NB-1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from darkflow.net.build import TFNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%config InlineBackend.figure_format = 'svg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "options = {\n",
    "    'model': \"cfg/tiny-yolo-voc-1c.cfg\",\n",
    "    'load': 1000,\n",
    "    'threshold': 0.6\n",
    "}\n",
    "\n",
    "tfNet = TFNet(options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = cv2.imread('demo2.jpg', cv2.IMREAD_COLOR)\n",
    "img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'label': 'Pothole',\n",
       "  'confidence': 0.102126874,\n",
       "  'topleft': {'x': 94, 'y': 107},\n",
       "  'bottomright': {'x': 188, 'y': 145}},\n",
       " {'label': 'Pothole',\n",
       "  'confidence': 0.1470562,\n",
       "  'topleft': {'x': 49, 'y': 106},\n",
       "  'bottomright': {'x': 237, 'y': 170}},\n",
       " {'label': 'Pothole',\n",
       "  'confidence': 0.115238294,\n",
       "  'topleft': {'x': 172, 'y': 110},\n",
       "  'bottomright': {'x': 343, 'y': 166}},\n",
       " {'label': 'Pothole',\n",
       "  'confidence': 0.121231176,\n",
       "  'topleft': {'x': 61, 'y': 143},\n",
       "  'bottomright': {'x': 224, 'y': 207}},\n",
       " {'label': 'Pothole',\n",
       "  'confidence': 0.20959628,\n",
       "  'topleft': {'x': 167, 'y': 150},\n",
       "  'bottomright': {'x': 342, 'y': 201}},\n",
       " {'label': 'Pothole',\n",
       "  'confidence': 0.10555748,\n",
       "  'topleft': {'x': 267, 'y': 150},\n",
       "  'bottomright': {'x': 353, 'y': 177}},\n",
       " {'label': 'Pothole',\n",
       "  'confidence': 0.13970935,\n",
       "  'topleft': {'x': 244, 'y': 199},\n",
       "  'bottomright': {'x': 492, 'y': 307}},\n",
       " {'label': 'Pothole',\n",
       "  'confidence': 0.14256592,\n",
       "  'topleft': {'x': 171, 'y': 186},\n",
       "  'bottomright': {'x': 670, 'y': 468}}]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = tfNet.return_predict(img)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(94, 107),\n",
       " (49, 106),\n",
       " (172, 110),\n",
       " (61, 143),\n",
       " (167, 150),\n",
       " (267, 150),\n",
       " (244, 199),\n",
       " (171, 186)]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_left = [(result[i]['topleft']['x'],result[i]['topleft']['y']) for i in range(len(result))]\n",
    "top_left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(188, 145),\n",
       " (237, 170),\n",
       " (343, 166),\n",
       " (224, 207),\n",
       " (342, 201),\n",
       " (353, 177),\n",
       " (492, 307),\n",
       " (670, 468)]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bottom_right = [(result[i]['bottomright']['x'],result[i]['bottomright']['y']) for i in range(len(result))]\n",
    "bottom_right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = [result[i]['label'] for i in range(len(result))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
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       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
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       "<!-- Created with matplotlib (http://matplotlib.org/) -->\n",
       "<svg height=\"252.099904pt\" version=\"1.1\" viewBox=\"0 0 363.6243 252.099904\" width=\"363.6243pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       " <defs>\n",
       "  <style type=\"text/css\">\n",
       "*{stroke-linecap:butt;stroke-linejoin:round;}\n",
       "  </style>\n",
       " </defs>\n",
       " <g id=\"figure_1\">\n",
       "  <g id=\"patch_1\">\n",
       "   <path d=\"M 0 252.099904 \n",
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       "L 363.6243 0 \n",
       "L 0 0 \n",
       "z\n",
       "\" style=\"fill:none;\"/>\n",
       "  </g>\n",
       "  <g id=\"axes_1\">\n",
       "   <g id=\"patch_2\">\n",
       "    <path d=\"M 33.2875 228.221779 \n",
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       "z\n",
       "\" style=\"fill:#ffffff;\"/>\n",
       "   </g>\n",
       "   <g clip-path=\"url(#pc126c89ded)\">\n",
       "    <image height=\"218\" id=\"imagee312a17971\" transform=\"scale(1 -1)translate(0 -218)\" width=\"320\" x=\"33.2875\" xlink:href=\"data:image/png;base64,\n",
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vjy8//wKhHCzbxmt3Kk1HWijLxnE88jJlb2fM/bt3gJIXr14TpQmWZSE0NXMdj8d897vfxfM8fH9FEAYMh8OqHaQkiiI6nQ6WJTnc3ebe0THHh/sMBj2upjOEUsRJxmSywhIlZRaBzplOZpVKVAqyLMa2FHmWYCuFZUkW82m9YqZpgrIq0O102sznM66uJgwGg1pMNg6FNE1Ra63RgKDWunY+FEVBkiR0Oh2Wy2XNuCzLIkmzysJgw6A2TLFyIBgd0LZttre3cV0Xf7kiiqL6/GYSOY5DXlT6jBm0ZuJVbM1pAKyomSFAGsc3+hyoAdFojWbiGIdLBTIxiA2jMZ9XbMUjiiLyPL+hYQLo9TUMsJkJZsDA/G/u1YCfub92u30DKAGiKK4mbp7XLKooCrrdLrZtE8dxrS02z2vqY4DKMEbbtimKCqCN6T6dTimKgp2dbZy1dtZkSAawmo6IJvsy7WeYdafTqed7tSipemEwDFhrvV40xY1F1rZtwnCFlNDueLTb7bqvbdtBKQulLLqdTr1omHoZUBIN54npG3M/eb655yYLBrCUVVtqSinyPCeKIg4OD+rxHwQBztrf0W63UVvjwce+75MkCYvForaP4zgmiOJ69SuKgtFoxL179yqBVUo67TZ5kdFte7iu4vBwn0cP7jHq91FSkmcpUZxxNZmQZjlb2zssJrO1iSTWGsw+Wpdr7cHGcRza7Tbvvfce4/GYZ8+eYTk2g8GA4+PjWtz1fZ+trS0WsxmOEuzt7oAuWS4WdDtt5rMZSZoSxhFFWWLZFqUuCYIQy3axbJuijNF5xmoxQ1Py+s0pyrZpt1vMp/PazG1OiNlsWjPA09NTlstKv4miiDhO2N3f5dGjB3ieRxxnfPHiBZbb4tXJGd3ukN3tPg/u7fPkg4e4rseLl6+wbAcpQJclnufQ6bQpi5x226XfH5GmGa2Wx/d/8H329va4uDin2+3g+2E9aAxTT5KkYhzOxnw1k9RMJjNAOp1O7dUzA0ojEA2Po2FlFRvIaRYDuIvFgmy9WBlWk6ZpbZ7m6wlgvPpGC6t0sEFtltu2vWFjZYlE1ODSnMC3hXrD5EwRAlotr74X0w6e560dJcWNiWUmqG54ojdm3gZQDDiZY815zeTKsgzf92vpoAKVmwBkWHAURTcWCcOCzL0ZzdAATp7nhGG41kpFzahWqxX9fp+joyO63Q5lUbWruWYTIFqtVi2TNOtjrAhjhhqmbRYNA3KGuZv7VkqRZ6wXTNNegtF4gG1bZHnGarUCKqkjChPKQmApi5bn1jKEYZqbBdemLDW6rJyduoQ8K8iyvGaCzf42xVIbLdm0seu6lGsZxERWmIVRCIH6V//y9z42es/u7m69SrmuSxDFtRbkeRshUUqJZTssFwtKnXP/7hFKldi25GBvm/3RiHt37vDw4UMur6eUWqBFNTmzOCWKEnRZUHlxEhAFQRCQJpU9H4Yhs9mMVqtVfZ5lZGtBPF6zAtu28X2f9957TK/rUZQ5+/t7dHudSsy1Kw2iP+yzs7OFkJr5fEZ/MEBrSNKIf/WHv897D+8TBitOT0/Iy4I0L0jTnCzJiKIqTGF7e5swDHn16hVKSba2t4jjuB4Ew+GwYjeWzcnZFadn57w9PeWTX/6G88mCKIO9gyOKJOS9h4d8+6PHtFuS8XjMoD9kNpthOy79QZc8z4jjkE63tTaL9JpFFJy8PWE6nZLnGcPhgH5/yN7eHltbWxRFwWQyodVqcf/+fRbzZT1oLcuqJ7kBLTOIzOQ23wtZebrNYDR9XrE+WUsk5vs6vGk9mYzpaIB1PB4jLYWzNgHNYmpAMIqiGxPLAKRSCiU2+pQBlCYImXo0zfTquwJlqVr/28gCmnIdJmLAqw65kZISaj0vSZIaIM21TZ3Ngtg0l03bdjqdOjymYnB2/Z2xtFzXrRmXmfxNUDHsJQzDGxqd0VyzfLMgb29vs729zfHxMYeHB9iWrMHRlCZDMwy61j0Ne7Ks2pw0i9AGZG5KFGYhStO8CvcpyrUX2sZxFbu7O9i2TRAGeK6LUooih263j1IWWZaTJBHz+Zz5fE4YVtKX4zhIKQmCmCAIK8+4kAgha+Zo2sj0SVMeyNKMOI5rH0G73cbzPH74L38IUBM8UwfbtlG7O8OPy7IayMvlsq7c/t4BXquFXjdEHMco26lYVRSji0pnQJc8uH+n8jCi6Xe76KKotC2pyArBIvBBCOI4QZR6bfpYSCUZDAZcXlySZQWg1qZCRKfTRinJeGuILgrmy4rq7+7scHl5ydbWiNFoSNtzuXdnH0tKzk4vcG0XKSR5maMsi4KSSc06bbrdDtfX17iujesITl6/4GB/j7t373F85z7nl1dcXFzR73Rot1tYlmIw6OO6DmlaOYn+/N/8OS9evMD3fXRJFb4SJxQlKMdlsZgThBErP8RyPXItmFzP6LZtsjSg3+vQ6ffJC41UNnGccXF+Rq/bw3NdBv0eZZ6zWswRymY2m6GUquK1ihRd5ORFim15lIVmLYGSpkkNJFEY10zFmJp5npKmCXm+AYAmgGitq1AMdD0hDbusVkyw1wPo9srrOW7NGI35Z+QTbaSr9YQyjMawEQEgSmzHotTFWgvVqIbu1dTYzP9NZtg0oy1L1V7gZghJxdKKur6GsZo2K9csxlhBzlqnbDoktK4aW0pZeZvLEtZAauZOFEX130Y/M+1s2tUsArd1rCY4uWvwMO3fbrdrAiCloNXy0Lqk3W6xs7PNaDzg+uqK+Xxe308TADXFmsU3Yi4tgVIWw+GQOI5vgN/meG6wxVartWaRFQBmuZHOHPI8q/V7qRRCVKZvURTkWRWiZFnW2rMta6ed0TwXiwVZVi3GnU6nXiyaVoIxm5UStcdaSsXkelLriq7rkiQJaZry9vSUy8vLmgiYRcDzPNTOVv/j+Xy6Pqkm8EPyrMTzWniOoiwKXK9Ff7BFEKf0hzugJTpJcGyLOExYLJYkaUGSlKyCEKfdxesOeHN6xRfP3tBud7Edi6LIcdadl6YZSZIRhQloRRjEDPsDHjy4S6fjEUZLlCq5e3cPpCbPq/CGYbdNsFqiswSbkvt3DxgOLASaqyufNCnx2j3OL89Y+EuiVUCegCNbdFs9DvcOmc+uUWR4quTo8IiLqytKLRFCsVz65FnGnTuH3L17jB+sODl5TVFkeC2XOA7xl0t2tnfodnqcn11T5ND2Bnh2mzROGPbHFDko5SKxSMMExwLbFiQpXF5HvHpzxWwV8vb8lMvJGeOtAcqyKTQIqZBKMl/OSNMMlGIRrAjCkG6/C7JksZwjscjSgqIoK28ZEqUqL3uewcpfoTVIKRBCkxcZyRrEl8vKc2y8l4axZEUV3mIGlgET0DiyYmWWlDiWRcv1KgdSUdaTxoCjMa+11qRZBrc0PsPydJaSZTGe46CLKgZOClBSoMubE9EwzKIovsICmoHURZHjOHbDdC8aIH/TqWDqrZSCUiM0lHmBazsIqvhDJSRCayypUELgOg6WrGIcXdtBi43joMm2gDpsqIpNrP4HvQboDUiZPjBgPhoNePDgPpPJ9dr7Xp1DKo1SJZYFnmfR77dxXUW77fDy5TPiKKnP2XQaSVWNA6UkCI2QoCxZm61loWsNsOlwyvNN6FOSxGRZZQkkSVzFAOsMITRCVNf03BZZViCQCG2BlpRF5eBTqhqHRbFhzE1nmelP12vXbNf0nwm2d+yKPGldhXEpKbAtRZbGtDs9RuMRrueR5RmWbeG4LhJBnuVkaUqR53Q7XbbGY/IsQ334wcOPd3d3SdOMNM3o9wcIIWvqv1wuWa78CqGznDRLEWVJEobkReV9u3PniN3dHRaLBZPJhLev3zCbz5leT/AjnyDy8dwWeVZFaN+9e7c+d7vd5uDggJ2dHdI04uXLF2hdkucZtm0RRTHT2YyiVPT7fdK4EtotJfFcl08/+w3BcsJguMXe7gFBGPLsxTOefPiUx48f8vQb75EkaeWR8myWyxnf/s43+M53nqDLiJevXpGkGWenl7x8+ZYgTPG8HpPrCVeXEyzLpSig2x1w/95DiqJksVgAkGU5UVjpDFLBD37wPYSEKArr926vg2Up+v0O3W6bPCs4O7tACMjyFCnhww8/qMJk4pSzs3Pm8zm9Xpff+Z3v8OrVGcEqptNqIzV0Wx6D3hBK8FcRSllMJpM63CVZe8KeP3/JarXk8vKSPM/Y3t7C9SqmkecF29s77O7u1uazEdyFkjVY2bZds5AoipDcFOmNCbcJk9mAnHGqpWmK1Zjct4vQJVLJGgSq+zPez4pNVg6mipXd9l6Lxr02w0Hy9c6NpvmrtV7HC26YZfPYpjPIsIU69KThjCmKYmOOZtkN5mhA14T4GCmgabKb76rwjrI2WW86hsBfe9bjOK5Db4S8ucvG9PfV1dWaaVamZFOnbB7XlAqa7Dpfs64m8PX7fXZ3d2vnWafTqeUe0x4bpitvjIHqHqrvjFaZJAlnZ2fMZjP6/T79fr9mf+bleR6O2wI2HuqNlquJ4yqAvd/v39CHXdetNnGsA9yNrlqWJdlaF+x2uwyHQ4qiYLFYVPU4PNj+eGdnZx1BX7mclTIDr9Iy0jxfa21tlvM5aZqSRnHdqFDS6VT0PA5D2m6LNImZL6dVwGSR0/JaCGExubrk5OSEXq/HeDxmPB4zm824uroAKk/W0dERrlt5jO7ff0i3O+Dk7TmtVot7d47Z2dnBdWzmsykCjdQ5s+kcjWA0GmFZir39vUonG3oVUEUps9kEITTttsP9e3c42Nvm4vKa07NzXLdDHJcUpSTPCvIsR6BYLnwCP6Lb6XNxcYUQkpbn0O/3mUyu0brEazlYtmQ0HHF5dcH5+RlpmtBut6rYLM+lLAvmixnz+bKKZ9IlCE1Z5miqTjp9ew6YSVFSlBlRpPHcNrYlGfX7xGEASPKiJAxi+v0+3W6XwWCAZVksFguCIODVmze4bmWK9vs9jo+P0LoaTDvbO2xtbdc6qjETKxNY1kDlOA5hGCJEtSUpW+8MaeqATdBoeuXMpPE8D2VbGOhrThohBGWeg4B+v8/x8XFt8rTbbbrdPkEQoHU1sG3bJggC4jiugbkJPBtQg1KXX5nsQgjQG9ZnmJ8RxA27NJpn04S+zTSNSV0UBTR0uqYe2TR3jVlm7tFax7o1w3HMd2VZ4vtV2NfJyQmtVotut7uWF+y6HY2ZbJib1hqBrK95wzEkjSVws03qRUlvQmXMAmbqZBYeA6xJsglrabaPGSemZFnlaZ9MJgyHQ3Z2dnjx4kWtDff7/fq3BrCre9A3HFXmc8tStDyXTqez3k5YxSGbNkyzTZhPlmUsl0vKsmTQH9zwhpv2c10XcbjX1w8fPqTd7rK9vcvbk1NOTk7Z3t7GaVUek8uzU3Z2dhgMBgRBwOnpOUq6tDseSikWiwlPnn6I67qcvT3h/vEdWi2btIj51ae/IY5LHLtDiYWkrFe68XjM/v4+l5eXnJy85t69Ax49eoRlWXz26Rd1AHKJYBVUMYA6S+j1erRcmyyJ2d/bpuUUvHjxijCK2d49pCgFSBvlKAZjB8fu8fd/9zPiOOW73/0uv/jFT3jv8QMe3Dnmi2fPeXt2yTc/+h3+03/6Gzr9AZ7bYnd7xMuXL+v9zz/9h5/x2/Lb8tvy/3/5b/7s39Q6qFlwK7anybMKfPv9PsPhkOVyWccmS1VFHcRxzKtXr/B9n729PcbDEY7j1OBowK8oCtRw0P64Mjkql30Uxfh+FSUdxlFF8YucIs8QAvI0osxyvFaPw6OD9S6DkuFoSKvVYnJ9Rct2+OijJwxHPZ4+fZ8sK1iuAvIUur12bT4Nh0MWiwXn5+fr/X8wHo/QGv7hF58Akvl8AUjCtQvbcyrWslouiMKQbqdNx7FxXYvBsM9yueDqaooQFi9fviKIfXRpEQQxUihm0xlCwNnZGWcn55ydXSGlw+HBMUfHRyRpim0rHj96wO7uDsvVgr/5+7/7pxwPvy2/Lf8gn911AAAgAElEQVSsygf/29PaSjDmb8XaC6otetThV0kj/Krb7dNut4HKYz4ajardXK7HcrmsIybSNK0jEsT/+G//O93r9YiihL/7ux+BFpUXtN8nynLQBb12i29940N2t7ewlCCKc96czTk9O8FxHFotGz9YYVkWTz54ny8/+ZSj422+9btPULZktUr42c+/JApLkFWmCpNdpd/vc3BwQLvj8uLFr9aR3hoprLWe4TKZzlGOzc7ODnkcVhklVgt0kbO3u82wJen1OigbslKjVIdnzy/ZPzgEr+BnP/k1cZwThQmDwYBO12Uxm2Npl/H2iCAOmC+u2dnbQViiouy9IT/84Q/59NNP+fd/8R/+KcfDb8tvyz+78l//6Z8D3GCAUkKeJTfMYyNnaK2Jk41maILMy7JECVk75XzfJ03TOhBeUUYfn52ds1wGOF4LoSxWvs9gNAQKkiSqNg0ryXI+Yzab0Ot3SfJKaPY8l6urS1yvcjuHQYgSksVqjpSaOEs5OTmnKAVRnLK9t8P27g7T6wn9fp/Ly0uKouBb3/4mx0cH7O/vc3FxiVIWh4dHtNttRqMhWoBtW1AIRsMxlm0hpUYKwfTqkkXgczWdECc5V9dz5vOAew/u8eQbT3nz+i1aCz784AnKqryEZVHiuV3SNObB/Ttsb424c/eAag97jmW5dWjQF//Di3+6kfDb8tvyz7A8+d+frD3lG6dWnufosqidZK1Wq9abqx0imx1BliUJgipxw9HhUa3/mUw5JsbVmp+f4fUGrIIEtztmtljQHgxx2y2GA8nFRcJiHiCkjdPtYjuC3zz/lI7TY3u7S5YWtHsdhltbRGGCsgSxv2S8s8fnLy8R4grHcciyjIvrCYtwXm1B8SzmwQK77bCKfc4uLrh7eEC/t8Wjhys++eQTZrNJ7YFsdTyKvCBONNcTn16vTXdosVzOOf7gmxRFwfPnz0mEZD6fMxwO+eknP+OLF5+R5zF3727z+PEBvt/jRz/6EbYLtlNU4RwtwdHwkDhc0ULiacnVLMRfveH+w4df6Zw/+N73+N3vPeXweIym4OWzS07eTJjNfeyeU2+1iqKI0WhEr9cjyxL8YIFA0W53SJMSpWz29g4YDTr48xMOjg75f//mb0mSjLPzS9rdPp5Q+MsVo/GAw8NdxlsDLFvyq199QhQJpFUFqU+nU+bzOauVz+/93r+gPxjXIQQm5mwjgFeieFMsN86JPLspyDfj4sw5NkG+Vi1nNL2qzb9hI9ibVbkpwMt1OAZsvH5NYb1OY9XwNgoh0OXGY9ncF2zqV5R57R1sOhm+umtk8/dtr/GN77W6cY5m/GTx1U0Jm5hEsbm3zf3Jr4T0mID05t5ncw0T21Y7e9ZbEk3fNq9XNjz1t73u7/LCv6sdmn1o4i+bCSxMXQqt0aiagZnEJicnJ3Xs6nvvvcdoNKqdWc0dMaZ/lVL8r//Lv7txP45dpb4SVUgoSlVe+ThJ0Bps211ndEkoCyjKDCnh4cNH7Ozs8Nlnn+GvoDPo3diiqbWm1+sBlZms/vS//C8+Pr5zDEJgKUWwmFFGPlaZce/BI4RwaHk9ilwym81YLFYIBOPhkMFoSJKkICUr369yjCURlJrpdAZUQZY7O7vs7x+wu7eH53XY3dljMplyfn6BEJKjw2NO375len1VZ8UoioKTkxOePn3KYDBg5QcUhSZJqpCOlb9gtVqws7PHi+dvePv2HCkskiRHCovDg2MOD49YzOfMpnNWS59Pf/MZV1fXdNpdpFCEUYKQFWCen5+hqXLuWbaFH1SbpvM84eW/PbnROU//3fsgS1ptD8/rEAQpZSG5vr4GUSIF9Lodet0O/nJJFAQc7h8QhRFoze72LrPplE67zWx6zReffUqZJ6x8n/5gROBH7O7tE4QRf/pnf8w3v/mEre0RK3/Fl18+p9sdsbt7xLMXb1gulwRBUG+tAtYC8bieUE3gMOEEZr+lickzHsA4TmtNpTkxmiEnzclkQmGam+tvA64BPwO05vgqENYFdL2Hthna0LyOOXbz3cZD2/x9dU0Tb7fxUm/MKHljEjcn/m2AurloWF9px/q3jfPf9oorcXMXSfPYd91Tc4ueAZTbCwK32qQZuoOQ9fVu3+ttj23Tu33b0317x08z8Lxu5/VKauqxs7NTj7+PPvqI7e3t2ltuFsnbXmhzjX/4s09uzLHv/YffrT3cph/SNAWxzk1Z6nUarj5RGJOkMcNhlfjhzZs3dc6CO3fugJaEYXhjoa6BuMxTHMfl3vERn37+HNfSPHr0GAvN8+evcewWjt0mzzWO3SZJAz741hN2RkOysmA+W9Yr4WAwqDKiCDg+2uPevXuEYcgvf/lLdnd3aXU7XF1NmM2qaO9//a//CM/zuLq64vrqirerBfP5HNu2uXfvHq1Wi9PT02oLXFTFHB4c7pCmMXmeoksIg4jdnUMuLi4AGI2qiPbPP3vO0fEBR0d3SJJq4/idO/fY2trizZs3pKmP2+7gOjZRGJCnCSdvz9jfL9jf20VagqvrGenN7a/VwLItrqdz2r0Wv/rNF0yul9y7+5gHjx8wmVYmfcurQh4stYXv+1xenLO3swtAFAZ88P7jymt+dsZ0cs4f/dEf87d//3ecvHyDlBZJbLYdao7uHrN7sMfuwT7X0yWn59d0uz2GwyFZlmGys5gJ3wQSoAanDYOh/r8Zw3V7ktYBwo3z3miH9YRsxuU1w0HMu9FkzL0a5mmOK8qiBpymtnObhdUAwCb7SBMkm/dl2eordbt9jneV5iRthusYxtkETzN5y69hVl8JwWmU2xlYbhcTF/euhcckGm0uCjWwsAHS2+dutsNX4gRv3d+77qsZ4yiEQAtBlm/aKAgCOp0OcRwzmUzqGEjDHpvnbWZ/ede1zOLdHHdSSjy3jee22dqqdoRNpzMsy2I8HlMUOb7v1zGcSZJU+/UXYb3TRErJ/v4+cRwznU5Rh9uDjwsEluNyNblGoXny+BHjUY9FmAES23IpCk2n0yWKQxxX8f7jR3itFtdXE376818wm8/ptLuEYcjh/iGWZfPs2XOkVBRFSZblZEVBklYrfWed9XmxWBCGIdfXVziW5OjoqI6vqhwsLV68eIXndZBSsb09ZjK54uLiHNdtYdsO0+mc1WpFp9NhtVrVwZd5npHEYZ0v7PDwkP39fV6/fg1CkmQ5eVGSxDEfPnnCcDhkss5Evbs/oizg8vKS1//T5Y3O+e5ffsRyuSLLUrIsx3FaFEWV4uvBw/v4vs/x8TGO4zCbzcjzvF7NTCaVNI2ZzaZAye7uDg/u3efzL76g1NWWwVILLMeh23bodtv8/Ge/II5Tut0Br169Js9Krq+v6HarNPXGze+6LrPZjIePHm/2967BbLMJvEoZJcQmH58pntu6AXrmvQmoTRBqxsq9a4VvZqkx7yY9VDUoq+wzTZBrmnDNc974nJu5DA3YVfe5ThrdiA00AGAYadPMvH0NU5omv7yVXQeoASpbZ6Zu7uowOzuE3mQvKcuyTghh4gzfBTyGgTe3/t0APHS9YJhSt4/asNZmerFmPzXbudmGtwH5XYtjM15PA6XmRt1N+Aps9h2bNmkyVjOmTFv99L+6GWb2rb/45o1dPCblWbWjrGQymRKGIUpZ5FlBEPp0Ou3awWEW04pEVFm/zf1lWVZLMdbRww94e3bB2aszVmGGoiSIIvrdbcbjPlmmq61q4TXdXh/H2+bzzz8lCXz2jw4Jw5APP/yQ/cND/FXIJ598wtnZBXfu3KHfHzKdztcd6oCWJElVkSwr6jRSAC2vg1KVHmJ2ipycnNTBnsPBNkIW9Pptfu/3v88vfvEJ08mK6WRJmoW4nuJ6UgVLjzo99g8e8vbtW7Isr5MfBkHIauVXjVZAUVYgG2dLvvjyGbalsCQkScawvcWr8JSzt6fcLp9+9hnj8ZjJZEWSZCgl1mbfBZPrLv1+nzevXnB0dEQUVNk6tra2WC0DPM8jyxP29nbodFrM5lNev3rDX//1XyOoUl912j0sx2Uym/Hm5JJPP3u2BqpLptMpw/GQP/mTP+Ev/v3/wXg8RutNRpHVasXTp0/rwWcGcDNYuDKVNszB7M29HXwK3DA7bycgMEHLRuNrluZgN+c11zDgYAY179DQmpO6CY7mc8RXD9okTvh6ras5gW+cj6+yoGYxwcbmOub+oyhiFYV0u90b+e3qtmu0p9FLgTpjzG1TvNnOhuUa8KiDk8viBnjeYK3i5sJwuzRN2earmY2luS2tedztgG3NZnw0d7s0GbJJcNA0gc35TZ1ujx0a5zWLUG0KyyopquOI9a6PgCwtEHLzzBKgtjYcx8FfxZyentYpt8y18zzHmvoxVqePKhWtApJwyfMXr7m6uGCZZ5SFIMs0uzsHXFyeonWGpgKVMAyrhx5dXNZbWkajEVmU8vz58zrJowlcvLia4NhenTdtMV+RpdWzCYaDTpUdZTBgOp2ytbXFBx98wM9+9jM6nS7LpU8QzsiLgF7vCffv32W1+JLF/BrH00RxRKvt4TiKb337G/z4xz8mSUMWs2pF8DyP4WDM1eWE1WqFsp3aE6RklWPt4GCXy4tzwigjDBMO9o/5ne9InvH/3Oic737v+xwdHeHYHpeXlzx7/kW1rc+zef/xI9I0JQp8Pv/0N3z09Anz+ZzjwyNepSdcX1+RpBHb4wHDwR5SlCRRTLe9xeHxMX//kx8zn1Uptra2djh5e4ZlWbz3wUecnLwmzXOGW0P+6v/+v9jd3eXDDz+k1+vx5s2bOiGpyaxtBrMxh5oAYSaQYUNNM6NZmhOzKebfLrfBw7ATk+24qXd95bhb+Qeb372Lnb0LAM3EetdkapbbjKrJcP4xAGx+Z4ApjmPOzs7AUuuU8Bvzvs6206hvsx4GLE3bN3dzNM10k4zC1E9KicS6cS/Nc5vWawLZ7fq/62VA8TZLNO9Nk70+hso5YepwG1ibEstNJ9VNlv8uE9gAYDM9l2VZpFm8ztpTbaRI00rztx1FEFQykHn8gdabHUn9fr8GPXO+1WqFtVj6LP0Ar90mDEN2xlvsjAZYFPjX57z/3gdMJjOEkNy/f5cgWHF0fMDJqzNWy4hHjw757NkLnn/5Yp0GyUOn0Ot3uHPnDtfXl4RhzO7uLpPrGVpqwsin2+6wt7vNw7WX9eXL59i2zevXr7m8vGQ2m9HpdPjGN77B559/SRxnDAYDTk/PefbsGYPBFmlWeZlmixPieJMT76c//TElMBwOcbaqNF62bTMajWoxtNDQa7WJowhpVQ/suby8JIoi/MWSv/UDRqMRi7W21iyHh4e8eP6KbrfL5eU1UlgMBgO6bZe352+IwipV1ipYcnB0yNXkml/++lfYloflOLS6HmGccD2Z0mq1ODu/IE0u+PLVS7784jmjrW2QirwskdIiTXM+/+xLvvmtb/Dee4/5h09+TpYlRH5C/3TA27enRFHM1tYWl5dXNetqDtgmYyiKEiEUCG4K1EiU9dVJYybObZOsyaY2AFF9lqabZKZNlmC8nhuTGSobuOH0qNPHvBuYKgC8DcINh4bQfB2tfBeTqa/7jxSlmuergGswGDAYDNBrPCrLEl1szqWUQugNczQTuln/21qgARMDXub3GwBcJ4rQGvRXHR6FvglyX1ea5r85vvlufrORAOSN85ZlidS6Yl5llahESklJgV5nJ9Jao8uSUlfhaqWowlqE1CihNpri1+iNAKXOkUKiKTcOHt1cTFRtKjcXlmbdBkcj/FWVOzNJErI8IUsTvJaD+vbTDz8edNroLGXY7XBxdk6a5uzsHVLkgrcnZxSF5urqmvl8iRQWaBul2sznPkq5WNLBsVziMCFPMkqdMhx1GQxbvDl5Vj2OMgxxXJdgNSdLIjzXpt/rEIU+21sjvv2tb3B5dYFUkl6/T1GWICCKY9IsxXUswtBnNNomKyvvU5KmXE/PyaMV3V4HIS0Wq4AHj9/Dsm22d/YZj0bYjoMf+Kz8FcpSFGWVFsiq0n2QxjG6LBkORwyHY7SQfPSd7zBf+Wzt7vOL//aXNyfDx5rVckEY+EShz7e++RF3jo65vDpje7/Lt3/nm7htlyhJef7iFUlWcHz3HpPZgkJDECaEcYZlt3n15gzH7bKKQ5a+z+7BHrZjoUWJ7y/ptDwCf0m/1+Vgf58kTomChCwpcR2Pk7enhGFEWWosu3rq12AwqifgbQ2w+qxKNa41CKGQQmHbLibzr2GLzSwxZjAZk6ROnrCe1FVC+SpS37IUlqUQsnqal2VVbNukW2qa49UcXe/RLUGtH24kpcJSFUi/U7wXVLGgSiLW16weQlR8rYAPG0ba9HI3Wcm72GY1+auMOBV7ptrHrYsqs4rW6KKAskQKDbqgCkjZMCuT6MEASTM11u2itYayAF2ipETJNcAIkLK6vmUplFqns7IqUEGU6/RoJboskEKsHx9UvXRZRShYypyT9VPvKu3wtke9vpcGIN4GR8oSdLk+l0aJ6gmGVfYciaC6nyJPq0znUiKpnrRnXuiCH//Jz2+0wff/43ep0l0ZYF7LMMXm8QxVn5SYbDRm/DYX3na7jW1bxElInqeUOidNE7IsJc8z1N72+OPz8zOCwAc0vu9TlgWz2YzlMsK2XTqdHsPBNlLaxFHGxcUFRZkjpcD3VzhOlWcPNNfXVwgBrZbLYNBDWZKTk1OUsqtnkLoOu7u7aK0Zj8dkWcZ0OuXs7JROp8Pp6RlxnKwrI1mtfMIwwuv0aHW6JFlGt+URR9WT0e7ePWY06DPa2iLNShzX4+z8nDwrefnyJbPJFN/3643bJycn9Pv9dZySwLYtOp02T558SBSFmKdhnZ6e1XndPvvvv7zROVv/87D2vBrTpdI6FH//ox/R8gYo6RLHOdPJkuOjO2RZWW/QNivV9fU1nU6HxWLGneMjijzn8GCfwF/R63ZptzzyrNo54/s+0+mUZ8+eYR5itVqtuLi4YDAYMBwOCYKA7e3t+nGeZgAbYdqYxbfDGzbAKL8CALDR1posoWlemQFZTUr1FfAo8s1zXI02Yx461DSTbk8+KcX6KWtVaepIt3Wz5v+3PaNN89voQk3nybtM7f/cy9Tvtpn5LtOymfGkWc/b7dlsD3vdJ57n1SnLbNvGdqqHETW97Tev+fWA3vy82SaVJrzphybr/Lq6m/rf7K+bYUK39cDmOLsdkvOjP77pBPnBf/zd+pjmdSxlf0Vm2CxSG5A2mqHxBhs9GqjjL3u9HpbnOYxGdwnDcJ2lYVinqk4Sm+vra+YzH8dJ69V7d2efMFquwaPS7oQQLBYFT59+yGq1IoxWfPnllwipOTo64oP3n/Ly5QnX15V5u7+/jxCC5XJJkiRsb28z6A/Z293nD//wD/mrv/or/JWP67o8ffKY08vJ2kMas4qrQONup839u/dYzCb8+tNP8YOYAsH+wQEnJ6eMRiNEkdPr9Tg6OuLi4qIWZg8PD2l3XJ4+fcqvfvUrVv6SUhfMZzNWS58kydja2qqfT9wsvV6PwWBQD4LRaMR0OmUymXC4/4DFPCTwU2bTJb4fEwQJcRwThj6Hh4c4jsP29jZFUfCTn/yEfr+LEpDGEW9evay266z3Ybtehw8//JCTk5N6IkRRxN27dxmYBz+tUyaNx+Pq8Y5rYDYph4QQzGYzwjDkzp079UBomjNNMGm+39ZvmhPfhDdU59mYMkZzrAHIsW5MVPOc3iZINo/blJsOGcNIzXHvNI3fUW6bhCbr8df9/j9X/jEQaJq/JsuyqbvRw5smuHkZUDYvJXiHXGCYzyaI/TbgNtvm67Ta2/JF9b75TTMc6bZTrDkOvq7UdVDqxn01Q17eBajN0lwohBAbTS/bZDJvPtekGsM3PdjmuqZtjRZt0qv5vo+lLIFUsPKrzCuaAkTJyl9QFm0cx8K2W+vU6Am2rUizGNe16XQ6HBwc8Pnnn6+fZGXVj9Cs0lKBZUu6nYqdzGazeoUxG5mb6WtevnyNUoq//Mv/E4DVKiDLCpK8IIpTlFUFQy4XCw4P9ml7DnHoc/r2nKurCcPRGLfd4eTkFM+rgqmfvv8+V1dX1Ta9NVAsFgsOD6tYoC+++KJmJYvFgsFgwP379/FXEW/fvr3xSElTDg8POT09ZbVacXhYecKfPXvGvXv3ePH8DUmsGQx7SOkwHo8B6HbbKCXY2dlhPp/XaauePn2KEJpf//rntFottra2OD2t0tsPBoP1fulqMEyn01rof/78OZeXl/zBH/wBX375Jaenp9y7d4+Li4s6/ZBZ5YbDYSVBOA7D4fAfHbzvih0zntumrmgAsKkhNT1/zVAZtc6K3O126/b8urCZprdVa10f+65ym2m9i5GZ3zUnsklh3wT05m6S5qQ1nzUncxMEbuugTf3JgKDRqJoeT8OwmoBgzquUQhd5/eQ88wyPZps2SxNItLj5CIF3tVHzPpr1vH1OA75NoL29UN5m4830Xub7d8Uz3u7D26XpKLn9yAAjHzSPr3T+zQOsTNiZAUkzXl23ehaJeRypGg/bH0dRiO+v2N/f46OPvsGdO8dIKQjjkG7PI81Cnjx9j6JMSdKAu3ePkEqyWi25uLzAti2msylRFGJbFkEQrvOPwf/H23v9yJLld36fMBmZkd6V9+Z60327e6an2Zwhx6yGDlqtqAcuBK60hJbAakVQf4HQL3oVIEgQIL1K0MuK4tLscLDEzHJ6ODPt+87t29ffW74qK72JiMyIDKOHyIiKzMq6fWeJ1QECVZkZ5kTEOb/zM9/f97e4uEDfMOl2NWRZodfrMj8/H6r277zzDp7n0Ww2cV2wrCGeB7puUC7PsLS0jCTFafZ0bNvFsYeIeGTTKooscHxwSDKdRZQkVDUFSKjJFJZpoyZVDvf3cByfJ+zrX/86pVIJz/PQtB7iqE7I6WmFbrdDPK5gmgP6/QG6ZpDNZpFlmScTJvD8/zbD4uIily5dwvM8nj59GmL+1tc2/SppWg/HGZLJqrieTS6XQVES6LpOrVbzi06N/pdEgWw6QbGQ5+T4iLmZGeZmZ0iqCeqNNv1RbZZgALXbbTY3N9E0jYODAwBqtRonJyc0Gg3K5TIzMz7h6fr6OpZlUSqVmJubm6qBRLWz6KAKPkejlMEqHk2N8geqr51EMWfBJHfdcVBw1PSJXjuqifqTQECIcNhF8WPRa0cn68vM1UmBE51o04RANCL6Mq0neq1gEQiOCyZgcP7ofU72KSpU5IgJGi2P6bg2L4P54I0HMS7q62S/EcRz/QlPOcUPGPwNxkPAFxg1fYP3cpEJHW0f/e5nY59/7W/fHtNUz/orheeOLiz+8x1nCQ/A1IEG2O/3/dz+pz7VXqvVQl5fXwegXq/z4oUfiV1fX6dUKmG7DtVqFU3v0uu1yGRULMug1arhumJYE3Z9fZ1KpUK9XieXy1EqKdiOieOY1Ot1Om0dc2DjuiLpdJparRaubgGfV/TBaJrmR9c8j93dXdRMlkyuiN7roCTTeDGRtZVV6rUTOt0WfctFkCUMY0CuUEKJqwhI6LqO4zhomhbif+r1ephfqesdLMtiZWWJXC6Hqqo8f/4cTeviOr6v6N133+Wv+OHYy1FVlcPDQ/b398lkMmHB6K2tLe7du0+/r/sOXEmg02lQLOXZ39/F0IfMzMwwHA5Jp9O8/fbbvP/++xwcHPDajUvkcjnq1RoJJR4SRXQ6HeKJ5Eioariuy+zsLJVKxWfHse0wt7FQKIT+vkQiwfr6+ljNC13XAc5pSdEBPzn4o7mp0ybq2eAkBDR/lVkT1Uwu0qzCvgjTnfCT2s1k9PKiFtVggutPm5xRbXKahjJ5X0GLLlTRjJyoYLhIc41q0VKE9imaJyxKZ5k8/5B27hlN3PtL9534bdLkjo6VaQGpr+wL5xfhM8F75i5xHCckyvVB9W7oNgjqLwfaY2B5NptNarUa/X7fr464OJN/T9c0RNknHajXmwxMi1QqTTqXRjd0ZCXG0ckRhVKeuKrQbDTIpLMkEr6m0+8PwqTz8tws9tBB03vUajUuX7rs17UAyqUytWaDdDaDbflm57NnL3BdD8cDJa7iuB7lYtnP1xUllpYWOTg4omfodLtdivkcsihgDgasLC+Qz+d562vfQBJEeloXQQDD0MlmcsQTMayBTibjF0UWBIGdnR0MQ0PTe6yvLZFPpxkYBoLnUSwVGAz6JJNJ8vki1WqNg4MDdv/5/tjLWf4/FsLBe3JyQiKhEIv5VassZ0g2nWF1bRlJEuh1O+Rz2RH3YY+ZmTLZXJrBwEAUQdO6ZDMZkskEPU2nWquhJhO82HnBvS/uIUoKpfIM3//+9/3gk3NWYjIoGtPpdBAEgaXVFVKZNHE1QalQ9PGIo0BOMJkn0+L86NlZYZlxv5Jfuc8vZBM7y/fFZ4528fCCSKIogBfUwIgUMHf84/3KXr6W6OfpCmMCaNpECGpRTBOqk8Iqus+kJjjpqI9OsMDJHv3t7PrSueOntehxgQAbewYjky3wYwXnCwoCBcpAVNMVghCtJ9DrabTbHZrNFoP+gFQ6YF32n+PY5l0syCaf1VdpgMEYn7Y4Rc8f/BbN/IhqiJPXntY+/J1Pxz5/7Qdv4rrnhd9weCbUApNWln2omM8k7fsCg3ETmMyWZaEoyshvnmRpaZnZ2Tmk21tX3rNMh5gUxxgMEZUkxmBIrdmh3qihG31sx8V2QI7F8RBxXFgszbO2toE5sOjqfVKZLKl0lpOTU2bKhdHqJ1Ot1JFEmV63RzyukEiruC5oxgBPkLGGHtlcAVmOYwxMVDU1oogfYA2MUdqPR1fXURMqKSXlQ25Mk5gMW9uXONw/pF47YX19gcXFMjgWogDZZAolIVOtnWIYGo5rs7q6TD6fwxr0+cadWyzOzuPZNu1GA6PXoVWrsbm+QVxNcXx0yNzsDA//6bgJPPu/5kmlVDzXIZ/PIooCg36femk44H0AACAASURBVKOGg4MkiXQ7HRRBxh7YFLNFTMMkribY2FgklZKRJItEQsLQNU4qJ8TiWU5rTRaX1nixc0h/4KAbQ1577XVs26ZSqYSR4yC4Ybvwi48+wgVSmSxzc/N4HkhyjNhEtG1c6/D8IjmSTzUkhPPHQ5QEhraFafbRtB6dbgfXc0mlVB88PkVbcmGE65L8SekJgIjnAvgQFd868UZRXW90lIvAmTkM40XJ/bIB5zW/qIkZTMDo91F/XtREC/5GNdqoiRs1VS8yh6P7R6PhwbMN4DWTfrCo+RteAw9ZkhCFILIxIjoQwPF8qJdl25zWajx49Ij9w0O2Ll0ioSSQpJiP2xR9an1hJAwvcgH4FxVC2NBIQoRbVJR6IwiOgOfDWIRAQfQXS89zcV2/KJEsn9VimeZHjD6LcEESBJBEBElEkCQcz+XjCQH41l+/heeCKEh4boi2IeCC8OuN+EgR1x2lOLoCjuthOy7xRBJZVgDRz9UWPEBCkuPkCyVUNUOz2UG6dePSe8mUSr6YQYqJqIkY129eoVTM0WsLKHKaeCyDGs9i9j0GhoOIjDPU2N3bQRQlpJhCtVonk8mDIDF0PQRZIZ3N02prOIjofYvX33id69cvc+XqJaq1U46PD1DVOPaogpPjusRH+aGtZsMnFBA9Lm1vkc3nsfoDDL2POehzWjlGUkSePXvCwByiDwyGjklMkjnY26d+2uDNN9/g6OSYb33rWywuLlKr1Xj48CHHx8ckU2k0vc9h5RRXkFhZ38SyPZZGpTE13WB1dZXd3V0O/kVl7OX8q9M/Zn9/n0Kh4JOz6jrf//73KZdnePZsD6s/IBGPYzs2c3NF1teXmZ2bodMzODjYo9VucPXqVWZn51ldWWN+bolqrRHmMZbL5TB9LkC1B5RUlmWhqiqVSoVOt0c64wcVrl+/TrvdBvxoqSxOnwi+sAnG/VmqUTC5g1zV4Ld83q897A8wECPwi6j2JjL9Wn7k1h3DJUcFUL/vl1RsNpvn4BeMKqlFJ1L0HNGJNrlFzclJn2Nwb4FvKHrMmGB3o8XBp5tl0eOiAi/6TKeZiLIs40aCSoGm5LounhvU6ThjFrq0vc2N69dQYjFi8pkP9FVaGOiQXuImeJmWG9Hmos8uKOwUXOMcMcPEsz/TNoUQ3xn0bVIAfu0Hb4XnjWImvQjQO5o9429CqFEHRAyO44DgjFxSPoj65KTCl1/e5+BwH3noWL4vLyaTz2c4Ojpib+eJv1K6KSRZQpbE0J+WTCZJJmQUyWIw9IsvW5bl+7YsB13v4wgp37TLZrl87Sa6rmN7J/z8o0+ZLSdGhUxsslk/cmwPHRqNGsXSDOlMkspxi0Ihh6510Q0XVU2gDSzSSRVL8mjUaly5eh1z2GXoCCSSGRz8h/DgwX22ty+zNL/GJx99hCML3L9/PyQnWFhYYHNzk3q9TndgYZs2x6dNdg8qSIpEwRyiJjOIAszNzbG+vs7PGcco/eQnP2FxcRHbtrl79y6xWIwnT56QzxdQFZWhNSCdTlMo5FnfWESRJTLpLB98ctePbNsGu7v7xBWVdrtDr2ty5eoNDg4OqFarDAYDvxpftwuMMlpGOY7xeBxd12k2m2TzRWqNOq+99hq6rqMoShh1FCR5rM9RjUQURR/EOxrU0chr4DcJUrsCmi1d14nFkyHiP8qv5nkeTERro1CbyRYVBEFRm2KxGPoDzwTG9OOibZrpC4Rmz3A4DPPNQ8ETEWqTpnRUyAUT76JI6GQ/LurXpOAM+ykIoW8vKpyjwlqSJUTR9+EKnktmlPAf+Bq/yjyf7FNwX9PwixfdS+BvCxaoyf2nacyTi0T0vSIIuJ770ucZvXb0LxESjOizEkURZeQOisfjY8f4bh5/4fej3y6mOfBxq2pSeU83DAaDPu1OEwSHeFxhdWkeNZnAMFpc2l7BsnocHj4nn1cZ2n3mZ8uoqRTHR8fE4iqipJBIqBimiccZELFWr2E7NtZwyEy5RKdd5fjIZ3LRNJ3vfucfkUplqFQqxOIxup026XQSa9AH/Kpps7Mz9HpdsukMgieRKxQYOjYxRaTT6+E4kEolyWdTzM3OICKSjCcolUq0NY16vR76z7a3t6lUKhwdHtPVBuwdHFCenWVhaRnH8Ugm03S6XfRel/39fSzL4t5/8XDspXz/Z9+h2WxyenpKPu/XQun3+xweHpJUEsQUhVa7QavVwrQMHj55wkcff0o2UwIBvvUbv04u67O69Ho6mmZgjXwbnufjJoPB5sOLlDBw8/jxY59uS1VRkykc12F1dZVSqRRGawVBCDXASVMRGPkrz/B9Y4KM89HKYHUXJb+6W+CDCarQASjyWbWyqGblD0Bf+wgG67jfTzqnJZxFdPHNtUiLsolE7y3qawruISg4PqmpRQXlJAA5ugW8hrZtU6/X/fdpmqNnGBsTDFGtM3jWk2bwOeFAJOVrIivEL1guIkkijuPX5o3FZAJ/anBMtNJZ9DzTNGQv8vlcdDay3ySLTEAaEkTgowuEn7Uz/v6in6MBqvAZCAK2c4bhEwSBj39nPAr85l+/MXXhGA7P8sqDRaDb7ZLL5ajWGqOSspkJbdwOnyFAPOEzEfV6XaStrcvvDawh9tAdwTGyyIJIUvXN4lhM4PHjL5mdLbK8PI+qxnBdi3arSU/rMb+wgOOJVE7qpNM5+saAwaBDOpkgm0mSiicwDZ20mkASPL+IuJoGFBJKhlq1iaGbDPomrmchSxKyJJCIK2TSKQTRI51UUeMKzWaLy5ev0Wi20AydrqaTzmZZWFilp3UYDHRajQYDXaPb6XDv3j082S+dNzs7S6PRwPM8ut0uMzNlfvPb3yYWkymXS6N6owbdbgdZgJPjY/L5PJZl8WSCEj//P6URRdEvztTrsbm5yc2bN4nLMlqvSUyCfC5PvligP3QxTYeEmiUeS9LvaxweHZBMqjiOR7PRZjh0WFhcIp/3M0wCvrJisehrzyOzeDgchi84mUzS7Wmsb6yHJmowsCXJJ+I85wMiEG7uaPMHV3RwT5otwSDyPA9GTCMBkr5arfLRRx+haRqFXD4UOJM4u8CUjfbhrG/nNYezCean1gVCb9pkmhRckxkGUQEwbZu8dlRoACGPYbVapVKpoOv6WD3laIApuLeLTL+oH1KW5TGhM7av6CELhCljkiiEKWS4LkPbvTCV7mX3Gt3zXPTbu1gAChO+5PHrXJxJE/XJBtFZQRBGaa7jmvekAHzr377JZPPHt2+GHx8f88EHH7C/v8/Ozg7Ly8skk2kKhULIQB88I6PvEzU3mw20Xi+sRxxXEkipZP69dCaHbbt0Gy1E/LzDTqtNtdGl09ExTYehCZ22jhJL0my2WFqcI5FUqdWayIpKIpFiaLl0u22WFoqUCnkqx0fMl8sMzQG108rIDySTSRcQkEarv4SuDUhnMgiSw7WrV33Ov3qVfD7D3NwcTx8/oq/rxOQ4CTVDq90loapsX9rC9jwMvY/jDJkt50koMW5du8L8zCxLS8t4MT/ys7i4SLVa9VlcFIWDgwOajTqe7ZDPZum0mriuTVyROT05YXFxkevXr7O8vMz73/vF2Iv47V98j2vXroV+udPT0xEA3OPm9S1EWcAaWnS6OssrWwhiHFyZeu2UbreDKArs7r6g2+3h2D6sxrSGNBqNEMTZ6/l1YRcWFvxsnJF/z/N8JtxcLkepPMPS8lK4GkYn7jQBeDYox/F10ckQ1c6iA93zPFxvfIAHHISXL19mZWk5jLgF2tErCUBvXAhFo7KCwCg6PT1Fb1JwRAVgdN9XFYCToOZg8g6HQ0zT5OrVqzQaDba2tkItd9L0m9bXac9ZFEU/f3aqAAQ/W1sYy+f1XJehPcR2vKma3rTrjwvAiIb5EgE4+VwmA1Hj+wnT72F0v7Ztj7KgjDP/sij64bCIlvyqAlAUJQzD4OjoiEajQalU4s033/TfB2elBqL3V6ke8OLFM05OKnTaHUqlMsmkiiRLSN/4tXffK5eKGLpOPC4jSQKLSwusLC+j91xiUgLPFojJMbSejtbVyOdy9C0DUYhx/cZtOh2Dbk/Hcf1iJJI3ICFLJGIiA6OHYfgFimzHQ/BkSqWST0cvCAxtF0lS0A0dJeah6V3KpRKbG5vcee11dF1DiSmoyQyeJ7C7dwiizwW2e7jHYFSECc/Bc4bIosDu3j7xeIJGs4kryZwcHWMOTF67/RqDfh8EgZgkMrQsCrksC/NzFHJ+ic7lpWVarRazs7Osra2g6xo/+/7HYy8i/j/6L8E0LTRN53d/93d4/fXXqZwck0hK1OpNhjYkUxkqp01SqSydbg/b1IknZAqFHEk1za2bt8nnizQajREI/RamabK7u8ve4QGO69Ks+1kdgaZ5enpKOp1mdXWVmBLHAwzDCAd/MJGjQZDJCeKbv+NR0KD5monvLI4OfM8FjzMhI0kS6XSatbU1kqqKPVpxozT8YwIQIQyESJKEz108HdMWnEdRYuExYZqcNxIHwrj2F5x3WtDhpUIh0oEogWx0IiWTScrlMsvLy6H2bRjGVA3sZQIQxn1jUuTZB/fgHy8iCB4I/kLlej6jiuM6WJaN612MTYy+78m+RJmrJ32AUW00qoV73ihu740D1acJwEkhaQ6H9A2DTqdDOp1GTaWQgmCP5+G63iiaK56PAl8gAAVBDFNRS6USd+7cCcHmgiCFeb+B9ue6Li9ePOG0WiURV0fA6DiZjJ/KKqWysfe0XhtEm3QqQU/rElcT7B8csLm+RSaj0m7XuHp1k9u3r7K8UkaOiRgDB0lK8vndB3S6fcyhhagIvPHGdTzbZGjqSJ5FKZ/DGloMBQnLFXGHJprWZTAwcTyH4dAESSChKpi9FqbRp16t8ezZc+7+8h66PmDguDiCQq3VBknA9Wyy2TTxmMzSwiz2oEepkGN+foHD4woOEmo2j5xIIuAHDgJ67Hq9jj0cYlkmoiywffka7U6PH/37nwAyp6dNZmYW8DybZquG61p8+o+/HHsRf1r/E5RYnOOjE0xzgGma7O0ecHB0RKXRQklk6JtDOh3/5R8eHCCJDouL+VF2iYJpWgyHDqIgk8mkkUSB46NDKqcVSrOzlMqzeAhsbm5Rr1XDYs/Vqs9O3Ww2eb7zHMexyeWyDIeWzzVoDhBFgXhMCSdygDM7S78SQh9gdFICeK44EkqBeepvoighiaIfBEFAEgQ8x8Fz3DFfVmDijWs70mhS+8wzohB8Pi+sAo3Lp/u3R1Aa0Yd9RI7zzz9OzBBMwKiZHBWSPvTE16hG6A7wvFDTkieoqgJBb9t2mEoJvn874F0MQLeBRvyyoMQklMZxPVzPZ1VGEML/XQ88QcbxRGxXwHYFHE/ARUSQZDwEEMRzUJazTQzrdUS/9xwHgZHgHd2/KIj+FlmwJnOMRUEcsdL4Y0CW5JFmKoSogEkN3PE8EAU0wyCdzaAk4giib/46roPnCj4ESpCIyQof/tZHY8/qaz9465wA9zyPZDJFrVYLWd4DkhNFUUDwC55H/YO2bdNpaXRaOo1aG9cRuXnjNYqFMp4rIIuCxJ07d9B1nf0XT1lbW2NpaQmj1OOLB/dGYFodRI9sLkMqpVIuz7K0lGbnxSHb29scn9Qxh2Y4SYvpBJ16nevXrnJ8fAyI9DpdGt0+pXyJbDZLs95geWmBk6NjysUsg4HB0tYlDMNAFEUsy8LzPH+guQ6Oq/mriKrSbrcxDAPDMFhbXcYyEvR6Per1Oo1Gg7m5ORqNBo7jcHJywve+9z2ePn3KyckJ6XR65MhVMAY6jUYDyxzy9ttvY9sup6en1Go1EvEY6YxPLz/Zvvj850iyQrmUYWV1EdNx6HQ1RCVOWo3z2q1bFPJFRFHmL/7ir1hamKPf11lZWWFzY5tWq0O/3+fg4JD9vUO+9vU32d7e8vuFwN7hET95/+/D/OlqtRqmD87Pz4epdEtLS8zNzyPLMsfHxxwcHJDNZtnY2MBTk2ODeHx1Dv5OX7lftUW1hSi+LXCU/yrnDISCoigkEolQqEQDMtPyfKPXmGq+TfwmRvp8bj9JHDMtAw3CsizgDOoxHPrclMViEVX1tYrT09OxojsX9THaJoMX0d9fJkh/lXc0edzYs4gsijBOYf9V5wj7OWWfaCCoUCiEAbNgTr9McwxaNBIevfajR484PT3lzp07IXxKVVXK5TK2YZ2L3Af+1k63gyRKlMtlUqkUhmHQ7XaR5ubn3rPtIfl8DmNEo2RZQ/Rej3ZXI5VOMzM3Qyqdod5o0dM0nj1/Tq+nARK242ENHeSYPILMgDUYoBsD+v0+SjxBXE0hSDKyHMPoO1jDITFJolqpUMilSCkS8zMlXCR6PQ3X9Wg0mgS8daIoUxhRZwWUTmEmhBJja2OdWq1GPu8z2SSTyRDOEU+oHBweYpoWmUyWO3feGK2OApIUo9PxoSbl8gyNRh3wkGSJ127foV6rUSqV+eXvj2uAf/T0H+M6FnE1gSdCq6fTG5GgMrTY3dnhyePHfP75Z8RiEo5tkUmnOD095sXzHVzXz332fXsddvd22Nraotls8ulnn/Hw0WNaLb+0Z7PRJCZLYU5jkP4zPz9PsVSiWCpxdHQUaiuXLl1C13WyE6QD46ZhwG13NvHOBMTFNEjTtmCgRx3kwWCdHMzTBnsUziCKYij4ApR/EGGdLsTHAbbjZvdZi05ySZTG+jF2vHQ+1S/qIgjOE4/HQxMsHo+HJBvRfk7bpmk0057X5Odpv73sfVwoWKZARwJtPRgLwbuMbtFzTrpNAvM4erzrujiui+u5oTk6+c7hrA+u6/LRb4+7mb7+g69NhcgsLCyyublJrVbj5z//OQ8ePECWZVZWVvC8Mz9rlDzBMi2Oj4+Zm5vjt37rt8ZIgOXl5WVOTyo0Gg0kzyOhxFheWkUSPGrNFqIEcUWl1+3R6+q88cYbpNNpPrv7KUMLLBuy2RILSwucVE9wbYNWs8Py8gqxWIxUKk0mk8bQNOYXl7j/6ICdnecIImRSKpLn0mm1WJifwZOEkBlmbm4OXdf9WguJBM22X1wolUqhaRrZbJZ0Oo1jWzx48ADP83nvZmdnuXLlCjs7O+zt7RFLqLimyc7ODuVymb//xQesrq6ipjOAiG1r7O7uU6lUyGQytFotLMvmwYP7dLvdsD5JtCUScba3t/nw07u0NYNEtoheqfsM2Ae7oZapKApvvfUGiqL4jDRWHwGJer3J7Ows3/72d7j7+T0qp8f8+Mc/RpZluj2NZCaLaZqIouhHe9sBg81iiG07PDykaA6wRz63arVKJuPXQK1Wq6QTargQBGmA4xPoDBc3pvF47sUTaEqLColJf6IgCGEqnqqqYaGrMT9YBPsnimIYkVZVNYw2A2MD2xe2TgiRiWock/2aFArT8GlBEyOCMvp9QGkVFRxBSYjAWpl2/5PnmeYvfNnzvOg9TAqUaItqyZPPRYpEyqMCMHoPQb+iz2+S3iwQWlEBFfhtJ+8vmpUTvUcQxt77RW3yfViWxWAwYGdnh3w+z9tvvx2W4zyD5vg8BYFvcHt7m+VlP0gXYCgDP7XcqHcoFn21sFk9YW11hefPd8hlUpTKZU5OThjaLslkmpnZBb588BSf/dfCdRz0Vo9MtsSjR49o99r8Z//p71CrVjg8qVE7OSGTSmP2e6gyGN0WelNne2WO+dk5UmmVk5MTDvf22dk/ASGGLCl4rsCgb7Ewv0Sr1ULTjLAK/OnpKeVyGcuy2Nvb48rlbQTHB7vGYjF2d3d58uQJy8vL5AtFPMkPiQ9G/H7B6rG7d0AhnWd21jct5+ZmKJWL3L9/z1eNYz7zzfPnO+deiqRk2dndp90xWN/c5qPPPqOYL2LpGqV8gdnZWSRZoFY7pXZ6xMLiPCk1hijBT9//GYqSYG9vj4cPH2EPXYy+5vsBJb+2aqPd4fLly5imSV83WFpa4sWLF9TrdURRDGumXL58mU63SywW4/nzHRRFDuEG+y92+P3f/32SyWRoFpwNKkIBeE4QeOc1iq8anNEBGrROp4OmaXz55ZehX2ZtbY2FhYUxX9Fk5DXg6gsWgP8/W5SvbxLeEgjqKDNO0O9oPyf7/FUm5eR+07TGyfaqJvCkgJHE85Hy4G+USv4comBiYZgWIHmV+4z2xXPP4yOn3WP0WQNhMsav//qvh+QgpmmOLeRB/4O/wUIVvLsAbdHr9ZAuX77ynj30aaL1Xo9Gve6j/IcOQywQRLK5PHIsju16SJLC0LHBs7l27SbF0gyeJ6LEFT9ahcPR4RGrG1tU6008QaKv9YgJHltry3QaTfReh3a7hRyLsbG5jSDFUJQkzWabwcDEd3LL4f+lcglN11AUhX6/H96Ur/UNkEWBRqNBKpUKsz329/dpNls4nhBmMziOw/Hx8SiCO0RVVI6PjsnlMzx9+oR6o8ra2hpvfe116vUTioUy+XyRuxNFm4v/8wxdrU+no3F8ckw2nUaRYjhDm1QiQSIep1jIoyZi7O7u4Do2airJ6toGWk8nm82RTqdJpdI4tsvyyhIbGxssLi5SKBbpmybdbo8bN25Qq1bptFt0R4LOdV3S6TSZTAbHdemPVkNF8QMdqqoyMzPDwDDY3t4mlUqNlWD0B6EwJgDHTDNenSH5ZXATWZZpt9s8f/6cQqEQTqJJjTo4NjCtgpV50o8Y7HumdXhj9xC9/mRfxj6/fIaOmXmT9xec5yIG7X/IFpx78pzTrjF5fxctWMGzCvoqS2cR/Em85EXvFMahSVFwfCAAp0XDwzjaRAuu6Xln710QBD74/odj+739w6+H9xB9H/IIcB9EewNfn88rqoTaZnRcxeRYWIUvSChQVdVXdFZX194rlUrgghKTGJpD0ukUer9PLJHAQ6DTbuO5EI8nsB1IJJIMbYueZuA4MD+/SLvTZmFujk67TT6bp3JaJ6mmUBNx0ikVJSaRSsTZWF/H6PfJF4rU6y0ePH4CkoxuDGg1GiGLriAImKaJaZrUG3XEkXls2zZzc3N897vfZXZ2lmRSJZtWASEseJTPF0aTyWX78jWKxRKtVhtN00mnM+i6Tw6azqQxTYO+0aNYLJBQVb748gG60WdrbZFuT6fT1s9R4s/874uIUgxRFLh5/Qa3b9ygenJMPCb7BZZcl0KhSDafZ2d/n3v3H7C0vMzjR8/odjUWFhZIpdLEYjF0zWBg9nnt5k36AwNj0MfoG8QTSe5/+ZDFhXk812N+fgHH8Vcvw9BQ1QR90wRBIJPJoGla+MINw6CQy5PL5UJG6PFJA2EUVowMdi+oE/Jqk3aS6in6m88uXuT4+DisAyyKIq+//nq4IEUDG6I4YpTBmwqNCfr/H1MABtkq0wRDEFUMvh/PhhjXAF9VWEW3yec3TRBNPotfRQAKgoAsXQwWn3b+oE0yiJ/972ML/TokhNApiArAM3NXDKFZZwQGwfaL738wdv1v/PDt8P+gv/59nLlbAlZoQRCIx1WCrBS///74EEUByxrQqDc5ODjwa2cnYrTaTR4+fIAseRZPHvyShYUFyuXZkNRQkhUMzyaTTZHP5WhV68QLBaSYSqdnUC6vhnz7H3/8Eb/xG7/hMz57MaqnTVKpFGo8RiKh0G73cUWR+y/2GfRfkEgkUAeOH8RwHEQRrlzdYraUGWl2AfDWQrBd0rkCXa0XgikVReHu3buIokg6nWRhYZGVtXW+uHcfx/F49nyHbKZIOpWnXvHLSg60HpZh0DAHLC4uEldVepZOf9Blc3mRW7du8dMPPuPy1TcwzQGG7vHRh5/xxptvnRscnmCDNCSdTDK/NMtg0Gd9a4V/+8O/YXV1m8HQ5t6TZ4hSjEQqzfz6TXZOdMxOl3Q6ze7eIYlEAkEQSKZTZNMZfvzjf4+aVIinEuzs7CIpSVaWN2k26rSbbQqFAvPz82QyKY6O92m168wtLNPpdEIfX7FYDOso375+g1KpNKqd2qfRaJBIJEin0yGdfzBSA4EjCPCSQOtL26SpJst+uYQ7d+7w5MmTkMK/1+uFWpQ/iRxEaTThBM8HAYtBx87nq47h5cTpfrIxJ/2EKTuBu/ZBuZ53TnAJwjhZazTVbtKHGWgd00zBaL8mfXfT+jwp+KaZltHzTD53SYgGbQKtKSh4dD41L9Ts8PGGvtQQwBsRD+AxdM4KlAMhiQP4sCkfnsTYouAJZwSlMJEq7o1T1gd0edHmOOPEst5onPoLZSRXevT843GV5892qVRO8HC4dGkDJS6zt7fH3u4h+XyR2dlZnjx5wunpaVggXc7l0wztAYLo0tPa1Bv1kUSNoyRiaM066UScmUKWdu0UQVZR5CRra2vUajVc1+XWrVvs7++zubmJYRjk83nW1taIKRKVyjErKyu0O01yuRyDvk/13Wq1qNVqzM3N0e/3+eCDD0in4iwtLXFwcMDq6irzS7PUajWGQ4fbm7fxPI/Hjx/7ecMjc7DVkjna2x+lkcUQBJ/yyvM8Tk7aIMQQBIXXX7/J3NxcmML19MVzBkN/9dD7A/YPj1hfX2fvsEpX6/H44WdsX72C7Z2vM7u1ukin06Ova9y9d49sNku32+PXf+N7NGt1pBHXYS5fJKGm6Bl9VCWGkJCZnZ1FVVVqtRrVapVGo0GjVmd1aZV6q44nOmSzBW6/9ib9vkNKVTD7Grdfu8lPf/oT/PKMIjdv3uTF7gH9gUWj0QjTswLQbkKO+avdiC9NkiQWFhb8+x0Ro77KhHrVNqlJyLIc1iAplUphH4Ii9YGWw6gkZlSoRM46ds5xweaMaYDR4y7SaqLniWpeFwnAVwEbT35+2bX/Q/2DX3X9i36LPudAa55sYeQWYaopG/Rz2vYPaYHwCorLT7bATJ+2aET7EIynw8NjPvjgI7/MxnDAF198Ti6fodfrUciXePLkCZ7nUalUQktRkiTktfUlkimFvb09hkMTQXBGhXd6qKisry1TzudwTItyZH/3LwAAIABJREFU2aHW0rAckb29vRBq8uzZM2ZnZ3n27FkYITs4OKA/0Gm1Gly7doW1tTWq1SrmQKPf77O9vU0sFuP4+BhBEHjrrTfoae1RAELi5PSEtbU1VtZWaLXafPHFF2FpwXw+H9Jbx2Jx4qJCIp7G6PuQFkkWaTar5Aspej0da+ggSh7NVo1Op0O1WiWXSZMVBNT4PLZtc3RSZWAOWdu4hHDqISqL5MtFTEM/93L2nj6mNDPLxqUt4mqKH/7tj3j9jbdQ1CSiUMc2B+QzaVQ1jtbXUJUEp5UKguP74oKC2oIgMDc3B4gcVk79glP5MkNHotVo02l3GZoGSlyk02mNVjqFxk4Tx3bpGX16moau69TrTd555+3Qj+aKZ1FTWfbrFkfZRiYH4z+0TU7KIKLb7/dD7SlY0Sejih7uBX24WND4OLJfXQBOmu9R6Mc00/VV7vVVf/uHCo1f9frj1GLn9wu1Nc8bqycc3WdSEw7uI1yw/gH3IAh+jZ8gzTPaggDG5FiNgrR3d3fp9Xpcu3aNfl/npHLE7Owsq2ubozl2hD10qdUaxGIxvvjiC0TRr1eUyWRIpVJIakJ8zycHmKHRrKPEY2SyaRzXRtN7zM/N4do2mVSadCZLoVhE61tIUiysNFYqlUZlNLuUSiUKhYI/8GMSGxvr2PaQ5y+ejSiUvLCymWVZIaC502nT03phTRDHcej1NE5P/SyIuKKSTqdJJpNhOchOp0OtVieXztHpdJBkEVkWabaadDotJFlA1/shnCKZTJLJZJiZmWFzc4NyqYhm9Lly9Rq//OI+qVQ2ZKGxBb9esCQKPPqDcR/gf3v8z/HwizYdnVQw+gPanS4H+/torRoLC4uk0mkeP3mKY9tomoZh6GxvblAul+n3+8zMzFAul0che5XdgwPiiSQDc0irXkeWRLqdFq/fucnDh19SrZ5imgNqtQaqmqTX07CGQ4x+n3g8zsLCPL1RorfneaSTqTF/kud5Y/inYGBFCUi/yqn/st8vmmBRLSQYuFHzTvBdRBdcbzwpP9pXn2T1rM+v2rcoDCTqd4v2dRqcJjppJyOpk5HsSTM6elzU7zb5bCd/+yrhMXWbuMdxjfasn0G2RDA3HHc6aD54T1HzdrwPZ/RY0d+ILDSBCyFa3EgUfUjKgwcPODw8pPovq2PX/OaPvhnx78UJYG5BXnYqlSKdTnPlyhV++MMfsre3R6GQ50/+5L+j3+/z05/+FMsakkql0XUd0zRZWlrCcRyWl5dJJBK0222kS1ub7xn6ANeB+fllLNMhlyuSTuewPaidVtF7Gt2uxpNnO+zsnyBIiVG1Mp+0s9PpUCgUmJubo1arYdv2qAD1EMPQMc0Bi4s+D5/jeGE1uGDlLRaLIxtfxnEE0ukc+XwJ07TJ58skkxn8GsS+thNogo7jUCoV6esdVFXBskyOj4/Y3trmG994l82NSzQbLURRBkTq9SaddpedF7sYukHlaA85nuTDTz5HN0wGfQPHMllanGWmVMK1HVZX1/jgt8ZBmtv/9xYuEq4Hjx4/RvBcUok4iZiIN7RIJOIszM2TSiUZ9A1y+ZzP6iGIHBwckEql6Ha79Ho9kskktWady1e3qFYrLC4sYg403nrrNrl8jGfPHvP6nTcwDB1Jkkml0j7r9swst26/hus5vPnmm2F2SK1Wo9vtsjA3RzabDQHF0ckeBJeC2iGTDvj/2Ns464wwRswwJhQmQNkvi2S/TBhPEyDRiS0IZz7JiwTMZEGmSV9dVFB/1YLyKoLsVQXeJINPwNc4Sbzg38O40I769byv0OUu9HFOsPmE/ReE0L8qCEJIjxcsJsOhTbFYZGVlhUuXLvH33/37sfO88+/ewXVdNE2j0+mMXFon/PjHP8YwDBYXFxEEgWfPnvHgwQNMs4/rDXnx/AWiKNFoNBgOHUrFWZaWlpAkHxvY7/fDlEbHcZAN3UQSFfAk7KFHqTjLaeXUz7FLq5guZNQUyWyeWKZI3/QYOiBztpJks1kURQmL8wSCbWibeJ6D5/l+t/39fbodI1Q/r1+/Tq/X48mTJ8QTSWLxBLbbp9UxaLZ1kskkRydVHwCd8F9oIPnr9Tr5fJ7l5UXmZ/LYtsuX9x+wubnN8+c71KodP2iSiAEyjuORy5VGGCARXTe4c/sah7UuuUKJ2XmVpCyy9/QRmYRKLC6z12pTFcd55gB2j05RYhKKHGO2XEaNK2xtrFEo5Dg9qfLo0SN+dvRTRFnh6rXrSEqcZrPJYDAIC4L3ej08z9eGl5YWkOIiyyuLDO0+2VyKw6PnvPnGDWZmyvz8F5/4RY7WNvizP/tz4nGVo8MTjMGAnb0XrKyssLzsB0SazSaO4/Dxxx9z86bv94zSUwmCQC6XG6tW9ipC41fZD17uS4z+5if9X+Br4/w5zoQWY/ZXtG8vMzUn/VhnTnbvnGk+1pfI56hw+yr3QVTYflXfJvs57drB9af9D/6jPIOaeBP3Mq7JRYuL84rP72VtXLgTPp/AHRKYtX56mkK1Wj0Hng6aaZpomkaj0WBhYYFCocDBwQG3b9/m9u3bAOzv748E3ZBeT2N9YwnHHVKrnfKNb/wa62ubPHr0GKPfo9uVOTo6YjAYoKoqiqKwvr6ObA49NE1jY6PMwLKRYnGkWBwFkVwxhz1IIOBy8/YtfvaLj4gpSTxRRMYLebeazWbIBReQdXqeh+1YzM/O0+/3abbqbG5so+t9BEHg9PSUn/38AxKJBMlUhlgsTq+nEY/FkEcrWLvV4erVq2RzaU4re+ztHVCv+oGTXC5HXE2w8+IFjaoPnl2YX+bp0+e4blBTeIgS9ymrAi0IIJvNUq9XuHvXYH5li7gEgjdEkeNsb2/x9MkjLl+5Rl8zSK1mzr2cRr3D0BqwurLkR1p7bT7+8CNu37rB0vIyiAJJNcvQdfnywQMEQaBcLpKMJ8NslpWVFWzb5uTkBK2vISoCCUVFkRVMs49IgsePH1Msr5JO+2r80La4ffs2lulSqVQxhn1mZuZ49uxFGOBoNBpcvnwZd5QHbds2c4sLxPxQKwh+cflg4gS4u191gP8q7dwknZgoQSAkbBGM2GQ7EzhToq1eIGzcqefzjxrPYAiuEcBcoueLfp4MFE1qpuH5L8AuRrXLqQImGp4WptzbBe2coJ7oe/Sv6zqh8BsHx4vn+zDZD08424LvPQE3UrhPEARG+OYRAiYIqoiIokyvpyMI3khOCCST6dC1MdmCipMBasEwDK5evYrnCQwGFj/72c949uwJ3/nOd0gkfC6A7373e3x5/yG//OUXPHjwBM8TiCsJbr92g6OjI2ZmZkIKu3q9zvHxMdLS8vJ78wvzxJQYkizR6XYY2kNEScTS6piGhuA59HodZMnj6OA5smBTmi3TaNTQDY1MJoM1NDGMvm8mYJPLphE8F0PXGfT7eK6HZQ1JppLouh+YUNMZhq6HI8h+uT+zSamQAcciLovEJIF2s4boOVgDAxEXSZTpGwZD08a2XBTZL4cXi6ksLa0wGJik02m+973vsLg4SzKl0GjWuHrtMmtry5RKebq9NqViHtexePP115gv5RnqHcx+F9e1mF9c5NneCddv3mYwMPn8n/xy7OW88zfvcOv6dSRETiunpNQU8ViC/aMjBo4DgsLAcun3TYb2gNu3r7G3+xwBOWSx2NnZIZ1Oc+PGDfSeztLcCqfHVVqNJrIkE1dTHB03OKl0efHigI2tLfb29xg6A8yhiShJVCoNBES0njHyn1p4nsve/oEPURoOGVimH/2OQAZEzibFNF/R5OQOoBTTzLRp5unkcdH9Jvf1AsDYaHJNMsVE27ipLo4otUYFgTwhPA6fp+RsE0TG2WcY62MsFgt9VFFz+KyPHlKEEcVnUBFGLDIisiSMfhP9Il6CEKDfxkzLc5qcECMoImXbDt1uj0ePHvP5559zcHiAJPnJ+5MCc1rA5uwiPnTFcV0fo+e5IQOLT6nl4XreqNyRT5Hleh6Cb9P5vfYC2IkQbn7QKSi85HN5eqLow/8EHzozdBwGpsnHn3yC7Q7R9BapZI6hBaqa5i/+4q/44ot77O7u8fjRM79yYzxBo97k3n9+d+w2vvmjd/E8l8PDA1zXZ6nvdLpUTmr8+Z//G/b297Esm8ODY955512fZKTZ44NffIxpDcll80iiH4NIplT++I//Bc1mk263y7vvvhtqmNK1a9feC+h9gmrpgQaXTcdZWFgIgxzJZJLl5WWMvk4uXyCbTQEumXQaRZGRZQklLrO5vuGnY+3vMzMz4zvl02lEQcAaDlCUGAsLSzSafjYInuvnvDZP2draRk2oGEafTCYb+g5UNT6qVeGzcMTjCQ4ODikU8sRivnpbKBRQVRXLsnj69Cn5fI5EwqeTD+oMq6rK/v4+SkyhXm+QSmc4rdbodDXiiSS7ewdcv3YLNZWh3faj0g//6eOxl/Ovqv+S/b096rVTv+h5JsPy8gIJNcHu/h6u49FqdTg9PUUQXBxnyPXr1xlaLpVKhWfPnqGqKqlUioODAzKZDDs7uwiCgKLEGAz6JNSEf99xldmZMttb61y/dplmo8HSwjJHh8dUTmt+cCflYwqLxdyIp08lJitks1kODw+5du0aqVQqnOCScF5YnX0OAMaBdhb9/HL231/l+1fZJttX7X9RgGF845zPMTAHO50OJycnAGPlKhmJ1JeeMyIsx/ouXCwAgwACBGah7LMy7e+haT3eeOONcC6+7Jm+apu275lwfXX4U3jdiWedzWb5u7/7Oz9f3bZoNGusr22j9fp88cV9Hj9+hG706PV65LKFkFT48PCQp//l+Bz79vu/yYcffsj9+/d59uwZpmmSSKh88vFnVGtV7rx+B8u06Pa69Lq9UKubm5vj2tVrzM3NYRiGXw4zHuOHP/wbdnZ20DQtZE1aXV1FzqSTYQ5tEGUdDod09R7b6z6ur1qtIstyWI9ibWOdR4/ukVRTI/BtH8syyKT9CXh8fDjKq3Rot5ujiedRKpXQtC5HlVNarQ7FfJGubuA4Npbp1+Ot1WromoEgSHQ6HRzHQdd1ej0r1GAODg5QYgkWFhb8TAfRY319PRy8+XweXde5e/cumUyKtbU19vcOmJ+fJxazWVxYot/vs7q+RbG0QLdnks3N0GzVyRXK/PDf/YitS9sAPhXYRNvb2+PZsydcuXQZNRmnWT/ls88+Y31zjZWVFYqFGWq1FpcvX+b9n/4ISfIjsK4Tw/M8Ll++zM2bN/nkk0+4ceMGjUaDXC7HysoKmtZla2sDQYQPP/yQ44MXtFotHnx5F89zSKVV9vr7GFqP/+QffTfEU929+xkry4uYpkkmk+buL+/TarVIp9PMz8/T6XTOALERk+X8JBo3Rz3XJ44TROGciTRpMkbbpAZ10XH/ob9d1L5aKIwLrMAEc12Xer3O0dFRWEIhCk7GnfSnRa95cR+FlwQXJn9Lp9PcuXOHd999B0EEXddDnNvkPb5K+1We38t8jhedQxDGYTD1ep3d3V2uXbvGwuIcL3Yek8vlePHcB/73tB6xmBDW7AgyMy5fvnzuOp1Oh9u3b3P//n1M0+T99/+eW7du0e12WV9bB0DTNC5tX+Lo6Cisj22aZsgR2Gg0yGaz3L17l9VVn+g4wBY2Gg2q1SrS8sLce5lUinKpSK166kdC7SH5XBbT1EO+s+XlZebn5zFNE13TmJub4fq1K+SyWaqnJ5j9AYau47kuruMX1E4k4jiOTTKpIssS9XqNxaUFWo0G5ZkyS0tL6JqOCCQTKkNTI5VKs7y0jGH0URTfr6hpGsVinuFwyI0bt0YPokc6nUHTerQ7LVRVZW1tbVSA3E/En52dYzAYUCgUefHCp6HqdLrEYgqapjO0XCxrSLvdIZPJcv/+A8qlWebn52i2GiQScfL5HHf/yUQu8P+S53vf/TaNRo3PPvuUXDZFXFH4zW9/i/nFJXo9nZ2dPSRJYmlpntnZsq/hdnRkWSabzbKzs8PJyQkPHjxgMBj4BaUMA8PQ2d3dodlsAKCqCb7x9jtIMvzau9/g008/BgQ63S43b9wkn8uSSqr81//VH/Lxhx9Sr1XpdloMXYekmgqLKgmCEGo0ztBPMA9qbQQT3S8eHQsFRL/fD6FOuq5jDqyw8tw0gTctmhxcJxqpnDapgn0mv79I04lqW8HxkxrSdJCzL8zHI8q+qV4qlVhbWwuhRGNEqxHf2WQ/A3btaBAleCZChH7rHIQkQgkFhCwl/b6B0TfGIEuvqlVPkq5+lWYdCD7XHddi/fs6C6ZMa75lfPacc7kc77//vm/t2BZzc2Vq1Rb/5s//Ctt2EAWBmZkSly5dYm11g3a7zeHhIT/60Y9w/4dx0oPD/+aAzz77DNu2uXPnDleuXOZP//S/59/+9d/QarU4PDpkY2ODwWDAlStXMAyDymkF13FptXzmKMuyEAQBx7Hp9bqkUqnQ1RGAr4Xf/t5vesEAOD09pdfrceXKFRzH4bTqq4rXrl3jk0/8SOT169cxDIOlpUXq9Tr7+wdsbV3CsT06nZ5vsioxyuUyg8EAXddJJBI4jkOz2aTf62LZLguLS+gDk/LMHLrexzT7FLJxZFkhlUxTKJTwPIF79+6NBmiQ5+qDiW/dfI1PP/0c27ZYXVsOzexOp0O32yWZTCKKIuvr6wyHQyqVCpIkcXBwQCwWI5PJkEgoHB0dhZTzi4uLuK5Lu93EwydbLBaL/D//71+PvZw/+qM/xOwbzJSKnFZPiMsSWxubGKbB/lGVykmNXK4EwMbmMnNzJVRV5f/6P/81y8t+X69evcrDhw9xXZfNzU2Ojk78HN5CjpmZEqfVCqZpkk9lWVpa5MnTB2RzPpFj37Dodgyu33yNjz/+GGs4oN1uk0jEuHLlCg8ffYkrKMQTSVqt1mjhuMHVq1cBiEdKZgqCELIba5qG/5MbEkZG8Xvdjs7W1iWWl5enCoKXCauLWnSyRT9/VQsESlQoXHTu8eu7YQrdZJ8nJ//Yce54KcbxY88whAGjTFi0SDh7fuec/ZFUv/HfxoM45wXuq0XYJ68XvMtJ0tfJvkT3jwruyeYK03JLIJlM8vTZY45P9nj3ne8gEOfg4JAPPvgFgujjca9eucn+/j5HR0c8f/4cxzHHzpFK+5UWRVFkZWUF0zRRlARKLMlgMGD/YJ9SscT6+jrxeJynT59Sr9fJZDLE4z7qAsBxHZLJBImET8PmOA75fJ5MJsPCwgLy1atXsW2b4+NjFhYWQrCgX6dinp2dnXAyAGHmRqvVotlsI0kSX95/TCLhsyvcvHEbOS5Sq9V8jkFJolAoYNs2M6UiyaVFTk5OKBWLXC7PUm81R4WMi9QqB6yuzvHo4WNSqRqW5ZcjXFpa4tKlDYbDIfV6G9u2qdVqLC4u0u22w5QvSZLo9Xrhi/O5/XzarN/7vd/j008/5dvf/jY/+MEP/H2NNulcmnwpjz7QkRSRRCxOIrVAt92m3W6H9XijrdfronXbmH0DBJf5mTJKPEbP8FkpZmdnicVUZFnm3r17SJIPBF1bW2N9fZ2HDx/yt3/7twiCwDe/+U329vbY2rpEp9NhODT56KOPWF5ZYnt7mwe/vM/AMFicX6ByeoSu95ClOIcHe3TaGkPXYXauzD/7Z3/In/3Zv0Y3fDD0890jrGGVTqfL8vJSyGAcANYDaqDhcEg2myWZTFIoFBAlB9u22N/fZzAwxhLRl5b881wEGH5Vv9R0M1J46WS7aP+XXTOqCY4dK06acmfCLwpXGaMQ887OeT7F7IzUIUolJQjjldjO9/EiF8K4bfmrLCgvc0tEhf75784D1b/qfQZfR7VPPyupTqVSwfM8fw7F0jx9+pRut8uNm1f4gz/4A378o/epVn2IWywWw2FcADqOQzabZXZ2ltu3b/OXf/mX+DU/HCRR4lvf/Ba3b99GFEUqlQo//8XPWV1ZJRaLhYpLvV4HYGNjg3rdT6gIyhtUKhW+/PJLpNWlhffa7TaCILC+vk6hkCOdTpFMqmxsbnDp0mVu336NcnmGw8Mjdnf3sKwhriMwHLo4tsdA75NMpLi0vYXtDml3WjSbDW7evEEsJlOrVXFdh0a9gYBL3/S1uEarztVrV9nb28N1hszMzvL02XPKpRL5fAFFiYMoEk8kcDyXLx88RO/9f8S9d4xkiX3n93mvql7lnLqruqu7OqfJPWF3NnADw4rLZRKp0wEn83yS7yzA55N1hiALMBb+w7AM24DoO8HQyUdJkClKlEhRqxO5Ky6X5O7Ozuzk2D2dQ3VXVVfO+T3/8aredPf2zK4A2VfAQ4cKL9X7vV/4hgpWiypc2gHcXi+1epXtWIxmq0W71UKSJNbXVzl58hTFchWTUaJQKHBsdo6V1RVaHYVTp0/h8Ti7vYO8Jp9fqVTYjW1ht5mx262Mjkb52YsHXeGefftpDHoduzs7GE0StUqFXD7PzRs3QZQYHR1jZyeOw+GgWq1QrdbUaW+lTrlcxuVyMTIygsfj4dKlS0xMTPHBBx9gt6uA78FImHq1RjjUx1h0mJHRKD944wfIisz5py4wOzfHzPQMpXKFL33xNe7fu0elVOb8hQu89oXXyBdydGRBZaRYVQOfc+fOsbe3p5pGC2CSjNjtdlxeDwajhMEoISjQbDYwmUwYjSYC/f34AgH6QiGGBofwev1YrTZNPHP/hXGgX/Yxj4NT4I+KYj6uvHtcwDsMOt7/v49ma4+GIKJ4cHrckTsHA7AiaConvdX1psa9qkkFSD8KtB9RPz7whzpNfbQo+5bDQ5XHB6CPGyztX5dwqOfZe05Rej1IRZ2+i4+UwgUBFFH1FekhX8R926eodnbq54kKou7R+yXJQCy2zc7ODi+88BJ2u5NKtcrM9AynTp8gk0nzzW9+k+HoMFc+vIzH42YgPEj8X+4c2Efhf4JgMKj1rjc3txEEgcGBIZ5++mnOnDnD0PAg+UKOn/zkbb72tV8kmVBdH5PJJLV6BZ/Py1NPXeDWrRsaZW5ubk6l6dbUmYPw6quvKdWq6m/rcFrQd+XXK5USFsmsEYedTqd2APv6+pA7CuVylWw2T7PRJhD04PHasVglspkyoqgaU/c4r1arlWw2y9jYGGazWXM3azabzM/Pc+nyFUSdEY9HdUkzGAxUq1VsXWn3vWSWUCikNYb9fj+CIFCt19hJxBkYGMBpVRkqhXyWPr8Pp8fLnXuLeB1mmrUyFouF0ECE1S3VHCkUUOloPd+NnnBAvVamkk8wd/wEeoPE//bN//vAyfn0S88yNjZGOBwmkUhw5coVotEojXqTdhsikQibm5vaMKZnC9jzkNjY2GBsTB2yzM3NcefOHZLpJJNjo6yvrZJMJjEadFhMRi6cn6dWb7C8voXF4UJn0FMt5gkFvKSzZSxWBz/72c947bXXKJfLrK6ukkql8AX62E3EaTQaDA4OMj09jcvlOjIj6gUItUTqHMjG9l/YBuHxwe2wtP7+i/Zwb/CjAN2PflbvfY97PCnQHhUU9pd8j9vO/c8dXn8PYKw3qAbler36uwr4R6OU7cfYCYKAwCOhzv2eG2LXs+hJ+/BJnzuwn/LHYxJlWe72nVVh3kuX3kPUt+gLDfD8p16kUqlhsdrIl8oYDAZ0MhoTptf6UNXD27TbLc224Hvf+x6VSoVf/uVfplAoUik3WFhYwOFw4Pf7efDgAZFIhIcPFzCaDNSqDQwGI2fOnOFP/vhPDmyrP+DHYDAwODjYFURRjbI6nQ6rq6uACpa22Wya9mR/f78mdtIDX/cwwD2h4BMnTvDVr36VH/7wh+qw5PjxE6/bbKrPrNFkwGKx4HQ6GRmJUimpF29Pd2tkZKRLKr5NmxYOpxOrzcnA4DCttoozajbbNBsNCoWC9t5ms4nVaiUcDhOPq5nR5OQkm5ub/OhHP+Lq1asMRiLo9arPbE9SvtFoaNpxHq+HdCZFR27T39+HXq/DYNBTKhUxWSxqA7mpMi1AIZfNYJCMOJxuKqU8LqfKAd6O7eD2+lX6Xaetii/odDidTvR6vQrXEQXsNjOTE1OMjo3z/dN/d+DkTH9nnLm5OZaWltjc3OTixYuk02nMZgtGowqzMZvNpNNphoaGyOVySJJEpqt36PerJ1f9Mjwkm83i8/vwuFyMjY0yPDxMdHiIY3OzFPJFJqdmKVfrZHNl6vUmZslIu9ViO7aLyWwhEAhw9epVLBYL8Xgcg8HAyuoaff19OJ1OTCaTVubub+wfXtSgouwr7Q5ebIfhM4eDyCd97kmvPRyUHvf8PxRe87iAcVRwOWo7e85pOr0aAA0GPTq9WjY2W52DtLL9AyEOZrZA1yCogU7/qJT+JBS6x+37gfchHrn9h29Isixrhu+lUhFBgHyhhCSZcDicyIqC3eFUxQc66n71hgqKonQFSHV0OqqqSrVa5fbt23Q6Hba2tiiVyvzw796kXq9TLBYJBoNMTEzwgx/8AFEU2NtLosh0h5D5j3CBX/jZCwSDQa5cuUKr1aKvr4+7d+9qClQ9AH9v6qvX6+nv70dRFMbHxzEaVduKubk5dnd3qdfr9PX1acpThq4DoM7t9rxerVaw2+0odGg06qytraEoMm6nm3LXKMnn87GxscHy8jITE+OEh0IEAkFqtQY7O3tYrDZ8viCdTotOu4XbrUrDA9pwYX5+nlKxTCKe4P69+9hsdo4fP4Hd7kAvGWh3VFZKJBLRBhnlcplUKoVOJ5LNZrBYzEiSAYdDnQDbHU5y+QI+nw+v29lViNFjs1gwmS1ksnmGI2F8XrfaaxsbZ21zm0QigcVkwuPxIMuyuv+Kala0u7uDosi0O7C4tMztry4cODknvz+H2+2m0+nQ19eHJEnEYjHsdgcmk4WxsTFarRZ2u516vc7IyAjPPfccyWSS8fFxGo0G/f39PHjwAEmSsFgsDEWHEFAol0qq+53JSDaTxu3y8t57lylXGrgvT4qDAAAgAElEQVQ8AcxmG2urq+gE1ZKw3mhqirg9w26n04mo01PpciZ7lDtVjKKjCc4+LgAevoD+/w6Aj+tD/X8dAJ+03T0/DVGnWorq9ToEkS74/GDv8nEBsFQqkUgkqNVq2O02bRrd6yv+YwTA/ZYGTwqABoOBRqPBrVu3umroaZxOD0PDUWxWBxarjVQqy8OHS9i79NNGo0EqleqaeeVRldtFTXF8Z2eXYlHF+en1euLxJM888wxOp5NYLMbKygo+n4/t7S0q1TqNeh2PR9WsLPxG4cC5sP/vdq5du6ZBZtbX1xkeHtZ4wQaDgaGhIXQ6nZYpFotF0uk0VquV48ePo9PpWF1d5ezZs5w/f55qtcri4iJ+v787cHSjO3Xq9OuhUD9Wq5XxiVEcDgflcpl8PoeICp0olUqaB6rZbObE6ZPkCwW2Nneo1VoMhIcBgUIhTzaXAbmtARNzuRxLS0uUy2WSiT3y+SK5XJ6TJ09hsVhZXV0jm80h6vRIkmo+U61WyWQyXacnBZfLRTgc1krKR3CBGtVaBZNZnY72B/2cP3+eYrGA3Gnj9nhB0Knm7OUS0WiUVDrDXibHiRMnKHQHMDs7O6TTaXw+H7Ozs9hsdgqlMiCSSmVY+ZWNAyfn2F9OUywWGRkZIRwOIwgCbrcbg97A9vYOGxsbiOKjQVCxWOTevXvdYc44169f105oo9GgUqkgIyO322xvbyHLMmfnz/AXf/4dyqUKs3PHWN/cZi+dpdVqMTM9yez0JNs7MYxGVerbYDCwvb1NvV7n/v37mMwWwgMDWjvBZDJhtVqp1WrYbLaPTDsf9csODjb2S2g9KQAevtAOw0weG1QPMVE+bgr8KFAfLOkO9xA/MsXt/v8wo+VxQaK3bVpwER6JN6jQGrV/ppa1qmJJvV5ndXVVbRd02SUGg1Fr9BcKBaLRKIFAQGU3mIz7xEqPPoa9ya12DvYNYA7L28uyjLhPneVxk3VFUTCZTEiSCpYfGRklPBDmUy+8iEGS6HRkGs02t+/co9lsYTYaSCaTBINBfD4fExMTqmNhNsu3/uMfUa3W0OsMlEsVzp+7gF5nYHcnzuc+9wrb29tdFfMqExMT5HI5/vk//wZ2uwWDQSIY7GNjY4PGbx8cghj+Z/WGHQ6HtUoymUxq+2K1WvnsZz9LKpVCEAR2dlQ9z3A4DMCtW7dYWFjQsJ0bGxuqN1C9TiAQIJPJkM1m0Y2MjL4uy52ucnAFnU7sWk/KICuaOY3BYMDr9ZLP5ykUSwxHx+jrH0SSrGQyOWQ6tDttoI2IyrgwmUyMj4/TbDax2+14PV5aLZlGo8neXopWq40sK5hMZmQFpK7doNFoxGQyaXaDalPeoDVaAwE/Op2oTmMrVbw+FbS6sbbCysoKy8tLmI1GtrZj1BstauUitUqZWCxGvdHE4wuopWLXB9ZsNmsCC4lEQtVG7IDdpvoXb/yL7QMn5wvXXkGv17O+vo7VqrYPVNJ2iHK5yvz8PCaTCVmWCYfDTE5OarzcnZ0dotGoFvhEUdUHrDfrrK4sE/D7Va3DQp7+viC5bIZKtUJb7uB0uxkcGkCkTTKxw9r6Bk6nC0mSWFlZIZVKYbPZGBsbw+P1sfjwIcVikUqlQiwW07QBXS6X1sQ/bHhzFMdWCwJPYEMcDiT/kAztSY+jMh1BELQSSMvQjqD0HbUNh/uRh9fxuG0TNPog2k9F6QoKKIIG2LfZbIyMjGhlodxRoReiKOJwODQLU4/HjSw/EgI4KqvrBbfe/vVe0wuahwc8KnbR8JGbyf6fvf3vlY69Csbr8bC6vkEqlUEQdGxsbOELBLl48Wn0ooDJZNIgZjqdjmw2yxtvvMHY2AShUJi//usfYDSa+MlP3qFUKtNuq6Vws9nUsrW33noLQRB48823KJUKfO0Xv84bb/wt09PT7PzawSGI9995kSQJq9WqtcJOnTrFhQsX+MxnPsOpU6e4e/cuXq+X1dVV+vv7KZVKFItFEokEU1NTtFotKpUKkiQxMTGhyWddvnwZQRDw+/3ootGR10FVVh0fHyWV2utq/HlYW1nravWpwOKeK5nT5UYnOSiX6zSaCoIooDOI1BplBLFNo1qjXFaHDvfu3aNQKHQP+iMdwfHxcYLBICaTiVAoRKPZJDoS1awT7XZ7l1WyC8iYzBKiTqDeqOF0OWg06rTbLQRBILmXwWaz4feqVDiLxYxBp8Pt9VIolvF7XTgdNgYGBjBbrNSbbdW5TUA7OclkErfbrcr66yUiQ6MaO+PSZw9OgcN/oIox9Ayy19fXGR8fZ2N9g729dLcHUmJgYIClpSXVLjOdxtLtVQ4PD+N0qv2VUCiktgv6Arz80ou4u97G9+7ewWI28+yzT6E36Lj47LOkcxnW1lcIBry4nTaGo6P4fAEcDge7u7ua1tlPf/pTRkbHSGfSqkdwt//ncrkwm83asRVF8YAIgPrzo7xTrUz7mEDxjxEADwSdI17Xy/iazeZHMqDHZaD7l/0Z7ZPAwkcFQEFQxVtVVRX5kbiAogZk9UZt1KxAdTodoqDaozYaDSwWC5IkdQeLLS2bhI9mwk8yL9rPDPlocH+8LeZh3GSvrDWZTEQig9y4dYvJqRmcTjeJxB7PPv8cS0vLvPP2W3i9XoLBIKFQiGvXriGKIrdv3yYUCmsQl1QqxcDAANFolOHhYba3t2m325pK0Wc/+1kURaHRqFGplPnZT99jZmaGvr4+lv/p8oHvQf438trxn5iYYG9vj0AgwPvvv8/Pf/5zcrkc8Xic3d1djQK7t7enJQC9IarFYmF0dJSFhQVisRjxeJxwOMzJkyfVDHB2duZ1UVTr+Fw+g9ms8nATiSQel1oqRaPD7O7u4HI5sdnsKIDN6sZismK1mDHoRRr1KtVKkenJMSbHJ/H5fOTzeeSOwkB4AJdTvbALBRWnltrLIMuKSoiWTDTbTbK5HLlcTsPrCILQTbVzNFt1Go0GA+EIm5tbNBstZKVDNpdneuYYFy9e5Oc/fYeTJ0/SqNcoF/PkslksNguK3MZisaITdNxfWMDh9rG0tEy9WmZgYEBTpO3dwYeGo6xvbpHOpGg2G9z7xXsHTs6ZH5ykv6+fZCLJQHiAUknV9gsGgpRKhW4fzkE8voteb+jK9euQJD1+f7cHUimTTqeQ5Q7FYgGPz8va6gpmkxlQsFlsnDh5QmVm6PR8eP06yb09dKJIMOBFoEMqnaXerPO97/8Vv/LP/hkjI6Pk83l8vgBWm4Ol5SX6+vpwOGxIkoH19Q329vaYnJxGp1PJ781mC0F4xMboiWbuD4DahfeEEriXWfQgEgiCKhzyhGB0VPa3P3M5/L79QcJkMmEwGLQL/Kgy+6ill431gsD+1+7fdoReztc9DoK4L0PubqMC3UJJE/5sNloIgki73UEQRDKZLIVCQfNukSQJn8+HXq/TjMN7+3F4X/eXvIdvDIf3G9Rt6QCyoqgb1X10UFCE7v+751GnUwVJZQEQBNpdvnw2l+fb3/4zkskEyyur7GxtcuL4HGtrazx48IArV64wMzNDpVLh2rUbGI1m+vr6CQSCGI0mnn32OTweLzduXCccVs2/ZmZmcDgc1OtV0ukUIyMjrK5u8JnPvMzeXopYLEbxvyse+B5MfWeKmZkZMpkMsVgMm83G0tIStVoDvz9AsVjqXjvqtdebFKtc6i3y+TzRaJSvfOUr/PmffxeTJOHxOPH7vCQScSqVMkajCeG5555ReobaPSXnHjHcLNXI5dRprs3qxGy2EuofoNlsYzLbNFDs3t6e6sPrcnHnzh0tFfV6vTidTtWg3GDQsp07d+4wPDysfRkNBgO7yQThgQgOh4OtrS0Guv2rnpadoiiaQXUPwDs6OkqhVKTelKmWioi0kVtNkok4n3n5Rex2K7FEnMXFFQL+IM1mG4vFhsVsU+kw9RK5XI5QKEQoFMJqtXLz5k0a7RZ7mT2ajQZWs5m/+7sfHzg5/82v/ytyuRwvv/wyu7u7rKysUC6Xicd3sFj0WC12Gg3V6EWSjJw/9xSNRoM7929pog1f/OIX+eCDDwgEVMHGnViC69evA3D8+HGGh4f5wz/8Q1xOH7IsMzU1hd1hZmtrg1q9jCAIWG16xsZUibHUXha7zYOi6Lhx/RZ6yczCwwWe/9RF3nv/HU6dOoUoGNjZSSIZrESjUcLhftxuFwi9zOARTGT/xdYLFvojskPtIlMEOspBqSntdfJHDbMf9zjqQt//934J+3+Mx4F9eEI1rtu364e3a3+PrdPpaKiJfD7PzZs3GRsbY3x8/NCUuIOoe3L5vn99mneHLKN0aWu9dfUYDi25g+qBoz5XrVbpdDp8//vfR5ZlRkdH8Xg8hMNhzSayZzTu9/v53ve+x+7uLtFoFEmSePDgAb/xG7+h+egqisL58+f5nd/5HfL5PJFIRGNdpFIpjh07puJ7MxlEEVptFQ3S399PNpslmUzSbDZ57bXXSO2pyIirV6/i9/t5uHhQDIFuddZqtZiYmCCZVC0jrBYn8Xi8K/phw+fzIUkS7XYbo0lHrVZjcXERu92uGXCh6HDZbFRreSYmo6RSKVKpMoPhIfRGk4FWu0G708RkljBbVNc1i9WE3MwTDg9SKddYXl7D4XDRbDYxm61IRiN9fX243W4URVVr7RkjmUwm7HY7sViMhYUFrWchSRKJRIJ4PI4sy7z44oskk0ni8TjT09NksnmWlpaw2+0sLCwwMDCgeeAWCgUSiQSAhutZXV2lVCphkEzYbRacDhcuuw29TiSZSjE2NobOaCKTUcVRz5w5SzKZYm1tDb8viMViYXFxEUmSSCaTGt7RbjEj4sbvDx7pV/Dw4UN8Ph+Li4sUi0Wq1SoGg4Hz55+i1VTBzg6HOr2qVEosPrzL7u4uX/ulf0K5XKZer9NqqZNyp9PJrVu36LTVVN9ut9Nqtfibv/kbtWHuDzE3N8fG5hr1umoy5HDaicd30OllyuUqrVabixef5W/f+CH9/YNkMhnsTg9DQ0NcuXIFnU5HvV5nL7kD6GnUS+zuxgiH+7tlHUAPBHvwItzfgBeP0uDrBRJBRFE4MgDqntzmU99/xNDi8ONJwfGTUuie/JlPeN0n/IweVs5sNuNwOAgGg9oEfv82HjVwelx2vP+YiqJIuytjpYg6KvXGvteqPfIe5rDZbPLNb35TMx9aWFjgV37lV3jw4AEulwuDwcB7772HIAicOXOGubk5+vr6qFarpNNpPve5z3Hr1i1yuRw//vGPsdvt/N7v/R4DAwOMjo5y9+5dPvOZz/Dhhx9y7NgxJEniZz/7mVZaB4N+Xv38awwMDPC7v/u7FAtVRkejOB1u3nv3MhMTE5hMqqjJQw4GwJ4FriiK6pS4oE6JJclEdGSIdDqNQdLhcNrY3t4mlUpx6tQJ4vE458+fZ2VlRXWmNJt59bOf58Orl3n11f+CRHIHp9OJZDCrrJJz86dfN5tMSAYD/X19mLvG3nKnQ61SopAvks3mcLt8yB2Fel3tvRSKavaUTCa1KVyz2dQkhe7du0e5XNaarD2wpMrpk3C5XPzkJz9BFEUCgQCtdptcLo/dbqdSqVAoFBBFkXK5zNLSkkZ9azabtFpqdtXf348sy5w4Psv01KQq5WU0srGxic8XYH1rk2tXb9Bpq94HuVweg0GPz+dFVtpUq6o6dTqd7sryqxNnFJmH9+6wE4sRGY7y4edvHDg582+cxufzEYvFuHHjhtZjkySJhfsPefqpp1lbW6XVavLKL3wGt9vB7Ow0f/Hd77G1tQWgBaZeQ3l6agabzYbJZCIWizE/P6/yliUda2sr/PCHf4eiKFy4cJ6FhUUq5SrJZAKTycLa2ibf//4PaLVkVlZWiUZHWF1bJ51J02jWOHX6BNlslmajRTye5OLTFxFFkcHBARRUW1JBUBBEBYGDU8beRfdxPUBB1GlRovf+3nN68RBUY98F3/t9f2P/cf25w1nR4WDxpF7e45b926k86XUcXNfhALi/dO1hAlVbiLbmvbE/iKm/7ru5HCr35Y6CIqvLI41DAUEQ6SDS6cggiBhNZkSdHgWBjqzQarUwmUwsLy/z4x//WPs7EAhoVNHx8XH0ej2nT5/mrbfeAqBarbKxsYFOp+Phw4d89atfZWNjg2g0ytzcHK+88oo2POhZOezu7rK1tYXVau0qJK3Q19enVWu//dv/A3/wB/8Bm81OJqOqQtlsdu7du48gqKLIrVaLF154gaufP2g7Yf5fzRgMBtxut4ZNzufzeDxu4vFd/H4foVA/+XwOSTJw7NgcrWadeq1KIr4LiozP5yWbzRAdjmCzWQkGA4RDAxSLqsz+pUuX0NmtptflTodyqUS1UmFrc5N8LocoCNhtFgYHIyiKwPZWjIGBCENDw+h0euqNpgarcDqdGsC5VCphMBgIBAKMjIyovcWulmAwGCQej1MsFjEajczOzmrE+t14HL9fbej3RDRtNpsW7ERR7HJlH8FxisUi6VQSpd1iL5mgWi7T7Mi02h0kk4lKtY6+66MhSUYqlRJGo8Tq6gp6vY58vkAqleLChQt4vV6azSYul4tms4HL4eC5515AVuDdly8dODm/nviXGvbv5MmTnD9/vmvNF8NsMmO328hk0hgMem7euo4sd1TLSquDdDpNo9FQYTiFAqVSiWeeeYa/+08/xGKxsLq6itPpZGxsDKPRSDq9x85OjH/7b38TQYBsNo/b7eGP/uhP2NjYpt2WeXD/ITarnUgkQjqdwev14PH6GBwcRK8XSSZ3u2odbo4fP8na6hpmi5G9VBK/341Cm1arqWIsDRL7aVkHQNH7JqgfuWAVELpDlf0y5yaTCZGDJev+3w/3uB4XBA+XiU8KeE96rlc+74eYaNul12stlp5GZi6XUz1o9vUbDz8OM1v270svsB8eZqiA84Pb9OgDBWS5J0oKnY6MwSChKFBrNjGbVaGPRCLBzZs3+Yu/+AsuX77MpUuX2N3dZW9vj/v372M2m5FlmUajgcfjoV5XMb69ae7Nmzc1GbAe8mJpaYlsNqu1YyKRCDdu3OD3f//3KZVKBAIBXnvtNe507WAzmYymwqTT6bQhQw/8XywWuXXrlqrwns/jcrk0Klo0GtWQF7l/nTtwTKN/HOX5558nFAqRSqWo1WpUKhVmZ2d56aWXaLfbnD17FklSBU10IuQzafr7glx8+ik8bheTE+OcPHEcUHA6HeTzRfb2UlitNlKpNCMjw+iGwn2vN+o1mo0Gep2IZDAgCpDPZfH7/TQaTawWO8lkCoNBIhHfo1gsYXeoUvFTU1Nks1lNVLJ3gqvVqiYo2oPE1Go1hoeHNZPufD5PoVAgFAqBIBCL7dBoNDTPinq9jiRJhMNhwuGwhl+yWCw0Gg3MZjPj42N4HVYajSaZbBa9QcIgmciXStTrLcySGafTidPpQFFktrY3MRr1WKxW2m2Zz3/+8+zu7rK+vo7RaOxCgCBfqiCZzGSzBa6/djAD9P+fHo1Uvbq6SrFYpFarYTKZCAa9GI0SrVaTY8eOqdI927uk01ne/sk79Pf309/fTyqVIhKJaLRAUL/QkiQxNDTEpUuXEEWR+bOniUQG2dmJYTZbKBSKKF1dvkw6z2BEvSl9/eu/xNTUNIVCAVmW8fmDWK1WqrUygaBPPTeCDrvdSbujGiXduXMLURRothq8++7PEQQFg96Iy+XWlDgOBC4+CiHRHoJIq91GkiQkSdJaHvV6HYPuIB7tcJDaP5R4UsDaHwSPkt76h2Z92qZ3/9/oZmkqfs/Au+++i8fjIRKJILcfyTUdBVk5Cs93VGb3aHkS31dAUbqeyQiIog5ZVmi3O1gsNtLZHB9cvsKHV6+pQ5Ziiec/9QKNZotMJo0kSdy4caMrBefG4XBgt9tJJpM8/fTTJJNJjS7Wq9IymYzmlZvP52m1WqRSKT788ENcLhfJZJJQKMT4+Dhra2tMTExo2Lv5+XkWFhZIJpPMzs7SarUYGBhgZWWNeDxBu90hk8ly+vQZWq02hUKR0dERHjx4wOzsLIFAgI1vbBz4Ok382QRbW1vY7XY6nQ7pdJpnn32WL3/5i/zt375Bq9UkldrjypXL2O02rBYT/+TrX0ORZXZ3d2g06uhEEZvNylZsQxVJyOQQMFCvN8nm0og60AX9ztcFAex2Gz6/l+3YFgoyzaYKTGw0GpiMZnK5Al6Pn0AggKII2B2q0GYulyORSPDcc89pbIqe5pbdbmdra0tTZK7VamxvbxMMBgmHw7zyyiuak1lb7iAraCfBYrFw+vRpcrkctVpNU+g1m83aRM1gMCAZ9JSyKlPEZLZitdmx2ewIgh6LxUYpryqfFIo5CoU8er2O559/Tn290cLNmzfJZrOcPXtW8xboKAKiZCWXL2Eym7n26sH0/PNXPsfU1BQrKyvYbDba7TYbGxtIkp5KJadN0lVYQIQH9x9SLlWZP3tGE2lMpVI8//zzPPfcc9y5c4fx8UkyGVUDsFAo4HQ6CYVCpDNJJMmAKOq0UqJardFqdjh//jxutwePx8u7P3+P2I7qOBcMBnE43aysrDAxMYasqFCEZqNFKpXBbreTSOzicjlYWl6kR38rFPIkEikCATV4Hg5yTwqArXYHk9lMLBbjpz/9Kaurq5r9p/4IQO7+AHE4uB0uefcH4icp0DwuOz1coh7O1LTn90FPdDodk5OTeDwe9fvcenwA7OEpD5ftHxcA99uBHnwI7Fdo7kn06/V63vr7t7l68xYLDx+SyebwB4JU63UcThcPFh8wN6PeBNvtNv39/cRiMTWAdzGpsViMtbU1TCYT2WxW03vssTx6VM1wOEwkEmFkZAS73c4zzzzDrVu3eO6551hZWcFoNDI4OIjVauXnP/85NpuNSCRCNpulWq0Si8XotKFWrTM2Ns7Y2DiyrBAODVBv1JiYGCcSibC1tcWJEye485U7B45A5D9GtGFHPp/nN3/zN9nZ2eb3vvlNoiNDJBJxRkdHCAT96PU6KuUKLzz/PC6Xixs3buDxeLoyczt86oVncbvdDAwMYTbZURRYWLjHw6UH6E6dmH1dFEV8Ph+DoUHmZuZ4cP8ByKpI4skTp9jb28Pn81Mo5mk120iSAUPXEEltsHa4f/8B+XwBSVIZCZVKhZMnTxIMBmk0GvuyNxuJRJLjx0/wxht/q420BweHsDusGAySNtm6ffs27a6vbqulkq47LZlatU671aFSrWAySlRKRZUlUqsj6vU0W23Q6TDodLSbNfR6Aw6ng0CgD79P1dprt2WcDhUX1zN2cru9yLJCrd4kX6hgs1lQZIUbX7x+4OQM/eEAuzu7hMJh7HZVOv+rX/0akqQnEPAiGYzodHpViHV1gy9/+asUCkUeLi0iGSS8Hi96g0rgfuONN1hf2yAYDDE/f5ZiscTJM6dwez0USwXWVpeoVis4nU7+3b//92SzRe7fW8DhcBEKhalUqjidLvx+H8dPHKNQKOD3+1lcWsEg6ejv8/PSp57FJBm4cfM2U9OzpNNpJL0eq9VCpVqhUChy5sxp7ty+BehotzuqsZIsHyz5BAWRR3ARzSMH0BuNVCoV3G43g4ODDA0NIYoiXq9X/SIdCl6Hs779WdlRkBZRfKTc/KTg90myv8c9ZB4JJsiyjKUrR1+tVtGLj/B1R8FzjiypnxgAVTeh/Rnt/gAoo/b1BEWgWCyxvLTCu+++x9r6OnuZDPl8numJSarlCl63B8lg6PruqAKhNpuDTkfm1KnTLC6qggSbm5vE43FMJpO23Y1Gg8nJScpl1dun97PXLxwZGcFoNLK+vk4ul8Nut3extqrHtt1uZ3h4GJ1Ox+zsLDMzM+zuqi2XjtzBZDJTLpdZX18nX8iysPCgWw1WSaVSnD9/nrfffpvqf189cC46/2NHC+AOh4Nr166xs7PLr//X/wpR0GE0mojtbGM0GqlWq7z80sv85Z99h9s3bzE7M4MC+Lukgtt3bvPg/kOWllbR6Qykc1kcTgcduYPw4vNPKz2ub0+5xG63a/Sdra0tbDYbe3t7CIJKjRsciHD27FOsrq6SSCRoNpvMzs6yt7fHysoKfr8XQVDpQT3ck9lsplyuksuqsJr5+XNUq1WNvO9wOyjXcmxubmIy2qjVavh8Aeo1VRxBbra0Sdru7i4+n4+pqSnVUU0vkM1micViqmdJtYper0eS9DhtBhp1kdReHrNJ9eMN9DnxeFxks2ni8SRWq13l9rZVL49arcL09AgCOorFIn/87T87cHL+9a/9l7RlWcVQKSIPl1cI9oepVqscm53kzp07hMNhMpkMiUSCaDSqqVXs7u4iiiLJZJKRkREmJycplCqIOhM2mxWFDorSolpTzdTXVx4wODjI3Tv3MRjMnDt7kVy2xOBghPX1FWI7W8zNzSEIgiYImcvl6B8c4Pq1Dzk2OUpsc41Op0MilaUtShglM52uDmA+n9fgPz6fj1bX8uCVV17RJppHX6RHT2WPmuYehsEcxu0dzswOv3a/kkuvv3hYJv7A+g4Fuf2vPWzDuF8c9Kj1H854P5I17nvuKDmso47RI93BRwG9XC5rVU2pWuPWrQXefvttvF6vxoboSUNNTU1htVpJJpN8+tOf5tKlS+TzeZxOJysrK5pwSU8M99SpU+j0ApKkZ2FhAavVqsFg3n33XbxeL4FAQJtar66uUq1W6evrQxRVwYH19XUmJiaYnp7G5/Pxp3/6p0iSxC//8i/zxhtvkM1mURSFkZERBEHQ0BFer5dWq8Xy8rLm0SHLMhcuXNACaiaT4d7dg1jb1774Gpubm1r2++qrr1IsFllZWcHhcBCPx1lbWyMSiTA2Nsa3vvUtIqF+jEYjoiji9/spFAqqJ7DBTC5f1KwwA4EA9+7dIxKJoDtz+sTroVBIa0hWKhWKxSKhUEjrf/QC35kzZ9Dr9fh8fra2YuRyOdxud9eXs8Tw8DDj4+OAQnWCMrgAACAASURBVDQaxeFwaPI0e3t71GpVxidGCQ/08/6l91TlZaVDLpdhK7bBsePHun0/E7Ozs8RiOyrwWlHwez3E47uYzSbcbhfhcIi9vSS+gJ9Gs069XicajeLxeLRhgk4nMjczycryGjrRQC6XJdgXIJFIYDQaaDYbDA5G6Jn9OB0u2u0OL730IvV6kUQiTjqTYuVXNg+cHOv/okPU6RD1elqtNqNjY2xsbiBJBrY2N/jVX/1VYrEYfX19mqnT6OgoPp+PZrOpQYVcLhfr6+uIOj1/8d2/5MGDB9y9dwej0cDS0kMePnwIcoez8+eYnz/HxsYmt27dJZvJ8s47P2U3vokst+jrC1Ao5JAkPa1Wg2QyTqVaJejzYjIYsVotiDo9l658iNlkJhFPYjAYKJdV2bJeL2d5eZlQOEy+kOf27dtMTU0dyGw+CaD5qJJOPCJgHM6OHvfZBz6nO2QBjnSpe9JnPS6oPel9+7dxf7l++O/92/dxn7//IQsCiCKiKneO1W4HQeSDS1ew2dVgFolEcDqdRCIRPve5z9FoNNjY2NA8dW/fvs2tW7cIhULkcjkmJyexWCx4vV5EUcRut/PWW2+Rz+cwGiVMJhMnT55EURRWVlY0tZSeV7fP59OCXy+RmJubo9FosLa2xpUrV1hbW9Nk7VZXV7l16xZjY2PMz8+zuLgIqFC1ra0tQqGQNik+d+4cU1NTvPTSSxQKBSKRCH6/Kkl39ysHbSdm/3JWG970KLhXrlzRjq/b7ebpp5/GYrFw6dIlvva1r5HPZpifn6cn8hyNRvn0pz9NKp3l+MnjlEpF3n3351SqZZxOBxub6+gCfs/rPTXlM2fOaOrNuVxO8+asVquYTCZSqRSiKGI2mbtqq4ragLRaNGmqZDKhcm7rdZUdEQwiCKoe4IkTJ9DpwWqz4HQ6cDrtFAo5rFYzX/zyF/nw6tWuL4U6xvd6fZSKZYaGhkin4gxHh8jmMkxPT7G6tkIg4MfldlGuVLTUXVVlsTMzM0Mg4Ofypfc5eeIkmUwKg96A0WhgeHiYhYX72GxWdnfj1OsN0uk0Y2PjtNsdknu7XLx4hi9/5UsMDQ3y3bkfHDg5n/rxUwwMhknupYgnVDrO3PE5vB4f+VyW27dv4/F4WF9fJ5lMkkwm2djYYG1tDUmSVPEDWcZqtZJKpfjpz37O57/wKg6nHUFQWFhYRK/XEw4PEB2OMDgYYXNzm2Cgj2PHjmF32OkL9jE6NoRk1OHxuDCZjVRrFSRJj8Nhw+fxUS6VuHvvLvH4LiazGafLw+b2Jk6HetNqNBraMCmbzarDC50O476LxGw2P5aw/0kDoO5Qs/+oKe8nCYC9133c9uzP6o4CTR+1jidtw1E9yE/Si3xStgjQQUCn09NotnA4Xbx/6QPefPMt4okEW5sxRkdHcbvdFItF4vE4e3t7LCwsEAgEtBuA0Wjk2LFjmolTKpVidnaWW7duaUIC2WyW02dO4XQ6uHHjhmYyvru7y6uvvkoul8Pr9VKv1zXF8EQiwcDAAO12G5fLpelyer1e1U1Nr6darWK1qr4zer2eRqPB6dOn2dzcxOl00tfXxzvvvIPBYGB+fp5r165px21+fh6dTkcul2N1dZXYr8YOnKPJ70zS39/P1NSUZo7e6XR48OABuVyOcDiMwWAgnU4zMzODx+PBoBO74g4jdDodrl27Rn9/P5LRyN17d8jnczz77DMkk4lHUl6DA/2vF4tFGo0Gm5ub3Lt3T5OB7w01enp+Pd5go1FnenoaSTIQDAZYXl7CarVgs1mpVitYrepgoAdVKRaL+7QB62QyGTKZLDqdWmLq9Xq2YzEMkoFnnnmG9fVNXn75ZRYWFrHZ7OTyGarlMk6nU9P+6vUGd+Nx+vr76O/vp1AoqAox3Ql0q9XEbrXicNoYHx+hPxQkGPQhGY0cP36CdrvJ6OgYkmTsUvcKlMsV9AaRcNjP8vIK9Xqd/3Tu7w+cnBd/8hS5fI65Y8cwGo0E+4IoKBSLRaYmJzGZTOzs7DA2NsbQ0BCFQoFXX32Ver1OrVZTgZ8PHyLLMi+99BJurxsE1Wj+3LlzTE9PYzKZEUUDZ8+cplKp4HF7icfjpFJpMhkVSqM3CORyWb7xjW9gsVhIp9OarancbBAZiHDm7Dw3b9/l3oMFzGYT4VC/mgFKajaQyWT4pV/6JT744AM8Hg/ZXA5Rp2N5eZnLly8TjUa17wGgudD1+K16vV4Tfth/ce8PQMKhEvNw5nc4kzoqWB3OvJ4UeBRF4Vvf+hbRaFQzftq/XY/7zCdlhPsn2L3/f1Qa/+MB2dpn6URaLbU62NjY4Lvf/a4mVhEI9NGoN8nn89TrdQRBJe5XKhXm5uaQJInZ2VmKxSLLy8sYjUZ2dnaYnZ3F7XaTzWapVCoMDQ0hyzLDw8PcuHkdg0HP4OAgsViM7W1VYbkHW1tfX2dwcFArWcPd/jbQveYb6HQ6+vv78Xg86HQ6dnZ2GB0dxWZTefY9hexUKqWhHc6fP4/f7ycSiTAxMcHExIRmtCaKqhDIzMwM77/8/oHjFPoPIfr6+lT8atfyoicofO7cOQRBZbv4/X42NzdVi450SlV079p2XLx4UZXmMkiYzGZOnjzJu+++q4mznjhxAp3P43pdp1MtKHuKqc1mk1qtptJLjEb8fj8rKyt4vV50Oh1er5fFh4v0h/q5d+8uIyNRkntJHA47qXQKUdAxPDxMvV5nd3eXVCqlqkjLMuVSBatVndIaDEb6+8M0Gi1SmTR2h427d+8yMBDh8uXLSJKRvWSK4egQcrtBs9mg1WoiigJOp4NUao/xyXE2NjbZ3NxEURQcDofWLwGFcH8f8XiMeqPC3bt3aLbarK6ss7a6QblSYHl5BUkydnUH08zMzCJJEvfu3cZgMPHw4TI3D7nC/dMHX+Kppy8wFB1mL7nHlQ8/JJ5Igtq2JpvN8tRTTxGJRFhYWMBmsxGNRllYWNDwjD11CovFQiaX5e13fszk5CQul5sf/vCHLC+vEh0eZXHhPo1Gg3g8ztNPP83q6hrHjh1jbm6Ws/PzxGK7PHy4TGx7h+ee+xR2u5NwaIDdnW0KhTzLK2ucOH2G8clJzGYjZslAu63QHwoxPz/PiRMnmJmZIRqNcu/ePSYmJylX1Gza4XDQ36/2VXoXgyRJGtav9wXuBcXHZT464fEDgf2v+7jHx2VW+4PVK6+8ok1OP+n7Pulz+7dn/8+PC4C9i1YN4DqabRmbzc7Nm7coFEsUiiUEUcf169cZiY5qwfrKh1fotNVWktvtRpIk7ty5g8vlYmhoiHa7zfT0tPa9n5ycpFar8aMf/YipqSm2trZ44cVPAYrWa1QxomomffbsWaLRKFNTU8Tjcebm5qhWqwiC6vVrMKh89ng8TjweZ3x8nDt37hzI4jY3N7HZbExMTHD8+HFtG3pzAKvVyve+9z0+9alPIcsywWCQN998k42NDUZGRnj/0wcD4OevfZ5EIqENXdbX1zXYTLWqDkwymQz3799ndnaWeDxOwOfF5XLRaDQYHR0lkUhQLBZ5sPgAk8mo6YiaTEYCAb+qKnX61PHXe+mkKKqeu/F4HJvNhtvt7gobxtU+nN/P9va2Vhpls1l8Ph87Ozuat+fe3h7h0ACNRkPjuYZCIZLJJKOj48S241jMNkai4wQCfWQzeVBEGs0WzVatO7lpdEVKHdhtDvL5HJ95+QVEUUCWO8hyh06nzeDgADdv3aTV6pBKpQiHw6ytrWlQEKNRYmNtFVlp4nQ68Hjc5PM53E61LB+ODkIXXd9oNBgbHWdra7sbZKHdkslmcjw4ZIz+6pUX+eu//gEfXr2memcE/Xh9fhqNOm6XR+v1Xb58uQsbUtjY2KDdbvP000+jKKpH8tramioXHvAxGBlAEIUuV9jH8NAIiUSSWqXc5W6aqdXqLC4+IBIZ5MqVy2SzRRwOF7IMHo+PH/3oLa5du8HKyhpnTx/DbrMxOTPH1Ru3EASRXDpNwOvi5++9TzabR69XucQ9zvXExAQdWSaRTGA0GgmHwxqkoCdTpigKi4uLfP/732dvb09rzPcC5FHByqB7PM7vqMDypADySYKVzWYjmUxqQ4XDUvf/uQJg73lJkiiVK+TKFf7yr77Hg8VF+kIhdAYDsd1dQuEBCtmcFoT+zX/7b1hZWeGpp57SsqxIJEI4HNaodhsbGyqgOJfTyuHf+q3fIpVKYTQa2dhYZ2JiXPPP7e/v5xvf+IamZjQ8PMzW1pZmGel2u7HZbBrl9Pjx43Q6HS5cuMB7773H2NiY1hrr6+vj/PnzWK1WCoUCfX19mvtiLya88847fP3rX2dra4tgMMjDhw+RJIn+/n5arRY3vnAQa7vzX+1QrVZVi81uRhqNRlXfbY+Hy5cva8rrPcGEdrPB9evX2dzcpNFoMDc3R6lUwuf3YjQbyeXUKfTJUyeQlQ4/eedtdKGA9/VCPo9kMOD3eMim0+hFAUFRqNYKtFp1zGYJq0miWi5QyKUJ9vnZ3dlCUdqq8KjLjcftpS8YxKA3IEkGGo0apVKBZGKHdDqOy2Xj5o1riDoD5UqJcDhIp9PE5bJSqeap10pEIqqvRrNWI9zfj9xuUS6XcDscXSHVHCMjo1y9+iGBoJ+19TVmpqZptRS8Hi+lXIGzp+dp1pvk8wWqxQoBfwidTiK2HadcVsHKhWIas0Uitr3bLd86XcXpLHqDSLVaYXR0FL1Bj0GSuHro5Ex/Z5r1jRhj41OkUhl0oh6H3YHfG+DOnTtUq1Wy2Sz9/f2YzWYePHig3R1LpRKKonD9+nX8fr+K1K83GBsZQS8aaDUaNJtN9pJxggEfNoeHar1FeGCI7dgOoyNRMqkMr732Kh9evc7g4CCCoKqjXLt2jZdffplIJEImWyA8MEwuX1SNacoVTp44SUcWKJdLHD8+h93toi0LLK+uIhn10Glilkw4HU5KXZPpYCDAK7/wCmarBUN3Ijc6Osrg4CBTU1OYzWY2NzeZmJg4MG3dT23r+cciiiiCgIzKGkEUQNCBICILAjICHQVUgJyIIiiq9WJ3QRR76GEQdCiiqP6v+7mKojJSOkoHnWRARqGjyKqpe3fRPSYTFQQBRffo89R1oa1PEXXa/wVB16X+qcYebUUVyeoCdFD2LbKggKhD0OkxGMw0mjIGycyduw/49rf/H8rFEsFAALnVplVvsLayyvHZOcIDQRrNWhdTWqHZamCxmgmH+3jzrR9iNKlCAffu3WV7ewu7w4astBgbG8FkVhkYmUya1dUVUukEc3PHuHPnLuHwAI16G7PZyqX3L3cnpgE2Nze0m2EPDtMbYH76058mkUhQrVYplUr4/X4URWFiYgKfz6fB1uLxOIFAgNXVVS5dukQ4PIjJaOXmjdvcunWHk6dOsrsbJ5vN0W63tAn49PQ0f3/xYJvJ+394kPQSkaFhspksof4QLqebRGyXm9dvkt5Lc+7ceYqFIi++8CLFXJZqtcLFixeJRqOcOHGCDz/8kFarhc/rI5XMEOoL47K7uH79Oo16g3BoAF0oGHjdZrOzuxunVK5Qqzdod2Q6soLRqNcanNl8DqvNht5g6I6ae3c9hVazw9LScpdbq5agsqwqVUhGHXq9yFNPXeDLX/kiy8vreDxuSuU8u7sxFDosLS2o2KBKA5vVqh0YWZbxetyYzSaMkoVUOsXGxjpOp4PFxQUVFF0sIAq6LnPFh8Nhp1QuEvD7sFntVCo1TEYz/SGVo6hmugJ9fUFEUZ2E9pRgSqUi7XaLqalJFWLTleO/dWhC1f9/9dGRFRQF+vtD1OsNqtUa+XyeL3zhCyiKgt1u58c//jGJRILTp9U+3uLiItlsFpfLRTAY1AY2RqORVCpNuayaU+l1IpHBAYaHImxs7WA2m9nd3aVQyFOrVgkG/BgMej73mVcYGBxkcXGRL33pSyiKwtLSkrp+h5NyRTViV+QOkkHPysoKZouFUMBHtV7j6vUbZHIFZEVheGgQpVXHbLSS3Etx+tQpHj5cRBBFJiYnEHUicleJW6/X43Q68Xq9GAwG+vr6PsIbPhhc1IDXywK7adO+5dGxFQRBdW0ThQNA4Y9kiYcyRqH3OcJHnjrweKKGzKHtPozNO/Dr/oVH28mhRRB6lDaFTKaAyWzhL//qryiVSuwlk7hdbuZm53jzrTcZGRlR+5Z6AUkydIUC+nA6HTgcdvb2kkQiEc6cOUO1WsXv97G1tcm5c2cZGAhz/fp1IpFItwcdo1DI89xzz2Kz2Xj//feYP3MeRVFvTj1qWTg0QLPVoFDId+X91eoul8vh8XgOKLA7HI4uPTNNqVRShYO7fOdS18rBZDJx9epVrl69SqVcZ35+Ho/Hwxe+8AXef/8Sz1x8lnq9xtjYqDbkaLVavPnUmwdOxa/F/gXz82do1GuMRIeJbW+xE9smPBAim83wpS9/kXKpiIBCXzDAwMAAMzPTmtT/ysoKIyMjjI6OUqupE+yeOZnJaMJhdzA7PYvuqXPzr8uyjN/v15DvnU5HpaS5bBpZ32g0ksvl0Ol0JJNJisWC1idqNttdiXsLa2vrXdCzkZmZGa58+AGKIrOwsNBlhaj6Z6fPnALAaJS4cOE8W1vbKPKjL1+r1WJoaIhUKsX9+/ep1Rro9TpA4fTpU6TTKY3SYzZZtWas0+nUTobVZiERT+B02RkYCJPJpBEEdZ0ul4dmo6U1+FOpFIODg5q/QU/avNlscvPLB1HqT/3oPF6v2m/Y2dlhfn6earWKoijk81kMBj0jI1EURaa/vw+/38feXpJodEQbGGQyGUKhkKZlNj09rQ47PB6Wlpa6x7UJosiF80+xG48xEB5gdGSISrnC0vJDFh485MbNG4RCIT744AP0ej3Hjh2jWq0SCAR455132NnZIR6P4/F46DXT11eX6evvxxcIUq7UmJiYwGoxobSbpFNZxsYn2NzcxGyxcP7CBQYGB1XtOlHlYPf6foLwSNxiv7DnR42XjrapVGPO0QwQdXm8iIJwhDbho2D4yQPggfcfCoAft75PUjqrT6vBtFAo8e1v/xlbW1u0Wi2ef/45JEni9u3bBAIBTaoqkYzzC7/wiqZPubW1Rbvd1uTqNjY2yOVy2oXucrlIpVK88sorWkDpcXN70nN7e3tYrXZWV1c1lZRAIIDT6SSZTOBy/b/svWeQJOd55/nLzPLeu65q3z093WN6emaAgRUIESQFLAD6pUSR1C2l3dPuKSTd7e5FKOIucKG71YeLW61WCkm3kk48mRMlUhJBSqAB4QjCDDC+x7SvdtXVZbq8r8qq+5CVOd09gwGoDerug96IipnqrMo3q7Lyyed9nr9xaudP/R2pNNRWq4XP51MsVYHx8XGi0SiRSERZbezt4XQ6CYfDxONxut0uv/zLvww9UbtR6nQ6Hn30USqVCoGAn25XZnFxkWw2S7FY5PxPnT9wXp58W1Gieeutt7h8+TKDg4NEIhFefPFF1tbWiMfjHD9+HLPZzPnz5zVL2Rs3bmgrE4fDgSAIPP/8N5mbmyMYDGp2EJVKhfn5eaR/9uRPPdft9dhJJpmYmMRisWK12mg0mpQrJeqNJo1mi2qtTrvdod3uUKtX8Xo8mM0WBfwpQyaTxeFwkM8X+lQ4hX6VSGzh9yvYomAwSLWqCA6Uy0Wy2Swul5PXXnuVW7cWGB0ZR5IkJiYm8Pv9XL9+HavVytTUFJFIFJ1ewmq1kMspUBhBECgWi0wdmWJnJ6H5EOt0EsnkDuVyCZ/Pg8Gg+H4YDHqCgSCdTpd4fB293sCxY8c0mXgVB1kul/v8XAU4O//pGwdOzuSfj2veGj6fj+XlZdpthV9bLOURRYHNzQ0Ghwbx+31sJ7YJh8JcuHCRSkURYZ2ZmWF5eVlr52cyGRwOh2KKHo0iSZJCmdvL0+60uH79OuFQkNxeug/i3sNisdLpK42Ew2FKpRK5XK7fnRWJRMKMjY0yNjaKqmBcKhW578xpgqEQz3/r7/D5g3i9XnxeN71Oi8mJo1ybv876+jrPfvxZRkZHqdaqtNotREFhY6hg02q1isViuQMjqAQ28UAQ2B/YDgagg88P4u3uVfe7F9bvHnaah5/vP+4fUwAU+gKk0YFBrl69xoMPPtiXjGogiiInT57kZ3/2Z7XmQ6Va5vr1eQ04nM/nabVabG1t0eyXSFR21f33348sy5hMJg3eYrfbWVtb48UXX+TJJ59kZWWFCxcuMD19DKfTidvtZnl5mWg02lc1d+ByORFFUQNINxoNrl27xsjICIIgUKlU2N3dRRSVUpGKyUwmk/h8Po11EQwGFeiJwYDN6qBWq9FutzWlmXQ6TbvdQq/XMTAwgM1m49q1ayx+7mCdPfoHYZxOB0ePTjE0NEilUiaR2GZ4eIiZmWmKxYLmXz49fZRWq0mlUmFsbIy/+Iu/YGhoCJvNxvnz5+n1buseOhwOIpGIdozCf/uvfqGnCg9YrVYSiQSCoACfK9W8puQSDofpdDpMTk6ytLRAKZdFp1Nkp0XBwMjIGJFIhE5H7vtfKOqsPdq0Wg2AvjGPX8ELnT7JrVu3MJkMLC8vIkl6JidmyOfzDA4OkkgkNNhNOp1meGhcQ7NPTIzx9y/8neYVUipWNViGKk1Vr9cplUqMjIzg9/v7KixtWq0W1WoVs9nM2NgYuVyO0dFR7c4lyzIjIyO89NJLRKNRKpUK/89ffu3AyfnvfvFf0e4bsKfTiuO8Yri8gcNp14CoKysrPPbYYySTSX77t3+bX/2Vf6vhnlQPYlX0cXp6mitXrmheKC6Xi0gkwndfepmXX3r5PS/ofxr//x6/+3u/gxp2TUYby8srvPjii/2MJsz4+Di/9Vu/xejoqCJi4fPRkVtYrQrWdnRUEQ1QlpBvMDk5SafT4datWxw9epSFhQVCoZBCb+yLT+TzeUZGRjCZTFrJQpIk9DozkUiEP//zPyeZTPLQQw8pZQyDUqZSFasNBgO5XK6fsFRptVp4PB5UyuzW1hbf/OY3+aVf+iUKhQIXLlzg7NmznD9/nnK5zOc+9zk2NjaIDgxrNe+9vT0kSaJSqWAwKoD90dFRrl27hiRJ/Off+s8HvrdXX/ou3//+91lZWaFUKiEIgqY4ZTQaOXfunLZ62tzcpNFoMDs7p0HlJiYmNEquKOq0G1W73dZuLK1WC+no9MxzOp2ejtwlEAhiMBiRdHpsNjvDI0NEozGSyV1ig0MgiNjsCrsjsbWhZQCtZodMJtuXd6pozJBut4tOrwg/ZjIZms0mLpcfWZZxe1zU63XK5RLpdApBEIlEYpqe2OrqKsFgUCNkVyo1EHq0Wk2q1Qrj42N96pGM3eFgZGQYQRSwWi1YbVZyuT3uu+8sHq+iUm2xWBFFCYfDRbFYwqA34/N7tA6SyWRia2sLr9erdcNUHNv1z9w8cHI+c/1T/Sy2TDgc1hR1I5EwstwiHl/j+vV5HA47OzsJEoltnnzyKeROt5+qi1qHL5PJ4Ha7uXjxItFolNHRUQ07GQ6HiUQHePUnXvvxX6n/NH4s45nLT2t1N78/yB/+4R9Rq9WYmJjgW9/6Fs1mE7/fz7lz5yiVSsTjcUZHRzAaleXnwsICwWCQVCrF5OQk2WwWWZa5dOmShsF8/fXXWVpaYmBggEwmg8/nAxTIkioqnEql0OuNlMtlZmZmmJubw2az8dBDD7G5tYHf78NisSAIipKT0WhkY2MDn8+naXSq5aF0Os1P/MRPHFj5zM/PEwgEOHnyJHq9XgFO925bZlarVc2dMBwOkU4rWoCqGdnlZy4f+N6OfX2KVDrF0aNTnDt3P+VyiVAoyPz8NfKFPHv9VeDI6Ah2uw2LxUyvJxAOh7VaZqPRQJIU3rDKcCsUCly7do21tTW2t7eRhoeHnzObzRiNRnZ3d7FarVitVnZ2dvB4XTgcDs38JxQKkU6nFUyNTqTZbDE4OEi91kTxQVA01JTMSYn8ExNjQE8DQsdiSh2sXCn171CKhI/L5e47xjXJ5XJ4PIqi8cbGBkajEZfLw14uq0habW5w4cK7eL1eDAY9ZrNFU7HNZDJ0Oh1CoRA+n4ednW1isSHarTatVhtJ0tFpK8s4m82iwQIsFosmAzQ3p9xJtra2GBkZ4c2Pvn3g5Az9UVTzVAAIBALcunULWe7gctnw+bx89KMfwel0MDExTrcrc+vWLVotWcuy5+bmtLvYysoKH/7wh6nX6+zs7KDT6XjyySfZ3d3lf/qf/5d/lAv1n8aPZ3zi6sdReynpdJZUKq3JyUejA32RTw9f//rXNSxfqVwkHA6xsbGBIAhEo1EajQZLS0sacuDUqVPY7XYNc5pOpzEYDFoWabVauX79ulayWFxcxGq1Y7PZDtBdFaydjXq9prkUWq1WTZWpVqtp+02n0/z2b/82H/7whzWAtk6nIxQKceTIEUKhEB6Ph0KhgMlkIp8vaqynWCxGt9sll8tx89YNZmamuXr1Ko1Gg1arxdVPXD3wvY3+3wpNLp1Oa1jiYrHI3NycBsCv1+s0Gg1UJpsk6QmHwzSbTW2FZjQaMRpNZLNZlpaWtAZPMplULD2eeeaZ52q1Gk6nE5PJhE6no9FoEIvFkOUuyZ1dJEnP/PwNRFFHdCBGtVKhXi3RarXIZrPYrA4qlSqhUAir1UZiZ0fJBOU2kUgEfyCATi9htpi5duUarWYDvU7CYbeRz+WxmC00GlVK5TzZvRSx4WEqtTqtDgyPHcHp9FKrFiiViqyvr1MqlZk6cpRqpUIms0ckFKJULDIQiZLaTeH1eNHrdOwmU4yMDrG4uEgwGCafL1CrNjEYTBiNBioVxUh5YGAAi8WiSdVnMimarSo2u41mo8HFZ64cODlfWP0c8XicWq3Kzk4SfV/9ZW1tnXK5QiFf4sqVeW5cV4JeMpmm1xVxu/wE/CF+8NoPCQRCpNNZ3n3nIoODwwxEYiQSSc1HNpPJIggirz3+6lJ4YAAAIABJREFUg3+sa/Wfxo9hPPXuM1itNjY3EvzxH/8xmUyacDjM22+/xejoqBYYwuEwwaAiQ9ZutzAaDezt7bG1tcXw8LAmLtBuK9eUw+FgeXkZl8tFu93m2LFjfVhKgEwmSyw6TMAfYmx0AkEQGBiI0mhUSe6q/rkR5G6HdDqJICg1smg0il6v15geTqeTUqmEx+Oj1xUZGRnjzJn7sDvstNsdms0WoXAEm81Ju9NFpzciShKNZguz2UajXtfYXvPz8zSbTX7/938fp8uB1WrBZDIhyzKvvfbaHYKos8+folKtYbLYWFxeodnq4A+GAJHTZ+7DZnfi9Qep1huEIgPkcjkWbt1gJ5mg25OJr69RKik+5ZcvXcZisfLYY49pTchHH1UaULpeT7HvW1tbQ6fTEQwG6Xa7GAwGCgVF6DQQCGAwGFhaWsLtdtNsNjXT4ng8TrFY1EQwp6dnSOwomCFRhJs3bzIwECaTyfLhJx7HanJw8eJFzGalq7y9va10l4QuotRj8sgR6s0GTpeL2OAIiBJtWWZlZQVBEDRRVYvFgk6nw+Fwkc/n6fV6bG9vAT0SiW08Ho8Gsmy3ZdxuN4IgUa83abcUfNPY+BCxWAyj0cjNmzfxeBSh04mJMURJptXqsrK8eseP+uWXX8ZutzM+NsH4+CTvvqs0N44cOUKzWafdbvPYY49rMIGZmSg3by5w/fpN6vUmQ0MjLC0pqhYej49Wq8Nbb7+B1WrFZrdgxkipVNIaMfvHl3/ui3zucz+zj6o2pC2RVNuAWq3G0pLidSIIAp/85Cd58803NdB6rSUzOXUEr9er+ZmoQ7hL/2A/LGR/gf+w8sk/ZBwGDt/pm3HwOFSe72HmyT1lru5xnPeab//zD7qPL/+LLx/YppMMNBttjhw5wic+8QmWl5f79apZ/H4/b7/9Nu12WwMYWywWRAmsVgVfGQwG6XQ6jI+PUy6XuXz5MvV6XUEnXL7MM888oyhW9z21i4UqQ4MjmuLyq6++isfjIRjya6pMGxtx4vE44+PjjIyM4Ha7eeutt7T6940bN7Sg7PF46MqKOrv6W6lVG5iMFiYnpkimdjXw9Pz8PA8++CB6vVHjmDcaDXw+H4ODgzSbTX7hF34BSScwNjZGtVql3W5z+vRp/iX/8sD35nR5uHLlCo899hhzc2cQBIHV1VXabZl2p4vBaCYajVGvN/nLv/waU1OTB/yI1GV5r9ejXK5SKBTY2dnRbGFVhWzp4Ycffk6SJLLZ7AEeoeK0pKjCms1mQqGQ5oBmNhlpt2qaGbHfHyQYDPV5qBYSiR0KhQLBYEDj5nY6bTweL1aL8kWOjIxoHD6lXQ35Qh5J1JMrFCmXFUziytoq2XSaXqdFJpPhzJkzzMzMaOrN3W4Hn9+J3W7lnXffRpbbVGtlKtUSzWYdj8fHxMQk3/jG81SrNSwWK612k7lTp1lZXSKZTGpfuloYzeX2KJWKxOPreDxe3vjIwSXwl9Y+jyiKlIol4vF1RFExHer1emxtbTI+Pq7xaG/dukU6naZQKPa72z4sFjOJxDZHj