(Model 8) word_Embedding_Chars_representation_Object_Value .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "import pickle\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DS = pd.read_csv(\"DataAnnotatedSubset150SentenceLength.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "DS['tag'].fillna('text', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>text_ID</th>\n",
       "      <th>word</th>\n",
       "      <th>tag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>47114</td>\n",
       "      <td>70</td>\n",
       "      <td>text</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1917</th>\n",
       "      <td>47114</td>\n",
       "      <td>kg.</td>\n",
       "      <td>text</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1918</th>\n",
       "      <td>47114</td>\n",
       "      <td>Pikkus</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1919</th>\n",
       "      <td>47114</td>\n",
       "      <td>157</td>\n",
       "      <td>value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>47114</td>\n",
       "      <td>cm.</td>\n",
       "      <td>text</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      text_ID    word     tag\n",
       "1916    47114      70    text\n",
       "1917    47114     kg.    text\n",
       "1918    47114  Pikkus  object\n",
       "1919    47114     157   value\n",
       "1920    47114     cm.    text"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DS[DS[\"word\"] == \"Pikkus\"]\n",
    "DS[1916:1921]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27741"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words = list(set(DS[\"word\"].values))\n",
    "n_words = len(words)\n",
    "n_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags = list(set(DS[\"tag\"].values))\n",
    "n_tags = len(tags); n_tags\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SentenceGetter(object):\n",
    "    \n",
    "    def __init__(self, data):\n",
    "        self.n_sent = 1\n",
    "        self.data = data\n",
    "        self.empty = False\n",
    "        agg_func = lambda s: [(w, t) for w, t in zip(s[\"word\"].values.tolist(),\n",
    "                                                           s[\"tag\"].values.tolist())]\n",
    "        self.grouped = self.data.groupby(\"text_ID\").apply(agg_func)\n",
    "        self.sentences = [s for s in self.grouped]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "getter = SentenceGetter(DS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentences = getter.sentences\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Pt.', 'text'),\n",
       " ('tehtud', 'text'),\n",
       " ('3', 'text'),\n",
       " ('plasmaferees,', 'text'),\n",
       " ('talus', 'text'),\n",
       " ('hästi.', 'text'),\n",
       " ('Eemaldatud', 'text'),\n",
       " ('700', 'text'),\n",
       " ('ml', 'text'),\n",
       " ('plasmat.', 'text'),\n",
       " ('RR', 'object'),\n",
       " ('112/60', 'value'),\n",
       " ('mmHg.', 'text'),\n",
       " ('Tgasi', 'text'),\n",
       " ('22.10.09.', 'text')]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentences[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    157280.000000\n",
       "mean          6.230697\n",
       "std           4.330117\n",
       "min           1.000000\n",
       "25%           3.000000\n",
       "50%           5.000000\n",
       "75%           9.000000\n",
       "max          54.000000\n",
       "Name: word, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# np.median([len(s) for s in sentences])\n",
    "DS.word.apply(lambda x: len(x)).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LengthsList = []\n",
    "for i in range(len(sentences)):\n",
    "    LengthsList.append(len(sentences[i]))\n",
    "np.max(LengthsList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Kokkuvõte:', 'text'),\n",
       " ('<ANONYM', 'text'),\n",
       " ('id=\"0\"', 'text'),\n",
       " ('type=\"per\"', 'text'),\n",
       " ('morph=\"_A_', 'text'),\n",
       " ('sg', 'text'),\n",
       " ('n\"/>', 'text'),\n",
       " ('nähtava', 'text'),\n",
       " ('patoloogiata.', 'text'),\n",
       " ('Kokkuvõte:', 'text'),\n",
       " ('<ANONYM', 'text'),\n",
       " ('id=\"0\"', 'text'),\n",
       " ('type=\"per\"', 'text'),\n",
       " ('morph=\"_A_', 'text'),\n",
       " ('sg', 'text'),\n",
       " ('n\"/>', 'text'),\n",
       " ('nähtava', 'text'),\n",
       " ('patoloogiata.', 'text'),\n",
       " ('Kokkuvõte:', 'text'),\n",
       " ('<ANONYM', 'text'),\n",
       " ('id=\"0\"', 'text'),\n",
       " ('type=\"per\"', 'text'),\n",
       " ('morph=\"_A_', 'text'),\n",
       " ('sg', 'text'),\n",
       " ('n\"/>', 'text'),\n",
       " ('nähtava', 'text'),\n",
       " ('patoloogiata.', 'text'),\n",
       " ('Kokkuvõte:', 'text'),\n",
       " ('<ANONYM', 'text'),\n",
       " ('id=\"0\"', 'text'),\n",
       " ('type=\"per\"', 'text'),\n",
       " ('morph=\"_A_', 'text'),\n",
       " ('sg', 'text'),\n",
       " ('n\"/>', 'text'),\n",
       " ('nähtava', 'text'),\n",
       " ('patoloogiata.', 'text'),\n",
       " ('Kokkuvõte:', 'text'),\n",
       " ('<ANONYM', 'text'),\n",
       " ('id=\"0\"', 'text'),\n",
       " ('type=\"per\"', 'text'),\n",
       " ('morph=\"_A_', 'text'),\n",
       " ('sg', 'text'),\n",
       " ('n\"/>', 'text'),\n",
       " ('nähtava', 'text'),\n",
       " ('patoloogiata.', 'text'),\n",
       " ('Kokkuvõte:', 'text'),\n",
       " ('<ANONYM', 'text'),\n",
       " ('id=\"0\"', 'text'),\n",
       " ('type=\"per\"', 'text'),\n",
       " ('morph=\"_A_', 'text'),\n",
       " ('sg', 'text'),\n",
       " ('n\"/>', 'text'),\n",
       " ('nähtava', 'text'),\n",
       " ('patoloogiata.', 'text')]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentences[6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_len = 150\n",
    "max_len_char = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "tag2idx = {t: i + 1 for i, t in enumerate(tags)}\n",
    "tag2idx[\"PAD\"] = 0\n",
    "idx2tag = {i: w for w, i in tag2idx.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "104\n"
     ]
    }
   ],
   "source": [
    "chars = set([w_i for w in words for w_i in w])\n",
    "n_chars = len(chars)\n",
    "print(n_chars)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "char2idx = {c: i + 2 for i, c in enumerate(chars)}\n",
    "char2idx[\"UNK\"] = 1 #For predictions\n",
    "char2idx[\"PAD\"] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx2char = {i: w for w, i in char2idx.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_char = []\n",
    "for sentence in sentences:\n",
    "    sent_seq = []\n",
    "    for i in range(max_len):\n",
    "        word_seq = []\n",
    "        for j in range(max_len_char):\n",
    "            try:\n",
    "                word_seq.append(char2idx.get(sentence[i][0][j]))\n",
    "            except:\n",
    "                word_seq.append(char2idx.get(\"PAD\"))\n",
    "        sent_seq.append(word_seq)\n",
    "    X_char.append(np.array(sent_seq))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = [[tag2idx[w[1]] for w  in s] for s in sentences]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13643"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sentences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = pad_sequences(maxlen=max_len, sequences=y, value=tag2idx[\"PAD\"], padding='post', truncating='post')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_char_tr, X_char_te, y_tr,  y_te= train_test_split(X_char, y, test_size=0.2, random_state=2018)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hidden_nodes = int(2/3 * (max_len * max_len_char))\n",
    "# print(f\"The number of hidden nodes is {hidden_nodes}.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Model, Input\n",
    "from keras.layers import LSTM, Embedding, Dense, TimeDistributed, Dropout, Conv1D\n",
    "from keras.layers import Bidirectional, concatenate, SpatialDropout1D, GlobalMaxPooling1D\n",
    "\n",
    "\n",
    "# input and embeddings for characters\n",
    "char_in = Input(shape=(max_len, max_len_char,))\n",
    "emb_char = TimeDistributed(Embedding(input_dim=n_chars + 2, output_dim=20,\n",
    "                           input_length=max_len_char, mask_zero=True))(char_in)\n",
    "# character LSTM to get word encodings by characters\n",
    "char_enc = TimeDistributed(LSTM(units=20, return_sequences=False,\n",
    "                                recurrent_dropout=0.5))(emb_char)\n",
