NLP_name-generation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating names with a character-level RNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# categories: 18\n",
      "['Arabic', 'Chinese', 'Czech', 'Dutch', 'English', 'French', 'German', 'Greek', 'Irish', 'Italian', 'Japanese', 'Korean', 'Polish', 'Portuguese', 'Russian', 'Scottish', 'Spanish', 'Vietnamese']\n"
     ]
    }
   ],
   "source": [
    "from __future__ import unicode_literals, print_function, division\n",
    "from io import open\n",
    "import glob\n",
    "import os\n",
    "import unicodedata\n",
    "import string\n",
    "\n",
    "all_letters = string.ascii_letters + \" .,;'\"\n",
    "n_letters = len(all_letters)\n",
    "\n",
    "def findFiles(path): return glob.glob(path)\n",
    "\n",
    "def unicodeToAscii(s):\n",
    "    return ''.join(\\\n",
    "                  c for c in unicodedata.normalize('NFD', s)\n",
    "                  if unicodedata.category(c) != 'Mn'\n",
    "                  and c in all_letters)\n",
    "\n",
    "def readLines(filename):\n",
    "    lines = open(filename, encoding='utf-8').read().strip().split('\\n')\n",
    "    return [unicodeToAscii(line) for line in lines]\n",
    "\n",
    "category_lines = {}\n",
    "all_categories = []\n",
    "\n",
    "for filename in findFiles('data/names/*.txt'):\n",
    "    category = os.path.splitext(os.path.basename(filename))[0]\n",
    "    all_categories.append(category)\n",
    "    lines = readLines(filename)\n",
    "    category_lines[category] = lines\n",
    "    \n",
    "n_categories = len(all_categories)\n",
    "\n",
    "if n_categories == 0:\n",
    "    raise RuntimeError('Data not found. Make sure that you downloaded data '\n",
    "        'from https://download.pytorch.org/tutorial/data.zip and extract it to '\n",
    "        'the current directory.')\n",
    "\n",
    "print('# categories:', n_categories)\n",
    "print(all_categories)"
   ]
  },
  {
   "attachments": {
    "image.png": {
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1QhAIZmxz4DtQJPkmSnskEBABfLNCo4IVbQwkkDcC6PnEXkZY7g0/xnu8GMIigC9f2G8NPXvYXbxRw1491RrzpRrxrKNI6ok8z5NAjglgDhK+7TGQQN4IoKcCjR4E0uLFi/NW/NKVd9++ffHqOIglPHOy6Lm285OwUaivQJHkiyTtkEBgBLCBHB5YPh8YgVWRxSkggV27dsWNLOaY4BUhDPkigN5rLMvHswf7LmHidzPDTTfdFO+f5CtPiiRfJGmHBAIjgIna06dPD6xULA4J1CaAxhWr1dC4Yo4L5xzV5pSXo/hyhuE3bFSJLRmatX0A8sFqR6zI8xEoknxQpA0SCIwAHhAYpmAggTwQWLNmTbw6CfvxZDFMkwdGeS0j3n9nJ9zfe++9TamG7UXH3nD1BoqkegkyPQkERuDgwYNxg4OHEwMJhE7ADs1g9RpDcQlg6BS9hHgVSjO2IsFQH97TV2+gSKqXINOTQGAEMFTh4+EQWLVYnIIRwJJxbHCKYWHs8MxQfALo2bHXvNGr4DAXCqIMk7nrCRRJ9dBjWhIIjAB6kfBg4JBFYBeGxelEYOnSpbGfYvVlM3oVOmXOD5kTwFxJPKcaPWcSm0xChNcTKJLqoce0JBAYATQ62FSNgQRCJYC9jtBA8h2CoV6h5pQLPUmYYI3XnDQyIA/staUNFElackxHAoERwD4laHz4Ys/ALgyL007g1ltvjX1069at7cf4T3kJYEUjJnVjwj7+b0TAa2yw35Z2dR1FUiOuCm2SQAYEMFGx0d/KMqgWsywIAfsyWg4FF+SCeqoGxMuIESPi1biNerUJ5kFh6E0TKJI01JiGBAIjgAcNepH4DT2wC8PixASmTJkS75fDjU3pEN0RwCaQeIY1Yn8s25vUXd7ScYokiQ7PkUBOCMybN88MGjQoJ6VlMctEwPYg7d+/v0zVZl0VBDCnEkIJC1B8B0zgvuOOO5zNUiQ5I2MCEgiPAMb1+QqH8K5L2Uu0aNGieM8uvGqEgQTSEBg3bly8Is13jxJ8EZO4XQNFkisxxieBwAhs2rQp/vbFpdSBXZiSF8f65dq1a0tOgtV3JYAX5GIyt+9nGkTSXXfd5VQciiQnXIxMAuERGD16dPzOq/BKxhKVlQCG1vCC2ttuu62sCFjvOghAHA0ZMsSgV8lnmDt3rvO0BIokn1eAtkggAwJ4gSRXDGUAnll2SwDDvzfffHO353mCBHoigB2zIbR97qeFITzMeXJ5pxtFUk9XiudJIGACq1ativcACbiILFrJCGByLPal8T1UUjKMrK4xBjuzQyhBMPkKI0eOdOp5p0jyRZ52SCADAtgbCUtnGUggBAL22z+WXDOQgA8CEDX48RVWr17tNIGbIskXedohgQwI4Bt7vS9wzKDYzLKgBDCHBBsDMpCALwJWeKNXyVfAcxNiKU2gSEpDiXFIIEAC999/f9wVHWDRWKQSEti4cWM834P7IZXw4je4ykuWLIl35PaVDfbuSvuOS4okX9RphwSaTGDatGmpb/QmF43ZlZAANjOdPXt2CWvOKjeDAJbv++pNwpsJMNcpTaBISkOJcUggQAKtra3q9xEFWB0WKccEtm/fHvci+d4AMMdIWHTPBObMmeO8fF8qAobc1q9fL0WJz1Ek9YiIEUggTAL4JoQhNwYSyJoAXifBlytnfRWKnT/eT4ln3oYNG7xUdMyYMWbixIk92qJI6hERI5BAeATwtmzs98FAAlkTsC9X3rJlS9ZFYf4FJzB9+nSD3bh9BOy8jf28egoUST0R4nkSCJAA3kM0ePDgAEvGIpWNAHYxpi+W7apnU19sAunryyHEPTbi7elluhRJ2Vxr5koCdRHA3khtbW112WBiEvBBABO2Fy9e7MMUbZBAjwQwl+jee+/tMV6aCHj1ye233y5GpUgS8fAkCYRBABMMq1d2oMt53rx58bE0kw/DqAVLkXcCXf0Qu2rjmz0aGvph3q9uPsqPuW9p5hKlqQ3sYENeKVAkSXR4jgQCIYA5SFjNZkP//v0NdjXGMZxjIIFmEOjqh3b3YvphM+gzDxBAr2WauURpaGH/JfSESoEiSaLDcyQQEIHqhgirPB544IFOwimgorIoBSZQ7Ye33HJL/MoIHGMggWYQsC+pxZyiekOaBTAUSfVSZnoSaBIB7BOyfPlyc/jw4XiIA//jGAMJNJOA9UPkOXToUDN16lT6YTMvAPOKtwLA3lw+Ar5wYnPJ7gJFUndkeJwEAiOA1z5gO/1NmzbFL2jE/zjGQALNJGD9EHliPhIWEdAPm3kFmBd23/Y1eRtCf/78+d1CpUjqFg1PkEBYBNDNjLlI2N8D4+gDBgww3OE4rGtUhtJYP9yzZ08skuiHZbjqYdURW074WlHZ0+RtiqSwrj1LQwIiAexsfNttt8UiCf8zkEAWBOB7K1eujIc96IdZXIFy5zlixAhv7wnEPl/SBpUUSeX2NdY+ZwTw7QnDG9ybJmcXrmDFhR9OmTLFYM8aX9/oC4aI1WkgASzbx6IBHwFiX1otR5HkgzJtkECTCOzYsSMeZsNcEPzPQAJZEIDvQahjx2L6YRZXoNx53nzzzd4208UEcEze7i5QJHVHhsdJIFACaJjww0ACWRKgH2ZJv9x5+xRJdkNU/K0VKJJqUeExEgiYALqGsZ0+AwlkSQA9SXxnW5ZXoLx545VMEEq+AnqSuttSgCLJF2XaIQESIAESIAESaDgBnz1JKCy+eGJuUq1AkVSLCo+RAAmQAAmQAAkEScC3SJIWwvQokvaMiAx/yCCtD/i+o9Lmy3j0UfiA70C/ol+5+IBv/6O92gR8i6Rhw4bFL2mulVuPTxUXB2FcPlBqOVk9x+hT9CkXH6jH12qldcmbcemrtXyIx/wTwAaQEyZM8GYY+yR194oniiT2lHntKfTmtccNseFhw+PiA/Q/+ouLv/iO69v/aK82Ad89SaNGjTIzZsyomRlFEkUSRRJ9wKsP+G54XOzVfMrVcdAlb8alQKvD1ZjUgYBvkTRu3DgzefLkmiWgSGID6bWBrOlldRxkw8OGx8UH6nC1mkld8mZc+mpNJ+JB7wR8iyS8xQA2awWKJIokiiT6gFcfyFIs1HrI1XMsy7ow7/yJrnp8jWnTE/A9J0kSXRRJbCC9NpDp3TxdTDYU+Wsosrxm6bwqfaws68K88+f76T2LMeshIIkajV3JHkUSRRJFEn3Aqw9k2bhrHpBSmizrwrwpkiTfLPM5SdRouEj2KJLYQHptIDUOKqVhQ5G/hiLLayb5kuZclnVh3vnzfY2PMY07AUnUuFsz8XwkvOqkVqBIokiiSKIPePWBLBv3Wg+5eo5lWRfmTZFUj+8WOa3vd7dJoosiiQ2k1wbS941Z1IZi8GWRueKzkVlxff4agpCvCf2vw592tyY+9seLO451vXY9+eFDwxMb//Pl7m3cflUSZ9zPuo/TNd+ifvbtf7RXm4AkamqnkI9K9iiSjoskNFjzr+JNXu/DS3ZF97P1lifU9Be9u2Ke94zI3Pq75vpc6w8jM+B7zc2zmdfA3cPkFM0su++8dgyNTBRFsZ91Z7snP9zVmth47und+0y/7yRxvvah7uN0l3/RjsvexLO+CEiiRpOHZI8iaURkpvw6ucn7f5c3eb0PLY2DSmnqLQ/Td/g0ehZe+KzIfPxtFa+9hyExlnxJcy6kurmWJY1I6skmRVLH/dMTK5xnaA4BSdRoSiDZ6/GqpnGMEOLM/ENk3vXaFvO0UyLzzte0mP+9ODLoKkbZ8LD40b9F5h9e1hJ/q8I3njv/nJxDV/SzT0tE0hlPj8y//3NyfF3vyHzmvIrBMfx86X2R2Tq444b5v/+IzIuek/QG/PhTkfnJpyPz8udXzORfdcTBN6xzzqqYp5ycnPv+JyKzbUhyfuD3kmNXfzUyH3xTiznrjEr8DR82UFbLFOXAsXec3dJ+zJ4L8a/GQaU0IdbRR5n+44OV+LrCbzcPTK7xJ95WMVN/HZk3v6Il9q1ffDbxXeSH3h/4Afz6Wx+NDL7ZQ/D0/laHr/zLW1viOKv+lhyDKEKat76qYpZcHZmXPLcS9yycetwf7+vXkdZHnUKwIfmS5lwIddKWwYok+Mm030TmLa+sxD7wq4s6/KraD5HPg4Mj8/n3ROYZpybPrgn/fWJP0h1/Smyd/pTIfPjNFfOX/+zck4Tn7m8+H5lXvqBinvrk5Hlc3Uv/hfckfrnoz4lgf9ZpkfnieyNz1zX590eNjzGNOwFJ1LhbK8HEbczrePJJkWmpROY/z4/M289uiRuDvt9ObrrffzG5if/t7ZVYzKAL+iNvSb5Nj/9ZIp5wDDf81ZckadCw4Nh3PxaZyz6epP/c/0vS4IGDc5UoMhd/oGJe/+KWuNHCsbYrkvRovPAZDxI0aq9+YWLP2uj1zeT8G16SPEgueHPFrO8TmZMqkXn+MzsEnrXzjQvy8QDROKiURttAhJ4O/gf/gKiGcMb/L3hW0nh87J8q5kktyTE0UqjL3y9NPkOwo7G78LwkPdLZxgVfAvB52bVJmupegHt7JaIf51/x/Ir53sci88CgfPiUy7WUfElzziXv0OJakQSfwTWHiMbzBT5g/araD1F++6zDUPC3PxrFX94Q3w63QQC97HmJjfNem/jhM5+afP7q8eG2330h+fzu1ydfViHSIISsv+E5C5t4xr7vjS2x2Mfnr3wg/z2cGh9jGncCnLjtOJna3tg/+ETy0Efvy/vPqcQ3OW58zMNAj9GGvsn5176oJRZU9vOlFyQ3tR1uw7d53LT/9KqOm/Ztr07SbOwbGTwMcB4PEdjHw+iUJyXHJv0yOWYfJFY04RsaRBzS4Zt+n+MiCt/Y1vRK0sAWhkIQx85V+dQ7O39GnJB/3N1dThFyXespW3XjZBszXHeIdthFLxI+w7fx2frLi59Tae8hhehHnF9+LokjiSTYsH7N4TbZ56rP1nONs05b7Vd2UjXmXsJn0KuN8lX7IT5DHOG8ff7g2YnPViRB1OMzetFt/dDjjmNWJOFLHj6v/GuSByZ94/P1X++c58XvT2yglxPn0Ztubeb1b7Xv8P/GEWBPkqMQ+PC5yTfo7iakbuofGQx9YSjtA+dUzNNPTW7Kef+b3LRdRdJfv5GcxzDcP76iJf7BQwI38szfR8bmB5v2ZsZwGM5DJGFYDv/jx357QjzbiOFbnG30MNRmbeDvDZcn6X74r5FBTwDKim+B1XFC/t/3bRFyXespW3XjZBsz9IZuH5r4FBo1+A++/SMf6y//+vYOX4A4Qhz0niKO9a9aPUk4T5Hk7p31XOOs06bxq2o/tD2a8Ck7tQBf6PDZiiTbs410tn5XfimJA5G0ZWDyP9LYZ6ftRUfvJdLYPIf+oOP5iR53pNk5rOOYtZ+nv+4exhQaAhRJjiIJ3bq4wfCtp9YNhS5dnEdjctVXkiENfO5OJGHIDecxFIb5RtU/GEeHsMF5zCuy+WEeFI5BJEGU4f+TT+p801shNeYnHY3e1z/c8bCBLTwk0D3+mjMrZvpvEzuY72TzCf2vxuGlNKHXV1s+21Dgm7ltzPAt3tqziwm6iiTM57Bx/nC8cbLfyK1IwjdzxLF+aBs4iiTJ02qfs6zz+DeNX1X74f0DkucNhnrxBQ11xhQAPMusD1339eTzp9/Z8dz68/E5SRBJmOeG+Og1r35u4v9hx0VRdZ6Wq+3Bokiq7Yc82pkARZKjSPry+5Ib86efSW5sfCPC0lbMBVp+XXLujWclNzUms9obcm6XniQ7h8l+iz/35R29PJhci7S4qdEo4UFgu6zxQMHkbByzw23POd7zNOvKJA1ufjskd/e1HSKp1lyj7