NMT_advance_fra_eng (1).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "NMT_advance_fra-eng.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "vc5ngHh4PPz1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# import the required libraries:\n",
        "import os, sys\n",
        "from keras.models import Model\n",
        "from keras.layers import Input, LSTM, GRU, Dense, Embedding\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.utils import to_categorical\n",
        "import numpy as np\n",
        "import pickle\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_SRkog90Prnf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Execute this script to set values for different parameters:\n",
        "BATCH_SIZE = 64\n",
        "EPOCHS = 20\n",
        "LSTM_NODES =256\n",
        "NUM_SENTENCES = 20000\n",
        "MAX_SENTENCE_LENGTH = 50\n",
        "MAX_NUM_WORDS = 20000\n",
        "EMBEDDING_SIZE = 200"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e281mVDVQOQG",
        "colab_type": "text"
      },
      "source": [
        "The language translation model that we are going to develop will translate English sentences into their French language counterparts. To develop such a model, we need a dataset that contains English sentences and their French translations."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YlZSuKwHQdF5",
        "colab_type": "text"
      },
      "source": [
        "# Data Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "umQGGqLOQ12u",
        "colab_type": "text"
      },
      "source": [
        "We need to generate two copies of the translated sentence: one with the start-of-sentence token and the other with the end-of-sentence token.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iah7wvJKQQy5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "15d10ca7-af21-48da-851a-5e654b85a96e"
      },
      "source": [
        "input_sentences = []\n",
        "output_sentences = []\n",
        "output_sentences_inputs = []\n",
        "\n",
        "count = 0\n",
        "for line in open('./drive/My Drive/fra.txt', encoding=\"utf-8\"):\n",
        "    count += 1\n",
        "    if count > NUM_SENTENCES:\n",
        "        break\n",
        "    if '\\t' not in line:\n",
        "        continue\n",
        "    input_sentence = line.rstrip().split('\\t')[0]\n",
        "    output = line.rstrip().split('\\t')[1]\n",
        "\n",
        "    output_sentence = output + ' <eos>'\n",
        "    output_sentence_input = '<sos> ' + output\n",
        "\n",
        "    input_sentences.append(input_sentence)\n",
        "    output_sentences.append(output_sentence)\n",
        "    output_sentences_inputs.append(output_sentence_input)\n",
        "\n",
        "print(\"Number of sample input:\", len(input_sentences))\n",
        "print(\"Number of sample output:\", len(output_sentences))\n",
        "print(\"Number of sample output input:\", len(output_sentences_inputs))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of sample input: 20000\n",
            "Number of sample output: 20000\n",
            "Number of sample output input: 20000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_nAgWjj4VF1g",
        "colab_type": "text"
      },
      "source": [
        "Now randomly print a sentence to analyse your dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X0S4pCxTRCMr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "787321ef-59a3-422b-b359-8a7337b87eaf"
      },
      "source": [
        "print(\"English sentence: \",input_sentences[180])\n",
        "print(\"French translation: \",output_sentences[180])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "English sentence:  Join us.\n",
            "French translation:  Joignez-vous à nous. <eos>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2rSbfS2-WDes",
        "colab_type": "text"
      },
      "source": [
        "You can see the original sentence, i.e. **Join us**; its corresponding translation in the output, i.e **Joignez-vous à nous.** <eos>. Notice, here we have <eos> token at the end of the sentence. Similarly, for the input to the decoder, we have <sos> **Joignez-vous à nous.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-hGrGE_LWWzQ",
        "colab_type": "text"
      },
      "source": [
        "# Tokenization and Padding\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IYXduD_iW62b",
        "colab_type": "text"
      },
      "source": [
        "The next step is tokenizing the original and translated sentences and applying padding to the sentences that are longer or shorter than a certain length, which in case of inputs will be the length of the longest input sentence. And for the output this will be the length of the longest sentence in the output."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ibQSA3kaWWbn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "25eebbc6-6637-44c9-f11e-5b8bd4376a35"
      },
      "source": [
        "# let’s visualise the length of the sentences.\n",
        "import pandas as pd\n",
        "\n",
        "eng_len = []\n",
        "fren_len = []\n",
        "\n",
        "# populate the lists with sentence lengths\n",
        "for i in input_sentences:\n",
        "      eng_len.append(len(i.split()))  \n",
        "\n",
        "for i in output_sentences:\n",
        "      fren_len.append(len(i.split()))\n",
        "\n",
        "length_df = pd.DataFrame({'english':eng_len, 'french':fren_len})\n",
        "\n",
        "length_df.hist(bins = 20)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uJnEP8llZ_Ao",
        "colab_type": "text"
      },
      "source": [
        "The histogram above shows  maximum length of the French sentences is 12 and that of the English sentence is 6."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eZSk_2kXY90w",
        "colab_type": "text"
      },
      "source": [
        "For tokenization, the Tokenizer class from the keras.preprocessing.text library can be used. The tokenizer class performs two tasks:\n",
        "\n",
        "1. It divides a sentence into the corresponding list of word\n",
        "\n",
        "2. Then it converts the words to integers\n",
        "\n",
        "Also the **word_index** attribute of the Tokenizer class returns a word-to-index dictionary where words are the keys and the corresponding integers are the values. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YQDnWoyPU6-p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "54c37c87-898b-41a8-bf83-e0019af994f9"
      },
      "source": [
        "#tokenize the input sentences(input language) \n",
        "input_tokenizer = Tokenizer(num_words=MAX_NUM_WORDS)\n",
        "input_tokenizer.fit_on_texts(input_sentences)\n",
        "input_integer_seq = input_tokenizer.texts_to_sequences(input_sentences)\n",
        "print(input_integer_seq)\n",
        "\n",
        "word2idx_inputs = input_tokenizer.word_index\n",
        "print('Total unique words in the input: %s' % len(word2idx_inputs))\n",
        "\n",
        "max_input_len = max(len(sen) for sen in input_integer_seq)\n",
        "print(\"Length of longest sentence in input: %g\" % max_input_len)\n",
        "\n",
        "#with open('input_tokenizer_NMT.pickle', 'wb') as handle:\n",
        "#    pickle.dump(input_tokenizer, handle, protocol=4)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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760], [5, 23, 317, 57], [5, 23, 1322], [5, 23, 1322], [5, 23, 2432], [5, 23, 2432], [5, 23, 1781], [5, 23, 1781], [5, 23, 1781], [5, 23, 119, 359], [5, 23, 119, 359], [5, 23, 600], [5, 23, 600], [5, 23, 54, 443], [5, 23, 601], [5, 23, 601], [5, 23, 18, 314], [5, 23, 18, 3496], [5, 23, 10, 1583], [5, 23, 10, 946], [5, 23, 10, 946], [5, 23, 126, 359], [5, 23, 126, 359], [5, 23, 45, 89], [5, 23, 45, 89], [5, 23, 24, 1039], [5, 23, 24, 819], [5, 23, 24, 819], [5, 23, 24, 819], [5, 23, 24, 819], [5, 57, 24, 296], [5, 57, 24, 296], [5, 57, 24, 296], [5, 57, 24, 296], [5, 42, 25, 198, 58], [5, 42, 1344, 1208], [5, 42, 166, 98], [5, 58, 88, 1338], [5, 168, 1857], [5, 1069, 88, 18], [5, 1069, 88, 18], [5, 511, 274], [5, 511, 274], [5, 148, 232], [5, 148, 369], [5, 103, 275], [5, 166, 88, 317], [5, 166, 88, 317], [5, 54, 1645], [5, 851, 32, 168], [5, 2433, 88, 126], [5, 2433, 88, 126], [5, 121, 82, 75], [5, 121, 3497], [5, 121, 38, 482], [5, 121, 372], [5, 121, 372], [5, 121, 372], [5, 121, 372], [5, 121, 554], [5, 121, 554], [5, 121, 1607], [5, 382, 173], [5, 382, 173], [5, 382, 173], [5, 382, 173], [5, 1401, 104, 584], [5, 2357, 3, 983], [5, 1186, 185], [5, 3498], [5, 263, 42, 4], [5, 24, 3499], [108, 434, 3500], [108, 434, 766], [108, 434, 753], [108, 434, 263], [108, 53, 138, 552], [108, 53, 25, 3501], [108, 53, 101, 32], [108, 53, 101, 32], [108, 336, 173], [108, 336, 88, 2]]\n",
            "Total unique words in the input: 3501\n",
            "Length of longest sentence in input: 6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jUQ47uZhZKQ1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "3f991e9b-f901-49d1-bd66-63bf2f40ebf6"
      },
      "source": [
        "#tokenize the output sentences(Output language)\n",
        "output_tokenizer = Tokenizer(num_words=MAX_NUM_WORDS, filters='')\n",
        "output_tokenizer.fit_on_texts(output_sentences + output_sentences_inputs)\n",
        "output_integer_seq = output_tokenizer.texts_to_sequences(output_sentences)\n",
        "output_input_integer_seq = output_tokenizer.texts_to_sequences(output_sentences_inputs)\n",
        "print(output_input_integer_seq)\n",
        "\n",
        "word2idx_outputs = output_tokenizer.word_index\n",
        "print('Total unique words in the output: %s' % len(word2idx_outputs))\n",
        "\n",
        "num_words_output = len(word2idx_outputs) + 1\n",
        "max_out_len = max(len(sen) for sen in output_integer_seq)\n",
        "print(\"Length of longest sentence in the output: %g\" % max_out_len)\n",
        "\n",
        "#with open('output_tokenizer_NMT.pickle', 'wb') as handle:\n",
