{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import torch\n",
    "from torch import nn\n",
    "\n",
    "from importlib.util import find_spec\n",
    "if find_spec(\"text_recognizer\") is None:\n",
    "    import sys\n",
    "    sys.path.append('..')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from text_recognizer.datasets import IamLinesDataset, AddTokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IAM Lines Dataset\n",
      "Number classes: 80\n",
      "Mapping: {0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9', 10: 'A', 11: 'B', 12: 'C', 13: 'D', 14: 'E', 15: 'F', 16: 'G', 17: 'H', 18: 'I', 19: 'J', 20: 'K', 21: 'L', 22: 'M', 23: 'N', 24: 'O', 25: 'P', 26: 'Q', 27: 'R', 28: 'S', 29: 'T', 30: 'U', 31: 'V', 32: 'W', 33: 'X', 34: 'Y', 35: 'Z', 36: 'a', 37: 'b', 38: 'c', 39: 'd', 40: 'e', 41: 'f', 42: 'g', 43: 'h', 44: 'i', 45: 'j', 46: 'k', 47: 'l', 48: 'm', 49: 'n', 50: 'o', 51: 'p', 52: 'q', 53: 'r', 54: 's', 55: 't', 56: 'u', 57: 'v', 58: 'w', 59: 'x', 60: 'y', 61: 'z', 62: ' ', 63: '!', 64: '\"', 65: '#', 66: '&', 67: \"'\", 68: '(', 69: ')', 70: '*', 71: '+', 72: ',', 73: '-', 74: '.', 75: '/', 76: ':', 77: ';', 78: '?', 79: '_'}\n",
      "Data: (7101, 28, 952)\n",
      "Targets: (7101, 97)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dataset = IamLinesDataset(train=True)\n",
    "dataset.load_or_generate_data()\n",
    "print(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 952)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.input_shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(97, 80)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.output_shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'A MOVE to stop Mr. Gaitskell from________________________________________________________________'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def convert_y_label_to_string(y, dataset=dataset):\n",
    "    return ''.join([dataset.mapper(int(i)) for i in y])\n",
    "\n",
    "convert_y_label_to_string(dataset.targets[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Griffiths resolution. Mr. Foot's line will_______________________________________________________\n",
      "be that as Labour M Ps opposed the_______________________________________________________________\n",
      "Government Bill which brought life peers_________________________________________________________\n",
      "into existence, they should not now put__________________________________________________________\n",
      "forward nominees. He believes that the___________________________________________________________\n",
      "House of Lords should be abolished and___________________________________________________________\n",
      "that Labour should not take any steps____________________________________________________________\n",
      "which would appear to \"prop up\" an out-__________________________________________________________\n",
      "Since 1958, 13 Labour life Peers and_____________________________________________________________\n",
      "Peeresses have been created. Most Labour_________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jX9Z2MqFIlke+3WTGiVgH0YxqPo/ip9hONC1ZvqPVoZjWavUZzTCLZqiLeZDlWS+Psu0mkyliCpPesWKb0eslJSUhKysLJpMJb731Fnp6ehATEwOn0wmXy4WkpCQkJycjKSkJ6enpKC4uRl5eHjZu3Iju7m40NTXB7XZjcHCQed+stT5u9bh3wnrEmXdTcJL1q3dyPk2Dtut60pf1jWgCiuy8tTzrRPLy8pCXlwev14vOzk74/bLFQiLztNrv5nr6lux3KCYmBhaLBampqZiZmYHP51vhRUkIwcaNG5GWlga3242hoSEsLCxIPWxW6/+y3yH+f1FM+02JiQqFQqFQKBTvF97Rkt/i4CyaJ0s08UJPgBC9Y/TEBPE8mXEnGzzKykA/xQG0aNzz+/gy8sKALA/0eFEY0LumXv7odz4tmSEeTXzRM4L0ysrH/dATS8R2Wc3QiGYA8O0v6zd0v8FggMVigdFohM/nk6ZJhRrqQSVLd7V+IhNUDAYDYmNjYbVasbi4iLm5OczPz+v2Tz5N2XXF81YzlsS8yepKFKKiCTQy0WOtRuNqBpjMKJfVL7/PZDJh48aN8Hq9GB8fx+zs7Kr54e8NQgiMRiP8fj9u3LiBCxcuIBQKgRCC7u5uxMTEwGQyIS4uDikpKSgpKUFZWRkKCgqwYcMGbNiwAeXl5bhy5QouXbqEgYEB+Hw+1sbi1L9oIsj71Ti9FcN7Pb8Dq4mosnNk+9cjUOnlL1pZCSGoqanB7t270dPTg5mZGXR3d7O0ZB4m68lftN++aPVvsViQnZ2N7du3o729HU1NTZidnY1Ix2g0YteuXaiqqsKxY8cwPT0tfZaKv1P8dCWZKKz3uyTLt14ZRCHq/XofKRQKhUKhUOhxS542fDwO0filn3QgJ4sVQ4+TDWbpIG9xcREGgwEGg2FFvBRZGuInnULFv6Gn05jonxg3h09bnHLBT8Pi60F8Y8mXR+/tMH9NOujlhQSZQMCfy9ex2DaiOCOWjU9DT+DiyyErA58f2f5oA/XVxCU+DX4KFY/BYMDCwgI7JikpCXl5eXC5XDh58iTm5uZ0xQ5a5mhCiCjoiXF6aL4NBgPi4+NRWFiIoqIieL1euN1u9Pf3IxAISA0XmmeZpxF/z4hTr2T3SLT65veJdavXHtGO49Pi+43MQJadx18jmtEl1lNKSgo+//nPo7GxEUeOHEFnZ6f0PhD7NG9Qjo+Po76+Hj6fD7OzsxFTKcPhMObm5hAKhTAxMYHW1lYcP34cxcXF2LZtG+rq6lBSUoLy8nIcPHgQL730Eq5cuYKBgYEVXhSiiKlXNlm9yI69FeNUry1XM4hXy/tar3ur6Ak2/HYxLlS0c6P1R3G/bLte20QTboxGI/Ly8pCTkwObzYb+/n709vZG9M211JMoEMmekau1M32WEEKQmJiIffv24Z577sGZM2fQ1dWFQCDAjgOWhJ3KykoUFxfD7Xajs7MTMzMzEb/dFP53k7++wWCI8ISVvcSh3+nvcjQBjK+LaMcoFAqFQqFQvN+5JU8bmVBCB5f8oE0v5gcvmvDp0O90wAm8LRyI4s3i4qLUYKRBbquqqmC32zE4OIj29naWP164EfPOp8VfUzTa+QHp4uLiinLQ88Ryywax9I8fCPPGiSjMiEKTGEeHTzta+9GBM3+saIiL6fFtSvMiBkfm05QFBBYFN1p2maC0uLi4wpOBr2fqZbNnzx4cPnwYRqMRk5OTuHLlygqRja8zWm983vT6JS+w8GWwWCzIycnBpz71KezYsQNmsxnhcBh9fX04fvw4fvrTn2J6enpFHB0xPhFf3zTPMu8yKpTy9U8Jh8MwGo1SISia0MILYzIBa60ihGiQiefLYj2JeZTFejKZTGxqU1ZWFlpaWjAwMMC8bWid0HvUbrfDarViYmKC9Q0AmJiYwPj4OObn5yP6qChK0jz5/X50dHQgLS0NaWlpaGtrQ1ZWFrZv344//MM/RF9fH06ePIlXX30VXV1dK+JnrVXAiHZctPt3LYLjerkV0SWaCLFeEWg1kSuasKi3X0+YFJ+VfFprEVZkfZ0KgZcvX0ZlZSXKysqwsLCA+vp6jI6ORjzHRdFRdh/xzyH+2SF7lop1RV9Y0ONMJhPi4+MRHx8Pm80Go9HI0qafdrsd+fn5iI+PR3FxMdrb2+F2uyN+t/R+mzVtKY5PfHw8kpOT4XQ6EQwG0dvbi9nZ2RXPLD4OjVh2XkzVE+OUWKNQKBQKheKDxi2LNqKhFhsbGzEtRFyxiIc3JKmQwhuz4ipPVCygLCwsRBV9MjIycOeddyInJwc3btxAV1cXMwr5N3x0cEvFBzGooewNoyygscyzRjbwFA0CTdNgNpuRnZ2Nu+++G729vTh+/Dh7C0rzSAe9vCgklp0a7aIRzNetzLARjQfxfNHwEb2VxLai16P1JRobYvuLsYtoPvT6jmhwaZqGqakpTExMIC8vD0VFRbh06dIKA4dvfzFNfjUyvm+LIg491mAwICsrC3/8x3+MlJQUfPOb34Tdbsf27dtRVlaG++67D/Pz83jmmWciVjeRCRd6bSLmUc/LgOZJbE++jmm/5g1Citjn+XzxnzIDWNYmsrRkhrT4v0yoCoVC6OjowPj4OAoKClBbW4uBgQE0NDRE5IUQgs2bN2PPnj1ITU3Fd7/7XfT397M2pdOhRKOYzwN/X8fExKCurg4PP/wwKioq8LOf/Qzf+MY3UFNTg0984hPYsGEDHn30UZSUlOBb3/oWOjo6Iu69tRj+a0XWF/QEjFtNWxSt3g3Ric+jTBiMdq3V7gW9cojXvhWxKJqwyG8T7zma75aWFoyNjaGsrAwlJSXYt28ffvGLX7DfL16Epd6CMhGO/12h/4v3oPh85qGipc1mQ3p6OoqKiuByuWCxWGAymWA0GiPynZ+fj8TERBiNRmRnZ6OgoAA2mw2zs7MsPV5Y4a9tsVhwxx13YN++fSgtLYXVaoWmafD5fLh58ybOnj2L5uZmjI+Ps3zxv+fiapAAVrxcEetcoVAoFAqF4oPEukUbasTSv/j4eBw8eBDbtm1De3s73nzzTbjdbgSDQQBvD/ZMJtMKMYeHN95F40o0AMSBtujxQY20hIQEpKWlwWazIRgMsrTp6h6EEDgcDjz00EPweDw4ffo0PB4Pyzf95AfyvGEnM4Zlg32Z0UHL4nK5UFlZiQMHDiAQCODmzZsYGBhgAhj1IhBj18imy8iEFno+L7SIRo34tlMmtKzVeBKPE+uGf1PL548vnzh9RRROaP3T7YFAANPT07BYLMjIyJAuVS0aRqIRJBo/sjqi6TqdTmzatAmFhYX48Y9/jPPnzwMAurq6sHfvXhw8eBD33nsvTp06hb6+PtbnxTaKVr+i8CK2D3+s3ipTaw0iyiMTasS65D9XS0tWFlk/Ec/jj33zzTeRmZmJuro69PT0oKWlBaFQKOKc1NRUZGdnw+l0oqKiAgMDA9J88OXgyygakQ6HA3FxcYiNjUVCQgKsVit6enrwq1/9Cg8++CCKiopQWFiInTt3Mm8bsd/z225VxFmPMLOWe/SdtqmYhuwc0WtKJmaJ5+k9Q3mRRBYTRvZ7IMun3rVWO57Pt+jlKN6XMTExmJ+fR2dnJzZt2oScnBzs2rULR44cibgeFY/F9Plryn4jxXzLRAz+GWo2m7F79248+uij2LJlCywWC5KSkhAbGwuTycR+vywWC7Zt28bOp7+bSUlJGBwchNlsxs6dO5GcnIympia0tbVFTDtOTEzEzp07kZ2djZaWFpw7dw7p6emoqqpCdXU1ysvL0dnZiXPnzuHEiROYm5uT/iaJgh7/HBCFIoVCoVAoFIoPEusWbajRbTQakZmZidtvvx233347XC4X0tPTYTabcfr0aTQ0NEQY6eKgjDeCZQNzPsCh3htGmdgQExODYDDIVshISEiAy+XC4OBgRBpUSKIDzvn5efT19aG1tTXqih/RDBzxLaw4/YIfbNP/qet6eno6YmJisHPnTrz++uuYnJxccU09Q5oeIxr50YwZ2dtj8RzRkJAZEfy1xKlE4jEyQURsW7Es4j4+TVqHgUAAgUAAMTExSEhIkAoi0YKA8u0mMx54NE2Dw+FAdnY2DAYDbt68iZmZGczPz6O1tRWxsbHIyclBXV0d6urqMDIysiLGjuzaeoYLhReyxLoT0xLrWO96sjKL6cjyLEtnLYKeeB/I2p+/Fj/l5KGHHkJmZiaKioqQnp7OluHmjeVwOAyXy4WKigocO3ZMVwTWE8f4Zw+Np2W327Ft2zbEx8cDABwOB9LT07G4uAiv14uhoSHdOo5mdIvXXC/R+lI0xGen3vl6/SHaOeL/0TxsZHnSyxeftuwZsp480d8uo9GIhYUF5rXJ50XvPP43hhACm83GfvcSEhIwNDSEiYkJ9PX1YWRkBJWVlaioqEBxcTHa2tqYWEPTFePBiddb7TuwMr4Mf0+YTCZkZmaivLwcLpcLhBBkZ2cjISEBk5OTrCypqamorq6G2+2G3W5HYmIi0tLSsGHDBoyOjqKoqAj3338/zGYzC64cDAYj2sJkMsFut8NsNmNsbAxtbW1ob29HZmYmamtr2fRCt9uN9vb2FaIVX/+y+yfac0+hUCgUCoXi/c66RBs6wDIYDMjOzsaOHTuwbds2jI2Noa+vDxs3bkR1dTUmJibgdrvh9XojBqh6IkA0I1EcoIlTb2R5XFxchMfjQTAYRHx8PLKysjA4OCg1DAHAZrMhLS0NtbW1mJqailjxgw6Aea8F6jkh5pEKQSkpKXA6nQCAqakpBINBhMNh+Hy+FfVAB69WqxU2mw179+7FpUuX4PV62ZtQcaAqM4x4ZMYwndZD640uXSxOSxGNa/rGNiEhAfn5+QiHw/B4PBgbG0MwGFwxdUuWF9Fw5reL8X/W8rZf7Dfz8/MIhUIwGAwwmUxrMjL1DCXekJYZkoQQWK1WuFwuLC4uMuNH0zTMzMygq6sLV65cwfbt21FdXY1jx46tEG3oNfhyiH0zmqHGH8v/yfq3nrGsZyjx+6lHGu0nYl+RpRsNvX6gJ3IAS/d7f38//H4/UlJSkJubi8rKSrYkMT1vcnISPp8PDocDxcXFiIuLg9/vj3rvyMQjitlshtFohMFgQEJCArKzs2E2m+Hz+dDR0YHJyUm0t7ejpaVlRTprFSH+q9B7bsruQVmb0HJZLBa4XC7Mzc0hEAhgbm5Omh7fL/n6oVDh2mg0IhQKsRXYZDHRxPxEM+LFPmq1WpGUlITc3FzEx8djZmYGIyMjGB0dxfT0dNQpiHyZrFYrm0KUn5+PjIwMOJ1O9PT0oL+/H6FQCMFgEAaDAZmZmdi/f39EQGJRZJHVt55IIT5H+GNFT0u6jYpMZrMZWVlZ2LZtG0wmEyYmJhAbG4u6ujo4HA4cO3YM6enpqKurQ1paGiorKzE4OIj9+/ejqqoKoVAI2dnZSExMxMjICLv23NwcBgcHkZ+fj8zMTFRXV+PEiRO4fv06WltbMTMzg3379iErKwv5+fnMM02vTWXtuJ7fB4VCoVAoFIr3G+sSbeiAyeFwYMeOHTh06BAmJyfx3e9+F0ajER/72MdQUVGBkpISXL16FR6Phxn+QORbfT4+jGxQDyDCsJXBHyu+wZ+cnITf70dOTg6Ki4tx6dIlliY9bmFhAV6vF11dXSgoKMDOnTvR29uLkZERtiSqLNgufx2632QywWazITU1FbW1tSgqKoLFYkFjYyOmpqYQCARw48YNtowqHUgvLCwgEAjAaDTCZrNh69atyMrKwvDwMKanpyPqXVZ2fpqQOJWLCk5WqxWJiYmIj4+HyWSKELWmp6cxNze3ItguX7aEhARUVVXh4YcfxtzcHNxuN65evYr+/n5MTk5iZmaGBQ0WDVW+XWVtJgZDFvsaRYwrQ9MC3ha+aDBgPeOT3y6+qTebzbBYLDCbzaxdFhcXYTab4fF4IgQCo9EIq9UKg8EQMdUOADweDxobGxEMBlFcXAy73Q6TycTiOPj9fta3gCWD1Wq1sukK1PgV64m2pSgI8AYgvYb4Bh/QX9FM9PSg5UtKSkJycjIAIBQKIRQKYWZmBpOTk6t6h8jqXWZ8rkVc0jQNfr8fXq8Xc3NzyM/Px549e3Dx4kXmjUZFG4/HA5vNhuzsbOTk5KCzszMiHV4w47fzfYb2ebqE/OzsLNrb2/HCCy/A4XCgr68PU1NTGB8fx9TUFPPK07v/1lJHa0U09tdynAw9A1gUEekUULHv0D6yYcMGJnR3dXWhr6+PBYkmZClYt8PhAAAEAgHMzs5GpEWfmS6XCyUlJbBarfD5fJiYmMDExAQ8Hg+L72UwGBAbG8tEXvq8WVxcjBC39bBarcjPz0dtbS22bt2K5ORk+P1+NDc3o76+Hm1tbZiamlrxXBCJjY1FUVER9u/fj4qKCrhcLoRCIVgsFhQUFGBhYQGNjY2Ij49HKBSCzWbD/fffj+PHj6O/v5/lXdYWotDF54N/BgCRXjp8u9BzNU3DwsICE9jT09OhaRrS0tLwyCOPIDs7G263G7GxsdixYwc6OjrwxhtvoLi4GHl5eSgtLcX27dsxPz+P3bt3IxwOw2KxYMOGDSguLobH42HTnObn59HQ0IC0tDRs3rwZ9913HzRNw7lz5zA+Po729nYUFhYiJSUFcXFxAFZOd5PVt/gcWe2eUigUCoVCoXi/sm5PGwAoKytDdXU1AOC5555DX18fAODkyZMoKyvDpk2bUFVVhdbWVjbVgDcy+f9F927RIBTfshmNxhVv/cXjNU1DX18fxsbGUFlZie3bt+MXv/gFW+6XTy8YDOKNN97AgQMHUFRUhIqKCvT09DCDjw6GTSYTLBYLCCEIBAJsSXIAbKpYXV0dDh06hPz8fABLA/z9+/cjHA7D7XbjO9/5Di5dusTKbjAYMDs7i+HhYczOzrLVN8rLy9HT04NAIBAR24UXkPj2oIgre1CX9Z07d+LAgQPIz8+H2WzG3NwcJicn0dfXh1//+tdoampiHiO88UpJTU3Fnj17sHnzZiwsLGD79u2455570NTUhNOnT+PkyZOYmpqKMMjo+dGC4IrtK/Y1mbjAb+ODLdO2mJmZWSEC8n2NN6yp8RkXF4e0tDTs2LED6enpiI2NZcGN09PT8dJLL6G7u5sJI4QQ5tlTUFCAkZERFnCY1u3IyAiLr1JSUoKUlBR4vV5cvnwZ9fX1CAQCMJlMyMvLQ1lZGbKzs+HxeHDhwgXm6UXbnq72QuNE8XVI2zw2NhYulwuTk5OYm5uT3kfiPaLXh1wuFz7/+c/jtttuYzGDRkdHcfnyZfzDP/zDiqkRYluKxj+ftigAiN4UsjajImw4HEZqaipqamqwZ88evPLKK+yY2NhY2Gw2xMXFwWq14u6778b3vvc91i6i0CXmn+6n7Ts9Pc1WvhkdHcWxY8fYVBoqjPECtMzo5Msq9m2x7tbCWg3WtXgu8MfS+5V6FlFR0uVyoaOjI2IKEX0WulwufOlLX0JNTQ3m5ubw5ptv4sUXX8SlS5fYsWVlZbjzzjthNptx+fJlnDt3ji0jbTabUVRUhM2bN2Pbtm2oqKhgU5YmJyfR3NyMCxcu4OzZs/B6vUhLS8OePXtgs9kwMzODqakpzMzMYGhoCGNjYxGeOWLfJoSgqKgIhw8fxo4dO3D16lX09fWhsrISt99+OzZt2oSzZ8/i1VdfxdjY2Io+QomJiUFpaSk+97nPweFw4MqVK/jxj3+M/v5+JCQkYPfu3di3bx927NiBuLg4LC4uwm63o7S0FE888QS+973vsWl9tB3oimZ8u8j6kyg68ytE0fyKccHofdPT04Pi4mL2m5WTk4OMjAz4/X4MDAzg/PnzePbZZ+HxeLC4uIihoSFs2rQJxcXFSEtLw8WLFxEKhVBbW8s8boaGhtgzkRCCGzduIBQKIRAI4I477sCTTz6JQ4cO4eWXX4bT6URZWRnC4TBGRkYi8ieLzca/6KH/8+0q1odCoVAoFArF+511x7SxWCzYtGkTEhMT0dvbi9bWViamjI+PY2ZmBvHx8bBYLLBarSygrs1mQ0pKCjIyMpCcnIyFhQX09vaira2NTdUBIj0GKHwQV9Hopp/0rTA9b2hoCMPDwzAajSgpKUFmZiY6OjoiplfRGCxXr15lS7Xu27cP4XAYzz77LCYmJthAcvv27Th48CAMBgN+9KMfobW1lU03ysjIwAMPPID9+/eju7sbn/vc5xAOh3HXXXfh0KFD7A1seXk5rl69yspFjdbp6Wl0d3cjLS0NZrOZxUjgV4Siq33wA1XesOfFDTqoz8zMxJe+9CXk5uZifHwcQ0NDAMDc5Kurq7Fv3z786Ec/wrFjx1h8Dt5wptOjLBYLJicn8cILL7Dgktu3b0dlZSXuuusuvPjii3jjjTdYEGjesKV5lXlW0Lagn/z19YxavqyELE1XcDgcWFxcRGdnJwtSy4uAtK15Dy+Xy4Wqqio88sgjqKioQDgcZm+PY2NjYbFYEAwG0d/fj/7+fhY3ZWxsDO3t7bjrrrvwoQ99CFeuXGGBrmk/NZvNSEtLw1NPPcUC21qtVnz0ox/FtWvX8IMf/ADbt2/Hzp072VQrALjrrrvw7W9/G01NTTAYDKioqMDmzZvh8Xjwk5/8JKL8tC+YTCZ89KMfxYMPPohnn30Wp0+fxtDQEGs/mjYfX0psE5qWw+HAli1b8OEPf5gJQLSfh0Ih1g9lK77QNPWm7PEeLeJ5YmBS4O2li2l9Ui+FjIwMfOYzn0F3dzeam5sBALm5ucjOzoamaXA6nXj44Yfxk5/8hHl38EYe77Ek9jm6oo/P50MwGITFYkFycnKEd4dYf2LZblWMWe281Txs1gt91qampjLBITc3F06nky0P/elPfxoXLlyIeEZbLBZ85CMfwYYNG+DxeJCcnIzDhw8jLS0Nf/qnf4rh4WHExcXhi1/8IiorK2Gz2fDRj34UFy9exF/8xV9geHgY99xzD+666y4kJiaiqakJf/u3fwtN01BUVISqqiqUl5cjLS0NWVlZeO655/DUU0+hrq6OPROph00gEEBHRwfOnz+PpqYmDA8PY2ZmhrUXfWmwefNmlJWVYWRkBD/84Q8xOTmJlJQU3Hfffdi8eTN27tyJUCiEf//3f2dtwf/e0Hr66le/CovFgh/84Aeor69nU15HR0fhdrsxPj6OD33oQyguLsbCwgJmZ2dhtVrx6KOPor+/Hy+//DKbKkXbQLxfZII1L2jQ/PHeJ/x3XvQZGxtDV1cXgsEgjEYj2traWMD9vr4+NDc3Y3h4mD0bJyYm8OKLL+LmzZuIj4/H1atX4Xa74XA48OlPfxpbtmzBbbfdhvz8fJw4cQIdHR3wer0wGo0ghKC1tRVWqxV79+5lL3Dm5+cxMTGBhoYGGAwGOBwO+Hy+FS8ZaDmpAM9D80cRf08UCoVCoVAo3s+sW7ShXiUGgwHDw8MRrumhUAjz8/NITU3F3XffjR07dmBwcBBXrlzBrl27UFhYyNzlFxYWMD8/j3/7t3/DW2+9xVZtokt98m/aRUOPf7steofQ8+bm5jA+Po7h4WFs3LgRt912G9xut9QlOxQK4fvf/z4+97nPobi4GPv27YPRaMTTTz+NmZkZxMTEoKSkBFVVVUhMTITf78ff/M3fMIPgzjvvRFlZGVpbW/H0009jeHgY8/PzePHFFxEfH89WosnPz2fu5NR7Ij8/H3fffTe2bt3KhIJ7770XKSkpOHfuHC5fvhwRuJHWAz/olxm7TqcTTz75JHJycvDP//zPaGxsZHUcHx+Pp556iolDmZmZSEpKYqKN6M1is9lgt9sxPDyMl19+GS+//DI2btyIbdu2YcuWLdi8eTO2bt2KN954A//yL/+Cvr4+3WDOMqGAD87Je2DoGamikZuUlISUlBTMzs6iq6uL1REVaPg0qGFQWFiIw4cPY//+/ZiensZf/uVfMo+xrVu34vbbb8fmzZsjRB+aN4/Hg4aGBgwODqK2thabNm1CY2MjAoEAzGYz4uPjkZKSgjNnzuDpp5+G3++H0+lEaWkpDh06hOrqanzjG9+AyWTC66+/jvr6ekxPT6OmpgYHDhzA448/jm9/+9uIj4/Hbbfdht27d2N0dBQ///nP4ff7V7xhNpvNqK2tRWpqKiorK9He3s5iTlD4N9f0kzf0eO83Gt8jFArh61//OtxuN2ZmZtgUFzEt8Tp6nix6x1PxRxQL6fFms5mtAEdFtYSEBHzxi1/El7/8ZSwsLKC6uhq5ubmYmJhAQkICUlJSUFtbi7feeot5d4j3Pt/n6H4qUAUCAdaHExISIgx42Rt/vSWKeTGHTvFxOp0oKChAbGwsgsEghoaGMD4+HhFXRVZfazVSZV4ifN0CS8/x5ORk3HHHHfi93/s92O125hmSkJCA2NhYAMBnP/tZtLW1YXR0FABYzK69e/eio6MDTz/9NB5++GHs3r0b+fn5uO+++/Cv//qvqKysRHZ2NrxeL4aHh2Gz2VBTU4M/+ZM/wQ9/+EPceeedMJlMOH78OF566SU2Deqtt95CVlYW7rzzTlRVVaGwsBAJCQlITExEKBTCL3/5S/T29sJsNiMjIwNVVVUoLS1FUVERZmZmMDY2hs7OTly4cAHXr19nItv09DTm5+fhcrmwceNGnD17FhMTEzhx4gQSExNRVFSEtLS0CJGT77txcXE4ePAgHA4H/u7v/g7Xrl2D1+tl4hCt23PnzqG2thaFhYUghDDBIjs7G08++STMZjOOHDmCrq4uJgYB8iXvxXYUBVeZ2Mc/N8PhMIaHh5knZUxMDNrb2/HSSy9hbGyMBWKm3mi0ruhUN+pRqGkaPB4PfvrTn2Jqago7duxAXl4ePvnJTwJY+v2ky4jTMnd3d+Ott95COBxGXl4e0tLSUFVVhZKSEgwODuL8+fM4ffo0xsfHMTs7i1AotGKFPf43SOaFo1AoFAqFQvFB4ZZWj6Ju7H6/n70BI4Rg8+bNSE1Nhd1uR1ZWFpKTk1FcXIwdO3Zgfn4ejY2N6O7uRiAQQFxcHB544AFs2bIF169fh9frXSE8iGIEHbjRga5oiNDzaIyZoaEhXLt2DeXl5diyZQt++tOfrnhbST8HBgZw5MgR3HvvvSgpKcHWrVsxNDSEI0eOIBQKYXp6GqFQCMnJydixYwe2b9+OCxcuMCPAbrejubmZDYY1TYPX60VLSws2btyIiooKbNiwAcnJycwAApY8G7KystgyrAaDAbm5ubDb7SgvL0dtbS3+7M/+DIFAYIXYwRuifJyX2NhYtmpIQ0MDGhoa2BQeTVtyyZ+amsLCwgJMJhNLw2KxsEE6rdfk5GQUFBQgKSkJ9fX1zABuamrC0NAQrl+/jp07d+Lhhx/GbbfdhqSkJPzgBz/AtWvXVhjL/FtlvSVc+TKKb5D5Y+jAnhqfKSkpbCoLf5w41cZoNMLpdOLw4cOoqalBd3c3/uM//gPt7e3MQycuLg7FxcWorq7GwsICOjs7IwypUCiE0dFRvPTSS3jyySfx8MMPY35+Hu3t7bDb7aisrITRaMTx48fhdruxsLCAkZERDA4Ooq+vD0888QQ2bdqEmZkZtLa2sqkFU1NTcLlcqK6uxoYNG0AIYYIZIUvL09P4Q6LX0uzsLIxGI1JSUuBwOCJEBtHLJCEhASUlJSgvL8e5c+fgdrtZu8/NzaG/vx/d3d3Izs5GaWkp3G43JiYmooo0ongq5k8UYlbbx293uVyw2+2YnJzEzZs3MTExgQceeAAFBQX4wz/8Q4RCIWRmZqK7uxsdHR24//77UV5ejgMHDqCpqSkiIDEVZngBhu9b1GD3er1sRTKbzRYRlJk/T/SyEaHHmEwmVFVVoa6uDkVFRUhISIiIm9PU1ISzZ8+ivr5+RfuuFz2Dn37Gx8ejoqICd911F6qqqnD58mVcuXIFU1NT2LVrF3bv3o3ExESkp6ejoqICeXl5LBaT3W5HQUEBEhMTcfLkSfT29uLkyZNwOByorq5mfX/Tpk2IjY3FhQsXUF9fD4PBgAcffBAbN27EQw89hJycHAwNDbF6pkY7APT19aGnpweZmZlMUBgfH4fT6URXVxeuX7/O4sWcOXMGxcXF2Lx5M3JycrBp0yaUlpaivLwczz33HK5evQqTyYRAIIDp6WkUFhbiwQcfhNVqZWVJT0/H6Ogoenp6IuqKfqeecjt27EBTUxNaW1tXBC6m/ZfGX6K/cV6vF//0T/+ET33qU9iwYQMefPBB2O12vPTSS2yFRbGtZC8W+OvQ68oCJ/MCKLA0XbS3txeXLl3Crl27MDU1henpaeaNxB/Lp8tP26L7qKfQjRs3sGHDBuTm5iI2NhYzMzOYnp5mQufk5CSLyRYOh2G32+FyuZCRkYGSkhIUFRXh0KFD2LZtG2ZnZzE1NYW+vj4W2Nvj8URMgeaf4XqCpkKhUCgUCsX7mXWLNgAigj9SIzw5ORl79+6FyWTC1atX0dnZCavVinvuuQdxcXHMEDxz5gybQrV37144HA72ho6Hn9IgDtD44LuioEMIQWpqKurq6pCdnY2kpCTmWZGWlsbiH9DBKW+s3rhxA+np6YiLi0NWVhbuuOMOLC4uorm5GWlpabBYLIiJiUF6ejo+9KEPoa2tja2sZLfbYTAYWNoUr9eLmZkZWCwW5OXl4ZFHHsGxY8cwMDDADPXm5mb09vaioKCArX4UHx/PgqvqvVXl4Y0yKjo4HA60trbC4/FETG+gQsQdd9zBvHKcTicsFgs0bSmApdFohMPhQGVlJUpLSzE3N8cMSrPZjNTUVMzNzTEDhhCCRx99FFu3bmVvdpubmyOMZbE9eTFFLJt4HL+d/58u256UlAS/34+ZmZkV9UE/qaBH48iYTCY0NzejqakJgUCAGWizs7OYm5tjYgiNc8EbDn6/H5cuXcKBAwewceNG3HXXXXC5XDAajdi6dStGRkbQ0NCA2dlZAEtvo2dnZzE7O4tXX30VxcXFbMpRMBhkcZIuX76MvLw82O12TExMYGZmBkajES6XC7t378bx48cRCASYMEjzRKcC5uXloaSkBG63my1zzxt81Dthz5497A34mTNn0NXVhampKYRCIXi9Xpw5cwaPPfYYqqqqUF9fj76+voi3+OJ0Db5/ygxRfp+ewCEeS49zuVwwmUwYHh5GS0sLOjs7ERcXhwcffBC1tbXo7u5GW1sbGhoa0NvbC5vNhqKiIvYMGB8fjwhoK7sG3+c0TWP3XjgcRlxcHCwWC+tbev1Ulg4lNTUVxcXFSE9Ph8/nw+joKGw2G3JyclBaWsqWkKf367thlPLlo3myWq3YvHkz9u/fj/LycrS0tOBXv/oVOjo6EAgE4PV60d3djeTkZHzkIx9BdnY2CgsL0dvbi1AoBIfDgZKSEhgMBrS1tSEQCKCnpwejo6NMLKfPSOqN2dTUhJmZGdhsNjz22GOorq6G1WrF7OwsEhMTYbFYmEiwuLiIYDAIt9sNg8HA7g8aMD0QCMDn87Gpek6nE0lJSayNaEwju92OmZkZFBYWwmazIT09Henp6TCZTNi0aRPLZ19fH65du4b+/n643W4AKz2kzGYzkpOTkZ2djV//+tcs8Lr4AoB63fh8PszMzCA2NhZutxvnz59HfHw8HnnkEeTk5ODAgQPsmXf9+vVVA+6L8PearK/x7b+wsIDR0VGcOHECs7OzTHinU8z45zIvQtJ6oOnQ+Gv9/f2YmppCT08PUlJS2Ipffr8fwWCQfZ+dnY0YI1gsFiQkJKCzsxP5+fnIycmBy+VCZmYm8vPzUVlZyaaY9fT0YHJyEoODgxgbG8PMzAwLzr5aEHSFQqFQKBSK9yPrFm2opwaNM0GnVdTU1GDjxo3o6OjAsWPH0NjYiI0bN+Lw4cOYn59nbzvpNICFhQXMzMxErMzDQ6e38Ea66DkhDnTp/6WlpbjvvvuQm5vLRIb09HTcdtttGBwcZEb51NQU2tra2IB3bGwMFy9ehNlsxt69e1FSUgK73Y7Lly+jsLAQ8/Pz6Ovrg9PpxJ49e3DhwgW43W4mcKSlpcHpdDJjj67aFBsbi4WFBcTFxeHDH/4wDAYDrl+/jpGREczNzaGhoQHXrl1j+aWr9fT39+PcuXPsbbPMOKSfvGeC3W5nU7EGBgZYLBZ6zMLCAm7evIlQKIS4uDhs2LABmzdvBiGErSpFvaXq6urgcrnQ2NiI1tZWAEBBQQH279/PBvF+vx+NjY0IhUJISkpCVVUVrl+/jpaWloi8RjMwZPtkHjbiOXQZX6fTCa/Xy2LqUAPSbDYjNjYWcXFxsNvtWFhYQF5eHhITExEMBuH1elmATvpGnXqJUZKTkzE2NhZhXC0sLGBgYAAnT57Eww8/jF27drFYTRs3bsTly5fZObzxMzMzgzfffBOPP/44nE4n0tPT4XK5EAwGMT8/j+bmZly9epUFM6YBoh0OB+655x5MTU3B7XZHlDU2NpaJUpmZmdi2bRsTlcbGxpio5nK5UFNTg127dqGgoAAxMTHYtWsXHA4Hbt68id7eXoyNjcHv9+PixYt44IEH2BK/ZrOZBULm4dtEnNYg9lPxu9gvxDan26gY5vV6MTExgf7+fhw9ehRpaWlITExEQ0MD6uvrmVfTiRMnmDdHZWUlBgYGVohO/LVl/ZAKgIuLi2zaIm/c8sdGe/NPDV6bzYa5uTm0tbXB7XbD4/EgNjYWxcXFSEpKYp5dSUlJzOvwnSATAQghyMjIwM6dO1FeXo6BgQG88MILuHnzJnsGNzY2oq2tDS6XC+Xl5cjJyUFBQQHq6+vh8XjYs4VOvbHZbEhKSoLdbmdiCxUHacDizMxMDA4OorW1FfPz80hOTmbPig0bNqCoqAgdHR0RYsjg4CCmpqYQDAaxsLDApqxRbzin04mtW7diz549SExMZOI/H+eqrKwMqamprB0CgQBaW1tZgPCFhQW0t7ejqakJnZ2d7D4R+wYvzIuiPIWPO0NF2PHxcVy5cgWTk5N4+eWX4XA4cPjwYRQUFODQoUPo7+9HQ0ODNGC1Xt+STdek5aPHUU8yut3n86G+vh5jY2Po7u5mgooouvKCK38t/gWJpi1NNZuenkZvb++K/PHCLL3f6BQsv9/PvDMzMjKQlZXFprZlZWUhLy8PhYWF8Hg88Hg8TMAZGRnB2NgYZmdnMT4+Dr/fz34TFQqFQqFQKD4I3JJoQ4UQOlB2OBz48Ic/DI/Hg6NHj+L8+fOIjY1l8/r9fj8sFgszrsPhMAoLC2G1WiO8EShicF3Ra0Jc7pTuo8b6xo0bUVhYiIKCAjZ9y2Kx4I/+6I+YG/fw8DDq6+vR3d3NVh4hhKCjo4O9CX/ssceQlpaGBx98EB6PB2fOnIHH48Fdd92FoqIifPazn8Xzzz+PuLg4xMfHszgv165dw8LCAtLS0rBt2zYkJiaira0NMTExyM3Nxcc//nHs378fzc3NaG9vx8TEBJqbm3H33XcjFAoxI3x+fp6tIMXHtOHbgp9aQr9bLBY4nU4WNFZ2bl9fHyYmJmCz2VBaWor8/HwcOnQI3d3dmJ6eRnJyMtLT0+H3+3HhwgX84he/QDAYRExMDA4ePIiPfexjSExMhNfrZXEJLBYLW6acejSIApvsjS4tC9/GQOSbXtEwoEYLFT7i4uIwMTEBo9GIhIQE2Gw2xMfHIykpCZmZmSgpKUFubi6GhobYymCJiYnIy8tjYgs1og8ePIji4mIEg0G4XC48+OCDGB8fx+TkJFsCORwOY2FhAa+++ioKCgqwZ88e7NmzByaTCdPT02hubobT6YTP52OeafTNts/nw/j4OJKTk7F161a2apTP54PH48Grr77KhE765tput2PLli1IS0vDq6++ivb2dvh8PhBCYLfbsXXrVvh8PlgsFtTW1jKD6Pjx4+ju7gYA1NXVYffu3YiPj8exY8cQCoWwf/9+1NTUYPPmzRgfH0dnZyeampowODgIj8fDgjKbTKaIqVmiwcj3R77N+baWeVrRdhanetBjw+EwEhMTYTQaEQwGEQwGWeyib37zm4iLi4PP52PCJAB0dXXh0qVLuPfee3HHHXegu7sbQ0ND0lhQotFL++3i4iLm5+eZYGAymSI8lvhyi995aPojIyPw+XxMPKCxeXp6elBWVoakpCSYzWbY7faI89biZaeHrGx0etbIyAief/555ulBrxMKhZig3t7ejjvvvJN5wxgMBiaS0sC2mzdvxqFDh1BUVIS2tjYcPXoUBoMBpaWlLCBtbm4u+vr6WNDe2dlZeDweWK1WVFRUICYmBr/85S/R2tqK2dlZ5vlIvW944Yfegzk5OXjkkUeQmpqK9vZ2dj/SKaZerxf19fUYGRnByMgIZmZmmMdGTk4OHnroIaSkpKCmpoZ58VBBn/e6o5+0D2RnZyM2NnZFzC4qmjocDhbAfGxsDKdPn8b8/Dx8Ph+eeeYZ9vykU4aitRtFDKRNj+FFFr5vir+X1Pvn2rVrUcVzGoRbvA9pGjR9floV7ynJ51cm4NL/acyb/v5+XLp0CXFxcUhNTcXGjRtRWVmJsrIyVFRUYPv27QAAn8+Hjo4ODA0N4fXXX8f169fZs0+hUCgUCoXig8C6l/ymg8LExETk5uYiIyMD999/P0pKSvCP//iPuHTpErxeL2JjY5kB5Ha7kZKSAr/fD6PRiMrKSvzO7/wOrl69ilOnTmF6epoZtMDK1V34lSWoIcfHmKB5o3+nTp3Cjh07kJWVxaa35ObmwmKxYGhoCL29vbh58yYuXLjAjBk6CF1cXMTIyAiOHj2KGzdusBgN7e3tGBoawuLiIq5fv44vfOELbPlXGoixuLgYX/jCFzAxMYHFxUU4HA4MDg7itddew+nTpxEOh3H//ffj4MGDyM3NRV5eHj70oQ+BEILZ2Vm8/PLL8Pl8uOeee1gsmYMHD+LSpUusrLKlr3lRhoo9Pp8PNpsNBw8eREtLC5viQ6cU7N+/H1arFWfOnEFGRgZyc3OxYcMG5OXlIRQKYXBwEMeOHcPp06fR1dXFhLWYmBhcunQJNTU1qKmpQUpKCpKSkrC4uIiGhgYcOXIEZ86cQW9vb0Tfkbnh8+1H885/yjw2eJEqJiYGcXFxrK85HA4WK6Gurg7p6emw2Wwwm80wmUzwer145ZVXMDg4iOnpaWzYsAEPPfQQ9u3bh1AohPj4eADAjRs38LOf/QxGoxGf/OQncd9998HhcOD5559HV1cXCwZMCEFCQgL6+/vZ9CYaA+UP/uAPsHnzZrz++uvo6OjAxMQEi5HidDrZ0uR79uxhwa8vXryI1tZW+P1+5OfnY8uWLSgoKEBPTw+effZZHD58GOXl5fj93/995plDV7xqbGzE888/j/j4eGzbtg3V1dX4yEc+gkOHDmFiYgI+nw8OhwMtLS148cUXcfr0aYRCIZw6dQpbt27Fzp07UVBQwFYRCoVCzFOLTpui9yRtSzEwLy96iPcm/V80GnnPPZkxaTAY2PQZfkWycDjM3sjzyx8DS1PRnn32WRb0uaamBm1tbejt7WWim5hHPj/UO2BiYgLj4+PMo6enp2fFcXzf5tMUxaHY2Fjs378fGRkZuHnzJt566y0QQtj0KJvNhrGxMbbCm5i3tSIKZnRbOByGxWLBtm3bYDKZmHcfn1/6nKXT9ujy1NSzga6klZOTg/7+fjz11FOoqKhAV1cXXnjhBRw/fhwejwdms5kJXgaDAfn5+SgtLUU4HEZvby9eeuklNDY2Yvfu