spanish_joke_classifier_tweet_only_LM_QRNN-LabelSmoothing[LM and Clas]_0607.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the tweets for the updated vocab, test classifer on humor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/bfarzin/haha_2019\r\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'numpy.core._multiarray_umath'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'numpy.core._multiarray_umath'"
     ]
    },
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'numpy.core._multiarray_umath'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'numpy.core._multiarray_umath'"
     ]
    },
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'numpy.core._multiarray_umath'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'numpy.core._multiarray_umath'"
     ]
    },
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'numpy.core._multiarray_umath'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'numpy.core._multiarray_umath'"
     ]
    }
   ],
   "source": [
    "from fastai.text import *\n",
    "from fastai.callbacks import SaveModelCallback\n",
    "import sentencepiece as spm #https://github.com/google/sentencepiece\n",
    "\n",
    "from prod.bin_metrics import Fbeta_binary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python code Examples (testing out tokenizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading the texts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = Path('./data/rnn/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_texts_df = pd.read_csv('./data/haha_2019_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>text</th>\n",
       "      <th>is_humor</th>\n",
       "      <th>votes_no</th>\n",
       "      <th>votes_1</th>\n",
       "      <th>votes_2</th>\n",
       "      <th>votes_3</th>\n",
       "      <th>votes_4</th>\n",
       "      <th>votes_5</th>\n",
       "      <th>funniness_average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>705196579758583809</td>\n",
       "      <td>Niveles de retraso mental: \\r\\n\\r\\n— Bajo.\\r\\n...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>678040651817213952</td>\n",
       "      <td>—Vamos Luke desenfunda tu sable, demuestra tu ...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>546750892213829633</td>\n",
       "      <td>- ¿Te ofrezco algo?, ¿Agua, café, mi corazón, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>965807211292364801</td>\n",
       "      <td>No se porqué me hago la cabeza deooos</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>638403841839484928</td>\n",
       "      <td>Quisiera saber que hago durante la siesta de l...</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id                                               text  \\\n",
       "0  705196579758583809  Niveles de retraso mental: \\r\\n\\r\\n— Bajo.\\r\\n...   \n",
       "1  678040651817213952  —Vamos Luke desenfunda tu sable, demuestra tu ...   \n",
       "2  546750892213829633  - ¿Te ofrezco algo?, ¿Agua, café, mi corazón, ...   \n",
       "3  965807211292364801              No se porqué me hago la cabeza deooos   \n",
       "4  638403841839484928  Quisiera saber que hago durante la siesta de l...   \n",
       "\n",
       "   is_humor  votes_no  votes_1  votes_2  votes_3  votes_4  votes_5  \\\n",
       "0         1         1        2        2        0        0        0   \n",
       "1         1         1        3        0        1        0        0   \n",
       "2         1         0        2        1        0        1        1   \n",
       "3         0         3        0        0        0        0        0   \n",
       "4         0         4        0        1        0        0        0   \n",
       "\n",
       "   funniness_average  \n",
       "0                1.5  \n",
       "1                1.5  \n",
       "2                2.6  \n",
       "3                NaN  \n",
       "4                NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_texts_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### apply pre-rules to text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## New Line char for replacement in text\n",
    "NL = 'xxnl'\n",
    "defaults.text_spec_tok.append(NL) #add a New Line special char\n",
    "\n",
    "def sub_nl(t:str) -> str:\n",
    "    \"Replaces \\n by xxnl\"\n",
    "    return t.replace(\"\\r\\n\",\"\\n\").replace(\"\\n\",NL+\" \")\n",
    "\n",
    "# def sub_br(t:str) -> str:\n",
    "#     \"Replaces the <br /> by \\n\"\n",
    "#     re_br = re.compile(r'<\\s*br\\s*/?>', re.IGNORECASE)\n",
    "#     return re_br.sub(\"\\n\", t)\n",
    "\n",
    "def spec_add_spaces(t:str) -> str:\n",
    "    \"Add spaces between special characters\"\n",
    "    return re.sub(r'([/#?!@,])', r' \\1 ', t)\n",
    "\n",
    "def rm_useless_spaces(t:str) -> str:\n",
    "    \"Remove multiple spaces\"\n",
    "    return re.sub(' {2,}', ' ', t)\n",
    "\n",
    "def replace_rep(t:str) -> str:\n",
    "    \"Replace repetitions at the character level\"\n",
    "    def _replace_rep(m:Collection[str]) -> str:\n",
    "        c,cc = m.groups()\n",
    "        return f' {TK_REP} {len(cc)+1} {c} '\n",
    "    re_rep = re.compile(r'(\\S)(\\1{3,})')\n",
    "    return re_rep.sub(_replace_rep, t)\n",
    "    \n",
    "def replace_wrep(t:str) -> str:\n",
    "    \"Replace word repetitions\"\n",
    "    def _replace_wrep(m:Collection[str]) -> str:\n",
    "        c,cc = m.groups()\n",
    "        return f' {TK_WREP} {len(cc.split())+1} {c} '\n",
    "    re_wrep = re.compile(r'(\\b\\w+\\W+)(\\1{3,})')\n",
    "    return re_wrep.sub(_replace_wrep, t)\n",
    "\n",
    "def deal_caps(t:str) -> str:\n",
    "    \"Replace words in all caps\"\n",
    "    res = []\n",
    "    for s in re.findall(r'\\w+|\\W+', t):\n",
    "        res += ([f' {TK_UP} ',s.lower()] if (s.isupper() and (len(s)>2)) else [s.lower()])\n",
    "    return ''.join(res)\n",
    "\n",
    "def fixup(x:str) -> str:\n",
    "    \"List of replacements from html strings\"\n",
    "    re1 = re.compile(r'  +')\n",
    "    x = x.replace('#39;', \"'\").replace('amp;', '&').replace('#146;', \"'\").replace(\n",
    "        'nbsp;', ' ').replace('#36;', '$').replace('\\\\n', \"\\n\").replace('quot;', \"'\").replace(\n",
    "        '<br />', \"\\n\").replace('\\\\\"', '\"').replace('<unk>',UNK).replace(' @.@ ','.').replace(\n",
    "        ' @-@ ','-').replace('\\\\', ' \\\\ ')\n",
    "    return re1.sub(' ', html.unescape(x))\n",
    "\n",
    "default_rules = [fixup, replace_rep, replace_wrep, deal_caps, spec_add_spaces, \n",
    "                 rm_useless_spaces, sub_nl]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Default Rules:\n",
      " ['fixup', 'replace_rep', 'replace_wrep', 'deal_caps', 'spec_add_spaces', 'rm_useless_spaces', 'sub_nl'] \n",
      "\n",
      "\n",
      "<function fixup at 0x7f11c098e268>\n",
      "<function replace_rep at 0x7f11c098e0d0>\n",
      "<function replace_wrep at 0x7f11c098e158>\n",
      "<function deal_caps at 0x7f11c098e1e0>\n",
      "<function spec_add_spaces at 0x7f11c09d6f28>\n",
      "<function rm_useless_spaces at 0x7f11c098e048>\n",
      "<function sub_nl at 0x7f11c09d6d90>\n"
     ]
    }
   ],
   "source": [
    "## apply the rules\n",
    "raw_text = all_texts_df.loc[:,'text']\n",
    "\n",
    "print(\"Default Rules:\\n\",[x.__name__ for x in default_rules],\"\\n\\n\")\n",
    "\n",
    "for rule in default_rules:\n",
    "    print(rule)\n",
    "    raw_text = raw_text.apply(lambda x: rule(str(x)))\n",
    "    \n",
    "all_texts_df['new_text'] =  raw_text #tokenizer adds xxbos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    niveles de retraso mental: xxnl xxnl — bajo.xx...\n",
       "1    —vamos luke desenfunda tu sable , demuestra tu...\n",
       "2    - ¿te ofrezco algo ? , ¿agua , café , mi coraz...\n",
       "3                no se porqué me hago la cabeza deooos\n",
       "4    quisiera saber que hago durante la siesta de l...\n",
       "Name: new_text, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_texts_df['new_text'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24000, 11)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_texts_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean up the imbalanced data to balance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of positive samples: 9253\n",
      "Number of negative samples: 14747\n",
      "Total samples: 24000\n"
     ]
    }
   ],
   "source": [
    "print('Number of positive samples:', (all_texts_df.is_humor == 1).sum())\n",
    "print('Number of negative samples:',  (all_texts_df.is_humor == 0).sum())\n",
    "print('Total samples:', len(all_texts_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "rnd_seed = 20190313\n",
    "np.random.seed(rnd_seed)\n",
    "\n",
    "idx = np.random.permutation(len(all_texts_df))\n",
    "test_cut = int(0.15 * len(idx))\n",
    "valid_cut = int(0.15 * len(idx-test_cut))\n",
    "\n",
    "df_train_all = all_texts_df.iloc[idx[:-(valid_cut+test_cut)],:]\n",
    "df_valid     = all_texts_df.iloc[idx[-(valid_cut+test_cut):-test_cut],:]\n",