06Ry+0xNDSITq90fXu9Hvl8HqfTid/v59UPHaz/zfzlFInEDslkkmeffZZ2u8Xa2hoWi0XjhbrdbgrlMoFgiC6wuhZH1OnweH1sJRI8+OBD6PR6zbT6QNfykPn5P5T0fzdNvx+1g3o3dRY1MB1+7Nfke79jfa/570bnu9tnvxfb45unvnng+cevfBJJUtzK/uqv/gqz2Uwmk9FUlsPhMIFAQAsSlUqF0bERvvWtb2qsB1EUtWVbMBikXC7zxS9+kXq9zt7eHpubm3g8Hur1OqlUhrm5OdLpNAsLCwwMDBCNRrFYzLTbrb7YiIHBwUFsNpviyLizo2H4VEMju91Os9kkFAqxsbGFzWbDbDYjiqLir21WmjQOpwI1yWazhEIhZFnWFNvpofQIWi0SiQTRaBSr1UqlWqbZVBqjlUqF7e1tzn/sIAym9u9rPPLIIxqsqlhUYHfnzp1jbW0Nt9vNq6++Srlc5lOf+hRms4lUUvkcw8PDRKNRpqamcDqdRKODdDqydo26XC6Wl5cxm81IDzzwwHN7e3saTgzAbDYrkb8r94NMVwMYdrtdisU8FpOeRkPx6q3VGhiNJiqViiLT0xXQ6SQCgSCzJ0+S6PsUOBxOmo1GXxqrrpkpKZmckx4ixWIJm8OJ3eZgbGKcoeFhRAF2E9tUKhUeeugh2u22Vn8rFgskEnHi8TX0eol6vYbDYUeSFKJ3vdZhYWGREydO4PX6kCSR0dERsllFTsjj8aDX3/4sOp2OQqHI6OgYbreH+fl5Fn5m5cDJ+am3nyAQCFApV7BabWxsbCKKYl96/LbZdLFYJBAIsLGxwalTs0QiEUrlIt/73nf7SjZO5G4HWe5gNCpyXXq9jqGhQcrlEktLi9z85wsH5v436V8kkdhhdHRUUd2VlQ6+yWRicnKSxcVFzp49Szq9R0fu4nK5EUWJer1BNBpjZGSUVqet+dGqNZ73CoBq4LkT23cnPewfEuTuFTjvdhzq2B/w3k/t5YMIFBwOeOrf1fFBXecOB8BnLn0cVVPv9ddfZ3R0lF6vh8fjYXh4WIOOJBIJSqVSX2C4RSwWZWBgQKvJq7WwWCxGqVTSJKw6nQ4f+tCHtCZeLDbEysoKR44c0cQQwuEw1VoFnU7SSlynTp0imUzSbDYpFArYbDby+TyCIBAKKeIik5OTXLhwgYFITBMtiUajvPDCCywtLXH8+HFM/SzP3BePVbnivV4Pi9lCPp+n0+loNfrl5WV8fi/FYkE7N/l8nkuHbCd+Xfp1nE4n7777br9eb9Mah6rOoSiKDA0N8fd///c4HHY++sQTxOPrtFttGvUG1WqNVrPFK6+8Sjy+TqFQIJfLaTJgb7zxBpLD4XhO7c5YrVaNxdBqtdDpJEUks6+Y7PF4+sZGCdqt20unfK7Yxw9aEdBhs7k4efIUZouZarWK0WAgl1MKo2aTkqonEgrXz2azUSwWFdK00cTw8AjhgQFCwTChcJB6o0E6tUujWuXUqVMYDAaSySQ3b97k3XffpVwu4fU6GB4e1lymOp0Og4OD3HffOYLBKMePn+Dq1Wu8+eYP+cxnPs2lSxdJJnfo9RR5H5PJpNl3yrJMLBYjnc5oDI/DQcj/O252d3fJZrLkcnl8Pj8WiwVZlpmcnMBisZBIJBgaGgIgEolw48YNBgeHyOcVyfyBgQGNGB+PryLLCh5re3sLj8dNsVjg9Ok5Xjj3vQNz1/9dhWAwpClhGAx6FhcXqVQq1GpKIXtiYoLE7i46nQ6jUQGk72drCOLtC9tkMh3M8g5d0PsDjfr/u2VJ9xJLVZ+rd/PDiiyHtfb2m4Lv36a+T/3/fqHUuwWw/XO/12P/ce/f537Zqw8aQAVB4PlTB6XTnr38CQRB0IyInE6nJvI5MTFBJpPRCAYDAwPodAqIPp/P0W63SaVSmu/t7OysBks7efKkhrRotxWx4LNnz/aNw/2alp+KG9QbdFgsCm9WlmXK5TJer7dft7fi8Xg07r9er9cyTp/Ph9GgwGdUk6XR0VHGx8f7qs9KEqMiP1wul/Y7TO7skEgkWFhYYGhoCFmWCQaDpNMp3G6X1kgJh8N8/+GD3tu7v6gYPs3NzZHJZLRAWC6XNWaUqgxltSoIj/RuCqPRxPr6Bqura9y4cZOxsXGefvpZtre3mZqaotfr9RXufTz66KNI999//3NDQ0NUq1WWlpbY29sjl8sB0Gw2NBBmr9fTdMgkSaBczFEuV/qT62g0lLrgxMQRjEYrrVYbs0mRvQkEAiSTO8hyh4FImGq1yt7eHoIgKJlUpcLx4ycxGk105S5Otwuf10+312VtfY2b168zHBvE5/OxsrJCPB6n1Wrh9/v7lDaBUqlCLpfHYDBhs9mxWm3Ua02sVg+5fI54fI1AwM/FSxcYHIqCIODx+DRFapV5EgqFyGT2SO1miQ3GsNttvHGoC/xvy7/S77oZsVptRKMxdnZ2+kbxIslkklOnTmm2n6rsfrlcwmw24XDYuX59HpfLye5uElEEj9eNTidhNBp4+JGHKRYLiJLI9x46iAH88sbP0Wgo/g0Gg4GXX36JkydPEggEWFhYIBqNcunSJZxuLydnTwEC1WoNk9micVcF8aCs04EAxJ1Z1/4s8F7yVe+3BFYDygdZku7/925zvN98P8r4IJnqB308P3swAD598VlarRbLy8tsbm6SSCSQZZnt7W2SySR2u514PI7X69UyQJPZSKOhZIZ7e3vEYjEymQzb29vakvLq1atUq1XS6TTZbBaLxaLU7vUmzaxcDTgKWL1LJpPm8uXLOJ1OrFYrTqeTVqvFhQsX8Pv92spKzeYmJib6y2Tje2orduSOxh5RIS2pVIpGo4HL6USv1zM4qFy7e3t7HDlyhI2NdbxeRbhYXYK/81PvHNjvF1a/gMPh4MaNG1itVubm5hAERWnp3XffZWZmhldeeYW1tTW8Xi8f+tCHuHr5Cltb29TrDVIpxcbD4XCSSqUYHx+n2WzSaDR45JFHNBEF6ejRo8/lcjkqlQpTU1M89NBDmn/A7m4SQVDoTmp7WWmbW/F6HBqfUK83UqvV0el0jIyM4Q9E6HZltrcT6PUSFotZkbLqtGnUa9r7ut0ujUaj3zxoYbHYcbs9lCpVnC47+XyOWr3GXjZDOBBic3OTzc1NxsbGOHnyJKIoYrc7qFVrpFNZBgeHKRUrhEIRQsEIlUoNm83dL5zL1Os1Pv7xZ1lfj2M0GojHN3G73UQiEYaGhkgmk+zt7VGr1bFY7BQLRSqVElcPcYGDv+tVAKaSjk5HxuPxEg6HsVgs5POKfpnFYtHukru7u7jdbtxuJ9HoAPPz1zCbTZw5cxpZ7jA8PEytVuX+++9Hr9fzrW99C51Ox/LyMtc/czD7/OibT+D1Kjivd955h3/9r38RSZL4/ve/z7lz52g2m9x33314A0G6gojBZMLt8dKWZS0ASv0geLdamnCXwHIvvb67BZK7LWXhdpZ4r2Xuft3Au2WRHyQAftDl6vvt8x/y+MbsNw7s/6l3nyabzfLOO+9QKBQ4ffo0W1tbmixVoVDAYrFoTYJCoUCv1+WRRx5mbW2NZrNJuVxGEARmZ2dpNps89NBDSJKE3+9ndnaW8fFxQqFQX8RA8dowmUya/eze3h5miwmLxUw4HMZkMuHxeMhms3Q6HaamppBlxYNjuN9x9vv9GjddDYB3tS0QBFqtFrIsa0tMlV4qiRJGoxG9Xo9Op9MaLafPzNFut9je3u6vNHW8+cRB69mTf3uSdrvN+Pg4jUaD7373u2xubvLss8+SSqXodDo88MADnDhxgpmZGXK5HPVqTfPoGR4exmq1sr6+jk6nY35+HqvVCqD5mdRqNaTPfO4Lz5mMZnR6I/lcjlKhgE7SEfR5MBhM6CQdw0MjVEsFNtfX6XbayJ022WwRWRZwub10ZJliKY/NbkOUIFfOoTcaaHW6DI2MIen1CJLI0soNLHqJ3F6GRquJJOmIDQ3TQ6DRbrGbTSEZdBj1VlqNDtFQgMsX3kBu19naTBOLDXLu3P0MDcVoNOp0ex0WFm/R7cmcOXuaWwuLeLxeYoMxBLFHq9WhJxqYPHIEm91BJptHlgXaco/NzR2MBj1+v5/FxUVqtduBuV6vcfToVN+NTeCdpy4cODmnn59FliESUUQj2+0Wy8tLdDptCoWCpkno9SoipioWy+PxsreXw+8PsLW1TTa7R6cjKxQ+g76vsg2XL1/m9OnThMNhXnz4lQNzf+zNn2RpdYlgJIzT7eWF73yPW4tLPPX0MzhcbvzBMHIPej0BvaSDbg+53UEUBCRBVNRQEBGRFBN0QBJ1CAjYLE463V5fVUVA0uluq54gAWJ/W49uXwyip+xBMVXvixgIooAoibefCxKCqO8HUqmPi1P2J4i6viKMogAjSjoEQYcgSPQ04pqiOqAYCSmvF0WdIrAgKsosCqdYed/thq2qVqDMBYqSizoX+/aH0Fey0f4uIuzf9l7vESQknQ6xfwx/e+KvD5yvzy78DDa7naPT00g6HSazmd1Uius3bhDrM41OnDjBtWvXaDabxGIx4vF1SqUSstzliQ9/jN1kGknS4/cFOXb8GO12B7fbg15noitDYjuJ1aKUrTKZXUZHldqix+tmeWUJp1OhtpotVhqNJkaTFYvVTqPRYnV1lVAozNpanK3tJLHYEDqdAQRF9NZstoFw+3wiCPSAHj26/RuNCs9RS2K7u7uKSoxOabTVajUkSWJzc5N0Os2Fi+8yMTGuZY25XI53njyYAf7Mrc8we+I4zVYTv9/H6blTVKsVNlZXcTns3HfmDM1WE71ex/nzb5NM7nDp8hWarRZmiwWnx02j1cLr9zE+MsHQ0Ch6vZFQMIJOp2N1dQ2f14/0yE/85HMIYLPaKBbzeNxuWo0GOp2E2Wil15VZj8fR6UTqjRpWq4VAIEAsOoTd7mBocJBarUqn06ZYzKM3GdhN7aI36LFYbDQbLQQRzBY989cuoev16Ha6IIrodQZ0egOlSgV/MIDVbsFhd1KrNCjlC6RSCfb2dtHrdIyPHyUSCXP58mVMJgW8nElnWFxcwGKxEg5HNPXYer2OJOkIRiKIoqFPmTPR6cgEgyGMBjO+gJ9apazR+wKBADMzMwiCgMfjod1uUakod97zhwLgP3v3KWw2G61Wi52dHebm5giFQiQSCWZnZ8lkMsiyzNLSEsFgUPMMVnmaw8PDmu9xNptFkkQl0+3r8gHasujVD/3wwNwPfv8cY2Nj/Pf/w7+nUq3x2X/+04yMjGKzO+j1FN6pEpx6BxoX6r+CICDuz1oQaTSa/Nmf/TnxeByzxaKJYHY6MqIocWdl8HZA0jiyCEqMOJRRKUM68D4OLbPvCHJ3mevwNuV9d5FlQc007/w7qNmnePDYtczx4HwHM9W7v0fdpr7nb098/cB8z175ZN+gy6DV5hYWFpidnSUUDCL3IV42m41ut4vD4WBwcBC328PVK9d45513mJiY0LjAC7cW+eEP3yA6EMNisWhlK5vNRrcnYzAoOL5Wq0k2m2FycqKv0mQjlcpitytm5q1Wi2AwyMTEJFarjb/5m7+l2WxqZl23P/PB306n07mt9C0ImidQLpfTzNNUNtPI8DCVSoXXX3+d119/nTNnzhAIBIAeS0uLnDp1ij/7sz/DbrffYYs5942T/N7v/R5LC4sU8nkGwhFeffkVHn/8cVKpFOVymVazyfLSEtevzVOuVnj0sccYn5jAZDZx/vx5rTPdqre1enun0yGb3cPt9iBJOqT7zz383N5elp1EAoEevW6XgN+Pw2HHoDfhdDpIpVK0Wg0EAa3WUC5V8Pl8GAx6UukUoihQLpcwmc1EBqJEIgMYDSacTif01dJWV5fxuZzodHoK5TJulwezxYrRZKLeaOAPBIhFYywtLuNw2Egktmi3632liRiiKGCxmNnZ2e1Lw+d44Nw5TGaTVj9RvTaq1SqjY2OYzQomSO3yiqKIxWLB7XZh1EsaKTyTyWgO9s1mE6PRqKHh3zpUnxj/kxFcLheVSkUzai8UCnQ6HQqFAoFAQMNJql7GtyEFG7hcLn74wx9qyjWLiwsUiwojQG02vfmmsiS49bnlA3MXfzXPgw8/xNPPPMMD5x7C6XRphWC1My4IivLwfomq/T9i8dAFbDQa2d7exmw2Mz4xoanq7n/v3cb+ACFJB9Vb9s8n8N5q0YfH/r/drT54t9fdbbzXEvVey+/D77vXa9/rdX9z/GAA/MS1TwNKUFZNstRivqNvRKTOPT09zfLyMouLi1y8eJEHHngAURQ1e0qXy6UtYUOhkNawsNvt1Ot1orEBkskdVldXNX/fnZ0dgD64Xgl8DoeDWCym2dqaTCZOnDiB1WrVVJ7vdR72l0VU/xC9Xo/T6exfW25CoRDPf+Mb3Lx5kxMnTnDu3DmSyaSy3eMiHA5x8+ZNjXwx/6mDZaajfzHJ008/rZWC1tbWmJubo16v88QTT/Diiy9q4gZTU1OcmD2JxWIlm81itSrqUFNTU+j1etwOJ6VSkU6njclkZGNjnXw+R7VaQXr2459+zm63EfD7GYiEKeTz0OspGVC5hk6n+IMODcUIh0MamLLVbCt2dtfncXvcBAJ+TCYjmxsbSJIBm9XK1tY67VYDUejRajaoV6oI3Q6tZotCqcTI8CgGo4mZY8dYWFwkm81TyBeRJEgmt0inkhiMRgYHh0EQMVsUVWSTydzHG9mw2RzEYlHC4QjLyyucO/cAe9k9vF4fmWwOi8WBy+XCYrFgMBj6istuRFHAblVwTbu7u5oBlCiKxGIxUqkU3W6XfD7P1U8dtOz7b+Jf1JoQNpuNpaUlTCYT7XabV199lVQqxezsLDs7O5oKhWrw3ul0WFhYYHp6Gp/PR7PZ5Ny5+5memUYURa3TPjg4qMj+/NzWgbl/0/OfqNUb6AwmBBSpMqvVqpkaqXdm9ccN7x8AZVlmbGxMwVeaTLRaLTqdDrIsH/DTODwOB0C1uXJHMPlHCIAfFK/44wiA+/GDf33soHDGx69+StumdjADgQC1Wo3Lly5hNBpptfqU0r6RfCKR4Pjx40xPT2sXuSo1pWaELpcLo9Go1RCHh4dJZ1L4/T4mJiZYW1vTnB1tNhuCIDB55KjmMLe3t6cZoDUaik/P4OAgsixjNBoPfL8Hz7OSZHQ6HdrttoJbHB3F6/Vq1g2XLl1ib2+P8f5vqlqtkkql2NnZYXNzk+9//0V2d5NMTU0BoNfrufzsQS7wp699gq2trT58zsF9992H0+lkdXUVg8GA3W6nWCzidruxWCxcunKZZkvhFo+OjhIMBnn55ZexWq1EI2HeeOOHbG1vEo0NcObMaXw+Ly63E+nJp559TpY7mIxGyuUivW6XZqNBrVYlEo6iN+golUpYLCbcHjflchkAq9WOyWTC5/MiSSKy3MHhsJPLFahVawhAqZin3apDr0ulXCE2MMDy0i1azRZyr8fExCQ9BERJwmSyMjg0QrlURKBLr9uh1aojyzKNegtREsnl9qjXFWhKqVQhFh1iYnySSqVMNruHTqfH7w/g8/mx2x3o9SZESanzqXLuu7u7hMNher0u2XSKbDaLavykgkTtdrtmjm21Wu/IAB3/h03jGarKL2tra0xPT9Pr9TRrwbGxMQRB0Aynut0uNpuNEydOsLS0hCAoSs7VaoVKtaIVwVUPhUAgwOsffuvA3E9eeopuVwBRooeArq+nZzabD9ydD9+tDwSRQz0C9TWyLNPpY7jUru/dAo+634MBqNt/3O4WvlcA/FHA1e819psUvVeg2/8ZDn8P7xVU7xbYDo+7zaUe098cPxgAPzn/Ge3/SiIxxFe+8hVeffVVDH0wejAYZGNjg3A4zM2bN7WOvgqdKZVKfY8bhZGSSqU0QK/KXmq32zRbDVZXV1hbW2N0dFQDXKsCBvWGYi5UKpU04/pisUixWCQej5NIJDh27JgGrVGNi9Rzpuh8mjScn/r5G40GV69e7atAD+ByKRCXpUWFOfY7v/M7DA8P0+v1+MhHPoLVakGSlGOanZ0lkUhw45Dk3H1/f6afqIhUKhXW1ta4cOECLpeL+fl5RkZGePTRRymXy5hMJvRGA41mSyNzvPXWW7hcLgDye3vodDrm5uZYWVlBFEXm5+cZHx9H12g0sFgsGPV6MumkIsFdqSCKgiIO0FHAz2qGdOvWLXq9HieOn+ov+1qk0rtYLCbMZoWdIaJjI74GQoe9bJedxBaSaCRhVVQlDHojJpMJg8GAx+dnaWWFQrGCr67gkzweC4mdDdrNDrMnlbR3aHiAdCrL9vYu7VaXRx95HJvNQSazR7fXZW8vjyiK7OwoAa5SUQJKMBTVANeiqAg0iqJIJqtomqlij36/n9XVVbxeL/Pz8zgcjj4otX3HBfDhD3+YeDyu6fiVSiVGR0c1pswXvvAFLl26xNbWFru7uxw/fhyLxcL6+jr1ep233nqLaFTxTREEAaNRT6fT0YQldTodr7/+Oo8//vgdc29ubDM8OkavK9DrdjUNO/XCPHyR7sfQadvu2OvBi/tez+/xTqVR8AHG/uD5o3RsD8x2KIjt3+c//DPce77/2v2pQgGf/exnGRoaIpVM8tprr9Hr9XjggQe4cOECExMT9Ho9Tp48qdz8+7S5RqPBxsYGR48e1W521WpVK9XU63XS6TQjIyOaQst+6bdYLEazpXSHTCYTm5ubWCwWTb9vZGREu2G0Wq0DwV8NhoDGplBLSirX1+Fw8PnPf55r165x8+ZNjEYj4XCYV155hc9//vP4/X5KpRJ/93d/h9VmZmJiQgtuwWDwju+q0Wjw7rvv8vDDDxMKhTS65+rqKmfOnGFoaIj19XXeeOMNkskkT3zso5rHMcDo6CiJRIJvf/vbjMSiHDt2jFu3buD3+7HZLASDfl5//TWkp5/95HN6vY7dZJJbN2/Qabdot1rYbFaajTbbia1+7QxqdaXFLkkSToeLUqnE9vYW0Vi0L0ejMA9cTjetVgNR7OFyO4iEI8zMnKBRrVOrlRBFiS7g9wcwGBUCtdfnx+sLIneatNt1SsU8FrOZ03OKjPfm1hqhcIi1tQ3GRsfxeUNEIgOKgYvH2ZfiUszZBUEglUohd6HbVe5ay8vLjI6OMtwvzFosZox6nSYKubu7i9ls1qS0P/EJJQVPpVJcO3R3GvojhQqkotNjsRjFYhGPx4NOpyOVSjE0NKQoX/eBnG63WwN/P/zww5pCx/j4OKdOzfbtRK0a+n9mZoaVlRXOP3nxwNzW33QydWQaQSchCQJ0746rU5sgd6/J3SMLEt9/yXf3R79Retfl452qz++17Pyg427Z3t0yt/c77vd7zd3mu9frDmeAn7r+We01qkFTrVbja1/7GpFwGJ/Px/DwMM1mk6e8ms2BAAAgAElEQVSffppGo6EZTLlcLg3WUalUNLl6vV6PJEnIsqxlg+12m+PHj/XVkcxabXp3d1dbrTSabdrtttaU6XQ6WnKgZnsqz1/9PtXGTKfTwdQvj6iP/YysVqvF+fPnkSSJQCCA3+/nxvXrPPXUU0SjUd544w18Ph8ejwe7w8bFixdot9vaSuflRw/iXQ3/q8S5c+dIpVJMTEywuLjIysoKJ0+e1EDXqpDJo48+itfvw+FUdAEAXnjhBR555BE+9rGPcd+ZM4AiCDwyMszOToJ6vaZ0onvdGu1Gj2plj1Oz09gsZl575WXWVm8SCEbw+XwKpqhc5Oq1JW15k81micViilXeTpqjR4/QbrdxOO1kMikQe7SaHRr1NoagkXRmF8koYLIbFdc3ucv169cRBB0njs8yFBumWCljs5qZvzZPu91mZGSUre0k6XQWg9HE1Su3GJ88yuSRaWqNJplijkAkxPWr7yKKCqvDblckf0QR2m2ZZrsNcpPBSJhSLo8sd4mvxinkckQG/ITDYVwuD7WaksYbDAZ0OgOvv/66dic9POx2O7u7u6RSKe0Oq/jypqk3FFiD2Wzm1q1bbG2vc/r0ad544w1MJpNyJ242uX79Og899BDXr1/nwoULrK+v4/V6+x26hoa6PzxK6Qz/+3/43/i1X/s1KtVqX9+wiNlswaA39V8lYjTq+gB2oG/ArY5mW8ao09NsVGnWSpiMFiwOJ7Kgp9tpsH+NvP/iPrzsPXh86jblbz0tMEv0Dq25D+7nzmxtP8Pj8Lbb+7h7EDq8NN7/t7uN98tG77aPw4F6/5z3GhsbG/zgBz8gkUgowgXlMpJeTyKZ5OzZs+zl87j7K5B6vc7o6Ci1Wg2v14vH48FjNPLVr36VqSnFuiIWizHh82oNr1QqhSiKGjXyN37jN/B6vXz605/GaLLh8/nZ29ujXm9oWoJGo4leD8WgvdnCbrcrep+JRL+u1yK9u9K/LnSUy2UtaahUKugNihnVxsYGkgjNRoXcXoq1tTWikQE21pRSTzTs57vf+TtkWebxxx9HlmVarRbT09NkMpk7vqsHH36UWr3OxNFp9oolKo0mwYEouYLiMX7ixAk2EkqNvRhfZ3h4mP/yB/8Jr9fL7OwsX/rSl9jc3OQ73/kOD5y9j5mZGTodePnlH3D27GlisSESiQS6jY047WZL8ZwIhdhp1LHazOhEDw6nHUknki/kSCaTmuKLKscjyzLRaJQbN26QTKYQBKGPMjdr0tPZbJZSuapwVxObdLsdTCYDzYbit+F0ONDpDCwvLVFv1qjXyzQaDZxOF9GBQW5cX0GnM2A0mOi0ZYKhCI1WE73BQLPTJp1Nabad3W4Xj8eFzaYIO1YqJUQRLlx4m5mZ4xRLeTrtLiI9/H4fBoPinqV4eRS4//772d7e7lsqGvF6vVrqv3+o7BYV71coFPpWgBscO3ZMw/4dO3aMtbU1rl69itfrZXhojEwmw82bNzly5AiXL1/G4/EAis+CKoM/MTFBvV6n1+vxb/iVA3Pn9zI8/uhjfOUP/4Av/osv0+12cDqdCIJIrwtaIOrdKQqgDrvdTqmQIxTwkU5W6fZaSDqBdvvOz3qvi/ru21TIy0Ej8f1jfzDZ7ySnbttfX3rv+Q4GHfXGfHi5///12P/5isUijzzyCLIs0+12+cY3voHb7UaSJE3d2GQyMTY2hsPhYHd3t38zV6Tq19bW+NKXvsSVK5cZGRnh4sWL2pLZ4XDgdDqp1ep4PF663S7/8T/+pqaArNPpqFarOJ1OTalFRSvs7OxgMBi0ko66lM5kFN8dl8Oh1dpUYYZGowFCF7vDy9LSEj/4wascOXJEEUsxm5mbm8PvVmpwPp+P559/np/9mc+xvLzMTnKbZ599lmQyyQ9+8ANNhn//CIQUfvSVK1c0Zkoul+P++++n3W7z/Le+2UehGJicnGR9fZ0vfelLGkRmdXWVWCzGl7/8ZbK7Sq3/5s2b+Hw+vvKVP9E8faQnPvLEc125qygpm800Gw3q9RpWswX6Kif1eh2DwUA+n0ev1zM2NobRaNY8AsbHxzUtM1EUqNWqNJstZmdPUSqVNf+NgYEw7XaTUqmCIEiYTVbKFcWEe2Zmhnxhj+3EJkaDgQfOPYggSLRa7T5ZfBtBEDg6c0zpRFusWKw2vv/i98jvZbS6RSAQwOv18tWvfpV8fo+d5BbNVoP5+Ws0mw0KxTyCAGaLkXq9DkCpVAJgbW2tjwFsU61WtIzs4iG12p946WEajYZGIleR9aOjIxSKBU1aXxAUEyKVCre2tq55nGazWbLZLOfOndN+lJ1OB6/Xy9TUFOfPnyeRSNwxt/k3DFy6dJlms8nfPv88M8eO4XK5++U39cJXurJyt3PX4n+hWMDrcfKdb3+L5cWb7OX3GBsfRwY4RFX7Ubqmd6vL3W0f+9932BRJ3aaO9zqW/fvf//xuAfD9PsMHfd17fSf7t/31sb86sM/P3vppbdv4+Ljm37G6uko+n+eVl1/mgQcf1Hy0HQ4Hi4uLDA4OotPpGBoaol6va00qxaDMwMWLF7n//vuJRCIav7pYLOJyuTUxjlKpRDCoeGCrzbVms6khItQlrNrpV2E1giCwubkJoDRLBMUg3WAw4HQ6NRbL0tIiLpeTr371q1y9epV8Ps+JEyc4c+YMnU6HTp9CG4/H8fl8hEIhTCYTE5OTxNc3GBwcpNVqsbu7y+Ih723/7/opFos4nU5GR0cRRVFrTN68eZNQKMS3v/1t5ubmeOWVVzRWycLCAhaLRWtI5nI5ivkC58+fx+1Wmrg///M/z6lTp4hGo+gqlQq7yaRiT5lO05M72B1WjDo9hr5Igqr07HAo9DdZljEaFP6fmiUVCgWGhobIZtN4vX5AZGlpBUGQOHXqNJlMhls3l2k1K9ATEUUdDzzwAFevzlOuFGl36pQrBfL5PLOzs6ysKBQWn8/H8pLSuRkbG9NqkNVaA5fXx2OPPUY6scnW1paGX3vppZcUlReDiNdnY2lJMR9CkBUBR6uS5pvNSm1DXW6eOHGCra0txsbGWFtb4dq1a5w+fZrDw+VyUSgUNAOljY0NOp0OGxsb2GwWzGYzOknPxvomw8PDtNsdJieO0JWXqFarfQWdGk899RTf+973iEajGrbr4sWLWjf6cHYE9L+bNUVYwm5jYzOu+YfUaspdmR6aDeXdLvZoLMz/+O9+FYtRT7tRwuPzMnffacw2bx/4fHscDkY/ylDnvddS9l7b7hagbr/vYJaoBoG7lQ3uddz3Wtp+0OO61/v2H0+xWMRms2nQE5PJxLkHHmBjY4Pp6Wn+6I/+iEgkwhe/+MUDWFSPx6PJ1TscDqrVSp93rig2q9QwVatTVVI2mRSFJoPBoB2nCpVS2RuiKGo+IGr9TBAERkZGtGW1vY8x3dnZ0eS3RFFkc2udpaUFJEni7NmzPPvss2xsbGh2smdmFbrq8rJimat2pustxbsnmUyyvr7O0aNH7/jeTpw4QaVS0RTpe72epgGaTCZxuVw8/fTTmnfJ8PAwLpeLWCyG1WrlT//0TzEajQwPD+NzuTl+/LgmQrG8vMzRo0epVCpIdoftuVKxhNVqwaDTUcjniMYGWF5cwtNXOalUKloqrooXRAdiDA4OaqKGDodDQ9m3O4r+VzAY1MCfwWCQ69fnCfh9ZDJZRFGRy2q3W+RyOba3N1hdW0UQFIbI6OgYmXSGmzfn8XjdjI1NKEXYjkwgEMDlcqPTG7BazDRr1X5AM+P3+/uUoAxyp0O7o3S5R0ZG2dzcolarMzo6Ri5XoNFoairQ29vbAESjUdLpNLFYVGN7vH6Ipzj+JyMA+P1+TXFFWXJXiEQGMBiMlMsVPB4vkqSjVqv31WUU9Yt4PE4gECCTyWhdvlarxcbGhlYA39nZIRqN8sqHDipCC8/1SCR2OHHiBJJe5PUf/qD/IwjhdiuacEaT8QCGT6UoqfW1b3zzG1yfv0rA52V0ZJDjx2dxun2IkgGdqL8jaArCnVp5aib0XlnXBw+WPxqe7732v3/ZvH85/H4c5vfKBg+Pe+1v/zF/feZgBviZm587cCNotVqEQiF+/dd/HbPZzOLiIh/96EeRZZn77ruPaDSK1+vVtPcAMpmM4q/TVytSl9CA5t+hBgqj0ajJuqndW9XUSH2vXq+nXldM1yuViiYvr95019fXicViFAoFhbdcLrO4uMgLL7zA6dOnue+++3C73YyMDmO323j00Uc5fvx43/IywPXr13n44Yf5g//z9+n1FAP1U6dOcfXqVbrdLv5AAIPRhCRJDA8PYzKZ7miCDPzhACaTiStXrrCwsKDZdLZaLZ5++mk6nQ7JZFITXVW/p7fffpsLFy7w6U9/mscee4xoNEqvr5xkNBoZHBykWCyyvb2tUPRGx0aeCwWDuN0ugn4/bpeT9Y04bqeLLqImNdTr9RSD9FxOsfiLKgoPSv1JYGlpiZ2dHZaXl6lUKjQaTRwOJy6Xm+ROElEQ6cgyoihw5sxZPB4PDruzjzlqUatX6PYU3N3jH3qCRqOJ3a7QewqFArKs3N29/kBf3QL0RiOlYoFSIcfm5iatlmIfWS6XmZqawu12MTIyysryGrIMcqeHx+MHJAwGo3bRNJtNTSGjWCz2fVhv/5jePoQDHPgvCswmHA4TCoVIJpPYbDYmJydZWFhClrvYbHa2trZxOJzE4+ucOXOWRGJbq3cofMRVpqentRM9NzfH2NiYthy6fPkyCz+9dGDu8T8dRZa7isCCV/FJmZ6e5p1332FocIhIJEynI6NSubrdrpZJqqodp06dJpXc5cjkBNNTU4TCUdweP52upOEK7xbs3m+81/LxXsvOuzVBPtjj7ni8/UH78PP/mscH/exfn/nLA3/fHwBV0YqvfOUrPPnkk4RCIWKxGIIgcO3aNQYHBwkGgzSbTY1iJgiCRpNTDYbUw1Hl3dUEpFQqaedczZjUzFitZZvNZq3mqBqem0wmTCYTjUZDIwWkUqm+zW2erc1NJicn+ZVf+RXMZjNra2t99ZgW6+tx7Ha7tmweGhpCkiTm5+exmIwMDQ1RKBQ0MVej0UihWAQUBpJKE33pkZcOfG9Tf6l4+46Pj2vL716vx/j4OG+88Qbj4+PaUr5cLrO7u8vu7i4zMzN4vV6q1aqmH+iw2djZ2aFarWq4SDVjlsYnxp8zGU2kUruUCgWajQY6vcSx6RkaLWXpOzk52Vep6HHmzBl2dnbotGWNKqaqydhsNiYmJkgmdxEEkWAwxJEjU9hsdqrVGk6HA6/Hw+bmFs1mi1w+i91hI5NNMzQUo4ci52O3uTlx4iTxeJytrTVcLieBQJiJiQnyRYWf22i26Mhd5HabfPa2EdLRo0dxu928+eabpFJphodHKJUqTE4eRa83odeZEEU99EQ6ckvDOCq2lgpX9+zZs3Q6bS1lPiyH9dT5j9HpKM2HdDpNKpWiWCxqGfD29jbT09NacFX10ZzO2ydyaGgIl8tFMpnUliGSJBGPx2k2m+TzeUwmE5c/ftAs5ufiX0QUJYXuZFRETQcGIrjdbr77ve/y+OM/qcCM+vAG9V+1JgmgN5iQRJGFmwsYDTrMZhtmq5MeOnTSnX68ajD5UXTx7vU4uJ8fTwA8XB/8cQbA/a/72vTBALi/Bvjqq68yODhILpfT+LuqBuDIyAh2u12hbvUBwOpQA5vNZtNAyOrw+XxUq1V2d3eJRCIablVdvsqyrC2Xu92u5iesYlDVup/JpBgHqWyVEydO9IWKHej7iU+xWKTdbmtgaLPFxO5ukmg0SjQa1YKKulx+8Nw5Td1cRT/U63UsNis+n1/j6mYyGd78yMFV1mdufgaTSaHSdrtd1tbW+orXKZxOJ/V6nWw22xdzUDQNY7EYo6OjWCwWxsbGtKatiIC9TztUpbpsNhs2mw3psUcffc5kNGIxmMnnizhdLsrlGm25h8vpYnb2FPPz17l5Y4FIeIBarYGhz/FVDVTUbqrVaqVWqwGKh0AoFKJcLmM0GslmFR/hVrtNPL5BfH2DZrPD4OAIOp2Rv3/hOzgcTgRBolgssLS0RLFYZmhwhEq53rfsNFPMF6iUyqRTKeRWk+ROAr1OYmhoiGazqTUTPB4PoVCIwcERZLmHw+Hk1q1bxKKDVKsV2u0WPRQ1ZkGAxcUFBAEikTDFYoF4fAMQcLncvHJIlv7+F85itVoVTnS5TCwW0wQE3G7FzW5+fp5yuYzdbtcaIXq9jng8rrm/jY2NaXfckZEh9vayWK0WavUKj//kh3A47bxw/0FB1OH/a5SHH36ErtxjcHAIn8fPws1Fzr91nkhoALfLhQi4XG72cgrpWxR09LoiBoNJsSpsNvD6PBw/OUs0NoLV7gJRQhKk94CXqEIBHywI3Pm4U9r+vR73et3+etrh197G/imQn/1z7hc5+Ic3iAXtcWfgvf3/vzr61QPv+sT8s5oDYSQyQDgc4cSJExw/fpzx0VHia2usx+NEwmG6soxBrye3t8fCrVuYjEbarRY6SUISRYwGAwajgXa3DSK88uortDttBElgeGSYRq2BxWBFJ+pYW1nD7/WjE3UY9UY24nHyuQyx6AAb6xuYjUYuXbyIxWTA43ISDAU0O81wOMzy8jKy3KZQKHJq9pRWp1OcGA39JkqLSCTM1772Nba3t1lfX8fn82nYQLfTpSnXxGKDNBpNxTqjIxP+f9l772DJrvu+83Pv7ds5p9fdL8+8efMmAxMwAAlBBAGKSRIF0pZoU7KSZa+5dtGutb2u2vKa5S1bVnJ5V7JMBwWba8s0TVqGySJpmiRAgESYCMy8Ce/Ni/1S55y7b+8fp8+ZeTNDMCy1Xts4VVPAzAvdfcPv/s739w2plOo6U6kUXzz7xX3HLfOXMqoRuLere/7z/4Hk2Bj1qjCDffSRRwkGAhi6TsgfwOoPcJh2er0+tWoVh91Bd8RtlJxGTdNUU2I8ce7cJ+vVGtVqdSQ2buNyuQmHIzSbLSqVKuFwhCNHjpDNZvH5fErb6nA4uHnzJrquK1xBgqDPPvvsaFK0pJ5KrVaLXq+PZQ2JRmMcOjRPLpfH4XDi9Xg4dvwox4+fYCw+BmjY7Q76/QFOh0eFnQuLHhdul4utdBqH3Y7X61G4XyQSodvtEgwGOXz4MEtLy8KddldoD2u1KnaHiWUNiMWi2Gw2xZR3OByKqOpwOFVS1/Nn/tO+k/P+V9+rcDXp1NHr9UbW4G6ViyBlcktLSyMrdItCoUCtVsMwDFKpFHNzc9y5s8TRY0cYDi00TeQk7Ozs8Oqrr3Ln59b3vbbzV+1kcxlspsFnP/vviITCtFstxlMpioU833r5JRJjcdY313DY7Rw8OEupWELXQdc0+r0BmnHPoEC0Uvu6qh/++sF+58Pey3fqyu79+/1f+16ldz+MP//uyB/te+3nrv3U6Npt4rC7aLc7SuroHEkpZSTDvdm5kUgETRORmKFQiMFgQKPR4KWXX8a0G9RqVQIBP3NzB1V8QiopuK4rd5axBgMK+RztVpM7y7dZXlrC53PhcjrI5zJcv/YG73zH45w7ewZ/wM/S0m3Sm+s0GjXG4lGuX3uDaDQyIlW7VKqbVE4JWluaF174Bh/5yEfUVFXeBydOnMDrdtPr9chms0rlFIlEWF1b4/KVK8qCLpfL8dr792eC/I3G31Bu7el0Ws0U4tEoX/nKVwgGg5w9c4al27fJ53JooIZGdruddqtFv9djK51WRHCZQywVXr1eDyMcDH4yHA7jdDopjDRzc3NzbG5uEggEiEajhEIhLly4gGmapFIpvF6v4mvJE1ar1ZSsRQq3pTdeo9GgXq8rY4DDhw+r7WM0GqVcLuPxuEmnN7CsIenNNKZpZzhkxNOD6ZkpZTog+XfASLgvmOGJRIKVFZHi1mw2R87WQoQuzRAllhkIBCgWCyr/V6omCoUCHo+HWq1Ou91mfX2di/flFcz9K5GkJQ0XK5WK6gDX1gShWbrjmqbJ/Pw8uq7jcjmFxbjDgcfj4bOf/Sz1ep2ZmWkWF6/z/PMiCOf69etEIhEcDgdXn9tvxDD82wOuXr3K2bNnsQaW2sJIS/yZmRmRtOe0kclu85+/+mXm5w/icNppNuu4PXaGwwe3dz/otvNP8s93Korf7fv/axbAzyzsL4Bj/3QMXTfweDwM+kP1kHU6nTQbDX7/93+fV155hRs3bhAIBPjMZz7D3NwcDqeJz++lUMjTbDYolQT9JRaLsrh4jY31dRx2O4vXrxOLRmE4pNVssLqyTKGYYzO9ztbWBk6nyeOPP8aTP/IOnE4nFy5cYGNjg8nJSb7yla8wPj5OJpul3mjwyiuvEAgEuHr1quIetlpNSqUSyWSCRGKMbrfDf/yPf0wqlaRarTIxIZRLy8vLVKtVBYtVq1UCPr/KLclkMly7dk3Y7Mdi9Po9NaVOJpO88KMv7Dtu5790XuGX09PT9Ho9XnzxRX70R36EyclJZkbqmWg0yuTkJOZIVy0J3u12m4mJCU6cOKHgO7fbzdbWFtvb28qj0/jpP/WRTzYaDYUfVKtVNjc3GRsbU1PQa9euKXxiZWUFl8uFx+NRsX2Li4tomsb6+rpKrZIOFK+99prC0rrdLl6vV+AAbkEX2d0VrtOpVBLDpmMYNnxeH5VKlb29DDabDZ/XT6lc5Ny5c+TzeRXSbJomdrudVktki9Tr9ZGCZFZRd8LhiMoRCYVCKuRZ6CebarImtcNSX9lui8FIPB7nv9wH0J76/AkAOp0OwWCQEydOqFyGSCSKaZr4/X4V0t5qtfD7/dy4schwOMTr9eJwOIjH48zOztLtdpiZmVa2VjMzM5w/fx6Px8NX3rH/tf+n3V/hqad+ZIRRGljWgFAoiK5rDAZ9yuUSljXg2o2LpNOrOJ0iwqBQyJLJbFGvVYhG46D9IBjZf90C+L1+71sVwO/V4eUHL4D/Zt9r//TNP83Ro8cE7tYWgzWbzcYnPvEJvvLlL6v0tWw2y+Url3HYhWrqm998gc9//nO4XE5KpSL1eo2bN29w/fo1ttKbFAt5bt28wdrqCl/7L1/l/GPnqFSLPP2ud5JMxTl56hgzs5OgDbCZGhsbKywv3WF1dYXDh+dJJhO85z3Psry8xM72FuFIiBMnjrO2uoKuwc7ONrMz04RDQb75zW+yuHgdr9fD+voa4+MpisUCXq+H27dvc/nyZdWAJBIJQGCQ48kkV69eVdf8mTNnsCyLcqVCrV7n9OnTtNttqtXqA5b4H37zw2pYIWkrfr8fl8NJKBRSwgyROCnudWnVJzF1y7LI5/MMBgNAWPk5nU6VwtdqtTDGk4lPjo+Pq2wMeYJkAdvd3SWZTCqbHLfbTSaTURy9bDarqCcOh0PZ3vT7fSqVClNTUyrTQI7ei8Wi0vP5fD4KhQI7O9v4fB4GA4tEIkksFqffl1QODcOmqw8jpsJChSKcKK4yNzcn0q8aDZLJJKurq5w8eZLFxRsKiJXEyrtxkKgiJXl8k5OTLC8vk0gkFXb56n3t+Qdfex+apik+YKvVYmtrSylYqtUqtVpNnSD5RAoGRT6B2+1WIUwOh4ODBw9Qq1UpFApMTEzwxS9+ka2tLS5fvszGL23ve+0PvPoeYrEo7XaLTqdNKpVkdXUFn8/L5ubGKDFuhpmZcX78x9/L2soqQ2uA1+Pi5uINNjfWOPvYO7GGDxaHh639BeMHK4DfTYr2wyg8Dzje3LPeqqi+1fpef+7eL99fAJ/99rMkkymGwyFXrrxBsVjihRdewO/3s7a6SjabVXxSubNqNOvk8zkOHjzIzZs3ef3115mfn+cb3/gG4+NJFo7M8973/RiJ5BjPvucZfuzH3sPWdprHzp7lv3z1y0xOTrC5uUEkEiYQ8BMOhwiFwsTjY8zPH2JsLE6lInZdX//617DbTaYmJ+h1OkRCId75jifQgZ2tLZr1OuOTgoydTCa5ffu2iqWV6pDDhw+r8DSnU9BbSqUShVyOvb09lSnk8/koFoscP3GCmdkZdnZ2hFKsWuXafbETP3vnZ5WZyPnz58lmsyKGQjdUxISk6MnUONnM9Pt91R3Kv7vdbtV9y/dRq9Uwzp05/Un5g6VSSf2iVquFpmmcPXuWWCzGSy+9RL1eZ3x8nEOHDinc4tChQ8qMczgcMhgMSKVSRCIRQGBkxWKRWCymUuxlBZcFRAQrN6lWywQCQS5fuozfH6DVEtMaXTPwej0jDLGn7OZluNL584+haZrKTpATMaHw0NSW1+VyYbPZqFQqI+Z8WY3wQ6EQjz/+ONlslnA4TLVaU7rKCz++3xF64p8llUY6n8+jaRoHDhwYYRADBRPs7Oxw7tw5HA4H2WyWZrOBz+fDMAxljDk2NsbLL79EJBqhVCqxvr7OsWPHmJiY4IknnnhgCPL4l89SKpeIxqL4A35ev/A63W6HxRuLHF44jM2m43Q6ePfTT/Gfv/IlEQqzm+H8Y+c5eHBOhEJHU9hsdlWcvt9O7Ptff9K//7uTmH/QjvP7/R3/9vD+Avjx3F9C1wXt5Hd+53cVRnXp0iW8Ho+ajLY7bYWjx2JR+v0OmUyWd77zCQ4ePIDT6SCVSvKj7/oRji4sUCmX6fd6tJpNDs/PYzdNbt26RTwWI5UaBwzGxpLkcoXRBBjWN9bxeD1UqxVM00Y8HmMsMYbD6cBummysr1Mpl9naSuP1eAgFg4QjYXb3MoTDIdrtFj6fl5s3bxCLRclmM2xtbSsysjRmlR4BhVyO2dlZEomEUqHEYjGarRatdot6vc7s7CypVIqvPP6Vfcdt9l/OUiwWCYVC1Ot1RVPrjjBUyacMBAIMBgNFh5H3llRjyW251En3ej1KpRKWZdFqtbAFAgHV0WmaRrVaVSx1n8/HrVu3CAaDmKZJMBhULeTKygqrq6t0u10mJibUBRCLCfJ0IBDA4XBw69YtlcJ+r8PEoUOHuHjxojLf1Gs8TPEAACAASURBVHV49PRJGg2xXczn86RSE+JJU20Igu8oYCUajVKr1ZSsbHV1FV3X8fl86LquHGplvJ8MjJHSvVQqRSaTIRwO43K5FM3gtddeUzjCgQNz+0LT713RaJS1tTVFMZAZCHt7e8zPL3Dz5k2F9e3s7CiHDSlVq9frmKZJJBJRITLLy8vMzs6ytLTE9vY2u7u7NBqNB1770qULaJoQnwvLnzQf/OAHCQR8TE5O0u93cTqdXLx4iRNHT+FyuulO9lldXmd1ZZ12u01y4ijxhNB+3ksd+U7F4+76wUaob9Vk/rAK4Ft1md/ra/ygv0PXv/PXXnnlFb7+9RcAgUt3Oh1effVVdF3HP2o2xsbGGO4JPN3r9ZJKpXjuwx9kZmaGUqnE0aNH+cM//EOeeuopYVSQ3uDkyZPcvn0b0zTJ7u1w+eLrzM8vMBZPUSrW2d3JMujD1OTsqMOsMDk5PmIliAS6crnI+voqZ86cwWkzcZiCv5dKxJmeniKXy+HzB4jGGircfDgc8uM/8QHu3LlDNCZMRKLRqJKEptNp1S3SH+D1eun1euTzeZxOJ0ePHuXa4iIuj7Diks3X/etrX/sa4+PjXLhwgZMnTxKNRsnn80QCwiGnWq2yvb1Nu93GbreTy+VUiLyk+MiiJ01hMxmhCZ6ZmaHREO5L2l/4pV8eyvxQl8tFrVZTnK9iMY/NZiOVStFutzl27BgvvPACR44coT0KOJdbQUk67Pf7JBMxdnd3R9V4oKYzNpsNXROgvTRXlO3r9vY2uk3QGfL5In6/n2g0yvXr1xVjXOB9d3WREssLBELq32TY972sfUnRkcAnMEpwKyiZkCwGsiB0u321Xf+tf/SP9p2cv/t3/jf1JJG+fl6vl3K5TCAQUkYGMjBabrENUxgstNttfAExjt/a2sLn82A3Tb74xS8SCoU4MDdLvV6n1WrxB7/36X2v/Tv/+HeVlCkxFqHRqO17eMkYxVK+QCDop9vusLp6h7WVFTK7O/zmb/4mi3d2cPsCJJNJ9ST8XtZbFYGBNhQyvNG6t5jYdBlKJL4uLnhxboy3did8i69952I1NOTr3X0vyuRVu6tckcocOQDrDe4agHo8Hhq1e64jXfx8q9VCx1AKBMuycNpQgoGf+In373svJ0+eYTgcMjU1xdraKrdu3eLY8SPcuHEdazAglYrT6XTwuT38xm/8Bt/+9rc5dfpRLl26SCKR4MDMjGIVSIVHt9tVcM76+jput5vx8XGq1Sq9Xk84txim0pi/8OI3eOKJJ9jc3FTnWqa+CVHBELvpQB/tCKTbeafbZXp6lma3Qzgc5c03RW5HsymGnabpIBwOc+nSJWKxGJFIhHq9rkxaX3rpJZ55+t1qR9loNdne3mZmZoa1tTU8Ho8q+L/0i7+077j9+q/+qlCyjAxgu90u6Y0NDh6cVV2mxAg9Hg+lUoloNCqiQUdcW7vdzp07d+h2+wr6crvdeL1elYtiPPmOd37S4/GoIiVBe0FC9Ks8T1ndJZam67oCIKU5ogAja1jWgGazqYDIXq+Lrmv0+z2swd2gadmuFotFQehsNul2e8qoUU5vNU3IzNLptApvNwyDQCBAKBSi0WiqC73Vaqlhi8fjodfr4XK51OsBquvs9bp3L4AREVnqSYfDu9Kxl9/zrX0n59wXTqsOM5vNEolE1AXZ6XTxeDxKiF6tVsX3Oh14fX4cTgfbOzs4HA4cDgehUIhKpUqhWGL5zgqnT5/h6JHjHDgwRyKR4vnTz+977Se//jSm6cDt8dLr9bHbHTRbHQYWGDY7Pl8ATbfh9fkYolEplwkGg9xYvI7P6xH4SSxFPJFUT075EPhu663wuqEGGrr6A9rd/x/KIsTouN7jWK3pI