{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "25a0cd07",
   "metadata": {},
   "source": [
    "# Text Sentiment Analysis of IMDB Movie reviews using NLP (Word2Vec and RNN) for MYM Intern Assesment"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a6ca3e39",
   "metadata": {},
   "source": [
    "## Assesment Objectives: \n",
    "### - Preprocessing the data\n",
    "### - Converting Text(words) to Vectors using word2vec \n",
    "### - Using the word representations given by word2vec to feed a RNN and training the model\n",
    "### - Evaluating the model and plotting the performance graphs\n",
    "### - Improving the model by Transfer Learning\n",
    "### - Comparing Accuracy of Baseline model, The model and Improved model.\n",
    "### - Testing the model (predicting the model with new review)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "84781652",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gensim==4.2.0\n"
     ]
    }
   ],
   "source": [
    "!pip freeze | grep gensim ##Checking the version of Gensim - Word2Vec"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "12e2dbaa",
   "metadata": {},
   "source": [
    "## The Data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7b25eb77",
   "metadata": {},
   "source": [
    "### Starting with 20% of the sentences from TensorFlow Datasets of IMDB reviews to check the RAM compatibility of the PC to train the model faster by splitting the datasets as X_train, y_train, X_test and y_test.\n",
    "### Then preprocessing the textual data to create input features for a natural language processing (NLP) model.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2079b965",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-05 06:40:17.849061: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow_datasets as tfds\n",
    "from tensorflow.keras.preprocessing.text import text_to_word_sequence\n",
    "\n",
    "def load_data(percentage_of_sentences=None):\n",
    "    train_data, test_data = tfds.load(name=\"imdb_reviews\", split=[\"train\", \"test\"], batch_size=-1, as_supervised=True)\n",
    "\n",
    "    train_sentences, y_train = tfds.as_numpy(train_data)\n",
    "    test_sentences, y_test = tfds.as_numpy(test_data)\n",
    "    \n",
    "    # Take only a given percentage of the entire data\n",
    "    if percentage_of_sentences is not None:\n",
    "      assert(percentage_of_sentences> 0 and percentage_of_sentences<=100)\n",
    "        \n",
    "      len_train = int(percentage_of_sentences/100*len(train_sentences))\n",
    "      train_sentences, y_train = train_sentences[:len_train], y_train[:len_train]\n",
    "  \n",
    "      len_test = int(percentage_of_sentences/100*len(test_sentences))\n",
    "      test_sentences, y_test = test_sentences[:len_test], y_test[:len_test]\n",
    "    \n",
    "    X_train = [text_to_word_sequence(_.decode(\"utf-8\")) for _ in train_sentences]\n",
    "    X_test = [text_to_word_sequence(_.decode(\"utf-8\")) for _ in test_sentences]\n",
    "    \n",
    "    return X_train, y_train, X_test, y_test\n",
    "\n",
    "X_train, y_train, X_test, y_test = load_data(percentage_of_sentences=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4352850a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, ..., 1, 0, 0])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d707253",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "fdffccea",
   "metadata": {},
   "source": [
    "## First, training a word2vec model (with the arguments that we want) on your training sentence. Store it into the `word2vec` variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f5c2e1b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import Word2Vec\n",
    "\n",
    "word2vec = Word2Vec(sentences=X_train, vector_size=60, min_count=10, window=10)\n",
    "word2vec.save(\"word2vec.model\")\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "81a82d8e",
   "metadata": {},
   "source": [
    "## Embedding the training and test sentences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "62a835d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "import numpy as np\n",
    "\n",
    "# Function to convert a sentence (list of words) into a matrix representing the words in the embedding space\n",
    "def embed_sentence(word2vec, sentence):\n",
    "    embedded_sentence = []\n",
    "    for word in sentence:\n",
    "        if word in word2vec.wv:\n",
    "            embedded_sentence.append(word2vec.wv[word])\n",
    "        \n",
    "    return np.array(embedded_sentence)\n",
    "\n",
    "# Function that converts a list of sentences into a list of matrices\n",
    "def embedding(word2vec, sentences):\n",
    "    embed = []\n",
    "    \n",
    "    for sentence in sentences:\n",
    "        embedded_sentence = embed_sentence(word2vec, sentence)\n",
    "        embed.append(embedded_sentence)\n",
    "        \n",
    "    return embed\n",
    "\n",
    "# Embed the training and test sentences\n",
    "X_train_embed = embedding(word2vec, X_train)\n",
    "X_test_embed = embedding(word2vec, X_test)\n",
    "\n",
    "\n",
    "# Pad the training and test embedded sentences\n",
    "X_train_pad = pad_sequences(X_train_embed, dtype='float32', padding='post', maxlen=200)\n",
    "X_test_pad = pad_sequences(X_test_embed, dtype='float32', padding='post', maxlen=200)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ea1e76af",
   "metadata": {},
   "source": [
    "### It's a good practice to check check the following for `X_train_pad` and `X_test_pad`:\n",
    "#### - they are numpy arrays\n",
    "#### - they are 3-dimensional\n",
    "#### - the last dimension is of the size of your word2vec embedding space (you can get it with `word2vec.wv.vector_size`\\\\\n",
    "#### - the first dimension is of the size of your `X_train` and `X_test`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d4770855",
   "metadata": {},
   "outputs": [],
   "source": [
    "for X in [X_train_pad, X_test_pad]:\n",
    "    assert type(X) == np.ndarray\n",
    "    assert X.shape[-1] == word2vec.wv.vector_size\n",
    "\n",
    "\n",
    "assert X_train_pad.shape[0] == len(X_train)\n",
    "assert X_test_pad.shape[0] == len(X_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6418b6d1",
   "metadata": {},
   "source": [
    "## Baseline Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3b477d35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of labels in train set {0: 2474, 1: 2526}\n",
      "Baseline accuracy:  0.499\n"
     ]
    }
   ],
   "source": [
    "# It is always good to have a very simple model to test your own model against\n",
    "# Baseline accuracy can be to predict the label that is the most present in `y_train`.\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "unique, counts = np.unique(y_train, return_counts=True)\n",
    "counts = dict(zip(unique, counts))\n",
    "print('Number of labels in train set', counts)\n",
    "\n",
    "y_pred = 0 if counts[0] > counts[1] else 1\n",
    "\n",
    "print('Baseline accuracy: ', accuracy_score(y_test, [y_pred]*len(y_test)))\n",
    "\n",
    "baseline_acc = accuracy_score(y_test, [y_pred]*len(y_test))\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b7a80da2",
   "metadata": {},
   "source": [
    "## The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "523cd8a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "# writing a RNN model with Masking, LSTM and Dense layers.\n",
    "\n",
    "def init_model():\n",
    "    model = Sequential()\n",
    "    model.add(layers.Masking())\n",
    "    model.add(layers.LSTM(20, activation='tanh'))\n",
    "    model.add(layers.Dense(15, activation='relu'))\n",
    "    model.add(layers.Dense(1, activation='sigmoid'))\n",
    "\n",
    "    model.compile(loss='binary_crossentropy', #compiling the model with rmsprop optimizer\n",
    "                  optimizer='rmsprop',\n",
    "                  metrics=['accuracy'])\n",
    "    \n",
    "    return model\n",
    "\n",
    "model = init_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5317ce64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "110/110 [==============================] - 9s 50ms/step - loss: 0.6751 - accuracy: 0.5749 - val_loss: 0.6679 - val_accuracy: 0.5980\n",
      "Epoch 2/100\n",
      "110/110 [==============================] - 4s 40ms/step - loss: 0.6271 - accuracy: 0.6486 - val_loss: 0.6284 - val_accuracy: 0.6433\n",
      "Epoch 3/100\n",
      "110/110 [==============================] - 4s 36ms/step - loss: 0.5775 - accuracy: 0.6980 - val_loss: 0.5818 - val_accuracy: 0.7040\n",
      "Epoch 4/100\n",
      "110/110 [==============================] - 4s 37ms/step - loss: 0.5377 - accuracy: 0.7329 - val_loss: 0.6517 - val_accuracy: 0.6653\n",
      "Epoch 5/100\n",
      "110/110 [==============================] - 4s 35ms/step - loss: 0.5152 - accuracy: 0.7520 - val_loss: 0.5543 - val_accuracy: 0.7300\n",
      "Epoch 6/100\n",
      "110/110 [==============================] - 4s 35ms/step - loss: 0.4946 - accuracy: 0.7657 - val_loss: 0.5921 - val_accuracy: 0.7267\n",
      "Epoch 7/100\n",
      "110/110 [==============================] - 4s 35ms/step - loss: 0.4811 - accuracy: 0.7769 - val_loss: 0.5399 - val_accuracy: 0.7460\n",
      "Epoch 8/100\n",
      "110/110 [==============================] - 4s 35ms/step - loss: 0.4625 - accuracy: 0.7874 - val_loss: 0.5263 - val_accuracy: 0.7587\n",
      "Epoch 9/100\n",
      "110/110 [==============================] - 4s 35ms/step - loss: 0.4564 - accuracy: 0.7880 - val_loss: 0.5222 - val_accuracy: 0.7433\n",
      "Epoch 10/100\n",
      "110/110 [==============================] - 4s 35ms/step - loss: 0.4375 - accuracy: 0.8023 - val_loss: 0.5429 - val_accuracy: 0.7353\n",
      "Epoch 11/100\n",
      "110/110 [==============================] - 4s 37ms/step - loss: 0.4271 - accuracy: 0.8109 - val_loss: 0.5867 - val_accuracy: 0.7433\n",
      "Epoch 12/100\n",
      "110/110 [==============================] - 4s 36ms/step - loss: 0.4118 - accuracy: 0.8151 - val_loss: 0.5874 - val_accuracy: 0.7247\n",
      "Epoch 13/100\n",
      "110/110 [==============================] - 4s 39ms/step - loss: 0.3998 - accuracy: 0.8229 - val_loss: 0.5303 - val_accuracy: 0.7547\n",
      "Epoch 14/100\n",
      "110/110 [==============================] - 4s 40ms/step - loss: 0.4012 - accuracy: 0.8214 - val_loss: 0.5168 - val_accuracy: 0.7653\n",
      "Epoch 15/100\n",
      "110/110 [==============================] - 4s 36ms/step - loss: 0.3857 - accuracy: 0.8297 - val_loss: 0.5377 - val_accuracy: 0.7500\n",
      "Epoch 16/100\n",
      "110/110 [==============================] - 4s 36ms/step - loss: 0.3727 - accuracy: 0.8403 - val_loss: 0.5561 - val_accuracy: 0.7640\n",
      "Epoch 17/100\n",
      "110/110 [==============================] - 4s 36ms/step - loss: 0.3616 - accuracy: 0.8414 - val_loss: 0.5625 - val_accuracy: 0.7460\n",
      "Epoch 18/100\n",
      "110/110 [==============================] - 4s 36ms/step - loss: 0.3502 - accuracy: 0.8483 - val_loss: 0.6253 - val_accuracy: 0.7247\n",
      "Epoch 19/100\n",
      "110/110 [==============================] - 4s 34ms/step - loss: 0.3420 - accuracy: 0.8517 - val_loss: 0.5606 - val_accuracy: 0.7540\n"
     ]
    }
   ],
   "source": [
    "#  Fiting the model on embedded and padded data with the early stopping criterion.\n",
    "\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "es = EarlyStopping(patience=5, restore_best_weights=True)\n",
    "\n",
    "history = model.fit(X_train_pad, y_train, \n",
    "          batch_size = 32,\n",
    "          epochs=100,\n",
    "          validation_split=0.3,\n",
    "          callbacks=[es]\n",
    "         )\n",
    "the_model_acc = history.history['accuracy'][-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "26e4350b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy evaluated on the test set is of 76.820%\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the model on the test set.\n",
    "\n",
    "result = model.evaluate(X_test_pad, y_test, verbose=0)\n",
    "\n",
    "print(f'The accuracy evaluated on the test set is of {result[1]*100:.3f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8d663411",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plot the accuracy and loss curves\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(history.history['accuracy'], label='Training accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label='Validation accuracy')\n",
    "plt.title('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(history.history['loss'], label='Training loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation loss')\n",
    "plt.title('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d9c43269",
   "metadata": {},
   "source": [
    "## Trained Word2Vec - Transfer Learning\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "92b6edab",
   "metadata": {},
   "source": [
    "### The accuracy of the above the baseline model, might be quite low. By improving the quality of the embedding we can Improve accuracy of the model."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1e80388e",
   "metadata": {},
   "source": [
    "### Let's improve the quality of our embedding, instead of just loading a larger corpus, let's benefit from the embedding that others have learned. Because, the quality of an embedding, i.e. the proximity of the words, can be derived from different tasks. This is exactly what transfer learning is."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "97d4ebeb",
   "metadata": {},
   "source": [
    "### Listing all the different models available in the word2vec using gensim api."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b07b77ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['fasttext-wiki-news-subwords-300', 'conceptnet-numberbatch-17-06-300', 'word2vec-ruscorpora-300', 'word2vec-google-news-300', 'glove-wiki-gigaword-50', 'glove-wiki-gigaword-100', 'glove-wiki-gigaword-200', 'glove-wiki-gigaword-300', 'glove-twitter-25', 'glove-twitter-50', 'glove-twitter-100', 'glove-twitter-200', '__testing_word2vec-matrix-synopsis']\n"
     ]
    }
   ],
   "source": [
    "import gensim.downloader as api\n",
    "print(list(api.info()['models'].keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bd25af6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's load one of the pre-trained word2vec embedding spaces. \n",
    "\n",
    "word2vec_transfer = api.load(\"glove-wiki-gigaword-100\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "062fa47f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "400000\n",
      "100\n"
     ]
    }
   ],
   "source": [
    "print(len(word2vec_transfer.key_to_index))\n",
    "print(len(word2vec_transfer['dog']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bc0a78fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to convert a sentence (list of words) into a matrix representing the words in the embedding space\n",
    "def embed_sentence_with_TF(word2vec, sentence):\n",
    "    embedded_sentence = []\n",
    "    for word in sentence:\n",
    "        if word in word2vec:\n",
    "            embedded_sentence.append(word2vec[word])\n",