    "df_test      = all_texts_df.iloc[idx[-test_cut:],:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test prevalence(n = 3600): 0.3938888888888889\n",
      "Valid prevalence(n = 3600): 0.3852777777777778\n",
      "Train all prevalence(n = 16800): 0.3838095238095238\n",
      "all samples (n = 24000)\n"
     ]
    }
   ],
   "source": [
    "print('Test prevalence(n = %d):'%len(df_test),df_test.is_humor.sum()/ len(df_test))\n",
    "print('Valid prevalence(n = %d):'%len(df_valid),df_valid.is_humor.sum()/ len(df_valid))\n",
    "print('Train all prevalence(n = %d):'%len(df_train_all), df_train_all.is_humor.sum()/ len(df_train_all))\n",
    "print('all samples (n = %d)'%len(all_texts_df))\n",
    "assert len(all_texts_df) == (len(df_test)+len(df_valid)+len(df_train_all)),'math didnt work'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((6448, 11), (10352, 11))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## oversample the less-frequent occuring cases\n",
    "# split the training data into positive and negative\n",
    "rows_pos = df_train_all.is_humor == 1\n",
    "df_train_pos = df_train_all.loc[rows_pos]\n",
    "df_train_neg = df_train_all.loc[~rows_pos]\n",
    "df_train_pos.shape,df_train_neg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train prevalence (n = 20704): 0.5\n"
     ]
    }
   ],
   "source": [
    "df_resample_pos = df_train_pos.sample(n=len(df_train_neg),replace=True,\n",
    "                                      axis=0,random_state=rnd_seed).reset_index(drop=True)\n",
    "df_train = pd.concat([df_resample_pos,df_train_neg],axis=0) #randomized again in DataBunch?\n",
    "print('Train prevalence (n = %d):'%len(df_train), df_train.is_humor.sum()/ len(df_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sp tokenizer from training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_prefix = './all_tweets_es_0606'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "## itos from m.vocab file: just read directly and populate the dictionary\n",
    "itos = [] #{}\n",
    "with open(f'{model_prefix}.vocab','r') as f:\n",
    "    for line_num,line in enumerate(f):\n",
    "#         itos[line_num] = line.split(\"\\t\")[0]\n",
    "        itos.append(line.split(\"\\t\")[0])\n",
    "        \n",
    "class SPTokenizer(BaseTokenizer):\n",
    "    \"Wrapper around a SentncePiece tokenizer to make it a `BaseTokenizer`.\"\n",
    "    def __init__(self, model_prefix:str):\n",
    "        self.tok = spm.SentencePieceProcessor()\n",
    "        self.tok.load(f'{model_prefix}.model')\n",
    "\n",
    "    def tokenizer(self, t:str) -> List[str]:\n",
    "#         return self.tok.EncodeAsIds(t)  ## tokenize + numericalize. We have them broken into two parts,just return tokens\n",
    "        return self.tok.EncodeAsPieces(t)\n",
    "        \n",
    "class CustomTokenizer():\n",
    "    '''Wrapper for SentencePiece toeknizer to fit into Fast.ai V1'''\n",
    "    def __init__(self,tok_func:Callable,model_prefix:str, pre_rules:ListRules=None):\n",
    "        self.tok_func,self.model_prefix = tok_func,model_prefix\n",
    "        self.pre_rules  = ifnone(pre_rules,  defaults.text_pre_rules )\n",
    "        \n",
    "    def __repr__(self) -> str:\n",
    "        res = f'Tokenizer {self.tok_func.__name__} using `{self.model_prefix}` model with the following rules:\\n'\n",
    "        for rule in self.pre_rules: res += f' - {rule.__name__}\\n'\n",
    "        return res        \n",
    "\n",
    "    def process_text(self, t:str,tok:BaseTokenizer) -> List[str]:\n",
    "        \"Processe one text `t` with tokenizer `tok`.\"\n",
    "        for rule in self.pre_rules: t = rule(t)  \n",
    "        toks = tok.tokenizer(t)\n",
    "    \n",
    "        return toks \n",
    "    \n",
    "    def _process_all_1(self,texts:Collection[str]) -> List[List[str]]:\n",
    "        'Process a list of `texts` in one process'\n",
    "        tok = self.tok_func(self.model_prefix)\n",
    "        return [self.process_text(t,tok) for t in texts]\n",
    "                                                                     \n",
    "    def process_all(self, texts:Collection[str]) -> List[List[str]]: \n",
    "        \"Process a list of `texts`.\"                                 \n",
    "        return self._process_all_1(texts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['🤓', '`', '🎟', '😇', '→', '😫', '🤨', '😲', '🏟', '📖']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "itos[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "mycust_tok = CustomTokenizer(SPTokenizer,model_prefix,pre_rules=default_rules)\n",
    "sp_vocab = Vocab(itos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = TextClasDataBunch.from_df(PATH,df_train,df_valid,df_test,\n",
    "                               tokenizer=mycust_tok, vocab=sp_vocab,\n",
    "                               text_cols='new_text', label_cols='is_humor')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>text</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>▁ xxbos ▁❤ .- ~ * ( ▁? ▁el ▁di vo 00 ▁d 3 ▁tw 1 tter ▁! ▁! ▁! ▁? ▁? ▁~ k 3 ▁di 4 vl 0 zz ▁ki 3 r 3 z ▁k 3 ▁p 4 rt 3 ▁d 3 ▁no 00 ▁n 0 ▁3 nt 13 nd 3 z ▁b 3 the ▁k 0 n ▁ki 3 n ▁t 3 ▁d 3 ▁l 4 ▁g 4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>▁ xxbos ▁❤ ️ -( el ▁di vo 00 ▁d 3 ▁tw 1 tter ▁! ▁! ▁? ▁i 0 ▁zo i ▁tu ▁ma 3 zt lo ▁? ▁ki 3 n ▁z up 0 ▁3 nz 3 ñ 4 l th 3 ▁? ▁d 3 ▁tu ▁k cuer p 0 ▁io o ▁k 0 noz ko ▁az t 4 ▁la ▁maz ▁in ty ma ▁p 4 r te ▁? ▁) ~</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>▁ xxbos ▁( ▁ xxup ▁o _ o ) ▁( xxunk _ ▁ xxunk ) ▁( ▁ xxup ▁o _ o ) ▁( xxunk _ ▁ xxunk ) ▁( ▁ xxup ▁o _ o ) ▁( xxunk _ ▁ xxunk ) ▁( ▁ xxup ▁o _ o ) ▁( xxunk _ ▁ xxunk ) ▁( xxunk _ ▁ xxunk ). ▁yo ▁con ▁el ▁móvil ▁en ▁clase .</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>▁ xxbos ▁—¡ idio ta ▁! ▁xxnl ▁—¡ h ist érica ▁! ▁xxnl ▁— que ▁tarado . xxnl ▁— tar ada ▁tú . xxnl ▁— te ▁odio . xxnl ▁— yo ▁también . xxnl ▁— ▁¡ ▁ xxup ▁jode te ▁! ▁xxnl ▁— no ▁me ▁hables . xxnl ▁—¡ ok ▁! ▁xxnl ▁—¡ ok ▁! ▁xxnl ▁— te ▁amo . xxnl ▁— yo ▁más . ▁: $</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>▁ xxbos ▁— mamá ▁, ▁¿ puedo ▁salir ▁? ▁— no . ▁—¿ me ▁das ▁un ▁calce tín ▁? ▁— ok ▁, ▁toma . ▁— el ▁ xxup ▁amo ▁le ▁ha ▁ xxup ▁dado ▁a ▁ xxup ▁dob by ▁un ▁ xxup ▁calce tín ... ▁¡ ▁ xxup ▁dob by ▁es ▁ xxup ▁libre ▁! ▁# ▁ xxup ▁jajaja ▁# ▁ xxup ▁hp quo tes ▁# ▁xd</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>▁ xxbos ▁ xxup ▁tengo ▁un ▁ xxup ▁gran ▁ xxup ▁anuncio ▁, ▁ xxup ▁una ▁ xxup ▁noticia ▁ xxup ▁que ▁ xxup ▁cambiará ▁la ▁ xxup ▁historia ▁de ▁ xxup ▁twitter ▁, ▁ xxup ▁que ▁ xxup ▁devolver á ▁el ▁ xxup ▁sentido ▁a ▁ xxup ▁nuestra ▁ xxup ▁vida . xxnl ▁xxnl ▁ xxup ▁pendientes ▁ xxup ▁hoy ▁en ▁la ▁ xxup ▁noche</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>▁ xxbos ▁— cuel ga ▁tú . xxnl ▁— no ▁, ▁cuelga ▁tú . ▁xxnl ▁— no ▁, ▁tú . xxnl ▁— que ▁t ... xxnl ▁—¡ ▁ xxup ▁pinche ▁ xxup ▁tonta ▁me ▁ xxup ▁colgó ▁! ▁xxnl ▁—¡ te ▁ xxup ▁escuché ▁ xxup ▁pendejo ▁! ▁xxnl ▁— ▁ xxup ▁jajaj á ▁¡ ay ▁amor ▁! ▁es ▁broma ▁, ▁te ▁amo .</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>▁ xxbos ▁¡ ya ▁llegó ▁santa ▁! ▁xxnl ▁ xxunk _ xxunk xxnl ▁( ・ xxunk ・ ) xxnl ▁_ | ▁ xxunk / ( ___ ▁xxnl ▁/ ▁l ▁( __ __ / xxnl ▁xxnl ▁ah ▁no ▁, ▁es ▁mi ▁papá ▁bien ▁pedo . xxnl ▁xxnl ▁&lt; xxunk / xxunk - xxunk ___ xxnl ▁/ &lt; _ ▁/ ▁ xxrep ▁4 ▁_ ▁/ ”</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>▁ xxbos ▁¡ ya ▁llegó ▁santa ▁! ▁xxnl ▁ xxunk _ xxunk xxnl ▁( ・ xxunk ・ ) xxnl ▁_ | ▁ xxunk / ( ___ ▁xxnl ▁/ ▁l ▁( __ __ / xxnl ▁xxnl ▁ah ▁no ▁, ▁es ▁mi ▁papá ▁bien ▁pedo . xxnl ▁xxnl ▁&lt; xxunk / xxunk - xxunk ___ xxnl ▁/ &lt; _ ▁/ ▁ xxrep ▁4 ▁_ ▁/ ”</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>▁ xxbos ▁ xxunk me ▁gustó ▁la ▁película ▁de ▁b rá cula ▁ xxunk con ▁d ▁ xxunk ah ▁sí ▁, ▁el ▁conde ▁b rá cula ▁ xxunk no ▁, ▁es ▁con ▁d ▁ xxunk ¿ qué ▁esconde ▁? ▁ xxunk ¡ ▁ xxup ▁d rá cula ▁es ▁ xxup ▁con ▁d ▁, ▁ xxup ▁mierda ▁! ▁ xxunk pues ▁qué ▁cochino .</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.show_batch(10)  #less xxunk, but still a fair amount..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### build and train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = awd_lstm_clas_config.copy()\n",
    "config['qrnn'] = True\n",
    "config['n_hid'] = 2304\n",
    "\n",
    "learn = text_classifier_learner(data, AWD_LSTM, drop_mult=0.9,pretrained=False,\n",
    "                               config=config)\n",
    "learn.load_encoder('twitter_es_enc_QRNN_0606_seed0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.layers import LabelSmoothingCrossEntropy\n",
    "learn.loss_func = FlattenedLoss(LabelSmoothingCrossEntropy)\n",
    "learn.metrics += [Fbeta_binary(beta2=1,clas=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.backends.cudnn.enabled=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
     ]
    }
   ],
   "source": [
    "lr_find(learn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot(skip_end=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 07:01 <p><table style='width:450px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "    <th>fbeta_binary</th>\n",
