idastar_rbfs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "b7c342575d9d497b89187341e8c82705",
    "deepnote_cell_type": "markdown",
    "id": "LRxLGblbtAnz"
   },
   "source": [
    "<h1 align=\"center\">Kelompok: Saya Budiman</h1>\n",
    "<p align=\"center\">\n",
    "    <b>Andika Rahman Teja (5025221022) || Mohammad Idris Arif Budiman (5025221114) || Rafli Raihan Pramudya (5025221266)</b>\n",
    "</p>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "cell_id": "2bdccfcb452e4d67bd273070806019b1",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 337,
    "execution_start": 1696741723428,
    "id": "vDJK8U7GtAn2",
    "source_hash": "a74bdad0"
   },
   "outputs": [],
   "source": [
    "import time\n",
    "import psutil\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from npuzzle import NPuzzleState\n",
    "from matplotlib.animation import FuncAnimation\n",
    "from matplotlib import rc\n",
    "from sys import maxsize\n",
    "rc('animation', html='html5')\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "cell_id": "ac6f18008eb24708a099401da47a9389",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 278
    },
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 378,
    "execution_start": 1696741723773,
    "id": "3PqKxlnktAn3",
    "outputId": "d96c55f4-2433-4e2f-f97e-928e7cad4e75",
    "source_hash": "fdebb7e8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# State Puzzle\n",
    "start_tiles = [\n",
    "    7,2,4,\n",
    "    5,0,6,\n",
    "    8,3,1]\n",
    "goal_tiles = [\n",
    "    0,1,2,\n",
    "    3,4,5,\n",
    "    6,7,8]\n",
    "\n",
    "start_state = NPuzzleState(tiles=start_tiles)\n",
    "goal_state = NPuzzleState(tiles=goal_tiles)\n",
    "fig, ax = plt.subplots(1,2,figsize=(6,3))\n",
    "start_state.plot(ax[0], 'Start')\n",
    "goal_state.plot(ax[1],'Goal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "150819a21acd4dc6b4bab86609b4e719",
    "deepnote_cell_type": "markdown",
    "id": "a2NeqNNgtAn3"
   },
   "source": [
    "# Fungsi Heuristik\n",
    "\n",
    "[Fungsi heuristik](https://theory.stanford.edu/~amitp/GameProgramming/Heuristics.html) dalam Algoritma _Informed Search_ adalah fungsi yang memberikan perkiraan atau estimasi biaya yang diperlukan untuk mencapai tujuan dalam masalah pencarian berdasarkan informasi yang tersedia.\n",
    "\n",
    "Terdapat berbagai metode umum untuk menentukan fungsi heuristik, yakni:\n",
    "- `Manhattan Distance`\n",
    "<p align=\"center\"><img src = \"https://datagy.io/wp-content/uploads/2022/01/image-2.webp\" width = 100% height= 100%> </h1>\n",
    "<p align=\"center\"><img src = \"https://www.101computing.net/wp/wp-content/uploads/taxicab-manhattan-grid-layout-paths.gif\" width = 100% height= 100%> </h1>\n",
    "\n",
    "\n",
    "- `Euclidean Distance`\n",
    "<p align=\"center\"><img src = \"https://miro.medium.com/v2/resize:fit:963/0*vjZlMGA9wN8uaXG2.jpg\" width = 100% height= 100%> </h1>\n",
    "\n",
    "<p align=\"center\"><img src = \"https://uploads-cdn.omnicalculator.com/images/manhattan_distance.png?width=425&enlarge=0&format=jpeg\" width = 100% height= 100%> </h1>\n",
    "\n",
    "\n",
    "Selain itu, terdapat beberapa metode khusus untuk menentukan fungsi heuristik. Salah satu contohnya adalah `menghitung banyaknya kotak yang salah tempat` pada kasus **N-Puzzle**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "da3569520fa446c894ae5af6741b18aa",
    "deepnote_cell_type": "markdown",
    "id": "gouxsHB9tAn4"
   },
   "source": [
    "# IDA* Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "cell_id": "2a4e91fedb994cf08b61168aad0ca35a",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 39165,
    "execution_start": 1696741724052,
    "id": "rGZvWiWVtAn4",
    "outputId": "913bf5bc-0619-42dc-d692-d82f2b7732b7",
    "source_hash": "7a36bf53"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compilation Time: 56.680044412612915 seconds\n",
      "Number of Iterations: 1265719\n",
      "Memory Allocation: 1265661\n",
      "SUCCESS\n",
      "\n",
      "Number of Nodes Explored: 1265738\n",
      "Step 1: Action - \n",
      "[[7 2 4]\n",
      " [5 0 6]\n",
      " [8 3 1]]\n",
      "\n",
      "\n",
      "Step 2: Action - left\n",
      "[[7 2 4]\n",
      " [0 5 6]\n",
      " [8 3 1]]\n",
      "\n",
      "\n",
      "Step 3: Action - up\n",
      "[[0 2 4]\n",
      " [7 5 6]\n",
      " [8 3 1]]\n",
      "\n",
      "\n",
      "Step 4: Action - right\n",
      "[[2 0 4]\n",
      " [7 5 6]\n",
      " [8 3 1]]\n",
      "\n",
      "\n",
      "Step 5: Action - down\n",
      "[[2 5 4]\n",
      " [7 0 6]\n",
      " [8 3 1]]\n",
      "\n",
      "\n",
      "Step 6: Action - down\n",
      "[[2 5 4]\n",
      " [7 3 6]\n",
      " [8 0 1]]\n",
      "\n",
      "\n",
      "Step 7: Action - left\n",
      "[[2 5 4]\n",
      " [7 3 6]\n",
      " [0 8 1]]\n",
      "\n",
      "\n",
      "Step 8: Action - up\n",
      "[[2 5 4]\n",
      " [0 3 6]\n",
      " [7 8 1]]\n",
      "\n",
      "\n",
      "Step 9: Action - right\n",
      "[[2 5 4]\n",
      " [3 0 6]\n",
      " [7 8 1]]\n",
      "\n",
      "\n",
      "Step 10: Action - right\n",
      "[[2 5 4]\n",
      " [3 6 0]\n",
      " [7 8 1]]\n",
      "\n",
      "\n",
      "Step 11: Action - up\n",
      "[[2 5 0]\n",
      " [3 6 4]\n",
      " [7 8 1]]\n",
      "\n",
      "\n",
      "Step 12: Action - left\n",
      "[[2 0 5]\n",
      " [3 6 4]\n",
      " [7 8 1]]\n",
      "\n",
      "\n",
      "Step 13: Action - left\n",
      "[[0 2 5]\n",
      " [3 6 4]\n",
      " [7 8 1]]\n",
      "\n",
      "\n",
      "Step 14: Action - down\n",
      "[[3 2 5]\n",
      " [0 6 4]\n",
      " [7 8 1]]\n",
      "\n",
      "\n",
      "Step 15: Action - right\n",
      "[[3 2 5]\n",
      " [6 0 4]\n",
      " [7 8 1]]\n",
      "\n",
      "\n",
      "Step 16: Action - right\n",
      "[[3 2 5]\n",
      " [6 4 0]\n",
      " [7 8 1]]\n",
      "\n",
      "\n",
      "Step 17: Action - down\n",
      "[[3 2 5]\n",
      " [6 4 1]\n",
      " [7 8 0]]\n",
      "\n",
      "\n",
      "Step 18: Action - left\n",
      "[[3 2 5]\n",
      " [6 4 1]\n",
      " [7 0 8]]\n",
      "\n",
      "\n",
      "Step 19: Action - up\n",
      "[[3 2 5]\n",
      " [6 0 1]\n",
      " [7 4 8]]\n",
      "\n",
      "\n",
      "Step 20: Action - right\n",
      "[[3 2 5]\n",
      " [6 1 0]\n",
      " [7 4 8]]\n",
      "\n",
      "\n",
      "Step 21: Action - up\n",
      "[[3 2 0]\n",
      " [6 1 5]\n",
      " [7 4 8]]\n",
      "\n",
      "\n",
      "Step 22: Action - left\n",
      "[[3 0 2]\n",
      " [6 1 5]\n",
      " [7 4 8]]\n",
      "\n",
      "\n",
      "Step 23: Action - down\n",
      "[[3 1 2]\n",
      " [6 0 5]\n",
      " [7 4 8]]\n",
      "\n",
      "\n",
      "Step 24: Action - down\n",
      "[[3 1 2]\n",
      " [6 4 5]\n",
      " [7 0 8]]\n",
      "\n",
      "\n",
      "Step 25: Action - left\n",
      "[[3 1 2]\n",
      " [6 4 5]\n",
      " [0 7 8]]\n",
      "\n",
      "\n",
      "Step 26: Action - up\n",
      "[[3 1 2]\n",
      " [0 4 5]\n",
      " [6 7 8]]\n",
      "\n",
      "\n",
      "Step 27: Action - up\n",
      "[[0 1 2]\n",
      " [3 4 5]\n",
      " [6 7 8]]\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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sazDowYMH+3yNtwHfvenbt6/X5+t/tzh+/Lj/haOQYmaBP//5z7j//vvRsWNHxMTEAABiY2NxySWX4LvvvsO5554LQBkgu7i4OKB1N8ZV1tatW6N9+/ZeX5OSkoLevXs3uPy5556L5ORkr69JS0tTB3w+fd8MHToUALBs2TL1Odf00KFDceaZZ+KMM87Apk2bkJ+fDwDIy8tTb6xy4YUX+qybr+xbrVZkZWUBCD77UnQEuaSkpGD06NGYOXMmfv31VxQXF2PJkiUYPnw4AGX0/muuuUb9IykQhw4dAgCcPHkSR48e9flwrdt1++XTtWzZ0uc2bDYbMjIyAEBtLER6xszVcTqd+POf/4yPP/4YVqsV7733Hi655JKg1tkUrv0GoMH9dvToUfV1vvYdmQ8z7c5isaBVq1Z44okn8O6776K6uhq33347fvvtt6DXHS78nmF8Zs3h5s2b8fjjjyMxMRH/+c9//F4uHJraCQQABQUFANDgP4Ia2n/1JSUleX3e9Uc7AFRXVwdQOgoHs2a2MTabDU899RQA5S66S5cuDdm6gbqyNpanVq1ahWT50/eNqyNny5YtOHLkCABg+fLlAOo6iVyvcXUQueafeeaZaN26tc9t+so+UJf/YLMvVUfQ6Ww2Gy666CJ8+umnGDNmDADllq9ffvllwOtyOp0AgFdffRVCuaSuwYfr9stEZmLWzDmdTowePRrz5s1DdHQ03nnnHVx77bWalcXF4XD4te/8/c8kmY9ZM+3N1VdfjTPOOAO1tbVut3slCjez5PCvf/0rqqqqMHnyZKSlpaG0tNTtUVNTAwAQQqjPhasTJDo6Ouh1+Dr7guRnlsz64/zzz1end+/erWFJQq9bt27Izs4G4N7RY7FY1A6g088acv1s6GygSJG6I6i+2267TZ3etm1bwMvn5OQACP4SioMHD/qcV1lZiWPHjgEI7j8RRHpglsy5zgSaO3eu2gl0/fXXN2ldoeDabwAv+aLQMkumG+L6r+HOnTtDvm7Xf/ga+o+xP6fV83uG3GTOYV5eHgBg0qRJSEpK8ni8++67AIB9+/apz73yyitB1CI8XJdt1D9D93QN7T+Si8yZ1ZqrrI3lydd81/IHDhxocHnXfG/7xvXP1GXLlmH79u04cOAAzjrrLPU44OuMIFcHkZZM0xGUmJioTsfFxbnNi4pSdoMQwufy/fv3BwB89tlnQZVj5cqVPrfz7bffqv/tcF2LSGRUZsic0+nEjTfeiA8++EDtBBo1alRQ5Q3Wueeeq16bvmjRorBsw5/3j+Rjhkw3RAih/qHa0CnbTZWWlgYA2L9/v8/X/Pjjj42uh98z5Gb2HBpBr169AAArVqzw+ZqG5gWLn9H6YvbMrlmzRp1u27ZtSNftKuv+/fuxa9cur685efIk1q1b1+Dya9eu9fmPlqKiIrexhE5Xv6PHWyePa/yiXbt2YcmSJWoHsR7Oxjd8R1BeXh62b9/e6OvmzJmjTrsO0C6uwaGKiop8Lu/qzf3999/x6quvNritsrIyVFVVeZ23b98+t7K41NbWqtdQdu3aFd27d29wG0RaYeYUrjOB5s2bB6vVinfffVfzTiAASEhIwI033ggAeOaZZ7Bv374GX9+Ugeb8ef/IOJhpqF+AGzJ79mx1DIBwfIHr2bMnAOCrr75CWVmZx/xly5Zh9erVja6H3zOMiTkE9uzZ0+AlL65LbHJzc9Xn7r77br/XHymuS8NXrVqlDvRbX2VlJZ5//vmwbZ+f0ZHBzDbe2VhZWYnJkycDUL6fDhs2zO91++Piiy9W/4ny+OOPe33Ns88+C4fD4XXeNddcA6vVioqKCjzzzDNeX/PUU0+hsrISMTExuOaaazzmuzp98vLyMHv2bLfnXFydRVOmTAEAdO7cGc2bN2+seuHXhDuN6cqiRYtEVFSU+OMf/yjmzJkj8vLy1HlVVVVi/fr1YuzYseqt2M477zzhdDrd1vHnP/9ZABAXXHCBOH78uM9tjRs3TgAQFotF3H333WLXrl3qvIqKCrF69Wpx//33i4yMDLF//363ZV237ktJSRE2m028/vrrwuFwCCGE2Ldvnxg5cqRaxgULFjRpXxQUFLg9XOt7+eWX3Z4vKyvzWNZ1q0l4uY0lUX3MnBA1NTVi1KhRAoCwWq1i3rx5AS0vhPvtYRu6bagvDd0e9tChQ6JFixYCgGjRooV46623xMmTJ9X5+fn5Yv78+WLEiBHikksu8Vi+sdvHT548WQAQ7du3FwcOHAi47KQvzLSSx4EDB4q33nrLY7vbt28XDz74oLBarQKAOPPMM0V5ebnHOoL9HN22bZuIiooSAMTw4cPVcpSXl4s333xTJCcni/T09EZvHx/O7xkUPsxh4/y5fXywn62u28c3dOt3F195r6ysFN26dVNvH/7xxx+LmpoaIYQQW7duFRdddJFIS0vz6/bxTdn+jh071HkffPBBo/WgpmFmhVixYoUYNmyYx2dnVVWV+Oabb8S5556rrvuZZ57xug5X5gC47UN/vfDCC+ryd911lygsLBRCCFFcXCwee+wxYbFYRGpqqs/vtffdd5+6bx955BFx4sQJIYQQJ06cEA8//LC67gcffNBnGVq1aqW+Ljo6WhQVFbnNf//999X5AMTtt9/udT31bx/f0L7Izc1t8jGuPsN3BH355ZduOxaAiI2NFenp6cJisbg936tXL3Hw4EGPdaxcuVJ9bXR0tGjevLnIzc31+KCprKwU48ePd1tnYmKiSEtLU7+8uR6n/3HkCuGkSZPEgAEDBAARExPj9kEAQDz88MNN3hen7wdfj0cffdRjWXYEkb+YOaX8ruVjYmJEdnZ2g4+5c+d6rCOcHUFCCLF582bRsWNHdRtRUVEiPT1dJCQkuNX/oosu8li2sY6g7du3C5vNpq43Oztbff9O/wJC+sdMu+cRgLDZbCIzM1PY7Xa353v27Onzy1koPkcfeeQRt+2lpKSoHVAjRoxQv5Q21BEUzu8ZFD7MYeOM0hEkhBBbtmwROTk56mvi4uJESkqKOr1o0SJ13urVq33WoanbHzZsmDo/KSlJbQcvvvhio/Ui/zCznp+ddrtdZGZmipiYGLfvn//4xz98riPYjiCn0yluuukmt+2lpaWJ6OhoAUCMGjWqwe+1lZWVbp1hruXr79cbbrhBVFVV+SxD/e2fe+65HvOPHDnitp98/QM50h1Bhr807NJLL8WOHTvw0ksv4brrrkOXLl0QFxeHoqIixMfHo0OHDhg5ciTmzp2Ln3/+2eutHAcNGoTFixfjoosuQmpqKo4ePYq9e/d6DMoVGxuLmTNn4ocffsDYsWNx5plnwul0orS0FM2aNcOQIUPwyCOPYMOGDT5vQxcbG4ulS5fiqaeeQqdOnVBZWYmUlBQMGzYMixcv9nlaG5FeMHPKabQu1dXVjd6m3dcpqeHUpUsXbNiwAf/9739xySWXIDMzEydPnoQQAu3bt8d1112H119/HfPmzQt43R06dMDy5cvxpz/9CVlZWTh27Jj6/vlziQ3pCzMN9O7dG2+//TZuvfVW9OzZEykpKSgqKkJUVBTOPPNMXHfddZg7dy7WrVuHNm3aBLx+f02bNg1vv/02+vXrh4SEBDidTpx99tl47bXXsGDBAr/uZMTvGcbEHMqlc+fO2LBhA+688060adMGQgjYbDaMHDkSa9asUcd9AYDU1NSQb3/+/Pm455570LFjR1RXV6vtgJeLhQ4zC3Tv3h3PP/88rrnmGnTs2BF2ux1FRUWw2+3o2bMn/va3v+HXX3/Fk08+GfC6/RUVFYW33noLb731Fvr16we73Y6amhr06tULr732Gt57770Gl4+NjcUHH3yA+fPn47LLLkNGRgZKSkqQkZGByy67DAsWLMB7772HmJgYn+uofwcwb4NAZ2dno2vXrgCUuwnqYXwgALAIwZHEImHIkCFYuXIlHn30UUydOlXr4hBJj5kjkgsz3TDuH4oEtrPQWLJkCS655BLYbDacPHmywT8yiYLBzJIvhj8jiIiIiIiIyAiEEOrAtEOHDmUnEBFpgh1BREREREREIbJ8+XLcfffdWLt2rXp5uBAC69atw/Dhw7F06VJYLBY88MADGpeUiMzKqnUBiIiIiIiIZFFcXIyXXnoJL730EgAgLS0NDocDFRUVAJRxQp5//nkMHjxYy2ISkYmxI4iIiIiIiChE+vXrh8cffxxLly7F7t27UVBQAABo164dBg4ciL/97W/o06ePxqUkIjPjYNFERERERERERCbBMYKIiIiIiIiIiEyCHUFERERERERERCbBjiAiIiIiIiIiIpNgRxARERERERERkUmwI4iIiIiIiIiIyCTYEUREREREREREZBLsCCIiIiIiIiIiMglrqFbkdDpx6NChUK1OM0IIVFZWuj0XFxcHi8WiUYmaRpZ6eNOiRQtER0drXQzpMMP6Iks9vGGGQ6+2thbHjh3TuhhB89bujYj5pUDJ8hnsjRHbvizHIm+Y4dCTJb+yfHbJUg9vQpnfkHUEHTp0CGeccUaoVkfk0759+9C6dWutiyEdZpgihRkOvWPHjqFZs2ZaF4NMgPkND34GU6Qww6HH/FKkhDK/vDSMiIiIiIiIiMgkQnZGUH0D2oZjrZFRWwvklyrTOcnaliUYstSjvu/ytC6BeTDD2pOlHvUxwxQIHof0hfmNHCO3/fpkaTN8PygQRm4vsnx2yVKP+sKRX54RRERERERERERkEmE5I8hl5nMPIiMtNZybCLmiUgemvPIZgLoexLvGXIHEeLuGpQqcLPU4dqIIE+5/RutimBYzrB1Z6sEMa2fz5s3IzMzUuhgBqaiowLx58wAAMUfXAACuGtoTttiwfl0JOeaXQsGIn8EVVTVYuOw3AMCI7H4AgJEjR8Jms2lZrIDJcixihrVjxPzK8tklSz3Cnd+wHs0y0lKRlZkezk2EXExsOawxym6xnto7melpSEqM17BUgZOlHqQtZlg7stSDtJOZmYmsrCytixGQiooKJCUlKb8UxwEAsjLSYIuL0bBUgWN+KRSM+BlcUVkNm03Jru1UlrOysgzZESTDsYi0Y8T8yvLZJUs9wo2XhhERERERERERmQQ7goiIiIiIiIiITEKTC12PFBajqMShxabdpCbZkZOZ0qRl9VIHILh6APqpS7D1oMjQS3sBmGFAnnoQBUqWti9LPSgy2F70he8HBUqWNsN6hF6kMxzxjqAjhcW49u4ZqHCUR3rTHmz2eMyffmfAO1xPdQCaXg9AX3UJph4UGXpqLwAzLEs9iAIlS9uXpR4UGWwv+sL3gwIlS5thPcIj0hmOeEdQUYkDFY5yPH5ZNNpmajfyfl5hDaZ8UY6iEkfAO1svdQCCqwegn7oEWw+KDL20F4AZBuSpB1GgZGn7stSDIoPtRV/4flCgZGkzrEfoaZFhzWrcNtOKzjlaj7zvDGppfdQBCLYegF7qEnw9KDL00V4AZlghSz2IAiVL25elHhQZbC/6wveDAiVLm2E9Qi2yGeZg0UREREREREREJsGOICIiIiIiIiIik2BHEBERERERERGRSbAjiIiIiIiIiIjIJLQdHtsft+31/7WHVgOfjQpfWYIVlwrkXgy07A9kngUktgSiogHHcaBwA7D9I2DPV1qXsml63g70faju99dztSsL6YssGZY5vwAzTPKTOcPML/kic7uXCTNMvsiS4Y7XAkP+1fjrFt8IHPw+/OUJJYPmV/8dQeX5Dc+PigFsacp0wW/hL08wblqrlNelpgKorQESmyuPNpcC+5YDS/4COCu0K2egUtoBve/WuhSkV7JkWNb8AswwmYOsGWZ+qSGytnuZMMPUENkyXOsEKo75nu+silxZQsHA+dV/R9A75zY8v/sE4PyHlemtH4S/PMGIigHyfwG2zQcOrARK9ivPJ7YCet0BdB4FnHEhMOifwPJ7tC2r3yzA4OcAqw04sg7I6a11gUhvZMmwlPkFmGEyDSkzzPxSI6Rs9zJhhqkRsmW47BDw/gCtSxEixs6v/juCGtP5euXn4Z+A4t3alqUxi0YBh1d7Pl96AFj1oNK723U00OFq4KdngbLDkS9joM4aC+T0AXYsBE7uMVwASAeMkmEZ8wsww2QeMmaY+aXGyNjuZcIMU2OYYf0yeH6NPVh0dm8grYMyvXWutmXxh7cQ17et3tkQWT3CW5ZQSGoNnHs/UHEcWP2Y1qUhIzJShmXLL8AMk7nIlmHml/whW7uXCTNM/mCG9UmC/Bq7I6jTqTMJKouB3Yu1LUso1FTWTVuitSuHvwY+DcQkAKsfV0JAFCiZMmy0/ALMMFF9Rssw80uhYLR2LxNmmEKBGdaGBPk17qVh1nig3eXK9K5PjTE4VmNanF83fXyrduXwR+dRQKsBwIFvgR0LtC4NGZFsGTZSfgFmmOh0Rsow80uhYqR2LxNmmELFaBm2ZQBXfQaktlM6rsrzgaPrlCsDDq/RunT+kSS/xj0j6Mw/AbGJyrTeLynxR2wycPZEZfrwj/oeKyU+G+j7D6DGAXw7SevSkFHJlGEj5RdgholOZ6QMM78UKkZq9zJhhilUjJjhmHggqzvgrAYsFiD5DKDDVcDwD5SBl/V+VpNE+TXuGUGuAWYLNwGFv2tblqBZgAtfBBKylVsCfv+I1gVq2MB/AnEpwI9P1Y1cTxQoaTJssPwCzDCRG4NlmPmlkDBYu5cJM0whYbAMlx0F1r0I5H0JFO0GaqsASxTQ7Byg9z1Aq4FAp5FAdTnww6Nal9Y3ifJrzDOC0joA2b2U6W06vt20vy6YCuRepEx/P0Xfp/W1vwrIHab88b7hDa1LQ0YlU4aNlF+AGSY6nZEyzPxSqBip3cuEGaZQMVqGD34LrJuulLO2SnlO1CqXhX1+E7DnK+W5rjcByW20KmXDJMuvMTuCOo9SftZUKLdrM7K+k5VbzwHAD9OAbfM0LU6D7JnABY8otylc9SAgnFqXiIxKlgwbKb8AM0x0OiNlmPmlUDFSu5cJM0yhIl2GBbDmSWUyKrqug0tPJMyv8S4Ni4pReuMAIO8LoOqktuUJRt9JQM/blOnVTwC//0/b8jTmvAcBWzqw6W2gaJcy2G99UTF10655tdXKg8hFlgwbLb8AM0xUn9EyzPxSKBit3cuEGaZQkDXDJ/cCjmOAPUMZN0hvJMyv8TqCci9WGghg7AFm+/4D6Pl/yvSap4CNM7Utjz+SWis/u92kPBpyyxbl58ZZwOrHwlsuMhYZMmzE/ALMMJGLETPM/FKwjNjuZcIMU7CYYe1ImF/jdQS5LikpzjPOLeZO13dyXU/umqeADf/VtjxEkWT0DDO/RMbGDJMZsd0TGZvsGU46o+4fxSeNPQizURirIyihBdBygDJt1Gsh64d49RPG6sn9bFTD83vfrYz6DgCv54a9OGRARs+wkfMLMMNERs4w80tNZeR2LxNmmJrKDBnuN1n5WesE9i3VtizeSJhfYw0W3XmkMoBUbTWwbb7WpQmc2zWdj8kZYqKGGDnDzC+RsTHDZEZs90TGJkOGE1sBIz4ButxYd4kVAMCi3D7+sjlA2z8oT215DyjerUkxzcZAZwRZgI7XKZP7lgOOfG2LE6iEFkDPvyjTtU5l2vW7NxtmAhtej0zZiCLCwBlmfomMjRkmM2K7JzI2mTLc7GzlASh3Da4uA2ISAKut7jXb5gE/PKpF6UzJOB1BLQcASa2UaSMOMGupd/JVVDQQ36zh18fENzyfyGiMnGHml8jYmGEyI7Z7ImOTJcOOAuD7R4BmvYCMroA9HYhLAWoqgRM7gaPrlE6go2u1LqmpGKcj6OC3hrnezqvSA8Yuvz/WTVceRN4YOcNmyC/ADJO8zJBh5pdOZ4Z2LxNmmE4nS4adlcCmOcpDVgbMr7HGCCIiIiIiIiIioiZjRxARERERERERkUmwI4iIiIiIiIiIyCTYEUREREREREREZBLsCCIiIiIiIiIiMgl2BBERERERERERmQQ7goiIiIiIiIiITIIdQUREREREREREJmHVasN5hTVabTpk29e6DqEsg9Z10Xr7FBg9vF/McGjXIUMZyHz00O6YYYo0PbxXeiiDXuhhX+ihDOQ/Pbxf/OwK7TqMWIaIdwSlJtlhs8djyhflAJyR3rwbmz0eqUn2gJfTUx2AptcD0FddgqkHRYae2gvADMtSD6JAydL2ZakHRQbbi77w/aBAydJmWI/wiHSGI94RlJOZgvnT70RRiSPSm/aQmmRHTmZKwMvpqQ5A0+sB6KsuwdSDIkNP7QVghmWpB1GgZGn7stSDIoPtRV/4flCgZGkzrEd4RDrDmlwalpOZYvgDlQx1cJGpLhR+srQX1oPI2GRp+7LUgyKD7UVf+H5QoGRpM6yH8XGwaCIiIiIiIiIik2BHEBERERERERGRSYTs0jAhhDpdW6v8LCp1ICa2PFSbiIiiknI4nUoFak6NGeWorEJMTIyGpQqco6LKox5FpeU49ZRhFJU61PbkUr+tUXhVVNWgorJa62IERJa2f+JkGaqqlDsIVFSdeq6kzHD1YIYjw9s+raioQEVFhQalaTqHw4Hy8lPfG041/BPFZbDbYjUsVeCYXwqULN+jHRVVKHc1+lNZdjj0Mf5GIGQ5FjHDkSFLfvl3sL6EO78h6wiqrKxUp/NLlZ9TXvkM1hjN7lDfJE5nLXYfLAQAHItTnpv75VrY4ox14C+vqPKox0PTF8ISZayTwGqqa9T25FK/rVF4LVz2G2y2OK2LERBZ2n5VVQ1+3rwXAGA/dRgtfGEhrDHRGpYqcMxwZHjbp/PmzUNSUpIGpWm68vJyvP/++8ovjnwAwI8b8xAdzfxqgfmNHBm/R8O+V30+Pj5eoxI1jSzHImY4MmTML/8O1l6482usvUFERERERERERE0Wlm7KnOS6n1ZjdYRC1AIxp85o3VbaDABQ2/wCwGD/yUB5ufqfmJxE5T8ZWUmAxWBdfzU1de1p5zFty2Im3+UpP0dk94PNYGcUyNL2q6uB7ERluvmpt6B5MhBtsGMqMxx5A9oqP2OOrgGKjXVGX1RlFVJqldzmZCnPZSUzv1phfrVh5O/RNc66/8C7PoOtR34AYo11RoEsxyJmOPKMnF/+Hawv4c6vwXYHERERERERERE1Vcj6KePiPP/reNeYK5CZnhaqTUSEo7IK875aCwDolXMBAGDMmDGw2+1aFitg9Qfmsx75AQAw8tI+sBvsGs/C4ydw87r1bs95a2sUPG/7deTIkcjKytKgNE0nS9s/UVKGB19cCADIOXVGwd8nXI3kRGP9V4YZ1s5VQ3siK8NYn8Enisvw40bllMSsU+3+vvFXIynBWJ/BzC8FSpbv0UWl5XhoutL2XRkeeWkfJCcmaFiqwMlyLGKGI0OW/PLvYH0Jd35D1hFksVg8nkuMtyPJYF96YmJiEHvq9NXYU6fB2e122Gw2LYvVJOrAfKfqk5yYAFucsUZ993bHG29tjYLnbb/abDa2fY04a4FoqzKwrOtykuTEeMMdU5lh7dhirYZr93ZbrDqYo+sU7qQE432XYH4pULJ8j3bWwiPD9rhYHos0wgxHhiz55d/B+hLu/PLSMCIiIiIiIiIik2BHEBERERERERGRSWgylvmRwmIUlTgaf2GYpSbZkZOZonUxNMf3g8xIL+0eCK7ty1IPihxZ2gzrEXrMsP7J0l5Yj9Bjfo2BbUZfzPx+RLwj6EhhMa69ewYqHOWR3rQHmz0e86ffaeoA8P0gM9JTuwea3vZlqQdFjixthvUID2ZY32RpL6xHeDC/+sc2oy9mfz8i3hFUVOJAhaMcj18WjbaZmpyQBADIK6zBlC/KUVTiMG3jB/h+kDnppd0DwbV9WepBkSNLm2E9Qo8Z1j9Z2gvrEXrMrzGwzeiL2d8PzWrcNtOKzjlaj9zt1Hj7+sH3g8xIH+0eCLbty1IPihxZ2gzrEWrMsBHI0l5Yj1Bjfo2CbUZfzPp+cLBoIiIiIiIiIiKTYEcQEREREREREZFJsCOIiIiIiIiIiMgk2BFERERERERERGQS2g6P7a+O1wJD/tX46xbfCBz8PvzloToxiUDX0UDuJUBKGyA2EXAcB07uAQ6vATb+D6g6qXUpiUJDtmMR80uNuW2v/689tBr4bFT4yhIs2fILMMPUOGZYv5hfaoxM+ZWRwTNsjI4gl1onUHHM93xnVeTKQkDz84FhM4D4ZsrvzkqgpgJIbK48WpwP7PkaOLZZ23IShZoMxyLml/xRnt/w/KgYwJamTBf8Fv7yhIIM+QWYYfIPM6xPzC/5Q8b8ykKCDBurI6jsEPD+AK1LQQCQ3Qe4bDZgtQN5XwC/vAIUblTmRduA9I5K72hVibblJAoHox+LmF/y1zvnNjy/+wTg/IeV6a0fhL88oWD0/ALMMPmPGdYf5pf8JWN+ZSBJho3VEUT6EG0DLnxBafy/zwZ+mOo+31kBFGxQHkSkL8wvhVLn65Wfh38CindrWxazYIYplJjhyGJ+KZSY38iTKMMcLJoC1/FqIDlXOV3xx39qXRoiCgTzS6GS3RtI66BMb52rbVnMhBmmUGGGI4/5pVBhfrUhUYbZEUSB63CN8nP3YuV6SCIyDuaXQqXTqf9EVhYr7YkigxmmUGGGI4/5pVBhfrUhUYaNdWmYLQO46jMgtR1giVZ64o6uU3pBD6/RunTmEBULZHVXpgs2AgktgF53AK2HAPZM5WBU8Buw+V1g/zJNi0oUNkY9FjG/FCrWeKDd5cr0rk+VU6GNwqj5BZhhCh1mOPKYXwoVI+fXyCTLsLE6gmLilZ1fUQTEWIHkM5RHh6uAbfOAVQ8Bwql1KeWW1AqIjlOmk88A+k8DYpNOjZTuAOKzgNyLlMeW94FvH9K2vEThYNRjEfNLoXLmn5TbpALGOyXdqPkFmGEKHWY48phfChUj59fIJMuwMTqCyo4C614E8r4EinYDtVWAJQpodg7Q+x6g1UCg00iguhz44VGtSyu3uJS66XPuAKpOAktuV26PJ2qUntF+k4EzrwC63AAU7QQ2vqFdeYlCyejHIuaXQsU1QGXhJqDwd23L4i+j5xdghil0mOHIY34pVIyYXxlIlmFjjBF08Ftg3XTg+FbloA8AolY5DfTzm4A9XynPdb0JSG6jVSnNwVKvyURFAysfAPI+Vxo/oNzSc+nflAMTAJzzV+W0XSIZGP1YxPxSKKR1ALJ7KdPbDHS7WqPnF2CGKTSYYW0wvxQKRs2vDCTLsDE6ghokgDVPKpNR0cqpWBQ+VaV100W7gb1fe3mRADbMVCZt6UBm94gUjUhbBjgWMb8UCp1HKT9rKoAdC7UtS8gYIL8AM0yhwQxrg/mlUJAyvwYhWYYl6AgCcHIv4DimTCefoW1ZZFd+pG66eJfv153YUTed1DJ85SHSE70fi5hfClZUDND+KmU67wvltGhZ6D2/ADNMwWOGtcP8UrBkzq8RSJZhOTqCKHIqi4HSw42/zlJvWoiwFYeIAsD8UrByLwbsGco0B6iMPGaYgsUMa4f5pWAxv9qSLMNydAQlnVEXipP7tS2LGRz8VvmZ2t73a9I61E2X8D0hkzDCsYj5pWC4TkkvztP3bZqbwgj5BZhhCg4zrC3ml4Ihc36NQqIMy9ER1G+y8rPWCexbqm1ZzGDbPOVnSlsg9xIvL7AAPW5TJksPczR7Mg8jHIuYX2qqhBZAywHKtKsdycQI+QWYYWo6Zlh7zC81lez5NQqJMqz/jqDEVsCIT4AuNwJJrevNsCi3i7xsDtD2D8pTW94DindrUkxTOfIzsHuxMj34GaDtZXUjoie0AIa9DGR0VX7/+TkA+j0ljshvshyLmF9qqs4jlUFYa6uBbfO1Lk1gZMkvwAxT0zHD2mN+qamMnF+ZSJRhq9YF8Euzs5UHoIyQXl0GxCQAVlvda7bNA354VIvSmdOK+wBbBtCiH3Dxa8r7UlMB2FLrXrPuRWDHR5oVkSjkZDkWMb8UMAvQ8Tplct9ywJGvbXGaQpb8AswwNQEzrBvMLwVMgvzKRJIM678jyFEAfP8I0KyX0rtmTwfiUoCaSuDETuDoOuWgf3St1iU1lxoH8NkooNP1QIergPROyodx6WHgyE/ApjnKe0MkC5mORcwvBarlACCplTJtxAEqZcovwAxT4Jhh/WB+KVBGz69sJMmw/juCnJXKztw0R+uSkAcBbJurPIhkJ92xiPmlABz8Fng9V+tSNJ10+QWYYQoIM6wzzC8FwOj5lZLxM6z/MYKIiIiIiIiIiCgk2BFERERERERERGQS7AgiIiIiIiIiIjIJdgQREREREREREZkEO4KIiIiIiIiIiEyCHUFERERERERERCbBjiAiIiIiIiIiIpNgRxARERERERERkUlYtdpwXmGNVpvWxfb1Ruv9ofX2yZz00O5CUQZZ6kGRo4f3i20/tOuQoQzkHz28V2z3oV2HDGUg/+nh/dJDGfRCD/tCizJEvCMoNckOmz0eU74oB+CM9Obd2OzxSE2ya1oGrfH9IDPSU7sHmt72ZakHRY4sbYb1CA9mWN9kaS+sR3gwv/rHNqMvZn8/It4RlJOZgvnT70RRiSPSm/aQmmRHTmaK1sXQFN8PMiM9tXug6W1flnpQ5MjSZliP8GCG9U2W9sJ6hAfzq39sM/pi9vdDk0vDcjJTTN3o9IbvB5mRLO1elnpQ5MjSZlgPMiNZ2gvrQWbFNqMvZn4/OFg0EREREREREZFJsCOIiIiIiIiIiMgkQnZpmBBCna6tVX4WlToQE1seqk1EhKOiCuUVVcov5UrZHQ59XDcYCIfDgfJT5cep+pwoLoPdFqthqQJXVOpQ25NL/bZGoeNtv1ZUVKCiokKD0jSdLG3/xMkyVFUpdxBwHZJOlJTBWdvAQjrEDEeGLJ/BsrR7WerB/EYOM6wvstSDGY4MWfLLv4P1Jdz5DVlHUGVlpTqdX6r8nPLKZ7DGaHaH+iZxOmux+2Ch8ot9r/p8fHy8RiVqmvLycrz//vvKL458AMCPG/MQHW2sk8BqqmvU9uRSv61R6Hjbr/PmzUNSUpIGpWk6Wdp+VVUNft6sHIPspw6jhS8shDUmWsNSBY4ZjgxZPoNlafey1IP5jRxmWF9kqQczHBmy5Jd/B+tLuPNrrL1BRERERERERERNFpZuypzkup9WY3WEosYJHItTpnMSlR5E65EfgFhjnUoWVVmFlFql/DlZynNZyYDFYF1/NTV17WnnMW3LYiYD2io/Y46uAYrjtC1MgGRp+9XVQHaiMt381ElZzZOBaKMdU5nhiDPyZ7As7V6WejC/2mCGtSdLPZjhyDNyfvl3sL6EO78G2x1ERERERERERNRUIeunjIvzPGvgrjFXIDM9LVSbiIii0nI8NH0hACDr1H8ARl7aB8mJCRqWKnAnisvw48Y8AHX1uG/81UhKsGtYqsAVHj+Bm9etd3vOW1uj8LhqaE9kZRgrw7K0/RMlZXjwReVYlHOqHn+fcDWSE411nTYzHBmyfAbL0u5lqQfzGznMsL7IUg9mODJkyS//DtaXcOc3ZB1BFovF47nEeDuSDHbAdNYClijlRCnX6WP2uFjY4mI0LFXg7LZYj3okJRjv/fB2xypvbY3CwxZrZdvXiLMWiLYqg1K6TkVPTow3XD2Y4ciQ6TNYhnYvSz2Y38hhhvVFlnoww5EhU375d7B+hDu/vDSMiIiIiIiIiMgk2BFERERERERERGQSmoxlfqSwGEUlDi027SY1yY6czJQmLauXOgDB1QPQT12CrQdFhl7aC8AMA/LUgyJHljbDeoQeM6x/srQX1iP0mF9jkKXNsB6hF+kMR7wj6EhhMa69ewYqHOWR3rQHmz0e86ffGfAO11MdgKbXA9BXXYKpB0WGntoLwAzLUg+KHFnaDOsRHsywvsnSXliP8GB+9U+WNsN6hEekMxzxjqCiEgcqHOV4/LJotM3U5IQkAEBeYQ2mfFGOohJHwDtbL3UAgqsHoJ+6BFsPigy9tBeAGQbkqQdFjixthvUIPWZY/2RpL6xH6DG/xiBLm2E9Qk+LDGtW47aZVnTO0XoEcmdQS+ujDkCw9QD0Upfg60GRoY/2AjDDClnqQZEjS5thPUKNGTYCWdoL6xFqzK9RyNJmWI9Qi2yGOVg0EREREREREZFJsCOIiIiIiIiIiMgk2BFERERERERERGQS7AgiIiIiIiIiIjIJbYfHDlTLAUDnG4BmZwP2TOW58nzg6Hpg63vA4R81LV6jMs4CcocBWd2BlLaALQOITQSqSoGiXcD+5cDmt4HKYq1L2rBoG9CiH5DZHcg8S3kktVLmrXsRWDdd0+KRTjG/+sD8UlMxw/rADFNTML/6wPxSUzHD+iBRho3TETTgSaDr6LrfaxzKz+QzlEeHEcCGN4A1j2tSPL90Hgl0G1P3e02F8rClATl9lMdZtwBfjQfy12tXzsY0Oxu4bI7WpSAjYX71g/mlpmCG9YMZpkAxv/rB/FJTMMP6IVGGjdER1PG6usa/ezHw07PAyT3K7yntgL4PAW0uBXqMB478BOz5SrOiNij/V6DkAHDkZ6Xns+qk8rw1Hmj7B6DfZKWH95LXgQ8uBKpLNC1ugyqKgMLfgWO/Kz/PfwSIb6Z1qUiPmF/9YX4pEMyw/jDD5C/mV3+YXwoEM6w/kmTYIB1B1yg/i/OApXcAwlk3r3g3sGQicP0yIDkXaHe5fgOwY4H352vKlXnlBcDl7wDxWcqpczs/jmjx/HbkJ+Ctnu7PnfegNmUh/WN+9YX5pUAxw/rCDFMgmF99YX4pUMywvkiUYWMMFh2fpfw8tsW98buIGuDYZmU6JiFy5Qq1/F/qphNytCtHY0St1iUgI2F+9YX5pUAxw/rCDFMgmF99YX4pUMywvkiUYWN0BJ3cr/zM6AJYoj3nW6xARldlumBD5MoVajnn1U2f3KddOYhCifklMjZmmMi4mF8iY2OGKUyM0RG0+W3lZ0pbYNjLyqlvLintgIteUZ4r3gNsnKVJEZssKhZIbKUMnnXhi8pzxXnA3m+0LRdRqDC/RMbGDBMZF/NLZGzMMIWJMcYI2rcU+GGaMhhWu8uVh2u0dKtduc3cpreBtc8B1aXaltVft2wDrDbP54/8DCy9E6itinyZiMKB+SUyNmaYyLiYXyJjY4YpTIxxRhAA/P4/4Ou/KANJAUrDt9qV6agYICYeiE3WrnyBchQA5flAdVndcwd/UIJedki7chGFA/NLZGzMMJFxMb9ExsYMUxgY44ygaBsw5HngzOFA/m/A8ruBwk3KvMxuwLkPKCOqtx4CLL4ROL5Vy9L65/0BddO2DKDD1cA5fwOu+hRY/zKw7gXtykYUSswvkbExw0TGxfwSGRszTGFijDOC+k1WGn/RTmDRdcDB74DKE8rj4HfKc0W7AHsG0P9xrUsbuIpjwMaZwBc3A0IAve8CzhiqdamIQoP5JTI2ZpjIuJhfImNjhilM9N8RFJMAdLlBmd70NuCs9HyNsxLY9JYy3fw8pWfRiAp+U66NBIAuN2pbFqJQYH6JjI0ZJjIu5pfI2JhhCiP9dwSltFWufQSAk3t9v644r246qXV4yxRO5UeVn8ltNC0GUUgwv0TGxgwTGRfzS2RszDCFkf47goSom05s6ft19sy66foDTxmNK7xGGfWdqCHML5GxMcNExsX8EhkbM0xhpP+OoKKddbfI6zwKsER7vsYSVXcKWUURULwrYsXzm8WPXd2iP9DsbGX60JqwFocoIphfImNjhomMi/klMjZmmMJI/x1Bzkpg61xlOqs7cOn/gLROACzKI70z8Ic3gZw+ymt+nwWIWo0K24CEFsDVnytBPf2UvYTmQM/bgUtnKkGpOAFsnKVNOf0VmwzEpdU9XAG32t2ft8ZrW07SFvOrT8wv+YsZ1idmmPzB/OoT80v+Yob1SZIMG+P28T/+E0huC5wxpO5RU6HMs9rqXrfzE+CXf2tQQD9ldgMG/lOZdlYCVaVK+WMS6l5zch+w5C+Ao0CbMvrrms+9X4Pa8y/Kw2Xbh8DKv0euXKQ/zK/+ML8UCGZYf5hh8hfzqz/MLwWCGdYfSTJsjI4gZyXw5Rig7R+BDiOAzO7KLfKEAEoPAvm/KTt6/zKtS+pb+VFgye1Ai35A1tlAQjZgS1N6bUsOAMe3AHuWADs/9j4iPJFRMb9ExsYMExkX80tkbMwwhYkxOoJc8j5XHkZUW23s8p/u/QFal4CMxsjtn/klMnYGmGEyOyO3f+aXyNgZYIZ1Sf9jBBERERERERERUUiwI4iIiIiIiIiIyCTYEUREREREREREZBLsCCIiIiIiIiIiMgl2BBERERERERERmQQ7goiIiIiIiIiITIIdQUREREREREREJsGOICIiIiIiIiIik7BqteG8whqtNh2y7Wtdh1CWQeu6aL19Cowe3i9mOLTrkKEM5D89vF9s+6FdhwxlIP/o4b1iuw/tOmQoA/lPD+8X235o12HEMkS8Iyg1yQ6bPR5TvigH4Iz05t3Y7PFITbIHvJye6gA0vR6AvuoSTD0oMvTUXgBmWJZ6UOTI0mZYj/BghvVNlvbCeoQH86t/srQZ1iM8Ip3hiHcE5WSmYP70O1FU4oj0pj2kJtmRk5kS8HJ6qgPQ9HoA+qpLMPWgyNBTewGYYVnqQZEjS5thPcKDGdY3WdoL6xEezK/+ydJmWI/wiHSGNbk0LCczxfAHKhnq4CJTXSj8ZGkvrAeZlSxthvUgM5KlvbAeZFaytBnWw/g4WDQRERERERERkUmwI4iIiIiIiIiIyCTCemnYsRNF4Vx9WBSVOlBTrYzaXXNq8O7C4ydQUVGhYakCJ0s9jNiGZGLE/S9L25elHkZsQ7Iw4r6Xpd3LUg8jtiGZGHH/y9L2ZamHEduQLIy472Vp97LUI9xtyCKEEKFY0f79+3HGGWcAAAa0DcUatVFbC+SXKtM5ydqWJRiy1KO+7/KUn/v27UPr1q21LYyEmGF9kaUe9THD4cP86oss9aiP+Q0vZlhfZKlHfcxw+DC/+iJLPeoLR355aRgRERERERERkUmE5dIwV4+V0e08pnUJQkOWelDkMMP6Iks9KDKYX32RpR4UOcywvshSD4oM5ldfZKlHOPCMICIiIiIiIiIikwjZGEFOpxOHDh0Kxao0JYRAZWWl23NxcXGwWCwalahpZKmHNy1atEB0dLTWxZAOM6wvstTDG2Y49JhffZGlHt4wv+HBDOuLLPXwhhkOPeZXX2SphzehzG/IOoKIiIiIiIiIiEjfeGkYEREREREREZFJsCOIiIiIiIiIiMgk2BFERERERERERGQS7AgiIiIiIiIiIjIJdgQREREREREREZkEO4KIiIiIiIiIiEyCHUFERERERERERCbBjiAiIiIiIiIiIpNgRxARERERERERkUmwI8gk3nzzTVgsFrRp00brohBRCEydOhUWiwVDhgwJy/o3btyIkSNHonnz5rBarbBYLDj77LPDsi0iIiKj2bNnDywWCywWC/bs2RPSdfN7O5G2VqxYoeY71ML9Hd5fIekIEkLgww8/xFVXXYXc3FzY7XYkJibizDPPxIABA3Dvvfdi4cKFOHnypMey06dPx9SpU/Hrr7+GoiiaO3LkCKZMmYLevXsjPT0ddrsdubm5+MMf/oCnn34a1dXVWheRKCTMnPv6X/78eYwbN07rIgckLy8P/fv3x4cffogjR44gJSUF2dnZyMzMBGD894+8M3Om65s/fz6GDx+OFi1aIDY2FgkJCejUqRMmTJggRf1I/5hFxaefforhw4cjJycHsbGxaN68Oa688kp88cUXWhdN91asWIGpU6fizTff1Loo0jF7PouKivDJJ5/gkUcewRVXXIHmzZur33cDaW+7du3C//3f/6Ft27aw2WzIysrCpZdeio8++ih8hZfExx9/jKlTp+Ljjz8ObkUiSCdOnBCDBw8WANSH1WoV6enpwmq1uj0/e/Zsj+Vzc3N9zjOauXPniuTkZLW+NpvN7XcA4sSJE5qUbfbs2QKAyM3N1WT7JBez537fvn0iOzu7wUdKSoq6D1555ZWQl+HRRx8VAMTgwYNDvu4HH3xQABDt27cXBw4c8Jhv9PePPJk900IIUVFRIYYPH+5W18TERBEbG6v+HhUVJV544QWti0oSYxaFqKmpEX/+85/VelosFpGWliaio6PV5+644w6tiykOHDggOnXqJDp16uT1szIYwX5vD+d3BDNjPuvapreHv/VavHixiI+PV5dLTk4WUVFR6u/jxo0TtbW14a1II3788Uc136EWbD7HjBkjAIgxY8YEVY6gzwi6+eabsXLlSkRHR+O+++7D9u3bUVlZiWPHjsHhcOC3337DM888g549ewa7KV378MMPceONN+LkyZO47bbbsGnTJjgcDhQXF+PkyZNYtWoV7rnnHsTExGhdVKKgmT33rVu3xpEjRxp83HTTTQAAu92OG2+8UeMSB2bjxo0AgCuvvBItW7bUuDQUCWbPNAA89dRTWLRoEQBg4sSJOHDgAEpKSuBwOLB27VoMGDAAtbW1uO+++7Bu3TqNS0uyYhaBhx9+GO+++y4A4K677kJBQQGOHz+O4uJiPP/887BarXj55ZcxY8YMTcvZsmVLbN26FVu3buVnpUkwn4qcnBxcdtllmDx5MhYsWBDQsnl5eRg5ciTKy8vRv39/bNu2DcXFxSguLsYjjzwCAJg9ezaee+65cBTdb+edd56ab2kF04u0fft2tefun//8Z6OvLy8v93hOhp7RQ4cOibS0NAFA/Otf/9K6OF7xjCAKFea+cQ6HQz0mjB49OizbCOd/+4YMGSIAiEcffdTrfNnfP7NhphVt2rRpMFNFRUUiMTFRABAPPfRQZAtHpsAsClFQUCDi4uIEADFixAivr3GdtZqamiqKi4sjXMLI4BlB+sN8Kmpqajyec+0Xf+o1evRoAUDk5OR4vVLmtttuU88SOn78eAhKrD9SnBFU//rGK6+8stHX2+12ddo1SNLevXsBAOPGjfMYV8ObxYsX45prrkHLli0RFxeHtLQ0DBo0CK+++iqqqqq8LjNkyBBYLBZMnToVVVVVePrpp9GjRw8kJCQgLS0NF198cVDXG8+YMQMnTpzAOeecg3vuuafJ6wnWmjVrMGLECGRmZsJut6NTp06YPHkySktL/Vr+l19+wc0334zc3FzYbDakpaXhggsuwPTp01FZWenx+n/961+wWCzo06eP1/V16tQJFosFVqsVxcXFHvP/7//+DxaLRT1zwqVNmzbqdaZVVVV47rnn0LNnTyQkJCAlJQVDhw7Fl19+6VedKPSY+8YtWLAAJ06cAACMHz8+LNvwR0FBAR5++GGcc845SElJgc1mQ7t27XDrrbdi06ZNHq93ZW/FihUAgGnTprm9N659Guj7R/rGTCsOHz4MAD4/01JSUtCxY0cA8PtzNRBjx46FxWLB2LFjfb6moQFk6y8vhMBrr72G8847D8nJyUhOTsaAAQPw3nvvhbzcFDrMIrB06VL1O+f999/v9TUPPPAAAGWskqDHyPDi9Cy98cYbGDBgADIyMtzGQfFnsOi9e/fi1ltvRatWrRAXF4dWrVph3Lhx2LlzZ0CDTa9bt069gUNcXBzatWuHe++9V/2u4eJa57Rp0wAAK1eu9GgHHDeoaZhPRXR0dJOXLSsrU8cAuv3225GamurxmkmTJgEATp48GZZ8198/1dXV+Ne//oU+ffogNTXV7TuwP4NFb9y4Eddffz1ycnLU79h33HEH8vPzAxpseunSpbj88suRlZUFm82GLl26YNq0aaioqHB7nWudc+bMAQDMmTPHox25yu+XYHqR5s2bp/YAfv311wEt+9xzz4ns7Gz1esDk5GSPMTbqKy8vF9dee63bdYjJycnCYrGov/fr189rz6HrWs5JkyaJgQMHqtdzpqamuq3P13+/G9OiRYugzgZavnx5wNdWnm7WrFlu11ampKSo4xp07txZvPDCCw3+Z+GFF15w25cpKSkiJiZG/b1Hjx7i0KFDbsusW7dOHTPh9B7dAwcOuO3bTz75xGOb7du3FwDE//73P7fnXb3lL7/8sujbt68AIGJiYtT/xOLU9eKzZs1q0r6i4DD3jRs6dKgAIDp06ODzNfWvsV6+fHnA22jsvwlLlixxq2tMTIxISEhQf4+NjRVz5sxxW6ZPnz4iOztbzX5CQoLbezNt2rSA3j8yBmZa0aVLFwFADBkyxOv8+mcEvfnmmx7zg/0s9+c/fA2dJVB/+euvv179fE5LS3Pbv3oYe4G8YxaFeOaZZ9TlGxpXs1mzZgKA+POf/+wxL9jPV1eWbr75ZnHNNde4ZSkqKkrNd15enrqdvLw8j/X88MMPIikpSX2N3W5XjyHJyclu7/fpy9fP+rvvvqt+LqekpLh93+/WrZsoKSlRl3ONYej6vI+JifFoB3Pnzg14nxDz2RB/P/u+/PJL9bU//fSTz9e5Po9HjRrlMc/1/ddX7hrj2j8PPviguOCCC9T94/qsdB0z6n+me7NgwQK3v5UTExOFzWYTAETz5s3djkO+6jB48GDx7LPPCovFIiwWi0hNTXV7jy+88EK3M7C+//57kZ2drW7HZrN5tKPvv//e730RVEdQXl6eWtju3buLbdu2BbwOf0+Rc51G1q5dO/Huu++qp4I6HA7xySefiHbt2gnA+2mkrjc8JSVFxMXFiddee004HA4hhHLArB80bx0WDdm9e7e67IoVK8SGDRvEDTfcIHJyckRsbKxo2bKlGDlypPjuu+98riPYL4/r1q1TBygbMmSI2LJlixBCiKqqKvH++++L1NRUNfzevjwuWrRI3f6VV14pdu/eLYQQorKyUrz11lvqh9gFF1zg1hidTqd6+cvChQvd1vn222+rBy0A4q677nKbv3//fp8hdrWJtLQ00bJlS/Hxxx+LqqoqIYQQW7duFf369VMDV1RUFPD+ouAw9w3btWuXun+efvppn68LZ0fQhg0bhN1uFwDEhAkTxObNm9Xs7t27V0ycOFH94Pv55589lnftO14aZg7MtGLGjBnq8hMnTlQHf62trRXr1q0TAwYMEADE+eef7/XUeL10BKWkpAiLxSIef/xxdf/m5+eLv/3tb2r5XnrppYDLR+HHLLp3BBUWFvp8XUZGhgCUf1SeLlQdQYmJicJqtYrnn39e3T8lJSXqP0Yb6gg6ceKEaN68ubqPly1bpnbA/vTTT6Jnz57qd+iGOoLi4+NFXFycGD9+vNi3b58QQoiysjLx73//W/0jdMqUKR514KVhocd8+ubvZ9/zzz+vvtbbpXMu1113nbqfTxeqjqDExESRmJgoZs+erZalsLBQHDt2TAjRcEfQrl271MGue/XqJdauXSuEUL4vLFmyROTm5rrl21cdUlNTRVRUlJg0aZIoKCgQQghRXFwsHnnkEXVZbyc+hOrSsKDvGjZhwgS1oBaLRZxzzjli4sSJYtasWWLjxo2N/tfJn0CsWrVKABDNmjVTD4Kn279/v9r7/csvv7jNqz+6u7ed6XQ6xaBBgwSg9KwHon7P5pNPPqmehWO3293uGGaxWMRjjz3mdR3Bfnm87LLLBADRsWNHr6GqX0ZvXx5dva4DBw70+uX2008/VZf/8MMP3eaNGDFCAJ53bxg3bpwAoDbk04M8Z84cAUC0adPGY3uuNhEXF6d2atWXn5+v9oS+8847XvcJhZfZc9+QyZMnq50shw8f9vm6cHYEuc5ImjRpks/l77zzTrXz93TsCDIfZlpZ/oEHHnD7b3v9u4bl5OSIhx56yOeXV710BPn6w1CIuj8u0tPT1T8MSF/MnsUPPvhAXfeKFSu8vubw4cPqazIzMz3mh6ojCICYMWOGz9c11BH0+OOPC0D5j/2OHTs8li0oKBCZmZmNdgQ1dEy49957BaDc4fN07AgKD7Pn0xd/P/tcbTYtLa3B1919990CgMjIyPCYF6qOIADi008/9fm6hjqCbr31VvU9cnUc1bd161Z1rLOGOoIa+q599dVXCwDioosu8pinm46g6upqMWXKFLdLDuo/mjVrJu655x5x5MgRr8v7EwhXZe+5554Gy3LFFVcIwPO/8K43vHXr1j4D+vXXX6tl3rBhQ8OVruf9999Xl4uKihJt2rQRS5YsEU6nUwghxKZNm9SBVwGIjz76yO91++PEiRPql9aZM2f6fN3555/v9cvjb7/9ppbtq6++8rn8eeedJwCIq6++2u35l156yeuBxDXo5sGDB0WrVq2ExWIR+fn56vyxY8cKQDlF/XSuNuHtdF+XCy+8UAAQDzzwgM/XUPiYPfe+1NTUiJYtW/rsYAklX1/yXF9MrVar1w8nl7Vr1wpAufzr9A5gdgSZDzOtcDqd4s0333S7FNn1SE1NFbfffrv6X7tQC1VHkN1u9zmAbv3BThv6AkzaMXsW6w8WffHFF3t9Tf2z22JjY/1et79c+yctLU1UVlb6fF1DHUE9evRoNM9TpkzxqyPIW0eSEEKsXLlSfU1ZWZnbPHYEhYfZ8+mLvx1Bro60li1bNvi6f/zjH2HLt2v/NNYJ5qsjqLa2Vr3SpqHL62666aZGO4Li4uLcLu2sz3XSRLNmzTzm6WKwaACwWq147LHHcPDgQbz99tsYP348evbsidjYWABAfn4+XnzxRZx11ln46aefmrSN77//HgAwa9Ys5OTk+Hx88803AKAOxHU61+BQ3gwcOBBWqxUAsHbtWr/LVltbq04LIfDRRx/hoosuQlSUsmu7du2KRYsWIScnBwDUwdtCZf369WoZhg4d6vN1vua56mq1WjF48GCfy1988cVurz99vZs2bUJ+fj4A5baAe/bsQadOndCiRQtceOGFEEJg+fLl6nKu6QsvvNDnNvv27etzXosWLQAAx48f9/kaCh+z596XL7/8EgcPHgSg3SDRrv1WW1uLrl27+txvf/jDHwAoA/cdO3ZMk7KSfjDTQGFhIYYNG4axY8fi/PPPx3fffYeioiIcPnwYCxYsQFZWFl599VX07dtXzbke9enTB8nJyV7ndejQAa1atQIQmmMehZ7Zs5iZmYk777wTALBkyRKMHj0aW7duRXV1Nfbt24eHHnoIr7zyCmJiYgBA/b4dDueee6663wNRVVWl3pChoe/WQ4YMaXRd6enpaN++vdd5ru/CADwGjabwMHs+ZdK/f/8mLbd7924UFRUBCD7f3bp1Q2Jiotd5kfhbN2RHz5SUFIwePRozZ87Er7/+iuLiYixZsgTDhw8HoHzBuuaaazxGv/bHoUOHACijhx89etTnw7Xu8vJyr+tp2bKlz23YbDZkZGQAgNqh4Y+kpCR1etiwYejVq5fHaxITE/HXv/4VALBhwwYcPXrU7/U3pn5ZG6qf64ufr+UzMzMRFxfX6PKn75tu3bqhWbNmAIBly5YBqOvkcXUSuX665u/evVs9aDXUEVR/357OdfCqrq72+RoKP7Pm3pc33nhD3eZll10W9PqawrXfamtrG9xvhYWF6jK+9h2Zj5kzPWbMGKxYsQKDBw/GV199hf79+yMlJQU5OTm46qqr8N133yEzMxO7d+/GQw89FNC6I6mh/VN/fiiOeRQ+Zs7iU089hVGjRgEA3n33XXTp0gWxsbHIzc3FM888g759++LWW28FAKSlpQW07kC4vt8G6vjx43A6nQDcO2tO11hWAf++CwP8PhxpZs5nMFztubHvna75DbX/YDU13wUFBep0JPJdU1MTQOkCE7ZudJvNhosuugiffvopxowZAwA4cOBAk2777TqYvvrqqxDK5WwNPiJ5W8T6b3KXLl18vq5r167qtK+eWyNy3VIaqOvocf301RHk+tm+fXufHVRkTGbJvTdHjx7FZ599BkC59Wwwt9cMhmu/ZWdn+7XfhBBeb0VNBJgn01u2bMHnn38OALjvvvu8/he1WbNmuPnmmwEACxYsgBAiYuUjMksWAeUPoPfffx+LFy/G9ddfj86dOyM3NxcDBw7EjBkzsGrVKvUPxY4dO4atHKH4HPfn1tFkfGbKZzBcHScnTpyAw+Hw+TrXWbcNdbQEi/kOY0dQfbfddps6vW3btoCXd11WFWwHSkOncldWVqqXRwTSQ9i1a1e/GlL9L4yhbDT1y9pQ/XzNcy1fWFiIyspKn8sfOHDAY3surrN66p8RZLFY1OfPOOMMtGvXDjt27MCBAwc8OopITjLn3pu33noLNTU1sFgsuOWWW4JaVzBc+62wsBBlZWWalYPkI3OmN2/erE6feeaZPl/XoUMHAMp/K0P9X1TXf/8a+g9ycXFxo+tp7LI11/xgj3mkHZmzWN8f//hHzJ07F1u2bMGePXuwatUq3HHHHYiJiVEvZ7nggguatO5wSk9PV/82cJ3d4Y2eLzGlpjNLPpvirLPOUqd///13n69zzevWrVvYyxSorKwsddro+Y5IR1D9a99Ov/zIdW1vQ/9Zc13D5/pve1OtXLnS53a+/fZb9dSrPn36+L1Om82GQYMGAVD+o+iL60umxWIJ6X/fe/Xqpe7D+mPwnM7V+XI6V11ramqwcuVKn8u7rkM999xzPea5Onx27dqFJUuW4NChQ+jRo4d6yiHgflbQihUr3JYjOcmce29mzZoFQGnX7dq1C2pdwXDtN6fTiS+++CIs2/Dn/SP5yJzp+uOMNPTlu/6l3b6u628q1yUu+/fv9/maH3/8sdH1rF27FqWlpV7n7dy5U/3HTrDHPNKOzFn0xy+//KJ+r3adpacnsbGx6h+wru+83jQ0L1j8nNaO2fPZkAEDBsButwOAz7Ol9u7dq/5Nfckll0SsbP5q164dUlNTARg/30F1BOXl5WH79u2Nvm7OnDnq9Olj6LgGNHQNuuSNq2f1999/x6uvvtrgtsrKylBVVeV13r59+9zK4lJbW4unnnoKgHKGT/fu3RvcxunGjRsHAFi6dCnWr1/vMb+0tBT/+c9/ACgDINfvSQxWamqqGpLnn3/e638Sv/nmG/zwww9el+/Ro4d62doTTzyhno5Y3+eff65++bzhhhs85rsGhQaAKVOmAPA828fV6fOf//wHhw8fBuDfIFqkP8y9p++++079r49Wg0S7dOjQQc3W5MmTGz2DoCmD0Pnz/pFxMNPu9fFVtrKyMrz11lsAlM/OhIQEv9fvj549ewIAfv75Z6+dQVu2bMGCBQsaXY/D4cDzzz/vdd4TTzwBQDljwXUTCNIPZrFx5eXluP322wEA1157LTp37hyydYfStddeCwD44IMPsGvXLo/5x44dw2uvvRa27fNzOvSYz+AlJCTgmmuuAaB81nr7jvrMM88AUMbPGTFiRMTK5i+LxYKrr74aAPDaa695Hah9x44dmDdvXtjKELJ8N/2GY0IsWrRIREVFiT/+8Y9izpw5brc+rKqqEuvXr1dvEw5AnHfeeept1V3+/Oc/CwDiggsuEMePH/e5rXHjxgkAwmKxiLvvvlvs2rVLnVdRUSFWr14t7r//fpGRkSH279/vtqzrNnEpKSnCZrOJ119/XTgcDiGEEPv27RMjR45Uy7hgwYKA94PT6VRvr96mTRvxzTffqPXcvHmzeqvzqKgosXTpUo/l69+erim3Y/75559FdHS0ACCGDh0qtm7dKoRQbnH4wQcfiLS0NPU2d95uObto0SJ1+yNGjBC7d+8WQijv4TvvvCOSk5PV9+j020y7uN5H12PRokVu8w8dOuQ2v0uXLj7rE8itFYO9bR4Fjrn35GqP6enpoqKiwq9l6t8advny5QFvs6Fbw27cuFG9/XXnzp3Fxx9/rNZdCCEOHDgg3nrrLTF06FAxfvx4j+Ubu328v+8fGQMzrRg+fLi6/OjRo8XOnTtFbW2tqKqqEt9//73o06ePOn/OnDkeywf7WV5UVKTmtl+/fupneVVVlfj4449FixYtRHp6us/PctdxKCUlRURFRYmnnnpKnDx5Ugih3JL7zjvvVMv34osvBlw+Cj9mUbFmzRrx5JNPik2bNqm3b6+srBRffPGFOOecc9RbYx89etTr8sF+vvr7HbOh28cfP35cZGdnCwCiffv2YsWKFeqtvH/++WdxzjnniLS0NJ/Lu+rgLev+bH/JkiUCgIiOjhbff/+9nzWnhjCfdQoKCtwervW9/PLLbs+XlZV5LLt7926RkJAgAIiBAweK7du3CyGEKC0tFdOmTRMWi0UAEM8884zXbbu+/3pr9/5o7Duui6/bxwshxI4dO4TdbhcARJ8+fcT69euFEMqt5ZcuXSratm3rlm9fdfD2Hd6f7c+cOVMAEGlpaWLLli0NV7gBQXUEffnll25/3AMQsbGxIj09XX0TXY9evXqJgwcPeqxj5cqV6mujo6NF8+bNRW5urseBr7KyUowfP95tnYmJiSItLU1ERUW5PX/gwAG3ZV1v+KRJk8SAAQMEABETE+P2BgEQDz/8cJP3xeHDh0XXrl3VdcXHx4uUlBT195iYGPH66697XTbYL49CCPHf//7XbZ+npKSIuLg49Q/BF154ocEPlBdeeMFt+dTUVBEbG6v+3r17d6/vn8usWbPU10ZHR4vi4mKP13Tu3Fl9zcSJE32uix1B+sbcuysuLhbx8fECgLjzzjv9Xi6cHUFCCPHdd9+JnJwct1xmZGSoH1yuR1M6gvx9/8gYmGlFQUGB6N27t9u64uPjhdVqdXvu/vvv97p8KD7L33jjDbd9npSUpH4W9+vXT/z73//2+Vle/3Px+uuvV9+LtLQ0t3XefPPNHn+ckD4wi4qFCxeq67BYLCI9PV39hycAcdZZZzX4B6AeOoKEEOLbb79VO3ddxxPX76mpqeLDDz9U5x0+fNhrHZraEVRdXS06deqkzk9LS1PbwYcffujnnqD6mM86p+8HXw9f3yMXL16sfncGlL9b62d83Lhxasfp6fTQESSEEB9++KHb94OkpCS1Ti1btlQzHBcX57MOTe0IOn78uMjKylLnZ2Zmqu1o9erVDdarvqAuDbv00kuxY8cOvPTSS7juuuvQpUsXxMXFoaioCPHx8ejQoQNGjhyJuXPn4ueff/Y68vegQYOwePFiXHTRRUhNTcXRo0exd+9ej2v0Y2NjMXPmTPzwww8YO3YszjzzTDidTpSWlqJZs2YYMmQIHnnkEWzYsMHn7dpiY2OxdOlSPPXUU+jUqRMqKyuRkpKCYcOGYfHixXj88cebvC9ycnKwfv16PP/88zj33HMRExMDh8OBNm3a4JZbbsH69esxYcKEJq+/Mbfddhu+//57DB8+HOnp6aisrERubi4mTZqEn376qdHba95zzz1Yu3YtRo8ejdatW6O8vBx2ux39+vXDiy++6PP9c6k/3k+fPn3