    "        \n",
    "    return np.array(embedded_sentence)\n",
    "\n",
    "# Function that converts a list of sentences into a list of matrices\n",
    "def embedding(word2vec, sentences):\n",
    "    embed = []\n",
    "    \n",
    "    for sentence in sentences:\n",
    "        embedded_sentence = embed_sentence_with_TF(word2vec, sentence)\n",
    "        embed.append(embedded_sentence)\n",
    "        \n",
    "    return embed\n",
    "\n",
    "# Embed the training and test sentences\n",
    "X_train_embed_2 = embedding(word2vec_transfer, X_train)\n",
    "X_test_embed_2 = embedding(word2vec_transfer, X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9270ed0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pad the training and test embedded sentences\n",
    "X_train_pad_2 = pad_sequences(X_train_embed_2, dtype='float32', padding='post', maxlen=200)\n",
    "X_test_pad_2 = pad_sequences(X_test_embed_2, dtype='float32', padding='post', maxlen=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cdee2b55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "110/110 [==============================] - 9s 53ms/step - loss: 0.6714 - accuracy: 0.5849 - val_loss: 0.6512 - val_accuracy: 0.6227\n",
      "Epoch 2/30\n",
      "110/110 [==============================] - 5s 41ms/step - loss: 0.6156 - accuracy: 0.6651 - val_loss: 0.6046 - val_accuracy: 0.6807\n",
      "Epoch 3/30\n",
      "110/110 [==============================] - 5s 41ms/step - loss: 0.5599 - accuracy: 0.7229 - val_loss: 0.5968 - val_accuracy: 0.6927\n",
      "Epoch 4/30\n",
      "110/110 [==============================] - 4s 39ms/step - loss: 0.5353 - accuracy: 0.7369 - val_loss: 0.6922 - val_accuracy: 0.6367\n",
      "Epoch 5/30\n",
      "110/110 [==============================] - 4s 40ms/step - loss: 0.5256 - accuracy: 0.7431 - val_loss: 0.5610 - val_accuracy: 0.7127\n",
      "Epoch 6/30\n",
      "110/110 [==============================] - 4s 38ms/step - loss: 0.4920 - accuracy: 0.7686 - val_loss: 0.6102 - val_accuracy: 0.6900\n",
      "Epoch 7/30\n",
      "110/110 [==============================] - 4s 38ms/step - loss: 0.4743 - accuracy: 0.7831 - val_loss: 0.5528 - val_accuracy: 0.7380\n",
      "Epoch 8/30\n",
      "110/110 [==============================] - 4s 38ms/step - loss: 0.4548 - accuracy: 0.7960 - val_loss: 0.4919 - val_accuracy: 0.7720\n",
      "Epoch 9/30\n",
      "110/110 [==============================] - 4s 38ms/step - loss: 0.4294 - accuracy: 0.8089 - val_loss: 0.5748 - val_accuracy: 0.7373\n",
      "Epoch 10/30\n",
      "110/110 [==============================] - 4s 38ms/step - loss: 0.4180 - accuracy: 0.8217 - val_loss: 0.5599 - val_accuracy: 0.7340\n",
      "Epoch 11/30\n",
      "110/110 [==============================] - 4s 39ms/step - loss: 0.3970 - accuracy: 0.8303 - val_loss: 0.4763 - val_accuracy: 0.7853\n",
      "Epoch 12/30\n",
      "110/110 [==============================] - 4s 37ms/step - loss: 0.3878 - accuracy: 0.8349 - val_loss: 0.7456 - val_accuracy: 0.6800\n",
      "Epoch 13/30\n",
      "110/110 [==============================] - 4s 38ms/step - loss: 0.3667 - accuracy: 0.8449 - val_loss: 0.4732 - val_accuracy: 0.7947\n",
      "Epoch 14/30\n",
      "110/110 [==============================] - 4s 38ms/step - loss: 0.3508 - accuracy: 0.8469 - val_loss: 0.5956 - val_accuracy: 0.7493\n",
      "Epoch 15/30\n",
      "110/110 [==============================] - 4s 40ms/step - loss: 0.3378 - accuracy: 0.8563 - val_loss: 0.5706 - val_accuracy: 0.7573\n",
      "Epoch 16/30\n",
      "110/110 [==============================] - 5s 42ms/step - loss: 0.3188 - accuracy: 0.8671 - val_loss: 0.4941 - val_accuracy: 0.7760\n",
      "Epoch 17/30\n",
      "110/110 [==============================] - 5s 43ms/step - loss: 0.3078 - accuracy: 0.8774 - val_loss: 0.5214 - val_accuracy: 0.7733\n",
      "Epoch 18/30\n",
      "110/110 [==============================] - 5s 42ms/step - loss: 0.2959 - accuracy: 0.8763 - val_loss: 0.6381 - val_accuracy: 0.7533\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "import tensorflow as tf\n",
    "\n",
    "es = EarlyStopping(patience=5, restore_best_weights=True)\n",
    "\n",
    "model = init_model()\n",
    "\n",
    "history = model.fit(X_train_pad_2, y_train, \n",
    "          batch_size = 32,\n",
    "          epochs=30,\n",
    "          validation_split=0.3,\n",
    "          callbacks=[es]\n",
    "         )\n",
    "model.save('my_model.h5')\n",
    "\n",
    "improved_model_acc = history.history['accuracy'][-1]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d8297abb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy evaluated on the test set is of 80.120%\n"
     ]
    }
   ],
   "source": [
    "result = model.evaluate(X_test_pad_2, y_test, verbose=0)\n",
    "\n",
    "print(f'The accuracy evaluated on the test set is of {result[1]*100:.3f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "070d098d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2Gf379ycgIIC4uDgWLVpke/3i8j1rmd1vv/1G48aNCQoKok+fPsU+pOTn5/P4448TGhpKtWrVeO655xg+fDj9+vW77Pd76tQphgwZQs2aNQkICKB58+Z88803xfaxWCy89dZbNGjQAF9fX2rXrs3rr79ue/3o0aMMGTKEqlWrEhgYSLt27Vi7di0AI0aMuOT8Tz75JD169LD9u0ePHowZM4Ynn3yS8PBwevfuDcCUKVNo3rw5gYGBxMTE8Mgjj5CRkVHsWAkJCfTo0YOAgADCwsLo3bs3Z86cYfbs2VSrVo2cnJxi+/fr14977733sj8PIYQQFafXtV6u9/3K9N/po48+on79+vj4+BAfH8+XX35Z7L/hxIkTqV27Nr6+vtSoUYPHH3/c9vqHH35IXFwcfn5+REZGMnDgwDKdWwghXIWM5NvBhTwzTV7+zenn3flqbwJ87POf8Pnnn2fy5MnUq1ePsLAwjhw5ws0338zrr7+Or68vs2fPpm/fvuzZs4fatWtf9jivvPIKb731Fm+//Tbvvfced999N4cPH6Zq1aol7p+VlcXkyZP58ssvMRqN3HPPPTzzzDPMmTMHgDfffJM5c+Ywc+ZMGjduzLvvvssPP/xAz549LxtDdnY2bdu25bnnniM4OJjFixdz7733Ur9+fTp06ADAuHHj+PTTT/nf//5H165dOX78OLt37wYgIyOD7t27U7NmTRYtWkRUVBSbNm3CYrGU6Wf6xRdfMHr0aBISEmzPGY1Gpk2bRt26dTl48CCPPPIIzz77LB9++CEAmzdv5vrrr2fUqFG8++67eHl5sXz5csxmM4MGDeLxxx9n0aJFDBo0CIATJ06wePFifv/99zLFJoQQomz0utaDXO9La+HChTzxxBNMnTqVXr168fPPPzNy5Ehq1apFz549+f777/nf//7H3Llzadq0KSkpKWzZsgWADRs28Pjjj/Pll1/SuXNnTp8+zcqVK8vwkxVCCNchSb4A4NVXX+WGG26w/btq1aq0bNnS9u/XXnuNhQsXsmjRIsaMGXPZ44wYMYIhQ4YA8MYbbzBt2jTWrVtHnz59Stw/Ly+P6dOnU79+fQDGjBnDq6++anv9vffeY9y4cfTv3x+A999/n19++eWK30vNmjV55plnbP9+7LHH+O2335g/fz4dOnTg/PnzvPvuu7z//vsMHz4cgPr169O1a1cAvv76a06ePMn69ettH1YaNGhwxXOWJC4ujrfeeqvYc08++aRtOzY2lv/+9788/PDDtiT/rbfeol27drZ/AzRt2tS2PXToUGbOnGlL8r/66itq165drIpACCGEuBxPut5fbPLkyYwYMYJHHnkEgLFjx7JmzRomT55Mz549SUpKIioqil69euHt7U3t2rVtN/+TkpIIDAzk1ltvpUqVKtSpU4fWrVuX6fxCCOEqJMm3A39vEztf7a3Lee2lXbt2xf6dkZHBxIkTWbx4McePHyc/P58LFy6QlJR0xeO0aNHCth0YGEhwcDAnTpy47P4BAQG2Cz5AdHS0bf9z586RmppquwADmEwm2rZte8VRdbPZzBtvvMH8+fNJTk4mNzeXnJwcAgICANi1axc5OTlcf/31Jb5/8+bNtG7d+rKjEaXVtm3bS577448/mDRpErt37yY9PZ38/Hyys7PJysoiICCAzZs32xL4kjzwwAO0b9+e5ORkatasyaxZsxgxYgQGg6FCsQpRme1NPU/y2QuEBfgQ6u9NWIAPVfy8MBrl/ytRSK9rvfXc9uJJ1/uL7dq165IGgV26dOHdd98FYNCgQUydOpV69erRp08fbr75Zvr27YuXlxc33HADderUsb3Wp08f23QEIchMg/PHIaq53pEIUSqS5NuBwWCwWxmdXgIDA4v9+5lnnmHp0qVMnjyZBg0a4O/vz8CBA8nNzb3icby9vYv922AwXPECXdL+pZ17eDlvv/027777LlOnTrXNf3/yySdtsfv7+1/x/Vd73Wg0XhJjXl7eJftd/DNNTEzk1ltvZfTo0bz++utUrVqVf/75h/vuu4/c3FwCAgKueu7WrVvTsmVLZs+ezY033siOHTtYvHjxFd8jhCjZ+ew8/u/X3cxZe2kyYzRASEHCHxLgbbsBEBrgQ1iAN6EBaju04LUQf2/CAn0I9DHJTTcP5QnXevCs631ZxcTEsGfPHv744w+WLl3KI488wttvv81ff/1FlSpV2LRpEytWrOD333/n5ZdfZuLEiaxfv16W6RMwdygcWQeP/wtV6+odjRBX5f5XK+EQCQkJjBgxwlY2l5GRQWJiolNjCAkJITIykvXr19OtWzdAjdJv2rSJVq1aXfZ9CQkJ3H777dxzzz2AarK3d+9emjRpAqgyen9/f5YtW8b9999/yftbtGjBZ599xunTp0sczY+IiGD79u3Fntu8efMlH2AutnHjRiwWC++88w5Go+p5OX/+/EvOvWzZMl555ZXLHuf+++9n6tSpJCcn06tXL2JiYq54XiHEpZbvOcGLC7Zx7Fw2APGRVcjIyedsVi6ZuWYsGpzJyuNM1qU38K7E22QgxL/4jYCe8dUZ2vHyc5uF0JM7X+8v1rhxYxISEmxT8UB9f9brP6gb+X379qVv3748+uijNGrUiG3bttGmTRu8vLzo1asXvXr1YsKECYSGhvLnn39yxx132O17FW7qxC5Ag7S9kuQLtyBJvihRXFwcCxYsoG/fvhgMBsaPH1/mxnP28NhjjzFp0iQaNGhAo0aNeO+99zhz5swVR8ri4uL47rvvWLVqFWFhYUyZMoXU1FTbRd7Pz4/nnnuOZ599Fh8fH7p06cLJkyfZsWMH9913H0OGDOGNN96gX79+TJo0iejoaP79919q1KhBp06duO6663j77beZPXs2nTp14quvvmL79u1XnbvXoEED8vLyeO+99+jbty8JCQlMnz692D7jxo2jefPmPPLIIzz88MP4+PiwfPlyBg0aRHh4OKDm5T/zzDN8+umnzJ49u4I/YSEql7NZubz6004W/JsMQO2qAfzfgOZ0rh9u2ycn38y5rDzOXsjjTGYuZy/kcTYrlzNZeZzNUttns/I4k5XLuQvq8UxWHrn5FvLMGmkZOaRlFK6CUSPEz+nfpxCl5c7X+4v95z//4c4776R169b06tWLn376iQULFthWC5g1axZms5mOHTsSEBDAV199hb+/P3Xq1OHnn3/m4MGDdOvWjbCwMH755RcsFgvx8fGO+paFu8jLhpx0tZ15Ut9YhCglSfJFiaZMmcKoUaPo3Lkz4eHhPPfcc6Snpzs9jueee46UlBSGDRuGyWTiwQcfpHfv3phMl5+f+NJLL3Hw4EF69+5NQEAADz74IP369ePcuXO2fcaPH4+Xlxcvv/wyx44dIzo6mocffhhQ6/v+/vvvPP3009x8883k5+fTpEkTPvjgAwB69+7N+PHjefbZZ8nOzmbUqFEMGzaMbdu2XfF7admyJVOmTOHNN99k3LhxdOvWjUmTJjFs2DDbPg0bNuT333/nhRdeoEOHDvj7+9OxY0dbcyNQIx4DBgxg8eLFZV5aSIjK7Ndtxxn/4w7SMnIwGGBUl7o8fWPDS0qwfb1MVA82UT249Mm5pmlk51k4U3ADwHZT4EIucdWr2PtbEcJu3Pl6f7F+/frx7rvvMnnyZJ544gnq1q3LzJkzbc1pQ0ND+b//+z/Gjh2L2WymefPm/PTTT1SrVo3Q0FAWLFjAxIkTyc7OJi4ujm+++aZY81tRSWUW6TWRcfm+E0K4EoPm7AlRbiA9PZ2QkBDOnTtHcHBwsdeys7M5dOgQdevWxc9PRmeczWKx0LhxY+68805ee+01vcPRzfXXX0/Tpk2ZNm2aQ44vv+fCk5w8n8PLP27n1+0pADSoHsRbA1vQpnaYzpGVzZWuTaLs5Frv2jzxei+/V27q6Eb47Dq1fc0j0GeSvvEIj2ava72M5AuXdvjwYX7//Xe6d+9OTk4O77//PocOHWLo0KF6h6aLM2fOsGLFClasWFFsmT0hxKU0TWPhv8m8+vNOzmblYTIaGN29Po9d3wBfL/t1KxdCVJxc74XLkpF84YYkyRcuzWg0MmvWLJ555hk0TaNZs2b88ccfNG7cWO/QdNG6dWvOnDnDm2++KfMEhbiCY2cv8MLCbazYo+ZPNq0RzFsDW9C0RojOkQkhSiLXe+Gyiib2MidfuAlJ8oVLi4mJISEhQe8wXIazOx4L4W4sFo1v1icx6ZfdZOTk42My8kSvOB7sVg9vk1Hv8IQQlyHXe+GyMiXJF+5HknwhhBAe4fCpTJ77fitrDp4GoE3tUN4a2IIG0vhOCCFEeWUUSeylXF+4CUnyhRBCuDWzRWNmwiEm/76H7DwL/t4m/tM7nuGdYzEZS7/8lhBCCHGJoiP5WafAnA8mSaGEa5PfUCGEEG5rb+p5nv1uK5uPnAWgc/1q/N8dLahdLUDfwIQQQniGoiP5aHDhNARV1y0cIUpDknwhhBBuJ89sYfqKA7z3535yzRaq+Hrx4i2NGdw+BoNBRu+FEELYSeZFJfoZJyTJFy5PknwhhBBuZXvyOf7z3VZ2HU8H4PpG1flv/2ZEh/jrHJkQQgiPY52Hb/IBc+6lSb8QLkiSfCGEEG4hO8/Mu8v28cnfBzFbNMICvJl4W1Nua1lDRu+FEELYX34uZJ9V2xHxkLLtovJ9IVyTrCckSq1Hjx48+eSTtn/HxsYyderUK77HYDDwww8/VPjc9jqOEMI9bTx8hpunreSjFQcwWzRubRHN0rHdub1VTUnwhbAzT7/eT5w4kVatWjn0HMJDWJfMM3pBeMPizwnhwiTJrwT69u1Lnz59Snxt5cqVGAwGtm7dWubjrl+/ngcffLCi4RVzuQvv8ePHuemmm+x6LiGE68vNt/D2b7sZNH0VB09mElHFl4/vbcv7Q9sQHuSrd3hCuBS53gthZ9bS/MAICIos/pwQLkzK9SuB++67jwEDBnD06FFq1apV7LWZM2fSrl07WrRoUebjRkRE2CvEq4qKinLauVxJbm4uPj4+eochhC72pJznqXmb2Vkw975/65pM7NuUkABvnSMTwjXJ9V4IO7OW5gdGqK+izwnhwmQk3x40DXIznf+laaUK79ZbbyUiIoJZs2YVez4jI4Nvv/2W++67j1OnTjFkyBBq1qxJQEAAzZs355tvvrnicS8u39u3bx/dunXDz8+PJk2asHTp0kve89xzz9GwYUMCAgKoV68e48ePJy8vD4BZs2bxyiuvsGXLFgwGAwaDwRbzxeV727Zt47rrrsPf359q1arx4IMPkpGRYXt9xIgR9OvXj8mTJxMdHU21atV49NFHbecqyYEDB7j99tuJjIwkKCiI9u3b88cffxTbJycnh+eee46YmBh8fX1p0KABn3/+ue31HTt2cOuttxIcHEyVKlW49tprOXDgAHBp+SNAv379GDFiRLGf6WuvvcawYcMIDg62jZxc6edm9dNPP9G+fXv8/PwIDw+nf//+ALz66qs0a9bsku+3VatWjB8//rI/DyH0YrZofPL3Afq+9w87j6cTFuDNR3e34X+DW0mCL/Sj17Vervd2v95fzGKx8Oqrr1KrVi18fX1p1aoVS5Yssb2em5vLmDFjiI6Oxs/Pjzp16jBp0iQANE1j4sSJ1K5dG19fX2rUqMHjjz9e6nMLF2cdtQ+qXthRX0byhRuQkXx7yMuCN2o4/7wvHAOfwKvu5uXlxbBhw5g1axYvvviibf7qt99+i9lsZsiQIWRkZNC2bVuee+45goODWbx4Mffeey/169enQ4cOVz2HxWLhjjvuIDIykrVr13Lu3LlLElqAKlWqMGvWLGrUqMG2bdt44IEHqFKlCs8++yyDBw9m+/btLFmyxJZch4SEXHKMzMxMevfuTadOnVi/fj0nTpzg/vvvZ8yYMcU+2Cxfvpzo6GiWL1/O/v37GTx4MK1ateKBBx4o8XvIyMjg5ptv5vXXX8fX15fZs2fTt29f9uzZQ+3atQEYNmwYq1evZtq0abRs2ZJDhw6RlpYGQHJyMt26daNHjx78+eefBAcHk5CQQH5+/lV/fkVNnjyZl19+mQkTJpTq5wawePFi+vfvz4svvsjs2bPJzc3ll19+AWDUqFG88sorrF+/nvbt2wPw77//snXrVhYsWFCm2IRwtCOns3j62y2sO3QaUJ3zJw1oTvUqfjpHJio9va71INd7O1/vL/buu+/yzjvv8PHHH9O6dWtmzJjBbbfdxo4dO4iLi2PatGksWrSI+fPnU7t2bY4cOcKRI0cA+P777/nf//7H3Llzadq0KSkpKWzZsqVU5xVuwNpZP7B64Ui+zMkXbkCS/Epi1KhRvP322/z111/06NEDUKV7AwYMICQkhJCQEJ555hnb/o899hi//fYb8+fPL9VF/48//mD37t389ttv1KihPgS98cYbl8yre+mll2zbsbGxPPPMM8ydO5dnn30Wf39/goKC8PLyumK53tdff012djazZ88mMFB96Hn//ffp27cvb775JpGRas5UWFgY77//PiaTiUaNGnHLLbewbNmyy170W7ZsScuWLW3/fu2111i4cCGLFi1izJgx7N27l/nz57N06VJ69eoFQL169Wz7f/DBB4SEhDB37ly8vdVoY8OGDa/6s7vYddddx9NPP13suSv93ABef/117rrrLl555ZVi3w9ArVq16N27NzNnzrQl+TNnzqR79+7F4hdCT5qm8e2Go7zy0w4yc80E+pgYf2sTWfdeiDKS6/3Vr/cXmzx5Ms899xx33XUXAG+++SbLly9n6tSpfPDBByQlJREXF0fXrl0xGAzUqVPH9t6kpCSioqLo1asX3t7e1K5du1Q/R+EmrAl9kJTrC/ciSb49eAeou+x6nLeUGjVqROfOnZkxYwY9evRg//79rFy5kldffRUAs9nMG2+8wfz580lOTiY3N5ecnBwCAkp3jl27dhETE2O74AN06tTpkv3mzZvHtGnTOHDgABkZGeTn5xMcHFzq78N6rpYtW9ou+ABdunTBYrGwZ88e20W/adOmmEwm2z7R0dFs27btssfNyMhg4sSJLF68mOPHj5Ofn8+FCxdISkoCYPPmzZhMJrp3717i+zdv3sy1115rS/DLq127dpc8d7Wf2+bNm6/4YeaBBx5g1KhRTJkyBaPRyNdff83//ve/CsUphL2cPJ/DuAXb+GNXKgDtY8N4Z1Aralcr/d84IRxOr2u99dylJNf7q1/vi0pPT+fYsWN06dKl2PNdunSxjciPGDGCG264gfj4ePr06cOtt97KjTfeCMCgQYOYOnUq9erVo0+fPtx888307dsXLy/5iO0Rio7k28r1T6opNHIDWrgwmZNvDwaDKqNz9lcZ/7jcd999fP/995w/f56ZM2dSv359W8L69ttv8+677/Lcc8+xfPlyNm/eTO/evcnNzbXbj2n16tXcfffd3Hzzzfz888/8+++/vPjii3Y9R1EXJ9sGgwGLxXLZ/Z955hkWLlzIG2+8wcqVK9m8eTPNmze3xefv73/F813tdaPRiHbRvMqS5gwW/TADpfu5Xe3cffv2xdfXl4ULF/LTTz+Rl5fHwIEDr/geIZxhyfYUek/9mz92peJjMjLupkbMfbCTJPjC9eh1rZfr/VWV9XpfVm3atOHQoUO89tprXLhwgTvvvNN2DY2JiWHPnj18+OGH+Pv788gjj9CtW7cy9QQQLqzonHzrSL4lDy6c0S8mIUpBkvxK5M4777SN4s6ePZtRo0bZymATEhK4/fbbueeee2jZsiX16tVj7969pT5248aNOXLkCMePH7c9t2bNmmL7rFq1ijp16vDiiy/Srl074uLiOHz4cLF9fHx8MJvNVz3Xli1byMzMtD2XkJCA0WgkPj6+1DFfLCEhgREjRtC/f3+aN29OVFQUiYmJttebN2+OxWLhr7/+KvH9LVq0YOXKlZe9sEdERBT7+ZjNZrZv337VuErzc2vRogXLli277DG8vLwYPnw4M2fOZObMmdx1111XvTEghCOlZ+fx9PwtPPzVRk5n5tIoqgo/junCQ93rYzLK6IgQFSHX+9ILDg6mRo0aJCQkFHs+ISGBJk2aFNtv8ODBfPrpp8ybN4/vv/+e06dV7xB/f3/69u3LtGnTWLFiBatXry51JYFwcUW763v5gm9B74jMNP1iEqIUJMmvRIKCghg8eDDjxo3j+PHjxbq6x8XFsXTpUlatWsWuXbt46KGHSE1NLfWxe/XqRcOGDRk+fDhbtmxh5cqVvPjii8X2iYuLIykpiblz53LgwAGmTZvGwoULi+0TGxvLoUOH2Lx5M2lpaeTk5Fxyrrvvvhs/Pz+GDx/O9u3bWb58OY899hj33nuvrXSvPOLi4liwYAGbN29my5YtDB06tNhIQGxsLMOHD2fUqFH88MMPHDp0iBUrVjB//nwAxowZQ3p6OnfddRcbNmxg3759fPnll+zZswdQc+0XL17M4sWL2b17N6NHj+bs2bOliutqP7cJEybwzTffMGHCBHbt2sW2bdt48803i+1z//338+eff7JkyRJGjRpV7p+TEBW16kAaN01dyfebjmI0wCM96vPjmC40ji5bKa8QomRyvS+b//znP7z55pvMmzePPXv28Pzzz7N582aeeOIJAKZMmcI333zD7t272bt3L99++y1RUVGEhoYya9YsPv/8c7Zv387Bgwf56quv8Pf3LzZvX7ixoiP5oObmF31eCBclSX4lc99993HmzBl69+5dbD7dSy+9RJs2bejduzc9evQgKiqKfv36lfq4RqORhQsXcuHCBTp06MD999/P66+/Xmyf2267jaeeeooxY8bQqlUrVq1adckSbgMGDKBPnz707NmTiIiIEpf1CQgI4LfffuP06dO0b9+egQMHcv311/P++++X7YdxkSlTphAWFkbnzp3p27cvvXv3pk2bNsX2+eijjxg4cCCPPPIIjRo14oEHHrCNMFSrVo0///yTjIwMunfvTtu2bfn0009tZYSjRo1i+PDhDBs2zNb0rmfPnleNqzQ/tx49evDtt9+yaNEiWrVqxXXXXce6deuK7RMXF0fnzp1p1KgRHTt2rMiPSohyyc4z89rPOxn66VqSz16gdtUA5j/UiWf7NMLXy3T1AwghSk2u96X3+OOPM3bsWJ5++mmaN2/OkiVLWLRoEXFxcYBaKeCtt96iXbt2tG/fnsTERH755ReMRiOhoaF8+umndOnShRYtWvDHH3/w008/Ua1aNbvGKHRgzocsVa1BYPXijxmS5AvXZtAuniQsSE9PJyQkhHPnzl3SJCY7O5tDhw5Rt25d/PxkSSfhPjRNIy4ujkceeYSxY8decV/5PRf2tu3oOcbO38y+E2p966Eda/PizY0J9JXmVKV1pWuTKDu51gtnk98rN3M+Bd6JB4MRxqeB0QTzh8HOH+Gmt6DjQ3pHKDyQva718ulKiErg5MmTzJ07l5SUFEaOHKl3OKISyTdb+HDFAaYt20e+RSOiii9vDWhBz0bV9Q5NCCGEuDzraH1AuErwobD5nnVpPSFclCT5QlQC1atXJzw8nE8++YSwsDC9wxGVxMGTGTw1fwtbjpwF4Jbm0fy3XzPCAn30DUwIIYS4movn44OU6wu3IUm+EJWAzMoRzqRpGl+uOcwbv+wiO89CsJ8Xr/Vrxm0ta9g6fAshhBAurWhnfasgGckX7kGSfCGEEHZz/NwFnv1uKyv3qeWFujYI5+1BLYgOkSUbhRBCuBEZyRduTJL8cpKRUeHJ5PdblJXZovH1uiTeXrKb9Ox8/LyNvHBzY+7pWAejrHsv3JT8LRT2JL9PbsaayBcdyZc5+cJNSJJfRtbl0LKysvD3l5Ep4ZmysrKAwt93Ia5kU9IZXv5xO9uT0wFoGRPKlDtbUj8iSOfIhCgfudYLR5Brq5uxJvJFR/KlXF+4CUnyy8hkMhEaGsqJE+ruXkBAgMwxFR5D0zSysrI4ceIEoaGhmEyydrm4vFMZOby5ZDfzNxwFINjPi2d6x3N3xzqYZPReuDG51gt7kmurm7KN5JdQrp+XBTkZ4Cs3s4VrkiS/HKKiogBsF38hPE1oaKjt91yIi5ktGnPWHmbyb3tIz84H4M52tXi2TyPCg3x1jk4I+5BrvbA3uba6GdtIfpFyfd8g8A5QSX7mCUnyhcuSJL8cDAYD0dHRVK9enby8PL3DEcKuvL29ZZRBXNbGw6cZ/