       "    <th>time</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>0</th>\n",
       "    <th>0.607904</th>\n",
       "    <th>0.502722</th>\n",
       "    <th>0.786667</th>\n",
       "    <th>0.749020</th>\n",
       "    <th>00:15</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>0.579344</th>\n",
       "    <th>0.487347</th>\n",
       "    <th>0.791111</th>\n",
       "    <th>0.755049</th>\n",
       "    <th>00:16</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>0.566550</th>\n",
       "    <th>0.512926</th>\n",
       "    <th>0.765278</th>\n",
       "    <th>0.748886</th>\n",
       "    <th>00:15</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>0.566239</th>\n",
       "    <th>0.459962</th>\n",
       "    <th>0.819722</th>\n",
       "    <th>0.771237</th>\n",
       "    <th>00:15</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>0.547051</th>\n",
       "    <th>0.528422</th>\n",
       "    <th>0.755833</th>\n",
       "    <th>0.746467</th>\n",
       "    <th>00:16</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>0.538483</th>\n",
       "    <th>0.450313</th>\n",
       "    <th>0.826667</th>\n",
       "    <th>0.779505</th>\n",
       "    <th>00:15</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>0.541693</th>\n",
       "    <th>0.470525</th>\n",
       "    <th>0.804722</th>\n",
       "    <th>0.775614</th>\n",
       "    <th>00:15</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>0.523182</th>\n",
       "    <th>0.454866</th>\n",
       "    <th>0.822222</th>\n",
       "    <th>0.786096</th>\n",
       "    <th>00:15</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>0.499513</th>\n",
       "    <th>0.447478</th>\n",
       "    <th>0.827500</th>\n",
       "    <th>0.789990</th>\n",
       "    <th>00:15</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>0.521285</th>\n",
       "    <th>0.456977</th>\n",
       "    <th>0.817778</th>\n",
       "    <th>0.787427</th>\n",
       "    <th>00:15</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>0.489611</th>\n",
       "    <th>0.462010</th>\n",
       "    <th>0.828333</th>\n",
       "    <th>0.797642</th>\n",
       "    <th>00:16</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>11</th>\n",
       "    <th>0.489259</th>\n",
       "    <th>0.433182</th>\n",
       "    <th>0.844444</th>\n",
       "    <th>0.803647</th>\n",
       "    <th>00:16</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>12</th>\n",
       "    <th>0.486299</th>\n",
       "    <th>0.439905</th>\n",
       "    <th>0.841667</th>\n",
       "    <th>0.806517</th>\n",
       "    <th>00:15</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>13</th>\n",
       "    <th>0.483867</th>\n",
       "    <th>0.453260</th>\n",
       "    <th>0.827222</th>\n",
       "    <th>0.796997</th>\n",
       "    <th>00:15</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>14</th>\n",
       "    <th>0.511987</th>\n",
       "    <th>0.448940</th>\n",
       "    <th>0.830556</th>\n",
       "    <th>0.799474</th>\n",
       "    <th>00:15</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>15</th>\n",
       "    <th>0.462581</th>\n",
       "    <th>0.451391</th>\n",
       "    <th>0.837778</th>\n",
       "    <th>0.804421</th>\n",
       "    <th>00:16</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>16</th>\n",
       "    <th>0.443250</th>\n",
       "    <th>0.440750</th>\n",
       "    <th>0.843611</th>\n",
       "    <th>0.807258</th>\n",
       "    <th>00:16</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>17</th>\n",
       "    <th>0.461265</th>\n",
       "    <th>0.480847</th>\n",
       "    <th>0.818889</th>\n",
       "    <th>0.792884</th>\n",
       "    <th>00:15</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>18</th>\n",
       "    <th>0.440997</th>\n",
       "    <th>0.448733</th>\n",
       "    <th>0.843056</th>\n",
       "    <th>0.806838</th>\n",
       "    <th>00:16</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>19</th>\n",
       "    <th>0.460870</th>\n",
       "    <th>0.449307</th>\n",
       "    <th>0.840833</th>\n",
       "    <th>0.806484</th>\n",
       "    <th>00:15</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>20</th>\n",
       "    <th>0.455949</th>\n",
       "    <th>0.456058</th>\n",
       "    <th>0.837500</th>\n",
       "    <th>0.804413</th>\n",
       "    <th>00:15</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>21</th>\n",
       "    <th>0.432098</th>\n",
       "    <th>0.459111</th>\n",
       "    <th>0.835000</th>\n",
       "    <th>0.804990</th>\n",
       "    <th>00:16</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>22</th>\n",
       "    <th>0.452197</th>\n",
       "    <th>0.452208</th>\n",
       "    <th>0.848333</th>\n",
       "    <th>0.809358</th>\n",
       "    <th>00:16</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>23</th>\n",
       "    <th>0.442237</th>\n",
       "    <th>0.466078</th>\n",
       "    <th>0.830833</th>\n",
       "    <th>0.801305</th>\n",
       "    <th>00:16</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>24</th>\n",
       "    <th>0.435585</th>\n",
       "    <th>0.452614</th>\n",
       "    <th>0.838889</th>\n",
       "    <th>0.804450</th>\n",
       "    <th>00:16</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 0 with fbeta_binary value: 0.7490196228027344.\n",
      "Better model found at epoch 1 with fbeta_binary value: 0.7550488710403442.\n",
      "Better model found at epoch 3 with fbeta_binary value: 0.7712371945381165.\n",
      "Better model found at epoch 5 with fbeta_binary value: 0.7795052528381348.\n",
      "Better model found at epoch 7 with fbeta_binary value: 0.7860962748527527.\n",
      "Better model found at epoch 8 with fbeta_binary value: 0.7899898886680603.\n",
      "Better model found at epoch 10 with fbeta_binary value: 0.7976424694061279.\n",
      "Better model found at epoch 11 with fbeta_binary value: 0.8036466240882874.\n",
      "Better model found at epoch 12 with fbeta_binary value: 0.8065173029899597.\n",
      "Better model found at epoch 16 with fbeta_binary value: 0.8072577714920044.\n",
      "Better model found at epoch 22 with fbeta_binary value: 0.8093575239181519.\n"
     ]
    }
   ],
   "source": [
    "learn.unfreeze()\n",
    "## 0.5 drop\n",
    "learn.fit_one_cycle(25, slice(1e-2/(2.6**4),1e-3), pct_start=0.2, wd=0.075,\n",
    "             callbacks=[SaveModelCallback(learn,every='improvement',mode='max',monitor='fbeta_binary',name='best_acc_model_Q')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6USDPXxHhYczMTuHjIBKGdwzEifUXTosMD2ve86SoopY3Nh1i0tBkIsKd/V376ilZLNl6hGW7i1v9WWjyGL79/CccKKvmma/MCHhGm1O0haGUD4/H8Nyag5w+vD+nBFieIlCR4WHcPHs4K/NKXdulzxjDva9s4b0dRfxs3nguGu98F9vsnDT2lVZzsKy6U+9fvruEpH6RnObw5AKnZSTEcMucEUwZ2vZ+F5117pgMUuOi2lyT8bu3d/L+zmJ+cvmpflXEdYsmDKV8rMwrZW9JlWuD3S3Nnz6U+OgI14oS/nbJTl5cn8+3zhvFl2a683fyljvvzEI4YwzLc0s4Y2SqY2MCPVFkeBiXTxrEu9uKKK8+cQLBfzcV8sgHe1gwfQhfcniKd6A0YSjl45nV++kfG8lF44ObX++vxJhI5p8+hDe3HKKgvMbRaz++Yq/9QTOUb5/vX+ntzhiVEU96QjQrcgMf+N5TXMWhY7V+Vaft7a6ZmkV9k4f/+qzJ2Fp4jB+8tIlpw/rz08vHd/kgd0uaMJSyFVXU8vbWI1xj7/TWVW6eY1WFfdzBciH/3VTIA29s48Jxmfz8Cnc/aESEOTlpfNyJbVuX2+VAQmnAu7ucOiiJMQMSeGmDNdW6tLKO259aT3K/KB750pSQ2Bu++yNQKkS8uC6fRo9hwfSubfYPTu7HZRMG8vzag47sVb18dwnffWEjpw9L4c8LJndJV88ZI1Mpqaxn55GKgN730e4ShqXGOjLFtze4ZmoWmw6Ws/3Qcb7+zAZKKutYeMNUMhKcL5fSGZowlMKahfLs6gPMzkntsjILvm47M5vKukaeX3MgqOt8WnCMO55eR3ZaPI/eOK3LWkqd2ba1ocnDqrxSR4sN9nTzJg0mPEy44bE1rN5bxoNXn8aErODK6jtJE4ZSwLJdxRSU13Dd9K4Z7G5p/OAkZmWn8tjyfZ3eH2FPcSU3Pb6G5NgonrxlOkn93C9G5zUouR/ZaXF8HMC2rZ8cKKeqvkm7o3ykJ0Rz7inpFFfUcduZI7hyclZ3h3QCTRhKAc+sPkBafDQXjHNvPURHbj8rm8PHa3lzS2CF6I4cr+W+1z/l4j9+RJPH8OQt012p+NqR2TlprMorpcHPbVuX7y4mTGDWSE0Yvu65eAzfu2A0d180prtDOYkmDNXnFZbXsHTHEb54ela3DiyePTqdURnxPLpsr1+Dx8UVdfzsjW2c9Zv3eXb1Aa6emsWb3zyTnIyu71IDK2FU1zex8WC5X+d/lFvChKzkLm0J9QQ5GQncdd4oxxcHOkFXeqs+7/m1BzHA/NO7eY57mPCVM0dw98tb+HhPafO4QEtlVfX8Y9kenvp4P3WNTVw1JYtvzh3F0NTuHTielZ1KmFgrt08f3n4RwWPVDWw6WM6d5+Z0UXTKCZowVJ/W2OThP2sPcPbo9JCYqTNv0mB+u2QXC5flnZQwjlU38M/leTy2fC/VDU3MmziIb543qlsG6VuTFGut1l6RW8K3zx/d7rkr80rwmNAsB6LapglD9WlvbjnEkeN1/PyK7hnsbikmMpybzhjGQ2/vYufhCk4ZkEBFbQOPLd/HP5fnUVHbyKWnDeTb549iVKa7pUs644ycNB5dlkdVXWO7+2x8tLuEuKhwJg8NnRlAqmOaMFTI2ltShccYRrr0G3RFbQO/eHM7pw5KZO6YrtuEpiPXzxjGw+/v4eH3cxkzMIGFy/Ior27gwnGZfOeC0Y5VmnXDnJw0/vbBHtbsLePcdv5Nl+eWMDM7lcgQ7KdXbdOEoULSa58UcPfLm+kXFc7b3znLlYVLv3t7F8WVdTx6w7SQqmPUPy6Ka6dl8dTK/SzaBHPHZPCd80dzWlZoF+cDmDqsP9ERYSzPLWkzYRwsq2Z/aTU3nTG8a4NTQdOEoXh29QHCxCqE192aPIZfv7WDhcvymDI0mU8Lj/PjVz9l4ZenOlreYkv+MZ5auY8vzxzGxCGh1y3iHQy+YvJgV6qjuiUmMpxpw9vftvUje3Gfrr/oebQ9qHhq5T4eentnl+381pby6npuenwNC5flccOsYfznjll8/8LRvLPtCK9vDGxtQnuaPIYfvbqF1Phovv+5Uxy7rpMyEmN4YN74HpUsvGbnpLHjcAUlbeyJ/dHuYgYkxrjW1ajcowlDUVJZR0llPZsL3NmTwR87D1cw7+EVrM4r49dXn8YD88YTGR7GrXOymTI0mZ8s2sqR47WO3Ovfq/azpeAY9102jsQYXQPgtNkjveXOT1713eQxfLynlDNHpXV75VUVOE0YfVyTx1BWZdXfX7r9SLfE8Nanh7nykRVU1zfx3O0z+aLPeojwMOGhaydS29DEj17