UU9bqq3+5GMcHMi7m3t3cuXPVu5dofVlYff3798f27dvx/PPPY9CgQUhNTUVRURGio6PRpUsXjB49Gu+++y6mT58e8Lr9ff/IGJhpRWZmJtasWYM33ngDl156KbKzs1FdXQ2r1Yp27dph9OjR+Pbbb/Hss882af3+uPXWW7F48WIMHToUycnJqKmpQceOHfH0009j5cqVfl+O9v777+M///kPzjnnHNTU1CAhIQHnn38+3nrrLcyZM8dtTCTSD2ZR0bt3b9x///3o27cvmjVrhpKSEmRkZOCiiy7C66+/jl9++cUQd7scMGAANmzYgHHjxqFFixaoqalBamqq+ndB/YHpXWOOhIrVasXSpUsxfvx4tG3bFmVlZWo78DWGGDWM+QydP/7xj9iwYQMmTJiANm3aoKKiAmlpabj44osxf/58/O9//9P9HbmuvfZarF27Ftdddx2ysrJQWVmJ7Oxs3HXXXfjll1+QkpICIPTZBpQxBVetWoVRo0ahZcuWKC4uVttRQzecOJ1FCPlHERsyZAhWrlyJRx99FFOnTtW6OEQUAcw9kVyY6YaNHTsWc+bMwZgxYwx1O2EyHmYxNGbOnInbbrsN7dq18zqOEFFTMJ/6MHnyZDz11FMYOnQoli5dqnVxvOK/hIiIiIiIiCKkoqJCPSP3D3/4g7aFIaKQKigowBtvvAFA3/lmRxAREREREVEIzZ07Fw8//DB+//139c5ONTU1WLVqFYYOHYrNmzfDZrPhrrvu0rikRBSoGTNm4Omnn8bOnTtRU1MDAKisrMTnn3+OQYMGIT8/H1lZWbjllls0LqlvVq0LQEREREREJJMjR47gySefxJNPPgmLxYK0tDSUlpaqnUKxsbGYPXs2OnbsqHFJiShQu3fvxksvvYRJkyYhOjoaKSkpOHnypNoplJKSgnnz5iEjI0PjkvrGjiAiIiIiIqIQuuKKK1BQUIAVK1Zg7969KCwsRExMDNq1a4cLL7wQd999NzuBiAxqzJgxiI6OxqpVq3Dw4EEcO3YMdrsdbdu2xaWXXoq77rrL50DeemGKwaKJiIiIiIiIiIhjBBERERERERERmQY7goiIiIiIiIiITIIdQUREREREREREJsGOICIiIiIiIiIik2BHEBERERERERGRSbAjiIiIiIiIiIjIJNgRRERERERERERkEtZQrcjpdOLQoUOhWp1mhBCorKx0ey4uLg4Wi0WjEjWNLPXwpkWLFoiOjta6GNJhhvVFlnp4wwyHHvOrL7LUwxvmNzyYYX2RpR7eMMOhx/zqiyz18CaU+Q1ZR9ChQ4dwxhlnhGp1RD7t27cPrVu31roY0mGGKVKY4dBjfilSmN/wYIYpUpjh0GN+KVJCmV9eGkZEREREREREZBIhOyOovgFtw7HWyKitBfJLlemcZG3LEgxZ6lHfd3lal8A8mGHtyVKP+pjhyGB+tSdLPepjfiOHGdaeLPWojxmODOZXe7LUo75w5JdnBBERERERERERmURYzghymfncg8hISw3nJkKuqNSBKa98BqCuB/GuMVcgMd6uYakCJ0s9jp0owoT7n9G6GKbFDGtHlnoww9phfrUjSz2YX20xw9qRpR7MsHaYX+3IUo9w5zesHUEZaanIykwP5yZCLia2HNYYZbdYT+2dzPQ0JCXGa1iqwMlSD9IWM6wdWepB2mF+tSNLPUhbzLB2ZKkHaYf51Y4s9Qg3XhpGRERERERERGQS7AgiIiIiIiIiIjKJsF4a5suRwmIUlTi02LSb1CQ7cjJTmrSsXuoABFcPQD91CbYeFBl6aS8AMwzIUw+KHFnaDOsResyw/snSXliP0GN+jUGWNsN6hF6kMxzxjqAjhcW49u4ZqHCUR3rTHmz2eMyffmfAO1xPdQCaXg9AX3UJph4UGXpqLwAzLEs9KHJkaTOsR3gww/omS3thPcKD+dU/WdoM6xEekc5wxDuCikocqHCU4/HLotE2U5MTkgAAeYU1mPJFOYpKHAHvbL3UAQiuHoB+6hJsPSgy9NJeAGYYkKceFDmytBnWI/SYYf2Tpb2wHqHH/BqDLG2G9Qg9LTKsWY3bZlrROSdGq82f4gxqaX3UAQi2HoBe6hJ8PSgy9NFeAGZYIUs9KHJkaTOsR6gxw0YgS3thPUKN+TUKWdoM6xFqkc0wB4smIiIiIiIiIjIJdgQREREREREREZkEO4KIiIiIiIiIiEyCHUFERERERERERCah7fDY/opLBXIvBlr2BzLPAhJbAlHRgOM4ULgB2P4RsOcrrUvZuI7XAkP+1fjrFt8IHPw+/OUJpZ63A30fqvv99VztykL6Ikt+AWaYzEmWDDO/ZEay5BdghsmcZMkw86s7xugIumktEFVvJO+aCqC2BkhsrjzaXArsWw4s+QvgrNCunP6qdQIVx3zPd1ZFriyhkNIO6H231qUgvZItvwAzTOYiW4aZXzIT2fILMMNkLrJlmPnVDWN0BEXFAPm/ANvmAwdWAiX7lecTWwG97gA6jwLOuBAY9E9g+T3altUfZYeA9wdoXYoQsQCDnwOsNuDIOiCnt9YFIr2RLb8AM0zmIluGmV8yE9nyCzDDZC6yZZj51Q1jdAQtGgUcXu35fOkBYNWDSq9o19FAh6uBn54Fyg5HvoxmddZYIKcPsGMhcHKP4QJAEcD86hszTI1hhvWL+aXGML/6xgxTY5hh/TJ4fo0xWLS3xl/ftg/qprN6hLcsVCepNXDu/UDFcWD1Y1qXhvSK+dUvZpj8wQzrE/NL/mB+9YsZJn8ww/okQX6N0RHUmJrKumlLtHblMJuBTwMxCcDqx5UQEDUF86sdZphCgRnWBvNLocD8aocZplBghrUhQX6NcWlYY1qcXzd9fKt25fCXLQO46jMgtZ0S2PJ84Og6YOtc4PAarUvnn86jgFYDgAPfAjsWaF0aMjKj5RdghonqM1qGmV+iOkbLL8AME9VntAwzv7ph/DOCYpOBsycq04d/BIp3a1sef8TEA1ndAWc1YLEAyWcAHa4Chn+gDDil997c+Gyg7z+AGgfw7SStS0NGZsT8AswwkYsRM8z8EimMmF+AGSZyMWKGmV/dMPgZQRbgwheBhGzlVnrfP6J1gRpWdhRY9yKQ9yVQtBuorQIsUUCzc4De9wCtBgKdRgLV5cAPj2pdWt8G/hOISwF+fKpu5HqigBksvwAzTOTGYBlmfonqMVh+AWaYyI3BMsz86o6xzwi6YCqQe5Ey/f0U/Z8Od/BbYN10pZy1VcpzolY5He7zm4A9XynPdb0JSG6jVSkb1v4qIHcYULgJ2PCG1qUhIzNafgFmmKg+o2WY+SWqY7T8AswwUX1GyzDzqzvG7QjqO1m5ZRsA/DAN2DZP0+IETwBrnlQmo6Lrgq0n9kzggkeU2xSuehAQTq1LREYlXX4BZphMRboMM79kItLlF2CGyVSkyzDzqwVjXhrWdxLQ8zZlevUTwO//07Y8oXJyL+A4BtgzlOsl9ea8BwFbOrDpbaBoF2CNd58fFVM37ZpXW608iFxkzS/ADJM5yJph5pfMQNb8AswwmYOsGWZ+I854HUF9/wH0/D9les1TwMaZ2pbHTJJaKz+73aQ8GnLLFuXnxlnA6sfCWy4yDuZXW8wwBYsZ1g7zS8FifrXFDFOwmGHtSJhfY3UE9Z1c1wO65ilgw3+1LU+oJZ2h9IICwEljDz5F5EH2/ALMMMlN9gwzvyQz2fMLMMMkN9kzzPxGnHE6guo3/tVPyNkD2m+y8rPWCexbqm1ZvPlsVMPze9+tjPoOAK/nhr04ZCBmyC/ADJO8zJBh5pdkZYb8AswwycsMGWZ+I84Yg0W7XQv5mDEbf2IrYMQnQJcb604tAwBYlNvmXTYHaPsH5akt7wHFuzUpJlHIyZBfgBkm85Ihw8wvmZUM+QWYYTIvGTLM/OqS/s8ISmgB9PyLMl3rVKZdv3uzYSaw4fXIlC1Qzc5WHgBQUwFUlwExCYDVVveabfOAHx7VonREoSdTfgFmmMxHpgwzv2Q2MuUXYIbJfGTKMPOrO/rvCLLUO2kpKhqIb9bw62PiG56vFUcB8P0jQLNeQEZXwJ4OxKUANZXAiZ3A0XVK4z+6VuuSEoWOLPkFmGEyJ1kyzPySGcmSX4AZJnOSJcPMry7pvyOo9IBhrrNrkLMS2DRHechq3XTlQeQiS34BZpjMSZYMM79kRrLkF2CGyZxkyTDzq0vGGCOIiIiIiIiIiIiCxo4gIiIiIiIiIiKTYEcQEREREREREZFJsCOIiIiIiIiIiMgk2BFERERERERERGQS7AgiIiIiIiIiIjIJdgQREREREREREZkEO4KIiIiIiIiIiEzCqtWG8wprtNp0yLavdR1CWQat66L19ikweni/mOHQrkOGMpD/9PB+se2Hdh0ylIH8o4f3iu0+tOuQoQzkPz28X2z7oV2HEcsQ8Y6g1CQ7bPZ4TPmiHIAz0pt3Y7PHIzXJHvByeqoD0PR6APqqSzD1oMjQU3sBmGFZ6kGRI0ubYT3CgxnWN1naC+sRHsyv/snSZliP8Ih0hiPeEZSTmYL50+9EUYkj0pv2kJpkR05mSsDL6akOQNPrAeirLsHUgyJDT+0FYIZlqQdFjixthvUID2ZY32RpL6xHeDC/+idLm2E9wiPSGdbk0rCczBTDH6hkqIOLTHWh8JOlvbAeZFaytBnWg8xIlvbCepBZydJmWA/j42DRREREREREREQmwY4gIiIiIiIiIiKTCNmlYUIIdbq2VvlZVOpATGx5qDYRESdOlqGqShm1u6Lq1HMlZXDWalioJpClHkWlDrU9udRvaxQ6zLC+yFIPZlg7FVU1qKis1roYAXFUVMF5qpHXnBq3sai03HDtnvmlQMnyGVxUUu6RYUdlFWJiYjQsVeBkORYxw5EhS36l+eyS5DhUURXeO4mFrCOosrJSnc4vVX5OeeUzWGM0u0N9k1RV1eDnzXsBAPZTRS98YSGsMdEalipwstSjprpGbU8u9dsahQ4zrC+y1IMZ1s7CZb/BZovTuhgBKa+owu6DhQCAY6eK/tD0hbBEGesEZuaXAiXLZ7DTWeuR4blfroUtLlbDUgVOlmMRMxwZsuRXls8uWY5DFRXhzaqxjmZERERERERERNRkYemmzEmu+2k1VkcoqquB7ERlunnSqZ/JQDTroYmamrr2tPOYtmUxE2ZYe7LUgxmOvO/ylJ8jsvvBlpSkbWECVV4O2JX/RuYk5gMAspIAi8H+bcX8UjCM/BksaoGYU5eUbCttBgCobX4BEB+vYamaQJJjETMceUbOryyfXbIch6pLSvBd3vywrd9ghzMiIiIiIiIiImqqkPXvxcV5jkNw15grkJmeFqpNRMSJkjI8+OJCAEDOqZ7Qv0+4GsmJxupBlKUehcdP4OZ1692e89bWKHjMsL7IUg9mODK87dORI0ciKytLg9I0ncPhUKetR34AAIy8tA/sBruun/mlQMnyGeyorMK8r9YCAHrlXAAAGDNmDOx2u5bFCpgsxyJmODJkya8sn12yHIcKCgrw97//3e25UOY3ZB1BFovF47nEeDuSDNZwnLVAtFUZEMt1GlxyYjzroZGKigqP57y1NQoeM6wvstSDGY4Mb/vUZrPBZrNpUJrgxLtO3Y5V/uBKTkyALc5Yd/pgfilQsnwGx8TEIPZUdmNPZdlut/NYpBFmODJkya8sn12yHIe8lTeU+eWlYUREREREREREJsGOICIiIiIiIiIik9BkDPAjhcUoKnE0/sIwS02yIyczpUnL6qUOQHD1APRTl2DrQZGhl/YCMMOAPPUgCpQsbV+WelBksL3oC98PCpQsbUaWephZxDuCjhQW49q7Z6DCUR7pTXuw2eMxf/qdATccPdUBaHo9AH3VJZh6UGToqb0AzLAs9SAKlCxtX5Z6UGSwvegL3w8KlCxtRpZ6mF3EO4KKShyocJTj8cui0TZTkxOSAAB5hTWY8kU5ikocATcavdQBCK4egH7qEmw9KDL00l4AZhiQpx5EgZKl7ctSD4oMthd94ftBgZKlzchSD7PT7J1rm2lF5xytR953BrW0PuoABFsPQC91Cb4eFBn6aC8AM6yQpR5EgZKl7ctSD4oMthd94ftBgZKlzchSD7PiYNFERERERERERCbBjiAiIiIiIiIiIpNgRxARERERERERkUmwI4iIiIiIiIiIyCS0HebbH7ft9f+1h1YDn40KX1mClXEWkDsMyOoOpLQFbBlAbCJQVQoU7QL2Lwc2vw1UFmtdUv/FJAJdRwO5lwApbZT6OI4DJ/cAh9cAG/8HVJ3UupSkJVkyzPwSGRszTGYkY7uXBfNL/pAxw2z7uqD/jqDy/IbnR8UAtjRluuC38JcnGJ1HAt3G1P1eU6E8bGlATh/lcdYtwFfjgfz12pXTX83PB4bNAOKbKb87K5X6JDZXHi3OB/Z8DRzbrG05SVuyZJj5JTI2ZpjMSLZ2Lwvml/wlW4bZ9nVD/x1B75zb8PzuE4DzH1amt34Q/vIEI/9XoOQAcORnpQfX1dNpjQfa/gHoNxmwZwKXvA58cCFQXaJpcRuU3Qe4bDZgtQN5XwC/vAIUblTmRduA9I5KL2+VjutAkSFLhplfImNjhsmMZGr3smB+KRAyZZhtX1f03xHUmM7XKz8P/wQU79a2LI3ZscD78zXlyrzyAuDyd4D4LOUUwJ0fR7R4fou2ARe+oIT499nAD1Pd5zsrgIINyoOoMUbJMPNLZGzMMJmRLO1eFswvBUqWDLPt646xB4vO7g2kdVCmt87VtiyhkP9L3XRCjnblaEzHq4HkXOWSnx//qXVpyMhkyjDzS2RszDCZkVHavSyYXwo1o2SYbV93jN0R1OnUmQSVxcDuxdqWJRRyzqubPrlPu3I0psM1ys/di5XrOomaSqYMM79ExsYMkxkZpd3LgvmlUDNKhtn2dce4l4ZZ44F2lyvTuz5VTiczoqhYZbCs3GFA73uV54rzgL3faFsuX6JilVHrAaBgI5DQAuh1B9B6iHJ9amWxMuDv5neB/cs0LSrpnAwZZn6JjI0ZJjMyWruXBfNLoWK0DLPt65JxO4LO/JNyqznAmJeU3LINsNo8nz/yM7D0TqC2KvJl8kdSKyA6TplOPgPoPw2ITTo14rvj1PWpFymPLe8D3z6kbXlJv4ycYeaXyNiYYTIjo7Z7WTC/FCyjZphtX5eM2xHkGmC2cBNQ+Lu2ZWkKR4ESiJgE5QEAB38AfnwKKDukbdkaEpdSN33OHcrI9UtuV27zJ2qUHt5+k4EzrwC63AAU7QQ2vqFdeUm/jJxh5pfI2JhhMiOjtntZML8ULKNmmG1fl4zZEZTWAcjupUxv0/Htphvy/oC6aVsG0OFq4Jy/AVd9Cqx/GVj3gnZla4il3rBSUdHAygeAvV/XPVd2CFj6NyClLZDZDTjnr8rI8MIZ+bKSfhk9w8wvkbExw2RGRm33smB+KVhGzTDbvi4Zc7DozqOUnzUVwI6F2pYlFCqOARtnAl/cDAgB9L4LOGOo1qXyrqq0brpot3uIVQLYMFOZtKUDmd0jUjQyEJkyzPwSGRszTGZkpHYvC+aXQslIGWbb1yXjdQRFxQDtr1Km875QTi2TRcFvyjWeANDlRm3L4kv5kbrp4l2+X3diR910UsvwlYeMR9YMM79ExsYMkxkZod3LgvmlcDBChtn2dcl4HUG5FwP2DGXaaAPM+qP8qPIzuY2mxfCpshgoPdz46yz1poUIW3HIgGTOMPNLZGzMMJmR3tu9LJhfChe9Z5htX5eM1xHkuqSkOA84vEbbsoRDUmvlZ3Vpw6/T0sFvlZ+p7X2/Jq1D3XTJ/vCWh4xF5gwzv0TGxgyTGRmh3cuC+aVwMEKG2fZ1x1gdQQktgJanBsnaNk/bsgTK4seubtEfaHa2Mn1Ix38gu/Z9Slsg9xIvL7AAPW5TJksPG++OUBQ+Rs0w80tkbMwwmZFM7V4WzC8FQqYMs+3rjrE6gjqPVEYar60Gts3XujSBSWgBXP25cu2mq9dWndcc6Hk7cOlMJfAVJ4CNs7Qppz+O/AzsXqxMD34GaHsZYIlWfk9oAQx7Gcjoqvz+83MAeGofnWLUDDO/RMbGDJMZydTuZcH8UiBkyjDbvu4Y6PbxFqDjdcrkvuWAI1/b4jRFZjdg4D+VaWelMoK61QbEJNS95uQ+YMlfAEeBNmX014r7lNsWtugHXPyacvenmgrAllr3mnUvAjs+0qyIpDcGzzDzS2RszDCZkUztXhbMLwVCpgyz7euKcTqCWg4Aklop00YcYLb8KLDkdqXhZ50NJGQDtjRA1AIlB4DjW4A9S4CdHysh17saB/DZKKDT9UCHq4D0TsoBqfQwcOQnYNMc4Og6rUtJemLkDDO/RMbGDJMZydbuZcH8kr9kyzDbvq4YpyPo4LfA67lal6LpaquBvM+VhzQEsG2u8iBqjJEzzPwSGRszTGYkZbuXBfNLfpAyw2z7emGsMYKIiIiIiIiIiKjJ2BFERERERERERGQS7AgiIiIiIiIiIjIJdgQREREREREREZkEO4KIiIiIiIiIiEyCHUFERERERERERCbBjiAiIiIiIiIiIpNgRxARERERERERkUlYtdpwXmGNVpsO2fa1rkMoy6B1XbTePgVGD+8XMxzadchQBjIfPbQ7ZpgiTQ/vlR7KoBd62Bd6KAP5Tw/vFz+7KOIdQalJdtjs8ZjyRTkAZ6Q378Zmj0dqkj3g5fRUB6Dp9QD0VZdg6kGRoaf2AjDDstSDKFCytH1Z6kGRwfaiL3w/KFCytBlZ6mF2Ee8IyslMwfzpd6KoxBHpTXtITbIjJzMl4OX0VAeg6fUA9FWXYOpBkaGn9gIww7LUgyhQsrR9WepBkcH2oi98PyhQsrQZWephdppcGpaTmWL4N0uGOrjIVBcKP1naC+tBZGyytH1Z6kGRwfaiL3w/KFCytBlZ6mFmHCyaiIiIiIiIiMgk2BFERERERERERGQSIbs0TAihTtfWKj+LSh2IiS0P1SYioqikHE6nUoGaU2NfOSqrEBMTo2GpAueoqPKoR1FpOU49ZRhFpQ61PbnUb2sUXhVVNaiorNa6GAGRpe2fOFmGqirlTggVVaeeKykzXD2Y4cjwtk8rKipQUVGhQWmazuFwoLz81PeGUw3/RHEZ7LZYDUsVOOaXAiXL92hHRRXKXY3+VJYdDn2MIxIIWY5FzHBkyJJfWT67ZDkOefsOF8r8hqwjqLKyUp3OL1V+TnnlM1hjNLtDfZM4nbXYfbAQAHAsTnlu7pdrYYsz1oG/vKLKox4PTV8IS5SxTgKrqa5R25NL/bZG4bVw2W+w2eK0LkZAZGn7VVU1+HnzXgCA/dRhtPCFhbDGRGtYqsAxw5HhbZ/OmzcPSUlJGpSm6crLy/H+++8rvzjyAQA/bsxDdDTzqwXmN3Jk/B4N+171+fj4eI1K1DSyHIuY4ciQJb+yfHbJchwqKSnxeC6U+TXW0YyIiIiIiIiIiJosLN2UOcl1P63G6giFqAViTp1Jtq20GQCgtvkFgMF6EFFervaA5iQq/8nISgIsBuv6q6mpa087j2lbFjP5Lk/5OSK7H2wGO6NAlrZfXQ1kJyrTzU+9Bc2TgWiDHVOZ4cgb0Fb5GXN0DVBsrDP6oiqrkFKr5DYnS3kuK5n51Qrzqw0jf4+ucdadjev6DLYe+QGINdaZ9bIci5jhyDNyfqX57JLkOBRTUal+p3P9bRZKBjucERERERERERFRU4Wsfy8uzvO/jneNuQKZ6Wmh2kREOCqrMO+rtQCAXjkXAADGjBkDu92uZbECVn9ALOuRHwAAIy/tA7vBxjoqPH4CN69b7/act7ZGwfO2X0eOHImsrCwNStN0srT9EyVlePDFhQCAnFP/lfn7hKuRnGissxOZYe1cNbQnsjKM9Rl8orgMP25U/u2Vdard3zf+aiQlGOszmPmlQMnyPbqotBwPTVfavivDIy/tg+TEBA1LFThZjkXMcGTIkl9ZPrtkOQ4VHDuBD+fPD9v6Q9YRZLFYPJ5LjLcjyWANJyYmBrGnThuLPXU5mN1uh81m07JYTaIOiHWqPsmJCbDFGevuZ95GS/fW1ih43varzWZj29eIsxaItiqD87lOyU1OjDfcMZUZ1o4t1mq4dm+3xaoDu7suwUhKMN53CeaXAiXL92hnLTwybI+L5bFII8xwZMiUXxk+u2Q5Dtliw3tNHi8NIyIiIiIiIiIyCXYEERERERERERGZhCZjgB8pLEZRiaPxF4ZZapIdOZkpWhdDc3w/yIz00u6B4Nq+LPWgyJGlzbAeoccM658s7YX1CD3m1xhkaTOsR+hFOsMR7wg6UliMa++egQpHeaQ37cFmj8f86Xea+qDJ94PMSE/tHmh625elHhQ5srQZ1iM8mGF9k6W9sB7hwfzqnyxthvUIj0hnOOIdQUUlDlQ4yvH4ZdFom6nJCUkAgLzCGkz5ohxFJQ5THzD5fpAZ6aXdA8G1fVnqQZEjS5thPUKPGdY/WdoL6xF6zK8xyNJmWI/Q0yLDmtW4baYVnXO0HrnbqfH29YPvB5mRPto9EGzbl6UeFDmytBnWI9SYYSOQpb2wHqHG/BqFLG2G9Qi1yGaYg0UTEREREREREZkEO4KIiIiIiIiIiEyCHUFERERERERERCbBjiAiIiIiIiIiIpPQdnjsQLUcAHS+AWh2NmDPVJ4rzweOrge2vgcc/lHT4plGtA1o0Q/I7A5knqU8klop89a9CKybrmnxiMLK6Mch5pcCcdte/197aDXw2ajwlSVUmGEyE9kyzPySmciWX4AZ1hHjdAQNeBLoOrru9xqH8jP5DOXRYQSw4Q1gzeOaFM9Ump0NXDZH61IQRZ4MxyHmlwJRnt/w/KgYwJamTBf8Fv7yBIsZJrORKcPML5mNTPkFmGGdMUZHUMfr6hrN7sXAT88CJ/cov6e0A/o+BLS5FOgxHjjyE7DnK82KahoVRUDh78Cx35Wf5z8CxDfTulRE4SPTcYj5JX+9c27D87tPAM5/WJne+kH4yxMMZpjMSJYMM79kRrLkF2CGdcggHUHXKD+L84CldwDCWTeveDewZCJw/TIgORdod7m+G44MjvwEvNXT/bnzHtSmLESRIstxiPmlUOp8vfLz8E9KDvSMGSbyZJQMM79EnoySX4AZ1iFjDBYdn6X8PLbFvdG4iBrg2GZlOiYhcuUyK1GrdQmIIk+W4xDzS6GS3RtI66BMb52rbVn8wQwTuTNShplfIndGyi/ADOuQMTqCTu5XfmZ0ASzRnvMtViCjqzJdsCFy5SIi8+BxiMhdp1P/iawsVk7z1jtmmMidkTLM/BK5M1J+AWZYh4zREbT5beVnSltg2MvKKWMuKe2Ai15RniveA2ycpUkRiUhyPA4R1bHGK6duA8CuTwFnhbbl8QczTFTHaBlmfonqGC2/ADOsQ8YYI2jfUuCHacogUu0uVx6uUcatdqUndNPbwNrngOpSbctKRHLicYiozpl/AmITlWkjnJIOMMNE9Rktw8wvUR2j5RdghnXIGGcEAcDv/wO+/gtQXqD8brUrD0C5dV5MPBCbrF35iEh+PA4RKVwDVBZuUu6YYRTMMJHCiBlmfokURswvwAzrjDHOCIq2AUOeB84cDuT/Biy/W2n4AJDZDTj3AWUk8tZDgMU3Ase3allaIpIRj0NEirQOQHYvZXqbzm9XWx8zTKQwYoaZXyKFEfMLMMM6ZIwzgvpNVhpN0U5g0XXAwe+AyhPK4+B3ynNFuwB7BtD/ca1LS0Qy4nGISNF5lPKzpgLYsVDbsgSCGSZSGDHDzC+Rwoj5BZhhHdJ/R1BMAtDlBmV609uAs9LzNc5KYNNbynTz8wBbRuTKR0Ty43GISBEVA7S/SpnO+wKoOqltefzFDBMpjJhh5pdIYcT8AsywTum/IyilrdLoAeDkXt+vK86rm05qHd4yEZG58DhEpMi9WPlvHWCcASoBZpjIxYgZZn6JFEbML8AM65T+O4KEqJtObOn7dfbMuunqsvCVh4jMh8chIoXrlPTiPODwGm3LEghmmEhhxAwzv0QKI+YXYIZ1Sv8dQUU7624t13kUYIn2fI0lCuhyozJdUQQU74pY8YjIBHgcIgISWgAtByjT2+ZpW5ZAMcNExs0w80tk3PwCzLBO6b8jyFlZd+pbVnfg0v8BaZ0AWJRHemfgD28COX2U1/w+CxC1GhXWRGKTgbi0uoflVFOy2t2ft8ZrW06iUJDtOMT8UlN0HglERQO11cC2+VqXJjDMMJFxM8z8Ehk3vwAzrFPGuH38j/8EktsCZwype9RUKPOstrrX7fwE+OXfGhTQhK753Pu1mz3/ojxctn0IrPx75MpFFC4yHYeYXwqYBeh4nTK5bzngyNe2OE3BDJOpGTzDzC+ZmsHzCzDDOmSMjiBnJfDlGKDtH4EOI4DM7spAWUIApQeB/N+UHb1/mdYlJSJZ8ThEZtZyAJDUSpk20gCV9THDZGZGzzDzS2Zm9PwCzLAOGaMjyCXvc+VB2nt/gNYlINKGDMch5pcCdfBb4PVcrUsRGswwmZEsGWZ+yYxkyS/ADOuI/scIIiIiIiIiIiKikGBHEBERERERERGRSbAjiIiIiIiIiIjIJNgRRERERERERERkEuwIIiIiIiIiIiIyCXYEERERERERERGZBDuCiIiIiIiIiIhMgh1BREREREREREQmYdVqw3mFNVptWhfb1xut94fW2ydz0kO7C0UZZKkHRY4e3i+2/dCuQ4YykH/08F6x3Yd2HTKUgfynh/eLbT+06zBiGSLeEZSaZIfNHo8pX5QDcEZ6825s9nikJtk1LYPW+H6QGemp3QNNb/uy1IMiR5Y2w3qEBzOsb7K0F9YjPJhf/ZOlzbAe4RHpDEe8IygnMwXzp9+JohJHpDftITXJjpzMFK2LoSm+H2RGemr3QNPbviz1oMiRpc2wHuHBDOubLO2F9QgP5lf/ZGkzrEd4RDrDmlwalpOZwgOVjvD9IDOSpd3LUg+KHFnaDOtBZiRLe2E9yKxkaTOsh/FxsGgiIiIiIiIiIpNgRxARERERERERkUmE9dKwYyeKwrn6sKioqkFFRSUAoLqkBABQUFAAm82mZbECVlFRgZJT5Y85VZ+CYydgi9XsRnFNYsQ2JJPCwkKtixAwWdp+UakDNdXKHQRqTt1IoPD4CVRUVGhYqsAxw9ox4r6Xpd3LUg8jtiGZGHH/y9L2ZamHEduQLIy472Vp97LUI9xtyCKEEKFY0f79+3HGGWcAAAa0DcUatfddntYlCA3Z3o99+/ahdevW2hZGQgUFBWjWrJnWxQgpI7f92logv1SZzknWtiyhwgyHjyyfwbK0e1nqUR/zG17MsL7IUo/6mOHwYX71RZZ61BeO/PLSMCIiIiIiIiIikwjLtRKynEkjC74fZFaytP2dx7QuARkJ272+yFIPihxmWF9kqQdFBvOrL7LUIxx4RhARERERERERkUmEbIwgp9OJQ4cOhWJVuhMXFweLxaJ1MQIihEBlZaXWxQiLFi1aIDo6WutiSKe2thbHjhm/21yWtu+tHkY8FnnDDIeeLJ/BsrR7WerhDfMbHsywvshSD2+Y4dBjfvVFlnp4E8r8hqwjiIiIiIiIiIiI9I2XhhERERERERERmQQ7goiIiIiIiIiITIIdQUREREREREREJsGOICIiIiIiIiIik2BHEBERERERERGRSbAjiIiIiIiIiIjIJNgRRERERERERERkEuwIIiIiIiIiIiIyCXYEERERERERERGZBDuCiIgkNHXqVFgsFgwZMiQs69+4cSNGjhyJ5s2bw2q1wmKx4Oyzzw7LtojMZuzYsbBYLBg7dmxY1v/tt9/i8ssvR1ZWFqKjo2GxWDBixIiwbItIduH+vCWiyHvzzTdhsVjQpk2bkK873J/x/rJGcmNCCMyfPx/vvfce1q9fj/z8fERHRyM7OxvNmzfHeeedh4EDB2LYsGFITk52W3b69OkoKirCiBEjDPvHRlFREVauXIl169Zh/fr1WLduHY4cOQIAmD17dqON4ciRI1i1apW67Pr163H8+HEAwPLly/kBRCHFvAaX1507d+KTTz7BihUrsGHDBhw5cgRWqxUtW7bEwIEDMXHiRPTu3TsCNQm9vLw89O/fHyUlJQCA9PR0xMTEIDMzE4Ac77/RMb/B5XfRokXq8vv27UNBQQEqKiqQmZmJnj17YuTIkbjppptgtUb0a1RIrFmzBkOHDkVNTQ0sFgsyMjIQHR2NtLQ0AMoftYDyRTUcX4DNhlkMLou+XHbZZfjyyy8BAIMHD8aKFStCVGKihjHTwWV67NixmDNnTqPbqa6uNuRnbCS8+eab2LNnD4YMGRLc3/8iQk6cOCEGDx4sAKgPq9Uq0tPThdVqdXt+9uzZHsvn5ub6nGcUs2fPdqtnY3U+3aOPPupz+eXLl4e9/GQezGtwef3uu+88lklKShKxsbHq71FRUWLKlClhK7/reDF48OCQr/vBBx8UAET79u3FgQMHPObL8P4bGfMb/Odtt27dPPJrs9ncnuvVq5c4cuRIWMo/ZswYAUCMGTMm5Ou+/vrrBQDRv39/cezYMY/5/F4ROsxi8Fn0Z53h+JwLRDg/b0lfmOngM+36fLPZbCI7O9vno6amJvyVacCCBQtEp06dxNChQ0O+7mA/411t8NFHHw2qHBG7NOzmm2/GypUrER0djfvuuw/bt29HZWUljh07BofDgd9++w3PPPMMevbsGakiaSInJweXXXYZJk+ejAULFgS0rMViQevWrXHllVfisccew8yZM8NUSjI75lXR1LxWV1cjOjoaI0aMwIcffojCwkKcPHkS5eXl+OmnnzBgwADU1tbi8ccfx6xZs8JYg/DYuHEjAODKK69Ey5YtNS4NnY75VQTzeXvttdfi9ddfx6ZNm1BeXo6TJ0/C4XDg4MGDmDZtGqKiorB+/XqMGTMmTKUPH1d+R40ahfT0dI1LIzdmURFMFk935MgR3HvvvUhNTUWXLl1CVEIi/zDTilBk+vrrr8eRI0d8PqKjo0Nc6sBcddVV2Lp1K5YuXappOcIpIudb7dixA4sWLQIAPPHEE3jooYfcC2G1okePHujRowceeOABOByOSBQr4m666aagrgV8+OGH8eijj6q/79mzJ/hCEZ2GeVUEk9f27dtjy5Yt6NChg9vz0dHROPfcc7F06VKce+652LBhA/75z3/i1ltvDUGJI6e8vBwAkJiYqHFJ6HTMryLYz1vX5VGna9GiBR555BFUVFTgn//8J7766iscOHAArVq1avK2Io35jQxmURFsFk83ceJEnDhxAjNnzsQ777yDLVu2hGzdRA1hphWhzjRpJyJnBP3666/q9JVXXtno6+12uzrtGoBt7969AIBx48bBYrG4PbxZvHgxrrnmGrRs2RJxcXFIS0vDoEGD8Oqrr6KqqsrrMkOGDIHFYsHUqVNRVVWFp59+Gj169EBCQgLS0tJw8cUX44svvgig5u6C7dnUume0TZs2sFgsePPNN32+pqHBr+ovX1JSgkmTJqFTp06w2+3IzMzEiBEj8OOPP4avAuQX5lURTN5atWrl0QlUX2xsLEaPHg0A2LVrF06cONHkbQWjoKAADz/8MM455xykpKTAZrOhXbt2uPXWW7Fp0yaP17sy7BqLYdq0aW7vres9CfT9p9BhfhXh/rzs16+fOn3w4MGwbsuXPXv24O6770a3bt2QmJiI+Ph4dO7cGXfddRf27dvn8XrXe+j6J9Lp76/r89vlwgsvdJvP8YICwywqQpnFefPmYeHChRg8eHDE/4HyxRdf4OKLL0ZqaioSExPRs2dPPPvss6iurvZr+RUrVuC6665T35vMzEwMGzYMs2fPhtPp9Hj9HXfcAYvFgmuvvdZjXnV1NZKSkmCxWJCVlQUhhMdrLr30UlgsFkyZMsXteVf7WbFiBUpKSvDwww+jc+fOsNvtyMjIwBVXXMHv4j4w0wqt/x4NVv2/R0tLS/HII4+ge/fuaqZcn5H+DBa9atUqDB8+HJmZmbDb7ejUqRMmT56M0tLSgAabnj9/PoYMGYL09HTEx8fj7LPPxksvvYTa2lq317nWuXLlSgCe38Prl98vQV1Y5qd58+ap1w5+/fXXAS373HPPiezsbBEVFSUAiOTkZI9rCOsrLy8X1157rdv1isnJycJisai/9+vXTxw/ftxjW67r7SZNmiQGDhyoXveZmprqtr5gr8erDwFcU3m6vLy8gK7lrz/GUF5eXsDb8+e61oaueXQt/8ILL4hOnToJACI2NlYkJye7jZsya9asgMtGocO8+hZMXk83Y8YMdX0FBQUe8+tfg92UsToaG7NgyZIlbvsqJiZGJCQkqL/HxsaKOXPmuC3Tp08fkZ2dLWJiYgQAkZCQ4PbeTps2LaD3n0KP+fUtlPn9+9//LgAIi8Ui8vPzPeYH+3nb2PgB77zzjoiLi1O3ERcXJ+x2u9uYRl999ZXbMq730Nf7e8stt4js7Gx1HWlpaW7z+/TpE3A9zIxZ9K0pWSwsLBTNmjUTcXFxYtu2bW5lb2hsnmA/S4XwHKMzNTVVHQ9m0KBBYtKkSQ2W45577lGXtVgsIjU1VURHR6vPDR06VJw8edJtmY8++kgAEBkZGaK2ttZt3unjEP72229u86uqqkR8fLwAIJYtW+Y2z7XMe++9J9q3b6+O1eJ6vevz//TjBzHTDQkk08GOj+NaHmhaN4br79Hnn39edOzYUW3zrv3j+sx2HTtyc3O9rmfGjBlu70dKSoo6FmiXLl3Eiy++6HP5+vvgr3/9q/o38Onv0c033+y23Ny5cxv8Hp6dnS327dvn976ISEdQXl6euqO6d++uHsAD4e/gWqNHjxYARLt27cS7774riouLhRBCOBwO8cknn4h27doJAGLEiBEey7qCk5KSIuLi4sRrr70mHA6HEEKIffv2uQXyk08+CbgO3gTzxdSoHUEpKSkiLS1NzJs3T1RXVwshhNi8ebO6/61Wq1i3bl3A5aPQYF59C+UfkldffbUAIJo3b+7xJU+I8HYEbdiwQf2jccKECWLz5s3qoHx79+4VEydOVLP4888/eyzf2CB1MgyGaFTMr2/B5rekpERs3LhR3H///eo+Pv1Lmks4O4K+/vprERUVJaxWq3jggQdEXl6eqK2tFbW1tWLr1q3iuuuuU/9o2Lt3r8fyjb2/wf7RTApm0bemZPHGG28UAMQTTzzhUfZwdgR98skn6vLXXXed+kdWeXm5eOWVV9z+gPRWjpdfflld/rbbbhOHDx8WQghRWloqXnzxRbVD6frrr3db7tixY2r7+eWXX9zmPf7442rGAYgXX3zRbf63334rAKWD2PVeurjKkpaWJrp27SqWLVsmnE6nqK2tFT/99JP6j9rc3FzhdDoD3l8yY6Z9a0pHUKtWrUSHDh1EXFycSEpKEmeddZa46667xPbt2/1aPtiOoMTERJGTkyMWLlwoqqqqhBBC7N+/X5SVlQkhGu4I+v7779VOvYsvvlhtC9XV1eLDDz8U6enpIi0trdGOoLS0NBEbGyteeOEF9T0uLCwU48ePV+u4dOlSj+VDNVh0xO4aNmHCBLVCFotFnHPOOWLixIli1qxZYuPGjV7/EKrPn+CsWrVKABDNmjXz2Ru2f/9+9b/epx9Y648C7+2sFKfTKQYNGiQAiG7dujVaZ3+YsSMIgPjmm2885peXl4sOHToIAOKPf/xjwOWj0GFevQtVR9APP/ygfoD4unNYODuChg4dKgDlv0W+3HnnnQKAuPLKKz3msSNI35hf75qS39WrV6vL1X9ER0eLW265RZSXl3tdLlwdQU6nU/2c/O9//+tz+T/96U8CgLjrrrs85rEjKHKYRe8CzeKnn34qAIizzjpL/YOtftnD2RHUtWtXdRveOkZee+01df2nl6O8vFykp6cLAOKGG27wuv76ZwevXbvWbd7ZZ58tAIh//etfbs9feOGFAoB45JFHBAAxfPhwt/nTpk0TAMSQIUM8tufaVlZWljh69KjH/A0bNqiv+e6777yW2cyYae+a0hHk+ixNS0tzO0MuNjZW/Oc///Fr+aZwvQfR0dFi/fr1Pl/XUEfQsGHDBADRtWtXUVFR4TF/2bJlahkb6ghqaJ/17t1bABDjx4/3mGe4jqDq6moxZcoUt0sP6j+aNWsm7rnnHp+3Yg2kE+Kee+5psCxXXHGFACCefvppt+ddO7V169Y+g/z111+rZd6wYUPDlfZDJDuCghWqjqD+/fv7XP71118XgHJ6XFFRUQhKTU3BvHoXio6g/Px8df906NBBlJSUBF0ub3x1BLmOG1ar1euto13Wrl0rAOW009Nv4cmOIH1jfr1rSn7XrVunnm7tOuUbgJg4caLXs21Cxddn6fLlywUAkZmZ2eB/6+fPny8AiM6dO3vMY0dQ5DCL3gWSxaKiItGiRQsRFRUlVq9e7bXs4bpt+2+//aaWdcmSJV5f43Q6RcuWLb2Wo/7ZRL7OHqmpqRHNmzcXAMS9997rNs91Sdnll1+uPldRUSFsNptISEgQRUVFIjY2VqSkpLh9Tg8ZMkQAENOmTfPYnqs8kydP9lnvtm3bCgAN/jFuVsy0d4Fk+qWXXhL//ve/xZ49e9R2W1ZWJj766CNx5plnquuaP39+0OXyxvUe1M+VN746guqfrddQfV2X5TXUEdTQe+Tq0D3vvPM85hnu9vFWqxWPPfYYDh48iLfffhvjx49Hz549ERsbCwDIz8/Hiy++iLPOOgs//fRTk7bx/fffAwBmzZqFnJwcn49vvvkGANQBu07nGmTLm4EDB8JqVW62tnbt2iaV0+yGDh3a6Lza2lqsX78+UkWi0zCv4VFaWoo//elP2Lt3L5KSkvDhhx9G/M49rv1eW1uLrl27+tzvf/jDHwAAZWVlOHbsWETLSMFhfkOnV69e6q1sHQ4HduzYgb/+9a947bXX0K1bN3z66acRLY9rvxcXF6NFixY+9/uECRMA+N7vFBnMYvDuu+8+HDp0CBMnTnQbpD0SXHW1Wq0YOHCg19dERUVhyJAhDS7funVrdOzY0etroqOj1e++p+9b1/PffvutOqD0Dz/8gIqKCgwYMAApKSno27cviouLsW7dOgBARUUFVq9eDUAZ8N2Xvn37+pzXokULAMDx48d9vsasmOng3XnnnfjrX/+K3NxcdeDp+Ph4XH311fjxxx/Rtm1bAEr2hRBhK0f//v2btNwvv/yilmvw4ME+X+fruFDfueee6/M9ikQOI3L7+PpSUlIwevRo9Y45FRUV+O677zBjxgwsWrQIhYWFuOaaa7Bjxw7YbLaA1n3o0CEAwMmTJ3Hy5MlGX++6herpWrZs6XMZm82GjIwMHD16FPn5+QGVjxQN7d/687h/tce8hk5ZWRkuv/xyrFmzBomJifj888/Rs2fPiJfDtd9ra2tx9OhRv5bxte9J35jf0IqKikL79u3x73//G+3atcN9992HP//5z9i+fTuaN28ekTK49nt1dbVf+ZX19sVGwyw2zTfffINZs2ahVatWeOqppyK2XRdXXTMzMxEXF+fzda1atWpw+Yb2bf3lT9+3gwYNQnR0NE6ePImff/4Z/fr1w/LlywHUdRINHToU3377LZYtW4bzzjsPP/zwAyorK2G32xvs7ElKSvI5z9VB4O8d0cyImQ6PjIwM/OMf/8CECROwd+9e/PLLL+jVq1dYttWsWbMmLVdQUKBOuzprvGks94D2OYzYGUG+2Gw2XHTRRfj0008xZswYAMCBAwfw5ZdfBrwuV2/5q6++CqFc9tbgo6HboBORJ+a1aVydQKtWrUJCQgIWL16MAQMGaFIW137Pzs72a78LIXjbaEkwv6EzceJExMXFobS0FO+//37Etuva73379vU7v6Q/zKJ/XGe2Pfvss7BYLCgtLXV7uOrudDo9npNBcnIyevfuDQBYtmyZ28/6HUHe5vfv3189S4XCj5kOnfPPP1+d3r17d9i24zobKRi+zuYxCs07guq77bbb1Olt27YFvHxOTg6A4E+FPnjwoM95lZWV6mUSTe1JNCpXz2RFRYXP1xQXFze6nob2b/15Ztu/RsO8+sfVCbRy5UrEx8dj8eLFGDRokCZlAer2e2FhIcrKyjQrB2mL+Q2OzWZDeno6AGDnzp3/z96dxzdRJm4Af9IkbdL7gpb7vkUUUFAOAQ88cEVRxBN00V13fyrousoiive1Aup64aKioogouqioIIcgoBwqKAoihXIVaKGlR9IjfX9/DJk2NGlzz8yb5/v55NNpZibzvpP3mUnezBG15YZrvZN+MIu+7d69GwBw7bXXIiUlpcFjzZo1AIA1a9aozy1evDhsy3fXtbCwEFVVVT6n87Xu3PPv27ev0eW4x3tbt+7Tu5YvX47y8nJ8//33SE9PV4+SGDhwIOx2O7799ltUVVU16Cii6GOm5dasWTN12H30ljeNrX+90FVHUP1rZZx8CGZcnFLUxn7dcp/r9+mnn4ZUjlWrVvlczurVq1FTUwMA6N+/f0jLMZqMjAwAwN69e72Or62t9es8U/dhrY2Ni4uLw+mnnx5EKSlamNemlZeX4+KLL8aqVauQlJSEzz//vNHziaPBvd5dLheWLFkSkWX48/6Ttpjf0JSWlqqHhzd2aHe4udd7QUFBxK7r4P6Fk/mNDmZRv9x1rampwerVq71OU1tbi5UrVzY6/759+7Bjxw6v07hcLvWz7xlnnNFgvLsjaO3atfj6669RXV2Nc845R20b8fHxGDRoECoqKrBs2TJs2LDBYz6KPmY6NOvXr1eH3dcL0pPTTz9d3U/6yn5T40IVrs/ZUekIysvL87kBrG/u3Lnq8MnnA6ampgIAiouLfc7v7oH9+eef8fLLLze6rPLycp+9+/n5+R5lcautrVXPUe7Zsyd69+7d6DJk476eyaJFi7w2vLlz5zb5qweg/HLjLRxOpxPPPvssAGDkyJFIT08PqbwUHOY1PNydQO7TwfTQCQQAXbp0US9gN3Xq1CaP4gvmInX+vP8UGcxv6NwfjhvzzDPPqNP5c0HIcBk+fDg6d+4MAJg8eXKjRykAzK+WmMXQNXVKjHufes4556jPjR49OmzLP/XUU9GjRw8AwGOPPYba2toG07z++us+P/uef/75yMrKAgBMnz7d6zSvvvqqelTBNddc02D84MGDYbVa4XA41Pfh5KN93J0+Dz/8MGpqapCcnKy7L/cyYKZD11THxdGjR9WytWnTRpcHBWRmZqqZe/bZZ72u/2+++cZn53E4hG0/7d/NxUKzePFiERcXJy6++GIxd+5ckZeXp46rqqoSmzdvFhMmTFBvF3fmmWc2uC3qddddJwCIs88+Wxw9etTnsm666SYBQJhMJjFp0iTxxx9/qOOcTqdYt26duOeee0RWVpbYu3evx7zuW7GlpaUJm80mZs+eLRwOhxBCiPz8fDF27Fi1jB999FFQ6+LIkSMeD/frvfDCCx7Pl5eXN5jX5XJ5TLN582Z1/o8//thjnNPpbDC/+3bSADzeA38tW7ZMnX/ixImisLBQCCFESUmJmDFjhoiPjxeZmZleb3krRN3t+tLS0kRmZqb44IMPRHV1tRBCiF9//VWMGDFCABBms1ls2LAh4PJReDCvdYLNa3l5uXr71uTkZPHNN98EvGz3bSsR5G2cfd0+Xgghtm7dKpKTk9XbS3/88cfquhNCiH379om33npLjBgxQkycOLHB/E3dttLf95/Cj/mtE2x+33zzTXHppZeKDz/8UBw6dEh93uVyiZ9++knccsst6msNGjTI661fQ93f+rp9vBDKvthisQgAYsCAAWLZsmWiqqpKHf/HH3+Il19+WfTv31888sgjDeZv6vbFgwYNEgDEmDFjvH4WIf8wi3VC+ezbGH9uHx/qvvSjjz5S57/66qvV9edwOMTLL78sEhISRHp6us9yvPDCC+r8f/nLX9TbipeXl4vnnntOWK1W9bV9cWfS/di6davH+HXr1nmMv+iii3y+lj/rIly3ppYNM10n2Ey/9dZb4vLLLxcLFy702L9WVFSIRYsWia5du6qvNX/+fK/Ldu8fg+3GaGof6Obr9vFCCLF69Wr1FvIjR44UO3bsEEIIUV1dLT788EORnZ0tMjIyfM7f2D7en+VPnTpVABCdO3cW+/bta7QejYlKR9AXX3zhsYECoHYauFei+9G3b1+xf//+Bq+xatUqdVqz2SxatGgh2rVr12DlVFZWiokTJ3q8ZnJyssjIyBBxcXEez5+84tzBmTJlihg8eLAAIKxWq/pGuh/3339/0Ovi5PXg6+Ft45uXl+f3/N4ad6gfTIUQ4oYbbvBYTnp6urpeb7/99kYbtjt4M2bMEN26dRMAREJCgkhLS1Nfz2QyidmzZwdVNgoP5rVOsHmdO3euOs5ms4mcnJxGH99++22DZUeyI0gIIdasWSNyc3PVZZjNZpGVlSXsdrtH3YLpCPL3/afwY37rBJvf+tkDIJKSkkR2draIj4/3eH7EiBGiqKjI67Ij2REkhBCLFi0SKSkp6jKsVqvIysoSCQkJHmV89NFHG8zb1Ifgt99+2+N1W7VqJdq1aycGDRoUcD1iGbNYJ9gsNiUaHUFC1H3pcj8yMjLUztghQ4aIKVOmNFqOyZMne3zOrT8/ADF8+HBx/Phxn8ufNm2aOm3z5s0bjK+urvbYHjz99NM+X8ufdcGOIO+Y6Trh3L9mZWUJs9msPpeQkCBefPFFn8vWQ0eQEELMnDnToy7p6enqPviUU05Rx3fr1s1nHYLtCNqxY4ew2WwCgIiLixM5OTlqOzq5Y7AxUTk1bOTIkfj999/x3HPP4aqrrkKPHj2QkJCA4uJiJCYmokuXLhg7dizmz5+PDRs2eL0V29ChQ/HZZ5/hvPPOQ3p6Og4dOoQ9e/Y0uJBWfHw8XnvtNaxduxYTJkxAp06d1LsJNG/eHMOGDcMDDzyALVu2+LytW3x8PL7++ms8/vjj6NatGyorK5GWloZzzz0Xn332GR555JGIrCcjePPNN/Hcc8/htNNOg91uR21tLQYNGoQFCxbg+eef9+s1MjIy8P333+O+++5D27ZtUVlZiczMTFx66aX49ttv1btEkDaY19DVP3zc6XTi0KFDjT6aOr0jEgYNGoQdO3bg3//+N4YOHYr09HQUFxfDbDajR48euP766zFv3jzMmjUr4Nf29/2n8GN+Q3fJJZfgtddew3XXXYdevXrBbrfj2LFjiI+PR/fu3XHDDTfgs88+w9dff61eMDraRo8ejZ07d+LBBx/EmWeeieTkZBQXFyMhIQF9+vTBxIkTsWjRItxzzz0Bv/b111+Pt99+G4MHD0ZiYiIOHjyIPXv2+HXqN9VhFuXx6KOP4tNPP8WIESOQmpqKyspK9OjRA08++SS+/vrrJu/ONWPGDCxfvhxjxoxBTk4OysrKkJKSguHDh+P111/H0qVLG73WWP3r/Xi79o/FYsGQIUManYZCx0yHbvjw4XjssccwatQodOrUCVarFSUlJUhNTcUZZ5yBe++9F7/++iv+9re/Rb1sgZo0aRJWrlyJiy++GBkZGXA6nWjfvj3uv/9+rF+/Xj0NLhKXOunSpQtWrFiBP/3pT2jWrBmKiorUduTP6e1uJuEuJWHYsGFYtWoVHnzwQZ/n8lLw2rdvjz179uCNN97AhAkTtC4OGRzzSmRczC+RPjCLRHJhpvXhuuuuw7vvvoubb74Zc+bM0bo4XunqrmFEREREREREREa0Y8cOfPTRRwCACy+8UOPS+MaOICIiIiIiIiIiPzzwwAP4z3/+g/z8fPWSEOXl5Xj//fcxfPhwOJ1OdO/ePax3Mgw3i9YFICIiIiIiIiIygi1btuCTTz7B7bffDqvVipSUFBQXF6udQq1atcIHH3wAq9WqcUl9Y0cQEREREREREZEfJk+ejJYtW2Lt2rU4ePAgjh49ipSUFHTt2hWjRo3C//3f/2l2Qwl/8WLRREREREREREQxgtcIIiIiIiIiIiKKEewIIiIiIiIiIiKKEewIIiIiIiIiIiKKEewIIiIiIiIiIiKKEewIIiIiIiIiIiKKEewIIiIiIiIiIiKKEewIIiIiIiIiIiKKEZZwvZDL5cKBAwfC9XKaEUKgsrLS47mEhASYTCaNShQ+stQjKysLcXHswww3WTLsjRHbvrdtkSxatmwJs9msdTGkUltbi6KiIq2LETJZ2r3MnyWY38iQZR8sS9uXpR7eMMPhx/zqiyz18Cac+Q1bR9CBAwfQtm3bcL0ckU+HDx9Gs2bNtC6GdJhhipb8/Hy0adNG62JIpaioCM2bN9e6GBQDmN/I4D6YooUZDj/ml6IlnPnlYRVERERERERERDEibEcE1Te4QyReNTpqa4HDZcpwbqq2ZQmXNXlal4CMxsgZrk+Wts/3g2KRkds9P0tQKNj2tSdLPepjhqOD+dWeLPWoLxL55RFBREREREREREQxIiJHBLm99sy9yMpIj+Qiwq64zIFpL34KoK4H8c7xo5CcaNewVIFzVtVg0fKfAACjcwYCAMaOHQubzaZlsQJWWFiInj17al2MmGXEDMvS9p1OJxYsWAAAsB5aDwC4fEQf2OIjutkOu6Jjxbjlnqe0LkZM2rZtG7Kzs7UuRkBkafeyfJZgfrVlxH2wLG1flnoww9phfrUjSz0ind+IfrLKykhHs+zMSC4i7KzxFbBYldViObF2sjMzkJKcqGGpAuesrIbNlgAAsKWkAACaNWtmuC/DpC0jZliWtu90OpFyovwoUerTLCsDtgSrhqUiI8nOzjbchfVlafeyfJYgbRlxHyxL25elHqQd5lc7stQj0nhqGBERERERERFRjGBHEBERERERERFRjNDkpPuCwhIUlzq0WLSH9BQ7crPTgppXL3UAQqsHUaDY9vWF7wfFKlnaviz1oOiQpb2wHuHH/BqDLG2G9Qi/aGc46h1BBYUluHLS83A6KqK96AZs9kQsnHVHwCtcT3UAgq8HUaDY9vWF7wfFKlnaviz1oOiQpb2wHpHB/OqfLG2G9YiMaGc46h1BxaUOOB0VeOQiMzpka3cXkLzCGkxbUoHiUkfAK1svdQBCqwdRoNj29YXvB8UqWdq+LPWg6JClvbAe4cf8GoMsbYb1CD8tMqxZjTtkW9A9V+u7gLhCmlsfdQBCrQdRoNj29YXvB8UqWdq+LPWg6JClvbAe4cb8GoUsbYb1CLfoZpgXiyYiIiIiIiIiihHsCCIiIiIiIiIiihHsCCIiIiIiIiIiihHsCCIiIiIiIiIiihHaXh7bXwnpQLvzgVaDgOxTgORWQJwZcBwFCrcAOz4Edn+pdSmb1vVKYNizTU/32bXA/m8jXx6iaJAlvzLhtohikSzbIuaXApF1CtDuXKBZbyCtA2DLAuKTgaoyoPgPYO8KYNvbQGWJ1iVtnCz5BZhhCgwzrC8S5dcYHUE3bATi6l3Ju8YJ1NYAyS2UR/uRQP4KYOlfAZdTu3L6q9YFOIt8j3dVRa8sRJEmW35lwm0RxRLZtkXML/mj+1ig1/i6/2ucysOWAeT2Vx6n3Ax8ORE4vFm7cjZFtvwCzDD5hxnWJwnya4yOoDgrcPgHYPtCYN8qoHSv8nxya6Dv7UD3cUDb4cDQJ4AVk7Utqz/KDwDvDda6FETRIVt+ZcJtEcUS2bZFzC/54/CPQOk+oGCDcvRA1XHleUsi0OFCYOBUwJ4NXDAbeH84UF2qaXF9ki2/ADNM/mGG9UmC/BqjI2jxOODguobPl+0DvrlX6U3seT3Q5Qrg+6eB8oPRLyMRecf8EpEecFtEsej3j7w/X1OhjKs4AlzyDpDYTDn9ZOfHUS2e35hfilXMMEWIMS4W7a3R1Lf9/brhZqdGtixEFBjml4j0gNsiooYO/1A3nJSrXTmawvwSeccMU5CM0RHUlJrKumGTWbtyEFHgmF8i0gNuiygW5Z5ZN3w8X7tyhIr5pVjFDFOQjHFqWFNanlU3fPQ37crhL1sWcPmnQHpHpaFXHAYObQJ+mw8cXK916Yiiy2j5lQm3RUR1jLYtYn4pWHHxQGJz5TSSfncpz5XkAXuWaVuuUBgtvwAzTMFjhrUnQX6N3xEUnwqc9jdl+OB3QMkubcvjD2uicgtAZzFgtQCpbZVHl8uB7QuAb+4DhEvrUhJFnhHzKxNui4gURtwWMb8UqJu3AxZbw+cLNgBf3wHU6v8uN14ZMb8AM0yBY4b1Q4L8GrwjyAQMnwkk5Si3oPv2Aa0L1LjyQ8CmmUDeF0DxLiWspjig+elAv8lA6yFAt7FAdQWw9kGtS0sUYQbLr0y4LSKqx2DbIuaXguU4ApgTAGuS8gCA/WuB7x5X7oBjSAbLL8AMU/CYYe1JlF9jXyPo7OlAu/OU4W+n6f8wsv2rgU2zlHK6e2xFrXIY2ec3ALu/VJ7reQOQ2l6rUhJFh9HyKxNui4jqGG1bxPxSsN4bDLxzBvBGT+CtvsC6R4GsnsDl/6s7vcRojJZfgBmm4DHD2pMov8btCBowFThlgjK89iHlECxDE8D6x5TBOHNdIIhkJF1+ZcJtEcUQ6bZFzC/5yVkEbH0NWHIjIATQ706g7QitSxUY6fILMMPkN2ZYh4yVX2N2BA2YAvS5VRle9yjw8+validcju8BHEXKcGpbbctCFCmy5lcm3BZRLJB1W8T8UiCO/KRcXwQAelyrbVkCIWt+AWaYAsMM64uB8mu8awQN+BfQ5y/K8PrHlZ5QIjIG5peI9IDbIqI6FYeUvzo/jUHF/BJ5YoYpCMbqCBowta7ncP3jwJZXtS1PuKW0BexZyvDxvdqWhSjcZM+vTLgtIpnJvi1ifilQKW2Uv9Vl2pbDH7LnF2CGKXDMsH4YKL/G6Qiq32jWPSpnz+HAqcrfWheQ/7W2ZSEKp1jIr0y4LSJZxcK2iPklN1OcchHTxrQcBDQ/TRk+sD7iRQpJLOQXYIapDjNsPAbKrzGuEeRxDuHDxmw0ya2B0Z8o5266e20BACbldnMXzQU6XKg89eu7QMkuTYpJFHYy5Fcm3BZRrJJhW8T8UiCSWgJXfO6lvQBIagH0uQ0Y+ZryZdN5DNg6R5ty+kOG/ALMMAWGGdYXyfKr/yOCkloCff6qDNe6lGH3/95seQ3YMjs6ZQtU89PqemxrnEB1OWBNAiy2umm2LwDWPqhF6YjCT6b8yoTbIoo1Mm2LmF8KRHYvYMgTyrCrEqgqU9qKNalumuP5wNK/Ao4j2pSxKTLlF2CGKTDMsL5IlF/9dwSZ6h20FGcGEps3Pr01MbLlCZbjCPDtA0DzvkBWT8CeCSSkATWVwLGdwKFNSqM5tFHrkhKFjyz5lQm3RRSLZNkWMb8UiIpDwNLbgJYDgWanAUk5gC1DOdWkdB9w9Fdg91Jg58fKF0y9kiW/ADNMgWGG9UWy/Oq/I6hsHzC7ndalCJ2rEvhlrvIgihWy5Fcm3BZRLJJlW8T8UiBqq4G8z5WHkcmSX4AZpsAww/oiWX6NcY0gIiIiIiIiIiIKGTuCiIiIiIiIiIhiBDuCiIiIiIiIiIhiBDuCiIiIiIiIiIhiBDuCiIiIiIiIiIhiBDuCiIiIiIiIiIhiBDuCiIiIiIiIiIhiBDuCiIiIiIiIiIhihEWrBecV1mi16LAtX+s66KUMFHv00O70UAa90MO60EMZKPbood3x8wRFmx7eK7b78L6GDGUg/+nh/WLbD+9rGLEMUe8ISk+xw2ZPxLQlFQBc0V68B5s9Eekp9oDn01MdgODrQRQotn194ftBsUqWti9LPSg6ZGkvrEdkML/6J0ubYT0iI9oZjnpHUG52GhbOugPFpY5oL7qB9BQ7crPTAp5PT3UAgq8HUaDY9vWF7wfFKlnaviz1oOiQpb2wHpHB/OqfLG2G9YiMaGdYk1PDcrPTDL+hkqEORMFg29cXvh8Uq2Rp+7LUg6JDlvbCelCskqXNsB7Gx4tFExERERERERHFCHYEERERERERERHFiLCdGiaEUIdra5W/xWUOWOMrwrWIqDh2vBxVVcpVu51VJ54rLYerVsNCBcHhrEKFuwIVynvgcOjj/MdAOJ3OBs/Vb2sUPrJkWJa273A4UHGi/O6N0bGSctht8RqWKnDFZQ61Pbkxw+HnbZ06nU6v21A9k6Xdy/JZgvnVlrOqBs7Kaq2LERCHswquEw295sS1V4vLKgzX9plhCoQsn6GLSysa5NdRWQWr1aphqQIny3Yo0vkNW0dQZWWlOny4TPk77cVPYbFqdof6oFRV1WDDtj0AAPuJohfOWASL1axhqQLnctVi1/5C5R/7HvX5xMREjUoUnNLS0gbP1W9rFD6yZFiWtl9RUYH33ntP+cdxGADw3dY8mM3GOpCzprpGbU9uzHD4eVunCxYsQEpKigalCZ4s7V6WzxLMr7YWLf8JNluC1sUISIWzSt0HF50o+n2zFsEUxwxrgRmODhk/Q7vzO/+LjbAlGOvHGFm2Q5HOr7HWBhERERERERERBS0i3ZS5qXV/LcbqCEV1NZCTrAy3OPFDaotUwGywetS46npAc5OVX1UtBWuBeGP16FqdlRjcQRlek6dtWWKJkTMsS9uPq6xCWq1S/txmynPNUgGTwbrva2rq2tPOIm3LEivc20zrofVAibGOJpCl3UvzWYL51YT7887onIGwGeyoPlRUqEfjuvfBzVKYYa0ww9Fn5M/QohawnjgVcntZcwBAbYuzAYMdVS/LdijS+TXY6iAiIiIiIiIiomCFrZ8yIaHhr453jh+F7MyMcC0iKo6VluPemYsAALknfgH4xy1XIDXZWD2hxWUVuG+WUo9mJ+oxdmR/pCYnaViqwB0pOoYPFi7UuhgxQZYMy9L2j5WU47utys/C7nrcPfEKpCTZNSxV4AqPHsONmzZ7POetrVH4XT6iD5plGSu/srR7WT5LML/R4229jh07Fs2aNdOgNMGrf3MGS8FaAMo+2G6wa4wwwxQIWT5DOyqrsODLjQCAvrlnAwDGjx8Pu91Y+2BZtkORzm/YOoJMJlOD55IT7Ugx2AbTVQuYLcqF4NyHf6YmJxqyHu4LYrkPg7MnxMOWYKyrvtviDXZMpYHJlGEZ2r7dFt+gHilJxns/vN21yltbo/CzxVvY7jUiy2cJ5jd6vK1Xm80Gm82mQWlCo96c4cQp2anJSYbbFjHDFAhZPkNbrVbEn8ht/Ikc2+12boc0Eun88tQwIiIiIiIiIqIYwY4gIiIiIiIiIqIYocl5NwWFJSgudTQ9YYSlp9iRm50W1Lx6qQMQWj0A/dQl1HpQdOilvQDMMCBPPSh6ZGkzrEf4McMULbK0e1nqQdHDNqMvsfx+RL0jqKCwBFdOeh5OR0W0F92AzZ6IhbPuCHiF66kOQPD1APRVl1DqQdGhp/YCMMOy1IOiR5Y2w3pEBjNM0SBLu5elHhQ9bDP6EuvvR9Q7gopLHXA6KvDIRWZ0yNbuQsB5hTWYtqQCxaWOgFe2XuoAhFYPQD91CbUeFB16aS8AMwzIUw+KHlnaDOsRfswwRYss7V6WelD0sM3oS6y/H5rVuEO2Bd1ztb5ytyukufVRByDUegB6qUvo9aDo0Ed7AZhhhSz1oOiRpc2wHuHGDFP0yNLuZakHRQ/bjL7E6vvBi0UTEREREREREcUIdgQREREREREREcUIdgQREREREREREcUIdgQREREREREREcUIbS+P7a+sU4B25wLNegNpHQBbFhCfDFSVAcV/AHtXANveBipLtC6p/6zJQM/rgXYXAGntlfo4jgLHdwMH1wNbXweqjmtdSv/1uQ0YcF/d/7PbaVcW0hfm1xiYYfKFGdY/5pdiiWz5BZhh8o+MbV8GBs2vMTqCuo8Feo2v+7/GqTxsGUBuf+Vxys3AlxOBw5u1K6e/WpwFnPs8kNhc+d9VqdQnuYXyaHkWsPsroGibtuX0V1pHoN8krUtBesX86h8zTI1hhvWN+aVYIlt+AWaY/CNj25eBgfNrjI6gwz8CpfuAgg3Kr4/unk5LItDhQmDgVMCeDVwwG3h/OFBdqmlxG5XTH7joDcBiB/KWAD+8CBRuVcaZbUBmV6WXt0rHdfBgAs55BrDYgIJNQG4/rQtEesP86hwzTE1ghnWM+aUYIl1+AWaY/CJl25eBsfNrjI6g3z/y/nxNhTKu4ghwyTtAYjPl8PWdH0e1eH4z24DhM5QQ//wGsHa653iXEziyRXkYxSkTlF+Df1+kHJZosABQFDC/+sYMU1OYYf1ifilWyJhfgBmmpsna9mVg8PzKcbHowz/UDSflaleOpnS9AkhtB1QcBr57QuvShC6lDXDGPYDzKLDuYa1LQ0bF/GqHGaZwYIa1wfxSLJEtvwAzTP6Rse3LQIL8ytERlHtm3fDxfO3K0ZQuY5S/uz5Tzus0uiFPAtYkYN0jSgiIgsH8aocZpnBghrXB/FIskS2/ADNM/pGx7ctAgvwa49Qwb+LilYtltTsX6HeX8lxJHrBnmbbl8iUuXrnjCgAc2QoktQT63g60GaZcW6GyBDjyE7BtHrB3uaZF9Uv3cUDrwcC+1b5PGyDyhfnVHjNMoWCGtcX8UiyRLb8AM0z+kbHty0CS/BqvI+jm7coFmU5WsAH4+g6gtir6ZfJHSmvAnKAMp7YFBj0ExKecuOK748S1Fc5THr++B6y+r/HX01JiDjDgX0q5V0/RujRkJMyvPjDDFCxmWHvML8UamfILMMPkP9navgwkyq/xOoIcR5RAWJOUBwDsXwt89zhQfkDbsjUmIa1u+PTblbuuLL1Nuc2fqFF6eAdOBTqNAnpcAxTvBLb+V7vyNmbIE0p9vnscKN2rdWnISJhffWCGKVjMsPaYX4o1MuUXYIbJf7K1fRlIlF/jXSPovcHAO2cAb/QE3uoLrHsUyOoJXP6/usPT9chUb1XHmYFV/wTyPldCDCgfoL/+P6DwF+X/0/8OmMzRL2dTOl+unApQ+AuwhRsaChDzqz1mmELBDGuL+aVYJEt+AWaYAiNT25eBZPk1XkdQfc4iYOtrwJIbASGAfncCbUdoXSrvqsrqhot3AXu+8jKRALa8pgzaMoHs3lEpmt/s2cDZDwC1NcA39wLCpXWJyMiY3+hjhimcmOHoYn4pVsmQX4AZpsDJ0vZlIGF+jd0R5HbkJ+X6BADQ41pty+JLRUHdcMkfvqc79nvdcEqryJUnGGfeq2xgfn0PKP4DsCR6PuKsddN6e47IG+Y3ephhigRmODqYX4pVMuQXYIYpcLK0fRlImF/jXSPIl4pDyt/U9poWw6fKEqDsIJDcovHpTPWGhYhokQKW0kb52+sG5dGYm39V/m6dA6x7OLLlIuNjfqODGaZIYYYjj/mlWCVDfgFmmAInS9uXgYT5leOIIKDuzakua3w6Le1frfxN7+x7mowudcMGvwAVkd+YXyJjY4aJKJKYX4pVbPsUIfo/IsgUB4jaxqdpOQhofpoyfGB9xIsUtO0LgG5jgbQOQLsLvJznaQJOvVUZLDsIFP4c9SI26tNxjY/vNwnoN1kZnt0u4sUhA2B+9YUZpkAxw/rB/FIsM3p+AWaYgiND25eBhPnV/xFBSS2BKz5Xrjvg/sVRHdcC6HMbMPI15cOq85hyCJZeFWwAdn2mDJ/zFNDhororuye1BM59Qbn7CgBseAYAD+0jg2N+iYyNGSYiPWB+KVax7VOE6P+IIADI7gUMeUIZdlUqV1C32ABrUt00x/OBpX8FHEe0KaO/Vt4N2LKAlgOB818BapzKw5ZeN82mmcDvH2pWRKKwYn6JjI0ZJiI9YH4pVrHtUwTovyOo4hCw9Dal4Tc7DUjKAWwZyqHqpfuAo78Cu5cCOz9WPqDqXY1DObSs29VAl8uBzG7Kh+myg0DB98Avc4FDm7QuJVF4ML9ExsYME5FeML8Uq9j2KQL03xFUWw3kfa48pCGA7fOVh0w2zVIeRG7Mr7Eww3QyZtg4mF+KCZLmF2CGqQkSt30ZGDC/+r9GEBERERERERERhQU7goiIiIiIiIiIYgQ7goiIiIiIiIiIYgQ7goiIiIiIiIiIYgQ7goiIiIiIiIiIYgQ7goiIiIiIiIiIYgQ7goiIiIiIiIiIYgQ7goiIiIiIiIiIYoRFqwXnFdZoteiwLV/rOoSzDFrXRevlU2D08H4xw+F9DRnKQP7Tw/vFth/e15ChDBRb9NDmmF/Sgh7eLz2UQS/0sC60KEPUO4LSU+yw2RMxbUkFAFe0F+/BZk9Eeoo94Pn0VAcg+HoA+qpLKPWg6NBTewGYYVnqQdEjS5thPSKDGaZokKXdy1IPih62GX2J9fcj6h1BudlpWDjrDhSXOqK96AbSU+zIzU4LeD491QEIvh6AvuoSSj0oOvTUXgBmWJZ6UPTI0mZYj8hghikaZGn3stSDoodtRl9i/f3Q5NSw3Ow0wzc6GergJlNdKPJkaS+sB8UqWdoM60FkXLK0e1nqQdHDNqMvsfx+8GLRREREREREREQxgh1BREREREREREQxImynhgkh1OHaWuVvcZkD1viKcC0iKo4dL0dVlXLVbmfViedKy+Gq1bBQQZClHsVlDrU9udVvaxQ+zLC+yFIPZjg6mF99kaUezG/0eFuvTqcTTqdTg9IEz+FwoKLixHbnROM/VlIOuy1ew1IFjhmmQMiyD3Y4q1DhbvAncuxw6OP6OYGQZTsU6fyGrSOosrJSHT5cpvyd9uKnsFg1u0N9UKqqarBh2x4AgP1E0QtnLILFatawVIGTpR411TVqe3Kr39YofJhhfZGlHsxwdDC/+iJLPZjf6PG2XhcsWICUlBQNShO8iooKvPfee8o/jsMAgO+25sFsNtZJCMwwBUKWfbDLVYtd+wuVf+x71OcTExM1KlFwZNkORTq/xlobREREREREREQUtIh0U+am1v21GKsjFNXVQE6yMtzixI8wLVIBM+uhiZqauva0s0jbssQSZlh7stSDGY4+5ld7stSD+dXG4A7KX+uh9UBJgraFCVBcZRXSapVf4HObKc81SwVMBvvpmRmmYBl5H1zjAopObHJyk5UcWwrWAvHGOqVKlu1QpPNrsNVBRERERERERETBCls/ZUJCw18s7hw/CtmZGeFaRFQcKy3HvTMXAQByT/wC8I9brkBqsrHOjZSlHoVHj+HGTZs9nvPW1ih0zLC+yFIPZjg6mF99kaUezK+2Lh/RB82yDJbhknJ8tzUPANDsRNu/e+IVSEmya1iqwDHDFAhZ9sHFZRW4b5bS7t35HTuyP1KTkzQsVeBk2Q5FOr9h6wgymUwNnktOtCPFYBtMVy1gtigXgnMf/pmanMh6aMTb3TK8tTUKHTOsL7LUgxmODuZXX2SpB/OrLVu8BbYEq9bFCIjdFg9TnHLCgfs0jJQkbou0wgxHh0z74JPza0+I53ZII5HOL08NIyIiIiIiIiKKEewIIiIiIiIiIiKKEZpcy7ygsATFpQ4tFu0hPcWO3Oy0oObVSx2A0OoB6KcuodaDokMv7QVghgF56kHRI0ubYT3CjxnWP1naC+sRfsyvMcjSZliP8It2hqPeEVRQWIIrJz0Pp6Mi2otuwGZPxMJZdwS8wvVUByD4egD6qkso9aDo0FN7AZhhWepB0SNLm2E9IoMZ1jdZ2gvrERnMr/7J0mZYj8iIdoaj3hFUXOqA01GBRy4yo0O2JgckAQDyCmswbUkFiksdAa9svdQBCK0egH7qEmo9KDr00l4AZhiQpx4UPbK0GdYj/Jhh/ZOlvbAe4cf8GoMsbYb1CD8tMqxZjTtkW9A9V+srkLtCmlsfdQBCrQegl7qEXg+KDn20F4AZVshSD4oeWdoM6xFuzLARyNJeWI9wY36NQpY2w3qEW3QzzItFExERERERERHFCHYEERERERERERHFCHYEERERERERERHFCHYEERERERERERHFCG0vj+2PW/f4P+2BdcCn4yJXlnBpNRjofg3Q/DTAnq08V3EYOLQZ+O1d4OB3mhavSWYb0HIgkN0byD5FeaS0VsZtmglsmqVp8UhnZMsw80uxRLb8AswwxRbZMsz8UiyRLb8AM6wj+u8Iqjjc+Pg4K2DLUIaP/BT58oRq8GNAz+vr/q9xKH9T2yqPLqOBLf8F1j+iSfH80vw04KK5WpeCjEKmDDO/FGtkyi/ADFPskSnDzC/FGpnyCzDDOqP/jqB3zmh8fO9bgLPuV4Z/ez/y5QlF16vqGv+uz4DvnwaO71b+T+sIDLgPaD8SOHUiUPA9sPtLzYraJGcxUPgzUPSz8vesB4DE5lqXivRIlgwzvxSLZMkvwAxTbJIlw8wvxSJZ8gswwzqk/46gpnS/Wvl78HugZJe2ZWlK1zHK35I84OvbAeGqG1eyC1j6N+Dq5UBqO6DjJfoNQMH3wFt9PJ87815tykLGZ5QMM79EDRklvwAzTOSNUTLM/BI1ZJT8AsywDhn7YtE5/YCMLsrwb/O1LYs/Epspf4t+9Wz8bqIGKNqmDFuToleuQIlarUtAsjBShplfIk9Gyi/ADBOdzEgZZn6JPBkpvwAzrEPG7gjqdqIXtLJEOcRM747vVf5m9QBM5objTRYgq6cyfGRL9MpFpBUjZZj5JfJkpPwCzDDRyYyUYeaXyJOR8gswwzpk3I4gS6Jy2BgA/PE/wOXUtjz+2Pa28jetA3DuC8qhb25pHYHzXlSeK9kNbJ2jSRGJosZoGWZ+ieoYLb8AM0xUn9EyzPwS1TFafgFmWIeMe42gTn8C4pOVYSMcDgcA