Handwriting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resources:\n",
    "\n",
    "http://forums.fast.ai/t/fastai-for-image-captioning/17003/8\n",
    "\n",
    "https://github.com/githubharald/SimpleHTR/blob/master/src/DataLoader.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T21:05:54.671793Z",
     "start_time": "2018-08-10T21:05:54.405046Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%reload_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T21:05:57.240933Z",
     "start_time": "2018-08-10T21:05:54.769737Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from fastai.fastai.conv_learner import *\n",
    "from fastai.fastai.text import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T21:05:57.357452Z",
     "start_time": "2018-08-10T21:05:57.305449Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "PATH = Path('data/IAM_handwriting')\n",
    "TMP_PATH = PATH/'tmp'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T21:05:57.597805Z",
     "start_time": "2018-08-10T21:05:57.419361Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mascii\u001b[m\u001b[m           line_labels.csv \u001b[34mmodels\u001b[m\u001b[m          \u001b[34mtmp\u001b[m\u001b[m             \u001b[34mwords\u001b[m\u001b[m\r\n",
      "\u001b[34mforms\u001b[m\u001b[m           \u001b[34mlines\u001b[m\u001b[m           \u001b[34msentences\u001b[m\u001b[m       word_labels.csv words.csv\r\n"
     ]
    }
   ],
   "source": [
    "!ls {PATH}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T21:05:57.723275Z",
     "start_time": "2018-08-10T21:05:57.668983Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "\n",
    "def open_image(fname):\n",
    "    f = fname if fname.endswith('.png') else fname+'.png'\n",
    "    return Image.open(f'{PATH}/words/{f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-08T20:50:34.465668Z",
     "start_time": "2018-08-08T20:50:34.384232Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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auue2ShDZgqT+/bdv33YTOIK6o1c0hQ6ZiLgLkQJMNUBFb99AzFkAEzOGkkow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\nc6+Hxw0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=L size=590x99 at 0x132CEB390>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open_image('p06-047-08-00')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Explore Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-02T15:30:23.331089Z",
     "start_time": "2018-08-02T15:30:22.795526Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# https://github.com/githubharald/SimpleHTR/blob/master/src/Model.py\n",
    "    \n",
    "imgSize = (128, 32)\n",
    "maxTextLen = 32\n",
    "samples = []\n",
    "chars = set()\n",
    "\n",
    "f=open(f'{PATH}/ascii/words.txt')\n",
    "for line in f:\n",
    "    # ignore comment line\n",
    "    if not line or line[0]=='#':\n",
    "        continue\n",
    "\n",
    "    lineSplit = line.strip().split(' ')\n",
    "    assert len(lineSplit) >= 9\n",
    "\n",
    "    fileName = lineSplit[0]\n",
    "\n",
    "    # GT text are columns starting at 9\n",
    "    gtText = ''.join(lineSplit[8:])[:maxTextLen]\n",
    "    chars = chars.union(set(list(gtText)))\n",
    "\n",
    "    # put sample into list\n",
    "    samples.append([fileName, gtText])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-02T15:30:24.732447Z",
     "start_time": "2018-08-02T15:30:24.228159Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(115320, 2)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.stack(samples)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-01T15:57:51.902986Z",
     "start_time": "2018-08-01T15:57:51.852599Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['A', 'MOVE', 'to', ..., 'the', 'garden', '?'],\n",
       "      dtype='<U32')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-02T15:30:29.322661Z",
     "start_time": "2018-08-02T15:30:29.273816Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(chars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### DataFrame (word images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T22:35:15.765984Z",
     "start_time": "2018-07-31T22:35:15.568520Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "word_labels = pd.read_csv(f'{PATH}/ascii/words.txt', delim_whitespace=True, comment=\"#\", header=None, usecols=range(0, 9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T22:35:16.664869Z",
     "start_time": "2018-07-31T22:35:16.615827Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "word_labels.columns = ['filename','result','graylevel','x','y','w','h','tag','value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T22:35:20.706228Z",
     "start_time": "2018-07-31T22:35:20.648668Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>result</th>\n",
       "      <th>graylevel</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>w</th>\n",
       "      <th>h</th>\n",
       "      <th>tag</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>75599</th>\n",
       "      <td>r06-143-04-06</td>\n",
       "      <td>ok</td>\n",
       "      <td>186</td>\n",
       "      <td>1323</td>\n",
       "      <td>1428</td>\n",
       "      <td>64</td>\n",
       "      <td>77</td>\n",
       "      <td>PP3O</td>\n",
       "      <td>him</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75600</th>\n",
       "      <td>r06-143-04-07</td>\n",
       "      <td>ok</td>\n",
       "      <td>186</td>\n",
       "      <td>1458</td>\n",
       "      <td>1469</td>\n",
       "      <td>36</td>\n",
       "      <td>52</td>\n",
       "      <td>IN</td>\n",
       "      <td>in</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75601</th>\n",
       "      <td>r06-143-04-08</td>\n",
       "      <td>ok</td>\n",
       "      <td>186</td>\n",
       "      <td>1532</td>\n",
       "      <td>1445</td>\n",
       "      <td>73</td>\n",
       "      <td>72</td>\n",
       "      <td>ATI</td>\n",
       "      <td>the</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75602</th>\n",
       "      <td>r06-143-04-09</td>\n",
       "      <td>ok</td>\n",
       "      <td>186</td>\n",
       "      <td>1654</td>\n",
       "      <td>1444</td>\n",
       "      <td>199</td>\n",
       "      <td>114</td>\n",
       "      <td>NN</td>\n",
       "      <td>garden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75603</th>\n",
       "      <td>r06-143-04-10</td>\n",
       "      <td>ok</td>\n",
       "      <td>186</td>\n",
       "      <td>1892</td>\n",
       "      <td>1453</td>\n",
       "      <td>36</td>\n",
       "      <td>66</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            filename result  graylevel     x     y    w    h   tag   value\n",
       "75599  r06-143-04-06     ok        186  1323  1428   64   77  PP3O     him\n",
       "75600  r06-143-04-07     ok        186  1458  1469   36   52    IN      in\n",
       "75601  r06-143-04-08     ok        186  1532  1445   73   72   ATI     the\n",
       "75602  r06-143-04-09     ok        186  1654  1444  199  114    NN  garden\n",
       "75603  r06-143-04-10     ok        186  1892  1453   36   66     ?       ?"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_labels.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "samp = word_labels.sample(8)\n",
    "info = {f'{PATH}words/{r.filename}.png': r.value for i,r in samp.iterrows()}\n",
    "\n",
    "fnames = list(info.keys())\n",
    "titles = list(info.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-20T21:12:26.148929Z",
     "start_time": "2018-07-20T21:12:25.665302Z"
    },
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IDMeVhBlE11H27+By/v4cHacfboT0QNz75YG4E8fbReTcbu56Im71/xG48jZF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\nFgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125a6b278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from fastai.fastai.plots import *\n",
    "\n",
    "plots_from_files(fnames, titles=titles, maintitle=\"Handwriting Examples\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### check errors -> look OK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T22:36:14.568207Z",
     "start_time": "2018-07-31T22:36:14.513088Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "errors = word_labels[word_labels.result == 'err']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T22:40:22.127377Z",
     "start_time": "2018-07-31T22:40:22.071113Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "samp = errors.sample(8)\n",
    "info = {f'{PATH}/words/{r.filename}.png': r.value for i,r in samp.iterrows()}\n",
    "\n",
    "fnames = list(info.keys())\n",
    "titles = list(info.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T22:40:22.897337Z",
     "start_time": "2018-07-31T22:40:22.467093Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mjI9x4hmr+ceCYbPntQBX4f4mJ+NqWn8mIq/bjrfy88BU3N/4y7im9Z8D1+EG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1273940b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from fastai.fastai.plots import *\n",
    "\n",
    "plots_from_files(fnames, titles=titles, maintitle=\"Handwriting Examples\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-23T16:49:03.530173Z",
     "start_time": "2018-07-23T16:49:03.516585Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10847, 75604)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(word_labels[word_labels.result == 'err']), len(word_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-23T16:49:21.068960Z",
     "start_time": "2018-07-23T16:49:21.052922Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64757"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove all err results\n",
    "# word_labels = word_labels[word_labels.result != 'err']\n",
    "# len(word_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Remove all rows where value count < 20 (word images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-23T16:50:02.525792Z",
     "start_time": "2018-07-23T16:50:02.146997Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9685"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort by label count\n",
    "pivot = word_labels.pivot_table(index='value', aggfunc=len).sort_values('filename', ascending=False)\n",
    "len(pivot)  # total num unique words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-23T16:50:30.911324Z",
     "start_time": "2018-07-23T16:50:30.905395Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "344"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# only keep values with counts > 20\n",
    "pivot = pivot[pivot.filename > 20]\n",
    "len(pivot)  # 344 unique words seen more than 20 times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-23T16:50:53.702369Z",
     "start_time": "2018-07-23T16:50:53.696317Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['the', ',', '.', 'of', 'to', 'and', 'a', 'in', 'that', 'was', 'is', 'for', 'he', 'had', 'be', 'with',\n",
       "       'as', 'I', 'on', 'it', 'his', 'The', \"'\", 'by', 'at', 'not', 'which', 'but', 'have', 'are', 'been',\n",
       "       'an', 'this', 'from', 'has', 'Mr.', 'were', '-', 'or', 'we', 'will', 'who', 'you', 'He', 'they',\n",
       "       'would', 'their', 'It', 'her', 'more', 'all', 'so', '!', 'than', 'being', ';', 'one', 'there', 'him',\n",
       "       'may', 'no', 'out', 'them', 'she', ':', 'In', 'first', 'man', 'when', 'said', 'about', 'up', 'A',\n",
       "       'into', 'two', 'like', 'some', 'what', 'Anglesey', 'new', 'But', 'its', 'if', 'other', 'me', 'can',\n",
       "       'very', 'Sir', 'my', 'much', 'people', '?', 'made', 'only', 'could', 'ever', 'our', 'years', 'great',\n",
       "       'over', 'This', 'told', '...', 'should', '(', 'time', 'well', 'most', 'before', 'do', 'these', 'found',\n",
       "       ')', 'under', 'year', 'now', 'even', 'And', 'must', 'life', 'per', 'back', 'God', 'any', 'then',\n",
       "       'still', 'men', 'good', 'There', 'us', 'last', 'way', 'go', 'Government', 'himself', 'here', 'We',\n",
       "       'little', 'many', 'What', '&', 'Naples', 'those', 'They', 'thought', 'make', 'such', 'against', 'went',\n",
       "       'too', 'Labour', 'upon', 'whom', 'off', 'work', 'country', 'As', 'through', 'When', 'number', 'cent',\n",
       "       'down', 'always', 'wrote', 'brought', 'because', 'long', 'going', 'same', 'One', 'did', 'German',\n",
       "       'described', 'get', 'put', 'after', 'your', 'see', 'return', 'better', 'also', 'end', 'She', 'Britain',\n",
       "       'today', 'While', 'town', 'policy', 'say', 'every', 'known', 'patient', 'place', 'how', 'without',\n",
       "       'where', 'perhaps', 'action', 'find', 'effective', 'London', 'give', 'At', 'just', 'children', 'House',\n",
       "       'later', 'Rome', 'Hahnemann', 'tremble', 'Lord', 'four', 'small', 'day', 'while', 'never', 'priest',\n",
       "       'THE', 'used', 'yesterday', 'Minister', 'home', 'Dr.', 'best', 'need', 'British', 'however', 'far',\n",
       "       'change', 'Germany', \"'s\", 'know', 'few', 'took', 'symptoms', 'high', 'Pearl', 'war', 'de', 'might',\n",
       "       'shown', 'since', 'among', 'left', 'system', 'point', 'though', 'right', 'strong', 'between', 'become',\n",
       "       'heard', 'doctor', 'seems', 'again', 'early', 'seen', 'world', 'three', 'fine', 'conference', 'For',\n",
       "       'each', 'course', 'word', 'nothing', 'got', 'yet', 'understand', 'done', 'near', 'almost', 'really',\n",
       "       'doing', 'West', 'am', 'fact', 'You', 'writing', 'part', 'own', 'past', 'both', 'days', 'Prime',\n",
       "       'talks', 'health', 'character', 'Commons', 'Northern', 'Europe', 'month', 'If', 'come', 'To', 'next',\n",
       "       'method', 'Macmillan', 'effect', 'believed', 'asked', 'By', 'cannot', 'face', 'advice', 'asking',\n",
       "       'Duke', 'success', 'quite', 'arrived', 'R.', 'death', 'Rhodesia', 'another', 'England', 'sent',\n",
       "       'returned', 'mind', 'usual', 'notice', 'France', 'full', 'given', 'having', 'aged', 'things', 'less',\n",
       "       'written', 'something', 'came', 'food', 'cure', \"don't\", 'search', 'agree', 'His', 'take', 'use',\n",
       "       'body', 'certain', 'themselves', 'young', 'week', 'Then'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value_mask = np.array(pivot.index)\n",
    "value_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-23T16:51:22.147985Z",
     "start_time": "2018-07-23T16:51:22.138215Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41383"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# apply value_mask to original csv\n",
    "word_labels = word_labels[word_labels.value.isin(value_mask)]\n",
    "len(word_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Numericalize characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-02T15:30:32.788568Z",
     "start_time": "2018-08-02T15:30:32.740103Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "itos = sorted(list(chars))\n",
    "len(itos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-02T15:30:36.036977Z",
     "start_time": "2018-08-02T15:30:35.746289Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "itos.insert(0, '_pad_')\n",
    "itos.insert(1, '_unk_')\n",
    "\n",
    "stoi = collections.defaultdict(lambda: 1, {v:k for k,v in enumerate(itos)})\n",
    "ids = np.array([np.array([stoi[letter] for letter in word]) for word in data[:,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-01T15:59:53.480220Z",
     "start_time": "2018-08-01T15:59:53.430341Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([array([28]), array([40, 42, 49, 32]), array([73, 68]), ..., array([73, 61, 58]),\n",
       "       array([60, 54, 71, 57, 58, 67]), array([27])], dtype=object)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-01T17:03:26.652479Z",
     "start_time": "2018-08-01T17:03:25.344038Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# convert to strings (as labels)\n",
    "strs = np.array([' '.join(str(l) for l in w) for w in ids])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-01T16:39:12.358714Z",
     "start_time": "2018-08-01T16:39:12.309909Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((115320,), (115320, 2), (115320,))"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids.shape, data.shape, strs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-01T17:03:32.034209Z",
     "start_time": "2018-08-01T17:03:31.984003Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# need to make strs a 2d array to allow for concatenation onto data\n",
    "z = strs.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-01T17:03:33.319378Z",
     "start_time": "2018-08-01T17:03:33.175125Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['a01-000u-00-00', 'A', '28'],\n",
       "       ['a01-000u-00-01', 'MOVE', '40 42 49 32'],\n",
       "       ['a01-000u-00-02', 'to', '73 68'],\n",
       "       ['a01-000u-00-03', 'stop', '72 73 68 69'],\n",
       "       ['a01-000u-00-04', 'Mr.', '40 71 13'],\n",
       "       ['a01-000u-00-05', 'Gaitskell', '34 54 62 73 72 64 58 65 65'],\n",
       "       ['a01-000u-00-06', 'from', '59 71 68 66'],\n",
       "       ['a01-000u-01-00', 'nominating', '67 68 66 62 67 54 73 62 67 60'],\n",
       "       ['a01-000u-01-01', 'any', '54 67 78'],\n",
       "       ['a01-000u-01-02', 'more', '66 68 71 58']],\n",
       "      dtype='<U95')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.concatenate((data, z), axis=1)\n",
    "data[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-01T17:03:47.299547Z",
     "start_time": "2018-08-01T17:03:47.174068Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>label</th>\n",
       "      <th>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a01-000u-00-00</td>\n",
       "      <td>A</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a01-000u-00-01</td>\n",
       "      <td>MOVE</td>\n",
       "      <td>40 42 49 32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a01-000u-00-02</td>\n",
       "      <td>to</td>\n",
       "      <td>73 68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a01-000u-00-03</td>\n",
       "      <td>stop</td>\n",
       "      <td>72 73 68 69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a01-000u-00-04</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>40 71 13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         filename label       values\n",
       "0  a01-000u-00-00     A           28\n",
       "1  a01-000u-00-01  MOVE  40 42 49 32\n",
       "2  a01-000u-00-02    to        73 68\n",
       "3  a01-000u-00-03  stop  72 73 68 69\n",
       "4  a01-000u-00-04   Mr.     40 71 13"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert numpy array to dataframe\n",
    "df = pd.DataFrame(data, columns=['filename', 'label', 'values'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-02T15:32:31.793460Z",
     "start_time": "2018-08-02T15:32:31.094274Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.save(TMP_PATH/'word_ids.npy', ids)\n",
    "pickle.dump(itos, open(TMP_PATH/'word_itos.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save to CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T21:06:02.178502Z",
     "start_time": "2018-08-10T21:06:01.794701Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ids = np.load(TMP_PATH/'word_ids.npy')\n",
    "itos = pickle.load(open(TMP_PATH/'word_itos.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T21:06:02.291714Z",
     "start_time": "2018-08-10T21:06:02.241772Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "CSV = f'{PATH}/words.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-06T23:34:53.002648Z",
     "start_time": "2018-08-06T23:34:52.811743Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# csv.to_csv(CSV, columns=['filename', 'values'], index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T21:06:03.102977Z",
     "start_time": "2018-08-10T21:06:02.960915Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a01-000u-00-00</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a01-000u-00-01</td>\n",
       "      <td>40 42 49 32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a01-000u-00-02</td>\n",
       "      <td>73 68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a01-000u-00-03</td>\n",
       "      <td>72 73 68 69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a01-000u-00-04</td>\n",
       "      <td>40 71 13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         filename       values\n",
       "0  a01-000u-00-00           28\n",
       "1  a01-000u-00-01  40 42 49 32\n",
       "2  a01-000u-00-02        73 68\n",
       "3  a01-000u-00-03  72 73 68 69\n",
       "4  a01-000u-00-04     40 71 13"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv = pd.read_csv(CSV)\n",
    "csv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T21:06:03.999665Z",
     "start_time": "2018-08-10T21:06:03.945420Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "115318"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(csv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get val_idxs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T21:06:06.795868Z",
     "start_time": "2018-08-10T21:06:06.737736Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17298"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_idxs = np.array(csv.sample(frac=0.15).index)\n",
    "len(val_idxs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocess (rotate, normalize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T21:06:59.228078Z",
     "start_time": "2018-08-10T21:06:59.179459Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f = resnet34\n",
    "sz = 64\n",
    "bs = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T21:07:00.464389Z",
     "start_time": "2018-08-10T21:06:59.405112Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "aug_tfms = [RandomRotate(5),  # transform type Classification\n",
    "            RandomLighting(0.05, 0.05)]\n",
    "\n",
    "tfms = tfms_from_model(f, sz, crop_type=CropType.NO, aug_tfms=aug_tfms)\n",
    "data = ImageClassifierData.from_csv(PATH, 'words', CSV, bs=bs, val_idxs=val_idxs,\n",
    "                                    tfms=tfms, suffix='.png', continuous=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T21:07:00.592166Z",
     "start_time": "2018-08-10T21:07:00.541640Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# pad ends of lines with pad token for language model\n",
    "data.aug_dl.pre_pad=False\n",
    "data.trn_dl.pre_pad=False\n",
    "data.val_dl.pre_pad=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T21:07:02.224652Z",
     "start_time": "2018-08-10T21:07:01.844467Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "denorm = data.trn_ds.denorm\n",
    "x,y = next(iter(data.aug_dl))\n",
    "x = denorm(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-08T20:52:34.723544Z",
     "start_time": "2018-08-08T20:52:34.673175Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 64, 64, 3)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-08T20:52:36.076596Z",
     "start_time": "2018-08-08T20:52:36.025102Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([50, 11])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### View image transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-06T22:59:38.425182Z",
     "start_time": "2018-08-06T22:59:38.376029Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def label_text(pred):\n",
    "    ints = to_np(pred).astype(int)\n",
    "    ints = np.trim_zeros(ints)   # remove padding (0)\n",
    "    return ''.join([itos[i] for i in ints])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-01T16:57:09.785299Z",
     "start_time": "2018-08-01T16:57:09.732540Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def show_img(im, figsize=None, ax=None, alpha=None, title=None):\n",
    "    if not ax: fig,ax = plt.subplots(figsize=figsize)\n",
    "    ax.imshow(im, alpha=alpha)\n",
    "    ax.set_axis_off()\n",
    "    if title: ax.set_title(title)\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-01T17:06:38.167108Z",
     "start_time": "2018-08-01T17:06:37.857669Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ljv/Bp9NpFiIZuDU2NsamT70QvIztmCt8c/v2bTQ2NrLJ880332TqpCcdLI5CoaBZTy5f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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1562de0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(4,1, figsize=(10, 10))\n",
    "for i,ax in enumerate(axes.flat):\n",
    "    t = label_text(y[i])\n",
    "#     t = data.classes[y[i]]\n",
    "    ax=show_img(x[i], ax=ax, title=t)\n",
    "    \n",
    "plt.tight_layout(pad=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-06T21:26:42.878340Z",
     "start_time": "2018-08-06T21:26:42.737851Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## taken from Translation notebook\n",
    "\n",
    "class Seq2SeqRNN(nn.Module):\n",
    "    def __init__(self, pre_enc, itos_enc, em_sz_enc, pre_dec, itos_dec, em_sz_dec, nh, out_sl, nl=2):\n",
    "        super().__init__()\n",
    "        self.nl,self.nh,self.out_sl = nl,nh,out_sl   #=> 2, 256, 30\n",
    "        self.emb_enc = nn.Embedding.from_pretrained(pre_enc) #=> Embedding(24793, 300)\n",
    "        # self.emb_enc = create_emb(vecs_enc, itos_enc, em_sz_enc)\n",
    "        self.emb_enc_drop = nn.Dropout(0.15)\n",
    "        self.gru_enc = nn.GRU(em_sz_enc, nh, num_layers=nl, dropout=0.25) #=> ([300, 256, 2])\n",
    "        self.out_enc = nn.Linear(nh, em_sz_dec, bias=False)\n",
    "        \n",
    "        self.emb_dec = nn.Embedding.from_pretrained(pre_dec) #=> Embedding(17573, 300)\n",
    "        # self.emb_dec = create_emb(vecs_dec, itos_dec, em_sz_dec)\n",
    "        self.gru_dec = nn.GRU(em_sz_dec, em_sz_dec, num_layers=nl, dropout=0.1) #=> ([300, 300, 2])\n",
    "        self.out_drop = nn.Dropout(0.35)\n",
    "        self.out = nn.Linear(em_sz_dec, len(itos_dec)) #=> ([300, 17573])\n",
