{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Points tracking - **`OpticalFlowPyrLK`**\n",
    "\n",
    "Here we want to develop a code that given the coordinates of an important point on the image it mantains its location during the video."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import required libraries\n",
    "import cv2\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the video\n",
    "PROJECT_ROOT = Path().resolve().parent.parent\n",
    "INPUT_VIDEO_PATH = str(PROJECT_ROOT / \"data\" / \"recording_2\" / \"Recording_2.mp4\")\n",
    "OUTPUT_VIDEO_PATH = str(\n",
    "    PROJECT_ROOT / \"data\" / \"recording_2\" / \"Recording_2_points_tracked.mp4\"\n",
    ")\n",
    "\n",
    "cap = cv2.VideoCapture(INPUT_VIDEO_PATH)\n",
    "\n",
    "frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
    "frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
    "fps = cap.get(cv2.CAP_PROP_FPS)\n",
    "\n",
    "fourcc = cv2.VideoWriter_fourcc(*\"mp4v\")\n",
    "out = cv2.VideoWriter(OUTPUT_VIDEO_PATH, fourcc, fps, (frame_width, frame_height))\n",
    "\n",
    "# Set frame position\n",
    "cap.set(cv2.CAP_PROP_POS_FRAMES, 70)\n",
    "ret, frame = cap.read()\n",
    "if not ret:\n",
    "    print(\"Failed to read the frame\")\n",
    "    cap.release()\n",
    "    exit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In a ractangular region of interest I track a point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I am giving  big random numbers for x_min and y_min because if you initialize them as zeros whatever coordinate you go minimum will be zero\n",
    "x_min, y_min, x_max, y_max = 36000, 36000, 0, 0\n",
    "\n",
    "\n",
    "def coordinat_chooser(event, x, y, flags, param):\n",
    "    global go, x_min, y_min, x_max, y_max\n",
    "\n",
    "    # when you click the right button, it will provide coordinates for variables\n",
    "    if event == cv2.EVENT_RBUTTONDOWN:\n",
    "        # if current coordinate of x lower than the x_min it will be new x_min , same rules apply for y_min\n",
    "        x_min = min(x, x_min)\n",
    "        y_min = min(y, y_min)\n",
    "\n",
    "        # if current coordinate of x higher than the x_max it will be new x_max , same rules apply for y_max\n",
    "        x_max = max(x, x_max)\n",
    "        y_max = max(y, y_max)\n",
    "\n",
    "        # draw rectangle\n",
    "        cv2.rectangle(frame, (x_min, y_min), (x_max, y_max), (0, 255, 0), 1)\n",
    "\n",
    "    \"\"\"\n",
    "        if you didn't like your rectangle (maybe if you made some misclicks),  reset the coordinates with the middle button of your mouse\n",
    "        if you press the middle button of your mouse coordinates will reset and you can give a new 2-point pair for your rectangle\n",
    "    \"\"\"\n",
    "    if event == cv2.EVENT_MBUTTONDOWN:\n",
    "        print(\"reset coordinate  data\")\n",
    "        x_min, y_min, x_max, y_max = 36000, 36000, 0, 0\n",
    "\n",
    "\n",
    "cv2.namedWindow(\"coordinate_screen\")\n",
    "# Set mouse handler for the specified window, in this case, \"coordinate_screen\" window\n",
    "cv2.setMouseCallback(\"coordinate_screen\", coordinat_chooser)\n",
    "\n",
    "\n",
    "while True:\n",
    "    cv2.imshow(\"coordinate_screen\", frame)  # show only first frame\n",
    "\n",
    "    k = cv2.waitKey(5) & 0xFF  # after drawing rectangle press ESC\n",
    "    if k == 27:\n",
    "        cv2.destroyAllWindows()\n",
    "        break\n",
    "\n",
    "\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fdb70e89700>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZsI5Uq9UKd2e73ZZmszlTP7YaPZpX8KeOXYrlv1+AGCpL80LICpuHkD+vJfKB//F4XIwVrxPq+migY/CzdrVaFotud6ytVh95+af2Q5m0i6RcJSxmqek4KaCXo1zf9RagSL6rztP09fNYuJ4kuWDHdfC08dAAlxFIi5pcFoha/ZNSnhYiuj8gCHG7SL1el06nI2tra8VhdjyDwGs2m4XA1Ja1199cH12vUNtTxiDUD6GbT/ib26e9BlYbQtZcyMLFJ9fyn4csrwfXAxYirxvCOtRtAi+w1Y98YD2CLEsFeeXOr5w+0v2L/ynW46JI89A8yk3OHNAAqF9DlsKnZejQAqCIPUktAWwJF/1c/0/RcrgeMYrVIbWsMgyRa7GWcd/w/5i7CBSaiLxbEwJseXl55n10fFyBQcJ6vU/MxRxTajRfWXXWAO71mbWhxQNAtnC8K8y4jjELLgQ4/HwRQjim7Vv14vIHg0ERhvbDLYo3U3AbofwwsSucrU38ts5jprQrNa5HZRXHRZC2ksu2I0VZ9wDQa2+IJ6w4MTpUAOiBXKrAzmH0mHWmhUxqXjlArMsNtSnHMk2ZaDnaaAgcy/Q5NERsZoElKLJrOUGwWcLMs5ZSANCzaq30sTGMlRcCPp2HBlxrrD2rz3LxecJrXo07h7yytGUkMusixTesQ30FG/dZipvRG3NPBui674fltp/jU0YeWfXx0qQo3J6hEsob4XclAHraw63SmnRdUimm2c9rFYIxQ5OV46VQDDxTQNcan5Q2YwJtb2/LlStXZgBuOp3OgJxXV35unaOziC0hXXerjd6tJxYIIR232zvSYP3XdbAEUSoIWpaWjsvttCjVGxDjyxjPsqsTfACrENYev6me+xXHK/TaoG5fjnLggel+WG+pczUEFNb/XIXLm6up/MiUagVa6XKBD3QoANAyo3W4FZ/JYv6csuelXAbMzdejMpZy7FkM6HTcVI2TaTgcyvb29gzzA3SsC5G98kOWUyieJRCttpQVVAzSMQCM9V/M6uMwrw9i2j7CQ2XH+sKLF5uXzGN8RILXgOH6ZD6p1Wp7XNGW0I3VPRXwdTtzn4XKz1WQvbBc8EsZU/071F+xtsfGIbf/DgUAatIgGOoQaxBThVaqdjIPpTIa4pYB7xRwigFlqAzWvDk9v1UB4Ts7O8UtLdZkRBt7vd4egAhtaLEEfGiya4oBZap1g/BGoxEsy1t7sgAwpX4I4z7gezMtLTokWFN5IFcBiFkSzC+cRtdfvzGD+XA6nc7c7QoXKW+gQd8gDNbkvJ4Sa605x9qxKFVp8+KHFKpQeG4drTliGS8xL0uojNS4oEMFgJqJeLJYNM/Axpg1Bi5c/iLrpxktpq3H6peSxvvP381ms9iYwMCHMYMgunLlily9elVEwtd8TadTqdfrZj0tQc7xvN2FXrs5zAJSK57VV5xHo9HYsyGD64sNPLG6cHtjIKLz4/nCfZOihYc28aSSB/BWPA1mVnpLcRDZfYkxPwfADQaDIhyuUvQLXslUq9UK/g0pCXruMfBy/bTihuvjLJDIoRBPW2GeImWFaXd+GZmp+VBbbB747ScdGgD0NAg8A8W0HU6Tov2WGaSQBubVM5SHpVmFSPdHKpiHfnvPmMn15QT8Iln9CiLOIzZBY22O9UeOINZ84fWfFTelLlbcFBAM/bYAw6pbSENPpTLegViYphDQh5Q9bo9+awQflkca8CUDfszKiPEk8tWC36JFyBaEeTIiVfHznnNdy4Ai0urfZdpeJu2BB0C2JKxnFoWAD+lSB7Os1uZpWvpjpVkExYRfSLO1SGtu+jcuQmbrZjAYFDs5caRhPB7PWHaxSWn9j5GlEMXI6wur39B31iYYxMdNNl5Z1hb0VAUpt02oV2icU3jc6hfW+nWcGEhzWk4HK42ttRRljNPyM/zH+iFbawA/LovdoeBly7Lj+lsvLrbmoO4nr30e5couT/bsB1kgrF8thnjzytRUOhQAiO/YRADFBjmmKcfyT6UY+IUsE0uQxISYVX5ISQDFLEZLoFjjot+s0O/3i9cM8RsY2DVYRpinUK6gwHfIcrImuNWPIaVNZPa4hlUHnX+qFh/iixj4eWMem28e2MQoZLWIyIylppc5vDJ1+UjLRyd4HblWqxWgqF2v2D2q68bjgRtqeLy5Dh7ghJSqlH7TFOvzVD6yCPXKqVvIWMnlEasuOXTgAVDEZxivo5mhYh02D/h54KvzZY0T/70t9PzbEhCe0AgxXYx0vgwEntXHYSxo2O3JZ/isN3/HxsayGnS7vXZaAsuypPQzbnNIcFljyGMRWmuD8LXKturuCS49B1B3LSg8ZSpGoTgp/GaVy8+88ddKYmiu41vnpzcA4Zv5lDfMAOwAirweCIDU4+EpCbq9KYI71j8xhT7U1xxmKeAhOVkWlNGG0P9QPrE5n0oHGgBjmp7XofpN0jGQiwlTK43FcDEtPnQmzRtw3QdWXWJ1DKXzQNcC25D1B9cPgx4us2YtvlKpuJtbdF11/b2JGrIkPPBDWAiMcwSQ9TsEgCEXqMcrlpDU664epShTIQpZWlb+FoUUOP6P9lubUhh8NH+w8oX/+rmIFO+LrFRubpKp1+vS7/dlMBgUd8o2Gg1pNpvSbrf3XL2Wyguacvo7lfdy8tXgZylYiyJPRuf0gZdvDh1oALRong60Jh9bOwiLgY5FqVp8zCLRZXoWSQj4Qv91WIqVOw8j6371Jp3V7ykC09OuU0AwZF3lpA2BrEUxUPa09FBeFoX6MNSvqWMb4y09t3R99HOrLgBDPueHvDQAYs3V2p3J8aGcIW8ozHyxOsDSu1bPqmcu+KUqDbosL44Os+SOJ4ty6r0f7dR5p8zrFDp0AJhLKR2WEic0iB7o8W+P2RapfVkWWm761Px1GbyhAP/xze4mXpPR/aYtSq3Be/3nKTZWmCdsQ0KYw/jgNd44kXJPJ1PIktZtZQFukd7ViD7m/xYQhdrJ9cwhS0kKga1VNw001WpVut2udDqdot85T25nr9eTnZ2dmbfXI1+2DvHS5Ol0OnObDKy9drtdvFVkaWlp5qL1er0+M+4M0FrZsyxRPW/4exGyIKbUxeTRPMRtsV5XlwuEXp/k5HOoADC3E1Pyy4kXEhwp2rwXD+Fl26Ytp1QLT2viXp6crzeJuY0AOwgF/dLTkObM64e6LhocQsCow1OUkJDg4HC453AxM4RoqkABwGshye67UN2s/PTY8NsQkN7iXwuEEJ5D3hxJBUFN3O5GoyHdbtd8pRUDfqVSmXHF83P0Cfq53+/P8GO9XpfRaCTValWGw6HU6/WZV26Nx2NpNBp73jqhgZDrxvWKzckUSywEClZ+llKhn1nle4pMqG5eeEgZt/LVypDXtlQ6FABoCfVFAqFXpqYUCy5mDcbyCjGfxUyLAs1QPH2wPVQXLQBZa/fi4JuFFQQZwMKrc2ycQmDrCYVQehZyvMHH2tTkkadMMFhZdYkpKRrw+NsDIquddxKBJ4bD4Z6X4uoPLEWtYLBSMJ1O5fr163L58mXp9Xpy4cIF2dzcLMay0WjIiRMnZGVlpcgHO5cZZEV27yPl8bLmiKYyyq4HCrFxS7X0QiCXyxseX6fkm1rXVDrwAGgJ2BzLLRa3rOUVEpYpgtSL55WTQrkWbUo8fBiQQnnxfxZUoWvMRGy3Ef4DZPD2dxZwMYpZF6HNJgx2Vr3xJvoylpNWarg8XvOyyNKs2TrQSoVu834rj4um0Wgk/X5/BgD5NUfot2PHjsnq6mrh0tQuS4DVlStX5OLFi3LlyhX5vd/7Pbl06VLRb0tLS3Ly5Em577775Pr163LhwoWZ9UARKa65g1tUK0XsehXx53lIKQlZRl4cKw/L+lsExSxSreRZSp+uZ065qXTgARAUsn5i5nkZ8z2FUkAw9Du1jpw2pPWHwnKIGZWPMFjXzlmaniWcNRDq+lr5WddihQDJIy9e7LVJMdAO9XHqeOr/ofJSytQupJCyoX/fKZQi2HmXKMI6nY4sLS1JrVaTVqtVrN2xq7pSqRQW5XQ6lbW1NVldXS2AC+CmN9LwXGB3tf5Ya9xwmab0s5YFZfgoNZ4uKxdcUuMvkr9yAfxQAKDnUliUNhMingBW2VpwsqC3nntl8GSzysQECwlGnS7m9tL5a1cnbyZggAMwcDmDwaDYWQfBpDfGIG7oVUX43tzclBs3bsw8Z8sIr7vhNCmatH5mgRzXz6prKF8eh5Bb1LKcLYVObwSKEafVF5SjbiGX9u0may7V6/Xizs5OpyO1Wk2Wl5dleXlZ6vW6dDodqdfrsra2JseOHZu597PRaBS/AYD33HOP3H///bK5uSlra2ty6dIluXTpkjz77LMyHA7l2rVrcvnyZWm1WrK0tCSVSqW4yGEwGMjm5mbBh7D2cA8uLuFm3kLdEd9S4nX79e+c/isLiPtlhSFNbjmLoEMBgHcCpWil1uT10of+a2Gr3Qae1mYJ0BgIcv5Y72BNVl/ppa0MESkEA85RcRvwzZsy+LfuM2jKg8FA1tfXC+ECbZ5fdWPtvMwBCgsAtRLjgaQeXz0+erOG1+/8mzdvIMxyGVltscbeuhYMZB0Uv1MJ1l69XpdGoyH1el263a6sra1Jo9GQtbU1aTabsra2JmtrazPvCkQaHpPRaFScBVxaWpL19XX5/Oc/L81mU27cuCGf+cxn5NKlS3L69Gk5fvy41Go12d7eLqxEuGP1OwdFZGb3KeoAF2zqeC6CbiXIoDxP/njxOCyUnxcnhQ4VAKYIunk1qNy6xIAvpU7WQFvCSafV4JbDXAx2vNmE3TyWUBaRwn2E5yIyc7N+q9WaEQAemHAYAyZcU6irjqetqzIAiLK0FWoBIJdnjbuloDBAW6TT6NtJUoQJkzf2mo+4TrqMFE9BjKx6WBYDW8pcB6988Jm+W7ZSqUiz2ZROpyPNZnPG2gMA4rd2v1erVTl16pQsLy/LYDCQ69evS6fTkWeeeWZGEdQ8h3AAKeaTyO7GqNA6bhkLyqP9lnc5bn4G+VAenkwKlVG2zw4FAGrrIEUjXmTZljCyQE9vmihTlxCIpmhT+rlVX3bVjMfjPUKFBY52k02nN9/pB013OBxKpVKR06dPy8rKygwAWuekGFR4B53ur263K2fPni3KFZHicm0GbX5epr9TNsHEANDakBN7HZK+K9Ta9DMPWTwEIVKtVs01rBA/5VihXnodJxUErbtkl5eXCx5dWlqS5eVl6Xa7xVk+8BY2wjDvNRqNoh4nT56UarUqy8vLUqvV5MKFC/Knf/qnM4ogyqxUKsU8GY/HxTzQvCwihaXq9VWoH8tQWXmTQqlLC9ob5fFXah0XAe6HAgA1WRM7FqcshfKOaS0pll+oLfq35e7UaUMKgqWhscBlyw8fXGXGYDMcDgsAxfb0SqVSuKm0wOENC9wOr09QV75s2Js42lqa11rRIK0/HG/RQkcrW6nKTYqGrZ9Z7cl1zVl1YEFnWZgpQtCaE1pBAA+y0iaya9XHvAYoB4oKNtAsLS1Jp9Mp1hVRHqdnxUa781FeGQUmVUakpFtE2boeHNfzHnhySZezH0Bt0aEAwNyJWSb/XK0kRUCWKRMAg9/MaJXK7kFfPOd4WlDEBCgYWV9ezb+vXbsm29vbM+5CbalUKjcX+ldXV/do3AA/3ORx5coVuXHjxozWbAGudeTBEqrcD/NqzyIiy8vLxUaKUJ5awOl4sFosQt9x3fXvWF3LzAldR+Zlz22VYtUtkiwBCcDDWjP4bjAYyMrKirRaLVlZWZFz584Va3YAPbhAkQ/4CRu24MEYj8eysrIi4/FYXv7yl8va2ppsb2/L+vq6iNzkC/ZswBMCEAav4sW63B6eK7H+1G0v07+eTFoUhWSXiMy0NdZmi0J559KhAECR/HWIHCoT37MGUvPXWjHIEvgAC5HZG0O0ZaYnmqeFQjDoNQ3NtOPxuNiNyVYchAqX3Wg0pNPpzMTjXXDLy8siInLlyhXZ2dnZ0ya0C4Ku1+tJv98vXE7T6XRGu4+tsZUl9AvqFgIovvbNGkOv/z3BEGtPGSvNy2MRFqzH14tWWMEDo9FItra2ihtbRG66xdfW1mQ0GsmJEydm7vMEj8GSYz4HT4nszp1OpyOTyUQefPBB6XQ68uUvf1muXr1arOm1Wq1iUw3XaTKZFDfLID8GwVwgmFex0OA3LwjmjKeliFv18yjmGcqlQwOAPHE9l8+8E89jPE8jS9WwYvWy2qPbycwM8PGsPc34Vp0R1u/35dq1a8VhY17PGo/HsrW1Jf1+X44fPy733Xdfsa1c77J7wQteIPfcc8/MwWN8sB19MpnIlStXpN1ui8js/aG81gKrEs8grNi61LfLlCHuN14f4h2S3ljp/tY7LlOUIk8JskDKiqfr5fGMrrulwMFttwjwCs0hLZgta0V/s1cC/NfpdKTdbkuz2ZyxDNnzgP4A//AmL03IfzQayfr6uly9erW4IYatRhbwWAsUkQJkAczj8Vh6vV6x3sruVJG9lx5Yc9Xii3n6PTVdyCMQqiMr0ProVE59dFzm7VwwXzgAPvHEE/Ibv/Eb8pd/+ZfS6XTkm7/5m+W9732vfPVXf3URp9fryb/+1/9afu3Xfk36/b689rWvlZ//+Z+XM2fOzFV22U7IyT/leYqLYZ56WhszeLclAJBfNaQFIKe1NnNAEKyvr8sXv/hF6fV6srm5Kf1+f8aigdB4wQteIA8//HDhcsLZLJzFWltbk263K81mU7rd7sz63Wg0ku3tben3+3Ljxo3ifJ/uL8SFBdhut2cAEMKPARCTTfd7qO/ZVQNXGEC90Wjs2dQSyo8FLueNfreEgMUbniLDeYQseuYBjzTIWPVj/vEUTjzX7bUA2AK1EF9acwxuxnq9LisrK8XRBxyDAH/BMuRjEMPhsLDOrD5mZW84HMrOzo585StfkWeeeUa2traKTS444oOxnk5vvvAZLtRWqzXTP/zKJWyI4XLBy1geiB2bCSnhXv/mgI1VHit7rJRq4vnEG+t4LlqKT6x8/b9MexYOgL//+78vb3nLW+RVr3qVjEYj+bEf+zH5u3/378pf/MVfyNLSkoiI/NAP/ZD89m//tvz6r/+6rK2tyVvf+lb5vu/7PvnDP/zDUmVak9CaoFozKUshTYr/zwPEMa2GmYYZUe80TRVYXI52c+IMH5/lgysQN2qsrq7KiRMnZHV1VdrttjQajWLtBeeyWq2WNJvN4gZ9gCwEBTRhLQxAqBMsS3Z1TSaTmTNdbD1qYZwKgABOBkBPG+f/Vjm6/8vwRghMdJjF6zFlLDQnFqVUWu23+tMLCwnZSqVS8AADCAgANhqNijOj3pq5ZbXwB4oYyFsiGI/HBb8ib1ZYrDms24r4ekxjcigEdCkyzErjyY4UnuZ281wO1T2FvHmYQgsHwI997GMz/3/5l39Z7rnnHvnUpz4lf+fv/B25ceOG/NIv/ZJ85CMfkW//9m8XEZEPf/jD8tKXvlT+6I/+SL7pm75pofWJAV0uOHoMFpqcKYyhBUNMm2Gm4/ryrjMuX+fDE1VrYwjDRMf5PWjTKysrcvLkSel2u/LCF75Q7rnnHjlz5ox81Vd9lXQ6HVleXi5eGcP3LEI7xjZzbBPv9/uysbFRCCcPAFFHBjveLIK1R7YYOK01Jqzx6z5HXSuVSnGvI4CdBRPSeG4h7ldLUw4JlhiF+MNrbwp5lkIuEOp5larYxerA1mGz2SyUq+Xl5eIbRxfa7bbUajX50pe+JF/+8pel1WrJ2bNnpdvtyurq6p5NTVDwJpOJ7OzsSL/fl16vV2xaOnnypPT7fbly5Yr0er3CsoElqRU7PhTPH1am+CXR3FfT6bRw40I5LNP/8yrknJ/HQzEQ5L6BFyUmO/eb9n0NEO6sEydOiIjIpz71KRkOh/LII48UcV7ykpfIAw88IE8//bQJgP1+X/r9fvEfO6/uBLKEWa4mEwM7kfDOKstto4Wz3nrNzMgL/iK7N7dAg4XwbzabMp1O5dSpU/LAAw/IsWPH5DWveY288IUvLK6FajQacuzYMel2uzN9oTd9wI2JHXzb29uys7Mj4/F4ZgON1VYID73Opw/W67Qp/WoJWgAhzjC22+0CiC13GX9bmn2qtszkabla0fHayuVqjd2rCz9nYRVK45Gn4IXa54Gfjgtrjw+846gCALBSqcizzz4rX/jCF6TT6ciLXvQiOXbsmNx3330FUCJvKG44StHr9WZucMGmmtFoJBcuXJhRFnnHJ9KAn9n9Ch4W2eUTeFs0fwAU+Y0TuX3v9fOtIm35eptgbjUQ7isATiYTefvb3y6vec1r5Gu/9mtFROTChQvSbDbl2LFjM3HPnDkjFy5cMPN54okn5F3velewLD3BdThTSAv2BsUqzwI+a4KnWoBWmLZOYnXnNugPNC98w42DCWwJ7larJcePH5+xtO6991556KGHZG1tTU6ePFms8+Gi4IsXL8pwOJxxd3KbsEayubkp29vbBfj1er3CkrMIdYA2rN/rxmcM8dFrgHo80E52n4J4swSEFl6MKiIzmxw8aw/11WColRE9vh4/6P9eGNrukTUvOD/vGcaOf3u8qutv1cGyANga4xuA8A0QwXhBmRqPx9JqtQqlDYobwAfANBwO5cqVK7K9vT3D52tra0V6rFNdv35dNjY2ZkAKilCn05G1tTUZDocFnwDAsGELddbjxHzJc9SKBwDktWT0PW/m8eSUHltP4fPGzkqjZaylnFg8HOJvr7yYQheqa4z2FQDf8pa3yJ/92Z/JH/zBH8yVz+OPPy6PPfZY8X99fV3Onz8vInEfdajzrHgp4Kef64PRKeliZYTyCbnLeBebXpNgyw7XNMFVo99dBxfmysqKnD17dsaietGLXiSvfvWrZWVlRe65557i3WhwGf3f//t/5f/9v/8nx48flwceeKC4ggqCq9lsisjNC613dnZke3tbrl+/XtQH72zTZIE1BM10Oi3WfDQIaCDg5xg7uMM0APKGIuwyhCWMTRDc/6gDW9bWS1jZEtOWJOqtrTVtkUEAWhY/54MyQxaqJouXYWEzGDC4W/NK56mfs0DXgtQK4/s+8WaHK1euyLVr1wqXeKvVmtk1jNtfAHg7Ozvyuc99TiaTiXzxi1+UZ555RlZWVuRrvuZriptfarWa9Ho9+fznPy/Xrl0r+AP5Ly8vy8mTJ2cO3I/H46IMWIu8jow+x5hoxYwFOgNfv98vfqMf9Fp0yHLSY2mBXq4M0nJXr+VpANMf3SdlKEfGerRvAPjWt75Vfuu3fks+/vGPy/3331+Enz17trhXj63AixcvFldbaWq1Wnt2UTF5llJIi+A0ocnraTpclvd7EQOUQpblp//zAj4EsmUNMqDA4sHbtpHnysqKHDt2TFZWVqTT6cyce6pUKtLv92Vzc1NarZb0er0iHYABecFdxB+RvefdeAz4tg1LG4bWHRMI7CqtVHbXkbhs3tHH29XRDgYE5GvVi8eE40MQap61NFsuQ1uMMcFnpbU0ccsSterHeWtQztHsrby8OcagyO/xw/hhswnzNt+/yf0PV+NwOJTNzc1ijW1jY2PmqrRerydbW1uyubkplUqlsPyhnECZ4zbp6/uss5sxgc/9jbnLyhzny3lZYxqSV3qOpACd1YZYHpbyFVOWvDpwnjq8jNxdOABOp1N529veJr/5m78pv/d7vycPPfTQzPNv+IZvkEajIU899ZS8/vWvFxGRz372s/LFL35RHn744UVXp6BQh4eETSg/raly+KIpJuj0RPAsECzmA3xgVfG5HOzu7HQ6BdhVq9XCesRGl263W7grIRDa7bZ81Vd9lZw8eVI6nU5x+Bi7RiFcALw7OzsiIjNrJ7qdvKMTbYPbCc8ZAHlsYhYgr8dYW83ZOtPCBG44rdXiN7vfAJrY4MMKB/e9tt65zswHloJngY0GNEsbt8rxgIrbBuDx8uK6eflpRYTrzC5oxGm1WsWOYvBWv9+XnZ0daTQasrKyMrMJhl+VBMBir8dwOCzOsn7qU5+Sdrst3W5XVlZWZDgcyle+8hXZ3NyU1dXVwkW/vr4uvV5Per1esW7d6/WKXabIWysg3B8x0oqryM054ll+rHxpmcR9rfnCkn0p8ovjsFucn/P8Y96uVHZfD8X1DylLoWfz0MIB8C1veYt85CMfkf/6X/+rrKysFOt6a2trhc/8TW96kzz22GPFtvm3ve1t8vDDDy9sB6ilxSI8x9yOgQ5PToQtYmBiTBhqBwsirQ1jpxosPghhfV/idDot1rq63a4sLy8X7R0Oh8VGEOyu4/5oNBrywAMPyLlz5/a8EobbNB6P5fr16zPWIISGbiv3M2vf+mwYC0w92bUVqYUGCw/djxYAIh+9kUHXXU9iBkXeSs8CwhImXhgLH698rX0zf3Be+jfno4ktG+1q98iyOEKKpAa/avXmq4Pa7bZ0Oh1ZXV2Ver0u6+vrhTLGZ01x3pQBEO5RAPeVK1fk+eefl83NTblw4YKMRiM5fvy4nD59WiaTiVy9erW4dQhtu3HjxszuT97x2e/3C1c+lD7NU0xWf2EM4C1hpQhHf3TfcRl8xtbq55hsSSVLMdLEfKE9JVrxypHNi6KFA+AHP/hBERH51m/91pnwD3/4w/JP/+k/FRGRn/mZn5FqtSqvf/3rZw7Cz0MW6IU0iDL5h5jI06hSKVRvS5DqtKwx8iI+gx3CIHjZNcTtgtsTh9ohaJeXl4tv655OfCzAY6GLNY3t7e1ih52eIJyGgYEtJy1QEFcDLYBVW3jIT0QK95YGGw/8LM1VP7eIrVVYhSgH9eQ2WRZoKi97oMljFLLcOB98azDltnKeIfIsGGsO6WfgW+wcxpovdh3jxcvs0gaQsNIHyw2btVBv8AjvxoSwxk50flMKLyNgPlnWrFZSrDms46K9WFoQkcLVy4olFDG+gUm/fWJRoBcaS92eEN/k8AjnuWjaFxdojNrttnzgAx+QD3zgA4su3qUyGoae2PidMnFjlOJq8OJYkweTjxflt7e3Z9bXeBMM589n9HDz/alTpwotGDvf7r33XllbW5MzZ87MCA19jKJSqZgHf6fTqWxvb8vly5el3+/LpUuXCm2aXWkafNAe1ia5TI4PYYHxGg6HMhgMpFqtFjf4c3xYxNgqz6DAeeOb1wVThYqlCCAPdouyQNXXc+l+0cqBxSMMRiHBo61/Lz8vjhb0HqDqvtTzSn/r3wAzPqIDReL06dPS7/fl+eefL0AQwKdftTUYDIozfEtLS8UmLj7rt729LSJSbAibTm+6PqfTaZE/K5lQ6rBRyuonDYIczs8xJpXK7lV/3A+wZNF2/MeF3BzXAiY9HhblPLOUXX1pvlZkNem+sRQfqw3z0qG5C1STpznkAqE1ES3mCAk8L19vQiCdjuMxpQYZCAdMUl7vY7ebrrPeYo6D77BM2u22LC0tFZOvUrHfPsHbtREGQT4YDIojD9iIoK8Ws9pm7WZkUEL/WAoJ14utQD0h2cJKtex0GZq8+Pq3Bg5tnUEYevlbdbZ4xwrTfWYJaI9HdVnec6vtFuh5fcT1Rz9AscGGGHbpg+dh+UEpw/EZXMAAN/50unv8ho+3sKeD7/1kq483mPHY6X6wKGYJiux9swgDO4elKuMpMiWFLP7R5eC7rMzVZc1bZ02HFgBBi9IWYqBn/dcCwkrvgWCKUGDiSQ+LCZoqrjDjScxaNd9ziZtOBoOBbG1tFeVh/QUbEDD51tfXi+MAOzs7Uq1Wi9fOoGxsD8eNL5cuXZLBYDBj+fFaou4/EZkBVAZFnly8kYU3zsDKWl5eLrRzgDfuiMQzWBmwoKzNKLGJj3p6QobT8RoPdiiiP2DRsgat2879FLK+uB+1wgSrgnfo6vxiFqLVVs3XFg/zRibPGtR9xm3hfqpUKrKysiLtdrtY6wMPTqdTWVtbm3kpM6xsbMSCkshWN6+/sffBAr4yFFPI2RuAD48VXq8Ey5aVQIC+pxiWrS++WYagHSm86MkyPf63gg49AGoGSwFEbwA8EMyJjzrMo4F5AMjuTuuIgQYLniTQolut1swFwgBETDZcOYVJuLW1JVevXpWtra3iLBY2EcBFNBwOZWNjQzY3N2Vzc1OuXbtW1Af9YW0XxzP99nRPGPNVaAyEsBJwUTL6EAA4HA5nDvLzZA4JqBTLyQMGb+LzGiErKqy86Hqxi5TB0VLIOD/LYtGbW7wx0cCm2+pZoFY/sBBNtQJ5bAFKIjff/s6ghV2e4/G4uKIPvIwznFjbQ795AIj+1+74XAtHU4jHuD4agNmNXqvVCouY+4YtxUUCiwZarbha/RLjBc0DOr/9oEMBgDEXjf6dSiGGyWUmZnKPAVIG2hKo0+m0sPIAfKylWkzH7k4+XKw/OPhdr9cLC44n5JUrVwor8MaNG9JoNAqw6/V6sr6+XgAgNr1YOz697dtoH7610OfnvJNTu64qlcrM5dnIA3XRmwYgWKzJzWPBmyx0fWJWuzW2epwgvPRuUVi3Vt/odUxNDFJM2nXNdWFLFWGeds/t93iP28sgyG5q8JkVh+uP9TfsAuVXC+GYRKVSKa7rg7IHhYeBD99wgTIQWr9TLeQc0nLLkmMoH4pktVotgBz9oi+G4Gc6zxh/hhQSPW/wbe0KDqW16uClD8XJoQMPgPulGYBCmk4ZgPSEj8foKWWNx+PiVhU+1I4NHtwOCB/9uhVYfhAMuEux3W4Xuz+vX78u6+vrMy4PAN329vaMBVitVosbXvh81Gg0Ks5TMWBh/YXfmK2FnhZS2jXE/Yp2Yq0HApJv3+DzW1AEACy8BV2PC4CAr7pK2UhikSU8eZx4t6h2uzEQhgRyTEjrcN5Ja1lCXhu11YcwfDOoa37E/0ajUVyfhyvyWKGBUsaWzfXr1+XSpUuytrYmX/d1Xydra2sFf2AO4J2VZ8+eLXgV4XixMo4v8I5Oy8qzfns7k1N5wArj8nX+4GO4Pfv9fnH9G+LwfagpZWsZEwImjoOP5jWrPyxlyCrH2qewH3TgARAU0mIsMzyFLNBDuPXbSq/rkJI2ZElY/+EW4e3ZPGF0WyB4YAFaH8uVwu9NQ/6w6LCxBa7Tra0t2dnZka2treJ8FNZYeI2NgQuCTmutqBMDIG6NYeGs+4i1Ui3EOK0GULZ4eHLr/vfixXjME3jIU/Myt8PiBYBIyELjelll6PzQJmtLf4hCeeM556sBEGvReiu/TqPdn9jRCSsPa+LYxVmpVIrzg9PpdGaTlxbavO7mKTY6LKbAehQCPw9wRfZelFCtVguXLp6FLs9OlYNlFf6Q0mVZgaF8cqzTWD01HRoADAkJJmsgcjS2mDBIAU2OZ9XLq7NXJoBoe3vb1FhZgEC48GXBfEsLhAqsveXl5ZkrznAbBV9EXKncvEpsdXVVarWabGxsyJe+9KXiJn22WBiwLCu3Xq8XAksDIK/DWFahnkj4wJ2LjS6Ih6vNhsNhsTYkMrsmFgJRdqeyQGEh5Y2jZ0F5pHfVYjxZcLPbDt9szVkgqAEb/Yxv9DWn13X2rD4dzwIwKDtY5202m7KysjJjVeN5tVotrkUEL4tIcZTh5MmT8qIXvUjuvffeoqzhcCjXrl2TwWAgp0+fljNnzhQbsSqVSnHRNcASyhyswVR5kQomMR7QeVogwun4bOPGxsbMEQzMz1yKAZ1WSELgp+csg54lK3PAq0z9NR0aAATldqDH1DGNNwaA3jO2NFLySyG4O7GbDaQPfgMMAGB8loiF0mQyKTaH8MI6dntCy2bwhEYtcvOSa+wgBVlrexbh6ioNgKzt81hBwLNw1mnhVmUArFR274+EC1TXj2/UsD5sJVkau14XTFW4LGVOp2UAi1mgqVq0TqPXN1P42msbC0zeqAT+abfbhVIGFyg8Gh4AwlWOq/lOnDghDz74oLzgBS8oNmyNRiPZ3NwslJx2uy3Xr1+XkydPFrwExQ51whEd7Ybz5naqhRLrI4tHLNBjgpsXG9J4+YDfl5lLsbZaAKaVQK2Y6bj6/7xUJo9DAYDa/aCfMXkM5pE1ULGO1iBnWXqW1mT9t9wqbElhHQOWE9yfoXYw2FmbTxgU+eA4H5Xga56QHmsN0EgBuMhTTxrr7Q3VanVmwqC92trT7l09NmydMRjp9S3ddu6vFIvNAitNWrBZ+cV4RFvzDDoaoMoIaSu9Hi8ul/slx4PC9dG8w8dYKpVKsX4FJYbdpajzZDKRdrstx44dk1arJZcuXRKR2RfMbm9vy3g8Lnh3c3NT/vqv/7q45kxfa2at+3rKi6U46P62nnt9EiOrfPQZ2oB+Go/HsrW1NaP4cvycsvm5bhtbm5anJBeYQvFTlLkcOhQACEqZiJoZc4AwJYzdYzzBOb4W4FYe2k2IMGztnkxuXue0s7Mj0+lUlpaWpNvtzkxovlyar0jiXZ74r9f+4IbiG/e73a5Mp9PiPkYw/WQyKc4Iity8KxGL8jgzyECL/9D60V8iUpwnTBkT7Z7iPrcmOR9wZqCE5Yo8GVw4fwtw2crT6dk69eJ4pJ97QKfdtXpNk+vLeYnMbnZBvdh6Zb611ms9pS1G1voz7p6FQlStVuXMmTPFS5ZhFV67dk3W19dlMBjIjRs3ZDwey/Hjx+X8+fMyGo3kE5/4RHGJ9Ve+8pXiKI7eQLK5uSmj0UjW1tbk+PHjM5dmY31Zt62sQLeojNJgeRlYgQSAY273+3358pe/XPQfrjDETln0fah+HvBxHHa566sKRXY3i2lFyspT52/NRWtuMuWMz6EBwFQgC6UF6c71NGIdF3lZ6WMDHQpn8OMbXvr9vvR6vQKoarVaMeFRtiUw8W39RjwGSAZyrU2yFQIgYWGJuFwWrEe4vFjA8sXc1pjqsbD62RsjS4hx2bocDVQWKPGYx0Azlzc9K0LXX7dfh3tWiafUeTxsla3HKMbLOk/mLc2D7XZbVldXi7c7wC2K7f5wUXc6HTl27JhsbW3JxYsXZWNjQ5599ln5/Oc/X8wHAB/vIka98EovazOR1xY99rH2plCqdaN5kI/K8JjgnCvmHzwumHM8pzWfptZD84BlASLfVKAK1WURygfToQDAMszmkZ6UPDlF/G27/D82SJpBLAsBH4AB7/LEcQccLeC6sbZrbTqwhA5/9M47bovlOu10OoWwWl5eFhEpdqMhjO8axUREWp6MEEwA8NCWe93Puq4xDdYaKxZq+M9WD/qAJ74Gv5AyhbxThYul+Xp9oMcVbmPOy8rDyzP04b6J5cXhWhkL8SaUJ3gWTp48WWyQOXfuXHGEBQDW7XZlMBjIQw89JP1+X5577jl57rnnpN/vy/Xr1wtlkecO3KLgQb5AYpEyJTWvFL7QPKrLYWWZrXnMayjKo9FoZq09BDJWHD0HIKP0m2VClAK6MUVjXjrwALhIRhXxJ7+348lLH6MQ6HEYmJkPuuP6Lrx6BYIOjK2PGTAAeVq3BYi6Xfqmebgx4b5aXl6W6XQ6A4DYLMN1gLbNLtBKpVLsZuXjFl6/pCgZekxCGqgFVAjX5Wm3p45npWUgteqmQSJmWYSEFMaG02iw1n1k1cUCPa04hMZBjxm3D2k98AMA4v1/J06ckE6nYyqjlnC+fPmyXLp0Sba3t+XLX/6ybG5uysbGhty4cUOGw2HhRsVRHT4HWHbjyH6Tp2gxEIns3lojIjPeIJHdc7YAP3hjWGGyxjjEi5A3+ghWiCz+DvH8fgHhgQfAXLImZUiYWMDnDYDFKB7QeXXS8dj1qV/DwkJFW0uWpm0JMPy2hIn13xNWVh9x2XC/8M4/XKuG+JPJpABHy2rJ1aQ1GIQ0TOTvTUpP8OiJaU1WCxhTy7DqG7JQ0V7eGarL0nXX9bD4n/PU5Vv56//aog3xEMoD+MFToN3pDNjIG2PdbreLez+PHTtWXJa9vr6+p0/Bb3qNlft8P8iSRd5zS5nS8awwEJZOKpWbli7AD8eavDODqYoOb8zTyo4nFzi9BXBWu2JjY8nXEB0aAMxptBUXHckaKSabfh4SKlZZrA1ZQt0CSREptFL48vkdZqxlwerygEtbYNqNy1ei8SYEPNcuUs+i1H3Ewmx5eXnP0QncOoM6wDW1ubkZ7NeQBRMiPbFS41pWnI5ruRut+sTWmTxLEfmxoNZCG0IMcbkNvKVfp+VyddtxJlBEijGCsmIJYl1nyx3GfKQ3YOFZvV6X48ePy/3331+86kdv3EB5fLUe+HdtbU3a7bYMh0NZWVkpLl5/9tlnZ64KRLt4rvDh+FtBltIQklHof3zzMxBfNVipVArrls8Bw+rjo0xeuR64suXHF7freWABX6g/vHheXXg+5VjwhwYAPc3Z0ij0b0/LydVedH6WRm9ZgzELkBlMHyrX8TW4eR+rnfp5bj5WG0F8wwdAjwGX3ava/cp10e314uUKr5AF5+XLQMXjzGlCVqUul9PEKEVLtsYy1ULVH7YAdTlWX1r/LUC3FDJ8ms1mYf0xf/CmD92/yBO8huvvsCOZrRRuL7tWPXmwHxSzAlEPy+oP8RGHicy+WBduUSgCiFtmzrCc4v+htli/NR+HQFDnZxkVqXRoADCHebx4ekLq9QaOZ+XPYVwvizG1qwCaFKw5LMZvbW0Vt1PotTFdL5HZC6F1O7RmD+bH2SFPoIB030CzhJWKuFtbW8UN/BBag8FALl++PAOAuH8U4CgisrOzM3MsQdeB+5C/NVmWhzXJeIx0XBYy+jfn4/FDaAJbAs1qs2cF4jc+rPWyJWhp48w7HgB7Cg+vF8WEFJcbE0rMU/j0+/3iKj2AGfLk/uFnvIEKgL20tFSsUfPxHb7oQR8f8WSFbnOqoJ6XYkoLx9NjqePiykJek6/VasV81JtjLJBmvrLW/UJK2H4Sr3+m0KEBwBi4MXmWQ2jShwYxprVrjUi7LzkMExMbXPSGF6+uAEI+ouC5KNmyFNkFQHbjhfoGcafT3Vs0AIDT6bQAQH47+7Vr14pJp2//wOTjtULu65ilYQGd54rhOLFx0+UgPdquLSJNIUEZ0+o97VjXm9ev9LlD5i3Ul8vXriK2FjTvWwphqP0xS0CXa/Ep3kk5Go1mbhricUB8XN2GW1FAtVpNlpaWpFKpyPLycrFrmd2BIrNXiullDm6Dp6gsklL7LAbQHnDh6BQISxHoNyioloLE+bHMsrxSur77SWyJ3pUuUBFfaw4xlNZQvMkYEsYW01nC2auX1qQwGQEs/Foj1CGkpXvt4XJDHw/krfzYhYlwrC/gnB+sQHZ9Ih6e880z1hVuehLmHJHwxitF0Fj5WQJRAxg/t6wsrZB48azf1n+rzRYfWM+tcnU8nZeVr1VH3V8WabC0rAuOqzfjcBqdL3s6wHN8FjCHB/aTLLnhKeqajzze88pB/2FZpVrdvQ6QN8aErEuuo66rJZuQnr9DlBN3HjrwAKgFCcI87chKa2m6MVDR+VoT2BLSHqOPx+PClbizs1NYfli853ejsebNG3ZQd902D7QtCxB15vzY7alvjMEl0ngGIYM8GAC1+5TBDlYfj41uB/cpr19od7KVj/Uslbguuu85X8s6089504K1CcVTciyB6AlD/UwDm+bXULmct+YD5KWFpddejw9Zc2d3GjwhfDE35gmUJd0m1A958ByaTqdy8uRJ2djYkCtXrhTHH9hbcDsoBHqW0qMVLL2LFek8OYi+hEcJ4IdXR7FCyre4cB1YOeFd6Z6MzaFbBX4ihwAARfYyRmjwQ0yuB8sDP0uoahCOfeM3f/imF8sC5LZxmSygrDZZpMu2tDmr3VppYJcl4vJ2dVh9cHXq9PzhenljoG+WD/Uv56EFR6qwszRs3S8x0NU8ZdXF4mGdl5W3FYfz5LqygOR0nuVgKX0p88PLI1RX8D+7l/nDcbViafEs54k1dZGbxyOwls5XnsXkQgotCkBDYGzxI6cLPdf1hKwRkRkwTNkYY8kuXU/NK/w9Dy1SUTnwAKgnoccEFjNoYWz9tsrQeaBcHQYKuesAcnh1kH6vH2u2yNtb27OeawLQeEcZOA+23PQdovwGCV0u5231RwpZgtaa8Jh8FnhbE9DSqMsS84tef9NtZkCqVGaPEXibUXTaVA3fUtIsBUdEZvpOp7cULNRBH42wbp7hPmd+QBi/eV1bE7BC8LJmC9hwnk0rItpCCa1NWbRIAZtL3njq/7F4If7Ac94HgLVTvpsVL2LGxhid1upbS+HxwM+bJ9zGFNkxz3gdaAC0BBxTDAQ1iLA1op95gGLVw7Ly8FunwZ2eeGs6u0JZ++U6QPhYm3NiYK13hnpgqq0zACAmBi+Uc3yEsbWXQyElhckS6rzzL7ZxCXmUIWtya+vKIwY7zscCIS9tCAQ9/kZ8S1HQfKYBVwMXnw3kuBwHH7hHmc+QR6VSmXmHIYeB33AMQu9iZquDXXVcRw2AIYuG88XvEKUK5zJkKSMpdfF40FL6MO5Qsnu93sytMbwxZjrdvXQbeXFavbnKqrMnj1L6wpPt/FvzdCodaAAU2bsGqLXoVAtwnoHztC09mSzNmF2enkZlWTVWnbWA0wCgwU/fyK/PWum3RfAF2SElIVVx8CjU5+gPBlxuLyszOi89aTSVAUTNezovzYO6LVZaztvjae+5Tu/xPj+3QNMTwtbYMjDrHbKeUNJzggEQwhVXlTEg8jlAtji1lwXHc2ApsrtvEbRf4JdSXgpvcdxQXbn/oXzweUH0uz5Yr0FHK3WIZ8nTlHmWAnxc/7J04AFQZHdLtoi9CSJFSFhhqUweEjJgLksDheaFDS/6LkIIF10fvUGFyVqQ5quOsBaHbeV4PQrfFM9huCwYYfr1RRbDhyzLRVClsnuHocjsNn/LMsSH3wCAb2sCpU4obpN2/2nNWBPzrFVmSJvXwObxtwYn/eE2aEFogR7qbLkzNfihbezq1eDK8bAbkXdoPv/881Kp3DwveuzYMWk0GtLtdqXb7Uq1Wi0usYbQRjqs+8Gth5uTtre3Q8N5x5KlkOjfUBy8NDqdyKxs0m58HBGBd0pEZpQQS86wvOIytcKXCoIx0nOtDBgeeAC0OpUntRXX+u8J6lwQZLKEjhZ6vNkl5lO3tHDOS5epJ0TIArTeEYgPgBL/rbZ7YfOCn5ee16MsIc9HR0K7RL2wnPp5+VggxfFCQsrS6D0tP1SXUH56jEL9YAkxywLUCl7MLcdAyeuBlUpFdnZ2ZH19vXhPIFyh4Ed4IobDYQFy/JZ3vBcT3pWUtzxYbfD62xrTGIX4JTW9x1OhMI94HKCA6I0xjUaj6DvtddH95c3XEPiF4ofqrcfprgRAkb0WWEjAWGlTQE8LAItY2OoLq/EccXjCauDj9vCakS7fYnCLCbSwqlQqM4CG83hsAbKFqLU6JgYiDov1VSpxe6yx1BOL14VgHVhjHDtHaNUjRtZY5JSRyq+WguSBbywvfHsfjsdWK/MTAyDv5ET/c3wm3VfsAh2NRnL58mW5du2adDodOXnypLRaLTl9+nTxKh/w8Pb2tmxsbMzMq36/X1iAmGN8pSBf3pACcKH+y+lvj4dTx6oscTs9hYSVRxEpjo+wvEA+lrUZq6OnFIcAMabszWtJHgoAFLE7KiQwQ7/x39J6Y4SJbL2xAR8+mwS3pxaWLHQ8AOR8dZtjIMibWnBPIlt7+vZ9CwQxMdgVaQnQHAoJcm6rJ3y8zRh6wllWss43Rp7i5bVn3smaIihT4luKYggA2T2mrW7wklZUuN8twGbCOLDyMplM5MqVK7K5uSmdTkdOnz4tnU6n2PmJN5xXq1XZ2tqSS5cuuQCIMPA6X4atN9BY/WRRisDPzXO/yeNTPR94Y8xgMCjis6LseapS6qDB2FsquBV9dWgAMESehWdpuimd7mmyFiOx1cUuOX3Di+VKsL71cwvwLBCxdn9aO0G9cC5Pg4/eLegpD1wHBk8Og7aONLp9HqBZ/e+5aTTFQIzjpbhZyrhiLC3aAjCrniHBpuMg3OJ7DwB1HMtTwXGsME/rZ5D0+hdKIw7G7+zsiIjIjRs3pNlsysbGRnH3LEAPH7w4l49q8Pla3rEMIOe14pCQ98bYmotWWmtO5Vg2Hn/o8jE3WZnWFhzH1UCI/oLlDHco8tKywqonvkPyxFMqPKXJ65McOvQAaAkWvUXeGsQcS4afY2JhFxqsKwh3fHDrQugWCtRLk6fB8W+uN5/rgWsTZ3s0CMEy5FcVaYblNiOudWSD28EL6PxWCHarTKc37xHt9Xp7+hXlMrCxQmH1A4d5Fgrn741zSDDrPLkf5iELsLx4nkKg41nCldOyJc8CEGEcX7dxOt09G8hCmPnXOk6h1wCZf+v1uqysrBTrfNPpVK5cuVLsCn322WelUqkUrwobDAayvr4uo9FIjh8/LsePH5d+vy8XL16UXq8na2trcvz4cdne3i4+a2tr0u12RUSKa8BERLa3t2eWJjxLMaYwcd+j37TAZz7ieNa4cxrED9WHARDHGvgCAB4LXS+2+tE36+vr0u/3i5dgV6u7L8XmzUi6PfjNyq/I7jnoEI+DcpSDVDoUABjTuD1NVv+3wC4H/LguYAZ9YFyvRWiBnSL4vPZagk0DvHX0Qcdjptdt1RpxyD3KxOXz+8f4gmNM1BDQMOjxeqsGIos8ENN9pn9baT1QtPK1/jPFNHkGOU/w6TQhy8VTCj0XNsZZWypePN4yr8v3QJgVCMTT7/7D0gHeFwnCJph+vy/Xr1+X4XAorVZL1tbWZDQayfb2tmxtbUmz2ZTl5eUiPuYf+Jyv7UOZDEQpioZH3Efcf3o8dTnWuHu8EOMxS5nWbbHOg0JpF5HCJcrl8z2/3v2qml90XTx+Dj0PtTWVDgUAeqSZxBIEIdArMwCYNAA5TMxKpSK9Xk92dnb2vDgypf7Wf10ufyO+BXq829M676cvo+YytADgi4atfgPxTrx+vy8iu2+u4DSsFHA5nkVnCdJQ/zBpIaSfeeQpH6mKiS5HCzddB0sAx4RfiFcsshRBa3ettvJjSqMFHNwXely9esBTMZ3eXJsCQELowm2JV/rwy115nZLX/5gfURbeQbi6urrnDl4PvENt0n2i22nlkzt2Ful+5R2wnodCAxvioY/G43Hx2in0P7xKiK/zYEJ7WcmAwsTPc9sWUgpjdCgB0BMc1mSN5aF/h4gtE/jKRaSYsHC76A0yIYbXAsQTJFY6XstjZoXrkQFPg6GnoemycMiYb4ex6gdg463qljWBhXakQ/s90IuRnixev+aSB8pe+bouIUtC86+XnydELStBE4O/NQ56/PV5Mb0ZRqf36qjbYvWhxbt4XRbejVmtVvcsLYhI8bojfqekBkDctAQFdXt7W+r1enH2dWlpqWgnb1LTZ828sdUueY9/9ZGokEJWhrg8yCNWHHRcLpvbi3pBLvB9vHwuMLShCISxxfiwOzbFkuX6hspIpUMHgJZGrcFDT3gOS6GQJs7Eaykis+8c48muJ5Il1DyB5glPLcxyPiGBxeXwZMe6TEjgWZOOBZ4uJwR+HqBZdQ1p7FZaL55FMdALkVWux78Wn1m8otPExlLnzePHbs+Yi1SnD4E4t9WqE/MFlDIAIO7MZeUN86xarRbHePTLczVfg9cBcizQ8eHNNHxI3OJF7ketFGCeVCqVPRa0lT4ki3L4Df2HuvI6XwpxOri1WabpMA9UY+2wZHDqPLXi3NUAKGIv+Gsh601az4LR+Wlg5QkLpuHzR9PpVDY3N2V7e7uYaCEBwGEeIOj26Q82m2AjDja+8HZmdonCOuQ1S10/PVn5nBB+Y42P68LHKdBH6B8toCyrwArncQgpCFZ65oPYpIoBqFXXRVEIyCx+ELFdXJ5A0WSNA/cRAwGEn+YHkd230ocUE+YRFraof71el3a7XZwDrFarsr6+LhcvXpRmsyknT54sjjUMBgPpdrvFcQk+BgGPAkARGzdgFV26dKlIu7KyUvAvPBt6HvPyhffhtnKYfq0Z95HH09aZ1RSFzbKSY3zKdeC30KAe6EP0JxQIyBteP/XKQRoQ5+nVJ0aWbE+hQwOAqVpmyOIro3lxGhb4mMzMCFh8D62z6fJiE8oqW1t9en2PwYjj8JbwWPtDII32ayBloOP+sawGK+9Q+7kMT5Gw2mGls/6nCI1FUoolqPmPLTZLaQuVpfMAYRx5nJBGW+0I47J5HVGXZY07f6AoNptN6Xa7BajiyMNwOJw51iAi0u12ZXl5WabTqezs7Mzkw/XFxg0cqwBf4oW5yBtgpa/R03Nbbx7xxoH5EPkjnT6akMLLobFlJZjr5W1U4fx12/iWHmzug8ILa5CVnlDe1tlbrQSkKKacviwdCgDMMY31JOZn+ncuMbNoFwEYD2