8foe7aZGOJnv1/McfRzwghnqP5omujShkOLQb9Pp9PG5XJiGAIuUAOlwYChZan/MrBwmCYOu4lhaAz6PRgOCXh9DHo9An4fnVYLj9ulHsB/dPhf7ztmof8zTi6Xp1IpU6mUeeTRk1y79gaxWIQj8wewGYLfN3dojonJCWLxGNlMhpMnThAJh7Esi8XFRdIjOkepVGJ8fJydnR3VcMhC5/V62dzcFMPAfo9isYBlDdQ9OjY2hmEYiqxcKBSQBOx+z2IskaTX6+PzB3A4nOi6gcPtolSusri4qPI2bty4yfT0DJubmySTSTUg9fv9fPWrX+VrX/sat27dEtS5XBany4lu3DUktixLsShcLhfZbPaBKfBPXhJ5IBqwlU7j9/l43/veR7crInrlA182NLL4ybqUTqdxOBwjepuTUqnEysoKhUIBQM0TjLFY7JP5fB6/3w+A3W6nXC6PJrpxlpeX1Yi7UCiQzWbxer0KwxNZpIKCIbaSGoN+n1Qq9cANJDowlwIm5c/ZbDaReO9xK0da+XQOh8P4fCLYKBaLKXca+SEE1UTcwPJpIPf/kvUun86yY5Qdk8vlVNilzMBot9s0m03a7btP22+/b78S5PR/fBS73YHb7SEcjpDJZDFNO5YlugqJ87RaLdVZmw47jUZTTdDvDZM2TZOvfvWrfPSjH1WcL8uycLvdfO7E/pjFj97+2F2Guw69Xld11HKAJazK+jSbTeKxKPlCHk2DYrGI2+Wk3h5wYO7QPouj73fQ8CB+poF2byGTCWo2sDT0oS5pxBi6gY5tFM0poio17vmjfvYt8EkMNGzf8ef4DtieoevYdPFevvniizgdDiLhMM1GA7tNw9DB0GDQ7+JymBj6EA0Lh8PE7XKgDS1MQ2No9alWiuSye7g9XgB+67d+i7WfX9n3eu2/1ccwNHK5HN1ei7W1FcLhMMFggHq1wvHjxzlz5gzz8/NUKhWi0SgHDx5kZ2cHt9tNo9Gg0WiQSCTUg3ptbY1wOMzW1hbRaFRpYhuNBqFQiKmpKdbXhQ/l1tYWBw4cYGVlhZkRdURib+FwWLEjms02L7/8Mh6PR2WAAwwsi06nq6y0XC4XgUCAnZ0d0uk0fr+fZrOpLLJkRvH58+dFNOdIg+x0OnnhhReU0qlQKLCyssL29jZOp/MBJYjt794lv5dKJWZnZ0ddo1spaaSjzXA4pF6vi+M92hLLibDQCQslyNGjRzEMQ0UJOJ1ObLJAvPHGGxw9elQ5m8zMzCiHlUqlojBAn8+niuXe3h79fp+pqSnC4TCVSoVSqUAkHGJlZYV4PI7L5SIYDLK3t6duUHkiNU0MKDod4Q4hNYyAcrqQ7rfSIUXTNBYWFiiVSjSbzRF9Z6AKoMRZpHNEuy3MH6VmstfrqcwPw7Cri8yyLBXgLA0my+XyA0UcIBwWmR+DQZNmU8RZLiws0Ol0VNcpXWbka0ejUVZW15XETfqzff3rX6fT6fDTP/3RUQHuEo/HqdVqWNbDt6aCbhOhWCwQDEUUhmKz2cTU2eVhMBjSH1SwsBGJJZidOcBS6hY2HQ4ePa085CRc8L2st8QKVednPPC9NuMhtJTRP1nfbZurPejuItb+be7+N2OJIjhavdHDzel0ovUt3KMO/d3vepe6ibThkGG3QTqdHkkw08peXV6Tuq7vG+DJDjo5eUiZDty/fD4xqZyemcDn89DtisHeu55+ihNHjrC1taVI8aVSiV6vx5UrV9Rw8MSJE+q6lMmHBw4cYHd3l8OHD7O3t8fMzIzi3LbbbdLpNMePHxcxp6Pw9UAgQC6Xw+v1ksvleP311zlx4gTBYJDXX3+dVrOrDE3S6TTFYpFMJkO1UafZ6nDgwAF1byQSCSYmJjh16tToOhUd3XA4JBwOEwqFqFarSgW1tLREt9vlAx/4ADdv3uTgwYNsb28zOTnJ3t6eMgC5dx08eFDNDk6dOqU4x/V6nUQiQalUYmxsjEajQb/fR9M01XTUajX8fr9KYPR6/RSLRaUFloa0TqcTm91uJ5FIqHZSjoklbqZpGrOzs+optL29zc7ODi6Xi4WFBXWgisUix48fx+VyUKtWiMVidDodKpUKTqeTYrHIxMQElUpFbY0lvqKCViwUblatVhUmKbe8cju7vr6O3+9X6VoisPuuA0ij0VDTbJnWZhiGykmQF7UMIpcHV2Th9kexmi1lxHj/yufziscoWf35fH6Eq9QYHx8nnU6rp2goFGJ1dZXhUFPj+52dHS5evEg2m+X97/8gve5AuYCsr68zPj7+0IIji2qn08HmsINhU9pfzWbicHtodroMLIjGxjB0HbPXxjAMpqZneO3VV5g7atBoNGi32+oB02g01EUkX1d2zaqu3Pd+BoMBfr+fSqWCx+Oh3+9TrVaVeN9ut6tuVsIJNptNXbD9fh+ny0O3K7okOZH2egV5XhuFncvfsd/GXcOyBvR6PUUsHgzEeTRsJtVqmV6vp6hSxWKRN6/cIB6LYVkWV69eJZ1O0+/3eeKJJ+h0OtxaFGlmw+EQj8dDJBIeaXY9tLsdjh07hq7rnD17hu3tbcrlMs8++wz/7vNf4ujRo9y6deuB85VMJomPRWk263zkI88RCgcol8vkclllpFGtVonFYoRCIbLZLKFQSOHui4uLTE1Nsby8jM1mw2azceXKlVHX1kRS2Or1ujALGZHwpTuM3P5GIhFisZja9mYyGa5evYrNZuPYsWN02n1lJirvs0gkQmpygiG6MvFot9vKtn91dVXRX4LBINFolNXVVVwulxoQLi0t4fP5SCaT5HI5JicnR4yIpiqcD7Mwk/ZfY2NjuN2i6xNdnbDes9lsKiag0WgouEmaCktVjdzZzc7Oks/nVUGVu1ybPFihUIiNjQ0lwbl16xbRaFgpQdrttgoCkooRt9utLnzDMLhx4wblcpFYNKJwxLGxMQU4F4tFfN6gspGXvCHLsgSo3O2ozk8Wpn6/r7pDuV2NRCLous6NGzeYnp6m0WipoUqj0SAejyvsTd6095sDgAipkXkKUs1iWdao+PtUQbh/hUNRIQzvVgS50uFm0B+ycmeNM2dPsb29LbJKRliLfEqNT0zR6XR48cUX8fl8BAIBHn/8cSYnp9lK79Lricl0PD5GtVpTSWD3LgngD4dDbIaJ3eFU2Gez1VZf87jFlmjQ6+Fye1nfXKff7+P1+YXKJxQiHo8r7pk0upQd+sNs3h/WKUoKgs2AoTUgGHDTbjfZ2Nhgb29PTMl3RB5srVZTQ6p3vOMdTE1NgWagG0N1bjqdDo1mGafLiWXpaksj8Wm5a7DbRb5Gr9/GsGkYNrA7bCMIZkDA72VxcZHf/I1fGw21BN5rGmJXEI0KGWSrUeeL/+l5NE2j0xdKmna7zcbmJlZ/oAYP6Bpro5jG40ePqfyW2ysrvPKqCLn60Ic+xD/k1/cdn0DQx/z8nLC1jwSpVMpUqxUOHJhhe3tbOYKbpkm1WlUUEsma8Pv9lEc4rtzVSNgpl8up4V0oFFLmAUtLS4oCIguerutcuHCBsbExms0m169f59SpU6RSKba2tvjj//CfME2TU6dOUa/XVVBYJp9j9oBQhnm9XnXs7HY7M6MBjXSSHg6HLCwsqOGo3W5XHL5MJsONGzdYX1/nAx/4AG63W7nbPOy6knWjXC6zs7OjPmO/31XXpIRvotEopVJJ2fnLIZa08Eunt6nVasRiwoDhxRdfpNsVRii269evs7m5yaOPPqrMDsfHx3nsscfI5YRzQiaTodVqEY1G0TSN48ePKy1weATUbm5ucubMGfb2dqhWyszMzNBsNrl06ZJqSc+cOcNWWkyH0+m0oqE0m01BBPX7lL28pmnKnFRu0+R2pVKp0O/3mZub4/r160xMTCn/M9M01X/lBFYWwHu338IRo610zFLJce90WsYN3r+63T7BoPjcYuoVxTBM3CPto9vtRtd1xTyvVqugoagt8/Pz+Hw+XnvtNTRN486dOyTGJkctfBu320uhUCIWezAtS1okicmkQb3Zvuv9pwuMy7Isur0+aDpOjxePU0zac9k9er0em5ubvOfIETwjHprb7ebGjRs0Gg2OHz+uiox0EPlOSz5MvvSlL3H16rdp1usMrJ4ip4r86D7e0RPcbrepgdaLL/xnIpEI8VSKcqVGNiu4m2JAJbq9P/tnfoHjx0+oCV+j0bg76XR4Wd9YIpPJqJukUCixsbHBeDJFe/QAZjggnd5S7t1el5tqtcrSLRFZcP36dSFLazbRPG7qHTEJLpVr1KsNpWCyO01FoL94dRG3x8NLr15iOBySTCS5ePEiX/va1+Af7j9Gjz/+mLpxa7XaSPyfwDRNVpcELjc1NcXW1hYTExMMRt51QobWotFoMDs7y+LiIl/+8pf55V/+ZXXsZaMibeharRYLCwvcuHGD9773vWSzWbrdLjdv3lRmBjdu3BAKCMPgc5/7nOKdSojq29/+NhMTE9y4cYOf+ZmfEceiVGJqako5Teu6SFOUDz9AZYVUKhW+/OUvK9jr3Llz6rp/7LHHWFhYYHJykkajQalUUruv+1ej0VDhTVI5JuAqUeCkEWw4HCadTit2hex65X2yvb3NoUOHVYaJz+fjkUceUS5MtieffIeq9v1+l1arQbGYx+NxUShmVcjQlauLPBl7kkw2y9TUFHNz87TbbSqVGoPBkEgkxp07q5imQSAQZGlpmdRYAlODQ9PTeDwuuvUq+ewuqVSKR44fpV6vU62UCPr9WAzRbDrRSIh2r4vPG6DZ7bG8tommacTDIUzTgWk6aLfbGIYNwzCZm5tXB1MeBJGNIFpsSZSUHaRpmqrQ3SulkRfUYDAQXW3PotcbPLQD1E2dfClPKBTC5rBRaZaVEiabLyqcsVCqUKqIGy8YDBIMBtXDwOl0cv78eXRdZLMyNDh69CgOh0mzWcNm0xgOH8zo0AHXSFmSiMdIb+8oMrfNdKqOumtZNJuiYGjaEB2LeDJBfzDAMHZ5/o8/w4EDB3jppRfJZDIiYOfQYWZnJrEGgsD+O//4/+JjH/sYn/vc5/j5P/eLpLe3Rq47IoLxm9/8Ji+//DI2m51Bt8N2ek9tuTweD3a7uHAdNovMXlYx8gHqtRbBQIg7SytsbW2J7U1PbGU9Hg8bq2v81q//Bu944p1YlsXY2BivvPIKtVqNYDBIqZpjMBDHuVQqjbbbQ9rNDouLt0mlUrzx5mWSySS1ehs0k2Kphs10MTY+Rb1eZ3MnA7odp9tPb6BRqjRoN3fotdtoNhshf1CFAcWTY/hH2RgBr1/56rndbjrdOpubm7i8dopk952vzF6R7a0M8XicSqVGoZAT0JI+ZOHgEUybm153QCgYp17rsLm5yeHDhynkBTOiUq7zwjdeGhW6DP/73/47CnKpVGrMzs5Sq9UIhUK8+cY1Tp06xcc//nGFkUnxwfLyMlNTUwQCAdbW1kgmk8RiMT72sY+JYoFNFQ05ZLTb7Rw5coROt6+aClmQut2uipI4duwYmqZx+/ZtwuEw73rXu5R1XiaTIZlMqmMni2WrVmfYE9tT3XoQZpKYZywWU3klzWaTbl88dIvlKnani3K9gcPlxjAduJ1OKpUKg8EQELLUZHKcpaUlBaNJLFjyFm2SvyY7pHK5TCgUEqRnl6lkIx/5yEe4ceOGCB4ulMjsFZWYWV4M+XxeEA53a0qO5vV6iURCrN65g2kaDPpdbt1cJL25rvCnQjGHYdro9sUNgKbTDAqn24nJSUybDfo9crmcMjUwDEMZNsiCUyqVlFRvamqKarVKPp9X2NTc3JzalgnVSk9NYyWmIHFQa2hhd9gYDB7sAC0G+AJe0Ie4PE71pCoWi3i9XgKhoGCvh4I4XE5lcCqJ2xJ/Mwwhkp+ensTvC1OtVlTI9Nzc3EPTsiR0IP3expMJhUlavT7dYVt0r50ODCxcDhOHwwSrT7vV4PDCHP/qD/4F4+PjLF6rkt5Yx+FwEPB5sfodXvrm1zn1yGn+l7/+CQKBAF/4wvM0GjU+/X//ARsbG1y4cIGJiQlOnjzJ2toa/V6HjfVVJscnFOZms9kUATwej7O2tkYqNcHS8h3CYcFZKxQKtNtdGFjMzR5QWM7K0vKIGlTh+LEJFq9dJRgMsnz7hhCzl4rsbm/hDbix2U12dzOjTlxMIuv1Ogdm59nZ3qPXHbC8vEI0EuX06dPMzc0pEv/NmzdpNBo891MfEdfZxASdERQiYQHDMBSoLrHBRCKhArfl8EzTxE1pGAYf5y/uO1+PPfY4ui6ws3AkwKVLF0bX4gEuX76M1+tleVlENkhj3tu3b3Pt2hvcvn1bwToOh2OkbQ8TDIZxu72cOnVKacc7nQ5/5a/8zywtC3L01NSUonrICbBUXklVhezyq9UqE+PTvP7666yvrzM7O6se2p1OhyG6clOR2J4k+dvtdvb29mi322q6fG9X+Oabb3L06FElqQX2xXFKl6f7l/Tsk3ZxbrebRqtFs91ibGyMyekpQCTtFQoFUfg6YlAaDgvoTrrX2O124vG40g9Lit2NGzew3blzB9M0yefzKlAkGo0Ki3htwO7uLt1ul9XVVRwOh5gkeXw0Gh0FVMrhgsPhYHNzE9M08Pp9tHtd+l3Y3t6l3mwqO5xHHnmERqOh7LUAsR3rdQmGw/T6Qzz+ANGY4OzVWi38bpeiw8hplNx+yxMpvQk7nY7auko+oM/nE9uVZFJhfr1eR4GkMhdVfhaPx6u6wfuXHMxYlkUsFlPHKBAI4Ha7KRQKhMPhu9KfpsDE7gWmZbaqNHsMBqJKx/zMM89w6dIlJSy/dz3//PMEAgEADh9ZUE9z6fwsIYV+p4XNALvDTim/x97ODs26MLqQCp1EIsG5c+eUoWQ8HicaT/Lbv/O7TE9PK+cQwzBU+taJEycIBAJsbGywsSFS8Hq9Hj/+gR/D7/cTi8VYW1tTbhyXL19mfDzJxMQEzzzzDGfPnuX27du4XC4qlQrT09Nks1nRqY0w2FKpRDgc5oUXXkDU8Q7Z3I7IXTYhkYxy5tw5djN77OyIaMRisch73vMesV2OiGHdYDBgamqKX/mVX9nHLjh27BjPPfecwpvr9TrValXdlFJYn8/nR4RfU2Fv8vzZ7XZlaiGn8HLode8S22tB0XjllVfweMSA4HOf+xyPnxU7gJMnT1Iqleh0Omxvb/OpT32KwaDH9PQ0jz76KO973/twOp3KNEHy6aSparFYxDRNoUCKRfB4PBQKBbWjkQMAKb+UhQfg5s2bzMzM4HK5eP/738/W1hbValUxFdrtNu1OTyksZLMgh1mDwYClpSXm5+dJJBKKMiepKMePHycUuisOkHCNvF4lLHX/kpnHEkbKZDK4vV46nQ57ewLKKRQKTE1NqSwUzSXs5gaDgZp4yzoh6T6tVosrV65QKBRIJBKiA1xeXiYcDis/LzkZdrudmKapXlTXdcbHx7ly5Q0Fusq9+cWLFzl79qx4coTDVMtlpmcOsJvexO5wEQzFYCg+/NbWlhrZyyQ0MUGtsru9zfTULL1WE5vpoNWs43H7hIpE11XOhQyXkURmh8OhTDHlCZJPon6/r1QbEhMUT++7F4SUq124cIG9vT2efPLJkVPHg08nn8+H2+1W7z+VSqFpGvl8nuFwyOzsLDs7O0otIovrxsaGKqiRSISZmRlarZawB/MLOksoFKJcLnPixAn29vYeeO3HHnuMQCAgOIr9nqIKSIC6UqmIC1WzKBRztOoNMtkdBr0unVaLIwcPsp3eUdveUCjE8vIymUyGTCbDxNQ05XKVarWM3W7D5XJgt9tZWVnh+rU3ePbZZ+l12zTqVU4/eoqPfvSjYtJvNwCLSqXC0WOHFSdtfOID1GstfL7ASOcaU8RdOfQIBoN0u4L+UygUlI/k2bOnsYYDjh49SrlcVkWqVCoRiSUYamJQJp/owsQiwebmJhMTEzz33HNKpy4xYeH001ZMBNmlPPbYY/TvGZANh0MmJiZoNptIocC9ki05PAPodkTHEovFHnojA8pK7qtf/QpvvvkmCwvzbGyIbJjt7W1CoRBPPfUUm5ubfOhDP8H8/LxiDEj7+r29PeLx+D4TAKluKhaLxONxNjbXKRQKKk/43LlzXL58mZkZMXQxTZNDhw6xt7fHYDBgenqaUCikHJ+ksknaWFXqNayh0NmPj48rYrbL5WJ+fp5iscjc3Bzlclnhgm63m93dXZUrIucF0qTA7XYT8gnKTS6Xe6jk0+l0qmGcxNUty8Lr9VIqlZiZmcHr9SoYq9vt4nd7OHnyJEtLS8olWwaGAaysrODz+Zibm1MPFJvsTORNJMmDojvxq3i8qampUdZHF5fLRalUIpVKkU6nmZ2dZXJyklqtRqPVwtXrYzpc9PpdJqZm6LQa1JtNysUy7W5HSbmkzXe32+XOnSW8HhcO00an3cTucGE3DTxOB2jWvtAWiTMNh0PFnZL/Lhn58v9lUZRFQ/D3Bmr4IbsAqcw4deoU8/PzRKNhNb29f0nTVLvdTqPRwOl0Yrfb8fv9qquTrbjsRmU4jDyJsnjKi3srvThStTTUhGx8fPyhry1BY7fbTb/fxzRNpW2UdIVatcz01EHcbifDQYd2q8ne9g7dXpuZmRkSqSSPPvoob775JtVqlUQiIbqyWp16o8r8/LzIOvGJLcrcoQPYNYOb10Vo/YkTJ9CHFs//h89z7tw5bAEf9XpVkdq9Xq+yVw8Gg2xubnHkyBHW19eVplsWJDnwEvb+ArM5dOgQ+XyW06dPK9hid3cX02anXKqwvLLO6TPnWFtbY2xsDJfLxfT0tKBwVQT+m8vllKTw5s2bdLtdSqUSwWAQm83G/Pw8fr8fj8dDNpvl5iiOUtNE+lowGBw5mduUFK3ZbCqXETkM8PuDhEIB9XB+2M3c6/XY2NjA7XbzwQ9+kMnJcWwMuXnzJoahcfLkcRqNGh6Pi6eeehLQVbckB3wzMzOqW5WdqiyIYhos7lMJt+i6rralzWZTdfq3b99mYmICuKuOkmFMt2/fVhjjYDBAtxnYDOH8XiqV1AO7Xq9z8+ZN8vk88/PzipqlaRrJZJKDBw/SJ9mdEAAAIABJREFUbrfVbknXdZaWljhy5AjRaJR6uYKmaYpJcv+Sg0nvqOvTNA3TZmMwtDhy5Aj5fF7tIgqFAj6fj2azybe+9S3VSc/MzBCNRnn11VfVACoWi+3jK9qEE0tLfbHX66kTmc8XsSxBF1leXhnJZ0oMBgPOnDnD2toaBw4c4NatWywsLIxcG0L0B0Mye1mmpieoFPL4PG6eeOIJrly5jN8nPqw8OJJ3FotF6Dbr2O2wu7uNhYZmmITCkZFqw6e2GBKfkQVscXGRw4cPKzsvSZfQdV09XaXCQm6Rhf5T0CNk9KcEgCOREMVSAb/fj4MHhyCyqErsURY0KdOT5hJra2skEglxoO9J9JIa43a7rfhcY0fHFU9OpsU9bALbarWU9XgwHFIXcL/fV1sJ0zSJxkQcQbtn0Wn3cDu9BKNxke0aiXL58mU+9alP8d73vpfHH3+cy5cv8+EPf5jXL17k2R97j9Jfyrzlzc1N2vUGf+FX/jymabK4uEilUiGVSrG7s83SUkPJE4XV0ya9Xo9kMkk+Lz7r1atXSSaTykBC+sRJZv7q6ip37txha2sLj8eDx+Nh6fYKhmHwrZdf5Z3vfCef+cxnMU2T2NgYr732GufOiSIoh0z3UiQymQyGYZBIJDh16pTSl8vsC+lIPD4+TiaTUYE+7373u/n0pz/N3t4eBw8epFAoCOC9WFRkYUm70HUdTR9QrpQeGvC9srLC9etvEolEGEsIPmA0GsXtcXLlwgWOHTumtLwSTmo2mxi66FAlgV+qo+4NMZqamqJUKqkHjmHcnd7LoiLVTNJ3UHZy3W5XYfevv/464VCMV199lSNHjqh7RuLMvb6ltMMScpDDqnq9rtxVKpWKir6UtnWdTkepQc6fP0+9XheSOofYXUrhwP3L5XLtU4R1Oh2G3S4ra6vK9UVmDU9PT6PrOrvpLcVNdTgcFAoF1tfXeeaZZ4jFYqrOuVwuVUtsImjZoax4JNeu1+vRaffpdvpspXf40R/9UW7evKmwB00T8pJGQ1z4KysrbG5uMnvwEL5wlGA4zOraBm6HjS986UsPfTL+MNfXeOFP/DXk+ge/+g/+RH7v7/3evwRQYvKH5QIvLCyoQrq6uqpMXCVlQhJBp+YWMG1OvD4PvpCfZqOGL5ygUMgTicU5cOAAp0+f5plnnuHrX/86H/vYx6hWq0zPTFIs5mm1xLZkcnJcYG82ncOzB8ns7jAYDJgcT1KrlEjEo4QCPkrVDqZNxDMahkEyIYpJLBbDsA25fv06Ho+HdDrN+Pi4SueSOwAZSyByXKbERdoekk6niSbH8LhDLC9tcOqkmCyurKzyM3/mp7lz5w7Hjh3jwoULI05oQ0W1yoFMqVQiFospnE1KtKQJx7Vr1xSh12az8YUvfIGTJ09SLBa5fv06vV5PUbZkkRbu32scP36cRFJYv0mQ//719NNPC/zL6jI1JbaYmew2J04c45FHHhkVsYJiIpimg4DfoWIrJTFbcjXtdjvBYFBhdZFIhHw+j2HYVdckifndbpdIJEImk1HbZUlbm5qaQtd1jhw5gs1wcPjwYTVA9Hg8XLlyBdPpULSTSqUyen93fUOTyaQqhJVKhXK5zMLCgtqVSUlqo9FQXL5SqaQ4svfi9fcueQ77/b6ynGt3u8zPzwOoAaFUoyWTSVKplNqmy/cUCASo1WqKLnX79m0VVTE2NoZ2bH5mePr0abLZLMvLy8o1GcA0nQpj8rk97O7ucuDAAZrtLpXWXUDUNE3Fydna2qLX6ZLP5+l22zzz9NN89vOf/39ZGv7HWb/92/9EdeCdToe/+lf/8r6v/9FnPqe6PdkNyKe8pABlMhlW1tZwOBxi0DA5OcIGBbThsNvQhnDu3DkyuSzVapU33niDn/rJD9Eb9EcSQItUKiH4ew4BWmt9C5thkMvnGR8fVwR3j9tNZ2DRbnVxuh0qk6VSqWAadna2RKxiPp9XfE45NLM0QU+KxsYol6s0m02Wbolo1TNnH6PRuMvFq9VqShfqdrup1SsKh67X6zgd7hHpuafCb7a2tggGw0jXl0qlqLikg8GAZDJJq9XaR5qXDIZIJCLez9ISuXyGJ598ks3NTSGje/e72djYUNPZQ4cOkcvl+Fv/69/cd77+3t//P7Db7SpyQtd1PB4Ppt3AGh2DVquF3ycGW5lMhkgkQigeFcfW5VaOx8LUQ8dAFJatrS3Fmc1kMszMTFFrC2dzj0uQpjc2NhhLJthOb6FZd7tjaUZw+fJlpqamyBSKGJogXm9ubhKPx9FthhpMtdttxsfHKRaLqsuUpsdyKi4HDnJLCnfzmt1utyqGAO2mwNblxP1v/M39x+0Xf+EXhPoslVTDH8uyCAVjjI+Pk8/nKZbyyrc0HA7TaVRF5siIfTIYDMjn85w7d46VlRWmpqaUZ6GEj7Rnn37HUH5gKWMql8tMT08rW6p8Po/DtCsXBt2w0RmgBPuGYZDL5RgbGyOXy5GKjyl8LpfL8fqlSz+k8vD2+m9h/dvP/BuFv5ZKJbSBpW6C4XC4z2XcH/LTG/SpVgS+bJomuazgdNqdDlXYJW0jnU6LLsSwKVDb7/erp3qlUsHr9Snu571OywKL0pTjUSqVUtvaVqulyNK7u7uk02kmJiZIJBJcuHCBAwenKJfLAsAfTR1lFm+rKeghp06d4md/9qP7jsWv/cbfU98bDAZVwRsMBgzad/Xt5XJZDUqKlTJev0+pqaT5sGEYQtzfE9Ndaa5bqVSEkqrXptaoqgJVqVSYn59XxgrDLoosfBe/9OP2eklvC9hhZ2dHuTq3223C4bCKvJVbcbnTkAFpcgghIx2kZnc4HKqtrHRtkWYkMnZDvt6n/smn9h23n/+Fn1ezgnQ6jcz0KOQrzMzMKD+CarVKKBTC63Ozubos9N6aRiKRUO9JTr/vHZLKYZg2MT42PHTokMIX5DROOKaguG17e3tkMhkBfusGLrdfaVql5XsoFEJHwzY6uHa7nXa7/cAW+Oc+9qfRNE1hN+12G7vpwOoMFHFzOBySSqUU928w8n+TKo5er6cueM0QPm0Si5POr8VikXBQCLOlqas8sd1eG9O0KS84OS2UxgumafK7/+wP/qRqxNvrf5D1z/75p2i1Wur6lPBGqVQiMTZGt9ulOaKI1Wo1IpEIe3t72EakYwn0S+Juq9XCrpuq85GBP81mE7vTyVCzFI3tXsVGLBajXq4pJkW/38fnE8ordB1fwK+GPKZpqsZG0zQajQZ2u13x7Q4dOoQMEJOCAtn9tdttRSVrtVosLS0Rj8eVM0+1WlXwQ7UqOv6trS3+/Wf//b7jduqRUyMmiltBJABnzpxTw8ZcLicMXFMpYrEIsVCQYrGoCi+gttjyc0v9u7zHbfPzC6OJlo1OU2QTeJweYrEYW5ktXC4XXr8PLZclNTGOx+clEAiwfFtgfqdOnRLaO7eTodXH7nIx7A8VTeRhDhn1mkiJG09NCDwgGKZUqjAxPi2kTgNIp7fw+EP0hzpX3lzE43GokyJP/vXr1wXwP3LkPX36NH6/n4sXL3L8+HEOHjzIcJScBsLEQOaGBEN+2u2WAmrHx8dVDsr94dP/v1hvZcx8v2fC8CH/9rB1//d9t78/7LXfXm+5trd3FWQRicQol6sjDin0+haGzY7bY1BvtLA7XBRLFQYWmJqBzWan2+1jmg4Mw6TRaLG7u0tud49kMim6a01TBhnNZhPTYadYLGO3O0f52MIn79KlKyRjcZWqaBiG0n4bpkm5XFZwSiQSIZvNKihsb29vZFLhJRQKsbW1pfz/ZHav2+1WssJDhw6pQVYymVTT5Xq9zqFDh2i1Wly8eJFms7lPd37v+smf/Eksy1IkcJlFfOnSBdbX10kkEhw9epSTJ48TDofp9XrK7eVen0A5cJTFT3bU0oDFVtzdGcVAiq3CwemjhEIh1tbWsOkGY7G4CEQyRBEpZnPk9zI0aw2OHp6nXMgr2crY2BidZgPDELiI1A7ev7rdPg6Hi2ZT6F7dbi+3b68QC6cIBsK0Wi2OLBzDtDloNYUleDotYjZPnz6t6AG6rjMxMUGhVFRT3Ha7zdNPP62efE63Q2FkkqdlmibWsI+ui0GOdKuRP+PxeEY+Yv8NrIcVuu/VZf7+7/tuf397fd9rbGxM4Z7SjFPKHyWRXxKwA4GAgpPkoCOdThONRtU2sFKpkEgklMOLHCBIZ2/NEPSu3d1dNTRxu92cOXMG+ndJyNKxRdM0BsMh7W5HDScajYaa6JbLZQV9Se2u3DEJ38y2stySNm7f+MY3FDbp9Xq5du0apVKJnZ0dvvWtb+HxeDh48CBOp4iJdblcvMzL+46bNFOQIgz5uRcWFjh//ryCDiTUIf+020IbL7u+ZrOpdoZSFit3u4J1MezSqXcxrAGdeo/NeolNBBBsDJx0a2IaMzsxRSaToZArCHJhMkGlUqFarTIzM4NvFE6u6wb+kF+ZJ0hnl/1Lx+Vyq5yOer3J4cOH6Q87mE4PO3tFZmZmuHr1KkePLXDx4kXSG+s899xzpNNpfD4fuVyOaDRKtVqlVCkrs0afz6cwB4lPyJMtwXTLsugP+spcIZvNqomrNAB42ARWro//xV9RriySPyi2/DYMXVMqEUn6lK/t9QXVDSB5cH/5E39t3+/+tV/7LdxuN9/4hrAxH0sm+XM/+9Hvvav7bus7dXVvF7s/kdXvWzSb9RH30D4KGnfi8wVoNNvs7YlurlAoUCqLJsJmOhgMLLLZPKVShd3djOJUxuNjdJtiQi8dzKUgwBcIYCGaALfbi6YZQh/dFg4qdl1TPFmJ23W7Xar1Om6vUALJ7frExASFQkFZpUlytJTrSXNcaV13rzOL1O1ubQlays7ODhsbG4RCIT784Q+rcHU5FX6YFO7ixYvk83ni8TinT59WGnebTafX69DrddQAttUSmSV23VBb3d3dXWVKAneJ691uV1HkDMPA5tQF7ud2OjF0oQW22+0Ud7do9Ey1fSzpWRGBN32ArZ1tcm3hwZVKxCmVSgJMdXpptkUXJbG8h3naBQOCF6RrNsbiScrlMuF4kN3sNuvpMnaXnZvL15g/egC7y8bMwSmmJsaVDlZKau61vJeSOOlqIQc02hAluZMpb/Kk6roY70uplzwwnU7nLU1CJRVAtuXSN69cLhMLB+gP+hi6jtftRNfB0HQM04HdbhtNHJ1UKiUqldIDv1s+yZ5++mmlPviXn/4jdF3n5/iZ0Rv43m/Az/77P+ZP/6mfuufNP+wDfe+/Dw0+9Tu/TTQaJZPJKMWA0+Pmz/7Zn9v3rf/634g8E5/LrZIBc7kcly9fZm5uTkzu+h2GGuiaDZ8vIByOd3IEg0Gy+RyVSkVlNo+Pj3P9+nVM0yQcDOH1epmamuKll17al+miGxaTk+MKZ+p2u4TDYXK5HG63n35P+NDJqXS32x3tMtJKhtdqtRTLwe1243AaFItFPB4Pfr+f5eVlxY0zdIfC3aRwX/IbG6kG6+vrNOqCbygB+LWNdQy7iWYzCUVjVOoNXF4f5ZrgyemDobKyGosL7uSbb75JNpvF4xBb093dXWVfV6/XsQBNH7mFazZqVUGqd7u86Aa06zXVBMipspT4dfs9ZbMvC8dgMGBycpJCoYBpmlQqFaWikVifjJusVCrKwUVOu9PpNGNjYxw7dox3vetdyMRIiTOWSqV929V7l91u58SJE8zNzSnlUC6XwR/wYg1FzdJ7OnpH3Kcut4Nh11LNi3Sukt0eoHZ1hmHcHYgM2n1cdif0oW91cJl2Bv0Bg/4AU9cZdMRgoNOz6LdbNCpleoM+uF3Ua010zUa5VKPT7jMxMYHDBtevXVF4gV17sJPq9gQ22GjWlKljNBoFhFFlMBhm2Ndw2T3sbe8yFhPOr8srd5g5MApFmZ6iWBZkVNPlJBgMMrQsdnZ2OHn8hLKq8nq9mHaDgdVDN1w0mlXC4SB+v4dWq6OmQ3JSKJ8Mb2UDFQqKwut06DSbVRUTUC4VsQZ9hbN0ul18Prco8C4Pvd6AfL6oOtSHaUcDoaAyiZVALTzci+97WfuK3w9h/Yt//k/FZ61VKVbKJMaFk3GxWHzge6sj8L7VbVJv1WjtimS/J556Am0oLsjB0GQsnqLRaoFh8ql//od86Cd+giEG/X6XUjHL/Pw8uq4Tj8eZmhyF/3QHrK2t8cYbb3Dy5Emy2SyBQEDgVppQ6uRypZFk06LbtXA4PAwtQZmQBr/S0EAqVPx+vyoScmgmZGg5lTAoYxwkiO9yDSgUMxi2IYZNY2D12NndotGsMbS0kRJmU6lPstksvU4X3bCYSMXpdRrMTIjPtb0rXKh1m4Px8XG1lZP69c30Hl2vSF0MR6PYpGLC76fRalIo5HA6nYTDUWYCU8oNqF6vY3fYaXY76Gh4/X6xMzF0LGugSNFSgiYnvYVCQalP7jVIkLGz1XKNQjYnJKWahsWQqYlpsWucPoB/pKl3OBwMEG49WEN6rSYhn1dlCd2/zp85LWAu06DZED6SyUSCfqcrYg1MA0u7S7MxLB3NNBR3UDZuEl6QxrnD4VDpqX0+H9rjc+NDGEliRnQFYGQLbypsbWihisLQphMYG1fTKanTs9lsZDIZ/EGfOnhTkzN8/kv7s23//C/+4r5cg1KpxNmzZ/nCF76giIxTU1PK1XVycpJer6PSsKQLs2VZjKWSbG5vYbeZhIJBuq02vc7INHFg4XC7sDtswHBEqWhi2EYtz9CmDoT0JZMqkVarxR98+t/+cKrG2+vt9fb6vtYn/vLHicViamAht7AyfVJu/+WgVaq85A4KGJmaeFTxlv6gkpRtGAa25MEDo4zQMu2uqMQ2mw27y4mt08M0BNal2TSGQ2k9r9Et5DC6XaxBj3xmV425+/0e+XZdWdBvrC498OFWV1cJBoPs7Agvu+PHj5PL5VhYWOD06dOKzS8KX49arUalUlKvYbPZVGKc1OL6PF6hhqjWyOzuMTU1hTmyiheFTXKabHR7AigNBsJKlH9vsNDDwnTechr6/yWm9vYU9u31P8CyLEtBV+VyWeGcEi+URfCuesbc91/ZwUvTCF3XlX+hlKV2u11sls0kmhpnbHKKXrujtjPtXg/DEttfTdcYWhaGTSRv9XsWhmVhA7TBAFO7C66ahk6z12U40Bn0ug/duslJlAgs8SruYavVUjberZYY+R85ckQx94fDIceOHVPE2FQqxdbuziirVaM/kiy1my2lq/SHgmxtbxKLRWm1WhQKBaKxsHKmkERbmYXS7/e5c+cOR48e3f+m7y1qb1X8vtO/Pez3/CBL4+0i+Pb673r99b/2CRx+h+rs7nWSkW5Vstu7Py1Q0mrkwEbkf5fUvR6Px9XvNU0TW7PVwe5woaNjc3qIJtyE42Iq5bM6Cufo9gaYo/xW025nOATdMOlbFo2uGMm3Wi1qlSq+sTipVGo0dOgCW/s+YCQSYXNzk2AwqKZE0uMOIJ1OK75OuVwmEokQCAi6irTZuXbtGv1+n1A0QrVRFx/qnsjLUqlETatQrlWpVEvcunWTRCJBOBxkd3d3JD8K77O/l35n0ob/O66HFUL59+/UKf6wOsK3i9/b67/j9eu/+vfQE7rK9pZZLtJebmJiQhU/uWuTnoKtVktkIvd6ivAsKT/3/leSt3Vdx3btzRsEAoGRC0oEwzBEIIkF1VYPw+kl5A2qXz4cDun2B2pyMxwOCQeC1BsNukOYWjhMawCGS8Tvybbz3rW6uqomsTLhaXNzk8nJSVqtFmfOnMHj8bC1tYVlWdy5cwebTZg4/j/svXmYZFlZ7vvbY8xTRmZEzjV2d1VX9UAPAg20gHJUFNDjdQA8IFc55+o9TkecERzBoVVQL1zto4IgKud6HMARuSBtF9BAD9VTVVdlZWVVzpEZ87RjT/ePFWvFjsysphtBPc9lPU893Rk7Ys/rW9/wfu8rE8pRnFC5XGbQd9A1jUajwbFjx3jggQfIZ7KEvR4zMzOk0ynV1O16TqQ1SlfutWSDsW2bQqHw+Z/WM8DN/cgP/Vcsy+Jt/MY1v/Oshgavfs3rOXbsGCdOnBiroH3LN7/qX7jzL+340R/9Kc6cOaPo3DOZDDuVdSW+NTc3x6233kqj0RB9uEOgerlcVkUh2bGTTgl5Q/W+DuEgUclVSSghiUxBsOmkUikVsciKoMSTNZuilSyXy6n0SiqVwg8cRaUmO4Zk1Vl6H1LESDLpGIYBoanSKRItkMlkaLbq6DoKlmHbNu12Wwn6hIGhCjuyYOa6Lp4/wDRFVCXJDSTuTebhZaeDLBDU63UFC5MiY5LAVzLFyL5eybI+Ozur7ucYZnAI6RIwMB899CLpL0/l0aVxCsNQ0OQN+flktHX40DFlkGR3l2VZ9MORUmHUsEnaLHmucu7L7hVJviBRHtHnIxmo5T/ZuRKGIebJ4zfS6/VImH0OzQq+sX7LYaJQpDEsBgQDn1g8S8JMUiqVWF2/SrPXVeV+K52mN3AxE0m8WAoTk81qC81MEEvtFzw5cuSIqCANjWMUyR01ijs7O0xOTg6rOrZ6WW644QZ2dnZEqHrTaR4/9yTFwgS5IcK90+kI6qPiJJ1+b5gDyA1ZVGy2K20WFhYwjbhiwomySsuK27XGG177Wv7gj/7oGU36X33Hb/OWn/iRA7e9455f5QffNL7tN97+i/zQT/wUAPf8ws/xpje/ZWz7b/327ygjEAV8HqRd8u9t/Mqv/CJf+eKX0GrXKU9PcerUKRLWHSwuzqPrOolEguXlZTF5fZep8gxzc3OC6GCI3Jca1WEYcvToUZXDlbRi8sUXolcCziS548IwVJNfQrSkhKL0JKQxleJLkpLe8wWEQho8CZORLOLy2I1Gg2w2q5i5Ax+F+ZSV8o2NDVLphOKClNVr2YbZbDbxPU3Rq8n54LousbhNGPpqLvR6PcWqIw16s9lU1yOo5qYU8kAaCUkGENXF9n2fUqmkyHHlfdQ0TWl8V6tVVVU1TZ1CNqFIX+V5SMyiNMIikqsShj7xuE0mk1O9vL1eT/E2SqLharWqVAC73a5ivJFqeLIAIiEvUtI2DEPFMiRb6PYuVtLY9no9sWD0/R5W0sTXTXzNpdVriIfcDkmmMgpE2Wg1xcMq5LHtOLOFHJcvX6ZWq6KFIYuLi4Ie+/wTTE2VVDNyZWtj30RYWxMsGuVycdhjvCYasdfWKBaLTBanRLtLeRJd1yhNFeh2BDNGKpGk2+6wvbnF6RtPsbmxSSaRIfBCNN9gd1dwo03Pzgn9h+VlWt0BQSjCYzMeY/HICcEpaFpouoXrhXQHfVLpNPFYHMOwcAb+vvOWY6/xe8fb3j5cUXw0Q6yG/+0nf1ps+6VfHCPKfOdv/Ca2LbRUdNPaF9Km7o0owcVivPOdv8XklCCZEBq33aEYTwzHBS/QMTQD1/9fA8l8882nWV9fZW6uTDJpk4qJheapp54in8+rDgLZHiU7dK5cucLJkyfVxHY9cH2P9c0N4SG5A7Ug+6HwjDq9LloYkEglFYO4DIGkOJbEGO6V3pQAd/nsLDuhQPRSU0YQBPfV4um6LrOzswo6omkaoR8Q+DbuoIs+5PabKuTx8XGGBlTqaUgPTlJFCUyrOK4zEN5iEMYVH6b8JzkDpRRsIpFQRl1i4aR8ra7r1Ot1lVOX/HzF4hSzs7MUCkUKBUHvJVJDA2zLoNsSbXPi3EWxsNNsoemCzafdbrO6uq46UqampjAN4Q2m02mmy4JJplar0e30qNVqYwZ2dnaWtbU1xSEgK7/yucn2O8lW5bquwgRLGrNoS12Uvdv3h1AZw8K240N6M0F/ph1ZnAnL5TInTpzg0UcfVawVg8EAQuEBep6n2HuFVW7Td3qCpDAMuXTpkqq+7FTb/1pz6V93XMu+XCsnF80TPl0f79MdJ4SffPNPY5qmakC3LEtJl9rxDP4QrqhpGt/yjV/7eXb+bz+OHjtKMplkfmGaY8eOUSpMqbBLenSSNinUDAJECCYBy1J6McQkn8+rUGhnZ0d4SLGYyh9LsfdisTgKkf1A5XtLpRLtdltBKOSElEZStk51Oh1MSxgEKRwOqLBLavZKcTHJYKzrOkbIWEgoAfx+GFLZFXROkixAkhPLHt/BYKBSMTK8lPRbvV6PRCKhxL+VxxmBgcj702q1BAfh1pbS0jh2TISgMsKSzQPRxUcaVZPRPWm328qbdF0HO2Yp/GAUP6vrOoGvKcZ13/eVLrT0QOX5K29siO6QBQuZy5P3RfYqy3OVqA1JGiGNprwGqein66YK06WnK7137cYbjoaHDx9mdXVVUd1LMZ5kQhQeGo0GjUaD5eVlbNvm9tufw/LlS+pGHDlyhPPnz4uVenV77zv/5fH/0/G2X7qHn/zxN4199oIXvoD5+XkSSdEJ8Iqv/QaefPJJtYKnUinK5TKu61JvtjFtwRqcz4s89PT0NE8++SQTxbLKXctG92jYB4zBmRTuSzcUqSigwiiZ15IGRE4kmSvUjUAZVdu2lUqaxKVJ4yblGGQrZWbIyyfJhuV5eUGAF4jvy1YyGZ5Fw/xUKqVCW+nBSa9IXnuj0VAtoFKYK51O02g0WF9fV56hTPdImvkoaalkS5bXDKi2tqRtKQZ0uS9BLNshFrfVuciwXLbC+R5KTyXKxiIps+R9k/o9MucqBZSiuVnJSpNOp5VXLiu5Cqs83Je8xyPdFl2FwNK4y/2aEhUv1dWnpwWV+tWrV7n7RS9RknISuydbk2QrTCKRoNfrsbi4SLfb5QpfNoBfHmIcxPKRz+d54IEHSKZEoeLs2bOqb/PQoUNKllWSqvphoIR1pqenlaCWJA2QC7RpmopvTxYE5CSTKmWe5+EFgkZNGjI50WS+SibfAcWY4rouA7erChhSUEfmo6SnAsJLjEoTyPBUeoWqgX84OSXnnsxxSo4+acCkYZfytNIzlHIM8ljJpOgOqdfrLC/xf/bWAAAgAElEQVQv0263FZPKwsICvu8rMPDOzg6pVEp1QIlQWlO5N8dxVPi7u1sBz1c5Z8kLaJom6XQStFCd79j1BQG2JSip5LOQOr/RgpU07nKf0nDJhUmG7rJQFO3Rl3/L78sFAlD7E9dmjfUBSyyxpmloxw7PhbKtZ3Z2VshTDl1QQkOxx8pKXLlcRtNCdnYrlEolHMfh6tWrVCoVLMviLz/091+6GfXl8b/8uO3220gkEhw7foggCJidmiGfz6uJVy6XlbGyYglM21I5O/myx2Ixuj1XwZbkBJMGQTJ+yES8LFwEQYBlmCrcAlQxRIZGUjh7ZASE9KNhCkNjmiadTkcl+uX5yHyiLJ4pvjk01Z4lz8NxHKxYjIE3yl1J4g7pQckCgLxuyawkj6WqwkOBMECFdplMRomiS4YUeX2O46hjSahXq9VA00UBIZPJqEqyVGMzgpEHJe+n8AJ7aDrKc46Kkrmui6HbygOXFWHpYUcr54Cq3EdTEID6vRRmj+5LLlby2LK/N/p7AMMYVaT3LsqmdD+lMtza2pq6WEJRPVleXlZVGLFzn1Q6qahstre3WVxcPLB/9mte9pX8/Uf+aeyzG647pCpLsjWl3W7jDanMK5WKgAo0m+pGxeJC81bie6Sa18b6Fj1H5IYkK3Uul8PzPA4dOiTovSdLhGGoaL5c3yWbzqAFIcvLl0nnstxy66388yfvp93toPkexw+X+dDfP/CFzvMvj2sMy7J4wQteQCotclnz5Tl836dcLqvwqtVqqSIEusbKygrXX3+9EuPpdDoYpliMpfcnGXnkSy6V2GTngJxoXc9XhQGZu4qCaiV1mgxXJYyi2+uOQa9k77hUCJRsyJIoQBq7MAiVJyp7XuPxOO4QViJ7YSVOTXpHsVhMhfhra2uqQCPDTSlLKQlWpWFKJpPK+5H5P/mvWq1SqVS4/fbbFSBYjIAgdFXYLwtEhiFCbd3XVRVahuICfQDuYPQ76Z3CsLU2ZCz0VODjYf5Nfi4NdDRlEWVvlvk8adiiEDZ57GjqI7pv+YxgZDijgGlT5hHuuusuVYmzbVsYisGo4iUJF4UbHpLLZ5WL3e/3lYjJ3nEQPEOSOa6uripN00KhwE2nTlOtVgmCkcCxgMzUKZUnldDLzMyMaLNbWSGbS1MwbebmZqjXq/i+y+qq0IX96Ec/SipboNly2N3dZXd3l2KxyMKheVbWNvAHLlPlWbr9PpevrJPOFbjrxS/B6/eIBV3gYAP48pfeSjIWxx0MCD0f3/VUviKVT6mXT0pCPvroo8TNGJpjYeUL6KkUjUaDk4cP8yvvvnds39/4ktv5i4+NSwjc82vvUrAh6aW0221S2QyJYR+27/v81q+/Tf3md9/zRywuziva9KWlJTVRs5kJpXl79uxZbr311lEYoetkC3lMTaff6bK5tsrZRx7i8UfOYidTzM7NqSrc9PT0iAYpn+Nbv+WVB1u96L17+cuFcYsbSnO3Xq+zurpKoVCg3+8zOTmpPJxgWHH0PE+FQ77vq+qjTHTLkE8SScgKoXz/1ITSDXZ2drAsS0EmZJJeTnKl9zs0qNG8YFTZT/aNRwsPEmsnjQhBqDBncrKbpgm6ThAG6nokGYFkWwFoNBrs7OyQzWZVnq9er3Pq1Cnl3TUajbE8m8TFBUHAgw8+SBiGzM3NYRgGJ0+e5OTJkyoslwbHsiycgctg4CqyYZmLMwyLmB5T+5QV8iiBgQxXo5g+UYUdrxyqwkPE05P3KqrkGM3VyXsrDW8UXyg9z6gnGR2j8xhRYkUNYBAEmKXSBAAPPHAGXTeVRyZC4SaptKisJBMpqtUqp06d5NLyErFYTLBauK6iqznI2J2+5Q4+xEfGPpsolVjf3saMx5lbXCSRyeC6A5449zjtdpu7776bT3/60wAUpyYoTk1w++23U61Wuf/+T/LI2Se44447qNWbnDx5iqfOneeRnbOUy2XB/oBBs97i+c9/PjvVGlcvr5BNJ5mbOcHy8jJLF5/ijufcxk6lycrlq9i2zfbGNqdvuZkrF66ws73Oc287fc1JnDDjBJ6GhkWoGwS6hmbYOF6XrB4nCAHNZOAGtB2HeDZLMp7ksUee4Ja5IprhEng9CpP7wda2uZ89Z2X5HJlMhmI2TjIVZ2NtncAUgNiv/fqXK9jAbzEygFsb2ww6DrGYRSITZzDo027ViMUtOs1dVpYf5/jx4wRBn1TSxvMCQi/Eilv0mw0++ZnP8OCDD1LbFcLVU1NTeE4fr9/jppMnqFarLC9d5IUvfCEf//jHWbm8BN9yzVumRiEdp1KpoKXTHJqZpdlpo5s2Rkyn2hRVwouXLov8TyZLNpvFtuMsL6+IMAlBXWbFfBWm9noC65nJZATcQdOVtyZDJUDlgSRVUhiGCkQbBAF+MJRLNYRx1Y24aP8MNEJPV4bMtm1lKKQBkTmyaAI/Ho/THfRVeE8gPNNOr0dxalJx8skChaSiA+h3HTbWNzE1nbgRw8gUCMOQhblFErEk1Z0agSvAy71EV4TVcYtkSixIcdviOTedFvk4DUWDr2kalqbhuSKPaCfidNod0Hxs3UCzh9VgXwhg+QOXgT7yfKOpAcuyCP0QPTQJPTBNCx0NP/AJPQ1NCwnDANAwZe4uBIIQTRMQMFM31HsvjV8QRMNcDR8fQgQkyBt5c0EQAjqEoKGhm5byoAPfG+4DxFlBGARoaAReiK6Ltl5T8phJbVzLMpifnx3y6aXZ3d3FcRyKxSJ9p0etLnjdyuUyk5OTrK2tKW3ceDzO53h87IU/SCrw2NHriMeSihK71xU9e33DJJvNk0ikeNnLvoaLFy9y+fIVstksf/InHySbzVIqlbj11lvZ2dmh13NYWlpSq4Nt2zSbTbLZrNDlXbpEoTjB4qEFscoHHjOz00pL1TBNbrn1JtbW1mg2A3zXYXNjg4W5Weq1a8N5/EBH1w0yWaFVsr6xRWkiz/GZG2jsVugPemCYxFMp/ABM20azDGYW5tneqZDLFUgnU6xvbu3bt53M7PvMG3RxOiGDrobj2HR7TVqNGgkjTtyyyWay+9IPuq6ztbXBYDBgZrZEq9Ugk01gYOP6DpomeBJDz8V1+liWjR6LkU4lsU2d5955O0sXzhOzDJWumCoKwzFZLJDPZXjs0Uf4jV+/R2CzYtcmkI2OdrstjOkwzEvYcba2d2juNjl06JAIc5MJwUmn2XTaPUEplZtQMAdd1zGGei7S05JGMBaLoYWoME1Oeglqluwg0eqvCqH0Ub4OIAg8hGMRqoR6NDelDBsjgLSsdgZBoPjuol7PwBOe18bGxvAYgSLw1DRN0dRvr4vtjV4PP3CVZ6ppGoN5ARyfmJjAsidIpUSXk6jujshFJRbOALQQ9BA816U3xDPKMRj0MS0DH195cZqmYaPj2z5hoCPJB6IVcwBDHzU6hGFIGAguQssK91Xgo/m3MBzh9KSXF+2skQbQdX11v0NN2M9RhXhkDAECzz3QI/QDF4Jwjwc4RAr87Ft+LGy1Wopnv1qt4vs+lUpFgTAty1Jq89IVjhIJHD58WJEs7s33vf47X8973/Pesc/e+jM/q4RXOp2OkLK7uMT6qmiHu3TpEnfeeSe1Wo1UKsX0dIn7z3xC4Z1WVlZ48MEHOXXqFJqmUdnaVjJ/8qU7duyYQKz7ntICkaQKk5OT7FR2SSRESCHDfske0Wo1yKeTfOQTZ649k788vqDx9p9/i2pU7/V6xNNZMtm8MkgyVHUchzAwlFe31wAxZN6OVhBlflgLUWkbGbpGQyhZJIni83Rdp++0VUElSq0UNXLRySonahQGIwV8pCSj67qUywLcLkXFJXwsn8+r83RdoSwne9+nJ6dUXrDTEQzQ0qDIHLdSiSNaZPD2FRKiUJIotEfeO8Mw8PzRNcKI+k7XDIWjk8eJDj0S5kbZWeS9ki10eyFGMiyV1yWNtVxYomGuTC95nifIcyNFrei1BvrI4MrzD4IAWzf2GUB5PabjuGQyOV71qm+i0Wiwvb1Np9Nhbm6BXC7H0tISa2tr9Pt9dnZ26Pf7Y+0mxWJRlaMP0tHwvf2iSJWtHU6cOCFc9UKCsw+f5cSJE9zxHKH3cestt+F5HosLhykUCmxXNjl8+DDVahXLshSB6vXXX8/ly5cZ9EXoMjU1RavVYn5+nmQyyec+9zle/NKXMD09Tb1eV32PjUaD6Zky8ViKbDatKLhk0nxzcxN3ELmWa5EfXGvbtUDQ7PnutQSI9m5/tgQIX+j57j3Hzwfm/gLO147F6TsDkskUpfI0bqirvFf0Zc/n82hYypOLGkgATRuBlhXsYhjyAspjiiqKyc9l+CrbwQBVaZYTWIWLkeqzPDdgVB0eViK73a5KA21tbSmVxLm5OVqtFhsbG8qYVqtC8kEm7mVvsm3bLC4uAmCEYlInErFhKBkqOI3j9NT5yZykPCdZKd1bZd1rEMMwVA6M6D0W90CPFEGk1ye9JUDl6xTVvD8eeUjjFfUW5fHlsxKGbfSSBMEIFhP1/sQ/Ddn6J0WNDMvCc8U5hEFAMNynpw17+91gbD+OPzoH+ayVAXRdl1QqxVNPPaW8vSNHjgh+wH6fcrmsePeOHTumSv3NZpNcLke/36darWIYxoEEAm5/P9//E489zOqVS1Sr1UjezuPCOUNRlc/MCBrwjY0Ndna3uf/Mx9E0jaNHRSdBqVTioYceEiHZ5CS5XE6xS8uLP336NGfOnOHYsWNsbm4Khbt0muuuu04UKHIFLl26xMrKsoJR5HIZ1tauUqs2xMlGJ/5eKqq92/aOa3WPfL7vXotk4ZkYwmud70H7fDpqrmdyPk/39zXONTOkspfGxtYMJUIlDYJM6LsDX1VQpQcoR2ROjnE4yjBRtbANj7MXdybDzajXubcSKXNeMOKQ8zxP0bjLFE4qlQJQIkESjLyysiLExYZFsUOHDinvUr6j8tgwnvcKXR8IabebY9VMXYdutz1W5ZTDMEbXEC3MSCMp72XUe5JkGrFYQu1PtI4Z6nlIj0reP3nuB43RAqUp7KPv+2P7APD8wVghQy5E0W6c6DUAGKaG6zm4nqPul3ofwpAQcA+A0GgR71NhFYffM586v6RoqTRNo1Ao0GqK1jbL1hUrs67rtFqtYW5pC8/zmJqaolAoYFkWa8M+3r1DO2AmfPy++w68ec9kfIxPPuvfPMnyF3y8axqJzycf+S/d/xc6rsVbeNB4tryF/8Jzfe8fvpcwFmKYNppukBh2EQDKMEWrqqY5TncU7e8M95yHzAGHYYi+p4Ngbz5Qfl96VcrLI1SenTQO0UKHrA5LELE01IIyfzOCWmiqyu6NN9441iI21ioWCeHGJ3wA/kB9X3lbYUhIKLB3hgZo+3K/A8cbM3DSq45WVoGxBUVc46gIIe+zLDTIsDrqzakQldGxJExH7lN6WtH9iWvwCQJ/372IPrPo98eG6x/8ua7hhaPfSvgSjLxWmWaI4j5NuRLKk5GWe3Jykk53pEeQy+U4cuQIV69eVbmyc+fOqX5FiRHcO66uRIzPFyOUfCah4hcrlDxoPBsD8EU2bj/6fd/LLc+9i4EP3b7DZD6PoaO6CGRe9jt47bM+13t/73d543f9Z/X3e9/3x7yeV49954Mf/Eu+lWdIuTX0An//D36fWFxQjenx4UQG/AA63T6mMd6uJieTpmmYhjkGT4l6gF6wn6xiMBiMeWpy4kX/lmGdhHrI/YqJOpoDgAr1dF2n1+uNCXLJkFWec7lcHoanjmockM0D8riy1Sza2xr1PkfhbIDBaKLv1cwQRioYu8bhFnTNGjNU8nrldUU93Kih0TAJgxA/EN5fGIb4njc0VvsNoAplGXV+yHsm9x2V7BwLa/EJAlftQ96DKGBcjr2GTvNHC4UM1eV+ZGuh7N+WMCYvcm9lFX/UGqkLbd0rV64wNTXF3NycotPpdl2ePHeOqakpEokUuzt1JiYmaAxqGLEYdjJJaXaWQd/B9Tx6zv58X3lmHjj77ELJvYbjmfz/s/n7i00q+myNXPT6nsVvu60d1q5cousGPPL4Oe6682bx8uo2h4+dIDNRAu/aLDZPN6LGD+D1r3v1vu9867c9C77B4T0W4OA4hOMVPolZk8WMWNxUq7Y2TJAHhGiGjh8G6KYBEc/GNq3xyRGMuB39QPDmyckfhCGaHmIMJ79hhAShT6tdAwRuMJFIYJFQeatoslw6AbIKLcNq5akaGhgGtpXEH7j02y2c4bEb/a4KoxN2jBDodbsMfA+0Ub5M10wFiDZNE9+Nrt6jl8Q0TbyhJxM16uI8wTeHFFGmJRjbNQ19aGA8LwBNA23YV6wJTr/QDzHCPoHrKcPieR4+Ic6gh21baEFIIAsRuvCq/YEwcPI8pJcczY/K85QLja7rhLqGM/ytSjcMNX9830fXDNVNohuaSkVomkZ/4CoweLvdGnvlfD+IFFPEQpFIJMjl8uAHTExMCMRCIq7umTk7O8vExIQSNjZNk4WFBdLpNEtLS7zgBS8SRQFX9FDWajWseBxLC7nuuuuIxWLUajU8zzuQQ0+T1vyZhJJP95kc/9Yatk93/C9WKHzAfv7bd/5HAsOk3+8x8DXuuOMOtMBhaekCXSegWJpjfuEInUZd/eav/vJveOWrXg7AH3/gf/Dq1zwDoN4B493vupfv+d43qr/f//738x3f8R3q7w984AO85jWvOfC3nucRBiOAvDQsMtQKNZk30sdIBIAxGjEYtXqBSL5Hw9togt8PXEzTUJV94QmM8m5y37IXVhpOy4qr40tPL1pkkEOyIKm8mDMqmO31lAhDZL5feo6GYWAbOgxD7qgymq7r2KGN3x+PpuQ+DcNDGyb7Y7GE8pY0TSPUNbVIeENvB0DbA1oO/ZF36IceYRBgMgJsN7tNda6iw2TkocreZBlOyvxd1OApjyySd5XvgjTymj5ScJPSpVEmILlfCVIHCHWNgSfyoMNbOzZ0XfyThBpzc3OUy2WymTyaJjrJZJulXIzNYrGApoUcP36UeDzO8vLyWIl+dnYWwzA4fPjwEC8niCJTyQz1WpNGo8H1119PvV7HPYBDL5cr8P3/5/fQ6/W4l/eMNhxgKL73jW/gXff+wfiHe8LYN//4D/ML/Nq+3779597CT7zl5/bv9IDxlp/8EYrFIru1KrOzs6pi2O12sW0bx/XwA11V3e7hngPP+9577+WN3/3G/QcYjv/yxtfyO4y4A9/z33+H1dVVPvV1n+LidRdUsj4IAnLPy3GGB/dfN/AT/8erGQQazsCj1e4wUZohlUxQKkyxsnKVFz//eZw+fZpEKsnz7xwBuKXxA+g7Xf78z/8S13XZ2dmmVquxvb3NO9/5TgB+/ud/np/+6Z9W3//rD4ueblk1ffe77iWesOl22/s6fra2trj33nvRNI3v/q7vHtsWBAGB7+6rSEqyUsMaN3LSI5DH3rtNTi7XGeybbApyYYDvo1qsZIglc3Ey1yiJZWXXkey1lbk96alKAxmFZsj/AsQj4HVpFKIepCw0SANumiaaaeBH8nvSwwFhKOLJEdO3fEdGWDnGDLAqXhDiDvOcMm2gCgxhiB4GyrPaa5QMzVRdLNGikeM4uF5fLTSSwEFul3R5cr8yDSD3K9IyLr4/eqVDEPjlcNyIaRoYhk4+nyeRSJCybSbL0ypV0Gg0uLh0CX+/iiYAiwuLLC4uCqo4e9SH3O8PVA1DhuryWsyHH3kIz/NotYQ7eeLECXbWK+iGxuzMAhcvXlRUN7KK2ncHWFYM6PEVX/E8ms0mExOTBwocl0rTip318419xg/2hcu/8Mv7jR/wjI0fQIiG5wccOXxUvBymRiKeJAwgm82ysV0hm81y9epVjh49euA+PvzhDz/tMb7lP76CXm80gd/xq79BGIpkcyqV4e4XvUTlPXoDB10POfPpBw/clxvqYFoszB6i2u4q7jcnGcMwLGrVBq1WB9ffn4MF+P7v/37OnXtCoflnZmZU5VyOvd7WzMyMog7v9XpcvnyZbq/Ngw8+uO+eSFbifD6/79iCVEMYnb1QF00bdWtIAPG1qouAmmSeJ3j9ut3u0BOKKRYYXdfR9BDbNpWRkB6i9GKkBychKJJNWcoiyGPJ57O3EBHNl8vvyiGNXtQwRQ2/PAcIGbjj1VHLMoeeqYWpjfJge4sk/nBBkcdWucIwwA9GRk5+fzAYEAYBoesojyxKJup6Pr2eq8hhZYeKXPwcp6P6jSXjuvRYPc+7ZtZFg6GxhgAwdLAsE820KJVnlLhRLpdTzRSSakx64v1+H9d12dzcZHVtQxnMgyrRly9foVLZpVqtc+jQIQXRCxlWzPEwh103kmLfXFxcVAfQdZ0rV64oDNPW1paiA5JaGd1ul+fccTumFVPWv1AojL0Y0TE9Pa1esC/2+N7v+S+8692/M/bZm374h7jn14T+xo/+yA/zK786bjDf/rZfIJFIDEHYAqIj+dGmpsrqBWm1Wpw8eVIY9T1z8s//4s9pt9vU63X2jjf/2E+RSFoYJmxubvKmH/wByqU5TNPm/vs/OfRALBKJUc+wF3q0uy1+5q0/CUHAz/BLY/t8zh3Px7QtAt2iPPCIxZN4fkjCMjlxw434mkGtVoNGldd+x+sIPIHV1HRTMXvIVXtra4vNzU2CQKjyvetd7yKbzWJZFh/84AfptIVn9tBDDykCSznBkqk4d955J7lcjj/7sz9T/bq+74+xJ0dHJpNBY78sggQaRynLoxVAGEFP5CQPAtEj7roC2W+aJo7jDPM8OdWXbJgCOyaNj+yUkGGaLJbIfY73r4oRNdJ7uxjk5FOwjkgeLhoCSyMrvaqR8YMwDLAsW31nb8Fj4I/Yq+W+Rh7fKDSXnpbrunhhQH/ofUk4kHzuvV4Ppy8MW6/do9PpCOB1q06z2abV7OANXSvLHJG3hoQq5BT3RSMIwn2em0w5xONxksmk+m+xWCSZTFIoFNT1W7aNbceVUZUkqBJnLPGU0vOuVCqsrq6OOVEHLZTSOK+srHDbbbdx/fXXY5om69vinQl10TlULpeVs2Zqmk8YuuTzaTKZjAJSrq2tUd1tYBgidtY0TfUqPvnEeW44cZpEXIjNWOYIfLnvRbeSGHoMwmsbwHt+6RdZvrJCiMnk5CTZbJY3vWlIpLknH3bPPfewvr7OiRMn8H2f97///TSbTWq1Go1GYywPmUxleOtb36pU7gFq9SaWHR+uCtrwtw0qlQqDwYCpqSlqjQbNdoterycog143fr6PP/64okPaO0xL58Mf/iuOHT/MV3/1V7O+tkU2l6ZarTI9PQkImvder6XohsyYTb3Rwh34YzkwOR49fwFn4FIoFElm0sQT4qWxMNE0k91Gk1ZvgBc4LCzO4Q96ZLNZjl9/Hb7vc/78eWKxDKlUimwuSSKeUV6bbMMKgoB4PM7C/BFlUHK5HIYhxHm2trYYuH2SyTiZTEbBPGRO6KBqpXwntD35DllI0HWdgHAslAKUQZS4NelB6bpOuVwWRQlHMMNEq4+y2d4P3DEPTBYhAOUZRcNm+b2D7v1BI3qdez2RKNhWGjcJmYlWYUPNwBmMHAbZQxyGAUHo4Tt9lReT5ymr/N1uWwGDo8JAvYFDtyeY2mUVNAgERb0fcmDoOD03hx5LE0/oQvSMkIEXit5a3UbXQzxPRA0LCwvMzMyQyWTIZDKKC1QaY3msarWq2g27PQc/EAqSyWSSdEa8V5gxZTBVOsQQmjyymFKv13niiSdYWlll4AZouk14gJMlh++DhkW71efP/+eHeOlLX8rdd9/N8593F64/7Jvu9dS75zgOZrMpejATiQRra2u0220SiQSHDh1C1xKKO2x1dVWJDL/kq16mbq4EsUpK6r3jzJkzY1igveOnf/KH8XyHcrlEIjWBbds0Go1rXmQ8keKWW29je3tbMAd7Ps7AY6I4xVRpeuwlnpmdH606Q+EU+fLHYzGK2QmKE1MA3HjyNFevXiUMQ8ozc8STsTFmkOiI6hnsHRcunOe5z7uDyckJLq9cpJCfot/vkk4niSeEYE0ylQCE5zE5OUkskYKlZdG50N9/vOmZWfqup0IGmVLw+wHZbIZULo9m2cQSJtbiDNXKBr7vU6vVxLUOe2AlzlPDUqt0Pp9XJJWSj67dbtNoNFSvrez6sX2TIBixdkgKKGlUDvIAdV3fB9obA/8Ow8tkMqkMRtRbkhNDfi7vu2w3i1aWVdEicJUHGPWwol6m/H7Uw937PK8VjkcNmTDwI4BxtIgSPWYUkiOgGeB64z3L0lPrO12cTlstwDIF1ev1hv86ygA2Gg11v0R1daDowWQuzTAM4skURjIvnqlhK5W2ZDqBFoI24bC+vi7yZEPPzw98EbuiU5wscfsdX6HkCfr9Po2m0D2ZmZlVJLWZTGasFQ1Qi5tkJrIsi0AbLXBBECgmHwnDk9omrVZLiKdpGoJY4dpDQyMchmutdot//ud/ptvt8twXvRBrCPQWeVh3BLB3BwPO3H+/IDOwbCFKE0LoeoSmQ6NeYWpqCtsImJrI0O/3eeCTnwBNvLTJZJJWq8XMzAyzs7P7TiqbE+wWsViaH/vhH6TVbCgqJZlzadab5Ap5shlRpYmZBn/0vvfzWllpHL6Hv/wrbyfw+iwvrQu2mr5A8hcmMor733Ec3vV/vxtdh2arysTEBBMTJdUv2ul0qFS28IM0a9WG6nTpOg6dQZ/77ruPXq9DNpOg02xRKpXg+8avqdtzuOGGGw4M+V/44pcIDQlTPMxsNq9eYEO3yWUnKE5Mks1lVPtUvVolZlrETAvf3r9QXL26Rq/XI58XL3Cob1IoFChOFjBNg/5gwKDbpbJeJZ1JMlnIk8nkFPTg0MJhYJT41Q1LeSXRcCmRSBAgGvYlv5wdiMS9ERjEU0nxOAYeBD5Ob4DrDyAGPcfFMvYbwNAyCBwPQ9cxzRGxpuwF1TyZmxs2+g9cXPG9SbEAACAASURBVNdXBYNQB0JDGV1N09F0TUBPQvE7+RyMUNxzwoAAbQj5AC0cTosgJBwaXgU3iYSvgAqz5DY1uSLYuX25StMiGBo8d+CNukt0TdwfAEMXjfwIvGEQ+vT7HQKfoWficOHCEhvrm1R2Nun1G8McaZxyaYYw1ESeLJunNDtDOi0iNtu2SafTwrvPZvHDQOn3RPOWsViMgSfIKHarDaq7df7xYx+nODnFTTefpl6tkHjsMa5cuUxtt4rvOiJ3a5oEgc7WVgXPC5iYmBzrrjF1kSce9ANs22Rnuz70WF16/SaVSkWFuKVSSRGumqat+va3t7ep1kUhqtvt4g18xe6zcvUyg14rYgeitFd7KuV7ttXqFc6df4yZuWmmp6dVWiO0bdKFgkjbbG5uqtDh6HXHsSyLpaUlNWGSySRr61cpFAq0W22OHz9OPp9nY71CvV7n6pUVgiBg5fIyR44c2TcBChPDik5K5LxCf0YpZsljSDLWy8vL+L7PxMQEQbjfX/c9B8/TKJcnicfjw6pOSHGyRGVrW4msTE4UxGQJYbeyw/knz+H7vnLbY7EYF586z/T0LKap4zi9YXhv8tKXvph2u8nf/82H8DyPam1333kEvksmnVSFo+goFotKWlPmT9vttvDauq7yWlavrjFwRWjm+z4JO6YM9N5x+fJl9aJLoZ5Br09LaygPoVQqMT87rXIn0pvzfX/ETmyaoBugjSrcUmBG9cwODaH0sAaDgeqy6DuOMGRDZhHXdek6bUIPCGME2v4FwfMCLFNTr2YYhmO5HOn5yFBa5n2EUbPHvDYFnQlDjHAUKptDnN7AH/H37XtmMr+o62MOadQARn8r84VyRP9feqLynKKwFythqf/3fR/Tio0fiwAt1NDCkPjwXYwnE5x78iL3nznDbrUK+DC8lzPTZV72Nf+BU6duwh2ISrVpj/Rv5bHb7TbdbpdGvcXa6oZSapP5ewkAzucnSOUKbGxs0G63+bqXfz2WZfGxj32UmXIJxxH63747ShuEQUBle5uHHnqIIAiUPGalUsHpOWxubtNqtYZRhPBcXc/FMAKuFfkdNKIA6L3PcNSH/XRe4GibfC7b29t87rMPiMhnWNQyDIPp6WmOHDmCWS7NkEgkME2TJx4/p8glW60WN998M7VajakpwUxRyBfZ3Nhma7NCo9FienqaYrFIOp1mcnLywHDx/PnzlEoldF3kGPpdEYOfOHGCtbU1pqen6XQ6ovAyUVSG7Wp1v+GJ2zaB59FuNlm7elWdZ2l2VujCVgWVUGVnS4lrV2s7pDNJoQ2bnOTIkUOEYcjRY4cJA0Ot9JmMyIs1Gg02N9Z47nOfS7lc5tixY3w737nvXCqVChMTE/s+bzabKmezsrKCFNmO2zHmp2eGuq91Op2WUvgaDAYkE4LKq93eT8N19913Kxae1dVVtre38TyPcrmswtednR12d3dpNptiFfVGFOELCwtjRs2wYuQKeWUUpVBOq9XC831FMhoEgQpNhbHRMDUd2wzQQigWC3jkiWVS6HqaYLD/+cetNIHXQza0780VSqEe6bFI43dQ+DnG7hKOCgPRa5VA3uiIdhe4Q+yhHPJ30rOLeoPRsbc4s3f/MB72SpKEqOGU1ydo6UNMEwYD8dv77ruPm266GcMQxihAGKCNjS3e/e7f4c1vfjOLC4fRTYNAg2Qmi+8Fih16KpXFjonr9n2fxLD7xvd9trY3WV5exnME1GtlZYWli8uk02lK5Sl2d3fRdZ1MJkPssGB2urx8Cc/pD42OhmXbfPYzn+Gzn/lM5KaEWOZIdlIMyaytE48nxrpEVKi+p/ATXYD2piSirZISxfBMhlzwHMfh8ccfH6VcIu9VPB7HTGWEiPPq6irHr7+BXq/HjcWiWvWPX38DjUaDer1OdWgMBfuFaF6fn5+nWq1y7ty5A5PIUmmuWq2SzWbJpjMq/pZQi0wmw/b2Nq1mg2ZDSAse1Fe8W9mmWq1SHJ7foYV5kSS9cIFEIsHc3By9Xo9CoUCjVqM/FKv2LYuYZeH0eqxeuUImk6HbbhOLJclms+gamJZBGFj0bZObbjqF74rJepBHducdt40l4KMjDDx6XZFDMw0N2zJ49OzDzM/Ps7OdH5IupASpQyqhErPuIFBg9IMe5sTEBEePHmV7e5tLly6pAkQikRB5xWRSEXVKSMHExIRCw8t+V8/zcFxfeV0SGOq6Q0lJ3VZhavSF63a7oBtYmoYb9tBCD8+N4aERtjsUJ238AzqBDDTsWIwgGIFgo8S50quLVkoVgHfPSxvFyoVBOBaWSk9DdZhERtRL0/d4gBIvJiegNIhyYkafgRxSxlFO7mj3iAyP5T3t9Uf3RDoIyshqAYQ6xWIR07Z5+de/gqtXr/LQww/TaG3iewGablKrNajWGhw9ZnNldZXKblV5d7JgsLOzQ61WI5lM0mw2uXr1KkEQKGGldr0+hqnN5Iscve44u7tiIX/Zy17G1sY62XSS7e1toT0ycAiDAMO0cKVEgGEQSK8uDHG9AMMQ8BaJs5T3InqPpAcqo43oYiaNVRSjJ/+W6QQJy3mmY29xau+CKkHe5uLQ4k9MTrK9vc1gMKDWaJDP5ynPzAjPKZUi1DQKxaII+4aTo1arKX4zqei1dxw7dkzk+YZVoerOLrOzs0rvQ5ItdDodlbSfnp4+0AOIJ2zK08IbbdbqaHpIIhmj1mhS3dnBMgzm5ubYXF8XYWepRGlyko2NDYrFIoPBgJXlZeLxOLOzszTqV6hUKhQKBWZmZlQ4CZAYVjsPYrkO3AGDMNg30QC6rSb9bpfQc4mZBovzcxxZXKBUmqbXc4bhqUgBLC8vc/HiRSqVCgvzhzl27Bjz8/P79zk05FL/YnZ2VvWVShGcer2uJqBMxsvq9+bmpvKKfN/HCyCZTon7GGH16XQ6pDOirzsWizEzM0M2m1Xg92QiQ7fdIGGk0TUX27ZZr1QIQrE4acH+Z+Z7g6Fx8xR4NgrslUY5avhkGA/jhme8R3R/tTXqXUSprSTeMAgCdNPEizTiSyycDK2jYOtox4eUgpTdCVLKMpFIjAGAZXFJoiesRFol9KOYx0QiQRB4JJNCW+T49Sdo9/rUW23SuTzV5ha6ZRGGIqf56+94J6lUhp2tyujm6qbAp+g6hmUhO0QEvEq8v6lkjuLEFObiEUoTRcHkXt3lwsXzuK7D7m6FXC4jojzPxRv0VRQin4cf8djDMBS5VQC0IXhZgHU9Lwp415VeiDR+8h7uTYPI9xJGMgRR+I/87kE2IeqxS1o++fzlNgWQH6YtZHogCALMVDpHcvgS3HBiiu1tIWup6Sbd3oB8YRLDMNjYrIiHGEsSBh4TExPMzc1RqVQ4cuQItVrtwCpguVwmnRYwkMFgwOL8gtI6dV1XSf3Zts2pG46rEn5pIrdvXxeffFKFEVNTU9S2tzEsEw2P06duYGlpielykUw6Tr1W4dLSebY2haJ8Kik0Ogr5NCsrK2yur2HbQmg58D2WLl5QhQHRSuTS6XQOvOnLl5YUj9rekUzE8T0Xa9gdkIjHhJTgoI+uC09BqKANhvRbOVX1/tSnPiUKSXukNaT4TbvdplarqQeazWZZW1sjFotx6NAhPM+jVqspbFytVgNEmCmZhnVdJx5PqhajTCZDpVJhfX1dhKLxtNL8kLALTRsSBwwGpBNJqtsXOHZsgcnJSfoDh2ZPp+900Q6o0rluC92w9nkD8uWVcBp5TdGQSXZyRL0KOWLGqIAhPQQ5ESQ5qsyFyoUVwI7HSaSSSoM2lUopo1Wv11WoJe9br9ej2WyK9MCwYUBWNdPpNFNTUyqEz+fzTE9PK3hKMPSe7Fgcw7Qwhvg6wzAwLRvfN+g7A7a2KmRzBQoTk3g+3PkVPWrNLTqdDq/+ttcShhrl0gyFQoF0OosRSyjPKLpY7AVAS5xdtVpl0HcIh5hAzbrEypVlgtCj73To9lqYhjDQTzx2lqtXrwp0R6fN0K9mampKETvIZxQEHmEw2Pdc5X/j8RELjqxiA4rTU2LxovokErsYRQLsjRDkiIbP8h0Fxha+vZV5EIZSUveZyXRKeQ1BEHCqfJqjR4+OMWsEQUBxapJ6vS5cy8CjXJjANE1mZmYUr9pBObFOp8PExISgvO/3KU6VSCaTqq/Qdd3h6uypyp9t25w7d27fvoLQZ7JYRNM0IZI9UyadyjA7Oy9u6vQMWhDS73Qx0JgsTKjwpNNsYekGO5UK2VR6mKcR4tzVndFxW/0uE5OTdPsjQOne8fZffce+z+R44/d8/zW3faHjW7794D7bfxdjf8Q+NgwzwDB0wFQeUBTq0u/3FdmuNLzSCMVisbHWqmhCPWaYKrkvu0HkPqVnLgsrpVJJof9N2yaWiCtIRKvVotPp0Ov12NnZUSG67HaQ+5QkqsDYAr6+vq46GCQfYLSVtNNzQDcxrBgBOn7gCw8Uh1RapC8ePnsWHw3NMEmkUkxMlUhm0rTqTTTTIGbFcTyXy1dXsUybEFNhJ6P5NVlwixoOVTTSdewwBAKqrarKjyWTcYHba/RIxkXl9YYbbqDVbFDf3VEg91KpNFYkGnmHfYJgxPgSZY6JeuUyNSENdKPRUB5Z1DuDUY5Vpi6koY8aM7g2u408R7lNFgSjx5MhslmePTZK1g5po7e220OL3YkkdJMUJoYKZK5LxwkJeh6GYRPPFAnNg3Nik+V5Bq5GvjCDF2jUujW2q7tMT8/hBxqabtDpNognDFKpLK1Wi+sWryeZTAK/O7av933wb55+tn2px14OwIM+j277QogRPh+hwhebyeZLPIyBwcDyCfAJ8dFD8H2XftcZFqRNLNOkND0lKKKSQiXQcfv4zoBsJo8RwvpOhV5PyKc2mnWa7R7tTk95QI1Gg1RCMDxvVNZYWFig1+tx+PBR5o8eJpkUoP2+o+EMDUQ8buMGBrqhEegeA0MnncuSS2coBEKPIvSEASDoimS/bjI3v0jfC2h3+2SzWTTfU4LofggBoxxqsy08x2Q6TTyeJJ1Oi1xdPEY+blNIprlQKHLfE5+mPFmiXq+TTKSIWWkIdFYuXyWdzgJRwtQRG000LQDguiOiUdME2xYGQ3zXIiTA6guPtVGtYQY6g1aPVqNNqTBF6eQ0C/PHePjhh1lZ2SAMQqZm5pg7dATTNBWkRaEDtKRatCTFvrQDOqOcoGmaxJIjGq3o9YwbsgA7ZkWq7COZS00ziMdSqkgnMaoy7JX3PMpoLbfruqnsnGnqanEwjxy+bsxSQ0RXU48wbPijmafrOjFFRxSgIdzhsYcxnMTfGX7n/lnxTLn/XnvAd/81uf++0PFMDd9Bxu7z/XYvldi/4/HXf/J+msGQXt4UaQ5N14gZcZKJvMq/ybxMGBgYpslkcVq8c25ILJUmmc1xZX2Dd/3u79HrtnnlK1/JK1/1TSoslTmiQqHAAw88wMfu+xhv/bmf5/WvewNblQbf/Z//K74ncH+mlSKZTOH7Hq7rkMtnhq1eAXpMTJJmvYHT7TFwHGo7uwwGA7Y3L1Ov1/D8kAfPPs6Np07zFc+7i+npWdxeG8uK0et1sKwRSWq722F+fh7DMOj1ekxNiDz0XKmEroVorosfBvS6XZ548jEKxQmKxcmhQdEolOfI5yeV1xNNBRxEcgqj5H8UMgQMRZ8EBEqGma4zYPniJY5ddx2lYpxSqaS0fzY2NghDDTuZJpMrYsfTIsqLpRShg2EYxOPmWIFNgrAtyyKdSFMoFITnLVl8hucTzfvKcN33fRGW97sKHiXRD/K7A2fkZEUB5HuRA37g4w6V78JQGztG1LM3n3jiCZUHkQwS3pBRIjlUV5NJTBkOm6aJO3DQNHHAgevg+0IoWTyF6BPh2h7Rl5r770tpKA4ytM+WDmuvR/lvSfP1RR7f+W3fQO+bIZbOEPN8er0uzZpgFHd9n0ZDMHMkYpZ6x1zXFYn7VApNC0in0mzv7mDbJu3egPLcIheefIKzj52jVJ5hdXVVFVK2t7e56aabWF5eBnQ+/Bd/g4HFoOeSjmeIxUQ7ZyKVwXEHGEaMIIwxGPTRdY1Go4GnCRjG2YcfYWt9g3wupzjyNAJK5Rksy+KRx8/xuc99jmw2y8ryEsYwHD19+jTxrMn25hq7u7ti0hu20tjVh5jXIPDpu32SsRg7OxXicVuF+ocOLTJVnubso4+wtLQ0wm8y8piiOa6DDKD8r5zsMAwrNR1NRxmwrueJ+6XrHDl6Qmk0r62tsbW1pZ5HLpcbOwdpdADqdWesAi47PSzLQg9RxZ8ovAVGjNyyCyRacXe93piHK/8/CIBwRJm/95qjC0QUmiSdt6i3qeiwJF+aTOxKd9Y0TbR6Q9F/x+Nx5faapkngDZH6oY8xBBNLg/glpWV/NuNfw1t6OiMtx7Wu4wskRv34P32K9/zef6fn9vmu7/oufN+lkM8TBgO6reawET2LhoEbhKTSafp9B2fgkcxk8fwOhqFhD0MaghBv4FLI5bi0dI5GbZd2u00mk+HVr389H/qzP+MV3/zN/PmfvZ9EzOA33/EOXnjX3WSyEyxd3qDa9uj26qTiNrPlAotz8/BCk7NPPMEr/rfXsnVlFd+1yU3nyecmCHWDJ889RRAETE1O0G7UBd/kxIjHLZFIgG5Trdf4xCc+QYiJ64XE0hkuXLjIxYsXCYNx+vXPfvazYhIEBu/7w/ejawb1aoNWo4uRt2k0mgyCbUzbAC2g2azT7/cwDINPffoMmzu73HXXXTi9Pul0WnAFDvGGpqkrB+HQoUMCFhQGfOz//SjLF56i2+5w8+kbadSqNGq7oqKeThGYaYrFIrlcjnq9rhoJfvu3f5Pbb7+do8euY35+nmazTq8nOojqjRq9bp/SVBnbGlFzKUNjjHKohr4HemaMPMBEPKly2JZlga6h6eC02woG1Wq1hJzF5DSVyhZPPPEEFy9eRDRI6ORyGTLpLLYVU17U2OEMK2KYQ1x3gOMMixqB6OyQLZNRT24v72N0m2mOoFjRXu0wDPG98fA5agyjEah01kQobIwV4WS6AoaiSNLDixYxLMtiEI48Ph0NwzaxUgk8z6PjCNFt1w3RfZ90PIauwz/d/2m+8gXPHd2hZxOSPpPc2bMxiv9eQsUvsmd35coVgtAnlclSnpll8dACW+sbJBMW/V6HRCLBmfvOkE5lsRNxgjDE9wMSyTRh6FPZ3WFne4uYZXHjiZMYIWTLGSqbWzx+9lEur1xifn6eXC7HoN1W2MRXvvKVbK2vcffdL+H4setJZyY4cuI5rG618bwmtgmHFmaJmQb1epO17V3u++jf4fU9wlBjfuEw5dIMXggTk0LBLxaLMbG4yLlzoltHVl0BvMAhn89zxx23sXTpPAsLc0yXJ0kmk7hOh2DIw5ROp5mZmSEMQ65evUqn0yNmJwhDIfbzgQ+8n0QixZkzZ/jaV3wdp266cdiqCGiCQsp1HYV/09FwwlHFUtd10coWhpgxg9myyNXFLZNiPsdOIs5kPkOvVefcY2eZnipQTMcwzIDCTEmA19evUtup8eTDDwv8ngYf+cjfc3prkze84Q2Yps6T5x6nOFnAC3Q2NrY4ffo02WyeSqWiunIcx1F4O8/zhoiCkfaxLBYEgYthWNh2fEQvpYcMXNFnG4/HCQNBKRbvtglCj0QyxlSpyNb2BrtVgxB/2HKXA3QFcB57tSMFkL2FBlOzFFWZ9GSv1UMfNWjRsRcBsNfwPZNtUtVuLyA7CALMQ4cOASNNT8kSYlkWlu8S6iGuK9SvbMMAd4Dh+eTiBoQOtiW413Z2hh0XlV3+8H2/y+sYp1h/RkZAg9/65bfyffzstX/zhYaYkfGWH/xBfu4d167kPtvxD//4txgxnf/nT/6IzYtLBKHOX/7DSPjpfX/yP/hP3z5iY37DG95At9dE/w4f/XU+tz/nVm65/iZitqhOvug/jKjnzz7wKTrOACuR5sraJjffegv/+Hd/Q6dbo5jJMD2/QLfXpzdwuHLlMo+cfYjQD8il4szOlugNHOwYuG7IdmWNWDLB1FSBbC5ObXuHD/3V/2T9ylWuLC1z4vh1nLjtOXzFi+7m4x//OLutHr/xf/0O3W6Xj93/afrtFsV8nm961Wu44aabQDd5+PHzdLQ1WnWdbCrF/OJxTp28HnB54P5/wojB3MIRVta2GQzq7NbWCXUb3YK+59DrNwl9k7/527/k+PHjpFIpzp07x/b2NpYRY2trC9u2eMELn89NJ44zUcjhOA6tdleFeLLVz7ZtrrvuOsLAI5NMYRgWvhdSqzX43ANnhkn8GrZtEgQ+YcgwbRMgKaZkpVGxBw9ED3lgWmiAqQt4TKtZxxt0KaQTHD2ywMf+8R+pbG5w+vqj3Hbqep469ziXzl/g9uI8XrfN5uYm/W6XVCpF3DTJ57L4esDlyxdZWrpAJptma2udj3zk7/FDA0NPMjMzRyaTQ+rySq9IGjTTNBUjkWEYqkAhiS+ioOwg8NBNDc93CRyxsKxdXlaqjh/96EfY3FxH0zRWV6/Q6bTI57Ncd/0xJiYmx/SHoyMIxjs3pPep62DqoSJJka2wBxnAcWM1Mlh7t4njjzzCvd6hPL+9BtDzgn3blAGUOgey/C+td6PRoF65pJTgVi6t0Ov1RP+f41DI5el2u6RSCTKpBDs7O5RKUzQ6NczkKD7/T9/2dbzvT/8WgB/431/NO3//j5/WmNQ2lp52+7MZhw8tcHnl6r7PLzz2EN/21S/mT//x4+qzV3/91/PHf/3X6u8X3f1V3PeJjz6j4zRqO/S9Lrs7WwKAG46vklHjB7Czu8Hi4jyu1yWbjzE3O4nmOrTaTVb2nK9uiP5Kr9tju7KBad0KmvBYilPD9kPNYH5hkXq9jucGbG9tsOZ2efyJs2DoLB46wuLiIU7fdAvxVJJ6s0atJv6122221jcI/YBOq41mWBw9foLzF0RfthVLEcckCEOKhTnmyiWWL61iWSkmZmaYnCyRKS2gByGWoWNqHv2ew/ql82hOj6SWprq7Ta2+RWYii+s6BBa4fki2kCUVNwh9BzQP3QhYubLE+sYVce1ewKkbriObSzMzNYGmhXTaLTTfZWa6xMAdVR4lxMY0TYLhgq2h0+s5dHtNnvvcO7h8+bIC3m9t7bKzu02xWODy5UskkjGV20qnUrTqjbGCArqJpoUYmuA4DMOQbCpN4eQNLM7OcGXpErh9nH6XuG1QLk7Qb02RisXQfB/fcYjbNoVcTjgaTp9abRfPD/nTP/1jZmdnRNg5USRmJUglJkklM3hugG3FVQg3cAYEfpQfsKpa/eT563pVwYFUFdQAzQhIZ1JkMhnK5TLLmQzdVotEOonj9HnkkYfHjMr09LTgijRjuLpIeY2FwFoAjIuQA0OMooZlQDKZVPyb1zKA0WOKv0fbxvcdjhnAKEv30xlA2x4nqJXbfN/HvPi5M2xvbbGxscHSUxdo1Op0Wq3hZKqOnai8gMFgEJnkwTg2hxAjQmQgjR+AGU/z4z/8A7zk+Xdy7z2/jI0QqnnfJx8F4BtuOMRj//Q59f27jswSy2X52MP7MYF7x0tvOclMzOSPHnhUfdbrjndqvP5rvgpdg0Bz8YKQb3zhncQMG8uwx2jNAVKpab7ya9/I4xeW0bSQhUmDBz/1D2Pf+cYXPZebn3cnjz36SXLpGAm/x0DTQO/xpu9/Hff85h/ynvd+AN3SeN1rhMjQV331V3J15Qq1yjbT0yV8J82FcyvsZivUdmt85G//Tu3/bd/97fx/tL1pkGTZeZ73nHPXvLlnZe1L7+v0rJgZADNDYAgCpAgSBBAQSRGkRIUcpO2Q/1j2H0p22P/sfzZDlGmSlgXaDjskLiblGQyAwWAHCMyG2XqZ3qtrzarct7uf4x83M7saA4YUXjKioqtr6aq+mfc753zf+z7vy//rHxJg0g4UojCP8cufZTiOKFbm8EwbT5oIAxrNfbbvXWfrzhVWFxd482ojO944Jq5r02ruc+vOLXL5EmWvkE3udcz199+mO+wjtKTpD0g19PtDwlhhWBbSlgy7Po6Xw6nOMTYd7tzb4/pei6XlVe7c2yRfLCGNDNU1V3CYrxR44S//NaaZ56Dj07ndZBiOmF8tsdXpU56f48qdO5QrcxjuBrY0sayYhWqecRdkGrK0ssbHPvw4qSHZPuwSqIiTS6v84Jvf41i1Rv7xRfxcdvOZdqYTGwwGCA3SsRBhSt3NM+52ufLeGxy4BW60R/RSwWckxDrksLlDrZrnoNFgsb6INBRCpozGA4JwTDFfJI4TXDeHIRW2YZPEiv44QgmbYZKwMFej22kjHYte5BPrhO+/8RZmrOg0RwSkSNcm1QpDmhiWSRiFKFNSLOczKkq3w9Mf+SjvvvUuB7t7LK9vUK7nwVIYVsbxk9LIeniGgSGNB4YG05vaNI8CUhMMw5zoAxNEAq5tokOBwGB5YY1abSFrOXS6LM4XCeKEnFsgSBL6o4DF42dxKvMoLbCmAFE5odpoPSE+3x/ETIvVdOihdISVy+HkPFwvj2VIBFnLQZj3J9siSUgnQfAoDUduRaGmUaaTZLvJTl2lYJrTDZtEC4E0NEaiMFKNoUCbEm3IrPiL+9bGowXT/G/+6X+erXIITClRSYpKUkSa4pkPkhymv5dUKff5HhKSByv6m60PGvohA5QurG9gGi7DzoiCaTzgHvCHQ154f3P2dwmI+IM0ic9dPAemxV+9897sY45h4CrBbz92kT996wr/8d//bf765fsF69d/7jlcK+tPJFojpUJ4Npa2OTxs07lz/+c++/RzvPv225QXTyKQ6DRmNBjy8Wc/wmLVo+xYtHZbOKZNPldgpEaQmox7AVbOxbIMXMfij/7g91GJxjDgf/k/vkS/1eeVb7zMoNdDKcjnCgz7Y/rtAf6oR+ewRRpF/NN/+Hnae3sEgy62ziGlTckpE6NIgjHBuEvoxznKiAAAIABJREFUdym4NqE/Znt3mys3LmMK8MdD3n17l7s7DR569DF+/hPP8/abP2RhcZ5qfYE7Wzu8/+5lFutzHBzuzSAI7VaPMNJ84xvfoDa3QKVS4cJDlwiDmBu373Du/AXm63Mzt8zly5dptVrMzc1ngzI3gyYE8Zh3rl2jH4aUXYdBFKENm1iPSMyI2NIMdcKJi3OkiSRKxrSaQ7Z3Nnn2wx/GFDAejtjavMPBsSUK9Tqj0Yj+YEzFq/Dzv/QrmEOfeG2VaLLrU0pxcHCAVgHLSxskIiZnethBzHq5zM0r71I+e4lTVgXLsYiSmGIx41gWCgWOra3j5UoY0wGCuE+aKZfL2JZLpDNJhRQmOTfP0soqhVJ5hrUvlIqMwsxi1Wm3+dDFS4wTRVa3NEJotEpoNQ8Y+GNWT67TGyo6zQZjP+bk6bP8o9/9Xf74f/oSwnJ59NHHKZfLjIY+hmWjUo3SilTFSO4f6Y6Kyo+KjqcnuwcnwTYg0UgKpRIf+eiz/MVfbhPHEds7DRzXw0klfqyozS/y0ed+hvnaPJ55f5jyAb+/IWfDhSlCzTRNhJG10Uwjk8y5toVtCeTkGivuO36OhloJDSq+v4GaSlYyOnVKHIekqZ4VwThOieOEJIknZCADY/L8JWgSFEEUzoKppvOO2RHY1BleXAiBpSS2ZaKERtjGA0e5oyuOY5hoJX7q5wCeWZwjNQSpbXHu8Uex3Byel3kiS/lCZolRGpQiTRK+cP4kfhwhheJzF4+RShPDdvCDgJzr8qsfeYJUGBhpApGPxCDSgs899ywnzp3HdS3e/sH3Zqip33zqCZCZ7uzzn34eO9FYemq50aSkpFohhMQf+zMpxe/86q/hFItcu7GJJTWmSKkWbNIY5qpVAn8faRYJw+x7CoUKFy8+glP28Hs93rR/TJSMMc37mKQ0TTG1MbMuTcWa7WaTzc1NypUir7/+OvX5Mn6/i1YJhoQgDNFSYFsO40SBSonGY0bdQ8Jhm2reAB1z89YNEhWT8xxc0yOOUg4PW2gl+Ae/+VvMVUp8/aV/y4efepqTZy+QKsEbP3iDrbubeHmHJ596hHNnL/Dee9fY3mpw0Gjy7W9/m/MXL5IkCevr68wtLDIa3x8KpGnKuXPn0FrP0gPNvEe73eb2jT0uv3+NRENiSKQw6cdjDgZdLuXPYbmSSEeIOMW2ckRRQrlcptPusb/foN8fZo19ldJq9lk5dgbbGLDTPOC6usvCxUfY6+zwztUrRPI+Mj9NU3q9Hm+9cxU7b2HIHFG3w6W1ed69vcWTZ55g7cQ58m7mP0+TaHbTdTodtDJot9vZgMLItGxpmuK5LlEYMQwzeEbey8hJU4jF/HyVoWnx+Iee5Ievv4Y/HnL+xAk+/7nP8b/9z18iCMYolZDEITrOhL2ohCQM8Ac9XNMgVy9z7949LjzyJH/8pT9lNI4QiaLZbOPYYkKMMTDNrIipNEtNU6nCkBJm0ZkZa3B2xBQgDIlrT6xzOtPtTietq6trnDv7ENeuvAdaEacQtrsgTFYfW8UxHfq9IcN0OIPxCiHuB0dZRwrdETdZFEVEaUK/lyG6VBKjkhjIArLSNAZ9n0N51LcLzIoVPBgyNc0TFiITRaMzN1dmRbQxZFY8kyRhEI5JBSgJnukiuS8PGh+BWZgWKQINGpIwxnJchFLYpkV6pACq9L4Pz5AScWQapI+koigBBhLDsUgm3lcMieU4pJNvGY9GOKaFZWS7MWEY2MJGpQHCAGmbxGmcgTm7bWzLw3ZchJQolXnAgzjBUBppmqRJglCaVE+CdgouzVaLXN6bBfLkDBMdR9lKrDUKjdJ6otGKMIzsydjb2+PgcB/LgDTocfb0BQ4PG3RaW+Q9nV1UpWaZB7aV41/8wR9z7sQJ7t7ZZu1kfZaPANNjwX1U0zSLYWlpiY9++Gm2tjfxhwOuXr3Kr332V/jKX/05cRhkoIdiiShV5HMFnNo89zoDAn9IOOzjSEUS+ZimZNAbZaCEcok4TimXq/gMef3V17CkxkBw6/oNvvPdv8HK5VEKkihrjB/bOIFp2hwedNm+l/V3G40Gx0+enOVsxEqTy2VHmyAIHgBiTl/EZ8+s8Yd/8g2uvv0my0vzrJ4/zT/8rd/kv/wvfo+5xQUK9QKtfpfVhXnG4x75UhE/8hHjMbu7e/T7A9595zJ5x8PzCkjTIE0FtuHw+COP8nxtjlq1ztuvvYExP0fjyg1M153ceBIhJaVyLbuJlMk4jun7EapQoTEaIdwcQQpGlJDL5UhiNbPPBUHAxY9e5Bs//BbD4ZD11TV67R6JkfWYgiBge2eb5eU1yqUq+XyeVqtFr9djfr5KpVLjyo33GfsB+WKJy+9f55vf/R7bBwfMT/BrcRxjWC6+H9IbjqinmoJXxA9CLMeh1x/yta+/zD/6j/4THr3wCHF/xGAw4KDRIk0zMMNo6DMc9QnDBISY5B1LpMjgrxqwJrubo321KI4hiSjkXJTOwKEpAqENPvULn2Z1/Thvv/M6zcYh