front page.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "from pytesseract import image_to_string\n",
    "import pytesseract\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import PIL\n",
    "from PIL import Image, ImageFilter, ImageEnhance\n",
    "import cv2\n",
    "import colorsys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**UNINSTALL AND REINSTALL**\n",
    "\n",
    "https://digi.bib.uni-mannheim.de/tesseract/tesseract-ocr-setup-3.05.02-20180621.exe\n",
    "\n",
    "\n",
    "Uninstall previously installed tesseract application and reinstall it with following link."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pytesseract.pytesseract.tesseract_cmd = r\"C:\\Program Files (x86)\\Tesseract-OCR\\tesseract.exe\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ORIGINAL**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "im = Image.open(\"C:/Users/SUYASH/Downloads/AC0020001/AC0020001-01.jpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Rescaling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "basewidth = 1700\n",
    "wpercent = (basewidth / float(im.size[0]))\n",
    "hsize = int((float(im.size[1]) * float(wpercent)))\n",
    "imz = im.resize((basewidth, hsize), PIL.Image.ANTIALIAS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Making Image Binary**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# im = cv2.bilateralFilter(np.array(imz,dtype=np.uint8),9,75,75)\n",
    "# imb = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)\n",
    "# ims  = Image.fromarray(imb)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dqtdu91rV21F02Ns1CEa5KjdIiKo8qN5PX8tgAvp4Tnox1JYGpauYVQaKVFYyaFdVWVnklp6Nlff3KKrI49dVRqpqJ2IU8E6Buvu66DHrZaqkweNxkBmN4ixrjOp2XpQeFMhdyXKjQe9i1U/XqPvQpi2fFs3LtC2jIDLqNx1X0S1p+ko/EqL289bJX+z27BqtmxfTnUoMstC+vklL0tZK4HmhgtSyLPdjyaDb76HNlmVpp8ANo1cfoyZ7vR7t2rxr2rwyEuRq1zkVh/fJ/pf7K7oXnbwjfdRqlzSvuh5wanfa1dDtpRjH+ojmbZiWWJ+VY2WpHtQry+13nvbl9hqQcr37q1wHMSHtZ9R/6qN/0PHH4akPxHZ/yU+I0923b5gUTJb1R/TB1mlZbrOrhzFq48g1SaQmHYUpDdvU7r/f+jYfver/3GtbuYvx00HPY1vl+A0cxb83O7CLQYDPxaPcLpvUPAsdkz/quKJhc3/6EMI3/QMxKk4mVZxNDL7nW0eqPHk87hobHVonW+1lTFsxLLVn4Pm2ze1B7tZNqtovtP+qx6ub0bK+yMaKPHqRb1AmKdNCec1G07VkmVk3EyHvL3lW46jc7VM5fUfL05ZcHTuub/OlqntDfvu48s/I1bdoL99vj0K3j0PV3UzjHJnUesy4XVZ65PKp7QGVVd77QabD+cK84KXx41X5ump+xn5be6Ql9fH1qJ2ur7T5NK7R25kHK0YvqCmXZxWm7dWbbYEbn4+ZKEFqs0ulO7M/dNBOg0f90+kNxFO313W3xfqtwYxiJ5enKXUb3FddnnWVVNGjeUlpadp0e1zFqFRvWW8+KDB9Rfuv+PVS8Rc6tOOHvuqviL31H270aaqXuVXHj9r5JYLJn/2vH4d/4TfhYnT33Y5+7Xnrum7jHPbaaVP3Eq6H6LqmugsuzarvPjn3w357wOwbuu4xNaVzE1Lm+2/33oaiyTZtl9QarGbjMe3QU7+T+8tJ1Tv8oUNtTnl5aJLG0/nj34vB3McmGv2eEEFqFFGWZSn/sHmwvI8KO9VveOI41Hzq6sGT29S8PHsN/cyGYTO3B+yHt6h45LVs9KCXLbq9mANx8tWODt0tfcB9pO4/OtjK+j4f/KTq/K/L1esR9K2edZ+SmxDdfc3RJfdXlOb1nz47uK3M9+0vT2+DCrmuoNrTfdOiNeX2ea1iKZ5fWjl+Ee0RKw8ej/Y/HT9pX9J9Qdmw7u1YypV1fGze3uFOcrlp+Ypz7l7GZ+zuK13vmbh3zpIuQ5rlH320yajc40anHs6pWdUdfj4bve+c1f11KkvZ8cY/YDf2FN/tsuNyfZybYuOu0lw+acpNPnTjxam5MupSsrxJo4/vSbV/uttns/7TpmVZhpM8m3rWre+lcnsWvteezx8LP/vxDZFJPUqM+sc8dHr5+LnoX85eH9z2XxjnZuZAZ8AnmnE2IcakHiRG/Yv2/+C3QYBDp3lHuxCe7Wq4fUiHRx2Hoh9Tps3tqFPxfPBhykta2pGqyWjVP+uMQer+wxL8O8qv+mjhcjieXCNHOQlzNmflwDSH7jaNTXEfVPibMwYpX/x/zPzKrcGZ6ZH5c9PpwM2Gj00bXi8j29q9k+Qf1PzBH7wOYnKW1NkYUf1ppxyT4l8zGcp56ruamn+Plt+Vp09g7uUfxF9zykyKf0o7xvTH439e6nxrvVp5GadSR26Dfp2tv3b4NfOKq0GHf/Zo808o5/UdcvTz8M+e98ikiC2lZUkXD21Xh4rhkFaqn7Y7Hjy6tqhsSju3/WjT+EfMPxpOVo4RpPgpzeSFWRyqL1iZPZnHk/66rI0mORj9O8rLv/vzqmbB7TZmuSvjI1PQ3Udoh76taV62vZxyqef1FQu6Srpyk11M7qw4aST/mkeuYii6/uTbN+e84wT/iurIPvnMtEf/PrZ0XXLtBUyzC5pmy5u2tHnVPB6t41++ACeVhml47paUyjtSjLdL3Ye0vB/FVmuupT96JHfHCRiZfS+aqX+jCyYHC/rrJ8d7HG48bYHQ84872Lf78rT6l/N0mRQ8yuf3HyYJ/vR7YEwKHvONO3rCP0t3HzzklztWIB5BCh4yG8AC3kF3HzxGhIIPkkkBEJYgBUBY4bv7Zj+k/Kq58139B3+5eb2ITv8PwLtEz6QmU6nS0Z9gebj+jTvs1PufVQDAywTPpGYx6mU76G6Tnw9kUi8MTK65AdgQO5P6QL4yvuvNkRiV8+Sn+B6w/WMRAP+62EFqWZbRdSnfParfYtT9P3+opaxErAJoBe/uW5YlTyLBKzvJHqmr7g588ldW0/pjruui79xHESCw0JnUZMDm5SnHkRtdP1hg2+CFyaMAepGD1HeO23vz+palDTAPR6zBLsQogIFQ3X3laE//M6p1ofZp+WNzza/e5TW0pOL3x9qfKSuvlcprrrNzHVRuJq3f29H8ZlnRzlTso2p4ysXu1rvhr+UG9bZvyK26/kf5dCYC5xMpk0rlv1sT+7pLb0cVrBcz3a6puvyb6o3Gvwye6v80K0aJVLnv24ZFLUU1bSvLRanbvPgn9fVOGtgvBjilQEGqDhez3zq9JQm7Y1VlXKiymf3fznxiannV+O3pevm2k3x/urG/orV5VG+xetYigNOK091X9MgtKd/60QadfYNpDql+nLtV93/bQ3e7167KQ9P36m7F60bdZcJV03JXd263KTsmi+b3eWC+FWteBcDZhQlS42GT0QF31P93PzKnMt04eEe+Kl6UwWlyvJ9EgX5xbtqahw+rEuWo07Rs7oJZ7tsqVgE/IEqQWmNUFy66YgP1lsvkIJ/v/+zEgO1mDpcONlq78zbysVm3Zj9TYlBvqp8O6x3WDXAOgcakDugP9dURvDmcDzKSdpyqq3C0YO8Q37dqmBZuBMZBDOrWD2YlzooD/IpIQWqdJtclD8u6qrlj3jxK5cusg9zO2R4mUpNkaGkWjyPbdLM1wg1f0byiYmJfHkwkSYOZ6EVTDwRWgFMIEqQOT0UbDfxMDv8pDROUXD/d3MWjPwiyUXrQzEHz6uB0eZw2AijAbwsSpFYbPV9dgpCq7q/hnIM67RrO7dtqwV5Ocg+E5cTAZkERZqoLnCZG+8td9ZPm3P4rpgG/IcrEiZvxJL/7mnbJOqdtMBnvEhYGA1VtoXWv40Gvbsf9DMJUbr019WPWjVk+aIrkW9yZ1nu7O8VtinoXmQUt4JyCZFJlNrQz4W3cazaal35dneqS13/7q40eaGgq/ttU0w4htQFo3MhqdTNvPd8aXC1v2j9PQI9kbwBBRcqkqp6zNFpV5CupLtZc5pqL43Y3RXt8sD+YSOX6oH+/xqrr9qsibhfPbnlRyv1LrS/K7V/GuvM03C/Az4gSpHJ12J/EqOrhdEZ3fWFScVTfSicOzOzrWjpYvxMr5jnipHzzrsz2lOerrtyHAjilIN1963yD4cG0mJlXTCGoDvmT438uNiv/V/yn3HtTwWZj+kavd+O7V7QXS+r2lJtXS8uX0U/SmLZv3VSMAk7pO2fY+/PPvj/af8o5cqdsNJxI8hX78HsQpbuvcIubX/sopGXJpzvc10NXAL8hXpBK958D/GojTna4//o7BvAO8YLUsnWxFNu8acBvCRmkluWbh9v05f3/wSkbDTAXZnZf44uH2/Me6c/bcoCxeJnU94+032/B487YZoBdUTMpABCkAIhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAI62u3RfLbEvwkN6iCl4p37z44M2dfP8+f+LN09wEQ1tcyKb0iAGf02VxSJgVAWIIUAGEJUgCEJUgBEJYgBUBYghQAYQlSAIQlSAEQliAFQFiCFABhCVIAhCVIARCWIAVAWIIUAGEJUgCEJUgBEJYgBUBYghQAYQlSAIQlSAEQliAFQFiCFABhCVIAhCVIARDWf99uwF1almXJxeP7s1cU/5tyZ/fdvXeXD7i//PSG5qRr3QDfECaTSsV/62W7xVO17A3mFae37fO4+8tPb3gHArw+4F8WJkjVSVG1bKN49fhtp/t58+mrpWVZlnQ4/JXlcrtgXOzBtgB8TZzuvlwcEfOS9mJBVTwtH+2Ruu3vHYfwNTl8oPMuL2U+OdpwvPRYYwC+Jkwm9Vdvj1GTHbznOJ7nO/y4KO0A/kVxMqnKbiJ1K/e1I+ib+hhvMe+havOyLEtOt4RqmEg909BrwihKAV8TNEjdPDpvby2fbp1gt3GaXBXJw61u3YZF9+EkarQtWuf/VU2+La7+Xeo2FcuufX2523/RqEHX5iVU3ytI1dBUvi9dt0zF3kZvRVHB904DAL51DJqe7i/3A2RxkF82StfZw73vLdfTxIsp5IMamx67XIaxtcZ63/eDfFUsN/VP+wKLYJOaZWWZsrrZlJLBXPy6qtxtmeoKB29Fnr1y4J+XPnpUiDkmdT1A7s1wG6Yz3apyotygxmJRLuNCamqs95OutdahrJz60T8dV9QvzbkssFNFVS7f/yl3vfN5moTrt8+ZBNgXKEil6p/bo3zoIHmPCbk+to42HtSY83p4b5vUF2+jWlns8rCIdvkyByK32yzdptdlOa/zJtK9bfNBpSqy3ipYN2yrvdV6a10ZEavdXUe6xCjgmwIFqWV0SNwat2+ylfk89LxRZDTSk/O0+Kjeslg7dNV005WxrNh0nmh1q2btScV/J60tNhV6gHOIE6TqXKGJG4NeumXpQ0Mu/rcuz+WyZuQnNVuUW432cHvabNNlR9ULuo+KNUE4t3uYdBtWOxrlgeU2t7wr10/ravtXVCwVzIAw4gSpuzQYlds4WKYmiSh6DTe22shdxk/mheuYlPu1+0f6wdBTO42iyvlax2PJRgDayxsBPi9akLqc8aciDRgrQkI/PSG3/1RzIdqD8S3JGIWH+eyBXD+pZmSk4eImjWomgOd+1X1pF5eGQbwZzJuqWjraqBscBPiWaEFqlboHf6mk7jhs051SFx/bMoMEp5zpV8SLNH8NqQtac8+kOGX33rC3b7tOGRUQQ7gg1R0eD8aoQW9fW+E9wekPwZPkYXSsnoWMPM662nGotjGD3Y73UCxtJuRVZdrBs+5pU08/TiWDAuKIEqTS5tjNzppi9fDCovpAnPs7jDdbVYNcG8fsqi8vpTSIcmuP32CaXruH8a7WqNIFlVSGqS49TFtVDlfMtwD4iihBqjaOOku1dBi4BjlJU/NolGd6XJ4dzNsNb7WmNh5eq5j3Ku47FDRGhcaR+B4Yt+q9RUO9fsCXxbh33/X4WU9GWCNE0UmVltttfpobOuUqoFS9fakvMpym0PQY5lGIWved0+VBWWuzxSSHmrS5iiKpCqbt3I+qxvpV1y0qljZlu9eW1zRKbAKiCJFJ9eMiy2hJqvujytkK4wqnyULuHhzUxLEqpRtdBLU+2B4r6vewTCue2J6233QKjobs+jdFsAK+LUSQag/g1WE9T4+Zt2LltT+Xu/9c7wGU13nquayqqyjfNy7LV9vUW66ri3Y088knO+uqy83+1o0Hga1b3rVoLXRddNuk2HT8nrbrAL4u0l3Qj23o/P4jvNPAmLugz5l59hFJjAKCiDFxgki2Z/4BfNC5MqllcX7/Md5o4PvOlUk5bn6O9xoI4HyZFB8hRgERnCuT4hPEJyAMmRQAYQlSAISlu49/2IP3KqxuxljfmfF+g8iXtAu4EaT4d93u8Xgwstzv3JhnT9o7A991v7KyYVxD+9sz/S8F5CXd7r68W99sH4P97Td78PaVN4dMS96oJJfFhXh6uvv4Z+38kFen+sWu8kaQ3734eef+zONtjjd5t8ZnIotoxGEyKf5Vj4eWOiPIt1uYrZ2G6XZbs2vJ++rb0rrw7SdYyrymvEtwWo7lPJcfcynTkVu1dUZTrk253cftlprtrwvkqmVtm0bhpi0/qOPetnS9+fFnbwfHicik4GFV91Rx+H38OHtwi6pYHbFuUePRfW/1J74iYIg5vIZMin9Y3s9RxlIbk26df6nMCerjdPcLMY/ssB+CKp7dfyym+nmBeeSajRGVO3k+xrQZ4WY0hD0yKf5dv3aw7MPX0z7y1hyemsG/TJDiX/XczzvmdpDo0CH2bz8lOZrmcFuWL49Tv5fHdvnAVIoDypY0r70e1lvEKHYE6u7rPrybBQOcBDc/yR7JgV+VfPqHJ9ftT/J3erXLNO9P/lzoaGJ5vfKZ1tRTFTb38Tbf2SunEidIVZ/W/e/cd35SuGpB+