+V8Dax9SLobV8RLl4b5ausWu9Oj+8jaw8RmgukzbshJFmtEyzPwS1TFafgFmmKg+o2WY+SWqY7T8AsywDhn3iCD3xbEKf1Gu1m0UP78OfPVXoOKI8r/FrjwA5RaA1kQgPlW78hFFixEzzPwSKYyYX4AZJnIzYoaZXyKFEfMLMMM6Y8wjgjK6ADl9leHtOr9VXn1mGzDs30CnS4HDPwErJikBBoDsXsAZ/1SuqN5mGPDZtcDR37QsLVHkGDHDzC+Rwoj5BZhhIjcjZpj5JVIYMb8AM6xDxjwiqPs45W+NE/h9kbZlCcTAqUrjL94JLL4K2L8GqDymPPavUZ4r/gOwZwGDHtG6tESRY8QMM79ECiPmF2CGidyMmGHml0hhxPwCzLAOGa8jKM4KdL5cGc5bAlQd17Y8/rImAT2uUYZ/eRtwVTacxlUJ/PKWMtziTMCWFb3yEUWLETPM/BIpjJhfgBkmcjNihplfIoUR8wswwzplvI6gducrPYWAcS6OBShXSI+zKsPH9/ieriSvbjilTWTLRKQFI2aY+SVSGDG/ADNM5GbEDDO/RAoj5hdghnXKeB1B7sPhSvKAg+u1LUsghKgbTm7lezp7dt1wdXnkykOkFSNmmPklUhgxvwAzTORmxAwzv0QKI+YXYIZ1ylgdQUktgVaDleHtC7QtS6CKd9bdIq/7OMBkbjiNKQ7oca0y7CwGSv6IWvGIosKoGWZ+iYybX4AZJgKMm2Hml8i4+QWYYZ0yVkdQ97FAnBmorQa2L9S6NIFxVdYdwtesNzDydSCjGwCT8sjsDlz4JpDbX5nm5zmAqNWosH6ITwUSMuoephNNyWL3fN6SqG05SV+MmmHml8i4+QWYYSLAuBlmfomMm1+AGdYpA90+3gR0vUoZzF8BOA5rW5xgfPcEkNoBaDus7lHjVMZZbHXT7fwE+OE/GhQwAGM+937uZp+/Kg+37R8Aq/4RvXKRjhk8w8wvxTSD5xdghinGGTzDzC/FNIPnF2CGdcg4HUGtBgMprZVhI10cqz5XJfDFeKDDxUCX0UB2b+WCX0IAZfuBwz8pDWbvcq1LShR+Rs8w80uxzOj5BZhhim1GzzDzS7HM6PkFmGEdMk5H0P7VwOx2WpciPPI+Vx5G9t5grUtARiNLhplfikWy5Bdghik2yZJh5pdikSz5BZhhHTHWNYKIiIiIiIiIiCho7AgiIiIiIiIiIooR7AgiIiIiIiIiIooR7AgiIiIiIiIiIooR7AgiIiIiIiIiIooR7AgiIiIiIiIiIooR7AgiIiIiIiIiIooR7AgiIiIiIiIiIooRFq0WnFdYo9Wiw7Z8resQzjJoXRetl0+B0cP7xQyH9zVkKAP5Tw/vF9t+eF9DhjKQf/TwXrHdh/c1ZCgD+U8P7xfbfnhfw4hliHpHUHqKHTZ7IqYtqQDgivbiPdjsiUhPsQc8n57qAARfD0BfdQmlHhQdemovADMsSz0oemRpM6xHZDDD+iZLe2E9IoP51T9Z2gzrERnRznDUO4Jys9OwcNYdKC51RHvRDaSn2JGbnRbwfHqqAxB8PQB91SWUelB06Km9AMywLPWg6JGlzbAekcEM65ss7YX1iAzmV/9kaTOsR2REO8OanBqWm51m+A2VDHVwk6kuFHmytBfWg2KVLG2G9aBYJEt7YT0oVsnSZlgP4+PFoomIiIiIiIiIYgQ7goiIiIiIiIiIYkRETw0rOlYcyZePiOIyB2qqlat215y4eHfh0WNwOp0alipwstTDiG1IJkZc/7K0fVnqYcQ2JAsjrntZ2r0s9TBiG5KJEde/LG1flnoYsQ3JwojrXpZ2L0s9It2GTEIIEY4X2rt3L9q2bQsAGNwhHK+ojdpa4HCZMpybqm1ZQiFLPepbk6f8zc/PR5s2bbQtjISYYX2RpR71McORw/zqiyz1qI/5jSxmWF9kqUd9zHDkML/6Iks96otEfnlqGBERERERERFRjIjIqWHuHiuj21mkdQnCQ5Z6UPQww/oiSz0oOphffZGlHhQ9zLC+yFIPig7mV19kqUck8IggIiIiIiIiIqIYEbZrBLlcLhw4cCAcL6UpIQQqKys9nktISIDJZNKoRMGRpR7etGzZEmazWetiSIcZ1hdZ6uENMxx+zK++yFIPb5jfyGCG9UWWenjDDIcf86svstTDm3DmN2wdQUREREREREREpG88NYyIiIiIiIiIKEawI4iIiIiIiIiIKEawI4iIiIiIiIiIKEawI4iIiIiIiIiIKEawI4iIiIiIiIiIKEawI4iIiIiIiIiIKEawI4iIiIiIiIiIKEawI4iIiIiIiIiIKEawI4iIiIiIiIiIKEawI4h82r17N0wmE0wmE3bv3h3W137zzTdhMpnQvn37sL4uUSyaMGECTCYTJkyYoHVRiCgMpk+fDpPJhGHDhkXk9bdu3YqxY8eiRYsWsFgsMJlMOO200yKyLCIiIqOJhe/BQXUECSHwwQcf4PLLL0e7du1gt9uRnJyMTp06YfDgwbjrrruwaNEiHD9+vMG8s2bNwvTp0/Hjjz+GWnbNFBcX45NPPsEDDzyAUaNGoUWLFmpDefPNNwN6rZ07d+Luu+/GKaecgrS0NCQlJaFjx44YPXo0XnrppchUQAIrV67E9OnTA17fsYh5DT6vK1euVKf15/HQQw9Fp1JETWDuQ99P19TUYM6cOTj//PPRvHlzWK1WpKSkoHfv3rjzzjvxxx9/RLYSEZKXl4dBgwbhgw8+QEFBAdLS0pCTk4Ps7GwAcrz/MmKmQ8+0y+XC22+/jfPPPx/Z2dlISEhA69atcc0112DdunWRrQBJI9azuH//frz00ku46qqr0LlzZ9jtdtjtdnTo0AHXXHMNli9f7tfrHDp0CHfffTe6desGu92OzMxMDBkyBP/9738hhIhwLYwtbN+DRYCOHTsmzjnnHAFAfVgsFpGZmSksFovH82+88UaD+du1a+dznFG88cYbHvVsqs6+zJw5UyQkJKjzJiYmiuTkZPX/tLS0iNXBH/v27RPdunUT3bp1E/v27Qvra7vXYbt27YKa/8EHHxQAxDnnnBPWcsmGeQ0tr99++63Iyclp9FE/s5999ll0KnWS8ePHCwBi/Pjxmiyf9IW5D30/ffToUTFgwACP+VJSUjzWX0JCgliwYEFEyh/Jfdy9994rAIjOnTt73bfL8P7LhpkOPdOlpaXivPPOU+cxm80iIyNDxMXFCQAiLi5OPP3005GvCBlarGcxPz9fmEwmj3omJiYKu93u8dzNN98sampqfL7Oxo0bRVZWljp9cnKyx/obOXKkqKysjGLNGoqF78EBHxF04403YtWqVTCbzbj77ruxY8cOVFZWoqioCA6HAz/99BOeeuop9OnTJ9CXNpTc3FxcdNFFmDp1Kj766KOA558xYwYmT54Ml8uFKVOmYNeuXSgvL0dpaSmOHj2KL774AhMnToxAyf3XqlUr/Pbbb/jtt9/QqlUrTctCwWFeFcHm9eyzz0ZBQUGjD/epG61bt8bIkSMjVAMi/zH3ilD205MmTcJ3330HQDlNq7CwEMePH4fT6cTKlSvRq1cvVFZWYvz48di/f38kih8xW7duBQBcdtll3LcbBDOtCCXTt9xyC5YtW4a4uDg8/vjjOHbsGI4ePYrCwkL885//RG1tLf75z3/if//7X4RKTzKI9Sy6XC4IIXDuuedi7ty52L9/P8rLy1FWVoZffvkFl112GQDg9ddfx/Tp072+RklJCUaNGoWioiJ0794dGzZsQGlpKcrLy/Gf//wHVqsVX375JSZNmhS9inkRE9+DA+k12rFjh9pT98QTTzQ5fUVFRYPnjN4TKoTw2sOJAH6V2LJli7BarQKAWLhwYQRKqH966QmVGfOqCDWvjdm/f78wm80CgLj//vtDeq1Q8IggcmPuFaHk3ul0qkfr+srUzp071dd75ZVXwlBiT5Hcxw0bNkwAEA8++KDX8TK8/zJhphWhZHrLli3qtJMmTfI6zdVXX60eKedyucJRZJIMsyhEcXGx2LRpk8/xtbW14sILL1SP8nE4HA2muf/++wUAYbfbxa5duxqMf/zxx9Wj9rZv3x7W8uuFXr4HB3REUP3zGd09fo2x2+3qsPvCh3v27AEA3HTTTQ2ur+HNZ599hjFjxqBVq1ZISEhARkYGhg4dipdffhlVVVVe5xk2bBhMJhOmT5+OqqoqPPnkkzj11FORlJSEjIwMnH/++ViyZEkANfdkNpuDnhcAHn/8cVRXV2P06NEYM2ZMSK8VjPoXlhVC4L///S8GDx6MrKwsj3Ot/blI1p49e/DnP/8ZrVu3Vs+1vummm7Bz586ALrK1adMm9cKVCQkJ6NixI+666y4cO3bMYzr3a7qvxbJq1aoG7YjXDVIwr4pQ89qYN998Ey6XCyaTCX/+858jthwAmDdvHgYNGoSUlBSkpaVhwIABmD17tt/nUX/00UcYNWoUcnJyEB8fj5ycHIwaNQqLFi3yOv2ll14Kk8mEf/zjHw3GHTx4UG0H/fv39zp/t27dYDKZMGfOHPW5k7cJhw4dwp133okOHTrAZrMhJycH48aNw2+//eZXnagh5l4RSu6PHTuGyspKAPDZvjt16oTMzEwAQFlZWdDLCsWRI0dw//334/TTT0daWhpsNhs6duyIP//5z/jll18aTN++fXuYTCasXLkSAPDQQw95vLfu9yTQ958ii5lWhJLpzz//XB2+5557vE7zz3/+E4By7c41a9YEvSxf3Plr7DNqYzd+qD9/aWkppkyZol5bJTs7G6NHj1aPYqTIYBaBtLQ09O3b1+d4k8mEm2++GYCyb/z1118bTPPWW28BAMaNG4cOHTo0GH/77bcjOTkZLpcL8+bNC6qcjeH34HoC6TVasGCB2hP61VdfBdTj9Mwzz4icnBz1XNzU1NQG19qor6KiQlx55ZUe5xumpqZ6nJc4cOBAcfTo0QbLcp+7OWXKFDFkyBD1/M309HSP1/P1a1gw4OevEmVlZerRQB9++GFQy6p/nvSKFSsCnt999MCNN94oxowZo54b7T5X2l2HvLw8dTl5eXkNXmft2rUiJSVFncZut6vXS0lNTfVoLyfPX78ndN68eeo6SUtLU9sIANGrVy9RWlqqzpefny9ycnJEUlKSACCsVmuDdjR//vyA14mMmFff/M1rY2pra0WnTp0EAHHeeef5nM7da+8rR/4s56abblJfw2QyeVzXYNy4cY0eEVRZWan+0nly1t3PXXPNNaKqqspjvmeffVYAEKeffnqD13znnXc8Xu/YsWMe4/ft26eOr/9rT/1tyqeffiqaN28ucOL88vrXS0tNTRU//vhjwOuKmPvG+Jv72tpadR/jzxFBK1eubDA+1P10U7/2LV261GNdWa1WtcwARHx8vJg7d67HPP379xc5OTnq/jYpKcnjvX3ooYcCev8pOphp3/zN9G233aZ+xvSlvLxcrefUqVMbjA91X+7PkSCN7cvd88+YMUN069ZNzXlqaqrH/njOnDkBl438wyz653//+5+6jA0bNniM++2339RxjV1j76KLLlLreDJ+Dw7f9+CAOoLy8vLUBti7d++gDtfy95C466+/XgAQHTt2FPPmzRMlJSVCCCEcDof45JNPRMeOHQUAMXr06AbzugOQlpYmEhISxCuvvKIempafn+8RrE8++STgOnjj785o+fLl6rS7d+8Wq1evFn/6059Edna2SEhIEO3btxcTJkwQW7du9fka4QqA+8Jc//73v9X1W1paKg4cOCCEaDwAx44dEy1atFDfo+XLl4va2lohhBDff/+96NOnj8jIyGgyAO4vgBMnThT5+flCCGVn/J///EcNxbRp0xrUgaeGNY159c3fvDamfpYb2+iG+uHxueeeU+f/v//7P3HkyBEhhHJ47vTp04XJZFJ37t4+PN59990CUDqQpk2bpnbaHD16VPzrX/9SX/vee+/1mG/z5s3qzrGoqMhj3J///Gd1RwdALFq0yGP822+/re7g6qu/TcnIyBCDBg1SPyRUV1eLpUuXqtuVIUOGBLyuiLlvTCC5v+uuu9Tpp0+fLgoLC4UQyukpK1euFL169RIAxFVXXeV1/kh2BG3ZskW9MOctt9witm3bpp42s2fPHvG3v/1N/eB/8odwIerWPU8NMwZm2rdAO4JSUlJ8TlNaWqq+3p/+9KcG4/XSEZSWliYyMjLEggULRHV1tRBCiG3btqnr32KxNHrqDgWPWfSPe/8ZHx+vlttt4cKF6rK3bdvm8zXuuece9XPmyfg9OHzfgwO+a9gtt9yiVspkMonTTz9d/O1vfxNz5swRW7duVVeCL/4E4JtvvhEARPPmzdWVcrK9e/eqvWE//PCDx7j6V3P31jPucrnE0KFD1Z62cPB3Z/TKK6+o0z799NPqBiU5Odnj7kNWq1W8/vrrXl8jXAEAIJ5//nmf0zUWgEceeUQAEDabTfz+++8N5j1y5IjIzs5uMgC+dnhC1G1IOnfu3GAcO4L8w7x6F46OoOuuu04AEFlZWY3e2SCUD48Oh0NkZmYKAOKGG27wOs19993nM0v79u1T78IwZcoUr/O7c2a1WtWdnxDKencv++SjFzt06CAAiAceeEAAELfffrvHePcRTBMmTPB4vv42pXv37l7Pn6//S9LevXt9rhvyjbn3LpDcOxwOceONN6rzuD+QuvPUsWNH8dRTT/m8K0okO4JGjBjRaKaFEOKOO+4QAMRll13WYBw7goyHmfbO30w/9dRT6rS7d+/2Os26devUafr3799gvF46ggCIZcuWNRhfUVEhunTpIgCIiy++OODykX+Yxcbt2rVLJCYm+vzc+vzzz6tlO7mTqL5Zs2ap09U/IkYIfg8WQsOOoOrqajFt2jSPQ5DrP5o3by4mT54sCgoKvM4fyIZw8uTJjZZl1KhRAoB48sknPZ53B6BNmzY+A/nVV1+pZd6yZUvjlfaDvzujJ554Qp02Li5OnHbaaeK7775Tx69fv16ceuqpAlB69euPCxf3+s3IyGj0C2xjAXCXsbGL006bNs2vAHgLkBBCrFq1Sp2mvLzcYxw7gvzDvHoXakfQsWPHhM1mEwDEnXfeGXJ5fPnkk0+azElxcbFalpPz6D6ayGaz+dzhHj16VD0t6+Qd4uWXXy4AiL///e/qc7t37xYARJcuXdTTY07+ING+fXsBoMGpKfW3Ka+99prX8lRXV4v4+HgBQHz++edep6HGMffeBZr7qqoq8cwzz6i/ytV/5ObminvvvbfBvilcfO3j3BmyWCwNjtSrb+PGjQJQTv86ubOKHUHGw0x752+m618seuLEiV6ncdcLgOjatWvIZTtZuDqCBg0a5HP+2bNnq98viouLw1BqOhmz6FtFRYXo16+fACCys7PF/v37G0zz2GOPqct1H9HmjbstA/D4kTIc+D24TsC3j7dYLHj44Yexf/9+vP3225g4cSL69OmD+Ph4AMDhw4cxc+ZMnHLKKfj+++8DfXkAwLfffgsAmDNnDnJzc30+li1bBgDqhbdO5r5YljdDhgyBxWIBAGzcuDGocgajtrZWHbbZbPjss89w5plnqs8NGDAAn376Kex2O2pqavDYY49FrCxnnHGG+r4FoqqqSr0Q5TnnnONzOvdttRuTmZmJzp07ex3XsmVLdfjki2WRf5jXyJg3bx6cTicAYOLEiRFbjruubdq08ZmTtLQ09OvXr9H5zzjjDKSmpnqdJiMjQ70g7snrdsSIEQCA5cuXq8+5h0eMGIFOnTqhbdu2+OWXX3D48GEAQF5ennpRvOHDh/us24ABA7w+b7FY0KxZMwDA0aNHfc5PvjH3ocvLy0O/fv1wzz33YMyYMdi4cSNKS0uRn5+PN998EyaTCU899RSGDh0a1YtFu9d7bW0tevbs6XO9X3jhhQCA8vJyFBUVRa18FBnMdGh69+6Nq666CgDw3//+F3fddRd2796N6upq7NixAzfffDM+/fRTWK1WAEBcXMBfj6LGvV9ubFxtbS02b94crSLFFGbRu5qaGlx77bXYtGkTrFYr5s2b5/E9To/4PRiwBDtjWloarr/+elx//fUAAKfTiTVr1uD555/H4sWLUVhYiDFjxuD333+HzWYL6LUPHDgAADh+/DiOHz/e5PQVFRVen2/VqpXPeWw2G7KysnDo0CH1C0w0pKSkqMPXXnut15C0adMG1157LebMmYOvv/4aLpcrInc+at68eVDzHT16FC6XCwAaDXlj69+t/vo4mXsDBQDV1dUBlJBOxryGl/tOWAMGDMApp5wSseW469pUllq3bh2W+U9et+6OnF9//RUFBQXIzc3FihUrANR94Bw+fDjmzp2L5cuXY9y4cer4Tp06oU2bNj6X6U/2mfvQMPfBcblcuOyyy7B161bceOONmDt3rjouOTkZ48ePxxlnnIG+ffti06ZNeOqpp/DII49EpWzu9V5bW4tDhw75NY+vdU/Gw0wHb86cOSgqKsLy5csxc+ZMzJw502P8ZZddBpPJhI8//hgZGRlRLVsgGlu/9cfp4bOSzJjFOi6XC9dddx0+/vhjWCwWvPvuu7jgggu8Tlv/s19FRYXPHynr16mxz4uh4PdgIGxd3jabDeeddx7+97//Yfz48QCAffv24Ysvvgj4tdwr9+WXX4ZQTl9r9GGk24XXbxQ9evTwOV3Pnj0BRPbXvHB0LvF2ssbEvAZv8+bN+OGHHwBE9mggPejVqxdycnIA1B0JtGLFCphMJrWT6OSjhtx/GzsaiLTB3Pvnq6++wtatWwEA//jHP7xO07NnT1xyySUAgA8//DBqZXOv95ycHL/WuxAC7du3j1r5KLqYaf+lpKRg6dKlePfdd3HZZZehS5cuaN++Pc477zzMnTsXixYtUo9C7dq1q8alJaOJ1Sy6XC5cf/31WLBgAcxmM9555x1ceeWVPqev33Gyf/9+n9O5x6WmpiI5OTl8Ba6H34PD2BFU36233qoOb9++PeD5c3NzAfg+1M1fjTWwyspKtYMl2B7BYJx66ql+TSeEUIf11sgyMzPV8Lh7rb1pbP2TfjCvgXEfDZScnIxx48ZFdFnuujaVJV/j3fPv27ev0fnd472tW/ehrcuXL8eOHTuwb98+nHLKKerpW+4On/odRUDjh6+T9ph737Zt26YOd+rUyed0Xbp0AaCcRhYt7vVeWFiI8vLyqC2X9I+ZblpcXByuueYafPzxx9ixYwfy8vKwdOlS3HjjjXC5XPjpp58AAGeffXbYl+3+dd99Wrk3JSUlTb6OP1+eAe0/K8WyWMmi+0ig+fPnq51AV199daPz1D+K/ueff/Y5nXuc+8AIPZHpe3BEOoLq99wlJCR4LvDEebf1OzpONmjQIADAp59+GlI5Vq1a5XM5q1evRk1NDQCo18eIhs6dO6Njx44AlNMtfHF/EE1NTUVWVlZUyuav+Ph49OrVCwCwcuVKn9M1Ni5U/rQj8g/z6j+Hw4F3330XADB27NiI/Urh5q7r3r178ccff3id5vjx49i0aVOj82/cuNHnB8zi4mKPawmdrH5Hj7dOHvf1i/744w8sXbpU3Sn6c240aYe5963+9UEa+yDuPjUrUoete+Ne7y6XC0uWLInIMrh/NSZmOjSLFy9GSUkJ7Ha7ej2hcHKfbrZ3716v42tra/26Vot7P9zYuLi4OJx++ulBlJLCIRay6HK5cO211+L9999XO4H8+XG0a9euaNu2LQD4PFqqvLwcq1evBgCfp5hpSabvwQF1BOXl5WHHjh1NTlf/fPq+fft6jHOfC1hcXOxzfndP6s8//4yXX3650WWVl5ejqqrK67j8/HyPsrjV1tbi8ccfB6D0NPbu3bvRZYTbhAkTAADvvvuu157EvXv34r333gMAXHzxxbq8aJ37sL/333/f6xfUoqIivPLKKxFbvj/tKNYxr+H34YcfqusiGqeFnX/++eqHR1/XIHn66afhcDi8jhszZgwsFgucTieeeuopr9M8/vjjqKyshNVqxZgxYxqMd3f65OXl4Y033vB4zs3dWTRt2jQAQPfu3dGiRYumqkcRwNyHrv768FW3goICLFq0CABw1llnRaVcgHIUkruTderUqU0eQRDMBde5f9UXZjryjhw5op4G+ve//z0i1wjq06cPAGDRokVev7zNnTu3yaN3AWDNmjVev2A6nU48++yzAICRI0ciPT09pPJSQ8yiwn0k0IIFC2CxWDBv3jy/j5A3mUy48cYbAQDz589Xby5S34svvoiysjKYzWZcd911AZUtWqT5HhzILcYWL14s4uLixMUXXyzmzp3rcSu0qqoqsXnzZjFhwgT1VmdnnnmmcLlcHq9x3XXXCQDi7LPPFkePHvW5rJtuukkAECaTSUyaNEn88ccf6jin0ynWrVsn7rnnHpGVlSX27t3rMa/7tnlpaWnCZrOJ2bNnC4fDIYQQIj8/X4wdO1Yt40cffRTIKlAdOXLE4+F+vRdeeMHjeW+3li0rK1NvH3jy7eO/++479ZZ0drtdbNu2rcH89W85t2LFioDL3tjtKetr7LZ5R48eFTk5OQKA6Ny5s1i5cqV6i8INGzaI008/XWRkZDR527x27doFtfylS5cKAMJsNotvv/3Wz5rHFua1Tih59VbWk2+X3hj3LR69tWN/zJgxQ53/zjvvFIWFhUIIIUpKSsTDDz8sTCaTSE9P95npu+++W31vHnjgAXHs2DEhhBDHjh0T999/v/ra9957r88ytG7dWp3ObDY3uC3te++953H71Ntuu83r6zSW6fp4++rgMfd1gs29y+USffr0Ues2efJk9Ta4DodDLFmyRHTp0kUdv3LlygbLDnU/3ditYbdu3SqSk5MFANG9e3fx8ccfq+tOCCH27dsn3nrrLTFixAivt8pu6vbx/r7/FB3MdJ1Q9uWffvqpmDVrlti5c6eoqakRQghRXl4uFixYIDp16iQAiD59+oiKigqvyw51X75s2TJ1/okTJ3rsy2fMmCHi4+NFZmamz325e7+YlpYmMjMzxQcffKDefvvXX38VI0aMUPfRGzZsCLh81DRmUYiamhoxbtw4AUBYLBaxYMGCgOYXQoji4mKRm5srAIiePXuKjRs3CiGEqKysFC+99JKIj49v9LMkvweH73twQB1BX3zxhceHfQDqhstkMnk837dvX/WDU32rVq1SpzWbzaJFixaiXbt2DVZEZWWlmDhxosdrJicni4yMDBEXF+fx/L59+zzmdQdgypQpYvDgwQKAsFqtHm8IAHH//fcHvsZOOHk9+Hr4+qD166+/ilatWnnUzf3Bzv3/4sWLvc6rhwAIIcTq1as9ypyYmKj+n56eLj744AN13MGDB73WIdgAVFdXi27duqnjMzIy1Hb0wQcf+Lkm5Ma81gk1r0II8fvvv6vrYsaMGX4vO9QPjy6XS9xwww3qa8TFxYmMjAxhNpsFADFu3LhGM11ZWemx03fPX/99ueaaa0RVVZXPMtRf/hlnnNFgfEFBgcd69PXBgB1Bkcfc1wkl9zt37hQdO3ZsULf69TKbzeK5557zuuxIdgQJIcSaNWvUD9LusmRlZQm73e5R5mA6gvx9/yk6mOk6oWR65syZHnk5uU5Dhw4VRUVFPpcd6r5cCM99KaB8VnaX4fbbb290X+7eL86YMUP9/JuQkCDS0tLU1zOZTGL27NlBlY2axiwq5XfPb7VaRU5OTqOP+fPne32djRs3iqysLPW1UlJShNVqVf+/4IILhNPp9DovvweH73twQOccjRw5Er///juee+45XHXVVejRowcSEhJQXFyMxMREdOnSBWPHjsX8+fOxYcMGr7dUGzp0KD777DOcd955SE9Px6FDh7Bnz54G5+HHx8fjtddew9q1azFhwgR06tQJLpcLZWVlaN68OYYNG4YHHngAW7Zs8Xl7tvj4eHz99dd4/PHH0a1bN1RWViItLQ3nnnsuPvvss6jd7tWb7t2745dffsEDDzygXkDa5XKhW7duuOOOO/Dzzz9j1KhRmpXPH4MHD8aWLVtw0003oWXLlqipqUF6ejpuvvlmbN682eMim+E+RNViseDrr7/GxIkT0aFDB5SXl6vtqKysLKzLMirmNbxef/11CCEQHx+PG264IWrLjYuLw1tvvYW33noLAwcOhN1uR01NDfr27YtXXnlFvWaRL/Hx8Xj//fexcOFCXHTRRcjKykJpaSmysrJw0UUX4aOPPsK7774Lq9Xq8zXq3wHM20Wgc3Jy1Av6mUwmXh9IQ8x9eHTq1AlbtmzBzJkzMWzYMGRnZ8PpdMJms6F79+74y1/+gs2bN+OOO+7QpHyDBg3Cjh078O9//xtDhw5Feno6iouLYTab0aNHD1x//fWYN28eZs2aFfBr+/v+U3Qw0+Fx/vnn4/bbb8fpp5+OjIwMlJeXIycnB3/605/w/vvvY+XKlcjMzIxoGd58800899xzOO2002C321FbW4tBgwZhwYIFeP755/16jYyMDHz//fe477770LZtW1RWViIzMxOXXnopvv32W9xyyy0RrUMsYxaV08rcqqurcejQoUYfvi5d0K9fP/zyyy+YPHkyunTpgurqaiQlJWHw4MF47bXXsGTJkgbXV9IbGb4Hm4SQ72qAw4YNw6pVq/Dggw9i+vTpWhcnZr322mu49dZb0bFjR58XuiViXoliD3NPJBdmOrLat2+PPXv24I033lCvNUrkDbOoD0b4Hqy/qxCTFJxOp/pL5IUXXqhtYYiIiIiIiIgizCjfg9kRREGbP38+7r//fvz888/qFetramrwzTffYMSIEdi2bRtsNhvuvPNOjUtKREREREREFDoZvgdbtC4AGVdBQQEee+wxPPbYYzCZTMjIyEBZWZkahvj4eLzxxhvo2rWrxiUlIiIiIiIiCp0M34PZEURBGzVqFI4cOYKVK1diz549KCwshNVqRceOHTF8+HBMmjRJ142fiIiIiIiIKBAyfA+W8mLRRERERERERETUEK8RREREREREREQUI9gRREREREREREQUI9gRREREREREREQUI9gRREREREREREQUI9gRREREREREREQUI9gRREREREREREQUI9gRREREREREREQUIyzheiGXy4UDBw6E6+U0I4RAZWWlx3MJCQkwmUwalSg4stTDm5YtW8JsNmtdDOkww/oiSz28YYbDj/nVF1nq4Q3zGxnMsL7IUg9vmOHwY371RZZ6eBPO/IatI+jAgQNo27ZtuF6OyKf8/Hy0adNG62JIhxmmaGGGw4/5pWhhfiODGaZoYYbDj/mlaAlnfnlqGBERERERERFRjAjbEUH1De4QiVeNjtpa4HCZMpybqm1ZQiFLPepbk6d1CWIHM6w9WepRHzMcHcyv9mSpR33Mb/Qww9qTpR71McPRwfxqT5Z61BeJ/PKIICIiIiIiIiKiGBGRI4LcXnvmXmRlpEdyEWFXXObAtBc/BVDXg3jn+FFITrRrWKrAyVKPomPFuOWep7QuRsxihrUjSz2YYe0wv9qRpR7Mr7aYYe3IUg9mWDvMr3ZkqUek8xvRjqCsjHQ0y86M5CLCzhpfAYtVWS2WE2snOzMDKcmJGpYqcLLUg7TFDGtHlnqQdphf7chSD9IWM6wdWepB2mF+tSNLPSKNp4YREREREREREcUIdgQREREREREREcWIiJ4a5ktBYQmKSx1aLNpDeoodudlpQc2rlzoAodUD0E9dQq0HRYde2gvADAPy1IOiR5Y2w3qEHzOsf7K0F9Yj/JhfY5ClzbAe4RftDEe9I6igsARXTnoeTkdFtBfdgM2eiIWz7gh4heupDkDw9QD0VZdQ6kHRoaf2AjDDstSDokeWNsN6RAYzrG+ytBfWIzKYX/2Tpc2wHpER7QxHvSOouNQBp6MCj1xkRodsTQ5IAgDkFdZg2pIKFJc6Al7ZeqkDEFo9AP3UJdR6UHTopb0AzDAgTz0oemRpM6xH+DHD+idLe2E9wo/5NQZZ2gzrEX5aZFizGnfItqB7rlWrxZ/gCmlufdQBCLUegF7qEno9KDr00V4AZlghSz0oemRpM6xHuDHDRiBLe2E9wo35NQpZ2gzrEW7RzTAvFk1EREREREREFCPYEUREREREREREFCPYEUREREREREREFCPYEUREREREREREFCO0vTy2v7JOAdqdCzTrDaR1AGxZQHwyUFUGFP8B7F0BbHsbqCzRuqSNS0gH2p0PtBoEZJ8CJLcC4syA4yhQuAXY8SGw+0utS9m0rlcCw55terrPrgX2fxv58pC+Mb/6wwxTIJhhfWF+KRDMr/4wwxQIZlhfJMqvMTqCuo8Feo2v+7/GqTxsGUBuf+Vxys3AlxOBw5u1K2dTbtgIxNW7InmNE6itAZJbKI/2I4H8FcDSvwIup3bl9FetC3AW+R7vqopeWUi/mF/9YobJH8ywPjG/5A/mV7+YYfIHM6xPEuTXGB1Bh38ESvcBBRuUns+q48rzlkSgw4XAwKmAPRu4YDbw/nCgulTT4voUZwUO/wBsXwjsWwWU7lWeT24N9L0d6D4OaDscGPoEsGKytmX1R/kB4L3BWpeC9I751S9mmPzBDOsT80v+YH71ixkmfzDD+iRBfo3REfT7R96fr6lQxlUcAS55B0hsphw6t/PjqBbPb4vHAQfXNXy+bB/wzb1Kr2jP64EuVwDfPw2UH4x+GYnCjfklMjZmmMi4mF8iY2OGKULkuFj04R/qhpNytStHU7w1/vq2v1833OzUyJaFSC+YXyJjY4aJjIv5JTI2ZpiCJEdHUO6ZdcPH87UrR6hqKuuGTWbtykEUTcwvkbExw0TGxfwSGRszTEEyxqlh3sTFA4nNlUPg+t2lPFeSB+xZpm25QtHyrLrho79pVw5/2bKAyz8F0jsqga04DBzaBPw2Hzi4XuvSkZ4xv/rADFOwmGHtMb8ULOZXH5hhChYzrD0J8mu8jqCbtwMWW8PnCzYAX98B1Or/Ct1exacCp/1NGT74HVCyS9vy+MOaqNzK0FkMWC1Aalvl0eVyYPsC4Jv7AOHSupSkJ8yvvjDDFChmWD+YXwoU86svzDAFihnWDwnya7yOIMcRwJwAWJOUBwDsXwt897hy9W5DMgHDZwJJOcqt9L59QOsCNa78ELBpJpD3BVC8S9nomOKA5qcD/SYDrYcA3cYC1RXA2ge1Li3pCfOrD8wwBYsZ1h7zS8FifvWBGaZgMcPakyi/xusIqn+bNluWcmXx0/8PuPx/wOYXgE0ztCtbsM6eDrQ7Txn+dpr+D4fbv1p51CdqlcPhPr8BuOBVoP1IoOcNwM9vAMd3a1JM0iHmVx+YYQoWM6w95peCxfzqAzNMwWKGtSdRfo19sWhnEbD1NWDJjYAQQL87gbYjtC5VYAZMBU6ZoAyvfUg5lMzQBLD+MWUwzlwXbKKTMb86xQyTn5hhHWJ+yU/Mr04xw+QnZliHjJVfY3cEuR35STk3EgB6XKttWQIxYArQ51ZleN2jwM+va1uecDm+B3AUKcOpbbUtC+kf86s/zDAFghnWF+aXAsH86g8zTIFghvXFQPk13qlhvlQcUv6mtte0GH4b8C+gz1+U4fWPKz26RLGK+SUyNmaYyLiYXyJjY4YpCPJ0BKW0Uf5Wl2lbDn8MmFrXA7r+cWDLq9qWJ9xS2gL2LGX4+F5ty0LGwPzqCzNMgWKG9YP5pUAxv/rCDFOgmGH9MFB+9X9qmMmPIrYcBDQ/TRk+sD6ixQlZ/ca/7lH5Gj8ADJyq/K11Aflfa1sW0hbza0zMMLkxw8bD/JIb82tMzDC5McPGY6D86r8jKKklcMXnyjmP7t5OdVwLoM9twMjXlKA4jwFb52hTTn94nAv5sDEPg0tuDYz+xMv7YVJum3fRXKDDhcpTv74LlOzSpJikE8yv/jDDFAhmWF+YXwoE86s/zDAFghnWF8nya4xTw7J7AUOeUIZdlUBVGWCxAdakummO5wNL/wo4jmhTxqYktQT6/FUZrnUpw+7/vdnyGrBldnTKFqjmp9X1PNc4gepy5b2w2Oqm2b4AWPugFqUjvWF+9YcZpkAww/rC/FIgmF/9YYYpEMywvkiUX/13BFUcApbeBrQcCDQ7DUjKAWwZgKgFSvcBR38Fdi8Fdn6shEOv6h/aF2cGEps3Pr01MbLlCZbjCPDtA0DzvkBWT8CeCSSkATWVwLGdwKFNSuM/tFHrkpIeML/6wwxTIJhhfWF+KRDMr/4wwxQIZlhfJMuv/juCaquBvM+Vh5GV7QNmt9O6FKFzVQK/zFUeRE1hfvWHGaZAMMP6wvxSIJhf/WGGKRDMsL5Ill/9XyOIiIiIiIiIiIjCgh1BREREREREREQxgh1BREREREREREQxgh1BREREREREREQxgh1BREREREREREQxgh1BREREREREREQxgh1BREREREREREQxgh1BREREREREREQxwqLVgvMKa7RadNiWr3UdwlkGreui9fIpMHp4v5jh8L6GDGUg/+nh/WLbD+9ryFAG8o8e3iu2+/C+hgxlIP/p4f1i2w/vaxixDFHvCEpPscNmT8S0JRUAXNFevAebPRHpKfaA59NTHYDg6wHoqy6h1IOiQ0/tBWCGZakHRY8sbYb1iAxmWN9kaS+sR2Qwv/onS5thPSIj2hmOekdQbnYaFs66A8WljmgvuoH0FDtys9MCnk9PdQCCrwegr7qEUg+KDj21F4AZlqUeFD2ytBnWIzKYYX2Tpb2wHpHB/OqfLG2G9YiMaGdYk1PDcrPTDL+hkqEObjLVhSJPlvbCelCskqXNsB4Ui2RpL6wHxSpZ2gzrYXy8WDQRERERERERUYxgRxARERERERERUYwI26lhQgh1uLZW+Vtc5oA1viJci4iKY8fLUVWlXLXbWXXiudJyuGo1LFQQiksr4DpR6JoT175yVFbBarVqWKrAOau0v4p7rGCG9UWWehSXOdT25Fa/rVF4ML/6Iks9mN/okSXDsnz+dDirGtSjuKyCGSavZMmvNPsuSbZDkf4eHLaOoMrKSnX4cJnyd9qLn8Ji1ewO9UGpqqrBhm17AAD2E0UvnLEIFqtZw1IFzuWqxa79hQCAogTluflfbIQtIV7DUgXO6axseiIKC2ZYX2SpR011jdqe3Oq3NQoP5ldfZKkH8xs9smRYls+fFc6qBvW4b9YimOKMdTIFMxwdsuRXln2XLNuhSH8PNtbWjIiIiIiIiIiIghaRbsrc1Lq/FmN1hKK6GshJVoZbpJz4mwqYDVYPUQtYTxzSt72sOQCgtsXZQGKihqUKXHVpKdbkLdS6GDGHGdaeLPWoqalrTzuLtC1LrGB+tSdLPZhfbRg5w7J8/kRFBWBXjozITT4MAGiWApgM9hM6Mxx9Rs6vLPsuWbZDkf4ebLDNGRERERERERERBSts/XsJCQkNnrtz/ChkZ2aEaxFRcay0HPfOXAQAyD3RE/qPW65AarKxehAdlVVY8OVGAEDf3LMBAOPHj4fdbteyWAE7cuQI/vGPf3g8562tUeiYYX2RpR6FR4/hxk2bPZ5jhsOP+dUXWerB/EaPLBmW5fOnw+FQhy0FawEAY0f2h91g1xhhhqNDlvzKsu+SZTsU6e/BYesIMplMDZ5LTrQjxWANx1ULmC3KBbHch8GlJicarh5WqxXx8crOKv7EYXB2ux02m03LYgXMW3m9tTUKHTOsL7LUw+l0NniOGQ4/5ldfZKkH8xs9smRYls+fAJDoPo3kRH1Sk5NgSzDYXYeY4aiQJb+y7Ltk2Q5F+nswTw0jIiIiIiIiIooR7AgiIiIiIiIiIooRmlwDvKCwBMWljqYnjLD0FDtys9OCmlcvdQBCqwdRoGRp+6xH+HFbZAyytBnWI/yYYf1je9EXvh8UKFnajCz1iGVR7wgqKCzBlZOeh9NREe1FN2CzJ2LhrDsCbjh6qgMQfD2IAiVL22c9IoPbIv2Tpc2wHpHBDOsb24u+8P2gQMnSZmSpR6yLekdQcakDTkcFHrnIjA7ZmhyQBADIK6zBtCUVKC51BNxo9FIHILR6EAVKlrbPeoQft0XGIEubYT3CjxnWP7YXfeH7QYGSpc3IUo9Yp9k71yHbgu65Wl953xXS3PqoAxBqPYgCJUvbZz3Cjdsio5ClzbAe4cYMGwHbi77w/aBAydJmZKlHrOLFoomIiIiIiIiIYgQ7goiIiIiIiIiIYgQ7goiIiIiIiIiIYgQ7goiIiIiIiIiIYoS2l/kOhjUZ6Hk90O4CIK09EJ8MOI4Cx3cDB9cDW18Hqo5rXUr/9bkNGHBf3f+z22lXFqJIky2/gPEybLYBLQcC2b2B7FOUR0prZdymmcCmWZoWj3ROtgwbLb8AM0zBky2/RsT8UiiMnGG2fd0xVkdQi7OAc58HEpsr/7sqgRonkNxCebQ8C9j9FVC0Tdty+iutI9BvktalIIoO2fILGDPDzU8DLpqrdSnIiGTLsBHzCzDDFBzZ8mtUzC8Fy+gZZtvXHeN0BOX0By56A7DYgbwlwA8vAoVblXFmG5DZVekdrSrVtpx+MwHnPANYbEDBJiC3n9YFIooc6fILGDrDzmKg8Geg6Gfl71kP1H2wIPJGugwbOL8AM0yBkS6/Bsf8UqBkyTDbvq4YoyPIbAOGz1Aa/89vAGune453OYEjW5SHUZwyAcjtD/y+SDmcz2gfQon8JWN+AeNmuOB74K0+ns+dea82ZSFjkDHDRs0vwAxTYGTMr5ExvxQoWTLMtq87xrhYdNcrgNR2QMVh4LsntC5N6FLaAGfcAziPAuse1ro0RJElW34BY2dY1GpdAjIa2TJs5PwCzDAFRrb8Gh3zS4GSJcNs+7pjjI6gLmOUv7s+U86HNLohTwLWJGDdI8oHUSKZyZZfgBmm2CJbhplfiiWy5Zco1jDDFCH6PzUsLh5o1lsZPrIVSGoJ9L0daDMMsGcDlSXAkZ+AbfOAvcs1Lapfuo8DWg8G9q0Gfv9I69IQRZZs+QWYYYotsmWY+aVYIlt+iWINM0wRpP+OoJTWgDlBGU5tCwx6CIhPOXGldAeQ2Axod57y+PU9YPV9jb+elhJzgAH/Usq9eorWpSGKPJnyCzDDFHtkyjDzS7FGpvwSxSJmmCJI/x1BCWl1w6ffDlQdB5beptweT9QoPaMDpwKdRgE9rgGKdwJb/6tdeRsz5AmlPt89DpTu1bo0RJEnU34BZphij0wZZn4p1siUX6JYxAxTBOn/GkGmekWMMwOr/gnkfa40fgAoPwB8/X9A4S/K/6f/HTCZo1/OpnS+HGh3rlLOLQwoxQhZ8gswwxSbZMkw80uxSJb8EsUqZpgiSP8dQVVldcPFu4A9X3mZSABbXlMGbZlAdu+oFM1v9mzg7AeA2hrgm3sB4dK6RETRIUN+AWaYYpcMGWZ+KVbJkF+iWMYMUwTpvyOooqBuuOQP39Md+71uOKVV5MoTjDPvVYL563tA8R+AJdHzEWetm9bbc0RGJUN+AWaYYpcMGWZ+KVbJkF+iWMYMUwTp/xpBlSVA2UEguUXj05nqDQsR0SIFLKWN8rfXDcqjMTf/qvzdOgdY93Bky0UUaTLkF2CGKXbJkGHml2KVDPklimXMMEWQ/o8IAoD9q5W/6Z19T5PRpW6YF4Ek0g/ml8jYmGEi42J+iYyNGaYI0f8RQQCwfQHQbSyQ1gFod4GX8yNNwKm3KoNlB4HCn6NexEZ9Oq7x8f0mAf0mK8Oz20W8OERRZfT8AswwxTajZ5j5pVhm9PwSxTpmmCLEGEcEFWwAdn2mDJ/zFNDhororoie1BM59Acjqqfy/4RkAPCSOSDeYX/2JTwUSMuoe7rtSWOyez1sStS0n6QMzrD/MMPmL+dUf5pcCIVOG2fZ1xRhHBAHAyrsBWxbQciBw/itAjVN52NLrptk0E/j9Q82KSEQ+ML/6Mubzuuum1Nfnr8rDbfsHwKp/RK9cpF/MsL4wwxQI5ldfmF8KlCwZZtvXFeN0BNU4lMO7u10NdLkcyOwGWJOUQ+AKvgd+mQsc2qR1KYnIG+aXyNiYYSLjYn6JjI0ZpggwTkcQAEAA2+crD5lsmqU8iKQmaX4B42X4vcFal4AMSdIMGy2/ADNMQZA0v0bE/FJQJMgw276uGOMaQUREREREREREFDJ2BBERERERERERxQh2BBERERERERERxQh2BBERERERERERxQh2BBERERERERERxQh2BBERERERERERxQh2BBERERERERERxQh2BBERERERERERxQiLVgvOK6zRatFhW77WddBLGSj26KHdMcPhfQ0ZykD+08P7xbYf3teQoQzkHz28V3oog17oYV3ooQzkPz28X9x3UdQ7gtJT7LDZEzFtSQUAV7QX78FmT0R6ij3g+fRUByD4ehAFSpa2z3pEBrdF+idLm2E9IoMZ1je2F33h+0GBkqXNyFKPWBf1jqDc7DQsnHUHiksd0V50A+kpduRmpwU8n57qAARfD6JAydL2WY/I4LZI/2RpM6xHZDDD+sb2oi98PyhQsrQZWeoR6zQ5NSw3O83wb5YMdSAKhixtn/WgWCVLm2E9KBaxvegL3w8KlCxtRpZ6xDJeLJqIiIiIiIiIKEawI4iIiIiIiIiIKEaE7dQwIYQ6XFur/C0uc8AaXxGuRUTFsePlqKpSrj7urDrxXGk5XLUaFioIDmcVKtwVqFDeA4dDH+dxBsLpe0I5CwAAf0NJREFUdDZ4rn5bo/BhhvWluLQCrhOFrjlxHT5HZRWsVquGpQqcs4p3c4gGWfIrS7t3OKsa1KO4rMJ426Eyh9qe3LgPjgxZMizL50+Hw4GKE+V3f5g4VlIOuy1ew1IFjhmODlnyK8tnaFm2Q5H+Hhy2jqDKykp1+HCZ8nfai5/CYtXsDvVBqaqqwYZtewAA9hNFL5yxCBarWcNSBc7lqsWu/YXKP/Y96vOJiYkalSg4paWlDZ6r39YofJhhfamf4aIE5bn5X2yELcFYH0KdTuY1GmTJryztvsJZ1aAe981aBFOcsQ7ErqmuUduTG/fBkSFjho38+bOiogLvvfee8o/jMADgu615MJuZYWpIlvzK+BnayNuhSH8PNtbWjIiIiIiIiIiIghaRbsrc1Lq/FmN1hKK6GshJVoZbpJz4mwqYDVaPGlfdr5C5ycovGZaCtUC8sX5VtTorMbiDMrwmT9uyxBJmWHuiFrCeOKp1e1lzAEBti7MBg/2aUV1aijV5C7UuRkwxcn5lafeoqFB/hXTvg5ulACaD/fxWU1PXnnYWaVuWWGLkDMvy+TOusgpptUr5c5spzzVLZYapaUbOryyfoWXZDkX6e7DBNmdERERERERERBSssPXvJSQkNHjuzvGjkJ2ZEa5FRMWx0nLcO3MRACD3RE/oP265AqnJxvo1srisAvfNUurR7EQ9xo7sj9TkJA1LFbgjRcfwwUIeTRANzLC+OCqrsODLjQCAvrlnAwDGjx8Pu92uZbECduTIEfzjH//weM5bW6PQyJJfWdp9/YtSWgrWAlD2wXaDXeuo8Ogx3Lhps8dzzG9kyJJhWT5/Hispx3dblZ/g3fW4e+IVSEky1raIGY4OWfIry2doWbZDkf4eHLaOIJPJ1OC55EQ7UgzWcFy1gNmiXBDLfRhcanKiIevhviil+zBWe0I8bAnGuvOKLd5gxyIaGDOsL1arFfEnDmGNP3FajN1uh81m07JYAfNWXm9tjUIjS35lafdAvYtSnqhPanKS4fbB3u5YwvxGhiwZluXzp90W36AeKUnGez+Y4eiQKb8yfIaWZTsU6e/BPDWMiIiIiIiIiChGsCOIiIiIiIiIiChGaHLeTUFhCYpLHU1PGGHpKXbkZqcFNa9e6gCEVg9AP3UJtR4UHXppLwAzTBQMtn194ftBgZClvbAe4cf8GoMsbYb1CL9oZzjqHUEFhSW4ctLzcDoqor3oBmz2RCycdUfAK1xPdQCCrwegr7qEUg+KDj21F4AZJgoU276+8P2gQMjSXliPyGB+9U+WNsN6REa0Mxz1jqDiUgecjgo8cpEZHbK1uxBwXmENpi2pQHGpI+CVrZc6AKHVA9BPXUKtB0WHXtoLwAwTBYNtX1/4flAgZGkvrEf4Mb/GIEubYT3CT4sMa1bjDtkWdM/V+srdrpDm1kcdgFDrAeilLqHXg6JDH+0FYIaJgsO2ry98PygQsrQX1iPcmF+jkKXNsB7hFt0M82LRREREREREREQxgh1BREREREREREQxgh1BREREREREREQxgh1BREREREREREQxQtvLY/vj1j3+T3tgHfDpuMiVJVxaDQa6XwM0Pw2wZyvPVRwGDm0GfnsXOPidpsVrktkGtBwIZPcGsk9RHimtlXGbZgKbZmlaPNIZ2TJs9PwSBUK2/MqE2yLyh+wZ7nMbMOC+uv9nt9OuLIFgfskfzK9+SZBh/XcEVRxufHycFbBlKMNHfop8eUI1+DGg5/V1/9c4lL+pbZVHl9HAlv8C6x/RpHh+aX4acNFcrUtBRiFThmXIL1EgZMqvTLgtIn/JnOG0jkC/SVqXInDML/mL+dUnSTKs/46gd85ofHzvW4Cz7leGf3s/8uUJRder6hrNrs+A758Gju9W/k/rqPSIth8JnDoRKPge2P2lZkVtkrMYKPwZKPpZ+XvWA0Bic61LRXokS4Zlyi+Rv2TJr0y4LaJASJthE3DOM4DFBhRsAnL7aV0g/zC/FAjmV38kyrDxrxHU/Wrl78HvgZJd2palKV3HKH9L8oCvb69rNIBS9qV/A46fOASw4yVRL57fCr4H3uoDfH4d8N0TwB+LAVel1qUiozJKhmXJL1E4GSW/MuG2iMLJqBk+ZQKQ2x/4fRGw/xutS+M/5pfCifmNPokybOyOoJx+QEYXZfi3+dqWxR+JzZS/Rb8CwtVwvKgBirYpw9ak6JUrUKJW6xKQLIyUYVnySxQuRsqvTLgtonAxaoZT2gBn3AM4jwLrHta6NIFhfilcmF9tSJRhY3cEdTvRC1pZohyapXfH9yp/s3oAJnPD8SYLkNVTGT6yJXrlItKKkTLM/BJ5MlJ+ZcJtEYWLUTM85EnlC9a6R5Qvk0bC/FK4ML/akCjDxu0IsiTWHW71x/8Al1Pb8vhj29vK37QOwLkvAKn1roye1hE470XluZLdwNY5mhSRKGqMlmHml6iO0fIrE26LKByMmuHu44DWg4F9q4HfP9K6NIFjfikcmF/tSJRh/V8s2pdOfwLik5VhoxwOl/81sPYh5SJSHS9RHu6rjFvsSo/uL28DG58Bqsu0LStRpBktw8wvUR2j5Vcm3BZROBgxw4k5wIB/Ke199RStSxMc5pfCgfnVjkQZNu4RQe6LYxX+oty1yih+fh346q9AxRHlf4tdeQDKLQCtiUB8qnblI4oWI2aY+SVSGDG/MuG2iEJlxAwPeQJISAM2zQRK92pdmuAxvxQq5ldbkmTYmEcEZXQBcvoqw9sNdKs8sw0Y9m+g06XA4Z+AFZOUAANAdi/gjH8qVyJvMwz47Frg6G9alpYocoyYYeaXSGHE/MqE2yIKlREz3PlyoN25Slvf8l+tSxM85pdCxfxqS6IMG/OIoO7jlL81TuW2c0YxcKrSaIp3AouvAvavASqPKY/9a5Tniv8A7FnAoEe0Li1R5Bgxw8wvkcKI+ZUJt0UUKqNl2J4NnP0AUFsDfHOv9zv1GAXzS6FifrUlUYaN1xEUZ1V6FQEgbwlQdVzb8vjLmgT0uEYZ/uVtwFXZcBpXJfDLW8pwizMBW1b0ykcULUbMMPNLpDBifmXCbRGFyogZPvNewJYJ/Pqe8gXLkuj5iLPWTevtOb1gfilUzK+2JMuw8U4Na3e+0sMGGOfiWIByZXF3oz6+x/d0JXl1wyltAGdRZMtFFG1GzDDzS6QwYn5lwm0RhcqIGU5po/ztdYPyaMzNvyp/t84B1j0c2XIFivmlUDG/2pIsw8Y7Ish9OFxJHnBwvbZlCYQQdcPJrXxPZ8+uG64uj1x5iLRixAwzv0QKI+ZXJtwWUaiYYe0wvxQq5ldbkmXYWEcEJbUEWg1Whrcv0LYsgSreqdxazmJXQvzbew3PkTTFAT2uVYadxUDJH1EvJlFEGTXDzC+RcfMrE26LKBRGzfCn4xof328S0G+yMjy7XcSLEzTml0LB/GpPsgwb64ig7mOBODNQWw1sX6h1aQLjqqw7hK9Zb2Dk60BGNwAm5ZHZHbjwTSC3vzLNz3MAUatRYf0QnwokZNQ9TCeaksXu+bwlUdtykr4YNcOy5ZcoGEbNr0y4LaJQMMPaYn4pFMyv9iTLsIGOCDIBXa9SBvNXAI7D2hYnGN89AaR2ANoOq3vUOJVxFlvddDs/AX74jwYFDMCYz+vO+ayvz1+Vh9v2D4BV/4heuUjHDJ5hmfJLFDCD51cm3BZRUJhhXWB+KSjMr25IlGHjdAS1GgyktFaGjXJxrJO5KoEvxgMdLga6jAayeysX/BICKNsPHP5J6TjZu1zrkhKFn9EzzPxSLDN6fmXCbREFgxnWB+aXgsH86odEGTZOR9D+1fo/b9BfeZ8rDyN7b7DWJSCjkSXDMuSXKFCy5Fcm3BZRIGTO8KZZysNImF8KBPOrPxJk2FjXCCIiIiIiIiIioqCxI4iIiIiIiIiIKEawI4iIiIiIiIiIKEawI4iIiIiIiIiIKEawI4iIiIiIiIiIKEawI4iIiIiIiIiIKEawI4iIiIiIiIiIKEawI4iIiIiIiIiIKEZYtFpwXmGNVosO2/K1rkM4y6B1XbRePgVGD+8XM0wUPD20Oz2UQS/0sC70UAbyjx7eK+6Dw/saMpSB/KeH94ttP7yvYcQyRL0jKD3FDps9EdOWVABwRXvxHmz2RKSn2AOeT091AIKvB6CvuoRSD4oOPbUXgBkmChTbvr7w/aBAyNJeWI/IYH71T5Y2w3pERrQzHPWOoNzsNCycdQeKSx3RXnQD6Sl25GanBTyfnuoABF8PQF91CaUeFB16ai8AM0wUKLZ9feH7QYGQpb2wHpHB/OqfLG2G9YiMaGdYk1PDcrPTDL+hkqEObjLVhSJPlvYiSz2IAsW2ry98PygQsrQX1oNilSxthvUwPl4smoiIiIiIiIgoRrAjiIiIiIiIiIgoRkT01LCiY8WRfPmIKC5zoKZauWp3zYmLdxcePQan06lhqQInSz2M2IZkYsT1L0vbd1bVwOmsBABUl5YCAI4cOQKbzaZlsQJWWFiodRFilhHzK0u7dzqdKD1RfuuJ+hwpOgZbvGY3aw2KEduQTIy4/mXZB8tSDyO2IVkYcd3L0u5lqUek25BJCCHC8UJ79+5F27ZtAQCDO4TjFbVRWwscLlOGc1O1LUsoZKlHfWvylL/5+flo06aNtoWREDOsX+62L4vDhw+jWbNmWhdDKrLktz5Z2r1s7wf3wZEhS4Zl2QfLUo/6mOHIYX71RZZ61BeJ/PLUMCIiIiIiIiKiGBGRY5Rl+RVvZ5HWJQgPWepB0cMMExmXLPmVBd8PCpQsbUaWfbAs9aDoYH71RZZ6RAKPCCIiIiIiIiIiihFhu0aQy+XCgQMHwvFSmhJCoLKy0uO5hIQEmEwmjUoUHFnq4U3Lli1hNpu1LoZ0mGH9k6UeWVlZiIvj7xDhJEt+vTFiu/e2HZIF98GRIUuGZdkHy1IPb5jh8GN+9UWWengTzvyGrSOIiIiIiIiIiIj0jT/JEhERERERERHFCHYEERERERERERHFCHYEERERERERERHFCHYEERERERERERHFCHYEERERERERERHFCHYEERERERERERHFCHYEERERERERERHFCHYEERERERERERHFCHYEERERERERERHFCHYEERFJ6M0334TJZEL79u0j8vr5+fm46aab0LZtW8THx8NkMiE9PT0iyyKS3fTp02EymTBs2DCti0JEAYr0/paIomvChAkwmUyYMGFCRF5/9erVuOSSS9CsWTOYzWaYTCaMHj06IstqjCWaCxNCYOHChXj33XexefNmHD58GGazGTk5OWjRogXOPPNMDBkyBOeeey5SU1M95p01axaKi4sxevRonHbaadEsdtjs378fn3zyCVasWIEffvgB+/fvBwDk5uZi4MCBuOWWWzBixAif8xcXF2PVqlXYtGkTNm/ejE2bNqGgoAAA8MYbb0SssVJsYl5Dy2uo8+tZSUkJBg0ahH379gEA0tLSYLPZkJaWBkD5ULx7924MGzaMX2zDgFkMLUurVq3CV199hY0bN2LXrl0oLCxEWVkZMjIy0KtXL1x++eW45ZZbYLfbo1UliiHMb2T2hX/961/x6quvAgDatWuH3bt3h7PYRCFh7kPL/fTp0/HQQw81uZzff/8dnTt3Dlu5o2H9+vUYMWIEampqYDKZkJWVBbPZjIyMDABK3QGlMyrincsiSo4dOybOOeccAUB9WCwWkZmZKSwWi8fzb7zxRoP527Vr53OcEeTn5wuTyeRRz8TERGG32z2eu/nmm0VNTY3X13jjjTc8pm1qnREFi3kNLa/hyHuo3NuLdu3ahf21X375ZQFAZGRkiF9//bXBeHfbefDBB8O+7FjDLIaepUsuucRj2qSkJJGUlOTxXIcOHcT27dujXLs6Dz74oAAgzjnnHM3KQOHH/EZmX7h8+XKP143Efi4QkdzfkvEw96Hn3r1PtFqtIicnx+cjLy8vInUYP368ACDGjx8f9te++uqrBQAxaNAgUVRU1GC8e/2sWLEi7Ms+WdRODbvxxhuxatUqmM1m3H333dixYwcqKytRVFQEh8OBn376CU899RT69OkTrSJFlcvlghAC5557LubOnYv9+/ejvLwcZWVl+OWXX3DZZZcBAF5//XW1J9Cb3NxcXHTRRZg6dSo++uijKJWeYg3zGlpew5V3vdq6dSsAYMSIEejevbvGpZEbsxh6ls477zw8//zz2Lx5M44fP46ysjKUlZWhsLAQzz//POx2O/Ly8nD55ZejtrY2irUj2TG/4d8XVlRU4JZbboHFYkH//v0jWHqi4DD34cv92WefjYKCAp8PI56O6f4MPW7cOGRmZmpbmIh3NQkhduzYofZuPfHEE01OX1FR0eA5o/eOFhcXi02bNvkcX1tbKy688EIBQCQnJwuHw9FgGm+9pmikR5koGMxr6HkNR95DFclfKCdMmNDoLyU8Iig8mMXoZOnVV19V1/OaNWtCKW7QeESQfJjfyOR30qRJAoCYOnWq+qu91kfi8IggcmPuw5N7rfeJkTwiqH379o2+v+72I80RQT/++KM67O4FbEz98/TdF1Dcs2cPAOCmm26CyWTyeHjz2WefYcyYMWjVqhUSEhKQkZGBoUOH4uWXX0ZVVZXXeYYNGwaTyYTp06ejqqoKTz75JE499VQkJSUhIyMD559/PpYsWRJAzeukpaWhb9++PsebTCbcfPPNAICysjL8+uuvDaYxm81BLTtc6q8fXxq74GUk1y+FD/Mael7DkfdoKC0txZNPPomzzjoLmZmZSEhIQJs2bTBu3DisW7euwfTudf7mm28CAObOnevx3rovmLlq1SoAwEMPPdTg/ed1HPzHLEYnSwMHDlSH3de9ioQlS5bg/PPPR3p6OpKTk9GnTx88/fTTqK6u9mv+lStX4qqrrlLfm+zsbJx77rl444034HK5Gkx/++23w2Qy4corr2wwrrq6GikpKTCZTGjWrBmUz56eRo4cCZPJhGnTpnk8724/K1euRGlpKe6//350794ddrsdWVlZGDVqFL777js/14q8mN/w53f9+vV4/vnn0bVrV9x///1BlSlY69evx+jRo5GdnQ273Y5u3bph6tSpKCsr82v+H374ATfeeCPatWsHm82GjIwMnH322Zg1axYqKysbTP/ss8/CZDL5POqpW7duMJlMsFgsKCkpaTD+L3/5C0wmE2644QaP59u3b6/ur6uqqvDMM8+gT58+SEpKQlpaGkaMGIEvvvjCrzpRQ8y9cT4Dh2r37t2YNGkSevXqheTkZCQmJqJ79+648847kZ+f32D6kz8Hn/z+ui9Q7TZ8+HCP8RE5+iniXU1CiAULFqi9W1999VVA8z7zzDMiJydHxMXFCQAiNTW1wfmB9VVUVIgrr7zS4xzE1NRUj3MVBw4cKI4ePdpgWe5fsadMmSKGDBmintOZnp7u8XqR+pX7f//7n7qMDRs2+DWPe3p/eo3rX2MomF5Gf37lb6wHV+v1S/5hXv0TTF4DmX/FihUhHfHX1C+UP/zwg2jdurW6DLPZLFJSUtT/TSaTePzxxz3mufzyy0VOTo6w2WwCgLDZbB7v7fz580VOTo6wWq0CUK7FcvL7n5+fH3BdYhWz6J9Qs/if//yn0flD3XcKUbdvdD/S09PVa0UMHTpUTJkypdFfPydPnuyRzfT0dGE2m9XnRowYIY4fP+4xz4cffigAiKysLFFbW+sxbs2aNR7l+emnnzzGV1VVicTERAFALF++3GOce553331XdO7cWd0WuKcHIOLj48WXX34Z1LqSBfPrH3/z63Q6RY8ePYTJZBIrV64UQgi/jggKdV8qhBBz5sxR3wsAIi0tTcTHxwsAonv37mLGjBmNlmPGjBke70VaWpq6nwQgTj31VHHgwAGPeTZt2iQAiLi4OHHs2DGPcfv27fN4bz755JMGy3Rn8/XXX/d43n20yQsvvCAGDBggAOU6LMnJyR7bmDlz5gS1rmIdc++fpnIf6hFB9fe5wVxHqKkjgt555x2RkJCgLiMhIcHjGkgpKSkN9oHu99DX+3vzzTeLnJwc9TUyMjI8xvfv3z+INdG4qHQE5eXlqY2yd+/eQV2Q0d/D5K6//noBQHTs2FHMmzdPlJSUCCGEcDgc4pNPPhEdO3YUAMTo0aMbzOsORVpamkhISBCvvPKKerhafn6+R9i8bXRDddddd6kfoNzlbkogOze9dARptX7JP8yrf4LJayDzR7Ij6MCBA6J58+YCgLjiiivExo0bRVVVlRBCiEOHDolp06apX1IXLVrUYP6mdpA8NSw8mEX/BJPFiooKsWPHDvHYY4+pH+aGDh3qddpQ952ffPKJOv9VV12ldoZWVFSIF198UcTHx6sfvr3tO1944QV1/ltvvVUcPHhQCCFEWVmZmDlzpprVq6++2mO+oqIitf388MMPHuMeeeQR9YMoADFz5kyP8atXr1Y/3J582H79D6k9e/YUy5cvFy6XS9TW1orvv/9edOvWTd32uFyugNeXLJhf//ib33/9618CgJg4caL6XDQ6gjZt2qRmbNiwYeoNEqqqqsR7770n0tPT1fx6K8fixYvV5V922WVi165dQgghKisrxVtvvaX+AHP22Wd7XALC5XKJjIwMr/vht99+2yO/d955p8f4vXv3+vwS7G5TGRkZolWrVuLjjz9W9/+//fabGDhwoACUU3aKi4sDXl+xjrn3T1O5d3+fzM7OFr169RJ2u10kJSWJrl27iokTJ4rNmzc3+vqR7Aj66quvRFxcnLBYLOKf//ynyMvLE7W1taK2tlb89ttv4qqrrlLzuWfPngbzN/X+hvJ5I1BRu2vYLbfc4tHTfPrpp4u//e1vYs6cOWLr1q0Nfq06mT+h+OabbwQA0bx5c5+/Ou/du1e9W8jJH4zqX+HdW0+4y+USQ4cOFQBEr169mqxzIHbt2qX+mnbDDTf4PZ8RO4K0WL8UGOa1ccHmNZD5I9kRdPPNNwsA4tprr/U5v/sXzj59+jQYx46g6GEWGxdIFg8ePKiW8+THpZde6vXuHUKEvu/s2bOnul/01jHyyiuvqK9/8r6zoqJCZGZmCgDimmuu8fr6zz//vDr/xo0bPcaddtppAoB49tlnPZ4fPny4ACAeeOABtf71PfTQQ+oX35O5l9WsWTNx6NChBuO3bNmiTqPVNZf0gvltnL/53bx5s7BYLCInJ8fj6JhodARddNFFAoDo2rWr1+u5fPHFF+rreytHjx49BAAxZMgQr9f6rH9kxAcffOAxbvTo0QKAuP322z2ev+mmmzzy27t3b4/xc+fOFQBE+/btGyzP3aYSEhK83vXz8OHD6lG/77zzjtd1Qo1j7hvnT+7rd+TExcU1uOOayWQSU6dO9bmMSHUEuVwu0aVLFwFAvPrqqz7n/9Of/uS1k1aIGO0Iqq6uFtOmTWtwy1b3o3nz5mLy5MmioKDA6/z+hML9pk2ePLnRsowaNUoAEE8++aTH8+5QtGnTxmdIv/rqK7XMW7ZsabzSfqqoqBD9+vUTgNLzuX//fr/nDWXnFqhwdQRFe/1S4JhX30LJazjm95evjiCHw6F+yDv5dJD6CgsL1XV38vvMjqDoYRZ9CzRLhw8fVg+xdmcAUI7S8faFKBx++ukndTlLly71Oo3L5RKtWrXyuu+sfzSRr1+Wa2pqRIsWLQQAcdddd3mMc59Sdskll6jPOZ1OYbPZRFJSkiguLhbx8fEiLS3N40vqsGHDBADx0EMPNVieuzyNfQjv0KGDACBeeukln9PEAubXN3/zW11drXZovv/++x7jIn2x6GPHjqmncbz22ms+pzvrrLO8lqN+/hs7VfLMM88UgHKEbn3PPfec1y/i7ovN7t+/X7Ru3VqYTCZx+PBhdbz7hg433XRTg2W529R1113nszzujuJ//vOfPqch35h73/zN/TvvvCOefvppsX37dvWItcrKSvHll1+q8wMQ//73v8NSrpP5+pzr7ljOzs5u9IjXhQsXCkA5dfRkeuoIitrt4y0WCx5++GHs378fb7/9NiZOnIg+ffogPj4eAHD48GHMnDkTp5xyCr7//vuglvHtt98CAObMmYPc3Fyfj2XLlgGAejGuk7kvoOXNkCFDYLFYAAAbN24Mqpz11dTU4Nprr8WmTZtgtVoxb948tGzZMuTX1bNorl8KDvPqXah51UPeN23aBKfTCQC44IILfK73Xr16qfP4WvcUecyid8FkqVmzZuotZysqKrB3715MnToVixcvxqmnnorZs2eHXK6TuetqsVgwZMgQr9PExcV5vcFC/fnbtGmDrl27ep3GbDZjxIgRHtO7uZ9fvXq1ekHptWvXwul0YvDgwUhLS8OAAQNQUlKCTZs2AQCcTqd6sfjhw4f7rNuAAQN8jnO/F0ePHvU5TSxgfr0LJL9PPvkkfvzxR4waNQpjx44NedmB2Lx5M2prawHUZckbX+Pq5/+cc87xOf/555/vMf3Jr/vLL7/g8OHDAIC8vDzs3r0b3bp1Q8uWLTF8+HAIIbBixQp1Pvcw86sN5t67QHJ/3XXX4Z577kHXrl1htVoBAPHx8bjggguwZs0anHHGGQCUC2x7u1h6pLjXe0lJCVq2bOlzvd9yyy0A9P/5OWodQW5paWm4/vrr8dprr+HHH39ESUkJli5diksvvRQAUFhYiDFjxqhfVAJx4MABAMDx48dx6NAhnw/3a1dUVHh9nVatWvlchs1mQ1ZWFgCoG+VguVwuXHfddfj4449hsVjw7rvv4oILLgjpNY0gWuuXQse81gk1r3rJu3u9A2h0vR86dEidzte6p+hhFuuEI0smkwmtW7fGo48+innz5qG6uhq33XYbfvrpp5DKdjJ3XbOzs5GQkOBzutatWzc6f2Prtv78J6/boUOHwmw24/jx49iwYQOAui+J7i+Z7r/Lly8HoHQUVVZWwm63N/plMSUlxec495cHf++IJjvmt04g+d22bRseeeQRJCcn46WXXgppucGoX9fG1k9T+fU3/yev2169eqF58+YA6vLZVH537dqlfvlsrCOI+Y085r5OOD8D22w2PP744wCUu459/fXXIZUtEO71Xl1d3eh6P3bsGADA4XBErWzBiHpH0MlsNhvOO+88/O9//8P48eMBKLdvDebWhe5fu15++WUI5bS3Rh/uWyBrweVy4frrr8eCBQtgNpvxzjvveL3FK5GeMK/B5VVPea9/m2mHw+HXuvd1tAJph1kMX5auuOIKtG3bFrW1tZgzZ06YSqoPqamp6NevH4C6L4ruv76+SLr/Dho0SP0Fm8KL+fUvv3//+99RVVWFqVOnIiMjA2VlZR6PmpoaAIAQQn1Ops4Lk8mk7n8DzW/nzp19dlCRNpj78O23zzrrLHV4165doRbRb+71PmDAAL/WuxAiamULhuYdQfXdeuut6vD27dsDnj83NxdA6Idh7d+/3+e4yspKFBUVAYDaSx8od6/o/Pnz1UBcffXVQb1WNLl/IWis59qfw/MivX4pOpjX6Mwfbu71Duj/kFXyD7MYOvevojt37gzL67m561pYWIiqqiqf0/lad+759+3b1+hy3OO9rVv3UQHLly9HeXk5vv/+e6Snp6Nv374AgIEDB8Jut+Pbb79FVVVVgy+aFFnMr295eXkAgClTpiAlJaXBY968eQCA/Px89bkXX3wxqPJ5U7+uja2fpvJbWFiIyspKn/P7m19AOSLIZDKpz7dt2xYdO3bE77//jn379jG/BsHcG1O41rte6KojKDk5WR0++RDKuDilqI31rA0aNAgA8Omnn4ZUjlWrVvlczurVq9VfIPr37x/wa7tcLlx77bV4//331UCMGzcupPJGS0ZGBgBg7969Pqf57rvvmnydSK5fih7mNfLzR8IZZ5yh/sq/ePHiiCzDn/efwodZDI0QQv3C2djpEsFw17WmpgarV6/2Ok1tbS1WrlzZ6Pz79u3Djh07vE7jcrnU00Xc102oz/2Fce3atfj6669RXV2Nc845R20b8fHxGDRoECoqKrBs2TL1FLLGTiuh8GF+9atv377qe1D/Gjwnc3e+nKx+/letWuVzfvd1XBrL7x9//IGlS5fiwIEDOPXUU9VTdgDPo4Lc2xLmV9+Y+9CsX79eHe7QoUNYXtMf7vVeUFAQsWvZuq/ZFI3P0FHpCMrLy/P5Aaa+uXPnqsPuX6rcUlNTAQDFxcU+53f3rv788894+eWXG11WeXm5z1/n8vPzPcriVltbq56T2LNnT/Tu3bvRZZzM3Su6YMECWCwWzJs3zxA7Qrc+ffoAAL788kuUl5c3GL98+XL1ApONidT6pfBgXhWh5lWveU9KSsK1114LAHjqqaeQn5/f6PTBXCzSn/efmsYsKkLJkvtDbGPeeOMNFBQUAEDYT4M89dRT0aNHDwDAY489pl54tr7XX3/d5xE/559/vvqFb/r06V6nefXVV9XrFlxzzTUNxg8ePBhWqxUOh0N9H04+WsD9pfHhhx9GTU0NkpOT+WNMiJhfRSj53b17d6OnXLhPr2nXrp363KRJkwIqX2PS09PV65j8+9//9npE/LJly7B27Vqv85966qno2bMnAODRRx/1ODXb7fPPP1d/RPWWX/dFoQFg2rRpAHzn96WXXsLBgwcBhH9bRv5h7hWh5L6pDpDKykpMnToVgPKZ9txzzw2obKEYPnw4OnfuDACYPHlyo0f6Agb4DO3v7cVCsXjxYhEXFycuvvhiMXfuXJGXl6eOq6qqEps3b1ZvdQhAnHnmmQ1uyXbdddcJAOLss88WR48e9bmsm266SQAQJpNJTJo0Sfzxxx/qOKfTKdatWyfuuecekZWVJfbu3esxr/tWemlpacJms4nZs2cLh8MhhBAiPz9fjB07Vi3jRx99FNA6qKmpEePGjRMAhMViEQsWLAhofrcjR454PNzleeGFFzyeLy8vbzCv+3bSCPKWdNu3b1dvo3nppZeq66+iokK8+eabIjU1VWRmZnq9Ba4QkV2/FD7Ma+h5DUfe3beoRBO3EPXF1+3jhRDiwIEDomXLlgKAaNmypXjrrbfE8ePH1fGHDx8WCxcuFKNHjxYXXHBBg/mbun381KlTBQDRuXNnsW/fvoDLTgpmMfQsrVixQgwZMkS89dZbDcq9Y8cOce+99wqLxSIAiE6dOomKiooGrxHqvvOjjz5S57/66qvVcjgcDvHyyy+LhIQEkZ6e7nPf+cILL6jz/+Uvf1FvOVxeXi6ee+45YbVa1df2ZdCgQR63L966davH+HXr1nmMv+iii3y+lj/rwt0mHnzwQd8rRnLMb/g++/riz+3jQ92XbtiwQZjNZgFAjBgxQvz2229CCOUW4e+//77IyMhQ8+utHIsXL1aXP3r0aLFr1y4hhNIG3nnnHZGamqq+xzU1NV7L4G4H7sfixYs9xh84cMBjfI8ePXzWJ5Bbk/vax5NvzH3ouV+5cqU499xzG+y3q6qqxLJly8QZZ5yhlu2pp57y+hoPPvigOk3998BfjWVg2bJl6ueGAQMGiGXLlqm3uBdCiD/++EO8/PLLon///uKRRx5pMH9TGXTvr8eMGeP1+3w4RaUj6IsvvvDYQAEQ8fHxIjMzU5hMJo/n+/btK/bv39/gNVatWqVOazabRYsWLUS7du0abHQrKyvFxIkTPV4zOTlZZGRkqJ0Y7sfJX1DcoZgyZYoYPHiwACCsVqvIyMjwmO/+++8PeB2sWrVKnd9qtYqcnJxGH/Pnz/f6OievR18Pbx++Qv0wK4QQDzzwgMdy0tLS1DCMHj1a3H///U12BEVi/VL4MK+h5zUceY9kR5AQQmzbtk107dpVXUZcXJzIzMwUSUlJHuvvvPPOazBvUx8Sd+zYIWw2m/q6OTk56vt/8ocR8o1ZDD1L9XMEQNhsNpGdnS3sdrvH83369PH5YTEc+05356j7kZGRoe47hwwZIqZMmeJz3ymEEJMnT1bnNZlMHvMDEMOHD/fozD3ZtGnT1GmbN2/eYHx1dbVISUlRp3n66ad9vpY/64IdQcyvu/zh+OzrSzQ6goQQ4tVXX/V4z9LS0kRCQoIAILp37y5mzJjRaDlmzJjhMX96erqIj49X/+/du7fX999tzpw56rRms1mUlJQ0mKZ79+7qNH/72998vhY7giKLuQ//fttut4vs7Gz1Rw/3Z8t//etfPssQyY4gIYRYtGiRxz7TarWKrKwsdbvgfjz66KMN5m0qg2+//bbH67Zq1Uq0a9dODBo0KOB6NEW5+m+EjRw5Er///js+//xzrFmzBj///DP27duH4uJiJCYmomXLljj99NNxxRVX4KqrrlLPjaxv6NCh+OyzzzBjxgz88MMPOHTokNdDrOPj4/Haa6/h5ptvxuzZs7F69WocOHAAlZWVaN68Obp3746hQ4fiyiuv9HnLvPj4eHz99dd49tln8e6772LXrl1IS0tD//79cdddd+Hiiy8OeB3UL6v7lnON0evt5h566CF06dIFL774IrZu3QqXy4XTTjsNEydOxK233oqHHnqoydeIxPql8GFeQ8+rEfLeo0cPbNmyBXPnzsWHH36IH3/8EUePHkV8fDw6d+6M008/Heeff35Qd3bo0qULVqxYgSeeeALfffcdioqK1FN0/DlVhxTMYuhZ6tevH95++22sXLkSGzduREFBAYqKipCQkIBOnTqhb9++GDNmDK688kqYzeaAy+evRx99FGeddRZmzJiBjRs3orKyEj169MB1112Hu+66C4899lij88+YMQOXXnopXnzxRXz77bcoKipCSkoKTjvtNNxwww248cYbGy3/8OHD8cgjj6jDJ7NYLBgyZAg+//xzn9NQYJhfY+wL/XHrrbeid+/eeOKJJ/Dtt9+ioqIC7dq1w5gxYzBlyhR8+OGHjc4/efJknHPOOZg5cyZWrVqFQ4cOwW63o2/fvrj66qtx2223NXp7+fp57N+/v3rqSH0jRozAb7/91mB6ii7mPvTc9+7dG//+97+xbt06bN26FYWFher669mzJ4YMGaJmUiujR4/Gzp078dJLL2HJkiX4/fffUVxcjKSkJHTv3h1nnHEGLrnkkqDW3/XXXw9AOe1769atOHjwoNf3PxxMQvBqnm7Dhg3DqlWr8OCDD/o8F5+Cx/VL4cT2RKQPzCKRcTG/RLGHuSdAZ3cNIyIiIiIiIiKiyGFHEBERERERERFRjGBHEBERERERERFRjGBHEBERERERERFRjODFoomIiIiIiIiIYgSPCCIiIiIiIiIiihHsCCIiIiIiIiIiihHsCCIiIiIiIiIiihHsCCIiIiIiIiIiihHsCCIiIiIiIiIiihHsCCIiIiIiIiIiihHsCCIiIiIiIiIiihGWcL2Qy+XCgQMHwvVymhFCoLKy0uO5hIQEmEwmjUoUPrLUIysrC3Fx7MMMN1ky7I0R2763bZEsWrZsCbPZrHUxpCJLfmXZB8tSD2+Y38hghvVFlnp4wwyHH/OrL7LUw5tw5jdsHUEHDhxA27Ztw/VyRD4dPnwYzZo107oY0mGGKVry8/PRpk0brYshFeaXooX5jQxmmKKFGQ4/5peiJZz55WEVREREREREREQxImxHBNU3uEMkXjU6amuBw2XKcG6qtmUJlzV5WpeAjMbIGa5PlrbP94MCYeT2Iss+WJZ61Mf8Rg8zrD1Z6lEfMxwdzK/2ZKlHfZHIL48IIiIiIiIiIiKKERE5IsjttWfuRVZGeiQXEXbFZQ5Me/FTAHU9iHeOH4XkRLuGpQqcs6oGi5b/BAAYnTMQADB27FjYbDYtixWwwsJC9OzZU+tixCwjZliWtu90OrFgwQIAgPXQegDA5SP6wBYf0c122BUdK8Yt9zyldTFikhHzK8s+WJZ6ML/aYoa1I0s9mGHtML/akaUekc5vRL9RZGWko1l2ZiQXEXbW+ApYrMpqsZxYO9mZGUhJTtSwVIFzVlbDZksAANhSUgAAzZo1M9yXYdKWETMsS9t3Op1IOVF+lCj1aZaVAVuCVcNSkZEYMb+y7INlqQdpixnWjiz1IO0wv9qRpR6RxlPDiIiIiIiIiIhiBDuCiIiIiIiIiIhihCYXmygoLEFxqUOLRXtIT7EjNzstqHn1UgcgtHoQBYptX1/4flCgZGkzrEf4McP6J0t7YT3Cj/k1BlnaDOsRftHOcNQ7ggoKS3DlpOfhdFREe9EN2OyJWDjrjoBXuJ7qAARfD6JAse3rC98PCpQsbYb1iAxmWN9kaS+sR2Qwv/onS5thPSIj2hmOekdQcakDTkcFHrnIjA7Z2t39Jq+wBtOWVKC41BHwytZLHYDQ6kEUKLZ9feH7QYGSpc2wHuHHDOufLO2F9Qg/5tcYZGkzrEf4aZFhzWrcIduC7rla3/3GFdLc+qgDEGo9iALFtq8vfD8oULK0GdYj3JhhI5ClvbAe4cb8GoUsbYb1CLfoZpgXiyYiIiIiIiIiihHsCCIiIiIiIiIiihHsCCIiIiIiIiIiihHsCCIiIiIiIiIiihHaXh7bX1mnAO3OBZr1BtI6ALYsID4ZqCoDiv8A9q4Atr0NVJZoXdLGJaQD7c4HWg0Csk8BklsBcWbAcRQo3ALs+BDY/aXWpSQKL1nyKwtuhyhQsmTYbANaDgSyeyttP/sUIKW1Mm7TTGDTLE2L5zdmmALB/OoL80uBYob1RaIMG6MjqPtYoNf4uv9rnMrDlgHk9lcep9wMfDkROLxZu3I25YaNQFy9K5LXOIHaGiC5hfJoPxLIXwEs/SvgcmpXTqJwkiW/suB2iAIlS4abnwZcNFfrUoSOGaZAML/6wvxSoJhhfZEow8boCDr8I1C6DyjYoPR8Vh1XnrckAh0uBAZOBezZwAWzgfeHA9WlmhbXpzgrcPgHYPtCYN8qoHSv8nxya6Dv7UD3cUDb4cDQJ4AVk7UtK1G4yJJfWXA7RIGSKcPOYqDwZ6DoZ+XvWQ8Aic21LlVgmGEKBPOrL8wvBYoZ1heJMmyMjqDfP/L+fE2FMq7iCHDJO0BiM+XQuZ0fR7V4fls8Dji4ruHzZfuAb+5VehN7Xg90uQL4/mmg/GD0y0gUbrLkVxbcDlGgZMlwwffAW308nzvzXm3KEgpmmALB/OoL80uBYob1RaIMy3Gx6MM/1A0n5WpXjqZ4azT1bX+/brjZqZEtC5FeGCW/suB2iMLNKBkWtVqXIDyYYQon5je6mF8KN2Y4uiTKsBwdQbln1g0fz9euHKGqqawbNpm1KwdRNMmSX1lwO0SBYob1hRn+//buPD6uut7/+HuSSTKTtUvSpoFuaKEUsEDZLQKCIi73sshiQQqIXHFBRLygUgTxAl69UkWRXxGwLBYKAirrLYu17FCUllJ6WUL3LW2SJp2ZpDM5vz9OtpI0nfV8z/J6Ph7zmJNZP5+Z73uS+eYsyAT5dRfyi0yRYXfxUIa9sWnYYIpK7W0Kxx8vTbvMvqy1UVr5tNm6ctFwZN/y1nfM1QEUmh/z6xd8DiE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      "text/plain": [
       "<Figure size 1500x1500 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class Node:\n",
    "    def __init__(self, tiles=np.array([]), parent=None, g=0, h=0, f=0, action=\"\"):\n",
    "        self.tiles = tiles\n",
    "        self.g = g\n",
    "        self.h = h\n",
    "        self.f = f\n",
    "        self.parent = parent\n",
    "        self.action = action\n",
    "\n",
    "    def isGoal(self):\n",
    "        return np.array_equal(self.tiles, goal.tiles)\n",
    "\n",
    "    def findBlank(self):\n",
    "        for i in range(n):\n",
    "            for j in range(n):\n",
    "                if self.tiles[i][j] == 0:\n",
    "                    return i, j\n",
    "\n",
    "    def hgf(self):\n",
    "        sum = 0\n",
    "        if self == start:\n",
    "            for i in range(n):\n",
    "                for j in range(n):\n",
    "                    if self.tiles[i][j] == 0:\n",
    "                        continue\n",
    "                    else:\n",
    "                        x, y = findGoal(self.tiles[i][j])\n",
    "                        sum += abs(x - i) + abs(y - j)\n",
    "            self.h = sum\n",
    "            self.g = 0\n",
    "            self.f = sum\n",
    "        else:\n",
    "            for i in range(n):\n",
    "                for j in range(n):\n",
    "                    if self.tiles[i][j] == 0:\n",
    "                        continue\n",
    "                    else:\n",
    "                        x, y = findGoal(self.tiles[i][j])\n",
    "                        sum += abs(x - i) + abs(y - j)\n",
    "            self.h = sum\n",
    "            self.g = self.parent.g + 1\n",
    "            self.f = self.g + sum\n",
    "\n",
    "    def genChildren(self):\n",
    "        global node_count\n",
    "        x, y = self.findBlank()\n",
    "        newTiles = []\n",
    "        actions = []\n",
    "        if (x + 1) < n:\n",
    "            new = np.copy(self.tiles)\n",
    "            new[x][y] = new[x + 1][y]\n",
    "            new[x + 1][y] = 0\n",
    "            newTiles.append(new)\n",
    "            actions.append(\"down\")\n",
    "        if (x - 1) > -1:\n",
    "            new = np.copy(self.tiles)\n",
    "            new[x][y] = new[x - 1][y]\n",
    "            new[x - 1][y] = 0\n",
    "            newTiles.append(new)\n",
    "            actions.append(\"up\")\n",
    "        if (y + 1) < n:\n",
    "            new = np.copy(self.tiles)\n",
    "            new[x][y] = new[x][y + 1]\n",
    "            new[x][y + 1] = 0\n",
    "            newTiles.append(new)\n",
    "            actions.append(\"right\")\n",
    "        if (y - 1) > -1:\n",
    "            new = np.copy(self.tiles)\n",
    "            new[x][y] = new[x][y - 1]\n",
    "            new[x][y - 1] = 0\n",
    "            newTiles.append(new)\n",
    "            actions.append(\"left\")\n",
    "        ret = []\n",
    "        for i in range(len(newTiles)):\n",
    "            child = Node(newTiles[i], self, action=actions[i])\n",
    "            child.hgf()\n",
    "            ret.append(child)\n",
    "            node_count += 1\n",
    "        return ret\n",
    "\n",
    "    def isExp(self, exp):\n",
    "        for i in exp:\n",
    "            if np.array_equal(self.tiles, i.tiles) and (i.f <= self.f):\n",
    "                return i\n",
    "        return None\n",
    "\n",
    "start = Node()\n",
    "goal = Node()\n",
    "method = \"\"\n",
    "m = 0\n",
    "n = 0\n",
    "node_count = 0\n",
    "iteration_count = 0\n",
    "memory_allocation = 0\n",
    "actions_ans = []\n",
    "\n",
    "def set_hardcoded_data():\n",
    "    global method, m, n, goal, start\n",
    "    method = \"IDA*\"\n",
    "    m = 100\n",
    "    n = 3\n",
    "    start.tiles = np.array([\n",
    "        [7, 2, 4],\n",
    "        [5, 0, 6],\n",
    "        [8, 3, 1]\n",
    "    ])\n",
    "\n",
    "    goal.tiles = np.array([\n",
    "        [0, 1, 2],\n",
    "        [3, 4, 5],\n",
    "        [6, 7, 8]\n",
    "    ])\n",
    "\n",
    "    start.hgf()\n",
    "\n",
    "def findOpt(lst):\n",
    "    opt = lst[0]\n",
    "    index = 0\n",
    "    for i in range(len(lst)):\n",
    "        if lst[i].f < opt.f:\n",
    "            opt = lst[i]\n",
    "            index = i\n",
    "    return opt, index\n",
    "\n",
    "def findGoal(num):\n",
    "    global goal\n",
    "    for i in range(n):\n",
    "        for j in range(n):\n",
    "            if goal.tiles[i][j] == num:\n",
    "                return i, j\n",
    "\n",
    "def IDAstar():\n",
    "    fmax = start.f\n",
    "    while(1):\n",
    "        if fmax > m:\n",
    "            return None\n",
    "        node, newfmax = limitedSearch(start, fmax)\n",
    "        if node != None:\n",
    "            return node\n",
    "        if newfmax != None:\n",
    "            fmax = newfmax\n",
    "    return None\n",
    "\n",
    "def limitedSearch(node, limit):\n",
    "    global iteration_count, memory_allocation\n",
    "    iteration_count += 1\n",
    "    if node.f > limit:\n",
    "        return None, node.f\n",
    "    if node.isGoal():\n",
    "        return node, limit\n",
    "    successors = node.genChildren()\n",
    "    min = float('inf')\n",
    "    for succ in successors:\n",
    "        newNode, newLimit = limitedSearch(succ, limit)\n",
    "        if newNode != None:\n",
    "            return newNode, None\n",
    "        if newLimit < min:\n",
    "            min = newLimit\n",
    "    memory_allocation += len(successors)\n",
    "    return None, min\n",
    "\n",
    "def printOutput(sol):\n",
    "    global goal, n, node_count\n",
    "    print(\"SUCCESS\\n\")\n",
    "    print(f\"Number of Nodes Explored: {node_count}\")\n",
    "    path = []\n",
    "    while(sol != None):\n",
    "        path.insert(0, sol)\n",
    "        actions_ans.insert(0, sol.action)\n",
    "        sol = sol.parent\n",
    "    res_arr = np.array([])\n",
    "    for i in range(len(path)):\n",
    "        state = path[i]\n",
    "        arr_tiles = []\n",
    "        for j in range(n):\n",
    "            arr_tiles.extend(state.tiles[j])\n",
    "        res_arr = np.append(res_arr, arr_tiles)\n",
    "        print(f\"Step {i + 1}: Action - {actions_ans[i]}\")\n",
    "        print(np.array(state.tiles))\n",
    "        print(\"\\n\")\n",
    "    return res_arr\n",
    "\n",
    "\n",
    "def solve():\n",
    "    global method, m, n, goal, start, node_count, iteration_count, memory_allocation\n",
    "    set_hardcoded_data()\n",
    "    solution = None\n",
    "    arr_state_print = np.array([])\n",
    "    if method == \"IDA*\":\n",
    "        start_time = time.time()\n",
    "        solution = IDAstar()\n",
    "        end_time = time.time()\n",
    "        compilation_time = end_time - start_time\n",
    "\n",
    "        print(f\"Compilation Time: {compilation_time} seconds\")\n",
    "        print(f\"Number of Iterations: {iteration_count}\")\n",
    "        print(f\"Memory Allocation: {memory_allocation}\")\n",
    "\n",
    "    else:\n",
    "        print(\"Wrong Method\")\n",
    "\n",
    "    if solution is None:\n",
    "        print(\"FAILURE\")\n",
    "    else:\n",
    "        sol = printOutput(solution)\n",
    "        sol = sol.astype(np.int32)\n",
    "        sol = sol.reshape((int)(len(sol)/9), 9)\n",
    "\n",
    "\n",
    "        row_count = int(len(sol)/5)\n",
    "        fig, ax = plt.subplots(row_count+1, 5, figsize=(15, 3*row_count))\n",
    "        for i in range (len(sol), (row_count+1)*5):\n",
    "            ax[i // 5, i % 5].set_visible(False)\n",
    "        for i, action in enumerate(sol):\n",
    "            state_print = NPuzzleState(tiles = action)\n",
    "            arr_state_print = np.append(arr_state_print, state_print)\n",
    "            state_print.plot(ax[(i) // 5][(i) % 5], f'Step {i+1}: {actions_ans[i]}', fs=18)\n",
    "\n",
    "    return sol, arr_state_print\n",
    "\n",
    "sol, arr_state_print = solve()\n"
   ]
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    "## Kelas Node\n",
    "\n",
    "Mewakili suatu keadaan dalam ruang pencarian.\n",
    "\n",
    "### Atribut:\n",
    "- **tiles:** Konfigurasi teka-teki.\n",
    "- **g:** Biaya jalur dari node awal ke node ini.\n",
    "- **h:** Nilai heuristik.\n",
    "- **f:** Nilai fungsi evaluasi (f = g + h).\n",
    "- **parent:** Node induk.\n",
    "- **action:** Tindakan yang diambil untuk mencapai keadaan ini.\n",
    "\n",
    "### Metode:\n",
    "- **isGoal():** Memeriksa apakah keadaan saat ini adalah keadaan tujuan.\n",
    "- **findBlank():** Menemukan koordinat kotak kosong (0).\n",
    "- **hgf():** Menghitung heuristik (h), biaya aktual (g), dan nilai fungsi evaluasi (f).\n",
    "- **genChildren():** Menghasilkan node anak dengan melakukan langkah-langkah yang valid.\n",
    "- **isExp(exp):** Memeriksa apakah keadaan saat ini sudah dieksplorasi dalam himpunan yang diberikan.\n",
    "\n",
    "### Variabel Global:\n",
    "- **start, goal:** Instansi dari kelas Node yang mewakili keadaan awal dan tujuan.\n",
    "- **method:** String yang menunjukkan metode pencarian (\"IDA*\").\n",
    "- **m, n:** Parameter untuk algoritma IDA*.\n",
    "- **node_count, iteration_count, memory_allocation:** Variabel untuk melacak kinerja algoritma.\n",
    "\n",
    "### Fungsi Utilitas:\n",
    "- **set_hardcoded_data():** Menetapkan data awal, seperti dimensi teka-teki, keadaan awal, dan keadaan tujuan.\n",
    "- **findOpt(lst):** Mencari node dengan nilai f terendah dari daftar node.\n",
    "- **findGoal(num):** Menemukan koordinat angka tertentu dalam keadaan tujuan.\n",
    "\n",
    "### Fungsi Algoritma IDAstar:\n",
    "- **IDAstar():** Algoritma Iterative Deepening A*.\n",
    "- **limitedSearch(node, limit):** Melakukan pencarian hingga kedalaman terbatas.\n",
    "- **printOutput(sol):** Mencetak jalur solusi dan informasi terkait.\n",
    "\n",
    "### langkah kerja algoritma IDAstar\n",
    "1. **Inisialisasi Variabel:**\n",
    "\n",
    "- Tentukan nilai awal fmax sebagai nilai f terkecil yang dapat diterima (biasanya 0).\n",
    "- Siapkan loop tak terbatas untuk mencari solusi.\n",
    "2. **Iterasi Utama:**\n",
    "\n",
    "- Lakukan loop hingga solusi ditemukan atau batasan pencarian mencapai batas maksimum (dalam hal ini m).\n",
    "3. **Pencarian Terbatas:**\n",
    "\n",
    "- Panggil fungsi limitedSearch(start, fmax) untuk melakukan pencarian hingga kedalaman terbatas dengan batasan fmax.\n",
    "Fungsi limitedSearch akan mencoba menemukan solusi dalam batasan yang diberikan.\n",
    "4. **Evaluasi Hasil Pencarian Terbatas:**\n",
    "\n",
    "- Jika hasil pencarian terbatas menghasilkan solusi (node tidak None), maka solusi telah ditemukan, dan solusi tersebut adalah node yang menjadi hasil pencarian.\n",
    "- Jika hasil pencarian terbatas tidak menghasilkan solusi tetapi mengembalikan nilai batas yang lebih besar (newfmax), maka tingkatkan nilai fmax untuk iterasi berikutnya dengan nilai newfmax.\n",
    "- Jika hasil pencarian terbatas tidak menghasilkan solusi dan juga tidak mengembalikan batasan yang lebih besar, artinya solusi tidak dapat ditemukan dengan batasan saat ini.\n",
    "5. **Pencarian Selesai:**\n",
    "\n",
    "- Keluar dari loop utama ketika pencarian selesai. Jika solusi ditemukan, solusi tersebut akan menjadi node hasil yang akan dikembalikan.\n",
    "\n",
    "## Fungsi Selesaikan (solve())\n",
    "\n",
    "### Fungsi:\n",
    "Fungsi ini mengkoordinasikan proses pemecahan masalah N-puzzle berdasarkan metode yang ditentukan (\"IDA*\").\n",
    "\n",
    "### Penjelasan Singkat:\n",
    "1. Pertama, fungsi menggunakan `set_hardcoded_data()` untuk mengatur data awal, seperti dimensi teka-teki, keadaan awal, dan keadaan tujuan.\n",
    "2. Selanjutnya, jika metode adalah \"IDA*\", fungsi memulai pencarian solusi dengan IDA*.\n",
    "3. Fungsi mencetak informasi terkait solusi, termasuk waktu kompilasi, jumlah iterasi, dan alokasi memori.\n",
    "4. Jika solusi ditemukan, fungsi juga mencetak langkah-langkah yang diperlukan untuk mencapai solusi.\n",
    "\n",
    "## Fungsi Visualisasi Matplotlib\n",
    "\n",
    "### Fungsi:\n",
    "Menampilkan visualisasi teka-teki N-puzzle selama proses pemecahan masalah.\n",
    "\n",
    "### Penjelasan Singkat:\n",
    "- Menggunakan Matplotlib untuk membuat visualisasi proses pemecahan masalah.\n",
    "- Memanfaatkan kelas NPuzzleState untuk merencanakan setiap keadaan teka-teki selama solusi.\n",
    "- Menggunakan subplot Matplotlib untuk mengatur tampilan berbagai keadaan teka-teki dalam baris dan kolom.\n",
    "- Melakukan plot setiap keadaan teka-teki dalam solusi untuk ditampilkan dalam bentuk grafis.\n"
   ]
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   "source": [
    "# RBFS Algorithm"
   ]
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    "class Puzzle:\n",
    "    goal=goal_tiles\n",
    "    heuristic=None\n",
    "    f_value=None\n",
    "    needs_hueristic=False\n",
    "    num_of_instances=0\n",
    "    def __init__(self,state,parent,action,path_cost,needs_hueristic=False):\n",
    "        self.parent=parent\n",
    "        self.state=state\n",
    "        self.action=action\n",
    "        if parent:\n",
    "            self.path_cost = parent.path_cost + path_cost\n",
    "        else:\n",
    "            self.path_cost = path_cost\n",
    "        if needs_hueristic:\n",
    "            self.needs_hueristic=True\n",
    "            self.find_heuristic_value()\n",
    "            self.f_value=self.heuristic+self.path_cost\n",
    "        Puzzle.num_of_instances+=1\n",