    "        self.out.weight.data = self.emb_dec.weight.data\n",
    "        \n",
    "    def forward(self, inp):        \n",
    "        ### Encoder ###\n",
    "        sl,bs = inp.size() #=> ([30, 125])\n",
    "        # initialize previous hidden state - _bos_ tokens\n",
    "        h = self.initHidden(bs) #=> ([2, 125, 256])\n",
    "        # get input vectors from embedding and apply dropout\n",
    "        emb = self.emb_enc_drop(self.emb_enc(inp)) #=> ([30, 125, 300])\n",
    "        # run above through gru w/ previous hidden state\n",
    "        enc_out, h = self.gru_enc(emb, h) #=> ([30, 125, 256]), ([2, 125, 256])\n",
    "        # output vector to be fed to decoder\n",
    "        h = self.out_enc(h) #=> ([2, 125, 300])\n",
    "        \n",
    "        # initialize decoder input - _bos_ tokens\n",
    "        dec_inp = V(torch.zeros(bs).long()) #=> ([125])\n",
    "        res = []\n",
    "        # loop through ~30 times -> for each word in output\n",
    "        for i in range(self.out_sl):\n",
    "            # .unsqueeze(0) - add leading unit axis -> treat as a sequence of length 1\n",
    "            # gru works on an entire sequence at a time but we're iterating through each part separately...\n",
    "            # not really taking advantage of rnn at all; could re-write using linear layer\n",
    "            # dec_inp - input to the embedding - previous result\n",
    "            emb = self.emb_dec(dec_inp).unsqueeze(0)  # embedding => ([1, 125, 300])\n",
    "            outp, h = self.gru_dec(emb, h)            # rnn => ([1, 125, 300]),  ([2, 125, 300])\n",
    "            outp = self.out(self.out_drop(outp[0]))   # dropout, linear layer => ([125, 17571])\n",
    "            res.append(outp)\n",
    "            dec_inp = V(outp.data.max(1)[1])  #=> ([125])\n",
    "            if (dec_inp==1).all(): break    # 1: padding token => stop, we're done (padding at the end)\n",
    "        return torch.stack(res)   # stack up list of results into single tensor and return it\n",
    "    \n",
    "    def initHidden(self, bs): return V(torch.zeros(self.nl, bs, self.nh))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T17:22:04.481427Z",
     "start_time": "2018-08-10T17:22:04.357818Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# based on seq2seq translation - old version\n",
    "\n",
    "class Seq2SeqCNN_RNN(nn.Module):\n",
    "    def __init__(self, conv_model, vs, em_sz, nh, sl, nl=2):\n",
    "        super().__init__()\n",
    "        self.nl,self.nh,self.sl = nl,nh,sl   #=> 2, 128, 20\n",
    "        \n",
    "        self.shrink = nn.Conv2d(64,sl,1)\n",
    "        self.av_pool = nn.AdaptiveAvgPool2d(8)\n",
    "        self.out_enc = nn.Linear(8*8, em_sz)  # (?, 10)\n",
    "                \n",
    "        self.emb = nn.Embedding(vs, em_sz)  # ([80, 10])\n",
    "        self.emb_drop = nn.Dropout(0.15)\n",
    "        self.gru = nn.GRU(em_sz, em_sz, num_layers=sl, dropout=0.35) #=> ([10, 10, 20])\n",
    "\n",
    "        self.out = nn.Linear(em_sz, vs) #=> ([10, 80])\n",
    "        \n",
    "    def forward(self, inp):\n",
    "        ### Encoder (Conv Layer) ###        \n",
    "        conv_feats = conv_model(inp)\n",
    "        conv = self.av_pool(self.shrink(conv_feats))\n",
    "        bs,c,h,w = conv.shape  #=> ([50, 20, 8, 8])\n",
    "        \n",
    "        reshape = conv.permute(1,0,2,3).contiguous().view(c, bs, -1)  #=> ([20, 50, 64])\n",
    "       \n",
    "        # output vector to be fed to decoder  (n_layers, rest)\n",
    "        h = self.out_enc(reshape) #=> ([20, 50, 10])\n",
    "        \n",
    "        # initialize decoder input - 0s (could use teacher forcing here...)\n",
    "        dec_inp = V(torch.zeros(bs).long()) #=> ([50])\n",
    "        res = []\n",
    "        \n",
    "        ### DECODER LOOP ###    loop through ~20 times -> for each letter in output\n",
    "        for i in range(self.sl):\n",
    "            # .unsqueeze(0) - add leading unit axis -> treat as a sequence of length 1\n",
    "            # gru works on an entire sequence at a time but we're iterating through each part separately...\n",
    "            # not really taking advantage of rnn at all; could re-write using linear layer\n",
    "            # dec_inp - input to the embedding - previous result\n",
    "            emb = self.emb_drop(self.emb(dec_inp).unsqueeze(0))  # embedding => ([1, 50, 10])\n",
    "                        \n",
    "            outp, h = self.gru(emb, h)        # rnn => ([1, 50, 10]),  ([20, 50, 10])            \n",
    "            outp = self.out(outp[0])          #=> ([50, 80])\n",
    "            res.append(outp)\n",
    "            # dec_inp = V(outp.data.max(1)[1])  #=> ([50])\n",
    "            # if (dec_inp==0).all(): break      # 1: padding token => stop, we're done (padding at the end)\n",
    "        return torch.stack(res)               # stack up list of results into single tensor and return it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T16:58:39.410632Z",
     "start_time": "2018-08-10T16:58:39.057845Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([50, 64, 16, 16])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feats = conv_model(T(x).permute(0,3,1,2))\n",
    "feats.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T16:49:50.062516Z",
     "start_time": "2018-08-10T16:49:50.005338Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([20, 50, 16, 64])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://github.com/BelBES/crnn-pytorch/blob/master/models/crnn.py\n",
    "# uses inputs of 320x32\n",
    "# convert the width dimension into the sequence length; height must be 1 already!!\n",
    "\n",
    "iden_conv = nn.Conv2d(16, 20, kernel_size=1)\n",
    "\n",
    "f = feats.permute(0,3,2,1)\n",
    "f = iden_conv(f)\n",
    "f = f.permute(1, 0, 2, 3)\n",
    "f.size() # ([20, 50, 16, 64]); 16 needs to be 1\n",
    "# f = f.squeeze(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-01T23:41:02.116249Z",
     "start_time": "2018-08-01T23:41:02.066163Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feats = feats.permute(0,2,3,1).contiguous().view(-1,64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T16:58:53.738111Z",
     "start_time": "2018-08-10T16:58:53.684904Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([50, 64, 1, 16])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pool = nn.AdaptiveMaxPool2d((1,16))\n",
    "res = pool(feats)\n",
    "res.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-01T23:41:04.457174Z",
     "start_time": "2018-08-01T23:41:04.401483Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([625, 200])"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin = nn.Linear(64, 200)\n",
    "res = lin(feats)\n",
    "res.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seq2Seq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**GRU**\n",
    "\n",
    "Inputs: input, h_0\n",
    "    - **input** of shape `(seq_len, batch, input_size)`: tensor containing the features\n",
    "      of the input sequence. The input can also be a packed variable length\n",
    "      sequence. See :func:`torch.nn.utils.rnn.pack_padded_sequence`\n",
    "      for details.\n",
    "    - **h_0** of shape `(num_layers * num_directions, batch, hidden_size)`: tensor\n",
    "      containing the initial hidden state for each element in the batch.\n",
    "      Defaults to zero if not provided.\n",
    "\n",
    "Outputs: output, h_n\n",
    "    - **output** of shape `(seq_len, batch, hidden_size * num_directions)`: tensor\n",
    "      containing the output features h_t from the last layer of the GRU,\n",
    "      for each t. If a :class:`torch.nn.utils.rnn.PackedSequence` has been\n",
    "      given as the input, the output will also be a packed sequence.\n",
    "    - **h_n** of shape `(num_layers * num_directions, batch, hidden_size)`: tensor\n",
    "      containing the hidden state for `t = seq_len`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T16:10:08.021288Z",
     "start_time": "2018-08-10T16:10:07.971240Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_block_size(net):\n",
    "    return net[-1][-1].bn2.weight.size()[0]\n",
    "\n",
    "get_block_size(conv_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T21:09:53.957393Z",
     "start_time": "2018-08-10T21:09:53.162101Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vs = len(itos)\n",
    "conv_model = nn.Sequential(*children(f(True))[:5]) #=> feature map of shape: (bs, 64, 25, 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T21:09:54.093208Z",
     "start_time": "2018-08-10T21:09:54.042203Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "em_sz,nh,sl = 64,128,20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T21:19:03.858967Z",
     "start_time": "2018-08-10T21:19:03.777859Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# based on CRNN - https://github.com/BelBES/crnn-pytorch/blob/master/models/crnn.py\n",
    "\n",
    "class Seq2SeqCNN_RNN(nn.Module):\n",
    "    def __init__(self, conv_model, vs, em_sz, nh, sl, nl=2):\n",
    "        super().__init__()\n",
    "        self.nl,self.nh,self.sl = nl,nh,sl   #=> 2, 128, 20\n",
    "        \n",
    "        self.pool = nn.AdaptiveMaxPool2d((1,16)) # (h,w)\n",
    "        self.reshape = nn.Conv2d(16,sl,1)\n",
    "\n",
    "        self.gru = nn.GRU(em_sz, em_sz, num_layers=nl, dropout=0.35, bidirectional=True)\n",
    "        self.out = nn.Linear(em_sz*2, vs) #=> ([64, 80])\n",
    "        \n",
    "    def forward(self, inp):\n",
    "        ### Encoder (Conv Layer) ###\n",
    "        feats = conv_model(inp)        #=> ([50, 64, 16, 16])  (bs,c,h,w)\n",
    "        feats = self.pool(feats)       #=> ([50, 64, 1, 16])\n",
    "        feats = feats.permute(0,3,2,1) #=> ([50, 16, 1, 64])\n",
    "        feats = self.reshape(feats)    #=> ([50, 20, 1, 64])\n",
    "        feats = feats.permute(1,0,2,3).squeeze(2)\n",
    "        sl,bs,c = feats.size() #=> ([20, 50, 64])\n",
    "        \n",
    "        ### Decoder (RNN Layer) ###\n",
    "        hidden = self.initHidden(bs)\n",
    "        outp, hidden = self.gru(feats, hidden)    #=> ([20, 50, 64]),  ([2, 50, 64])            \n",
    "        return self.out(outp)                     #=> ([20, 50, 80])   \n",
    "   \n",
    "    def initHidden(self, bs): return V(torch.zeros(self.nl*2, bs, em_sz))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T21:10:00.667458Z",
     "start_time": "2018-08-10T21:10:00.609978Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# categorical cross entropy loss\n",
    "# list of probabilities for each character in vocab; target is correct character\n",
    "\n",
    "def seq2seq_loss(input, target):\n",
    "    targ = target.transpose(0,1).contiguous()  # need to switch the axes to line up\n",
    "    sl,bs = targ.size()   #=> ([13,50])\n",
    "    sl_in,bs_in,nc = input.size()  #=> ([20, 50, 80])\n",
    "        \n",
    "    # tweak 1: align sequence lengths (input is always 20 but target often is less)\n",
    "    if sl_in>sl: targ = F.pad(targ, (0,0,0,sl_in-sl)) # rank2 tensor requires 4 padding values\n",
    "    # (padLeft, padRight, padTop, padBottom) => (before 1, after 1, before 0, after 0)\n",
    "    # sequence length: add as much padding as necessary at the end\n",
    "    \n",
    "    # input = input[:sl]\n",
    "    targ = targ[:20,:].long()\n",
    "    # cross_entropy expects rank2 tensor but we have sl * bs so we need to flatten out both\n",
    "    # combination of LogSoftmax and NLLLoss\n",
    "    return F.cross_entropy(input.view(-1,nc), targ.view(-1))  #=> ([1000, 80]), ([1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T21:27:26.637896Z",
     "start_time": "2018-08-10T21:27:26.583879Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rnn = Seq2SeqCNN_RNN(conv_model, vs, em_sz, nh, sl)\n",
    "# SingleModel => way to handle learning rate groups -> treats whole thing as single group\n",
    "# easy way to turn pytorch module into fastai model\n",
    "opt_fn = partial(optim.Adam, betas=(0.7, 0.99))\n",
    "learn = RNN_Learner(data, SingleModel(to_gpu(rnn)), opt_fn=opt_fn)\n",
    "learn.crit = seq2seq_loss\n",
    "learn.clip=10.\n",
    "# learn.metrics = [accuracy]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T21:27:59.554306Z",
     "start_time": "2018-08-10T21:27:27.961904Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9852786ff8564d63b307f67a81682635",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  2%|▏         | 41/1961 [00:31<24:30,  1.31it/s, loss=3.74]"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
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      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-6a28f8f6e85b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlr_find\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart_lr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1e-4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msched\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Projects/DeepLearning/fastai/fastai/learner.py\u001b[0m in \u001b[0;36mlr_find\u001b[0;34m(self, start_lr, end_lr, wds, linear, **kwargs)\u001b[0m\n\u001b[1;32m    328\u001b[0m         \u001b[0mlayer_opt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_layer_opt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart_lr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    329\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLR_Finder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_opt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrn_dl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend_lr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinear\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlinear\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 330\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_gen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlayer_opt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    331\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'tmp'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Projects/DeepLearning/fastai/fastai/learner.py\u001b[0m in \u001b[0;36mfit_gen\u001b[0;34m(self, model, data, layer_opt, n_cycle, cycle_len, cycle_mult, cycle_save_name, best_save_name, use_clr, use_clr_beta, metrics, callbacks, use_wd_sched, norm_wds, wds_sched_mult, use_swa, swa_start, swa_eval_freq, **kwargs)\u001b[0m\n\u001b[1;32m    232\u001b[0m             \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreg_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreg_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclip\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclip\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp16\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp16\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m             \u001b[0mswa_model\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mswa_model\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0muse_swa\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mswa_start\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mswa_start\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 234\u001b[0;31m             swa_eval_freq=swa_eval_freq, **kwargs)\n\u001b[0m\u001b[1;32m    235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_layer_groups\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_layer_groups\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Projects/DeepLearning/fastai/fastai/model.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(model, data, n_epochs, opt, crit, metrics, callbacks, stepper, swa_model, swa_start, swa_eval_freq, **kwargs)\u001b[0m\n\u001b[1;32m    138\u001b[0m             \u001b[0mbatch_num\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mcb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 140\u001b[0;31m             \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_stepper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    141\u001b[0m             \u001b[0mavg_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mavg_loss\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mavg_mom\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mavg_mom\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m             \u001b[0mdebias_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mavg_loss\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mavg_mom\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mbatch_num\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Projects/DeepLearning/fastai/fastai/model.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, xs, y, epoch)\u001b[0m\n\u001b[1;32m     48\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m         \u001b[0mxtra\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mxs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mxtra\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp16\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    489\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    490\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 491\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    492\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    493\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-22-4c25c0e0a9ce>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, inp)\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0;31m### Encoder (Conv Layer) ###\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m         \u001b[0mfeats\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconv_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minp\u001b[0m\u001b[0;34m)\u001b[0m        \u001b[0;31m#=> ([50, 64, 16, 16])  (bs,c,h,w)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m         \u001b[0mfeats\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeats\u001b[0m\u001b[0;34m)\u001b[0m       \u001b[0;31m#=> ([50, 64, 1, 16])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m         \u001b[0mfeats\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfeats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpermute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#=> ([50, 16, 1, 64])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    489\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    490\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 491\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    492\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    493\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     89\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_modules\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 91\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    489\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    490\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 491\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    492\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    493\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/batchnorm.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     47\u001b[0m         return F.batch_norm(\n\u001b[1;32m     48\u001b[0m             \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrunning_mean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrunning_var\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m             self.training or not self.track_running_stats, self.momentum, self.eps)\n\u001b[0m\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mextra_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mbatch_norm\u001b[0;34m(input, running_mean, running_var, weight, bias, training, momentum, eps)\u001b[0m\n\u001b[1;32m   1192\u001b[0m     return torch.batch_norm(\n\u001b[1;32m   1193\u001b[0m         \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrunning_mean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrunning_var\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1194\u001b[0;31m         \u001b[0mtraining\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmomentum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackends\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcudnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menabled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1195\u001b[0m     )\n\u001b[1;32m   1196\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "learn.lr_find(start_lr=1e-4)\n",
    "learn.sched.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-08T21:32:44.879405Z",
     "start_time": "2018-08-08T21:32:44.830070Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lr = 2e-4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Determine size of dataset (300,125)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-20T21:51:15.162051Z",
     "start_time": "2018-07-20T21:51:10.062271Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# create a dictionary comprehension of image sizes in the dataset\n",
    "size_d = {k: PIL.Image.open(PATH+k).size for k in data.val_ds.fnames}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-20T21:51:18.162229Z",
     "start_time": "2018-07-20T21:51:18.157161Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "row_sz,col_sz = list(zip(*size_d.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-20T21:51:18.929314Z",
     "start_time": "2018-07-20T21:51:18.924390Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "row_sz = np.array(row_sz); col_sz = np.array(col_sz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-20T21:51:20.687717Z",
     "start_time": "2018-07-20T21:51:20.540199Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 2752.,  3277.,  2014.,  1023.,   406.,   168.,    49.,    18.,     4.,     3.]),\n",
       " array([   1. ,   85.1,  169.2,  253.3,  337.4,  421.5,  505.6,  589.7,  673.8,  757.9,  842. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x127bdb128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(row_sz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-20T21:51:24.235200Z",
     "start_time": "2018-07-20T21:51:24.108113Z"
    },
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  629.,  1338.,  2883.,  2678.,  1270.,   537.,   262.,    94.,    20.,     3.]),\n",
       " array([   1. ,   23.3,   45.6,   67.9,   90.2,  112.5,  134.8,  157.1,  179.4,  201.7,  224. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gElg19P5C4OBwg6q6G7j7VBaSZHdVjZ/KZyw3bpPjuT2O5/Y4Xi/bY6lPAT0BrElyUZIz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12c9dcdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(col_sz)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "heading_collapsed": true
   },
   "source": [
    "## Vanilla Resnet (word images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-25T21:41:48.012893Z",
     "start_time": "2018-07-25T21:41:47.205320Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "learn = ConvLearner.pretrained(f, data, metrics=[accuracy])\n",
    "learn.opt_fn = optim.Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-24T16:02:17.080396Z",
     "start_time": "2018-07-24T15:56:09.171060Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d2358fda87be4c38bbc3794ddbef4ded",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0%|          | 1/550 [00:01<14:01,  1.53s/it, loss=6.68]  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception in thread Thread-4:\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/adamschiller/anaconda3/lib/python3.6/threading.py\", line 916, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"/Users/adamschiller/anaconda3/lib/python3.6/site-packages/tqdm/_tqdm.py\", line 148, in run\n",
      "    for instance in self.tqdm_cls._instances:\n",
      "  File \"/Users/adamschiller/anaconda3/lib/python3.6/_weakrefset.py\", line 60, in __iter__\n",
      "    for itemref in self.data:\n",
      "RuntimeError: Set changed size during iteration\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 72%|███████▏  | 396/550 [06:06<02:22,  1.08it/s, loss=12.5]\n"
     ]
    }
   ],
   "source": [
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-24T16:05:48.019961Z",
     "start_time": "2018-07-24T16:05:47.526442Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xfSmrquOe91eRk57ETacM9jokaUVDo2P6ok0cPyCP9unRcyV1yBK+c+4m4CYA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\nAPbU9LwNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x130d2f748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-24T17:14:40.506600Z",
     "start_time": "2018-07-24T16:24:23.996492Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e5156cd8f6164528ab35d82cba00eea7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch      trn_loss   val_loss   accuracy                   \n",
      "    0      2.363408   1.935103   0.545191  \n",
      "    1      1.931388   1.610802   0.594007                   \n",
      "    2      1.669592   1.416459   0.629451                   \n",
      "    3      1.526673   1.29501    0.65555                    \n",
      "    4      1.36705    1.214377   0.676494                   \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.2143766782278789, 0.67649428085253704]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# min: 2e-4, max: 1e-2 => 1e-2/2e-4 = 50\n",
    "lr = 1e-2\n",
    "learn.fit(lr, 1, cycle_len=5, use_clr=(50,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-24T17:16:06.769450Z",
     "start_time": "2018-07-24T17:16:06.604118Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "learn.save('resnet_5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Explore Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T15:10:34.788518Z",
     "start_time": "2018-07-31T15:10:34.703954Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "line_labels = pd.read_csv(f'{PATH}/ascii/lines.txt', names=['filename','result','value'], escapechar='\\\\', delim_whitespace=True, skiprows=23, header=None, usecols=[0,1,8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T15:10:36.303388Z",