        "#    pickle.dump(output_tokenizer, handle, protocol=4)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[2, 64, 5], [2, 1046, 5], [2, 4544], [2, 4545], [2, 4546], [2, 36, 4], [2, 30, 4547], [2, 75, 725, 5], [2, 20, 4548], [2, 4549], [2, 30, 2705], [2, 4550], [2, 2011, 5], [2, 315, 5], [2, 367, 5], [2, 4551], [2, 2706], [2, 4552], [2, 2012, 5], [2, 1046, 5], [2, 3, 930], [2, 2707], [2, 14, 517, 5], [2, 3, 66, 1245, 5], [2, 204, 562], [2, 2708, 487, 5], [2, 2709, 5], [2, 4553, 5], [2, 1551, 5], [2, 20, 59, 1551, 5], [2, 455, 5], [2, 4554, 5], [2, 2013], [2, 2014, 130], [2, 4555, 130], [2, 1246, 130], [2, 14, 4556, 5], [2, 1047, 5], [2, 4557], [2, 4558], [2, 1048, 4559], [2, 4560], [2, 4561], [2, 2015, 99, 211, 810, 5], [2, 2710, 99, 268, 810, 5], [2, 3, 6, 4562], [2, 3, 6, 2711], [2, 3, 1049], [2, 3, 6, 657], [2, 3, 6, 658], [2, 14, 563], [2, 14, 322], [2, 204, 811], [2, 14, 2712, 488], [2, 3, 94, 84], [2, 30, 659], [2, 2016, 5], [2, 16, 8, 2713], [2, 4563], [2, 23, 323, 4564], [2, 212, 4565], [2, 16, 1247, 12, 2017, 5], [2, 7, 152, 15, 8, 2017, 5], [2, 16, 4566, 5], [2, 23, 291, 2018, 5], [2, 1247, 12, 2017, 5], [2, 1248], [2, 564, 4], [2, 4567, 150, 4], [2, 455, 5], [2, 80, 2714], [2, 10, 61, 562], [2, 10, 4568], [2, 10, 456, 1249], [2, 10, 4569], [2, 390, 20, 52], [2, 2715], [2, 83, 1250, 5], [2, 73, 1250, 5], [2, 73, 2716, 5], [2, 83, 2019, 5], [2, 83, 147, 5], [2, 73, 147, 5], [2, 73, 4570, 5], [2, 83, 2717, 5], [2, 73, 2717, 5], [2, 73, 4571, 5], [2, 83, 457], [2, 83, 489, 5], [2, 83, 812, 5], [2, 73, 489, 5], [2, 73, 812, 5], [2, 73, 2020, 5], [2, 73, 2021, 5], [2, 2022], [2, 1552, 5], [2, 4572], [2, 4573, 5], [2, 4574, 5], [2, 2718], [2, 1553], [2, 1554, 5], [2, 1251, 5], [2, 4575], [2, 199, 5], [2, 2719], [2, 2720], [2, 303, 368, 5], [2, 490, 368, 5], [2, 726, 368, 5], [2, 813, 368, 5], [2, 64, 324, 52], [2, 4576], [2, 518, 5], [2, 727, 5], [2, 2023], [2, 4577], [2, 2022], [2, 605, 5], [2, 64, 43, 70, 2721, 5], [2, 605, 5], [2, 2722, 5], [2, 2723, 13, 4578, 5], [2, 605, 1252], [2, 64, 269, 5], [2, 4579, 5], [2, 931, 23, 5], [2, 2024, 20, 18, 128], 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43, 209], [2, 3, 57, 70, 12, 2086], [2, 3, 57, 27, 9, 9405], [2, 3, 57, 27, 21, 9406], [2, 3, 57, 27, 21, 3775], [2, 3, 57, 27, 9, 3776], [2, 3, 556, 12, 18, 2594], [2, 3, 556, 13, 700], [2, 14, 4530, 18, 880], [2, 3, 556, 13, 165], [2, 3, 9407, 13, 165], [2, 3, 556, 22, 1219], [2, 3, 556, 543], [2, 14, 4530, 543], [2, 3, 159, 9408, 25, 441], [2, 3, 159, 9409, 25, 441], [2, 3, 159, 9410, 25, 441], [2, 3, 159, 9411, 25, 441], [2, 62, 17, 183, 1831], [2, 62, 9412], [2, 62, 3944], [2, 62, 17, 4531, 325], [2, 62, 25, 4531, 325], [2, 3, 9413], [2, 62, 23, 67, 12, 70, 17, 3958], [2, 62, 212, 9414], [2, 80, 22, 9415], [2, 3, 398, 2704], [2, 80, 86, 2704], [2, 14, 48, 2704], [2, 14, 48, 9416], [2, 14, 48, 9417], [2, 14, 48, 1341], [2, 62, 9418], [2, 62, 139, 13, 552, 12, 112], [2, 2152, 112], [2, 171, 689, 167], [2, 171, 689, 321], [2, 427, 13, 3920, 3921], [2, 3, 331, 8, 3922], [2, 62, 99, 18, 1019], [2, 3, 11, 4532, 27, 2587], [2, 3, 111, 405, 4422], [2, 80, 86, 305, 4502], [2, 14, 48, 1506, 240], [2, 62, 170, 4533], [2, 3, 11, 398, 8, 391], [2, 3, 93, 8, 48, 391], [2, 62, 20, 2696], [2, 62, 23, 1876], [2, 3, 11, 4532, 27, 2587], [2, 62, 1990, 450, 18, 454], [2, 14, 48, 273, 23, 9419], [2, 62, 9420], [2, 62, 9421], [2, 62, 9422], [2, 62, 9423], [2, 62, 366, 832], [2, 14, 48, 195, 1282], [2, 62, 395, 240], [2, 62, 497, 240], [2, 62, 155, 23, 254], [2, 9424, 12, 18, 2594], [2, 62, 1073, 240], [2, 62, 2287, 240], [2, 427, 4063, 26, 9425], [2, 62, 9426], [2, 427, 54, 289], [2, 62, 168, 3732], [2, 62, 168, 9427], [2, 3, 152, 634, 8, 215], [2, 3, 11, 9428, 41], [2, 80, 11, 86, 8, 4533], [2, 80, 11, 86, 8, 9429], [2, 3, 331, 8, 2243], [2, 3, 331, 8, 2244], [2, 80, 11, 86, 8, 2243], [2, 80, 11, 86, 8, 2244], [2, 3, 331, 8, 9430], [2, 3, 331, 8, 9431], [2, 3, 331, 8, 23, 67, 12, 337], [2, 14, 1790, 17, 1230], [2, 3, 6, 361, 574, 20, 18, 128], [2, 14, 914, 20, 317], [2, 3, 9432, 77, 9433], [2, 3, 6, 361, 20, 9434], [2, 3, 6, 361, 20, 18, 2682, 12, 1999], [2, 3, 6, 361, 75, 3905], [2, 3, 94, 23, 9435, 569], [2, 100, 4534, 569], [2, 100, 4534, 525], [2, 3, 94, 70, 12, 42, 332], [2, 311, 20, 498], [2, 3, 355, 9436, 20, 498], [2, 171, 9437], [2, 171, 9438], [2, 3, 1875, 145, 242], [2, 3, 22, 6, 1194, 20, 9439], [2, 3, 22, 6, 1464, 20, 1196, 1591, 1592], [2, 14, 9440, 13, 952, 4535], [2, 9441], [2, 9442], [2, 3, 11, 13, 9443, 41], [2, 3, 11, 22, 9444, 8, 110, 114], [2, 3, 4268, 8, 51, 12, 255], [2, 3, 11, 9445, 41], [2, 3, 11, 9446, 8, 160], [2, 3, 3348, 110, 52], [2, 3, 22, 68, 50, 9447, 20, 179, 1933], [2, 1542, 1608], [2, 14, 475, 31, 164], [2, 14, 475, 31, 164], [2, 14, 3906, 20, 376], [2, 3, 9448], [2, 3, 496, 137, 452, 23, 266], [2, 3, 496, 2485], [2, 3, 496, 2486], [2, 3, 1803, 708, 514, 138, 130], [2, 478, 17, 553], [2, 478, 537], [2, 478, 145, 606], [2, 478, 22, 9449, 606], [2, 478, 22, 4269, 9450], [2, 478, 22, 9451], [2, 478, 22, 2328], [2, 9452], [2, 3907, 58], [2, 3, 9453, 326], [2, 3, 238, 509, 12, 751], [2, 3, 238, 99, 77, 154], [2, 3, 2344, 573, 4270], [2, 3, 94, 9454, 13, 225], [2, 3, 9455, 13, 225], [2, 3, 94, 138, 324, 42, 9456], [2, 3, 1258, 76], [2, 3, 13, 4536], [2, 3, 18, 4536], [2, 3, 94, 20, 386], [2, 100, 892, 25, 1711], [2, 100, 892, 17, 1711], [2, 3, 13, 9457, 75, 494], [2, 3, 43, 13, 413, 603], [2, 3, 9, 13, 413, 603], [2, 9, 22, 4537, 214], [2, 9, 22, 4537, 275], [2, 3, 11, 9458, 278], [2, 3, 3636, 278], [2, 3, 3643, 573, 101, 4535], [2, 3, 3911, 1524], [2, 3, 2483, 89, 114], [2, 3, 2483, 89, 132], [2, 3, 13, 9459], [2, 3, 22, 3135, 20, 18, 1351, 50, 242], [2, 9460, 38, 1869], [2, 3, 43, 4538], [2, 3, 9, 4538], [2, 3, 9461, 110, 52], [2, 311, 70, 17, 1545, 347], [2, 3, 1087, 99, 26, 2480], [2, 3, 94, 481, 13, 467], [2, 3, 13, 1038, 20, 77, 782], [2, 3, 13, 1038, 20, 891], [2, 3, 43, 1038, 549], [2, 3, 9, 1038, 549], [2, 3, 4539, 89, 52], [2, 3, 4539, 89, 114], [2, 3, 9462], [2, 2484, 347], [2, 2484, 20, 500], [2, 3, 6, 9463, 137, 458], [2, 3, 6, 17, 359, 750, 130], [2, 3, 6, 25, 63, 236], [2, 3, 6, 17, 409, 758], [2, 3, 6, 17, 409, 1126], [2, 3, 6, 9464, 9465], [2, 3, 6, 25, 180, 785], [2, 3, 6, 17, 9466, 12, 202], [2, 3, 6, 2653, 23, 1190], [2, 3, 6, 9467, 23, 1190], [2, 3, 6, 17, 183, 190], [2, 14, 17, 183, 148], [2, 3, 6, 17, 183, 189], [2, 3, 6, 17, 2659, 1182], [2, 3, 6, 25, 1946, 2561], [2, 3, 6, 17, 2659, 4540], [2, 3, 6, 25, 1946, 4540], [2, 3, 6, 25, 1843, 1630], [2, 3, 6, 17, 409, 954], [2, 3, 6, 17, 3646], [2, 3, 6, 25, 9468, 9469], [2, 3, 6, 1409, 12, 237], [2, 14, 801, 12, 318], [2, 14, 801, 12, 18, 242], [2, 3, 6, 40, 182], [2, 3, 9470, 1861], [2, 3, 6, 406, 298], [2, 3, 6, 406, 507], [2, 14, 314, 289], [2, 3, 6, 4274], [2, 3, 6, 9471], [2, 3, 6, 452, 2670, 27, 114], [2, 3, 6, 452, 4541, 27, 114], [2, 3, 6, 452, 2670, 27, 132], [2, 3, 6, 452, 4541, 27, 132], [2, 3, 6, 635, 20, 386], [2, 3, 6, 20, 18, 2447], [2, 3, 6, 20, 59, 951], [2, 3, 6, 20, 124, 951], [2, 3, 6, 9472, 130], [2, 100, 6, 9473], [2, 100, 6, 9474], [2, 3, 6, 2630, 249], [2, 3, 6, 2630, 539], [2, 3, 6, 651, 23, 1999], [2, 3, 68, 966], [2, 3, 68, 3179, 12, 4236], [2, 3, 6, 495], [2, 3, 6, 1244], [2, 3, 6, 9475], [2, 9476, 50, 396], [2, 3, 6, 23, 67, 12, 22, 4369], [2, 3, 6, 23, 1065, 12, 9477], [2, 3, 68, 12, 42, 332], [2, 3, 68, 42, 1139], [2, 100, 55, 1231, 12, 9478], [2, 100, 55, 407, 110, 114], [2, 100, 55, 407, 110, 132], [2, 100, 55, 407, 12, 132], [2, 100, 55, 407, 12, 114], [2, 137, 100, 55, 407, 110, 132], [2, 137, 100, 55, 407, 110, 114], [2, 3, 245, 25, 9479], [2, 3, 6, 9480, 1051], [2, 14, 18, 526, 36, 1280], [2, 14, 18, 526, 36, 22, 1280], [2, 3, 22, 81, 4185], [2, 3, 6, 1341], [2, 3, 6, 9481], [2, 3, 1740, 12, 292], [2, 3, 6, 12, 18, 434], [2, 3, 111, 12, 18, 434], [2, 3, 22, 9482, 12, 4198, 186], [2, 3, 94, 2078], [2, 3, 94, 414], [2, 3, 94, 297], [2, 3, 6, 20, 4376, 3982], [2, 3, 6, 792, 89, 114], [2, 3, 6, 792, 9483], [2, 100, 6, 167], [2, 3, 104, 791], [2, 3, 6, 23, 195, 180, 607], [2, 3, 6, 23, 180, 607], [2, 3, 6, 99, 13, 9484], [2, 3, 6, 9485], [2, 3, 11, 6, 1021, 476], [2, 3, 6, 1032, 79], [2, 3, 11, 68, 27, 9486], [2, 3, 152, 6, 259, 8, 437], [2, 3, 152, 6, 259, 8, 1263], [2, 3, 11, 68, 27, 1489], [2, 3, 6, 195, 167], [2, 3, 6, 89, 1076, 346, 167], [2, 3, 605, 240], [2, 3, 1377, 50, 4281], [2, 3, 9487], [2, 3, 11, 6, 8, 1858], [2, 3, 11, 6, 8, 9488], [2, 3, 11, 6, 8, 17, 2000], [2, 3, 11, 9489, 8, 18, 9490], [2, 3, 11, 6, 8, 9491], [2, 3, 11, 6, 8, 9492], [2, 3, 11, 6, 8, 3365], [2, 3, 11, 6, 8, 3835], [2, 3, 11, 6, 8, 3836], [2, 3, 11, 6, 8, 9493], [2, 3, 11, 6, 8, 17, 3386], [2, 3, 11, 6, 8, 2038], [2, 3, 6, 9494], [2, 3, 11, 3552, 8, 58], [2, 3, 11, 6, 8, 1806], [2, 3, 11, 6, 8, 3929], [2, 3, 11, 6, 8, 1494], [2, 3, 11, 6, 8, 3373], [2, 3, 11, 6, 8, 3374], [2, 3, 11, 6, 8, 1252], [2, 3, 1169, 41], [2, 3, 11, 6, 8, 1941], [2, 3, 11, 6, 8, 2354], [2, 3, 11, 6, 8, 400, 9495], [2, 3, 11, 6, 8, 103, 58], [2, 3, 11, 6, 8, 103, 2580], [2, 3, 11, 6, 8, 103, 2581], [2, 3, 4469, 41], [2, 3, 11, 6, 8, 23, 67, 12, 9496, 20, 682, 3884], [2, 3, 11, 6, 8, 1140], [2, 3, 11, 6, 8, 1808], [2, 3, 11, 22, 2123, 41], [2, 3, 11, 2123, 41], [2, 3, 11, 6, 8, 1806], [2, 3, 152, 6, 8, 9497], [2, 3, 152, 6, 8, 9498], [2, 3, 11, 6, 8, 163, 298], [2, 3, 11, 6, 8, 163, 507], [2, 3, 11, 6, 8, 1016], [2, 3, 11, 6, 8, 2348], [2, 3, 11, 6, 8, 95, 748], [2, 3, 11, 6, 8, 1810], [2, 3, 11, 6, 8, 2357], [2, 3, 11, 6, 8, 95, 519], [2, 3, 11, 6, 8, 452, 9499, 27, 129], [2, 3, 11, 6, 8, 3392], [2, 3, 11, 6, 8, 9500], [2, 9501, 31, 45, 8, 79], [2, 3, 11, 6, 8, 163, 249], [2, 3, 11, 6, 8, 163, 539], [2, 3, 11, 6, 8, 54, 190], [2, 3, 11, 6, 8, 54, 236], [2, 3, 11, 6, 8, 71, 4027], [2, 3, 11, 6, 8, 124, 1778], [2, 3, 11, 6, 8, 59, 1778], [2, 3, 11, 6, 8, 124, 464], [2, 3, 11, 6, 8, 59, 464], [2, 3, 6, 175, 59, 637], [2, 3, 6, 175, 71, 637], [2, 3, 6, 714, 59, 4542], [2, 3, 6, 714, 71, 4542], [2, 3, 94, 505], [2, 3, 6, 20, 18, 9502, 9503], [2, 3, 6, 23, 9504, 9505], [2, 3, 93, 122, 12, 9506], [2, 14, 122, 12, 1380, 488], [2, 3, 6, 4279, 12, 58], [2, 3, 6, 3763, 12, 129], [2, 3, 6, 54, 495], [2, 3, 6, 54, 1244], [2, 14, 155, 289], [2, 14, 155, 589], [2, 3, 6, 147, 20, 500], [2, 100, 55, 1231, 12, 527], [2, 100, 55, 155, 1231, 12, 527], [2, 3, 6, 126, 9507], [2, 14, 170, 1927], [2, 3, 6, 23, 227, 4543, 249], [2, 3, 6, 23, 227, 4543, 539], [2, 3, 6, 163, 118, 79], [2, 9508], [2, 3, 6, 163, 23, 434], [2, 3, 6, 314, 585], [2, 3, 6, 314, 1674], [2, 3, 6, 163, 585], [2, 3, 6, 163, 1674], [2, 14, 314, 863], [2, 14, 163, 863], [2, 3377, 530], [2, 3, 6, 1491, 466], [2, 3, 6, 1491, 964], [2, 3, 6, 195, 466], [2, 3, 6, 195, 964], [2, 3, 6, 17, 2593, 343], [2, 3, 6, 23, 67, 12, 481, 42, 1431], [2, 3, 6, 793, 462], [2, 3, 6, 3917], [2, 171, 2618], [2, 3, 6, 59, 9509], [2, 14, 48, 3603], [2, 14, 48, 420], [2, 80, 86, 1418], [2, 14, 9510], [2, 14, 12, 2609, 1331], [2, 14, 77, 9511], [2, 3, 26, 55, 214], [2, 3, 26, 55, 275], [2, 100, 55, 195, 1945], [2, 14, 262, 2930, 12, 132]]\n",
            "Total unique words in the output: 9511\n",
            "Length of longest sentence in the output: 12\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XPCKJUsWZ0x6",
        "colab_type": "text"
      },
      "source": [
        "Now the lengths of longest sentence can also be varified from the histogram above. And it can be concluded that English sentences are normally shorter and contain a smaller number of words on average, compared to the translated French sentences."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YZlCVIG5af9r",
        "colab_type": "text"
      },
      "source": [
        "Next, we need to pad the input. The reason behind padding the input and the output is that text sentences can be of varying length, however LSTM expects input instances with the same length. Therefore, we need to convert our sentences into fixed-length vectors. One way to do this is via padding."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hj-vUQjOZx10",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "0eefd318-e328-461e-f1aa-ae07b23a68fc"
      },
      "source": [
