zhabib3/Handwritten Alphabet Recognition.ipynb

## Handwritten Alphabet Recognition.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Handwritten Letter Recognition\n",
    "Using EMNIST dataset, train a resnet neural network to recognize the hand-drawn english alphabet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.vision import *\n",
    "from fastai.metrics import error_rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('/home/zeeshanamin3/.fastai/data')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "root_path = Path('/home/zeeshanamin3/notebooks')\n",
    "path = Config.data_path()\n",
    "path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First unzip the dataset files\n",
    "import zipfile\n",
    "\n",
    "with zipfile.ZipFile(root_path/'english-letters.zip', 'r') as zip_file:\n",
    "    zip_file.extractall(path/'english-letters')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PosixPath('/home/zeeshanamin3/.fastai/data/english-letters/english-letters/Train'),\n",
       " PosixPath('/home/zeeshanamin3/.fastai/data/english-letters/english-letters/Validation')]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = path/'english-letters'\n",
    "path.ls()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfms = get_transforms(max_zoom=1.0, do_flip=False, max_rotate=2.0,\n",
    "                     max_warp=0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Then we load the data in the DataBunch via specifying dataset path \n",
    "# Since there's no separate valid/test set, we will specify valid_pct = 0.2\n",
    "# to select 20% of the data as validation set and rest as training set\n",
    "data = ImageDataBunch.from_folder(path=path, valid=\"Validation\", \n",
    "                                 train=\"Train\",\n",
    "                                 ds_tfms=tfms, size=40).normalize(imagenet_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A',\n",
       " 'B',\n",
       " 'C',\n",
       " 'D',\n",
       " 'E',\n",
       " 'F',\n",
       " 'G',\n",
       " 'H',\n",
       " 'I',\n",
       " 'J',\n",
       " 'K',\n",
       " 'L',\n",
       " 'M',\n",
       " 'N',\n",
       " 'O',\n",
       " 'P',\n",
       " 'Q',\n",
       " 'R',\n",
       " 'S',\n",
       " 'T',\n",
       " 'U',\n",
       " 'V',\n",
       " 'W',\n",
       " 'X',\n",
       " 'Y',\n",
       " 'Z']"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.classes # These correspond to each alphabet or label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x432 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.show_batch(rows=4, figsize=(5,6)) # Here's a preview of our labelled training set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the model\n",
    "We will begin by training our CNN with the resent34 architecture without any initial tuning to see how it performs. Initially we will train it on 4 epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn_model = cnn_learner(data, models.resnet34, metrics=error_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.307256</td>\n",
       "      <td>0.373288</td>\n",
       "      <td>0.122667</td>\n",
       "      <td>10:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.208801</td>\n",
       "      <td>0.272651</td>\n",
       "      <td>0.090896</td>\n",
       "      <td>07:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.162623</td>\n",
       "      <td>0.222449</td>\n",
       "      <td>0.076792</td>\n",
       "      <td>07:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.147212</td>\n",
       "      <td>0.221822</td>\n",
       "      <td>0.078074</td>\n",
       "      <td>07:26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnn_model.fit_one_cycle(4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn_model.save('stage-1') # Accuracy ~= 7.8%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn_model.export('stage-1.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As shown above, with each epoch there was a decrease in the losses and by the end our model could recognize the correct alphabet with an error rate of ~7-8% Now we will try to optimize the model by choosing a better learning rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='0' class='' max='1', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      0.00% [0/1 00:00<00:00]\n",
       "    </div>\n",
       "    \n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table><p>\n",
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='54' class='' max='6259', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      0.86% [54/6259 00:06<12:35 0.3092]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
     ]
    }
   ],
   "source": [
    "cnn_model.unfreeze()\n",
    "cnn_model.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnn_model.recorder.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='0' class='' max='1', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      0.00% [0/1 00:00<00:00]\n",
       "    </div>\n",
       "    \n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table><p>\n",
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='99' class='' max='6259', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      1.58% [99/6259 00:11<11:34 0.1602]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets try plotting on slower lrs\n",
    "cnn_model.lr_find(start_lr=1e-9, end_lr=1e-6)\n",
    "cnn_model.recorder.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like the loss mostly increases in the range 1e-9 -> 1e-8 so lets try training over this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.156813</td>\n",
       "      <td>0.220640</td>\n",
       "      <td>0.077646</td>\n",
       "      <td>11:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.145739</td>\n",
       "      <td>0.221011</td>\n",
       "      <td>0.077789</td>\n",
       "      <td>11:17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnn_model.fit_one_cycle(2, max_lr=slice(1e-9, 1e-8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn_model.save('stage-2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing the results\n",
    "With fine-tuning, we were able to improve the error rate of our model to ~7%. \n",