ejrrak8e9h6gyDNeKlBlBq/yqPXCm8dqccjOt3SP30Gj0WKg6UmzcjXWY7t4nXuIwzF6e6rOwU2Tx3XUq/po3imkj/v+d72159LXi7jSbvvTn1e8Lc3c7D5k/PdvuDRRl57/L6SBcx3Gjspid06AouTSV1Pqo7Mt8rFBR0vOFNPT1RSX3PyyFGubfsze99v221I+sFxiQeK1x01w7/ZWfpynpg4fr3GZ2fLa8q1u4Ota6H2h23u8+eeTkiOXZB87Iqtqf7SrWutwwwLbuJkUqvD37Rnrg55jT9dGnPb9m+D6VPldTMbe2gPTI++mfVhZbj1GY48wzamuz9V88SH48AGg13ljXXhnCK3JpBImVSRAewXXJajpY9W9vA2xaUxT+zuXdNvq+tmHvHEm/naqz+/Ye8zNx1hOfh327pr3aw9w2oGZYrE6enrvSa7eFE9abi02+IMoZXviRSkesWF/ZejSSoPwEVHW2rKFZ1quaxrXZbKStq9peKftcOl+aKNE4dbne2/S3EkSblsxVIeKu8HnrJzJFcH0zb9mRxn2y3KbqNJ8SW3706z+a2DshgVLOot/xRlidgRbLtt07XrkXXjGHsPcTGnj0RplCjFlmBBqv7apOZBPalqEF7SbRCgWF7dlvpW0f2QWywY7q1cuXOkuQeg20BEqlaU29+Kdq8rrf+OXk//ZBgp7zWkIjJVL3FZmkND+fakevZaMfySri/vuuo+M6sqUu+t/ptFk9pzg4tncquRSydoekm6nJb6BKHNpx7N2canKu2yZ4e5qjsYDuown4/DIo5JtQf3ZdzdkdsFTcFqRKHpZatuxrm03SV7359UF8pLLmpM/eWea7XX40l5B+3pDsYldo5EfU/oYFxlsNuqQf1w06Gt21Gu+NKyPDb0VGjehvbMJoBIbYGnBQxSZQi4Hs5zO8kgF2UuyUMu13QH0qJIEx9ysfFobzvh5LYyFduPNsp5rTOv/yv3fmvmfYu2jrSUb07ziuvmtEvW4FM2pNsoD1/nfYP2qNffpC5V5aP721E81w/WvHF9/Wv0m8bB3TZsFbjU+niMTWn4V+orOlZzE+dz2bM83X/pNJ8YviFQd9/9GLzMT8pnB+S83OellyvLnGwcvaq9z/bWdII0idS0cZN9tY2s87jbK2+Ccj14VYS1SQfpvbIq7s37DZuWDUcJ7n+ce1/esI8sX0ts1BRDO7h3cIvUvf2DTtmj1e6/O2Wna9D3cvw3PtAvqtOPAwIFqUJzpB4fuIselnF4yGXgGmRi5daDvR0NOremNDnP9vev6R3qokfb/zgIwTOjQL5/dHv8iD3pi8zNmrBR6vF2NYOL+X6MzdXfqiiaLtnpbeyuHUka/flTXUO5roiR9elX3XmdivJ1lt7vq132WKn28U197dP4laZlfTXla4BGnO6+Yhxm2pHVyV2MGgSzaTTbevZwjBo37bbhYMiiz6i6jZtMaPjiRkG6qqWupDyCPaQ7DpfP0mDdtYs17MEnTx7vb9H0yNaP5punP6ZCr3wnX9/BFvbvzNnFyqTqZGQSJYbZR7nB+k9qDvC3f/cO9H2Zw90yzdOiO2hQXRo/HTSh6g68Pbx1AXWvcVZXTrNemFuRJqMctHmdxNfFqmlvYsxOncdbtbHF1ilHO3x1qAXbrXvkbGu+9LHYvGHn/O/g+SaMxMmkJgbH76e6+tseuGG9s1g0DGyjbVK5pEovRlFm4BaIyz1OgsauQV1H6xi/D7lbVXSWbrSAX+cPzfuEyqRGmcPSHmCbjGiajHS98bldNT5wD5dujPC2996bPUnNq2v2k9uX0IwvbBidw47qGjSkbdAkGuc1xKeqfNeI4dvkEPbTNk/h4K/CZFIbOcyRvvzhiEm1Vb5cODWsqYx0Bzv2rrNo02BwejQCNHy6sTLn+3VeW6nKoSh2qWs07F0Hr3mlVRqaB+vqZ6MRc36aPzNvEyWT2ohRL6ktF2umx87JaFe5/eXJbq/gOkqUN5Ow4t8mos4acl2cu7JTxUvP/W0wugYdPScuEq8uFLcT451n/zJ/Xd4qRpC6dU7NJit0Z/ZF110aF2wmGKR24aWWdPlnVsmRRi+5bUS1vltzv9tQrp/elxbNHUSU7oVM50HcaijqajYeTbjY1Yb40U2Uur5LgOeE6e5blmXaEdUvufZeDbulXnKA3J6GlWerNmb2TgbcpvtYlvH9H5qiG6exg8kX94ePvzcHpl44pQZeLVaQKuRlWa73GcpL7obpbzcwKgtebmd0//8iY8j3f241XddXx/Dp3gbxo8lG+lkQ113013wVLWiK39pcVFa8kmbNtnsDmp1WS/tHuW5nt/ktbNbv4rqzun156RsC8JDvDBekEx24Are1a1o1wWFny7CvCojts/ezijEmFdRoAncQaXnkTkmDrUO+KoBG2O6+AAIP+k/myj9wsxsxCjgDmdSuwIfz55omjwJOQya1J+Lh/E/9kGIUcB6C1I6Qh/NqSuFgzd7GIV8UQE9331zgQ/mhy7H+UAQgBpkUAGEJUgCEJUgBEJYgBUBYghQAYQlSAIQlSAEQliAFQFiCFABhCVIAhCVIARCWIAVAWAFvMHv8pyT+9iPGl9+7yNXzzfruP5Dx2R9PftYLf5KjfasAPiReJpXu/zlQMj3/67mp+ufAfm/r/rDPTzr+PgKEFS9I5ft/Dpd+bj/NjzLt7Pf50JSu26fq+cukScvkPcAPiBSkrofbo0fXPx/s8+bTel/XDq+cHz72pyUty/1/t+evktaKW3l5NFKlzYAn6gGfFylIPeahgPYaf9jb4xHjYL0vrlgkAkIJOnHiGzva2G8ZEPNj7UtNHvLyVzcNKw/vyQAWEE6YINUeIVNzWN/sjFvWIlXpe3C5hIpUPlm3Lau+P011mUJO7Z6u8+ju0+nqNUvK91VprS8PXmUuWjoKa7faB1MSy/eg2ax5A8unxVbXpq2Vle9hVfMr5w0CbIrS3Xeba1f8W0+/64NY9zC1pe9TFi5rruu7UZymqtu0uGaPzUG+DQLX4Zy6DdWrSvVoVCqLpHJ2Rapm5g22GUxJHM/KKMPXqMRkLseoSLtI2gV8QpQgdetL607Q5wfDrmhuTvtTXXIS7fpFw3i47i3nXEz2S7OkIufbmnyfSDhLP8YtnTW33MnugtmLm0y4KF7adiySSQGfEKa7L9c9Ysty6XpK93XdBrNnZTdZ7iro8qMm2jyQIVyal3LTvrYzLBcNyKl9NcUU+LQWzGUsyUUf4OTNGHfA1V2R6f7yctmneI/rqd2g3P3oDQZ4uyiZ1LIsdbayDB/fpLR7pFyPrPWlUE0Qmx9y90bB2vW3drYBqnuc2yl5XW1t1+I8B9traCNd2nlJBtdN+8n4ZYwC+JpQQeruHk0mnWnT+xJN06tlWXJO7cLjB+C65DBGjWps2z98PfMYdX/WTKI40MvX1D3pkmxKVi0cviCAjwoTpOr+rX5dczwtrztteunKSRPdMff1h948avNwWKt7WjUutQFsWHET5+ajV+uzSco2HpCqVk7H2wA+JEyQahRHye4uCN3xtxkvKQagNgaabkfgZj5FrlYO9lnvelSmmNBdrOm71LqNR2FpP0rkep/DJrVTM+rHdRjrWqHTD/iaKEGqnDZRhZ7bDYmOVNAWLI/a49GXyboHZk8sgzZPJ2DMkp3Lq8yzguN91cUG6eRwRG5fSmvAFJuA7wozu29Ll9SUKdCyXGamFVMp0n3O2mhA6Nixd2uUp53XsFVhm63UaVYRWtpgMqmyG46rovKsHX3QS9M1XaXzxgC8WcAg1eYh9Tl934HVbr4GrHw9pldF6/Gg4bF6FMbSvR+wjlHT9Gt//mHV6nsF9xf6wsBwIEZ1SVn1hr+yMQDHBQxStVHXWZMflUEjF4WvYWVwYdF9WTffb9iE9Tjdxaiu5lGBtFSH+Xq86t7Ubu5H+XSrjdupzi2tHHYmjl7CtS3iEhBApCC19tJNj9fN4bWfnnDLRnIapWPF83X9GlNyWbwp3BZfn5VtLgreyhVhswxW6b7LtSmj63S7Ibk22BR322v7Rdfkr81EL81u3+ayqq5LtSopgAGfEWXixMU0Zcrl8bY5vrZlmxlsG1Ouq/WTuoZ1H5iMsJlvrQZzO7bbc2R2xaB0Hi/e3EAkAr4uUpC63Feh/N9kEkEeHH1z9XApf3g339fmaklT5Vbo2TrYl3Xn3Ky5vapy42vL8v0/5Su4b5jbDbr9317hVsjM03+u71LR+mKXReh+9S9WATzgOyMPkSc3x21b3JYB/5DpLX/eItKY1Nel9Ta3wYxm1AP8vkjdfd+Wwl4T9NjFxQA/Q5BqBYxRN4GbBvAWgtTdwfsvfU/gpgG8hyB1V08ujydw0wDexMSJVdwoELdlAG8lkwIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAI62sX87pnKj/JhdfwUu44Aa/k7AteSncfAGF9LZPSKwJwRp/tLpBJARCWIAVAWIIUAGEJUgCEJUgBEJYgBUBYghQAYQlSAIQlSAEQliAFQFiCFABhCVIAhCVIARCWIAVAWIIUAGEJUgCEJUgBEJYgBUBYghQAYQlSAIQlSAEQliAFQFj/fbsBD0iXf/JuiapUqkrkdkFRXbo9vpXIqS2zbtu0oinZ7ONeum9gaoql/mGxQSo2rd+OUQPyaA3AifxUJlUe1VMbJ25F2uWpeJC6JfWDcZ19yVm51D1O0/Ub26eU0qxN2w0AOJefClJ1rjA8UPfpRJ/k3Jbk3CyZxp5iRaq3bPZVLi+SnUMNrHaT11Y2q7ocSgYFnNeZuvvyboKQ01J0i6V83+pyJF8rui1Lw0N4vh3ocxVI0rptZY0Fad3rrSWjNpcvZC14q+e+4PaoKFL0+ZWNS+WqlJfBeyVUAWd0vkzq0NF2o1AuV66pTZfXtGnU+mQ4HDXYY548ym3p8a6W1C2ZJF5r24evW3wCTut8QeqYXKQc5eKlSkaerbx8UvbvDcLBcMhoYDyGNgxfRZBrYlS7j/sA1az/ESC2M3X3jVKLXlcgXfvoNg/UXYiYFL515u01496dOE5+ut2lOsOrmlSOeA3ypfHYFsAv+K1M6miCtBPtRuGhVs5b36hrc5ZfN4mib1tfb1+jGAX8sDNlUs/10aUjW6b1mqhWMZ50qWrQ2Tfd8UacKWYMDnddJWHrRIrdHK7eC8Cp/VYm9aw0n3bQz6U4cvjfvVCq63vcnHhRLhiledP5EociNEBYPxekjnZ87ZQbrz4epY7ufePy4FnY2gtOVWehCAWc2pm6+3Zt9dhtHK3beLBRNBcXYFXVr1vuhoi2Y3EykWLYnKLHL61rh5MK8zK4rRPAuZwtSD00Q6/aZjhHe7ZgmjJNh64OOTQ9sbz+uNum2v94vKufJChGAWd1su6+44fb6t4TuyWP73B2F4lq8VYudmTxRmDZfE2DFfr7gDM7WZDaVd3CqF2RRuvmadWjx/dqpt9lJsbs2qzU/Lvd3/h4JiQ0Ab/hRN19Dx9422N7KgaTunsdbVafliWPb/O3LMdnhd93V+2qCKu5X1o0YT/ZKqPkOmkd4LzOk0kdOt7eLj4aZENp2IfWL62GdfpronYauBmvutkQk+kR423SePH6ZG2A2AT8iPMEqV1rYKovo20nQZRRKA3Wd/XdrzbaTLaKGLU3ZLW1bnIJ8H337dSINSiNqp9e/gVwCo92Vb1or48eOdeLYNuFRwahRouKjCOPRoGOTqfou+5SV6YqOJ/5sG7dvqhBC9di3Xy/1KwUpIAXmo99vMPZMqlZB1kXZSaxoMpY5oFo8ru+t8rHj3PV1dbU8Mx0jGKb4VbjmfPt9BAxCjivkwSp0SBRP/dhfTY9MI9Hm/b3n4uZepNLeXP9vNlBVWx6G6NJFXnJ62tqpwwWK/p2dPdWBziTk3T3ARCD7j4AWJZFkAIgMEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgrP++teMHfvsPzsNv0MBLfS1IwU9y9gUvpbsPgLC+lknpFeGHffanS+GjPttdIJMCICxBCoCwBCkAwhKkAAhLkAIgLEEKgLDiXMx7ndWYrw+ensGb7luv8yRNBwY4pTCZ1C2ipD9OwU/3WlK7EICTCZNJ5SaTelKxcS6j1EYulZY21UrBc681WVyq5tc56B8zUoAAwmRSS87Lsiz5cljNTx5bq4BzqTA3XX/HKomce6Xiv5d/LulnuaQpDHBOcYLU3Z/zmJzzvYYDNXVF8rHtviU1/96fdOlgXwzgXMJ0912l/Je7nt0P1VUVeec4PYxSYXWJUl4nnRT9gJe3Ii3bXZ0AsUUKUrnuZ2uOuPejcW6SrXXs5dLplesVZZbRzPdLl13W+2jHePJSHvH/dsCvBpPeYC8cA5xMwO6+ahZAPdhyHYDpOrraR1VdwwLX6X/riE5al9ejPmmw5knVYNLzcu7i8KzWh8fjAEKJF6Suec1wAnmfhNzLjUey0jWiDQ/WuXtQzdh4/bE9t3t7qpLgQ2YALxSpu2/JVaTJ6zy7ew9f25+V1oIpN9sX9d7LXvvsUrEmFSuXYmUqV7+I2ALwkFCZVNt5NQsQuU5+jlc+H1fKo2dVx2M4r46gAPGEClJ3aVlyEVCaqJLvnXJNyOmP2XXf2L3KYqpE1624JlLtbuPlQfFaBPBSobr7SteOvFsYGl6oWukO2P1FrfWdGpp8bO8a2GgBYac9Od1fRLSWAxwWKZPqAkMeHmAHPXPb/YLl2mpiRK7GotZZGM0Eh1s29/cp6C/zl4vJAM4jWiZVjTV1E6wHwShVV091l0+11T94dO/iUpArYw/dSEMiBZxepEyqMh0BGkxD36vrViDnpZzcvt6JdXjp1fAapDMd8XPAUTSAh4TJpG65UnMP7/omFPMN28NxqmpL5RjN+LZ+qf63LF6mU3/JpF51v4l7zG0m079thwDfEiVIzW6YsP17G7k4Xlf/zo2u4O1Xbi560qvmi9/rmQTMIjIvYlRY1cchb6xbcru0GId95M/rA8EpxezuK65TusxauM5d6GZS1Bc0bdSV6zLNk6aaYt56e3uHP3zFX3R06GJdSrfp8qm8lZRD0nldP7P+ePCtiQCTO0P8udJ/4JhcTVVcn+d6VS4W8wVHJuiUl1eMuqv7dVvPZm2oa9rfZt6mz2iyyI0D1KNt3Mpdm0LtnOHn34qUf/G49NnZxTEzqYel9K/cfSE3D6tMc11TLIYHxPrMfPNb/ZJ3ItbbeU5RxqT+5jbL4V/4RLSvsY1ak2L8a6pk+nY6f+jahWPl3iSvycxbIlTeqrYdDJwfVVyp+Dk/kkkd/gbCuTybDp+3Z+H+il/8fX7gnWw71fmm38ikRCjOqB76aKeqLoNIk/uS7SDOsmYL5UUUdWpV1rA+Tsu47nzgqP35iTpv2teRah85A3Bg+qufyaTgdLaucssP9ne941h43myMH/IbmRScUjHmUdzb68BGa25zn8N3mdFZ5D5rlnR7Vs/4S4Paytzq1ri0pLw5knPf7l1GeV+/JNeFl/L93GhdnUiOR5vqRLIsVyerXaPaH8CrS0uyDpFJQSBbR9MvJDb9gT+Go+NLn7nEZnjxZr+C58ik4CvSfKxnKybUPzGTmuxiO+Op86X6eqjRgbbNA/bzqddZx7j6vK9tS9WuXCU313d5Ohnv2GVkuWpE9QtCeVlSymWJVJdut6hLsk+Qgm+oD/cPHK7eGify8Gm+tO9Lh9Rhl9ndy96O5yaVP7qNsPQ4QQq+5366Xf9zdOOlue/CaAJGt6Qf1dkscF82yrje55J2pCVXQ3WzrKhbUm/2PtcErwvuG8lbsR1HGJOCuIbHuenB71iH4UCaPjnp0bSYqv++qFoMEh56l/pC53xzP00mBWHUc9K7EZLbNIbbeEs5sjSrqXtSLMw767uVXziopuvcxL1c6ktmDWmb2Y5BhXkBJyCTgi8oDvfrCXla7vPWUpfRPBEgjmyy3vayiWt5tPA7cwxrDx7f39fenCO8Hb9PJgUfl7qnOTUpSz2brb6Ep8wsRhfqXNdczt67g3q+1t7kUakqXdxw4shY1Ps61gavLLe51CCzumaZXwohacnjnRc/6iqXOkqQgm9Zu3+q29Udm0X+xO626vj74fwzR91jR/cnJnd/4YoqDvmh35OCMGKdKMdqzbZm7mGq5+i1103Vj8oKrjlnTtV2ZdXNDovMZ9DTWW95SxzXfbWV1wnu7dyj2ed5/ioNvycFsCyXk+imG/QyZ+Ro4vfG/r4HE16DV8+SScHrRcldmiwDXkEmBQDLspg4Ab9PHsWJyaQACEsmBb9LDsXpyaQACEuQAiAsQQqAsAQpAMKKNnHi2MWHg9tZPneHy/IqcGPMANHEyqTqHy2YF5stevTOI/s/ugPAF4XKpI6GiVzGssl9ow/deSnXUWq0wXfuG7WtyBrLH1N4tsRG8cnGAB8SKZO6HCpz3v25gmqL2/9yHq3ZkW+7nO0zBUywiqxx9HN1R0qkKvOcFp9UD/ApgYLUejp/KErl4n+Xf3L1a80PJgDjwpHvhFv+bt2zJe7FtouLUsCXxAlSZf/T4cBwSYFGsy1yPlpJsc9THIzvaWOfP14L7JdoH6zvVl5PE4qodYo3BvhBocakavdDZJqNrqQitJQH1Hx9sq5vD7WbAey+n7Tu/QWDMy/6he1Px4ssQAHfEyZIjQ/h1988S0Wac53JcI0hqfg9zUs8SbfgcglweX1y8Gh7308aLDv4aqbVRpmHUb6h94VN424/H5r6VfAm7bd064PXHzQO9nC3u6tOYvN0TVH1wzt6yv4VOe1b8ItTneJ09y3FWzv6oE5DTP8HKZekwaP5jqpFaz/gK5KJXPz3NXU9X+JwVygE56PcvwU/9p5EyaQGYSCn/sylWlQUKKZa5HRfU/VUrV2AuydnuayqWfa8H/vkwDusX7jbHN03fnHWOUO3A0nZb5OXtKTrN//WR7PXmGebO97ukaNOlB+Dfr1QmVSluFSnjS07f7g8e3pwpltb7DUxKqTNd+RXP/MwkctOhtN+/k/b8IkomVRlnO3k9sk8bJRritlq3Tbljtru33XixXKPUb/21/+118O/ZecgcbCK2YBzuyCn3RPep89kn97wfvj64e9yyCA19MQU8TbY1QOjOxsOFvzk52DnPT38nsHJjCcK3VYMP/JhY0HUdr1CpCBV5jT3E5zq1ghL+8fo/zQpT2Yo5DZVarZbliWn0RF7f4bNOV3PCzemmK+rfu21cxr15Sejset6Hmp9Opv7XpXy6fgbsHEUaGb+1ZXfjlX1NSvj71fumpiKl9JeMjNqV/XtLK6W+b1T6khBKq1vcdM9V0yWTt0HtZ0dUWn/UpOJMH0vYLswp+gDkwenq95LHHg1PzoSxw+pJ1I9W8nmpu++VDD/PawMmhj9eHVcpCB1cWikaTo3Yi08OMCOZkQUD/L1Tz0+W1sC5/rLcqQr9JmvmjDFd7QpRTPV7nYJ5XqlUFoTkfuSJeV833rLziUb/TUxZS5Vzga+ZU/5egBKRan2gpg+zetfaJ+r3esppjTXL+KJkZHQogSpfPuDXp4t5Qfw4j5b9PohuOVVbUVL80mtPhj9CFXXkdgkVZfI9eczndfOuxhFy9sXaR5PB1cnL9VE/Wn7Igdn/k25eNB+u9buv/zcieWTp6PlN2z+Zfql+PERUYJU+afr/rxbI0VtR+1aoKmm6rXtK+13OukZ/IP3ZmLrt3b2LRgnnFt+rXeb02hzn+1D+6xv60BAGG06WvJAbEntKXD1lZwdgvqlg0NQ+qFuvKPCBKkmE1iaG/CVWfO6JtebVNUtt7OoauboUl22VxUvdpruKUZeS/79o/GSD1e+fwWKNLH+4Oa9Et23o+0+KAdx/7nvBFQeDQuj8YJRQNrLqXzxLuIEqSaYLHUEyv2i0d+wnhJQllvn3KSN0Dbczys+LG/4vDVdmZN3Y1SiTSIH3YDL/d6Fvil8T+q+hKNk4rLsyY/q9DCy2Zzj1V5+kq4PSGkWum5rpy4Dbf9ORhXnjhMpbf9ldjff/JT+UlJQBNLcLDxWIjclL/9p3pufeKs4sUc/gS/+xP5x6ChtPl2WZb/BwytiZpX9sDCZ1K2b6dbndOQTt0aevZP+6q8aepLeEW2QebhEu2wc687+NnFy19lUZT/1ZOSpSS3Ki5C6svXitWC3USpL1X00O+2eDpQ3zXigjr36flmYIHXzQMqTmkc72+xPYgO+74WH5zqqHekh24weMbvYTn/WveM7r6/PlPqLHA7Vci27H6SOzA2FV4l5NDuD4fUj1fhoe9HR7QCQ2wX1w9ItV6qXpMHKXJW7/VHHkWy9w2d7aOr3Xk/3S83yvsRN+eL7u1YcyeFe4bMf7yhjUjkvxdjo0az69t9m9GVadrcYcDLbIeSB8ZsXHRq+OGL0m4NVUTIp+CUyqajaC38/s8Pl03t9r38zkwKATriJEwBvchvS+eQO+wEwHiKTAngf0emPZFLAv+Ljw0IvuT31P04mBfAm0qi/k0kBvIn86e9kUgCEJUgBEJYgBUBYghQAYQlSAIQlSAEQliAFQFiCFABhCVIAhCVIARCWIAVAWO7dB0RV3p/1L7fBO82NyNeGfvynr8KSSQEQliAFhJWXZVnynzOKv9ewLMuyJL+88QWCFBDVP9ff9aJo+lOMSQEc8Nk0SrS6iROk6k/AH/5C9dDj3+sD4FuiBKn2JCU9HVXS4NFf6gM+6fa9nX9lRylNrhbm+bzA+0nsoJZc1N5Wka5b56aBzY6aM+P+lHm0tK+pb1xb5t85okUJUi9z+yOm3HxstzfJO0tCeXg+7fgVVss+PEf3kbw5nezPw2ddo8bBr3txlH9L9917ar36N8+1IwWpfD/6pKePQdXHNV/rS8vun/dEf/1U/PtIoBqUXZelp+p8ld13P/XxlF+WlzQ9Cty+1U2P/nr8SMuScvmsrXtZ0lr5rWDxvcrlgSTfT+BS9d/qKHPbbVNrumdL5YHpVsNab1rrzeO9lY37107S4szuK6e1/G2KS15yXcNObXm/CO+1G3ce+HPyr8tVPjPu2CtXzxYd/KBtFktFqb5gf6Dz4e5FyaRe9Le5nWOs1R2p+FSflDXb/Es7m06JstKPuHXR3PZ5okyWN9v7IF5Tj2Ox4fo0F8ubwat+y1RvM959v2kdGG8f8dt/7p/3soFdcxiIEqTu7kOX5RhmsWrp/tzL7vGuOJ7Xn6O0dgk2A6rlKOW66C+BoWv1H+UHPt25G9aZdJ3n5ztan7bbid81Kotp8M8IF6SWpYg1zel+GbdSf1KSl27VqOK6VLuvsvr749SuCeGBhuwUffdg8h8Nukm+0Ao+pBmGGdgasdq3vd3ghG68+6Ofwn7mXr30kZfx2J5/RcggtXWk7FbN5mrWq/u8bLKvIl+Ke+guvqBln/d9QZk29qnh4Ji/DvIWPYnlwHN50Li9l/VZRJN7XjevCmwkk/V3tspg69eTh6+wGFH+x77A/7CtMPW3b2yEE9Fgh5xvChWk1gPuOnGmnDuzzM6y7jN66rOM5gBeHnTX/TQ9yXVrqor/5rWf+zIP7JeUOec9D1zfw3tTmjZVVSzl1nUOep+61G1cZJup36zZe7186TYpH1UfgMnewqW6vEValrzX2d18CNrz1tlH5NhnJ013v3Wx1iyqPpYS/osf7jiz+yqzw+i6aJA4TLeoVqZlyeWg6zB7zvOH3/6U3HKI8chc0WmZyuXVo2nyWVU6/KfZoJG2SuTcL0+p+HukumAzNN6uhbn6C55Hy69r2zgyXzcpVS69f8OeP6mthx0O7vq3Rcqkpu9/lRvVpydliBmdouf1M3rvoCqrKz6+qT6PGnT6HXoVb5fvH+O8VJeCLcVLTrcMtHjHxlnoRqWX/16fNXta7tV3fYGpStaKZHU04aE+rc11Rtx00l7/HLl6sUtaL9zORR7Jmd3+hqn67+3spT5Dq9eOg869TNGv0KxqN5tVn9v9F6XGmdKy5Hvnw+VENw2WrldOrc1pX/4aB/+lz3i0TGr7vU/LKIWo128tTuUZTmpOd3K3/T36pWG69S39eFR95lgf1dfMI28lIdOUdb8h5Xlq+ybm6/9tKbO/1C1uCm5VtbcjzmMUOup1y7J850uZ5gHpwMaHtvwX86W5SJlUJ+X2fOfqcP/eLREoKhhsW+ylj1TLsjFu9R3jRo4ziLrjdKMr/z6M9JdX2Meo6//S9Ry0K3+kb/++qGvb7Xz7eIcv4Y372icLt3rqi2fDDr/xB2XeNzju+d7aav/xrMTE/YvxhWtFviZaJlVIt0GWW6dbEUvaM+507zUa/umqDsLpedj2prE+E/UoTrty1NrLu7kZgv423HPdwcYbdbCfPU8W571MC37cP/mhD5RJ9Ynw8NkkjRjXtyxtGlSej9+Phnntch7eiqHskPp2sLq1dDrhcBiidt633UoPuI0YPbhV9aZub1wnU9GuC4D3Gw28/bowmVRK63+7VbdEKS/9UNJtSXsQTvf/rPWmfnDpvtvRupeNRaXUN/v5yibLJ9nTzuPr80GlaWNf42bNt5i/ieOUcL60/yMPGgIf4/P2ZlGC1OgPvdH3nLtT7/Yo2IxEjSbpHZsr8GgCN7I1Cvx0dcuBvs1jK9a142DxkMGcp+3Kyni01cS0DGNXqDktfFF9nrtx1vvi3Ra7e790Pd/9l67ECNTdt+kWKrqJNfeLfTe2vR3HiqK57La7B6DbGP/t31vxS+n89PHwQCMfkmZPy1tHXNOacXS9frNyt6gZEbpfzNvcGqp6dtsij7YrW9btbPRbIU3H41pPnUPl62yMpZjz0b9O4OS+M8SyP3SR1rPmwy38N86pm5SwzRBTt2p77Ob+Llcx6rnD/WhPZRQq/jhlMpiGW1RnFsXAYFkutY/anX7NvzT5in/OZz/eUbr7Cmkdg3pkq2VZIhyd3i5PMv1+Luvtv7csNOdlyZeLpe75zVqmmxxZbz6e0lKtuxS97mB0f4lJq+/tG7fg9vCy/n6x17plWcehnQLnES+T6s7oD7Ww6av6d5wmffy3cot/69Xyj/nsxzvqmFTR63QkjhqNCM2fB3hSwO6+1YEpX7V/7+z1FIf/f6YrFni5oEHq0QPavz0W8anprwCfFq+7bzxw/sBW/xLRCfhtQTMpfs4/ehoB/E28TIoHnOPIf45WAhHJpAAIS5ACICxBCoCwjEkBodzmrObDNzYY3MC5vThv8DMI7zS40+RO4e5mz4Zyr2RSwMntHs9jX6uxezPOf5tMCojkfv/7V0aWVN1T//1ykQ6tv5/DM2RSAK8nKr2ITAqIqP8Jsa2yx7Z+pM4PapsvvpVkUgCEJZMCoklLTrnJe6Y/PV0s2/pducuwVF6WlOtqj6Zc5aDW1gDXaO9149daykmH/c9Lj8r9e3mWTAqI5y8/x3po0+8d6kP2OAYmkwIiuaYNaVluudQlqUj3Z+vjWrl2XudtZW4zl3VX/dL1t+36jCdV225frpWXtCxpLX+fBrjcC6Y6v7rNEqza8U+RSQFnlfN9dvlzG5f/vtvDc+onvxb6rwUqmRQQy/bFRZdj9K07sB6dWqaH8KcuWOqKdkNRuSh1GUhrN6jbU44ypSW3e0i52GkqtirLGZMCCC9WPpH/7R8HfyuZFBBJuqcn4zhUTpS7Lrk/SUvOo61udf4tsqU83UG5m63lW1Hs9pJFupogBUR0PKCk5t93ET2+QncfEMuRGQaPznYY1flgUEujSroRqtalobct1xr62xOmZgpIMSewnzyRXnprw9AEKeD37B7Djxzj03jWw1z3exuzldM9jjdI0zL/gL9cMveHvcqc+Wluev28+pef2t+B6u4uUdxxYr2UKKd2q9uS3O/kuYN+eaOLW52zn69q7jRRtGGwtmjq+I4To31/1mc/3jIp4NS6PrNRsjGNRE93m70qoblfJ5zrBVzJpOD1ZFKvcqLpbp/9o3/zIyaTAmDLP5RumYIO8CdbN19/6x7/CTIpgPM5SSfo38mkgKiu9+k7xwH5k408xRvyIjIpILp/qXeLhkwKiOokCcNJmnlSMikAwhKkAAhLkAIgLEEKgLAEKQDCEqQACCvOFPTq56D/XlFuF4wrTsXS7979HoBOzEzqL5fupeqfprK2YtcIAkQWJ5OqvPI+KLmKUnm+rtz9kf1/7QdHivRvcmfLMkEc/9jBrESdTn7+xpkApThBKt8P+pP05lBMuBx7m5+xrH8Bc78FB354+nZI/8bhO627nr9Vy71x1x+By32BQYnBiuVLL5N/VnWm1J+Sdb/Ry0+L2N03jlTH85a85HHZyfb3xWm7XNuSyV4+Zf5rcKn8NzUL58WXtcJhZgYfMfi0pcGHlH9EnEyq+GhOOuEO1pGXwcf4sc/14Wj4BdcTy3TJb/IsftxWpGVQKK0P8rX0ki75Ulk23dbIpfic9bQq3/5773Zez8xuH+yT3CGd5wUKUiPrmEha7ofUajilWnBJHbbTgPvH/F6uTUnWj33X03DbSTVWc1+Vqn8GjXjV12m7niLKVC/Ll5mTev60lfOL2N23Ws+pigNvqlatC1K7VVfbPbykps6ypy+lZh9lp1kabHWfTnh5lqpOtrr+z33VmgDc9qDmnPOty3I8D3JQF3xAWpa89sCn/lG3gQj22+IEqVFH9O1BXv8djVU1RgfVlPY/ysVAbF7qz37RkrL2eu7C1sE875Z4uW6obbBq3yUsZ4GKD7kMKfvAcROru6/+ZJbDKbmYeNePw1yCV8r3eXwbO1j75IpJAqnoDMtF1nMd+2n3Ws6Da3d/6KVFsd2qPEwK4a1iflX4mjiZVKsYxGmubaqWdWFp4yPen54Nol3zNOc1txq3ZOPh2eXmX/gc2RTLsgTLpLpEahRU6jzq4MzUR1a1/Xd52JKmkf1G8TzfJnMugC8JFaRu0jKaz1Ne29eNDJXrBrV1Nc1qHxXoZgeOD9m5z+q+79JfOV8LZ+dT/OPCBKnxLIVmWepXtB79xN7zoHUm4aCqvtateR6hvjd59qYdSI/uiauLUfio/Q9cbubn8rPCBKnGxtS0ZZTNzHvktrKrtFHkWEXtui8ey9tgVMyqb4ocbeRGNyfAR4QLUk1vXh9Gjk1Dr7UpWdeB140kFXlVrpfMnlVN/sIgzubMwtE0vd0m3kKa81U+7cj3p74AhJ8VI0i1oWgcF/KSUxfFlmLJtAuuiRn9zIv7Dq/3kMjNnupcpK6ub9C4FS+MW6MYeIsluS9x3X0ZbYq0K7dvOwTk0/mvihGkarsTxYsi0xGXesthF/d6YdNg6kW/qljdXVRVXHM1Pbd7d3bVvydlgM3L8D3aalRO5vXxDeuXOi/9FzzlspQP6M8LGKRux/vqqtu666mc0rCZpfQxrLgwd11WVnRbs1bcteSafrQTK3IqS89e11+t8/WqS4qr6Y6pDM+DndeBNKci1bvNUGlGthwK+Jj+PE+v3r/sO2fK7xmRDzgD/C2KuDGZULiWGJ5tll/5vGxUqCPwWaZDPq35TKfB47oEH/fZj3fcO0486B/K/YtJHrlZ2JXI3br6+bq+vb6/7B/8J95WQrp+lsWof9ePZFIHLqCCz5FJ8cNkUgCwLMuvBSlnrwA/5UeCVHstLgC/IOAU9KeITwA/6EcyKQB+kSAFQFiCFABhCVIAhCVIARCWIAVAWIIUAGEJUgCEJUgBEJYgBUBYghQAYQlSAIT1tRvM9r+yCT/ArY7hpX7lLugQQ2r+Bf5Edx8AYX0tk9IrAnBGn+0mkEkBEJYgBUBYghQAYQlSAIQlSAEQliAFQFiCFABhCVIAhCVIARCWIAVAWIIUAGEJUgCEJUgBEJYgBUBYghQAYQlSAIQlSAEQliAFQFiCFABhCVIAhCVIARCWIAVAWIIUAGEJUgCE9d+3G/CAtCzLsuTdEktVLlUlcrugqC9dH98L5NSWWVdN9pxHOx03pq4iLe3eJ3UN9pzvxSa732pxvWryOtr2D9/cepv+vRz85Y6+6rqFWx8B4LecKJNK1T8bJbaKp277VDzoCjcPUr9qWHLWxlEDJ7taliWlUV0br//4m1M/Hbys5nWkneeDbZrG9+9uv7uy6nlrgX/JeYLU6IDW6HKTbtngJLxPCm5Lcm6WTGNPeYwd76Wtsamu3/lRG7Fn+H4M190C+vR1XBs+fd5XnHMXbzZe2vzP0j4TruDfkr7SdfJMl01a++rm26bb2qbzK68r7/XkZe3h67e4rV+aMDZowxoNizV1R1Z9kM3Fv+Od52pvw/ZutHopyg/ej0FLmtbXr6N7raPnRbmq4sH7eORVN4+GfyXgK4Z9929znkzqVcemXNaT50e/No2atmOYsW1usV2oy/2Grfq7dn9P5yiDNq2LLu9wHhWr3vLhuywawT/vPEEqP3TEysWp/NXtBL9fM6xgY3HfVTbv/kp96b7UqDlVkGwnLGxteF3e9Jflpe1+G+60fR2j9k57UAfv9yN/tHkkmzwDft+ZZvcdMzrM5d0E9ejR79I9mo/0kubbvh/MB9LROHrM0b2/8vjft3/wjg3DTzXTIs+COvDPOE8mdcTkQLtzfHzCeDTp2B72C49HwwYGy2c5105T0nODPVvTNur6jlU9GTqb1Q/8vDNmUn89lA5qSes1UWX5+jw+p2XYfTdp0OZYVdOkXGUQw0DzyKtOzb+bLUj3/Y93sdPbty6vkqHdFCi1r7qcaNE0EfhnnSyTeqL358iRrjs8jhXT9f6oiQnHosOglnKIrJwf91AlG8Xa1zp97dWKI3+hbtTstu1GTEyPjUsCP+CMmdSmo4exPH9WRcI6m0nLsmxM20/dg0GBNk+aHHqn0xOKynKVAD6fYubRjPiD21fT8+5vzaiTbuMt75fOem5nwQ34TecKUjuJ1OzYujcP4fiQzzBKjZ/t7bQ6nLezDDa2Oyb3te62ZH/t9P2/ZIbjhHSN3LlZ2LzqURJoRAr+cacKUn+KUe0Rsnp6+Ph3LH4ciHpFjOqnGzR7GVaXlskhPC35QCvvJaZDTLMdb1fb77q/A+LkVY+al1O5RJiCf86ZgtRDA1K3wrez+/nxbbTmnoj015imZdrhd6iBW0Fh0poHlJM7quaMZkbMmtL14m3tsPh38ia3nYmDFzn9G+jbg3/ciSZOPBQC+vP5deGBNGW078cKXac07DX4SOycJlIPaGPUw72f3UhaWTD15Vp/DTaSKPg3nSeTenxiX1s6FRlQP7t7e9JDGv/UxKNNGPRqDXfe5ST9a7lvWL+mB8az3pmkVM0YzBGsG7++6kGOmsqcSqyCf8xpgtR9gvXmobXsM2sO4uMg1y8dze27HBpns/pScfjcPJKOern2wtjBmib7rNq/m9XNX0dauueX0adBneUkwdksiwPBu3ooNsG/6kTdfdeLgKbHqzUb2T+klZ1W+1c/peJ/23vfjAMHRmIOTmbbyyqb7Kxv27yC4esYxKim77AbQNqcpzJcPl54j1EGp+CfdJKf6igPXeO5AFuDUKNFddbQTibrpkP3FY32WwaHQQfdoPnbzcyDzcodNJ2D5XSJ4fsxa8rW69iRuzd0OAOk+TPt/HHGe5q+tcAn+amO3jxGDZ8sy+gu6NONil/rvT3YTsby+HGuju17+cN8YkS5ZpB29I8OhJM8i6v3Am3Zgz1s41jU7HF/w0P7e+D1Aj/iHEFq2I202981WjhaUczBm028znnJebxyPBdj/nR/EGpt56Bl90d50to8Cg/9PJF5kZ3V28tz39BlP2DdSu/8SWVQ8O85SXcfADHo7gOAZVkEKQACE6QACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwor5e1LuIMpZud8XvFTMIAVn5QSLn/PdMy/dfQCEFTaT0msC8H3f7hyQSQEQliAFQFiCFABhCVIAhCVIARCWIAVAWIIUAGEJUgCEJUgBEJYgBUBYYW+L1LncmyOXTw7dO6na7q+VHVTvc9iC7Y2faMuxzR5sC8B3nS2TSsV/H7mp1LBk2i/ylDT+Jx3aw1+acXTbb9+MC+CgswWpi9z8OzcJDHUOdbSygyaxLwkOAA86T3dfLg/x+eE+sVSXLmvLS3plD9goEuVDASq9tR8uLfn2ul+zm7S8rCqAsdNlUusA0tEBqXzbYFTPu+Sci10eO5Jfx4sOvrRuj5dNd2p/YYxalp09AvzZ6YLUPbq85PB4D1UvTqTyvcpqD5t7WUs+15jtzV4elI+NsAH8yXm6+yaH2YPT1WYFLr2AxTy8fO36u2UclwJlR9xgj0XH1/zQncvRqap4LjYsM51m5aXPsCx8b0Rd8tr+oolbAaXutSt7Qasl9/bmNOq91PcHvMOZMqlRqnBopGd89OyXpfV/aVmWJaWUqgWTPRZTDm+hpk7R1o68YfG0bpCKrdf/3h6tj4cNuLb20u5+ffG8KDCdL1lPTlzbm24tGbRg0DaAPzhTkKqP+Mv6eOfomAfRLQ3yjO2+v1sg6FeUjci3XU5bk/o4cN+umrVYVDsbULt3feZ6r+OkLlfL2hc9a+646ePXJ5MCXuw8QWrUn5SW25F541g7PXI2G1YZV9EL2EaVYVaW58/65t0qb2urpkysr23S+uG6jeAxWpXaCJjraFm96917bNoE8G7nCVKDtGl3ptpacqf/a7Oy0XF7s6rB1qPewlsnYHesL8fEupqK57MgkbffleHdNzYqyO2ywX6nYRDgL040caLqoKu7rsbqDq3yQqlr/pDTZfl2MGi3G6R02zEhFbu/13DrF9zacHNp6la3HZjtXI+lesvusyv6iRNNOrdusnFWIEAB73CaTKqcYzYYGxmMOqV7qrF9ml9VVmcQo5kag+P/zgG6yKXSrYa2s698MgnAs4Gpg3L34FLL3rS8cTQ1RQL4iLMEqYcPitXE6WVyGG7CR7GfKnLdIkgzT2HUriYRKSd5lPs9EDGbRs7a3Wx2MEmbvp66QqEI+LKzBKmlGxqqQsf2TIXB/Ljd/fS1jArsLi+iVJP+bQaANiwuTe41nLyxVWFdbb1d3isxmL54cIcAf3OmManCo8fHaVxpjr9l5OjCwVbH2AM5xyyWbbymvvY832Huyre1969y3p75eoD3O1EmNXELL+0Evqt5erO1+KGD8GAy+aSm3Wp3osvuZvMdTOYzzq3zzvsu0K6m2bsP8DcnCVLNrLR+zfAQmQaPluHlSWWxbquu7vkBuZ8btz6YRZs2BownZeRDoWhzB6N08bJ8NLL25M4AXukU3X3XUfzbhID+UqP5hrl7tJQzwkfdZtPE6DJjvd/hrBH95IN7Dbms7bEQOy2/sXxs8HrWKL0501zgAj7kDJnUcMZdkWNszSJI3eVE47gwmsswPRQPJg9M5usVi6eRdNTAqtpZO7avo+odeT2DRSIS8D1nCFLttVD1vzsXl+ZroGiLrWFgrSbn24P7NVPl/4Zzu6tG1MfzXF54VZW71FbsuHtZudlo0I5xaMnF46bCQ6+nWF28KU3R4TsK8Hr9fQs+stedQ9xjc8bjODrRIaqztx94uf4Xw9/7G+KtU4xJncJZAytAYGfo7juFYkrEaRl9AqIRpF7r/DFKpAICEaS4SP0kSoBvE6RepJywd0qDaYEA32bixKuc/th++hcA/CCZFABhCVIAhCVIARCWIAVAWOedOLFx5Wx7G+/2R/7aW+xdb2Oe7iUmvwq4MTm7bEl9I9xxrZOqR/e5Ha2pXnga777bY3kH8413p1nQ3Yk31U+H9bTbDJpXNnB4G93BjXfzwRt71L/sUr63dYHZ2929U3UV7VuYylLmn8BLnfbefRs/0tEelNqjanvY3bkwaFzPtNQ8yAx/FaTZZFjsXnQYpJpDY3mA3rjV+9F3p2vHqM6unnaj4vHk3ZmGimq7fCwObLyF9frZ623eqTRafq8v1aUGNxvO7pnFeX373n1n7e47ekjcX99dH5TzE5c7Tbco7yfereyCzrjYVv3TKh/aarbN+lbcX8cr35z7XoZlUvtkFMrmMWa6sPiNx9nPVxaxsIpjKRXPJr/s4mJoeKXzdvcty/TodzmY1Uli2VOUr2cC5c8P5utm1xJp0gdV1ZNuR7A27JVb57VArnqs6nDSpghrY4od5EGP0vVmRuX5e9n3VDS2qqN9UcN3JxftX1/H7dHszbnvf9g7V707ebrb5i8469xN45O6qol50Io2P7qXv7496fbS10pu7/Qyfg+X0fK8fgg+e/IJP+OsmdQrzQ6Axeq7Js94xa4HFT/dOVTHsKblW32j8xrH706X8rV9YMuxfPRg2jEYVjpkHNeWov11/GravJ4kFOVzk/jdX/kkIQT+4qxB6sgRMFX/jCq5r9+sraznob6c7TZu9RD+5Vh3y7raKDEY4jlY4VZrdseS5tUeX7vGlWajJ7rW6p68vXa02Xj/eDxGl9ItTF9/NDLP+xaBDWcNUq+zG6MqD0WP7QP4YMep+O8D/hDStveVxlNY9saPXrPr/Z2WRVNKLw0Cs5En4LN+P0hdDjLTQ1w62ms3OFhtHb/edGwbNbUeuZnojuCHGpie7tJ8MAQN7e56zXEn4X3nDORoipibf49o37gz33wYvujcEyf+aHSorJfNpqcfOMiOg2OqZzFMdrWTNYwWpOZAuk4Z2Axd81VtvUtOffmcLgWq5fVUjq0d5/EbPBqBGtaWb9V070JRzRNmEyMG0u4lDMBf/HQm1Q3mbxcbyeUh+PFz4W6Lalgjd4fh3ePduAn90p2Owzx4tGe7bc/kCU+O2TVlcl7nNkxmLj5YdXEiMX3RxdTAahKJiAWv9dNBalnuHVbDY8ft6HLkwPJUx9cseOzt6bG9jAZjDnRQXXOtvZ1thvAH6hlvvTer5ZHKhuHomeB5nwW//aco2z9/HeUfx9QJeNjvdvfl5dIVdWgidJdZlHPDB/VMO+3aejYaN2vL5obzkaWme624ycEoxchpWdJuR1VeUl1x/+4M8o08aE5X8e5u/+5Pg0Cjzs2++lkUE4zgVX4lk0pX3eLdLSe9RPX243r+OFG8G+DZaMixXW7U8OS4/bDG8vg9eB3FJO9nDWo9ttUx+/nPXvn7hce3D919Dkfu2m7KBPzBrwSpm53DThr8sxmlXu0Nx6v89H2c2jygfnfKcrdH1eLyyPzw/nd2O3g+X/gns6bfXmG7Po8XN0NYt7+IDj74o18LUkeut9zappPq9Zuzzh86Ig2u7sz9/gbNmbVudLq/sekjtucQHKzj2PnD1dGhwo0q/lpybzhv6T9tw4HLvrcUOO5XxqSmX/9mRT9hermNPzzRk7YTUYrab5s3QzUP3YR+K+bcJmMP9nEvv7+z0bszrjLNOuQ2QuRo//fa07ITnfNafmP9jtTu8ujfbynLr5+VNaEsltdvQTVCJ0rBo876Ux3XA8HWsHW+1VM+vcnLePnSh6Omnk3lQWqUyOVybf8anup/LIrntuWDGQ33Y20qSjSvefKm9QXbeg639Oi7U0eB/i27vphyiKwt16erzQuoy6d6af/OFE/7N7T4uJTtvj4RozghP9XxJ8M+pGaMY31avbH3ORFdF1rTbdd3ox1s2+1BHi19qPPr6L661zgtejug3jacbDm4T8XozXkgRm00LQ/enTor2X3z6hhVbTNuSBWjlnR/e6osK1fFp7nc9Z/7G3SdUXH9PD3ceQksy+mD1DCgr10ruTm+3I86uTwCNXUMbsx69CqgSalcPMj9w6ZgrjY5puhNWnfWtnr0OvO97MF3p2/Z0bt/TxpTPZi8O7eMqI8aRZOXfn3uy1bvbnfrouqdXF99VW/9kRrvBXiVs3b38W85OH4U1tnbzz9Mdx8cdPIeMzEKnvArs/v4bS+6CcWXnLnt8GWCFHyCPAqeIkhxDmc+yJ+57fBlxqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLD++3YDgH9I+nYDeEz+dgNkUgDEJZMCPun7p+acikwKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAI679vNwD4p6VlWZYlT59vb3ezX/6x9lyrTOMyuW5AtfO0LDm1L2dZltzX9bpG/zKZFMBDNoNLHhcQkJ4lkwK+KQ/SlfqInrolt2K3NZOE58n2FDvMS1rykqod5XvBUdKXlmVJ92Vr+cuj9JYm/zaZFBDL0aTjY8nJ63aUB4/YJpMC4kjL0h3Ax8fzvFvijw255VJ/qWOtoEsYRamDZFIAtRfmcnKnv5JJAd+XmkP4Le24zom7DOWM0qx2g+VWJnXJS67LzoPGX2LUaIjtwWqpyKSAANKyLEuazKHIxeOn5I1nrzR4AX8qh0wKiOc6FW6wdGODyxbX1OueiV2jX1387TlN2tyFCPUImRQQ3+yYn/8+vYHYZFJADOl6WdKyLN3UunFuVa+9bJGX1I1wbQwUTeS0kw3NjHe0JnbPNedfJpMCYsuPB4vXBAGhJAKZFPBtObW3aTiiLDeKY8+lQmV1bUbW1J+bZymvd5do66tvm6F78jiZFPBNKeVxMHg8jym2eCL7OlTvn8r8fZN/kUwK+LLmAqbqceoKTc0O+m9JW9IoDqZ1zCndhsemuxejjpFJAXEd7vrrLub9gDR8eLDRYtRBMinga64XMV1+3qK8wun63+aHm9otxzccvz0Y32x867YVjfsIU3WtVfFvtzxdbqJ+f1n59qRqjvj0CJkU8DV7waJOUJ4+uHcbvjNMzOt2n4lnbM9eedtedz6ce+uBc/rDjDu+o/+TffaPKJMCICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICy/JwVQKH//qXx+X7y3oDOpaVysLJRTsXK09b9wS3mZFMAjdiJD97Pyf4gk/0IQ2iOTAiiMcpbbr+rme4lc/xRwVWJdd/mJ3jytKTXPLw/SrY6Uc11rWfpf+QVFmRTAn+Q+e+JlZFIAf5D+EKL+Gtv+hR86FqQA5l7Wq/aO7rnfD1GCFMBQOjTycySXuZbJ/8oo0msZkwKYet1o07+Q9byDTArgSWlpc6k2W5pdRVXPDXx0fbrW/S+QSQE0zNaLQyYF8IycBnlSf9uIycZ/Wr9/l4vfIZMCeEbqHvAGghTAUJpGn7U7MB+6+cO8pr7o4ZKP1HpighRAp+9uy8vyVNL0nuGtf2fQTJAC6A2j1CVGXTKYXBTczGk240m9ZUppWrrZxz8TpQQpgCfcY8ZT4eIe7pqttyobrPsHOvzmYfute907ufiHThPgX3KCm83drkG634t8uS4YP+60N5boajqo/xWqcvhrffzuN7T/k332jyiTAnjIZ+Psv37NlkwK+JwTZFLUZFIAMCFIARCWIAVAWIIUAGEJUgCEJUgBEJYgBUBYghQAYQlSAIQlSAEQliAFQFj/fbsBwD9pfFPw+a8pravXh+Utwfe3+7MDu+PlZFLAN7ziSH+0DlHlxGRSwFfky88sLdefW9r58YN76fpHntalW9u9RCoeiXqfI5MCvu08x/zztPRnyKSAb0rLck2kHgsA6Z5XfVAWpT5OJgVAWDIpIIJLitKPIOXh0tI6plVuVY5W3R+Pqz8+B/CVcwU5RiYFfF962fyGzb3MVgg7ccmkgG/KTei45UDlZLolp1xP/mu3Wpqt+jrTcq2irf5a4oHGmt33STIpIIpZPjWeIvFoXpS2tzpGePo0mRTwPek6VHQ/9ldBYGc8Ko0jRns1VS7XFHnUc/HmdRdecYhMCviio91urw0Nk1kUBCSTAr6nDEprYpSrTGfczXcdZ+rqykvKax6Vui1z9eBhItunyaSAr0qDR48GkLr8MJAYTDopmRTwbVU+Vfy3XjbZYJnlTWvRS2KWbo+fHIxakuukvkAmBXzTM/1nj0QJ/XMnJ5MCvqUONjkdyKPS5T956a+KmoSjYnyrHOrKwxIPNJfPkEkBX/a3g/+sj+/BqmVcQU0uNHj3Xnc+O0/3GgOhuVnD6fR/ss/+EWVSAIQlSAEQliAFQFiCFABhCVIAhCVIARCWIAVAWIIUAGEJUgCEJUgBEJYgBUBYghQAYQlSAIQlSAEQliAFQFiCFABhCVIAhCVIARCWIAVAWIIUAGEJUgCEJUgBEJYgBUBYghQAYQlSAIQlSAEQliAFQFiCFABhCVIAhPXftxsA/NtS8Tj3K6pFu1UcKc2pyKSAMMqAJeCwLDIp4MtyWi4BKQ3XXJfvRqy83Er3RdN17VNhb92wSdi69pbNaB/Xm3KcTAr4rr3Ddj5Q5hvH/iPtfngjWjIp4Ltu6cY08ThwYM+z7ZdbbvWC6JCbYbI1PVv/rZO2fN15vZ5HyKSAIM7ROSZB+iyZFBBDqv7N3bP2URkZrtlSSss6AnUpkury9YrczCCcDX9V+c9zAUkYe5JMCghl3Gd3zGYkmK7c2ioda85fGs0mmRTwfVWWlJci/8lLWnK6DevcluclLUsqB4fWZGcdDrpvf6/tOjiUlpQvG10W1ps28n4Emg2HlSV4jkwKCKKah/7sJVM5D9OfVK5vqt6u/dC+ZVJvI5MCvq4aFMr1oqpQmSXlal09ca7durji6log5bQs+ZKM/T3NyWlphreGDZBPPU4mBXzb8NhdJScHh4ZG1Uy2zK+MGBKp95FJAaHcx4e67Of+vM6jbtdBzSJFOaZUTQhs1g2sQ2NpM6TtX4h1G06TSz1KJgV8U5vp5Mnjbrvm35FXRoTNaLaf6D2fCv7rZFJAJOVVTn2UuQ/tpHtuk6p/llvSU1zyNB6gOqTeR1NNOQR2ZHafOPUMmRRwGjuH+QMJzet2WK7qd3xbkpswJ6N6lEwK+J7Rncvv1zJdr5lalqW4UmpZHzW1lMM+Zb2pHsBalkmG1d1uYruHb75qzauKF5bygXyLnkwK+Jr9ROWZw3pqHx+4HPexWkftkiO9x/aUlbftdWdEc289cE4nmt62eXP2ifqGg+1IVK6Wn0X/J/vsH1EmBfAu4/h2phj1dcakAAbmt694YNvmpkui0+NkUgCEJUgBEJYgBUBYghQAYQlSAIQlSAEQliAFQFiCFABhCVIAhCVIARCWIAVAWIIUAGEJUgCEJUgBEJYgBUBYghQAYQlSAIQlSAEQliAFQFiCFABhCVIAhCVIARCWIAVAWIIUAGEJUgCEJUgBEJYgBUBYghQAYQlSAIQlSAEQliAFQFiCFABhCVIAhPXftxsAEE26/pvvD/N9eS4L3Ms1C8pVTb15UpAxmRTA34g7bySTAmgUmVFellTkUeXSS3RKy5LydYtbuevyJnpNsy02yKQACEsmBXxTN7pTLp/0o6VlY+Urpad2ovfvlWRSQBx549lmUX6VIAV8W87LkpdchZ1xDEoppa31L27UsqRuIKlf8pi/bv+vEaSAENqoEy9Tyn9sUrxXdAbGpIBvypPH47lwHz3OX2bspdwuWbUDZ1sDaUt52RWHyaSAkMLkHWmnJQfjjvD0HJkU8G2pe3xNWa7XGlXH91wX/cgcv/W/tTaz2myMKPUUmRRwJp/t8AuTzv27ZFJAHJfUKRXPrr1tuViyLEv+/F3wihY0HYD7V1OlJRe3/3vu6qt/lUwKiK87qn/+MP+pqe/UZFJAJOUwTy5um3e7Hd6XmpPasbBmfSXVsSyV22dDU48RpIAoZsfvPJhaEZlA9EKCFPB9dZqS24WfVuU+1fN6pl9aLvc/rwbRikd1bK221214kDEp4Lu6+wQNY9TX7iY07uZ7hvzqGTIpIJI8GNL5eNYxv4vgqCmPNE8C9SiZFBDPtVdM7oFMCvimcvLcbXgnF+EpLaN+MvPB/xkyKeAUBlPmxKh/gEwK+Kbxzxzm4brRUn6cTAqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCw/DIv8E31j8L71V0aMingm4QlNsmkgK/KabmEqrQsS1rysqQ2dKXq+a38bP0r3fO83KR8q/maulRZ5e3ltuvoyaSAGEIfq9NGJArd8NOTSQFB5Gsg2DnofywmrM0ps5/Lv1UuVGR/9zQv5aVLEZc2f+qTQloyKSC2I71pb5Tv/ykDzFZmVZflb2RSQBR5SdejezXQk7pjfhMzilGpdF/9qia1/20fpn5t82w6aiWSHSCTAs4mT5+87bCf39Erp6fvAJkUEEmuhnqWpRsAWm5LL4NYdRrzwsN+mteV1t2V403T4kvZQOnTY2RSwLflsq9ueSBteWsq8rJo0rdyb0SLlUwKCKM+dne5zJpR3ZKTL3SYtbfISP3CrvShESqGBCkgokeO5V8f2xF23keQAr5pmJgM18+nz33UYzvOzSREHmVMCogq6tBN1ay3zPvjTiYFRNAMR3V35+vL5+v/NifiPW+jv/Gh3V0u/qonIaauyWnJb3kV5yeTAk4vaMa15YRN/g6ZFPA9qfjvmqCkfOQg3t0s/eW3m1jKi7Rylf2UqV6bc41SolviN91NgOkfMQlSQDhp9vQcP5Go4+6FvvNm9mdAj60Hzul9v/z0FtXUwmFut16G3P5GVHOP9KL8OEmM+sb0f7LP/hGNSQEcMA4taWMdr6C7D2AiTx7Pys3KB7nE65RkUgCEJUgBEJYgBUBYghQAYQlSAIQlSAEQliAFQFiCFABhCVIAhCVIARCWIAVAWIIUAGEJUgCEJUgBEJYgBUBYghQAYQlSAIQlSAEQliAFQFiCFABhCVIAhCVIARCWIAVAWIIUAGEJUgCE9d+3GwD829L6MKf8vXYQk0wK+KoiLqV5Kf5VMing+/JyD1Gpilv862RSAIQlkwJiyJdcShpFSSYFBGFIip5MCoghLYM4lcu138iyqv2m++NbQ8tnuVxRDrPJDv9AJgVElPOytEf3Mx3rB83nGTIp4PvWVCQtOeUlLWlZcplXfeV4nzaeXZflW36VlnQdVstLuqyo1vAcmRQQzFkO6PksDT01mRTwdcOj/ddDwDyPWsemlrTdI5kHy3iEIAV8W3sUv8YAs/0QpIBI2nQllnysSan4t5j3x1MEKeCb4oWiuxy5cf8MQQpg4rlrnarglhez+/7E7D6A95GM/ZFMCviedP3vaFJcW+5Eych61dSJGh2UTAr4mmGakSbJx1dykud2mvzeyMvIpICv6Q7k+cDjjxrvuLiLRFOgSPmut/ITrP5GkAKY2sqkDDd9giAFMLV3adTljhKpGjRbN8npmluJZ08zJgUw9Wh0mXTu6fN7mkwKYGp2Z77pHfvO++MiQcmkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgrLA/1eE3wjglP80ALxU2SMEpObva4x3iITG7+5yNArCEzaREKfhJyZebx8TMpABgCZtJAT9JHsWDZFIAhCVIARCWIAVAWIIUAGEJUgCEJUgBEJYgBUBYghQAYQlSAIQlSAEQliAFQFiCFABhCVIAhCVIARCWIAVAWIIUAGEJUgCEJUgBEJYgBUBYghQAYQlSAIQlSAEQliAFQFiCFABh/fftBvxBWpZlWfJega5UqpdNnnbPBwtzW6BqzL1QypNqlmVJ18f1+iVXu2gbmSZNaaq57aXf96Q1aWlak9t3Y91f2nrnAV7jxJlUqv6ZF7g8Tv3iNHp6aKcPFF2WlJbUbtHttK0x1QtGjaybMmrS+B1KoyU7zetX9bUAvN55g9T+ITKP8prUHubvhXL1z9IsHi3LSxdNxu3r6hjnOGWBKvUbt7lq/jCtGeaafcmuNet70hYWmICPOnN339onNi2QlmWp+trWw3ZaliXlW6Hi0D1/nupl66K+B22plqd8b8agNWvha3fb2qy1P/H+qGhz3ZRrJVXLUr4vbHoV82Zr1mrad2Pj1QK83HkzqfzAmEgzoLKbWhzKp+oYtdmauq2jllfL6tV78WCy46PtOfI+jksYkwLe7rxB6sgxskoy7tvlZvNBqXmMqrrEUlHfrBstXzOSQW9a2yPYhaN2Hxt9en1ru1kdZSa405qlSc+62gUo4CNO3d33cKfTOzqpyn7B5tCd+uP58YP7I209UGtelrSkPIg3jxq8KoA3OXEmdT3d3zladnPPh+VHM9Qnz5tJDHU/3vamO2s3EqmNCvZm6pULt96rSVsHi41HAR9z3iCVpvPqBmXXh3/LAHZn5c13fGzvuyH0gfX15MbB27WdVs33KkoBn3LaIPXIgXIrGzmWivRr8l4Tmtl0o8Tpb/1mwxYMR+GOSPdKt3faj3QBvM9px6TygQNlWqqersn1saPKm3LVxUhp0MM36k/LqStd7z/3L6KboJFGC+8BbtvGJbnDvsWiNdWmg11NXxXAS502k7oNRh08qc/Nv1tLJ/avnW0u7d3POo7kYq+R8/bgXRnP92q6lQd4u9NmUst+MlXNuD5Q6lDxZVnSfMftTPZ5b1969kBfzVUfdWQeuvC2zyqnSZ2kCfie82ZSy+hKoL5InuQQXS9esXy4n2XrcH3ZRb+jJkYMOu4me96cpDdvznCzfLu2aedisEE14/pmkQ/g1U4dpJZl2TnRPzpbYmNhs/I1B+gjE7vHE9onlwxP9nGsa24rMAN805m7+15tOP1ur0uxmTlRbtrJ6+px+DgeGS6p0cPjXWnUjVe0Zi+5ErqAzzp9JvXg1TyXhWlNSpoZbWsvYGqe93tt40zVmbdu3remml2x3/aNNg82yxul72Fo1ppmSVHfUlSrqw/4lFMHqSPdc/NV7T2M+nLNWFUebbZ/xB52221ftDvtDTw6s26c9wyHmg5dWfX05VcAf/KdC142O5aObX+Vm4XtLSHquy6UTx6YSX3rVluTilEnX9uUYnnqH1eP6uYOn7RL1ofT0DbrUSzr71uzrm3nacxeLfBP+ezPcp86kxoEhn5caVz6kRh1L9bdWejIX2qasKX13yrFmc4v769QykWBtnGHrtIatmZcX1P71kqAVzl3kOrv/dAePMcX705++mJ749uCXPwCU54Wnk1bz/d/ihJVfU3lR69X2rvTbl9JnrRmvL+85HUfYhTwGefs7gPgS3T3AcCyLIIUAIEJUgCEJUgBEJYgBUBYghQAYQlSAIQlSAEQliAFQFiCFABhCVIAhCVIARCWIAVAWIIUAGH99+0GDB38MUIIx2/QwEvFDFJwVk6w+CnfP+vS3QdAWGEzqe/HbyCYz/4k7JdFeLEROgZkUgCEJUgBEJYgBUBYghQAYQlSAIQlSAEQliAFQFiCFABhCVIAhCVIARCWIAVAWIIUAGEJUgCEJUgBEJYgBUBYghQAYQlSAIQlSAEQliAFQFj/fbsBAM9L9dP8hzpyXVvuSuTLv0/tg2fJpACWLvYIRUHIpIDzSsstwclLej6Pykta0nXrfFmWyspykWPl+zZ8wmmC1C3dXh//+UNSVrlUefya/b9GUfesO6EomestHtzNkutvF/yy3Pb3vXl3H90by3KiIPUZ9eH9VQf7NKltUv+zu033f0Qp/h158OhP1VyejaPRi06QecBpxqTKPCSXi16r+mS+NEaN6tw/K0spHT11e+QU74Fq4R80+n5kkekrzpNJVZ3CL+kRrmt5ff1X7eyja93jIHHrTSh7FY7lRfcOwnRfMNpuXSrd4oesH/02CSq/FPfP/Lpg+2ytHhMol14HwlJfiy/Wa50mk+q89ZPwwsovH+Lcjncd3EV+6PytnzM7bM2loK8SPG78vfFtep/zZFIXl9P/9O4PxYt3kJvq5nWXKVzeLjrf2YEcyXeK39GmRbk4GUtLmfgUG9x7M3Lb57BmT6lKw+57Gew63Sv01Xq1swWpxjgZL9et81Orj9DRfOb+YV/ruNbUdByMLvy7P9yOGqMXsU6CuH72q70W35rRTI+6M7FIntK1UPdGFO9O+z1z9SI/bfuznW7fuHmxvHUU4u9OFKQG/Ve3Q/mBGJDqYZ556WpWRnGetBQRJy3Fp3ejVZPdjONRbtbeQk3V/jQ4sxu9its2xUvIzYupxq9yuS/DVZxUnUvlZnkuCy7X8NJ+m9ZC6yYpzyYa5csUi7Q+59VONyaVRo9HkWL92OQunDw8YS6XA0PDJgxblauINy621ZZc7nv4apvpt7dXfc2o6pQq35YOv0i+XfyK4dS8v1TYXx3VfImmZ6z83XkyqVR9VNb+qY0JAmvXdCr/KefmtJtcsoiu3FLtvOuj7sJW2d+9EaPW3rW0mb407e86L9sGtoXasFa/EdedTy5TFLw4p7ysX6q01J/6+sFmFZcHqfhSj4lQ73KuTOp62F0739oZCV3p0fNx3EhVjjGKAuvTvF3V1oE9Lcv9KqX+U7/5Qa+H0/qiuV5cdBsWk/nKVzhqvZjE+dVddse1pXe3zsV/eZPzZFKVI/Md2g66Kut4fD+XKuvMqJhrMNnz1s7KQag6SSonZtyHjtoO9UGFxVjZvTVbXY1l1/ztufu+cD71h77/qk+Gcrsvx0b3/ajD/5ljCo86U5DKXZ/b7HNRrmvnIcwO3MXGuS3X9T+n8ZP9U7E+0dlLu7od5TT63txWVVFqY9xrnMXt9GhAPJeJC8Ov0bHuua1v4PY5Hp9wmiBVRaeN+QgzVfZz+7evIN8/8XsH9rrCR06pRqdkqXo6P+fLg2Xlkq4vsEvIBmu7sO2kkLO4f6irD+1tZDk3pZbl+jUeff1vX8J2pLqeFXEtlcuTwcnoAC9xmiA1deBkqSiS7/85oBzkmZ13pcGyrfI7exslgN1EjS4szqs7tnRZfMf4GZO+vT98vIe9gBKqDzlLkLp3cA0/GcfmLlQJw+wTtjEk022cprvaaMJwxGq80ya4trG2LnqNcak4p5wkZm0jup2LVZzGoDuhW9OVGR8H5pOItr4Rl69mvk7p8t15vRPN7hv/+feGpuoi89mns5GqNPqnaVd/S/G0UX6wg+1iB6YcpbrDMLUrtxqzvj2zM1A4C5/f33OWTOoujw/AI1uZx0bZZTvh6hOhbk7ddJ8bYWIwUrXR/tF4bnfLiu2dznfmhhOc0aznfWeDV+3YrKN3OUWQug1V3p433VnDUcuD0Wu8g2G53B3Qc6raUG82HaZqd7MWL6Pd4CcSmyezAmsFRfvuZUYjavOgfGj0C87sdZ9vMeotztDdN+6uqkJW6te3W9/K7CU4z8w5zV38XIeaummFwwzp0LTxpkDqFtU1bW1ftWy6yajzEOK6TsR75CP79xiV87IkvyP6NmcIUnU3Vm5mY+f6P2XJnJclV+Xv0STnruJ166K2nMvC+f5Ptcdy+8Gi8V4GxW915vV11isu2Vzu7uaXixdWFL0+yfWS7g0rd1u/kXIoTqYYVxUzfsR3hh9msxSOrj+LN7yOe5V64vj3/FPT5yK82L3f9/6EM2RSp5Qe7HQ4Xi3Av0