ZEnA11JaOHK/lt0t2cuaoNC6b4M5+3X3d+MFJJMZEsKKVulJbCo5xrKaBOdod1SNpwujjSqvq8PZEvbfD/b2lfXk8ht+/s4uv/ns9ozIT+O+dc5g67OQumOz0eH540Rje21HEK3bp58564I1t1Dd5+Nm87t9boLcKDxPOGJnG8tyTy517y5m3tShRhTZNGH1ccYXVzzxpSDJbC49z6Jizm/i0paK2gdufXs+f39vNtVOz+M/tM9utf3TzGcOZPjyF+/+7lcPHOtc19cHOIt7cfIi7zs1heFpcZ0NXfpidk0pBeQ0HWmzb+tHuEsYNTCQtPrqbIlPB0ITRx5VUWt1R808fAsDSLmhl7C2p4spHPub9nUXc//lx/OaaCR2W4Q4LE35zzQQamwz3vLI54K6p2oYm7nt9K9npcdx+dnYw4Ss/NJc795ktVVXXyIYDR3V2VA+mCaOP87YwZmanMjQllqXb3U0YH+ws4vK/Lqe0so6nb53OTbNH+N01NDwtjnsuHsMHO4t5cV1+QPf969JcDpRV8/MrxhMd0XW76fVVI9LiGJgUw8c+27au3ltKQ5PR8YseTBNGH+ed+pieEM3cMRkszy2hpr7J8fsYY/jbB3u4+Ym1ZPWPZdGdczijE2WtvzxzGDOzU/jZG9v83gM7t6iCfyzbw1VTBnfqnipwIsLsnDRW7Clpnq790e4SoiLCOixMqEKXJow+rriijtiocOKiIzhvbAZ1jR5W5vm/a5q/XlyXz6/f2sElpw3k5a/N6nShv7Aw4bfXTKTJGO55ueOuKWMMP371U2KjIvjRJWM7dU/VOXNy0iivbmDboeOAtRvfjBEpXbpfunKWJow+rriijvQEawBy+ogU4qLCec+Fbqln1xzglMwE/rpgMrFRwRUYGJISy48uGctHu0t4bs3Bds99eUMBq/eWcc/FY3SgtYudMTIVsMqdHz5Wy+6iSt2OtYdzPWGIyEUislNEckXknlZeHyoi74vIJyKyWUQusY9fICLrRWSL/edct2Pti0oq65o/SKMjwjlzVDpLdxQFvd7BV25RJRsPlnPN1CzHprJeP2Moc3LS+MWb28g/Wt3qOUer6vnl4u1MHdafL04b4sh9lf8yEmMYnRnP8tyS5sFvHb/o2VxNGCISDjwMXAyMAxaIyLgWp/0f8IIxZjIwH3jEPl4CfN4YcxpwI/C0m7H2VcUVdaT7/OY9d2wGh47VNncjOOHlDfmEhwnzJg9y7JoiwoNXn4aI8MOXNrda1uTB/+3gWE0Dv7hyPGEhVFywLzljZBpr95Xx3vYjpMZFMXZA6FbaVR1zu4UxHcg1xuQZY+qB54F5Lc4xgPenKAkoBDDGfGKM8RYQ2gr0E5E+16ewr6TK1RpPJZV1pCVENT8/9xSrwqhTs6WaPIZXNxRw9uh0xyvOZvWP5ceXjuXjPaU8s+bACa+t3VfGf9Yd5CtzRjBGP6S6zZycNGobPLy19TCzc9I0cfdwbieMwYBvJ3O+fczX/cCXRCQfWAzc1cp1rgY2GGNOqmYmIreLyDoRWVdcXOxM1CHi0LEazvv9h7y19bAr169v9HC0uoH0+M8+yNMTopk4JNmxVd8rcks4fLyWa6ZmOXK9luafPoQzR6Xxq8XbOVBqdU01NHn48atbGJzcj2+dP8qV+yr/zMhOITxMMEa7o3qDUBj0XgA8YYzJAi4BnhaR5rhE5FTg18Adrb3ZGLPQGDPNGDMtPb13bfeYV1xFk8ewt6TKleuXVn02pdbX+WMy2JRf3rxGIxgvb8gnqV8k5411Z4MiEeHXV08gXIQfvLQJj8fwz4/2sutIJT+9/NSgB9hVcBJiIplo7+OhC/Z6PrcTRgHgO9qYZR/zdSvwAoAxZiUQA6QBiEgW8CpwgzFmj8uxhhzvOgOnqrS2VFJhrfJOi4864fjcsRkYYy2yC8bx2gaWbD3M5RMHubpYblByP/7f58exem8ZD761gz+9t4sLx2Vy/rhM1+6p/HfDrOFcOzWLgUn9ujsUFSS3E8ZaYJSIjBCRKKxB7UUtzjkAnAcgImOxEkaxiCQDbwL3GGNWuBxnSCo46m7CKK60rtuyhTFuYCIDEmOCLhOyePMhahs8XO1Sd5Sva6dmce4p6SxclkeYCPdffqrr91T+uWLyYH577cTuDkM5wNWEYYxpBO4ElgDbsWZDbRWRB0Tkcvu07wG3icgm4DngJmPN6bwTyAHuE5GN9lfobLzcBT5rYQTfNdSaz1oYJyYMEWHu2AyW7SqmrrHzq75f3pBPTkZ8c5eEm6xZUxMYnhrL/106jkHJ+tusUk5zvYPXGLMYazDb99h9Po+3AbNbed/PgZ+7HV8oK3S5S6q4svUxDIDzxmTw7OoDrNlbxpmjAh8b2ldSxdp9R7n7ojFdVkY8MzGG979/jpYtV8oloTDordrgbWEUVdS5MrW2uKKOhJiIVks1nDEyjeiIsE6v+n5lQz5hAldObjkpzl2aLJRyjyaMEOXxGA6V1xIfHUGTx1Bq74rnpOLKExft+eoXFc7snDTe23Ek4FXfHo/h5Q0FzBmV3u4eF0qpnkUTRogqqayjvsnDxCFW/78b3VLFFXWktdId5TV3TAYHy2rYU1wZ0HVX7S2loLzGtbUXSqnuoQkjROXb3VFTh1pblhZVOJ8wStppYQDNaycC7ZZ6aX0+CTERXKjTWpXqVTRhhCjvlNrJ9h7XbsyU8q1U25qBSf0YNzAxoIRRVdfIW58e5rIJg7SMtVK9jCaMEOUd8J6UlQw43yVV29BERW1juwkDrFbGuv1llFf7N4ayeMshquubuGZq1w52K6XcpwkjRBWW15AYE0H/uCjS4qMcTxjenfZarvJuae6YDDwGPtzlX52ulzfkMyItjil2V5pSqvfQhBGiCo7WMLi/tStdRkKM411S3jpRHbUwJmYlkxoX5Ve31MGyalbllXH1lME6vVWpXkgTRogqKK9hsL1aOTMx2oUWRuurvFsKCxPOHZPBBzuLaGzytHvuKxsKEIErp+jsKKV6I78Thoi8IiKX+laSVe6xEoa1hiEzsftaGGCt+j5e28j6/UfbPMcYw8sb8jljZGpzolNK9S6BfPg/AlwH7BaRB0XkFJdi6vOO1zZQUdvI4P7WB29GYgylVXU0dPAbfiC8CSM1ruOEcebodCLDpd1ihGv3HeVAWbWuvVCqF/M7YRhj3jXGXA9MAfYB74rIxyJys4hEuhVgX+SdUjs42RrDGJAYgzGfDVQ7oaSyjuTYSKIiOv4RiI+OYGZ2Ku9uP9LmOS+tP0hcVDifO3WAYzEqpUJLQN1LIpIK3AR8BfgE+BNWAnnH8cj6MG/RwUHNXVJWK+DwMefGMVru5d2RuWMy2FNcxb5WNnOqrm9k8ZbDXDphoG5YpFQvFsgYxqvAR0As8HljzOXGmP8YY+4C4t0KsC/yrsHwdkllJlqJw8lxjJLKug4HvH3NHWPv9d1Kt9SSrYeprGvkah3sVqpXC6SF8WdjzDhjzK+MMYd8XzDGTHM4rj6t4GgNURFhpNnjCxl2C8PJ8iDFle2v8m5pWGocORnxrSaMl9cXMDQlltOHpzgWn1Iq9ASSMMbZu+ABICL9ReTrLsTU5xWU1zAoKYawMGstQ2pcNOFh4ujU2o7KgrTmvDEZrN5bSkVtQ/OxwvIaVuwp4aopg5vjVUr1ToEkjNuMMeXeJ8aYo8BtzoekCsprmrujAMLDhPT4aMe6pKrqGqmubwqoSwqsbqmGJsPy3SXNx179pABj0O4opfqAQBJGuPgs3xXH7/VBAAAgAElEQVSRcKD9uhKqUwqO1py0liEzKcaxFkZJOzvttWfqsP4k9YvkXXvVtzGGl9bnM2NECkNSYh2JTSkVugJJGG8B/xGR80TkPKz9t99yJ6y+q66xiaKKupP2pM5MiKbIoRaGv3WkWooID+OcU9L5YGcRTR7DhgPl7C2p4mpde6FUnxBIwrgbeB/4mv31HvBDN4Lqy7xTZ09qYSTGcNihFkYgq7xbmjsmg9Kqejbll/PS+nz6RYZzyWkDHYlLKRXa/J40b4zxAH+zv5RLmhft9W+ZMKI5VtNAbUNT0PtMBJMwzh6dTniYsHjzId7YXMjF4wcQH61rL5TqCwJZhzFKRF4SkW0ikuf9cjO4vsi7015W8oljAhn2WgwnuqWKK+sRgZTYwIegkmOjmDqsP0+u3EdFbaOWAlGqDwmkS+pxrNZFI3Au8BTwbzeC6ssKy2sQgQFJMSccb16858BajOKKOlLjoogI71wdyfPs2VKDk/sxMzs16HiUUj1DIJ8Y/Ywx7wFijNlvjLkfuNSdsPqugqM1ZCREn1TjaUDzau/gE0agq7xbOm+stVe3rr1Qqm8JpPO5zi5tvltE7gQK0JIgjvPdB8OXt56UE2sxOrNoz1dORjzP3TaTyUOTOz5ZKdVrBNLC+BZWHalvAlOBLwE3uhFUX1ZYXnPSlFqApH5WZVknWhiBFh5szayRqUEPviuleha/Eoa9SO+LxphKY0y+MeZmY8zVxphVLsfXp3g8hsLy2pNmSAGIiCM77xljrC6pIFoYSqm+ya+EYYxpAua4HEufV1JZR32Th6w2dqzLTAh+tXdFXSN1jZ6gWxhKqb4nkDGMT0RkEfAi0LwpgjHmFcej6qMKmvfBaCNhJMaw/dDxoO5RYq/BSEvQqi5KqcAEkjBigFJgrs8xA2jCcEjLfTBaykiM5oOdwbUwmhftxcd0cKZSSp0okJXeN3fmBiJyEdbOfOHAP40xD7Z4fSjwJJBsn3OPMWax/dq9wK1AE/BNY8ySzsTQU3y2NWvrCWNAYgxV9U1U1jV2enV1caW2MJRSneP3p46IPI7VojiBMeaWdt4TDjwMXADkA2tFZJExZpvPaf8HvGCM+ZuIjAMWA8Ptx/OBU4FBWHuIj7bHU3qlgvIaEmIiSIhpfYv0TJ+1GPHpnZvRXNLcwtAxDKVUYAKZVvsG8Kb99R6QCFR28J7pQK4xJs8YUw88D8xrcY6xrwWQBBTaj+cBzxtj6owxe4Fc+3q9VmEbazC8vDvvHQlib+/iyjrCw4T+nSgLopTq2wLpknrZ97mIPAcs7+Btg4GDPs/zgRktzrkfeFtE7gLigPN93us7bTffPnYCEbkduB1g6NChHYQT2vKP1pDVxvgFOFMepKSintS4KF2hrZQKWOeKCVlGARkOxLAAeMIYkwVcAjxtryj3izFmoTFmmjFmWnp6ugPhdJ+CNhbteX3WJdX51d6B7uWtlFJegYxhVHDiGMZhrD0y2lMADPF5nmUf83UrcBGAMWaliMQAaX6+t9c4XttARW1ju11S8dERxEWFB7UWI9g6UkqpviuQ3+QTjDGJPl+jW3ZTtWItMEpERohIFNYg9qIW5xwAzgMQkbFY03eL7fPmi0i0iIzAatGs8Tfenqawgym1XplJMUGVOA+2jpRSqu8KZD+MK0Ukyed5sohc0d57jDGNwJ3AEmA71myorSLygIhcbp/2PeA2EdmEte3rTcayFXgB2Ia1Few3evUMqQ6m1HoFs9rbWxZEE4ZSqjMCmcz/E2PMq94nxphyEfkJ8Fp7b7LXVCxucew+n8fbgNltvPcXwC8CiLHHam5hdJQwEqNZf+Bop+5xrKaBhiajXVJKqU4JZNC7tXN1b06H5JfXEBUe1uGHeWZiDEeO12HMSUtiOhTM1qxKKRVIwlgnIr8XkZH21++B9W4F1tcUHK1hUHJMh9NdMxJjqG/0UF7dEPA9mld5x+saDKVU4AJJGHcB9cB/sBbg1QLfcCOovqitfTBaat5IqRNrMbwtjAxtYSilOiGQhXtVwD0uxtKnFZTXcNaojteRDPBZizFmQGD30MKDSqlgBDJL6h0RSfZ53l9EenUxwK5S3+ihqKKuwym1cGI9qUCVVNYTFR5GYj8delJKBS6QLqk0Y0y594kx5ijOrPTu8w4fq8WYtvfB8OUdsC7qRMIorqgjLT4KES0LopQKXCAJw2OXIgdARIbTSvVaFbj88mqANnfa8xUTGU5ybCSHO9XC0K1ZlVKdF0jfxI+B5SLyISDAmdhF/1Rwmhft+dElBd7Fe4Gv9i6uqGNgko5fKKU6J5DSIG8B04CdWCuyvwfUuBRXn+LdaW+Anx/mGYnRneuS0lXeSqkgBFJ88CvAt7CKAG4EZgIrOXHLVtUJheU1ZCREEx0R7tf5AxJj2H2ko61ITtTkMZRV1esqb6VUpwUyhvEt4HRgvzHmXGAyUN7+W3qvJVsP8+f3dndqxXVLBeU1fndHgTVTqriyjiaP//c+Wl1Pk8doC0Mp1WmBjGHUGmNqRQQRiTbG7BCRU1yLLEQdq27gJ4s+5bWN1saA8yYNYlhqXFDXLDhaw6mDkzo+0ZaZGE2Tx1BaVUdGgn/dWCXNq7w1YSilOieQFka+vQ7jNeAdEXkd2O9OWKHp/Z1FXPjHD3lj8yGum2FNGFu5pzSoa3o8hsJjtX7NkPLKsNdiBFLmXOtIKaWCFchK7yvth/eLyPtY+2+/5UpUIaayrpFfvLmN59YcZHRmPP+84XTGD07knW1HWJlXyvzpnd8atqSqjvpGT8BdUmCt3xjvZ8tEE4ZSKlidWvJrjPnQ6UBC1aq8Ur7/4iYKymu44+xsvnP+aGIircHpmdmprMorxRjT6cVw/u6D4asz9aRKtPCgUipIwezp3avVNjTxwH+3MX/hKsLDhBfvmMW9F49tThYAM7NTOHK8jr0lVZ2+T2G59aHvzypvr7T4aEQC29u7uKKOmMgw4qO1LIhSqnP006MVGw+W890XNpJXXMUNs4Zxz8VjiI06+Z9qVnYqACvzSslOj+/UvQrsVd6BdElF2vtmBLIWo6Sy3k40WhZEKdU52sLwUd/o4aElO7nqkRXU1jfx71tn8MC88a0mC4ARaXFkJkazKq+s0/csOFpDQkwEiTGRAb0vMzE6oAKEupe3UipY2sKwbT90nO++sInth45zzdQs7vv8uA4/xEWEmdmprMjt/DhGQXltQOMXXpkJMRw6FljCGJYaG/B9lFLKS1sYwPr9ZVz+1+UUV9Tx6A3TeOjaiX7/xj8rO5WSyjr2FAe28tqroLymUwkjIzEmoBaGFh5USgVLWxjAxKxk7jhrJLfMGUFKXGCziGaN9I5jlJGTkRDwvQuOVnP68P4Bvy8zMZrSqnrqGz1ERbSf9xubPJRV15Oui/aUUkHQFgYQER7G9z93SsDJAmBoSiwDk2JY1YkFfBW1DRyvbQxohpSXdy2Gd5/u9pRV1WMM2sJQSgVFE0aQRIRZPusxAuGdUtuZLqkBAey8V9S8NasmDKVU52nCcMDMkamUVtWzK8AKsp2ZUuuVkej/znveVojOklJKBUMThgO86zFW5QXWLdWZVd5en+3t3XGXVIm2MJRSDtCE4YAhKbEMTu4XcCHCgvJaosLDOvVBnhIbRUSY+NUl5W1hpCVoWRClVOdpwnDIrJGprNpbiieAPSoKymsYmBxDWFjg6zfCwoSMhGi/9vYuqagnLiq8zQWISinlD00YDpmVnUp5dQM7j1T4/Z6Co9UMSgq8O8orIzHGrxLnujWrUsoJmjAcMtO7HiOAbqnC8tpODXh7DfBz8V5xRa0mDKVU0FxPGCJykYjsFJFcEbmnldf/ICIb7a9dIlLu89pvRGSriGwXkT9LCFfOG5zcj6Epsaz0c+C7vtHDkYrOlQXx8reelLfwoFJKBcPVhCEi4cDDwMXAOGCBiIzzPccY8x1jzCRjzCTgL8Ar9nvPAGYDE4DxWPuJn+1mvMGalZ3Kmr1lfo1jHD5WizGdm1LrlZEYw/HaRmrqm9o9TwsPKqWc4HYLYzqQa4zJM8bUA88D89o5fwHwnP3YADFAFBANRAJHXIw1aDNHpnCspoFth453eG5Beeen1Hp5p9YWtbORUl1jE8dqGrSFoZQKmtsJYzBw0Od5vn3sJCIyDBgBLAUwxqwE3gcO2V9LjDHbXY02SLOy0wD/1mM4kzDsnffaGfgurawHdNGeUip4oTToPR94yRjTBCAiOcBYIAsrycwVkTNbvklEbheRdSKyrri4uEsDbmlAUgwj0uL8Sxj2or2ByTGdvl/z3t7tjGMU66I9pZRD3E4YBcAQn+dZ9rHWzOez7iiAK4FVxphKY0wl8D9gVss3GWMWGmOmGWOmpaenOxR2583MTmH13jKaOhjHKCivJj0hmuiI8HbPa09mgt0l1U7CaN7LW1sYSqkguZ0w1gKjRGSEiERhJYVFLU8SkTFAf2Clz+EDwNkiEiEikVgD3iHdJQUwMzuVitpGthYea/e8wk5unOQrsV8EMZFh7c6Uam5haMJQSgXJ1YRhjGkE7gSWYH3Yv2CM2SoiD4jI5T6nzgeeNyeWe30J2ANsATYBm4wx/3UzXic07/PdwXqMgvKaoGZIgVUpNzMxpt0xDG8LI7UTpduVUsqX67UijDGLgcUtjt3X4vn9rbyvCbjD1eBckJEYQ3a6NY5xx9kjWz3H4zEUlNdwwbjMoO+XmdD+4r3iijoSYyKIiex815dSSkFoDXr3GrOyU1m77yiNTZ5WX/fulBdslxRYZc69+120RsuCKKWcognDBbNGplJZ18iWgtbHMZyYUuuVmRhjLwJsfZC9pEJXeSulnKEJwwUzm/fHKGv1de+U2s5szdpSZmI0NQ1NVNQ1tvq6tjCUUk7RhOGCtPhoRmXEt1lXqtDbwghy0Bt8Vnu3MY5RUlGnLQyllCM0Ybhk1shU1u0ro6GVcYyC8hoSoiNI6hcZ9H3a23mvpt5qeWgLQynlBE0YLpmVnUp1fROb808ex8g/WuNIdxT4JoyTWxglupe3UspBmjBcMqOdfb4LHViD4ZWR0HY9Ke/WrFoWRCnlBE0YLkmJi2LMgIRWF/AVlNc4MkMKIC46goToiFZbGLrKWynlJE0YLpqZncq6/WXUN342jlFZ18ixmgbHWhhgrcVor0tKB72VUk7QhOGimdmp1DZ42JTfvIlg8wwpp8YwwKqS214LIzVey4IopYKnCcNFM7NTEDmxrpR3DYZTXVLgLQ/SyhhGRR0pcVFEhuu3WSkVPP0kcVFybBRjBySekDDy7RZGlqNdUjEUVZy82rukso40bV0opRyiCcNlM7NT2XDgKLUN1r7bBUdriAwXR2cuZSZG09BkOFrdcMJx3ctbKeUkTRgumzUylbpGDxsPWuMYheU1DEzqR1iYOHaPttZilFRqHSmllHM0Ybhs+ogTxzGcnFLr5d3b23erVmOM1cLQhKGUcogmDJcl9Yvk1EGJzQv4Chxc5e2V0cpWrVX1TdQ0NGmXlFLKMZowusCs7FQ+OVBORW0DRypqHV2DAdY6DDhxtXdJha7BUEo5SxNGF5g1MpX6Jg//23IYYyDL4RZGdEQ4KXFRJ4xhFGsdKaWUwzRhdIHTh6cQJvDS+nzA2UV7XhkJ0drCUEq5ShNGF0iIieS0wUms2WdtqOR0lxRYM6WKKrSFoZRyjyaMLjJzZGrz44FJMY5fP7NFPaniijrCxCqCqJRSTtCE0UVm2eXO0+KjiYkMd/z6mYkxFFfU0Whv2FRSWUdKXDThDq73UEr1bZowusi04SmEh4kr3VFgJQyPgdKqekBXeSulnBfR3QH0FfHREVw0fgBDU2Jdub7vau/MxBiKK+u1jpRSylGaMLrQw9dNce3amS3WYpRU1DEyPc61+yml+h7tkuolfFsYzWVBtEtKKeUgTRi9RGpcFGFilQc5XttIfZNH60gppRylCaOXiAgPIy3eWryne3krpdygCaMXGZAUw+HjtbqXt1LKFZowepGMBGtvb21hKKXc4HrCEJGLRGSniOSKyD2tvP4HEdlof+0SkXKf14aKyNsisl1EtonIcLfj7ckyE6MpqvDpktIWhlLKQa5OqxWRcOBh4AIgH1grIouMMdu85xhjvuNz/l3AZJ9LPAX8whjzjojEAx434+3pMhNjKKuqp7C8hogwIalfZHeHpJTqRdxuYUwHco0xecaYeuB5YF475y8AngMQkXFAhDHmHQBjTKUxptrleHs071qMbYeOkxYf7eg2sEop5XbCGAwc9Hmebx87iYgMA0YAS+1Do4FyEXlFRD4Rkd/aLRbVhgx7LcbWwuOkJegqb6WUs0Jp0Hs+8JIxpsl+HgGcCXwfOB3IBm5q+SYRuV1E1onIuuLi4q6KNSQNsBPGsZoGHb9QSjnO7YRRAAzxeZ5lH2vNfOzuKFs+sNHuzmoEXgNOqq1hjFlojJlmjJmWnp7uUNg9k3e1N+gMKaWU89xOGGuBUSIyQkSisJLCopYnicgYoD+wssV7k0XEmwXmAttavld9pn9sJJHh1riFrsFQSjnN1YRhtwzuBJYA24EXjDFbReQBEbnc59T5wPPGGOPz3ias7qj3RGQLIMCjbsbb04kIGQlWK0NbGEopp7lerdYYsxhY3OLYfS2e39/Ge98BJrgWXC+UmRhNQXmNtjCUUo4LpUFv5QDvOIa2MJRSTtOE0ctowlBKuUUTRi8zJCWWiDAhQxOGUsphuuNeL3Pd9KFMH55CQoyWBVFKOUtbGL1Mv6hwTstK6u4wlFK9kCYMpZRSftGEoZRSyi+aMJRSSvlFE4ZSSim/aMJQSinlF00YSiml/KIJQymllF/Ep0BsjycixcB++2kaUNKN4bRHY+u8UI5PY+ucUI4NQjs+p2IbZozpcEOhXpUwfInIOmPMtO6OozUaW+eFcnwaW+eEcmwQ2vF1dWzaJaWUUsovmjCUUkr5pTcnjIXdHUA7NLbOC+X4NLbOCeXYILTj69LYeu0YhlJKKWf15haGUkopB/W6hCEiF4nIThHJFZF7uvC+j4lIkYh86nMsRUTeEZHd9p/97eMiIn+2Y9wsIlN83nOjff5uEbnRodiGiMj7IrJNRLaKyLdCJT4RiRGRNSKyyY7tp/bxESKy2o7hPyISZR+Ptp/n2q8P97nWvfbxnSLyuWBj87luuIh8IiJvhFJsIrJPRLaIyEYRWWcf6/bvqc91k0XkJRHZISLbRWRWKMQnIqfY/2ber+Mi8u1QiM2+5nfs/wufishz9v+RkPiZwxjTa76AcGAPkA1EAZuAcV1077OAKcCnPsd+A9xjP74H+LX9+BLgf4AAM4HV9vEUIM/+s7/9uL8DsQ0EptiPE4BdwLhQiM++R7z9OBJYbd/zBWC+ffzvwNfsx18H/m4/ng/8x348zv5+RwMj7J+DcIe+t98FngXesJ+HRGzAPiCtxbFu/576xPIk8BX7cRSQHErx2dcPBw4Dw0IhNmAwsBfo5/OzdlPI/Mw59Q8fCl/ALGCJz/N7gXu78P7DOTFh7AQG2o8HAjvtx/8AFrQ8D1gA/MPn+AnnORjn68AFoRYfEAtsAGZgLUaKaPl9BZYAs+zHEfZ50vJ77XtekDFlAe8Bc4E37HuFSmz7ODlhhMT3FEjC+uCTUIzP53oXAitCJTashHEQKwlF2D9znwuVn7ne1iXl/cf2yrePdZdMY8wh+/FhINN+3FacrsdvN1knY/0mHxLx2V0+G4Ei4B2s34bKjTGNrdynOQb79WNAqluxAX8Efgh47OepIRSbAd4WkfUicrt9LCS+p1i/1RYDj9vdef8UkbgQis9rPvCc/bjbYzPGFAAPAQeAQ1g/Q+sJkZ+53pYwQpax0ny3TkkTkXjgZeDbxpjjvq91Z3zGmCZjzCSs3+anA2O6I46WROQyoMgYs767Y2nDHGPMFOBi4Bsicpbvi938MxeB1UX7N2PMZKAKq5unWXf/n7DHAS4HXmz5WnfFZo+bzMNKuIOAOOCiro6jLb0tYRQAQ3yeZ9nHussRERkIYP9ZZB9vK07X4heRSKxk8Ywx5pVQiw/AGFMOvI/V5E4WkYhW7tMcg/16ElDqUmyzgctFZB/wPFa31J9CJDbvb6MYY4qAV7GSbah8T/OBfGPMavv5S1gJJFTiAyvRbjDGHLGfh0Js5wN7jTHFxpgG4BWsn8OQ+JnrbQljLTDKnlEQhdXcXNSN8SwCvDMnbsQaO/Aev8GefTETOGY3hZcAF4pIf/s3jQvtY0EREQH+BWw3xvw+lOITkXQRSbYf98MaW9mOlTiuaSM2b8zXAEvt3wYXAfPtWSMjgFHAmmBiM8bca4zJMsYMx/pZWmqMuT4UYhOROBFJ8D7G+l58Sgh8TwGMMYeBgyJyin3oPGBbqMRnW8Bn3VHeGLo7tgPATBGJtf/fev/duv1nDuhdg9724M4lWLOA9gA/7sL7PofV59iA9dvVrVh9ie8Bu4F3gRT7XAEetmPcAkzzuc4tQK79dbNDsc3Bal5vBjbaX5eEQnzABOATO7ZPgfvs49n2D3guVpdBtH08xn6ea7+e7XOtH9sx7wQudvj7ew6fzZLq9tjsGDbZX1u9P+uh8D31ue4kYJ39vX0NayZRSMSH1dVTCiT5HAuV2H4K7LD/PzyNNdOp23/mjDG60lsppZR/eluXlFJKKZdowlBKKeUXTRhKKaX8oglDKaWUXzRhKKWU8osmDNXjiEiTXWV0k4hsEJEzOjg/WUS+7sd1PxCRkNy7ubuIyBMick3HZ6q+QBOG6olqjDGTjDETsYqs/aqD85OxqnqGJJ8VvEqFNE0YqqdLBI6CVStLRN6zWx1bRGSefc6DwEi7VfJb+9y77XM2iciDPte7Vqz9OXaJyJn2ueEi8lsRWSvWfgh32McHisgy+7qfes/3JdaeFb+x77VGRHLs40+IyN9FZDXwG7H2YnjNvv4qEZng83d63H7/ZhG52j5+oYistP+uL4pVJwwReVCsfU82i8hD9rFr7fg2iciyDv5OIiJ/FWsPhXeBDCe/Wapn099sVE/UT6zqtjFYZabn2sdrgSuNMcdFJA1YJSKLsIrejTdWgUNE5GKsAm8zjDHVIpLic+0IY8x0EbkE+AlWbZ9bscpBnC4i0cAKEXkbuAqrzPQvRCQcqzx7a44ZY04TkRuwqt9eZh/PAs4wxjSJyF+AT4wxV4jIXOAprJXS/8/7fjv2/vbf7f+A840xVSJyN/BdEXkYuBIYY4wxYpdcAe4DPmeMKfA51tbfaTJwCtZ+CplYZSke8+u7ono9TRiqJ6rx+fCfBTwlIuOxSjj8UqyqrR6scs6Zrbz/fOBxY0w1gDGmzOc1b2HG9Vj7m4BVI2iCT19+ElZtnrXAY2IVdnzNGLOxjXif8/nzDz7HXzTGNNmP5wBX2/EsFZFUEUm0Y53vfYMx5qhYVXTHYX3Ig7U50Uqs0ta1wL/E2h3wDfttK4AnROQFn79fW3+ns4Dn7LgKRWRpG38n1QdpwlA9mjFmpf0bdzpWfax0YKoxpkGsKrMxAV6yzv6zic/+fwhwlzHmpMJydnK6FOsD+ffGmKdaC7ONx1UBxtZ8W+AdY8yCVuKZjlWw7hrgTmCuMearIjLDjnO9iExt6+9kt6yUapWOYageTUTGYG2zWYr1W3KRnSzOxdp2E6ACa2tar3eAm0Uk1r6Gb5dUa5YAX7NbEojIaLGqxQ4DjhhjHgX+iVW+uzVf9PlzZRvnfARcb1//HKDEWHuWvAN8w+fv2x9YBcz2GQ+Js2OKxyqmtxj4DjDRfn2kMWa1MeY+rE2NhrT1dwKWAV+0xzgGAud28G+j+hBtYaieyDuGAdZvyjfa4wDPAP8VkS1YVVJ3ABhjSkVkhYh8CvzPGPMDEZkErBORemAx8KN27vdPrO6pDWL1ARUDV2BVsP2BiDQAlcANbby/v4hsxmq9nNQqsN2P1b21Gajms5LVPwcetmNvAn5qjHlFRG4CnrPHH8Aa06gAXheRGPvf5bv2a78VkVH2sfewKtxubuPv9CrWmNA2rFLbbSU41QdptVqlXGR3i00zxpR0dyxKBUu7pJRSSvlFWxhKKaX8oi0MpZRSftGEoZRSyi+aMJRSSvlFE4ZSSim/aMJQSinlF00YSiml/KIJQymllF96VWmQtLQ0M3z48O4OQymlepT169eXGGPSOzqvVyWM4cOHs27duu4OQymlehQR2e/PedolpZRSyi+aMJRSSvnF9YQhIhfZ+wPnisg9rbw+VETeF5FP7L2FL7GPp9rHK0Xkr27HqZRSqn2uJgx7n+OHgYuxtpRcICLjWpz2f8ALxpjJWFtRPmIfr8Xaz/j7bsaolFLKP263MKYDucaYPGNMPfA8MK/FOQZItB8nAYUAxpgqY8xyrMShlFKqm7mdMAYDB32e59vHfN0PfElE8rF2PrsrkBuIyO0isk5E1hUXFwcTq1JKqXaEwqD3AuAJY0wWcAnwtIj4HZcxZqExZpoxZlp6eofTiJVSqsscrarnt0t2UFTROzpK3F6HUYC14bxXln3M163ARQDGmJX2fsRpQJHLsSmllGtKK+u4/p+r2XG4gvyjNfxp/uTuDilobrcw1gKjRGSEiERhDWovanHOAeA8ABEZC8RgbUivlFI9UkllHdc9upq9JVWcNyaD1zcWsjm/vLvDCpqrCcMY0wjcCSwBtmPNhtoqIg+IyOX2ad8DbhORTcBzwE3G3jdWRPYBvwduEpH8VmZYKaVUSCmqqGX+wlXsL6vi8ZtO54/zJ5EaF8UvF2+np2+J7XppEGPMYqzBbN9j9/k83gbMbuO9w10NTimlHHTkeC0LHl3F4WO1PHHzdGZmpwLwrfNHcd/rW1m6o4jzxmZ2c5SdFwqD3kop1eMdOlbD/IWrOHKslidv+SxZACyYPpTstDh+9b8dNDZ5ujHK4GjCUEr5bcfh4/z6rZAkvv8AACAASURBVB1U1TV2dyghpaC8hi/+YxXFFXU8desMTh+ecsLrkeFh/PCiMeQWVfLCuvxuijJ4vaparVLKHbUNTfx1aS5//3APjR7D8NRYvnj6UNfu9/GeEg6UVpMSF0VKXBT946JIjYsiMSaSsDBx7b6dcbCsmgWPruJYTQNP3zqdyUP7t3re507NZNqw/vz+nV3MmzSIuOie9/Hb8yJWqoepa2zilQ0FzMlJY0hKbHeHE7DVeaXc+8oW8kqquGrKYFbnlfHOtiOuJYzq+kZueWIttQ0nd92Ehwn9YyPpH/tZEukfF0VKbBQZidFcPH4g6QnRrsTVmgOlVrKoqG3gma/MYEJWcpvnigg/unQsVz3yMf9Ylsd3LxjdZXE6RROGUi5av/8od7+8mdyiSs45JZ0nbp7e3SH57VhNAw/+bwfPrTnAkJR+PHXLdM4anc79i7by3JoDVNc3Ehvl/EfIsl3F1DZ4+MuCyYxIi6O0qp6jVfWUeb+qreelVfXkFlVytNo67jHw8ze384VpWdx+5kiGprqbnPeVVLHg0VXUNDTx7G0zGT84qcP3TBnan0tPG8ijy/K4fsZQMhNjXI3RaZowlHJBVV0jD729kyc+3sfAxBgunTCQNzcfYlvhccYNSuz4At3srU8Pc9/rn1JSWcdtZ47gOxeMbk4OF47L5ImP9/HR7hI+d+oAx++9ZOsRkmMjuXj8ACLC/Rtm9XgMeSWV/Gv5Xl5Ym8+zqw9w6YRBfPXsbE4d1PEHeaDyiitZ8OgqGpoMz35lZkDf0x9edApvbzvMH97ZxYNXT3A8NjfpoLdSDlu2q5gL/7CMx1fs48szh/H2d8/ml1ecRlxUOP9Ytqe7w2vXkeO13PH0Or767/Wkxkfz2jdm8+NLx53Qkjh9RAqJMRG8s+2I4/evb/Tw7vYjnD820+9kARAWJuRkJPCrqyaw/O5zue2sbN7fUcSlf17Ol/+1mo/3lDi2BiK3qJIvLlxFY5PhudsCSxYAw1Lj+NLMYbyw7iC7jlQ4ElNX0YShlEPKq+v53gubuOGxNURHhvHiV2fxwLzxxEdHkBQbyXUzhvLG5kMcLKvu7lBP4vEYnl19gPN/9yEf7Czm7ovGsOjO2a32yUeGhzF3TAZLdxTR5HF2IdqqvFIqahuDarlkJMZw78VjWXHPXH540SlsP1TBdY+u5oqHV/DWp4c6HXOTx7DpYDnzF67CGHj+9pmcMiChU9f65txRxEVH8KvF2zv1/u6iXVJKBckYw//sLpyj1Q3ceW4Od87NISYy/ITzbp2TzRMf7+PRj/J4YN74bor2ZHuKK7n35S2s2VfGrOxUfnXVaQxPi2v3PReMG8BrGwtZv/8o00ektHtuIN7aepjYqHDOHJUW9LWS+kXy9XNyuGX2CF7ekM/CZXl89d8byE6L446zs7li8mCiIz77HhljOFrdwMGyag4ereZAWTUHy2rItx8XltfQ0GTISIjm2dv+f3v3Hld1fT9w/PUGBARBURHwfk1EMzVKzVx2sbRat3XR1nX9arW1tdqltt+2X22/ba21tdraql/ruqbdy5plZne1FPOGikqKXBRUUEAQub1/f3y/xx2RywHO4Rzg/Xw8eHDO93becIA3n8v3/ZnG6AG92hxbQmwk3z1zNPe/k8WK7P2cNrr9X29HkM5+q7q39PR0zcjICHYYphspKqviF29k8t7mIiYMiuf335jYbJ/5j19ez1sbdrP87rPo16vjZvM0RlV54pMd/PG9bUT3COPnF6RxRfpgRFqetnroSC1TfrWU66YP4+cX+qdiT129MvW3yzh1RAJ/++bJfrlmw+u/k7mHxz7+isyCMpLiozhnXBJ7y4+QV1JJ/oHDHGpwf0nf2EiGJPRkcN8YhvaNYUhCDGelDiC5d/sHq6tq6jj7jx/TJ6YHb91+epunC9fVK499/BUJMZFcPbVtM9dEZI2qprd0nLUwjGkDVeXF1Xn8ZvEWqmvr+encVG46fUSL/e7fPmMkr3yZz7Mrcrjr3LEdFG3jHv9kB/e/k8V545P49SUTGBDn+x/BXlERTB/Vj6VbivjvC8b5lGRasjb3APsPHQnIQDo4U3IvnDiQC05MYXl2MX//OJs31+1mYJ9ohiTEMG1kP4b0jWFIQk/nc98YegXwXonoHuH8+Lyx/ODFdby5voBLJw9u9TXyD1Ry14vrWZVTwhUnD25zwvCVJQxjWimvpJK7X93Aiq+KmTqiL/d/YyIjWujC8Rg9II7Z45J4duUuvn3GqKDdvPXqmnzufyeLCyem8Mi8yW3673Z2WhI/fyOT7XsPcUJS2/ryvS3ZVEiPcOHM1AHtvlZzRITTx/TndD90e7XXRScN5MnPdvDgkm3MnZByXDdmc95cV8DPX89EgT9deRKXTm64Np3/2aC3Ma1QXlXDNf/4go35pfz20hNZcPM0n5OFx62zRlF6uIYFq3IDFGXzPtq6l7tf3cBpo/rxxytPanNXyOw0p4ieP2ZLqSpLNhUxY3R/4qN7tPt6nUVYmPCz88dRcPAwTy/P8emcsqoa7li4ljsWruOE5DjeuWMml03xrSuxvSxhGOMjVeVnr2eSf+AwT914CldPHdqmP7ZThiYwdURf/vHZTqprO7YQ3Yb8g3znhS8ZkxTH49eefMygb2slxUdz0uDevOeHhLFlTzm5JZUB644KZaeN6s9ZqQP424fZlFRUN3vsqp0lzP3zp7y9YQ93zT6BF2+Z1qHVAyxhGOOjF1fn8db63dw1+4Tjisu11q2zRrGntIo31zVcgDJwcvZXcOPTq0mIieTZG08hzg//yc9OS2J93kGKytq3BOmSTYWI/KfV0t38dG4qFdW1PLJse6P7a+rq+cOSLOY9sZKIcOHlW6fz/bPHtOpeFX+whGGMD7YVlXPvW5s4fXR/bjtjVLuvN+uERFKT43j8kx3U+/lehsbsKz/CdU+tol6V5246lQF+KkkxO81pEby/pX2tjCWbCjllWF/6B3nmWLCMSYrjqlOG8M/Pd5Gzv+KYfTv3V3D531fw6IdfcfnJg/n392cypYkCh4FmCcOYFhyuruO7L3xJr6gI/nRV2/v8vYkIt80aRfbeQyzLCuzy9YeOOMX89pZX8dQNpzAqse33DzR0QlIvhvWLadc4Rs7+CrIKyzl3fPdsXXjcec4JREaE8cCSLMDpAl2wKpfzH/6UnOJK/v7NKTxw+UkBnbnVEksYxrTg3kWbyN53iD9fNblVU09bcsGJKQxO6MnfP8oO2NKd1bX13PbPNWzeU8ajV09psvR2W4kIs8clsSK7+Lh7GHy1ZFMhQLccv/A2ID6am2eOZPHGQpZtKeLbz6/hp69tZPLQPrz7g5nMPTEl2CFawjCmOW+uK+DFjDy+M2uU36dhRoSHcfPMkXyZe5DVOQf8em1wyn385JX1fLp9P7+79MSALQ06Oy2J6rp6Ptm2r03nL9lUyPiB8Z2y9Lu/3fK1kSTGRXHTsxl8uHUv/33+OP5501RSevcMdmiAJQxjmrRzfwU/e20j6cMSuPOcwKxdcGX6EPrGRvLYx/4vSvj7d7N4Y91ufjj7BK48ZYjfr+9x8rAEEmJ6tKlbam9ZFV/mHmRON29deMRGRfDri8czbWRf3vjuDG7+2siQWjAq4AlDROaIyFYRyRaRexrZP1REPhSRtSKyQUTO99r3U/e8rSJyXqBjNcbjSG0d31vwJRHhYTw8f3LAZqP0jAznhtOG80HWXrIKy/x23X98tpPHP9nBNdOGcvtZo/123cZEhIdxVmoSH2TtpaaV61UvcZPMeRMsYXjMmZDCwlumB6Qse3sFNGGISDjwKDAXSAPmi0jDwjM/B15S1cnAPOBv7rlp7vPxwBzgb+71jAm43y3OIrOgjAevOIlBfQLbHXDd9GHERIbz+Mc7/HK9Ret38+u3NzNnfDL3XTShQ27omp2WROnhGlbnlLTqvPc2FTKyfyxj2lHIz3ScQLcwTgWyVXWHqlYDC4GLGxyjgKegfG9gt/v4YmChqh5R1Z1Atns9YwLqvU2FPLMihxtnDO+Q+wL6xEQy/9ShLFq/m/wD7St9viJ7Pz98aR2nDE/gz/MmEd5B3RlfO6E/URFhreqWKq2sYeVXxZw7PrlDkpppv0AnjEFAntfzfHebt3uBa0QkH1gMfK8V5yIit4hIhohk7NvXtkE3YzwKDh7mx69sYMKgeO6Zm9phr3vT6SMQ4MlPd7b5GuvyDnLL82sY0T+WJ687pVV1idorJjKC00f3Z+nmIp9nfC3LKqK2Xplj3VGdRigMes8HnlHVwcD5wPMi4nNcqvqEqqaranpiYmLAgjRdX01dPd9fsJa6euWv86e0q2xGaw3s05OLJw1i4ercFstDNLS3rIq7X9nAZX9bTnx0BM/ceCq9Yzq+HtPstCTyDxwmq9C3VeTezSwkOT6aiT6shW1CQ6ATRgHgPT1jsLvN203ASwCquhKIBvr7eK4xfvPQ0m2s2XWA31w6ocUFhALh1jNGUlVTz7Mrcnw6vrK6loff386sBz/itbX53DhjBIvvmMnAAI+5NOXscUmI+FaM8HB1HZ9s38e545NCahaQaV6gE8ZqYIyIjBCRSJxB7EUNjskFzgYQkXE4CWOfe9w8EYkSkRHAGGBVgOM13dSn2/fx94+/4qr0IVw8KfBlohszJimOc8Yl8ezKHCqrm74Jrr5eeTkjjzMf/IiH3t/GGScksvTOM/jFhWn0iYnsuIAbSIyLYvKQPj4ljI+37aOqpt6m03YyAU0YqloL3A4sAbbgzIbaJCK/EpGL3MN+CNwsIuuBBcAN6tiE0/LYDLwLfFdV6wIZr+me9pZXceeL6xid2It7Lxof1FhumzWSg5U1LFyV1+j+Fdn7ufAvn/HjVzaQ3LsnL986nb9fc3JQWkSNmZ2WzMaCUvaUHm72uCWbCukT08Ovy7uawAt4URJVXYwzmO297ZdejzcDM5o49zfAbwIaoOnW6uqVO19cx6Ejtfzr5mn0jAzuzO2Th/Xl1OFO6fNrpw+jh3v/R/beQ/xu8RaWZe1lUJ+ePDxvEl+fODDkunNmpyXx+3ezeH9zEddOH97oMdW19SzbUsS545M7vNqqaR97t0y39uqafJZnF3Pv18f7ZdU4f7h11kgKDh7mrfW7KT50hF+8kcl5f/6EVTtLuHtOKst+eAYXTxoUcskCYPSAXozsH9vsGhmf7yimrKq229eO6oxsiVbTbakqT362g3Ep8VwVwNIZrXXm2AGMTYrj9+9m8T9vbqKypo6rTx3KD84ZQ79OUP57dloSTy3fSVlVTaOr5y3ZVEhMZDgzQ2CJVNM61sIw3daKr4rZVnSIG2cMD6kbx0SE288aTVHZEaaO7MuSH8zk15dM6BTJApyEUVOnfLT1+Pui6uuV9zYXMWtsYofeJ2L8w1oYptt6evlO+sVGctFJA4MdynG+ftJApo3sR2Jc50gS3iYPTaBfbCRLNxcd971dm3eAfeVHrDuqk7IWhumWdhVXsCxrL1dPHRqy/+l2xmQBEB4mnD1uAB9l7T1uzfIlm4roES6cmTogSNGZ9rCEYbqlZ1bkEBEmXDNtWLBD6ZJmpyVTfqSWL3YWH92mqrybWciM0f0bHdswoc8Shul2yqtqeDkjnwtOTCHJT2tbm2OdPro/0T2OLUaYVVhObkmldUd1YpYwTLfzckY+h47UcuOMEcEOpcvqGRnOzDGJvO9VjPDdzEJE4JwArfxnAs8ShulW6uqVZ1fmMGVoH04a0ifY4XRps9OS2F1axabdzsJQSzYVcsqwvp12bMZYwjDdzIdZe9lVXMm3TrfWRaCdnTqAMIH3Nhexq7iCrMJyzh1vrYvOzKbVmm7l6RU7Sekdbf3oHaBfryjSh/Vl6eYiekU5M9Hs+965WQvDdBtZhWUszy4+pkaTCazZaUls2VPGC1/kMn5gPEP6xgQ7JNMO9ltjuo1nlucQ3SOM+acMDXYo3YZnidtdxZVWyrwLsIRhuoWSimpeX1vApZMHkRAbvDUjupvh/WMZM6AXAOfZUqydno1hmG5hwapcjtTW21TaILjutOF8vHXv0cRhOi9LGKbLq6mr5/mVuzh9dP+QKWHenVw7bRjX2h31XYJ1SZku753MQgrLqrhxxvBgh2JMp2YJw3R5Ty/fyfB+MZw51greGdMeljBMl7Yu7yBrcw9yw2nDQ3KFOmM6k4AnDBGZIyJbRSRbRO5pZP9DIrLO/dgmIge99v1eRDLdj6sCHavpep5evpO4qAguTw+dFfWM6awCOugtIuHAo8BsIB9YLSKLVHWz5xhVvdPr+O8Bk93HFwBTgElAFPCRiLyjqmWBjNl0HUVlVfx7wx6umz6cXlE2v8OY9gp0C+NUIFtVd6hqNbAQuLiZ4+cDC9zHacAnqlqrqhXABmBOQKM1IaWwtIrc4so2n//Pz3dRp8oNpw33X1DGdGOBThiDgDyv5/nutuOIyDBgBPCBu2k9MEdEYkSkP3AmcFy/gojcIiIZIpKxb9/xawibzml1Tgnn/fkTzvzjR9z31iZKK2tadX5VTR0vfJHLOeOSGNrPylEY4w+hNOg9D3hFVesAVPU9YDGwAqfVsRKoa3iSqj6hqumqmp6YmNiR8ZoAeXvDbr755Bf06xXJlemDeXZFDrMe/JDnP99FbV19yxcAFq3bTUlFtU2lNcaPAp0wCji2VTDY3daYefynOwoAVf2Nqk5S1dmAANsCEqUJCarKE598xe3/WstJg3vz6q2n8bvLJvL292YyNjmOX7yRyYV/+YwV2ftbvM5Ty3eSmhzH9JH9Oih6Y7q+QCeM1cAYERkhIpE4SWFRw4NEJBVIwGlFeLaFi0g/9/FEYCLwXoDjNUFSV6/cu2gTv12cxQUTU3j+pqlHaz6lDYxnwc3TeOyaKRw6UsvVT37Brc+vaXJ84/MdJWQVlnPjjOGI2FRaY/wloFNHVLVWRG4HlgDhwFOquklEfgVkqKonecwDFqpnLUdHD+BT9xe+DLhGVWsDGa8JjsPVdXx/4VqWbi7ilq+N5J45qcfdMyEizJmQwqyxA/jHZzt59MNsPnhoLzfPHMF3Zo0m1msW1NPLd9I3NpKLJzU6XGaMaSM59m9055aenq4ZGRnBDsO0wv5DR7jp2Qw25B/k3q+P53ofZzQVllbxwLtZvLa2gAFxUdw9J5VLJw8i/8BhznjwQ747azQ/Om9sYIM3posQkTWqmt7ScT61METkRFXd2P6wjPmPnfsruP6pVRSVVfHYNSe3ajW25N7R/OmqSVwzfRj3vbWZH768nuc+30VyfBThIlw73YrdGeNvvo5h/E1EVonId0Skd0AjMt3Cml0HuOxvyzl0pJYFt0xr89KdU4Ym8Pptp/GnK09iz8HDLNlUxPknppAUH+3niI0xPrUwVHWmiIwBvgWsEZFVwNOqujSg0Zku6d3MPdyxcB0pvaN55sZTGd4/tl3XCwsTLpsymPPGJ/Pal/nMTrOFeowJBJ8HvVV1u4j8HMgAHgEmizMi/TNVfS1QAZqu5anPdvLrf29m8pA+PHn9KfT14+p3sVERXDt9uN+uZ4w5lq9jGBOBG4ELgKXA11X1SxEZiDMV1hKGaVZ9vfK//97CU8t3ct74JB6eN5noHuHBDssY0wq+tjD+AjyJ05o47NmoqrvdVocxzXpuZQ5PLd/JjTOG8/ML0gi3UuPGdDotJgy34myBqj7f2P6mthvjUV+vPLMihylD+/A/Xx8f7HCMMW3U4iwpt7bTEPdObWNa7ePt+8gprvT5HgtjTGjytUtqJ7BcRBYBFZ6NqvqngERlupTnVuSQGBfF3AkpwQ7FGNMOviaMr9yPMCAucOGYriZnfwUfbdvH988aQ2REKBVHNsa0lq/3YdwX6EBM1/T857sIF+HqqUODHYoxpp18nVabCPwEGA8cvYVWVc8KUFymC6isruWljDzm2p3XxnQJvvYRvABk4ayIdx+Qg1O63Jgmvb62gPKqWq63uk7GdAm+Jox+qvoPoEZVP1bVbwHWujBNUlWeW7GLtJR4Th6WEOxwjDF+4GvC8CyovEdELhCRyUDfAMVkuoDPd5SwtaicG06zRYyM6Sp8nSX1v26V2h/i3PUdD9wZsKhMp/fcyhz6xPTgokkDgx2KMcZPfJ0l9bb7sBQ4M3DhmK5g98HDvLe5iP+aOcLqRRnThbRmltTNwHDvc9yxDGOO8cIXu1BVrplqg93GdCW+dkm9CXwKvA/UBS4c09lV1dSxYFUeZ49LYkjfmGCHY4zxI18TRoyq3h3QSEyXsHjjHkoqqrne1qUwpsvxdZbU2yJyflteQETmiMhWEckWkXsa2f+QiKxzP7aJyEGvfQ+IyCYR2SIij4hNtwl5z67IYVRiLDNG9wt2KMYYP/M1YdyBkzQOi0iZiJSLSFlLJ7ml0R8F5gJpwHwRSfM+RlXvVNVJqjoJZwbWa+65pwEzgInABOAU4Awf4zVBsC7vIOvzS7neptIa0yX5lDBUNU5Vw1S1p6rGu8/jfTj1VCBbVXeoajWwELi4mePnAws8L4tThiQSiAJ6AEW+xGuC49kVOfSKiuCyKYODHYoxJgCaHcMQkVRVzRKRKY3tV9UvW7j+ICDP63k+MLWJ1xqGU3rkA/faK0XkQ2APIMBfVXVLI+fdAtwCMHSoFbhriz2lziKKKb17tvka+8qP8O8Ne7h66lB6Rfm8VLwxphNp6Tf7Lpw/xn9sZJ/i3/Ig84BX3AWbEJHRwDjA8+/qUhGZqaqfHhOE6hPAEwDp6enqx3i6jTsWrGPT7lL+ds3JnHFCYpuu8eLqXKrr6rlmmk2lNaarajZhqOot7ue23qxXAAzxej7Y3daYecB3vZ5fCnyuqocAROQdYDrO9F7jR9v2llNRXce3nlnN7y49kStPGdLySV5q6ur55+e5zBzTn9EDegUoSmNMsPk0hiEi0SJyl4i8JiKvisgPRMSXetWrgTEiMsJd4nUesKiR66cCCcBKr825wBkiEiEiPXAGvI/rkjLtU3q4hoOVNXzvrNGcNqofP3l1A39aug1V3xtrSzcXUVhWZVNpjenifJ0l9RzOWhh/Af7qPn6+pZNUtRa4HViC88f+JVXdJCK/EpGLvA6dByzUY/9KvYKzyt9GYD2wXlXf8jFe46O8kkoA0lLieeqGU7ji5ME8smw7P3p5A9W19T5d45kVOQxO6MmZqQMCGaoxJsh8HZ2coKre02E/FJHNvpyoqouBxQ22/bLB83sbOa8O+LaP8Zk2ynUTxtB+MfQID+OByycyKKEnf35/O0VlVfz9minERfdo8vwte8pYtbOEn52fSniYTaU1pivztYXxpYhM8zwRkalARmBCMh3paMJwy3iICD845wT+cPlEPt9RzBWPrTw6i6oxz63cRVREGFemt27cwxjT+TSbMERko4hsAE4GVohIjojsxBlrSO+IAE1g5ZZU0jc28rhWxBXpQ3j6xlPIP3CYSx9dwZY9x9+nWVpZwxtrC7hk0iD6xER2VMjGmCBpqYVxIfB1YA7OPRJnALPcx3M9B4mILanWSeUWVzZZJHDmmERevnU6AFc+tpLPtu8/Zv/La/I4XFPHdafZVFpjuoNmE4aq7mruw+vQZQGO0wRIbknl0e6oxoxLief1757GoISe3PD0Kl5Zkw9AXb3y3MpdnDI8gfEDe3dUuMaYIPJ1DKMlNtrZCdXU1VNw8DDDWihDntK7Jy/dOp2pI/vyo5fX88iy7Xy0dS+5JZVcf9rwjgnWGBN0/qrhYHdYd0J7DlZRV6/NtjA84qN78PQNp3LPa859GnFRESTFR3He+OQOiNQYEwr81cIwnZBnhpSvCx1FRoTxxytO4vtnj6H8SC3XThtGj3D7ETKmu/BXC8O6pDqhXSUVAAzr5/vKeCLCXbNP4KKTBjKif2ygQjPGhKBWJQwRGYBTchwAVc11H57tz6BMx8gtqSQyPIykeF+qvBzLakYZ0/34WkvqIhHZDuwEPgZygHc8+1W1JCDRmYDKLa5kcN+edoe2McYnvnZA/xqYBmxT1RE4LYrPAxaV6RAtTak1xhhvviaMGlUtBsJEJExVP8Tu9O7UVJXcYksYxhjf+TqGcVBEegGfAC+IyF6gInBhmUA7WFlD+ZFaSxjGGJ/52sK4GKgE7gTexSk7fmGggjKB17DooDHGtMTXhPFLVa1X1VpVfVZVHwHuDmRgJrC8y5obY4wvfE0YsxvZNreRbaaTsBaGMaa1mh3DEJHbgO8AI90y5x5xwPJABmYCK7e4kv69ooiJ9Ne9m8aYrq6lvxb/wrnf4nfAPV7by+3ei85tV0lFq+7wNsaYlsqbl6pqjqrOB4YAZ7llzcNEZESHRNiNVdfW84s3Mo+uu+1PeSWHrTvKGNMqvt7p/T84g9w/dTdFAv/08dw5IrJVRLJF5J5G9j8kIuvcj20ictDdfqbX9nUiUiUil/j2ZXUNa3MP8Pznu3h7wx6/Xre6tp7dpYd9LjpojDHg+30YlwKTgS8BVHW3iMS1dJKIhAOP4gya5wOrRWSRqm72HKOqd3od/z33dXBvDpzkbu8LZAPv+Rhvl7CxoBSArMLjl0dtj/wDlajS4joYxhjjzddZUtWqqrjrXoiIr2VKTwWyVXWHqlYDC3Hu6WjKfGBBI9svB95RVf/3zYSwTE/C2FPu1+valFpjTFv4mjBeEpHHgT4icjPwPvB/Ppw3CMjzep7vbjuOiAzDWSv8g0Z2z6PxRNKlZe52WhZf7TtEdW29367rSRjWwjDGtIZPCUNVHwReAV4FTsC5ke8vfo5lHvCKqtZ5bxSRFOBEYEljJ4nILSKSISIZ+/bt83NIwVNxpJav9h1iVGIstfXKV/sO+e3aucWVREWEkRgX5bdrGmO6vtYsl7YR+BSnntRGH88pwJld5THY3daYploRVwKvq2pNYyep6hOqmq6q6YmJiT6GFfo2T6eUFwAAF3hJREFU7ylDFS4/2fn2+XMcw1OlVsTKmhtjfOfrLKn/AlYBl+GMJ3wuIt/y4dTVwBgRGSEikThJYVEj108FEoCVjVyjqXGNLm1jvjN+cdGkgUSGh/l1HCO3pNLuwTDGtJqvs6R+DEx2S5wjIv2AFcBTzZ2kqrUicjtOd1I48JSqbhKRXwEZqupJHvOAhe7A+lEiMhynhfKxj3F2GZkFpSTGRTGoT09GD+jFlkL/JAxVJbekkumj+vnlesaY7sPXhFEMeP/FKne3tUhVFwOLG2z7ZYPn9zZxbg5NDJJ3dZm7SzlxUG8AUlPi+Gz7fr9ct7iimsrqOrtpzxjTai3VkrrLfZgNfCEib+JMrb0Y2NDkiaZdKqtryd57iDkTUgAYlxzPa18WUHzoCP16tW+gelexO0PKuqSMMa3U0hjGZJxCg18H3sC9DwN4E2d9bxMAW/aUUa8c08IA2OqHbqk8q1JrjGmjlrqkpuCUBPkG4O9ptKYJngHvCYPiAUhNdj5nFZZz2uj+7bq2p4UxOMEShjGmdVpKGI8Dy3BuqMvw2i44rY2RAYqrW9tYUEb/XpEkx0cDkBgXRf9ekX6ZWptbUklyfDTRPcLbfS1jTPfSUrXaR1R1HPC0qo70+hihqpYsAiSzoJQJg3ofc59EanI8WX7qkrLuKGNMW/h6p/dtgQ7EOA5X17F9b/nR8QuPsclxbC0sp65emzjTN7tKKqyGlDGmTVpzp7fpAFsKnQHvCQ0SRmpyHEdq68kprmjztatq6igqO2ItDGNMm1jCCDGeCrUNWxjjUtyB73bc8W0zpIwx7WEJI8RszC+lb2wkKb2jj9k+ekAvwqR9NaWsrLkxpj0sYYSYjY0MeANE9whnZGKvdg1851oLwxjTDpYwQkhVTR3b9x7iRPf+i4ZSk+Pa1cLYVVxJbGQ4/WIj23wNY0z3ZQkjhGS5s6Aajl94jEuJJ6/kMOVVjVZ6b1FeSSVDrKy5MaaNLGGEEM8a3g1nSHmkJjslQrYVta1bKtfuwTDGtIMljBCSmV9KQkwPBvXp2ej+sW7C2NKGmVL19WrrYBhj2sUSRghpasDbY1CfnsRFRbRpHGPfoSMcqa23FoYxps0sYYSIqpo6thWVN9kdBSAipKbEteleDE/RwaH9YtscozGme7OEESK2FZVT28yAt0dqcjxbC8tpsDhhi2xKrTGmvSxhhIiNTdzh3VBqShzlR2opOHi4VdfPLakkTGhyfMQYY1piCSNEZBaU0rtnDwYnNP8H/ejaGK3slsotriCld08iI+wtN8a0jf31CBEbC5w1vFu6R8IzU6q1A982pdYY014BTxgiMkdEtopItojc08j+h0RknfuxTUQOeu0bKiLvicgWEdksIsMDHW8wHKmtY2th8wPeHr2iIhjStydbWlkixBKGMaa9Wlpxr11EJBx4FJgN5AOrRWSRqm72HKOqd3od/z2cdcQ9ngN+o6pLRaQXUB/IeINlW+Ehaur06JKsLUlNjidrj+8tjIojtew/VG1FB40x7RLoFsapQLaq7lDVamAhcHEzx88HFgCISBoQoapLAVT1kKpWBjjeoMjc7duAt8e45Dh27q+gqqbOp+PzDtgMKWNM+wU6YQwC8rye57vbjiMiw3DWDv/A3XQCcFBEXhORtSLyB7fF0vC8W0QkQ0Qy9u3b5+fwO8bGglLioyN8/oOemhJPvcL2okM+He+5B8Pu8jbGtEcoDXrPA15RVc+/zRHATOBHwCnASOCGhiep6hOqmq6q6YmJiR0Vq181toZ3c1JbOfBtCycZY/wh0AmjABji9Xywu60x83C7o1z5wDq3O6sWeAOYEpAog6i6tp6sPcev4d2cYf1iie4R5vPaGLkllcRFR9C7Z4+2hmmMMQFPGKuBMSIyQkQicZLCooYHiUgqkACsbHBuHxHxNBvOAjY3PLez21ZUTnVdvU8zpDzCw4SxSb6vjbGr2Ck6aGXNjTHtEdCE4bYMbgeWAFuAl1R1k4j8SkQu8jp0HrBQvepduF1TPwKWichGQID/C2S8wZDZQknzpqQmx7Nlj28lQvJsSq0xxg8COq0WQFUXA4sbbPtlg+f3NnHuUmBiwIILAZm7S4mLimBYK/+gj02O48WMPPYdOsKAuOgmj6urV/IOVDJ7fFJ7QzXGdHOhNOjdLW0sKGP8oHjCwlrXXZSa4g58t1AipLCsipo6ZVhfq1JrjGkfSxhBVFNXz5Y9Za0a8PY4WlOqhXGM3GKbIWWM8Q9LGEG0vegQ1bWtG/D26BsbSVJ8VIszpXJLKgC7B8MY036WMIIo08eS5k1xSoS0lDAqCQ8TUno3Pc5hjDG+sIQRRJm7S+kVFcHwNq6Cl5oSR/beQ9TUNV1ia1dxJYP69CQi3N5qY0z72F+RINpYUErawNYPeHuMS46nuq6enfsrmjwmr6TSuqOMMX5hCSNIatsx4O3hWRtjSzOVa3NLKhliA97GGD+whNFGe8ur+CCrqM3nZ+87RFVNfbsSxqjEXkSESZMD32VVNRyorGn1PR7GGNMYSxhtcKS2jhufXs23nslg8cY9bbrGxvy23eHtLTIijNEDejW5NoZNqTXG+JMljDb43eIsNu0uY0jfntzz6gYKDh5u9TUyC0qJjQxnZP/23VCXmhzH1iZaGLlulVrrkjLG+IMljFZaurmIZ1bkcOOM4fzzpqnUK9y5cB119S3XdPKWubusXQPeHqkp8ewuraK0sua4fZ6EYSvtGWP8wRJGK+w+eJgfv7KeCYPiuWduKsP6xfLrS8azKqeERz/M9vk6dfXK5t1l7eqO8mhubYzckkoSYnoQH21lzY0x7WcJw0e1dfXcsXAtNbX1/GX+FKIinMX/Lp08mEsmDeThZdtZs6vEp2t9te8Qh2vq2jXg7TEuxVMi5Phuqdxiq1JrjPEfSxg+enjZdlbnHOA3l57IiAbjDr++ZAID+0Rzx8J1lFUd3zXUkGfA2x8JY0BcFAkxPZpsYQxt402BxhjTkCUMH6zI3s9fP8zmipMHc8nk45ckj4vuwcPzJrOntIqfv57Z4hoVGwtKiYkMZ2Rir3bHJiKMTY5jS4MSIbV19RQcPMzQvj3b/RrGGAOWMFq0/9AR7nhxHSP7x3LfxeObPG7K0ATuPGcMi9bv5rUvm1qF1pFZUEpaSjzh7Rzw9khNjmdbUTn1XgPvuw9WUVdvZc2NMf5jCaMZ9fXKD19aT+nhGv569RRiIptfb+q2WaOZOqIvv3wzk5wmynXU1Sub9/hnwNtjXEocldV15B2oPLrNptQaY/zNEkYznvxsBx9v28cvLkw7OrjcnPAw4aGrJhERHsb3F66luvb4ooA79x+isrrOrwnDszaGd7fULresuU2pNcb4iyWMJqzNPcAD725l7oRkrpk61OfzBvbpyf2XnciG/FIeen/bcfs3trOkeWNOSIpD5NiptbkllUSGh5Ecb2XNjTH+EfCEISJzRGSriGSLyD2N7H9IRNa5H9tE5KDXvjqvfYsCHatH6eEavrdgLUnx0dx/2UREWjfWMPfEFOafOoTHPv6KFdn7j9m3Mb+M6B5hjEr039hCz8hwRvSLPWZtjLySSgYn9PTbOIkxxgQ0YYhIOPAoMBdIA+aLSJr3Map6p6pOUtVJwF+A17x2H/bsU9WLAhmrVzz87LWN7Cmt4pH5k+kd07ab3n5xYRoj+8dy50vrKKmoPrrdM+Dt7/UpUlPijmlh7Cq2KrXGGP8KdAvjVCBbVXeoajWwELi4mePnAwsCHFOzFqzK498b9/Cjc8dy8rCENl8nJjKCh+dN5kBFDXe/ugFVpb5e2bS71K/dUR5jk+LZVVJJxZFaVJXcYlsHwxjjX4FOGIOAPK/n+e6244jIMGAE8IHX5mgRyRCRz0XkksCF6cgqLOO+tzYxc0x/vv21ke2+3oRBvfnJnLEs3VzEP7/IZWdxBRV+HvD2SE2JQxW2FZVzsLKG8iO1dpe3Mcavmp8n2rHmAa+oap3XtmGqWiAiI4EPRGSjqn7lfZKI3ALcAjB0qO+D0w1VVtdy+7/WEhfdgz9dOandRQE9vjVjBJ9u38//vr2Zm2c6SSgQCWOcO1Nqa2E5Ye6YiyUMY4w/BbqFUQAM8Xo+2N3WmHk06I5S1QL38w7gI2Byw5NU9QlVTVfV9MTExDYH+qu3NvPVvkP8+apJJMZFtfk6DYWFCQ9ecRJx0RH89cNsoiLCGDOg/Xd4NzQ4oSexkeFkFZZblVpjTEAEOmGsBsaIyAgRicRJCsfNdhKRVCABWOm1LUFEotzH/YEZwOaABJlTwsLVeXxn1ihOH9Pf79dPjIviwStOApxigf4e8AYnMTklQsr+c9NegiUMY4z/BLRLSlVrReR2YAkQDjylqptE5FdAhqp6ksc8YKEeW4RpHPC4iNTjJLb7VTUgCSN9WAJ//+YUzklLCsTlAZg1dgC/vfREknv7r/XSUGpKPP/esIdh/WLo3yuK2KhQ6nE0xnR20lKhvM4kPT1dMzIygh1G0Dy/ModfvLmJ4f1i6BsbyWvfmRHskIwxnYCIrFHV9JaOszu9u5BUt3xJTnElw6ysuTHGzyxhdCEnJMUdfWw37Rlj/M0SRhfSu2cPBvVx1r+wKbXGGH+zhNHFeNb4tru8jTH+Zgmji0lNcRKGtTCMMf5m8y67mGunDWdQnxiSrKy5McbPrIXRxST3jubqVqzfYYwxvrKEYYwxxieWMIwxxvjEEoYxxhifWMIwxhjjE0sYxhhjfGIJwxhjjE8sYRhjjPFJlypvLiL7gF3u0/7A/iCG0xyLre1COT6LrW1COTYI7fj8FdswVW1xydIulTC8iUiGL/Xdg8Fia7tQjs9ia5tQjg1CO76Ojs26pIwxxvjEEoYxxhifdOWE8USwA2iGxdZ2oRyfxdY2oRwbhHZ8HRpblx3DMMYY419duYVhjDHGjyxhGGOM8UmXSxgiMkdEtopItojc04Gv+5SI7BWRTK9tfUVkqYhsdz8nuNtFRB5xY9wgIlO8zrnePX67iFzvp9iGiMiHIrJZRDaJyB2hEp+IRIvIKhFZ78Z2n7t9hIh84cbwoohEutuj3OfZ7v7hXtf6qbt9q4ic197YvK4bLiJrReTtUIpNRHJEZKOIrBORDHdb0N9Tr+v2EZFXRCRLRLaIyPRQiE9ExrrfM89HmYj8IBRic695p/u7kCkiC9zfkZD4mUNVu8wHEA58BYwEIoH1QFoHvfbXgClApte2B4B73Mf3AL93H58PvAMIMA34wt3eF9jhfk5wHyf4IbYUYIr7OA7YBqSFQnzua/RyH/cAvnBf8yVgnrv9MeA29/F3gMfcx/OAF93Hae77HQWMcH8Owv303t4F/At4230eErEBOUD/BtuC/p56xfIs8F/u40igTyjF514/HCgEhoVCbMAgYCfQ0+tn7YaQ+Znz1zc+FD6A6cASr+c/BX7aga8/nGMTxlYgxX2cAmx1Hz8OzG94HDAfeNxr+zHH+THON4HZoRYfEAN8CUzFuXs1ouH7CiwBpruPI9zjpOF77X1cO2MaDCwDzgLedl8rVGLL4fiEERLvKdAb5w+fhGJ8Xtc7F1geKrHhJIw8nCQU4f7MnRcqP3NdrUvK8832yHe3BUuSqu5xHxcCSe7jpuIMePxuk3Uyzn/yIRGf2+WzDtgLLMX5b+igqtY28jpHY3D3lwL9AhUb8GfgJ0C9+7xfCMWmwHsiskZEbnG3hcR7ivNf7T7gabc770kRiQ2h+DzmAQvcx0GPTVULgAeBXGAPzs/QGkLkZ66rJYyQpU6aD+ocZhHpBbwK/EBVy7z3BTM+Va1T1Uk4/82fCqQGI46GRORCYK+qrgl2LE04XVWnAHOB74rI17x3BvlnLgKni/bvqjoZqMDp5jkq2L8T7jjARcDLDfcFKzZ33ORinIQ7EIgF5nR0HE3pagmjABji9Xywuy1YikQkBcD9vNfd3lScAYtfRHrgJIsXVPW1UIsPQFUPAh/iNLn7iEhEI69zNAZ3f2+gOECxzQAuEpEcYCFOt9TDIRKb579RVHUv8DpOsg2V9zQfyFfVL9znr+AkkFCJD5xE+6WqFrnPQyG2c4CdqrpPVWuA13B+DkPiZ66rJYzVwBh3RkEkTnNzURDjWQR4Zk5cjzN24Nl+nTv7YhpQ6jaFlwDnikiC+5/Gue62dhERAf4BbFHVP4VSfCKSKCJ93Mc9ccZWtuAkjsubiM0T8+XAB+5/g4uAee6skRHAGGBVe2JT1Z+q6mBVHY7zs/SBqn4zFGITkVgRifM8xnkvMgmB9xRAVQuBPBEZ6246G9gcKvG55vOf7ihPDMGOLReYJiIx7u+t5/sW9J85oGsNeruDO+fjzAL6CvjvDnzdBTh9jjU4/13dhNOXuAzYDrwP9HWPFeBRN8aNQLrXdb4FZLsfN/opttNxmtcbgHXux/mhEB8wEVjrxpYJ/NLdPtL9Ac/G6TKIcrdHu8+z3f0jva71327MW4G5fn5/Z/GfWVJBj82NYb37scnzsx4K76nXdScBGe57+wbOTKKQiA+nq6cY6O21LVRiuw/Icn8fnseZ6RT0nzlVtdIgxhhjfNPVuqSMMcYEiCUMY4wxPrGEYYwxxieWMIwxxvjEEoYxxhifWMIwnY6I1LlVRteLyJcicloLx/cRke/4cN2PRCTdf5F2fiLyjIhc3vKRpjuwhGE6o8OqOklVT8Ipsva7Fo7vg1PVMyR53cFrTEizhGE6u3jgADi1skRkmdvq2CgiF7vH3A+Mclslf3CPvds9Zr2I3O91vSvEWZ9jm4jMdI8NF5E/iMhqcdZD+La7PUVEPnGvm+k53ps4a1Y84L7WKhEZ7W5/RkQeE5EvgAfEWYvhDff6n4vIRK+v6Wn3/A0i8g13+7kistL9Wl8Wp04YInK/OOuebBCRB91tV7jxrReRT1r4mkRE/irOGgrvAwP8+WaZzs3+szGdUU9xqttG45SZPsvdXgVcqqplItIf+FxEFuEUvZugToFDRGQuToG3qapaKSJ9va4doaqnisj5wP/g1Pa5CaccxCkiEgUsF5H3gMtwykz/RkTCccqzN6ZUVU8Uketwqt9e6G4fDJymqnUi8hdgrapeIiJnAc/h3Cn9C8/5buwJ7tf2c+AcVa0QkbuBu0TkUeBSIFVVVdySK8AvgfNUtcBrW1Nf02RgLM56Ckk4ZSme8uldMV2eJQzTGR32+uM/HXhORCbglHD4rThVW+txyjknNXL+OcDTqloJoKolXvs8hRnX4KxvAk6NoIleffm9cWrzrAaeEqew4xuquq6JeBd4fX7Ia/vLqlrnPj4d+IYbzwci0k9E4t1Y53lOUNUD4lTRTcP5Iw/O4kQrcUpbVwH/EGd1wLfd05YDz4jIS15fX1Nf09eABW5cu0Xkgya+JtMNWcIwnZqqrnT/407EqY+VCJysqjXiVJmNbuUlj7if6/jP74cA31PV4wrLucnpApw/yH9S1ecaC7OJxxWtjO3oywJLVXV+I/GcilOw7nLgduAsVb1VRKa6ca4RkZOb+prclpUxjbIxDNOpiUgqzjKbxTj/Je91k8WZOMtuApTjLE3rsRS4UURi3Gt4d0k1Zglwm9uSQEROEKda7DCgSFX/D3gSp3x3Y67y+ryyiWM+Bb7pXn8WsF+dNUuWAt/1+noTgM+BGV7jIbFuTL1wiuktBu4ETnL3j1LVL1T1lziLGg1p6msCPgGucsc4UoAzW/jemG7EWhimM/KMYYDzn/L17jjAC8BbIrIRp0pqFoCqFovIchHJBN5R1R+LyCQgQ0SqgcXAz5p5vSdxuqe+FKcPaB9wCU4F2x+LSA1wCLiuifMTRGQDTuvluFaB616c7q0NQCX/KVn9v8Cjbux1wH2q+pqI3AAscMcfwBnTKAfeFJFo9/tyl7vvDyIyxt22DKfC7YYmvqbXccaENuOU2m4qwZluyKrVGhNAbrdYuqruD3YsxrSXdUkZY4zxibUwjDHG+MRaGMYYY3xiCcMYY4xPLGEYY4zxiSUMY4wxPrGEYYwxxif/D1M/rk73IKa7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()\n",
    "learn.recorder.plot_metrics()\n",
    "#learn.recorder.plot_lr(show_moms=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('qrnn_F1_metric_90_dropout_0606')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds_t,y_t,losses_t = learn.get_preds(DatasetType.Train,with_loss=True)\n",
    "interp_t = ClassificationInterpretation(learn, preds_t, y_t, losses_t)\n",
    "interp_t.plot_confusion_matrix()\n",
    "\n",
    "preds,y,losses = learn.get_preds(with_loss=True)\n",
    "interp = ClassificationInterpretation(learn, preds, y, losses)\n",
    "interp.plot_confusion_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "----------\n"
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<span style=\"font-family: monospace;\"><span title=\"0.089\" style=\"background-color: rgba(229, 239, 249, 0.5);\">▁</span> <span title=\"0.185\" style=\"background-color: rgba(210, 227, 243, 0.5);\">xxbos</span> <span title=\"0.318\" style=\"background-color: rgba(176, 209, 231, 0.5);\">▁ayer</span> <span title=\"0.565\" style=\"background-color: rgba(85, 159, 205, 0.5);\">▁comí</span> <span title=\"0.174\" style=\"background-color: rgba(212, 228, 243, 0.5);\">▁con</span> <span title=\"0.271\" style=\"background-color: rgba(191, 216, 236, 0.5);\">▁un</span> <span title=\"0.834\" style=\"background-color: rgba(15, 91, 163, 0.5);\">▁adicto</span> <span title=\"0.295\" style=\"background-color: rgba(183, 212, 234, 0.5);\">▁al</span> <span title=\"0.671\" style=\"background-color: rgba(53, 133, 191, 0.5);\">▁ajedrez</span> <span title=\"0.270\" style=\"background-color: rgba(192, 216, 237, 0.5);\">▁,</span> <span title=\"0.683\" style=\"background-color: rgba(50, 130, 190, 0.5);\">▁teníamos</span> <span title=\"0.327\" style=\"background-color: rgba(173, 208, 230, 0.5);\">▁un</span> <span title=\"0.523\" style=\"background-color: rgba(99, 169, 211, 0.5);\">▁cubre</span> <span title=\"0.247\" style=\"background-color: rgba(198, 219, 239, 0.5);\">mesa</span> <span title=\"0.176\" style=\"background-color: rgba(212, 228, 243, 0.5);\">▁a</span> <span title=\"0.327\" style=\"background-color: rgba(173, 208, 230, 0.5);\">▁cuadros</span> <span title=\"0.153\" style=\"background-color: rgba(216, 231, 245, 0.5);\">▁y</span> <span title=\"0.221\" style=\"background-color: rgba(203, 222, 240, 0.5);\">▁le</span> <span title=\"0.371\" style=\"background-color: rgba(160, 202, 225, 0.5);\">▁tomó</span> <span title=\"0.215\" style=\"background-color: rgba(205, 223, 241, 0.5);\">▁2</span> <span title=\"0.269\" style=\"background-color: rgba(192, 216, 237, 0.5);\">▁horas</span> <span title=\"0.646\" style=\"background-color: rgba(60, 140, 195, 0.5);\">▁pasarme</span> <span title=\"0.445\" style=\"background-color: rgba(128, 185, 218, 0.5);\">▁la</span> <span title=\"0.881\" style=\"background-color: rgba(8, 79, 153, 0.5);\">▁maldita</span> <span title=\"1.000\" style=\"background-color: rgba(8, 48, 107, 0.5);\">▁sal</span> <span title=\"0.136\" style=\"background-color: rgba(220, 233, 246, 0.5);\">...</span></span>"