    "Now we will observe the confusion matrix for the model i.e. which categories it got confused between the most."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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S7cfkL/ZKM1sY7Nulomx/U3GOb5jZzLxcL5rZN4L9D+TlfdvMZpjZJxPuUVW5zcz+zczeyn9XmZlF8rnAzF4ys3fM7DYz2472bWdmt+b7XjKzC1or14aAc26rlh+AlwGcTtuuBwAz6w3gIAAPJmZ7CoC7KvbNAPAXAJ4Od5jZiQD+N4DjAAwBMAzAd2MnMrPdAJwDYHGw60oAewDYFcAxAK4ws5NaKfdJZeVuS7nMrD+AWwD8PYDtADwF4MZ2lmuDwswmm9nyqo8OM3vezIab2XVm9o/BviF5+7NJJP9CWxns+ysze8rM3jez60r2V37sBOl2MLPfmtlrebv5qJkdRvuPMbPnzGxF3jbdamaDIrcFZraTmb1asW9IXp41efmOL0uXp41+lKVeY7txznXKD8ClAJYAGEjbjgcwKZfPAfB2nm5bZJ350QCujuTZG8BbAHqV7DsUwGcBfBHAwlbKNhTAOgBDKvZfAeBAAJsA2BPASwDOp/37Atgklw8DsIqvs43lvgzAPACDAQwCMBvA5RX5jMjPNQbAVgBuAPA72v9bZA3bVgCOArASwIjOeubN8AOwEMDxJdvPAHBHLl8C4JFW8nkawIGtpHkEwCXBthsA/DP9fxyAJa3kMxFZB10oO4BFAMbR/9/j512Sz74Anq3Yl1yu/B2aEtTfdwHsVadcG9oP2QfHOgDLAJxbsn83AC/k8nUA/rHkeNfShlScw7eVJfs+BeBMAP8D4LpgX//8vT8XwOYA/h3A4xX5DAPwNQADAWycP/c3AWyV7x8AYKdc7gXgqpZ3KFLuSwFcU7HvMQA/BLAFgLMBrACwfUXafwHwMIC+APZG1pec1NZrbO+vU1SyZnYAgP+LrJPhr+ZTANyVj6B+COB7zrlrnHMrnXMfOecedM79eSTr4wA86px7P9zhnJvqnPs1gAUJRbwIwEPOuYVlO51zVznnnnbOfeicmwfgdgBH0v5nnXMftvwLYFMAO9cpN4CLAfzAOfeqc24RgB8ga9DLuBDAeOfcQ8651chGAJ8ys63zEdTZAP7eObfaOfcIgDuQfUSIrO7dmZLQzAYiayyeqXGeEchGoC3MADDAzPpVnOtcAGudc3cF2/sC2KkkrxGRc8eusS3lKqR1zr0DYD6AETXLtaFxEYDHkXWGF5fsPxXV2olUKjUczrlbnHO3IfsID/kUgFnOuZucc+8h0wbsZ2Z7leSzwDn3Q+fcYufcOufczwFshmyQAOfc68651+iQdQB2r1NuMxuObBDyHefcu865PwB4DlmbVcZFyPqH5c65OQCuxvp2Mfka20uHd5hm1gfAzci+qiYHu1te6D2RdTA3tzH75EavFS4C8MuUhHnnPhrArGD7BDN7D8ATACYjU1tV0daGrKrxCRuy+QDWAhie/9Y5555PzKuncTLSG7FTANzt8s/ZNrIVsq/fFlrkrcOEubrtnwH8r4p8+PgW+WP5ELGGOrlcJWn53HXKtaFxEYDr89+JZjYg2N+IdqpuHpUfO60daGb7I+swX6Btu5jZCmQahq8jG2VWHb8pMu3XfRXlWuCcW0XbStunhI+y2tfYVjq0w8w7l18CmIngxprZMACb5iO2lq/a0GbTGm1p9KrKOBrZ6CG1s74S2X27ljc6505D1kicAuAe59xHkTxi5S5ryLaqsGO21pBV7evRmNkoAG87515JPCRmv2yN1QC2of9b5FUlab8L4NfOuRcr8uHjW+SyfFo+VPcCUOqo1MZyhWn53G0q14aGmR2FzHb7e+fcNGQN9QW0f0sAh6BoK/96bgdckXc+z7ZyDm4r20qtdsDMtgHwawDfdc75451zLzvn+iBTg34LwNxINmMAzAg6xTrlau2jrNPauo4eYf4tgJEALi75Ouev3xZVwsDUjGs0elVcDOAPuUqztXP+FbKvyVMr1MAfOOcmIvvKLHVWSih3WUO2umJ001pDVrWvp9MWdeymyGzpZV/JKcwCsB/9vx+A151zZeqz4wB8JXdqWIJM6/J7M/tb59xyZB+UYV6zSvIBgBMB/NE5t64B5SqkzdX9uyFTg7W1XBsaFwO41zn3Zv7/DSiqZY9DZv99j7Z93znXp+WHzNYcoz0q3Ta3A2a2BYDxyOyA/1KWxjm3DNlg6PaIs1LsQ7Mt5Wrto6zT2roO6zDNbCyAbwI4xzm3oiQJN1rzALyCav11Ge1Wc+QV41wkqGPN7PPIvQqdc6VeX8QmyBqUMlord1lDVtX4hA3ZMGTG+Ofz3yZmtkdiXj2JtjRAo5E5zrxdlSD3YNwcgAHY1Mw2N7OWd+tXAL5gZvvkqqVvIbN1lXEcsg/M/fPfa8icwH5MeX3LzPrm9pk/j+TV2jW2pVy3AhhpZmfn1/ltZPekZXTRlnJtMOTtx3kAjqaPnK8is5+1vJddqY4FIh87ZYlzL9/bkDlyXdZK3psA2AEf76xaiJV7FoBhZsajwNL2KeGjrE3X2C46wpMI2UhxCYAvVOzfAtmocnPadg6yYfTnkD2AjZB5dv68Io+HAIyJlGEjZB5TJyPzat0cwGZBmgvyfdbK9VyYX8/eJfv2ys+xBTJnn88gsyOWelQmlPtyAHOQecjuhOyhx7xk30bWqPcG8BsUvWR/h8xTtjcyJ6Ue7yWLzAN7KcgjEZnzwMMVx38fwNdbOcdkZM5e/BtL+78G4PX8WV0L8o7On++FiWXvBeAXeT6vA/haxXGGrIHZoZVyJ5cLmZfmXGS2q8kgj/LUcm1oPwB/hswzdhcAO9LvIWSOey3PcBc65jq0wUsWJW1lSZpNkLVv/4JMjbo51nvtb5+/92fn2/8N1V6ymyIbWd5WUZZPIfM32SjP9/cAnq7IaygyG2Xs/j2ev1+bAzgLcS/Zf0Wm1u6LrM1djPVessnX2O5n3kEV6dt5BVhd8vspgNMATCg57iRkrsOrkTVqk5GpP8N0H2v0StKMxccbsclBmnuQeV6Fx45GpgZt+f9FAB+E15Hv2xuZo8+q/IE/CeCsijKllNuQ2XuX5b+rQB16fu7R9P8FyOYavoPMe3c72rddXvnfydNc0BHPuzv9kI0Ifh9s+wqA2yrSzwawT1eXu43XeCiAqV1djg39B+Bu5B1jsP08ZB/YIwHMDPZdh7Z1mKVtZZDmypK27kraH/vY+Sm1ZUfnx64J2rrR+f4v523hO/n1/Q7ArhVl+isAP2ql3EPy8ryLTMvIH4cXIlP5t/wf/SiLXWMjf5afrFMxs58gq0g/qXn8echUvec1tmQdS3ct94aEZcEiVjnnHsv/3xyZ2ugB51w4oXwzZC/mv3Z+SetjZocC6Ocye7roIszsCgD9nXNXtCOPdrWVXYWZ3YWsw2zvdJqmojKyRAczHdnQvy4rkM3r7G5013JvMDjn7m2RcweshwE8AOBHJWnXIlMFdSucc1O7ugwCQKaObU87B7S/rewqJiN7rzYoumSEKYQQQnQ3FHxdCCGESEAdphBCCJFA1IZpZkn6Wg5CE1PxpqarYqONiv0751GVX51j6sLnSj0P35MwmM9HH8WCBXU+zrnKVVMaSWq9a2bCesfPsjxoU0YzmEiqyteZZeP7t27dug6vd81e5+o8kzrtUdXxqXnE6n3quTqr3QvvaXB9pTdcI0whhBAigajTD391xb6KmWb4Qq5D7Pp4X52vn84c5XYkPWmE2WitSer7U1XXmrEOVWlHYu9IeB0px3RGvWuGOhcjpf40Y1vSDNqKOmiEKYQQQrQDdZhCCCFEAuowhRBCiASSI/3U0TnHvJDqpGs0dbwVG+3h2Owekz2VOvbImD2zvc+yEV7ljfY+rFMm1ek0GtEudIX9cENvzzTCFEIIIRJQhymEEEIk0FXB15Pd7Ovk0eihP+dXp9wx9VwscMGGoMLYEGlvfWBCtemmm25amvfatWsL6RqpXm1EvUs9pr3qxJ5KiomoEe1FnWlSLHP9BYB169aVyiHN0Nal1DmNMIUQQogE1GEKIYQQCXSoSjY2zK4zBA+H+6zOqqOejUVLYZUXqxKaQV0lmof2ekb36dOnsG/PPff0MqthX3jhhUK6lStXtnqemNq22VWePf1dqKO6boRndqpKdrPNNvPydttt5+WBAwcW0r3//vteXrZsmZeXL19eSPfee++1uXxMI+pLSh4aYQohhBAJqMMUQgghEuhQlezGG29c+J9Vm3WWtdphhx0K//fu3bs0HR8fqnH5XO+++26pDAAffvihl1esWFG6PSRVXVAnXU9XUXUljfAirVpq6eCDDy6k+/nPf+7l2bNne/m73/1uId0TTzzRalk32aT4enPdbYbgIIzqdz0aHYwiNb8BAwZ4+bzzzvPyZz7zmUI6Vr3eddddXr711lsL6ebPn+/lVDNaV5gVNMIUQgghElCHKYQQQiSgDlMIIYRIIGrDTLWhbbXVVl7eddddvXz44YcX0rHNke2boS66apHZfffdt/A/2zTZzbmVxWi9zK75bKcEgA8++MDLL774opdDG+Yrr7zi5blz53p5yZIlXn7rrbcKx6TaCWTXaQ5ituXUhZN5Hx/Tq1evQjq2ud97771eXrx4cZvLmmpvT61n4Xu5+eabe3nQoEFePuqoo7w8adKkwjH8XvA7FitfTyRmN0+tf1yX+FlxWwkUnyvXmdCvg6c5cd79+/f38o477lg4ZujQoV7eYostKstwzTXXeJntnmEdqbNweiOnomiEKYQQQiSgDlMIIYRIIKqSTXXh3W233bx8/vnne3n06NGFdDwkj00rqdq3/fbbF9KxKjicwtJCbNjN0SU4IgVQnALD6tpQ7fHmm296ed68eV6eMmWKl++4447CMatWraosk2gOOnJty6233trLHCUFKKrHWN0fqseqzBZcPztyEQIA2Hvvvb18zjnneJnf+zCS0fjx470cRi+KnaunU9X+cj1g1ShQNIntt99+Xg6j8XDer732mpfvvvvuQro5c+Z4macssbo3NDFwXR8+fLiXjz322EK6119/3csTJ04s3Q5Umxk6awqeRphCCCFEAuowhRBCiAQaEulnyy239PLgwYO9zENwoKieqVKhhvAQ/I033ijsW7BggZfXrFlTmneoxt122229HFONMXxN4XCfVbesmuZ07O0ISCXb3Yh5LNbx1OP34pBDDimkY+/Dfv36eTlUdTUDHCj+rLPO8jK/B++8807hmBkzZniZVbJaC7ZIanQw9jZlFTkAnHvuuV5mNXmokmU1Pnv6P//884V03N5yearMAyFsQgv7BlbRTp061cscsB1ov0q2vfVKI0whhBAiAXWYQgghRALqMIUQQogEkm2YMd3vSy+95GWOSB/aBXfffXcvsz471M+zXZBtIDxVAwCmT5/u5UWLFnmZ9fqHHnpo4RheHYJtMKFLdipsL2U7E8s9PWpJd4Tre2ijSY3uw3AeRxxxhJdPPfXUQjq2YY4cOdLLjzzySCEd1/eqd7MRdkEuD9v/AWCXXXbxMkf64fPyVDKgOB0hNrWsp9swQ6qm+PGUDq4vQLGt42cVwhF8qp5PWIaq6Uux58Z5b7PNNoV9XH+4LX755ZcL6XgqYEfW+yo0whRCCCESUIcphBBCJNCQaSUcjYFVsn/6058K6diVmFWyIRxwl92Kn3322UK6pUuXepnVCqz+euihhwrHXH755V5mlXGokk0d7jNcBg64zipm0TZCdWiK+qeuSqbKNT1Uu6a41IfHsApqp5128nIYrDoWkLyKqnK3Nzg1UFzg4MQTTyzs46kArBrka5gwYULhGJ5KEiurzBjVVNXNRqgiua0K63ZswYy2loHrCwDsscceXuapVmzuA4oLZlQtyq5IP0IIIUQXow5TCCGESKAh62GyWoC9WjlYL1AcXsci/XB+HNkhjBrCKlAuH0fwCYP88lp9HMEnRmyNQY5ccuedd5bKYblFnJgXaoqqLlZXY8dXeSKmBl9nFVHoIX7ZZZd5+aSTTvIye6ECwOrVq738zDPPeDmMeFJH7VR17aF5ZMSIEV7+0pe+5OVwPdqdd97Zy7xAwf333+/l++67r3AMr4fZkQHuuzsxUwTPAhgwYICXeRYCEDd7MVUmhvAZ1AnsX1Xn2GMWKJosOCJcLMJVqod6I9EIUwghhEhAHaYQQtlnPHcAACAASURBVAiRgDpMIYQQIoHkBaRTqbJnlv1fRZXeOywP20GrIuGfcsophWM4uk9Mx8/n4sV7w2kqbKvkfQsXLvRynakCPZlU23lqPanaF1u4PBbph/exDZKni3z2s58tHHPmmWd6mW1NYd5sI+cFpHk1nrKyl5UtpHfv3l7eZ599vMx2fQA46KCDvMw+AKFdlt/nJ5980stXX321l8OpZeFC2KKc8DlyPWPbMT+fT3ziE4VjODJTbPpJahmqbJ2NWLyZ23K+1tSVUGI0cnFpjTCFEEKIBNRhCiGEEAk0JNJPe0lVjYXpWEU0atQoL48bN87Lhx9+eOUxsaE6T1nhRVVvvvnmQjp2m+eA2DHqqkR6CrFn3pGkqm7YBZ4XSz7++OO9fOmllxaO4Yg+4VQShs/LC6a///77lcew2opVW6EKdf/99/cymyr4fQGAXXfdtbSsoVqYFz/g9+KBBx7wcuj6n1qnG6GK686E94mnWvBzPP30073M04GAj0fTqcqbzQBvvvmml0MTWtXizY0g9Z1PqT8d2Wb07FophBBCJKIOUwghhEggWSWbGgS7jhdiqlcjR/ABimrY8847z8sXXnihl7fccsukMrAKFigGlL/99tu9/OijjxbSLV68uDS/GHU8HHsSqR6q7b1fseN5HcdwTUdWfZ1zzjle/sIXvlB5TOozZ9Vr1eICQDFSCnt7s6fuAQccUDiGPXcPO+wwL4fvFaveOIIPmyYA4MYbb/Tyr371Ky/HAngzsfvf01Wy4fWzSp+9mNnklFrnwsUgVq1a5WVe4OK1114rpGMP56ooO6nq0FjfwHmnRvNprxq3tTxa6Nm1UgghhEhEHaYQQgiRQLJKNhYEO1XVWkeFxqonXicNAM466ywvH3PMMV5m1URsfTaWWbUKALfeequXb7jhBi+/+uqrhXRVE4I7S624oZNa72KkeuBxoOeRI0d6OQx+wcHT2SyQWu8YVocBxYAXVQG3gerJ65///Oe9zB68QFF1y96voQcuL5Jw/fXXe5nXugWA559/3susxo0Fz0+lIz0ym5WYlzY/fw5AwZ6wqW3Je++9V/ifA2Q88sgjXg69/jkAS3s9/WMq2aqgCHXO02g0whRCCCESUIcphBBCJKAOUwghhEigdqSfKrtQewNnA0W9PEe1uOSSSwrpjjzySC8PHDjQy6ku6Rwwmm2WQDGoOts36wRSl52ybbR38eZwcVr+nwNU89QKoLigOC+WzHULALbffnsvs92SI+GwLQgoTkXhRX/ZXggAN910k5f32GMPL4cLobM9f+jQoV7eZZddvBxGeuH7+vLLL3t50qRJhXQ8RYRt9jzNBUibZhCjEVMBNiT4mvv371/Yx1NJDjzwQC+HdZ3h+8vPat68eYV0f/jDH7zMi5bzlCKg8c845ZhGR+1p76LlGmEKIYQQCajDFEIIIRKIqmRT1aupbvtV+zhgNFBUeZ199tleZhUsUHSt5zxiEXxYBXbvvfd6ecKECYV0vI4fR8aooyrqieql9hCrT6we5XVPWe7Xr1/hGJ5CwdNAeE1IoBisfIcddqgsA0/DmDNnjpfvv/9+Lz/88MOFY7785S97mdVtvGYhUFS18jqVYdQervsczYrVZhy8HShG6uG1W/k9AIApU6agjDrvdiPajZ4C3wOuf0BxDdWqdi8GTyUJp8Y98cQTXl6yZImXw+lGdZ5xnT6k0dPuGlm3NMIUQgghElCHKYQQQiSQ7CWbGrmkzhA6DJDO6oeTTz7Zy6G3YpU6gtWwrDIDgHvuucfLd999t5dfeOGFQrquiDSSeo97EuGajmPHjvUyR+BhL8JQncUetOzVGkbPYfjecyQUoFhXHnzwQS+ztyF7JQLA6tWrvczPedCgQYV05557bmlZQw9FruO8huGyZcu8PHny5MIxrCZmNVxY96tIVb2lquFS896Q4brJQfDZSxsomhx4PdbYjACuM1wv2NwEAPPnz/cyq27DZ9LeKFuNSFeHRqr+NcIUQgghElCHKYQQQiSgDlMIIYRIIGrD7Ei9MtsfQzsOTx8ZNmyYl3l6QAiXlfX1vNIIUFz0lm1Tde2HjVwMuqfacUL4nu69996FfRdccIGX2b6daptgu05oZ+RIPRzR6ZZbbimk4ylIHBmF3fBPOOGEwjFsf4/ZTjk6D69kEq5qwlNGXnzxRS9Pnz7dyz/96U8Lx/B70ei6phV4inD7xs80jMzD9ZYjTXHdBoCDDz7Yy2zDjN13rt8zZ870chiFitvBmL0v5R2r++w7MroP0966qRGmEEIIkYA6TCGEECKBhk8riR3DsPohXKD30ksv9TKrr2LnXLlypZdZLXXfffcV0rELPtNeFWxb8kiljnv+hgZPrQCKavmq+xN7RlxPpk6dWtjHqta33nrLy48//nghHQfj5+lHrHa96KKLCsdwfY89V55+8stf/tLLYT3mqSB8TayGe/vttwvHVC12Hk5NqLvo84ZKHbMLm5l4wXEOjg8UA6FzlJ2ddtqpkI4X/2b4WYULQz/22GNe5sUknnrqqUI6fv6cX0dOIwpJXWi6q9EIUwghhEhAHaYQQgiRQLJKNhb1oYrQG5DX9zvttNO8fMYZZxTShZFaqs7J6qenn37ayz/72c+8vHDhwsIxYUDhtlJHddtoNUXqedubVyy/joTLunz58sI+VqlzFKcFCxZ4OSzzO++842VeA5Wj3QBFb1OuJ6zyBIoetLvttpuXOUoPezUCxSguXJ7Zs2cX0vG6rByJitevBIqqW14cgOWQqmcpFWzjTS077rijl3lN39Dru0+fPl7m9SfDqGbclnJZ+dmxlzdQVMly+8jmhjCP1ADpHGmKVf9h+8r1MRYovqq9rHPvQxNDIz24NcIUQgghElCHKYQQQiSgDlMIIYRIoPa0kqp9bKvZb7/9Cul4+sixxx7r5b322quQrkpfH7rJT5s2zcu33367l3kBXLYXAZ0XwafZpql05nSY9sJlDVcK4RVBeKWQ1157zcvh9bBdhVdmCBfSZdtkjL59+3qZV0lhG+aAAQMKx7D9hqOucL0FgJtuusnLbLess3pOd3rm3ZGYnwLbI7ndChcMHzNmjJerpigBH48QVFaG8Hn36tXLy1wfwzZx6dKlXmbbZMy2zedim2jYRvO7UmcB8jqEzyJ1ykrKO6ERphBCCJGAOkwhhBAigWSVbAwOLjx06FAvc6BsoBjxgiNZhOqGKjfgZ599tpCOg2JPnDjRyzwVITVaRYzuFHGn0REzuuJ6uayvv/56YR+rMDkdu6+HZWY38zou5mHQ/1GjRnl53LhxXubpA2HerO7lSFRcb4GiyphJVR/FVHSp00e6U31vFKnXyXUp9k6xSpYj+ITPYMSIEUn5pdTbMCrWYYcd5mVeiD00c/D/rK6N1RcOAM/Ri2JTOmLbU9utFFNZR9ZZjTCFEEKIBNRhCiGEEAlEVbJVQXnDfRyZh9ey/PSnP104hj1oq9RkQFG9xp5jt912WyEdBxQOI/pUEaoMqsqQOsTvLG/aqnKHeaSqQJqZmCqoKpJNR0YkCj0Wzz77bC+fc845pceEXq0cmYejEnEQ9RipdZDrSWrUn7qq255CSpux5ZZbFvZxBChWWXIbCKQvIJASCSdUybIH7ujRo70c1ouqSFGp6lU2WYTRfFLfS35f2MTXu3fvQjqeQcEevUxYhqpIRnWi12mEKYQQQiSgDlMIIYRIoLaXLKsgDjjgAC/z5O1wDbc63nc8KTb07gqDYrcQ82SrGpJ3ZID0uhPIWbUQU6/VodFB2juKOqqpkKp1IFM9Ty+77LLC/yeffLKX+T3g40N11ksvveRlDpjACwjEiAWu5utLrSed5VXYHUmtF1WBAQDg4osv9vJxxx3n5Z133jkp75CqdLF6UUVYN/l/nrGQGmgg1q6ntresaj311FO9HPYhHLhk0qRJXubA841uKxmNMIUQQogE1GEKIYQQCajDFEIIIRKI2jBjbs7Dhg3zMkeUGDlypJdDXXmqDZMD+P7+97/38vPPP19Ix/afqkVV6+jhwzxidGRUlI50729mu1WqTSTVjpJqr2U7yq677uplDrAOFKOm8HQRtieF7vBz5871MgdVD+0tVdcRqwt1oqTIhlmkjp8BT4UI/SkWL17sZbavxdrE2Dn5+XO7xxGFwgWkY/lVEWs7+X+233JAeY4ABFT7k8TsxPxO8ILxQDGKW5WtMjYFr857VMi71RRCCCGEUIcphBBCpJCskg2HuRw8ndWz4XpvVcRUIHwudis++uijC+l23HFHL7MKhNc/DIf0HFw4NgSv2heqAVLUph05ZaUnEzMZ1IGjsJx55ple5gUFgKIaltVjgwYNKi0bAMyZM8fLPD2qrvpYNJY6UXb43Q8jO7FZ6b333is9vi1wneO6NHnyZC9zGwjEFySoIvU+cHQ3jiJ0+OGHF47h6EMxMwDfy3nz5nl56tSphXQcGSt1jdhGvjsaYQohhBAJqMMUQgghEqgd6YfVDOyptWzZMi/369ev8viYCo3VsBxVJVTJsmqCh+qsvuDhPVBU0bLqNrV8fH1AUfVSFQw4lVS1Ylhufha8ryMjXnQkjVY/VqmZwnVYt99+ey9zxKowisuMGTO8vGrVKi8PGTKkdDtQ9PB+44032lzWOvckdc1BkQ4/E/aKDoOvsyc1e0ynBjQPPV65Hbvrrru8fP3113uZI0gBxTVY6xBrj7iu8zXsueeehXTseR6rz1Uq2VmzZhXShWrnsrzrzi5Q8HUhhBCiQajDFEIIIRJQhymEEEIkkGzDDPXCjz322PpMyBbEdsvjjz++cAxHh2D9f6jX532DBw+uLNPw4cO9zDpx1t2H00rY5sj2vlCnzmXgfc8++2whHUdwWbp0qZdTowjF9Oacju/x66+/XkjHi2fzlAW2t4bPb0ObvlCn3OFKCDw9avfdd/cyL2gLAPPnz/cy169jjz22NA1QjO7DNvaw7lctdis6j9T7zgsns/0bAPbaay8v8xSM1NVFwvpzxx13ePm2227zMr/vjfZZSI3G9tRTT3k5bPP5HeNpW2G95ykidabDMKmrzYQo0o8QQgjRINRhCiGEEAnUnlbCUyimTZvm5f/8z//0cqgOZbXFHnvs4WWOGgQUVR2sxg2nAVSpPWOqkv79+3s5pv6qcoHmoNwAcOKJJ3q5jkokNdoQl4dVegCwaNEiLz/55JNe/tWvfuXlBQsWFI6pWri4rjqjq2EVT2qAZVbBAsA555zjZVbDhu75PGWA6ydP73nwwQcLx/BC6KL5SJ3Cw20QTzfiyFBAcRGKMCA5UxU5iNtUAHj00Ue9zOra9qovY8TMRVVmOFa7AsW2OHWBhKqA7TE6q53SCFMIIYRIQB2mEEIIkUBtlSwPgdljir1Iw2gnHPFi55139nKoNuV0+++/v5f79u1bSNenTx8vs6qV8wvVczzcZ7UCR6QI0zGhx2TqmoxM6vqFVYRBhzkIPZeP1TqstgWqVbIhsbXlOoNUFXFMDVv1jEJVGXtkc93gKFJAMaoLH8NmimeeeaZwDL8jTKopQHQsqWpYbrfGjh3r5dNOO61wDJuZWC0Zi3DDC0OE7yubt7iexRbIaO+avrF7wu8AmzbCtpzb1TptYqys7c2rDhphCiGEEAmowxRCCCESUIcphBBCJFDbhsmwrnzlypVenj59eiEd65/Z/TiM9M+u+myHC21O7M7MdjyWQ3026/nZLXzgwIGFdKGtsio/pipifmhb4OgXfE3hfWDY5sj3OMyfI4mwnLoobuqCxs1MrMx8fWHUFa53TLhCDT8nrmtsg+LVSYDiVKDUOiSaA24LdtttNy8fc8wxXg5X6ah6l8OpZ+znwRFznnvuuUK6sA620Oj6Umd6Fl9TOAWL86iKnhYexxHYYhHKUheQ1wLSQgghRCejDlMIIYRIoCEq2dRpEjy8Zjf7cPoJw4HFw/xYVcJqzi222MLLoTqU8xg6dKiXOdh2mF9sSM/5V6kpwghF7HLO0xI4gkyYHy/S/eKLLxbSsds6w9MhQlVJavD1uouxNoo66stYvYvlXUelzmpcXtz2tddeKxzDUYBiaCpJ1xCbzsOqRH72bBIKVa1V9YfV9