     "start_time": "2018-07-31T15:10:36.247441Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>result</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13346</th>\n",
       "      <td>r06-143-00</td>\n",
       "      <td>ok</td>\n",
       "      <td>In|the|train|going|back|to|London|I|sat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13347</th>\n",
       "      <td>r06-143-01</td>\n",
       "      <td>ok</td>\n",
       "      <td>beside|#|Catherine|.|She|had|the|stories|open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13348</th>\n",
       "      <td>r06-143-02</td>\n",
       "      <td>ok</td>\n",
       "      <td>in|front|of|her|,|but|she|said|:|'|Philip|'s|a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13349</th>\n",
       "      <td>r06-143-03</td>\n",
       "      <td>ok</td>\n",
       "      <td>I|wish|I|went|to|that|school|.|Did|you|notice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13350</th>\n",
       "      <td>r06-143-04</td>\n",
       "      <td>ok</td>\n",
       "      <td>that|girl|who|said|hullo|to|him|in|the|garden|?</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         filename result                                              value\n",
       "13346  r06-143-00     ok            In|the|train|going|back|to|London|I|sat\n",
       "13347  r06-143-01     ok      beside|#|Catherine|.|She|had|the|stories|open\n",
       "13348  r06-143-02     ok  in|front|of|her|,|but|she|said|:|'|Philip|'s|a...\n",
       "13349  r06-143-03     ok      I|wish|I|went|to|that|school|.|Did|you|notice\n",
       "13350  r06-143-04     ok    that|girl|who|said|hullo|to|him|in|the|garden|?"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line_labels.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-27T15:01:17.859962Z",
     "start_time": "2018-07-27T15:01:17.776170Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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fDn4BqKlk1jKtR0Eeqp+jRgmAbjN/ktkIilTU3UMT9dDyhX7+yscvxgsPPbji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tE1Yw21SJxhUMPEIKpgDaPceVHDpV9c55ibfHwACFFRXYRGc8mnMDFfmD5ZPDRbS+vxPXpzEfzY\ng/0kSGL6rSmRsbEwkJXqw+JZMYy0rCE2xfLSL86U0n7s+8syQ+GOOuCHdq4kx0sXE7rxfnRejahV\nSCwgN08msCn6wefWexKWjHs3XltQv2oNZrp4dimJIxp64HvrPZJDURZDUOX3lF1aCFnzviYaYkSQ\nxAlBwpnpwE57jI09z2/LnLVCNrnkUCslhcUzYt26Bwux0SZ1qsGohbqcfL0bzMilaWGxy1462Cwy\nUIOJCQxwlqJPaixD7ZFITXPlT8Kx6fLU//jgvjWJDMDyl2/dqq5MHNm+b2XT14qJKCG4cGtRy/YW\np0vTN06eu7mEvcaGP1ljHaEPg6PVLgkFWrCgikxx8fgFQ3X/1qZBpbtUSqTYKxxLZiJnasBVUude\nyCOKxshGx5QlBsMJLCCoVrFXlUV3eAwNgI9Yk3Wx1zv2zk1urN7/veXCUZKkyA6+EoKmDZtO12pa\nzYcPmFgYUkW1pQLZNjGKdRoNmSuvv3ELs0QpdQ4ttLYMJCsDA7fy8nqZRQuIwsk4ULQRYOpz9vXy\nR7IcVZLH4K1temJauJd1v0f8+kzX9j/uPzvKCC2Ptd6xOei3eCg70j1zAcATJi+3I19oUyyvKYsd\nIRKvbKN3NsUmpHztxq1/fSWGlMLSzOFOIPSrCgBhvFNRYTp5TYBDsbZfbkcTEVQBBZUFRctubVSd\n3XV/QwGSgXJmXo0d+fqL/SFAVolV79qcoBtZbckRJciTC1/OSjaon5aNbY/1KwH1fHnrppJd3u++\nXpf3W2EAu+SwPapKAHpb6KCcTopupTAmn2ThjTc7FVCrmCQLnMLHY3/qQWJt/NcuWqS6O3mDCVUL\nMmoQUQwFkYvzdd3uv/DKpJjhZ0cwhRkKdmD1PTnvzDz9+esT84wmW7m3WSTHCUEg1VdOLbmggpJW\nD/dOvTEHgNE2lj1XOKB7KcKq5z7qBhzYPcxWDJIIAjqG2hNgEnRIkBDj1Rsi2canh5pswCij2H+I\nGWnn6GREaO61aoyhyIDD87M1LR7vVcXaB50y8PWf3+R813PXjgn5/lUF3aWg6K4oL9xEl+9qGyNG\nQTQshYZjzh/8wVBOqBLr8oN5cf3uxgeJyQ786GADUiATF873xA+3NZgsYsKAl/6nWTIwPswFVd0v\nT0rCVTYSOi8G63LurakAyLb12PMDDsXMHV8g21ot6VuGgBCp/fT1T4HM4PiWfe2zR2Jz+95WbqiG\nLPHS0Y+FjS7bvc8tWqc9JichZfHLY+AH9yIJoBCTT50b+bI2AqnW9m7dOeH8aQbQ9v5+JQcBSNGk\nasSt2VZ9Wtlsk3v/wyRjjz9oZ44rmJ2PemXHwFVVqYDd+mJb0YK7dAqIlShAhXL6r04HHLj/pREL\nInfuSN2/woukK1cY+ne3rQOLyduYkql1/r0FJxRMsXO/R64z20v14ZuCdn3TisrNyKh25If7x3OL\nqqCUNCXsddv3IiVigm2MUWbIJjRoSZGge3xaKIYerXjg6eU5WKMuJFjMhBAgaXn8svTay9sMMWMy\nAawCRUC+eonZzNeRNNv2p5sbNXSbFgIaiIqcyIqKWhskGRAXqV9/iO9VcO5aWobkCLNNL793Tvhm\n99C6Z7YOeRsBlZzpnqtZ0c6kG6ePzGNdN8Z2PbXBg1E2BXCOwOAYjapYjvOnf7UYg9u4NngCSXd+\n2GatbXUvjySyVgP7Eq2PbDCakBSTdszpBwacM4K1BYYaQ9n58p3xl40Vk3ypiVSo4nju1SnOWvc9\nNoBeGdCU2PxqhswGt/zLgFnjJ8+MA0SnksBrxpwwAxAy0dmUpj58fdbYfN+1U3WUS8c2cZUjjR+p\ns2O7H2KyCQAQNCpQAlicQqQ6c6KITSZIxra8KM/cw7LfK46kwuzZYsuroGIiUonhmzpZG3vWzF7T\nYEDE0G9ee4bE9aWydFqQNRLRWoOBlVRbaO8fP33uxs2lDidmIjOyjkjwdr7565AWmGzE5iNtQLCI\nAILCNolVBZh/rQNAYka+NwxgRCV0z3exyJb/c08NgEVWV55/Y4mdGxsiYhXWmJamPgPyg1dKQN7g\nRK1olAsSU5ZrVPudG5KdnonKQGgUJGQCAGHuGAP1rfC2KQn16uclm7x/86XoGkMLi/X8q7v3FQAN\n+/U+iEhdna4di8lGmDNQYkigEOfnarNi7bs3iPq/v5XQ8pnopdn+umvccW5SNfWr1+a6As60vrca\nQQExk4p6i5+dz3w7JWeay+Pnr83XamoS3rLOijd3oAz5ekx9GQTa6xxTIxEIkkvokzciNWr0CVAw\nhC8rMNnO8cKEjEyKTf0tExbKVBcyGmyrGCLVqn9+gvqimzoeXGNr2wXkW29+Wa647+ns5AybxopW\noK8NJXwLdKbUFOs3JaMqAhCr670ymcbgw4MQjZOq7H1ygk1jZefDW5K5bMfBplUQUJi6mezy5wc9\nSAyexC2+f7FMbvSpJtYN7J7qJjO6xiF74qSG46ljJcRG5Ve/tKJFLDz1MaAsW078LUt0LYtf+dPN\nvdrtWONTfXnJrHpirCFaM1lKM0cWhmOkfMMgOqUkqCK9qzw20Umjm7f62kYyFAuZe//nK/6PHHMk\nYyHZOw89/XlQNNjYREi1NQEl9m78etkPh/quTQm2x668HwMN7RsNpw6F5Lc1vQ1d68IUGhQY2L3d\ncbeJkx8HG8kokRKf/+uTEVv3/5vlOZLcuf4B1iuiqb7s1VlrndEoCQAXDUM0vQunKlZGN/T0MKHP\nhVyaeLNGs/LRptfA0g2mse7grlFSA7VDjJFrhYlPX1xxDyXyC5AYSAEYMZmAlNTUN1Acx8b4y/c3\nMnTEGJMhimBAAcrpz3uJpK/UJoAksqDMkmLvzDtlSHaJXNF6dP/ahnjTgEAeQKT0glmUGDPHYEwS\nxxCpueH58jBHuZiaLGxk6Nn7Pjp0o7cQZqY3P75pUDg3KaVyLnJr1dUvpy7Fkj22V7ywd9CpihVN\nSFZyYUwYwBJA9+rPJ7pRBlYy1z5TurOtvvWTahF6gDVYsF69AOQJUpU7k3DVzSqeO3MASaG2STn0\npm4dnb7e2jdSyZINvWHpeJ+rhgtvXQXKtr00EAwSxpRLr6CvNOjYVy4eCoL1yI4BFLZiwKjIbXFR\nIFEV1Xr+jTeST/7ZH1/9d0dV6/cfNn0J2rveXcKJI3s93paHi13lopY+qCA5cDE4JAQyxiYyRkG+\nu0/1LUBZa8uyOGgrq43UGzDRsd59QC7SbEfIbB1jNb9ZCa2prG8tiVu5yRIIOCwAGiGxtQq579u0\ntHDx9eNLUZI12SN7PKq1QHdKJFRBwbuVqzJXg/EAAMJV5iGACt88b4wkKg6Mk3XqY1A8XbJk6/9Z\np+U2NAmlietI5FY1HRsQpNCrjk0lxfGbnGlpI1mqoHfl/V7M+jfmwvdiJX8VC7cuGSOdGsWBOkkA\ngL1LUe2y1QWqoC5cnE6Ur8fDs9B3cM+vjkiYfWV1oerucGoYtotHlZDtA+sNRwIDmgRCnOmkvnT4\nywh92/cXqnHuXHDZsr5vFsL6R3S6Jz5+x2w+RKz5zkcaVPuEKuJ7N46+VWL/2jOVmoFVZ/52IXBS\nEMp293tIzP7bt2mJQte65W0kLaVgAEBkVUQWcGQBEhmuZYaiK+5rEXhDwsiI+JuEOC/dMEqt3Tki\nkCZDUO9esT7rHF1IfvVe7ip3v3itpu0/Gbh6mKIbeiF35lt28/webh5D27h/OJGQjZrp1Ae3orj+\nrety1ZgsweSbS9i4b925w1Eyu+KFZRlRbVHrz3umb9u6vPbJGULoXf4ssnJzY4EaefKqmObmcctZ\nBl2CMp17ZV5VuDn4x2sdc0zdkzck5GtGvf2WrnswTs3adZHFZdy99G7of2YnJkM+Ekr85IpdM1sr\nrsvFGKs22u7U2f8w/OIXmob3tnM1SFZS3X3n384tU7YkoeFR9c5DnzppEliXtVqIRpVYeqc+XHpu\nnaslpmw8f/NK5GLHxjT3UQ8oTyBKDebeF2AFsj2PWLDEvbOnqHARZGbB1Wde/bI2+Z6fjnkPAqKU\n7nAo5kkkzF8sQe5b30ITbBYoA4mq829MaHI1Dh7YkBljbIgAZyYEB3+43kaF1E2Ghv50uzGEEWxE\nFSBZOH/5i/b374FwXpFjVE0iIJ4FmQzMzFSGqL3h0Z0uS0Y1KlquvMFYaC1m7qxIpbPJqqIDAJOi\ndtLClVdOB/Li3cDeB9YXuckAAXMgSGCUgCpkQauICJAUgEHcqn2Xb/UyrBsGnBI0VvzR9l8e7YaB\n3vnJP2v3V8wplpd76uTs5S5yQNu3fMv+9QWa5OoA6ojYAiuz1aTGxlu/PK/qWy/tywsE1LvkO9q8\n2EUxalHQJRGrGj0rIRkc/N6FJeh8tK5tcjCh6izN3vpsorTbn96xsNjfiF3r6mgNRa6+uFCW2fKH\nhzTHsg25Qe8AvlIebP++u2QC1UsX1pNaiUglKErkuutAUC7cD7H3xeulkH/g5eXj3amrEqavrjQ2\n5vetPsXx6LEDLjGQVtjplAvX+38C12Zrm4+0csseRCBPiFVAMf+SYgx5Pyzf4XszASgftK08EXxd\nOc/vgiLVHVOMvLi8QRLg9oZqkpmfiGrXjOWiiqAIoM5ExhBbgA3rWwOr3puuEpr8vr4CBQHoDj/s\n5pmYXN63a8BAoRwICQkyUcqrul642gNTk1+9v0kQrJJUE2cFYP2ddEH/ADBHQZ6qyWu+C4CIwaeU\nbh3pgbaWnaopH8u9SQo89cF8ZM02NP6ALiSoP7qSbJJZBeHfhFtMqBjRuTrniNo5GaS5Ytn7S1iM\nfX/tWH2Sw+eHRvtZ2cjXmmmo1SRUzrS/N6jIahEFTEyxi9AeulCpHf3BeMYC0Os2+vcP3sWu/f1L\nX/v359MOS5HslhfaZFSjxyRp7uhfz3rXt/Osgjbn35rtoUVMmO/YU2TV3SiTfh9KMTk3ak2GyUAC\nEWSJZMhYYjBgRSHJzUuomI+zACbETAh+q2jNC4tsaN2BAcgsC7CF1atGYfHSW9Nh9OC4KTneen2O\nhu4b6R6bqslvWgW3Zcm+C6ob2lcsaW6AAJKK3NC8h9mDYwWrssSFt05Z43c0T8yritu1kVisVS6v\nnxU3sCs3RIoUGHpvX6sUs93Lm6QcZTLYbMVt5eMihdD99BwnAecPbgNhANc92qsYVxbeUvxW9+mS\nZTIEKZGJcmLR+DWayBqApPXEubhu7Scg+XLHiGqE0rW3T17Ijs5H21oGitFJhUHnT9yCAYs2qRUL\nd2OImo8M4JrbVliIRGhS781X/Y9XmtQ7fYPr3i+vJmiteWpw8exhVbfxQAMhkxTmri4iuN1PDmNO\nRmc+WOrfu/Bp4vP/YfP5MxOl83v/9RpAJEqc7lhzEdGLzF3uWWkju2CU6XZopDxxlqNqwcufbYJx\nFIFEQuJ864FWZjQ2+5Ta7ZVNI8ZKpUiRF2dPf3S2otFn7uWJCE3fVPWMySIwJDBhPhoj/Tt+2F94\ndMg1OYIykRWKDGn62HylMJRbR55qK1mqlk7HK5cv3aqc5q7v0X2jg64AIFRFEQPRE3KmQuSTi6oo\nkHFMKI6nL9cJ3GoXrYFEEXJZ88SljlebOkLJhN6189cvXQnApqskfmzb9nUrnLcaM8HFC1M71xjr\n6uDYEjLENPfK4UTY/OHz/U3LIHCXcEsekzNsAUBQACDZZMDAAHFyjeE101W89eoDpMgLUx+cK6Uq\nR3f8NEvdEUBTtlmNjaqzn386W5tlT2zzXPWRjZYpQ/5qObIdcKVYgerLx433FiOois5f7cLpK2yk\n99mezYun3o1atsdfGnP6wPT/d047/7l/r3Wx2Hp9sZp8dfkqjM7EXn3sk+vTi6M/gUWmcmCsYdEJ\n2sDMmjJCwvJelv0e8fin5pmVmM4Bkh91NTQIwzdswwrGBgTpRDSQd35jgGrn3LwYWOY9qNU6B1BU\nDA4tWQWTXLPoW/HGUQTTt7KpgF/LnXgb0yVr3Wzud6mBSBGJb2d/gRRS93gNEbK+R9flJmNJEeKl\naQH/2HcnwLgjiLx0bl4VdDC6vmGYJUBBV/7yOGNjS97NuLGMAOuI8dg73aRu0wpBcyeL/JvR/STG\nmNMX24caao0yAEy910M0o8aIMMVTJxKzO9ahbMWPN2ab/+L/NqmdDx9wDrn+WvoUZO2kqNx6fAUQ\nWeDaomPAxWtkZr+U2jSfWlkIVvXkguPBrdldPJnfw/RbF3TLM/9OhJr3b3fBIpOKwNKxNycw738u\nAYDpe/tKnVpjc0uUtQ6MGCrMt2xWvQ1aWoDKjSOyM5DQGuA8JnZIik6Ctahqlj5aJGdWjXsfNaox\npOm3PD1xOiVtPLvaQCQFpALGMuxUb1yNmG3tgxSu/PqMNB550HU+6kA2dn+fAbonusHfgYIqDUIv\ncwgZpiSWsHDbNyUuhAWrmU/Rw/Dg1WOBwKx+OMutZQDb/XRC843bTPSUTEVcf/7mkhrT2uBZXUyl\nlhkkMoKKKnWY+KQ0RpU2PTfoHQbpXj1Rql22JjfwbWPnjpUchkCa6oXDvWLP+pZDiqTI4cp0/2hM\n4L2VHDGp8MKn/6ka23WeyYwOitqs7hnvyokTkm8C0toB1kbuMvTqoowggwcbRgWFYnXsP3T/i4NN\nrGMPrL3IvuDRP9qk+nHQbOjFlV6BEkjZUyK/dpvDaEx59ax/+vn3Pwlm6dPPQ0zU3P5fbMjEJimM\n0J3jg1xLd0ZRmn2xLLMcxKU6GOSbv5iPCSM17ltZaJYsGqzqSk3z6T5iMipDjVTO1eQUSGU6h/ra\n4YvXb0TIV9xLkiZ3OYWaKpMEoSp7rqElW8fFrueWO6s2aXLGcsSCWKIuytQ77y5E6zc+366dF4yJ\npD72V2VVpyoWfnD77j1tbKOIRyGiSJgyUoEYXcptlqyocECFDLk6+R8vVOqGdmUOEiJHVOmcmEvc\n4+ZYG0IqP31jshMCMCxhX7F68MmVme9DTipSdz/+TzOP/W/7WY32PCnokpl573CXTPHQ88Muia2x\nHe+cSWIClzq9lq8BEEiSqKJaFTXqxl6eOY4Lhy6viVVx9caCJpv1P/ZgJ/hBWVhbGWcMUkj1x+9O\nxGLk2ScbdUL15JN3CRxK9TubvP1p/fal7oLy5M3VLihyoFiXX/7HGTtbMSedevP6pdNdVbvuRxsz\nH1sHj3wS4qW3tmTO9D/6xZwuHHvvgOu6wfkjE59MRmMaAHNOkxnOBFBUSYhB5pfobgGePxzPby3G\nCjAdkJitM5kXJfqGbtAipPle9EC3GkMFtgEASqNQn64w8+ucEEZ0oIJJ0JMTEEXMUoar/IljZN1Q\nU5lRsn/M1f0TTDKy6R/uI2KDYAHYWcfKwtIpe9dFEhCs6bOGjRJUc0c7kq8Y+ReYJSUuP//FF4Bm\n8PuDmlCwqBZnDh9ZlPy+56IVgHUWAwHMH60YnW0MGv4DPODeQi4CejViNJWlAADXXo9ozbYsS2LL\n+Xc6aNOFpH7dHz9gONt84DUJp46PkQX52uJdlc7RHqFb+cywt0aSZgqRIFyeAYIOKqx7eshwwurz\nBGjbei99wJc+AGqUswreNYu2KKjhwJ2PfnY9QeOxNW/2kOKxWqix6ul3T6Jb8UCDRJy5l/Nt6Vgv\nmLRwYldfyEGByZAYG0EUBQGNCNdh5r0gkB9oNSAXUglEKZnfpOElismG7+szTpAC5gDKXC1cYOzf\nt0FcPf/Ge4vDa58s6muXDLceP5AZUPmOC9gznut+45ypcyZMMzfKIu97pEmpdIR17+gt1ub9g7/o\nINv2/auttWQgwtT7Ndg1fRkYcrHKZz//2RIBuqF1ZXBFE0eHp41WChYxcbR6/XrVdOLNI2sIkhJ2\nX73F2DiwEUNxp7fpn0IJTJBkBLh36QY2HxkBtGpDKno3Jh9bXryvKo0kwaYEcfLD/zy/9kdrDwVr\nRnMiDVRojJc+uJUPLQcyzQSBgO4SM1ux49MO+tXrfUKq0FQTf3dr2X1tw2R73ro6Bb/iRwdc72aH\n0a7dQdyk5EvIfPDZwLaWcUpGpmnN3pEtA5w6GaPxyx98ZC1GC5ShIqU7uQtdyDRcqY31BaEzyUAC\nj6qdMzc4YMzhgacyUrIaUWHpFJrl6ywBSaQ+0+i4bidHy7Xg1MWzX15DFcJ+9+2CDLfRwKpIxsZA\nVsr5Yx8tf355bus687vGIYcOUMzRoKKTUjj0vjg9caIOLNmmFhsTFIrapXi8063BYla0X3iwL7MU\nLSmE0CIJtpEpBCAmzTQCW0kWACyFCk7/7AtF6N8zGnOgGBnCrUvvn+5hgsaO55cbhPLI9aqMpSDk\nq1Yt27kuj1keU2YrWVw6/D/M02yNlBx5lwKUnXMfHKmh2974XIGKWqFJd2lmaK1a6MG7uzZbxGjB\nIQdnaikIastqVj11dakb5k5REJuoYcfGdu1bigSuHKlDs8mYktRn/3YqZMsPPN4HgQYUUJVUUfQr\nVbt2X1p99eJri+nSf/izlc5aGztL0+d+ftFTxUYYw5GT0gObD72we1DIyfKfXL/E9bmJdm54y4v/\nfirN/80h0/FjV67WCaCx6gcAxmsxsKZwBjDFAjCD2FUEM34Py36vGB5UiN1bi5DcyHA/q9I3JpPQ\nZqUC9t6mHxxwCrf132xYOBmNX76KhDRLKgqM1iRSCzaIAUwu8Dyi8IB2cmc8At/Bel3T1zFm5Ok2\nojIpIpABJQHpnHrb784lJZdvXudBIQF3e6c/DcY3R/8FDuDYnTnx7yeWDYWV9z+EbEgVcObNV0ox\n63+6+hi40Bx1NqqmqxcQfROqspFM+M7CxHUtohF3NERD7pIBgEuzAqaxGdSYKkxfsQLIUqz/4wfa\nIbnWOlfL4icPZ5QpfF3pl8jUF6Vk+VNrvQIhqgr3ZpcaV2sASS7ve3msya4KV14NnK1ztb2HPuCz\nAdL0B6UYP7AtU4tJIi5MH/7FVMCRB56BG2wEusKt5oPFdXDF4ysdCKRvInn5CuKtFMV8+sn/fkdh\nlK0BABXnIbFkrKrgiOPMDINZ9eSQZfUukrB1kn7TSzd7vGeKLUOZJgNgGQQageTkDDRXHPCmmnz/\nRGXb/2qZ750OCWmH1wxQv2Mf91JCWra8lVtjODFDVyQ2799kM5tqSOHi35ecrd4/f06VGlsebfik\nxpAuHr0uWbG3cIgWSpr97H+cCSRMA8Xlz59ex9K37BJXE2zQxqASO+d7vEi+sWtX5m0dW9W1EwDQ\n3Nk2Dr4lX09ERABva0Rd/CC4VWtVRIyACTOHVmzMp24mo01LPjGUcz97d7b18uNTfVdsnwHDFp12\n4/m/OhJaL6zwpEBsLMhdeBnGX7o4R2awER3VSeOtV4/B8AgrkRErncjFsu8f8DEduSxm/Z+N+gzR\nsofx9TPQ3LLFWwMm+c1PPrwhH93M0xEcutU/3t/XQDEGYizcnVlAvKUEEwHUl9PZiHGKkMhL1TtU\nJkY14y+OIBhlQM66N85ha/dKFG+6Md86Pt+9/pcP3Z+5slt+cuQiSyUZLdv2vc33Uo2z8iqfe+8Z\ny1IuxvlfHq/HkjjTThguP5AFpMSWSQ1EDXF+4urFL2Y9gFoiD2QssmqO4pZb9Uaw0dj/RH8mpCoA\nwVkjVGAU5QyjVjnYVCumLKoQL/Vmzv7qXKlu9IGX+kwAAxB7l187vRSS5v0HfrCiTVYgABpnA+HA\nn65turZmGRsTtJq7dOj4hBlZ16g0iWukTqxn3v9yIoWQb/rJdmPZMGcAdwnzDT96IoWrf/u/HjV4\nO4mX1FgCQZsUsdi/9PObWnEerPi+TcMPrrIKjoYr68qmkRQXgI7+/Y0kQxvu92qGkNEANlAMJsSv\n9AG3pH/jpguHQjySb109WFC6fvTwtVkWVDArpiqBRQqFHfrJgwO5Ahaw9ZGbS/Xc6XWi3H5i8e8m\n68kZTdbGhMYNbX9pN8CBs/O0dYsnwmhDZHCRZ5Kq33IPy36v8EIBe905RrutSJaB7mbd3P7lVI0N\nzOfV4ewB7biMPUCmsfrwmgLs6reWSBAogeHoLKghVkD1zNqZJDADD/SxM6BwR5N9dNlNzbfuKXKp\nxRm1xmmyzDHe+LefFQ2W6Jtr/nQ4GSRNibofz0eXber/7jq8d8Tilx98sPDwi/Xs9r4+nwubUM+8\n8upsbQee3Vhf7qmhNrBBnnp/NomNTrhqm++uysQMlKD5aBsNmdtqyVxEgLF2FNGGzPYisZrBLc/e\n12czDo1d/bdU6k7DRRPd14zL1eVpVrvmAYeIAVCidE7/zdJzcxFcHan17P2ZGsritQXL2Xrv7iX9\nGbpspm/WkOVbxoUlROqUp399uqx52YvPtRdt7SSr1A0/vedX867Y8pADxJiMv4cYdOyiC50LzaWY\nlblDBVR0AMxYWxRIpKALn3RBcUWbExCgB60t2H/wYvlWhGx9Q0VFLZiAADbMvltTc/+mor756heL\n2N68ogWds4FgYMRgou+sqnWthhX/i42ZBDCgIgs3wbCsb/qMc6q7C0dmorb3D304J1AMP7ksQyLV\nKPVlSfm20SBQJHX1sb+7JUwAafaNE5Mja9X7ARslxhqcsZDK+auswRQjL40T5C6EhY/mEZr37ckk\n/7a5ayMGwSL4LqfjX6aRvYNAWmNmJSzt2OCqatpXWU61cFi68tondfH4I40mcegfbXgCFl44/sqH\n2Hz0B8MNUGWPQfO70Me7lgJCnwQnDmZPv/NBWv7iIBEQmBpK09jy4n3NPMXNfd3i4e0FxKrwQDTy\nUmdh9w9HchKAlG1Z0V/Asp8ef2VKbP/6P94+4qjKoC5MBomyOxEXNBKEBDVounlG/1fjMVfxUomU\ns8qOG333L88yZLYK1rnFmiCwBQje6VBxVeOnX25eO37pysL1FG3upbnzqX1j90T204hc/63uK+bO\nfNKr5+y27481wnBjIcD1hMLiciYSTljemvrsy8lEfcWAP1JKmqzzwiSDWUrk7ztc9ledNrWeHm1I\n4LZUyVqTGA0mSDarTVFmxKI1Ou0xgcydPnbq5rRqNvKjJ/stYUjl9Nlzp692AbB/4xMP9YNRSHZ8\n3uTDx26AVDhu0WRaEkicuXbyneulbe1/WrlcxKITLix13rpi0Qa797/akltQtqrW3OVpa25YdzJ1\nPxh/YoVTBmWwAakMjQFVInL69Ni/n5TK9g9tmRl/2Tfy0GsVDdeoFvu8mclrM3n8jQvJNdc9NtaE\nzNgA6ssanRqXvlKjYo0lGXpi6qx23/14fGtaqm9MljF5SpSv/9G1n3dDMN4P/+SZlmMDCrb51MlD\naen0g2ha2P99+esqRI3iyJm+8eceHc0BNj9/eOiFZU4SKTnIuWf4ZhA09yCTfu8gRdXT88k0t1og\n4+5qzwIAgAgv1MQV9q/SPkkEAImlPCUEZrNjazUaAgu/KaYmwUITYFZSXSWyY/v7wDhQ0jsp4zSG\nFfkmWnFaWRJJWTRBIc68+gXT1Cyrb+xe31ADYFiWTn+eBLMHh/4FPOCbf3NibujxDR49GQ8G08KV\nt969hSZ/6rEWcIwDo325lbj0waFSydS5A/+d+0gBYLrbBXSb+sE6BRIBgP2H38HRl8dzqxFofOwa\n4uDYk3sHLRIIpbHNtzCeOfKMpvS17Rjl6b+bZJ9vbiZRNkCVzH3w6vni1AQrR6SRh1vAttLFkxSo\nb2de3Iv3t+fX0zZ0FJ3dQWWF9dLCsYmT0z2yg8++NBKaG09VgFT0739s6aTa9r4xw+A8mHuiE5om\nlJStXeOBtc4QJHkAMEatYeSQcRXqyGrau22dm0AGQHMUyH7jDPIS6MguY0i1EjEGgKW+PlVj8wFT\nTf/qrQXNt3xvyAZi1v79Qw3LQvdWJ/aP6KjWvs0WlDHV9eS7ZTR21Bg1GCR88HHC9ob7O9eUm275\npmaGrBg1Ld4AgRFnSEC4e/bnV5WcgsGrkzww4nJurHPCl68UXtmUceHDs+yRsgc3oWiXBLpnSjX9\nz7cFGb+lzpSJBoEkIsSZw0s0stuDktMExqw3LolwhZmCCTr37q9usT/w/KA3pVhIDIYTL332l9fU\n7fpxv03WVByy/M6WNACkWCnBVMwjhtnXXp809/3FZm81QbZ5YNH27/vR6obBDPe8fOLW5qYXdUYS\nZ9l9kbcMeRSLEbloMduBfSvXHo1mx7plWKeGs5KDwXiXzUqxNsWG1yt0x26Md0WCp8jR88Kij2jc\n6kdbLVSLAohkjC/rs70+FGOlWvbjXx6tUjjyhYOKRUCzgWXP7hkusrvJPv0eHjk8I+E//bJ/caky\nI3v37l3u0I9tu6mmd6PPorXCFCKl2ZNnPpwpnd/xRGtkevqchE8eH4koqKWqmFX/y+O915K29ixL\niYnA+JBhAlBRo1qBJjYGFKkhEYG0unni1Yu2w9K/7rEn2oaspOsn3r5Sl4ymve2x+/qty6LlPPvp\nLRzoutkytj5fNWoDUuyGuYtX3l5IUVo/eGpYwfHNpaVbn8/RrObkmnv+1XqXkAyrZQt3IWy1K3/y\nP16Knb+99dJKa8iAT7AwffrSo/cRAueEcHDwcmfRrF7bZ+IyWyn1k0yOOhxKVnPMl079x8Ug+bIH\nx0wyFWTWADswSlJ/tTzYeoSQP9r499fKDlXXKggahRy5xtiG5zZumvhIAPOxHz/R55kRophixWNn\nF8pDre+vZLL9z9jTx7tcGNPoH3o8e2oAEWDw8c3DgygOMUMFsFGYBYDvJfNwr1BWBbhViylGCXL8\nFlR8UstCUkRavzojZAUATKkkUb9+R2aQjWElAEEwt1kqEygG5O7HHbGNNU31kZCSQf564zVfQaE3\ncXKgEU0/RnKQAAjKpfPvp1W7FxeFqG9/YZBY1dDNn82rzXdu9r93rSPvb53fNtz8zhFhgJtnu373\nupZHJlIty3OfnbjcS9nQg380ZqrCMLYMcDX/wZsz0SAQNRse0ndnwloSkKL92KAj8HI7Pr/qe+fk\n8WfbqBYlW/+jV+PKFXtX55nDYMmmvqfPz6TJf+cfy4H0a0rKz/4PX6oR2tFwiEQAWk6/emnkoXUf\nsRiwjf2rc83R9OZPRfYDw5nFezgdR1fPJEFwzut14ur0zYlLERINLnviuRbl+pT59Yxfs33PlsFT\n6FJa1zCZOrk362RygpEwOJPliBA8glNAIRGDVoWEJXVusSmefriZIYMqMnAkS795FhamJF8+DMEQ\nG2BGC1WaeHuGW/vHdPrnv6rEb/+THR5l4VZ0y5/LiVAMftUI+eL/enag+X947JtvtgZYOrzOo7OW\nUW+9NwNosqaC2FjX5/6ujH74sVXHbolrDD7cD9EhqXCYmKJifHc7s5XCzBevnldtt6dEhaTvoV0e\nbXO8qMqp4yv7xZULX578aAEY/YZHG5ShhbwzM5HEtVchMBv8dtV/4oSAxbGtT5zhfM9ytWw0SxiJ\nrKJrZQqAS92lS1dfv0bFw//FWG5cXxG7vx7YojH0Pv37i1rs+ekQqEfIKiOId3O+kaMmb2pfzt36\n6N3p4r7/ZiNSbRnc5qdvLBx4cMiSA6a+Hz1xeZdjS6RRIOb0oFf1BC4AGYNobMqWr3iIU5bnwtIz\niF4igruz3YEAlK0emEn1Ra7FeyeJTUFhKVWoef/OVQ2JYEDJ+DqND11DAx6tajDFwYHWB12IXKOI\n8Y0VB7cNbbJov5El/yvY9xeHjnBn5oaB5vCLT4xkAirtfaeup1v/+Xs7mpCnFJeCOf/JiV5ZUeOx\nHw3ZrLH1rPDcpfWNYLyI+rq2q9p/U7Fp7+3XpOii2j6sxUZQRkxUZaayaEUsZGXVq2Yvf3JkIVA0\n+eYfbxkgLkP1xa/PLloA19r0wrZ25o2xVoG5uU6L8slTp/PwxdTD24oJufH51M3pugTV9sbdA51u\nOXvpyrHKL2kUUjP6yPOrMwWESIRkK/F3NPdcc/sP/tNNXvjwzNrN/ctjQ8PsiVs3i6d782awHmdb\n1Ns2OemC9NloATM7uxMAAG6ANe3YPflph6Gx4sk1rlVA6CEgIfqkSQ19pT7DKhjj7AP97741I1UU\nVLXo2u2DY+P3OV/8WXFzauSBXZv7M0IwgrnG/MH3j8Tp17J/1XZkV/107tQll2Ru4/q+YrBNhADt\nxrCSo0jqRUERtAZQ+Betgk7IsVyoRClD1Mpa/ka2QL1NDCZ2VTMT9REAUKsvjrG4R9qkpEl7LVA0\nwIqKEgjBYqB48/Najd3dLMRGy6R3Irkudi4/bSaP3J+pM5iiOrCC3Lvxq6nB7x34y2DQbBjH4GtL\nEsrZG4mzgR/0m9/z3t78RTG15cGndnz3yTlfS7GhgVVCY+3C5auvz8511Yz/ySPjLmgbtbo115bO\nZ38zzwYEDbUzk+4oj/bNYLKSZ/3Okk3mtgCF2/Gv25vb5Cw7To1H1vEo9mWqDlER2umR3n+/yNf/\nbbar8fv2BwB8ekbQs7ZtZqx4DvXlDya3fX/r6QjBGrvu2SHygloem2FqrG/fjdL39zH0Az3VVVJI\nrzVLjrfYBrZ+xa7nVjeJyK9NF/2qH69tZHILGlVrhIStIsK9EHFMR6wNFBtHScBYrD0oJTKKyAbV\nOBCBaydBGuv7TJYMYbRWFBX1N79kogJeIkERgDA9f37PLpSZC+Aa++z062/1KFvxk+3e9hY+nKV8\nxYqckyX4J+o6F07Pz+RvfIsDeCiSTIuJ5JOY6u1f95I1PjfBVjVPHpozrnnwPr1UMenw1oZTsSJo\nwpc9MKu2WRMowcR/vqHJ7dn5H7psENf+oE+jx9Etn8X6w5U7IHUOvT4dkwlF/1MrqAAGhGq+AqCV\nA1bJZPztjBtBo8qJia+937HD+xuWEpIq9wwEzLA5fEnqs+/euHqpy87t/OnyzLFfc8yG41tafYu3\njr96i7Ohn27z5BGArPuG2i9y/VOhe+UzN/X5pZ60n/yj1aQxqUEa+SPhwjsC9Y0AeWO1E3CIPchY\nmRxaox7YK1ogC8CSkUEymqgh9W0WYM30zs9TFEx22eZpY4Md6eMy5eTQpO5ppgCNB58fRCZjDAKR\ni2tffjUMNLxGQKPQ3tkqjs/0iDRrDm9bvn+F92RIhOUeRMcHXlgTryrU+fYtD6zqw6gFcrFr9i8X\nF09P71gxumpp/trVG40rgZP6tU8/NWjUtwadWp4KzkIdLVTUSGW4UYEpxoTFCqay4TVq8Ajs0aQm\nShGJTWKIceH8x0dv1iGKsaP7v7+2JRGWTl5571ztyLm1TxxYlguQBRXje4DOiF/5+ERP5qYvbWpM\nhsmOcgpikIY3HP90empxMWnPlcnalLc3v7xn0Ao3oDaGie5aaep09LH8f56oer2L77fzqt/0MDaX\nPT4OA5LHeewjD9zUFkdCD2Qr6t7+nmFj5qcPvXKxB275/ofFFhgEFYiUI0C6rQrFVbRw9dfHdz1u\nkxX0tV/X7HvzqhZ1RD+y0Ty9bLAFLSS/5d/cuL5rpJmrIFIyzigWAwfPLcr8of/z7QHX7v2nty5R\nCgMMxCV5iFGMH+7r0uDGb7/q9w5JYDsTCAItUiUkkG8IZgnRfGBE8hiBKAcAMNXxpWQa6wCtWOTC\nAAKATYSaWC0mYzhMTDH4tbsMEhi0ICZ+vc3eXPXwZRMuTPgmCFmxSFGqcPzwp/DigepmcNTa12AM\nRmuuLv7dDbatxzea38/PvHoV9KNT1/8v331uuoHCW6tWDCb0MnP0/ZmFfAkaK7/3VD8lzgZczdc/\nGHCT708q5dAzYLeiMfekGfpVmDw42L0RBUEQKQHA2Nju2//nAAC8Nw2lDBJQxmwAh5747GMK1//7\n7+3wq1u/f8G1uy9MK5OzpIFqWDr1744NPHsgTgmSuOZTK8iaWrF7Ch20nmxixK8t5fp69N/f9+Hr\nC5ZNmsAeaBCTFSMbvrd5wFvNVBrr/8uJjSuIZP7QpLT7cyBKBi3CN3ON/xaHBQDyR17MJTmtjCEE\ntaoA4gBQEdXOfhgNNtdbTZ7JGETCyL/1YqOwXR4jOkqdE387N/df78IwPx0zD72P3pj1/Q8/u6Wh\nXE193CsGHy0yypVEv7q5nK1L+lZ3PEBATZVEFdblJx90BSTNTqzMUkzV668E8Jvvz2bPLCHp+tVO\n0JLlUPXmg7djllJQmD10I0W87/nW8JwQ5TtHyWBIfQ9/uUQ335lanJyd7kgyAey6fZZEGB3ADAOa\nIUQP6dvqTKkFYDXK5aWryT211hvKhElSk1TRVW71mZJPnGTVVBSP/HRZnovm2z+cloW/XyxOnZup\nqNj53CbfuK2V7QH4rkewrNh5JcbjV7qVcrPvj55YWVjHPSeecTSiKFkjHpi8EyscrPXBKBsDQgaV\nBbNKgcQKiZADTCoBtZFQk5LVu8jiKdgsDO84tliiW+bBSO1FU1WVLq/d5qeGRIzLOQBkqq7/hf1T\ng4NkXBS0yrzhv7pw5Oy8G1u+fl1/nnmDAQBqvJc6E5dt+jdnjo32Da0bbaoXVHHGDB388tOKeUYH\nl011SqxsaaxZvuW5jU2XSGhbezbo4X1r+5RVIZFjS7UL1jtr1Ip6dLEyRNZEp3V0MwOEVFkDFc9+\neezQZKpUKRt87ODQihbWvVufvD8dwGTF8Pd2byh8ij4hOtBg0QVXmcaBC4cXA89+BFAjqtEME9L8\nKwBRmR0nEeOaK5ZtfHTUA6Sc2KEIq/PlnR82sTJ0oHf44gz1Up3XSxYHdz2xctS46HxWG1Q1VgWx\nIQHyWC+0lt3+HhlZOvP6p1MRGqt+vKPddcGh9VZTaNiEfLuOo0YbU/jiUPesWmuSWrbQ7N/89uTC\nwoIff+rBkSyLRRJXCPWvTrlTTQhsgACI8sFHThz57YH/NbDKBsAQJ0xZJYaSe376g/zgvwTH029B\necRqUQmKVu4KBdRvCkSi1lGBqQAABEQACNXkKUAzsLzJxpJQfpvPAB0kNJoYXUpoVw50GuOPjXtj\noBZCA3coYMxa21de0Wu//klmaxWxMQmXJ/+fZZem7ZWOU7tms8uJVUnDh58h+r49Durfk9nZcTkg\n8eU/YG6Wu+Bu/ndj6wCHpi9dDrUt7cie55a1CxSA1e2A5cl91etnNBWPlG9DlvcB2zvQa36r4fo7\n7aGXhqxhNebrxEexv5bMREMkMRURjAz/yfS1Sq/8u4HGQ/+n37/g/nWH/vuepk6wIGjS1b86HIdG\nCDkJYrbnfmeSNTEsLBD67RuYbO8eKF8yu6OtF2/dyqoACQh8c8WmgzuGC1DQiFx4t80mMovXT6K4\nPQ2VaK2A3ksQ+nJQRPfYGoeC5rYYjYhTpkQGmFRsWjoX1a4aL4wARrJwm7v0H3aHlc7JF08UWJvq\n2L876/ZtAtHFyGLKj3++CNnWl1c5J4BXL6Vi2W5LRpD+qfG5vTmPze3f4mZX+zKemTGMTqrjf3NL\nLJZSH9lhOHXeeb3LfvjlHeWFa4XYkYPe2ITIyhqTs7YwyByn/+6jpLjzf7d8fvtFAtPc0bKWatPc\n9tgvKjhyNKEGL9EZLJ4dzLMya2Cs6stdIb+tnRtl8y2rxwxIsiixvPza9Wz5vsJU0EDAzEdFlDI1\nX77xeSBRAytfWrFvSMkQtHe88HfzMvHXWie2xd7/cv0IkkrpCAHAQLrLmhpd5yiVKbA1o0++OFIo\nk1gXhVyQDBOQepSC2QiD1UCEgmSCQYoJwCSyaoWtsZ6JKICxEDKlaBwwQMA7mkcGoHD2kVNfLlHf\nsmZGiUU1udYoklv25ObMq6BoHsioEdPXt8HniGoRULARt29/+VtN553RbI6uf+Sf/vHWih+HI1UK\ndXldgSAl1z+26amxfgdCCMXGF/5uKZz9fz3zbEsUCTCpWjZGG2TZ2iphVCvWWI2awvzF+fN/MZpY\noNubPPnh6aWSSbA98uiOjQ1wcf74kZPXIUH/lvV+7f193ngi5ugxj8AGDTYTjP5APqwkYhKxaPPW\nqvkLGiNoQmIBtK4xsGnbfct9yxqjDc+ECFSLQH4X+Q2D+diPnjz+N725xeZwtji0fv2u8eAaqZdT\n0UTQ4FyZkdNOblkG83+QOLLl1LFfne9GaG398y0FF4kNJRFiqD0wZlznStFLxgun5nBm3DoBAIMo\nw80N9a2JmYHVa5v9Cb3LIqhV52sHBEhgf1ONZPPV/5u3j6Q9d7x1ygRQVchKJoggWQ4vNjdsXfGH\nPQnfAOROqgIURaQsIeHdeG1ufwEp15C54X0ueKvsACROTEY1K4aRFBSZmIRAFdCgWrDMmFO29c//\nMu378SAwkgraO1rOMd/xvX/f6x4af2zAGaTapN7ixM8uJB3arp8uujrfOpRZNoCc5q8wGr9re6vw\nv3de/fDUJTJrfvoHTM2evZ9xmpw+kTvTjUGE+x54bs8gOUJjaGBlV+TSf5i6yFhsfuS0T9Doy0Dx\nu8sRbnvstfE/2p6TerKgtvd7H0D0iUnFAKMXCDnhlhf+uldrb8l1v+YAHnBrXPD1AsTkA1/860Pa\nt300mRX9JTQ2/Hi5TyiA1cXzc+L3DVgPxT1Y+Kpm1Z/fun7pSnnRV3mjf3jHyjWtJgXPhDGp0T5g\nZdbJirG1o+ksIARzT9qaMwlttmGzd5GUraAICnEyCgIAYiHV129Gm63KwCGnRFYFUQj/oSl608Nv\n8NSv/FhMR39xnUZfWAGiPa85XTpc48CyF8ZJOMn865WxW/tD4UEV/0mejxNl6+/8yv4jlo9OydRn\nP3AQlj7824va2HDjhvKxUzvs7BuvzbNpv3Rfu/68ZrX7t7YUjAPgkKbO1dat1Br0xhtvJOxf/xfr\nyTx35Yt87PndRsk4oOEfLX7Rw15pjWkv2Oj8vl2Fc5ohdrSaBoJsTdux5qDfxGD3m3ULJolh6Xwx\n7weeW29qKRhABSk0UBPy8h/PXUpA+c4Hn+zLk7EmOBh+8Pp7PSkVBNqPPbPe1U5AAek25djdxhW3\n45ETF0mtG9r20q6BzIbAFi0k7FIfg5GUVyU10CkSoCfCBkTlDBjIiwEOlIHRLlFKHgGFLJaaxBIA\nIeR3c4GNsA7/2UM/r5/6QdOQUQUqgl278vzml55sJnVYCalHQElVamaGyQAYxih/gBV9dzi4r+/v\nzo1dvBINmLyP3PJH1o41LXKGyMaPvzz5Qbd3epYfta6pBCgJiir5fhPJYFZL4aIDEIK6Ux/6n7oj\nP+kY7N2qPn37Qg1RwI+tenDn6oZWvpx444NJobxvxe5Hl3ubZ5ZrtYYqiCDoDVhFIF35o9H3l3pZ\nzWBa+di+LWf/u0VVI4CebaPVv/bgyLhtZ5kBp2IU2BARqZq7mFxoEawv2uumy1PLVw0uNEy+rCHS\nc325kCFSYzSSJc2IyhwL95sXrnz31LFzGqFv6ws7TTCQZ0hSJ2rY+rY4TUHxtiexcCWKm7MWvmG7\n+j3TzDeWHbzbF76y6re9w8ayZ+8+yB8KJ2ibax56JfUfHLZoEES/qQo6QuQ8pdazGym3BhQAHF+v\nEN2YgDVMSEBYFkxiAAApA0ZBxNaT3P1e2ycUUSK5o8CmYuO+9SfK6z/3+wedUw7diY8/uBhN9r0H\nZjvO2zUPtbylkJl5nTvBhCMvrMiQf+9ia/7iVcQ/3/sHTM7YH7WuXI6a5i0EJN8eOvjU8gZ6I+pT\nNvjklY7MfayMfY8+NPy5c1neJIL43Rm5tv23/+3v/nPo9z4ghigyYAQyqKjB5PZZefV6SMBfOywS\ng1SXg3bLqROfH4v5jp+MZ2nrk6/Jqr/YYpxJgMin54Nt73QU9V46cBTzWDRX7llauNl3dU1fKyuc\nAxVktqKKiQ0wYKpu9kSbYw4gvyPxyp0wcIvdigeGbAUeDWoPLVoUjrkTYSJGiBcDkF/XalijgCYl\n65QE/yGG0//Sycudt6+s0tlTS81tf7ZdweFscpW+MRez9S/va/jIWB27YOzgo02XHCGbfxIj//tp\ndpu+TeZv7Jm/TtX10Kivf/irqbz52O6jr5Tp5s+6+ScfzKtrv/AE1W4ZO7fx4WayhBoFGqVZUHHD\nLsUrv3y3soMPfX+Vx2zrn9tVT69v5c6zAU+Nf7XtFzN5gaMbNp7+tDIb/3QkR8qYIiIBs+sbTmqM\n8rd88JIxBUYtv3yz7vvjJyA5BAZUA+gwsVeftv3XF7rnFg88PZRnnhwDSdLlP3IfzfXAmbHnnxxx\nzrJHEoLK8De0jjez5n9z+q+vmb7xZzY3Co0KzmNkIaAMDTJiQgOAKqAE6ACEha2CATCihIoqho2i\n9QApOEzRGraOhRCA7kyKmEArbGcjm9fZ9Q1ScQjK4JrbzcUtW3Ig56AJlZBRdACJhVgrNWLU6T3k\nSe4NrqK1/2Y2m3jlWsvaHXsAh3KkDKXhGZUZRv64OtrlW//x0paFVXlRCPTZZScjzNUh5VZIIAGW\nxpul0+cPnZ0d/+PCUJz82cSFaUFq50Orn1o7aD0GOXbhnStdbfcXe7+/TApPagCSI6Xm7fkKloBF\n/JAZeXj+5oJ3/dynZ/aNN3YfYdf2pvBD4xuWDbSLLEEDBUmdAichSP/4en09fvOytJcB/PB3/vwV\nivwmAMDQV/94+c0zQtwYeuDRZZjUmQySBhXvojqMyRg2BJZcrLuSQjH4L6qQ8P9XWOz7cX1z9zNt\nFCBW+KYQNMfmQ8dOxy2PD+agiVIGEHpTzLZ/ZyMDYhFACxD1H4PCtycra28CAPAAcFdZdjWN1U/d\nmClv/O2NZ/rIp5lTHx+qIjUPvNS2W7sWn13fr4RQoln8NFrte3QnMf7+FTds+Nf3OhX/BH17V06e\n+HimU5qYFc0dO9atbHkgRwHAmL5Hln59lYPmw9tfXr2g6splRXRov7sH/I3wSsnbaDkBsSBmYOL4\nD/YeulJXfY9/zecNudZMghsX+OTRG7NR+7f8dKu1MPryppv37cySIIgKLkSyawaJDN3LCWlAjeQu\nb46tN9szw6SxVZF1yKDWOlO7SJxw/gqDhRyNAcQ84b2kyH/0l538Lx7I1BixwVuTDAGYhkiy6lRN\nSos3AdT0e2MA0SvmAIDmt30xdvWTf1mFs6cximk2Ng0hoMUSEl4HN/SjPU0SBZj82RIWj20wDhDU\n3iaW+UdUZFZs+jZhc7vS9bD+/7V3Zj12XMmd/0fEOSfz3lsri/si7tTWapFq7Wq15O4WJUG92Ggv\nYxhoeFZgXgzMJ5jvYsyDBzY88GBgY8bT06vb6kXqRfvWUksiKZISyWJtN/OciJiHW5SohRJLUolU\nVf4eCll1C7ey8maeOLH941T12vd/e5533Pq18S0nf0b5pdfLYoZMHXlgM5gPP39+13f2OpmzI5jl\nIOoHBufzyb/77Xy97dbvbAwe0Dvcn9oZWIO6W3Tat6l6Nuyd2LSdr8+vxb/YP5bRCoF682FnatJk\nKoX6OV9mbL8CyM1mHz3pg229seKMVCLIKZlIsTiWDh9uFxfGNpSajTMycY2w79v7Xjh2fvzIF/eO\nUXSoOxhajZrVP+RP18Dkngfffw4XrhoAfED1AnDBgakBvOPqvJMj+cj1dwzoAwmTu979llMH3n8m\ncQUKNJ+IEpx6m9OGzfNVCIM+WWDAPLm5R/FcX/fvfv4P55qT/+cHPpa0Qjj4J9d+n+Z+/9uvRiwG\np0xe2mbxzSe/d1pt+u47+xr01V/MLpQwvuuBHVNT41VhK6d/+XeL86E38Y29MzsnUQIzWWDSHJO2\nwZndNDtzJpmcnEpNacfMqRyaSvHPx87ecYB4fGnTIHGAp5hcwaIQYuKgDmWvcGkh4Y/LPz5rmOK9\nR/e3/UAaxRbLWNLYF43sZUg9pliYbamFBnhvjRjgAEQMtt514fvL2PoNkA/84d/P/fEOUC69IgCG\nxx6nGHftZ4Mlo+wEvsyqzA9AyQe3H//Bm81rJ1/c2ew79tKLS43FrV85urnXe2T38e33jlkQ1C2k\n98XhsdM3/MlEzMk/gQbkJak2T+668+zS69bM79+6qxeZKYx0Qk19w4Py9+cDb3vkzgkuuZqn3PRV\n8PGLsD6SCCS8O/ASMXHN+zJNb59+O77/9aKPHx+ebRtOW7/84J7IQaTaQmEM2RFYAxkFnhB3XZmL\nquAGYtF7hOAKS4jB4EACivkiJTaaiCkPggR2kHsEVhCC/su/vOhfwbJZdWIyZiNyhp0yDmObSTXR\nchGS6zvzjKx/z1NPaEPg6YP3zkxWDq52iGGp6fW+emOfHD2ae/m46saj45ojiPDe4fLXPjP1n2+5\nHAngMO/SPvfX1aunW+rfdHSG4/2vnCwLC9aKTN37R9uTeLr2u+eu2VIlZyUKYOuNjZ/1uZfqU889\nn9OmI49MCjsopS96jOKuITA7h7E/uJekVxWr/myu3hGNURjU03E7uPH0+J7pvsA41pf74bkrLZ45\nbv39m8VYAtQdJcCWYghmHtuY+hsiRGJxd4iIO2/beISbeoz6laggA6MYsAAras5Z50TXFHIsW3aw\nOVySa1UCyIdRSuRK87XTU4/9tGkUBZkpRpkYLEYrZzYVPy8wHZ46/drjxxcXY9X/2r/ZoBbjzpt+\nUo1vuOcPdiazYG7n/vmXvztnvd179z0wyRWxqzKLeWR4CwgEw+gh6pDiJDB98flt2vvwh5z9xUrM\nn/qlOfhCCdN7d+/U8Q3sUZHrsjTsjZmH2nP0lliJWsLi+SfPNimMrxED/DGoUY//8btTq/PPvenO\ng2AwtqBRPcSP349TNV7v+rPBj461i4891Q7mrAVXW7913wyHuPeatl8nciCS0uC2Q+fnN2wLIZCt\nguFTRwz1Xiy22tT9KFCLCYSQ4U408cDGn5/bcs/ecVDv/GLTJ/HYrsTCrDZW7Xrg6ZOy8KKCrBq/\n4+HNCbUS9+oFglFTgc2GZ8D1hhQZHzxU+BI4+yITJ2E2tgjNwiTI0dkBkpDMm4Iyy5HUxDU5DCtq\nQ/qAPwoiY6QCFDeuJr88+9qWb+6pnQAnEAjM5e1Wdq73/af/8WsZ+tSXHtoN0YDiU71F0d74t749\nw4Sgw9P/mqnau2XcjEuE+3tEtR+wfXdXlxPUkAMbF3D2OJFYdf3RTcnDzV/53wsLFml6/L6vbwuu\n4pNfKJINIHYChDn2YvvES2K60G4+8tDmPpu0Nfk4u0ZSYcRGKYVBKzAmrq/3zBqiRXNV5skvvfqb\n3pcrCTDkS03Xfg8lKLyZ++Hx3v1/uDUGYaBlCwQvglaCCfdbcqeIrNykUedYNIQ6uAmqLO5Uk72z\n0VlNrfo1hlO0UIGdoxYPwlpyABquBDA2rbD1q1+Yefo1zRRFJrbfu61/x/eXzvz3576yzWUxn2x+\n/9zxhYLBppmbd98xQdEp7XxgeOL6hw7UiLCmLP76R0/N5jh9z9c3trEX2CiYU5CQS6W5qZNwKwQ2\nkphX0VlYMd/97gf9dJac1D1SDEZQz5CXftQqhY3r1wB/AP5Wa+QQAgFGJKASPoE2pAhv+c6Nf/O0\nljlfMEpV2v3wHTOhMnjfwfAiIFENum2DYhQ9XIWbqc5kVJCr2goqDwhWijmDxamFTN97ZCmOVybW\n3w5IzaSaPuY4u9VAvPril360ZEZhvLd3z0N7QgijAC2FQrlQcgzPvK5mxMgxriQ+3DIJWazMgext\nxeQsEAa8EALDlpJQOfema1t50wttYB3WK9H6eC8XBB+cvARxIau/tv0fDn9jospgeAnkTgDxhXqA\naWD7u1t4GROT8xEbb31kY4UinMv5F012Hp2kVGBtaOU9UYA777zMkwtbvvq35xtuPabDf7QzCev0\nt7f98NdNHW+858gEwJFb7mWqWkusrAz1UO841pQzoojTR+/fNiZcvHaLHhQM8VbgJDzAJWZM1zfe\n+Pbx5ZYe2BJiaY89pXFyKkZmAiI5QBaNKUPNhEG5YmdNPSNycijHoPC+l8YoaHDyURGlua1IoHGd\nQwjsi72QFpUCcZt7VWEeBvIGztoyuYwNvvvWY68M283txC1bJmT86PlH27d+/fK0tYszx+c0N7ke\n/8rd05t6Y2ymIfEj7yrY/q/PtzJ5w9EbNkizkNySORd2tlTlxrhic/ESwa58WTvLKwshGsykdqgp\nReTFV+dZc90Z4IuZe71VocUYSaMAXkDl42+MOTg4TV7/zS2PnxuGHnqTR3Z+cfcUG8j7hlE7qAlS\ni6AhCruz68cXwLgkJM5iECEIs3pwFide9HFvhYkbH+tHIbaSGw3ukjyG9tM/j49NLFN/cc0PTiim\nb7rtxplUBSqBjUC1zY0lMVadfXmOUB3qBXG2cvkVZMkplGBuFiIPya0CuRKsRErqRUsRktwOG4k7\nJoQERgHw/PEfnHfSIxfeo4d9jwDLicL4nhc/8KzDnjv+JzZ++RubY+s1LFT19Cm66XA/WbRQil9S\nHeajqGbuevbZM5xk020PbatjsNhuuX/39tP9fXdvpjQIMNRUIAxiDwYrJjx564vHyFuMbfrG16Yq\noqCcqIQhBRg5yMs7Ce1PCRJSmv/x8TwYMx+pIQBUmHjUw2UgZkcgN7A7kQPkDkd0dqdgweBt5QDM\nhGDvbwDsuBRjwCiHNCpVWt5Yvbf7b9OBO5aPTkha3PbQuZcWz557GRZP6NADZnZd+8CeMgjGsTDi\nexa+Uzy47u4j2+oq98aUJUMiDwmUI0UQjCBWF694ST+Jj/QZQeYBkknFs6mYnH/umaEjHN7TGeCL\neOMZJ+L9E0KBndy5kF9KaPKjIQ6tBZ289eDuM6/Shu1xx/WD0JecgyB7dCKCQdu+cGoQiWBEaRVu\nJgLEBCZqwRgJhVmj90ubEgWDt1RFNS/DhQUuWksgft/TdAVRD9iz7ciJtxYPXjtd9SwTJxOIumC8\npsI5oDyzqGHnLdHNsZINMZGrZI+iC7XUJuwEcwYzElwskIh6OQ1pJ/f0o0BAXjlW0mv86cM8+XA9\nf+imzUnY1GsO2//9Ey9d19e5VFGOGK5oXuK7iLv+7c9/0fruew5MIxK7w/vX7R5yv7aegmg0t55Q\nIYMRLKgXOfL7H58vvZk7b792IjlTRu0ZFqwFK4dYhPlTLioIln3p9V/52Nbrp0gI1EQUFoIKA6RE\nglw4ASxQcmQRAO4AOZkjEpEHTgCIQSbInQ+8WrDyeDr0pz9+6WSDoYfMFWTrw7fMjHEdXBis79O+\n3Vjf8eCuHohZhiVyiDrPKCyZJLgnOBBUkpVi482V+a9WgEYsBRITN4/JfOHUD3+35HHD4bozwBdj\nKGHitjqA3IlAycLKlA0vJkZEoMI03tWA+S7vLAA1wmoUA1yEZ0J0A3GRwObEksURSkkMcLFIZMxt\nbIQ49naSF7+adpWxKKd4wwEQBsFzFDF2E2WAWRks0HrONO7qg+DOK7qaBAI7EjIyAoyJyUFFaxJQ\niQTN2haJu+6ZhBKcnMg/q3rTD2aAwbtbAcOOHfdfOE6XVYN4CcSxe9udplPjkWvyQtRzbcbHxVxK\ntPkqmhUJBrGY1aJoKFxv+daOx87d9IVd4zGyiaovMagRCo6WjIO5fVyX/BJY4XbhyYVQ3bKVyAls\nbJHgFOBqLAAQHDlq2yOxHCM5RloGToDmPrEu3yhEDSq0l5gq2vHJ6WfS0j+8/6XTw2efKQPpb949\ntmd/qnpGhkIc3p96+9t3Dpc7gkZb3ovTGAEA+peft7hyiFiozNrMFB1y/oV/eqbJccN9k59+Gfbn\nmbcWC/UO7hyqRDaBkzaePv+1GZYahlsx9BkkJaAhkFsIrN5KiMqZSTVFce/Fs2W++rAW9c8eZlBB\nzUxCOYCLJcAYoEJs1gRSbYPy9CBAxVbi/alGdhSqkJwBz+IgEBCETEmNLYo0Z4qVPLYEsMDUPKzV\nktkeAGx++9sP3YaF0ZcEYPK6S/XKrdbimHLzyg8y9w/3oisZRYgyAcYwISdXokhAYSdwUgE5VGBM\nrhYDU0sEH5WLVwBGo2M6VgMmhYhtmAzNXS8+d8OGqj8RKVAqxoFZyU0+enrO55mkQq6cFOQtzj35\no2eYJsP+6ydW1/X6vLFoHuO1/YqNmODkXqnaVeQLfjxckoCCkQvQeAV4hIOCG3sEAnvPlSL3+slb\nOVsiR1m1Jv6V4x7QVBFwuEWAkjIBDrioArEQLZySeuL2GNjaCivIGoi4OrG7myC2DY8CrNDAZsFZ\nqRVthyeWLCzNVgkRrkxSLm9mT8dqYaWc+eEZhJt39Y0rc2KAR+EMwIQcBAZ8lFt0EPyd3moJcPIE\nGIgxGogAshXNmOxYAYN3/NZdhz/oF66ihNeqQFyStTXgXubPPfrPZxZZ9l+7dcNk6AzwRZzkGON0\nDxKhokCO8pGS0p8DBEIwCkyeI7gNVhUSaADDPAJcYGwCdonanudgRa6i0n5nIIAA1WjkhOWl0imH\nINBGiBfmwtgtBx1OZitJNxoTuQez0TLMcVkgh2HMpRBZFKX2nGpSpFiZM5ngaro865Ko5dSz8I23\nToiOZiRjFFwmICpQPAEAtZYMTBfaxgw8eqJp+beNAWVeA095x1VMcWpNMmvm5vj3Hp09H/o3fnPD\nxMT4qqmFfi6ZRZWmDlRcknoghpuHsgYeTYIjcySYUOVKAaKAOLmyOBkFUBtaJ+8j8PxwQHw1JcRY\nSxIoQ5jECU4whjKDHTALRGHj9EL98KC1AdXvm0TwYXiOCAa3ENFKBbi7kpiTOBzIQbO21AtSlSUD\nmMEM8NWfdlrbZF1446z0br8+lKjL+WWyUV4C4s6mxmQSR5FycpCTNemd/ixCIx4YFwowrqLG9441\nB7sSinF548RPftpYvfHAt64jj7ia1tkrz6lAcd9mqwiMzFDiVS6P+mwgd3AIGElEUYAKojtBR1aG\nqIkSxZZUdhDK6dkZ9suVBPwsyCXA4YqRyqQTlNxbqogYZNGJaPN/fCnsZjc3WlHDiwiYBJmMPRkA\nMlNWoaZmdymeireJct1qjxB4JBpuncd0pWmO/yyXzV/qQ91HQvgEccZIyqQUEQHYsZzYVSZXinKx\nSku1nPN1grGxd59px2rhgW1J+dzsU//61Hyott121566aj13BvhiZpP1pxKVEJicERap+pTbF68U\nDHEiH2UujV2ZHMxATmTusTixVEseg/LMGCyUq6ggZVRxfHFeN2BUHxRwYYeUjn689x6ZUk8gkMPI\nUUYbleg5kIjCJbQ+hyz9QRp1mX60znjHauPti6+FdNfBHmUPbiSFnYyLgAjFYwDeVhUD4AKQ0LKf\n684jOTL4yOq20d3RWeCOVUIDTH1BFn/9kxeX+tXBr+yfib6gxqkzwBdxxy/nt9wzSEF8JOjfXxuP\npLIRQEqcACUayUy4kZOJMRMgrKA6zW/rnWUearI1W+b7HkyAUdmVZxcXNzgHFZjDA4gQTOBNJGTy\nzJbgIFobN8bnGCovn9Et11fQ6G0srnmAIuSqlVMAYOQMp9FuypT5bQPrNArvaBjlL0BI8BWN8Ojo\nWBEaUQrw8ivfe71JOzd++VBPcgMp3uWAL+aR7Sf23DjGDPKRl+NrZKUlf7vFgko0l0TECoaQewlw\nULAC4rEwjMde3inerpMqXyIoj0p0opsJJJAjAixOgIMM4mnzG4Ejs0CorN6Y1Y7LpjnxNPUPXZco\nBCx69KUBIE7RtI0jsQ0iLFe0j4a7Lh8aEZI5QAGA8PLzTcBF4y86Oj5dFpTp7As/Pfb7uH3q6xs3\nTamzDzQU6daSizh8+EqfwaqQKyYsOwRgciZXFooFAYCzWRaLJJRTRQI9mZWvohTwqsJOgsW+QeBU\nApHRyPAyyAnk3kYmHVsMrC2Ys1E0gLoG+ivL3HPD+uZvpJKYEDVan0bRDI4EUvZRqQADnqM5v535\nVRAuBKLfNsqjSUi+NjbbHVcjDXT+Z4+dPt27Z9fmXf2xBA81hlzWQo1Rx0dQAYA3SCMTbASJcDgR\nLCdCKaX2ogQtWRL5Gcf6yYdpMKoaWKi8rUAQJ2N40WCRnKjEYEILmaCFbFEogqyLV15pFp5YHLtz\n6zD1BB5AQo6RlAMRIHCYk7sAiCSuywa4CJhGdfReZBThAEaiLt1S2LFqtFiYffR7pa1uf6DW8RSk\niBnRsHh31619yAF4VLKCCL/wmZsbSgV4kFhESVoHD0zyqXXU5ErB3RAtiZuhQTRmh1uMebRUs7tK\n0JhD1TMkdxNXDV3TypXljQUe3zIWKlZDBOBQ5lEfsBOUSD1IcWcC/GKVJadRszBFayMhJ4wUojs6\nVg9ffPKXT81vPLLv0DU2J+xsTc+1ra3Tgl4vEAuQ3BWBlrf+xBwBOBM3kGCAVTtfKZ6WxsL6UWhR\nByh4yS0aikuRISAhFS8OITJXpt1PLk1s4WDubOz0KQsbd6yYV+bT1pk6mgrbSCKNLsSTCRCnBLPo\nrvx2JTRgASpvx3Y4wUdam8sf57qJ+nR81sy98sNn+YY7bhikmNy5AoRbjIXQpbLWBU4g5ZH0rQEO\nY1oOtQaSkQCUoy5c9Yh78wzmdXJnmCOA1JFPPvZmvW/TRJqiEkAgzhAqWolxk2pIO+tM6kxGgtyF\njq4su/e+eN0UMRtBjMlzHLWGjUQpkSOBAXp3wzYDCXahFhrvjEgcKUJ3WtAdq8Q/vXJs87W37SFy\npwFMqcc5xWS2fhyddYw7Ac6u8AB3Rw4M0MgRLhFgMJZEKHO1xQva4p9gDPLnC2NxB5s03/9v531m\ny6Fb7uqTw9gNTC5s7Gq2VGxulkrPPJADzqswtrljBVx3vt0bBUUAMODJCYBbC4uBEZfd2ZANbsw0\nSvaONNQAAORq/Lb68/KLHR2rwrN0x77padHKkAtHDFA4JNKuDWk9QGQElKKLdd/hUOKCQKAcKLiD\n4MVDyWwmM9FQlzbR1TOLYVVhhjHBLT9/ZmgLx0/tW0TwyLAgBcJqOQSP6J3rDYhKNAI50YpmDnd8\n+mz79reBDyiceq9E6IfcxuvkDu+4Crj25l0xikRuRPIS0mzUqFmZVzY6teNzCgOw+Ve+d93hiZ4r\nOY/kJwKc2ADSAl9qxiRWoW6W5ue2r5vFiQBxuBVc42awN/6xv23TZAohkFMJpmIq4oFYFznA2Ue9\n4eukTbqjo+OT81cXDgYApi9+pTPAax8HuZa5J/76mZlDB+8fS31iMRECvCQvAWqWvB8ie0GOuiAh\n8DqJxxEAyuRaH7n5iaF589RbM/tuu37DoCiDlyrTPGia2WMl0lTMgYEcqaB0uZuOjo5PTLeOrH0I\nICY8/3z7xplf/nTXrbu3TTU88AJhcY1UEIwtiGCotTZxIrnxOomxOgGIJsB1f/W/fjE8u7j0+plX\nnrrt6G703UHFIDnPHT/GLPvHxIwoAY2vmwhBR0fHKtIZ4LWPOxH74LbfPL04tKdf/s3goXur8VqI\nSYnYi5FxURcipiABeXHYWy9TOkwAwLUqcc+f33f2VyceHzb8xv+dvXv3JnhkQ2shn/3NmyZhmoyM\nLKKN5uukRq2jo2M1WSfr7LpGmYA4f81/+NVjTyy1+fSbf/OrHfcdqqNpYqgRjIRNvHGjzLXOntgV\n1kuOk4FGXNy45jDV3vjWoR+cLWXpX5645sYxpPktfMzJTn9fufQTmHKMrSZWrCOtko6OjtWiM8Br\nH3E3xljev+PWfznz6Mnsp87WL3/9pq3QQhFRAddY54wlQ1/O1zoUXi9lvmTKiJ7gFFPSwfj0nhd/\n98KZ80N7Xpi47Q0boXax51rvHLC6aQxeuo6Vjo6OT4HOAK99RhNsuUQ+sH3+8A9+Puu5vHD2F3u3\nbZvi8bqRkqegWd0RFbUEy7KyqfafZzhTcABkcCFOuPVLJ178fy+3c1mFqQkclkzEc/+2gwCDGERE\n6PqAOzo6PjGdAV77mCICGto+V+MbDh75xRuvL7VvnX8ybdo3E/rbN0bK6IGgzAvF6qYeUuB1U2ZU\nAUVchdmMQx6f4pl9N/7smZNnSxjm2EJUS8LYoQcHxBoN5O7LAww7Ojo6PgmdAV4HBMCHzXRPmbm3\n5eg9xx9/4cT5tpodnuo1OjbT31T45mSyaXDuxCvzrZlbW60nWb4AWIBzymbOXA+m9p8+/btz+eUz\nbbvxeG9J4tR9d21PbMbiRM6grgiro6PjE9MZ4LWPswPRIcYKiqE/tnv+xHPHXz1ebGmpnT2VU+s/\nm55f2jXx5olhq73+JkphvYSgAQBGCTAmzmIuRDHEDQdzXpod+sZTb5yYmb1p/xS7UQLRSPpwXV2e\njo6O1aETIF/7NC4EQKEJngtC6646f/KxF0/kZq4U9iaGqeG8h5xDzya2/ZdrBqL1lT7tjo6OjrVN\nZ4DXPgskAgcAU1YgAq7W0PDUacMzv196Y9ZJVRfZWTluOvDg7X2BrJskcEdHR8eVoTPAa58lIXKo\nB6dSHOgBSiUn1VbnY7tw/OnNOP67Fj2t3rK9h6/d2pfI3ZiOjo6OjtXl/wMoXeSfKkny3QAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=L size=1920x113 at 0x12E99FEB8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open_image('e04-008-03')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T15:10:56.917086Z",
     "start_time": "2018-07-31T15:10:56.655443Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>result</th>\n",
       "      <th>value</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13346</th>\n",
       "      <td>r06-143-00</td>\n",
       "      <td>ok</td>\n",
       "      <td>In|the|train|going|back|to|London|I|sat</td>\n",
       "      <td>In the train going back to London I sat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13347</th>\n",
       "      <td>r06-143-01</td>\n",
       "      <td>ok</td>\n",
       "      <td>beside|#|Catherine|.|She|had|the|stories|open</td>\n",
       "      <td>beside # Catherine . She had the stories open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13348</th>\n",
       "      <td>r06-143-02</td>\n",
       "      <td>ok</td>\n",
       "      <td>in|front|of|her|,|but|she|said|:|'|Philip|'s|a...</td>\n",
       "      <td>in front of her , but she said : ' Philip 's a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13349</th>\n",
       "      <td>r06-143-03</td>\n",
       "      <td>ok</td>\n",
       "      <td>I|wish|I|went|to|that|school|.|Did|you|notice</td>\n",
       "      <td>I wish I went to that school . Did you notice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13350</th>\n",
       "      <td>r06-143-04</td>\n",
       "      <td>ok</td>\n",
       "      <td>that|girl|who|said|hullo|to|him|in|the|garden|?</td>\n",
       "      <td>that girl who said hullo to him in the garden ?</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         filename result                                              value  \\\n",
       "13346  r06-143-00     ok            In|the|train|going|back|to|London|I|sat   \n",
       "13347  r06-143-01     ok      beside|#|Catherine|.|She|had|the|stories|open   \n",
       "13348  r06-143-02     ok  in|front|of|her|,|but|she|said|:|'|Philip|'s|a...   \n",
       "13349  r06-143-03     ok      I|wish|I|went|to|that|school|.|Did|you|notice   \n",
       "13350  r06-143-04     ok    that|girl|who|said|hullo|to|him|in|the|garden|?   \n",
       "\n",
       "                                                    text  \n",
       "13346            In the train going back to London I sat  \n",
       "13347      beside # Catherine . She had the stories open  \n",
       "13348  in front of her , but she said : ' Philip 's a...  \n",
       "13349      I wish I went to that school . Did you notice  \n",
       "13350    that girl who said hullo to him in the garden ?  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line_labels['text'] = line_labels.apply(lambda row: row.value.replace('|', ' '), axis=1)\n",
    "line_labels.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-26T19:22:22.717853Z",
     "start_time": "2018-07-26T19:22:22.657344Z"
    },
    "heading_collapsed": true
   },
   "source": [
    "### Tokenize Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T15:11:16.946802Z",
     "start_time": "2018-07-31T15:11:14.486024Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "tokens = Tokenizer().proc_all_mp(partition_by_cores(line_labels.text))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T15:11:18.099111Z",
     "start_time": "2018-07-31T15:11:18.046600Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4964"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax([len(o) for o in tokens])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T15:11:20.432181Z",
     "start_time": "2018-07-31T15:11:20.381407Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['each',\n",
       "  '1',\n",
       "  '1',\n",
       "  '/',\n",
       "  '4',\n",
       "  'in',\n",
       "  '.',\n",
       "  'by',\n",
       "  '1',\n",
       "  '/',\n",
       "  '2',\n",
       "  'in',\n",
       "  '.',\n",
       "  'by',\n",
       "  '14',\n",
       "  'in',\n",
       "  '.',\n",
       "  'which',\n",
       "  'fit',\n",
       "  'into',\n",
       "  'the',\n",
       "  'recesses',\n",
       "  '(',\n",
       "  'c',\n",
       "  ')',\n",
       "  ','],\n",
       " 26)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokens[4964], len(tokens[4964])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T15:11:24.035124Z",
     "start_time": "2018-07-31T15:11:23.981555Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile([len(o) for o in tokens], 99)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Numericalize Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T15:11:45.506424Z",
     "start_time": "2018-07-31T15:11:45.429385Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('the', 6491),\n",
       " ('.', 5561),\n",
       " (',', 5558),\n",
       " ('of', 3221),\n",
       " ('to', 2708),\n",
       " ('and', 2615),\n",
       " ('a', 2429),\n",
       " ('in', 2044),\n",
       " ('\"', 1601),\n",
       " ('he', 1210),\n",
       " ('that', 1187),\n",
       " ('was', 1154),\n",
       " ('-', 1112),\n",
       " ('is', 1004),\n",
       " ('it', 983),\n",
       " (\"'\", 977),\n",
       " ('for', 890),\n",
       " ('as', 771),\n",
       " ('with', 766),\n",
       " ('his', 761),\n",
       " ('i', 746),\n",
       " ('had', 742),\n",
       " ('be', 731),\n",
       " ('on', 729),\n",
       " (\"'s\", 629)]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq = Counter(word for line in tokens for word in line)\n",
    "freq.most_common(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T15:11:49.965198Z",
     "start_time": "2018-07-31T15:11:49.916033Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "max_vocab=40000\n",
    "min_freq=2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T15:30:17.249311Z",
     "start_time": "2018-07-31T15:30:17.099438Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "itos = [word for word,count in freq.most_common(max_vocab) if count>min_freq]\n",
    "itos.insert(0, '_unk_')\n",
    "itos.insert(1, '_pad_')\n",
    "\n",
    "stoi = collections.defaultdict(lambda: 0, {v:k for k,v in enumerate(itos)})\n",
    "ids = np.array([np.array([stoi[word] for word in line]) for line in tokens])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T15:12:02.606529Z",
     "start_time": "2018-07-31T15:12:02.556502Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13351, 4085, 11704)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ids), len(itos), len(stoi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T17:22:46.155923Z",
     "start_time": "2018-07-31T17:22:46.105607Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def idstoline(ids):\n",
    "    return ' '.join(itos[i] for i in ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T17:22:52.876258Z",
     "start_time": "2018-07-31T17:22:52.824055Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a t_up move to stop mr . gaitskell from'"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idstoline(ids[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T15:14:02.389817Z",
     "start_time": "2018-07-31T15:14:02.329692Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.save(TMP_PATH/'ids.npy', ids)\n",
    "pickle.dump(itos, open(TMP_PATH/'itos.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-30T15:00:19.913436Z",
     "start_time": "2018-07-30T15:00:19.852197Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "ids = np.load(TMP_PATH/'ids.npy')\n",
    "itos = pickle.load(open(TMP_PATH/'itos.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T15:54:31.610358Z",
     "start_time": "2018-07-31T15:54:31.255422Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>result</th>\n",
       "      <th>value</th>\n",
       "      <th>text</th>\n",
       "      <th>ids</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13346</th>\n",
       "      <td>r06-143-00</td>\n",
       "      <td>ok</td>\n",
       "      <td>In|the|train|going|back|to|London|I|sat</td>\n",
       "      <td>In the train going back to London I sat</td>\n",
       "      <td>9 2 871 159 116 6 200 22 712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13347</th>\n",
       "      <td>r06-143-01</td>\n",
       "      <td>ok</td>\n",
       "      <td>beside|#|Catherine|.|She|had|the|stories|open</td>\n",
       "      <td>beside # Catherine . She had the stories open</td>\n",
       "      <td>1392 143 1299 3 44 23 2 2316 506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13348</th>\n",
       "      <td>r06-143-02</td>\n",
       "      <td>ok</td>\n",
       "      <td>in|front|of|her|,|but|she|said|:|'|Philip|'s|a...</td>\n",
       "      <td>in front of her , but she said : ' Philip 's a...</td>\n",
       "      <td>9 1003 5 35 4 28 44 55 77 17 832 26 0 2303 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13349</th>\n",
       "      <td>r06-143-03</td>\n",
       "      <td>ok</td>\n",
       "      <td>I|wish|I|went|to|that|school|.|Did|you|notice</td>\n",
       "      <td>I wish I went to that school . Did you notice</td>\n",
       "      <td>22 1115 22 164 6 12 397 3 107 33 436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13350</th>\n",
       "      <td>r06-143-04</td>\n",
       "      <td>ok</td>\n",
       "      <td>that|girl|who|said|hullo|to|him|in|the|garden|?</td>\n",
       "      <td>that girl who said hullo to him in the garden ?</td>\n",
       "      <td>12 235 52 55 0 6 59 9 2 1431 57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         filename result                                              value  \\\n",
       "13346  r06-143-00     ok            In|the|train|going|back|to|London|I|sat   \n",
       "13347  r06-143-01     ok      beside|#|Catherine|.|She|had|the|stories|open   \n",
       "13348  r06-143-02     ok  in|front|of|her|,|but|she|said|:|'|Philip|'s|a...   \n",
       "13349  r06-143-03     ok      I|wish|I|went|to|that|school|.|Did|you|notice   \n",
       "13350  r06-143-04     ok    that|girl|who|said|hullo|to|him|in|the|garden|?   \n",
       "\n",
       "                                                    text  \\\n",
       "13346            In the train going back to London I sat   \n",
       "13347      beside # Catherine . She had the stories open   \n",
       "13348  in front of her , but she said : ' Philip 's a...   \n",
       "13349      I wish I went to that school . Did you notice   \n",
       "13350    that girl who said hullo to him in the garden ?   \n",
       "\n",
       "                                                ids  \n",
       "13346                  9 2 871 159 116 6 200 22 712  \n",
       "13347              1392 143 1299 3 44 23 2 2316 506  \n",
       "13348  9 1003 5 35 4 28 44 55 77 17 832 26 0 2303 3  \n",
       "13349          22 1115 22 164 6 12 397 3 107 33 436  \n",
       "13350               12 235 52 55 0 6 59 9 2 1431 57  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add ids to df\n",
    "line_labels['ids'] = [' '.join(str(p) for p in o) for o in ids]\n",
    "line_labels.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### load Wikitext103 pretrained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-30T15:05:50.299351Z",
     "start_time": "2018-07-30T15:05:50.250231Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pre_path = Path('data/aclImdb/models/wt103')\n",
    "pre_lm_path = pre_path/'fwd_wt103.h5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-30T15:05:54.456900Z",
     "start_time": "2018-07-30T15:05:53.835463Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "wgts = torch.load(pre_lm_path, map_location=lambda storage, loc: storage )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-30T15:06:15.623100Z",
     "start_time": "2018-07-30T15:06:15.533411Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(238462, 400)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dec_wgts = to_np(wgts['1.decoder.weight'])\n",
    "row_mean = dec_wgts.mean(0)\n",
    "dec_wgts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-30T15:06:34.830968Z",
     "start_time": "2018-07-30T15:06:34.650137Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "wiki_itos = pickle.load((pre_path/'itos_wt103.pkl').open('rb'))\n",
    "wiki_stoi = collections.defaultdict(lambda: -1, {v:k for k,v in enumerate(wiki_itos)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-30T15:07:08.990015Z",
     "start_time": "2018-07-30T15:07:08.933178Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def create_emb(dec_wgts, itos, em_sz):\n",
    "    row_mean = torch.from_numpy(dec_wgts.mean(0))\n",
    "    # embedding: simple lookup table - input=index; output=word vector\n",
    "    # embeddings: rows = vocab size (4085), columns = determined by wiki pre-trained weights (400)\n",
    "    emb = nn.Embedding(len(itos), em_sz, padding_idx=1)\n",
    "    # learnable pytorch module has a 'weight' attribute => Variable\n",
    "    # 'weight' attribute has a 'data' attribute => Tensor\n",
    "    wgts = emb.weight.data\n",
    "    # iterate through vocabulary and replace found words w/ pretrained vector weights if available\n",
    "    for idx,word in enumerate(itos):\n",
    "        wiki_int = wiki_stoi[word]\n",
    "        wgts[idx] = torch.from_numpy(dec_wgts[wiki_int]) if wiki_int >= 0 else row_mean\n",
    "    \n",
    "    return emb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-30T18:26:15.455220Z",
     "start_time": "2018-07-30T18:26:15.076882Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'collections.OrderedDict' object has no attribute 'size'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-55-0807491be555>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# emb_dec = create_emb(dec_wgts, itos, em_sz)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mwgts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'collections.OrderedDict' object has no attribute 'size'"
     ]
    }
   ],
   "source": [
    "emb_dec = create_emb(dec_wgts, itos, em_sz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-30T15:08:21.617295Z",
     "start_time": "2018-07-30T15:08:21.561011Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "torch.save(emb_dec, TMP_PATH/'embedding_decoder.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T15:32:33.237693Z",
     "start_time": "2018-07-31T15:32:33.141597Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "emb_dec = torch.load(TMP_PATH/'embedding_decoder.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-27T15:24:11.362174Z",
     "start_time": "2018-07-27T15:24:11.299921Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "wgts['0.encoder.weight'] = T(new_w)\n",
    "# weight tying\n",
    "wgts['0.encoder_with_dropout.embed.weight'] = T(np.copy(new_w))\n",
    "wgts['1.decoder.weight'] = T(np.copy(new_w))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-27T15:24:14.902409Z",
     "start_time": "2018-07-27T15:24:14.726117Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "torch.save(wgts, TMP_PATH/'pretrained.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-30T15:03:26.227020Z",
     "start_time": "2018-07-30T15:03:26.178191Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "wgts = torch.load(TMP_PATH/'pretrained.h5')\n",
    "dec_wgts = to_np(wgts['1.decoder.weight'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Save DF as CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T15:33:32.508013Z",
     "start_time": "2018-07-31T15:33:32.459649Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "CSV = PATH/'line_labels.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T15:54:56.516593Z",
     "start_time": "2018-07-31T15:54:56.444744Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "line_labels.to_csv(CSV, columns=['filename', 'ids'], index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T15:55:01.674936Z",
     "start_time": "2018-07-31T15:55:01.599700Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>ids</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a01-000u-00</td>\n",
       "      <td>8 31 1310 6 499 40 3 948 38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a01-000u-01</td>\n",
       "      <td>0 110 64 161 120 1311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a01-000u-02</td>\n",
       "      <td>15 6 24 103 27 8 386 5 161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a01-000u-03</td>\n",
       "      <td>893 500 3 40 3 1622 833 45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a01-000u-04</td>\n",
       "      <td>204 135 8 2147 25 2 707</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      filename                          ids\n",
       "0  a01-000u-00  8 31 1310 6 499 40 3 948 38\n",
       "1  a01-000u-01        0 110 64 161 120 1311\n",
       "2  a01-000u-02   15 6 24 103 27 8 386 5 161\n",
       "3  a01-000u-03   893 500 3 40 3 1622 833 45\n",
       "4  a01-000u-04      204 135 8 2147 25 2 707"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv = pd.read_csv(CSV)\n",
    "csv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-30T15:01:05.896971Z",
     "start_time": "2018-07-30T15:01:05.846294Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13351"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T15:55:06.919407Z",
     "start_time": "2018-07-31T15:55:06.865125Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['filename,ids\\n',\n",
       " 'a01-000u-00,8 31 1310 6 499 40 3 948 38\\n',\n",
       " 'a01-000u-01,0 110 64 161 120 1311\\n',\n",
       " 'a01-000u-02,15 6 24 103 27 8 386 5 161\\n',\n",
       " 'a01-000u-03,893 500 3 40 3 1622 833 45\\n']"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CSV.open().readlines()[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Get val_idxs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T15:55:17.393738Z",
     "start_time": "2018-07-31T15:55:17.341432Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2003"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_idxs = np.array(csv.sample(frac=0.15).index)\n",
    "len(val_idxs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Model Data (rotate, normalize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T22:07:09.612258Z",
     "start_time": "2018-07-31T22:07:09.561568Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "sz = 500\n",
    "bs = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T22:07:10.660760Z",
     "start_time": "2018-07-31T22:07:10.465047Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# use this md object to load image data w/ transforms\n",
    "\n",
    "# These values were generated initially with tfms_from_model(resnet34)\n",