3ay/bdmyBY2Njbh+/ToGBwfZVDSn04ldu3bBbDbjtddew+DgIBPU/H4/wuEwMjMzWbs3Njbi5s2buHbtGpqbm5kHEf/MaW1txbVr13D77bfj8OHDeOCBB3Dw4EGMjIzgxo0bePnll1kMJ9o/6ZSs22+/HadOncLVq1eZCEefq8nJyXj88cdRU1ODxsZGvPDCC+jq6mLCn8fjwY9+9CO89tprcDgcaGtri+iL4rNd/J/3eOGDH/P3GIAVU7dEUYWKM7wgw8eQ4dMT+7bshQm/j/fS4b1lxXuCpkFFbK/Xi87OTrz22mswm83Iz89HSUkJysrKUFZWhuLiYtTU1KCzsxMtLS0R3kQKhUKhUCgU73fIegwCk8mkUc+DgwcPIi8vDyMjI8jLy8Nf//Vf4/z58yx2RHx8PLZs2YIvf/nLMBqNGBoawjPPPINAIIDbbrsN09PT+P73v8/irfCiDT/ApgGJ6eBRXMWFbqPLhMfExMBqteLxxx9ny9DevHmTLVX+93//93jzzTfZtCFxVSaaHr02HdzSgTAfzPaxxx7D7t27kZycDEIIc4sHlt4OHjt2DOfOncPg4CAbKMfExCAxMRFZWVlIT09n0wra29vR29uLcDiM2tpaVFVVISYmBlevXmVTpMS3qrSu+DqjnhelpaX49re/DZPJhEuXLqGxsRF+vx/JycmorKyE3W7Ht771LRYMkwYedTgcGB4exsDAAJs6RAfg9M9sNiMuLo6tMqNpS1MCJicnmTcOffMttiuFL4s4sKfGPw2STF35AUS8/Q6Hw9i9ezc+85nP4ODBgyxOQyAQQFNTE7q6ujA4OIjx8XF4PB40NzdjdHQUi4uLqK6uxq5du1BbW4v4+HiMjo7i4sWLOH/+PIaGhuD3+2G1WlFXV4cvfelLsNvt8Hq9GBkZwdTUFBYXF+FyuZCTk4OJiQmcOnWKLT9dVVXF4jXFxMRgbm4OHo+HrUTmdDpBCMGrr76K3NxcFBYWIjU1lYkTHo8Hfr8fLS0tuHLlChoaGjA5OQmr1YqqqioUFBQgOTkZ8/Pz6OnpYUFk5+bmYDQaYbfbkZOTw5ZNpp4NTU1N6OnpiYhxZDAYmDBis9mY51JhYSGGh4dZnc3OzkbcK+Jbdl78FBHFHdHYFINTU6h3w6FDh/Dxj38cjY2NOH78OK5cuRKRB1lgcpPJhK985Su499574fP58OKLL+Jb3/oW6yP8+WKeaLDiAwcO4BOf+AR27dqFp556Cq+//joTfcR7UeYRw3sSPfDAA3jiiSdQU1ODkZERXL58mcWXmZ2dxS9/+Uu8+OKLLA7UrRqkMk8mSkpKCv7qr/4KFosFR48exXPPPRchwPH17nK58KlPfQqf+MQn8NWvfhVnz55FTEwM9u7diyeeeAInT57ElStXMDo6iuHhYRaUXtM0WCwW/PSnP0VCQgK+//3v4+LFizCZTADApsfSqZklJSXYvXs3tmzZgqysLBYXjMa2GR4exrFjx3D27Fm2WiG9RkZGBiorKwEAAwMDGB0dhdfrZcu88+0jBnemfSsnJwfbtm3D3r17sXXrVtjtdng8Hpw4cQLnzp1jgndcXBx+93d/Fx//+McxNTWFM2fOoKWlha0MFxcXh/3798Pr9eLo0aOor6/HwMAA87ITvbPo84v2RV4MWUv78i82xDTpPv65S39H+X2i2CrziqHpygKFA29PxRL7K+1TstXkxN8s0TuI99KkzyeTyYS4uDgWk44GS56bm4taXwqFQqFQKBS/ZVzWNG2LuHFdnjZ0INfS0oLy8nLU1NQgKSkJR48exaVLl9gKR9TNni79nJycjLi4OGzZsgXd3d1MiKBTR4DIgSG/IlVsbCxcLhcIIejv71/xlpz/o8ZWMBjE66+/DgA4fPgwcnNzYbVa2Tlzc3NstRLZW3I6wKeDTjoYBsAGix6PB8888wxeeeUVJlzQwSQVGiYnJyPm39Py0ak2ra2tbDBPl0ufn59HfX09bty4wbbTgSlvBAIrl8qm0Okjzz33HB599FFUVFSgqKgIfr8fExMTuH79Ok6dOoXW1lbWZl6vF319fYiJiWGGEf/Glq+rcDjMppnR/6kxK75Fpeh5IvBvgcVjqFEjDvqpoUA9QRobG5GQkIDBwUFcvHgRbW1tGBkZYdNQqHAUDAbZdW/cuIHOzk68+OKLMBqNCAQCCAQCER4soVAIFy9exFe+8hXcfffdKC0tRWJiIux2O8bGxnDt2jW8/vrraGhowOjoKJsycfPmTRw9ehQZGRnYtGkTnE4nm15E276lpYUFD3Y6nUhMTITL5YLZbGZLbNOAprQMwWCQiTjUsJmfn2eCCu2DwWAQHo8HbW1tMBqNbB9fNr5NaT3TGD+Dg4O4ceMG5ufnmaDKG4h8+/GGIb1veZGV7uM9c2g7UkQDm+8Li4uLmJycxOzsLFvymzfAqVcMfx36/HnuueeQl5fHPK5MJhO7F2VTpXgRKhwOY2BgAM3NzSgrK8PIyIj02SOWRUyP7j937hzy8/MRFxeH/Px8VFRUoKGhAd/5zndw8+ZNdHZ2Ynx8fIVwtRqiUBTtXBpbJDs7G1u3bsXY2BgaGxtZLBm6nPeGDRtQVVWF6upq/OxnP8OVK1fg9XqRlZWFhIQELC4uoq+vD5cvX47oU7wXlc1mY14Ug4ODbKoTL5T7fD7cuHEDXV1deOmll5CYmMi83eg0qvHxcRbPhA+mHgwGWQwmTdMihH9eLKBp8fVKn+ehUAg9PT0YHx9HfX09CgoKUFlZid27d+POO+/Enj17MD4+juHhYYyMjAAABgcHkZGRgQMHDmDnzp2Ym5vDwsICWlpa8POf/xwNDQ0YGhpinjm8ECh+ih4xIjJvFtrm4rOWfoqCDN3O3+v8SoM0LdlvIZ932e8kXx5RlNF7XtBj+Tzzzw36P20nPoYQnQbJl1OhUCgUCoXig8AtrR41MDDAhJvCwkK2wsjExAQLuEuXi6XTDEwmE8rKypCeno7h4WG2PDN1dQ+HwzCZTLBarTAYDHC5XHA4HEhJSYHL5UJ3dzd++MMfwuv1SqfX0EEiHQDS6T1DQ0Ooq6tDXV0d3nrrLTQ0NMDj8axpwEwHkOIAmRof4+Pj8Hq9EVO4gLcHueIbffpHB8rUK4Uf3FJPEX4KDk1DjBUgGob8W2Wfz4dXXnkFY2NjSE5OZu75g4OD6O/vR09PD3sjTeuNikO88SqmzXs6Ue8hMX98HfDni3EORDGG/+Q9bMQ24t9O06W3z549i0AggJGRkRXeW6LBRoOa0mCh1PDn24Ye6/P50NTUhOnpaaSnp8NisSAcDmNmZgZjY2MIBAIYGxuLMCjpKjxDQ0Nwu91syWhqiFLxjPbVqakp9Pf3w2w2s4C7fBwc4G2jiApDYrvzBjO9p2ZnZ1eshMMfL7YxvSYN9ioTJqKJFbxX3FqFB1lavLGoaRpb6rutrQ1jY2MrpgPy33mDsaurC9/73vdgt9sxNDSEubk5qZEp5ofuGxwcxJEjR9DR0YGOjo6I6Vl6+dZ7pkxMTOCVV15Be3s7UlNT4fV60d/fj+HhYUxNTUWstsM/39Zaf3r7+PoJBAKor69HQkICSktL4XQ6MTo6ypZ/pkG7qYhJxXha57GxsbBarUy09fv9EdPm+P5EY3NRcYSuxgZExiyj/XRychIDAwMsIDgA1hdpvYsCLg3aLrYFfw+L7cELivQ5TpfoHh8fh9vtxrVr11BcXIzy8nKkpqYiNzcXBQUFIIQgKSkJDQ0NzPttbm4OwWAQfX19bGUlPs/itUWxUmxHPfGD38cjEzxl36NdVyYAUWge+KlOevnlr8X/Dojp8e0vXkPWhuLvqkwIUigUCoVCoXg/s27RJhwOw+/3o7m5GadPn4bRaERmZiYOHTrEVk2KjY1lwWDfeustBINBtuR2SkoKEhMTkZycjKmpKRiNRmYwG41GWK1WmEwmxMfHQ9M0xMXFwe/3o7+/PyIfssEwP9Cdm5uD2+3G+Pg4RkZGMDAwgIaGBrZKiUwwENPnPUv41Tr4t4H8QJU3FMQ3z/x+UQgSA7HyA1Jx2obeIJ4vOxVUurq6MDExAafTCYPBAL/fD4/Hw94O80IGTV9MT/Z/NMNdzN96jAnREODLKx5H2yUQCKClpQWtra265+nVrWggyK4FADMzM2hpaUFHR0fENebn52EwGJiXEe0ji4uLzDOEBkcW+xqdMkENR+oFpGeA8W/F+bbiBTGx3sU+KWsvWRuIfUnvPBkyY0omyPD7+D4u2zc2NoazZ8+y1bR44UXsM8DbdeP3+3Hu3DnExMQwISva9UR8Ph9aW1vhdrvh9/sj6nM90Puxo6MDfX19sFgsmJubixCRflPw92MoFMKFCxdgs9lQU1MDl8sFl8vFPPz8fj/Gx8fR09ODnp4e3Lhxg3nJ0GcE/8kb0fzUHCpq8tendSDLGxVo6T0gto/4zKXbxP16zwpZnfDXJ4Qw70ufz4fe3l60tLSgra0N2dnZ7DcrKSkJ3d3dOHXqFG7cuMFWPqTBwmn5xPs4migie87q7RPLqlcu2TMsmrCzWhqyPK52fdn/Iqs9Y/TyrMQahUKhUCgUHzRuSbQJh8Nwu93MaN2zZw82b97MYpAEg0GMj4/j6tWrOHLkCAKBAGpra1FRUYHc3Fw2AM7KyoLD4QAQOTBfWFjA3Nwc+vv70d/fj6amJpw/f54ZAqIhSr+Hw28HKKbHTU1N4dy5c7hy5QozkngDWzRsow2SRa8ScWlVcbAuG6hrmsamCvCeJ6L7OZ8mTYsXjkShQTwPeHtqycTEhK5RLKtPmVEb7Q0rb0TzQhRtT168EtPTK3c0bwNRDKMeK7J4R2L9i2Xn0xTLQuGXruUFEt5IEz/Ft/kyI5MXbsRriXkUjVlRhOKvSdOUvcnny0bLsprQshbDiTeqV2s7MS96RibdPjk5ifr6eulUOb3v9HNubk73npbVs1hvVNBYi2ATbT+tH+rhRa8l5l3Mj3icSLR9/H56THNzM/x+P9ra2lBYWAiXy8WmlPb396O1tRU9PT3w+/0sRhLvWUf7p9lslvY94O3nDr2+yWRi0zBlnhd8/mWihF7b0uP59hSfmbL0eJGaf57SvIfDS8uZj46OsngqNpuNBTx2u93MS5E/h88v/wwSvVTW2m4yjxSab7FtZfc0j1gvsueBjNX6ZrRt/LmrPYtXE5z4z7UIrgqFQqFQKBTvJ25pehQVZtxuN37yk5+gvb0dRUVFSEtLg9/vR0dHBxoaGpiwQ8jSik5nz56F1WqF0+lEVlYWcnNz4XQ6mafC3NwcpqenMTIywlzN6dKvNB4DP/jTM7z5gTo1NqjRRAMb8mnIRBBq/PLiDO95w5+jZ6xRA4fmk3pl0LRpHuibZhp/RByo03zR/RQ+r7zoxW/XM2DoH/VCAFYuFyuKMdQ4oYYcLxJEE4Bk3/UG4DLjS2xjepxonIgBbfk0+Prm25e2UzRDWSwn3w94LwRZfxIDkIr5Fw0w/jwx77Qvim3Il1nsn/QY2mZ8PVMPIN5jgpZTXCZery3WKs7I0uH7ruiNIBqKMs8w0TAUDX3Zs0AmZvFpiKKZXtq3il6/58svlj1a3UfLi0ysCofD6OnpQW9vL06cOLFC4OPTpM8T2u/oFEoa/0ZsD+DtKUs9PT2wWCywWCywWq3weDwr8sdPQ+TzJ5tGGa18/FLVYpvx9xhfp3wa/Ln8/UWfCzTe08TEREQ90Xzy09pk01ejlYPWq6xcMlFGnHIl6wcyIYw/XxSQxGei+Nykzzz6P02HT5dPgyJ7vvHtI06HoufIxFlZeZR4o1AoFAqF4oPCukUbOkilBt/09DSOHz+O48ePswEXP0im3gQxMUvLyIZCIUxNTaGrq2vFm0MRXojgDWC9Y2n+ou0Xg5bS9MXBtpgHmjY/CBWnSNE06cCfBj2l54kGiSg4iJ4zNG+iaMKXhx7HC0O0TmUCjTjI5/PEiwLUi4ovM80nv41+F8UNeh2avhibha8vWUBl0eCWeRmJ4gQ17EWDgq9f/pr0urzhxK9cQtPlg/nSFcX4/ri4uBghgNB258VBsa1EoUIUEvj/xX4hiix8HfLH8HXK1zFfH/S7LFbRap4lojHF55EXxcT7Rjyfr2v+f77ssuvK8iNLU89w5kUq8Xpiu8gEHxlrfQ7xx/DtuJoQI54rph1NXOKFBuDtfiuKDkBkvfOeZfTZn5OTs0IQoccbjUbcvHmTeVLabLaI/sk/O/nyi/e8rLx6/YevA1mZ+fLwaYtel1SEFe8nPm1e1KFp0DLJfqP4Zwbft2gbiNtkoolYH7zAIvY52b1GryXzwuHPkeWPloHvP+K9IOu/Yswsvmx6v7F8vYuijtg/1nKfKBQKhUKhULwfWLdowxti/KCVrjrEDxh5I1c27UNmYIuDPmqg6r3Z4wd1vLs6vz8cDksH4byRIeaFv45oINMyUw8cvTd//DKqvGHIiwR08C3WG2/cy4xFOoDmz+GvTwUD3gDhyyLWm7hN9Aqix9GBv8x45adS8AaxuE9sL7He6DaxHcRPWZ+h0xmoVxYvpPB1KzMgZQYJIYQJWECk6McLXbzBJgpb/PF8GYCVxjFNT+wHsoDCtJ1kBi5/Ht+/RaOe9g/ecBWNVjH9aF4xsvaUGaV829JyiPe6mE+x7ui5/LF8nYseZNEEF5n3jawssnstmlAibtMTdd4NI3StYg9fZ7yXGf8MpEIeLS8Nbj46OopgMIisrKyI/fz9ND8/jzfffBMjIyPwer2Ymppi95nsPubrWryn6f+i8CJ7ZojniL87PDKRZLW244+JVtdieqIwyO+j9c0LIjIxV3Yvis8Xfpueh4soqIiiCN0mCu+8NxM9nn/2yO5vse55oYk/TiyDmC+x/fSEJ4VCoVAoFIr3K7c0PUo2kBWny4jH8m7nvJeCaEjTc8QBKp9utPPpdXivFL03kvy16KolouggGiT0HGqoh8Nvx9HhB+B83YgDY9EopXmULV1M06VTu/jBO389vh74NPjyy7wW6LH8J39stFVAxDaOJiyJxqL41lkmDvDb9YxdWT3pGVZ8/xNFA3q+2Oa0/mTeOzR9apzw/Z/eDwBWTGkS80Sh5/P5EOuWXkusV4vFEtF/+PLyHmFiG0fz/pIJTbwBuprQoFdW2X0uprWWvhHtWvx5oteReI5MpJEZoeL1xP4hlm2t+ZQhu967SbSyy7YTQtgKSx6PB06nE0lJSWy6FIX2z/HxcbYqFV3mWzTixb4GyJ8hst8CWVn4Z6Pedfg0+XLy50UTk6OlSfua3n5xu14++ftrLeeLv5/8M3CtdSKWV0xzreWX3cey60RLm88Lnw/aNnp1rFAoFAqFQvF+5ZYCEYuGn7ifH2CKb8XEqTQ8/Hn0TR1FNKx5g0xvwMqnqffmkj9PRHyzSNMTDVi+HkRDkz9ejFfD51HPkOXzGc0Ilg2Wo8WGEL1leGOFryfZQFxP/OHLz+dDZnjJyiwz0qMJAzIBBni7D4h9j69HsY+KeV+LSCAzeGX1JV6bIvZF/ny+7WT9Qcwnb8yIbSDzNuDvGzHP0crAn6M3pYLftpZ6FOuUT0smHMnOF++FaMet13jVO1Yso16+18pqeb8Voj039JDVVUxMDObn5zE2NobW1lZUV1cjMzMTIyMjEaIg/QuFQhFxiPi0o+VJry9Fy/dqAsBayqj3m6F3D4jHisdEy7+eGMmXI5ooKhOYoh0nS1/vHFm+ZWWT3bdiWaMJmHrtLHvG3UreFQqFQqFQKN5P3FJMG/F/3vOFN6QBubt2NAOUFwT0pk7x0x1EI5gf9IqDRnEQLDO0ZeIMnz/6J5aR9+oRyykb5MsMA1FU4A0KMfiqrF308isTNsRryxCFHL1tfLnFtPn+IQp1MiFDzKc4JULWX0QBTuw/dJueMSUKVLK307K30HrGm6wvRUPW7qIhpGcs0fPo1EQ9Q4heR5Z3vWlDfPqy8suO1Ts/Wh3IngXR+qbYX9ZqwK1moItp610z2nYxvbVyq6LXrbIWg13sIwsLC5icnMS1a9eQmZkJi8Ui7bfRjGvZ/tXECzGdaPdWtPtSLz1Z/lbrs3pp6AkysmuI1xM/o9WdTMyU/baJ+9faL8W0ZM8W8Ti9cq5VxFotj7+pe0GhUCgUCoXivc4tiTa8oSzGoBANbD7WB79PnLrEG/jA254veqKGaKjzn6KAJBuA0v2ikc4PGvmpKLxnAh8HRHRF58vJl0dMh6/PaMYqn7ZsGotsgMvnQxYHQtO0CO8LWdwEURSjQplsdSsxH3y56T7+PL7d+KlrfB/Q60+yepbVlWzwH83w0DPAVjPexGNkYo9MhBHLES3eitg/xbKvZqgCK1dcinYsJZo3HJ9/MV2xjWX3hcxQE+tDrx2jtbNevciMR1kZogkOq4koa63X1c5dC7J7QDTk1yr6yI7l64v3jvT5fGhsbITL5cLIyAjzphFFG/H5wfcFWcyiaPXB938+LRExLpFevfDInjN6/+vVp/i7Fi1gPp9XsVx8emIfF/PCexHKnkF8WmsRklYTs+g+0bNHT2QRf0+jiUiyfOtdZ7XnsUKhUCgUCsX7EbKeAZDBYNDi4uIAvD2IogFEAblnQzgcjghYyA+6qOhBg7zSNPjYILzwQAhhK/mIAgZvDPDn8sKLLLgqzS8fo4Zup8E49YQEehyfBz4eAQ3eSM8RDWdZDCC+vvg/flUofnArE8T4+hfzLiIb8POBkvmyifkSr2U0GiNWzOLPEeueT4OWb61GH39dMR98v+LLKKuvtRj0hBAWK0bcThFXxuL7H98G4r0mrlpEz6MrrslEJz0jiZ4n7qPXFwOhrvbmnM+raIRGE3P0hBSZKMDXVTQxk1+FS9YnxP1i/sV+KmM1Y15kLUbubwK+PWUG7FqFmvXCr97GX0dPKBHRE1H0WK3/iMeuZZ9ef9frE2LeZdMtAf0ptxRZnB5ZPvl9svtIPIb+9orChl6ZVxOc+DTEY/j8iMKbXnnE8/TyKD6Hxam7egIujWGmUCgUCoVC8T7isqZpW8SN6xJtCCFjAHrezVwpFAqFQqFQKBQKhUKhUHzAydM0LUXcuC7RRqFQKBQKhUKhUCgUCoVC8Z/D6pPvFQqFQqFQKBQKhUKhUCgU/+ko0UahUCgUCoVCoVAoFAqF4j2IEm0UCoVCoVAoFAqFQqFQKN6DKNFGoVAoFAqFQqFQKBQKheI9iBJtFAqFQqFQKBQKhUKhUCjegyjRRqFQKBQKhUKhUCgUCoXiPYgSbRQKhUKhUCgUCoVCoVAo3oMo0UahUCgUCoVCoVAoFAqF4j2IEm0UCoVCoVAoFAqFQqFQKN6D/B+mnHNlWC7BRgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ira2t1NbWXnBZl4LJxoHX66WqqorS0lLcbvd5z4qgoCCSk5O54YYbKCgoICoqioyMDCIiItizZw8tLS2XuwkajUaj0Wg0mj8TLkq0CRTaYfY48OcNYj3ebExbDXV5jN3vQJ4c5vKt1zHX3Wzoy+3+vDlkSJVVQAgkclgJ5Olj1y67cKapGpvW69mFKgXyqAlUd7vjrf1o17cXgrW/HQ4HLpcLl8ulwqSku/rw8PCkb+X9bbca2WNjY+pYeU2n08nAwMB5ZdmNF39tmKyt/sqYjEDi6YWcN53rhoSEsHbtWkZHRzlz5gw1NTUThJvLyYXU/VL2rx0Oh4PQ0FAGBwenJDL8pYk6DoeDiIgIYmJicDgcVFVVXZCnjMPhYOHChRQWFnLy5Em6u7vp6Oj4VPsr0OesqamJjz76iIGBAWprayfsdzqdREdHs3LlSj7zmc9QXl5Of38/OTk5CCFoa2ujra1Ne9poNBqNRqPRaIALEG3svBwCvdmXv/151Vg9F6wTervz/E36JzOIzUa8VSCZDOs1Za4es7AyWdvMxr+1Teb6+wvFkblh7LxT5DUC1cFOrDIfYxZzzMdLgcScn8jfNezqMVXjw1+7zKFGcp/L5SI0NJS4uDiio6MJCwvD5XLR29tLR0cHXq+XgYEBRkdH1c/IyMiEuth5Xdldd3R0VG0PDw8nISGB0NBQysrKVJJRu/ab76E8306MtBsTUxX8/I33qQoLU/U6mgpCCNLT0/n7v/97AJ599lleeeUVamtrr1gDdDLhzl//TFUwjYyMZPbs2Zw9e5bOzs5P1Tvkk0YIQVBQEPPnz2fjxo0MDAzwz//8z0pQnW5ZixYtYtGiRTgcDiorK+nq6vL7TLocOBwOgoKCGB4ePu9Zbq1rV1cXR44c4ciRI+c9AzweDzk5Odx88814vV4effRRcnJyWL9+PSkpKSxfvpyDBw8yNjZ2xX5uNBqNRqPRaDSfHNMWbayGvRnrdrPHzFQ8MORxVg8U+Tdge4753Mk8VszXtxrodt5DgbxKJsPsZeRPbLETbqz94E