Example1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "#Loading all required libraries \n",
    "%pylab inline\n",
    "import cv2\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "import pytesseract\n",
    "import matplotlib.pyplot as plt\n",
    "import statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting matplot figure size\n",
    "plt.rcParams['figure.figsize'] = [15, 8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading image form directory\n",
    "img = cv2.imread('image2.jpg',0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(322, 681)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# showing image\n",
    "imgplot = plt.imshow(cv2.resize(img, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC),cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for adding border to an image\n",
    "img1= cv2.copyMakeBorder(img,50,50,50,50,cv2.BORDER_CONSTANT,value=[255,255])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "img123 = img1.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Thresholding the image\n",
    "(thresh, th3) = cv2.threshold(img1, 128, 255,cv2.THRESH_BINARY|cv2.THRESH_OTSU)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vR0dHBxYvXqztR18WALBnzx5ERkYiODhYKwv9xXpP+9TnbcWKFUhMTMTkyZMRGBiI5557Drt37+5z8GRPcHAwABgud5X18REaGorw8HCEhYXh8ccftzg+1IkDPD09LdpPUVGRdi6Kj4/H5MmTUVtbi7vvvltrm2q7VNvzI488gvDwcISHhyMiIgLh4eFau0xJScH06dO1OjGSx/6cL/V1ZDKZbOq9pKQE06ZNQ1BQEJYsWWIxEU9FRQWcnZ21PKvnvL62fz3rc5ee/jynfubYO0daB0/f//73ERQUhNDQUIfnLrVt6r8EHDNmDKqrq7XzRV/Ol4DjzxJ7x4eaB/Ucp7YVBk9EZMNIP7+h/uMYqP6z17fd1dVVHn30UYt0n376qYwfP97iNfrHdXV1Mm7cOAkNDZXS0lIpLS2V0NBQGTdunEUfeHuD92NiYiQmJsZi2bJly0REZOPGjdqyadOmSUFBgTbBRWZmpt3Bwvr8paWlCQB54YUXpLS0VBvzFRgYKIcPH7ab7tixYzYTPFiXR05OzqD0l09KSjI0psBeOZeVlWkDnxVFsTvWRp9PAOLm5iabN2+WkpISrSz8/Py0OlL3Yy9f+jEVUVFREhoaKi4uLlJaWipvvvmmREVFGRpnYT0Yf/369dLZ2SmVlZUOJ3Y4deqUeHh4yHPPPSeVlZXS3NysTcZgXf+XLl2yaDfWbUeddED/HgFIeHi4xXN75dKTvXv3yvz580VEpK2tTcuDvfFQFy5ckIqKCvn000+loaFB24Zad6tWrRIAsnbtWikrK7N7LD366KPi5eUloaGhUlJSIps3bxYAMnPmTC2NdVlMmzZNioqK5NNPP7UoCyP7bG9vF5GuY0Udv7J+/XopLS2Vd955RxvT1ddjIicnx6IuAgICtDGWevPmzXM4Zk0tN/3xoU5M8MILL9gdi2Y9ccD8+fPl/Pnz8vDDD1vsZ//+/XL27FmHEyiEhYXJ2rVrJTs72+5ECREREXLq1CktfW951NexvUkRrM+X9tqpdb3HxcVJdXW1bN++Xfbv3y8iXcdxWFiY3H333XLw4EGbCT2sz5Eixs9XvdW3kXbS2dmp1dW9994rGRkZsm/fPikqKurx3DVhwgSLMVYpKSkiIjbnCyPndEefJceOHRMXFxdxcXGRc+fO2aSNj4+321aIaPQDJ5G4ulx77bUCQJycnASAzJkzR0S+uWjKz88XZ2dnu689fPiwNrhaPyDYUbCiD9acnZ0FgLz55psSFhYmQNcA7YqKCm2/alr1g1Bv69atdj+MDx8+LI8++qi2Th1wrr/Iy8rKEhGRpUuX2qSzHogcFxdnsd/LHUCp70ktZ31ZpKenG7ooACAbNmwQs9ksImIxq5l1HTnKlz4PkZGRUlNTIyIi+/fvtyiLnmb7+uqrr2TatGna9k+fPq2t62liB29vb8nMzLTYVl5ent16sJ48w17bsQ6gXnnlFfnyyy8tLk7tXUQ6Ul9fb3OxtGHDBpsg6umnn5bW1lb52c9+ZnHxp5aZ/kLNulz0+YmLixMActNNN4lIV5k/+eST4unpaZGHno4h9aLP6D71x5R+sgd1xjX1eO5vAKW+3tPTUyIiIiQ8PFzCw8Pl2muv1SYfcHS8ODo+9G3E+vjQl82YMWMkPDxc3nrrLfnyyy8lODhYAMjOnTstysN6366urhbt8uuvv5aQkBAtrf6caCSP1m1Ofy7Sny/1X0ToX6PWqf69KYoiP/7xj+2+74SEBG25/lxi7+L/cgZQKrXNTZgwQU6ePCkitucJ/YyI1ucX9bPMet/qcvW4c5QvR58lNTU1EhISIl5eXjbnCP25ddeuXf0qJyK6MhkNoNiFb5TIycmx6MZXUFCAFStWwNnZGQUFBVi5ciVOnDhh8Rrp7rbg6uqKmpoaxMXF4dixY9r6Y8eOIS4uDjU1Naivr9eW6/ukd3R0ID09Hffddx/+7//+D5mZmVi1ahVCQ0MB2O/uoSotLcXKlSvtvh9XV1dt3AnwzYDzDz/8ENnZ2di1axcSExMBAM8//zz8/f0BABcuXAAABAYGYvz48drr3d3dHRfeZVJdXY2amhrtseqOO+7QHluPX7O2YMECODl1HbZq100ANnXkSH19vZaHsLAwBAQEAAC8vb0BAHl5eVr3GHtELMfdAV3dCg8dOgTAto7V7lzLli3D+vXrtQkG1Lbn4+Njdz/W3SGNDH5PTk5GRESE9l4URTFcLmpeUlJSLLrzpKWlYcOGDRZjIF544QVcf/31eOutt+Dv74/CwkKtO9iKFSuwceNGAMBdd92lbUftmqrPj9omP/vsM/ziF7+AyWTC2rVr8emnnzosC3VsiL4rW1/2qW9f+skeZs+ejfT09AF34evo6MDHH3+MnTt34rXXXsMbb7yBN954AxkZGYiKiurxtfq2qa9vfVuzPj7UslEUBc3NzSgvL0dkZCQiIiKwY8cOZGVlad0xHbWh3NxcLc2RI0cwadIkVFVVAQCmTZuG7OzsPuVRX94ilpMi6M+XRUVF2ngs/WvUrm/6eo+OjsZvfvMb7fnUqVPx29/+FgDQ2NioLdefSwBo54rhpN5r8MyZMzh+/DgAyzGNW7ZssRgrGhwcbHFe0L+HhIQEbTIX9TOut+519fX1Fu3G09MTABAQEIDo6Gg0NTXZnCMyMjK0z5kr7d5uRHR5cBKJUcD6otZkMqGtrU0LQNra2pCVlYWwsDCLAbOKouDQoUO4+eabAQAnT57EjTfeiKNHj8LJyQleXl7ahXFPF1bqhdiUKVO0mfb0+3Hk9OnTdgeuq3myd+FrfeNHEUFoaCg+++wznD59WpuQ4Ic//CGysrJ63P/llpiYiF27dsFkMiEoKAhA1+QEs2fP1tL0FuipF1W9XWg4ok+Tm5uLyMhIdHZ24vz58wC62sq2bdssZpzT04+7U2d/rK6u1i6ImpqaLNInJyejtLQUubm5+PnPfw53d3etbejb3mBQ2796YasGQn29iLRut2lpaQCAlStXwmQyoaWlBUVFRQCAd955R7v4Aywv8GfPng13d3c4OztrF47AN8eSGkS3t7fjj3/8I/bu3Ys5c+bgpZdecjjxxq5du7B06VKLWe/0+/z3f/93NDc3w2w2292nvn3pL9BdXV0dzp7XV0uXLkVRUZHNhaejYFllpJ4cHR8iXTPCPfjgg9rx1NPsiGpg39nZaZFP9cskJycn7abC+kkSjLYlNZ29SRHUcvb398f48eNx5MgRi9fY4+/vj4iICC0f7u7uWLVqFebMmaN9WVVZWYmwsDBD+bucPvnkEzQ1NaG1tRULFizAj3/8Y+zatUtbv3z5csyYMQMzZ87scbZAka6b+N5xxx1YsWIF2traev3CCegqV327mTt3rs1xqf/8ERG7nzNXww2Xicg4BlCjgL2LWgA4fPgwUlJSEBISgvLycou0qsbGRlRXV0NRFDQ2NqKiogL/+Mc/cP78ebS0tMDd3R0dHR09fgtnb9YtIx82CxYswNatW7F06VK7eTJCURR0dHRos2v94he/wGuvvYa33noLhw8f1i50RwL9jHPV1dVYvHgxtm3bhi1btuBHP/pRn7Y1GFN5R0VF4b//+79RV1enXWSYzeZep5O39yvUnXfeCQB4+umnccMNN+B73/seDh06hOrqari7u6O9vV0LrtS8nz17VhtQPxiTFwyltLQ0uLu7W8zMBwAPPPAApk+fjpkzZwKwvMB/5ZVXMGnSJDQ2NmLMmDEwmUxobW3VjqV9+/bh7bffRlpaGjo6OrBv3z7s27cPLi4uNr96qaZMmYINGzZYLFP3qSgKioqK8NJLLyE+Pl77VUy/z54M1jTQr732GqKjo22WD8a0825ubgDsz4rm4+NjMRNmTyIjI5Gfn4+4uDht2YoVK/Dhhx9qefXz80Nubi4A4xPJqNQAzWQyYenSpRYBg1rOS5cuNfwlj/VkDSICFxcX3HTTTWhsbERQUBBiY2P7dS4ZStbByG233Ybvf//7OHjwoHZu3rx5M2bMmNHrtux96dKfngVPPfUUZs2a5fDzracJMYiIVAygRgl7v0JduHABOTk5CAsLs/n1SdXe3g6g61s6s9mM8ePHG/owGwyOuvCpeQJgM0OSPc7OznjwwQdx5MgR7NmzB88++yzuuOMOh7+iDAb1Ak69oAMsZx+zLmd1WW1tLZKSknDp0iU4OTmhoaEBy5cvH7J89iQ4OFi78LfW0zeu9gJ29ZfK0NBQi250AHDddddhw4YNuOmmmyy2k5qaivT0dJugZKTSz8yndqHr6eJv5cqVFl1N9To7OxEQEIAnnngCIqL9uuXovlMq618i9NTjRJ2JzSh1W2r7HSj9LwqDzd4vcKrOzk7DQeCWLVswa9YsAN8cywcOHNC+uJk6dSp27dqldXHtK32AtmHDBhQXF6O0tNQmD0ePHsUXX3zRp21bn0taW1tRV1eH999/H6dPn+5XfoeCyDfTxK9YsQLbt2/HL3/5S/zwhz/Ea6+9pqV79tlnkZiYaPc+gdbUL9fUXwiNftGmd/vttyMkJKTPryMi0hv+DtI0KNSLqYULFyI4OFi7wHN1dcWiRYss0ugFBARYpLfWW/AyEPoufPby5Orqqu1f/02jPk/q488++wx79uxBUFAQHnvsMQCOp+MeDCaTCadOncJXX32lLcvIyEBzc7PDbzDb29uxZcsWHD9+HGfOnMEPfvADfP311/3av73uaX39hv+rr77SxnNYl2lv37iazWYEBwcjMDBQWxYcHKwFTpcuXQLQVU4VFRVISEjQuu+p+3B1dcV3v/tdQ3m9nDcGdtTmvby8EBgY2GO7Ui/gTSYTysvLbS7w9uzZg4aGBu0LCwD4yU9+Aj8/P+0eQgD6dNPUnqbP1+/TEUVR0NbWNqAbtVrnZyiPvcFmMplQXV2tjZ8EusbJqDcbb2pqcjh1vhEREREW4zH7Q20X6rGpP5dUVlbimWeegY+PD5qbmwe0n4Hky5r6C2htbS0++OADHD9+XPsFvq2tDYD986iR/XV2dvbp/KH317/+Vdu/vXwbXUZEVzcGUKOI2WxGenq6zUDx9PR0hzcZjYmJsUivHyQNdH0I7t271/BA/J5Y9/GfOHEiIiIibLpvxcTEIDs726J7jT6P+jwpiqJ1AwOA9PR0+Pj4oKqqSruHin7fg/lB+Oyzz6KsrEx7vnr1ahw9etRh+j179uCJJ54A0NV9cd26dQgJCcGmTZsM51PfXau6ulobQK4fqN4TNRBxcnLCkSNHMHXqVO1+SxcvXsSePXsMdVdRu0/pxzzl5ORo407UrlRqu1PrQj/+DrAcAG8vn2o6tVvWnj17BqUt2tPU1ISioiK7v3qKCKZOnYojR47Az89PW758+XIcPHhQe279vvPy8iy28+STT2pdbNUy9PPzQ2lpKQICArT3rf8FT089PvV1ZN1tzXpciH6f+mBL/6VETk6OxYD8vhwv7u7uFsf26dOnERcX1+djzUiQPGfOHGzdunXAAZp1vaWmpuLw4cNQFAWurq7ar4bqfb6SkpIM5xH4ZgIeNb3+V3VHeeiJdb3rzyWxsbFYs2YNAOBf//qXxeuGYvIc/TYbGhq0LuCOzJkzR/tMUb8wUs/Xaj321m3Ynri4OIefbXr6XybVX8PU49L6800NUNXeDP/6179w4cIFduEjIhsMoEYR9eLnzJkz2jL1AtfeTEXqh4I+/ZEjRxATE4NXX30VhYWFKCoqwgMPPGARGFgHQj19SOsvOBobG1FSUqJNbjFlyhSsX7/e7nbb2tosuj6tWLHCYZ7i4+Nx+PBhODs74+6778brr7+ORx55xCK4qaurw6lTp7T3rM9zXy4W9Wm2bNli0U3p7bff7rHbkr67X3V1NUpKSvDaa69ZdOGzzqf1B/fu3bu1x6mpqTh06BD8/f21YEgfIKn079XX1xeBgYHo7OyEk5MTzp07h29961soLy/HG2+8gWeffbbXMtBLTk7W3pfajtRAXg2A9b9MWdNf7Ovz7O3trQ3eB4Dz58/j3XfftQharduhmo++tE+9goICzJo1Swso9XWtPvfz80NZWRmuueYaAMB//ud/al341PetdvVTZ8h79dVXsXfvXvzv//4vsrOzkZI0M+vCAAAgAElEQVSSggMHDmDHjh3YsWMH8vPz4e/vj4KCAm1/+gkdrIPeKVOmoLKyUltvdJ/W29WXvXpBq+6rqqoK5eXlhi4cc3JyLILa//mf/9HyoueovlRq27ROq0+nnhPU85n1cWy9TcD+r7X6Y7WmpkYLQEVE61qqysvL0yZnMJLHwMBA7RcOJycnm5kg1Ty//fbbWjdC6/cC9Fzv6qQUTk5OKCoqwoQJE/Dxxx9j9erVFtvYuXOnxa9SjvLcF2q7URQFhw8fRmpqqrbO3jlU3zU0NzcXZWVlNr84WedT33bUsrTOc29tQeXr66u1ezXYOnToEAoKCmw+SxRFQWVlJb797W8jNTUV8+fPH9Cvj0Q0ihmZ63yo/3gfqMF1+PBh8ff3t3vfFHsOHjwo0dHRFvfe0P+dOXNGS2vvRrrr16+X5uZmh3lR75ui/v3gBz8QEZE1a9ZYLLe+YaXIN/e36SlP+nt8qH8iIvPnz7dYtnTpUhGxvQGnvf325MCBA3Lw4EGbm/WqZZGVlWVxg1OVeq8d9W/ChAly//33y/nz5+3mU0Tks88+0/L4ne98RwDHN0pV7x9jXUfW9VNXVyd+fn42eb/vvvtEpOd7QOmp6eLj42XcuHE291JR66W0tNQifyIiLS0tvdb/sWPHLNbfe++92rovv/zSph2+/PLLFjdbNtI+RUQaGxslMzNTXnnlFQEgY8eOlcrKSrtp1fdQW1srjz32mLi4uGg3hlYtX75ckpKSxNXVVcuDl5eXjB07VrsHze233y5BQUEyfvx4WbBggRQXFzu8Yay9Y0jfRozuU3+fKvUmpWvWrJGamho5fvy4tg83NzeJj4+XRYsW9VhmWVlZNjdvtT4OmpubpaCgQAoLC23q5eWXX5bMzEyLY2UgN9KNioqyu00Rx+et48eP2xzHUVFR2r7V7c+ePdtwHnu7ke769etFRLT75jlqpz3Vu711y5cvtzmXAN/cY8m6DByVVV/rOyoqSgoLC22OA5X+prSBgYESGhoqycnJNvcF098LKiYmRiIiImTGjBly9913y/79+/vUFvrzWaIe2/r7CgK8kS7R1QYG7wOlyAjo25uQkCDWXV2of9QB0ZWVlXjiiSfwwQcf2J2xSiW68S7V1dVITk7WvjGsrq7Gvn37EBAQoKVbvHgxTpw4AS8vr64biTk54YsvvsDOnTu1b8Gtt11bW4s5c+bA29tb61L4+eef4+abb8bMmTPR2dkJRVHQ1NSEyMhIbNu2zWYAck5ODtzd3W3yZJ2msbFRW//ggw9i586d8PT01PYLdH2jfOONN2LmzJnaGAd1v0YsWbIE2dnZiIiIgJubm8U33NXV1aioqEBubq7NQP7i4mIsWbLEJj9FRUW46667LJbrB7a/+OKLWLduHcLDw/HYY48hIyNDK7PCwkL4+/tb1KO+jhRFsagf6zoJDg5GS0uLzX77YvHixfjjH/+IkJAQbfvqdr7//e/j8ccft5lIQV8H9uq/p/Kyfo9qO6yurkZzczPGjx+v1UtP7VOldtEKCwvDhAkTUF9f32N70Je19TJ9+enbZE5OjnZcWU9RXlFRgWuvvRYTJkzAHXfcgZdeeknbTk/HkDpmSj0Getqndf5WrFiBvXv3oqysDPPnz8fHH3+M2tpaxMbGWkzA4qg96MtM/aXQ3nFw4sQJrFy5Ejt27LA4XuwdK2oe6+rqtF9mnJycICI25WLdhkQEra2t2kyi1sef0faiKApaW1u1iQpcXV3h6+uL2NhYbNq0yVAe9eXt6HxZXFyMF198ETk5OXBzc7Nppz3Vu9p+7K3Tn0v050LrMlC7ADs6V/VW3/outq2trTh16hRSU1ORkZFh89oHH3wQ+fn5aGhosGiT9s7Z+nJ75ZVXEB4ejoqKCkRGRmLChAmG20J/Pkusj4+cnBycP38eGRkZNtOaE9HopShKvojYv2DQp2MANfroL/CMXhDbuyi0Xtfbtnq6sNRzdI8blX4/jvbZ23u0tw/1gtPR+zRSVn0JMPR5tFcOjvJjffHc03uy3nZP+XO03f68N3uvsd6uo3U9tTX1dY7KxTpg6Auj7dNe/vvK3muN7t/6tUbS9GWfRo4Xo1+6DBZ1m0brw0gejJ63jOrt+FHTqG23v/vtqSx6ykNP54fejhkj538jrOvI3n57Wm5dbo7ek9G20NfPEkfpBqsNEdHIxwCKiIiIiIjIIKMBFCeRICIiIiIiMogBFBERERERkUEMoIiIiIiIiAxiAEVERERERGQQAygiIiIiIiKDGEAREREREREZxACKiIiIiIjIIAZQREREREREBvUaQCmKEqooyi5FUY4oinJYUZT/r3u5r6IoOxRFOdb936d7uaIoSrqiKMcVRTmoKErcUL8JIiIiIiKiy8HIL1AdAH4pIlMBzAHwc0VRpgL4FYAsEZkMIKv7OQAsBDC5+285gD8Peq6JiIiIiIiGQa8BlIh8JSIF3Y8vACgBMBHAIgBvdyd7G8Di7seLAGyRLvsAeCuKMn7Qc05ERERERHSZ9WkMlKIoEQBmAfgcQJCIfNW9qhpAUPfjiQAqdS+r6l5GRERERER0RTMcQCmKMg7A/wB4TETO69eJiACQvuxYUZTliqLkKYqSV1tb25eXEhERERERDQtDAZSiKC7oCp7+W0QyuhefVbvmdf+v6V5+GkCo7uUh3cssiMhmEUkQkYSAgID+5p+IiIiIiOiyMTILnwLgDQAlIvKibtXfAPy4+/GPAXysW/6j7tn45gBo0nX1IyIiIiIiumI5G0iTDOBeAMWKohR1L3sKwDoAWxVF+SmAcgBLu9f9A8BtAI4DuATg/kHNMRERERER0TDpNYASkT0AFAerF9hJLwB+PsB8ERERERERjTh9moWPiIiIiIjoasYAioiIiIiIyCAGUERERERERAYxgCIiIiIiIjKIAdQVyGw2D3cWRpWhKE/WEREREQ2lrnnbaDgYmcacRhiTyYRHHnkEn3zyCQCg61Zd1FctLS1ISkpCRkZG74n7yGQyYcmSJcjNzYW7u/ugb38kMpvNmDhxIhoaGtDS0sJ2SX0mInB3d4ePjw9Onz4Nk8k03FkiIhpRzGYzTCYT/v73vyMmJma4s3PVYgB1hfrkk0/w0EMPITU1Fa2trcOdnStSZ2cnfHx8hmz7Tz31FBoaGuDkdHX80BsQEIC5c+fiqaeeQlJSEtsl9Zmbmxtyc3Oxdu1a7N69G7W1tcOdJSKiESUgIADXX389zp49ywBqGDGAuoKlpqYiJSVluLNxxRORQf+1RESQkJAwqNu8Evj4+CApKYntkgbEx8cHM2bMGO5sEBGNSMHBwcOdhave1fHV+CikKAq/4R8kQ9HV7GrtviYibJc0IK2trezXT0TUA54jhx8DKCIiIiIiIoMYQBERERERERnEAIqIiIiIiMggBlBEREREREQGMYAiIiIiIiIyiAHUKOJoVpaeZmvhTC50OfSlnY2ENjkS8kDGGamv/tYp2wIREVnjfaBGEUVRkJeXh5qaGowZMwYuLi7w9fXF1KlTtXsdNTU1Yd++fRg7dizc3d2RmJg43NmmUU5teyUlJThz5gxEBCaTCe7u7gC67qre0tICRVEQFBTU440Bm5qacPToUTQ3N6OzsxPx8fHw8PAYcB6bmpqwd+9ejBs3Dl5eXpg+ffqAt0lDr6mpCV9++SViY2N7vZ+boig4cOBAr22HbYGIiHrDAGqUWbt2Lf75z39izJgxqK+vR2BgII4cOQI/Pz8AwMmTJ3HrrbcCAJYtW4b33ntvOLNLVwFFUVBVVYXY2Fg4OTkhIiICNTU1qK+v19JER0ejvLwcQUFBOHXqlMNtnTx5Ei+++CLef/99AMCuXbswb968Aefx5MmTWLhwIQAgKioKx44dG/A2aegVFhZi/vz5qKmpQUBAQK/pjbQdtgUiIuoNu/CNImazGRkZGbh06RJyc3MBADU1NYiOjsa5c+cAALNmzUJxcTEiIyPx3nvvwWw2D2eW6SrxH//xH/D09MSpU6dQUlKCffv2aevmzZuH0tJS7N27F19//bXDbYgIZs2ahffeew/z588HALi6ug44b+p2T5w4AQAIDAwc8DZp6LW1teHQoUMAgKSkpF7Tm83mXtsO2wIRERnBAGoUMZlM2mOz2ax9+J87dw7R0dGora0FAJw9exYdHR02r7FnsPv/czzB1cdsNmPbtm2ora1FUFAQAGhtEei6EAaAjo4OpKamGtpma2urzTJ7bau39qbv9lVdXQ0AcHJyskljxEDS8bjou+zsbDz66KMAADc3N1RXV2ttSU8tW/Vc197ebnc9AK0tNDQ0GM6HkTbWn9cREdHIxS58o5CIYOrUqThy5Ij269O5c+eQlJSEY8eO2XxwX7hwAQcOHEBnZydMJhNcXFzQ0dGhjRHQjxuYN28eioqKcP78ebvPrccVtLe3Y/fu3RARTJw4Ed/61rd6HatAo0tvQboqISEBGRkZMJvNMJlMdttOTxRFQUFBAerq6uDs7IzExER4eHiguLgYTU1NMJlMaGlpgdlshqIomDBhgt322NjYiLKyMlRWVtq0WX2e1H3OnTsXLi4uFmNsXFxcLPalprEeD2Y2mxEQEIBZs2bxuOijSZMmISAgALW1tTh8+DBSU1OxZcsW3HDDDVoa6/L29/e3CY4URdHaiOrIkSMWafTjovz9/REdHY1PPvkEERER2pg9fduwbhf68X9OTk5ISEiAp6enzXK1zRIR0QgnIsP+Fx8fL9Q34eHhkpmZ6XB9Z2eniIjU1dWJn5+fAJBp06bJ+fPnZf/+/RIWFqal3bFjhwCQKVOmiK+vrwAQALJr1y4REVm2bJm27PXXX5fFixc7fL53715tu21tbbJhwwYBIJ6enuLu7i7nzp2zyB+NLr21S1VOTo7WZpKSkuymWb9+vQAQb29v8fb2lpqaGm1dUlKSAJCcnBwRETl48KAcPnxYbr75ZgkPD7dYFxUVpe3L19dXoqOjZezYsTbb1OcJgPj4+Fik0bfn6OhoiY6OFgCyYcMGaWtrExGR++67z+L1M2fOtElTU1Mj3t7eMnbsWAkPD5f58+fLmTNnRITHhYhIZmamhIeH95imoaFBjh49KufPn5eAgADt/Jaeni6tra0i8k1Znjt3Ttzd3cXd3V2ioqLEzc3Non2IiEyZMkWrt8DAQJu2WVBQoC2bOHGi/O1vfxMAEhQUpG3jD3/4g3auU+tcpOsc7OXlJb6+vjJ16lQBILt375a6ujoZN26cxfIdO3YMZlES0Shl9LOW+g5AnhiIXYY9eBIGUP1i5ODRB1H+/v4CQEJDQ+W9996TqKgoLd3+/fvl7rvvFhGRiooK7UJBf4Exf/58bbmIyMKFC+0+37hxo1y6dElERHbu3CkA5Hvf+56IiHh6esq4ceMsLlppdBmMAKqtrU02btwoAGTRokUiIgJArr32Wi2NdQClBknHjh0TEZGYmBhZt26diIikpKRo+zp69KiIiBw7dkwAWBwHap5cXFxk9erVcuHCBYv9qu15wYIF2msWLFggACQ3N1dERJqbm7Wg6dSpUyIikpCQIABk586dIiJy7bXXCgCpqKgQEZHi4mIBwOOim5EAKisrS9zc3EREpLKy0iLwVctZpCtY9fDwEDc3N618582bZ9F2vvrqK4mKihJPT095+umnRUS0oEzfNk+cOKHt46c//am0t7fL7bffLiLfBPtq0PS9731PAMiJEye0tvn++++LiMgPf/hDqaiokHvvvVcAyDvvvKMt379//0CLj4iuAgygho7RAIpjoEYxRVEgIvDz80NpaSn8/f1RWVmJe+65B8HBwQC6AujExES8//776Ojo0GaosqaOOVEHV6vdYKyfr169GkePHgXQNS5BTfPll19i8+bNuHjxImbNmjVE75hGgz179iAtLQ0AUFlZif/6r/8CAPj4+Dh8jTreb/v27SgrK0N7ezt+9atfobS0VJsuHfhm7FVNTQ0AwNnZ2WbWv5SUFDz33HMoLCwEAO1YUdtzc3OzllZ9nJOTAwB46KGHcPDgQQBdXRejoqKQl5dn8Xp1e9u2bUNpaSl27twJADwuDGpra0NeXh7c3NxQVlaGjz76yGK9l5eX9njOnDm4cOECSkpKtFn6rMdJJScn4/jx4ygtLcXvfvc7ALCY5ESljpGbP38+Xn/9dTg7OyMjIwOZmZlYtWoVEhIS8MADD+DUqVNaF8D29nbccccdcHJywrJly/CHP/wB77zzDkJDQ7FlyxasWrUK9957L373u9/hnXfeQWJiIsdGERFdARhAXQX0QZR6EdHZ2QkA2riOPXv24N1338UzzzzT47asB9pbP9+8eTNmzJhh8ZoTJ07gxhtvxDPPPIPZs2fju9/97uC9ORp1XFxctMeHDh3C73//e8yePRvx8fG9vvbFF1/ELbfcgmuuuQYzZszA+PHjtQlT7CktLcXKlSstlqlfFhgduwUAaWlpKC4uxltvvaW1/+bmZtxxxx0OZwpcu3YtFi5ciFdffRVz5szBHXfcYXh/V7PPP/8cq1atQmxsLBYsWIC//OUviI2N1dbn5ORoQZK3tzeAnieFUAPajz76SDufqf/tsZ7AZOzYsQCAw4cP44YbbsC8efPg5eWFGTNmoLW1FevWrcOKFSuQnJyMp556CuvWrUN7eztEBOvWrcOqVauwevVqbTnHwRERjXycRGIUEd0g9ObmZpSWlmqD09UgKjc3F5MnT9bStbS04Pnnn8fq1atx/fXXo7W1VVun/+ZeDZDUb9EdPV++fDlmzJiBmTNnakFaSkoKMjMzLfKqThRAVxe1jdprW+o6X19fBAYGoqamBqmpqTZtR/8adTstLS0AgD/84Q9YtmwZAKCqqgpeXl4Ws66p7VX9P2fOHGzdutViW47atjrlv36WPvXxsWPHEBUVhXvvvVf7BaqhoQHr169Hfn4+srKybPL6+eefIywsTNtWWVmZkSK8qokISkpK8NBDD+HPf/6ztryyslIryxUrViAhIQE33HADkpKSUFBQgN27dyMuLg6A47azYsUKpKen49ixYzZ1r09v3V7V81x0dLT2qyXQ1f4CAwNx4MABvPTSSwCANWvW4Mknn0RcXBy+853vAADWrVuHMWPG4Mknn0RiYiIWLFgwmEVGRERDgL9AjSKKoiAvLw87d+7E888/j7i4OFRVVUFRFC0oamtrg4eHhzbjVElJCVavXg0AuHjxotZdCgAyMzMhIqiqqkJVVRWAru5VZWVl2je01s//8pe/aN/A+/r6wsvLC0ePHsXBgwexY8cOrTsTg6erk6IoaGlpsQiKqqqqUF5ernU5VWeQHDduHI4ePYqioiKt3bS0tKC8vFxrj+p25s6dCycnJ9xzzz3YsGEDjhw5gltvvRWVlZUWvwDl5uairKwMn332GQAgMTFR2666rerqapSVlWHPnj1a/qqrqzFhwgR4eXmhvLwcZWVlKCsrQ3l5Oby8vLRfPF5//XVER0cDAPbu3aulsc4r0PWLx8GDB5GdnY309HQ8/PDDg13co0prayuefvppPPjgg1izZg32798PoKvurG8Inp2djYsXL+LVV19FSkoKHn/8cWzYsAFHjx7VzlXW9aGyrvuqqiqIiJZe316BrvNcUFAQioqK4OHhYdH+6urqcNNNN+E3v/kNdu/ejZ/85CdYuHAhJkyYgOLiYuzYsQOffvopHn74YXh5ebH7HhHRlcLIQKmh/uMkEn3naADh7bffLmPHjhUPDw8BIPfcc4/F+ra2NnnuuefEw8NDRLoGvf/2t7/VBkcHBwdbDMg+c+aMxcxi6ixmjp47OTnJF198oe1LnbUsKipKgoKCtEHXNDoZGdiqn9FM/Vu2bJlNOnVgfmhoqNZuCgoKLGaFBCDFxcVy/vx5CQoKEgDi5eUlPj4+kp6eLiKWk0gEBwfLddddZzMLn3WeoqOjLWakVI+jvszCZ31s6POqzsIXFRUl48ePl3nz5snx48cHrR6uZI4mkSgsLNTK8brrrrNoE9btCYDk5+eLyDcz7Hl5eTmsD/3Me9Z1f99998mZM2cctte2tjZ54YUXtHXW7W/8+PHi6uoqEREREhAQIK+//rqIiNx5550SFBQkYWFhEhMTIwAkKytriEuXiEYDTiIxdGBwEgl24RtlnnrqKfzsZz/D2LFjYTKZLLpKAV3jS1auXImFCxcC6OqWsmrVKqSkpMDZ2RltbW0YM2YMgK57n/j4+ODRRx/F/fffb3FvG3d3d4v76qjPm5qaEB4eru3rscceQ1xcnNbgepoIgK4O1157LbZv364N9m9vb9fanN7jjz+O+Ph4mM1m+Pr6aq/9xS9+gYcffhguLi5oampCSEgIPDw8sHPnTtTX16O9vR1ff/01br75ZgCwGAP11FNPITY2Fm1tbZgwYYI2JlCfJ7PZjJaWFu340edP354By/tAAcAjjzzi8FjR5zU3N1e7/4+IwN/fH5GRkRbdcMnSpEmT8M9//hPXXHMNLl26ZNEm9O0J6Dp3RUZGAgC2bt2KpqYmtLe3O6yPXbt2ob6+HgBs6n7cuHHw8fHR9mHdXl1cXPDoo48iLi4Ozs7ONu3v008/xdmzZwF0da1Wl69atQrLly+Hk5NT14xO3feBIiKikU9RLwSGU0JCgqhddMiYiIgIvPHGGxb95R1dfPV1+WDoadu8SBy97LVLvYG0i/6+Njk5Gbm5uQC6xsqEhIQY3udg5c9IOh4XXbKysvDTn/7UZnZEewajTvpb7gPZNuuaiAait89a6j9FUfJFJKG3dBwDNYo4+kDu6/KhzMtQ75dGtoG0i/6+Vp2yHABCQ0MtBvr3pS0OJH9G0vG46NLa2trjLHh6g1En/S33gWybdU1EdGVjFz4iGtUyMjLQ1NSkddu69tprhztL1IPk5GTtvlpEREQjEQMoIhq1RATTp0+3u5y/AoxMXl5eiI+PZx0REdGIxS58RDRqsQvVlYt1REREIxUDKCIiIiIiIoMYQF2hzGazNgUz0UhhMpng7+8/3NmgK4yR2WBHwoyxREQjgYuLC8+Jw4xjoK5QEydOxNy5c+Hj48ODiEYEk8mEiooK3HLLLRgzZgzbJfWquroaOTk5iI+P7zWtoijIz89HcnIygoODL0PuiIhGHpPJhJMnT9rc55MuLwZQV6iGhgY89dRTSEpKQmtr63Bnhwj+/v645ZZb8MQTT2DOnDlsl9Sr1tZWREVFGU4fFRWFjIwMuLm5DWGuiIhGLn9/f8ybN8/iJvF0+TGAukK1tLQgKSkJKSkpw50VIs2YMWMwZ84ctksaEl5eXrjtttuGOxtERMPK29sbZrN5uLNxVeMYqCuUoij8hp9GHBFhuyQiIhpC7CI//BhAERHRiKC/KOC3q0RENFIxgCIiohFBURTk5eVhyZIlMJlMFuv4jSsRXc14DhxZOAaKiIhGjIaGBuTm5tosVxQFhw4dwr/927/BbDbbBFhEfWE2mzFx4kQ0NDSgpaWFN26mEUk91/39739HTEzMcGeHdBhAERHRiOHk5ORwet6zZ8/i7Nmz+Pzzz1FbW3uZc0ajSUBAAObOncvZbGlECwgIwPXXX4+zZ88ygBpheg2gFEVxB7AbgFt3+o9E5NeKokwC8D4APwD5AO4VkTZFUdwAbAEQD+AcgLtF5NQQ5Z+IiK4SIoKQkBDMmDFjuLNCo4Cfnx9uueUWzJw5c7izQuRQSEgIu++NQEbGQLUCuFFEZgKIBXCroihzAKwH8JKIRAFoAPDT7vQ/BdDQvfyl7nREREQDoigK2tvbhzsbNEqYzWbU1dUNdzaIetTe3s4upiNQrwGUdLnY/dSl+08A3Ajgo+7lbwNY3P14UfdzdK9foLDmiYiIiIhoFDA0C5+iKCZFUYoA1ADYAeAEgEYRUW+DXAVgYvfjiQAqAaB7fRO6uvkRERERERFd0QwFUCJiFpFYACEAZgOYMtAdK4qyXFGUPEVR8jgYmIiIiIiIrgR9ug+UiDQC2AXgBgDeiqKok1CEADjd/fg0gFAA6F7vha7JJKy3tVlEEkQkISAgoJ/ZJyIiIiIiunx6DaAURQlQFMW7+/EYADcDKEFXIHVnd7IfA/i4+/Hfup+je/1O4fQhRERERER90tLSgs7OzuHOBlkxch+o8QDeVhTFhK6Aa6uI/F1RlCMA3lcU5XcACgG80Z3+DQDvKIpyHEA9gGVDkG8iIqJeiQhnsCLDjLSXy9WmetqPvXVs66NTUlISfHx8hjsbZKXXAEpEDgKYZWf5SXSNh7Je3gLgrkHJHRERUQ+ampqQk5ODMWPGwMXFBR0dHUhOToaLiwuArqnPDxw4gJaWFly6dAlz5syBl5fXMOeaRipFUZCXl4f6+nqMHTsWHR0d6Ojo0AKTCRMm4Fvf+tZlCVas8wIAly5dgq+vLxISEtDU1IR9+/Zh7NixcHd3R2Ji4pDmhy4/s9mMjIwMAAyQRxojv0ARERGNSMePH8edd96JMWPGoL6+HgCwceNGPPHEE1qaF198Ee+//z4AoKCgALNm2XwnSKRZu3Yt8vLyUFlZCW9vb/j7+6OjowPV1dUAgMrKSvj7+1/2vABAaGgoEhISkJGRgfLyctx6660AgHvuuQfvvvvuZckTXT4mk0l7zOBpZGEARUREVyQRQXx8PC5duoSysjLcd9992LdvH9LS0gBAC6Lee+89HDhwANu2bUNMTAy/ySWH9N/4R0ZGIiMjAzNnzgQA1NbWYuLEiYiPj0d5efmQtyPrvADAiRMntPUzZszAu+++i+3bt2PLli0wm80WF9xENHT6NAsfERHRSKG/eG1ra8OMGTMwZ84ceHt7Iy0tDRs3btTWd3R04OzZs71usz9zHulf09PrOZ/SyKcPQMxmM+rq6gB01V1AQADKyspQV1eHU6dO9Rg8Ganr3tJY58VsNmvPOzs7sX79emzZslqIfYwAACAASURBVMVh8ORo+/1po4O5LaLRgL9AERHRFU1EMH36dGzatAkAcMMNN2Dfvn1YuXIlFEXBE088YXGxqygKiouL0dTUhLa2Nri6uqKjowN+fn6YPn06mpqasHfvXowbNw7+/v6Ijo7GP//5T3h4eNg89/X1xdSpU7VfIxRFQUlJCU6fPg1FUTB37lyL8VjFxcWora1FYmIiPDw8hqW8yBgRgZubG4CuuhMRvP/++7h06RJWrVqFDz74AABw4cIFHDhwAJ2dnVAURRsnBXTNoFZWVoYZM2bg4MGDOHv2LEwmE1JTU+Hq6or8/HycO3cO8+fP19qJo7w4OXV95202m/H888/jX//6F7Zv324RPKljAsPCwhATE2PRlr28vBAeHg5PT08UFhYiNjbWIk9qWz1w4AAaGxsxb948uLi4aG27sLAQdXV1Wj6CgoIQExMDAGhvb0dOTg6cnZ0txmkRjVoiMux/8fHxQn0THh4umZmZw50NIgtslzRQmZmZEh4e3ud1eo8++qgA0P7Ky8tl2rRpFm0zLCxMAEhgYKCWLioqSkRECgoKtGUTJ06Ujz/+WBRFsfvc399fKioqRESkra1NampqxNvbW3x8fASArFu3Ttra2mz2m5WV1f9CokHR2/nquuuuk/Xr10tRUZFkZ2fLW2+9JePGjZPHHntMREQ6OjpERGTHjh0CQKZOnSq+vr7i5eUldXV10tnZKatXrxYXFxf505/+JAsXLpTw8HABIK+88oqUlJTIwoUL7bYTa1FRUeLr6yunTp2STZs2yaJFiyzyoFLbrnqc6NtydHS05ObmSl1dnQCwydOGDRtEROQHP/iBTZ6OHz8u8+bNk+DgYImMjBRvb28JCwsTEZHm5mb5/e9/r+0nLCxMbr/99n7UCBnFz9qhAyBPDMQuwx48CQOofuHBQyMR2yUN1EADKPWCMi0tTbugy8jIkPHjx8vOnTu1dMuXL5cJEyaIiGgXsUlJSdr6EydOaK9/4IEH5Ouvv3b4/K677hIRkdzcXImKihIA0tbWJhs2bBAAkp2dbbHfsLAwOXjwYH+KhwZRb+er66+/Xry8vCQqKkqcnZ0t2p4+cMnLy5Of/exnIiLy5ptvasFUbW2tRSCvBtoLFizQlrW1tcnatWsFQI95mTt3rvaawMBAi3WdnZ0W/4uLiyUkJERbr7bl+fPny4kTJ+RHP/qRwzzt2LFDRERWrlxpkafZs2fLqVOntG3+6U9/ksjISBERyc/PFwDi5OQkv/zlL+2WEQ0uftYOHaMBFMdAERHRqGEymWA2m7FhwwZtMoklS5bgq6++gru7O4Cu8VCbNm3C6dOnUV1djZMnT9psR51xbf78+di8eTPy8/PtPl+wYAG2bt2Kixcval2YAOBPf/oTHnjgAQQGBmLnzp1oaWkBAGzatAnl5eWYPn06x4iMcKdPn8bu3btx7NgxnDx5EhEREaiqqgIArRsbAMTHxyMtLQ1//vOf8dBDDwEAPD094e/vj+LiYkRGRqK4uBihoaEAgObmZgBdE0K4uLjgscceg7+/v8U2rV26dAlAV3tzc3ODq6srDh06hJKSEq17odpNVe2Sp1LbcnNzM06fPo23337bYZ7U6dLXr1+PKVOmaG30888/x9dff439+/fjtddew89//nNtJsK4uDgUFxdj0qRJeP7557WxWpzQgkYzBlBERDSqqBeiq1at6nW66ZUrV6KsrMzh+vb2dgDfTFhh/VxVUlKCtLQ0lJaWAgDWrVuHb3/726ipqcGvf/1rlJSU2GybMwGObE5OTjh//jwAICQkBMuWLcMzzzwDoGsSBzW4qK+vx80334y//vWveO211yy20djYCLPZjMbGRpvtq8uWL1+Ourq6HsdANTY2wtfXF2+++SYef/xxtLe34/bbb8fmzZu1/BihtjlHeerJ2rVrkZKSgr/+9a/YtGkTGhoaLPKnBk49BYJEowVbORERjSrqN/J+fn44fPgwpk6dCuCbWcGcnZ3x+OOPw8PDA4sWLUJsbKzDbakXpupFofXzrKwsLFu2DImJidi4cSOmTJkCAHj66afxxz/+EZ999hkKCwsxefJkm23zF6iRTVEUtLa2ao+nTZuGt99++/9n787jmrrT/YF/DiGSICEhBlC2RIiAFIuyKHWhWGvp4rTa1c7ctjiLnTtzbbV16diZX53xjop2WuvcTseOXV/3th3njs7c6rgAbiguIChR1IqKgJR9cUuAhOf3B5xjAgGDoiw+79eLF5wlJ0+S55ych/P9fg/eeustyGQyCIKAwsJChIaGwmq14quvvpLuMSbmR1NTE4hI2o498ark8uXLoVKpYLVau4ylubkZ3t7eCA4Oxi9/+UtkZWVhzpw5WLt2rRSP/Sh9ziiVSgQEBABoy2NnMYmef/55nD59Wrpq+8Mf/hBTpkzBli1b8N577yE2NlaK3/51iu8VY4MdF1CMMcYGpJsVIEQEPz8/NDc3A7hxYvfCCy9g7dq1eP/99/HMM8/A19cXAKQR1wBg+PDhAIAhQ4YAgLROx+mYmBh8/vnnAIBJkyZJJ8EzZ87ElClTkJSUhKVLl6KqqgovvPACxowZg4iICJhMJj7R7IfsixCZTOZwBTMhIQErV65EWloalixZAqCtOWhjYyOio6Oh0+mk/BB/63S6TtvpmEMGgwH+/v7SOmJe28fi7u4uFWVDhgzB5MmTsXTpUvzP//wP0tLSsGjRIqnJ3LVr16SbSts/z+7du6X7SalUKvj7+0vriLkvrvvNN9/Ax8cHWq0WALBjxw4kJSXhkUceQWRkJNzd3aXmquLrFKdvVsgxNii40lHqTv/wIBI9xx0IWX/EecluV08HkcjNzSUPDw/S6/Wk1+vJw8ODcnNzHdapqqoiALRt2zYiIiopKSEvLy/SarUUGRnp0NE/NTWVysvLHeZFRESQVqvtcjo1NZWIHEfh8/T0JIPBQN7e3rRu3TqyWq106dIlUqlUPApfP9HV8WrWrFkUHBxMAEij0VBwcLDDqHIrV64kAGQwGGjGjBn05ZdfdpknGo1G2o7BYJAGGQFAWq2207y8vLwuYwHgEMvx48fJzc3NYfS7Z599loiI1q9fTyqVigwGgxRTYGAgbdiwgWbOnCk9pry8nFJTUzvFFB0dLc2rqamhiIgIUigUZDAYyGAwSK/LaDQ6TM+ePfsOfmL3HvuBOMRBQoj4u/ZOgouDSPB9oBhjjA1YRqMRmzZtkv6D3tTUBKPRKC0narsBan5+PkaOHAkACA4OxpEjR1BeXg4igkKhgEwmQ0tLC7y8vODj44Nt27ZBrVbDZrPBYrFAJpNBoVB0mm5paYFSqQQAyOVy+Pr6Ijs7G+Xl5QDaOudPnz4dMpkMAQEBOHToECoqKpCQkHCX3ynmqqVLl6Kurg6enp6wWq1obm6WrsQAwJtvvomEhATYbDb4+/sjJiYGAQEBIKJOeSJeqbFarbDZbA75ZrFYpL5UCoUC165dQ2hoaJexAJDusQQAer0eW7dulfLUftkrr7yCgIAAKJVKuLm5QS6XQ6fTITIyEnFxcViwYAHMZjN8fHwwb948zJkzB3K53GlMnp6e2Lx5s5TTQNvVOXd3d1gsFgiCAJlMJr121ntkMhmefvppLF26lO+r1c9wAcUYY2zAUqvVePzxx7tcLvaHsu/nREQYPXq0dLNTZx599NEexUHto6B1t20iQlRUlNQni/U/RNTliar4GcvlckybNs1hWcfpuxGLWq12mqdEbTcAnjFjhtPHduzzFxsbe9NYbra/dHx+bp7ae7Kzsx0G7GD9A/eBYowxNqh1PJm7Eyd34ja72zafVPZ//enzu9VY+jrP+vr5BxuFQsEjG/ZD/IkwxhhjjDHGmIu4gGKMMcYYY4wxF3EBxRhjjDHGGGMu4gKKMcYYY4wxxlzEBdQAVVFR0e1dxBljbLAhIsjl8r4Ogw0SHW9wy1h/JJfLb3rTcHb38TDmA9SBAwcc7nXCGGODnUKhQFFREcLCwmCz2fo6HDaAyWQylJSUICUlBUqlkk9QWb8kk8lw/vx5vr9WP8QF1ABERIiLi+vrMBhj7K6yWq3QaDTYtGkTampq+jocNoDpdDqkpKRg4cKFSExM5BYdrF/S6XRITk6G1Wrt61BYB1xADUB8jwXG2L3IZrNBo9EgJiamr0Nhg4BSqURiYiImT57c16Ew1iWNRsNX3Psh7gPFGGNswOCmVqy3EBFfeWL9Hh/z+icuoBhjjDHGGGPMRVxAMcYYY4wxxpiLuIBijDHGGGOMMRdxAcUYY4wxxhhjLuICijHGGGOMMcZcxAUUY4yxQaOrEavs5/OoVqwrznKjJ/lyJ3KLc5ex/ofvAzWIENFN7xHlyjp3mrMY+kNc7M7q6jO+2Wfvam7cbg7dShy3ksu99RjmnCAIKCwsxPfffw8AMJvNePjhh6FQKJCfn4/Q0FCo1WqYTCZUV1cjISEBKpWqj6Nm/YUgCMjNzUVdXR1kMhmSkpIgl8u73AdbWlpw4MABuLm5QalUIiEhoVfjEZ/32LFjsFqtiI+Pv6Xt1NfX4+TJk7jvvvvg4+PTqzEydi/iAmoQEQ/8VVVV8PT0hLu7OywWi3TQDwgIwOjRo+/ayVhjYyMOHDggxXL9+nVotVrEx8ejsbERhw4dgqenJxQKRa9/6bD+Rcy5U6dOoby8XJrumJMtLS3Yt29ft8uLiooctiGe4AiCgBMnTqCystLhed3c3BxOku3z32KxID09HSNGjHA4MWlqakJ6ejqUSiVaW1vh4+PT6cTF/sRm5MiRUKvV0rz8/HzU1NSgqakJ06dPh4eHh8Nj8vPzUV1dDZlM1uV+Kb4Xfn5+GDNmzB37bAaLK1euoLi4GJ6enkhISEBrayuGDx+Ouro6rFmzBg8++CBiY2Oxfft2pKSkYMaMGSgpKUFmZiYeeuihTu8/F633rhUrViA3NxelpaVYvXo1UlNT4evr22m9lpYWrFu3DgsXLgQAzJ49G19//XWvxiIIAkpLSzFu3Dh4e3ujsbHxlrZz8uRJLFmyBGlpaXzjYMZ6AxH1+U9cXByx3jFr1ixSKpWk0WgIABmNRjIYDKRQKEihUFB1dfVdiyU3N9chluDgYJo1axYRER0/fpwAEAB68cUX71pM7M7S6/WUkZHhdFl1dTUpFAoCQJGRkZ1ysrm5mVatWtXt8tWrV5NGoyFPT0+KjIwkALRq1Spqbm4mIiJ/f38aMmQIGQwG0uv1ZDQaCQBlZmYSEVFra6u0rX379tGf//xnGjJkiJSXRETXrl2jdevWkbe3N+n1evL393dYbq+mpoYA0LZt26R5RUVFlJycTCNGjCCVSkXr168ni8XSabn9fllbW+sQn9lspuXLl0v78L0kIyOD9Hp9j5cdPHiQIiIiCACp1WqqqqqSls2ePZtGjBhBACgrK4uIiObOnUshISFUUFAgrXf58mVKT0+nI0eO9NrrYf2Xs+OV1WqV/p40aZLDPijun6KsrCzpe2zq1KmdHt9bnnvuOQJAEyZMuKXHd4y74zTr35zlaXfftez2AMglF2qXPi+eiAuoXmN/4D527BiFhoZK01VVVSSXyykkJISI7vwB1H77HWMRffXVV/TSSy8R0Z350mF3X3cH9aCgIJLL5bRo0SIiupGTQUFBRNR2cgygy+W7du2STlbOnj1LRESLFi0iANJzPvXUU1RcXNwpph07dkjTtbW1tHLlSgJAr7zyijRfzMGjR4/SY4891il++xw1m81UUlJCPj4+BICys7OlZRMmTKAzZ85I0yqVir799luny6uqqsjDw4NUKpXDCf/Ro0el1zpx4kSn7+dgdSsFVFNTE61bt47uu+8+AkDnzp0jorbjkP3nFhwcTNu3b+/yucUT4jlz5jhdfrMTUT4xHVhudhI6b9486Z9/FRUVDsvMZjN9+eWXFBISQgAoMTGx0+O7yoeezBfzNzw8XDoWurKtmy1jAwcXUHeXqwUUN+EbRGQymfR3TU0NbDYbgLYi2dfXF2fOnEF0dDSKi4thMBi63A61N10Rf98K+8fZxwIAra2tWLNmDfbs2YNt27bBZrM5xC7G0HE79rH1hu621ZvPw27Iy8tDdHQ0rFYrfH19kZeXh8ceewxA23seHh6O1atXO10ul8ul7Vy+fBkAsHr1avztb3+T8usf//iHtC1BEFBSUgKj0QidTgcAqK6uRmhoKK5evYply5bhnXfekdaXyWRoaGhAfX09Vq1ahdzcXKnZnrhcdOHCBfzud79DfX29w+uz2Ww4dOgQgLY8d3Nzg5eXl9SEz365+BpPnTqF0NBQjB07FpcuXQIRITY2FufOnUNYWFhvvfWD2oEDB/Daa69J0+Xl5QgNDYUgCA6fW3x8PAICAlBQUICGhga0trZi4sSJMJlM8PT0RH5+PoKDg7Fy5Urs3LkTcrkcCoUCLS0tUv+WxsZGZGVlYfjw4YiPj8eVK1eQk5MDX19fbmo5iNhsNqxbtw5KpRLvv/8+xo8fj4sXL0rHloyMDCxatAhlZWWQy+Vwd287nfrXv/4FDw8P+Pv7Izo6WmrKPmTIEIwfPx7e3t4QBAF5eXkYN24cjh8/jqqqKiQnJ2PIkCHIy8tDTU0N3N3dHZoeWywWKBQKAMCWLVugVCql5r8Aumz+bL/Mz88PBoOB+/wx1gu4gBrExAJALIa++eYbXL9+HUuWLMFf//pXAN0fdMW+GmPHjkVBQQEqKyshk8kwbdo0HD9+XJoW+6C4EovNZsO7777rtHgSv2hCQkIQHR0NAMjJyYHZbEZraytiY2Ph7e3do5jE12XfpwWA9OUGtPV3KSwsRGRkJDIzM6UTI9a73Nzc0NDQAOBGsS9+XkBbjjQ3N3e5XKvVQq1Wo7GxEXv27EFsbCwuXLgAg8HQqX+CmG96vR45OTmIjY0FACQmJuLq1auIj4/Hq6++ip07dyI4OFjK+XPnzuGJJ56AVqtFZGQk3n33XQwZMkTKFQC4evUqWlpa8PXXXyM8PBy/+93vpAJOJpNJOefm5obS0lKMHTsWfn5+Dq8LgHTCdenSJQDAM8884xB7RUXF7bzd9xSxQAUAg8EgFcz2bDYbNm3aBKCtr4p4DDx16hQ+/PBDfPbZZwAALy8vPPTQQ7h+/Trq6+ulPiepqan47LPPUFRUhB/84AeYNWsWNm3ahJycHEybNg0hISG4ePHinX6p7C6RyWSw2WxIS0vDSy+9hNjYWFRWVsLf3x8AoFaroVAoUF5eDqDtHzwVFRV49tlnQUTw9/dHcXExzp8/jyeeeAIAkJWVBa1WC6vViri4OHz44YfIyMjA5s2b8f777+PRRx/FwoULcf78eVy8eFHqnwcACoUC9fX1+Pvf/46f/exnMJvNANqOHz4+PlixYgWWLVuGqKgoVFRUwGaz4dy5c9BqtUhLS8NvfvMbGI1G/OUvf0FycvLdf0MZG2xcuUx1p3+4CV/v2717N2k0Gjp27BhlZWXR559/Tl5eXjR//nxpHYvFQsuWLSMAFBUVRVqtltRqNdXU1BARUWlpKQGgDz/8kB577DHS6/UEgL788kuaMWOGNL169WqpD4ozu3btIq1WS8XFxbR+/Xp66qmniKhzs728vDwC4NBEZ/bs2VJTpuzsbKnPSVcxGQyGTv1i7Pu0hIWFkUajkZoyms1mevvtt8nPz4+qqqoIQJf9XdjNddesIDg4mFasWOHQVC0rK0v6LDIyMkihUHS5nIiooaGB3nnnHQJAn3zyCU2cOFFqsmffXEX8+9lnn6WcnBxpvtinAe39GrrrGyjm/4gRI6R5165do9WrV9PUqVOl7ep0Otq1a5fDY3NycqR+fhs2bHD6fogx7t27l4KDg4nIcZ84cOAAN+FzcZn4XgGgjRs3drt98T2eOnUqAaB9+/Y5TI8dO1Zat7KyklQqFd13331dbq+goIBCQkJo7ty5N3llrL9xtRnUyZMnSaFQSMei69ev0/r162nOnDn03XffdWrCd/z4cYfmdiaTicLDw8lkMtGPfvQjCggIIHd3dwJAzc3N9P/+3/+T8re0tJSIiIxGI6Wnp0vbmDBhAgGgH/3oR0R0o4mz0WikyspK8vDwoF//+tdERPTZZ59J3+vV1dUOzYEPHDhw+28cu6u4Cd/dBe4DdW8T2/IbjUZyd3d3OOkQTyBycnK6POgWFhbSK6+8Ih14S0pKiIho2rRpBIBMJhMR3Tgh3b17d5exZGdnS9vx8/NzWCaeRIq/TSZTp3be4onNuXPn6OWXX75pTL/5zW8c+sVkZWVJHXyJiD788EMKCwsjoht9TcaPH+/wnNwn69Z0d1CfP38+ASC5XE4mk4mys7Ppvffek4qHwsJCUigUXS4XXblyhebOnUsA6NNPP+0ylsOHD0t/i/kVHx9PXl5e9OabbxJR535WHde/dOkSJScn08WLF4moc/8oMR9zc3MdHj9r1iwKDAwkNze3bvePK1eu0AcffOB0IBUuoFxfZl9AAehU0DozceJEAiCdUIrTACggIEBa7+zZswSAdDqd9M+ljsctkdVq5X4nA4grJ6Hid4HJZCK5XE7Xrl2jPXv2kFwuJyKiffv2ddpPO+apOG3fFzM0NFT6ziIiCgsLc5juGFtQUBCFh4cTEVFLS4sUU2BgoLROYWEh/elPfyIPDw+Hou7cuXNcQA1gXEDdXa4WUHwj3UHKYrHAaDTi7NmzOH/+PAwGA8rKygC0NaUiIsTHx8NiseCHP/whPvroI/z85z8HAHh7e6Ompgaff/45wsLCYDKZEBwcDABSswGxD8oDDzwA4EZzJGfE5k3Tpk2Dh4cHhgwZghMnTuDUqVOd+lrZN9kSNTU1AWhrqvDFF1/cNKalS5dCp9PBza0tvSdPnoxdu3Zh+/bt+Mtf/oJf/vKXUhOf2NhYmEwmqRmGqGMM7PbYbDa8//77mD9/Pvz9/aUmUG+88QaefvppAMDo0aNRWlra5fKmpiasW7cOCQkJ+MMf/oB33nkHP/7xj7Fq1Sq0tLR0es6kpCT861//AuDYLO7gwYN49913HfpZdSTmZUBAANatWwe9Xg+g7V4qq1evxo4dO7Bjxw6sW7cOBoMB27Ztk/JQbCpWVlaGBQsWAOi8f1B7H7/Tp09j8+bN+Oqrrxz6CbKeaW1tdZhWq9UO0+L77QqFQoGXXnpJmm5uboafnx9qamoQHh6O0tJSh+bR9tzc3Ljv5CAjfhe4ublBLpdDr9fj+vXrUpNbZ98VHh4eDjnn4eEBQRDg6ekpzbPZbA63XLBarQ7TzuIQvwvF40l9fb103FizZg2ioqJQUFCAuro6KWaAmwMzdidwATUAuXIyIN4zBwCCg4Px3nvvITg4GHV1dRAEQVre1UFX/FK42UF9zZo18Pf3l/quOCMWcxkZGcjJycGIESMQGxuL2NhYKR5XXtPQoUNdiunll19GTU2N1C+rqakJH3/8MZ588kkcPHgQNTU1Ur8TwHnRxnqX2J/g/fffR2lpKS5cuIDly5fDYDBg7dq1sNlsICLodDqnywEgPT0dy5Ytw9///nd4eXlh2bJlWLx4MX71q19h37590nOJuaTVajv1zXPWD6uhoUE60bAnCAIsFgsOHjyI1NRUlJSU4IknnsCMGTPw85//HK+99ho0Gg2Ki4vxzjvv4NSpUw7bFfv7hYeHw2KxOMQnCAL2798PIsLu3budDqTCXKfVaqV+ZgAc8gFo+ywPHjwoHeOAGyeXYsd88TOKjo7G4sWLcfjwYQBAVFQUCgsLodPpUFdXhzfffNNh21euXMGuXbtgMpm4eBoEuvouioqKwokTJ1BTU4Nf/OIX0v2exPwR88lsNmPXrl1SsdTa2oo9e/bg6tWr2LNnj1TsC4Lg0HfvZtNms9nhe9ZqtSIvLw9PPPEECgoKsHjxYqSkpOCjjz7CiRMnHGISt+Pm5ibFyxi7PVxADUCCIODo0aOdbqjn7D9eIoVCAYVCId1YEgAOHz7c5UFXPOB2PIh3PCCPGjUKlZWV3d7ZXKFQwGq1AmgbvOHixYv4/vvv4eHhAb1ej9raWilW+6tRHZ8zMzMTQNt/37qLaePGjYiIiJCWp6enY/HixWhubsann36KEydOOFwRsH+vevKfauY66jCK3cqVKzF37lzMnDkTQFvRYf+5d1wOAEqlEmazGf/85z+leWlpaTAYDA5Xb8TR96KiojBs2LBOcWRlZaG6ulp6vqysrC4/93379uE//uM/8NlnnyEkJAQWiwXFxcW4cOECzpw5g7fffhsAcObMGcTGxjpcCRFf75UrVxy2LwgCysvL8frrr0ujAIrr2q/X8cSMc9M5IpJObufNm4fJkydjwYIFWLlyJfbv34/MzEzk5eXhZz/7Gb777jsAwMWLF6Ur8hkZGQDarlj6+/sjNzcXw4YNwwcffCBtf9iwYcjMzERgYCA2btzokG/iIBIzZsy4y6+c3QniDenT09ORmZmJf/3rX8jNzQUAlJaWAgB+8IMfAGgrusX8KSsrQ2VlJS5cuIB33nkHFosFp0+fxieffILf/va3qKmpwW9/+1t88sknOH36NOrr67F7924QEcrKylBbW9tpOjs7W/remz59OqKiopCTk4OsrCx8+OGH2L59OzZs2ABBEKBWq1FYWIgzZ87g4MGDANquPJ05cwb79++XYszIyHD4hw5j7Ba50s7vTv9wH6ie8/DwoK1bt3aan5OTQxkZGZSWlkZqtZry8/Olm0LW1taSXC4nuVxOp0+fpi+++IL8/PwoODiYTp8+TWvXrpX6Ta1du5ZOnz5NarWafve731FrayuVlpZKgzSIy8UblaalpZHZbHaIpaGhoVMsYsf71tZW+uKLLwgAeXl5UUFBAZ08eZKysrJIoVDQ8ePHKSsri44fPy49h1wup82bN5NOp+syJvExYtyVlZWdtinGYz+tUqk63T+I9Vx37bJzf+kdmgAAIABJREFUcnKooKCAVqxYQQBoyZIlRHSjj0FDQwNlZmZ2uVzsIwWAVqxYQVlZWXTkyBGaNm0a5eXlOTwXAIdcE9n3wyoqKpKeSxxcpaGhgbZu3UoZGRm0d+9e+vOf/9ztjZ4tFgupVCqHzt45OTm0c+dOysjI6BSf+B4AoPXr11NNTQ3t2LGDdu7c6TDYhdlsprS0NAJABoPhnsrNW+kD1bHf0euvv07BwcEUEhJCer2e3N3dpc75RG33C4uOjqakpCRSqVR06NAhImrrExcQEEDz5s3r9Bwmk8npADM8iMTA1dXxatasWeTv7096vZ48PDykz/3IkSMOx4OcnBxSqVSUlJRE0dHR9OSTTxJR203Dg4KCaNiwYZSamkrNzc308ssvU3NzM6WmptKwYcNowoQJ5O3tTaWlpfTCCy9QTExMp+nw8HAaMmSI1J+ztLSU3N3dyc/PT4pD7A8lPmdgYCAZjUZKSkqi+Ph4h+no6GhSqVQOxxrW/3EfqLsLPIjE4Obv7087d+7sNH/WrFmkVCpJo9FINwC0/9IXbyBqMBhoxowZ9OWXXxIAioiIIK1WK3Wi1mq1FBERIU2Xl5dTampql8sBdDqJzc3N7TKW48ePSx3sAVBISAj927/9G508eVIq4gwGg1Q8oX00s5kzZ3YbU8fHzJkzp9M2xXg6Ts+ePfvOfmj3gO4O6rNmzaKQkBBp5Lva2loiunHym5ubS56enl0ub25uplWrVknLDQYDqVQqWr9+PVksFofnAuBQ1Iiqq6ulIiwyMrLTKHy5ubnk4eFBer2eRowYQdOmTXM48e5IzL/MzEyH1ymefHWMT3wPAJCvry/df//9FBISQv7+/g77qTgipfhzL+XmrRRQHZ08eZIyMjKkn2+//dYhR44cOUJ79+6l7Oxs2rZtG9XX1xNRW45lZGTQyZMnnW7XfmAS0eXLlykzM5MKCgpu/uJYv9LV8cr+nyBbt26VCo6GhgbKz8+X1mtoaKBt27ZRdnY27d27V/pnpZhHO3bsoGPHjlFra6vD7x07dlBWVhZt376dzGYzHT16lPbt29dpevfu3bR161YpPy0WC3377be0c+dO6fvW/viYkZFB6enptGvXLsrOzqasrCyH6b1799K2bduooaHhjr6vrHdxAXV3uVpACeRisxBBEGQAcgFcIqIZgiCMBPANgGEAjgJ4iYiaBUHwAPAlgDgAtQBeIKLi7rYdHx9P4iVy5hqDwYBPPvkE06ZNc5ifm5uLqqoqeHp6wt3dHdevX4dWq5XuayTe98lms8Hf3x8xMTHIzMyUmlgpFArYbDa0tLTAZrNBoVDg2rVrePDBB1FYWIirV69CLpfDYrFIy2UyGRobG/HAAw84dN4W7+vkLJbGxkYcPHgQarUaNpsN169fh06nQ3R0NPbv34/W1lapb5TYBDA5ORnHjh3D5cuXYTabncbU2toqPebatWvQ6XQYM2aMwzZlMhnc3d1hsVikafG1JyQk3NXPcbDpKi+Bttysq6uDTCaT7jdGdk02xXzx8PBwuhxoy9+ioiJp0A+z2Yzp06c7NOkEgKNHj8JoNDrko7itU6dOoby8XJq2v/eZfQytra3Q6XQYN26c06alQFvzrcbGRkyYMEG6OWVubi7q6+vh5ubWKT7xPfD09ITVaoXVaoUgCGhtbYWPj4+0n9rvH/Y3cb0XZGZm4ic/+QmKi4t7tEzU1Wd1s+XO5ufk5OCjjz7C+++/j8cffxwHDhzodvs3e27Wvzg7XrmSH32RA862y/l2b3CWp91917LbIwjCUSK66c1Ae3Ij3dcBnALg3T6dBuB9IvpGEIQ/A/gJgI/af9cTkVEQhNnt673Qo+jZLSGiLm8AKx5o5XJ5px3O1R1QvBmpq9RqNR5//HGnsajVajz66KNOHyfeONCZsWPH3lJM3W2zY2z8hdT7nOVmx/e6Y744+yzkcjlGjx4tFTxdPVdcXFyn+WJB7uzx4nM5y9nuTqg6FjU32wdvdoNm+zg67h+cm6652XvU1XJn8xsbG/HZZ5+hvLwcX3755U23z5/PwOdKfvRFDjjbLucbY33HpUEkBEEIAvAEgA3t0wKAhwD8b/sqXwAQe3s/1T6N9uXTBN7L74qB8sXen2LpqD/HNpDdypf/rX4Wt7If9NZjbncfHCj78L1iwoQJ2LlzJ1asWIGwsDAeyIMxxhgA169ArQWwGICqfXoYgAYisrZPlwEIbP87EEApABCRVRCExvb1a3olYsYYY+wuUKlUmD59OgC+AsgYY+yGm16BEgRhBoAqIjram08sCMJcQRByBUHIra6u7s1NM8YYY72KiyfGGGMiV5rwTQLwpCAIxWgbNOIhAB8A0AiCIF7BCgIg3pn0EoBgAGhfrkbbYBIOiOhjIoononhfX9/behGMMcYYY4wxdjfctIAiol8RURARGQDMBrCLiH4EYDeAZ9tXewWAeHfL/2ufRvvyXcQNxxljjDHGGGODgEuDSHRhCYA3BEEoQlsfp0/a538CYFj7/DcAvHV7ITLGGGNt/ZDkcnlfh8EGCZlMBp1O19dhMNYtuVzOA9j0Qz0ZxhxEtAfAnva/zwMY72QdC4DneiE2xhhjTKJQKFBUVISwsDDYbLa+DocNYDKZDCUlJUhJSYFSqeQTVNYvyWQynD9/HgqFoq9DYR30qIBijDHG+orVaoVGo8GmTZtQU8MDu7Jbp9PpkJKSgoULFyIxMRFNTU19HRJjneh0OiQnJ8Nqtd58ZXZXcQHFGGNsQLDZbNBoNIiJienrUNggoFQqkZiYiMmTJ/d1KIx1SaPR8BX3fuh2+kAxxhhjdxU3tWK9hYj4yhPr9/iY1z9xAcUYY4wxxhhjLuICijHGGGOMMcZcxAUUY4wxxhhjjLmICyjGGGOMMcYYcxEXUIwxxhhjjDHmIi6gGGOMDRpdjVjFI1kxV3SXJ72RQ5yHjA0OfB8oxhhjgwIRQRAEnDp1CuXl5QAAs9mMhx9+GAqFAvn5+QgNDYVare7jSFl/JQgCcnNzUVdXB09PT1itVjQ3N0Or1SI+Pr7H22tqaoLJZMKoUaOgVqul7VdUVGDKlCmci4wNUFxAMcYYGxQEQUBdXR1iY2MBAMOHD0ddXR3WrFmDBx98ELGxsdi+fTtSUlLualxiYccGhhUrViA3NxelpaXQaDRQqVSIj4/Hpk2berQdi8WC//zP/8Qf//hHbNy4Ucq7FStWYPPmzcjNzUVcXNydeAmMsTuMCyjGGGODQm1tLUaOHAkiQmlpKXx9fQEAL774IpYtWwYAGDp06F2NSSyejh07hpEjR/IVh37OZrNJhVJYWBg2bdqEmJgYaZlMJnN5W4WFhfj973+PCRMmSHlnv32Ai2vGBiruA8UYY2xAa2lpwR/+8AdERkbiypUrOHHiBHx9fUFEsNls+Prrr1FeXo7g4GBcv34dgPO+KK72T+nJeoIgoLa2FuPGjUNeXp7rL6oHz8X9anqPfYFks9lQU1PjdFlHzj6D2NhYmEwmXLp0CRaLBQDg5sanXYwNBnwFijHG2IC2f/9+LFy4EAAwYsQI6WRVEASHk974+HhotVppmdhXymw2Y/r06fDw8AAANDY24uDBg/Dy8oJWq8XIkSOxd+9eeHl5Qa1WY8yYMQ7r6HQ6REREYPv27Rg6dChaW1sRFxcHlUqF0tJS6QrGyZMnIZfLYbFYoFAoYLVakZycjGPHjuHy5cuQy+Uwm80AgCFDhjg816FDh+Dp6QlPT0+MGTMG2dnZcHd3l9Zhd4fFYsGZM2dw//33Y+vWrVAqlfD390d0dLS0TlNTE3bu3IlRo0bBYrHAzc1NusrU8WqkIAg4ceIEKisrpYI7KSkJcrm8r14iY8wFXEAxxhgb0MTCBwByc3MREBDQqWlUx6ZT9n2lFAoFVq1ahSeffBIjRozAuXPn8NhjjwEAoqOjsWfPHmnaaDTi7NmzOH/+vDQvMDAQf/rTnzBz5kzpSsTu3buRnJyMN998E/X19QCA3//+91AoFHB3d0dRUREAYMOGDdi6dSs2b94MAPDz84NMJsP3338vPdeFCxfw6KOPAgB+8pOf4I033sDUqVMBACEhIbh48WIvv6PMGYvFghUrVmDVqlXYuHEj5syZA4vFAn9/fxQXFwMAWltbsX79erz11ltITk7GzJkzoVAopEK+tLQU48aNc+iL9/DDD6Ourg5RUVE4fvw4Vq9ejfnz53MRxVg/xteSGWOMDRp///vf0dzc3Klfif2VqOrqahgMBhARSkpKUF9fj7fffhsBAQGoqqpCbGwszp07BwDw8vLCsGHDpGk/Pz8AwLhx46R5ly5dwpYtW3D16lWpqBoyZAgAYOPGjVKxs2/fPly4cAFnz55FcnIyAOCnP/0p/vrXv2LRokUICAhAZWUlCgsLAbQNggEAY8eORVVVFZ577jls2LABzc3NMBqNiIuLkworducVFhZi+fLlaGlpwf/+7/+ivr4ehw8fhs1mA9B25em//uu/sHXrVly/fh3r16/Hq6++Ch8fH7i7t/2/+s033wTg2BfvwQcfRE1NDY4dO4axY8di8eLF2L9//91/gYwxl3EBxRhjbNB47bXXcODAgW7XSUxMxJUrV3Dq1ClpoInRo0cDAB544AEAQEVFBYAbfVbEaXvivKlTp+Ljjz/G0aNHpatN9pqamgC0FW6i5uZmAMC5c+cgl8uxevVqXLp0CUDbVQofHx9UVFSgtrYWVqsVxcXF2LhxI6xWK8aOHYuzZ88iNzcX69evl07g2Z1DRFKfprCwMPz3f/83gLbPVCzO09PTsWzZMuzYsQMtLS0IDg7u1Adq48aNCA8Pl6YB4KOPPkJJSQkeeeQRHD9+HIDjVVXGWP/DBRRjjLEBrbW11WG640h3HTv4azQaAI5FkbiOuKwnxAJJJpP1eJCAjoWZzWbDmDFjcOTIERQVFWHs2LHYsmULFi9eDADSlQx7PDDBnSde0aysrITVanW6ztChQ0FEqKiokJrfNTQ0wN3dHQqFQlpP7AMHtBVgYWFhGDNmDHbu3MlXFBkbIPioyxhjbEDTarVS0zqgramcPUEQcPDgQdTV1QEAJk2aBMCx8Lhy5YrDMpG4jnhFwP4x4kmw/Tri3/YnzB3Xt39Mx/XEqxkWiwUjRoxAWVkZFixYgN27d0tXmq5cuYI9e/Zg//79MJlMPAx2L7IvtgVB6HQlyMPDw+H9tp+Wy+VoaGjAlClTpOWHDh3C1atXsWfPHmmAEKVSKV0lTUhIQENDg3T18fLlywCc5w9jrP/gQSQYY4wNWESEqKgonDhxAsuXL0d+fj4WLFgAs9mMKVOmoKmpCT4+PvjZz36GDRs2IDExEevWrcOVK1eQn5+PpqYmKJVKaLVapKamYt26dQBuXNWqqKjAmTNnpD4pZWVlKCsrQ2BgIDIyMjqtI15RysjIQFRUFBQKhXSFKiMjA+PGjcOlS5dQVlbWaT371xQdHY19+/Zh1KhR+MEPfgDgRnGVk5ODadOmAeBBJHqbIAjIzc2Fu7s76uvrkZ2dDW9vb1RXV8PDwwPZ2dmora2VcmD37t2ora1FRUUFRowYAbVajaamJnz33Xf4xz/+gSVLlgAAfvOb3+Dpp5/GsGHDYLPZ8NZbb8Hb2xspKSn4+OOP8c033+CJJ55AZWUlAOd5wRjrR4ioz3/i4uKI9Yxer6eMjIy+DoMxB5yX7HZlZGSQXq/v0bLW1laH6ddff52Cg4MpJCSE9Ho9ubu7U2lpqbSu1WolIqIXX3yR/Pz8HJaLywoKCig8PJzi4+MpMDCQjEYjJSUlUXR0NL3wwgtUWlpKKpWKkpKSHNaJj4+npKQkUqlUlJOTQ0REc+fOpfHjx5NKpaLS0lJ64YUXKDo6utN6HV/H9u3bKSQkxCEuMTaj0UhxcXE0d+7cnry9zE5Xx6tZs2aRTqejCRMmUHh4OAUFBZFer6eQkBAKDw+nmJgYKQe8vb0pJiaGnnzySSIiqq6upqCgIId8mDdvHlVVVRER0ZNPPil99p6enmSxWOitt96i4OBgCgwMdJo/7N7mLE/5u/bOAZBLLtQufAWKMcbYgNZxuPK1a9di7dq1ndaj9qHNZTIZbDYbvvrqK4flNpsNMpkMRIQxY8bgzJkz3T6v2NyqO1arFevXr3eY980333QZm8lkQnV1NfR6PUaOHIm4uDgAN64+ibGdPXu2U9zs9nUc7v5mGhsbpb+tVit0Oh1KS0udrmu1WvHPf/6z0/yVK1di5cqVTh8j5gVjrH/hAooxxtig0V0hYX8i6mw9cV5vnrA6G/TBGfE5Z8yYgZKSEnh5eeH8+fPYtGmTQ4HkLDYunnrP7byXN/usXc0Fe1w8MdY/8SASjDHGWD9z9epVLFiwAACPsscYY/0NX4FijDHG+oktW7agtrYWbm5uUCqVAPgqBGOM9TdcQDHGGGP9gNi/qeM8LqAYY6x/4XYBjDHGWD/grFDi4okxxvofLqAYY4wxxhhjzEVcQDHGGBsQiAhyubyvw2CDhEwmg06n6+swGOuWXC5H2+2JWH/CfaAYY4wNCG5ubigqKsKoUaPQ0tLS1+GwAUwul+P8+fNITk6GRqPhE1TWL8nlchQVFfFInP0QF1CMMcYGhISEBGRmZqK1tZX7BrHbQkRQKBSwWq2w2Wx9HQ5jThER3NzckJCQ0NehsA64gGKMMTYgqFQqPPTQQ30dBmOMsXscXxNkjDHGGGOMMRdxAcUYY4wxxhhjLuICijHGGGOMMcZcxAXUAGWz2eDr69vXYTDmgIcFdl1vjvo1mDrB63Q6yGSyvg6DMcb6LR7avO/xIBIDVGBgIJKSkuDj48M7EesXZDIZSkpKkJKSAqVSyXnZhYqKChw4cABxcXG9tk2ZTIann34a2dnZUCgUvbbdu00QBJjNZtTW1iIsLGxQFYaMMdYbZDIZzp8/P6CP9YMBF1ADVH19PZYuXYqJEyeiqampr8NhDDqdDikpKVi4cCESExM5L7vQ1NQEo9HY69tdunQp6uvrB/T9Qjw8PHDo0CG8++672LRpE2pqavo6JMYY61d0Oh2Sk5NhtVr7OpR7GhdQA5TFYsHEiRMxefLkvg6FMYlSqURiYiLn5V1GRIiPj+/rMHqNUqlETExMX4fBGGP9kkaj4Sv0fWzg/qvyHicIAv+Hn/U7RMR52QcG001lm5qauPknY4x1g4+RfY8LKMYYY4wxxhhzERdQjDHGGGOMMeYi7gPFGGNsQLhy5QpycnLQ2to6qJotsruPiKBQKGC1WrkvCeu3iAhubm5ISEiASqXq63CYHZcKKEEQigFcAWADYCWieEEQtAD+CsAAoBjA80RUL7R9q30A4HEA1wGkElFe74fOGGPsXpKTk4Np06bBaDSipaWlr8NhA5hcLkdRURE0Gg00Gg33KWH9kpinmZmZeOihh/o6HGanJ1egphKR/ZiybwHIJKJVgiC81T69BMBjAEa1/0wA8FH7b8YYY+yWtba2wmg04uzZs30dChsEwsLCsGnTJh7xkfVro0aNQmtra1+HwTq4nT5QTwH4ov3vLwDMtJv/JbU5BEAjCMKI23gexhhjDIIg8JUn1mtsNhvfa4z1ey0tLdxkuR9ytYAiADsFQTgqCMLc9nn+RPR9+98VAPzb/w4EUGr32LL2eYwxxhhjjDE2oLnahG8yEV0SBMEPQLogCKftFxIRCYLQowbE7YXYXAAICQnpyUMZY4wxxhhjrE+4dAWKiC61/64CsBnAeACVYtO89t9V7atfAhBs9/Cg9nkdt/kxEcUTUbyvr++tvwLGGGOMMcYYu0tuWkAJgjBUEASV+DeARwCcAPB/AF5pX+0VAP9s//v/ALwstEkE0GjX1I8xxhhjjDHGBixXmvD5A9jc3oHNHcBXRLRdEIQcABsFQfgJgIsAnm9f/19oG8K8CG3DmM/p9agZY4wxxhhjrA/ctIAiovMAOo3xSUS1AKY5mU8Aftkr0THGGGO3gYh4BCvWY87ypqtcGmg5NtDiZaw/6sl9oBhjjLF+pbGxEQcOHIBSqYRcLofVasWkSZMgl8sBtA19npOTA4vFguvXryMxMRFqtbqPo2b9mVhgnDp1CuXl5bDZbNBqtYiPj3dYbrFYcOrUKYwbN65fFiWNjY04ePAg1Go1WlpaoFQqER4eDrVaLe0TNpsN169fx6RJk3i/YKwHuIBijDE2YBUVFeHZZ5+FUqlEXV0dAGDNmjVYuHChtM57772Hb775BgCQl5eHcePG9UmsbGAQBAE1NTWIjY0FAPj5+WH8+PH429/+hv3792Py5MmwWCxYsWIFli9fjpKSEgQHB99kq3ff+fPn8dhjj0nTs2fPxuLFizFu3Dh89NFH+Oyzz6BSqWCz2bBv3z7ExcX1YbSMDSy3cyNdxhhjrM8QEeLi4nD9+nVkZ2cjMTERALBo0SK8++670npff/01wsLCYDKZpKsFjHVn7NixkMvlKCsrw8WLF/G3v/0NWVlZWLJkCQCgsLAQy5cvBwC8/fbbfRmqU0SEcePG4dy5cwCAqVOn4uuvv5b+ebBu3To8/PDDOHnyJK5du4a4uDjeLxjrAS6gGGOMDUj2Taaam5tx//33IzExERqNBosWLcKaNWuk5VarFZWVld1u71ZPIO0f1902+AR14CAifPDBBxg2bBgAoLW1FVOmTMGBAwdgtVoRGxuLkydPQqfT4Ysvvuj02K622ZPn78myjvPEfaOiogIAYDabpWUXLlzAuHHj8PHHHyM4OBhE5FITxNuNqafbZKw/4yZ8jDHGBjQiwpgxY7B+/XoAwAMPPIBDhw5h8eLFEAQBCxcudDg5FAQBJpMJjY2NaG5uBhEhKSlJ6jcl9h3x8vKCTqdDREQEtm/fDpVK1WlarVZjzJgxDtsW+86YzWZMnz4dHh4e0glqYWEhKioqkJCQAJVKdXffKOay559/Hj/+8Y9RUVGBV199FVqtFkBbrrm7t506NTY2oqamBqNHj8bp06exZcsWhIWFYfTo0WhoaMChQ4fg5eUFtVoNg8EAlUrl0B8vKSkJxcXFKC8vl/JDzEMxV77//ntMmzYNx44dQ1lZGUJDQxEVFQWLxYKMjAwolUr4+/sjOjq629czZMgQAG0F1SOPPIKdO3di5MiRDoVTTk4OzGYzWltbMXHiRJw4cQI1NTVwd3dHfHw8vL29pZgCAwMRERGBrVu33jSmlpYW7Nu3T3ouQRCk/BcEAfn5+Rg7diwKCgpQWlqKKVOmcH8s1v+J/3noy5+4uDhiPaPX6ykjI6Ovw2DMAeclu10ZGRmk1+t7vMye0WgkAASA/Pz8qKGhgcLDwx1yc+7cuTR+/HgKDQ2lwMBAqq6ulpbl5OSQSqWi+Ph4WrRoEVksFvL09Ow0PX78eJo7dy4REbW2tkqPnz9/PgUHB5NMJqPS0lIiImppaSEionnz5lFwcDAVFBT09K1hvay741V1dTXJ5XKSy+W0ePFiab7VapX+zs7OJgA0depUIiKSyWQ0f/58IiI6dOgQqVQqKUfEz/upp56imJgY8vb2puLiYlqwYAGFhIRQYGAgBQUFOeThvHnzKCAggIiIXnrpJZLJZPT6668TEdHFixdJJpNRSEgI/fu//3uXr/HAgQMEgB577DFqamqiWbNm0apVq8hms3Va96mnnqLo6GgKDw+nqqoqevHFF0mn05Fer6djx445xCTuB67EVF1dTUFBQRQYGEghISGk1+vJZDJJy59//nlqbm6m1NRUAkC5ubldvp57kbM85e/aOwdALrlQu/R58URcQN0S3nlYf8R5yW5XbxRQVVVVNGzYMKmIunbtGsXExFB6erq0TkhICAGgTz/9lIxGI6nVapo3bx4REZnNZkpLS5Me/+STT9Lnn3/udNr+++vkyZM0b948AkB/+MMf6Pjx4zRt2jTKy8sjIqIXX3xR2kZmZuatv0msV3R1vBKL4draWlIoFASAjEajVByJRZRYQAGgmTNn0qeffkoApPV++9vfEgAaP348ZWVlEVFbIQ2AfvWrXxERkVKppF//+tfU2NhIRqORvL29qaamhl566SVp23K5nDZv3ixtPz4+npKTk8lkMtGKFSsoJCSky9coFlAKhYLmzJlDmzZt6vQ6RcXFxWQwGAgAHTlyhAoKCqi8vJzUajWZTCZ65ZVXpJgA0FNPPUUfffQRAaDExESnMeXn5xMA8vDwoJKSEvrkk0/Iy8tLKqCee+45AkDu7u708ccfU3p6OjU0NPT8wxzEuIC6u7iAGuR452H9Eeclu123W0CJJ4U1NTVSERUfH08ApJNYIqKCggLasWMHERE98sgjBIBiY2Ol5UePHiUAFBkZSdXV1WQymZxOT5w4UXrM2bNnKTExkQDQxYsXiYgoKChI+u/94cOHae/evbR79266fPlyj98b1ru6Ol7ZX2U6efIkZWVl0e9//3sCQEuWLJGWZWZm0ogRI6i2tpZMJpOUE/Z5lJaWRkajkXbt2kVERKmpqfTxxx9LeVpcXExERK+++ioJgiAVL5WVlRQZGUkAaPXq1UR0IyfDwsKopKSEiIiysrJcKqDEH51ORwaDQbrSJcYh/p48ebLDvrJkyRIppoqKCimmyMhI6epqWloaDR061GlM9fX1lJ6eTlarlS5fvkwymUyKxWQy0ffff086nY6WL1/u9KoY4wLqbnO1gOJBJBhjjA0agiCAiDBs2DCcOXMGOp0Oubm5AACZTAagbUCJMWPG4JFHHsHLL7+MnTt3AgAUCoW0HYvFAgDQ6XTQ6XRoaGhwOu3h4QGgrf/IqFGjcOjQIQDAhAkTEBERgbKyMowdOxZ5eXkYP348kpKSkJyczP2f+jGZTIann34aOTk5iIqKwuTJk7F06VKsWLECaWlpWLBgAYC2fkXff/893njjDURHR0s5YZ9HcXFxqKyslPr0fP755xg5cqTU7+iDDz6Aj48g7Gu1AAAgAElEQVQPUlJSMHz4cABtfYb8/Pyg0+kAANOmTQMA3HfffUhLS4O7u7s0bHpTU5NL95+Kjo5GcXExDh8+jEuXLiEgIAB1dXXS/iJuo7W1FQDg5tZ2epieni7F5O/vL8Wk0+kQFBQEAJg4cSJ8fX2dxqTRaDBx4kSMHDkSzzzzDNavX4+goCAsX74cYWFhGD58OLy8vJCcnCw9J2MDAWcrY4yxQcW+iDp58iSioqIA3Bjxy93dHQsWLIBKpcJTTz2FsWPHdrmtjieUHad3796N2bNnIyEhAWvWrEFkZCQAYOnSpdiwYQOysrKwfft2GAyGTtsW42H9T1ZWllQI2Ww2AMCvfvUrfPrpp9i4cSOAtkJcpVJh2bJlAOC0AJg0aRLWrFmDN998EzU1Nfjuu+8QHx8vffbffvst5s2bh1mzZqG8vBx+fn5S8S7mmjjt4eGBZ555BlevXu3x69FoNNDr9QgNDZVG5hs+fLhDEeWM+B6IxZ3VanWITdy2OL+jwsJCBAQEwGaz4auvvkJMTAw8PDyQnJwMpVIJoG0/aGpq6vFrYqwvcQHFGGNsQLpZAUJE8PPzQ3NzM4AbQzu/9tprWLt2Ld577z0888wz8PX1BXDjahJw44RRHL1MXKfjdExMDD7//HMAbSfL4onkzJkzMWXKFEyePBkpKSnQarV49dVXER8fj1GjRsFkMrl05YD1jaFDh+L+++9HWVmZdOWytrYWr7/+urSOTqeDv7+/VBx3zCObzQaFQoH77rsPZ86cga+vL6xWKzQaDQRBwPPPP4+ioiL89Kc/BdBWdFdVVSEwMBBA51yrqqpCTEyMQzHu7+8vjQooFnrAjX1DzGNxhEkA0Gq1aG5uRkhICEJCQlBVVSXlYnl5OYC2mwcDkG5OHRYWBgDw8fFxiK2qqgqJiYnQ6/VOYyorK0NjYyOio6Oh0+ng7u4OInK4AiuTyeDv7+8QN2P9nivt/O70D/eB6jlu/8r6I85Ldrt62gcqNzeXPDw8SK/Xk16vJw8Pj06jeFVVVREA2rZtGxERBQcHEwAKCAiQ+nSIP6mpqVReXu4wLyIigrRabZfTqampRETU3NxMVVVVpNFoyNPTkwwGg0M84sAV4EEk+oXujlcmk4mOHj1KU6dOJW9vbzIYDKRQKEihUNCVK1ek/k5oH2DCYDA45MTs2bOlbV2+fJlMJhNt3brVYYCEkpISUqlU5Onp6ZCHWq2WIiIiHKYjIyNJrVZ3maf2zyfKy8tzyOPZs2dLA5qII94BILVaTZ6enlRQUECXL1+W8letVtO2bdvIw8OD/vjHP9LMmTM77RfdxfTiiy8SEdHq1aul9TUaDQEgjUZDBoPBYcRMMTbmiPtA3V1wsQ8U3weKMcbYgGU0GrFp0ybpv/5NTU0wGo3SciKCr68v8vPzMXLkSADA1q1b0djYiJaWFukqgUwmQ0tLC7y8vODj44Nt27ZBrVbDZrPBYrFAJpNBoVB0mm5paZGaIsnlcvj6+iI7O1v6T759PFu2bEFjYyOsVivi4uLu5tvEeoCIpHsYvfvuuygvL5c+44CAAHh5eSEoKAjbt2/H0KFDYbFYIAgC3NzcOuUEtV9tiY6OdrhXExEhODgYhw8flu4DJeahxWJxyEtx2j7nxDzdvn07PD09HfpdiUJDQ6U8FmMKDQ0FAPziF7/AnDlzIJfLYbFYYDabERwcDJVKJeWv2WxGSkoKDh8+jPvvvx+TJ0/GggULIJfLne4HXcU0f/58xMbGtp10urvD3d0dVqsVNptNet3Xrl2TYmNsIOACijHG2IClVqvx+OOPd7lc7N8h9nMiIocb33bl0Ucf7VEc1N4Rn4gwevRojB49utNyV56X9T2xORsRITY2FrGxsQ7LiQgajQYpKSkubeu7777Dq6++iuXLl2Py5MnS/K5ypSfsYxBzUKRWq53mMREhISHB6facxRQTEwMA3fYV7C4muVwuDYTB2GDBBRRjjLFBzf6k8k71OxK329X2ub/TwHM7n+XRo0cxadIkaLVaRERE4Omnn+5UiPV2Tri6ve7W66uYGBtouIBijDHGGOtFYtNSuVwOnU6HqKgoh0FKGGMDGxdQjDHGGGO96GZNSxljAxsPY84YY4wxxhhjLuICijHGGGOMMcZcxAUUY4yxAUEc0Yux3iCTyaDT6fo6DMa6JZfL+QbD/RD3gWKMMTYgKBQKFBUVISwsDDabra/DYQOYTCZDSUkJUlJSoFQq+QSV9UsymQznz593ep8v1rf4ChRjjDHGGGOMuYivQDHGGBsQLBYLjEYjzp4929ehsEEgLCwMmzZtkm4Uy1h/NGrUKFgslr4Og3XAV6AYY4wNCIIgoKWlpa/DYIOEzWZDTU1NX4fBWLdaWlr4hsT9EBdQjDHGGGOMMeYiLqAYY4wxxhhjzEVcQDHGGGOMMcaYi7iAYowxxhhjjDEXcQHFGGOMMcYYYy7iAooxxtig0dUNUe3n801TWVdczY3bySFXHss5ylj/xveBuocQUaehMJ3N688GWryszc0+t+6W3+pn3tXjejq/p3HcynZ667nvdeL7dOrUKZSXlwMAzGYzHn74YSgUCuTn5yM0NBRqtRomkwnV1dVISEiASqXqclvs3iIIAnJzc1FXVwdPT09YrVZYrVaHXAgICMDo0aNdzpGWlhacPXsWFRUViI+Ph7e3NwoLC1FRUdFl/gmCgP379+O+++6Dj49Pr75Gxtjt4wJqkBEP6C0tLdi3bx+ICEQEnU6HcePGScubmppgMpkQHx/f1yE71djYiO+++w5msxlyuRyNjY144IEHoFarkZOTA4vFApvNhuvXr2PSpElQq9V9HTLrhnhSUl9fDzc3xwvf/v7+iI6OBgCHvBUEodOJSktLC4qKilBeXu5wYtyRuP6xY8cwcuRIKT/s8z89PR1KpRKtra3w8fFxui9YLBakp6djxIgRDsvtT4jsT4DE7efn56O6uhoymQxJSUmQy+VdLu/qZKypqQk7d+5EUFCQw77LuiYIAurq6hAbGwsAGD58OOrq6rBmzRo8+OCDiI2Nxfbt25GSkoIZM2agpKQEmZmZeOihhwbMsZHdeStWrEBubi5KS0uh0Wig0+mke/FUVFQA/7+9e4+Pqr7zP/76EhJCSMyFcCeES+SmCIRYaIsaRMW6XS+tdqXWiuuq2+6qFbUKXYto5abd/sSuAm23tta1UqVqsSAXsdwECReJgDHhGoFAQgjhFkKSz++PmTnMJAEmCpkA7+fjkUfmfM93znzPfM+ZOZ/5Xg6wc+dOUlJSTnteVlRU8Pzzz/PCCy9QUlLC8uXL6dmzJ1dddRUlJSUsWrSI7Ozsep/7+OOPM3nyZIYOHXo2dlNEvorABXYk/wYNGmTSMOnp6bZgwYJ611VWVtqkSZMMsJ49e1qHDh0sOzvbCgoKzMzs6NGjNmbMGEtOTjYzs5qamkYrd7jWrFljt99+uwHe35o1a8zM7O677zbAEhISLC4uznJyciJcWgk41XF5yy23WLt27Sw9Pd3S09Ota9euFhMTY+3atTMz33E7ZcoUA6xXr14WGxtrsbGxVlxc7K2fNGmSl56cnGzTpk2zJUuWWHl5ufc6geO5sLDQAJs7d25IOQoLC2369OmWkJBg6enp1q5dO7vllltC8lRWVtrixYtt2rRpFhMTE7L+6NGj9swzz1hqaqoBtmjRopDnFhQUWHZ2tnXt2tUAmzhx4knX197HgOrqanvhhResZcuWlp2dbVu2bAnZt/PZggULLD09vcHrzMxKSkosISHBWrRoYXv37vXSb7/9duvQoYMBtmTJEjMzu++++6xLly62fv16L19lZaU99dRTFh0dfUb2RZq2+j6vqqqqvMfdu3e3devWhazfu3evRUdHW3R0tO3bt8/MTn1erl69OuR7bPPmzfbDH/7QW162bFm9zwsux4Vw3svJ1Xecnuq7Vr4aIMfCiF0iHjyZAqgv5VQnz4IFCwywJ5980kvLy8uzwYMHm9mJD/RevXrZ7t27G6W8DRH8ZTFs2DDvSyfg4MGDds0119iOHTvqfY5EzsmOy+CLgWBFRUV24403mpnZBx98YIANHz7czE5cqHTu3NnMThzX0dHR3sVx4EJk4cKFIdstKCiw4cOHh1wwB46RmJgYS0xMPGUZJ06caIDdddddddbXviCqfQE0ePBgy8vLMzOzxx57zACbPHmyVVZW1llfex/NzCoqKuyFF16w6667zszM8vPzrXfv3lZYWFjve3i++TIBVGVlpT3//PNeUJufn29mvjoPrte0tLQ6AbWZL2A1M1uyZIkBNmbMmHpfv/bnzOmWpWk73UVo7fWB+t23b5+1aNHCEhISQgL12gLH1ebNm73vssWLF5vZie+2wOdTbTqWJEABVOMKN4DSJBLnITMjMTGRp59+GoCamhp69uzJihUrqKqqIjMzky1btpCXl8egQYPqPPdk22zI6zdkXe202t2YAG88w9atWxk4cCAzZswgLS3NO5BP17Xpq5apoduUUFFRUXXSysrKKC4u5p133gHwurkF6rxNmzZ8+umnHD16FPC93z179mTNmjW0adOGqqoq/vCHP9CzZ09qamq8PAA/+MEPmD9/PgMGDKCiogLwHVfbt2+nRYsWVFdXU1hY6JXFzIiKiuL48eNMmjSJMWPGMH78eF555ZWQ9WZGZmYmmzdvZtiwYXX2qbq6mhUrVtCzZ0+qqqqYMmUK1157LY8//jiLFy8G8NYfP368zj4CzJ8/nyeffJL333+fqqoqMjIyeO211+jWrduXr4Dz3NKlS3n00UcpKSmhQ4cOIXUefOxlZWXRsWNH1q9fz+LFi/nwww85ePAgzZo1o6ioiBUrVpCWlsZDDz3EvHnzWLRoER999BFLly4lNzcX5xwHDhxg9uzZ5OTk4Jzj4MGDfPDBB956OX855zAzUlJS2LRpEwcPHmTIkCHe+mPHjjF79mwWLlzIpk2bvO7KRUVF3uda4HgMLCclJbFp0ybmzZvH8ePHgdAuyAcOHGjMXRSRMGkM1HmoU6dOHDt2jJiYGIqKikhJSQF8H8rNm/uqfOfOnQDceeedAPz973+nW7du9OnTh7KyMvLz8zl69Cg1NTUMGjSIhIQEb+zRkSNHuPLKK9m2bRu7du3yPuwDYz2Cx2B16tSJXr168d5779G9e3f69u1LRUUFCxYsoGXLliHjX04lKSmJoqIirrvuOubNm0e3bt1CAqdVq1Z55f3GN77Bp59+SklJCVFRUd5rfJkyBY+5qW8/N27cSGlpKcePH+fo0aMaj9UAy5cv5zvf+Y53sZuSkkJiYiLbtm2jtLSUlJQU5syZw3XXXQf4Ll4qKyspKysD8I7lY8eOecdBYOD1Rx99xN69eykoKPDyATz00EN0794d5xx33nkn//3f/01MTIxX34sXL2bMmDE89NBD3HXXXcybN4+0tLQ6Y5SCL4iCBV+sB153//79JCYm0q5dO+DExVF0dDQ1NTX84x//4J//+Z+957Vs2dIbNB68jU6dOn2l9/t81qJFC+9xTk4OHTt2rPPDSnV1NbNmzQLg9ttv54033gDwxqB885vfZMuWLcTHx3P11Vdz5MgR9u/f713AZmRkkJ+fz9atW7n11lu54YYbmDVrFqtWrWL48OF06dKF7du3N+JeSyQEjqndu3cD0LZtW2/dq6++yrhx47zvovz8fNq0aXPK7a1du5ZZs2bx9ttvM2nSJEaPHk10dDSFhYUMHDjQG7MnIk1MOM1UZ/tPXfga7nRdpfbt2+f1087NzbWNGzea2YluAR988IGlpqaamdn69esNsKSkJCsvL7cNGzbYHXfc4XVRCozxCIw9Amzfvn2Wnp5u0dHR1r9/fwNsypQpZuYbY/LMM88YYJ06dbJ3333XAIuLi7Pt27fbtGnTLCkpyWJjY085nsHM7Bvf+IYB9sYbb9iQIUOsqKgoZD8CgsdLbdq0yX7wgx9YWlqaxcbGWteuXc3MGlymiooKe+qppywmJsZatGjh7efTTz9tNTU1VlhY6HUZSkxM1Hgsa1i3gsWLF1vbtm3N7ER9lpWVWUJCgsXHx9u7775rN910k5d/wYIFFhsbaxMmTAjZTteuXb3XLCgosN69e9vmzZvt3nvvDekiU7sbYWCMVOD4MPOdF507d7YlS5bYRRdddNIxSsuWLfOOzZONYQjsU7t27eyTTz4JSVu1apV98skn9swzz1hMTEzI8+rrpna6sT/nky/ThW/ZsmXe+T916lQ7duzYSbcfOA4CXagC9ReozwEDBnh59+zZY61btzbAhg4dama+LsTB3SnXr19vXbp0sfvuu6+huyoR1tAufMECx1zguBg/frwBVllZaa+99pr3XRPIW/vzIrAceM7Pf/5zA7zXu+22207ZxU8uHOrC17hQF74LU6DLQEpKCjt37qRDhw5kZmaSmZlJaWmp9+tZdHQ0JSUl3HnnnfTr148tW7ZQVlbGZZddRt++ffnTn/7Eo48+CkB8fDwAL7/8MiNGjKCwsJCUlBS+9rWvUVJSwrp16+jduzc//elP2bRpE//1X//Fk08+Cfhaut555x3MjFatWpGens7y5cvZv38/K1eupLq6Oqz9+pd/+Rceeughdu7cSWVlZZ1fll9//XWvS1VxcTGvvvoqO3bsYOXKlTRv3pw9e/Y0uEy5ublMnDiRNm3aUF5ezrp167jkkkv4+c9/zu7duxk7diwlJSWkpqby+eefc/jwYQYNGqSufWGoqKhg06ZNXH311cCJX3UTExN57bXXMDO+//3v8/bbb3vP6dixIwBjx45l4sSJLF26lM8//5y9e/d67/m//du/sXDhQrp3786MGTNo27atd04Etw6ZGZ07dyY3N5edO3eyZ88ewHdefPHFF0ybNo0DBw6wY8cOqqurGThwYIP30TnH559/Tp8+fbxjKrCfEyZM4IYbbuDFF1+ksrKS1q1bh3QplC/vwQcfZNmyZSddX7sLVW3r1q2jR48egK91IS8vj4suuoiioiLKy8vZunUrt912G+A7jvr168f27duZPn061dXVOv8vEMGzf+bl5TFu3DiGDh1Kt27dGDduHF/72tf4p3/6p9NuZ/PmzURHRzN+/Hh69uzpHT8zZ86kZ8+eXgu9iDQtCqDOM4GxIAAJCQm89tprPPXUU1RUVDBlyhRv3fHjx0lNTeU3v/kN4OsiBHjd/QAee+wxEhISaNWqFQD3338/3/3ud72uRM899xwXXXQRP/7xj/nss88AOHz4MFOmTOG5554DoHfv3jz11FNe/tjYWH7xi18AUF5eXmdK61P5xS9+wR133OF1qQl80QS2Eeg/HhxclZeXU1VVRbt27RpcpoyMDGbPns3s2bOJiYnh3//939mwYQOTJ0+mQ4cOPPvss6SmpvL444+HdOPQOIjT27hxIzNnzmTy5MnAibrcvn07kyZNYurUqRw6dMirJzMjIyODp556itjYWJ5++mnuuOMO7rnnHo4cOeId97///e9DAq29e/d6dRN8YRuoo7KyMo4fP87EiRMBqKqqIiEhgQkTJgC+cVjjx4//0nV6//33853vfIe+ffuGpI8dO5a///3vPPbYYwCUlpZ6P1jIVxcYT/dlJSUleY9bt27N+PHjKSgo4Nlnn6Vfv37eZ2nt46JZs2Y6/89zgc+RQDfbwK0Z4uLi+N3vfscrr7zCjBkz+OUvf8l//ud/nnZ7gS7JQJ0fB2svi0jToQDqPBMVFUVOTg7f+c53iI2NZejQoYwdO5YJEyYwefJkHn74YeBEC9R9990H4P3KFfyrWtu2bUlOTiYzM5NDhw7xf//3f3Tr1s37QH/hhRdITk72WrBSUlIoLy+nbdu2XstCamoqnTt3Zt26dYwaNYoBAwaQlpYGhI5dOZ133nmHffv28dlnn9GjRw+vNc2CxjkELqKDg7Jjx455X3gNLVNSUhKDBg3i17/+NbGxsVx77bWkpqZyxRVX4JwjLS2N+Pj4kEHEEp59+/axceNGunTp4tXh2rVrGTVqFH/605/413/9VyZMmMD48eN5+OGHvTFDo0ePZs2aNcyePZu//e1vrF+/nsmTJ3Pddddx6623kp2dTbdu3cjIyPCCosDEE/Uda4MHD+app57ipZdeAnytma1bt/bKBXj1/WU888wz3HPPPSFjdMyMrKwsLrvsMh599FGee+45UlNTvWBSGq52a1IgiA7UYXBLdzgtREeOHAl53uDBgwGYMmUKI0eO5IorrvC6cQSrqalRC9R5LPBZVVpaSp8+fUhISGDFihVs27aNmJgYevbsyTXXXMOwYcMYOnQo/fr1O+02g1uYah87OpZEmi4FUOeh0tJSVq9eDZy4ABgzZgz/+7//y8yZM4ETLVAzZsw45bY+/vhjqqur2b17N5999lnIzSXfeustHnzwQe699166detG27ZtvSAm8KUQWO7fvz/Tp0+nuLj4S+1T9+7dWb58Ofn5+VRWVno3yAwEUSfTrl07byD+4cOHG1SmNWvWcMcdd/Dtb3+b999/n+9+97vEx8eH3DXezE7aFUjqChyPrVu39t73QHBSXFzMtm3bvNnmxowZw4QJE3jrrbe850dHR9OnTx+GDx/OZZddBuDNJPnEE0/wm9/8ht/+9re89dZbXHLJJYDvordPnz51ymJmREdHM27cOJYsWQLA0aNHKS0tDSlrUlJSSMtuuPsIMHToUOLi4kLWB/Y3cNwOGTKE+Ph4unbt6pUh0CJcVVUF+H7tDkz8InUNHTqU559/3mttzMzMpLi42Huvo6KiGDZsGEuXLvXSAkFt+/btgRMzfQ4dOpTf/OY3XHHFFV53v0GDBvH888+TmprqBebOOZxz5Obmkp6ezv33309UVJRaDM5xwedvVFQUqamp3rJzjuLiYtq3b09NTQ3btm0jPj6eoUOHMmnSJO66666QbQXO3/bt23vHW2BSiZiYGG9dQPPmzb3JZmovK5gSaWLCGSh1tv80iUTDnWoAYeBeJnfccYeXFrjBZMeOHc3Md28ZwH72s5+Zmdnnn39ugA0bNsx7TuAeFoEbggYmogjo1KmTdz+bhx9+2AD77LPPQraXnZ1tZmbFxcXWunVr+8Y3vuE9Pzc313r06GFmJ79pYGCgd+AmwAE9evSwVq1a2Z49e7y0QDkD94B55JFHvEke7r33Xjt8+HCDyjRnzhxvoonDhw+bmdmQIUNCXrd79+6Wm5tbp9wXqnAGtq5evdqGDx9u27dvD0kP3Ofpscce89LWr19vUVFRdbbx0EMPWXx8vE2bNs0qKirqfZ2MjIyQGzDXdy+qvLy8kPo/evSoTZs2zX74wx+aWd37NAXqOPi+LoF7lB0/ftzbzm233WZbt26t83rBeQI+/vhja968ubes+0B9uRvpVlZW2oYNGyw/P9/i4uK8CWTS09PtoosusqlTp3rnce2bdOfm5lp5ebl3I+cbb7zR+xwJWLhwobVs2dLMQs/1hQsXGmBdunT56jsvjepkn1e33HKLpaWleZMrZWRkeDf/DkwsU/smuoHv3Z49e4bcnHvNmjUhx1pKSor16tUrJG3Xrl02atSoky4HPsPkwqRJJBoXZ/JGukAS8CbwGbAJ+DqQAswH8v3/k/15HTAVKADWA5mn274CqIY71ckzd+5cA+yvf/2rXXTRRSEf+gcPHrTc3NyQD+/evXtbSkqKt3z77beHfGAXFxfbggULbP/+/XXKEBsba717967z5RC8vV69elliYqK3PGrUKNu1a1fI69W2Zs2aOhc5gTIFf7EEZr9bv369lZeXW1JSksXFxYWUqXPnzvbOO++Ycy7sMo0cOdLMzCZNmuS9d8FfeomJiSHL+oLzCedD/W9/+5s1a9bMzEIvRCsrK23SpEneRUiHDh1s+PDhXuCQk5NjcXFx1qVLFwNs3Lhxp3ydvXv3GmBz5szx0m655RZr166dpaenW4cOHSw7O7tOcB64EOrdu3e9s/DVviAKrv9bbrnFK19CQoJ3Ad+iRQtvhsbaZQjex4Dq6mp74YUXrGXLlpadnW1btmyp836dr75sABVsw4YNtmDBAu/vb3/7W0ig/fHHH9s//vEPW758uc2ZM8f7bKusrLT333/fVq9eXe92V65cWSetvLzcFi5caOvXrz/9zkmTcrLPq1WrVtn7779vS5YssUWLFtn8+fNDjqfas9qanTgO5s+fb/PmzbNVq1aZmW9m0Tlz5njH2wcffGDz58+3JUuWeMff0aNHbfXq1bZ48WKbO3duneWysrLGeUOkSVIA1bjCDaDCvQ/UC8BcM7vVORcDxAFjgYVmNsk59wTwBPA48C3gYv/fYOBl/39pJEOGDOGTTz6hX79+vPbaa7Rs2RLwjQuIj4+nc+fOzJkzh8TERKqrq6moqCAqKorY2FiOHz9Oy5Yt6d69u7e9Bx98kCeffDJkYDXA7Nmz2bNnD2ZGbGwsUVFRVFRUUF1d7W2vvu3Hx8eTnJzM3LlziYuLCxl3FdC9e3dGjx7Nj370I6Kjozlw4IBXph//+MfcfffdREdHU1FRwdGjR0lLSyMhIYHly5d792wKbDc1NdW771NSUlKDyjR69GhuvPHGkG0G72dsbCyHDx8Oeb/k1K644go+/vhjIHQQfmCMU1ZWlvcBFRivBr5JPf7yl7/QvHlzoqKiuPLKK0/6GmZGmzZtWLt2bcgNaMeOHesN+q6pqSE1NZUePXqEjKXr378/CxcupKamBuccHTt2JDU11cvTvXt35syZQ1xcXJ1jc+zYsZSWlhIXF0dVVZXXHejYsWNkZGSctAydO3cOKUOzZs24//776datG507d65z3zM5OTOjb9++dSbuCF5/+eWX15seHR3t3XcMfPeXe/nll/nVr37FDTfcwLJly+rUQ0JCgje+UnV07jP/GMVw8p3sOAjOk5iYyPXXX3/a7WVmZlN/iVwAABsvSURBVJ5yWUSamNNFWEAisBVwtdLzgA7+xx2APP/j6cDI+vKd7E8tUA13sl8fTvcL9Zf5BTs6Otrefffdr7ydUwl3e6fKF6kyyQmn+1Us+D09E3XekOedbDvhbP9s5zkb5+256ky0QJ0p8+fPN8BGjBhRp6VSzn36FV/OBWqBalycwRaobkAx8HvnXH9gNfAQ0M7MdvvzFAGBkY+dgOAbmnzhT9uNnHWn+/UznF9HV69ezbPPPsvy5cs5cuQIv/71r0N+lQ13Ow0R7vZOlS9SZZLwBb+nZ6LOG/K8k20nnO2f7Txn4ryVM2/w4MHMmzeP1q1b12mpFBGRC1c4AVRzIBN4wMxWOudewNddz2NmgfElYXPO3QfcB9ClS5eGPFXOsoyMDK+b0bFjx7j22mtDpmEWEbkQJCQkcO211wLqniciIieEE0B9AXxhZiv9y2/iC6D2OOc6mNlu51wHYK9//U4gLej5nf1pIcxsBjADICsrS/NzNiGJiYlh9QEXEblQKHgSEZGA094HysyKgELnXC9/0nBgI/AuELjpwV3AO/7H7wI/dD5DgANBXf1ERERERETOWeHOwvcA8Jp/Br4twN34gq+Zzrl7gO3A9/x5/w7cgG8a8yP+vCIiIiIiIue8sAIoM1sH1Nena3g9eQ34j69YLhERkRDmn2pc5EyIiooiNTU10sUQOaXo6OjArNbShITbAiUiIhJRsbGxFBQU0KNHD+8eWyJfRlRUFDt27GDEiBG0bNlSF6jSJEVFRbFly5Z675cpkaUASkREzglVVVUkJSUxa9YsSkpKIl0cOYelpqYyYsQIHn30UYYMGcKxY8ciXSSROlJTU8nOzqaqqirSRZFaFECJiMg5obq6mqSkJPr37x/posh5oGXLlgwZMoShQ4dGuigiJ5WUlKQW9ybotLPwiYiINBXqaiVnipmp5UmaPH3mNU0KoERERERERMKkAEpERERERCRMCqBERERERETCpABKREREREQkTAqgREREREREwqQASkREzhsnm7FKM1lJOE51nJyJY0jHocj5QfeBEhGR84Zzjo0bN7J7924Ajh49yjXXXENsbCxr166le/fuJCYmNmqZzAznXKO+pnw5zjlycnIoLS0lLi6OqqoqKisrSUlJISsrq8HbO3bsGLm5uVx88cUkJiZ62y8qKuKKK65o9GNRRM4MBVAiInLOO3jwINu2bSMuLo7LL7+cmpoa2rdvT2lpKc899xxXXXUVmZmZzJ07lxEjRjRq2ZxzrF69moyMDF0wnwMmTJhATk4OhYWFJCUlkZCQQFZWFrNmzWrQdioqKvjFL37Biy++yMyZM73jbsKECfz1r38lJyeHQYMGnY1dEJGzTF34RETknLdhwwZuu+02MjIyiI6OZseOHWzdupUDBw6waNEihg0bBkCrVq0atVyBLltXXnkly5Yta9TXloarrq5m1qxZ7Nixg+7du/Phhx+yY8cOZs2aRXV1dYO2tXHjRp599ln69OnjHXeB7ZsZgwYNUpc+kXOUAigRETmnVVZWkpOTQ3R0NAA5OTm0adMGM6O6uprXX3+dXbt2kZaWxpEjR4D6x6KEezHbkHzOOfbu3Uvz5s1JTk4Oc48a9lq6CD9zoqKivMfV1dWUlJTUu662+uogMzOT3Nxcdu7cSUVFBQDNmumyS+R8oC58IiJyTlu2bBkPPPAAAF27dqWqqgrwdZ0LvujNysoiJSXFW7dp0yZ27drF0aNHufbaa2nRogUABw4c4KOPPiI+Pp6UlBS6devGP/7xD+Lj40lMTKRfv34heVJTU+nVqxdz586lVatW1NTUMGjQIBISEigsLKR///6Ul5ezfPlyhgwZwtq1azl06BA1NTVkZ2ezbt06ysvLiY6O5ujRowDExMSEvNaKFSuIi4sjLi6Ofv36sXz5cpo3b+7lkcZRUVFBXl4el112Ge+99x4tW7akXbt2XHrppV6eY8eOMW/ePC6++GIqKipo1qyZNwbOOce6devo1q2bNybq008/Zc+ePV7AfeWVV3o/BohI06QASkREzmmBwAfg6aefplevXnXyBLpOBZSWlpKZmQlAbGwskyZN4sYbb6RDhw5s3ryZb33rWwBceumlfPjhh95yRkYG+fn5bNmyxUvr1KkTL730EjfffLPXErFo0SKys7N55JFH2L9/PwCPPvooI0eO5MUXX+SVV14B4NVXX+Xtt9/mrbfeAqBt27ZERUWxe/du77W2bt3K9ddfD8A999zD6NGjvS6JXbp0Yfv27WfmjZRTqqioYMKECUyaNImZM2dy9913U1FRQbt27di2bRsANTU1TJ8+nSeeeILs7GxuvvlmYmNjvUC+sLCQgQMHhozFu+aaaygtLaVv37588sknTJkyhZ/85CcKokSaMLUli4jIeeO+++5j/fr1ddKDW6KKi4vp2rUrZsaOHTvYv38/P/vZz+jYsSN79+4lMzOTzZs3AxAfH0/r1q295bZt2wIwcOBAL23nzp3Mnj2bQ4cOeUFVTEwMADNnzvSCnc2bN9OxY0d+//vfe2l33nkn//M//8MTTzxBx44d2bNnDxs3bgSgffv2AAwYMIAdO3bwve99j9/+9rdUVlaSkZHBoEGDvMBKzr6NGzfyzDPPcPz4cd58803279/PypUrvbFRx44d49e//jXvvfceR44cYfr06dx///0kJyfTvLnv9+pHHnkEgNTUVG+7V111FSUlJaxbt44BAwbw05/+lKVLlzb+DopI2BRAiYjIeeOPf/wj/fv3P2WeIUOGcPDgQTZt2kSbNm0A6NOnDwBf//rXASgqKgJOjFkJLAcLpA0bNowZM2awevVqr7Up2LFjx+psI5DWvn179u7dy8SJE9m5cyfga6VITk6mqKiIvXv3ApCWlsYbb7xBVVUVAwYMID8/n5ycHKZPn97gyQ2k4czMG9PUo0cP/vSnPwG+YDwQnM+fP58nn3yS999/n6qqKtLS0uqMgZo5cyaXXnop7733ntdd8+WXX2bHjh1cd911fPLJJ0Boq6qIND0KoERE5JxWU1PjPf7e977HRx99FLK+9gD/pKQkoP6gKLCuIQLBUFRUVIMnCVi2bBn9+vXzylhdXU2/fv34+OOPKSgo4JJLLqG4uNjLH2jJCKaJCc6+wBimPXv2eGPsamvZsqU3xi4QVJWVldG8eXNiY2O9fK1atWLcuHFs3ryZgwcP0qNHD/r168e8efPUoihyjtCnroiInNNSUlK8rnUAK1euDFnvnOOjjz6itLQUgG9+85vAicCjuLjYG8MSWBcQyBNoEQgOVgIXxcF5Ao+DL5hr5w9+TllZmVdGOHHhXVFRQYcOHSgpKWHAgAEh2zl48CAffvghS5cuJTc3VzfpPYOCg23nXJ2WoBYtWoS838HLZkZRURHFxcVe2ooVKzh06BAffvih1+K0a9cu8vPzufTSS+nTpw9lZWVe62N5eTlQ//EjIk2HAigRETlnmRl9+/bl008/5YEHHmDo0KE8/PDDTJw4kaVLl7Jw4ULWrFnDvffey+effw7A1KlTGTVqFGvXriUvL49OnTqxZ88eHn/8caZOnQqcaNUqKioiLy/PG5PyxRdf8MUXX2BmLFiwoE6eQKvWggULvG5bgRaqBQsWYGbeNmrnC96nSy+9lEWLFgHw3e9+N2T9qlWrGDZsGFdccQXf/va3z+wbeoFzzpGTk8O6devYv38/y5cvZ926daxduxYzY9GiRezbt887BgLLRUVFdO/eHfBNKrJ582amTJnCY489RklJCU8++SRbt25lz549lJWVMWvWLNavX8/w4cMB+POf/0xeXh579uwB6j8uRKQJMbOI/w0aNMikYdLT023BggWRLoZICB2X8lUtWLDA0tPTG7yusrLSNmzYYPn5+RYXF2exsbHWtWtXS09Pt4suusimTp1qhw8f9vLv2rXLhg0bZl27djXAJk6caPv27fPWL1q0yAADrFevXpaSkuItjxo1ynbt2uUt15cHsDVr1piZWUZGhpe2a9cuGzVqVL35aguUYe/evSHpCxcu9J7bpUuXBry7Euxkn1e33HKLpaWlGWBJSUmWlpZmI0eODKnz2sfAyJEjzcxs0qRJBljv3r0tMTHRAEtNTbVnn33WzMxGjhzpbTcxMdHmzJlj7du3t7i4OOvVq1dYx4VcWOo7TvVde/YAORZG7KJpzEVE5JwXHR1N3759AV8Lze7du711te/zZGZ06NCBX/7yl94kAIF775j/XjyDBg1i0aJFNG/enIqKCqKiooiNjeX48ePEx8eTnJzMnDlzSExMpLq62ssTHR1NVFQUBw4c8FokZs2axYEDBzh06BDJyck88MAD3H333URHR4fkq66uDpkt8LLLLmPt2rXeTYED3cIuv/xyr2yJiYmN8v5eSMaOHUtpaSlxcXFUVVVRWVlJmzZtSE5OZu7cucTFxdGqVauQ5UCXu9GjR5OVlYWZecdDSkoKF198MQAPP/wwP/rRj6iurubQoUOMGDGCDz/80GvRCkx5HnxciEjTowBKRETOG+bv0hcIpurjnMPMGDhwYJ3nBoKUhIQEsrOzT/la4Qz4N7M6N7oN3H+qtqioKO6//37mzJnDzTffzNSpU0lJSQkpV31lq71evjwzIysr66TrA/duqm/ZzIiOjva65dW37csvv7xOeq9eveq9d5mINF0aAyUiIueNcAOJ+vKdjSCkoducO3cuhYWFvPjii3z/+98PaxsKns6cr/Jeqp5ELhxqgRIREWkiZs+ezb59+2jWrBktW7aMdHFERKQeCqBERESagPq6+6l7nohI06MufCIiIk1AY3UrFBGRr0YBlIiIiIiISJgUQImIyDkhMMuZyJkQFRVFampqpIshckqB2ytI06IxUCIick5o1qwZBQUFXHzxxRw/fjzSxZFzWHR0NFu2bCE7O5ukpCRdoEqTFB0dTUFBAc2aqb2jqVEAJSIi54TLL7+chQsXUlNTo7FB8pUEblpbVVVFdXV1pIsjUi8zo1mzZvXeP0wiSwGUiIicExISErj66qsjXQwREbnAqU1QREREREQkTAqgREREREREwqQA6hxVXV1NmzZtIl0MkRCa1Uq+qtTUVKKioiJdDBGRJksz80WexkCdo6Kiohg8eDDt27fXSSRNgma1kq/KOUdZWRllZWWaaU9EpB6ama9p0Lt/jrrhhhto27YtNTU1kS6KCAAVFRUMHjxYx6V8aTU1NbRt25bBgwdTUVER6eKIiDQ5FRUVpKen07Zt20gX5YKmFqhzUHV1NS+99FKkiyEiIiIiEWJmuqVDhKgF6hyk8QEiIiIiFzYFT5GjAEpERERERCRMCqBERERERETCpABKREREREQkTKcNoJxzvZxz64L+yp1zP3HOpTjn5jvn8v3/k/35nXNuqnOuwDm33jmXefZ3Q0RERERE5Ow7bQBlZnlmNsDMBgCDgCPAX4EngIVmdjGw0L8M8C3gYv/ffcDLZ6PgIiIiIiIija2hXfiGA5vNbDtwE/AHf/ofgJv9j28C/mg+K4Ak51yHM1JaERERERGRCGpoAHU78Lr/cTsz2+1/XAS08z/uBBQGPecLf5qIiIiIiMg5LewAyjkXA9wI/KX2OjMzwBryws65+5xzOc65nOLi4oY8VUREREREJCIa0gL1LWCNme3xL+8JdM3z/9/rT98JpAU9r7M/LYSZzTCzLDPLatOmTcNLLiIiIiIi0sgaEkCN5ET3PYB3gbv8j+8C3glK/6F/Nr4hwIGgrn4iIiIiIiLnrObhZHLOtQKuBe4PSp4EzHTO3QNsB77nT/87cANQgG/GvrvPWGlFREREREQiKKwAyswOA61rpe3DNytf7bwG/McZKZ2IiIiIiEgT0tBZ+ERERERERC5YCqBERERERETCpABKREREREQkTAqgREREREREwqQASkREREREJEwKoERERERERMKkAEpERERERCRMCqBERERERETCpABKREREREQkTAqgREREREREwqQASkREREREJEwKoERERERERMKkAEpERERERCRMCqBERERERETCpABKREREREQkTAqgREREREREwqQASkREREREJEwKoERERERERMKkAEpERERERCRMCqBERERERETCpABKREREREQkTAqgREREREREwqQASkREREREJEwKoERERERERMKkAEpERERERCRMCqBERERERETCpABKREREREQkTAqgREREREREwqQASkREREREJEwKoERERERERMKkAEpERERERCRMCqBERERERETCpABKREREREQkTAqgREREREREwqQASkREREREJEwKoERERERERMKkAEpERERERCRMCqBERERERETCpABKREREREQkTAqgREREREREwqQASkREREREJEwKoERERERERMIUVgDlnHvYObfBOfepc+5151ysc66bc26lc67AOfeGcy7Gn7eFf7nAv77r2dwBERERERGRxnLaAMo51wl4EMgys0uBKOB2YDLwKzPLAPYD9/ifcg+w35/+K38+ERERERGRc164XfiaAy2dc82BOGA3cDXwpn/9H4Cb/Y9v8i/jXz/cOefOTHFFREREREQi57QBlJntBJ4HduALnA4Aq4EyM6vyZ/sC6OR/3Ako9D+3yp+/9ZkttoiIiIiISOMLpwtfMr5WpW5AR6AVcP1XfWHn3H3OuRznXE5xcfFX3ZyIiIiIiMhZF04XvmuArWZWbGbHgVnAN4Ekf5c+gM7ATv/jnUAagH99IrCv9kbNbIaZZZlZVps2bb7iboiIiIiIiJx94QRQO4Ahzrk4/1im4cBGYBFwqz/PXcA7/sfv+pfxr//AzOzMFVlERERERCQywhkDtRLfZBBrgFz/c2YAjwOjnXMF+MY4/c7/lN8Brf3po4EnzkK5RUREREREGp1rCo1DWVlZlpOTE+liiIiIiIjIBco5t9rMsk6XL9xpzEVERERERC54CqBERERERETCpABKREREREQkTAqgREREREREwqQASkREREREJEwKoERERERERMKkAEpERERERCRMCqBERERERETCpABKREREREQkTAqgREREREREwqQASkREREREJEwKoERERERERMKkAEpERERERCRMCqBERERERETCpABKREREREQkTAqgREREREREwqQASkREREREJEwKoERERERERMLkzCzSZcA5dxDIi3Q5pF6pQEmkCyH1Ut00Xaqbpk3103Spbpou1U3Tpbo5c9LNrM3pMjVvjJKEIc/MsiJdCKnLOZejummaVDdNl+qmaVP9NF2qm6ZLddN0qW4an7rwiYiIiIiIhEkBlIiIiIiISJiaSgA1I9IFkJNS3TRdqpumS3XTtKl+mi7VTdOlumm6VDeNrElMIiEiIiIiInIuaCotUCIiIiIiIk1exAMo59z1zrk851yBc+6JSJfnQuOcS3POLXLObXTObXDOPeRPT3HOzXfO5fv/J/vTnXNuqr++1jvnMiO7B+c351yUc26tc262f7mbc26l//1/wzkX409v4V8u8K/vGslyXwicc0nOuTedc5855zY5576u86ZpcM497P88+9Q597pzLlbnTmQ45/7XObfXOfdpUFqDzxPn3F3+/PnOubsisS/nm5PUzXP+z7T1zrm/OueSgtaN8ddNnnNuRFC6ruPOsPrqJmjdI845c86l+pd13kRARAMo51wU8D/At4C+wEjnXN9IlukCVAU8YmZ9gSHAf/jr4AlgoZldDCz0L4Ovri72/90HvNz4Rb6gPARsClqeDPzKzDKA/cA9/vR7gP3+9F/588nZ9QIw18x6A/3x1ZPOmwhzznUCHgSyzOxSIAq4HZ07kfIKcH2ttAadJ865FGAcMBj4GjAuEHTJV/IKdetmPnCpmV0GfA6MAfBfF9wOXOJ/zkv+H/h0HXd2vELdusE5lwZcB+wIStZ5EwGRboH6GlBgZlvMrBL4M3BThMt0QTGz3Wa2xv/4IL6LwE746uEP/mx/AG72P74J+KP5rACSnHMdGrnYFwTnXGfgn4Df+pcdcDXwpj9L7XoJ1NebwHB/fjkLnHOJwJXA7wDMrNLMytB501Q0B1o655oDccBudO5EhJktBkprJTf0PBkBzDezUjPbj+8iv87FpTRMfXVjZvPMrMq/uALo7H98E/BnMztmZluBAnzXcLqOOwtOct6A70eenwLBExjovImASAdQnYDCoOUv/GkSAf6uKwOBlUA7M9vtX1UEtPM/Vp01nv+H74Oyxr/cGigL+nILfu+9evGvP+DPL2dHN6AY+L2/i+VvnXOt0HkTcWa2E3ge3y+0u/GdC6vRudOUNPQ80fkTGf8KzPE/Vt1EmHPuJmCnmX1Sa5XqJgIiHUBJE+GciwfeAn5iZuXB68w3VaOma2xEzrlvA3vNbHWkyyL1ag5kAi+b2UDgMCe6IQE6byLF30XlJnxBbkegFfrVtcnSedI0Oed+hq+L/2uRLouAcy4OGAv8PNJlEZ9IB1A7gbSg5c7+NGlEzrlofMHTa2Y2y5+8J9DFyP9/rz9dddY4vgnc6Jzbhq9LxNX4xtwk+bslQeh779WLf30isK8xC3yB+QL4wsxW+pffxBdQ6byJvGuArWZWbGbHgVn4ziedO01HQ88TnT+NyDk3Cvg2cIeduNeN6iayeuD7UegT/3VBZ2CNc649qpuIiHQAtQq42D87Ugy+AYrvRrhMFxR/X//fAZvM7L+DVr0LBGZsuQt4Jyj9h/5ZX4YAB4K6YsgZYmZjzKyzmXXFd158YGZ3AIuAW/3ZatdLoL5u9efXr7pniZkVAYXOuV7+pOHARnTeNAU7gCHOuTj/51ugbnTuNB0NPU/eB65zziX7Wxiv86fJGeacux5f1/EbzexI0Kp3gdudb9bKbvgmLPgYXcc1CjPLNbO2ZtbVf13wBZDp/y7SeRMJZhbRP+AGfDO9bAZ+FunyXGh/wFB83SfWA+v8fzfgGwOwEMgHFgAp/vwO34w7m4FcfDNdRXw/zuc/IBuY7X/cHd+XVgHwF6CFPz3Wv1zgX9890uU+3/+AAUCO/9x5G0jWedM0/oDxwGfAp8CrQAudOxGri9fxjUU7ju+i754vc57gG49T4P+7O9L7dT78naRuCvCNmwlcD0wLyv8zf93kAd8KStd1XCPUTa3124BU/2OdNxH4c/43WERERERERE4j0l34REREREREzhkKoERERERERMKkAEpERERERCRMCqBERERERETCpABKREREREQkTAqgREREREREwqQASkREREREJEwKoERERERERML0/wEj+NSDbGN2uQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imgplot = plt.imshow(cv2.resize(th3, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC),cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to flip image pixel values\n",
    "th3 = 255-th3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5eHKmsrISQP9GRLQ/PkpLS3HhwgVs377d5vgQ6R44oKmpyab+xMbGauei3NxcFBYWIigoCO+++65WN9V6qdbnl19+GaWlpSgtLUVJSQlKS0u1epmVlYWTJ09q+8RIHvtzvtTvI6vV6rDfv/Wtb+H06dOoqqpCRkaGzUA8N954Izo7O7U8q+e8vtZ/Pftzl57+PKd+5zg7R9oHT++88w6qqqpcnsfVz56enm7zI2BLSwtCQkK080VfzpeA6+8SZ8eHmgf1HKfWFQZPROTAyG2qwU4Y/tt1V21y1uShra3NocnR4sWL5eLFizbv0f+vNhG6cOGCREVFSVRUlFy4cEGam5tt2sA767yfn59v04xQRGTXrl0CwKG5h36Ai5SUFJv3qB1/9fnbunWriIisW7dOoqKitD5fVVVVEhMT43S5qVOnOgzwYF8eA9Xcw2iTGGflPH36dK3jc1dXl9O+NvZN+FpbW2XVqlUSHR2tlUVtba22j9TtOMuXvk9FYWGhXLhwQdrb2yUqKkpWrlwphYWFhvpZ2HfGT0tLE0VRJCwszOXADhEREdLU1CRPPfWUhIWFidls1gZjsN//9s2E7OuO2iTSvklYSUmJzWtn5eIuxcfHy/79+wWAeHp6anlw1h9q/PjxEh4eLosXLxY/Pz9tHeq+e/HFF0VEZPPmzTJ9+nSnx1J6ero0NDTIhQsXJDo6WlatWiUiInl5edoyzppM3XTTTbJ48WKbsjCyTQ8PD+1YUfuvpKWlSVRUlDz88MNany6Rvh0TCQkJNvuiurpa62OpTwcOHLDZP/rjRS03/fExa9YsSUpKknXr1tmUvbqs/cAB+/fvFx8fH3n55ZdtthMXFycTJ050OYBCaWmpbN68WZKSkpwOlHD+/HmbZpO95VG/j50NimB/vnRWT+33e25urgQHB8sdd9yhnT+DgoKktLRUdu/eLRaLxWFAD/tzZF/OV73tbyP1RFEUbV+99dZbcs8998itt94qN910k9tzV3l5uYh808dKbb5rf74wck539V0ydepUaW9vl/b2dpkwYYLDsjk5OU7rChMT0zWR2AfqWkrFxcUiImK1WkVE5NChQwJ8c9E0Z84c6ejocPremJgYrXO1viN/YWGh0y9ifbDW0dEhIiIrV66U0tJSEenuoB0eHq5tV+WsH8u9997r9Ms4JiZG0tPTtXlqh3P9RV5ycrIAkHfffddhOfuOyLm5uTbbHeoAyr6cMzMztelr1qzR1uHuokBEZMOGDTJmzBgBYDOqmf0+cpUvfR7OnTsnQUFBAnT38dKXhbvRvuz73d1www1O64b9tuvr6yUlJcVmXXPnznW6H+wHzzDSByo1NVUiIyMlPz/fZbm4S/7+/g4jJm7YsEFEbIOo5557Try8vOTVV1+1ufhTy0x/oaauJzMz0yE/ubm5IiLy0UcfaWX+y1/+UhobG23y4O4YUi/6jG5Tf0zpB3tQR0NUj+f+BlDq+xsbG+X8+fNSUlIiJSUlUlxcrA0+4KxuuDs+9HXE/vjQl83XX38tJSUlsmLFComMjJSKigoR+eY84WoAhba2Npt6OW7cOJsfCfSDJBjJo32d05+L9OdL/Tb071H3qf6zdXV1yR/+8Aenn/vo0aNOzyUijhf/QxlAqUmtc+Xl5RIZGen0PKEfEdH+/KJ+l9lvW52uHneu8uXqu0QdnKOhocHhHKF+94mILFy4sF/lxMTEdNUmQwEUm/CNEomJiTbN+ObOnYv09HR0dnZizpw52Lp1K6ZMmWLzHrXZQnt7OyZOnIjjx49j+vTp2vzp06fj+PHjmDhxIiZMmKBN1/d38PDwQGpqKv7whz/gn//5n7Fo0SJs3boVZWVlAJw391BFR0dj69atTj9Pe3u71u8EALy9vQEA3//+9zF//nwkJydr/ZLWr1+P2tpaAICPjw8AoLq6GhUVFdr7W1tbXRfeEAkODsbEiRMBACEhIdr0jIwM7X/7/mv29u/fj66uLgDfNN0E4LCPXJkwYYKWh9LSUtTU1ADo7ssAdDdVU5vHOGPf7w4AcnNzMWPGDACO+1htzrV7925s3LgR2dnZ2noAoL6+3ul27JtDuur7pZednY2SkhKtCauIGC4XNS/Z2dk2zXm2bduGtLQ0mz4Q69evx5EjR7By5UrU1tYiNjZWaw6Wnp6ODRs2AAD+53/+R1uP+l59ftQ6+e1vfxu//vWvYbVa8eSTT+L22293WRZq3xB9U7a+bFNfv9R9AQBHjx5FamrqFTfh8/DwwN13343bbrsNq1evxqOPPopHH30US5cuRVFRkdv36uumvmmWeuzb5x/4pmxEBGPHjkVERASKi4tRUlKCRYsW4bbbbtPOE67qUEJCgrZMTEwMvvjiC4SFhQEATp06hfnz5/cpj/ryVhQF1dXVuHTpklY+6vly9uzZWn8s/Xukp+mbfr+fPXsWv/jFL7TXZ86cwc9//nMAgJ+fnzZdfy4BoJ0rhpP6rMEbbrgB06ZNAwCbPk7Lly/HiRMnAHxzflHPR8A35QEAOTk5SE1NBfDNd1xvzesmTJhgU2/U80NNTQ3Onj0LX19fh3PE0qVLte+Zq+3ZbkQ0NDiIxChgf1FrtVrh5eWFm2++GUB3B/GUlBRcuHDBpsOsiGDGjBnYt28fAGDy5MnYv38/pk+fDhFBQ0MDZs6cCQBuL6zUC7GCggIUFBRoedJ/8TkTGhrqtOO6midnF772D35UFAVlZWX49re/jdDQUG1AgrfffhspKSlutz/UcnJykJycjM7OTlRXVwPoDljU/jZA74Geuo+dXWgYuVjSL5OYmIji4mIoigJfX18A3XVlyZIl2kWKPXWfzp8/Hx999BEsFovWpwMArr/+epvls7OzER0djYSEBPzud79Da2urVjf0dW8gqBex6oWyGgj19SLSvt5u27YNALB161ZYrVaYzWbExsYCAB555BHt4g+wvcA/evQoWltb0dHRoV04At8cS2oQ7enpiTVr1mDevHk4cuQI1q1b53LgjeTkZOzZswf33Xef022+//77GDt2LEwmE6ZOneqwTX390l+gt7e32wRUV2LPnj2IjY11uPDU11VnjOwnV8eHoig4deoUdu7ciaNHjwIATp8+jdOnTztdXg3sx4wZY5NP9cekrq4ujBkzBmaz2WaQBKN1SV3O2aAIajnX1taioqICMTExNu9xpra2FiUlJVo+WltbsXXrVhw5ckT7sSo8PBwXLlwwlL+h9J3vfAd+fn7w9vbG/v378eabbyI5OVmbv3PnTuTn5+PEiRPasedqQJj29nZkZGQgPT0dXl5evf7gBHSXq77eZGVlORyX+u8ftS7Zb7u37zMiurYwgBoFnF3UAt2BSFZWFsrLyxEREWGzrMrf3x8hISEQEfj5+SEiIgJ33XUXrr/+epjNZrS2tsLDw8Ptr3DORt0y8mWzf/9+3HfffdizZ4/TPBkhIvDw8NBG1/rNb36DVatWYeXKlbBYLNqF7kigH3EuJCQEe/fuxZIlS7B8+XK89dZbfVqXuwsNo4qKivDwww8jMDAQXV1daGlpgYeHR68Xus7uQv3pT38CALzwwgv49NNP8f7772PmzJkICQlBa2srPD09tSBNzXtISIgWfA3E4AWDadu2bWhtbbUZmQ8AXn31VZw8eVILovQXamvXrsX58+fh7++PlpYWdHZ2wmw2a8dSfHw8VqxYgW3btsHDwwPz5s3DvHnz0NHR4XDXS1VQUIC0tDSbaeo2pWfQiXXr1ml3VOy36c6VjJ6nt2rVKpw9e9Zh+pXUVVVbWxsA56OiNTQ02IyE6U5xcTHmzp2L48ePa9PS09Nx7733anmtq6tDQkICAGPnMz01QLNardizZ49NwKCW8549ewz/yGM/WIOiKOjo6MC+ffvg5+eHqqoq5OXl9etcMpjsg5EPPvgA77zzDmbNmqWdm1evXo38/Pxe1+XsR5f+tCx44YUX8Nlnn7n8fnO2rxk8EZG9kX3VQoY5uwvl4+ODpKQkXLhwweHuk8rT0xNA9690JpMJFRUVhr7MBoKrJnxqngAYGkWqs7MTO3bswLe+9S3Mnz8fzz77LDIyMlzeRRkI6gWcekEH2I4+Zl/O6rSgoCBkZ2dj/Pjx6Orqgr+/P3bu3Dlo+XSnqqrK5u6JnrtfXJ0F7OqdyrKyMptmdABQWFiItLQ07N+/32Y9WVlZSE1NdQhKRir9yHxq8y13F39bt27FwoULHYbAB7oviGtqavDSSy9BURTt7pbJZMKGDRtgNpud1l/7OxF66nGijj5plLoutf5eKf0dhYHm7A6cSr1jZMTy5cvx2WefAfjmWI6Li9N+uDlz5gySk5O1Jq59pQ/Q0tLSMHPmTERHRzvkYfr06bjpppv6tG77c4nZbEZgYCAeeOAB3HDDDf3K72DQDxOfnp6OO++8E7/+9a/x3//931i1apW23LPPPotjx45prRfcUX9cU+8QGv2hTe+9995DeXl5n99HRKTHPlCjhHox9de//hWVlZXaBV57ezv+/Oc/2yyjV11dbbO8vf4MgWuUvgmfszy1t7dr29f/0mg/BDvQ3Y9k/vz5qKqqwm9/+1sAA/OLtytWqxWRkZGYNGmSNm3p0qUYO3asy18wPT09sXz5ckybNg033HAD/vjHP2LcuHH92r6z5ml9/byTJk3S+nM4G9beHZPJhMrKSpsLzMrKSq1Pk/q5rFYrbrzxRhw7dkxrvqduo729HX/5y18M5XUw66HRbTU1NaG6utrt0Mn6HzAiIiIcLvASExPh7++v/WABAK+//jrq6uq0ZwgBwF133WU4v+6Gz9dv0xURgZeXV5+22Vt++vvIgOFgtVoREhKi9Z8EuvvJVFdXa81bXQ2db0RJSYlNf8z+UOuFemzqzyXh4eF4/vnnUV9fj7Fjx17Rdq4kX/bUfoFBQUG4//77MXXqVO0OvPojmbPzqJHtjRkzpk/nD72lS5fCy8vLZb6NTiOiaxsDqFHEZDIhNTXVoaN4amqq0wsaRVFw+vRpm+X1naSB7i/B+Ph4wx3x3bFv419eXo6SkhKH5lunT5/G/PnzbX5F1+dRnycRQUhIiNbHIDU1FfX19QgLC0NgYKDDtgfyi/DZZ59FVFSU9vq5556zGYTDXlJSEl566SUA3c0XN23ahPLycjz++OOG86lvrhUSEqJ1INd3VHdHDbK6uroQExODM2fOaM9bGj9+PJKSkgw1V1GbT6nN8oDufaT2OVGbUqn1Tt0X+v53gG0HeGf5VJdT929SUtKA1EVnfH19tQEh7MtfURScOXMGMTExqKur06bv3LkTs2bN0l7bf261H6LqxRdf1AbcUMuwrq4O0dHRqKmp0T63q2aU6vGp30f2zdbs+4W8+OKLWj8bfbCl/1EiMTHRpkN+X46X1tZWm2M7NDQUx48f7/OxZuQHgE8//RT33XffFQdo9vstKytLezZUe3u7dtdQpPsZeocOHTKcRwCYNm0ajh8/ri2vv6vuKg/u2O93/bnks88+wzPPPAMANk0FgcEZPEe/Tj8/P60JuCuHDx/WvlPGjx8P4Ju6r+5H/R1ro3Jzc11+t+np70yqd8PUAYrsv9/UADUmJgZJSUlYuHAhfHx82ISPiBwwgBpF1C8lfTMO9QLX2UhF6peCfvmYmBicOnUKa9asQWxsLGJjY/Hqq6/aBAb2gZC7L2n9BYevry+io6O1L6+CggJs3LjR6Xq9vLxsmj6lp6e7zFNubq72wNZ3330Xjz32GF5++WWb4CYwMBCRkZHaZ9bnuS8Xi/plli9fjry8PO31ihUr3DZb0jf3Cw4ORnR0NFatWmXThM8+n/ZlvWDBAu3/zMxMWCwW1NbWasGQPkBS6T/rpUuXUF1djTFjxqCrqwsBAQH4/PPPERERgcceewzPPvtsr2Wgl52drX0utR6pgbwaAOvvTNnTX+zr81xfX6913ge6B6d48MEH8eyzz2r73b5s1Hz0pX7qzZkzB5999pkWUOr3tfq6rq4OUVFR+OqrrwAA/+///T+tCZ/6ufUX32q9jY+Px/e+9z3Mnz8fBw8eRFxcHBYvXozFixdj7ty5qK2txezZs7Xt6Qd0sA96CwoKEB4ers03sk11ffr16steDVDVbYWFhSE6R3b7AAAgAElEQVQiIsLQhWNiYqJNULts2TItL3qu9pdKrZuA7flKv5z6OdX59sex/ToB53dr9cfqxIkTtQBUURStaanq5ptvRmlpqeE8VldXa3c4urq6HEaCVPO8YsUKrRmh/WcB3O93dX91dXVh9uzZuHjxIu6++24899xzNuu47bbbbO5K6cvAWVkZodYbEYHFYkFWVpY2z9k5VN80NCEhAVFRUQ53nNzlUx989qUuqC5duqTVezXYslgsmDNnjsN3iYggPDwc//jHP5CVlYUDBw5c0d1HIhrFjIx1PtgJwz/m+6hKMTExUlNTIyKOz01xliwWixQUFDg8DFc1adIkbVlnD9JNS0sTs9nsMi/qc1NUb7/9tgCQp59+2ma6/QMrgW+eb+MuT/pnfKgAyP79+22mvfvuuwI4PoDT2Xbdpbi4OLFYLA4P61XLIjk52eYBp2pSn7WjKi8vl9dee018fHyc5hOALFiwQMvjhx9+KCKuH5SqPj/Gfh/Z75+AgACpra11yPsbb7whgPtnQOmTulxOTo40Nzc7PEtF3S9RUVE2+QMg3t7eve7/qVOn2sx/6623tHmRkZEO9XDt2rU2D1t29fntk6+vr6SkpEhqaqqIiFy+fFnCwsKcLqt+hsDAQNm+fbu0t7drDzZV044dOyQ7O1va2tq0PDQ0NMjly5e1Z9BkZGRIZWWlXLx4Ufbt2yczZsxw+cBYZ8eQvo4Y3ab+OVXqQ0qffvppCQoKkilTpmjbaG1tlZycHNm7d6/bMktOTnZ4eKu+zJOTk8VsNktsbKzExsY67Je1a9dKSkqKzbFyJQ/SLSoqcrpOd+etKVOmOBzHRUVFMmvWLElMTNTWf+TIEcN57O1BumlpaQJAe26eq3rqbr87m7djxw6Hc4nIN89Ysi8DV2XV1/1dVFRk84B0+6R/KG1VVZVcuHBBsrKyHJ4Lpn8W1MmTJ+X8+fNy4sQJ2b17t8TFxfWpLvTnu0Q9tvXPFRThg3SZmK7BZOg5UIqMgFvTPScuGgBqh+jw8HC89NJLuP/++52OWKXS93cJCQlBdna29othSEgI4uPjUVNToy23d+9eTJkyBQ0NDVoH4djYWJvnrdivOygoCIcPH9ZGyUpNTcWtt96Kjz76CHl5eVrfDz8/PxQXF2PJkiUOHZATExPR2trqkCf7Zfz8/LT5O3bsQHJyMpqbm7XtAt1Dh3/88cfIy8uDoig22zUiIyMDCxYsQElJCVpbW7VfitW+FBEREUhISHDoyG+xWPDee+855Cc2NhZ79uyxma7v2P7Tn/4UmzZtQmlpKX77