1eVlPvrMszz59Ecy8rMf0W91PwCGsCwLYRp45Uwn6HkelmXNen1CCIrlCqkXIVFolU6iQEGQEI4Gsx3jUbaiEAL7CM7saL9QCE2chpMFT82KYPZ1giQOwQWlNTIMSFRKkMREYYqeTNHjOJ7dg2maYkqtZ3CHnGUh0hQJGQ5K3IcbiEnhmz7kUfLHkY8bgJIGUZJgODZRHGNpTW/QJ18sUcwXOLh9E5Wk2bRHKZSGKAoxpcQ0XCLbwnVz5GoGItLkXQ+vOkfOtsgXXLxCEWnZSLdEp9untXsXKQQ6UWBIxoHPoWqTaoVlmFhCoJOYyfVHSIEhLGItMNJsS7+wsECr1cLI5VheXqTXGuDHIfduX6bVanLm3BqmkWQI+GA8azo7jsWbb77OuNvB8zIKSLVanWVtWLmYVXcRSxgz72hpAoe4ffs2C4sZhadcrtLvdFFpTBL5BEHAMx/7OC/9n39BrW7R77SxLI/WQSujDguT+fl5IBuITFXuhmEyjlIMKdETf2gxXwClGfT66KGPlCZpkqWQHR5Ob7CYYrGc0YZLpQcyJaZQTdexZ9osgD/7sz/ji1/8IoZh8C/++R9x49YtatUy4+GIH965zde++hKPXnqIOIpQCmpzcyRRimW5RMMESzoMeiMuX77C+voxgiBgZWGZ92/cZn1jg1Rprl27xub2Fo8/9ST1+UVGcUipUiVXLBL4EWHgz4TB06MN2kfmirilEsXFOpFpstNosjIXY+ksv1rmDJ566ikKXp5nn312lm9RrVZng4OpeNc0TW7fvk25XJsVs6l2djwe49kuuzv7+FGMCEL2Wh1efestTl04R28YEitJmAhMS5IqjbQcgkST84oEMbQ6HbqDAcMw4Z133yXnFVifn8eyJslofohpzk3iKeaIoswd0el0Zn7onZ0dtNazCfBR32yW3BYTTsG4loWUJmEUMTc3x8//wi/xiZ//JK1Wi/rcAoVyZoDoD0ZEQUyhUJhp9aZH0iiKUIGiPxzQ3GvMwAalSnm2cNbrdSrVMpZpkEZhlguksteOa8rZIGPqE46iiDRJiMb3ozaP+pqVSnCK1sRdUkApPWMRZsHyFikaLaDq1hn2Bxzs7fP6G2+gk/tOkChNZj/bTLW8D5LWciL8nOzmjnAlxYNtPoT46aRXoQUiESid4BSzI18QhuQKNsPxmGLO463tnWyyphUkCmEIpBK4lsdgEPO7/+Q/4+bONgM/wNQG6+sbNPs+QRxw4dwp3n//BoetPiuFGqP9HjpJkel9G59XKNBpdRGmIBj3yecKExKvRmhFKrNUZcN0EEowNzfHo49/iMrKComULCwuI1RKkig0JoVikQifP/lX/yNhmiL0FN4o6A9aSDOgN9zm1Mr6TA3vOA61+iqRmmKQEtrt9kyIqSa5HqPRiHq9Trd5kOW2+mPCYEiURgzCkNjUaEci8x7DYcxfv/QKUjks1U9m5F4VTujZisODFlGYIISJ1DHNxgGVgpfhjHQ2cEqVplaukFtaxnYkhwfZoOv06bN4uSqYkvVjx3ByOcIw5Ec/+hG3N+/xzLPPfYCNOAUCLC8v8xtf+AJpGrO9dQ/bMMl7LtevX+O73/4OrfY+oRqyca6GU/SwhUe7PaDZHbJ9r0mvOyDn5jGkxWgY4OUK2LaHdAy6nQMcHdLdvkPYa9INwc0X+PgnPsZXvvZVuv32bALc8zOyjBeDcPM8/6mfY2/nBqPY5+/9/d+gMHeKXuse/X4H1xEzRFipVCJJMsJRp9NhcX6B9mF79n+c7miiKJoV/2nSYRiGSCE4dfo8n3FdWv0mFx65RN52mK/WaA0TkiDkkyfOk7NsMA0SoTFME0MLbNdFC+iNh4zCgHd+/EPu3bnGyY0TPP7442ysH0cpTbfbx3E98gUbJWvZKWJ9dYZKO3X+7MwTG8cxg8Fghn06PDxEx5CmIXEYYSsPlUbkcnmCUNEdBswt1fjQqYvAJDrUD1DJANM0iVQ4k91MWyDZQqNJ48wYoVXWexy3e4xaXbRW3DYTXNvBNg0ECp3EaJUV0MEwmPUmj06Gtdbo9EFy0Cy8SiZ4ZZulpRVKpQqFfCmrL0GYoeZMOxsUCYFn2jhKYA8j/MMDxlE4s0xOC3jWnsKcgaQzKzLIyY5QH0GcH4VICq1QP9H3mz4kTCqynK2krueRak0un0E7u51OtmNTs84kWgkMZaPSlFSYfPmb36bZ6/LbX/wHaLfAv/k3f0WpWuLUow/z0ve+x6g74u/+8gZIC44cz6c9Ct/3s+lbmk4cKtmESaMRSqPkxCepNZZl02w26ScJRi7HcDRid/MOea+U9fGEJJaZQ6bf93HDGJlkEFVpaOrzZTbW50n8Ea7Mz44KYRii5IRwkgSz3cOUun1z0Of5n/0Yb775JrYQXL16lY21Nb74xS/S6o047HSIpcYsOJQW6hhFwbFinflqDb/XJtQBvV53Yux3USITrja6Q4TO+nVJkiB1dl3y+Tzrx09x/NgFlEppHOzQaOzMenmO0+fYqRMUSiXMCVvtkUceYf34Caq1OdRPxIr9+q//OoZhMBwOKbkGne4QU4usHREnGErSOmhl2RdmzOHBAUFcoD8coSOXzTs7IAx8P8N3qSg7phQKJXq9HstrC3Tbk+O+P6S0UGcU+riWSXl+ni984fP4vk+5XGZ3dxelFKurq1RTm56Gg6DHzt42lmvzzVe+zhPPWJxcr9PrRSTxCDURAhsYbN7dIUkSms0mu4Usr6VSrE3cFplweNqjKhYzfl7G9MtOHcVikZX1NWpJHUOAVCpzS5VcHjp3geZ+g/ZhM8vunfSmRCoQlsSQkkfPnOL111+j2djFFIrtHdjdvcdDDz3M2bNnqVbniMIEYhM/yATj3W73AyLjqeviKKE9c3clRGEGk2g0WvR7I5rtLuPRPidOncTIORiyjee5s3t4CsR1XfcBmOvRAui43swvbRgGwjQmR1lI4hHj8YjIlKRJRJqE6CRFKWj1xzOr27TIzaIQjuRbT4XfcRzjh0OOnV1jYSFBSrJ2QBATTMhNwnKzAqhBugU6hx1uvn2Zvc1N8pUi5UmaoR8ExGmMjuOsBzgrbEKCOGKgOHLMlQ+oiuUDv6Rx5KisAaSBUmAYJqEfoaRPqVJlqT4PlqLZ2MXWKWmaoE2JUGAqGKc+MueA4bK908TJe1SqcwzHI+JRiFuxMRKTzl4X07SxXZuYgMGwTc4UmLEkFgaFUol+r8dacQV3SpZQYJgShCaRILXGAKQlECKm3doibW7P+oOuaTBWAo0kVAm73TaJJfEcF09IxnpMuVbENhySUNIbh5giIY8iTSS2VSCKQpQ0MYwqURAwHLYZdfsMSmMc28O0NIetA/xwxDCBbj/lsYcf5s+/8TrR2Of4sZOUrDyjwwHnTi+yvrbM7b0WfqKprq5x48YNmq0haRxhiJR+p02ajMGMSFOJMBwG4y5WzkNIk939fbYPmrx37QaGMMnncyyvLrG/f0B/PKIyV8YtFNg/aHLm7HmKhTJaayrFAoZO0erBACEpBTpNEEoxCjS5YpnOYMB+o0GpUOD67Tt0+j1AY0uPdGAzDlOah306h9ukqSBIYoKBj1cqo9OISChyFkR+xk+MpI2wDPZHMfY4YhSGdNoHNA7bSGmy19ihvrhAo3GAkC5S5NjXktF4zDAcUXAqjEaana1NTu6/z2t7V7lw8TSGdNCmwJQG/nDM7t42F889RsEusb+dOS/KpWAyWUxxCh5+GGK5DqP+YLJDSinmPFSSsnn3JnfvbT4gtAUYDBMeOXsGKRVvX/lx5ixxLDQxYqJf9IOAJ5/+bXqDNmHiY1mCqN8jjlPe+MH3ufneZT772c9nO7thwGCYcPXG+6xsrJMvFTBkJk8RwsAuZJkxiRAYORNQCMcCpXE8j2J1ntW1U2itGQ6HDCdk92xCDFobYBhoFLliBcctMBr3GA9H97NlJqVAoBkNOuzfvkscxkjXJtQJoT/AH3Vp6xaur6nIHHnLIZUJY52QJIq4q1COiXYkUZrgGBJTgdKCpFahurLI/Nwci6ZLyXJJdMLe/jZG0MeKFWGsafXH9No9hJo40VKDSBukCRRR2ElMIepRaLchHOGP+5CzCcIAEUQ4cYr54NH2QZzVA5/7iQ2fOCr2/YnPJWgSlWaNWS1QCuIwolAoMBwPGA76GGGIlDpL74JsV2YJhCHZ398niVJMW+G6HqEfUcrlcQ0LYs2zH3mWfr9PFAVYlklKmkX7CUEQxtheJgFwDHOGDLeRJGmCEqClyCasUkz0QROpQpZSm8kLlMZAomVGltAqxrULlKplwl6HbhgS2gJfSz772S8SpG1uXX97MpHKEUcJtzfvcuz0Rb70pX/FeDzk5MYitVqdfm+YBRK5Bq5TIF+sYOfLLM/P4zoOaQI6VSzXq+xe86nPrbCzs0P/ziFLJ84iDMH+QYPTZ87j2jm+/OJf897ld3FNzaWHLyKFiROA43oYIqV7uIdXyGM5NkEymRIamrn5OpVKjeFwnO3iSiXSVJMvZDvf6eT6qNfyg4p/OVv5DQSlSgaabB4csL+/nxnsewP6zS7upsFoPMB1bQpekTSF7u42p06c5LDRYBz42XAqSWZFtjo3h5QZkLPZbPLoo4/iui6l0lJGZC58hDAOeOfty9y4vYNWBonS+GFEFAREQczx46fQWhEHA0yngGFYJHFGMs8VSxiFEs899xx/8VcvsjS/wOLiIrlcFqPoOA6DwYAPfehDHB60uXr1KuE4w3AZCK5fvw5KMz8/z9ra2mwXNnVhCOmSRCHVWoXPff5XSLQiSVPQKSrMCmCiUg4POjz99EdofPlFhDD4ueefz64rBnGc8tabrzPoj3j9x28xGMe0+10Kc1X+zi/+IsePH6fg5pibKxLpFJCkcTJxkGSJdWg9bb9NCp2m5rpUZsXanD2nR/FUSiWY5soDx//pMCFVMSJdQvRT7ty8hZmzOVFykYcGaZzS2+8ThXvYboyVKjAdUm3ia8m2GaOiGD1OcaZHXMDRkvq+g97xiAt5eonFSAtiCZ1ojJifp1BdxE4tusMxSZTgCEkaJKTRmAiTSEEzjXGGPQIdUtYhejBm2D9gGEdYrk3RdPCEyQcRzv9fPQyJnXMx7WzEHURZ2nvg+4Rjn6KQKJ1MjqGQojFMA2kaWWPZ80BKXNshtGwee+yxGeH43LlzNJvN2U0aRdEELSTQMovrPIrIEUKCvo/zzlBfP5Ht+lOO9NnXyYyOu7uNdjKBZ2M0ACGwnTx5q8Cnfubv8O3vvkQy0uBmzedSucjzzz/Pi1/7Bu12E2nA1SvXCMN4MoBwOGgc8qMfvskw8CkbHlpD6I8YjmJ6hw226mUWF5YZjscsHi+Rt4tEiU8aJ0SRz93bmxwetrHsIs888zzomB+/8SqOLcmX6ty9u4kgJmdq/ChEGBYri3UMYc9yWOM45vDwMOudTmJOV6pzlEolCoXCAzjyqdn86J9Tj6pWKSpOGA4zluTu7i6Hh4eMx+PZ8anf7xPFAaYpiUREtzuY9dem+RYZ7bvG7u4u16/fJOd5WZSi0cMfh7z143c4duwY/tigPl/DdCxKpRInT57k3k4zo5akilu3bjEa90jigDAMZ9kYruFycHCA6xiUCnl8P8AUJoV8Cd/32d/fJ02zVsLCwsIDINRarUbBKyKUZvPubQoFl07Xx3EdSqUSruvO4KjTHdZgGFGr1QjiCCElpmNj2la215gUpDRNMRyb4XhArfRDbNNhdXU5AwikkmvXrvPGG2+wubmZZYgsrvDsx5/h2uYdxsGITq9NfyBpHOwyV89oOq5VxJBgWx5CWpleTt6vgHEc38feaU2aPhgpcP/NQGs1K+pHycuQaYLXPr1O8Y3XeO1HP2D3/U3mOgMuiDzPzl8iKhfILYWMRZvmYY9BJyaKXG4ddpCDiLrpkDdtAiXopyF+GtFOxvT8DjgWnvDQ2iBUKQkpzSTFLi/gjRWJYRIFIe1Bj9gPyOWLJIZJLAwaaQD9DqPGPkYSIVWM0ilSgR5HKJmg+f+pAAZRSIQGKTAsE21IosmqPu4N8MdjChqU0NnUhux9OVG6Q9ZkDqMIWxo4ts2lS5e4e/duVlwmTevpBEnKTAYhtZh5JY+itE3XQSRqVvikYWQFUD9IlZ2JMY8ghcTkJp9OGHu9HkaakrNcCtqicX2L73zzh4yG+5QdbyYD6Ha7GI5DnARE8RgdaVLfx7IcxuMBnudRrVZpt/o4+QI5LwuWLrs2qt1AxCE5CVutNqGSqFs3ee7nfpGXv/M9Gs1DSmWPjz/3yzz62FPYbpbXe+Xyj4miiP2du5SqVfKFEjfefw/fSLiYy6M1aJkx96ZkH631zKo0Ho+5efMmp88Ys0yRqfbrqP/S9/3ZAGT6ccfLzeRQWfxihWq1yqDfzeRCTomx38KyLHq9HrYVc3DQZOP4xgxqOhplBJpqtZqBEnIFhDSBDA5w/PhxOp0Oo5HP9vZ7PPzIQ2ztblNfmOPdd64QBBlx+MTJU6xvrBEnPs3Dff7lH/0Pmdi73caLJe9eeYeF+RpPPHqJaqVCmmi+9c1X+PGP36Ren+Py5QZvvfUWv/d7vzd5bWRhQP1eH8/NU/TyuK7L8vIyli1wLHsG6bTtLHphOoVN0j7z83N0B0PiJKVcq1Ku1DIFRJo5WRw3A6eurx/Dc/P4ozGQFZ0ERT6fo9vtoLVCCHBsePrJR8AVnDp7ijhOMaVJvVJCJV1IAkZBxMFBG7RFtbxAoVyBI26nqZTpPlT1weCoBwTWHCmKR04BAJbpkiSa0488jFvOcfXP/wIZhCzX5lgvWSTrJoVTIYWVOrL+IUjnYeCx+y+/zc2/eZMlcpRlntCS7Pt9tlKfH8qUfpoSxxrfzE5s3TChpWMazQ69d94FJ08MuDkHnSboMCSKEoZKMU4SLFOQjno4aUw+jrCExLCyPq4jTbqJIhIm5lGu1k/SZY8+jn4dgCF+eoanBqRtoaIQJ+cy8n2ElW3zLceh22oiETiOTSoMfDRCCtySh49meW2FZruVqbQNA4ng3t1NRrHA87wHVO9xHBMEWXRfzrKQiZo00QsAEwS3QShC3ImhnMkTm6QKIe9HIk4nTTMJgRAoYZCqGGcifk2lpOTlCW6H2K5FVVjkwohHVlfZbwT0woMZeqdQ8Gi1mlRrRQbDZpYXMbfAaDzgS3/6v/PP/tl/xb3NHYIgwi1IHrt4nrwKGO7dxQgD6uUS3e6QFAsr59DpdvmDP/zv+ejHnmdvfxNDu3ztKy/glWo8+dzHKFfrfPVb32OpmuPMyROcf+JpiuUqn/rkz3JwsEmv1+Oppz/CTqNJzrEY5YY0m03q9Tq1Wo1iMQu9P3v2LF6+gOu6s+D26c08/bNSqcw4bdPrFauUcOzT7jQZDfo4jsMzzzzD4kKdnFfg3PkzvPSVv+TO3VuzIdAUs95qtWbN9um/32w2SZXksceeYDTKdI5bWzvUajW2tnYoFGqUSiXquXlM22BlZYV27zaNRoO/9xtfZHtni1SFVMoFNjY22Nq6y40bN5hfSpirLXLQ2OPmzds88djjdFtdXn31NVIVUq7kKVfyPPvcf0ine4ht2wyHQ+YWV5ii4wbp/cySarWKazuzVsE0fH4KKqhUs+Bz03YoFCv0BwGj0SFJDLabyziaBniOxWGvzdrSItfff48gHFFQmaRlbX2JT37qeb76lZcJfB9PRYhRj7Mbq+zeu8uZs+cRIoOSmnYegYUhNcurq9y7t8NLr7zEfH0Ry/Fm+SXFYhHP81hcXJwsQPZsgZtqOw3DIFWKSOnZsdfzPKbZNpk4GRxp4eVrnLn4GMd/q8TVl77G2+9dZckRPLyyRGlDwqrD3ovfYfd6l5q5jhW62LkqsZmjJ11qJ46xZpvc2dqi+uQjnDxzisL8HDmriDRtlOegTJM0iUA4KAwM28Kw5URnGIEShFrjpzFIgVQKEUcMYk3CfbCCKSQizWLU/713gD/tiPi3PaI0oVyrZn0U0yQBUp2Jhw8bDUwhSaOY1FQkaLTO0OhhnFIqZf0ne+JjDMc+3/z6K+y0B3z6059+wAAdRdEM1AoPhp5MaRgz3Zo0Jw1qDZMJ9fT/dZS0Mb1ISqksACeKycfFmfDTMAzypSLoBNMzuXbnOq/+zavMLThYnsCa/C5KKfYbu1QqJX71177AaDRipT7HYXOP4ycXSZXP+QtnWJgfYec8wl6HJOwRtJvYKsG0cvSDBCtXYKexz+L6KuNxnyj2QSfYhkE3GrBc3qC+MMfc0iqrGyt0du+QjiKczWWWVzQq9hkN+gwGQ8IkZjAakoT2jOs4Ho9pNLIc5UqlAmRHvXq9/lOv0fQ6T4vfzIdqmQwnR91SqcRhozELE3ro0iNIKXnyySdptg4YjQbYdtZLUijMya7JtTNOgc4+AAAgAElEQVQt2cbGBrdv38ZxC+TcPKVihVMnz3Djxg3G4zGW6XDm7DmklKyurlKqFKlW6rxz+SbD4ZBbt25Rn5+jWFog51p87nOf4+Bgj5s33udnf/ZneemrX8a2JPv7B1y+fBXbsGk1OwRBNhDwPI/f+Q9+h9//579PPp9/ALQwnY4aRuYc8fI2ZvlB3tz09OH7PsOxT6mYXVd/HJLEKcVSmVytQJCGSBz80YC9vS0O9nfY3bmLKRV3796l2+2ysX6CwWDE6dMnqf7GrxH0+yTtFtu3brJ+8SGiUpm9zS0Wl1eIEoVZzE0WK4lpWiyuruDdukGURpS9jC49TT9M05QbN24gpaReX6JWq82O8tPergGg7sd9Hp3caj2JAEgFOgXpeuRPneGp35qn+8Y7bH/nh/hvHrJ2T1MxQvy9efRBgSg/z9Cw2DQOCVMQjkty2MfM55l/7hM89Xd/iaSYI5GQN1x0ohnrhFgnVDFBG2htkJCiDUWahqSxYNgfEWuFV8gKvaENhNK4aYo6MpxKdPYcJSr9f1EA/xbWnhIgReZFDOOYVAi0NGa4ok6rTc51sdMUXwUP/HNxmlCqlGnF2fEsl8sR+D63b91ipE1c1+XNN9+k3W6ztLQ0m0od3YFO8dx64vIwTZMkTEiEkckOtUJaJtLKCCez33vyxE6P1kJrhJoE60iBm8/RCwP6wwGxLTEMG99Mefz5D9MXAVHSZuS3Zvm6vu9z69YtPvKx5zi808wcBk9colCyeOHFvyYIBzx86XE21i2qlTnu3n6HnCmQrkk8DCmWqxRLNTrDMZ6X+XULxTy9bhNNiuNaVColTpxcZ319Fadc4/jpE5xYrdDeus7m5h0ODjs4psKSimK5QHswJEoTiO9jwafZwpVKBdu2WV5epl6vz4LKp4Vueo1+8jUx20Gr+4FEKsmo4rdv32Y0GvG1r32NtfUVknQwKzKOIzFNB2nK2fdOn7spIbg8IadM0+7K5TJJksxCjqbZzFEU8fLLL6O15sTJE5w6dYpUJezs3KPdOmBpaYmNjVVe+frXeOajz+LkXJJ4RDAecP3a+6hY89BDD3EsWmZvbwchBF/+ygvYtomU4LrTCWlGfk7CaNK0T7Cs/GwxgQfhnvczLkyQFt1ugO3mKZUqKDR5N4fWKXE4pj/oMBx1aR020CQMBlloUBSH2LZJGIbMz88TOS6LK4uYXoGxaTFwbEg1rd0Gq8dO4EcC2zZxbRNpWRTKJerLC+xs7uKNRjOR8lSSNS3s+/v7vPbaa2xsbGS9zkKB9fV1cp6XSeOOWN2OFkClFLYyAINRoulJgVOt4n78Weynn6Jz9XX87h660cCdE6iiwS4CuWjjPvsEMp/Dqs3hLSxjWQ6lWo2mFqi8iWEK4nGEKbKBpilMVCTQSiJU1ptEZtrWWGgG/phYRVi5LIbUxMTAIBf7SNKMJo1kFAUkJsQIzKP6Pv2BSe/9948eD4UQJEcq4FHvnkYSRhG4JiMRUs15qCilP8xCbnqbm4gkoTf2sV0LW2j8JEHZmqjvU5+fp3HYZjAeUcp79IM+xfkSG8U5Fqt5hkNNOTePYcAojonHXawggFQgnDxhEiEcj7ybo+C4GEma7S5ThZyqxpVAptOb+mgwsyYKsuNLFCWYtoU2LXpBRK1SIe72CPWQfCSxbEHj1l32btymmpMcHLYxowThGnjSIRqFEIFn5OjstwnDkE5jADiMhxDFBhsnT7C5tU3Pb7O0MI+jA97dvMx/+o//MX/0J1+i2e8jpMHGmTN0Q1ipr9FoDSkUaiRBQGNvjzu3F3j+U7/EeBRw7txDfPmv/zUkCbXFZaSwMUVCPGhhyyIyTSCIMVwbxzZxbBPTECzMz82se3OVKuV8NvxIJyQOpRRC6cxyNVnkAJLJjk/q7LWTc10Wa3UaBxlkYXV9jXv37lKtlTFNwTtvXMMWFoYhcKyEfD6HsExMx+XwULG0OMf+zi7b2/ewLAMpRtzdfAfP8xiOR5imSbPVQhsDmv0GB91dCvseFx46x7Hzc7x37zWWTl9gt/Mqje0mja0WUpv0m33OnzpD3PL5/ne/w8OXzqPGY/bubbH00edp9AbMbfS4duV1ltfXicOIu/cazJWXMAyDxu49VlaLWYFzTIb9PrYpcN0StuWRxMHMkWCYBkpIPK9I4gcU8ikSjWnC8kKR8TgkGjRIEcRJhgCTaUQpb2DWq5w+fZq7d+8h8Mi5FdIkc3Lk89lU2lqoIGoVpGXRurfNdmuH+bkFkiBgt3GXyurZLNFRSlzXwDQEZ46fwu/5mJacQU6DIMqm4YmaiPMNKpUS7XYzs1cOeiwuzmUtI8MhUSFSZggUy55Iy8hsro4p0TqLN/DjDAAR6BRVq+A8/hFMnVJyXAwklumg0xgpQ4RlgmEgLJsYhcBAC005SVBooiTMMo8NgZ4OKp2JL1gJojQrgFJKTGwK1gglcuTNPCIVWG7m5HHsHFEQYBoGSZpSsGyiJGtVGOue818fXdEfKIBH3v/J6dDRhxZH3hBo06S2tkQiNeV8HksbOLk8lx56mB999SXiMECqjFKsyTyNlmth2S6PPvMMzSDg/LkLrK+s4HgOQRygo4RzZ8/wytdf5vvf+y5SgGNbFByb/ZvXsBKIEpCFPBvnz/Oj11+lViphiQStFFpl/Y10Gogy2RZPV0Df92FybDZNkyBMiJTCD0MSrXDzHujsmkhlcthuY+ccvKJHrBOanS7atDHcPJXqHGGSZPGM0qCxf8h45FMpVmgcNHnj1TcYjX1q9TqVie1of28fKTIwZ+PwEEUmeF1dW6U3GnHYHSPdPOsbx0njEKkTdnd32N1v0G73OXP2PCePneSpJz/E+uoam9v7mJZJqeCBTnFsi1a7g9ICKbMjm+24FEtlgiCk3e6QLxTR2qDb7WW9IttGCvFBF5CevJHpQ4XI9HQSwXA4IIrCyS5cMRwOePfdd8jnPdCaMIwY9voZdCOJiZKEdqtJPudhTY7kURQSxxF+MKJULJCqlE63Q6OxT75cZHt3m9DvYTkKf9zm3s5N7m5e49TpRaKoze7u+3Tae7z2o+/w5us/QMcB33rly/T7Dd54622+842X2b51k3GvzSvf+Ba7jQNW1tfIF1zGoxGnTp+ikM9DqjClpN/r4do2eS+HZZqkacLVq1c5ffo0URQjSIHsRrx19x5v/vgtbt2+jesVmKt4CAFpnGRGA5Vy5fJlXnrxBXa2tlleWsBxBIVCjsXFJRzH5atf+Tr+OGJrawdpCGq1ucmAxaI7GHJju0enG9Dp+PQ7Pn/5ly9w59Y25WINlSaUPA+YBE5JcFwX18mxv9fAcWxyrkcu52FZzqzXJyUYhiQIfEDjeTny+TzFUpFUG5mnXghQGS1HSjAQCJ0l/iEz4o050dgKNFubW1y7foOdgyZbzRYHwyG+gADNOAoYxwm94YhxEJAm6aT/mKDiOCNSZQjAbHiXalSaoFSKUilpGiMMMTFRZH73+WqNYr4wW6ijMMIPAg4P9jPwa6dDs9Via3ubmzdvcufO3QePwEdzVwHMn8wb+Pd8TI+SocqOBqP+EDnBJU2PrUkQZ43jSRGK05RUCZI0ZW9vj8NGm+OLS+SLLq+++iq1XHZE293dnfV+pgTrbFtuobXIjO6TI0mapviJD0phCIlQmRtESolpGIRRhONkL4Kp2Xv6fZbt0en38JOIcq1Kp9vNNIQalFnArs7RGI4pLi1jmDbDNKPglqUgVAlRmjCOQzrdLpVadaJHDLFNgzOnTvKJT32CwWBAr93C8fIkSlMs19nf3qJYWeT67QPyxSqxlthOnqIh+M3f+CKNZovvf2uPcrmEIiUMhrz81RcY9Mf82hd/m4KXI2eVEMLAMG0q1So5UxPHIUKa1BertA+bsxf+NM1rSn559NFHyUT++oMLndYfeJ6Pvj+FJfi+z5UrV5BS8OSTT3L8xAaDXpcb16+xfW+LUiHPwA9wHYvRYDTpOZl0Oj1arQ4XLizR7/dJkihrQUx+12PHjiHtTCqThGNif0ivM6ZYyrMyXwelSMYR/aiHY2S2sY6Z8tFnnsD8mQ/x4r/9vzCsCndv3qLbcon9Lv1uh7evXmbj+Dp3tu9Sq9WwTYuc7RCHmVYwn8/P0PizTJwpGMQwcCyZxXtOjvAvvPACtm2ztLLGseVStkBMLGTTYclbb73FyVNnCMMnyRcsbNuaDRlM02Rzc5MoilhaWuDsmfMZ4MIfYZouV995g/n5ebbv3SMOI+arC4zHYzZv3iWOQkylWFpfhTQhNgXakBSqZZaXl9nZ2aFUKqMn4JCsTw6G4cyGgb1eb2Z7y05DR2qCaTIY9Ll9+ybbm/doNQ9I03TWO5x6gBcXF6nWqtTrD3NwcIAQgitX3uO73/0m3XabSs5mfn6eEydOzH5WNoSJsQzxgJ7yfmawfqDPOn09GkJiGQJ/OGD/oEG322UUBph29vsbAkLfxzRN7ty5w/Xr1zl+/Phk53jk8QHW1//DTA2tdWamt5khqvL5PFEU0ev1KHn5WVNdMwk1SVNMx8MrFtg7aDBsDahduMg7b79N3s0sR7VabWbLqdfrNBoNBoPBEY2aMXtxTvsUlmUhdMY7tKQxQ39JKfGKBfL5fCZKjrKbTUpJGgSkaGr1OTqDPkIIuoN+pt8SklPnHyGXy7G9vcVBf0Rtvkzq5DEtC2UIMA0KbonTp0/juB5CGBwe7JPEGcni4OCASqWU6bwch/l6nYXqPDr0kabL5au32T/ssiCLdHrbSNOiWJunWMxz9dp1huMxXs7CcnIIS1Owirz6ox/gugU+85nPoIVJzitm/aVU0+70qFbLaCSGaaMzPDAIAyHN7Oa1XZgAG+r1hQeEr39bAfzJRxRF5HI5TlRO0Gg0CMOA1157jQ8//SRz1Rp7e3v8ymc+x3uX36HdbBLFATkvA0z0er0J9kmyuLhMoVBie/seF84/RH805O69TRYXl8mXSywuLtM97FAoenQ6HU4fP4kmoTpXIU1TRmGbGzduUS4tYKwVeOjiYxw/vsHNG5u8+MJXEWlCkgbcunOP1Y3TdEcjwtGA0M8Wy73tbVaWlynkPEa+TzGfn5n9pxPSYrE4gWE4OHY2xJsuANPX//Rrp+y6XC5Pvz9kioCaagyzKawmTaPJwCVDdpmmyVtvvQPAJz/1CXK5HAUvx+pKBa1Cjq3XWV9ZpdPp8OM33mRv+xZJ2MeWAss2WVhZQSUp4STZbnV1mVbrkNFoSC7nTUgqcjIAzO6bUqk02YVnpJbxeAxmfvYcp2nK1tYW7733Hpu371ApF6lUKg/4a0ejEa1Wi1q9ysbGBmsrC+RyOZ547BJBEHB40OCVl17k2rWrjMdDHnnkEUzTmVwrTW4C3XAch1wuN/MhZzDmzEOs0ixjWk60wo5t8tqP/obvfOubvPvuu1g5B2EYrK6u8vijj+J5HoPBgP39/Vl+TKfTwfx3vainj6Or/b/re6bePreUsf88J9tOj0YjisUiUmfYHq11hrKaFKRyrUqCIk4zQa2JQKaayA84dvIYzWaTarVKq9WiUCiws7Mz23kkQUKqBWXPo9lsziQVSkWkE8abKSSu42RPKpAYEA37k2KYpzXRrJk5h153SDU3R5TEzFUWaO70KXglXC+LDrRtm6eeejrLZzBNrl29jVV0EUpT8DwKuQIVr4DAwB/6lFbW2dq6y7lz53AMg+b2DncGA45vHEP4Pi9++UXKXoGCm2M4HiNENnUOQ4NyvsSjTzzBlffe49Uf/Q35YhFluPy3/90fZP05LKIg4b33rvOdH3yX0w+dJxUS17VwvTzL6xsMuj3Wj5/AK1awbYebN29SnV9k9dgJrEkY0PHTZ7Esi263O9sdHBXHTvuER50gR1fjJEkymYQBDz/8MLdu3WRlZYl7m7ucPHmST37y5+kPe6weO86drXvk83mIxwwHI5I4RQqD1ZU1rly+mk0ypUG73SdRCrTJ3u4hy4aD5xU4f+kstdo8F89fQKgMWbV59y7Xr19nd6+HSmrkCzW+/8a3qP+TCyTK5Or1Bq6TZ9Bv8ouf+WVW1pa5fXeXDyuDVneIawq6zQbxqE/rYJdL5y+ikoTGbovFtWMUi8XZ0O3SpUuzayNldpKJooi1tTU+//nPI4SgUqnc3/WlmvF4jG3bLCws8Au/8AvZ4iNERu7OZbuVg4ODbCeYKzEc/t/MvXmMJNl95/d5cUfed93V1fc908OZ6Zkhh7eGQ/FekvJShi3DtGjZ8D/7j2FYgmRBawuQbSzg1VqmbcAwpF2Ilu1dW5K93iVFUSRHJIczPXcf1V1V3V13VVblnXFH+I+XEV3dIkekRAEbQAGFqq7szIj3fu93fI8+N2/e4tq1a3hewOc//zmKpTzPP3dJDm/iGGe0w2MXTqPGfV4a7aMQcOfm27IsndDfSrU6M61p+qMuFy+d5fo7y+i6HBT4E8+TJH6g67e0tMS9e/cYDodU6w3cUGS5UK/X49q1a+zsbFOpVKiUi1mGnP69FAQRhN6QlVs3sj2qqGQA+89/4bNY1t+n3+/jOA5vvPFGBkIPPTkcPQq/SrPA7vCQTvuAfrdHOHaJw3BCZxzR7hwAYOpadrisrdzhsN3GNE3K5TKNRoNyuYxpmiwsLPzdAKEf0GgmqqumnOC22+1JX0KO6Y/q9scklKoVlMk0TQhBv9/ncL+NoWqMRiMqlQo7OzuZ7liasmcyPUgF3zSFT5IEx3VQhcDQpPKH40gbRM/zsFU7C+btdjuDCKS2ecPJNNJxXQzDwAsCkuEIEWiExRKvvfxDut0uFy9eJHEDHIa0qlNYmsHu5had/TZ3V++hKbosV3F5683XCVyPJI7JWTZuv8/9ewUunFqi2+7ij8f0DzoYiiDyPS4//jgf/8SnWF1d5Y03XiNJIsl0iSBfm5YKwVGCZvgUa0W2+we8+vZrOG5EGBpUCgV0zSBfKmNaFqouoUif/vSnWV1dzURAU9qXrpsZli0Mw0z9Ax5Q4Y4+t6OLUzJwfDa31tnZ2ZmY4eTwSz4526ZYLlCt1wgRxKqgNtVkpPTotg/QNOmNOx67mKaJ7wcUijLzL5fLJELCc85evMDq6ip/9u1vcvWZ97Jyf5XPf+7v0e/sYxcLLCwuMjtXxzLyvPzDa7zwwov4vs/u/ha6aRDGCSgaN1dXef6Fn2Pq2CnW7+9yoVAmeSnmtddeI4ki9nZ2aZarWJbFxv11hGFnwp2jkSzbq9Xq5F4oKIqa4RuXl5dxXZdKrcGZpabcC1E8KaelmOj3v/99avUmFy6ezfZLFEfZQE5VdXK5AhcuXOD++t2svPTGHnbOolFtUCmV2d3eoXfQpV6pM9VoEakqiVDpdQ5p1JrkrRx797fZ39ym2KrQbLaYmZ1id2cfy7IwDCnWIVAfTHUnQPbUcCrGnGRfnoT2DIfk83mmGk0EcZZsPFy2gqKEWDkzY49I2FTEcNhnZdAjSRKKxSLDocwCr169KgNI7D9EZEjfRxBF5As2mlAgjNjb2iH2Awwhg6QfuajGhPqnKri+R7/fJ0GdSImNMwUf13Xpdrt/d1Q40zTJlUrZSZPLyXIlCAJ0Q3mIeQEyq9BNg42NDTrdrsQCajqe6zLo95mamsuMfFqtVkbHCcNwot0mUPUcjUaD/d2drJdnTPp9QRCgIpu4/qT3d+bcuQwn2Ol06Ha7bGxsoGgauWKBwXBIuV6l0+mQr5bo9HokponiJ6wtDycnVsL+1ibNxpTsOSgKagLd9gHvvPEWlmoSRR7lRoXHn3iMG+9c52BvH11Rib0AwoiD3R3Gjok39DBUi/m5GZozs1RaNcxSnW9+689ZW1nlwoULJEL27IqVOqGiEScqQo1Ai7DLFlpO4Ac+xXIVAwWEVAmWMCSTME5QdZ3h2KXWaBGGIeVqXXI7E4EfheialvVGU7ZNesgcHYQ96M3w0IHnOA6Li4tEUUgY+tRrDRShEkewvrPN7/3e7/EHX/un/Adf+TIffup97Fs29+/fJ3UjTA8v1/HodfsMRmMSIcU65IDGwCrpfOUr/x4vv/YDVtdvYCgGM/N1ND2iWTuGO/bI59+HohrEwuX2net85rMv8tbiLDdvXafSarG5t8+T73mWmbnTaJrF7bXrfO+lIXMzU4gk5tq1V6iWZRZ3eHiIbduUy2V2d3cZDodcuXIlg+KoCjKTs6ysDPQ8j263mxmL67opzca6/UkvTXryhlGVmdkWQggJdfF9SkUp1tput9nd3eXGjRvE8efQVIPQV2lU5gn9kFNLl2jv7TPu77N07AzVhRrO2OPShSfQMCmXaqiqSX884ubWHV5//TUqFTlU6XQ6zM3Nyf5b/EDlJUkSKpVKBidLSFDUB7RJiZUsyDVCnDF5Ug3FNAM0NAVFcg+IwiALlCAVpVRVYzweYtsmo9EA35eJRhJJjnQK18naZUKgGwqaUOjst+l3O3jDMcNeXx4cuowpY89le2eHg26H/nCAaT0o4Y/aBsRxjHZU5eXRad9R3Nej/cGHlVofdgdXNY1yrkjOLDIOQ7p+wFKlyvrybUyhkngBgTqZvvoeITFRBGqlyd2767j7B8RBTK6cJ5fPk8SCpWNzBIGHahqopoHQDTRFx++PqSo6HX+A0C0iAYeHHfxYKpUYcYLrehneDcDUdaqVCpeefi+HvSEgYH2dsXubON7Cd3wiNSYSAtOy8cI2YW+AN3alco0hCKMERTVxvTG5vE65VsCLfELNRCgKw94BSjhGKCG6bvD4YxeJ8yViYZEv13FGQ3wR4CoCq1gh8GKE0BiMXd7/sedRcnmMQolv/uvvkrdNTpw4Q7Fc4nDY57CzT2N6ChEEBK6PMAxcP2IwCBCJha1EeI5PEEfMtmroSYLvu1kjuVSqsLm5y8LCAnt794ljKfEEGqYl2TbuJAtMF5+m6ZkReboqoiTOJuOaphFGEYmqsXjiJHEsfTpqtSmCwGM0HnK430foBr/8H/3HJGj86n/+m9y7cZM4iugO+lkGkgJxxyOfSm0601Cs1Wrk9CJL504SWzabB/s0W7N0dvdRY7i/ukYUxMy2ZihOtRCqjabp5PMF7t69z/nz57l0/jFuvb3MYuskN99Y4ezxy1KuLQz4+Mc/yze/+RIoFs2ZGpHYYbffp9/p0g+G6GbIcJxD1QSO22Fv/z7Hjh1LNeBwvSHtnU2ef/Yqvh+ikjAcjKmUaxOco0qvd0Cn2+W973taZi3eiGFfg6lphBDYpoXvjUjwQA3ZaLfpuyNGrsQfFnN53DjEtC2CaMhhv4swNZzIpzbdwrZzVCs2UzMt6rUmYRAQhTFazuaZ5uMIIbjxzk0Cu0en0+FgK2Bqeg7MUtbzkwfQUJqex5LqJxXjYzoHPfJ6kUq+gqkpqLqPoSlZfFCVBF1FVl1xDl3VEar0ExYI2bdTBZaR9h6NycELodBJEsiZGpqhSQ+UVMJfaKhRjNobc/fePe6t3aXXkZqN8WSCbE2k52whGAz2cUOXMWNss5iJt2hC2hLI7DJ5uAf4br29n/R3sRKBbmLqBt7YwSqW0QxZf/9g0rNTFYGSTMrXJCFRJHskVyrT39khb1hgQBj6rNxdA8Adjbl27RUUTaU51ZIbLogJ/QjhecQC/DjEDUK63T4//4lPsTg3yzf/t/+V3d1drJzU+QPwdB1z7PDytWv8z//LH1AolNCShJl6GUNI8LOLoFqv0R30J9aTIstkfddDIFB1g1xBxbAtzJyJnsiT0AsD2gd7hJFHI18lShKW79zm7fUtfNfjyuOXudPrEUQRdpzgOw7CV6nWigRxxNbePvm6IK8VmV08TqtWZXPnHvbYZWd3i4XFJUqlEsQR6qSVoKjaRHUnplauoZs2o0GPwWDA4GCbuekpokh6S/h+mIHEP/7xT2Tc6jiO6U6GPqoqqUZSiFIQIV3AftQqSIAwihCK1MUTQloLmIZNpVrH9xxqjTp37txh5Dr8o3/03/F7/8P/xMh1+NV/8A8oFMsIRWPQ73P5stS/29nZ4e7aBlPTs+zu7tLtDVBUnbn5RTY3tvG8gKtXn+XMiZP86n/6n2EoKu3dNpcvXkLVTMZjh/v317l8+TJf+9rX8P2AkydPATD42h8SqAob+3t8/bvf5hOf+iQDb0Dg+Tz//Pv5xV/8RW7fvs3v//7vM7+wyNLSEndXbrJ8a5VeTw6ULMsgjALu3lvj4oUzRLGHpglm55rEcYJAxTAsDEN7QBubZFb5fD7rmaYEgbTV4DjOhHLnoeoaV95zhbfffjsD/vuKRqhE6LrK2bOnOTg4IJfLcez4Itdv3WT5zm1Onz49QTKoCCVBMxLpXRbI4cv5M6dpVOX7UBSFoeNyd6vNvbX7DAaDrAwunDiBksh+XBB4DIbbhEGH6fkSQsRYpoouzEwEmMlBqOs6qgBNxJhmjKZJ3KxQFIQyQRckWpY4qQjyloGum2iaghK6iInGpz2ZFQz6A/bau9y+9jKeJ83dmUyydUPFVFRpFiViECqGpoArcTRWXvbmjwqshnGAH/p/8wB4NOt7+DVAnyhejAMXHdk4DVzpNWHEEaqiEcaRlAZKYuBBqTUajTBNeVPTvoGERIRUbJsTJ05w6dIlAClISUIsJAxhOBqzu9cGoNftsqsIhsMxlpVjOBySEr6jKCEMowz2oqoq1kRIcnC4h6mpRGEk/2aiox9FEaY96Zm4HoVCgcCP6Xa72dRZU1VUElSR4HnScSyIQuxckf5wlJWQh4eHkpRtmtnkNAwjtra2QBF85OdeoFCf4nuvvc3nv/hFNGC3vcnMfJP7W3eBiWTRJJWPJv//0tIScwvH8ENZzoa+C6HLjgGktoMTX9ter5f1V1LRyZTqlT7PdIIPshp4N1BU+ndpP7FarVIsFjOoje97mKaJG/hcunSJ73z3uxw/dZJarcba7WVOnTrF3t4ehUKBQqFAs+AoWsMAACAASURBVNnk7toGvu+zuLjI/v5+1mxXVZX9lZv4js/+zj5JIri9ssbZpRNESOXmdnuyDnqSUfGhD32Ifr/PKA4489hFFs6dIs4bvHnzJrU3ZyX8x5MKKa7rcu7cOX7lV36FP/mTP6HT6fCRj3yW555zuL++yve+/126/T4bWxKFcOL4nOynmRq2XSOKJsgGBJpqZPczHRKlZd1Relk6RZ2ZmaFer5IrlSmUirSadb7w2c+wNLeApim4vgOGQbksvUiKxfIDYQOh0D7oYBo2zcYUAhWBFBlR1YQkJoN7pftX7jODCxdOcP7MsWyg5XkeEQmGYeJHAkXolEp5NFMlEgGmbaFbJmok0NUHKuiKLnuAuppgaNGkHyhQNYGShAghJ89qUpQtFFXB0HREIuW1ksAncCRVT7YQDhmPpVzbqN/DmcQCIFMzkiUtFHKWpO3pEpMqEtBRpAkTCkIR2TNRdA3V0B/uAf6kQe6vC4BBHElDI9/HiiI0RaVcLDEeDFEmWQvxhJ9HQpxIiprruvR6PRlsTDMbeMzNzdFqtThz7hxPPPNcZmaT+iIUfJ8QlUQoPP30Vb7zymsc7B9SL1VIhEJ/2McwzKzHESfSN8DzpG+wYRiYus5wOJRBN45QVbk4/CjM8IppzysMQ1zXRVUMmfEoqWpGDEksDcIViIIIYap0+2Nml45zY2OLUqE4If+72KYuCfW1CvudfU6ePoGqGRw/eQJfsbhw8TLleoNrP3yZ8xdOsbA4jW3ns+yCZNJLnWAbW60WG1s7XLt2jd39A4p5m6Ktc7B9j8W5WdkDVWSwfHwCDbBtO8O4PTrYSHFvP8mVYbImU7uUyvgAoqRQLJbZ3N6lWqnjuj5BENHtDXj+/R/kxo0b3Fq+w7ElHc8PEYrG3MI8di6H47nopsGJUydxXJepqSkW5xcn4NiEN954kw9/8EMYQqVYrqApCpVKhSRJmJubo91uUyhIREKQxBw7d5qX33xNBqRqif/n6/+K+ztbKAdD3nnnHX7hF34B3/dpNpt85Stf4etf/zprd7d5z3uuML+wRPugww9efol8vsY715fpdHrU61r22YWQ7IX02aR7JZXXT1tLR7Gn4/E4w8JduHQRu1jik5/8JI7jkLdz2KZJ6Af0BgN+eO11NE3jl37plyY9w4A4jjlx/CQ3bt7C9wNM05pkPEe53CKjHKZQHCEEoeczGh7ijL2JMrhJ4I+J4gCNMjmrTrlg0GzMcuHSk7z+9jsEcULouYSjAZomudHRpApA1YkUBZcEJZDCqYobTyTnE0QCkTfCCwOJ+Bh0ScYdxoM2/nhE5EpIWpohO47UX9QNlUKhkEGEwjDEcZyJZFvMaNCTjn5WTlZGfoA3GjPqDx7CvSbKA39g7WhT+yeFxDwa9I5CZGIFDNti6DoMRkNqU7MoiSJlh/yAaBL0FE0l9H38MEDNWRiaNDvZ3t6m0+lkZPwXXniB7e1tisUi3W6X//cb3+Tq1au0GlNy4mQaOAOB5/t4ieDrf/4tfD9kZnoe1/WlDJD6wNF+MBjguA66adNqtWg2m5RKFXrtNnZOBjTfGTOc2GWmC8bz/Ym6TJBp4KmKzIymZ5qEYUi/32Nhfhp3PJDYQsUgiBMSVePtd27QarXodbo89eQTrK2t4I6HKIoiy5iyhVkw6XYGHHY7BEqOcrXG91+9hmkZ5Gs1nCBkOPZJREA5X8megxCy0zwajdjd3aXZbPLk089QzNsULI3u3gbvvPkGjuNQqRU4d+4Sa2tr1Ot1er0erVZLPrtMGolH+KxHTre/Zj0clRRLN7plFtB0m25nyLFjJ9ndP6TZmKGQr4Cis7m7z/b+AfudHuvbuwwceTC12/tZYAiCgHubG4xGIxYXF+mOhvy7X/p3sCyL+blF9nbbRFHErZVV3OEAayJP9ad/+qdYlsWtW7c4deoUr377JYqNGoamYVg6h90O+VwO3/P4wNWrdDqdDOyc+ga/+OKLdLou3/jGN+h2u7z0vZfp9cY8duUZrj77Qa5978+4t7bD8ePHqVRL6JNmfOp7k+6vLEiIB4ZFKRA9bfirqkqn02Gnd0hERLNaIQ5CDvf3WN/YYGXtLvOzJ/nwhz9Mqzmd3X9VUXFdn4985OcmA6t4EvRkjmMYFrEqufL9fj8TvtB1nXzBZrBzyExDcoA9z8Pp+7xx7RpbW7uMxwbCyBPEOgEavppDtyWWV1U8kokVp2maRMnEnzhWSCJLOrVFIePBgLdffw2RJPjOkCAcEkSxZHTEDi3b5fyxFrWCQRyToTHm52fJWwVZgagq7gSVHYZhBilK8YFxHJIEIUkEtqLR3WszDjxEAoYlFXvSIVUaDLWjmK6f5jq6MR7+XmaAQlNpTU9NQM4JSgKh5+MmHgpgKJrUX55kC8IwMwFNRVE4PDzk+vXr7O7uyp7S5johCSfPX0TTtMyZazgaoYcBAQmJMCanholl5di4v8rhYTer++fm5qjXm9y5c4cgiFhdXSWXy6GqKvV6nbwuEEJO3kJNJ1fIM+wNJwMCpCNcEEherPLAHS3FyNXrdSxdQ5mcrqEXgpowchyq9SYHIykT1ev15Eg+CFCFBHxaOYPjx49zUO7hhzGjwEETFmfPX6BWL5HPW0ThGMf1KRREJvn16HX+/HkUTUWoOnHoQ+xTKpUkwb1SxbSl0GcKfzg6mfu7uoIgQDclhlBRFK5cucLVZ57h/uYGMzMzhL4USDg4OGBxcZHZ2VkGgwGqqkyEEyR2c3p6mm63y4ULFxCqQa3WyIC7iUD2Z32fwcE+x48do9vtsr6+zgsvvMB4PKZer3Ny4Rjv/8iHuHnzJoPhkNgPGI7H2IrO22+/zczMDHEcS7n44XDCw82jWRFf/NJnuL28ws7eLrdu3SaONS5ffpKpcpHvfe8lVu6sEych58+fpdms4wcuyhHZuKMZ4P7+/kOqRek0WQiBaugE3pAg9Ag9hU77gJu3l9ne3eHS40/y3qsfZmFhgSh6eN8mkLV2jsKXftSVlt7SI2NM3rSpTWA/5Iv4jkezWqdRaxAlMYf9CD/JMQo0NtoeQaQRIxWc0hgSDh6I5yZJDGqMmmioiQJ6mXx9kb2dLYLIlBmaBokWY6gucdwmjCb4YctA12WAHQx6mStdEHjo+oOWQuokJ0t2B0UBXSiEfkSxWOTiufOMfY8gkvatvV6PQX/0EIpBSzOIH0V9erfr6L89Oi2OJzdAn4zGwzCkaMnNqmkaSqTg+j6aNTEKjxTcIECNYw4ODmg2mxiGQaPRYHt7m2PHjnH58mW2Nu6ze9CewAi6jAbyhBiNRhiuSxAmCFtnNJQG3sNen16nixtIRHuUxKzcXZPCpgLplcoD1kqzUsEf9/F8CWXI1xvEJNRqtQwVn95s3/fRNRXTyGVqx0mSYBgaqiIpcZZhoto6g0FAvihVgnVfz3QUHcdBFQlCgdnZacI4YGZuGsvOs7a2RqkxTz4nQFVIhIrjuqgTuf4ojDNmwqPPxPd91EQjCWMUYjRVwgeUSVlYrbeyBVssFtnY2KBcLmcwhp+mCvhJL9l3jcnl8hx0OoRxxPRMHaFpVMpF3PEIP4w5deYc586dQwjBcCxVeE6cPpXhwaanp7H393EDHyMWxMEDi8YgChm7Dqurq9RyeZr1Ibu7u5w/fz5ToFleXmZ3cwsjUTg2Pcfb19/h2NQs5WqFMIqwFAkNefXVVzl58mQG6YjjGMuG0fiQs+dO8H/93/8CReisr2/yxBMRxxZPksQq/+yf/QHf+LP/j0988kWuXn2KQqFAckQpJKNZ6jq3bt1iMBiwsLDA2bNnswxnPB5TLpfZ3+5KObFxm7t3Vhg4Yy5evMiLP/9xynYTy7Ky9ZjdZx4Y179b8EvL30zUNoJysYKm6IhEToJN3WJhbpH1zTVm5/J4cRcR+ezvD+g7IUIvoZkWYVwkmbgkOr6DmEBJEkUOxpQ4Qk0SDEXQmJ3FTxK21++SoBEicYFeFBOjoCoGpmkQJ94kQ1NQNYFQElRN4HkxnuNk84J0D8hAHhCGvlSiNwzUGKZbUwTIqkbEkaz+HIeR6xAEgex9z1r6bx4xlmfin0YCiEfNPh65iY9+nyQJEQKzUkU3NcZuH9sSVIo2lVyeH770EiICQzdRUQnDmAhBmICRz+F4Pt3DA3KWyeb6fVQBx48tcu2VH5Iv2oRegPAD5lvTbN29T94yeOfGOziRj1Eq8JnPfZ4g8jjY34HApb29gRImaFGIloSIWJKpA0WnMT9He9Bj7IwYDnuUykU6vT6xZhCi4DkuBTOH0xtiCk2yv4WCoqr4boitaxgiQlcSpqYbCBXG3oCpWoGiVWBnYwdCgWqY9EYDNNNEVXMoho5WyNHt9Qm8gGalzlSjyczcAoVcjfX1XfKlJifPnKU+06JgF7BNiyQKsAyF7770FwA8fvkKpUo9e05xFBGHAZahoQvQkgCViCgI+PNvfYe9gy4Hhz3CMCLyQ/rdHuPRiN2dHaIgZNgfYBoGhqYiO3YTdIeUt0D8iPnvo22Qo+2Uo1+qphJFAaqq4IxHlIsFauUyahzTPThEJIJqpUoxXyTwA/Z291BQuLe2zsXzl8hZBRq1JuVihVKhjG3myRsmUegy7HXpdw4o53Mc7u6yNDdHuVTk+vXrJEnCuXPnuHfvHsvLy0RRRKVWozU9xdhxWF9fp1Iuyx6bYRI4LqdPnsTUdbY3NxkPRxiahq5qeI5COV9HxCrPXb3KE49f5q03X2VzfRW92iJA44/+zz/G9UOOnzzOfnsHzx+Sz5tyX00CQUG38UZjvv+dl7h27RUMTWV6ZgbTyhHFgqHjY+fybG7eB6Gys9um2prhIx/9OB94/0dp1KcI4oQgComSREqJMVFyEuIhjuyP2rNHe2FZf83SpahHHDJ0RvhRgBf6KLqKphm0ylOMh31ymuDcsWlGO5sk3X2CwSGBokAkJdAUIYjQEKoFMeiqFEsgkgIKKlCtlikVKxx22hAPMdUI4QXYIsEuRFRrFoVJVeL7fqZdOBwOcT0H2zLQNYUkDnGdEUkcoipSGCUIQxzPxfFdNEMllzexDA1VqOhHKLLVcoVquUy5WOKnq3t/witVnQ3DUMI1gJ2dHUmT0h80i6MkzhRYVFXNWBqpyrMQgqmpKfrDAZvb2xx0O9SmmvQHA+ndqSrkCnmeu/oMZ0+d5tixYxwcHEg0/sodojhGqAqheCB5VSpJvNOpkydpNBq4E5ZHqi8nEslpzRkase+SMzQiz8FQoFiwUUiyz5ROSTNivJBgVz8MUTQd1/dgUsa1Wi16gy4HnUNCzydv2eRNOXBpVetEQch0o0n/oMP8zCyVcplhp5f1iI4CONNexlGqUNrXSAOTzLqkXeZoNMK2bTmFdV10XadcLmey9bOzsxmZ/ae5jga5d7vS3laK5Uv5nWn2nZYynudlvhrlcpnp6Wmq1SqGYcgydDKlPlqx2LZNs9nE8zy2t7dxHIfDw0P29vaYn59HCMEbb7yBZVmYpsnc3ByOI43n8/k8rVaLnZ0dKpUK4/GYra0tms0mc3Nysnvnzh1effVVNjY2WFlZYW9vj9/4jd/gt3/7twmCgEqlwl98419yd+Um//C3/guuXHkPZ89f5nNf/BJ9N+TevfXJkCIhCEPcidZlvdXk8uXLmSF72ldOsZdBIFEBZ8+e5xM//ymeeOIJSqUSzqQ/+je90tIvXSvpJDX9fzVNag+mA8NKpYJuWFgTu4dCIccLL36Aj/zcc3zyY88yn3cpxofk/F2soI3h7mO4OyTOHn40JiIhUhVcVIaxiaeUMarTNOdbJCa4iQt6ghuP8UIHzXzAJkktPvMTLraiKJkZffo50n1tmuZD+/jodD393jRNCoVCxoARQqDOWfpv/riF/JNmgEevWAhEPo+iK3iBi6oqWIbFsbljvPbDV9EyuIjMKRRNJRYQ6yrBRJAwDYCqqtJsNhk7YwzbJAgjFhePMXIc8sUidrFAtVRiul5hd2eH1bV73FlZwfUDcoUCxUpZyjkRIYjxwgBdNzh/+XGmZme4cfMmgefhux6WLjfZoN9HIaKaNwkdB12RcJJ8KU+jXmEw6BOGYGgq1uQmyl5mgmHodNq7VKsN1lZWGY49whgWFpaYnT/GMAykO9zxJRLHxVAE3mDE5z/7Gd6+cZ1XX3mNWzeXqdanOHnqNM3pGXrD8eReSJDpt7/9LfK5Ak8/eRU7XySaqNsCmSinXNgPmBn37t0D5LS7UCiwuLBILpfLFlUaHGVQ+ek206OskB/1dfTfpp4Z6XNOFVAsy5KSZMgDtFKpMBgMmJ6efqhHnZY/zmhIvpDLbBEODw9ptVrkcjn6/T7z8/Pouk6z2WR5eVmWz7aNoqoUigVu3LiBZVnUajX29/cpFotEgZxce56XQXgy0ycUpqenKZVKfOxjH+PkyZMIIXjve9+LpcNbb77J7//B7/PGm2/z97/0Jc5fvERrbo4bb77OyuoalmXTrDcn3tsJrVaLuflZ5ubmKJbKKIpKFEn5tbEzZnllmStX3sPnPvM5FhYWcccuIlEIohBxRKUpPQh+kkHm0edx1NslSWIsKxUjiLLkw/d9WlMtTMMgFgJVl2ZfmqZh5XSG/T0KyYBwsE3JjBFBFxH00cIRsd/HD31UAhSRACpRohLEKlGSUKmYxIQMRyOSMEYEI0zNpVG2yelGhkqQa1nJ1o1t29k6T6foIMtg98hUXdd1PM+bKMgLie113Qkcjizp+jsJgEohDwpouoKua5xcOkE5X+bVH74i3cOShCSM8MKARFUQuoaRsxG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      "text/plain": [
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     },
     "metadata": {
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     "output_type": "display_data"
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   ],
   "source": [
    "for i in range(len(top_left)):\n",
    "    img = cv2.rectangle(img, top_left[i], bottom_right[i], (0, 255, 0), 7)\n",
    "    img = cv2.putText(img, labels[i], top_left[i], cv2.FONT_HERSHEY_COMPLEX, 0.6, (0, 0, 0), 4)\n",
    "plt.imshow(img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now processing video in real time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parsing cfg/yolo.cfg\n",
      "Loading bin/yolov2.weights ...\n",
      "Successfully identified 203934260 bytes\n",
      "Finished in 0.02119302749633789s\n",
      "Model has a coco model name, loading coco labels.\n",
      "\n",
      "Building net ...\n",
      "Source | Train? | Layer description                | Output size\n",
      "-------+--------+----------------------------------+---------------\n",
      "       |        | input                            | (?, 608, 608, 3)\n",
      " Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 608, 608, 32)\n",
      " Load  |  Yep!  | maxp 2x2p0_2                     | (?, 304, 304, 32)\n",
      " Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 304, 304, 64)\n",
      " Load  |  Yep!  | maxp 2x2p0_2                     | (?, 152, 152, 64)\n",
      " Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 152, 152, 128)\n",
      " Load  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 152, 152, 64)\n",
      " Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 152, 152, 128)\n",
      " Load  |  Yep!  | maxp 2x2p0_2                     | (?, 76, 76, 128)\n",
      " Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 76, 76, 256)\n",
      " Load  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 76, 76, 128)\n",
      " Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 76, 76, 256)\n",
      " Load  |  Yep!  | maxp 2x2p0_2                     | (?, 38, 38, 256)\n",
      " Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 38, 38, 512)\n",
      " Load  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 38, 38, 256)\n",
      " Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 38, 38, 512)\n",
      " Load  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 38, 38, 256)\n",
      " Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 38, 38, 512)\n",
      " Load  |  Yep!  | maxp 2x2p0_2                     | (?, 19, 19, 512)\n",
      " Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 19, 19, 1024)\n",
      " Load  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 19, 19, 512)\n",
      " Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 19, 19, 1024)\n",
      " Load  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 19, 19, 512)\n",
      " Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 19, 19, 1024)\n",
      " Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 19, 19, 1024)\n",
      " Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 19, 19, 1024)\n",
      " Load  |  Yep!  | concat [16]                      | (?, 38, 38, 512)\n",
      " Load  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 38, 38, 64)\n",
      " Load  |  Yep!  | local flatten 2x2                | (?, 19, 19, 256)\n",
      " Load  |  Yep!  | concat [27, 24]                  | (?, 19, 19, 1280)\n",
      " Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 19, 19, 1024)\n",
      " Load  |  Yep!  | conv 1x1p0_1    linear           | (?, 19, 19, 425)\n",
      "-------+--------+----------------------------------+---------------\n",
      "Running entirely on CPU\n",
      "Finished in 9.121592044830322s\n",
      "\n"
     ]
    }
   ],
   "source": [
    "options = {\n",
    "    'model': \"cfg/yolo.cfg\",\n",
    "    'load': \"bin/yolov2.weights\",\n",
    "    'threshold': 0.4\n",
    "}\n",
    "\n",
    "tfNet = TFNet(options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "This file removes frames from a videofile.\n",
    "The resulting file will look faster when played back at normal speed.\n",
    "The idea is to create video that can be processed by yolo and look normal speed \n",
    "'''\n",
    "\n",
    "# import cv2\n",
    "# import numpy as np\n",
    "\n",
    "capture = cv2.VideoCapture('demo.mp4')\n",
    "size = (\n",
    "    int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)),\n",
    "    int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
    ")\n",
    "codec = cv2.VideoWriter_fourcc(*'DIVX')\n",
    "output = cv2.VideoWriter('reduced_frame_demo.avi', codec, 60.0, size)\n",
    "\n",
    "i = 0\n",
    "frame_rate_divider = 3\n",
    "while(capture.isOpened()):\n",
    "    ret, frame = capture.read()\n",
    "    if ret:\n",
    "        if i % frame_rate_divider == 0:\n",
    "            # frame = cv2.resize(frame, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_CUBIC)\n",
    "            output.write(frame)\n",
    "            cv2.imshow('frame', frame)\n",
    "            i += 1\n",
    "        else:\n",
    "            i += 1\n",
    "        if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "            break\n",
    "    else:\n",
    "        break\n",
    "\n",
    "capture.release()\n",
    "output.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Processing a video for YOLO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/pothole_detection/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/opt/anaconda3/envs/pothole_detection/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/opt/anaconda3/envs/pothole_detection/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/opt/anaconda3/envs/pothole_detection/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/opt/anaconda3/envs/pothole_detection/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/opt/anaconda3/envs/pothole_detection/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "/opt/anaconda3/envs/pothole_detection/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/opt/anaconda3/envs/pothole_detection/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/opt/anaconda3/envs/pothole_detection/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/opt/anaconda3/envs/pothole_detection/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/opt/anaconda3/envs/pothole_detection/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/opt/anaconda3/envs/pothole_detection/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/leoadlakha/Desktop/Pothole_Detection/darkflow-master/darkflow/net/build.py:15: The name tf.train.RMSPropOptimizer is deprecated. Please use tf.compat.v1.train.RMSPropOptimizer instead.\n",
      "\n",
      "WARNING:tensorflow:From /Users/leoadlakha/Desktop/Pothole_Detection/darkflow-master/darkflow/net/build.py:16: The name tf.train.AdadeltaOptimizer is deprecated. Please use tf.compat.v1.train.AdadeltaOptimizer instead.\n",
      "\n",
      "WARNING:tensorflow:From /Users/leoadlakha/Desktop/Pothole_Detection/darkflow-master/darkflow/net/build.py:17: The name tf.train.AdagradOptimizer is deprecated. Please use tf.compat.v1.train.AdagradOptimizer instead.\n",
      "\n",
      "WARNING:tensorflow:From /Users/leoadlakha/Desktop/Pothole_Detection/darkflow-master/darkflow/net/build.py:18: The name tf.train.AdagradDAOptimizer is deprecated. Please use tf.compat.v1.train.AdagradDAOptimizer instead.\n",
      "\n",
      "WARNING:tensorflow:From /Users/leoadlakha/Desktop/Pothole_Detection/darkflow-master/darkflow/net/build.py:19: The name tf.train.MomentumOptimizer is deprecated. Please use tf.compat.v1.train.MomentumOptimizer instead.\n",
      "\n",
      "Parsing cfg/yolo.cfg\n",
      "Loading bin/yolov2.weights ...\n",
      "Successfully identified 203934260 bytes\n",
      "Finished in 0.03949904441833496s\n",
      "Model has a coco model name, loading coco labels.\n",
      "\n",
      "Building net ...\n",
      "WARNING:tensorflow:From /Users/leoadlakha/Desktop/Pothole_Detection/darkflow-master/darkflow/net/build.py:105: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "Source | Train? | Layer description                | Output size\n",
      "-------+--------+----------------------------------+---------------\n",
      "WARNING:tensorflow:From /Users/leoadlakha/Desktop/Pothole_Detection/darkflow-master/darkflow/net/ops/baseop.py:70: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
      "\n",
      "WARNING:tensorflow:From /Users/leoadlakha/Desktop/Pothole_Detection/darkflow-master/darkflow/net/ops/baseop.py:71: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n",
      "\n",
      "WARNING:tensorflow:From /Users/leoadlakha/Desktop/Pothole_Detection/darkflow-master/darkflow/net/ops/baseop.py:84: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
      "\n",
      "       |        | input                            | (?, 608, 608, 3)\n",
      " Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 608, 608, 32)\n",
      "WARNING:tensorflow:From /Users/leoadlakha/Desktop/Pothole_Detection/darkflow-master/darkflow/net/ops/simple.py:106: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n",
      " Load  |  Yep!  | maxp 2x2p0_2                     | (?, 304, 304, 32)\n",
      " Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 304, 304, 64)\n",
      " Load  |  Yep!  | maxp 2x2p0_2                     | (?, 152, 152, 64)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/leoadlakha/Desktop/Pothole_Detection/darkflow-master/darkflow/dark/darknet.py:54: UserWarning: ./cfg/yolov2.cfg not found, use cfg/yolo.cfg instead\n",
      "  cfg_path, FLAGS.model))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 152, 152, 128)\n",
      " Load  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 152, 152, 64)\n",
      " Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 152, 152, 128)\n",
      " Load  |  Yep!  | maxp 2x2p0_2                     | (?, 76, 76, 128)\n",
      " Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 76, 76, 256)\n",
      " Load  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 76, 76, 128)\n",
      " Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 76, 76, 256)\n",
      " Load  |  Yep!  | maxp 2x2p0_2                     | (?, 38, 38, 256)\n",
      " Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 38, 38, 512)\n",
      " Load  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 38, 38, 256)\n",
      " Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 38, 38, 512)\n",
      " Load  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 38, 38, 256)\n",
      " Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 38, 38, 512)\n",
      " Load  |  Yep!  | maxp 2x2p0_2                     | (?, 19, 19, 512)\n",
      " Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 19, 19, 1024)\n",
      " Load  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 19, 19, 512)\n",
      " Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 19, 19, 1024)\n",
      " Load  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 19, 19, 512)\n",
      " Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 19, 19, 1024)\n",
      " Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 19, 19, 1024)\n",
      " Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 19, 19, 1024)\n",
      " Load  |  Yep!  | concat [16]                      | (?, 38, 38, 512)\n",
      " Load  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 38, 38, 64)\n",
      "WARNING:tensorflow:From /Users/leoadlakha/Desktop/Pothole_Detection/darkflow-master/darkflow/net/ops/convolution.py:28: calling extract_image_patches (from tensorflow.python.ops.array_ops) with ksizes is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "ksizes is deprecated, use sizes instead\n",
      " Load  |  Yep!  | local flatten 2x2                | (?, 19, 19, 256)\n",
      " Load  |  Yep!  | concat [27, 24]                  | (?, 19, 19, 1280)\n",
      " Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 19, 19, 1024)\n",
      " Load  |  Yep!  | conv 1x1p0_1    linear           | (?, 19, 19, 425)\n",
      "-------+--------+----------------------------------+---------------\n",
      "Running entirely on CPU\n",
      "WARNING:tensorflow:From /Users/leoadlakha/Desktop/Pothole_Detection/darkflow-master/darkflow/net/build.py:145: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
      "\n",
      "Finished in 8.648632049560547s\n",
      "\n",
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     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-18db39bb7ad8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0mret\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcapture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m         \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtfnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturn_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m             \u001b[0mtl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'topleft'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'x'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'topleft'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'y'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/Pothole_Detection/darkflow-master/darkflow/net/flow.py\u001b[0m in \u001b[0;36mreturn_predict\u001b[0;34m(self, im)\u001b[0m\n\u001b[1;32m     82\u001b[0m     \u001b[0mfeed_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minp\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mthis_inp\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m     \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     85\u001b[0m     \u001b[0mboxes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfindboxes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m     \u001b[0mthreshold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFLAGS\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthreshold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/pothole_detection/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    948\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 950\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    951\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    952\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/pothole_detection/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1171\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1172\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1173\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1174\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1175\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/pothole_detection/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1348\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1349\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[0;32m-> 1350\u001b[0;31m                            run_metadata)\n\u001b[0m\u001b[1;32m   1351\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1352\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/pothole_detection/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1354\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1355\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1356\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1357\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1358\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/pothole_detection/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1339\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1340\u001b[0m       return self._call_tf_sessionrun(\n\u001b[0;32m-> 1341\u001b[0;31m           options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[1;32m   1342\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1343\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/pothole_detection/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[0;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[1;32m   1427\u001b[0m     return tf_session.TF_SessionRun_wrapper(\n\u001b[1;32m   1428\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1429\u001b[0;31m         run_metadata)\n\u001b[0m\u001b[1;32m   1430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1431\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_tf_sessionprun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "'''\n",
    "Here we are processing the reduced_frame_video\n",
    "and using it for YOLO for every frame and hence \n",
    "we can use the seperate frames to form a video \n",
    "by combining all these frames\n",
    "'''\n",
    "\n",
    "import cv2\n",
    "from darkflow.net.build import TFNet\n",
    "import numpy as np\n",
    "import time\n",
    "\n",
    "option = {\n",
    "    'model': 'cfg/yolo.cfg',\n",
    "    'load': 'bin/yolov2.weights',\n",
    "    'threshold': 0.30\n",
    "}\n",
    "\n",
    "tfnet = TFNet(option)\n",
    "\n",
    "capture = cv2.VideoCapture('reduced_frame_demo.avi')\n",
    "colors = [tuple(255 * np.random.rand(3)) for i in range(5)]\n",
    "\n",
    "while (capture.isOpened()):\n",
    "    stime = time.time()\n",
    "    ret, frame = capture.read()\n",
    "    if ret:\n",
    "        results = tfnet.return_predict(frame)\n",
    "        for color, result in zip(colors, results):\n",
    "            tl = (result['topleft']['x'], result['topleft']['y'])\n",
    "            br = (result['bottomright']['x'], result['bottomright']['y'])\n",
    "            label = result['label']\n",
    "            frame = cv2.rectangle(frame, tl, br, color, 7)\n",
    "            frame = cv2.putText(frame, label, tl, cv2.FONT_HERSHEY_COMPLEX, 1, (0, 0, 0), 2)\n",
    "        cv2.imshow('frame', frame)\n",
    "        print('FPS {:.1f}'.format(1 / (time.time() - stime)))\n",
    "        if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "            break\n",
    "    else:\n",
    "        capture.release()\n",
    "        cv2.destroyAllWindows()\n",
    "        break"
   ]
  }
 ],
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