/xLYguiDjYhzOzDJPS/nV25/k+h11dbvuqGIk1jZnuSMP/N5on5bfAP6/cfOt6jar8sjPivxH9sA2dFEvK2Nor2t36P62whz3zS+FW1H6KumGgNn7ITrfGFDp+tD9nPZ5/Z4UOYdI04drjN9grZHngPt60AACAASURBVE08j/FjWXbNE8ftM5kiqbP/8VNtAhRJjiLJ9rjgRsOE6EuOiwtMbsW3afToQMQM+X6ycsMKGKxQw/AGVnvgJsfkadjCwwVzPzAxG0MaY3+aTDyE0MLNjnkjiA+7EGIQU5hbhGNWJP38wuQzViohX0zGxXlMWsRDwTZ6tUQSHlCIi0mW1ULNPmRC/lvbpfVHQ65rPWWrbijSNGbWX/AtH3PhsMrttFMSP7ntj0kDhGPwG/RC/vqiZPgN8TFsjLJCnOM8eipHXp6sqqunDiGm1Xta7ZQh1jFtmdL4VbUfwu6n35U8pzApG9MX3vTS5LP1IUwfwApi+BHmy6E3007cxnMXNuz0hY++tWJuviIy55+bTBjHSk6c75onjlEk1fY/Hq1NgBO3HUUSbjJMCsSNjJsXczvQHYzuY5zDN23M7YHoQA8TepCsUEJPT/XyaIyjIw2OQxTBHhoabC9gtw3AeUysxWokTNDG/3bC9S3HJ25DfKGnCcutrQ2UCZPKkd42erVEEs7boROM9+NzXn5qu7T+aF7q7VrO6oYiTWNm/QWrKe3wMfwSAt/mjfkjGJ6DuEejg0UD+IveARsH5+GPuBcW/6XjuD2f9796T6udMs880vhVtR+iruh5f92LEx/BFz8sZsEzrNqHsEu37XFCXGxjAp9CjxJs4IspvqDahSqYk/S3Szt8rWueSEORVNv/eLQ2AfYk1SEK1vbuPA/IPuQgWuxkRBzD0Fn1pGocw8o1G9/+xQ1fnQ7H8fBZenXHUmscs6vZbBezTY8ls6v/3jHGb49Lf2Efy2bRU4Wl21Lc0M7Vdmn90dDql1V5rEiyohp+CT+pVR6cq3XcHsO8ETt0bI8V5a/e02qnLAoX13rgy5zdZ65WWkwxqPW8rI6LoTO7grj6eJH/r+1FPOqbAEVSHSKpGTcgep4wfIdvP/gWhW/3+CaFfT+kB0uasmGStp0Ejj2a0qQJKY7vmyGkumVZlq4iKcuyhJw3/S9/z4yQ/cm1bL79j/ZqE6BIClwk4cYZ9eNkiA1CCft7oKsZPUauN1XX+JgDgGG+n13YfU9B1zQhfa7t0vqjIdUty7JgXhvmEl3+yfp9LMt6NDpvvafVTtno8tJ+sfy5thfxqG8CFEk5EEl8uNV+uPm+Gci5Nmdyqc2F/lebC/2lOVx8+x/t1SZAkUSRVHePVFYPxdourT+aVT2Yb3MaFd+c9Z5WO6Xv8tFePv0q7XWr7UU86pvAxIkTzYQJE7yZlURXYV5wm9aJGa+xDylvXnvcEK9XY69X0fjS/+gvWfq0b/+jvdoEJFFTO4V8VLJHkcReK6+9VrIrup/N8oHHvPPX4Lp7mJyCPpA/H8jymsnexLO+CEiiRpOHZI8iiSKJIok+4NUHitRIZVkX5p0/gaZpoJnGnYAkatytGSPZo0jKSQO5E3s75aCsGgeV0rChyF9DkeU1k3xJcy7LuqTJG8+EtcPOKYzITVPnkONofIxp3AlIosbdWp0iSZMh0/gnMHXqVNOvXz//hmkxdwR8PyByB4AFbieAZwKeDQwkUCYCvp+Bkr0ee5LKBD70ug4bNsz079/frFq1KvSisnwNJCDd0A3MlqYDIoBnwNChQ83w4cMDKhWLQgLNIRDU6rbmVJm5pCUwZ84c06dPn1gs3XrrrWbPnj1pkzJeQQhQJBXkQjpWA/c67nl8UcIzAM8CBhIoIwHfz0DJHnuScuhhx44dM0uXLo2/Rfbu3dsMHDgwnni2evXqHNaGRXYl4PtblGv+jN88AmvWrInvbdzjuNfRc4R7H88ABhIoKwFJ1GiYSPYokjREA0pz9OhRs2TJEjNu3Lh4zhIepEOGDDFoSFeuXGkOHz4cUGlZFB8EpBvah33ayIYA7lXcs7h3cQ/jXu7bt298b+Mex73OQAIkIE+01vCRnqkUSRqiAafZt2+fueOOO8zYsWPNgAED4gctJneOHDnSzJgxw6C36ciRIwHXgEXriYB0Q/eUlufDIIB7EL1EuCdxb+IehSjCPYt7F/cw7mUGEiCBEwn4fgZK9iiSTuRfqCP49rlu3boTHsZ4KLe2tppJkybFPVGc25Sfyy7d0PmpRXlKinsLPUG413DPWUFU/eUF9yh7isrjE6xpfQR8PwMlexRJ9V2rXKbGw3j9+vXxxE98ax00aJDp1atX/E0Wcx9Gjx4dTxBF1//+/ftzWcciF5pzksK8urhXcM9gcjXuITuPCPcW7jHca5hsjXuPgijMa8hS5YNAW1tbPFfPV2kpknyRLLidvXv3muXLl5tp06aZUaNGxQ95PODxgwf+mDFjzPTp0+M47HnKzhmkGzq7UpUnZ/g+7hPcC7gncG9U3ye4d3APIQ7uKQYSIAG/BHw/AyV77Enye+0KaW337t3xiho8+DF/ws51whwKNBA4NmXKlHhIYfv27YVkEFKlpBs6pHLmvSw7d+6MfRq+DR+3PUN27hCO4Z7AajPcIwwkQALNIeD7GSjZo0hqzjUtZC74lnzPPffE853wjRpDCti/BY0I9nLBcuVbbrnFLFy40GzZsoXLlj15gXRDe8qiNGawlB6+CR+Fr8Jn4bvwYfgyfBq+jQnW8HX2DJXGNVjRgAn4fgZK9iiSAnaEvBbt0KFD8cqdWbNmxcuXsZwZS5nR8NgJ4xhTXrBggdm8eTPFk+OF9j0e75h9LqNDDMHX4HOY01U9gRq+CR/FNhrwWaw6gw8zkAAJhElAEjWaEkv2KJI0RJlGRQDLnjds2GDmzp1rJkyYEL9Woat4wgogfKvfunWrKo8yJJJu6DLUv6c6wnfuvPPOE1aTwdfwKg/4HnwQvsjtMHqiyfMkEB4B389AyR5FUnjXv3QlQkOFJdBY+TN+/Pj4W331sN2IESPil3hiIuzBgwdLx6drhaUbumvcIn+GL2AIDPOC4CPVw2ToGYIvwafgWxRDRfYE1q1sBHw/AyV7FEll864c1Rc7EGPoo3pJNVYRQUChR+Cmm24yt99+ezynJEfVqruo0g1dt/FADaB3CENluOa49vAB+AIWEWA1GXwEvsId5gO9gCwWCXgk4HsbFOmZSpHk8cLRVHMI2AYTc3PQYGIYxW5TgHkl8+fPN9u2bWtOYTLIxfcDIoMqiFni2uEa4lra5fUQRegdgkiCWOJwrIiQJ0mg0AQkUaOpuGSPIklDlGmCI4CJtliKjTlNtqcBE8WxOgliavHixYVZmSTd0MFdmB4KhNViuDa4RrhWuGb4wTXEtcQ1PXDgQA9WeJoESKBMBHw/AyV7FEll8qyS1RV712ASOCbqDh48OG580euEZd4Yntm0aVMuiUg3dOgVAnOwxzWwk/ZxbXCNcK2431DoV5DlI4HsCfh+Bkr2KJKyv94sQRMJYBk4lnljoq9tpIcNGxZPDM+LaJJu6CaiTJUVmE6dOtWAMXqIwBzscQ1wLRhIgARIwJWA72egZI8iyfXqMH6hCOAVE/PmzYt3VLaTgdHLgWP1vLcOk4ix8spXwPu+MPSEUD0nCcdwzldAmVF2bQAzsANDO8keO1NjyT1fZaOlynQkQALVBCRRUx0v7f+SPYqktBQZrxQE8CoK7K6MOTHo+cCycsyNwa7MLgF2IA58BczdwQaICNU3NI753AUaK8VQdpcANmBkl+CDHRjyFTUuFBmXBEggLYHqZ2DaNFI8yR5FkkSO50pNAG9qx95MeKO7HSrCzdR1ZRV2c37iiSdOYIW5Nj4nHVtBZG/oauF0QuaKA9h3CJOnuwbUrev+VGCActghSzACK77dvis9fiYBEvBNoLo33Ydt+0ytZYsiqRYVHiOBGgRWr14dL0uHYML+POgtwb48EEKTJ082jz/+eKdUmKC8cuXKTsfq+YCNESFE7A2N/3HMV1i1alVcp2p7qBPqhjqirqizfcExluiDCQMJkAAJNJOAfQb6ylOyR5HkizLtlIYAeo6wbB3zbiCYxo4d2y5cHnvssXYOa9euNXiDvK+wcePGeFjLvrsNQ1w45itggjXKbAPqYvNCHVFX1Bl1BwMGEiABEsiCgCRqNOWR7FEkaYgyDQkcJ7Br1y5z4403xgICw2tYufXoo4/GZ9Hzgl4XX4ICr9bAvB8IFyyZx/++XreBITWU1e5YjTqgLnbrBNQRdWUgARIggawJSKJGUzbJHkWShijTkEAXAhAXGCe3K7oefvjhOAaGpLrOYeqS1OkjxAp+YBe7T/sKDz74YGwT9lB2u9IPdbLCyVdetEMCJEAC9RCQRI3GrmSPIklDlGlIoBsCmLuD1V0QS4sWLYrfRo/XaPgKGOrCJGmsQluyZIkvs/GrPu688864zCg76uBz0rm3gtIQCZBA6QlIokYDR7JHkaQhyjQk0AMBTKiG2Jg5c6YZM2ZMD7HTn96xY0e8ogyryvC/r4Ayoqwos8/J4L7KRzskQAIkYAlIosbGcfkr2aNIciHJuCTgQGDZsmWx6IDweOSRRxxSylFhDz++AspmbaLMDCRAAiQQMgG7oMRXGSmSfJGkHRJwJIBl+hAgmNvjK1hB48seVsnBJgWSL6K0QwIk0EgCkqjR5CvZY0+ShijTNI3AnhGRyfvPmqHnBl+HPJQxjR80zTGZEQmQQGYEJFGjKZRkjyJJQ5RpmkYgTcPIOPkXkr6uYdMckxmRAAlkRkASNZpCSfYokjREmaZpBHw1nrRTDiHVNMdkRiRAApkRkESNplCSPYokDVGmaRoBiptyiBtf17lpjsmMSIAEMiMgiRpNoSR7FEkaokzTNAK+Gk/aKYfYappjMiMSIIHMCEiiRlMoyR5FkoYo0zSNAMVNOcSNr+vcNMdkRiRAApkRkESNplCSPYokDVGmaRoBX40n7ZRDbDXNMZkRCZBAZgSwpQreX+krUCT5Ikk7TSdAcVMOcePrOjfdQZkhCZBA0wlIokZTGMkee5I0RJmmaQR8NZ60Uw6x1TTHZEYkQAKZEZBEjaZQkj2KJA1RpmkaAYqbcogbX9e5aY7JjEiABDIjIIkaTaEkexRJGqJM0zQCvhrPvNvZ3RqZKz4bmT9e3L1omn9VEmf8z7qPUy+H24/nMa6BedRTxqY5JjMiARLIjIAkajSFkuxRJGmIMk3TCNTTYBYp7Y6hkYmiyDzvGd0LoCHfT85f/snu49TLpN93knJ87UONy6OeMjbNMZkRCZBAZgQkUaMplGSPIklDlGmaRqCeBrNIadOIpGbUlyKpaa7PjEiABLohIImabpKIhyV7FEkiOp7MmkAzGn5tHn/6SmRec2bFPOPUyHz6nRXTdkVH78rWwZH55kci89IzInPqyZH5h5e1mKE/6Dg/6LLIvPz5FXPd1yPzo3+LzHNOj8x5r62Y5ddFZvBlkXnlCyrmH1/RYm78eZLGiqTnPzMyGOqCPeR78fsrBudQhxE/Smz+/MLkc3Uev/l8ZM58dsW88zUtZtSPO8qxrndkPnNexZzx9Cj++dL7IoOyWyZ3/Ckyb3llxZz+lMh8+M0V85f/ZE9S1vcE8yeBshOQRI2GjWSPIklDlGmaRsA21qH9/fNxsQDRctnHk2Gup50SGYgOlPWCN1fi4bHXvbjFXHpBZJ765ERc3HB5cr73t5LPEDvnvrwlFjAYTjv/3JZYWP3z65L0EFmwZ0XSaackwuurH4oM8kOa334hidPnuM1vXNA5D4gc2IEIQ/xnnxaZh4Yncd76quTYdz+W1APnP/f/KnGeiPOy5yV5IO2F51XMM5+afEb+oV0TlIeBBEig+AS4T1LxrzFrmJJAiA0xyvSS5ybiYtaViVhA78+Hzk16i2ZfmQgJ9A5ZMWKHqd5xdkssLqygOeuMitnVGpmZv0/SQKRM/21iE+fwecX1HSKp+jzmIOEzhBbKZG1akWQ/o5do+/Hepje9NLGJ/Kb+Okn/T69KRBFsvO3VLaalEpmNfSMz+VfJ+Rc9p+M8ep2QJ0VSSgdmNBIgAe8EpJ4fTWaSPX710hBlmqYRCFEkYTgKQgFiwoqP6nL2+mZyHr1J9vhd1yTH0BODY1bAoHcGn63Np5wcxaIJx2xv1M1XdIikk6rytDafdVpnm11F0qfe2VGOL78vKQdE21+/kfyPniUM7eHnuacnxyCibG/XR97Skf7KLyXnKZKadgswIxIggS4EJFHTJWqqj5I9iqRUCBkpKwJWZIT0F70sEElPPqlD0FSX75qvJucxT8keX9MrOYbhMhyzIskKGhyDTfT62DT/9vak16ZaJCG97Z1a9bckzdNPrW2zVh7ID/lAJF19SfL/G15SMT/+VNTpZ9Gfo3i+FOJW18MOM1IkZXVHMF8SIAFJ1GjoSPYokjREmaZpBKxgCO0vJjJDQCz4UyJQMDR10bsrpu+3IzPxF8k5zOex5W79YXKsu6ExxEsjkhAHAgbxMQEbnzHvCZ+7iqKunxGnWiRhAjjSY04UzuFn88DIYE8m/D/mJ8n5s8/sEHtffG9yjCKpabcAMyIBEuhCQBI1XaKm+ijZo0hKhZCRsiJgG+/Q/n7nXxKxgAnWw3+UzAuC4MD/EBnnnJX0An3lA4lwwmo1nMcQFupSS8DgfHc9SduGJPlVoiheoTbwe5FBDxDSoBeols1aeVSLJEwGf/FzKgY2f/m5yIz9aWQwdPfGsyoG+T0wqGNyOCan/+FLUfvE7UvOT/IM7bpk5afMlwRIoHkEJFGjKYVkjyJJQ5RpmkYgtEbYlmfLwMigV+VJLYl4wSTtq77SIRzuvjYy73tjSyxiIGQwFwkiww6V1RIwkkja1D/J51UvrJhfXxQZzF1C/A+cUzE4pxFJSIOJ5xBFsIW6vOu1LebO4z1VOI8J6XZlHnqsrj4+lIi6WxYh/W2aYzIjEiCBzAhIokZTKMkeRZKGKNM0jUBIDXCtsmBl2r29uhcM6JFZe3xbgFrptcfQC2TFkdZGdTrYqt4fqfoc6oh5WNXHQv2/aY7JjEiABDIjIIkaTaEkexRJGqJM0zQCoTbGLFeYoqlpjsmMSIAEMiMgiRpNoSR7FEkaokzTNAIUI2GKkVCvS9MckxmRAAlkRoCbSWaGnhmHRiDUxpjlClO8hea/LA8JkIB/AlLPjyY3yR57kjREmaZpBChGwhQjoV6XpjkmMyIBEsiMgCRqNIWS7FEkaYgyTdMIhNoYs1xhiremOSYzIgESyIyAJGo0hZLsUSRpiDJN0whQjIQpRkK9Lk1zTGZEAiSQGQFJ1GgKJdmjSNIQZZqmEQi1MWa5whRvTXNMZkQCJJAZAUnUaAol2aNI0hBlmqYRoBgJU4yEel2a5pjMiARIIDMCkqjRFEqyR5GkIco0JOCZwNGjR03v3r3N0KFDvVmGLdiEbQYSIAESKAoBSdRo6ijZo0jSEGUaEvBIYPr06aZXr15mwIABHq0mpiCSYBt5MJAACZBAEQi0tbUZCBtfgSLJF0naIQFPBB566CFzww03xD09EDKjRo3yZPlEM7CNPPCDPJE3AwmQAAnklYAkajR1kuyxJ0lDlGlIQEFg/fr1prW11fTp0ycWLH379jW33HKLwpIuCfJCnhBLKMPw4cMNysRAAiRAAnkiIIkaTT0kexRJGqJMQwIpCBw+fNhMmzYtHkazPTkQKePGjTP79u1LYaExUZA3ymDFGsqGoT6UFWVmIAESIIGQCUiiRlNuyR5FkoYo05BANwTuuOMOM2zYsHYBgvlAAwcONFOmTAlyAjUmdaNsKCPKanuZUAfUhYEESIAEQiMgiRpNWSV7FEkaokxDAscJLFiwIB5Csz1FEBr9+vWLe2p27dqVO04o8/jx4+M6WNGEukE0oa4MJEACJJA1AUnUaMom2aNI0hBlmtIS6CqKICAgikaPHm02bNhQOC73339/XDc7l8mKQcytYk9T4S43K0QCuSAgiRpNBSR7FEkaokxTGgKLFy/u1FNkRdGYMWNKOekZE71RdwhDK5jwF6IJrBhIgARIoNEEJFGjyVuyR5GkIco0hSWwYsWKeNUXGn473IRelLFjxxayp6jeC4neM7CxPU2WGVbOgSUDCZAACfgmIIkaTV6SPYokDVGmKQwBrObC0vjqnhGs+sJ+QqtXry5MPZtVETADu+qVc2ALxlw516yrwHxIoNgEJk6caCZMmOCtkhRJ3lDSUBEIrF27Nu4tqm7IBw0aZObPn1+E6gVVBzAF2+qhOfQyUYAGdZlYGBLIFQFJ1GgqItljT5KGKNPkigCWuWMPoK7L3DFMlMcVaLmCX1XYHTt2xENz1eK0f//+wW6PUFV0/ksCJBAQAUnUaIop2aNI0hBlmuAJYMNErDirbpAhkmbPnh182ctSwFmzZsWbWNp5TJjXhGuW5UabZWHPepJAnglIokZTL8keRZKGKNMESeDAgQPxHj/VwmjkyJHmvvvuC7K8LFQHgc2bN8fvr7PDcriG2K8J15SBBEiABKoJSKKmOl7a/yV7FElpKTJesASwY7QVRviLicMY2mHIJwFcu+rJ37imuMYMJEACJAACkqjREJLsUSRpiDJN5gQefPBBM2TIkPYJwdinhz1GmV8W7wXANcW1tT1MuOa49gwkQALlJSCJGg0VyR5FkoYo02RGYPr06e178mAOC+cYZXYpmp4xrrXdjwl/4QsMJEAC5SMgiRoNDckeRZKGKNM0nQD22bETfPEeMQ6nNf0SBJMhrj18wG74Cd9gIAESKA8BSdRoKEj2KJI0RJmmbgLbt2+PN3DsyRA2DbPiCEv2GUigmgB8wool+EpPAT1Q8D0GEiCB/BJoa2uL5yX5qgFFki+StOONwNy5c+PegO4Mzpw5s30eCt4VxkACEgH4iJ23BN/pLqAHat68ed2d5nESIIEcEJBEjab4kj32JGmIMk3dBLAfTq0VS1u3bm2fd4Il4Awk4EIAPgOxhB4j+FLXMHXq1Hirga7H+ZkESCA/BCRRo6mFZI8iSUOUaeomgJ2Wu76aAiuXMLSGBo5zjupGXFoD8B34EHwJPlUd1qxZk2qYtzoN/ycBEgiLgCRqNCWV7FEkaYgyTV0E8KJTfNvH60IQsMOynVeC14cwkIAPAvAlK7rtLt7wOfjaoUOHfGRBGyRAAhkQkESNpjiSPYokDVGmqYvA3XffHb9HDUZWrFjRLpC2bdtWl10mJoGuBOBTduI/fA1hwIABZtmyZV2j8jMJkEBOCEiiRlMFyR5FkoYo09RFAKuQMHcEYgnf6vHDFUd1IWVigQB8y/rZkiVLYt/D6hgGEiCBfBKQRI2mRpI9iiQNUaapi8CgQYPiFUb2G/6WLVvqssfEJNATAfgYhBJ8Dqvb4IMMJEAC+SQgiRpNjSR7FEkaokxTFwH7rR4N1qZNm+qyxcQkkJYAfM0Kc/ggAwmQQD4JSKJGUyPJHkWShijTqAnYhgqN1YYNG9R2mJAENATgc/A9CnQNPaYhgTAISKJGU0LJHkWShijTqAnYBgpzQxhIIAsC8D3rh1nkzzxJgATqIyCJGo1lyR5FkoaoQ5rPXvOg4Q8ZpPUBB9dKFTVtvoxHH4UPMJBAHghIokZTfskeRZKGqEMaNj5sfFx8wMG1UkV1yZtx6aupnIqRSCBjApKo0RRNskeRpCHqkIYNDxseFx9wcK1UUV3yZlz6aiqnYiQSyJiAJGo0RZPsUSRpiDqkYcPDhsfFBxxcK1VUl7wZl76ayqkYiQQyJiCJGk3RJHsUSRqiDmnY8LDhcfEBB9dKFdUlb8alr6ZyKkYigYwJSKJGUzTJHkWShqhDGjY8bHhcfMDBtVJFdcmbcemrqZyKkUggYwKSqNEUTbJHkaQh6pCGDQ8bHhcfcHCtVFFd8mZc+moqp2IkEsiYgCRqNEWT7FEkaYg6pGHDw4bHxQccXCtVVJe8GZe+msqpGIkEMiYgiRpN0SR7FEkaog5p2PCw4XHxAQfXShXVJW/Gpa+mcipGIoGMCeAF1RA2vgJFki+SCjtseNjwuPiAwsXEJC55My59VXQmniSBQAhIokZTRMkee5I0RB3SlLHh+dPEPab1tofNpf13dNpt/Et/3WYuupYNkeQTDq6VKqqUV1HPufjf5655MPbV/rMe7uSrRWXTU71SORUjkUDGBCRRoymaZI8iSUPUIU1PD6Uinr915SNmz8HHzU9v2BU3PL8dt9ss3XTYPPb4E+bA4cfN3DWPmP9q3clGqcYraxxcK1XUIvpXT3Vy8b/PX5e8iuOhA8foj3wtSap7ipGyJyCJGk3pJHsUSRqiDml6eqAX/TwaoZ0PP24OHTlmhs9/2Exbfiimt+Who2yUKJIa7gM9+R9FUueeXYdHG6OSQGYEJFGjKZRkjyJJQ9QhTdFFUK36TVl2yGzdc9T8ePhO8/2hO2Jh1G9mMpyB4Y3Djx2LCX7h+uQB/cXrt5m2JQfNtr2Pm0eOHDPrtz9mrrp5T8Mb0Fplz/qYg2ulipp1fbLI38X/rEjatf9x8/ORu8y9Dx4x2/c9bobOe9jgXBblzzLPVE7FSCSQMQFJ1GiKJtmjSNIQdUiT5QMvq7wXrn80JvTfo5Lhtupy4BjCtn2PtzdAd244HB/btOsxc/PSg3GvEw5cedND7XGqbRT5/xiEx19FZtVd3Vz8z4okDA9D2C9YlwwL4xL8euxu+p9HX6QpEvBFQBI1mjwkexRJGqIOabp7kBf5eHeN1PcG74jnKgHfdVP2xg3Q5a07Y5r7DnWIpqtv2RsfW/nAETZSDr5WK2qR/ay7urn4nxVJYPfb8YkowqIDhHF3HqD/1XIqHiOBjAlIokZTNMkeRZKGqEOa7h7kRT5eq5H67qAdZveBx2NyYxbub298IJYQ0JtkmXx74Pb42P5HOoSTPVf0v3HFPf4qOq9a9XPxPyuSHj16rH14DWIJYcG6Dp+slU8Rj3l0PZoigYYRkESNJlPJHkWShqhDmiI+SHuqU9dG6juDtscC6dgTTxg7N8na+Pv0fTFNrHizx77WNxFJhx4t34ojB9dKFdUyKX3J5AAAIABJREFULdNfF/+zIql6dZsdEqZISuVijEQCTScgiRpNYSR7FEkaog5pytQ42bpWN1KYqI2J2Ah/nbavXQjZuL8cnXxrf3Bvx2q3P7Y9FMdHOhuvLH8dXCtV1LJwq66ni/9RJHWenJ7KqRiJBDImIIkaTdEkexRJGqIOaaof3mX5v7qR+tu0pKcIK9puXXGo0w96mCCiNu5IRNTUZYfMNZP3mgf2HI0JX3t83lJZuKGevkOZ2Nm6uvgfRRJFku97jvYaT0ASNZrcJXsUSRqiDmnsg7tMf6sbKcw/6i5gyTW4fGvAdnPXpkfN48eeiKNiLtLA2eXcAbk7VtrjZfI7W1cX/6NIokjS3ltMlx0BSdRoSiXZo0jSEHVIYx/c/Nv5YVyLB/ZNuqTP9tINsVWzcHCtVFGrbfP/nn2w7IxSORUjkUDGBCRRoymaZI8iSUPUIU3ZH7qsv1vD7OBaqaKSvxv/svNK5VSMRAIZE5BEjaZokj2KJA1RhzRlf+iy/m6NtINrpYpK/m78y84rlVMxEglkTEASNZqiSfYokjREHdKU/aHL+rs10g6ulSoq+bvxLzuvVE7FSCSQMQFJ1GiKJtmjSNIQdUhT9ocu6+/WSDu4Vqqo5O/Gv+y8UjkVI5FAxgQkUaMpmmSPIklD1CFN2R+6rL9bI+3gWqmikr8b/7LzSuVUjEQCGROQRI2maJI9iiQNUYc0ZX/osv5ujbSDa6WKSv5u/MvOK5VTMRIJZExg4sSJZsKECd5KQZHkDaW7obI/dFl/t0ba3cPkFOTvxr/svGRv4lkSCIOAJGo0JZTssSdJQ9QhTdkfuqy/WyPt4FqpopK/G/+y80rlVIxEAhkTkESNpmiSPYokDVGmqYuA5JB1GWZiEkhBgP6XAhKjkEDABHzfw5I9iqSAHaGoRZMcsqh1Zr3CIUD/C+dasCQkoCHg+x6W7FEkaa4Q09RFQHLIugwzMQmkIED/SwGJUUggYAK+72HJHkVSwI5Q1KJJDlnUOrNe4RCg/4VzLVgSEtAQ8H0PS/YokjRXiGnqIiA5ZF2GmZgEUhCg/6WAxCgkEDAB3/ewZI8iKWBHKGrRJIcsap1Zr3AI0P/CuRYsCQloCPi+hyV7FEmaK8Q0dRGQHLIuw0xMAikI0P9SQGIUEgiYgO97WLJHkRSwIxS1aJJDFrXOrFc4BOh/4VwLloQENAR838OSPYokzRVimroISA5Zl2EmJoEUBOh/KSAxCgkETMD3PSzZo0gK2BGKWjTJIYtaZ9YrHAL0v3CuBUtCAhoCvu9hyR5FkuYKMU1dBCSHrMswE5NACgL0vxSQGIUEAibg+x6W7FEkBewIRS2a5JBFrTPrFQ4B+l8414IlIQENAd/3sGSPIklzhZimLgKSQ9ZlmIlJIAUB+l8KSIxCAgET8H0PS/YokgJ2hKIWTXLIotaZ9QqHAP0vnGvBkpCAhoDve1iyR5GkuUJMUxcBySHrMszEJJCCAP0vBSRGIYGACfi+hyV7FEkBO0JRiyY5ZFHrzHqFQ4D+F861YElIQEPA9z0s2aNI0lwhpqmLgOSQdRlmYhJIQYD+lwISo5BAwAR838OSPYqkgB2hqEWTHLKodWa9wiFA/wvnWrAkJKAh0NbWZnAf+wrSM4EiyRdl2klNQHLI1EYYkQSUBOh/SnBMRgKBEPB9D0v2KJICuehFL8b69evN0qVL42pWOySO4RwDCTSSAP2vkXRpmwSaS6C6DfGRs2SPIskHYdrokcDevXtNa2trHK/aIXEM5xhIoJEE6H+NpEvbJNBcAtVtiI+cJXsUST4I00YqAlYQWYesbrhSGWAkEqiDAP2vDnhMSgIBEbBtiK8iSfYoknxRpp0eCcyZM8csX748nnCHiXf4H8cYSKAZBOh/zaDMPEig8QQkUaPJXbJHkaQhyjQqAhs3bjSTJk1qF0n4H8cYSKAZBOh/zaDMPEig8QQkUaPJXbJHkaQhyjQqAkeOHDH9+/c36EWaMGFC/D+OMZBAMwjQ/5pBmXmQQOMJSKJGk7tkjyJJQ5Rp1ARuvPFGg5/x48fHf9WGmJAEFATofwpoTEICgRGQRI2mqJI9iiQNUaZRE1i8eLEZPXq0GTlypFmyZInaDhOSgIYA/U9DjWlIICwCkqjRlFSyR5GkIco0agI7duwwffv2jX/wPwMJNJMA/a+ZtJkXCTSGgCRqNDlK9iiSNESZpi4CvXr1MvhhIIEsCND/sqDOPEnAHwFJ1GhykexRJGmIMk1dBAYOHGiGDBlSlw0mJgEtAfqflhzTkUAYBCRRoymhZI8iSUPUIc2eEZHhDxmk9QEH10oVNW2+jEcfhQ8wkEAeCEiiRlN+yR7vCg1RhzRsfNj4uPiAg2uliuqSN+PSV1M5FSORQMYEJFGjKZpkjyJJQ9QhDRseNjwuPuDgWqmiuuTNuPTVVE7FSCSQMQFJ1GiKJtmjSNIQdUjDhocNj4sPOLhWqqgueTMufTWVUzESCWRMQBI1mqJJ9iiSNEQd0rDhYcPj4gMOrpUqqkvejEtfTeVUjEQCGROQRI2maJI9iiQNUYc0bHjY8Lj4gINrpYrqkjfj0ldTORUjkUDGBCRRoymaZI8iSUPUIQ0bHjY8Lj7g4Fqporrkzbj01VROxUgkkDEBSdRoiibZo0jSEHVIw4aHDY+LDzi4VqqoLnkzLn01lVMxEglkTEASNZqiSfYokjREHdKw4WHD4+IDDq6VKqpL3oxLX03lVIxEAhkTaGtrMxA2vgJFki+SCjtseNjwuPiAwsXEJC55My59VXQmniSBQAhIokZTRMkee5I0RB3SsOFhw+PiAw6ulSqqS96hxd3dGpkrPhuZP17cvQ8NviyJs+L62nEeGp6c/58v1z6POt9+VRJn3M+6jxMam0aVJ5VTMRIJZExAEjWaokn2KJI0RB3SNOphlke7aPDmX+W3IVr8l8j8+FORQWOYRyZdy+zgWqmidrWfp887hkYmiiLzvGd0f20venclPn/r72rH2dWa2Hju6bXPg0e/7yRxvvah7uPkiVs9ZU3lVIxEAhkTkESNpmiSPYokDVGHNPU8sIqUdsqvk4ao/3f9NkSXXpDYRa9DEXg5uFaqqHlmkkYk9VQ/iiS3+yKVUzESCWRMQBI1mqJJ9iiSNEQd0vT0EM/D+a2DI/PNj0TmpWdE5tSTI/MPL2sxQ3/Q8fC9+quRefnzK+bar3Ucu+T85NjM3yfDJc8+LREzZzw9Mv/+z5HZ1D85f/65LWb4jyLz2he1mKc+OTJffG9k0DiCy7+8tSW2u+pvyWcIIeTz1ldV4vNIe/JJiV0cR49AHnhKZXRwrVRRpbxCP2dF0gufFZlpv4nMW15ZMS95bsX86qIOH/mPD1Zin5j5h+TaPzg4Mp9/T2SecWpkzjmrYib8d+If1T1Jd/wpsXX6UyLz4TdXzF/+M4lje5LQK/mbz0fmlS+oxD75zte0dOoB/cJ7Ej9c9OfIfPxtFfOs0xK/vesa+l8qp2QkEqiTgCRqNKYlexRJGqIOaUJviNKU74I3V+Jhj9e9uMWg5wZiBsMgN1yeNAq//FzyGQ2LtQeBgziTfhmZ8T9LhBU+o1G6+pLIQHjhM2y96DmVWISh0cIxawdiDJ+XXZvY7dor8LsvJKINcb77saQhtfnn9a+Da6WKmlcOKLcVSRDWr3h+JRbNJ1USH4H4QZyPvCXxzcm/Sj5f9vHkPIbovv3RyJx1RnLeiiQIoJc9L4lz3msr5sLzKuaZT00+f/X4cBv8Cj717te3mP+9ODIQaRBCDwxK8oAP4zzE+vve2BKfx+evfCAR73lmnsqpGIkEMiYgiRpN0SR7FEkaog5p8vzARNlnX5k0GM85vWPej53D8Y6zW+KGqieRBDt2WMwOt9kGEI3LjN8mjc+gy5K8II6QpieRhDjPf2aShnOSajslGOX1p9pH7KRqzGuDz3z/E0m9uookiCOct3OUWn+YfLYiCWIK5yHMLZfPnJeIHiuSrE+t/GuSByZ9I831X++c58XvT2wsuTo5D0Fmbeb1b20v4lESCIuAJGo0JZXsUSRpiDqkyevD0pa71zeTBgC9SfYYhhXQaOAbOI7VI5LQM7D9+PDa0uONDb61wy5FkoOjdRPVXrM8/rUi6ckndfgIxBJ8Dz2VqFO1SNo2JDmH8+ipxHkM1eKzFUm9v5V8RjrL5MovJccgkrYM7LDxj69oMfh59QsTEfW9j3UWSdVDzrYXdOewJI61nbe/3bgRD5NAUAQkUaMpqGSPIklD1CFN3h6SXct7zVeTRuPT7+xoVNb0So6ddkrSIFiR9IvPdjQQ731Dx3AbbHbXkwQbthcI39zRoD391MSOFUn4pg4bmMdU3eDhmP3Wb210LX/ePju4Vqqoeat/dXmtSKpe3WYXANQSSfcPSPzjSS2RwdAsbK3v09lnrvt68rnan/98fE4SRNJ9/ZLzLZVk1SRWTtqfYcfn4VULM1te24NFkZTKLRmJBOoiIIkajWHJHkWShqhDGvsQzevfib9IGg3M47B1sEMYdljs75ceb3jelQgpNBR23hLmJCGdFUl9v518tg0gRM+df06Ojbw8sYO5T0jzoXMToYX88HnEfyXnba8AjlmRBHv4nPcfB9dKFTXPPKyPpBVJqKv1O7vVxJifdPYZ+/nsMztEPxYLwA/tcJvtFbJz4dBDhR/LkiIplesxEgk0jIAkajSZSvYokjREHdLYB2te/2JFGVYJoRHBxFSIHKz6wWcMXaBedkgDK99++pnIvOcNLebMZydxbr4iiYMVSUiDYbvpv+2YlItv7Fg9NOB7kXn9ixNRhG/usItJs0jzmjMr5tcXJcNv6CXASjnL0/Y2/eFLkcFwnT2e178OrpUqal45oNwakfTpdyV+h0nZ8Kk3vTT5bH0Gk6+fdkriV5jkDb+xE7exIhP5WkH/0bdWDPwXqygxLGxX0FEkpXI9RiKBhhGQRI0mU8keRZKGqEOaPDdStux3XxvFq3ggWPCDRgWNS/UQ1+WfTFYAYf7ID/81Mt84vn8RvrnDDobMsHwb6THPwzaAL3hWMqfpKScntj/4ppZ4WA1pIL4wrFI5vqFg2xXJxoLoLbBlw7YDEGewa1fF2XN5/OvgWqmi5pGBLbP1EZeepOXXRQY9kfAHbAOAhQLwj2qfwS7dtscJcbGFBeKjRwl5Y1gXq94g4HEcc5L+dmmHz1EkpXI9RiKBhhGQRI0mU8keRZKGqEMa+8Avwl8MOazt3dFYdK1T12GJrufxeWPfJH3XBhCf0TjVStPdcRsXk3TtRF17LK9/HVwrVdS8cqi33Ot6dxbxXe1hzpL1xa7n7GcMG2847q/2WNH/pnIqRiKBjAlIokZTNMkeRZKGqEOaoj9UtfXrKpK0doqWzsG1UkUtGh/Wp/YXCV9cUjkVI5FAxgQkUaMpmmSPIklD1CGNr4dX0exAJGGTwDe8pGMCbdHqqKmPg2uliqopA9M0VoiEzDeVUzESCWRMQBI1mqJJ9iiSNEQd0oT8QGTZwmsMHVwrVVRe4/CuccjXJJVTMRIJZExAEjWaokn2KJI0RB3ShPxAZNnCa0AdXCtVVF7j8K5xyNcklVMxEglkTEASNZqiSfYokjREHdKE/EBk2cJrQB1cK1VUXuPwrnHI1ySVUzESCWRMQBI1mqJJ9iiSNEQd0oT8QGTZwmtAHVwrVVRe4/CuccjXJJVTMRIJZExAEjWaokn2KJI0RB3ShPxAZNnCa0AdXCtVVF7j8K5xyNcklVMxEglkTEASNZqiSfYokjREHdKE/EBk2cJrQB1cK1VUXuMTr/HOAry+plHXNZVTMRIJZEygra3NQNj4ChRJvkjSjhcCkydPNn379vVii0ZIwIXA7t27Ta9evcz8+fNdkjEuCZBAQAQkUaMppmSPPUkaokxTF4GjR4/GDVVra2tddpiYBNIS2LJlixk7dqzp3bu3mTRpUtpkjEcCJBAgAUnUaIor2aNI0hBlmroJHDlyxAwcONAMGDDArFu3rm57NEAC1QSOHTtmVq5caW666aa41xLiCP9DKN14443VUfk/CZBAzghIokZTFckeRZKGKNN4IYAepblz55o+ffqYoUOHxo2aF8M0UjoCEN0QRRMnTjSDBg2Ke4z69+8fC6LVq1e388D5CRMmtH/mPyRAAvkjIIkaTW0kexRJGqJM45XA4cOHzYwZM+Jv/GjYZs+ebXCMgQS6I7B582YzZ84cM2rUqLg3Ej1F8J1x48aZhQsXmv3799dMCpGESZ8MJEAC+SUgiRpNrSR7FEkaokzTMAKLFi0yQ4YMiXsC0ABW9wI0LFMaDpYAhs02bdpk5s2bFw+VYYgWgggT/4cPH26mTJkS+wh6JdME9iSlocQ4JBA2AUnUaEou2aNI0hBlmoYTwCokNGhoDPEDJ965c2fD82UG2RHAkNmaNWviXkUIZPQMQRBhOBaC6JZbbjHLly83Bw4cUBeSIkmNjglJIBgCkqjRFFKyR5GkIco0TSWA3qTRo0fHDSZ6EmbNmmUOHTrU1DIwM78EMBwGwYPtICCAIIQhiPr162dGjBhhpk6dGvcQ+R52xco2Ttz2ey1pjQSaTUASNZqySPYokjREmSYTAhhSwXyTYcOGxQ0qthBYvHixwZAMQ7gE0CuIYVQ8iDBBHz1D2KsIKxvHjBkTz0Fbv359U64jeqMoksL1FZaMBNIQkERNmvRd40j2KJK60uLnXBA4ePCgmTlzZtzQosHF0u6NGzfmouxFLuSuXbtiIYvl9nZuGXqIsOIM4mTBggVm69atmSGASBo/fnxm+TNjEiCB+glIokZjXbJHkaQhyjRBEcBGgVjWjR4KzGPBqifMb2FoLAHMDVq6dGncQ1QtiPA/RBJ6/bZv397YQjhax3AbRZIjNEYngcAISKJGU1TJHkWShijTBEsAw2+2wUbvUpa9FsFCUhYMm35Onz49nkMEQYoeIswRw7L722+/PThBVKuaEEkoLwMJkEB+CUiiRlMryR5FkoYo0wRPYNu2bXGPARrywYMHx70anLuU/rKhJw4Tq7EaDPwwpInJ1Vh1honzeR3a5HBbeh9gTBIIlYAkajRlluxRJGmIMk1uCNhdvTFJGL0f2EgQ85kYOhPAKjL0wqH3zS69BzP0umDSdXebM3a2Ev4niCTUkYEESCC/BCRRo6mVZI8iSUOUaXJJAL0fI0eOjIeJMC9l7969uayHr0JjTyI8HOwGjZhcjZ6je+65p7BzurDlAEWSLw+iHRLIhoAkajQlkuxRJGmIMk2uCWBTSgwbYSgOS9D37NmT6/qkLTzqeeutt7ZvoYDhM/QULVmypDSvgcEO3RRJaT2G8UggTAKSqNGUWLJHkaQhyjSFIADRAJEAsYQelCKuiMPqMvSe2N4ibNSIlwqXRRh2dVSKpK5E+JkE8kdAEjWa2kj2KJI0RJmmUAQwyRu7PmPOErYPyHu47777YtGH3ashANFrht6itO83y3v9pfJjJ2/2JEmEeI4EwicgiRpN6SV7FEkaokxTSAKYo4PJyvjJ29YB+/btM9OmTYtf6wGxByGwYsWKQl6neioFRhRJ9RBkWhLInoAkajSlk+xRJGmIMk2hCaC3AT0waFC72zYAvTP1vGi1O4CwCdtpw8qVK+NeMJQXvWHLli1Lm7SU8XBNMQ+NgQRIIL8EJFGjqZVkjyJJQ5RpCk8APUmYx4NeJbx7rGtAY7t27dquh+v+DJuwLQUIt9mzZ7f3GkHU+X4RrJR/ns9RJOX56rHsJJAQwBxSvGXBV6BI8kWSdkpHADs0o5cGQ3HVARstYgK07zBv3ryavUGYT4T9ndDIYzgNy/VRBgY3AtgxnD1JbswYmwRCIyCJGk1ZJXvsSdIQZZrSEMCKNwgS7DhdLYp27NjRkMYWDThsVwds8oheLYijoUOHniDYquPyf5kARNLo0aPlSDxLAiQQNAFJ1GgKLtmjSNIQZZpSEcBQFt4HB6E0Y8aMuO4Y8sLO1I899pg3Fugtgs3qeVB4J5p9JQh7jupHDZHkMuer/hxpgQRIwDcBSdRo8pLsUSRpiDJN6QgcOnSoXSjZOUN40/0DDzzgjQVswaYNGHOHQELDzuCHADbTpEjyw5JWSCArApKo0ZRJskeRpCHKNKUkgJVnGHrDHCXsp7RgwYL4fWe+YGAvI9hEj5LtudqyZYsv87RjTNwTSJFEVyCBfBOQRI2mZpI9iiQNUaYpLQHsR2R3r54/f368aaMvGFixgY0g0XuEn7K/W84X12o7GC7F+/sYSIAE8ktAEjWaWkn2KJI0RJmm1ATwSg9MokaPEuYQ+QqwhV2yIZDK+toQXyy7s8Phtu7I8DgJ5IeAJGo0tZDsUSRpiDJN6Qlg7yTb4+ND0MCGtVdrX6bSA/cEYNasWZyT5IklzZBAVgQkUaMpk2SPIklDlGlIwJj2oTEMv9UbYAMiCcNtDI0jAJHE4bbG8aVlEmgGAUnUaPKX7FEkaYiWMM2eEZHhDxmk9YFQbxGIpBEjRoRaPJaLBEggBQFJ1KRIfkIUyR5F0gm4eKAWgbSNI+NRSMEHQg14nQtFUqhXh+UigXQEJFGTzkLnWJK9cJ9mnevATxkToPih+HHxgYzdtdvssXUDRVK3eHiCBHJBQBI1mgpI9iiSNERLmMalgWRcCqpQbxGKpFCvDMtFAukJSKImvZWOmJI9iqQOTvxPIEDhQ+Hj4gOCK2V6CiJp+PDhmZaBmZMACdRHQBI1GsuSPYokDdESpnFpIBmXgirUWwQvKaZICvXqsFwkkI6AJGrSWegcS7JHkdSZFT91Q4DCh8LHxQe6caPMD8+bN48iKfOrwAKQQH0EJFGjsSzZo0jSEC1hGpcGknEpqEK9RSiSQr0yLBcJpCcgiZr0VjpiSvYokjo48T+BAIUPhY+LDwiulOkpDLe1trZmWgZmTgIkUB8BSdRoLEv2KJI0REuYxqWBZFwKqlBvEfYkhXplWC4SSE9AEjXprXTElOxRJHVw4n8CAQofvfAZfFlkrvhsZFZcX9vG7tbk/B8vrn3eB/uHhid5/M+XG5dHdTkFV8r01G233caepEyvADMngfoJSKJGY12yR5GkIVrCNNUNYMj/L/5LZH78qchAFIRSzo+8pWKiKDKTf1W7TDuGRuZ5z4jMG8+qNKzMu1qjuAzPPb12GXyzCvUWmT9/PkVSqBeH5SKBlAQkUZPSRKdokj2KpE6o+KE7Ar4b0UbZu/SCRAygd6ZRebja7UkkudrTxKdISjwbImnYsGHduTmPkwAJ5IBAW1ubgbDxFSiSfJEssR1Nw+yaZuvgyHzzI5F56RmROfXkyPzDy1rM0B90iJ1/eWuLefnzK2bV35JjEEL4/NZXJT0w55/bYk4+KRFJON7vOx1pq8sy8w+ReddrW8zTTonMO1/TYv734s49T0h3zlkV85STE/vf/0Rktg1JbG0emBz7xNsqZuxPI/PqF1biMgz8XjKcdt5rK+YFz4rMry6KjBVqViSN+FFkPnxuizn9KZH5p1dVzNKrE5s7hyU23/bqlljYDbos+Xzd1yPzm89H5sxnV+JyjvpxR33QU4Zzr3xBxTz1yUk95l/Vcf7BwZH5/Hsi84xTk7pM+G/2JOH2veOOOyiSSvwcY9WLQUASNZoaSvbYk6QhWsI01SKjUf9f8OZkWOp1L24x6BFC449hqhsuTxp/iCZ8XnZt8rlr78jvvpCIK8T57sciM+03HaLBlhnzgp58UmRaKpH5z/Mj8/azE5t9v53E7f2tJE8ImW99NBFBsPe5/5cIMYglfIYQes2ZFQNRhM/POT0y731Di/nwmyvmpEoSZ9aViU0rkl7y3Eps5/UvTvJ88ysSUYThNtjAkBvKacvwlldWYsFo83j2aR1iDnVFmne/PhF5L3xWZJ51WmQeGJTYuOzjHTa//dHInHVGUs6yD7fdfvvtFEklfH6xysUiIIkaTU0lexRJGqIlTGNFRqP+zr4yadQhNux8IvToQAi84+xETPQkklC25z8zSWNtdC2vFQ8/+EQiJtb1jsz7z6kYCAnEfdnzkvRtVySf0SMDQYVyoAfLChp8nvHbJM6Ln5MIEGvD5vH7LybnrUiy52HD9njddU2HTSuS+hwXamc8PTLbhyY23vTSJI+Zv08+23qu/GvyGROyUabrv558hi18vvV3yefWHyafyy6SFixYQJFUwucXq1wsApKo0dRUskeRpCFawjRdxYbvz72+mTTi6E2ytiEg0NA/86lJQ+9DJGG4CzYHfC+xafPCXwz34Rx+bI8Mjtt8MWRlRdIpT4oMerJw3trEcBo+256gS85PPluRhCE5nK+2CTFmbXYVSZ96ZweLL78vKReE45aBHeX8x1e0GPxg2A/l/t7HkqFBWw/UCflB4OEYRRJFUgkfX6xywQhIokZTVckeRZKGaAnT2Ma9UX+v+WrSiH+6Shis6ZUcO+2UzsJiyfG5PJv6n9jw2x6W7nqSMDwFsYCela51sfbQy4N5QvY8erKQZsxPThQ0iPOvb08EyqRfJmmw5B/xu4okpLc2MScJcW78+Yk2bU/SNy7oiI//ER8i6b5+yf/o4cJKvuqfYT+IzP0DkvNPaukQcuv7JMcokiiSSvj4YpULRkASNZqqSvYokjRES5jGNu6N+jvxF0kjjuEum4cdIkJPDo596HgvkBU4I/7rxIbfiiT0zlg71X9tj8xPP5Ocxxyji95diecfIR6G+yBG7HwiiCX0GuHY3deeKGiQJq1I+u9/T/KEgMPEdNhc+H8n2uxJJCFPzJlCejs/C/Wwk8tx3s7nspO5IdAQv+wiCRO3hw4dWsI7mFUmgeIQkESNppaSPYokDdESpkHD28gfrATDijI05F/5QMVgIjVWbuEzhq+QN1ah4TMmTP/6omQYDL0lmNBsy2aHxv7wpah99Zg9h7/Tf5vYwNAWhvjQ2wObF56XDG39/MIrK575AAAgAElEQVTkMyZVD/l+ZD72T0kZMCEb6bsOjeFYTyIJaZEHJldjzhBWneEzhshq2UwjkuxWBx99a8XcfEVksLIPE8axcg82P/2uJE9M+sbQop3TVM0K8Rr1E+otsnDhQoqkUC8Oy0UCKQlIoialiU7RJHsUSZ1Q8UN3BBrVmFbbRU/N+96YDG1BRGAuEsSOHTrDvBpsA1A5vhIM83kgdtBrYu1c+7WOXhoskbfHq/9CqEAsIA+sdMMQH4aoEAcTpbHk3/b0QIThPCZ447xGJGHVG/L626WJUML/WB1nJ353tZlGJGFoEMLOTiqH4IJ9W8/l10UGqwSRF7YB6P/dpE7VrGzcRvztzo+yPr5o0SKKpKwvAvMngToJSKJGY1qyR5GkIVrCNI1oSLuziWGjtcdFSa04EAi1jttjmKxsJyzbY7X+Io/quUfVcSDMVv+9Y05P9bl6/7eCq147SI/yb+jbPQ/kZUWmj/zS2gj1FoFIGjx4cKjFY7lIgARSEJBETYrkJ0SR7FEknYCLB2oRSNs4Ml73gqVMbGr5UAjHFi9ebIYMGRJCUVgGEiABJQFJ1GhMSvYokjRES5imTA0861q/0Av1FqFICvXKsFwkkJ6AJGrSW+mIKdmjSOrgxP8EAhQO9QuHMjEUXCnTUxRJmeJn5iTghYAkajQZSPYokjRES5imTA0861q/IAz1FoFIGjRoUKjFY7lIgARSEJBETYrkJ0SR7FEknYCLB2oRoHCoXziUiWEtHwrh2JIlSzhxO4QLwTKQQB0EJFGjMSvZo0jSEC1hmjI18Kxr/YIw1FsEIok9SaFeHZaLBNIRkERNOgudY0n2KJI6s+KnbghQONQvHMrEsBs3yvzwXXfdxZ6kzK8CC0AC9RGQRI3GsmSPIklDtIRpytTAs671C8JQbxGKpFCvDMtFAukJSKImvZWOmJI9iqQOTvyPBJwI9OnTx0yePNkpDSNnS2DZsmXsScr2EjB3EqibgCRqNMYlexRJGqJMU3oCra2tplevXubo0aOlZ5EnAHfffbcZOHBgnorMspIACXQhIImaLlFTfZTsUSSlQshIJJAQWLdunRkwYEDc0B45coRYckZg+fLlFEk5u2YsLgl0JSCJmq5x03yW7FEkpSHIOKUnsHLlyvjFqBhimzt3LnuQcuoRFEk5vXAsNglUEWhrazMQNr4CRZIvkrRTKgKHDx82s2fPNv379zd9+/Y1M2bMMDjGkF8CEEncAiC/148lJwEQkESNhpBkjz1JGqJMU2gCa9asMaNGjTK9e/eOX4aKN8czFIMAe5KKcR1Zi3ITkESNhoxkjyJJQ5RpCkdg586d8bcT9BjhZ+LEiWb37t2Fq2fZK3TPPfdwTlLZnYD1zz0BSdRoKifZo0jSEGWaQhA4dOiQmTVrVtxootdo9OjRZvXq1YWoGytRm8CKFSviife1z/IoCZBAHghIokZTfskeRZKGKNPklsCxY8cMXnKKJfwQRsOGDTMLFy7kROzcXlG3gmMCPrcAcGPG2CQQGgFJ1GjKKtmjSNIQZZrcEdi4caMZO3ZsvLcRlvDPnDnTHDx4MHf1YIHrI0CRVB8/piaBEAhIokZTPskeRZKGKNPkggD2MZozZ048vIKl+xMmTDBbtmzJRdlZyMYQ4HBbY7jSKgk0k4AkajTlkOxRJGmIMk3QBLZu3Rr3GtnVaRheYyABEMDKRfQkMpAACeSXgCRqNLWS7FEkaYgyTXAEMNcIc4sGDx4czzUaP3682bZtW3DlZIGyJbBq1ap436tsS8HcSYAE6iEgiRqNXckeRZKGKNMEQwDzirD7KobT0EPA3bCDuTRBFgQ9SdgclIEESCC/BCRRo6mVZI8iSUOUaTInsHfvXoPeIgypjRw50mBiNgMJ9EQAWzxwuK0nSjxPAmETkESNpuSSPYokDVGmyYwAxNGYMWNicYRdsbEJJAMJpCXAnqS0pBiPBMIlIIkaTaklexRJGqJM03QCWKkGR0bP0bhx48yePXuaXgZmmH8Ca9eu5XBb/i8ja1ByApKo0aCR7FEkaYgyTVMJYBk/5hxh40dOxm4q+sJldu+991IkFe6qskJlIyCJGg0LyR5FkoYo0zSFAJbyY/4IfrAqiYEE6iWwbt06069fv3rNMD0JkECGBCRRoymWZI8iSUOUaeomcODAAYM5RbUClvNPmzYtHlqbOnVqrSg8RgIqAhRJKmxMRAJBEZBEjaagkj2KJA1RpqmbAOaGQAh1Dbt37457jvB+LfQkMZCATwIbNmzgcJtPoLRFAhkQkESNpjiSPYokDVGmqZvAvHnzzLJlyzrZwcojTMyeNGlSp+P8QAK+CEAkcbjNF03aIYFsCEiiRlMiyR5FkoYo09RNAMv4d+zY0W4Hm0D26tXLDBo0yGAlGwMJNIIA9tPiZpKNIEubJNA8ApKo0ZRCskeRpCHKNHUROHr0aNxQYe4RwowZM2KBNGTIEHP48OG6bDMxCUgEIJLYkyQR4jkSCJ+AJGo0pZfsUSRpiDJNXQQeeOABc9NNN8U2MC8JPUgQSIcOHarLLhOTQE8EIJL69u3bUzSeJwESCJjAxIkTzYQJE7yVkCLJG0oa8kFgyZIlZsGCBQb7H2EOEobYsNqNgQQaTWDTpk3sSWo0ZNongQYTkESNJmvJHnuSNESZpi4C+BYwf/78WCBhFdu+ffvqssfEJJCWAEQSe5LS0mI8EgiTgCRqNCWW7FEkaYgyTV0EMHEWPUjYRZuvF6kLJRM7ErjvvvsokhyZMToJhEZAEjWaskr2KJI0RJlGTQCiCHOQ8IM9kRhIoJkE7r//fg63NRM48yKBBhCQRI0mO8keRZKGKNOoCWB4DQIJ3+gZSKDZBDZv3hz3YDY7X+ZHAiTgj4AkajS5SPYokjREHdJ89poHDX/IIK0POLgWoyoIbNmyhcNtCm5MQgIhEZBEjaackj2KJA1RhzRpG0fGo5CCDzA0lgBFUmP50joJNIOAJGo0+Uv2KJI0RB3SUPxQ/Lj4gINrMaqCAEWSAhqTkEBgBCRRoymqZI8iSUPUIY1LA8m4FFQOrsWoCgIUSQpoTEICgRGQRI2mqJI9iiQNUYc0FD4UPi4+4OBajKogsG3bNk7cVnBjEhIIiYAkajTllOxRJGmIOqRxaSAZl4LKwbUYVUEAIombSSrAMQkJBERAEjWaYkr2KJI0RB3SUPhQ+Lj4gINrMaqCAEWSAhqTkEBgBCRRoymqZI8iSUPUIY1LA8m4FFQOrsWoCgIcblNAYxISCIyAJGo0RZXsUSRpiDqkofCh8HHxAQfXYlQFge3bt3NOkoIbk5BASAQkUaMpp2SPIklD1CGNSwPJuBRUDq7FqAoCFEkKaExCAoERkESNpqiSPYokDVGHNBQ+Jwqfz13zoPny37ZxJ/Iau7E7uBajKgjs3LmTPUkKbkxCAiERkESNppySPYokDVGHNBRJHSLpa323m1tXHDIPH3rcPH7sCXPPlkfNdVP2UixViSUH12JUBYFdu3ZRJCm4MQkJhERAEjWackr2KJI0RB3SUCR1iKRJdx2Myc1a9YgZNu9hc+Dw4+aJJ54wP2rdSaF0XCg5uBajKgigJ6l3796KlExCAiQQCgFJ1GjKKNmjSNIQdUhTJpH0xeu3mbYlB822vY+bR44cM+u3P2auunlPLIAwxDZh8QEz/Z5D5qJrE+EEsYRwzeSO3qSrb9lrNu54zBx+7JjZuueoGXfnAfOF6zuEVtF5OrgWoyoIUCQpoDEJCQRGQBI1mqJK9iiSNEQd0hS9Ua+u350bDsdkNu16zNy89KA5dORY/PnKmx46oacIwmf7vsfj8z8envQkXTtlb/z54KPHYrG1+aGj8eeZKx85IX11vkX6P64wfzWMwO7duznc1jC6NEwCzSEgiRpNCSR7FEkaog5pitSAS3W5vHVnTGXfocfbBQ16hRBWPnCk/RhsfP66B82i44IKf63dB/cmouhXY3bHxyCkjj7+hDn2xBPmG/22t8ez8Yv418G1GFVBACKJw20KcExCAgERaGtrMxA2vgJFki+SCjtFbMhr1QkTsBHQm2TPf3vg9vjY/kc6hBME0sL1yTDb2m1HzFf+nqxyw1Ad5idhQveX/tqx8g1Ddgi/HpsIJ2u7qH/jyvJXwwjs2bOHPUkNo0vDJNAcApKo0ZRAsseeJA1RhzRFbcy71uvv0/fFVOau6Rgaw2o2hEOPHouFE+Yi3bEuGZJDD1K1GIJYQjhy9In2OUvIA71QCL8bf+KQXdcyFOFzXFn+ahiBvXv3siepYXRpmASaQ0ASNZoSSPYokjREHdIUoeFOU4dfjt4dU8GQmY3/x7aH4mPoDcKx1tsejj9DINnJ2zYu/mKoDuHy43OUEAcTuBG+NYDDbTEI/qqLAEVSXfiYmASCICCJGk0BJXsUSRqiDmmqRUCR/8fqNaxKQ5i67FC8Yu2BPckcI0zIRq/So0cTwQORhP2S7A/mLoHNiPmJiMIw3J9u3tOp16nI7Krr5uBajKogsG/fPvYkKbgxCQmEREASNZpySvYokjREHdJUN4BF/x+9PXdtejSeVwREmIs0cPbDsQDCXkjdBayEAxvMV8KSf2wfgPDY0ScMhu8u6V2OXiQwYGgsgf3791MkNRYxrZNAwwlIokaTuWSPIklD1CFN0YVRrfphVdolfeoTNl/vu73mkFyt/Ip0zMG1GFVBgCJJAY1JSCAwApKo0RRVskeRpCHqkKZIDTjr0vhNLR1ci1EVBCiSFNCYhAQCIyCJGk1RJXsUSRqiDmkoLBovLIrE2MG1GFVB4ODBgxxuU3BjEhIIiYAkajTllOxRJGmIOqQpUgPOujRe8Dm4FqMqCFAkKaAxCQkERkASNZqiSvYokjREHdJQWDReWBSJsYNrMaqCwIEDB0yvXr0UKZmEBEggFAKSqNGUUbJHkaQh6pCmSA0469J4wefgWoyqIHDo0CEOtym4MQkJhERAEjWackr2KJI0RB3SUFg0XlgUibGDazGqggBEEnuSFOCYhAQCIiCJGk0xJXsUSRqiDmmK1ICzLo0XfA6uxagKAocPH2ZPkoIbk5BASAQkUaMpp2SPIklD1CENhUXjhUWRGDu4FqMqCEAksSdJAY5JSCAgApKo0RRTskeRpCHKNHURkByyLsNMTAI9EGBPUg+AeJoEckDAdxsi2aNIyoFDFK2IkkMWra6sT1gEjhw5wuG2sC4JS0MCzgR8tyGSPYok58vDBPUSkByyXttMTwISAYokiQ7PkUA+CPhuQyR7FEn58IlClVJyyEJVlJUJjsDRo0fZkxTcVWGBSMCNgO82RLJHkeR2bRjbAwHJIT2YpwkS6JYARVK3aHiCBHJDwHcbItmjSMqNWxSnoJJDFqeWrEmIBCiSQrwqLBMJuBHw3YZI9iiS3K4NY3sgIDmkB/M0QQLdEqBI6hYNT5BAbgi0tbUZtCO+gtQmUST5okw7qQlIDpnaCCOSgILAsWPHOCdJwY1JSCAkAr7bEMkeRVJIV74kZZEcsiQIWM2MCFAkZQSe2ZKARwK+2xDJHkWSxwtHU+kISA6ZzgJjkYCeQO/evfWJmZIESCBzAr7bEMkeRVLml7t8BZAcsnw0WONmE+BrSZpNnPmRgF8CvtsQyR5Fkt9rR2spCEgOmSI5o5BAXQTYk1QXPiYmgcwJ+G5DJHsUSZlf7vIVQHLI8tFgjZtNgCKp2cSZHwn4JeC7DZHsUST5vXa0loKA5JApkjMKCdRFACIJE7gZSIAE8knAdxsi2aNIyqeP5LrUkkPmumIsfC4IUCTl4jKxkCTQLQHfbYhkjyKp28vAE40iIDlko/KkXRKwBCCSsKkkAwmQQD4J+G5DJHsUSfn0kVyXWnLIXFeMhc8FAYqkXFwmFpIEuiXguw2R7FEkdXsZeKJRBCSHbFSetEsClgBFkiXBvySQTwK+2xDJHkVSPn0k16WWHDLXFWPhc0GAIikXl4mFJIFuCfhuQyR7FEndXgae8Elg/fr1ZunSpbHJaofEMZxjIIFmEYBIOnLkSLOyYz4kQAKeCVS3IT5MS/YoknwQpo0eCezdu9e0trbG8aodEsdwjoEEmkWAIqlZpJkPCTSGQHUb4iMHyR5Fkg/CtJGKgBVE1iGrhVMqA4xEAh4IQCQdPnzYgyWaIAESyIKAbUN85S3Zo0jyRZl2eiQwZ84cs3z5cmMdEv/jGAMJNJMARVIzaTMvEvBPwLYhvixL9iiSfFGmnR4JbNy40UyaNKldJOF/HGMggWYSoEhqJm3mRQL+CUiiRpObZI8iSUOUaVQEMFm2f//+pq2tzUyYMCH+nxNoVSiZqA4CvXr1MocOHarDApOSAAlkSUASNZpySfYokjREmUZN4MYbbzT4GT9+fPxXbYgJSUBJgCJJCY7JSCAQApKo0RRRskeRpCHKNGoCixcvNqNHjzYjR440S5YsUdthQhLQEsBwG3uStPSYjgSyJzBx4sR4NMJXSSiSfJGknboJ7Nixw/Tt2zf+wf8MJNBsAuhJOnDgQLOzZX4kQAKeCEiiRpOFZI89SRqiTFMXATRS+GEggSwIoCfp4MGDWWTNPEmABDwQkESNxrxkjyJJQ5Rp6iIwcOBAM2TIkLpsMDEJaAlQJGnJMR0JhEFAEjWaEkr2KJI0RB3SfGvLHMMfMkjrAw6uxahKAhBJ+/fvV6ZmMhIggawJSKJGUzbJHkWShqhDmrSNI+NRSMEHGBpPgCKp8YyZAwk0koAkajT5SvYokjREHdJQ/FD8uPiAg2sxqpIARNK+ffuUqZmMBEggawKSqNGUTbJHkaQh6pDGpYFkXAoqB9diVCUBiiQlOCYjgUAISKJGU0TJHkWShqhDGgofCh8XH3BwLUZVEoBIwsuVGUiABPJJQBI1mhpJ9iiSNEQd0rg0kIxLQeXgWoyqJECRpATHZCQQCAFJ1GiKKNmjSNIQdUhD4UPh4+IDDq7FqEoC2KNrz549ytRMRgIkkDUBSdRoyibZo0jSEHVI49JAMi4FlYNrMaqSQJ8+fSiSlOyYjARCICCJGk35JHsUSRqiDmkofCh8XHzAwbUYVUkAw207d+5UpmYyEiCBrAlIokZTNskeRZKGqEMalwaScSmoHFyLUZUE0JO0a9cuZWomIwESyJqAJGo0ZZPsUSRpiDqkKYLw+dytQ8zbfvI18/Hh/8fdwxu8g7qDazGqkgBEEnuSlPCYjAQCICCJGk3xJHsUSRqiDmmKIJLO/+svTRRF5o3/8alciKTPzx1u3vKDr5hvbp6di/JW+4iDazGqkgBFkhIck5FAIAQkUaMpomSPIklD1CFNdQOY1//zJpLOueTCWNR98/5ZFEkOvlqWqBBJ27dvL0t1WU8SKBwBSdRoKivZo0jSEHVIk0dhdNHMIeaMc19nnnT6U81ZH3yXeff//LBTT9Jr/v0j5mkvfZH5+Ig/m1NfcIb5h0svisXI19dOM+d89UJz2lkvNC1POcU895yzzYf7/q5dqJx9YZLuk+OuN2f+81tMyylPNs8997UGPT+WU0823vPHy+O833vVj9vTvOHiT8bHPjOxj3nJ+95hopOfFJcXZfzg9b9oj2fzCPmvg2sxqpIARZISHJORQCAEJFGjKaJkjyJJQ9QhTcgNcq2yYYjqaS89MxYZL3zHueZVnzzfPOn/t3cm0HIV5b7vJMzIKAoogyAqigNcEZBBUARB9CKC94LiRUAQFHWpzws46/Pd9y7Xp6C8GwiZE0g4GU4SMpIEMk8kZJ7InLwsxpewGJYry+Wqt/7Vd3c6J+fU6aqu3kP3b6/Vq8/ZXd9XtX9V3fXfXw37qCPs/2d9qzzcJuGk4bdjzjzdCqJP//J7VoicesVF5fMfOs0omtPr0IPt/18c+D/t5yd/7tP2/0OOf6c586YvWXv5kVBKytKdj/Pv/471ccHP767YvO+qS+y5r4z8s1FZJNDkV+Ltq2P/s5IuySPP7x5Ni6SBBCSSXnzxxUBrzCAAgawJuERNSNlc/hBJIUQ9bPLcIXdWtutGP2wFxmHveXdFXEgoSXQkIunUKy+2/5/2xUvNXVum23Rfm9zXnjvo2KMqc4GSYboTzvuYTZPYfeI7N9r/v7Npmikd0Mva3Tx3mKnFR3ciSdckEabyMifJo6G2UNI+ffogklqovrnU5iPgEjUhV+vyh0gKIeph05kQyfO5yx/6uRUYEjRJOS/6zfftuY4i6bI/3FdJ87kHf1a2u+KiyjkJH4kVRaLkKxFJV/b+TSXNsWd9wKa5tu1BU4sPRJJH4yNppwQQSZ1i4SQECkNg3LhxRsIm1oFIikUywE8iNIryftl/3GtFy/uvvbwiZC75tx/Zcx1FkuYWJdeluUISRNV233q+3Z7rddgh+4ikawY/ULF79yfPsmm+POyPphYfiUg6/747Kz7ee8m51oeG21QeIkkBDbWFTBBJLVTZXGpTEnCJmpALdvkjkhRC1MMmERFFef/SkAes4DjqjFMqIuTMG6+x5/YTSaMfrqS5dsRDNo3mMyXXenXf39tzmsCtc0kk6VP/7Xb7v4bDkvlDN80YYmrx8dk/3W99nvGVz1sfGu5L5j51FEl3bS4PBSblKcK7R9MiaSABzUnauXNnoDVmEIBA1gRcoiakbC5/iKQQoh42ReiYq8t4xwtTTK/DD7VC5Oy7v24u/PU9eydu33LdPmJH85cSWy23P/YjZ1i7D998rbn8z78wR5x2kv1fQ3hKl4gkrYjTUN0HbrjKfp4Islp83LJ4lLWRuDr3J7fZVXKHnvgue+7atodsPhJlimqp7N+YM6xSxqSseX73aFokDSSASAoEhxkEckLAJWpCiujyh0gKIephk+cOuauyXfnobyvRmWM+dFplGEwRJdkkYqdaJOn8zfOGm/d+5lNWoCRzkSRUkgnUid3Fv/uhOeT442w6Cabrx/WuCJnufCiff/jhLebAo480PQ46wJxzz83mo7fdYH0pCqbPtT1AEqGqXgWnz/L+8mhaJA0koOG2HTt2BFpjBgEIZE3AJWpCyubyh0gKIephk/dOuavy3b31GXPbyvFBouLOjVPNrcvG7mebiKREXN26fP80SXm68lH9udIk/3d8135LenU8n/f/PZoWSQMJIJICwWEGgZwQcImakCK6/CGSQoh62OS9U06zfB1FUpp5FyUvj6ZF0kACiKRAcJhBICcEXKImpIguf4ikEKIeNkXpnNMop3bcPvi4Y8z1Tz1SuAhPGnyUB0fjCSCSGs+YHCDQSAIuUROSr8sfIimEqIdNWp0r+eR/vlEtdeTRtEgaSEATt7dv3x5ojRkEIJA1AZeoCSmbyx8iKYSoh00tHSNpmkPgxKhHj6ZF0kACjz32mNm2bVugNWYQgEDWBFyiJqRsLn+IpBCiHjYxOk58tI6I8mhaJA0koOG2LVu2BFpjBgEIZE3AJWpCyubyh0gKIephg8BpHYETo649mhZJAwkgkgLBYQaBnBBwiZqQIrr8IZJCiHrYxOg48dE6QsujaZE0kIBE0tatWwOtMYMABLIm4BI1IWVz+UMkhRD1sEHgtI7AiVHXHk2LpIEENCcJkRQIDzMI5ICAS9SEFM/lD5EUQhSbugi4GmRdjjGGQA0EFEnavHlzDSlJAgEI5JFA7D7E5Q+RlMcW0ORlcjXIJr90Li8HBBRJQiTloCIoAgQCCcTuQ1z+EEmBlYRZOAFXgwz3iiUEaiMgkbRx48baEpMKAhDIHYHYfYjLHyIpd9Xf/AVyNcjmv3quMGsCiKSsa4D8IVAfgdh9iMsfIqm+usI6gICrQQa4wwQCXgT69u1rNm3a5GVDYghAID8Exo0bZ9SPxDpcfRIiKRZl/NRMwNUga3ZCQggEElAkacOGDYHWmEEAAlkTiN2HuPwhkrKu7RbM39UgWxAHl5wyAURSysDJDgKRCcTuQ1z+EEmRKw933RNwNcjurUkBgfoIIJLq44c1BLImELsPcflDJGVd2y2Yv6tBtiAOLjllApqTtH79+pRzJTsIQCAWgdh9iMsfIilWreGnZgKuBlmzExJCIJCARNILL7wQaI0ZBCCQNYHYfYjLHyIp69puwfxdDbIFcXDJKROQSFq3bl3KuZIdBCAQi0DsPsTlD5EUq9bwUzMBV4Os2QkJIRBIoF+/fmbt2rWB1phBAAJZE4jdh7j8IZKyru0WzN/VIFsQB5ecMgFEUsrAyQ4CkQnE7kNc/hBJkSsPd90TcDXI7q1JAYH6CDDcVh8/rCGQNQE2k8y6Bsi/oQQQSQ3Fi/NuCEgkrVmzpptUfAwBCOSVQOw+xOWPSFJeW0ETl8vVIJv4srm0nBDo378/IikndUExIBBCIHYf4vKHSAqpIWzqIuBqkHU5xhgCNRDQnKRVq1bVkJIkEIBAHgnE7kNc/hBJeWwBTV4mV4Ns8kvn8nJAQJGk1atX56AkFAECEAghELsPcflDJIXUEDZ1EXA1yLocYwyBGgggkmqARBII5JhA7D7E5Q+RlOOG0ExF27hxo3n++eftJVU3SJ3TZxwQSIsAIikt0uQDgcYQqO5DYuTg8odIikEYH90S2L17txk6dKhNV90gdU6fcUAgLQLMSUqLNPlAoDEEqvuQGDm4/CGSYhDGR00EEkGUNMhq4VSTAxJBIAIBRZJWrlwZwRMuIACBLAgkfUisvF3+EEmxKOOnWwIzZ840K1asMEmD1N86xwGBNAlIJKntcUAAAsUkwGaSxaw3St0Ngc2bN5sJEyZURJL+1jkOCKRJYMCAAYikNIGTFwQiE0hutGO5dfkjkhSLMn66JbBnzx6j3Y51FzB27Fj7t85xQCBNAkSS0qRNXhCIT8AlakJyc/lDJIUQxSaYQHt7u9Fr9OjR9j3YEYYQCCQgkbRs2bJAa8wgAIGsCbhETUjZXP4QSSFEsQkmsHjxYtPW1maGDx9ulixZEuwHQwiEEkAkhZLDDgL5IOASNSEldPlDJFc6Cr4AACAASURBVIUQxSaYwMsvv2z69OljX/qbAwJpE9CcpOXLl6edLflBAAKRCLhETUgWLn+IpBCi2NRFoHfv3kYvDghkQWDgwIGIpCzAkycEIhFwiZqQLFz+EEkhRLGpi4CGOwYNGlSXD4whEEpAImnp0qWh5thBAAIZE3CJmpCiufwhkkKIeth8d8dMwwsGtbYBj6ZF0kACiKRAcJhBICcEXKImpIguf4ikEKIeNrV2jqRDSKkNcDSegOYksWig8ZzJAQKNIjB+/Hi7jUws/4ikWCQD/CB+ED8+bSCgiWHiSUCRJESSJzSSQyBHBFyiJqSYLn9EkkKIetj4dJCkRVB5NC2SBhJQJElbUXBAAALFJOASNSFX5PKHSAoh6mGD8EH4+LQBj6ZF0kACiKRAcJhBICcEXKImpIguf4ikEKIeNj4dJGkRVB5Ni6SBBLSykkhSIDzMIJADAi5RE1I8lz9EUghRDxuED8LHpw14NC2SBhJAJAWCwwwCOSHgEjUhRXT5QySFEPWw8ekgSYug8mhaJA0koInbzz33XKA1ZhCAQNYEXKImpGwuf4ikEKIeNggfhI9PG/BoWiQNJIBICgSHGQRyQsAlakKK6PKHSAoh6mHj00GSFkHl0bRIGkhg8ODBZtGiRYHWmEEAAlkTYJ+krGsgYv6tKHz+ZdEIc9kf7jMX//5H5u5tz7LjuMeu6xGbHq66IIBI6gIMpyFQEAKuyE/IJbj8EUkKIeph02oi6Y51k0yPgw40pVLJHPOh08xdW6abr8963HzyR98yd2+fgWDqRjB5NC2SBhKQSFq4cGGgNWYQgEDWBFyiJqRsLn+IpBCiHjatJpKueuz3ViB98GtXVwTRx27/mj1HVKn74USPpkXSQAISSfPnzw+0xgwCEMiagEvUhJTN5Q+RFELUw6ZoIumrY/6P+dA/XW0OOf44845T32s+/I1/NLcuG1sRPDfNGGJO/uwF5sCjjzQHv/Noc+oVF5lvzBlmP//cgz8zBx93jBVEBxxxmLU/6bLzTenAA+w5+fv8X35prnzkt/azy/7jXnPO979pDjr2KHPiBWebby5oM1c++ltzxGknmXedfab58rA/7s33mUHmzBuvMYee+C5z7FkfMBf87C5z5wtP2881rCff5917h/1fEatTr7zYnrv+qUcqPopQFx5Ni6SBBIYMGWIWLFgQaI0ZBCCQNQGXqAkpm8sfIimEqIdNETrmpIx3rJ9seh12iDnk3ceaC399jx0iK/XsYc647gorNG5dOsb0OvxQK3hO+9Jnzfuu+oz9W2Lp22snmevaHzanfP5Ce+6dH/2AOfvum8wFP7/b9DzkYHvuE3fdZL469j/N5Q/93P5/3Mc+aI77+Ies8NHwnMTX4aecaE789Nn2c/2dlO3dnzzLnjv//rsqeciPPr9z41Rz1BmnmNIBvczXZw41n/3jfTatxF5iX5R3j6ZF0kACiKRAcJhBICcEXKImpIguf4ikEKIeNkXpnFVOCQyJFUV1vr1mohUYXx3X23xt0mP273Puudl+/pF/+UpFfCRC6aLf/tCeu+LhX9k0H731+kqaQ45/pz2XzElKRNLhJ59g7t76jLlh/KP2c+Wt/FQWfab/NQlcZbn0339qxZU+u+mZQfYzRakSvooYKf0JF3zcRqYOPeFdlWtI0hTh3aNpkTSQACIpEBxmEMgJAZeoCSmiyx8iKYSoh00ROuakjBIxR77/lLJgOfAAc+KF55iLfvsDo8nYSnPy5Z+2nyURHJ07//7v2HMf+ea1No2PSEoiVBo2k8DpefBBVjTJr4bxdO7atoesX03+vuAX37XDaEmk6cjTT66IJNkkIk52X37if+/zmT4vwsujaZE0kIBE0rx58wKtMYMABLIm4BI1IWVz+UMkhRD1sClCx1xdxttWjDNnf/cb5tgPv9+KFAkODYlJQL33knPtOU3OTmwUQVIazRfSOR+R9NHbbqj4kQ/NZ0r8ajhP5ySSNCeq1KunHQY89ye3GQ256bOOIunjd/6zPa/PvtDndxVfic8ivHs0LZIGEpBIYuJ2IDzMIJADAuyTlINKiFWEInTM1WW8fdX4yoRoDXVp3pBEhyI5Z91ynf377O/dXBEgigbpcwkX+XGJpLs2T7dpkuG2WkXSp3/5PZtHMjH7KyP/bP9/x/veWynHdaMftudOuuw8o+E9TQavnnBefY15/jtWu8NP1wQkkubOndt1Aj6BAARyTcAV+QkpuMsfkaQQoh42ee6QO5Ytmddz2jWX2QnW17Y9WB5+69XT3L56grlx+iBT6tHDHHDUEeaS//Fjc+6Pb7NDZJrsrUnd8teZSEqEliaDayWcr0hK0p9w3sfM1X1/b1e3HXDkYTZvzaO644UpdiWbhuuSFXISbqd98dKKiOp4rXn936NpkTSQACIpEBxmEMgJAZeoCSmiyx8iKYSoh01eO+OuyqV5P1qCL5HR46ADzHsuOscKkyT91f3/zRx+UnlStdIcc+bpdlVb8nlnIumyB/61ssJNq90S0VNrJElzok669DxbJm09cPmff2FX32kI7h2nvsdokrjKorIn5dBKOZ1TeZJzRXj3aFokDSTw+OOPE0kKZIcZBPJAwCVqQsrn8odICiHqYVOEjrmzMmpF2Xc2TetSYNy2crzRlgGd2XZ2TpOzk32NOvu8lnMqU7JCTunlTzt612JblDQeTYukgQQQSYHgMINATgi4RE1IEV3+EEkhRD1sitI5U858rH7zaFokDSQgkTR79uxAa8wgAIGsCbhETUjZXP4QSSFEPWwQH/kQH0WpB4+mRdJAAoikQHCYQSAnBFyiJqSILn+IpBCiHjZF6ZwpZz7EnEfTImkggaFDh5pZs2YFWmMGAQhkTcAlakLK5vKHSAoh6mGD+MiH+ChKPXg0LZIGEiCSFAgOMwjkhAD7JOWkImIUoyidM+XMh5iL0ebw4SaASHLz4VMI5J2AK/ITUnaXPyJJIUQ9bBAf+RAfRakHj6ZF0kACEkkMtwXCwwwCOSDgEjUhxXP5QySFEPWwKUrnTDnzIeY8mhZJAwlIJM2cOTPQGjMIQCBrAi5RE1I2lz9EUghRDxvERz7ER1HqwaNpkTSQACIpEBxmEMgJAZeoCSmiyx8iKYQoNnURcDXIuhxjDIEaCDzxxBNEkmrgRBII5JVA7D7E5Q+RlNdW0MTlcjXIJr5sLi0nBCSSZsyYkZPSUAwIQMCXQOw+xOUPkeRbO6Svm4CrQdbtHAcQ6IbAsGHDzLPPPttNKj6GAATySmDcuHFG/Uisw9UnIZJiUcZPzQRcDbJmJySEQCCB4cOHI5IC2WEGgTwQiN2HuPwhkvJQ4y1WBleDbDEUXG4GBBhuywA6WUIgIoHYfYjLHyIpYsXhqjYCrgZZmwdSQSCcgEQSw23h/LCEQNYEYvchLn+IpKxruwXzdzXIFsTBJadMgOG2lIGTHQQiE4jdh7j8IZIiVx7uuifgapDdW5MCAvURQCTVxw9rCGRNIHYf4vKHSMq6tlswf1eDbEEcXHLKBCSSnnnmmZRzJTsIQCAWgdh9iMsfIilWreGnZgKuBlmzExJCIJDAk08+aaZPnx5ojRkEIJA1gdh9iMsfIinr2m7B/F0NsgVxcMkpE0AkpQyc7CAQmQD7JEUGirt8EUAk5as+Wq00Gm6bNm1aq1021wuBpiEQuw9x+SOS1DTNpjgX4mqQxbkKSlpUAookIZKKWnuUGwLG7rataFKsw9UnIZJiUcZPzQRcDbJmJySEQCCBtrY2RFIgO8wgkAcCsfsQlz9EUh5qvMXK4GqQLYaCy82AgCJJU6dOzSBnsoQABGIQiN2HuPwhkmLUGD68CLgapJcjEkMggAAiKQAaJhDIEYHYfYjLHyIpRxXfKkVxNchWYcB1ZkdAw21EkrLjT84QqJfA+PHjzdixY+t1U7F39UmIpAom/kiLgKtBplUG8mldAookTZkypXUBcOUQKDiB2H2Iyx8iqeCNpYjFdzXIIl4PZS4WAURSseqL0kKgI4HYfYjLHyKpI33+bwiBjRs3mueff976rm6QOqfPOCCQFoERI0aYp59+Oq3syAcCEIhMoLoPieHa5Q+RFIMwProlsHv3bjN06FCbrrpB6pw+44BAWgQQSWmRJh8INIZAdR8SIweXP0RSDML4qIlAIoiSBlktnGpyQCIIRCCASIoAERcQyJBA0ofEKoLLHyIpFmX8dEtg5syZZsWKFZXdUvW3znFAIE0CEklM3E6TOHlBIC4Bl6gJycnlD5EUQhSbIAKbN282EyZMqIgk/a1zHBBIk8DIkSOZk5QmcPKCQGQCbAEQGSju8kFgz549pm/fvkbP3NEeF/pb5zggkCYBiaTJkyenmSV5QQACEQm4Ij8h2bj8EUkKIYpNMIH29naj1+jRo+17sCMMIRBIAJEUCA4zCOSEgEvUhBTR5Q+RFEIUm2ACixcvNtrxePjw4WbJkiXBfjCEQCiBUaNGmUmTJoWaYwcBCGRMwCVqQorm8odICiGKTTCBl19+2fTp08e+9DcHBNImgEhKmzj5QSAuAZeoCcnJ5Q+RFEIUm7oI9O7d2+jFAYEsCGi4jUhSFuTJEwJxCLhETUgOLn+IpBCi2NRFoH///mbQoEF1+cAYAqEEJJImTpwYao4dBCCQMQEt/pGwiXUgkmKRDPCz64mS4QWDWttAQBPDxJMAIskTGMkhkDMCLlETUlSXPyJJIUQ9bGrtHEmHkFIb4Gg8Ac1JIpLUeM7kAIFGEXCJmpA8Xf74VQ4h6mGD+EH8+LQBj6ZF0kAC2n4CkRQIDzMI5ICAS9SEFM/lD5EUQtTDxqeDJC2CyqNpkTSQgCJJ2u2dAwIQKCYBl6gJuSKXP0RSCFEPG4QPwsenDXg0LZIGElAkCZEUCA8zCOSAgEvUhBTP5Q+RFELUw8angyQtgsqjaZE0kAAiKRAcZhDICYHk0VaxioNIikUywA/CB+Hj0wYCmhgmngSYk+QJjOQQyBkBl6gJKarLH5GkEKIeNj4dJGkRVB5Ni6SBBBBJgeAwg0BOCLhETUgRXf4QSSFEPWwQPggfnzbg0bRIGkhAImn8+PGB1phBAAJZE3CJmpCyufwhkkKIetj4dJCkRVB5NC2SBhJAJAWCwwwCOSHgEjUhRXT5QySFEPWwQfggfHzagEfTImkggbFjxxJJCmSHGQTyQECRYH2PYx2IpFgkA/z4dJDNnnboj0um3w/ii6Zf/HPJzH0gvt8s6iOgiWHiSaC9vT3qc588syc5BCBQJwGXqAlx7fJHJCmEqIdNFh1tHvN8bWjJnHhMyXz5vB5Rn2U3+TclUyqVTN/vI5I8mmVLJ0UktXT1c/FNQMAlakIuz+UPkRRC1MMmj4IlpEwL/1AyV5zd0xxzeMkcd0TJXHVOD/P8n8rCZNVfSua043vsI4CG/bR87r4bSmbJn0rm5ON6WDFz6IHl81seK5mbPtPD2k38VclcelZPc8iBJXPO6T3N4j/W5vffbymZYw8vi6R3HVky/3Rx8YWSR9MiaSABRFIgOMwgkBMCLlETUkSXP0RSCFEPmxBBkjeb9b1L5h0Hl8XIV87vYb70qbLgkVja1q9klj5Y/kwCJyn7Y/eUz91xZcnI/oaLyjanH9/D/OBLJfN/B5TMlWf3tMLp+KNL5pufLZmzTi6nSfx053f0/SXzifeVfXzhnB7mT7cjkjyaZssmHTNmDMNtLVv7XHgzEHCJmpDrc/lDJIUQ9bBJREOR3398bVnw3HbFXhGSCKX/dUv3IknXPuW/hsWqh9uu/mRZFN1zTdnvS4NL5oCe5bwkkLoTSfJ711Xl9Ay3eTTKFk/KxO0WbwBcfuEJMHG78FW49wKKLI6Ssn/hvyI+j3x3r0j61Y1lcXLr53vUJGZcIql/1WTuj59aFk7jfoFI2tuK+CsmAUWS2CcpJlF8QSBdAq7IT0hJXP6IJIUQ9bBJhEaR3y87qzykNeRHe0WSIkiaMH3zZXvFzMdO2Tsp+8Fvlz/XcJuu3SWS2u7d6/dTZ5RFUvvPavNLJMmjMZLUEkAk0RAgUGwCLlETcmUuf4ikEKIeNkUWR0nZb7+iLHh+9I97xUwyx+jXN5XnJfUolcxBvUrmlSHlNJpELRHVUSRpiC3xmwy33f+1ss3/e7xkNLFbdpoorvlO3flNRFKf7+0tW+K/iO8eTYukgQQYbgsEhxkEckLAJWpCiujyh0gKIephU8SOumOZ5z1QFitHH1Yyf7i1ZO69vmRXoh1+cHlSttJf8MFytOn6C3uY732xvIJNYieZx7TsobL40Sq04T8tme39SyYRSdoa4C93lsyNl5SjSB98z14h1Z1fiTTlo9V2U39XfKHk0bRIGkhAkSQ9RZwDAhAoJgF9f9lMsph1t1+pOwqOov7/xE/2LuOXKNFKNO1RlFyPhsyS1WnnfaCnefJfy+JFw3FJmmvOLQupXj1Kdpl/IpK0lP+EY8rpJZimVYmd7vxWby/wD1Wr65I8i/a+XwPiRHQCiKToSHEIgVQJuCI/IQVx+SOSFELUw6ZonXR35d3cp2R29N8rfDqm39q368+UVvsjaWNJ/Z2IpEm/Lv+/4ZGubbvzq3J1LEsR//doWiQNJOD6QQx0iRkEIJAigdjfYZc/RFKDK7aIHXVaZe4oktLKN8/5NLg54t4YO9SmH0UOCECgmARcoibkilz+EEkhRD1s8twhZ1027bitOUrT/3tzRIFi8PRoWiQNJKD5DIikQHiYQSAHBGJ/hxFJGVZqjI4TH60jojJsqi2TtX4QEUktU91caBMScImakMt1+SOSFELUwwaB0zoCJ0ZdezQtkgYS0KqYmCtjAouBGQQgEEjAJWpCXLr8IZJCiHrYxOg48dE6QsujaZE0kAAiKRAcZhDICQGG23JSETGKgcBpHYETo65jtDl8uAnE3mPFnRufQgACsQm4Ij8hebn8EUkKIephE6PjxEfrCC2PpkXSQAKuH8RAl5hBAAIpEoj9HXb5QyQ1uGIROK0jcGLUdYObI+6NsZO2FU3igAAEiknAJWpCrsjlD5EUQhSbuglMmDDBtLe31+0HBxDwJaC2N3r0aF8z0kMAAjkhoL5D3+NYh/xNnDixU3eIpE6xcLLRBKZPn26efPLJRmeDfwjsR+DZZ581w4cP3+88JyAAgWIQUN8xbdq0aIVta2szU6dO7dQfIqlTLJxsNIHZs2ebxx9/vNHZ4B8C+xFYtGiRGThw4H7nOQEBCBSDgPqOuXPnRius/M2aNatTf4ikTrFwstEEFi9ebAYNGtTobPAPgf0IvP7666Z37977necEBCBQDAJ9+vQxmzdvjlZY9UULFy7s1B8iqVMsnGw0gdWrV5v+/fs3Ohv8Q6BTAo8++qjZsmVLp59xEgIQyC+Bv/71r+aRRx4xf//736MVUn3RihUrOvWHSOoUCycbTeDFF180uhvggEAWBDRRc9SoUVlkTZ4QgEAdBCRm+vbtW4eH/U0fe+wxs2nTpv0/MMYgkjrFwsk0COhu4M0330wjK/KAwD4EduzYEf1udJ8M+AcCEGgIAS26mDRpUjTfikipL9qzZ0+nPhFJnWLhZBoE+vXrZ1atWpVGVuQBgf0IaPL2lClT9jvPCQhAIJ8EEkHz0ksvRSugbpg0/N7VgUjqigznG05AyzjppBqOmQy6ILB161Z7B/n22293kYLTEIBAngho65gBAwZELdKCBQuci4gQSVFx48yHgBo8+9X4ECNtbAIS6k888URst/iDAAQiE1AUSfNY165dG9Wzdt93bWyMSIqKG2c+BDZs2MDkbR9gpI1OQCtl9MM7b9686L5xCAEIxCOgzSNjR5FUOu2RpH37ujoQSV2R4XwqBDQWrDFhDghkRWDdunV23yS9c0AAAvkj8Morr9ih8Ub0FZq0/eqrr3Z50YikLtHwQRoEtB18zGfwpFFm8mg+AvPnz7dCaefOnc13cVwRBApOQJs9jh07NvpV6MZIy/9dByLJRYfPGk5g6dKlbCrZcMpkUAsBTeDUXaX28OKAAATyQUDzhbTZY8zNI5MrGzNmTLcPu0YkJbR4z4TA3/72N9sxvfHGG5nkT6YQqCageQ8MAVcT4W8IZEdAjwrR93H37t0NKYQ2pVy5cqXTNyLJiYcP0yCg1UUMuaVBmjxqIaAHXerZbkuWLKklOWkgAIEGENDzPRXZjfmMtupi6hmO8t9dhAqRVE2NvzMhoC+B7ha6a6yZFI5MW5KA5iroB3Ty5Mktef1cNASyJKA5gvr+rV+/vmHFmDhxYk3bfyCSGlYFOPYhoDHnOXPm+JiQFgINJaAVNdoVXsuOd+3a1dC8cA4BCJQJaN8iRXJdK85isNKNubah6e5AJHVHiM9TIaCx59gPLUyl4GTS1AQU3Rw/fryNdCLim7qqubgcENB3TfuWNWKpf/XlaSiv1v4GkVRNjr8zI6DOSF8O9qrJrArI2EFAYX8tFVZUidVvDlB8BIEAApofpO+WRhQaNUm7uljaUmDGjBnVp7r8G5HUJRo+SJuA7tRrVfdpl438ICAhr6ePa66EliXzzDfaBATqJ7BixQobqdU+SGnMS9XDcfUd3rNnT02FRyTVhIlEaRGQSJo5c2Za2ZEPBLwJvPbaa3bCp35o9YBmbWPBAQEI+BHQTYae3am5QcuXL/czriP10KFDjeY91XogkmolRbpUCCSrivRMLQ4I5JnA1q1b7dPD9SOvhzUjlvJcW5QtTwTmzp1rxZEeMJ1mRHbTpk02iuTTvyCS8tRyKIsloAcOaidUDggUgYCEveY4KLL09NNPmzfffLMIxaaMEEidgESKVoxqxGD16tWp5695T9ow1udAJPnQIm0qBLT0U0tAG7WJWCoXQSYtR0AdgAS+xJLukGm/LdcEuOAuCGzfvt1omEtR12effbaLVI09rUdgaXGQ77wnRFJj6wXvgQSScGytk+sCs8EMAtEJaHXOU089ZTuEZP8vhuKiY8ZhAQhoKX9y46CnKvgMc8W+PEWv1K/4HogkX2KkT42Avly6I+eAQBEJ6I5VOwcrxK/IqFbvNHr/lyJyoszNR0D73g0cONBGVbWzdZbiSHT13dOQeMiBSAqhhk0qBDShT+FZbfzFAYEiE5A4GjVqlG3P2m9Jm+ax31KRa5SydySgvY4ULdJvtuYd6RmIeYigvvDCC/YmJXTXfERSx5rm/1wR0BOa9aXTIyI4INAMBFatWmXa2trsXbaGALT3UqMfwdAM3LiGfBLQXDw9pFxz8fRey6M+0roSTdfQTUnIMFtSRkRSQoL33BLQagQJpbfeeiu3ZaRgEPAloOE47Q+jvWLUwejuW0MT6nQ4IJBnAhs3brQbqmoitH6b1W7feOON3BV55MiRdk5UPQVDJNVDD9vUCKixaxKs78qE1ApIRhCog4CGJZYsWWJGjBhhV+BoDpPuynUHnMfOp45LxbSgBLRaU3N7JIz0Gj16dK6iRh2xPvfcc1bAaRiwngORVA89bFMloImA+nJyQKDZCWi+0tSpU83gwYMrw3LqoLQnEzcKzV77+bg+tbO1a9fa3amTiJHm1ek5hnk/JOgUnY0RlUUk5b22KV+FgO62dYetqBIHBFqFgNq9nm+lDkrzK/Tjr6iqNlzV3i9p7ljcKsxb9Tq1wEAboiabo2rOnCJGmvxclENbcGgIUCtLYxyIpBgU8ZEaAS0l1RdAq4M4INCKBCSKNDSnyJK2F5BoknjSZPDZs2czCbwVG0XgNUtQaBWahnb1u6qX5shpmLfeYarAItVlphuK5AaiLkdVxoikKhj8WQwC+mIny6iLUWJKCYHGEdCwiIbhJk+ebIYMGVLp7DQ8rSiAOjz2Z2oc/6J41g2mHgWi1ZTag05DaBLYajN69mAztBGJPd08xBySRiQVpYVTzn0ISCjpS+7zNOd9HPAPBJqYgLYU0MRVRVzVCSYdou6yNTlcj4bQfA12tG/ORiCRsGXLFlvPigxp2EyCSCsoVf+KODaDKKquPQkkte/YbRqRVE2ZvwtFAKFUqOqisBkT0IN3NbdJESd1KMn8Jg2xKOqkzlOTxZctW8aQXcZ1VWv2EkN6LtqCBQusINbz0arrVRGjKVOm2EijhqKa9dCTGSQAG7GzNyKpWVtNi1yXhJK+HPpxaOYfgRapTi4zAwI7d+60u9pLPCnqoLtxRR2SuU76bulZdIo+SWQpPd+1dCvqtddesyvNFAFSdLB6uExRQk20bm9vtxGiVosQaiGPImWNWsCASEq3rZNbAwjoB1tDCvpxZ0+ZBgDGZUsSUKejVU3qmDWsreiTJoor8iQBpXd1TuqwtfJOEQsN8amT1nAfQqq2ZqNokETQmjVrzJw5c6wIklhNtjypZi3Bqjk3qpNWE0Od0UwiZ43caBiR1Bl5zhWSgO6k9MPNM7EKWX0UumAE9KggdeyaGK5ndml1nSIayXBPEo3Sd1I3MLqR0bCIIiGaKKyHoGofnq1bt9rHDjUqEpA2VokeddovvfSS3WxRz56cMWOGvW5FPdSxi0cyTyzhpIi4BKcm24uPVjBu27Yt+hybtHk0Ij8JcPHSljDi3cgDkdRIuvhOnYB+jPSjox8YDghAIFsCiuyqo9cwncSU9uBRJESREgkqiQWJKgkpdXiJYND/Oq/PtaGmxIMEliJWuhnS8J+EmaJXemzRzJkzrX8JL3339cxHvfScPL20qqv6lZyXSNPKQH2mR8RI0Gh+jyI6+i2RWFGZ9dgN5amyS+io/BJ9ivYomtZR8Og6dE7XoDTDhg2z4kdDmooCad6XxKGmC3D4EdDcOkU0xT+NaCUiya9+SF0AAvoB1I+sfkwbfZdRABwUEQKFIqAojKLBej6YhM6iRYvsXj4SLFq+LnGk4T9FXDTZXIJFQ4GK0EhQqQOVuNJL0Qa9kv+T9+R88q7zspNwU+crX/IpYaY8JIz0e6IomMqg+VkSfdrMUyJLcLgO/QAACxVJREFUK8kUWWvExOFCVV6DC7tr1y4rPFUnaR2IpLRIk0+qBLQRmu7g9OOn8X4OCEAAAhAoLgHNj9PNr0RqmgciKU3a5JU6Ad11KoyvcD8HBCAAAQgUj4CiiBrC1FBo2gciKW3i5Jc6Ac030B2IQrSEw1PHT4YQgAAEggjo91rz0TS3S1tPZHEgkrKgTp6pE9AEUs0x0GRKzXPggAAEIACB/BLQvDT9Xut3O/Yu2j5XjUjyoUXawhPQ6hWFbbVcmahS4auTC4AABJqQgCbG63da71kfiKSsa4D8UyegJaRJVEnLfjkgAAEIQCB7AlpkowU3Gl7bvHlz9gUyxiCSclENFCILAlq+q3Culvxq4zcOCEAAAhDIhsCsWbNs9EjbO+Rp6xZEUjbtgVxzQkCbkWmTOIV29Z7l2HdOkFAMCEAAAqkR0IaaulFV9EiPWsnbgUjKW41QnkwIaCM4fVG1Ck7Pn+KAAAQgAIHGEVC0SLuZ6wZVO5mnsXt2yNUgkkKoYdO0BLSfku5otAmlnkvFAQEIQAACcQnoqQjJ7+z27dvjOo/sDZEUGSjuik9Adzh6bpOiSnrMgZ6xxAEBCEAAAvUR0JMQtGhGv616rEsRDkRSEWqJMmZCQOFfPUBTO3ZrQzPt28EBAQhAAAJ+BDTXUw8J1tCanrlXpO1XEEl+dU3qFiSgL7RWXOgLrgdeIpZasBFwyRCAgDcBReWnTZtmI0ea87ljxw5vH1kbIJKyrgHyLwwB7do9ZswYK5b0lHCG4QpTdRQUAhBImcD8+fOtOBowYIDZsGFDyrnHyw6RFI8lnlqEwNtvv2304NxkztK6deta5Mq5TAhAAAJuAkuWLDF9+/a1E7Ob4cHiiCR3ffMpBLokoDlLejq1NqTU3dKiRYu6TMsHEIAABJqZgCZia8WaXgsWLGiaS0UkNU1VciFZEdC4u3aL1d2TBNOkSZOMHn3CAQEIQKCZCehGUc9X0+9ev379jJ6N2WwHIqnZapTryZSAht60Ei6Z5L1t27ZMy0PmEIAABGIT0JQDbQSpKQeKomvfo2Y9EEnNWrNcV6YEdu3aZR9zoh8RPbBRoei87iibKSgyhwAECkNAq9NGjBhhbwJ1M1jkCdm1Qkck1UqKdBAIICBhJIGkuy1Fl/QDk5enWwdcDiYQgEALEtAwmm729BumFb6vvfZay1BAJLVMVXOhWRN46aWX7H5Lii5p/pL2DynSpmpZ8yN/CEAgPQLaHVub6Sa/V5p32YrRcERSem2OnCBQIbB06VL7QF3dmWnPJa2Ma8UfoAoQ/oAABDInoEUoihpp40c9aUCPEFm7dm3m5cqyAIikLOmTd8sTeOutt2xESQ/UlWAaPny4aYa9RVq+YgEAgQIR0FwjPS5Ev0FapaYVa5qczWEMIolWAIGcEHj11Vft8420z4hC3KNGjTLr16/PSekoBgQg0EwEdu/ebfd5kyhS1Ei/N9u3b2+mS4xyLYikKBhxAoG4BHRnp+fFaf8RCSZN+FaESeFwDghAAAIhBBS5njFjRmUStob6NbzG70rXNBFJXbPhEwjkgoAE0/jx4+1kb4XDNU9g4cKFzGHKRe1QCAjkm4AWh8yePdsMHjzYRow032jevHlmz549+S54TkqHSMpJRVAMCNRC4JVXXrErTjSHSSFy/fDp0SgaquOAAAQgIAJaoq/fhUGDBtnfCb1LKDHPyL99IJL8mWEBgVwQUOhcP3zJDt+ay6R5BRqWY6VcLqqIQkAgNQJbt261D97WHKNk1az2aGObkfqqAJFUHz+sIZAbAnokylNPPWVXp+hHMoky7dy5MzdlpCAQgEAcAhI/mk+k+YrMXYzDtDMviKTOqHAOAgUn8MYbb9iH7mr+kiZ+66W/Z86cydBcweuW4rcugU2bNtloUbJliN4nTpzILv4NbBKIpAbCxTUE8kJAk7+1w3cyNKc7zyeffNLMnz8f0ZSXSqIcEOhAQLv0a6fr6u+tIkeKIDGM1gFWg/5FJDUILG4hkGcCen6cHjmgITkNzemlVS+6K129ejUrX/JceZStaQloaFxL9KsjwFqmr0nYEkwc6RNAJKXPnBwhkDsCL774op0ErrtUPVdOoknv+l+Tw/U5BwQgEI+A9ibS8Jl2t5YQSobFE1Gk6C9H9gQQSdnXASWAQO4IaA+VVatWmUmTJlWeMactB7SUWHs2aQUd4f7cVRsFyjGBbdu22TmB1TciWpE6bNgwGz1igUU+Kw+RlM96oVQQyB0B7dGkOUwjR46srKDTj7zmNuluWA/CRDjlrtooUAYEFHnVd0VbcgwYMMDuVaRIkaJEuvHQDQjflQwqJiBLRFIANEwgAAFj92LStgNPP/20vRtOhunUGSjiNHbsWKN9WrZs2cJjD2gwTUtg165d5rnnnjNjxoypPO4j2YJDW3IsXbrUaE8zjmISQCQVs94oNQRyS0BzKRYsWGD3bNJkcK2kU6ehqJNW6UyYMMGuzlFkigMCRSGgbTW0qEFRU0VTFSHSDYHatv6WSFq0aJHd7boo10Q5uyeASOqeESkgAIE6CWiO04YNG+ycDHUwAwcOrHQw2utFQ3ZawbNs2TI7SZwHbtYJHPNgAlpFpuiPVn+qXSZ7EmlOnqKlmkMkoa/oEQsagjEXxhCRVJiqoqAQaD4CGoZYuXKl7ZCGDx++z9257tIlptRRaWsCRae0GojnTzVfO0j7iiTCNZE6iXgqwpkMFyeRIU2wnjp1ql2koGehcbQmAURSa9Y7Vw2BXBPQs+c0bLdkyRLbUSn6pD2dNGSnTkwvPaNKE2E190l7y0hsMYSX62pNrXCaFC0RpIjQM888Y4fCtPdQ9RCZhoE1d2706NE2wqmFBwjw1KqoMBkhkgpTVRQUAhBICGiy7Jo1a+xuxOPGjbOb7yXDIhJQ6gD1vyIEElhaUaT9nrR1gTpPJtImJIv5LjGs+tfCAEUZFfWR4KmOBiWRSEUo1UYkpFX/WmrPA6CLWe9ZlBqRlAV18oQABBpGQPOfJISWL19uRZQ6US3FVtQpiSRofkkiphSR0gTztrY2uweUJubqsQ+aQ8UwS8OqaR/HqjMJHzFX9EeCVqsmNRlaw62qH4neZBGABJAEkc5LBCvtwoULrf3rr7++j2/+gUA9BBBJ9dDDFgIQKCwBdcyaeKs9azQ3RRN129vbbVRKYkqdcLJ6SYJKf2u4T521OmcN30h8aZm3bPXwYHXUilZo7pR8t1LEStEZXe+rr75qh0oleDQEOm/ePPvcQG1CqoiPxKrmmollZ3z1mSKASqsIkOYFKWIkrmJKFKiwX7lCFhyRVMhqo9AQgECaBBSd0DCNOn6twJOo0vCNhvHUkatDl2jSvCmJKAmAJOohgZWILJ1T5EppJMSUXoJALw0LKZol4aV5MpprpVVUykMiTHNrJMQkGLRRoaJdipZJPOilvxWF0TwufaaXlqRLuKm8Eiuy1WvOnDk2yqaHp8qnomfJS1EZCRrlryiNIjkSNiqrBIzKn1yfRE4Slet4nUqja5SglA/503UoT5VTLLWSjE0V02zJ5OVLAJHkS4z0EIAABDwJaEKwBIEeLKzIlYSWRIwEjESLhIpEkKImEkWJSFFkKxEqElESYolgkQDRS4IrEV0SMcl5zdGRsNFLQiV5SZDJh176Wz61rF3vEjMSfBJpKsPkyZPt1gwSNiqrxI3Kr0iZJtZrOPLNN99ks1DP9kDy4hBAJBWnrigpBCAAAQhAAAIpEkAkpQibrCAAAQhAAAIQKA4BRFJx6oqSQgACEIAABCCQIoH/D4bo2p0JZYt/AAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the network\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "class RNN(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, output_size):\n",