+4mgzpJeJPNLPWJ5AhajWOrPUMJEBMVkdz/ez2yetI3G43c+fO5fbbbyc/P5+wsDDVx06nk6GhIQYGBqivr6exsZHm5mYaGxspLy+nvr5eechIL52hoSGGh4cnXNeuXg6Hg61bt3L77beTkJDAj3/8Y7Zv335e7iZ5rBR7Anka2d0Xf8LZdPp2Kn1/sZ41dgQFBXHmzBkWL17MHXfcQXR0NL/4xS9ob2//xL0iLqR9dmKpmckEMvO9CwoK4rbbbmPr1q089dRT7N27l6ampr84bxp/GIZBaGgoWVlZLFmyhLa2NiV6XAhyDOXn57No0SJOnToVUDy5lEjvmM2bN7Nr1y4aGxttBVszdvscDgfJyckUFhYSFxfHr371K1paWmhqasLj8VBUVER2djYzZszg5MmTgM5to9FoNBqNRvNfnQsKjwoktMi/pSHjcDjweDx4PB7GxsYYGBhgYGDAr2EkJ+FOp/M8LxL5Y5cbwc5oms5k3p9wYF1ZylyunWFvrYe53vLNqVkM8idWWL1LAnnQ+Kuf1ZicqneAP6FqKuKOGbtVufx55fjbJ8+T+8PCwli2bBnf+MY3OH36NDt37qS3t5fh4WHcbjcej4eYmBjy8vJISUkhNzeX4OBgFT5VVVXFvn37qKqqoru7m97eXurq6s4T1eza4Xa7SU9PJzU1ldjYWB566CHKysooLy9naGjovLpb+0lukwKYHMswUTS061t/YkGgfVMRdgIdN9lnybpfCEFPTw+PPfYYHR0dLFmyhA0bNuBwOPjJT35CX1/flMafv+vZHTtdo3kyLvYc8+dNLvnsdrvJzMzk1KlTNDU1qf1/CeJNoDEin9NDQ0M4nU7i4+OJiYmhv78/oDDm7xpHjhxh+fLlZGdns2jRIt5//33q6uou6wo+8jMbFhbGpk2b2Lx5MwMDA+zatYvm5ma/n1W7Z7vcFhkZSUpKCmNjY5w5c0Z5Hp04cYKhoSEyMzNxOp0AE156yLLl7z/n8XO5x7+/78c/5z7TaDQajUbzX5dpizZ2XgPmCaXD4cDhcBASEkJaWhqLFy9m6dKlBAcHMzQ0RHFxMR988AHl5eV+jR1/S3DL/dbQJPM+uzrZ7bfDKpCYPYWsk2SrOGXdbneO7D/zMuN2dbUzhv3V3yyy2HntWOsSyKPDnwE2maeR3X4hBMHBwcTFxbFkyRL27NlDZ2en7Vtjaz/7K1/+Hhsbo7S0lGeffRav16vCnuTYCw4Oxu12Ex4eTlRUFCkpKcyaNYuZM2eyZMkScnNzVWiF1+vl6NGjHD16lJKSEnp6etTSu3KcmXMYVVZWsmfPHpKTk1m2bBnz58+noqJiQl1dLhdRUVEEBQXR3Nx8nsAoBTxZ9nSExsmOnY4oIseidZ/T6SQqKgqXy0V7ezsjIyMBjSy5fWxsjObmZrq6unj88cdpamri6quvZsWKFWRkZFBaWhpQhJyuITpdo3+qguXFYhZpu7u7cTqdJCYmEhERETDM8GKvGeh5eLnwdx9lHwQFBSnR3ul0snDhQpqamqbVB3JclJaWcubMGeLi4khPT6eoqIjnnntOidyXup0Oh2PCiwJAfbZdLpdfwd6K+QWG0+kkNDSU6Oho5RUIMDIyQnNzMy6Xi5GRETo7O3E6nQQHB5OTk0NaWhoxMTG4XC76+vpoaWmhtraWtrY2+vr6VMjWp8lUn2P+XvBcqvo7HA7i4uKYP38+2dnZNDY20t7eTm1tLd3d3QwODqqQ2el6MGo0Go1Go9F8GkxbtPFnGMgJaWxsLHl5ecybN4+ZM2eSmJiIw+FgYGCAqKgoDMOgpaWF8vJyv2VaPR7sJoP+vDOsx1v325Xpb18gMWWyN3mBhI6plCPLmkwwsZYbSKSaTIixCkSTGekSu8m2EIKYmBjWrVvHxo0bGRkZYffu3fT29k4wzgMZlnbi3cjICLW1tWzfvp3q6mqGhoZUvaWRJQ1jaTBWVVVRVlZGQkICc+bMISkpibi4OLKyssjLyyM5OZm5c+dSV1dHRUUFZ8+epaamhvb2dhXKMTY2xsjICAcPHqS2tpaoqCj279/PqVOnJhig0qPgxhtvZGRkhD/+8Y/09PQAEBkZSWZmJj09PWr8T/X+XqhxYT5Pilpwfribuf4xMTHcfvvt9PT08Morr9DV1eVX1LPWaWBggOHhYaqqqnj77bdpaGggPj5+QtJYO9HR/P9U2xfoMzzZtgtlqh5M8ndvby9hYWFkZWWpZLz+Pi/+6irvm1WcMBu95nxX/oTPS9UPk5Ul6+N2u8nNzSUnJ0d5HRUUFLBjxw4ljE5FqJPX6+np4fDhw6SnpzNnzhxWr17N22+/TXt7u99cVReD9Rk1MjJCSEgIQUFByhPUuoKh3XeHuS7ye1L+9ng86rjh4WFaWlro7++nt7eX+Ph4Nm3aRHZ2NnFxcXg8HvXSwjAMOjo6qKur48CBA5w8eVI9Wy8VU3nxYRVg5DN4ZGTEti5Op1PleYqMjMTj8TAyMkJXVxctLS1UVlae57UoBS+Xy0ViYiKRkZEMDQ3R0dFBR0fHeeGpHo+HvLw8br75ZpKSkvB6vfT399PV1UVvby9er5e2tjbq6upoaGigtbWV/v5+hoeHL6vXlkaj0Wg0Gs2FcsGrR8GfjHyHw0FsbCz5+fnk5uaSnZ1NamoqwcHB1NXVUV1dTW9vLykpKbjdblwu+8vaTQQDGarWCbI/zOWYvQmkoSMn/P68WuxElkATO/ObZn/77Lbbed3YbbOeZ/e31QPH3H5/IpldHwQ6NpBxJITA4/GQmprK3LlzaW9vp7i4eEL+mED9ZC7HfOzY2BgtLS00NzczODhom6RYTrwHBwcZGhrC6/XS1NSEy+XizJkzpKenk52dTVFREUlJSSQlJeF0Opk5cyazZs2isrKSkpISSktLKS0tpaenR42Tmpoa6uvrcbvdHDt2jNbWVnU96WUTHR3N6tWr6e/v5/XXX6evr4+QkBBmzJjBxo0bqamp4dy5cxOEk+mIFVO9B2acTiezZs0iOTkZgNraWs6dOzdB9AKfURkWFsb69evp7Oxkx44dyvvIem1/43FsbIzBwUFKS0tpbGwkIiKC9vb2gO0LNI787ZcCWXh4OK2trXR1dU3aD4G4EGNtsv7v6ekhLCyM5ORkIiMjcblcE8bLZGV7PB6ysrJISkqira2N4uJilbspLCyM+Ph4lRi7ra2Nc+fOTRBGrZ5cQggVemM1UC9E7DB7o5gFUxkStnjxYhISEqirqyMjI4NZs2aRmJhIQ0PDeTlhAnkLgU80KS0tpaamhsLCQmbPnk1hYSG7d++2Tdrrrz3T/azJ40ZHRyd42Fi/V6wijvyxbhseHmZgYACXy0VsbOyEOvX399Pf34/L5WLjxo0UFRXR1NSklgCX4yY1NZX8/Hzy8vIAaG1tpaKi4rIIDoFejDidTuLi4oiLiyM8PJyQkBBcLhetra1UVVXR09Oj6hwWFkZmZiZz5sxh5syZhISEEBISAkBfXx/t7e0cPHiQw4cPqxBq8IWlxsXFMXfuXHJzc4mOjmZgYICqqipOnDhBdXX1hM9TaGgoaWlpzJ07l4GBATo7O4mKiiIpKUnVr6enh+bmZurq6qirq6OtrY3y8nLOnj17QYmyNRqNRqPRaC4nF7x6lPzb5XIRHx9PYWEhGzZsIDU1Fa/XS0lJCUePHqW0tFS9qc/Pzyc6OprW1lbbN6P+BBM7UcF6jjXcyM6TQwiB2+0mLCyMiIgIwsLC6O/vp729nb6+vgkCgJxoy0mpYRhqBSLzj9xuPtd6XWv/+etPu+PN26ZqVE3mfWRXtt32yQSVQMcKIRgcHKSpqQmn08nKlSt566236O7upru7W/UbcN7baomdwQmoUABrn5s9EaznyeMbGhpoamri7NmzxMTEMHv2bLq7uzl06BD5+fkkJiYyc+ZMVq9ezbFjx3jxxRc5ePCgEpqkW/3Q0BB9fX0TVpaSbXG73cogiYiIoLOzk5iYGBYsWMCtt97KmTNnePHFFxkYGJjwOZhMCLQeM9V7AxAcHMw111xDUVERLpeL/fv3s23bNqqqqiYsXS6Rn5GIiIgJQsNUxp9s0+DgoBLYzPUNJDSal1QXQjA8PGz71l4KGqtWrSIzM5OdO3dy8uTJy/6m3Fpff0KDbGtHRwcej4fY2Fiio6MJCQlhaGhI7Q8JCVGfFbu8SElJSWzevJnly5ezf/9+SkpKcLlcJCcnk5eXR0FBAfPmzSMxMZHS0lLeeOMNKisrMQyfV5rX61XPNiGEChkEaGlpUcuxBwUFERYWpgSFyVa5krnKIiIilPeJvHc9PT0EBQWxZs0aZsyYQWdnJ8XFxWzcuJHs7GwKCwvxer2qvbIv3G73hBXe7D7DLS0ttLa2MjIyQkpKCjfeeCPFxcU0NzcrAdJ8jvV7wLp9KpgFMPM2c7kul4uEhARGR0fp7u5WooMUycyhOH19fXR2duJ2u8nIyODo0aPnXTM8PJzPfe5zhIaG8vTTT3P27NkJYp98Ri1fvlx5M1VWVk65TdNFinPmlwtut5ukpCSWLl3KjBkzSExMVGO8srKS1157jWPHjjE4OEhISAg5OTlcf/31LFu2jPr6erq7uxkeHiYiIoKUlBSWLVtGQUEBDQ0N1NbWMjw8rIStNWvWcNNNNxEWFqZylzU2NhIbG8u2bdvwer3qngQFBREcHMzw8DClpaW8+OKLpKamkpiYSEZGBhkZGSQkJDBz5kyCgoIYGRmhpaWFF198kdraWr/58jQajUaj0Wg+LS4oPMo8yY6Pj+euu+7immuu4ciRIzz11FMUFxfT3t7O6Ogow8PD6vjS0tIJSYStbynNZZsn7tZJtnUCbhZKZOJPeW05WXa5XLjdblJSUli7dq3Ks9HU1MQf//hH9uzZo4wImUtAGjLf+MY3cDqddHZ20tDQQHV1NZ2dndTX1zM4OEhnZ+cE7xHpSWSe/Jnfusq2WfO4+DPCrcaH3d/WvrTuD1S+3bWmUhe7cs1/9/T0cPbsWcbGxkhKSuIzn/kMLS0tlJaWKgPEuoy5GbnNLOpIgUxuMxsS1uW0zSKDORdTcHAwgBoTDQ0NPProo8TGxrJixQrWr1/P7Nmz2bBhA3l5edx33320tbWdVy+7v4eHh+nr62NgYIDY2Fji4uJUXpuxsTFiY2NZtGgRERERysi80KV9p+NFIFfJAsjPz6egoIDVq1fzox/9iJMnT9Lf369ENBmukJSURGJiIvX19ecJCv6ubye0SgKtPAd/yqUjr+vxeDhz5gx1dXXnled0OklPT+ev//qvCQsLo76+nvLycnp7ewPWb6pMxRvDn4glt8s8JUNDQyQkJJCWlkZsbKzyhAkKCqKoqAghBGfOnFFiC/zJSF64cCErV65k9uzZVFRU4HK5SEpK4oEHHmDVqlWEhoYyMDBAeHg4K1asYM2aNezcuZPo6Gja29v54IMP+Oijj5TwtW7dOtasWUNHRwfPPvssdXV1AOTm5rJhwwaqq6s5ePCg31WupAgRERHBwoULWbduHbNmzVLhO263m+LiYiW27dy5k6NHjyrvm3vuuYetW7dSUlJynqGdlpZGY2Mj/f39tl4z8lkhQ/BiY2O54YYbOHr0KNu3b1f5l+Rzwvp8MN+fQPfd+gJB/h0REUF4eDhBQUFq5Tl5bFRUFA899BBVVVVs27ZtQvijPEaGRMkXBUFBQSxbtowjR44oAVg+x+Li4sjIyGBwcJDQ0FCVEw58z6+enh6qqqqYM2eOyuPldDonze1j/vz5E7isx0kvGY/Hw8DAAIODg+p+/e3f/i0FBQW0tbXR1tbG0NAQUVFRFBUVkZOTwz/8wz/Q3NxMXl4eGzZsoLCwkNdee43t27czODiIw+EgPDyc/Px8tmzZwooVK1i0aBEdHR14vV7i4+NZuXIlX/nKVygvL+eXv/wl4eHhXHPNNSxcuJAtW7bQ0tLCjh07VP8MDw/T29tLb28vg4ODnDhxgsOHD6swQ+kNuWzZMtauXcvq1atJTk4mKSmJ4OBg+vv7AVR/yh/zOLoQrzSNRqPRaDSaC+WCRBsZQjFnzhzuuecesrKy+MlPfsKRI0dUKInEvDqOnFRZDWqraGGeoFuPka795uOkIZGVlcUDDzxATk4Oe/fu5d133+XYsWOMjY0RHBzMDTfcwG233YZhGCqGfdasWXznO9/h4Ycf5tixYwwNDTFr1iy2bt3K+vXr6e/vZ9myZSqcyjAM9aa8r6+PJ554gmeffZZz585hGAYej4drr72W1NRU/vjHP6p8C+bJvzk/xVT6W55jPd6fB5J5wu12u1XdpZBlFpOsxqn5t51XkPxtvWd2nlMDAwOcO3eO119/na997WvcfPPN1NXVMTAwQE1NDUIIW88IcxvMK26Zt1lXYJJv4s1l2K3AJZN/ejweoqOjiYqKIjg4GIfDQWNjI6+//joHDx5k8+bN3HnnnSQnJxMVFUVXV5cSIGV5dobl2NgYfX19dHV1kZaWRkJCghKu5P0AyMzMpKOjQ92LS2EEWHOfwJ/up9fr5fHHH2f79u0sW7aM2267jaKiIp588klefvllnn32WUpKStRy6ZWVlaSkpFBQUEB1dfUEA9vsuSQJZBRPxWB2OBwsXryY2267jTlz5hASEsLIyAgdHR387Gc/4/jx42rVIXkPgoKCiIiIUOJYeHg4AwMD5xnsEvO9m6yvL5Xg09HRQUVFhfJwiY2Npbm5mYSEBO6//342btxIe3s7jz/+uAr5i4+PJzY2lurqahX+NTg4SFdXF5GRkXzrW99iwYIFvPzyy+zYsYPGxkays7P5X//rfzFz5kxuueUW9ZxctmwZ//RP/8RHH32Ey+WioKCAuXPnUlVVRXR0NA0NDTgcDq655hruuOMOmpqaiI6O5rnnnpvg4SQ/Z9HR0SxevJgvfOEL5OTkqHAsgJSUFOLj41m3bh0lJSU8/PDDVFVVMTg4iNPp5N1332Xr1q3k5+dzzTXXMDY2RkVFBSEhIaxcuZJ77rmHl156iR07dpy3RPzo6CgOh4PQ0FBSU1PJzMwkNDSU0NBQvvWtbxEVFcWHH35IZWUl3d3dfj+jk2F9/skfl8uFx+NRHnRyNTrDMAgODmbhwoVs2LCB48ePs2vXLs6dOzfB2JdlejweMjMzmTdvHjExMdx0002sXr1aeeNIQXpsbIzIyEiEEPzgBz9QLyBGRkbo7++nr6+P1tZWjh49yv79+zlx4oTfPDLmMTmVcWtuv9PpJDw8nIcffpjh4WHeeustqqqqWLlyJV/84hcZHh7mn//5nzl27Bg9PT3Ke+ihhx5i7ty5zJ8/nyNHjpCTk0NhYSEul4s33nhDeX/J79OysjIOHTrEwoUL8Xg8BAUFqZxIRUVFdHZ28v3vf5/u7m5cLhdNTU3U1NRw1VVXcccdd1BVVUVlZaXyguzp6WFgYGDC/MPsEdvf388rr7zC22+/zQ033EBiYiJlZWXqXmZnZ7Nw4UL6+vqorq6muLiY0tLSKa2Ap9FoNBqNRnOpmZZoY34rumbNGrZs2UJ0dDSPPPIIJSUlEwwmQAkFZoPbn3eN+cfsim1OWCnPk3/Hx8czb9480tPT8Xq9/M3f/A3Dw8NkZGRw66234nK5qK+vp7e3l2uvvZavfOUrvPrqq3z00Uc0NjYSFRXF6tWrufXWW1m0aBEVFRW43W7mzZtHYWEhMTExJCcn8/HHH3Po0CEVKiVj93fs2MHrr79Oc3MzY2NjuFwuIiMjue6664iKiuKjjz5iYGCAvr4+HA6HcsU2e/TINsn/pUeG1UPEGoYjsQopctLvdrvJzs7mlltuISsrC4/Ho97OHjhwgH379ilPD6tAY74n5nsmseZo8DdWZMjA+vXr8Xg8hIWFce+995KXl8cbb7zBiRMn6OjoYGhoiJGRkQkhOGaRRZZnNajM3jXmc8xeNXailMvlIjg4WHlEhYSEkJuby4wZM5g3bx6zZ88mKSmJnp4e3nrrLVpaWiasJGUev/LHbBwODg5SUVHB/PnzycrK4sSJE2pJcunBNWvWLE6dOqXCU6bi2WG+z9ZtbrebrVu3EhkZycGDBzl9+vR593FsbIympiYlZq5fv55vfvOb3HDDDeTn5/Paa6/x1ltv4fV6OXz4MFdffTVr167l1KlTNDQ0MDw8jMfj4cYbb8Tr9fLhhx/S2dkZcAzIce/xeFRYnLXuQggiIiL40pe+RHR0NO+99x6nT59m/vz5XHXVVdxyyy3U1dXR2Ng4IQ9QW1ub6vf09HTS0tKUh5/1GjKvzOzZs3n//ffPC027XEiDVCa8Xrp0KQUFBdx1112MjY1RXV1NdHQ0aWlpZGVl4XA4uP/++2loaOCxxx5T91COqeXLl7N06VJefvll3n77baqqqhgeHqanp4cf/vCH/PrXv6auro6jR48SHR1NYWEhd911F8eOHcPpdJKQkEBQUJASb2UYmsyHUlhYSE9PDwcPHqSsrGyCV2BaWhpr1qzh9ttvp6WlhQcffJDu7m4KCgpYunQpsbGxtLW1ERkZSXp6OlFRUbjdbiU41NXV8R//8R/cf//9fOYzn1Fld3Z2kp+fT0ZGBitXruTEiRN0d3efJxrJ55sUL2T4TEZGBg8++CBf+tKXKC4uZteuXbzxxhtUV1cDTHtlKbO4bn4uysTBUlSQYThZWVn81V/9FW63W60S5na7lbhv9rTJycmhqKiIoqKiCc//8vJyBgYG1PeFfE7MmzeP2NhYhBB0dHRQVlbGq6++ytmzZ1VYr/TsM9fdTjid7BljFejk79TUVObNmwf4PnNz587llltuITw8nNdff53Ozk513uDgIM3NzVRUVJCXl0dSUhJhYWG4XC5CQkKIj48nIyODsrIyJVgHBweTnJzM8uXL6ejo4Pjx4yrnT2RkJAkJCbS3t9Pf36+E/oqKCtVXmzdv5vOf/zw/+9nP6O7uVvccfGFm5nbLH3lvxsbGeOmll9SLjejoaLKysggJCaGrq4sZM2Ywe/ZsVq1axZkzZ/j9739PR0fHlEQwjUaj0Wg0mkvFtEQb+cZx5syZLFy4kODgYP7whz9QUlJCX18fcL7XTFBQ0HkeFeZjwsLClHEbFxdHaGgoY2NjpKam0tPTQ2VlJadOnaK8vHzCKjYyT8rKlStxOp28/fbbPPnkkzQ0NLB582bWrFlDTk4Os2fP5vTp01x11VV4vV4OHjw4YcJ35MgRtm7dqt6ednZ28tFHH1FXV0dhYSFf+MIXSElJoa+vj6NHj9LW1qY8Gpqbm2lubp4QAjY6OkpPTw+ZmZkq/Mrj8TBz5kw2bNjAW2+9pcIFZB8EBQWRnJzMpk2b2LlzJ5WVlQwODk4Qc2T4lew/aahnZGQQHx/PyZMn1cQ9LCyM/Px8vvSlLzEyMkJ9fT3R0dEkJiaydu1a5s6dS0ZGhnqbbr5fgFqOtqury3a5avNv6/2WSDFFegjIlVeysrK44YYbWLZsmUqwefr0aZ5++mmV68jqJWH1oAkkKNnVySwApaSksH79embMmMGSJUuIiYmhsLCQ733ve0RGRgJQVlbG7t27OX36NOXl5epeyHAIc/9HRUXR2to6QSAZHR1VBuOcOXPYvXs3Xq+X4OBgZSjMnz+fN998c0IupakYlnYeUOAL3bj22msxDIOamhpKSkrUGHM6nSQlJdHZ2cng4CD9/f3U1dXx/vvv09HRwZe+9CXS0tL47Gc/S2RkJP/5n//JiRMnaGpqUoZ6d3c3xcXFREZGsn79egCKi4vp7e2dkLjT3OdOp5OcnByuvvpqQkND+X//7/8pQ9zcDiF8uVtSU1M5e/Ysp0+fVtfPyMggKSmJ5ORktfqLuU2yrJUrV6p7dOTIkQkGv9PpZPbs2Xz5y19WywD/4Q9/oLOz87xcPZf6Lbph+JaqXr9+PfPmzSMzM1Plenr22WdZsWIFy5cvJyQkhKioKGXYj46O4vF4SEtLIzo6Gq/XS2trK/PmzSM4OJjm5mZ6enpUOwcHBykuLubMmTOMjIxw8uRJwsLCyMnJITMzk7i4ONra2vB6vSpccd68eSp3SElJCS0tLWRmZlJYWMh9993HT3/6UxobG1W/5ubmsmTJEjweD6+88gp9fX185jOfITg4WC1vnpmZqZL1yiWqpfA+NDTE/v37WbduHQsXLmTt2rU4HA6OHj2qxA7ZRzKxtDWkbmhoiNbWVhobG0lOTlbPXimexMbGqtwyjz322JTukXnMmgVf8zgdHR2lubkZIQRz587l6quvpqOjg8TERObPn09ycrJa3SglJYXk5GQVEiaTjsty5D2Tz/Tm5maee+45ampqVK40GcaTlpbGVVddxYIFC4iPjycnJ4ctW7awZ88eTp48SVVVFYA63u571txG+be/54i1X6R3kxSr1qxZo75nDMMXbjdjxgza29vVak5yxTT5+ZehlkNDQ4SGhnLXXXdx9OhR3G43kZGRREVFqSTdzzzzDHV1dQwNDU0QtcLCwggJCVHC7/DwMI2NjRw4cICZM2cyY8YM4uPjlTeebF9ISMh5wr55TI2Ojqr2OBwOhoeH+fjjjxHClzMrPT2dNWvWMHv2bJYsWUJXVxfPP/+8yqt2KTwkNRqNRqPRaCZj2uFRDoeDrKwsEhIS6O7u5tixYyoHgZwcyTj1rKws5s+fj9vt5vjx41RXV6sVXhwOB8nJyVx//fUsWrSIpKQktQSow+EgKiqK3t5eGhsbWbRoEYcPH+b999+nvr4e8BliaWlppKWl0dbWRmNjI6WlpXi9XqKiosjPzyciIoK0tDQqKirIyspSYSvyLbvMD2EYBrW1tSo0pLGxkY6ODjo7O8nLy2PlypXMmzePs2fPcuzYMZV/wBqmIyfltbW1LFu2jFmzZnH27FkMw2DBggVs2bKF4OBgysvL6e7uVsa6nOwvXbqUoaEhOjs7aW9vJzY2ltzcXEJDQ9m5c6fywpFGVHx8PJ/97GeJjo6msbGRpqYmRkdHiY6OZunSpeTm5vL000/T2NiIx+NRk/758+ezevVqXn755fOWqw4NDWXLli04nU7eeecdWlpazgszkklIh4eH1eTVvN/sJdXV1cVbb73FjBkzVC6ImJgYoqKiyMnJobOzE5fLRWho6ASDSWInBpgnylbjwxoCYy7P4XCwZs0abrjhBtLT00lJSVGriSxYsACHw0FPTw/t7e2qjpGRkYSFheH1etX15KpFt956K9HR0fzsZz+bsH94eJiamhpGR0fJzMwkNjZWrTwj+2/hwoVERESopbCnO/G365fo6Gj1dtrtdqvP5MKFC1m+fDk7duzg3LlzKidIY2Mju3btwu12c80115CXl8eCBQtISkqivr6effv2sWXLFhYvXqy8xQYHB8nIyCAkJIT169cjhJiwCpX5PphzSEVERKi31NZVs+SxwcHBREdHq7CQxsZGjh49ypo1awgJCSE4OJjR0VFCQ0OVyHn27Fm6urpISEhgyZIlOBwOIiIiOHjwIL29vcpYDgkJISkpiTlz5nDrrbcihGDXrl1UV1fT3d3tdwn0i0FeV4oY4EtMfPr0aQ4cOMDhw4dZvHgx4BMj+vv7CQkJUavsJCcnk5ubi9vtpra2Vn0WhRBqxSjpzWgYviWxt23bBsCJEydUaJ5cbrylpYVTp06RlZVFamoqq1evpq2tjdOnT6sl7QESEhIoKiqivr6eJ598Eq/Xq/KspKenq9V5Fi1axIoVK5R3Q0xMDOB7jvT19annlfyROX7eeecdwsPDSU1NZeXKlSQlJZGXl6c8HZYsWUJLS4sKtTU/V6RHBPhCXNxut3puuFwuRkZG6OzspK2tTXkuWr0I5b2ZzmdueHiY2tpa2tvbSU9Pp6ioiJaWFvr6+mhububkyZPcfffdxMTEsGHDBnJzcxkcHKStrY1nnnlGibPt7e3U19fT3NxMWloaLpeLsLAw+vr6qK2tVUIV+J5X586do7OzkzNnzjBjxgxycnLIzc0lJCSEgoICamtrqa2tpbGxkdbWVmpqalQiaauHqtXjyE64sRNyzEmmk5KSqKmpYffu3Wo1p8jISEZGRhgcHFTeQpGRkTidTgoLC0lNTVUJhV0uF2vWrCEzM5O+vj5aWlro6OigsrKShoYGjhw5opI4G8afkjanpqZSWFjIRx99pMQus4BXUFBAZGSk8iKTzxRzrjn529xuOb7Moc9ShHc4HHR2dqocd0uWLOHqq6/mwIEDVFRUfCKeehqNRqPRaDRwgatHJScnExoaSl1dnXIVNr81Dw8PJy8vj40bN3LVVVcRFBTE7t272bFjB6dPn6a7u5uwsDCuueYabrzxRjW5khP9mJgY9QYuJCSEzMxM0tPT6enpYfv27UpwCQkJUcapNLYBzp49S0dHB/Hx8URHR6sJp1ztJCgoCKfTSWZmJkuXLqWxsZHi4mI1sZY/FRUVbN++nRkzZlBQUMDJkyc5cuQIXq93gscA/GkyKF233W43BQUFlJaWIoRQ3kQbN27k/fff5/Dhw8prQL79i4qKYtWqVZSUlCCEICcnhxtuuIHIyEiVAFkucx0cHExCQgIbN24kKCiI1157TQlSUVFRzJ49G8MwOHDggPJg8Xg8ymU9Pj5ehUaY3fcjIiLYvHkzHo+H+vp6jh07pjycZF1lTgmZV0Au3Ws1jqTX0dtvv01hYSGbNm0iIiJCvUGVxpcUOMxMJmRMxfgyHyN/YmNjGRsbU+KeXD62vr4eIQQjIyNERUWxZMkSMjIyKC0t5eDBg5w8eZK2tjaGh4cJCgoiJyeH+++/n+DgYB577DElEIDPwGtoaMDr9ZKYmEhmZiYAHo9HiT75+fmkpKTQ3Nw8YRxMBTvDSxpM8fHxREZGEhoayuDgIG63m/Xr17NhwwYqKipoaGhQb5ZlwuE333wTQIkmaWlpVFZWsmPHDhYsWEBOTg5r1qzB4/HQ0NBAcHAwYWFhbNy4US0/XV1dPSHfgxxPMiwpOTlZGV12iWZ7e3vp7u4mPT2dpUuXqhVd5CpD0ntCJitOTEwkPT1dJd2dO3cu+fn5rF27lpiYGIKDgykrK6O1tZWhoSGGhoaUp0RBQQFhYWHExsayd+9eiouLzwu9ulicTieRkZGsXLmS3Nxczp07R3NzM0eOHOHjjz+mpqYGj8ej2tXR0aGEWhnauHr1ambMmIHX6+XkyZN0dHQoz5g5c+aolb/a2tpUn7711lsqr4fb7aa+vl55gRiGwfHjx5WQOH/+fMbGxlR/hYeHq1WN4uPjufvuu6msrOSjjz6it7dXidRhYWFcd911ZGVlqc9Vd3c37e3tKifYwMAAvb29E5LOy5XXPv74Y7UikPQCGhoa4ty5c0p8q6mpUUKEHE9Op5PExEQSExMZGRnh6NGjpKenk5qaCvg+d21tbZSWlnLo0KEJAq4Z83eV9TljFYUlUrTZvXs32dnZDA4O0t3dTXl5OadPn+bcuXPMnj1braSUkJBAY2PjBK8UwzBob2+ntLSU48ePq++46Oho5syZQ01NjUpqL+s4NDTEgQMHOHPmjFpCfeHChaSkpFBYWMiiRYtoa2ujoaGBhoYGTp06RUdHB11dXXi9XhV+6XK5aGtrm/CyQNZL9oWdYGP9PPf19XH27FnefvttOjs7CQ4OJj4+nqCgIEJCQpRHVWdnJ6dOnWJkZISIiAgGBwdpbGykq6uL+fPns3DhQpqbm+ns7KS2tpbi4mLq6uro7e1V1wJUTqgZM2awYcMGGhoa1AIALpdLfZ8PDw8rQc/lcqnwNSkoBmoboEK35Gp/MuS5r6+PU6dOqc/LnDlzmDdvHtXV1ZdF6NVoNBqNRqOxY9qijcxTEhYWpt7YW8M7kpKSWLVqFUVFRQQFBREdHc2dd95JYmIi27Zt4+DBg+Tn5/PVr36VkydPsn37diorK9UqKHPmzOG+++4jKyuLsrIywsLCSE9P56abbuK9995TrtPybVpoaCjh4eFqgj84OKgSV8q3rS0tLeTm5jJnzhx6e3uJiIhg3bp15Obm8vzzz3P27FklPkgRZWxsjH379nHq1CnWrVtHQkIC0dHRaglj8wRfvkmWYQper5cZM2awadMmnE4nc+fOpa+vj5SUFL7yla/wT//0T1RWVqrcErW1tfT19TFz5kzWrFlDVFQUWVlZrFy5krCwML74xS/y4osvquVug4KCSElJITw8HI/HQ35+Ph0dHbS1takcIlLYkaFrYWFhKgSjsrJygncUTEw2mpKSwvXXXw/A6dOn6enpUX16/fXXc+2117Jz505effVVGhsb1RtN6+R/ZGSE9vZ2fvnLXyqvK5n4Vyaa/cMf/qAEN7PBZPWokdusmA0vfyKOfNP8/PPP89577xETE8Ott97K5z73OZqbm3n22Wdxu90YhkFGRgbp6elkZWUxc+ZM1q1bx1NPPcWHH35Ia2sroaGhJCUlkZ6erlz+zR4PMlSktLSUtLQ05s2bp8LTpBEbHh5OYWGhSuRqFr2mKt5Y+1nmwpAroXi9XjweD4sXLyYuLo6YmBhCQ0OVAS4Tno6MjKicMeHh4cTGxioD/4MPPsDlcpGSksK1117L0NAQLS0tDA0Nqc9kUlISr7/+OhUVFeqzZzWYo6Ojuffee6mrq1PeAObjmpubOXToEKtXr2bVqlUUFhZSX19PZ2cnkZGRLF26lKioKGJiYjAMg/Lycp555hlOnTqFYRjk5+dz1VVXsWnTJgoLCykoKODdd99l//791NTUqGS+AwMDtLS0kJKSwr333svChQvVajYtLS2T9r1dWKCdd1hwcDDz58/nkUceobW1lV/84hccO3ZMhWQFBweTnZ1NamqqEmS7uroIDw9nZGSEhIQEvvzlLxMUFMSbb77J3r17GRgY4MyZM9TW1jJz5ky19PfBgweprq6ekC8rLCxM3W+v16tWMKurq2PHjh04nU6uu+46Vq9ezeLFi9XqYgcOHGBwcJAlS5aQl5fHww8/zPe+9z0OHz5MfX09x48fJzU1lVmzZuF2uyktLeX06dMcOXKEmpoa5syZw9e+9jWVL8e6opsUeF577TXa29tZtWoVSUlJlJWVUVtby913301GRobKb3LgwAElpoeHh7N8+XK1vPW2bdtYv349d911F6GhoQAqF0xnZ6ftsuVWjz2r0Gx+ppuPHR0dpbW1lX/9138lKSmJ3t5evF4v/f39qq3PP/88PT09DA0NUVpaytGjRyktLVXCCfhEmOLiYlwuF6mpqcycOVOF95WXl1NbW6sS6MpxJZeDl0unv/TSS8yfP59ly5YxZ84csrKymDFjhlphqquri8rKSs6ePUtnZ6cKcdq9eze7d+8+zwtHttHO01HWWfZVWVkZ+/bto6SkRHnYFRUVERoaqupYW1vL3r17aW9vV+KKFE5jY2O57777KCgoUC8RZPvky5eenh4A9X9xcTEbNmygqKiIrq4uduzYQVtbG+Hh4cyaNYt58+bR2NiovB1lcnK5ypZsm9mrRn4fyDlCSkoKGzduZNmyZRw4cIA33niD7u7uCeGkMk/PjBkzcLlcE8Q1jUaj0Wg0msvJtEUbQIXHREREKJd0s+Hc0NDAtm3beOGFFzAMg2uuuYavf/3rrF27lu7uburr69m6dStut5vHH3+csrKyCXHlVVVV9Pf384Mf/ACHw0FXV5d6y+rxeJRhIifDCQkJLFiwgNraWjW5lxNFr9dLX18fL7/8Mt/+9re5++67WbhwoXL1/sMf/sDOnTuV6GA2MsyeID09PfT29qrlQK0TfpnnRoad7Ny5k61bt7Jy5Uqam5s5ceIEu3bt4otf/CIbNmzg1KlTvPTSS1RWVipPif3795OXl8dtt93GTTfdRFdXF01NTaSmprJ161ZWr15Ne3u7qqu8BwD33Xcf8+bNY//+/XR1dVFTU8Py5ct58MEHeffdd0lMTGTWrFnEx8dTXl7OU089pYxm81Koo6OjVFVVkZWVxXXXXUdBQQHHjx/nwIEDtLS0EB0dzbXXXktaWhqbN28mKiqK3/3udzQ0NExY5clOZHE4HOrtr3xDmpWVxZe//GUeffRRVYa5f+08cMy/Jf6MMfPxY2Nj6m1zV1eX8iBqaGjg97//PQMDA6o/IiMjyc3N5eqrr6aoqIh7772XsbEx9uzZo1Yek14m8fHx1NfXq0m8dLN/6623WL58OStWrGDp0qUEBQXR3t6uElpv2LCBAwcO0NjYCHBRb25HR0cpKSkhNzeXgoICGhsbaWtrIzExkezsbIKDg9m0aRMDAwN8+OGHyjByuVwqyWxcXBw9PT2Ul5cro+65557jo48+Iisri+joaLq7uzlx4gRpaWl8+ctfZvbs2dx4440sXbqUd955hw8++ECt/JOUlMTMmTOJj4/H4XCwevVqHnzwQX7zm98ozzZpQA4NDfGb3/yGQ4cOsWzZMmbPnk1cXBzZ2dm0trYSHR1NZWUl77zzDmVlZcrLRN4DmX/ozTff5LrrrmPLli1s3rxZrc4kwwYPHDjAd7/7XT772c9yyy23qPxGGRkZ/PCHP5w05MFf+J4ZOX6WL1+Ox+NRHnJSwI6OjmbWrFncfvvtALzwwgscPnyYwcFBlVNrzpw5JCUl8fLLL/Paa69RUlKiwu5+8pOf8Ld/+7fk5uYye/Zsbr31VpWY1uv1Mjo6Snp6OjExMSoHR2NjowoVKisro6WlhT179jBv3jyysrIoKSnh4MGDdHR04PF4KCgo4OGHHyY/P5+HH36Yp59+mnfeeYcnnniC9957j29/+9tkZGTw/e9/X60QBb5n5bJly1i4cCGbNm3C6/Vy4sQJJfhKAcTr9fLuu+8qAUk+O5KTk9myZQvr1q0jOzubuXPnsm/fPvr6+liwYAEbN26ko6OD999/n48//litWvT5z3+ehIQENeY8Ho8y4q33azJx1J+3zdjYmPJikcfJ58ro6Cg7d+5kz549SsA1h7PKMqVX1fHjx3nqqae49957Vf6zRYsWKe9Gc24vcxkjIyMMDAywY8cOdu3aRWhoKOnp6WRmZpKQkMDKlStZvnw5a9eu5dprr1VejUNDQyxfvpwDBw6o54U14b2d55FMvuxwOBgYGOD06dMcP35ciRm//e1veemll9Q8QIbMSq8qq6dOU1MTDz/8MDfddBNr164lLS2NjRs3snHjRrVU99mzZxkYGFA5rITwJcpev34999xzD9dffz19fX243W7CwsIoLy/n/fffJycnh7S0NJWvaXh4WIUnm5fvlnWSzz7wicryeX/ttddy55138swzz/Dxxx8r77O+vr4Jy7xrwUaj0Wg0Gs0nhZhOXL/D4TAiIiL46le/yvr16/F6vfziF79Qq+PAn5LGSiHA6XQSEhLCQw89xOrVqzl8+DC/+93vuOmmm7j22mt56aWX2LZtGzU1NUqMkaFM9957L11dXWRlZamQjZ/+9KcMDAzgdDopKiri5ptvZuXKlbS2tvJv//ZvHDx4kE2bNnHjjTdSWVnJf/7nf1JeXk5QUBD33nsvmzZtIjw8HK/XS0lJCb/73e/UW7qRkRHcbrdaDjovL4+FCxeqZUvfffddjh8/rkQqaQTJSbV8s+x0OgkLC2PZsmV0dHSoEJiQkBD++q//mnvvvZfe3l4effRRXn31VVpaWhDCt4LOTTfdxJw5c2hububYsWOcOXNGJYmVeQBkzpvm5mbKy8vZtGmTSvYqDeDBwUGVVyAoKAhAJUA9duwYFRUVtLS0ABAaGkp8fDypqanExsaq/BdyBRiZCFnmzWhublY5coKCgqitrWX//v188MEHNDQ00Nvbq7yWnE4nERER3H///SxdupRnn32Wvr4+rr76am644Qbcbjf79+/nm9/8pkrE6c9wtvNusDPA/E2m5QQ9ODiYBQsW8MUvfpH169fz+uuv88gjj0zwPgHUfdy4cSNf/epXee+999i2bRtnz55l1qxZPP7442RnZ/PjH/+Y3/72t9TX1yvhRYaaff/732fhwoUIITh9+jRvvPEGubm5fOc732FwcJAHH3yQN998U+V6mW5uGzNLly7l29/+Nvn5+YyMjNDT04PL5aKrq4uMjAwVFtbQ0EBZWRnnzp3D7XaTnJzM4sWLGRwcZOfOnTz11FMTEm2ahT1AiX0JCQls2rSJDRs2MHfuXIQQDA8Pq3ElhG/Fm9LSUgYGBrj33ntxOp0qqW55eTl79uzhmWeewTAMlZ/E5XKpperNYTUySav8/MnPnuxv87kJCQmsXr2anJwcXC6XEk5lwu6goCBuvvlm7rrrLlJTU3nxxRd55JFH1DPoYnA4HISEhDBr1ix+/vOfExISQn9/vwphkuLo22+/zauvvkpzczP9/f0q71FGRgZ///d/z/79+9m5cyd1dXUTnq8Oh4P4+HiWLl3K/Pnzyc3NJTMzU61YVFdXx8GDBzl+/LgKFZX9J++l0+lUBqh5uWnZrx6Phzlz5vDcc88B8N577/Hcc8+xe/duIiMjeeCBB9iyZQuPPPII+/btU3lAZNjpv/zLvxAVFcULL7zA66+/TllZ2QQPRtlPZs8HmQtt06ZNXH/99cybNw+326080dra2ti7dy8ffvghp06dUgK6rOvGjRtJSEiguLiYp556SglJ5ueJv2Xq7bCKv7Ke5uW+zeVJUcosfphFEHm+/AkKCiI7O5tvfetb5OfnU1NTw5tvvslLL700wRvGn+egrJP0OpVlyvE/Y8YMMjMziY6Opr+/nw8//JBjx45NWPpa9okUd2RbzeXNnTuXn//85wwPD/PEE0+wfft2Ffpmfslhrpv04DOHosnPqHlekJiYSFZWFrm5ucTHx5Oenk5ERIR69gwPDzMwMIDH46G8vJywsDDmzp2rvnvk81Y+74aHh9V3blVVFdu2bVOCpRTXzPdS1i8kJISMjAxWrFjB5z73OTX2+vv7lReQTOr/4IMPUlZWxvDwsPIs1Gg0Go1Go7lEHDIMY6l147RFm7CwMNatW8dNN93EggULKCsr47HHHuPMmTPKjd1sGAQFBZGYmMh9991HYmIi+/bt45VXXmHx4sV8+9vfVol7y8rKqKyspKWlhcHBQdLT08nPzyctLY2RkRFKSkp45513qKqqUpPF+Ph4brzxRm699VbS09NpbGykurqa+Ph4qquref/993n//fdVeJBcfnrVqlUqwW9XVxf9/f20trYyODhISEgIERERhIaG4nQ6OXToEG+99ZbKN2BeKtgaFiYngFK8CQkJURN7s0Hz6KOPkp+fT1lZGS+99BLPPfccXV1dCCGIjo7G4/GoN6ryDb30TJGTx7GxMRVaFRcXx+rVqykoKCAxMVEtLd7f309jYyPBwcEkJiaSlJREdHS0yt0il9qW+YAaGxupqanh+PHjNDc3ExsbS1pamkowKo3t5uZm3G43hYWFLFu2jJkzZxIREUF7ezu9vb10dXXR3d2tjKrExEQcDgfvvvsuhw8fxuv1EhMTw4wZM5g1axZ79+7l5MmTyhCwrhZlxV+oVCDvm/Hxq8K/HnroIa677jr6+/t5/PHHefnll5X3iZzUh4aGkpqayk033cTGjRv51a9+xc6dO2lqaiI9PZ3/8T/+B7fffjsNDQ3cf//97Nu3T+WnkeM/OzubjIwMhoeHaW5upqOjg4SEBF5++WWio6N5+umn+f3vf8/+/fsnzeMzGWFhYaxfv55rrrmG3Nxcuru72b17N3v37mXRokWsXbuWvLw8oqKilAEzOjpKf38/+/btY+/evereW+tiDSMBcLvdREREqATXBQUFpKSkEBUVRUVFBadPn6ayslIlhV29ejXXX389TqeTM2fOqFwdtbW1EwxFq9eU+ZpmQ9kc4mA2DoUQSviV+avMnyd53KpVq7hqPOfW888/z5EjRy5ZclE5fhYtWsS8efNITEwkODiY/v5+lcPj3LlzKpzGbExKo7u3t1d5IwFK8JDGtMfjISQkhNDQUEJDQ3G5XCqkRJ4rc2CZBUGzwWq+x+Z+dTqdxMTE8IUvfIHW1lZOnTrFuXPn6O7uxuPxUFhYyA9+8ANKSkp4+umnOXLkiFq5x+PxcMcdd3DHHXfQ39/Pe++9xwsvvEBDQ8MEwc3aX4BKpC29iDIzM/F4PJSVlXHixAklsJvD8AzDUAmcpcDc3t5+XiJeq8jiL5TSvB/sc2NNJaxK7jOLn7Icc+6V/Px8rrvuOpqamjh06JBKDD2VsWgWU+VnwSxeyuTvhmHQ3d2tvr8me1bKOsr6/fu//zsDAwM88cQTvPHGGyosTI5Zu3pJ0cbcH7KPXC4XTqdTJfmVyY5lmJu5TbJeMtQqOjpa3e+wsDD1skKKn319ffT29qrvIilAm0VD6Xkjk4Q7HA6VqysuLk6Fv0oxtKuri9raWpXDSH73msPDNRqNRqPRaC4BtqLNtMOjRkZGKC4uJi0tjfj4eGbPns0DDzzAiRMnKC0tZXh4WOVTke7uCQkJhISE8NFHH6nwnZMnT/LHP/6R5cuXk5iYSHx8PAsWLFArM8m301VVVZw5c4aSkhLliSHfFLa3t7N3714Mw7f0aGZmJuHh4ezZs4dDhw6ppcil0dPc3MyuXbuoqqoiPz+fOXPmkJKSQkxMDMnJySrfRHd3N1VVVZw9e5YTJ05w9uxZlWjWPAn3Z9TKCaI5V4mcxNbU1PDrX/+ar3/96yQkJKg3jB0dHQB0dnaeNwmUbuNW5JtdGfJy/PhxwsLClPEmkzvL1ZnkakjSS0bmP+js7FThQl1dXTQ3N6vErdXV1Xg8HlUPucKWDHUqLy8nKyuLWbNmERUVRWxsLBEREXg8Hrq7u2lqamL37t1qwtvR0cHw8DDd3d20trZy5swZmpub1X0396U154LZ6JlKmIq/Y8yr2kRFRbF582ZmzpxJe3v7BFHO4/EQExNDXFwcL7/8MocOHVKhNm1tbTz//PNER0ezb98+tUy71QOkurpaJbWU3kpDQ0Ns376dpUuX0tzcrEIDp4NdH/X19XHw4EGampqIiYlhcHCQuro66uvr6erqoqKigrS0NGJjY/F4PMTGxqpQuoqKCmpra89b3cmu/+X/8q12T08PLS0tlJeXq3svV/Dxer0MDw/jcDjUak3SA0cu62wWiPzdc2vbredYBR75Fhw4z6iUxxYXF9PW1oYQgqqqqosSzOz6q7+/n+PHj6ucOjKERIqkfX1954kJ8tlWX19v2y/ytzQazYlb5fPALPLY9ZGdOGjtd2nkv/LKKyp/ixSABgcHKS0t5ciRI+Tl5bF27VqGhoY4duyYyk+yc+dOsrOzlbDb0dHBCy+8MEEYNSMFB7n6U0lJCQ0NDSoEVyZqlnUw30f5rJXivCzPfMzFGtZ2oqX5b7vxKzHnipHlSPF9dHRUhQf39/er50sgAddOFDJ79YyOjjI8PEx/f/8E0SSQEGSuv/lzJHOSnTx5Uj0nzCKjLDdQmeYxJ8uW49ych0kKQFYhyCzoArS2tipRXApTgMqTJb3xzO21ej2ZX7xI78qBgQH1nSi9ESMjI1VoWE9Pz4TkzhqNRqPRaDSfFNPytBFCGNL9etasWaxbt46rr76avLw8Ojs7qa6uZmhoaEJ+F7fbrZYsPXjwIBUVFWqp1rS0NAoLC1WulbCwMLUSREtLCw0NDRw/fpzy8nK1Cgz8aZlO6WKdlJTE/PnzycvLo7u7m0OHDlFVVYXX6z1voirfqCUkJJCdnU1ycjJxcXE4HA66u7vV0rstLS1UVlaqFVHsDChZj/G+MfeT+tv8dhtQS15//vOfJzk5WXkQ1dTUqImpdRJsnfSbhSD5t3TZNydZlMebvQ/My25LA1K6gJsnulZhxOr6L8uU3hYyh4YU6MC3JG9TU5NaxceaAFN6H5nd9e0MokD9Oh3Mb1RvuOEG1qxZw4wZM4iPj6e7uxshBP39/cr7Sq7M09LSwocffqiSZct6R0REsH79eo4cOaJWZbIaGNYxIH+kZ9SpU6coLi6mqalpWoalv7AwORbk/TLfT7m8cGhoqMqvIsPdzMsE+6tHINFMXluOc6uRZOdFY/V8sBJItDGPb+s1zMf4ux/m+pgN30uJ+Rp2OUPMeV6mO9bNn39/+637rO02l20VFYAJ99KM9Aa67rrruOOOOxBCcOTIET744APlhSA9q2655RZyc3Oprq7miSee4ODBg+flQJPXNF/P6j3l7x5NVcC12+/P08bOY8a8zU7AtJ4/Fey+O/wJdXbfL2aRw+6zYrfN/J1lh/XzIj1Gr7rqKrVaYFtbW8BQTrNIZOeFI8u1+2za1dvu+846fqzhXdb2msP/zNeV9bPeC/N2832QgrYsK1A4r0aj0Wg0Gs0FcPHhUUIIQ+adCA4OJiUlhaKiItatW0diYiJOp1OJHnIJ37q6Ovbt20dpaal6626elLvdbrUEdUxMDOHh4fT29lJRUUFra+t5xqR5cmWeEEpBQr6BNhuf5kmk/Fu6Zss6yAmZ2dA1u1Sb+gCY6PZu3m49xopMfijfIA8ODqqVmaxYJ+PWcs0GmNWYsJuwmye71jeZ1h+Y6KFgnbyar282sKzGqdlANfeX+Rr+DAB/xtDFGNdSvIiMjCQ7O5v58+dTUFDA4OAgaWlpDA8PU1dXR1tbG62trRw8eJCWlhaVj8RfnWQbrIamnagg+0y+4TWPuwtpj53BDf4NcX8Gu/z7Qvs3kBFqZ+xa6ziZEWgd3/7Ok/17OYSYC8HOMJWGpAzT8CceTKVMq1eJVRgzh5r4E8/8XcPfc0Q+w+69917WrFkDwJEjR3jxxRc5d+4cw8PDhISEqNDC5ORk9uzZw89+9jPl0WiusxRCZV+YxWerEGg3hszCtvW4yUQWK+a8VtbPs12/mMvzJ4hZ77O/MW1XTzvRzO5Y87PX3A/yev4EG391sI4V8ziyjll/n2t/LxzM9QkkJE5FiAJUCJi1f82fDbv+ly87rO2wfqeazzd/Z2nRRqPRaDQazSXm0uS0cbl8EVXy7ZZcwSEuLk7lYpFx5X19fWo1CetE0jD+FNduN5mUkzqr8S8nTVYhxTrJsnrXWK9t3i7rYMWau8YqzNidY30raJ6k+jOuzWVaJ+p2E0pz/c2iibm/zP1irpu1HOv9t5v4WtttPc9cDztPDauIYxYurJNpaz0vtdFtnqzL8SfrYl5hxJzzxTAMv6KatZ7+hDI7/Bl+02mL9X4EEjXMnxNrey60HnbnWT9v/s6ZzvX8GYX+COSBIM8P1OaLvS/Wcy/FtezGlhl/7TXf80DGsfVc83PWTjgJCgoiNTWVe+65h+XLlxMUFMSpU6f41a9+pVbyS0lJYdOmTXzuc5/D4XDwj//4j+zfv195L9oZx3YignWf2Si/2OeEdTxK7z9/xwYai/7u5XTFuUBimjzH6sVi91y39qdd/QI9K8xl29XHOj7kNn/fceY6WMUamXtK7rMKeNbrm/vA7vtF1sWuL8xhWNZ+Myeato4Ncz1kyKsWbTQajUaj0VxCLp2njen/CRMqmUvFHJYgjXI7zw5pFFg9OszHWSdUk4k28lgzgYQMcxnWiaw5YaE1FMGfkW5nvFr3WSeLwIQ+C2TE2RllZq8Nf+dbJ67mya61z63GnJ04IK/rzwC3CweRfWqtu7nt/tp9KbEaDea3wRJrf/nDblz5K+uT4JPqw8nq4E9E+qTrIfk0+0MyWV9Mdb8ZO0PW7nyzEHGh/WL3bHa5XCQkJLBmzRq2bt1Kfn4+zc3N/OIXv+Do0aP09/eTl5fH9ddfz80330xJSQl/93d/N2G1tOmOFX+G9HTGmr9+EkJMEFis3z9WMSlQ/cwCgRTMrOdOVlc7Qci8z5yI2HysWcwKJFxK7NpsJpDoZDcGzSu/yW1SiPH3/WvnQWQ9xk4gkeda2yy3mevu73vbrkwZcmX+3jJ/d0jRRq8epdFoNBqN5hJy6UWb8W1qImM2wO0mrFYjPlCMujxP/m8VBOwmf/4mZXIC56+t1smidZI2FWPczhCyq5/VwJhs8j6Va/hrm92b18nqONl+f288/bVFijfmvDWBhK5PCn/GV6A+u5BrXAliwZXAVPviShCdLgeXs11mgzjQMRfa/+ZnlnW/DDMNDQ0lLy+PO++8k6KiIrxeL++88w7vv/8+NTU1pKWl8cADD5Cdnc0jjzzCoUOH6OnpmVIIm/n6F9N//u7BVIQYf+eZ+93uORhI1A3UpkBtte4zly1/mwUY60pMgfD3/TnZ94Qd/r6X/NXdn+gvj5Pfy+ZzzOVY628Vy+R+uxc+dvh7YWH+X3vaaDQajUajucRcmtWjAk3SzHkK5G9zrg/r5EYeZ01Ca72GeaJm3hdIeLBONK1GuHUy5q9ca53Mk2N/E1K7SbW1XZOJKHYilRmrYWB3PWvogPkcuzLtyrero115skzz/+Z7br7WlWCQ2/W/P2PL7ngzgcaCxsd/9b64nO2fStmTPY/Mx1mftf6ey3KbTGheVlbGk08+SX19PevXr2fFihVkZ2dTUlJCZWUle/fuJS8vj4ULF3LmzBm1ktRU636xfRhIGDe3eaqfefMxds+AQM9763Y7QWc6mJ+51ntn9hQN9F3pry5yn523rBm7NtiVGaif7bb7E9DlNqunkbUP5EuDyb6z/dXB7nvPKpJpNBqNRqPRXE6mLdpMNjk1b5vszag06gNNyOwmseYJqt31zWKM3QTOX/3t6hHIW8Zfef7aMtV9U+lfc/0CCVnmfpisHoGEGqs3ivUe2JXh775cyfgzEDSfHLr//8RkzzjJdPrM32d9sv3+ng/yp6enh/Lyct58802am5tZvHgxmZmZxMbGkp+fz9DQkFptzt+qQoG4nM+PqT7LptLvU63jVIQaq8AxWd0nE4emwmTi1lS+J63lTHfM2X3vBBJ0pjuHkGVb2xTou9SuPvpZpdFoNBqN5pNg2qKNP6YyKbuQMu2uMdnbOrtz/E22L0bIkFyI63iga/rbdiHXNZ87mUfIdMUlu+3TEZyuJC7GW+bPpY1/Dui+nDoXI2JcjNAs95uvLz3pBgYGKCkpoampiba2NhYvXkxeXh6pqamMjY1x7tw5qqurVSLiKwnrs/JCzp3O8YEE7uley04gsV7D7gWEGX8vIwIJGlOpm3XfZN/V1jYEKs8w7Ffdu9D6TSZM+RN4NBqNRqPRaC4n085pI1ePMm0Dpva2z3z8VI6b7I3eNOt+2SZZ06nTpa7HVPv/QvvtYuv058CfW301Grhyxq2doe1wOHC73SQkJDB79mzmzZtHaGgoZ86cYf/+/bS0tKhVBf3V/9Ns3yfhFXgprhHIE0tut3tpYT3H7nvE3zF217sY/Ak1VsHJLtG+3ffeZN615nP8rSxp12ZrWJRh6Jw2Go1Go9FoLjmXJhGxVbT5r8rlntRP12CZbPJuDmm6nPw5hEBpNFciV4oIMxWm+jmXCWXlKkcyx9lUEhBrLgx/wsafc3/7E3MgcDifdcECfyKUlUDeVuZVpbRoo9FoNBqN5hJzSRIRt46MjJy7RBXSaDQajUaj+bNheHj4066CRqPRaDSav1yy7DZOy9NGo9FoNBqNRqPRaDQajUbzyTD9JTw0Go1Go9FoNBqNRqPRaDSXHS3aaDQajUaj0Wg0Go1Go9FcgWjRRqPRaDQajUaj0Wg0Go3mCkSLNhqNRqPRaDQajUaj0Wg0VyBatNFoNBqNRqPRaDQajUajuQLRoo1Go9FoNBqNRqPRaDQazRWIFm00Go1Go9FoNBqNRqPRaK5AtGij0Wg0Go1Go9FoNBqNRnMFokUbjUaj0Wg0Go1Go9FoNJorkP8fphNiM/qlP5kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(10, 20):\n",
    "    plt.figure(figsize=(20, 20))\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    data, target = dataset[i]\n",
    "    sentence = convert_y_label_to_string(target, dataset) \n",
    "    print(sentence)\n",
    "    plt.title(sentence)\n",
    "    plt.imshow(data.squeeze(0).numpy(), cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "data, target = dataset[10]\n",
    "sentence = convert_y_label_to_string(target) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([97])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "h, w, s = 28, 6, 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from einops.layers.torch import Rearrange\n",
    "slide = nn.Sequential(nn.Unfold(kernel_size=(h, w), stride=(1, s)), Rearrange(\"b (c h w) t -> b t c h w\", h=h, w=w, c=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "patches = slide(data.unsqueeze(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Griffiths resolution. Mr. Foot's line will_______________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 50 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# remove batch size\n",
    "n = 50\n",
    "patches = patches.squeeze(0)\n",
    "fig = plt.figure(figsize=(20, 20))\n",
    "print(sentence)\n",
    "for i in range(n):\n",
    "    ax = fig.add_subplot(1, n, i + 1)\n",
    "    ax.imshow(patches[i].squeeze(0), cmap='gray')\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([158, 1, 28, 6])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patches.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f8797c56cd0>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 20))\n",
    "plt.imshow(data.squeeze(0).numpy(), cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from text_recognizer.datasets.transforms import Compose, AddTokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_transform = Compose([torch.tensor, AddTokens(init_token=\"<sos>\", eos_token=\"<eos>\")])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IAM Lines Dataset\n",
      "Number classes: 82\n",
      "Mapping: {0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9', 10: 'A', 11: 'B', 12: 'C', 13: 'D', 14: 'E', 15: 'F', 16: 'G', 17: 'H', 18: 'I', 19: 'J', 20: 'K', 21: 'L', 22: 'M', 23: 'N', 24: 'O', 25: 'P', 26: 'Q', 27: 'R', 28: 'S', 29: 'T', 30: 'U', 31: 'V', 32: 'W', 33: 'X', 34: 'Y', 35: 'Z', 36: 'a', 37: 'b', 38: 'c', 39: 'd', 40: 'e', 41: 'f', 42: 'g', 43: 'h', 44: 'i', 45: 'j', 46: 'k', 47: 'l', 48: 'm', 49: 'n', 50: 'o', 51: 'p', 52: 'q', 53: 'r', 54: 's', 55: 't', 56: 'u', 57: 'v', 58: 'w', 59: 'x', 60: 'y', 61: 'z', 62: ' ', 63: '!', 64: '\"', 65: '#', 66: '&', 67: \"'\", 68: '(', 69: ')', 70: '*', 71: '+', 72: ',', 73: '-', 74: '.', 75: '/', 76: ':', 77: ';', 78: '?', 79: '_', 80: '<sos>', 81: '<eos>'}\n",
      "Data: (7101, 28, 952)\n",
      "Targets: (7101, 97)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dataset = IamLinesDataset(train=True, init_token=\"<sos>\", pad_token=\"_\", eos_token=\"<eos>\", target_transform=target_transform)\n",
    "dataset.load_or_generate_data()\n",
    "print(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "data, target = dataset[0]\n",
    "sentence = convert_y_label_to_string(target, dataset) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([], [])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 20))\n",
    "plt.title(sentence)\n",
    "plt.imshow(data.squeeze(0).numpy(), cmap='gray')\n",
    "plt.xticks([])\n",
    "plt.yticks([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "from text_recognizer.networks import VisionTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "tt = Transformer(3, 3, 512, 8, 2048, 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "vt = VisionTransformer(6, 6, 256, 82, 8, 118, 512, 256, 0.1, 79, (28, 16), (1, 8), \"gelu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchsummary import summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VisionTransformer(\n",
       "  (slidning_window): Sequential(\n",
       "    (0): Unfold(kernel_size=(28, 16), dilation=1, padding=0, stride=(1, 8))\n",
       "    (1): Rearrange('b (c h w) t -> b t (c h w)', h=28, w=16, c=1)\n",
       "  )\n",
       "  (character_embedding): Embedding(82, 256)\n",
       "  (position_encoding): PositionalEncoding(\n",
       "    (dropout): Dropout(p=0.1, inplace=False)\n",
       "  )\n",
       "  (linear_projection): Linear(in_features=448, out_features=256, bias=True)\n",
       "  (transformer): Transformer(\n",
       "    (encoder): Encoder(\n",
       "      (layers): ModuleList(\n",
       "        (0): EncoderLayer(\n",
       "          (self_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (cnn): _ConvolutionalLayer(\n",
       "            (layer): Sequential(\n",
       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.1, inplace=False)\n",
       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "          )\n",
       "          (block1): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block2): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (1): EncoderLayer(\n",
       "          (self_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (cnn): _ConvolutionalLayer(\n",
       "            (layer): Sequential(\n",
       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.1, inplace=False)\n",
       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "          )\n",
       "          (block1): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block2): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (2): EncoderLayer(\n",