    "\n",
    "\n",
    "    def find_actions(self,i,j):\n",
    "        actions = ['top', 'down', 'left', 'right']\n",
    "        if i == 0:\n",
    "            actions.remove('top')\n",
    "        elif i == 2:\n",
    "            actions.remove('down')\n",
    "        if j == 0:\n",
    "            actions.remove('left')\n",
    "        elif j == 2:\n",
    "            actions.remove('right')\n",
    "        return actions\n",
    "\n",
    "    def __str__(self):\n",
    "        return str(self.state[0:3])+'\\n'+str(self.state[3:6])+'\\n'+str(self.state[6:9])\n",
    "\n",
    "    def find_heuristic_value(self):\n",
    "        self.heuristic=0\n",
    "        for num in range(1,9):\n",
    "            distance=abs(self.state.index(num) - self.goal.index(num))\n",
    "            i=int(distance/3)\n",
    "            j=int(distance%3)\n",
    "            self.heuristic=self.heuristic+i+j\n",
    "\n",
    "    def goal_test(self):\n",
    "        if self.state == self.goal:\n",
    "            return True\n",
    "        return False\n",
    "\n",
    "\n",
    "    def generate_child(self):\n",
    "        children=[]\n",
    "        x = self.state.index(0)\n",
    "        i = int(x / 3)\n",
    "        j = int(x % 3)\n",
    "        actions=self.find_actions(i,j)\n",
    "\n",
    "        for action in actions:\n",
    "            new_state = self.state.copy()\n",
    "            if action is 'top':\n",
    "                new_state[x], new_state[x-3] = new_state[x-3], new_state[x]\n",
    "            elif action is 'down':\n",
    "                new_state[x], new_state[x+3] = new_state[x+3], new_state[x]\n",
    "            elif action is 'left':\n",
    "                new_state[x], new_state[x-1] = new_state[x-1], new_state[x]\n",
    "            elif action is 'right':\n",
    "                new_state[x], new_state[x+1] = new_state[x+1], new_state[x]\n",
    "            children.append(Puzzle(new_state,self,action,1,self.needs_hueristic))\n",
    "        return children\n",
    "\n",
    "    def solution(self):\n",
    "        solution = []\n",
    "        solution.append(self.action)\n",
    "        path = self\n",
    "        while path.parent != None:\n",
    "            path = path.parent\n",
    "            solution.append(path.action)\n",
    "        solution = solution[:-1]\n",
    "        solution.reverse()\n",
    "        return solution"
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    "### Kelas Puzzle\n",
    "\n",
    "Kelas ini digunakan untuk memodelkan permainan teka-teki 8-puzzle. Berikut adalah atribut dan metode utama dalam kelas ini:\n",
    "\n",
    "- `goal`: Atribut yang menyimpan keadaan tujuan teka-teki (nilai ini tidak terdefinisi dalam kode yang diberikan).\n",
    "- `heuristic`: Atribut yang menyimpan nilai heuristik.\n",
    "- `f_value`: Atribut yang menyimpan nilai f (kombinasi dari nilai heuristik dan path_cost).\n",
    "- `needs_heuristic`: Atribut yang menentukan apakah heuristik diperlukan.\n",
    "- `num_of_instances`: Atribut yang menghitung jumlah instance Puzzle yang telah dibuat.\n",
    "\n",
    "#### `__init__(self, state, parent, action, path_cost, needs_heuristic=False)`\n",
    "\n",
    "Metode konstruktor untuk membuat instance baru dari Puzzle. Ini mengambil argumen sebagai berikut:\n",
    "- `state`: Keadaan teka-teki saat ini.\n",
    "- `parent`: Parent dari instance (instance Puzzle sebelumnya).\n",
    "- `action`: Tindakan yang menghasilkan keadaan saat ini.\n",
    "- `path_cost`: Biaya path dari parent ke keadaan saat ini.\n",
    "- `needs_heuristic`: Opsional, menentukan apakah heuristik diperlukan.\n",
    "\n",
    "#### `__str__(self)`\n",
    "\n",
    "Metode untuk mengonversi objek ke string yang dapat dicetak.\n",
    "\n",
    "#### `find_actions(self, i, j)`\n",
    "\n",
    "Metode untuk menentukan tindakan yang dapat dilakukan dari posisi tertentu. Ini mengembalikan daftar tindakan yang valid.\n",
    "\n",
    "#### `find_heuristic_value(self)`\n",
    "\n",
    "Metode untuk menghitung nilai heuristik berdasarkan jarak Manhattan.\n",
    "\n",
    "#### `goal_test(self)`\n",
    "\n",
    "Metode untuk memeriksa apakah keadaan saat ini adalah keadaan tujuan.\n",
    "\n",
    "#### `generate_child(self)`\n",
    "\n",
    "Metode untuk menghasilkan anak-anak dari keadaan saat ini.\n",
    "\n",
    "#### `solution(self)`\n",
    "\n",
    "Metode untuk menghasilkan solusi dalam bentuk urutan tindakan.\n"
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    "def recursiveBFS(initial_state):\n",
    "    node=RBFS(Puzzle(state=initial_state, parent=None, action=None, path_cost=0, needs_hueristic=True), f_limit=maxsize)\n",
    "    node=node[0]\n",
    "    return node.solution()\n",
    "\n",
    "def RBFS(node,f_limit):\n",
    "\n",
    "    successors=[]\n",
    "    if node.goal_test():\n",
    "        return node,None\n",
    "\n",
    "    children=node.generate_child()\n",
    "    if not len(children):\n",
    "        return None, maxsize\n",
    "\n",
    "    count=-1\n",
    "    for child in children:\n",
    "        count+=1\n",
    "        successors.append((child.f_value, count,child))\n",
    "\n",
    "    while len(successors):\n",
    "        successors.sort()\n",
    "        best=successors[0][2]\n",
    "        # print (\"Best Node = \",best, \"\\n######################\\n\")\n",
    "        if best.f_value > f_limit:\n",
    "            return None, best.f_value\n",
    "        alternative=successors[1][0]\n",
    "\n",
    "        result,best.f_value=RBFS(best,min(f_limit,alternative))\n",
    "\n",
    "        successors[0]=(best.f_value,successors[0][1],best)\n",
    "        if result!=None:\n",
    "            break\n",
    "\n",
    "    return result,None"
   ]
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   "source": [
    "### Fungsi `recursiveBFS(initial_state)`\n",
    "\n",
    "Fungsi ini digunakan untuk memulai pencarian solusi dalam permainan teka-teki 8-puzzle menggunakan algoritma Recursive Best-First Search (RBFS). Fungsi ini memiliki satu argumen:\n",
    "\n",
    "- `initial_state`: Ini adalah keadaan awal dari teka-teki 8-puzzle.\n",
    "\n",
    "Langkah-langkah yang dilakukan oleh fungsi ini adalah sebagai berikut:\n",
    "\n",
    "1. Membuat sebuah objek `Puzzle` dengan menggunakan `initial_state` sebagai keadaan awal dan menginisialisasi atribut-atribut lainnya.\n",
    "2. Memanggil fungsi `RBFS` dengan objek `Puzzle` sebagai node awal dan `f_limit` yang diatur ke `maxsize`.\n",
    "3. Mengembalikan solusi dari `RBFS`.\n",
    "\n",
    "### Fungsi `RBFS(node, f_limit)`\n",
    "\n",
    "Fungsi ini adalah implementasi dari algoritma Recursive Best-First Search (RBFS) yang digunakan untuk mencari solusi dalam permainan teka-teki 8-puzzle. Fungsi ini memiliki dua argumen:\n",
    "\n",
    "- `node`: Node saat ini yang sedang dieksplorasi dalam pencarian.\n",
    "- `f_limit`: Batasan nilai f untuk pencarian saat ini.\n",
    "\n",
    "Langkah-langkah yang dilakukan oleh fungsi ini adalah sebagai berikut:\n",
    "\n",
    "1. Memeriksa apakah `node` saat ini adalah node tujuan dengan memanggil metode `goal_test()` dari objek `node`. Jika ya, itu mengembalikan `node` sebagai solusi dan `None` sebagai nilai `f_cost_limit`.\n",
    "2. Menghasilkan anak-anak dari `node` saat ini dengan memanggil metode `generate_child()` dari objek `node`.\n",
    "3. Jika tidak ada anak yang dihasilkan, itu mengembalikan `None` sebagai solusi dan `maxsize` sebagai nilai `f_cost_limit`.\n",
    "4. Membuat sebuah daftar `successors` untuk menyimpan nilai f, indeks, dan node anak-anak.\n",
    "5. Melakukan loop hingga `successors` tidak kosong dan melakukan langkah-langkah berikut:\n",
    "   a. Mengurutkan `successors` berdasarkan nilai f.\n",
    "   b. Mengambil node terbaik (dengan nilai f terendah) dari `successors`.\n",
    "   c. Memeriksa apakah nilai f dari node terbaik melebihi batasan `f_limit`. Jika ya, itu mengembalikan `None` sebagai solusi dan nilai f dari node terbaik sebagai nilai `f_cost_limit`.\n",
    "   d. Jika tidak, mengambil nilai f dari node kedua terbaik (alternatif).\n",
    "   e. Memanggil kembali fungsi `RBFS` dengan node terbaik sebagai node saat ini dan minimum antara `f_limit` dan nilai alternatif sebagai nilai f batasan.\n",
    "   f. Memperbarui nilai f dari node terbaik dengan nilai f yang dikembalikan dari pemanggilan rekursif.\n",
    "   g. Jika hasil tidak `None`, itu keluar dari loop.\n",
    "6. Mengembalikan hasil dari pencarian dan `None` sebagai nilai `f_cost_limit`.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "cell_id": "a0ff02147e70457d9aa60968958f19eb",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": true,
    "execution_millis": 13452,
    "execution_start": 1696740010979,
    "id": "Lxll64qftAn7",
    "outputId": "d7dbdbb2-e164-4a52-87c3-63d9b4605546",
    "source_hash": "4e8dc9e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start Node:\n",
      "  7   2   4 \n",
      "  5       6 \n",
      "  8   3   1 \n",
      "\n",
      "Total nodes generated: 77\n",
      "Total execution time: 13.929097890853882 seconds\n",
      "Memory usage at the end: 170.546875 MB\n",
      "Total number of steps: 26\n",
      "Maximum depth reached: 26\n",
      "Actions that are taken:\n",
      "left, top, right, down, down, left, top, \n",
      "right, right, top, left, left, down, right, \n",
      "right, down, left, left, top, right, right, \n",
      "top, left, down, left, top\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x1500 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fungsi untuk mendapatkan penggunaan memori saat ini\n",
    "def get_memory_usage():\n",
    "    process = psutil.Process()\n",
    "    return process.memory_info().rss / (1024 * 1024)  # Dalam megabyte\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    state = start_tiles\n",
    "\n",
    "    initial_state = NPuzzleState(tiles=state)\n",
    "\n",
    "    print(\"Start Node:\")\n",
    "    print(initial_state)\n",
    "\n",
    "    start_time = time.time()  # Waktu awal eksekusi pencarian\n",
    "\n",
    "    result = recursiveBFS(state)\n",
    "    end_time = time.time()  # Waktu akhir eksekusi pencarian\n",
    "    execution_time = end_time - start_time  # Waktu total eksekusi\n",
    "\n",
    "    current_state = Puzzle(state=state, parent=None, action=None, path_cost=0, needs_hueristic=True)\n",
    "\n",
    "    row_count = int(len(result) / 5)\n",
    "    fig, ax = plt.subplots(row_count + 1, 5, figsize=(15, 3 * row_count), sharex=True, sharey= True)\n",
    "    for i in range (len(result), (row_count+1)*5):\n",
    "            ax[i // 5, i % 5].set_visible(False)\n",
    "\n",
    "    total_nodes_generated = 0\n",
    "    num_steps = len(result)\n",
    "    max_depth = 0\n",
    "    res_arr = np.array([])\n",
    "\n",
    "    for i, action in enumerate(result):\n",
    "        children = current_state.generate_child()\n",
    "\n",
    "        total_nodes_generated += len(children)\n",
    "\n",
    "        for child in children:\n",
    "            if child.action == action:\n",
    "                current_state = child\n",
    "                break\n",
    "\n",
    "        max_depth = max(max_depth, current_state.path_cost)\n",
    "\n",
    "        current_state_str = [[current_state.state[0], current_state.state[1], current_state.state[2]],\n",
    "                             [current_state.state[3], current_state.state[4], current_state.state[5]],\n",
    "                             [current_state.state[6], current_state.state[7], current_state.state[8]]]\n",
    "\n",
    "        state_print = [\n",
    "            current_state.state[0], current_state.state[1], current_state.state[2],\n",
    "            current_state.state[3], current_state.state[4], current_state.state[5],\n",
    "            current_state.state[6], current_state.state[7], current_state.state[8]]\n",
    "\n",
    "        state_print = NPuzzleState(tiles=state_print)\n",
    "        state_print.plot(ax[(i) // 5][(i) % 5], f'Step {i+1}: {action}', fs=18)\n",
    "\n",
    "        res_arr = np.append(res_arr, state_print)\n",
    "\n",
    "    print(\"\\nTotal nodes generated:\", total_nodes_generated)\n",
    "    print(\"Total execution time:\", execution_time, \"seconds\")\n",
    "    print(\"Memory usage at the end:\", get_memory_usage(), \"MB\")\n",
    "    print(\"Total number of steps:\", num_steps)\n",
    "    print(\"Maximum depth reached:\", max_depth)\n",
    "    print(\"Actions that are taken:\")\n",
    "\n",
    "    for i, action in enumerate(result, 1):\n",
    "        if i < len(result):\n",
    "            print(action, end=\", \" if i % 7 != 0 else \", \\n\")\n",
    "        else:\n",
    "            print(action)\n"
   ]
  },
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   "source": [
    "#### Fungsi `get_memory_usage()`\n",
    "\n",
    "Fungsi ini digunakan untuk mengukur penggunaan memori saat ini oleh proses. Ini mengambil penggunaan memori dalam byte dan mengonversinya menjadi megabyte.\n",
    "\n",
    "#### Blok Utama `if __name__ == \"__main__\":`\n",
    "\n",
    "- Mulai eksekusi kode utama dengan inisialisasi keadaan awal `state` untuk permainan teka-teki.\n",
    "- Waktu dimulai dihitung menggunakan `start_time`.\n",
    "- Pencarian solusi dijalankan menggunakan fungsi `recursiveBFS`, dan hasilnya disimpan dalam variabel `result`.\n",
    "- Waktu akhir eksekusi dicatat dengan `end_time`, dan total waktu eksekusi dihitung.\n",
    "- Objek `current_state` dibuat dengan menggunakan keadaan awal.\n",
    "- Plot disiapkan dengan membuat subplot berdasarkan jumlah langkah yang diperlukan dalam solusi.\n",
    "- Variabel `total_nodes_generated`, `num_steps`, dan `max_depth` diinisialisasi untuk menghitung total node yang dihasilkan, jumlah langkah, dan kedalaman maksimum.\n",
    "- Array `res_arr` juga dibuat untuk menyimpan keadaan permainan pada setiap langkah.\n",
    "\n",
    "#### Loop Langkah\n",
    "\n",
    "Selanjutnya, melakukan loop melalui setiap langkah dalam solusi yang ditemukan (`result`):\n",
    "- Menghasilkan anak-anak dari `current_state` menggunakan metode `generate_child()`.\n",
    "- Menghitung total node yang dihasilkan dan mengidentifikasi anak yang sesuai dengan langkah yang diambil.\n",
    "- Memperbarui kedalaman maksimum jika diperlukan.\n",
    "- Mengonversi `current_state` menjadi format yang sesuai untuk dicetak dan memplotnya dalam subplot yang sesuai.\n",
    "- Juga memperbarui `res_arr` dengan keadaan permainan saat ini.\n",
    "\n",
    "#### Informasi Akhir\n",
    "\n",
    "Setelah semua langkah selesai, mencetak informasi penting sebagai berikut:\n",
    "- Total node yang dihasilkan.\n",
    "- Total waktu eksekusi.\n",
    "- Penggunaan memori pada akhir eksekusi.\n",
    "- Total jumlah langkah.\n",
    "- Kedalaman maksimum yang tercapai.\n",
    "- Langkah-langkah yang diambil selama pencarian."
   ]
  },
  {
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    "deepnote_cell_type": "markdown",
    "id": "V301wfDqtAn8"
   },
   "source": [
    "# Analisis Perbandingan Algoritma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "1d93e64c070e49c196a61c7466c2c40b",
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   },
   "source": [
    "### Kelebihan IDA* Algorithm\n",
    "- Langkah yang diberikan selalu yang terbaik dan tercepat berdasarkan pemilihan heuristik\n",
    "- Pengguna dapat leluasa mengoptimasi programmnya sesuai keinginan (bisa mengefisienkan memori penggunaan, pencarian parsial, dan sebagainya)\n",
    "\n",
    "### Kekurangan IDA* Algorithm\n",
    "- Memerlukan waktu eksekusi yang lama\n",
    "- Tidak efisien untuk permasalahan graf yang dangkal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "4a5942f7c8294f89a33b8a248c3537f1",
    "deepnote_cell_type": "markdown",
    "id": "D1D0JoKctAn8"
   },
   "source": [
    "### Kelebihan RBFS\n",
    "- Selama heuristiknya tidak melebihi actual cost, maka algoritma akan menemukan solusi yang optimal\n",
    "- Dapat mengurangi jumlah node yang dieksplorasi dibandingkan dengan algoritma BFS dan DFS\n",
    "### Kekurangan RBFS\n",
    "- Memerlukan penggunaan memori yang besar karena adanya proses rekursi\n",
    "- Jika heuristik tidak konsisten, algoritma memerlukan banyak waktu untuk melakukan backtracking\n",
    "- Tidak cocok untuk permasalahan dengan ruang lingkup yang besar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "324b561a6e204cc2bc43a5c1e1a6e55b",
    "deepnote_cell_type": "markdown",
    "id": "112t52ZqtAn8"
   },
   "source": [
    "# IDA* vs RBFS\n",
    "- Waktu eksekusi: RBFS (35s  vs  __±10s__)\n",
    "- Memori yang lebih efisien: IDA* (__±1 MB__  vs  ±303 MB)\n",
    "- Total node yang dibuat: RBFS (1265719  vs  __77__)\n",
    "- Keduanya berhasil menunjukkan langkah dari start ke goal dengan optimal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_bXbALevvU48"
   },
   "source": [
    "# Sekian, Terima kasih\n",
    "\n",
    "Saya Budiman -2023-"
   ]
  }
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