        "encoder_input_sequences = pad_sequences(input_integer_seq, maxlen=max_input_len)\n",
        "print(\"encoder_input_sequences.shape:\", encoder_input_sequences.shape)\n",
        "print(\"encoder_input_sequences[180]:\", encoder_input_sequences[180])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "encoder_input_sequences.shape: (20000, 6)\n",
            "encoder_input_sequences[180]: [  0   0   0   0 464  59]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "juymGRF5bDUe",
        "colab_type": "text"
      },
      "source": [
        "Since there are 20,000 sentences in the input and each input sentence is of length 6, the shape of the input is now (20000, 6)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e4wlW59CbZt8",
        "colab_type": "text"
      },
      "source": [
        "You may recall that the original sentence at index 180 is **join us**. The tokenizer divided the sentence into two words ***join*** and ***us***, converted them to integers, and then applied pre-padding by adding four zeros at the start of the corresponding integer sequence for the sentence at index 180 of the input list.\n",
        "\n",
        "To verify that the integer values for ***join*** and ***us*** are 464 and 59 respectively, you can pass the words to the word2index_inputs dictionary, as shown below:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FNTu1gbfa2jf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "b98f0109-131a-4327-c326-f3ab658c292f"
      },
      "source": [
        "print(word2idx_inputs[\"join\"])\n",
        "print(word2idx_inputs[\"us\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "464\n",
            "59\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c1K1SJQucTbU",
        "colab_type": "text"
      },
      "source": [
        "In the same way, the decoder outputs and the decoder inputs are padded.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f8eOgYfUb0EO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "8c9cd205-d1be-43c8-a5a2-6c9d9479d56c"
      },
      "source": [
        "decoder_input_sequences = pad_sequences(output_input_integer_seq, maxlen=max_out_len, padding='post')\n",
        "print(\"decoder_input_sequences.shape:\", decoder_input_sequences.shape)\n",
        "print(\"decoder_input_sequences[180]:\", decoder_input_sequences[180])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "decoder_input_sequences.shape: (20000, 12)\n",
            "decoder_input_sequences[180]: [   2 2028   20  228    0    0    0    0    0    0    0    0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wF9WNh1fcsdP",
        "colab_type": "text"
      },
      "source": [
        "The sentence at index 180 of the decoder input is <sos> Joignez-vous à nous. If you print the corresponding integers from the word2idx_outputs dictionary, you should see 2, 2028, 20, and 228 printed on the console."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ra7H9gBAcaMR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "36507fd7-8b1d-4c52-b774-ab042f5b784c"
      },
      "source": [
        "print(word2idx_outputs[\"<sos>\"])\n",
        "print(word2idx_outputs[\"joignez-vous\"])\n",
        "print(word2idx_outputs[\"à\"])\n",
        "print(word2idx_outputs[\"nous.\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2\n",
            "2028\n",
            "20\n",
            "228\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0DRTlTzWn8OM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5429860b-c469-49de-cc54-023430450490"
      },
      "source": [
        "decoder_output_sequences = pad_sequences(output_integer_seq, maxlen=max_out_len, padding='post')\n",
        "print(\"decoder_output_sequences.shape:\", decoder_output_sequences.shape)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "decoder_output_sequences.shape: (20000, 12)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B9Sh9cViduI0",
        "colab_type": "text"
      },
      "source": [
        "# Word Embeddings\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C5Cp6ac7eRZ1",
        "colab_type": "text"
      },
      "source": [
        "We already converted our words into integers. So what's the difference between integer representation and word embeddings?\n",
        "\n",
        "There are two main differences between single integer representation and word embeddings. With integer reprensentation, a word is represented only with a single integer. With vector representation a word is represented by a vector of 50, 100, 200, or whatever dimensions you like. Hence, word embeddings capture a lot more information about words. Secondly, the single-integer representation doesn't capture the relationships between different words. On the contrary, word embeddings retain relationships between the words. You can either use custom word embeddings or you can use pretrained word embeddings."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oo7j1JWyehMz",
        "colab_type": "text"
      },
      "source": [
        "For English sentences, i.e. the inputs, we will use the GloVe word embeddings. For the translated French sentences in the output, we will use custom word embeddings."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-RHgs-GRezLC",
        "colab_type": "text"
      },
      "source": [
        "Let's create word embeddings for the inputs first. To do so, we need to load the GloVe word vectors into memory. We will then create a dictionary where words are the keys and the corresponding vectors are values, "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k-UIFAa7ddt1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from numpy import array\n",
        "from numpy import asarray\n",
        "from numpy import zeros\n",
        "\n",
        "embeddings_dictionary = dict()\n",
        "\n",
        "glove_file = open(r'./drive/My Drive/kaggle_sarcasm/glove.twitter.27B.200d.txt', encoding=\"utf8\")\n",
        "\n",
        "for line in glove_file:\n",
        "    rec = line.split()\n",
        "    word = rec[0]\n",
        "    vector_dimensions = asarray(rec[1:], dtype='float32')\n",
        "    embeddings_dictionary[word] = vector_dimensions\n",
        "glove_file.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IoKYra2-foaR",
        "colab_type": "text"
      },
      "source": [
        "Recall that we have 2150 unique words in the input. We will create a matrix where the row number will represent the integer value for the word and the columns will correspond to the dimensions of the word. This matrix will contain the word embeddings for the words in our input sentences.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1PpnsuMpfNwZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_words = min(MAX_NUM_WORDS, len(word2idx_inputs) + 1)\n",
        "embedding_matrix = zeros((num_words, EMBEDDING_SIZE))\n",
        "for word, index in word2idx_inputs.items():\n",
        "    embedding_vector = embeddings_dictionary.get(word)\n",
        "    if embedding_vector is not None:\n",
        "        embedding_matrix[index] = embedding_vector"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tOuMuJekf0UE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        },
        "outputId": "65ea04a6-572e-4dae-8c6c-f2007b7ba306"
      },
      "source": [
        "print(embeddings_dictionary[\"join\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-3.7239e-01  4.7634e-01 -1.9666e-01 -1.1641e+00  1.2820e-02 -4.7063e-01\n",
            "  4.4522e-01  1.8911e-01  5.6740e-01 -6.1915e-02  2.7159e-01 -1.7613e-01\n",
            " -5.4897e-01 -1.6782e-01  3.0910e-01  1.0253e-01  4.3412e-01 -4.0300e-02\n",
            "  3.4782e-02  2.8017e-01  1.6735e-01 -5.3730e-02 -5.9838e-01 -6.7251e-02\n",
            " -7.0371e-01  1.0674e+00  4.1123e-01  2.6227e-01 -3.2041e-01  5.2882e-01\n",
            "  3.6446e-01  1.3661e-01 -2.2680e-01  6.6556e-01  3.4684e-01 -4.1284e-01\n",
            "  5.5050e-01  3.5691e-01  3.6985e-01 -3.1312e-01  9.2343e-02  1.7105e-01\n",
            " -1.5200e-01  6.4297e-02  2.2327e-01  2.9222e-01 -7.3356e-01 -4.6585e-02\n",
            " -2.1011e-01  1.2690e-01  3.1638e-01 -5.1846e-01  2.2601e-02 -1.6426e-01\n",
            " -8.4231e-02 -6.5978e-02 -1.4436e-01 -2.2150e-01  4.2886e-01  3.0813e-01\n",
            " -1.0191e-03  5.9619e-01  1.7514e-01  5.3788e-01 -6.3198e-01  1.5236e-01\n",
            " -1.9268e-01  5.8482e-03  6.8425e-01  5.9576e-01  4.4315e-01  3.5061e-01\n",
            "  1.8628e-01  8.6317e-01  2.1811e-01 -2.6925e-02  1.4113e-01 -6.5218e-03\n",
            " -1.8793e-01 -7.6275e-01 -1.9529e-01 -3.8076e-01  2.4531e-01 -9.6168e-02\n",
            "  9.9905e-01 -4.4919e-01  2.7007e-01 -8.1641e-02  2.0172e-01 -3.0940e-01\n",
            "  2.1268e-01 -3.3724e-01 -4.4451e-02  3.7221e-02  3.9271e-01 -4.4238e-01\n",
            " -3.6380e-02  2.6630e-01 -1.9518e-01  4.9542e-01  4.6434e-01  2.8676e-02\n",
            " -3.2328e-01 -2.2912e-01 -1.0846e-01  7.4751e-02  1.9630e-01  9.2830e-01\n",
            "  3.4753e-01 -2.2845e-01 -5.7626e-02 -1.9475e-01 -1.5422e-01  2.5627e-01\n",
            "  1.0862e+00 -2.4259e-01  2.6479e-01  2.0851e-01  5.9924e-01 -2.0119e-01\n",
            "  2.8459e-01  2.2108e-01  8.1928e-02  6.4911e-02  4.5134e-01 -9.2248e-01\n",
            " -6.3541e-01 -8.6245e-02 -5.4689e-01  2.1964e-01  3.0496e-01 -2.5695e-01\n",
            "  2.2484e-01 -3.0984e-01  1.2758e-01  1.0109e-01 -3.2874e-01  1.2935e-01\n",
            " -8.7196e-01  1.2193e-03  1.2921e-01 -1.2158e+00 -9.7884e-01  3.1955e-01\n",
            "  7.0111e-01 -4.4321e-02  1.3345e-01  4.0505e-01 -1.5676e-01 -9.4797e-02\n",
            " -4.9323e-01  4.7612e-02 -4.1518e+00 -4.0886e-01 -3.0467e-01  1.4018e-01\n",
            " -1.0525e-01 -5.1683e-01 -2.0616e-01  9.2721e-01 -7.0207e-01 -2.1796e-01\n",
            "  2.2035e-02  8.5640e-02 -2.3397e-01 -8.8407e-02  1.9469e-02  2.5186e-03\n",
            "  1.2422e-01 -1.3315e-01 -7.0345e-01 -4.5398e-01  2.0099e-01  2.6786e-01\n",
            "  2.6263e-01 -1.5726e-01  3.2610e-01 -1.0904e-01  3.9328e-01  1.2629e-01\n",
            " -3.7834e-02 -3.8130e-01 -2.3811e-02  1.1780e-01  8.3620e-02 -1.7298e-01\n",
            "  1.8870e-01 -7.4950e-02  1.2175e-02  1.7848e-01  7.5212e-02  1.6401e-01\n",
            " -3.5318e-01 -1.0642e-02 -4.9625e-01  3.8459e-02  3.9650e-01 -8.5031e-02\n",
            "  6.6139e-01  3.9729e-02]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LgoHeGrkghcL",
        "colab_type": "text"
      },
      "source": [
        "In the previous section, we saw that the integer representation for the word **join** is 464. Let's now check the 464th index of the word embedding matrix.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iaWaL8EegHgF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 867
        },
        "outputId": "44963034-0987-4ac8-f83a-bef767306e24"
      },
      "source": [
        "print(embedding_matrix[464])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-3.72390002e-01  4.76339996e-01 -1.96659997e-01 -1.16410005e+00\n",