299i6dKl2i+9s2fPRm1trc1+1O8j6RmZTd0/9vtEHXjEfrt9sXfvXvzoRz/Cl19+qa1fXc8777yD3/zmNw51Q78PnO1/d+Vl/xnVOhASEoKxY8eisrJS2y/u6qdq7ty5OHToEEpLS1FRUYEJEya4rQ/uBgnRl5++TiYmJmrHlf0Q5TfeeCPOnz+PixcvIiMjA+vWrdPW4+4YUstN/Zzutmmfv/T0dMTHxyM6OhoHDhzA3XffjaCgIOTl5dkMwOKqPujLTL1T6Ow4mDx5MrZu3Yrbb7/d5nhxdqyoeQwMDNTuzHR1dUFRFIdysa9DiqLAbDbD19fX6fFntL50dXXBbDZrAxW0tbWhvr4eeXl5ePzxxw3lUV/ers6XM2fOxE9+8hMkJiaira3NoZ662+9q/XE2T38u0Z8L7ctAepoAuzpX9ba/1Ts5anlFRkYiMzNTu/uot2PHDsydOxf+/v42ddLZOVtfbmvXrkVpaSluvPFGFBcX4+LFi4brQn++S+yPj8TERPj4+GDp0qUOw5oT0aiWKyJxvS3EAGoU0l/gGb0gdjdwgLOLQ6PrcDbN1TNuVPrtuNpmb5/R2TbUC05Xn9NIWfUlwNDn0Vk5uMqP/cWzu89kv253+XO13v58NmfvsV+vq3m9DVLhrlzsA4a+MFo/neW/r5y91+j27d9rZJm+bNPI8WL0R5eBoq7T6P4wkgej5y2jejt+1GXUutvf7borC3d5cHd+6O2YMXL+N8J+Hznbrrvp9uXm6jMZrQt9/S5xtdxA1SEiuiowgCIiIiIiIjLIUADFQSSIiIiIiIgMYgBFRERERERkEAMoIiIiIiIigxhAERERERERGcQAioiIiIiIyCAGUERERERERAYxgCIiIiIiIjKIARQREREREZFBvQZQiqKEK4pyQFGUM4qinFYUZW3P9AmKonykKEpRz1//numKoijpiqKcUxQlX1GUOYP9IYiIiIiIiIaCkTtQnQB+KiIxAOIB/FBRlBgAmwDsF5FpAPb3vAaAOwFM60mrAfx+wHNNREREREQ0DHoNoESkQkSO9/zfDOBzAKEA7gbwZs9ibwJY0vP/3QDekm6HAfgpijJpwHNOREREREQ0xPrUB0pRlEgAswEcARAsIhU9syoBBPf8HwqgTPe2L3umERERERERXdU8jC6oKMp1ADIA/FhEmhRF0eaJiCiKIn3ZsKIoq9HdxI+IiIiIiOiqYOgOlKIonugOnv4oIu/1TK5Sm+b1/K3umV4OIFz39rCeaTZEZKeIxIlIXH8zT0RERERENJSMjMKnAHgNwOci8hvdrPcBrOj5fwWAP+umL+8ZjS8eQKOuqR8REREREdFVSxFx3/JOUZQkAFkATgLo6pn8M3T3g9oD4EYApQDuE5FLPQHX7wDcAeBrAP8mIjm9bKNPzf+IiIiIiIgGWK6R1nG9BlBDgQEUERERERENM0MBVJ9G4SMiIiIiIrqWMYAiIiIiIiIyiAEUERERERGRQQygiIiIiIiIDGIAdRUymUzDnYVRZTDKk/uIiIiIBlP3wNc0HDyGOwPUd1arFS+//DLuuusuAMBIGEnxamQ2m5GdnY1ly5YN+LqtVisyMjKQmJiI1tbWAV//SGQymVBeXg5/f3+YzWbWS+ozRVHQ2tqK+vp6hIaGwmq1DneWiIhGFJPJBKvViu9+97s4derUcGfnmsUA6ip111134ZVXXkFWVha8vb2HOztXpTFjxqC+vn7Q1v+rX/0K/v7+6Orq6n3hUaCmpgaZmZl44YUXcOjQIdZL6rO2tjYkJCTgZz/7GRYsWICgoKDhzhIR0YhSU1ODI0eOIDg4mAHUMGIAdRXLysrCwYMHhzsbVz1FUQb8bomiKMjJcfv86FGpvr4ehw4dYr2kK1JfX4/8/PzhzgYR0YhUUVEx3Fm45rEP1FVKRPgL/wAZjKZm12rzNUVRWC/pinh7e7NdPxGRG2PG8PJ9uHEPEBERERERGcQAioiIiIiIyCAGUERERERERAYxgCIiIiIiIjKIARQREREREZFBDKBGEVcjV7kb0YqjXdFQ6Es9Gwl1ciTkgYwzsr/6u09ZF4iIyB6fAzWKiAji4uIQFBSE1tZWtLe3o76+HmfOnNGedeTr64v4+HhcvnwZra2t1+SzimhoqXUvOjoaoaGhUBQFVqsVLS0tAAAPDw+YzWaICKqqqtw+GNDX1xfTpk3DuHHjMGbMGOTm5qK5ufmK8+jr64t58+ahubkZjY2NfDjhVcLX1xf/9E//hLy8vF6f56aeH3urO6wLRETUKxEZ9gRAmPqWSkpKJCUlxWF6RkaGfP3111JXVyciIlVVVRIQEKDNj42NFdWuXbuG/XMwja7kql6GhYVJW1ubtLS0yOeff67VT1VBQYG0tLRISUmJ2/XHxsbKrl27tPctXLhwQPKtPy4KCwuHvRyv5ZSSktJrPVDTwoULRUQkKCjI0PJG6g7rAhMT00hPrr5rmQYk5RiJXdiEbxQxmUxYtmwZxo0bh4SEBADAxIkTcfbsWQQEBAAA8vLyMHPmTBQXF+PBBx+EyWQazizTNeJ3v/sdmpqaEBkZiW9961u49dZbtXmffPIJoqOjMW/ePIwfP97lOhRFQV5eHh588EEcOHAAANDe3n7FeVPXO2XKFABATU3NFa+TBp+XlxcsFgsA4NChQ70ubzKZeq07rAtERGQEA6hRxGq1av+bTCZUV1cDAAICAnD27FkEBQUBAIKDg+Hh4eHwHmcGuv0/+xNce0wmE5YsWYKgoCBUVVUB6A7sVV5eXgC6m/JlZmYaWqe3t7fDNGd1q7f6pm/2FRISAgDo6urq0zoGYjkeF303f/58/Md//AcAoLW1FSEhIVpd0lPLVj3XeXp6Op0PQKsL/v7+hvNhpI71531ERDRysQ/UKKQoCs6cOYOYmBjt7lNAQAAOHTqEadOmOXxx+/j4IC4uDiaTCVarFe3t7fD09NT6COj7DXzyySeIjY3F9ddf7/S1fb8CT09PLFiwAIqi4Msvv0RBQUGvfRVodOktSFfl5ORg2bJlWj10VnfcERHMmTMHgYGB6OjoQE5ODpqbm2GxWODn54fOzk6MHTsWJpMJIoLy8nKn9dHX1xdRUVEIDw93qLP6PKnbzMzMREdHh00fm/b2dpttqcvY9wdTf+gw0oeHbH3xxReoqalBUFAQLBYLsrKy8Mgjj+Dw4cPaMvblXVtbCz8/P5v1iAgsFgt8fX215WfMmGGzjL5fVF1dHc6ePYvvfOc7KC0t1fpI6euGfb2w7/+Xm5uLpqYmh+lqnSUiohFuuPs/sQ9U/1Jv7V8VRREAEhAQILW1tSIicvLkSfHx8ZG4uDgpLS3Vll20aJGIiEPfFLWPgL7fwKOPPir/+7//6/J1fHy8tl5PT0/ZsGGDiIg0NjZKS0uLTJgwwSZ/TKMrGW2XnZCQoNWZ7Oxsp8ukpaWJiEh9fb3U19fb9HPJzs4WEZGEhAQBIBaLRWJiYuTvf/+7lJSU2MwrLCzUtlVXVycFBQVy+fJlh3Xq8yQicunSJZtl9PW5oKBACgoKRERkw4YN4unpKQDkjTfesHl/Xl6ewzJBQUFSX18vly9flpKSEtm/f79MmjRJAB4XgLE+UH5+fjJ16lTx8fGR6upq7fy2Zs0a8fLysinLCRMmSEtLi7S0tEhRUZG0trba1A8A8vnnn2v7raqqyqFu6vtFlZWVyfe+9z0REamsrNTWsWnTJu1cp+5zoPsc3NDQIHV1dXL69GkREUlKSpKAgABpbm62mb5o0aJhL38mJqaRn9gHalCToT5Qwx48CQOofiUjB48+iKqpqRERkQsXLsgDDzxg0zk6Li5Odu/eLQAkPDxcu1DQX2B8/PHH2nQA8sEHHzh9vX79ehk7dqwAkOTkZBERef/99wWANDY2SnNzs+EO30xXXxqIAMrT01PWr18vIiJ79+4VACIiUlxcrC1jH0CpQdLUqVMFgOTn58vGjRsFgGRlZWnbUudPnTpVRESKiooc8tTe3i7PPvusXHfddTbbVevzvn37tPfs27dPRL754cBsNmtBU0REhACQY8eOiYhIcnKyAJDi4mIREQkPDxegO/gTMT4QwmhPRgKo5ORkaW1tFQASGhoqemo5A93BalNTk7S2tmrle+DAAZu6ExISIoWFhdLY2CjPPfecANCCMn3dnDx5sraN//qv/xIPDw957733BPgm2FeDpvfff19ERCZPnixFRUUiInL//fcLAHn77bclPDxc3nrrLRERefjhh7XpcXFxw17+TExMIz8xgBrUxEEkrnUiAkVRUFdXh+joaNTW1iI8PBy7du1CZWUlgO4mLjk5OXjggQfg4eGBBx54wOm61D4naudqtRmM/evnnnsO06dPBwC0tbVpy0RGRmL16tW47rrrcPz48UH6xDQaJCUlYdu2bQCA8PBwPPzwwwCA+vp6l+9RO/vfeeediIqKgpeXF1588UVER0ejtbVVW07te6X+7ezsRGRkpM26Dh48iKeffhqxsbEAoB0ran0eO3astqz6f1JSEgDglVdewaxZs7R1nzt3DnFxcTbvV9e3ZMkSREdH47bbbgMAHhcGeXl54eabb0ZbWxuioqJw77332sxvbGzU/j98+DB8fHwQHR2t1RH7flLZ2dmYNm0aoqOj8dRTTwEA4uPjHbar9pE7cOAAHnvsMXR2dmLp0qVYtGgRtmzZgmPHjuHVV19FZGQkYmJiAHQ368vIyEBXVxd2796NzZs345FHHkFZWRmWL1+OLVu24O2338bPf/5zPPLII8jJyWHfKCKiqwADqGuAPohSLyLU0fekp19HYmIiHnroITz//PNu16VeRIhdx3v19erVq5Gfn2/znilTpuDjjz/Gc889h6NHj+KDDz4YuA9Ho05HR4f2v8ViwZNPPomjR48iNze31/f+5Cc/wYcffojLly8jPz8fFRUV2oApzkRHR2Pr1q0209QfC+wHk3Bn27ZtsFgsWLlypVb/x44di4yMDJcjBf7sZz/DBx98gB/96Ec4fPgw3nvvPcPbu5bdcsst2LJlC/Ly8rBv3z784Ac/QF5enjY/MTFRC5IaGhoAABMmTHC5PjWg/f73v6+dz9S/ztgPYPL1118D6K6rhw4dwieffILGxkbk5+fD29sbmzZtQnp6Og4ePIgXXngBmzZtgqenJxRFwaZNm7BlyxY899xz2nT1XEpERCMXB5EYRfSd0MeOHYuoqCitc7oaRCUkJKCoqEi7ODSbzfjpT3+K559/HkeOHIG3t7e2Dv0v9+ry6q/orl7v3LkT+fn5OHHiBMaM6Y7PDx48iEWLFtnkVR0ogK4tah11VrfUeZcuXUJ1dTUmTpyIrKwsh7qjf4+6HrPZDADYvHkzdu/eDQAIDQ1FY2OjTQCl1lf176effor77rvPZl2u6rZan/X1Vv1/2rRpOHfuHN5++23tDtSECROwceNGzJ07FykpKQ55vfXWW3HhwgVtXVFRUUaK8JqmKApiYmLwyiuv4IknntCmh4eHa2WZnp6OY8eO4fDhwzh06BDmzJmDBQsWaHf4XNWd9PR0pKamYtq0aQ77Xr+8fX1V60VBQQHmzJmjLR8aGoqamhrExcVh3bp1AIBnnnkGv/rVr3D8+HH8/e9/BwBs2rQJLS0t+NWvfoVjx45h//79A1lkREQ0CHgHahSRnlHAkpOTsX79enz22WcICwvT9zWDl5cXmpubtSZ30dHR2l2n8ePH41//9V+19S1atAiKoiAsLAyhoaEAupsqRUVFab/Q2r/+wQ9+oP0Cf+nSJTQ2NmL69OmYNWsWFi9erDVnYvB0bRIRmM1mm6AoNDQUERERWpNTdQTJr776CtOnT0dsbKxWb8xmMyIiIrT6qK4nMzMTXV1d2LVrF9LS0hATE4O//e1vCA8Pt7kDlJCQgKioKCxYsABA98h/6nrVdYWEhCAqKkprlhcaGoqQkBBUVFSgsbERkZGRiIqKQlRUFCIjI9HY2Kjd8Xjsscdw9uxZAMC8efMQFRWFiIgIh7wCwLJlyzBr1iwkJSUhNTUVv//97we6uEcVb29vPP/889ixYweeeeYZ3HLLLQC6992DDz5os+yCBQswfvx4rFmzBllZWdi+fTvS0tIwffp07Vxlvz9U9vs+LCwMiqJoy+vrK9B9nquqqsLs2bPR3NxsU/8CAwOxf/9+/OIXv8CCBQvw+uuv44MPPkB5eTksFgsWL16M22+/Hb///e/R2NjI5ntERFcLIx2lBjth+DuMXXXJVQfCjIwMuXz5sjQ1NYmIyDvvvGMz39PTU5566ilpamoSoLvT+5NPPql1jq6oqLDpkD1p0iSbkcXUUcxcvbZarTJr1ixtW+qoZYWFhVJZWSkZGRnDXnZMQ18v9Uk/oplq165dDsupHfMvXLig1ZvY2FibUSFFRGbMmCE+Pj5SWVkpIiINDQ1y6dIlWbNmjQC2g0hUVFTI2bNnHUbhs89TQUGBzYiUf/zjHwVAn0bhsz829HlVR+ErLCyUixcvyoEDB2TKlCnDvv9GQnI1iIR+H509e1YbwMFZfRIRmTNnjgDfjLDX0NDgcn/oR96z3/dvvPGGTJo0yeZ9+vrq6ekp69at0+bZ17+LFy9KW1ubfPHFF1JdXS2PPvqoAJA//elPUllZKaWlpZKfny8itgNgMDExMblKHERiUJOhQSQUGQHtrXtGi6M+KCkpwaOPPurQ3CMuLg5BQUFoaWlBZ2cnWltbtV/ZVd7e3pg5c6Y2Xe0D1dnZCU9PT7S0tADoHhjik08+QUxMDK677jqbZ9u0trbaPFdHfe3n54dPP/1U68itfzaKoiior693yA+NHq7qpZ76TB21f4qXlxe+/vprh3qh1h2TyYRLly4hJycHvr6+mDZtmvasJT8/Pxw+fBgNDQ2IiYmBv78/vLy8MG7cOOzbtw9tbW04dOgQ5s2bBwBITU3FZ599Bm9vb+05UPZ58vDwgNlshtVqRUtLi03+3D0HCoDT50Dpjw01r/rn/yiKgpqaGj4LqkdKSgpee+01h8E9fH19ER8fj6+++grjx4+3qRP6+gTA5jykPuPJy8vL5f5Q646iKA77/quvvsKZM2ewcOFCNDQ0OK2v9udQff2zWCwIDg4G0H23TJ0eFxcHf39/dHV18TlQRNQnRr5rqd9yRSSu16WMRFmDnTD80eZVl5z9+uDqGTJ9nT4Qyd26+ayb0ZuMPp+sr/Ou5L3qkOciIqGhoX3a5kDlz8hyPC66k5FhzAdyn/S33K9k3dzXTExMV5J4B2pQE4cxv9a4+uW6r9MHMy+DvV0a2a6kXvT3vUFBQdr/X375pTY8uZFtDlT+jCzH46Kbl5eX21Hw9AZin/S33K9k3dzXRERXN47CR0Sj2tKlS+Hn56c12/riiy+GO0vkxqFDh5CYmDjc2SAiInKJARQRjVqKouDUqVNOp/MuwMjU2NiI3Nxc7iMiIhqx2ISPiEYtNqG6enEfERHRSMUAioiIiIiIyCAGUFcpk8mEmpqa4c4GkQ2r1Yra2trhzgZdZYw8QJYPmSUi6tbe3s5z4jBjH6irVHl5OTIzM1FfX8+DiEYEq9WKG2+8ER9++CFaWlpYL6lXISEhSExMRG5ubq/Ligjmzp2L7OxsVFZWDkHuiIhGHqvVismTJ2vP7KThwQDqKuXv748XXngBhw4dgre393Bnhwi1tbX48MMP8dJLL+Hw4cOsl9QrLy8vnDt3zvDy586dwz333IP29vZBzBUR0chVW1uLTz75BJ6ensOdlWsaA6irlNlsxqFDh3Dw4MHhzgqRpqWlBYcPH2a9pEHR2NiIv/71r8OdDSKiYVVfXw+TyTTc2bimsQ/UVUpE+As/jTiKorBeEhERDaIxY3j5Pty4B4iIaETQ95vjr6tERDRSMYAiIqIRQUQQFxeHjIwMWK1Wm3kclISIrmU8B44s7ANFREQjhr+/PxITEx2miwgsFgv+7//+DyaTySHAIuoLk8mE8vJy+Pv7w2w288HNNCKp57rvfve7OHXq1HBnh3QYQBER0YjR1dWF1tZWp/OCg4MRHByMW2+9FUFBQUOcMxpNampqkJmZydFsaUSrqanBkSNHEBwczABqhOk1gFIUxQwgE4B3z/L/IyLPKIryTwB2AwgAkAvgERFpVxTFG8BbAOYCqANwv4iUDFL+iYjoGqEoCsrKypCfnz/cWaFRoK6uDh9++CFOnDgx3FkhcqmsrIzN90YgI32g2gDcJiI3AYgFcIeiKPEAtgDYLiJTAdQDeLRn+UcB1PdM396zHBER0RUREXh5eQ13NmiUMJlMCAwMHO5sELnl5eXFJqYjUK8BlHT7quelZ08SALcB+J+e6W8CWNLz/909r9EzP0Vh6ExERERERKOAoVH4FEUxKYqSB6AawEcAigE0iEhnzyJfAgjt+T8UQBkA9MxvRHczPyIiIiIioquaoQBKRKwiEgsgDMAtAKKvdMOKoqxWFCVHUZScK10XERERERHRUOjTc6BEpAHAAQDzAPgpiqIOQhEGoLzn/3IA4QDQM98X3YNJ2K9rp4jEiUhcP/NOREREREQ0pHoNoBRFCVIUxa/n/7EAFgP4HN2B1Pd7FlsB4M89/7/f8xo98z8W9n4jIiIiIuoTs9mMMWP6dL+DhoCR50BNAvCmoigmdAdce0Tk/xRFOQNgt6IozwP4DMBrPcu/BuBtRVHOAbgE4IFByDcREVGvFEXhCFZkmJH6MlR1yt12nM1jXR+dsrOzUV9fP9zZIDu9BlAikg9gtpPp59HdH8p+eiuAewckd0RERG74+voiISEBra2t6OjogIeHB7Kzs9HR0QGge+jzuLg4mM1mjB8/HocPH0ZjY+Mw55pGKrW+TJgwAZcvX4anpyc8PDy0wKS8vBwFBQVDEqzY5wUAxo8fj0uXLiEnJwe+vr6Ij4/H5cuX0draipwcdikfbUwmE5YtWwaAAfJIY+QOFBER0Yg0depUZGRkoKWlBRMmTAAAbNiwAS+99JK2zE9/+lM88EB3Y4jZs2cjLy9vWPJKV4fNmzfj5ptvRnh4OBoaGlBTUwMPDw9MmjQJABAeHo7a2tohzwvQ/VDVY8eOYdmyZYiIiMDf/vY3AMCuXbvw0EMPDUmeaOhYrVbtfwZPIwsDKCIiuiopioLc3FyMGzcOUVFReOONNzBv3jxs27YNALQg6sEHH8TNN9+MJUuW4NSpU/wll1zS/+JfXFyMpUuX4sSJEwCAoKAglJeXIzc3FxEREYNej+zzAgBTpkzR5ufn5+Ohhx7CnXfeieXLl8NkMtlccBPR4GGvNCIiuirpL149PT1x8uRJfPrpp2hoaMC2bduwYcMGbb6HhweCg4N7XWd/nvuuf4+79/OZ8iOfPgAxmUwIDAwE0L3vampqMH36dAQGBiIyMtJt8GRkX/e2jH1eTCaT9nrMmDHYuHEjli9f7jJ4crX+/tTRgVwX0WjAO1BERHRVUxQFp06dwuOPPw4A+PTTTxEfH4+tW7dCRPDSSy+hq6tLW15EYLFY4OvrCy8vL63vVG1tLU6dOgVfX1/MmzcPzc3NqKurw9mzZ3HHHXegqanJ4XV9fT3OnDmj3Y0QEURHRyMsLAwigszMTJv+WBaLBUFBQcjJyUFzc/OwlBcZo4TV9nYAACAASURBVCgK2traAHTvO0VR8OCDD2LcuHHYsmUL7r//fgCAj48P4uLiYDKZICJaPymgewS1qKgo5OfnY9asWQgODobVakVWVhba29sxd+5cBAQE4MCBA1o9cZUXNUAymUxYv349Fi5ciDvvvNMmeFL7BJaVlTnU5cbGRly4cAFNTU2IjY3FiRMnbPKk1tW4uDj4+/vjk08+QUdHh1a3Y2NjERQUpB1LVVVVOHXqFIDuHzASExPR2dmJcePGaf20iEYt9YQ/nAmAMPUtlZSUSEpKyrDng4lJn1gvma40paSkSElJSZ/n6VN6erro3XjjjXLy5EmbullaWioiIlVVVdpyhYWFAkBiY2O1aWVlZfIv//Iv0tXV5fR1TU2NhIeHCwDx9PSUoKAgqa+vl0uXLomIyMaNG8XT09Nhu8nJycNe1td66u18dfbsWUlLS5ObbrpJEhMTZcWKFdLc3Czbt28XAGIymQSALFq0SERETp8+LXV1ddLQ0CABAQGiKIo8++yz0t7eLk888YT85S9/kZKSEhERSU1NlejoaPnggw+c1hP7VFhYKHV1dRIRESGrV6+WvXv32uRBTWrdVY8TfV0uKCiQ+Ph4CQgIEBFxyNOGDRsEgLz99tsOeZoyZYocOHBAKioq5Ny5c1JfXy+lpaUCQMxms2zevFnbTmlpqWRkZAz7/h3Nid+1g5pyDMUuwx08MYDqX+LBwzQSE+sl05WmKw2g1AvKrVu3ahd099xzj1y8eNEmaNmxY4eUl5cLAPnLX/4iIiLZ2dna/MmTJ2vv37lzp4wbN87l6z179ggAiY+Pl6KiIhER8fT0lA0bNoiISGJios12S0tLxWKxDHtZX+upt/PV4cOHpaGhQYqKiqSjo8Om7ukDl7lz58qrr74qAGTlypVaMBUYGCh6aqC9b98+bZqnp6cWfLjLyz/+8Q/tPVVVVTbzFEWx+TtjxgwpKytzqMv79++XyZMny5tvvukyT4sWLRIAsmXLFps8HTlyRCIiIrR1PvHEE3Lu3DkBIHPmzBEREavVKi+99JLTMmIa2rrLdEXJUADFPlBERDRqWK1WmEwmpKWlaYNJvPfee5g0aRJaWloAdPeHevzxxxEaGoqQkBCbjvmqkJAQAMCBAwewevVqzJkzx+nr/fv347777sN1112HpKQkdHZ2AgB++MMf4tVXX0V1dTVSUlJgNpsBAI8//jgiIiK0wSxo5AoNDcWCBQswbdo0TJ48GSUlJQgLCwMAmyahubm52LZtG5544gm88sorAIDGxkbU1tZi5syZKC4uxsyZM1FWVgYAGDt2LIDuASE6Ojrw29/+FrW1tTbrtDdu3DgA3fWtra0N7e3tmDFjBqKjo7XmhT0/SCMkJMSmP5Ral8eOHYsbbrgBK1ascJmnr7/+GgCwceNGbbh2ALj11lsxfvx43HLLLVi1ahVefvllbSTC48ePY+bMmfjiiy+wfv16ra8WB7Sg0YwBFBERjSrqheiWLVt6HW5669atiIqKcjnf09MTALSLU/vXqujoaGzbtg3R0dEAgE2bNuEf//gHJk6ciH//93/XpuvZr4NGlq6uLlx//fUAgC+//BK7d+/G888/D6B7EAc1uJgwYQI++ugj3HPPPVi1apXNOnx9fWEymeDr6+uwfj8/PwDAzp07ERgYiPb2dpd58fX1xaVLl/Bv//Zv2L59Ozw9PbF3716t39+YMcYu59Q65ypP7mzevBkHDx7EPffcg8cffxz+/v42+VMDJ3eBINFowQCKiIhGFfUX+bq6OsyYMQNnzpwB8M1FZmdnJ7Zv347m5mb8+c9/dvtcKPU96oWn/euUlBTs3r0bOTk52LBhgzZ4wC9/+Uv88Ic/xLe//W3Mnj0bRUVFDuvmHaiRTUTg7e2t/X/q1CmsWLECL774IqxWK0QEMTExOH/+PDw8PPDQQw/h+PHj2vJA9yASiqJodyD11GlPPfUUmpubteDcGW9vbzQ2NqKsrAz/+Z//iaSkJLz++uv48Y9/rOVHP0qfM62traioqADQXY+d5Um1Z88eREdHa3dt33nnHRw8eBDf/e538ZOf/ATHjx+3eb/6OfWfnWg0YwBFRERXpd4CEEVRUF1dDS8vLwDf/DL+7rvv4sc//jHWrVuHjIwM1NTUAIA24hoAVFZWAoB2V6C6utrp6xMnTmDlypUAgIMHD8LDo3tw27179+LgwYPIzMzEL3/5SwQHB+Pdd9/FyZMncfbsWVgsFl5ojkD6IMRqtdrcwczJycHmzZuxceNGbNmyBUB3c1BfX1+cOnUKtbW12kh6aj2pra11WI99HSopKUFVVZW2jFqv9Xnp7Oz8/+zdfVhT990H/vchJASF8hSe5MmKDxRTRYgVhTEpqFh73Xerv7l1v6trnave7e7auRbUbl3tttbW2tq5e7vntlZrr9Xe3Q+r6+xKp6JWLCoIFoo8CARSECQQCGJCIHx+f+A5JjwZFU3Az+u6Phech5x8kvPNyfnmfL/fI5UXi8WCvLw8bNmyBT/84Q+xYcMGvPnmm1KTuYkTJ0o3lbZ9ntTUVOl+UkajEc3NzdI6YtkX1/3BD34Ag8EAg8EAAFiyZAmOHTuGL774AuXl5ejt7ZWaq4qvU5y+XkWOsXHhVgeAGI2A8zuMjbngDoQcrhhcLjluNW50EImEhAQym82k1WpJq9WS2WymhIQEu3UCAwOJiCgjI4MAUEREBHV2dlJrayuVlZXZdfTftWsXhYaG2s0rLy+n1tbWYad37dpFgP0ofF1dXVRTU0MdHR307LPPkkwmo0mTJpHRaCQiHoXPFWK441V2djbV19cTEZHBYKD6+nrat2+ftHzjxo1ERFRbW0uffvopPf7448OWE4PBIG2npqaGKisrpeWtra2D5sXFxQ2bCxFRfX29NMLdrFmzyGq1Ssvq6uro73//OwGgNWvWkNFopJqaGiknnU5Hq1evpk8++UR6TGhoKO3atcsup9raWvr666+leQEBAVReXk4mk4lqamqopqZGel2VlZV203v37nX6fh1PYTsQhzhIyEhll2NUwqFBJPg+UIwxxsasCxcu4NFHH5V+1VcoFLhw4YK0XLwB6pw5c1BbWwsA0Ol0mDt3LsLCwiAIAsxmM3p7e6FQKHD58mUYDAYsXboU7e3tcHd3h1KphNVqhclkGjStUCikjvc9PT1oaWnB/PnzERYWBqC/adOhQ4dgtVrR2NiIxMREBAcH8z1yXNiWLVvg7++Prq4uyOVyKBQKtLW1ScvfeustnDlzBjKZDM3NzTh37hwaGxulezXZlpPe3l709PRALpdDJpPZlTdPT0+pL5XJZIKXl5dURofKBei/uiTmUldXh2XLlknlVLz/EgC8//77aGhogNlsRl9fHywWC1pbW1FeXo7CwkJs374dnp6eMBgM+P3vf49du3bBYrEMmdOVK1fwyCOPSGUa6L8619PTA09PTxARrFYrent7YTabb/fuuatYrVZkZ2djy5YtfMxwNY7Usm53wPm1zTEX/OsDhysGl0uOW43RuA/UwLD95Xao6dGIgUNJO5IHh3NjqOOVK+2/m83F2eXM2c8/3qKpqYkWLVpkN4+/a29r8DDmjDHGGA3oazRwejSfY6Rt347nZaPLlfbfzebi7HLm7Ocfb8SriMy1cAWKMcYYY4wxxhzEFSjGGGOMMcYYcxBXoBhjjDHGGGPMQVyBYowxxhhjjDEHcQVqjAoJCZFuDskYY3cDQRCk4coZu1UDb3DLmCuyWCzXvWk4u/P4PlBjVFJSkt29ThhjbLwzmUyYNm0aqqurIZPJnJ0OG8OsVisiIyORk5MDk8nEJ6jMJVmtVkyZMgUmk8nZqbABuAI1BgmCgMLCQmenwRhjd5RcLkd7ezuWL18OlUrl7HTYGKbX65GTk4Nt27YhPz8fHh4ezk6JsUH0ej2OHj0KuVzu7FTYAFyBGoP4HguMsbuRTCaDwWDAuXPnnJ0KGwdMJhPy8/Nx4sQJZ6fC2LAMBgNfcXdB3AeKMcbYmOHmxl9bbHQIgsBXnpjL42Oea+K9whhjjDHGGGMO4goUY4wxxhhjjDmIK1CMMcYYY4wx5iCuQDHGGGOMMcaYg7gCxRhjjDHGGGMO4goUY4yxcWO4G6LazuebprLhDFU2bqS83I6yxWWXMdfD94EaRwRBuO49ohxZ53YbKgdXyIvdXsPt4+vte0fLxq2WoZvJ42bK8mg9hg2NiBAbG4vQ0FAAgFKpxOHDh2E2mxEXF4fa2lp0dHRArVYjMDAQBQUF6OzsdHLWzFUQETQaDfz9/WG1WnH8+HH09PQM+xmUy+VISkpCX18frly5goKCglHNR3zeuLg4uLu73/T2/fz8EBsbi7KyMhgMhlHNkbG7EVegxhHxwB8YGAiTyYSenh54enpKB/2GhgaUl5ffsZMxHx8fLFiwQMpl4sSJaGtrQ0FBAXx8fJCYmIiuri6YzeZR/9JhrkUsczExMQgLC5OmB5ZJuVyOlJSUEZdHR0fbbUM8wSEiqNVqBAcH2z2v1Wq1O0m2Lf9KpRKLFi3CxYsX7cqgh4cH0tPTYTab4ebmBoPBMKiM2p7YiCfltvMCAwOhUChw6NAhdHd3D3pMUFAQrFbrsJ9L8b1obm5GaWnpbds344W3tzeioqJgMplw5swZuLm54eLFiwgICEBmZiaOHTuGoqIiZGRkICcnBwcPHkRkZCQefPBB5ObmDnr/udJ699q0aRPmzp2LiIgIZGVlYffu3WhpaRm0nlwux7p167Bt2zYAwEcffYTHHntsVHMhIkRERKCoqAhGoxE+Pj43tZ3Y2Fhs3boVWVlZyMvLG9UcGbsrEZHTAwBxjE5kZ2fTlStXyGAwEBFRZWUl1dTUkMlkIpPJRCqV6o7lkpCQYJdLfX09ZWdnEwCaNWsWiT788EOnv28coxNarZbS0tKGXKZSqchkMhER0fnz5weVSblcThs2bBhxeWZmJhkMBurq6qLz588TEdGGDRtILpcTAGpqaqLu7m6qqakhrVZLlZWVRESUmppKAEgQBGlbycnJtHbtWuru7pbKJQCaMGECPfvss9TR0UFarZaamprslttGQEAAERFlZGRI86Kjoyk3N5caGxvJaDTSmjVryMPDY9Dy2tpa6TX6+/vb5adUKukXv/iF9Bl29n69k5GWlkZarfaGlyUmJlJ5eTkREbW3t1NgYKC0bO/evdTY2EhERElJSQSAdu7cSXV1daRWq6X1vL29KT09nTQajdPfB47bH0Mdr2QymfT/l19+afcZFD+fYiQlJUnfY0eOHBn0+NGKjz/+mIiI8vPzb+rxA/MeOM3h2jFUOR3pu5bjlqPAobqLsytPXIEavbA9cM+ePZuqq6ul6cDAQLJYLFRXV0fA7T+A2m5/YC5iPPbYY7Rnz55BuXOM3RjpoK7T6chisdDWrVsJuFYmdTodAf0nx0Q07PLU1FTpZGXq1KkEgLZu3UpEJD3n/v37KSoqalBOixcvlqb9/f1p48aNRES0e/duab5YBuPj4+mzzz4blL9tGVUqlRQREUFtbW1ERJSYmCgty8/Pp+nTp0vTRqORli1bNuTywMBAMpvNZDQa7U744+Pjpdeal5fn9P16J+NmKlAKhYKeffZZKikpISKiKVOmENB/HLLdb/X19bRkyZJhn1s8IX733XeHXH69E1E+MR1bcb2T0B07dkg//gUHB9stUyqV9Pjjj1NdXR0REZ08efK65eVm5ovlt6KiQjoWOrKt6y3jGDvBFag7Hg5VoLgJ3zhitVql/1UqFWQyGYD+pigtLS2YPn06vvnmG0yePBlarXbY7YhNV26lCYvt42xzAQA3NzdkZmZi4cKFWLp0KWQymV3uYg4Dt2Ob22gYaVvcfOf2mDNnDr755hu4u7ujpaUFc+bMweeffw6g/z2vrKxEVlbWkMt7enqk7dxzzz0AgKysLHzve9+TytcjjzwibYuIEBkZiQsXLkCv1wMAAgMDUVNTAy8vL2zevBmvvPKKtL7VaoWvry/8/PywceNGaDQaqdmeuFw0efJkvPzyy/Dz87N7fTKZDImJiQD6y3lfXx8uX74Mi8UyaLn4GmNiYlBbW4vi4mKpaeLZs2cRHR2N6urq0Xrrx7WkpCTs2LFDmp40aRJqampARHb77cyZM2hoaMCsWbPg6+sLNzc3nDx5Evfffz+6uroQHx8PnU6HF198EYsXL0ZPTw9MJhMUCoXUv8XHxwff+c530NTUhIKCAnh7e0Oj0aClpYWbWo4jMpkM69atg8lkwvr163H69GlERUVJx5a0tDS8+eabCA8PR09Pj1TOli5dCovFIjW9FZuy9/T04PTp0zAajSAixMfHo6ioCLNnz0ZQUBCOHj0Ki8WC+Ph4qFQq9PT02DU9ViqVMJvNAIBly5bBbDZLzX8BDNv82XZZc3Mz6urquM8fY6OAK1DjWF9fHwBIlaHHHnsMEyZMwBtvvIHvf//7AEY+6NLVvhrnzp3DrFmzEBwcDKvVisOHD2P27NnStNgHxZFcZDIZXnjhhSErT+IXjU6nk05ENBoNJkyYADc3N5w9exZGo/GGchJfl22fFgB2/Uo8PDxw3333oaKiAmlpadKJERtdfX198PX1BXCtsh8SEiL9T1f7OA23vK2tDR0dHfDx8cF3v/tdnD17Vvox4NKlS3bPJVZ+6+rqMHfuXJw9exYAkJ+fDy8vL5w5cwY7d+7E4sWLUV9fL5X56OhoHDx4EG1tbTh//jwyMzNhsVjsTownTpwIhUKBxx57DJWVlfjVr34Fd3d3KW+xzPX19Ul9F8T8bE/me3t7AQDh4eEAgOzsbLvcQ0JCbuXtvquIfcwAQKvVShVmWzKZDCtWrADQ31dFPAbed999eOaZZ/DjH/8YAHD58mUcOXIEEyZMgJ+fn9TnZPfu3Vi1ahWmTp2KTz/9FPv27cOKFSug0Whw5MgR1NfXIyoq6na/VHaHWK1WyGQybNiwAXv27EFRURGCg4PR3NwMAOjo6IDZbMakSZMAABaLBSEhIcjOzoYgCGhubsbkyZNx77334rPPPgMAJCcnw2AwwN3dHYWFhXjmmWewaNEiPProo1i/fj0+//xzvPnmm4iOjkZUVJTUPw8AzGYz/Pz8sGLFCvzlL3+Bp6cnACAsLAwGgwEvvvgiNm/ejLKyMoSEhEAmkyE6OhptbW3IysrCb3/7W1RVVWHNmjU4evTonX9DGRtvnN18j5vw3Z5YuHAhGQwGmj17NiUlJdETTzxBnZ2dtH37dmkdDw8Pevnll4mI6JtvvqHW1lZqb2+ngIAAAkBhYWFERPT000/TwYMHSavVEhHR448/Tp9++qk0nZmZKfVBGSpSU1OptbWVoqKiaM2aNbR//34CBjfbi4uLIyKya6Kzd+9eqSlTYmKi1OdkuJxqa2uJyL5fjG2flgsXLpDBYJCaMiqVSvrNb35Dzc3NFBgYSEQ0bH8XjuvHSM0K6uvradOmTXZN1ZKTk6V9kZaWRiaTadjlAMjHx4c2b95MRESrVq2ivLw8qcmebXMV8f+///3vdv1ZxD4NRP39GkbqGxgeHk5ERI2NjdK8CRMmUGZmJh0+fFjabktLi9THSgyNRiP181u9evWQ74eYY0pKCtXX1w/6TCxYsICIuAmfI8vE94qI6Hvf+96I2xff4yNHjhARUXJyst302bNnpXWDgoLIaDRSSUnJsNtTq9VUV1dHO3fudPp7x3Fj4WgzqNjYWDKZTNKxyNPTk9asWUPvvfceTZ06lYjsm/DNmjXLrrndzJkzqaKigtRqNX3wwQfU0NBAPT09REQkl8vplVdekcpveHg4AaDKykpKT0+XtpGfn09ERB988AEB15o4V1VVUVBQEJnNZvrNb35DAOjJJ5+UvtdVKpW07by8PFqwYIHT33eOWy+n3ITvtgb3gbqbQ2zLX1VVRT09PXYnHeIJhEajGfagGxMTQ7t375YOvBEREQSADh06REQkdbwWT0gXLlw4bC6JiYnSdpqbm+2WiSeR4t+ZM2cOauctnthMmTKF3n///evm9Otf/5qIrvWLSU5OpsOHD0vbe/rpp+nChQsEXOtrcurUKbvn5D5ZNxcjHdS3b99OREQWi4VmzpxJiYmJtH79eqnyEBMTQyaTadjlYnh5edHOnTuJqL8SNVwuDzzwwKBydubMGers7KRt27YRMLif1cD1J02aRLm5uRQZGSmVl4MHD0rrieUxISHB7vHZ2dmk0+nIarWO+PmYOHEirVu3bsiBVLgC5fgy2woUEQ2q0A4VeXl5RETSCaU4TUTU0NAgrSeeILe0tEg/Lg08bokhk8m438kYCkdOQsXvgpkzZ5LFYqEJEybQd7/7XbJYLAT0f78M/JwOLKfitG1fzOrqarsBTC5cuGA3PTA3nU5HFRUVBIDc3d2lnGyPXTExMfT000+T2WwmomuVuilTpkg5cgVq7AVXoO54OFSB4hvpjlOenp6oqqrCtGnTMGXKFGi1WqmpUF9fHwRBQEFBAZRKJf72t7/h6aefxp/+9CcA/U0TVCoVnnzySVRXV+P++++HTqeTtgtc64OSn58P4FpzpKGIzZsOHz6M7u5uWCwWzJw5EzExMYP6Wtk22RJ5eHgA6O/X8MQTT1w3p9deew16vV5qNnjixAmkpaUhIyMDTz31FP74xz9KTXzOnj0LtVotNcMQDcyB3RqZTIb169fjnXfeQXNzM/75z39i//79ePvtt7Fv3z4AQHl5OSIiIoZd7uHhgXXr1uHMmTN4/vnn8corr+C9997Dxo0bpaZ/to4fP46lS5cCgF35SkxMxAsvvGDXz2ogsVw2Njbi2WefRV1dHYD+e6lkZWVhyZIlWLJkCdatWwetVoulS5dK5VBsKhYREYHt27cDGPz5EPv43XfffXj00Ufxwx/+0K6fILsxbm72X2UdHR120zdy81Gz2YwPPvhAmlYoFLh06RJUKhUqKysREREhlSfxr6ivr4/7To4ztk2Me3p6UFdXhwkTJkhNbsXvGVtms9muzJnNZhARurq6pHkymczulgvu7u5200PlIX4XiscTPz8/6fs1MzMT58+fx6xZs+Dv7y/lDHBzYMZuB65AjUGOnAwQERQKBQBAp9Ph5z//OXQ6Hfz9/aXas1wuH/agK34pXO+gnpmZiebm5iFPYEVKpRJVVVVIT0/H3LlzcfHiRRQVFaGoqEjKx5HXdOXKFYdy2rNnD1QqldRx38PDA2vWrME//vEPJCYmQqVSISwsTFp/qEobG11if4L169cjIiIC9957L1566SXU1tbiZz/7GWQyGQRBgF6vH3I5AKSnp2Pz5s1YsWIFLl++jM2bN2Pr1q3YsmULUlJSpOcSy1JbW9ugistQ/bB8fX2HPAkiIiiVSixYsAC7d+9GZGQkDh48iIMHD+J///d/sWPHDrS3t2Py5Ml45ZVXcN9999ltV+zvV1lZCaVSaZcfESEpKQkAkJqaOuRAKsxxbW1tdv3gbMsD0L8vExMTpWMccO3EV+wXKe6jkpISbN26FfPmzQMAlJWVITY2Fnq9Hv7+/njrrbfstu3t7Y3U1FSo1WquPI0Dw30XlZWVYebMmVCpVPjDH/4g3e9JLD9iefL09ERaWpr0feXm5obU1FR4eXkhNTVVquwTkfTY4aZt+/Z5enpK3+lA//dgQkICDh48iFmzZmHr1q3IycnB008/DbVabZeTuJ2+vj6752CM3TyuQI1BRISEhIRBN9SzPfB3d3fbfZmbzWaYzWY0NTVJJxHz5s0b9qArHnAHHtQHHpCrqqoQHBw84p3NzWazVMFqbm5GVFQUQkND0d3djbq6OgQEBEi5CoIw6GRWnE5LSwPQ/+vbSDmtXLkSFRUV0vuxaNEibN26FQqFAqtXr4ZarbY7sbZ9r27kl2rmuIGj2G3atAl//vOfceDAAQD9lQ7b8jpwOdBfjjw9PfGf//mf0rwNGzZAq9XaXb2hq6PvlZWVobW1dVAeKSkpCAwMlJ5PHERlKCkpKfif//kfrFq1CvX19VAqlZg8eTKmTJmCGTNm4Le//S0AYMaMGTh79qzdlRDx9Xp7e9ttn4gwadIk7NixQxoFUFx34K/WwLXyzWVzaIIgoKysDGq1Gr///e9x4sQJbN++HZs2bUJSUhLS0tIQHx+Pv/zlL5g+fToAICoqSvoRJT09HUD/Fcvm5mbMnTsXra2teO6556Ttt7a2Ii0tDd9++y1WrlxpV97EQSQOHjx4h185ux2I+m9Iv2jRIqSlpWHp0qXQaDQAgMjISADAP//5TwD9lW6x/ISFhSE4OBj33nsvXnnlFSiVSsTExGD16tV4+eWXoVKp8PLLL2P16tWIiYmBn58fHnzwQQiCgPDwcAQEBAyaXrBggfS998UXX6CsrAwajQbJycn46U9/ioyMDPzkJz9BX18fOjo6EBsbixkzZmD+/PkA+n8cnDFjBpKTk6Uc09PT7X7QYYzdJEfa+d3ugPPbO465MJvNtHTp0kHzNRoNpaWlUVZWFrW3t1NcXJzU2d3f358sFgtZLBaaMWMG/ehHP6Lm5maqr6+nGTNm0HPPPUdE/f2mnnvuOZoxYwa1t7fTSy+9RIIgUHh4ONXU1BARScurqqqIiCgrK4uUSqVdLj4+PsPmIggC/ehHPyIios7OTlKr1RQbG0vJyclkMplo1qxZlJycTLNmzZKew2Kx0COPPEItLS3D5iQ+Rsw7KCho0DbFfGynjUbjoPsHcdx4jNQuW6PRkFqtpk2bNhER0euvv07AtT4GPj4+lJqaOuxysY8UEdGmTZsoKSmJNBoNHTp0iOLi4uyei4jsypo437YfVnR0tPRc4uAqPj4+tHTpUkpLS6OUlBRau3btiDd69vDwIKPRaNfZW6PR0KJFiygtLW1QfuJ7QES0Zs0aCggIoMWLF9OiRYvsBrtQKpWUlZVFREQ1NTV3Vdm8mT5QFYSR7gAAIABJREFUA/sdvfPOO1RfX091dXWk1Wqpp6eHwsLCpOX79++nkpISOnbsGBmNRpo3bx4B/X3iGhoaaMeOHYOeQ61W0759+4acz4NIjM0Y7niVnZ1NTU1NpNVqyWw2SwMLaTQau+OBRqMho9FIx44do5KSEjpw4AAB/TcN1+l0pNfradeuXSSXy+n9998nuVxOu3btIr1eT/n5+dTR0UHh4eH00UcfUXFx8aDpiooK6u7ulvpzhoWFUU9PDzU3N0t5iP2hxOfU6XRUWVlJx44dozNnzthNl5SUkNFo5BtFj7HgPlB3PHgQifEcTU1NtGjRokHzs7Oz6cqVK2QwGIio/waAtl/64g1Ea2tr6dNPP6XHH3+ciIjKy8uptbWVRK2trVReXi5Nh4aG0q5du4ZdTkSDTmITEhIG5SJ+Ec2aNUvqYE9EVFdXRx988AHFxsYSUf8IaTU1NVRZWSmts3r1avrkk09GzGngY959991B2xTzGTi9d+9ep+/XsR4jHdSzs7Oprq5OGvnO39+fgGsnvwkJCdTV1TXscrlcThs2bJCW19TUkNFopDVr1pCHh4fdcxGRXaVGDJVKJVXCzp8/P2gUvoSEBDKbzaTVaqmxsZEOHTpkd+I9MMTyZztoge3J18D8xPeAiOjSpUt07tw5qquro6amJrvRH8URKUV3U9m8mQrUwIiNjaW0tDQpli1bZldGNBoNpaSkUGJiImVkZJCvr69UxtLS0ig2NnbI7doOTCKGt7c3paam2g0AwDE2Yrjjle2PIEuXLpUqHD4+Pnbfcz4+PpSRkUGJiYmUkpIirSeWo8WLF9Ps2bNJEAS7v4sXL6akpCRasmQJKZVKio+Pp+Tk5EHTCxcupKVLl0rl08PDg5YtW0aLFi2S8rA9PqalpVF6ejqlpqZSYmIiJScn202npKRQRkYG+fj4OP2957i1csoVqNsaDlWgBHKwzbYgCDIABQAaiOhhQRDuBfARgAAAhQAeJyKLIAgeAPYASADQCuD7RKS9zrYdS4JJtFotVq9ejcOHD9vN12g0CAwMhMlkQk9PDyZOnIi2tjbpvkbifZ9kMhmam5tx7tw5pKWlSU2sTCYT3N3dIZfLIZPJYDKZ4OXlhWPHjiE2NhZeXl6wWCzw9PSETCaD2WxGb28vfH198dVXX9l13hbv6zRULj4+Ppg/fz7a29vh7u6OCRMmQK/Xo7S0FElJSZDJZFLfKLPZDHd3dxw9ehRxcXG455574OnpOWRObm5uEARByluv16OkpMRum1arFT09PfD09JSmxWaBfP+nWzNcuQT6y6a/vz+sVqt0vzHbAUTE8mKxWIZcDvSX3+joaKn5lVKpxKFDh+z6CgBAQkICLly4YFcexW3FxMRIN6wlsr/3mW0Obm5uaGlpQXFx8bA3VtZoNPD19cWpU6ekm1NqNBr4+fmhr69vUH7ie9DV1QW5XA53d/f+0Xzc3GAwGKTyZ/v5sL2J690gLS0N7777LiZPnnxDy0TXuwn2cMuHmq/RaPDMM89g/fr1+Oyzz5CUlMQ34B5HhjpeOVI+nFEGhtoul7e7w1DldKTvWnbLColIc921HKllXf2A/hzAhwD+eXX6YwA/uPr/nwA8ffX/ZwD86er/PwDwfw5s29m1zTEXQ/36MNLwua40tK4r5TKWchsLMdyvYkO9r9d7r292X9zM52C0HnOrn8Gx8hm+nTEaV6BGK9LT04mI6PPPP6fo6Ginvzccoxv8Kz7HWAi+AnXHY/SGMRcEIRzAMgB/vTotAHgQwP93dZX3ATxy9f//vDqNq8vTBO79fEeM9EuUK/1K5Uq5DOTKuY1lQ72v13uvb3Zf3MznYLQec6ufwbHyGb5bnDp1CosXL8aLL76I6upqHsiDMcYYAMDdwfXeAZAFwPvqdACAdiIShzL7FoA4LnQYAB0AEFGvIAgdV9fXj0rGjDHG2B3Q2dmJf//73wC4uRRjjLFrrnsFShCEhwFcIqLC0XxiQRDWCIJQIAjC3dGwnzHG2JjFlSfGGGMiR65AJQH4D0EQHgKgBHAPgN8B8BUEwf3qVahwAA1X128AEAHgW0EQ3AH4oH8wCTtE9GcAfwZ4EAnGGGOMMcbY2HDdK1BEtImIwoloMvoHhThCRP8vgFwA/8/V1Z4AIN7x8h9Xp3F1+RHin+4YY4wxxhhj44BDg0gMYwOAnwuCcAH9fZzevTr/XQABV+f/HMDGW0uRMcYY6++HZLFYnJ0GGyesViv0eu6ezVybxWLhAWxckKODSAAAiOgogKNX/68B8MAQ65gBfG8UcmOMMcYkJpMJ06ZNQ3V1NWQymbPTYWOY1WpFZGQkcnJyYDKZ+ASVuSSr1YopU6bAZDI5OxU2wA1VoBhjjDFnkcvlaG9vx/Lly6FSqZydDhvD9Ho9cnJysG3bNuTn58PDw8PZKTE2iF6vx9GjRyGXy52dChuAK1CMMcbGBJlMBoPBgHPnzjk7FTYOmEwm5Ofn48SJE85OhbFhGQwGvuLugm6lDxRjjDF2R7m58dcWGx2CIPCVJ+by+JjnmnivMMYYY4wxxpiDuALFGGOMMcYYYw7iChRjjDHGGGOMOYgrUIwxxhhjjDHmIK5AMcYYY4wxxpiDuALFGGNs3Bjuhqh8o1TmiJHKyWiUIS6HjI0PfB8oxhhj44IgCCAixMTEICwsDACgVCpx+PBhmM1mxMXFoba2Fh0dHU7OlLkqIoJGo4G/vz+6urogl8uhUCjQ1taGgoKCG96eh4cH7r//flRVVaGjo0PafkhICL788ksui4yNUVyBYowxNi4QEfz9/VFUVAQAuHjxIgICApCZmYljx46hqKgIGRkZyMnJuaN5iRU7NjZs2rQJc+fORUREBNrb29HZ2YkzZ85gxYoVN7QdpVKJX/ziF1i3bh1WrlwplbtNmzZh+fLl0Gg0KCwsvB0vgTF2m3EFijHG2LgQEBCA2tpaCIKAiIgItLS0AAD27t2LzZs3AwAuX758R3MSK0989WtskMlkUkWpuroay5cvx7lz56RlVqvV4W3Fxsbil7/8JU6dOiWVO9vtA1y5Zmys4j5QjDHGxjS5XI7nn38e5eXl8Pb2hlqtRktLCwRBgEwmw2OPPYZJkyZBp9Nh4sSJAIbui+Jo/5QbWY+IEBAQgKKiIsTHxzv+om7gubhfzeixrSDJZDKoVKohlw001D44e/Ys7r//foSFhcHT0xMA0NfXN4rZMsacha9AMcYYG9OSk5Oxbds2AP3N9jw8PAD0N+mzPek9c+YM2trapGViXymlUolDhw6hu7sbAODj44P58+ejs7MTBoMBtbW1+O53v4vOzk50dHSgtLTUbp3W1lZUVFQgIyMDXV1dcHNzQ2FhITo7OxERESFdwZg5cyZ6enqgVCphNpvh7u6Oo0ePIi4uDvfccw8sFot0om2xWOyeKzExEV1dXTCZTCgpKcGCBQvQ29uL9vZ2lJaW3rH3+m6nVCoxY8YMfP3113jooYdgNpvR3Nxstw88PDywePFiVFVVQalUoq+vT7rKNPBqJBFBrVYjODhYqnAfP34cPT09znqJjDFHEJHTAwBx3FhotVpKS0tzeh4cHLbB5ZLjViMtLY20Wu0NLVuwYAGJJk2aRABIEAS7dWQymd20v78/mUwmMplMZDAYaO3atRQaGkoAKD4+Xtre119/TQEBAdJ0ZWUlAaC4uDhpnk6no//4j/+gvr4+ad7ChQsJAH388cfSvIsXL1JtbS1VVlZK81avXk379u2Tppubm6mxsXHY5/rrX/9KsbGx0nRdXZ3T99lYjesdrwYuVyqV9Otf/5osFgs98sgjZDAYyGQy2ZVJNzc3WrduHV25coU+++wzWrNmDVVUVEjlISIigoiIlixZIj2mqamJLBYLFRcXExFRZmYmyeVyp78/HK4RQ5VT/q69rVHgSN2Fm/AxxhgbN1asWAGFQjGoX4ntlajAwEBotVoIgoDIyEj4+fnh1VdfRWNjI4KCgnD27FlER0cD6O8z1draKk2L/aqKi4uleeHh4Xj44Yfh5eWFf/3rXwD6ryABwMqVK5GbmwsA+M53voN7770X06dPx9GjRwEAf/3rX/H9738fb775JhobGxEcHIzY2FgAQFNTk/RcQUFB+Pvf/46f/OQnUCgUqKqqQmFhIT7//PNRfw/Z0GJjY/HSSy9BLpdjxYoV8PPzw7x58yCTyQD0X3n67//+byxbtgwTJkzA2rVrsXPnThgMBumK0ltvvQXAvi/esWPHoFKpEBcXh6KiImzduhXJycl3/gUyxhzGFSjGGGPjxo4dO5CUlDTiOvn5+fD29kZMTIxUISorKwMAfPXVVwCAkJAQAJAqYuK0LXFebm4u1qxZg/j4ePj6+g5aT2xSGBQUJM1TKBQAgOjoaPT09CArK0saej08PBwGgwGhoaEICAiAu7s7oqKisHLlSri7u6O4uBjTp0+HRqPB2rVrpRN4dvsIgiD1aaqursbjjz8OoL8yLlbO09PTsXnzZixZsgRyuRw6nQ5qtdquD9TKlStRWVkpTQPA008/jYiICHzxxReIi4sDAKk5KWPMNXEFijHG2Jjm5mb/VTZwpLuBHfzb29sB2FeKxHXEZTdCrCDZ9nVx1MCKmUwmQ2lpKR544AFMnToVxcXFePjhh/Hmm28CAHp7ewdtgwcmuP3E/RocHAx396G7j1+5cgWCICAkJES64uTr64ve3l6YTCZpPaVSKU0HBgaiuroapaWlWLx4MV9RZGyM4AoUY4yxMa2trQ2XLl2SplNSUuyWExESExPh7+8PAMjLy5Pmi+655x67ZSKxciJeEbCtrJjN5kHriP+Ly2zZzhtuPfFqhoeHBy5evIjw8HC8/fbbSE1Nla40eXt7Y+HChUhOToZareZhsEeRbWWbiAZdCeru7rZ7v81mszTd09MDX19fHD9+XFo+f/58eHl5YeHChdJVJ5PJJDXRO3PmDHx9faWrj97e3tJ2GWOui0fhY4wxNmYJgoCysjKo1Wq89NJLmDNnDrZv3w5PT08cP34cSqUSBoMBf/nLX/DUU08hPz8f69atg7e3N+bMmQMPDw9cuXIFbW1t2L17N9atWwfg2lWtkJAQzJgxQzrhDQsLQ3h4OBoaGpCenj5oHfGKUnp6OsrKymA2m6UrVOnp6SgqKkJYWJh0wmy7nu1r+uabb5CSkoKqqir885//BHCtcqXRaHDkyBEAQH19PaKiom7re3w3ISJoNBr09vbCz88PCxYsgNFoRGBgILq7u7FgwQIEBARIZeDBBx9EQEAAQkJCcPHiRXR0dECpVGL69Ol45JFH8MYbbwAAfvvb3+KTTz5Ba2srZDIZXn/9dRiNRuTk5GDNmjX4/ve/j88++2zI8sMYc0HOGHVvYMD5I26MueARWDhcMbhcctxq3MwofANH3HvnnXeovr6e6urqSKvVUk9PD4WFhUnriiPyffjhh9Tc3Gy3XFymVqupoqKCzpw5QzqdjiorK+nYsWNUUlJCH330EYWHh5PRaKRjx47ZrXPmzBk6duwYGY1G0mg0BIB27txJp06dIqPRSOHh4fTRRx9RSUnJoPUGvo4lS5ZIo+zZjiKoVqupsrKSCgoKaOfOnU7fZ2M1hjteZWdnU0tLC+Xn51NFRQXpdDrSarVUV1dHFRUVVFxcLJWBjo4OKi4upgMHDhAAUqlUpNPppPJw+vRp2rFjBwUGBhIAOnDggLTvu7q6yMPDg7Zs2UL19fWk0+mGLD8cd3fwKHx3PBwahc/plSeuQN1c8IeHwxWDyyXHrcbNVKBsY+Bw5bZhW0EZaj1x3sCKzK2Eu7u7Q+uJz6lWqyk1NZWio6Np+vTptG/fvmFfgyOvmWP4GOp4dSvv5fX2taNl4Xr7m+PuCq5A3fFwqALFTfgYY4yNG7bDlQ9k23dlqPXEeaPZp2ioQR+GIj7nwYMHERkZicuXL2PKlClYvnw5ZDLZiLmN9JrZjbmV9/J6+9rRsmCL+7cx5pp4EAnGGGPMxXh5eeHtt98GwKPsMcaYq+ErUIwxxpiLWLZsGVQqFfr6+nDlyhUAfBWCMcZcDVegGGOMMRcgCAJKS0sHzeMKFGOMuRZuwscYY4y5gKEqSlx5Yowx18MVKMYYY4wxxhhzEFegGGOMjQmCIMBisTg7DTZOWK1W6PV6Z6fB2IgsFgsEQXB2GmwA7gPFGGNsTLBarZg2bRoqKyuhUCicnQ4bwywWC6ZMmYKjR4/CYDDAzY1/T2aux2KxYNq0aXyrAhfEFSjGGGNjQkFBAR588EHIZDLuG8RuiSAIMJlMkMvlkMlkzk6HsSEJggCr1YqCggJnp8IG4AoUY4yxMaGzsxO5ubnOToMxxthdjq9ZM8YYY4wxxpiDuALFGGOMMcYYYw7iChRjjDHGGGOMOYgrUGOUTCZDS0uLs9NgzA4PC+y40RyWdjx1gtfr9TziFGOMjYCHNnc+HkRijGpoaMDx48dhMBj4Q8RcgtVqRWRkJHJycmAymbhcDiMkJARJSUkoLCwctW1arVZkZ2cjKSkJZrN51LZ7pxERPD09ERAQgOrq6nFVMWSMsdFgtVoxZcoUmEwmZ6dyV+MK1Bjl5+eH1157DSdPnoSHh4ez02EMer0eOTk52LZtG/Lz87lcDkOhUODChQujvt0tW7bAz88PfX19o77tO6W7uxuJiYl44YUXsHz5cqhUKmenxBhjLkWv1+Po0aOQy+XOTuWuxhWoMUqpVOLkyZM4ceKEs1NhTGIymZCfn8/l8g4TBGFc3SfEZDLh3Llzzk6DMcZcksFg4Cv0TsZ9oMYoIuJf+JnLEQSBy6UTjKebynp4eHDzT8YYG4GbG5++OxvvAcYYY4wxxhhzEFegGGOMMcYYY8xB3AeKMcbYmODt7Q2NRgOZTDaumi2yO08QBJhMJsjlcu5LwlyWIAiwWq0oKChAZ2ens9NhNhyqQAmCoAXQCcAKoJeINIIg+AP4PwCTAWgBrCQig9DfeP13AB4CcAXAk0R0dvRTZ4wxdjfRaDQ4cuQIqqqqoFAonJ0OG8MsFgumTZuG9vZ2GAwG7lPCXJJYTh988EHk5uY6Ox1m40auQKUSke0dMjcCOExErwuCsPHq9AYASwFMuxrzAPzv1b+MMcbYTZPJZKiqqsL06dOdnQobB6qrq7F8+XIe8ZG5tMrKSr5K6oJu5SeX/wTw/tX/3wfwiM38PdQvH4CvIAiht/A8jDHGGIiIrzyxUSOTyfheY8zlKRQKbrLsghytQBGALwRBKBQEYc3VecFEdPHq/00Agq/+HwZAZ/PYb6/OY4wxxhhjjLExzdEmfMlE1CAIQhCAfwuCUG67kIhIEIQbqh5frYitue6KjDHGGGOMMeYiHLoCRUQNV/9eAvAJgAcANItN867+vXR19QYAETYPD786b+A2/0xEGiLS3Hz6jDHGGGOMMXbnXLcCJQjCREEQvMX/ASwGUArgHwCeuLraEwAOXP3/HwB+JPRLBNBh09SPMcYYY4wxxsYsR5rwBQP4pH90crgD+JCIPhcE4QyAjwVBWA2gDsDKq+t/hv4hzC+gfxjzVaOeNWOMMcYYY4w5wXUrUERUA2D2EPNbAaQNMZ8A/HRUsmOMMcZugSAIPIIVu2FDlZvhytJYK2NjLV/GXNGN3AeKMcYYcyk+Pj5YsGABzGYzenp64O7ujry8PPT09ADoH/pco9FAqVRi4sSJyM/PR0dHh5OzZq5MrGDExMQgLCwMMpkMbW1tKCgosFuuVCoRExOD4uJil6yU+Pj4YP78+Whvb4dCocCVK1dQVVWFjo4O6TPh7u4OT09PnDx5kj8XjN0ArkAxxhgbs6ZOnYrs7GyYTCb4+/sDADIzM7Ft2zZpneeffx4/+MEPAABz5sxBcXGxU3JlYwMRQaVSoaioCABw6dIlnD59Gt/73veQlJSEvLw8KJVKvPjii3jppZcQGRkJnU53na3eeffeey/+9a9/SdMfffQR3njjDRQXF+OZZ57BqlWr0NnZCZlMhpSUFBQWFjoxW8bGGCJyeqD/PlMcNxBarZbS0tKcngcHh21wueS41UhLSyOtVuvQsqu3zyAANGPGDDp58iSJXnjhBbvHXrhwgdRq9aDHcdy9MdLxSqfTkdFopICAAGlecnIy5eXlEQCKj4+XytqePXuc/loGhljGp0yZQkRER44csVvu5eVF//73vykiImLQYzhcK4Yqp/xde1ujwJG6i6M30mWMMcZcim2TKblcjpKSEnz11Vdob2/Hm2++iczMTGm5u7s7goODh9qM5OpgSTfM9nEjbeNmt8/uPDc3Nzz33HNobW2Vpk+cOIGkpCS4u7vj7NmzmDlzJvR6PZ544gm7xw63n29k/99oORo4T/xshISEAACUSqW0bPLkyTh79iyeeuop6HQ6CILgUBPEW83pRrfJmCvjJnyMMcbGNEEQUFpairVr1wIAvvrqKyQmJmLr1q0gImzbtg19fX3S+kQEtVoNHx8fKBQKuLm54fjx41K/KbHvSGdnJ1pbW1FRUYGMjAwYjcZB0x0dHSgtLbXbtth3RqlU4tChQ+ju7pZOUGNjYxEcHIyCggJ0dnbe2TeKOezjjz/Ge++9h5CQEOzcuRNtbW0A+stab28vAOCee+6BSqXC+fPnERMTg2XLlqG6uhrl5eXw9fVFYmIiOjs70dHRgbq6OnR2dtr1xzt+/DiioqIQFhYmlQ+xHIplJTQ0FIcPH0ZcXBzCwsJQW1uLsrIyKJVKpKWlwWw2o7m52a4MDkUs2yEhIfjiiy+wePFiaLVau4qTRqPBhAkT4ObmhpMnT0KtVkOlUqGnpweFhYUwGo1STg0NDaioqMBDDz103ZzkcjlSUlKk5+rr65PKPxEhLi4O586dw6xZsxAREYEvv/yS+2Mx1+fs5nvchO/mgi/fcrhicLnkuNW4kSZ8w0VVVZXUvKq5uZl8fHyooqLCrmzu3LmTTp06RdXV1aTT6UilUknLNBoNGY1GOn36NG3dupU8PDyoq6tr0PSpU6do586dBNg3f9q+fTvV19dTb28vhYWFEQByd3cnALRjxw6qr6+XmhNyOC9GOl6pVCqyWCxksVjojTfekObLZDLp/8TERLvmcb29vbR9+3YCQPPmzSOj0SiVEXF/79+/n4qLi6mjo4OioqLo7bffprq6OtLpdIPK4Y4dO6ihoYEA0J49e6i3t5feeecdAkCRkZHU29tLdXV19Mc//nHY17hgwQIiIvrss89IoVDQvn37aMOGDeTm5jZo3f3791NJSQlVVFRQYGAgffjhh9TS0kJarZZmz55tl5P4OXAkJ5VKJb2+uro60mq1duX///7v/0gul9OuXbuIiCghIcHpZcOVgpvw3fFwqAmf0ytPXIG6ueAPD4crBpdLjluN0ahABQYGkl6vlypREyZMoOLiYkpPT5fWqaurIyKiVatWUWVlJbW3t9OOHTsIACmVSsrKypIef+DAAXriiSeGnC4oKJC2GRsbSzt27CAiovXr19OsWbPo0KFDFBcXRwDoww8/lLaRmprq9Pf6bo/hjldiZdjf359MJhMREVVVVUmVI7ESJVagiIg++eQTWrVqFRGRtN6vfvUrIiI6deoUJSUlEdBfkSYievXVVwkAXblyhX7zm9/QPffcQ1VVVdTR0UEBAQG0Z88eadsWi4UeeeQRafunT5+m3NxcmjlzJm3atInq6uqGfY1iBcpkMtF7771Hjz766KDXKUZUVBTV1NQQEZFGoyG1Wk2hoaHU3t5OarWadu/eTbb2799P//Vf/0VERF999dWQOcXFxRERkdlspoiICFq1ahV1dnZKFaiPP/6YiIh6enroqaeeovT0dPLx8XF62XCl4ArUHQ+uQI3n4A8PhysGl0uOW41brUCJJ4UBAQFSJerMmTNERJScnCytp1arafHixQSAcnJyiIiosLBQWi4OEnD+/HlSqVQ0c+bMIafFQQUA0NSpU6WBLCIjIwnoH4xA/PX+gQceoJSUFFq4cCF5e3s7/b2+22O445XtVabY2FhKSkqiTZs2ERHR66+/Li1LTU2lxsZG8vf3J7VaLZUJ23KUlZVFlZWVUoV5165d9NRTT0nlNCoqigDQn/70J+rr65MqL0FBQXT+/HkiIsrMzLQrkxcuXJAGf0hOTnaoAiVqaWmhmpoa6UqXmIf498svvyQikip8r7/+upRTcHCwlNP58+elq6tZWVnU2dk5ZE6+vr6Unp5OMpmMvL29qbe3V8pl5syZFBISQi0tLfTLX/5yyKtiHFyBckLwIBKMMcbuLkQEQRDQ2tqKGTNmQK/XQ6PRAIDUd8Xd3R2lpaX44osvsGfPHixevBgAYDabpe2Ine71ej30ej18fX2HnO7u7gbQ33+kqqoK8+fPBwCcOnUKFRUVCA8PR3FxMeLj43H69GkcP34cR48e5f5PLsxqtSI7OxsajQZlZWXIy8vDli1b8OKLL2LDhg3Yvn07AMBisSA0NBRvv/02SktLpTJhW44KCwsREhIi9el58sknUVtbK/U7eu6552AwGJCTk4OmpiYAgEKhwKVLl6DX6wEAhw8fBgB888032LBhA3p7e6Vh0z08PKRtjaSkpASTJ0/GvHnzEB4ejsbGRvj7+0ufF3Ebbm79p4Xi9KJFi6ScmpubpZz0ej0aGhoAAHl5edDr9UPm1N7ejpMnT6K2thbZ2dlYu3Ytvv32W/zyl79ETU0NmpqacPnyZeTm5tr1U2TM1XEFijHG2LhiW4maOXMmysrKAFw7Oezt7cX27dvR2dmJAwcOjHhfqIEnlAOnU1NT8dFHH6GgoACZmZkoLy8HALz22mtYvXo1kpOTkZGRAa1WO2jbPAKZ60pJSZEqQjKZDACwZcsW/PjHP8bKlSsB9A+O0NnZic2bNwPAkBWZvLw8ZGZm4q233oJKpcK0adNQUFAg7fuHH34Yv//97/HJJ59g0qRJuHQSCPrEAAAgAElEQVTpEjw8PABcK2tiZb67uxvZ2dmYOHHiDb8ecSCLmpoaaWS+pqYmu0rUUMT3QKzcie+FmBvQX0lydx96TLLY2Fg0NjZCJpPhhz/8Ic6dO4fu7m4cPXoUJpMJQP/nwHaUQMbGAq5AMcYYG5OuVwERBAGXLl2CQqEAAOkX7h07duBnP/sZfv7znyM7OxstLS0Arl1NAq6dMFosFgD9N1MdavrcuXN48sknAQAnTpyQTiT379+PEydOIC8vDzk5OWhra8POnTtRUFCAyspKqNVqh64cMOfo6upCSUkJwsPDYbVaAQABAQH43e9+J62j1+vR3NwsVY7FMiGWI5lMBrPZjG+++QYzZsxAS0sL3N3d0d7eDiLCxx9/jGnTpuGvf/0rAODVV19FUFAQGhsbAQwua0FBQSguLrarjDc3N0tXVsXKDXDtsyGWY3EUPgBoa2uDQqFAfX096urqEBQUJJXF0NBQu+cUb05dXV0NoL+yZJtbUFAQ8vPzh80pPDwcPj4+KC0thV6vR29vLwRBwOXLl6X1rVYrmpub7fJmzOU5u/8T94G6ueD2rxyuGFwuOW41brQPVEJCApnNZtJqtaTVaslsNg8axSswMJCIiDIyMggA1dfXExFRQ0MDlZWV2fUR2bVrF4WGhtrNKy8vp9bW1mGnd+3aRQBILpdTYGAgGQwG6urqopqaGrt8xIEriHgQCVeIkY5XarWa4uPj6fDhw9TR0UE1NTVkMpnIZDKRl5eX1N+JiKiyspJqamrsysTevXulbXl7e5NaraalS5faDZAQERFBRqORurq6pL5FREStra1UXl5uN33+/Hlqb28ftpzaPp8Y4gAOtuuIA5qII94REbW3t1NXVxep1Wry9vaWym97eztlZGSQ2Wymn/70p/TJJ58M+lyMlNPf/vY3AkCZmZnS+gaDgYiIDAYD1dTUUGVlpbS+mBvH9cspf9fe1nCoDxTfB4oxxtiYdeHCBTz66KPSL+IKhQIXLlyQlguCgJaWFsyZMwe1tbUAgIceeki6B5R4laC3txcKhQKXL1+GwWDA0qVLpaZJSqUSVqsVJpNp0LRCocCVK1cA9P/K39LSgvnz5yMsLGxQPsuWLYOvry/c3d1RWFh4J98mdgPE+4oBQGZmJkJDQ6WmbA0NDbh8+TIaGhqQkZGBy5cvw9PTE0T99zcaWCYEQUBnZydKS0vt7tUkCAJ0Oh0eeOAB6T5QYjn09PS0K5fitG2ZE8tpRkYGurq67PpdiWpra6VyLOYkfgb+8Ic/YNeuXbBYLPD09IRSqcS3336Lzs5OqfwqlUrk5ORg3rx5+Prrr5GXl4ft27fDYrEM+TkYLqd33nkHZ8+ele6h1dPTA7lcDplMBkEQYDKZ4OXlJeXG2JjgSC3rdgecX9scc8G/PnC4YnC55LjVGI1hzAeG7XDNA4duHq0YOJrZSDlwuEZc73g1Wvty+vTplJubazcK5O0oE45ub6T1nJUTx/DBV6DuePAVKMYYY4xs+hrZ/n87nmO47d+u52W3z63sy4SEBOTl5aGtrQ0VFRXYt28fzp49e8PbuRGObm+k9ZyVE2NjDVegGGOMMcZGkdi0tLe3Fy0tLTh//rzdICWMsbGNK1CMMcYYY6Ooo6MD//rXv5ydBmPsNuFhzBljjDHGGGPMQVyBYowxxhhjjDEHcQWKMcbYmCAIgjRcOWO3ymq1Qq/XOzsNxkZksVj4BsMuiPtAMcYYGxNMJhOmTZuG6upqyGQyZ6fDxjCr1YrIyEjk5OTAZDLxCSpzSVarFVOmTIHJZHJ2KmwAvgLFGGOMMcYYYw7iK1CMMcbGBE9PT1RVVWH69OnOToWNA9XV1Vi+fDnOnTvn7FQYG1ZlZSU8PT2dnQYbgK9AMcYYGxOICAqFwtlpsHFCJpNBpVI5Ow3GRqRQKPiGxC6IK1CMMcYYY4wx5iCuQDHGGGOMMcaYg7gCxRhjjDHGGGMO4goUY4wxxhhjjDmIK1CMMcYYY4wx5iCuQDHGGBs3hrshqu18vmkqG46jZeNWypAjj+Uyyphr4/tA3UUEQRg0FOZQ81zZWMuX9bvefhtp+c3u8+Eed6PzbzSPm9nOaD333U58n2JiYhAWFgYAUCqVOHz4MMxmM+Li4lBbW4uOjg6o1WoEBgaioKAAnZ2dw26L3V2ICBqNBv7+/ujq6oJcLoe7u7tdWWhoaEB5ebnDZUQul2PatGkIDg5GYWEhjEYjYmNjERwcPGz5IyIkJSWhrKwMBoNhVF8jY+zWcQVqnBEP6HK5HCkpKRAEAYIgoKWlBcXFxdJyDw8P3H///SgoKHB2ykPy8fHBtGnTMGHCBFgsFvj6+uKrr75CR0cHNBoNlEol3N3d4enpiZMnT6Kjo8PZKbMRiCclfn5+6Ovrs1vW3NyM0tJSALArt0Q06ERFLpcjOjoaYWFhdifGA4nr254w28738PBAeno6zGYz3NzcYDAYhvwsKJVKLFq0CBcvXrRbbntCZHsCZPu8QUFBsFqtOH78OHp6eoZdPtzJmIeHBxYvXgydTmf32WXDIyL4+/ujqKgIAHDx4kUEBAQgMzMTx44dQ1FRETIyMpCTk4ODBw8iMjISDz74IHJzc8fMsZHdfps2bcLcuXMRERGB9vZ2tLS0QKFQoK+vD6GhoQCAsLAwtLW1XfdzqVQq8fzzz+NnP/sZVCoV5s+fj6qqKhw7dgwqlQqpqak4evTokI/dunUrsrKykJeXdzteJmPsVhCR0wMAcdxYaLVaSktLG3KZXC6nDRs2EBFRRUUFNTY2Um5uLkVHRxMAUiqV9Oqrr1JbWxsBIEEQnP56BkZcXBzt3buXbMXFxREAeu+994iIyGg0UldXFyUkJDg9X47+GKlcZmdnU1NTE2m1WtJqtVRTU0Pd3d3U1NREQH+5zczMJCKi8vJyMplMZDKZSKVSScs3bNggzW9ra6O1a9dScnIyeXt7S88jluewsDAiIlqyZIldHmFhYbRmzRoyGo2k1WqpqamJsrOz7daRy+WUnJxMa9eupe7ubrvlSqWSfvGLX1BLSwsRES1cuNDusdHR0ZSbm0u1tbVERLRx48Zhlw98jWK4ubnRunXr6MqVK5Sbm0uTJ0+2e23jOdLS0kir1d7wMgAUEBBARqORzGYzBQYGSvP37t1LjY2NRESUlJREAGjnzp1UV1dHarXabr+//PLLZLFYnP4+cNz+GOp4JZPJpP+rq6tp9uzZdssDAwPJYrGQxWIhf39/Akb+XMbHx9t9j02ZMoXef/99aXrBggVDPs42j7vhc88xfAxVTkf6ruW45ShwqO7i7MoTV6BuLkb68KSlpRER0a9//Wtp3vTp0yk/P5+Aawf08vJyCgkJcfprGRi2XxZHjhyRvnTEeV5eXvTvf/+bIiIihnwMh/NiuHJpezJgG8HBwXTgwAECQKmpqUREdOjQIQKunajodDoCrpVri8UinRyLJyKpqal2242OjqZDhw7ZnTCLZaS7u5va29tHzHHjxo1ERLR79+5ByweeEA08AcrPz6fp06cTANq6dSsREWVlZZFcLh+0fOBrBEAeHh60bt06ysnJIQA0depUOn/+PIWFhTl9/96JuJkKlFwup+eff16q1E6dOlXa57b7tb6+flCFGuivsAKgpKQkIiJ67bXXhnz+gceZ601zuHZc7yR04HJx//r7+///7d17fFTVvf//18odJBBCuISbchNOTGmIqeV2LAjlIt4e0AfeftbjaZXHaZV+e+EmVrSePqj1dtRvfx6s/aG1HPvlC7b29Hi8YSyCwhEQkXINBAjhloRJDCFxyPD5/TEzm5kkwAQhk8D7+XisR2avvWbPmll7z+xP9lprW11dnX3xxRdRgXrDFN6v+vfv7/2WjR492uDUb1v4+6lh0r6kFE4KoFo8xRRAaRKJi5BzjqqqKh5++GEAEhIS2LFjB8OHDycpKYkNGzbQr18/Bg8ezPr16xs993TbbM7rN2ddwzxr0I0J8LpNXHHFFWzYsIF7772XkpISr4ti5HMuRJ2au02JFggEGuVlZGTQtWtXbr75ZgCvm1tKSgoAZWVl5Obm0q5dOyD4ee/YsYNhw4ZRVlZGUlISd999Nzt27CAxMdErA/Dqq6/y7W9/m40bN3rPNzMuv/xyvvzySxITE+nTp49XF+ccgUCA5ORk5s6dy8KFC1mwYAH/9E//FLXeOceGDRsYMGAAhYWFjd5TYmIiw4cPZ8eOHSQlJTF79mzeffddHn/8ca699loAb31ycnKj9wgwfvx4HnvsMSZOnEhSUhJFRUXceeed7Nmz55w//4vd6NGjefLJJ8nKyuLgwYPe94aZRe17n3zyCaWlpQwdOpRrr72WMWPGkJ6ezsmTJ+nRowcjRoygpKSEZ599lgkTJjB27FiGDx/O6NGjyc3Nxczo1KkTN9xwAwUFBZgZ6enpjB071lsvFy8zwznH0aNHGTJkCOnp6axZs8Zbn5qaypQpUxg3bhxDhgzxuiv36NHD2yfDeeHlyspKhgwZwoQJE0hOTgaiu/p26tSpJd+iiMRIY6AuQvv37yc1NRW/30+PHj04evQoEPxSrq+vB6B3795A8EQTYPLkyRQXF7Nt2zYyMjIYOHAg7du3JyEhgfXr11NdXe2NPbrssstYuXIll19+Ob169fK+7MNjPSxiDFZpaSnbt2/n+uuvp7i4mC1btpCWlsa4ceOoq6uLGv9yJpWVlfTo0YN33nmHCRMmsGfPnqjAqaCgwKvvRx99RG5uLllZWQQCAe81zqVOkWNumnqfOTk5dO7cmZSUFNLS0jQeqxlGjBjBn/70J9LS0gA4evQoVVVV9OvXj8zMTI4ePcrkyZN55513ALz9KiMjA8Dbl1NTU739wEIDr0eOHEm3bt0YOHCgVw7g2WefZffu3Zw8eZJXX32Vn/zkJ/j9fq+9r732WhYuXMizzz7LK6+8woQJE9i3b1+jMUqRJ0SRIk/Ww6/buXNnqqqqOHToEHDq5OjEiRMkJCTwrW99i//8z//0nldXV+cNGo/cRmlp6Vf6vC9mX375pfe4oKCAAwcONPrHSmJiItOmTQPgj3/8I7feeiuANwZl9erV9O/fn2PHjvH+++/Tvn17Onfu7J3A7ty5kyuvvJJ+/fqxbNky3nzzTaZOnUpBQQHvv/8++/bt4/LLL2/Bdy3xEN6nevbsCQTHcIbdddddPProo7Rr146EhAQGDRpEWVnZGbeXn5/P1KlTueWWW5g7dy5PP/00J06coE+fPlFj9kSklYl39z114Tu3dLauUpmZmV4/7auuusqGDBlicKpbwNixY62srMwAy83NNTMzn89n6enplpOTY6+++qrXRSk8xiM89sjMLDMz0/bs2WN+v982btxoZmazZs0yCI4xmT9/vpmZlZSU2I033mhmZjU1Nda3b1+bMWOG+Xw+q62tPeN4BsBWr15tZmbTp0+3jz/+2Lp37x71PsIpcrzUkCFD7NVXX7V9+/ZZbW2t7d6924Bm1yk1NdUWLFhgX375pdXV1Xnv8+c//7k556xXr15el6HKykqNxzrDftlUGj16tB0+fDiqPTt16mRffPGFVVdX24033mh//vOfvfLjxo2z2tpamzdvXtR2du/e7b3mgAEDbMuWLda/f3978cUXo7rINOxG2Lt3bzMzb/8IHxclJSU2atQoq6qqOu0YpZEjR3r75unGMITf06FDh2zo0KFReQUFBTZ06FB76KGH7Msvv4x6XlPd1M429udiSufShW/kyJHe8f/AAw9YSkrKabcf3g/CXajC7Rduzw0bNnhlu3XrZuXl5WZmtnLlSoNgF+LevXt7ZXJzc23v3r22aNGiuH92Ss1Lze3CF5nC+9yHH35ogD388MNmZpacnGy3336791sTLtvw+yK8HH7Oo48+ambmvd7SpUujvr+ULt2kLnwtnjQG6mJOpzt4IgOLrl272t69e83v91ttba034BVO9fV/9dVXDbArrrii0cnkE088YWZmBQUFBsGA4q233vLGYixdutQ6duxogG3ZssULXl5++WWL9NJLLxlgR44cMTOzV155xQAbOnRo1NiPplLkj8xtt91m+fn5jU6OGp4QhfuYh19j586d1r1792bXqaCgwOrq6qykpMR7zc8//9zMzHr27Gm///3vzcysrKzMunXr1mQbXGop1i/1tLQ0u+++++y1115rtO7GG2+06upqq66ujsofMmSI1dbWmpnZvHnzbPTo0XbllVdaTU2NjR8/3gArLCy0nj17es85fPhwkwFOuI2uuuoq8/v9XmDe8LhoaowSxBZAQXDsYWFhoTcBSjgtX77cSkpK7PDhw2ZmVlFR4Y3pUwD11QIos8Zj4ppKZzqh3bVrl1euS5cuVlVVZTt37rSOHTtabm6uffTRR1H7UTglJiZe0sd/W0tfJYAKj4X88MMPbfDgwWYWDLJLSkpsx44dtnbtWi+oPlMAFTm+d/v27d53WVPLSpdmUgDV4kljoC5FCQmnmrS6upo77riDBQsWkJaWxuzZs711KSkplJeXc++99wKQmZkJEHW/iSeeeILq6mqOHTsGwKJFi1i+fDkHDhwAYNasWXzxxRf85je/4R/+4R8A6NChA7Nnz2bWrFkAbNu2jUceecQrX1dXx0MPPQRAx44dG01pfSbz589nyZIlXpea8HiX8DbC/cctottOx44dSUpK4vDhw82uU1FRETfccAM33ngjfr+ff//3fyc3N5c5c+Zw8OBB5s+fT3l5OY8//jhHjhzxXjPy9aVpOTk5TJ8+nTlz5gCn2vLyyy9n7ty5zJw5kw4dOnjt5Jxj165dPPLII9TV1fHwww+zZMkSXnrpJa/rJsA999zj7Z+//OUv6datm9c2kWPWwm2UkZFBcnIy8+bNA4L7UHV1NfPnzweC47AWLFhwzm26aNEiXn/9dbZu3RqVv3DhQqZMmcITTzwBBI+/J5988pxeQxoLj6c7V5WVld7jiooKFixYwMCBA5k/fz6bN2/2vksb7hcnT57U8X+RC3+PhLvGZ2RkEAgEOH78ON///ve55557mDFjBj/5yU94/vnnz7q9cJdkCH7/RO4/DZdFpPVQAHWRCQQCFBQU8Prrr1NXV8fq1atZuHAhDz74IHPmzOGZZ54BwO/3k5WVxYsvvgjgjUOJvKfOkSNH8Pl8fPrpp1x22WXccccdFBcXe1/oP/rRj/D5fGzevJl+/fpx9OhR0tPTOXLkCO+//z4A5eXl7N+/n7y8PF5++WU2btxISUkJED125WxuvvlmsrKyGDJkCLt27SIzM9Mb0BveRvgkOnKbqamp3g9ec+tUWVnJ+vXruf/++6mrq+Pdd9+lvLyclStXYmaUlJRw7NgxPv7442a1kUCXLl3Iyclh3759UQOmX375Ze68804WL17Mgw8+yIIFC3jmmWe8MUNPP/00w4YN8wLbr3/968yZM4d33nmHZcuW8cEHH7B7926Kiop48MEHAdixYwf5+flN7mv/8z//wyOPPMIPfvADIDhOpry83KsX4LX3ufj5z3/O7373u6gxOs451q1bx6ZNm3jyySeZNWsW5eXlXjApzddwPFo4iA63YXiSkci8MwlP6hF+XniigNmzZ/Paa6+xatUqbwKbSAkJCZpc5iIW/q7KzMxk27ZtVFdXM3z4cPr164ff72fHjh289957FBYWsnr16pjG94Z/eyH6H6BNLYtI66Gj8yKUmZnJ1VdfDZw6AVi4cCH//M//zPTp04FTV6Duu+++M27rmmuuITExkezsbIYMGRJ1c8lp06bx3HPP8dvf/pY9e/Zw5MgR7/XCPwrhH4DPPvuMGTNm0LVr13N6T7t372bEiBEMGjSIlJQUDh06FBVEnc7hw4e9gfjt27dvVp3y8/P5wx/+wF//+lcmTpzI8uXLqa6ujrprvHMu6gdQziy8f1RUVHifezg46datG1dccYU321w48A8P/IfglYVt27axYsUKNm3aBODNJPmrX/2Ke++9l3vvvZdp06Z5Jy+zZ89udAUIgm134sQJHn30Uf7xH/8RCO63Xbp0iaprZWVls05kIk/WV61axfHjx6PWh99veL/9+OOPOXbsmPe+09LS6Ny5MwBJScF5fnw+H7169Yq5DpeaVatW8bOf/cy72rhhwwa6du3qfdaBQIDCwkJGjx7t5YWD2vDkHuGZPletWsW9997LypUrvUlBNmzYwM9+9jPKy8u9q5Xhbhy5ubns3buXRYsWEQgEdMWgjYs8fgOBAOXl5d6ymdG1a1cOHTpEQkICV1xxBTU1NaxatYq5c+fyyiuvRG0rfPweOnTI29/C+6jf7/fWhdXX10dNShG5rMBcpJWJpZ/fhU7Ev79jm0tn6v/acBwHnLrBZGlpqUHw3jJmZo899ljU8vvvv+89J3wPi927d5uZeRNRhFNJSYl3P5unnnrKzMwGDx4ctb3CwkIDLCsry8rLy2316tXe83Nzc62oqMjg9DcNDI9rCt8EOJyKioqsuro6auxRuJ7he8A8+eST3iQPL774orVv375ZdZo0aZKZBSeaaN++vQH20UcfRb3url27vBtxauxDbP2y8/Pz7b333rO+fftG5Yfv8/TrX/86qj3q6+sbbePf/u3frLq62mbMmGGpqalNvs7OnTvN7NQNmJu6F9WVV14Z1f5paWk2Y8YMb0xcwzFQ4TaOvK9LeAxDUlKSt52lS5d6N7+NTJFlwqmgoMBOnDjhLes+UOd2I93k5GTLycmxgQMHWk1NjTeBzJ49e6yqqsoeeOAB7zhueJPuq666ytLT070bOb/xxhve90g4jR071o4fPx61H4Tzzcz27t0b989OqXnpdN9Xy5cvt3379plZcHKlHTt2eDf/Dk8s0/AmuqNHjzaz4M3rI2/OnZeXF7WvVVRU2LZt26LysrOzbfHixaddbjiGUunSShoD1eLp/E0iAWQAy4BtwFZgBJAJvAvsDP3tHCrrgOeAImATkB/D9uP9YbW5dKaDZ+LEiWZmdsstt1hVVVXUl36HDh3sqquuivry3rp1q1VUVHjLr732WtQXdlZWlo0bN84yMjIa1aG2tta2bt3a6Mchcnvbtm2zyspKb3nx4sWWnZ0d9XoN30NeXl6jk5xwnSJ/WMKz3+Xm5lp6err5fD6rqamJqlNJSYnddNNNdvLkyZjrtGTJEgNszpw53mcX+aNXWVkZtawfuLPvl+F0ww03WCAQMIg+EU1OTrY5c+aYWfAk5MCBA/bee+95gcPVV19tNTU1tnfvXjMze+SRR874Ol27djUzs0mTJnl5y5cvt0OHDtmePXvswIEDVlhY2Cg4D58Ibd26tclZ+BqeEEW2//Lly736ffHFF94JfF1dnTdDY8M6RL7HcEpISLCZM2fa8ePHrbCw0AvGLoUg/VwDqMiUk5Nj48aN89KUKVOiAu2CggK79tprbfjw4TZp0iTvuy05OdkmTJhg+fn5TW73mmuuaZSXnp5uY8eO9f6RotR20um+rwoKCmzChAk2atQoGzNmjI0fPz5qf2o4q23kfjB+/Hj79re/7U2+1KlTJ5s0aZK3v4XLjB492tv/0tLSLD8/30aPHm0TJ05stNypU6e4f1ZK8UsKoFo8xRRAxXofqGeBt8zsO865FKA98CCwwsx+5ZybC8wF5gCTgUGh9E3ghdBfaSFr1qzh61//Op9//jl33HGHN66ptLSUY8eOUVpayuTJk6msrCQpKYm0tDQCgQC1tbWkpKRw/PhxiouLve0999xz/OIXv4gaWA1www030L17d5xz1NXVUV9fT7t27UhMTPS219T2jx07hs/nY9KkSdTU1ESNuworLi7mqaee4oUXXsDv95ORkeHV6Te/+Q2LFy/G7/fTrl070tLS2L9/P9XV1YwYMcK7Z1NdXR1mRkVFBdu3b2fKlCn4fL5m1enpp5/mjTfeiNpm5Pusra2lQ4cOUZ+XnNmHH37INddcAxDV3Sk8xmndunXe+JKysjLv/kdFRUV85zvfIRAIEAgEWLly5WlfI/zcYcOGRbXNwoUL6dy5MydPniQhIYGysjJ27doVNZbus88+47rrriMxMREzo7S0lPLycq9McXExkydP5vjx4432zYULF5KZmUlNTQ3Jycled6CUlBSKiopOW4fS0tKoOpw8eZJFixZRXFxMSUlJo/ueyek559iyZQtbtmw57frIrsiR+SdOnPDuOwbBe0r94Ac/4Mc//jFvvvkmo0aNatQO1dXV3k2V1UZt3+n2j6bKnW4/iCxTVVXFW2+9ddbtbdiw4YzLItLKnC3CAjoBxYBrkL8dyA49zga2hx4vAm5vqtwZXiPe0WabS7FMY95UOpf/YPv9frvxxhu/8nbOR73OVC5edVI6lc72X7HIz/R8tHlznne67cSy/Qtd5kIct201nY8rUOcrjR8/3szM3nrrrUZXKpXaftJ/8ZXaQtIVqBZP5+0KVD+gDFjsnPs6sB74EdDdzA6GyhwCuoce9wJKIp6/P5R3ELngzvbfz1j+O3r11Vfz4IMPMmrUKNq3b8/9998f9V/ZWLfTHLFu70zl4lUniV3kZ3o+2rw5zzvddmLZ/oUucz6OWzn/1q5dy4QJE6ioqGh0pVJERC5dsQRQSUA+8ICZrXXOPUuwu57HzML/IY2Zc+4+4MxTwElcFBUVed2MUlJSeO+996KmYRYRuRRUV1fz7rvvAuqeJyIip8QSQO0H9pvZ2tDyMoIB1GHnXLaZHXTOZQPhO4mWAn0int87lBfFzF4EXgRobvAlF1ZVVVVMfcBFRC4VCp5ERCTsrDc3MbNDQIlzbnAoaxywBfgLcHco727gjdDjvwDfdUHDgaqIrn4iIiIiIiJtVqyz8D0ALAnNwLcbuIdg8LXUOfc9YC8wPVT2TeB6gtOYHw+VFRERERERafNiCqDMbCNQ0MSqcU2UNeCHX7FeIiIiUZxz+GqGm1MAABkmSURBVP3+eFdDLhKBQIDy8vJ4V0PkjPx+P865eFdDGoj1CpSIiEhc1dbWMmjQIHbt2uXdY0vkXAQCAfr27cvbb79NbW2tTlClVQoEAvTv35/a2tp4V0UaUAAlIiJtQnJyMpWVlUydOpWsrKx4V0fasPLyct5++22efPJJ1qxZQ2pqaryrJNJIeXk5H3zwAcnJyfGuijSgAEpERNqExMREfD4fn332WbyrIheB2tpa1qxZw6pVq+JdFZHT8vl8uuLeCp11Fj4REZHWIiFBP1tyfjjndOVJWj1957VOahUREREREZEYKYASERERERGJkQIoERERERGRGCmAEhERERERiZECKBERERERkRgpgBIRkYvG6W6IqhulSizOtJ+cj31I+6HIxUH3gRIRkYuGmZGTk0N2djYAaWlprFixgrq6OvLy8iguLqaqqqpF6+Scw8xa9DXl3JgZBQUFZGZmUlNTQ3JyMikpKRw9epR169Y1e3upqal87WtfY+fOnVRVVXnb79GjBx9++GGL74sicn4ogBIRkTYvPT2dyy+/nNraWj755BMSEhI4ePAgXbp0YdasWfztb3/j008/ZdKkSbz99tstWjcz4+qrr6aoqEgnzG3AvHnz+MY3vkGfPn2orKykurqaTz75hGnTpjVrO2lpacyfP5+ZM2cyffp0b7+bN28eU6dOpaCggPXr11+ItyAiF5i68ImISJt31VVXsWzZMoqKijhx4gR9+/alf//+dOrUibFjx1JYWAjAsWPHWrRe4S5bK1euZOTIkS362tJ8iYmJTJs2jb59+7J7927GjBlD3759mTZtGomJic3aVk5ODg899BBbt2719rvw9p1zrF+/Xl36RNooBVAiItKmpaSkUFBQwIkTJwAoKCigrKwM5xyJiYncfvvt9OzZk5KSEi677DKg6bEosZ7MNqecmdGtWzfq6+vx+XwxvqPmvZZOws+fQCDgPU5MTCQrK6vJdQ011QYbNmzga1/7Gr169aJdu3YAnDx58jzWVkTiRV34RESkTRs1ahTPP/88AMXFxSQlBX/azCzqpPeTTz7h6NGj3rohQ4bQq1cv0tLSeO+99/jyyy8B6NSpEyNGjKC6uhqfz0dxcTHf+ta3qK6upqqqis2bN0eVqaioYPv27UyaNImamhoSEhJYv3491dXV9OnTh88++4yOHTsyatQo1q5dy7Bhw+jQoQMJCQl88MEH5OXl0bFjR/x+v3ei7ff7o15r+PDh1NTUUFtby+eff87IkSOpr6+nsrKSzZs3t+THfUlLS0tj8ODBbNq0ieuvv566ujoOHz4c1QapqalMmDCBnTt3kpaWxsmTJ70xcGYWNRbPzMjNzaV79+5ewL1y5UrvnwEi0kqZWdwTYErNS3v27LFx48bFvR5KSpFJ+6XSV03jxo2zPXv2NGvdyJEjLeyuu+4y51yjMomJiVHLmZmZVltba7W1tebz+WzGjBmWnZ1tgOXn53vb27Rpk3Xp0sVb3rFjhwGWl5fn5ZWUlNhNN91kJ0+e9PLGjBljgC1dutQi9ezZ0xYvXuwt33nnnbZs2TJv+fDhw3bgwIHTvtZLL71kOTk53vLevXvj3mZtNZ3t+6rh+rS0NPvFL35hfr/fbrnlFvP5fFZbWxu1TyYkJNjMmTPt+PHj9uabb9p9991n27dv9/aHPn36mJnZxIkTveccOnTI/H6/bdy40czMZs2aZcnJyXH/fJRaR2pqP9Vv7QVN62KJXdSFT0RELhovvvgiQ4cObZQfeSWqa9eu7NmzB+ccffv2pXPnzvzyl7/kwIEDdOvWjQ0bNjBgwAAgOGaqoqLCWy4rKwNg48aNXl7v3r254YYb6NChA//93/8NBK8gAUyfPt0bfzVgwAAOHDjAPffc4+X94Q9/4Ic//CG/+tWvOHDgAN27dycnJweAQ4cOea/Vt29fli5dyve//31SUlLYuXMn69ev56233jq/H6CcVk5ODj//+c9JTk5m2rRpdO7cmW9+85ve2KjU1FTuv/9+pkyZQvv27ZkxYwaLFi3C5/N5V5SeeuopAMrLy73t/u1vfyMrK4u8vDw+/fRTfv3rXzN69OiWf4MiEjMFUCIictH47ne/y2effXbGMmvWrCE9PZ0hQ4Z4AdGWLVsA+PjjjwHo0aMHgNf1KrwcKZxXWFjIfffdR35+PhkZGY3KpaamNtpGOO/QoUN07dqVefPm0atXLyAYkPl8PrKzs+nWrRsAJSUl3HrrrSQlJbFx40auvPJKCgoKmDFjRrMnN5Dmc855Y5p27drFXXfdBQSD8XBwPn78eB577DEmTpxIUlISJSUl5ObmRo2Bmj59Op9//jlTpkzx8v7lX/6FPn368M4775CXlwfgdScVkdZJAZSIiLRpCQmnfsqWLl3K8OHDo9Y3HOBfWVkJNB0Uhdc1RzgYihzrEqtRo0axefNmr46JiYls3ryZa665hoEDB/L3v/+drl27euXr6+sbbUMTE1x44Xbt3r27N8auobq6OioqKoBTVzwzMjKor6+ntrbWK3f8+HEeffRRBgwYQHp6Ort27WLz5s1MmDBBVxRF2ggFUCIi0qYdPXqUI0eOeMsNAygzY/jw4WRmZgKwevVqLx+CVxH69esXtS4sHJyErwhEBit1dXWNyoQfh9dFiswLlwtfsQrXJXzinZqaysGDB8nKymLjxo1R20lPT2fMmDGMHj2a3Nxc3aT3PIoMts2s0ZWgurq6qM87ctk5R3Z2Nl27dvXyRowYQYcOHRgzZox3xalnz54MGjSIzZs3s2XLFjIyMryrj+np6d52RaT1UgAlIiJtlnOOLVu2kJuby/PPP8+qVat45plnmDdvHqNGjWLcuHHk5+fz29/+liuvvBKAmTNn8vLLLzNs2DAGDx5MaWkp3bt35/HHH2fmzJnAqataPXr0YPDgwd6YlF69etG7d2+cc4wfP75RmfBVrfHjx5OWlgacukI1fvx4nHP07t3bO2GOLBf5nv7+978zduxYAJYvXx61vqCggMLCQj788EP+67/+6/x+oJc4M6OgoIC8vDw6d+7MyJEjycvLIy8vD+cc1113HV26dPH2gfByjx492L17NwClpaUMGDCA2bNn88QTT5CVlcW//uu/0q9fP7p3705GRgZTp05l6NChvP/++wDceuutDB48uMn9R0RaoQs5u16sifjPuNHmkmZgUWqNSful0ldN5zILH2DJycmWk5NjAwcOtJqaGqutrbXdu3fbnj17rKqqyh544AFr3769Vz47O9tWrFhhxcXFZmY2d+5cy8zM9NaPGTPGm+lu27ZtVlFR4S0vXrzYsrOzo2bXa1jGzCwvL88A27Fjh5eXnZ0dNQtfZLmGKVyHrl27RuWPHTvWe65m4Tv3dLrvq+XLl9u+ffvMzMzn89m+fftsyZIlUW3ecB9YsmSJATZnzhwzM9u6datVVlaamVlZWZnNmzfPAFuyZIm33crKSps0aZIdPHjQampqbNu2bTHtF0qXVtIsfC2eYpqFT/eBEhGRNu/EiRPeRBDf+MY3yM7O9tY1vM+Tc46DBw/y05/+lG7duhEIBLx774TvxbN+/XrGjh1LfX09aWlpBAIBamtrSUlJ4dixY/h8PiZPnkxlZSVJSUlemRMnTlBfX09GRgbFxcUATJ06lU6dOpGeno7P5+P5559n8eLF+P3+qHKJiYlRswVu2rSJYcOGeTcFDv3DkXXr1nl1O5cxW3JmCxcuJDMzk5qaGpKTk0lJSeHIkSP4fD7vXl/Hjx+PWg53uXv66adZt24dzjkCgQB+vx+fz8fOnTsBeOaZZ3jhhRdISkrisssu4+2332bMmDHeFa26urpG+4+ItD4KoERE5KIR7tIXDqaaYmY45xqNLYoMUqqrq/nggw/O+FqxDPh3zjW60e2GDRuaLBsIBFi0aBGTJ0/mz3/+MzNnzuTo0aNR9Wqqbg3Xy7lzzrFu3brTrn/77bdPu+yc48SJE6xYsaJZ296+fTvbt28/xxqLSDxoDJSIiFw0Yg0kmip3IYKQ5m5z0qRJ9OnThwceeID/+I//iGkbCp7On6/yWaqdRC4dugIlIiLSSkyZMoWsrCxOnjzJ8ePH410dERFpggIoERGRVqCp7n7qnici0vqoC5+IiEgr0FLdCkVE5KtRACUiIiIiIhIjBVAiItImOOfw+/3xroZcJAKBAOXl5fGuhsgZ+f1+nHPxroY0oDFQIiLSJgQCAQYNGsSOHTtISUmJd3WkDfP7/fTv358PPvgAn89HQoL+nyytj9/vZ9CgQVH3h5PWQQGUiIi0CevWreO6664jMTFRY4PkK3HOUVtbS3JyMomJifGujkiTwjdkPtO9ySQ+FECJiEibUF1dTWFhYbyrISIilzhdsxYREREREYmRAigREREREZEYKYBqoxITEykrK4t3NUSiaFYr+arKy8s1YFpE5Aw0M1/8aQxUGxUIBFi7di0HDx7U7EHSKmhWK/mqTp48SefOncnIyNBMeyIiTdDMfK2DznDaqDfffJMjR45o9iBpNdq1a8fatWu1X8o5S0xM5MiRI6xdu5Z27drFuzoiIq1Ou3bt2Lt3r3ohxZlrDVPBOufiX4k2JDExUf95EBEREbmEOed0S4fzb72ZFZytkK5AtUEKnkREREQubQqe4kcBlIiIiIiISIwUQImIiIiIiMRIAZSIiIiIiEiMzhpAOecGO+c2RqQvnHP/yzmX6Zx71zm3M/S3c6i8c84955wrcs5tcs7lX/i3ISIiIiIicuGdNYAys+1mlmdmecDVwHHgT8BcYIWZDQJWhJYBJgODQuk+4IULUXEREREREZGW1twufOOAXWa2F7gZeCWU/wpwS+jxzcDvLWgNkOGcyz4vtRUREREREYmj5gZQtwGvhR53N7ODoceHgO6hx72Akojn7A/liYiIiIiItGkxB1DOuRTgJuD/NlxnwYnomzUZvXPuPufcOufcuuY8T0REREREJF6acwVqMrDBzA6Hlg+Hu+aF/h4J5ZcCfSKe1zuUF8XMXjSzglju9isiIiIiItIaNCeAup1T3fcA/gLcHXp8N/BGRP53Q7PxDQeqIrr6iYiIiIiItFku2PvuLIWcuwzYB/Q3s6pQXhdgKdAX2AtMN7OjzjkH/G9gEsEZ++4xszN203PONav7n4iIiIiIyHm2PpbecTEFUBeaAigREREREYmzmAKo5s7CJyIiIiIicslSACUiIiIiIhIjBVAiIiIiIiIxUgAlIiIiIiISIwVQIiIiIiIiMVIAJSIiIiIiEiMFUCIiIiIiIjFSACUiIiIiIhIjBVAiIiIiIiIxUgAlIiIiIiISIwVQIiIiIiIiMVIAJSIiIiIiEiMFUCIiIiIiIjFSACUiIiIiIhIjBVAiIiIiIiIxUgAlIiIiIiISIwVQIiIiIiIiMVIAJSIiIiIiEiMFUCIiIiIiIjFSACUiIiIiIhIjBVAiIiIiIiIxUgAlIiIiIiISIwVQIiIiIiIiMVIAJSIiIiIiEiMFUCIiIiIiIjFSACUiIiIiIhIjBVAiIiIiIiIxUgAlIiIiIiISIwVQIiIiIiIiMVIAJSIiIiIiEiMFUCIiIiIiIjFSACUiIiIiIhIjBVAiIiIiIiIxUgAlIiIiIiISIwVQIiIiIiIiMVIAJSIiIiIiEiMFUCIiIiIiIjFSACUiIiIiIhIjBVAiIiIiIiIxUgAlIiIiIiISIwVQIiIiIiIiMVIAJSIiIiIiEqOYAijn3I+dc393zm12zr3mnEtzzvVzzq11zhU55/6Pcy4lVDY1tFwUWn/FhXwDIiIiIiIiLeWsAZRzrhcwEygws1wgEbgNeBx4xswGAj7ge6GnfA/whfKfCZUTERERERFp82LtwpcEtHPOJQHtgYPAdcCy0PpXgFtCj28OLRNaP845585PdUVEREREROLnrAGUmZUCTwL7CAZOVcB6oNLM6kPF9gO9Qo97ASWh59aHync5v9UWERERERFpebF04etM8KpSP6AncBkw6au+sHPuPufcOufcuq+6LRERERERkZYQSxe+8UCxmZWZ2QngdWAUkBHq0gfQGygNPS4F+gCE1ncCKhpu1MxeNLMCMyv4iu9BRERERESkRcQSQO0Dhjvn2ofGMo0DtgCFwHdCZe4G3gg9/ktomdD6983Mzl+VRURERERE4sPFEts45x4FbgXqgU+B7xMc6/RHIDOU9/+Y2ZfOuTTgVWAYcBS4zcx2n2X7CrBERERERCSe1sfSOy6mAOpCUwAlIiIiIiJxFlMAFes05iIiIiIiIpc8BVAiIiIiIiIxUgAlIiIiIiISIwVQIiIiIiIiMVIAJSIiIiIiEiMFUCIiIiIiIjFSACUiIiIiIhIjBVAiIiIiIiIxUgAlIiIiIiISIwVQIiIiIiIiMVIAJSIiIiIiEiMFUCIiIiIiIjFSACUiIiIiIhIjBVAiIiIiIiIxUgAlIiIiIiISIwVQIiIiIiIiMVIAJSIiIiIiEiMFUCIiIiIiIjFKincFQo4B2+NdCWlSFlAe70pIk9Q2rZfapnVT+7ReapvWS23Teqltzp/LYynUWgKo7WZWEO9KSGPOuXVqm9ZJbdN6qW1aN7VP66W2ab3UNq2X2qblqQufiIiIiIhIjBRAiYiIiIiIxKi1BFAvxrsCclpqm9ZLbdN6qW1aN7VP66W2ab3UNq2X2qaFOTOLdx1ERERERETahNZyBUpERERERKTVi3sA5Zyb5Jzb7pwrcs7NjXd9LjXOuT7OuULn3Bbn3N+dcz8K5Wc65951zu0M/e0cynfOuedC7bXJOZcf33dwcXPOJTrnPnXO/TW03M85tzb0+f8f51xKKD81tFwUWn9FPOt9KXDOZTjnljnntjnntjrnRui4aR2ccz8OfZ9tds695pxL07ETH865/885d8Q5tzkir9nHiXPu7lD5nc65u+PxXi42p2mbJ0LfaZucc39yzmVErJsXapvtzrmJEfk6jzvPmmqbiHU/dc6Zcy4rtKzjJg7iGkA55xKB3wCTgRzgdudcTjzrdAmqB35qZjnAcOCHoTaYC6wws0HAitAyBNtqUCjdB7zQ8lW+pPwI2Bqx/DjwjJkNBHzA90L53wN8ofxnQuXkwnoWeMvMhgBfJ9hOOm7izDnXC5gJFJhZLpAI3IaOnXh5GZjUIK9Zx4lzLhNYAHwTuAZYEA665Ct5mcZt8y6Qa2ZDgR3APIDQecFtwFWh5/y/oX/w6TzuwniZxm2Dc64PMAHYF5Gt4yYO4n0F6hqgyMx2m5kf+CNwc5zrdEkxs4NmtiH0uJrgSWAvgu3wSqjYK8Atocc3A7+3oDVAhnMuu4WrfUlwzvUGpgAvhZYdcB2wLFSkYbuE22sZMC5UXi4A51wn4FrgdwBm5jezSnTctBZJQDvnXBLQHjiIjp24MLOVwNEG2c09TiYC75rZUTPzETzJb3RyKc3TVNuY2TtmVh9aXAP0Dj2+GfijmX1pZsVAEcFzOJ3HXQCnOW4g+E+e2UDkBAY6buIg3gFUL6AkYnl/KE/iINR1ZRiwFuhuZgdDqw4B3UOP1WYt598IflGeDC13ASojftwiP3uvXULrq0Ll5cLoB5QBi0NdLF9yzl2Gjpu4M7NS4EmC/6E9SPBYWI+OndakuceJjp/4+Gfgv0OP1TZx5py7GSg1s88arFLbxEG8AyhpJZxzHYDlwP8ysy8i11lwqkZN19iCnHM3AEfMbH286yJNSgLygRfMbBhQw6luSICOm3gJdVG5mWCQ2xO4DP3XtdXScdI6OefmE+zivyTedRFwzrUHHgQejnddJCjeAVQp0CdiuXcoT1qQcy6ZYPC0xMxeD2UfDncxCv09EspXm7WMUcBNzrk9BLtEXEdwzE1GqFsSRH/2XruE1ncCKlqywpeY/cB+M1sbWl5GMKDScRN/44FiMyszsxPA6wSPJx07rUdzjxMdPy3IOfdPwA3AnXbqXjdqm/gaQPCfQp+Fzgt6Axuccz1Q28RFvAOoT4BBodmRUggOUPxLnOt0SQn19f8dsNXMno5Y9RcgPGPL3cAbEfnfDc36MhyoiuiKIeeJmc0zs95mdgXB4+J9M7sTKAS+EyrWsF3C7fWdUHn9V/cCMbNDQIlzbnAoaxywBR03rcE+YLhzrn3o+y3cNjp2Wo/mHidvAxOcc51DVxgnhPLkPHPOTSLYdfwmMzseseovwG0uOGtlP4ITFvwPOo9rEWb2uZl1M7MrQucF+4H80G+Rjpt4MLO4JuB6gjO97ALmx7s+l1oCRhPsPrEJ2BhK1xMcA7AC2Am8B2SGyjuCM+7sAj4nONNV3N/HxZyAMcBfQ4/7E/zRKgL+L5Aayk8LLReF1vePd70v9gTkAetCx86fgc46blpHAh4FtgGbgVeBVB07cWuL1wiORTtB8KTve+dynBAcj1MUSvfE+31dDOk0bVNEcNxM+Hzg3yPKzw+1zXZgckS+zuNaoG0arN8DZIUe67iJQ3KhD1hERERERETOIt5d+ERERERERNoMBVAiIiIiIiIxUgAlIiIiIiISIwVQIiIiIiIiMVIAJSIiIiIiEiMFUCIiIiIiIjFSACUiIiIiIhIjBVAiIiIiIiIx+v8BbFAOfQFBTzoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imgplot = plt.imshow(cv2.resize(th3, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC),cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize kernels for table boundaries detections\n",
