUbBd09Y6ircW1qjJbSCH9aZkM3R6CilVe9xxjCfzxkOPQjM4aO95XJJlg1Cr/4K/NfWn41pR9xgrTdMrTxnl473aj9oLZnPPYhn5YyE6xAyAu01UTJCSXsM/KekQsUSFekOUV6WJfPFgsHaujeQPfChsy3/ui9LOQHTxdzXn5dLkijBOiiqGh2nOOfHq5FVF12JxpcF8jiB3FA3FZNySe3gcDqlthYrPIo91ppEauJUUk4qokSDVrjlHAKILj5/EWE9y1kdxyRK+/S7liyKJddu3eDN61dQ+Trl1IttDAc5kVY4F6OVItZRKB34O1Fr3WlASO2NTB7n/PyNK3zXBz/K61cvk2c5D1y8xGee+wpplFMLuHz1Cu9+4gmefvJpqsWMX/7qs0RZxvcPh56UxRgWixnXd2/zscffy7wsmJcFm8N1cILDyQRpHZuDEWkcc3P/Ds42XNzcYbnc89wCQlKUJbtlxRN5yvsvPMTzl6/w+q1b/IUf/wne8/TTfPHF1/nEx76d29ev8aUvfonv/r5PsLO5xcHMR8pWW4aZ5/wup0viOPLob+eYJTkRgo3RGpsbm4yHQ87sbPPo1iZXLr/BIE5RSYyTwmcWqpqnHn2MUZIxO5ogpSKO4kCYIkm1Fw/BOmKhGMb/hstrfrPL2xna320a9bRtH3svBAbnI3IdlK9EmFyVBClonPXeVPt5b2lT6i2JyFud1/3O7X5LFHuRkGVR0ISoeTwecffuPmvjEX/4D36K7/rYR3n+ua9xdmeLWCvKgD6UShHFMU2gThXca6Q7Q326NfxXuLRQxLZ6ufqsq2Z2oKzTo9n7XfPf63JseyevXdunLkRXsjm53qln2Y6zFB5MFri5OyePVVxvhY96kyQhTdMVQtr5jJOvN/rIc7kseOChBz2vc1kyHI3QWrO1uY3qCEtWDIKz2YzDw8Pu5QFuvj9bh5qqVjFaxV2aUGtFmiasra0xHA5J05Q0z8kHQ7JsQBwlWGuZzxccTQ6ZTo9YLudY6znAl8s5e3t32d/fZbGY0fa8O+EpUOumwREEe0LtT2lNnCRkgXO/1WmO47iri7f62oPBgDzPu+PNsgStJUVRMJvNWCwWnYPiOhW4UGZytvdMePDpcDTi6PCQNEnAWE+IEnlylq4ltLYs5kuWRUGUpVgCsFUIjLGUVY1r8THOBhVBRyIFsZLkSUyiPcFGpDW1s9ze36WovGpa6xhFasXjMDmcIJ1kkA1QKCKpGA1H7B3sY6wlzTL2Dg98psMZposZe5M9JosjsnHOjZtXaeqSUZ4zygahnu5rw4nSxCoikrrDJszmcypXU9YlZVmAEmztbPHg+fOev6LybVraCT7xbd/OwXLKwjXsHx1y7bU3Obq9z3d97/fw6vUrfOnrX+XGjRtoAbu7d7h9cJfIWnb3d5nMJmwORiwXC4qmYhinDKKYuqm4ub9LXdc8evZC0OTz825VVWwOx7x46wobozXe/cRTDNbG/Hf/4O/yEx/+BD/767/I/uSQhy48wIULF/nc73wegEGeE0cRpm5YzOZUZYlWgqZufIkpy3ng/AXfgYSkXCwY5RlPPfooH/nAB3jg3AUWs7nHigRyrM31dTZGY3AwHo4Y5QM0AoUHMGeJf4aTKCZPMwbZv+E17HY5bWI9zRi/3bonI/GTddH+dk/+3b4/Fi3fz+CH9dr6oFIqtIP5pV8PO5YbPeW4+8u9rUHtBo9H5z7CEkwmE3Z2drh27Rbnzm7zye/7bn7gk5/kyhtvsLG9RREAJ0ppqtqnqqTQnvCgMaennd9iTN/O+N3Poeq//+aNp+j93w9Cf9XV9jj2Ornvd8Jgn3r9e/eZj9p6tXx57334lrX+8Hlf9nK1Lb89i29hShJvMPs9zCrUdH2EH9qtrCXJBjTGMl8sqWtDY72j1ycraUlD2hbHNE0ZDoeMRiOiyH/mDWLiZS2jCK2iVfeFs5RVQdNUGNtgrME4T96htSZNMga514bPsow41PAGA197H4/HDIb+7yzLVqC6HgiuLMtjwLKq9BrGZVn2QHM+C9AHzBVFRVFUVFUdjEjZCeqo0LrVGvMkSUjbV5x0zoASEuFAS9832zQ1xWLelZi0Up4SWfjSnG08cRMhFb0slkRxjGkMw+GI7Z2zVI3BNTVeJ8YhTA22RmEZZRmRFJ5pDGis4eqN657bW/hnv8WjtDzrWRKTxTGx8vTO4+GIqqy8prXUOOGBgNO5Z5ZclgWVqXHSMZtN2NncYDmdopwjVqrrtpHWEUsZtrsiMyqKgkZ5lsW6qljYmu94/4e4eusaF9Y3MVXJopjzPd/+Hbz55ptBzbBhkOVcOHuBcT7g9ddeIx8NubF7h73JAYNhjo4UMtFkQjGbTSnKgnGeM13MmJRLzmxuM4hiqqJgUi6Ioojz69tI5wGCDo/OT+qCaP0Mn3v1BdaSiB/90Ee5OV3y6Re+xgeeeITf+M3f4sqNW3zXx76To6MjsJ661kuPRkjhSLRPiydJgsB36owGI5qqwTrfJTHMBmyPxkRVzbsee4KL5877EpHznBqmatjc2MRUdQ+Q6UtZkfKOpg70pFG4197J5V9pSrw/ydoQ7bpg/MRb/PattvN2k7fsTZz9enl/cu3XsI/JDDoXCFzoUoqiBQkZe89+jx2LW3FO9/ex+jFdkGld4DN3jjzPqeuaLI35wR/4JB/58AeRzmHrEqkUxhoiHRPFGmkMSkW07FMiJG7vMdnC5zyFEB2C1YqTP3lro/1Waf9wwvdd994Tv08WpNuSu+f3/a6D9v0362i83fdCiK5tsL9n2b8vQ1Zeivs7KSfLA61xXzlmotsurHAU/t5U96zfbtMY74y133nD6tHLnt7z5HPgaFXIjtW1pZexXUWf3V66+jRYL74QaEallTTN6p6WrJ6psiyRrY47gto02K5cJIK4SptVcB5MJT31qBCgpOg6HURoQxOCrufedccvaB0XgX/+hAxo8cZTP7YOj5Q+zS5Dul1KfE0xZJ+scx2yXGkPRYmUZjqdBhY0r22fxHGLVCNL0sB77tPbTvhavY5iisawd3DI7d1dH/EnCbYsiJRE4YilYH00wFjLEodWEo1mf3fPZ0XCPOK7NwRRHLOcLVkbDEm1pxl1xjDMBxzevEG0cyYwLDqyfEhZHDBdzCmakspUQY4zojGKYZqRSIUWAhtqrG3LqgWM8POw1tpjxaXnGRDGX68LZ85x+fJVxioh1xFREvHUw4/wK7/1adJBxtZghK0b7uzdZTPPeeT8Ja7fvcX+0SFCOs5sbpAkMdkoZ5SkmKbGOcs4H7Aol+xODtna2CDVMVVZUZiaLMsYJSk4L80qItCRZjabsvHAQ9y+eYW7B3vsZEO+9f0f5rNf/zzf9dhjFIua3aMjqqriscce49Wrr2Gkd/h0AAimcYIpqs4Ra5+lqqoQ0jPf5Yl31KSFh89f5O7N27x4+wrOGJIoQiKwdd3NC1IIIqm9rnibgfUR0jsSWJxc/rWgxNtoxvkVgOPTffdbYPXIn768FSCtnTC9EQ6qU32D3QeVCU9p2I90PGBrZbDafUXRShP5vpHqKfbrtAvoHQGDcQYRopSjownf9pFv5ds+8q2cP3uGYj4nEqFfG19nlUqRZpkXtw8sKK2D0G73ZCq87T9uJ6/fy7U7/cU3+br/NTrt/vAvjiG3Re/f+zl1b+X03e985YmXkvKYcT6539OO+6QTcDICb2/mdqJYGZkT53+slOG31yKUEQIdxeiQOm8zOO2uV8Y5UHSG0k5LItS2Tq3Sxm7VFhVasTxHQTD6tOxsbfZG0Eb+fYY+J6Cq64CiXtWOjfGpxKauuuMSInCuONtpDvhSiOfNb2l1j1+rFq0eHYui2zHToYXROairGmMsTVPTVL1o3dKNbQtCc8636yzn88DHDVrKQO7in5U8APuqslw57eFV1w3zxZJFUZJEmjxJyBIfGUvnnZLRYODBZC4AkeKExWyGlp7Loe+oZXnmNQ+C4TB1TVNVJEmCNR6AaoxFSUUSJx44V3ka0KIqsM4wGg0pi4ILZ8+S6whpHVGkPUFLOxdKGbgpgiSq1t18LIVEI5CRYhynuLphPR9wcXuHREiu3LnJEw88xEM755EWdg/2QUsurG2yORwhkohJuWB3fxeUIE4ThgEtLaVklOdUTc3u5JDRcESkFFVZUpnGZ2ykAtvyyQuiKGZ/sWCoFMMkY3dyxOu3bnB+exOhFItlxYUHLuGwvPzSy7zrXU97HETj2S7zLCdNErI0CWPgW3OllF3giPSCOHEUMUhSEiFZywZsra1h6wZbe7llrRRN3QRJZT9HaCGJ206KFhQihG+rewtw8u9l+X0x2O2/p02qwfk4FiG5E+9XRocwYd0btfffC45PlN0+j02avu+Y0HlI73etYEeSpoGRbRVR2/65hJpkFEVdRHbqGLgTxyfEW75vjPHbTZIwMTv+1J/+aYaDnKODA6Kg8qVjP8E3pgltBgkWun7UbqRcdwD3jFXgCXnbcvY9NfC3W/ql6G4g2mh59YUfx95N3RrldnzlcXKcfir52Ou0VNOJe6/9u7+v0wxr3yuUouUG6DkZ92kv7Nbvb6v398l9dRNicCJbHfWutt+Oc+hBdW5F7evwhsgqiYy8rGNtfHuSxXXGs82kaK39JB3Hq37iY8/DSgxGqRZEFlLJcUyWZaRpGmrrCXmeMxgMGQ59LTuKYobDAVmWhnq43/5gMPQylWF9JVVQdBP0+8+d8yh40/h6e1GUXmayqkKN0WADK59zdHVtEBjraJrAoNjrSW8zEU1jOza0uln1aouACWjPGbwjFMcxTTgOKQRaaXCEfnRBliYI4dPG09kMpRTDwZC6bpBaM1xbYzAceiR4kpBEMUp6RTKs9XVN5UljlJTkme/tlVL6PvCmoXVxhoMBSZp2TlJ7HlJ6ApmD/QOq0jNDWuOo6hqhBPOFr98TtjGfHPHEIw+TJwkEsqgs9kbGo9eDsZFekzxJYlzjOdellKRCcTA75Inzl1guF2yvb/DQ2Qu8+frrGGH5A5/8JI9ffIA8zSDSOCmZ3N1jYzDi3KULECsuX7/CdDn3XNuRL5tEWpOnGQ7YPzpgmKcIBIui8DXtLO2MtQlZRx1FHDUGMz1gJx1So3jhzk2e/8ZX+ehjT/Hs9Zs8/cy72BiP+NrXv8bm+iaPPvIoOMfG2hpbGxvEUUSaJKyvrZGmiSecEl4zHiEQIaujgvGNlaYuSzbGa1hjqENrYV1VaK286FLieRKkEMTaZzpbR79ts1U9sqx3YnnnDbZrCQMCGUDLGObwMpJh7jahxaRbT/bYnggRgTMYCTLW6CT2oDElidLEq9LgUFGEZLXPYynLcCHKIEWXRLGv4xhHJCQaibRAYxkkGe991zPURcl85inrpMOzn1lHEsekSYoW0gMRWEVRx9Lo1voJVIIRlropvSpXaCVpJ9OWEEJrTdlAFOcM8gG7t+/yn/1H/1u2hhm2KbCuQUQaF8c0je+7HWS5bytoGlwT9LuF8FKbJ9DgJxcnVg5SN/b3iVZPvb6nRckIn6okEIT0EdzC+bssvLp9h5cTLQeY11pynG7suuPvzmuVJfCAouAahPDNCf9qt+nDOnns1f4mmEsQATl9DPgWMjESPLfK6TXrdmza1qw2em6zM77PVRHrhDTNybIBQmmiJEVIvUr/WhcEVUKWx1QIt6SOImIBFglWoh0kkefozlLfSuIaL8mKFZRFzXJReMKHOO5q2Hmee2lMEaJf4yVp48QL6mgdEUcZw2SNWOVI6aNaJSNAUlQNZV0jdUwaD8nSMWkyQIqYQT5iPl/Q1A5rJJKIQTZCq5TRcJ0kzpEiRoqEOBqQpSMGgzWSeECWjRiONpEqRYgIr2QG1vg2LYRiMp2zLOdUldfSLoqCLMtRQUyjaWqEdGS5Pz/d9pXrCB0nJFlGGmru4LCmxlmvEx4Jj8IuqhojBE4KmqpkYzRgMEiRsaB0NbPlnNgJmvmSfJBDLDiY7XP9+mUyJBtJSiQlVkGtHbWryYViazgmCmnTYZzQaMfMFrz05itMmzkqVWAakgakEag4Qyc5+XDM9s4ZH0XXJYPxAKUEy+mEejLh7OZFbt28C6ohHSiUc8zvzHn60gNEbk4ae9lMXRu2NzeYTo5I8gSVKUQEQoOwDYNIklQOmca4cQq2Qc7m5Bc3UDguXXiAtXPn+bUvf5Gf/s5v44k8x87m1FXDrKj5zd/5PM0o5c7RAcmsYiRilrHmc28+z3ecu0Qdpxip0UhSp8jrMTeW+7zrgU0SIq7fnbC33OWDF86i9YBp01AJhwx97mcHY+7uTYmiiM21EXGqeeHwLhfG6zxxfoff/OxvMjM13/PJH+J/+h//Jk888AgPn72AXVaUswVpHFMsCrY3NtlYW2OxWHDz9g129+9SFIXXhSBE2lJSAg2gSuO11xU0wpBFGisdi9kS6TyHgY40jw42cbFmkA6Ia4FaNsjGYqJ3NsJ+x2vYxvkWhVZ5Z7GYE8cxg8GgA4hEccTW1hmstZRFcUwNqm2scc4F2Tu6Fo/ZbMbDDz/McukfVucCIxk9lSHnZQCTxBv13d1dpPQUpVVV+WhDyC4iBf9g53nO17/+dRaLBVmed/bOA3I8mrWdeKUIiHNWkeg96VDX/u1JFRCia9lphRaiKOLu3bucv/Qg88WCvcWCH/u3fpQf+ZEf4blnv06URB0tYx1qKCs9YI7tfxVBfxPR8O/DIjiWDLnPL05NxN/zydtF9G1veX/N48azO5pTI+F7j6D3n1ill4/9pk/3eto2xPF2xfY423PpsgVqxYlurTuWjWkj6n6WwlmHdU1Qj+qn2oMue11T24JE+SjQGEPVLLuItBXRaB3G9rnpn0tVVTSNl6v0KWvvWTmC2l44doHAGNdFs/2xbevHa+P1bqyc8zKQeT44JmjTPudFURzLoAAMBgNsYwKLV9JhACyO8XjstZx75ZXjrXBtGt50AjKtE11VVecwt+Pv8CCwSEc4IRkNBmgtARucLc86tywWnRNqBdzd3eWpRx7n9uSA1958nTevvMHm9hZpnOAag3QWZxtsA0kUs76+zuHRhCxOWRpLXXuw3ZmtTYwxHB4eMsyG5IMBtw8OydKENM+wEuqyRDjffqaU6gB5xBFIwXQxp1nbZHdvj8IsPJmJ0WxsbmITS1H4fnYZKbyuOV4ys3YUtaByTRAWgmg8Yj6ds7/cYzwY8ehjjzObL3jvBz7AIB9w/eZN8jji4x//ONduXKcxhsuXL1MJxx//8R9jZ5zz2huvkUSx14rA8OwLL/DB84+BcOQ6JsfDo3oAAQAASURBVFobsLWzw/7uAcMoYmM8QmpNHchiNsdrq6DLOQhAvYEV3JIl5cFt3vfIEzx98UH0/oz/75c+zX/8x36KF599jtmdfdz6eb7zY9/O4f4BEkESAJZJnLCxsUEWBJ2qUGaI4xik8tzstSEOmQCpPGhMa0UURx53Yp2nz61W95F3ehtGo1EHfoyiiDj1rbfUKzvzTizvuMFWkTfUxvlaTz4agvMC8MVyydr6OsPhcEWhaL1aT5sCxAWebus1cG2QJJRS8q53vYvbt293UWpbm+oezKCdi9CgJNPplCzLOrrDqqr8BKRVN3lEkRcE2N/fp6qqLj3bTmotIKg9jm5SJkSrvFXa2BuBuml8i07kU3c+GvCpus3NTRbzKQLBM08/yU/+5J/g5ZdfQmoZoiCDdY4kSYjkquYJx/tr38pgf1MG8OSRi9OJTk5O9idOlw7VRi8dfWK791u6iJm3sPunrRci5f72+0bzbTMI4XqujH7/aOw92+wvJ/kDuuPpjV1rkFpqTQfdw31suy4Y8DZbEBxSRFvXtt5wi1bkJsaJBEPtUdwuZBhckKwUQZVKBgfGgVIRzvUIU4ToniEpha+Nu3B8gX+9RbkLIb3utHPHBHbapTYNDuX5AcJ51XUNLDGmpmlWPdz9oWyPoS6boCXgeqlr1W3bWotUXnvaWZ/5KJYVSkmc9YCytuauVNyt2xLHtIC79npY22BdA6nAEbJkAWjm1awSkkixsAYJGOdrwctpicVwNJuyu3+Xw8khSZqysT4mDyIRulpS29qDSQFTNyQ6Jk8sJT6zNptOOffgQ5R1xWRyxObGNlVZECeafJAzX8yp6xIhvTphGsW+Lt/U2DhFChcUCTNM0+CsYTAYsDbYZG1rk0Y17C/2OSqm1LZCR8r3h2cxs4UAZ3FGAJJFVfHsKy9xJhsz2T/khTdf5w/84A/zzGNPYyNLtSypy4KHzl+gRe1vb2+xtj7m+u4dbly7xns/8d186AMf4oXnn2dRLEjSBCsXHJb+71T7UoOQkA0zioMjxvkQWzrmZYGQgjMbGzTOeKZHZyFIvBr+/9T9WbAtS3rfh/0ys8Y17vlMd759p759ex4AohtoAMRAkAAIwiQIwGTQDIpiOBQCpRcpLEX4waLDEQ47JMsPVthBh8IOhm2KpAwYBGEAJNCNBoFGN3q4fce+45n2Gfa8hpoz/ZCZtWqtvfc55zZOS3SeWGevoSorK6sqv+n//T/NhfEGcyqu3blFkFX89Gc+z3/1u/+Ur7/+Cj/0/ItMjya8+srL/MxP/ySvvvoyo16frMwsatsVvI/CkP39QxdiSAmjGCkVh4eHLttBtHz8aAs6VkK0BEbIEF+HAiHcPSdYX1+nqWuEMYTKktPEUUSVz89fd76H9vBd4lK27kmfCmMw5HnOcDRqq1bN53P7MHfAUgv3o3TWgn1Ioyji4sWLTKfTVtCtxmQRouXIFtDmnHZdmN4y8Iusz/X0bEdLNI6mYw20QBW9tECtirOzrSvRWkYW6OJBQYtjzaYzPvT0U/yln/4JemnCyclJy1eO7cFVlVmwV5nOmM4TrA8af/7A8epTzQnMrhHdCr3u60F7u8dv3TnufD5PKJ8XV77Xi6WYtXSfO1XJRMeVLoR189vgd8uO17UAV93orMy3d4V7cMEihXChxLb+BGOzDxYpY2HLuBVGEWnao9frEScpQRi1PNhCSILQ1pCXKrDc6f4cwKUwOj5zKZxVYmPY3RxtT3/rY/CRS5dSwYLW14YV/BhD6toSogSBcve1PZWu9W/vY+v+NsbGu7W2yq1NB4OiKNGNaWPbCw5xg+dst1O3qHznx9G9Fh7wtwhbgOURx+Wx107xtcJMIlpUseWch3mekReZoykVBMpzgkMvscAzJYWziCtCpRgNBvSTFAX044SD/X2GwyHz+Zy9/T3KqrAEKDPLUFbXFVVZoIREuzLAVVVT1hW1rql0jXLXw+cKG2NYH6+ThBHrwzH9pIcwhqLMUUowSFMGSY84CAmFrYpohCAvK2opGI7XGI7WOJln/Nmrr1CWdl0sG5t3/uxjj1FXFUJId2zFaDjkhWef5/aNXY4ODrlw4SJbm9s0ZU0QhuxPjplmM3pJzCC1ykVjDOO4x6g3pKgrTuYzlJBsj9YstkFb4SeMo4MNFCav6KmILM+5sXeHKhD82Mc/x9df/TZJr8fjjz3GYNDnxrXrPPn444z6A4b9AYO0R+xi2E2trWAVgii0qY1VbVnvsjwjL0tbnMY9ax6cBt21dlkhD5RibTy2oEXjMTDS5ek/XAv7+5KH3bU6PDq13+8zHo8BLOVbXtjHoWOV+AfKOGFp0zJkC2DZ399vF7FWaAsnXMXCEvAuMM+YBF1mJN3WwLaxLCvcYbGA+HPw7Syh2C6qne/uJTS8R8C7En1+7nyesbGxwac/9Qk++9lPs7+/15YfXFI4WHCd++OfUiA6ltkHuVYftJ0S7K08dm+E//tgAvVeFvB5seKlfc4VvIs+znp/1ufTx/SIZXtO3Rj84r1YfjmB3wXOda1ojDmt7bnvVxUnG5/vKI+ineylcSrHuBQnCWnaI+31HCNe3OZcJ0ligWVR6DjDreCWHoPhhacxbfxXdJ4b3Yag7D8b37ZIWG1M++wsCshYZHfT1K2C0S3AE8dxGw5DLIS0cO8b5z7Whvazr2rnBbyvLhaGoQW4IV3YaHmOPM7EK+lhGBK4et3WY2bnXzsXp7XEbc6z6NC0GmOQgWSWzSmrAqkESRKTRKEjTIEkDIjDACkWAjuJE4a9Pr0oQSFJooTZbEaSJORlweHRIbPZlCSJmU8nYHQLvAuDoOWp9tXOGqOpzSKMAVY4mEYzHAwJZMCoP2TcHxGHEU1dIYVg2O9Zl7VYpHhJYelVUdYjur2zw4ULF3nt3Xe4tX/XXn/seT+6s+P8hlaBMsYwHo149qmnONrbJ5vOSeKUYX9IGifEUcTeySG7d2+TJDFroxGNbsjLgnF/wCDuMc8LjmdTwiBgazCmyHMa3SAFBP68JLZwiJEERjAtc169e4PPPP8Se3f3uHr3FslowPPPPcv7773L+njMeDhkY7zGsD8gVIp+2mc6m4Fbf5UDmuVFQVEWZEVh31clVWOxQWHg6rI7w8uGreTS2huEIf1e3yoCYJW7zv3yMNv3pbxmNy3Kvz7ykY9w9epVptMpVVU5sgBb8LwtgiBtBZraaXVxbJmI4jjmrbfeaq3zrtBSSqGrukXC+ljdYDBogV1dDdvvlyRJa+l3t1ksrAuOXeue71p2yy58f97dxb97obyALgqfMxqQJIlNySgq/tbP/gw/+AOfQTe2FN/aeOhcid4SVw70Y5u/WbqpOUvHfYCb5J4uYh5MkK+i3peDyvdxf3/A3+4lWM3K4nzedue5toUQbQnN7ratkdU2w6q7e3V8/nchRFvmtOsJaWPVvkdnVXeF9dIRvXLq3dbCu3kNVd1YN7RpUDJY9O8ElFdI/bH9/R+GobtFlo9n075srmxjtHONW6SyWPKYWPezaapWgBhs2MYLR79vXS9Qzl2qVG/leq9Roy09r587Py5LDWorQCVJglIS3YDnHo/jpLOPcH0HrdLr51A675ufZxtOa9AWJIMQAt3U6MatFY5lTEYRd/bv2EqkwlBVJalSzLMZta6QAtI4Ym08RmgsSUdtCGtX7UzbIj5JnBJmlrK0H6fMplOGvYFDpUuKIuPgcJ+NrQsYGoLQovzjMCRKU4a9AQUNeZ5Zb4iCRtvYtnCoe4wkUCFplNBgGKZD2BYcT084LI4QBob9AWWWI5GEKsJIbJGQBm7duMHN/ogL4y0+/KFneeVf/zbffP1VHr9w2eIJQkkqFaQDmlnDdDrFGEjCiP3bt3n04mUeTZ/gD/7oK8hA8vSTT5NffYcbs/d58913ePrKY6yvr6ECwTzPwEAkQybzjJNsTpqmbA2GXM/vUDc1cRiQhCFmNkVWGrUzpsxyRnEKfclXr32Xxzd3eO7i4/z2V75EmCb81R/9Se7u7vLm66+zvb1OWVdM5zPqpqHX73Hj2g3rgYpii07P5g5rZbFLWZFTNzV902PY61ulsqOAYmwRm7puCJznKgxsCLapG0LONhoeVnvoArssSwaDAWVZkmUZQgh+/Md/nO985zvs7e21AC8vkLzFKwAaTeO+7/V6bO1sc3xywvvvv0+SJHg39iLmZsFjXmuezSyNXK/Xs5za2hB468gJOCFsvOHo6MjW13WuPe8J8HwSQixqLxutrabtyBqWrCPnBj3r4rQgNaeI2IXS7pxlOTdv3eF//g/+PX7+r/xljK65eeM6o9EAoC1PGEcxSZK2ngP/8r9/L5zgfmzn3VD3jFOf1wS0E/PnuE/vJVjP2cEZ96evgRCuWtIZD9CpczzrmGJxqUW7zUKgdP9297WuU1cJywEiveBtf8fKaa8orArrritXd/Ka/ba+SpYUChUq6rJy1qiwloMR1FXTjtHeK4vMBCmlw1/Y+8fHjL3F6SlS67p08fDlhUhKSVWUGCMIsRZt0kupqsoRgjRoAUEgieNRe5/6e1ZrbclKnFYkpbIlOMPQ1rk3hjRN2+OVdWW9FTaBu03DsmO047MeLI3BVrLq3ocL8qRFCpgQEBibuhUqQV6VlEWGLksa45SGRpNEMUJIytLmCseDPrM8YzadoOuSfhyxNR5TzC0V6CwzBKUkDCW9YY/QUawqqWz1JinZ37vLM08/zWQ2YTAeEgUh+/sHXLx8hZ0L2wwGPYoip6wqlAoYD8fcnRyAI+1ssJ6X7OCQurSlQjELbEGvN6AoSvppnwvbF5npDGEEg8GIvUmGlIEVWtJQa00gJFI35MWck9kJ1XzOztY2f/i1r/LSU8/yxBOPcnFnm8nuHdL+mCeeeILf/bd/TC9Nefqpp3n+Q8/yj//r/wObj13hx370i9zY3eWrX/0TNh+5wIsvvshb71+ll6Rc3NqhF6dMZlPWt7dQMuB4PmNWlWysr7PZGzKbTdCmpp8OGfUG7B4dsj4ccfPkhFgo1sYj1kcjHq8K/nf/3X/L//KX/x5fe+M7vPbdN7mYDvnox17i+O5d1gZjJvOpi5sHzGc5AsWg10epwIZatOHk5IT+0OKqTOOqwM00g16fQAq0MGhhs3+MEARIi33ooG2kwRUDsSleURi7UNW/40xnSimOjo4IgoCNjQ3W19f58pe/vCQYq7K0p6ltRRvp+NH9QhG5PNCDw0PyPG+pDz1opIu21lozGAw5PDxsY1JFUbTWN9gp9fGurrBecil697lYWBB+YdJCLD3893KrrlpIXqGoqqodt807zfjUJ1/iV371l5kc3GU2ObFVZYKApqnac/auwOWSg4tFr3vcTmLUA12rh+WuaS1tefr7eykFwNnC7gNop+edgRfW57XV47Zjbd3ObrulnRZHXB1vt3W9Nb6YxKrl2P177zk6by4WrvCkF1Eri2RVQehQrYtwkLcwPVisGzf2oSUpl58Dr2xbq9uTniwXw4m8u9A/J7XNUy31cvwYaEtt+lCQL9ThrW8pZav4+ipfXomp6orSPfthuKgZXzcNdd4sjUkGNmc9SdJWqbX9CPeyDFo+j1vX1qUsY4WpLdUrEmQUEYYgVUAv7aOkoqpryrICaUuGlnnBuD9kMLI52Hene1Qbm6BtqdEwiggNHJ2cYGLY3toinE6Z7t7k2Q89w+7dG9y+fYvtzU2ENOSVjYvHUYhx+dd5ltFkGYFU5FnhqoxVzLPcEpokKXEUMxwMaQhtzLY3RIU2dTQNe2yM15lWM2YnM9JkCHKPMIqpFZRVQVM29KKYZ596nEEvoapywiTko8+9wB/8yR/z9u51drY22Bz02dcVcW2oqoZ+b8gLz63zzLMf4sZ77/N3//bf4Rtvvcbv/va/Iu4P+NQnP8M7776FCKCOJLeODzieTXn2sR7vXbtGMh4gjGGeZ1SmYXN9i34UMc3mNKahl8aM+j200ZzkM8Jao3sBNw/uUmYZP/TsSxzkE/7o1W/yt3/hb3Dz7Xf5rX/1r/jP/5P/BOOKugQqQIqA48mEIEiQLtSjlL2emaskJ4QgTdOlWuwn8yk9rVy1NpupI7tpWhoMGm30kve2n6b0ej0CuZyN9DDaQxfYxpGqJ0mCMHB7d5emql12rq1A1K7sS67BBSpbKcVkMqFqGkvXF4atcIrjeCmG3e/3OTk5cbmlC4unO4Faa6IgoN/vc+vWLUJHtuB/98LbpjcsuyaVUmi5WPjahZfuYs+phfisRdyP2cbQI/6j/+gfcnx8RJnn1oUnhSVvkN7CDxZpXGfENpfm3aMquwCl/8Hakh16/lbnzM1ZwvM8QbZ63q1LvDM3i9DFg7n+hRAOzdER1kKcIawXb+7ltu+CHLvjasd7xjmcNS5bZ9p6eIyhBVXaSle4cFJNVQmEWdRx9zpb160shGgLaHQxGV6xaLrhFSf4lfQYCv9c2bHZR9FQFoW9p8GRu2j3u7FsWUEAWtM4L4EX1tLxpHusiQ85GawyGwWL8I8fn0+bKYrcWsYOcOef225YywNIV5UiH8cOQ5zhUFOKCoEFulm8QoOQlq60qKpFWVLngSgKS/AyHo5QUrG5vs7m9iaHBwctU6LAEJsaWRdkecabb32XjWSNj7z40ZZt7LkPPcPXvvlVLlzcdveEnYuTkxMiF7LQWlNXFUVdE49GgAs3CItYB+tNlELQ7/XIaw0I0rTHdDajljWN1CgZEAcRe7N9qyypgDCKKcuGJrfg3ySOMbpBNDXCKBoBk4MjhuMxu0d73Lx5k/6lS6xtbdJUWI+BsrWiy6KAIGZydMiVS5f4qP4o712/wauvvsb2eIyMGgZra1y/eZN30jEfffw54iiivz5GIqkrW6az1+shEVRVYVnHgpAkjKzxFQTIUNI4r6lpNG/fvs6PffiT/PN/+Rsc3rrDIxcukr30Eb7yR3/EZ156icM8s0lDwpH2KOkYAuUCuOjKvw5Gw1ZhN+5+LeqaCNqa4o22bJRNVVrmX2Naz1e75mjtjE57D3sv0MNqDx10Fidx6/r1DEbe/SZYIOisS9Ahnd0iIZwGXtc1dVVhPHMTPnq2EPpKyiVqv1VLqfsZ7Dps3ANw3iLeMkU5V6V0FhKdY7eLMmAclaEnh+9a9bAwyASWwEMGirJuSHs9fuzHvshzH3qabHriOJbtImY0YCRhEC7AM+6cupSSq83Oz2lKxz9PWwVAnd+8oLu3cFy1Ru/n/r5XrNh9OBOp3+5/n1Ev97EQ1nZc/qMnURFWzxSr23kXcddlDAKLcPVsVT6Vyh1yWflzi6/93hW88OlxwtjnAAGmsX8dAY1NsbKCp25qxw7VUFf2sx9jK/w7XpluloJPe6ycgMLYY5ZFQTafU1cVTW1f9nwciloKS7+olMsSkAipHJAtsBkbStn4qkOgN9q0qWmexc1fayGMzRVGI5WlErZ/g9ZLYNHdUcuuFgTKcb4bbAqeIQwkdV22xChCaJSyfDla25g/WARw5Dx6jWlcoRNLJlOWGQqNqQswxsXFAzSaRlf2eNoS1wx6Y9ZGW1CVRL0EFUYkYUqqYibHE/qjATdu3+Q4mxKEis3hgEGaUsxz1obrxGGKFLbk597+PoNhH1NbQSvDkKwqieOIIsvQwjNVCIa9oY3IKcOoN2AUD4kIqZsCFSrQVhHT2HKmOOS5RJCEMWmYoDwmwq3LBhAKelGIKEpUoLh7cszdk2OyqqIfJ6AFUgUgbQw9m80JVEhtbM1uJQUb4xFbG2MOjg+os5xHNraJVMjto33e2r1KOkzYHPSRlWFSFWhpeHQwxmhDaTQmEKRJSD+KbQpf4Kh2m4ZQSIyS3JoeMTmasD0a8ycvf4OD2Qmf+NjHeef9d8mqwip2AkJpAXazbE6SxDZMqg11VVGWBSKw/PRFVWCwgEuEoCgLGgGxChEIKm0pYU3d2BCCw2t5pj0hBJXRjNI+wzBF182DrUEfoD10CztJUyuoS6t9yw74AxYLSAvasl8ig8WJ13UNxqYyYazVbtHkBtM4TV3YmG6R5a0FfpagBufKMzaXcbV6StcabwsXGIMw7gZe6teKXq01ygh03TjDTLT7e4WjBRZpB/bRDRpQYchjjz/OX/uFnyOfTaApkUFoBYex6SlS2oe3jV8am8rj+12yJNvztKkH2Fn6QNfsQd3PnR1Of+Xt+jO6upc1eq/jn7eN93C0FrSzKIUb21kW/JKycIZQX43R2m69VWldxqa7PavKx8ICdbdqxyuiF3u1l8ymKonFxoC1tKRUtAxxLs4shEBTuzLbNvyhNQQh1u3ulASDodaWZSlQYXsPeUHtEeseAOaBmX4OgiCwd3ltFzMfkgJbO1sahdaNZRgMAyIZ4HnFpbBC2QtXeyyNya3yYIyl4fVpn0opgjBw3gCNkIY4DqmqxgprKdtx+ayPKIoXir629J1VXdhULCwPeRiGlK78rBXMyglrHGjV96vaecgLm/srJGgamqogDBJqbKqOUgEqDGhoqHVtC1nUGrQkDHpsbvQJjCHq9agrQ9JAKmNmJ1MuPXKJ777yJnvHh/T7fbbXxpiqIlIhW+s7SCmoRUOSpBwdHdEf9pgf1LbITxxT64Y4jsizGWKQUht77wzjATqfIgIY9QYkMiBQAXk5J+kNKEpDrSsa4zyIoaSqS4u+DiK0MYQyAjGjNA2BDEAJhFCMox6jOEbliqP5nKMyozCaEAlaOMZE680o88Ly20u4ffM9ZvMZg17K2vgSB4d7ZNM5l8frbK1vUNQ133r3DdaHIy6oHqJsmFYZBIanRhvoRlMBMlH004RBlNA02GehqJEuB1oLSa0bvvXGa3z4mWd569Y1dna2+MiHnmE0HHBweMAwjp230j6bWTZnY7yOdMjzyoUm414MyuIkbP69xUnVTU2NpqcipJCUxgIahTaoYJHOKVx/UkpqCWu9AUmU0FQ14j7lmD9oe+gCu1tcwLd2MWfZavPCzaZYWHL86cnE1qE1C0Yj37xA980f40FjsV1Bd5711qZRdXJqOyfSrtgLtyeLL1nE1duFX9llNO2l3Lh5m09+8mP8pb/0Ezz91JN89/XXSaKoBcJ4C73r4vOuvS7RxpLlu+oaf8C5uNc+51m9XlCe9dtighYW5AdtDyK4u0dq61OL5e2WLtmKIGblHM5XVRwwbDlxDzAdYS2W7tFVl75xKFIpVx8za00uxthNJfQpZN3NnWu7oxjqxrrJwzBGBp3SnUIsCWB/T/uxdePa4MBYYYB0WQhBENgiE4UhTfskSY8wtMpx4zxQja4JQxuHlsqGbLpx8kVYyiogKgmW+c5ZsBcCrmhHQ6Mr6lqTZRlNs3gWVlMtjTFtWc6mqVpOcoFBqkUKpV8vqqpGdNiphIvvKwe8snNi3cvKVa6qa41uJGkQIKcZYWALP+RZTpGUVHFJEIYkcUKaJEgkl3aukMYpMoVZbY0SKSWz+Zymqdnf32drvMaVtQ3QtqhIEsVk+ZymromiCGO0LaOaRES6JgyUI+KwLmgN1FVFLjKyaEasDUZI4rRH6HAMgQppmpo8L7A5cfZaeMVFufVNuJCHvcWtZ0cKqxT1kgHb29tsiYZbd++Q5Tm1YyPz3j7L3CdAWEIZXVkmyX6vx539u8yyOb/4Mz/Lb/z+7/Hme++wORxhas3Vq1eZX9rho08+YwvGVBW9IGJzbR2AuqlJo5h+mhKEAXlZIEyf+WxKnKZUpiGoazYGA759+Baf/twn+AuPXCI7OuY3f/e3+Q9++W/zld/5XdY+9DhzXTKtLd/+0OWfV1luFdumJssyLmysURubPqfrhqopCFWASgLqIkfJTrEaYdOM66K0uAro5Grb58fXdV/iC3lI7aEL7FMxsrMiqoKWNzyIoxboUlUVKgrJsowkSc4UJF1KxA+CkF517Z4XS+wufODuZWmFbnft9qhy5/9sj9Hty/9WG818MmFt3ONHf+Tz/MgXPs97772HkpJ+r0fVNFROIHdzybvu/UVZxPPTfx72zXFeO9divpeh/uBYuD9XOzU2oZZIXcRZ23T27b6s59YDF60HQ9Cypdv7xb0QoD2trnZ7CECtuOmdZbgq7FdGshiPBNGmF5o25ziOY+IkdkJpuW+/UOjGnDo2sBTv9YKWTlUv7dzdSgXOdS0xpm4te4OgrBp6vZggsPFWrwj453gBjqxZW9sg6DyzXdDkYkyu5KUKGAxGLS2pH3fXE2YAT0mqVA+bbbNQFJSwseYoiZeelwVhSkxV18ymM+bTKXEc0+vbVMrZdE5V1xgDKogdFscgjSFwnBBBELC+voU0IVEY0pQlSoVsbWyBFiRxSlXYOHqSJDS6otdPOTzeYzLdonfpMsfzGY9evsLmxhrTaYDEUFQZcRJxcLjHMFi3tQK0JgoD+mmPXhxTKkWDcZWsoCobmtow7PcxYY1p6na9yPIZuqmsZ6ADqBXOKvQhBn8NAhlSljVG58jeOoNen+3hGu9dvcrewT57R4dW2YDW0JKO5jmOI05mU5SrCf1YHLN/cMD/5Z/+E37hL/8cV997n2++8Sqlabh4YYd3vvUKX/yRHyE/rGnKkn4YcmFjg6oomU6n9MOYQdpDSMlkNqOfXOGwOUDFEcaFVK9sbCE//Sm++sq3+Ye/+nc42r3NV778JV7/7lt86EMf4qjKCXsJoig4Oj5i+4ktjo6O6KWxnQuF9U6MBty8eZNQBVQYTG1pa/PZnJ3xOqbWBC4PfjI9Zl5WDHo98DLAPUNeHkWRk2ltTfeH1x56DNsXy5AGlFu03FrZNv/waa1J05Q8z9vgvEWChmcKpG6soGtJ+N+77+8lvFYXsa7lrbEkAecBpAzGxg99GphZtma6Vn/dNMzLgiRJOD4+5h/+2n/Ipz/5cW7v3sTUlkawLEpqV+u4SzvadeF2EbNL53bOOd7Lxb0aR36g1tnnrBh017I9t+cHOOR57umzhyTO3XbZou4OQOAqOuP5rlet0HsdU4oFa5lS1uIKPDmKVATCIlKVsq5JW3giQgYRQlmGMUvGsbjOsHwPLq4tDv8gkcISihjjPOXCpYwJb1Ev3OveKu1WOoNF7n73fLsxOG/J2rzngDCI8JgKyzamncs8cuEam42RZRlFUbQKZRha/vter8dgMKDf7zOd2kpSRVFQO8EhVUfxaBnT4jY27b1K3pIG2lSz1XCX92B4UN1sPicvi3Z8s9mM6XTK8fExR0cn7B8ctFTEvV4KaIrccjLUTUMQhPT7A+LI9ruzuYZpKmaTCYNenzTtEYYRSdojjRMCqaiLklDE1KVNtYuCiDhMWpKW8foaRVkwm01dOp1h1B+wPhyxNhjQT2Jb7SuNubm7S55nlHWBbmpCFRAIST/tEQYBAkPT1FRN1VbrEyh3n9gMF5sKqDHCxtyrukCbxmEU7DompSR04KhASMbDIUJr5tncxm7jmK3hGr04IStKjqZTe88IqOuKIFjUJQ8Ca2jhiFzy0pKP/Njn/gL/7J/9M6ZVzksvvshjO5d45513KEPJyf4hFTVJEDKOewx6fcqqIssy1qIewzBBCkGtG+q8sFXQEAgXWjmaTXn6iSe5ffU6f/jlL7PWG/A3/8pf45/8v/8Z6eOXaUxDWBpSrYhViAkkUZogIsW8nFOUheWu15rpyQxda+IoslUQjfWA5HlBGAaUZcnx8XG7NhTeum6BlLLFgngl2Gc2Pcz20C1sWLZiVu1rr9V7ylFPIeq/k1KiEKcEqW9tnLnT33nt1CJ4xjhObd+6M8+xYrUV2K2rbsUN2rWMVaBIo5DdW3f5n/ziL/DiC88TBQHTkxOiMGhRlmBL+XWrO3WRvf4m+H63M+eydTs/2Jx33dPnjfms63eW+/1Bx3qeMgHWJl54QU4Ldq8mrAp737oKohDSFrd3n5Xwlcnctg5YaTnmF/sbU/kN3MNhS3+1z4Zpa4oBy1gKXLzSxpDtuVhgWUUTRaRBQBBKEIt7xt+LnmTFpzcBrTBcFeRl2bTZErA4gcVzaUE/wCItrGkcCtcRsyDAxYTtfIERsuVQ8FOwGs5ZUpg7ikV3jO2z7MfcUWKtR6peSuOKoqQlT1lY6jb8YKuhBW2OvseeJHHaej/QhkY3JGFEpBVJEBAIQZHnzEXIYDBCGoVSgc1Hr5vW46KEVXqCwGIRjDEMB30OT06Yz+c0Dkk8n88tpkZbulWJIApDDiZH4OKuStnc7bqsicOYWWHrdhsasjwnCXs2BRWJMY6a1TgDJ5AgXQqcq+qntaVtE8amBMZh5BSxhbVd1xWahn4vZQNBGscUTc1JZseuHAGNZWATlj9bKvI8wxhr/UexLfi0e3OX559/nrt37jAajuj1ejz11JN87Rtf593332N46RniKGIUWIIcGus52ByM6MUJVV4RhIoiyy0VqrbPV20arh/epdY1n3n2Rd5880220gE/8YNf4NkXnufPvvVNntjcRjYao20qcBRGLgVLtHTU/X6fIs9dOCREBSFoQ1k3NFISCYWRCqRF1yOUZafLc0K/XnfDv511RCnZsmg+rPb9EdidRa/7MK4uwt5K7T5UTdO0LkDfznJdn2cBP+i4zm+mXVjPFNhYK6cFurFYgNqFshNLbLRmY2Odv/TTP0kUBOjaMvh4jUwq5YhZZOtB8AvWAlij2/4e1HOwOv/n/X6vPjpf3vOYy2PywKrThCL3G+tZ297PW7D6fvWvdYcL5w7vCut777foeyGs7T3aseg771vBDq6Gut3bXkNbAaqdJdefsTeP3biDhWiFkwYjsfuahbPAGEvuYBygSgUhjV6gwv3fbs3wriA/tZ2UFpndcUF7WmBLe2mrF3XDRV6p9Z6Lrit+0awlGKe9xXm58/VzteoN6943ywJbW0FiOv2wCIsJsQCXat20fft+7JjtuJUPeRnj4xeApzldhBXqqqYQOYFQDNMe/SQlm2fUQUoUxpiezSSp64pinqMd8MjzSPtzbOqG0WCARNjqanWNCgOK0lYvNNYsBrBCRVviF2OMq+NsY6ZRENLMbSqSrfpmEEo66lpjketOYDeNjTfb+6/BmMWctPMrFFEYu/x4uw5pY9esqqlZ6w0oK0OkQvK65GQ+I8sKlIpaT0zgaoZ7ciiprDInjK2CJkLF2mDE3t4eWZ4jw4DBYMDGeI2rN6+zk64jpWAYpaAb6wEThs3BkEQFFE1uKWRVQNE0SAewbDCcVDn6+g0+99wL3Epj9ifHvPzG63zqox/n5a99nSc2t9FKWjY3IYikYlaXqHABDE7jlFk2ZzgaMc8LijynrBvyouQ4n7Ge9pmVFdKl1xlhr6Ffj4V7hrtKcDf0NJ1OeZjt4Rf/YHGjnruNu8B5nrdAlZa60JjOQ7gsdM4TPr6dF9s9a59ly6mz8LuFsV3YVvrxbt+u1m4XuEV6mbeUq7KiKAp+7Iuf58PPPU8+m1GXBXFsq8I0TYMRixSbVVac1VScU+e6MqffazvPpYzo1ov+3o6z2uep+T7Hsr3Xvme9cACvZfS26PzrEHOsCO3V43f7te5rn/tsC1es3t/d+9OY7mz5vs/OTPBz3BX8vp9WOXP9WS1+6Uos4ujS35Us7b96PK8krpa6tBZW0JIWeeS0kIIwCgmj4BQPuHLpVt7tLoStludj2FVVUZYlZVkuHdMY64VQTkH1Aq47j10Odv8s2amxqHBMF4Vuv2/DFIENT9SVLadZVzVN7Y+9TDhkjKHWTbv4LlnyxqAdgYzSms3RiM21NerKV+AShGHkGLOsG7mq7UIu1cLzpqSkrirWRmPL6d005FWFcKlDvgBSIK2QD911yPKMqioJo4j19XWqsiSJYluVyxhLXRqH1oLXPhPBV3vzhs9CkcKYRd0Gt4pJKYnCyHkELN2mVwazsiBJYmKhiIKAqqk5mc+YTKYIYRbXT3XTA61rHGNcBo1gc3ubPMtYG4yQUjDN5mR5zqOXH2H/6JB3dq+hTcOoZ4uCaEdEst4bEAnZZgeMBgPn0XEKlW5ASuazOWjNFz7/BfprY37/T77C5Y0dxjKyIQ4JOgys+1pbwJ4PCygZEIYRZVGRpD0m8zm7+3vcuHOH3f193r5+k6u373A4nXE4nXJ0MiGvSmbZ3Hqq9MJztYqzaBwuqSgKHmZ7+Ba2MdbEkIvF59Qib0zrDpQGcMAFrS0N4HQ6pZf2sJzGCy3ZP1D3cw/fy/pe3X91oewuQMJZPmKh0Lf7+AfBw/ZNZxGR0t5oQgguX7jIf/xr/5Br775FEkeYpqEuC1QYImTI8XTCsN9vz88vhl1wDnwwgF23fa8C1jcDrGpf4pxtz+vnflbzg1jip3+zlt/ymMTSJ2ApbCEE7X232t2qsmSv/zJmojXKnNDx7l3f2vdKnsJsdC3xbthkMRjf4zJJj83H9qQoAHZB1Z3c6aqpTx3Dv1bnzbvEhRAdQWq5wH1qCuC4jUyrjPp+m6Zp+4AFO5xQnRKieKvWgclq01L++mN3rRBd+7j2AmW+sNYNTVM5pUIRRYrAMawZYygceYu3UP2Cbju3io7GUDcLRcaWGLXWf9PU9NNee06+3rTWunXlh0ZwYWOTMpTcOnRleG3BTRq3RgVBwHR2YmPDgbLwvKYmTVPKvGDj8jo9x742yeeko4FFGzc1SkniJKEyNVVp3bTHk2OkChmvb7Kxts7+7bv0077lhRCWRU0FAXVZudxy01YUA6irerFmOsvZr1vGunYcJWuAkDYnPY5tvF3nGbNihgJCBGtrI451xqzIOZlOGY9G7p50z4ZcVKWK49ieUxQijeHw6k2CQcJga53J1WvkRUE0HnCoc8ZpyjtX32U43GBtNATd0EhBVZYMkx7KQD6fo3XDqD/g+u4ula6pjKHSmmEUMtjZ4k++/Q1+4qd/irV0yJ33b/L7v//7/OgP/hCvvvcGfeUL48SUZUk/SZnlOVr77ATLyX90MuHazV32jiyorDKaII7J5gVH84xaSm7tH3Czf5uLFy5YZdEsk/OsWtnaaPr9Hg+zfV+qdQHWs6xPC1Z7IvZBStMUWKRC6cYWte/3++jGkkR0F+zuAvRBBPfq53vFVluhKyy4od2+44Zb3Pimjdd3keV1XZPnOR9+4QX+N//of83e3p4979pWoBEOxaq1pVWN46RdQH1f3TKiq8Lkf8z2/Y2kf7DeTeelBS43tPO9vTh2ToVFd8NpYX1agbDANP9b14p3R1sSil3r7DyFsFUCpbQZEgEgOlb2Gd4H28/CipZSUDcNs9mMw6MjDg4PmM0mlq6yKCjLckngeIHc1p531tmqNemR50mSkDpaxTRNl4A0vk//DHZrWnfPuXu8sixbgVoVJdk8Yz6fM5vNyLLMulCxTIaBU1L9sYC2ulbgqlTN53Om0ynT6ZTZbMZkMuHo6IiTo2MbG3YKh0fphqH1DoShsmlZQUAcx6RpTJrac43jmKIoWrpiT4vsqYG11pRZTpnlhCrgysVLbK5tWheys1qDQGJoKJsSbbSNXWNBp2mckM/mJGEMxpDlOdP5zGLtpQU/Vq6Ocj/tMZ/PGThrsgXyRSFFVtLr95FunvIi5/D4iLopMdrGp4XwhFISNJbPOghBQNNY5L6pG1s1pNEW/S4F0lgFazgcEgYhZVUync1sYaSmYWtri6SXklcl8yKjKn0WgquepkWbGWCrpkl0XUOjufDko4RBwMHNWyT9HunakJOTE4JSE68Nubl/B6kUm+sbrWenrEr6UYwyUBalVSgbTRrFiDhCRiHKQHUwIUhidmfH/Pqv//cMheLX/u7f57e+/G84CBt2ti+QaomY5cjGQOwIvYocCcRhRKTs/XXr9h1u3d3naGZ5zSdZxvG8INeawhiO53Nu3LrNm2+9zauvvdZ6YOz6vOCn71Zj7KU9nnrqqQ+0pt2vPfxqXa5kmcbYeJMGuRQ3EWhjU2D8ImDBGc5R45B11i29TPbQ1fTPWhi7Wo6tjlW0qQu+chFYgdpLU3Rj+Yg9S1UYhhhlUYla2yo7lSNkaEFLrg8jjSWAb7CoWmVJ91Uccfdgn7/w+R/iZ37qJ7i0vcadO7cBgwyC1irzwiONF24ub534m/+s8+yeb9fsv5/ysrTfyj4LJ+tpkblqw672sfzZuW/N2dvdayxL2zoh5t+L1d/deJwj3AEE6ShULFJX3MiF8LFlf5bei+LPrfv+7LH5qK0/hun880Afg0E0EmMWJVIBPI81gDAKaQRQg+XTsvSjmCXL3LuE0QEShXZuvNFoxNbWFv00IenZsrWtUHX52V0XnbdGhRCtIljXtXWDRxEqUAsBW1YIYV25XlG06GwXc5aAcTPrQG1KSYLOuXZdzsql4PT6vSVNqatUxJGln6zzphXQfgGsa3s+HpluXHzUL5ZesArnhUDYcoeN0Q4c6pHli/umqhxuRgUkQYhJtK3UpV0OuLY53ZXjOW9CSVUWRMAzGxd47/1rVEVBGARUVU1TCdL+mCifElSasJHEKiKJI+JIUcmSw2zCsDdkNs84PD7maRHQD2MCDAWGWmiMMKyPxxwfH1M0x+Q19Mohg7hHJCN0GNJTATQhRksOi5qPPv4kVaHJs5IoCiCSNEogtIEa3r12gzuTY0o04WTOFz7+WWqVoR3xlC40T158lD+Y/SH1rMJUBqEFommYVhNKqTk+vEMzndNow3fu7PLic89YzIM2KKEQgcI0FaSKqiigMY5BzVBnJYOoz5WLV9g7OKDIC3pRzPFsgq4bNtbHVNTsz47J9TZrwToyUARCM6tqDquKIDZsqIhXpCLAEOYlUVaRDfocTafsXNzmT958nfF4g1/88b/I3/zVX+L3fus3+as/+pNkgeSkysmrBmpDqEJmRUaSxERBxOHRMTWKf/G7v4ccDwjTFG0M0yzDZHPevjajMhIZBMyzQ27PS3YO7zK+tM3jmxtEFdTTAsYp9CKaWU6gBcpYtH5ePlyX+MNP6/ICxiwoRFthqrWzupfdCK2l0hVOYhnM1BVoq4uptxr8AuVdW5FbCHy8rTG6LelXOrh9FC54i/2avxqFXLWSjDHUVW39hsKyO9XasjNNpxMeffQR/sIP/AAvfeRFZtPJQpA496xfqGwe4+kY7qqQPldwfwB39+mYL8uCkdPCut23FY6nvONt335L7/s8T1ifFSdevZ7e2b26zdLLj0uIpfEtjbkV9mBWYttLWy5Z0N0xrMbVTTsJRizcvPaXhTi3u6zGtWk/48Ba3fG3ozJONLbmKy5tayHU26k2i+pXlhd5OXe/e0xvVfv7v03j6niSvKKr5MKtveDf9nHsxZLh09y6960/TjfXOs9zy3DmLOiyLBfPpVKtstENRS1wHRaJ7V/+WZJCtlb0Ij/bzk6XwMW4+atd4Y6isHF1q9z4MTVtLFoqW1c8CEKSJCYMLcNZEIYkUczacMgjly9TlgUIYfnMpWQynfH+9WtoBywzQBwlLSYmL3O0adBGt4VMTGPd1RKBdNn9UigbIgC7vbYEJQLDbDZx24KuG8qqYlbmlr5VScJAoYQkm83p93ocHB1xe/+AW3t73Lp9l5uHB1zdu00cJ7aKXKNtemAYuBRc6ebZphEWdYVQgiSKiIIAgyBz9LQIXCzfhysXYSXvgayaxq7/GKIwopekjAYD1kdjhv0+VVHQNJq7+/vcunubQtc0CObzjNFwQBCGFFWJbmpG/R6NU6qE0Uhj+QDyPKff6xHGMe/t3uBPX/4mT19+jKqsuX5wl3lVEEhlUey1rSQnBEhlU9JsAZCCK489Rpz2aICsrDiZTS3tbhCQJglREFBXNYcnE67evMW333iDg/kckcRE/dRe13lGoCxoLi9y5rMZa+M1Hmb7PgrsBbhkFaAFi8XYrOxznrDy78+KNfrvuwLVLyKLVClLCOHrmmoX10G4baRwyEwP9lkWGKeOi02hENKCmaqmpnI8yz/6hS/w0gvP04ttPL5reXQtH1uZq1laYO+FBL9XGOAs4be073kXbKWP77Utju+XlGUwVXdsZ71ffnX29+LY+bhb0XzfPlYEsbj/dl1lQC6O1PGrLL4xfkwr3y8rAKa9n5bv6eU5W8x9R3HpvD+1v/uv65I3zrwUQrb3/Grz914Xp+EtWa21LdjRyePuxuPaeDpOlxAsPXNdwd397J+1rgDvsvb5zIiuQn6ve5yVY/ixtkLChQf8eS0Y0Vw5zRWgXPeaCGE5p62CEjnUuMJoY9nGYlsnYdDvt+egQkVDw+7tXd65cZ2j2ZRSa4RStg9pS52WVUlj/PlX7VpnKZBdfQWnAIXBoqJa3VTopkIIw2RyghDCIrMxoBv2jvZRUYDW1q0eSEHtGMeOp1P2J8ccHJ9wdHzC7ekJ33jrDYyUKCPbFCqjFEEYunQ3q8Q1TU1ZlQgl6Kc9V5BEU9Z1i5JehFQ8cFgtnhdhPV+WP74mikL6/R79Xo84DEmjmFiFqDAkLwr2Dw+4vX8XQklVV66Yk4vFG+ilCbqu0XVFEkUM+wPrFakbgsC61I9nE/7kW39GMc1YX9/g7VvXuXt8aL1JwmY8VHW1JC8wVpnrOxxRnhfM55nz3Albg11AIAVJqAiV5GQ25fV33+OVd99nd3KCSSMEElFab60Rlq2triuSjkH4MNr3JTi6+sAtWUUr1irSxfVWFqyukO4Cr7ras//OGONiagthHcfxUg3qsnT1t92iEwQB2pg2xUIqtcSr7GNC3UWpa0nYuJqxVraSaGGYZjMee+wR/urP/SyXd7Y53t9fQhH6GKKnrxNCLAnsroVyvzldzO05LurVFytC+0zBttr3+QrA0rE6254S2p39zxamZ/zm7QgflPZkJ06U+n+rCtW5/Z8xR6fO041V0o11L97bbjqCua2OJZaE5eJlZ9xWUFt127jQgf+qM7TVOfE/28voFnVhixXIDpLao7x9iVmvDK9arKvx9pYAqPGFdkSLAAZaIegR33VTOwCTPw93Rl5J7xyrOybglHCvO6Ggtka3WQC/VtHlYMNPtjLYwvXuhbB9zkum01l7rr4vsICofr/fxqiVO6ZXnK37XLRjamqN1oamtuVtA2WpK+uyctZe46y0iveuvc+to33euXGd0hhUFCNVQBqlmMbQ1LVbzO0YbZ71QlD7ezAKIoaDkWMQqymLjHk+JQgFk+kJUgriKCIOQgIh2Ds+QEvDPJ9RFHMUhlBKEDArcg5nU06yOVlZcVBk/NHL3+JwanODJdIypilFnKaLGw2sN6IsEVLQT1MiFVBXFfMio6wrV9RloQBiuhb2IgugrCtqrQnDgH4/JU1sbFo0hmGcMhgOSXoJ02zOG+++DYmviV5TlSVNXREHIUkU0NQ1TVmxPhpx5fIlC94VAtM0bGyso6Xgzffe5Vuvfoedixd4//ZNrt66yf7JEU1T0U9ipuWcpqlQUhC4LANjNLdv3WI+nXNydMJ8mjHojygrGzYq8wzRVPSTiI3REKEk1+4e8Ptf/yZ/+uabHFS5xUbIAOkedRFYZrR8nvEw20OPYa8KttZVLSRCLeJ83TQouzDa5pcAo03LdesfRM+K5hcTz2frubfbh9o9gGtraxwdHSGERVXKwKGvMa1AX815NuZst68/n1ao1po4jpnOZqgoZDgesr9/wj/6L/4L1oY9pkdH6LIgTdMWWev5jT3D02w2O+VNWE078a/unC6PaTFr3e0EKwL6A1y/B/39fkKwtROXeLPvoyCcIeTPO64XSmeNxzqdWw99qyyuHs8LD0Vnw5X+fP21NpPaX4NTEQq/s1lyY3vBrzuKgDHWpS7OuUii+0ZoWIDdcXyljiNaooKQqs5b1C7CjrVViBGuRKetUHQuJgKfSmhJQ7zV2q3p6+9Nn67l9+1aXLAQzl556NYY8IK6qqpWqLcWpeNl8LFpL8j9+AIVUFcleZG1KTOeu9nGvu2kGdOQpmmbcVG7FBsPdrMKvkQbgdYNYWDZrJRUSGFdvFEUYQCVWBDobDqlampG43X2Dg9QQcAwCGi05vDoiCCO+bOXv8VLL36MOExsZb60z8HxPrppiJIImWVkmSUYCcPQ5lOXzr0sIIkTemnK3ckdBDOyPOfu3m3Sfkzd2EpbcRwTCcOsKVFpyN3Du5g4pSkyyiJlbW0Djebg5JCT2RQtJaPxGtVccGP/Dv/221/nhz/6ObbXNtmfTjg6OGSwsW6JdbWxlazqmlI3BGFAL44IhaAuS2bZzFJIu6wAqaRFxZsFl7yFdFiF1JdDPjo6YjQacunCBTY3Ntj943/L/tE+upcwHI5oqpL3dm+wNzmmP+ojhWQ+m5HNMwZJakOHRiNQXNje5sp4kz94/VU2xpvMpjOqccWlnYuMooh/8v/9//D3/uYvU2cF352+z83kNo9vXuTpR58kHfUJZrG7t10BkGzOZHJCIQR1USKQ9OKUuphZ70AgiBSEpkGZms2NTQ5OZrxxY5d5U4MS/OpP/AyTSQ4oKmpUGBJLxcHe/tkP+PfYvi/EKd3mrWIfqzUu3tsYQ+3yNltCis5+Ahun0s6l6NGccRy3D2/YcTcURdE++HVdMxqNyPP81GIihLAPvUOEKqXI87yNKVuN6/SCtpoTHYYhx5MTRutjyqbh6OiE/+w/+4958onHuPn++wijGfR7aLcQdV1xWusWJe4507suQz9P30vrCu3V9r07vB9Ou7+Af/A+zvQquOatViPvoxy0Fv1KX+cdG4Ewho6OdGZrFS3tRLSxIDdLn0Jr+Z466Dl9WmvSKgE+17mumyUrdDabtQqsfy78/eYVwaZpnGtzoQQuC2p3/znXsW+rcWq/bzd+7IV7N797lVvA3+O+MEK/3z91jG5t+q7C4JHnoXIlZ5HObSravrshgSzLl+pid92gXomQSqGEwjSKwBU4MY1u2Qd9qlpRlghw9bcVeZZxfHBoEelxhDENTVMzGiTMD+bs3rpFtbZBVTmSE2MFQRxZDECWzW3de2XvDV+CFSkxRpCECePeGllRU9UNuzev8+iVx0jimOPJjL6rlGYO9iAQXL91g4kMSJVifTRivL4OBvq9Pr0kpSoKjGm4tLHJo1sb/NaX/g2b400+v7FJGka8+q2XmWAJXTDGsi4GyuJypCKVIb0gJFYBtbEMa7XjkGi1SBciMS5Fz4extNHM5lP6gx4Gw3w+J4oifuLHfpyvf/3rvLp7lShQiDRllh3w+3/8ZeI4RClBEIWEUYic2zAATc24P2KYpgRS0VQ1oVTMs4Jr165zeXuH8doaJ+9X/Nmbr7PW7zM5uMP+SYYMFd985zWefuopW6ik0WjZkCQRgRT87E//FL/5+7+PNoYo7kFdEUpBGobMijnDXsLGeGTTAoUkTnqkvT53jw747S9/icfXLvCDz73A/sm+JWqpDaWuH3qGz0MX2EtW3soiavSCtSuOo6VKPNroNo9QYC9+o+2D44WwR357oe0FYdNYbdpbzFEUtZr0AtyyzPrkF5uu9eBdbW4r/Aq6mgYD1oIZjYbMZ3MGwwE/+NnP8As/9/O89eabpFGAlIJGNzROYbEE+fGSwPbu+u5ieT9Bfb6lbbobdWGxS997y7MrHx5EWD6oZd39fdVtep6wPfX7qgDt7LfkwYEFoE+IRUqXPg28Wj12V1jLds1ZeCd8O/NqnGNZL5TDpsVwgGgpQm3M0nVxjyn0CqYlwXCC38cLcVa1VITKWpVhYFOyvLDz7uFunNoLLo/AXr3XWqvf+FSl5e263gjgTLDYqjXu9+m64FeBZn68q2En78buKtoea+KFgTGWZcwqCaJNT/MFURbpeZqmqZf6tAqBVU6kkq0XrGpKEIIojlqFuqkXlZeCwGZxXLl8kaKqKPOMQAo+9NQTvH/3PZ54/lnu3r1FXRSkiSXlGA4GGN0QR5Y3fZ4VlGXhztkGRqSw17QqKypVMeyNyeuaw5Mj9g/2ePSxxwniCH1iC2yEYeTc/ZqZycmynDRQNEpzOD9kc7jJY5cusT855u0b15gdHXB59BiPPf4I1956h1fefoPNjS2ef+IZaDQnx0eUjxQIsAAzbciK3MaIEYzSPuPBgOPGptdlee6yMwxN3bQARC1szejKNJYH3YEV5/M5w+EQKSWz6ZT5dMbnPv1Zpl8zHB/PKHVF3Et4/+Z1tjbXiaLQ4op0w2w6JY1i8iyz9ctDS85iar+aSRqj2Ts8oioKPv3SJ/nWm69yeWODREiEEuzNjvnWzbe5sLVNvzfAVA26MfTihEcvX+bEVIx7iU33SmKUDDjZOyLqJ8wmJ2yOh1y+dIFRb8DVazcotCFVClEXHB4f8c9/57fY3ljn4tYaRTanLCuUEIRxcv6D/j2073sMu8uC5BGcYbCMVF1trVu0axU7wexjwV1L1C8K3ort5px26RK1thSAXmBrramK0sXtPPhl4bb3/Z+1uAlp66cKNM8/+wx//a/9AtPjY1vTyRi0sBR6S+fVESDe3dcF5HRj890F9AO1VuO9T4z6fq7pc/Zdfd/9ffHZemwX+LAOmKvzt/vydq4859j3Gn/r+m3zmvEumnOF9tJY26O7bTqv1XHIM8a+QDR7vIOnp1QO5bxc7KOrTDks3VI775pLuRiT7HgPpBBt/nQcx2eWpLTPkLWafD1qD/hctYy1NrYoTVWfaYlXVWWR32fUZ19S0r2QlspZbjauF4UhYRDauKew7F6BWqTBebBYURRtfrl/Ptq4touj+9xq7y0DnPU+aL/zqZ1pmtLv99s5atPm6FANSwtO8sK9rmtbEMgssDShkqA1cRiihK3ntjEeM4pDnnniMeoyt0U+jAVijUcjMJBEMUkc2zh2Vbnrubg3lVLW8ms0/bTPoD8kDAKybEZVFe5+8ex7TsmQikY3aAWV1EzrnFv7twlCxSCOuDhe4/LaOptpSmQaLm1u8uKzz3B4dMBbV9+lDmHrwg49FDhwXS9J6aU271o3DbEKWBuMGA2HlFVJURUUDiXfJdxZKNyugIyLe6eJDVl4JS0IAgIZ8N6773FhY4v1/pDQAdWOpidMJhMa3VA1NQ2G/sCSzJRliRT2HgqkWpIBcZxQa83B8TEBkrKq2J8co3oRybBPUdfcPtznu9feJfS5/Q6lH4cB2WzG9voaW2tjhnFMPwhIg4CqKEjSBCkFWtdsra9xYTRku5fw2PqYR7d3GI3Wub5/wO999d8y93UffHy/k078MNrDpyZ1gsYjsIW3oDuu5K4ru92vs/9qf/6B67r2usCsVZeXX0i61qjW2gI/nPDHWCKTLm2f0YuYcdeKWo0lW03SMM8ynnr6KX7wc5/lw889y9HBPnEUuYpfdIKOCxejXxz9ouoXolUw0GrrjuG8GKQXive7Pojzt1oScP51jmDmzN9wQtq/uv3d61/n93MUCX+AJc9AV7B2XeCrf8/qyh/zHsrLWdvL7svxRi+lJIkFpamSy4hkrxQuTuKeh1w5ddG62LU2LduZjb3KU5bq6n0lhCW4aFxajj9dfz91AZa+AtjiLqAVmB6E1lVk23M/hb62gtqnqxlX6MJ7DjCWa7uL5u66wFexLl10PIi2nOPCsjat5d69R70r/Cz0e1uO1IUDus/HkjJt98IYz5UdWDa1pmE8GLKeplzc2CCUksZZ/lEUMegPEDiijjDEAFVdtufTvT5+se/FPXpJzwq6xhIx1U6hEC6EaMdthbgIFESKkpo7h/to05AEARu9Po9tbfPEhQtQlawP+zz/9NM0Vcnu3VtMmoLR1gab6dApTyGDtMewN8DXCU/CiLXhkGF/QFmVtnpWXS/G4cbVfVCktEaQcNfIKxhWuCvSXspkMkE2hjS0ILqmbgjikIP9A+q6ojHW7Z666m9eMfD9dq+hUgEqDKm14e7du2yO18mrijpUqH5CGIWYuuHV996irGtsRoBV2PP5DKG1FcYbG6wP+kRSsDUeEwUB6+trIGA6mZBGEeMk4dGNdR5ZH3Flc5PtzR1KKfjqay/z3o1rVHXT4hNE8O+4SxywDyNiqQiC1pogskQNCMiyeatNC7fK+22F8LR3y7mWgQOGeFeoNgYFrbvZP1yqo637WJjnE/YPdp4XjkTfUYJ6Pmb3YApn4Jvu6uoXJaWYlxlRlPBzf+Xn+PwPfo6923dI44SmrgCbEuA9Ck3TtC5Lr7lHUbSEfl+14leFcvswr7TVxfFe7V4C6ZSitPjh3O3OfO+qDJ3ZpxegnX7Fyu9WKC1oD0+5+t3+orO/6ViewJJlvaoInlYQPnhb6rtzXCO9gLCkET6ut3yNOq7x9tQWSkh7vm7hpiM0PV+0MVb5LIVAZBlRGBPFsU2j6dxTS0IAgxDW+9QKVumqfK2cl9+/248HEi2d/zn3Xiv83DZ2obb9dIWwb3meOwV/oTh1XfreG6e1E/RuDi2+xbrG7cK98BRMTiZI5Qvq6BZgpI2mqZsWIW6ce6N2KG5bc1wShkFbAMWYDgLaQFGWDAYDyrqmyPOW8OSoPyRWklE/paxBNzW9tEeaJMxOpkSBdedKAXVVgnRKlbP02nkUwuYtxylpnCClZDqbohsXE5WSxikaZVFZV7ipUUKgBRzNJpxMjkmimHGvTxqFXNhY42vf+Qb9JObKzg5JGDHL5uxNjlBJxNZoTKwCkIogUlRCow7uIgSkSczIQD9NaRxosdFN6yEwxuBci+197O+bfn/QAtUsRsii6geDIc8/9xzf+Na3wUjSOIHSsLm9ze7VG+SFteDb9dOxYkZBiGThAdFGo43Np+4lKbGSvHv9Gj/06U/y+jtvcFLmhCZhPByhZc4bN69ye+8uF9c2idMEU9cc7O+x89gjTLM5w54mywp2s4LHL18mPY4hlExOjphMTlAC+knElQsXLAiRGXtZw/Vej9snd/nKV/+En/3hH2Vj5wKZq172MNtDF9i6bpa0WO9WM1ISJglFU1NXFUIb6qJEOXe0t5yNsexFnqJPCNHSG0ppgSZS2XQEpL1BPcWpj4/7B94vEJ520Ze0m0wmS/EwjXVLek2NpqFxiO6qLuyiEYZooEGQDHpcf+MW//v/7T/iEx//KIEUTPIJcRwD2gJZcA8jNYnjEF7Ezqxm6rX7s2LXXcvFfz5TaN/HKuz2sfr+Xtvd6/uzPtvv7h2vNq1VJ6CTMNUVpuCijqvWvEtVYeX6SmXx3Us5+H4Bb3Sbdy9gCZFtCRTufb7duT9vfrrKhz8jo8Qivt6xCKWUFkDmYn8dChIaY9Cmab3lAk1d5SgRIZXNcMAaqoRRRG8wIIliBsM1ojik8ba/EDRVuVCE3aInVYAKFEniMh0at4cT3F0PklcWtBvMUl63soQgSim0WUaPdz1Qft6MMeSFpUy1XgC5lDFRluUCT6IXlr6QNozmr61vQRiCsAjfRntEuQ8t+QpKEqNtelpRLFz3UkJZ5O6cbAhFStGC4GxrqOuSssydUl0jReSutEFI6A0GyMCm8KRhjFJ2fxUNqMqSRy5uUhQNZW1omphBskXRs+efxiHba2sUWd7ej568SSmJjBSFrsmyCZvDIYJH2d29zRvvvUM8GFDViiQJMUhkJZG1oKBBCdBZQZNnqLTH9b3bPHrxCdL1MWGVI+aaUTqkyQwb6xd4/NHHmczmnOzeYjQY8HI+4UPJZYwwVKZhEAfsJEOKeYHYTNC6Al0xapxXoszJqhlCaZIoYFbkyBqCOMSUlU2Fk5q4FzHJJnb9NAZpDJFUHN2+w+VLl/ipH/wCX/7a19g7OOKZJ1/k9Tdfo6Hmnf07nJQFaRISz2J2j04o0Vwe9tgIQw7nGbOopg4MmgFZVmB0RRQIxusDbh3uc/HCJU4Ojtg7yTGbGwSR4rlnnuFLf/oV/uIXvsgTlx5h993r7GzucGn9Avt3DpjLgjBQyCvbXFzf5MmtDb67dxNNSS9JyIuMZz78Arfu3uHxJ58i01d5+bXvcmE4pKbiS6++wkde+gibG+uIvEI+3DTs7wM1qc+ldNpgEARUVcXly5c5Ojqy6OimQQnBYDDg7t27S2hPj9oeDodtOkZXoLWpVx33m1+cutt1NfQuWhbOtib9Qtk09RLoJo6sMlBUJUEYEgYB7759lX/w7/9dvvCFzzM5PuLg8GhR4cgdr9IWter3Xxx7Uc2lrqtT7v3/v2/30B+8J2Uhms/e91RM95xuuwjl847Xuk+FWFIQui7T71UL7grrs477oG01Tgye2teOu1sn3VvRTV1Ty6BVUnFlnK3bN26FgdbagR8bdLUIvzgHwBJPwKqV3FUSV+eqamqMXqRVdgvW+O3bCmBhzGw+tc+gtjHaXq9Hnuetsi2lInFxZIRZjq+3rlSnqPvfaoMxNfMOYt5npQihrAItBGFoQaCem7xuahq9KC7i3fs+TNUlNOqC5HxecK+XUJQ2jm8QKBUSBCGDwZC9/QMuXLhAkkgm04KqNoxGIw727xKGMZvrWwShtZqLytZ4VkohmxoMKOw6JoVlG0vihMcfeYyvfvMbjKIEXZdEoSKKe/SSGKSg0RZprkyNEJpKVraoktbEQYwSgqouGA6H5FlGrzfgyUcf52QyQWkYj0bEcUwYWYNDmJogiIhCm8FitCEOQ8aDIVvrG4D1EBZ5znQyYZD0GI5GFPMZh7NjpDGW/AVLVSqFaLdv6hrigLiXcnh4iE4bXvrIi/RvrfGVb32NZ59+mltXr3Ht5g1UlJKkMUkccXxs+eIH/b7NrDme0x/0kEYjauvP1bVGS8VwMOTw8JDLFy9Y5c55BOrceWIxvPXO24hGc+niNnd373I4ObLkLMIgqoq1tTU2NzfZOzggjkOiMgCtOT46ot6+wHA4ZDafsTke8/lPfJI/eetVxhe2+MbX/pTbewdMrhRcXBvbamIPsT38PGxpXcaiI2yvPPoIJ5NpS0kopGWY6dKH+oejruvWve01Y1i2eqwrZhGzq4tyiTGpu/Atx+XORmH7rQ2gWivQ3qg+HhbHCUVZUuU5n/7kx/mVX/4l7ty+hRKCNEmo67IdX90hgPfWxGIhWVjWXfDcA8/vBxQGD2pdf9D9z36/HNY48/cVR/RZlvgpIShEa12D9xKvWOCd/u4XPlh1U5+1zffslfDuef/5HtfKVf9dCMU2XqxdCpd9iUa3pQ8FHVpOd3+lSUrpSj5KgXWd64WCK7RsqzX5UIyuF2mEXjG2w3Vc/C7NyWtLi9N010+AxhXHcDnZfl/fj4+v54FlHqNp2nmZ51mLzLaAM5exIVyMe0WhajNCOjgPjwc4S+ExpqEoModgFq07VqmAwK05bkvyPD91PbW2vOK2DoEjwKGh0RVVLahry5xmQZLWbyJlQFHk5HlOEMYEoWyLdaytraEUqDAgSnrs3rhJ02iE8OVbrQekcWG6pm4wGpIo4cL2DtvrGxweTxkOh8QqQFclseOVEKYhAAIVkoSKJIqJowRdayIlMVJhkIRhTBxEZJMZo6TXMo0Vs5wkSgjDGCkNyuVfDwcDlJYIbUjCmM3xGpc2txn1h/b+cSGDUAU2vCgs+LHKcluUxBiSfo9ClZR13WriRhtQggZjq2IBj1++wnA05He+/K+5ePEiKgo5OD7i6HjGeDAkr3KkEKyPx0RKkWe2rGaAIBCGMAwQNNRVg+qFGASzLAMp0Rgm0zlJEtlrk0TcObR4o43xGoPRkOl0ymDQByWI64p+f0CZFWxvbnD16DZlXqClYmtjgyov0MIq1eM0oX/5Eid1zut7tymygrv7h5zM51ze3KSeLlj9HkZ7+NSk7q+PJff7ffKiIHcPKHiL9nSheuuSXhAtrIJafOtaSGeBsVathe5DuAqSWYpHGkvWYlyuq7carMZrl9NRf8hf/8VfpJfElGVB44qDeDSwF8yBq/i1PJblBejPG9+4l9A5S5Ddq58H7et+789/eZjW8vZdvu62vyXh7V+nwW1LyNT2Oto+lZBt4QmgTRk8c58zxvtg57Tqtl+Z7zOuw6qr4BSArlOWtuXWN9aCavdxcc+6stWwyqqk0QsObw/iaq1Os1AK24peK6EYD8oKAoUKOvnKarmimHShiXs9Y6uYDP/eo75XsSaN1tbqbRbVxJYzJ1wdbBtfcFaf718gZUAQhC2g1Vp03he5IGAC6c7bCvqmscqGR7x7pcPjZpbwMy6Vygr+hefOGg12zUjilLKoODk+cXnxmjAInMcuBCRSBkRBRFM1VGXtFDPa3H6fu9/UtkJVIBTDdMATjz6OqWrWh2MUMJtObNjEgduCIGDQG7AxWmdjbYNBb4RpXEhGQ2MMoS+yUlX004RRv4/QMD0+IQkiN4cxcZgQhzHjwdgBdG3seNwfsr2xSRzFHe5zR2VaN9RV3aLs2/CWVC2wzt8jlVMWgygiKzJ005AGEdvDNV586hny2Zxaa4qqpKor+r2UWTYniSLGg4EjuSmoypJeFGGct8cquJazIE4STqYz532w+d/WG6updUPRVMyyOUcnx/QG1tsTRxFpHNNPUjbX1lFKkiS2OFMaJ+xsbbExXnPASYeDEBAKzSM721BWXLpwkaqumWYZIrAo+YfZvi952N6d5B+go6OjFnEohGitjq6A8xeza5GepTmvHqsrpM/6vdu6QJizWpv3ahY5o14rr8uKUX/ACy+8wBd/5IfZ39+1KFGwudaOYtAYh5DsVBiD5RirX4TOjUufNbaOtXE/6/Be3z+IlX2v7e9nZXe39paYfy9YFpKnthHtm24nS9aW/WsX6lNj69io7Xx1vvP7r467jTevnssDzFVnp1PCePnnBcq521pMmVMoRAeA5t9IYYPXXrvWxgo4YQwiy4iTCGTg9reeoRbE1J1KKVums9Xr4U8BJ5yt+70zP+0GgHvGdSedZ1WB9sI9CAJbPlKIdtHuPtceVOSfASUWlKj+WF2h7ZU83VG+fKlPrRfCI44jtPMyBMoWEAEoq4rAHd+mqJVLFMZ2vgUyCDF6sQYIBEp68hT7vnudDIYksRUA9/f3SXo54/EG/d6okwliFS1p7Joyz46p6ppGa6q6amPyQrjQR2OLJSVByGOXrvD662+wNhhR6pp8NkUJKJra1QmPGA6GbI7GrPX6DHpDilmOaZzypG2GTlVbtrThYEijISsKsumMUCrr2lcSpCZUilF/SF1YEG2oQoa9AdvjdRtaCCMkEs+8K91a1+A9NAHS1yN3YEGfFVM1NrUrVooiL9C6wdQNom74yDPP8/VXvkVTVVRViRQwGvQ5ODggCSP6SUroPC1VVbI+GHK3ObYFXELrrajrhjCMmM1mtpYFBtPUSCzCfDqdEQ8GCCWZZXNXz7u2aYcqIBCKXhxjjCZQiqZu6KcpFza3SKKIeTZFhhY7YxXJhmE/JY0innzkMQIVMJ3NrBIQ/jue1uUFdhzHKKU4Oj5uXeH+d6B90LxrGBapT2VZnknQ7/fvauHdFI6zhPtZFvi9rMaqWvB9a60Jwog4jsmznGc+9CF++W/+EgGGk8NDYlfHF2yeZt1oVBCi9TJBhNfGhVjMT1fj7M6Lbw8qLM6zpu91jvfr50Etze5vNid5YRBLJxIkCskiDaN7btIJ66U+veCTYuFePkPIn3UmXWHthYEfx1njdm/a1/2UkdX3vhk6grGVaWZJ+bvXnNv93fgcb7pH6HlL16eKdc/cWon2Xqrq0haK0Lq1qL2CYF3eDVWtl0JEXSWwaRqq2qZWVWVpBUlHkHbPumkaapeG5S32bux6ldvc3+u9Xo/EkQd5bEvXmvXpm0spkC0y3obQQLT7dC1qJSwbVhdMqJRAKa8oa5pmkTZmjE3ljF0VqTROUMLmV+vGYQSqimw2pypriqIiL0qaGnRjrfqqrCmLmqa2AllJSRzH7O3d5c6dW2T5nDgOKPICoy39aFXYeRuNRhyfnJCXBUWVM8tmHE+PUYFEO5d0ozV1VaGEZDwYcnn7AsMkRWkQ2tBPEizK3xCHMf10wPponQubF0mjBOHi4UZbYpYgCCiqkrzMkIEg7kXEcWirdmlQMiSQIUooAhGQpj1XT9vybveTlK01W7e61+u116TRhl6/766pzZVOk5S011sUSXGWePs8uIJJYRyhjc37p9HUecUXP//DpEFInRWESrG+vsaNmzcJhCQSirCTP7+zuYXyzwEG6XAdVVW3qWBN3RCHCbqs6UcJs+mEQAWMR0PiOOLg4ACprKfHOOVC1w3T6dQSFAWKKAhQQlBkBVVZkSQJ83lG2VREvQSta55+7DEurG8y7g/JZnOuXr3GlUtXznz2v9f20C1spQKb8lCWzGZzyrJgPB5T6YbJZEIURURhBFozm81al5NnAZtOp0tMTWdZP8bQUjQCbfGQ80BlYAVqw7J1epaQFAgwAt2AsXcxu7u7fOHzn+fnf/5n+eTHX+Lb3/oGW1tbHB8f2xzQyCLIdWXTtsbjsU25cIqKJ9PwbEt+wbhXnPUsoM/i/M8/z7Pag1rY99vuwSxP4Qwx6wZvt29joYs+vAV3lmV9lgLSbnOG58WXMVwVzGcS8/h5xY2p/fBggrp7XbpX0IPj2mvrX93vVsbh3bpeT0GINi2tJZRxsVrrarSKbZIkpFFClPZZX18nK8sWSLYKGGuc21W6uTPGgF6AzbTWdsFq3L3mtgPaMpT+PvbgTSFsJSM/F97F7TnA/XdAi8Lu1n2PoqilJvU83920x7woMCyoXX1M3KZaqXYsWmuaqlwoP347Uy8sb7dogyU9iWLrnsZZ7XESunFYt6rAZZtIY5/Z3CH6sIJnQWNs0exIG9M2uuHyxQscHNwhL3LKbMZo0COb1yRrY05OTijrylZzqi0pyNH0mNl8RlkWRHHA8d4hWxtrhM6Ca+qKWtpn5bHLj1IJbXODh2OEkJwc3SVEEkpFICSBDBgNR86iVe09oYSkKAo2ttb5wz/6Mjf3bvPUkx/iyuOPcHP3NnVVEwiFcmEDgyFQISiJcUVLlJKMh0NEGHBycsLJZGILmRhLDY0U9Hp98umUkoowtumGY6XY29uz+dpOodPasj2uDYdg8bnEUcrd3X12xmP+2k/9DP/sN3+TP/32y9zdv4uKQrY2NulFsVWssDpt7LKGZGSvQ9M0mDDk+OiQF557lv29u+iqZNwfUM0zAg1r/RHDtMf6cMzFnR1ee/k1rly5gjA2vt4N0a5vbJDN5qg0Zn28hjD2+Ts5PmHUHyKVYNaUjEcjnlIx04MjPvT0k9RFzrvvvsunX/zoqfXnz9MeusBOkoSyLMkyW6VkNBpZ8ElR0E9scfC6LJfcWePxmKIomM/ny3HjzmLrLaYuuchZFl93UfTbd5sGVOfhPjX+KLFFAoqM8doas/mcRx59hF/91b/JC889w1vffYN+L6ZuSkuRqiSV43YGWN/cpHGVjTxxAtCJyzWYTkzxXhbY/1jtQQV8u82pdyv7rAjDM/t0EktgMGL5ui+NZ9HdqTEs3Q/us7mPgnNWiOFBFaG2j87mHt3sBfZqT+LUGdjPZuVbm4qlzlQ4vJvRs44VReFyqgG98D4tUPSLspH4+xDToq/ruqasioXl6QSqkpLGEZi0YDSHCIeFJ6ybJz2fz9tnzlvMGEMcRTRKOeEL2WxO2utRFgUg2ipcbcw4Us6lbZaqpzUOXLe4L2y1qXBlXFVTLvjQjY3lSiUdEC6nqmqUFO3zuQoAFUK0bnIpJSpQCKTFDtQ1WpcEoQBhaHRFUc6hEsgQLl++yLXr13j33bd48skPMeivc3JybFkWgaKqCJOY92+8z8c+/QlUHFLmU/LpjF4Sc/fgLlHYYzgYEciAqqxQKmRjbZ07h4dIEdDrDdBSkc6OEbpBaE1TWtBVGMZUmaU+1Y226YJNgzY1s2LOjb2bvHf7OpkuSId98jJDYAiCEIUjfXICX0iJEYa8KpEaMDYuP5tlzOZzssqu9ccnE2uADVNqbWt1m8qC+QaDAWEUINz6V2s7JimlzevWUAsNUrGztcXe3i02x2v8wMc+RYPkK1/9Kj/2Az9CHcWsjcaYWdFijPb29qxbPQkQBnRVtnSoWZZxYXMLXVVUWcbjlx+BsubK9gV21jcZpn3SMCUOQuqihtii37uegKauQRvrIzRQlxW6rkkGfVdCsyEIJaMoRNaSXtPw5KWLHBwf8sZrr7B7csTOvZePD9QeusD2hTS8BuwXhDYeLGzes9Y2tcMvOl0q0a5LrLvYeu3eu8raOPMZDtJTLtz2B06lDXUXaO0WBM9UM51O+V/8p/8pFy9cYDqdIIRGCeHQnKpF83apRsuyQghJFC3K/i0s69WR+kXiwYXEeQLlXlaxf++twPP2OUuofi9Wedd9fa/+F/vYxcx0Lc2z9jNnz9R5Lu+zFLkHafeay+49uWRje3e436Yz1tNYC680npMl0D0fY89bOqWmBZ01NbWrQKV8amRHYFvXo0RgLHoca236mKJXmJVShNFiKfDPZMt70Emfa62PRlOvKM/AkhW+cGNbIRo46s32GXfPr481+2N7T5cxFolcd5R3IRYlQsEW60BYelMhunzmEt0YqrKysW/pYrhVRenK6JpOaKAoikX4wX3vGRnLKqcpLbCvKmsXQ7UKgDGaRtdoUxOqBGMq1sZj7t69zZ08o6lLTo4P0Y2wHgUlmZcFR8fHFvsSKpRRREkEWjGbTzB1xWw+s9SkYYhxLvde2sfs73e8HXY99TedaRpoNJEKKBoLstLols1x0O/znddfYW17k4PDQ67fuon81jdQclGWVWrjitfa2L9UEm3s9TNVg9IGmSRcuXKFNO1zOJsxzzP6/QF7d28zPz5mbTggbhruHuxz9f33efLJJ+n1e9RlxTzLqOuyDRlWVUWS9pBBSFk3xAbW+2Moa2KpuLC1zdPPPINSdh48rwFS0B8MmM/n9v5z5WZFbd3ha2tr3Lh2nc996pNIbbh+/C4fffEjvPXG6zRo+i4MouuaC9s73Ni9RW8waNN5izx3GQIRwj0HaZJCoVHS5pQ3dUOgBBLJyeERG4MNXvzQ06SBQgpDbzjg3Zs3+Ninzllkvof28IlTltDPCzR015Xm3SLGGJvb6CzUpUXVe0A7LlZvsQhjmaKk03r8IrYao15yK3M65ctv092vahqEkigUk8mUL37h83z8Yx/B1JVNJWhdvaI1iZSrT6yUQtfL7kO7wHjEq24X6O7CfVY7zwNw1na+3cuSXAjQ9gu7jxeQZ4CQVvs69dt9R+c3cov5igUuhFgI5q4i1XF/nzqGk+rdOPbpGHV7UkvC/37jX1XuzMqGdq6ckG4v31J0vj1P0y6kK9fQGdiLUzRdD7Stmmls4RtfU9xFcVtXoFSqZeoKk9gSbvh70vWnlBOu0k2sMeimBhwTnUuttM8EBASnniNgKbXSK9RWmZWEIrJ9sACRKtVRqt09LzoUreDZqSyftp/KpmmoGxvTtvnTDmwnrSq3AKHVnZm2rlrdNOR50ypAVV0hlaKuDFVddoCtriSvlG2xCimUnTYhiaLYgUdFy3xomc8CwCAVBEFEHMXWO+GqbBkkYaBIoh66yDCNYNAfsT6e8fZrb/DMiy/xnbe/y86FS2yvb9BLUvbNIVsbm4hG01Q1aN3G3LO85uBkn/6gTxwnCBWihMSSuBjiMEBoyWRqGAxHXHvvPTZHG0gH2DXGGhVBFFE1NVoYKlOxd7BPOkwQCnTZUFUNe/v7NI1g0B+jhbYVxKSxgr6GYZJSY6yRJQxlXRGUOaaq2dja5CCfM5nNuLp7A5XG9ETIjZu7SKUYjoc88sijvPneOzx26TKDtIcMFE3ha4ELhLs3/X3UCAiSiLJpiKTk4miMfuRRmtKgYjuuGhBSMooCnrl0iW+99j49IFUKE0hy3RBHCQZJVlT0oojxeINx3OfxK48yzaas9UYkKkKXDWmc0hjDZD5jNBwSKkVZFESuMlzjoKsBkpKGUEpU5cYhFEIEGAxVXbG+tkZVVoQi4OLORd57/xoPs30fuMRxlbGWa153gV9eU6+qivlsTlksauu22rkfoPBF3r32La2VZYwjBzinOIdYoFJXra2WkaozJt80BpdAQqAUv/JLf51ICWpHhG+MABkgpa29a9GjFl2ohHSF6mUrOFZBb6dBcGeD5brjbdvq2r/y+yqg6Cyrs/0saEFdtr7y+SCze/XrxOOZ41rs715CtOCTpbGL7jhc2pDj+V2dG4MfNy2ft2iPJWkLgHjFqnMOp9zrQrRKX1ehWT2eB5UZAVpYgJgHxS3/89O6fJ5LSuHSbFlRvNBbjJsrD0G2PxiHGm7R4sIyhgmpSNKEKArt/ef5y5Wy7k1liyxYASVc4RsXC/e0khiXEmYtBu8JApZyrFf5vXEWaNABjC1KyIr2fIyxGRRhFKGCABUEBGHYLnC19jzezTJ4DVvlLFAhYRhZ96yQGGOfTuVSJ5WUaNOQ5XNm8wnzbEZdlzR1Qd0UGFMDVnGo66r1ytn879AB+SRKLj577dFoMBqkDFEqJo56jIZrpGmfKE4JghilYgIVEQYpQZyQRj3qSjMYjNne3OGdN94kTmJeef9tXr32HnvTKUnSQyG4cvESVVFQFwW6Q0SjgYPJAfsnB8zyOTKw49LaWnRJHJJEERhBfzjm8HhiQYsePV0WRIENEyAFRkJlam7evsljjzzCMO6xOVxnfTDGaM2NvVscVlM0DQiNFhoj7HylYUQkJLGyqXOVrjFlyXQ6ZW19nTBOuH1wwDvXrpLXBVEac3Rywt7BAVXTcOWRR6jqmt07d9g/OqKsLfeGNtZallJaI6euCAOBFppSQo0hkIrNtM+HNrYZhSFhIhDKhrw1hlEg+cTTz5A1NYmUDFRAJCRCSYq6Zn1tg8nJlLq247h16zbPPf40j1y4zLg/IhQBNPa56g8GnMwmLYK9qSriOEZXNaV22AsDpbFFSSSWDrWoauoG6yHQNQSSqixJ45RHLlzm7s1bPMz2fanWdb/WzdPUbYDz9OLsra62bGL3O5xF1l3/OvuvWtu2JF24VFnGu6q7pBFhFJFnBVVV8ZN/8Yt89rOf5fbuLYo8s5aMtCZSN+2sy6TWzeX0MXRrHXxvKPDlnT74Lg96vFU+6XOViM5r8d3pbc/cX3RmoWNdr8ZvgSUa0bPa/Vz29zsPON9N3vV9+KJj3f+7CofAeXzwILNlK/VewEKfD2zM8nZLik57rAWlaFWVFGVJWVqglmVEw/VpF9q6Lqnr0lpljn4zjuMlDIgfn79va5dL6z1EuXMLtvWjnWvQPze6WVaUrUBfACu1UwTqyuZYewXau0O9Ra4CRRTFpGlKFC3K7nZffk4CFdrqX+25eg+eVVKiKLJWqVDgyEJsDjSt4mFzwe2YPNK9aRqmsymz2cyWj8yy1ptgjAWQ+sphh4cHHB4eUlUlVVW2fAxlXqLiiF6/RxonxGFIb2jplWOluH17l9t3blFWJScnx4zHY5evXbfhw5OTE4JQMc/m3N2/y+HxITIQZFlGv2+LgfiKZ7a2uSZNEubzOVVZogJFlmUIoSiryik+EjQEUjFMh4QqYHtzk63NTaIgsJSb+ZxYCGRt0LUGGRCGMfNpRoiiFyX0wgil3f3fi1FGcOfWbd66+h7JoM+7r77J6++/w+d+8HNcuXyJf/0Hf8CX/viP+Fs/94voQPKdd9/ixu4uW+mQqmnIalcXAlvRLY3itpiKpRO2yP/RaMylnR2oGwZJSiCgzgsGccL2xgbZZMp6f8ggSamKkrW4z/XZIZc3t5FlQxqEXHnkMr/+x7/PY6MtNtfWW+yCv7+2t7fbyo1xkqCNob8+pjqaUuYFKIGKQ1RZWwOzF3H38ICbt3bZO9pnVmTEgx5NUxMoxdpgyM7aBqpZ8Cc8jPY/iMA+a8G8L9jKreJnbXMuwnplG3/srnDuLlRLMTxX/q0qCwb9mE99/CP8w1/7D3nzu6/T76dtERGl1FJc3oNk/KLnt+ke0y9W/66By3y7102wJJjO2fdeqsDqtW8VLf/5rI6NP97pnhUC5S3rJa/A6RTA1j3ehjHu3z7IFVpSKlpT3LT+7fsDCsUpj3n7i/cSGUNVW4KNIAwIQmuleg9SURTkWeaERoUxzdI8eAFeVQVlmS/oQiObrpgkSVue0j4LQYvC9q7kOI6XXu2zUpftvd0tidmSs7h7v24W6V9dK3oxP4YglPTThCRZCOwlpQx7P3j2NH8cYwxK2loBvZ4tMdrr9WzM0whEp8SpRaanBIEkCCRxEtLrJ4SRIooDQBNGirQXE8UBNq+kceA5hVKCpqnseAPr5UjThNFoyGCYEiLJmoZaC5IoZWt9i8tPPMa8mDOKYmYnR0znE1Sk2Ds8YLQ2YjDsYdAURU5Z2ZrQk9mMvC44nB5xa+8Wdw/uMCunHE9OSFKLzM/yGYgaJaHXSyirgrIukEqggoBGNygkkYrQtaEoKh65/Ci6MWyMNriwucOjFy/zxKOPESlLIdrkNdIoojAmTnuEaQ8RBJZ4paigrEnDkNsH+1za3GFaltQCNsdrjEYD/sbf/VvMj0745//8X1BWNX//3/v32csm/K/+T/8VX/jEZ/jYM8+xd3LEb/zJl9p7yWhNHMWEKmB6MkEYGPT6NkxgjKO2DXnyyqNspAMCJJEKGPUHXNzYYnM4ph/HXNnaYXO8gUZQzub88Ec+ybXDuza2bQT7e3t8+NEneGv3PaI4QiAoy8payXXFeDQim81dGl9JURaESOZYa5q8pp5mNKFkvLWBAq7t3+arr32bf/MnX+GVt17jaHZMPOiRFxn7+3co5hM+94lPfIAV5f7t4VOTri7Q58RVu4vt0gLuV7DOStZd9IywlI4aW29aiLOtsVU3vHGLShrFrQXQ1d5PTk4YDofc3TvgJ37ix/if/srfYDo5tqhaaAFlvphI01RL6FiPCk+SpAO2q9r49arAPte6u4dFdq/vHtRiPxXLXvltyTK9zzEl4FFsptv3Ga5nYBHXdW2J1OOc8Xe/t8rBskveKl6n3d2r47DzKlsB8YGbYOH4Ngv091lgstXvTmEzuh9FB6sgxCklpWXN8wxoznXd1DVGSOfNsUhi2vCDXGxvNAsOEnMqTNTemw4M5t3hKrAI7zzP22291RzHsatuJdsx+t/taQTOeWLPzdOh2rTGyr239aytBWxrE5dVCQKC0DOeLSCSTVU7Cz/xM9/+tSE07/bWQM2gP3JegUUKqMULLLAtVVWSZXNL75qmLqxvaJqqdf/7okFFUVkGrTi2l9AYjG4o6xKtDUkSE6qEE1Ogak0sIApj1re2qYuSS+vr7O7f5uDgLjdv7/Lk00/xyiuv8PGPf5Q0TambkiyfIwOJVBCmKY2GaT7jeHJEP0yZTyasbayDsZk2SRQQVDVJHAENng1PKYWp7XMRqNCisMuKtfEGRZYTqhBTG8IgYGttg16ccvPGVVuSM02YHM1584332dje4vKlyxTTGYEwoG02QYnmW3/2ZxycHNKYhnw257VXvsNb777BX/0rP8+3vvFnvPH2d3n9+rv8nb/4s/z6n36Jf/zr/5SPPfMcH372Gd67eYPf+YN/w+f/wg/xyMY2ujEUVWFT5bDWcxLFBMp6Ceq6JooiqnnO0cE+eVZbRsq6Iokj5vMZkZK2fKkK2J0c8pNrW7ylv0uj4Mb+LXbv3uA/+MVf4qvf/jof6b1IIEN0QCtnlBCMBkOqsuT45IQgjkijmL2DfYIwYJCkpFHMcVGSDFLefvMtDibHFFIjQsWtySH/7T/9J3z8xY/wsedf5PLOJdIoblMXH1b7/pTX5P5uwdW2tHCtfO//NmaRI9fZwntW2wcRFrFwWCBPvVXmFymvyReFzRW/fXefT33yY3zhh36AR65cZnd3l9Shb7vkLt19uwAbjzL1lnVVWZDP6nmdNS/3ErgPIsweZA7v11crpMXZALTVPjzgr4Pxuv+xTrnJz1Y8VgXwcqzavyxWovsdnT2690a32Sl6MMWo/c0Bxc7cor2ei37vd893vTWr/bgP7hly8eVGL/1m+7DPglKKxsXZfKlJf4zVV5ewp4snQZrFeISNcyfO1bqq+EgpSdMUY6zQtS+BEMGp4xoXLlBCIJRCSVsTu1GOwEQ3No7b2NRPKSVxuuAJd7OFafTSc+ibD1F1pgWtDdPp1ApXaZUWq4xU7f6rnOdZlrXn1iU9WlCk2m3n87kLLVhL37IaCooiRyNRaYBCY+qaqtHWYyEEO5ubxO9KTFNRVBXvvv8eVx59hIOjQ9I0Jo4jsnxmlf/KguaqurJCtBdRZz72a8cYJzGiFoR1QRKEKCEwWluLsSzphwm6MW2cv4uZqYqaUAXIQJHGEVcuXebN99/lpJjx6nff5M7dPS48coW3r17lv/+Xv8mLzz/P8089ydZoaEMPCIJRn/yNGVWWs7a9yY//+E/wh7//u/zG7/xLPvqh53jp+Q/z2ltv8ut/+Ht85IUPEwaKq3d2ScKQrdE65eWLfOeNVwmffZGN0ZgkjAmkoshylMduGNMWDtFotjY3mEwnVI0gHqQMm6G9f0PFoN8nkgGBsQQoX339ZV56/Gn2jg84mR4zThNunhxxeXOHpqwQkURKZcGcjnHt4s4Ok+mU46MjRusjsrok0lZu5E1FrmvW+0O+++abfP2V7yDTiF4/ZVZkNJNDSmm4c3LIN177Dldv3GBnY4sLW9v3XAc+aPu+UJP6vw8Sx1vseLqv1cXfmAW14eKHhet01Zr327aL1Bkx5tZ1rRt6gz4/8iNf4PnnnqEqy4V7s9Ofbz5Hc1VYQ7fo/Soi/MEW8vu1ewnFBxXuZ/bF8tw9yHH9ctndfHW7cwW3WOnrnGOeTYqyLNhP97H697SusHyg02M/qxlAdBUtPri9ft7166LPfWzPh1oaV1wB4dnPgkVsWUkC40CQwispp6+BF85d71N7fLEQYmfd091+qqpyrFYFnuPbX5Plv7ZVHd4FPzQlpaVQNcamRbnCH175E0J4cDue9a2rNGj3bDYaPBDNGJvuqV36lRfmxv1W11VbzrPbvIesO0fdrJaqqulW2etynAtHpVpVFULVCGMZDW06nSIQArRhNBwwSBMMkJUFUimyPOfypQtk2ZS8yImjmKzMbIpZL3UpeA1RpMjnFb3eGsbYuYsCRVE2xCoiViFxEqGkshzbdQVRCnjSJjsLtqiSrQceCIUSkihOGA4GRGHIt777CvOjKXGYsLO1Q3N0wGvvvYuOA/rjAf1+ShInliVsbUwUhAz6fQajAXk+58PPP8+ffvdl3n//PXY2tnjuqaf5yivfYHd3l8sbW8yyGbMsI84sGvu9wwOu375pQ4vrEaErHEVjGd6MEC0pVlHkXL5wgXk2J69BBJK19TWKIkeGin4vRWkBwhAguHZrl5ceeYokm9GkPXr9lG999w3+0sc/w9HxMSAJI+GOISiLgvW1NebzzBYCGduUusZg+fRDhVGC2XTKyy+/zObOFloJjvMpuS4IQ4sP2Ts5ZjafczKbktUljTR84oMtD/ds35cY9oNY14aOu9q5Ke3zurwAL8UBW2/5MnHKeUFUv13XHb1Kg+oZmo6Oj/nMpz/JD3zuM6yP1zg6OGTg8sSN7hQqaBoQi5xrHxfvVubqkjDcX2lZtnbOFXAfoJ1lWT3o94tx4U2nxauzLbBEHXq/Pk+f99kKwlJfbptTqPJ2aGe73jHd2/q0IFkSkv4e9JuePaN0dlje97w9zpqvc7ZbnK/fzrvIW3Fji0J0ik4EgXKuck+KErbFKSxCfFkh9W5oL/y7yG9/v/rnxAOz8jxfIisCK9R9+GeBHC9djLp0JWPrtl/PYlY6MqGyKimrgrqplq5hECiSJCaKAgIVtM9T6F7+fKQvSCKtQG90TVVXFoRXLWLnYIlC6qZqQ1NeGCtXHcweW3Y8ZaYtBlIUxanr5+fezp1u+9HackoboaGprRKjpGX6UgG6qomikPXBECUkx5MJWzvb3L57h/WNDbQxZNm89ZSUVdnyweumRjcVUlpSKtWhX83zOf0oJVIRG+N14igmz4tFCVKvqDQVta4ZDAft+uXDDWEQYYzlSP/qt7+GDg3PPfcMV7Z3eOzKY8RrI94/vMPbt25wcHJCr9ej1jX9OKI36HPh4gXWR2O++Wdf58pjV/jIs8/xzjtv895777Kzvc3HP/IS33z9FYos5/LWDsPBkJv7d2mahmF/wK39u9w6vMtJNqMyi5BiS2mqFAbL77GztdWCA6umYXN9nWw+QwhBL46J4hCtsOA5bbgxOSRKEy5sbpP2erz1zrv0BiMwlpWyqV1hHCFcgY/UhTs1WZ4zSvvkTYVQkiRNMGhe+e4b7N65zRc++zl2NjcJpbKVzNY3iOKUWV4wLQqOi4y700Ou3r157rP/vbTvO+jsTCEhhM1sE914X2dx6grpbl/GnF78zMIqsR/PSp2ifVi9dQ20cSofc/61X/s10jjhYH8frRuLxnRowpZdytgSebBcTGS1Tx9b9WM6e17kKRf/WfN1lkD02561z3nX4H79L/V5lvQ6R2j7WPQHGcfq992/vkkhlliuln8/SwgKQK7I10VseXl858PlzjsPf6utYv7bI7S6zfnXqjuus+/T5X4CpWx1JKVawd3yYbu+vOCAxT3lBZwfd11rB/oqF8KzAw7z7t/G0VJ2WQLbAhhiwePtz1xrL7Dzti+Pnq6qynJhK2+FFsznMyaTCdPp1OZdu8IVHoQkpUSb2ioozppUarFW1Etoc0kYBm5/C6RLkoQ0TUnTpKNnmg6ALsViGWz8fDgcEUUJvuJXkvRI0z5J0qPfH9I0lrBkPBozHA7b/pMkbec5COz5icCgy8ICADHIMEBWmnw+xwgLzopkwK07dzg6Oeb5F17ga1/7GmmacPHiRW7u3qSqa5Jej0DYVFGahsODfdI4oioKq5QFlp+9KAuGUZ8oiNjZukC/N6BwKbK10RRlTpbPmM5OmMwnBKEdZxSFNHVDUVTUteZkMuPo6IQqhvHWmFEv5fjOXebHJ0yrnOOm5O3d67z9/nuEQvL2++9wdHuPoi4ZjoY88cgjXNze5v/8f/u/8uSVR/nCFz7PYT3n//7P/yk/sPYYH/3kx/jD775MXpbsDMYERnA8nxLFEToQvLd7nTfefYtpNifpp9SmIU4sINIbRkpIIhmwPhojgGw+Z2djE6lB1xX9KCFNEghsiOKp7R3+9I2XqbE85/OsYGe0wavvv0ua2FRIf58DJFGMrmv6vR79QZ/jyTGJDLhzckjZVKA1t2/f5jd+51/yxR/9IpeG6/SjhFBIwsogiob14YjHHnmEMI7ZPz7ivevXOMmzM9eY77V9X2LYXev4vN+FPGORXqyt9s+yYb3YrLvQYQW9YLm/VZa07vc+NpckCSA4PDrhv/yv/kvG4zGHt28iBKRp2loYXskIhC3AEMUxVV60SkAXdNPNPb/HDLEsLD6YY/VeguChtfO8Fuf/9MG6F3CauPNB9vPC8Hs6Kt8L4MyO1bcH2f/s4whh3fveLl8V1ot9F+dpwJWeXL6vtDHObZgznU5pGgErZR9tk0hpyxxa67nsPJ/Os4PlElh4Vux52noAsxYV7oGao9HIxXIjmsayVVmBKBZ0oK5/iyIyqEgRxb3Wgvfx4rouKUuLCJZKLJgShRXEURS5ePIiDbRLhyqEI12pfVEd61qX7hzCUGGMZVOz+0WWaU1ZoTebzQDrcfOsbj6uHUWR4zhvyPMFAYsJBL7+tfVONARBxDyfMQ77ZMKQV5aBrto/IurFGGlz8wMh0cZweHTEE489zlNPPcXh4T51U3Lx8mV2b9+w+BihUHFEIAXzyZR05zHmkzkb6ZAkEfSqnq1XjSAWAb0kxTQ1E204OTlha22dw8NDjBDs7e9z684u331nwEvPf5TZ0YRaWF72qm545PKj7O8fcWlnh6oumB0fMabP9s4F0v4AtdbjcDLh/WtXST/7eT790sd57f23ePuV11i7dIGtF57nsy+8wPz4gP/jP/5v+Ks/9TP86Cc+yyvxG/zn/4//hr/31/4GSWn47s1r1GguXbzI8fExRV2ipaHMS96/eY2Tg0N+4KOf4sr2BSaTCWU+cx48iwxPZMB6L+FWeIhixtbGJsUkZ200IglDqrIkEZJmmPDmwS3WJg0He4dMplOaouBjn/0cX/vjP+VXfv7nKKqKSZ4hsPdPEiUcHx8TxxGj8Zhrb95kWhVcHK6ztb3Nm9ffp5ll/MUv/Agfe/HDvH3zGrERPLF5kUk/Y5rbVMD1y1cYDEccHO5x9+4dru4+XOKU7wtK3FutXQvau56NsbmkaEEpyg7nsX0YLdBAtpziIFpuVyltNR0pxKK6kRDUZdVW+fFuav9Qd9NIaDT9/oDJbE6S9pnlGWkS8TN/+Sf5gc9+nPfffguBdKX4BFIFC9Rsx2rRVd3SK3bj1t49fi+Fxbtml79beFqXgWjLnNpdGsvzLO2zjnuutXvONl2X9f36uN9Yuu+1O3XhwGCr2/jPygutJRf2ebHq7vuFK7mV6J35Xvpf+DiuaO853/z0LQ4hOtsvjrTcc+e6SdN+qzWY5ux7YeGGtzeAcSqM1oa6MWgjwEhrRXi6TcBoEMZamBiQKkQqsdSzMbXL8154gaRU9NMhWZ5ZCl53LeqqpmSB+ta6QTeWUlQFAQZcxTDLjpbNpmAMGisM7f1bIYRyFJ+qhX4oFVCbGl1DHFoSF0FoXciOelRKH7pqiOOQsrRFNxotqBtABAgpXGW8mqbJaUoDjVpK1aydO1ooRRiEZFlmqSXFIn1O6xxjLIMZuoGmxoAlcSlyt41DlTsymGkxt0JZKtI0pawK5tMJbSqhu9+MMexnB6ggIIwiWwt7I8XUDdP9A0aDAXdOJhzeustnPv0pvvXNP+OTH/8IaRJwfDJlejJj0O8znU+oyhlRGCJEyLwsiNKEo4N9JA2CBokhTRPCUHBxe52NJKGvFE2e88bV99mfZ2z0hpSTGUVZMxqtk5gIVTTUuqZSDUIWaAouXdhke32TzeEGSgSYUDLY2mD/cI8Xhmu8NTtirit2y4wvvfwtPvzYDumFj7FbFLz37tv84e/9DmtG8OM//leow5Tf+cqXeOLRx/jUxz/JPGz4jX/1L/nYSx9le2uTo+kxx7NjW9qyrJhMTxgOR4SDkP3pjD/8ztf4wY9+ms3BmMODCYcnx8T9HsM4ZXt9AyFgMBywWeRkx3PWB2NUcYQwNXEYkYQxk8NjPn1pyEEcc1xMGMmErdEatw4O2H76ca7t7zJIU8JQQVPSjyMMIKMAGSmi2qLC7x7sMVpfI38r586du2z0+/z0F36U3Zu3SYOIJIwowpJUNGysj8mznM2NDW7eukWqBZc2tzmcnJx67v887ftCTeqFrRey3g3t85S7buDWwhALIIzvp9u6cezVuLBfsL2F611mp+LCHTKIwhUuePrpp/mlX/ob3L19G4EVzGe5qVct6a7wOMtS8nH50+18N+zptgDznLXNg1jaXQG3pAx8D/3dy5q/l9D2f61a1h781DbdMa2oEw+sFCxbw15DcNep6x9YUozOnqOzr5X1i7cqwplepO4IVoQ5HfHuBCZOETidnmiV1cAVYTh1vVxvngK3Ox4bk+6UxxQS0JRl7uLhtLFtrRtHretrU7d0RstzLJ2HwPH4+znsIqu7ISdwteRrWwlLUCFY5gKv68paxJ0MC8/UZklbgtbiLcsCpbDzpQ1V2Th3tLAsZtqGobRecC545d2vSQuUuQ83RG1IrIs7adcwrQkDhTaWLc5SstpqaS3fAlA3miBQrbHSKvNG0xgL90hTC9pq6ppbu7s88cTj7O3tEUUBcZKQHWeIwLLCVZUrm0qFsKRg9pyrCiOtYtfUDfEg5OLOti3aUVWURcn1GzcoG8Ntc4sL65uYQFHmGhko5vOMJE0pGkv4UuY5axtbYODk8JD9MOZCfw11RRHGAZc2Nnn7vT0wmryueOv6NYwpWN/J6JmGzeGIWhv+9NVXuXPnDp/46IdpwpobN3f58h99hZdeeIHp4THv7l5n1B8wiHvs54eUokFo0+It6qYhKwuef+pDHB8fY3KbI62CgLwoELUmNILBeEQUhSglOD465PGNbaLQ8hKkUcx6f0g+e58L46c4Oa6QStPvpawNR9y4do2f+MIXmJ4ckEQR/TimKiza3wiJULa6mUTST/tcu36djz//ImhNP+2xs73D2mjMnb0DRCiIwpBer0egI5I4Ya0/Ik1TsmGG1oaTbIbQD9cD+tAFdtM0LRtSN2WlS8Tgre9VcgTfToHK/PecIRzNosi8d9t1Bbj/DSydnTaGKAiYTU547rln+fznP8+Tjz/GtffeIYmCJavWH8M/8EIsigt4IpUuqG118V5Yaosyime1+7u1z3fDPohgFWd9fw/h/P1ys5+221d/X8Ss/3zHEfd1XC9ywH3q0QL4d2pb8HDlleD1qoLWGUNXEenk/wIrBUwWo/U4Dh8sFy4m7cttmk6fSlqGMBxbmI/LCmEW3ihUe995gdhUdce74UMLjnUsUHgaWd1Y0hY/N36AxhjLRmV8Spj3fgWtp8wKbKekCUEUWXSycPW8Zau0W6GNcHSpbo665C3d5103/jer8tS6QcrQPpeNQQRWoFqq1YooipfWEevNc1652nospKuXrWtLUCN8HrvDDNRNY9nYOkq7khITWbpUqRQIgaoblFp+zrs1EiLHGJckCXEUkc3mRFFEVufWM2AMQlmUd5hEBMoStDSNoZHO+FG2RK+n3K2qkjBQhHGMEoJQKaIwpG5q9o8PLfNZFFGbinlRUuiGaT5nkIxQUlJXNVVeoLApfLrKmJyccDKdUJuGtJeys7lF/9q7VI2lsN09PuSJRy6zf+c2+7d3CcKUC5cv0x8MePm1bzBcS3j80iM0ZcO777zD3t4el69c5o233yJ0YEIVhBT5DAkkcWIVkw4R1aA/ZO/mbeIkIeqllHlGpUtOJhN6/T5pkpAkMYeHBy3pjxGGIFD044SmKNne2OSV2+8xSvv2GHWDLmsCIanwqb2mXdMtbb2iaSyV7ng05tU3Xif9+KfQdcOg3+fC9s6iRK0DgcZRTChioiCk5wBzG+N1C2CsKgYtb8DDaQ8ddOa10y4Iq2sRd4WgLQm44hY947v2/VkH7Aj+rlXvP/umjaZsauI0odYNaRLzQz/4A/zEj/8YB3t7REG41L8fa1fB6H7vz89bC+cRoyy7c88ThvdGEp9pp58jqIVwC2Ln1VqvsPius/39+l39fnW/JYv3jP66vgY3G51/dP7e/xwfqAkrwHyPZmkEtOh2IxxgTt5HwLdCw+2+MtZTVrbj0/XqhzzrWtHJLe/2p6316PcTAhcqcm54sRAontpTKkGXPMZv45HjXVDXWQAyH7uVQhKFIbFjPQuURaL751kq6WjnvTAGJa1r3vZr8DziwpriaN2QJKk7fmTBRGlC2u+RpilxkhLHScsYqFTQfvZKvxW0FjsSBjFRlBLGKVEct+ltcZwSpz2ipEccpwgjSJOUMIxQyivXmjAMSJLIukOFsRSZRcEsmzPL5mS5ZRyrdUPjPH++mtjivJ0B0BgwomVSs0qTcBZ+0wL76roGYc9t0O+ztb6OEoK9vTuMhiOqumaezQmD0NaXRljudRU4dU5QG00QhlYZcTdDVVl6WKENoVIMez02N9YZr61xOJswrUuuH+1z8+iASZFzOJ8xyTOm2RwlJHFgEey1E3CbozUwhul8SlaVJEnCxc1ttgdjhpEtjXxYZjz92OPUBq7ducXh5IjN9TV+8kd+hChN+e1//btkJyc898RTPPfcc3z1m39mrc/BkFmRc5LPCeMYk1doCXEct2DFfr/PjRs3eOKJJ8jyjPl8DkAUhiCgdNkGvV6PtfGYo6MjS9UaRzTGghQTFSBrzfbGBkeHRy59UHO4f8DO1g53bt2h1+sBUBQWz+HvdQzoukZJyebGJrPZjCSOqcuKNLZIcFNryyPgPFGBlKRhTByE9KKEWIWMh0M2xmus90dsj9fvtbJ84PbQLexV1iMfR/ZglNb15YonWHL6xaLuhbYx5p580t04q7dyYWENt1q5d49j19HR2ojXXnmTf/D3/x5f/OEfJglDru3eZHNtTFUVbfzRa+Z+IYEFkK1LPbqoc30e9ah3gN7Lnbx8Tmf1cNbv57mwT7t2F99/ULf2vbbpfu5ej3P37xxfrGyzHK8+PeYHEd5nW8fahYnvcT5nXreO0sUCKPagGq4fs+HseV/0bcX1vbIj2nvZ5RjXurG5tlrTCFuIIYkHp0JNC9fzgjO5cshwP8YFK1/d3u9gC3OshpwsiUvTsqJ59y8CtFkw+nkF0Y+hPxi1aVLd/jy1qtaaKApJk7hFsXefaZveFS2liiklCaKAKrf9hnFEoxtbSMO5vqcnE5CCOFkoK1k+Z575UIFAFIVTCkyrvPi1ys/Pgp2tYT6fE3qXv1DoGkRjU9fW1tbaZ8+747upcqUx7GxuEaRD/l//3f+TX/6VX+L6zetEcUgYRRydHFEbi+YPpC0/qoVGIJjMpja80DRgNEVZUZU1+wd3ePbxp8mnc+rGzkt/1Gd65waNlFRzS8MqaLh66xaDpE9pGjaGA9IgRDaG+cmEK5cusrEVc+P6NbI852QyoR/22N5Y56lLV8huXuXm9Jg6huL4kE9+6oe4Opnz8mvf4ve+9Fs8uj7gf/bLf4d/8Vu/zu/9/u/w9JNP8dFPfYard27w8ivf4dLOBZpAcpL9/5j772Dbkuy8D/xl5rbHXP/8e/XKdFVXV3uLbjTQsGw4EiQlcUhRJCVSFI1ISjExEzOhmIjhTGhmQlKMjVGEZvQHI6QQNRIFgSRAD5EDgA0Qpg0a3dXV5d3z79rjts3M+SMz99nn3PtevaqunkBWvLr3HrN3bpdrrW9961tz5pMTR76LXEMTrOdISMXNmzeJk4TrTzzBrVu3ODw45PpTTzA9OgLTcHx8xPbeHtvb21R1TVGVSCWpmho5FGyNRiSRIs9STk6OOToeYesSO6/5+Mc/zVuvvMQHH/sCbV1RVzVZki6DPVzb2TiOyQbOyXTa+oY8HTIaDrGtRgJKKl96Z6FtEQh07excHiXY4Rh2DSPvHLxf43032HEcM5/Pl5GAlGRZRlVVnQ4xQrgTLE/DzyuLxAMWsHWDZK3L7fQj+47R6aPkKEmIpeRb3/ouP/vTP8G/+Sf/dQSGmzfeYHtzI2QDPeq5jKLXkYLgkcFqzvwsSPRsA/7exruKNt/Dbk9t/+E+xrvb9trmzorQwyL//8/RLa7+XnvY/jvn8V1OMkDZ/TKpwMzGk8zCuZFCsN4qwKWBlhB+97cNJZDuXi/KhYOb5bJhRti/+ykRQiEkDEeDzqC46M8SJ0v97rZdGvA+uuRaCUoQujP0/eMEiGKPSPkjNKbfo9r5TUpJD2U6uFBK6WRCF/OVZ27FaQ4qatJF7a6crKVqC7fv1nTbT5KIWA46Po02mul06valZDd369MOTivc1SIv2fTL/L8r01w6KU0IOpAovxbUdc3BwQFW0DVMccfmcvDan4NYRWwMcj79iY/z/Ld+n6c++AEmswkn0wmN1gwGA+bzOZvDTSQRSrp2qsfHh1zcvoQwAq1dlKeU4ujogHu5q8VWSUxVFXzjG9+gzmJkljFbzEmSmCyN2T+Z8MadO1y9fBFmE8ZRwnY+6q7hE1efZHo85fDwiKosaVvXseqx8xd47ebbzI6OkDsjfvkrv8r/4pOf5/r5y+wf3eX+0R1+8R/+fT73wRv8sZ/9w/yLccq3vvNt3v7H/4if//LP8M9/6yvcmu4ziDLi1nK8KMjPbdHun2AipwQnIq8bECleeuVlPvD4daqy4rbnFm1vb9Mcz9g/OiLNcoabYy5dvMTbb79NNshBSlSsyPOUopizsTHiicevkw9ThsMB6WCTF155iZ1hzp1799jb3mI4GtEaTV0WKN+NTCjXqcBqSPKMt2/cACBLEiIhsVq71s7KNVTRrUc8tGCQKUxjGAwGyFTRpA2JSt7dgvEO4/tCOgtwW8gbhb870QHvVQlrfSMHAJczE0J4aTo61vhZ0Uk/zx3M/jo0HupFg9jD8XzG3s4m//P/8G9Qlgusbtkcj8EaFmXlNX1dDWgfCu/nqMPrfe/5dO56NTI5/V7v8xYP4cKjWNp3grCF/18wfw+by6Nu81G+F6Q7HZlqNeqmB8H3o+xT++ob7fVtrH1+/edynB0Dr99HQqiQOXYWUCzrmkN02P/uGVvsDH5/u1aACdC5CHK4HiJ+hLFuCPu3RDAugZSVxBFWKrdAiGgFFu/DyeF3Y0IjEXMKGQrHEdAprIfZe+ezS/9o7SILAmEUECEilhjddn2ljQlIV+siEgxNa9Gm6crBwPeM1zVJ4uDwoNsfjsGRwPB16K6ZSVWVLn8vDGVV+HPiFshivmAymZDnOXESIyN33orFgqZpkZHyqSHRaS04tbMlgS5UmiwlWAU2GHIrnJHwEsWDwcC15u2qXjxxVogOQtdGY8oKqQzXr15jVs5c5zDrHAupNSCwVngCnUcMI8l0OuXSzmXwaYckci0258WEk9kR53fPc/PODb7z2mucu3COe7MJwggGeY7BUixK8ijl5sE+JJILm5uQamIr2Tl3gaIoyKKE7fE2bdVCaxhkOff3D9jd3mY7H5JagYwSXrxzn1dfep6D22+xEWdc+eAneXz3Ar/+G7/CUTrnI48/yzgd883vfpvf+frvcvHCeW4f3GNezKnqmihWpCjmRpOp1Om/W0scR1RVxfHxEfWFS2xtb7EoS27dvs1HP/Qh5o0lLwrapqGpa65dvcrdW3edoZWuJn84zFECzm9tIZuGS3uX2doYc3T3iP2D+3zgiU9x5959Njc2GCcpk5NjYs8taJqWRmti4brH7e3tcXh8BEIQKYd46LbtArbWGLQ1xDIiSmKm0ym7u7sOwRKC7e1tjo+PH+m5f9TxfSGdhQcuGLi2bRmNRp2oSNDi7mqnvYGW9BZge/ZCGXKJfchbSbUSrcMSzoIl9KaE4q/8lb/MeDTi+GAfgSGJIrSxqEjR+vxQX3iiHx30S9X6Hvjq3N5leCv8wT4gdHsYTP2gv+3qiytGcCWXfIZhfjeGvXsduit5xpe6X9ej7P52+8Z9fX8PhufPmsk7vUIHVXdVYGJ5BGdVIZw9Ts+tI7KFqD38c95n2Ly/DvYUhy3Mo69z7SB1b4hD6shD01qLLsK2LJtbuH+r8w9kq0AIJRDa/H6bugb/vov2l6WZCLDGYoRGi2CwnCyn29ay/FJKidHhORLdvuNI4Qz8UiNaKm+crHHZsch1zKqqsju/ASXT3pgtOTEuj5+kHsJvnJNgLTRtS5y4xTuKY6eOJl2ueTAYdYGBMRq0dprjImzbnzBj0dZJpjoinz+ffi5KJY6Y5pEKABmp3lzpnBlXeeKOs21bTGs4v7uDOWgptHdcrEVFEcY41URrTFBkxWJZLObUVYXRPoKXDhXa2BxzdHLEYDBg/+SQewf3iDc2qMuSZ55+HF21HM9mFI1BRIpFU3Hn+JA4EsTWklpBa1xTFikUO1s7oC2xUERSUjQV48GICzu7XNre5SRSFAb2J0fcvHOTxsClc5f48LPP8fWXv8oLb73GyGRsjzd56vHHefHNV2kwJEhMHFPqhrZsoKxRiTO0UkrHtG9b0izj6OiIuq7Jc9d97eadW2Ad0jIcDDG4lq2DwYD9NrRL1d29GCvJue1tTFlxaWePrc0Njm7tk6QJRyfH5I2mrGqv2S6RUahfsb6EGIqiYLS5iZ4svDPg2poabTqUWCjXmCcSkrqpSKKERTEnz3KUcvft1ubmQ9aQdz++b1ri4ffQKKDfKONUVHJGFN1XETsN155WiepD4evfqyrXCebpDz7Fz/3szzKfHrkFBwevCaFQkaDVnuzTM9ghku5H3Mtev6vH0jfY/df6v599ziAs3Q+yl+9svB4SFb+XiHkN0n8nx2Gdmf2eovR34Zx8z6NvrK2DB945qn4AatK9SZfvXkEKhCMOuesQsuFuv3Y5ERxEvpazX/tM8O+scXoG2pcwYZdduJZR9ur9J4QTUAGQwvgo2fNO/Gcj5ZaEKFqt0rDCOrEj7erEgxCJu+dN55dEkaLxhx5Y4y5y91oLvpGJNS4Xa/FsXZxhdxF17Vniy8qMgJhJGfn9RMRx5JALAZGKQSiwAm0NSeQUyEKU65AdF4UJ6Zjkuqk7TXJ3TjyXwLh1IRyDEaGMjc5gW6GJPNzv0gqCRCQrAjfhX1i8jTHo1jkWSRQRSUVTzb3BNo5wa5xBFnZ5zYR10X9Vu/pz5cl/bdtilKZqKw5PjijrkiiN0UbTlDXbow2OFwfIxpAoR1hDSSZVweFsSoZgJCMWRYGxGlCMRxvY1pB6ApyW7pqe393h6rnzzGb7NLYhG4yIkpyjw33u3buNaBs+8dzH+VcvfJPXb7xJc/4CW+d2GQ1HTI+PGW1sIKzuUKKmrn2deXgO3LM1yHOm05lrYJIPyfMM3bSuHFcqBnlO3bYY4YRohoMBZVn6FIEkVpJESrYGQ5Q2jJKMjWxAHjsBnrv37/KB3YuUZcmiKBgNc+qqYD6fk6au1WzTGu7fu+/8bOui/yhytf3aE9UsFunJagrhv59Q1ZUT5BLu+Ry8zzns950lHkURZVl2hlNrzfnz591JtUvN7TDOgpVXyCtidQE7a8Hs1113JJsg1u8fqCuXL/On/uSfIIsT5tNpp09s/IXW1vgSj9UcYL+uu2+wz86tu599R4KV3/EJSJZ/E16z3a8PGg+KiN9LVPywbfTPdTDG7zRs7/8P28ejzPdhhnvltbN2FyyaNyDhlNP9XIpoiJXvPKpTIM7+J1aZ76fmK3vR9gO3LPz8Vh2HM1X7giNgl8THkJNuGlfD6nS9Pc9CO5GSQDgLUX6fcAas9KwO73X3uwWBY5PTlT8tGetxlJDECUmSLv+lGeFCBZi7qkrKsqQsFtRV6YhmjetxvZRKbbpmHWVZdgS3LMvJswFZNiTPh7Sta2aRJK7GOU5S0jjrntflWiK7/dd1S1XW1LXbx3w+YzabUhSFk2v15zGsV8afY8FSprX156kTZvLn0vrPOaffEb6Aro+3Ma6JSzFfEEWKsiip6watTZdzj6IIqXosfekYzFpryqpAG3e9qqri1t3bqDji3sE+QkkuX7lCWVZgHZlsdnwCbcsoz2nqiihNsEpxOJ1yMJlQ6paT6QSsE6TKk4zxYEiWpEynU5I0QZuWne0trly8gC4KJtWES+eu8tyzH0PlGd9+9Xl+72u/xWc+8Ak+9cwnqJXmtbtvc+vOba7tnEfFijaCclFA0zIaDilpSLz50a0GY53s62DAvC4oytLNJ8tJkoTJyQlOWjYly3KSKEU3hvPnzzsxLSnJ0oxRPiCNIxIZM0pSdFESA1fOnUdYy2QyJUlSirKiKCtGwyFN03D33j2MhdSrX969e4979+5T+WsspSJOEmqvS69bTeSrLFQcgRSuLFBJWq+lD0vy9fs1vm857HCzJ0nC/fv3uwcglKSgZCcRGm7UpbC+RsZObawfQfehypBvA9cAIJaxK3GwoMRSJe3g+JjHn3yCH/2JH+WP/KEf45VXXiGSDjpTKgZhKErnFWmtXXNzIZbz8CIIfcnCPuS1jOjxP9eiaZ+r7xZc9yZ+AmCXBDnp9ysjD70DrdYY3M2MsSsW/az89Zk2TKwakRV044zPnt7AEsw+y5gu+1yvffcdnK11x+CMHQNnGfBgFldnb7p8NF2EsmpYQzDtwhZBD3zwC7r2HrWwp4/11Dw7w29XHS1/fYUQPpcLCEOL7ngZWNvVnNvgWABu95ZISGTsDGDVtCCUzwd7h9YahIyw2rjqBpW6Omfh2ixGUhJL/xxYp+QmhWtT2dqme0YDYRNWHeVwL653nOscVwtlvYBAfGuNzwO6ciPhjbm1ljTNumdHypgockbc+RwedbDGk78c76RfDpplsTvLVtO0BVo7J1y3LXVdeuNVE3rPSyVI4pTFYtGl2UIJmwGSJEVFwuU+jepIsgIJInLG1/eXXkyOGIxHtMZQlgVYF+XUdc3W1hbpcIiKY1Sk0KWbc13XhFxzawxBs3p7extjofYO0mN7Vzi8f8zRZEIjNBs7GyRJwmJygkpTDJJIKGIV09iGJEu4e/8uKhUMBjlFMSUeZ9w+PAAjkXFKbWFeVeTjAW/cfIMLO7sMhWQ2nxOJBpqaQT5gPp1yoz7BiBiTJMRWkxqDFYImSZC+OmZY1izKBYONMZcuXST57RqTD/jV3/h19gY5zz75JG+fG/PVl17ghddf49/41/4Eqml588ab3Ds84GB6Qjocubpk3dJaTaQkSZxQm5a6taQqQgHz2ZzdzU1+/+AlmqrCVBWRtVzY2uGt2ze4+tkrnBwe07ReL71p2HvsCs+cu4hA01SaIUMujMbMKsmFqzugay4ON3nmo1f5u//5f8aXf/QnuX3/HtevXEHJiKO7xzx2+XFeePk1dHQPdXDM5sY2P/D5H+I/+j/9Tb74Iz/I9GRChGC8tcFsckIeZ1SlJiHBakutKyIBxrREKmE+nxHHCRuj8Upp8fsx3neD3batw/s93BE8S2DJ/jTGlXOtRdb934M8peXhuWGBIJaxM3Sqx2hVEo0lTRL+2B/5ef7oH/k5bt26tWIkwgKVJMlKn92VSD3kWNYinX7U7fKJwXDLlW2DdSUAwjsaHhIS/vjdIuZxL+H0lMM5lFISS9cHvK5rYhU9MD7rm6d3yqKvwKTv8NnVrb+Xdx/wnfcF4l5xQ7q/rBXvPKnuM++Sc/AoszrDAXkYWrGCyAAgMUJjRGg0EiBbZ/xVFJGlGYM8I0WwubmBYSn6E0hakXApHWtsR3zq54TD/oJmd9+Id0Q1q1fmGI7LGEM7b2lb11lKSbWiER4g/iiSNLUXM7Ku5aTA0urQIMfNwa0TDWmaeaJXH1FYPnfaGOIocgplWUZZSh8cyJU5RlHkVKjkMrK21qKtR9R8KqH15WYhj22Rzkk2bRdwxFFEEsfYPMe0mros2d7epixLptMp2lq00RSTBWnm1j2pnKiKEC736pCFChAgnTN2fHzM3rk9DmYnHC+mLsqME+bWicJEcnnP1Lrt1iKtNVVd0eiWeVHS6IYsyUnSCIlke3uTW3fuIiNFoxtiFZGmKTaOWFiYT2edgty9owM2hxmV1izqyqdEbMeDbZoGqzWJitgebfDY+Uu8/uJ3eHNyn69+522GWcLHnnuOn/83/m3+k7/1n/Nf/J3/ih/5yGd56tI1Xr37ltuG0dRNgwBiqXr6AwKMwRposTS164feVg0nk2P2NjbJ8pzRaMTrd26wWCzQfdlpv2ZvbW/jayYYjEbs7uwgY8mFc+eIo4iyKhimAz71iY9TLgqmdUl6/4CmbEkHCff27/On/2d/mr/1C/8ds/1jxtmQ13fe4q//h/8B//Xf/q8ZbW3y9d//Ji+//BI/9+WfYjHxJXa26fSWa20YpAnzxYI0dvdS1dRsb269hxXkweP70vwj5H+DQWo9s67LR2FRKFq7rBM9yyifZdBPL/QWjKPZG6A1rlxjOBxy89Yd/vpf+ct88Qc+j7RwNJms1Fn2c01Axxbt58H7xLUQdYcRPPr+4tKfs1SuRlUb60SlXeiHsQK3DlrXKtArPhmfO1PKPeBxHLuHG597tF02s8v90Dsf/bzzOxnEs8/l6VPbj66/H2N9Do86/7PmtJpHf/D3w7G7YNw7S9Dl0UK0K9bIgO510f1/OUV3LTpf+mGIgehdp55xDH+fOnbvVziH0TG9O5lOAVo4uV1k0hls02pXgwu0dbNyP68253D7DVB0n+y2zM2erg8PCJRzwBOMcQxfQnoLQ9u0aKOd+IcV3fPfnR8UUrnvuVwztI2rx3aOvqZLtNjlfSFN05VXuXm2vZI5d12TJO7Sckosy8qEcCV2xhgarX1fguXxRFFEEkckSUQTOcNtrWU2m3WlanjkcDab0bRtlw5AwO7eDkp5g99zejonhqXjphDUdcnm5gajwYDJYkK1WJCdP0eiItqOtiBdzl1rZOTEYyyW0l+zWtfISLkmdco1yRikCVin6qYBWo0UkitXrvAbX/06lx57DISkrWus0cybBqME82pBGiW0xiCUJI4SF0xYi7SWcZ7zxLWr/OLv/Tbbac5HPvt5nn/9JX7tG7/DhfPn+Jkf/jG++q9+m5feepU4S7wSn6RaNNSNC9ocX8J2Yi9NqxHKKdW1Tevq5YHj6YSqaRiNNxiOhhhrWZSFk+r1aKs2mrKqGA6HzGZzZos5m8pV/bRas7W5gZLK38fwhc/9AL/xL/4lbaS4u5hRGc35ZsyxOeZv/4//A5//xGf4nW98nee/8wKXFxc5d/0qm9s73Lx9g63NMVu7u7z6+ut87NnnuHXzFptbWxgLs0Xd3YMuGMM9A23LfDE/exF6j+N9z2H3FYoCYWuFvGJXWd6W00b6rEV8dcHovRkuoFefEVISpwmHxxM+9alP8qUf+iKb4xGTo6PuK32tc+hFJN6zX88XhgXsYcY5zLOvSdxlNLumHbYjEYW6Truy3SXkb62TUbXGLP/1HZoOcu/nv+1DjMV7GKL73+rLD4G5u9fW4O53/Pz3eTwIoXFv9nLLYa69Y/fCZT0qmTPlpvev24RYVhOAj467e2YJ5p81r/4MOwa3/9399CVi9IyYdbnlSCpiFRErRSQdw1fJ1fKkkHICVl7rom6fEgjPYF9CuJPl7G0vUjFR5Htwq17Pas+KdqfDrKwDLtcdewRJ4ZTB6Oa0bHtqe/95UQuWqbAgchJ+z7KsU0gD0UHrwQmLoqhTe8uyjMFg0DnozmmIOuW40E9ASUmaxN15UD49lmYZ2hhUFJOmGVmWkaUZUezy3W1Pn6HrN+5JZcHAC+FSOALLME/JophqUbg2kkkW7hzHblfL65BlKQjhCGH+WsZxhLGuUkBJkMKihHCGQwBKAIaT42M2RkMW8zlat45BH8fsT06Ytg3TckHZul7ldV1hrZNUBdBtQywlVy9eYpRm7B/s07aa4WBAmia8+tor3Lp1k2efeZo4S1jUZefwrGq4O4fSakPku5c5lrhzSKM4IosTJvMZi7pESOnqyH36NHAmwnNT1U7fY1EULKoSlUac29tlNpuSRDEYS1O7Pu2JFJSLGSpLOZxOubW/z1S3zKqaN27c5PmXX2ZzZ4cPfvg5Tso5B/uHXLh4maKsGG9s8tj1xzk6OgFcsxj8mt3V++ueSJdfw4uyfPC68x7G+x5hB2Wzfg5qXSlsNcqgWyvfTXS1EoSIoAe7hJvB8sf+6B/m4oXzVIsFdVmQZHHn7fYdiZBv7xAAu6y3hiXxrI8GhOW1H9Wuw4bWWle3aZxakWuq4A1YYOZ6pm54oPulNtYzXFei6geMbr/Wri38pyPx9zoelIN+4Oce8fPvbTLv54Z6951dMlZheU+GiNva3r3Xm4P10depYZeG2N02pxX8lj6XCBaazvqL5WvS3x/C630HIx6ijsAAx4IKpWCsOsFlWXb3fac6aK1f8Huok0/dmK4ebfksh+fHGov0+fTwXp9zEmRKXUeu04IubUvnuIaUhjGBKNrQO3NA6Ge/NPzufNjO2AaEys0hRPv0nJ2l1kOsFEa33vBG7jmzrk7degPjDJYjfzmDvSwfjeIYpbxAinU+uRbGyZq27UqlCdDl9KWQjtMinIPU1E5ta5hlFMWCtm5IkoR5U3bfUz1d9SzPKcuiKwUDJ0RTtw3WaKSwRFKSKtcxzeCEVyJpmU8nbIyG7E/n7rXBAKliJrMpSkqOF3NHxhUSUxnauCWJIqQS6LYliWMu7O1xdWuXe8eHDG6nIC0Xt/cYxCkvvPoSX/z0Z9ncGGEmmrptSNWSD+SeL9d5LjxXjmfhXDKMwQrLKMuZFQvmVUFrnSzteDxmPp+zNdpESLdOR76bYhzHFMWCsi7RwnBud9cZ8jimMgZrDY1uKOYTslixqGsWZUmN4P5iTqwNs6Lia89/m+ee/RB75y9wc/8+t27dJhvlWCTGClQUs7G5yf7+AVabLvhy10DRtkvek5JL8Z/3c3xfyrr60HFftSj8DHneYLz7EUl/O31jGEaIMpwNct9pG7evLMtodMtiPuMzn/4E/9of+6O89uKLSGMZDQZosWTJ9uezXsLVX8wCyews6L4/Z2fog3PiYwvtcmRSSlQU02gXMUvlmtMLFTkoy/p2go3xuUS6aCh45GeRF9bRiO5crb32sOj2UcejbmPlc2vRdf8z36vxXn8MxBmvrX7yjFQKQfhkdW7LT9gzv229Feh/sg9GhBIut62exe4D9pY1x6q3bf9JF9UvP7iMut2HjTFYsdQKCN91SBMrxjE8YwEBC/dFpywmxSmntBUg7Ooz2Bld4VjmQkqE8TKlBOczPFMx1mriKD71PC/5H93RQjBinpnbFyxx84/82mI89J10kXRVNd1imWUZBwfHXuKzV9NuLXXbkue5r3nWNLqFRmLrhpXhF3p3BQRIifWe1mw2YzgcYowvt/Lw9HCYY+1S4jTkrkOdsRDCleK1LTaKqKoCoSCLY7ZHG9RlxWI6J0qTzosT1kXZYVuO2Of1yT1a6ZA6DUajgHGakccpjYGqaUnjmOEgJ85Sbt0/YJSlNG1LXVUMRkOiNOXw6IjDyQnjwYBBklHXLYuiINnYIIri7t7YHo556txFbukFb9y9RWLhsQsX+OHP/yCv/ONf4De/9tt84PoTbG2MuXt4QNs2NLp2EX0Ud/dtCI5SGVHq2tW74yLSrdGI6WTGdLGgrmskls3NTQ7u3uPqxctI31M8yzLf/Mb9K6qSo+kxeZYSZwkbwxELqYhiJ36jy5Inr1/nn33jm+SjMUk+4MbdOwzT1LVizVN++/e+wWAw5PqTj/Pqt75LtjEkTTPeevsGSRTxx37mZ/ntX/s1Lp475yuRlv3Z26qmquqeyp3sUJz3a7zvBjuUb3Ue7VrEum7kDBD1It3+WIebzxrhIcwGOU3bUtUVO1s7/F/+s/+MG2+8gRIQxarLKYQ5Bfg71IlrrSnL8pRn3BdOWV+4QsF+gNyUkjRNQ1lWHXFse3uPKIrY3d0lNLyv65qyqlhUTsheYcmzjChSHB8fcXR0RFUVDIdDhHD9xcPiup5LXAIUq/nWh41HRzKW+3m/PvewKL1/BI9yNHbt97M/3ztDK+jHadCh72ysoyUiRL32rD2v73J145bV8sXTTujp4FzIEIk7ZMYYg8a6khHtZDlN24LSFEWEtQoVLdNRNrJIu5RDlXhBoChyRiOUbWntEalVY7w++g5jX3/APdPutcgbfW1WNbSTOF5RXeuf65Dz1toR2AJZNUmcIIq1hla3lFWBLV07xjhaNvQIDO80zVAqcFA029u7gCVSEqOXveoNDgVsjasJVh4tqOt6ZY1yCEADAqd77bk3bl8pAFnmpJazNHVCK6bBSIm2tiuvq6pqqYzV1WC7Zit12zBUIyIVsbO1TZok3D64zzBOnCqcdk6DwAUj0+mUCxcuUJYeKVQKTMtiUZLEEVK4Bf38zjZ37x2wP1+wmM9dTfrWGGVBWUvbVsQqxjQN08mUnXN73Lt/j7uHh+zt7DLIh1gapvMZURKjksjNxzoHLR8O4d4xP/EjP8brb7zG73/n21z4+kV++ks/zrd/+3d55a3XUWnMeDB2ZW+N61MOsYuO7XL9HA03KdqaFrcmHx8fc35ji4PjY6bFjKJcMEwGjMdj3nztddqmJYpdikjFMVbCfD4nyVLmxZw3b7zJ5saYw+Mjoki5BjWRIhkktDcLfvoP/TilyvjN3/8Gbx8dcPXcRebC8NbhPbbTISJPOGwr7nzjkOce/wBFWyHSlCSOKFrD//fXf50nrlzl9VdfYzTeIB/kKARpFFPi0A7dtmifqvkDH2EH47JunNcXqr43f9YC8UikKP9dmTkd3ZPDIz7y3If5q3/p30NoS1MV3ptdqpZFUbQso5Khy5DoYKwAr/Xz1uvzCsppmW+nFoy9E2hJ2NvbYzQcYxHcPzzilRe/y0svvcRLL73MzVs3OTw6YlHWGCxJFLMzHLO7s831x67xQ1/8QX7kR7/E0dEBs8UM7ReSJElW5tKP/JxJOhuGPuu8fj9HFz2/hwhaPOD3732EFIZ89PuKtXTHO8xp3dnAOrWz7jW7WmXQ3we9z6y/J3zo7pxCRaQikiR1kptSIOKE8XiEscsuXOG7XXWDtWhv/GyzJE1aXCrGeoZTFEWOwISrjTVdlLmcW19fISirSSFQMoiEKDKZdc+7FKaL7LuuYH5eoZuV8X2R83yItbqLIIVH35I4Jc8GxHHcdb8ypgWcsx1FEVobmsahYSqKaOoSa13Nc+CJKKWw/lmvWxdRR8p3DcQhGCE3HBx7qQRWSOrKzylS5IOh72OdEEdJ1zbSzcOpp1lA+3OwfM8grEtnaAvD8Yg4isEYojQlSxNmZYWKYwQC49MFRmuX151MuHblKnHknJo0SRBpxsnkmFgqlJDEQrCZDTi3ucXBdEHbuoDG2JbxaBMpNFfOneN4OqdFMBiPmZ1MuHD+AgcnJxydTNgcjIlURN1UnMznjMdjIiWxCJq6IW4sx/MZN+/fJUsynrn+FLas+ZVf/Rd89oPPEZ3kzMsCrOgQzZAiCfejlJJ5VXJxvAseSRkNhhwdHfHBZ67Qas1sMWe+WDCMcsajEQpBWRSOaa4UdVURqZSD/X2uf+BJpFAc7h8SS0VVV2yOxkifFpjOptC2vPbyy1y7eI7P1c/xys0bvH6yD61lc3cPHUkiA7Qth3XJW/fvUNQlB7MTLpy/gE0TXnz7Bp/69Gf57ksvc+fwiHHlyJ8B6YnjFKPbzgE0+g+4wQZWFqb1+mkAPOHirAUMHhypPSgCTwY59+8f8qEPPsNP/viP8ZEPf5j9u3fQrWYwHDgRBq07rxzoWKFKqU6YITSlD+8/qDVo/7jCdwEuXLjIeLzBa6++zq/96r/kOy++yKtvvc3JyZSiWLgGKHnO9rmLPLaxwf7hAXfu3OH+wRGT4ym3bt3hW9/5Dr/0S7/Ev/3v/Dn2zm2jVOpgpaZCyuXlemD+2L35SOfvUce6serv+1Hmsc5leH9y2UseAd2+eogM4oy3DEvd9tUt9XBnB0W+o7Fey0V3cPjacOHnA6Nr95F1o91zzKyrfGitk/u0vePup26UiLtUk2MUR650sMHtvzsRq2IO4Zq4s+N6+EoE0vrEjvTRjDeyfThAmwbdNk7/QEh044hFLqIMUbvqOnL1jzec32DMQaB9v+ssTSB1nzfW0LY1ZVlTFBUq8lROv41g9MHplgtPFrNRRNuCUgLhn1UhBLUvC8rj0OxEdumsNEmJ46SD4uvaCbpYvez4Fvgrm5ubzGYzdFPQepGTRtddZ8LQ6MgkqecW9Il4gqoqGY/HtHUD2ninR7Gzu0sjXKQWYPGQYljMFz6oUB3Zbry1ia4bpPS1+/7f5niDSN4jjp0jV5QLpBDs7WxTVSVZEtMiWExnnCwWjLc2mMymTGZTyqZmnGTIOKJuat93XKF9CuSnfuzH+ZX/19d5/oUXuLy9x2MXL/DJj3+Cl/6nt3n75g0aoZed5RoHEcdYEMvOcFEcOweIJUSeZhnF/n3yNENbTVEWFEWBHrQko5F32BrMwBIpxWI+Jx6kFEVJnudkWU65KGkXBbdu3+L89m7nZKlIsTEaMz06ZmPnAkcH+9y7e4fBaMCMmuNiTlvXKBURpylplnK8mNKgMbHicDHFStjb2uLv/YN/wA9+9rPcuXWTyXTC+e3tpY5I26K8Y1JVFYOtP+DduvqLkjUGs5IgdP8T7oMuqu2TddZhwT586b+vpJMQNbrFCkGe5yzmM0ajAT/0xS/wmU9/kqp09XpY6xsOLCO+EF33tcIDg1EphfQ3lVMeclKKK/CdNcsHuqqQKmI03kAlCfcPj/nV3/xtvvP8C7z66mvcvH2bo8kJFkFdVWxsbLC7scnWzg4Ae+fOcffePcq2pKwrZFVwsphx8/YtBv9gmz/85T/EtYsXUK2FdLmwPtDcrUDkp6/LaZ4Aa7nYB2x27Xo8aN8rRtrvYNUYhfV+aXBOw/OrhML+++G+Ca9buZydsCxLq9eB9Xeqy7Z02eqVKDkYb7/fFSQDJzcqACtsd7zrJMogfWnF0lAFMZNwXMYaJ6jCkg+9PCeOEFYLjbJuH4ZlZGwjV8IS6metn6/w50TgIGvht9daUP6zpvesSumLC1uN8Y6Ae358NyICUc3PLeRYAznNk8+iTjPc5VeVlEQyQkRLkmmAlrUJLXGlJ2Uur4DxqQfnJDheiCv1ijx5yxl4hEJrZ4Sl8k6MUqBdP3C37eV9FFsBxsHmSI+4BRWytukck46DIyTaNq5kKgponNPdzkL7RWMcMQnrVK9wxya07u53442eOz8C2UjauvXOgDv2VhuGWc7RbIrAGeZYxU4IJ01pJ41rmGQlEgUqZjsf025WTGbHLpUQRUTAxd1zZMkNSt1ikFS1IVENO7ub3L+zj2k1+G5je1vbzKs5Cyx3TibsHByxeekKUkmXf65LVD5ECMv9kwOiNOOxvXOMs5yimHN3/x61bXns0iWKk2NQruuWxXp98NyV2xonRmOsAS91q4VBWEssJGkUY7SmsoYkiimKiv3jY7Y3d9iYlwzHIxZ1yaht2BgMiYWkbhqy4QBrBVmcsbOxxXdv3iIfD0iyhFgqEqGwjXtG0mzAS6+9xltH+0x0w2DRUFcl8xhU3aBSCcZQVO45MAKkimm1YbooSVTK0f4+h2WJTlJMVXM0mVNrTZ6lpEnsVA2txXin4/0c3wels+XCZ43FoDvIaXUlhrZpe7Wf4SP9fHZv+bTgRPgNkSdkNbpFRRGTu/f4Qz/5k3zh8z/AlcuXuHvrJkns4LfG6/wuGxXQeadAZ6z7LPGz8o0BHuy/J4QkTlKMkBwcHPGV3/wtfukf/iNu375D02pfBuJg9tJH12mWYq2r7Uyy1HmcaAyuzGGU5kyKKX/vn/1Trl+5ylaSszUc0Ha6WHTndyVXuhalBQN3FjfAndFV0/ZOow+1npV3XjHmjxhBn+VEPDhi76ygc5qEN8T9VIDt7dt7ecuYRnSTXXdq+qVX/UgUVpnPy88v99k34rard15uzyxvbBet2mVLzeX2AjztjSirTVrAUEuD6s3XAmiL0S7Hi1Vgl9fIPS/Kq+P5DXnfOPJNKqxeyo6KyPdfbr2R8Q0oQrOeLr/da3hhhfXRozdISrgSI2OwOjgOFiWlz+dBYG8jwAbVQAsiMMqNcVKdRrtF3TsPURz7c+PIZ0JKrNE+IranSjJNYH/j9cH9VYpURNM2TqRDCUf4bBqaqkQ39Wq+2fNbCt8KOIgZzedzoGE4HKGkg9GFdAqLcRz7GnSDaRp/zG5eaZr6+bvceTFfkMaJ05Cwjp+QZwm2qlBSkcYpsXLnM0pjGqsxrUYiUNJ1ahvHOe14k/l86tCKKCISkq3tXYZZxrQs0BqqypDHmmGWMEkjKl0hkAyzHG1hNjvGZBn7sxm37u1zdeccSRbRaEFVLBilKTJOmRUzFpMDnjh3gXw05vbdO9y4fZM3b73Fhd0t3l7MsFIgpEIbS9Nq8lBV4J9dp0VhiGJFYzUCiIWD9CMpmdUVeZJR1zX7J8dcaivio5bBaMRkNmWzqUmiTRKlmLUNg/EGddOiEGwOR5xMp4yKGekiY3s4JjKSalFgrCXPB3zzpe9ybzHDxDFN0aCrBhNHRBIS/4yWZYlMEyw+VeSPZ//kBKKYF157jceuXkNaycHxMVo4Bbvze9uOnChAEu6V92+8/xF2eDREt350kZbwXvNSNtINZ1zCIrh8zXgRFKxFW9dWr6wqdvfOkWY5Rycn1HVNpFL+2l/9K2wMhxzs77uG5lVBnufuIe+8XNMJpwTD3NfvtdY6CKyDRE1HJFsyyPGMzZZzFy6wf3jEV379K/ydX/xFXnv9bQajgaP0R8tjnE7dRWuahtu3b5FlGXu75/n2t77loHopkDLq8uFSSqjh937vm1za2OAHPvlJl0saLXvXvptc7FljGQ0/HCZ/FPj7VFQfIvf3ML8HHVcXAfcjeaFOfW45AcIkTt9b7zCHcFZED/7tNrviQ5oux9xDsVeQhDPPQd+peIc5uNvfdqQfZ4i9ERMC6zkYVVVhzfI9R95yQivaR8lCCFeX6u/xPhEsPAtat51YRpcWWrtHAnFSxQqlbGfAXWML7xkYtw4YY5jP5z2xIoclBFa6tRZtIhf1W7HUb7DO0Q68kqA61lQtUax84w8XyVobr5zajvjW6M5dAyc6YY312151QvM8X2H3KqWYeKGlsOiG+RrjOjb15Za11rSmRSBpmiDp6mB6h0A45yTyzUesNu48+05nAusDEk0+yBgyxLYa3Th9apXEtE3jRHKANI1RC1cnHTcxWZKhvJGIYqdclyYxoljQNg2VFcwzRTstiCJFPsgRIka3lt/85tf4yJMfINoYoRcF+7Mj3rp/h49+8BkMmpODI5SK2dvNuXblMb76ra9TFAX5aMxgkKNNzd27d2nawinGKUXduHukf58FOB8jOsUyYy2RV4JsrGacDpiVTi1M15qydgTeeVljJRRlSdnWvirfKdSZVtN4DfhYwMbGBotFwcnJieukJRtMa0nSjJdef4VZW5ILRR5FmCGMRgmxrmhpadqaSMA4TtDWtUTVxpAPXOe3tnH68y+/+gobm5tkCIqyYHtni5PZlI2NEZESjtcQx7ic1Ps33v/2mnhEAPeAdFFBH9p8xMW8q2nzXXeEjdgcjZhNZyAleT7g9p37/Jf/xf+DvZ0d5rMpTVOh8BfSuOijW76F6PLWfXJJEGDoL179Rayu645gVlUV0+mMT/7AF/g7//0v8P/57/8OL3z3RaIkYjgeMp3OuzSflC7iUEpgtWV6fIJUkhMpuHfvnjvGtnF5t0ghhIu8o9wtQC+/8jLPPX6dT3/8Y55ROXzweXsQFn7meO+G/swtrc/pe9z8Ozkj9lG2/8DPvMOXRTDyrORsu/v3EXbdwefrzo5Y5l77+fGVz6wNrbUzjlHkCVaKpmmZz+c0jUENB6hUkES5e/B623Hn0eVxQ112U9UOSvYjqHQBSCVQvlmFBYRy0Gvbtq78SS+Z5FESo3VDIKWZdqnsJUWEtQ521kajEomQdhWdQuIC9shF3waqqnXpJgmxXBrhFaUyNE2j0XrZsCSOIrQNJDmwtORp4mvFTcjPEBjpXXcx6Lbbb4LST5MdHBx0FR/hunUENms6R0JrTdaV8SxTbqEktKoqpFztBGitpfWyzdq4DmZFUTg4eDIniRPyPMcKwXxeYKzvCpUkvgOYZpiPWLQ1u9u7NFWJ1c6I67Zmb2uLk7rkaN7QWsulwSZqd4P4VkVZzrk/PWSmDX/tT/85/pu/9wuoaotERWhZ8eKtN/n8Zz7N5OSEwXiDFsHxZMq5zW1+9Es/Qllrfuebv0eL4fLli2ztbPP2W0dY4dZe7YlXgYgnQnokjkmF4Ghy0pXhJnGCxjCvSs5vbrF/cszOeExVVkwWc+qmQZcVjDLm5YJ5saDSDSJSDLIcrKVqaoqiQEWO9Hvz8C7jrTGtR5KqqkZZzW9+46u8dOsGz1x5kt3tc9ypFkyPDuBgiswk86IgVgmP7V3iZjlja2vbVfTMC8qy5Ny5cxwWc7Is5YWXXuTSzh4XNrd48Y03eOLaZW7dvUeWxAzTjNFgwMZo/CgrxiOP9x8S73uzOP/RZW1CTs29cfYCaH104l5VarWjUBzHzOcLRxQrKoSU/Jk/9Sf50g/+IK+99hptU6/01A6wdaTkEpaHJQwoVtnsxhgnywddXXYSxUS5YjafOXgnH/Dhj36c//T//H/l7//yP+To+BiZxMSpg7qV6udl/UmOIhCBhKH9eRHkeUqSxNhQ5y1wC6aS3eJurWua/o6EsUexJCs4q1h9LSSzTxn+BzcOeVTo+72MB0enPNA56T4vBGJdneTMjZze1inI367Gl0viZP9k2l5IbB94LQScOqY+BB8iT/RqHlsI0Yl5OFU+SZqkpMpiPfvYSAN2GbWuM8YdJC+I06TLz7r73wuGePg27Ct8Zhl5L5+ZThVMquWhepKXYGmMwhEquVQR62swhONpW1/6qcAYLwTTS1G1bUvbNBjfOzqgXeAaeWSZK7MK4i9SCBdF614Ky9juuPoCTjLoUvvt9qVbR6ORc5a8gQ0VJcFIByje5aYjF/VZfKex1RLSgOy1beMMi08tBKMmpXTM7Lomy3PGgyGL+YLCwNbuLuQZUZIio8hFfbr1hlvR1g0b+YhJoylmc/SmYTI9YJRnDJOE6cxSVw0H5YLyzRk7O9to4PJwQJTk/PKv/CMubu1SxjEkEULArK3ZPzgilTFGCsqq5Hh2xHz/EP02/NAP/zDzpuWbz3+bt27c5PGnrrOzt8tkMqFuXbc4a12tdL83QhInxEnMvfv32BiOKJuajSR3nbSKGU/sXuXVG69w/fEPsn98zPF87lTFgFhIjLHMioKTYs4gdcefJwnaaObFAiNqtDUcT07YOb/HyWKOMGC0pSrn3Jsc8PSzT3Mx22Y6mfPKG6+S5RkfvHSJ79x4naeeepLnnn6WXKZ8+5/+Q3Z3dzukwFq4d+8e4/GYu7dvOaKgkmSDjArL8XyB1C1JuoMRkqKq2Ri/v2vk+88SFx7ewa9Djzxfd0JWFk/poEaL0/lelI4N2DSOiXftsWv82T/zp7nx9tvEkcRqB8UFoRGllFMas3SLQN9b79dbL/teh0VCI4WTvlNS0bSGja1txhub/MIv/j3+wT/6ZxwcHTulnSjGGFgUjmDQFzoJUUcnwRfILyqirRvPh3JdnaSSxFJS+cVxMBi6xiRGdyKy62Zo5RT38qVnn2G6Ty8/0osCg9Xpq3mt5IV56PV8YP753Rr23n5OE+V8uuJhE3noPFedqXW/Zd05sf1zasMne5/uGXQR8qeec9GfvxACE3LeZ5yPs4y5m5h7LkKLx7At19wDtK+DjpPENZnp97hmmT91jseyb3zfIQpqYyGqDJB6MNKhMUifNOeer8iVTQW0wK7qLXT5ZIwjkWlAuAfaGEurzdp56iuV2e41a7WfoyPFSeGY36Gb2Hw+dwiZJ74ppUDXS7WpLp2w1PRutavDDvOM47gzokopsizzz+DANfjoCUG5FISLjKtqmULT1iJ8mmapvmZ8BN/SNHUnnBIch9AYKY5jBLi2nWXFY5cu88orr3L/7j2MUGzll7AiYjjaoJhPKIqiQwdta4mymFjF1DJyef1FQZ4mZNKtKTZJGY43GeqGum6IlKSsFhweHnJud5fxcIu51pzULoI1puZgesKl4Yabo7akUcx4kPLi268wrWriNGW0Meb2vdu8+fYNtsc52zs7HB8dURRFl/+tqspH3W7dTePEke0sNE2NzEdgLXVVMRoMXG4/SYjjBG2nFHVFmuaA60tdVCWHJ8fk5y/Sti1ZmlG0Dj6X0rCoSlprKKrKpRCM68xoJOxduoCNJJPFlFlRkGcpm+mA49mU7e0tnnn8SfZGG/zmb/0Oe9vbnBwdsVg4oZzhaERZldR1zWjDIQBHsyn29i2yOEGdTLiwu828qhFINgYD7uzvn36mv4fx/hts77kK0c8e+eE9/ZWPn/qlt5j4mkXCg4WTBCyKkiuXL/EjP/xDXLp4kddf/i5ZljpaljwdITmij1hRMwtebWC46l6UGxrYR/6BK8qC0XiMRvD8d1/kl/7hP2b/4MDnEx1hpGlbpFCOLMPqwh8ckWXUI3D9Z6HRxqUO4ogoimmrmsZo0jxla2uDLHceapRGZ9uhHmy7elZPn+dHM5yrlqwzOCEC7732MDh3Jbo7ay8Pm8v6W6LX1KMzsLbjRoQRIODwu/tqP0IWvdd6UfYZcwoLeT9StQRo3HZBdW+HdE7nWccQXuu9HqK81Y+IJSTPknuxPhdXNrNEkZZRNB2C5Z4ZLzvacTZsl1910qGOoR1F3viyvE/Dv1Mwbof4LCcfDJmQve96L1PJ2DsD3vkwxpWCIbpSSyGsM+b0EbClcxIchThWTgNbOm6JkK75iYP2BaY16LZFoFG47zk1wWWduDUG4XPz0uuXR175zEXGDglo2hobcq09B9yl1Kpu7QivBWJqIFcZ038tZF0dNBxqtPuOlcDJdlZVRT4YsLO5yeRkyssvv8JjSUycZiAVUZQQRTH1bE6apkghkLh6dZtbkjRnNDKYsiRRrjuWxjpyVRqRCIO0BoEhTmM2N7e5c/8E7VMOAoEVlv3jIzZVzCDNEFiM1qgs5tKFC7x59z5HsznT2ZxFWTKdzdnZGlFVFa3WK45sKKE1WiOsK8mKPDrjWrEKN8dWE8Ux1rdnDfn+RVVybrSJNYY0TijrmqPphPPnzqFLF71PipKqkSSJpGxq0uGA1qccrDaoSCNixc65PcqyZlEUlLohjRQxksPZMeevXEAay8G9fW7evoXcGTMcDmnqkjhSKyI8g8HAEdG04Xg+J40dMXGj1TRVAxYXXP5B1xKXLPv8Clhym4VYLljiQZD42rC+n7B09P0kSbHGkmU5H/nwR/jyl3+Cg/17xErS9JSK+tJ3Fp83kqvt9/pweL+Mw3pcK7CLlSdQnLu0zUuvvs4v/v1f5qu/9/uMRgOqunYIgDXo1pLECYVusGaVg70sW1k+oE3TkqauXADotIaLtqU1hivXL7Kzt0OeZyBcLvyBp2nFcPXyDiw7T60M0TPp4pRbtWZMlw0nuk/2HJD3a6wcwxmeienwAbEkNtKDqP28ncFZvuZKiPpbCk7j6plZPwc2fJR1B/CM/raWpdRoZ4RXjf9yL2LleoRytPB7mJkgwPHewIYUjnC93I02Lhdto6UgRahw0EsD73K2vfeMI5W5si3Z5VWTOIF4GY33YfN+iilsN9ScBjKbVJ5QBN1zF445NJAwQS1LSEA7oY848gbSkcH6csBC4AmZLhdcN42DILPUPZe+Q58xmjzPnaGoXNSmlIOqOxIceNJT7MqK6tqVlQWkTSm3ViiHZhS+dnw+n7M5HqNU/5ysNkUBx86WuqVpG4x2Dj+4dJYTmJEgXF9yJaUTV+nB7P17pW1b5rMJu9vbLIqKr3/nO5RJzFNPPkmjDXGSkg8G1If3HEfGtYciiVMiFRPHKdtbGfrkmEw5xbiirpgeT1HbY0ajlLo0JElEujGmXTQcHh6SDYaQRE42WcLdk0O2kpQ0ijC6pWxKbGz54DPPUMuIl7/6Ne7dv8+sXNDqljRLuX17H7Tx6OiyGVIkJbVHOZSUZEkCxjVKscKlQaSxGAmRb3GqIlcGN1/MSS4lVIuCLElYlAWT2ZSqrlGNqxqqmgYhBVmSUBvNYLyBEYJWG3TTIo1BEDMYZEgNc1WghUEah2osdM0gzzk+PmZ6NGFeFcSF4oNPfwDT1jTaUBUFVVm55i/aECcZ0mha3TKvawZNw8HJCblvujNsaq/Y9/6N97/5h1+UgqI2nLWwBzWss7exjOrc+qc9yWb33AVu37rFT335x/npL/84169d5Tvf+n22NzbdokRvsfAGJU5i8myAFJJFOe8Wm7PUzEKeIlIxMnIyq2VZsrt3julszu9+7Wv80//pnyPiiMVsTmshUs4TVEpRLAqiWKEb3S1irEVqrW8GEkXSwzlxl6fW2i08ti757Gc+y9BaBsMBm1ubTMy79NRE36y5sWIollfi4ZvBlb6cBaG/tyF4Rzvfj+SFoL/8P4yQFqKUVSO9fgZOG+sHzmEN1j6zPM72zrLfvwhpBCEeONdHHcEw4HO5xpqObRsJsD7ydaQoFwFofIQqIeRSgrGNVIorsV6yd4PedSBpSinObDUbfga4dzaZEvrQy0h2xE1jHQQuPJOlaWofZSmUcuQtVwfMEmK3S9nPoGhmrRMUiZWixRHmqrbu6q7btnUcayGXTo1fW9I065AGfEpMKlf6ZqxTLwtlY334PqTN6romz/OutaZbK1q/76jLdbuOZe6YolgRNbJjhgcUwBHEnIOlTUPtHSoZRV3qwWhNHEUMh0MwlqKcIqxla3uTT33mM/zdf/EvMBY++exHMNb1TxiNxg5m9+uWlC7/3LaW0XjEoK4ZZjkxkrZu0IMEMa3QwwSZxaiqZXFwyO+8/Bo/9aWfY7i1xas33uatuzep25a7iwkX6y2Ov/s8J5MjdGS5+oHHuby1y+d/4PPcODjmlbff4vb+XSaTqeuCluecHB3RaoOKE5rQx9s7JiE1MRqOlk2VcEzxREZURjNMMw9vK5I44Xg2JUkS6kVJnmYIMaWoKoqqJLfu/i/rCisswzyntQaEompqF1RpjbIRWrdUpqU4WTiHDEiEoqgrNne2HcdAWha2YTaf89jlXQZpgjSWxWSCNhAlKVub27z40kts7+4y3ByRD3MiBLP5gsV0yhNXr9EKweFkylOPX/uenv/18X1ROgO80V5brKTrR8uDbfWp0TQNFjh/7iI3bt/i8qWL/PE//sf50LNP89LLL7K1vYWuXe5NSBeNO+Pt8lJxnNC0LW3rWsZlWbbSArSvtmatxWr8ogWxUtRScvHSJf67X/gf+cpv/isabUgHQ6xuSIVAemjHak2cKGyrSUItqnHdubCWpm1QynmSIpLdwhcIGW6hsFRVzV/9q3+Fl996laefeJILFy9htCbNUrpahu/BBgST8859VX0qop/D7r/1Hsa7MV5CiEdjg7/r8Q6G+gGjW9i/572foSXudnD2571TKb16lvTymdPplHnZEI2GJIPIG4bI52lXnYXglLZtS1k03tgs00DBUAaiV9hnSB91cHdve03TEMVRV0McnqfKG2cplasJjiLK8oSqqpbHagNMmnTs69BEI89dA42iWGC1S0sF56EoClrdIMSSbR1FESKKqKoaIVw0nw9ysC1t02OOSxeFGpaNUpzhVYT2ukopfx4VOzs71HXNzs4OSoiuYiXku+MopSgWKLVUS0yzpbF3ehSOVJamKUqFKF+RJJmT54yU761duRaQjTNQYGmqisVsQbaxzSc+8Ql+49vf5jvffYlfHf0an/noR9jbu8Ctg3udxGoapxhjKSsH92Z5znAwZmd7j/HREaIqqI8XJFcvooRAGqfFPTyX8wlSXnjpFd68fZN8NGSwtUGUZrx04w02pOKPffFLzCZHfOvVF/jKN3+Xm19/iT/yJ/4UVy5d4sK5CxxOTvjsD3yO4+MJ8+nMQef+vLT+fIaWpcFXHg6HnJycuPvJky3TOKZoazYyj15KQRzFzBYLJ4AiJSZOSaKIRV1RNjV5lDtovW1RcYRQChkp7h4eEAlJU1RkccLWZk7dNlRlwebmJjIbwNGEo8MD5rrh4sYOb7z+OrUxREnKRz/6Ub7wpc/xT/7JP2H/8Jgnn/wAl65c5eVX3+Du7TuMhmPyNEfJCIOgbF0P9KaqeevmLebjEbtbG/DGsq3o+zG+LwZbqWUbveBRhZyxq7u0FG2NFCBVdCryc96ig7mxFhlHlG1JYxr+t//7/w2PXbnCfDollRGRhnptIQ0SjkG/tmkatF4yFddLt1ZKzpTvqONbAqZ5xsHRIV/96u/y+qsvM0gUTVUgRQwBbmxdjirLUmrtxA1CflJhlypSJtRagnbpctIkQQtBUTXsbG/zh//ET/HyC89TFXOe/tEf49rj12iqGqH92fHwUT+qQwiwIXfoT4INby+hZHAkZIVvbL/6UWeiVwyH7UqFllyDUGdv12zf0gt7cARslp9dg7RX86bKnVuzBlsLCIWC9oxIvQ89PziVvFqJ8MD5mjWo3VoPsXpyU6uxwiDX5mGwICRSLL8XhpQCjMT6XK30aE/jI2fb7UtDlx8VaBthPWQMijhKGQyG5ANNNBoQidQr83ndZim7+9fBuA6SVVFMnEh0q0EoVBS5vGlXxhSuBWgtOmN6SlbUi0sYbUBKpLVo3VDXLWAQxqLbiraauS1qSOOkU3MDQZrmGCucsBHuNg4lQNZaV8ImJVjfatNa9nZ3mCwK5wwkMSpaNjdxtILgOLgueWWxcPluIWnbmkVTeSVDH9ETHBDdiSm5Z1NzcnSElIIsy6mNa+GY5xkgKIqCwhbOiZaWqqmo2orEOiMVSkoB8tEQq91a4NAHqFuHOriSVXct4nTg+pjHTg8i39phL8/JE0ey/VNf/in+21/6uzz/2gskw4inHnuMYZzT1JbNzW2OJycM8gE7O1ss5gsiAXXTcPX8eW4f7/P8jdc40C2X9B5CZAyiAZnRVK3m5mLBYTnnx7/4edqi4ehkwkldEW2c5807hxwsah6/+Djnti7w+M03+K1vfZ1//Bv/iGrSspEnfO4zH2cgW944vkdCiok1SIESUEmLbFtaYdkajBwEXTSc29zlaDIlSxNM22CEYDDMmJ0cM9gc0tY1aZIySFPu3b7DVj5genzMIMtcPbRusUWN3hsxm89dCiWKMEoxmUzZTHKwkJ7b6JzKPBsyTmJofSXQxhCpDdPb94lURoUiiSSjRBIx5+TebW7sz/nkMx9mM1fcf/tV3r5/l4t7V0lNS5kIDIakdrD6vKyQScQcTVXMOTQtm80f8DpsKU/DgKf/xgvY2yVsBWAtxjhiWdP4TjlxAlIwmc74c3/23+KJJx6nXixoyookipwEoJQd6crVcQov1YgnyCyVmvrw5rqWuSO6hfm6xXYwHPDmG29wsH9IWztltaKqyJOUtl223VzmyR0hKLC+hbBobXz0EspJBEnsDE/duJv1ueee46Mf/jDT2ZTnv/Vt/vJf/PPs7e646EpJVqQzV6Jscfrv0zSr7v8C1y2nh4kv095r18jt6iwI2p6O9N91NLx63h8Wfa/5IGfC+N333yUE/Y6G+6ztG5+0JhgJVqNvsTqPLrfNMvLtUJ3wXnc+Q9MMsWpMPcO2f/xaa2IBaZp1TTvAt6v1UWNwnPtNbrQI7tqSBOag/S4H0G0/ROf9nK02ve5W/rit/18gnoF7lgV0feqjSPnj933fhejKo9w9ZXrXwKWJ6rr1xCS84IurWaZtsSwJo/2yK4EkiWMiNSKkYFxkHAST3HG67xrSdEAUxQQlw/7QelnS1nX88rl4Uzg1tfDsn5ycdEx7BEglSaXjryivJx6276JuZ5yNT6WEChW39gnquqGtWtrWMMoynrz+GPeODpnOphwdHxN5JzLPB5RVhVTSwb5V6SsVLJFyOelYKYbDgZ9X5BW9DLJ1bUnzLOPNN9+gXJTMqpo6joi2RtA2vH3jJlmjGWUp2xubfOqZD3EoS27uv05Zt+wOt9mQMYvZnI3BDlZIujoO77g6gagUazRlVXHh3HmquiZP044QORoMmS3m7G5scziZsjkcMRqNaNuWqqlJ04RIRuRpRhLHLIqCLejOX1EUHBwdUbeNE5kSvimHAN22NK0jsyUqRginYx4nMYNhzsnJhKbVZLlzzPJBxts39tkexVy6tMvB3X1u3Tzg4vYuF/bG3L+/jzAJNK7XtjHGzS9JaLXBGE1R14jF4oFrynsZ3weDvQq2djk4lnk0hGeoIsCGxUL4PqemW1iyPHeLgbFcuXyZP/7zP4/UBtO2CGuwJpCMlq0wLUGwJO4esv5iI8BB1cb6hTIsuPg5ht9Ft1DduXOHqq5dHsZ393H5umX04W192CRCLVsPau1qZKVU3QJv/H62d3a4eu0a1x67hrWGV156mU9+8uN87GMfI40CO9XDolI9AiRuV350xnrdaQqTXn7k7Hy2eGcQ+ZQ57+WZz8o5L52kZVS9+v6SUbvcyXKup7Pzpye0QsJ730Y4l/56d8Hdw/ex4hTSh9dd6saK5XaD44nx1Q129fthfy73XCPrxkWIIjq1r26fK+jF8nn03kL3t+w5aQExWXdw/R/OoRB0DF8hIlei6J+xwEwPx+KiSY9w4XvYy6X0qwnll1J6w2n9M+Lyn+HGD06MacGIcM6W59V1SHLrQJxkS4ddyV7DCYcSaO0UsgI0DqrjsYRjVsrSts2KYx6kXbvriev61fgbQ4DvZX6GM23BmHYVvfDoitEC47uVxVlMqxtMrbHabf/Jx65Ttw11VXN8fMLWYAgI4jghy52ASNNoqrr2x+CcI6Ui0jghSVKquqE1llGWkkiBLQqMNmSDnIFKSXDEvAVAlFDKijduvg1Fwc5oCGjyPGVnnBMnN5nOKoqicF3UfOqGRnTXPjQ1adsWmQ9oW01RlQxHQ+qqYms8RusWI1pGG1vc2b/PU5evc+v+PjuRYuzLwY5nE/I4AakYZBmDNGNSzHkMd39IKSmr0hll5YR9gjMUHKzA1cjj1K/7BiUFeZZwPJ07VDaKiNOMOM34zss3+eCTl9G65P7xMciEn/zCF/j2Ky9QWUvUtGiWZEkVxwjfNQ3t1Nxmf9ClSdcNdr+d3nIx8cxX41iTxmiXe/Df7eqk44RFUZImMT/75T/Ec08/w8svfZdYuZuweQALL3jbQc0skGRk98CYzmiHutUQJRmzjGSC9354eEiSxOTDAWXVeGidjqgTvu+IPt4R8dChEhIjvWpb5EpIhMsFsLk55kMf+hCf+uTHuXnjbb72u7+LtJL/4K//+0RKouuatnXQYyDPvFMkGyK2vkF7t8SnFVY0ZwSta38/wrRWP78Gg/f3GaKt/usWuny2XdmbX8SDUyGCMX3X4f5Dx4MQIxcQO8+rTyxc3uVnGFDhDKGU0rW8BFeK6I2OFZ6qZU2H9kipvIa324Tx7FvaBqqS+WLOsFfxEKDwPts7OK79qNs5zKt9rbvyL2u7z4bthGc4wMfGavccCN+JSkVI35AlNM4Bi9VNj4QmsT5Ct0Dko962cYSuJE5oTdOl0aI4dpBm0zqD37pnV4iltoIUvT7WHieoGoOKRBftKCWJZYSxPmWnIoTnn4SWnVmW4ghsmsZ3HsvznKoqsX4+rp7YdOc5oBNSSsYbG70o3HTf6UftxmjapnElo7Zxjkq4X6zTNXek1MyVfgntWP3GcO3iRe4f3Gcym3F8csIgy9GtawGaximtV8VzSnYuxpVKkSYJgyTBGsNkOmV3MCDb3GSQxDSNBmOIo4jnnvoANIajkyn3TiZMY0EVFdw6PCAR0JQLFkeHJOc3uD54jHPZiIVcMCnm3JufuJx8v0xNOKnUOFKdKI2uW4qyZGNzk6ZpyJOM49kJ0gqGWc5JMWdjMGJRlQghGOUDjNHcOzrk6cuP0UpHLNvIBtw+uE8iFCpxNfS6dgY7TVNUEju50mbpbGnfHcwhQyCsRQpIogjr+3GrOEYol5d++94d/ugf+hxf/fYL7J8cc+3yY/z4F36Ar/yrr1CZCNlUICxaCSdDKgRaG69aKTDasFj8ATfY6xBj34CHRS0AJrXvKW2NcQppFi+DqBmPx5xMJyil+PBzH+Ov/ft/jW9/4+ukkSKsbir26kIryk2yq4nsM1zDw9xXblpnwK7D41JKsizj1u3bPP7447RW8rVvfgspFPO68lGG+647TidN2HR5C0EUpxgEVdFA7eD63b1dPvvZz/KlL32Jk5MT/of/7r/lcP8en/n0J/k//sf/ByYnRxzu7yNx0FkSx+im7SKxs871+jiVww3H2P9+9/PRDZwIIc2jzMPb1r6BftA21/O9XVlSl6NfnbgllAc9wKnofbY3lXc4rnc+JiEch8BK13wiGOUw15BO8QfRHQs4A9NFXmE7NkS4oXzLPR+t0XS91ywdyzYAQ4EZjhA0Td1B1LDqSAYDsn5vK6UITIjO6WSppmZ9uWGj2xVBFZfasR0M3aWZrHN+l85urybd67Y17bLPdHhGQsoliiPiJKaqKt9lywsY+daWIlKOse2j9CD32fo66dVyTcVwOEJ6R1lrNyfXL9v6gEA7gprXEG/btjPS/bWjLEuiKO5en83mTlKzaZhOXcONJEmI4phhnBKE0oLRDsx5pWJ/DzghwyDU0q8xL8uShWeVu6YojiUvhYC2YTDI2R6NWcznFOWC1rbuPDbungqKa6lXXTS+UdJ4MGRnvMn9k2NOyhJz8RL5cMxmntI0DaM8Q0eK3//ut9ifTtlfLJhaSMZjdqKcC3vn+dTHP861c3t854Vv881XnieOJImV5ElK0S6YNS5/O53OIPHqjsY1RBnGGUflnCRNaZqWsqnZ2tpCWMjTjIOTYxrTkKiIeV2SpgllUyME5EmCEpK7h/t86LEnaW3LIMvYyIe8Ur5FJCRxErsMlQUVKUa+50KrHfyOXYr/SJ+iSOKYKJI0s5I0iRhkmXMIlaKsG4SwfPBDj3Nwb8H+/RmXLp3n6rVtfuHv/G0Wk4qFqdndGSIjxbR1jlIex4574p/p4WjUESbfr/F9Y4kvaxRZWTDCYqGUoqZEIrxSmHH5aKUYZhkiUpRVzc/+7M/wl/78v82dG2+TRookVl0E4HVIfc2jewiCR+6YmmaVaOYfohAtnDVi7xE3bUPtiSFYy2Q65dq1q1x57Dp//5d+mQgHB/YZ5n1nANwNE9TPvvD5L/D00x/g2rWrbG1tMpmc8P/8v//fuHtvn09/7EP8pT//Z/jRH/kR7t65RSRcKUtoSFKUpXsIQ+eldxhLA+aNtOiR1N4NRPy9Rqrv4esPK9t6LxsUaz/fj9EhA97ghn/r6BKcBVH30gBiebzCIwWN1lirfSmdoGk1i8UCo32kJYVDe/KcARHRYEA22iJOsm4PfS2Csiw7I9xnOjvjuu4HLR3efteq8LxI6aQYhXWGUEWqq8po29b1dnYzQIVWl9I14lk2/3DHFZTEhBDESYKxZoUl7hwS92xXZUFdV+SD3Jcu+dersnvGQ0OS8HeWDZjPS8+pWebpHRQdiIuGMmh5W8fM7ufrgc4oC7GUJF0sFlhr2dra6j4Xx7FjNuN7WXcEON2r31bda4vFnKIoO4b6MmWBl/Ns3PlsDQpJmqbQ1Fzw7PWT2RRrl0pirW593bKb42w+d6mLPCeNYzbTjBMVcf/omDhxPaQzIRkPxyRKMW9bHtvZ4RPXn2Iw3MDIlN96/nlenxzwzTdeJhkmDEaf5Iuf/iwbUcyL09vcqU44aQpSqdiLc2ZyTmFapNbEQiKMS/vko4y70yOPLipa7SSfR+mQPMk6xzXcI+78OycwlortzS0OZxOEkogGBnHK5nBEJKTvGico6wqjNYOBSw0E1CQ4TZGKEBaH1AwlSRQzTFLqQca9wykb4yEHRycoNcRYyclkzgefuc43v/kqGxsjrl3aIxGGf/V73+JoUvODP/rDSCG5cec2R4f32NzapKoqqqoiywekWeZSV/YPPEvcs2l7EN16hBUMWxzHK6Qmi2NNIwW3797nyz/5Y/zcl3+CSxfO8+qL32V3YwtrWs82BRBdl6S+GlMg6gTHIOwzLDTrEUd/fmVRIJUiiWNUpFgsFnzxh36Qv/Vf/W1Obt7iQx/5MP/On/+z/PN/8isURcFisXC1kMaitYtAtra2GI1GXLv2GJ/89Gd46onrDIdDnn/+2/z6r/8qr77yKtYKrl+/xN/4a3+Vjzz7NIM05fD+PtqrN4WHODSk161ZyUOuj9OvhZyvr4gXfdNlTn2+e3cNon6n8aDPPSh3LXrzWIHe167J+ve6+T9kfmcTyB7dQek7Oe/0wWCwVwhkXZR3ev59GD00xwkGX4lgVJbnwPjXVShrEsJHXU6Uo648C7soGNaQDQZdLi9Jki4aDnXNYR7a6wD0UZUO+VKhpMs5hlHsvt9f/Kx1PbFjGVO3LcIs76V+WZj1PciFEAwGgy7SDM/f6t+ufCvKBxSVc04dUlXTtg1COIPVNA1CBJVC0Tnjg8GgqycPRtl42DyIufTTA1ovtciFcI5C0zSu+YnPwQfnZjgcYqzreKa1pqgq95rWTKdTQgOQOE7QrQVhfd31MucfSsUsDhJ3kHfURdnhWrm1yylpJXlOJGNM2yKtcB2w2obt8Yiq3qZta2bHR6RJgsXQzGuMI+2DMByfHDn1RyWIleD8zjYmTXjj3l1EFDNbFGwkKVcuXWCUD2lMQylbvvbmdzmaF6h0xN07h5y/eJEEy8233uJ/OjnmpZ1dLu7usjne4O6dY+qqRseKQrt1eWdri+PZAXVTgUjQxpDFCbpxlTfuYkA5L9kcjBwHwt8/tbXEKqJsGgZp6tqvNg2X9s5xd3bEvKmQ2hCpmDTLGKU5x/NZt65HKmKcD5h5B7e1ng3uUcEg4COtg9WFMcznCy5dOM/zL72Bka6CopYt1ggmh3Omes4TV59ga5xzdDBhVmh++md+hGvXLvLPf+0b3Ns/ZJwPMG1LpCKyJEFYg24b95zEf8CFU1YiiP6rPWMTFucoirBeoF8bA1LQ6JayqLlw/jw//iM/zJPXrnJycJ88Tamb2jF9fJ1pICsvoTpBYHp2sJ9YNkQwvWYA68YijCRNHTu1qonSmPl8xpUrl/nSl77Ib3/tG3z967/H7t4WTz/1AUca8h5cmiVEUdJtL88HDIZDynLB8y88z1tvvMWdO7epy4LHr1/lU5/8JB/58LNcvXKFREnX3MBop30uhOtgow3aGh/5uG5e/fPZ/xl+D9rkD7w6QhCqsNcNy7uBxt/r6EOXZ6Ui3Dz8/1fS1ct8NQF2Fqdnvb7NdwcSPBgGXx9SSrRvuNE/rvVtdAkE73SEYwvOmGZ9BINtOga2IyYtjblSEUmaoEixHtaV3jHFWHSr0bb1MoqhrMudTyHFcuH0o3teCOTN3rOCXTFwYKkb7Xs+ezKZkit1/QGOtZ5ZGfKa/RRU6JoFtuONSOEaijhd8wZwMpZCgPUoWpKkjrzZfdc43eqe022tRbeN66Hjeo4SSYGMI9dzwIDWktY0NG1Dq1u0caxzK7xj3BMBCLKnYdt109A2jU9vgLYGaTXGAMKs3HORz2dGasniF9JJsgKdExECDkeOqr0DoDG40tS2dY1P8jhmmKWMs9wxouuKOFIeSbAgHfJYVQVpGlFVBaZtGaY523EMSA5PTlBJSjsaE8mINIqJjWGQJWwNcnbGO3zg6Q9zfDTnzft3ebWYstAtldFUbcNbx/fJ84TcSmIENYY5rqObMq63dSscCqq1JkkSr8wXjlUxm07ZHAzRxnSoz6KuGPk89njg2ovWVcXFvXO8ePctZsWCDZmgIkcqG2UDTuZTx4zwjuH5nT3KxdtUPnckhG9m02rS3J1TawyREORZytbGiFILRsOcyaJ2z450aaTp8YwLF3eJYsnhwSGT4znPPPsBolRy++ZtDvbvo4Ti0t45yqaiqBuEEqg4RsW+jPl9XlK/LznsdcPcfz38LnsPcdu2rgRKOgnSpmn4iZ/8ST70zDNkseL46Jg0y6jryufdRG9Rd5FBMM5aG7TRXQ/dbvSgy/5c119TSmGlY6uD67yT5ymf/swn0AJOpjNu377FtQvXnYfsa0LTJEVIp0fcNC1VVTFfLDiZHhNFiqP9QzY3Nrj+7DM8+8Gn+dQnP87WxthF6bPZkkAjZSc/6ebtIznZHfHK/Fd+Fw+PjENUGKLsh37uoa+H6/jATbzjOMthWrlHoGPUn96ZP46V/b+/T8ZZ56DPDHbXxCmQLe8jb5cfMpfl4QgnYGGXhxIuj6dqeQDJie4gvFKXoNuXEC7yclGa6u5l4yPX8K/vvAorVgwnLA1pUP4KuWMVon2zjpBZr5nv0RvvOBsTIHfTaSFgBW1vf8vKCVd7jvBGywqs9Ne917Pb3QDLZzfML8zZGMNisSBA1nHsyUZ16cl37RJBkE4Jzhq3D6N9Gq7/7PlSNSch6kl30kXeylcnBMJc7JnI/uCIkwhrdcdJAOFElCKIfMmnkE4BsX9fu/psi/DGSylX799ajcEZfdM65TfdNmRxzPbGmKoqmU4nbO9uI6XfthQIiReYEdRNRdu6/PAgdZHpweEh461drLY0VeNIaUASR4zznDgeshGlNLKG1tBoTZJFRGmCihST+Qyhhsg4QkQKbWqscaWsZVk6KF46x8h4xKOPcEipWEznjPIBta+BN8awqCvGgyGTxYxxPsRqQ11V7G3vMFvMmVcFG4lT9FNxxDDNXDcuAUJIRsMh53f3uHv7Dto3FzHCXeuu/3ZrPInXkCYJ25tb3Dk45PKlSyzevNnd+9ZoFoVhd3ODsqhoigoVSx6/cokoinj55RsYWjY2xmyMRtiJZlrNkdbpnyOEO3bfSe79Gt830tk65NyHyENEXJWVW1Tw3XKiiHI248L58/y7f/EvYGcnzKdTJ7rujTCITqBfKQXWeJKKl0U0jkwWYMG2bR0bfG1O63MMf9eVY3BmWU6jG1QkKYo5W5ub/NiP/BBPPfUEv/wP/iG/+9vfxGjt28g5r1obvxBL5WUYFYNBxhe+8AW+/BM/znMf+iDnd3eJlWI2PeHtN99kPBqR9G7oJHYki8rDaK41p8sFdXKvfWPif+30qB9ybQJz852N21nUtHf4Ru8rDzLk/eg6bPesayA8hHWKObd+2P59hyq8E62sP793a9xPM70FSwfDV9F2hta/231P9Mh36w5X50OtbH/5vrXGl6ooJ4GJI5ktFgviZo4wmjhZkA9GK89e3xF1JVVejtcs2c1hH+FvJ6m5FB4SShJJhTIKY5e8jzhOfETndbCti+rDPWysJsjDSinRrTOsAcoORtpa63LgxmBt67SkjSGOnYCKYzy3COHywqEFaGhgEsqrqioQQJ1oTCCkpZ4cZrwueRwnHlYXGG2x3nhKfw7i2Dn5SrqIWnqnqKgqJ73qTk5n+A29jnytZuBbSbrXHMtfSdWlDYzRGO3Qk6bVzm22tpMrVt5xAMcxaNqWVjeo2Dlk7ry0JEnK7s4uSikmk2O2tjddgxMVjL07xyqSVFVD27pypmGaMR4OOT6Z0I43MdZS+v7bW3mOkDUVmlt3b/L8S69zfP+E8bkLLmgXEm0NlW4YoqitoUoljbLYoiVtLHmSMj2ZI5UhjhRCukqdJE666wxOQXIxnzFIMwq9II0TGqOZlwU7G1scz6bsDTeoFhV1VbNzeZOqrimbGhM7R1ZKxTDNuHtyn3GSYQXkWc7u5hbDPHfyuCKmNcZL4xqH3ojWpZW0IY1jsvGIW3fvc/XiZW7d3WdeusoibTRSGQ73D2nzETtbG2xtDckHgsFgl3v732b33Dab4w103TCdzdyxCiibFqskUZai0j/gpLPQWzbAbP02fZ3hxiX/0yRlVhSOSh/FaGvRBv7T//h/R9oWnJQL181Kyu7hWZZRGYxxOrVpnlGWJdqzPJMoRuL0ik27lB/tG4d1gYQwZCRpjaYufQ9WlaKMQU8KYqX48NXH+dz/8n/NG7N7vPza69y8dZOTyaQjj+RpxqXzF7h06QKX985zYbQDuJKCsixZHB7QeHLMOB8gLWCchxiEV1q7lIP0nGICkC08TEr46ccSOV6F+0Vn2UJ0KHvbPG24OmO5DpefaeTenYFcNc6WNVu9+p6fvIs4w34MWNmlRQJL/DQl/tHntP77w7/T24kQqMip9BmcsAkA0s1XWNFF3M5am+46IiRKOmOMpOtvrLW7fx2DWyJRKJ8TxVrqsvGpJEWaRUQyRmQJ2kCrTZfDVkqSZGmX122Npq2cw5unWefI9nO+VkBT1S4vLHwlhzZUjYfDxdLRBidSkaYu3922uhMHklLSlA11U2KtJkliFFnnULe+S1bkhYNCbSymodaOnCakJEIQpQlaK5qmRmDJkth158DlyNumQQrBxmjg66st1rTOWAwGGGPI8rzbb5Qk4HPewblo/XNZVzV11ZDnA5LEwfDaWsqyoK1LBnmOipOunSaAEk7SVSrJbD6jqkpms5mP2J3ccFXXxHGEtE7v2lgnGtWV1lnrUgbSpxXCtqUlTyKqStMsCs9HGHQwjLSWve09XGVNiW41cZIRJxlVdYREujIm61qOWqV45sJV6sNDhhf3uH10j4s7mzzz9FPMyoq9zW22ckUzKSmSgp2ndvnBL/0gr711h4OXX+bo4ARba6Lzko3NiPNJzq2Dt2kmh6goYlLWFG3JcCtHzgs2swHZeMy3Xn+VnWzIaDjEtIYEyXY64Nb+HS5u7zG/c5ed0ZjGal69f5svfehT/PLv/w6Xdy8xn9fMm4a9rT2SCHRZMslKZJOQqZgLO3t86/g2qW6pdItuavLWcH33HM8fHnL+0gXmZcmicM/PMErJU0U+GGCAotbIxnDt0lW+853vshPHSGOYGYPMU2rjhHXGviIjiRJsCcfzu1gz5fGrH0cvKl5+4WXeKmf8yBd/mC0juHPzbRbCwM6YNw8PH31ReoTxvhvs9VIq42GeLrekneximqRM53MGwwFZlnEynTEcDPn3/sKf5WMf+xjf/fa3uvrp03lJ0cF8URR17FBHYFjWffeVmvrjQeSmMPqQfpf/Bkzb0s5mTmEnS/nCsx9DffRTbqHsQaRNWVN7xuDt6W23PZa5SGHXSHLfywl/9MByzTC9z8mVdzGsfcCkey+v22AfK76HPPsy0n34Z/q/h3h9LfoH1r2MEKE65vWjXckA9fZRH6fWJ30Ntu0cM+1rrpVUJEm8vC+1ayOpvAEfb2z4iHPJctZad6QXlyMWLOaLLspM07R7jtq2paGhrmsH/0q1UvEAvc5LQd1LuWjQGShBkmTEsSLPc0K3qiiWlPNmJf0kBAglOofBzdcRt9q2oaq8Y+KZwtaCaZ2AiVUNHq4AIEliYunTYcbFvNriou5enXQfOj8+PsZaR0IaDAZdGdTSiDfdPZHFCSQ+32wNURxRFo6dHiUxZe000pWKnHPiSYKhFKyqKvdanjKyDjVytd7WEVa9xjoAPl0gpYOWhVg2YXHyyhrlHSqApm2IRITR1t8/oLV1+tbGcQhaoymbEkzL1saIz37q0/zaV3+Li088wXCQce/+XQBmiwWXty8yyIfU9+5y4/ZL3H77HjYZsbOzw2Q+ZbZYcPv2XfayS7RJSxNHxFubbI42uX7lSY4ODzgoZ5RaMp0WyFnNvFW8cXBEG2ckozHjNENLONy/y5OXL3F4PCGLc4ywTI4njPOMTEq2RiMO7t1nVjjVtkhEzIsFm4MxdVwTRZKBh80HG0Myj6iiXCnuzvY22xubZFlOGi98I6eCnZ0d0JZIBPW9GCkVW1tbTO/ukyQJQyE4LhdeAz1yinPaUFUNd27fZDga85nPfJKT+YKX77zNXVnwpS9+hi985Fn++b/8bW7bmIPjKYu3XuOpJy890prwqON9N9h9gYYwOm8yPLT43JxuGQ1yTk5OyLOMz3zyY/zpP/Vv8vabbxJ7T/us6CxsP/wrfW9aBwfJFWO7Lj961rb6hmy9reCS6LOs7W6rCt3UqEUEUnTlYgLhVG6wWKNdQ/tenaoUqstXLkViTPeg9seDiHv+F/fjXVyX06P/7TMM2nuIWh80HuYgrZCFPJzct+edjGqIpsPvPVi998ba1teN9VnzOOt74ZW1c2TPvne6a7l2TMtv6t7eRfd7d2/6YxHSaQw4CFV2CILRLdhwL/upWMf9aMoSY+bESepaRNqlIpczuMscrsubq1Pqf0CHDq20+hRLJraxLhoNkbnWmqJwoiQhT661pa77LpalaSCJs5Uc5jIl4q+1h4CtFRjtOCnug560Iz3jWypfArZ0HpSKEcqjLqbtEI0kyfwa5PP6xnbweusFNKwxNLWr+0a4FplOXc7ddK1uQThnyvScFRVFPZjbOTWRZ3y7zl2yi96nsxnaGJJFghBgHFJOEiVo7QRiYu8Aae16YVtru6ZA/dSe8cRc16HA6eQZnLyxELK7p+IkweD4DY7VHxElCca0PPeBp7l/dJ9nrl9nb3uLqlw4gqIw1GXB7uYW169eQyO4fOk6dw9nnBwf0dQtaZqzc26PSVWQJjlKg543HBVHvKrf5s07tzicT8hlhNWWom5YoKkPayY03Lp5l3PjMZvDAYtFwQeefZZff+FFso0tJAZaQ6pi9HxGEgkEGqNbsmHOMB8wXxQ0m61rq4lid7iBqRqyJEUhHS9BWFQcu65e2pCrCDkYICzc2b/HcDxinOS0TYuxGhUnDIdDYhVx5dJF7k2mFNMZEZJhPmI2PaYWMUVVI4RlMpvzgQ8+TVE17L/+KsNxwhc+/CkeH17gn/3TX+Wrb76OSjM+/OTT/NBHv8zGH3RIvO+Bw1K1LMDh0ifkG1+DoLWmrmo+/7nP8XM//dNY3VIs5mRx3EWe6+SYYKj7Cmohd7weGT/KWGeYwtKg9rfRF4kAHJnGLxDuu05oAugibiF8T3BvqUV4rx9x9ObSJ+b1f/Y+cPbf/eh0JVf8MMhX9L747vPWp4f1+1+ey3efLw4zWvby7miGXcK3/299POi1zvKvfWb5e5j32dsJ23hwhB0W8KUmNyuf92dnJX3hE/DdUbp7Sfp0gOy+Gfbh9unnKZYkroAwrZSQLXfkvuvJlKGyoU8Q7SNZrmpDr75n++fJeuOkMEah1JK4JcRSRMaGxh2m7p7J7rwpx8hwRnVZvih8j2yERXkinfS5cot1YknaIETbq5lWRFHSRa5SKaKIlTXI2tb1wjaaOEqwxrW0LBaLjmEfxwnWhnXDkcGs1MQy7XLNddOgvRgNrT/P0ollzIuCpm66dc744CQQSYUQWGE6pTStLZJlRUu/fr6PDCxRCLf2GKO74CDUrydp1rGhA9cnkJ+sBI1hPp2RRRGPX7nC5mgIRlPXTilPDgduXsYiEURC8vabbzEpNSeLOa2P/BdFibGaLSNJVIbVU6bTKZGIqUxLPhpwfrCBbgz3TqZMZycUWBZNS9tMmc7mJEJAXfK1b32HWaMxUYzEIHEd3mSriaRPEWKpdcvO5hbzskRrl9tvVEuWptA4Jwx/v1ksSZowzAfY1jlSUeocxqauqZuGKBsjlENMrHGOwmNXrnDz/j7jXFO2mtmhQ6Li2IniVHVD21TU3mE1umFzY0w+HrM33OXo9j7ffeN1Hrt+mYubm1wY5hzducFLhyf8hX+L9218X4RT+otY+Hu1pMOJikgpqKqS69ev84Nf+AKf+PjH2L97xxk1JZ3a0ppx60PJQgr/8PpFJeTHzdnlW+9m/v2f/eOQUiLiuFv48A+S8IL3rumRxUoP31pzatv9SDksvGflVM96bcWsirWGEw85loccLetG6P0c7+Ya9D/XmUaxhKg70907F49ONjvLAD/qWEaND8p99+HXfvmdXU942CUpbH3bznlzjl84zuAIuvu7t2+WEXCSZSuSo53B8DW/YQaOPyKXbOhehL3+zHbT9dFs6IQVnrUg+RvIau51SRAxCvM22jVHCNvqjI+gq+QIx+3mpnyTEK8rbnrNOgL0Itzn3NW3K0iA1hr8XAL8bu2ydM1oQyQVBom1LVobR/ryUW2Ye7i2obVmX3Spc7ih00wIUXCrNcI7REGNrp/7d5G57VrrWrM8Jx1y6M9JlxqxgbTr2eXGdudHt04kJIpTt15aV48c1hUrXamnsQbdNNA0bAwGoDV1WaKNpqgK0rYlTmIWs4K6qkjiiGK2YJANyI2h0A2Nbjk+mZDbmAM1ZV5X1FqDgNEgZ7CRYSPJdjYAq8jGG5SvVxAppFG0xnJSNS4n3zb82m99lYO24cL5CbmSKOlapUYycufRM/bnxYLz27vMZgvnlGhHxFQqIrLeBrj8Egan5TEaDJnPZkRCeu6C8F3sWmIZoZSgNa3jahjN+d09TmYL5lXjGqMYS1VWDPOUOEmpmxbdOMfo+PiEJIk5f26PYTpGFvCdN95ApIbPfexDjI3g/q07/M53vsNbR5OHLSzvenxftMT7BjbcbH3FHysE2t9EVhv+9J/8E3zus5+irkqaumKQZcuogNOGyzXh8ApLvvWdXDPWDyKV9bd31uLUh/LXme7hWIDOczZYWuPKAMJDBL4jFqbLVfb32f0tHGy1Pq/+7/3XDD0zc4ax7iMEZxnJh0fa36PhfojdfFgK4kHzMwCil7M+4yunYeuzhgxbe8hYHvuqg3EG8uCs6anrs24AA0u4P7prg8WuOXK2Z8jXER+ttSeUCdfW0i9aCkiShNFo7KMpurk4JGjdOaDL164/p30krHNMhVhqY/u65/4xNp6QtnTG7Ypyl7WWptUosdxf/3xlWdatDUYbh2ob454na5HSGdLG14E74ljcYRFLqNgSxy4ab7WLnAMKFgxxOA60wcqwfcVg4IwDrMq4GmPIsow0SSmqCosrr0rTlNgjfMY69URrXBlbEJkJ104IVwMczr7x5zhNks5gt7WD2kM+v65rpNYM8gGBN9C0jtnsHKSl+Iz058BdB92l5VYEbPy+lVKMx2MOJydUhVNqU8MBUZKghbu++XDI0XyCNprt7S0+8qGPo6OMl2/f5fXbNzk4OKAoShqrmdx7nYWuUJHk8vlzfPbDzyFEyxv7t5kvZoyHW+xuX+bo/l1uTg7J8hEmUqgoIU4H6HnBi2/fZq5LIqnYHg7QCCohybIR06J0JW2xYjafcuX8Bb65/wKERh5aY61hEKc0bePZ/q7xSyQUwzynKUvXqSxOEJHrVqZNSxxFOIaFkyyNVYQ1lgt757h/dMxiOmGUZsyt9VUVirJyxL6rly5w//CIKxfPMx4MiLTi+GDKjeO7fOITT/PMxXO88K2X+Narr/Pq/j4bly8+eNl5D+N9N9ih+D/c/P2HtMudxS4PdLB/xF/+d/8cP/PTX6YqFtx8+y12tneIlWQ+nzvmpFx2xlHKNScPkUaIaIKR1cZ4sYj2lLHtj3WD0X8/UssHv7/f8LmwOMW9SM94KMbHCO41XCcmVM9AuVXZ/bp23h4WVZ8aQpyyU+8MfwfI94Fv8z0Z7bXtvnNk/fD9LL972hN4d8jJuzmeB3sdIRVi7WlYXnooWilX6tQ2VbfXVWdl9bXu2RCii8rt2v3hYOUahDOYtpdLb7V25X9FwXA0QqrAmrdLAZVOVtQZTBmpjoh2lmPbd1jD0Fq7hZIlWhaIcOGeMWZZCRIMvpSKJMkwbbP6PAKmaVgsFoBrIymlJE+zztjXTYO2DmcJEXQgx/Xv/6YuMVohRO6JVu5frXVXLhnHMWma0LYtwyz3zokzUm3rpDSVUsuSL5/7j73McZRErgLFGIQ0zKaLrn679g0mkJI0S8DSMcRdwyE658KlASTWE2OVEmi5hL+7cyQE2rrSJaQTmTG67qJsISzGOCckGwxp2wUCF7kruRSS0rZFCFc9oFToC+6kZiujWTQVSmgq3bJYLHjt7Tcpm5IWzXQy5flvfZdpoSmjmGxzk43xmPnxhGldcW44IhcDklg6Y9uWtKZkPj1EFwVxlrM9Svng1cvcemGfjTjiuGxocYTjwhRoocjyAW/fuMEriwXSWL5y/VuowYgmihnv7JCkKUdHB1w7d5HfLr8BOPTVYKiahnPbO8zKwvUrz3IiJE1ddw6OiiKUlMRCsLe1zc2794iuSAbZiFq3tLplsZjzzDPP8uKLrzJOU85tbnHv+JiN8+fY39/nKAjUxIoWyWJRsrWxSVWWzGbH1NR89LMf4bPPfIK/9V/+N+xLy9alC/zcZ36Kv/jTP3/mevJex/cNEg8LwbLG093gg8HAqYkJ+MTHPszf+Gt/nbdef422rtja3ELrlrrRDkbplJdMdxNK33Iy3NzBm3T70GdG1mflsh8UZZtAjOlFwn1j2B2b7THicXWKYa7Bp7Ysc/hCLKtzz4LBH2nIR/18yH2uA8Zi7edZo28o369xtiE847KszGIZH7z7tMbqvh/lM4bTTTtPuVXeVoil9Q3vCHEKnVm/vtJvcj2tITzMa6UrP2l9NYHx0ZmMpNfAduJCMlYul1s69nBRLBiMxh4yXZUKdVB7aHMoWSwWTqRHRZ3QSIiYgwTp+vxdk5GYDrb3MHGaZF4ExKB1Q9vW/l53hsUZ7Yi2rjpjKITLrequS57P/WtNWZadYTa6cde/F5G7iN8pAkoRSFVecMQax0WMFUq5UrcgouLy0k4Rra1qFsUc18ErIo5T12hFCO9wBaU0TdXMe6z9JYSfex1019FMdh0DozRxuX2vdx66ktV17Z0m4fPgLcPBgDRJyDOnAe8QhAatW6cj3mqSJF2iFWZ9rfKERyXQuvHlca5zmdYNCN/IxVqUd9IWiznWWsZbm8ynJ07q0zYIFaG14Xg2oaHBRrC7t83F3QtM5pqDquH+8Qnz2Zw8Tnjr/l2++OGPIuuGqpyD0UxmU3Yu75Ec5ujK0BY15XzGM49d58XDu1wZnue7b7/FrC7IspSTquDclUvMT/aJRwPGozFlWfP3f/VXGaQJ+cYWl3e2yfKc/ft3+dC1Jxz07zUyWgx1U7G3vc1br71I0zSkccI4HzBtNY1wZZdCKoSKyNOUx689zosvvozB+raYLkqXSnDn5g2uXbnMvCiYTGfkly7z7Vs33LMqJePNLfJBxmJRcnR0wmA4pipKZAJb20P20m3+7i/+I7Jzu3zumetcvXie8zLlV3/h7/Pxj3/hwUvPuxzvu8Hue/NAB6sFibrx2MF3s8WCv/k3/yZ3bt1GIEjjxC1Wuu3EQoIqUzD2UeRY2W3bOg9XutKUDvrraTH3CWlhXsHwnzXnMAzLtn0WOg3hcCwhemisdguT24AXZ3HEHgdXu/ItJZZknr7BDp23TkVbfjw0uu7/yYOj7e799W94uPRsO/o9QuNnDXt6jt1ba/Bv/1zZ7rcHzfUB23z0j/a+cdq9CWPJOXDnM9zfnWPWn/c7IBxdqscu9yWEq3uW3mCHbWItdgUxcvdw27hGF7ZtiIQr/dG9zwgBSgQIOqhuuQMIhFBnBEIf6iVhLjw3/dSSM9h0DnF4hlzJkoPitW48C1sQOnU5AlmC1W1XphTH8YqTEBxxY1y7XaWEf4R6LHb/bNS121+4BnEUQZsQHAnt5S+tMpSNIwf5pwxrnZhSJGGkhj7NEBFFCdrrd0eRQvt2nEJK8HlUTOtr4R1xK0mSjtEdODXaGpqiIFYR2ouv9PPSwqfLQvlVP32nPbwLdKzwqrG4JiouQox8C1OnMLdURAtNT6QslvdeC6bV1HXl6vtxDH7n/winwDifIbEkg4xsMMBaKJoKIpdmrOcNu8MdLly4wMGbb9H65hoowbaKibAczE7QbcPmMCeXKamOWMwa4jjHxBlN02Kl5uKFi+j7c8ZpglACmopRFmN0xd72FouipKxbVJaRphlat/zG736NSxsjrp/b5elrVxhlrnY+jSPyPKUyTqVuNBhyPJ1Q1AuMbhFIIpUQxQbqmlq30CqywYAL585jG83R0RFZlqE98bBpGqpZQRQlxJFkazzGRIq9eodbd+4yHI/RxjKdzRlkGaPRmHJReSW1lHklSecn7F3JufLkdfI2ZnrzPl+5fZdX7tzlP3z0hegdx/dB6Wy5gIUHXynloLs4Yjqfs7e7x8/80Be4cuEc+3fvgo8khIBIOYZj6FMdbvhA2lhviymsq4/WQSDlATXX/bzcO41gyx56nKesZP+1pdGRZ0TFD4LD139/0GfW93tq+70oy/29vg0PwZ713Qfsy4Tcbf9La7npB45g70IjkjPntP7xHnt+xQHpbbD/nT4S0j8e67f1jgb17DRJ35FwzlGvfvoMR0lKSaRiGt/rmBC3i1VHwqmKiS5Sc993xk96pS3At9N0EX1rXJmR9a8plbgFXSnnbHjWa6s1wtfv9s+Lta7i2zcLAAAk8klEQVRG1/3p0AIjlpBskM10Z8xBxkL4a299A4xeU51gOAFfxigAiZJLY1RXJUmckmY5sDRabm1w+tvW+p7vxmKMM1LuHKymocDS1BUCp3xmTQs4QljTuJ/O8EmsxpVLBvKnUKhY+UXdksQRUkUgI0AhY4dGGNE6aNlrfUdRhDvtSzlYxx5eNlQJpLk4UkS+x7aL6p3bqSLPhJeC2Euegk/hdcTVZW27lIKynCNshCRCW0NRLmjqmjiJO8fO9USXjMebri6/adGyIRsMEDhib9O2ICTKChoBMo5o65qmqpCRQJEiIklTG1oVI6XBthbRthSLgnO7Q5qypTSWVklSa0nTIcfzGbNyRiQElgFaSLSRmEYjBxEqkhgD82LB1a0dVLLJ5M2KxaQmUYoiTdCVE7NRVhAhUEnsOxMmzKcTbpQVs/mCSVnxmWc/zeZoC+2iKBKgMS3ZKCeOFFWlqa1GIXyrCUGcpFSLuW+RGpEoxRNXr3L3YJ9r166ys7lDU5Ro3XJ0fMKsnGOFIcsiWmuIG+1UNr2HpVvDbDLlwoVzZK3lpCgpdINKU5S1nNvbZRhn7N+5y62b97g3mTDe23jAevPexvtvsP3DGiBirQ1xnDjdXSHQwKXLl/jXf/4PMzncRwmLtsYTqnpMVesWjeCBBy9cexaqFK6swfqI46ya6z4JK/x9poFYf02I3iK62tMbPIoQVmD3qdVtWPfaWaP7ylp+v4MLz4DqHYS+uuAv8+LLF5fHe5a70Z+foEvFrs5quckuMlvNPa5aw/6fD3Fx/Bz751Gsbeusa7OybREU2kSAKbrvrXxHrJ6oU+fx1M8HQg3d9/sRtl+6l9M64xpGUewbt2if95RLKFx49rS13gjYzvhbXImWi7dXz770hjVEzZGKiZPMCT8oVwIjpXRtItdKKcNxGOP0wbU2K/Pu2hDGUfC4CZE6Xkq0f1t1KJoVvlwJNF74RSrHFhea1jpkTUhJliTOQFvnCDhlN8ced4RUAwbPGveG0pqlUcNxVeqydekqI2l9z2Rw5ZROR1vQ6gZJ7Jp/dA6O9V2dXCQvpADrJEqNcbn7VjcE587B+so17RD4RhyO3FbXNVI5AlqrXU/0fu+CcK47py9yzo2xvo+5Z9nXPc0KF0y4unFtQLc1uhG00hneopizWCwYjcakSeIMctsi4pStrQ3H+G61J0kJokhRtDWNbolljARK05JkKUIb59zhcsGNbmhQWN8WVFgL2qAiweR4wqKoqI3FKOUaviROw7vVrS/Bg1oItJAIa4ik9Y1XoLYtm8mYa49d5bX7dzg4OWE8GHJYl0gtaOsWCb4WXUBdEaUpSZZTVRVvH55w8+CILzz7KsiIylhMq4nAOSBJxMZwyM3FMa0xKLksUVSxux5N01DMF2RK8cGnnuIrz3+TJ5KYc3t7VJMZALOipNINIhKkWYReLDCLgkGasqhrZByDkpTzOaONMRsy4bXZjIVt2IwlJkoYRmPKacX9oxMm1YLheMATTz1x5rryXsf3hSXeNA0ykp7F6SCHjY0NTk5O+PwXPs/P/NSXuXDhAjdu3KCpajKfxwkygUmSdLDTemTch6VDLs3Y5cK3TjbrG+11A/5ex4OitIflyr/34Zbw/jEFQ37W/laj6bXoMUSvdvXV3kZW3wu7ea+n7qxT8CDA4KGn6xHPpVj/42HfC++9E5B+Op/eOVdn7NLlYR1UujS0ZyML7nQHE722V2u6UqUQ/Uq/KCUy7nK+URwRqcjXYzsGdh9yDUa5KIoO1g7PUnhP2yU82/qcsxROzMXtVyKIwbPBi6J0uvlesCUcZ+ikJaUkimPq2vVrdl2/fKWItYDpDJ0Uxil2Ca+5bZYpBzdXRyIajzbQPsVmjKGsFuR5TttqrHVptLZxlSOtabs1omkatI+M4zh2+cvWRfjOFEdeZMYTSIV7TrDacW6ikGPWRElCVbt1S3oGehTHFJ593V+HlFIomyCl+zuk3MJ7/YAg5KK1tv7YSmqvuhbHCeOx4xy4cidIVYqIU8pyQRqnJHFC07YcHR4xGOSc3D/BSseBwFrmsym75845Z0S5stimaZidTBhu7mHrFikMutIUVc0HH7/ML//z32SaKGJcLX4ZGUTrnikpI5SKHcnOK7wZbZA+HWCEYXtjm/3jYx779FU2BkOGWca5vT1eu3+HjeEAmpoojn1P65rNOMNUtWNzD1KiNGK+f8j/+3/8O1gFn/j4JxAqhqZBtIamKdkcjVD3HcojlSLPB1RaM5ssiJRCCUmkFGmak+7F5P7Z0CH9Clw4f55FXZImCYmKKOYFMonZiCKmd+5SJw3JeEi2vYGZlcSXcmZVSSsNWZK6hk9aYxGcu3CeC5cvEScprT3NqfpexvtusJumIUuzLm8dx44x2ZQVT1y/xpd/4sf57Kc+yWuvvUbsH9ZASJFSellDR9YID/O6alqfaNb6kofvr7F8tHFWRP9+bLNvGB72uYeONasiHmKfHvTeo5q27214x6T7zcGsHdj9ns/po3zvEdCXR/iW9I1pbBw7FalG95zOJbTafd+6wzK47oj9YX00KtWyZDDsNKQnmqahidouhxtKhPrHsI7ghNdDntg5f4HtrlyfZWvRrfFGw+1ZSYWQzuDmee4if2uoa9cPWQpc9Oaj8DiKaJrSBeoK6Am0QIiofR22UF1ZWFVVgKs2qaqie7ZUz3mXUoKM0Fa4ngNWEIkIKxS1bijnU9I0Jc9z0mzo5CnnC+rabVsIpy5nBWizVBML584YF/1pI7yEpSPQ7u7uOtJdulyr6roizwedIejzbqT/rrW2I/+5GuylOEqo2c5zlzqoSycb6xjuKfhAqCwKpH89yzKm8zmz2QLTaldnbAzT2YS9C3vESUxrNBIn85plGTJy56bRBqEk0udxaRuSrQFNtaCJBKPBNjfv3WFOxUa8zWO7F8HCb7zyPIPhmMiLzyRJ2smnDrIUYSFLc4y1nMwmnN/Y5ujokEEccXl314msSMuVc+epqooaSIXg/GDM6OIGv/a13+GZZz9E9fpbKG1RaUy5OcKmQ27duck//cpv8OXP/gCfe/Y52rqkqgrG2cB1luvEfqApK5qyxkFbtmPzb29vsrOxBcZSVzXD2HXTOj/Y4/a9uwilupruDzz3LC9992Vs3ZCkKVEcc1JM+Zkv/nF+5Vf+CcloyIWtDTbyAaUsOZpOidKMja0NEIL5fM7tm7fecf14N+P9VzrTYKMgDOA8+52dbW7fvs1/9O/8r3j66Q9wcnSEblqyxJ2sIBUoWea+Os97TcyhD/H1PdmwGPVLS8J4vyLrRxlnQrS90Z/vO23HgpNmfGCEu2o6H3qcK/AzHid021idil35sRL4iZUfnPraGsR91gQe7Vqcpsqtvv3oRnt1HqtR7Tt9fhXVgWBs33GfShJJBxW3WqHrYECXLo9YO8aAA6wYWtsFt12Uuay+t52xUFGETl3kGvK9bdt2xKQl5Or2uI5c9XkhAe4POXslbQcthhSIEwhxzGex9pzGcUzTVDTVknEeRa6EyRjfa7sX+dd17TpnEeBrZ5SNdUiBlII8z1FKeGja6Xa7PtMwlmOapkVFjsUupDuv8/mU4WDkm5k4pM8aSLIcYZd1zw71F0Q9IpySyuWwvdFtfOcsKV3jkLpuuvsnrDdSKoZ56pqttA2h53g/Zx/6KCh/XQLLPVw355x4aVJfzy2VQhWF0whXiqapXDrN7zsfDkmSiPl02qVjpBRMJhPHIG9aWiER0pKmMYfHRxRV6TrAIZG+/ng6LdgjYWpqbBRzYec8v/Gdb9Mozc/8xI8xnS745svfJR4k1HXpNLatQImIWMSUi8JJw2rt7tvIteNMEsecb4uKcZozzjJ027A1zHht/z7PfeBpXnvjde6WJZceu8pTFy9zcbzJ/6+9M3uy5EzP+u9bcjtbVXVXV3e1lm5Z0kiEwNaMbRgzOAKIkbkwOBz2HQEmAiL4F+af4ZobuODKXEIE4cBmMLOAZ6QZqTXqnl5rrzpbZn4LF9+XeZaq6kXTmlBDPjfVfU6ezO/kycznXZ73fR8psBISreibhGIwQmUHfPH4MX/+X/4rn/3sU/7Bt/8uN3e2GQ6GDNKcVGkcntlsxmQyochzyqoMUyGdZTKZMBz2ubZ1FW9DtOnKaJNqPieRikHRCymYuibPMp4cHJBnKTs7O5zOZsyOTxle3eCHP/gxP3lwlzdu3uDmlW1GvT6z6RQB1KZClCHtNT474+H9e89+YLwAvpKQeChtIpY8pNy//5B/9Wf/nG+8+w7eWmaTCYPBoK3LTmJ+upmb23rUkbScj0MzZCiOD/qaUHPNkjjtMsX1emj8aXjZXvnz5NAv/BysEKDnYjK57FgXGQaL4y8e+hf7y0uv+9UI+aWrXwvxrr152afOYzn/vB7OFl/eu3/6eV89zsXbtpR6KakvvyYEeKVRUofKAVg1MKVY2raNH6xmIsSysbBYRghVN0NALFLFMyVWBZrL3c9g0b50EbpeqMOXJ+o1gQzRlAfKJe/cL+qsgzpZtqHdhoDrqsbEiX0A1pWxF//C4Gn2EbyzELL10TqZz+chX91OE9OU8xqdaLxXOGcxxrUGQvgesj1fAGlStKk2ay3eGrTOgljNWpQKojEpY5RBKaraLLxgobA+9CD3LMrKqqpiPp0hpWQ+L1d6fVeViOmHRf27EKJtwdzOg25+D87fp8ud55pcOkicq4NwKs0RIjRrqevQMKTf74e8vvdonZC6lMrUmKoOYztVyO0fHR9zcHzC7vVdPr/3C+blnFGxQVUaUp1wUpcUgwFKa/YnE+4dPOG33v2ADSQWz2aSsuUT9m3w8nGQJim9XkE5neAbD9eJqJMJYjLjgtBPuDA7YnP7GvcePmBrY0A1mzPqDTDesff4EbPxhERJdm/c4PjslPm8RHroe0U/7VHXFQfTCR/fv8vxf5vyx3/wETdu3kT/WFPNQ+vRWRnqsoejEWdnZ5jKxHPvKauKq5tb7O3tMZ/NyPKM+XQK3jMs+szjHPbr13bYO5txIgWTakZdh0l2yjg+f3SfiXTcvL6LNJ47n37G5tWtcF+p0HfeujBi9P3337/gWfLl8ZUQto/k6jzUxnD79m0++u5H4CzlbBZU4X6hIAca87p96DTPx6YlIsQe3NA+YJZrri/KX6/jItL+KsPm6zdjqza+xOteXdja+sQzvM4LjhdfXXptiWyWVERP8/pf5Ow8j9f6zPdXNhW/Qgj8ace52HC7LCrw9G0u3FH4I0MtqNIKLrheV7/3YhBGu82SP90ERRbCr3gfGIPSQZyFWHjObqnOeUWQKQXWGEC0c6DXCVsIEVW+TU8CuQjB+6DEXs292jYU75quZe3381H01cyxXr1HTVzLsure+8WMaO99FJWG9TlvWoNFRCKUUoacezQe8B4pNEKo1otWSrTvGeuCIRUjV00fc4mI4tfYNauta/coEcR0WmuMrvA+dD5TUVmfxUlqzfNsOTLoo1EQ5pG79jnnn2LA60bhLlWbE24MMfCgQmSsqmsSncSGVdFoi4bQdDqltnUcDaq5e/c+vf6IG9vXODk94fDkMPTjcZ4szdBIhklBZR0PDh4jpea9m2+ghI/h74ThcMD+fIapLXVlUDKhV/QpJ2coJUIXOCGpY1259EHN3QywybOU7atb2Kri+OyEPMmYiSkYhzAWkUjGsymJUGz2BsxVwsnZmKS2vH7tGnt7T7DADMf9wwN+dvcu79++jVYJznkmsynG1PT6BUWeU5UlWisqE8R487pkczjk4Mke1dJEN5wPHrq1+CRFbmyy3T/kpJyHLmoqpuUqw4nyyCJDo6hnJZPJlNdvvcm0rhnPZ1hCc59Ea/L0az/8w4fuQDoJJQUI/uiP/pA333idzz75ONT+ZVlbwwi0iuzlXFqwps/nptdrRC/CZZ7mZV7pMqE22/46ce54Yp3ExKKUeY1rnm+ty+fDtQ/Sc1utk0grhRKLYz8XKS9HFc7XvT8bopHFLe0xeh5fZm9r5yisqQkBr/jWkWvF0r8v66q28HzPe9mijYio2JzELJFms11z3a/nGPzS655wT4WDxTRJS4CL+8A62x5TSEltouIZcZ6wrQ2HkosOfispphiaDj++iBGu+Fok7MbY9t6HHHqc4OXtgsyVCiFtRRKN8Kb0MijF6+iFO+dWqjLSNKWu60UHwygOstYGY8OH6F2SpJiqIssy6ujtkyRxjQJjFi1KZSynsnUV5w/Q9m1QKhgV1gaVNdLjRKgGcM6BCgaCloosSbFZjTGGIgtqbeeDN9WkIRYNa6JaPI79jSd2hbDXIyDNb6CbXL8QgGwjIY3hIghh+Nl8FlTTMijRjbU4b8n7PaqyYjqfMRptgBQ8efKEb314m6ubW9y8dh2cZ1JOcdaTJDmbeZ++SBlPT3mwv8/tG6+xu7GFVwIvPWmW0N8aoo72mc1mTCdzBJIiLziM5yBRGqU0lasxtSFVoSytrssQudASrRVv3rjOnXtfcH17h8nRGWU5o8hy8l4R6sDnc3p5wSDvY43Hz2Zcv73L+OwEKSDv9XDO8rM7v2B7YxOpEjyS8WRCqjW9XoGSse5f6/A7RQ97Z3M76AhqQx2n2XkXzymBaJM8Y3f7KnvTMaPBAOvHoa68MsxHmoEeMp/MUFWJ1pp+r8+orhlPJ23FhVKK+fL41JeAl07Y1lqyXhBODAY93nnnbf7ln/0LfvD975PgQx/dOKijKfxvBCpNaAlnGRbDVtG6bM1fVL71PFgno5ehHH8aWZ4nv2eHxM953u1fH3OZz+epLkjn/H5XQvRP3dsluJC0F+Hi5T1fnj9+zkjHpZ73i+P8/psyseU1N0S9vu4FOa8uW5z/66PuIHp2WmvIMmyc2X7hWkQwULz3rF+JgZhBsEquEL042YSCV0fRNnlqJRYNSoB2fKaMytmGdFty8aF5UV3XZFmGVkkgrhh+d3FYRlmWJEnSKtCFCKImJRYitrYxiwhqcRU7jkETJVuUdTURCCFCN8SyjOHmGIYuitCnuy5n7fNCa42pJVoJJB7hHSISWijZqsOUrSiA1UK0M8NNXeOJJXdC4bxlPp+2HnsYcBLOQ5rnpGkWZiKfTLHWUJYVZQyltuctNplZPKtiaFwuUhVa6jiv2sR+2IsUQ5PCaEb5GmOooxBO6zT+RjZ2e3Oh/Eor5vM5RVqspDeKPmxtbDKZTjg9PaM/GtHvD9gYjSh0xrXNbZyxPDrY4+hsAlKwsbnB3DgezsYcnI75k+/+Ido4HIbNJKUuhkymZVCgn54wPx1jjYm16pZEK4RwKCFIpCLVCcPBkLRXMK8qdKKxE8ve3mN+8/Z7nB0d8vt/5/eYn035HEuJQ1WWD959j7/84V/z6PiQK4NNfue9D/jLv/oLzHFKniX0spCv3t/f50yPOZnMmBlLYmtqWzEcDiOX1CRakmcp02lod531MtJGsDcec3JyEvrrW0ddmzAkRCm8EOxe3+He4ydcGQwZVxWTqqIATuo5t0abCOepbI3BMZ5NscaQJymVMWAdNhrsLxMvnbCzLMMaw3Q24zvf+ft873vf45NPPiFRikGR4+J4Mp0FgQYsHuDOe/JegZSSs7OzVj3ZCGdaT+ISgl3Oy70MQn6Z+DJeu1/7d9Ov/FdFo8p9Xlzsj7/Ilr/qmte89KdHtp/+2XNoiHod65bJcx40noKGelUkFi+jabDkyT7PNdE8pJerI1QsockTiU0kOinI0rQ1cILwq267hAGt1qMxeOVyXpo1ncfy+kRDeFH1nCiUztv8c1VVLRn18qIlR0kIFXspqeYWU9t2gEi4H0O4fZmkmrVPp1OklJwcH7eVJuPxaTB+ImE1Xn1o2bq472sTPNnRcIPT09OY11VoFUqYprM69PcWAqUVWjUGD2i9iAwkqcIYj07AWY+ta4SPNeZOMBoMWofDmKAmNxBFVrIN91dVhSUMBtFao1WCIhhJFo/3tv1Nmu2NMZi6Dh6pCq0101TG52XwvlUsTzN4yAUKCTGvPasrnjx5wuuvv87+4T77h/s4JXjnG+/ijOF4/5BUKbZGm0yrOf3xmJm1vNbb4i8+/RseHO7x7Q8+5Pb11/j0Jz/m6nCAxVNKh0g0N2/e5MHdB3gHSmrSNEMqHw2sMB51XhrmsxmuNgitQuNfJRAqGI0b/R65ShhubnJj+xpHR4ccH5+iN/r89x/9L27ffBOzWTI+OeMX9+4x2tlgLgyTyZhhmrM93MSMZ5Szkr3H+zzeP0TtXCErCjavbHB2dIoSkizL6DvLeDJm72ifa4MbSCEo8oLxdMLhwSHvvvMOs8PT9voTiWJuwnn8W2/e5s7/fExpa1yhqcsa0oINnfPgwQN2dq7w3rvvc3oars9hf8BkNsV6B0oxq77mHrbq5zx88IQ//eN/xj/9J3+AtjXVyQn9XsF0Pg11m1FktvzAkkqG3q9iofRetla9dW395WUEvC7wal5bVpY/D14GwV+YD34B7xoi1Yhzr/C89HnuGE8lp2ab9Xdks8D49/w5fp41rUb4xQWGVEMeYmUdfimPvci/s5I2WI8grL8u1lMM54554YrX1v/8aZM4tnhxDKlJsx7WekxdYXyYqiR9k9eMYWYfxgQ24qFlAZKKZT2NcMs5R1VXeJVgq4rhKHQHa75/OwnLxPC3irPqVajXFgTPtukCBaDTQPQKHWZHW8t0MiVNs+CVWsPcWkwdqjrm0yDu0UrhrOP09JQkTfF4TBwBGdoMO6wzbRRNSoVzoVY8RAICKdZVTTmrYimnIC+KODsapNJkWYGMSnnrbCTsUD8tRZgDEEL1mrKs6PUG8TkTusdl0aapTRmuP0LTJutBS01WbKwo2J2HyXQe0nhp2nams9bhE8F0Ngtd5YQgTRNsVbdKcO8BqSj6o/a6qcqKCoOSiqoqg6I9EruQkjTLyTJN3pME+UBjVnuSJI3q+NXRm9IYvBJtxGPQK8h6BbP5HOkFr1/bRVpBeTxl55vXODw4pUizUI7lXBiWMR1z841dTrzj0ckpmdb8w9/8kNmTJ2xd3UbiSbzn+nDA7mDIv/vz/0zlHZYSnUFeJGiv2e6NKKuSs/kpCMWo6HMqDenRjFk5o5fn5EnKeHKGLSuu7F6jlwheu7bFeLJD+ajmejHi00f36QvFTIbc8TDrs93f4cEvv2BwfZdBXpAIyWu7r/Gjj3/Krapie2MTVzk+/vkXFP1NNvqbzMZneCfIkpztK1fJ04yqMkwnUzZHI8q6Yv/4kLfw1FoghcYJ8HVQu18bjPjldI8PfuNt5nccP3/4gKzXJ5l6/mb+ED+f8mbvLd4Y3uDf//X3+ebb77KzscWDacncevI8C+1eXyJeOmHvPTnkd771Id/59re5ubvLk8ePKfKsFY/U1GiXkMX6N2hy1/LcQ7GF961gZUVp+xweymX7fBGP99J1XbKfiwyH5/ncudcvIZlVIdmFe7nk1YsI+4LvdO4dsfpn6bjP+h7PMlIuJu3F8Rdp4TUSXgu7r+9rNT2w/NqqUvlZa7xs3c/+3CLELqORqnQgqkAG7lyUY/3Xabzd9WOG3HAopQp1zYu+1ev3xuL0Napyh2QxxKa5n6y1eEGr8vYu9N62zjMvy6h9oDWiy1noDd4Y0C72465NFQ8aFM7WWvI0bXOFEAyHJkfdTvATccKUNQtDJa69VZtbh5eBsJ3zsXSq6W9OO6lLShfakqrQOcxFMZ8gtK00tplZHifqeY+TPs65Dh43IszCDoZLHVPPq9oYu5SaM7E1ckO+4Yjh+ltOOQghonMSGs6IRiMhRNvlDSHIMr2iP4DFPO5Qmx9+D1NWpFnaXkvOe2pTU9cV3mYM+n2Ggz7m9ISz0+P2OpFSUWQFG4MNQLK1eYU7d++ClLx24yaDXp/5/DjMdkgStNJ475AGbt3YReUZ9774gkQqCp0wzHsIH/LZUipq6zEOdK9Hv9drnlixd3cVR11aEi3JtKaX52wMh1BZtBRUs3mcuhW6Zs7nJVc2trjz8C5yc4uta0NOTsfcuLnLZDLh9PSUa1tb9HsDfvrTj/nd3/oQrXRrEOZFzmQ2RbpwTpM0RSrFyckJZ+Mz5pMpmUrajoFaqpjW8iGVgoiT1wSuqrEpXNveJisKDo6O2D86psgLsiQ2/apAeEEinxXhezG8dMLu9wq++4//EbfffANirijXOZ7QU7cJmXm9yMMppYIKb8m7Xq+3Xg4lroe9n4avKhz+LKL++uEy7/rrg19V7Pesz18kYPxqBIbr3rlsw8Y25iRDR6j1UP8yhUeFcVti127CYhKWhKgiXj7mRcZpG/5eM2qakPSyLsRHD9ZYg/MOJVVQUcvFMIskDfXNy7lvIcDYUHsd1hcFY3mYiLV8/zbk1pQ6KanbHtvL37/ZV7Pe9oxETUcTcfA+ChWFxCPQSdoqpz2RjGU0dkXTXlbE/64aYKH/v0JKTZrl4HSb729+36bRyco15Jue80u/p4gGhw9NZcTSOdRxKIiPSwnDPZpzFEWRMe9vjMXUFd7plrgB6jgQJRC6papcGPphgmI/SRL6/YJZNebocI/NzevhGoLokQ+xtSfPBtx78L8ZXNng1utvxuY/Gl/NUJlC6yQo3IXl9s1dNq9uc/T4CcpDKjX9PMcaSy8rqCvLwdmY0/GMW0fHSJ2EiEtzXcSwuTUmDlpSFHnG1saI2emETOuoSQjh/16v4MnhMde3Rmip4m8ZygSvXr3K/PA41JY7GwVgBZ999ilvvXErXDt+0SpbKgU+RJOUDm1mZ7NZMPZk0/89pLKcs2RZikQE402G/gqVswgvuH71ClJK7u89wTrPoD8AIM+yVpPSS3NeJl46Yf+93/4mv/+d30M4x+TsjH6vCPmYRJHnOVVd4+xC3d3WcarVsHUrXGlu8jVV+EXqbng6kX7ph/OFQqvzodhfCy5Zy+UQfJ3I+uJw+P/7UEqT6ASrTeyj7WP5cePuB4v8oohHEKOtk3Bz/TUtRpffW3jQzf8X61BLxLQQpDWfafLkJva7FiJMxJNS4qMXDZDlORKxaEAiBFqnYOrojTcEH47bDOYRcjWS1hrf0YN0sUta45423d2a7l6h1CwYMYmQ4EDIRsgWBGVCShKdxjXTfsfGHghefSDD4J2raFTJaAAEck+0xrrQG1tGtzt4ybLN4TfnzhhDqjUmEinQ1kZba8MDXy3EaD6qx9ue/V6039sThpy0jkqMChlTgw/GnjFu8R18MIZMrBRw1ra957XW9Ps58zrj+PiA7au7EMlEy5BmmM9rcIqDkxP+9tu3ufXa67jSkiQpulLoJAm9BJzHe8P2aMDbt2/z/b/6H2AdGkGeZJjakCc5D06O+eXjJxycnXHl88/pVYaytkipSHSCNlUoKQyrDc1RegUb0jPUGfcP9hj2e+GmUJKrV67w8PEevTTnxs5OiMw4Q2/QhyRh7j1eCuZxjOvv/va3+E//8T+wu70TvXtBZYLWopkq32hLmlbaw6KPdE3HvRDx8AIGgz5FlpIoSaIkeZ5SljOwlo2ihzE1D/f3GYyGDPp9pidn5FkOSKqyZLM/fMaT4cXw0gn73/6bfw3GUZbzoJIkiD5kHFeZ5zl4WtJuCTt2N2pu4GX1amORf1lcRNgvxHtfD64LaMONL/KBCzZ+YeL/deLlLu6r8aKfD821p7XCJRpjFHUtqZdSO0/9bEvoq9u2k6n86jCP9RRB89Vb49c4jKsXTTyWaqlDmHAxbKe579oyJbcYzhOM6MYTjA2PtCLXYmkc6KLBSjUvw74bEZ7351oOK6XQJMELwoGJgyiaTK512CZ3q0I3srAfgTUWqHEuhM6VdC1ZNs8TFSeAJUkaz0fwXlujQCoQS88gnaC1BVthoho+jKxcCObSNBgzZVkyGgzaTmeu2YcNojQfWyx778OoSucwNW1aACFIhWjD6XUVOpo1YkPPoqucESb8XwgGxQYQUhneOaQQDPr99jcXQpAXKSOf8eDBUTjXxKEnKkQbZrOK8bgk6RXsXN3mSn/I4eFDhkWPXt6PExRBJCleSOpyzOawjxOO2oUxw0pKamtQKuHug0ccVSUb168zd56TgwNq60hVgnee6WRCL8tDuZS1aKUosozclty4dYsffvKTMAqTfexkwo3rO/z8i7tkOmFzOOTo7JTTyZi33voNfn7nc65sbXHv6BHWO7SW3Llzh48++oj/84Mf8Y1vvMvGaMhsPmUwGLB/eLjosOdccCKritHOLuPjk6WmQg6EY3O0yajfI9eaREqGvYJ6XnI6OcKWFcezmsPxmFu33yLXKXtnY4rRiDwTuHnF1dHmM58RLwLx/5un06FDhw4dOryKeLkZ8Q4dOnTo0KHDV4KOsDt06NChQ4dXAB1hd+jQoUOHDq8AOsLu0KFDhw4dXgF0hN2hQ4cOHTq8AugIu0OHDh06dHgF0BF2hw4dOnTo8AqgI+wOHTp06NDhFUBH2B06dOjQocMrgI6wO3To0KFDh1cAHWF36NChQ4cOrwA6wu7QoUOHDh1eAXSE3aFDhw4dOrwC6Ai7Q4cOHTp0eAXQEXaHDh06dOjwCqAj7A4dOnTo0OEVQEfYHTp06NChwyuAjrA7dOjQoUOHVwAdYXfo0KFDhw6vAP4vWrQBggNim7EAAAAASUVORK5CYII=\\n\"\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n }\n }\n ],\n \"source\": [\n \"# load image\\n\",\n \"raw_img = plt.imread('../figures/demo.png')\\n\",\n \"h, w = raw_img.shape[0], raw_img.shape[1]\\n\",\n \"orig_size = torch.as_tensor([int(h), int(w)])\\n\",\n \"\\n\",\n \"# normalize image\\n\",\n \"test_size = 1100\\n\",\n \"normalize = Compose([\\n\",\n \" ToTensor(),\\n\",\n \" Normalize([0.538, 0.494, 0.453], [0.257, 0.263, 0.273]),\\n\",\n \" Resize([test_size]),\\n\",\n \" ])\\n\",\n \"img = normalize(raw_img)\\n\",\n \"inputs = nested_tensor_from_tensor_list([img])\\n\",\n \"plt.axis('off')\\n\",\n \"plt.imshow(raw_img)\"\n ]\n },\n {\n \"source\": [\n \"## Model Inference\"\n ],\n \"cell_type\": \"markdown\",\n \"metadata\": {}\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"outputs = model(inputs)[0]\"\n ]\n },\n {\n \"source\": [\n \"## Post-processing Results\"\n ],\n \"cell_type\": \"markdown\",\n \"metadata\": {}\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"out_logits, out_line = outputs['pred_logits'], outputs['pred_lines']\\n\",\n \"prob = F.softmax(out_logits, -1)\\n\",\n \"scores, labels = prob[..., :-1].max(-1)\\n\",\n \"img_h, img_w = orig_size.unbind(0)\\n\",\n \"scale_fct = torch.unsqueeze(torch.stack([img_w, img_h, img_w, img_h], dim=0), dim=0)\\n\",\n \"lines = out_line * scale_fct[:, None, :]\\n\",\n \"lines = lines.view(1000, 2, 2)\\n\",\n \"lines = lines.flip([-1])# this is yxyx format\\n\",\n \"scores = scores.detach().numpy()\\n\",\n \"keep = scores >= 0.7\\n\",\n \"keep = keep.squeeze()\\n\",\n \"lines = lines[keep]\\n\",\n \"lines = lines.reshape(lines.shape[0], -1)\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"source\": [\n \"## Plot Inference Results\"\n ],\n \"cell_type\": \"markdown\",\n \"metadata\": {}\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {},\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": \"\",\n \"image/svg+xml\": \"\\n\\n\\n\\n\",\n \"image/png\": 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\\n\"\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n }\n }\n ],\n \"source\": [\n \"\\n\",\n \"fig = plt.figure()\\n\",\n \"plt.imshow(raw_img)\\n\",\n \"for tp_id, line in enumerate(lines):\\n\",\n \" y1, x1, y2, x2 = line # this is yxyx\\n\",\n \" p1 = (x1, y1)\\n\",\n \" p2 = (x2, y2)\\n\",\n \" plt.plot([p1[0], p2[0]], [p1[1], p2[1]], linewidth=1.5, color='darkorange', zorder=1)\\n\",\n \"plt.axis('off')\\n\",\n \"\\n\",\n \"\\n\",\n \"#plt.savefig(\\\"../figures/demo_result.png\\\", dpi=300, bbox_inches='tight', pad_inches = 0)\\n\",\n \"#plt.close(fig)\\n\",\n \"plt.show()\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ]\n}"
},
{
"path": "src/engine.py",
"content": "\"\"\"\nTrain and eval functions used in main.py\n\nmodified based on https://github.com/facebookresearch/detr/blob/master/engine.py\n\"\"\"\nimport math\nimport os\nimport sys\nfrom typing import Iterable\nimport json\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimport numpy as np\nimport torch\nimport datetime\nimport util.misc as utils\n\ndef train_one_epoch(model, criterion, postprocessors, data_loader, optimizer, device, epoch, max_norm, args):\n model.train()\n criterion.train()\n metric_logger = utils.MetricLogger(delimiter=\" \")\n metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))\n header = 'Epoch: [{}]'.format(epoch)\n print_freq = 10\n\n\n counter = 0\n torch.cuda.empty_cache()\n for samples, targets in metric_logger.log_every(data_loader, print_freq, header):\n samples = samples.to(device)\n targets = [{k: v.to(device) for k, v in t.items()} for t in targets]\n \n try:\n if args.LETRpost:\n outputs, origin_indices = model(samples, postprocessors, targets, criterion)\n loss_dict = criterion(outputs, targets, origin_indices)\n else:\n outputs = model(samples)\n loss_dict = criterion(outputs, targets)\n except RuntimeError as e:\n if \"out of memory\" in str(e):\n sys.exit('Out Of Memory') \n else:\n raise e\n \n weight_dict = criterion.weight_dict\n losses = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict)\n\n # reduce losses over all GPUs for logging purposes\n loss_dict_reduced = utils.reduce_dict(loss_dict)\n loss_dict_reduced_unscaled = {f'{k}_unscaled': v for k, v in loss_dict_reduced.items()}\n loss_dict_reduced_scaled = {k: v * weight_dict[k] for k, v in loss_dict_reduced.items() if k in weight_dict}\n losses_reduced_scaled = sum(loss_dict_reduced_scaled.values())\n\n loss_value = losses_reduced_scaled.item()\n\n if not math.isfinite(loss_value):\n print(\"Loss is {}, stopping training\".format(loss_value))\n print(loss_dict_reduced)\n sys.exit(1)\n\n optimizer.zero_grad()\n losses.backward()\n if max_norm > 0:\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)\n optimizer.step()\n\n metric_logger.update(loss=loss_value, **loss_dict_reduced_scaled, **loss_dict_reduced_unscaled)\n metric_logger.update(lr=optimizer.param_groups[0][\"lr\"])\n # gather the stats from all processes\n metric_logger.synchronize_between_processes()\n print(\"Averaged stats:\", metric_logger)\n return {k: meter.global_avg for k, meter in metric_logger.meters.items()}\n\n\n@torch.no_grad()\ndef evaluate(model, criterion, postprocessors, data_loader, base_ds, device, output_dir, args):\n model.eval()\n criterion.eval()\n\n metric_logger = utils.MetricLogger(delimiter=\" \")\n header = 'Test:'\n\n id_to_img = {}\n f = open(os.path.join(args.coco_path, \"annotations\", \"lines_{}2017.json\".format(args.dataset)))\n data = json.load(f)\n for d in data['images']:\n id_to_img[d['id']] = d['file_name'].split('.')[0]\n counter = 0\n num_images = 0\n for samples, targets in metric_logger.log_every(data_loader, 10, header):\n samples = samples.to(device)\n targets = [{k: v.to(device) for k, v in t.items()} for t in targets]\n\n if args.LETRpost:\n outputs, origin_indices = model(samples, postprocessors, targets, criterion)\n loss_dict = criterion(outputs, targets, origin_indices)\n else:\n outputs = model(samples)\n loss_dict = criterion(outputs, targets)\n\n weight_dict = criterion.weight_dict\n\n # reduce losses over all GPUs for logging purposes\n loss_dict_reduced = utils.reduce_dict(loss_dict)\n loss_dict_reduced_scaled = {k: v * weight_dict[k] for k, v in loss_dict_reduced.items() if k in weight_dict}\n loss_dict_reduced_unscaled = {f'{k}_unscaled': v for k, v in loss_dict_reduced.items()}\n metric_logger.update(loss=sum(loss_dict_reduced_scaled.values()),\n **loss_dict_reduced_scaled,\n **loss_dict_reduced_unscaled)\n\n if args.benchmark:\n orig_target_sizes = torch.stack([t[\"orig_size\"] for t in targets], dim=0)\n \n results = postprocessors['line'](outputs, orig_target_sizes, \"prediction\")\n\n pred_logits = outputs['pred_logits']\n bz = pred_logits.shape[0]\n assert bz ==1 \n query = pred_logits.shape[1]\n\n rst = results[0]['lines']\n pred_lines = rst.view(query, 2, 2)\n\n pred_lines = pred_lines.flip([-1]) # this is yxyx format\n\n h, w = targets[0]['orig_size'].tolist()\n pred_lines[:,:,0] = pred_lines[:,:,0]*(128) \n pred_lines[:,:,0] = pred_lines[:,:,0]/h\n pred_lines[:,:,1] = pred_lines[:,:,1]*(128)\n pred_lines[:,:,1] = pred_lines[:,:,1]/w\n\n \n \n score = results[0]['scores'].cpu().numpy()\n line = pred_lines.cpu().numpy()\n\n score_idx = np.argsort(-score)\n line = line[score_idx]\n score = score[score_idx]\n\n os.makedirs(args.output_dir+'/benchmark' , exist_ok=True)\n if 'data/york_processed' in args.coco_path:\n append_path = '/benchmark/benchmark_york_'+args.append_word\n os.makedirs(args.output_dir+append_path , exist_ok=True)\n checkpoint_path = args.output_dir+append_path+'/{}.npz'\n curr_img_id = targets[0]['image_id'].tolist()[0]\n np.savez(checkpoint_path.format(id_to_img[curr_img_id]),**{'lines': line, 'score':score})\n elif 'data/wireframe_processed' in args.coco_path:\n append_path = '/benchmark/benchmark_val_'+args.append_word\n os.makedirs(args.output_dir+append_path , exist_ok=True)\n checkpoint_path = args.output_dir+append_path+'/{:08d}.npz'\n curr_img_id = targets[0]['image_id'].tolist()[0]\n np.savez(checkpoint_path.format(int(id_to_img[curr_img_id])),**{'lines': line, 'score':score})\n else:\n assert False\n num_images +=1\n\n # gather the stats from all processes\n metric_logger.synchronize_between_processes()\n print(\"Averaged stats:\", metric_logger)\n\n # accumulate predictions from all images\n stats = {k: meter.global_avg for k, meter in metric_logger.meters.items()}\n\n return stats"
},
{
"path": "src/main.py",
"content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\nimport argparse\nimport datetime\nimport json\nimport random\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nfrom torch.utils.data import DataLoader, DistributedSampler\n\nimport datasets\nimport util.misc as utils\nfrom datasets import build_dataset, get_coco_api_from_dataset\nfrom engine import evaluate, train_one_epoch\nfrom models import build_model\nfrom args import get_args_parser\n\ndef main(args):\n utils.init_distributed_mode(args)\n print(\"git:\\n {}\\n\".format(utils.get_sha()))\n\n output_dir = Path(args.output_dir)\n\n print(args)\n\n device = torch.device(args.device)\n\n # fix the seed for reproducibility\n seed = args.seed + utils.get_rank()\n torch.manual_seed(seed)\n np.random.seed(seed)\n random.seed(seed)\n\n model, criterion, postprocessors = build_model(args)\n model.to(device)\n\n model_without_ddp = model\n if args.distributed:\n model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])\n model_without_ddp = model.module\n n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)\n print('number of params:', n_parameters)\n\n param_dicts = [\n {\"params\": [p for n, p in model_without_ddp.named_parameters() if \"backbone\" not in n and p.requires_grad]},\n {\n \"params\": [p for n, p in model_without_ddp.named_parameters() if \"backbone\" in n and p.requires_grad],\n \"lr\": args.lr_backbone,\n },\n ]\n optimizer = torch.optim.AdamW(param_dicts, lr=args.lr, weight_decay=args.weight_decay)\n lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, args.lr_drop)\n\n if args.eval:\n dataset_val = build_dataset(image_set=args.dataset, args=args)\n\n if args.distributed:\n sampler_val = DistributedSampler(dataset_val, shuffle=False)\n else:\n sampler_val = torch.utils.data.SequentialSampler(dataset_val)\n\n data_loader_val = DataLoader(dataset_val, args.batch_size, sampler=sampler_val, drop_last=False, collate_fn=utils.collate_fn, num_workers=args.num_workers)\n else:\n dataset_train = build_dataset(image_set='train', args=args)\n dataset_val = build_dataset(image_set='val', args=args)\n\n if args.distributed:\n sampler_train = DistributedSampler(dataset_train)\n sampler_val = DistributedSampler(dataset_val, shuffle=False)\n else:\n sampler_train = torch.utils.data.SequentialSampler(dataset_train)\n sampler_val = torch.utils.data.SequentialSampler(dataset_val)\n\n batch_sampler_train = torch.utils.data.BatchSampler(sampler_train, args.batch_size, drop_last=True)\n\n data_loader_train = DataLoader(dataset_train, batch_sampler=batch_sampler_train, collate_fn=utils.collate_fn, num_workers=args.num_workers)\n data_loader_val = DataLoader(dataset_val, args.batch_size, sampler=sampler_val, drop_last=False, collate_fn=utils.collate_fn, num_workers=args.num_workers)\n\n base_ds = get_coco_api_from_dataset(dataset_val)\n\n\n\n if args.resume and args.frozen_weights:\n assert False\n elif args.resume:\n if args.resume.startswith('https'):\n checkpoint = torch.hub.load_state_dict_from_url(args.resume, map_location='cpu', check_hash=True)\n new_state_dict = {}\n for k in checkpoint['model']:\n if (\"class_embed\" in k) or (\"bbox_embed\" in k) or (\"query_embed\" in k):\n continue\n if (\"input_proj\" in k) and args.layer1_num != 3:\n continue\n new_state_dict[k] = checkpoint['model'][k]\n \n # Compare load model and current model\n current_param = [n for n,p in model_without_ddp.named_parameters()]\n current_buffer = [n for n,p in model_without_ddp.named_buffers()]\n load_param = new_state_dict.keys()\n for p in load_param:\n if p not in current_param and p not in current_buffer:\n print(p, 'NOT appear in current model. ')\n for p in current_param:\n if p not in load_param:\n print(p, 'NEW parameter. ')\n model_without_ddp.load_state_dict(new_state_dict, strict=False)\n else:\n checkpoint = torch.load(args.resume, map_location='cpu')\n\n # this is to compromise old implementation \n new_state_dict = {}\n for k in checkpoint['model']:\n if \"bbox_embed\" in k:\n print(\"bbox_embed from OLD implementation has been replaced with lines_embed\")\n new_state_dict[\"lines_embed.\"+'.'.join(k.split('.')[1:])] = checkpoint['model'][k] \n else:\n new_state_dict[k] = checkpoint['model'][k]\n\n # compare resume model and current model\n current_param = [n for n,p in model_without_ddp.named_parameters()]\n current_buffer = [n for n,p in model_without_ddp.named_buffers()]\n load_param = new_state_dict.keys()\n #for p in load_param:\n #if p not in current_param and p not in current_buffer:\n #print(p, 'not been loaded to current model. Strict == False?')\n for p in current_param:\n if p not in load_param:\n print(p, 'is a new parameter. Not found from load dict.')\n \n # load model\n model_without_ddp.load_state_dict(new_state_dict)\n\n # load optimizer\n if not args.no_opt and not args.eval and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'epoch' in checkpoint:\n optimizer.load_state_dict(checkpoint['optimizer'])\n checkpoint['lr_scheduler']['step_size'] = args.lr_drop # change the lr_drop epoch\n lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])\n args.start_epoch = checkpoint['epoch'] + 1\n elif args.frozen_weights:\n checkpoint = torch.load(args.frozen_weights, map_location='cpu')\n new_state_dict = {}\n for k in checkpoint['model']:\n if \"bbox_embed\" in k:\n new_state_dict[\"lines_embed.\"+'.'.join(k.split('.')[1:])] = checkpoint['model'][k] \n else:\n new_state_dict[k] = checkpoint['model'][k]\n\n model_without_ddp.letr.load_state_dict(new_state_dict)\n\n # params\n encoder = {k:v for k,v in new_state_dict.items() if \"encoder\" in k}\n decoder = {k:v for k,v in new_state_dict.items() if \"decoder\" in k}\n class_embed = {k:v for k,v in new_state_dict.items() if \"class_embed\" in k}\n line_embed = {k:v for k,v in new_state_dict.items() if \"lines_embed\" in k}\n\n model_without_ddp.load_state_dict(encoder, strict=False)\n model_without_ddp.load_state_dict(decoder, strict=False)\n model_without_ddp.load_state_dict(class_embed, strict=False)\n model_without_ddp.load_state_dict(line_embed, strict=False)\n print('Finish load frozen_weights')\n else:\n print(\"NO RESUME. TRAIN FROM SCRATCH\")\n \n if args.eval:\n test_stats = evaluate(model, criterion, postprocessors, data_loader_val, base_ds, device, args.output_dir, args)\n #print('checkpoint'+ str(checkpoint['epoch']))\n return \n\n print(\"Start training\")\n start_time = time.time()\n for epoch in range(args.start_epoch, args.epochs):\n if args.distributed:\n sampler_train.set_epoch(epoch)\n \n train_stats = train_one_epoch(model, criterion, postprocessors, data_loader_train, optimizer, device, epoch, args.clip_max_norm, args)\n \n lr_scheduler.step()\n if args.output_dir:\n checkpoint_paths = [output_dir / 'checkpoints/checkpoint.pth']\n # extra checkpoint before LR drop and every several epochs\n if (epoch + 1) % args.lr_drop == 0 or (epoch + 1) % args.save_freq == 0:\n checkpoint_paths.append(output_dir / f'checkpoints/checkpoint{epoch:04}.pth')\n for checkpoint_path in checkpoint_paths:\n utils.save_on_master({\n 'model': model_without_ddp.state_dict(),\n 'optimizer': optimizer.state_dict(),\n 'lr_scheduler': lr_scheduler.state_dict(),\n 'epoch': epoch,\n 'args': args,\n }, checkpoint_path)\n\n test_stats = evaluate(model, criterion, postprocessors, data_loader_val, base_ds, device, args.output_dir, args)\n\n log_stats = {**{f'train_{k}': format(v, \".6f\") for k, v in train_stats.items()},\n **{f'test_{k}': format(v, \".6f\") for k, v in test_stats.items()},\n 'epoch': epoch, 'n_parameters': n_parameters}\n\n if args.output_dir and utils.is_main_process():\n with (output_dir / \"log.txt\").open(\"a\") as f:\n f.write(json.dumps(log_stats) + \"\\n\")\n\n total_time = time.time() - start_time\n total_time_str = str(datetime.timedelta(seconds=int(total_time)))\n print('Training time {}'.format(total_time_str))\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser('LETR training and evaluation script', parents=[get_args_parser()])\n args = parser.parse_args()\n if args.output_dir:\n Path(args.output_dir).mkdir(parents=True, exist_ok=True)\n Path(args.output_dir+'/checkpoints').mkdir(parents=True, exist_ok=True)\n\n main(args)\n"
},
{
"path": "src/models/__init__.py",
"content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\nfrom .letr import build\n\n\ndef build_model(args):\n return build(args)\n"
},
{
"path": "src/models/backbone.py",
"content": "\"\"\"\nLETR Backbone modules.\nmodified based on https://github.com/facebookresearch/detr/blob/master/models/backbone.py\n\"\"\"\nfrom collections import OrderedDict\n\nimport torch\nimport torch.nn.functional as F\nimport torchvision\nfrom torch import nn\nfrom torchvision.models._utils import IntermediateLayerGetter\nfrom typing import Dict, List\n\nfrom util.misc import NestedTensor, is_main_process\n\nfrom .position_encoding import build_position_encoding\n\n\nclass FrozenBatchNorm2d(torch.nn.Module):\n \"\"\"\n BatchNorm2d where the batch statistics and the affine parameters are fixed.\n\n Copy-paste from torchvision.misc.ops with added eps before rqsrt,\n without which any other models than torchvision.models.resnet[18,34,50,101]\n produce nans.\n \"\"\"\n\n def __init__(self, n):\n super(FrozenBatchNorm2d, self).__init__()\n self.register_buffer(\"weight\", torch.ones(n))\n self.register_buffer(\"bias\", torch.zeros(n))\n self.register_buffer(\"running_mean\", torch.zeros(n))\n self.register_buffer(\"running_var\", torch.ones(n))\n\n def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,\n missing_keys, unexpected_keys, error_msgs):\n num_batches_tracked_key = prefix + 'num_batches_tracked'\n if num_batches_tracked_key in state_dict:\n del state_dict[num_batches_tracked_key]\n\n super(FrozenBatchNorm2d, self)._load_from_state_dict(\n state_dict, prefix, local_metadata, strict,\n missing_keys, unexpected_keys, error_msgs)\n\n def forward(self, x):\n # move reshapes to the beginning\n # to make it fuser-friendly\n w = self.weight.reshape(1, -1, 1, 1)\n b = self.bias.reshape(1, -1, 1, 1)\n rv = self.running_var.reshape(1, -1, 1, 1)\n rm = self.running_mean.reshape(1, -1, 1, 1)\n eps = 1e-5\n scale = w * (rv + eps).rsqrt()\n bias = b - rm * scale\n return x * scale + bias\n\n\nclass BackboneBase(nn.Module):\n\n def __init__(self, backbone: nn.Module, train_backbone: bool, num_channels: int, return_interm_layers: bool):\n super().__init__()\n for name, parameter in backbone.named_parameters():\n if not train_backbone or 'layer2' not in name and 'layer3' not in name and 'layer4' not in name:\n parameter.requires_grad_(False)\n if return_interm_layers:\n return_layers = {\"layer1\": \"0\", \"layer2\": \"1\", \"layer3\": \"2\", \"layer4\": \"3\"}\n else:\n return_layers = {'layer4': \"0\"}\n self.body = IntermediateLayerGetter(backbone, return_layers=return_layers)\n self.num_channels = num_channels\n\n def forward(self, tensor_list: NestedTensor):\n xs = self.body(tensor_list.tensors)\n out: Dict[str, NestedTensor] = {}\n for name, x in xs.items():\n\n m = tensor_list.mask\n assert m is not None\n mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0]\n out[name] = NestedTensor(x, mask)\n return out\n\n\nclass Backbone(BackboneBase):\n \"\"\"ResNet backbone with frozen BatchNorm.\"\"\"\n def __init__(self, name: str,\n train_backbone: bool,\n return_interm_layers: bool,\n dilation: bool):\n backbone = getattr(torchvision.models, name)(\n replace_stride_with_dilation=[False, False, dilation],\n pretrained=is_main_process(), norm_layer=FrozenBatchNorm2d)\n num_channels = 512 if name in ('resnet18', 'resnet34') else 2048\n super().__init__(backbone, train_backbone, num_channels, return_interm_layers)\n\n\nclass Joiner(nn.Sequential):\n def __init__(self, backbone, position_embedding):\n super().__init__(backbone, position_embedding)\n\n def forward(self, tensor_list: NestedTensor):\n xs = self[0](tensor_list)\n out: List[NestedTensor] = []\n pos = []\n for name, x in xs.items():\n out.append(x)\n # position encoding\n pos.append(self[1](x).to(x.tensors.dtype))\n\n return out, pos\n\n\ndef build_backbone(args):\n position_embedding = build_position_encoding(args)\n train_backbone = args.lr_backbone > 0\n return_interm_layers = True\n backbone = Backbone(args.backbone, train_backbone, return_interm_layers, args.dilation)\n model = Joiner(backbone, position_embedding)\n model.num_channels = backbone.num_channels\n return model\n"