GeUEhpk54MWoAByOAOgtUXW/fTl1x7ZVvCld0kXA9YunqzDIMkh+v2WW1OpRBgaEAJpdfCSOcd06pD9cqpa6h+4D0LTLx2piqBADF2LYKv9Vq2nmNMKePKAhL8AR6GgMWaOgQxu+BarZZ0Op3iZbeWNVyp7LpWJ5OJ9Hq9Ygdos9mcsU4Y1PHNb7DQ2/69diKcFQfOky8B8BTc3DnALl1tqXv1Q1n8n3kLa3287oo3yMMdnVLXGF9qntfyWSsMmgdT6VAAoCZvYrN1pOPpNGWAEIzCm2BQHsIYFFPKsASE9ZutPjA9zud0Oh3pdDqF1qt3gupNMRZjepPc0lAhIPgYxWAwKKxffaej7odQm1PJmkSclwdCVjyPLAugTP1CFp5VB+tZDEhSQDBVAWHrhRU8y1LUdQ7xjcjeK+oqlUqxQQX8tLW1JSI3N1Dg/BnK73a7srKyIjdu3JjZHcpWkIjMAODOzk6hLEJRBDFYQcDjP8776nkYUiwsJYHvAeYNN5ym7BzAeV684SFFpllzHfUB6FWr1Zm3bUD2YG7relg8rsvUaTxihcQ6tnHXAWCKJqs7xtMumEICFPGZWdgtAs0VDKK1ZY9CIBMK5zawFajdnVoR0GGpdYrFw39LOHBZqQI4VrYHHrFnug4W6ITCvLogjgdkHuB5Vp8Xz8rPE5wh4IzlizieIGfh4/GSlX9IWEFpHAwGsr29XcwngJRuH2592dramnmnJisqKBPHJOAyxWF4XvfS9fQ+3I+W9abbyXJDf2K8mgIeHA6LDf1g1ceTMxZPssLHG2IYvFP5M9ZPKQpsbD7H6MADoAd+IQBMAUyPKXTYdDqdeYM7wK7dbkutVpONjQ25cuXKzDkcdnPwIIY0ZP7WWjJATB910Btf+BC6tR4oMnsQNkcDtbRWhEPg8EFjALAuax5KmVipSo1Oa40Z8tHWBSjVKmQrw6p/imBkINI78hDmgSrn4dWPFTvmU+YdXj9kQWkJd02aB2CZjcdj2dzclGvXrkmlcvMNEefOnZtxuSHP7e1t+exnP1u8vQDWGaxAlFOtVuXEiRNSqVTk+PHj8sADDxTtuHr1agGeXFeMMS9n8JyBDOD1slBbuW+4v2LzzCKk0XJhPB7LxsaGVCq7r4zKIWs5BEoJW4DYBFOpVAprEHLF2kGLMfD4LcajOUpAtI2lUmXQv/23/1YqlYq8/e1vL8J6vZ685S1vkZMnT8ry8rK8/vWvl4sXL85VjqV5emGxZxZTeh/WUgF+0+m0AJ5qtVq884/v0GPyJkFIIGrNFm3R4KZfcxQ6/iAiezTFUD28+nM4/uu68E5RnZf30eXxOFpj6lkjsXieJROLZ1naXn6pYQjn79Q6p/RHLCy1TM+7oPvE4z1LGA6HQ9nZ2ZGNjQ25ePGiXLhwQUajkaysrBRn9Zg3sKPzwoULcv369Zk5yRZKtVqVTqcjy8vLcurUKbn//vvlzJkzBcACOD1FKHSbEsCR7xvVH74S0boTOEYpirLIzbmM10J5sidEely5bL3XIaUtoXnMZfLv2DyYl/bVAvzkJz8p//7f/3v5+q//+pnwH/qhH5Lf/u3fll//9V+XtbU1eetb3yrf933fJ3/4h39YqhytFVgd5E1qJj04VjytZfF6X7/fl83NzRkrqt/vFwv4eONCaLdlrE4e02Atj9/swLdi8PEG7R7VAs3KP6TBe+lD/e31b1niuoXGGGVb6VLzx/+UNFa5Vpmh/LnvU9NWKrP3Tlq7MVO161DbWPB7z3PyL8MTeuw1MTholx3C9TqcPupglQmAEJHCJQtFj8vlvtB14f+8pqifI07IqizTZ5q3uN4WoV686Q0WIR+1sNps/bfy99LpOPit4+k1yBDtGwBubm7Ko48+Kr/4i78o73nPe4rwGzduyC/90i/JRz7yEfn2b/92ERH58Ic/LC996Uvlj/7oj+SbvumbFlqPmHYdEsSeBqMtwMFgIL1erzijNBqNinWK6XRa3E7R6XSk1WrNuEpTGTekOWFDSbPZLM5N8SaYdrtdnAfCBxtfPIulDKVaGF775gVETq+38wMQrHLLlqEFh5WfFd8TOrH8UkCQAYn73uLlHDDVwpw3wVjt9MK9fpqXLB7zXMGVyu57IlEPtl5C7kJL2KIMAF+I/1lxBj8C6Pr9vrt2GQJjLkv/9+KnKPs6HuqK/gPYYddsrVaTfr+/54wzL3lofvcopmx6/ZEia5j2zQX6lre8Rb7ru75LHnnkkZnwT33qUzIcDmfCX/KSl8gDDzwgTz/9tJlXv9+X9fX1mY8mzWgxIRwT9h7jWQCoF4BFZEbTZCHCWl0qA+iyrXjapRRzd4Ihc8HPA7TYfx0WY+qUiR8aV+t5CKC9/2XI0vxT+8Orv34ea7suNyWflH7y4rGA4/86zEub0u85wt+ar1A8cR8ovvljhVkfBkzvXFuoP0J9EyM9J1J5Pjb/rGcp81RvhrE2xfB45JInAzz5kDN/98UC/LVf+zX59Kc/LZ/85Cf3PLtw4YI0m005duzYTPiZM2fkwoULZn5PPPGEvOtd70ouPzTh8Ju/QSmmud6qjHeQ4dDt6upq4XvXWiTcG7VarXiVSIg8ENAbMkRk5uW22PoMF6j1aiN+FZHlCrX6NKWfdJoygi4l39y4UEDYCqhUZm8yyTnK4CkiIQvQq58FmDHLr4wg0fnjXJ1Fuk6WEjaZTArXO2+y4TgsDK0L4K32eMomwrw+jc3nXq8nvV5PqtWq7OzszLgqJ5OJbG9vz4BcSh9bY5cCbJYFqIHDa0cqWbyvwc3jec2Dui58MJ5v2IEFyDKK5Yu+/D6X2IjwlIAcWjgAfulLX5J/9a/+lfzu7/6utNvtheT5+OOPy2OPPVb8X19fl/Pnz5txQ1oz/04V6JoRNQDyGqCISLvdngljodDv94tXtcAl6ZXDvz1Tn8N4XU9fcea99khro7qPdD9YgjiVQuBqlTNvnBCIow90W8q0C/Xh7xjpcbcEN9cvVm5qmVZ+Vl04Hv+2dsIyH+n1cS2ceG1cl+f1R2xuegBjzZnBYCBbW1sF8POF2ePxuNiolrJjUssZy5Pi7Q7m+nkfK34ZiimzXrg13/U8gUKj1wBFpDh3yGf1+F7SUD1CFOunXFo4AH7qU5+S559/Xv723/7bRdh4PJaPf/zj8nM/93Py3//7f5fBYCDXr1+fsQIvXrwoZ8+eNfMEYFhkmfaetWf9BsUEuw7H1me+4Bp5gzmwJZh94pbLpAzpNlt3euoP9w0fel+EVbYIWiT4aQuK03pgvIgJ5VFZsNJCJwbYOp7mE89yxXNt8Xn5QbhB848d3PbGLGbpLGpMAHi4Uo0P1ovsWjRwb+qXxoqEXY2W1RebV6wwaCs3R8iH4ujxjMWzfmt5pccM/YV7U2H1IT3OXOpjT5aikyODLeUplxYOgN/xHd8hf/qnfzoT9v3f//3ykpe8RH7kR35Ezp8/L41GQ5566il5/etfLyIin/3sZ+WLX/yiPPzww9nl8UTUAOdZftr1iEHOAUEcuuUjEAA7kd13aMEVifS8wI0wrWGltFn/B/C1Wq1i8wufA2y32zN9ogHQauOtphQQTgXqEAAwz4D2q90Mutqy4nrpeqSCoEUWWLHVlpOHzo8FvuZhaP06H/54ALwI8IspNNghXavVio1ioMlkUtyYwhs8LGXJCmPh7sWxSNfx8uXLe2TBPJZhSCnx4vG35hX+huIDA6Ba3X0bh8juO0D5xieRvZegp7Rnv+bnwgFwZWVFvvZrv3YmbGlpSU6ePFmEv+lNb5LHHntMTpw4Iaurq/K2t71NHn744dI7QEOaGT+fx8qxGEAv9DIzWDcvwE0JsOJb0FlIIS9ujz7IDKuPrzfTh9uts0qcp560noC2mNR6rvPXNK+VaU0CPcG14Lbipwp+q8xcQeSBka6/pQGngqCnwMSsOSut1WdWGmvOgecsCwYCUyt7qYCcS5a1wB94S9AOfmGziOx5q4F1VlHPIet5qF4euFikx5L7Tyv0nI++ZIHrGrrxRiR+kQPXBR/rEgDNB5bMS6FQPI93Y3RbboL5mZ/5GalWq/L6179e+v2+vPa1r5Wf//mfL51fCOBiQlkkvP6Gb3ygIQ4Gg2KjCyYPXtaJoxGcT6VSkaWlJel2u7K0tCSnT58ubqyAFbm5uTlzphB3INZqtZl7RPFZWlqSpaUlaTabsrKyUmi42AjT7XaLMKzHwm9vTRxP0wMxqFs73/i3tcNU98eiKTVPrpMW6t7E1VY7wqwFea6LpYjxbw/UQ+DGddXrbl57rXK1YMK4WWMPENB1YTeoXuOZTqfFm8TZ9c+vFeIXJVu8Ny8ocnuxcxObwrrdbsGnm5ub8pWvfEUuX74sJ0+eLA7Gw8uDeSQixVEF5BMCIB4nVm4hLyBTRqORbG9vz/Spp8hxfnyNGwj9yrKi1+sVt9fglqqdnZ0C8OHCxga96XRatN2yCpmQdjQaFV6vbrdbnEfmNqOv0O/WGHsKpvWZl24JAP7e7/3ezP92uy0f+MAH5AMf+MDceYdcDjEhxBTS7DXj6tscMLGHw2FxC70lZOEeWFpaktXVVWk0GsUtDdVqtZgAAFgRMQUL2tFut+XYsWPFmT+2LPEaGayftlotmU6nxflD5BGy+Cww8JgvxowhcAo9s8rxLJ5Y+R5AaUGj22e12esP1uothYLBS1ve+tvrD83PMfDz8rMEtZU3h/Fyg1akNIBCSPM1gAAiFoaYL94yxDyCjtNirQpeGMxHgAHO8bZaLbn33nuLtvBl28gTdeUdj159eX0UvAIZwjvGMectS1LzKOYwlGQuG23k+AA6tn5RHo8xPErT6XTPWzFCShn6hIEXnihrnmjFOGfcDxwA7jfFhAaTJUBjwtwCP31YFUzearWK7eE4oI4jDwgTkeLKJT6ThLrhFhdYfpgk/X6/uFGmUqkUmhbHh/UH7ZQPvGMSoxwt7Lg9+mYM7hfuH72RBh/eYZpihaeQBpkyafHbAq55J1ZKvay+zyELyNmVpIHUEiycTgs2q+0sxFh50ptfWKtHHD5qweEaADG38J9BJiQ4c4hvSsKyAfNpt9uV1dVVaTabhSBP5bWQIsJ15nnIc0V7TCxlTWTXEscOVlhe+gywJssbw32MZ3wbjR5bjm/VDXkinn45Mqfh9lqUOwdTjByLDgUAiqQ12tJ4Y1qGyO6hdjAHDtPqg5/YXSYiM+4R3oGJsjc2Nopy+IwUQBRnq3Dh7NbWlmxvbxdnKLGQf/z48YLpK5WKdLtd6XQ6xa0wqAe7InR/aQGIcnlDAKfhSQEhh3B9vCKF2ctQGRDktNZNG/PuzrUsOyuOV6dYe0JWorY2LcDTYXrzmIjt2vaUBQYxHmeOAzcY542XqWLOQOHibfWshJVVSnSaTqdTLAtgjqBe9Xpdjh07JqPRSDqdzh7rKDQ2Xl/r+aLjoq/QP3qdHt88JtPptFB0Ua/BYDBzthjzHW1DGlZ68IxfbyQiMx4t1JXXbvm51TbwBS4j12+JRxooH1q2WP2pxz9F0UilQwOAoNQO0BNaP9O/tWbjWUUMCnwbi6V5icieb24HC1LkCWuv3W4XH9a62dK01uEsJslhGmuCxj6xsuaxuFLqGkqTOrnKkAdWFqWOgQeC2tKzNHNP6fHiWO0J1Z35mueLniOWBQEAhYVjAW5onlpgY9UfdYLVhPnc7/cLiweCPgZgIfL4npUOT3kIKaeYU6xU84uoOV+dF8rAGiCUDM1HnqEQartWJtHPKWvUVt9ZY1u270N0KAAw5H6wKARwPNC8WM+vO+LXpfBA85k/1ua8NQA8w7c+ZA/NrVaryfLycrGx5b777pOlpSU5e/asnD17Vkajkayvr8twONzj+tSXbrOg0X2B+k8mk+JAPzOxxVgMtvpmmdBGmNtBlrWXMrlzyHLFWCDoWQj83MtXh2lAtCxBz7LTZFnByKdarRZ8z/HAqzwvNF9bbeI2sCeB3W08B/XYhZQrCyy3trZkfX1dqtWqXL58WWq12sybWq5duya9Xq+4sAJWFd8Yo3kF8z7E37pttVptZv7jPzap6Ly0YtBut+XEiRNSr9fl6tWrcv36dWm1WsVyCN8IdfLkSWm1WrK2tlacu4YCv7GxIdevX5ednR35yle+Ipubm7K5uSnr6+szfd/v94v9CfzCYAZHtAXtglUqIoUsSplzek6E4ug+8pTuEB14ALQEjvXcExJ4ZlkCDG68TmGd5WMLzLL49KTR10JZ4If6V6tVWVlZkbW1NVleXpbz58/L8vKynDx5Uk6ePCm9Xq9w2VivPdJ9FLIamJG06yVEej1F57UIylV0mCwFRwvUecmyoFhrZ9LgaD1LsbisvPS3xfOcjklv6OB8ub+0Qge3OVz2lkXAZbDVowUXAyCsNJHdOWLVMdZHIjfvFMbb5CGcNzc3ZWNjY2ZHKvoLQOJZR9w2Pa/0uGhrT0QKUABA9vt913rjsHq9LsvLy9JoNAoAQ/lwj7bbbVlaWpIzZ87I8vKyPPDAA/Lggw/KdDotAH9jY0Nu3LhRKAVXrlyRSqVSXAmHccQOVeTPa4xa4UJ7eMORt4/AAsGQMuh5P/CsjKw58AAo4pu8njYeImvS6s0v+lUpPPDY0cnaHK+LiNyc4Fh/YEDla5j4PE21WpVjx44VRyhwnGI6ncrm5ubM2SV9FpCBCGXrdrKlijBuJ5hXT2JmOh1mAW+oz1PGJhYnVJ4FfoumkAIWS8fjoPvDeqafW3lxPqnCwRJKOn+2+HkHJzZi6Xmh+z42LxlQwHP6Basie19NZrWF+wEbyPBb5KalAqURR4XgNYHwRz2scqx5ZCk7un16vZSViphShHU+gDPPVZFdT0673ZbTp0/LsWPH5OTJk7KysiLT6bS4sYXn8+rqqgwGA1lfX9+zyQ/7DbhvvPmFdoxGI6nVasV+iel0Wrw4t1aryWAwmFFurLZ64+n1711pAeaQpaHzM3zrD9YIsAmG34CsrUSAkd4MwtRut2VlZaVwwWDDCTQzvBYFeddqNbnvvvvk+PHjsrKyIidOnJBOpyPb29ty+fJlEZFCY9XuT72wri0D3Wb8500wvJsLkwsAzpYmr3+yoIxRKsN6AGdpzTw+aBNb1ouy+hZBHl/yhA/xribPskutiya9EYX7kBWkZrO5B0BZkFrfrOjpsnnzBG/i0ptlYqAN4kvh0R+Y25XKzXO1jUajcPnxPKhWqwUI6HJiVgyXxxuHWIHQ7k+twPD41+v14tVq1Wq12GyCONgnsLa2Ji984QvlzJkzsrq6KseOHZPpdPdsJs4QLi0tyZUrV6RWq8m1a9cK0ALV63VZXV01287ECgnWVPXSEPiB7yvWu1YZxFjB0gCovQb4TuV1kUMEgKnCITRRLOsAk06/AkV3spUva3VMYAwwBFt8lUqlmKgoF9opPjjHI7J76SwYLGfdLWQlWExmxfG0rxwtTPdbipUXqhOHa5D32mQJsjJ1D5FnsVmWHsePUQg8rXw9suJZdeI+YwADSLHFxAoUiAEMLlO+6CEkwPDcm1tWm5BO119kVoHTG9di/WMpkF6d9M7JkLWI9rGyyvG5zuhjHKVCf6I9rVar2PEK6xzHoni5RLeTy2RgZpCzLFVWjnjvBFuqWqn2+tmSx7oPQ/IqhQ4NAIJiFp4VpgFPu296vZ5cu3ZtxjXAVhDShQBBZFcbhv9dRAoNs9FoyNLSkrRaLXnRi14kDz74oAwGA7lx44ZMJhM5d+6cnD17tnBtWFYXT2JmLKt9Vp/ovLTmb7k9+bOItb8U0EYZvLFAl8vCHx9YDHDJ6PUk71tTqsXhpdXt9Fw7ut1lSCsCVl04rmfNaMDAfbPb29sFj2LLe7vdluXlZalUKjNvQFlaWprZ4bmxsSEXLlyQXq8nzz//vFy7dk1arZYcP3585qiEZxWxVaUteq+92gMwnU4LyxVHnCqVm9agyK7XAwqpzo/PC3rr7shXRIp+4z7ls3ew6nA+mOsM7w52cSLvzc3N4qaXVqslX/VVXyUnT54sZBTkFMpAv2JN9MaNG/Lss8/Kc889J9euXSvAUfMAtxtWmVYe8QzLOehDfWEAv46NXbEYG74hCHwXUn49b1uMDhUA5ggJS4BZHwDW1tbWzC0H3s6vEAAiP7yWhW+DWF5elhMnTsjKyoq89KUvlW/8xm8sBMNgMJDl5eViUvLWZa6PNwF1mz1hzfW1Dq9aoALywDHV+kgl5MvuXe1y1X3OE4k3M3F+qeDHlBM31qZ5yLJMNJh5QiwHBPG7Xq/L0tJScWXW+vq6VCq7Lnjc/Vut3nzvXr/fL8JYMbty5Ypsbm4WSsn6+noxD/RaogY/kb1HCrQQ5vjaWuOjTHBF4pYkXCeo55SVP19P6PUXrkhEWXyEgS04KNYAF62A4mJ73pjDeW9vb0uj0ShuscEc4Qv6uQ9Go1GxC/bq1avy/PPPy9bW1gyIeLwQsgCRN/oN/Ypz0QiD8qGtee02F5GZ+Y56aKu4jNJ9qADQopAprScF/9YfToOPt2MO33oweKLCVXHmzBk5ceKErK2tyUMPPSQrKyty//33F1uacd1ap9ORTqdTCAvsssL6BYMBb4Dx2mnVWfeNB/Ch9nrPUygHLC2LUwMgBAlbsPhm4WkpKjl1zolbBuxy02lNmfPhOFa+Ogw8BaHFa+HoQ1gYq6urBZ8uLS0VaXFuFZtkINybzaYsLS3JdDqVbrdbWEcMCFbbNACKyIw1wtYJt0m3n12fbJnqo0PIw7IAeSkD6/Vs0aBs7a3gOjBQ8aF4fbE0v98TeZ0/f75Iu7q6Kq1WS174whfKC1/4Qmk2m/KZz3xGPve5z8ny8rIsLy/PyI+rV6/KpUuXZH19Xba3twvLfnV1tZAvfCOUJUdD8wb9wy7Q0WhUWPee7EAfsYy1DvJb6XOtwEMFgFpDDFlB+G1ZfLz7kfP0BKql/Vlls+Dd3NyUSqUiX/3VXy3f9E3fJCdPnpSXvvSlsrS0VDD6ZDIpFq6x6wubZXCDgmZOfvcfM4K1G4/bFepPTSEtzGt7CuUKeeuN9lwH1qTR73DtIh4fePYUhLJkgUkK6FhtLVtmKO8UC7BavXlFWKPRkBs3bsxYMhCOeH7vvffK2tpa4aaD9TMejwvXfaVSKTZZdLtdOXXqlHS7Xbl69apsbGwUQDsej2fGlechuz5jfcbzOKSkTae7a5ieK12vZXIYAzCfG0R9oSToW5MwtxHGuyOZHyuVSuHOZNfhq171Kjl16pSsrKzIgw8+WGzkaTQa8jd/8zfyy7/8y/LMM8/I+fPn5cEHH5TJ5Ob5P+z4vH79epF3rVYrjluNRiO5ceNGcVcxH29BW0PylTevwJ2JzYPYuKPzQn6QX1peefJdg99dC4CaQpNfd6ol/DhMu0GYga0BwbdmYv4WuXk907Fjx+T48eNy/Phx6Xa7M4vEmEx8vREvZkMzAnN6rgDdTh2HhTPX1wJLS7B7faB/x7TIVPJA2Arz2haiVCBkAek9Sw0rA75aobPytsbb6gevb8Dr4DOR2XU3WDA4f8ZuN5HZ24mQHwPDeDwuhLZVFw+krfHW8fibeQBhbPlxnFxlzAvHh4FP1wFWsb45ylIw8QxrlVBQ+IgU2tPv9+Xy5cvy3HPPSaPRkE6nMwOA29vbsrm5KfV6XdbW1op1OsiX0HKKHhtrnLRhgW/+WGPLspUVC6/Pua9yZcmBB8BQo1PAz7IA9XZtHDxltwDu2bR2hOp6QVAgrNlsFgy3vLxcbGrZ3t7ec94QvvJOp1PczoI6sIaJOrDw8drn9U8MtCwmCwmhEDOWAT0m3S6EWZNSj7feoWZdbJBC1sRMSRN75mm8sTqE4lnCP1Q3PeahfBEPd2yyZVSpVArLqtfriYjsmVf1el1WVlZkeXl5Jk/dRg1klgKH51pZFZHCsuL4lpeE8+RyWBnGvOP1ZwAdLDQAipUH1wtvdMB6abW6e+MOE5SEwWAgzzzzjIzHY/n85z8vnU6ncDWzq/DatWvyuc99Tm7cuCHPPPOMXL16VURkZt/B6urqzJsx2MOEC/v1GUOuO88xi1dgAcKyRx6wAHn3rz5gL7Kr7IcMlXnowAOgSDlBZIGD1lbwu1q9eQYIZ3XwfjC4KbVfnz/WgGETAVxFMPlxdgbrfnxHITMAC2ytJbKmrc9YxazRlH72QHJeQEM9c/LhNumJiOdWXO5DdnnrNDkU48FUa9B7HgMu63kZaya1bladIEjBuwA5CHR+4wlAA8ofrBi9BKGPA1h1Y4vBAu4Qn/I8RZuYMJc0cAH8MHcZCPGb3zgBpRS7vjkvyJLpdFqc54OLmduDpZHt7W25ePFisTEPRyDwH54hdj9ub2/L888/PyMj7rvvPrnnnnuKfCHbdnZ2inf7Ib1uu+YDqy/xjI+9oAwsPfAVcDpfDbLIz7Icy9KhAEARe8JaApDDY9oEaz3sasR/LtvadOIJvUajUWheN27ckL/6q78qNgRAG15aWpLhcCibm5syHo9ldXVVVlZWisnEgIk6omze4m0BXwwEcy23EJjmAqxFFqixgsIaPsLZZacFasgFMy+lgFZKO1PThJ6H6hLLj/sZwAUBDaHdarWKjTEiu9v9+SA5LAu43KbT6Yz3BOXg8DYTLCKuj6VosALA7dTfOg+dl9df3vzAjm6EMbDDysFmFuyExLEQLgM7O/nMnLY+Oe/p9KZ7E0cf0I84P8zv/2QAw7wA4K6trUmn05FKpVKAKL/pRrfXmoOh/tWEOcigBzDkNXqr7Vb/W5Q7nw88AFpaB5MHeiGzWk+EarVaMJTewSSye/WQBaiWNdLpdGRtbU1qtZp88YtflL/8y78sNK9qtSqveMUr5GUve1lxSe1wOJRTp04VF9uePHlyRoAAVCHw9XZnq07WJNP9poXAfoJfiLgP0RbeXYb/ev1EZPft2NCG2cWMPKzNFGWAMWeiooxbZUWHxskitqjZddntdovNEp1Op9hIgaM9m5ubsr29LdeuXZPJZFIcwt7a2ipccLiei5WYVqslq6urxTnC6XQqly5dmnkpa6iuvB7vWb5svVjuTv5m5Vf3G8L05hCdHyytjY0NuXjxogyHQ9ne3t7zqiW8PX11dVXuu+++mRf16vxg2a2vr8uNGzcKfm80GrKyslIo1ysrKyIye6E/jlngYn24QIfDoTz//POFwtHr9WYUSRD3Lyva3OfsetaKK+YbrMvJZCK9Xq84csJX0PF4eVa79z9n7h54AAR52okVZlmD+reOxxPEYgoL6Lwwnhz9fl9u3Lghg8Gg2AV3/fp12dzclJ2dHdna2ip2zPExCMsNoXdnWe31AE0zmaVRW/F0nreKNBjqOujNGtbarsUDXlkeWWOcSmX6K6TslcnTU374NysIemMEhKvITQsQAnRnZ6fIB8od3iiANy5wehamerOMp1RyHS2B6PFlyNL28tPlW0LXGhfUnxUx9tBUKrsbWqAIQNnmtMjLAm6AB3aJ4tiJiMwcKOfduFhPZIWAL8C2+gjl67ZxH3lyQ/cT6qSXIHgHLPez17c8HtZYxejQACCTJdwsa0gLUSs+BojdQNrUxwI0NBuQpZVUKhXZ2dmRzc1NEbl5P+GpU6dkNBoVWtt0OpWLFy9KtVqVtbU1qVarcvz48WKLuT5TBC2dtTD9jd96YT7kZtDM7cXTv2NKgKaYQNJxIRR0e7k9+tgHC1q2AGNuUA8kdd9Zk7QMKCK/GOXmnWqhW/lqoT0ajeTq1auFmw99+eyzz8qVK1eKG0Z4DHhurK+vF8oc8t/a2io8IFgvYxBNJbYENW/zGOllAq/tlqKIOdTpdPbMNciLarUqJ06ckG63K71eT9bX12U6nRbgw67AjY0N2dnZkVOnTsnLXvYy6XQ6srW1Jb1eT7a2torbcq5duyZXr16V4XBYrN/x/MYtMnyMQLdxPB7L9vZ2cQ8x3oSBseEjG9ozAouQlSItUz3jQFuOaH+v1ysAHJt4Op1OsS7ZarWkUqnMvPEG48HHZLjNvAM5RocSAEEW+OkPP+c0HI47Cz1NFJPWcoVwPmCEwWAgly9flslkIvfff7+cOnVKxuPxzBbma9euSbfblTNnzhSHU5eWlmYGHcyPg8n6dhOAJV86y64GboP+rXeuclti2lZIA/TipxJPZFh+ehMS1mb0BiWeeN5YWWV5cXTfad4JpY1RiqWXmrceQ4RZZPEuKwr4bG9vzwjI6fSmyxIKBtaRILhYEF67dm3momtdLs+rnHZyWk+5Y6XVm/+huQGAgMUFEEQY5mK9Xi8usIcCXa1W5dSpUzMHzYfDoXzpS1+S559/Xs6cOSNf//VfL91ut/AAXblyRarVauHyXF9fl1qtJvfcc4973s3zdKB9rKTrvuPNKNbygGU08Hzn31oGaOsPshCyitsDmcVnRy2ZVKnM7rzF2KbSoQZATdZE0syfu0mCrZCUCQsNDVu+oe0wsK2trcnKykqxMQaH4PmeT31xL874MDDyLjT9bfWHNeFjwtOzIkLgaJWdS5yeXaB4xhON41mCIQUEdRu8cJ02BdytdFa4rs8iFA1vbGPKgZWOdzBirYrrx3MlRDltQtme0NV11QDnzQHv2/I26LprpQFgybtGEVfk5v2ga2trxSY4zFOkg2J8/Phx2dnZKRQ8T56lUqivrLHiPtauUGtex/oHyqr3gduYLVzOl2Ugrx/edQAYAh0t5CzLj4UiX0zLcUPCCJNC3xSh64iB7Ha7hWsTdyriWb1el/Pnz8u5c+eK+ohIsTbArg64CXhCIh+Ox8yJNYGYFcQTntcJ8CwmoCzBMi/o6by9yabHk9cD8VyPb0obPIGR0i5PqIf6MQUI5n2eEg98xYJPg0ClMrudH3FbrVaxW1G/vkhbXYvgD22NaMGs5zMruSErlNusrxpEPpABaCd2OC4tLcmpU6cKLxF/RESOHz8uq6urxZlgzGu89uiee+4prjk7ffq0rK+vyzPPPFPcylOGPPDUym3IU4DjHCEl2ZqX3Ee8GQ2yjjcXsSKgX0fFY8wAaG1q8+hQAGCMLAbXE9HS3HIshFRNn0127c7ERbntdrs4EwUwZmvP+uhrwcA0rMExKPBuLas9qW3S7csVYvMIPT2e/NuyAlPHNJa3ppilpuOUBaWyfJgDvCFN3hKQ+Ga+RlrsOmT3FPjPAr2YZe1ZbDovFowcpq0/kdnD8V6Z+sNkKdE6jJUD3KXKsgZ9pG/CQb0wvwECOCyfo7x57Qul88ad+8UrJzYnLOXTMkzwgUfLK4u9bzEPA9OhAkBLCGhzGmGaaXmrPO+c0oxtCQgLPLFFn91zALd+vy8XLlyQyWRSuD5arVaxNbzX68n169dnbmiAG5TdmJb2za8e0X51rM2kuHdZYFmWYqpbyfodA5RU8sBMj53e4BQrt2ydUiy2nHiacgVO2Xpoy84aWyuOBRJ41RCUrslkUoBADqUAtgZn8D7AgucwC3D+WP1gtVNk9zVGlcreOzwrlUpxJpJfv4UdsrwpCOciR6ORbGxsSKVSkatXr8r6+rpsbm4Wm2CwUQgvzgZZ/K/bY8XJ4VXdb55CEMtHZPcIE/oU8g3LQbj1CvISN+Vg45RWNHQ53DcxOlQAyMQTg01s1qzYFQEAZPPcW0QOaYEsdBlkYOE1m81icZvfur60tFS8dbnf78vGxkbxAlx+y7u1jgcARBx+4SUDIKfxwMMSsiGwjAFJSMsrS179OQxj7U2UWP3KkOU+Clk0Vn3KAFlZ8sBPW038TFtQHjiIzJ6P5TcC8DnVnLpqYezVG2VbSirC9NKAxSeh9orY7yFkLww2z7HrF5cE4FVELBdGo1Gxe/bq1avFBeFf/vKXi2vJ4B7UN7Todlpt1vVMIctyZosrh195/NgwYAWfD/JjFyhuzlpaWpqR1SxjWVbflWuAIr4mpP9zh1m3hOA/Opq1SU+ohbQuZhp2WU6nU1ldXZVTp07J8vKynDlzRlqtVvFqGFh/liaq281aJnY/isgeC9Ca5BYI6jbo57E4um+s//NYWinf+B0Cy1D9rHI1yMXa4LmCPPDxaD/BUYN1TLB5ioMlkCC0eT5pgNVlh+qphTGXn6NYsMvMyzNUH08GsLUJZXpzc1Oee+45qVQqcv369RkAFLl5LKTdbhfHEcbjsdy4cUO2traKCwYwd/V78bz6zDO3Qv0csvxTlGnky2nY8wbLGd46WIp8dMkyUGLKrkcHHgB153vaj7b20IF8Tx2HjcfjwlUBrcSbJLrztcUE8IOmA5CbTCby0EMPyYtf/GJZXV2V+++/X1qtluzs7BTnY/glktxergM0R2iTSINzNSBeaNZ9aDFvpbL34C0/t+Lr/DxBmUMx4NJjLjK74zPFBZoyaWIWU0p8T1BrIMhJU5ZC1g2PZ0i48H/2ogD0YLlwfChoPG4xZcICu5ASpuvP7YCVBv5OIW9c+DeDONyj/X5frl+/Lp/5zGdmLpjGb4BmpVKRGzduyBe+8AXp9/uFNwevjGq1WrK0tFRsBEkZ91B/piphltLHyryI7BnLlPogPUB/Or25e3g4HBbn+PDiX7YEJ5PJnjPZmg9y3OsHHgBBHsPrbwYpveHF+o/3knFHW0LAAkHLPcJAKCKytLRUHHtYXV0t1kyw/TfVzaDv7kM5XFfLLVtWU7TI0/jwzApPpRDw4TcLIS2svTReOTnA4vEFh+u+sQRQSFu24qdaPDltink4PGLFhq09aPZcvp5LXvtS6+zFC/Ub5og3ZtaYeu23+A79AaC7du1asSaIPsGRBqS7evWqPPvss9Lv9wsv0HA4LK6IY7ehVR+P91P7K9QPXpyYp8AL0xYg79Hg4w9wnfPeDOt8IueXI1sODQCK2K4cfiMCOo7fdKytNp7IfJBcTwjNiNr9g0usYcUB8HA0AesE99xzjxw7dkyazWZxmJ1fQYJyYjubuH44dMsTJjQ5QgyD/mLiewrRZg6DhjYcDgstFxqcLi+VWUNgZ7XPWihPBb9FU4pgDwk2L4+U8jxgiYGZ1ccxhclSImNletanF+bVyRN8LKQxr9mCYYDmI0XsytTlhhQZ1Gc8HsvW1lZxNZzI7hlg3qjGAIi0/EYHeHOQH1zKqLOn+HFYSFHKJV1fyxJPUZb0GyLAM2g35AcsQNxgw/Xm8nlN1zvob9GhA0D+5t+safD79PRgsYUEgR1zP2mmm0x2r3ZqNBrFS24rld1b9HFx7cmTJ4vNL1jgZgDUG1fw8QSobifCEIc30MSI89IMDXDl/tP5sparLdLY5AsBlCeg+bl2e+aCn9XHHpjsF5UpK6feLHR1uDc+oTqBJzwA9PKz5mtqmfzcsgA4f1YKeQ0QCiO/0ojPvnI/WX2m+xfxcE6PlVlcFjCZTGbedTedTotD75YXClbR1tbWHlnjKXq6H5i0/Eolnld6vqeAH+IhH7b04G3jV2hBscabNSDDWJZxWKVSuft2gXoTxHJnaMYB5TKNyN51Jk7L1/NAI8FN7JPJ7vk+3kqtd2xaGlaMQgyo2xiaAAizAJBdR2g7W9ucRueT4wpNAUEdLyYIUgGY46S4g1Lrv9+0nwDtKR2W8GfS1pRX11A/x1xt2vMTIswpvlEJlgaft0VcS+HylDCvD2Jt4bnuKQQ5PGvF9Sz4Mh6CUL25HE8hAwDq2160TIVM0fKQrWet2OTsMTjwAMgdbw2aFtawjLDjyLKwUsrU6xx8zKJSqRQuv16vJzdu3JBmsyndblfOnz9fXHCLjTa43w8bbaApsqYa0pLBcCnCL2YJ6b7AeSWdDuDObWetjn36IjJzH6LlCk2ppyWA9HPvmRVvv6lMOWXrljr+ZfIIzQ1PuWTFCJYOK4keGJa1AHPaj/mFTRVYqsA5NC5T315jtVX3E6+FMigvku9ywHCRZBkIGgRDRgTy4CWo4XBYyDu4OyE/YPkhnXWNo/59V22C0QCIiWBZTppx9e4lq1Njrkae8Fw25w0NBzedAwh4TRKgwm4Yq326Hrp9HmktOUa6bTzRWbhZFiAzt+4vrkuszvzcsvJi4DYvCJZxEXH9rPDcfHKeefFD/KOBw7NmQn1pxfXmjy7fo9BzzzMTA0E9P/Hh25P4TQLT6XTmTlNWpD03KD/bT2scZS4qPIXXNf/E5FGIuC8BbJp/IDPQr54SovO7qyxASxhqAIRWAK0CH725JUdAsRYDi0dEivM829vbsrm5KdVqVU6ePCmdTqd4mwNufRmPx9LpdPZsydaaFbcrpV4WhYScJVDQJqxJMlNBgxaRmfVUK0/Ew5sB+HVMZbRYS8jqcJG9r7fS+Vll57hn95ty+TGWl0Wekohvb30ppHx447Io8uaABTiepwSbS+DFwEYTXDzPCi34mz08LKwt4NA8pq3bGDh64GLNmZhykqLwWTImZ8y4P/SdsSnyCPH49izuX1ZWtHuV07O7OpUOPACK2BNPW3cMVLzzK+feOF2mBkCAG4Djxo0bsrKyIqdOnZKVlRXpdrvF+by1tTWZTCbFRdg88LygKxLfbsx1SoljTSQNkOgjAKDuW6xb4uAuuzyxroKrjCqVSrEjbjKZzOz0CtXBa18MAD2hoONatB9ae641EKpfWTDRdfDqpDVzD/xCYVZ+i1YmQnX33KkMgDgviwsp+E3p4H3MafzXl03ouYQ+8M7apvSDth5DgB8CPC8sl3SfenWGvNVgHZI1mh/hjrY8aiGvnm7jXQeAmix/dGyiIg7nYZH27+u89Fog320HdwrWG0SkuP5H131eCgld7hMLdNAu62iIbjsLBF7zE5E9L9+1BCe329JuvbrnPE/Jdx6K9fV+ucK0cOKyrHJT+9saI0vBCCkiujyvXKtvcsMsIR2aR+wGRXqAIh+FgJLGm2KgHGKzhb7z1ys3pf6LokXkZ7lxPevLAiivbSEAC8lmPjuNseAd7XwM4q67Cg3Eg6bP2PB2YiZ9uDRELOz1LsdKZfdVKCgDRyBOnjxZHHsYDAbS6XSK16Owxg0NcxECM5aHBYKcRruMmTHZ2mPtGNu+sQGI3UwMgDk+eq6rDrMEto4fmlQ55VsT19NkY27rWzHGOePPZK1r87qKVnp0nlp5Sq2bFxZza4rYb3RggNNkvY0FL19ttVozbYCHg1/Rg2NK4H1WirmOHojodljKyKJonny9eukwdk3qNVPOI2bN8i5yLVtxRITL87xkOXPrUAGgSNifrTtVC4GQWwW/eVOIJWgZADHRcLmryO6uMlxybZn9i5gMXpt0naG9MjHIW21l4We9z4tvz/HqELNGvPbo/yFrI5ReU0gb94SznuC5ZcUoFyhTQQ9xQ4pFyMqz8uO0Zeun4+R4Qzyg9AATAMkbYfRbVsDnfCaQlV+8AZ4BOKe+Mb73nu0XWKaQlh9MGowsBUDH5bwsOQ3iY2WWpc2bBrUnKkQHHgCtSQpG1i48rbXGAA/ETK8v9J1MJrKxsVFYPxiY1dVVOXny5Mybni9cuCAXL16Ue++9V44fP77nBZkxoaTDLPel1wYvz36/L5cvXy5uWUAd0F4+UA/a2dmRra0tEdk98sAaH45zTCaT4sZ77+aKFPLaHxLKsUV4rxymkNWH57n9HspPU0iAzEte37HV5wGgN4aWy/xWCOuQpcjfuIQCu7F596e+YJoBki087yiHFt4h8NJxvHys+F54qG90GakUUga9Z7m8allwIOyXwLhBrsDF6bleb7sF+Oyzz8qP/MiPyO/8zu/I9va2vOhFL5IPf/jD8spXvlJEbnbeT/zET8gv/uIvyvXr1+U1r3mNfPCDH5QXv/jFc5WrwY0Hiy0aFir6N4h/8+W++MbEmUxuXnt2/fr14qqjZrMpp0+fllOnTkmn05FutysiIl/5ylfkypUr0u/35Wu/9mtngFlbS8zoniC2LJAc8IOL58tf/rJcuXKlKJ+vbPIYCuWi/vzuQtQBF93iN48Fj5Fuu24/1zmFcgGWy0l1Z4YUkFQK5WfFWSSVsf5CY8FxF+nJiJHXP5Y10m63ZXV1debWFwY/nlO8qUPzeqhdMYCz4lnPdDwNvClloP2ejAtR2Xg5bkiWLWx5I99KpTLzeiRsqGMLntNb+cRo4QB47do1ec1rXiPf9m3fJr/zO78jp0+fls997nNy/PjxIs773vc+ef/73y//4T/8B3nooYfkHe94h7z2ta+Vv/iLv5B2u1267BRXWko8zVQMgLAA+cweFmix5tdsNmVlZUVWVlak3W7L0tKSVCoVWVtbExGRlZUVEZnVKEMMjG8LjGLgF9JE2aWDiwFgubEACO2U5Q0/+hwgbxLgNlj1C7kSUya6jpcSN8cVmRIe0phT65QTN6X+KQI2J03M8vCAMUSxdqQK71g+UGC1EOU3E7BFOJ1Oi/U+XJ7B7n7LWta0aCUgNjZl88xx3XI5IVnKzywFE/9Dih8DnAa+MhanpoUD4Hvf+145f/68fPjDHy7CHnrooeL3dDqVJ598Un78x39cXve614mIyK/8yq/ImTNn5KMf/ai84Q1vmKt8SwPRA+b5kREXHzB5v98vXlQJZm+1WsX5oeXlZanX67K6uipnzpyRdrstZ86ckePHj0ur1Spuc7///vtlNBrJ8vKyVKvV4pLcUFvwYQawBj0keFiL5bbxxAZ4WVqZB4AaREWkuLcPAgV5Wussnvao6587wVOVoEXllUOLaAP3R6gdueAXEuKeVWilBV/cKYR6Wa9mwne1Wi08GNgNKrKr4GETzHS6++o0Bj+9N0D3x7wgFUs7DwCXAZGy3g/wrCXXrDI4Hq8BevVOUbiYFg6A/+2//Td57WtfK//wH/5D+f3f/32577775Ad/8AflB37gB0RE5JlnnpELFy7II488UqRZW1uTV7/61fL000+bANjv92du+F5fXw/WIWVwUkGELSQ+8M2uVFxw2+l0ZHV1VdrttqysrMjS0lJhAfLg4XdoB2rMp51r7fFvPXnxm90lbNmFGApKgrYAYflBq/YYnYV4rjWSGn4nkB6DXODynnmadcyaLKNUhMDP+307xsSbL7xRSwMVeJXPCHI8yABWjDnvFMC7FX2xKCs0JHNYTnj85+VnKb0hD5NW4nPkYQotHAD/5m/+Rj74wQ/KY489Jj/2Yz8mn/zkJ+Vf/st/Kc1mU974xjfKhQsXRETkzJkzM+nOnDlTPNP0xBNPyLve9S7zGdar2ArRZ8/YXSHiW4kaGBjwcKaNmRuHwbFIW6/XZXNzUwaDgSwvLxeH3AEG/HolAIJnBWlNieuqgdPKQ1uA+M0aLbRa/Nb96vUVE/Kr1+vF7TpoM8LYSrYmgjWJbrXwnNeV4hGPYwi4y7qgUuuwiHixOnJ65ptQvqE8yz5LJc8bxBYsb36DBcigGXLfeRvurHm5CApZ5YsgLZss3o65SLlvmEewvFSpVKTX6xXLS1tbWzIej4vlI0u50O28rW+DmEwm8spXvlJ++qd/WkRE/tbf+lvyZ3/2Z/KhD31I3vjGN5bK8/HHH5fHHnus+L++vi7nz58Xkd3tsTwoeisza2wsbCyByzs+2cpDGfzp9/vFjjK88mhjY0Pq9bocO3asuPoMVhDqywvv1qI614/bwgOeI4wQn/uCb3kBCMY0V6tM1L9erxcX1zIA4g3P7XZbOp2OO+ljFkss/iJoHqHq1YcFhVdObrmLFpwi6cLSAzaPh/Vvjl+m3aE8U4nLttaWRGQPAEJJ5H6CWx95lm3LoinXus8lT/n2vFYhXkG/4/YskZuuaqTvdDrFOWMQ78pluQ1iN3eMFg6A9957r7zsZS+bCXvpS18q/+W//BcRETl79qyISHEcAHTx4kV5xSteYebJ5+gsCmmbIcGkyXLzYLIwo8M6ZI0Qz2Dt6QHXawUARdYmdf1y3WQxVyIDPB9d0NYvx48RKwkAde0CjblzvTbNC5ReWSFA15M7lEcqEIVAP1aHUN11+lxQiQlKz4vA9QzxSazvQnFCpNNaQlj/LwMKOk0ZxSU0T/eDUts3j1KSU0bq/AAQslXJH36LjJZRGgBv6znA17zmNfLZz352Juyv/uqv5AUveIGI3NwQc/bsWXnqqacKwFtfX5dPfOIT8i/+xb/ILo+FqrdJRIOaRczselcXLBjOa2trqzjjtrKyMgOKsLJg9QEE8AwDqV2hluak3TPzEGuy1i0vsAy5D7hvvDyZAWu1mvT7/QL8kC/3R2pdc+OmgJXuS2/Sx4TBIqw2C8Ry85w3P0+hsOaM5/bKBZd52m3NhXnmhjW/tCIosrvUohXelDbvlyW2aNpPYA6VCXDDuit21GMzEnbX48Xhev2Vv0Uk6yTBwgHwh37oh+Sbv/mb5ad/+qflH/2jfyR//Md/LL/wC78gv/ALvyAiNxv89re/Xd7znvfIi1/84uIYxLlz5+R7v/d7S5WpNcKUgbS0R8siEtl1dbDGy2t7DCDWxhYGCbgi+SycZwHi23M5WG2xyubf1jZuroe2VDkPr10iuxtf+DdAsOyF45pSLJaQRRWykHUeVp/OA1axcubJg2ke8OPwEFhbVqEVP1SmVvRSKVcpyfEQhOYZWyg837WVfETlqFKpzBgGem7wXaCWUsaUswN54QD4qle9Sn7zN39THn/8cXn3u98tDz30kDz55JPy6KOPFnF++Id/WLa2tuTNb36zXL9+Xb7lW75FPvaxj811BlBklwH5ZYqwdkI3wFhaMIexZcTWU6fTKTTDSqUinU6neKkmnmk3ESaSdfWS951i4eC5Ftg6nvabT6fT4hA7bm7htULthuDf6BuUg+MPWC/l2xtSaJEAk1pGTDtPAc2c8nS+qfFvNbEAspQ09rqI7LUYteLF6e5Ui0h7CnS98dEXOeg5fkRpNJ1OpdfryWAwkNXVVXnggQekVqvJV77yFXnuueek2WzK+vq6dDodOXv2rNx///3SarWKDXWdTqdYGuP+39jYSK7DvtwE893f/d3y3d/93e7zSqUi7373u+Xd73733GXpiSoixaLpYDAoji/kaAWWRoHPzs6ODIdDqdVqxSYXnAdst9uyvLxcmOwsPHnSACR4XdGy+GKA5vWHJYSQB7cFdcJri7i/cOiXXxjKFwh756REZMY6ZrAs41acFxA8we3l7T1flJtykfH3Eyi1xaOtYFwRxnWwNnRZoJgDgrcLVCz3L/qDv3XcO0F5OSg0nU5lZ2dHxuOxHD9+XF784hdLs9mUCxcuyJe+9KVi6anb7cqLX/xiGQ6Hxavk2u22nDhxotgdyor99evXk+twaO4CZYLFBmGu39yQk3fsPwsEAAMDhb61wBKolkXqaaIWhSxFzkNr5Vx/bkPMbaTzB5hi8Rm3bWC9EVfGhfrSopjLM4UsJSJFSMWsgVSK1f12W3ypfRtygVoW4qLqlpqnFy937PYTcC1F1prfuo/xbbUlx8Wry86pM6fP6dMU+cUb6RCfz19DjvClHbp/ILdCZ6stOvAAKLJ37W44HMr29rYMBoPCxM7ZGaQHjd2FcHmK7N5vyXeA4oYYuP/0JbtW/gjTbfKeWe23/vO3ZWGymxNt6Ha7UatZ9zWsv+3tbZlOb56ZajQaMhqNClfFqVOngm2ItSkWnkohxYPL8NzRZcoLKTKLsHoXYSWHlL2QR0KvI4f4OEWhsZS0eSlV4bHillHCrPmuQU3zoeWpCFmVZV3JOelSyvXmkwXYFmGMx+NxYQ3yDnmctd7a2iouQNnc3JTJZCLdbrd4RRIfW+FLU2J0KAAQxJ0JbUG/s6tsnvjAotP3XOrXqug1vnkE6TxCIMUCRP1xphL/U4jfG8jvM8R9qTgbGALTWP1z03A8SyjHyNN6y9ajTPk5FnJOXcrm4VmAMb7WlnfO2IXyXSRpUPfqwv9TwDlmtXmWnu5f7vtFKEYhxS+VQv3FcVLrCw+S9kDxlY18Zllf4ciy/7ZugrnVpJkRndDv92c6ap67CTl/WHSwKsFk+lZ5vlOw1WrNvGEhdGO5Ztoy1oD1m/Pi4xmTyaRYx6xUKgVobW9vS6/Xi2rA7OZFP/FOU3ZB51IqCGghYglPS2uNCVntltFxy2rhi6BbWa7l6vR+e3XVAt4DmhxrbRGU6l0pYwnmlG8pFSEvkAWYVr33m0KWrRfPejYej2V7e7uQ17hABAfgr127Js8995xsbm5KvV6Xbrdryp7RaHR3rQEyoYOHw6H0er2ZjTDzElyFADZYPAgHuIFqtZo0m81igwlvJuF4muaxNLw0nvACAPKuzZWVFen1enL16tU96S3idjH4icjMOqylgKS6wkJxUgAf5AkRL+9Q2lxhHRNYOXSrQTfW9jJWdmq5uZZwCniE8vGUJvwv63YM1ZXL1la1ZwHqOIumlDyt8fEs2RD4AQABbgyAkN+XLl2SL37xi8VZQLxhRyuog8FArl27ltzOQwWAIAjimNU3LzPr++x0vhYj6/J1+H4JNz3xrA+sWNx7qp9zenzz7tB6vV5cKBy6/Dr0f95nofytyZprYafWLxQ31+ooU35I6JSxZnIEmi4rJ0yXEZpXoTboOuaAYEpYKsX6KuSFKAtuZevr1UHnXbZvvTKRXu8cZ2WGNzbqjTDz1OFQASAGBu/94k0qTLzrCOlyCRYUCAOmD8d74He7iBkM7zzDb2ziqf7/38QMIPQArVKpFOcdobHxwXdsCIJ7NfcoyjyCJwYO1m8vTko584AVx7EsmBhZwLGI+ljlWGXycYBQGiutDluEhRwq+3aRZe0h3OsLL/2totDYpY5TrN6QRdPpzQ0vMCparVYhm0R25QFf1FGr1Qp5xXctx14zx3SoABDE7j2RWQDUAmse7R8fDCKHM4WsP47D9QrFDdUplVBnBjasBcKKSznKAeuv2WxKs9mceQcgHwfJrV+ZNqWktSZwWQs85ILVeabmm5JfappFkObLUL+l8qjnFVgUCHr5c94p1v9+eWNSlC5L2UhVpHP7L8T/ngUek3H4H6sHy042KPiF41pmcz14WQlKNjbypdCBB0AGImgHo9FI+v2+TKfTwooBsXUYW4/jMvDNa4G4kaDZbM7sokSZvGNUREyXYAotSvvTjNNsNgtGm0wmhbUmcvM+vXa7PcOgHhCin3EnKoPi8vJyUQ6DY0q75hHsIfdfzNVWhkKu13nzmje/1HzKAI+2ZFIEnleutoz0wXqvfrEx1PxWxvOTo8ToPtHlaTCzlArLg2QBZIo3I1RPK43XlynKQmyMPBoOh3L9+nWpVqszr5k7ceKEVCoVabfbM67QwWAg169fl+eee66QNZBTt/V1SLeD2CyGKY1XamAjCgg7QvGanhAAWkzP779bW1srNrnwizSxIQYMo12uXlkpzJiqveo8QGB6aEkAQ86rWq3K0tKSbG1tFXE4H2tCNxqNwh3abrcL0EPf4Fwgp2PXRazui9TGU1xOiy7DC0tNGwpfpHtM90WKMGQ+tyhkLeQCckjA7gfl8p8GNg7PtcoYKPnCfM47VB7q7XkSQhZlypjz3cZW3bgMz7LH88FgIJcuXSryq1ar0ul0ZG1tTer1uiwtLRUyfjgcSr/fl0uXLkmv1yvOGmN36F17DAIdJOJrTtqKScnfCsOxBn3ri7VelgJWqYxYxnrCf61VasuO6xp715ku3zsHycdDyoJCSAjpenBfp7oQrbRlnntpUvIqA44pIJJKllXMZXhpuF451gQ/81yqobCU+pUZzxiPlrGQQ25FDtOKQQ5opo4T//cs05S6xurHwJdSd092W5th+OwxXkmHnexYR0ylAw+AIrMdA+0BlzBbVh5uaClblsjNxdZut1tYPu12u/gNN6hOx58y5XqWWGqdmWI7NOHa1cqFLhtveefdo+yX5+vVNIPnviIphSwteD/yDlHqpJ+XygByiDwQDMXD/0qlMrOG48VNfZbyPEZllU6RxXseLGUhZGWljoVXz5CyOA/PlMmjTF/yuuB0Oi12f+KIG7xO2LMgclMm47B8Kh14ANQCGkIWglm72abTaQF++nUmFnnMx9ef8XurtPvTq68VHgtLYfJYHtq14jEnAEz3kZUfAx2A1bK09T19ZZWBUPtyyNJ858mvrGIzTz7z9p+lVMUsrRCgWXXLATKPX8uknZdCgjunTmWBPNVLZZXJ35ZSWCZfDc45HrRUWaXbzG9/5/tBa7XazPtMcSYb+z9S6cADoMhe66rRaBQ+Y21h1Go16XQ6RafBZLbMZmuAIdix9ofdjwDAZrO551YY67OfZDGdtj69bxAsOk6j+wngBwDEcQd+mSWfKbzTqIyAjuW1X7Rfwh20iD64XbQffZMCfpZHx/L0WL89ZdbKy8rbqq/1No4csiz7/SatkCOMXy7AmwkZCHFTjPYs3VUAyJYfPs1mU1ZXV2UymRRvYQdhcbVarRaXr2r3jR54vPoFv3H+ZGlpSZrNZvFeqlarJZ1OZ88doLcSAL2J5MX1rDBc4cb5WROMd7ziNzYeQSlI2eySQ6mTPEdDnYf2AzgWmee8VubtoFzvwH70V6zfeE5Y8yTFgxMDz1A9vHJFylt6qW7jRSpMWjayPOFzfrz2V6vVCgOmUqkU3yJyd50DRIeJ7N7TybeSaMDRVksOg6As3tyBNUbr8msL7GLlhZgq1xWUMrm8vNFWzgtMz3HZ9andoJ4rtEybQu3UlOrOtNx6qe6dmHDz3IEpecXil4m3aLpTLMZcoNRpU56ljk/K3ApZcGVJ55/jomSaJ52XJmUexJaL+KNf6I1lFc435+rLAw+A2GovMrtBZTqdzryhAIQt+Y1Go3iDeYqghGDvdDrSbDal2+0WLlC8CZ6tHYBxCABSma2MJmxpiNa3R/xmCMRl9yfqzut/sAD1OxF1+xdhAceE16I139y6hKxuLyw0RiEhkUOhduaC2p3o1l4Exfog5XmK9afD5wVBS1m1xig1rAyF8s7pN5ZfLH9hAeK8oLWZ8a5ygWLtSWS3o3HGD8+1BcgCOtUtiTjYHanXufSbHlLdnjmWQUxApbhYYmWC+Ko0pLHaoI+AWBa2BX6pk6IsxaxDHVfzSJly5rHaQsrKoqiMkPPGKaTMpVjm+2FBLtqbEFNWPA+Ll0cKCC6KcpRAzfupcyf0P3epR3tg2MoD8OmlLoAhpxe5yw7C89sH0AlYGPXOg6DTcOUXaxl6pyIzBAZiMBjI1tbWDPgOh0Npt9szxy5CbsAcVwBoXgFrhekJChcDXuqLMC8PDwBjV6jdSRRyzS6yrp7ASwG+mEDL8Sbcjv5ni0QLWQsYtbu9rCKXQrnzygI+L77liUmxEK0wnZ/38Vz7i6JFKy+h/kR7tra2ilcldbtdmU6nxdEzyBzwVrPZTC77UAAgbneBltDv92delqgZAq/YAACKSLHlny1Gnox8zQ7eN4jXdwyHQ1lZWZG1tTVZXl7eY1ny0QCrPvtBnkURmojow+l0WlwtZL3Pj/uH28Z3ijIoiqS/YHe/6Ha697z+18+9/xog5qlvDET3kyzF0nPVef3jWWBlaRHg59UlBH5WuhDI6zxYKfVAw+OXeZQgLRsXTdr6g+zBzV7Ly8vS7XZlPB7L6upqcWk/v3LurnKBTqfTopPYTGamsCaX3jqrLRbE89yZ2lT3mCHFxcoMa9U3pQ+s39Z/hIUmn8ju5hYrrgWAGuA9l2JK26zJZYWF+lwTC5CQsC1Lob70nsfS8LM7zWoWibvcQ3EXUV5O2K0oI7U/cuN5Sqv3X4Of99uj/ea1nD7ha9a0TOcNiViCyr3g5MAD4HA4lJ2dHZlOpzOHIvHbE9wiuxtiYOno2weQFhtdmDF4s8exY8dkZWVFut1uFtDF4u0HWZPNA7mY1WYBoOfyza1jSlgofeo4zNPPqUCmhZQOSy0rd03lVvX9IgDH6qM7gWIKZAykOCw1XgjkQvXUcVneYWmn7JxcNIXG2uoT7LPodDrS7Xal2+3K0tKSLC8vF+e+cSfoXXUVGi5HZRcoX44t4lsg7DvGoW+473gtEB2v1wOxEQa7QJvNZtI6XsqaUyyel3cKeZPOohAQauDzLL/9WJNIVSJC/