       "          (self_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (cnn): _ConvolutionalLayer(\n",
       "            (layer): Sequential(\n",
       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.1, inplace=False)\n",
       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "          )\n",
       "          (block1): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block2): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (3): EncoderLayer(\n",
       "          (self_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (cnn): _ConvolutionalLayer(\n",
       "            (layer): Sequential(\n",
       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.1, inplace=False)\n",
       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "          )\n",
       "          (block1): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block2): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (4): EncoderLayer(\n",
       "          (self_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (cnn): _ConvolutionalLayer(\n",
       "            (layer): Sequential(\n",
       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.1, inplace=False)\n",
       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "          )\n",
       "          (block1): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block2): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (5): EncoderLayer(\n",
       "          (self_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (cnn): _ConvolutionalLayer(\n",
       "            (layer): Sequential(\n",
       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.1, inplace=False)\n",
       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "          )\n",
       "          (block1): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block2): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (decoder): Decoder(\n",
       "      (layers): ModuleList(\n",
       "        (0): DecoderLayer(\n",
       "          (self_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (multihead_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (cnn): _ConvolutionalLayer(\n",
       "            (layer): Sequential(\n",
       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.1, inplace=False)\n",
       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "          )\n",
       "          (block1): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block2): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block3): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (1): DecoderLayer(\n",
       "          (self_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (multihead_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (cnn): _ConvolutionalLayer(\n",
       "            (layer): Sequential(\n",
       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.1, inplace=False)\n",
       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "          )\n",
       "          (block1): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block2): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block3): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (2): DecoderLayer(\n",
       "          (self_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (multihead_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (cnn): _ConvolutionalLayer(\n",
       "            (layer): Sequential(\n",
       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.1, inplace=False)\n",
       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "          )\n",
       "          (block1): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block2): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block3): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (3): DecoderLayer(\n",
       "          (self_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (multihead_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (cnn): _ConvolutionalLayer(\n",
       "            (layer): Sequential(\n",
       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.1, inplace=False)\n",
       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "          )\n",
       "          (block1): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block2): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block3): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (4): DecoderLayer(\n",
       "          (self_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (multihead_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (cnn): _ConvolutionalLayer(\n",
       "            (layer): Sequential(\n",
       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.1, inplace=False)\n",
       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "          )\n",
       "          (block1): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block2): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block3): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (5): DecoderLayer(\n",
       "          (self_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (multihead_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (cnn): _ConvolutionalLayer(\n",
       "            (layer): Sequential(\n",
       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.1, inplace=False)\n",
       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "          )\n",
       "          (block1): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block2): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block3): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (head): Sequential(\n",
       "    (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "    (1): Linear(in_features=256, out_features=256, bias=True)\n",
       "    (2): GELU()\n",
       "    (3): Dropout(p=0.1, inplace=False)\n",
       "    (4): Linear(in_features=256, out_features=82, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==========================================================================================\n",
      "Layer (type:depth-idx)                   Output Shape              Param #\n",
      "==========================================================================================\n",
      "├─Sequential: 1-1                        [-1, 118, 448]            --\n",
      "|    └─Unfold: 2-1                       [-1, 448, 118]            --\n",
      "|    └─Rearrange: 2-2                    [-1, 118, 448]            --\n",
      "├─Linear: 1-2                            [-1, 118, 256]            114,944\n",
      "├─PositionalEncoding: 1-3                [-1, 118, 256]            --\n",
      "|    └─Dropout: 2-3                      [-1, 118, 256]            --\n",
      "├─Embedding: 1-4                         [-1, 97, 256]             20,992\n",
      "├─PositionalEncoding: 1-5                [-1, 97, 256]             --\n",
      "|    └─Dropout: 2-4                      [-1, 97, 256]             --\n",
      "├─Transformer: 1-6                       [-1, 97, 256]             --\n",
      "|    └─Encoder: 2-5                      [-1, 118, 256]            --\n",
      "|    └─Decoder: 2-6                      [-1, 97, 256]             --\n",
      "├─Sequential: 1-7                        [-1, 97, 82]              --\n",
      "|    └─LayerNorm: 2-7                    [-1, 97, 256]             512\n",
      "|    └─Linear: 2-8                       [-1, 97, 256]             65,792\n",
      "|    └─GELU: 2-9                         [-1, 97, 256]             --\n",
      "|    └─Dropout: 2-10                     [-1, 97, 256]             --\n",
      "|    └─Linear: 2-11                      [-1, 97, 82]              21,074\n",
      "==========================================================================================\n",
      "Total params: 223,314\n",
      "Trainable params: 223,314\n",
      "Non-trainable params: 0\n",
      "Total mult-adds (M): 23.93\n",
      "==========================================================================================\n",
      "Input size (MB): 0.10\n",
      "Forward/backward pass size (MB): 0.86\n",
      "Params size (MB): 0.85\n",
      "Estimated Total Size (MB): 1.81\n",
      "==========================================================================================\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "==========================================================================================\n",
       "Layer (type:depth-idx)                   Output Shape              Param #\n",
       "==========================================================================================\n",
       "├─Sequential: 1-1                        [-1, 118, 448]            --\n",
       "|    └─Unfold: 2-1                       [-1, 448, 118]            --\n",
       "|    └─Rearrange: 2-2                    [-1, 118, 448]            --\n",
       "├─Linear: 1-2                            [-1, 118, 256]            114,944\n",
       "├─PositionalEncoding: 1-3                [-1, 118, 256]            --\n",
       "|    └─Dropout: 2-3                      [-1, 118, 256]            --\n",
       "├─Embedding: 1-4                         [-1, 97, 256]             20,992\n",
       "├─PositionalEncoding: 1-5                [-1, 97, 256]             --\n",
       "|    └─Dropout: 2-4                      [-1, 97, 256]             --\n",
       "├─Transformer: 1-6                       [-1, 97, 256]             --\n",
       "|    └─Encoder: 2-5                      [-1, 118, 256]            --\n",
       "|    └─Decoder: 2-6                      [-1, 97, 256]             --\n",
       "├─Sequential: 1-7                        [-1, 97, 82]              --\n",
       "|    └─LayerNorm: 2-7                    [-1, 97, 256]             512\n",
       "|    └─Linear: 2-8                       [-1, 97, 256]             65,792\n",
       "|    └─GELU: 2-9                         [-1, 97, 256]             --\n",
       "|    └─Dropout: 2-10                     [-1, 97, 256]             --\n",
       "|    └─Linear: 2-11                      [-1, 97, 82]              21,074\n",
       "==========================================================================================\n",
       "Total params: 223,314\n",
       "Trainable params: 223,314\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (M): 23.93\n",