},
{
"path": "src/models/letr.py",
"content": "\"\"\"\nThis file provides coarse stage LETR definition\nModified based on https://github.com/facebookresearch/detr/blob/master/models/backbone.py\n\"\"\"\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\nfrom util.misc import (NestedTensor, nested_tensor_from_tensor_list,\n accuracy, get_world_size, interpolate,\n is_dist_avail_and_initialized)\n\nfrom .backbone import build_backbone\nfrom .matcher import build_matcher\nfrom .transformer import build_transformer\nfrom .letr_stack import LETRstack\nimport numpy as np\n\nclass LETR(nn.Module):\n \"\"\" This is the LETR module that performs object detection \"\"\"\n def __init__(self, backbone, transformer, num_classes, num_queries, args, aux_loss=False):\n super().__init__()\n self.num_queries = num_queries\n self.transformer = transformer\n hidden_dim = transformer.d_model\n self.class_embed = nn.Linear(hidden_dim, num_classes + 1)\n\n self.lines_embed = MLP(hidden_dim, hidden_dim, 4, 3)\n self.query_embed = nn.Embedding(num_queries, hidden_dim)\n\n channel = [256, 512, 1024, 2048]\n self.input_proj = nn.Conv2d(channel[args.layer1_num], hidden_dim, kernel_size=1)\n\n self.backbone = backbone\n self.aux_loss = aux_loss\n self.args = args\n\n def forward(self, samples, postprocessors=None, targets=None, criterion=None):\n if isinstance(samples, (list, torch.Tensor)):\n samples = nested_tensor_from_tensor_list(samples)\n\n features, pos = self.backbone(samples)\n\n num = self.args.layer1_num\n src, mask = features[num].decompose()\n assert mask is not None\n hs = self.transformer(self.input_proj(src), mask, self.query_embed.weight, pos[num])[0]\n\n outputs_class = self.class_embed(hs)\n outputs_coord = self.lines_embed(hs).sigmoid()\n out = {'pred_logits': outputs_class[-1], 'pred_lines': outputs_coord[-1]}\n if self.aux_loss:\n out['aux_outputs'] = self._set_aux_loss(outputs_class, outputs_coord)\n return out\n\n @torch.jit.unused\n def _set_aux_loss(self, outputs_class, outputs_coord):\n return [{'pred_logits': a, 'pred_lines': b} for a, b in zip(outputs_class[:-1], outputs_coord[:-1])]\n\nclass SetCriterion(nn.Module):\n\n def __init__(self, num_classes, weight_dict, eos_coef, losses, args, matcher=None):\n\n super().__init__()\n self.num_classes = num_classes\n\n self.matcher = matcher\n\n self.weight_dict = weight_dict\n self.eos_coef = eos_coef\n self.losses = losses\n empty_weight = torch.ones(self.num_classes + 1)\n empty_weight[-1] = self.eos_coef\n self.register_buffer('empty_weight', empty_weight)\n self.args = args\n try:\n self.args.label_loss_params = eval(self.args.label_loss_params) # Convert the string to dict.\n except:\n pass\n\n def loss_lines_labels(self, outputs, targets, num_items, log=False, origin_indices=None):\n \"\"\"Classification loss (NLL)\n targets dicts must contain the key \"labels\" containing a tensor of dim [nb_target_lines]\n \"\"\"\n assert 'pred_logits' in outputs\n src_logits = outputs['pred_logits']\n\n idx = self._get_src_permutation_idx(origin_indices)\n target_classes_o = torch.cat([t[\"labels\"][J] for t, (_, J) in zip(targets, origin_indices)])\n target_classes = torch.full(src_logits.shape[:2], self.num_classes,\n dtype=torch.int64, device=src_logits.device)\n target_classes[idx] = target_classes_o\n\n if self.args.label_loss_func == 'cross_entropy':\n loss_ce = F.cross_entropy(src_logits.transpose(1, 2), target_classes, self.empty_weight)\n elif self.args.label_loss_func == 'focal_loss':\n loss_ce = self.label_focal_loss(src_logits.transpose(1, 2), target_classes, self.empty_weight, **self.args.label_loss_params)\n else:\n raise ValueError()\n\n losses = {'loss_ce': loss_ce}\n return losses\n\n def label_focal_loss(self, input, target, weight, gamma=2.0):\n \"\"\" Focal loss for label prediction. \"\"\"\n # In our case, target has 2 classes: 0 for foreground (i.e. line) and 1 for background.\n # The weight here can serve as the alpha hyperparameter in focal loss. However, in focal loss, \n # \n # Ref: https://github.com/facebookresearch/DETR/blob/699bf53f3e3ecd4f000007b8473eda6a08a8bed6/models/segmentation.py#L190\n # Ref: https://medium.com/visionwizard/understanding-focal-loss-a-quick-read-b914422913e7\n\n # input shape: [batch size, #classes, #queries]\n # target shape: [batch size, #queries]\n # weight shape: [#classes]\n\n prob = F.softmax(input, 1) # Shape: [batch size, #classes, #queries].\n ce_loss = F.cross_entropy(input, target, weight, reduction='none') # Shape: [batch size, #queries].\n p_t = prob[:,1,:] * target + prob[:,0,:] * (1 - target) # Shape: [batch size, #queries]. Note: prob[:,0,:] + prob[:,1,:] should be 1.\n loss = ce_loss * ((1 - p_t) ** gamma)\n loss = loss.mean() # Original label loss (i.e. cross entropy) does not consider the #lines, so we also do not consider that.\n return loss\n\n @torch.no_grad()\n def loss_cardinality(self, outputs, targets, num_items, origin_indices=None):\n \"\"\" Compute the cardinality error, ie the absolute error in the number of predicted non-empty lines\n This is not really a loss, it is intended for logging purposes only. It doesn't propagate gradients\n \"\"\"\n pred_logits = outputs['pred_logits']\n device = pred_logits.device\n tgt_lengths = torch.as_tensor([len(v[\"labels\"]) for v in targets], device=device)\n # Count the number of predictions that are NOT \"no-object\" (which is the last class)\n card_pred = (pred_logits.argmax(-1) != pred_logits.shape[-1] - 1).sum(1)\n card_err = F.l1_loss(card_pred.float(), tgt_lengths.float())\n losses = {'cardinality_error': card_err}\n return losses\n\n def loss_lines_POST(self, outputs, targets, num_items, origin_indices=None):\n assert 'POST_pred_lines' in outputs\n\n if outputs['POST_pred_lines'].shape[1] == 1000:\n idx = self._get_src_permutation_idx(origin_indices)\n\n src_lines = outputs['POST_pred_lines'][idx]\n \n else:\n src_lines = outputs['POST_pred_lines'].squeeze(0)\n\n\n target_lines = torch.cat([t['lines'][i] for t, (_, i) in zip(targets, origin_indices)], dim=0)\n\n loss_line = F.l1_loss(src_lines, target_lines, reduction='none')\n\n losses = {}\n losses['loss_line'] = loss_line.sum() / num_items\n\n return losses\n\n def loss_lines(self, outputs, targets, num_items, origin_indices=None):\n assert 'pred_lines' in outputs\n\n idx = self._get_src_permutation_idx(origin_indices)\n\n src_lines = outputs['pred_lines'][idx]\n target_lines = torch.cat([t['lines'][i] for t, (_, i) in zip(targets, origin_indices)], dim=0)\n\n loss_line = F.l1_loss(src_lines, target_lines, reduction='none')\n\n losses = {}\n losses['loss_line'] = loss_line.sum() / num_items\n\n return losses\n\n def _get_src_permutation_idx(self, indices):\n # permute predictions following indices\n batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)])\n src_idx = torch.cat([src for (src, _) in indices])\n return batch_idx, src_idx\n\n def _get_tgt_permutation_idx(self, indices):\n # permute targets following indices\n batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)])\n tgt_idx = torch.cat([tgt for (_, tgt) in indices])\n return batch_idx, tgt_idx\n\n def get_loss(self, loss, outputs, targets, num_items, **kwargs):\n \n loss_map = {\n 'POST_lines_labels': self.loss_lines_labels,\n 'POST_lines': self.loss_lines,\n 'lines_labels': self.loss_lines_labels,\n 'cardinality': self.loss_cardinality,\n 'lines': self.loss_lines,\n }\n assert loss in loss_map, f'do you really want to compute {loss} loss?'\n return loss_map[loss](outputs, targets, num_items, **kwargs)\n\n def forward(self, outputs, targets, origin_indices=None):\n \"\"\" This performs the loss computation.\n Parameters:\n outputs: dict of tensors, see the output specification of the model for the format\n targets: list of dicts, such that len(targets) == batch_size.\n The expected keys in each dict depends on the losses applied, see each loss' doc\n \"\"\"\n outputs_without_aux = {k: v for k, v in outputs.items() if k != 'aux_outputs'}\n\n \n origin_indices = self.matcher(outputs_without_aux, targets)\n\n\n num_items = sum(len(t[\"labels\"]) for t in targets)\n\n num_items = torch.as_tensor([num_items], dtype=torch.float, device=next(iter(outputs.values())).device)\n if is_dist_avail_and_initialized():\n torch.distributed.all_reduce(num_items)\n num_items = torch.clamp(num_items / get_world_size(), min=1).item()\n\n # Compute all the requested losses\n losses = {}\n for loss in self.losses:\n losses.update(self.get_loss(loss, outputs, targets, num_items, origin_indices=origin_indices))\n \n\n # In case of auxiliary losses, we repeat this process with the output of each intermediate layer.\n aux_name = 'aux_outputs'\n if aux_name in outputs:\n for i, aux_outputs in enumerate(outputs[aux_name]):\n \n origin_indices = self.matcher(aux_outputs, targets)\n\n for loss in self.losses:\n \n kwargs = {}\n if loss == 'labels':\n # Logging is enabled only for the last layer\n kwargs = {'log': False}\n\n l_dict = self.get_loss(loss, aux_outputs, targets, num_items, origin_indices=origin_indices, **kwargs)\n\n l_dict = {k + f'_{i}': v for k, v in l_dict.items()}\n losses.update(l_dict)\n\n return losses\n\n\nclass PostProcess_Line(nn.Module):\n\n \"\"\" This module converts the model's output into the format expected by the coco api\"\"\"\n @torch.no_grad()\n def forward(self, outputs, target_sizes, output_type):\n \"\"\" Perform the computation\n Parameters:\n outputs: raw outputs of the model\n target_sizes: tensor of dimension [batch_size x 2] containing the size of each images of the batch\n For evaluation, this must be the original image size (before any data augmentation)\n For visualization, this should be the image size after data augment, but before padding\n \"\"\"\n if output_type == \"prediction\":\n out_logits, out_line = outputs['pred_logits'], outputs['pred_lines']\n\n assert len(out_logits) == len(target_sizes)\n assert target_sizes.shape[1] == 2\n\n prob = F.softmax(out_logits, -1)\n scores, labels = prob[..., :-1].max(-1)\n\n # convert to [x0, y0, x1, y1] format\n img_h, img_w = target_sizes.unbind(1)\n\n scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)\n lines = out_line * scale_fct[:, None, :]\n\n results = [{'scores': s, 'labels': l, 'lines': b} for s, l, b in zip(scores, labels, lines)]\n elif output_type == \"prediction_POST\":\n out_logits, out_line = outputs['pred_logits'], outputs['POST_pred_lines']\n\n assert len(out_logits) == len(target_sizes)\n assert target_sizes.shape[1] == 2\n\n prob = F.softmax(out_logits, -1)\n scores, labels = prob[..., :-1].max(-1)\n\n # convert to [x0, y0, x1, y1] format\n img_h, img_w = target_sizes.unbind(1)\n\n scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)\n lines = out_line * scale_fct[:, None, :]\n\n results = [{'scores': s, 'labels': l, 'lines': b} for s, l, b in zip(scores, labels, lines)]\n elif output_type == \"ground_truth\":\n results = []\n for dic in outputs:\n lines = dic['lines']\n img_h, img_w = target_sizes.unbind(1)\n scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)\n scaled_lines = lines * scale_fct\n results.append({'labels': dic['labels'], 'lines': scaled_lines, 'image_id': dic['image_id']})\n else:\n assert False\n return results\n\n\nclass MLP(nn.Module):\n \"\"\" Very simple multi-layer perceptron (also called FFN)\"\"\"\n\n def __init__(self, input_dim, hidden_dim, output_dim, num_layers):\n super().__init__()\n self.num_layers = num_layers\n h = [hidden_dim] * (num_layers - 1)\n self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))\n\n def forward(self, x):\n for i, layer in enumerate(self.layers):\n x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)\n return x\n\n\ndef build(args):\n num_classes = 1\n\n device = torch.device(args.device)\n \n backbone = build_backbone(args)\n\n transformer = build_transformer(args)\n\n model = LETR(\n backbone,\n transformer,\n num_classes=num_classes,\n num_queries=args.num_queries,\n args=args,\n aux_loss=args.aux_loss,\n )\n\n if args.LETRpost:\n model = LETRstack(model, args=args)\n\n\n matcher = build_matcher(args, type='origin_line')\n \n losses = []\n weight_dict = {}\n \n if args.LETRpost: \n losses.append('POST_lines_labels')\n losses.append('POST_lines')\n weight_dict['loss_ce'] = 1\n weight_dict['loss_line'] = args.line_loss_coef\n aux_layer = args.second_dec_layers\n else:\n losses.append('lines_labels')\n losses.append('lines')\n weight_dict['loss_ce'] = 1\n weight_dict['loss_line'] = args.line_loss_coef\n aux_layer = args.dec_layers\n\n if args.aux_loss:\n aux_weight_dict = {}\n for i in range(aux_layer - 1):\n aux_weight_dict.update({k + f'_{i}': v for k, v in weight_dict.items()})\n weight_dict.update(aux_weight_dict)\n\n \n criterion = SetCriterion(num_classes, weight_dict=weight_dict, eos_coef=args.eos_coef, losses=losses, args=args, matcher=matcher)\n criterion.to(device)\n\n\n postprocessors = {'line': PostProcess_Line()}\n\n\n return model, criterion, postprocessors\n"
},
{
"path": "src/models/letr_stack.py",
"content": "\"\"\"\nThis file provides fine stage LETR definition\n\n\"\"\"\nimport io\nfrom collections import defaultdict\nfrom typing import List, Optional\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch import Tensor\nfrom PIL import Image\nfrom util.misc import NestedTensor, nested_tensor_from_tensor_list\nimport copy\n\n\nclass LETRstack(nn.Module):\n def __init__(self, letr, args):\n super().__init__()\n self.letr = letr\n self.backbone = self.letr.backbone\n\n if args.layer1_frozen:\n # freeze backbone, encoder, decoder\n for n, p in self.named_parameters():\n p.requires_grad_(False)\n\n hidden_dim, nheads = letr.transformer.d_model, letr.transformer.nhead\n\n # add new input proj layer\n channel = [256, 512, 1024, 2048]\n self.input_proj = nn.Conv2d(channel[args.layer2_num], hidden_dim, kernel_size=1)\n\n # add new transformer encoder decoder\n self.transformer = Transformer( d_model=args.second_hidden_dim, dropout=args.second_dropout, nhead=args.second_nheads, \n dim_feedforward=args.second_dim_feedforward, num_encoder_layers=args.second_enc_layers,\n num_decoder_layers=args.second_dec_layers, normalize_before=args.second_pre_norm, return_intermediate_dec=True,)\n\n # output layer\n self.class_embed = nn.Linear(hidden_dim, 1 + 1)\n self.lines_embed = MLP(hidden_dim, hidden_dim, 4, 3)\n\n\n self.aux_loss=args.aux_loss\n self.args = args\n\n def forward(self, samples, postprocessors=None, targets=None, criterion=None):\n if isinstance(samples, (list, torch.Tensor)):\n samples = nested_tensor_from_tensor_list(samples)\n \n # backbone\n features, pos = self.letr.backbone(samples)\n\n # layer 1\n l1_num = self.args.layer1_num\n src1, mask1 = features[l1_num].decompose()\n assert mask1 is not None\n\n # layer 1 transformer\n hs1, _ = self.letr.transformer(self.letr.input_proj(src1), mask1, self.letr.query_embed.weight, pos[l1_num])\n\n # layer 2 \n l2_num = self.args.layer2_num\n src2, mask2 = features[l2_num].decompose()\n src2 = self.input_proj(src2)\n\n # layer 2 transformer\n hs2, memory, _ = self.transformer(src2, mask2, hs1[-1], pos[l2_num])\n\n outputs_class = self.class_embed(hs2)\n outputs_coord = self.lines_embed(hs2).sigmoid()\n out = {}\n out[\"pred_logits\"] = outputs_class[-1]\n out[\"pred_lines\"] = outputs_coord[-1]\n\n if self.aux_loss:\n out['aux_outputs'] = self._set_aux_loss(outputs_class, outputs_coord)\n\n return out, None\n\n @torch.jit.unused\n def _set_aux_loss(self, outputs_class, outputs_coord):\n # this is a workaround to make torchscript happy, as torchscript\n # doesn't support dictionary with non-homogeneous values, such\n # as a dict having both a Tensor and a list.\n return [{'pred_logits': a, 'pred_lines': b}\n for a, b in zip(outputs_class[:-1], outputs_coord[:-1])]\n\n @torch.jit.unused\n def _set_aux_loss_POST(self, outputs_class, outputs_coord):\n # this is a workaround to make torchscript happy, as torchscript\n # doesn't support dictionary with non-homogeneous values, such\n # as a dict having both a Tensor and a list.\n return [{'POST_pred_lines': b} for b in outputs_coord[:-1]]\n\ndef _expand(tensor, length: int):\n return tensor.unsqueeze(1).repeat(1, int(length), 1, 1, 1).flatten(0, 1)\n\nclass MLP(nn.Module):\n \"\"\" Very simple multi-layer perceptron (also called FFN)\"\"\"\n\n def __init__(self, input_dim, hidden_dim, output_dim, num_layers):\n super().__init__()\n self.num_layers = num_layers\n h = [hidden_dim] * (num_layers - 1)\n self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))\n\n def forward(self, x):\n for i, layer in enumerate(self.layers):\n x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)\n return x\n\n\nclass Transformer(nn.Module):\n\n def __init__(self, d_model=512, nhead=8, num_encoder_layers=6,\n num_decoder_layers=6, dim_feedforward=2048, dropout=0.1,\n activation=\"relu\", normalize_before=False,\n return_intermediate_dec=False):\n super().__init__()\n\n encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward, dropout, activation, normalize_before)\n encoder_norm = nn.LayerNorm(d_model) if normalize_before else None\n self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)\n\n decoder_layer = TransformerDecoderLayer(d_model, nhead, dim_feedforward, dropout, activation, normalize_before)\n decoder_norm = nn.LayerNorm(d_model)\n self.decoder = TransformerDecoder(decoder_layer, num_decoder_layers, decoder_norm,\n return_intermediate=return_intermediate_dec)\n\n self._reset_parameters()\n\n self.d_model = d_model\n self.nhead = nhead\n\n def _reset_parameters(self):\n for p in self.parameters():\n if p.dim() > 1:\n nn.init.xavier_uniform_(p)\n\n def forward(self, src, mask, query_embed, pos_embed):\n # flatten NxCxHxW to HWxNxC\n bs, c, h, w = src.shape\n src = src.flatten(2).permute(2, 0, 1)\n pos_embed = pos_embed.flatten(2).permute(2, 0, 1)\n mask = mask.flatten(1)\n\n query_embed = query_embed.permute(1, 0, 2) \n tgt = torch.zeros_like(query_embed)\n memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed)\n hs, attn_output_weights = self.decoder(tgt, memory, memory_key_padding_mask=mask, pos=pos_embed, query_pos=query_embed)\n return hs.transpose(1, 2), memory, attn_output_weights\n\nclass TransformerEncoder(nn.Module):\n\n def __init__(self, encoder_layer, num_layers, norm=None):\n super().__init__()\n self.layers = _get_clones(encoder_layer, num_layers)\n self.num_layers = num_layers\n self.norm = norm\n\n def forward(self, src,\n mask: Optional[Tensor] = None,\n src_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None):\n output = src\n\n for layer in self.layers:\n output = layer(output, src_mask=mask,\n src_key_padding_mask=src_key_padding_mask, pos=pos)\n\n if self.norm is not None:\n output = self.norm(output)\n\n return output\n \nclass TransformerDecoder(nn.Module):\n\n def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False):\n super().__init__()\n self.layers = _get_clones(decoder_layer, num_layers)\n self.num_layers = num_layers\n self.norm = norm\n self.return_intermediate = return_intermediate\n\n def forward(self, tgt, memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None):\n output = tgt\n\n intermediate = []\n attn_output_weights_list = []\n for layer in self.layers:\n output, attn_output_weights = layer(output, memory, tgt_mask=tgt_mask,\n memory_mask=memory_mask,\n tgt_key_padding_mask=tgt_key_padding_mask,\n memory_key_padding_mask=memory_key_padding_mask,\n pos=pos, query_pos=query_pos)\n if self.return_intermediate:\n intermediate.append(self.norm(output))\n attn_output_weights_list.append(attn_output_weights)\n if self.norm is not None:\n output = self.norm(output)\n if self.return_intermediate:\n intermediate.pop()\n intermediate.append(output)\n\n if self.return_intermediate:\n return torch.stack(intermediate), attn_output_weights_list\n\n return output.unsqueeze(0), attn_output_weights\n\n\nclass TransformerEncoderLayer(nn.Module):\n\n def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,\n activation=\"relu\", normalize_before=False):\n super().__init__()\n self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n # Implementation of Feedforward model\n self.linear1 = nn.Linear(d_model, dim_feedforward)\n self.dropout = nn.Dropout(dropout)\n self.linear2 = nn.Linear(dim_feedforward, d_model)\n\n self.norm1 = nn.LayerNorm(d_model)\n self.norm2 = nn.LayerNorm(d_model)\n self.dropout1 = nn.Dropout(dropout)\n self.dropout2 = nn.Dropout(dropout)\n\n self.activation = _get_activation_fn(activation)\n self.normalize_before = normalize_before\n\n def with_pos_embed(self, tensor, pos: Optional[Tensor]):\n return tensor if pos is None else tensor + pos\n\n def forward_post(self,\n src,\n src_mask: Optional[Tensor] = None,\n src_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None):\n q = k = self.with_pos_embed(src, pos)\n src2 = self.self_attn(q, k, value=src, attn_mask=src_mask,\n key_padding_mask=src_key_padding_mask)[0]\n src = src + self.dropout1(src2)\n src = self.norm1(src)\n src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))\n src = src + self.dropout2(src2)\n src = self.norm2(src)\n return src\n\n def forward_pre(self, src,\n src_mask: Optional[Tensor] = None,\n src_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None):\n src2 = self.norm1(src)\n q = k = self.with_pos_embed(src2, pos)\n src2 = self.self_attn(q, k, value=src2, attn_mask=src_mask,\n key_padding_mask=src_key_padding_mask)[0]\n src = src + self.dropout1(src2)\n src2 = self.norm2(src)\n src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))\n src = src + self.dropout2(src2)\n return src\n\n def forward(self, src,\n src_mask: Optional[Tensor] = None,\n src_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None):\n if self.normalize_before:\n return self.forward_pre(src, src_mask, src_key_padding_mask, pos)\n return self.forward_post(src, src_mask, src_key_padding_mask, pos)\n\n\nclass TransformerDecoderLayer(nn.Module):\n\n def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,\n activation=\"relu\", normalize_before=False):\n super().__init__()\n self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n # Implementation of Feedforward model\n self.linear1 = nn.Linear(d_model, dim_feedforward)\n self.dropout = nn.Dropout(dropout)\n self.linear2 = nn.Linear(dim_feedforward, d_model)\n\n self.norm1 = nn.LayerNorm(d_model)\n self.norm2 = nn.LayerNorm(d_model)\n self.norm3 = nn.LayerNorm(d_model)\n self.dropout1 = nn.Dropout(dropout)\n self.dropout2 = nn.Dropout(dropout)\n self.dropout3 = nn.Dropout(dropout)\n\n self.activation = _get_activation_fn(activation)\n self.normalize_before = normalize_before\n\n def with_pos_embed(self, tensor, pos: Optional[Tensor]):\n return tensor if pos is None else tensor + pos\n\n def forward_post(self, tgt, memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None):\n q = k = self.with_pos_embed(tgt, query_pos)\n tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask,\n key_padding_mask=tgt_key_padding_mask)[0]\n tgt = tgt + self.dropout1(tgt2)\n tgt = self.norm1(tgt)\n tgt2, attn_output_weights = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos),\n key=self.with_pos_embed(memory, pos),\n value=memory, attn_mask=memory_mask,\n key_padding_mask=memory_key_padding_mask)\n tgt = tgt + self.dropout2(tgt2)\n tgt = self.norm2(tgt)\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))\n tgt = tgt + self.dropout3(tgt2)\n tgt = self.norm3(tgt)\n return tgt, attn_output_weights\n\n def forward_pre(self, tgt, memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None):\n tgt2 = self.norm1(tgt)\n q = k = self.with_pos_embed(tgt2, query_pos)\n tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,\n key_padding_mask=tgt_key_padding_mask)[0]\n tgt = tgt + self.dropout1(tgt2)\n tgt2 = self.norm2(tgt)\n tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos),\n key=self.with_pos_embed(memory, pos),\n value=memory, attn_mask=memory_mask,\n key_padding_mask=memory_key_padding_mask)[0]\n tgt = tgt + self.dropout2(tgt2)\n tgt2 = self.norm3(tgt)\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))\n tgt = tgt + self.dropout3(tgt2)\n return tgt\n\n def forward(self, tgt, memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None):\n if self.normalize_before:\n return self.forward_pre(tgt, memory, tgt_mask, memory_mask,\n tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos)\n return self.forward_post(tgt, memory, tgt_mask, memory_mask,\n tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos)\n\n\ndef _get_clones(module, N):\n return nn.ModuleList([copy.deepcopy(module) for i in range(N)])\n\ndef _get_activation_fn(activation):\n \"\"\"Return an activation function given a string\"\"\"\n if activation == \"relu\":\n return F.relu\n if activation == \"gelu\":\n return F.gelu\n if activation == \"glu\":\n return F.glu\n raise RuntimeError(F\"activation should be relu/gelu, not {activation}.\")\n\n"
},
{
"path": "src/models/matcher.py",