ZjjhkuphzepQ+CXOmbMF6lk8VSo/rlh+nmKxZOjIOTSvIqTlzamMKbWOUXxDPVhyrz16hCTGxbv325LkNvIb9rBhSN4fylfQbm0tJRVhwMPgCJ7D1LqZ6H/IJjObAmi8xkoAa767Q+8+cXS4Lh8K2yR5E0MaxJ5BGDTm4LwjMFN932KlnkrJhjqEqP9Ar8Q4MWUDq+sslZdrjIVi5fSDh3Py9PjyxjQpFoRuZQCSKG6eh+vTdOpf6BeL+nwby9MK5zT6eyNWJqXLKX7VlmJ3ryx+J3rGbpsIGfsDzwAagHMt7akAiKsObzVHIfo4QrlM26wMnFLAXZL4rU/Insng+V+2C+XhDfpvHj8nJnO2rADsm52yQE/rsN+TrTU/LXASAVN/ZvDeBdyLG7ZeudQSp6pFkUI0L30OcqCLsfj5f0GQE+YhtquwSiUrwVm/Mza9q9f/aY9XgyCPJ+xwU/zQe76/H6QpyCgrlqmQDbrc8kis6cCUujAAyCTtb7mDaqOC6HP61gapHIZRE8EzZQcz9LCrLhWGaEwb7J6z5li62Qh5SK1v1La6KWJUSjP1DbFyovVxVMyUtJa9Soj2HPThsY8xltWObG2zwuSVpleWCrFxqlsmAeM1rxMCbc+et5aYXc6eX3E94Dq36wopNKBB8AQo8aEMJ7zJIJlx52KZ9zJo9FIer2ejMdj6fV6hSsUIKovjUY92L2q21FGG9NAbgFdSEh4eXF9Qy5Qq765ysJ+TMoy4JeSZ6gPrbHQQqwMCHmKUyrlCr5Y3+WAuKd4Wc91mOXmWjT4pSqQ+rn1sZ4hTHsDPHALWYUWb2kXqQZBkb2K7J0KhKE+HAwGxQH3jY0NGY/Hsr6+LsvLy8Vr23AkLYcOPACK2AzraTwc1xLifAUYhL9mKjAeDtTjyjV9d6b1gkfrHXghrS3U5pR1q1wtWAtdDWaxicPx92uSpQBR6rN5Lc+QdRUSYKm0KM09JQ/m3Vx3aRneC4Ff6Jn3PLXclDxCeaeAeQqwhoAtBfxicax659KtBkurntPp7lG3er0uvV5PKpWK7OzsFEaIvt0rlQ4FAIqkD7AlsFIFPIOf5yrTWph+ruvq5cOCLyR48duLa7l6dfu9iTsP0Oh2pKa1lIEyZaeSLifGQ6H2eP3IylAqeeWUFUixseT8+Vv/DqWx0nNfpIJGCEhSwTKVYnIj9ixF7qT2uwdyof/Wb1DICkyl/QI/q136wx44GCP88gGR3U2LuPMZhkgqHXgAREd5mlAKpVgtHiNq0kcvQhMWv2PrTilMqEHQ8//HhJBVdi4IWiCcSvttPXqUKhxShF1IO09tlx4vr36p7uZQeo7jtccLC82L0Jxkb4rIrrtT56nr5YWVAT+dTyjMe5YDgmWAUET2bHLxNtpoPrP6bTKZZB8Wn5e8tluAhzriP9bzWq3WzLEHHFuDF67X6+0BxxQ68AAIytUCcwVsioC0JocFIBjwVOArC4axsFDdddp5SbdXg3RZKjOOOm1I0Obk54Xl5uuNu6dYpOaZyhNlw2Jgif+ecPbA03qmv+fhoVwQXHTeoX7z+iGkaC3Kbb5o8pQFr338LbK7UVHLEQbQXE/LoQBATxvi75Q8Ul1uIa2bBwNrfoivN8BoEAgBYuqajDVpQvHmpXkUjtsxSVNdgSmUMpHL5mu5z62w1D704nl5puSn01hzMAW4PIUhVTCWUVxS2xYKD9Xd+63b5lm+LNCt37oOob7MWcO32rzIOZoKfminftGBloecvlKpzLwhIoUOPADqDpxHCMUmf6q7CYQNLwyCXNYifPRc7xywt35rWiRYhOpyqxfZ76R8QvmngOA8dVkU+HnPU/gsRXENAaJXr7KUmm8I6L24OQDqKbRWX+j/GiTmof0AvxjPeMpBTFaJ3KXnAEOMkpJWJG93YI7GrZlUr+sgP37mubZCLqvQxEiJZ7VRC9zYJJ/X9RIS8LFNMxbF3H66/738Y3X2wuZRxsq4sXPyC7nmPQoBlSekY3mnAJ5Vlk5j/S9LOYqA1cbQuIfALBX0YnFEwi5zqx3W/xyZqOtn3WxjfYfySiUYF7kGisghAECrw8t0MgtS7zunPrFJAKvQeyO9B56xtnhMp+uk01nlh8rx4s+jMYaALDeNSPokWhT4hYRVLmmwSqmPRbH1xBwQCYEU/w7xoJenB4TW+TkvbWo7YlQGAK3XE3ln+mJn/ULXneln1k0woc1SnhfK6lfrDHCoH/TVbVadrf5Nnd+hcbHWB1PowAOgSHyS5ebDNI9A18yI+nmuLX5WZlKHNK6cfEIUAxzPckxJlxJ3XkLbY6CSE55a59DkDfEe1zmlb63+9MJy6qmfa6HpCVIrzPofA81bBYKhPEPxvDqk1NdSHFLjemG5PMnlhaw/z3ugvy15bJUXmoupLn+vvBgdeAAMTUYrXmwgF1UnT7uy4moXqK5napnz1HcReXtWYKwt8yzSx/ItK+RDeer089Y5pS6eezl1Y4tVZu56ojfHPAGdE4+fszArC3q3cz6ElIFYfP3xjniFwubZRxCrb4i3vLrFFBudPz585zBvIByNRjIYDIrD8cPhUDY2NqRSqcjS0tLddwwi1rEhplgkCGoGtMoN1W8e5vXyvpspB7hTlJTbTSlrMCmA6MVP7S9PqKXE8/LTaWJlxMpLbUcupdYhBM6hNF76FPDT5cwrDzR/hPgoVmZKf2gDgBVqBkB9GTauoxSR4uUEqXQoAFAkPNgpwmvR1qC1MMtAxwPNz2KUwmApGleusOB0+wUGnsJQZp0vd93W+59ibdxuQh+VWbcNtd3LJ/SdCmAc38rDe56aXw7lpskBG2/upcSLlW3VwVJqYkZBDLRzNs+kgLnI3rU9NgA0+Ok0fN6vUqkU16BVKhVpNptZN8Gkx0yk8Xgs73jHO+Shhx6STqcjL3zhC+Unf/In9zDNO9/5Trn33nul0+nII488Ip/73OdKlTedzi4El6H9EOi8KKtBkOvsvdcqhSwGi2mJOQKP27KITS4p5YTCcvqoLC9YfenFi032suN6uygmxMG3oe9YvBBfhm440b91WXr+h8YnJpxT41ttjd3SkpKnd5g7VIbXr6GxtfowVyZZafXbGXRe7OLUhkKlUiluc8HLydkVivLg/gTonThxQk6fPi3Ly8vFXc4ptHAAfO973ysf/OAH5ed+7ufkM5/5jLz3ve+V973vffKzP/uzRZz3ve998v73v18+9KEPySc+8QlZWlqS1772tYUZm0vWQFsd62nJ+ymkLOBIAaFY2LyMb4V5jHqr3H9lADpnolr9E4qfQyn10IqEt56X2+eLGJ+QsLae6zCdzosXy9fia+uZVZcYKOQAYk48XT+rT7z/oX4J9YFOGypTxOep2Lhb5OUTqmtKftra85RuzhtAimvScsBPZB9coP/zf/5Ped3rXiff9V3fJSIiDz74oPzH//gf5Y//+I9F5Gbln3zySfnxH/9xed3rXiciIr/yK78iZ86ckY9+9KPyhje8Iau81EFj0q6fVMsjRyha9WKNJwZWKW6/lAmXWtf9pOk0zW1quUViaxCpwn+/Ntpwnrn84YXx5LbiWG7z1D5OrYMOi/Gcjhvjb52vdyOK9VJhHcerV6hNMUpJ4wFj6BhDKI6VxnvdUSi9VqTY4vLaEfOgpXiAYnVDnFAe2lum+xlv4eGX+zabTTl+/Li0223Z2tqSnZ2dwKjN0sItwG/+5m+Wp556Sv7qr/5KRET+z//5P/IHf/AH8p3f+Z0iIvLMM8/IhQsX5JFHHinSrK2tyatf/Wp5+umnzTz7/b6sr6/PfETS3VU5lGolpgqbWL1SwTtlwu83kJWlMoCzn+uMKWF3KnlAvmgrkMMs3guFWfl5fayFrwa/VLAItcmLWya/UN45YbHyPcDkvtHvK/UoxQPm1TG2HsfpdV4p/aHrqetr1VG/JaLRaMjKyoqsra1Jq9XKms8LtwB/9Ed/VNbX1+UlL3mJ1Go1GY/H8lM/9VPy6KOPiojIhQsXRETkzJkzM+nOnDlTPNP0xBNPyLve9S63TA8UrE5MycObuPgdEzaWNhaLzxaEp9Vb7QwJHatO3v8UyrVy9tN9avVtSn97/FAWBGPpeHHf4587DYBDSoLVZ179Y5tsQjyZAkix8q0ydJ3m7fvU9oTq5LXTAw4vPw9UPECJtcXKB79j7QrVNWYBWu3m9LwLFIA4GAykWq1m3QMqsg8A+J//83+WX/3VX5WPfOQj8jVf8zXyJ3/yJ/L2t79dzp07J2984xtL5fn444/LY489VvxfX1+X8+fPi0jaJKxUKtm3hKcMppcupV6x8iw3gM4vtV6hCVIGBPcjbi6VdYemKiWLIAv8rPrkUtk25KZD3fTcsYSTBhkdZqXnfKyNHTG3XA4ApiqvXhmhvHU8LjPFetWfkMvTyj+0aYXdn7VarUjDddbWpCak1X2SAtwer+R60LiuePcf7vwcDoeyvr4uvV5P+v1+1pxaOAD+m3/zb+RHf/RHi7W8r/u6r5MvfOEL8sQTT8gb3/hGOXv2rIiIXLx4Ue69994i3cWLF+UVr3iFmWer1co622ERaxZe58eYPVfAWmnmcZ2maI1l89bPrXrfSvCwaJ7yQ9ot5z2PgpCijOXmGcqH/+u+8YRhLqUAkFWXnHw9EImVnxqP5/1+zRcrnsdnobmtwSVUZui5tvy0GzO1f+eVV7G+yamXBagAbqwL5ho6C18D3N7e3rPYWqvVioo99NBDcvbsWXnqqaeK5+vr6/KJT3xCHn744ezyQouzWktCvJRB5+231uIxx+HyLNLMGnMNpoLfPBSbPDnht4putUWZouFaafj7VlEZQXWr66jL1cIs5RPKx/p4llPOJ9SOVOAK8VCMn0Jz32qviH2PZyqPl10vXAQ/saFibQKqVCrSarWk3W7LeDyWzc1N2d7eltFoVNyvnLMTdOEW4Pd8z/fIT/3UT8kDDzwgX/M1XyP/+3//b/n//r//T/7ZP/tnRQPf/va3y3ve8x558YtfLA899JC84x3vkHPnzsn3fu/3lioz5GpCp1jxQToNuw2Ykbx4ISoDNEhnlZsyMUPlLwI8j+j2gsc8VnCZjTNlLadUCgGhFddLa8W5FeOUUk+EheZyDOxj7eQ4kF+59bYsRm8fQq4syiXOmw2YZrMpzWZTJpOJbG9vS61WmwHAnIPwCwfAn/3Zn5V3vOMd8oM/+IPy/PPPy7lz5+Sf//N/Lu985zuLOD/8wz8sW1tb8uY3v1muX78u3/It3yIf+9jHpN1uZ5XFvm02f63dUakbMrSA8DQpCyCZWGOx0qZQiOFTSW++0My8KLfcQaSYdp8SHhN+i3JLlqmr9Sy3DrnglysQLeuFv736hqyiWH0W5UnwLED9PGa1lf1oQrtYJnpAhj7UfRyTa7qNoX4oK1OsdjIAdjodabfb0mq1pNFoFNefNZvN4oB8Ki0cAFdWVuTJJ5+UJ5980o1TqVTk3e9+t7z73e+eu7x6vS6dTqfwA0+n00IbwO/pdFrcLMB1YLKYAKa09cqiWq0mjUZD6vV6oXHEBMk8E28ehuJyuS387Vmbt5JSrZt5rKCyFOsLSzPWtAhLah6PQ5n8vDQpFk4sPQtgvtuRn1lrOjEPDuLgGb8mSANCTn9Z61Se4PcsJf3Rt8jwfwhzK0yXw5ZPvV6XZrM5U2f+zeCnAcMCTyst11mPlTU2OYqe1XbQ8vKynDp1SpaXl6Xb7Uq73Zbl5WVZXV2Vbrcrm5ubtxcAbzXxwOtFUB4oPjiJZ5osTdFb/8N3CNhSBz9FqGmw0pZdmd+W61j/5jKtenEe81KqS3heRSK1HB2WAoJakQr1a6h8r6wcJSFEIQWQ41h1js2jnLqEtH0LYLhOsXxZWOtx4fQ5oG21yQJE3bZQe1MBM5Qv6sheq1Se1f2Sq6yHFIF5yBs7vvWFr0tDe++qN8JDY6lUKjNmstZSJpNJsQ045LLkicbuAC0IQq4C5BMCkFQQ1WRpZdZvj/liLjkrvQdyiwCkVFpUGWUtHotyBKiX3svbG8NUIM2tQyrPcLoyZem0eo6y1m9t77f+x+rM/ZXirfEoFfQt4R8KC/GR13YNjvBY4Vqwer1egIHHL+hjEZnpFwZQr20sX1PakUucv4gU7s52u13UC2f+ptOpfOELX5BGoyGDwUA2NzeTyznwAKgtQISB9IClDCooNFmYUTgfK+9Q3UOMlmt1cdgiheOi8sspLxS2n5TjqtmPssooQjnlWVZDmXws70GIPPCzFFUPAL15GqqnV2f9PCWfHAWB44esvhgocpgVh8ENSzKwjDwA9GSWZUFq+cT1927qmZcsBQdLXa1Wq5DLcJsPBgPZ3t4WEck+DH/gAVBE9gxWjCGRRodxXqwZ5xALBWsgrbrmuNxSASJkscWsu0VaGDlUBhBC1kmqQuJNWm9CW+OaGhYqL4XmUQhS2x6zMmP9xf1mXXPmvT2BwxFfryt5AGG1wWoP4qcCd6wvrLwta8gCjtDHa5duH8BAvz0hRCElKMRbnrISurM0RJYss8oEKKN9yJtfg4S8Go3G3ecCrdfre876aXDhQdEMYjEErxmGXJkW8UT3tG64bb16pDJPikvUS88T2wLFw0axNlnaeCiOJdw84XfQ+rMMD2jhCAvEWpfHR29cwwYIEft2GF2eBxZ6ByS3y/rt9QH/TlHQPAuWwYNfF4Q+0mEhHmLZBhCoVquFdeTV01MEUtsqIkVdh8OhDIfDYvMh11/X1+uvnPlVq9UK1y76ud1uy9LS0kx/LC8vZymIBx4AUywqC8AsYRcaMI9ZQul5ADltTMu6VXQYgC5U/1SLp0yakIYfsgisfCwFxKMct11u31jzpgwIamsuZMVpcLDCOI0WoKF+1W0ItSfmCfH6KwTM1th7z7y+4We6bJHZXZupF3PgmScnQ2SNLYfpNui8rXJTeQzpUFatVivWBGG0dLvdmd3+MTrwACgyC4J6AVdbcCJ77zbEM2/QckinzxFCtxKMUibYnQCO1oRZVL6hsBQgtEDPEnhl6mJRSj/cjnFDm9mS4W3sGsS0NTSdTgsrgkFPb4Gfp10WwFlxYu2MpfHAPQT4nvvQAni0AR4knIMLKdacj1biGUg9A4J5G2OmX3rrbWCy+isFmPXRs52dHZlMbt4D2u12ZTgcygMPPCAvfOELpdFoSKfTkXq9LktLS6Z89+jAAyB3lj7qoAeTBzJkinP6RQoTXWbMHXArKEXQ3w0UUgZSwDKk0adQjNdylAAW9rHy5+VxSzh6bkAt5PXZXd6wYQl/hJdViBatSGlQQZilBKWAn2cB6zzh+sSrgLAOlkqWfGTjQbeJ2wDws976bgG5VW5K/fgznU5lZ2dHer2ejEajAuS63a684AUvkE6nI8ePH5dmsymdTkeGw2FyXxx4APTI8/WXEUw58ZliaW6nBagpRbtdZN6plFOHkHC0LImYJlo2LCfP0LOUNad56hPLMzevFKsH8ULuzpy6enXXwt1LE+vPVLegF27NcQ3yHtijfA1YlqUG3veAS//2+sBKDxBKqa+VB+eVSrBy8X4/AG6j0SgOvsPqg9s3pW6aDgUAesJDu0M97dKz+NCxnjk/T31vt+WVUtY82vbtoP0Ah1BaC1TLUqoltsgxyVmLTikXgorBjF2g+OZ3uWkXKFNsI0sueHluwrL9aYF2jgXouT11vbSbUq/3YbOd1Q5dFvcD5xFygSJ/y4K38kV+ngLA39aOVdSF1/jwpveVlRV56KGH5OTJk3L69OliYwx4p16v370WoKfdsHAJmecW8+nfi65ravjtIG/dxOon1hRTrWYv71St27LsLSCJAVWqpe6l1c/nKcMLK+su94DVE5ahsNx+8vrG+0DQ6vp561ZeO3NAbhFz2+ujFGskBBKWHOPfHk+E5mCIFzye0BaglZd2u4f4VeehN+4gLXZ+VqtV6ff7xY7/brcrS0tLxdonFChYiHfVMYgQoHnxUs3kVE1zUXSrwS8ksEEe+HkKg6V1e/mngJ8HiKlhljC2nnthVh+lCnov/1A4noUEHCjH3aufpyoaXj6WJcD1siyj0GYY/u+BX2zMU63oULty04baGSPPnanbiUPtvNcBOz85H50+pPR71jHnZVmoofriKkoefwZPjo//1p2j+GBNr1qtFkcuGo2GDIdD6fV6cvnyZWk0GgUAioisra3dfbtAPbKESBmNaz8opiXtN6WAn0U5lnFu/83jnrqV4Odpwinadqg+HsWALmQJWPFDgjBUDv8PCddYOoR72+it9s3DBzrPeeY111HX2bOUQvW0wERbUrq+1hWMIR7x6qNBLyQvOQ8LPLlulhKjec+z6iuVSgHueO9frVaT4XAo4/G4cHHu7OzI5cuXi/OIGxsbMh6P5dSpU9Ltds2+sOhQAmBsYEX2Ts4Qw6VMKuu3VyYzeKxOobLmpUXkVUaghNLoyZ+avzc5vXhlw1LAr4zV58VPsaCtPvLqk1OHUB6WYESdUsYiZ8548zK1PZqPFjHPPF7QyoEVzsLeU75jfD+Pd0qDb2ycQmPt1UG/dUbPaS8PAGCtVpu51q3VaslkMimuQ2s2m1KpVGQ0GhWAOBqNZGtr6+46BmEROrFSqcz4g60DufwsxBCexoJ8PCaHdosyvHWORQJbCi2qvHkmYOhZ7gRfpLW+CPCbFwRzrLQyVlxKfC3AOR/v2ivMH7YEQn2BfJCGSd8ik9pWHc/6nePJ0HW2+gX5pACgrqdlQaW0LYUvOH9dV/3KNx3PAj9uh66r1TehOcMgz3Vtt9vFGx86nU6xGWY6ncrq6qqcPHlSOp2OiIgMh0PZ3t6WK1euSL/fl8FgIK1WK9gvTIcSAEVs7SYGOKlCx9MYQwOtJ0VIMITqqJ+l5JMTL0Zam8t9zvFQr0VRGSHvhelnOeCXW6+Uvkq1pkLPLP7WeXvtDvE25xmzKLwyvDj6WS4AaitLg3LIExGqf44ilDo/Od0i54VVFvpBAxBTSjt1Xa3+5nQhq5d3f+J8IyxBuH3xDsBWqzVzBdtwOJTBYCC9Xu/utQB5AuqPSHgyI31M2MxDiwCg3Dw1E6bmmWt1WBMqJ/0iSI8zft8p5NWlrAWn+xxhKULLSou6pAh/T/NHHparywIlq54xgLR4T5Nn9VkeBk+R8yim9KS0IVbXULmxuafz9sbZyitFued8dRiDnCVHdD58/AJeO7z6qNlsFscc8Bb4tbU1OXXqlHQ6naIMuD17vZ602+099Q/RgQdAz9KzPiHw2y/yLMD9BFpdfi7lWr+xNLeCYpbMvHkflDz2A/w9C8cTjCyoc1yNobmSUj9NnkzQ/1MANbXcHMsxVN+cMmNzMvQ8VWGN5e9Z2R4o45vvMcXvZrNZXPGGz+rqqqyursra2pqcPn26cI1Wq1XZ2toSkZvXpVWrVRkMBsltOPAAKDK77sD/LaYPUY6rwkrLDB7Scvn/nQAcB6WsRbjWrGe32krMLa8M38bAI9XSs/LLBahYWSELLKWM2+FZicVP4S8LOPDbyj9FVug4oXelWtZwjHJ4LcX65EP9vLvV8mzg6rydnR0RkQIA+/1+0UbvbRgeHXgARCei0Xw/3Xg8njk7w7cTWBti2IWmGUW/Hsnr5MlksmcrMMpDnbRVertB8FbQnQC081oU8xDzQg5pN6IOiwmkkFAN1UfnEbLOPGvQylO3h8+O6fZxfG9dJyR89W/PCoy9Py9GPJdT6haqkxdfp7PCdDv4KjF+/Zq2wGL5c70sntIXnrNlpw0T3WYdT4chz8FgIDs7O8WaH3aI1uu7b75HHnfVOUDN0JYWYTGZp5WmMq33jNNarpXJZJI1QDnkgWnMJTFvvinpF0Hz5OO58VLi4X+Z8lPS6H7U/GP1cy6Il8nDi5trjcUAOaZUhuZkqoUYE+y3Wwn1xjqmwMSsOAt8dNoylDIWIRnMygfkob66kvOF9Scisrm5OXNMApeCe+lDdOABUGSvBoQX5Far1UIzgJagwUBPbEtLTRlQjn+7JpOeOBr0rOe5+aaEH3QKubO88FSgsygk+FOtvtw6pYACvkOv6rGe6wPuXl3Ys4L/XC6/HSKUT46SEXtjzCLIUpZiVp3VB/jvpQ9ZkSwTp9NpEBzKKHZaprD1qQ0R68Ya6w5SawlL//fkL4PkXXUXqGZqmPuNRkNqtVrRKbC82EWJ9JoB+Kwel4NvNtctC9QbPK/++0Exi83Ssg4ToC3CWuT/IYEWE3bz1kULwVwLLCVvnbYMAPI3wCukVOq5qwHQuzqtTNtAyHORXhg9RikKk0W5888CcR3GV5KlWNk5ZYJwlpDDPfmo62J57DywDhkfqP94PC4sxRQ68ACoiTtV/9ed5014fFvamDcIlgZmMVtZkLG0QitO7JnVLi9PrSTwJC9rTXqU4vqxrNpQHhZZbc+poxdW1jLx4oYUtFjenrVgjXkM2Ll9IasvZg1aL8llK4/TIgzCTFuDKeQJUH51jn5Whrzxz+UxbQXqfEIKtAcwnqXrzSkvf47HdbXKxXcIALk+MXnqAZ7V/hQFUdOBB0BLE4BPGNZfpVIp7pHTr/DAt36tB+fNA8rbdkO7l2IAmUOpkysEuN53iFk0w3rlzAt+qXlaEym1HintzaUY+C0CaMsAa6qlEQN0fPhGFh1mxWOww2Fl7N7TL82Fdyb2eiALCEJzSr9KCR9sBtHzepFkzdcQP+jyU5Rd/dyTN16/gXDRti5XK7xWeRzmyclQ2SJ7LUUtY3EtmidfdX3Zc5BCBx4ARfZOaO2PxsBoqzDkqrAEcsrHqteiJ5hHixTuKWWFNMlUwJnHmtPxU0lrvGX6rYy2uYjxSbX8tNDyBJqXTwiI2IqzwrSFNx6Pi5s6+G3iAEYLAK12xRRKy0LRwtnapW3lEaOY0lPGAkS6XHnh9UmOwqjnBIeFwJTL1X1tuZlDiq5Orw2LUFtE4oqGRQceAEODi0GAVcgLr5iwetKUKbuMtWIJKJ0+tT63EvhAqWAfa0dsspcRCCHFBt9lgXkeyrVCU+tgCSnreSw/y/rToBoKY+uO3ZjsvsS85HrpdUBvwwvnkTJfOIz7gMvS+e4Hef2pFUhNqfWJyaH94OVQXTzAAphxW6H46E0zsPxwJRquRfMs+9y5BTrwAChiC11MMgAgOlRk786oFLIml1d2inXo5Vs2/HbQfk6sstazB265ykTZCbVIyhWAFoUsQR3HsvRS+xNgB4tOAyCUTevC65DV54Xz79jcQpu1wovffA4xpiyU5fkUPkq1dHSaWPxFgaBndVt18KxCnlfT6U0X+Wg0knq9vsd6rNfr0mw299wHqsu0yk2lAw2AVuOtOFq70AOjNUImTyDGJqjWOHWckDabS7dSw2OyBOki6pFiqVgAZVknVtrUOuQ82w+FJbcO+hlbOyl1DAGgBYg6DGt6+A0gZBDUafT88+aFF57iibD4NLX/UvsrlFdMZqCO81iBsTQpSsJ+5J+TB8I1YMbO980jdw40AIrMLvbiVgB+Bs0Bm2DG4/GMWxQdbN1GocGPtUhMbhEp8uT6cBpovXDt8I00i1iEDzFTWSojuBcFwrF8QhaMDlsUhcqY1+oMlRcKCwmvkGIQAgR8M1jpYw4McPg/Ho9lZ2dHer3eTF5Y92Or0CoX88RSIDmupfnzHIttvOD6a7C0BGmob2PjkZN2UZRjtZapS0heed6ukKHibYJhl2doA4yW23eVBSgy2+l6cumJYR0KtTpOTz49ES1NlsGN4+lJbWm/uj2abhcYhQSCZVVoQZMzGVOf6zKtsJT+ClneKYCWCrq5Y5ebXxnw9eJ7fOrxvP6MRqM9FxHDxQWQFIlbECkAbcXzhC9/e3X3+sUKjwFgKM5+E8+f/VAItZzUz6z4obz0d+ij41h1iJWp6VAAIN8H53WIvnKH1wY1UDExkOF/SCBYL9b1DtYzQHN5Gkjm7Z9FpvUETGq5IW07lM6jsv0TA4GUtBowytbNA7WQgLXqkVtuqExW6vShdpG9FiK7O7kO7PrkQ9leG/Rc4rOD4BkNZt5/kBa0iIu6WrtCQwLWCg+BX4yvPGW7zNxdlBcmRDwOut0hC9qqG2940R+94UUfh0A46oHvHP4/8ACInUI88SyrjgEQ4DedTt2zgZaGiDxFZt/uzlvCdZkiMnMdG09ShGm3DQ9mWQK4l017EGiR9fRAh59xmTHLYd4w67L2RZYB0t4NT6njs1VaAWTwg7WHPLEphtuky7XabgEg0lk3OVlKpkdaUdUX1OcqoB74WTwTopAVfCdRzINjfeM3g5XI7PWVcHtark/vzHWo7BQ68AAIsrQ+3eHcmZ6ZbWl2etBCAkK7QHWYFrSeG7TMYHp5xOLp9oYoVTu1tMFQuTlkaZ45lKMlxsoJPS8LUDEhsogyRGw+80AwphiIyMy6uJ4bVh6hPL3yvbmo84x5KkJ9myLgrf9l5qqWU1y+V4+yoBzKM4dy5o7V7xrovctEmGKu0dy6gQ48ALJGGHJ91Go1aTabM1qqyK57kjemhLRv3tI9HA6LVy6JzLpSeFARBu1mOp3uMe8914fFsJ4w03FS4sXyzUlfJt4iKBXsU8F9XuXDSxsDXn5u3WaRAoihcixBqN1GGgj5kLolcJjwyhom69alEKHM6XT3FhldPz13kEZ7VTRYhsoLKcGhtPOQpaSnKpipeVuAXTbvWN94FjTXRYN7o9EQETE3vYR27nsAmEsHHgA1eR0CABIRU9vATlBvgC3BAG1X/9baqS6jUqm46456EmiGjdVP94MWeqn5hSh1Es0rIBZJMfDIsbpSy/LSpvBYSlovXgoAiviWtNcHmm80n+LGFy9eCjEAWhagyO47N60yygBJyKpLSRNTaizSfZ8C1hw3py6LsAA9r1RMubAsdgZB70rJWP4MfpasS6UDD4CYeCGNWVPs/k6QJSB4UPUaIH7rvDzBovPLmQSpfbNoSywnz3kY806hHKsrFi8FmG4V+Gne857zM70OaCmFei1