       "==========================================================================================\n",
       "Input size (MB): 0.10\n",
       "Forward/backward pass size (MB): 0.86\n",
       "Params size (MB): 0.85\n",
       "Estimated Total Size (MB): 1.81\n",
       "=========================================================================================="
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary(vt, [(1, 28, 952), (97,)], device=\"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = vt.preprocess_input(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 118, 256])"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = vt.encoder(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "trg = torch.tensor([10, 62, 22, 24, 31, 14, 62, 55, 50, 62, 54, 55, 50, 51, 62, 22, 53, 74,\n",
    "        62, 16, 36, 44, 55, 54, 46, 40, 47, 47, 62, 41, 53, 50, 48, 79, 79, 79,\n",
    "        79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79,\n",
    "        79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79,\n",
    "        79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79,\n",
    "        79, 79, 79, 79, 79, 79, 79])[None, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "t, tm = vt.preprocess_target(trg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VisionTransformer(\n",
       "  (slidning_window): Sequential(\n",
       "    (0): Unfold(kernel_size=(28, 16), dilation=1, padding=0, stride=(1, 8))\n",
       "    (1): Rearrange('b (c h w) t -> b t (c h w)', h=28, w=16, c=1)\n",
       "  )\n",
       "  (character_embedding): Embedding(82, 256)\n",
       "  (position_encoding): PositionalEncoding(\n",
       "    (dropout): Dropout(p=0.1, inplace=False)\n",
       "  )\n",
       "  (linear_projection): Linear(in_features=448, out_features=256, bias=True)\n",
       "  (transformer): Transformer(\n",
       "    (encoder): Encoder(\n",
       "      (layers): ModuleList(\n",
       "        (0): EncoderLayer(\n",
       "          (self_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (cnn): _ConvolutionalLayer(\n",
       "            (layer): Sequential(\n",
       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.1, inplace=False)\n",
       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "          )\n",
       "          (block1): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block2): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (1): EncoderLayer(\n",
       "          (self_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (cnn): _ConvolutionalLayer(\n",
       "            (layer): Sequential(\n",
       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.1, inplace=False)\n",
       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "          )\n",
       "          (block1): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block2): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (2): EncoderLayer(\n",
       "          (self_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (cnn): _ConvolutionalLayer(\n",
       "            (layer): Sequential(\n",
       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.1, inplace=False)\n",
       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "          )\n",
       "          (block1): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block2): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (3): EncoderLayer(\n",
       "          (self_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (cnn): _ConvolutionalLayer(\n",
       "            (layer): Sequential(\n",
       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.1, inplace=False)\n",
       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "          )\n",
       "          (block1): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block2): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (4): EncoderLayer(\n",
       "          (self_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (cnn): _ConvolutionalLayer(\n",
       "            (layer): Sequential(\n",
       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.1, inplace=False)\n",
       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "          )\n",
       "          (block1): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block2): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (5): EncoderLayer(\n",
       "          (self_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (cnn): _ConvolutionalLayer(\n",
       "            (layer): Sequential(\n",
       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.1, inplace=False)\n",
       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "          )\n",
       "          (block1): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block2): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (decoder): Decoder(\n",
       "      (layers): ModuleList(\n",
       "        (0): DecoderLayer(\n",
       "          (self_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (multihead_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (cnn): _ConvolutionalLayer(\n",
       "            (layer): Sequential(\n",
       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.1, inplace=False)\n",
       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "          )\n",
       "          (block1): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block2): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block3): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (1): DecoderLayer(\n",
       "          (self_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (multihead_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (cnn): _ConvolutionalLayer(\n",
       "            (layer): Sequential(\n",
       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.1, inplace=False)\n",
       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "          )\n",
       "          (block1): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block2): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block3): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (2): DecoderLayer(\n",
       "          (self_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (multihead_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (cnn): _ConvolutionalLayer(\n",
       "            (layer): Sequential(\n",
       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.1, inplace=False)\n",
       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "          )\n",
       "          (block1): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block2): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block3): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (3): DecoderLayer(\n",
       "          (self_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (multihead_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (cnn): _ConvolutionalLayer(\n",
       "            (layer): Sequential(\n",
       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.1, inplace=False)\n",
       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "          )\n",
       "          (block1): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block2): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block3): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (4): DecoderLayer(\n",
       "          (self_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (multihead_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (cnn): _ConvolutionalLayer(\n",
       "            (layer): Sequential(\n",
       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.1, inplace=False)\n",
       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "          )\n",
       "          (block1): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block2): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block3): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (5): DecoderLayer(\n",
       "          (self_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (multihead_attention): MultiHeadAttention(\n",
       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (cnn): _ConvolutionalLayer(\n",
       "            (layer): Sequential(\n",
       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.1, inplace=False)\n",
       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
       "            )\n",
       "          )\n",
       "          (block1): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block2): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (block3): _IntraLayerConnection(\n",
       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (head): Sequential(\n",
       "    (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "    (1): Linear(in_features=256, out_features=256, bias=True)\n",
       "    (2): GELU()\n",
       "    (3): Dropout(p=0.1, inplace=False)\n",
       "    (4): Linear(in_features=256, out_features=82, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vt.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[-0.4344,  0.4939,  0.0382,  ..., -0.0808, -0.0290,  0.4399],\n",
       "         [-0.4350,  0.5146,  0.0356,  ..., -0.0634, -0.0289,  0.4271],\n",
       "         [-0.4303,  0.5245,  0.0435,  ..., -0.0755, -0.0267,  0.4240],\n",
       "         ...,\n",
       "         [-0.4477,  0.5377,  0.0596,  ..., -0.0866, -0.0283,  0.4457],\n",
       "         [-0.4475,  0.5435,  0.0606,  ..., -0.0900, -0.0293,  0.4440],\n",
       "         [-0.4488,  0.5476,  0.0689,  ..., -0.0914, -0.0276,  0.4411]]],\n",
       "       grad_fn=<AddBackward0>)"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vt.decoder(t, x, tm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[-0.4179,  0.4755,  0.0407,  ..., -0.0609, -0.0870,  0.4562],\n",
       "         [-0.4262,  0.4845,  0.0361,  ..., -0.0497, -0.0847,  0.4459],\n",
       "         [-0.4237,  0.4900,  0.0409,  ..., -0.0573, -0.0812,  0.4434],\n",
       "         ...,\n",
       "         [-0.4477,  0.5053,  0.0394,  ..., -0.0489, -0.0815,  0.4589],\n",
       "         [-0.4469,  0.5069,  0.0407,  ..., -0.0500, -0.0808,  0.4573],\n",
       "         [-0.4464,  0.5079,  0.0416,  ..., -0.0510, -0.0801,  0.4570]]],\n",
       "       grad_fn=<AddBackward0>)"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vt(data, trg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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