OQeo/0lhB1qxvgH3GK2X3n9Z6k+D678C21A8QhkzoXcQn4pwhS71HMyntxxbl/BPCrdPe9yfsiiNgE/DtkUgCEJZMCTu2Rm3Qera29lWZ7S5jRLdQ+0cfx0M1lptufqzdGJgVw2HcP8OcKL68hkwLO7JbEpOK/9frhLahnlV0Kr9V0Gw5+ci7lD+VReX2xz8knvMxSJgVwDueLMC8gkwJ+36FEZ5AjHav9g8HjdnXLvxOvBCnghz14MD/0+wX/4sjQ9whSwC+4JRepSzPq3xUdbLgW2/lttWU2z+/zYav6uZ5inCwPl1YbnYwxKeDsHu772jpab94buv950s919j26nx/pEZRJAWeWl3T9YbxczeRLzZJyHuBwjl6vikO5/n24dSefj1H11VKXl5+uv4laLL3/955efuf3A/9IJgX8W546Um8OVkU/9p86p5JJAefWXx81GyjK7dM0XJ2bUZ3yWZmUffZHc9bkr8iNildQjq01USmfOU4JUsDvey57Ou+R/YcIUsB5lRnUZcRlL7A08/Ryt7yoOpe5UlsgDZe+0TBmjqPv4N6Cp424ghTwI1L78A/H5Xt42pjHfYmK0Qekzk6QAs4s5b1Za6l9kgcxrI9nufpntHpzsvqbPJO/feoe7e9hdh9wWtdJAn/pyrrNirgexaeBJ2hv2TXc3luXTjnNfItMCjirVEy4q5YPlkzuhT66OcWy1DewWJen+5JvBK1+Pl8qnq6Dbamb3Zeq3LG/VXxkMingtNYD8+CwO06KuiP4H/Z6Ct/olHyl76SGaaeHdG898C96awoQLb+I0J5xGz7bsvN09/XXzW1fSbdOzhn90vNN7pYsTS5fPqtq2rp5Y920aV31jS+711HewLKpedTuuhXDd6dZWGz//a8DQO803X1dx2vqllTFi7Vb2W4adGnPOqn3JgMVQ5f9o9E26f5PGtV4W50G9aVq8bjSjTafvQMA3sH3IqLTBKmNM/1+1fZnrUtNtlKtwc1W+jL1sm7Ca739/RLCLmRsRrRmUZ16FR3yuayx3qiLV9InILjzdPdNfopy3GO6rhr3iN0O0WlZlpSLmwVfc5p83135O5hNjMrlgzYU3Jd1dS25iIv1PZtTGwVnNd9X30vlMuysD8taB0uzYAVXvgYxnSaTWo5/hqphm+c/eN2Wsxg13Xh+k+RqGGqnoc1tWw68oHaoKW0sBYjsTEHq6tjBNRe5yiOBatL1l7oYNVOmJk0wqCdQFPu8x6pxsljOrr2/rLzzssZN2LmIHiCWEwaphz1+RB7lNqNk6M9t6Kbgbc3xOLqvccG+X08qBYT3g0GqPfY+Fk3KsadikOrQxI1JLlSPBdV1zQNFNwGi3tfe65IrAb/gZEHqrYfe22z03Z20M8lTaifbHdnddY5D1xXXVr5f02gePcAvOFeQKi9hnQWChw/WxdXBKdW3V1nTn61ZFMuh2DbOjLpl08uvNl/X5JopgJM7zxT01zs6W25wJdWw2KH8qejsu9+5ebxlXsPTbs1ZJgX8pHNlUm+YPF3mTX3lw8lw3ThRvsy/28lm+rruSdtkwyPTKe71GIQCftDZgtTF1kH7gYN1Nzv9uQP9H2Lm/Tdsxrsf3dn5raRjQCyn6+77y6+btVVNpipUN0I6urt8u3lFe+eInbry/b4UkzYuZcVvlr50V3x4Xv3FOPgBHp8WlmPLZbV/OXvNR/rr2XDOTOq9Z/yvrvzIjIqjpDrAP+VMQergAbo7O3rgwF5OH3xkn09YJ+SNo1S3enIvjF41tFbc3G+09FiNEFfO13Hh/ED3+Kxo/SPyD1U5rsstyP7sdN19F80nZzADPFUB5/BtVPNm59uhqeZFf1+92+6T2v4MdXv91WhXqYujs6YOvx3jpZP2AX9zi3k6/J53pkxqehhN9br+89DdYfVgxe3qjUnrTRhsmrRrkk3d/nPwvoGTaDy6y5PpgPygv001Wk8X03K/Z6bvybedJ5NqUpw1bvQzCnIdItr+smLTakFa8rptnbD0J0JlStKM3I73Przqqe/x25jtnqu9FfdgSus1Vddf4KhuQZHL7eul3YXCvpH8M7Y70POsGB92miB1O5zWcaV5eFPGrSK72Yw0TYmUZ2vW6ps952Z1tbBscFdw72twv8SqjiZF85ra25J9w3SV8yPWY8K4K7z9tbjrwvvmzfPpbkYVTnZWP+rrEfceca7uvmW5DpPWl8T2WXmuT4PG/V1VkfujgwGjr7CoJg8e9fXkNfgsy9KfyJW/NrKzx37hfdvcr9sYNh4vhsD+dK5Vd1b4AgT0nQG9tHM43Fv/dW85G3KKBdtGk4n67oVc9YBX69YsKK2L7o/Lfo+yryIX5S7Dvqmqo95ZvjeibNJrXuynjdvw2ZadL5P6WWIUvNnGF2wvHxt2STy0B55zmjGpWHJ6+dmE8SF4Vnlbh3pAdk1qbjOOiolNqSxezcrK3ebrJK16nLlemtLBYMZhglQQh6/kAmZGp3ov/FaNR0dSdhnUOwlSz8lLeumAnhgFf1FP16unSpWzaAeX1PeXEKZBP0kabt5ekM/rCVLPyi/t7svh54pAVIOps1cbwaO+vvKxKLNf2pf5dQSpp732Y+hDDS+RD4ac+9UpaftSqbXc7r6qy+2Lf3y7/8LsPuC8Hrl/9DLqrruZ3X+ivWj36LVUegBfRSYFnNV9gl2zbHy7h1Q+SEV61N5a7TLk3G/bVXtferuGPlXZ1GhkS171KJkU8Cv6NOdQRHhv1uM+Fn/jjhPAWUS4CcND/nIoi/Bi3XEC4Kd9P9CcnTEpgLcweeIVZFIA7yKR+jOZFMBbiFCvIJMCICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMJy7z7gPP6pG4v/Uy92SiYFQFgyKeA8/qEbi8f4Zd7vk0kBEJYgBUBYghQAYQlSAIQlSAEQliAFQFiCFABhCVIAhCVIARCWO04AJ5aW5XYfiuvtETbu07Bf4tBG+/eCSE/shzGZFPBL3h8ZItwr6B8ikwJOLK8hI++mOJcSDwWZW+HcLZlvkO9NSYt86q9kUsCZPR4DntoiyZ++RCYFnFpOh28Ynp7LavLxPKoJgbKov5NJAT/j2IQGzkQmBZxaWh/k+6y6QTTKVfG89IVuIW4dh5oHtduswlHZ6+P7xMP7mlzWd10j2dohkwLOrIojkyP+dwJBt1fx6BkyKeD8ylGpfP1vKtYu9wGpe/aTro/bfCkVhXKT6qRqCuG9wmUdFhvnRvOcTODaI5MCzi0/e6Df3C5vFNnspWvXmBf4NzIp4OzydBgpXxOjVAWdnNZs6l6w2uqqz7HuA015O/bcd9TWvWZcT841/NfIpIB/zU5u87rgIQ79nUwK+AWp++/NNOdpR5fKTcfbrHP1Rjuazxcc7/3o5V3/NpkU8Kty9c/Mp8aMRKSnyKSA89q5pKm68qlIfvJ9bt9cNwFiGmUm+VSz062ZGMwJUsCPaC/GXWa9cvvW62/bBakptRN1HryfLR1BCjirawhqevVmgSkvqfz5qSKn2ihUVrlWXfyAVVq3yt0Wl5tgzBvEPmNSwL/gwYRmFEDkRN/wnRmSezesckMroPfh6XDv/DWo/bojzP0bt+GzLTthd193n5KlnUD6sjew/A3o4iRqdo717I+c9T+rBvw63/djzhek0jLI/1LVG/2q9PA2R2e6t6Zo+uMYqQQSonhf155Ow8ecL0gty+7dhV8bo0Zr+ga0GdTxgNPevH8WZJNfo4YP82X7vtNNnOgix1vi00DOy7LkfLmX5XSeTpnRPXDCdKv3vm0v7awHTuTpu+L+c84WpAa3a3zrrpbc7e7Q3vPRghvXY4yKHa8X4Aecs7tv22sO4rdwOLkf8rB40R33RCNycYH6pHoRCj7jjZ0yb6v5N50sSKXx03r+XboNEFWX3BXddGWP3eCKver3nivNp6tJ68oev9vPA9xvvTKdIljc/HL9Nba2heW8jPpCxKXfybBxAGd0tu6+Sv9bL8WjlNJy+bmx64H/fuH3dMBoXb19J+TlXuGsolT898ho0kZrlpRS1Zxyt2uZcTWGr4BzO1eQqqfM1V1g67NyRHJ4C8nJj22WaU5/9+Tq1l1dte2dVbr9zGdSDGZi1Lnc2pi167GLouMJJTIp4NzO1d3X9c3lKue5Pyt/P+Z+Z62qA2zSGXYJPilfqhjPBC/3dyvd1nbtcMyX0JRTuUk3Oz0Xl37dd1v1F1aNyWvrU/lic9GW8asDOJtTBanUpiabN5qoBouqw/m48iMN6KrekLsHs+GuMkoVe9rey3CtwAT8ljN193WT3/LGP8VGtxVVqjPcw7EJ3rmYpjDd296u+iJt8JpsN5gjcWsUwK85USaVquN4Nzw1z7KWalLFLWQ1nWPDyXHD57n5d7t0vdUsXbvneKnrESyfzrO94dgbwNmdJ0jVUeLoEbkeDeoi0yD92Kh5cFOI8fSJYvSp7HWsZ3rUVVct2351m12b8I8ZdK7srjlQme9UFGfq7msUucUg0yhD0XpUL+Lb/OM8u+po3v/W/1OtbWJObiue1jpY2W9bz+QACvrBT+80mdS4x6s/Mk+mJtwfFGdKaZRKPdZpttn/Nnra7i89csv2NHvqe8i/Kxed5KNvU17Wn8c98k3J6+lf/3XtO0i2TaYR84CzZFKHYsc4kVqWnJdUXO6aNqp7LB/prlYart5pe5o83q1+Wco5+PDvup8Etl0ig4cfJ0b9zUkyqXHsuMad3Dxre9eWW2zKy/3+ePeNinkUSx0O9nv72rOkJqiMugxnedMo1LQxapBIrZ2Y8K/Lbb9E/Y05+DW5TNrdOvd75Pvmu/l3ZwhSqXmYZh1/e5uXo1FpfXh5lMplo7rb5L+p4fr1aKPf0jwdTFK/7Wzj+1Wpmzour5eBf5GP/A86Q5C6KnKklDevyx2NQe1sMNh03fGg0CCw3PKn3T0V0a7J3C4xePhFG3e2zwrpA+Tf0XSrj0/gLqdtl+6H+yncaFg3FblU6nKq4ot/P+dNt5qL0rNLTkTRx51lTGpZllxNP8jLcr1L33357Vm71WXVfdtJR3VRx4Fugn4u3+13C697ymvDrp/5tP604ToXPZevqdzy9vJyvTQXL6GajJHLJb4K/Ds2e+byOBL90WDKoK/c2zwwt+yVe935m+6tf7d3TOe+vyZzxeFJ/YS7NgituVJuJuOtz5Ylp6LQePz5+m9aupL3iq6rywYMans6To5e7KeN2/DZlp0ok/qUrrfghbUC33d8DkXTqdIPWPN2JxqT+oyt+emv2gHwUuVg7LgrbpjMjHvy031calRmnQdcrs/3k9v2kpK1V95X/zmC1Nhbstl1KOodtQP8HkFq6PVRpJzzJ0bB221ce3gbmGq79suZfY3uSzv/Fo9GbMZZGUcYk2rUs+ReXXHzCPi70f35Dtyz75HutyNf2lzOveVlZFKtt33EfHbh5fZ+yWDnnmLtvL7bv6m4FmVcRSqmVWzP2y1q+c5s6pOTSQEn9uB0hKNR4olZDgLQe8ikgLMa3bGsTGgGqVCuCs5udrbeb6Ksoip9u29FKhdsx7YPzu8r7wF3cjIp4Fc8cUzeChyDdd0iE8vfzR0ngLN46a0O1lGlmEebv7zY2/0E//rK3HEC4CvSz6dAvzJLw5gU8M/6zftArMNyEe7/91cyKQDCkkkB/6YfmgH3y2RSAIQlkwL+QfKns5BJARCWIAVAWIIUAGEJUgCEJUgBEJYgBfBT8uWuhOk3foLRFHSA3/Mz93sSpIDzu/26Ez9Hdx/wG8SonySTAs4tLfny60nFkid/APFX4tyvvI5lkUkBv+BFR+WfGcj5ITIp4NRS8d+rp0PWL2Ugv0ImBZyZH9z4cTIp4LzuaVS+/3Od6HdLrXJR7r6g6da7D2ilqrjAF4FMCvgBa9jZCS3V6tnFrsJTHDIp4LzyNflJ9//lcmVV7vLf68y/XGZTablmU3mwKd91piBVfYaun6o1xx+UGS6qOwHGw639nqoP/71Toagj163Is0qq3c1avnZetC0D/s4kvhM5W3dfap6k9RRqXKZ9lupHw7LVmsuTlOolqdgyrQuLdg0qab8aD7Q8+VbBjpyb/rsyV6pvZJebTr/8Cze5+1FnC1I3xU1Q8nI80dg51B//nLapWT7QitwWeOhrIUrBI56IOiJVRGfq7vu7NbSlZVnSGuRql6k+KbdLduo85qGvwahPErgbz5fIy7IsOe3dQmLje00YZ8qk2jzk0OG+u9BvFpke2vdSx4+92ja+Coca4vwO+EedKUiNrm94SDk3IXedb/NtbvvealfzvJyCAbxXN7SbxqO4KS2309t0PxwUz31fAzpVkFqlF2QXL0hPUhX36jWyH/isdTJUGn39nDme0/nGpIYfv0ozvScvSzqw1XONWSbBLrel/rwbYCzl7iuSBt+ae9f8feVgq7okAZwrSN3GOZ8/aA/ueTIZeb0HtuODq4cK3gsd+Q64Ugr2peHDZfnT1+ZNJ7Y86lxBqggYxz5AxcVM/fW8XZfc4FP59yToNs9oUHW3u40X5QsDA92cvqGUy7W7nR++bZGcdEzqEdUQ6mYev99xN6x/vmrza/HQNRm+NfAHeszP62SZ1LL88eO2TkBP5ZNRye4GEcPqUpGAVbc4ulfSZFGPXyf1ilkiAKd01kzq0d6+4Ujpn8+uNroXqod/viGGuyIB/6azBamHU4qc1/tyDa5n2t3V3g5H1279dbrDaOxXlIInXaem+w6d09mC1EM3jBjdr7VPro6r5gGWV/mOKnq0cpMmADonHJP6g3wfQfrDhRDDiPSiOJLmN9UwIRae46tzaqcOUmnwqH1ytYanA51zfQUpX2oYtSLv3k5ldmVvFZFyVc/gayVKAf+e03X3PWjjprSDKeBHQ83k/io7PXzD35Mqngx2UD3VpQ78c84XpIofbsqTo/no+a1snhUtlhX/5Ommazuap9cNcr3xcl02bW3ealseLgX4dd/pQ9qble3KIKD3T924OcKLHbfhsy07XyYFwD9DkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgrP++3YCZ9O0GwDPytxsAvyVskIJTcnb1Xv/U+/tPvdgp3X0AhBUzk9JlAsAikwIgMEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCw/vt2A/4qLUuer7qvTJd/crqtzOvCpSi3ls7l+lxWMqxh1IhUrUrN2twUWxenuki/22pxXlKeFErNNk3FZRPT7F0E+KKzZ1Jp6Y/+xap2ZTr2ZFmWJaW0sfayrCwxqGC6at6mZUmz19MUv5dKqd/mssvUbJOa9UVle3sF+IqTB6kyWWqNEphcPpjmDpdac24XdfVvZB87kWnUptvT3KwZJmnjl1Uu2Q6yYhJwBufu7ts+0uaqNy5dw8q1K29Zrp1/bb9fEflu61NZyWXjVNeQlqXuMEvLrZL7qnLXTYhJ9fKqkWU7mzSq63C81pNyV6RuQVlN//oBwjh1JrUeh5+3nYatKVc/nLNTw3j1aJRptREs8u2/dQBas61cl52+N13iBhDXmYPUQ+f/qQk8j1bbpUmblVw7DO9bTlK1wR7vCdOg/pTKfKqPfm3/XtGAbZudnwDfc+Luvhf3Ud166wYRqJyAkJclLSlvpyjjKJbuoWQ8hrSMaq22z4cj7VQbMlOZjwEEc95M6nCM2pjf14z0PLWnR8d0ZnMYduuo5+qVYaUIr/NI2+xjMgEeIJTTZlJpKScUbBfNk/SlMpgMuM5vz9WyqkR7LdIyDRGD2ibVNmU25tkvS55d4TRenMqVuZvwARDKWTOpQ+FpUKhaNL1Qqq157/lS9dT9OTfprpzqdzt+eLgFufxXLgWEddIg9exFSA9V3F+eO10wHGRqnn0iFmykb2WpQeQGiOic3X3ltU6bpUYJRx6sv90uKRU3TrqVTWWPWBdt8u4RvhkIGka6vBMp2g0PxN5xgpWb/eT+Ei+AOE6aSS3Lki73LTqSBezel6i4bvb+n2pRF7lmz0a1H7Ne6bTTlfnERVam7wEndd4gdbMXBAZ3atgpvLunx6LU8qrevmcu71p2M7Ccl90wDvAl5w9SB3OEebF7ItUcqUcTAh9PSLoQkZtK0jLI38qkarTvdflueFmnGtZz2AFO4JxBKuecd2+nMJgb3q6vMpyHLnZK9Rb7l1LNng2qLZ6Xc9yX5XJn9nb70cVPhxsDENo5J04csTdJfRJX7lGgC17tTYeOTIMfpDEDdd5Uhb4qEpctWBs5aUJq76DUbl3FO6ELCOmcmdTqiYPrPMK0c+zWY/vkNknjRetmB5KzOvTlftUkXWvrH+9qFKOKiJcWw1FAaN+ZfLx//4eDtfS3aa2TjtyWa4PQbaNUP22P+e3M7XXq+dpjOLsrRdGn2L3oWa1TfVZWLZm9zD5c9VPtAY5IHz1inDiT2uptm87mKy+cWmPR7datt6dNdpH7GDXYw/qomgKRh4UnzTuW1XRTLLquyeZlFq9suCcxCgjqxEFq68ha3fYnDwdf2iN9dVyv6m7WXot0K0cXDjerBv2LTa15p8B2/fMHfXOb1wAQ0Zm7+wD4ON19ALAsiyAFQGCCFABhCVIAhCVIARCWIAVAWIIUAGEJUgCEJUgBEJYgBUBYghQAYQlSAIQlSAEQliAFQFj/fbsBQ37RnJPyEzTwWjGDFJyU8yt+y/dPu3T3ARBW2Ezq+/E7HL9n/Dnea1hi9AzIpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsP77dgOAH5Ku/+bq2fXpge1W+b5wd2N+mUwKgLBkUsDr5Conujw7kgndtqsysNznV/xzZFLAK+mc46UEKeCVLtlP2k2CtkrkhyPd/v44Kd19wDv9NbOSmf3jBCngtfJgrl5rs0TKD4YmadQPE6SAKHL1DyyCFPBK6R5gUhtr1quettOoZRylxldgtVlbUer+MLXlBMEzEaSAF0oHIsDO1PLHYsi0c/G2lyQqnZogBbzaGoWKSNFdQ1VKxX9H6u1TvZOlzK8updKSq/L5Vsrw1dmYgg68XB48el2dL66Y0GRSwHuso0t9HjWSU17KVGe0fS6Wt2FqPJqV69Exse1sZFLAB20FiRcHkD4ouuT3hGRSwKtsxYA6L3q81tn8h3FtudjkOh4lQJ2TTAp4vcdvbPT2vV3illB1NjIp4L0uc/HqZfOokgeXWJXlZ1FmP/ocuRMG4QhSwOvk2bPN66e64JGeDCj1zAt+gSAFfMogRRqUmedZ46ufygKzX7A6smdCMiYFvMZD4z2Hyq4X3651H96LjOo3yKSAV2jn4K0Zz+0eEOucu6VIdtLSPyqW5GL7nPva7trldYmcmt1yFjIp4POeyrpG26TZinq5tOq0ZFLAK4zu9TBcd3Ce3/wuEbPappM2NvZCeDIpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLBOfjHvet+VeHto7ttS/lxBerbOl6ivvZ/fynNj5XbV+fGbeTZ7i/NmAd917kyquOfkm+568vweLndrSdXTweOv22zMM695WdJfX2Lq/hvsTQM+5dxBqrN3JLse9158H+U0qjAX/13Ke14WN+AML09e3NihculAhXnw+ATvFvB6p+7uu3WoFf9u/rLakpZ0vff/drlKuYdpM0bbpea+Yut+82Z971b+4s48XORi1bF369ZH90zOk+uNyl+8++6bBXzXT2RS8ztRDovmA+XmexisfOoIGuKwmzbaseZ7D77A7dJPvPtB3izgC86cSTUn2/tn8H+YZpE3Kx9WuN2cbx92L/nh1m903wLYQy29JYobGx1/sx5Id4EfdeYgVbh3MqVb59xl6doDle/r07oqt7+RtlY32MO9UF6qSlKzelxBqZlO8bVZfvn2Wq7vWr6vKQqVK9Pem5XKuNa8WWtQrOs4OurkR+vgH3TmIJX3BpdS8U8zSeyy2XXjKrL1tbTzyJu6UlFfv/ONBU1jPq18Fal6M+sYtb6Na5kHhp2qN6t8wWukHrz8VK0p31zpFfxTTj0m1QWGdZbD9Vl3QMv1ovEU8ea32ga/7jmafjY4dE+Pvs3jD0+vTkv5yoeB6bbmkhjlpsy4ve2bdc+U+r/CqI62VJPWfevNAr7q1EGqOizm5Xo4TLcH94PsstwOtHktWM8Dv2x032y4h6Lm4sCb75WmZXRAvmnn+u2Vf5821DYz4utmjcsUb1Y7a6V+s3K9WXOO0L/fQ+ubJYuCf82Zu/uWYY/feMLy7Og2ihuzPQxqrvqh+m1Hzb2Nit3Kb0/J+IR6rKd5GVXr8mBZtb7+c+S6aC577+rdDGos+2Ob9n4mVD35dxFH4aVOHqT6KDWKUffOqmG/0yN7WP8ZxsJ8LT+t9haRZhV8St/fNohRuS3cNHcUWcpRq/rNWnIb+7rsrKm6eLO++2495OvnHPBbzt3dtxS5TJofXGv9omsQm9wJ4baHNWEaJmrHD06P39nu/coXULYujcrkZbndtWN4PlAN4pV/jWrdKCntpHGzgH/F2TOpI91l3cyAwbn5vZbBoTAXx8lqxGV9WGybxtExleVTM1TzHe1o0vWfnZ7RPFhWLrn3ZdaBu36zmkg/ebPWN37U6fhuPxUUI54Y7Ttnq5cTN7wToWPgxEFqPF7RnczvdFKNO70Ge1gnY7/28/fxT0EbjtNoZfP+NY3MO29EMcV8FPT73c1F+JIA33PeIJWODKCPQ1Dz7yV/6E9+hnvI7aP5RINR8/K8k+wb0nop8nBK+DxWdU2+vX9VZtsnsbNuvvlbUBY4z9AU8BqnHpNKk8cbE+5GJQbbT5fmZUlpVLqpb97bt7eHdys7Kmc7b1cMXs2k5jqQ5fub1fX23f89+gY8Uhb4GacOUtMD18bBbCfT2d5DzjsHy/2jaF76xOtLucH+lISNN2sUt+oKr29W95akNmR1b+lmwyRS8E85b3ffsiz3w9l1QkN/b6K65K33LtX3VU1F6Bn0Yl33kO8H3NytT+Xqqt6ijr7v8F735/qxumynaUvxaNre+5Oul7N4Qc2TvoIuyjXJ1sbedfrBv+O0mVQ/8jEu0kSUvVQn9QfrWy2HpkxXW252AebyQerXv8Vw4Gnr+WYtk+7C6s16eh5j9ddbq3tosj9weqcNUs386dvk6esxMd//U5XPRbnqzj9pveXOqK+pPNLm3K9aW5DzstbczAm41ZL7tbks90bzmJSX6mUsxbtZvm/3N6ubEpHbl1ktLFbVb1a5m0HTclXdx94nIIqv3YB7c79761/tvr/QHUmfflemrSj78wK06B1ivNevc84Ld87Z6uXEDe+MX8lnX99pM6lX0n8EEJMgxeNEdeBDTj677zUGM/OYyf1kc4B3EaQajrqP8G4B76W7b1naSW5sKqZFereAN5NJLcsiNj3GuwV8ikwKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsPwyL3Aeabh0/8eiU1+ururI700PKuHtZFIAhCWTgnfqT75Hp+PRTtG7fOXhpqWntjomL+laeVrykordzPea29dUvOXj7OxIJW/X7u9fzPcEKWDPc0e8t8Wo1+/ieD2/c/A/C0EK3qk9+b7lAH2pSEe/XLbz09nDptw8e2RgqVhbpVw51CvslIljr//gfD7fey9jUsCmSNHzLods1Ttsv9DfikdDMin4oMkxJd6h5nY6HivDu+jerWteNHgXZ/nHbV39qE3L1nyrrSOVJbr6XidX//Z72H9t5yeTgg86zcEjXtjcdz2S53ZJb+/Vddt9PXGbt/jbLXs7mRR8Tho8DjrPL93/c21NnTzcBkpGY1fVaX8ql72rqddRpTxKcFKfTo0HBq8vKKclL0tO16mDS7ovu1dSvvTxn/GV6imL5f4uf6P65QX46LyYIAUftjHtOVr+MphScF2SuoPhpO0fmZUw38fjex/PTrjJ1T9lkTDRIdqH6K8EKfiUJkvK4+NJXlKQI16b9+Xlnl5UmVZVYFDHLSF5l/F0tn7S20iV/jXT/wZbjMe4PvfX6uf55aXO7H6PIAXv1IzFp3VpO5m6XHyeY806wWK59pKV0nrof8tLGkS+fjjp2crLpCn3q34tYQlLkIL3Ks/zJ/PPirWT9OrDyjs6lM/L6WazE/fqAJ7ec9lOOUIzDvbXdu9e6pXrHHBy9tDUss77+2z2Mt/bJZIG+OS8gSAF75e62JObf9dnZz3STI7VLz+Ej9+fdl7DZuFu481ig2rvu4zkR2OUIAWfcO332p8uEfVAU0fZ5m55m5lK1Fd0VfRIVheFze/w8On8aWtf49gcLXz+jeuk4NPSznH76xflDP1l5kPIF3RVT04PHlL/RTIpeJ9yNKqfP5C6srdLdd7fsmOqjCmV10vVa4tSxbpPvKIj0+269bdpiuPKxslfe2+JWzb17r/VXltu7VmWX+3wk0lBNL93rAn5imZtOt7W776qPutLw6VnJ5OCj+imTsyu7QlwkOnmzLWz5o6d4N+2e90rWodfBvWmukyxuI+Rl0u3ijYu+bZgmBqt+dUlC7s++srfajwiFuBj8yYyKfiEMD14B9y66dplh7cfVfBmaSNgHHvvp6Xi/+l+N0Ati0wK3uZ2Zl9dq1NcedSc35en6N/UNLta3I6rNfMSi1vo3S4+eu0rGl/CNL2waVJisih3j/Lon0G5d6kTx2JZcZ1YLgsOyp+fTAre4/DpbXqo9LcdaOhpXsvZ5CZc/hu+M49o0OX90Pp/k3flc37tvT7nqfU5W72cuOGd8Sv57OuTSQEQliAFQFiCFABhCVIAhCVIARCWIAVAWIIUAGEJUgCEJUgBEJYgBUBYghQAYQlSAIQlSAEQliAFQFiCFABhCVIAhBX25+P9uOeId+Vznnyvf+XX7iCIsEEKTinqeUTUdm07Z6uXEzc8IN19AIQVNpPSa9JJ3pWP+bX3Op3y5Zyz1cuJG96JkBHKpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwvrv2w2IJV3/zV9txS+5vKO5eHx7c8s1AGMyqVLaL8JDUvVP+WS0BqAhSJWc1r9YKh/MnwBM6O6rZAfNV0rLsr6nqXh8e7KkZUlODc7j/u3I5bP+CbyMTIr3uh+zcl7ycg9NnFv3J5Qa8yYyKT4mbzz7J9zyx/rx2VKQ3GXD6dr2fKYItTb1DG98/SEpn53t4/M4mRTfJK86FX8qPk8mxWeV2cS/ddQbv9Z1qC7FPxnOt3/613JbdZJRxtsFECf6AOZb0toN6OYynf09MineJy/LklJKRViqYxQ3J35P8uRxaPk2Pnpu+f6fHyaT4o1yc6KaihVnON9+m/J9ycV/z2Geftzmc4Z3O1k6Ry5Vn9qNOiLO8TqeI0jxRuuUr3JkPdWjv/+G3321v/vKwvjlELRPkOJ9BldD5euTNsf6p6ThszMkIMuym0fxOU3w+tHOCWNSvFX/tblFqzwv8pvyj73QH3s50bVnAPWdMEclfoVMim/5l6aft68zd6NS5xmku7c2p+Z11VdN8VrVeF/qn/8smRSfkeov0i9/qZ51rvfkXK3d9EMv5RfJpHirNT24DUxdb+R3P9/+B067u2vDqtd83jfgX5wA8xXl7UnWyaC3L1F9Q8XfI0jxXmvXeWr60f/dg9uPHkz4hrXn+Fc/VoIU7zOZwpfLNb/6zWql4t/18clefTEMklORH+Y+PzyTszY8D35S9AcZk6qklKp/+Zvbt7+6vr+6Sv6sx4d/WzPAWNxx9nTfm9M1uHDmtj9AJlWqRvYdPl9gPvjyL729/Z3PR8MMJ5DXOHQbFLk9Xqo14eXrdMqTHefLX2a7L/n1abIyqdIp70LGLzj9QeZk35d0yrSv9wMvYZdMqnKybxrnl7t8JLKNnwQ7Q/NX+VSdk8WHJLUfmfvFauf6AzxAJgVwEvNRiJ+NUTIp+Jhxd/LvHl1CO9fbPk9hz/U6niGTAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAjrv283YCZ9uwEheVc+58n3Or+2FfCvCxuk4JSinkdEbde2c7Z6OXHDA9LdB0BYMTMpXSYALDIpAAITpAAIS5ACICxBCoCwBCkAwhKkAAjra1PQXezGT3L5BLxUzOuk4KycfcFLfae7z9kmAAck8QKAqEycACAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCchd0gCfdbnqfD90F9VJ6XHJdd69zZ4t/hUwK4I/8Qsv7yKQA/iAfDlHbJXPx76Vcypdt0j+dS8mkAJ6U/+Xo8SEyKXi1clDhePlyOGK8db02PbCH8d7KCmc1PbCXtFeyfl/apOKUh/vLUNTljzEaVdoeU6rL1flS+wf6d8mkIILL8cmJ+cn4e72fTApe7dEsJzdJyOVkvB+IWOvdPsPeHsIoBkbytb6N6uqaxq/runTnBa9tv7XvllGd/0h/fQNzl0PtjSndc63qE9DnVf8ymRRE8tlcKpfRgmeNg3zemVGR0rLc33t/gimZFLzLbUCiO1Tl2TjUaPv7JuNn9yNdWTpVp+izTW9DKl2DmzbVQyvjCtvXuu53XZ+LMusgTv9erG9PU0lMt5dW/qFvqVXaH1bq/kKPzBf8F8ik4NPqOLFXat5TdKCSncN7rjZ/6ZHxT2GlbVDkGLXh+Tf0pC/4LWRS8D65yHRup9ypfVRnLqkY4bhunLqAVFxKcymWykGh67LrcMhyL5+X1CVP9TjTWiIXbVyW8qiZr8MlaWlSndQ+2koJbrUsRW3ViFde9x/4iJ2at71+u/Ky1/jZjSrSIL/6V8mk4COOX/D5VMFR6LmueegY30/WeKj4y+Thw3DWWCKovItMCt4nNf+268Z5wjoH7jo1LNVrU3UNTVrKZ21Nafh4Vqab91euaRqab0P/dan71n9TtjuwdR5eTuWzwTBbsU0xOFfMr1xTy6VIZ4Onkh8gk4Lw+vCwvHkeYLr/p1m2uWCrTf/OgbYL3ce3vP9HZraSScG7XDOiPJ7DdzttfuaCmH74Y3xQq2f8zTOc+sql+tH12ThEHc6ZbqNih4++5whq417JYtBuXn6ZvN15XOLfJZOCzzh2gv34afjeBtvX6ixLmf/0Zbd/gmK0Mr06CzhbTvF8JsXIoV9BAR4wu8qoHx+qlzWb3hes08e6WnOqn98ftXtoR6TKvfXr+lpnr2A21tW9sqL9s3zqvk0etPU8zj5xPhqZFLzYQyfSo6tpH9lXfyB86Xn8gcpGicPezRb2/MTh/SdeRAAyKYgsHfzR15fvdVkcHohAJgXBfS1SJMMrfJ/ZfRDVNyNEf3kPfIVMCmjIn4hDpzMAYcmkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAhLkAIgLEEKgLAEKQDCEqQACEuQAiAsQQqAsAQpAMISpAAIS5ACICxBCoCwBCkAwhKkAAjr/+7pGLKwHG5pAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=1700x2200 at 0x20161EF8908>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im = cv2.adaptiveThreshold(np.array(np.array(imz,dtype=np.uint8),dtype=np.uint8), 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 31, 15)\n",