8MOdh5XpflNawTz6u3NaFtHlmYUnkWu9cKe5NrqhkoayQcIDIezSZCZBlXrOT8uIUpBkvwKMJlM8gdbCFEppGXk8H+/7ua7jYWl+f/p04ihHWpLab7waHKtF6ISspghS60SU2xOPqik/2ySdNgXLs2odwAffPABsbGx+Pn50bFjR9atW3fF/adOnUp8fDz+/v7ExMTw1FNPkZ2dbXt94sSJGAyGYl+NGjVy9LchhBAeKd9sYVbCIXpOXmFL8Ae3i2H5Mz249xqZey+EEMIDZZ0GzQIYICC8+GvWpD9TknzhunQdyZ83bx5jx45l+vTpdOzYkalTp9K7d2/27NlD9erVL9n/66+/5vnnn2fGjBl07tyZvXv3MmLECAwGA1OmTLHt17RpU/744w/bv728pGBBCCHKakPiacb/uINdBaX5zWqq0vw2taU0XwghhAezJvABVcF0UR5hXUYvQzrsC9ela/Y7ZcoUHnjgAUaOHAnA9OnTWbx4MTNmzOD555+/ZP9Vq1bRpUsXhg4dCkBsbCxDhgxh7dq1xfbz8vKSxiZCCFFOJ8/nMOnXXSzYlAxAiL83/+kdzxApzRdCCFEZXG4+PhQm+bKMnnBhupXr5+bmsnHjRnr16lUYjNFIr169WL16dYnv6dy5Mxs3brSV9B88eJBffvmFm2++udh++/bto0aNGtSrV4+7776bpKSkK8aSk5NDenp6sS8hhKhs8s0WZiYc4rrJK1iwKRmDAe5qr0rz75HSfOEEZZnC16NHj0um5xkMBm655RYnRiyE8Egldda3knJ94QZ0G8lPS0vDbDYTGRlZ7PnIyEh2795d4nuGDh1KWloaXbt2RdM08vPzefjhh3nhhRds+3Ts2JFZs2YRHx/P8ePHeeWVV7j22mvZvn07VapUKfG4kyZN4pVXXrHfNyeEEG5m3aHTvPzjdnannAegRa0QXr29Ga1iQvUNTFQaZZ3Ct2DBAnJzc23/PnXqFC1btmTQoEHODFsI4YlKM5Iv5frCheneeK8sVqxYwRtvvMGHH37Ipk2bWLBgAYsXL+a1116z7XPTTTcxaNAgWrRoQe/evfnll184e/Ys8+fPv+xxx40bx7lz52xfR44ccca3I4QQujuRns1T8zZz58er2Z1yntAAb17v34yFj3SRBF84VdEpfE2aNGH69OkEBAQwY8aMEvevWrUqUVFRtq+lS5cSEBAgSb4QouKso/QXd9Yv+pyM5AsXpttIfnh4OCaTidTU1GLPp6amXnY+/fjx47n33nu5//77AWjevDmZmZk8+OCDvPjiixiNl96zCA0NpWHDhuzfv/+ysfj6+uLr61uB70YIIdxLvtnCF6sP87+le8nIyS8oza/Ns73jCQv00Ts8UclYp/CNGzfO9tzVpvBd7PPPP+euu+4iMDCwxNdzcnLIycmx/Vum5gkhLss6Sh9YQrm+zMkXbkC3kXwfHx/atm3LsmXLbM9ZLBaWLVtGp06dSnxPVlbWJYm8de1aTdNKfE9GRgYHDhwgOjraTpELIYT7MVs0jp29wNqDp5i/4Qi3TPuH137eSUZOPi1rhfDDI12YdEdzSfCFLq40hS8lJeWq71+3bh3bt2+3DQKUZNKkSYSEhNi+YmJiKhy3EMJDXWkk35rkZ5+D/JxLXxfCBejaXX/s2LEMHz6cdu3a0aFDB6ZOnUpmZqat2/6wYcOoWbMmkyZNAqBv375MmTKF1q1b07FjR/bv38/48ePp27evLdl/5pln6Nu3L3Xq1OHYsWNMmDABk8nEkCFDdPs+hRDC0TRN4+T5HI6cucDRM1kcPXOBI6ezOFKwfezsBfLMxW+GhgZ481yfRgxuF4NRmuoJN/b555/TvHlzOnTocNl9xo0bx9ixY23/Tk9Pl0RfCFGyK83J9w8DozdY8tRofkgt58YmRCnomuQPHjyYkydP8vLLL5OSkkKrVq1YsmSJ7U5+UlJSsZH7l156CYPBwEsvvURycjIRERH07duX119/3bbP0aNHGTJkCKdOnSIiIoKuXbuyZs0aIiJKKLcRQgg3oWkapzNzVfJeJIm3/jv5zAVy8i1XPIaX0UCNUH9iqvrTrGYID3erLyP3wiWUZwqfVWZmJnPnzuXVV1+94n4yNU8IUWpX6q5vMKjR/PPH1M0ASfKFCzJol6tzr8TS09MJCQnh3LlzBAcH6x2OEKISOpSWyVdrDpOYlmlL6rNyzVd8j9EA0SH+1Arzp1ZYADFVCx7D/KlVNYCoYD9ZBs+Nefq1qWPHjnTo0IH33nsPUFP4ateuzZgxY3j++ecv+75Zs2bx8MMPk5ycTLVq1Up9Pk//eQohysligf9GgCUfntoJITUv3Wf6tZCyFYZ+Cw1vdH6MwmPZ69qk60i+EEKI4nLzLXzy9wGm/bmf3BJG5iODfQsT92KJfADRoX54m9xq0RQhbMo6hc/q888/p1+/fmVK8IUQ4rKyz6oEH0puvAfSYV+4PEnyhRDCRWxKOsO477exJ1WtVX9tXDh9mkXZkvoaof74eZt0jlIIxyjrFD6APXv28M8///D777/rEbIQwhNZ5+P7hYLXZaa0WefqZ0iSL1yTJPlCCKGz89l5vP3bHr5ccxhNg6qBPrx8axNub1UDg0HK60XlMWbMGMaMGVPiaytWrLjkufj4+MuuriN0lHESjq6D+JvV/GUh3MmVOutbBckyesK1SZIvhBA6WrozlfE/bCclPRuAAW1q8dItjaUhnhDCff38JOz+Ge6cDU1u1zsaIcrmSp31rQIlyReuTZJ8IYTQwYn0bCYs2sGv29Ua4LWrBvBG/+Z0jQvXOTIhhKgAcx4cWK62k9ZKki/cz5U661tJub5wcZLkCyGEE1ksGnPXH2HSr7s4n52PyWjggWvr8cT1cfj7yHx7IYSbS94EeZlqO3WbvrEIUR6lGcmXcn3h4iTJF0IIJ9l/4jzjFmxjfeIZAFrWCmHSHS1oUkOW7xJCeIjEvwu3U7aBpsm8fOFebHPyZSRfuC9J8oUQwsFy8s18tOIAHy4/QK7ZQoCPiadvjGdE51hZt14I4VkOrSzcvnAG0o+VvM64EK4qo2B0vjRz8rNOgcUMRqnEE65FknwhhHCg9YmnGbdgG/tPZADQMz6C1/o1o1ZYgM6RCSGEneXnwJG1ats3GHLSIXW7JPnCvZSmu35ANcAAaCrRv9K+QuhAknwhhHCA9Ow83vx1N3PWJgEQHuTDhL5NubVFtCyLJ4TwTEc3QH62GgGt1x22fatK9hv21jsyIUqvNCP5Ji+V6GelqZJ9SfKFi5EkXwgh7GzJ9uO8/OMOTpzPAWBwuxjG3dyI0ABZFk8I4cESC0r1Y7tCVHOV5Kdu1zcmIcpC00o3Jx9UYp+VVri/EC5EknwhhLCT4+cuMOHHHfy+MxWAuuGBvNG/OZ3qV9M5MiGEcALrfPy610JoHbWdIh32hRvJPgfmXLV9pZF8gMCCJW8z0xwbkxDlIEm+EEJUkMWi8dXaw7y1ZA8ZOfl4GQ083L0+Y65rgJ+3NOMRQlQCeRfg6Dq1HdsN/ApWDTl1AHIzwSdQv9iEKC3rkni+weDtd+V9pcO+cGGS5AshRAXsPJbOSz9sY1PSWQBaxYTyfwOa0yhKlsUTQlQiR9erEdAq0VCtvlo2L7C6KmU+sQtqtdM7QiGuzpqwB16lVB8K5+FLub5wQZLkCyFEGeTkm1l/6AzL95xg+e4THEzLBCDQx8SzfRpxzzV1ZFk8IUTlYy3Vj71WJfig5uUfWKZK9iXJF+6gNJ31raw3AqyN+oRwIZLkCyHEVaScy7Yl9Qn708jMNdte8zIauKFJJONvbUKNUH8doxRCCB0VbbpnFdWsMMkXwh3YOuuXYiTfuk+mJPnC9UiSL4QQF8k3W9h85Cx/7j7B8j0n2XU8vdjr4UG+9IyPoGej6nSNCyfYz1unSIUQwgXkZqnl80A13bOKbK4epcO+cBdlGcmXcn3hwiTJF0II4FRGDn/vO8mfu0/y996TnLuQZ3vNYFBz7XvGV+e6RtVpEh2MUUryhRBCObIGLHkQXAvC6hY+H9VMPabuAIsFjEZ94hOitGxz8qVcX7g3SfKFEJWSxaKx41g6y/ec4M/dJ9hy9CyaVvh6iL833RtG0LNRBN0bVqdqoKxxL4QQJSq6dJ6hyA3QanFg8oXcDDibCFXr6RKeEKVmLb0PKkvjvZOgacV/94XQmST5QohKIz07j3/2pbF89wlW7D3JyfM5xV5vEh1Mz0YR9IyvTquYULxMMuokhBBXlVik6V5RJi+o3hiOb4aU7ZLkC9dXlpH8gHD1aMmD7LPgH+awsIQoK0nyhRAezWLR+GnrMb5Zl8SGxDPkWwqH6wN9THSNC6dnfHV6xFcnKuQqa+IKIYQoLuc8JG9S23WvvfT1qGYFSf42aHKbU0MToszKMiff2w98QyDnnCrZlyRfuBBJ8oUQHuuffWlM+nUXO44VNs6rFxHIdfHV6dmoOu1iw/D1MukYoRDCUxxKyyQ8yIcqla0RZ9Ia0MwQWgdCa1/6ujTfE+5C08rWXR9UWX/OOXVzIKKh42IToowkyRdCeJwdx87xf7/uZuW+NACq+HrxQLd63N6qBnWqBeocnRDC07zy0w5mrUrkpVuacF/Xuld/gyc59Ld6LGkUHwqb76VIki9cXG4G5F9Q26UZyQdV1n9qf2GZvxAuQpJ8IYTHOHomi3d+38sPm5PRNPA2Gbjnmjo8dl2cNM4TQjhM/YggNA2+WJXIiM6xmCrT6hu2+fjdSn49siDJP5cEF86Cf6gzohKi7KyJuncg+JRyQCCwYF5+ZppjYhKinCTJF0K4vbNZuXywfD9frDpMrtkCwG0ta/DMjfHUrhagc3RCCE93R5uavLVkN0mns/hz9wluaBKpd0jOkX0Ojm9R25cbyfcPhZDaKslP3Q6xXZ0WnhBlUpbO+la2Dvsyki9ci7SOFkK4rew8Mx//dYBuby3n05WHyDVb6FSvGovGdGHakNaS4AshnCLAx4shHdR89JkJh3SOxokOrwLNAlXrQ3CNy+8nJfvCHZSls76VdV8p1xcuRkbyhRBux2zR+OHfZN75fQ/HzmUD0CiqCs/d1IgeDSMwyFq1Qggnu7dTHT5deZBVB06xOyWdRlHBeofkeIcKSvUvN4pvFdkM9vwCqdscH5MQ5VWWzvpW1lF/axWAEC5CknwhhNvQNI2/9p7k/37dze6U8wBEh/jx9I3x9G9ds3LNgxVCuJRaYQH0bhrFr9tTmJWQyP8NaKF3SI6XWNB0L/YqSX5UQYd9GckXrqysnfWL7itJvnAxkuQLIdzC9uRzTPp1Fwn7TwFQxc+LR3s2YETnWPy8ZRk8IYT+Rnapy6/bU1j4bzLP9mnk2Q0/s04XJu1XTfILyvVP7AJzPpjk46dwQeUZyZdyfeGi5K+sEMKlHTmdxdu/7WHRlmMA+JiMDOtUh0d7NiDMkz9ACyHcTvvYMJrWCGbHsXS+WZfEoz0b6B2S4xxOADQIj4cqV2k0GBoLPkFqibJT+6B6Y2dEKETZ2Obkl6XxnozkC9ckjfeEEC7pTGYur/60k+veWWFL8Pu3rsmyp7vz0q1NJMEXQrgcg8HAyC51Afhy9WHyClb78EilnY8PYDRCZFO1LSX7wlXZuuuXYyQ/LwtyMuwfkxDlJEm+EMKlZOeZ+XDFfrq9tZwZCYfIM2tcGxfOz4915X+DWxFTVTrmCyFcV9+W0YQH+ZCSns1vO1L0DsdxEguS/KuV6ltFFpTsS/M94arK013fJxC8/NW2jOYLFyJJvhDCJWiaxvwNR+jx9greWrKH8zn5NIkOZvaoDnx5X0ea1QzRO0QhhLgqXy8TQzvWAWBmQqK+wThKZhqc2Km2S5vkS/M94erKM5JvMEjJvnBJMidfCKG7rNx8np6/hV+3q1GvmqH+PNO7Ibe3rIlROuYLIdzMPR1r89GK/Ww8fIatR8/Solao3iHZl3UUv3pTCKxWuvfYknwZyRcuKDdL9YyAss3JBzXyfzZJmu8JlyIj+UIIXR05ncUdH67i1+0peJsMPH9TI5Y93Z3+rWtJgi+EcEvVg/24pXk04KGj+WWZj29VvTFgUB3MJRkSrsbaWd/LD3yrlO291pH/TPm9Fq5DknwhhG7WHDzF7R8ksDvlPOFBPnzzwDU83L2+LIknhHB71gZ8P289xon0bJ2jsbOyzscHNXe5Wn21LaP5wtVkFJTaB1ZXJfhlERiuHjPT7BuTEBUgSb4QQhdfrTnMPZ+t5XRmLs1qBrNoTFfaxVbVOywhhLCLljGhtKkdSp5Z46u1SXqHYz/nUyBtL2CA2C5le6+1ZD9V5uULF2MdhQ8qY6k+FDbqkwoV4UIkyRdCOFVuvoUXF27jpR+2k2/R6NuyBt8+1Jkaof56hyaEEHZlHc3/eu1hcvLNOkdjJ4n/qMeo5uAfVrb3WjvsS/M94WrK01nfSsr1hQuSJF8I4TSnMnK45/O1zFmbhMEAz/aJZ9pdrfD3kfJ8IYTn6dMsiqhgP9Iycvlpy3G9w7GP8pTqW0nzPeGqbJ31yzOSX/CeDOmuL1yHJPlCCKfYeSyd295PYN2h0wT5evHZsHY80qMBhrLOfRNCCDfhbTJybyfrcnqH0DRN54jsoDxN96ysI/lpeyHPw/oUCPdWkZH8QFlCT7geSfKFEA73y7bjDPhoFclnLxBbLYAfHu3M9Y0j9Q5LCCHsQ9Ngz6+Qfe6Sl4Z2qI2vl5Edx9JZn3hGh+DsKP0YnD4ABiPU6Vz29wfXUCX+mhlO7rZ/fEKUl21OvpTrC88gSb4QwmEsFo0pv+/hkTmbuJBn5tq4cH58tCsNqpdxeRohhHBlCx+Cb+6C1R9e8lJYoA/9W9cE1Gi+W7OO4ke3BL+Qsr/fYJDme8I12brrV6BcP/sc5OfYLyYhKkCSfCGEQ2Tk5PPwVxuZ9ud+AO7vWpeZI9oTEuCtc2RCCGFnDfuox9UfQNbpS14e0SUWgN92pHD0TJYTA7OzxL/VY3nm41tFyrx84YIqMpLvHwZGr4LjSMm+cA2S5Ash7C7pVBZ3fJjA7ztT8TEZeWdQS166tQleJvmTI4TwQE36qeQ19zz8879LXm4UFUzn+tWwaPDl6sPOj89ebPPxu5X/GFHSYV+4INtIfjmSfINB5uULlyOfuIUQdrVqfxq3ffAPe1MzqF7Fl3kPXcOAtrX0DksIIRzHaITrXlLb6z5Va8lfxLqc3jfrksjKzXdmdPZxNgnOHgaDCWpfU/7jWJvvpW5TvQyE0FteNuQU9NMoT3d9kA77wuVIki+EsAtN05iVcIh7Z6zjbFYeLWNC+emxrrSuXcZ1lIUQwh017A212kP+Bfh78iUvX9eoOrWrBpCenc+CTck6BFhB1lH8mm3AtwJ9VSIagdFbzV8+d9Q+sQlREdbRd5MP+IWW7xjSfE+4GEnyhRAVlpNv5vnvtzHxp52YLRp3tK7JvAevITLYT+/QhBDCOQwGuG682t44C84UL8s3GQ0MK1hOb9aqRPdbTi+xIMmvyHx8AC8fiIhX29J8T7gCa2IeGKH+Py4Pa5l/hiT5wjVIki+EqJCT53MY+ula5m04gtEAL97cmHfubImft0nv0IQQwrnqdVfz1S158Ndbl7x8Z/sYAn1M7D+RwT/703QIsJw0rch8/Aom+VBYsi/N94QrqEhnfavAcPWY6Ub/XwuPJkm+EKLcth09x23v/8PGw2eo4ufFzJEdeKBbPQzlvRMuhBDu7rqX1eOWryFtX7GXgv28GVjQo2RmQqKTA6uAM4cg/agqs4+pwHx8qyhJ8oULqUhnfSsp1xcuRpJ8IUS5/Lg5mYHTV3H8XDb1IgL58dEudG9YgbvgQgjhCWLaqyX1NAssf+OSl4d3jgXgz90nOJSW6eTgysk6il+rHfgEVPx4tuZ7Uq4vXIC1xL48nfWtpFxfuBhJ8oUQZWK2aLy5ZDdPzN1MTr6FnvER/PBoF+pFBOkdmhBCuIaeL6rHHQsuGa2uFxFEz3h1Q/SLVYlODqyc7DUf3yqquXo8fQhyztvnmEKUlzUxL29n/aLvlSX0hIuQJF8IUWrp2Xk8OHsDH604AMDD3evz2fD2BPt56xyZEEK4kOgW0LS/2v7z9Uteti6n9+2GI6Rn5zkzsrKz93x8UPOXq0QDGqTutM8xhSivTHuM5EuSL1yLJPlCiFI5lJZJ/w8SWLb7BL5eRt69qxXP39QIk1Hm3wshxCV6vAAGI+z9FY6sL/bStXHhNKgeRGaumW83uPgycqf2Q0YKmHyhVgf7HddWsi/z8oXOrI33KjIn33qDIOsUWMwVj0m4D02D/By9o7iEJPlCiKv6e+9Jbn//Hw6czCQq2I9vH+7E7a1q6h2WEEK4roiG0HKI2l7+32IvGQwGRhTMzf9iVSJmiwsvp3fob/UY0wG87bgsqq35nszLFzoruoReeQVUAwyqF0fWKbuEJdzE2SR4oyZ81gssFr2jsZEkXwhxWZqm8dnKg4yYuY707Hza1A5l0WNdaFErVO/QhBDC9XV/TnWkP7iisOS9wB1tahLs50XS6Sz+3O3CzbrsPR/fSprvCVeRYYfu+iavgkQfab5X2RzbpJZNNeeB0XVSa9eJRAjhUrLzzDzz7Vb+u3gXFg3ubFeLbx68hupV7DiSI4QQniysDrQdrrb/fE2VdRYI8PFiSIfaAMxMOKRHdFenaZD4j9q213x8q6gW6jF1p5Q3C/3k50L2WbVdkTn5IPPyK6vkTeqxZht947iIJPlCiEukpmdz1ydr+H7TUUxGAxP6NuHNAS3w9TLpHZoQQriXa58BLz84shb2LS320r2d6mA0wKoDp9idkq5TgFdwcrdKWLz8oWZb+x67Wn113LxM1WVfCD1YE3KDCfzDKnYs6bBfOR37Vz3WkCRfCOHCNh85S9/3/mHzkbOE+Hsze1QHRnapi8EgDfaEEKLMgqOhwwNq+8/Xis3ZrBUWQO+mUQDMSkjUIbirsE4xqN0RvHzte2yjCao3VtvSfE/opeh8/IqWWlsrAaRcv/KwWODYZrUtI/lCCFe1YNNR7vx4NSfO59AwMohFY7rQpUG43mEJIYR76/IU+FSBlK2wa1Gxl6zL6S38N5nTmbl6RHd5iQVN9+w9H99Kmu8Jvdk661eg6Z6VdU5/piT5lcapfZB7HrwDIDxe72iKkSRfCIHZovH64p2Mnb+F3HwLvRpHsuCRLtSpFqh3aEII4f4Cq0GnR9T28jeKzUFvHxtG0xrB5ORb+GZdkk4BlsBiKTIfv5tjzmGbly9JvtCJbSS/gvPxAQILBkUy0yp+LOEekjeqx+iWqvmiC5EkX4hK7lxWHiNnrefTlWpO5GPXNeCTe9sS5Otaf6yEEMKtdXoU/EIhbQ9snW97uuhyel+uPkye2UWWYDqxAy6cAe9AqNHaMeewdthPkXJ9oRN7dNa3knL9ysfadM/F5uODCyT5H3zwAbGxsfj5+dGxY0fWrVt3xf2nTp1KfHw8/v7+xMTE8NRTT5GdnV2hYwpRWe0/kUG/DxP4e+9J/LyNfDC0DU/fGI/RKPPvhRDCrvxCoOuTanvFJNXVu0DfljWoFuhDSno2S7an6BPfxWzz8a8Bk7djzhHZVD2mJ0PWacecQ4grsTbJC5RyfVEOx1yzsz7onOTPmzePsWPHMmHCBDZt2kTLli3p3bs3J06U/D/H119/zfPPP8+ECRPYtWsXn3/+OfPmzeOFF14o9zGFqKyW7z5B/w8SOJSWSc1Qf74f3ZlbWkTrHZYQQniuDg+q0b6zh+HfL21P+3mbuLujWk5v1qpEnYK7SGJBkm/vpfOK8guG0DpqW0r2hR7sOpJfcKMgQ7rrVwr5uYVVSI6qdqoAXZP8KVOm8MADDzBy5EiaNGnC9OnTCQgIYMaMGSXuv2rVKrp06cLQoUOJjY3lxhtvZMiQIcVG6st6TCEqG03T+GjFAUZ9sZ7zOfl0iK3Kj2O60LRGiN6hCSGEZ/MJhG7PqO2/34a8C7aX7rmmDt4mAxsPn2Hr0bP6xGdlMcPhBLUd66D5+FZRzdWjNN8TerDrnPwiS+hpWsWPJ1zbiR1gzlXTsKrW0zuaS+iW5Ofm5rJx40Z69epVGIzRSK9evVi9enWJ7+ncuTMbN260JfUHDx7kl19+4eabby73MQFycnJIT08v9iWEJ8rOM/PkvM28uWQ3mgZDOtTmq/s7Eh5k56WRhBBClKztCAiuBeePw/rPbU9XD/bjluaqmmqm3svppWyD7HNqRYDolo49lzXJl5F8oQd7dte3JvmWPMg+W/HjCdeWXKRU3wWXmdYtyU9LS8NsNhMZGVns+cjISFJSSp6PNnToUF599VW6du2Kt7c39evXp0ePHrZy/fIcE2DSpEmEhITYvmJiYir43Qnheo6fu8Cg6av5cfMxvIwGXuvXjEl3NMfHS/fWHEIIUXl4+UL3Z9X2P1Mg57ztJetyej9vPcaJ9OyS3u0c1lL9Op0d3zHa1nxvq2PPI0RJ7DmS7+0HvgVVkVKy7/mOuW7TPXCBxntlsWLFCt544w0+/PBDNm3axIIFC1i8eDGvvfZahY47btw4zp07Z/s6cuSInSIWwjVsPHyavu8lsC35HGEB3nx1f0fuvaaO3mEJIUTl1GqoKu/MOgVrptuebhkTSpvaoeSZNb5aq+NyeoecMB/fKqogyT+5B8x5jj+fEFbm/MKGj/aYkw+FFQHSfM/zJf+rHl2w6R7omOSHh4djMplITU0t9nxqaipRUVElvmf8+PHce++93H///TRv3pz+/fvzxhtvMGnSJCwWS7mOCeDr60twcHCxLyE8xfz1RxjyyVrSMnJoFFWFRWO6ck29anqHJYQQlZfJG3oUNA1e9Z5aqq6AdTT/67WHyck3Oz82cz4cXqW2Y52Q5IfWAd9gNbc1ba/jzyeEVVYaoIHBCAF2+lxUdF6+8Fy5mXByl9qWkfzifHx8aNu2LcuWLbM9Z7FYWLZsGZ06dSrxPVlZWRiNxUM2mUyAaiZWnmMK4anyzRZe+WkHz36/lVyzhZuaRfH96M7EVA3QOzQhhBDNBkD1JpBzDhKm2Z7u0yyKqGA/0jJy+WnLcefHdXwL5J5XS/5Z58s7ksFQpGRf5uULJ7J21g+oBkaTfY4pHfYrh+NbQbNAlWgIds2VqXQt1x87diyffvopX3zxBbt27WL06NFkZmYycuRIAIYNG8a4ceNs+/ft25ePPvqIuXPncujQIZYuXcr48ePp27evLdm/2jGFqAzOZuUyYuZ6W/Omp3o15IOhbQj0dfDcSiGEEKVjNELPF9X22um2hMPbZOTeTmo61cyEQ2jO7tKd+Ld6rNPVfonP1VhL9lO3Oed8QoB95+NbWcv+pVzfs7n4fHwAXT/xDx48mJMnT/Lyyy+TkpJCq1atWLJkia1xXlJSUrGR+5deegmDwcBLL71EcnIyERER9O3bl9dff73UxxTC0+1NPc/9X2wg6XQWAT4mptzZij7NLj9dRQghhE4a3aI+JB7bBCunwE3/B8DQDrWZtmwfO46lsz7xDB3qVnVeTM6cj29lG8mXJF84kT0761tZbxhkSJLv0Wyd9VvrG8cV6D6sN2bMGMaMGVPiaytWrCj2by8vLyZMmMCECRPKfUwhPNnvO1J4at5mMnPN1Arz59Nh7WgcLT0m3FJ2Omz7FlrcCb5V9I5GCOEIBgNc9xJ8dQds+Bw6j4GQWoQF+tC/dU3mrj/CzIRDzkvyzXmQtEZtO2M+vlVUkXJ9TXPJ5aiEB3LESH5geMGx0+x3TOF6kjeqRxceyXer7vpCiJJpmsb7f+7jwS83kplrplO9aiwa01USfHe2+gNYPBZ+H693JEIIR6p/HdTpohrP/f227ekRXWIB+G1HCkfPZDknluRNkJcJ/lVVvwBnqd5ENT/LSoOM1KvvL4Q9WEfb7dVZv+ixpFzfc2WdhjOH1HYN1x3JlyRfCDeXlZvPmK//ZfLvqivx8E51mH1fB6oG+ugcmaiQEzvU446FkJ+rbyxCCMcxGOC6gpt5/34Fpw8C0CgqmM71q2HR4MvVh50Ti3U+fmxX1TPAWbz9oVqc2pbme8JZrB3wA6VcX5TBsYKl88LqQoATp1KVkST5Qrixo2eyGPjRahZvO463ycCkO5rzyu3N8DbJ/9pu73Siesw+CweWXWlPIYS7q9MJGvQCSz6s+D/b0yM6xwLwzboksnLzHR+HbT5+N8ef62K2kv2tzj+3qJwcMpIvS+h5PGvTvZquW6oPkuQL4bbWHTrN7e8nsPN4OtUCffj6gWsY0qG23mEJe9C0wlIwgG3f6ReLEMI5rntJPW6dDyfU+svXN46kdtUA0rPzWbAp2bHnz8+BI2vVtjPn41tZm++lyki+cBLbSL495+QXJPl5WWotdeF5kgtG8l14Pj5Iki+EW/p6bRJDP13DqcxcmtYIZtFjXWkf67olQ6KMMtMgN6Pw33t+kQ8LQni6Gq2hcV9Ag+Vq1SCT0cDwgtH8WasSHbuc3tENkJ+tEp6IeMed53KimqtHKdcXzmIbybdjub5PEHj5Fz++8Cy2kfy2+sZxFZLkC+FG8swWxv+wnRcWbiPfonFri2i+e7gzNUP99Q5N2FPBnFyCa0JYrBoR2LtE15CEEE7Q80XAALt+ss37HNSuFoE+JvafyGDlPgd27E4sKNWP7apPd3trkn9qH+RdcP75ReViMatGj2DfkXyDQUr2PVn6cTh/XDUKjW6hdzRXJEm+EG7idGYu936+li/XHMZggP/0jue9Ia3x9zHpHZqwN2upftV60GyA2t72vX7xCCGco3pjtWwmwJ//BSDYz5tB7WIAmJlw6HLvrDjbfHwdSvUBgiIhIBw0i226ghAOk3Va/a5B4bJ39iLN9zyXdRQ/ojH4BOoby1VIki+EG9h1PJ3b3v+HNQdPE+Trxaf3tuPRng0wyFrCnum0NcmvW5jk718KF87qFpIQwkl6PA9GL9j/BxxeDcDwzrEYDLB8z0kOnMy4ygHKIe8CHF2ntmN1aLoHagTU1nxvm/PPf3QjfNwdDix3/rmF81mXuPOvCiZv+x47UEbyPVaytVTfdZfOs5IkXwgXt2T7cQZ8tIqjZy5Qp1oACx/pTK8mkXqHJRzJOpIfVhcim6o7xuZcVcIrhPBsVetB63vU9p+vgaZRNzyQnvFqdPCLVYn2P+eRdepvTJVoqFbf/scvLb2a72ka/PofOL4Zfn9J/Vt4Nkd01reScn3PZR3Jd/GmeyBJvhAuy2LR+N/SvTz81Saycs10bRDOj492IS6yit6hCUcrOpIP0LxgNH+7dNkXolLo9iyYfOFwAhz4E4BRXdTfg+82HuXchTz7ns82H/9afebjW+nVfO/AMkjeqLZTt0PSaueeXzifrbO+HZvuWUm5vmfSNFuvFFdfPg8kyRfCJWXm5DN6zkbeXbYPUB/uZo1sT2iAj86RCacoOpIPhSX7h/6G86n6xCSEcJ6QmtD+PrVdMJrfpUE1GkYGkZVr5tsNR+x7Pr3n41tZk/zU7c4bTdc0WPGm2vYumGO77hPnnFvox6Ej+QXHzJQk36OcOQQXzoDJB6o31Tuaq5IkXwgXc+R0FgM+WsVvO1LxMRl5a2ALXu7bBC+T/O9aKeScLxxhsI7kV62nlmrRLLDzB91CE0I4UdexKuk89i/sXozBYGBEZ/U3YdaqRMwWOyXBuZmFo9ixXe1zzPIKb6g+QOekw9nDzjnnob9UPwKTLwyerZ7b9ZPqoi08lzUBt2dnfStrI79MB66GIZzPOh8/qjl4uf6gm2QNQriQVQfSuO39f9idcp6IKr588+A13FnQVVlUgKap5XLcgbVUP6Aa+IUUPt9soHrcJiX7QlQKQRFwzcNq+4+JcHAF/VtGEhrgzdEzF1i6005VPUlrwJIHwbUKq4f0YvKGiHi17ayS/b/eUo9tR0CDXlC7M1jyYeMs55xf6COj4GZ6kJTri1JKdp/5+CBJvhAuQdM0Zq9O5N7P13EmK48WtUJYNKYLbeuE6R2aZ1j6MrweDSf36B3J1V1cqm/VtD9gUCNOZ5w0wiWE0Ffnx8AvVK0dP/t2/N9txOxqs+lp/Jcv/7HT37PEIqX6rrBiS2SRkn1HS/xH9T0w+UCXJ9RzHR5QjxtnQn6u42MQ+nDkSL6U63sma9M9N5iPD5LkC6G73HwLLyzcxss/7sBs0ejXqgbzH+pEdIi/3qF5hvxc2DATzDmwf5ne0VzdxU33rIKjC0tpt3/v3JiEcIIPPviA2NhY/Pz86NixI+vWrbvi/mfPnuXRRx8lOjoaX19fGjZsyC+//OKkaJ3EPwxG/gqt71VLfV04TYuTPzHT522mHx/M2S+Hwc4fVcl9eR0q0nTPFdia7zlhGT3rKH7re1QfBIDGfSEoCjJSYdcix8cg9OHIOfnWZn7Z5yA/x/7HF85nzofjW9S2jOQLIa4mLSOHuz9bwzfrjmAwwLibGvG/wa3w8zbpHZrnSFwJuefV9ql9+sZSGpcbyQdoXlCyL0m+8DDz5s1j7NixTJgwgU2bNtGyZUt69+7NiRMlj4Tl5uZyww03kJiYyHfffceePXv49NNPqVmzppMjd4LIJnD7+/DMPhj+E7R/gLOmcKoYLhB64EeYPwzeqg9z74at81ViUVo55wu7RevddM8qyknL6CWtUfPxjV7Q9anC503e0G6k2l73qWNjEPpxZHd9v1D1ewUyL99TpO2BvCzwCYLwOL2jKRVJ8oXQyf4TGdz+fgLrE89QxdeLGcPb81D3+hhcoVzSk+wpMrJ3ar9+cZTW5UbyARrfBkZv9eH3xG7nxiWEA02ZMoUHHniAkSNH0qRJE6ZPn05AQAAzZswocf8ZM2Zw+vRpfvjhB7p06UJsbCzdu3enZcuWTo7ciUxeULcb3DKZA/eu5Y6ciXxuuQVzcG3IvwC7f4YFD6iE/6uBsGn21ROMpDWgmSG0DoTWds73cTWRBUn+mUTITnfceayj+K2GXvq9tx2hkrQjawpH74TnsFgKk3xHjOQbjYU3D6Rk3zNY5+NHtwKjewzESZIvhA62Hj3LoOmrSD57gbrhgSx8tAs9GzngQlPZaRrs+bXw36cO6BdLaV1pJD+gKjS4Xm3LaL7wELm5uWzcuJFevXrZnjMajfTq1YvVq0ter3zRokV06tSJRx99lMjISJo1a8Ybb7yB2Xz5Bps5OTmkp6cX+3JXbepUI79me17LvZsPWnwPD/0N1z4D4fGqid7+pbDoMZgcB7NuVSPS6ccuPdChv9Wjq4zig/o7F1xQkZG6wzHnOLoRDiwDg0mtYnCxKlHQ5Ha1LaP5nif7rGquCI4ZyS96XGuDP+HebPPxW+sbRxlIki+Ek63an8aQT9bYGux9P7ozDaoH6R2WZzq+BdKT1dJIoLYrMnfV0fJz4dxRtV21Xsn7WLvsb//OeetIC+FAaWlpmM1mIiMjiz0fGRlJSkpKie85ePAg3333HWazmV9++YXx48fzzjvv8N///vey55k0aRIhISG2r5gY9125xGAwMKqLuhH45dokciOaw/XjYcw6eHQdXPcSRLVQy24mroRfnoEpjeGzGyBhmholh8Kme7Hd9PlGLifSwSX7fxeM4re8q+SqKYAOD6rHbd9B1mnHxCH0YZ2P7xcCXr6OOYc03/Ms1pH8mm31jaMMJMkXwol+25HCiJnrycw107l+Nb5+4BqqBrr+WptuyzqKH3eDaloFrj2afzZJfSj3Drx8CWH8TeDlD6cPFs6lFaKSsVgsVK9enU8++YS2bdsyePBgXnzxRaZPn37Z94wbN45z587Zvo4cOeLEiO3v5ubRVK/iy8nzOfyyrcia7hHx0O0/8PBKeHwz3PhfqNVBvXZ0HSwdD++2hOnXFpaiu9JIPji2+d6xzbB3CRiMcO3Tl98vpqOKI/8CbJ5j/ziEfhzZWd/KVq4vI/luLz+nsKrITZrugST5QjjN/A1HGP3VRnLNFno3jWTGiPYE+XrpHZZn27NYPcbfDNUaqG1XnpdvK9WPvfxSVr5BEN9HbUvJvvAA4eHhmEwmUlOLr/uemppKVFRUie+Jjo6mYcOGmEyFcyMbN25MSkoKubklL3vm6+tLcHBwsS935uNl5J5r6gAwM+EQWkmVPVXrqmX47l8KY3fBzZNVF32DEVK2qpuKVetDcA0nR38V1uZ7jkjy/35bPTYbCNXqX34/g6FwNH/9Z2C5/FQQ4WYc2VnfyhXK9fNzVf8eqfqrmJTtahpUQDXX6V1SCpLkC+EEn/59kGe/24pFgzvb1eKDoW2kg76jnT2iPiAajNCwT5Ek34VH8q/UdK8oW8n+AtVASAg35uPjQ9u2bVm2rHCJS4vFwrJly+jUqVOJ7+nSpQv79+/HUuT3f+/evURHR+PjU3mqo4Z2rI2PyciWo+fYlHT2yjsH11BrwI/4WXXqv+09aDEYbnrLKbGWSWTBSP6JXfZNrlO2qQaFGKDbM1ffv9lA1Sn9TCLs/8N+cQh9ObKzvpUrlOsvewU+7Ai/PieJfkVY5+PXaHP5ARgXJEm+EA6kaRpvLdnN67/sAuDBbvV4c0ALvEzyv57DWUv1Y66BwGoQbk3yXXgZvaIj+VcSdwP4hsD5Y5C0yuFhCeFoY8eO5dNPP+WLL75g165djB49mszMTEaOVEuZDRs2jHHjxtn2Hz16NKdPn+aJJ55g7969LF68mDfeeINHH31Ur29BF+FBvtzWSo3Cz0g4VPo3BoZDm2FwxycQ1+vq+ztb1brgHaBK5e15Y9Y6it+0v5rWcDU+AdD6HrW97hP7xSH05ZSR/OrFz6WHgyvU47qPYenLkuiXl20+vvuU6oMk+UI4jNmi8cLC7Xy4Qn1Aea5PI164ubEskecs1qXz4m9Sj+5Qrl/akXwvX2jcV21v+86xMQnhBIMHD2by5Mm8/PLLtGrVis2bN7NkyRJbM76kpCSOHy+cdx4TE8Nvv/3G+vXradGiBY8//jhPPPEEzz//vF7fgm5GdokFYMn2FI6dvaBvMPZiNEH1Jmo71U4l+yd2wc4f1Xa3/5T+fe3vAwxqJN+VK8FE6TllTn54wbmusoylo+RdUL/zVqumwYpJ+sTi7oqO5LsRSfKFcICcfDOPf/Mv36xLwmCAN/o3Z3SPK8z9E/aVfQ4S/1HbjW5Rj0WTfFe9m336oHosafm8izUfoB53/gjmPMfFJISTjBkzhsOHD5OTk8PatWvp2LGj7bUVK1Ywa9asYvt36tSJNWvWkJ2dzYEDB3jhhReKzdGvLJrWCKFj3aqYLRpfrjmsdzj2Y2u+Z6cO+39PVo+N+0Jkk9K/r2o9iLtRba//3D6xCH1Z58kHeXC5fuoO0MwQEA593lTP/fUmrHxHn3jcVc55OLlHbctIvhCVW2ZOPvd/sYHF247jbTLwwdA2DO3oPo06PML+P1STlPCGhY2VqtYDDOoGQNYpXcMrkcVSuKzV5ZbPKyq2m5pPeOE0HFju0NCEEK5tZMFyet+sS+JCroc0iLNn872TewsblXZ7tuzvtzbg+/cr116GVZSOU0byC46ddUqfpo3HN6vH6JZwzcPQ6xX172WvwuoPnR+Puzq2GdAguJZjp3c4gCT5QtjR2axc7vl8LSv3pRHgY2LGiPbc3Dxa77Aqn93WUv2bC5/z9oeQgnWx01xwXv7542DOAaNXYZxXYvJS80oBtleCkv29v8O8e9W8wi1z1YU3z0NKk4WooBuaRFIrzJ+zWXn8sDlZ73Dsw9p8L9UOI/kr3wE0dU2IblH299e/Tt18zTkHW+dXPB6hL9tIvgOTtoBqgEGtYKHHwMKxzeqxRiv12PVJ6FHQ1+S3cVKVUlrWUv2arfWNoxxk/S4h7CTlXDbDZqxlb2oGIf7ezBzZnja1w/QOq/Ix58G+pWq7aJIPalT/XJIq2a9Tctdu3Vib7oXEqAS+NJoNVM2gdi+G3CzVJMoTHVkP8+5RN0GKMaj+BRGNoXrBV0QjCI9TfQuEqCRMRgMjOsfy38W7mJlwiLvax7h//xdrSf3545B5SjVQLY9TB2BbQWJelrn4RRmN0P4BlRyt+xTajnCrLtuiCE0rMpLvwHJ9kxcEVFUJfuZJ548C20byWxU+1/05dXM8YSosHquuk9bGkqJkye45Hx9kJF8Iu0hMy2Tg9FXsTc0gMtiXbx/uJAm+Xg4nqNGWwAio1a74a67cfK+0TfeKiukAIbUhNwP2/eaYuPR2NgnmDlEJft1u0P5+qNMV/KsCmupjsGcxrJwM398H07vA69Hwfns18r98EuxYqNYKlt4FwoMNahdDgI+JvakZJOx3wSlJZeVbpXDqUkWa762cokZT426s2JzaVkNVx/8TO+CwrGritrLPgTlXbTs68darw35+TmHTveiWhc8bDNBrInQcrf794xhp3ns1x9yzsz7ISL4QFbbzWDrDZqwjLSOH2GoBfHlfR2KqeuiIqjuwluo37K06NBcVHqceXTHJty2fV4Yk32CAZneou/Lbviss3/cU2enw9WA1ChLZHO76BnyD1Guapp4/sUt9ndylEvkTu9RNnrS96mvXosLjGb3V70BEI9W5u3ojVQVQte6lvytCuJkQf28Gtq3F7NWHmZlwiK5x4XqHVHGRzdSNvJRtUK9H2d9/JhG2zlXb5ZmLX5R/KLS4EzbOUhVUsV0qdjyhj8yCUn2fKmoanyMFRahrk/WczpK6Ayz54B8GoRf1hDIYoM8kyM+GjTNhwYNg8oEmtzk3RneQmaYGGqB4RYSbkCRfiApYd+g0932xnvPZ+TSODmb2qA5EVJEyYd1oGuz5VW3H33Lp69YmfK6Y5JdnJB+g+UCV5O9bqkYo/ELsHpouLGY1Mn9iJwRFwtC5hQk+qA8qQdXVV73uhc9rmirvPbFTJf0nrTcB9qiKhxM71deOBYXv8fJTUx/6vlv6qRJCuKDhnWOZvfowf+45QWJaJrHhgXqHVDFRzdWNuvJ22P/nfyrZqdcTYtpXPJ72D6gkf/fPkH4MgmtU/JjCuayj6o7srG+l10j+8S3qMbpVydNKDAa4ZYqqaNg8B74bBXfNUYMjotCxf9VjtQbqJp+bkXJ9Icrpz92p3Pv5Ws5n59M+Noy5D14jCb7eUrerOfde/iWP+ljL9U8f1Kfb7ZVYl88rTWf9oiKbQXi8Kmffvdj+cenl95dg3+8qAR/yDYTUKt37DAb1wbtBL+g8Bm7/AB74E54/Ak9shaHzVZfhFnepMkYvPzWisfkr+PM1x35PQjhY/YggesRHoGkwa1Wi3uFUXGRBh/3yNN87ewT+naO2uz9nn3iimkGdLurGwcZZ9jmmcC5ndNa3ss75d/ZIftHO+pdjNMJt70GzAWo1onn3yko9F7POx6/ZVt84ykmSfCHK4Yd/k3lw9kZy8i1c16g6s0d1JMTfW++wCu3+BaY0gf3L9I7Euayj+PV7ltyELiQGTL7q7vW5I86N7WrKU64PKqltPlBte8rcug0zYE3BEj/9PrLPBdZohLA6aqSi65Nwx8fw0N/wwjHoN13tkzAVdv5Y8XMJoaNRBcvpfbvhCOnZbt6HwrqM3sk9kJ9btvcmTFXJS+y19m202uEB9bhhZtljEvqzddZ3wkh+kE5J/sWd9S/HaIL+H0OjW9VAwTdDIDHB0dG5j2Pu23QPJMkXosy+WJXIk/M2k2/R6NeqBh/f2xZ/Hxeaz2sxw+8vQnoy/D5elS9XFtaR7PibSn7daCocKU9zoZL9rNOq1B4gLLbs7282QD0eXKHmkLmzA8th8TNqu+dLqueAIxlN0GoIdBqj/v3DIyqhEMJNXRsXToPqQWTmmvl2w1G9w6mYkBg1BcmSB2ll+P8y/Rhsmq227TWKb9XoVqgSrUaEi/b8EO7BqSP5OpTr5+eqKWlQunnkJm8YOEM1psy/AF/fCUfWOTREt6BpRUbyJckXwqNpmsbUP/YyYdEOAEZ0jmXKna3wNrnY/0a7fios/T6xAw78qW88znIuuaBEzQAN+1x+P1ecl28dxQ+KKt8yeNXqq4u5Zlad5N3Vyb0wf7j6PprfCd2ecd65e72iRvxyM2Du3arpnxBuyGBQy+mBuilttrjxjV6DQTXdBNV8r7QSpqmKrdqdILarfWMyeUPbkWp73Sf2PbZwPNucfCck+dZzZDoxyT+5S/3u+4WUftDAyxfu/BLqdlfXwK8GFs5Hr6zSk9V/N6OX6g3ihlwsOxHCNVksGq/8tJOpf+wD4KleDZnQtwlGo4utk6tpqkQRIKCgs/Kq93QLx6n2FpTq12p/5Yu3Ky6jV96me0VZS/a3f1/xePSQdVqNIOScg5iOaq6gM9ehNnnBwJlQpQac2gc/jK5cVTDCo9zRpibBfl4knc7iz91Obvplb9aS/dI23zufqrqGA3R/1jF/R9qOUKt1HFlb2ORMuAdr6XygMxrvFXwOc2aFnbVUP7pl2X73vQv639TurK7DX/Yvf8NLT2Adxa/e2PGrMDiIJPlCXEWe2cLY+ZttTYxeua0pT/SKw+DMBKS0Elequ69e/nD3t2AwwcHlcHyr3pE5nnU+fqObr7yfKyb55Z2PX1TTOwADJK1WDafcSX4uzLtH/RxCa8PgOeoDh7MFRcDgL9VyQrt/Vp25hXBDAT5eDOmgls6amXBI52gqyNZ8r5Qj+aumqWaatdqrrvqOUCUSmtyuttd96phzCMdw5ki+tVw/86TzbhrbOutfoene5fgEwtB5ULMdXDgDs29XFXaVUfJG9eim8/FBknwhrur577fxw+ZjeBkNTB3ciuEFZZAu6Z+p6rH1PWoOUdN+6t+r39crIufIOQ+H/lbb8VdJ8sPj1KMrJfm2kfwydtYvKqQm1OmstosuD+fqNA1+fgoOJ6h1i4fOd05DpMup1Q5uelNt//la5ZnuIjzOsM6xmIwGVh04xa7jbjz9xFoqm7L96olSxknVuBPUXHxH3ozv8KB63PatqkQS7kGP7vrmXMg+6/jzQZHO+q3K936/YLjne4hqAVlp8EVfOHXAXtG5j2PuPR8fJMkX4or+3nuS7zcdxWiAj+9tS7/WNfUO6fJStsGBZWr0vnNBE7HOj6nH7d/DOTdvwHQl+5epi2jV+hDe8Mr7Wkfyzx2BvAuOj6007FGuD4UN+Nypy37Cu2r5OoMRBs1SpXF6aztS3SjTLPDdfXDmsN4RCVFmNUP96d00EoBZCYn6BlMREY3Ude3CadVQ70pWvw95WVCjtVpG05FiOqhEKD8b/v3KsecS9qFpzu2u7+0HviFqO8MJHfbNeYUl9jVal/84/qFw7w9QvQlkpKgR/bNJ9ojQPVgsRVYokCRfCI+TnWdm/I/qj+XwzrFc3zhS54iuIuFd9di0f2GzlRqtVTMxSz6sna5baA635xf1GH/T1UduAqqphjRQ2KBQb/Yo1wdo0k81iUnZCmn7KhyWw+36Cf6YqLb7vAlxDv5QXloGA9z8jvr/58JpmH+v69wQEqIMRhYsp/fD5mROZ7rpcm/efoU3b1OvMEc46zSs/0xtd3PQXPyiDIbC0fz1n6mVbYRry81QHeTBOSP5UGRevhOS/JN71FJ4PlUq/nkisBoM+xGqxalBkS/6Xv0mm6c4fQBy0sHLzzUGHspJknwhLuOD5fs5fCqLqGA/nr4xXu9wruzMYdheUKLd5fHir3Uu+PeGWYXLtHkScx7s/U1tN7rl6vsbDOqiBa5Rsp93Ac4fV9sVHckPrFY4B9XVR/OPbYYFDwIatH8AOj6od0TFefvBnbPBv6qa47j4aWnEJ9xOuzphNKsZTE6+hW/WufFInK353hXm5a/5UCVxkc0vv4yqvTUbAH6hcPYw7FvqnHM6y/4/4Ms7YOciz/nbZ52P7x0AvkHOOaczO+zbSvVbgtEOKV5QdRi+SA0cnUmEL25z7nKAerE23YtqoVbTcFOS5AtRgv0nMpj+l5qDNKFvE4J8vXSO6CpWf6CWHat/3aXNVhr0UuWOuedh4xf6xOdISWvUXDf/qqore2lYS/ZdYbT7TKJ69A0B/7CKH8/WZf871/1gln4MvrlLldXWvw76/J/eEZUstLZaP9hghM1zCuf6CuEmDAYDowpG82evTiTPbNE5onKyNd+7zEj+hTOw9mO17aiO+iXxCYA296ptT1tOb+lENQVw/r3w9eDCa5U7c2ZnfSvruZxRrm9tulejlf2OGVwDhv8EITFq5ZnZt0PmKfsd3xV5wHx8kCRfiEtomsaLC7eRZ9a4rlF1+jSL0jukK8s8BZtmq+0uT1z6utFYODd/zUeqk7knsZbqN+wDRlPp3mPrsO8CzWRs8/Fj7fPBtNEtqsTs1H7XXNopN1Ml+OePq5tPg2ap5etcVf2ecP0Etf3rc3Bkvb7xCFFGt7SIJjzIl9T0HH7ZdlzvcMqnaPO9kqz9WJXXVm8CjW51XlwA7e4DDCohdoVrij2kHytYzcCglgrc9xt8cA2sfMe9P0M4s7O+lTNH8osun2dPobVV6X5QFJzYCV/2UzfWPJV1JN+N5+ODJPlCXGLBpmTWHjqNn7eRV25r6ppL5RW1/lM1xyy6FdTtXvI+zQdBUCScP+ZendevRtNg92K1fbWl84qqVl89ukK5vr3m41v5VoGGvdX2dhcr2bdYYOFD6uZDQDUYMrewP4Ir6/IENL4NLHlqVKsylCsKj+HrZeKea6zL6SXqG0x5WZP8U/vVjcKiss+pUn2Abs/Yp0y5LKrWLfyba+0J4O72/a4ea7WH0Qmqt0/+BVj2KkzvCon/6BtfeTmzs76VdSTf0XPyLebC6Szl7ax/JdXqq9L9gHDV9+ergWplI09jzlPfH8hIvhCe5ExmLq//sguAJ65vSEzVAJ0juorczMISxS5PXH4k2MsXOj6ktle957pl3GV1YpeaC2nyLdt6yLZl9FygXN/a/K8iy+ddrJm1ZH+hSqxdxZ+vqWZ7Jh8YPKfiPQicxWCAfh+q5l/nj8O3I9UHASHcxN0d6+BjMrL5yFn+TXLDEbig6gWJmab+7he17hOV6Ic3VM1H9dDhAfX47xzIydAnBnvaW5DkN7wRIuJVuXb/j1WCl7YHZt0CC0dDZpq+cZaVMzvrWzmrXD9tr7oR4x1YOJBhbxHxakTfPwySN8DcoZ7zedLqxC61YoZviFqxyY1Jki9EEW8u2c3pzFwaRgZx/7VukID8O0d1/w6LhSa3X3nfdqPUH//U7XBwuVPCczhrqX69HmVromNNqC+c0X99Y3stn1dU3I3gGwzpR+HIWvsdtyI2fw3/TFHbt70HdTrpG09Z+VZRNyZ8qsDhf2DpBL0jEqLUIqr4cmvLaMCdR/NLaL6Xc171pAHo9p/ST9myt3rXqYQg5xxsm69PDPaSnwMHV6jtuBvVo8EALe+CxzaoJUYxwJav4b22sHGWa91MvhI9RvKdVa5vK9Vv4dj/D6Kawb0L1bTAQ38Xjnp7Cut8/BqtnF8VZGfuHb0QdrQ+8TRz1x8B4I3+zfE2ufj/HuZ8WP2e2u782NX/qPuHQZthajthmmNjc5aiS+eVhU8gBNdU23qX7Nu7XB9UZ3jrvFRXKNk/vAoWFazycO0z6sOiO4poCP0/UttrPnD9FQyEKMLagO+XbcdJOZetczTlYC3ZL9p8b/3n6mZt1frQ9A594gKVDLS/X22v+9S9RzcPJ0BeJlSJVt3Fi/IPg75T4b6lahWD7LPw0xMwo/fl+yW4Ej3m5FtvKDh6mpe1B48jSvUvVqO1auoMsOtnx5/PmZI9o+keSJIvBAB5ZgsvLlSjA3e1j6FdbFWdIyqFHQvhbJIqBWt1d+nec81o1Sn84PIrL0XkDs6nQPJGtV2e5ZJcYV6+OV/9NwT7l643G6Aed/ygzqOX0wdh7t1qPnuT26Hni/rFYg+N+0LXsWp70WOQukPfeIQopWY1Q+gQW5V8i8aXaxL1DqfsIq3N9wquXbmZavoZqLn4ejfwbDVULc12YqdKlN2VtVQ/7obLTwGMaQ8ProDek8AnCI6ug4+7wW8vuvZ0BV2664cXnNvBUxuKLp/nDI37qsfdHpbkH/OMpnsgSb4QAHy28hB7UzOoGujDc30a6R3O1WkaJLyrtjs+BN7+pXtfWJ3COYur3ndIaE6z51f1WLMtVCnHCgjVCubl67mMXvpRsOSrngJVatj32PW6q+Z2WWlwaIV9j11aF86qpZcunFZ3/vtNd/vyNwCue0n1gMjLUjcwLpzVOyIhSmVkl1gAvl6bRHaeWd9gysparp+6Q5WHb5ip/r6F1lHNZfXmHwotBqvtdZ/qGkqF7PtNPVpL9S/H5AWdHoFH16kbuJoZVr8PH3RQvVdcsZpBz+76eZmXNo20F4sZjheUzdtz+bwradgbjF7qppanrCqRdwFSd6ptGckXwv0dOZ3Fu8v2AvDizY0JC/TROaJSOLBMLW/jHViwfE8ZWJfT2/4dnEu2f2zOYk3y48vQVb8o2zJ6Oo7kW+fjh9Wxf/Jr8i68obPte/seuzTM+fDtCNUMKLim6qTv4+KNLEvLaIIBn0NIbTXdYuFD7jMnVVRqNzSJpGaoP2ey8vhxs5v9/a8Wp26I5mbAyd2FN7qvfVr9vXMF1gZ8u35Sy9C5m7T9qvrK6K163ZRGSE24czYM/VbdcElPhnn3qBu8ZxIdGW3Z2UbynZjk+wSBV8FAjKNK9k8dUDcRvPxVA0pn8A+D2K5q21NG849vVTerAqsXTul0Y5Lki0pN0zQmLNpBdp6Fa+pV5Y42bvI/tfXDTdsREFDGqQU126jlcCz5sPYju4fmFDkZhY2BKpzk63gH2hGd9YtqXtBlf/fPkOfEObiaBr8+q6aFeAeqBL881RauLLAaDP5SJR17l8Dfb+sdkRBX5WUyMqxTHUA14NNccbT1ckxeUL2g0u7XZ1Ujs5AYaDlE37iKimwKdbqqRGHDTL2jKTvr0nmxXVSz0bJoeCM8skb1XTF6q4qAD66Ble9Afq79Yy2r3Cx1gwic213fYCg8n6OW0bOW6kc1d27zSWvvH0+Zl3+syHx8V18+uxQkyReV2m87Uvhz9wm8TQb+2685Bnf4nzp5k+poaiwolSsP62j+hllq6SF3c+BPMOeoVQWqNy7fMaxz8k8f0G8U1hFN94qKuUbdjc5JL/zw5gzrPoENnwMGGPCp6vbriWq0glv/p7ZXTCqcyyqEC7urfW38vU3sTjnP6gOn9A6nbKzN9xJXqseuT4GXi1XfWUfzN85Unerdia1Uv3f53u8TANePh9EJajAh/wIsexWmd4XEf+wXZ3lYu9ubfNXqM84U6Ogkv6DpnrNK9a0a3aIej65TfZLcXbLnzMcHSfJFJZaRk8/ERWruzcPd69OgehmWYNOTdRS/+SAIqVW+YzS4ASIaQe552DTbfrE5S9FS/fLemAmto0Yb8rPV3Hg9OGL5vKKMRmhW0HHaWV329y2FJc+r7RteKfwQ4Kla310wZUaDBfcXVmcI4aJCArwZ0FZVrc1wt+X0rM33QPUxaX2PfrFcTqNbVGf6zJOwc5He0ZReznlILGgYeLX5+FcTEQ/Df4L+H0NAOKTtgVm3wMLRjm9AdznWdeqDqjt/lNbRHfZty+c5qemeVXANqNlObe9e7NxzO8Ixz+msD5Lki0psyu97SUnPpk61AB7t2UDvcErn1AHYVfChofPj5T+O0QidxqjtNR+BOa/isTmLOV+VR0P5S/VBlX5ak2u95uVb5ys6aiQfoFlByf7e3yA73XHnycuGtR/DtyNBs6gP3xX5HXUnff4ParVXVTHz7lVloUK4sBGd1d+cZbtTOXzKQc3AHMHafA+g65Pg5atbKJdl8oZ2o9T2uk/0jaUsDv6lVkGpWg/C7fCZyGBQy6U+tqHg52GALV/De21h4yznV9BZR/Kd2VnfypHl+haLc5fPu1jjgpJ9d5+Xf+Fs4WdBGckXwn1tTz7HrFVqFPW125vh5+3EOUwVsfp9lUDF9YbIJhU7Vos71d3l9GTYvsA+8TnD0XWqW7tfKNTuVLFj6TkvX9McP5IP6s5+tQaqYmHPL/Y/fn4OrP8MprVW82Rzz6syzVv+5xFz2krFy0c1ngqMUGt4//S4a3aWFqJAg+pBdGsYgabB9L/cqDN2dCv1/1nV+tBmmN7RXF6b4apS7Oi6wlFWV1fRUv3L8Q9T05ru/0NNt8g+Cz89oZrzOfPvpB6d9a0cOZJ/5pC67nr5qQpNZ2tUsJTeob/de6UZa1+D0Nqq544HkCRfVDpmi8YLC7dh0aBvyxp0a6jDXd3yyDgB/85R212eqPjxvHzV8nug1hp2l6TEWhLWsHfF10W2Jvl6LKOXeVJ1w8WgLiqOYjAUjuZvs2PJvjlPjca81xYWPw3nj6n5/7dMgXsWuN48WUcLrgGDZoHBBNu+VVUNQriwB69VDT+/WXeEact0XEq0LHyD4LFN8NBfpV86Vg9VIqFpP7W93g2W09M0NdUKVAM9R6jVDh5YAb0nqRsgexar1VecxdZZX4fPfI6ck3/sX/UY2bTin4nKI7yBurlgyXdu7x9787D5+CBJvqiE5qw9zNaj56ji68X4W8rZtE0Pa6erZnO12kOdzvY5ZrtRqvt56jbVCd3VaVrhaHT8TRU/np7L6FnnbofEOL7k1Npl/+ByyKxgoy1zvrrZ9F5bNRpz7ggERcFNb6sP3+3vq3wJvlVsV7jxv2r79xfh8Cp94xHiCrrGhTPuJjXyN2XpXj5YruNyomXhF1z2zu966PCgetz2HWSd1jeWq0nZCuePq88Ddbo47jymgobBdbupfzszKbSN5Ec675xWjizXt45A61Gqb2Xrsv+TfjFUlIfNxwdJ8kUlk5qezdtL9gDwbJ94qgf76RxRKeWcVyXRAF2etF8ZdEBVaHOv2l71nn2O6Uhpe1VybPKBBr0qfjxdk3xrqX6s488VHgdRLdSd9l0/lu8YFjNsmQcftIcfH4Gzh9XoRO9J8MRm6PggeLvJ/0+OdM1oVTlhyYf5wyH9uN4RCXFZD3Wvz7N94gF4+7c97lW67+pqtVfTpfKz4d8v9Y7myqzJdr0ezulzYG3s58wkP9NDy/X16qxflHVe/v4/IO+CfnFURHJBRUTNtvrGYUeS5ItK5bWfd3I+J5+WMaEM7VhH73BKb+MXqqlXtbiKNZsryTWjwWBUy9KlbLfvse3NOopft5t9RnKsSf7ZJOcvdeTo5fMuZh3N3/Z92d5nsaiRqA+vgYUPqpssAdXghtfgiS1qVMaVy2adzWCA26ZB9abqQ+W3w11jjWghLuORHg14+oaGAPzfr7v59G9ZIcIuDAZoX7Cc3vrP1I1SV2Vd/tNRpfoXi7tBPR5e7diGsEVl6Fiub72xkGnnJF/TijTdc3Jn/aKiW6mqxLwsOOAGVaEXyzhRsMqSQd+fo51Jki8qjb/2nuTnrccxGuD1fs0wGd2kKVh+Lqz5UG13eVx1xrensFho0k9tr37fvse2t912LNUHdeH1DQY05y995oyme0U1LVhK73ACpB+7+v4WC+z4AT7qDN/fp6oo/MPg+gnwxFb1u+gT6NCQ3ZZPIAz+EnxD4Mha+O0FvSMS4ooeuz6OJ3vFAfD6L7v4/J9DOkfkIZoPVH83zya57nzlzFNwdL3arujSeaVVrb5qnmjJg0N/Oeecuo7kF9xYyD5n35u+ZxLVMU0+EKHj9FODoXC5XHfssm+djx8R7x5TgUpJknxRKWTnmRn/gxqlHtmlLs1qhugcURls/051wA+KghaDHXOOzo+px23fwrlkx5yjojJOFH4QaWinJN9gUB82wPkl+84eyQ+NgZhrAO3KqyloGuz6GT6+Vo1Cn9wFfiHQ8yWV3F87VjW/EldWrT7c8YlqMFWtvvs0thSV1pO9GvL4daq66bWfdzIrQRL9CvP2h9YFU+LWuWgDvv1/ABpENlcNRJ3F2SX7tpF8HZJ8v1AwFjTFs+e8fOt8/OpN9O+FY52Xv+dX1bvHnRzzvKZ74CJJ/gcffEBsbCx+fn507NiRdevWXXbfHj16YDAYLvm65ZZbbPuMGDHiktf79OnjjG9FuKj3/9xP0uksokP8eKqgLNEtWCyQME1tXzPacXPlaraBOl3VPOK10x1zjorauwTQCsrCatrvuHrNy3f2SD4UluxvL6HLvqbBniXwSXeYd7daCs6nCnR/TiX33f+jGl6J0ovvo/oVXDO68iwnKNzaUzc05JEe6sbnxJ928uXqRH0D8gTt7wMMcGCZPsu1Xs0+J5fqW1lL9vctdfxN0LxsyDmntoN0KNc3Got02Ldjyb51eUY95+Nb1e4E/lXVEsdJbtZ0NnmjevSgpnvgAkn+vHnzGDt2LBMmTGDTpk20bNmS3r17c+JEyf8TLFiwgOPHj9u+tm/fjslkYtCgQcX269OnT7H9vvnmG2d8O8IF7T9xno//VhfWCX2bEuSrwxIj5bXvdzWS6hsM7UY69lzW0fyNs5w3R64s9vyqHhvdcuX9ykqPJD87HbLS1LazRvJBTcswmNSSO9YPm5oG+/6AT6+Dbwar+X3egXDt0/DkVuj5AviHOi9GTxNSS+8IhCg1g8HAf3rH81B3tbze+B938PXaJJ2jcnNhsYWj1htn6RnJpcz5BSP5QFxv5567ThfwDlBd/VMd3A/IOnpu8lGj6nqwJvkZ9hzJt87Hb2W/Y5aXyauwZ9QuNyrZ1zSPXD4PypHkx8bG8uqrr5KUZJ8/+lOmTOGBBx5g5MiRNGnShOnTpxMQEMCMGTNK3L9q1apERUXZvpYuXUpAQMAlSb6vr2+x/cLCwuwSr3Avmqbx4sLt5Jk1rm9Und5NdVg6pSISpqrHdiNVybQjxd0I4fGQkw6bvnDsucoqt0gzF3vNx7eyJvlpTkzyraX6AeHOHR0PioB63dX29u/Vz/TzG2HOAFWu5h0AXZ6AJ7fB9S+r1ReEEJWKwWDg+T6NuL+rugH5wsJtzF0niX6FtB2hHjd/7VqNOI+uh+yzqm9ArXbOPbe3H9QtuB45umTfOnoeGKFfVVWgnZfR07Qiy+e5SLM4a5f93YvdZ4ra2cOq+sDoDVHN9I7Grsqc5D/55JMsWLCAevXqccMNNzB37lxycsrXlTo3N5eNGzfSq1fhUlhGo5FevXqxevXqUh3j888/56677iIwsHgDqBUrVlC9enXi4+MZPXo0p05dfm3onJwc0tPTi30Jz/D9pmTWHjqNv7eJV25visGdSmaT1kLSanXnueNox5/PaITOY9T2mo/AnOf4c5bWweWQfwFCakOknf8I6zGSr0epvlWzgpL9v96EL/vB0XXg5Qedxqhu+Te8CoHVnB+XEMJlGAwGXrylMSO7xAIwbuE25m84om9Q7izuRqgSrSq49izWO5pC+35Tjw16gdHk/PNbS/b3OjjJ17OzvpW9O+yfOwIXzqjkNLKpfY5ZUfV6qkrA9KOqYtAdWEfxI5s6Z/lIJypXkr9582bWrVtH48aNeeyxx4iOjmbMmDFs2rSpTMdKS0vDbDYTGVl8dDUyMpKUlJSrvn/dunVs376d+++/v9jzffr0Yfbs2Sxbtow333yTv/76i5tuugmzueTlSyZNmkRISIjtKyYmpkzfh3BNZzJzeeOXXQA80SuOWmEBOkdURgnvqscWgyE42jnnbH6nakqTngw7FjrnnKWxp0hXfXvfqLE23stKUxdMZ3B2072iGt8KJl/Vf8HkCx0fVsl979f16ToshHBJBoOBl29twvBOddA0eO77rXy/8ajeYbknk1dhAz5XKtnft1Q9OrtU38qa5B9dB1mnHXcePTvrW9m7XN86H796Y9dJTr39IK5g4NZduuxbm+552Hx8qMCc/DZt2jBt2jSOHTvGhAkT+Oyzz2jfvj2tWrVixowZaE4o0/j8889p3rw5HTp0KPb8XXfdxW233Ubz5s3p168fP//8M+vXr2fFihUlHmfcuHGcO3fO9nXkiNyt9gT/9+tuTmfmEh9Zhfu66pBMVcTJvQV3+w2qfNpZvP2g40NqO2Gaa5RbWcyqIRxAo5vtf3zfKmqEBZzXFEnPkXy/EBg4QzXUe/xfuOlNqBLl/DiEEC7PYDAw8bam3HNNbTQNnvluCz/866IrsLi6NvcCBji4wvlLtpbk3FE1F95ghAbX6xNDaG219JtmgQN/Ou48GdZyfR2TfHuP5Ltaqb5Vo77q0V3m5ScXVBx42Hx8qECSn5eXx/z587ntttt4+umnadeuHZ999hkDBgzghRde4O67777qMcLDwzGZTKSmphZ7PjU1laioK3/ozMzMZO7cudx3331XPU+9evUIDw9n//6Sy3F9fX0JDg4u9iXc27pDp5lXUFr4xh3N8Dbp3mOybFYVjOI3ugXC45x77naj1Nzs1G3qw4jejm5Qo+y+IapRjyM4u2Rfz5F8UKP5PV+w7yoFQgiPZDAYePW2ZgzpoBL9sfM3s2jLMb3Dcj+htQuT6U2z9Y0FCufB12qvb/+Vol32HcU6D16PzvpW9p6Tb2265wqd9YtqeKOaQpC2B9L26R3NlVnMhTdLZCQfNm3aVKxEv2nTpmzfvp1//vmHkSNHMn78eP744w8WLrx6qa+Pjw9t27Zl2bJltucsFgvLli2jU6dOV3zvt99+S05ODvfcc89Vz3P06FFOnTpFdLSTSp6FrnLzLbz0wzYAhnSIoW0dN2seln4ctsxT212edP75A6oWlhWues/557+Ydf5i3A1g8nbMOawl+85K8k8nqkc9RvKFEKKMjEYDr/drxuB2MVg0eGreZhZvPa53WO7H2oDv36/0b8BnK9V38tJ5F7Oef/9StWywI7jCSL49y/U1rbBc3xU66xflFwJ1u6ntXT/pG8vVpO2D3Aw1sBUer3c0dlfmJL99+/bs27ePjz76iOTkZCZPnkyjRo2K7VO3bl3uuuuuUh1v7NixfPrpp3zxxRfs2rWL0aNHk5mZyciRarmwYcOGMW7cuEve9/nnn9OvXz+qVSveICojI4P//Oc/rFmzhsTERJYtW8btt99OgwYN6N1bpzlHwqk+++cge1MzqBbow3N9Gl39Da5mzYdgyYPanSGmvT4xdHpElfAdWAYpDl7a5mqsS+fZu6t+Uc4cyc/PUQ1zAKrWc/z5hBDCDoxGA5PuaM7AtrUwWzQen/svS7ZLol8mDftAUKQazd37q35x5GUXVuo11Pmzce1rwKcKZJ1yXLM220i+h5TrpyerCkeDyXWa7hVl67Lv4iX71vn40S1V3wwPU+Yk/+DBgyxZsoRBgwbh7V3yqFpgYCAzZ84s1fEGDx7M5MmTefnll2nVqhWbN29myZIltmZ8SUlJHD9e/CKyZ88e/vnnnxJL9U0mE1u3buW2226jYcOG3HfffbRt25aVK1fi6+sijSmEwxw5ncW0Zao86MVbGhMa4KNzRGV04SxsKPh/p+uT+sURFgtNblfbq9/XL460/ZC2F4xehSV9jlAtrvB8jnY2CdBUB1o9O/0KIUQZGY0G3hzQgjta18Rs0Rjz9b/8tuPqjZJFAZM3tC6oQN2o41K1h/+BvCyoUsP+K9aUlckb6vdU245aSi+jyBJ6erFWEWSdUmXiFWEt1a/eGLz9K3YsR4i/BTBA8kZId+GpPdbO+jXb6huHg5Q5yT9x4gRr16695Pm1a9eyYcOGcgUxZswYDh8+TE5ODmvXrqVjx46211asWMGsWbOK7R8fH4+madxww6Uf+v39/fntt984ceIEubm5JCYm8sknn1zSwV94Hk3TePnH7WTnWehUrxr9W7vhfOONMyH3vGpE08CBSW1pdH5MPW77Fs7p1GjJ2lU/tqsqAXMU60j+6QOOKxe0Ktp0z52WdBRCCMBkNPD2oJbc3qoG+RaNMV9v4o+dqVd/o1DaDFOPB/6EM4n6xGBdsi7uBte4DllL9h2V5LtCd/2AaoBBNRms6EoCtlJ9F2u6Z1UlEmIKmqLvdqElIy9mHcmv0VrfOBykzEn+o48+WmL3+eTkZB599FG7BCVEeSzZnsLyPSfxMRn5b/9mGFzhwlUWedlqfXpQHfWNOjcLrNlWNbqz5MO6j/WJwbZ03i2OPU9YHVX2lpcF5x1cfmpruhfr2PMIIYSDmIwG3hnUkltbRJNn1nhkziaW77ZT13BPFxYL9a8DNNj0pfPPr2mw7ze1rXepvlWDgmXXjm0qHHW3F3Ne4fK4es7JN3kVNjisaMm+dSTf1ebjF9WooGTfVefl5+dCiurf5YlN96AcSf7OnTtp0+bSH0br1q3ZuXOnXYISoqz2pZ5n4k87AHi4R33qRwTpHFE5bJ0HGakQXBOaDdA7GqXz4+pxw0zITnfuuTPT4EhB1VB8H8eey+RdmHQ7el6+nsvnCSGEnXiZjEwd3Iqbm0eRa7bw0Jcb+WuvnTqHe7o2w9Xjv1+pJNSZTu1XFQQmH6jb3bnnvpzgaIhqobb3L7vyvmVlnY9vMIF/mH2PXVbWmwwVvZFh7Qjvap31i7LOy0/8p+KVC45wYgeYc9XvhF4rHTlYmZN8X1/fS5a8Azh+/DheXp7XtEC4ruw8Mws2HWXgR6u44X9/k5qeQ2y1AB7pUV/v0MrOYoZV09R2p0fBy0V6CcTdCOENISfd+Uv+7P1NlbVFNVdLDzmadanCUw5e8kXv5fOEEMJOvExG3r2rNX2aqkT/gdkbWLlPEv2rir9ZzQ/PSFHXOmeyni+2K/i60ICIo0r2i87H17tCMsgOy+ilH1cDQgaj/v0UrqRqPajeFDSz83/HSyN5o3qs0do1pqw4QJl/22+88UbGjRvHuXPnbM+dPXuWF154ocQ58kLY297U80xctIMOr//B2Plb2HD4DCajgRubRPL5iPb4eZv0DrHs9vyi7q77hRbe4XcFRiN0GqO213zk3BEHW6n+zc45n63D/gHHnuf0QfUonfWFEB7A22Rk2pDW3NAkktx8C/d/sYGE/Wl6h+XavHyg1d1qe+Ms557bWqqv99J5F7PGc2AZmPPtd1xbZ30XaHQbaIck31qqHx4PPgEVj8mRXLnLfnLBSg41PLNUH8qR5E+ePJkjR45Qp04devbsSc+ePalbty4pKSm88847johRCC7kmvlu41EGfLSKG//3N7NWJZKenU/NUH+eubEhq56/jk+GtXPPMn1Ng3+mqu3297vWnXWAFoNViVn6Udix0DnnzLugmhKBE5P8ggoQR5brWyxw5rDalnJ9IYSH8PEy8sHQNlzfqDo5+Rbu+2I9qw+c0jss12ZtwLf/j4JVV5wgOx0Or1bbrpbk12qnSqezz8HR9fY7rm0kX8f5+Fb2KNe3luq7atO9oqzz8vcvg9wsfWO5mLXpnofOx4dyJPk1a9Zk69atvPXWWzRp0oS2bdvy7rvvsm3bNmJiYhwRo6jE9qSoUfuOb/zBM99uYWPBqH2fplF8MaoDfz/bkzHXxREZ7Kd3qOV3eBUkbwCTL3R8WO9oLuXtBx0fVNurpqmbEo528C/VBC+4pvMuZNZl9ByZ5J8/BuYctSRgcC3HnUcIIZzMx8vIh/e0oWd8BNl5FkbNWs/ri3dy4GSG3qG5pmr1C+bEa2puvjMcXAGWPFW5Vs3FpjYaTVD/erVtz5J9V+isb2WPcn1rZ31Xno9vZZ1umX9BVWi4itxMOLlbbXvwSH65JtEHBgby4IMP2jsWIQA1av/z1mN8sy6JTUlnbc/XCvNnSIfaDGpbi+runNRfLGGqemx9t2uUk5Wk3X2wcorqRHroL6jXw7Hns5Xq3+S8uVLWcv0zh1XXVUf0RbA23QutrTrtCiGEB/H1MvHRPW15+KuNrNhzkk9XHuLTlYe4pl5VhnasQ++mkfh6ueGUOkdpO0JdUzd9Cd2edfx1wVVL9a0a9obt38G+pdBrgn2OmVGQUAe6wOcru4zku0FnfSuDARr1hTUfwK6foXFfvSNSjm9RPZ+qRKumjx6q3H9Ndu7cSVJSErm5ucWev+222yoclKicdqek8/XaJBb+m8z5bDUfy8to4IYmkQzpUJuuDcIxGj2sOUbqDnXH2lBk7rsrCqgKre+BdZ9AwjTHJvkWC+xdoradVaoPUCUKvAMhL1N1Ho5oaP9zSNM9IYSH8/M28fnw9izffYKv1yWxYs8J1hw8zZqDp6ka6MPAtrUY0qE2dcMD9Q5Vf41uhYBwVeW1f6m6se0oFotKnsF1k/z61wMGSN0G6ccguEbFj+lKI/kVnZOfcUL9rmBQo+TuoPGtKsnf+6vq62Ty1jsiSC4o1ffgUXwoR5J/8OBB+vfvz7Zt2zAY/r+9+w6Pqkz7OP6dSU9IAiGkAqH3DhJpioqCooJiLyB2RGVl3XV9XcW2i2VXXZVFRVHsWBEbrCBFutJ77xAgQBKSQMrMef84mZBAAgmZzJmZ/D7XNdccJmfO3IdJcnLP89z3Y8MomrrrWpPc4XC4N0Lxa7n5hfywaj+fLdnF8hKj9g1iwrjpvIZc360+cZF+NGp/qvlFHfVbX+19U+dOdf4D8Pu75pSrtDWQUE1dXfctMzvHBkea3X89xWYz34O0VeaU/epI8rV8nviZ3bt3Y7PZqF/fLD9ZsmQJn376KW3atNGMvxoswG6jX5t4+rWJZ2/GcSb/vpvJv+/iQFYe78zdxjtzt9GzaV1u7t6Q/m0TCA60uOu5VQKDodPNsOANswFfdSb5aauKrq21IKVX9b1OVUTUNWvz9/xufiDR1Q2NiL2pJr+q0/WLm+41977+TeVpkGp+kJWbbi6n1/QiqyMqUY/f2do4qlmlf6uOGjWKxo0bc/DgQcLDw1m7di1z586lW7duzJ49uxpCFH+0bl8WT05ZQ+o/ZvLXr1axfFcGgXYbV7RP4KO7ujPn0YsYeVEz/07ws/ab09IAeo2yNpaKiGlsfhgB1Vubv+FH8755PwgMqZ7XKE91L6OnkXzxM7fccguzZs0CIC0tjUsvvZQlS5bwxBNP8Oyzz1ocnXiD5NphjL60BfMfu5gJQ7txUct62GywYOthHvpsOT3GzmTsz+vZkZ5jdajW6HKHeb/5f5C5p/pex1Xn3qSv9yzTWxZ3L6XnVd31iz5oyDl0bn9DuerxfWGqvos9AFoVzcr0li77Gskv28KFC/n111+JjY3Fbrdjt9vp3bs3Y8eO5eGHH2b58uXVEaf4iWlr0nhrzlZW7M4ofqxhTDg3dW/A9V0bUC/Sw0mdldZPBWch1O/uO909ez4M66bAqsnmuqfJXUvf3HER3fizee/JqfouxcvoVVPzPS2fJ35mzZo1dO/eHYAvvviCdu3aMX/+fP73v/9x//3389RTT1kcoXiLwAA7l7aJ59I28ew5mssXv+9m8h+7OZCVx9tztvH2nG30alaXW7qncGmb+Jozuh/bDBr1gR2/mQ34+v6tel5nk5fX47s0vxRm/cNsEuiO/jjeNJLvmq7vyIcTGeZqApXhS531S2p1FSz70BzEufxlc3lmq+QeOTngkuTfI/mVTvIdDgeRkZEAxMbGsm/fPlq2bElKSgobN250e4DiHwzD4PWZW3h1xibArLXv3zaBm7s3pGfTuv5Xa18R674z79sOtjSMSqnfFbrdCcs/MS9QW2eW7pga3dD8wKJ+NzPpT+wIwZWouzyyDQ6tB1uAeaH3tOIkf6v7j20YcGSHua3p+uInCgoKCAkxP5ydMWNGcV+eVq1asX//fitDEy9Wv044oy9rycOXNGfmhoN8tmQXczYdYv6Ww8zfcpjYWsFc17UBN3dvQErdGlC73/UOM8lf9hFc8Bdz9NOdctJh71Jz29uT/ISOZkKecxB2LYQmF577sRyFkFu0lKM31OQHhUJIFORlme9JpZP8oun6vtBZv6QmF5olmMf2w77l5t+SVtlXNBhdp7HZb8qPVTrJb9euHStXrqRx48akpqby0ksvERwczDvvvEOTJhqdktMVOpw8+d0aPluyG4A7ezVmRN+mNWvU/lTHDphL58HJKfC+4spXYcCLcHCt+UfD3mXm/aGNkLnLvK2bYu5rs0NcGzPxT+4Kyd2gXqvyOwi7RvFTelb+4ucOrr4I1TGSf/wo5GWa23Uauf/4IhZo27Ytb731FgMHDuSXX37hueeeA2Dfvn3UrVvX4ujE2wUG2OnfNoH+bRPYfSTXrN3/YzeHjuXx1pytvDVnK32ax3Jz94Zc2iaeoAA/Hd1vdSWExUDWHnNN8RZuTsS3zAAMSOjg/d3E7XbzQ/4Vn5hT9quS5OceBgzzb5FwL/l9FFHPTPKzD54sEayInMOQaf4d7TNN91wCQ8z3dO03sOF7i5N8Vz2+hTF4SKWT/L///e/k5Jh1U88++yxXXnklffr0oW7dukyePNntAYpvy80v5KFPlzNzw0HsNnhmUDtuPz/F6rCst+F7wDB/ydRuYHU0lRcYbE5zSuoM5xU9diLTrBfbu/Tk7dh+OLDGvC370NwvKNysJytO/LuaS8rZbLChaOm8VgMtOClOjuRnH4ATWRAa5b5ju5ruRSZCUJj7jitioRdffJFrrrmGl19+mWHDhtGxozmNdOrUqcXT+EUqokFMOI/2b8mofs2Zud7szP/b5kP8tjmd3zanE1srhBu6mZ35G8SEWx2uewWFQqdbYOGbZgM+dyf5rqn6Lfq797jVpTjJ/wX6/+Pcj+PqrB9e1/2zI85VrTg4svVkbBW1v2gEOqYphEa7P67q1vpKM8lf/wP0e9q6OPYW/T/6SplsFVQ6ye/f/+QviGbNmrFhwwaOHDlCnTp1ijvsiwAczs7jzkl/sHJ3BiGBdl6/uTP92yZYHZZ3cE3VbzPI2jjcKTTa/MS95KfuWftKJ/17l0P+Mdi1wLy5RNQzG6DsWmj+uzo7DJ9JaPTJaYKHt7j3IqCme+KH+vbtS3p6OllZWdSpc3L2zb333kt4uJ8lYuIRQQF2BrRLYEA7c3T/89938cUfezh0LI//zt7K+DlbGdKlPi8N6eBfpX5dhppJ/qZp7ls+Dswp666yOm+fqu/S5CKzbC99o7mk7bnOfvOmenwXV11+diU77PvqVH2XZpdCQLDZ2PjQRqjX0po4XGUrft50DyrZXb+goIDAwEDWrFlT6vGYmBgl+FLKzsM5DBm/gJW7M6gdHsSn96QqwXfJKVpGBHxvqn5lRSVB66vMT22HfQ9/2wUjl8Cg/0K3u8wRfXug2Wl283QwHBDX1trp7NVVl6/l88QPHT9+nLy8vOIEf+fOnbz22mts3LiRuDgv+sNafFKDmHD+0r8VC/52MW/d1oU+zWMxDPhq6R4m/LbN6vDcq15LaNjTvA4u/8R9x92zxJxpF17Xd6Yoh9WGhueb25t/OffjeFNnfZeIc1xGr7izvo813XMJjTJXdgBY/701MWTtg+w0s3wjsYM1MXhQpZL8oKAgGjZsiMPhqK54xA+s2pPBkPEL2HE4l/p1wvh6RE+6pvh3c4tK2fADGE7zF3VNS/jsdvMPmc63wpWvwH1z4PG9cNcMs86/63Cz5t9K1VWXX9xZv4a95+LXBg0axIcfmqU4GRkZpKam8u9//5vBgwczfvx4i6MTf2GO7ify0V2p/PMasx755ekbWbUnw9rA3K3rHeb9sg/B6aa/tV1T9Zv1854p6xXhar5blSTfG0fyXQ0AKz1dv2gk35eWzztVqyvNe6uW0nMtnVevdeWaQvuoSncweeKJJ/i///s/jhw5Uh3xiI+btfEgN72ziPTsfNomRfHNAz1pWq+W1WF5F3+cql8VQaHQ4Dw4/3646jVomGptPK5GOIc3u/e4mq4vfmjZsmX06dMHgK+++or4+Hh27tzJhx9+yOuvv25xdOKPbu7egAFtEyh0Goz6fAU5eYVWh+Q+ba6G0NpmA9uts9xzTNd6874yVd/FFe/2uVBw/NyO4UqkvaGzvsu5TNfPPQIZO81tXx3JB3NpZJvd7HCfucfzr1/cdM+/l85zqXSS/+abbzJ37lySkpJo2bIlXbp0KXWTmuuLP3Zz96Q/yM130Kd5LJPv60FcZKjVYXmX3COwbY653VpJvlcqnq7v7pF8TdcX/5Obm1u8rO7//vc/rr32Wux2O+effz47d+60ODrxRzabjReGtCcxOpTt6Tk8PXWt1SG5T1AYdLzZ3F76ftWPl7EbDq4zE6umF1f9eJ4U1waikqHwOOyYf27HcCXSEV40Xf9cRvJdo/h1GpmlDL6qVj1oUFSGseFHz762YZxc1aoG1OPDOTTeGzx4cDWEIb7MMAze+HULr/yyCYBrOyfzwpAOBAf66VI3VbHxJ7PeLr4dxDazOhopS8mafMMwu/5XVX6uWQcGGskXv9KsWTOmTJnCNddcw/Tp03nkkUcAOHjwIFFRblydQqSE2uHBvHpjJ26esIgvl+7hghb1uKqjmxrVWa3rMFg83lxS9lgaRFahn5FrFL9Bqu+tCW6zmVP2l35gnkfzfpU/hjeP5FemJt8fpuq7tL7SbLy8/ntIvc9zrzvnRbO5s80OjauwLKMPqXSSP2bMmOqIQ3xUocPJk9+t4bMl5tqdD/Rtyl/6t1QjxvJoqr73q9PYvAjkZ5t/YLljTeGjO8z70Gjf+0NL5AyeeuopbrnlFh555BEuvvhievToAZij+p0714wpkWKN85vUZWTfZrw5awv/9+1qOjesTf06frCiQ1xrc7Rz9yJzGbk+fz73Y/nqVH2X5pcVJfnTwXix8h+6F4/ke2GSX5np+vtXmPe+PFXfpdWVMP3/zFH13COe+Zto2Ycwe6y5fcW/aswgm4Za5Zzl5hdy30dL+WzJbmw2eG5QW/46oJUS/PIczzhZY6ck33sFBkPtFHPbXVP2VY8vfuq6665j165d/PHHH0yfPr348UsuuYRXX7W4iab4vVH9mtOpQW2OnSjkT5+voNDhtDok9+g6zLxfOgmc53hOBcdPlgf6apLf+EKwB5kflJ/LijfFI/leOF2/IAfycyr2HF9fPq+kOimQ0N6c1brx5+p/vU3/g+//ZG73eRTOu6v6X9NLVDrJt9vtBAQElHuTmuFwdh63TFjMzA0HCQm0M/7Wrtzeo5HVYXm3TdPAWQD1Wlm3PqhUjLvr8ovr8Zu453giXiQhIYHOnTuzb98+9uwxmyl1796dVq1aWRyZ+LugADuv39SZWiGB/LHzKG/OcnMvFau0GQwh0Wazte2zz+0YO+aZ9exRyRDf1p3ReU5ILWjUy9x2zUqoKKfTXLIYvGskP7gWBIaZ29kVqMs/kXlydR5/mK4P0Ooq8766u+zvXQpfDjM/UOh4C1z89+p9PS9T6ST/22+/5Ztvvim+TZ48mb/97W8kJibyzjvvVEeM4mV2Hs5hyPgFrNidQe3wID69J5UB7apQM1ZTaKq+73B7kq/l88Q/OZ1Onn32WaKjo0lJSSElJYXatWvz3HPP4TzXEUiRSmhYN5znB7cD4PWZm/ljhx+s/hQcDh1vNLeXfnBuxyg5Vd+XZ1i6ZiFUNsk/fsRM7gAiYt0bU1XYbCXq8tPPvr9rFD+6of+U+7UuWkpv668Vn81QWUe2wSc3QEGu2XTy6td9++fgHFS6Jn/QoNMTlOuuu462bdsyefJk7rqr5kyDqIlW7cngzg9+Jz07n+TaYUy6szvN4rRE3lmdyIItM81tJfneL9bNSb6m64ufeuKJJ3jvvfd44YUX6NXLHHGbN28eTz/9NCdOnOAf//iHxRFKTTC4czJzNh3i2+V7GfX5Cn4a1YfosCCrw6qarnfAknfMLuTZByvXPM4wYFNR+UyL/tUSnsc0v6yohns+5GWbo/sV4RolD4uBAC/7XqhVz1wmsSId9oun6vtBPb5LXBvz76Gj22HLDPf/XZyTDh8Pgdx0SOgAN3zofd8DHuC2mvzzzz+fmTNnuutw4oVmbTzITe8sIj07nzaJUXz7QE8l+BW1+X/gyDNHiOPaWB2NnE21TddXki/+ZdKkSbz77ruMGDGCDh060KFDBx544AEmTJjABx98YHV4UoM8O6gtDWPC2ZtxnCe+XY1hGFaHVDXxbaH+eeAsNBvwVUb6JnOqf0AINL6geuLzlLrNzKXjHPmwfW7Fn+eNnfVdXOUDFZmuv2+Fee8vU/XBHFF3jeavd/OU/fxc+PRGcyQ/uiHc+iWERLr3NXyEW5L848eP8/rrr5OcnOyOw4kX+uKP3dw96Q9y8x30aR7L5PvOJy4q1OqwfMe6KeZ9m0E1brqQT3Il+Ud3gKOgasdyFEKmufqERvLF3xw5cqTM2vtWrVpx5IgfTJsWnxEZGsR/bupEgN3GD6v289XSPVaHVHVd7zDvK9uAzzW1vVFvCI5we1geZbOVmLI//cz7llTcWd+Lmu651KrEMnrFnfU7VVc01nDV5W+aDoX57jmmoxC+uhP2/gFhdeC2r6u2BKWPq3SSX6dOHWJiYopvderUITIykokTJ/Lyyy9XR4xiIcMweH3mZv761SocToNrOyfz3rDziAytedNezlleNmz+xdzWVH3fEJlkNsZxFkLGrqodK3O3eZyAEIh0w3J8Il6kY8eOvPnmm6c9/uabb9KhQwcLIpKarHPDOoy+tAUAY6auZXt6NdX7ekrbayAkypzWvOO3ij/PX6bquxQn+b+YpQgV4dUj+RVM8k9knZxR6A+d9Uuqf545oyEvs3Lf2+UxDPjpz7DpZwgMhZsnQ70WVT+uD6t0Tf6rr75aaok0u91OvXr1SE1NpU6dOm4NTqxV6HDy5Hdr+GyJOQr5QN+m/KV/Sy2RV1lbfoHCE+Z0swT90esT7HZzNP/AakjfDHWbnvuxiuvxG5nHFfEjL730EgMHDmTGjBn06NEDgIULF7J7925++ukni6OTmuj+C5vy2+ZDLNp2hFGfL+er+3sSHOijv3uDI6D99fDHe2YDviYXnv05JzJh10Jz21eXzjtVo95m4pa1Fw6uq9hqAa6p8N7UWd+lotP101ab91H1vat5oDvY7dDqCvP7esMP0OySqh1v7r+KmlTaYMi70DDVDUH6tkr/1rvjjjsYNmxY8e32229nwIABSvD9TG5+Ifd9tJTPluzGZoPnBrXlrwNaKcE/FyW76uv/z3e4Evuq1uUXd9bX8nnify688EI2bdrENddcQ0ZGBhkZGVx77bWsXbuWjz76yOrwpAYKsNt49cZORIcFsWpPJv/+ZaPVIVWNa8r++u8r1o196yxz9ljd5v7TByYo7GRvgYp22XeNktfy4en6rqZ7iX7UdK+k4qX0fqpcOcqpln8Cs543t694GVpfVfXY/EClk/z333+fL7/88rTHv/zySyZNmuSWoMRaR3PyuWXCYmZuOEhIoJ3xt3bl9h6NrA7LN+XnwqaiC5Km6vsWdzXfU9M98XNJSUn84x//4Ouvv+brr7/m+eef5+jRo7z33ntWhyY1VGJ0GC8OMWfOvT1nG/M2VyA59laJHSCpCzgLYMWnZ9/fVR7oL1P1XUpO2a8IfxjJd9Xj+9tUfZfGF5jlKNlpZh39udgyA75/2Nzu/Qh0v8d98fm4Sif5Y8eOJTb29CkjcXFx/POf/3RLUGKt12ZsYsXuDKLDgvjk7lQGtKu5TSuqbOtMKMiB6AbmRVp8R2xz876qSf7RHea9mu6JiHjMgHYJ3JLaEIDRX6zgSI6bmntZobgB3wdnrkl3Ok+OdPvLVH2XZv3M+12L4HjG2ff3h5r84s76fjqSHxh88vt0/feVf/6+5TB5qDlzpcONcMkY98bn4yqd5O/atYvGjU//YzUlJYVdu6rYoEosZxgGMzeYvxhfuq4D3RrFWByRj9NUfd+lkXwREZ/25MA2NK0XwcFjefz1q5W+u6xeuyEQXAuObIUd88rfb/8KM7kNjoSGPTwWnkfENIbYFmA4YNuss+/v1d31iz54OJFRfmf5/BxzKUTwv876JbmW0tvwQ8WbKoI5gPLJDeZAWpO+cPWb+jv7FJVO8uPi4li1atVpj69cuZK6deu6JSixzs7Duew5epygABu9m/lZkw9PKzgBG6eZ25qq73tcNfTH9psrJJwLw9BIvoiIRcKCA3jj5i4EB9iZsf4gHy/aaXVI5yakltmAD4qai5XDNYrftK85SupvKjpl3zBK1OR74Uh+aG2wF/U+L280P201YJir8kTGeyoyz2t2qbn60JFtcHB9xZ6Tcxg+HmJ+oBXfHm74yD+/36uo0t31b775Zh5++GEiIyO54AKzCcacOXMYNWoUN910k9sDFM/6bbP5y6ZrSh0iQir97SElbZsF+cfM5diSu1kdjVRWeAyE14Xcw+boyblMl8s+aH7KbLND7Ybuj1HEItdee+0Zv56RkeGZQETOok1SFH+7vBXP/rCO539cT/fGdWmZEGl1WJXX9Q5Y+j6snwq5R8xr1KmKp+r7WT2+S/NLYeGbZpLvdJa/Ys3xo2YPA/DOkXy73Yzr2H4zUY1OPn0ff5+q7xJSC5peBJummaP58W3OvH9+Lnx2kznLMroB3PolhEZ5JlYfU+mR/Oeee47U1FQuueQSwsLCCAsL47LLLuPiiy9WTb4fmFvUnKZPcy/8pehr1k0179tcraXTfFXdorr89M3n9nxXZ/3o+vqUWfxKdHT0GW8pKSkMHTq00scdN24cjRo1IjQ0lNTUVJYsWVLuvh988AE2m63ULTQ0tCqnJX5qeK9G9G1Zj7xCJw9/tpwTBQ6rQ6q8pE5mwufIh5Wfnf717EOwd5m53fxSj4bmMQ17mGULOQchbWX5+7lGx0OjITDEM7FVlmtJvPJWTCjurN/JI+FYqlXRlP2z1eU7HfDNPbBniTkb4ravISqx2sPzVZUeqg0ODmby5Mk8//zzrFixgrCwMNq3b09KSkp1xCceVOBwsnDrYQD6NNdU/SopzIeNP5rbmqrvu+o2g92L4PDWc3v+0aJ6fE3VFz/z/vvvu/2YkydPZvTo0bz11lukpqby2muv0b9/fzZu3EhcXNlTbqOioti48eQSaVrmVcpis9l4+bqOXP6fuWw8cIyxP63nmUHtrA6r8rreAT88Yk7ZP/+B0jXIW34BDPODgEg/bZgcGGLWX2/4wRzNT+pc9n7e3Fnf5Wwd9v29s35JLS83ZzymrYKjO6FOGTmlYcDPfzXf+4AQuPlzqNfS87H6kHMeXmzevDnXX389V155pRJ8P7FidwbZeYXUCQ+ibVK01eH4tu1z4UQm1IqHBqlWRyPnqm5T8/5cm++p6Z5Ihb3yyivcc889DB8+nDZt2vDWW28RHh7OxIkTy32OzWYjISGh+BYf78e1q1Il9SJD+Nf15tTnSQt3MnP9AYsjOgftroOgCLMh266Fpb+2abp5769T9V2K6/L/V/4+3txZ38UVW04ZSX5+LhzaYG77+3R9MGc1NOxpbm/4sex95r0Cv78L2GDIBEjxs8aS1aDSSf6QIUN48cUXT3v8pZde4vrrr3dLUGKN34qm6vdqFkuAXaMhVbJuinnf+iqwB1gailRBVTvsayRfpELy8/NZunQp/fr1K37MbrfTr18/Fi5cWO7zsrOzSUlJoUGDBgwaNIi1a9eWu29eXh5ZWVmlblKz9G0Zx129zd/Hf/lqFQezTlgcUSWFRkH7IeZ2yQZ8jgLYWtRxvoW/J/lFpQh7/jAbsJXFmzvru7hiyy6j8d6BtWA4zdH+yBoyHb1kl/1TrfwcZj5rbg94QTNkK6jSSf7cuXO54oorTnv88ssvZ+7cuW4JSqzharp3gerxq8ZRcPKXlH4R+bbYopr8w1sqt7SLi0byRSokPT0dh8Nx2kh8fHw8aWlpZT6nZcuWTJw4ke+++46PP/4Yp9NJz5492bNnT5n7jx07tlTfgAYNGrj9PMT7/XVAS1onRnEkJ5/RX6zE6fSxZfW63mHer51iNuAD2L0Y8jIhPBaSulgVmWdEJZkd1TFg68yy9/GFkXxXkl9Wd33XVP3EjjVnWbhWA837XQtL9ynY+it8N9Lc7vkwnH+/52PzUZVO8rOzswkOPr2BVFBQkD4V92GZuQWs3J0BQG/V41fNjnlmZ9fwEtOPxDfVaQzYIC+r/GVuzkQj+SLVpkePHgwdOpROnTpx4YUX8s0331CvXj3efvvtMvd//PHHyczMLL7t3r3bwxGLNwgJDOCNmzsRGmRn3pZ03p23zeqQKiepCyS0B0cerJpsPuaaqt+sX81o9OsazS9vyr4v1OSfabp+TarHd6nd0PxQw3DCxp/Mx/avgslDwVlolqr0e8baGH1MpX8TtG/fnsmTJ5/2+Oeff06bNmdZ9kC81oKt6TgNaBZXi6TaYVaH49vWfWfet74SArQMoU8LCoXaRaN9lZ2yfyLTXH4PNJIvchaxsbEEBARw4EDpOukDBw6QkFCxJmJBQUF07tyZLVvK/lkNCQkhKiqq1E1qpmZxkYy5qi0AL0/fyOo9mRZHVAk2G3QZZm4vnWTOMnOtG9/iMuvi8iRXXf6WGWbH9VO5PpSv5cUzU880XX9fDeqsX1Krq8z79T+YDfg+uc5cirpRHxj835rxAZYbVfp/68knn+S5555j2LBhTJo0iUmTJjF06FCef/55nnzyyeqIUTzg5NJ5GsWvEqfj5BIgmqrvH+qWmLJfGa6p+hH1IMQH12QW8aDg4GC6du3KzJknp986nU5mzpxJjx4Va7DkcDhYvXo1iYk1pIZVquSm8xowoG0CBQ6Dhz9fTk5eodUhVVyHGyAwDA6thzVfm/e2AGh6idWReUb988zl8Y4fhb1LT/+6L4/kF5ww30+oGU33SnLV5W+bBR8PgewDENcWbvrEe5dC9GKVTvKvuuoqpkyZwpYtW3jggQf485//zN69e/n1119p1qxZdcQo1cwwDOZuMj9JVJJfRTsXQG46hNUxP3kU3+dqvpe+uXLP01R9kUoZPXo0EyZMYNKkSaxfv54RI0aQk5PD8OHDARg6dCiPP/548f7PPvss//vf/9i2bRvLli3jtttuY+fOndx9991WnYL4EJvNxgtD2pMYHcr29Bye+b78po1eJzQa2hU14PtxtHnf8HwIq21ZSB4VEHjyA42ypuwXj+R7cZLvGsnPPVx6NsLBteb09PC6EF3fmtisUq8VxDQFRz4c3gxRyXDrl+b3u1TaOc17GDhwIPPnzycnJ4dt27Zxww038Oijj9KxYw37xMlP7Dicy96M4wQF2EhtXNfqcHyba6p+q4EQEGRtLOIexR32t1bueWq6J1IpN954I//617946qmn6NSpEytWrGDatGnFzfh27drF/v37i/c/evQo99xzD61bt+aKK64gKyuLBQsWqHRQKqx2eDCv3tgJmw2++GMPP6zaZ3VIFedqwHeiqNTAVadeU5S3lJ5hlBjJ9+Lp+uGxgM2sQXc1UATYt8K8T+xUc5ruudhs5qpUYCb2t30N0cnWxuTDzrlgeO7cubz33nt8/fXXJCUlce211zJu3Dh3xiYeMq+oq37XlDpEhPhoDXn2IZj6EHS+7eR0H09zOmH9VHO7zWBrYhD3q9vUvK/sdH2N5ItU2oMPPsiDDz5Y5tdmz55d6t+vvvoqr776qgeiEn92fpO6jOzbjDdnbeHxb1bTsX5tGsSEWx3W2dXvZk5lPlg0A6G5ny+dd6pmRctt7l8Jx9Igsqh3R16W2ZQQvHskPyAQwmPMkfycgyf7B+x31ePX0IHTng9DfjZ0uhXiWlsdjU+r1Eh+WloaL7zwAs2bN+f6668nKiqKvLw8pkyZwgsvvMB5551XXXFKNTpZj+/Fn3iezcrPYNPP8PXdkH6Oa5pX1e7FZv1QSDQ0vtCaGMT9XMvoHdkGjkrUbGokX0TEJ4zq15xODWpz7EQhg8bN59cNB87+JKvZbCdH86Mb1LyEqFa9k8sFbplx8nFXI7vgSAjy8kbSrp4B2SXq8mtiZ/2SIurCwH9Dsp8vBekBFU7yr7rqKlq2bMmqVat47bXX2LdvH2+88UZ1xiYeUOBwsnCr2QH8Al9O8l2NVwqPwzf3mGvVe1rxVP0rIPD0ZSbFR0XVh4AQcBZA5q6KP+/oDvNeI/kiIl4tKMDOf2/tQpvEKI7k5HPnB3/w7PfryCsso3O7N+kyFHqNgqvfqHlTu6HsKfuuRnbe3FnfJaKoD5ZrXfjCPDiwztyuqSP54jYVTvJ//vln7rrrLp555hkGDhxIQEBAdcYlHrJidwbZeYXUCQ+ibZIPLye0b5l5b7Ob27/927OvX2qqvrrq+xW7vcSU/QrW5RfmQeYeczumSfXEJSIibpNUO4xvR/bkjp6NAJg4fztDxi9g26FsawM7k6BQuPRZaHqR1ZFYw5Xkb511cnDHFzrru5zaYf/genNAIbQ21E6xLCzxDxVO8ufNm8exY8fo2rUrqampvPnmm6Snp1dnbOIBvxV11e/VLBa73Uc/Bc5Jh4yiEdYr/mXez3kJ9pSxrEp12bsUsvaa08Oa1NCLrT+rbF3+0Z2AAcG1Tn5SLyIiXi0kMICnr27Lu0O7USc8iDV7s7jyjXl8vXSP1aFJWZI6mw3s8rLMkkko0VnfF0byT5muX3Kqfk2cmSFuVeEk//zzz2fChAns37+f++67j88//5ykpCScTie//PILx44dq844pZq46vF9eqr+vuXmfd1mcN5d0O46MBzmtP38HM/EsG6Ked9ygPnJuviXukV1+RVdRq9k0z1dqEVEfEq/NvH8POoCUhvHkJvv4M9fruSRySvIzqtEXxapfnb7yQZ8rin7PjWSX/S3t+uDiZredE/cqtJL6EVERHDnnXcyb948Vq9ezZ///GdeeOEF4uLiuPrqq6sjRqkmmbkFrNqTAUDv5j482ri3aKq+qwHLwH9BZBIc2Qq/PFX9r28YsE5T9f1a8TJ6FRzJL26616hawhERkeqVEB3Kp/ecz+hLW2C3wbfL93Ll67+xek+m1aFJSa6lAzf/Yt4X1+T7QJIfcUqSX3L5PJEqqnSSX1LLli156aWX2LNnD5999pm7YhIPWbA1HacBzeJqkVTbyzuQnomrHt/ViTOsDgz+r7n9+7uweUbZz3Pb6y83G7IFRZz8RFn8S3GSX8GafC2fJyLi8wLsNh6+pDmT7+tBUnQoOw7ncu34+bz72zacTsPq8ASg6cVmP6aD6yBj98nu+hE+MEO15HR9RwEcKFoOsaZ21he3qlKS7xIQEMDgwYOZOnWqOw4nHnJy6TwfHsU3jNNH8sFsQpN6v7n93UjIPVJ9Mbi66re4zPuXa5Fz40rys/ZAfu7Z99fyeSIifuO8RjH8NKoP/dvGU+AweP7H9dw56XfSs/OsDk3CY6B+d3N7yy++NZJfcrr+oQ3gyDOXYdYAgbiBW5J88T2GYTC3qOmeT9fjZ+01f6HbAiChfemv9XsaYltAdhr88CfzAwF3M4yTSb6m6vuviLrmDBEwy0DOxjWSr876IiJ+oXZ4MG/d1pXnB7cjONDO7I2HuPw/vzF/i5pQW67klH1fqsl3xZhz6GR/qcQO6uUjbqEkv4bacTiXvRnHCQqwkdokxupwzp1rFD+uDQSHl/5aUBhc+w7YA81EfNUX7n/9tNVmQhcYBs0udf/xxXtUtC7f6YCjO8xtfRovIuI3bDYbt52fwtQHe9E8rhaHjuVx23uLeWnaBgocTqvDq7lcS+ltmw3ZB8xtn+iuXxSjIx+2zzW31XRP3ERJfg3122ZzFL9rSh3CgwMtjqYKiuvxO5f99aTO0Pdv5vZPj5r1Wu7kGsVv3g9Carn32OJdKprkZ+0zL9j2IIiuX/1xiYiIR7VKiGLqg725uXtDDAP+O3srN7y9kN1HKlDOJe6X0B5qJUBBLhSeMB/zhZH8oFAIiTK3t8w075PK+XtWpJKU5NdQcze56vF94JPOMymrHv9UvR4x67XysmDKCHC66dN2wzi5dF6bwe45pngvV5KffpYk3zVVv3ZDsAdUb0wiImKJsOAAxl7bnnG3dCEyNJDluzK44vXf+HHVfqtDq3lstpNT9gGCwn1n4MU1mn+8qHeUOuuLmyjJr4EKHE4WbTsM+Hg9vtN5crmR5DMk+QGBcM1bZvf7Hb/Bov+65/UPrjdHdQNCTk4VE/9V0ZF8Nd0TEakxBnZI5KeH+9ClYW2OnShk5KfLePybVRzPd1gdWs1S8u8wX+is71KyQWBwpHr5iNsoya+BVuzOIDuvkDrhQbRNirI6nHN3ZBvkZUJgqFmTfyZ1m0L/f5jbM5+BA+uq/vquqfrNLoFQH/5/lIopTvI3n7mJo5bPExGpURrEhDP5vh6MvKgpNht8tmQ3V785jw1pWVaHVnM06Wv2YALf6KzvElFihavEDmBXaibuoe+kGui3oq76vZvXw2734Q6ernr8hPYQEHT2/bveAc37m/XS39wLhVVc+kZd9WsW16frJzLPvCTjEXXWFxGpaYIC7Pylfys+viuVepEhbD6YzaA35/PRop0Y1bG6j5QWGgUNe5jbvlCP71IyVk3VFzfyiiR/3LhxNGrUiNDQUFJTU1myZEm5+/bt2xebzXbabeDAgcX7GIbBU089RWJiImFhYfTr14/Nmzd74lR8wtzNrnr82LPs6eUqUo9fks0GV78B4XXhwGqYPfbcX/vQRji03myu1mLAuR9HfEdwOEQ3MLcPn+H3yZFt5r2m64uI1Di9msUybVQfLmpZj7xCJ09OWcOIj5eRkZtvdWj+r/115n1iB2vjqIySsw7UWV/cyPIkf/LkyYwePZoxY8awbNkyOnbsSP/+/Tl48GCZ+3/zzTfs37+/+LZmzRoCAgK4/vrri/d56aWXeP3113nrrbdYvHgxERER9O/fnxMnTnjqtLxWRm4+q/ZkAH6Q5Bd31q9gkg8QGQ9X/cfcnvca7Fx4bq+9bqp53/QiCKt9bscQ31O3qXlfXl2+YWj5PBGRGq5urRDeG3Yefx/YmqAAG9PWpjHw9Xms3pNpdWj+rcswuGcW9PqT1ZFUXMn+AUmdLAtD/I/lSf4rr7zCPffcw/Dhw2nTpg1vvfUW4eHhTJw4scz9Y2JiSEhIKL798ssvhIeHFyf5hmHw2muv8fe//51BgwbRoUMHPvzwQ/bt28eUKVPKPGZeXh5ZWVmlbv5qwdbDOA1oFleLxOgwq8M5d45C2L/K3K7oSL5L66ug022AAd/eCyfO4f3WVP2a6WzN93KPmKs4ANRJ8UxMIiLidex2G3f3acI3I3rRqG44ezOOc91bC/h2+R6rQ/NfNps58BMUanUkFedK8oMiTv6NIeIGlib5+fn5LF26lH79+hU/Zrfb6devHwsXVmyE9b333uOmm24iIiICgO3bt5OWllbqmNHR0aSmppZ7zLFjxxIdHV18a9CgQRXOyrv9ttmsx/f5UfxD66HwuLm+6Ln8Uhww1lziLGMXTH+8cs89vNWc7m8PhJZXVP61xXedLcl3Nd2LTIIgH/4QTURE3KJ9/WimPtSbi1vFkVfo5JHJK3n+h3UUOty0nK/4tqROZgPpFpdp2V1xK0uT/PT0dBwOB/Hx8aUej4+PJy0t7azPX7JkCWvWrOHuu+8ufsz1vMoc8/HHHyczM7P4tnv37sqeik8wDIO5m8x6fJ9eOg9O1uMndjy3TqShUXDN24ANln8M63+o+HNdo/iNL4DwmMq/tviuus3N+/RyknwtnyciIqeICg3i3aHdePAi84Pid+dt5473f+dojur0a7zaDeHRzTCk7BnMIufK8un6VfHee+/Rvn17unfvXqXjhISEEBUVVermj3YczmVvxnGCAmykNvHx5PRc6vFPldITej1sbn//MGSX3QfiNJqqX3O5avKPbANnGWsgH1WSLyIip7PbbTzavyX/vbUL4cEBzNuSztXj5rF+v/+WiEoFhUZp6TxxO0u/o2JjYwkICODAgQOlHj9w4AAJCQlnfG5OTg6ff/45d911V6nHXc87l2P6O9dU/W4pMYQHB1ocTRXtXWreV7Ye/1QXPQHx7SD3MEx96Mzrn4PZVG3/CrDZodWVVXtt8T21G0JAMDjyILOMukpXZ3013RMRkTJc0T6Rbx7oScOYcHYfOc61/13Aj6v2Wx2WiPgZS5P84OBgunbtysyZM4sfczqdzJw5kx49epzxuV9++SV5eXncdtttpR5v3LgxCQkJpY6ZlZXF4sWLz3pMf+eaqt+nhY/X4xcchwPrzO2qjOQDBIbAte+YidumabDswzPv7+qqn9ILInz8/1Eqzx4AMU3M7bKW0dN0fREROYtWCVFMfbAXfZrHcrzAwchPl/Hy9A04nGcZaBARqSDL54aMHj2aCRMmMGnSJNavX8+IESPIyclh+PDhAAwdOpTHHz+9Mdp7773H4MGDqVu3bqnHbTYbf/rTn3j++eeZOnUqq1evZujQoSQlJTF48GBPnJJXKnA4WbjVT+rx01aD4YDw2JPrlldFfFu45Clze9rjJ0djy6Kp+lLcfG/r6V9zTdfXSL6IiJxB7fBg3r/jPO69wPzgeNysrdw96XcyjxdYHJmI+APL52zfeOONHDp0iKeeeoq0tDQ6derEtGnTihvn7dq1C/spdSobN25k3rx5/O9//yvzmH/961/Jycnh3nvvJSMjg969ezNt2jRCQ31oSQ03W74rg5x8BzERwbRJ9PGeA66me8ldzeVS3OH8kbBpOuz4Db65D4b/DAGn/Hhk7Ia9fwA2cxk+qZlcdfmndtjPz4HsojIhjeSLiMhZBAbY+b8rWtMmMYrHvl7FrI2HGDxuPhOGdqVZXKTV4YmID7M8yQd48MEHefDBB8v82uzZs097rGXLlhhnqJ222Ww8++yzPPvss+4K0ee56vF7NYvFbndTYmwVdzTdO5XdDoP/C+N7wZ4lMP81uODR0vus/968b9gDImt2f4carbxl9I7uMO9Da0NYHU9GJCIiPmxw52SaxdXi3g//YHt6DoPHLeDVGztxaZv4sz9ZRKQMlk/XF8+Yu7moHr+5H9SRu0byq9p071S1G8LlL5nbs8fCvhWlv66p+gLlL6OnenwRETlH7ZKjmfpQb7o3jiE7r5B7PvyD/8zYjFN1+iJyDpTk1wAZufms3pMBVCLJL/TStVtPZJ5seObOkXyXjjdB66vBWQjf3Gs2+QPI2ge7F5nbmqpfs7lG8jN3n/z+gBLL5zXxfEwiIuLzYmuF8MndqQzrkQLAqzM2MeKTpWTnFVocmYj4GiX5NcCCrYdxGtA8rhaJ0WFn3jk/B6Y8AP9MhPU/eCbAynCNrkc3rJ7u9jYbXPka1IqH9I0w4xnzcdf/Rf3uEJ3s/tcV3xERCyHRgHFy9B60fJ6IiFRZUICdZwa146UhHQgOsDN97QGuGTefHek5VocmIj5ESX4N4KrH73O2rvqHNsGES2DFJ+ZI9opPPRBdJRXX43euvteIqAuDxpnbi8fD1lmaqi8n2WxlN9/TdH0REXGTG85rwOf3nU9cZAibD2Zz9ZvzmL3xoNVhiYiPUJLv5wzDYO6monr8FmcY+V79FUy4CA6tLxqlBLbP8b5p+9VVj3+q5pdCtzvN7W/vh53zze02V1fv64pviC2qy3eVjoCWzxMREbfq0rAOPzzUmy4Na5N1opA7P/idt+ZsPWPzaRERUJLv97an57A34zjBAXZSG8ecvkNhHvz4Z/j6LsjPhkZ94MElEFHP/PfuxZ4P+kz2LTfvq6Me/1SXPW/WV2enAYb5wULthtX/uuL9ijvsbzXvHQXmEougkXwREXGbuKhQPrv3fG46rwFOA174eQMPfbac3HzV6YtI+ZTk+7nfirrqd02pQ3jwKSsmHt0JE/vD7++a/+7zZ7h9irk8XNNLzMe2zPBcsGeTfchsdoYNEjtV/+sFR8C1E8AWYP5bU/XF5dTp+pm7wXBAYCjU0vKKIiLiPiGBAYy9tj3PDW5HoN3GD6v2M2T8QnYfybU6NBHxUkry/ZwryT9tqv7Gn+HtPubIeFgduOVLuOQpCCj6IKBZP/N+y0wPRnsWrnr82OYQGuWZ16zfDa58FZpdCl2GeuY1xfsVL6NXNF3/SImp+nb9WhUREfey2Wzcfn4Kn95zPrG1glm/P4ur35zHgi3pVocmIl5If436sQKHk4VbzV/+F7ia7jkK4Zen4LObzOXokrvBfb9Bi8tKP7npRYANDqyGrP2eDbw8nqrHP1XXYXDbVxBeRrmD1EyuZfKOH4HcIyc762uqvoiIVKPujWOY+mBv2idHczS3gNsnLuGTxTutDktEvIySfD+2fFcGOfkOYiKCaZMYZSbrk66C+f8xd0i9H4b/DLUbnP7kiFhIKupgv/VXzwV9JsWd9T2c5IucKqQWRCaZ24e3wtEd5raa7omISDVLqh3Gl/f34JrOyTicBk98u4Yvft9tdVgi4kWU5Psx19J5vZvFYt8x15yev2sBBEfC9R/A5S9CYHD5Byiesu8FdfmGYd1IvkhZStbla/k8ERHxoNCgAF65oSN39zavO499s4rvV+6zOCoR8RZK8v3Y3M3p2HByL1/DR4Mh5xDEt4N7Z0Pba85+AFeSv/VXcDqqM9Szy9wNuelgD4SE9tbGIgKll9HT8nkiIuJhNpuNJwa25ubuDTEMeGTyCmasO2B1WCLiBZTk+6mM3Hx279nF+0Ev027jG2A4ofNtcPcMiG1WsYMkd4XQaDiRcXIU3Squ149rA0Gh1sYiAieX0UvffHK6vkbyRUTEg2w2G88PbsfgTkkUOg0e+HQZ89WMT6TGU5Lvp9Yumcn3wU/QN2AlBIbBoP/CoHEQFFbxgwQEQpOLzG2rp+yrHl+8jSvJ37UQCnLNpRajy+hvISIiUo0C7Db+dX1HLmsTT36hk7sn/cHSnUesDktELKQk398YBiwaz/mzbyXZdpj0kIZwz0zofOu5Hc9b6vJVjy/expXk55i9L4iuf+YeFyIiItUkMMDOG7d0pk/zWI4XOLjj/d9ZszfT6rBExCJK8v3JiUz4YihM+xsBOPjBcT7rrpoC8W3P/ZjNLjHv9y41lwqzgtMJ+1ea2xrJF29RO8XsEeGiqfoiImKhkMAA3rm9G90bxXDsRCG3v7eYzQeOWR2WiFhASb6/SFsN7/SF9VMx7EE8VTCM0c5RdGuRUrXjRiVBXFvAsG4pvcNbIC/LLDuo19qaGEROFRBYutGemu6JiIjFwoIDeO+ObnSoH83R3AJufXcxOw/nWB2WiHiYknxfZxiw7EN4tx8c2QbRDfjpvPf50NGfbo1iCA8OPPsxzsY1mr9lZtWPdS5c9fiJHc3ESsRb1C3RxFIj+SIi4gUiQ4OYNLw7LeMjOXgsj1smLGZfxnGrwxIRD1KS78vyc2HKAzD1ISg8Ac37w31z+fZgAgC9m8e653VK1uU7ne45ZmXsVdM98VJ1m57c1ki+iIh4iToRwXx0d3cax0awN+M4t727mEPH8qwOS0Q8REm+ryo4DhP7w8pPwWaHS8bAzZ+TH1ybhVsPA3BB83ruea2G50NQBOQchANr3HPMytinpnvipWKbn9zWSL6IiHiRuMhQPr47leTaYWxLz+H29xaTkZtvdVgi4gFK8n3V5l8gbRWE1YGhU6HPaLDbWb7rKDn5DmIigmmTGOWe1woMgcYXmNue7rJfmA/7V5nbGskXb1Nyur5G8kVExMsk1w7j47tTqRcZwoa0Ywx7/3ey8wqtDktEqpmSfF+1bZZ53/4GaNyn+OF5W9IB6N0sFrvd5r7Xs6ou/+A6cORBaDTENPHsa4ucTUJ783szsSOE1LI6GhERkdM0jo3g47tSqR0exMrdGdz1we8cz3dYHZaIVCMl+b7K1em+6cWlHp672Uzy+7irHt/FVZe/exGcyHLvsc+keKp+Z7C58UMLEXcIjYZRq+DO/1kdiYiISLlaJkTy0Z2pRIYEsnj7Ee7/eCl5hUr0RfyVknxfdGQbHN1hrtHdqFfxwxm5+azakwFAH3fV47vENIaYpuAshO1z3XvsM9mrenzxcmG1ISjU6ihERETOqH39aCYOP4/QIDtzNh1i1GcrKHRY0FBZRKqdknxftLVoqn6DVAiJLH54/pbDGAa0iK9FQnQ1JB0lu+x7yr7l5r3q8UVERESq5LxGMUwY2o3gADvT1qbx169W4XQaVoclIm6mJN8Xuerxm1xU6uHfNh8CoHczN4/iuxQn+TPB8MAFIT8XDq43tzWSLyIiIlJlfZrX481bOhNgt/HN8r08+d0aDE/8XSciHqMk39c4CmFb0XT5EvX4hmHwm6sev4Wb6/FdGvWCgBDI3AXpm6vnNUpKWwWGA2rFQ1RS9b+eiIiISA1wWdsEXrmhIzYbfLJ4Fy/8vEGJvogfUZLva/Yth7xMCK0NSZ2KH96WnsPejOMEB9hJbRxTPa8dHAEpPc1tT0zZL1mPr6Z7IiIiIm4zqFMyY69pD8Dbc7fxxq9bLI5IRNxFSb6vcXXVb3Ih2AOKH55XNIrfrVEdwoMDq+/1PVmX7+qsr3p8EREREbe7qXtDnryyDQCv/LKJd3/bZnFEIuIOSvJ9zVnq8d3eVf9UriR/53woOF69r6XO+iIiIiLV6q7ejRl9aQsAnv9xPZ8u3mVxRCJSVUryfcmJLNi9xNxuejLJzy90snDrYQD6NK+menyXei0hqj4UnjAT/epyPAOObDW3kzpX3+uIiIiI1HAPXdyM+y5sAsATU1YzZfleiyMSkapQku9LdswzG9HFNIE6jYofXr7rKDn5DupGBNMmMap6Y7DZoNkl5vaWmdX3Oq6l82qnQETd6nsdERERkRrOZrPxtwGtuP38FAwD/vzlSqavTbM6LBE5R0ryfYmrHr9EV32guKt+r2ax2O0eaFDnibp81eOLiIiIeIzNZuOZq9tybZdkHE6Dhz5dztif13Mw64TVoYlIJSnJ9yVnrcev5qn6Lk0uBFsApG+Cozur5zVUjy8iIiLiUXa7jZeGdGBgh0TyHU7enrON3i/N4v++Xc3OwzlWhyciFaQk31dk7ILDW8zkunGf4oeP5uSzam8m4IGmey6h0dAg1dzeWk1T9l3T9TWSLyIiIuIxgQF23ripM+8O7UaXhrXJL3Ty6eJdXPSv2Tz02XLW7su0OkQROQsl+b5ia9Eofv1uZpJdZMHWwxgGtIivRUJ0qOfiqc66/GMHIGsvYIPEju4/voiIiIiUy2630a9NPF+P6Mnke8+nb8t6OA34fuU+Br4+j2ETl7B422EMw7A6VBEpg5J8X1FuPb6Hls47lasuf9scKMx377Fd9fj1WkJIpHuPLSIiIiIVYrPZSG1Slw+Gd+enh/twdcck7DaYs+kQN76ziCHjFzBj3QGcTiX7It5ESb4vcDpg+xxzu0Q9vmEYxU33PFaP75LQASLqQf4x2LPEvcd21eMnd3XvcUVERETknLRJiuL1mzsz69G+3JrakOBAO8t2ZXD3h38w4D9z+WbZHgocTqvDFBGU5PuG/Svg+FEIiSqV+G5Lz2FvxnGCA+ykNvbwMnN2OzR1Tdl3c5d910h+Umf3HldEREREqiSlbgT/uKY98x67iPsvbEqtkEA2Hchm9Bcr6fvybCYt2MHxfIfVYYrUaEryfYGrHr/xBRAQWPzwb5vMqfrdGtUhLDjA83FVx1J6hlFiJF9N90RERES8UVxkKH+7vBXz/3Yxfx3QkthawezNOM6YqWvp/eKvvPnrZjJzC6wOU6RGUpLvC1xJftNTl85zTdX3cD2+S9OLABukrYZjae45ZsZOOH4E7EEQ3849xxQRERGRahEdFsQDfZsx77GLeW5wOxrEhHE4J59//W8TPV+YyT9/Ws+BrBNWhylSoyjJ93Z52bB7sbldoh4/v9DJwm2HAQvq8V0iYk9OqXc1Bqwq1yh+QjsIDHHPMUVERESkWoUGBXD7+SnM+nNf/nNTJ1olRJKT7+Cdudvo8+IsHv9mFdvTc6wOU6RGUJLv7XbOB2cB1E6BmCbFD6/em0luvoOYiGDaJEZZF5+7p+wX1+Nrqr6IiIiIrwkMsDOoUzI/j+rD+3ecx3mN6pDvcPLZkt1c8u/ZjPx0GRvSsqwOU8SvKcn3diWn6ttsxQ+v3ZcJQMf60djttrKe6RmuJH/rr+YqAFW1d7l5r3p8EREREZ9ls9m4qFUcX97fky/v78ElreJwGvDjqv1c/cZ8vl+5z+oQRfyWknxv55oG3/TiUg+v3mMm+e2Soz0dUWnJXSE02uz+v2951Y7ldJw8hkbyRURERPzCeY1ieO+O85j2pz5c1LIe+Q4nD322nPGzt2IYhtXhifgdJfneLHMvpG8Em93srF/Cmn3mNCfLk/yAwJO9Aqo6ZT99ExTkQFAE1GtZ9dhERERExGu0Soji3WHncXfvxgC8OG0D//ftGgodTosjE/EvSvK92baiqfpJXSCsTvHDJwocbD5wDPCCJB/cV5fvarqX2BHsFiwJKCIiIiLVKsBu4+9XtuGZq9tit8FnS3Zx16Q/yM4rtDo0Eb+hJN+blbN03qYDxyh0GtQJDyIpOtSCwE7R7BLzfu9SyD1y7sdxNd1TPb6IiIiIXxvWsxHv3N6NsKAA5mw6xPVvLWR/5nGrwxLxC0ryvZXTeXIk/5R6/DV7T07Vt9ksbLrnEpUEcW3BKBHzuXCN5LuW5RMRERERv9WvTTyT7zuf2FohrN+fxTXjFrBunzrvi1SVknxvdWA15B6G4FpQ/7xSX1q910ua7pXkGs3fMvPcnl+YDwfWmNsayRcRERGpETrUr823D/SkWVwt0rJOcMPbC5mz6ZDVYYn4NCX53srVVb9RHwgIKvUl1/J57ZK8KckvUZd/Ll1SD6wBR77Ze6BOY/fGJiIiIiJeq0FMOF+P6EmPJnXJzivkzg9+5/Mlu6wOS8RnKcn3VuXU4xc4nGzY72q6F+XpqMrX8HyzK372gZMj8pWxr8RUfW8oQRARERERj4kOC2LSnd25tksyDqfB375ZzcvTN+B0aok9kcpSku+N8nNh10Jz+5R6/M0Hssl3OIkMDaRhTLgFwZUjMOTkMn/n0mV/73LzPklT9UVERERqouBAO/++viOjLmkOwLhZW/nT5BXkFTosjkzEtyjJ90a7FphT16PqQ91mpb60pmiqftukKO9ouldSVery1VlfREREpMaz2Ww8cmkL/nV9RwLtNqau3Mft7y7haE6+1aGJ+Awl+d6o5FT9UxL5NUVN99p7U9M9F1dd/q6FkHes4s/Lz4FDG8xtjeSLiIiI1HjXda3PpDu7ExkayJIdRxgyfgE7D+dYHZaIT1CS743KqceHk0m+V3XWd4lpDDFNwVkI2+dW/Hn7V5rL70UmQlRi9cUnIiIiIj6jV7NYvh7Rk+TaYWxLz+Ga/y5g2a6jVocl4vWU5HubY2lwcC1gg8Z9S33J4TRYt99cO7StN3XWL6l4yn4l6vL3uqbqd3V/PCIiIiLis1rER/LtAz1plxzFkZx8bn5nEdPW7Lc6LBGvpiTf22ybbd4ndoSIuqW/dCibEwVOwoMDaBwb4fnYKuJcltIr2VlfRERqrHHjxtGoUSNCQ0NJTU1lyZIlFXre559/js1mY/DgwdUboIhYIi4qlMn39uDiVnHkFToZ8cky3v1tG8a5LNssUgMoyfc2W38170/pqg8nm+61SYwiwO5lTfdcGvWGgGDI2AWHt1TsOXvVdE9EpKabPHkyo0ePZsyYMSxbtoyOHTvSv39/Dh48eMbn7dixg0cffZQ+ffp4KFIRsUJESCDv3N6V289PwTDg+R/X8/TUtTi0xJ7IaZTkexPDOGM9/uo95lR9r6zHdwmOgJSe5nZFpuznHoGj281tjeSLiNRYr7zyCvfccw/Dhw+nTZs2vPXWW4SHhzNx4sRyn+NwOLj11lt55plnaNKkiQejFRErBAbYeXZQW/4+sDU2G0xauJP7PvqD3PxCq0MT8SqWJ/mVnZqXkZHByJEjSUxMJCQkhBYtWvDTTz8Vf/3pp5/GZrOVurVq1aq6T8M9DqyFnIMQFA4NUk/7smsk36uTfCg9Zf9s9i0372OaQFid6otJRES8Vn5+PkuXLqVfv37Fj9ntdvr168fChQvLfd6zzz5LXFwcd91111lfIy8vj6ysrFI3EfE9NpuNu/s04b+3dCEk0M6M9Qe58e1FHMw6YXVoIl7D0iS/slPz8vPzufTSS9mxYwdfffUVGzduZMKECSQnJ5far23btuzfv7/4Nm/ePE+cTtW5puqn9ILAkFJfcjoN1u1zjeRHeTqyynEl+TvmQcHxM+9bXI+vqfoiIjVVeno6DoeD+Pj4Uo/Hx8eTlpZW5nPmzZvHe++9x4QJEyr0GmPHjiU6Orr41qBBgyrHLSLWubx9Ip/ecz4xEcGs3pvJNf9dwKYDlVjCWcSPWZrkV3Zq3sSJEzly5AhTpkyhV69eNGrUiAsvvJCOHTuW2i8wMJCEhITiW2xsrCdOp+q2uabqn16Pv/NILtl5hYQE2mlWr5aHA6ukeq0gKhkKT8DO+Wfed2/RSL7q8UVEpIKOHTvG7bffzoQJEyp8jX/88cfJzMwsvu3evbuaoxSR6tY1pQ7fPtCTxrER7M04zpDxC/hg/nYycvOtDk3EUpYl+ecyNW/q1Kn06NGDkSNHEh8fT7t27fjnP/+Jw+Eotd/mzZtJSkqiSZMm3HrrrezateuMsXjFFL6CE7BzgbldVj3+XnOqfuvEKAIDLK+yODObrcRSejPPvK9G8kVEarzY2FgCAgI4cOBAqccPHDhAQkLCaftv3bqVHTt2cNVVVxEYGEhgYCAffvghU6dOJTAwkK1bt572nJCQEKKiokrdRMT3pdSN4JsRPTmvUR2OnSjk6e/X0f0fMxn5yTJmbTyoxnxSI1mWLZ7L1Lxt27bx1Vdf4XA4+Omnn3jyySf597//zfPPP1+8T2pqKh988AHTpk1j/PjxbN++nT59+nDsWPnTd7xiCt+uhebId2SiORJ+irV7XfX4PvJHSUXq8rP2w7H9YLNDYgfPxCUiIl4nODiYrl27MnPmyQ+GnU4nM2fOpEePHqft36pVK1avXs2KFSuKb1dffTUXXXQRK1as0FR8kRqmTkQwH9+dytNXtaFNYhT5Dic/rt7P8Pd/p9cLv/LStA1sO5RtdZgiHhNodQCV4XQ6iYuL45133iEgIICuXbuyd+9eXn75ZcaMGQPA5ZdfXrx/hw4dSE1NJSUlhS+++KLcxjyPP/44o0ePLv53VlaW5/9AcNXjN7nIHAk/RXHTvSQvb7rn0vhCsAVA+iY4uhPqpJy+j2sUv15rsyu/iIjUWKNHj2bYsGF069aN7t2789prr5GTk8Pw4cMBGDp0KMnJyYwdO5bQ0FDatWtX6vm1a9cGOO1xEakZQgIDuKNXY+7o1Zg1ezP5aukepqzYS1rWCf47eyv/nb2Vbil1uKFbA67okEitEJ9Kg0QqxbLv7spOzQNITEwkKCiIgICA4sdat25NWloa+fn5BAcHn/ac2rVr06JFC7ZsKX/N9pCQEEJCQsr9ukecoR7fMAzW7PWB5fNKCqsNDbqbMxS2zoRud56+z96iJD9ZS+eJiNR0N954I4cOHeKpp54iLS2NTp06MW3atOIZf7t27cJu9/JyNRHxCu2So2mXHM3jV7Ri5vqDfPnHbuZsOsQfO4/yx86jjJm6livaJ3J9t/qkNo7BVsYAm4gvsyzJLzk1b/DgwcDJqXkPPvhgmc/p1asXn376KU6ns/hCv2nTJhITE8tM8AGys7PZunUrt99+e7Wch1tkH4S01eZ2k76nfXnP0eNkHi8gKMBG83gvb7pXUrNLzCR/SzlJvurxRUSkhAcffLDcvwFmz559xud+8MEH7g9IRHxaSGAAV7RP5Ir2iaRlnuCb5Xv46o89bEvP4etle/h62R4axoRzfdf6DOlan6TaYVaHLOIWln4kPnr0aCZMmMCkSZNYv349I0aMOG1q3uOPP168/4gRIzhy5AijRo1i06ZN/Pjjj/zzn/9k5MiRxfs8+uijzJkzhx07drBgwQKuueYaAgICuPnmmz1+fhW2bY55n9AeatU77ctriurxWyZEEhIYcNrXvZarLn/bHCg8pcupYZQYyVeSLyIiIiLVJyE6lAf6NmPmny/k6xE9uLFbAyKCA9h1JJd//7KJXi/+yu3vLea7FXs5UeA4+wFFvJilxSiVnZrXoEEDpk+fziOPPEKHDh1ITk5m1KhRPPbYY8X77Nmzh5tvvpnDhw9Tr149evfuzaJFi6hX7/Tk2WuUrMcvg8/V47skdITwWMhNhz1LoFHvk187sg1OZEBAMMS1tSxEEREREak5bDYbXVNi6JoSw5ir2/Dz6jS+XLqbRduO8NvmdH7bnE5UaCBXd0ri+q4N6FA/WtP5xedY3nGislPzevTowaJFi8o93ueff+6u0DzDMM5Yjw8U1+O39ZV6fBe73Zyyv2qy2WW/ZJK/b7l5n9AeAssutRARERERqS7hwYEMKZqqv+twLl8t3c3Xy/ayN+M4Hy/axceLdtEivhbXd23ANV2Sia1lcQ8vkQpSBxurHdpgLiMXGAoNT18myGy65xrJ95Hl80oqbym9varHFxERERHv0LBuOKMva8lvf72Ij+9KZVCnJEIC7Ww6kM0/flpPnxdnMX1t2ct8i3gby0fya7ytRaP4KT0hKPS0Lx/IyuNwTj4BdhutE30wyW96MWAzGwseS4PIopUT9qkeX0RERES8i91uo3fzWHo3jyXzeAHfr9zHZ0t2sXZfFvd/vJSnrmzD8F6NrQ5T5Iw0km+1s9Tjry4axW8eV4vQIB9quucSEQtJncxt17k6CmH/SnNbI/kiIiIi4oWiw4K47fwUvhvZi1tSG2IY8Mz363juh3U4nYbV4YmUS0m+lQrzYOd8c7vcenwzyW/ra033Sjp1yn76RijIheBaENvcurhERERERM4iMMDOPwa347EBrQB4b952Hvhkmbrwi9dSkm+l3YvNZDciDuLL7jC/1tVZP9kHp+q7uJL8rb+C01GiHr8z2H1wdoKIiIiI1Cg2m40RfZvyn5s6ERxgZ9raNG6ZsIjD2XlWhyZyGiX5VnLV4ze9CMpZmsPVWb+dr3XWLym5G4REw/GjZlf9fSWSfBERERERHzGoUzIf3dWdqNBAlu3KYMj4BWxPz7E6LJFSlORb6Sz1+IeO5ZGWdQKbDdr4YtM9l4BAaNrX3N4y4+RIvpruiYiIiIiPSW1Sl28e6En9OmHsOJzLtf+dz9KdR6wOS6SYknyr5Bw+2XyuadlJ/pqiqfpNYiOICPHxhRBcU/Y3/AgH1prbaronIiIiIj6oWVwk3z7Qiw71ozmaW8DNExbz8+r9VoclAijJt8722YABcW1OLit3irV7XfX4PjxV36XpJeZ92ipwFkB4Xajd0NqYRERERETOUb3IED6/93z6tY4jv9DJA58u493ftmEY6rwv1lKSb5Xievyyu+pDiXp8X+6s7xKdbH6g4ZLUpdw+BCIiIiIiviA8OJC3b+/G7eenYBjw/I/reeb7dTi0xJ5YSEm+FQzjZJJfTj0+nJyu39aXO+uX1OySk9uqxxcRERERPxBgt/HsoLb83xXmEnsfLNjB/R8v5Xi+ltgTayjJt8LhLZC1BwKCIaVnmbsczclnz9HjALT1h5F8OFmXD6rHFxERERG/YbPZuPeCpoy7pQvBgXZ+WXeAmyYsIl1L7IkFlORbwdVVv+H5EBxe5i5r95lT9VPqhhMdFuSpyKpXwx4QHgtB4VC/m9XRiIiIiIi41cAOiXx6dyq1w4NYuTuDa/47n62Hsq0OS2oYJflWqEg9ftFUfb+ox3cJDIE7p8Ndv0BErNXRiIiIiIi4XbdGMXwzoicNY8LZfeQ4Q8Yv4PcdWmJPPEdJvqc5CmDHb+b2merx9/pZPb5LbDNIaGd1FCIiIiIi1aZJvVp880BPOjWoTUZuAbe+u5jvV+6zOiypIZTke9qe3yE/21xCLqFDubu5puv71Ui+iIiIiEgNEVsrhM/uOZ/L2sSTX+jkoc+W89acrVpiT6qdknxPc9XjN+kL9rL/+7NOFLA9PQeAdslK8kVEREREfFFYcADjb+vK8F6NAHjh5w08+d0aCh1OawMTv6Yk39MqUI+/rmgUP7l2GDERwZ6ISkREREREqkGA3caYq9ry5JVtsNng40W7uO+jpeTmF1odmvgpJfmedPwo7FtmblekHj/Jz+rxRURERERqqLt6N2b8rV0ICbQzc8NBbnx7EQePnbA6LPFDSvI9aftcMJwQ2xKik8vdrbgeX1P1RURERET8xoB2iXx6z/nERASzem8m14xbwKYDx6wOS/yMknxPctXjNy1/FB9OjuS3V5IvIiIiIuJXuqbU4ZsRPWkcG8HejONc9cY83p6zVXX64jZK8j3FMEok+eXX4+fmF7L1UDbgh8vniYiIiIgIjWIj+HpET/o0jyWv0MnYnzcwZPwCNqZpVF+qTkm+pxzZBhm7wB4EKb3K3W39/iycBsRFhhAXGerBAEVERERExFNiIoL58M7uvHRdByJDA1m5J5Mr3/iN12dupkCj+lIFSvI9ZVtRV/0GqRBSq9zd1uxVPb6IiIiISE1gs9m4oVsDZoy+kH6t4yhwGLzyyyaufnN+cQmvSGUpyfeU4qXz+p5xN9cPczt11hcRERERqRHio0KZMLQb/7mpE3XCg1i/P4tB4+bz8vQNnChwWB2e+Bgl+Z7gKDQ768MZ6/EB1qizvoiIiIhIjWOz2RjUKZlfRl/IwPaJOJwG42Zt5co35rFs11GrwxMfoiTfE/YuhbwsCK0NiZ3K3e1EgYPNRUtoKMkXEREREal5YmuFMO7WLoy/tQuxtULYcjCbIeMX8PwP6zier1F9OTsl+Z7gqsdv0hfsAeXutjHtGIVOg5iIYBKj1XRPRERERKSmurx9IjNGX8C1XZIxDHh33nYG/Gcui7Ydtjo08XJK8j2heOm8i86425p9Zj1+26QobDZbdUclIiIiIiJerHZ4MK/c0In37ziPxOhQdh7O5aZ3FvHklDVk5xVaHZ54KSX51e1EJuz5w9xucpYkv6izfntN1RcRERERkSIXtYpj+iMXcHP3hgB8tGgn/V+dy9xNhyyOTLyRkvzqtv03MBwQ0xTqpJxx1+LO+kryRURERESkhKjQIMZe255P7k6lfp0w9mYcZ+jEJfz1q5VkHi+wOjzxIkryq5urHv8sXfXzC51sTCtqupekJF9ERERERE7Xq1ks0/90AXf0bITNBl/8sYfLXp3DL+sOWB2aeAkl+dWtgvX4mw8eI9/hJDI0kAYxYR4ITEREREREfFFESCBPX92WL+7rQZPYCA5k5XHPh38w6vPlHMnJtzo8sZiS/OpkGNDvGeg6HBr1OeOua4vq8dslRavpnoiIiIiInNV5jWL4aVQf7ruwCXYbfLdiH5e+MocfV+3HMAyrwxOLKMmvTjYbtLkarnoNQqPOuKurs377+pqqLyIiIiIiFRMaFMDjl7fm2wd60SK+Fodz8hn56TJGfLyMPUdzrQ5PLKAk30us3nty+TwREREREZHK6NigNt8/1JuHL2lOoN3GtLVpXPyvOTz/wzqOagp/jaIk3wsUOpys3180XV+d9UVERERE5ByEBAYw+tIWTH2wNz2a1CXf4eTdedu54KVZjJu1hdz8QqtDFA9Qku8FtqXncKLASURwAI3rRlgdjoiIiIiI+LA2SVF8ek8qk+7sTuvEKI7lFfLy9I1c+PJsPlm8kwKH0+oQpRopyfcCa4qm6rdJisJuV9M9ERERERGpGpvNxoUt6vHjQ7157cZONIgJ49CxPJ74dg2XvTqXn1arOZ+/UpLvBdbs1VR9ERERERFxP7vdxuDOycwc3Zenr2pDTEQw29NzeOCTZQweN58FW9OtDlHcTEm+F3CN5LdLUpIvIiIiIiLuFxxo545ejZnzl748fElzwoMDWLknk1smLGboxCWsLVrtS3yfknyLOZ1G8Q+URvJFRERERKQ6RYYGMfrSFsz5y0UM7ZFCoN3G3E2HGPj6PP70+XJ2H9Gye75OSb7FdhzOISffQWiQnab11HRPRERERESqX73IEJ4d1I4Zoy/kqo5JAExZsY+L/z2bp6eu5XB2nsURyrlSkm+xNfvMevzWiVEEBujtEBERERERz2kUG8EbN3fmh4d606d5LAUOgw8W7OCCl2bxnxmbycnTsnu+RlmlxdaqHl9ERERERCzWLjmaj+5K5eO7UmmfHE1OvoNXZ2ziwpdn8eHCHeQXatk9X6Ek32KrXUl+cpTFkYiIiIiISE3Xu3ks343sxZu3dCalbjjp2fk89d1aLn11DlNX7sPp1LJ73k5JvoUMwyjurN9WI/kiIiIiIuIF7HYbV3ZIYsboC3luUFtia4Ww83AuD3+2nKvHzdOye15OSb6F9hw9TtaJQoID7LSIj7Q6HBERERERkWJBAXZu79GIOX/py58vbUGtkEDW7M3ilgmLefWXTRrV91JK8i3kGsVvmRBJcKDeChERERER8T4RIYE8dElz5vylLzed1wCA/8zczJ2TficjN9/i6ORUyiwttGaf6vFFRERERMQ31K0VwgtDOvDv6zsSEmhn9sZDXPXmPNYW5TXiHZTkW2j1XnP5PNXji4iIiIiIrxjStT7fPNCTBjFh7D5ynGv/u4Cvl+6xOiwpoiTfIoZhnFw+L1lJvoiIiIiI+I62SdH88GAfLmpZj7xCJ3/+ciVPTlmjpfa8gJJ8i6RlneBwTj4BdhutEtR0T0REREREfEt0eBDvDTuPUZc0B+CjRTu58Z2FpGWesDiymk1JvkXWFE3Vbx5Xi9CgAIujERERERERqTy73cYjl7Zg4h3diAoNZPmuDK584zcWbj1sdWg1lpJ8i6zWVH0REREREfETF7eK5/uHetM6MYr07Hxue28xE+ZuwzC0zJ6nKcm3SHE9fpI664uIiIiIiO9LqRvBNyN6cm3nZBxOg3/8tJ4HP11Odl6h1aHVKEryLXJy+TyN5IuIiIiIiH8ICw7g3zd05NlBbQm02/hx9X4Gj5vP1kPZVodWYyjJt8DBYyc4kJWHzQZtNJIvIiIiIiJ+xGazMbRHIybfdz7xUSFsOZjNoDfnM23NfqtDqxGU5Ftg7T6z6V7TerUIDw60OBoRERERERH365oSw/cP9aZ74xiy8wq5/+NlvPDzBgodWmavOinJt8CaParHFxERERER/xcXGcond6dyd+/GALw1ZyvD3l/C4ew8iyPzX0ryLaB6fBERERERqSmCAuz8/co2vHFzZ8KDA5i/5TBXvTGPFbszrA7NL1me5I8bN45GjRoRGhpKamoqS5YsOeP+GRkZjBw5ksTEREJCQmjRogU//fRTlY7paWv2mtP12yYpyRcRERERkZrhqo5JTBnZiyaxEezLPMENby3k08W7tMyem1ma5E+ePJnRo0czZswYli1bRseOHenfvz8HDx4sc//8/HwuvfRSduzYwVdffcXGjRuZMGECycnJ53xMTzuak8/ejOMAtE3WdH0REREREak5WsRHMuXBXlzWJp58h5P/+3Y1j329ihMFDqtD8xuWJvmvvPIK99xzD8OHD6dNmza89dZbhIeHM3HixDL3nzhxIkeOHGHKlCn06tWLRo0aceGFF9KxY8dzPqanuZruNaobTlRokMXRiIiIiIiIeFZUaBBv396Vxwa0wm6DL/7Yw/VvLWT3kVyrQ/MLliX5+fn5LF26lH79+p0Mxm6nX79+LFy4sMznTJ06lR49ejBy5Eji4+Np164d//znP3E4HOd8TIC8vDyysrJK3arL6r1mPX5b1eOLiIiIiEgNZbPZGNG3KR/emUqd8CBW783kqjfn8cXvu8kr1Kh+VViW5Kenp+NwOIiPjy/1eHx8PGlpaWU+Z9u2bXz11Vc4HA5++uknnnzySf7973/z/PPPn/MxAcaOHUt0dHTxrUGDBlU8u/IVN91TPb6IiIiIiNRwvZvH8sPDfehQP5qM3AL++vUq+rw4i//O3kJmboHV4fkkyxvvVYbT6SQuLo533nmHrl27cuONN/LEE0/w1ltvVem4jz/+OJmZmcW33bt3uyni063d6+qsr3p8ERERERGR5NphfHFfD/7vilYkRIVy8FgeL03bSM8XZvLs9+vYc1TT+Csj0KoXjo2NJSAggAMHDpR6/MCBAyQkJJT5nMTERIKCgggICCh+rHXr1qSlpZGfn39OxwQICQkhJCSkCmdTMVknCthx2PwG1Ui+iIiIiIiIKTQogHsvaModPRvzw6p9vDN3GxvSjjFx/nYmLdzBwPaJ3HtBEy1DXgGWjeQHBwfTtWtXZs6cWfyY0+lk5syZ9OjRo8zn9OrViy1btuB0Oosf27RpE4mJiQQHB5/TMT1pXVHTveTaYdSJCLY4GhEREREREe8SHGjn2i71+XlUHz68szu9m8XicBpMXbmPK9+Yxy0TFjFr40Etu3cGlk7XHz16NBMmTGDSpEmsX7+eESNGkJOTw/DhwwEYOnQojz/+ePH+I0aM4MiRI4waNYpNmzbx448/8s9//pORI0dW+JhWWqOp+iIiIiIiImdls9m4oEU9Pr47lR8f7s3gTkkE2G0s2HqY4e//zoDXfuPLP3aTX+g8+8FqGMum6wPceOONHDp0iKeeeoq0tDQ6derEtGnTihvn7dq1C7v95OcQDRo0YPr06TzyyCN06NCB5ORkRo0axWOPPVbhY1qpOMnXVH0REREREZEKaZsUzWs3deYvA1rx/rztfLZkFxsPHOMvX63iX//byB09G3NLakOiw7REOYDN0DyH02RlZREdHU1mZiZRUe4bde/3yhy2HMzm/eHncVHLOLcdV0RE/F91XZtqKv1/ioj4rszjBXy2ZBfvz9/Ogaw8ACKCA7ipe0Pu7N2Y5NphFkd4btx1bfKp7vq+LDe/kK2HsgGN5IuIiIiIiJyr6LAg7r+wKb/99WL+dX1HWsZHkpPv4L1527ngpVmM+nx58SzqmsjS6fo1ybp9WRgGxEeFUC+y+jv5i4iIiIiI+LPgQDvXda3PkC7JzNl0iAm/bWP+lsN8t2If363YR69mdbn3gqZc0DwWm81mdbgeoyTfQ1SPLyIiIiIi4n42m42+LePo2zKONXszeWfuNn5cvZ/5Ww4zf8thWiVEck+fJlzdKYmgAP+fzO7/Z+gl1hQtn9dW6zqKiIiIiIhUi3bJ0bx+c2fm/KUvd/VuTERwABvSjvHnL1dy8b9n89mSXX7fkV9Jvoe4RvLbK8kXERERERGpVvXrhPPklW1Y8LdL+OuAlsTWCmb3keM8/s1q+r48i48W7uBEgcPqMKuFknwPOFHgYPPBoqZ7yergKyIiIiIi4gnR4UE80LcZv/31Yp68sg1xkSHsyzzBk9+t5cKXZzFx3naO5/tXsq8k3wM2pB3D4TSoGxFMQlSo1eGIiIiIiIjUKGHBAdzVuzFz/3oRzw5qS2J0KAey8nj2h3X0eelX3pm7lZy8QqvDdAsl+R7gmqrfNjm6RnV1FBERqYxx48bRqFEjQkNDSU1NZcmSJeXu+80339CtWzdq165NREQEnTp14qOPPvJgtCIi4otCgwIY2qMRs//Sl39e0576dcJIz87nnz9toPeLvzJu1haOnSiwOswqUZLvAWv3uerxNVVfRESkLJMnT2b06NGMGTOGZcuW0bFjR/r378/BgwfL3D8mJoYnnniChQsXsmrVKoYPH87w4cOZPn26hyMXERFfFBIYwC2pDZn1aF9euq4DjeqGczS3gJenb6T3i7P4z4zNZB73zWTfZhiGYXUQ3iYrK4vo6GgyMzOJiqp6Yn7VG/NYvTeT8bd24fL2iW6IUEREahp3X5u8TWpqKueddx5vvvkmAE6nkwYNGvDQQw/xt7/9rULH6NKlCwMHDuS55547677+/v8pIiKVU+hw8v2qfbz56xa2HsoBIDIkkDt6NeLOXo2pExFc7TG469qkkfxqll/oZGPaMcBczkFERERKy8/PZ+nSpfTr16/4MbvdTr9+/Vi4cOFZn28YBjNnzmTjxo1ccMEFZe6Tl5dHVlZWqZuIiIhLYICdazrX53+PXMgbN3emZXwkx/IKeePXLfR+8Vde+HkD6dl5VodZIUryq9mmA8fIdziJCg2kfp0wq8MRERHxOunp6TgcDuLj40s9Hh8fT1paWrnPy8zMpFatWgQHBzNw4EDeeOMNLr300jL3HTt2LNHR0cW3Bg0auPUcRETEPwTYbVzVMYmfR/Xhrdu60CYxipx8B2/N2UrvF3/l+R/WcTDrhNVhnpGS/Grmqsdvp6Z7IiIibhUZGcmKFSv4/fff+cc//sHo0aOZPXt2mfs+/vjjZGZmFt92797t2WBFRMSn2O02BrRL5MeHe/Pu0G50rB/NiQIn787bTu+XZjHmuzXszzxudZhlCrQ6AH+3Zq85HbC9puqLiIiUKTY2loCAAA4cOFDq8QMHDpCQkFDu8+x2O82aNQOgU6dOrF+/nrFjx9K3b9/T9g0JCSEkJMStcYuIiP+z2Wz0axPPJa3jmLPpEG/8uoWlO48yaeFOPluym+u61WfEhU1pEBNudajFNJJfzdbsO7l8noiIiJwuODiYrl27MnPmzOLHnE4nM2fOpEePHhU+jtPpJC/PN+olRUTEt9hsNvq2jOOr+3vw6d2ppDaOId/h5NPFu+j/2lyvWnZPI/nVrFVCFNknCmmXpM69IiIi5Rk9ejTDhg2jW7dudO/enddee42cnByGDx8OwNChQ0lOTmbs2LGAWWPfrVs3mjZtSl5eHj/99BMfffQR48ePt/I0RETEz9lsNno2i6Vns1gWbzvMG79uIaVuOJGhQVaHVkxJfjUbe217q0MQERHxejfeeCOHDh3iqaeeIi0tjU6dOjFt2rTiZny7du3Cbj85ATEnJ4cHHniAPXv2EBYWRqtWrfj444+58cYbrToFERGpYVKb1CW1SV0KHE6rQynFZhiGYXUQ3kZr54qIiLfRtcm99P8pIiLexl3XJtXki4iIiIiIiPgJJfkiIiIiIiIifkJJvoiIiIiIiIifUJIvIiIiIiIi4ieU5IuIiIiIiIj4CSX5IiIiIiIiIn5CSb6IiIiIiIiIn1CSLyIiIiIiIuInlOSLiIiIiIiI+Akl+SIiIiIiIiJ+Qkm+iIiIiIiIiJ9Qki8iIiIiIiLiJ5Tki4iIiIiIiPgJJfkiIiIiIiIifkJJvoiIiIiIiIifUJIvIiIiIiIi4ieU5IuIiIiIiIj4iUCrA/BGhmEAkJWVZXEkIiIiJtc1yXWNkqrRtV5ERLyNu671SvLLcOzYMQAaNGhgcSQiIiKlHTt2jOjoaKvD8Hm61ouIiLeq6rXeZmhI4DROp5N9+/YRGRmJzWar0rGysrJo0KABu3fvJioqyk0Rehd/P0d/Pz/w/3P09/MD/z9Hfz8/OPs5GobBsWPHSEpKwm5XtV1VufNaD/7/Perv5wf+f47+fn7g/+fo7+cH/n+OnrrWayS/DHa7nfr167v1mFFRUX75jVqSv5+jv58f+P85+vv5gf+fo7+fH5z5HDWC7z7Vca0H//8e9ffzA/8/R38/P/D/c/T38wP/P8fqvtZrKEBERERERETETyjJFxEREREREfETSvKrWUhICGPGjCEkJMTqUKqNv5+jv58f+P85+vv5gf+fo7+fH9SMc/Rn/v7++fv5gf+fo7+fH/j/Ofr7+YH/n6Onzk+N90RERERERET8hEbyRURERERERPyEknwRERERERERP6EkX0RERERERMRPKMkXERERERER8RNK8t1g3LhxNGrUiNDQUFJTU1myZMkZ9//yyy9p1aoVoaGhtG/fnp9++slDkVbe2LFjOe+884iMjCQuLo7BgwezcePGMz7ngw8+wGazlbqFhoZ6KOLKefrpp0+LtVWrVmd8ji+9fwCNGjU67RxtNhsjR44sc39vf//mzp3LVVddRVJSEjabjSlTppT6umEYPPXUUyQmJhIWFka/fv3YvHnzWY9b2Z/j6nSmcywoKOCxxx6jffv2REREkJSUxNChQ9m3b98Zj3ku3+vV6Wzv4x133HFavAMGDDjrcb3lfTzb+ZX1M2mz2Xj55ZfLPaa3vYc1ja71pXn7teJU/n6997drPfj/9V7Xel3ry+Ku91BJfhVNnjyZ0aNHM2bMGJYtW0bHjh3p378/Bw8eLHP/BQsWcPPNN3PXXXexfPlyBg8ezODBg1mzZo2HI6+YOXPmMHLkSBYtWsQvv/xCQUEBl112GTk5OWd8XlRUFPv37y++7dy500MRV17btm1LxTpv3rxy9/W19w/g999/L3V+v/zyCwDXX399uc/x5vcvJyeHjh07Mm7cuDK//tJLL/H666/z1ltvsXjxYiIiIujfvz8nTpwo95iV/Tmubmc6x9zcXJYtW8aTTz7JsmXL+Oabb9i4cSNXX331WY9bme/16na29xFgwIABpeL97LPPznhMb3ofz3Z+Jc9r//79TJw4EZvNxpAhQ854XG96D2sSXevL5s3XirL48/Xe36714P/Xe13rTbrWn84t76EhVdK9e3dj5MiRxf92OBxGUlKSMXbs2DL3v+GGG4yBAweWeiw1NdW47777qjVOdzl48KABGHPmzCl3n/fff9+Ijo72XFBVMGbMGKNjx44V3t/X3z/DMIxRo0YZTZs2NZxOZ5lf96X3DzC+/fbb4n87nU4jISHBePnll4sfy8jIMEJCQozPPvus3ONU9ufYk049x7IsWbLEAIydO3eWu09lv9c9qaxzHDZsmDFo0KBKHcdb38eKvIeDBg0yLr744jPu483vob/Ttf50vnStMIyad733p2u9Yfj/9V7X+orz5ffQk9d6jeRXQX5+PkuXLqVfv37Fj9ntdvr168fChQvLfM7ChQtL7Q/Qv3//cvf3NpmZmQDExMSccb/s7GxSUlJo0KABgwYNYu3atZ4I75xs3ryZpKQkmjRpwq233squXbvK3dfX37/8/Hw+/vhj7rzzTmw2W7n7+dL7V9L27dtJS0sr9R5FR0eTmppa7nt0Lj/H3iYzMxObzUbt2rXPuF9lvte9wezZs4mLi6Nly5aMGDGCw4cPl7uvL7+PBw4c4Mcff+Suu+46676+9h76A13ry+dr14qacr3392s91Mzrva71vv0eevparyS/CtLT03E4HMTHx5d6PD4+nrS0tDKfk5aWVqn9vYnT6eRPf/oTvXr1ol27duXu17JlSyZOnMh3333Hxx9/jNPppGfPnuzZs8eD0VZMamoqH3zwAdOmTWP8+PFs376dPn36cOzYsTL39+X3D2DKlClkZGRwxx13lLuPL71/p3K9D5V5j87l59ibnDhxgscee4ybb76ZqKiocver7Pe61QYMGMCHH37IzJkzefHFF5kzZw6XX345DoejzP19+X2cNGkSkZGRXHvttWfcz9feQ3+ha33ZfO1aUZOu9/5+rYead73Xtd7ky++hp6/1gVUJVmqWkSNHsmbNmrPWhfTo0YMePXoU/7tnz560bt2at99+m+eee666w6yUyy+/vHi7Q4cOpKamkpKSwhdffFGhT9p8zXvvvcfll19OUlJSufv40vtX0xUUFHDDDTdgGAbjx48/476+9r1+0003FW+3b9+eDh060LRpU2bPns0ll1xiYWTuN3HiRG699dazNr3ytfdQfJM/XuuhZv386FrvX3St9w+evtZrJL8KYmNjCQgI4MCBA6UeP3DgAAkJCWU+JyEhoVL7e4sHH3yQH374gVmzZlG/fv1KPTcoKIjOnTuzZcuWaorOfWrXrk2LFi3KjdVX3z+AnTt3MmPGDO6+++5KPc+X3j/X+1CZ9+hcfo69geuiv3PnTn755ZczfrJflrN9r3ubJk2aEBsbW268vvo+/vbbb2zcuLHSP5fge++hr9K1vmJ86VoB/nu9rwnXeqg513td60vzxfcQrLnWK8mvguDgYLp27crMmTOLH3M6ncycObPUp6Ml9ejRo9T+AL/88ku5+1vNMAwefPBBvv32W3799VcaN25c6WM4HA5Wr15NYmJiNUToXtnZ2WzdurXcWH3t/Svp/fffJy4ujoEDB1bqeb70/jVu3JiEhIRS71FWVhaLFy8u9z06l59jq7ku+ps3b2bGjBnUrVu30sc42/e6t9mzZw+HDx8uN15ffB/BHHHr2rUrHTt2rPRzfe099FW61leML10rwH+v9zXhWg8143qva/3pfO09dLHkWl/l1n013Oeff26EhIQYH3zwgbFu3Trj3nvvNWrXrm2kpaUZhmEYt99+u/G3v/2teP/58+cbgYGBxr/+9S9j/fr1xpgxY4ygoCBj9erVVp3CGY0YMcKIjo42Zs+ebezfv7/4lpubW7zPqef4zDPPGNOnTze2bt1qLF261LjpppuM0NBQY+3atVacwhn9+c9/NmbPnm1s377dmD9/vtGvXz8jNjbWOHjwoGEYvv/+uTgcDqNhw4bGY489dtrXfO39O3bsmLF8+XJj+fLlBmC88sorxvLly4u7zb7wwgtG7dq1je+++85YtWqVMWjQIKNx48bG8ePHi49x8cUXG2+88Ubxv8/2c+xpZzrH/Px84+qrrzbq169vrFixotTPZV5eXvExTj3Hs32ve9qZzvHYsWPGo48+aixcuNDYDWpGhgAABclJREFUvn27MWPGDKNLly5G8+bNjRMnThQfw5vfx7N9nxqGYWRmZhrh4eHG+PHjyzyGt7+HNYmu9b53rThVTbje+9O13jD8/3qva72u9YZRfe+hknw3eOONN4yGDRsawcHBRvfu3Y1FixYVf+3CCy80hg0bVmr/L774wmjRooURHBxstG3b1vjxxx89HHHFAWXe3n///eJ9Tj3HP/3pT8X/H/Hx8cYVV1xhLFu2zPPBV8CNN95oJCYmGsHBwUZycrJx4403Glu2bCn+uq+/fy7Tp083AGPjxo2nfc3X3r9Zs2aV+T3pOgen02k8+eSTRnx8vBESEmJccsklp513SkqKMWbMmFKPnenn2NPOdI7bt28v9+dy1qxZxcc49RzP9r3uaWc6x9zcXOOyyy4z6tWrZwQFBRkpKSnGPffcc9oF3Jvfx7N9nxqGYbz99ttGWFiYkZGRUeYxvP09rGl0rfeta8WpasL13p+u9Ybh/9d7Xet1rTeM6nsPbYZhGJUb+xcRERERERERb6SafBERERERERE/oSRfRERERERExE8oyRcRERERERHxE0ryRURERERERPyEknwRERERERERP6EkX0RERERERMRPKMkXERERERER8RNK8kVERERERET8hJJ8EfFKNpuNKVOmWB2GiIiIVBNd60Wqh5J8ETnNHXfcgc1mO+02YMAAq0MTERERN9C1XsR/BVodgIh4pwEDBvD++++XeiwkJMSiaERERMTddK0X8U8ayReRMoWEhJCQkFDqVqdOHcCcXjd+/Hguv/xywsLCaNKkCV999VWp569evZqLL76YsLAw6taty7333kt2dnapfSZOnEjbtm0JCQkhMTGRBx98sNTX09PTueaaawgPD6d58+ZMnTq1ek9aRESkBtG1XsQ/KckXkXPy5JNPMmTIEFauXMmtt97KTTfdxPr16wHIycmhf//+1KlTh99//50vv/ySGTNmlLqwjx8/npEjR3LvvfeyevVqpk6dSrNmzUq9xjPPPMMNN9zAqlWruOKKK7j11ls5cuSIR89TRESkptK1XsRHGSIipxg2bJgREBBgRERElLr94x//MAzDMADj/vvvL/Wc1NRUY8SIEYZhGMY777xj1KlTx8jOzi7++o8//mjY7XYjLS3NMAzDSEpKMp544olyYwCMv//978X/zs7ONgDj559/dtt5ioiI1FS61ov4L9Xki0iZLrroIsaPH1/qsZiYmOLtHj16lPpajx49WLFiBQDr16+nY8eOREREFH+9V69eOJ1ONm7ciM1mY9++fVxyySVnjKFDhw7F2xEREURFRXHw4MFzPSUREREpQdd6Ef+kJF9EyhQREXHalDp3CQsLq9B+QUFBpf5ts9lwOp3VEZKIiEiNo2u9iH9STb6InJNFixad9u/WrVsD0Lp1a1auXElOTk7x1+fPn4/dbqdly5ZERkbSqFEjZs6c6dGYRUREpOJ0rRfxTRrJF5Ey5eXlkZaWVuqxwMBAYmNjAfjyyy/p1q0bvXv35pNPPmHJkiW89957ANx6662MGTOGYcOG8fTTT3Po0CEeeughbr/9duLj4wF4+umnuf/++4mLi+Pyyy/n2LFjzJ8/n4ceesizJyoiIlJD6Vov4p+U5ItImaZNm0ZiYmKpx1q2bMmGDRsAsxvu559/zgMPPEBiYiKfffYZbdq0ASA8PJzp06czatQozjvvPMLDwxkyZAivvPJK8bGGDRvGiRMnePXVV3n00UeJjY3luuuu89wJioiI1HC61ov4J5thGIbVQYiIb7HZbHz77bcMHjzY6lBERESkGuhaL+K7VJMvIiIiIiIi4ieU5IuIiIiIiIj4CU3XFxEREREREfETGskXERERERER8RNK8kVERERERET8hJJ8ERERERERET+hJF9ERERERETETyjJFxEREREREfETSvJFRERERERE/ISSfBERERERERE/oSRfRERERERExE/8P8krNtKo8bGAAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the accuracy and loss curves\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(history.history['accuracy'], label='Training accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label='Validation accuracy')\n",
    "plt.title('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(history.history['loss'], label='Training loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation loss')\n",
    "plt.title('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "81d63cb7",
   "metadata": {},
   "source": [
    "### There is a significant improvement in the accuracy after Transfer learning."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "379655e1",
   "metadata": {},
   "source": [
    "## Comparing Accuracy of Baseline model, The model and Improved model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "44b32cc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Graph\n",
    "plt.plot([baseline_acc, the_model_acc, improved_model_acc], marker='o')\n",
    "plt.xticks([0, 1, 2], ['Baseline Model', 'The Model', 'The Improved Model'])\n",
    "plt.ylabel('Accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "043e1753",
   "metadata": {},
   "source": [
    "## Predicting the model for new review."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "645e44d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Masking, LSTM, Dense\n",
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "# Load the pre-trained Word2Vec model\n",
    "word2vec_transfer = api.load(\"glove-wiki-gigaword-100\")\n",
    "\n",
    "# Define the function to embed a sentence with the pre-trained Word2Vec model\n",
    "def embed_sentence_with_TF(word2vec, sentence):\n",
    "    embedded_sentence = []\n",
    "    for word in sentence:\n",
    "        if word in word2vec:\n",
    "            embedded_sentence.append(word2vec[word])\n",
    "    return np.array(embedded_sentence)\n",
    "\n",