    "\n",
    "# main LSTM\n",
    "x = SpatialDropout1D(0.3)(char_enc)\n",
    "main_lstm = Bidirectional(LSTM(units=20, return_sequences=True,\n",
    "                               recurrent_dropout=0.6))(x)\n",
    "out = TimeDistributed(Dense(n_tags + 1, activation=\"softmax\"))(main_lstm)\n",
    "\n",
    "model = Model( char_in, out)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         (None, 150, 10)           0         \n",
      "_________________________________________________________________\n",
      "time_distributed_4 (TimeDist (None, 150, 10, 20)       2120      \n",
      "_________________________________________________________________\n",
      "time_distributed_5 (TimeDist (None, 150, 20)           3280      \n",
      "_________________________________________________________________\n",
      "spatial_dropout1d_2 (Spatial (None, 150, 20)           0         \n",
      "_________________________________________________________________\n",
      "bidirectional_2 (Bidirection (None, 150, 40)           6560      \n",
      "_________________________________________________________________\n",
      "time_distributed_6 (TimeDist (None, 150, 4)            164       \n",
      "=================================================================\n",
      "Total params: 12,124\n",
      "Trainable params: 12,124\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\", metrics=[\"acc\"])\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.utils.vis_utils import plot_model\n",
    "plot_model(model, to_file='word_Embedding_Chars_representation_Object_Value_Model_plot.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Kasutaja\\Anaconda3\\envs\\myEnv\\lib\\site-packages\\tensorflow_core\\python\\framework\\indexed_slices.py:433: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 9822 samples, validate on 1092 samples\n",
      "Epoch 1/10\n",
      "9822/9822 [==============================] - 67s 7ms/step - loss: 0.0332 - acc: 0.8945 - val_loss: 0.0136 - val_acc: 0.9447\n",
      "Epoch 2/10\n",
      "9822/9822 [==============================] - 70s 7ms/step - loss: 0.0105 - acc: 0.9658 - val_loss: 0.0075 - val_acc: 0.9775\n",
      "Epoch 3/10\n",
      "9822/9822 [==============================] - 68s 7ms/step - loss: 0.0065 - acc: 0.9800 - val_loss: 0.0042 - val_acc: 0.9872\n",
      "Epoch 4/10\n",
      "9822/9822 [==============================] - 67s 7ms/step - loss: 0.0046 - acc: 0.9859 - val_loss: 0.0034 - val_acc: 0.9898\n",
      "Epoch 5/10\n",
      "9822/9822 [==============================] - 70s 7ms/step - loss: 0.0039 - acc: 0.9878 - val_loss: 0.0030 - val_acc: 0.9906\n",
      "Epoch 6/10\n",
      "9822/9822 [==============================] - 68s 7ms/step - loss: 0.0035 - acc: 0.9886 - val_loss: 0.0028 - val_acc: 0.9906\n",
      "Epoch 7/10\n",
      "9822/9822 [==============================] - 66s 7ms/step - loss: 0.0033 - acc: 0.9891 - val_loss: 0.0027 - val_acc: 0.9914\n",
      "Epoch 8/10\n",
      "9822/9822 [==============================] - 67s 7ms/step - loss: 0.0031 - acc: 0.9899 - val_loss: 0.0025 - val_acc: 0.9913\n",
      "Epoch 9/10\n",
      "9822/9822 [==============================] - 66s 7ms/step - loss: 0.0029 - acc: 0.9903 - val_loss: 0.0024 - val_acc: 0.9922\n",
      "Epoch 10/10\n",
      "9822/9822 [==============================] - 67s 7ms/step - loss: 0.0028 - acc: 0.9901 - val_loss: 0.0023 - val_acc: 0.9920\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(np.array(X_char_tr).reshape((len(X_char_tr), max_len, max_len_char)),\n",
    "                    np.array(y_tr).reshape(len(y_tr), max_len, 1),\n",
    "                    batch_size=32, epochs=10, validation_split=0.1, verbose=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "hist = pd.DataFrame(history.history)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4g+joaMaOHctzzz2Hn59fpc4fPXo0+/btY+jQoU7ntGjRguXLl/PFF1/QsWNHnn/++WqV74MPPiAiIoKBAwfSt29fTCYTgwcPdryu1+uJj4/n4MGDREZG8tBDDzF16tQrFol/4403+PHHH2nTpo1jbF5JLVu2ZMOGDSQlJdG1a1fuvPNOoqOjHUuoVVdlyqjVatmwYQP9+vVj/PjxREREMHbsWDIzMwH75/TDDz8QFhbGiBEj6NChA5MnT+bChQsAuLu7s2LFCo4fP06XLl144okneP311ytVvscff5wRI0YwcuRIunbtyv79+3nhhRec0gwdOpQvvviCLVu20K1bN3r27Mn//vc/R88H2L+Qxo8fT1FRkQy1EPWStP2XSNt/SU21/aWZMmUK//rXv5g+fTqdOnXijTfeIDY2lvvvvx+w//G0detWhg4dSnh4OA8//DAPPvggU6dOBcDLy4tZs2bRo0cPbrjhBr7++mvWrFlDu3btXF7W+k5RKzswRzQ4vXr1ol27do5lYYS4FkyePJmffvqJXbt21XVRhLgmSdsvROXIE/caib1793Lo0CF69OhBYWEh7733Hj/99BMzZsyo66IJUSnZ2dns3buXuLg4Fi9eXNfFEeKaIG2/ENUnQXIjMn/+fI4ePQrYx5d+9dVX9OnTp45LJUTl3Hrrrezfv5+RI0fKhD0hqkDafiGqR4ZbCCGEEEIIUYJM3BNCCCGEEKIECZKFEEIIIYQoQYJkIYQQQgghSqi3E/cuf6pXfRcUFFTlp/00BI2x3o2xztA4613dOrdo0aIGSlP/SZtd/zXGejfGOkPjrHdNtNnSkyyEEEIIIUQJEiQLIYQQQghRggTJQgghhBBClFBvxyQLIYQQQtRXqqpSWFiIzWZDUZS6Lo6TM2fOcOHChbouRq0qr86qqqLRaPDw8KjSZyVBshBCCCFEFRUWFqLX69Hp6l8opdPp0Gq1dV2MWlVRnS0WC4WFhRgMhkrnKcMthBBCCCGqyGaz1csAWZROp9Nhs9mqdI4EyUIIIYQQVVTfhliIilX1M5MgWQghhBDiGpORkUH//v3p378/Xbp0oWvXro79oqKiSuUxZcoUkpOTy02zbNkyVq1a5YoiM2zYMA4ePOiSvGqD3CcQQgghhLjGBAQE8M033wAwd+5cmjRpwoQJEwD70AKLxYKqqo5Ja6WJjY2t8Dpjx451WZmvNRIkCyHqnqqi5OejycpCycxEk5GBJisLTW4uqkYDWi1otagXf1dqW6e7dG7xtk5nT3P5dmnnlvGFIlwjJ0dh3ToPoqOLCAuz1nVxhGhQUlJSePDBB+nWrRt79+5l+fLlxMbGcuDAAQoLC7n99tuZMmUKYO/ZffXVV4mIiCAyMpJRo0axefNmDAYDcXFxBAUF8frrrxMQEMBDDz3EsGHD6N69O9u2bSMnJ4d58+bRrVs38vPz+cc//kFKSgpGo5GUlBRmz55N586dyyznZ599xqJFi1BVlf79+/Pcc89hsViYMmUKhw8fRlVV7r//fh588EHeffdd/ve//6HT6ejQoQNvvvlmrbyXEiQLIVyrqAhNZqb9JyvLaVsp47gmMxOlkrcHa4tqNMJ339V1MRqkwkKFf/7Tn+nTswkLy6vr4gjR4CQmJjJv3jxef/11AJ577jn8/f2xWCyMGDGCIUOGYDQanc7JycmhZ8+ePP/887z88st88sknTJo06Yq8VVXlq6++YuPGjbzxxht89NFHvPfeezRt2pTFixdz6NAhBg4cWG75Tp06xaxZs1i/fj3e3t7cc889fPPNNwQGBpKZmcm3334LQHZ2NgBvvfUWO3bswM3NzXGsNkiQLER9YLWiZGejuexHycpy2sdisfeIXt7zWUrvaJV6Wyvqeb3Yq6okJ+P++++VC37zyg56VDc3bP7+2Pz8sPn7YwkNdWyr/v5Or9n8/bF5eaGoKlit9vfo4m8sFhSbzXnbYrGnKd622VAuHrt825FHBduG666rvc+/kWna1Iafn43ERPkKEg3DtGk+HD6sd2meHTuaeeWVnGqd27ZtW7p06eLY//zzz/n444+xWq2kpqaSmJh4RZDs4eFB3759Abj++uvZsWNHqXkPGjQIgMjISE6ePAnAzp07eeyxxwDo1KkTJpOp3PLt3buXm2++mYCAAMDeo71jxw4mTpzIb7/9xrRp0+jbty+9e/cGwGg08vjjj3PrrbdWGIC7krRQQriKxeIc3Obk2APfy4JdRyB88Zhj//z5crNW3d1RdTqnYE6p4lI2Vyvw8vJoNNh8fe2BrZ8ftubNsUREOAe4xdsBAY50qqcnXCMzwt2DgiAtra6L0SApCkREmElIcG1QIYSw8/T0dGwfO3aMJUuW8NVXX+Hr68vjjz9e6kM33NzcHNtarRartfShUMXpLk+jqmqVyldW+oCAADZt2sTmzZtZunQp69atY9asWfzvf//jp59+YuPGjfz3v/9l8+bNtbIOtATJov6x2VBycsrsUVVyc6GK/yFdRavR4Jea6hTsOrbL6UEFsHl4oPr5YfP1xebri7VFC2wdOtj3/fxQLx63+fra0/n4OPYpbfHzqvawltJrWmpPaok8UFW8W7QgS6t1BL+qr6+M2xVXxWi0sHq1AVW9Zv5uEqJM1e3xrQ25ubl4eXnh7e3NmTNn+P777/nb3/7m0mt0796dL7/8kh49enDkyBESExPLTR8VFcWrr75KRkYGPj4+fP7550yYMIH09HTc3d0ZOnQorVu35tlnn8VqtXL69Gn+8pe/0L17d1atWkVBQQFeXl4urUNpJEgWNcNqdQp0Swa7ZfaoZmej5OTYb7GXQdVo6i5Ac3PD3cfH3kvq64ulVSvUzp1LDXQdwW5xoOvu7tqyKIp9qIVOx+XvVk38+eAVFIS5gl5Vq9U+1rT4p6DAed9+TLnimPOP8zlFRQpaLej1aqm/dTr14ltQ/Lu0Y2X9vrSt1aro9c6/W7ZUkBEXNcdkMnP+fBNOn9bQokXt3hURojGJjIwkPDycvn370rp1a7p16+bya4wbN45//OMfxMTE0LlzZ0wmEz4+PmWmb9GiBU899RQjRoxwTNyLiYnhwIED/POf/0RVVRRF4V//+hcWi4XHHnuMvLw8bDYbjz32WK0EyACKWtU+8lpy6tSpui5CpQUFBZHWSG7LKnl56H/9Ff3+/Xjl5VGYmlpqoKvJKf+vatXd/VIgeXlgWTLQLKWHVfXwqLOup4b0WVsskJOjITtbufjbvp2drXEct28rWK0e5OQUlRvoms3V/0w8PGx4eICHh4qHh4rBYP+t16tYrQpWK5jNxb/BalWwWMBiKfv31WrfXmXr1tNVPq9FixZXfe1rUVXb7J9+cuPvfw/io4/S+dvfrrz1W5Ma0v/jqmiM9a7JOufn5zsNa6hPipeAqy0WiwWLxYKHhwfHjh3jvvvu48cff6zVJxJWps6lfWbltdnSkyzKZrOhPXYMtz17HD+6I0ccY2FVDw88Lg9og4OxmEwV9qiWOXxAVImqQl6eUiKwLSvovTL4zcsrvzdep1Px9bXh46Pi4wM6nQYPDxVvb5sjmC0Z1F76oYLXL/24u7v+b57ikSgVBdL236W/1rRp2b0g4uqZTPYvs6NHdbUeJAshXCsvL4+7777bEaS+/vrrDeKR3dd+DYTLKFlZuO3bh744KN67F01WFgA2Hx+KbryRwn/8g6KoKIq6dCHQaGx0vRLVoapQVAQFBfZe2Px8xbFdUKC54vjlr1++ff78pWC3OBC2WsuPLr29bfj42PD1tQe8bdpY8PGxb9t/1IuvX7ltMKiO4PVa64G6bCQKlwagVO2mWVCQKvP2alBAgI2gICuJiTJ5T4hrna+vL19//XVdF8PlJEhurKxWdEePOnqI9Xv2oL/4aEpVUbBERFAwZAhFUVGYo6KwhIU1molaViuO3tbin6ws+77NpiE93bvMQPbyn8uPVxTMlqQo9iDVYFDx9FQv9uCqBATYaNeu/EDX3vtr7wGuhcm/QlSbyWSRZeCEEPWWtE6NhObcOfR79+L2yy/2oHjfPjT5+QBYAwIwd+1KwfDh9qD4hhtQvb3ruMRXp3i8bXFwW9z7mpWlcdq3B8DO++fPV/THgPfFIQQ2p0DWYLAHqMHBlwLckq+Xt315fnU47FqIWmMymfnkE09stkbzN7gQ4hoiQXJDVFSE/tAhRw+x25496E6cAEDV6TB36kT+3XdjjoqiKCoKa5s29T4iU1U4fVpDcrKelBTtFYFtyf3c3PK/cT08Lu+JtXHddVYiIuwPOCjujS3+8fO7tN+2bQD5+WnyhS6EC5hMFvLzNfz5p5ZWreTx1EKI+kWC5GudqqI5dco+bKK4l/jgQZSLC4Vbr7uOoqgo8saMwdy1K0WdO9frSXMWC5w4oSU5WUdiop6kJB3JyfafkoGvh4dzABsSYqVjx+LA1jnYvTydr699JYXq8PKCwkIXVFQI4Zi8l5CgkyBZCFHvSJB8jVIKCvB59VU8vv4abWoqYF9toigykryxY+2T66KisNXT5agKC+HYMR1JSTqSki4Fw8eO6SgqutSrHRxsJTzcwl135RMWZiE83EJoqAV/f5vLlx0WQtQuo9EMQEKCnpgYWeFCiKr4+9//zqRJk5weDLJ48WKOHTvG7NmzyzwvPDycpKQkUlNTefHFF1m8eHGpeb/44ovccMMNZeazePFiRo4cieFix9uoUaNYsGABvr6+1a8UMHfuXJo0acKECROuKh9XkCD5GqQ9eRL/8ePRHzpE4W23caFHD8xRUZg7dIDLHitZH5w/r1wMhHWO3uHkZB0nTmix2ezBsEaj0rq1lbAwC337FjqC4bAw+wQ1IUTD5OurEhxsJSFBvoqEqKo77riDzz//3ClI/vzzz3nxxRcrdX5wcHCpAXJlLVmyhOHDhzuC5A8++KDaedVX0jJdY9y2bsV/4kQUq5WM5cu50K9fXRcJVYW0NI1TMFzcO5yaeml5BTc3ldBQC507m7nzzgLCw82OnuHqDn8QQlzbTCazBMlCVMOQIUOYNWsWFy5cwN3dnZMnT3LmzBm6d+9OXl4eo0aNIjs7G4vFwjPPPMOtt97qdP7JkycZM2YMmzdvpqCggCeffJKkpCTCwsIovGxc4bPPPsuvv/5KYWEhQ4YM4amnnmLp0qWcOXOGESNG4O/vz8qVK+nRowfr168nICCAd955hxUrVgBw77338tBDD3Hy5ElGjhxJ9+7d2b17N8HBwbz33nuOILs0Bw8e5Nlnn6WwsJA2bdowd+5c/Pz8WLp0KR988AE6nY7w8HDeeusttm/fzr/+9S8AFEVh1apVV/1kPmmZrhWqSpN33sFnxgws4eFkLFmCNTS0Topy/rzCZ58Z+O03LQcOBJKUpCcr69J44SZNbISHW/jLXy4QHm65+GOmdWsrDWBtcSGECxmNFj74oAlWK7JkoRBVEBAQQJcuXfj++++59dZb+fzzz7n99ttRFAV3d3eWLl2Kt7c3GRkZDB06lAEDBqCUMUn//fffx2AwsGnTJg4fPszAgQMdr02dOhV/f3+sVit33303hw8f5sEHH+Tdd98lPj6egIAAp7z279/Pp59+ytq1a1FVldtuu42bbroJX19fUlJSWLhwIbNnz+aRRx5h3bp1DB8+vMw6PvHEE/z73//mpptuYvbs2cybN49XXnmFhQsX8tNPP+Hu7k52djYAixYt4rXXXqNbt27k5eXh7oIxmRKyXAOU/Hx8n3oKz88/p2DIELJiY1GbNKn1cmRkKCxd6sV77zUhJ0dDUJBK+/Zw220FjmA4LMxMixa2+r5YhhCinoiIMFNYqHDihJZ27WTynrg2+Uybhv7wYZfmae7YkZxXXik3zbBhw/j8888dQfK8efMAUFWVmTNnsmPHDhRFITU1lXPnztGsWbNS89mxYwfjxo0DoGPHjnTo0MHx2pdffslHH32E1WrlzJkzJCUl0bFjxzLLtHPnTgYOHOh4/POgQYPYsWMHAwYMoFWrVnTu3BmA66+/npMnT5aZT05ODtnZ2dx0000AjBgxgkceeQSADh06MGnSJAYOHOgI6Lt378706dO58847GTRoULmPm64sCZLrOe3x4wQ8+CC6o3tub5EAACAASURBVEfJef55cidOrPXl2s6c0fDOO1588IEn+fkaBg8u4PHHc+nb15e0tPRaLYsQomExGotXuNBLkCxEFQ0cOJDp06dz4MABCgsLiYyMBOCzzz4jPT2d9evXo9fr6dGjBxculD85trRe5hMnTvDOO+/w1Vdf4efnxxNPPOE0FKM0qlr2XKLLe3e1Wm2FeZXl/fff5+eff2bjxo288cYbfPfdd0yePJk+ffqwefNmhg4dyooVKwgLC6tW/sUkSK7H3L//Hv/HHgMg48MPuXDZ4PzacPKklkWLvFixwhOzGYYNK2DSpFzHsk1CCHG1LgXJOi67wyvENaWiHt+a0qRJE2666SaefPJJhg0bdqk8OTkEBQWh1+vZtm0bf/zxR7n59OjRg9WrV3PzzTdz9OhRjhw5AsD58+cxGAz4+Phw7tw5vvvuO0fPrpeXF7m5uVcMt+jZsydTpkxh0qRJqKrK119/zfz586tcNx8fH3x9fdmxYwc9evTgs88+o2fPnthsNk6dOsXNN99M9+7dWbNmDXl5eaSmptKhQwc6dOjAL7/8QnJysgTJDZKq4rVwId4zZ2KJiCBj6VL7Az9qSXKyloULvVm1yoCiwF135TNxYi5t20ovjxDCtby8VFq2lMdTC1Fdw4YNY/z48bz11luOY8OHD2fkyJEMGjSITp06VRgsjh49mieffJKYmBg6duxIly5dAOjUqROdO3emT58+tG7dmm7dujnOuf/++xk5ciTNmjVj5cqVjuORkZGMGDGCIUOGAPaJe507dy53aEVZ3njjDcfEvdatWzNv3jysViuPP/4458+fR1VVHnroIXx9fZkzZw7btm1Do9FgNBrp06dPla9XkqKW1y9eh06dOlXXRai0oKAg0tLSXJKXkpeH35QpGL76ivw77iB7zhzUi+N6atqhQzrefNObtWs9cHdXuf/+fCZMyKVFC1up6V1Z72tFY6wzNM56V7fOrhgHdy26mjZ71KgATp/WsmnTOReWqGyN8d8zNM5612Sd8/PzHeNu6xudTofF0rju+lamzqV9ZuW12fKnez2iPXaMgPHj0SUlkf3ii+Q98kitjD/es0fP/PnefPONB15eNh57LJeHHsojKKj04FgIIVzJZLLw44/uWCzICjhCiHpDmqN6wn3TJvwffxxVqyX9f/+j6K9/rdHrqSps3+7G/Pne/PijO35+Np56KocHHsjDz69e3lwQQpSwb98+4uLisNls9OvXz2lMItgn0MTFxbF3717c3d2ZOHEioaGhFBUV8dJLL2GxWLBarfTs2ZO77roLgNzcXGJjYzl37hxNmzZlypQpV73WaEVMJjNFRQq//64jLKxx9X4JIeovTcVJRI2y2fB64w0Cxo7F0ro1aevX12iArKqwaZM7d9wRxF13BZGYqOPFF7PZufMMU6bkSoAsxDXCZrOxdOlSnn/+eWJjY0udnLN3715SU1OZP38+Dz/8MEuWLAFAr9fz0ksvMXv2bGbNmsW+fftITEwEYM2aNURGRjJ//nwiIyNZs2ZNjdeleDLw0aPSbyOEqD8kSK5Dyvnz+D/0ED6zZ1Nw552krVmDtVWrGrmWzQZr13owcGAQY8YEkpqqYcaMLH766QwTJuTRpIkEx0JcS5KTkwkODqZ58+bodDp69erFrl27nNLs3r2bW265BUVRMBqN5OXlkZmZiaIoeFx8zKXVasVqtTqWf9q1axe9e/cGoHfv3lfkWRPCwy0oiiqT98Q1pZ5O6RLlqOpnJi1SHdElJ+P/4IPoUlLIfuUV8saNq5HxxxYLrFljYMECL5KS9ISGWpg3L5P/+78C9HqXX04IUUsyMjIIDAx07AcGBpKUlHRFmqCgIKc0GRkZ+Pv7Y7PZmDp1Kqmpqdx6662Eh4cDkJ2djb+/PwD+/v7k5OTUeF0MBpU2bawkJEijJK4dGo0Gi8WCTgbSXxMsFgsaTdX6huWTrQMeGzbgN3kyqrs76Z98QlGvXi6/xoUL8Omnnixa5MWJEzo6dDCzaFEGt91WKI9+FaIBKK1HpOTDAMpLo9FomD17Nnl5ecyZM4cTJ07QunXrSl9/06ZNbNq0CYCZM2c6BePV0bmzhuRkj6vOpzJ0Ol2tXKe+aYz1rsk6q6pKRkZGvVxFwmazNbqe7orqrNfrad68eZmP5i5NpYLkiiaH5Obm8tZbb3HmzBn0ej2PPvqoo7Fdu3YtmzdvRlEUWrVqxcSJE3Fzc6t0ARsUmw3vefPwjo2l6IYbyFi8GFtIiEsvkZ+v8OGHnrzzjhepqVpuvLGI6dPT6d//gjwqWogGJDAwkPT0S0+8TE9Pd/QAX57m8uWvSkvTpEkTOnbsyL59+2jdujW+vr5kZmbi7+9PZmYmPj4+pV4/JiaGmJgYx/7VLrPVrp03X3/txalTadT0V0RjXAoNGme9a6PO2nrY8ySf9ZVUVXVqM4uVtwRchf3OlZkcsnr1atq2bcucOXOYNGkSy5YtA+y3+tavX8/MmTOZO3cuNpuN7du3V3TJBknJzibggQfwjo0l/667SFu1yqUBck6Owvz5XvTo0Yzp030JDbXwySdpfPllGgMGSIAsREPTvn17Tp8+zdmzZ7FYLGzfvp3o6GinNNHR0WzduhVVVUlMTMTT09MxhCIvLw+AoqIiDhw4QMjF9ig6OpotW7YAsGXLFqeHB9Qkk8mCxaJw7Jjc4BRC1A8VtkaXTw4BHJNDWrZs6Ujzxx9/cOeddwIQEhLCuXPnyMrKAuxBdlFREVqtlqKioit6MRoDXWIiAePGoT15kqwZM8gfM8Zl448zMjQsXtyEZcuakJOjoW/fQiZPzqBbN7NL8hdC1E9arZZx48YxY8YMbDYbffr0oVWrVmzcuBGAAQMGcOONN7Jnzx4mT56Mm5sbEydOBCAzM5OFCxc6bk/edNNNdO3aFbA/vSs2NpbNmzcTFBTEk08+WSv1MRrtbVZCgo6IiPp3+1oI0fhUGCRXZnJImzZt2LFjBxERESQnJ3Pu3DkyMjIIDQ1l6NChPProo7i5uXHDDTdwww03uL4W9ZjHunX4PfEEqqcn6fHxFHXv7pJ8VRX++18vFizworBQYfDgQiZPPk/nzvLlIkRjERUVRVRUlNOxAQMGOLYVRWH8+PFXnNemTRtmzZpVap7e3t5MmzbNtQWthPbtLWi16sXJe4W1fn0hhCipwiC5MpNDhg0bxrJly3j66adp3bo17dq1Q6PRkJuby65du1i4cCGenp7MmzePrVu3csstt1yRp6sngdSmUicGWK1op09H+/rr2Lp3x/rJJ/i4cHjFf/+rYfZsHXfeaeOll8x06KAF/FyWf2XIJJDGozHWuzHWuS55eEDbthZZBk4IUW9U2BpVZnKIp6en4zaeqqpMmjSJZs2a8euvv9KsWTPHxI8ePXqQmJhYapDs6kkgtankYHElKwv/SZNw++478u6/n+x//xvc3cFFddqwwYOpU/0ZPLiA+fMz0WhclnWVyMSAxqMx1ru6dS5vEogon8lk4cgRWQZOCFE/VDhxrzKTQ/Ly8hxLoHz77bd06NABT09PgoKCSEpK4sKFC6iq6jQ5pKHSHTlC08GDcf/xR7Jef53sWbPsAbKL7N+v57HH/OjSxcz8+VlUcck/IYSot0wmC8ePaymU0RZCiHqgwp7kykwO+fPPP1mwYAEajYaWLVsyYcIEAMLDw+nZsydTp05Fq9XStm1bp97ihsbjiy/we/JJVB8f0lauxFzij4mr9eefGsaODSAgwMZ772VgMDSuNRCFEA2b0WjGZlNITtbJ/AohRJ2r1OCviiaHGI1G5s+fX+q5d911F3fddddVFPEaYLHg8+qreL31FkXR0WS8+y62i6uBuEpursKYMYHk5yusWZNOs2Y2l+YvhBB1zWSyB8YJCXoJkoUQdU5mSFwlJTMT3ahRuG3eTN7o0WRPn46rV8K3WODRR/1JTNTxwQcZsjySEKJBatfOgl6vyuQ9IUS9IC3RVfKdNg3lxx/JmjOH/HvvrZFrvPyyD5s3ezBzZha9e1+okWsIIURdc3OD0FDLxWXghBCibsm0r6ug5ORgWLcO2wMP1FiAvHRpE+LivHjkkVxGjcqvkWsIIUR9YTRaSEiQ/hshRN2TIPkqGL78EqWwENuoUTWS/zffuPPyyz4MHFjAv/6VUyPXEEKI+sRkMnPihI78fNc8lVQIIapLguSrYFi5EnP79qguXsUC4OBBHRMn+tO5s5k338xCq3X5JYQQot4pnryXlCS9yUKIuiVBcjVpf/8d9507KRgxAhTX9nicPq1hzJhA/PxsLFuWgaenLPUmhGgcjEYzAEePSpAshKhb0gpVk+fKlaiKQv7w4Xi4MN+8PIWxYwPIzVVYvTqd5s1lqTchROPRtq0Vd3eVxEQ9UFDXxRFCNGISJFeHzYZh5UqK/vIXbC58BK3VCo895s/hw3qWL8+gY0dZ6k0I0bjodNC+vUzeE0LUPRluUQ1uO3eiO3mS/BEjXJrv9Ok+fPONB//+dzZ9+8pSb0KIxslkMkuQLISocxIkV4MhPh5bkyYUDhrksjyXLfNk6VIvxo/PZexYWepNCNF4mUwWTp3Scf68rHAhhKg7EiRXkVJQgGHtWgqHDEH19HRJnt9+686LL/oyYEAB06bJUm9CiMbNZLJP3pPeZCFEXZIguYo81q9Hk5vrsqEWhw7pePRRfzp2NLNggSz1JoQQxcvA2SfvCSFE3ZAguYoMK1diadmSop49rzqv1FT7Um/e3irLlmXQpIks9SaEEK1aWTEYbNKTLISoUxIkV4Hm9Gncf/iBguHDQXN1b11+vn2pt+xsheXL07nuOlnqTQghwN68hodbSEiQnmQhRN2RILkKPFetQrHZyP/7368qH6sVJk3y49AhPW+9lUnnzrLUmxBCXM5kspCYKD3JQoi6I0FyZakqhvh4iqKjsYaGXlVWr77qw4YNBqZPzyEmRpZ6E0KIkkwmM2fOaMnMlBUuhBB1Q4LkStLv348+KemqJ+y9/74n777rxbhxuYwbl+ei0gkhRMNiNMrkPSFE3ZIguZIM8fGo7u4UDB1a7Ty+/96dF17wpV+/Ql5+WZZ6E0KIskRE2INkmbwnhKgrEiRXRlERhjVrKBwwANXXt1pZHDmi45FH/DGZLCxalClLvQkhRDlatLDi5WWTcclCiDojQXIleHz7LdrMzGoPtTh7VsOYMQF4eaksX56Ol5cs9SaEEOVRFPsKF0ePynALIUTdkCC5EgwrV2Jt2pQLvXtX+dyCAoUHHgggI0PDsmUZtGghS70JIURlRESYpSdZCFFnJEiugCYjA49vv6XgzjtBV7XG2maDyZP9+PVXPYsWZRIZaa6hUgohRMNjNFpIT9eSliZfVUKI2ictTwUMa9agmM3VGmrx2ms+rFtn4KWXchgwQJZ6E0KIqih+PLVM3hNC1AUJkitgiI/H3KkTlo4dq3TeRx958tZbXowZk8f48bLUmxBCVJXJZL/7JkMuhBB1QYLkcugSEnDbv7/Kvchbt7rz3HO+9O1byCuvZKPIWvhCCFFlzZvb8PW1yeOphRB1QoLkchhWrkTVaikYNqzS5yQk6Hj4YX+MRvtSb1UcxiyEEOIiRQGj0SzDLYQQdUJanrJYrXiuWsWFPn2wNW1aqVPOndMwenQABoPK8uUZeHvLUm9CiJqzb98+4uLisNls9OvXj2El/qBXVZW4uDj27t2Lu7s7EydOJDQ0lLS0NBYuXEhWVhaKohATE8PgwYMB+PTTT/n222/x8fEB4N577yUqKqrW61bMZLKwdq0BVUXuygkhapUEyWVw/+EHtKmpZE+fXqn0xUu9paVpWLUqnZAQaw2XUAjRmNlsNpYuXcoLL7xAYGAgzz33HNHR0bRs2dKRZu/evaSmpjJ//nySkpJYsmQJr732GlqtllGjRhEaGkpBQQHPPvss119/vePcIUOGcPvtt9dV1ZyYTBY+/FDD2bMamjeXJTSFELVHhluUwbByJTY/Pwr7968wbfFSb/v26Vm4MIsbbpCl3oQQNSs5OZng4GCaN2+OTqejV69e7Nq1yynN7t27ueWWW1AUBaPRSF5eHpmZmfj7+xMaGgqAwWAgJCSEjIyMuqhGhYon78m4ZCFEbZMguRTK+fN4rF9Pwe23g7t7helffFHLunUGXnghh4EDC2uhhEKIxi4jI4PAwEDHfmBg4BWBbkZGBkFBQeWmOXv2LCkpKYSFhTmObdiwgaeeeopFixaRm5tbQzWoHFkGTghRV6TVKYVh7Vo0hYXk//3vFab9/HMP5szRMnJkHo88Iku9CSFqh6peOedBKTFot6I0hYWFzJ07l7Fjx+Lp6QnAgAED+PvFtm/FihW8//77TJw48Yp8Nm3axKZNmwCYOXOmUzDuSkFBEBSkcuKEF0FBBpfkqdPpaqy89VljrHdjrDM0znrXRJ0lSC6FIT4eS2go5kpMVlmzxkC7diqvvipLvQkhak9gYCDp6emO/fT0dPz9/a9Ik5aWVmoai8XC3Llz+etf/0qPHj0cafz8/Bzb/fr14/XXXy/1+jExMcTExDj2L7+Oq4WHB7Jvn+KyawQFBdVoeeurxljvxlhnaJz1rm6dW7RoUeZrMtyiBO3x47jv2GFfG7kSUW9iop6oKBt6GS4nhKhF7du35/Tp05w9exaLxcL27duJjo52ShMdHc3WrVtRVZXExEQ8PT3x9/dHVVXefvttQkJCuO2225zOyczMdGzv3LmTVq1a1Up9ymMyWUhK0lFKx7gQQtQY6UkuwfDZZ6iKQsHw4RWmzc9XOH5cy+jRspKFEKJ2abVaxo0bx4wZM7DZbPTp04dWrVqxceNGwD5s4sYbb2TPnj1MnjwZNzc3x7CJhIQEtm7dSuvWrXn66aeBS0u9ffjhh/z+++8oikLTpk15+OGH66yOxYxGM+fPN+HUKQ0hIbLChRCidkiQfDlVxXPlSop69cIaElJhcnvPhkLHjtK9IYSofVFRUVesYTxgwADHtqIojB8//orzIiIi+PTTT0vN8/HHH3dtIV0gIqJ48p6ekJALdVwaIURjIcMtLuO2cye648cr/Rjq4tnWEiQLIUTNMRrty8AlJkq/jhCi9kiQfBlDfDw2T08KLz55qiIJCXrc3VXat6/hggkhRCPm76/SrJmVo0dl8ocQovZIkFysoADDl19SOGQIapMmlTolIUFH+/YWdNK5IYQQNcpkskhPshCiVkmQfJFhwwY0ubmVWhu5WEKCjogIebqeEELUNKPRTGKiDpvM2xNC1BIJki8yxMdjCQmhqFevSqXPyVE4dUqH0Wip4ZIJIYQwmSwUFGg4eVJb10URQjQSEiQDmtRU3LdutS/7pqncW1J8289kkp5kIYSoacVtrTyeWghRWyRIBgyrV6PYbFUcamGfQFK8NJEQQoiaU3zXLjFRJu8JIWpHpf4k37dvH3FxcdhsNvr168ewYcOcXs/NzeWtt97izJkz6PV6Hn30UVq3bg1AXl4eb7/9NidPnkRRFB599FGMRqPra1JdqopnfDxFUVFYq7BMRUKCDoPBRsuW8iARIYSoaT4+KtddZ5WeZCFEramwtbHZbCxdupQXXniBwMBAnnvuOaKjo2nZsqUjzerVq2nbti1PP/00f/75J0uXLmXatGkAxMXF0aVLF/75z39isVi4cKF+LQSvP3AAfUICWf/5T5XOS0jQYzJZKjs6QwghxFWKiDA77uIJIURNqzDES05OJjg4mObNm6PT6ejVqxe7du1ySvPHH38QGRkJQEhICOfOnSMrK4v8/HyOHDlC3759AdDpdDSp5PJqtcUQH4/q7k7B7bdX6byEBB0mkwy1EEKI2mI0WkhO1mGVG3hCiFpQYZCckZFBYGCgYz8wMJCMjAynNG3atGHHjh2APag+d+4cGRkZnD17Fh8fHxYtWsQzzzzD22+/TWFhoYurcBWKijCsWUNh//6ofn6VPi0jQ8O5c1rHU6CEEELUPJPJzIULCr//LitcCCFqXoXDLVT1ykcuK4ritD9s2DCWLVvG008/TevWrWnXrh0ajQar1UpKSgrjxo0jPDycuLg41qxZwz333HNFnps2bWLTpk0AzJw5k6CgoOrWqdKUL75Am5GB7sEHq3S9w4ft9e/e3ZOgIAM6na5WylvfNMZ6N8Y6Q+Osd2Osc31XfPcuMVFP+/bSnSyEqFkVBsmBgYGkp6c79tPT0/H393dK4+npycSJEwF7UD1p0iSaNWtGUVERgYGBhIeHA9CzZ0/WrFlT6nViYmKIiYlx7KelpVW9NlXk/957aIKCOHfjjVCF6+3c6Qn4ERycTlqajaCgoFopb33TGOvdGOsMjbPe1a1zixYtaqA0AiA83B4kJyToGDSojgsjhGjwKhxu0b59e06fPs3Zs2exWCxs376d6OhopzR5eXlYLPbG69tvv6VDhw54enri5+dHYGAgp06dAuDAgQNOE/7qkpKRgcemTRTceSfoqzYRJCFBj4+Pjeuuk0c/CSFEbWnSRKV1a4tM3hNC1IoKe5K1Wi3jxo1jxowZ2Gw2+vTpQ6tWrdi4cSMAAwYM4M8//2TBggVoNBpatmzJhAkTHOePGzeO+fPnY7FYaNasmaPHua4ZvvgCxWwmf8SIKp9rn7RnpsSoEyGEEDXMaLQ4HuYkhBA1qVItTVRUFFFRUU7HBgwY4Ng2Go3Mnz+/1HPbtm3LzJkzr6KINcMzPh5zx45YOnWq0nmqau9JHjKkoIZKJoQQoiwmk5ktW9wxm6t8E1AIIaqkUa7yq0tKwm3fvio9Ya/Y2bMasrI08qQ9IYSoAyaTBbNZISVFepOFEDWrUQbJhvh4VK2Wgv/7vyqfWzwWzmSS5d+EEKK2Fbe98uQ9IURNa3xBstWK52efceFvf8PWtGmVTz961N4wy4NEhBCi9rVvb0GjUUlMlLEWQoia1eiCZPdt29CmplZrwh5AYqKOwEArQUGysoUQQtQ2gwHatLE6OiyEEKKmNLog2RAfj83Xl8L+/at1/tGjeoxG6UUWQoi6YjKZZYULIUSNa1RBsnL+PB7r1lEwdCh4eFT5fFW19yRHRMh4ZCGEqCtGo4WUFB0XLtR1SYQQDVmjCpI91q1DU1hY7aEWf/6pJS9PI+ORhRCiDkVEmLFaFX77TXqThRA1p1EFyZ7x8VjatcPctWu1zpdJe0IIUfeKh7zJ5D0hRE1qNEGy9sQJ3H/6yb42cjUflVfcIBuNMtxCCCHqSmioBa1WlWXghBA1qtEEyYbPPgOgoBoPECl29KiO4GArfn6qq4olhBCiitzd7YGyBMlCiJrUOIJkVcVz5Uou9OqFtWXLameTkKCTh4gIIUQ9YDRaHA93EkKImtAogmS33bvR/f57tSfsAVitkJysl/HIQghRD5hMFo4f11JQUNclEUI0VI0iSDbEx2MzGCgcPLjaeRw/rqWwUJGeZCGEqAdMJjOqqpCcLL3JQoia0fCD5IICDF98QeHgwaheXtXOpnjSnvQkCyFE3Stui2VcshCipjT4INlj40Y0589f1VALuLT8mzxtTwgh6l7bthbc3GSFCyFEzWnwrYvnypVYWrSg6OabryqfhAQ9rVpZaNJEVrYQQtQP+/btIy4uDpvNRr9+/Rg2bJjT66qqEhcXx969e3F3d2fixImEhoaSlpbGwoULycrKQlEUYmJiGHxxOFpubi6xsbGcO3eOpk2bMmXKFLyu4i5cTdHroX17mbwnhKg5DbonWXPmDO7ff0/B8OGgubqqJibqZKiFEKLesNlsLF26lOeff57Y2Fi2bdvGH3/84ZRm7969pKamMn/+fB5++GGWLFkCgFarZdSoUcTGxjJjxgw2bNjgOHfNmjVERkYyf/58IiMjWbNmTa3XrbKMRjOJiQ2+r0cIUUcadJBsWL0axWazP0DkKhQVQXKyjogImbQnhKgfkpOTCQ4Opnnz5uh0Onr16sWuXbuc0uzevZtbbrkFRVEwGo3k5eWRmZmJv78/oaGhABgMBkJCQsjIyABg165d9O7dG4DevXtfkWd9YjJZOHlSR15e9R4QJYQQ5Wm4QbKq4hkfT9GNN2INC7uqrFJSdFgsioxHFkLUGxkZGQQGBjr2AwMDHYHu5WmCgoLKTXP27FlSUlIIu9hOZmdn4+/vD4C/vz85OTk1VYWrVnx3T3qThRA1ocG2LLpDh9AfPUrWa69ddV7FE0Nk+TchRH2hqlfOj1AUpUppCgsLmTt3LmPHjsXT07NK19+0aRObNm0CYObMmU7BeG3p0cP++9Qpf/r3t1X6PJ1OVyflrWuNsd6Nsc7QOOtdE3VusEGy56eforq5UXD77VedV0KCHo1GJSxMepKFEPVDYGAg6enpjv309HRHD/DladLS0kpNY7FYmDt3Ln/961/pURxtAr6+vo4hGZmZmfj4+JR6/ZiYGGJiYhz7l1+ntvj4gIfHdezeXciQIZXv8Q4KCqqT8ta1xljvxlhnaJz1rm6dW7RoUeZrDXO4hdmMYc0aCmNiUEt8aVRHYqKOtm2teHi4oGxCCOEC7du35/Tp05w9exaLxcL27duJjo52ShMdHc3WrVtRVZXExEQ8PT3x9/dHVVXefvttQkJCuO222644Z8uWLQBs2bKFbt261VqdqkqrhbAwmbwnhKgZDbJlcf/uO7Tp6Ve9NnKxo0f1MmlPCFGvaLVaxo0bx4wZM7DZbPTp04dWrVqxceNGAAYMGMCNN97Inj17mDx5Mm5ubkycOBGAhIQEtm7dSuvWrXn66acBuPfee4mKimLYsGHExsayefNmgoKCePLJJ+usjpVhNFr46Sf3ui6GEKIBapBBsmd8PNbAQC706XPVeRUWwu+/a7n99gIXlEwIIVwnKiqKqKgop2MDBgxwbCuKwvjx4684LyIigk8//bTUPL29vZk2bZprC1qDIiIsrFrlSXa2gq+vrGMvhHCdBjfcQq7MJQAAIABJREFUQsnMxGPTJgruvNO+2vxVSk7WYbMpMmlPCCHqIaPR3jbLkAshhKs1uCDZ8PnnKEVFLhtqUfw0p4gImbQnhBD1zaVl4OTJe0II12pwQbLnypWYO3TA0qmTS/JLSNCh16u0aydBshBC1DctW1rx9LQ5luoUQghXaVBBsi45Gbe9e+1P2FNc8wSmhAQ97dtbXDFyQwghhItpNPbJe8V3/YQQwlUaVJBsiI9H1Wgo+L//c1meCQk6edKeEELUY0ajRcYkCyFcruEEyVYrnp99xoW//Q1bs2YuyTIvT+HkSZ1M2hNCiHrMZDJz9qyWjAzX3EEUQghoQEGy27ZtaE+ftg+1cJHingmZtCeEEPWXTN4TQtSEBhMke65cic3Hh8Jbb3VZnsUTQYqXGBJCCFH/FLfRMnlPCOFKDSJIVnJz8Vi3joKhQ3Hls6MTEvR4eKi0aWN1WZ5CCCFcq0ULG97eNpm8J4RwqQYRJHt89RWaggKXrY1cLCFBR3i4Ga3WpdkKIYRwIUWRyXtCCNdrEC1K4aBBZOr1mKOjXZpvQoKem2++4NI8hRBCuF5EhJl16zxQVZetACqEaOQaRE+y6uNjX/bNhS1jVpZCaqpWJu0JIcQ1wGi0kJmpJS2tQXytCSHqAWlNylA8S1om7QkhRP0nk/eEEK4mQXIZihta6UkWQoj6r7itlmXghBCuIkFyGRISdDRpYiMkRFa2EEKI+q5pUxt+fjaOHpWeZCGEa0iQXIajR/UYjRaZACKEENcARbE/eU9WuBBCuIoEyWVITNQRESHjkYUQ4lphMllITNSjqnVdEiFEQyBBcinS0jSkp2sdjzoVQghR/5lMZrKzNaSmylebEOLqVeq+1L59+4iLi8Nms9GvXz+GDRvm9Hpubi5vvfUWZ86cQa/X8+ijj9K6dWvH6zabjWeffZaAgACeffZZ19agBhSPaZMgWQghrh1G46XJe9ddJ2vcCyGuToV/bttsNpYuXcrzzz9PbGws27Zt448//nBKs3r1atq2bcucOXOYNGkSy5Ytc3p93bp1hISEuLTgNal4drTJJMMthBDiWlHcsSGT94QQrlBhkJycnExwcDDNmzdHp9PRq1cvdu3a5ZTmjz/+IDIyEoCQkBDOnTtHVlYWAOnp6ezZs4d+/frVQPFrxtGjOvz8bDRrZqvrogghhKikwEAbQUFWmbwnhHCJCluSjIwMAgMDHfuBgYEkJSU5pWnTpg07duwgIiKC5ORkzp07R0bG/7d35/FNVWkDx383S/c9aaksZW8BAQGroDMqS6eyDFoXVBAdhmFkB0FBQGQcfWEAB8QKiALC67iBjpbBFcGFGXkHWQVFWqpF9u4LXdI2yX3/CA3doKVNmjZ5vp9PP0lu7j33HEpPn54855wcQkJC2Lx5M2PHjqWkpOSq99m5cyc7d+4EYOnSpRiNxoa0xyHS0nT07Anh4fWrg06nc2l9XcUT2+2JbQbPbLcnttkdREebSU6WtZKFEI1XZ5Cs1jJNWKm2LlpCQgKbN29mzpw5REVF0bFjRzQaDQcOHCA4OJhOnTrx448/XvU+cXFxxMXF2V9nZWXVtw0Oparwww+RJCSUkJWVX69rjEajy+rrSp7Ybk9sM3hmuxva5tatWzuhNqK+unUrZ8sWP1QVWcJTCNEodQbJBoOB7Oxs++vs7GxCQ0OrnOPn58eUKVMAW1A9bdo0IiIi2LNnD/v37+fQoUOUlZVRUlJCYmIiM2bMcHAzHOf8eQ0FBRrJRxZCiBYoOtpMUZGGs2e1tG0rm0EJIRquziC5c+fOnD9/noyMDMLCwtizZ0+NILeoqAhvb290Oh27du2ie/fu+Pn5MWbMGMaMGQPAjz/+yPbt25t1gAyVJ+3JyhZCCNHSVPTdyck6CZKFEI1SZ5Cs1WoZP348ixcvxmq1MmjQINq1a8eOHTsAiI+P5+zZs6xevRqNRkPbtm2ZNGmS0yvuLLL8mxBCtFzR0bZPAZOT9QwZIsvACSEarl5TgPv160e/fv2qHIuPj7c/j46OJjEx8aplXH/99Vx//fUNqGLTSk7WEx5uISxMVrYQQoiWJiREJTLSQnKyrHAhhGgc2ZaompQUnYwiCyFECxYTUy7LwAkhGk2C5EqsVlsem0zaE0KIlis62kxKig6rfCAohGgECZIrOXNGS0mJRkaShRCiBYuJMWMyaTh1SuvqqgghWjAJkiu5PGlPRpKFEKKlqujDJeVCCNEY0oNUUrFLU3S0jCQLIZq/w4cPs2nTJqxWK0OGDCEhIaHK+6qqsmnTJg4dOoS3tzdTpkyhU6dOAKxdu5aDBw8SHBzMihUr7Nds3bqVXbt2ERQUBMDo0aNrTNxu7ir68OPH9cTHywoXQoiGkSC5kpQUHa1bmwkKqrnLoBBCNCdWq5WNGzeycOFCDAYD8+fPJzY2lrZt29rPOXToEBcuXCAxMZETJ06wYcMGlixZAsDAgQMZOnQoa9asqVH2iBEjuOuuu5qsLY4WEKDSpo1ZRpKFEI0i6RaVHD+up1s3GUUWQjR/qampREZG0qpVK3Q6Hbfeeiv79u2rcs7+/fu5/fbbURSF6OhoioqKyM3NBaBHjx4EBAS4oupNIibGbP90UAghGkKC5EvMZkhN1UmqhRCiRcjJycFgMNhfGwwGcnJyapxjNBqvek5tPv/8c5588knWrl1LYWGh4yrdhGJizKSm6jBLly6EaCD5LOqSkye1lJUpMmlPCNEiqGrNtDBFUa75nOri4+O5//77AdiyZQtvvPEGU6ZMqXHezp072blzJwBLly6tEow3BzfeqOGVVxTy843ExFR9T6fTNbv6NgVPbLcnthk8s93OaLMEyZdUfCwny78JIVoCg8FAdna2/XV2djahoaE1zsnKyrrqOdWFhITYnw8ZMoRly5bVel5cXBxxcXH215Xv0xy0aaMHwtm7txCDwVTlPaPR2Ozq2xQ8sd2e2GbwzHY3tM2tW7e+4nuSbnFJSooORVHp2lWCZCFE89e5c2fOnz9PRkYGZrOZPXv2EBsbW+Wc2NhYdu/ejaqqpKSk4OfnV2eQXJGzDPDdd9/Rrl07p9Tf2bp2NaMoqmxPLYRoMOk9Ljl+XE/79hb8/GRlCyFE86fVahk/fjyLFy/GarUyaNAg2rVrx44dOwBb2kTfvn05ePAgM2bMwMvLq0raxKpVqzh27BgXL15k0qRJPPDAAwwePJg333yTkydPoigK4eHhPPbYY65qYqP4+qpERVlk8p4QosEkSL4kOVlHdLTkIwshWo5+/frVWMM4Pj7e/lxRFCZMmFDrtY8//nitx6dPn+64CrpYTEy5LAMnhGgwSbcASkshLU0n+chCCOFGoqPN/PyzjrIyV9dECNESSZAM/PKLDrNZkTWShRDCjcTEmDGbFdLSZDRZCHHtJEim8nbUkm4hhBDuomJJT5m8J4RoCAmSsXWgWq1K584ykiyEEO6ic2czGo0qk/eEEA0iQTK2ILljRzPe3q6uiRBCCEfx8YGOHc0yeU8I0SASJGNLt5BJe0II0UTMZvxfew1Npc1QnCUmxizpFkKIBvH4ILmkROHXX7V06yb5yEII0RR0J08StGQJQc8+6/R7RUebSUvTYTLVfa4QQlTm8UHyiRM6VFUhOlpGkoUQoimYu3ShcOpU/D74AO+vv3bqvWJiyrFaFX7+WUaThRDXxuOD5IqP4WT5NyGEaDoXp0+nvHNngufNQykudtp9KlLpZPKeEOJaSZCcrMfLS6VDBwmShRCiyfj4kP/CC+hOnybwhRecdpuOHc3odKrkJQshrpkEyck6Onc2o5P+UwghmlRZ//4UjR2L/4YN6L//3in38PKyLQUnK1wIIa6VBMnJOpm0J4QQLlLw9NNYw8MJefJJKHdOXxwdbZZ0CyHENfPoIPniRYWzZ3UyaU8IIVxEDQoif/Fi9MeOEfDaa065R0xMOadOaSkpUZxSvhDCPXl0kFzx8ZuMJAshhOuYhg2jZNgwAleuRJuW5vDyY2LMqKrCiROSciGEqD+PDpIrPn6TjUSEEMK18v/nf1D1ekKeegpU1aFlR0fbBkKOH5cgWQhRfx4dJB8/rsPX10q7dhZXV0UIITyaNTKSgqefxvvbb/HdutWhZXfoYMHLSyUlRfKShRD159FBckqKnuhoMxqP/lcQQojmofjhhynt35/g555Dk5npsHJ1OujSRbanFkJcG48OD5OTdZJqIYQQzYVGQ/7y5SjFxQQvWuTQomNiyiVIFkJcE48NknNyFDIytMTEyKQ9IYRoLsxdunBxxgx8//UvvL/4wmHlRkebOXtWR2GhrHAhhKgfjw2SK3LTZCRZCCGal8KpUymPiSFk/nyUwkKHlFmxipFsKiKEqC+PDZIrZjlXzHoWQgjRTHh5kbd8OZoLFwhctswhRVashy+biggh6stjg+SUFD2BgVZat7a6uipCCCGqKY+NpWjcOPw3bUJ/4ECjy4uKsuDjY5W8ZCFEvXlskFwxaU+R9DQhhGiWLs6bhzUykpA5c6CsrFFlaTS20WRJtxBC1JdHBsmqCseP62XSnhBCNGNqQAB5S5agT04mYO3aRpcXHW2WdAshRL15ZJCcmakhL08jk/aEEKKZK42Pp2TkSAJfegldamqjyurWrZwLF7Tk5TmockIIt+aRQXLFpD0ZSRZCiOYv/7nnUH19CZ47F6wNn0dSMXnv2DHJsxNC1M0jg+SKj9tkJFkIIZo/a0QE+YsW4b13L35vv93gcir6/B9/lCBZCFE3jwySU1J0hIVZMBplZQshhGgJSh58kNJbbyVo8WI0Fy40qIw2bSz4+1tlJFkIUS/1muZ7+PBhNm3ahNVqZciQISQkJFR5v7CwkFdeeYX09HT0ej2TJ08mKiqKrKws1qxZQ15eHoqiEBcXx/Dhw53SkGthm7QnK1sIIUSLoSjkLVtGRFwcwc88Q+769Q0pguhoM8eOyQoXQoi61TmSbLVa2bhxIwsWLODFF1/k22+/5cyZM1XO+fDDD+nQoQN///vfmTZtGps3bwZAq9XyyCOP8OKLL7J48WI+//zzGtc2NVW1jSRLqoUQQrQslk6duDh7Nr6ffILPZ581qIyYmHIZSRZC1EudQXJqaiqRkZG0atUKnU7Hrbfeyr59+6qcc+bMGXr16gVAmzZtyMzMJC8vj9DQUDp16gSAr68vbdq0IScnxwnNqL9z57QUFmpk0p4QQrRAhRMnUt6jB8FPP41SUHDN18fEmMnIUEhNldFkIcTV1Rkk5+TkYDAY7K8NBkONQLd9+/bs3bsXsAXVmZmZNc7JyMggLS2NLl26OKLeDXZ5ZQsZSRZCtGyHDx9m5syZTJ8+naSkpBrvq6rK66+/zvTp03nyySf55Zdf7O+tXbuWCRMm8MQTT1S5prCwkOeff54ZM2bw/PPPU1hY6PR2XBO9nrwXXkCTkUHQkiXXfPmwYSYMBpUxY8I4c0brhAoKIdxFnX9Kq6pa45hSLZk3ISGBzZs3M2fOHKKioujYsSMazeX422QysWLFCsaNG4efn1+t99m5cyc7d+4EYOnSpRiNxmtqSH2dOWOr1y23BBEW5pgydTqd0+rbnHliuz2xzeCZ7W7uba5IhVu4cCEGg4H58+cTGxtL27Zt7eccOnSICxcukJiYyIkTJ9iwYQNLLgWWAwcOZOjQoaxZs6ZKuUlJSfTq1YuEhASSkpJISkpi7NixTdq2upT36UPRn/5EwPr1lNx7L2U331zva9u1s/DJJ2bi47U88ICBf/4zi+uuk0ncQoia6gySDQYD2dnZ9tfZ2dmEhoZWOcfPz48pU6YAtqB62rRpREREAGA2m1mxYgW33XYb/fv3v+J94uLiiIuLs7/Oysq6tpbU08GDIURGKlitWTjqFkaj0Wn1bc48sd2e2GbwzHY3tM2tW7d2Qm1qqpwKB9hT4SoHyfv37+f2229HURSio6MpKioiNzeX0NBQevToQUZGRo1y9+3bx7PPPgvAHXfcwbPPPtvsgmSAi3Pm4PPppwTPmUPmjh3g7V3va/v0UXnrrWweesjAgw8a+Oc/swkPl0BZCFFVnekWnTt35vz582RkZGA2m9mzZw+xsbFVzikqKsJstqUv7Nq1i+7du+Pn54eqqqxbt442bdrw+9//3jktuEbJyTqioyUfWQjRstUnFS4nJ6fKaHht51SXn59vHwgJDQ2loAF5v01B9fcnf+lS9KmpBL788jVf37dvOf/4Rw7nzml56CEDOTkeuSKqEOIq6hxJ1mq1jB8/nsWLF2O1Whk0aBDt2rVjx44dAMTHx3P27FlWr16NRqOhbdu2TJo0CYDk5GR2795NVFQUc+bMAWD06NH069fPiU26MosFTpzQ8cgjxS65vxBCOEp9UuHqc05DNVWK3FWNGoXl448JWL0an0cfRe3Ro16XVaTSDB8OH35oISFBxyOPRPDZZ2aqfVDqVpp7CpEzeGKbwTPb7Yw212t6b79+/WoEtvHx8fbn0dHRJCYm1riuW7dubN26tZFVdJxTp7SYTBqZtCeEaPHqkwpnMBiqpIzUdk51wcHB9pSM3NxcgoKCaj2vqVLk6qKZP5/wzz5DnTCBrKQk0NQ9Ilw5laZXL9iwwZvx48MYNgzeeSebwMCaf1y4A0mb8hye2G5npMh51OdLKSkV21FLuoUQomWrTypcbGwsu3fvRlVVUlJS8PPzqzNIjo2N5ZtvvgHgm2++4aabbnJaGxzBajBQ8OyzeB04gN8bbzSojEGDSlm3LpejR/U8+mgYxcWyjrIQwsOC5Irl36KjZSRZCNGyVU6FmzVrFrfccos9Fa4iHa5v375EREQwY8YMXn31VSZMmGC/ftWqVSxcuJBz584xadIkvvzyS8C2WtGRI0eYMWMGR44cqbHDanNUct99mG6/naC//Q3NuXMNKuPOO02sXp3L/v1ejBsXRkmJgysphGhxFLW2pLVm4FwDO7qrmTIlhAMHvNi7t+aM7sbwxI81wDPb7YltBs9sd3Nf3aK5cUaffS20v/5K+ODBlN12GzmbNtn2oL6Cq31v//lPX2bODGHgwFI2bsy5lkUzmj35OfYcnthuSbdopJQUveQjCyGEG7K0b29bFu6LL/D56KMGl3PffSW88EI+X33lw+TJoZRLdp4QHstjguTyckhN1dGtm/R4QgjhjoomTKCsd2+Cn3kGJS+vweWMHl3M4sV5fP65L9OmhWKWsRUhPJLHBMlpaTrKyxXJRxZCCHel09m2rM7JIeh//qdRRY0bV8yiRfl89JEvs2aFYLE4qI5CiBbDY4Lk5GTbpD0ZSRZCCPdl7tmTwokT8X/nHbz27GlUWRMnFvHUUwV88IEf8+YFY5VN+YTwKB4UJOvRaFQ6d5aRZCGEcGeFs2djbt+ekLlzaewyFTNmFDJz5kXeftufZ54JpnlOdRdCOIMHBck62re34Ovr6poIIYRwJtXXl7ylS9GlpRG4alWjy5sz5yKTJhWyebM/zz8fJIGyEB6iXjvuuYPkZJm0J4QQnqLs9tspfuABAtato+TuuzHXc8vq2igKLFxYQGkpvPpqAD4+KnPnXnRgbYUQzZFHjCSbTLaJezJpTwghPEf+M89gDQ4mZM4cGjvzTlHguecKGDOmiJdeCmTVqgAH1VII0Vx5RJD88886rFZFtqMWQggPooaFUfDcc3gdPoz/6683ujyNBpYuzee++4p54YUg1q3zd0AthRDNlUcEycnJegC6dZORZCGE8CQld9+NafBgApcvR3vmTKPL02ph5co8Ro4s4fnng9m82c8BtRRCNEceEiTr0OlUOnaUIFkIITyKopC/dCkAwfPn44hZdzodvPxyLnfeWcLTT4fw9tsSKAvhjjwkSNbTubMZLy9X10QIIURTs7Rpw8V58/D58kt8k5IcUqZeD6+8ksugQSbmzg3m/fdl6SQh3I2HBMkyaU8IITxZ0bhxlPXtS9Bf/gLZ2Q4p09sb1q/P4dZby5g1K4R//cvHIeUKIZoHtw+Si4sVTp3SyaQ9IYTwZFqtbcvq/Hy0Tz3lsGJ9fWHz5hxiY8uYPj2Uzz+XQFkId+H2QXJKSsV21DKSLIQQnszcvTuFU6ag/cc/CFy2zCH5yQB+fipvvJFDr17lTJwYypdfejukXCGEa7l9kJycbAuSo6NlJFkIITzdxdmzsYwfT2BiIiHTptkW0neAwECVN9/MJiamnD//OYx//1smwQjR0nlAkKzH21ulQ4fGLSQvhBDCDej1WNaupWDBAvySkjA89BCanByHFB0SovLOOzl06GDmj38MY+9eCZSFaMk8IEjW0bVrOVqtq2sihBCiWVAUCqdOJWfdOryOHME4ciTan392SNFhYVbefTeb1q0tPPpoGAcP6h1SrhCi6bl9kHz8uF5WthBCCFGDaeRIsrZuRbl4kfC77sLrv/91SLnh4Va2bMnGYLDy8MMGjh6VQFmIlsitg+T8fIULF7QyaU8IIUStymNjydq+HYvRiOGhh/B9/32HlHvddVa2bs0mMNDK6NFh/PSTziHlCiGajlsHySkptr/eZdKeEEKIK7G0b0/Wtm2U3XQToTNnErhihUNWvmjb1sLWrdl4e8NDDxlITZVAWYiWxK2D5IqVLWQkWQghxNWoISFkv/UWxQ88QODKlYTMmAGlpY0ut0MHC1u22DYvefBBAydPygQZIVoKtw+S/f2ttGkjK1sIIYSog5cXeStXUvDUU/h98AGG0aNRHLDyRZcuZrZsyaa0FEaMCOellwIoKFAcUGEhhDO5dZBcMWlP49atFEII4TCKQuGMGeSuWYPXoUOE33UX2rS0RhfbrZuZDz/MJja2jOXLgxgwoBUrVwaQlyfBshDNlVuHjykpsh21EEKIa1eSkED2li0oeXkYR47E67vvGl1m165m/vd/c/j000xuuaWUFStswfKyZYHk5EiwLERz47ZBclaWhqwsLTExko8shBDi2pXdfDNZ27ejhoRgePBBfJOSHFJu797lbNyYy44dGdx+eykvvxzAgAGtWLIkkKwst/21LESL47Y/jRWT9iRIFkII0VCWjh3J/Ne/KOvXj9CpUwl46SWHrHwBcP31Zl57LZdduzL53e9MrF0bwIABEfz1r0FkZLjtr2chWgy3/SlMSakIkiXdQgghRMOpYWFkv/02xffeS9Dy5YTMng1lZQ4rPybGzJo1eXz9dSbDh5vYsMGfW25pxaJFQZw/77a/poVo9tz2p+/4cT3BwVZatbK6uipCCCFaOm9v8hITKXjiCfy2bsXw8MMoeXkOvUWXLmYSE/PYvTuDhIRi/vd//bn11lYsWBDM2bOydJwQTc1tg+SKSXuKzIUQQgjhCIpC4ezZ5CYm4rVvH8a770b7668Ov03HjhZWrMjn3//OYNSoYt5+24/f/CaCuXODOXVKgmUhmopbBsmqCsnJeslHFkII4XAl991H9rvvos3KwjhyJPr9+51yn6goC8uX5/PttxmMGVPMe+/58dvfRjB7dghpaRIsC+FsbrlH5oULGvLzNZKPLFokVVUxmUxYrVaUZvJRSHp6OqUO2H2sJblam1VVRaPR4OPj49Lv0eHDh9m0aRNWq5UhQ4aQkJBQ5X1VVdm0aROHDh3C29ubKVOm0KlTp6teu3XrVnbt2kVQUBAAo0ePpl+/fk3bsBagbMAAMrdtw/CHP2B84AFyV63CdNddTrlXmzYWlizJZ/r0i7zySgBvveXPe+/5cs89JcyYcZEuXWTDLCGcwS2D5JQUPSArW4iWyWQyodfr0emaz4+nTqdDq/Wskau62mw2mzGZTPj6+jZhrS6zWq1s3LiRhQsXYjAYmD9/PrGxsbRt29Z+zqFDh7hw4QKJiYmcOHGCDRs2sGTJkjqvHTFiBHc5KeBzJ5YuXcjavp3Q8eMJmzyZglOnKJw6FWfl+V13nZXnnitg2rRC1q0L4I03/PjgA1/uuquEmTML5XeeEA7mlukWx4/L8m+i5bJarc0qQBa10+l0WK2umxicmppKZGQkrVq1QqfTceutt7Jv374q5+zfv5/bb78dRVGIjo6mqKiI3Nzcel0r6scaFkb2u+9SfPfdBP3tbwTPmQPlzv0UMyLCyqJFBfz3vxlMmVLIF1/4MHhwBI89FsqPP0rfIYSjuGWQnJysx2i0YDDIyhai5WkuKRaibq78XuXk5GAwGOyvDQYDOTk5Nc4xGo01zqnr2s8//5wnn3yStWvXUlhY6MRWuAkfH/JWr+bizJn4v/MOhrFjUfLznX5bo9HKggUX2bs3nZkzL7J7tzfx8RGMHx/KkSN6p99fCHfnln9y2la2kFFkIRoiJyeHBx98EIDMzEy0Wi0GgwFVVfn444/x8vKqs4xZs2YxdepUunTpcsVzNm/eTFBQEPfee6/D6u5J1Fo2tKgetF/pnKtdGx8fz/333w/Ali1beOONN5gyZUqN83fu3MnOnTsBWLp0aZVgvLnT6XTOqe/y5Zh79sRr8mRa3Xcf5qQk6NDB8fepxmiE5cth/nwza9ZoePllH4YN82XYMCsLFli4+Wbb99tp7W7GPLHN4Jntdkab3S5Itlptu+099FCxq6siRIsUFhbGF198AcCKFSvw9/dn2rRpmM2X//BUVdU+ea02L774Yp33GTdunEPq66kMBgPZ2dn219nZ2YSGhtY4Jysrq8Y5ZrP5iteGhITYjw8ZMoRly5bVev+4uDji4uLsryvfp7kzGo3Oq+/w4Xi99RZhjz2G9re/JWfTJsr79nXOvWoxaRKMGaOwaZM/r70WwG236Rk40MTjjxcybFhQi/o+OYJTv9fNmCe2u6Ftbt269RXfc7t0i7NntRQXa2QkWQgHS0tLY/DgwTz11FPceeedpKenM3fuXIYNG8agQYOqBMYJCQn88MMPmM1munfvzpIlS4iLi2PkyJH2TmzZsmWsX7/efv6SJUsYMWIEt912mz0/trjcbQ8JAAAZLElEQVS4mD//+c/ExcUxZcoUhg0bxg8//FCjbn//+98ZPny4vX4VI6U///wzo0aNIi4ujjvvvJPTp08DkJiYyJAhQ4iLi2Pp0qVO/Xdzls6dO3P+/HkyMjIwm83s2bOH2NjYKufExsaye/duVFUlJSUFPz8/QkNDr3ptbm6u/frvvvuOdu3aNWm73EHZb39L1rZtqL6+GO6/H59PPmnS+wcFqcycWcjeveksWFDA0aN6EhKM9OypZ/r0ENav9+e777woLpbULiGuxu1Gki9P2pPl30TLt2hREMeOOTa3sEePcp57rqBB16akpLBy5Ur76OL8+fPtI5OjRo1ixIgRREdHV7mmoKCAAQMGsGDBAp599lneffddpk2bVqPsinSOHTt2sGrVKt566y1ef/11wsPDWb9+PT/++CNDhw6ttV5/+tOfePLJJ1FVlalTp/LVV18xePBgpk6dyuzZs4mPj8dkMqGqKjt27OCrr77io48+wtfXt0pQ2JJotVrGjx/P4sWLsVqtDBo0iHbt2rFjxw7AljbRt29fDh48yIwZM/Dy8rKnTVzpWoA333yTkydPoigK4eHhPPbYYy5rY0tm7tqVrO3bCfvjHwl97DEKFi6kaOJEp618UZuAAJWpUwv54x+LePddP/btC2TPHm8++MAPAI1GJTrazA03lNO7dxl9+pTTvXs53t5NVkUhmrV6Bcl1rcVZWFjIK6+8Qnp6Onq9nsmTJxMVFVWvax0tOdkWUERHy0iyEI7Wvn17+vTpY3+9bds23nnnHSwWCxcuXCAlJaVGkOzj48PgwYMB6N27N3v37q217GHDhgHQq1cv+4jvd999x9SpUwG4/vrriYmJqfXa//znP6xbt47S0lJycnLo3bs3/fr1Iycnh/j4eHs9Ks596KGH7Eu3VU9RaEn69etXYw3jivaCLc94woQJ9b4WYPr06Y6tpAezGo1kbd1K6OOPE/z88+jS0shfvBiaePUaPz+V8eOLmDvXl6ysLNLTNXz/vZ4jR7z4/ns9O3d6s2WLLXDW61W6dSvnhhvK7cFzTIwZvcwDFB6ozp/U+qzF+eGHH9KhQwfmzJnD2bNn2bhxI4sWLarXtY6WnKzjuussBAfXnJgiREvT0BFfZ/Hz87M//+WXX9iwYQMff/wxwcHBTJ8+vdbNNypP9NNqtVgstW98UHFe5XNqm2BWXUlJCQsXLuSzzz7juuuuY9myZZhMJqD21SfqU6YQDuPrS+4rr2Du0IHA1avRHz5M6cCBlN14I+WxsVjDwpq8Sq1aWYmPLyU+3vbzqqpw7pyWw4f1HDmi5/vvvdi+3Zc33/QHwMdHpUePcvr0KaN373L69CmnUyczHrZ0uvBAdQbJldfTBOzraVYOdM+cOcM999wDQJs2bcjMzCQvL4+MjIw6r3U023bUkmohhLMVFhYSEBBAYGAg6enpfP311wwcONCh97j55pvZvn07/fv356effiIlJaXGOSUlJWg0GsLCwigsLOSTTz7hnnvuISQkhLCwMHbs2FEl3eL2229n7dq13HXXXfZ0i5Y8mixaAI2Gi/PnY+7SBf/NmwlYtw7l0kRYc8eOlN14I2WxsZTdeCPmmBiaOvpUFNuufm3aWBgxwvYHpqrCyZNavv/e69Kos5533/Xj9ddtU5n8/a306lUx4lzGDTeU0769pSmzSYRwujqD5NrW0zxx4kSVc9q3b8/evXvp1q0bqampZGZmXnEtzurXOpLFAqmpOn77W8/aPlcIV+jVqxddu3Zl8ODBREVFcdNNNzn8HuPHj2fmzJnExcXRs2dPYmJi7NslVwgLC2PUqFEMHjyYtm3b0rfSSgIvv/wy8+bNY/ny5ej1etavX8/vfvc7jh07xvDhw9HpdPzud79j7ty5Dq+7ENWVjBpFyahRKCUl6I8cwWv/fvQHDuD99df4vf8+ANaAAMr79r0cOPfti1ppxZGmoijQsaOFjh1LSEgoAWy/Y3/+Wcf33+svfXmxebM/paUBAAQHW+nd+3LQfMMN5bRuLYGzaLkUtY7PHv/v//6P77//nkmTJgGwe/duUlNTGT9+vP2c4uJiNm/eTFpaGlFRUZw7d46JEydy/vz5Oq+tUH3NzbKysmtuTEoK9Orlxfr1Zh59tOk2EtHpdFWWx/IUntjupmhzeno63jJzBrBt/Ww2m/Hx8eGXX37hgQce4L///W+z2ZGwtLTU/klZhfqsI+2Ozp075+oq1FuzWx5LVdH++iteBw7YvvbvR/fTTyiXdnQs79qVsthYyi8FzubOneEKyy9ejTPaXV5uS3M8csTLnq