kBx8YYbb7zRy1OnTi2k42kl7V1kPDZVKxZlh9Nxm80LqvOiHECxXeX2LXynlixZ4mWOShberyoaoZLVAtJCCCFEg1CHKYQQQiRQWyWbotKLDelTI0rEhsnsOcpy6vCcVZv3339/5XlTy1el0gthz1iOXhR61rGah1V6y5cvrywDq6PZsy6MDlT1nELP0UavsZdC7N5XRR6qs45kmI6vle9X+Cw5egkfw2sThqqkrriPIp1YneG2hb1Xb731Vi+H5pQhQ4Z4md9rNjEBwMSJE708fvx4L4desVX1p44KP2aKSK2n3AZNmjTJy3PmzCmk48hIe++9t5fDqG08G4IDzYcLTVS1t414v7QephBCCNEg1GEKIYQQCVgrHqB+Z0d673XVGoyxwOId6R1aFQA+Vh6+J6F6tSpvTteIe+qc65RZ9Y2ud1Uq+lAtxJPSeR97KANFlSw/v6VLl3p53rx5hWNiz0zE6Yx6F6tzVWvtsmll0KBBhWM4MAubOdasWVNIx3WGA66nmlCq0jSCVNMWtzlh8A9+P2JrG7MJg1Xgoaq1kdfYismw9OI1whRCCCESUIcphBBCJKAOUwghhEggasM0M78ztK+lTgupVagaUzrqlKGzAvY2gti1pixinRpUvZXoN51iw+R6V7KvdHud5xVOoWH7FNtYQntSla2Zoyml2iybrZ41I51hw6zT1nFdDBegT62nVVOZ6tAM7VlqMPiObNcbgWyYQgghRDtQhymEEEIkUDvST2e596aWoU7Enc6i7vU1iWqiS87bGYQu6x0ZjUfrXG548Luxdu3apHQhVcHOm11lmWr6qTqmEeet0863V22tEaYQQgiRgDpMIYQQIoGoSrbRqs3OUkuxmiNW7mbwKouR6mlXVdbYNTTD9dWhveXuqmeeYj7o6DKINOqo91K90MPtKevphse111O8ESaiOmrh1IU5UkktTyP7HY0whRBCiATUYQohhBAJqMMUQgghEkiO9NOQk3VTt2mmu9qYGmEz6KzVSrjebSg2vmao092Vzqh3G2+8sX8odfw1VE83LBTpRwghhGgH6jCFEEKIBKIqWSGEEEJkaIQphBBCJKAOUwghhEhAHaYQQgiRgDpMIYQQIgF1mEIIIUQC6jCFEEKIBNRhCiGEEAmowxRCCCESUIcphBBCJKAOUwghhEhAHaYQQgiRgDpMIYQQIgF1mEIIIUQC6jCFEEKIBNRhCiGEEAmowxRCCCESUIcphBBCJKAOUwghhEhAHaYQQgiRgDpMIYQQIgF1mEIIIUQC6jCFEEKIBNRhCiGEEAmowxRCCCESUIcphBBCJKAOUwghhEhAHaYQQgiRgDpMIYQQIgF1mEIIIUQC6jCFEEKIBNRhCiGEEAmowxRCCCESUIcphBBCJKAOUwghhEhAHaYQQgiRgDpMIYQQIgF1mEIIIUQC6jCFEEKIBNRhCiGEEAmowxRCCCESUIcphBBCJKAOUwghhEhAHaYQQgiRgDpMIYQQIgF1mEIIIUQC6jCFEEKIBDq0wzSzhWb2upn1pm2XmtnkIN0RZjbFzH5mZj+h7Zua2TsV2w6PnPfvzOyfS7abmX3TzF42s7fN7Hdmtk0knwfMbGmedoaZfZL2nWpmj5jZCjNbYmZXm9nWrdyPnczs1Yp9Q/LzrTGzuWZ2fCSfXmb2i7xcS8zsa8H+4/I81uR57hor14ZIXvfeNbNV+TOaYmaXm9nH6ryZPW9mw3P5YDObYGbL8+Nmm9k/mVnfVs53r5mNK9ney8x+mr8Hy8xsvJkNSij/xWbmzOzSkn2b5c+3tC4FaUvfhXzfBWb2Uv4+3WZm20Xy2d/MpuV1apqZ7U/7zMz+zczeyn9XmZm1VrYNiWZr6/J9XzazF/N24ikzOyqSz2/MbHGe9nmud2Z2uJndl9ffpWZ2k5kNbOV+bGZmb5rZViX7tjOzW/Nre8nMLojkE61bsXrZITjnOuwHYCGAtwD8HW27FMDkIN0/AvgWgAsBzKLtnwAwB8DMYNs7ADaNnPcRAEeVbL8YwFwAOwPYCsDtAH4ZyWdfAJvk8mEAVgEYmP9/AYCTAGwJoC+AiQB+2sr9uBTANRX7HgPwQwBbADgbwAoA21ek/RcAD+fn3RvAEgAn5fv6A1gJ4FwAmwP4dwCPd+RzbsZfXveOz+VtAZwB4EUA1wbpdgPwQi4fAWA1gP8DYEC+bRcA3wUwNnKu3nk971Wy7woAMwAMyJ/HrwHc0krZ++b1dCaAS0v2fxPAQwBeTbgPVe/CiLw+j8nfhRsA/K4ij80AvATgqwB6AfhK/v9m+f7LAMwDMBjAIACzAVze1XWgC+pbM7V1h+XHHgTAAHwJwFIAG1fkM6Kl/gLYK29TDsr/PzlvT7ZB1t79AsDdrdyP4wFMqtj3WwA35vXuqLy9GlGRtrJutVYvO+Q5d0Il+t8AlgHoE6lETwM4ML8pHwHon2+/AsB3kDV0vK30QeT7+wJ4o6xiALgZwDfo/yMAvAdgy4RrOTRPe2jF/k8BeK6VPG4B8KmS7cMBvA9ga9r2cFWjA2ARgHH0//eQN3YAvghgCu3rDeBdAHt15LNuth+owwye4UcARtK2rwD4r1x+BMB/1zjXGQDuqNj3PwCuov9PBTCvlfx+CuAvAExG0GECGIqsYT0ZrXSYrbwL/wzgBvp/NwBruQ7SvnF5nTPa9jLWf6RNAfBF2vcF9LCPtCZs6z4NYCr93xuAQ/7B38q17AlgMYDzKvYfCGBVK3n8EMDXSrb3zuvZcNr2awD/WpFPZd1qrV52xK8zbJhPIXvxv162Mx/aDwDwjHPuVWRfCKPz3WOQdRxTgm0PRc53IoA/OufWlZ0u//H/vQDsUZVZrp57D8AT+XU8VZF0DIBZkXw2zdPcV7J7BIAFzrlVtG1Gvj3Mpy+AnfL9ZWlH8D7n3DsA5pfl1dNwzk0F8CrW1yUAOAXAnbkq7RMA/lAj61MA3Fmx7/8BONIydfyWyEYWE6syMrNDARyMrNMs478B/B2yj6DWiL0LYT2Zj7whq0j7rMtbpJxnUVHnUFF3ewDN1NZNBLCxmR1mZhsD+DyA6chGjqWY2U/MbA0y7cZiAHdVJI22dTlV78RwAOucc8/Ttlh9idWt1uplw+ksp59vA/iymW1fsu8UZMP7lot+EMCY3NZ0KIDHkVWklm1H5mmqOBXVD3oigEstsxduC+Bv8+1bVmXmnDsNwNZ5Oe9xzn0UpjGzE5Cpe78dKdcYADOCTrGFrZCpJZiV+XnL0rbsL0vblrx6Iq8B2A4A8g7sEGT1qS+y98E3KLm9ZEVua/lWJM+TUV3nnkf21bsIwNvIVOj/UJYwb9h+AuDLFfXsLGQmglujV7ie2LvQ1joXSxvuXwlgq55mx8xplrZuFbKPv0eQaa++g2yk5irSwzn3F8ie6Whk2rD3wzRmtm9+jd+oysfMhiFTI88r2d3W9ilWtzq9reuUDtM5NxPABGQqi5BTUHzoDyHrXEYhG3WtQfbQW7ZtgWy09zHySnYCgLsrivILZPrzyci+kB7It0edJ5xzHzjnJgI40czOCM55ODL7zznBV1NIeJ3MamT2AWYbZJW+LG3L/rK0bcmrJzIImdoMAI5Dpr5+D8ByZCoy78zgnLvCOdcHwK0ANinLzMxGAXjbOfdKxfn+B5ntsh8yddQtqB5h/gWyL+bHSs7TG8BVAL4cvbr16Vt7F9pa52Jpw/3bAFgda5w3VJqorbsU2ahyBDJb32cATDCznVop/zrn3CPIVMZfCs65O7K6+9fOuYcj2cQ68ra2T7G61eltXWdOK/kOgD9H1mAB8GrKo1FUUz4EYD9kN73locxC5qhzKoAn8waujEMALHTOLS3b6Zz7yDn3HefcEOfc4DzfRfkvhU2Q2Xpayn8AgDsAfN4598dWjo2p7WYBGGZFL9v9UKL2cM4tR6Yu2a8i7Szelze0u5Xl1dMws0OQ1b9H8k3+meSq6yeQ2aLbQuy5AtmzuM45t8w59z4yleqhZta/JO1xAM6yzPN5CTIb+w/M7EfIzAZDADyc77sFwMA87ZCSvKLvAj5eT4YhM0+UffTNArBvMGLcFxV1DhV1twfR5W1dnu9459zzebt3N7J244jEawjbul0BTALwPefcr1s5NvZOPA9gEzNjM1isvsTqVmv1svF0lHHUrTeEH0//X43Mk2xy/v+xAO4vOW4RgNcBnEHbxufb/ilyvn8A8O3I/u2QVQIDsA8yL8QvVqTdC5mqbQsAmyL7QlsL4MB8/8i8PJ9OuA9DkX1BxtI8DuD7yEYjZyHuJfuvWK9G3AvZi9DigLE9MrXE2Xle/4Ye5oAR1j1kX52nIbPl/ipIswv9fxQyz8L/DWCHfNtgAI8CuLLiPA8BGBMpx7XIVGPb5vXo7wAsqkjbB8CO9JsC4Gv5sZsE+z6FTL28I8qdPlp7F0YgUxGPRjby/Q1a95L9a2Sd6l+h6CV7OTJHpEHI7Ouz0DO9ZJuprbsYWec0DFl7dwKANShx/gOwA4Dzkak4N0ZmG30HwCfz/YPyd+cbVeejvLbIr3vzSJrfIdP09Uamdo55yVbWrdbqZYc8506uRDsj8zRtqUTfB/D1kuN+i0w91o+2XYHMy+vEyPmeAnBwZP9wZC7Ka/Ib+7Vg/0+RTw1BZmt6AtnwfgWAJwGcRWmvzcu4mn6zKs77VwB+1Mq9GoJMVfxuXka+b6ELei9k6uW38xcrvI7jkRnu383zHNKRz7kZf3ndezd/fiuRTdv5S+SdC7IPnpklxx2GTJ20Iv/NBPBPXBcp7bbIXPU3iZSjH4DrkXkzrkA2uj2U9k8ETUUIjp2Mkmkl+b6xiHjJTUnb7gAAIABJREFUtvYu5GkuQGZffQfZFKvtqsoF4AAA0/J7+jSAA2ifIVMXL8t/V4E8F3vCD83X1hmyTvXl/B2YA+CztP/vAEzM5e2RfYCvyNuU5wD8OaX9Tl4ebutWV5z3NAATWrlX2wG4La93LwO4gPaN5rxbq1uxetkRP8tP2iWY2Wxktr/ZDchrADIvsJ1cV15UCWZ2F7IOs0qvLzoZM7sCmfv+Fe3I4zxk9fe8xpWs/TTzu9BT6UFt3U+QfYj+pNXE3ZBSR4bOwMw2Q6Yea3cFytkW2UirqSpQzmSsdzASzcFCZKqv9rACwP9tf1EaTjO/Cz2OHtbWTUf736umpUtHmEIIIUR3QcHXhRBCiATUYQohhBAJRG2YZiZ9bUAseElnqbc7swx8ro8++qhTIreo3jWGsJ60t27UqXcbbbRRZbpYeTq73sXqXNV1p5Y/NV1q3lXHh/f6o48+FiyqTedqhrauq3DOlV68RphCCCFEAl3mJdsIUr7OGv2VHTtXo6n6Su3Mr7sN/Uuyu5Baj1NHNqnnSs2DRzd8TOoop6Pf07aQOrLqyNFdKnz8unXFGOx1RrkdqaHaENoSjTCFEEKIBNRhCiGEEAmowxRCCCES6NY2zJitsixNo8+ZSuqygF1lZxDdjzr1ob31MPU8dcrTTDbMOtcZHlPVNoXXybbO9r7jsefbCI/XOnbzDa2t0ghTCCGESEAdphBCCJFAt1bJMs0w9O8sN+466pW6Kq9UNZ7oOlKf0Wabbeblrbbayst9+vQppOvbt6+Xt9hii9LtALDJJuubj6VL169jPG3aNC+/915x/eP2qnublWYof0zdW4fwmtQWaIQphBBCJKEOUwghhEig6VWyjVZfdoXqpK7nWJWnHavWQj788EMvt1clA3w8gonoGsI6w8+FVaMbb7yxl7fddtvCMYMHD/bykCFDvDxs2LBCuuHDh3u5X79+pduBorp2xowZXv7+97/v5enTpxeOWbNmDcpoBpVme4hF+kmN0hXz+k+J+tWI9z2VINZv6faQ7v6MAY0whRBCiCTUYQohhBAJNL1KtiMn8NbxFK2jUqnrkcr/b7755l7eeuutC+lYHfTOO+94edWqVUnnjREGdBbNAT/zbbbZxstDhw718hlnnFE45vTTT/fyHnvs4WVW6QJFFRvvi6nnWXW74447evlzn/tcId28efO8nKqC7Gxi11mlDo29J3UCNMTSNcMygp2p/m0mNMIUQgghElCHKYQQQiSgDlMIIYRIoOltmHVohoDrMWI2DYbtluedd56XjzjiiEK6TTfd1MtPP/20l3/zm994+e233648T8w1XdE9ug62pW255ZaFfSeddJKXzznnHC/vueeeXt5uu+0Kx3B0nxUrVnj5lVdeKaRbsGCBl7muDRgwoJCuV69eXmZbJ09nqTstqSttZI14B6oW1A7txeyPwO/78uXLC+k4YlJVe8RTioDqqS2pxCL9VMl1Fwyvk0dXoBGmEEIIkYA6TCGEECKBDVIl24i1I9ubR2rwdSZUqXCw69GjR3t5zJgxhXQc+YfVZBMmTPAyTzcBgA8++KC0PM0QGUlkcH0YN25cYd/555/v5bFjx3qZVbdTp04tHPPoo496edmyZV7maSAAsN9++5XmF6pX27sebXdR96dcZ+wYVsPydB4AOPbYY728/fbbe/n+++8vpJs5c6aX33rrrdJzhqrMOu8uP+PweVdFlIqVYe3atV7mqTfdtV3RCFMIIYRIQB2mEEIIkcAGo5JlFQGrDsIIHKlqgUaodauoyo+95ABgl1128fLee+/t5YEDBxbScVl32GGH0vxCFQqrSuqonETHwGow9jZlr1gAOOqoo0qPeeKJJ7z8wAMPFI55+eWXvcwes/379y+kY5UsmwXCusGenC+++KKX2VN79erV6InwO8X3mu8tAFx44YVeZpUsHwMUvWTZw7kRkbi4/vTu3dvLHKAfKLYtrKpnNWwYXWz27NlefvPNNyvL0F1UtBphCiGEEAmowxRCCCESUIcphBBCJNCtbZhVi+VyRJL333+/cAzbXdi+wtMsQhq9wklVHrwgLwDstttuXma7ZWjrfPfdd73M18F2yjoL0orOh+1JbDPiugAU7Y4vvPCCl//4xz96mesFAFx66aVePuCAA7wc1juG7VNsOwOAJ5980svjx4/3MttO33jjjcIxqatzdAdi7zg/x5122snLo0aNKqRjmyZH7AqnET333HNefuaZZ7zMC8aH00C4fOzXwVPPgGK0oZ133tnLp512WiHdvvvu6+U+ffqUloFt2QBw9dVXe3nlypWlxwDdp93RCFMIIYRIQB2mEEIIkUC3VsmyC/SRRx7pZV609rXXXiscc++993qZVUqLFy8upGtvAODYVI0q9UOoUuHrY3VNmI5VZTx1gKP7xFTOonngusJqq5jKatiwYV7+5je/WXlMVaSWmMmATRq/+93vCuluuOEGL0+bNs3LXNdS36NYoO/OIKZerYqEFXsmfH8PPvhgL3PELqCoHuX8QjU5v/9VhOXhYwYPHuzlcGrLoYce6mUO3s9qe6Bo6uK6xM84NB1wtKmFCxd6OVTVdwV1oppphCmEEEIkoA5TCCGESKDpVbIxFQhHw+CoFIcddljlMeyxxkycOLHwf6oKM2Vty5haitWr22yzTWEfq0dCz1iG17p89dVXvcwBtuuqmLuj92JdUtVtKfckptaL5c1qWH6Wv/rVrwrp2AP2oIMOKs2Lo8MA1V7l7CUJAGvWrPHyVVdd5eW77767kO7555/3cswjuw6d7TWZGu0q9T3iBRFYHcpymB/LYeD8+fPne7nKM5ZNOEBRpXr88cd7OVQLs0qf8wjbnCqVPm8PA/nzeqpz58718uuvv15IV6fO1JmxUOd4RiNMIYQQIgF1mEIIIUQC6jCFEEKIBJrehplq72F7DUfwD1f2YPvm9OnTvfzUU08V0oXTUeqUL4Wq1SkAYK+99vIy20TCFQp4QVl23Wa7UjgVhYnZD7oiAkdHrhQTo5Hnii22nArbEsMFhdktn+33fO9C2yRHbuGpBWzrBooLTd91111eZhsU8PFIQi10l6gtHQnb/3jFl9BPgZ8Xv9e84gtQjKDDdWu77bbzMtspAeCYY47x8iGHHOLloUOHFtKx3ZLLE/px8PNmeyvXs3D6C/uMhKviMKk2ZL6vLHPZwuhujUQjTCGEECIBdZhCCCFEAk2vkmXCoToHT1+yZEmpzAGsgeIirezizaoNIF0ly9RRRbFagcsGFFUnrOoIF2ldsGCBl+fNm9ewsjUDqe7mHbkYeKrKOjbFKHU6QlVZeYoJACxatKj0GI4c88lPfrJwDE8FYHPGzJkzC+l4CgtPZ2AVcWq5U2mm6UuNqEv8LnPbEgY+r1r4gJ8vUDQ5cbvFU4o++9nPFo5htXu/fv28HJoLOG8274TReDhyGKuWR44c6eXwOfL1slkppOrdCe/X7rvv7mU2Rbz00kte5ulOwMenV7UHjTCFEEKIBNRhCiGEEAk0vUo2puZirzJWF4Uqy6r8OEJFSnDjsjIwdVRRrK4JAxezWofLGqqLn332WS+HnoztKVtXwWWNrfGXuo5nR6phqwg9mVNh1W2q+o/rBteZyy+/vJBuxIgRXuYg/ffcc08h3W233VZanhipXo5V19Fd6mfqdfK93mWXXbwctjN8fznvMB2rH3ldyjPOOMPLRx11VOEYNvewCj5sH1n1+uCDD3qZA+oDwJZbbulljhbE5Ukl9l5zuUOP3lNPPdXLn/jEJ7zMa7CG7WOVSlbB14UQQogOQh2mEEIIkUDTq2SZUD3EQYN5TTeeLBsOu/l/VgvE0sVUf5yOvRCZ2DHs9cUqBqA4IZjL+uabbxbS8VqeMXV0VRliZY0FPOgKqlRYDKsogaJ6NFX115Eqwtg9rVKBhs+L69rOO+/s5U996lNe3nvvvQvH8H155JFHvHzjjTcmlSGV7qJeTaVO8G5eA3PXXXf1clg3+V7zhPtQFcmBB4499lgvcxsY5s288sorXg6DIvC6wHfeeaeXwwDprP7la4qZDlgVzO9hWMdYBX3ggQd6meszABx33HFe5jVDZ8yY4eXYfahq/8PyVdFcraEQQgjRpKjDFEIIIRJQhymEEEIk0PQ2TNaJh4GL2Y36lFNO8TLbdEJ9Ntv42Pa3dOnSyvOmli/V9sP6era3Dh8+vJCOy866d7YLAEXde+pUhNTra689q73UOX9oi6i61phbeep9ZDsIu8NzHQSKi53zgt9hNBXeF5teM2DAAC8fffTRXv785z/v5T59+hSO4Yg+zzzzjJf5PQCK9tEUm3Fdmi3ofwupUYeqFlEGiu91uKgCw3V15cqVXj7yyCML6ThiWVX7Fr4rHKXp1ltv9TIH1AeK0zA4SlrY3vJ5Wa6aCgUUg8ZzPQsjq40aNcrLl112mZd5EWyg+B488cQTXp4wYYKX+R0KqdNeF87f5iOEEEKIHog6TCGEECKBplDJxqZ0sNojdJPntS15H7sbh3kvX77cy+w2zdvrkqpGYlUZr9cZBl9neG26cP3CKjVedyVVHVon6k8MDg7Nz4jVn0BRHcURWHh7GPS/au2+0HWf1zNlV/lQdXvEEUd4+dOf/rSXeTrCrFmzCsf8/ve/9zKveRlGQmG1V0fWp9SoOV1N1T3gtimsI/xe83qTIXzd3G6FbR1PMeOA5Gxi4ohfQHEN1fvuu8/LXK+A4vNnNSWrlYFineb3I6ba5Oks3E7tueeehXQXXXSRl8eMGePlMPg611tWM3NdTw22XmcKnUaYQgghRALqMIUQQogEmlIly16kvI7bMcccU0jHHlTsiRZT9bAnGqthWU3WlrLWgaNkcKQOVrsARQ86ju4TrnnJayW2V4VWJyBxZ9Le+89qr1CNxlGX9thjDy+z2hUoqpP4WXJw6rVr1xaO4SgurPoNr4e9tTmodbge5uGHH+5l9qhktdf48eMLx3BQ9XDNQKYrPKObqZ6lloWfY2zhhFCtyLBaN/RqrkrH61LOnj3byzfccEPhmIceesjLrBoN27qq5x2Wh9tYbqO5nQojjfH6mty+hVHNeJYDq7D/9Kc/FdLxNXGgeL4nqdRpSzTCFEIIIRJQhymEEEIkoA5TCCGESKDLbJisPw4XS+UoEAcddJCXTz/99EI6dr2uWikkhG08q1ev9nIYHabOCgVVkfDDsrFtiuUwIgjbvThKy5QpUwrpFixYUFmmDY2kRV4D9/AqW9OJJ55YSMfTM/i5cD0BijYgjqbCNsIweg7bJvv27VtaHqBoLz3vvPNKjwGKEV64Ts+ZM8fLd9xxR+EYXjSaCe9XR9ow67xXzUTqNBCOkhObJsX7uJ6G6bh94ufIz5inDQFFe2JsxZ6qaUSDBw8upOvfvz/K4GsIpz8x+++/v5fHjRtX2Mf2Un6neDoMUGz7wul17SWl3muEKYQQQiSgDlMIIYRIoLZKtioqQh11DkfFAIouxp/73Oe8HKqvWIVRVYZQBcSLpb7wwguVZaoThaQqsG8YxHivvfbyMqs9wnvHqsCbb77Zy88991whXeqUmBS6i2oMKD6XmNqrKioOL0YLADvuuKOXWaV67bXXFtKxaztH5uFpSmGAfH62rKIPzRGseuXnetJJJxXScRQWDnD9X//1X14OXfJZxV9Vto6mO9WvMrie8bMKVYzhO192fOp5gGJEqIcfftjLrIaNBR1PDXTPU2DYHAYUp11VHR+e5/jjj/cyTxEcOXJkIR3X9euvv97LvKA1UGyz6yyWoAWkhRBCiE5AHaYQQgiRQFQlGxvmshqnyiM0VPVwHhyMmqOqAEX1BqsBeE3BkKp1+1asWFFIxx5YHIUiFuEmdYjPsBcjR4MBiutesudZqBLgaDHsZblmzZrKsqaqJphmW5cwtg5kVTqODhJGEfniF7/o5UMPPdTLXAeB4hqRP/7xj708bdq0QjpWj7EqiQPkpxJ6RnNdYbVVqOLjyE8ceJoDa4dBqFNVr3XqUHenTj3bZZddvLzPPvsUjuF0sbak6t0L1el//OMfvcyesbyWZV3VOqthOXoae4oDxchYVarNcNEBbrOrFiAAioHiJ02a5GU2NwDFOs1liJnhqu5/eL9S1OUaYQohhBAJqMMUQgghElCHKYQQQiQQtWHG3IXrLNjLOmyOjDF69OhCOo4IwTrwmI6Z97H7fLhY6ty5c73c6EWj2X7LUUD222+/wjGDBg3yMtsPQr0+R7LgsoYrYfRUqlz8eeoIUJxWwvXpqaeeKqS77rrrvDxx4kQvhyswtHfhY64n4UooHN2HbfvhFKpHHnnEyxxhaMmSJV4Op7ak2iZ7it2SSb1mjkLGbVi48k1sRZqq87INfPLkyYV0EyZM8PLTTz/tZW7r6voi8NQmtmGGvhe8Gg/DNsxwxSVuB7k9C989nh7D9ttwFZKU55S64lKqPZnRCFMIIYRIQB2mEEIIkUBypJ/U4X5sSgBHJ+HFoDkaBFCM/MPqq1iAdJZ52gVHxQCKbsp1Fh2NwSoLjuYTLnwdul63EAb55oDrHNS40SrZZlbBxQJFs/qH7/cJJ5xQOIbVQjx1JAxWfdNNN3mZ3dcbrb7k588LQQPAGWec4eXYQrp33XWXl++9914vc92Puc0321Sirib1mjlIOKvMuY4B6VGo2AzDi3rz1AqgqMLkKUVMqoox3M6mshEjRniZ1c9AdZD2WPQcNmfw9Cw2IwDF6Fl8TOq714ipUEkLO9TKWQghhOhhqMMUQgghEkhWycaG+1VD/9CritVPHEyaPbOAjwekbiG25iWra1m1ySo4oBiguBHDeC4TR/644IILvHzyyScXjuEgxKw2Yw9HoKh24+gysYgyG5o6LXzmXDdYDXvmmWd6OfRY5ADp11xzjZd/+9vfFtJxHWLVUkpQ5pCYpx6rvUJ1PXv7slchlxsAxo8f7+VQlZ9SBr6+MF2d692QCNWKVRHKWGUZO4YJ391XX33Vy7/4xS+8zJ6wQDFiWeo7XpUutv4wRyELPV6r2nmuL6EZgBeKYJPHrbfeWkjH5rHUqEtcHm4nQu/wRqIRphBCCJGAOkwhhBAigdoq2arhPgeJPvLIIwv7LrroIi8feOCBXg5VBFWqsZgqgo9hz7MwGACnq6O+DNUS22+/vZd5/ThWOYeqDYYDwLNXLABMmTLFy+Hk+aoybWgq2VDFw4EHWMXPKvCwPi1YsMDL7GEYWxygvWrYsJ7w/6wi5qDaIY8//riXWT0PAIsWLfJynYUCYkGoeyJVgbyBYn1i9SUHIAmpUiuG7/GcOXO8zMEJQvMMqxmr6lnqc2Tva6C4zjAH0gg9f6vgusTB4IFioHiesRC2y3XevdQ6XKVKrtNWaoQphBBCJKAOUwghhEhAHaYQQgiRQLINMwbruocOHepljloCFIMVs60z5pIdswtVBT5nO1cYVYeDnacGzubzcKQPoDgt4Pzzz/fy4MGDvRwGzmYX6tmzZ3uZA2oDRXtAe12lUwMSt3ZcZxOWkxfqZZsxT2HiqEgAcMstt3j52Wefrcy7vbbgVFsiP9d77rmnsI+nQfGi4SwD1bb42DVsaPbtzoIX+WYb37Bhw7wcu9f8rNieDhSfP9vXw2heKc8unILFdj0