    "# Define the function to preprocess a new movie review\n",
    "def preprocess_review(review):\n",
    "    # Tokenize the review\n",
    "    review = text_to_word_sequence(review)\n",
    "    # Embed the review with the pre-trained Word2Vec model\n",
    "    review_embedded = embed_sentence_with_TF(word2vec_transfer, review)\n",
    "    # Pad the embedded review\n",
    "    review_padded = pad_sequences([review_embedded], dtype='float32', padding='post', maxlen=200)\n",
    "    return review_padded\n",
    "\n",
    "# Load the trained model\n",
    "model = Sequential()\n",
    "model.add(Masking())\n",
    "model.add(LSTM(20, activation='tanh'))\n",
    "model.add(Dense(15, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n",
    "model = load_model('my_model.h5')\n",
    "def predict_sentiment(review):\n",
    "    # Preprocess the review\n",
    "    review_padded = preprocess_review(review)\n",
    "    # Predict the sentiment\n",
    "    sentiment = model.predict(review_padded)[0][0]\n",
    "    return sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "faf5685a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 679ms/step\n",
      "Positive review\n"
     ]
    }
   ],
   "source": [
    "review = input(\"Enter a review:\")\n",
    "sentiment = predict_sentiment(review)\n",
    "\n",
    "if sentiment > 0.5:\n",
    "    print(\"Positive review\")\n",
    "elif sentiment == 0.5:\n",
    "    print(\"Neutral review\")\n",
    "else:\n",
    "    print(\"Negative review\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "30d4a32e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/gradio/inputs.py:27: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
      "  warnings.warn(\n",
      "/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/gradio/inputs.py:30: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
      "  super().__init__(\n",
      "/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/gradio/inputs.py:30: UserWarning: `numeric` parameter is deprecated, and it has no effect\n",
      "  super().__init__(\n",
      "/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/gradio/outputs.py:22: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7861\n",
      "Running on public URL: https://ef9fee4af1830c79fa.gradio.live\n",
      "\n",
      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"https://ef9fee4af1830c79fa.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 743ms/step\n",
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    {
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     "output_type": "stream",
     "text": [
      "2023-06-05 17:41:09.620915: W tensorflow/core/framework/op_kernel.cc:1780] OP_REQUIRES failed at transpose_op.cc:142 : INVALID_ARGUMENT: transpose expects a vector of size 2. But input(1) is a vector of size 3\n",
      "2023-06-05 17:41:09.621087: W tensorflow/core/framework/op_kernel.cc:1780] OP_REQUIRES failed at transpose_op.cc:142 : INVALID_ARGUMENT: transpose expects a vector of size 2. But input(1) is a vector of size 3\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/gradio/routes.py\", line 427, in run_predict\n",
      "    output = await app.get_blocks().process_api(\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/gradio/blocks.py\", line 1323, in process_api\n",
      "    result = await self.call_function(\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/gradio/blocks.py\", line 1051, in call_function\n",
      "    prediction = await anyio.to_thread.run_sync(\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/anyio/to_thread.py\", line 31, in run_sync\n",
      "    return await get_asynclib().run_sync_in_worker_thread(\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/anyio/_backends/_asyncio.py\", line 937, in run_sync_in_worker_thread\n",
      "    return await future\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/anyio/_backends/_asyncio.py\", line 867, in run\n",
      "    result = context.run(func, *args)\n",
      "  File \"/var/folders/d2/90pdbpy11hx6fnbddlzkff340000gn/T/ipykernel_92516/1865062254.py\", line 35, in predict_sentiment\n",
      "    sentiment = model.predict(review_padded)[0][0]\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 70, in error_handler\n",
      "    raise e.with_traceback(filtered_tb) from None\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/tensorflow/python/eager/execute.py\", line 54, in quick_execute\n",
      "    tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n",
      "tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error:\n",
      "\n",
      "transpose expects a vector of size 2. But input(1) is a vector of size 3\n",
      "\t [[{{node transpose}}]]\n",
      "\t [[sequential_1/lstm_1/PartitionedCall]] [Op:__inference_predict_function_58960]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 29ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-05 17:41:36.584429: W tensorflow/core/framework/op_kernel.cc:1780] OP_REQUIRES failed at transpose_op.cc:142 : INVALID_ARGUMENT: transpose expects a vector of size 2. But input(1) is a vector of size 3\n",
      "2023-06-05 17:41:36.584461: W tensorflow/core/framework/op_kernel.cc:1780] OP_REQUIRES failed at transpose_op.cc:142 : INVALID_ARGUMENT: transpose expects a vector of size 2. But input(1) is a vector of size 3\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/gradio/routes.py\", line 427, in run_predict\n",
      "    output = await app.get_blocks().process_api(\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/gradio/blocks.py\", line 1323, in process_api\n",
      "    result = await self.call_function(\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/gradio/blocks.py\", line 1051, in call_function\n",
      "    prediction = await anyio.to_thread.run_sync(\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/anyio/to_thread.py\", line 31, in run_sync\n",
      "    return await get_asynclib().run_sync_in_worker_thread(\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/anyio/_backends/_asyncio.py\", line 937, in run_sync_in_worker_thread\n",
      "    return await future\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/anyio/_backends/_asyncio.py\", line 867, in run\n",
      "    result = context.run(func, *args)\n",
      "  File \"/var/folders/d2/90pdbpy11hx6fnbddlzkff340000gn/T/ipykernel_92516/1865062254.py\", line 35, in predict_sentiment\n",
      "    sentiment = model.predict(review_padded)[0][0]\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 70, in error_handler\n",
      "    raise e.with_traceback(filtered_tb) from None\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/tensorflow/python/eager/execute.py\", line 54, in quick_execute\n",
      "    tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n",
      "tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error:\n",
      "\n",
      "transpose expects a vector of size 2. But input(1) is a vector of size 3\n",
      "\t [[{{node transpose}}]]\n",
      "\t [[sequential_1/lstm_1/PartitionedCall]] [Op:__inference_predict_function_58960]\n",
      "2023-06-05 17:41:40.561062: W tensorflow/core/framework/op_kernel.cc:1780] OP_REQUIRES failed at transpose_op.cc:142 : INVALID_ARGUMENT: transpose expects a vector of size 2. But input(1) is a vector of size 3\n",
      "2023-06-05 17:41:40.561098: W tensorflow/core/framework/op_kernel.cc:1780] OP_REQUIRES failed at transpose_op.cc:142 : INVALID_ARGUMENT: transpose expects a vector of size 2. But input(1) is a vector of size 3\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/gradio/routes.py\", line 427, in run_predict\n",
      "    output = await app.get_blocks().process_api(\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/gradio/blocks.py\", line 1323, in process_api\n",
      "    result = await self.call_function(\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/gradio/blocks.py\", line 1051, in call_function\n",
      "    prediction = await anyio.to_thread.run_sync(\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/anyio/to_thread.py\", line 31, in run_sync\n",
      "    return await get_asynclib().run_sync_in_worker_thread(\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/anyio/_backends/_asyncio.py\", line 937, in run_sync_in_worker_thread\n",
      "    return await future\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/anyio/_backends/_asyncio.py\", line 867, in run\n",
      "    result = context.run(func, *args)\n",
      "  File \"/var/folders/d2/90pdbpy11hx6fnbddlzkff340000gn/T/ipykernel_92516/1865062254.py\", line 35, in predict_sentiment\n",
      "    sentiment = model.predict(review_padded)[0][0]\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 70, in error_handler\n",
      "    raise e.with_traceback(filtered_tb) from None\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/tensorflow/python/eager/execute.py\", line 54, in quick_execute\n",
      "    tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n",
      "tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error:\n",
      "\n",
      "transpose expects a vector of size 2. But input(1) is a vector of size 3\n",
      "\t [[{{node transpose}}]]\n",
      "\t [[sequential_1/lstm_1/PartitionedCall]] [Op:__inference_predict_function_58960]\n",
      "2023-06-05 17:41:45.918540: W tensorflow/core/framework/op_kernel.cc:1780] OP_REQUIRES failed at transpose_op.cc:142 : INVALID_ARGUMENT: transpose expects a vector of size 2. But input(1) is a vector of size 3\n",
      "2023-06-05 17:41:45.918572: W tensorflow/core/framework/op_kernel.cc:1780] OP_REQUIRES failed at transpose_op.cc:142 : INVALID_ARGUMENT: transpose expects a vector of size 2. But input(1) is a vector of size 3\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/gradio/routes.py\", line 427, in run_predict\n",
      "    output = await app.get_blocks().process_api(\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/gradio/blocks.py\", line 1323, in process_api\n",
      "    result = await self.call_function(\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/gradio/blocks.py\", line 1051, in call_function\n",
      "    prediction = await anyio.to_thread.run_sync(\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/anyio/to_thread.py\", line 31, in run_sync\n",
      "    return await get_asynclib().run_sync_in_worker_thread(\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/anyio/_backends/_asyncio.py\", line 937, in run_sync_in_worker_thread\n",
      "    return await future\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/anyio/_backends/_asyncio.py\", line 867, in run\n",
      "    result = context.run(func, *args)\n",
      "  File \"/var/folders/d2/90pdbpy11hx6fnbddlzkff340000gn/T/ipykernel_92516/1865062254.py\", line 35, in predict_sentiment\n",
      "    sentiment = model.predict(review_padded)[0][0]\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 70, in error_handler\n",
      "    raise e.with_traceback(filtered_tb) from None\n",
      "  File \"/Users/pavankumarhm/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/tensorflow/python/eager/execute.py\", line 54, in quick_execute\n",
      "    tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n",
      "tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error:\n",
      "\n",
      "transpose expects a vector of size 2. But input(1) is a vector of size 3\n",
      "\t [[{{node transpose}}]]\n",
      "\t [[sequential_1/lstm_1/PartitionedCall]] [Op:__inference_predict_function_58960]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n"
     ]
    }
   ],
   "source": [
    "import gradio as gr\n",
    "import numpy as np\n",
    "from tensorflow.keras.preprocessing.text import text_to_word_sequence\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from gensim.models import KeyedVectors\n",
    "from tensorflow.keras.models import load_model\n",
    "# Load the pre-trained Word2Vec model\n",
    "word2vec_transfer = api.load(\"glove-wiki-gigaword-100\")\n",
    "\n",
    "# Define the function to embed a sentence with the pre-trained Word2Vec model\n",
    "def embed_sentence_with_TF(word2vec, sentence):\n",
    "    embedded_sentence = []\n",
    "    for word in sentence:\n",
    "        if word in word2vec:\n",
    "            embedded_sentence.append(word2vec[word])\n",
    "    return np.array(embedded_sentence)\n",
    "\n",
    "# Define the function to preprocess a new movie review\n",
    "def preprocess_review(review):\n",
    "    # Tokenize the review\n",
    "    review = text_to_word_sequence(review)\n",
    "    # Embed the review with the pre-trained Word2Vec model\n",
    "    review_embedded = embed_sentence_with_TF(word2vec_transfer, review)\n",
    "    # Pad the embedded review\n",
    "    review_padded = pad_sequences([review_embedded], dtype='float32', padding='post', maxlen=200)\n",
    "    return review_padded\n",
    "\n",
    "# Load the trained model\n",
    "model = load_model('my_model.h5')\n",
    "\n",
    "def predict_sentiment(review):\n",
    "    # Preprocess the review\n",
    "    review_padded = preprocess_review(review)\n",
    "    # Predict the sentiment\n",
    "    sentiment = model.predict(review_padded)[0][0]\n",
    "    if sentiment > 0.5:\n",
    "        return \"Positive\"\n",
    "    elif sentiment == 0.5:\n",
    "        return \"Neutral\"\n",
    "    else:\n",
    "        return \"Negative\"\n",
    "\n",
    "# Create a Gradio interface\n",
    "inputs = gr.inputs.Textbox(lines=5, label=\"Input Text\")\n",
    "outputs = gr.outputs.Textbox(label=\"Sentiment\")\n",
    "title = \"Sentiment Analysis\"\n",
    "description = \"Enter a text and get the sentiment prediction.\"\n",
    "gr.Interface(fn=predict_sentiment, inputs=inputs, outputs=outputs, title=title, description=description).launch(share=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f45c4e95",
   "metadata": {},
   "outputs": [],
   "source": [
    "!git add Sentiment_Analysis_Gradio.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bfaff667",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[main 8b4a38f] User Interface for custom trained model\n",
      " 1 file changed, 841 insertions(+)\n",
      " create mode 100644 Sentiment_Analysis_Gradio.ipynb\n"
     ]
    }
   ],
   "source": [
    "!git commit -m 'User Interface for custom trained model'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3e6d3355",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enumerating objects: 4, done.\n",
      "Counting objects: 100% (4/4), done.\n",
      "Delta compression using up to 8 threads\n",
      "Compressing objects: 100% (3/3), done.\n",
      "Writing objects: 100% (3/3), 146.82 KiB | 1.44 MiB/s, done.\n",
      "Total 3 (delta 0), reused 0 (delta 0), pack-reused 0\n",
      "To github.com:MYM-Onboarding/Sentiment_Analyzer.git\n",
      "   c6304a9..8b4a38f  main -> main\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 87ms/step\n"
     ]
    }
   ],
   "source": [
    "!git push origin main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55498423",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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