7x0096zGbl0j0t9O5dTps2FiIiLBiNVsLDrYSHWwgPtxIRYcXX13kpUM3ue91EPLHdzlgCrs7fNPVZi9PPz88+a1pVVaZNm0ZERARlZWV1XlvBEWtu/ve/PkAYrVvnkpXVdCkXnvifETyz3U3R5tLSUrTNLNnPVX8Q5efn8+CDD9rvXbGqRlPUpT5tLi0trfH/4WodrhC1UhQsHTpQ0qEDJffdZztUVIT+8GG89u/H68ABfD/9FP933gHAGhxMWb9+ttHmG2+kvG9f1MBAl1Rdr4eePc307GlmzBjbMZMJfvrp8mjzDz/oOXxYT05O7f2av3/VwLniudFoC6KNRov98dJ8WyGaRJ1BcuX1NMPCwtizZw8zZsyock5RURHe3t7odDp27dpF9+7d8fPzq9e1jpScbGuOrGwhhHsIDg7ms88+c3U1hGhyqr8/Zb/5DWW/+c2lAyran3++PNp84ACBK1agqCqqomDu1s0eNJfFxmLp2LFJl5urzMcH+vYtp2/fcuDyxl7l5ZCdrSErS0NGhpbMTA2ZmRWPtuepqTr27NGSl1f7SHlAQM2AuiKIrn7s0oI2QjRYnUFyfdbiPHv2LKtXr0aj0dC2bVt7esXV1uJ0huRkPe3bm/Hzk9nrQggh3IiiYOnShZIuXSi5tG28UlCA16FD6C+laPhu24b/m28CYAkLs6VnVIw2V1q60VX0eoiMtBIZaQWuPphVVgZZWRqysrRkZNgC68sBte0xOVnHf/6jJT+/9oA6KMhKeLiCj084fn5W/PxU/PxUfH1V+/PLX9Yq79V+joqPjyo51h6kzpxkV2lIftugQeG0b29h8+YcJ9Toyjwx7QA8s91N0ebi4uIqS601B5J/Xrvavleemm4hOcnNgNWK7sQJe4qG/sAB9KmpAKhaLbRvT1l4ONZWrbC0aoUlMvLy81atsEZGovr7u7gR1660FLKyqo5IVzwvLPQlN7ec4mKFkhKF4uKqXyUl15bbrSg1A+iqr61V3gsKUgkMtBIUZCUwsOL55Ud/f+cE3W77f/wqXJKT3FKUlcEvv+iIjze5uipCCCFE09NoMMfEYI6JofjhhwFQcnPxOngQrwMH8LtwAU6dQn/0KN5ffIGmpKRGEdaAAFvAfIUg2tKqFZaICJpTcrC39+Ul7KozGr3IyrrywJnVCiZT9cC5tteaWgLsyq815OZqKC7W2Y8XFSlYrVePgDUa1R48V36sCKqvFFxXfgwIUBsyj1PUg9sEyb/8osNsVoiJ8azRLiGEEOJK1NBQSocMoXTIELyNRrIrRtpUFaWwEG16OpoLF9Cmp1d5rklPx2v/frTp6Si1bBJkDQm5HDzXMjJtjYzEEh4OzXy1F40G+6ivo6kqFBcrFBQoXLyoqfF48eLlx4KCy4/p6RpSU3X28ypWCrmagICqQXVoqA69PrTGqLe/v/WKqSTVR8Gb+beuSbhNkFwxaU82EhGice6//36mTZtWZWOQV199ldTUVP72t79d8bquXbty4sQJLly4wDPPPMP69etrLfuZZ57hhhtuuGI569evZ+zYsfZtox955BFWr15NcHBwwxslhKhKUVADAzEHBkKXLlc+T1VR8vJqBNEVgbT2wgV0qaloMzLsG6RUZjEYsEZEYA0KQvXzs335+6P6+WH190f19bW9vsox1d8fq6+vbUZgC0oIVhTw97elVFx3XcOWpVVV22ohVYPrmgF3QYFCYeHlQDsvDwoKdI1KLdHpqqaU+Ptbr5JmcjkA9/VV8fYGb2/V/uXjU/FIldfe3rZgvLl+W90oSNaj1ap07iwjyUI0xt133822bduqBMlJSUksXLiwXtdHRkbWGiDX14YNG7jvvvvsQfI//vGPBpclhGgkRUENDcUcGoq5W7crn2e1osnJqTWI1mRkoLl4EU1uLsqZMyjFxWiKilBKSmodpb4SVaOpEmTbg+ragu9LxzQREfharZfP8fPD6utb5bXq5wfNZO316hTFltni62slIgKgZkpJbWrLz7VaqTUvuyJ1pKio9pSSoqKq6Sb5+RrOn7+cUlJcrGAyNTzfQ1GqBtBVA2uqvL4cXNcMtg0GDSNGKA79VKB5/q9ogORkHR06mGXJFyEaacSIESxfvpzS0lK8vb05ffo06enp3HzzzRQVFfHHP/6R/Px8zGYzc+fO5c4776xy/enTp/nDH/7Al19+SUlJCbNnz+bEiRN06dIFk+nynIF58+bx/fffYzKZGDFiBE8++SQbN24kPT2dUaNGERoayvvvv0///v359NNPCQsL49VXX2XLli0AjB49mj//+c+cPn2asWPHcvPNN7N//34iIyN5/fXX7UF2hR07dpCYmEhZWRmhoaGsXr2a8PBwioqKWLhwIUeOHEFRFGbNmsWIESP48ssvWbx4MRaLhbCwMLZu3er8f3whWiqNBqvRiNVoxNyzZ/2vKy9HKS62fRUVoSkpQSkqsr+ueE9T+dilcyqOafLzUc6ft5+vKS5GqdTX1GfTedXLyxYw+/pirRZAq7UE1dYrHK8SiPv6grc3qpeXLQh38XCpRnN5ZNvRKgfgpaUKJpOCyQSlpYr9y2SqeM6l95Uq79d23PbcNpJe+VjFcZNJQVUv/7sOHixBcq2Sk/V07y6pFsK9BC1ahP7YMYeWWd6jBwXPPXfF98PCwujTpw9ff/01d955J9u2bePuu+9GURS8vb3ZuHEjgYGB5OTkMHLkSOLj41Gu0Pm/8cYb+Pr6snPnTo4dO8bQoUPt7z311FOEhoZisVh48MEHOXbsGH/605947bXXeO+99wgLC6tS1pEjR9i6dSsfffQRqqry+9//nltuuYXg4GDS0tJYs2YNL7zwAhMnTuSTTz7hvkubMlS4+eab2b59O4qi8Pbbb7N27Vr+8pe/sGrVKgIDA9m1axcAeXl5ZGdn88QTT/DPf/6TqKgocnNzG/rPLYS4Gr0eNTgY9VI6Vf3GSevBYkEpLsbg7U1uxeh1ScnlgLz616X3NNWOa3JzUc6erXrMdO0LBKiKYg+YK77w8kKtCKIrjld+7e2N6u19+b1Lxyq/rlJmpfeU665DV1ZWNXh3YrqKMwPwq1FV2/rbpaUK/v4GwLG7LbtFkFxSAidParnnnpozdYUQ1y4hIYFt27bZg+SXXnoJsO2ouXTpUvbu3YuiKFy4cIHMzEwibJ8D1rB37177NvQ9evSge/fu9ve2b9/OW2+9hcViIT09nRMnTtCjR48r1um7775j6NCh9iXXhg0bxt69e4mPj6ddu3b0vDR61bt3b06fPl3j+vPnzzN58mQyMjIoKysjKioKgH//+9+sXbvWfl5ISAg7duxgwIAB9nOutFOoEKKZ0mptuxAajVgcPQPNarUH1VW+KkbCK16bTFBWhlJailJWhlJWVuU1l45Vfq3Jz7eloFS8V1YGla5Xyus/GFi9V1YV5Yqj39Y6Rs6vOnru728LwF2wxIai2OaGenmpGI3g6FXv3CJITk3Vo6oK0dEykizcy9VGfJ1p6NCh/PWvf+Xo0aOYTCZ69+6N2Wzmgw8+IDs7m08//RS9Xk///v0prSOnsLZR5lOnTvHqq6/y8ccfExISwuOPP14lFaM2V1vS3dvb2/5cq9XWWtYzzzzDY489Rnx8PHv27GHlypX2cmur45VGx4UQHk6jsU8sbHJWa9WguVoQXfE82MuLi+fPX3GEvPLouVJcjC4np8YxxXJt4/oVud7o9ag6Heh0qHq97fFSyon9dfVHne7ydXU8XqkMJSwM+vbFkXm3bhEkV6xs0a2bTNoTwhH8/f255ZZbmD17NgkJCfbjFy9exGg0otfr+fbbbzlz5sxVy+nfvz8ffvghv/nNbzh+/Dg//fSTvRxfX1+CgoLIzMzkq6++4pZbbgEgICCAwsLCGukWAwYMYNasWUybNg1VVfnss89ITEysd5sKCgqIjIwE4L333rMfv+OOO9i0aRPPXfqDJC8vjxtvvJGnn36aU6dO2dMtZDRZCOFyGg1cyne+WmKDajRiasywqqraRrMrUkwqB8+VcsWVkpIaATjl5bYRb7P58mPl5yYTSmFhzXNquYby8msK1jWHD2OVILmqYcNMdOiQSceOEiQL4SgJCQlMmDCBV155xX7s3nvv5Q9/+APDhg3j+uuvp8vVlo4CHn30UWbPnk1cXBw9evSgz6Wtca+//np69uzJoEGDiIqK4qabbrJf8/DDDzN27FgiIiJ4//337cd79erFqFGjGDFiBGCbuNezZ89aUytq88QTTzBx4kQiIyPp16+f/bqZM2eyYMECBg8ejEajYfbs2QwfPpy///3vTJgwAavVitFo5N13363fP5wQQrR0FTnU3t6ooaEOzvS9RlZr1aD5Co+hAQFYQ0Icemu32pbaVTxx+0fwzHbLttSeQ7alvjbSZzd/nthuT2wzeGa7nbEttWxkKIQQQgghRDUSJAshhBBCCFGNBMlCCCGEEEJUI0GyEM1MM50mIGoh3yshhHBfEiQL0cxoNBqPmyTXEpnNZjQuWDxfCCFE03CLJeCEcCc+Pj6YTCZKS0ubzYYW3t7edW4a4m6u1mZVVdFoNPg4cD1OIYQQzYsEyUI0M4qi4Ovr6+pqVCHLCQkhhPA08lmhEEIIIYQQ1UiQLIQQQgghRDUSJAshhBBCCFFNs92WWgghhBBCCFeRkWQHmDdvnqur4BKe2G5PbDN4Zrs9sc2ewlO/t57Ybk9sM3hmu53RZgmShRBCCCGEqEaCZCGEEEIIIarRPvvss8+6uhLuoFOnTq6ugkt4Yrs9sc3gme32xDZ7Ck/93npiuz2xzeCZ7XZ0m2XinhBCCCGEENVIuoUQQgghhBDVyLbUjZCVlcWaNWvIy8tDURTi4uIYPny4q6vVJKxWK/PmzSMsLMxjZtEWFRWxbt06Tp8+jaIoTJ48mejoaFdXy6k++ugjvvzySxRFoV27dkyZMgUvLy9XV8vh1q5dy8GDBwkODmbFihUAFBYW8uKLL5KZmUl4eDizZs0iICDAxTUVjSF9tvTZ0me7h6bqsyVIbgStVssjjzxCp06dKCkpYd68efTu3Zu2bdu6umpO98knn9CmTRtKSkpcXZUms2nTJvr06cMTTzyB2WymtLTU1VVyqpycHD799FNefPFFvLy8WLlyJXv27GHgwIGurprDDRw4kKFDh7JmzRr7saSkJHr16kVCQgJJSUkkJSUxduxYF9ZSNJb02dJnuzPpsx3fZ0u6RSOEhobak8R9fX1p06YNOTk5Lq6V82VnZ3Pw4EGGDBni6qo0meLiYn766ScGDx4MgE6nw9/f38W1cj6r1UpZWRkWi4WysjJCQ0NdXSWn6NGjR40Rh3379nHHHXcAcMcdd7Bv3z5XVE04kPTZ0me7O+mzHdtny0iyg2RkZJCWlkaXLl1cXRWn27x5M2PHjvWoEYmMjAyCgoJYu3Ytv/76K506dWLcuHH4+Pi4umpOExYWxsiRI5k8eTJeXl7ccMMN3HDDDa6uVpPJz8+3/4IJDQ2loKDAxTUSjiR9tnuTPlv6bEf02TKS7AAmk4kVK1Ywbtw4/Pz8XF0dpzpw4ADBwcEet7SMxWIhLS2N+Ph4li9fjre3N0lJSa6ullMVFhayb98+1qxZw6uvvorJZGL37t2urpYQjSZ9tvuTPlv6bEeQILmRzGYzK1as4LbbbqN///6uro7TJScns3//fqZOncqqVav44YcfSExMdHW1nM5gMGAwGOjatSsAAwYMIC0tzcW1cq6jR48SERFBUFAQOp2O/v37k5KS4upqNZng4GByc3MByM3NJSgoyMU1Eo4gfbb02e5K+mzH99mSbtEIqqqybt062rRpw+9//3tXV6dJjBkzhjFjxgDw448/sn37dmbMmOHiWjlfSEgIBoOBc+fO0bp1a44ePer2k32MRiMnTpygtLQULy8vjh49SufOnV1drSYTGxvLN998Q0JCAt988w033XSTq6skGkn6bOmz3Zn02Y7vs2UzkUY4fvw4ixYtIioqCkVRABg9ejT9+vVzcc2aRkWH6ynLCZ08eZJ169ZhNpuJiIhgypQpbr8k2NatW9mzZw9arZYOHTowadIk9Hq9q6vlcKtWreLYsWNcvHiR4OBgHnjgAW666SZefPFFsrKyMBqNzJ492+2/3+5O+mzps939Z1j6bMf22RIkCyGEEEIIUY3kJAshhBBCCFGNBMlCCCGEEEJUI0GyEEIIIYQQ1UiQLIQQQgghRDUSJAshhBBCCFGNBMlCCCGEEEJUI0GyEEIIIYQQ1UiQLIQQQgghRDX/D/6+FWwJ+HWTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"ggplot\")\n",
    "plt.figure(figsize=(12,12))\n",
    "plt.plot(hist[\"acc\"])\n",
    "plt.plot(hist[\"val_acc\"])\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# Plot the graph \n",
    "plt.style.use('ggplot')\n",
    "\n",
    "def plot_history(history):\n",
    "    accuracy = history.history['acc']\n",
    "    val_accuracy = history.history['val_acc']\n",
    "    loss = history.history['loss']\n",
    "    val_loss = history.history['val_loss']\n",
    "    x = range(1, len(accuracy) + 1)\n",
    "\n",
    "    plt.figure(figsize=(12, 5))\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(x, accuracy, 'b', label='Training acc')\n",
    "    plt.plot(x, val_accuracy, 'r', label='Validation acc')\n",
    "    plt.title('Training and validation accuracy')\n",
    "    plt.legend()\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(x, loss, 'b', label='Training loss')\n",
    "    plt.plot(x, val_loss, 'r', label='Validation loss')\n",
    "    plt.title('Training and validation loss')\n",
    "    plt.legend()\n",
    "\n",
    "plot_history(history)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(\n",
    "                        np.array(X_char_te).reshape((len(X_char_te),\n",
    "                                                     max_len, max_len_char)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def charindx2word(Tword):\n",
    "    word = []\n",
    "    for i in range(len(Tword)):\n",
    "        if(Tword[i] !=0):\n",
    "            word.append(idx2char[Tword[i]])\n",
    "    return \"\".join(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word           ||True ||Pred\n",
      "==============================\n",
      "Üldseisnud     : text  text\n",
      "rahuldav.S     : text  text\n",
      "regulaarne     : object text\n",
      "mm             : text  text\n",
      "Hg.Vesikul     : text  text\n",
      "hingamiska     : text  text\n",
      "pehme,valu     : text  text\n"
     ]
    }
   ],
   "source": [
    "i = 1000\n",
    "p = np.argmax(y_pred[i], axis=-1)\n",
    "print(\"{:15}||{:5}||{}\".format(\"Word\", \"True\", \"Pred\"))\n",
    "print(30 * \"=\")\n",
    "for w, t, pred in zip(X_char_te[i], y_te[i], p):\n",