    "stats = A([ 0.92025,  0.92025,  0.92025], [ 0.12774,  0.12774,  0.12774])\n",
    "\n",
    "# aug_tfms = [RandomRotate(5), RandomLighting(0.1, 0.1)]\n",
    "\n",
    "tfms = tfms_from_stats(stats, sz, crop_type=CropType.NO, aug_tfms=RandomLighting(0.1, 0.1))\n",
    "data = ImageClassifierData.from_csv(PATH, 'lines', CSV, val_idxs=val_idxs, tfms=tfms, suffix='.png',\n",
    "                                    continuous=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**Dataset**\n",
    "- x: images (with aug_tfms)\n",
    "- y: array of ints -> represent words in a line\n",
    "    -- pre_pad=False, pad_idx=1, transpose_y=?????"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T22:07:12.680428Z",
     "start_time": "2018-07-31T22:07:12.625573Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# pad ends of lines with pad token for language model\n",
    "data.aug_dl.pre_pad=False\n",
    "data.trn_dl.pre_pad=False\n",
    "data.val_dl.pre_pad=False\n",
    "\n",
    "data.aug_dl.pad_idx=1\n",
    "data.trn_dl.pad_idx=1\n",
    "data.val_dl.pad_idx=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T22:08:04.319222Z",
     "start_time": "2018-07-31T22:07:13.698121Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "denorm = data.trn_ds.denorm\n",
    "x,y = next(iter(data.aug_dl))\n",
    "x = denorm(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T21:50:01.345593Z",
     "start_time": "2018-07-31T21:50:01.293502Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'labour has to have an adequate number _pad_ _pad_ _pad_ _pad_ _pad_ _pad_ _pad_ _pad_'"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ints = to_np(y[7]).astype(int)\n",
    "idstoline(ints)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T21:50:31.017728Z",
     "start_time": "2018-07-31T21:50:30.964688Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 200, 3)"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[7].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### View image transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T21:54:37.686144Z",
     "start_time": "2018-07-31T21:54:37.631511Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def show_img(im, figsize=None, ax=None, alpha=None, title=None):\n",
    "    if not ax: fig,ax = plt.subplots(figsize=figsize)\n",
    "    ax.imshow(im, alpha=alpha)\n",
    "    ax.set_axis_off()\n",
    "    if title: ax.set_title(title)\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T22:08:21.104184Z",
     "start_time": "2018-07-31T22:08:20.559048Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3++np6aGlpaXZIT52uVyOb3/727z++uskEgkikQjLy8sq3BZ5ADqTJCIiIuJCSZKIiIiI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dLBY5YKSb/fV2RTqiwkRjIrMJfLoVqtolwu486dO5o3jSZiPr/b7SoiwFpa8thqtZo2d9k4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1373a40f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(4,1, figsize=(20, 20))\n",
    "for i,ax in enumerate(axes.flat):\n",
    "    ints = to_np(y[i]).astype(int)\n",
    "    t = idstoline(ints)\n",
    "#     t = data.classes[y[i]]\n",
    "    ax=show_img(x[i], ax=ax, title=t)\n",
    "    \n",
    "plt.tight_layout(pad=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T17:32:47.159343Z",
     "start_time": "2018-07-31T17:32:47.097741Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MLI2yETGV5Myc7ne70SwtMAmxcMIFkhA0DTA59Bcg92bcNv3Sim/fjn4pO7bD1Oj87PS2\ndclTXzmnGbck3iwK7FluUYARCaQzqUVISWYCkK1NKuQl1QHY+SeXY9f46rObnbW5BEgaE8QYsBRG\nChknA6QgeJPCJhkhSMAdYqnBzCqgcCYWQpCQ0LYT+exk94JkdmVsRAXh3mbf+RYLJ7NJGHnfobx2\n+vlKLymbUmFPdqEYs4N5VXZUgU859cifI2l9HLibWwZQhZMXOd3LOwWljhAKgmP++pe164+/3P3V\njXd0871zdq7RT+Qg1mHqU6SmhhHCWQKZYVsoVN6bPTin6N5+aGkpChABPEaqIBWYMogY5NXhXjz5\nGxPxsMx0KSmu0tiG1vRbV1+9MmRhdsrmmonWJWUaI0YobPsFqGwn87rcH2T7WYMvZcd22L9tT8bx\nYjMKECnhKykWWRhE6o24hAlzJQ/dqMUcGRZMvReyjuDJu3AFMPxvT4Y58rw8/OnvlFkHN5msJkgz\nwNyICGEqMAoykRJFktnVxZ2IoYRAiUaMoJrOyEQTUOa2i3R36vnxNB78k5KACQWocmFvpB5GEVpA\nGTd25ugv2syzr5lAGq3nilDK8NIwgVg7SCMMy+fcAQMkJSzFMrXIJN9aJWVnksnkguzuvP3o0PR4\n+MYotL9wDu1RXbbK67oBiGRKzbQUNicTIvIkTGUbL8uwHua5ueAnpY17oJItOSxrQQYlWIlGBiW1\n0JpEbUtNM38OKHmCU2maU1J4jfP57Lz02gokR67eKgZJgJEkDgRvp6/LX8d8dc0vrBSM2w4MvZy0\nuR3kzrtlfe1yPRgqMqjvMATVeSZAIZxmJU0JYUMNlkS6ITKEOZNZwUgQgKNPr9rht94odvTZGjmO\nPjWiIHimJ7IQAO6BEGdiiTE5pxYcDUnBkZTDla8zaVpHjCRJ4cQMZGz76a8HKWqbNbEy24hAZRqv\nPFtJdy7efDLEE1s8vRqscAQRihLmwqKqSEoxYZWIlJk+d3zGfXTzKakKHM1Na3/jttIAClThUiX6\ng9PYnN5rv7p2x+61cbr3UO+0j3pSFRMtokwZqVBBQpjEbRsa/2JVaa/bu8hOxZOmxhXQSmCXqsnZ\nrErXlYiiSSn6DUqljgkswTOKHJ1kPsyy1fPRpai3Eo2SdakdIJxFgo1Etq6rZ8xXnzyO7XQ9/pu8\ntDu2g9Fbj482D05fwyRgSgdxgTO5NxKoW5ILezBgIE4GOLR1aFJaS8mm0agCDx7Hzvjr7dG8ffnV\nvkQ+c/9Q28w4KTQ4jBVGlELGYSUiFfAqGclMlpZJ7Ej3TB9iBoJnEADbsmF7JRxcXttLdhBnihCB\nupS0DYODEwTWVztb9ZGUwWEcAWcxNoYgIQlyksELgZ431Ucnnqx0CjhfdEvmSFCAsCHmlkktEzdu\n3bvsHp8/r0nzD4eo11arJJxAJslaEuEmKskMa8WBJKu6DU1D+9ibyMvCCikaR0ZwigYJT4VD1RpP\nRJQ+ClxIvjGxcqdZIxCHc5K1qU17zWe7I828EWfdCDJDOD0tKYtFkmw9R1fKMsrYgmTrQval3bEd\nXK+P6vsctU+Mm2wZ7CkeTGN6mkNNYrTGWoKJqVmmgtqYnqo1NmmTEIDNvsqv3b3zasz4ij0QlO4R\n1FNSskVC0rwlITNBRIBEA0PQBk5wSplrVxGjNepLnzElC6jF1tulvXLtDFNZJCUCUYGAN+LZrC9z\nrRyjJVOU22Uo8rS5m0XENDUuJiyGGKcJLIiY2N3x3IZSLvq95UymYW2yRx5BShFGDGUyM09oXUjd\nKWePvjGX0i8cWkrXfbThdqDNONjNwO5ARotw8mSwYBsHlFetauLy2s5tai7umcyyGbOl5zi2TUDM\no7ESJzRy6zG6vzNETKXkJlrWWvrZ8XhU7mTfEwpIhHpVcncnpr6KhlSftt+MK3119PjxBU2+dTH1\njXkS/8Cg4WLIJGE4y2xeqoUIDZMNudsTwpiFa9FqlqmwSGZlpALijYi7jsgdwOeb5Y1bpbvrG+x6\nBAjwYOFKrEgoKJN4oeEZksGs4iFjAi4CeIzeKjI1UzU9krQPB5tH2/Z1aeZeZjk5gLApM6ZgFgCp\nEpuhCTNnvXm4f2Bfuksmo3kmuzMXiShckemcJMuq9tyRRkYlDnMjm2xGygCRFIsqIOFI4V53XmEZ\nhk/Ov/7//0vC/AYdnUTp5t3erMCbQ5EOJiF9lqCQidyKphGqe6WZnR8uNSm1imDiknAKsEKFqRSm\nLBysNNpWJNjPBVEWcJ0bUxNJn3XH3UE3V4+JJOHMRKpkkzGFW2uWtBW/39/clcUwkTJttu6DfSk7\ntkPI/BoiH5g3Sfd1Y2XhrtdCkTXwzGtEBI8mwZKkEsG9FG/MNlFBo1oAfK/nbqcu7gxX8XQzKGmJ\nv6gJCVdQDhMrB4KQAoAjQIUxwkl7JFMtM28eXAu30YjTQyOD6nN7gJ6bzRh335GnG2/mFMkMrg72\ncQOk1XknsBBZ7pydr2bjahMO4m62EBXPlsqe2WAxeMJTtDxvvCM9HMhMIu2hkPRQN3GkigRM0KR7\nDTOWT063cgS/DDRc1t7Xm263doKAZFJfaQSSSChrWiTGbVxGOwf9MuvpZuoqmAAuxByZrIFsFG4h\nAtK0JCbuu29OH91IpogIVtA04YKJj8ZiZ20zBBCYXJSpTQFrYY5N9HPa/oVe16ucjDDbfinJthf4\nFWWmdNntnJ5ushJIlAXN3M3oWexR2NuUHtT13JqTY2hCZqP7CGJimI/mAK72dkHhh9c0P/kyjUgK\nLIgMNk5hEys4GcnK6ZnuHkxTdhEeLMwqSNZgj8skQhrSLDOEYise7L+OMga1kTRDWKXWjrKiWaM2\nAdT1sJZMJE9yOB20Z3YOD84R6o70lNorCzGD4+eINFKmW6CwsJROEVXCC0UByB1EIew2f1N2hqcf\nb+MEfikou3vzaXVuLQMYLTMtJ4i0yOyEOVmYmLdxa/PRakLD2DbWFfYYx8YZVJMT4i0cnIaIyBD3\n7OmbUxuoFakUJMqMqmXeohy+uinKhDEZIggEC1NClfqYkuv2s8SuvdOJZv4C+g2/lB3bIfKidK3Z\nvFRSSUamkYBhWXwcowp3PZgzslQ3qUkMigjuxTLSELSCAWhZNgjkzt7e8JS1MKNYmhepM6Z0JZh5\nju5EtSABEEqXI1IYBLh7kiDoUOtCWKGlqNZYJ2/bJVrnS17t1Y6aO+YzShUNEPWlVmwmJ606Dkmr\n3eF6f28wZklt5lzYxyCDpk2NSNkppMVzjx9StIgsIqWQw4eYdM5gJ0lLC2YAZPqOy0J+dX1WossT\nPFlNx6BxUHU3AioFkC0CNlqpRWbbyKvZWTTKi6sbN39tYZQZlSs6JoIpUa9KIGO3xBREpIhv0JgV\nMjMU8OTo2fXavNwgs1G6jgVImHkwc0pBREbXo/0C7I6yd23RM0O2frW/lB3bIQiXZewwehNNVeWs\nkxOkEiGHySEIVibApZTgVEuEUZoCSnCZ7YMB6I3Vglj3vyPJjLBokXPOjNbAzDxRZJgLaVCANMNA\nKl7RCeANTBDzkRvzNLTRHCxK0N3Z1idgllm9Mc+6i25WeTIXp9DaSYhyP+8mHxv3dDUd7Mwv7594\nSvhknJZKFl6sUQVcQBt3LkXleT89IuKKieewYFKlCGuEEqWUvgo8o2WnbGdGm60cwS8DMr/Tz3g8\n3WlBKqWq1L6KhLSV5GAMNg/fylzg+a49zuHo/sUum7uUIMCzDc6omoDAESQJIlEfh/RvzI0VZkg3\np0KRLoezRVxePXly2lpoEW9OyUjLtLAABmUuvHXZ4d2NWF3C8bKu/JcVH4ZkiVjN3dGTJbEF+Fnr\nKd3xaW5OjcVYCjQiNQpZgTqpWioaW5oCsFwsBKMuKY6ETEaVhGJMcAgVmJsKJSXKJqtasrGYaCIp\nkRaVK3SSAKYgYfBftSvY+gRM5qDV2c19Z45gjwCg6TDrnckrKJNLR9evnazolRHr7GCiCEEom0Q4\nWd+CtHoEU3teu2PgKIaR4QJwiEAoLTo0FFLkwGAH7961/NUtDUTIzS7tatlHECzBPlBJdExE3CU6\nEBp0K/2szgTTpauN0VmkqyObsFITSuFp9K5ZL5mtJFSA6RsjO5iKRFohUje3K6XpslldsvMomcmj\ntBJdK5maZmJtJrn15Kfq0vl4ctdpuLblpV7Kju3AcuPW6dS/Nud0nigIYCECeOJsVGPDQqAxuyT3\nlCFJ3EghoKS0YI9nH+s4HO7WiKa+Hi5QPZ1IJqKRrTQhJlPryBOe1QgM9DGAAaYkULEgV8C4kFQk\nOBMmGQqUbWtBerlaeamlTwMFo6mSUaa29TxcgkpODi/9cvlZufQplMSGhQ4S3FjYLLUmNWJnjJXK\n8wo7BTujG6nlzLISp6MbdWInZGRWV0/h0p/0W29Z/c3FNA824/nhtZ2loAbYvVgXns5EQWytcM3t\nzO/YGezsq1Kv9xoKp8JjokOoNKPW9cWzKgezuGUhBE/fFCHfepqiTyuUAGJNjkv3NqYIXJhYVUkl\nCUncIqeOn1X7bhfubtRZtFn3sr7jl5QsBxk3D5INCGtmbZVIi7QJ4DgeKdM9urn75AFhnrknsbhN\nkzkXgHwyAOF2/JA06r7Pa2oR2DAK1zlqSJum0CkJyQAxGGBhYRHRAAGsvZCEyyTBlWshUyGk/0Im\nIXCZZ+veWOZVOgRKPE2RzbWTCUXcmiEhxF3I2ReXs0WVrDUmQEuJaWhOTjyGMBEp0XP3ulUCB6C9\n9FEXRSNjGsKGMaZNaw09JasIL2q0b44j/RdN752Nm9MBN0onDliu3BvTZEkpsXElD37uYNPfBdZ6\nNbV+h5mEC4Oh/YySg6uQuBsnCSI2yVRzctt6XvnfGWpNSmQGg4WlNSxzLLy5mNyTnbknGJEIp7Aw\nsfI0bd3usMjrLLEs2x8z9dLu2A4siKtlDwcJQJxgBzGIeiGWeUdBwkTRDVGExqlMAlVzCDF5CByS\nDGDDeTzYjLu9+ckwbTqAaPIuUylSiENmhCSQjfN0mrpILWikiiRkgpMbkSAmUTPmSAlmjFZ029fl\nZtodd0O6WjIzOBhkWTpw1qQKT2dmioFvxg7vbkgyiEhTCKE8Tdw5R4pzIIyUnlvVCdfg9GE2pymd\nHcQBO/8Ct/clRHNsMR/BPfer8XgrR/DLANVFnV2uPt/fBXeNgnjmhciYaapBykhh6PM2wP87LR2r\n2Woz3pgtM90hqQS4a1qwgTJyUrigpPCzmOA3pt1xEiX5RIQI1TWfXPpyJqNtrgc4GGD1ak7kLE4x\nKxr0C7A7/Hjaw3i5s3332EvZsR0E89cm9kvvShAGKSoWLIFgD3UBCYlEUFRv3BErzHlgkLClBoXw\nMy17GHdWQcl1f5B5UjCvUCSyOCmlc0UaU0pQnzFQbcRTRB9GQpmE5iRDSSUhSqKkFCJPZsmtT08W\nOz/f81d2M9MYRN40hCWmLDDS4qkZYrNxZ+/j11+vTQBWjUgkI3THokimtyLVCk3M8Zw7JiDVtU9P\nykbJhWha/ezT1bXvfGumxk46tR4yheWvcKxcdP/odLdyJyOncxmlinigQjI5g8iJsI1hL9PlsBeP\npdxdBmvTDlSmoIzgBuZsJWfkUcnT+mRLfHNi5Zxi8EgvZEChq8uokrzqiTPTiicxJqoRBI6iSZzb\nT37K2FxIrlW230j3pezYDkzz6z+yvcvJUSm6RLJSmrNycugEB5Jj5BrOvu7mYId5qGiCiCzE6Vm/\n6bbZ2+GIju+Os8ioWVgtS3JCeFKgCUMtlI2Jg4AISgQnOcCcGdQ10SQ4TKHItdQUxdbHHVHfYvgn\ncPdK0XMWI6YYsubI2gweIdSaJhYzszZ2obzq2Tt4UBJnhhWpCm+JpPDndZsQoXPl2gJBRM7OotMX\nX1ytLpevKbuQ+Iw3l7s3psXdrRzBLwOTEfWL2bTXA56StqniFKRARgDM3Gg7E5CPi4z3NeaY1aix\nKWPHLiJNapIxLCTVZSOa4plM36Ae7MwsXskpqXDI6qpNG1n0dU3w6gSK3kOBIHbtzATJ2989k1wd\nvVLw/L3fnpuXsmNLhDfJ/roSN23CCqPGjQiMgPSUgTCZeQv2pCkr9xneJjKm8IA6Agrg8sl4zZLE\n+tX140Y+CJNBc6OdOWhMzhxJQsIlSQIuwpxWZNBkiVJa1qaUBJBPWQLUERyg2Ioq+dcoyziUh7XV\nvCqwQqkZmdCYipA6CUgCM2E5FN18GN+t1JBTEoLZclIhOBmpZU7JND7vfkdhJ5dGAjMhDevA9fDP\np89Xf7x7qDoqN1fq2ypO39rKEfwyIJbdsrt37Xu9RLAX7ExjK9U5AWRm01IQE2/BTL1Mvjh++Mrr\n35GJXWsjMBhAXFEpQGHyjbdxF0ogiHt+Y+rKmQEHFRN4y/DjTev7vqqt5traUnwchBIhFBMkiCjD\nfNspGaIp7csLIt66c++l7NgObm3drbGY02hVgImSA5SFgXQkJWcYnLVJFIqJPShYoelMDZyJEsMS\nqHfs8HoGoV9uGlpbuDgjLdDYyZKSoOTUADDZxD0kiNlSkgkMlAxGBFoQSS0GUngA2+/qWdNmi3vv\ndsOs5xFOpIipi9AOxsLJlEY5LIqyXN//6E8vvrePLmLIq34eU52rGyi9tJ5btaD+ub+HkZ/NT2Fy\nJktXT9drvDK5/ye/P1cMFUS8Om1XszvbOIFfCqLOdh4v7IAQLWo05T46kcKIsZPJQowltuKr91Wn\n5zV918GUXaNcdwyqjeAEF2Zn0Ewc6i7m8s0ZKz+xBHt0BTEWuUysKjUu4pVDunAIZwSX8AwPQXgV\n20bM6G9gbpfit7oct+4e+8Y8iX9gbIIuF+PFKfmqv1oQOIQ7kwRHUmYUT2LNYDCYiOoocG3owqa5\nUiY4HQpgt7S3ehB3Guu3EOSFJ0g09nSZQKo0UTTWpBCOEAU4wDVSnvWdHSklk8ZaglojlgjiGv78\n5RLPi+5xzoeKGYmbI2OCqkGRTpriLtmgNRrv1K/u7j7+cNrfs8Xti6YzGIGe6WqdcQRL8PPHypOj\nMEcYp49zyUJkJLdvP05cnT56U8icKtr58Vn0h1s5gl8GKDkuTnc2BwOlohRMyemSCC+Z1CGFtzTD\n1HG2fyprhFCM6ECl0EiUXEMjIW7ZKmUGTaNw25IE+7nQFA0eIjV7dxnbzFvtrJEQIzO5OjFXIIXT\nQ1pwtK3ftyKHU1sejq3fvuw4pfGz+59fvMmvfk9vbXu1Xx2knivPV7GuSyVJZf6L+dhu2dW0MKAQ\nqWwkuCm5BmLiMKjAkiKR1BjAxfG3rpfOJHu5m2BltiDmGRsRJJUCoFCHNGo56zM4GjEkBQkYcXFL\nal0tUCSQbpRZhbD1Fv9tfe3o0byUzjyWmFTAZKaKFM8wBEy7NlnSzbe/mF79as/ODvY7ur7qhJQC\nPFA1EQJlmwhJz+typ0punOItuuLJwRD3+Xd/ZNnj5C4JqcTkD47H5Y1tl1J9cyFPGapMSJvB1EKS\np4B7SsKZggSMzTYyK9jWG6r9XhEiodBiNUvCEjDyUkaSxmyhPErxkvHNmf0kScgJ4MhuHKeyd1KF\neLE/Y8CEKD2dkZGWQhKGWlC2bncw93dOotWy/YQ0Nf/x/+vJQvDwTX1n24v9CkHTcHBhM4aAi3do\nMELWAEkYRK0EwskEodxPoslszOrJiTCFlQydANzdtJZTnz5d789dJAyaSclAJoOcPVjSIRnClknC\nRBlBhEBIjGMHEi5jgoJH4dAgwJqUbasmglPY+SyhFCEKSdc6dq7P5JYFaHTiTI64vLr1/Xf2Z1S6\n9GtIzyjcJLKwAeGFZYzn1jmLCRcKTyicLV0J0PrmzlnhR/l2IQr29Iv5vt/d3cYJ/FLQqFxnf/ID\nYg2oJCawF49nPWCcgn3N4tsIji2Wy6ud3RtzajMSJ3Z4QCw6h0QYBcNY2a1yCR66rY+6/LtDFMGc\nTQWROT5dzbOjulMlxTRCQOIEjuybMzPZ2OVWamT+BsPmxG+dWyvbDwypP/npZrN7/slV98PdX91M\nk68dzosPN7tO4dU5ZUKWKBmpEgQYF3gz4TBKdlWJTGYm1JbsQqnkXfMOwMmNua2W635aUVyMok5k\nEMLkhSLTK9GKOhfPjh06DRrsYAgolNIpKkNDTJICWaGIZ3MsMrZtQfvs5vpy0yeRkXBjJKFVZWR0\n6S7OUahJGdxlvrJZ2ROvito8hYQIEOWsDmImYqzKcw73Y0nkWCCSHi21gsOzX96kODn09Z4BA9nT\nh3pIb35jis7+Fi6iPfro6vWrd2dzBhNzFIMxBwzevchwahU+uG6CK7BkJUoLFC9sVEDE6mFSaCsF\nA9m80Grn2sybuFekeEOnbIxwZHiZlAOKnBlRP9HW55X9nRkSxaonUzilRfB6Ppyf69CqwtbC4IIJ\npCOGJbUgDIqtF6DWYL/SzMwt58EAfPmz+1c7NSefcLTtxX6V4POHVemsESV1tQsGWZCkB7K1iNFK\nx1ANtmzuyKRO2Q2FZ5kRUk1jAjCuB2yo5uzidO3d0FgiWYRZ4QUtiDOVoFrcnSavMGMRmjJjsiSd\n1zBGmKZbEmUbjHL0SLJtx8pzfri5uHXcGqkIiJPVNYjcZGrJSaaaGeQupAvqRxhnWmSEdH0lyxxH\nc+ZS2daRz93CunlrE4MoQ4TT4MSFrX5fbLZjf/zE2NMvf7I29m+MH/1vI+zpj97/8v4nP+ZEpqpW\nAs/TrCW/2CRWyhQOG09TOb15g4GZwBLWGDYSM7tuoxlIha5XuVeJrTlNaycSxWAgKKsakc5UBVK8\nNdde69avxL8rwWw5mFpjRuh6fTWGDm29psmDCouIj5MBHDXNiGZqz+10ff5dWbUzdrLtN47QP/2j\nL+WV3+iP/5RPP9n2Yr9CmPfluJep+QjBYCSm4u5QG2pW44wEUaPeyFA4n3UDb4IkEHrZhBE6ANcf\nbto1Vl/x0MlEme6omYHoLDxkYnQ8BUqyOzWpDniXmIghGhkkEURUNgI0lmzeG7NMJlvPNtfxT093\nVsfuxtRYegsUKjyIpkMIyhxVLQQj21G13bFEsCd0XdiTXEgsKmDGzYF43nqnjKkIR9fEMgXkFqU5\n4s7+tHdxiu9cd+jqquGi27u59TrcF8SHE03q7t7uegnKbMI5hpARQC9mQVrMXnt0ta41c0RJBAcR\nkhSjg1gjC4luwwkS0c2ku1MgSE6SGJlJyrN5LQmS5iRTiKcnOKl8DU/p4sM/6y9//y0pYUqpFAIE\niBAO5lYnblUAZyRRBjEwUDVOkozkkYjId6CNaNMDI8/ci5a9p3TBe5tVn9lUuzEyfSTN5lnFtdow\nQ9t6jq7S9NWTZqR1+7Hyo/Z6/B+/j5P1xn91E02+fjrK2dkZv7qyriYaoVEiIMQkWiIyNVJEg4oJ\nhN1QaLIobpRJ1qZCzgHgaIh7N/oWKOevFiFmKAxIybEIuaRHDiIRRkMp6o5wImaksDeEcHA3hWlJ\ndgSnFrIikK3M8vmbbI6Xm93D6+MOaVPN9OAI70U9eksnr3AyoGB2/d59Xk610maA1BJoFKgewdxY\nQCHc8NwjJDSkIKlRIk0KTMmapN74zn3w+d0buLy8vX44njB+fecbo9D+LcTjp9c2R7NXZoRBK2+G\nIuCE15FedIgXgw5jOMJGS0fhST66lC6HSQSWYG3Q2EYXwoeX652dW+8UF4k6cg2wGDibUlAowM3C\nW+8oEupfS37HZ//6atMWr+x4ELw+S94LCiVO9pRkdJlJTkmZkMxwZSJxNqYmiqmA8Gy+4li55Mgp\nOY56cTI9WcdBBMeURJFefRIIc1RzH4S3H66Z2jBlrcITtl1Yrm8djcvXFzxqp4str/Wf5//90eYP\nf6P3KQpYG0GTwyk8OUTZyX2qAqYghDSIszOKNOs9Q9MImdK5V4OJs2hEs783ceh66/aFRemUnJtk\nAC0Bp4QiM6OSpWeRoBCaarCHskSQT7OSmQrNYADZcO97TUJ49cmruiECeULTkz1HQmTQRAWTWidg\n98go7iQES3KJtMrpYMqkOhpHFyLZPJTGLT/ycrAupusKNJLE5EiDNjjVnBygCE8OA5Xb2lpMqeiT\nS0xMNOVCwhjBFMkIiKE95/cgmpnEidyoC0gDKeLL1W63qbPXyY++ys35RbVbb37js9Vj5VGulW6/\nA4wIIilCBJP0fKEcqAYko1t/+u2eNZMtMTx4nDdeuaEAOY17smbaSorQzoeyulULpamYJEKcJd1A\n4qFEU5lhsHTOxplEiRfXeH70YLNZnluM0rEmGEEkITDOFGrCSSMraeazFqMeYEoFiBJduMyyMVDU\njAMhAKH5imxxOA3HswphUCNOt5rSSjLCWNF5tRf0Wa09QDR98eftrZuvLxo0BUAajX1qUkMj1Lcu\nxrOJtj8qRG/84IN/XEbW139WZtte7D/LJx9dbF69riWJokl4pkB5VEJSY6p1EKZ6OiMgWFgAJeZU\nyhItqUtjwMYKDggjB/0FhIv+Voy7BZbntKhhEgwwuFF1QQkOVsgohjSOnEUwHBxNyQgyj+SmyU1C\nAPD6YDi5MWdvjIO5IgHJgQiJtSavNlPBvE+qxIhVzzxosqHN3FIyJGXWEkLF0yVFaDJh9xi80ra1\nIJ98mOeNUpKBINWgMOYkn2LIOXJgchUaFMv57tV9f6cUKCHMZyloI1wlS8CIxT2fO1BqwhnORNoH\nJZ7l3rNMC+H+av1oVb1+5XU+o//mZvEnmgSfNpcida6FLZhZvbiP7ScPZzd/i6tEuCKE0rWRd4Dl\ni0Spn4ers8cf6LdvqUTB2LTro6GV4MxkbF7kVdccUBbjbDX5Wmuy+/qnH350sfvW/7AU9tQ+qHKL\nbXxNQ3+xSdmwxEiFaADlxkULYUJNpMZUJJ3YJy6hga8hYhA6blqSB6FpioOQRB6GnhMCYsyQGdEq\nTeLEmhTpmVKDSCyTGICma0PDzNMdZfd0drpKjXZ+i9IEUAdvgjoySG1uWShf1NYPtyrjo3/1452f\n3v6/3UGrKRzMiF4RI4un0uUXs0ll6982oMtr37pz0JL36t9zh8rNft9O9xAwFcZYSmYEKzXGWEI8\nNYpPXabVDubOIRMXjrRAghsVM/KiJpSNg1xa/P3lgpfh8mE5xCFHCWcJZieeZ2vicOoTG03OQMuk\nIG5MAMy1cHNqnMnNtCSAN23Mr17fDX8yX59ncQ9hbySpRk3Wjz/+8Mbirds3i5MbeKxgiMAQqkZi\nIfCpBkyIJIPSiTxgTz7YvKl7qwUh9rd2CtOnm3y6Oy8tCzZRy4BkKxQkSCVmnoASrCVRDu6d2plp\nTFSUfeqCXJJ7TUQDZRMSzef+IJpAa7uaiY6cZsLZqFksv3vx4Uq/t7q226/Pc7n43u1KGpSRdvL+\nx5juHv5OChRdmjQX/+Bnj+O1V3eZweYJFqJgiYnYf2Fhkger86v5QT8vMZWZqxEJ0lNLerzYTUHo\nud+58/FeQZExtW0++B+fdsPF2W/9tjB6F/JAbGXKZC/Tqf/6kllH78Q5NYhcKV0oyZxAKUTFsdph\nAo/84vbh4dUm5GRTK2dJZjhvujQlaQkg4UIEwCtcVU3SQMTBgqZBDBcIAYOqQ7wFWLVRt55vSKQH\np5FXICFcTJ05EAx3kL7o/KwkDJv7986wqk+fXu8yMS6JKSiaEXNSQbcXJ+RO23ePqe3YorDF/sHl\nttf6zzNOm6HxpkitQ+s6cxb2kdMExT2C2WlVBaXzEAvinFQgLSbmnGJGQxCSIkk9HIQN53RUSS+f\nPBiv3dpj0QQaxrkITeDtumsyMubrRd/MidHSJSBGuimUnXsNahp1zGrm7JQswekkEWn6LK80QwjA\nLg4kViNsotm+OtYVmR0kg1SmB390ejwN917/g0Nh4qTYGHo0UY5WKFGm0JbsUhAQSk8BArj4n382\nnZwc3PjBgmx/a6fgqhljZGoSwOBk6igFaE7MlEISKe4etuge7HromKWF1I6QIkzZFNRNSEKCntth\n8axmo98B8Nee94M+2RH9/X9ki25v7/z/AQB4Spkx3r/370o9XsvOd2bJ7kKInL56cu1TPHiyB84p\n3ZIMUB8ZQs89Bvfn5uqrr4J2diSYpuwowBxN0AKaL5YllrTW2dRuxHo30mxlZx98vl6e6bUdjhRP\nJhLoVmoTZOfjVs7FYjHPHLOmq1oHEiJEtOIJNNLMerjWZ8WuL7zm/qShYWCFgSgFHUg4qHPTMSlB\nCVAHnnyeALsiDJIsIUhniCfQRqXhaLU+kMOOM+at3tnLzdV+VbC24gOKQGtgIgoEUQ9ye7GYUSux\n/tH/eFKv2urwwzd47mONHBsXamzMYdlitljtHRm6F3burSDsabr64Y/r4vA7t4UkNQfV1jx7IX28\nOCwhhOwe9Z/Lox8tf3fZMaUjNckJEPGmASRTCEwdFQA3zNrEVjxTGMY8+ZzSvZ84iCIrjFKeIx1f\n21hONn2FEg/gMYtzIgBQIUasMR+VpcAiIZbFGEZEJplIdiQkSkFMU9UmGSnCOWY++UlcvHfjt16B\nOmeUGfJi5ts26Syp0MU4Xl2vz+IT1Kx6T0LBUaeGSSIbmxQRtol96hKaTSepiHAenxVEAEfD4umi\naHOp0rkWcebkcT0nnfzkf/s82r3DzQ/L7+zOIeZkviAltyQ1R4iCWTzNKnQiMm2Zkn50zPjy2r1P\n33+jHby5tVMYdaYXQZDUUXQKFUAkPVOHOdHMpxxIxmlk0Wvj4tp0dlMLLBTFBJxBGUQjiYycOcjX\nlOGoEnRyIsNZXSpN9S+CYkKedPVD64b1auz+9e1ddgh8KGkXn88nam7hkRh97gQBaSpi633s/4r7\n571WRuvIwJkUnknBlkh+sYMJ9KMX2YsW5pvLz08+/izT5nKwCEY6NRDxdrouL8p8c+1N2Vy+Xh2Q\nxiCPPmkiStPerLI1KoygnkkYL5oWAKC/c/oUN7rMRtZxYizmPTclcEwckeRNiCJNOOEBuEMlGikH\nPws2OSAo50/vXfHx+juvlrhqdUgavpx2iwPAxJEET5NKFAImf9FgB4Aa7dGfH5/XW48ZP/39edA8\nw9r65NvUm/jkCBZt83I1LV7cTBRin3j19KuvTur4FZa9LS2kgtKp4xD9sr5TZQrB3u5suP/l05/J\nbws6JbHJOjXiNJpN654ZVNI1gbWKBNSpHyioRqCFENXk5GzCTJYC9OHP87bNspYzSkiaLtO9MVoW\nDyJKDtQaYiFjeggRV1ekJ1zmlp4zcxFiYg/qJCOb8dyMo7RHHw3TyXC2vMajGEuEZIb4lh10pGQn\nh0fH50kVo2uru02QUrJJcbjMwo2AQauRw7mGZlK2ZuJT1zshPRjA3iqb1SG8LaeTJsyika26yxRX\nH35cFpV1ffyBfvewiFMumjAYjZCa4lZcoWAitujIWACCXa3y3fmoZ5fDePvXt3cKy1lU3znXwkwT\nJSFTHYLmyUnRpqkT9rA+mCio4XxYRlRumpgkWcwlSD2SCeDwryegzYgM1oPdkVXV/uIjS+I2Pqrr\naifLCfJev98Va0wZR1+ePsmZ1KlvGbRwdkYLFI7IXM//5Q+PlweVXjnQ5e6MpKeI5yxh/DuxrGe2\n62ZTDUIgmNo4I4riVF5MGy+gmN86uppJ5ukPP/p8I48yx7t3/8WhZjC1JkzwrUhJ/WrZcj6sO0ZI\npHC4cVAb+0gh0IxSOQMUUGIKmvKF04euhk0/f6oI44og4eBqyQQQg5hbezZMLWOGjORnvYWd2QqF\nC9KsAGh69qPNYHp65+FdgVzZuYvurPelVKDE2Db9kgA4sgxaIjnpRYsucjr7os7604PV/curq5uZ\nHKK+f9C1qSQ3FvLRdvbHiYhf2EBrVAw4/+hDPH3Vv7r2g3mnMrlqRtck0PRy+i4r23jx8f6OnH54\ndfnHr/fFAkSeXns3QsxIY7CZJwW8dcI8JbILV+OcaCxgQUc+AhwcyBAB4/lazxw+vJpTiI/UM5Fi\nopJTkgSKNaAJi66amQjlhOCM9YaeHl6jUWnKAmeiBIdvJCRIJTOSffPZV+X4Meum1aBQogjem7Dt\nFrISuljj9topXaikRMAFITHLqwU7uYBozBnaUDmnORyeBSHELO7sBHEGsFyO1U5nLIfDznLBaMUn\nlkgitvvv73XTXjmfl83jq38+RwsmjkwihXAWZ7bI2iJTE5FSCMwjNZod/NZNbr/xcLqzu7e9U8iT\neMJ1iYY0LeIJ5piUUpqGGFSIyLALz3Fe2YdjM+JJI9wTsaZppP3OEJDk9Pia+krXFhPujnFwdkOE\n/nIeT42gi4e5wCuvvb/Q3c++O2fWQkx+7xibHvWiyh5puhMy2r2rvX2WXi6v170PV/c3sqt9t/+D\n7yw3s+3UGR4Nrc2zLKmRwFjYUYIoGzvJCzkomhB18PU7I6/+3f+0vn7+QBre+O9+f7+6wGwsIgHb\nSvezhmk2rR/uXuPGYVwhU2gaCjIplEaWIMRIRW3igtzk/ouu2e8NV0/QdkjDGKVBMYXPzJSTU8Yk\nlwBCJDkoG8g03CXAkGiY28AKFHvw74nvXlFqJxHe+S5L9IshQ6ds558fLb53jTjAaJwNHCEvHOm3\ns482O97/5lv/n/XrXasSJFYJQ2qSlDETRF09fKOL6YX1LFJDHn90f8RygGxsKA2CGMGsDmE9nlXG\nCJxdvaY2218NFx/f6DDRRCKCbAHVjYOUw4SSuEjYGB3xKAC56IQSbl2mpcVcwkVnEWRN9Hm2f33J\nwyNkRggzBS9SDOEK2CRkmlMitCTBGOBo9vjj8WH+k28twB1DnThAlgwEU8vSxJT8iy++kM2x7R1P\nO1pCvVEFELLlFKyYztZX8zigRENJLzYqeUThoJIpMBOhGSWHOGPeOpoQpjSi65uOVODPZs4uDh9O\n6/M7nDpb7PU+akybWYcEfPWT492ds3f3/+y49/v70zSrwQQjDvIA55UqgmQciqcARJFw4wxJ+87N\nnvrFb273FFDWba5LjoRQcsJ5AjZcsh/WtZoUaVnCKKLs3rxvO4/WWihQJBxh9vRnq3L3B3MJMeJQ\nfE0tHbza7v7D8iaPjPgrN/7kOZw8jtXeb7WvovYLzrV0zAI7f3x+0Ka82glnGCTJcv3pY/324TUn\nx8O2GFLOU8er65O+W8etTElCXG/lUMdZ6VpMSgHjLq3VTIkX64gsGc0OFY8vxsfv0fe/c/av1n7t\nD/5gvyPPzKqSE5tsJd6xXD9cLJse1KaKzIiikHw2m7XpmOdHsxsVoYk0zokmf/Eik3q17rDcDJWk\nxuiFkZJsJGTJHBwsAYcn5cTt9KOdeLNX0kl0MlYhcDEC2um//+Gv6Vzz0e0WxjLjXmX/8+mupcf6\nwcenX6T8xkwhMkWSaHK+cOLs5ur9k+727JXfPH+c39tvmRrB6aHUJIOQ2IxHYz87X5evoeFw+Lh+\n/2KyV2493tldjlo5mT3QWKlY6uqgz+ysPaU20VPp+jjelBpRMyvFwJBwpWa1JBuzcINmxliIWwo8\nhdlk6iOIRAjBQumRZFr0eXxWS8bFcQsZNYJRM+LUz65XH6tIRYnMps8KeTTZFLb685NP1rq+ucgQ\nDEXZkJaMjOlk0EqCmtOjf3mfrvVvfnnx0T9ZqlWAyLJsKWnkr9F02VEuDmnsxYM5P3+1E6tTGunM\nMku4AIywTBgHTVDXyFLMg3ysrhFMAPblsn75ZgJS62xTqk0Xn9y9vdBG0xS7uoo3b96995Mv2uFp\nF6QxzgAgQsNDAYhHyKQT9yCQaQSqTeOw283oxVWT/wJTzmQ+Y1OIZxN2cgSNl3LopkozmAlDsJGk\n8uoPuZ2e7GSsU3nSGL/6czm6uP64/eZCnMhCiL4el3tsov/ug6+uHd3xiPyrjpA5nn9lZ8P3d9dv\nnkyPv98RVW/SxyYWg86OiwzTVMhcq/r4EOPpwWbe5y72Py/o8mozl/nu2b99+vtRt2LUHmJc7Ssh\nU6iTiKRwmCAYbi/0JDlH4129eV7PPn33+v8p/mXSbPntPYlCkqEtmX1L9QJXx5tK7focyAbxRLNq\nHJGCaLE5/clDvPnW3XlGBGuyWHlxpc+/mA5uxvE1RlK0GUeLgnQlN+NZsxAKJXh04Wdnf3qVuyff\nOlzILMOInIJY04CrL/746tHby88NUzpRd7Fb1qdfoePoDfL0q6un3flp15wZEgI203zuwZf/EXb+\n4aODxW+9trP4P7dbbAE2FuFogkBoTD6dfNldndTT/rnraP/3i1UaP60x07cO/nh9g6dnPRqMRpsj\nB1LVdUTmmHa9o+7mcVynxdmCaqpXuJdMZltXrg5lJU9XgoqM6lN2CcmgxlzS1BMUhi6DE0wFY3se\nD2nZGbM2Qj8QU46m54/t0fdviW96mboMA4wii5iHY4r1e1/6WJ3OdxfqvIi0NJQmmpufvedv/BZJ\nIFbvfSrT4Xfo4tSfvsXCPnTP/EXbHnvc2eFbRzhaPPkeElMFazK0aRCNFMnNkeJJkcIcUeGalBxU\nZBw1akQ6nAAs737e8QZWpr5bds5+9Wc//s68F5iN2D/B9QNZfvvw31xM59e4JHUBxFioreZc2Ep6\nZpHoInUMaUPl9OQmxOsZbTtHaDMyzWsLZ6RzCJeEbR79cPpvFksNcadEkmXrYbHX+i5GaWXG4ez5\n+M+OsG4XV5/d7UBwbgL9ehR6caCG3Oc7PQf/pZWg6ccPy3x2u54lIGVWM9l4JOa5Xb/+xqeP5uvZ\nNC9IirZ6fOTzqa1uGuF7cbqIoejxZvpydXBx7Te/rpj+36R7ctlO1nU+FeWES06zOvLEiokXL3Yn\nufS6X219Spvbf7C32rDE3rf7EEZOdZMiyvzC995/kgCGh93OjDgITePq/EG5flstQ5GXD7/8tJ1e\nBF+fuYamSiy+Bptu/3tfDF/NrIrFZlI8q6NFTqilarLGyCUcEyPv/a/jSbn2EQ6uByNJAzBKt9KA\njO72uJ8P/LALgU5jN7T1l9cnpBHaKft+p9PUKREHU6xY469iaz8vfnaxOF1e3+N8zStJyVBtlkQx\nmzIn4fWPjk6tfOUf3vwatOPcHA9jHuze3Xnng9ODQxFHuOeqCCeS9NaimWaO8e1r/enpHjarowdv\nawEJgjgzG5XZs7peSpMggyBViZk4ElEckQwyEMjcPWdiVCfX2p7HCyvT6o3lp7uFZyaluU4/frDI\ngXnh6Z5CNKIGkjISHBlX//ZyWi/y8sqbV7gxK0MpPVd/cn929uYcGS3GZUQ3t0d7r+wj1AOUkZ3K\n1rOfZdRR5rq3pOBK4a9OG1WYLMzYAxYiPIVM3jkxgtSYwOyRPNmsjhE1IACyH/rI1WwdX3Zvp2np\nhsdvwECJI8vNm3e0tGX5vZ/xKoBktRQyvTo+pjeXXefBxaSM0xLBkjoBYlOe60LYvoaclf88ejWs\n6rqKRIqMpXqQ++bD+939w5kQDV0j12C1kUUnQS4blZIkPPnZJx/0V5uDS1+d3SipFJT1a9J9XZAy\nKxoH6wP+K3ePJ9P+5s40axenMr/5rmgjUnA7W61nueTHa9+EtFoz0dmae/uyvHbFivnvbCTad339\n+b+d6PHZv7nxylZkx7VP2+ruQCULwKnp6usC91HUn0tL+98xkUTrbv0EP+vjzWXhbqbdW8ULxdSX\nMUJItE28DTdvfzhV2pc2Q0Ai4viL4/Vb3XVpaTx98vHZMF6s79y85h0RLHq32YvbdLM7w2Na7NEI\ntODN6cWN69yeDbRVECSl90DmQONn/8ty2fn66k9u7s7EJCKgQZGhQB0vh5tvTvPZ5bIiaN5frZHc\nzWhTefXgErIje2qL5JiyZ1KntOfKHvpP4EdlF78+F5Qo2UkSu4c1RVkXJYYP06P6ZJqd7nU0vGjR\naqTff7Tcx81Dfe38y64UhU/MsktpzBMrnQ2U1ma/flJpvVo+yb3xeKQEAjAJ1UlQRxt7aVwqZyYc\nmpKJoXTNmEVbUA0NyswCCgJjYAt+Lv2rdHw8nXhlEw4pdvXp6esdiTaqcpWcUalxVU+PcCr56CjH\nBRZv3qioyJbIKVN5cjw8PjkgiJHG5stufbPvLsv1PGUJkU5IXCK/hny5/yyuKtd13TjSlUgad2DA\nzatMPaw0HkrY5Sdny5m/sZvOJJmgcEJBZEros9lP6GvaV5EbyLTuOUIPdi4+Gw46tc10Xi9eL1xa\nvjqs5hxNOYPS6/jF54/Ov/fPREwoY5AW7dkUvhoJoWN7oHulbH3203DjgSRRSEl406F4Mm9aHX/6\nG0c35hBPD6ZslMnRXV97nKVwYmD45cNuvNq9s8nYr67ERsj8emSHNi2zzk5vbzZL9viL28ijlsw8\n0DaMpItlWjZGITw+vdydHchubnanOSUCOvro8ngj770xY/TZEceeDTvrj+2Rnfyo2/9atvkfsaiQ\niy9fRTaFImK9i2ykI7dRyws9SXJrTfY265Obr71RlPtbtPdajg/vaqwckhRD1q9LcP9Nrr++4S4q\nZaSnsk8TXDKqIjcffPQQ3WF9uhmzEMggA2iMF25/Ef35cvSnd2r6WP34j+K7mC2zEFl0U2bhknDV\nNunx/dfOD6+V8cudL3+jGRkrZckJhARWHyzyjt/bbGifSZvIsMm4dvMKs0w8utL9qeKsLJNz7BxQ\nalnyBZM94rKtrwe3RQQHDMRERZUyBVPhNg0P4oSuXTsievGRsH1cHrWOltc7Pfi1N2VK84kqskwW\nqjPXIxzd7igX73xEzPb5htbLvNScrJ2e59u7ZMqgEj4Un4EcSIdzegqzgEomQzURhqSU7Hjk8GlG\niOd7oa9/e/gkH6PmqiIb58Ti58evzEKIenA4ogt4MxKSqW0+tdPrb47x9j5neIiIc3KGhuhKpykn\n4hxPH53Wce/ayf765kJQwymIImnbogPRWlDL9bDphGCpOuZUwRkjLWUEMY8TtR/9K9q9E49/96CG\nEDklsSU4uZ8aRRiAfic20Tgvj+5YRij0u3lUv7p4MyWmJ1e/PkewVHv9sZ/tUE86lcI58vUPvpIf\nVOKVMmIcHu8tlhKZJNny6k9OT6ebFbbtbojdTlFuE+eU6TNlL0GWHF8d32CW0luKJaURw/T2rQdH\n4zuTOhvQYm3XTsoBpwXS81nF8df02DQJi37vZPry+0jRv9DZR/LH57SZ2skHpyLLrmaFO3J973J5\nc0/XJrCrXp3Ewza7bf1ko8uxE0hwUCGS7g/9Z5XiZ/qHX8s2/yPmlCv+ATKdZBJkPxKGthoXp6dv\nH77Qvc6CiZ0XRvzOQj1me1dNO7lZ2D8bVbpbwsm8lVj5Yn7rqsI6K3XkXJ8+0aPZ0kYB0XivVdr9\nzvHn48NbVYVZsmT7GmoDZS+uai0RsVqKndybjw8PFprglAbXyAZiTLI73cPN5au/fv7vLp40q4AC\nmWZBigTKW0eH18erh91/fT2Dqd8s6GRWFwNJhtdrJzwrHG4KdDwW1ggGXjDPuT2KfrcHj1ScIrok\nDXJOR1UzcpmVc6eRFp3pC6dU23A6q/NLXXTZ39xdL/Py3sf7/0iravOam6oD3/t27RTXrk+xc3ej\nmzOZBpnp+v6n5/7kDxeFjQ2koZzNwQzXSDawLmwwqVaEw1nKtBG1qlkndrWkYs9VgMczvZllHGMG\najXj6rzT9vAtFYqUCIZdZN8BlMTU+dMfno31nccPu07dgtMJGSANnez6taPX+0nRovl8PgsfJI5y\n0vCpehOh8BdLavw7EHxos0VbWUVmkzAJt54aHOSZ7sGJx//zV9c2j3x+c5Fqpk6jqIeQwKZMKgIA\ni/2HO9d8cl/vCKR6HLxyfO/hd16b+fH67OnvvlKE2bPvjt/f3b/ZJWfTdrqZJw0f7Ow3zwraPHp8\nKXt3qxPTlMOff9hmD754a77YdheaO99+b7bxoUljmgmS2RjzHSrrLzd2TRsaY2AuqU1dbn52lReW\nbGymcXK8O7xx98kp7zJVV0SgNftaCieuHC3ffGBDD/8Pc2zHXK2dN914Zcv+1VuaMjEx5QavbnZn\nO6ir9dWr18UzpIVsalNt1dxgEhwbSB/yB0/OOY6/2ErJDMfqKp9WQCKRSUmtTadfFDv74uifvdC5\nSEZpekBX7bW5MvW7T67P75Y5m6++2BsuNr95+5p6vFi/xb+F5Y3HMQMMJspjO3vYrpY+broSDd3D\njX/7rr979PTz20wuAEX34mULQKUmGaIDF2o54UE53V0ISWcoHI10IhH14fKcVVa7Wfau73CUsS9m\nsFTyAHB6uXMRT37Wbn53xk60mN9b+kqaU+fDqL6e3Xp6dY3zYmeWXD0j2MuLTltjrb6OmuMccJFN\nVImEWMho0Mh1fef+KeFO3dSvoWnk+vhkNpbbnRfQzlzWX/7ks+U7t5SiOElx7eZjwsFgGtrnV9eH\nO3W+cRe+uNzsTKczZqEWpYxN2sT9yP18khwlLCxJ2MnU06O0Lj07HqowCEiK55unE/B3Ng/P5oUo\nFezR0xNuEzfrWWxUufqzi7dfOWjs3EGMSvSHN9ePryvBSE8TSzIpLmRPDp/2e6VQU6qXg1wfZIXz\nrkwzBDMkyBPbbnUV0u36+e7O64qoUoKQETVCgGz6rA9uHP3Rx9P9Tm36/C1xtg0JI5UNxT2rTK0H\nQPO9x/bw0c37q9N/LiJIlPn08Og1GNvyu90eNCzHp08efTzcunbvnyyjdfn03gy0t38+rGrV0oYv\n7zVaTTe6kExEG9VmD/5kb8e27bPqFjvBU+RkMmskJYKR5eDGBZ/nq2GMgkhhtginejtgDy76qbB0\nGC4uhn6a+Npw0LspwsXb13R9cTad9SU3xw/nqn/pgz6S8Xw9vy5fnLXD3W8dUoBDaBweHl3DcN0v\n1E+lp3jW17eb4vwiX3mluCI9KDWz8ezarQ93rz1ef/SDr2Wff5NM2unbZBwg5ohWJE9/9nQlOK6f\nvf0i09Y9mgpk78mpqlCiv302Hr3uxK3/3ZOvuPvsvX90p6ftpCb2puMVKZsYrdAuu/3VlCjCoJv3\n/fD1ggdrQbSilAShr8FWjmKKzYPvlJyThU41j7783pw5KXVycETfwmiy+xfHx+Wf9sNbZ3f2iSaz\n4OxSmhFG4OTR8cMnu0e3frt4CnFH3J7u70+1OJE5aH746AHvYpmjMMjEo8OLDtUwy3W/A6pTIWLg\n8uqwl5qaAROP7Nvhrz390frW3Xm8eF7JMEw7lNYBkQIm//Cjk/HDxaJQ0QyFmq+Ol88KysqySZaN\nLT9/tdo0PRpy+cVrhZxmE4HX50/X32OJSTI9UxBQadJII9MZEqzG3pTUS3oNgrTn8E3K/C4/na4a\nQUEWVCbJ1XB0jRk5SdHhZ/diH0sFYqJo0+7bZ3fni99dcKBMH3zM/tq3ZiwBQOxspRpCbTrl+frh\nztOPHx9Mi5KNGVzHhNG2U4wK2eKtH1/d/OzO0jOzJm9KH1MFCOQinpqX9z+7sqzDYrh3ot30+OF8\n/9osikmOgPDqWT8rz1vvn/Kj/a+Obu9xolHq7vWzmy3ycpy31w4KIXTzIZ0aph/ilbdBuRnlqtWB\nLq0KUYN9+ZT5CsOsJExQ+lU38f2r4WvoK/df4HDnscwvpr7LCIeD0sHz/R2+OsyAtkEqwj0IHHK4\n2zbD5FWzhMOvU1kOR9em15eMBCmzfF3tOrkid63U8Ys3Nf4yR/fJIOf5pF/fWMNv3RaHE3v69Hi6\n4kvqL57ibNkwdh15HfWVH15O/eErfQFIiiMt2Lm+9Wf2qe1sxSO6OHjvQNIKkjy0j84H/+zRh23/\n4XCx9+aL/DJnbZkH1766uBdCjV/fcDuYk8ms3z24+d4Dxsf5BuVW/lWb8y1KUIks82m8vMTeuruR\nYxg9qa9OmlPI+f40UyQ8YSO9cN9ilhtPDkfaxHidudTDeYvDjMZMXXMbXedaR4S18cIm2ZeDtz+8\nODmMzNYWhcWJGnfAJ5+fPM6Se+/O0jl92D8i7WbngiaS6zPaoXJxfrm/qMhgejYz8EXdHSdeXe80\nocwM8+OvxuP91w0plJkNA+f5gn7wxI7eDH7hfHay8bwsrlUTSnVPEtktYwsRRHBAh4v+y7vOsyDQ\nzvc+4Di6uXllXm342bmfd8fDXBkNmYEnx/zF93fMnVoPjkItk0ORLTjSJOEhRZ0zGwuX9Odqs0Pz\nw/Xp0Sd3lYgYdaK9vc8PPhlISwZ5pfHxeRzn6TWEZtq02o/uN3e+M3bGTJv3HnVxfqjZpzu6y+OL\n003XiUdinMXlv/90yHd3K4ekBaig8OZFD/a/gFjYOOVpmmGYkbOzxNRxZAQoOJKHzdHRfHV99+z7\nZ3l8oz18/xM5+PZ3dhMckRrWTdEAuKfUsy/euHzygxkqZYmURdudb/jK2o2rQ00u41M988XJ1GZ/\ncncnUzsY0X4rzUsGx3jWanRPj/qd4gHiV24/sZPy3u35NqaI/nWCV+tcffQqzUsQAZ7U3Hfmu2dy\nMSf4NJZajTjCEbrTn5TVZ90dlwnWqI5wv871NfaweYQyfV3ThyLJj9eH07SiQn/pQd9wW+GR4hb5\nW2/0aZxNazDF1XpR+5jZhbFlT0aUyftX67dppy9SoTkxnGpkyp3FV3YdW3m7BLs708WDG1IyY3RO\n39x/7/7TG3G+Wp2+0IoclCk8rjVztcgcpzbrM1zIUudv+0kZHtY7Wxng0XS5MxtaLRMpz2UxG6UM\n6yklg16DrJvw5TTogkQwiIjXF7c7aGe/tvbgbD731sdid+1XFxd7AnBSPPzhUfcvrgWD9OLDxzK9\nulR//e7Fqdfj41u7jEkzajQGMJSdg+nV/363KIfTbr3xMD6/U+lqN5purnZD9y8un94dS6Y0Roek\nfNGCvVME7w4dF07LePi/OO/o4aIDgUmd8+r4bEaHZ6vL19588SoEf3x5ebB5R4DIVjCtjdpeN+xR\ncDAodO+K3v+NXRqEROrOQFnOu2aJMp9lR/s1ozYPbbiKlZ5OrZuSOsekburRqHAGwouHICicCyUB\niEZ4rm71tT659NiMO8pwJ2uH3dXq4EaJTaXEtDlrKDYSp8KEMHVTfXRDu0riga4szzefbG4SiV9s\nnpzMztfFuPTaPT6a2WwIvL2TA3mdGlen5xRrPwfG0mesp/3L69JBIqiYlGwJZ01LojY8ON4nfu2f\nfopZt1xcffLkLO4d3+pIRIInYuBZy3Ge6d7jz/pX6TQ0A5Nm3b9xOlzSZ5dX83evIzCs3r8a8vre\n+c7ULjuV6eqixsyyC81khPrlbdy5f3JrswiASy6fHJ3sfjXuv3Aa338Br8EHs8en+ypGLGnMMNb5\nztp1PTAqU6YGCOwe9dWfzJ/+9LXNzIWcl5v+2h/fuPXltysIgigR/vw92P+TUFk3FiUvm8jyl1Ne\nDoYV3qf5/mXZeWOhXJO6NFgbL6dH17/YLd3MdvdzfJaF09e94+vz0ggOQ47RAxRhs1ePL6zfzsv1\n7sXV8cXZoFNl4jEt3vvjo5P6eptf6vtXL/TL6dWi0BiVxcU/+eqkv+MlY4JB9t/97MFZd7Szt405\ndH0/9AiPSUb1aPs74/6+zGgU7tYH7aqbfFaeHtyILjIkLEAvnilMdpWTn11d3+eWNFs+nK97piBq\n6zKdPP00fm1WOFm63evrw9+bUeqNXFH4/cvXX29RApBw4I0/v8n9H/zuK0WzKQtdbG48tbO631Oo\nrAb26xt/fKcwBQyN0yBkL2gM1O5VslEBY5A9+TjX12+cLMyLKzWKZK945frnO93Z+FxNPf6TXH3V\n9vcO97n4BCSS2pJveQbU3RSkvLzMo2XHArK2c/jw5EnF7rRby7WNt3xjnj56K0Sr9+/vjfNpSoTU\n5opkcaYAm+m06RScIQ5x5nByRj5fImwsbj049eF4v/LkAM/s4ur8LieIgzjl7JjLaGms3kDA8anF\nm68iuIVYf2u+iuPzxYLb6pP3T6bW104JcnBr/Hwa20zf+H4mc3oflJOpLbYsO5hQ9hGil0EKQRjC\nWcJL5cbBDJ9W57a7+I1vf3fVXewPDy4i1+OTR3udJItFij9rZ1blcJ797Nrr33XFmimEJmJcbPKD\nR93JIWd4tB/Huztv9e9/WPcvDxhEx7ejPNy5exMTJ/MgO3WYbkbqoDI5ut16hmEK33bCQN93i3le\nffqala7BsgCkY11krPrLqTKzWCh7OgjkWoOGwj4WkNxY+NMS6wXaWDUMEYB8PT6rnKBtxnRzyVNO\ns794Fe5+NsjuBT3Id373mkKszRuIYWfobbXiujn3t6cEN2dBKQd6Yo2tJlxSkQHhVst8Pp7PXv9a\ntvkfM55t6vnTYZbaEVOLk88vH/lbv/ZJKp3e/70X+OGVqq+62d3Dx6urHUwt19meuqp5KZm2eJve\n+4Jn/TZi5fXW4Vl+/m4lV4ej7I+Edbvq2Fv4ary4XGxiYsKGi9YYg+TFWwvUg7f+zSuXs4fvZiNH\nXJ3tPN47uj6jEG6Tr57qEBaqbVN1fqNkSt1f7bZHX3762s3jGdy4GTEw9Mlv/7O9ToLUgKnU1lc6\n72qQxeV4cfOy9WcMSiIiBbH7M3vlBdjF1RWfQxOsPmo5drl8cruJhYZRTq57uhxu7H9+/+nX4Djt\nn9aHrwgyOSlTNjqXq70R7hEamnr3x3H++HVhhk90+MqDcx5tHptaFnsPbi3UKzWaNSfy4/m4ltGr\ngDSDo0wODhjT1fsfzV6/frCEdRzRVBBk0eG5LLSkm7t7j7/6trVCwkSz5SMTDkBy0gRFN8aUx0uh\nJKTGx0dcBtPw4KQxN/3w4fxgX8cvfnLv+FTuHS2UQq79ztn8SvLV3/m9XQEgkjO3Z40bt9s7OxN8\ne2e9t7zY7Lg7GEYVJEzJnGoEO/nicXeg35l1e+j18tPLcd9Ge/rdatUJ4CRmAUDTXmvjvI/L3+4N\nmmWUttlYOT1I6OXp5IVYb33E3781LHY2lyzptLxWhpjrLQePXDdX7dqVnb3x4as7sHCcrfVsnaD1\n16TG/+3o7BVdnuO8yATANF0V5tIq4cl6R5sPc28ixpnsbYbaVhOj47TcnD6lSkN/uLPAVMKYS9LX\n89CU01Z7r13gaHez7OIv3tL54rSt+zZ75Xdv9srUV0PhIVGtzfHw0VUUpktSjBSExnLw+dW3D+Qv\nRkCEcwHr5LJok27HnJPNxer/396fftlxXAmC513MzN3fFvuOHQR3cZFEaq9ca6pqqk6fmVNfZv7G\n+dKn16rqyqxOpTKlTEokxZ0giDUCiD3ixdvczezeOx8oZWV2VzbJDKAJKf33JXAOAv7cDP7c3cyu\n3VuNY0nR2IE0t29Xhbu2+rAzDlKf68iWtAPW6c77xgxzf6acc/wiJ44y+Dh0B+7CE6nfwSXuFZk9\nCqBryKOOZ4MLpaFLk48o9zWd6YYTZou1cvB2/jgrYoI9bApQJEyxqO6Mqp1nlchrfbYriZHAMujR\n7mKzMIe5BlraiEefnvr50+tlYwiiAeDYCUuATCaOBAbLj2aTWLoSkDMB5FtcnSA2JEDmYsGZMoVz\n9iH5B/F4d9L1GSGOHxzp6giPszej5EhRhgddgfLCaHSyd+ncHTWZURwscZHpi/3YvQ2335x4hoSW\nOBtRVaa3P0mqUHSZHeakTZ9J/cLRdDg+UuXCh+AwzOHOoZH3ZKboWOq9WzunWprMmlt/e3jn/U/v\nSvB1Y947MhF0X3d+lOc2qkE4zdoQabIw8GfxeKhOGyLNKbkBLOTbSuq6nvM0d4o4E1VAsNCvPxqN\nDnA0Gt4/vLc39nRvrJq9Lwv3/MbClX//b66X3rKUAAYATPC1trz/EygyzPXK8en9GvSLqC4PFhtD\nSlLXEpt4cGx5eGM+GIc+zYadUCwsye0xOhFTsdJ9cVPznfkN3zw8KF7oZnUZxCFEKMs4fKhFtd7L\nCWEaXVNiudgvhwk9aLGUcr/CPakzmmTHs3QEB2egasnqe5/fGfF8dfJo9kRKUP99dPHkuMx1DUxE\nBRmJoEeVbjmbEMbsKioDQxPNQegtXxul8HAIpOBhPPbN6aEflozqLBObymMKDFMkqvp0cHoYs+bJ\nb9YKiksvXOr3FqFTMGoWNe9SIixLWhwdn945o+IiF44hhq43yscN6CloRiAQ4MBZFKk+CLR8/YlU\nb8b5uTMN+w0jQz2dNic7o8nSC9+aW57FvHmu+SQGNo2uP5jJsXE536dcBJVMzrKiYtgI3enx0ZOo\n36EFY5lvZzNoVLjoV+OPdBwMTbm+e+vzm81sYpcY1DuCqnKPo0xjSg/vjKDeHiHljGFtReuHew1Z\nPVaY7RxqFQ186Vgf7NQJspG/pAh7xcDd2jOPOM0AHiD1C0ZhEiuQnJX96VhtgOMayNF6lZrh2bGC\nMrsQyEMGYnDnXLArYrFAo7NmorOkNoyLD+9PB4rMzESk8eDRzc99OXhYzN0ZnnuMfmYzWu5iJiKQ\nXE+5pDCts0YigAKjW/lcOse/ujAfeoSp3phDa+qjyAzzbm5I95/z2cxQMZf5uM5XrkEmYhCSo7+Z\n9FdeR1Ciw1GlxzlfZmJA4ozBctNR+lqzewgwv0Wd3VnpAcwMKVL96NfXXUgOEGJjjob1FQGERIhl\n2Yh8srbAXqkJ16f73e39C+Ofdz4dFVyUHm9fCaQculdOiubgpCw0EsJZCGhAnFH0ydYNRMhKYTA+\na2pTR2qiAmpODc3MpFDgB3I2KEtnhN6qHgxrwk7HZY8MVDXoc2IASOCe33ej2z/qAhISimFJu02l\n1Yj95asl+GgwHRSzfuKqgSQeDag5zebAPGXkatZPqfiM572RAAx1tjftlEfDRy8/6bVyCWWp43AW\nm5KBsyDVjlydKTzz8M5MxDxBdJKxBBOf/cZnIXJlgCgw7/LssNcvF705A3OkYvCYBkoRLMD1w2MY\nafi7rb6uByuWF2A/ETBYrcEYnVGfD8aLg5vUcNlf9WpzHSCtz3QXApmpgPoSCA2MlIBg1mueyHjO\nD14+mTbhcKFUU5S0Pd4abb22jl1L6XwvAaEmYp7SZG64Pw6cOlsfj+9cvuIQIBOaC/2QI95dmX88\nDfkHHz0oIx3vxMzgYoF10ew8GrDUoURaunp/mE4mXa0cUYwVZk/5vBW/AYDnv3fch3vDRyvmTau1\n21NqPq8TSCx1srcfS0WCbDNdxOMgjsvJ3AuhWDhWLktCalxyLgN0DqfNeLcDZEJGrL1pA3h6iYEA\nYWGWV3JRHo8dCGYEzo6RVM/5/KULh6d892d/VAQEFuw/ako/RSaMZJbj/tmhXiPgtaOSz/9euPcw\nTHYhBs5MkkDFJ/Uh5qAUIJujS5uc+9O/PIw5ZuKFZ6qF+sEv7ozGNaf76dHug5lkwgyiUOfh7U8V\nUZJkrfPDT07e+9WRiDL6skkVDPdrKwnAnAL5UsDh13lJYA3La/PjyVAtAsaIHai4l6fTplHNErQ5\nnj66NxWVSCnD3PxAJ78+bdREO8XKd37QKe3B+3/5fp4N5uYHVZgSB4yy7rdHZf3Z/VQAFYq5yUpg\nls+/Y///mgmUc5cppP3tSKoqSCpABMhchgJJKMEp9nJAjdEgSfKLi1YEEADLApVC8AAACMXmnPNu\n24iy+uDA8mKS4bzvZNv0SkbcuXb9ecLO/FJVZxPnsEe7n+0co8fsvFl3Wc+Od/YXAmp9uv352ezI\nrRhPT594gXriS8uFrM7vAYJkU80kkgHLyclJs5/Bq2SLHNhSQ1QsT4v6NKrzBRgSPRo2w0cbc0rk\n2Vsy5K+VmPkfh85hUa1kglT7sv/bOz1WV1alv/Dw41GcRHMeCLQRuNKnXJTHaTo5SzSJirPpWNP+\nvo4amWhsAJoYm2kEpzgb5Qk2nScSAB46GxubXv761lCRZLb/CE6K1aXCDUoL8fg8R85MgZ0Pp5MQ\nupqoVBd274MTACJwINgZHw/vPImi1Eyr09jpqAKiSxlkJ80dRfO+brR/ETDvHMjGciJRLwRo/4SS\n9f8nPjzzp4PRApwiG6SUY2zKQeEJvE3e2yk4PppKaoCmG3l2zEUA7i4t5vnFNUsjIvRIwAQw34Pi\n5IOZGcc4E8OiO08Pbp4RkrmwvpDdg91iYcsBiXABEJsG3XkL0PTWO53OdLhbes9+fquXtSnraGrI\nmpqTk2o83WQ/KCez3tm5Hx67PDrz5CxbbMxz6HVPbh/fuk+YrUmijgaX5qPO3vrl0VRzdliUIebD\nnz+aHP/tg+H93Xf//HgGmDVqOh1NZg8/nmhRgGZS2Tu482g25uDA+ludTgHyibJ5TEAk6hyAfq2l\nfivcEManZ/czOsjgfSefjSb7SoogocDu6WnzYHaSWZJmrLY2dICndxUFIAN2/vhfff9ap6HB4Ac/\n+nG/Wlwi0RyVyothNMbZf7g7c2hVURKQIwvhSZeZdgU7GvQ6nj4XsgRgCoomDEYzIlWOD6Fbnknk\nAkx8SE13KSykBbA6ZRIRyfBFmTylAY/hZFtyKJwJFaxCdhIW1mooMrtsPHjj1UU7yZ0+4HGWZDC7\neTJ7NBvkzISeJR+Nd+OZeLQ8/JuPdu5XL3ez83ryJGYh/r7InTmeT3EWm2aWDBVcMEuPHu3fPpg+\nnDjGmCzH5ADIhHg2qsOeSE4INrdU1qSHvcBIkDWLmT9v2bXfUALf0OILc7Yotfx2r6836F73eSX/\n+vPGO3QeAXJM1L82X5Y2mWWcVWig9bRRrWMvHt+eBPMVAJFlBFJs4vbwmObOn/76v0XcwsVDWx/+\n9axupkd33005L7zQJ+hcoPqcib6IbJaBeiSDAjkU06amOycAwGVZEljx3Iwmd6aPpx3/QFMuX+jN\nNQ2j40Dgu6MxMM5UDF24suYOfj4cr7vK+6IoOoVGewyplBVpBXu+3BlrbEgbzFTO16oEo+3d0TQ+\nPIoqJNo9rkugejJFX4KrarBcpoyETFoDrK+UOrt/EjM64MxcXKzc8aEvXUKF+av+EHe3TwYUCAPG\nDF6skfPWfiqXVhYP66O3zlKOWbeevzKv3kmWJqtZvP/Rx/wvNwt0Hke9hXP3E5w2s8q5YEqEKDQZ\njo4Oj6MaN40iZ9d/GYZxOPpw8ieVFTJ+MNwHSO/tXzv75DAy4gfPfLerHnDWPDjNU42z3EBAAteM\ni8M0HmsAoM2b886fnkRQ9YBZyEvtABG+Vjyd0PwYVY7jANR5mVLRG9FsuODFTAEGs/Rwkg8UgqKP\nDrY+5E54hIwmzgBdxStyWL283HOTeR3PMSphYnf5tXewfOj+1//vEqOhBm3AOTv3Jp0vo2a1Ug6j\nhVmtBkLg0YPFWeDgACqSkyMtubuEAAaW/Mr2oHyoPDvrlgGUci4TUAaAppSyXw0P7591IqJ5zY6O\ndvKVZmKdaVbKXrNfH9bTLF6pyGbeACDvUZrWwQslq+vh0KaD3pQxfvDeJMxe/vE7k/pkZ3th/cn2\nArvOyh7WeecaOISG2ECyNtN9z7N451K3Qy6ZI0heBCXHgrQ+y1mhVAgbnzhUUYJZQAQpqLHHEKAJ\nAACWVVNY+6Q/+eRa+duMnQmhunJzthbg3rWC1ByBcmLClz8EPB5mj6MelhEMDXKsBrEuznLTdcAo\nwJKdmPcFu9CffyLPZJc6z9z9+HMZ/sfvrO++dzRyk7jZc0Cu07HzFTbhpFRYDvN3Fjkzy+Ibp55m\ntxc6Kh5UgaoldzauzhcH/N/meGWhPixHa5oZqJgV8wcnc+ujRWZSK1lk6GS1zOoUI7jzhil9Ac3P\nLaUYYdxTh7PRNCTI08azTN4/hJlMH1ouMc/iiWpZITfAHrHsPyxHU0iRAHwUgPnN+1ZNZ+yQMmXE\n7vWHI1p5MO2yNAZz/fE0DiSqGQsYERYGct7pjmLzzb9I+Sx/tLgSkkw/vnd2Vv2JS0Ecabz7zh7+\n0boPMpjXrt+fXDhnPxXVges5ckb0RbHv09o0jiMSGKkGF3rPTD/Bun7n5Pn7c/2P3z9rROo0uukS\nlCVk959Gby73NMrNvzorGr390/8ugFkGktmj2HGFZQRfLu/hyaP4HGmDZIAkQJGz+a91g/a4brcO\n5urhHCMC8diOMOSPtwIrWuLuhbfzTHsMmh16Ky4u3E/l3t3rzGyRMFp3OhvHco1sszhK1geAhEhL\nf9j5qLoJ9//i1QuVBJwxx1yhhCf9yk3GoVseTVy8MxcUEcEyoDfBpOLQNDdzrHMLJhDMyG3NGptk\nsrMLwURMCcqYKwAIJvTS3UO//4t/5VCcOshxWE1DUTDARMDEMjPtzYYn63OLRwMDTfWje9vjcPxp\neqXHBLO7H57ujm3688lrxds36yn0npu7drMZLD+48YQ7gTVc/mTiqod1H0kBUJJlctMhhOxOP73C\nSAXFhJazFcTu0s6+7SUjTWLlC0f3o8sTNJ8waCoiP66S2QlTmYVGbuVUwf7u2WFAF954B7uTvQ9f\nHBAYZjBP5pZePjlycTYLL6waheQoxeHp3qdHQnw8n9NvasGIqEItNFe8tPxE1jtM3fyL94f17Gx3\n4/NR7fzk+WfKjihuXDiMn5/nyMSRSaE7V/DtVyqP8dLGYW/704uXwASSEVq18ElcPXpcLfn7Hx2t\n28zbaNLFFKjRam4QUzkPClm1uvDWZHj2/UtEKOo1CxT6GHIhIqtb3rWz/tmCcbL7jxJMH8VMGcaP\n9qfTHoCBisq4OolrwRNbdpQUpzPf9+phWliTAbqbA0kr25cMvEMQtKq+V5/lnbAQma23Vp4Qfn8e\nRBA8nVWcqy/y756Lbbz84Kge/o94Y63z8N4H01wfR+FZZbl59N7Dw1dv5AwM1Wrs98/dUWZKB2KS\nWFAdNil6l+zscAvYkyI7LpeffZg2p/rZqb9bTGwBDA6zQqZail6otv/X0U9WWQ9/dVILMR3dfT7E\nigRhseNtYckEkMrBra7OwhYpoAF5NcCQFJW/RsCJqbn5a4fV2fGFwiznOqt1+nj/iDKXOTvssvPj\nw0nXFZxBw+KVWXlw9BflZWJkE2rkzqmtH141qzZnUh1EdGxec/Wa3e8k+c93fnhjgUVTIGugCfCE\nt8UZoy3PPYJmdvOZBa9MpoosnqM5BJWYmonzRQfBlDL7sj4ZnfVh0jFJ5gxQFLwCgID6C4v9nG69\nsUCsDRlat5gMy8pTPJkcLpkhCTWzXRhd4pOHr3hp7v7q2Hsfbu/HxY2e3PvpQa4l4/bo/oVbByN0\nVy+jJas/unT4ZPsACvULK9OmjON5LQOgCJPA3dtRpvOLK8M9rkpURsoyK0wz923hMNUO1VDCwnz/\nJBef7V2TXKABgjE9njkrpy6x5aWz4w4oavyiYrs31c6NE4qHk+2D1xfK4E1YQcH9KPysSbEWSxot\no0J8uPvpAygrd7QJZ1eVkQSDAKXDM99f2Hgy6x0GBtf/6M+OfP70Xb80xfkX/6Bvpth95uZorXOe\nI0dXRHMJB8vz0PQsQ//C2QjP/qpaJYu1g2Szh6GZPYnlMTFYiS5+5p9nql0yd+lTjDqh0ouX2f2j\nmvpr4DgjWRCkiO78Q09D57pL0714d6UndnYY2Xw+XM9hcnd0JoadDhNzJNqKs5NxD3nWFGXhd7jJ\nTpCwp1YpAG69+E4NpwebkAER0vizh47Jv5tfCAjYo30c9+eUgaQQy0BUM5x3d4ry3LfoZ9vpmH69\nOrqb85jm8P61Ehjt+O3j2cqNggIpW3807J//v6uaP907XQKPWUMUHQ2zzvjh3/5orpMVXeNA3fr/\n43j31v3xichS3Zu/8O3TP78nYtJf/t7nozt58ue7axv8/qMs2PNzJx+ubjIiCNScq5mKBFCdb4a4\nr7HBAghNDEAIy6/34EPO5haXjvOD5wkSNQfj4dxqykcPq65Dptpd/fjDXG1/NKi4KUTK3ovb4WRy\n/8/+X4uIpIBwaxjnRhlL7V4/K3Hvlz9GT2Ye5l8vw1vH5Z37136wWXVomiWFfX/eAd2XyA7N5q7e\niRTOtjvBNKSc+4ZIrMnATGMKOlo2FAfCKFjj8MC0KnGcO6IQAL+4UZKVjX9mf+ybm99zHthKTe9t\nV0iAM394ctzrMGTsbPzZrZWTB8Pm4OAUtt/dZ6OyGh38dGX18uH20bE02HPN4dmOIs+//sYaLi1/\nPpX6XCusX0FiQ66N7C3qF8hGAFYOP9nRbGtuaL/8I/ZMQKiTWUWsPD/4rC4YsII6lvEo9NNgdmu+\nYkGQlLtGjykXIjV1B7jryuGDuzfCb+5Gik5h8TsP5k4mveO/fvaabwKBqw0Uru1+MFbCO//9my/0\nuOa9z98fTuzCuBneXOYJsIJTB2YW90/qYmvFnkjsM3N0xY0hn6hwnedl8d9cJwOmLlb98yVNVcys\nSqxFfXSn6vKEn2+2073D3rNXzECSjd456jTTJxGZmIA3XnonvTu9TApGGTr9YX/v7WeKgOXO+9tg\nWH+8KstcUgOgneQ1nnt7OzXUWb8/WcF7Vzsw3qnBl83Rzaur01ufHgzGCrOzpsrOOztteH6aWLNC\ntijDe+Xqi6RsYI4AuLi0f3t25/jfrntUARc/l6recNNPbX3Foe4OAcIn1+YZkUSDsM9SnHfUlKtc\nXJ3BjjVHUYzduHu1c/8XL5SQ7h582nT+xfcqs4kzGw66c+d+OfbHqWp2u7niMqmejrd7S6cTGH9W\nfKcqDISdB8P1/lya+t7x3by2ubSxdaH39nZZwisXffetYvEEdj9cC4fjGC5Mijj8cKXTMZwBbl68\n1ewcDUows7lrb92SOJQaukrqREgA2b5WzkuXES3MneLxwRoTSy24Cv6MPtjqBDPz2j055UW8+waZ\nV9Epr1/5MDl361ev9ApDZhyfqe8NAllYn9+h4t4rBZKyoVt4feth8jz6cOfZXu/aZ4czvXR6ZfvJ\n7P39LTYWcheXTuen4zt0pfApoYjLxl6BICoyNbnURKAEiaG8eq8zOl24aLOeVxdEUVgCAFjiIBev\n7h6evb92kZ1UZNj4npS+T8Xkweg6owO2uZdGe2M2cv9hs/kUsS6fH88gprh/7LUJVvZT71ZO4zmo\ntl6d9zx/JT2Y7W8/0T4ASKK9tfcbKh7+7PVARU6kqJMDYcX1HPffXfshRIecp7eWs4MGu8ENevvz\nZVIt0nHn4idFd3Z38FoAUI6Y3GNKx4eAHsnK1c9n/uDSbzerNyUyxDm9cZjmH/Fw/GLPGaJCEEuT\nBmMo9MHso6vd3L+1s3/G8+vz7/LJW0sXQdWbMpAglV209X58Is8OBe7Z0o+vvv3LThlvzM1tbSJY\nUxKU3vfP9T6L6lRVqCNRxwjYaRaf3ZtNq//y7r/sVHMdN771dhyerTyJxbFKVC99WDdpVBBMLTfg\nw2i8+4E++2D95rbSuswN37/9ky3zVpeO4XHkQlBN3rneOB0fXobxh0dxGiq6ewfGP9+fLji07e3Z\nIggw19O1k2bMNCqYct46ekTLP6lybYEpAWRcHKSZu/df/nTR2QzjflPW18vTEEfhjQthb+ZqwAd7\na85Rk1HFiJw7bwGagkB7L4GODzWPU5UWlsb8qN5mOR4fDvAHr3eMxJm5Ge9V537ULwTt1B9u9lAt\nGfL2bUqdbplOf2l/rI4adpnUrFz50XPjcXqIq5uroZTnVmYuL1RVnqt+dcfFo3Ta1AjuhdU/75U7\nf9V7lRjVwunUiv2NIF5cd+uvp+PivT/w2LBoRqbKLPHXek000ihltzyY7C2TJz7+8ASabjG6c6cX\nzFjlOIZY40dHXVJCJ1Je3eG65r+6/cq1ovCz2/e7yS32DRD639l+1I+HqyzmyGtBK//dO5+MAWc3\npfM3IxdrXX72CW9tqCwhQPncMZTjD07cegcB+04EEVhVPEgH69j0T5d8jp4B/dbDEEcLU3GpIGAi\nmjArAAhGKtevKW3f/ekPLg44MszOOmnFV5vzsd6L77/aJXPUf6H755+ObT6djce7jVvY+O7hh0dT\ngmEJHcSLLy/9+rPBWIRuPP/KilEuLmwPYmqebCeAN+5evvLpIY/hp/+ug5BUmtN3j9PQfffiJ4fF\n7MOLFz1xjg//43eXGdF3F8oK379AWgSsb+83F60/OnjLvt1JpN4xJXssOZUUkrHZ3PrbJgfQ+c33\nuetqcAXNv3j06QRo7p1H373MBA7GJ0e/vN10mnLd3PSzY56x9i9sh5df7fU+xUdZQDQANl456+xg\n/NICFE9kb6ACKbiFaun6zqC/2Q1cmmBJgjS02bluFJkRDZytzN/hjteo0W89f3Iq7uB/ovVvLcPd\nt+5Fqd5cfVwt+XsceJy/+rCYPFhk8plAhvXhlbiX9nEnVgPrbu3hsP7LS1uXihQqFp3K4nk/E9kp\n92dhVBxZPD0cV8vD3tH4p7dmn87P5laffadfHS2SD9YjfTT/iz+cQ98znX44HmY72O2aopYZAAh4\noxwDvb303Yrqhx98eC+El6u73f2zZry1dDvPzqqy+bPu5coxgnNUMwCfb/8/MAZu5r9z9ZO3uhNY\nuyLPpF9P0vCTlc3prLxx45VO9sbBlc0rD/q75y4ic+F4x5/ce/c1Ijadjo5lb7T0r25/Xs/eCdX1\nhfrEZVZKhbmLkFEa7wEt2GYUKEtK3TdX/nJ2y+FJlYobL77O9x6KO/nVxqqLNjke4/RkT4Ui5Dzd\nnVja3n+mRJs6Q0WnBPlrjdAMNJUwt6hHny2tZ673H57BAjuYPpjbqBBAR2cpQ5zc2QigaoDu4nff\nSj4f37/54tZy/+Ct7Rn6Ny94MJfK+eOop00hkCGSut615bW37VgmTVn19GLx6vULTzoXovdNTb1r\nR++5pnP2//v+y3OFy6jJE5hJomTB5wF1JtkjKRNQb+6XdbyvKz/uW0GzoDkZxyWAYI0v9bmcj8a3\npz96YQ7q5vaj2CvQXbt2K5zZX+JLg5AyJ7d8CwqpZ+Azz9344aZe/+jmDhazrutsvPGcm7tysst6\n5fmlkkkRN68/dPak56wwiF78g9EQ3GnxH79/eaD54IOdT7mB9TdXT08Luvs/v/pSP8z8wfFEhQtq\nVlZsFE47TELH9x2uXLhbHU7fSs8OeoUHjPJ41juKKaJhga/euRV302/3a8mUHZbkqp9s/G9glB+M\nXhosecxn7+w+LOLlV6vhWX/ixnSWwkl3tfrexfjadJj4EiA0HoJZhNH2fbu63nkyVSkryWQEvv/y\ny2TqCmdm7E2acu4gnGsuJGg2r0K8tq9ndQ+xB8216eysHJ/grbdX8yOpsPrOnzyJeuUOslu9Nrp4\n9214uQLKZ78cHbnB5KQ6IoGw/vpKuH1nb1Ls/vrF57W70uMUz/9cLkSBBr1MsHMSBKuJW0ecfnTX\naseLb145TnxwA0igP3+/GU//lz9YXKjBDh/cg7mCf757qQhrGSOAM57f/MSA/6IZVGcff76L1bM/\nnL/3kbfd2WfvxGEN3fHi3Z/3BlWpRIQ4JeZzRlFQNvLgw+DqGGu30MHZpc9u7xD3inHx2nN97102\nzjp3be8o7D9/zn5aWr47HMz+Rr4fTPLJ7Qdo1Q+uLDfNQfrwoLu+uekgaigA2VkJENEpAJXoFJ0l\nH+ZfuHTy4GRvn3T9xysdfXVyT7v3/pf/56KlB5+5SeLPv12Iw9nkVs/NnJ3l5Lg0k4A1R1fWX2fc\nIUilug07Pj7pd+alPjiW+uXTSYp37/3oUj/ir97rj60/5V8+s4neWaPsuxf2lceT0VEZoK5txnjR\nGzlA98IxjB68UDowBI2OyvDjG+9/UMJsDsprzxR9Xzzh/R31tOSS/OrzH8XqZDf+p8PnFpZIgQVV\nsUAl7L06PJvS+xfYA2QySQu1Ds72fymv6EYvjfF0JCvxIoBRATOYv3i8d8B7P7/34kX/4L/QrFzu\nZLp6d2aLe/9bur7Oenrw9mFVlnOD7ZF8q/j25jJDWLixfTJTW3j+8hrb5lZdO6ZOgQBs0H9h+2+q\ntSfbCQBqoqtv/KcsUM7+6miliJ98OpRR5X54sfruycHCySc8XL0adz6dvyTZQN0z97eLemeVc8zb\nJ+OwtXmvmh/t/fTRt571kYnl8dyUzYsnEt9bu19Pp/m390TlCN7LZCM82JODIYz2Cyv6duSPmsXV\nq8/Hu7+gLs5QIZQ3rl1fc3zxX9/f2XgZhDJ7TtNcQu5tXcL0ZCpdqBBGBSxRRFg8aypxRlbNrQ/9\nK+c5stNITpPOka+7jpxBLObX50SLgyY2hzkXzeVn31h/EtFjSRvm9d1Hs/pXCxss079+Gzv/9tm9\n9ybZBusvXViu3GDu6E6YlDsfN7L50vX9u6+e+6KNTqLa6sNRTPe3DveG/dnSnkIit3J04+Vnw+rR\n+M6NZVBzcz3m/Gn/D0f1MP3seMQbS/v3pkePLvwRuP1NmBAuvXg0Ss30p4uPpE7kBj/YqMrVe5/H\nTjOdjfhbL9zr13ff4/JbA8+ShbQO57wyIiISZQ4XFCMh+Lmlqw92HkV95uLSnHMsJjjNytpZH5z7\nDtddX5iMGv82bvXz0eHJ7mz5Xzznnp07/eu8frIzHC44ywTMRsmbZPTgKLEJOk6FNCm5uf7GbCKM\n/Q4wvDg72B+XD/7Dc3On7+4c13O69xd/smwY7/x8HKsCP7hcgKowmoDizKJ8jaE0I0k5LZnC0WdL\nV/Wzu2M3eOXmdG402j958aJW/8NwYj0tYPvnfzLwtRLFRwdX/a14j3A860+jM9f5/pVMHojDhbV9\nf/BgIV77r8e//OZ5+/LrQGlcYKGNl98dzaY8e/tReu7icqeDACiNiRpfXtuf2cFH30YQCZhP3xsP\nQl3Et/YWwrc+6nZ/UbnNNQCgugPRis2jo9OJ3v340SUVd+bWv1v58tmzTx4NS/rFB68ulW8das2L\n31/gB9q9VPQ9onRw4caEAByU6LOjQbIkBRmqenKLP17gJ1He7h9gzq77Svz4eDQ7oXGnN5zczt2i\nvHgj8NafvPe54Gz6cJw/6tzYCIBIGsCbf/CtQdZ4/yhf/dZ8dffTcW/wye0/vd6fadDH8+qbQckn\nVVwZjPuj3+ZCBGtYG6ipXPvXO+8f0aQqT8e5qxAd3L1weU1Xtu6cHXUh+JWrzywVlJ1bXXi2KGHz\ni3++CLD5RIrN/vasSZjUAYKLnrMiEKIDNP/aw/K58xxZoQsavV/auNsxJFYECat/+tGnu74OjQFN\nV/7g+tUnMewAFRBafOZMTyf/+erluaOHI1p6afXCM9NPt9bnS2APKysnz83un3m3O/uZfHz83Nvf\nO/eHZnDVxQdn4tz99VQe1Rf/ePbuzd0I+Py3b3Tt++7Dnbf+KDjuvzTdHZ7Q37rNj26t/LKZq669\nee/n96vb44vDJv8YGl/KpT/8q9nYMZxit2w2//S5ylXOj1LireHVC68N5rc3jx7IWVGs9ynKqDqZ\nxf/3uc78vzVf9w+vuSOBAKTLpzfPdUkAAODK6w+nU+3/VEMeB7fHN66XZGtrK4elP1tYeNZRgU5U\n2JkpoZOm8JrAo7gMhKSsvLiS64IYBVfewD87jJdOfmGybdVVNxx9WL1a1Ltvj8dhcS7f/O6CkQU2\nTQGSa6qvN2eVSV210vXl3sefTm6flqOr68vupk3invztnJsKrVy8zfXsvfDGEgDF7eGF7gZ9Hhgn\nbAo8u/Tjf9FxHsXAL740DLM7S4+luvU/jWebhQy9b8unD9FjPHE3353feqk75wJLQ4CenXbWTn65\neiEYW6r3x71+rwPMu2715naudq8PrqwCgJUipIlf6KSPfYbPbvv1suOen68oL74Jszqf6Mpfocy6\nZ9b5o0tB1xipMlIG6yr3I4OoC1pqUp8LcGKqQsa8sXreXa5filJyXrrfu/r23Ud17u9yPhrzyrNv\nrC54wEvdTnXroblACotLRQLMx8i1+lGTiOO+s8urRVXwB6Oz7N4a9vig73DrcZxWiErqAODyrUP/\n6JnfrJVnh85mZSVcUgf+elJMp0ljmV0Ref27m1WZ5q6OZxon3f5yQA0CZEV5/vJsX9U/Ps9/8fvn\nOzKa+eRR/JW97jMdAQSMTb+3sfGu3oKx5MpevbES7Em0FKHIRnP9K+/XH93evHIrbewu90Kvb9eh\nECJDj+AW9PLZHu082Kq6C5vnvwJM2GF5+ZNqJnfLX06t+92F5ZVv7ZzxanV9jggvfWInH77WQ9+5\nMhlbHr33q/HRQ6LO6k+2Fudv1dfiZ1uDCmCOAObhJ++dfl41FQ6quR+/WBEgrn1/crQt3bWrQZc/\n2isf6Nr7k1UoD5s06G086SrPYLmgzL2N7aOH5z5Wd3n9I2dnbt80dWblhZ90kZzH/pZeTlg55ywC\nYDJ16AAyuwTkPCCbAEvmzF4Ru4hoJtz7tv7F3v3qoKqtfGF1m4ajv3p7/egs5rBe9mq9+wKDdwpW\nqIoj+lpzk2QI7PvPPXq0cJQfqCvD9/rdwcYn2xgt79R2ofzTxV/sHYx20vGfdArZecdvhurbVy89\n2MkxNsXF/r+94jsAhoBcXHh+/+jB4fdfO3f//RP5miEjEqz96Jm/ec/TGUx7MDt9ZC+uLZCbU4VY\nn526w+P9P/+DLedkvPP2aY6DP+wfPnTWHerg9eeX1ua/mCXAoKjgN3/sHuUyNzNfzV/6XleUpPud\nxXeOJppxOHZZVl+/3LUiuSDkFGrnDIhUPTABRs2ciLJBHSojUl/AY3qN/7+gAVE9byaE++5sBDly\n1fvhxYoKa/x679l3350s1PnCxW8XTjlBlQfeynVt/PTDYtFtoPOXFtY+vF0s7Z6Wdrf75uNZ3MfQ\n50xMnaFK/LtgwFIoQVFAk6HoP9u9dXPkD7SYWoC5517acCpE/W6CZIGcQwjZIMfOY0qT8o1SsobN\nrFr/V1S6ZGLoHUb/yqtnu6fmJsudjZK8exLTvBHMCc6/9sno4aPJ+HA2v7H4vaUSHKLL5A0NxHuy\nor8ZX9A6uJLPH6ZNJISw9tpZPBzuJc7zL/fT4vKNrK5ywSXZeO72rnMvlzHNX4hHUzmZGLL3N37y\nDFcb3z2T6WR9xQEwmPGAF3YATi4sXLywtYaaHZewsrp+Tb0vOA2+M9rtjwc3E0zXP51dinMXnnAi\nB4AQWBl57tK9c78YEix8f/iQZk3OJLPOM/9+s3BBDLiCilHUARbiwAExYVRQQ1ShoADOIgRgR7Uj\nU85onnHxB5f+YlSfUggv/OvR6ZYdzvZPqtn8pcWXP9636UTMs2VUJS7li71tX5WBWdIQ1t48iWfV\nlUdxsPhsl+deWT27eXtl8l6onn/1Gi1/+jf3Gtz+HzpX0r3bnW8vh2rpwvDQTWd1b7235JQMSskh\nW//5g17qL5+3+/7JMEhyQjFY9+LC1Xv59smsWfG6O7pdvci99bml8cF7N2MY5fRZfW1zKZ78bKeI\nnbkXl+xworPiytKGZ0YEAHZJUbm28vLCw7uT7v24uTl/dZ4ABf3ct9buPrpzMpsh+J+8vooWmJJa\n7RmBkESFCDhxoaJfJOE1YVbGKAR63sxsX84yM2coLi298OvBg7uHeLH/+pXLngNbYMNi7bUDX8x1\nQx/UlKRslriZdCXwzk3ev7TSdcqrg4sH943rPl7uwLUv/8ivICdnmhyExRd/1pxOfjOxSmgOAYVR\nMHSubLx+fHx7dKR4eeXqyhwRCTOjOCfkCkYCMyKv59tZ8XSY/+Y+OgghYUBe/vnoON1dXokv/9h7\nF9WhKTkvQoRGikFYCIUfQ8FPAoWM/vJLf1PDLMbOpS4bcFfRgUegcunNWX/6l/dWeX46uz5NJTgb\nNsv++89ywQhzdSoLBQBCM6zEl5esSYNQFk4ykAoSdjqq2TJBkZ/Z3H/4vPOjtfXp4uWtwRNOwQow\n+OJHZ+VH5z4Uor/47967ubh9AkUFP/jhBe99NgCHGRyROjByIL7RQojJsGgAidWUOTlBcSZOhbKy\neWPCdPnfj+93L+y+dnVx2X3cTPZlVPUvX3m1z5994OZiQYDMmiOL7yT9GpGwliwEUO6/PP/5ZwfF\nFdn8/mYpulDmrat3rryAS6vzIfRLkZMyfmB/y1C9tlYJB/LrVCOiCyAMoJEwEvrlP947ubpy7v77\npzLSrlgSY7S515/bX/yozvFoqzzJ0w+p8XRjejp+YVnr3p59vovL41jjULd+tNzDblZLjn0Q4goA\nXBb0GfuYz1Y3X86QMgQsXGhMgzlZXYLdh5+MNpcvbHY8YBB1NSGYdUihiNkpklPJgAHAKIpXxz4b\nEjl70u/MCJSBsFQZzK1Pnju53b3WWygZSgP01Diu5q+LUSGK4Hz2qV6sbXpndWF2Jxx1n+uXqKKd\navGivZljExbo8WxPU2eiBCy80j0Y/zZ4a/6xHLv19ZBrPGHXhc6Nd8axs/ziZpcNGDRpZZSFKCGD\nhtIMmiDgzp+TxBgVQvA/DP+7i0Sv/suSK6cyK4EZMaFb/TeP3s2/iPPPVt+Z2zz6HD7bvzS68t1v\nFUGFjUtvYvrbOcSlL/24F859wt8Qj1ptrrw2ftTs5+7KKwvOITg0ydCUkkmdgQTBSF4VTQDImBXB\nMGeNpiUoELInUnNZCCrpzq2+RBoqc1suFKizlc3Xnuu6jZsL8eG9G5mDif+izs/XKvPGliMzUum7\n68/txNCfn/fKaJ1c9F8ksi6bAA6+U/3vZ2OqyzS7vhrQWwSHUhRoxNlAIGQTVDBe6ufu/21z0f8n\ndU8AouuLAVLZ71188dF2vLmPcSqNFaF4dxjLC/9u9N5kOg46aaalOXavX2R1TA6AGATQZp2/f0v7\nxwJMzr0o9sR8tVyee14VEBUy2OLSsUs537tr5dz1ClQrAa5KE06GgR5PqI/DWp3DpNXC6n7a+fKv\nf+tJ+bsrZA1++P/5sl8+V+KV/8qUEEC4/5p9rkvLbyx0hFW9okMQcJlwLizvXPw1wdZlps2Lk2t5\nvpirggk4MADPqk96pfBp0IceAFz9R//egUKNSKTsTNiSQlYjEKIcoychbxwJi5QxojNlk6o7iAhA\nUm2tvvjA0fpcnx2tzO/Q6f7FJTUgYQUk0q/zVWdAMkAxokFYlUShqJJaSs6VhYh5JsmQBy/pz8bg\nLSz/8AVPZJy8ICAZRPSCpiAO1QAkOHviI8R/lNcqWQBiSQVC9u6Z9Vdmr9872z7Bk2S10/nw/Lcv\n4caj92/bUZr2683Z4I0XSsdeMhkxGALKE9lj9tSxGSChQPaxdFu7OG3qm2fMr83N2DGYYxEN6DB/\nMYl3fhhnBMgA0n95u9l5ksFRraePEQEr5UrevN7E9YWKyMjEg0cF8ZQT4cLg+repV3UYkQabmSlk\nBvMm3hwU9ngS+v6Oc8nIiJ00TSgKUHTRFAWyR0edjEwAQElw5qWCREBEhBAwA1tBoXvNjArM0hSb\nD2vcfjGFDA1XXpQb+jpf9USAbFJGCRACRISQjAArFsAqqXo0NoTud1be2z/b6q+9WKAyaIleUgYi\n0gQIOYgpCDqz4mvVS3+8OJM3NY0OvakPwl0crL90Npy6e839B53V+PIL8849uz7fu4UNQfDXbrxQ\nAUCCQkgoGznxT3rf91NCwYGRGRjaOk3rw7mjGf2bVytRihSQgQDMASZ8PF/ZBgtpoPGSYeXyh48O\nrzyWo7Z+R/xmSWDpHx3If+HZ3/w89z7231fOZUbITrkkjEAgVY0gHEgdTZwTIFMjUV+YeoNspuDA\nKQNblo4yOEEDKOPzs3fj5GQ+kAIqgilg8zUmrb76uvYzf/xPaOj/zZwmhhiUmZCA1BIZYNHvubyZ\nm2Guqm6PVKF7Y/HKw9rK60uLC14TQXTgMs8AAZ78UvbToaAowB4VQ63z3vJBOhluXQzk0IIaTj0i\nMiYMj2mBBiEIMyMGsI2/xZvffSxHbbX+WXGQyatlghSMMpDWhGgAnFF8oV5joIzBxUpjZOCggmRA\nDMLJmUWvIJ4zY3cVR/rRWpfBezLI6PUxFKb/HTX46r/6wyd3Fr8juACx5H3ME4Qw8gd36/jCXJSu\nAwGSQgHArJT4uKYKTMgrqymVz9/evvV4Dtpq/bNCzqkoGhOREUNCIIjZgCu2lFSxMPVmuRDjEJDE\nGETNgNhCYGeqQCaW1S+shM7OUC1YoyIgtXxza9Wt3yGUskPLCahQNwvN/vBw8YWyJBKhQlSRyBRV\n6Hzl8f4Ohg420oBHxd4fvPHPYd2z1XrcXBRMVuaM1KCBoEgiQEY0Lk2NfPacDByZIooJsIGTrERl\nMkrsvBlC9tRwf46Gg4OraIUCZHB23gqLrX8WDEkzm3Hm0uWNjyeTeXxmlXxphAg+J2VPKgyPK7pl\nAeC/RiFsffvxHLTV+ueFsgGpsRmyB2PMBM3MSGJs1AUqzAOQN5UEkIldQarOIStqxzuPjGjqSp2x\nY1gv9USanJMhE+FjCotp/Z4zz0aWkoIarVishwtv9NEUlVizagBUshwTtEOEVusp4YroiC17AQZk\nJadKphN2ELFgnxxaBlYBZ+iYANRbA4hIEQgAgSB1KaHDTJeRxg+21xERPCRJTzpZbev3QvIZVBiV\ngaHY7J/UeuEiAxKJglAnCyGZj44fUw72Vqt1btSIAclQmZkpAxhAKF1A0AJTncAURGdKhkLBEAlT\nishG5CiDDyVVXVIlVinXWCfwQDM6EFEqnniuvdbvg9oAJVvwDIq8MHCdaiMAMEIgBcqEBipADh/T\n3sBWq3VuhCWRFYuQkwE7R96XjtA5LIBrjbNGM3tfIiRAg1RnSVqwEiUKAQ3NkolBctgJi92myHeT\nkbOUAQHPWSWr9c9CIRgKzFHQTG30KBWdDcqETJoNRUydgSnDN7hfp9Vq/UMuF2o+mlBSB41jUgWz\nTGAAldfEDsybZkSIRI6SI1CJIeKUnEByBAAMCgF89cIe5dlkjqwQIAP7PcgT13riAqWmYUAwdHn/\nbj3pCXoml8kRIAKWqMzJPMY2+KLVekpQycHMOQ+oBM5iY8RQkIopBYqlA4ZsSIhApgaUCAk9MDrv\ng4McJYO6wgwU55f2x2kIBkmJtH10tL6KZMoFKVqWaHZrRnFxrvROHKYsSo4ZUc07VW4HHq3WU4JY\n0OqEHDwnBUdiUViMnJLl2hGT5JQmgKEEbCYQvBkSoWfPpurAoCQqvbNkxoN67B6BghmSAzh/rv3W\n7z+vpObZE4KmeDKyrcWzxiTP0KF3uc5ZjRggY7thqNV6WhBzSuyE0BObZHbmhdWBA+CEplmTmTk0\nzUTeGyA5klRHVBUzMXRNrDOoQZPx8sLk5PhYMqCKcdkubba+HDISKWQ0dPHOqOq9uLgzEiDKwBrV\nNNYKAFpoG/Tdaj0tnKIyGTTgDZEYQe2LwjimrtKcjM0jgWlmAB8gA7IWXDvWhtEALQFBTWAuRFt8\n5bizvzNnDi0zpDbOqvXlUCmyMmGOsv+5LNoBgAo7D8YSBDR6IyGn8s8lw1er9fRzNnUFSMhRvVJg\nVURKDBhRp+RLESNFQ0BiEQ9KkjxZYjJiBTAWdSEJOALzubhx60BF0QsqtlMMra8imwYARWU+vXmW\nLkx33Co7x0CEzASmRU5UI1KbqKDVelo4R06C5QwCQASoCI0RJFbBwhTMIJNLCBG8akYTFswMEcqQ\nCROg5wykmlxCM3dB0glQNkAxnD2eOm+t329oZCApO5ztzXSlth5E51lBMjoCrpAM2SR8raT+rVbr\nCaJcoAON6pi9ZBGH6rgxQg3YaC3IRpAAchYAYaeKlpMSsyYBY2KITU7ELAJAxWIzPjwWJQTJ1s5Z\ntb6cGIG4Clh0uq/FhWcGPJlYagzBkWYQi+rBYUlp8k2fa6vV+oJLiNQ0qasuswKaKQB5lwGIVSgr\nBMhBG+O6DC5nZCIDQzTAgDMy9CCCoOpJlcLC6gej60sJKbjYPjtaX0FF9TSo8xlsP9Pas/r5oU6a\n4DMZo0YgMRQ1ItN23NFqPSUIWDISK4IHNPZkmhMng6DoArOa5pypYqiCB++9M1DyhJq1ETZE48Kh\nd84IiXBwsTO6NVOqfLsLuPWVMLsAqslzSi5e7XMnyoEYSE5CvnBgKDNBjBP751sQptV6yjgSQkVG\nU5/NI4FrNAhr45gio6oz9NC4MllEIzBBgAzgCAyzsLpGvI7RKSUOVjvXvVRLl20GTtvNga0vFyEX\nmNVRTgfl8psdK4OYsQKWYEIKiOCmFQFC55s+11ar9QUicpbZJOWYk6ZGyTvJAgWDYBYuUF0noKkn\nzWmmmFO05lTFsRlgJhcc+i6KUTAznPQXbHhzlDVrojYspvXlJJMkIqxV8niwVPS2XPPRFNF5bVJw\nlhXA2GrsQLthqNV6SricnXlzoNAEZ+owMTamYB5AnBgjSMoGCYU8REuOzAsjNJ5rL07rIjMhF5I1\nnpW577YOl0azkhiY2n3lrS/HLJasdqoHUkhB1erydpnFmbJpgwyRGUs1Te0saKv1tHCE2fsMguyM\nzIuBOgaPYKAAAqWPjXIp6rwAuMxmbGUSdixdVQPMpVlAIUDnxqWHDh6fXllxYu03vfVVoOXoMxEo\nFLyB5gYkqDotDTGAineZEif2hm39jlbrKeFcIWpoqKnDyqDKWZmEWREMmNTMCzaKkFmcAoNlCUyM\ngtlY0ZwBGJk4UHYF0crg03TvYpdRIJXfdPtaTz8j65BmY2Rvl01qnfACsuPGiIFrc06Tq5SkTVTQ\naj0tyIkRaVLvLedEpnU0EBMDduhcxxNDQFKUWBu44JyBQ4VsBIzAjgQcE6JJlMPdxDTXn80+fzDL\n6Nu0p62vALFwiEwaj4M6dpq7vYOxpIiUZrPkyMh3CtBkRfimz7XVan2BFJIkIDPSVOfGyBVmTCAI\nBE2ss5kiMWclBwgkGb3PqigiiiJZAUWVPJqe3K6Tuc7lBdy5ZYgR27Xy1pdjMEuqhPXBQX8pcHe+\n5+q9hOxINDdJLQKqD0Y5ftPn2mq1vkANBGJ0CGhMOUULHhQDgmZ1npKZFZRqYEJGdsmYET05lzUB\ncAYDK1jqmNNsb+FCiDPb6PT5pM4M0objt74CbQSBFOL4YJXNEVapHAomh0WnP9dB0YSajcWab/pU\nW63WFyg3AkihE1CVEIMTIY9ZyChl8gU1DqMicqKsSR2DSlTMESIghy4xWWPBCXmcSOGJqHMjjLPP\nZzm3z47Wl9MZmLFR1rC22mRUouDPJsjmPNmZOstZNTdi1g5kW62nBDGBzAgoi4kSGTADkDmkiAhi\nwrNGGcQCOdDY1IqWLCUsPamBMhGYZWTLMjwDBK98aemGDT2jb2N0W19uFgrFOIvY3ZyjsjQsNkNf\nKTOBA3NCpTlQdQW32ztaracFOQPCpk4YDYy8I0uWm4wIYppQgAszREaTxplRtjwzk4LFM0pOlhOz\nmSSF2G8ASZ1fLfeM0FM77Gh9BQxNNi58UdmxLhTs/KLetQSxVlUjiJERg3OGofqmz7XVan3BkVcK\nyRoj9KLZGaowKaiwS5YrJJIMIuwUjYAsZ/YYTHKHLNUFOTNyThRSY4VlB0z916bjz/oVt6V6Wl8B\nIzggNWnULWEm9eF01BzNFVx7NiCU4EVyZmy3DLVaTwuKGRGRyUgiEkQRQiNHph5RVTRHcFQAJGMA\nUc++48gFZrNGnQN1pikmlOmvP4lOkybtXl4P759l03bg0fpynshyTJZk0Y9VFLF3pVd1KqSSzHlG\ntjyLAExtgrRW62lBILNaHZoXxAyU1aJAyuosMWPBYDkBpMoZkecv1kQQiRAFDCjGHDM509zce++U\ntaCkJsArbgcMim+6ea3fAeQRAAtQLMq9GbJFpjQa1p5VmUHR8gwMMRu0hShbracEOWUiYNcoGEAm\nyeZMQSQ4cIFZM4KoN6gAhYJjdJotN9lhRgYyBhRj1fr+uFoktbKA5OZuze5PQNq1zdaXS40QkVjh\nB8/N9pMnlELSPikC1CIJ2LMZBq9t/Y5W62nhNACbJgrkQGeowXmuAyVBMWGLKOBBAQkJMQsxf1GI\nPGc2omSOgVNWjGlndKweHQoEuvBoOiSkdoqh9RUEQzR1KKEMvS6VQ6669X4GJTOmLISE0SOENhli\nq/W0IDPLKSp6E22AEFNOREoBSVVERdVQ6tgANrMsmhr1Ds0IgCB02QQiSkwIgOTRNIknv7I4c6c5\ntCkkWl8uRyIk54RjYwM2G1Sb1EmZiYmUCqe+qDpgINDmQmy1nhIEjtGMolFUrqHwzpkhoxkaKCIg\nYDLKTczIoMnybFLPDL0HTJPky8IMPIVi1n/paqkGikbYXdfDe1K3KSRaXw4lJUeghAlKJgHleZ87\nDJwx1JGBG1MuNcU2UUGr9bRwQcxBUotFSNT1RKJGqAhICKBBKzAK5KTmDoAzlMxsiJjNHDVeQY2A\nAdBZj5oSSsiKfuXGGTahnbNqfTkyKTgTOh5EnXZcytRbfedk1sUCgSR7VEEhRsht0Her9ZQgE0Rw\nXFBKjrzThplFyalTMQziEMiyopQFZg+EoeOoF6A2AyCnkJPOTCyNZ5N7VhZexchjH7dHvmhTZre+\nnKHGZIqpJuueNWCAzvqjIQGqlgElmiGKArbFxFqtpwWpw5wVxZATg4Il9GipRiWPKUFSE5aUHUhi\nhZmEsheCofdsHoPHIqDLTuJIJGOODBnJYjqbn8+xHXe0vpwjFTEF7hh1hoihwNDdmAPEzJAN8jjJ\nTCUDttdTq/W0oLrJSqSIwDzLyYhVnVKwGKkoAQ2BAgGbOSZDMpGcJ02GDBVpAyKYgB3PzqIMPGJj\nzpK4zpLfOYJ23NH6cuwCqqKK5MOzygQplGvueCaMjSmqMXkw1gTSzlm1Wk8J582TmDXEIORd43Iq\n0AAb9pYVEGORnUdhzESiHtUUWAwsNyWAAmEHCKOE0A+FUyMDQvTzRzyatWlPW19OAQtQAQAang0z\naATunUYxDSakGhmtgGwYub2gWq2nBDGnnJNZ1GxoKUWgPGWXHQO4oFT46EiUUZXMICNkKD3OuCgd\nKahHpUBGKQIkFWQiVzDwXHN4t42obH0FRITOEIl5Lp2MKTYK/bKHalmBCdgFZjHPXLaJClqtpwSp\n5UzARQiOCbjjAJEgxgysZggmkBPEWANmcsQG5CSmHieuyDtpjExUTKcnQzNynl2uG7G1tYZ8G6Pb\n+nKWczNjl4Ec6+lxfaBpOoLqlEAM1PnCQ64NMrK1byOt1lPCZVEGh+q8RDMLhhYQ2RxGjAFVJBcR\nHXAKkAtOYpyUAmUyUqwVsw8mWbnxmQ2y8+Cz+jS4MtGmTZnd+nJq4jwlZ0iLiYdSNl0/niY1Fa9Q\nA3PDXhyZQdP9pk+21WoBAIBDc6YoPgmEBnwTkC1zsEwUWBEYspkmT5SDRFKNIZckpAKiiIZRAgKI\nH3caLgiMZlSAoIVED+e+6ea1fhcQsAkQJun7R3B9UQ0GC74UIgyCSBIUnGUhbIMvWq2nBIXAbFkz\nqKhDKgOqkioiaUpW+JQDZHQGzguAkrI5YsqQpZkZegCZNbMsomNn00aasaFmTVoup3v1N9281u8A\nxOCRAjbY9RfrW/s1Abl5igaIYAjSJGCE9sHRaj1FSACNCZBdcKFANHYKIOiAcvaUi8JVDlBN0zSh\nL4oA2tTZsEDy5ImJEJE5nQ3H1gg4QxPLwXzJ19vUda0vR+SMnEWnFHp28J9rKjGeTA4SlCCoDrQx\nIxe1KNsLqtV6Sjgwo5yhsKgOwAGBeROJpZFRTgQAUgqmLJ0iWmRLiCJKSl+k14VcYurLpLc4qqMy\nW6Ekhll7W5OdzW+6ea3fAcLIakldkbpXr4etKEWoFC6FnHIJGJ3PtSfL1Ob0b7WeGg7JmHwWDRJJ\nxemwqxp8NlA21YBAERpvFhRL0gQO0OfEDtUEhZML5FFLm2swg7AXJWP20PSWdtvUda0vFziqZStB\nQIqlkXvQJ5OFsylCIjAALAhcgpJw1tZ+arWeEs4pGJhvPASYUEXacRkBPEZAcgCRyKsnxzlDR1CB\nkLD0pkCNA4gOMwSJ4Dd7I/dorTFxbOhQmMer7Qx168tlZFUlBxnAb04fnm2gzZpjdq40zVZJQjZm\n+GJo3Gq1ngaEBCnH5BWMu5VlVyAIIAqRWpYpJhXzLOw4hADeI1lWwzySAtRXDqcxmcO40pXmWCEr\nKKHU6sjObn3TzWv9DpAmNw1pipYELi3n7Z8fncRYDYhUs2MR1yTDnLNxO2nVaj0lnAg4b40CSpML\nnzOAN0ioxKwiSBWYZlalwgFYdj5naAiRlAhUFIsYg3LRXxxG3b/ifEqOMply+X7vm25e63cBm9MM\nmREdVBe3T+7mrU8WeBEtE5rkUktTREWv7SRoq/WUcAnQYRZxpiGgORBhgIwOzRI6DtiQKnFmwsYZ\nNAxmDGwFZWOL5NFH0gzu2cHh6cEFMHKUM6H5wfU2ZXbry5kaMGXyDpPk9TdPRqfFQVxf7pPWIbMR\nIEBTImRq95W3Wk8JZ+w4G4EpS/JowAQVGlJUK7NxlhSyJ3WaGkDDHF1AAjD2SSyzJDSfiaS6TF3o\nT7oUNJEiadm5ufCn33T7Wk+/ZCymgrUD5F7aeP3j/fXe3E/6OTEbUxIzCJqgyNC+jLRaTwnnJCfQ\nZJyDJwFRctQo5ECswmCCtYA6YQBRVCtVARRMxQoVFe+ckiYAorzgj9PmvIKnzgQIF7ZWvunmtX4H\nUNGAenKAMwQoitfWdvrLN9Z9dpzJoHApsaIH8djGWbVaTwlX5IQhIquZGpk6AphScKQqxAaGwWuD\nBKjsVQuIyYGCwyhjj94AMZdeTdxgNR+NzuLzfQ+WmRETPPymm9f6HbAEnd/86YtsVYuX/w+/0I42\nWq2njpswQTLDEpuMiAFRa3SkEX3ZZGKF4NDVgGzqDDABmI8AELgWtcIscxJU9W7h4dlZIxdKQvKc\nEH3Zvia2Wq3W7yMHQiDsRMtACUw5amZi8CaCTg1UNahlZywKlsSRJUbmlBxnMGdqpsCMOMi7c3vy\naPeaC8m5hM2kDcdvtVqt30fOqTCZZRIzyhmJGdUZkURPnBMZk7hugywuk4FZCSpmqMYApkAMYgSF\nRKyuPYqdg6Nhb2WlI5Pmg503v+nmtVqtVusJcApe1EhtygDgiCnPvPkGsasaSVkxoZJ6ZHQquUBS\njGqOElohGRAzmPmkrF0/3909G0a3s7o1N7m3K6NvunmtVqvVegIc5ghaasgADACagdkEyXLhBRpC\nUgWnrOZByCuigqdGEBkogxigGUTzTA1U/goNTz7r4Mb2ldmD8dbL33TzWq1Wq/UE4FgiFZIdJGbW\n2mtiBIeWvDMhIYKUAlJuAjsBYGsYUYyUAZDjOBUdAovkFSA7Snl46+bhsFrqVyLf2vo/Rsy0Wq1W\n6/cAHqv5TgMiwCZlYuXE6DVFdmAojiHVHaWGfCrBDEQEesmgLsqMGDOSBzMBiuhQDBiaye1H3irq\nbs0XC990+1qtVqv1+P3/AXhO35/FUWI1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=L size=1595x148 at 0x12DC07198>"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image.open(PATH/md.trn_ds.fnames[8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T17:26:31.454041Z",