    "if(th3.shape[0]<1000):\n",
    "    ver = np.array([[1],\n",
    "               [1],\n",
    "               [1],\n",
    "               [1],\n",
    "               [1],\n",
    "               [1],\n",
    "               [1]])\n",
    "    hor = np.array([[1,1,1,1,1,1]])\n",
    "    \n",
    "else:\n",
    "    ver = np.array([[1],\n",
    "               [1],\n",
    "               [1],\n",
    "               [1],\n",
    "               [1],\n",
    "               [1],\n",
    "               [1],\n",
    "               [1],\n",
    "               [1],\n",
    "               [1],\n",
    "               [1],\n",
    "               [1],\n",
    "               [1],\n",
    "               [1],\n",
    "               [1],\n",
    "               [1],\n",
    "               [1],\n",
    "               [1],\n",
    "               [1]])\n",
    "    hor = np.array([[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to detect vertical lines of table borders\n",
    "img_temp1 = cv2.erode(th3, ver, iterations=3)\n",
    "verticle_lines_img = cv2.dilate(img_temp1, ver, iterations=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgplot = plt.imshow(cv2.resize(verticle_lines_img, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC),cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to detect horizontal lines of table borders\n",
    "img_hor = cv2.erode(th3, hor, iterations=3)\n",
    "hor_lines_img = cv2.dilate(img_hor, hor, iterations=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgplot = plt.imshow(cv2.resize(hor_lines_img, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC),cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding horizontal and vertical lines\n",
    "hor_ver = cv2.add(hor_lines_img,verticle_lines_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgplot = plt.imshow(cv2.resize(hor_ver, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC),cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hor_ver = 255-hor_ver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgplot = plt.imshow(cv2.resize(hor_ver, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC),cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# subtracting table borders from image\n",
    "temp = cv2.subtract(th3,hor_ver)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgplot = plt.imshow(cv2.resize(temp, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC),cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = 255-temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgplot = plt.imshow(cv2.resize(temp, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC),cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Doing xor operation for erasing table boundaries\n",
    "tt = cv2.bitwise_xor(img1,temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgplot = plt.imshow(cv2.resize(tt, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC),cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iii = cv2.bitwise_not(tt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgplot = plt.imshow(cv2.resize(iii, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC),cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tt1=iii.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgplot = plt.imshow(cv2.resize(tt1, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC),cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#kernel initialization\n",
    "ver1 = np.array([[1,1],\n",
    "               [1,1],\n",
    "               [1,1],\n",
    "               [1,1],\n",
    "               [1,1],\n",
    "               [1,1],\n",
    "               [1,1],\n",
    "               [1,1],\n",
    "               [1,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hor1 = np.array([[1,1,1,1,1,1,1,1,1,1],\n",
    "               [1,1,1,1,1,1,1,1,1,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#morphological operation\n",
    "temp1 = cv2.erode(tt1, ver1, iterations=1)\n",
    "verticle_lines_img1 = cv2.dilate(temp1, ver1, iterations=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgplot = plt.imshow(cv2.resize(verticle_lines_img1, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC),cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp12 = cv2.erode(tt1, hor1, iterations=1)\n",
    "hor_lines_img2 = cv2.dilate(temp12, hor1, iterations=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgplot = plt.imshow(cv2.resize(hor_lines_img2, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC),cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# doing or operation for detecting only text part and removing rest all\n",
    "hor_ver = cv2.add(hor_lines_img2,verticle_lines_img1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgplot = plt.imshow(cv2.resize(hor_ver, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC),cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dim1 = (hor_ver.shape[1],hor_ver.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dim = (hor_ver.shape[1]*2,hor_ver.shape[0]*2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# resizing image to its double size to increase the text size\n",
    "resized = cv2.resize(hor_ver, dim, interpolation = cv2.INTER_AREA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#bitwise not operation for fliping the pixel values so as to apply morphological operation such as dilation and erode\n",
    "want = cv2.bitwise_not(resized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgplot = plt.imshow(cv2.resize(want, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC),cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if(want.shape[0]<1000):\n",
    "    kernel1 = np.array([[1,1,1]])\n",
    "    kernel2 = np.array([[1,1],\n",
    "                        [1,1]])\n",
    "    kernel3 = np.array([[1,0,1],[0,1,0],\n",
    "                       [1,0,1]])\n",
    "else:\n",
    "    kernel1 = np.array([[1,1,1,1,1,1]])\n",
    "    kernel2 = np.array([[1,1,1,1,1],\n",
    "                        [1,1,1,1,1],\n",
    "                        [1,1,1,1,1],\n",
    "                        [1,1,1,1,1]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tt1 = cv2.dilate(want,kernel1,iterations=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgplot = plt.imshow(cv2.resize(tt1, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC),cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# getting image back to its original size\n",
    "resized1 = cv2.resize(tt1, dim1, interpolation = cv2.INTER_AREA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find contours for image, which will detect all the boxes\n",
    "im21, contours1, hierarchy1 = cv2.findContours(resized1, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to sort contours by its x-axis (top to bottom)\n",
    "def sort_contours(cnts, method=\"left-to-right\"):\n",
    "\t# initialize the reverse flag and sort index\n",
    "\treverse = False\n",
    "\ti = 0\n",
    " \n",
    "\t# handle if we need to sort in reverse\n",
    "\tif method == \"right-to-left\" or method == \"bottom-to-top\":\n",
    "\t\treverse = True\n",
    " \n",
    "\t# handle if we are sorting against the y-coordinate rather than\n",
    "\t# the x-coordinate of the bounding box\n",
    "\tif method == \"top-to-bottom\" or method == \"bottom-to-top\":\n",
    "\t\ti = 1\n",
    " \n",
    "\t# construct the list of bounding boxes and sort them from top to\n",
    "\t# bottom\n",
    "\tboundingBoxes = [cv2.boundingRect(c) for c in cnts]\n",
    "\t(cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),\n",
    "\t\tkey=lambda b:b[1][i], reverse=reverse))\n",
    " \n",
    "\t# return the list of sorted contours and bounding boxes\n",
    "\treturn (cnts, boundingBoxes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sorting contours by calling fuction\n",
    "(cnts, boundingBoxes) = sort_contours(contours1, method=\"top-to-bottom\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#storing value of all bouding box height\n",
    "heightlist=[]\n",
    "for i in range(len(boundingBoxes)):\n",
    "    heightlist.append(boundingBoxes[i][3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sorting height values\n",
    "heightlist.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sportion = int(.5*len(heightlist))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "eportion = int(0.05*len(heightlist))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#taking 50% to 95% values of heights and calculate their mean \n",
    "#this will neglect small bounding box which are basically noise \n",
    "try:\n",
    "    medianheight = statistics.mean(heightlist[-sportion:-eportion])\n",
    "except:\n",
    "    medianheight = statistics.mean(heightlist[-sportion:-2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#keeping bounding box which are having height more then 70% of the mean height and deleting all those value where \n",
    "# ratio of width to height is less then 0.9\n",
    "box =[]\n",
    "imag = iii.copy()\n",
    "for i in range(len(cnts)):    \n",
    "    cnt = cnts[i]\n",
    "    x,y,w,h = cv2.boundingRect(cnt)\n",
    "    if(h>=.7*medianheight and w/h > 0.9):\n",
    "        image = cv2.rectangle(imag,(x+4,y-2),(x+w-5,y+h),(0,255,0),1)\n",
    "        box.append([x,y,w,h])\n",
    "    # to show image\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgplot = plt.imshow(cv2.resize(image, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC),cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv2.imwrite('imagegen.jpg',image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rearranging all the bounding boxes horizontal wise where every box fall on same horizontal line \n",
    "main=[]\n",
    "j=0\n",
    "l=[]\n",
    "for i in range(len(box)):    \n",
    "    if(i==0):\n",
    "        l.append(box[i])\n",
    "        last=box[i]\n",
    "    else:\n",
    "        if(box[i][1]<=last[1]+medianheight/2):\n",
    "            l.append(box[i])\n",
    "            last=box[i]\n",
    "            if(i==len(box)-1):\n",
    "                main.append(l)\n",
    "        else:\n",
    "#             print(l)            \n",
    "            main.append(l)\n",
    "            l=[]\n",
    "            last = box[i]\n",
    "            l.append(box[i])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculating maximum number of box in a particular row\n",
    "maxsize=0\n",
    "for i in range(len(main)):\n",
    "    l=len(main[i])\n",
    "    if(maxsize<=l):\n",
    "        maxsize=l   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ylist=[]\n",
    "for i in range(len(boundingBoxes)):\n",
    "    ylist.append(boundingBoxes[i][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ymax = max(ylist)\n",
    "ymin = min(ylist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ymaxwidth=0\n",
    "for i in range(len(boundingBoxes)):\n",
    "    if(boundingBoxes[i][0]==ymax):\n",
    "        ymaxwidth=boundingBoxes[i][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "TotWidth = ymax+ymaxwidth-ymin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "width = []\n",
    "widthsum=0\n",
    "for i in range(len(main)):\n",
    "    for j in range(len(main[i])):\n",
    "        widthsum = main[i][j][2]+widthsum\n",
    "    \n",
    "#     print(\" Row \",i,\"total width\",widthsum)\n",
    "    width.append(widthsum)\n",
    "    widthsum=0\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#removing all the lines which are not the part of the table\n",
    "main1=[]\n",
    "flag=0\n",
    "for i in range(len(main)):\n",
    "    if(i==0):\n",
    "        if(width[i]>=(.8*TotWidth) and len(main[i])==1 or width[i]>=(.8*TotWidth) and width[i+1]>=(.8*TotWidth) or len(main[i])==1):\n",
    "            flag = 1\n",
    "    else:\n",
    "        if(len(main[i])==1 and width[i-1]>=.8*TotWidth):\n",
    "            flag=1\n",
    "           \n",
    "        elif(width[i]>=(.8*TotWidth) and len(main[i])==1):\n",
    "             flag=1\n",
    "             \n",
    "        elif(len(main[i-1])==1 and len(main[i])==1 and (width[i]>=(.7*TotWidth) or width[i-1]>=(.8*TotWidth))):\n",
    "            flag=1\n",
    "    \n",
    "        \n",
    "    if(flag==1):\n",
    "        pass\n",
    "    else:\n",
    "        main1.append(main[i])\n",
    "    \n",
    "    flag=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "maxsize1=0\n",
    "for i in range(len(main1)):\n",
    "    l=len(main1[i])\n",
    "    if(maxsize1<=l):\n",
    "        maxsize1=l  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculating the values of the mid points of the columns \n",
    "midpoint=[]\n",
    "for i in range(len(main1)):\n",
    "    if(len(main1[i])==maxsize1):\n",
    "#         print(main1[i])\n",
    "        for j in range(maxsize1):\n",
    "            midpoint.append(int(main1[i][j][0]+main1[i][j][2]/2))\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "midpoint=np.array(midpoint)\n",
    "midpoint.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "final = [[]*maxsize1]*len(main1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sorting the boxes left to right\n",
    "for i in range(len(main1)):\n",
    "    for j in range(len(main1[i])):\n",
    "        min_idx = j        \n",
    "        for k in range(j+1,len(main1[i])):\n",
    "            if(main1[i][min_idx][0]>main1[i][k][0]):\n",
    "                min_idx = k\n",
    "        \n",
    "        main1[i][j], main1[i][min_idx] = main1[i][min_idx],main1[i][j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#storing the boxes in their respective columns based upon their distances from mid points  \n",
    "finallist = []\n",
    "for i in range(len(main1)):\n",
    "    lis=[ [] for k in range(maxsize1)]\n",
    "    for j in range(len(main1[i])):\n",
    "#         diff=np.zeros[maxsize]\n",
    "        diff = abs(midpoint-(main1[i][j][0]+main1[i][j][2]/4))\n",
    "        minvalue = min(diff)\n",
    "        ind = list(diff).index(minvalue)\n",
    "#         print(minvalue)\n",
    "        lis[ind].append(main1[i][j])\n",
    "#     print('----------------------------------------------')\n",
    "    finallist.append(lis)\n",
    "\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#extration of the text from the box using pytesseract and storing the values in their respective row and column\n",
    "todump=[]\n",
    "for i in range(len(finallist)):\n",
    "    for j in range(len(finallist[i])):\n",
    "        to_out=''\n",
    "        if(len(finallist[i][j])==0):\n",
    "            print('-')\n",
    "            todump.append(' ')\n",
    "        \n",
    "        else:\n",
    "            for k in range(len(finallist[i][j])):                \n",
    "                y,x,w,h = finallist[i][j][k][0],finallist[i][j][k][1],finallist[i][j][k][2],finallist[i][j][k][3]\n",
    "\n",
    "                roi = iii[x:x+h, y+2:y+w]\n",
    "                roi1= cv2.copyMakeBorder(roi,5,5,5,5,cv2.BORDER_CONSTANT,value=[255,255])\n",
    "                img = cv2.resize(roi1, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)\n",
    "                kernel = np.ones((2, 1), np.uint8)\n",
    "                img = cv2.dilate(img, kernel, iterations=1)\n",
    "                img = cv2.erode(img, kernel, iterations=2)\n",
    "                img = cv2.dilate(img, kernel, iterations=1)\n",
    "                \n",
    "                \n",
    "\n",
    "                out = pytesseract.image_to_string(img)\n",
    "                if(len(out)==0):\n",
    "                    out = pytesseract.image_to_string(img,config='-psm 10')\n",
    "                \n",
    "                to_out = to_out +\" \"+out\n",
    "                \n",
    "            print(to_out)\n",
    "                \n",
    "            todump.append(to_out)\n",
    "#             cv2.imshow('image',img)\n",
    "#             cv2.waitKey(0)\n",
    "#             cv2.destroyAllWindows()\n",
    "    \n",
    "\n",
    "            \n",
    "            \n",
    "           \n",
    "    print(\"--------------------------------------------------\")\n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating numpy array\n",
    "npdump = np.array(todump)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating dataframe of the array \n",
    "dataframe = pd.DataFrame(npdump.reshape(len(main1),maxsize1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data = dataframe.style.set_properties(**{'text-align': 'left'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#storing value in excel format\n",
    "data.to_excel(\"output.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data"
   ]
  }
 ],
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