    "    if t != 0: #PAD\n",
    "        print(\"{:15}: {:5} {}\".format(charindx2word(w), idx2tag[t], idx2tag[pred]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word           ||True ||Pred\n",
      "==============================\n",
      "Üldseisnud     : text  text\n",
      "rahuldav.S     : text  text\n",
      "ebaregulaa     : object text\n",
      "mm             : text  text\n",
      "Hg.Vesiula     : text  text\n",
      "hingamiska     : text  text\n",
      "pehme,valu     : text  text\n"
     ]
    }
   ],
   "source": [
    "i = 14\n",
    "p = np.argmax(y_pred[i], axis=-1)\n",
    "print(\"{:15}||{:5}||{}\".format(\"Word\", \"True\", \"Pred\"))\n",
    "print(30 * \"=\")\n",
    "for w, t, pred in zip(X_char_te[i], y_te[i], p):\n",
    "    if t != 0: #PAD\n",
    "        print(\"{:15}: {:5} {}\".format(charindx2word(w), idx2tag[t], idx2tag[pred]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pred2label(pred):\n",
    "    out = []\n",
    "    for pred_i in pred:\n",
    "        out_i = []\n",
    "        for p in pred_i:\n",
    "            p_i = np.argmax(p, axis=-1)\n",
    "#             out_i.append(idx2tag[p_i])\n",
    "            out_i.append(idx2tag[p_i].replace(\"PAD\", \"text\"))\n",
    "\n",
    "        out.append(out_i)\n",
    "    return out\n",
    "pred_labels = pred2label(y_pred)\n",
    "\n",
    "def pred2labelV2(pred):\n",
    "    out = []\n",
    "    for pred_i in pred:\n",
    "        out_i = []\n",
    "        for p in pred_i:\n",
    "#             out_i.append(idx2tag[p])\n",
    "            out_i.append(idx2tag[p].replace(\"PAD\", \"text\"))\n",
    "\n",
    "        out.append(out_i)\n",
    "    return out\n",
    "test_labels = pred2labelV2(y_te)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-score: 89.6%\n"
     ]
    }
   ],
   "source": [
    "from seqeval.metrics import accuracy_score,precision_score, recall_score, f1_score, classification_report\n",
    "print(\"F1-score: {:.1%}\".format(f1_score(test_labels, pred_labels)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           precision    recall  f1-score   support\n",
      "\n",
      "     text       0.94      0.87      0.90      4461\n",
      "   object       0.95      0.89      0.92      2064\n",
      "    value       0.88      0.82      0.85      1577\n",
      "\n",
      "micro avg       0.93      0.86      0.90      8102\n",
      "macro avg       0.93      0.86      0.90      8102\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(test_labels,pred_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      object       0.14      0.93      0.24      2075\n",
      "        text       1.00      0.92      0.96    405697\n",
      "       value       0.06      0.91      0.12      1578\n",
      "\n",
      "    accuracy                           0.92    409350\n",
      "   macro avg       0.40      0.92      0.44    409350\n",
      "weighted avg       0.99      0.92      0.95    409350\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn_crfsuite.metrics import flat_classification_report  \n",
    "report = flat_classification_report(y_pred=pred_labels, y_true=test_labels)\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word           ||True ||Pred\n",
      "==============================\n",
      "/106           : text  value\n",
      "90             : text  value\n",
      "ebaregulaa     : object text\n",
      "160cm          : text  value\n",
      "Pikkus         : object text\n",
      "157            : value text\n",
      "Pikkus         : object text\n",
      "KORRAS.        : object text\n",
      "KOORMUSTES     : object text\n",
      "155/95         : value text\n",
      "24-tunni       : value text\n",
      "Pikkus-174     : object text\n",
      "Nõustamine     : object text\n",
      "=              : text  value\n",
      "kõikumised     : object text\n",
      "140-90mmhg     : value text\n",
      "100            : text  value\n",
      "sööki-6,6m     : object text\n",
      "regulaarne     : object text\n",
      "Pikkus-        : object text\n",
      "178            : value text\n",
      "kaebusteta     : object text\n",
      "regulaarne     : object text\n",
      "Pikkus:        : object text\n",
      "162,7cm        : value text\n",
      "Pikkus:        : object text\n",
      "162,7cm        : value text\n",
      "tarvitanud     : object text\n",
      "135/82         : value text\n",
      "139.0/85.7     : value text\n",
      "139.0/85.7     : value text\n",
      "139.0/85.7     : value text\n",
      "Pikkus         : object text\n",
      "164            : value text\n",
      "Pikkus         : object text\n",
      "162            : value text\n",
      "Pikkus         : object text\n",
      "184cm          : value text\n",
      "Pikkus         : object text\n",
      "184cm          : value text\n",
      "min.Pikkus     : object text\n",
      "189            : value text\n",
      "min.Pikkus     : object text\n",
      "189            : value text\n",
      "Kordusrets     : object text\n",
      "Pikkus         : object text\n",
      "46cm,          : value text\n",
      "Analüüsid.     : object text\n",
      "184/88         : value text\n",
      "Analüüsid.     : object text\n",
      "184/88         : value text\n",
      "regulaarse     : object text\n",
      "Pikkus         : object text\n",
      "175            : value text\n",
      "Pikkus         : object text\n",
      "175            : value text\n",
      "Pikkus         : object text\n",
      "170            : value text\n",
      "Pikkus         : object text\n",
      "170            : value text\n",
      "Pikkus         : object text\n",
      "165            : value text\n",
      "Pikkus         : object text\n",
      "165            : value text\n",
      "170            : value text\n",
      "Pikkus         : object text\n",
      "154            : value text\n",
      "Pikkus         : object text\n",
      "157cm.         : value text\n",
      "Pikkus         : object text\n",
      "157cm.         : value text\n",
      "Pikkus-        : object text\n",
      "150            : value text\n",
      "sööki-4,3m     : object text\n",
      "rr140/100      : value text\n",
      "Pikkus         : object text\n",
      "183,           : value text\n",
      "tuhmid,kah     : object text\n",
      "130/82         : value text\n",
      "RR-monitoo     : object text\n",
      "Rf             : text  object\n",
      "regulaarne     : object text\n",
      "90             : text  value\n",
      "145/80mmHg     : text  value\n",
      "98%,fr.93x     : object text\n",
      "140/90         : value text\n",
      "Pikkus-        : object text\n",
      "159            : value text\n",
      "Pikkus-165     : object text\n",
      "Pikkus-165     : object text\n",
      "140.80.        : value text\n",
      "171            : text  value\n",
      "Pikkus         : object text\n",
      "175            : value text\n",
      "Pikkus         : object text\n",
      "175            : value text\n",
      "86x´,regul     : object text\n",
      "130/90         : value text\n",
      "Tervisekon     : object text\n",
      "halvasti.R     : object text\n",
      "min.Pikkus     : object text\n",
      "162            : value text\n",
      "min.Pikkus     : object text\n",
      "162            : value text\n",
      "õhupuudus,     : object text\n",
      "sääki-14,0     : object text\n",
      "52Pikkus       : object text\n",
      "166            : value text\n",
      "52Pikkus       : object text\n",
      "166            : value text\n",
      "cor:akts.r     : object text\n",
      "120/80mmhg     : value text\n",
      "140.70         : value text\n",
      "regulaarne     : object text\n",
      "rr150/90       : value text\n",
      "Pikkus         : object text\n",
      "160            : value text\n",
      "Pikkus         : object text\n",
      "160            : value text\n",
      "Pikkus         : object text\n",
      "161cm.         : value text\n",
      "Pikkus         : object text\n",
      "161cm.         : value text\n",
      "Pikkus         : object text\n",
      "174            : value text\n",
      "Pikkus         : object text\n",
      "174            : value text\n",
      "Pikkus         : object text\n",
      "168            : value text\n",
      "Pikkus         : object text\n",
      "168            : value text\n",
      "regulaarne     : object text\n",