"content": "\"\"\"\nModules to compute the matching cost and solve the corresponding LSAP.\n\"\"\"\nimport torch\nfrom scipy.optimize import linear_sum_assignment\nfrom torch import nn\n\nclass HungarianMatcher_Line(nn.Module):\n \"\"\"This class computes an assignment between the targets and the predictions of the network\n\n For efficiency reasons, the targets don't include the no_object. Because of this, in general,\n there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions,\n while the others are un-matched (and thus treated as non-objects).\n \"\"\"\n\n def __init__(self, cost_class: float = 1, cost_line: float = 1):\n \"\"\"Creates the matcher\n\n Params:\n cost_class: This is the relative weight of the classification error in the matching cost\n cost_line: This is the relative weight of the L1 error of the bounding box coordinates in the matching cost\n \"\"\"\n super().__init__()\n self.cost_class = cost_class\n self.cost_line = cost_line\n assert cost_class != 0 or cost_line != 0, \"all costs cant be 0\"\n\n @torch.no_grad()\n def forward(self, outputs, targets):\n \"\"\" Performs the matching\n\n Params:\n outputs: This is a dict that contains at least these entries:\n \"pred_logits\": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits\n \"pred_lines\": Tensor of dim [batch_size, num_queries, 4] with the predicted box coordinates\n\n targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing:\n \"labels\": Tensor of dim [num_target_lines] (where num_target_lines is the number of ground-truth\n objects in the target) containing the class labels\n \"lines\": Tensor of dim [num_target_lines, 4] containing the target box coordinates\n\n Returns:\n A list of size batch_size, containing tuples of (index_i, index_j) where:\n - index_i is the indices of the selected predictions (in order)\n - index_j is the indices of the corresponding selected targets (in order)\n For each batch element, it holds:\n len(index_i) = len(index_j) = min(num_queries, num_target_lines)\n \"\"\"\n bs, num_queries = outputs[\"pred_logits\"].shape[:2]\n\n # We flatten to compute the cost matrices in a batch\n out_prob = outputs[\"pred_logits\"].flatten(0, 1).softmax(-1) # [batch_size * num_queries, num_classes]\n\n out_line = outputs[\"pred_lines\"].flatten(0, 1) # [batch_size * num_queries, 4]\n tgt_line = torch.cat([v[\"lines\"] for v in targets])\n\n \n # Also concat the target labels and lines\n tgt_ids = torch.cat([v[\"labels\"] for v in targets])\n\n # Compute the classification cost. Contrary to the loss, we don't use the NLL,\n # but approximate it in 1 - proba[target class].\n # The 1 is a constant that doesn't change the matching, it can be ommitted.\n cost_class = -out_prob[:, tgt_ids]\n\n # Compute the L1 cost between lines\n cost_line = torch.cdist(out_line, tgt_line, p=1)\n\n # Final cost matrix\n C = self.cost_line * cost_line + self.cost_class * cost_class\n C = C.view(bs, num_queries, -1).cpu()\n\n sizes = [len(v[\"lines\"]) for v in targets]\n indices = [linear_sum_assignment(c[i]) for i, c in enumerate(C.split(sizes, -1))]\n\n return [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices]\n\n\n\ndef build_matcher(args, type=None):\n return HungarianMatcher_Line(cost_class=args.set_cost_class, cost_line=args.set_cost_line)"
},
{
"path": "src/models/multi_head_attention.py",
"content": "\"\"\"\nThis file provides definition of multi head attention\n\nborrowed from https://pytorch.org/docs/stable/_modules/torch/nn/modules/activation.html#MultiheadAttention \n\"\"\"\nimport warnings\nfrom typing import Tuple, Optional\n\nimport torch\nfrom torch import Tensor\nfrom torch.nn.modules.linear import _LinearWithBias\nfrom torch.nn.init import xavier_uniform_\nfrom torch.nn.init import constant_\nfrom torch.nn.init import xavier_normal_\nfrom torch.nn.parameter import Parameter\nfrom torch.nn.modules.module import Module\nfrom torch.nn import functional as F\nfrom torch.overrides import has_torch_function, handle_torch_function\nfrom torch import _VF\n\n# Activation functions\ndef dropout(input, p=0.5, training=True, inplace=False):\n # type: (Tensor, float, bool, bool) -> Tensor\n r\"\"\"\n During training, randomly zeroes some of the elements of the input\n tensor with probability :attr:`p` using samples from a Bernoulli\n distribution.\n\n See :class:`~torch.nn.Dropout` for details.\n\n Args:\n p: probability of an element to be zeroed. Default: 0.5\n training: apply dropout if is ``True``. Default: ``True``\n inplace: If set to ``True``, will do this operation in-place. Default: ``False``\n \"\"\"\n if not torch.jit.is_scripting():\n if type(input) is not Tensor and has_torch_function((input,)):\n return handle_torch_function(\n dropout, (input,), input, p=p, training=training, inplace=inplace)\n if p < 0. or p > 1.:\n raise ValueError(\"dropout probability has to be between 0 and 1, \"\n \"but got {}\".format(p))\n return (_VF.dropout_(input, p, training)\n if inplace\n else _VF.dropout(input, p, training))\n\n\ndef _get_softmax_dim(name, ndim, stacklevel):\n # type: (str, int, int) -> int\n warnings.warn(\"Implicit dimension choice for {} has been deprecated. \"\n \"Change the call to include dim=X as an argument.\".format(name), stacklevel=stacklevel)\n if ndim == 0 or ndim == 1 or ndim == 3:\n ret = 0\n else:\n ret = 1\n return ret\n\ndef softmax(input, dim=None, _stacklevel=3, dtype=None):\n # type: (Tensor, Optional[int], int, Optional[int]) -> Tensor\n r\"\"\"Applies a softmax function.\n Softmax is defined as:\n :math:`\\text{Softmax}(x_{i}) = \\frac{\\exp(x_i)}{\\sum_j \\exp(x_j)}`\n It is applied to all slices along dim, and will re-scale them so that the elements\n lie in the range `[0, 1]` and sum to 1.\n See :class:`~torch.nn.Softmax` for more details.\n Args:\n input (Tensor): input\n dim (int): A dimension along which softmax will be computed.\n dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.\n If specified, the input tensor is casted to :attr:`dtype` before the operation\n is performed. This is useful for preventing data type overflows. Default: None.\n .. note::\n This function doesn't work directly with NLLLoss,\n which expects the Log to be computed between the Softmax and itself.\n Use log_softmax instead (it's faster and has better numerical properties).\n \"\"\"\n if not torch.jit.is_scripting():\n if type(input) is not Tensor and has_torch_function((input,)):\n return handle_torch_function(\n softmax, (input,), input, dim=dim, _stacklevel=_stacklevel, dtype=dtype)\n if dim is None:\n dim = _get_softmax_dim('softmax', input.dim(), _stacklevel)\n if dtype is None:\n ret = input.softmax(dim)\n else:\n ret = input.softmax(dim, dtype=dtype)\n return ret\n\n\ndef linear(input, weight, bias=None):\n # type: (Tensor, Tensor, Optional[Tensor]) -> Tensor\n r\"\"\"\n Applies a linear transformation to the incoming data: :math:`y = xA^T + b`.\n This operator supports :ref:`TensorFloat32`.\n Shape:\n - Input: :math:`(N, *, in\\_features)` N is the batch size, `*` means any number of\n additional dimensions\n - Weight: :math:`(out\\_features, in\\_features)`\n - Bias: :math:`(out\\_features)`\n - Output: :math:`(N, *, out\\_features)`\n \"\"\"\n tens_ops = (input, weight)\n if not torch.jit.is_scripting():\n if any([type(t) is not Tensor for t in tens_ops]) and has_torch_function(tens_ops):\n return handle_torch_function(linear, tens_ops, input, weight, bias=bias)\n if input.dim() == 2 and bias is not None:\n # fused op is marginally faster\n ret = torch.addmm(bias, input, weight.t())\n else:\n output = input.matmul(weight.t())\n if bias is not None:\n output += bias\n ret = output\n return ret\n \ndef multi_head_attention_forward(query: Tensor, key: Tensor, value: Tensor, embed_dim_to_check: int,num_heads: int,\nin_proj_weight: Tensor, in_proj_bias: Tensor, bias_k: Optional[Tensor], bias_v: Optional[Tensor], add_zero_attn: bool,\ndropout_p: float, out_proj_weight: Tensor, out_proj_bias: Tensor, training: bool = True, key_padding_mask: Optional[Tensor] = None,\nneed_weights: bool = True, attn_mask: Optional[Tensor] = None, use_separate_proj_weight: bool = False, q_proj_weight: Optional[Tensor] = None,\nk_proj_weight: Optional[Tensor] = None, v_proj_weight: Optional[Tensor] = None, static_k: Optional[Tensor] = None, static_v: Optional[Tensor] = None) -> Tuple[Tensor, Optional[Tensor]]:\n r\"\"\"\n Args:\n query, key, value: map a query and a set of key-value pairs to an output.\n See \"Attention Is All You Need\" for more details.\n embed_dim_to_check: total dimension of the model.\n num_heads: parallel attention heads.\n in_proj_weight, in_proj_bias: input projection weight and bias.\n bias_k, bias_v: bias of the key and value sequences to be added at dim=0.\n add_zero_attn: add a new batch of zeros to the key and\n value sequences at dim=1.\n dropout_p: probability of an element to be zeroed.\n out_proj_weight, out_proj_bias: the output projection weight and bias.\n training: apply dropout if is ``True``.\n key_padding_mask: if provided, specified padding elements in the key will\n be ignored by the attention. This is an binary mask. When the value is True,\n the corresponding value on the attention layer will be filled with -inf.\n need_weights: output attn_output_weights.\n attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all\n the batches while a 3D mask allows to specify a different mask for the entries of each batch.\n use_separate_proj_weight: the function accept the proj. weights for query, key,\n and value in different forms. If false, in_proj_weight will be used, which is\n a combination of q_proj_weight, k_proj_weight, v_proj_weight.\n q_proj_weight, k_proj_weight, v_proj_weight, in_proj_bias: input projection weight and bias.\n static_k, static_v: static key and value used for attention operators.\n Shape:\n Inputs:\n - query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is\n the embedding dimension.\n - key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is\n the embedding dimension.\n - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is\n the embedding dimension.\n - key_padding_mask: :math:`(N, S)` where N is the batch size, S is the source sequence length.\n If a ByteTensor is provided, the non-zero positions will be ignored while the zero positions\n will be unchanged. If a BoolTensor is provided, the positions with the\n value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged.\n - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length.\n 3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length,\n S is the source sequence length. attn_mask ensures that position i is allowed to attend the unmasked\n positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend\n while the zero positions will be unchanged. If a BoolTensor is provided, positions with ``True``\n are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor\n is provided, it will be added to the attention weight.\n - static_k: :math:`(N*num_heads, S, E/num_heads)`, where S is the source sequence length,\n N is the batch size, E is the embedding dimension. E/num_heads is the head dimension.\n - static_v: :math:`(N*num_heads, S, E/num_heads)`, where S is the source sequence length,\n N is the batch size, E is the embedding dimension. E/num_heads is the head dimension.\n Outputs:\n - attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size,\n E is the embedding dimension.\n - attn_output_weights: :math:`(N, L, S)` where N is the batch size,\n L is the target sequence length, S is the source sequence length.\n \"\"\"\n if not torch.jit.is_scripting():\n tens_ops = (query, key, value, in_proj_weight, in_proj_bias, bias_k, bias_v,\n out_proj_weight, out_proj_bias)\n if any([type(t) is not Tensor for t in tens_ops]) and has_torch_function(tens_ops):\n return handle_torch_function(\n multi_head_attention_forward, tens_ops, query, key, value,\n embed_dim_to_check, num_heads, in_proj_weight, in_proj_bias,\n bias_k, bias_v, add_zero_attn, dropout_p, out_proj_weight,\n out_proj_bias, training=training, key_padding_mask=key_padding_mask,\n need_weights=need_weights, attn_mask=attn_mask,\n use_separate_proj_weight=use_separate_proj_weight,\n q_proj_weight=q_proj_weight, k_proj_weight=k_proj_weight,\n v_proj_weight=v_proj_weight, static_k=static_k, static_v=static_v)\n tgt_len, bsz, embed_dim = query.size()\n assert embed_dim == embed_dim_to_check\n # allow MHA to have different sizes for the feature dimension\n assert key.size(0) == value.size(0) and key.size(1) == value.size(1)\n\n head_dim = embed_dim // num_heads\n assert head_dim * num_heads == embed_dim, \"embed_dim must be divisible by num_heads\"\n scaling = float(head_dim) ** -0.5\n\n if not use_separate_proj_weight:\n if (query is key or torch.equal(query, key)) and (key is value or torch.equal(key, value)):\n # self-attention\n q, k, v = linear(query, in_proj_weight, in_proj_bias).chunk(3, dim=-1)\n\n elif (key is value or torch.equal(key, value)):\n # encoder-decoder attention\n # This is inline in_proj function with in_proj_weight and in_proj_bias\n _b = in_proj_bias\n _start = 0\n _end = embed_dim\n _w = in_proj_weight[_start:_end, :]\n if _b is not None:\n _b = _b[_start:_end]\n q = linear(query, _w, _b)\n\n if key is None:\n assert value is None\n k = None\n v = None\n else:\n\n # This is inline in_proj function with in_proj_weight and in_proj_bias\n _b = in_proj_bias\n _start = embed_dim\n _end = None\n _w = in_proj_weight[_start:, :]\n if _b is not None:\n _b = _b[_start:]\n k, v = linear(key, _w, _b).chunk(2, dim=-1)\n\n else:\n # This is inline in_proj function with in_proj_weight and in_proj_bias\n _b = in_proj_bias\n _start = 0\n _end = embed_dim\n _w = in_proj_weight[_start:_end, :]\n if _b is not None:\n _b = _b[_start:_end]\n q = linear(query, _w, _b)\n\n # This is inline in_proj function with in_proj_weight and in_proj_bias\n _b = in_proj_bias\n _start = embed_dim\n _end = embed_dim * 2\n _w = in_proj_weight[_start:_end, :]\n if _b is not None:\n _b = _b[_start:_end]\n k = linear(key, _w, _b)\n\n # This is inline in_proj function with in_proj_weight and in_proj_bias\n _b = in_proj_bias\n _start = embed_dim * 2\n _end = None\n _w = in_proj_weight[_start:, :]\n if _b is not None:\n _b = _b[_start:]\n v = linear(value, _w, _b)\n else:\n q_proj_weight_non_opt = torch.jit._unwrap_optional(q_proj_weight)\n len1, len2 = q_proj_weight_non_opt.size()\n assert len1 == embed_dim and len2 == query.size(-1)\n\n k_proj_weight_non_opt = torch.jit._unwrap_optional(k_proj_weight)\n len1, len2 = k_proj_weight_non_opt.size()\n assert len1 == embed_dim and len2 == key.size(-1)\n\n v_proj_weight_non_opt = torch.jit._unwrap_optional(v_proj_weight)\n len1, len2 = v_proj_weight_non_opt.size()\n assert len1 == embed_dim and len2 == value.size(-1)\n\n if in_proj_bias is not None:\n q = linear(query, q_proj_weight_non_opt, in_proj_bias[0:embed_dim])\n k = linear(key, k_proj_weight_non_opt, in_proj_bias[embed_dim:(embed_dim * 2)])\n v = linear(value, v_proj_weight_non_opt, in_proj_bias[(embed_dim * 2):])\n else:\n q = linear(query, q_proj_weight_non_opt, in_proj_bias)\n k = linear(key, k_proj_weight_non_opt, in_proj_bias)\n v = linear(value, v_proj_weight_non_opt, in_proj_bias)\n q = q * scaling\n\n if attn_mask is not None:\n assert attn_mask.dtype == torch.float32 or attn_mask.dtype == torch.float64 or \\\n attn_mask.dtype == torch.float16 or attn_mask.dtype == torch.uint8 or attn_mask.dtype == torch.bool, \\\n 'Only float, byte, and bool types are supported for attn_mask, not {}'.format(attn_mask.dtype)\n if attn_mask.dtype == torch.uint8:\n warnings.warn(\"Byte tensor for attn_mask in nn.MultiheadAttention is deprecated. Use bool tensor instead.\")\n attn_mask = attn_mask.to(torch.bool)\n\n if attn_mask.dim() == 2:\n attn_mask = attn_mask.unsqueeze(0)\n if list(attn_mask.size()) != [1, query.size(0), key.size(0)]:\n raise RuntimeError('The size of the 2D attn_mask is not correct.')\n elif attn_mask.dim() == 3:\n if list(attn_mask.size()) != [bsz * num_heads, query.size(0), key.size(0)]:\n raise RuntimeError('The size of the 3D attn_mask is not correct.')\n else:\n raise RuntimeError(\"attn_mask's dimension {} is not supported\".format(attn_mask.dim()))\n # attn_mask's dim is 3 now.\n\n # convert ByteTensor key_padding_mask to bool\n if key_padding_mask is not None and key_padding_mask.dtype == torch.uint8:\n warnings.warn(\"Byte tensor for key_padding_mask in nn.MultiheadAttention is deprecated. Use bool tensor instead.\")\n key_padding_mask = key_padding_mask.to(torch.bool)\n\n if bias_k is not None and bias_v is not None:\n if static_k is None and static_v is None:\n k = torch.cat([k, bias_k.repeat(1, bsz, 1)])\n v = torch.cat([v, bias_v.repeat(1, bsz, 1)])\n if attn_mask is not None:\n attn_mask = pad(attn_mask, (0, 1))\n if key_padding_mask is not None:\n key_padding_mask = pad(key_padding_mask, (0, 1))\n else:\n assert static_k is None, \"bias cannot be added to static key.\"\n assert static_v is None, \"bias cannot be added to static value.\"\n else:\n assert bias_k is None\n assert bias_v is None\n\n q = q.contiguous().view(tgt_len, bsz * num_heads, head_dim).transpose(0, 1)\n if k is not None:\n k = k.contiguous().view(-1, bsz * num_heads, head_dim).transpose(0, 1)\n if v is not None:\n v = v.contiguous().view(-1, bsz * num_heads, head_dim).transpose(0, 1)\n\n if static_k is not None:\n assert static_k.size(0) == bsz * num_heads\n assert static_k.size(2) == head_dim\n k = static_k\n\n if static_v is not None:\n assert static_v.size(0) == bsz * num_heads\n assert static_v.size(2) == head_dim\n v = static_v\n\n src_len = k.size(1)\n\n if key_padding_mask is not None:\n assert key_padding_mask.size(0) == bsz\n assert key_padding_mask.size(1) == src_len\n\n if add_zero_attn:\n src_len += 1\n k = torch.cat([k, torch.zeros((k.size(0), 1) + k.size()[2:], dtype=k.dtype, device=k.device)], dim=1)\n v = torch.cat([v, torch.zeros((v.size(0), 1) + v.size()[2:], dtype=v.dtype, device=v.device)], dim=1)\n if attn_mask is not None:\n attn_mask = pad(attn_mask, (0, 1))\n if key_padding_mask is not None:\n key_padding_mask = pad(key_padding_mask, (0, 1))\n\n attn_output_weights = torch.bmm(q, k.transpose(1, 2))\n assert list(attn_output_weights.size()) == [bsz * num_heads, tgt_len, src_len]\n\n if attn_mask is not None:\n if attn_mask.dtype == torch.bool:\n attn_output_weights.masked_fill_(attn_mask, float('-inf'))\n else:\n attn_output_weights += attn_mask\n\n\n if key_padding_mask is not None:\n attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len)\n attn_output_weights = attn_output_weights.masked_fill(\n key_padding_mask.unsqueeze(1).unsqueeze(2),\n float('-inf'),\n )\n attn_output_weights = attn_output_weights.view(bsz * num_heads, tgt_len, src_len)\n\n attn_output_weights = softmax(\n attn_output_weights, dim=-1)\n attn_output_weights = dropout(attn_output_weights, p=dropout_p, training=training)\n\n attn_output = torch.bmm(attn_output_weights, v)\n assert list(attn_output.size()) == [bsz * num_heads, tgt_len, head_dim]\n attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)\n attn_output = linear(attn_output, out_proj_weight, out_proj_bias)\n\n if need_weights:\n # average attention weights over heads\n attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len)\n return attn_output, attn_output_weights.sum(dim=1) / num_heads\n else:\n return attn_output, None\n\nclass MultiheadAttention(Module):\n r\"\"\"Allows the model to jointly attend to information\n from different representation subspaces.\n See reference: Attention Is All You Need\n\n .. math::\n \\text{MultiHead}(Q, K, V) = \\text{Concat}(head_1,\\dots,head_h)W^O\n \\text{where} head_i = \\text{Attention}(QW_i^Q, KW_i^K, VW_i^V)\n\n Args:\n embed_dim: total dimension of the model.\n num_heads: parallel attention heads.\n dropout: a Dropout layer on attn_output_weights. Default: 0.0.\n bias: add bias as module parameter. Default: True.\n add_bias_kv: add bias to the key and value sequences at dim=0.\n add_zero_attn: add a new batch of zeros to the key and\n value sequences at dim=1.\n kdim: total number of features in key. Default: None.\n vdim: total number of features in value. Default: None.\n\n Note: if kdim and vdim are None, they will be set to embed_dim such that\n query, key, and value have the same number of features.\n\n Examples::\n\n >>> multihead_attn = nn.MultiheadAttention(embed_dim, num_heads)\n >>> attn_output, attn_output_weights = multihead_attn(query, key, value)\n \"\"\"\n bias_k: Optional[torch.Tensor]\n bias_v: Optional[torch.Tensor]\n\n def __init__(self, embed_dim, num_heads, dropout=0., bias=True, add_bias_kv=False, add_zero_attn=False, kdim=None, vdim=None):\n super(MultiheadAttention, self).__init__()\n self.embed_dim = embed_dim\n self.kdim = kdim if kdim is not None else embed_dim\n self.vdim = vdim if vdim is not None else embed_dim\n self._qkv_same_embed_dim = self.kdim == embed_dim and self.vdim == embed_dim\n\n self.num_heads = num_heads\n self.dropout = dropout\n self.head_dim = embed_dim // num_heads\n assert self.head_dim * num_heads == self.embed_dim, \"embed_dim must be divisible by num_heads\"\n\n if self._qkv_same_embed_dim is False:\n self.q_proj_weight = Parameter(torch.Tensor(embed_dim, embed_dim))\n self.k_proj_weight = Parameter(torch.Tensor(embed_dim, self.kdim))\n self.v_proj_weight = Parameter(torch.Tensor(embed_dim, self.vdim))\n self.register_parameter('in_proj_weight', None)\n else:\n self.in_proj_weight = Parameter(torch.empty(3 * embed_dim, embed_dim))\n self.register_parameter('q_proj_weight', None)\n self.register_parameter('k_proj_weight', None)\n self.register_parameter('v_proj_weight', None)\n\n if bias:\n self.in_proj_bias = Parameter(torch.empty(3 * embed_dim))\n else:\n self.register_parameter('in_proj_bias', None)\n self.out_proj = _LinearWithBias(embed_dim, embed_dim)\n\n if add_bias_kv:\n self.bias_k = Parameter(torch.empty(1, 1, embed_dim))\n self.bias_v = Parameter(torch.empty(1, 1, embed_dim))\n else:\n self.bias_k = self.bias_v = None\n\n self.add_zero_attn = add_zero_attn\n\n self._reset_parameters()\n\n def _reset_parameters(self):\n if self._qkv_same_embed_dim:\n xavier_uniform_(self.in_proj_weight)\n else:\n xavier_uniform_(self.q_proj_weight)\n xavier_uniform_(self.k_proj_weight)\n xavier_uniform_(self.v_proj_weight)\n\n if self.in_proj_bias is not None:\n constant_(self.in_proj_bias, 0.)\n constant_(self.out_proj.bias, 0.)\n if self.bias_k is not None:\n xavier_normal_(self.bias_k)\n if self.bias_v is not None:\n xavier_normal_(self.bias_v)\n\n def __setstate__(self, state):\n # Support loading old MultiheadAttention checkpoints generated by v1.1.0\n if '_qkv_same_embed_dim' not in state:\n state['_qkv_same_embed_dim'] = True\n\n super(MultiheadAttention, self).__setstate__(state)\n\n def forward(self, query, key, value, key_padding_mask=None,\n need_weights=True, attn_mask=None):\n # type: (Tensor, Tensor, Tensor, Optional[Tensor], bool, Optional[Tensor]) -> Tuple[Tensor, Optional[Tensor]]\n r\"\"\"\n Args:\n query, key, value: map a query and a set of key-value pairs to an output.\n See \"Attention Is All You Need\" for more details.\n key_padding_mask: if provided, specified padding elements in the key will\n be ignored by the attention. When given a binary mask and a value is True,\n the corresponding value on the attention layer will be ignored. When given\n a byte mask and a value is non-zero, the corresponding value on the attention\n layer will be ignored\n need_weights: output attn_output_weights.\n attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all\n the batches while a 3D mask allows to specify a different mask for the entries of each batch.\n\n Shape:\n - Inputs:\n - query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is\n the embedding dimension.\n - key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is\n the embedding dimension.\n - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is\n the embedding dimension.\n - key_padding_mask: :math:`(N, S)` where N is the batch size, S is the source sequence length.\n If a ByteTensor is provided, the non-zero positions will be ignored while the position\n with the zero positions will be unchanged. If a BoolTensor is provided, the positions with the\n value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged.\n - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length.\n 3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length,\n S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked\n positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend\n while the zero positions will be unchanged. If a BoolTensor is provided, positions with ``True``\n is not allowed to attend while ``False`` values will be unchanged. If a FloatTensor\n is provided, it will be added to the attention weight.\n\n - Outputs:\n - attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size,\n E is the embedding dimension.\n - attn_output_weights: :math:`(N, L, S)` where N is the batch size,\n L is the target sequence length, S is the source sequence length.\n \"\"\"\n if not self._qkv_same_embed_dim:\n return multi_head_attention_forward(\n query, key, value, self.embed_dim, self.num_heads,\n self.in_proj_weight, self.in_proj_bias,\n self.bias_k, self.bias_v, self.add_zero_attn,\n self.dropout, self.out_proj.weight, self.out_proj.bias,\n training=self.training,\n key_padding_mask=key_padding_mask, need_weights=need_weights,\n attn_mask=attn_mask, use_separate_proj_weight=True,\n q_proj_weight=self.q_proj_weight, k_proj_weight=self.k_proj_weight,\n v_proj_weight=self.v_proj_weight)\n else:\n return multi_head_attention_forward(\n query, key, value, self.embed_dim, self.num_heads,\n self.in_proj_weight, self.in_proj_bias,\n self.bias_k, self.bias_v, self.add_zero_attn,\n self.dropout, self.out_proj.weight, self.out_proj.bias,\n training=self.training,\n key_padding_mask=key_padding_mask, need_weights=need_weights,\n attn_mask=attn_mask)\n\n\n"