+Pozgw4uqc/u2ySbF7qHqmlauXOllfteAot2S7bJ1bI4hsmEKIYQQTYo6TCGEECKB2ipZdrVmNSxPpwijmPTv39/LsWgOVUPoMEpGVaQHVveOGTOmcMxLL73kZXZtjk3b4LKGUWQOPvhgLx922GFe5oggYbl5usCdd97p5VAlywHAmc5Uk3a1Gi9UM7FKlmUOOh6utffkk096mSMm1YleVXZc2TGx+8ZlfeWVVwr7Xn755VbzFo0ntsYpR5RiMwBPKYvBqsiwbvI7z2tbpgZSrzOlKFTJchvGpo1YnWM17NSpU73M7RkAzJ8/38tV7VlI6nqzqarWRpqfNMIUQgghElCHKYQQQiRQez1MDhJ9yCGHePmss87y8u677x7No4XYkJm9Q8M1ATliBatK2Wt33LhxhWPYg5LVfayqDWGVLEcoAoqev3xP2Its8eLFhWM4cDbLHL0FqFbxtdeDszsRqmTZ+5gjrbB6fcaMGYVjli9f7uVYFJGq4OSNvnepnt8p22Pp6npG93TC+zZq1Cgv80IR7HEfwl6b8+bN8/Jjjz1WSPfCCy94OeYJn9J2pq5/yrMIgKLnb+gZy/A7xqpWXtsyfPdipq4UUtXMneUdrhGmEEIIkYA6TCGEECIBdZhCCCFEArVXK2G9N0eKGDlypJdTF1UN4WkYbHO84YYbCul23nlnL7OtksvGizoDwIUXXujlPffc08uhfbRqAWG21wJF+wbbEDhSx7Rp0wrH8P9st4ytniEy2KbJ9ha2/6TWu5iNj59F6oK2deyMjYg21MhIJuLj7yH7JvDUOCZ8Bryo96xZs7zMCyoDxakkTGrUHiasp3wMly9Mx+eK1Rn2xeAIPjyVhBe6DvNr9KpKdd69OtPFGI0whRBCiATUYQohhBAJJKtkw2E8T+Ng936OcJMKByMHii7LPO0iXER3//339zJH42CVbMjAgQO9fMIJJ3h57NixhXRVw/NwUVV2LWe3a1bx3njjjYVj+PpY3atoLh8njJL06quvlsocdaXRQdU7c4HlOmVtNtUrtxXdcXHqsK3jqDbLli3z8k477eTlUIX6xBNPeHny5Mle5mkk4bn4XtVZwDx2r1OnZ8SO4SkjrIblhQGa3azU3vJohCmEEEIkoA5TCCGESCCqko2ph1iVwN6KqR6FCxcu9HLoRfrQQw+VynwMUBz+V3mvcbBkoKhS5SDtsbIyofqCo8jw2pb33nuvlzn4d3hMeyP4bOiEKp7p06eXyueee66XTzvttMIx7EF78803e5nXxgQa68UX88CtE+mnM6m6D6kehqnqvzpqws4gLNfTTz/t5QceeMDLHGmMF6MAgEmTJnl5zpw5Xg4DkDeynqXeT25/gGLwdCaMPHb//fd7ee7cuV5ms8mGblbSCFMIIYRIQB2mEEIIkYA6TCGEECKB2pF+OAr9m2/+//bOPfiO4srv30ZCEhICIQmQQEggXgKBQAKBeVjCSDyMYc0uATuAcR4u71bK5a21E1dqKylvNq59eHdD4k0cQhzMYq/jXS8bgoN52UYyAvGwsMA8ZSQh0BNLSKAXFuDOH3N/7W+3bvc9v7n3d++V9P1U/arO705PT89MT/fMOafP2RzkLVu2ZPdZvXp1kDlSBNspgdi2xEsHUnsW2zQffPDBpu1hGQBmzpwZZLZ7jhgxAhbYhRqIXa0XL14cZD4nTiAL7L1UYoB+tGf1gpJdhjM/sJ2IM+NwIm8gti+NGzcuyKltmSOZsF2HlwsBsU203XtkzaxQJ0JJ6k/A//NyqDR7Bdv2WU6Tp7MNj5dbcEaOks2u05Ff2qHUlhUrVgSZl7bxcpPUhpl7/ru57CJ3Tmn2pHvvvTfIvOSNx2sgtuWyHXR/t1sy+sIUQgghDGjCFEIIIQy4kkrgoIMOChvTchy4fMGCBUGeP39+kNNoFY899liQlyxZEmRWeQCxGoePWwpIzIwdOzbIqXpu3rx5QT7xxBOb7lOCl44A8Xmwio/VMNYlBim9VlOleO+7ontxzoUTLwVS50grHLXpi1/8YrQPq2F5CdQ777wTlWP3/29961tBZnUjEEemYvU698c0CHWuXN17zNeFo2sdfvjhQU6TAfOSKu7v6fKq0aNHN5WnT58eleP9OILN7bffHmRWWwL5Z7Z0HbrR74YNGxYakLaRxx2+Hnz+aT/l+88q/VKS6E6TU8mm/YJV8qxmT58PPo/cfawb2amf1PONNjTtc/rCFEIIIQxowhRCCCEMFFWyrBprsi3I/BnP6q9UJctqipyKqtn/g4XVI6nKk9vKqoiSlyxfI/YCBoBdu3YF2drufTV/YS9UsqkaPpezkvvdFVdcEe0ze/bsIM+ZMyfIHLAfiL1A+R6lXs3sKc0qRzYlpJ7f7O3NORBLKmeWU1Ueq1fZtMAmkbSfHXXUUU2Py20DYi9XjliVeldyrtpNmzYFOZfjMaUU8ShRWw95v2PzU0ovPFmt/aKb5KK4DWXu2F4hlawQQgjRBpowhRBCCAMdUcnyZ3hJHcpqJav6stO59VjFV2orw9tS1Vgub5213fKS3ZtSv8vB9zX1+mT1JXs5sooSiD2/586dG+R0UTqraNlD9bjjjsvWzf2E+1AaFIE9cvn+pwE4eOE417Fnz54gp0kNuN3s6ZsG42YzA3tKpqppPo9OBnNI6SeVbG7cS59jvt91rsdQjgtp3fzslNptCVCQqmQ74RHeC6SSFUIIIdpAE6YQQghhQBOmEEIIYaC2DXMosS67aHd5Rp3oEtYA6UNZd6/olg2zFGGqk/ecA5AD8dKUyZMnBzld2sKwfZNtpxxxJ62D7TpscwT2DlY+QGrr5P95GQf/zks9gNgGycfNJQNIsQbZ3t9smElbgtyJoOOdDEZf1+7Jdsc6icBLWJMJ7Ctjnb4whRBCCAOaMIUQQggDZpVsPy5/6IeIOf0WNHgo6TeVbO7ad1MFbr3/Q6nO7JWqtJPq8VId3eh3QznWWesbSpVsHfrBtNUrpJIVQggh2kATphBCCGFgeOsiFUPpHVoXS/3d/PSv420m9qZFbsSmv/fKZGBVqeXKdbp/5vpgJ+pO6YcxYCiwqtat59aJ+izqb2t70mg87Zo26tzjfbFfAPrCFEIIIUxowhRCCCEMaMIUQgghDPQs0k8dm1Onk6rWsR/UsROk2Ur6YTlMHfohW0k/X7uhtN2VEvPWyeLTieUw3brm3V5W0qJckDsdPSe3v7WOofbX2Ndt04NBy0qEEEKINtCEKYQQQhgoqmSFEEIIUaEvTCGEEMKAJkwhhBDCgCZMIYQQwoAmTCGEEMKAJkweym+XAAAgAElEQVQhhBDCgCZMIYQQwoAmTCGEEMKAJkwhhBDCgCZMIYQQwoAmTCGEEMKAJkwhhBDCgCZMIYQQwoAmTCGEEMKAJkwhhBDCgCZMIYQQwoAmTCGEEMKAJkwhhBDCgCZMIYQQwoAmTCGEEMKAJkwhhBDCgCZMIYQQwoAmTCGEEMKAJkwhhBDCgCZMIYQQwoAmTCGEEMKAJkwhhBDCgCZMIYQQwoAmTCGEEMKAJkwhhBDCgCZMIYQQwoAmTCGEEMKAJkwhhBDCgCZMIYQQwoAmTCGEEMKAJkwhhBDCgCZMIYQQwoAmTCGEEMKAJkwhhBDCgCZMIYQQwoAmTCGEEMKAJkwhhBDCgCZMIYQQwoAmTCGEEMKAJkwhhBDCgCZMIYQQwoAmTHFA4Zx7zTm3MPntdufcZ51zlzjn1rbY/5hmZZxzU51zO5I/75z7Yqaecc65v3HOvdn4+6O6ddF+DznnLm/yu3PO/blzbkvj76vOOVeo50bn3Brn3E7n3D3OufG0bbxz7v80tq1xzt1YapMQ+xNdnTCdc4ucc1udcyMz21c4505pyBc6537snNvunHvbOXevc26G4Ri3O+c+2+R355z7inNuXaO+Rc65mYV6jnfOPeKc2+Wce5kH2cHW1din6UDb6lhNyo50zt3hnHvHObfROfeFZPuCRh27GnVOK7VLAACuBPADY9mrADyQ/ui9f917f+jAH4AzAfwawN2Zem4FMBrA8QDOA/Ap59w/r1kXnHNjAJwDYHGTzZ8FcC2AswDMAnA1gN/N1DMTwP8A8CkARwPYBeDrVOS/AdjT2HYTgP/equ8fiDRezHY3XnY2Oee+6Zw7lLaPcM5tds4d2hg/3k1ekC4o1H2jc+47TX6/KaljV+NF65xMPac1xti3nXOvOud+m7ad7pz7aWO83uqc+6Fz7vQW5xzOqck284tWqxc859zZzrlljfNb5pw7u9SujuK978ofqoHhAwBvAbi+yfYTAbzakC8AsAPA7wMYC2A8gK8A2ALg+BbHeR3AlCa/3wBgPYDpAIYB+FMAzxTqWQrgPwE4BMB1ALYBOLJOXY19PgPgG4M9VpOyfwrgUQBHADgNwEYAVza2TQTwNoDrAYwC8BcAnujWPd4X/gC8BmAh/T8LwHMN+RIAa1vs/48AfsdwnC8DeKSwfTOAufT/HwJ4tE5djTK/BeDezLbHAXyW/v+XuX4B4E8AfIf+PxHVBDkWwJiGfApt/xaAP+v1fe23P+5nAI4F8DxfJwALAfywIS8C8JlB1P1tADcbyv0zACsBuCbbhgNYAeALjTHsUgA7B+4tgHGoxmzX2P75geekcLxwTk22/W8AfwfgUAAXN8apmZmyvwvgFQBTGtfuRQC/19g2AsAaAH8AYGSjXWsAjOjGfe3mF+YtAJ4AcCeATzfZ/jH85i3/qwDu8t7/F+/9du/9W977fwfgKVSDR1Occ7MAbPPeN/uSOwHAEu/9Ku/9B6g6XdM3psZX7hwAX/be7/be3w3g56gms0HVRVyFJl8xhmOl3ALgP3rvt3rvXwLwP1E9GADwOwBe8N5/z3v/LoA/AnCW5cv8AOYqAPdZCjrnDgYwD8DDhuK3APibVlUm8hlt1FU6j5kAnqX/n2381rKs934lGpNk4+8D7/0KY10CgPd+HYD7Ed/fpuNBK5xzBwG4DE20HE34NKpx1DfZNgPAMQBu9d5/4L3/MYDHUGkW4L3f5r1/rbGvQ/Wxc1KL4+XGuDGoxrN/773f4b1fAuDegWNl2v1X3vu1jWv3V/jNGHcJqsn+P3vvf+W9/1qjfZe2aFtH6PaE+beNvyucc0cn268CcJ9zbjSACwF8r0kdfw9gLxtNWkdm23cBnOScO6Ux8H0a+U43E8Aq7/12+o0HhsHU1WqgbXUsrucIVJ08N/ilg91OVG+YGtDy8ItaK+YBeDa5V3vhnPswKpXlPxSKPQDg3zrnxjrnTgLwL1CpaOvUBQAfRf48DkX1Rj/A2wAOZTVXoexA+bEttokMzrnjUI1NP6OfzS9qCeehGi82tzjmNFT99a5ckcxv0Uubc24bgHcB/DUq7UOJ3DkN9kWr9II3E9WXLr8EPFeoq6N0ZcJ0zl0MYBqAv/feL0M1iN9I20cDmIvK/jK+0a4NTaraAODIwqFKg98GVKrMVwDsRqW2/INM2VYDw2DqAsoD7WAGoUNpe7OyGtAGgXNuHKo37ceNu1i/Cj4N4G7v/Y5Cmc+j6ju/APB/UamsmmlGWtblnDsTwDve+zcyRXYAOIz+PwzAjsyXR1p2oPz2FtvE3tzTmHCWoBrb/gQAnHPTARzsvX+Fyn7NObet8fdMoU7rC94tqFT8qzPbXwbwJoB/45w72FXOYvORvLR578cBOBzA5xBP+BGZcxpgsONS6QWvp2Nct74wPw3gIXor+g5itewCAI831IhbUTk4TG5Sz2QAv2x2AMPg92VUk/JxqOx7/wHAjxuTdUqrgWEwdQHlgXYwg9AO2t6srAa0wXEFgB811OoWWn4VOOcOQfUCVVShNswMN3nvJ3nvZ6J6Fp+qU5ehXS+gcvgZ4KzGby3LNgbCkajsXSsADHfOnWys60DnWu/9OO/9NO/9v/Le72783mzS+3yj7Djv/ZxCndaXtqIa33v/HipHsI+h8oP4IioN3l4vbQ1N1W0A7nLOHZWpsjSRD3ZcKr3g9XSMG/IJs/HQ3wBgfsOrcyOqr7GznHMDD2Z44Bs3ZymqgSLlBjT3AgRaD35nAfi7hl78fe/9nagcZ5rZHl8AMN05NzbZ/wWSrXVF51fjWAHv/VZUX7e5wS8d7MagctrQgNYcszrWOXcCgJHe+5dbFP1tVE5bj7So70Tn3ATn3DDn3EdRebJ+pU5daH0edwH4gnPuWOfcMagGxzszZf8WwDXOuQ83+s8fA/jHhi/BTlROT3/snBvjnLsIwMdROf4IO7XUsc65Sag+GkpfoGjcl2PQQo3vvX/Oez/fez/Be38FKifGpzLFD0L19XlsZnvpnAb7olV6wXsBwKzEnDCrUFdnGWqvIgD/FJVn7FQAk+jvJ6gMu0DlUTaV9rkYlcfW51F9ah+BajDZCeDMzHHuAnBLoR1fRqUaORrVzf9Uo75xmfJPAPhLVF+QAwPXkYOtC5WD0KoW1yh7rCZl/wzVS8MRqL6oN+A3XrJHolJPXNeo688hL9n0+r2GypvPNa7dUbTtEgBvZPb7HID/aqj/QVROWenvH0b1ljzw/4Cn9S4AywFcYa0rKXM4Kq3L8EIZh8qR7q3G31dBnpOo3to/TP/fiMrbfCcqdfF42jYewD2Nba8DuLHX97Qf/5B4Y9Pvh6Dy9h9Fvy2CwUsWlZ37DkO521E5+7QqN6sxTowG8K8BrEb1UghUjkWzUXnIHgbga43+OspyTk3KfBeV2WEMgItQ9pL9PQAvoZqcj0E1GaZesr+PSvPxOXTRS7YbHecBNCbG5PcbUKkCzgDwfJPtFzc60g4AHsAbAM7PHGOvwa9JmVGo1pBtAPAOqre0K2n7bQBuo/+Pbxx/Nypb5UJrXclxWw60LY51EyrP14H/RwK4o3HcTQC+kNS1EJV9YnejzuO70ZH2tT9UzhNPJb/9FoDlmfI/AHBVr9vdpF03oPIN6Hlb9Bfdl9fQfMK8GsD/S35bBNuE+Q8A/kmLMqNQvXAvaLLtDwHcT///BSoT2A5UXrwn0bbrG+PIDlQvZD8AMCtzzL3OqUmZ7IsW9n6ZbPWCNxvAssYY9wyA2d26r67RgJ7hnPsSgIne+y8VypwF4MeoLvKDTbafh2pSOm/oWloP59wPULVt0C7kYuho9JkJ3vv7G/8PB/C/ALznvf9Mk/JfAvDX/jd2qL6g4ayx3Xu/tNdtEa1xzn0d1QfC11sWjvcbjuoD40Tvfer00lPqntO+yPBeNwDVm9j3SwW89886564FcL5z7kfe+/ebFMuuz+wxi9DaBiW6jPc+2Gqcc4ej0mAsQ+Us0az8V7vUtEHhvX+o120Qg2I5Wox3GcajWsfYV5Nlg7rntM/R8y9MIYQQYl9AwdeFEEIIA5owhRBCCANFG6ZzTvpaEfDeZ1NCdZJ+6He8zGt/MVtYz6nfzr0b/a4f+lyvyN3vgw7Kf0/9+te/NtXNdXDdab9qHqWxM/2vTt25PqcvTCGEEMJAqy/MIFtnet4nndmtbyWWulPafRNJ6+6HN+sDlW594ZTueZ3+bv1qKzGU52utW31//6POuFznKzLdx1pHJ8fvtK5c3dZnktEXphBCCGFAE6YQQghhQBOmEEIIYaBow6yjV+6VV5O1rlwdstv0D92yYXbCU5S3dcLG3q7fQJ1jiv5gKO3cnR6XrW3N7VOyM9a5Dt26JvrCFEIIIQxowhRCCCEMDGnwdamERB261W9S1c+wYcOCPGLEiCAPH55/THKqpHTR93vvvddUfv/9OI9Au8u39MztH9RZojTUy4b4uPxMjBo1KsgTJkyI9uFtzK5du6L/N2/eHGR+Pj744IOoXJ2liZ18PvSFKYQQQhjQhCmEEEIY6Id8mHvRSe/AdusaTB2iM7TreVpStbKK6LDDDovKsTrpiCOOCPLYsWOz9bPKKKeyAmIV1LZt24K8ZcuWqNxbb70V5HfffTfIrKYCYtVUJ7zHc6jvd486KtVcrFagnuqW9+HnBoificmTJwf52GOPDfIZZ5wR7cPPDh837fcvvvhikNevXx/kdevWReW2b9/etN11vGzroC9MIYQQwoAmTCGEEMJAx1WydYJRWz+hUw+pTqqLpHrqH0rBnEueqDnGjRsXZFYZzZs3Lyp3ySWXBHnmzJlBHjNmTFSO1a25/p62e8+ePUFes2ZNkJ988smo3MMPPxzkp59+Osi//OUvo3Ksrk09CQdIVWpMqa2i96T3rs79qjOmsXc4q2AB4JprrgnyzTffHOQ5c+Y03R+wq4XZ5HD//fcH+bbbbovKPf7440HevXt30+PUTaRRel4G0BemEEIIYUATphBCCGFAE6YQQghhwLWwM/bcsNduMOq6y0pk09wb7/3Q+WsTw4cPDxffarc+5JBDgnzKKadE266++uogX3zxxUE+9dRTo3Jss2G7pcW2AcR9qJRIl+2ZqZs82yrXrl0b5J/+9KdRObblPPvss0HesGGDqa0l+i1yUDf6XT+MdYx1PCrZ+3P1jRw5Mto2adKkIH/84x8PMtv0gdiuf9RRRwWZn72DDz4424YSfH5bt24N8gsvvBCV+973vhfk7373u0HmSEEpNYO5N91JX5hCCCGEAU2YQgghhIHispJ2VTOp23+ujlRFwMsA+NM/Lcf1s2s9R1VJ3fE5ygq7MveD6klUlKLY5NQrrDZNI/hMnz49yDNmzAjycccdl6273chPad/n+liFxTIATJw4McjTpk3LtpWjq3CEosWLFwc5Vc/mohKl6FnoPZ0OpH7MMccE+bzzzou2XXnllUE+55xzgnzSSSdF5Q499NCmx+UEAm+++Wa0D0fq4XGZx3ggNo+waeTss8+Oyq1evTrIS5cuDTJHyCqZcUpzmkV1qy9MIYQQwoAmTCGEEMJAz4Kvjx49OshTp06Nts2aNaupXIq4wupVDuzLHoTp/5s2bQoyey4C7QfzlVpr6MmpUNP7xf2EVbdWk0EngjlbzRvcPu7vJ554YlSO++vGjRuDzF6FqTlCEX32P0p96cgjjwzyRRddFOTrr78+KnfVVVcFmZMTpM8Hq/R37twZ5Ndffz3IS5YsifZ56aWXgswe4WxuAIDrrrsuyKwKTk0WHPSdzRTLly+HBeXDFEIIIbqAJkwhhBDCgCZMIYQQwkDRhmnNNpLbJ7WZsE6co0tceOGFUblrr702yBwJP11WwrYp1q9zpIjHHnss2ofrWLZsWZBTF/zUpjnAUEYbEhV1steUlqLktnXadb8Opbr5efnVr34VbePlUexSn8vg0OpYjPpu/2F5JtIyvCSDx9SFCxdG5VI74QDp+M02yJdffjnI9913X5C/+c1vRvuwnwjXd/LJJ0fl2N7KS6bSjCm5JYclrP3ZtFzMdEQhhBDiAEcTphBCCGHAvKykE8HJ2U3+zDPPDPJll10WleMA2RxdwqqqGzt2bNP9gTg4MKtnOWoEAKxfvz7IvGQldbXOqftyUYhKKAD83pSiceT6Q6q+fOedd4LM7vDpfWEVf6kNufbUSZhuvecciB2I++uiRYuCvHLlSlN7Sqjf9R7rWMfleKkeAMydOzfInDg9jYSVM6OlZimOIsWq1x/+8IdB5sTmaX38fPEYDcSRiHLPIQAcfvjhQebliHzubJYA7OOvBX1hCiGEEAY0YQohhBAGOh7ph1WRHDUCiANfs7qAfwdiNWqqAh1sG1IvK/bIZdVEmkORPWg5FyFHEQJilUMpH2KufXW8Nvd3cte0BKuCVqxYEW37xje+EWRWZZ577rlROf6fvfjSQNGs1ud7ae2rrDJOg1W/8sorQX7uueeC/Pzzz0flXnzxxSCvWbMmyKx+Kqmz1df6G6v3NI9h6WqDD33oQ0HmVQmlfsH956GHHorKcf5Jzsda8sxmOFrV5ZdfHm07//zzg5zOGwyb0XieYDVuen6d7Ov6whRCCCEMaMIUQgghDGjCFEIIIQyYbZhWewhnW0jthxwxn3XWaXJc1tFbbVi57CKpizJHu+cIF+zWDMT6dtaVP/nkk1E5Xn7CtqmSvUj2IzvWfse2l7fffjvahxPXcmYPjlYCxHYZ7g9pRBHur2zrZDt42vdzfTq11/B+3AbuZwAwYsSIIA9llhXRf/C9Z9skR/MB8ktJ0v7CNkge3+6+++6oHD8fuYTNaZ/j/jx79uwgz58/PyrHzxg/K+kSkVdffTXI7FvC5VL/EUtUOiv6whRCCCEMaMIUQgghDNReVpKLasNuv6wuAOJPclZfcfSGEqWoKDlVXboPLwmYOHFitg1HH310kFkFwjIAPPHEE0F+4403gtyJINhibyz3PL2+HKmJkypv3rw5KsfJl7mfTJkyJSp3wQUXNC3HkUdK95j3SVW3HLGEVb/pszRhwoQg87KXUgJpXnpTWn4i+htWr3LEtEsuuSQqx+YnHrd27NgRleOlTLx0hCP7AHGCitz4z+MmAJx33nlB5ohu3G4gNuUxaVu5f/MSMe7b6Xhbx6yXQ1+YQgghhAFNmEIIIYSBjkT64U9yDrA+bdq0qBx7/dWJ5lPyNrV6CuZUeqmqlVULl156aZDT3HGsguYgxKtWrTK1QQwOSxDz1Esud71LeTNLaiaOTHXaaacFmT39SgGkc17lQPz8cF9jr3Ig9s4955xzgszRWTgoOxBHBOKA9KyyFv0J90ceR1kNyypYYO8xbYA0WtkjjzwS5O9///tBZk9YIB7r2Lub5TTX5vXXXx9kVs+yOQyInz02Z3GfBeJxNTU5DFBKkFEqZwnSri9MIYQQwoAmTCGEEMJAUSVrDdacC1bAn+AAcPzxxweZvQGtgcpLC1Ktat06eeb4nDigMRCrDzZt2hRkzl8olVfnqBOs3rpwmVVO7MX3iU98IirHaqfp06cHORdUfzBtYLi+VL125JFHBpm9dllVe+qpp0b7sLqNF32nAeBLz6PoDePHjw8y3+PTTz89yOx9ncL9LM1Zyfefg6+kSQd4PGcTAfdFDk4DxJ7ebIZL+xi3ic0K3/72t6NyS5YsCfL27duDXMckUydPpr4whRBCCAOaMIUQQggDmjCFEEIIA0UbZsm+kgsgzfpsDmAOxNF0cpEdgLw9Mv09Z8+qY6e02pzGjh0b/X/SSScFec6cOUHmiBSvvfZatE/ORqTlJxVW27nVLpjrG6XrzX0tXUrE/T3Xj633smR7z7Un/Z/bwEtg2NaVbuNzkM2yPyj5a5xwwglBZjvhWWedFeTUhsl9kBMScGQfIA64zv3iYx/7WFTummuuCfKsWbOaHpdtrUBse9+zZ0+QOYg6ANxzzz1Bfvjhh7Nt3bp1K5pRGv87mThdX5hCCCGEAU2YQgghhAFzpJ+SmpPVlKyGTVWy/LnP9aXuvTt37gwyuxunbs4lN2oLdT7P02Ny1A1eisC56NIg3xxlReqwvbHel07ngcypa1N1aEmta6m79Hudc2KVLPfPUlSqkklE9Aa+97zsDoiXj5x77rlBTsdEhvPAcpIIVn8CwMqVK4PM41YatYeXknBUq/fffz/I6Vi3YsWKIHP+2WeffTYqx4HeObpPmg/TMjYMpSlLX5hCCCGEAU2YQgghhIGOBF9nlSxH80mDAedUqGnkifXr1wd527ZtQeYcmkCsjqirhhss6XHY85e9Etlj9vnnn4/2YTVDeu5iaGk3OlCnqeMFbEXq/v6H7zGrzNkLFYijObFnPpP2JQ6ezrkjOUkEEHue8hi9cePGqNyyZcuCzCpj9n5NvVqffvrpILMaNl05kOa9HGAo1at1ViXoC1MIIYQwoAlTCCGEMKAJUwghhDBgtmGWIuGw3psT4PIyEiBOqsuu+uz+DACvv/56kDlJ6KRJk6JyHP2+3SUmVlK7EB+XXa3PPvvsIKc2A84OULJhdjJCxYFEJ64Vu8qn2WZ4Gy+JKi3V6PQSGAulutWfekN6T9huOXXq1CDfcsstUTle4sHZk3JJz4E4e9K6deuCzMvagDhDyQMPPBBkjrgDxOMbR3Tj5yO1TXLd3NbSfGKN2tbpbEWWZ1FfmEIIIYQBTZhCCCGEgdrLSizBpOtGbGE1FycJZfflUv2lT/o6n/vWRNWsguZA1xw4GQBWr14dZD4/6zKATqgpcvfpQFbVcb/bsmVLkFetWhWV4+VNrB7jJUZ1A+m3q6K1LlPp1rIZEZOaqTga2k033RTkyy+/PCrHiZhZ9c/3MR0fOUn4iy++GOR0CUdu3GHTAxCreNlUxn0ujdrWbtSuToxH7baB0RemEEIIYUATphBCCGGgtko259XEcuo1WPLoYthzlPOmTZgwISrH3lkcVYgjD7FnbtruEnW8tjjY9bRp04LMAY0B4Oc//3mQSx6znVQlpCgKTPlepv0mt19OtZ3WXervTJ28rrm2pfvzPS/df3lnDx2crAEAPvrRjwb52muvDXK6IiDXH3nMWL58ebTtRz/6UZB/8YtfBDm999b7zerWVPV6oKAvTCGEEMKAJkwhhBDCgCZMIYQQwkBHspUwnci+wLZJTkDKrsxArJfnBKvsql2KDjRixIgg17ExAfE5sc2Wj8NRMQDgsMMOCzJHCuKoGM2OlUM2p3qk14ptO5wlh6OkALHdmZOd8z3vdBaSTiSatpZTH4rJRfPipR4pPG5xZB1+9gFg+vTpQeZMTzw2pfD94cxHqQ2TM4dwfy7VJ8roC1MIIYQwoAlTCCGEMNCRSD+syioFreako0y6/ISXZ3B9nMAUiFUOnKR55syZQT7ttNOifWbMmBFkdvHmpShArKItqTwtEYHS5TCshuHoGW+88UZUjtV9fI27qU47kCLCcF/bvHlzkFeuXBmV4+QAvGQoVf8zVhWqNVC05Tgl80Fp+YlUdDETJ04MMidyXrBgQZDTa3j//fcHmQOSp8mfp0yZEuTcmAPE94T7KSd//tnPfhbtw9GqDtRlIJ1GX5hCCCGEAU2YQgghhIGOeMmydyd7Y3FgcSD2ImSVA6tggVhlefHFFwd58eLFUTlWlbGa7JlnngnyySefHO1z5ZVXBvkjH/lIkDmgdkqdgNbsWccqYiBWqXDA7p/85CdRuZdffjnIHNGjboBjxupZeyCp5/hc2ZyQBqvm/K18L0rq63ZV26XIPDkPb+t9TctxfYoIFXuvXnbZZUHmYOmpWenUU08NMnvMpuYZHt+s151NNbxSYOnSpVG5t956K8jWMexAet7roC9MIYQQwoAmTCGEEMKAWSVb8tpiTy0OLM6qCCBetDtu3LggjxkzJirH+SNvuOGGIJ955plRueeeey7InLOQVWapupeDGLMKJFVrtRsEm+vj3JgAMG/evCAfe+yxQeaA7QCwaNGiILN6NvWmTVWGg+VA8oS1wvc/7Rs5Fag1uUAd6tRXemZLqrcDUQ3LKtXU5MH5azmvJJuf0uAkc+fODfLs2bObHgeIx6eSSpaf8cceeyzIt956a5DTvK1pIJQcUsPa0RemEEIIYUATphBCCGFAE6YQQghhoGjDzCXHTWFdOQdI5wgXQBx1h+2ZqV5/5MiRQeZIGLwEA4iDrLMLNS/bSGFbA9sW60Y7sZTj8wHiJSd8DrzsBoijgvCSk4cffjgqxxGP2H7L55TaRKw2Wtk3ytGdmJKdMXcvrNF4Sm2yRJsq1a1IP2W7LQdP52eNI49dccUV0T78zLOd0mpXTsvxUpK1a9cGmZfW7dmzJ1u3nvHOoC9MIYQQwoAmTCGEEMKAeVlJ6bOdVQEctJqXmADxshBWyaaqSFYfcF44DoIMxCrakhqWYfVvqgrOtYEpqW5y+6S/s6qMg76nS2BYfcznx2ohAFizZk2QWSVbB6nn9qZObss6uUw7wYG4JKQTlJYR8fjGy7vuvffeILPpCIgjjPGyubrPEydp4OUjbIrSvR969IUphBBCGNCEKYQQQhgoqmStn/i5PIJp/koOcM4Rd6ZOnRqVY1VrLjIPEHubslzHOyzN3cn/83VIo4Bwm7itrEoueU/yNt4n3ca5O1NvYVYtW6O5WFEUoHreqnXUuHXblKsvNXWwVzir+zdu3BiVa1etv6+TXk9+/tetWxdkjrgzf/78aB9W0aaRzErHGiAdj9avXx9kVsmy96ySKAw9+sIUQgghDGjCFEIIIQxowhRCCCEMdCSBNOv42f6RLn+45557gsw6eY7sD8TLTyZNmhRktlMCef1/yWbIbWU7QZrsmu06nLy5tESEbRXc7tTmaLVhsd2BbaepbTlnzyrZcq22N9kw98YSnaWO3ROolwyaj8V2dI4UBQALFy4MMtu+0r7PGTkOFPi6p34KfH3ZXxLssGwAAAWPSURBVICj+aTPON+HUoJmfpbZF4SjCwFxBLW33367ad0lZMPsDPrCFEIIIQxowhRCCCEMdEQly7A6gxOsAsBTTz0VZA7M/vTTT0flZsyYEWROvsrRgYD8cgqraoxVUWlS5lyi2FRdM2rUqCBzMugLL7ywqQzE6htWBe3evTsqx21aunRpkNm1HYiD3+ciltSNAiJVjj2BdCfqrkOu76cmDF5mwnJaTsTw9eUxZ9y4cUGePn16tA+PCyVYNc7L8O68886oHD//bM7KtTOlNCYqQpAdfWEKIYQQBjRhCiGEEAbMKtk6QbnTMjt27AgyBzROVbccTHz58uVBTvNKWjxCS+0seaWxlyy3Na0vp6Lhc0i9hY844oim+7N6BojV1i+99FKQX3311ahcul+urYz1GvXCS7bd3H11989dk5JnYx2Vdena1zEtWPYHYvWv9f4fKFjVkjkv2TRxQq7uNGcljxN33HFHkDn/LQBs2LAhyOzdX6ePSAVbH31hCiGEEAY0YQohhBAGNGEKIYQQBjqSQNoK21BYD79169aoHNsT165dG+SSHTWny0/19dZlF7xUgynZCbjdfE5siwRiewfXl9o3ODks18eRh4C9l7oMUCeJcb/bsyx2mtIyEM4IM3r06KgcR2rKRXcBYvs239vJkycHme3UzdpkoXSuFntrur/VxrUv9YehoHTdcktJOBE8EEf6YdIsJPxcP/PMM0HmyD7N9muGNZqXqI++MIUQQggDmjCFEEIIAx2P9FMipxJK1aG83IPlOsepu2SijlqK25pTpwLtu3Wn6j1LW+suD+mFKqeOWpF/TyNCcfJtllmFCgATJkwIMqth0/vHKnVeEsVBsVl1l1K6plbVbbv3pbQcQSrZ/LPCanxOsJAuK+H+w/Wlph5WvXLiilKChdw9KS1RKnGg3+/BoC9MIYQQwoAmTCGEEMLAkKpk08/7nDfnUB+XaVf9UNrHqipplzoq3TrqmV5R8l7OnQeryjh4PwDcfPPNQb700kuDPHXqVFN7UrNAztOag5in7c7ds5IajeuuY1pInzf2tCw9iwdiJJiSZz3DiQ+WLFkS5E9+8pNROVb3cyD2NHHC4sWLg8zRfFKP+Vxb6zzXCr5eH31hCiGEEAY0YQohhBAGXAtVj+l7P/e5X0d9Waq7mx5clsDuKb3yMMtdf5brqsMTVVVX9LXDhg0LF7LkLcgyqy853ygATJkyJcjjx48PcpqzMBecPCWnwip5uOb2KQVIb1f9n7aHkx9w8IU0v2KaiKDXeO+HvN+Vxjq+RxyQgPvSggULon1OOOGEILOqftWqVVG5Rx99NMgciL2Ox2udAP2iObk+py9MIYQQwoAmTCGEEMKAJkwhhBDCQEdsmMk+Qe6VzZHpRBs6ndB4KK+L9TrUuV7dsCUBdntSjjRYOgdcL9kp211SY00MXWpDnaVJVhs721F5qUwa2Ltby7+s9JMNMxeUn6NEAbEdne93ah/evHlzkHMJH9LjMp0e32TfrJANUwghhGgDTZhCCCGEgY6rZHuFVLKDa4Mln2JKL1Syda59J5b+tBtNpW6A9XajrrTb7sHs1y36SSXLdDqIfp1oTlLJDg1SyQohhBBtoAlTCCGEMGAOvt6PEW7abYP1nNpVlfTD9el3tVuOVLVlUVnWPddc5KBOe412Oth1PwTM35/JeSFbPZI7MXbuK6af/R19YQohhBAGNGEKIYQQBjRhCiGEEAb2m2Uldej08gNrElorQ+lKbj1mYovteaSfDtSd3dat69oPywc6XfdQ0u1lJWmkKH6W+z1zUQ4lkB4cWlYihBBCtIEmTCGEEMJAUSUrhBBCiAp9YQohhBAGNGEKIYQQBjRhCiGEEAY0YQohhBAGNGEKIYQQBjRhCiGEEAb+P7W2TuhhrBd0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "interp = ClassificationInterpretation.from_learner(cnn_model)\n",
    "interp.plot_top_losses(9, figsize=(8,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Note: The top losses come from really stylized/inverted depictions\n",
    "interp.plot_confusion_matrix(figsize=(12,12), dpi=60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Going through the confusion matrix above we can see the main culprits of the losses namely \"I\" & \"L\" or \"G\" & \"Q\" etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To use the model in prod, we will export it\n",
    "cnn_model.export(\"model1.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "defaults.device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Alphabet classes\n",
    "classes = ['A',\n",
    " 'B',\n",
    " 'C',\n",
    " 'D',\n",
    " 'E',\n",
    " 'F',\n",
    " 'G',\n",
    " 'H',\n",
    " 'I',\n",
    " 'J',\n",
    " 'K',\n",
    " 'L',\n",
    " 'M',\n",
    " 'N',\n",
    " 'O',\n",
    " 'P',\n",
    " 'Q',\n",
    " 'R',\n",
    " 'S',\n",
    " 'T',\n",
    " 'U',\n",
    " 'V',\n",
    " 'W',\n",
    " 'X',\n",
    " 'Y',\n",
    " 'Z']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "learner = load_learner(path=path, file='model1.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/anaconda/envs/fastai/lib/python3.6/site-packages/torch/nn/functional.py:2854: UserWarning: The default behavior for interpolate/upsample with float scale_factor will change in 1.6.0 to align with other frameworks/libraries, and use scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details. \n",
      "  warnings.warn(\"The default behavior for interpolate/upsample with float scale_factor will change \"\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "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\n",
      "image/png": "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\n",
      "text/plain": [
       "Image (3, 400, 400)"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here we test our model on a custom test image\n",
    "img = open_image(root_path/'e.png').resize(40)\n",
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_class, pred_idx, outputs = learner.predict(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 E\n"
     ]
    }
   ],
   "source": [
    "idx = pred_idx.item()\n",
    "alphabet = classes[idx]\n",
    "print(idx, alphabet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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