      "Pikkus-192     : object text\n",
      "Pikkus-192     : object text\n",
      "südamepeks     : object text\n",
      "Pikkus-        : object text\n",
      "173            : value text\n",
      "Pikkus-        : object text\n",
      "162            : value text\n",
      "Pikkus-175     : object text\n",
      "kontrollil     : object text\n",
      "166/97         : value text\n",
      "rR             : text  object\n",
      "Analüüsid.     : object text\n",
      "118/80         : value text\n",
      "Pikkus         : object text\n",
      "166            : value text\n",
      "Pikkus         : object text\n",
      "157,5          : value text\n",
      "Pikkus         : object text\n",
      "157,5          : value text\n",
      "Pikkus         : object text\n",
      "168            : value text\n",
      "Pikkus         : object text\n",
      "168            : value text\n",
      "140            : value text\n",
      "169cm          : text  value\n",
      "Nõustamine     : object text\n",
      "rr100/60       : value text\n",
      "/100           : text  value\n",
      "rr170/100(     : value text\n",
      "24             : value text\n",
      "186            : text  value\n",
      "Pikkus         : object text\n",
      "170            : value text\n",
      "Pikkus         : object text\n",
      "170            : value text\n",
      "min.Pikkus     : object text\n",
      "170            : value text\n",
      "min.Pikkus     : object text\n",
      "170            : value text\n",
      "võileib).R     : object text\n",
      "170/100        : value text\n",
      "Pikkus         : object text\n",
      "175            : value text\n",
      "140/90         : value text\n",
      "kompenseer     : object text\n",
      "140/70         : value text\n",
      "100            : text  value\n",
      "Pikkus         : object text\n",
      "177,5,         : value text\n",
      "100            : text  value\n",
      "124/78         : text  value\n",
      "pearinglus     : object text\n",
      "163            : text  value\n",
      "Pikkus         : object text\n",
      "49cm.          : value text\n",
      "170cm          : value text\n",
      "Pikkus-        : object text\n",
      "174            : value text\n",
      "tervisetõe     : object text\n",
      "159cm          : text  value\n",
      "Pikkus-        : object text\n",
      "175            : value text\n",
      "Pikkus-165     : object text\n",
      "13-101035-     : value text\n",
      "13-101035-     : value text\n",
      "13-101035-     : value text\n",
      "13-101035-     : value text\n",
      "13-101035-     : value text\n",
      "13-101035-     : value text\n",
      "13-101035-     : value text\n",
      "13-101035-     : value text\n",
      "Pikkus         : object text\n",
      "160            : value text\n",
      "sööki-6,3m     : object text\n",
      "Pikkus-        : object text\n",
      "163            : value text\n",
      "Pikkus-        : object text\n",
      "180            : value text\n",
      "Amb.m/õ,te     : object text\n",
      "id=\"0\"         : value text\n",
      "140            : value text\n",
      "Hba1c-7,0%     : object text\n",
      "110/70         : text  value\n",
      "kg.Pikkus      : object text\n",
      "163            : value text\n",
      "kg.Pikkus      : object text\n",
      "163            : value text\n",
      "kg.Pikkus      : object text\n",
      "163            : value text\n",
      "Pingepeava     : object text\n",
      "118/75         : value text\n",
      "Pikkus:        : object text\n",
      "161            : value text\n",
      "Pikkus:        : object text\n",
      "161            : value text\n",
      "RRmonitoor     : object text\n",
      "110            : value text\n",
      "110            : value text\n",
      "110            : value text\n",
      "ebaregulaa     : object text\n",
      "regulaarne     : object text\n",
      "Pikkus         : object text\n",
      "157            : value text\n",
      "Pikkus         : object text\n",
      "157            : value text\n",
      "03.06.2010     : value text\n",
      "127            : value text\n",
      "uimnasust.     : object text\n",
      "140/85.Cor     : value text\n",
      "120x1          : value text\n",
      "120x1          : value text\n",
      "rr140/90       : value text\n",
      "AVR            : text  object\n",
      "130/80         : text  value\n",
      "Nõustamine     : object text\n",
      "=              : text  value\n",
      "reg.puhtad     : object text\n",
      "Pikkus         : object text\n",
      "166cm.V/s      : value text\n",
      "Pikkus         : object text\n",
      "166cm.V/s      : value text\n",
      "Turseteta.     : object text\n",
      "statiinrav     : object text\n",
      "transparen     : object text\n",
      "134/75mmHg     : text  value\n",
      "120kg          : value text\n",
      "RR-monitoo     : object text\n",
      "135/93         : value text\n",
      "Pikkus         : object text\n",
      "170            : value text\n",
      "Pikkus         : object text\n",
      "170            : value text\n",
      "*10.06.201     : value text\n",
      "Pikkus         : object text\n",
      "170            : value text\n",
      "Pikkus         : object text\n",
      "170            : value text\n",
      "Pikkus-        : object text\n",
      "164            : value text\n",
      "158cm          : text  value\n",
      "170            : text  value\n",
      "RR-monitoo     : object text\n",
      "Pikkus         : object text\n",
      "172            : value text\n",
      "rägivad.Tu     : object text\n",
      "Pikkus-        : object text\n",
      "163            : value text\n",
      "130/80         : text  value\n",
      "130/80         : text  value\n",
      "Pikkus         : object text\n",
      "156            : value text\n",
      "Pikkus         : object text\n",
      "156            : value text\n",
      "R120-82mmh     : text  object\n",
      "=              : text  value\n",
      "100            : text  value\n",
      "168cm          : text  value\n",
      "Ülekaaluli     : object text\n",
      "135/80         : value text\n",
      "Pikkus-159     : object text\n",
      "Pikkus-159     : object text\n",
      "160cm          : text  value\n",
      "158cm          : text  value\n",
      "170/90         : text  value\n",
      "ujulast.Hb     : object text\n",
      "26.10.2015     : object text\n",
      "05.11.2015     : object text\n",
      "26.10.2015     : object text\n",
      "05.11.2015     : object text\n",
      "/106           : text  value\n",
      "90             : text  value\n",
      "meeleolula     : object text\n",
      "100/70,        : value text\n",
      "Hba1c-5,7%     : object text\n",
      "169cm          : text  value\n",
      "söönuna--1     : object text\n",
      "üldseisund     : object text\n",
      "120/70.        : value text\n",
      "120x           : value text\n",
      "p/s-12,5kg     : object text\n",
      "120,1          : value text\n",
      "*12.03.201     : value text\n",
      "24             : value text\n",
      "Pikkus         : object text\n",
      "166cm,         : value text\n",
      "rr140/90       : value text\n",
      "145;           : value text\n",
      "regulaarne     : object text\n",
      "120,2kg        : value text\n",
      "162/90         : text  value\n",
      "Pikkus         : object text\n",
      "170            : value text\n",
      "100,eelnev     : value text\n"
     ]
    }
   ],
   "source": [
    "j = 0\n",
    "print(\"{:15}||{:5}||{}\".format(\"Word\", \"True\", \"Pred\"))\n",
    "print(30 * \"=\")\n",
    "\n",
    "for i in range(len(y_pred)):\n",
    "    p = np.argmax(y_pred[i], axis=-1)\n",
    "    for w, t, pred in zip(X_char_te[i], y_te[i], p):\n",
    "        if charindx2word(w) != \"\": #PAD\n",
    "            if idx2tag[t] != idx2tag[pred]:\n",
    "                print(\"{:15}: {:5} {}\".format(charindx2word(w), idx2tag[t], idx2tag[pred]))\n",
    "                j = j+1\n",
    "                \n",
    "                \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "325"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.utils.vis_utils import plot_model\n",
    "plot_model(model, to_file='ObjValue_plot.png', show_shapes=True, show_layer_names=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}