},
{
"path": "src/models/position_encoding.py",
"content": "\"\"\"\nVarious positional encodings for the transformer.\nborrowed from: https://github.com/facebookresearch/detr/blob/master/models/position_encoding.py\n\"\"\"\nimport math\nimport torch\nfrom torch import nn\n\nfrom util.misc import NestedTensor\n\n\nclass PositionEmbeddingSine(nn.Module):\n \"\"\"\n This is a more standard version of the position embedding, very similar to the one\n used by the Attention is all you need paper, generalized to work on images.\n \"\"\"\n def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):\n super().__init__()\n self.num_pos_feats = num_pos_feats\n self.temperature = temperature\n self.normalize = normalize\n if scale is not None and normalize is False:\n raise ValueError(\"normalize should be True if scale is passed\")\n if scale is None:\n scale = 2 * math.pi\n self.scale = scale\n\n def forward(self, tensor_list: NestedTensor):\n x = tensor_list.tensors\n mask = tensor_list.mask\n assert mask is not None\n not_mask = ~mask\n y_embed = not_mask.cumsum(1, dtype=torch.float32)\n x_embed = not_mask.cumsum(2, dtype=torch.float32)\n if self.normalize:\n eps = 1e-6\n y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale\n x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale\n\n dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)\n dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)\n\n pos_x = x_embed[:, :, :, None] / dim_t\n pos_y = y_embed[:, :, :, None] / dim_t\n pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)\n pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)\n pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)\n return pos\n\n\nclass PositionEmbeddingLearned(nn.Module):\n \"\"\"\n Absolute pos embedding, learned.\n \"\"\"\n def __init__(self, num_pos_feats=256):\n super().__init__()\n self.row_embed = nn.Embedding(50, num_pos_feats)\n self.col_embed = nn.Embedding(50, num_pos_feats)\n self.reset_parameters()\n\n def reset_parameters(self):\n nn.init.uniform_(self.row_embed.weight)\n nn.init.uniform_(self.col_embed.weight)\n\n def forward(self, tensor_list: NestedTensor):\n x = tensor_list.tensors\n h, w = x.shape[-2:]\n i = torch.arange(w, device=x.device)\n j = torch.arange(h, device=x.device)\n x_emb = self.col_embed(i)\n y_emb = self.row_embed(j)\n pos = torch.cat([\n x_emb.unsqueeze(0).repeat(h, 1, 1),\n y_emb.unsqueeze(1).repeat(1, w, 1),\n ], dim=-1).permute(2, 0, 1).unsqueeze(0).repeat(x.shape[0], 1, 1, 1)\n return pos\n\n\ndef build_position_encoding(args):\n N_steps = args.hidden_dim // 2\n if args.position_embedding in ('v2', 'sine'):\n # TODO find a better way of exposing other arguments\n position_embedding = PositionEmbeddingSine(N_steps, normalize=True)\n elif args.position_embedding in ('v3', 'learned'):\n position_embedding = PositionEmbeddingLearned(N_steps)\n else:\n raise ValueError(f\"not supported {args.position_embedding}\")\n\n return position_embedding\n"
},
{
"path": "src/models/transformer.py",
"content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\nDETR Transformer class.\n\nCopy-paste from torch.nn.Transformer with modifications:\n * positional encodings are passed in MHattention\n * extra LN at the end of encoder is removed\n * decoder returns a stack of activations from all decoding layers\n\"\"\"\nimport copy\nfrom typing import Optional, List\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn, Tensor\nfrom .multi_head_attention import MultiheadAttention\n\nclass Transformer(nn.Module):\n\n def __init__(self, d_model=512, nhead=8, num_encoder_layers=6,\n num_decoder_layers=6, dim_feedforward=2048, dropout=0.1,\n activation=\"relu\", normalize_before=False,\n return_intermediate_dec=False):\n super().__init__()\n\n encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward,\n dropout, activation, normalize_before)\n encoder_norm = nn.LayerNorm(d_model) if normalize_before else None\n self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)\n\n decoder_layer = TransformerDecoderLayer(d_model, nhead, dim_feedforward,\n dropout, activation, normalize_before)\n decoder_norm = nn.LayerNorm(d_model)\n self.decoder = TransformerDecoder(decoder_layer, num_decoder_layers, decoder_norm,\n return_intermediate=return_intermediate_dec)\n\n self._reset_parameters()\n\n self.d_model = d_model\n self.nhead = nhead\n\n def _reset_parameters(self):\n for p in self.parameters():\n if p.dim() > 1:\n nn.init.xavier_uniform_(p)\n\n def forward(self, src, mask, query_embed, pos_embed):\n # flatten NxCxHxW to HWxNxC\n bs, c, h, w = src.shape\n src = src.flatten(2).permute(2, 0, 1)\n pos_embed = pos_embed.flatten(2).permute(2, 0, 1)\n query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1)\n mask = mask.flatten(1)\n\n tgt = torch.zeros_like(query_embed)\n\n memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed)\n \n hs = self.decoder(tgt, memory, memory_key_padding_mask=mask,\n pos=pos_embed, query_pos=query_embed)\n return hs.transpose(1, 2), memory#.permute(1, 2, 0).view(bs, c, h, w)\n\n\nclass TransformerEncoder(nn.Module):\n\n def __init__(self, encoder_layer, num_layers, norm=None):\n super().__init__()\n self.layers = _get_clones(encoder_layer, num_layers)\n self.num_layers = num_layers\n self.norm = norm\n\n def forward(self, src,\n mask: Optional[Tensor] = None,\n src_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None):\n output = src\n\n for layer in self.layers:\n output = layer(output, src_mask=mask,\n src_key_padding_mask=src_key_padding_mask, pos=pos)\n\n if self.norm is not None:\n output = self.norm(output)\n\n return output\n\nclass TransformerDecoder(nn.Module):\n\n def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False):\n super().__init__()\n self.layers = _get_clones(decoder_layer, num_layers)\n self.num_layers = num_layers\n self.norm = norm\n self.return_intermediate = return_intermediate\n\n def forward(self, tgt, memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None):\n output = tgt\n\n intermediate = []\n\n for layer in self.layers:\n output = layer(output, memory, tgt_mask=tgt_mask,\n memory_mask=memory_mask,\n tgt_key_padding_mask=tgt_key_padding_mask,\n memory_key_padding_mask=memory_key_padding_mask,\n pos=pos, query_pos=query_pos)\n if self.return_intermediate:\n intermediate.append(self.norm(output))\n\n if self.norm is not None:\n output = self.norm(output)\n if self.return_intermediate:\n intermediate.pop()\n intermediate.append(output)\n\n if self.return_intermediate:\n return torch.stack(intermediate)\n\n return output.unsqueeze(0)\n\n\nclass TransformerEncoderLayer(nn.Module):\n\n def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,\n activation=\"relu\", normalize_before=False):\n super().__init__()\n self.self_attn = MultiheadAttention(d_model, nhead, dropout=dropout)\n self.linear1 = nn.Linear(d_model, dim_feedforward)\n self.dropout = nn.Dropout(dropout)\n self.linear2 = nn.Linear(dim_feedforward, d_model)\n\n self.norm1 = nn.LayerNorm(d_model)\n self.norm2 = nn.LayerNorm(d_model)\n self.dropout1 = nn.Dropout(dropout)\n self.dropout2 = nn.Dropout(dropout)\n\n self.activation = _get_activation_fn(activation)\n self.normalize_before = normalize_before\n\n def with_pos_embed(self, tensor, pos: Optional[Tensor]):\n return tensor if pos is None else tensor + pos\n\n def forward_post(self,\n src,\n src_mask: Optional[Tensor] = None,\n src_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None):\n q = k = self.with_pos_embed(src, pos)\n src2 = self.self_attn(q, k, value=src, attn_mask=src_mask,\n key_padding_mask=src_key_padding_mask)[0]\n src = src + self.dropout1(src2)\n src = self.norm1(src)\n src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))\n src = src + self.dropout2(src2)\n src = self.norm2(src)\n return src\n\n def forward_pre(self, src,\n src_mask: Optional[Tensor] = None,\n src_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None):\n src2 = self.norm1(src)\n q = k = self.with_pos_embed(src2, pos)\n src2 = self.self_attn(q, k, value=src2, attn_mask=src_mask,\n key_padding_mask=src_key_padding_mask)[0]\n src = src + self.dropout1(src2)\n src2 = self.norm2(src)\n src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))\n src = src + self.dropout2(src2)\n return src\n\n def forward(self, src,\n src_mask: Optional[Tensor] = None,\n src_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None):\n if self.normalize_before:\n return self.forward_pre(src, src_mask, src_key_padding_mask, pos)\n return self.forward_post(src, src_mask, src_key_padding_mask, pos)\n\n\nclass TransformerDecoderLayer(nn.Module):\n\n def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,\n activation=\"relu\", normalize_before=False):\n super().__init__()\n self.self_attn = MultiheadAttention(d_model, nhead, dropout=dropout)\n self.multihead_attn = MultiheadAttention(d_model, nhead, dropout=dropout)\n # Implementation of Feedforward model\n self.linear1 = nn.Linear(d_model, dim_feedforward)\n self.dropout = nn.Dropout(dropout)\n self.linear2 = nn.Linear(dim_feedforward, d_model)\n\n self.norm1 = nn.LayerNorm(d_model)\n self.norm2 = nn.LayerNorm(d_model)\n self.norm3 = nn.LayerNorm(d_model)\n self.dropout1 = nn.Dropout(dropout)\n self.dropout2 = nn.Dropout(dropout)\n self.dropout3 = nn.Dropout(dropout)\n\n self.activation = _get_activation_fn(activation)\n self.normalize_before = normalize_before\n\n def with_pos_embed(self, tensor, pos: Optional[Tensor]):\n return tensor if pos is None else tensor + pos\n\n def forward_post(self, tgt, memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None):\n q = k = self.with_pos_embed(tgt, query_pos)\n tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask,\n key_padding_mask=tgt_key_padding_mask)[0]\n tgt = tgt + self.dropout1(tgt2)\n tgt = self.norm1(tgt)\n tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos),\n key=self.with_pos_embed(memory, pos),\n value=memory, attn_mask=memory_mask,\n key_padding_mask=memory_key_padding_mask)[0]\n tgt = tgt + self.dropout2(tgt2)\n tgt = self.norm2(tgt)\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))\n tgt = tgt + self.dropout3(tgt2)\n tgt = self.norm3(tgt)\n return tgt\n\n def forward_pre(self, tgt, memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None):\n tgt2 = self.norm1(tgt)\n q = k = self.with_pos_embed(tgt2, query_pos)\n tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,\n key_padding_mask=tgt_key_padding_mask)[0]\n tgt = tgt + self.dropout1(tgt2)\n tgt2 = self.norm2(tgt)\n tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos),\n key=self.with_pos_embed(memory, pos),\n value=memory, attn_mask=memory_mask,\n key_padding_mask=memory_key_padding_mask)[0]\n tgt = tgt + self.dropout2(tgt2)\n tgt2 = self.norm3(tgt)\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))\n tgt = tgt + self.dropout3(tgt2)\n return tgt\n\n def forward(self, tgt, memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None):\n if self.normalize_before:\n return self.forward_pre(tgt, memory, tgt_mask, memory_mask,\n tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos)\n return self.forward_post(tgt, memory, tgt_mask, memory_mask,\n tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos)\n\n\ndef _get_clones(module, N):\n return nn.ModuleList([copy.deepcopy(module) for i in range(N)])\n\n\ndef build_transformer(args):\n\n return Transformer(\n d_model=args.hidden_dim,\n dropout=args.dropout,\n nhead=args.nheads,\n dim_feedforward=args.dim_feedforward,\n num_encoder_layers=args.enc_layers,\n num_decoder_layers=args.dec_layers,\n normalize_before=args.pre_norm,\n return_intermediate_dec=True,\n )\n\ndef _get_activation_fn(activation):\n \"\"\"Return an activation function given a string\"\"\"\n if activation == \"relu\":\n return F.relu\n if activation == \"gelu\":\n return F.gelu\n if activation == \"glu\":\n return F.glu\n raise RuntimeError(F\"activation should be relu/gelu, not {activation}.\")\n"
},
{
"path": "src/util/__init__.py",
"content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n"
},
{
"path": "src/util/misc.py",
"content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\nMisc functions, including distributed helpers.\n\nMostly copy-paste from torchvision references.\n\"\"\"\nimport os\nimport subprocess\nimport time\nfrom collections import defaultdict, deque\nimport datetime\nimport pickle\nfrom typing import Optional, List\n\nimport torch\nimport torch.distributed as dist\nfrom torch import Tensor\n\n# needed due to empty tensor bug in pytorch and torchvision 0.5\nimport torchvision\nif float(torchvision.__version__[:3]) < 0.7:\n from torchvision.ops import _new_empty_tensor\n from torchvision.ops.misc import _output_size\n\n\nclass SmoothedValue(object):\n \"\"\"Track a series of values and provide access to smoothed values over a\n window or the global series average.\n \"\"\"\n\n def __init__(self, window_size=20, fmt=None):\n if fmt is None:\n fmt = \"{median:.4f} ({global_avg:.4f})\"\n self.deque = deque(maxlen=window_size)\n self.total = 0.0\n self.count = 0\n self.fmt = fmt\n\n def update(self, value, n=1):\n self.deque.append(value)\n self.count += n\n self.total += value * n\n\n def synchronize_between_processes(self):\n \"\"\"\n Warning: does not synchronize the deque!\n \"\"\"\n if not is_dist_avail_and_initialized():\n return\n t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda')\n dist.barrier()\n dist.all_reduce(t)\n t = t.tolist()\n self.count = int(t[0])\n self.total = t[1]\n\n @property\n def median(self):\n d = torch.tensor(list(self.deque))\n return d.median().item()\n\n @property\n def avg(self):\n d = torch.tensor(list(self.deque), dtype=torch.float32)\n return d.mean().item()\n\n @property\n def global_avg(self):\n return self.total / self.count\n\n @property\n def max(self):\n return max(self.deque)\n\n @property\n def value(self):\n return self.deque[-1]\n\n def __str__(self):\n return self.fmt.format(\n median=self.median,\n avg=self.avg,\n global_avg=self.global_avg,\n max=self.max,\n value=self.value)\n\n\ndef all_gather(data):\n \"\"\"\n Run all_gather on arbitrary picklable data (not necessarily tensors)\n Args:\n data: any picklable object\n Returns:\n list[data]: list of data gathered from each rank\n \"\"\"\n world_size = get_world_size()\n if world_size == 1:\n return [data]\n\n # serialized to a Tensor\n buffer = pickle.dumps(data)\n storage = torch.ByteStorage.from_buffer(buffer)\n tensor = torch.ByteTensor(storage).to(\"cuda\")\n\n # obtain Tensor size of each rank\n local_size = torch.tensor([tensor.numel()], device=\"cuda\")\n size_list = [torch.tensor([0], device=\"cuda\") for _ in range(world_size)]\n dist.all_gather(size_list, local_size)\n size_list = [int(size.item()) for size in size_list]\n max_size = max(size_list)\n\n # receiving Tensor from all ranks\n # we pad the tensor because torch all_gather does not support\n # gathering tensors of different shapes\n tensor_list = []\n for _ in size_list:\n tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device=\"cuda\"))\n if local_size != max_size:\n padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device=\"cuda\")\n tensor = torch.cat((tensor, padding), dim=0)\n dist.all_gather(tensor_list, tensor)\n\n data_list = []\n for size, tensor in zip(size_list, tensor_list):\n buffer = tensor.cpu().numpy().tobytes()[:size]\n data_list.append(pickle.loads(buffer))\n\n return data_list\n\n\ndef reduce_dict(input_dict, average=True):\n \"\"\"\n Args:\n input_dict (dict): all the values will be reduced\n average (bool): whether to do average or sum\n Reduce the values in the dictionary from all processes so that all processes\n have the averaged results. Returns a dict with the same fields as\n input_dict, after reduction.\n \"\"\"\n world_size = get_world_size()\n if world_size < 2:\n return input_dict\n with torch.no_grad():\n names = []\n values = []\n # sort the keys so that they are consistent across processes\n for k in sorted(input_dict.keys()):\n names.append(k)\n values.append(input_dict[k])\n values = torch.stack(values, dim=0)\n dist.all_reduce(values)\n if average:\n values /= world_size\n reduced_dict = {k: v for k, v in zip(names, values)}\n return reduced_dict\n\n\nclass MetricLogger(object):\n def __init__(self, delimiter=\"\\t\"):\n self.meters = defaultdict(SmoothedValue)\n self.delimiter = delimiter\n\n def update(self, **kwargs):\n for k, v in kwargs.items():\n if isinstance(v, torch.Tensor):\n v = v.item()\n assert isinstance(v, (float, int))\n self.meters[k].update(v)\n\n def __getattr__(self, attr):\n if attr in self.meters:\n return self.meters[attr]\n if attr in self.__dict__:\n return self.__dict__[attr]\n raise AttributeError(\"'{}' object has no attribute '{}'\".format(\n type(self).__name__, attr))\n\n def __str__(self):\n loss_str = []\n for name, meter in self.meters.items():\n loss_str.append(\n \"{}: {}\".format(name, str(meter))\n )\n return self.delimiter.join(loss_str)\n\n def synchronize_between_processes(self):\n for meter in self.meters.values():\n meter.synchronize_between_processes()\n\n def add_meter(self, name, meter):\n self.meters[name] = meter\n\n def log_every(self, iterable, print_freq, header=None):\n i = 0\n if not header:\n header = ''\n start_time = time.time()\n end = time.time()\n iter_time = SmoothedValue(fmt='{avg:.4f}')\n data_time = SmoothedValue(fmt='{avg:.4f}')\n space_fmt = ':' + str(len(str(len(iterable)))) + 'd'\n if torch.cuda.is_available():\n log_msg = self.delimiter.join([\n header,\n '[{0' + space_fmt + '}/{1}]',\n 'eta: {eta}',\n '{meters}',\n 'time: {time}',\n 'data: {data}',\n 'max mem: {memory:.0f}'\n ])\n else:\n log_msg = self.delimiter.join([\n header,\n '[{0' + space_fmt + '}/{1}]',\n 'eta: {eta}',\n '{meters}',\n 'time: {time}',\n 'data: {data}'\n ])\n MB = 1024.0 * 1024.0\n for obj in iterable:\n data_time.update(time.time() - end)\n yield obj\n iter_time.update(time.time() - end)\n if i % print_freq == 0 or i == len(iterable) - 1:\n eta_seconds = iter_time.global_avg * (len(iterable) - i)\n eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))\n if torch.cuda.is_available():\n print(log_msg.format(\n i, len(iterable), eta=eta_string,\n meters=str(self),\n time=str(iter_time), data=str(data_time),\n memory=torch.cuda.max_memory_allocated() / MB))\n else:\n print(log_msg.format(\n i, len(iterable), eta=eta_string,\n meters=str(self),\n time=str(iter_time), data=str(data_time)))\n i += 1\n end = time.time()\n total_time = time.time() - start_time\n total_time_str = str(datetime.timedelta(seconds=int(total_time)))\n print('{} Total time: {} ({:.4f} s / it)'.format(\n header, total_time_str, total_time / len(iterable)))\n\n\ndef get_sha():\n cwd = os.path.dirname(os.path.abspath(__file__))\n\n def _run(command):\n return subprocess.check_output(command, cwd=cwd).decode('ascii').strip()\n sha = 'N/A'\n diff = \"clean\"\n branch = 'N/A'\n try:\n sha = _run(['git', 'rev-parse', 'HEAD'])\n subprocess.check_output(['git', 'diff'], cwd=cwd)\n diff = _run(['git', 'diff-index', 'HEAD'])\n diff = \"has uncommited changes\" if diff else \"clean\"\n branch = _run(['git', 'rev-parse', '--abbrev-ref', 'HEAD'])\n except Exception:\n pass\n message = f\"sha: {sha}, status: {diff}, branch: {branch}\"\n return message\n\n\ndef collate_fn(batch):\n batch = list(zip(*batch))\n batch[0] = nested_tensor_from_tensor_list(batch[0])\n return tuple(batch)\n\n\ndef _max_by_axis(the_list):\n # type: (List[List[int]]) -> List[int]\n maxes = the_list[0]\n for sublist in the_list[1:]:\n for index, item in enumerate(sublist):\n maxes[index] = max(maxes[index], item)\n return maxes\n\n\ndef nested_tensor_from_tensor_list(tensor_list: List[Tensor]):\n # TODO make this more general\n if tensor_list[0].ndim == 3:\n if torchvision._is_tracing():\n # nested_tensor_from_tensor_list() does not export well to ONNX\n # call _onnx_nested_tensor_from_tensor_list() instead\n return _onnx_nested_tensor_from_tensor_list(tensor_list)\n\n # TODO make it support different-sized images\n max_size = _max_by_axis([list(img.shape) for img in tensor_list])\n # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))\n batch_shape = [len(tensor_list)] + max_size\n b, c, h, w = batch_shape\n dtype = tensor_list[0].dtype\n device = tensor_list[0].device\n tensor = torch.zeros(batch_shape, dtype=dtype, device=device)\n mask = torch.ones((b, h, w), dtype=torch.bool, device=device)\n for img, pad_img, m in zip(tensor_list, tensor, mask):\n pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)\n m[: img.shape[1], :img.shape[2]] = False\n else:\n raise ValueError('not supported')\n return NestedTensor(tensor, mask)\n\n\n# _onnx_nested_tensor_from_tensor_list() is an implementation of\n# nested_tensor_from_tensor_list() that is supported by ONNX tracing.\n@torch.jit.unused\ndef _onnx_nested_tensor_from_tensor_list(tensor_list):\n max_size = []\n for i in range(tensor_list[0].dim()):\n max_size_i = torch.max(torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)).to(torch.int64)\n max_size.append(max_size_i)\n max_size = tuple(max_size)\n\n # work around for\n # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)\n # m[: img.shape[1], :img.shape[2]] = False\n # which is not yet supported in onnx\n padded_imgs = []\n padded_masks = []\n for img in tensor_list:\n padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))]\n padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0]))\n padded_imgs.append(padded_img)\n\n m = torch.zeros_like(img[0], dtype=torch.int, device=img.device)\n padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), \"constant\", 1)\n padded_masks.append(padded_mask.to(torch.bool))\n\n tensor = torch.stack(padded_imgs)\n mask = torch.stack(padded_masks)\n\n return NestedTensor(tensor, mask=mask)\n\n\nclass NestedTensor(object):\n def __init__(self, tensors, mask: Optional[Tensor]):\n self.tensors = tensors\n self.mask = mask\n\n def to(self, device):\n # type: (Device) -> NestedTensor # noqa\n cast_tensor = self.tensors.to(device)\n mask = self.mask\n if mask is not None:\n assert mask is not None\n cast_mask = mask.to(device)\n else:\n cast_mask = None\n return NestedTensor(cast_tensor, cast_mask)\n\n def decompose(self):\n return self.tensors, self.mask\n\n def __repr__(self):\n return str(self.tensors)\n\n\ndef setup_for_distributed(is_master):\n \"\"\"\n This function disables printing when not in master process\n \"\"\"\n import builtins as __builtin__\n builtin_print = __builtin__.print\n\n def print(*args, **kwargs):\n force = kwargs.pop('force', False)\n if is_master or force:\n builtin_print(*args, **kwargs)\n\n __builtin__.print = print\n\n\ndef is_dist_avail_and_initialized():\n if not dist.is_available():\n return False\n if not dist.is_initialized():\n return False\n return True\n\n\ndef get_world_size():\n if not is_dist_avail_and_initialized():\n return 1\n return dist.get_world_size()\n\n\ndef get_rank():\n if not is_dist_avail_and_initialized():\n return 0\n return dist.get_rank()\n\n\ndef is_main_process():\n return get_rank() == 0\n\n\ndef save_on_master(*args, **kwargs):\n if is_main_process():\n torch.save(*args, **kwargs)\n\n\ndef init_distributed_mode(args):\n if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:\n args.rank = int(os.environ[\"RANK\"])\n args.world_size = int(os.environ['WORLD_SIZE'])\n args.gpu = int(os.environ['LOCAL_RANK'])\n elif 'SLURM_PROCID' in os.environ:\n args.rank = int(os.environ['SLURM_PROCID'])\n args.gpu = args.rank % torch.cuda.device_count()\n else:\n print('Not using distributed mode')\n args.distributed = False\n return\n\n args.distributed = True\n\n torch.cuda.set_device(args.gpu)\n args.dist_backend = 'nccl'\n print('| distributed init (rank {}): {}'.format(\n args.rank, args.dist_url), flush=True)\n torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url,\n world_size=args.world_size, rank=args.rank)\n torch.distributed.barrier()\n setup_for_distributed(args.rank == 0)\n\n\n@torch.no_grad()\ndef accuracy(output, target, topk=(1,)):\n \"\"\"Computes the precision@k for the specified values of k\"\"\"\n if target.numel() == 0:\n return [torch.zeros([], device=output.device)]\n maxk = max(topk)\n batch_size = target.size(0)\n\n _, pred = output.topk(maxk, 1, True, True)\n pred = pred.t()\n correct = pred.eq(target.view(1, -1).expand_as(pred))\n\n res = []\n for k in topk:\n correct_k = correct[:k].view(-1).float().sum(0)\n res.append(correct_k.mul_(100.0 / batch_size))\n return res\n\n\ndef interpolate(input, size=None, scale_factor=None, mode=\"nearest\", align_corners=None):\n # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor\n \"\"\"\n Equivalent to nn.functional.interpolate, but with support for empty batch sizes.\n This will eventually be supported natively by PyTorch, and this\n class can go away.\n \"\"\"\n if float(torchvision.__version__[:3]) < 0.7:\n if input.numel() > 0:\n return torch.nn.functional.interpolate(\n input, size, scale_factor, mode, align_corners\n )\n\n output_shape = _output_size(2, input, size, scale_factor)\n output_shape = list(input.shape[:-2]) + list(output_shape)\n return _new_empty_tensor(input, output_shape)\n else:\n return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners)\n"
}
]