     "start_time": "2018-07-31T17:26:31.401839Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from fastai.fastai.plots import *\n",
    "\n",
    "def show_augs(n):\n",
    "    x,_ = next(iter(data.aug_dl))\n",
    "    x = data.trn_ds.denorm(x)[n]\n",
    "    return plots(x, rows=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-31T17:26:52.290243Z",
     "start_time": "2018-07-31T17:26:33.057508Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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z+MOpQLu5lyg5KVlC8S37BzByWw8ln9FokSq3Stinym2hPzNpdB/5GUmH831k\nvcawsLD4M8MSZ4u/DbaF5ENfAGBXmD5cBnGujHA6ZWnDGyNzb4CO4bi5jQFUhoW0+4jQmFSJiR8T\ndiYIjQHUUHteinPrMVILO5FlU4/TTAciDVpdTSSEaDD7zueqA8VCNtNRO5AsHudsABVhjhPMsYv1\nvjuNWXVYYimnAtgRqqNjnK+DZfPK+VqcUm43BrA7FH3MGFkAaih2M49LTThB03N6VhnZAMrC1Cca\np2BEJ2YUqlq7xm4ANjdIG35+tmjYzWvkuMs1Eb3vc2spAUy9JLxZXdNMlWfkAiM2naHk79YI0fWV\nSmzuteOAE++WhDfpm+VCkt6Yp8p9w8Cccw5XcqClJNAJdpT2cCrQUC3To0ZZFWBkeYWk83iWhaWN\ntgA2N0QPF+inpDE9AWwNSRshetIy3aJnibOFxZ8bljhb/G1gEiQ/pfU169jSmOyWj51pZfQQGWXy\nZLbnQXSYBJbJLWcxNi1/ARKZLJqkio/j/nFyjHQANWH+RQeTdjORxn+QDaA6woQrxgyD++fQHqAT\nlXItoZtu6XYii4BzHxKpymf0lU9VHdbv5I6QPBluV/TO5wKoJvLop+x+fodjAKAyLG2Xh/XYwttD\nEjPZR1SXr7ctgFXBJkreTCR1/j4hojPzgX+tkayMO/eJpXZVPnD6l5Jy27WdEs9UyVisvgk45y4h\nsEm7ZdTcxMS/egWYcfkAJQcyhXA2cI7154H1T0h2v2b1Mk5lB0JDxYHyrEaOF0vv7kG6YtVUWulI\nLlblHLc+qTmyi6Tt5iQ0ALCWUq4v2ieZDgP0HLzTCpiy5SQld07frsqHpmxU5Z4AKolwu2lSEoY+\nWbWwsPjzwhJni/0aoQhbY51h1nkciJ/TkjSgW+7M9jjrmt/l3BO2nmrL1UZ/mnqiv7omwXZyJ6iL\n6KmFqx3OmwX9WsLUjYBBMiqJZNYRKQhEpIVcAGVEOjhD3r6wJANpC2BBgIhKRI6pDAmp7AjgxYpD\nlbw3KJa/fQ2i90QecMf2fkreUCkEc0elEMdlucAxH12n5IQ9ct71o4ALnipUcjBVxtq/W8ZixZ3A\n0NtEz1tDVmq6bfOeAYYWFolelTxBSat3qHK/jcCYG4cpuT5dzsUZtxc8Btw+4RI5Fy2PeOrouXgR\nqJuZo8Q0XkYZACQvlnEjQyjqJBEfAKBaDMmobC33vqGRTgLXnU/TUL/Uub36M1h+gqwINewTgpmY\nqbs5JTcRd4eqKnlmXNv0ZDKbNzaVsk8mEYmpdZreyj1CjsPGRGnZ7uaqXFkjk4hgnf7+LVxZIGWv\nuIg8HxGr94C2wIVvyaQk7JMdFhl+AAAgAElEQVTrP7LbGlV+oaXxv+XwDltYWPwxsMTZYr9GmC2u\nRl0sFwy2cIaIIHL63ywAleHoRJotjrkAVgTFYlgeEnK3vUHYyDAAT5R3U/LuoBC60lrRm5ULnLxs\noJLrQ+RmEZTzLvsH0Pu7y5TMpCBQJcSk5GKgz4dCFhklQ4DeRCRd1XKu1GIpL7sHuPJa0UsuFXIT\n9onex18CN5w9RMmeMkqfHRRSdfImYObg05Vc20LIXIQmEde+Cnw8to+SQz4HkvEq8N1z3ZXoJtN5\nJIVu/j+A1FUyNmHjHzKcQCQ4kVxEehg+7AP2qGJ+Y0kJ3j19MymNxNBJryupkYf8k6GTyumTJDW3\n7prDz18RnrzzXkRDUFsDGam1p6MIz/9L2mALrG5hL8RLgyT1daKLXZG476PxwalTo/cpwn0ahzd6\nS2bHXWRhT3Yz0b0Jz/SYqSS+/p0hzjxYhKdPlBTefpdYnxu560nvJjzX9SklhbVViol45sCnEA1l\nYSbp4/D4CU8qyctjof3rjMHsf0a/j22M58ySZQuLPy8scbbY78AbcG7cfpwqz1kiS7kllwKnPjla\nyWkl8gH+/kng0utHKjlELpG1TeSDtvRe4PxbZLk6mBrdSr3kPuDaKVcpuV7cLUH8GsNuBZ59Tqyi\nbvKeCLPvyBHA7u/EYljXVAinK0W39tXXkWuBRz7UjbOqNL1mLcpVOcmru210b1eiyhk+2oh3qG45\nL7hhlSqnJgg5SdKICnDw48tV2UMEMdGt9733jB/kXGS1Lg/q/qRtJ6xENCR69PbOHDpXlZnc+Hmg\nUYizB4meRyOBhbjyzA+U1Mwr/rC5CXtJbwIe6PyitO9yXobv6NsW9feQ4WbC7h5OvsuATpA1FxHD\nFSDTuCeMNOovrzCYfWIizX7NMM4VikR/L8z2zGv5D8pCeiru+bXidvL53vaq/O0iKRcPBy6bPVzJ\nfAsaUqTfG68FTv3gWtELSh+KrwJOe0dWDhJ3yjOYvF3aWPgIcNt1Q5Vcly5tVOdKefldwNlzRkhH\nkuT+HHKAvGOvtQCeqJD3u12iuH4w+kLf8OyNsZJlYWHx68ESZ4v9Do09Qqweyv1Wlbc1+5i0xuC5\nS8RixsQMKMKUux5UEpMs3cJXiBk3TSM9J1/bQjx9fXQr3r5IoqZ369DnlFRSL+4EC/blg5F+6C5V\nrqkTZl9b7dP0QvVyXQ1kPq039HZspXV42sCE44GFK4SouKiOiQR6AQtfJR9T4kBstZ3aHXjvxd5K\n9rChlk576zTglef7KpndIrRJxCHA5593VWLEI3rcVxwCPPXtkVIXkL57akVvVGfg7fuPUXKzT0ql\njQ3Ah90y6eRS9uT0UuXjNgE3XC2uFVsGCmsr7C7PYA/o7ikpZFkNGV7yTCr5OfO6dMs0P598jOnB\nXhNxJlmVRIKdLc56+/y81xlt1zj4e3sMt6LSUIYqf1UpJJjPeyqALfUy7genb1Hl7sdsotZG4bYz\nZykp0yMTxXQ3R5AZiZdOfIiuifs+Cs+eKlZwdhfaY1i3T530mZIWky/0twvag9H8cxmztFmymbGS\nlcLA7E459IOU3d06q3LfBcD4nYco+d/NFsHCwuK3hyXOFn8bmOSBP5LL63JVuTeAmbuPkroy8XPc\nUSbm4vWtgPOeE/cEXwWRyr3ysV8wAxg+WvQCjcliSFbqZZOBCc8Mkrp0aaMhk3rfC9hXLYSrVZZY\nO5s1lzBZANCv8wpVPixNNip1TNxKWmPw5gnTlcTkBhiNL06Nvvxfp/GeQjw/IvrkwJxEPDH8vqjt\n6aSlEA9c+UhUvXpjknPjP15V0ppaIRlLKnLByGwhFuLy9UK+3MaDEaJNa+WHtdDqqv8hRGVPZ+lH\nbb7RyLUysSlsKZbzbv4STS2bCB2TYN5EZta5OSSgYbVlFyGP5qbkrGfCS6sAQXIZMC3C1RHpY0Cb\nzegopg2LlTRRqKc+dAWwrDZPyTvq5D0zr3Hu9naqXEuuSWVbhXiP6QyM/ewcJfMkz10n7a2aCFzy\nhFicPcSpl98NXPmoWIj9ZTKeSXtkjM5/BfjsosOUHEqVccnJpb7/C6jMlzEsHy8TyNoCfQWg/iPx\nk85Llfe7W9q3mt6k7AUkWfcOC4vfA5Y4W+zX4Ji0G4LyMS4A8G1tWyV/uFMsOZd3AL7ZWqDkWtoU\nZHo0N5AltCGNQqHl6XrbjqEjU4RkmRuk6jtRbNwG+ug26B/FugohIOuDsglqg0tICg4H/q/4ACV+\n7RXLMVvxlhYA5867XPpQIx/+4ouAPu/JBjZ3LbkC7JTyyjuAi+8RPY7OFSKj+qpbdaLCpJX5+vK7\ngaueEqttoKlURlKlXNIGmLTwFCVnpMn4pfh0MnJQU3KLoHKKR9c78hIhuglGBIbDbpyvygV+8WNO\n1Nj3aPTPFetfBQ3GZ1XynPUB8OQemaAVV8sGtlVbJVTbugLgoqUXK9mXIH1qIP/2H/KBi78X//Fg\nvfy9h+pEr+QS4LzPxI0B9CyUXAz0/07GneHzieV8eSvgsu+kT8Ga6MS5pA0w5s0LlczD6amR53vY\nLcAzL4qbkpdMsFX59O4cBpRukGfcnSbj7vLr96pDe1ktOPyIYlVu72fXh0Lce8kTSmJfaKAQTw+T\nFSWd9OuW6aKXZyuJVw5StPZGYtZVU5TEk5kUzZ1nDJ7u8DyiIdejuylZX2gLi98fljhb7NdIdcvH\nrm+STlKva1wctQzchyWHvYifxo34eMA9SuLW9c/ZSHxy+hREQ1Czpo3D20eKi4hpaROMwTv9ZJMR\nW4j1896Idw+XpeZ6Bx9S4Da8dbhYd/Ul9Bvw8SliSeaPve67WogXRt2DaNA3phXiySEPKIldFUx/\n4icvFb008snVLeJj8EHvBxEN+t2ejFE5HyppD4V7M62lR6VLhAPeyAkA6QkyEVtSJUvylQ0yOxgG\nYM52cVtxETHNSxFfckB3A+rVWFYE+jVhv+3xuKPTm0pq5BYf/mSNsN+BV3rKhji2ENdr92AMXjpW\nngufNlI34LXDH1US32/9ubgVs3s9hp/GGDx31vSoNez6ABTijkufUdLaOpk4lNbpkY1z28hmy607\nxMUoYx5t2BsM7J0p7k3/t0dWDr4MyPUO/hi465qLlNyQLOP09Wxg0ExaKWolz2CnNrJic3IbICdB\nmP5BPt44yK5YQBefHmbQCfkJqT+tZGFh8YfAEmcLi/8BaW4ncquDQ9bqYet0GzZHJKjXfFR10htw\n8FF1OyTHMNv+7zjTMQIqO+iZy/9M9M06Bm+Wc1MkCbMPTpvqTDcD9tfl6zfHjOsakZ9r2KWHJ2vj\n2yl1xmTjn+kLVTkxg/x1Nf/fR3Fv25eV5Ew+gSFZXyEazPublyCEW5846HBy6TCfC58xrYgHpquG\n03WZftc86dH6YXShqUfcjJomS9lvbHid1P416VM7Gac9RzDZLMSlY99S0rObJCzcjm/ZfxjwEJFO\nfG++Vpd/29eIhvq+klEQnwLPlh2hxMk5C6McYWFhsb/AEmcLi5+BSbslScP4HKD/8sFK3lst1qRg\nUD7o684Fen8i7glgS62xTN5v7jWiFo7uqlEyFDjnvatFL4EYCGVNKbkEOOGDQqojgpAiZGZNHjBo\nhSy7p/uESH6UB0zYIhndmvvFT5itr7cCeHOfhNLLSBBCnEV+vN0AbGoQCyIvjfsNZ2OO8VzPfrcG\nqWRCl6glG9EJps8hjJtJ8j2aD7EOzXJLHNC05jO5Z5JqkkqWOVtgwJgcOEWcMPvOhDvWZIiPi9Un\ntgmb50p2mNiYKyWmv/Z/sCecoslr64XQLqgSH98NVeKa8V4rYMi3l0j/1siKQOsXJPb1+auA1zuL\nC1NagmwcTOfMgTcBNc1o098kIcAAUPGeuDod3ERcP05s9AppjbVk2cLibwRLnC3+NuCkAm4A79bI\nkuoH5RKZYXoOcNJKiSHMS+2ZiUIIAeDIZhtU+RDKEpaTwKl7J+DRPrIM7XGMw3sDXj5aXCaYmOiR\nCkbj2VMeRjTo7Y3B1GPF5aQ0KITVXP5uniIWvsqgnkhiXbkQl4U14p4QLBHic2tX4KWZxyvZW8XR\nLaStgY8D/x4nkw1vNSVeoWDaX74OFI2VZBF1GbShMk3fUDnoEZkcOHmjrLwDOIF8tV3sP07F4quA\n81+izWLk/rxmPHD+O7JZzNdMnoXmjWX8/q8VMGmTPD9ZiZK2uXWyuBl0ATC3Rja6pbuNWNAEziTI\nkw3eoAcAG2gjXnVY3ATKGsQa2w3AnMqDlMxJY7rlA+M3/0PJq3ZLcpDKPXK/Sy4F+j8vIRvZX5mN\n7ytvB0bcIWPGXjGp2+TBOON1YMaEM+Uay+TZ33kIuTscDaCU3Gzy5QatulE2BwJAu/lyHLvf5CTw\nhrqJmHqHuPro7iNFmNJxNqKhkVtfpdgdknvMSYe8lD4oFUANpYEPaxMq0UsCUBeRe8wh5zhBUpah\nl+hy3qBpYWHx68ESZ4u/LRbWFKjyu6vEJ3V6D2Df40IQ01+UneyyHQxAGFgkOTXwxfmSxnjfueLz\nuKIAWF0nPpZd/ZIEI9OjE3GOr+vhtNBGpr+mFMfNqy2Ta2o4yi9WuDCVQ6m6NXJagSTi0LnnvXit\nqySc0HpxOAsj8W7h3Uri1Nw1RhSM++8W32XdusmtX49n747uMx0wom88N2wq1clfmk4qCzG9n0xe\nOB01hzcDgEP6rFblkn36BAMZRGho811JaRNNbelC2YjpCchN+Zx0bjoQmDqrv5LZgOvfIwM48CFg\n2PNXIhrc9RQh4jbgxpkXRdXj+dS1dwDPvn2skkN+qZzaHZi/so30KVFIW25umdbk4cdJPO6WfnEl\naZ+kb74bf8OzSvLSReob8a7Hnf8W32peVUjTwscV4pWzZcOenpad/YfH4szM76Oey3QBYj9xb0Rf\npWBd7tMeIyX6AkqryJsDeSLbG8CyoPQ3ECH/fnr2+wD4njZzhsjuz65DxwGoCRNx9ljibGHxe8AS\nZ4u/DRrIu9gL4MS0pUo+6HCOATsOJ97whZKe6Stho7Lm6a9MQnNKXTzrWyqTUhh49yKJnjC9v0T3\n6N1vmSo/nQ+UNEhdC9pw5DWW3Z2W4U3XgpoI+7ny8TpifXL9LvZl5Q+/fragg2dAsuEykeZma1p8\nftyx/HWTKXFKIhGfpi7dgtvDtztq2cQ9eeIbq4fcuwtvHx19I6KOG/B6/2lRa8xMf28OmRxVr0ZL\nWViE1y+SzaXsT23GFX/9SmmPx8+cbLwwUEIC6i4tI/FeP6njGMw6Mf03Ts2U92cXPbcldfokYu6+\njqrMbhfFe2VSsqw1cNk8If2hbWIFD6dQFJXLgDPniJuSh6K8NFolz2a/R4FRU6JPNtjqvXQqcOGD\nshKRSCHnFswARowT16nULUSIa+UZ7jsPmHrW2UquzRXrfk1TGb/vZwJXTuGIMnKufRLgB+tHAYO/\nkIyfWZSsqLZeOr+iNbCVYpU3tvlPLCx+F1jibPG3QSiiE64sDhvl1WzJ+AdtAju+n1jWtvZlC2QR\nMl6RNr5ZLLFc8z6EjiVrVbHgB7Eql74vfsH4Ahj2g4TuOq+DLCkfkixuIG0BbKdoD2wVM/1Omcwm\nkjXa6zL8el1MiPVxqqC04kyx2AKXCyBAJDjWRkRGLELM1miNYBv+GJXEhPiYurDOJMrD0XvicZnX\nS1kKyd2hHYBSIojOLjfA1gZxG2CXiWry9+0K4J1KcRFaXSORJJZQFIjvWgGXr5L43rsqhJjVV1Lq\n9CHAKa+R+wTFK+bYxWsm6O4ofPnrxgBnvELhB8mlpSFNrrd4ODD2i7OU7KUQiynJ8k7c2hV4f30n\nJedliWW6c1NZAQGAkw+QSCLedvKktUtivdG49TjZHMj3rvpUjmBRhFHXvoRoMOOFD7roIyVx8hIA\n8FwsG0XXlcrKROoq3eLckCbnTp5frMqJuySeN2YC2dOjbzb0n6Mt3yBhq7RXlyETQN47AQAtPA6z\nVQsLi98Mljhb/G1QFxFSmQrTrUG3ijIBS4cQgXSfnv72ipz/U+Wzmkr83896dtL0Vj8mBCl9iXwU\nmyzRfSWbPynLty8efbQqv9O1iyr3bws8s1uy4GV5xb+yuU98q7sAeL9a+sHLwezLWZQLvEaZ2niT\n3hAA8wMSlJpJYAWR964A5lSJu0syTUo4ZnRbAJ9W69nUoqELgI+qxVLpdYgk0QN6CuYgWUi31Qt5\n7dEKeHi3ZATkmMl7auU6vskDzpp3hVzHp0JSFz0ETBwtcZLDDha+b14CJo69RMmBxkLYa1rIQ3fJ\nTcAjb52kZJ4PeCvp4TwJKN0s/U3JEveeZnn6hK9Lj2JVbuQTPTM6CLujJHn0TZnH9hFL8qYqmSju\nDehksXG2lu9OgeNMA4CHUr2vWyvJhErKaQCPAD56u6cSUzfLM/NBUxmLq+4E7plxrrRNr0+TpUIw\nh30GPDZSiH2AzLG11N6Qe4An3zxBydo+xiOA0mKynhNJDXTXXazCt4gbS/vGkrCka7L+f3HrBvmP\ncNN/jA/fkdYovHi+WP3Z0l+pWf0naFlSLSwsfh9Y4mzxt0GlsbGmnJbDd4Vk41M7AK9XSLipd7cI\naQ1RpIuFrYAhX1yqZFeZkNGUUiIqhwCZ38oXuYH23m3rpcd5LT1G+tSQLB/qsm36xqcPl0mfEvwy\nIQhT/67tBEyZ3w/R4EsSslTUCbhv+XFyHUR0h7QHblksG90aKFNbeLf0fVRn4LHXhAR6q6Qfvgpp\nb9jDwHMTpL3UEiH9nlJxn7iqFHirs5BFJwwLA2/2LJAfWhPJbyvWYbwOfPqyEDPmkVoAixMB37dp\nSqzXhx1lHYWA1VNmx7Bft/ztOFyun9OAh43QasGmJJN1193gvO5eX0fZ8qATp41lYhV1uYT0BgLk\nn9ALWFQqWRW9Xp3ortwrlu/EBOlflyydBHZuIpZgL2U2yfDqLjLH5q9T5QM7SYpsfTJUhFsvlKQf\nbMHPSeDY14WYOWJaVD3dlWQcJj/wYFQ9M5Pl0wMlzrRbizBShDdPvh/RENZimN+ICa3fURJPGs2w\nf7w6VE2TV3PVg8Hj1Nm3w1HPwsLi94Elzhb7NXhH+tJ6sR4VAJhdLkTqtXUHq/Kq1sDsl/oqOW2T\nfPxcHLf5NKDgORFrJQABaNUdAFAp0bXgaSeWun6tV2l6tw0Q5+hMCuPWSNtEOBpvHicf+zKy/K6p\n5xi143DOQeLusXqfdGpTRSMwmIxFKvRIDeF1YnX1UfQE3sAGAJTRG0EKqVulr35j69FEKpOE7KQ0\n0Ylyxpci9268XpU5zjIA3LpsrvSPiIpOkK7HS8PFT5jdQnTSUogXrxE93S2kEC9eIclgeDOj3sZI\nvHcOZYgjkuXXiNlo/N9JvLGR3WX0DZUfnChk0fR3F9yCt3pET0qiJ9qZECPhza2Y1ZkiwFCNx3Dv\nmdxSyKKZzp5xTfanqsxj4TPC4PXyS7i3SidzPsyEPxTxxtho24g32mouQTqaeZyjmaQR0eVNrmbI\nvUYUEcXt6IOur2SluZyS+uj7ALw04W+bEF8CFQsLi98Oljhb7Nc4fdU/VXn7O5JJ7IwpwJfjeim5\n9VJKxzwAKHheIl9UdZXl5e299A968WD5MGY3FYtpYkT/sLraimWV/RTf/eIQVZ7eA7jjCYnMkUjE\n1Fsr5e+eA64aJT6qFQXSHkUdw7CbgFf+T64RTcQSlpcty8kAcFIH8S/tklJKNaPw2HkS7YAJgk5M\ni/DAhOgZ4vTsdkV4459CApkg1oTZ+n4LRuW+ryQmFl7DitdIyyroTJAyySoai+jxZsZAjE2ZKW5n\nP27uBzsR+w3ymexiUi2/V4d1y7RXI/ryu3neFJrYxfItb0SVIYOH+1zR2/AYZNFPYddixYzWiD41\nYca+Zqmpx3lsuY73LZibVfkemNeo60nZnBxwXQ214Y5hIWaYfvv8HGuxtP9Lj2VOtGNTbFtY/NGw\nxNlivwb7Zdan6XUVrSnMk6+FVld+mMjso2oYyeDdLGRvR4NYcb3JOjXzJ4rcoYlYTHsfvJ60RmLi\n5WLt0yzOWkiukXj0HiGfTAj9Lt1CuuBssWiGI04f+8m4s/lnSjIJ0oE+sZDzxkEzigZb7niYko0Q\neS08HJeWLHpGbNymHNYrRoJGfwxiymBSxITLjETCvu8pbv0i9c1YMtah/yI+8ZHPZIfYu163TpyZ\nEHupDbdxvYl0//l+B43+8fWb3uP1VJfmlolN0IyiEsOKy+B753W09Ovn0o7/rzEjParyG/3LcGiv\nLmLecYG5gZifmQx3dD97QI/sov1uXCNv0A06vo/6u+WU/MbCwuKPgSXOFvs15nWX1MfozjWFWHjj\nQw5HXY+v7nvUoY5RhNVDoici0TEeiw978afVAPRPqXKo0ZOSdPHFt2Sb4f7f9Zp4UhzrGK29qT+t\n9Bu0l58Qn152nOdtGaO9eDdjxXuuZHf0rHrJ0H+Pt71473fzGNcY6/p/SZ/ivT/x9j3V7f9ppZ/R\nXqyxiPca23v/92eL0TbOZ9/CwuL3hyXOFhYWFhYWFr86Qg7WfXeMOlOP96nURMQti1dAsgFsaaii\nuujttTX0eE0iF8BOzgBJyxmJLqFKqUafOOujxd8DljhbWFhYWPxtUBEWl6LGADYGhUgxlWsHYBuR\nrIBDNsyuABbViVtRoot9kuWgjgDWBIWYcfxxji3dG8C3AWnja0rL/vVeyer4Wg5w8qrTlLxhp2ym\nDVaKC1nJUOCA54cr2b9HCGHSDunfD08Ax1whoRi9VeJ+8umnwEkDKEFNklAHT7W4oX30LXBqXwkD\niF0Spi+0V/ZVfBQGTs2VyEVO+CgMnJ57SFx6Q/OP+p/0PgoDg/OOjFr3S/t04lkXK7l8vGxe1VZC\nLf5ysMTZwsLCwmK/BlsIp+0R0nNrC+DqjecouYSyGS7PA/p+c5WSG+qj+1ZvHAic+fEIqhMy6qLU\n2cXDgZPeLVSyby+l1aZwhhsKgcGvSHsc0zuthNj7kUDp+xKuJ5JJdZm6z7U7Twh7o64yGejQSI9Q\n0/XmxaLn1aOUnP6ExKxvlyihCTtoyaNuwuQPJdQQpyb3GLsJphVLMhiOUhIyfNpZj1EZ0fcH3LRh\nQVS9kOGBP3SNhP/xGdFMrlmnRzn6D5p69JjlEzfOV2We9JibZO98YYYqH+TTYtREPY/FXwOWOFtY\nWFhY7Nfg5fTxTZZpdW+2e9fhqFux4qinlOQc0WIcNp42w6GOMQob+0cPF6hjJNYOimfvRBGWXue0\nT4MxGmuOfuan1QBMz/3Ose7qxiUONbo/tvP+C338Ovni2y8Qr96R/vg2UZ6bWuFYd1pywKFGJ+mH\nJUbf1Guim48s8zYiyn4DS5wtLCwsLP42MAlMLEJjyY7F/wL7/OyfsHfVwsLCwsLCwsLCIg5Y4mxh\nYWFhYWFhYWERByxxtrCwsLCwsLCwsIgD1sfZ4i+DuoiEPeIMX5zdKwn6Dno3omeSixVH1Iwd6hSz\nMzFGG2HaXZ1o9N1N89Uw7TRPMvQSaOe16SvHfeI2uO1EQ88J5nXE8stjPfManer4HphjG2/fY91H\nJz3zOrh/DeBj9PPG+1xw3/l5TAVQE5Z4sx4ti2B854rXN5KPN/vnhJ+6xnj75DSeDPOZdoL53uqx\ne+X4bOgh3cpCkgxleV2uKl+eA9y+u6OSdwdlA1t9WD57j+QAV5VKWvrakGz6ejoHuG7boUou8O9W\n5QFpssGwIOaVWVhY7G+wxNniTwsz3uqOkMRK5ZTGqcaOZyY0TAEClN65MYB9YdlBzSmTs6EH2k91\nSUxUE0wQw/+VvFkQ0tIfRycSSUYfk7X0yUIkEuFMOE2CyTKTG+7Pj8Rezsspjk1Cw+etJHKYDWBb\nSMJXcbClNLf8zXgRe5zi6bs2kYExoeL7EZZyFpzvN6c+zgWwl547P02UvIiPmAL6eHL7IeNcTmPG\nyAWwl/Ri9X1HqJbqpI2ysGQi7Ang5SqJ+bs5mKnKY5oDN+yQcG3LK5pH7dP7OUDXbySub6A6egbE\n4sFA59lXKznik05FKJ15yZVA+zlXKpnDuCFN7m/xIOCkT66Nei7UyXtweQfgteKDlZydKiHYmvir\ntcNSPPK/0il5m1Z3XPoKVc5JkGgMTRwyPlpYWOz/sMTZ4k+LcESPiZnmYIVjkvKjtU8+tF46xmOQ\nSs4GZcJLNMbJmuaFTrC5v0xukgAEqQ0/nTfRpZN+ThPMVkut34691vv6I6lscFZ2QMgYdyfi7DXu\nRwrJOrnTiXKFw3VpmbpgkE+6LvO5CGj9cwbfb71tvb0dIbmOaorR2gvA54E0Je8Li7Vza1Di/xa1\nAK7efKqSy+vlnropxu87LYHzVgj5rKkTMlZRLiG4igcDfX8YquQEj1xvJCJjtqgl8I9FoseorpWn\nZm0r4OZFZyg52S/3Y0xnYMlesdx66Vx+jz7hG97pC1XukLhVlXMSOObtWHx21j1yLprw6u/j9Vhw\n0v1K4udH1xuL+f3uU5I+Meb7eD0+6T5TzuuW9yzBmKLc1ewHak9/Fk5IKldlXm2y2eIsLP6+sMTZ\n4k+LgEG4qkn2uTgdqk7g3FTHpIo/rOnQl4D9MT6ETBxNQhzWLMnRLYsAUKddC1sjpe106FZ2Jpwm\nSQ1oFs3oek7RVAH92lOhk1mPZs0HqkiXx7CclrwbA1gdFHLGpLIsJMvkAwE8XXGQkldViUVz/b4m\nqvx5LtD9q8uUHGqgZBHVct6Sy4DDXi9Ssrte+u4KS3n9KODA18Xy6d3njnrM6puBgdNGil6lXO+C\nGcD4O4couS5TjqvNFr2iTsA334mbQDhN7lVKptxfE83ShHC63fqz3zx9nyr7PM5W78bJ0n5NUMhi\noF6foHk87GaiTyj31ct9DIVlnJK9OnF+tbQ71XVR5VSvWHBfyQeGrr1AyU39YvlN9Mi4PJ0LTNh+\nrJLTE+Q6jk9frsr9AM5yVzsAACAASURBVJTR0DSi18JMs1weFsVARPrkpec7G8Aeeuf4LcuGOVG0\nVmYLCwtLnC3+ZGC/SdOhoZI+4kH6xKW5dBcMJyLpNdxk2ZIVMAgsWySZiJvWzupIdLcQ8yO+J+SK\nqgci210BLCbrZHG9EMltZNEc2wIYVXqCkneQFbSsViyVX7cAen0vKV8DASFPoaAQ0Y0XAr3fE/Lp\nqZIx21AE9H3keiX795D7w0pxffjkM+C2I06R9nfoGcn+g4Fh4OMD0+gXIlJURhhofdFqkbtK2uF6\nZkuXAS0/lT41JBJxNu5Vyma5Zjc9XEEjv0IwRcqukPHQuLlOyt5qXS+hhjKh0QSjOqKfbOtWcZNI\n8Msz11CnT+R2VUunkryil2AQ7DCR4HSfkMWmybp7Qo8WW1TZ7dLb6J0tmdW4/WbefZreVQWfqXIw\nItdoEvHBud+ociNPTdRjAOCwtPWqXBMW8p7iir5CAQD12iqPfl7tfeQK47moN2SGuVpiYWFhYYmz\nxW8GJsG15NLAVtvGAHaH5KPOFs0y2qhTAKCkQcjj+vpsVa6iDUJjAdy+8xglryzPUeXNexup8qoB\nQM8vJJ1ueK98qIuHA72eEatjApGiCBGnVbcBZ94ipNJTL333Vkv5q1eBawbJuVyh6B/qj78CJp03\nWPTqovtCj10EbB7RRsnl7YVU1WZTB08E6pbKNTdkyv1wZehkJJEsoSm5dVpd3nGbVLlZspAnr0G4\n8t4Wi2kiLY2zewIAHLRAxrOJV8hyqkfP2nX+YiFwae6V0j839+8G3H6vZGPzu2TMfBp1Gom3/nW3\nkjzR9xoCKMSrl4trQY1G7grx0Lj7//sQAH4tdW8hXrvw3qh6+piNxhcnTIuqV6cN2Q14/eAno+oF\nNJI6Gbe3fV1Jq+paqPInZZ2043YH5JkxyffmWnnPAvQOLg6JC8eozsDkNScpuaZO9NiafUl7YNIS\nmVAlJcr9yUiS+33OAcDi6nwl5yaKi8SeMM1koG8IDLidNx5qRFpbGdLBk1zn58LCwsLiR1jibPGb\ngTdjsWW2zFjif6lSlrXf2NZNlTcukg/1hkLgpomyTJ62RYhfWQchvWOnA+/MlZ3woSQiBQk6geMP\nvK+ZbpFrfYSQxVapZVL2l5FWIYaOektJTcm3Uyd3o3Dj05LyNpOsbl6N3I3Hg6/Ek2p3oqbnHH+h\nEO9fNDlqjW59H4fPekl77KoB3IHn27+kpGAMAnJDs4+oDfnd7N/VTb5ANCS7dNZyeooQ52AMq2BH\nr9w7PpdpK0xzR2dFZtspRCRToE8wWniib1A120h2RXencOqD2V9zzNjRgls2rbsempTmeveq8kXN\nvtb0bmg1R9p2sR/8/RiX+17U/qVpJHUaZncVMs993xNmL/zbMO4gOdfy2paq/M3O1lr7b3xxmCq3\nflPG/R2vjMYZ7wMTLhUXnso8OdeeruSacz3w6O4+Sr6qyeeqnGzcAj/JXuMZZN9oCwsLC8ASZ4tf\nERzdIAnAdvrCL67LU+WP9oo/5Mx84P6l4tvoWiX+sHnf0Ie6EGgyf4/IIfpUdxTrMwCEE4U8uJKl\nEwl+nerlNxFi0aWRvpt+cO63qtzOt12Vczy6Nfb01NWIBtNjurOP/FejHvEjnIiV6TPNetxeLIIZ\nIHN5JZXzAWwOCQHhZfIcAIvr05XMy+a8xF8APXIDW1b9Bok03VikT3pkE74WJjQmIealdh53k9jz\naga3YS4AVIed7xC7C9WQJd3/M8gYg331+R6bjwG3kUb++I2Mrnbyyf2pi8jE1XRHOIDcLsxntVUC\n7wuQkaoxxikI7ruUa8L69tU1AfFj/36PWJW3rKb3th+QQ9ze89kCVfZ1bq+fN1k+W3s7kL/ywTs0\nvcuyvlTltt5UOKF5gnOduXnXwsLCwhJni/8JXwXkwzp4zghVLh4ODLx7lJIzV5Glbi194EqAgvOW\nKDFypFicN52o+4OW3SPnOq75GlUenf4aaRXiozOmKCnRkbNMwGMHvKgkk4z18peoMltnzc+o05ZC\n095YHY7OFhfXiyvJAACTyM1k/i4hGVu3iC9syRDg6JkytiDSwp4Aq28GTnlqtJLDXvIFThXF4hHA\n+e/KveMV7uLhwOXvi4WPB8pXJle/ZgIwdOp1SvYEiKTSAC5+ADjnLnFvYU5d30j0lt8FHPmGuMuk\n5MnEIzdDwoJ9mAtcV9JfyV3TJbpDB79MhgoAbGgQgtTILaTSa9z9usjPj5gQMCy/TDLdWsxx/Tgn\n0u83yLbT5lXTB5dlfuZ2hMR3Ph/AinpxxzA3b35QIys97+yRkG6Ld4jrx/I84KQ5hUpO2iyfkqTd\nct6FDwOf33KEkmszKQ56S/0at50qU53k82VyfVKrlZreVffNVuWOPvkvyfPwWPwbnXyG87qFhYXF\nrwBLnC3+J/ybwm61+9d3UjEcyH7o6yhHcEyJH+FpIjFl1/5TPvBH9Fmm6Q1pLe3lJHCYKL3FavJL\nDThsHsoHUBEWGlwelvO2A7CUCG0pbczbUNtUlae2BE6j8F97t2aosn+b9GH1zcCAyUJgmWNRFzDg\nHmDuU7JczUhqpMvBNkL8TuogxKJvBpOMQjxzkYTuYsuvbgUepU02dDeOUZhLdbHcQl4dKT7EASKf\nQXYMRyGmXS9uIWvrZJxX1TbX9Lp126CkMvLJ3VKuD8aybXLcD8vF99u3W/owcBwwdPZwJXsreROh\ntLXyduDSe4QQJhAD/uEJYNAkIfN1jcniSkbW1bcAZzxGExYKxsATlg2FQN/ZMgHiKCC+vVJeeQdw\n0KsyKYmkU7hF2lC47jzgiC/+JeeqpBBse+V5XD8aGDGbJkOEgTcA4985T87FfD1LX21Jz5HJzGHd\nxbXpyIy1pFWEm6c8oaSmHnGrSdHe20J8ciw/q1LDccABoF+STIgqaaKQ7I4VqNHCwsLi14Elzhb/\nEx5v86oqT5h3gla34QWxHrvXCzFtvNqwvmYKEWozWnbgawuvYeD1QwqkvXSJzLC3r/hKfvsCMHga\nhyeTJpJ3y0f2m1lA0ZVCMhJ3yAf9uIXAI+f/U8kN6fJBrm5OLOg5IPSpkH60FyKQdAi5lQDoNmip\n1FE83HMz55FWIWaPEp9kPzE63b2hEHP6TFeSF+zSoJ0WeYZriRNi+d46LVabVnU+t+7jq2u2SZAN\ngTkeiaTQK2mjpndH/puq7CdTum61vQUf9orH37sInw+UsSWDuOZyABTi6etlY19YsyQXYea4qUpi\nS3WKtsGuEJ9cKZMItj7r92ckPjhHNiLyZMOtxRMuxOdnih5vHNQjSYzHZ0dNx0+jCP83SMYiZNR9\nQX1y3no3Dm/3mEH9FaQYoRMPTZT7rUel0fX4GWQ3ncqwvFfp0N1H+Nm0bhUWFha/ByxxtvifkO0R\nS+CjLb/R6tb2fUqEvlLUU/qOxNufvawkTuBRaSTKuG/lR1QnH0k9UsFIvFwYnXzqn9WReP3JB0iP\nX4Xb8PZbzyAa9Mx8I7Hg+niIyu2YmR99Q5yJ9t6UqL+baZCd9Exke+Jbrs5yO0d9juUDysh30DP7\n3pz6FCu1dHuvP+rvZpKK5h7pe6zEFHwdTmmlAaCTV54UM8sh1zmlAQfiH/eChOh65ri0/AVj65T2\nHIg9FvH2PddBz+x7hsOzZZ63MenFei7ifR4tLCwsfgtY4mzxu8P8KHq07H5STvToVDdeshivntMH\nHYg/M1isD/yviV96nniP+y2vI9b9/jnHqd8N+ZdkcYvVB709T4y6X9b+L9GL93j9Xfrf2/g5x/1c\n/NLzWlhYWPyRsMT5TwDOHseWITdiW8ZY75eci6Ng1NDvOQC2NcjyKvsYpkNPBa1n1pJPdZJxLtbj\n6AGpMdoz4z1zVr2wFnFBRiAdQBWFwXNK3Wvq8aaqLOixpbUkKkRHzL47IdU4F4+TmycKcL4/scaC\nYfaP23BCEvTr4PTW5rm8DnQsFc73hxPINIY+Fk4w78+voed0jYxY/TPP5USik+A87ub9DmkJdKJv\nIDX1GHxMYozzmv3j58w8l9N7G+tZdWrPfEc4YyVfkfmfU0kuMry61BPAG9ViceYkQV/vbavKr+QA\nxy4Xd6ud++SYYL38n607D2j76aXSpyo5V8kVQOu3rlCyK0h+8Y3lmjYOBM7feJySD0orVeWLGkk6\nb9nqa2Fh8VeGJc5/ELQMefTRMT/GbIVx+qB5oZOWmjC3p6eX3etACsy01clu6ceukO5juC1EIcm0\no0SvrXEuL5FWM300Z+ljglgT0cni1gYOJybtZbhNH8joH3EOd5YO3XfSRFALXSZjXUflVABlDsSZ\nScGPenIu9v/lsUgEUBWO7pPMfp2Nod9jP7VRQ16pqQDKQtIex2fmO5AEndBUhvVxLwtF31ToNVJz\n8+SDJylmtkXW47TI7P8b6z4yfkydHl0vZNzv3XSv/A5jAaM9r+HiUK49M1LmtzYX+rgzleXQd20B\nrAnKO8LPNPsudwOwvkHeb4/mgqG3t5gex0pKe745KL74Q5oDk3Z3lfbI1emm5sDJK89U8ta9suG1\nrlZIZfEg4MCZEonFv0v6y4mAFj0InPwv0YuQg7q3SsZ57vvABVdSlI5S2XhY1k02585/Crh12kXU\nnlxv2mZ6DnoDZe9JdJAa2n/QOFeisgDA4a2LVblNym6trqjPB6qcQRsbOQMiMA5XN/9YSX7a9Ojk\nYmNhYfHXhSXOfxCckoP4DaLLhJj9cJlE/2hB4t3l8jUxLYSJGhEPRy0DOtlJMTaO8SYeDqdVEdbP\nFXKIw2ueK+DQ92bGbvo25F/K/s8eg/owGeXrMGPtxrLNMclMjrE8z/eLI3iYsYpZDiA6KQf0iQPX\nmLGAnWIDm3ZJJ7JsTl5YbmrETGvm8SEaeMJjnrvGwUKaBf1+m8+WEzwx/InjjePM2BWS6y2nuMPZ\nAD6qEdvgkhqJPz45FyjaJFbMvXXi47uzUgjS0pZA7/eFBHrL5Dnmx2L9aOCsxyWqho8yWifuFc35\nTwMDHpIQfkwW2b1/xSTg8mnXKpnDKXNgkyGTgBfelrCH7gYZ25sOBPa8JUlKWi6W/x/fZkr+Mwgo\nGC97GjxZEi6x8hhJjw4AO3vI9R9zykJVPjKdo2+MwS3TJfoGxwjn0IFAkbZ5kzdo6lkeR+HRq2UP\ng5cmfB7tLtyOGynhi9uw+p+QoofCU20Y2TCbevh/muuivzsWFhZ/XVji/CcA70I3CQ0ThkCkIerv\nAJBMO8rZeriDLHodAewORbfO8VmzoZP5NLdOHJlIJtJHIs0w3TWhTVtOrhqATpaZ6AbIomd6I2e4\nxZpmWmnrHEibaT1M40mE8cHkPtbQsr5Jovl+mbF3GU080a8xZJzXyTHHfC6SHSIIuA0XBPOao/UB\n0MesPoalluMV76LYwH0AvF/dSsk7GsRSySnRJ7YErt9yupI5NnBVmRDRkkuBw96Q6CgRyvrorpOx\n2HgN0O9xCf3GQUQSy/V4wmfcS3oUViNEwb6XTgWmTDtX6nxSN3kqsPBLScYRypFxSU7XV3Ja5EtU\nlbZdxYrZLW0LaRXhloueV1KWpwrRUYSnh0lq7mSyaPq1jbGFeK1IonnUE1vWY1MX4oULJfRbyIgq\ncv91DymJ03a/v6sLGMUTJT5z1jKaaJfqY9HqZgkjWXyz/L6loLcqX7IBGD/mciVX5tH7crSEnlye\nD0wqlRCYeUmSxCiVbn5PANtD8gymuWuprPcvpK2G6W9gmjv6G+kz3nVORJNhw+JZWOzXsMT5D0Kq\nw5+rufs9lQgiu3eY1j6WNeuhWydIGXFa+FIcLNOAvoQeyxLo5G/5X2TRgUiaBLuCCFws1w8mt/Es\n9wNRUhw7ZKozx4KhWYuNvrO7wy4yBfJyej8Ab1eJn+amelle31gj5edaAP2Wna/kihohsDWV0nbx\nYKDHHLFAguIE80WVDAOOfEEsn56A6K2ZAJysxSSW60qoEb0Vk4ApDwvhZGtnoIkcM7Er8NWKA5Ts\nTZV7mtNCSBAA9OohSW4KkoWIZrNpFiPx78FPSZ1HlvjTOBYhivBG4d34aRRi1g0Sjs1jhIX7bCBH\nbHFKuX0LXuwsUVn42TIJVx+/+MPyk1qnPz5o6onuHmWubDAJZKtomkt/F53aA4B2FC6wIEHuQd9k\nthA/hDcGS3zvsDbBZ5J+Iy5dLcmEllHK7SUVuptT+UA5b3ClZKvEciHA+Cew7K2OSlxLlvns74Rg\nYyFw3wh5RwKZFNu9sVzx4unAwEcofCUN07LJwCnT5dn37iN/8gopf/c8cOFAcUdZf5mMxQ/HSdQd\nClxpYWHxF4Ylzn8QzCX6/8AN091B9DhUm3k8b8Bi66FJPoNaFjNnP89gDALrRCT5w//jknx0X16T\nfHISA053XEakMhvA/Dr59JQGZWm4hjJMFLUAppVJ/Oid9RLvec0+SfH7YS5w1DyxcNXskEgcJcOA\nXi9JogsmkmF6Y9aPBo6cLnqcBIPDGK+6DTj7DllqT6QPsK9SFPu9DbzW7xAlN2wRUgUQWQwDjS4X\n0rFzRDNVzuqix4/ue9AqVe6YIqnDOasecD2eP/d+JekkqxDvXyGEM9nBNQUoxNuUAIWfJ/2JLsL/\nnSjWU77fQe2ouzAh911pQyOB+vPYM3GnKseaJsWKVa3puXgip4OJr9dh/wHgTJbNFQCPU8pt47xO\n42lmIuS+ezSSbmYijBG3m+r0bIa6XiOyxur/Jfr73Ttpc9SyO5O1HsDcno9JHw4jv/0wj20R3r5K\nnjOOwa0n2rkNjz4mVnUnv3CgEG8Pl/b0ONuFmDNC6pzjZ4/EtGfFSt/Fx+tjNnuhhcX+Bkucfyfw\nhzURutuFHiGCNyYBtREhSNrGOfqYpMPcOCefBs6ilwtgKaXa3RUSq041mQiHAThtsew0371LyGfJ\nxbp1MmWLfEDYxXDpFKDnu+LnCQ99uKrlc7zxGuDkGWLV8dG+ndRSucavXwYmXzNYyaFEucY9naW9\noonAE18fLedKpTS+KbpLR06GWCezmm3X6i4/6RNVPjRZMtilGf6WT14pH2cfEYaA5m9ZiB5DJa34\nJ6s6qHLGd0asYh9le2spm5vq22ZraiUDxQ/34lM+VeXDkteT1h0YlfOhknhTojlty6FlbtMJJFkj\nUk5WVh2xyCJvkGOy7DFWIjipCJMdk8yyrPmFG3pcF3KYQJowx4ndWOrpna4xUm6zvz9vqGTXpiyj\nPSZmASPtt2ZZplOZ3vda2pkY96c+Rp3TmkrAOIaTyCBGWnG+30xMTav61pBcDT8LpgsYE3g/6VUa\nPXciy2b/uE/mtbOsd0PvvOniYWFhsf/CEuffCdtC4mNXAGBdUD4S64OSxnkJbUya1AIYuL6/kjdT\nquF9G6S88VrgikIhqemLhAQ2bJRl0o/CwJQDdD/F/8B9cCdVHrYQwKti3U1ubhAfShscPl4soW0a\nl2l64495S5ULfLtUuZGbQ6mNxKyhstmHP3a6VWcUpjz4YNS+Jxupez8+RdoLOVqkbsXUA2YrqTys\nE9jGCbKD/oVd4su5ulwI7NetgPM+kOyDnioigbVy3rU3AqvuOlDazha9fW31D/CuB4TunZgrJLh7\nMidQGYcnhwlhz3IL6U00SEEzD3/QnazFOjEzianTKoVpweXRrQ7zZFAni0x8zHTpDLaKMtEzSSpf\nYaxoGU4wSWQ5WcHZN7gAwO6QU+IdHbtCsoJR2kCT1QaZhBYBuGvH8UourhYT7CZ615flA4NWSiSJ\nIBHMqlqZ8K5qCRw99xolR2qJiFZKeUMRcOwsWQHhW7DuBqD3bFlFcddFnxivHwWc/Ky04a5nPRnP\nNeOBMx6WiTGvyrA/+opJwMCHxWUiZRtNUNKk7cUPAGfcJudNqCV//A3yv/LxV0B/crOo6iQGiCM6\nyHs1qyXwPaV9L0iQFZsCAOW0xMSTmUBEnoN2ALbT/U5zicsJvy85AHZSmEunyZupl+GWFTWbHdHC\n4o+HJc6/IdjHN2B87MvDspy3NSgf1rKgc/ION1ngwkn6R3tPF/lT391VLJWuSK6mxxt6+EMYMVhG\nuRhF4QrrxMJNpLB2jXzgl0PK6APcveREaYPIU4TGYm0rYMBXw5UcDlJHKij81dXA+S9RxADytc3q\nIB+77/OBh3aLxfnglE2qXODTQ03xjvw8Y2PWGSni29k/VXw73dpw/hvfnTYV0aAPZxFevf/eqHp6\nGMAizD34+ah6ZtzhggT295Y23P8VfSN6hBEzZnAsi5weTo397OX3bABbGuSZXlMv7iNPbjpKlT9v\nCRz1iqxYNF0gbWTOk8nV+6uAS48Qn2m2xIcyJYLFh/OAM8kNprwjder/2fvuwDiK5OunDdpVjpZl\nSZaFsw0mmww2JoPJmBx85o4DEyVHTM4c0eR0YDhMMMFg0nFEg00+wAack5yDbFlxJe1qV98ffEy9\nam3v7QFH+s37a3qnt6dnpmfmdXXVq0JhZitOA85YfJIcN03ud68MOe51ZcA7zTKJTKdJyc7Qk9xc\nL2t9251Eunhlcpnl0frbO2XKxLY4IMst2f5uql7/XHFHaWwX9rk1qF0BcnJEJq2BEvxEjfcPT+wC\ndWoX0jaQRGItWW2NU+z2EQUpbqaVmHZ6N10OlD8kyhQp6eTGwGP1RqDbR9L3Dp/0oWZHPakN55B7\nCwVvphgToC6kzZe/QNqrzqSX257AVffSpIRcqxdeAxz9pkxEsoplhYpdh+ZWAO82ikEilLHU2c71\nyDkVA1jdTuOYJrK8KlEMoI4uYZHXJcsuXPyW4BLn/yHYOtDXr19+B6TJi3JQ6jxnOzNnsar3dK+X\nnW121fDvxIu04/DRX2+LW0+jEp+OvD3uHtbTBarw1ikSBKWJVBVmnUjBU9Zl7ip8vvdDTolJm1aw\nuAIf7ycBNLysr0ngGLxLgVmMLEU+b8SNxZ85JZtLDACU+NgabZd786tsbEY9S39NP+6AUt+ILysI\n2H3fTWTS2PKq4+r2bNrKWUZAZa5Hyub44bosJec3bn2+V8jTtoF1zvaOBWtUPZ7obMyQyVZdX+2O\nsvSvlDKCumvcKjRxNbKQYq0mXItXiGVxMQVKfhQVpYzrBgH3viSqDcHNUu8vdwA33naaNGgZ+l8/\nAIy/9q/SX+p7e5r86Zh7gYduFs1kD/k0pG8iM/CbwOcvbO8UW4ukXnsu1dsfaGqWc+6SL0SvJVO/\nf3Y8UHzfe6bzhLIS5/9phlNiveIgR86hCpfc/YxT4pWDjBQO+rsaE798H/EQU7P1ybjsCQmo3BKV\nyVFQBXlW4qq/TnVKHFx7/7IhYKygeVfGEnJZ+0BPkovvEtUPTwYZLq4BesyQa715ezFw0JwJOFIH\n7w4Iytg3/b0Z7NrlN+r5re9wFy5c/NpwifNvAKwC0SnojxiDTbv3v4GNVCfy+zOVAIIWItWpTWIW\n6bTcaKptaP9VmhwYdtsckq1rJB/vZiPrX2MSSUm+70d8H1qzrtK7pt+/T9IRn5iaCWUYTOa5D2bS\nj0SE3R5UZpetS0TKdXbE5BA1hhK72bBqw9guM1W9adtNkT6JB0snK/2bZ8afKGlU4Y3TpV7UqvKi\ngxIZjTE9CZ12mtQzfdXvmyiTvHSPTSm6Cg9fLW2wS0e6skxXYuq1MgllV6JmddwJePJ8Wdng9rT7\n0Xg8tecjTomJaVh5Q1+L0d3egw2Dgqvj/p5hKHOwWwNf95polqr3SbPoOq8Pi0LGwnpZlRi2DXDO\n5xLDkLJcLOns7n3keODKKac75VSZG8DfRAPyMMBfIyO5dQeZAHQ5WlahAOCweWJyL/GvUPuuve8R\nxEPEkPc7q+gjp8QTh6DhipTl4VVIaSNijH5zUurChYvfDlzi/BsAZwv0GeE+TDh5mZyDBtOgrZ0B\nuq2mRThLtceJI8wXvD3phw3msZjE2VL3AnZN4k7t08eZ5fLMyQDX00Rc989mBQaAIq9YnmxpsL//\nn91NgmFau+P14fv24qfjNhGKxU/HbWYsZE1vvi6mhB/DPK6tbrTTJI/2JfKZtpJbu3qL3Vdd6xXb\nZdFMghy/bROmCwYn5oiR6dtjKH14lDKH1AsZfeI+NpI6zIZ2scTvAWAV+UlHOuK/tncGsDwsVvsw\ntb2iTX4fCuC21Yc65eVbxFo6vwI4/T1JM526nlY2yI954TXAWXdJXAXrYneQ7/vh9wIv3HmgU27N\nl33t7JU2FGjfItbjjgK57l3LdezENocIuQ16ZXyvb85W9bbfR1ysStLEDaYiqJVn8slNKxTTMqEb\nSI+cr3swxTZpSgy+3zx+soxJmJtx0IWL3y5c4vwbABPYVohPZR6ArVGxlDRaLKTfp49mbWXZzqLs\ne2YSEa+FbJswSSVrUAeIVDYZpFJpS8NOdLmeTVYvEzrdMZOqdJOwc1kpOOjjsjJJPV2zCgCr2m3J\nKATZ0C4ujR3xA4n2ADA/IqSA1UzqomJZGwVgSr34XzZS4pD5TeLz+nQJcMIi0ajlD3BLRIjO56XA\nIXNEHaU9Gv+jPe9oYPAnIs0XocDV5ScDO8wkH/RWSn6zTo61+HLg+Jspux1bxMlj4rvbgBGTKTDN\n4hr8zWTghL9JcFc78Yh2cutddCVw7BPiMw2WYyMZwQXXA8c+LcFnvub4ZHnhdcDIO4UQcvDZVw8B\nF1wjPq9RS56Lrx8A/nrpJdIlGqycbOWLx4HRY8Vv3xeijIrfyHL/iFXArRPEGhsqpGeJIi1HTAZu\nevCUuH2KUgK7a24CFn+wTfzOHwN4mmQch7vIDfJl65Wclt3FX8HnJ+UQv56ENxzSjHjoiOl74CsU\n/+9IPflxN2o/7sUbxM88RoGcWE8D7WDgy4Vyjl+nxQ9CHTsQuOoTyQbpqZMxPaovcPn0U51yoFb6\nm7lG7tWIqcCtJ8l154lDh1f6985HwHnj5H5H0smPew+5fiv/CsxskX1D0+wrRS5cuPjl4RLnXwlR\ni4tDJx9atooS+suXEAAAIABJREFU8cs0rLS5RJBVEhGDLLL1dCVJ2PHS8B4AFoTlS8sR5MMAPNUo\nJG4dyds1EZO4uRR4uG4Hp8xay1vCwoLuLwH+Un2UU94UkmXetZtIOeQ0YMiLQpCCNaShmynnsXQi\nsOtTQpCYmLHFbNGVwJCnWRVA6i2+HDh4CiX9CJB/dp58gFf+BTjgZelTB/mtc7KRlecAZ7wh5JNV\nB/y54hc8qi/w9yWSTY2tluF2banc3CTmuuIsWa/Oywypett3Eb3mfL8QmAK/JjOjB37obOcoB87L\ncMtuLyIeWCECqMIJ58nyPy9lNymGWYk9TpG0y/URyi5pSrrtIdemgwh7Squ2OEey5FlSBt084xnr\nKefV2ipj2iRwDf3kPqZE9L6a3WWfJ0smiqkBTcyaTxILZ+988SE+oHAh1arC+JuedErsT2z6/95z\nu+hsB6yBiJV48uL4QahmIpeXzpRYB+0mUIVXjrEFvHIbl2HGng9I+2xhV/fxOkzbTdwd7Eokl+Ol\nPSQmQmXzU9PkK/DM7n93SuzjvEm5iIzB9ftNl3o0e1vYogMvd+olrhuLarSffSSHn2kZXF3m6Pvd\n8e/vEA++3nqC4iX5mjZ6hbNsJgD08HGSH9f67MLFbwkucf6FECK/20wALzYL4ZxDqYqj9BG7tQQY\ns25/p7xgqwQ3rVko/oHVFwJDrhUrGa/68Qryvx8D9rte6uUul4rtZP2YPR2ovExk1kIkn/btncBt\nU05wyjF6+bflywfu5h2A19eJA2tuUKxJqR67BFk/Ug8YXLhS7TtuyGdmdQBAoZ+tw1W44ujnnZL9\ng16Ja4991imxsglQhROOFvm3hnb56LYbS+377Dbf2Way0xTR5sge/bRO9A9gCzEAtEfJL5W+rP5U\nI1NkWB7dDY1CGDwp+iO7cKsQAT+psqT7tfXwtQ2DnO2GsPR9ZF/g5sWyrJ/mlzETJgv26P7As8t3\ndsrZwba4/wGA1TT200kdpN1wpRjSV5ba26JyvisaVOYMoEiO1dEk18zTYhDsEJldo6TGENBEtMOb\nwM/ET1bhTCH2GQF9Pfn8wyRp9kmdZIa8EMBzNbtJGz75D09shgFYS+mju1B2RFP7mt092LVgcVje\nHYMA/Kt5oFMO0cRmRwDP1EmfasLxSdsj5cAN6ySIcnOrTOTqWmUy9Hk5cNyHNGkMSZ/4/qy4BDhy\nhrybAlvoOaD3yoqLgBNfFqs/2xmCm+Q/J18PXPn6CKfMKkRBsmxjJ2AjTdbLcklIHsCwneT5ZjWT\nA49dQLUmqOyIXYj05no+p3q34IHJIiPpUTEBfB8nYRu/S5ZduPitwiXOvxCea5JUs6MAXPqGLO3l\nLCK/TPIBvHUH4L2ZkgUvmkGWF8OnMlQcf+nZjF9q7SL11nelj2ymbm/j3uSz6NWkLVROVrcQkROj\nC0ysGtqMRB8E9k1c0yhW5uY2Ijo7Aa8sEXLXTu4EuTlCMi7dFpi+UbLvsc9rC0lBjewLTF2/R9z+\njAWwuEkIZwaRuyxfq6qb65ePcL90Icemb+wpZV8420Xqw8oW4pswZefH5bgUWJSjtMCuwvt7iLXP\nLoR2HZ7f9gnrXsFkPNpHJhHaJngznh30mFOy+RMDN2Hajo86pVSyLOpApztwX0/Rzw4pbVwmzvfi\nkDyx4n1Kqcjn1RSD0VEr4yS1nlZoNusBGVwp9VjOMJKpCbu3mfzWDYuzf5OMoaY2GbeNXv38rF8g\n44ckidHhp8IewGcfivRdkFwBWJoNs4C/jRXJtNZc6W9ziXZHOXPGaKfMZNGfI6T83H7Ao4tlZSMv\nXcbw5dtpScxMElvONsY+y/gNzJSVjUJ/o6p39e6i515A6by1+sYYPH2k6LRzMGN2Spuq98qx8S3i\nrUbA3kvHSYAmy73pzJNX4gVKj66fpTtxVcmbcY9lZl7cnQIqW+kZSTUs7LYkRFkei9+PCxcufnNw\nifMvhJHZm1T538fHX1LVgViV+OQUjrpnSTfGWEwfdRv+Myrx/NmyRMsWD/2Cr8KbR8bvHzAObw+X\nNqLWYKwxeG67x52SPfzvVvXhYoQUF7kGL+/+YNz+5nv4mt2Aqb1ETkunBOcG78AjPcUyrbKg4W7c\nVi5tcN9NZZNrun5A+4hwGQF2p2dVS584dboRNNiTSLrOdAcrUhMk/bClVjZdghIFW6an8Lbd3zKL\ndcattcz+0STMOO4gkrTbOSiSdn8t+JBqXYvXj4pPpDSxr8SMUaK+YQYYCsbghePEKhg23Bj+cbKo\nauhAQa43DlOOlrHaSssyjTGONBiLPx/xjlP6YLOoT6yNVKhehQrk2WrpSpPBUsNani/jJ4VWWNhn\nGABCK4X0t0TEmo2DgDc/ERcrnjiwy9I1g4Cn3tlXjkX12BXp3H7AjdNEFy5GEwdue/EVwMgnL3TK\nnEG0pVj+s2wscMxz5IpFc/osWqD6+gHg5EekXluB3KtAuZD3hWXApHWHOeUhuYuc7VEAlpOwM2cN\n5aRLxQDq6R6n04pauvH4cfIffue4iU1cuPj9wCXOvxDMlNscwKY0jg3fZ1ZZ4JeuSW6KibMGUuy3\ntQdpF6tUxQaR6EpBLWaf9MeAlhs9ul5OksocOUqqjq3bur1cap8/OqYkXqjDboNlcOBkegLtYr6e\nrIDyfTk+AUuUxtnPyimdPqzxVTW8RpY+7ntEaSvbVTrskwiNzn72PDmyZ/BrjMW3RpuEmIMo2a+Z\nkwJtC2BZRNQemHB+E+rubN9aDkxYeZxT3tQsS9y19WI5XVoOHP35uXIeW6Q9Jn3V5wMnPSWBfcEt\nsu/b24ExEylTZBtd9yYZc++/Ddx4vOg9t5ZIcFtDdzn3EfcDzzx8kFNOlJBoyz5CiLPzxBpdEtRp\n5Eu6irRaWZZs56dq3/dj9hUXgjSv1me++mDxaWcLsU43PxYPHSO+y6bLiGAMbj9NVizqKMPeF03a\n/7diX2G+i9eIKxoMH3R+LaRvkH1dZ2u1jO5vi+V76wAZF1sMd6Zl9YXO9sDMdWof60lzMpwM412X\n742vsmFOtLlsSoC6cOHi9wGXOP9C8HRKnEEKB2S5SiRBloj4sJZxY0d8HeNMow0m7+keu8XDTM/c\nSGQnRP6bLKlUALsEm9n3JpVh0U7omA7zJIJJJKB1sflY5gSgMRbf1zoPWoGDg5M4NXUAWnfZJJw/\nIA12K3MiCTueRJiSfSaB/wHcn2wAG6PSRiNZxRo6xAJZBGBWq5CHT5t6O9s3lwIXrRrulNeHxAK3\nepP4Gi8/BThuCgVvUk6N1EY53y8eBy46TyyL/iZSXvmu2tketgW4r48kJuHEFB1hIiltQPsJcs75\nFMyVDwqwOgkomCYENpImz5lJUru/IwTJO5NSG95eiex/SrKi2LZC/Fq7aFek5m2EcLUUkO55F32/\n6bIreLVXBHzkh90jb6uzvX3OWlVv767Lne0samRZqIuqt6BB3F2UG9X2wMPVknmzrZ1UechXfXYF\nMH7+8U6Zs4G20wTqu22AC2fLJMKbSjrl7Le/E1BP/SgvFgm6A4s5oBK4jmIT2J/Yfwm/Ia7H36ZJ\nECEHVGrSOwbTBjwJG/ZNExcUfjeZK0A6ZT1LJ8qOIqONHyP56cKFi18fLnH+hdBOxMkPwyJpXTbW\n8FiSXgBQ6s+Jlu41kSTrYUzIQik0ucs1iGmJl6yY5HvbanxMWLaOJw5muueAcnGwL/Lb3AlMIq4n\nH+w+oEkLW7rN5CAB67G0NdtGls0+JbIyM7jvrKrBSV3SAGyMyv1qtUxkigB8S0Fhz20a7Gx/skB8\nhleOAq69+SynnLOCJl7vALUXlcixW4Q89VlJWS5PAcqvkQxs3q7i4xvtQdZDaEutp40kBitKVL3o\n/hJsuLWHjKW2XH0Bl10sRD9M+r+mUkH7KLFItoYpiNCIF1h1rvQpcvquat/C2/rL/9KlXooRTLBm\nOD3vaUJgA0FdL9JT/Iv9y8UK3uMxSduMG4Def5Zr3RYS6/EXPK5iwHeHybVecnFPabsvqzQAhUGx\nJB/R9Vu1b3wv8evNJitzrkoXfgse3U4IJ7u0pKpn6VpMH3K/U2I3Bh2sexWeHij++NxCwHhe9koT\nf2ImrM2GvjUfizPxme9EWzIhs6wTNWno9NmUAbKTjr6X9qXChQsXvz+4xPlXArtuqCX5Tkt78qI1\n9BdUyWshy50yzimfZLtPaqKMgGzVZPJtLktyspCE52ghlaaF2NanH5tliy26THSzoTWetaU7gWY0\nwbS2cyKSRMlHbJZp8z7yB5ldZ/INxZJ9ghud7d27v+psR7tzrQmYdqUtS99E3PmiLMm3kR97XUwr\ng5+zWKydjTFZ8mYfX+BKHHr3TKfEig7mxOagu0XZJEDEVLsVVeKCY99wSlleIXc6ScUkXNH3NafE\nrgV+ld3tOtyyi8iYrQ4X0L4JOGHwv53SZlKcWNHA9QAPWVYjdXKO7VFtmfatkjLHk676U29Vb+F9\nEkRYXCwuGF3Ttd54yQwpn5QjPvypKlj1agwvmOuUTDeLVIvcXTjBahgHsgaN/zOB5WMFPcbk0vIc\nJ7LL8n/8xvtB32N7eyZZ1u2zLF78tgFNluNP2/9/md+XrsXZhYvfJVzi/D8EazUzUTYTkWiylKLq\ncYY4dh/gJb88AHX0htYvddkugHbp4Nd2p48JbTcaKa352EwkTUJodyeQD2kaks9uxy4Z7MdcTyQ3\nD0AtWWe5B8EEhLUTMaWPmi0TIdCZ3NvaYyRK9R2xpPA2YZvWmCQgkoAUMBJNPnhp26vcTLTfbBZZ\nJNk3dFWbJpWrW8XFYxGlXa5tEVeK6wYBU5eKhTxM8nvhkFCYsQOBO98TuTx/g1zRDMocfer9wOV3\njHLK6ZvJP7lZxs9BrwIPH36wU44ukcnAxTHgm51pMjNArPm+rqQhfCBQ/qSMn7Zc6RNLOwJAcFP8\nCx+oN0jlBrFObmwQt4tNHeSCsS/w/mxRnpkZ3V7+3yjHOfUa4OonyH2CFhhG/A2YePfZTjlznVyb\n1Dp5bme+BUw88c9OOZwnk4O6nnJ/5twPHPK6yMwduIu4ugzNFReMCgBr2uXNyOnMc43JIFuZWeXF\nTFvN4Ocl0buuswEhyefH8nsnC3aC2AIXLlz8PuAS5/8hVEa8TmmM41uSzXrZHrFI6cA5veSrlj3Z\n/9dYhlZkmTMHdiKp8YPKAO0K4lX+fKaFOL4PsUkIc+gc2WWCJw2A9klOZAVOtSy9mpkDGeYHjTMx\nMjKSTJFtTho41bk6bodpdYsflMkfcHPy8mPi8U3ywNcwnMgCR9epztC0zqDgqe0Dwlr3SFuu6o0q\nmO1sN+STxVlZkm/APduLLysvhUdVvYm4/7DHqe/Sp9ooB4FVYdT5r8s+yvfcZKYAfFjcE2oa+6ld\nq54XYuqhCUU0qsdq9fH0jNTTdpvxPIalnL+QfKvfJ9/qx4CeL4irRSSXnhc/XYuLgOJPKMFGFrlH\nperjsrqhSn0NIEQqFizVFw3qsV89XCYL7enkzlSoYywyu4kVvI20uhsN6zurlNjiCgA92QymcD1P\ngnp297XUBHEGppvaD+ikPGOxHpsrT2asggsXLn5/cInzLwQmvZkAaslHlT+5/IIvBtAQk484v6xN\ncsPybCwLt44+VKUA6ihwJ0sFyWjSy1bcTn7SHCRDfTJdMNiyyv/hetkA6ukcE8mY8XWqI1/eDEMa\nqjkWn/hlJLCqJjpuogXVqFoFIMs53e8A9IqD7f+AvmZ8j03ywGW+LlFjUlJD16mW0nuvbZckJGcA\neKBWrLtrWmXfo2XAOUtPcsobKdlK3SbZXnk2cOYroiGcEn/OhOVVWrUig2Lb2Mr62VTgqovFoplC\n99TXItfy0HeB2/8kaZFT2uX6+bYIYRu1EHh+0iFx+9SWQ3f4caDmHz2cYkcaXc/hAL6Tc27ZRp7h\nwi7ah7h3L9H0Ls+QYL7ytFqqpbMtMky3lZFPiztKvlfOy6vkAcfjxlsk+x5PRBp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qVeHp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\nw07RAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1360f6b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_augs(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Determine size of dataset (2000 x 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-30T21:15:10.854374Z",
     "start_time": "2018-07-30T21:15:10.158431Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# create a dictionary comprehension of image sizes in the dataset\n",
    "size_d = {k: PIL.Image.open(PATH/k).size for k in data.val_ds.fnames}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-30T21:15:30.201385Z",
     "start_time": "2018-07-30T21:15:30.150609Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "row_sz,col_sz = list(zip(*size_d.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-30T21:15:49.738310Z",
     "start_time": "2018-07-30T21:15:49.687166Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "row_sz = np.array(row_sz); col_sz = np.array(col_sz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-30T21:16:09.499644Z",
     "start_time": "2018-07-30T21:16:09.310733Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([    9.,    16.,    25.,    21.,    27.,    27.,   116.,  1075.,   625.,    62.]),\n",
       " array([  100. ,   313.3,   526.6,   739.9,   953.2,  1166.5,  1379.8,  1593.1,  1806.4,  2019.7,  2233. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9y7FJ8krgo8Dbq+qbizWdp3bejc+5HABL/mxED6rqeJueBD7O4BD92dlTO216sjXvdczO\ndjy6Gaeqeraq/reqvg/8DYPXD3Q4NklexuDD/wNV9bFW7vq1cy4HQPc/G5HkFUleNTsPXA08wWAc\nZu8+2Ak80OYPADe3Oxi2Ac/PHt6e5852PD4JXJ1kQzslcnWrnXdOuwb0WwxePzAYm5uSXJTkSmAL\n8K+cp++7JAH2AUeq6j1zVvX92hn1VejF/hhcif8yg7sS3jXq/ozg+b+GwV0YXwCenB0D4BLgEHC0\nTS9u9TD4D3e+AjwOTIz6OazCmHyQwamM7zH419iu5YwH8HsMLnxOAbeM+nmt4tj8fXvujzH4ULti\nTvt3tbH5EnDtnPp5974DfoXBqZrHgEfb33W9v3b8JrAkdepcPgUkSVpFBoAkdcoAkKROGQCS1CkD\nQJI6ZQBIUqcMAEnqlAEgSZ36P/ygYcS5tOMzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12efc69b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(row_sz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-30T21:16:31.568001Z",
     "start_time": "2018-07-30T21:16:31.399168Z"
    },
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  86.,  429.,  699.,  464.,  224.,   82.,   15.,    2.,    1.,    1.]),\n",
       " array([  45. ,   72.5,  100. ,  127.5,  155. ,  182.5,  210. ,  237.5,  265. ,  292.5,  320. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JOfp/AjYnuSbJJcAtwP4VOI4kaQgjv6KvqueTvB34e+Ai4INV9fiojyNJGs6K3DBVVQ8A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\nnEEvSZ0z6CWpcwa9JHXu/wAOyxdOan0AJwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12efb6b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(col_sz)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "heading_collapsed": true
   },
   "source": [
    "## Initial Seq2Seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-30T17:53:32.739672Z",
     "start_time": "2018-07-30T17:53:32.691086Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "vs = len(itos)\n",
    "f = resnet34\n",
    "conv_model = nn.Sequential(*children(f(True))[:5]) #=> feature map of shape: (bs, 64, 16, 16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-30T17:42:52.713203Z",
     "start_time": "2018-07-30T17:42:52.108468Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "z = conv_model(T(x).permute(0,3,1,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-30T17:43:13.048200Z",
     "start_time": "2018-07-30T17:43:12.995862Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([64, 64, 16, 16])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-30T17:46:10.623806Z",
     "start_time": "2018-07-30T17:46:10.574388Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([64, 64, 256])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.view(64, 64, -1).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**GRU**\n",
    "\n",
    "Inputs: input, h_0\n",
    "    - **input** of shape `(seq_len, batch, input_size)`: tensor containing the features\n",
    "      of the input sequence. The input can also be a packed variable length\n",
    "      sequence. See :func:`torch.nn.utils.rnn.pack_padded_sequence`\n",
    "      for details.\n",
    "    - **h_0** of shape `(num_layers * num_directions, batch, hidden_size)`: tensor\n",
    "      containing the initial hidden state for each element in the batch.\n",
    "      Defaults to zero if not provided.\n",
    "\n",
    "Outputs: output, h_n\n",
    "    - **output** of shape `(seq_len, batch, hidden_size * num_directions)`: tensor\n",
    "      containing the output features h_t from the last layer of the GRU,\n",
    "      for each t. If a :class:`torch.nn.utils.rnn.PackedSequence` has been\n",
    "      given as the input, the output will also be a packed sequence.\n",
    "    - **h_n** of shape `(num_layers * num_directions, batch, hidden_size)`: tensor\n",
    "      containing the hidden state for `t = seq_len`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-30T18:01:02.886801Z",
     "start_time": "2018-07-30T18:01:02.839149Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "em_sz,nh,nl = 400,256,2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-30T18:01:23.164182Z",
     "start_time": "2018-07-30T18:01:23.050341Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class Seq2SeqCNN_RNN(nn.Module):\n",
    "    def __init__(self, conv_model, emb_dec, vs, em_sz, nh, out_sl, nl=2):\n",
    "        super().__init__()\n",
    "        self.nl,self.nh,self.out_sl = nl,nh,out_sl   #=> 2, 256, 20\n",
    "        \n",
    "        self.out_enc = nn.Linear(nh, em_sz, bias=False)  # (256, 400)\n",
    "        \n",
    "        #self.emb = emb_dec\n",
    "        self.emb_drop = nn.Dropout(0.15)\n",
    "        self.gru = nn.GRU(em_sz, em_sz, num_layers=out_sl, dropout=0.35) #=> ([400, 400, 20])\n",
    "\n",
    "        self.out = nn.Linear(em_sz, vs) #=> ([400, 4085])\n",
    "        self.out.weight.data = emb_dec.weight.data\n",
    "        \n",
    "    def forward(self, inp):        \n",
    "        ### Encoder (Conv Layer) ###\n",
    "        \n",
    "        conv_feats = conv_model(inp)  #=> ([64(bs), 16(h), 16(w), 64(c)])\n",
    "        # reshape conv_feats to feed into gru\n",
    "        reshape = conv_feats.permute(0,3,1,2).contiguous().view(64, 64, -1) #=> ([64, 64, 256])\n",
    "        \n",
    "        # output vector to be fed to decoder  (n_layers, rest)\n",
    "        h = self.out_enc(reshape) #=> ([64, 64, 400])\n",
    "        \n",
    "        pdb.set_trace()\n",
    "        #GRU inputs: (([1, 64, 400]), ([2, 64, 400]))\n",
    "\n",
    "        # initialize decoder input - 0s (could use teacher forcing here...)\n",
    "        dec_inp = V(torch.zeros(bs).long()) #=> ([64])\n",
    "        res = []\n",
    "         \n",
    "        ### DECODER LOOP ###    loop through ~20 times -> for each word in output\n",
    "        for i in range(self.out_sl):\n",
    "            # .unsqueeze(0) - add leading unit axis -> treat as a sequence of length 1\n",
    "            # gru works on an entire sequence at a time but we're iterating through each part separately...\n",
    "            # not really taking advantage of rnn at all; could re-write using linear layer\n",
    "            # dec_inp - input to the embedding - previous result\n",
    "            emb = self.emb_drop(emb_dec(dec_inp).unsqueeze(0))  # embedding => ([1, 64, 400])\n",
    "            outp, h = self.gru(emb, h)        # rnn => ([1, 64, 400]),  ([64, 64, 400]) expected 20, got 64\n",
    "            outp = self.out(outp[0])          # dropout, linear layer => ([64, 4085])\n",
    "            res.append(outp)\n",
    "            dec_inp = V(outp.data.max(1)[1])  #=> ([64])\n",
    "            if (dec_inp==1).all(): break      # 1: padding token => stop, we're done (padding at the end)\n",
    "        return torch.stack(res)               # stack up list of results into single tensor and return it\n",
    "    \n",
    "#     def initHidden(self, bs): return V(torch.zeros(self.nl, bs, self.nh))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-30T18:01:42.524212Z",
     "start_time": "2018-07-30T18:01:42.465881Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# categorical cross entropy loss\n",
    "# list of probabilities for each word in vocab; target is correct word\n",
    "\n",
    "def seq2seq_loss(input, target):    \n",
    "    sl,bs = target.size()   #=> ([20,64])\n",
    "    sl_in,bs_in,nc = input.size()  #=> ([20, 64, 17573])\n",
    "    \n",
    "    # tweak 1: we may have stopped early; if seq length is less than target, add some padding\n",
    "    # necessary if difference in length btw target and input sl (french longer than english)\n",
    "    if sl>sl_in: input = F.pad(input, (0,0,0,0,0,sl-sl_in)) # rank3 tensor requires 6 padding values (before and after)\n",
    "    # 3rd dimension (sequence length??) add as much padding as necessary at the end\n",
    "        \n",
    "    input = input[:sl]\n",
    "    # cross_entropy expects rank2 tensor but we have sl * bs so we need to flatten out both\n",
    "    # combination of LogSoftmax and NLLLoss\n",
    "    return F.cross_entropy(input.view(-1,nc), target.view(-1))  #=> ([3750, 17573]), ([3750])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-30T18:02:02.676502Z",
     "start_time": "2018-07-30T18:02:02.625555Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "opt_fn = partial(optim.Adam, betas=(0.7, 0.99))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-30T18:02:23.238219Z",
     "start_time": "2018-07-30T18:02:23.018782Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "rnn = Seq2SeqCNN_RNN(conv_model, emb_dec, vs, em_sz, nh, 20)\n",
    "# SingleModel => way to handle learning rate groups -> treats whole thing as single group\n",
    "# easy way to turn pytorch module into fastai model\n",
    "learn = RNN_Learner(data, SingleModel(to_gpu(rnn)), opt_fn=opt_fn)\n",
    "learn.crit = seq2seq_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-30T18:10:16.972708Z",
     "start_time": "2018-07-30T18:02:43.243747Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "22cd766d0c9a4a2aa0015a56408355ba",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/178 [00:00<?, ?it/s]> <ipython-input-47-03c9d21b4d2d>(29)forward()\n",
      "-> dec_inp = V(torch.zeros(bs).long()) #=> ([20, 64])\n",
      "(Pdb) !h.shape\n",
      "torch.Size([64, 64, 400])\n",
      "(Pdb) reshape.shape\n",
      "torch.Size([64, 64, 256])\n",
      "(Pdb) n\n",
      "> <ipython-input-47-03c9d21b4d2d>(30)forward()\n",
      "-> res = []\n",
      "(Pdb) dec_inp.shape\n",
      "torch.Size([64])\n",
      "(Pdb) n\n",
      "> <ipython-input-47-03c9d21b4d2d>(33)forward()\n",
      "-> for i in range(self.out_sl):\n",
      "(Pdb) n\n",
      "> <ipython-input-47-03c9d21b4d2d>(38)forward()\n",
      "-> emb = self.emb_drop(emb_dec(dec_inp).unsqueeze(0))  # embedding => ([1, 64, 400])\n",
      "(Pdb) l\n",
      " 33  \t        for i in range(self.out_sl):\n",
      " 34  \t            # .unsqueeze(0) - add leading unit axis -> treat as a sequence of length 1\n",
      " 35  \t            # gru works on an entire sequence at a time but we're iterating through each part separately...\n",
      " 36  \t            # not really taking advantage of rnn at all; could re-write using linear layer\n",
      " 37  \t            # dec_inp - input to the embedding - previous result\n",
      " 38  ->\t            emb = self.emb_drop(emb_dec(dec_inp).unsqueeze(0))  # embedding => ([1, 64, 400])\n",
      " 39  \t            outp, h = self.gru(emb, h)        # rnn => ([1, 64, 400]),  ([2, 64, 400])\n",
      " 40  \t            outp = self.out(outp[0])          # dropout, linear layer => ([64, 4085])\n",
      " 41  \t            res.append(outp)\n",
      " 42  \t            dec_inp = V(outp.data.max(1)[1])  #=> ([64])\n",
      " 43  \t            if (dec_inp==1).all(): break      # 1: padding token => stop, we're done (padding at the end)\n",
      "(Pdb) n\n",
      "> <ipython-input-47-03c9d21b4d2d>(39)forward()\n",
      "-> outp, h = self.gru(emb, h)        # rnn => ([1, 64, 400]),  ([2, 64, 400])\n",
      "(Pdb) emb.shape\n",
      "torch.Size([1, 64, 400])\n",
      "(Pdb) n\n",
      "RuntimeError: Expected hidden size (20, 64, 400), got (64, 64, 400)\n",
      "> <ipython-input-47-03c9d21b4d2d>(39)forward()\n",
      "-> outp, h = self.gru(emb, h)        # rnn => ([1, 64, 400]),  ([2, 64, 400])\n",
      "(Pdb) n\n",
      "--Return--\n",
      "> <ipython-input-47-03c9d21b4d2d>(39)forward()->None\n",
      "-> outp, h = self.gru(emb, h)        # rnn => ([1, 64, 400]),  ([2, 64, 400])\n",
      "(Pdb) n\n",
      "RuntimeError: Expected hidden size (20, 64, 400), got (64, 64, 400)\n",
      "> /Users/adamschiller/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py(491)__call__()\n",
      "-> result = self.forward(*input, **kwargs)\n",
      "(Pdb) q\n"
     ]
    },
    {
     "ename": "BdbQuit",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mBdbQuit\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-51-f034a61e3f18>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlr_find\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart_lr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1e-7\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msched\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Projects/DeepLearning/fastai/fastai/learner.py\u001b[0m in \u001b[0;36mlr_find\u001b[0;34m(self, start_lr, end_lr, wds, linear, **kwargs)\u001b[0m\n\u001b[1;32m    328\u001b[0m         \u001b[0mlayer_opt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_layer_opt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart_lr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    329\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLR_Finder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_opt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrn_dl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend_lr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinear\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlinear\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 330\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_gen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlayer_opt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    331\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'tmp'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Projects/DeepLearning/fastai/fastai/learner.py\u001b[0m in \u001b[0;36mfit_gen\u001b[0;34m(self, model, data, layer_opt, n_cycle, cycle_len, cycle_mult, cycle_save_name, best_save_name, use_clr, use_clr_beta, metrics, callbacks, use_wd_sched, norm_wds, wds_sched_mult, use_swa, swa_start, swa_eval_freq, **kwargs)\u001b[0m\n\u001b[1;32m    232\u001b[0m             \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreg_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreg_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclip\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclip\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp16\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp16\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m             \u001b[0mswa_model\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mswa_model\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0muse_swa\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mswa_start\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mswa_start\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 234\u001b[0;31m             swa_eval_freq=swa_eval_freq, **kwargs)\n\u001b[0m\u001b[1;32m    235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_layer_groups\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_layer_groups\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Projects/DeepLearning/fastai/fastai/model.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(model, data, n_epochs, opt, crit, metrics, callbacks, stepper, swa_model, swa_start, swa_eval_freq, **kwargs)\u001b[0m\n\u001b[1;32m    138\u001b[0m             \u001b[0mbatch_num\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mcb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 140\u001b[0;31m             \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_stepper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    141\u001b[0m             \u001b[0mavg_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mavg_loss\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mavg_mom\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mavg_mom\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m             \u001b[0mdebias_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mavg_loss\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mavg_mom\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mbatch_num\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Projects/DeepLearning/fastai/fastai/model.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, xs, y, epoch)\u001b[0m\n\u001b[1;32m     48\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m         \u001b[0mxtra\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mxs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mxtra\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp16\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    489\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    490\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 491\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    492\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    493\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/bdb.py\u001b[0m in \u001b[0;36mtrace_dispatch\u001b[0;34m(self, frame, event, arg)\u001b[0m\n\u001b[1;32m     52\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_return\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mevent\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'exception'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     55\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mevent\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'c_call'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrace_dispatch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/bdb.py\u001b[0m in \u001b[0;36mdispatch_exception\u001b[0;34m(self, frame, arg)\u001b[0m\n\u001b[1;32m    108\u001b[0m                     and arg[0] is StopIteration and arg[2] is None):\n\u001b[1;32m    109\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muser_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 110\u001b[0;31m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquitting\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mBdbQuit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    111\u001b[0m         \u001b[0;31m# Stop at the StopIteration or GeneratorExit exception when the user\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    112\u001b[0m         \u001b[0;31m# has set stopframe in a generator by issuing a return command, or a\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mBdbQuit\u001b[0m: "
     ]
    }
   ],
   "source": [
    "learn.lr_find(start_lr=1e-7)\n",
    "learn.sched.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# spatial attention model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": []
  }
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