    "        super(RNN, self).__init__()\n",
    "        \n",
    "        self.hidden_size = hidden_size\n",
    "        \n",
    "        self.i2h = nn.Linear(n_categories + input_size + hidden_size, hidden_size)\n",
    "        self.i2o = nn.Linear(n_categories + input_size + hidden_size, output_size)\n",
    "        self.o2o = nn.Linear(hidden_size + output_size, output_size)\n",
    "        self.dropout = nn.Dropout(0.1)\n",
    "        self.softmax = nn.LogSoftmax(dim=1)\n",
    "        \n",
    "    def forward(self, category, _input, hidden):\n",
    "        input_combined = torch.cat((category, _input, hidden), 1)\n",
    "        hidden = self.i2h(input_combined)\n",
    "        output = self.i2o(input_combined)\n",
    "        output_combined = torch.cat((hidden, output), 1)\n",
    "        output = self.o2o(output_combined)\n",
    "        output = self.dropout(output)\n",
    "        output = self.softmax(output)\n",
    "        return output, hidden\n",
    "    \n",
    "    def initHidden(self):\n",
    "        return torch.zeros(1, self.hidden_size)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "def randomChoice(l):\n",
    "    return l[random.randint(0, len(l) - 1)]\n",
    "\n",
    "def randomTrainingPair():\n",
    "    category = randomChoice(all_categories)\n",
    "    line = randomChoice(category_lines[category])\n",
    "    return category, line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def categoryTensor(category):\n",
    "    li = all_categories.index(category)\n",
    "    tensor = torch.zeros(1, n_categories)\n",
    "    tensor[0][li] = 1\n",
    "    return tensor\n",
    "\n",
    "def inputTensor(line):\n",
    "    tensor = torch.zeros(len(line), 1, n_letters)\n",
    "    for li in range(len(line)):\n",
    "        letter = line[li]\n",
    "        tensor[li][0][all_letters.find(letter)] = 1\n",
    "    return tensor\n",
    "\n",
    "def targetTensor(line):\n",
    "    letter_indices = [all_letters.find(line[li]) for li in range(1, len(line))]\n",
    "    letter_indices.append(n_letters -1)\n",
    "    return torch.LongTensor(letter_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def randomTrainingExample():\n",
    "    category, line = randomTrainingPair()\n",
    "    category_tensor = categoryTensor(category)\n",
    "    input_line_tensor = inputTensor(line)\n",
    "    target_line_tensor = targetTensor(line)\n",
    "    return category_tensor, input_line_tensor, target_line_tensor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.NLLLoss()\n",
    "learning_rate = 5e-4\n",
    "\n",
    "def train(category_tensor, input_line_tensor, target_line_tensor):\n",
    "    target_line_tensor.unsqueeze_(-1)\n",
    "    hidden = rnn.initHidden()\n",
    "    \n",
    "    rnn.zero_grad()\n",
    "    \n",
    "    loss = 0\n",
    "    \n",
    "    for i in range(input_line_tensor.size(0)):\n",
    "        output, hidden = rnn(category_tensor, input_line_tensor[i], hidden)\n",
    "        l = criterion(output, target_line_tensor[i])\n",
    "        loss += l\n",
    "     \n",
    "\n",
    "    loss.backward()\n",
    "    \n",
    "    for p in rnn.parameters():\n",
    "        p.data.add_(-learning_rate, p.grad.data)\n",
    "    \n",
    "    return output, loss.item() / input_line_tensor.size(0)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0m 16s (5000 5%) 3.3842\n",
      "0m 41s (10000 10%) 3.0339\n",
      "1m 7s (15000 15%) 2.0392\n",
      "1m 34s (20000 20%) 2.4242\n",
      "2m 2s (25000 25%) 2.8325\n",
      "2m 30s (30000 30%) 2.7743\n",
      "2m 57s (35000 35%) 1.4324\n",
      "3m 25s (40000 40%) 2.2695\n",
      "3m 51s (45000 45%) 2.7919\n",
      "4m 15s (50000 50%) 2.3314\n",
      "4m 38s (55000 55%) 2.2704\n",
      "5m 2s (60000 60%) 2.2223\n",
      "5m 25s (65000 65%) 2.8978\n",
      "5m 48s (70000 70%) 2.5232\n",
      "6m 11s (75000 75%) 2.1350\n",
      "6m 34s (80000 80%) 2.9972\n",
      "6m 52s (85000 85%) 2.3060\n",
      "7m 7s (90000 90%) 1.6370\n",
      "7m 22s (95000 95%) 2.4500\n",
      "7m 37s (100000 100%) 1.6219\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import math\n",
    "\n",
    "def timeSince(since):\n",
    "    now = time.time()\n",
    "    s = now - since\n",
    "    m = math.floor(s / 60)\n",
    "    s -= m * 60\n",
    "    return '%dm %ds'%(m, s)\n",
    "\n",
    "n_iters = 100000\n",
    "print_every = 5000\n",
    "plot_every = 500\n",
    "\n",
    "total_loss = 0\n",
    "all_losses = []\n",
    "\n",
    "n_hidden = 128\n",
    "rnn = RNN(n_letters, n_hidden, n_letters)\n",
    "\n",
    "start = time.time()\n",
    "\n",
    "for _iter in range(1, n_iters+1):\n",
    "    output, loss = train(*randomTrainingExample())\n",
    "    total_loss += loss\n",
    "    \n",
    "    if _iter % print_every == 0:\n",
    "        print('%s (%d %d%%) %.4f' % (timeSince(start), _iter, _iter/n_iters*100, loss))\n",
    "    \n",
    "    if _iter % plot_every == 0:\n",
    "        all_losses.append(total_loss / plot_every)\n",
    "        total_loss = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sjpark\\Anaconda3\\envs\\torch\\lib\\site-packages\\torch\\serialization.py:360: UserWarning: Couldn't retrieve source code for container of type RNN. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    }
   ],
   "source": [
    "torch.save(rnn, \"model/NLP_name_generator.model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15d12d53080>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(all_losses)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation with confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Roveki\n",
      "Uanten\n",
      "Shakov\n",
      "Geler\n",
      "Eren\n",
      "Roure\n",
      "Sera\n",
      "Pare\n",
      "Alara\n",
      "Cha\n",
      "Han\n",
      "Iun\n"
     ]
    }
   ],
   "source": [
    "max_length = 20\n",
    "\n",
    "def sample(category, start_letter='A'):\n",
    "    with torch.no_grad():  # no need to track history in sampling\n",
    "        category_tensor = categoryTensor(category)\n",
    "        input = inputTensor(start_letter)\n",
    "        hidden = rnn.initHidden()\n",
    "\n",
    "        output_name = start_letter\n",
    "\n",
    "        for i in range(max_length):\n",
    "            output, hidden = rnn(category_tensor, input[0], hidden)\n",
    "            topv, topi = output.topk(1)\n",
    "            topi = topi[0][0]\n",
    "            if topi == n_letters - 1:\n",
    "                break\n",
    "            else:\n",
    "                letter = all_letters[topi]\n",
    "                output_name += letter\n",
    "            input = inputTensor(letter)\n",
    "\n",
    "        return output_name\n",
    "\n",
    "# Get multiple samples from one category and multiple starting letters\n",
    "def samples(category, start_letters='ABC'):\n",
    "    for start_letter in start_letters:\n",
    "        print(sample(category, start_letter))\n",
    "        \n",
    "samples('Russian', 'RUS')\n",
    "\n",
    "samples('German', 'GER')\n",
    "\n",
    "samples('Spanish', 'SPA')\n",
    "\n",
    "samples('Chinese', 'CHI')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch",
   "language": "python",
   "name": "torch"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}