            "  1.28199998e-02 -4.70629990e-01  4.45219994e-01  1.89109996e-01\n",
            "  5.67399979e-01 -6.19149990e-02  2.71589994e-01 -1.76129997e-01\n",
            " -5.48969984e-01 -1.67820007e-01  3.09100002e-01  1.02530003e-01\n",
            "  4.34119999e-01 -4.03000005e-02  3.47819999e-02  2.80169994e-01\n",
            "  1.67349994e-01 -5.37299998e-02 -5.98380029e-01 -6.72509968e-02\n",
            " -7.03710020e-01  1.06739998e+00  4.11229998e-01  2.62270004e-01\n",
            " -3.20410013e-01  5.28819978e-01  3.64459991e-01  1.36610001e-01\n",
            " -2.26799995e-01  6.65560007e-01  3.46839994e-01 -4.12840009e-01\n",
            "  5.50499976e-01  3.56909990e-01  3.69850010e-01 -3.13120008e-01\n",
            "  9.23430026e-02  1.71049997e-01 -1.51999995e-01  6.42969981e-02\n",
            "  2.23269999e-01  2.92219996e-01 -7.33560026e-01 -4.65850011e-02\n",
            " -2.10109994e-01  1.26900002e-01  3.16379994e-01 -5.18459976e-01\n",
            "  2.26009991e-02 -1.64260000e-01 -8.42309967e-02 -6.59779981e-02\n",
            " -1.44360006e-01 -2.21499994e-01  4.28860009e-01  3.08129996e-01\n",
            " -1.01909996e-03  5.96189976e-01  1.75139993e-01  5.37880003e-01\n",
            " -6.31980002e-01  1.52360007e-01 -1.92680001e-01  5.84820006e-03\n",
            "  6.84249997e-01  5.95759988e-01  4.43150014e-01  3.50609988e-01\n",
            "  1.86279997e-01  8.63170028e-01  2.18109995e-01 -2.69249994e-02\n",
            "  1.41130000e-01 -6.52180007e-03 -1.87930003e-01 -7.62750030e-01\n",
            " -1.95289999e-01 -3.80760014e-01  2.45309994e-01 -9.61679965e-02\n",
            "  9.99050021e-01 -4.49189991e-01  2.70069987e-01 -8.16410035e-02\n",
            "  2.01719999e-01 -3.09399992e-01  2.12679997e-01 -3.37240010e-01\n",
            " -4.44509983e-02  3.72209996e-02  3.92710000e-01 -4.42380011e-01\n",
            " -3.63800004e-02  2.66299993e-01 -1.95179999e-01  4.95420009e-01\n",
            "  4.64340001e-01  2.86759995e-02 -3.23280007e-01 -2.29120001e-01\n",
            " -1.08460002e-01  7.47509971e-02  1.96300000e-01  9.28300023e-01\n",
            "  3.47530007e-01 -2.28450000e-01 -5.76260015e-02 -1.94749996e-01\n",
            " -1.54220000e-01  2.56269991e-01  1.08620000e+00 -2.42589995e-01\n",
            "  2.64789999e-01  2.08509997e-01  5.99240005e-01 -2.01189995e-01\n",
            "  2.84590006e-01  2.21080005e-01  8.19279999e-02  6.49110004e-02\n",
            "  4.51339990e-01 -9.22479987e-01 -6.35410011e-01 -8.62450004e-02\n",
            " -5.46890020e-01  2.19640002e-01  3.04960012e-01 -2.56949991e-01\n",
            "  2.24840000e-01 -3.09839994e-01  1.27580002e-01  1.01089999e-01\n",
            " -3.28740001e-01  1.29350007e-01 -8.71959984e-01  1.21929997e-03\n",
            "  1.29209995e-01 -1.21580005e+00 -9.78839993e-01  3.19550008e-01\n",
            "  7.01110005e-01 -4.43210006e-02  1.33450001e-01  4.05050009e-01\n",
            " -1.56760007e-01 -9.47970003e-02 -4.93229985e-01  4.76120003e-02\n",
            " -4.15180016e+00 -4.08859998e-01 -3.04670006e-01  1.40180007e-01\n",
            " -1.05250001e-01 -5.16830027e-01 -2.06159994e-01  9.27209973e-01\n",
            " -7.02069998e-01 -2.17960000e-01  2.20350008e-02  8.56399983e-02\n",
            " -2.33970001e-01 -8.84070024e-02  1.94690004e-02  2.51860009e-03\n",
            "  1.24219999e-01 -1.33149996e-01 -7.03450024e-01 -4.53979999e-01\n",
            "  2.00990006e-01  2.67859995e-01  2.62629986e-01 -1.57260001e-01\n",
            "  3.26099992e-01 -1.09040000e-01  3.93279999e-01  1.26289994e-01\n",
            " -3.78339998e-02 -3.81300002e-01 -2.38109995e-02  1.17799997e-01\n",
            "  8.36199969e-02 -1.72979996e-01  1.88700005e-01 -7.49500021e-02\n",
            "  1.21750003e-02  1.78479999e-01  7.52120018e-02  1.64010003e-01\n",
            " -3.53179991e-01 -1.06419995e-02 -4.96250004e-01  3.84589992e-02\n",
            "  3.96499991e-01 -8.50310028e-02  6.61390007e-01  3.97289991e-02]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AXgArIbzgqXA",
        "colab_type": "text"
      },
      "source": [
        "You can see that the values for the 464th row in the embedding matrix are similar to the vector representation of the word **join** in the GloVe dictionary, which confirms that rows in the embedding matrix represent corresponding word embeddings from the GloVe word embedding dictionary. This word embedding matrix will be used to create the embedding layer for our LSTM model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E4zJ1qkSgwPb",
        "colab_type": "text"
      },
      "source": [
        "**Creates the embedding layer for the input:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mq9hCDQwgTtv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "embedding_layer = Embedding(num_words, EMBEDDING_SIZE, weights=[embedding_matrix], input_length=max_input_len)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zjnm7SOpiZyj",
        "colab_type": "text"
      },
      "source": [
        "# Creating the Model\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v7r0kn0si7kY",
        "colab_type": "text"
      },
      "source": [
        "The first thing we need to do is to define our outputs, as we know that the output will be a sequence of words. Recall that the total number of unique words in the output are 9511. Therefore, each word in the output can be any of the 9511 words. The length of an output sentence is 12. And for each input sentence, we need a corresponding output sentence. Therefore, the final shape of the output will be:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l86ScpjAg3NL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#(number of inputs, length of the output sentence, the number of words in the output)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kGbPaXOdjhwr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "60557654-8037-4e29-8c2b-4200debf4a67"
      },
      "source": [
        "decoder_targets_one_hot = np.zeros((\n",
        "        len(input_sentences),\n",
        "        max_out_len,\n",
        "        num_words_output\n",
        "    ),\n",
        "    dtype='float32'\n",
        ")\n",
        "decoder_targets_one_hot.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20000, 12, 9512)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZtMLgewjm-Yu",
        "colab_type": "text"
      },
      "source": [
        "To make predictions, the final layer of the model will be a dense layer, therefore we need the outputs in the form of one-hot encoded vectors, since we will be using softmax activation function at the dense layer. To create such one-hot encoded output, the next step is to assign 1 to the column number that corresponds to the integer representation of the word. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KTo25u64joMw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i, d in enumerate(decoder_output_sequences):\n",
        "    for t, word in enumerate(d):\n",
        "        decoder_targets_one_hot[i, t, word] = 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pb80AaowoHg9",
        "colab_type": "text"
      },
      "source": [
        "Next, we need to create the encoder and decoders. The input to the encoder will be the sentence in English and the output will be the hidden state and cell state of the LSTM."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7lRHdbM-nCfd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "encoder_inputs = Input(shape=(max_input_len,))\n",
        "x = embedding_layer(encoder_inputs)\n",
        "encoder = LSTM(LSTM_NODES, return_state=True)\n",
        "\n",
        "encoder_outputs, h, c = encoder(x)\n",
        "encoder_states = [h, c]"
      ],
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N86Lh0vgoQCH",
        "colab_type": "text"
      },
      "source": [
        "The next step is to define the decoder. The decoder will have two inputs: the hidden state and cell state from the encoder and the input sentence, which actually will be the output sentence with an <sos> token appended at the beginning."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xP6ra2fWoLL3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "decoder_inputs = Input(shape=(max_out_len,))\n",
        "\n",
        "decoder_embedding = Embedding(num_words_output, LSTM_NODES)\n",
        "decoder_inputs_x = decoder_embedding(decoder_inputs)\n",
        "\n",
        "decoder_lstm = LSTM(LSTM_NODES, return_sequences=True, return_state=True)\n",
        "decoder_outputs, _, _ = decoder_lstm(decoder_inputs_x, initial_state=encoder_states)"
      ],
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y2SY3VJToh8i",
        "colab_type": "text"
      },
      "source": [
        "Finally, the output from the decoder LSTM is passed through a dense layer to predict decoder outputs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SxAS83WqoTM8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "decoder_dense = Dense(num_words_output, activation='softmax')\n",
        "decoder_outputs = decoder_dense(decoder_outputs)"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KvTYVm5-ombi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "outputId": "630265cf-4a38-41ce-f829-0007968a030d"
      },
      "source": [
        "#Compile\n",
        "model = Model([encoder_inputs,decoder_inputs], decoder_outputs)\n",
        "model.compile(\n",
        "    optimizer='rmsprop',\n",
        "    loss='categorical_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "model.summary()"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_7\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_6 (InputLayer)            [(None, 6)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_7 (InputLayer)            [(None, 12)]         0                                            \n",
            "__________________________________________________________________________________________________\n",
            "embedding (Embedding)           (None, 6, 200)       700400      input_6[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "embedding_2 (Embedding)         (None, 12, 256)      2435072     input_7[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "lstm_2 (LSTM)                   [(None, 256), (None, 467968      embedding[1][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "lstm_3 (LSTM)                   [(None, 12, 256), (N 525312      embedding_2[0][0]                \n",
            "                                                                 lstm_2[0][1]                     \n",
            "                                                                 lstm_2[0][2]                     \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 12, 9512)     2444584     lstm_3[0][0]                     \n",
            "==================================================================================================\n",
            "Total params: 6,573,336\n",
            "Trainable params: 6,573,336\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VD1Se2XKoxwq",