OxxOJC019xZoXL9TOGI+G+m/eOXOr+T3Hq+PxkB7rFEWE84i5OkOKgGXlWbI5dh4wVpZFBx4A+fqlGHFH1mq1GfdlqlnNwoB3ybEblPPRO0h1PiEte15ahOanqWw+ixYKi8ovBDhWWZZATwVJKzwFrLy8QwIpltarv5eeQc5Kw65SKy2nic0vSxjvJ1lKaKyeobzmqXeOC5bLsOoaUxJyAFDHS6lXSP7EwE6Hpx6Kz6UDD4B68CxNxBqwkE+ZJ0OIIS3tyAI0rpcXD3XSv3MnhFVH7X641RpqqE5WWBkBlNMur/9jYBISzDn5pYBWLF4IbFPz8+rk8bW+ABvf3iF3EftFtrFx8gDZmk+hPLQFkgq8OZ6LlDEJ5WPxbRnr1ap3DIgtsNPhKQDoKQ0p8zgmhy1XpwbAeeRZNR5llj7+8Y/L93zP98i5c+ekUqnIRz/60Znn0+lU3vnOd8q9994rnU5HHnnkEfnc5z43E+fq1avy6KOPyurqqhw7dkze9KY3yebmZqkG8CtVQsQDp1+1gQ8vxOoFWa9svtVe33TPWjFfCaWfWbfhxz45NE/aUD45HyarP7XrOSYEQnmkAGeoLyyQ88aHw604emxDt6l48WOfUH5asGslzxNw0+l05mgD3rQ9GAyk3+8Xn52dHdnY2JDNzU2ZTCbF64b00Qj+DIfDPR8dR8/pmDBPGecY2Kf0dco45NbRW37x5oFn7VnyxxrbnLHhZ945a4+vLV5E/bVs5bB6vb7nEmzwlT43bY1jDmUD4NbWlrz85S+XD3zgA+bz973vffL+979fPvShD8knPvEJWVpakte+9rXS6/WKOI8++qj8+Z//ufzu7/6u/NZv/ZZ8/OMflze/+c25VRERmenwUByLuNPZxLYGh9No4eEJOU/AxeLEJm3Zfor1R+i5nkj6mRdmlZubv0ee4ODvEMXq7QGDFZajCKTkrZ9ZAjYlv5SxDqXXChwLQwbFwWAws/6nlT8rHy2wtXJYVqhx21LDQkAYG0+dT2pfxyweUMrSDPIPAY+Ooz96nCxFvixf634JtdOSySlLVGX4JNsF+p3f+Z3ynd/5nW4FnnzySfnxH/9xed3rXiciIr/yK78iZ86ckY9+9KPyhje8QT7zmc/Ixz72MfnkJz8pr3zlK0VE5Gd/9mfl7//9vy//7t/9Ozl37lxWffhogQcqmrRPWWT2oLy+G9TrWD1hoPnq3Z3TqX2vIdJYzKHLiblvct2FZZ/HNFLUJddC089y2zQvebxSVgCnCMiYIrCIeDpuqhvQszD1zSwAQbYePPCyhKPl0swd97IA6eW1X3wXc9Pr55Yr1lomSSULlGA4eAqWJz91vla4lYcFetZOUO9A/KIp2wIM0TPPPCMXLlyQRx55pAhbW1uTV7/61fL000+LiMjTTz8tx44dK8BPROSRRx6RarUqn/jEJ8x8+/2+rK+vz3xAON/XbDZnXJghYk3Dun7HsgCtAWChAI2WhQbi4NvTMGMa7zyApeu737QoJuXJn6qBL4K0wM4tKzV+DNRC+YTAL9ZfMetGW2rsSsMxh+FwWLhAe72e9Pt9GQwG0uv1pNfrFdagJ1Q5f8v6CCmvsX7PpdvFW0yh9S79OxSWUral3HgWeMiStNoUU8C89moQ5OUolsn7QQvdBHPhwgURETlz5sxM+JkzZ4pnFy5ckHvuuWe2EvW6nDhxooij6YknnpB3vetd5jPNNJ4WouN7H+uuOUuTxzP8ZwHCLlKdXodp4aDLSdEK9bPY83km960A0ZRyvbbEwAHfHtB5aa2yvPp545n6LKXclHbGyOJNzcspIKoBTPNhrN8W4d3QcyXFixILy51/nMc8tCgl0psb+O3xvcd7ZSxOy+Lj35b8jrmEQ230wjw6ELtAH3/8cXnssceK/+vr63L+/PnifwwANSEeXJ6YwEgLt4DlyuTffMcnhIW+lYZdrPpWC3b/YPHX0gIXOSHKAGfKs1tNORppCAC1ZmzF8zRgK9z6z6TLsOoeEkixuF5865k17rglSb/FQQMdXnOENUB+GS4rkhbx0gOHzcvnobnque1QNo8bL39wnNw6xKhse3OASPM6yyovXihM1yO1vp61p60+yEG9+UXzqh6zXOUPtFAAPHv2rIiIXLx4Ue69994i/OLFi/KKV7yiiPP888/PpBuNRnL16tUivaZWqyWtVsstN6RlWOF6MERkzxVllgtUxBdwSAsBh7dMMEBa2g9PTl4/DGmyobZaWivHjaW3ykh9djsoxzoKjV1IG/YAUAuSFDCKAWRqPiltipFVN8tF6rnMeJeg3nUYa5u21nIU1xTyytbWHf/25k7oWaisVLLqpPNJVVKtesR4WcdhcPH6zKtbqH0hS8+77NpTphYBfiILXgN86KGH5OzZs/LUU08VYevr6/KJT3xCHn74YRERefjhh+X69evyqU99qojzP/7H/5DJZCKvfvWrs8ucTCZ7JiHvCPXAzNNCvIVYbY5DM7Z2uHmf2K6rUFxvcFMAjeOGBHJM47vTwM+iEPhZ/622h8Jy87M+VpoQAHtrdaH8Qv3jjbMHXlymtzmGLb/cTQtWG0M8n0shJcEaA6vdsfEsS6ngMS95fZojPxDfa3MorQYzS8ayFZh6LZo3P1Mp2wLc3NyUv/7rvy7+P/PMM/Inf/IncuLECXnggQfk7W9/u7znPe+RF7/4xfLQQw/JO97xDjl37px87/d+r4iIvPSlL5W/9/f+nvzAD/yAfOhDH5LhcChvfetb5Q1veEP2DlCRm7ew9Pv9PZMHZHUarD0GRbgfkZ7fHC+yO/B82z3/5htl+CJedqfim92kLDg4Htc95lLKIWYOBnP9LJTuTqUQWKUAlvXMAyaR8CHvFGVDp9V5eGlCbZ53nKy+sjapaMUNfFzmFVwoD/3JyweLAgCrTMsCDMWzaJH1y13rSqGQQpEC4l6dYv3C6Rn89MYWgF6j0ZBGo1G4QC3Dw5NXZcFPpAQA/q//9b/k277t24r/WJt74xvfKL/8y78sP/zDPyxbW1vy5je/Wa5fvy7f8i3fIh/72Mek3W4XaX71V39V3vrWt8p3fMd3SLValde//vXy/ve/P7cqIrJXe+RwTZbbxTPLLatPl+sJSk+jZfdmiBkRl9thpYm5R0N9pt2vqQL0TgXCHPDz0oSelQnTv1PT6Dgp8VLKZQoJtphQDOWZ6qoLtWO/iOc+/sdAMLTelrMWl0rz5qndl6lKlxfOYSFXrcVPVhwvzJO7ngxOnYsxygbAb/3Wb41qDO9+97vl3e9+txvnxIkT8pGPfCS36D00nU73bCxBuPWb6yiy9919bLVNJpPCKhS5uU6JuAxUnB5lsfk+nU6L/6PRqFgbRD3YIuR6jEajGauS674oS9ADwVCaO4k8JYKfhZQN/h1LGxIkFuh6z9k7EQI9L71VXmp+IObL0NoK4oIP4fGwFEe9qUWDDcI8Aa/XifTvRZLmeWsuWHWz8tnvusaI682b9vjbcpfjm+VOCEw85UB/WwAXOlbGm1544wtvfknp2zLgJ3JAdoGGCCCYStoKBNPw2+K9jTGaWbjTNajyzlIGPFyczTvNtAC21jAtwZTaPzE3RgrT3EngF9NUQ5PBAqtU8IuVEQO/WL1iwFemnZ4CEOIf8LfnHdGkXfUWn6aAXyxsEaTBLsTXljV7u4AuRCFvjsW3qWFMnpLgKS6pSru1F8PbiKjrlTJfYnQoAJABQyRtMRZpORwdzjfC1Go1GY1GrpajQZAPDSMu36HHAKnrojVruEJ5BykLGQZmr92xCasZOsREBwEEQ0BmxbPyDAmCUN7ehEwBrZQ01v/c/ELKkO4/y43PlkIIFHU+Xjm6TrcCYCxhrgFRg6SWGZblfCeCI8jj+VTylCHrw/HYpWlZgnrNz9r8gvpb7Uidsx4deACEpSWy1zUTI46LTuPjC8jXOovChLRs9eEMFSYGgxa7Nq3nXD8LJHmyLWKjQIo2fCeAX2odUiynXLDygDUEpiFQCmmtKQBnhcXAEL/Z6+CNOwNgaFdybF5YfRQbx5w5HCOvrBAIWs9jCgSXdacCYazfYyDO8ojlmF6qYeCzdtczyNVqNWk0GsEd91z31PmSSgceANHBnmUXI83cGDT+H9J+UB5rybDa8M3aMwsfkb1n/xAvtAnGEnAxAAu1X5cTojsZCENApdPlhlnPY0BmhZcBt5R4KSATsmg0WGme1kcfQu3LnX+h356LLZSP1U4O1+SBYOi518Zc8IuBjhXfCwv1e0hRA6UaDaFx8uKHrETveaiuMQUzlQ40AFYqFVleXi52mKLjtra2ZHNzMygEOA/+5tthGo1GYc15F2UjT30uCuHT6XRGs+E6MBjyOqHFKNPpdEZrQhhvO9ftStGkUwX3nUAWAPG3DvOsDw8oQzfBxPIOAYE3cecFwFi+Vlx8s5bORxmGw+Ge86gI48uuEaaBMQUYeJ0wBnQ63NsliDghQEkFmhQl0JtbZedNLgim8qY3D6zyNRjG4ntjoOuj4/FxCD4D6N36wt/6t9c3qXSgAVDk5i0xa2trM502Ho9la2srKNytiYZwfveUtjA9TYWZDGmxdog1QJ68qBtfoQZghStVHyjWeXD9+Ru/U9YIvf65Uyk08UNxPEGh48fi5dQhlF7H02302pQSLyU/3Q5+/Q2uNMM3wE5fj8Y7sEMAbAlTbQVosoRqrrWQa41xWq/+WsBz2Dxlp6SJ8TiH63iLJGs8dLiVRoOgNZZ6NzG3jX8vqk0HGgArlYo0Gg3pdrszk1i/EcITHtbWXDwT2XWv6sPr/NGaDgMbg5mIzNw3ivRszXmbXrgstj71xEwBOmuihYTmnUTW5Pa+cwAsFs8Ls8qeNyzUttg45eQnsrvbmC0+/b4/ACD4WANeijIg4oNEzEsRswxTyKsLzwVr7lggF4oXS5tKXj4xsvjbGqOUcjm/kKWn0+q6M9iFbt6yyshR+MrKqwMNgCIinU5Hjh8/LpPJRLa2tmQ4HBZnShh8ROxO0ptP2BWJARoOhyYA6nz1cQcug9cC2Z0K0oDG+VqT3wvzmAdhniDab61xHvLakgtYPE6paXPyTc0vJcyb/KHfufkhDO/ym0wm0u/3CxDEa40AiJzOcn1a7fWsv1SrQX9bno95yJoT+rlVpzuRuN+t6+oseRIijJ1neVugaOWtwY43voSuPvPaZoWVBcIDD4CWi9LaSRTqoJj2yb+9SegNUOoH8Tk//SzEBFbdY5qpFebldycAYkwjjAGVlcb7ndrXi8gvFdxS65WTH8Kta86sXZ8e74YoJHBjgOZZgPzbEry5VqImPRf1b/2NZ1YdQ32UW8eU+evJjTIUGhMvTMse62NZfjq/kMzOkYUhOvAAyJOXO7der++5od4DPz2htbYUup7HE7j6ZhkR2WMVcn3AEOz+xHPWjKx1wVDdPIvR68s7nayJbW1e4fi5oJirdMwLvLkgavVHrB2h9sHC0xexayHmgV6KEmY942/9OxTHsxo9QRqimPUX8qggvX4WandqvULEMsVSSCw5ptNzHVLGyLqVKpfq9bq0Wq2ZF95qeZaSv6WM6fDkOuU24k4jD7DgZsQVZql5eRqTBSKW9edpY3p9jy/Jxm+Og2cMply2fhZyTTAQHgayNNwUsCkDSqHnZerg1Uu3L1SHedupBSRfdh0SnF7+Xv1E7N2F+rdnUejnsTCrDC//UDgT5g7azOVqINFpUvKNxfHCLeFvfWL5eBRSPmKyxJOf+k3vFgCmktXOMnQoADDUCRZQxfLTcUPgogdPTxKvrvq6KRHZA+QANw2cFkjqNUqui1d3bl8qpUxcL76XNpan9dwT9DHBr+OFtMd5ATO3Lrp9ofxSnnnlaevBWivKEaSavPlgPQ9Zgh7Yxc7pes9S6xwjDXwWEHqgH8rLG98Qr1jjZJ3ZjI2dp4Tg27O2OZwNDy5fv2SZ0+olrJBiNQ/QeXTgAVDEFoaWwNdn7bzO9TQYXqzlw+ohxtfP9D2fCNNAht9a06xUKgUT8WudvDozoyGNrpengaWGhcgSbNwfKfnpPtQCgS3oHPDRYTlA42mgqYCoywzFS6mzV1cLxHR/paz9gUKA5fGgF+YBVCgsdAOJlXcZ6yKFUgEuloeeu9yXMZ7jsRMRV5FJodR5qK82Y6rVasWLy/mcaK/XK5alUJ9Op1OAX71uw1Dq3JiHDgUAguZl9BiIhCwpZuQUbVy7LjmcQTAkoDmOPvYhMmsd6jVSBp/QJNxvKsvYZYAnpPB4+YXqGqtD7FlKvazyY/FCbbJ4x3sWyl8kbAFx2pgi5Vl83rMca9EqL1SXGMXaauWZohhY/RRSelIA0kobqkMqhRQXbZ3reum4KQrKfsqhAw+AWqCzpcZhIjYDWEynmZzL0GdZNOOyVsfaGIMdwrUFKDL7iibv0DunZZeDJo7HZVvMtwhrb7/JE+DapcfxdVzrmRcWAhYRSSrXqodXbqxeqfVMFRjoO9784h1uF9m77qX/W8QvlbbihaxBK8zaEGZRzLpcBOn5ZT3LSRvjIcs9HXJfW/l4dWSZlEpahuD4mYgU/MRHHzqdjiwtLUmr1ZJ2u11YfzFlYT/pwAOgiO320DsqY2lF9gpKDwAZQLybCzwhgufIlwFQAyoDboqW7PUJ9w1PPC1MvH6508jq35jwiAmFFOCxyk7JLwTEVllW2li8lPxE9rrY4EKzNsGkCCPNV16cUHgI/PQzPSdj5YUUvEXQfoGgHr+Uj3ZZh/jDi5MKglrWisiMO3M4HBbPEA7ga7VaMxdgewbKraBDAYCpkzWFtGarwy1wtbRai4k08IHZEBfaN4fpdUuEcdmehq3vLNVpOb5mfK8Nt5MwyfHbAqJc8POAwhMeMbAtE6bz3894LCgtwAsBLv5b5cRA0CIL7PiZFc7POI9Q/qnhIosXwnrOe+V7AOrxuMf3sXbkjlGsTZXK7oUhfKsV1vxqtZo0m02p1+vSbDaL3yHrfZ565eZ54AFQD/4iOtXSPNlFGToXGCufgY/ra00UtjI5Hb+8VzO6ZdVZdYTgC9X/dgNgSPh6oJcjHFLALwUwF5U21k4rXm5+7Oa0zv55m184Hz3fPGUsRjyvQlZgLIzzuxMp1wrVfGxtcrHGIoVnvPJSScsuBjiWi7DwWq2WLC0tSb1el9XVVel2u8W4L9ryKzP+Bx4AQYsGQaYQ2IWAj8N0/SyhzBtgmOlFZg/O8yF7T7jHhJGVLiTMbrVwSRXAsfje81Qgiz1fRNpY/RcZz1MWQoLVy09bfbkauGcBWmBnxeO4Vr4elbEUUtN48fS8DMWx/qeMm5WHV8ecunvEira26HjXOdYGvff9LQoAy9ChAcBUCyw1Hx2Gu0Xh455Op8Uhe758m12WIM1Y1oCza4/dCJYFyAvHPLF0WZYFyEcrYEGmrKXcakqxJlIA3hMOKWAWA9uQpRbLMwWcQ0ItV/Pnci0rz+tLaxw8Ic5xU62zlKMMHrE3I4fK8HVqGi+eB9oxMExRVEKKoUeWTAqlD7Ufl/zzEStYhHB78mY9fQvW7ZQzhwYAQYsCQT1AGFy8MR4+bwCIdbQghbTQqlR23ybPu01B+iwiymRhYIGh1rB5HZNBl/O73RQSbrmabooFFgMmnTYF1LwyUrX9lDblAHyq0PS0c80vFghZcyBkqXngZ8X3+Nmq60GhkIJsjdUiwE+Xq/PW/R9TNiETIVsAeo1Go7D+wCfePoTbQYcGAGNaey5pENFWlNZcNQhxGNcvVB63ge8P1aDEblEmrX17wGhp6hr09Aaa26GxpQhxHd8ChRSgsOLp3149UvLWZcTyttpkxU2NF5ofHoDn8Kwn0GIKoX6eIhhj4ZYAt8iq+60iPZ+sMdFHm0IgaOUbIy0bcvojFoflpJafueBXxspPpUMBgOic0AK+ly4VMDGgMOXxwlrWfFKtP09Q8QTGBJhOpzMuA9ayuD5cT3xb9eGX/QLk4FK1gDOkue83hQR1GQCMxdPlpgBlKojhW1/cnVOeVY6lwXtxdZj3sdrp1UXzQgjQNHk8qq2hHECNPZsnblnSbfB4hknf8IJ44CHrqrNUuWeNEfNTSGm3wMwCOsgm/rbi3W46FAAosn/uD8uKsiauZiZvgEP11CCIb96dJzK7zpjSbmZuy/3AB/HvJEoR3NYzK24o/xAYedp1KG8vrZd32TL0+McsgZBC4ZEHrDlWQoq14MXbD1eZ1U/7JYy9NoUUFoSnKEkpYBqrT8ozPI99UuKGyub6h4B4UXTgAVALFn2hNJMHYjHSms50Oi0OcU6ne29sSZ34sfaI7C4wIy+24NBer2x9PZp2d6DucLNyW+8EC1ATW1D4r5+HLBgvnQ73tGodngqAVrpQOTnPPMsiRXng+cLlsMJn1TtE1pyLCb0cML3TKBW8rWcx3gnxT0wBzO2vkJXtWXjWRz+z3vxglZFSPy3DdNvL0IEHQFAK+OE7Bn4Wc7K7k9/SYPm4rQFCHfX/WDzdHoAWAxjqYjEVg53uC85fCz+9rflOAEINgJYmHRIOqWWEwmICyIoXC7PWeqz8UtqRIlQZ/LQ7zeL9MsCHMG8eemlS874TqCz4eWTxlgeI1hhpz1BOHXQaTqvBDeOqXZsWCPJb3/nWl7J9xHXVeZQBwUMFgPguqw2kktaCtDUIRrJM+FThogWRFlpWPA3A7O7Uwk1PKB2X8/Mot5/nFWRaw021sFLqmyJ0dFiojJz8YvmEwmJp9DML9FIBXVOugPX+7ycQenNwP0DVA55UZdjjLWucPDmn54eewzqNlg+6vpbhkPrx1v6sfrN+W21ZtGw/8AAI4T2dTmfuM0ztqBAzhYg3oDQajZn39U0mk5ltv1yWVb5FeHs8GAJuylqtJqPRaM/RC4uB9auPrLpoBsdvnjzWxIhNPk2LEjgsFCw3iGdNpeSH7zIglgJYMaCOgV1IyQvF599864v3BngR+9VSixI+t9ui28/yc/L2AE4/866si/FqTn0s8I6BnzYA2PprNBozHwZCrqsFiosGuRAdeADUNE/nxdw/FgNMp9MZRrCudvKYM6fOyIMnA1t5Wkuy2uIBctk6IU+un9Y6LfCdl7z2xbTiUH46Xg4YeWGLyMPLd560ISswpW8ssiwJ/cxLk5LvraJ5rAxPAYz1q+77GDB6YInfGsxy53LMOk+1AL0lIl0nT7EuU//csTt0AAhKcd1ZzBfrRAwsDnmyRTYcDkVk9r5FL69UoctMADcrjmAwc3mXZnMeqLuOx3Xh9mvGxXPc/1epVIpDrv1+XzY3N2eEKjRB7ut5KZQHhHtOGn4eAhjvmfXby8PKJ6VML10oLy8u8yYuXECY7pPYPEj5nWOB7Cfl5G8BuJYR89Q3xjMYE/07xQLUYxa6tN8iy+rj9Ttro4v+rz/aE2XJIU950P3myeeyisuhBUCRtAFP6XidJ0AAwNNqtfacERwMBsUrQVKYNaSJaw2JhZUFdhqwdB7achSZfQ8hMxP+67itVqu47LbRaMjGxkYBgKPRqLg2rtFoFOVZ4FSWrH7yNO2UiRHTxK14niBDmD7zlwtoXv08YEsJs157NM/YhKw7z3pIjYt23y6KgXwqoMT4iEmPrWetW7yl56wHFLo83R4GPpG9m2B0nBAI6t2fnNaql1V3z0KMtS2FDg0AlpkoIYETIq3J4Ho0Pk9ngZBXhhVu7ejifKC18xshplP7Xk/Ow5sw+nygdRMMgySsB1YEeHLi+Aa+cy4oCFFszFJA0cs3BlRWuRZ4efmFBKGVn+YdSwB6bwqwwligMgDG+D8EcjmUY4GgPrE03EeLrEduHroeuWV4Cpg37njO3159PBmUMq7aC6SBzwO82C5yq/8QrvvS481U+RqiAw+APJFF0twTunPnAT+kx6BD+MMKEpnd0BITlNPp7mYeLlOXoxeTAU58Mw0sUg1+mjEtLVAfq+B4cPVWq1UZjUaFCxT1GQ6HMhgMZha/Uy0CC3z085DQLguAHFdPqBThZG0aiQkr9KnW5tklyXwG16VWNpDGAkDkg29LqHr9ZAnQsrQI0LmV+e43pShcLA9YefHyscoQCbsHPWAC32mwA8A1Gg1pNpvF72q1Wnh8cPTBO/vn1SOlvoumAw+AZSlVawiZ43gGxsDaHASGdkfq8jxBDmbXQG1p/bCwuG58TpHraVkTHJ/jaQuQ645yEYcnKdeVJ2zIMtUUAsEcAMydRFpR4Dy8cYqBiaXwiMwqNanxLODTIMxh1jpSqE0WOIdAMBUYUxQfHWY9vxVCsSzYhxQ5qwz9W/OCxVMeD3rlpboVY+3yLD8LJL1XHsVAUNcxFQSteDnjd6gAUA+W9Tw1H5G9TGW5BkR2AQmDD1CERQjKYXIWaAxI7JeH9QVrD9984NRiRq6Px6SWBYgPts9XKpViAxCvLeFuUXaZWlewxTRXaww8AAyBj0chN4xXJsfTa2ixOln8ZIGTBi+tZFifmLvTA9tUUIkJLu+/l1cM7G4X5dYlNb41xz0lhsfO4q0cHud4KWPIc5/v8sScxqF2WHj6g3D93AJAbg8rXLdC0QEdCgD0NBvPvA9RSMvl3wA4kV1ByK7JkNvPAz9LoOq6MaOwJgYmw+tHEM4gx0wIa1W322qf7lMAnifI8AJM3VYrrgdCMSBJBcEUsrRjbbVbdeDxCu36DQEgW8kMrOyyhCLlgZz1G/l4wjPWRzEg8+aFJmsOploFh4U88AspMtamF4vnU8r2xokVPm3laasOYMi3uugwfvEtA6BeDwSxd4P/30o6FAAI2u8JxYDC//GbrT5mJA8IQZ5Wj2ea2PWo68PCG3XxzuNoq8yqV6i++O1ZzDrMmowpaay8UzTilMmkgU7/98rTiorlYrTapIHWA0CUweDnWXMhgRriJ69/ci26UF5WnmXpTgVK3V+x8fcUuZSxC/FYTv/ExlXLCgsg+WPd+Zlan7LeIC8shw4dAFprTTHK6UTrHB3CxuNxYfnU6/UZF6UGJy1ERcTV/nQbGXS53XBRwLrjG2PYhcFgjfpr61JbeFbdNbinWhc6Tz0OqYCWAjgx8kBPf1sbUPTmhFiZFqB64Kk3UCFMC0ZvE4zmn9RduHcqyBwECglxa5ys395zb1xzgCaFNNDptT29yxMyBZticCRMe6FSyuU2heZjqpxJoUMFgEypTJEirLW2ZAGDdh1oTUmni2l4oTrqnWBcR5SL/5PJZA8Ia9dnGUby6hljTt2fFqjlANqitcIQyGoh5VmAKfnHANDqBwv4YmFWvt6Y6zmj+f5OIa0AIsyrq35mxU2JE6uPF+aNbe4nVNa83i8PSEMWIJ6zSzTHAmSZpdulQdCbl/PSoQXAeUm76qznvN6Hb5zL0xfB6kGEwNL3l4aYH+ks4g0nqDOsOOxQBSCyVofnmmmt7cuWtaf9+yFG1VqeZwF66S3yxiYlrUdW/7O1x0cLNPjk5I3/Gjyt/BYlNO9EQMul0JinxE9JP08/WYAX8vJ48z+Wb0pdteKuvy1w47U93HjF63wa8LD2xxdf5NbtdlG2v/DjH/+4fM/3fI+cO3dOKpWKfPSjHy2eDYdD+ZEf+RH5uq/7OllaWpJz587JP/kn/0See+65mTyuXr0qjz76qKyursqxY8fkTW96k2xubs7dmEWRNzAhbchzF/BvzpfXdbCrksEQpLUgjj8ajYrPcDgsvgeDgQwGA+n3++4HcYbD4Uz5qENKmK6D3qWod7LFPilCIDZungtHf0ICj8eF26f7W/dP7OP1pbfzL3UcrDTeDsK7lVLbn9tPXnyLl3P4PmQtWhQD9RDoWQAI4AP44dsCPevjbXy50ygbALe2tuTlL3+5fOADH9jzbHt7Wz796U/LO97xDvn0pz8tv/EbvyGf/exn5R/8g38wE+/RRx+VP//zP5ff/d3fld/6rd+Sj3/84/LmN7+5fCv2kSxLxRKemsEsgcuAmSroLa0tRKmCVIOYJ0w9a8KzSrl8TwCUAcdUoZHysfK4VWWH6pRiGaSEeeP0/2vv6mPkqq77b8azM7OfXtaJvSzghEZRIYQQgovlUKlEWAEHNQ3QRiDHsUgVRGuHr4iSD5E0qlKHJG3ClyDJH8kfgeZDilNAIpGDHQiSMWaNm1KoQ1QHCGGxwPjbuzO7c/uHdZ7PnD3n3vvezHp31u9Io5l57757z7n33PM75369nMLUyrCndi8mMrfsQAhcYynN0Ka1v09bEWo5ltqiu7S8tqK3aZ5NPQS6atUqrFq1Sr23cOFCbNq0qenaPffcgwsvvBAvv/wyli5dihdeeAG/+MUvsH37dixbtgwAcPfdd+MjH/kIvvnNb2JkZCQVP6QM2jBdqxQ7Pya9p0Lh2MQwGSY6KYHy4uATC34kI//WiAwilSP5ogUy2jJmnk7bYhFazsznoPi3/K3Jl5a0do4deuG88mv0oVN8tDk+TU7+OyRzrLdv8Qc0L2iR835aubx/nKzUTvl5/cp8tXaKdXDoGfrmz7ST5Pwd7+PaXj7+SiPt1BcZKVLePlus1Zsc/ToRNONzgPv370ehUMDg4CAAYOvWrRgcHEzADwBWrlyJYrGIbdu24YorrpiWBw3bER04cEAtKxawZoI4CDrnpnlG/BrQbISJZ00pQgqkGTurgxKPmgEl8JN5y7NGtXwtj1e7p8kw1wCQImbLcFkeeEz0pYGcDwytZ610/PqJBL2ZKittf+a6a/EUm2eWaFBzZnyOjqZPvrZPw5dPHs0uaIv3OEDKKFAufJGryUMr8i0bciJpRgFwfHwct912G6655hoMDAwAAMbGxrB48eJmJkolDA0NYWxsTM1nw4YN+MpXvuIty1KwLOQzYFo6Im34kxQEmL7dwMpfAwaenq/yjOGRDG2hcOzkFuC4h0mAyJVcRn18iwXJJLdTSLDwRUlSTvnbSh+qI19ekjSvmuqYz8vRgpeYSItfayWK4x9NB0LgJ/+HQMEaPfHVY+yUQCuUFfz471YcAQ1weH5W+/P2lA6Ub67bcoq4TFIeLdLiv/m2LfrmQMdHrCjC4yNCPO9CoYByudwU7dEz/MQXX/uH2sOyaTMV3MwYANbrdXz84x+Hcw733XdfS3l9/vOfxy233JL8P3DgAM444ww1rc9z8qW1jIdF1rPU+KQQjUYjAUC+GlNTbqlssuNxoyn5k8MH2n3g+L4yepMEDYtKsNO8QH7QLfFIQMijJppTjKlHLr9Vx7KeYp4NGb1QdMaB3AI9HwBSfWj5a2XJa9xpkfriK9dHIcOjGdFQPcaCZFpql7FrNxBrozTymztSvD0lAGpTBiHnXWsnC3Co73K+uXPLIze+ypNWcmrv8atUKqhWq4kdkCtD+d7iNEO3XM99YBcDhGnafEYAkMDvpZdewubNm5PoDwCGh4exZ8+epvSTk5PYu3cvhoeH1fwqlQoqlUomXtrljaYpi4MgB0BrBSJ/XvIcY5R8XqkP/Cny83UgXiYfLuUb5WWH52+ziFXWkFHXvHEtn1ZIRmdWhBUbAVqgKfOOnccLgS9dj9F5zYjKe2nzyEpZwK6VyE4rl8scw0+o7fk1re0s4PTxx2WOidqttJqjLT/W6nZr6DO0sprkkU69RbwdNGDkv1txltoOgAR+L774IrZs2YJFixY13V+xYgX27duH0dFRXHDBBQCAzZs3o9FoYPny5S2VLT0sTtxYt5ukd0VDgvy3dli0zMO3ckoqAP8mcKL7fGiT0lgGVQIc/affctiWIkYCQPL++LCnFqmGAFaSFh1Z9Z62Ta1yfUbJAkLLEPIIUEsvh4nTyBkT3caCl2W0QuAWe22mKIsjFHIguF7E6pTPUZHOjrRP0uGy8tNki20nKZsEOXnCCwc2et0RnfDS1dWFarWKSqWSbHfQwNJXf2n6quynGvjxe1kAMTUAHjp0CL///e+T/7t378bOnTsxNDSEU089FX/7t3+LHTt24JFHHsHU1FQyrzc0NIRyuYyzzz4bl112GT796U/j/vvvR71ex/r163H11VenXgEqKUbwVjppjFGhNKQIcghUG1vneWvRnzR8/Hor3hHvgJZXyP/LVy9ROQTyWnQj+ZHy+vizDINm4FvpWBr5Ij/67QNAC8C5IQwBoFZHISfOV9da2hCQ+aLBEwl4IYqNPkJ1J5+R93yRH/+t9YeQM8WvpbFlPhDU+rK8zgHR2vYg9wDydQCaHll1mTYIiU2fNbBJDYDPPPMMPvShDyX/aW5u7dq1+Od//mc89NBDAID3v//9Tc9t2bIFF198MQDggQcewPr163HJJZegWCziqquuwl133ZVJAG6IuWGJiSA0ajVClABYKpVQqVQSkKFJ5KmpqWTTOr1gNuTtaRFAqENq1yVo8XLkhwyHBDtK32g0mq5Z5VoGyfeML9KyyosxyprcMt8YD1MDaF+Ex/PQ3h6hlenzgjn/Ft+a4aPvVsCvFZKOW6tkycjzlw4i3dfyor4JIJkHk/2AyyJly7LgxcrPktfnMMt0HOAKhYIa7cl5PAl+tPCF21rilQ9/+nihZ6TTbvU136ddlBoAL7744qCxC9HQ0BAefPDBtEWbxBXa2rvlo6wV6zPC2rBAuVyGcw7d3d1YsGAB6vU6Dh06hKNHj2JqagoTExPmxHFMxOIDfHmfl8MNgxbF0n+a0+SrUPlcIH8uBvB84B3yiDVDH2tMeX4awMSAn3PT389nPS9XBQJQ94Ba4KVd4/xLMPfVTUzUYKWz0rcCYq2CYFY+fX19amoKtVoNAJKFX2kiwNCeP0qn6ZSPr1AUp6Xji1L4gpdCodB0aosPALu6ulCpVJoWuUi5ZV1rTnGon2g05wBwLpL0SjTvy6K03lcsL1whOJA455rel0fDCTydxgv3mCz+LbLShOqFKytFe5bnaXVC67+Pj7SGgfOYBgSt6z7A1Tqxr3Na6S0eLGCzrtF1jc+YNggBoeW4tCuCC5Ufm4fvWRkRSr4tQJF6HWNHfO2vXbMcvqwU6p/cKdcOvpCLWrRrIYeKZLDahOu475qPYhyFGOp4AOT70LSzKXlEaFG7vQpfh6R7NPRJ+/Lq9Xoig8azTwbLAIdk1jo2v86X4vNv3hm0pdKys4XqhPPg85BDlMWYaulCixLoWmwEyO+FFibJ37GOhWaUQnLGUggE05ZlRaGtAKDvGi9POnNcR4lo2oJ+FwqFpqjQV8+hoW/rGr+XlbQ+qAFcuVxuGtqUESDfEygjQEofs5ZBqxuuM7w/EI8+vdKI0vE2PKkAENCHQEMnd2RRtJhnNIPE//OhBx4NOueSSFCWFRPBWZ0nxmu17lnKyO9JWS3wC5XH840BwZDHH9MJrI7mA0DJp8/YyWdCMslrxJ/lrFjRjOV9twqCVj5SF3zl+sCvndGkdU3Wj1y5SPfk4RV8a4/Vxy2g4/f5t3UtxH+oDrToTEZ//EBrbTWotRiGfod4kBTSySxtb9mEkwoAacJaO+Q55my5dkZ+RBog8N9cqcrlcrJSlJbOk6cpPSSZH6WJAXfZIbnx14wrH+7kcsm8pPHi/2V5lmJqQJ7WG9Z4zRoFSgC0DJaWzmfcQpGiJAk6vihMesCWwfHJHUsxefvK489KJykLtWpYqS3IZvDISetrMmKke/TtO+PT5wjJ/uKTSetvvghQApgEOA6EtBGeR4fyWVkO/fe1p+bg8m95cISVj0ZZ9WdeAODExEQChNqbDXwRVMgDS0shpaD/dMYm7bWhVWd8q0Gj0UgOZqa9dvRtGV7Ly7SMM/2WHZ17xRz4tKhJKj2XP9bAxUZIGlnGIhQlWte0YUpt0ZBvG0Nsm6QhzbunNuGLkyhq8QFVq9FWbJ5aNMLvtRIB+MoORaSyfRqNBmq1GqamppJ9b1o6SmvVLz8RyfrwvOi3vKbJ4IvsLEdbHnKtLXThICePOKNvfjyaHPrUAFHrf9b+YLou69RqOx4UtGq3OxoASRm10/s1siqsneAXIhkRcTDkq7MmJydRKBxfZRmKHCzZZKTH87EiCh/PGghadUrKGquoaaKjmaSY6E9ei7nPr2lptXqSRkEaCsvw0++ZigJ9ecSAkQZ+aXgJRccWH9LJ4XlZICWjMi0y5L9DzlxIPzSy6sbqs1pd+z580YsGkNZzWhmyrmT9SRti6bF8jteXz8FLo0cdDYAAUKvVcPDgQTjnEg+OL83XaCaNqwYqPGKg/9yjIq+RfhPvBOy+F5yS18+BJhTdWXxq6WUaXgYHaC1PzVDEtksIBLUOKO/7yNdxYqJRzVhq32ko5Jxp0QH95nNZvtV6rZBW35a373s2Zt9YDC8h/ZDE59vr9bo6f0v9je/NpQ+96odIO/0oS+Sn8e2rD61P877F+7g2lyfn9OSWB1okQ4tg6OQXDpT8d4hv2VYSHC0gtByWWIc6hjoeACcnJ1Gr1eBc83vcOMVEfWnA0kprKYHmUZKxovcGUqNOTU2hq6srWR1KoCifl51A84g0z8tHUrE0pfVFe1qd8udCAKjl66t/GRlZcmjPW5EWL1v+1kDQdz+GWgUmDejSgJ8Fqj4+Y8HPihS0fHyk3edtnyYfAkC5D5PrkXWsn3w1mOxrvo3vlMbn2GVtJ1//o2/+kcOYMvqTq0BjosDYdrXskwV8XD5ZD1Y9pemHHQ+AXPHSbHyn73ZHgzEGTXpS3HMrFApNAEidTiqsBqqaTJr3ZYGK7KwhWULpLUCOrfOsfMbwnrZ8Hy+aRyuvyfRp9Y6nl7pAeadxLmLIBzBZQCsWqLKQzFPTO+0ZDgjcGZXvg+TzadIgh4BuJuxMiIg/a2iTgx1FexQhyxfiyhdiW04XL9dqD2mLNPsUI5cFiGn7wLwAQP6OO8v74uk1QxzTYSxK07FJkcgTleBN+3SoI9KwC80J8sUpUiY5ySw7q0+2rB00BiQ4aXMvWjrrmiZXbKTK06QBzVBeIcqiW5buAmEADEVMMbz6jFuaPLTf7aAYMPaBIhEt+qDFdBQd0r5cigYJIIioLUJDn5S2lVGCtA4dr3NrGwNf8EKr0UulEsrlcrIwj0eAEgR5WbH6EerjsY5hFgfSoo4HQEm+igkZ65h8YzyWmA6pGRo+h8MVl4CRIj8ZRXDeeP4xgBRL7fRgfcY9RL4oKmvHiAFN33NEWjtkJUs++vZ54mk8cy1f61oa2axnLHD2PR9TjgZ2IX3QRkv4nDrPRzoxoahPk61VRyo2nSYXd5j479BH07WQDvn40pxBTcdOFBjOCwBMY3BaBYUsw2taeg52MhLs6upq2vLAoz9ark2eqlzwIzf1ch4KhebjzLhMaSiNN+szeD4+soJIrIHIUo6MOi2j2yp/kkctD82g8fxDwCDvxUZ8WXQ/NkJISzEgbRlWOjWqWCwmi8zkezt5H+LtTump/8nRF23Pn4//NHLGPCfBW3uPH48C+WHXXV1d006H0bY+ZHG0tPUZPlA9ETRvALBV711SuyIenyGSUSWBF3/rOq3Ikse78U27cljQMgCa0U4L6GnnNSTY+MAvlJ+UVbun5W3xlRaMrDrmPGS95itXe5YbNwvIrPJ8IOjTnyyk5RdyJGLzlflrMvH8eVo+bcCnHywdlAdb84M2rIjQ4llei4kM0/ZRWlXO5dYiPG0lqLZR3qczcliUlynrw+q7s0UdD4DcW7M6bxaAtCiLcbXATxoB6bEBx0+jn5ychHPH5zu552nxJOXm5VlpYqndz/iGjmQ9+YA8pn3SGpUYQAhFVWny4hQCQe2gAZ98PkPmux/Dl8aj9lw7ATZElsGlD4EFkZxjJ+NOq81l5GcBn+bg0u8sMmQhDk4cyDTA4wteQkOhlHcavtL2Tfrtc3gBewVsLHU8ABJgcHDgyj3b4OfLi6flikb80vAmXxBD+5NoywcNj2p8hrwtrVOmjeqsa2k91hA4+qKILJS2A1u8yLwscPJFKWnKtsqQ/MgyYnhOw5+vX/mejQXGdhI33vTh+yapD8lohvpcsVhMXllGfY5A05r7o+e1NpNy+6gdei4jPjn8KT/yHYE+58jqR75rvP/yoWPLXmkjXL4RoDQ0LwBQVk4Mxc4DWZFlu0CV5yc7DO+kpLDOueTbWpYs8+ayyPI0WTVKK6+VVxqw1tKEjGts+/vAgSg0jBVrGNoFgFq5Wl6WbCGj5HtGjljEypClPULXQ3Uohz3lPeo7fF+flZ+M7ihvOUeYRi6p6zH2JKu+cFCX4G99+DmoWp+KcbDkdW3Ey9ffY0EuxsnwUccDIKCfRZcVoKRSx3QKTq16saSgAJoiQvLI+EkWNHxDHmlIYaSyxESHM0GaAefD2Bo/MZ1O66wkh2UUsxhYLa0citTaMTZPizTgSSuT9kyM45CF71b7gi9PzrNsY6tcPmJSLpdRKBSa5gD5yUt8qqFQKCTnDQNAtVpFsVhErVbD0aNHM/MvdTJLRB0qi293oPUEtOWhUqmgWq2iVCol3/wcVB/4xfAl24qvWeAnXvE0styZtEPAPABAq+J8FFupWlQWys8CzDRKzAGBnuedm1avTUxMNG2TsN7y3oo8aXhOcy8EXj7DHvI2NU8zLdClAQSpe6FOncWgWUOvFl9pQTANtRvYYtuzVSLDS3vcqM9w0ONHDgLHV1TyRWfkiMrVnjEypU2TJS1/JjT0SWsM+ApQeeSbj5c0AYDUP58d0gDQchJaAcuOB0CLfJ6iRpaBkdFeGoBNq+A8mqDORUBI3/x1JZVKBT09PUk0SJEgASR1eF4G58+KZDVFk0ASq2xpI49Q+izlaWlCQJEGHMnQSF7bBX4+SgOC1v8QAM0UWW0VY0R5nVYqlSSis3SLRk1oIQsHL/rPI0C+yAVAsgjNuWNnDk9OTibz8Wll9d339SsNeEj3CoXpL7+lCI/sRE9PD7q6upJv7dzPWGDKStqcqbQ90v5Ih9YCYAsgfTQvAFCCHCkBD7NjKiZmTD8EgvJ+LGgSz5IHCWCUhry0BQsWoF6vJ9+1Wg0TExNNndxSGPptRY4x4+2hevCBGAcHbSk1X+Ah71ntlHZIWgMFyZf1jGZsLQAPgVGMHDGyafWr0UwYt1iy6k9elw4cf547i319fVi4cKF3qNk5h7179+LIkSPTAE6WwaNBvt2B0nEwjLErMY6eT7/lCBAvly9WqVQqSXRLw5m9vb2oVCoYGBjAwoULUS6XMTAwgHK5rAIPH3lqp9Om1ZU23RQCQ8lPq1NQ8wIALYqtjNCkbFrSht40j8bn9cnnNaPMF8bwlbDathCNH981zh/n0SePlk6TV5MpxkiEKDTUaVEIBK30rQCgjy9NDp8jZdWbDwB9PJwIIEwri+856hMWAEqnjsDMAjF+X27e5vd43j7eQvd97cR51/SMD2/ycz3pw4c6y+Vy03UO6jHt4bNX1jMxpNWlL107aV4AYIw35vNm2tnheWPG5iuBSF7n+VIavnm3Xq83nV9IE/f8lS8yP16mBoK8TItnK18tndZ5OaDLCDBkvLV7mjcfCwCWo+HbZ2c9H1NGiLJGsu302jkvaQA5RL76s9L5eJucnMTExIQKJtw20PCn1HHZx7Qh0NDQnSVnrH7IPqfVNxFFeYVCAdVqFdVqFQsWLEB3d3fTeZ6lUgl9fX3Jd29vb9O8H9kPyYvkKSSj77+8bkWBmkPC//uuafnG0rwAQI24UZbX5L12Rn5UjrwuO6XmoUpeOc9cUel56gT0G0AyHCojQAl8/LcGghY/vijLMto+8JMf/qzPm7coNrLw8WnxFTLcss2sstoJThY/WcqwnIp2Oo2x4MfThNqcr+7U+h5f5Wk5ytyoyvduhobuLL59beLTU21YkOcnhz1pBWdPT0+yupMAkO5ROn76C8kSkiPmWiyF2lPaZQvsNMclNpLkNK8AcKaMS1qixtEAVwMS7b4kHpVQ/qTMdOwR3V+wYEGy2q1QKDSdGarNtWk8yDe5awAq5ZG8av81sPGR5mGmdVh8nTgEzLEnrcQa86w8x8jcCvjF8tGOfDT9SFsWr49arda05YTf5wDII0AJeFaUEXuupyZb6JuIRhpozlE6Hfw5bVWnPMGFp+PnfHL7QOX5HLUs5HOiOck20OrfesbKLwvNSwDMYiRngnw8+EBIM/pSLvrmq0R553DOJUBIQ0TFYjFZ9WVFK1IRJYjHRAQh0EgLgq2Qlrc2V6Txrw2BWhGhLC8mSkjDc0inNf6Ij3aR1fY+spyFtO1uGUQAOHr0KMbHx5scFg5efD6P9vLx7Q3yVBffUWcWLxKk5D2f08QjMpq24DrK5/z5m9tD7/Hj0SBt++D3tYg4pl1iQFPTV2m/0n5mguYVAFqkNYbszFk6t4+0iEkCnK8jafnJTsQXu0jPj/+X+VsGXCtD8muBtM9r1WTzGcG0UaEWkYbyi+HPSmuli+U/S9oY/dQM0ImOBmPqOuREEGlDX/K+vO4DQBnRhSIQn/HVRmx8euHTHR/o8H6iHWmmneTC02vRoex7Wr+06nwmyNfGVjrfs2l4njcAKBs2ZAAsBW0nyU7iA5AYfrTorFAoJK9Qog5Or3ahvUrk+XKg1Doiz19uxJcySeJyxXZ2LW2scdT4sYxvTEfn/+Vwckx+Ph5ihs186bg+x+SjGdQTYchi2tvij5M2FBYTOcg8tI8c0pR7Ap1zSb/hz1l6y+XlIMVJAo+sC3qGRm34tULh+P4+HuV1d3djwYIFTfN9NLpD12j/X7lcThbL8KPOtPbjPIYAJ8097X5sG2q/rXRpaV4AoGUcYoAwa94h0sJ/uu7jyTIOfO6PgxR1EDqZgjoJgKa3ydM1rYOGiPPri2BlB9Jkygp+oXbQ6jSUr1U+B0AtvczT4gfwr6q1nB4tutD+W1GIlIu33UyRr95D4KGR5dnzj/YuPi1dbKRH17RVoJx3a8WyT/+l48m/5dvWZURH83d8qJNvYNeGQPlGdz4MSvlrbRZql1b0R2sffj0WWDVAbIU6HgBlg/JO7/Ma0pYRQ6FGtDZ2hyKJkOGkjgCgKdojhaeNvZIHqViFQvPiF1l3vK5bja55ntJAy99aHWh5x4Kc9e0z3KEyffLR/1iHJ9YQhcBelmsBaKtk8Z8VAKXTEBPx8Wd9+WqLWjSgo8hf1rUFdtbwIt2z5p05qFIa4pPmAgkg+TmetLqTn+ZCG+EJ7LgM3C5QH+fznxrv2lFv7QBBucpWW5jkiz4tQMzCY8cDIFcQfoRYTGeJoSzRI2+UUOdPm7fMz7ljb4fo7u5GuVxOIkXyYPnycKls2lJvy/Dw8riMrZCl5FI+S37Or/XbB3DWM9qQlaRQu1nt7JOHt5PPwPtA2Vc/Wl6ttmNIp30LjizHSA6/c30OGbqQkdTsgyQ+9B1qf+2bL6LiACfbhS9ukbaLXoBNkRw5uXS02cDAQNOGd74whu/1A44v9OGviwOOLbiha3RdAmBsZExfgoMAACMzSURBVBZLHIypXX2AKMuyoketjWOo4wFQ8zCJTmT0J0mLnmR+PkNg8aFFTtxj5W+T569PoiFSCYSUJ89PizC0crPUh+++jJasyDdUvs/QhkCSvi2dSkPyuVDU7ANcrU185YV4jskvDVkgGHI26Ldsf86nBKtQv7ZAUOZh5eM7Us2SUZNV+1j1w4GSH+HIty3IA6vl+/u0xS4cVOToDgcLaU8APQLkadOSBCkt6rPa2vfbdy1EHQ+ANCzQaDSSw2llw1uVa1EWA5+GrGiE37ee0+7TsAc3auTZ0YZZ4Lhn6ZxL6oqe4Z3DF61Y4BQjswVoGmlt4OOHPyOjyDSGyrrWDpJ1kAZw5bMhxyrURu2ULeTcpQETaZy58ab7PJ31WzO22gZ3jScaeozhlepZGwKVEaDMQz5DIzmcx3K5jP7+fpRKJSxcuDA52aWnpycBSz5UyrdUkE3kr0rjeqFFhZxi7KRWL6FnrQiQt5MEwFA0mpU6GgBJiSi6oX1vmrFIU3kzCX6ynFjwk89YRp7kpJVslJZ3FFImUj6Zf0xdtQqCMXVsgaAP8Cyj5gMKCwRngrT2tvQgJgK28pPpQ3y0SpajkaW8UFSgpaFr8tsHgDwt55nSWnXs0zdLpyQ4aPfk/txC4dh7C/v6+tDV1YX+/n709/cnTn+hcPw9hQTCcu6Sv8ZJzv/zfZFcdv48p1iACxHtTQ45J5qD027qaAAksjpXyDOMNcJpyKc4cm7B8sK1ziLvW9EEOQQAps0JSgDknpiPf2lwsyhjDPBp8khKa0gt0LAMlAaqPhlCUVbontSBEJBr5fqcKM1Ax/KolTUT5AM23wiO1be1fHwRoOaY8f7E/8s0lvNEH7kf17IP8u308mBrPpIj8+EycOAGjo/saFG0POhby3smKGb40wJi7X8r/M4LAPSR7ABEM+Hh+xqDOgGBk49CvGnDM/wZOh4NOOYBcq+QgJC8Tb4KTHpk3EDwMrjx4L9lXUij4fMuYygL+PkiQjI88lgo/qzGewxQa/e1SCEmrayvELDGAl5sfVrtFYoSZFoZkWrPy+ExvpgrBIDaNa7LfDW0BECNdwlu8rg1DnA8D7lZnT/D5+K4DpJdIBtFi11oWJSn4fJyHjQbx4GchjstmyjrULZXLFn1ycvlJ/JIXjSHRqOQ4x5D8xYALS9CGrVYLztt2UQho2eRLwrxRUfc06QOw/cJUjrqnM4dn7+Q/EpepExaRCTTWJGVZQRj5E3rvITqjHvQGnBqxpWeTQMCWpmhZ9PqQAwPWUBQUgwP2jOh6zGRQVoADOUD+Ec2qI9I3aXntMiP/vPrIeeYygHQdIYnX+ii1Zl0TLX8ZR1pdWA5gFrePnuhPS/LlTJovGmyaul910I0bwCQPAgZ1ksvkFOMEUkLgqH0aYxGDBjJtHIFGIEgDYPQiz6pM5EnJk+OCXlhWoQSMugWCHLSAGimSBos7oFaIGjlwykWcOXzIXlDwBvDm7zW7jrO0l8sPeB9V9NJ6xvQIwufXss2486QBBBOViQvHT9tCFSLMClyHBgYQF9fX9NGeLlXUOajjdrw+pBnnkp+5O+sEaB83rrvKz/mWsgxiqF5AYBccDmmrV2bLWqXsQl599KoU/3IVWHUKWivIICm3z5vUv728cLJB35amhMFglSuxkvoeZ8D4CuP//d50ho/oYgiBHJpHbEY5zGroZT/LQOnrQ4MRRHWRz5j1YfMU97TwI6DJndMJcm2LxaPv829p6cHAwMDTXxJ+ek3H17lYMvl4nZQk8Mnu3UvjQ75HJdQOo0nX5umoXkBgLFDSZbxiPF0YipYluEbHjkRRJ2Bhj+5h8jnRQAkG2+r1SomJyexf//+aR6zNl8QG71wCoGm73ds9OIDXs0otLrM2gLsVkBci7Kt+/KalLNVvkK8aNe1PkbXrQ85ZfRtbYC3gMzHeyhao980/FgsFpMTVygyc84lTiQnPq9H8+s8Mjty5EgTj/Ige77oRQ53cv7S6miMzYkdefDlK21oTN/U7K4mo/U/jQ5YlBoAn3jiCXzjG9/A6OgoXnvtNWzcuBEf+9jH1LTXX389vvOd7+Bb3/oWbrrppuT63r178ZnPfAYPP/wwisUirrrqKtx5553o6+tLLQCRHE4gsiqrVYOtXePDr7ITUJlpz+HMStzgU8ei9wbSCRPVajUBQW5UxsfHk+FQvk+nVqs1DaMUCoXEYyVqBxDKDmIZrrTl8Dqha3JhgMWb1Vll+/IysvKbhXzRtvyt3efXrPx96bj+aM4KrxdteJKPTNBCFRqul9FLWs9fc0J9YNjX14dTTjkFlUoFS5YsQW9vbzJnPjU1hTfffBNHjhzBkSNHcPDgwaYFHfxIMuLt8OHD2Lt3LxqNBnp7e1GtVptk4Uec0cKXmMVyGu/ymnb4vfaMlkaLuK2ytd9aOl8Q4us/RFokK6PGNECYGgAPHz6M8847D5/61Kdw5ZVXmuk2btyIp556CiMjI9PurV69Gq+99ho2bdqEer2Oa6+9Ftdddx0efPDBtOxMI59XkoVC4Bfr9WaJlNpFcpiFdwznmo+eAo7JQF4vKZysy9gowIocfF6lrDcL/GLqUnqkEhRiPMi0nrcWbaYhn95qhoO+s5QV0m8fLxYIWnxaURwHQ+1YLF5WFmNn1RcnrnsESNVqFT09PclcHM2THzp0CPV6PZmbs+qdO7sczGV5fMWotuBF8um75usbPv3WnrN0TSvbqgOpDxoIhvpXbP87IRHgqlWrsGrVKm+aV199FZ/5zGfwy1/+EpdffnnTvRdeeAG/+MUvsH37dixbtgwAcPfdd+MjH/kIvvnNb6qA6SNSIDLa5FHxs/Vmmqji5SS074Dcmaa0RhtA0/BTX18fCoVjJ8rQm7QpAgwtSrAicMmf7AyaDO2sM3kUFCdpvOW9VoCsHWR52T6HL039WY6AdBi0byJtEYZWp9o+VDqXkl+TCzfkt0+/NXk4oGqGnh8nWKlUkleM8VeK1et17Nu3D4cOHcLk5OS0xS1TU1M4fPgwisUi+vv7kymF7u5uTE1NoVqtJtsa6BDr3t5e9PT0JIdc8+FW2Zby4P9YR9JqC5+T1Y6+qaWRB+rTN3e4ffy1k9o+B9hoNLBmzRrceuutOOecc6bd37p1KwYHBxPwA4CVK1eiWCxi27ZtuOKKK6Y9MzExgYmJieT/gQMHkt/Si+IrHFsxoGmNnmwo7QT42Yj+0hBfJUqdtF6vY2JiIrnHD9ZtNBqYmJhoGkaUFDJUPmOueem+TinLtbxgzQhaRr9Vame7W5FwKJ2PYiJgCTq+dqaPFjnSNW1fHs1BSwDUHCwfr1o6jW+tvembb0Anva/VapiYmECtVsPBgwdx+PDhJC3ZHZJpYmICxWIRAwMDKJfLySuJpqammt7fV6lUknf7dXd3JwCoOWncsedAyNP5HP6YqMvnNMfqno942VbEqeWdhs+01HYAvOOOO1AqlXDDDTeo98fGxrB48eJmJkolDA0NYWxsTH1mw4YN+MpXvqLes4ZPLOMX23ChIQTpVWoNIiey0xrDtI3sM+iWEZAGikfTJAMpHAdA8oppTpHXvTRckqdQ1ELfoeGemOtafWjGWcvL+tY6seQ39D+GshodGTVYEXkomgo5NL4oWl7jRov3VfqQXoWGQH2/Lf4t/ac6krxOTEzg0KFDyTFj5XK5yfHj7c/zo74wOTmZgCJwLKKk+Xa+wZ0AkL++iBxnjVfuVPPDLSgd30soT7Dx6U7avhELVKHyrFEg2SY+Z0peS2sz2wqAo6OjuPPOO7Fjx462Rjuf//znccsttyT/Dxw4gDPOOAMAEq+Lf3xRQqvevQQ+YPoKQgISDQBngyxvWhoX6ljUybhXS9/cI6ZhUQJGunb06NFph21LksbZB47trjcue+h1PdpRWJx/H38+gOS8+IA+BKIaiFEaqYMyPV/8FHMkVigas9papuVgR85TvV5PdEYueOG/fU6eVa6VTkZDpO8HDx5ErVZDsVhEd3d30+uGiH++spryonwoAiTZyuUyFi1ahEKhgGq1miwco1GW7u7uJPIjYJP8ky7S4jp616fPIfGRr61CozI+vQ85JBykuFxU/7weNT2Q5UqATGsr2gqAv/nNb7Bnzx4sXbo0uTY1NYXPfvaz+Pa3v40//OEPGB4exp49e5qem5ycxN69ezE8PKzmW6lUUKlU1HvSu7O82RiKHRbQOqaMCuhZ7XdMuTNBlvcveQGOv6OMPErnXNMQMzcY1NnJONBiAVmuRtwISR6s3z6Dpskr7/k6igZaViQWcmpi02nPyOe1fLhBieEjDaX15n1GWOqdFgFyMNHmgKx+HVOuTCcNprxGukvOQ71eT4ZDpQ5a5crIjBwtWuHJ3+NHC1+4vmmRDx/+5CMzmn3SfvNrsg58oGY5clkdU+6o+Wyk5QTKPOTvNHy1FQDXrFmDlStXNl279NJLsWbNGlx77bUAgBUrVmDfvn0YHR3FBRdcAADYvHkzGo0Gli9fnqo8rUNpkYSPtEoPeTEW2HLl5JGFPJ3FMnIx4BsrR4hv7Tk+9MI7F03IU/3yRUa0vWJycjLxiovFYjLnUa/XE6PCDYhWL7Gy+GQMPesDNa29eDuG8vCVmeaeNDI+sPaBoJU3peULLeRiDsuwSudJ8iDT8Q/pDx8y56/n0vb8yfKta7EAyPmWMvA64jpOC3RoHYLWJjzaJmCjoU2SkyK3arWavOWBANC37YF45CtEKQKk0RdLZ3wAzfVYjoRIx57XDf+Wv4msRXKka5rdk89IneQRYaFQaIq+sy54TA2Ahw4dwu9///vk/+7du7Fz504MDQ1h6dKlWLRoUVP6rq4uDA8P48///M8BAGeffTYuu+wyfPrTn8b999+Per2O9evX4+qrr069AhQ4PpylzTvFGEPruszHZwz4Pf52eqD5jfWW1+UrJwYEfV6bj1dNTqn8nHjEJ98yQfMetNeQjMb4+HiinNrbp30yZCEfCMaUKY2CHELk9+Xzsc6KRT5Q9j2jRQsh0KV2lACiGSz6zXVDOpsSYOR8MF/cogGg3CZg1YkGtpIs4NPykXlSnfPDqTn/QPMeXx7Z0fAlDXHSN18kxrdY0BCoNUwt64GAkgMgTy/5svKTjiyXg9e3fFOM5Ie+NV0juyB1hT8r21LaHu1Zzr/sy1n6X2oAfOaZZ/ChD30o+U9zc2vXrsUPfvCDqDweeOABrF+/HpdcckmyEf6uu+5KywqA5qX7luEOkc9bous+AyE9pXasQuVlS94so6A9J/kM5cVJgqg0mFJxqQPysfyY9klbR1qn8eUj5bAMXxqSZaZxvGQaK70mVyyv0snS8uB5WXUq00sZLD3j88faXLK20lPqp+ag8fsxQBdDsj4pOgOQLPaiuW6KDvlcHb9Gc4W0orNarSYjJYVCAd3d3U2LXvg7AEOySOdL6pDPcePE31DBf0sn3VqMo4GPdp8iaEs+qZOSLOc8Jr9YSg2AF198cSpl+8Mf/jDt2tDQUFs2vQNoWnxBwwGx4XBawyc7uDxBhQ9/ymE0QB8+CJUXszhBM2Iar3yeRXue/9aUnudLw2f8PYN8WJSGjmiIlAMiDYtaMnBP0JI9NnrU7lmG3DLosqNSGsvIxOqVL00WUI4pRxo5rVxg+mHSGljKOuO6RhEd75Pym+u3xYvPSKaxQzH1SXUzMDCAM844A4VCAYcOHUKtVsP+/ftx9OhRAMfWJFSr1QTI6VqpVEqiu66uLrz97W9Hf39/EzjR3j9aBaodkq3xDqAJMOWH2x0O0NLm8GhPe10Tp9B2FMsB4fe5rnE9yuKwcBtC39zRy5Jnx58Fyj1Ly2jG5GH9tzqm5u0C9hBZWvDz8aeRNEQan5riWkBied9E0jskB4DSUZ3QmYqlUikBPl+ZvqjZZ8RaiQBk/hJktbytDief0fjVIrMQGFrlhNLR9bR6FxsZSx3gfUJub7A2uPsiHp/HL3+H5LGuyzagIUYATcOEfGRDbubm0R8BIO0B5NMglB8Hs1i91nRF8i/7kHaPT9PIwzp4ubxtSHbLUdTsRVqHRdMjjUJgl0bXOx4A+Z40fmJDDPmMiBYJyA6ubQDnE9UWGFoU4wVq6TTP3DmXRMZ8/5KMAKWXFhMdSKLORFGhNBbEQ6FQSIaR5Mo/TZ5QPcSQz7jItpEdkF+TeVjtKg2Odc/HZ4yXHOOk8dOQYgFT6jgA1cGUw9r8Gn+tFl/cIrfThMiqhxgADLWLjJ54mjfeeANvvfUWnHPJQQ8TExM4fPhwAh61Wi05Ko2fHFOpVNDX19e02IWiPQ6A9FvKYTkTPnsk5eLPaNE7B3qtPqQd8Q1Tc374b74ug39zWTSbqj3ra3sNLNPYiI4HQBqfd84lEUbWFUFEIcOjNSYplVzWrK0iTMuLpXg8DTeeZGTI+GjGyve8xYdG3CvkAMi9XO4scJ4IlGV+9C2j2hCPFkkQ9Dkm1LaFQqFp6FbzkOU16c1b0V8Mn9z4WxFQqB7k/Ivm4ct86RrXFwmAoWscAPkQaGhRRAzFyq61gwQ9GQURvfnmm9izZ48K1BTZ0XYfivborRHVahW9vb3Jb0pPQMnn5zTDbQGP7A9yRSSXj67zoIDXg1b32pQNr2uf/eC8WKApbaYmBx8hkOl8QYQE1DQ0LwBQm6/wkTZEIIHMIlIOfvwRXwlmDSlkAUHNQGnX+TU536cprgY6Uv40QCiNNpdV7n9yziWHCIeAnfPWalQYY3AtMJY8WkBoAYq8L5/38ZwmEpTXNCAmnQhFv7wvcSDwASDXOU3/5Id4C+mUJW9Mvci+Th9tOJPSaYdp0KrOSqWCRYsWYXBwEL29vSrY0Qkv8mBrKbtsG36NnC9LJ6U8MrLjZXIQ1xxKeoa+NYdL413L0wIjTU4rbcheha6noXkBgHwvkWaEOWkKJK9L4o1PSsknkPlJEdzLs04RyUKWAaH8+aZdvigoxinwKTXn3wdW0pBIBebbI6iDj4+PN0UKPhDU/ofA0yIJLBq/lh5pTo0mO//WPHSffDH3QgZJ45/6CJ1UQlGL9MilF66t4rWAjZ7juudb8WnJmgb4tLzk8LpsY187UP3wduvv78fQ0BD6+vpw3nnnYXh4OCmjWCyip6cnme+jDy2MkQdH8LaxdIPqXSMJdjKqpbnHWq2GI0eOJHbRB2SaLmv1lAV0JK9yDthaECX1UiMJ5GltbccDoFQsUkhuJKU35/vNSQ4nyEiH0vAlzVoEGNMoPi9HGnoNALmiWEdchZRXAwLtmnxGS8tl5w5Do9FIIsBSqZR0hBBIWGVov2PlbKVzx9RVlnwkabrr87T5Nc2o0lmVND+ozeNJMOMRIP+WUaEc4vR9ZL3J31J+/jvksEn+fOk0UORDhjSVQUOb/f39GBwcxKJFi1Cr1TA+Po5C4dj2Bn5aFT3LT2zhvEmA5d/a75BDJgFMizx521k6x7c6WfbLsgEWT1revI5lej53bZGvz6QBwY4HQDlXwSsuVIkcqKxNz0RaByalJC+PVjvK/GIMrZW3fEYzILyzawsWLI9b4yE2uvLd17xtqheKmgn4isViEh1qbajlq+Uf40BYDgwHg6zEQYfzJcuTz8h02n2eRt7T2tYnB7UDPUuRGjdMXKdkm0iw4+VZAKjJE0PW87G6LOe+aHQkRNSHyuUyRkZGMDAwgFNOOQVLlixBd3c3Fi5ciGKx2LTCk159RPUFTB9O5HXDwZET/ecrROXCEQsIOdjy7RlaBOqcm2bzfEDmCxI0ndc21nN90Zx0qYPSqdIiRU0/0rxMuKMBUFaSPLkgdj6QgEsqpGW0uOHiYMeVMGbxi2a4NBDUjJ2UU9aBZoBiKNa4xKTl8vP5EGkkaL6DL2iy8vaBnRaVSUMo08jRgpAcmvdLZViRJX/W52D46tUqVwNNnxw0N0Xgpw1tAmGd0oyV7I+avsbqo6XrsfrJZSEjT0OCWl3K/7QV4vTTT8fw8DCGhoYwPDycnOFJwEf7+vr7+9HT09N0/B//kL7zvYPlcnka7xwA5RsfqL20Z7jd4W1Gq1C1iF07gk2zbzJK0+pLpuMRqFxUw3mR8khbx08KkttpLH2c9xEgCTs+Pp785x2PAMk5l7zShD8nAUtuHgX0MF4zLnyYRANALQqwDJxmjKwX0EqjoK2g8h1llIZ4h9AAhqexyqPr8k0StCpULpv3lS3L1f7z3xIASTd4G9OqVE7a8Ay/J7+l8yQ7I5Ut6yk26tZIA0A596Xxyo0LPSd10BcBavXPn5M6qC3GkvJbsmUBQi2il3XDr2sRNvFPq1r53DrfaF4qlZJN8hIAiRfSadJzckLkqJMWNXJeeN+QuiXXHzh3/GW+vA14WXJEgH9rwOcDQP6b149M75sL5v/5XDIBINdbSwfpzNaYflRw7bCQJ5j++Mc/Jq9DyimnnHLKKSdJr7zyCk4//XRvmo4EwEajgV27duE973kPXnnlFQwMDMw2Sy0Rvd+w02XJ5ZhbNF/kAOaPLLkcM0/OHXun48jIiHlcIVFHDoEWi0WcdtppAICBgYE51wBZab7Ikssxt2i+yAHMH1lyOWaWFi5cGJXOD4855ZRTTjnlNE8pB8Cccsopp5xOSupYAKxUKvjyl7/ctPm0U2m+yJLLMbdovsgBzB9ZcjnmFnXkIpiccsopp5xyapU6NgLMKaeccsopp1YoB8Cccsopp5xOSsoBMKeccsopp5OScgDMKaeccsrppKQcAHPKKaeccjopqWMB8N5778U73/lOVKtVLF++HE8//fRss+SlDRs24C/+4i/Q39+PxYsX42Mf+xh27drVlGZ8fBzr1q3DokWL0NfXh6uuugqvv/76LHEcR1/72tdQKBRw0003Jdc6RY5XX30Vn/jEJ7Bo0SJ0d3fj3HPPxTPPPJPcd87hS1/6Ek499VR0d3dj5cqVePHFF2eRY52mpqZw++2348wzz0R3dzfe9a534V/+5V+mHR4912R54okn8Nd//dcYGRlBoVDAz3/+86b7MTzv3bsXq1evxsDAAAYHB/H3f//3OHTo0AmUwi9HvV7HbbfdhnPPPRe9vb0YGRnBJz/5SfzpT3+ac3IA4TbhdP3116NQKODb3/520/W5IksMdSQA/vjHP8Ytt9yCL3/5y9ixYwfOO+88XHrppdizZ89ss2bS448/jnXr1uGpp57Cpk2bUK/X8eEPfxiHDx9O0tx88814+OGH8dOf/hSPP/44/vSnP+HKK6+cRa79tH37dnznO9/B+973vqbrnSDHW2+9hYsuughdXV149NFH8fzzz+Pf/u3fcMoppyRpvv71r+Ouu+7C/fffj23btqG3txeXXnpp8haSuUJ33HEH7rvvPtxzzz144YUXcMcdd+DrX/867r777iTNXJTl8OHDOO+883Dvvfeq92N4Xr16Nf7nf/4HmzZtwiOPPIInnngC11133YkSAYBfjiNHjmDHjh24/fbbsWPHDvzsZz/Drl278NGPfrQp3VyQAwi3CdHGjRvx1FNPYWRkZNq9uSJLFLkOpAsvvNCtW7cu+T81NeVGRkbchg0bZpGrdLRnzx4HwD3++OPOOef27dvnurq63E9/+tMkzQsvvOAAuK1bt84WmyYdPHjQvfvd73abNm1yf/VXf+VuvPFG51znyHHbbbe5v/zLvzTvNxoNNzw87L7xjW8k1/bt2+cqlYr7j//4jxPBYjRdfvnl7lOf+lTTtSuvvNKtXr3aOdcZsgBwGzduTP7H8Pz88887AG779u1JmkcffdQVCgX36quvnjDeOUk5NHr66acdAPfSSy855+amHM7Zsvzxj390p512mnvuuefcO97xDvetb30ruTdXZbGo4yLAWq2G0dFRrFy5MrlWLBaxcuVKbN26dRY5S0f79+8HAAwNDQEARkdHUa/Xm+Q666yzsHTp0jkp17p163D55Zc38Qt0jhwPPfQQli1bhr/7u7/D4sWLcf755+N73/tecn/37t0YGxtrkmPhwoVYvnz5nJIDAD74wQ/isccew+9+9zsAwH/913/hySefxKpVqwB0lixEMTxv3boVg4ODWLZsWZJm5cqVKBaL2LZt2wnnOZb279+PQqGAwcFBAJ0lR6PRwJo1a3DrrbfinHPOmXa/k2QBOvBtEG+88QampqawZMmSputLlizB//7v/84SV+mo0WjgpptuwkUXXYT3vve9AICxsTGUy+WkUxAtWbIEY2Njs8ClTT/60Y+wY8cObN++fdq9TpHj//7v/3DffffhlltuwRe+8AVs374dN9xwA8rlMtauXZvwqunZXJIDAD73uc/hwIEDOOuss7BgwQJMTU3hq1/9KlavXg0AHSULUQzPY2NjWLx4cdP9UqmEoaGhOSvX+Pg4brvtNlxzzTXJWxQ6SY477rgDpVIJN9xwg3q/k2QBOhAA5wOtW7cOzz33HJ588snZZiU1vfLKK7jxxhuxadMmVKvV2WYnMzUaDSxbtgz/+q//CgA4//zz8dxzz+H+++/H2rVrZ5m7dPSTn/wEDzzwAB588EGcc8452LlzJ2666SaMjIx0nCzzmer1Oj7+8Y/DOYf77rtvttlJTaOjo7jzzjuxY8eOaW9571TquCHQt73tbViwYMG0VYWvv/46hoeHZ4mreFq/fj0eeeQRbNmypeltxcPDw6jVati3b19T+rkm1+joKPbs2YMPfOADKJVKKJVKePzxx3HXXXehVCphyZIlHSHHqaeeive85z1N184++2y8/PLLAJDw2gl6duutt+Jzn/scrr76apx77rlYs2YNbr75ZmzYsAFAZ8lCFMPz8PDwtIVvk5OT2Lt375yTi8DvpZdewqZNm5reodcpcvzmN7/Bnj17sHTp0qTvv/TSS/jsZz+Ld77znQA6RxaijgPAcrmMCy64AI899lhyrdFo4LHHHsOKFStmkTM/Oeewfv16bNy4EZs3b8aZZ57ZdP+CCy5AV1dXk1y7du3Cyy+/PKfkuuSSS/Df//3f2LlzZ/JZtmwZVq9enfzuBDkuuuiiadtQfve73+Ed73gHAODMM8/E8PBwkxwHDhzAtm3b5pQcwLGVhvLN1wsWLECj0QDQWbIQxfC8YsUK7Nu3D6Ojo0mazZs3o9FoYPny5SecZ4sI/F588UX86le/wqJFi5rud4oca9aswW9/+9umvj8yMoJbb70Vv/zlLwF0jiwJzfYqnCz0ox/9yFUqFfeDH/zAPf/88+66665zg4ODbmxsbLZZM+kf/uEf3MKFC92vf/1r99prryWfI0eOJGmuv/56t3TpUrd582b3zDPPuBUrVrgVK1bMItdxxFeBOtcZcjz99NOuVCq5r371q+7FF190DzzwgOvp6XE//OEPkzRf+9rX3ODgoPvP//xP99vf/tb9zd/8jTvzzDPd0aNHZ5Hz6bR27Vp32mmnuUceecTt3r3b/exnP3Nve9vb3D/90z8laeaiLAcPHnTPPvuse/bZZx0A9+///u/u2WefTVZHxvB82WWXufPPP99t27bNPfnkk+7d7363u+aaa+aMHLVazX30ox91p59+utu5c2dT35+YmJhTcoRk0UiuAnVu7sgSQx0JgM45d/fdd7ulS5e6crnsLrzwQvfUU0/NNkteAqB+vv/97ydpjh496v7xH//RnXLKKa6np8ddccUV7rXXXps9piNJAmCnyPHwww+79773va5SqbizzjrLffe7322632g03O233+6WLFniKpWKu+SSS9yuXbtmiVubDhw44G688Ua3dOlSV61W3Z/92Z+5L37xi00Gdi7KsmXLFrVPrF27NprnN998011zzTWur6/PDQwMuGuvvdYdPHhwzsixe/dus+9v2bJlTskRkkUjDQDniiwxlL8PMKeccsopp5OSOm4OMKeccsopp5zaQTkA5pRTTjnldFJSDoA55ZRTTjmdlJQDYE455ZRTTicl5QCYU0455ZTTSUk5AOaUU0455XRSUg6AOeWUU045nZSUA2BOOeWUU04nJeUAmFNOOeWU00lJOQDmlFNOOeV0UlIOgDnllFNOOZ2U9P894/7JaWOC/AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# take region of interest ( take inside of rectangle )\n",
    "roi_image = frame[y_min:y_max, x_min:x_max]\n",
    "\n",
    "# convert roi to grayscale\n",
    "roi_gray = cv2.cvtColor(roi_image, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "\n",
    "# Params for corner detection\n",
    "feature_params = dict(\n",
    "    maxCorners=20,  # We want only one feature\n",
    "    qualityLevel=0.2,  # Quality threshold\n",
    "    minDistance=7,  # Max distance between corners, not important in this case because we only use 1 corner\n",
    "    blockSize=7,\n",
    ")\n",
    "\n",
    "first_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "# Harris Corner detection\n",
    "points = cv2.goodFeaturesToTrack(first_gray, mask=None, **feature_params)\n",
    "\n",
    "\n",
    "# Filter the detected points to find one within the bounding box\n",
    "for point in points:\n",
    "    x, y = point.ravel()\n",
    "    if y_min <= y <= y_max and x_min <= x <= x_max:\n",
    "        selected_point = point\n",
    "        break\n",
    "\n",
    "# If a point is found, convert it to the correct shape\n",
    "if selected_point is not None:\n",
    "    p0 = np.array([selected_point], dtype=np.float32)\n",
    "\n",
    "plt.imshow(roi_gray, cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "############################ Parameters ####################################\n",
    "\n",
    "\"\"\" \n",
    "winSize --> size of the search window at each pyramid level\n",
    "Smaller windows can more precisely track small, detailed features -->   slow or subtle movements and where fine detail tracking is crucial.\n",
    "Larger windows is better for larger displacements between frames ,  more robust to noise and small variations in pixel intensity --> require more computations\n",
    "\"\"\"\n",
    "\n",
    "# Parameters for Lucas-Kanade optical flow\n",
    "import time\n",
    "\n",
    "\n",
    "lk_params = dict(\n",
    "    winSize=(7, 7),  # Window size\n",
    "    maxLevel=2,  # Number of pyramid levels\n",
    "    criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03),\n",
    ")\n",
    "\n",
    "\n",
    "############################ Algorithm ####################################\n",
    "\n",
    "# Read video\n",
    "cap = cv2.VideoCapture(INPUT_VIDEO_PATH)\n",
    "cap.set(cv2.CAP_PROP_POS_FRAMES, 70)\n",
    "\n",
    "\n",
    "# Take first frame and find corners in it\n",
    "ret, old_frame = cap.read()\n",
    "\n",
    "width = old_frame.shape[1]\n",
    "height = old_frame.shape[0]\n",
    "\n",
    "# Create a mask image for drawing purposes\n",
    "mask = np.zeros_like(old_frame)\n",
    "\n",
    "frame_count = 0\n",
    "start_time = time.time()\n",
    "\n",
    "old_gray = first_gray\n",
    "\n",
    "while True:\n",
    "    ret, frame = cap.read()\n",
    "    if not ret:\n",
    "        break\n",
    "\n",
    "    frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "    if p0 is not None:\n",
    "        # Calculate optical flow\n",
    "        p1, st, err = cv2.calcOpticalFlowPyrLK(\n",
    "            old_gray, frame_gray, p0, None, **lk_params\n",
    "        )\n",
    "        good_new = p1[st == 1]  # st==1 means found point\n",
    "        good_old = p0[st == 1]\n",
    "\n",
    "        if len(good_new) > 0:\n",
    "            # Calculate movement\n",
    "            a, b = good_new[0].ravel()\n",
    "            c, d = good_old[0].ravel()\n",
    "\n",
    "            # Draw the tracks\n",
    "            mask = cv2.line(mask, (int(a), int(b)), (int(c), int(d)), (0, 255, 0), 2)\n",
    "            frame = cv2.circle(frame, (int(a), int(b)), 5, (0, 255, 0), -1)\n",
    "\n",
    "            img = cv2.add(frame, mask)\n",
    "\n",
    "            # Calculate and display FPS\n",
    "            elapsed_time = time.time() - start_time\n",
    "            fps = frame_count / elapsed_time if elapsed_time > 0 else 0\n",
    "            cv2.putText(\n",
    "                img,\n",
    "                f\"FPS: {fps:.2f}\",\n",
    "                (width - 200, 30),\n",
    "                cv2.FONT_HERSHEY_SIMPLEX,\n",
    "                1,\n",
    "                (255, 0, 0),\n",
    "                2,\n",
    "                cv2.LINE_AA,\n",
    "            )\n",
    "\n",
    "            cv2.imshow(\"frame\", img)\n",
    "\n",
    "            # Update previous frame and points\n",
    "            old_gray = frame_gray.copy()\n",
    "            p0 = good_new.reshape(-1, 1, 2)\n",
    "\n",
    "        else:\n",
    "            p0 = None\n",
    "\n",
    "        # Check if the tracked point is out of frame\n",
    "        if not (25 <= a < width):\n",
    "            p0 = None  # Reset p0 to None to detect new feature in the next iteration\n",
    "            selected_point_distance = (\n",
    "                0  # Reset selected point distance when new point is detected\n",
    "            )\n",
    "\n",
    "    # Redetect features if necessary\n",
    "    if p0 is None:\n",
    "        p0 = cv2.goodFeaturesToTrack(frame_gray, mask=None, **feature_params)\n",
    "        mask = np.zeros_like(frame)\n",
    "        selected_point_distance = 0\n",
    "\n",
    "    frame_count += 1\n",
    "\n",
    "    k = cv2.waitKey(25)\n",
    "    if k == 27:\n",
    "        break\n",
    "\n",
    "\n",
    "cv2.destroyAllWindows()\n",
    "cap.release()"
   ]
  }
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