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<span style=\"font-family: monospace;\"><span title=\"0.038\" style=\"background-color: rgba(239, 246, 252, 0.5);\">▁</span> <span title=\"0.142\" style=\"background-color: rgba(218, 232, 245, 0.5);\">xxbos</span> <span title=\"0.253\" style=\"background-color: rgba(197, 218, 238, 0.5);\">▁tipos</span> <span title=\"0.087\" style=\"background-color: rgba(229, 239, 249, 0.5);\">▁de</span> <span title=\"0.117\" style=\"background-color: rgba(224, 236, 247, 0.5);\">▁te</span> <span title=\"0.099\" style=\"background-color: rgba(227, 238, 248, 0.5);\">:</span> <span title=\"0.237\" style=\"background-color: rgba(200, 220, 239, 0.5);\">▁extraño</span> <span title=\"0.060\" style=\"background-color: rgba(235, 243, 251, 0.5);\">▁</span> <span title=\"0.064\" style=\"background-color: rgba(234, 242, 250, 0.5);\">xxnl</span> <span title=\"0.191\" style=\"background-color: rgba(209, 226, 242, 0.5);\">▁te</span> <span title=\"0.206\" style=\"background-color: rgba(206, 224, 241, 0.5);\">:</span> <span title=\"0.320\" style=\"background-color: rgba(176, 209, 231, 0.5);\">▁la</span> <span title=\"1.000\" style=\"background-color: rgba(8, 48, 107, 0.5);\">▁cre</span> <span title=\"0.401\" style=\"background-color: rgba(147, 196, 222, 0.5);\">iste</span></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from fastai.text.models.awd_lstm import TextClassificationInterpretation\n",
    "from matplotlib.pyplot import cm\n",
    "txt_ci = TextClassificationInterpretation.from_learner(learn)\n",
    "(_,idxs) = interp.top_losses(5)\n",
    "for idx in to_np(idxs):\n",
    "    print(\"-\"*10)\n",
    "    txt_ci.show_intrinsic_attention(df_train.iloc[idx,1],cmap=cm.Blues) #textify breaks?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save(\"haha_20190516_tweet_vocab_labelsmoothing_lmAndclas\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate test file for submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tweet1</td>\n",
       "      <td>Historia #36.\\n*Entra corriendo y gritando al ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tweet2</td>\n",
       "      <td>estoy tomando helado en el centro y pedi tiram...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tweet3</td>\n",
       "      <td>— ¿De 15 o 20 centímetros?\\n\\n— Ven y averígua...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tweet4</td>\n",
       "      <td>Que fea soy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tweet5</td>\n",
       "      <td>#OMG, VAMOS EN RETROCESO RESULTE CON 40 SEGUID...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id                                               text\n",
       "0  tweet1  Historia #36.\\n*Entra corriendo y gritando al ...\n",
       "1  tweet2  estoy tomando helado en el centro y pedi tiram...\n",
       "2  tweet3  — ¿De 15 o 20 centímetros?\\n\\n— Ven y averígua...\n",
       "3  tweet4                                        Que fea soy\n",
       "4  tweet5  #OMG, VAMOS EN RETROCESO RESULTE CON 40 SEGUID..."
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.read_csv('./data/haha_2019_test.csv')\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6000, 2)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Default Rules:\n",
      " ['fixup', 'replace_rep', 'replace_wrep', 'deal_caps', 'spec_add_spaces', 'rm_useless_spaces', 'sub_nl'] \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## apply the rules\n",
    "raw_text = test_df.loc[:,'text']\n",
    "\n",
    "print(\"Default Rules:\\n\",[x.__name__ for x in default_rules],\"\\n\\n\")\n",
    "for rule in default_rules:\n",
    "    raw_text = raw_text.apply(lambda x: rule(str(x)))    \n",
    "test_df['new_text'] =  raw_text #tokenizer adds xxbos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_tst = TextClasDataBunch.from_df(PATH, df_train, df_valid, test_df,\n",
    "                               tokenizer=mycust_tok, vocab=sp_vocab,\n",
    "                               text_cols='new_text', label_cols='is_humor')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.data = data_tst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds_t,y_t,losses_t = learn.get_preds(DatasetType.Train,with_loss=True)\n",
    "interp_t = ClassificationInterpretation(learn, preds_t, y_t, losses_t)\n",
    "interp_t.plot_confusion_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds_tst,y_tst,losses_tst = learn.get_preds(DatasetType.Test,with_loss=True, ordered=True)\n",
    "interp_t = ClassificationInterpretation(learn, preds_tst, y_tst, losses_tst)\n",
    "interp_t.plot_confusion_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text ▁ xxbos ▁¿ le ▁pusiste ▁a ▁tu ▁hijo ▁bec kh am ▁hace ▁16 ▁años ▁y ▁ahora ▁odia ▁el ▁fútbol ▁, ▁a ▁la ▁humanidad ▁y ▁a ▁ti ... ▁? ▁. ¡ normal ▁, ▁ya ▁hay ▁que ▁ser ▁gilipollas ▁! ▁."
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_tst.test_ds.get(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>text</th>\n",
       "      <th>new_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tweet1</td>\n",
       "      <td>Historia #36.\\n*Entra corriendo y gritando al ...</td>\n",
       "      <td>historia # 36.xxnl *entra corriendo y gritando...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tweet2</td>\n",
       "      <td>estoy tomando helado en el centro y pedi tiram...</td>\n",
       "      <td>estoy tomando helado en el centro y pedi tiram...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tweet3</td>\n",
       "      <td>— ¿De 15 o 20 centímetros?\\n\\n— Ven y averígua...</td>\n",
       "      <td>— ¿de 15 o 20 centímetros ? xxnl xxnl — ven y ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tweet4</td>\n",
       "      <td>Que fea soy</td>\n",
       "      <td>que fea soy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tweet5</td>\n",
       "      <td>#OMG, VAMOS EN RETROCESO RESULTE CON 40 SEGUID...</td>\n",
       "      <td># xxup omg , xxup vamos en xxup retroceso xxu...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id                                               text  \\\n",
       "0  tweet1  Historia #36.\\n*Entra corriendo y gritando al ...   \n",
       "1  tweet2  estoy tomando helado en el centro y pedi tiram...   \n",
       "2  tweet3  — ¿De 15 o 20 centímetros?\\n\\n— Ven y averígua...   \n",
       "3  tweet4                                        Que fea soy   \n",
       "4  tweet5  #OMG, VAMOS EN RETROCESO RESULTE CON 40 SEGUID...   \n",
       "\n",
       "                                            new_text  \n",
       "0  historia # 36.xxnl *entra corriendo y gritando...  \n",
       "1  estoy tomando helado en el centro y pedi tiram...  \n",
       "2  — ¿de 15 o 20 centímetros ? xxnl xxnl — ven y ...  \n",
       "3                                        que fea soy  \n",
       "4   # xxup omg , xxup vamos en xxup retroceso xxu...  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_cols = ['id','is_humor','funniness_average'] #need value in last col.  Try 3?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_df = test_df['id'].to_frame().copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_df['is_humor'] = to_np(preds_tst.argmax(dim=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_df['funniness_average'] = 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>is_humor</th>\n",
       "      <th>funniness_average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tweet1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tweet2</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tweet3</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tweet4</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tweet5</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  is_humor  funniness_average\n",
       "0  tweet1         1                3.0\n",
       "1  tweet2         0                3.0\n",
       "2  tweet3         1                3.0\n",
       "3  tweet4         0                3.0\n",
       "4  tweet5         0                3.0"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_df.to_csv('sub0520_1.csv',header=True,index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id,is_humor,funniness_average\r\n",
      "tweet1,1,3.0\r\n",
      "tweet2,0,3.0\r\n",
      "tweet3,1,3.0\r\n",
      "tweet4,0,3.0\r\n",
      "tweet5,0,3.0\r\n",
      "tweet6,0,3.0\r\n",
      "tweet7,0,3.0\r\n",
      "tweet8,0,3.0\r\n",
      "tweet9,0,3.0\r\n"
     ]
    }
   ],
   "source": [
    "!head sub0520_1.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  adding: sub0520_1.csv (deflated 84%)\r\n"
     ]
    }
   ],
   "source": [
    "!zip  sub0520_1.zip sub0520_1.csv "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "!cp sub0520_1.zip /mnt/awcnas4_AWX/tmp/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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