    "im = Image.fromarray(im)\n",
    "im"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Things required to be extracted**\n",
    "\n",
    "1. Vidhan Sabha name\n",
    "2. Part No/Bhag Sankhya\n",
    "3. Details of part and polling area/\n",
    "4. No. and name of polling station\n",
    "5. Male/Female/Total\n",
    "6. File Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJsAAABVCAAAAAB76MmUAAABDElEQVR4nO2Z24rDMAwFpdL//2X1oTXrbXqBscyGZeYlTkzIcCw5gWTFabn8tcAHdGPoxtCNoRtDN4ZuDN0YujF0Y+jG0I2hG0M3hm4M3Ri6MXRjnNntCu7JMaj57HiyylJu+fbCYYZAcouIqMfjM+4hZeQjrOoRC5Rb16O/AnKrcThKjqmMyPWS6+rTejNegdbbp6XNiBbBrtx2FCHN7UtqLazmtvM34ko7ZUTNOdU802C9mNvWvY736eCezw7Jf/YdMm1flVNl1ajALhZyy6eV/Hn5Ny0w6dPfnZkvxj3x9dZb727X8LmwjTP3qW4M3Ri6MXRj6MbQjaEbQzeGbgzdGLoxdGPoxtCNcQOZmSG5rJgg1QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=155x85 at 0x20161F0E240>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crop_rectangle_vs = (705, 110, 1100, 202)\n",
    "# cropped_im_vs = im.crop(crop_rectangle_vs)\n",
    "crop_rectangle_bs = (1462, 110, 1550, 205)\n",
    "# cropped_im_bs = im.crop(crop_rectangle_bs)\n",
    "# crop_rectangle_mg = (830, 737, 1010, 770)\n",
    "# cropped_im_mg = im.crop(crop_rectangle_mg)\n",
    "crop_rectangle_psn = (159, 1550, 1050, 1630)\n",
    "# cropped_im_psn = im.crop(crop_rectangle_psn)\n",
    "crop_rectangle_psa = (170, 1670, 1125, 1725)\n",
    "# cropped_im_psa = im.crop(crop_rectangle_psa)\n",
    "crop_rectangle_male = (820, 1965, 955, 2050)\n",
    "# cropped_im_male = im.crop(crop_rectangle_male)\n",
    "crop_rectangle_fem = (1020, 1965, 1175, 2050)\n",
    "cropped_im_fem = im.crop(crop_rectangle_fem)\n",
    "crop_rectangle_tot = (1400, 1965, 1565, 2050)\n",
    "# cropped_im_tot = im.crop(crop_rectangle_tot)\n",
    "cropped_im_fem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "# To input only first page of pdfs, we will input all jpgs path whose file name is ending with '-01.jpg'\n",
    "onlyfiles = glob.glob('C:/Users/SUYASH/Downloads/AC0020001/*-01.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We require file name for every file our code reads. To read that we will input file name from the path.\n",
    "For example, when I read onlyfiles list I have created above, it shows paths such as\n",
    "'C:/Users/SUYASH/Downloads/AC0020001\\\\\\ **AC0020001**-01.jpg'\n",
    "\n",
    "Here we require highlighted text. To get that we will split the string with respect to '\\\\\\'. Our O/P will be as list as follows.\n",
    "\n",
    "['C:/Users/SUYASH/Downloads/AC0020001','**AC0020001**-01.jpg']\n",
    "\n",
    "Then we will take second part of the list('**AC0020001**-01.jpg') and split it by '-'. Our O/P will be as list as follows.\n",
    "\n",
    "['**AC0020001**',01.jpg']\n",
    "\n",
    "Here first item of the list is our requirement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def img_to_txt(x):\n",
    "    s = x.split('\\\\')\n",
    "    s = s[1].split(\"-\")[0]\n",
    "    im = Image.open(x)\n",
    "    basewidth = 1700\n",
    "    wpercent = (basewidth / float(im.size[0]))\n",
    "    hsize = int((float(im.size[1]) * float(wpercent)))\n",
    "    im = im.resize((basewidth, hsize), PIL.Image.ANTIALIAS)\n",
    "#     im = cv2.bilateralFilter(np.array(im,dtype=np.uint8),9,75,75)\n",
    "#     im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)\n",
    "    im = cv2.adaptiveThreshold(np.array(im,dtype=np.uint8), 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 31, 11)\n",
    "    im = Image.fromarray(im)\n",
    "    cropped_im_vs = im.crop(crop_rectangle_vs)\n",
    "    c_vs = image_to_string(cropped_im_vs,config='-c tessedit_char_whitelist=abcdefghijklmnopqrstuvwxyzQWERTYUIOPASDFGHJKLZXCVBNM --psm 6')\n",
    "    cropped_im_bs = im.crop(crop_rectangle_bs)\n",
    "    c_bs = image_to_string(cropped_im_bs,config='-c tessedit_char_whitelist=0123456789 -psm 6')\n",
    "#     cropped_im_mg = im.crop(crop_rectangle_mg)\n",
    "#     c_mg = image_to_string(cropped_im_mg,lang = 'eng',config='-c tessedit_char_whitelist=abcdefghijklmnopqrstuvwxyz -psm 6')\n",
    "    cropped_im_psn = im.crop(crop_rectangle_psn)\n",
    "    c_psn = image_to_string(cropped_im_psn,config='--psm 6')\n",
    "    cropped_im_psa = im.crop(crop_rectangle_psa)\n",
    "    c_psa = image_to_string(cropped_im_psa,config='-c tessedit_char_whitelist=abcdefghijklmnopqrstuvwxyzQWERTYUIOPASDFGHJKLZXCVBNM --psm 6')\n",
    "    cropped_im_male = im.crop(crop_rectangle_male)\n",
    "    c_male = image_to_string(cropped_im_male,config='-c tessedit_char_whitelist=0123456789 --psm 6')\n",
    "    cropped_im_fem = im.crop(crop_rectangle_fem)\n",
    "    c_fem = image_to_string(cropped_im_fem,config='-c tessedit_char_whitelist=0123456789 --psm 6')\n",
    "    cropped_im_tot = im.crop(crop_rectangle_tot)\n",
    "    c_tot = image_to_string(cropped_im_tot,config='-c tessedit_char_whitelist=0123456789 --psm 6')\n",
    "    return c_vs,c_bs,c_psn,c_psa,s,c_male,c_fem,c_tot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C:/Users/SUYASH/Downloads/AC0020001\\\\AC0020001-01.jpg']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "onlyfiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "vidhnsbha = []\n",
    "bhag_snkhya = []\n",
    "main_vil = []\n",
    "PS_name = []\n",
    "PS_add = []\n",
    "file_name = []\n",
    "male = []\n",
    "female = []\n",
    "total = []\n",
    "for i in onlyfiles:\n",
    "    a,b,d,e,s,m,f,o = img_to_txt(i)\n",
    "    vidhnsbha.append(a)\n",
    "    bhag_snkhya.append(b)\n",
    "#     main_vil.append(c)\n",
    "    PS_name.append(d)\n",
    "    PS_add.append(e)\n",
    "    file_name.append(s)\n",
    "    male.append(m)\n",
    "    female.append(f)\n",
    "    total.append(o)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "front_pange = pd.DataFrame(np.column_stack([vidhnsbha, bhag_snkhya,PS_name,PS_add,male,female,total,file_name]), \n",
    "                               columns=['vidhan_sabha', 'bhag_sankhya','PS_name','PS_add','male','female','total','file_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vidhan_sabha</th>\n",
       "      <th>bhag_sankhya</th>\n",
       "      <th>PS_name</th>\n",
       "      <th>PS_add</th>\n",
       "      <th>male</th>\n",
       "      <th>female</th>\n",
       "      <th>total</th>\n",
       "      <th>file_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BURARL GEN</td>\n",
       "      <td>1</td>\n",
       "      <td>M. C. PRY.(BOYS) SCHOOL</td>\n",
       "      <td>VILLAGE MUKHMELU R</td>\n",
       "      <td>446</td>\n",
       "      <td>393</td>\n",
       "      <td>839</td>\n",
       "      <td>AC0020001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  vidhan_sabha bhag_sankhya                  PS_name              PS_add male  \\\n",
       "0   BURARL GEN            1  M. C. PRY.(BOYS) SCHOOL  VILLAGE MUKHMELU R  446   \n",
       "\n",
       "  female total  file_name  \n",
       "0    393   839  AC0020001  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "front_pange.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# front_pange.to_csv(\"front_page1.csv\",index = False)\n",
    "front_pange.to_excel(\"front_page1.xlsx\",index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# front_pange.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'393'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_psn = image_to_string(cropped_im_fem,config='-c tessedit_char_whitelist=0123456789 -psm 6')\n",
    "c_psn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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