        "colab_type": "text"
      },
      "source": [
        "Let's plot our model to see how it looks."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_v6W3oDkovGi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "outputId": "5d89c1a7-b2eb-4643-8d5d-1fbe11bd4488"
      },
      "source": [
        "from keras.utils import plot_model\n",
        "plot_model(model, to_file='model_plot4a.png', show_shapes=True, show_layer_names=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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+vlAo5HA4pqamISEhVVVVbG+NjY2zZ8/mcrkODg7/+Mc//uu//osQ4uzszAzBc+PGDXt7ex6P5+vr+8cff8iP/PLlyzNmzGBvExw7dqyPj8/PP//MtJ49e9bExGTHjh09X1hSUjJ+/PhRo0Yxr0pMTBy2gzpw4ICTk1Nfn5aTJ08yHcbExFhYWJiZmYWGhjLDfzo5ObGjFNE0PXny5NjY2G7H9erVq5iYGDs7Ow6HY2lpuXjx4vLy8p07d/J4PELIa6+99v3338t/Sxk6MtwMhg0CAICRgqI1dRZsFkVRWVlZKryTbM2aNcePH29sbFRVh5pA0w4qMDBw//79Dg4OQ9F5aGgo0YE78LKzs8PCwjT/L3TQdOT/EQBAF+joJW9mjBsto/aDYi+Xl5WVMWdD1RsPAAAADA8dLSiVd+/ePapv4eHh6g5QDWJiYioqKn777beVK1du375d3eHoijVr1rAfvBUrVsg2nT9/PjY2tqura+HChXZ2dlwuVygUBgcHl5WVKdi5RCJJSkpydnY2MDAwMzObMGFCdXU1IeTUqVM7d+6U/Q2Tm5vLhjFmzBjVHR8AAIwAOldQbtmy5dChQ83NzQ4ODidOnBh0P2+88YacOwkyMzNVGHO/VHVQSuLz+W+88ca7774bFxfn4eGhrjB0kIWFRUFBwf379zMyMtiV27ZtS01N3bJlS1dX16VLl44ePdrU1FRcXCwWi2fOnFlbW6tIz2FhYcxgoiKR6O7du05OTi9fviSEBAUFcblcf3//Fy9eMFsGBwfX1NRcvHiRmRgJAAB0yzDdq6kEosBc3qBThuFhDpFI5O3trd6uFHwoZ/Xq1UKhsNvKL7/80tXVVSwW0zQtkUjee+89tunq1auEkMTExH57PnbsGEVRZWVlfW0QGRnp7e0tkUhkV0ZFRbFzu8uHh3IAALSGzp2hBFBERkZGfX29pnWloMrKyq1bt8bHx3O5XEIIh8M5ffo028qMHlpVVdVvPwcOHJgyZYqnp2dfG8TFxZWWlqakpKgiagAAGMFQUILWomk6OTnZ3d3d0NDQ3Nw8JCSEnTo8MjLSwMCAGW2KELJ+/XojIyOKop49e0YIiY6O3rRpU1VVFUVRzs7OqampXC7XyspqzZo1NjY2XC7Xx8eHHX1zQF0RQs6dO6fa8Ud7Sk1NpWk6KCio11axWEwIYYadkqOjo6OkpMTLy0vONubm5rNmzUpJSaG191F0AABQBApK0FpxcXGxsbGfffZZfX39xYsXnzx54ufnV1dXRwhJTU2VHYgqLS0tPj6eXUxJSVmwYIGTkxNN05WVlZGRkRERESKRKCoqqrq6+saNG1KpdM6cOU+ePBloV+TPh/G7urqG7sDz8/Pd3NyYgUt7Yi55+/r6yu+ktra2o6Pj+vXrs2fPZspod3f3tLS0brXj5MmTnz59euvWLVUFDwAAIxEKStBOYrE4OTl50aJFK1asEAgEnp6eX3/99bNnz7755pvBdcjhcJiTnR4eHunp6a2trYcOHRpEP4GBgS0tLVu3bh1cGP1qa2t7+PBhr6PT19XVZWZmRkVFeXt793X+ksU8fGNpaZmYmFheXl5XVxcSErJhw4ajR4/Kbubi4kIIuX37tuqOAAAARh4UlKCdysvLX758OXXqVHbNtGnTDAwMek4UOQhTp07l8/nsBXSNUl9fT9N0r6cnvb29o6KiQkJCCgoK9PX15fdjaGhICBk/fryPj4+FhYVAIIiPjxcIBN0qcmZHzHlfAADQWRx1BwAwJJjhbIyNjWVXmpmZtba2qqR/Q0PDhoYGlXSlWu3t7eTPcrAbKyurjIyM8ePHK9IPM2Uocycow8DAwN7evtvTPMy0mcxOAQBAZ+EMJWgnMzMzQki38vHFixe2trbKdy6RSFTVlcoxFV6v0yZZWloyb4sijI2NXVxc7ty5I7tSKpUKBALZNR0dHexOAQBAZ6GgBO00YcIEY2Pj//znP+yaK1eudHR0vPnmm8wih8Nh54ocqKKiIpqmp0+frnxXKmdlZUVRVHNzc8+m06dPC4VCxbsKCwu7efPmgwcPmEWRSPTo0aNuowgxO7K2tlYiZAAAGPFQUIJ24nK5mzZtOnny5OHDh1taWm7fvr127VobG5vVq1czGzg7Ozc1NeXm5kokkoaGhkePHsm+3MLCora2trq6urW1lSkWu7q6nj9/LpVKy8rKoqOj7ezsIiIiBtFVQUHBkA4bxOfzHR0da2pquq2vrKy0trYOCwuTXRkeHm5tbX3jxo1eu9q4caO9vX1ERMTjx48bGxtjYmLEYvGnn34quw2zIzljVQIAgC5AQQlaa9u2bUlJSQkJCWPGjJk1a9brr79eVFRkZGTEtK5bt2727NnLli1zc3Pbvn07c9HW29ubGQxo7dq1VlZWHh4eAQEBTU1NhJD29nZPT08ej+fn5+fq6vrTTz+x9ykOtKuhFhgYWF5ezow3yep1qMiOjo76+vq8vLxe+zE3N7906ZKtra2Xl5dQKLx69Wp+fn63kSmvXbsmFAonTpyowvgBAGDEwUM5oLUoivrkk08++eSTXlstLCwuXLggu2bXrl3svydPnlxdXS3bamJi0vO03yC6mj9/fktLi4KHMDj/+Mc/0tPTc3JyVqxYwa50cXHp+Sz2iRMn3n77bXt7+766srW17TZOkKzGxsYff/xxx44dFEUpHzYAAIxcOEMJoJBeH3PREGKxuLCwsKKignlExtnZOSEhISEhgRlLsi+dnZ25ubmtra3h4eGD229cXJyXl1dkZCQhhKbp2tra4uJiZvx2AADQKSgoAUa8pqamefPmubq6fvjhh8ya2NjY0NDQ8PDwXp/OYRQVFeXk5BQUFPQ1p458ycnJpaWlZ8+eZYa0zMvLEwqFfn5++fn5gzsKAAAYuVBQAvRjy5Ythw4dam5udnBwOHHihLrD6e7rr7+m/3T48GF2fWJiYmRk5JdfftnXC/39/Y8cOcLOQj4geXl5r169KioqMjc3Z9aEhISwYciOXgkAALoA91AC9CMpKSkpKUndUQzG3Llz586dOxQ9BwcHBwcHD0XPAAAwEuEMJQAAAAAoBQUlAAAAACgFBSUAAAAAKAUFJQAAAAAoZWQ8lHP58mV1hwAahBlgPDs7W92BDC3mY6/Fh1lTU2Nra6vuKAAAQAWoXidk0yiYhANAWy1ZsuT48ePqjgIAAJQ1AgpK0B3Z2dlhYWH4TAIAAIwsuIcSAAAAAJSCghIAAAAAlIKCEgAAAACUgoISAAAAAJSCghIAAAAAlIKCEgAAAACUgoISAAAAAJSCghIAAAAAlIKCEgAAAACUgoISAAAAAJSCghIAAAAAlIKCEgAAAACUgoISAAAAAJSCghIAAAAAlIKCEgAAAACUgoISAAAAAJSCghIAAAAAlIKCEgAAAACUgoISAAAAAJSCghIAAAAAlIKCEgAAAACUgoISAAAAAJSCghIAAAAAlIKCEgAAAACUgoISAAAAAJSCghIAAAAAlIKCEgAAAACUgoISAAAAAJSCghIAAAAAlIKCEgAAAACUgoISAAAAAJSCghIAAAAAlIKCEgAAAACUwlF3AKDTampq/v73v3d2djKLz58/NzExefvtt9kN3Nzc/ud//kc9wQEAAIBiUFCCOtna2j569Kiqqkp25c8//8z+e+bMmcMeFAAAAAwMLnmDmn3wwQf6+vp9tYaHhw9nMAAAADAIFE3T6o4BdFpVVZWLi0uvn8Px48f/+uuvwx8SAAAADAjOUIKaOTk5TZw4kaKobuv19fX//ve/qyUkAAAAGBAUlKB+H3zwgZ6eXreVUqk0NDRULfEAAADAgOCSN6jf77//bmtr29XVxa4ZNWrUW2+99csvv6gxKgAAAFAQzlCC+tnY2MyYMWPUqP/7aRw1atQHH3ygxpAAAABAcSgoQSP87W9/k12kaXrRokXqCgYAAAAGBAUlaIQlS5awt1Hq6em9++67VlZW6g0JAAAAFISCEjSCubn5nDlzmJqSpukVK1aoOyIAAABQFApK0BQrVqxgnsvR19cPCQlRdzgAAACgKBSUoCmCgoIMDQ0JIQsWLDA2NlZ3OAAAAKAoFJSgKYyMjJgTk7jeDQAAMLKobBzK7OzssLAwlXQFAKAk3Rxht+eMUwCgCzQh43FU211WVpZqOwSd0tnZmZWV9f777/e75eXLl1NSUnTh8xYWFhYdHe3t7a3uQEYM5rOh7ijUBp8WkLV3715CyMcff6zuQIaW7nwj9KQ5GU/FBeXSpUtV2yHomoULF3K5XEW2TElJ0YXPW1hYmLe3ty4cqQppSHpVC3xaQNbx48eJbnw168g3Qq80JOPhHkrQLApWkwAAAKA5UFACAAAAgFJQUAIAAACAUlBQAgAAAIBSUFACAAAAgFJQUIIOOXv2rEAgOH36tLoD0UTnz5+PjY3t6upauHChnZ0dl8sVCoXBwcFlZWUK9iCRSJKSkpydnQ0MDMzMzCZMmFBdXU0IOXXq1M6dOzs7O4cwegBQHW1NlWvWrKH+1G0GDeUTICGkq6tr7969Pj4+3dYnJCR4eHiYmpoaGho6Oztv3rz55cuXTFPP9Jibm8sGOWbMmMEeqxqgoAQdoglDv2qmbdu2paambtmypaur69KlS0ePHm1qaiouLhaLxTNnzqytrVWkk7CwsO++++7IkSMikeju3btOTk5M0gwKCuJyuf7+/i9evBji4wAAFdDiVGlhYVFQUHD//v2MjAx2pUoSYEVFxcyZMzdu3CgSibo1XbhwYcOGDdXV1c+ePUtKSkpJSQkNDWWaeqbH4ODgmpqaixcvBgQEqOKIhw8KStAhgYGBzc3NCxYsGOodicXinj9SNdZXX32VmZmZnZ1tYmJCCPH29vb19eXz+Q4ODomJic3Nzf/617/67SQzMzM3N/f48eNvvfUWh8OxsbHJy8ubMGEC0xoVFTVp0qSAgACpVDqkxwIAytPiVMnj8ebNm+fq6mpoaMisUUkCvHXr1qeffrp27VovL6+ercbGxqtXr7awsDAxMVm6dOnChQvPnTv35MkTprVbeqQoSigU+vn5ubi4qOqohwcKSgDVy8jIqK+vV3cUCqmsrNy6dWt8fDwzAiiHw5G9zuXo6EgIqaqq6refAwcOTJkyxdPTs68N4uLiSktLNWQAXgDQBGpPlapKgJMmTcrJyVm+fDlbp8o6c+aMnp4eu8hcyJY9kakd6REFJeiK4uJiOzs7iqL2799PCElPTzcyMuLz+Xl5efPnzzc1NbW1tT127BizcWpqKpfLtbKyWrNmjY2NDZfL9fHxuXLlCtMaGRlpYGAwduxYZnH9+vVGRkYURT179owQEh0dvWnTpqqqKoqinJ2dCSHnzp0zNTVNTExUw2H3JzU1labpoKCgXlvFYjEhxNTUVH4nHR0dJSUlvf40Z5mbm8+aNSslJUWLr6YBaAGdSpUqSYAD9fTpUx6P5+DgwK7RjvSIghJ0ha+v7y+//MIurlu37uOPPxaLxSYmJllZWVVVVY6OjqtWrZJIJISQyMjIiIgIkUgUFRVVXV1948YNqVQ6Z84c5iJFamqq7BxfaWlp8fHx7GJKSsqCBQucnJxomq6srCSEMDdcd3V1DdvBKi4/P9/NzY3P5/faevXqVUKIr6+v/E5qa2s7OjquX78+e/Zs5kvF3d09LS2tW3KcPHny06dPb926pargAUDldCpVqiQBDohIJLpw4cKqVasMDAxk12tBekRBCbrOx8fH1NTU0tIyPDy8ra3t8ePHbBOHw3F3dzc0NPTw8EhPT29tbT106NAgdhEYGNjS0rJ161bVRa0abW1tDx8+dHJy6tlUV1eXmZkZFRXl7e3d1893FvPwjaWlZWJiYnl5eV1dXUhIyIYNG44ePSq7GXNL0O3bt1V3BAAwTLQvVaoqAQ5IUlKSjY3Njh07uq3XgvSIghLg/8f8XmR+dvc0depUPp9/79694Q1qaNXX19M03euvc29v76ioqJCQkIKCAn19ffn9MOwalMkAACAASURBVLcNjR8/3sfHx8LCQiAQxMfHCwSCb775RnYzZkd1dXWqOwIAGG5akypVlQAVd/Lkyezs7MLCQuYBIFlakB456g4AYMQwNDRsaGhQdxSq1N7eTv4sB7uxsrLKyMgYP368Iv3Y2NgQQpj7ohgGBgb29vbdbmbn8XjsTgFAW42UVKmqBKigzMzM5OTkoqKicePG9WzVgvSIghJAIRKJ5MWLF7a2tuoORJWYFNbrkOOWlpZmZmYK9mNsbOzi4nLnzh3ZlVKpVCAQyK7p6OhgdwoAWmkEpUpVJUBF7Nu3r7Cw8MKFC8bGxr1uoAXpEZe8ARRSVFRE0/T06dOZRQ6H09cVnxHEysqKoqjm5uaeTadPnxYKhYp3FRYWdvPmzQcPHjCLIpHo0aNH3UYRYnZkbW2tRMgAoNFGUKpUYQKUg6bpmJiY27dv5+bm9lVNEq1IjygoAfrU1dX1/PlzqVRaVlYWHR1tZ2cXERHBNDk7Ozc1NeXm5kokkoaGhkePHsm+0MLCora2trq6urW1VSKRFBQUaOawQXw+39HRsaamptv6yspKa2vrsLAw2ZXh4eHW1tY3btzotauNGzfa29tHREQ8fvy4sbExJiZGLBZ/+umnstswO5IzViUAjEQjNFWqMAHKcefOnV27dh08eFBfX5+SsWfPHtnNtCA9oqAEXbF///5p06YRQmJiYoKDg9PT0/fu3UsImThx4oMHDw4ePLhp0yZCyLx58yoqKpiXtLe3e3p68ng8Pz8/V1fXn376ib3bZt26dbNnz162bJmbm9v27duZ6xTe3t7MYBlr1661srLy8PAICAhoampSy/EqKDAwsLy8nBlujdXrWGgdHR319fV5eXm99mNubn7p0iVbW1svLy+hUHj16tX8/PxuI1Neu3ZNKBROnDhRhfEDgGrpVKpUVQIsKSnx9fUdN27clStXbt26ZWNjM2PGjIsXL/bVW0/akB5pFcnKylJhbwDyDcPnjZkpa0h3oQhCSFZW1tD1X1FRweFwvv/++3637Ozs9PPzy8jIGNyOnj17xuVy9+zZM7iXK06Xc9FQf1pgxFmyZMmSJUuGdBeakCoV/KtfvXq1UCiUXTNsCVC+XtNjVFTU6NGj+32t5mQ8nKEE6FOvN2trGWdn54SEhISEBGYsyb50dnbm5ua2traGh4cPbkdxcXFeXl6RkZGDezkAaKwRlCrFYnFhYWFFRQXzEMywJUD5ZNMjTdO1tbXFxcXMYO8jyMgrKKdNm6anpyd/krd+ffTRRyYmJhRFlZaWKtJ69uxZgUAgO8XnUMjJyXF0dKR68/rrrw+iQy1+r0CFYmNjQ0NDw8PDe705nVFUVJSTk1NQUNDXlBLyJScnl5aWnj17VoUjusHgaOZfaEJCgoeHh6mpqaGhobOz8+bNm+V/wbNKSkrc3d1HjRpFUZS1tXXP8aKHjmzGHjt27IoVK4Zt1zBoTU1N8+bNc3V1/fDDD5k1w5AA5euWHvPy8oRCoZ+fX35+vsr3NbRUdapzOE+6+vv7T5o0SclOmKlIb968qUjrmTNnTE1NT506peROFeHk5CQQCJh/S6VSkUhUV1fn7u4+uN609b0a6s9bbGwsM3jv66+/fvz48aHbUb/IcF3ELCwsjImJGYqec3Nzk5KSpFLpUHTek+ZcABp+inxahjObKW7WrFlpaWmNjY0tLS1ZWVn6+vrz5s1T/OV//etfCSHPnz8fugj7IpuxNdBQX/LWkFSp/F/90CVA+ZRPj5qT8UbqOJQURQ3n7gIDA+X8dhk6enp6PB6Px+O5uroOuhMdea9UKykpKSkpSd1RDKu5c+fOnTt3KHoODg4ODg4eip5hEIbtL1QsFvv7+8vOCi2HsbHx6tWr9fT0CCFLly7NycnJzs5+8uTJa6+9NsRhDsyADkoXaE2qHLoEKJ82pceRd8mbofyFM/lllgqLMJqmjx8/3m0OuoHKzc0d9Gt17b0CAE2QkZFRX1+v4MZnzpxhqknGmDFjCCEikWhIIlPCgA4KQKcMd0HZ2dn5xRdf2NnZ8Xi8iRMnMqdqU1JSjIyMRo0a9eabb1pbW+vr6xsZGU2ZMsXPz++1117jcrlmZmabN2+W7aeysvKNN94wMjJixikoLi6WvwtCCE3Tu3fvdnNzMzQ0FAgE//Vf/yXboZzW4uJiOzs7iqL2799PCElPTzcyMuLz+Xl5efPnzzc1NbW1tWUu+7IBJCUlubm58Xi8MWPGODg4JCUlLV26lGk9d+6cMuNs6dR7BQCqNaC/0NTUVC6Xa2VltWbNGhsbGy6X6+Pjc+XKFaY1MjLSwMBg7NixzOL69euNjIwoimJm4IyOjt60aVNVVRVFUc7OzgON8+nTpzwez8HBgVkcUNrUtIO6dOmSh4eHQCDgcrmenp6FhYWEkI8++oi5+dLJyenmzZuEkJUrV/L5fIFAcOrUKdJHct61axefzzcxMamvr9+0aZNQKLx//76CYQAMOVVdO1fwKv4nn3xiaGh44sSJ58+fb9myZdSoUdeuXaNpetu2bYSQK1eutLW1PXv2bN68eYSQ/Pz8hoaGtrY25tGn0tJSphN/f39HR8eHDx9KJJJff/31rbfe4nK5v/32m/xdfPbZZxRF/fd///fz589FIlFaWhqRufNPfiszYta+ffvYjQkhP/74Y3Nzc319vZ+fn5GRUUdHB9OamJiop6eXl5cnEomuX79ubW399ttvs+/AmTNnTExMEhIS+nqLut2RExUVdfv2bdkNdOe9kkNz7hoZagQDwQyQ7nw2elLk0zKgv9DVq1cbGRnduXOnvb29vLx82rRpJiYmjx8/ZlqXL19ubW3N9rx7925CSENDA7O4ePFiJyenQRxFW1ubiYlJZGQku6bftNntHsrhPKh+76E8fvx4XFxcU1NTY2Pj9OnT2YFgFi9erKen9/TpU3bL999/n723VU5yJoRERUXt27dv0aJFd+/elbNreliGDdIEuvxXrznHPqwFpVgs5vP54eHhzKJIJDI0NFy3bh39Z5HU2trKNP3v//4vIYQtpK5evUoIyczMZBa7PWhSVlZGCPnkk0/k7EIkEvH5/Dlz5rCvkn2URH4r3UcKFovFzCJTUVVWVjKL06ZN+8tf/sJ29X/+z/8ZNWrUq1evFHgXaZqmnZycuhX9vRaUOv5eac6f0FBDQTlQuvPZ6GnQBWVff6GrV6+WrZauXbtGCImPj2cWh6ig/Oyzz1xdXVtaWhR/Sa8F5fAc1IAeymFuN6yvr6dp+vz584SQHTt2ME3Nzc0uLi7Mwxlyviu7HVq/UFBqPc059mF9KOf+/fsikWjChAnMIo/HGzt27L1793puyTw1JpVKmUXmLsC+5gP19PQUCARMqdTXLiorK0Uikb+/f689yG/tFxMtG157ezuXy2VbOzs79fX1ZW8P6pdAIHjx4gXz7+joaEX2rpvvVXZ29uDCGFkuX76s7hBGErxdyuj2F9rN1KlT+Xx+r0lbVU6ePJmdnf3DDz+YmJioqk+1HxSLyc/MqI3vvPOOq6vrt99+u2XLFoqiMjMzw8PDmeyn+HelImpqarQ+VTJ/9Vp/mL3SnIw3rAVlW1sbIeTzzz///PPP2ZU2NjbK96yvr88ki752wcySaWlp2evL5bcOVEBAwO7du/Py8ubOnVteXp6bm/vee+8NqKCUlZKSopKoWNr0XnWba1VbpaSkqPxjADA4hoaGDQ0NQ9R5ZmZmcnJyUVHRuHHjhmgXvRrSg8rPz9+9e3d5eXlLS4tsUUtR1Jo1azZu3Pjjjz++++6733333ZEjR5gm1X5XlpSU6Eiq1JHD1FjD+lAOU4Xs3btX9hyp8sW1VCptamqys7OTswvmNNirV6967UF+60DFxcW98847ERERpqamixYtWrp06cGDB1XSs/K07L0ajpP46kZwyXuA2GfLQOUkEsmLFy9sbW2HovN9+/YdPnz4woULw1xNDsVBXbx4kZn/+vHjxwsXLhw7duyVK1eam5t37twpu1lERASXy/3nP/95//59U1NTe3t7Zr1qvytxyVu7aU7GG9aCknkMudcJV5Tx008/dXV1TZkyRc4uJkyYMGrUqJ9//rnXHuS3DlR5eXlVVVVDQ4NEInn8+HF6erq5ubmSff7+++8rV65UPjZdeK8AYIgUFRXRND19+nRmkcPh9HUdeUBomo6Jibl9+3Zubq6xsbHyHQ7IUBzU9evXjYyMCCG3b9+WSCTr1q1zdHTkcrndBlkzNzcPCwvLzc3ds2fPqlWr2PVD9F0JMKSGtaDkcrkrV648duxYenp6S0tLZ2dnTU3N77//PoiuOjo6mpubpVLpjRs3IiMj7e3tIyIi5OzC0tJy8eLFJ06cyMjIaGlpKSsrkx3sUH7rQG3YsMHOzq6vecMKCgoGNGwQTdNisTgnJ8fU1HRw8Yzc9woA1K6rq+v58+dSqbSsrCw6OtrOzo5JIIQQZ2fnpqam3NxciUTS0NDw6NEj2RdaWFjU1tZWV1e3trbKL9Hu3Lmza9eugwcP6uvry045u2fPHmaDgaZNNR6URCKpq6srKipiCkrmctD58+fb29srKirY8YlYa9euffXq1ZkzZxYsWMCuVOF3JcDwUe1J1343e/XqVUxMjJ2dHYfDYUqT8vLylJQUZn7M119//dKlS1999ZVAICCEWFtbHzlyJDMz09ramhBibm5+7NgxmqYPHTo0e/ZsKysrDoczevToZcuWPXr0SP4uaJpubW396KOPRo8ebWxs7Ovr+8UXXxBCbG1tb926Jb913759zLBkfD4/KCgoLS2NidbFxaWqquqbb75hSj17e3tmOJ4LFy6MHj2afYf19fXd3d1zcnKY8M6ePWtiYsI+2Sfr5MmTPR/xZn3++ec0TevUe6X8500LEFzyHiDd+Wz01O+nZaB/oatXr9bX1xcKhRwOx9TUNCQkpKqqiu2tsbFx9uzZXC7XwcHhH//4BzMkrbOzMzMEz40bN+zt7Xk8nq+v7x9//CEnqtu3b/ea9Hbv3s1sICdtlpSUjB8/ftSoUYSQsWPHJiYmDttBHThwQE7GPnnyJNNhTEyMhYWFmZlZaGgoM/ynk5MTO0oRTdOTJ0+OjY3tdly9JuedO3fyeDxCyGuvvfb999/LeUtZeMpb62nOsY/Iubw1XFpaWnR0NLv46tWrjz/+2NDQUCQSqTEqzTTo90p3Pm8oKAdKdz4bPan807J69WoLCwsVdqgJNO2gAgICHjx4MESdo6DUeppz7CN1Lm+N9ccff0RGRsre+2JgYGBnZyeRSCQSCfPjEhh4rwA0HzPGjZZR+0FJJBJmCKGysjLmbKh64wFQ3kidy1tj8Xg8fX39jIyMuro6iURSW1v7z3/+84svvggPDx/0TZDaCu8VgC64d+8e1bfw8HB1B6gGMTExFRUVv/3228qVK7dv367ucABUAAWligkEgh9++OHXX391dXXl8XgeHh6HDh366quvmPlsQBbeK5U7f/58bGxsV1fXwoUL7ezsuFyuUCgMDg5mhrJXhEQiSUpKcnZ2NjAwMDMzmzBhQnV1tSIvTEhI8PDwMDU1NTQ0dHZ23rx5c7dnrYqLi2fMmMHn821sbGJiYrqNPNVX66lTp3bu3Kn280m6acuWLYcOHWpubnZwcDhx4sSg+3njjTfkXCbLzMxUYcz9UtVBKYnP57/xxhvvvvtuXFych4eHusLQNWvWrGF/yaxYsUK2SfnkSQjp6urau3evj49Pt/Vy0mPPFJebm8sGOWbMmMEeqzqo6tq55lzFB12gO583ovBdcV988cWCBQuYwZNHjx596dKltra2Bw8ezJkzRyAQyE4ZLMfChQvd3NxKSkqYc8ZBQUHdZv7sy6xZs9LS0hobG1taWrKysvT19efNm8e2/vrrrzweb+vWrS9fvvzll1/GjBmzcuVKBVtTUlJmzZrFzqrXL935bPSk+KcFdATuoZTF3D5bUFBw//799vZ2dr1Kkudvv/02Y8YMQojsdMcM+emxW4rr6uqqqam5ePFiQEAAO/O78sc+DFBQwog0DJ83kUjk7e2t9q4ULBG+/PJLV1dXZoZfiUTy3nvvsU3M9O6JiYn9dnLs2DGKosrKygYRZ2BgIDMNMWPp0qWEEPZR1rCwMAcHh66uLmZx9+7dFEXdvXtXkVaapiMjI729vSUSiSKR6HIuQkEJ3QxDQakJqVLxglIoFHZbqZLkWVpaumjRosOHD3t5efUsKOWnR7qPFBcVFTWyCkpc8gboXUZGRn19vaZ11avKysqtW7fGx8cz8xhxOJzTp0+zrY6OjoSQqqqqfvs5cODAlClTPD09BxHDmTNnZOfMZK7UiEQiQohUKs3Pz581axY7qvP8+fNpms7Ly+u3lREXF1daWor5JwE00AhKlT2pKnlOmjQpJydn+fLlhoaGPVvlpEeGdqQ4FJSgzWiaTk5Odnd3NzQ0NDc3DwkJuXfvHtMUGRlpYGDAjMlHCFm/fr2RkRFFUc+ePSOEREdHb9q0qaqqiqIoZ2fn1NRULpdrZWW1Zs0aGxsbLpfr4+PDjlE8oK4IIefOnVPtKM2pqak0TQcFBfXaKhaLCSH9PufU0dFRUlLi5eWlkpCePn3K4/GYZ1cfPHjw8uVLZoRnBjN6H3NzkvxWhrm5+axZs1JSUmiaVkl4ACBLR1JlTypJngMlmx4Z2pHiUFCCNouLi4uNjf3ss8/q6+svXrz45MkTPz+/uro6Qkhqaipz3YGRlpYWHx/PLqakpCxYsMDJyYmm6crKysjIyIiICJFIFBUVVV1dfePGDalUOmfOnCdPngy0K/LnkCVdXV2qOsz8/Hw3NzdmMOeemKs2vr6+8jupra3t6Oi4fv367NmzmW8Cd3f3tLS0QSQ4kUh04cKFVatWGRgYEEL++OMPQoiJiQm7AZfL5fF4zH+E/FbW5MmTnz59euvWrYEGAwD90pFU2ZNKkueAdEuPLC1IcSgoQWuJxeLk5ORFixatWLFCIBB4enp+/fXXz549G/RckRwOh/kF7+HhkZ6e3traeujQoUH0ExgY2NLSsnXr1sGF0U1bW9vDhw97nbGjrq4uMzMzKirK29u7r5/gLOapQ0tLy8TExPLy8rq6upCQkA0bNhw9enSgISUlJdnY2OzYsYNZZB7Zlr3iQwjR19dnfv3Lb2W5uLgQQvqaUgUABk1HUmVPqkqeA9ItPbK0IMWhoAStVV5e/vLly6lTp7Jrpk2bZmBg0HM63UGYOnUqn89nrwqpUX19PU3Tvf7C9vb2joqKCgkJKSgoYEZRloO59Wf8+PE+Pj4WFhYCgSA+Pl4gEAz0S+XkyZPZ2dmFhYXsSUfm5iSpVCq7WUdHBzN2vfxWFnOA3U5bAoDydCRV9qSq5Km4numRpQUpDjPlgNZ68eIFIcTY2Fh2pZmZWWtrq0r6NzQ0bGhoUElXymhvbyd/loPdWFlZZWRkjB8/XpF+bGxsCCHMzUwMAwMDe3t7RW5IZ2VmZiYnJxcVFY0bN45dydwy1dLSwq4RiUTt7e3MHuW3spj6kjlYAFAhHUmVPakqeSqo1/TI0oIUh4IStJaZmRkhpFtOfPHiha2trfKdSyQSVXWlJCYN9Tr0t6WlJfMmKMLY2NjFxeXOnTuyK6VSqUAgULCHffv2FRYWXrhwods3k4ODg4mJyaNHj9g1zA1SEydO7LeV1dHRQf48WABQIR1JlT2pKnkqoq/0yNKCFIdL3qC1JkyYYGxs/J///Iddc+XKlY6OjjfffJNZ5HA4EolkcJ0XFRXRND19+nTlu1KSlZUVRVHNzc09m06fPi0UChXvKiws7ObNmw8ePGAWRSLRo0ePFBlFiKbpmJiY27dv5+bm9kyXHA4nICDg4sWL7M31BQUFFEUxdybJb2UxB2htba344QCAInQkVfakwuQph/z0yNKCFIeCErQWl8vdtGnTyZMnDx8+3NLScvv27bVr19rY2KxevZrZwNnZuampKTc3VyKRNDQ0yJ4kI4RYWFjU1tZWV1e3trYyGbCrq+v58+dSqbSsrCw6OtrOzi4iImIQXRUUFKhwLAw+n+/o6FhTU9NtfWVlpbW1dVhYmOzK8PBwa2vrGzdu9NrVxo0b7e3tIyIiHj9+3NjYGBMTIxaLP/30035fe+fOnV27dh08eFBfX192muY9e/YwG2zdurWurm7btm1tbW2XL1/evXt3RESEm5ubIq0M5gAHN0YmAMihI6myJxUmTzn6TY8MLUhxKChBm23bti0pKSkhIWHMmDGzZs16/fXXi4qKjIyMmNZ169bNnj172bJlbm5u27dvZ641eHt7MyNcrF271srKysPDIyAgoKmpiRDS3t7u6enJ4/H8/PxcXV1/+ukn9uabgXalWoGBgeXl5d0ei+51uJ+Ojo76+nrZMcNlmZubX7p0ydbW1svLSygUXr16NT8/nx2ZUs5r+x1aaPz48YWFhT/88MPo0aMXL1784YcfHjhwQMFWxrVr14RCYbfr4ACgEjqSKntSVfIsKSnx9fUdN27clStXbt26ZWNjM2PGjIsXL/bVW0/akOJUNeWO5kz+A7pg+D9vzCSww7lHBlFgMr2KigoOh/P999/321tnZ6efn19GRsYgIlHmtUp69uwZl8vds2ePIhvrci5S5NMCOmX45/JWS6oc9NSLw5M8+9VrisPUiwBaq9d7tzWBs7NzQkJCQkICM5ZkXzo7O3Nzc1tbW8PDwwe6C2Veq7y4uDgvL6/IyMjh3zUADJTGpkpCiFgsLiwsrKioYB6CGYbkqQjZFEfTdG1tbXFxMfN44giCghJAG8TGxoaGhoaHh/d6gzmjqKgoJyenoKCgr2kh5FDmtUpKTk4uLS09e/asCkeDAwDd1NTUNG/ePFdX1w8//JBZM9TJs1/dUlxeXp5QKPTz88vPz1f5voYUCkqA/m3ZsuXQoUPNzc0ODg4nTpxQdzi9S0xMjIyM/PLLL/vawN/f/8iRI+xEugOizGuVkZeX9+rVq6KiInNz82HeNQAMlIanyq+//pq9Pnv48GF2/ZAmT/l6priQkBA2SNmBgTUfxqEE6F9SUlJSUpK6o+jf3Llz586dq+4oVCk4ODg4OFjdUQCAQkZKquxJXclTm1IczlACAAAAgFJQUAIAAACAUlBQAgAAAIBSUFACAAAAgFJU/FBOaGioajsE6BUzS5WOfN727t17/PhxdUcxYvScSE2n4NMCskpKSogOpEqd+kboRnMyHkUrNilQvy5fvpycnKySrkBn/fHHHzdv3pw/f766A4ERTzeLKt38QgVCyM2bNwkhkydPVncgoB6akPFUVlACKC87OzssLAyfSQCAAVm6dCkhJDs7W92BgO7CPZQAAAAAoBQUlAAAAACgFBSUAAAAAKAUFJQAAAAAoBQUlAAAAACgFBSUAAAAAKAUFJQAAAAAoBQUlAAAAACgFBSUAAAAAKAUFJQAAAAAoBQUlAAAAACgFBSUAAAAAKAUFJQAAAAAoBQUlAAAAACgFBSUAAAAAKAUFJQAAAAAoBQUlAAAAACgFBSUAAAAAKAUFJQAAAAAoBQUlAAAAACgFBSUAAAAAKAUFJQAAAAAoBQUlAAAAACgFBSUAAAAAKAUFJQAAAAAoBQUlAAAAACgFBSUAAAAAKAUFJQAAAAAoBQUlAAAAACgFBSUAAAAAKAUFJQAAAAAoBQUlAAAAACgFI66AwCdJpFIXr58yS62tbURQp4/f86uoSjKzMxMDZEBAGgwkUj06tUrdrGjo4P8v8nT0NCQz+erITLQVRRN0+qOAXRXXV2dUCjs7Ozsa4PZs2dfuHBhOEMCANB86enp69evl7NBWlraunXrhi0eAFzyBnWytraeOXPmqFG9fw4pilq2bNkwhwQAoPlCQ0P19PT6atXT0wsNDR3OeABQUIKa/e1vf+urSU9Pb9GiRcMZDADAiGBpaenv799rTamnp/fuu+9aWloOf1Sgy1BQgpotXryYw+nlXl49Pb158+aNHj16+EMCANB8K1as6PWmNZqmV6xYMfzxgI5DQQlqZmpqOn/+/J41JXIiAIAcISEh+vr6PddzOJygoKDhjwd0HApKUL8VK1b0fC7HwMDgvffeU0s8AACaz8TEZMGCBd1qSg6HExwcbGpqqq6oQGehoAT1e++997oNb6Gvr79w4UIjIyN1hQQAoPmWL18ulUpl13R2di5fvlxd8YAuQ0EJ6sflchctWiT7O1sikSAnAgDIFxAQYGxsLLvGyMho3rx56ooHdBkKStAI77//vkQiYRdNTU3nzJmjxngAADSfgYFBaGiogYEBs6ivrx8WFmZoaKjeqEA3oaAEjfDuu+9aWFgw/9bX11+2bBmbIgEAoC/vv/8+M00OIUQikbz//vvqjQd0FgpK0AgcDmfZsmXMVW/kRAAABc2ePZsdcnLMmDGzZs1Sbzygs1BQgqZYtmwZc9Xb2tra19dX3eEAAIwAo0aNev/99w0MDPT19ZcvXy5n+hyAIYWCEjSFj4+PUCgkhHzwwQd9TcYIAADdLFu2rKOjA9d2QL3+n9Gka2pqfvnlF3WFAjBt2rSnT5+OHj06Oztb3bGA7lq6dKmSPSCXwnCiaZqZVOzhw4fV1dXqDgd0hY+Pj62t7f9dpmVkZWWpLzAAAI1AKw25FAC0XlZWlmze62UO5V7nBgUlZWdnh4WF6cJ7S1FUVlbWoM/xnDhxYsmSJaoNCUBBzN+pqnrThb930BB37twhhHh4eAz0hfhugsGhKKrbml4KSgA1QjUJADBQgyglAVQLjz4AAAAAgFJQUAIAAACAUlBQAgAAAIBSUFACAAAAgFJQUAIAAACAUlBQarSzZ88KBILTp0+rOxCQ5/z587GxsV1dXQsXLrSzs+NyuUKhMDg4uKysTMEeJBJJUlKSs7OzgYGBmZnZhAkTFBydOCEhwcPDw9TU1NDQ0NnZefPmzS9fvpTdO0vonAAAIABJREFUoLi4eMaMGXw+38bGJiYm5tWrV4q0njp1aufOnZ2dnQrGDwA6Bd9Ncij/jUAI6erq2rt3r4+PT7f1cnK+2vM2CkqNpgtjg41027ZtS01N3bJlS1dX16VLl44ePdrU1FRcXCwWi2fOnFlbW6tIJ2FhYd99992RI0dEItHdu3ednJy61YV9uXDhwoYNG6qrq589e5aUlJSSkhIaGsq2lpeXz50719/fv6Gh4eTJk99+++3atWsVaQ0KCuJyuf7+/i9evBjImwEAOgHfTX1RyTdCRUXFzJkzN27cKBKJujXJyfnqz9s9Z3dQfpYI6EnD31uRSOTt7a2SrkiP0fO12Jdffunq6ioWi2malkgk7733Htt09epVQkhiYmK/nRw7doyiqLKyskEEEBgYKJVK2UVmzN7Hjx8zi2FhYQ4ODl1dXczi7t27KYq6e/euIq00TUdGRnp7e0skkkEENkKp6u9Uw//eAVga/lkdWd9NKvlGKC0tXbRo0eHDh728vCZNmtStVX7Op4cxb/d8P3GGEgghJCMjo76+Xt1RjDCVlZVbt26Nj4/ncrmEEA6HI3sByNHRkRBSVVXVbz8HDhyYMmWKp6fnIGI4c+aMnp4euzhmzBhCCPOjViqV5ufnz5o1i53PYP78+TRN5+Xl9dvKiIuLKy0tTUlJGURgAADKG0HfTar6Rpg0aVJOTs7y5csNDQ17tsrJ+Qw15m0UlJqruLjYzs6Ooqj9+/cTQtLT042MjPh8fl5e3vz5801NTW1tbY8dO8ZsnJqayuVyrays1qxZY2Njw+VyfXx8rly5wrRGRkYaGBiMHTuWWVy/fr2RkRFFUc+ePSOEREdHb9q0qaqqiqIoZ2dnQsi5c+dMTU0TExPVcNgjR2pqKk3TQUFBvbaKxWJCiKmpqfxOOjo6SkpKvLy8VBLS06dPeTyeg4MDIeTBgwcvX760s7NjW52cnAghzH088lsZ5ubms2bNSklJoXF5CwD+hO+mXqnkG2GgZHM+Q415GwWl5vL19f3ll1/YxXXr1n388cdisdjExCQrK6uqqsrR0XHVqlUSiYQQEhkZGRERIRKJoqKiqqurb9y4IZVK58yZ8+TJE0JIamqq7ASmaWlp8fHx7GJKSsqCBQucnJxomq6srCSEMHf1dnV1DdvBjkT5+flubm58Pr/XVuYCh6+vr/xOamtrOzo6rl+/Pnv2bCbburu7p6WlDSIXiESiCxcurFq1ysDAgBDyxx9/EEJMTEzYDbhcLo/Hq6ur67eVNXny5KdPn966dWugwQCAtsJ3U69U8o0wIN1yPktdeRsF5cjj4+NjampqaWkZHh7e1tb2+PFjtonD4bi7uxsaGnp4eKSnp7e2th46dGgQuwgMDGxpadm6davqotY2bW1tDx8+ZM7qdVNXV5eZmRkVFeXt7d3Xr1UW8/CNpaVlYmJieXl5XV1dSEjIhg0bjh49OtCQkpKSbGxsduzYwSwyj2zLXhwhhOjr6zM/lOW3slxcXAght2/fHmgwAKBrdPm7SVXfCAPSLeez1JW3UVCOYMyPEuZXYE9Tp07l8/n37t0b3qB0RX19PU3Tvf4Y9fb2joqKCgkJKSgo0NfXl98Pc5fM+PHjfXx8LCwsBAJBfHy8QCD45ptvBhTPyZMns7OzCwsL2ZOOzH08UqlUdrOOjg4ej9dvK4s5wG6nLQEA5NDB7yZVfSMormfOZ6krb3OGeX8wnAwNDRsaGtQdhXZqb28nf5aD3VhZWWVkZIwfP16RfmxsbAghzA1DDAMDA3t7e0Xu3WZlZmYmJycXFRWNGzeOXcncltTS0sKuEYlE7e3tzB7lt7KY+pI5WAAAldC+7yZVfSMoqNecz1JX3kZBqbUkEsmLFy9sbW3VHYh2Yv5iex1C1tLS0szMTMF+jI2NXVxc7ty5I7tSKpUKBAIFe9i3b19hYeGFCxeMjY1l1zs4OJiYmDx69Ihdw9yENHHixH5bWR0dHeTPgwUAUJ5Wfjep6htBEX3lfJa68jYueWutoqIimqanT5/OLHI4nL4uQMAgWFlZURTV3Nzcs+n06dNCoVDxrsLCwm7evPngwQNmUSQSPXr0SJFRhGiajomJuX37dm5ubs/MwuFwAgICLl68yN7AXlBQQFEUcxOP/FYWc4DW1taKHw4AgBxa+d2kwm8EOeTnfJa68jYKSq3S1dX1/PlzqVRaVlYWHR1tZ2cXERHBNDk7Ozc1NeXm5kokkoaGBtlTU4QQCwuL2tra6urq1tZWiURSUFCgsUMzaAg+n+/o6FhTU9NtfWVlpbW1dVhYmOzK8PBwa2vrGzdu9NrVxo0b7e3tIyIiHj9+3NjYGBMTIxaLP/30035fe+fOnV27dh08eFBfX5+SsWfPHmaDrVu31tXVbdu2ra2t7fLly7t3746IiHBzc1OklcEc4ODGyAQAYGj9d5MKvxHk6DfnM9SVt1FQaq79+/dPmzaNEBITExMcHJyenr53715CyMSJEx88eHDw4MFNmzYRQubNm1dRUcG8pL293dPTk8fj+fn5ubq6/vTTT+wtHevWrZs9e/ayZcvc3Ny2b9/OnAz39vZmxm5Yu3atlZWVh4dHQEBAU1OTWo53xAkMDCwvL+/2WHSvw/10dHTU19fLjhkuy9zc/NKlS7a2tl5eXkKh8OrVq/n5+ezIlHJe2+/QQuPHjy8sLPzhhx9Gjx69ePHiDz/88MCBAwq2Mq5duyYUCrtdBwcAXYbvpl6p6huhpKTE19d33LhxV65cuXXrlo2NzYwZMy5evNhXbz2pLW/LTpuj4VMwjWjD8N6uXr3awsJiSHehCKIzUy9WVFRwOJzvv/++3y07Ozv9/PwyMjIGsRdlXqukZ8+ecbncPXv2DP+u1QVTL4KuwXeTqgzPN0K/hi1v93w/cYZSq/R6RzAMEWdn54SEhISEBGYsyb50dnbm5ua2traGh4cPdBfKvFZ5cXFxXl5ekZGRw79rANAmuvDdNAzfCIpQY94ecEG5Z88e5ubTr7/+eigCki8nJ8fR0ZG5aWDs2LErVqzoa8tbt26Fh4c7ODgYGhqOGTNm0qRJ7OCf4eHhlFxnzpyR3VFfY6gmJydTFDVq1Kg33niDOR0NuiY2NjY0NDQ8PLzXe7EZRUVFOTk5BQUFfc2gIIcyr1VScnJyaWnp2bNnVThwGqjctGnT9PT0lJy686OPPjIxMaEoqrS0VJHWs2fPCgQC2XmKh1RXV9fevXt9fHwUf4lsAu/m9ddfH0QMuvA+g/KG+huhX2rO27KnKxU89c3cFXHgwAEVnz9VmJOTk0AgkLNBWVkZn8+Piop6+PChWCy+f//+5s2b/f39mdawsLAffvjhxYsXEonk999/J4QEBQV1dHS0tbXV19evWrXq9OnT7I4IIWPHju3o6Oi2C6lUam9vTwhhu5VvqC8rxMbGMmPJvv7668ePHx+6HfWL6Mwlb1ZhYWFMTIy6o1Cl3NzcpKQkqVSq7kCG20i85O3v7z9p0iQlO2FmXr5586YirWfOnDE1NT116pSSO1XEb7/9NmPGDELIII5R9ptCKpWKRKK6ujp3d/fBRaKt7zO+m1ROXd8Iw5y3e76fQzUOpVgs9vf3l53uczjt2bPHzMwsJSWFWXR1dd2+ffvixYuZRYqiZsyYIfvjgKIofX19fX19Pp//5ptvynb15ptvXr9+PTc3NzQ0VHZ9Tk6OUCjs9jyaGiUlJSUlJak7Ch01d+7cuXPnqjsKVQoODg4ODlZ3FKAoiqKGc3eBgYFyTsCo0K1btxISEtauXdvW1kYPfHZ7WXp6ejwej8fjubq6DroTbX2fh5QOfjep6xtB7Xl7qO6hzMjIqK+vH6LO+9XY2Njc3Cz7RJiBgQF74eDYsWNyTjWvXr36vffeYxfXrVtHCOn59GtycjLzIBsAgHopf3lLfqmkwkKKpunjx48rOLPopEmTcnJyli9f3usEJIOTm5s76Ndq6/sMoBIqKCh//vnnv/zlL3w+39TU1NPTs6WlJTo6etOmTVVVVRRFOTs7p6SkGBkZjRo16s0337S2ttbX1zcyMpoyZYqfn99rr73G5XLNzMw2b97Mdnju3DklB5qaNm1aW1vbO++88+9//1vJo3vnnXfc3d1/+umn+/fvsyv//e9/i0QiLTspBQDDrLOz84svvrCzs+PxeBMnTmQuPg40YRJCKiv/P/buPK6pK+8f+ImEJIRNQEAEURahgihabVmljmNrxQVtFVptSx0dEX0AdUakaosIuFSBYlFHS51pqywuBVHRjg9StYpSFbC4sQioVBYRWRJNQu7vj/trnpQlBBKSAJ/3H3313nPPud9zTW6+3OWc0tdee01XV5celuXy5cuyd0EIoSjqyy+/dHR0ZLPZhoaG//znP6UblFF6+fJla2trBoPx9ddfE0L27t2rq6vL5XIzMzPfffddAwMDKysr+tatJIDY2FhHR0cdHZ1hw4bZ2NjExsYuWrRI8aOn4C8FjjOAkknf/+7FM5QtLS0GBgY7duzg8/lPnz5dsGBBXV0dRVHvvfeenZ2dpMoXX3xBCLl27Vpra2t9ff3MmTMJIadPn66rq2ttbaVfRyooKKA3PnXqlL6+flRUVFcBdPsMJY/Hmzx5Mt1BJyenHTt2PHv2rNMt6Wco582b19WOHj58+NVXXxFCwsLCJOvnz59/6NCh5uZmojHPUGoOMvieoYQBQ8XPUP7jH/9gs9nHjh17/vz5Z599NmTIkPz8fKqHJ8zp06fb2to+fPhQKBT+9ttvb775JofDefDggexdbNy4kcFg7N69+/nz5zweLykpiUg9vSe7lB4gcM+ePZKNCSH/+7//++LFi9raWm9vb11dXclz5zExMVpaWpmZmTwe78aNG+bm5m+99VZPj+ebb77Z8fnFnv5ShIaG3r59W3oDHGcKv03QWx2Pp6IJ5W+//UYIOXXqVLttOk0om5ub6cX//Oc/hBDJd/v69euEkNTUVDm70W1CSVGUQCD46quvXnvtNTqtNDMzo6d7akeehLKxsVFXV9fIyIjH41EUVVZWZmVl9erVKySUncKXFvovVSaUfD6fy+UGBATQizwej81mBwcHUz08YbZ7WaSoqIgQ8o9//EPGLng8HpfLnTFjhqSW9OsgskupLhIdPp9PL9JZUWlpKb04ZcqUN954Q9LU3//+9yFDhrx69UqOo/h/Ok0ou0W/VSmt04RykB9n/DZB73Q8noq+lGNra2tmZrZkyZLQ0NDAwEA5R2Sg3/kSiUT0Iv1ginJn89TW1g4JCQkJCbl27dr27dvpt2ru379vZGTU06YMDQ0//PDDgwcPpqamfvrpp/Hx8cHBwSwWi55/vUfavdkzUMXHxx89elTdUQD0WMeZ0/rO/fv3eTzeuHHj6EUdHZ3hw4ffu3ev45Y9OmG6uLgYGhrS6U5XuygtLeXxeNOnT++0Bdml3aKjlYT38uVLDocjKW1ra9PW1tbS0upd4z1laGjY2NhI/39YWJjsjQfzccZvEyhO0WcodXR0cnJyvLy8YmJibG1tAwIC2s07pHZvvvnmjz/+uHLlyrq6ugsXLvSuEfrVnP379zc2Nh49ejQoKEipMQLAoNPa2koI2bRpk2SIxMrKSh6Pp3jL2tradJ7R1S7ovNnU1LTT6rJLe2rWrFk3btzIzMzk8/m//vprRkbG7NmzVZZQSktISJDkfEqB4wwgTQnDBjk7O2dlZdXV1cXFxW3fvt3Z2bmrkcD71MWLF2/cuLFmzRpCyHvvvZeWlsZk/l/vPvroo3379vX6ZO3q6urm5paXl7dixYqFCxf24jInbTD8bcRgMNasWYOHwaE/Sk9P9/f3V82+6EwiPj6+2ytnPSISiRoaGqytrWXsgv7T+tWrV522QF/o6qq0pyIjI2/cuBEYGNjS0mJhYbFo0SJFXrjUHAPsOOO3CXqq46AEil6hrK6uvnPnDiHE1NR027ZtkyZNohdV78aNG7q6uvT/v3r1ql0Y9DvaisyVTl+kPHbsGJ2zAgAogn6VuNNJUxRx4cIFsVg8adIkGbsYN27ckCFDfv75505bkF3aU8XFxWVlZXV1dUKhsKqqau/evb3+g1wpfv/9908//VTxdnCcAdpRQkIZFBR07949gUBw69atyspKNzc3QoixsXF1dXVFRUVzc3NPH47Mzs7u0WAQQqGwpqYmNzdXklASQubPn5+ent7Y2PjixYvMzMwNGzbMmzdPkYRy0aJFw4YNmz9/vq2tba8bAQCgcTicTz/9NCUlZe/evU1NTW1tbY8fP6ZfE+wpgUDw4sULkUh08+bNkJCQUaNGBQYGytiFqanpe++9d+zYseTk5KampqKiIukBC2WX9tTq1autra1lz27cOz39paAois/nHz9+3MDAoHd7HJzHGUBe0m/oyPO21+7du83NzQkhurq6CxYsqKio8PDwMDIy0tLSGjFixMaNG+lpf27evDlq1CgdHR0vL6+IiAh6IPHRo0dfunRp+/bthoaGhBBzc/PDhw+npqbSDRoZGaWkpFAUdebMGX19/ejo6I57P3HiRMcX9yROnDhBb/bTTz/5+/vb2dmx2WwWi+Xo6BgZGfny5UvpppqamqZOnWpsbEwIGTJkiL29fUxMTMcdDRs2bPXq1fTK9evXX7lyhf7/TZs2DR8+nK7r5OR06dIl2ccNb9IBaD4VDxv06tWr8PBwa2trJpNJpxfFxcUJCQk9OmEeOnRo2rRpZmZmTCbTxMTkgw8+qKyslL0LiqKam5uXLVtmYmKip6fn5eX1+eefE0KsrKwKCwtll+7Zs4c+9XG53Llz5yYlJdHRjhkzpqys7MCBA3S6NmrUKHpInZycHBMTE8lZWltbe+zYscePH5fnMF69etXT09PCwoKuO3z4cA8Pj59//pku7fUvxaZNmyiKwnGm4bcJeqfj8ezNsEHQC4Pn2OJLC/1Xf5zLW8MlJSVJD+L76tWrNWvWsNlsehQ2UJZeH+fB81nFb5NydTyefTWXNwAADHJPnz4NCQmRfriQxWJZW1sLhUKhUKijo6PG2AYSHGfQBH01lzeADEFBQZLxNZYsWSJddP78+YiICLFYPH/+fGtraw6HY2lpOW/ePHq8t25FRUU5OTkZGBiw2Wx7e/v169dLP1QUHR3N+LN2w4gIhcLY2Fh7e3sWizV06NBx48ZVVFQQQk6ePLljx462trZedBY9UlePMjIyJI0MGzasFz0FBeno6GhraycnJ9fU1AiFwurq6m+++ebzzz8PCAiorq5mdC0gIEDdsfcnMo5zrx8YHeQUPMvRxGJxfHy8h4dHu/Wyz4GyaeAvwv+Rvlw5eC59q97gObZEjtsKK1asMDY2zs7Ovn//vvSzrZ9//vmcOXOampqEQqGJicmlS5daW1vLy8tnzJhhaGj45MmTbvfu4+OTlJT07NmzpqamtLQ0bW3tmTNnSkq3bt3a7vPv7OwsXX3+/PmOjo55eXn0SXnu3LmSaTMSEhJ8fHyeP3/eg2OBHqm1R2Kx+PHjxxcvXpw1a5aJiYk8vcMtb6W7ePHiX//6VwMDAy0tLUNDQw8Pj6SkJKFQqO64BppeH+fB81mV57eJpvhZjqKoBw8eeHp6EkI6TvIk+xwom+b8InQ8nkgoVUQFx5bH47m7u6u9KTkTSktLy3Yrt23b5uDgQE8sJhQKZ8+eLSmi50CTfmuqK76+vvRrYTR6yLGqqip6cevWrd9//31XdVNSUhgMRlFRUVcbhISEuLu7y/9biB51SvU9Cg0NRUIJ0Cn8NrWjlLNcQUHBggULfvjhB1dX144JpexzoGya84vQ8XjilvfAkZycXFtbq2lNyam0tHTz5s1btmyhB/tlMplZWVmSUnqoprKysm7bOXXqlPTkEPSNTjkHtN+3b9+kSZNcXFy62iAyMrKgoCAhIUGe1tCjrmhOjwBABfrRb5OyznITJkw4fvz44sWL2Wx2x1JFzoGafP5EQqlZKIqKi4sbO3Ysm802MjLy8/OTzO0bEhLCYrHogSQIIatWrdLV1WUwGPX19YSQsLCwdevWlZWVMRgMe3v7xMREDodjZmYWFBRkYWHB4XA8PDyuXbvWi6YIIWfPnu3ReG+9kJiYSFHU3LlzOy2l5/PsxcNAT5480dHRsbGx6XZLgUCQl5fn6uoqYxsjIyMfH5+EhAT6jzPZ0CM5qbFHACCnQfLb1EdnOdnkPwcqUlcF508klJolMjIyIiJi48aNtbW1Fy9efPTokbe3d01NDSEkMTFRes6opKSkLVu2SBYTEhLmzJljZ2dHUVRpaWlISEhgYCCPxwsNDa2oqLh586ZIJJoxY8ajR4962hQhhH5QVywW913HT58+7ejoSA+01hF9o8HLy6tHbfJ4vJycnOXLl7NYLMnKiIgIIyMjFotlY2Pj5+eXn59Pr6+urhYIBDdu3Jg2bRp9mhs7dmxSUlK779XEiROfPHlSWFiIHg2AHgGAnAbJb1NfnOVk6/QcqEhdNZ4/kVBqED6fHxcXt2DBgiVLlhgaGrq4uOzfv7++vr7X0ycwmUz6D0onJ6e9e/c2NzcfOnSoF+34+vo2NTX13RTtra2tDx8+7HQg4pqamtTU1NDQUHd3967+auxKbGyshYVFdHS0ZM0nn3xy8uTJR48etbS0pKSkVFVV+fj4FBcXE0LoF+VMTU1jYmKKi4tramr8/PxWr1595MgR6TbHjBlDCLl9+zZ61N97BAByGiS/TX10lpOt4zlQkbrqPX8iodQgxcXFLS0tkydPlqyZMmUKi8WS3A5QxOTJk7lcruQmhUapra2lKKrTPwrd3d1DQ0P9/Pyys7O1tbXlb/PEiRPp6ennzp3T19eXrBw5cuTEiRP19PRYLJabm9uhQ4f4fH5SUhIhhH7SxdnZ2cPDw9jY2NDQcMuWLYaGhu3OmHSQ9N/l6FG/7hEAyGmQ/Db1xVlOtk7PgYrUVe/5EwOba5DGxkZCiJ6envTKoUOHNjc3K6V9NptdV1enlKaU6+XLl+SPj3s7ZmZmycnJzs7OPWowNTU1Li4uNzd3xIgRMjZzcXHR0tJ68OABIYSe3o1+UofGYrFGjRrV7vlreohgOmAZ0KNuqb1HACCnQfLbpPSznGxyngMVqavi8ycSSg0ydOhQQki7r2hjY6OVlZXijQuFQmU1pXT0J7jTIVVNTU3pwyK/PXv2nDt3Licnp93pryOxWCwWi+nTh56e3pgxY+7cuSO9gUgkoqf3lRAIBJKAZUCPZNOEHgGAnAbJb5Nyz3KyyX8OVKSuis+fuOWtQcaNG6enp/frr79K1ly7dk0gELz++uv0IpPJFAqFvWs8NzeXoig3NzfFm1I6MzMzBoPx4sWLjkVZWVmWlpZytkNRVHh4+O3btzMyMjr9pr3zzjvSi/n5+RRFubu704v+/v63bt0qLy+nF3k8XmVlZbsRFuggzc3N0aP+3iMAkNMg+W1S1llOtm7PgYrUVe/5EwmlBuFwOOvWrTtx4sQPP/zQ1NR0+/btlStXWlhYrFixgt7A3t6+oaEhIyNDKBTW1dVVVlZKVzc2Nq6urq6oqGhubqa/kGKx+Pnz5yKRqKioKCwszNraOjAwsBdNZWdn9+mwQVwu19bW9vHjx+3Wl5aWmpub+/v7S68MCAgwNze/efNmx3bu3Lmzc+fOgwcPamtrS889tWvXLnqDJ0+epKamNjY2CoXCq1evLlu2zNraeuXKlXTp2rVrR40aFRgYWFVV9ezZs/DwcD6fv2HDBuld0EHS30AZkaBHGtIjAFDcIPltUtZZTrZuz4H99/yJhFKzfPHFF7GxsVFRUcOGDfPx8Rk9enRubq6uri5dGhwcPG3atA8++MDR0XHr1q30RWl3d3d6wIWVK1eamZk5OTnNmjWroaGBEPLy5UsXFxcdHR1vb28HB4cLFy5Ing7paVN9zdfXt7i4mB7lS6LTobAEAkFtbW1mZmbHom6Hzpo5c+amTZusrKy4XO6iRYs8PT3z8vJMTEzoUiMjo0uXLllZWbm6ulpaWl6/fv306dPtRu3Kz8+3tLQcP3687EjQIw3pEQAoxSD5bVLKWY4QkpeX5+XlNWLEiGvXrhUWFlpYWHh6el68eLGr1uRsWdPPn9LT5mC6sL6j+mNLz5etyj3SSK+mXiwpKWEymTLmjJJoa2vz9vZOTk5WKMpeqa+v53A4u3btkicS9EgTekTD1IsAXcFvkzRNOMtp2vmzKx2PJ65QDmSdPlysIfh8/rlz50pKSuhHgO3t7aOioqKiouixsrrS1taWkZHR3NwcEBCgqkj/T2RkpKura0hIiDyRoEdq7xFFUdXV1ZcvX6bHQAYADaGxv01qP8tpzvmzF5BQgno0NDTMnDnTwcFh6dKl9JqIiIiFCxcGBAR0+kw0LTc39/jx49nZ2V3NZNB34uLiCgoKzpw5Qw9CJk8k6FGfxN21dj3KzMy0tLT09vY+ffq0iiMBgH5KvWc5zTl/9ob05Urcpuk7Kj62ERER9FxMo0ePPnr0qMr2S8l3W0GGc+fOhYeHKzEepcjIyIiNjRWJRL2oix6phiI9ksAtbxhs8NvUKc08y/WdXpw/Ox5PBiX1jGd6erq/vz/Vq0nBQbbBc2wZDEZaWpr0fKwA/YWyvqeD5/sO/d3g+azit0m5Oh5P3PIGAAAAAIUgoQQAAAAAhSChBAAAAACFIKEEAAAAAIUgoQQAAAAAxUi/8k0PHwAAMJgpOgIHzqUAMAjIGjbo8ePHV65cUWNwAP3XoUOHfvrpJwsLiwULFnh4eAwZgsv//ZXiA4vgXNqPtLW1Xbx48ccff6yvr/f19V28eLG6IwLoHzw8PKysrCSLf0ooAUARDx8+3L59+7fffjty5MgNGzYsXbqUyWSqOygA6JxQKExJSYmOjq6oqAgICNi8efOYMWPUHRRAf4WLKABKY2Nj869//evBgwczZsxYtWqVg4P95E5RAAAgAElEQVTDgQMHRCKRuuMCgD8RCATffffd2LFjly1b5ubmdufOne+++w7ZJIAikFACKBmdVpaUlNBp5ZgxY5BWAmgISSq5fPlyDw+Pu3fvfvfdd/b29uqOC6DfQ0IJ0CdGjx5Np5Vvv/326tWrkVYCqJdAIDhw4ICdnd3y5cv/+te/lpWVfffdd3Z2duqOC2CAwDOUAH2usrIyLi7uX//61/Dhw9esWRMUFMRms9UdFMBg8erVq//85z9RUVH19fWffPLJ559/bmlpqe6gAAYaJJQAKoK0EkDFWltbv/nmm507dzY2Ni5btiw8PHzEiBHqDgpgYEJCCaBSVVVVu3fvPnDggJmZ2dq1a1esWMHhcNQdFMBAQ6eSO3bsaGpq+tvf/rZhwwYLCwt1BwUwkCGhBFADpJUAfaSlpSU5OXnbtm2tra1Lly6NiIgYPny4uoMCGPiQUAKozaNHj3bt2nXgwAFTU9N169YhrQRQRHNz8969e3fu3CkQCJYuXfrZZ5+Zm5urOyiAwQIJJYCa0WnlwYMHhw0btm7dur///e86OjrqDgqgP6FTyR07dohEouDg4PXr1xsbG6s7KIDBBQklgEaoqamJj49PTEw0MDBYs2ZNSEgI0kqAbjU0NCQmJn711VdisXjlypXh4eFGRkbqDgpgMEJCCaBBamtr4+LiEhMT9fX1165di7QSoCvPnj3bs2fPV199paWltXr16rCwsKFDh6o7KIDBCwklgMah08o9e/bo6ekhrQRop76+/uuvv05ISNDW1l61atWaNWsMDQ3VHRTAYIeEEkBD1dXV7d69e8+ePbq6uuvWrfuf//kfLper7qAA1Kmuri4pKSk+Pp7FYq1atWrt2rUGBgbqDgoACEFCCaDhkFYCEKnL9vgiAGgmJJQA/QB9jy8+Pp7NZgcHB+PCDAwekvfV8GAxgCZDQgnQb0jSStzvg8FAMqIWhj4A0HxIKAH6GbyRAAMeppIC6HeQUAL0S/SYKQkJCUwmc/Xq1UgrYWCorKyMi4v717/+NXz48DVr1iCVBOgvkFAC9GMYig8GjIqKim3btn377bdWVlZhYWFBQUFsNlvdQQGAvJBQAvR7kslChgwZ8j//8z9IK6F/efjw4fbt27/99tuRI0du2LBh6dKlTCZT3UEBQM8goQQYIJqamvbt27d9+3Z6DroNGzYgrQQNV15evmPHjm+//dba2jo8PBypJED/hYQSYEBpbm7eu3fvjh07RCJRcHAwpjYGzXTnzp3t27cfOXLE0dExPDx88eLFWlpa6g4KAHoPCSXAAIS0EjTWb7/9tnPnziNHjrz22mvr169HKgkwMCChBBiw6LRy586dQqEwODh4/fr1xsbG6g4KBq/bt29/+eWXhw8fdnJy+uc//4lUEmAgQUIJMMC1tLQkJSXt3LlTIBCsWrUKaSWoXlFRUXR09LFjx1xcXDZt2vT+++8zGAx1BwUAyjRE3QEAQN/S09MLDw+vrKyMjo7+97//PWrUqNDQ0JqaGnXHBYNCYWHhokWLXF1dHzx4kJaWVlBQsHDhQmSTAAMPEkqAQUFPTy80NLS0tDQ6Ojo9Pd3e3j40NPTp06fqjgsGrKtXr86ZM2fixIklJSVpaWm3bt1CKgkwgCGhBBhEkFaCCly5cmXOnDkeHh7Pnj3LzMxEKgkwGCChBBh0dHV16bQyJibm6NGjdFr5+++/qzsu6PcuX748Z84cT0/P58+fnzx5ks4s1R0UAKgCEkqAQYpOK0tKSui0csyYMUgrodcuX748Y8YMb29vOpWkM0t1BwUAqoOEEmBQk75aeezYMfpqZXV1tbrjgn7j8uXL06dP9/b25vP558+fRyoJMDghoQQAwuVyQ0NDy8vL4+Pjjx07Zmtru2LFCqSVINvly5f/8pe/eHt7v3r1Kicnh84s1R0UAKgHEkoA+P/YbPbf//738vLyxMTE06dP02nlkydP1B0XaJzz58+7ubl5e3traWldvXr18uXL06ZNU3dQAKBOSCgB4E+QVoIM58+ff/PNN2fMmKGvr3/t2rX//ve/bm5u6g4KANQPCSUAdILFYtFp5Z49e86cOUOnlY8fP1Z3XKAeFEVlZWVNmTJlxowZBgYG169f/+9///vGG2+oOy4A0BRIKAGgS3RaWVZWRqeVdnZ23aaVT58+bWlpUVmEoLiKigoZpZJUct68ecOHD8/Pz//vf/87ZcoUVUUHAP0DEkoA6IYkrTx48OD58+ft7Ow+/vjj8vLyTjfesmXLzJkzeTyeioOE3vn222+nTJnS6d8AYrE4Kyvr9ddf9/Pzs7Cw+PXXX7OysiZPnqz6IAFA8yGhBAC5sFisjz/++O7duwcPHrxy5crYsWM//vjjsrIy6W0eP36cnJx85cqVWbNm8fl8dYUKcvr++++XLVv27NmzpKQk6fVisfjo0aPjxo3z8/OztLSkU8lJkyapK04A0HxIKAGgB6TTyqtXr9JpZWlpKV26bds2QghFUb/88svs2bNfvnyp1mBBlmPHjgUGBlIURVHUtm3b6IuUdCrp7OwcEBAwbty44uLirKysiRMnqjtYANB0DIqi1B0DAPRLQqEwJSVl69atlZWVAQEBK1eufOuttwQCAV3KZDKnTZuWlZXFZrPVGyd0dOLEiYULF9LZJCGEyWTGxsaOHj168+bNJSUl77333tatWx0dHdUdJgD0G0goAUAhQqHwu+++i4mJefHiRXNzs1AolBQxmcxZs2YdO3ZMW1tbjRFCOxkZGe+//75YLJY+/+vq6goEgo8++uizzz6zs7NTY3gA0B8hoQQAJaiqqrK3t5fOJmlMJnPOnDnp6elMJlMtgUE7Z8+enTt3bltbm1gsll7PZDI3bNiwdetWdQUGAP0anqEEACXYvXt3p+tFItHJkyc/+OCDtrY2FYcEHf3000+dZpOEEJFItG/fPrxKBQC9g4QSABT19OnT/fv3d7w8SWtra/vxxx+XLl3aMYkBVTp//vzs2bM7zSZpjY2NBw8eVHFUADAwIKEEAEXt2LFD8i5Op9ra2n744YegoCA8Y6MuFy5cmD17tlgslpHWt7W1RUdH4918AOgFJJQAoJC2tra6ujpnZ2d9fX3JSm1tbRaLxWAwJGvEYvE333wTEhKijhgHu0uXLs2aNUsoFEo/eMBkMtls9pAh//9XYMiQIcOHDx8zZszNmzfVFCYA9GN4KQf60NWrV+Pi4tQdBaiOSCRqbW3l8Xj0f1taWlpaWng8nkgkkmzj4OAwfvx4NQY52Dx79uzSpUuSfwIGg8HhcHR1dfX09Lhcrq6uLvcP0n8AwMC2du1ad3d3dUcBAwreu4Q+9OjRo2PHjr3//vvqDgT+5PHjx3l5eX3x78JkMg0NDQ0NDdutl040W1tb6+vrhw0bpvS9d3Ts2DE3NzcrKysV7EszCYXC6urq1157TZI7cjgcJI6D3LFjxxYuXIiEEpQLCSX0uaNHj6o7BPiT9PR0f3//wfDvwmAw1qxZs2jRInUHAqBB8BcF9AU8QwkAAAAACkFCCQAAAAAKQUIJAAAAAApBQgkAAAAACkFCCQAAAAAKQUIJAHI5c+aMoaFhVlaWugNRsqCgIMYflixZIl10/vz5iIgIsVg8f/58a2trDodjaWk5b968oqIi+dsXi8Xx8fEeHh7t1kdFRTk5ORkYGLDZbHt7+/Xr17e0tMjZpuy60dHRjD8bN26cdHWhUBgbG2tvb89isYYOHTpu3LiKigpCyMmTJ3fs2NG7WdcVPFboUa97lJGRIWlENaNxAXQKCSUAyGUAT4JgbGycnZ19//795ORkycovvvgiMTHxs88+E4vFly5dOnLkSENDw+XLl/l8/tSpU6urq+VpuaSkZOrUqWvXruXxeO2KcnJyVq9eXVFRUV9fHxsbm5CQsHDhQjkDVqQuIcTf3/+77747fPgwj8e7e/eunZ0dnevMnTuXw+FMnz69sbFR/taIMo4VetTrHs2bN+/x48cXL16cNWtWj/oIoGQUQJ9JS0vDZ0wDafi/C4/Hc3d3V0pThJC0tDTZ26xYscLS0rLdym3btjk4OPD5fIqihELh7NmzJUXXr18nhMTExHS794KCggULFvzwww+urq4TJkxoV+rr6ysSiSSL9GCZVVVV3Tbbbd2tW7d+//33XdVNSUlhMBhFRUVdbRASEuLu7i4UCuWJhFLSsUKPpPWuR6GhoSYmJt0GRsn3vQDoKVyhBADNkpycXFtbq8YASktLN2/evGXLFg6HQwhhMpnSN/ptbW0JIWVlZd22M2HChOPHjy9evJjNZncsPXXqlJaWlmSRvlnZ8UJmpxSpu2/fvkmTJrm4uHS1QWRkZEFBQUJCgjytKetYoUfSlNsjANVAQgkA3bt8+bK1tTWDwfj6668JIXv37qXn8cvMzHz33XcNDAysrKxSUlLojRMTEzkcjpmZWVBQkIWFBYfD8fDwuHbtGl0aEhLCYrGGDx9OL65atUpXV5fBYNTX1xNCwsLC1q1bV1ZWxmAw7O3tCSFnz541MDCIiYlRWWcTExMpipo7d26npXw+nxBiYGCg3J0+efJER0fHxsamT+sKBIK8vDxXV1cZ2xgZGfn4+CQkJFByPOTQR8cKPVJijwBUAwklAHTPy8vrypUrksXg4OA1a9bw+Xx9ff20tLSysjJbW9vly5cLhUJCSEhISGBgII/HCw0NraiouHnzpkgkmjFjxqNHjwghiYmJ0nMhJiUlbdmyRbKYkJAwZ84cOzs7iqJKS0sJIfT7B2KxWGWdPX36tKOjI5fL7bSUvunp5eWlxD3yeLycnJzly5ezWCyl1I2IiDAyMmKxWDY2Nn5+fvn5+fT66upqgUBw48aNadOm0bn+2LFjk5KS2uUlEydOfPLkSWFhYbd774tjhR4pt0cAqoGEEgB6z8PDw8DAwNTUNCAgoLW1taqqSlLEZDLHjh3LZrOdnJz27t3b3Nx86NChXuzC19e3qalp8+bNyotaltbW1ocPH9rZ2XUsqqmpSU1NDQ0NdXd37+oKVu/ExsZaWFhER0crpe4nn3xy8uTJR48etbS0pKSkVFVV+fj4FBcXE0LoFztMTU1jYmKKi4tramr8/PxWr1595MgR6TbHjBlDCLl9+7bsXffRsUKPlNgjAJVBQgkASkBfeqGvUHY0efJkLpd779491QbVG7W1tRRFdXqByt3dPTQ01M/PLzs7W1tbW1l7PHHiRHp6+rlz5/T19ZVSd+TIkRMnTtTT02OxWG5ubocOHeLz+UlJSYQQ+mlOZ2dnDw8PY2NjQ0PDLVu2GBoaHjhwQLpZuvs1NTWy994Xxwo9Um6PAFSGqe4AAGBQYLPZdXV16o6iey9fviR//Ki3Y2Zmlpyc7OzsrMTdpaamxsXF5ebmjhgxoo/quri4aGlpPXjwgBBiYWFBCKEfV6WxWKxRo0a1e8tER0eH/HEoZFD6sUKPiLJ7BKAySCgBoM8JhcLGxkYrKyt1B9I9+ne606GwTU1Nhw4dqsR97dmz59y5czk5OXp6en1XVywWi8ViOknS09MbM2bMnTt3pDcQiUSGhobSawQCAfnjUMig3GOFHvVFjwBUBre8AaDP5ebmUhTl5uZGLzKZzK5ujqudmZkZg8F48eJFx6KsrCxLS0ul7IWiqPDw8Nu3b2dkZPQ0m+y27jvvvCO9mJ+fT1GUu7s7vejv73/r1q3y8nJ6kcfjVVZWthuhhu6+ubm57EiUdazQo77rEYDKIKEEgD4hFoufP38uEomKiorCwsKsra0DAwPpInt7+4aGhoyMDKFQWFdXV1lZKV3R2Ni4urq6oqKiublZKBRmZ2erctggLpdra2v7+PHjdutLS0vNzc39/f2lVwYEBJibm9+8ebOne7lz587OnTsPHjyora0tPf/erl27um2527pPnjxJTU1tbGwUCoVXr15dtmyZtbX1ypUr6dK1a9eOGjUqMDCwqqrq2bNn4eHhfD5/w4YN0rugu09nMDIiUdaxQo8U7BGAJkBCCQDd+/rrr6dMmUIICQ8Pnzdv3t69e+Pj4wkh48ePLy8vP3jw4Lp16wghM2fOLCkpoau8fPnSxcVFR0fH29vbwcHhwoULkkfTgoODp02b9sEHHzg6Om7dupW+befu7k6PK7Ry5UozMzMnJ6dZs2Y1NDSovrO+vr7FxcX0iIMSnQ74JxAIamtrMzMzO20nLy/Py8trxIgR165dKywstLCw8PT0vHjxYletydlyt3Vnzpy5adMmKysrLpe7aNEiT0/PvLw8ExMTutTIyOjSpUtWVlaurq6WlpbXr18/ffp0u1EP8/PzLS0tx48f320flXKs0CMFewSgEVQzIQ8MTho+xd+gpYJ/lxUrVhgbG/fpLuRBejX1YklJCZPJlDEznkRbW5u3t3dycrJCUaq25W7V19dzOJxdu3bJE4kmHKtuDfge0TD1IqgXrlACQJ/o9M0GzcTn88+dO1dSUkK/6GBvbx8VFRUVFUWPCNiVtra2jIyM5ubmgIAA5cbTdy3LIzIy0tXVNSQkRJ5I1H6s5DGwe0RRVHV19eXLl+mJAADUBQklAAx2DQ0NM2fOdHBwWLp0Kb0mIiJi4cKFAQEBnb6fQcvNzT1+/Hh2dnZXs6r0Wt+13K24uLiCgoIzZ87QQy3KE4l6j1W3BnyPMjMzLS0tvb29T58+reJIAP5E3ZdIYSCT59bql19+aWpqSgjZt2+faqLqVFtbW1xcnLu7u/xVjh07JpmZ19zcfPHixV1tWVBQ4O/vP3r0aBaLZWJiMn78+K1bt9JF7R7z7ygrK0t6R5s2bep0F7t37yaEMBgMR0fHn3/+WXbkfX3LOyIigh7nfPTo0UePHu27HXWLKHZr79y5c+Hh4UqMR8NlZGTExsaKRKJe1NXMY4UedUrB7wVAp5BQQh+SM3GhX+NQY0L54MEDT09PQsiECRN6WtfOzs7Q0FDGBkVFRVwuNzQ09OHDh3w+//79++vXr58+fTpd6u/v/9NPP9Eve/7++++EkLlz5woEgtbW1tra2uXLl2dlZUl2RAgZPny4QCBotwuRSDRq1ChCiKRZ2QbPs6344QToCN8L6Au45Q39A5/P9/Dw6IuWCwsLN2zYsHLlynYvUSrLrl27hg4dmpCQMHr0aA6H4+DgIHmvmRDCYDA8PT0NDQ2ZTKZkjba2NpfLNTU1ff3116Wbev31158+fZqRkdFuF8ePH1fW+IgAAAC9gIQS+ofk5OTa2tq+aHnChAnHjx9fvHhxp/OtKe7Zs2cvXryQHv6GxWJlZWXR/5+SkiLjGawVK1bMnj1bshgcHEwI2bdvX7vN4uLi6FF7AAAA1AIJJWicn3/++Y033uByuQYGBi4uLk1NTWFhYevWrSsrK2MwGPb29gkJCbq6ukOGDHn99dfNzc21tbV1dXUnTZrk7e09cuRIDoczdOjQ9evXKyWYs2fPKjiq9pQpU1pbW//yl7/88ssvCgbzl7/8ZezYsRcuXLh//75k5S+//MLj8d5++20FGwcAAOg1JJSgWVpbW+fOnfv+++83NDSUlJQ4ODgIBIKEhIQ5c+bY2dlRFFVaWhoWFvbPf/6Toqh9+/Y9fPjw6dOnU6dOvXXrVkRExK1btxoaGj755JMvv/yysLBQ8XjosW/EYnGvW1i/fv3kyZMLCwu9vLycnZ137typyGDdQUFBhJD9+/dL1uzevXvt2rW9bhAAAEBxSChBs1RUVDQ1NTk7O3M4HHNz8+PHjw8bNqyrjZ2cnLhcromJyQcffEAIsba2HjZsGJfLXbJkCSHk3r17isfj6+vb1NS0efPmXrego6Nz5cqVr7766rXXXrtz5054ePjYsWN//vnn3rX2ySef6Orq/uc//6Fn8igvL8/Pz//www97HR4AAIDikFCCZrG1tTUzM1uyZElkZGRFRYWctehBakQiEb1ID88mFAr7JsYe09bWDgkJuXv3bl5enp+fX21t7cKFC58/f96LpgwNDT/88MPnz5+npqYSQuLj44ODg+nu9xRjECCE+Pv7qzsKAM3Si9MFQLeY6g4A4E90dHRycnI2bNgQExMTFRW1aNGiQ4cOSd6J7u/efPPNH3/8MTg4eN++fRcuXFiwYEEvGgkODj548OD+/fvnz59/9OjRu3fv9i4YevCggc3f3z8sLMzd3V3dgQBokG6HvwXoBSSUoHGcnZ2zsrLq6uri4uK2b9/u7OysyB1ntbh48eKNGzfWrFlDCHnvvffS0tIkowIRQj766KN9+/bxeLzeNe7q6urm5paXl7dixYqFCxcaGRn1rp1Fixb1rmI/4u/v7+7uPhh6CiA/JJTQF3DLGzRLdXX1nTt3CCGmpqbbtm2bNGkSvdi/3LhxQ1dXl/7/V69etesC/Y72+PHje90+PX7QsWPH6JwVAABAvZBQgmaprq4OCgq6d++eQCC4detWZWWlm5sbIcTY2Li6urqioqK5uVmVD0dmZ2f3aNggoVBYU1OTm5srSSgJIfPnz09PT29sbHzx4kVmZuaGDRvmzZunSEK5aNGiYcOGzZ8/39bWtteNAAAAKI26p+qBgUyeKf52795tbm5OCNHV1V2wYEFFRYWHh4eRkZGWltaIESM2btxIz1p78+bNUaNG6ejoeHl5RURE0IOBjx49+tKlS9u3bzc0NCSEmJubHz58ODU1lW7QyMgoJSWl2yCvXr3q6elpYWFBfyOGDx/u4eEhmQ77zJkz+vr60dHRHSueOHGCng6xUydOnKA3++mnn/z9/e3s7NhsNovFcnR0jIyMfPnypXRTTU1NU6dONTY2JoQMGTLE3t4+Jiam446GDRu2evVqeuX69euvXLlC//+mTZuGDx9O13Vycrp06ZLsLmPqRYDBDN8L6AsMiqL6NGGFwSw9Pd3f3x+fMU0zeP5dGAxGWloanqEEkIbvBfQF3PIGAAAAAIUgoYSB7N69ezIGYwsICFB3gKB+QUFBko8EPSS+xPnz5yMiIsRi8fz5862trTkcjqWl5bx584qKiuRvXywWx8fHe3h4tFsfFRXl5ORkYGDAZrPt7e3Xr1/f0tIiZ5uy60ZHR7f7qI8bN066ulAojI2Ntbe3Z7FYQ4cOHTduHD3m68mTJ3fs2EHPDtVTCh4r9KjXPcrIyJA0ImMaCIA+p+Zb7jCgDZ5n9fqXwfPvQuR4VmzFihXGxsbZ2dn379+Xfrb1888/nzNnTlNTk1AoNDExuXTpUmtra3l5+YwZMwwNDZ88eSJPAA8ePPD09CSETJgwoV2Rj49PUlLSs2fPmpqa0tLStLW1Z86cKWe/ZNfdunVru/O8s7OzdPX58+c7Ojrm5eUJhcLq6uq5c+fevn2bLkpISPDx8Xn+/LmckdAUP1boUa97JBaLHz9+fPHixVmzZpmYmMjTO3m+FwA9NSh+VEBdBk/i0r+o4N+Fx+O5u7urvSk5E0pLS8t2K7dt2+bg4MDn8ymKEgqFs2fPlhRdv36dECL91lRXCgoKFixY8MMPP7i6unZMKH19fekXzmj0A21VVVXdNttt3a1bt37//fdd1U1JSWEwGEVFRV1tEBIS4u7uLhQK5YmEUtKxQo+k9a5HoaGhSChBjXDLGwCULzk5uba2VtOaklNpaenmzZu3bNnC4XAIIUwmMysrS1JKD9VUVlbWbTsTJkw4fvz44sWL2Wx2x9JTp05paWlJFumblXIOd69I3X379k2aNMnFxaWrDSIjIwsKChISEuRpTVnHCj2SptweAagGEkoA6BxFUXFxcWPHjmWz2UZGRn5+fvfu3aOLQkJCWCwWPVYRIWTVqlW6uroMBqO+vp4QEhYWtm7durKyMgaDYW9vn5iYyOFwzMzMgoKCLCwsOByOh4fHtWvXetEUIeTs2bM9Ghm0FxITEymKmjt3bqelfD6fEGJgYKDcnT558kRHR8fGxqZP6woEgry8PFdXVxnbGBkZ+fj4JCQkUHKMA9BHxwo9UmKPAFQDCSUAdC4yMjIiImLjxo21tbUXL1589OiRt7d3TU0NISQxMVF6zJGkpKQtW7ZIFhMSEubMmWNnZ0dRVGlpaUhISGBgII/HCw0NraiouHnzpkgkmjFjxqNHj3raFCGEfh1BLBb3XcdPnz7t6OhIj3XaEX3T08vLS4l75PF4OTk5y5cvZ7FYSqkbERFhZGTEYrFsbGz8/Pzy8/Pp9dXV1QKB4MaNG9OmTaOT+7FjxyYlJbXLSyZOnPjkyZPCwsJu994Xxwo9Um6PAFQDCSUAdILP58fFxS1YsGDJkiWGhoYuLi779++vr68/cOBA7xpkMpn0xU4nJ6e9e/c2NzcfOnSoF+34+vo2NTX13fTura2tDx8+7HTI+pqamtTU1NDQUHd3966uYPVObGyshYVFdHS0Uup+8sknJ0+efPToUUtLS0pKSlVVlY+PT3FxMSGEftHY1NQ0JiamuLi4pqbGz89v9erVR44ckW5zzJgxhJDbt2/L3nUfHSv0SIk9AlAZJJQA0Ini4uKWlpbJkydL1kyZMoXFYkluVSti8uTJXC5XcgNdo9TW1lIU1ekFKnd399DQUD8/v+zsbG1tbWXt8cSJE+np6efOndPX11dK3ZEjR06cOFFPT4/FYrm5uR06dIjP5yclJRFC6Kc5nZ2dPTw8jI2NDQ0Nt2zZYmho2O7vBLr79NVoGfriWKFHyu0RgMow1R0AAGiixsZGQoienp70yqFDhzY3NyulfTabXVdXp5SmlOvly5fkjx/1dszMzJKTk52dnZW4u9TU1Li4uNzc3BEjRvRRXRcXFy0trQcPHhBC6ClG6edTaSwWa9SoUe3eMtHR0SF/HAoZlH6s0COi7B4BqAwSSgDoxNChQwkh7dLHxsZGKysrxRsXCoXKakrp6N/pTofCNjU1pQ+LsuzZs+fcuXM5OTntEnfl1hWLxWKxmE6S9PT0xowZc+fOHekNRCKRoaGh9BqBQED+OPxAYioAAB44SURBVBQyKPdYoUd90SMAlcEtbwDoxLhx4/T09H799VfJmmvXrgkEgtdff51eZDKZQqGwd43n5uZSFOXm5qZ4U0pnZmbGYDBevHjRsSgrK8vS0lIpe6EoKjw8/Pbt2xkZGT3NJrut+84770gv5ufnUxTl7u5OL/r7+9+6dau8vJxe5PF4lZWV7Uaoobtvbm4uOxJlHSv0qO96BKAySCgBoBMcDmfdunUnTpz44Ycfmpqabt++vXLlSgsLixUrVtAb2NvbNzQ0ZGRkCIXCurq6yspK6erGxsbV1dUVFRXNzc10sigWi58/fy4SiYqKisLCwqytrQMDA3vRVHZ2dp8OG8Tlcm1tbR8/ftxufWlpqbm5ub+/v/TKgIAAc3Pzmzdv9nQvd+7c2blz58GDB7W1taXn39u1a1e3LXdb98mTJ6mpqY2NjUKh8OrVq8uWLbO2tl65ciVdunbt2lGjRgUGBlZVVT179iw8PJzP52/YsEF6F3T36QxGRiTKOlbokYI9AtAESCgBoHNffPFFbGxsVFTUsGHDfHx8Ro8enZubq6urS5cGBwdPmzbtgw8+cHR03Lp1K33rzd3dnR4MaOXKlWZmZk5OTrNmzWpoaCCEvHz50sXFRUdHx9vb28HB4cKFC5In1XraVF/z9fUtLi6mRxyU6HTAP4FAUFtbm5mZ2Wk7eXl5Xl5eI0aMuHbtWmFhoYWFhaen58WLF7tqTc6Wu607c+bMTZs2WVlZcbncRYsWeXp65uXlmZiY0KVGRkaXLl2ysrJydXW1tLS8fv366dOn2416mJ+fb2lpOX78+G77qJRjhR4p2CMAjaCK6XhgsMLUi5pJ9f8u9HzZqtwjjfRq6sWSkhImkyljZjyJtrY2b2/v5ORkhaJUbcvdqq+v53A4u3btkicSTThW3RrwPaJh6kVQL1yhBABV6PRFBw3B5/PPnTtXUlJCv+hgb28fFRUVFRVFjwjYlba2toyMjObm5oCAAOXG03ctyyMyMtLV1TUkJESeSNR+rOQxsHtEUVR1dfXly5fpkf8B1AUJJQAMdg0NDTNnznRwcFi6dCm9JiIiYuHChQEBAZ2+n0HLzc09fvx4dnZ2V7Oq9FrftdytuLi4goKCM2fO0EMtyhOJeo9VtwZ8jzIzMy0tLb29vU+fPq3iSAD+RN2XSGEgwy1vzaTif5eIiAh6xrnRo0cfPXpUZfulFL61d+7cufDwcCXGo+EyMjJiY2NFIlEv6mrmsUKPOqXg9wKgUwwKU8tDn0lPT/f398dnTNMMnn8XBoORlpYmPVc4AOB7AX0Bt7wBAAAAQCFIKAEAAABAIUgoAQAAAEAhSCgBAAAAQCFMdQcAA196erq6Q4A/uXr1Khk0/y50ZwEAoE/hLW/oQ/TbxOqOAgAA/gRveYPSIaEEAOge/es7SC7rAgD0FJ6hBAAAAACFIKEEAAAAAIUgoQQAAAAAhSChBAAAAACFIKEEAAAAAIUgoQQAAAAAhSChBAAAAACFIKEEAAAAAIUgoQQAAAAAhSChBAAAAACFIKEEAAAAAIUgoQQAAAAAhSChBAAAAACFIKEEAAAAAIUgoQQAAAAAhSChBAAAAACFIKEEAAAAAIUgoQQAAAAAhSChBAAAAACFIKEEAAAAAIUgoQQAAAAAhSChBAAAAACFIKEEAAAAAIUgoQQAAAAAhSChBAAAAACFIKEEAAAAAIUgoQQAAAAAhSChBAAAAACFIKEEAAAAAIUgoQQAAAAAhSChBAAAAACFIKEEAAAAAIUgoQQAAAAAhTAoilJ3DAAAGufw4cPJyclisZhefPjwISHExsaGXhwyZMjf/va3xYsXqy0+AABNgoQSAKATRUVFEyZMkLFBYWHh+PHjVRYPAIAmQ0IJANC511577f79+50W2dvbl5SUqDgeAACNhWcoAQA699FHH2lra3dcr62t/emnn6o+HgAAjYUrlAAAnSsvL7e3t+/0JFlSUmJvb6/6kAAANBOuUAIAdM7W1nbSpEkMBkN6JYPBmDx5MrJJAABpSCgBALr08ccfa2lpSa/R0tL6+OOP1RUPAIBmwi1vAIAu1dbWWlhYSAYPIoQMGTKkurra3NxcjVEBAGgaXKEEAOiSmZmZj4+P5CKllpbWW2+9hWwSAKAdJJQAALJ89NFH0ndyPvroIzUGAwCgmXDLGwBAlqamJlNTU4FAQAjR1taura0dOnSouoMCANAsuEIJACCLgYHBzJkzmUwmk8mcNWsWskkAgI6QUAIAdGPJkiVtbW1tbW2YvBsAoFO45Q0A0I2XL18OGzaMoqj6+nodHR11hwMAoHGQUAIoQbuxrwGgn8JvIkDvMNUdAMAAERYW5u7uru4oBoWrV68mJCSkpaWpcqcFBQUMBmPChAmq3Km/vz8+VypDf67UHQVAf4UrlABKwGAw0tLSFi1apO5ABoX09HR/f38Vn7tEIhEhhMlU6R/h+Fypklo+VwADBq5QAgB0T8WpJABA/4K3vAEAAABAIUgoAQAAAEAhSCgBAAAAQCFIKAEAAABAIUgoAWBQOHPmjKGhYVZWlroDUZ3z589HRESIxeL58+dbW1tzOBxLS8t58+YVFRXJ34hYLI6Pj/fw8Gi3PioqysnJycDAgM1m29vbr1+/vqWlRc42ZdeNjo5m/Nm4ceOkqwuFwtjYWHt7exaLNXTo0HHjxlVUVBBCTp48uWPHjra2Nvl7BwDKgoQSAAaFwTYczBdffJGYmPjZZ5+JxeJLly4dOXKkoaHh8uXLfD5/6tSp1dXV8jRSUlIyderUtWvX8ni8dkU5OTmrV6+uqKior6+PjY1NSEhYuHChnLEpUpcQ4u/v/9133x0+fJjH4929e9fOzo7OR+fOncvhcKZPn97Y2Ch/awCgHBQAKIwQkpaWpu4oBgt6SHN1R9ElHo/n7u6ulKZ6/bnatm2bg4MDn8+nKEooFM6ePVtSdP36dUJITExMt40UFBQsWLDghx9+cHV1nTBhQrtSX19fkUgkWaQHy6yqqpInPNl1t27d+v3333dVNyUlhcFgFBUVdbVBSEiIu7u7UCiUJxJpGv65AtBwuEIJAKBMycnJtbW1agygtLR08+bNW7Zs4XA4hBAmkyl9o9/W1pYQUlZW1m07EyZMOH78+OLFi9lsdsfSU6dOaWlpSRaHDRtGCOl4IbNTitTdt2/fpEmTXFxcutogMjKyoKAAc94AqBgSSgAY+C5fvmxtbc1gML7++mtCyN69e3V1dblcbmZm5rvvvmtgYGBlZZWSkkJvnJiYyOFwzMzMgoKCLCwsOByOh4fHtWvX6NKQkBAWizV8+HB6cdWqVbq6ugwGo76+nhASFha2bt26srIyBoNhb29PCDl79qyBgUFMTIzKOpuYmEhR1Ny5czst5fP5hBADAwPl7vTJkyc6Ojo2NjZ9WlcgEOTl5bm6usrYxsjIyMfHJyEhgRpkDzkAqBcSSgAY+Ly8vK5cuSJZDA4OXrNmDZ/P19fXT0tLKysrs7W1Xb58uVAoJISEhIQEBgbyeLzQ0NCKioqbN2+KRKIZM2Y8evSIEJKYmCg9F2JSUtKWLVskiwkJCXPmzLGzs6MoqrS0lBBCvyMiFotV1tnTp087OjpyudxOS+lb3l5eXkrcI4/Hy8nJWb58OYvFUkrdiIgIIyMjFotlY2Pj5+eXn59Pr6+urhYIBDdu3Jg2bRqd648dOzYpKald7jhx4sQnT54UFhYq2C8AkB8SSgAYvDw8PAwMDExNTQMCAlpbW6uqqiRFTCZz7NixbDbbyclp7969zc3Nhw4d6sUufH19m5qaNm/erLyoZWltbX348KGdnV3HopqamtTU1NDQUHd3966uX/ZObGyshYVFdHS0Uup+8sknJ0+efPToUUtLS0pKSlVVlY+PT3FxMSGEfvnG1NQ0JiamuLi4pqbGz89v9erVR44ckW5zzJgxhJDbt28r1CsA6AkklAAAhL48Rl+h7Gjy5MlcLvfevXuqDao3amtrKYrq9PKku7t7aGion59fdna2tra2svZ44sSJ9PT0c+fO6evrK6XuyJEjJ06cqKenx2Kx3NzcDh06xOfzk5KSCCH005zOzs4eHh7GxsaGhoZbtmwxNDQ8cOCAdLN092tqahTtGwDIjanuAAAA+gE2m11XV6fuKLr38uVL8kfi1Y6ZmVlycrKzs7MSd5eamhoXF5ebmztixIg+quvi4qKlpfXgwQNCiIWFBSGEflyVxmKxRo0a1e4dIx0dHfLHoQAA1UBCCQDQDaFQ2NjYaGVlpe5AukfnUp0O7m1qajp06FAl7mvPnj3nzp3LycnR09Pru7pisVgsFtMpsp6e3pgxY+7cuSO9gUgkMjQ0lF4jEAjIH4cCAFQDt7wBALqRm5tLUZSbmxu9yGQyu7o5rnZmZmYMBuPFixcdi7KysiwtLZWyF4qiwsPDb9++nZGR0dNsstu677zzjvRifn4+RVHu7u70or+//61bt8rLy+lFHo9XWVnZbhQhuvvm5uY9CgwAFIGEEgCgE2Kx+Pnz5yKRqKioKCwszNraOjAwkC6yt7dvaGjIyMgQCoV1dXWVlZXSFY2NjaurqysqKpqbm4VCYXZ2tiqHDeJyuba2to8fP263vrS01Nzc3N/fX3plQECAubn5zZs3e7qXO3fu7Ny58+DBg9ra2tJzJO7atavblrut++TJk9TU1MbGRqFQePXq1WXLlllbW69cuZIuXbt27ahRowIDA6uqqp49exYeHs7n8zds2CC9C7r7MsaqBAClQ0IJAAPf119/PWXKFEJIeHj4vHnz9u7dGx8fTwgZP358eXn5wYMH161bRwiZOXNmSUkJXeXly5cuLi46Ojre3t4ODg4XLlyQPJgYHBw8bdq0Dz74wNHRcevWrfStVXd3d3pcoZUrV5qZmTk5Oc2aNauhoUH1nfX19S0uLqbHm5TodFBGgUBQW1ubmZnZaTt5eXleXl4jRoy4du1aYWGhhYWFp6fnxYsXu2pNzpa7rTtz5sxNmzZZWVlxudxFixZ5enrm5eWZmJjQpUZGRpcuXbKysnJ1dbW0tLx+/frp06fbjUyZn59vaWk5fvx42TsCAGVSzwQ9AAMLwdSLKqSCKfJWrFhhbGzcp7uQR+8+VyUlJUwmU8bshRJtbW3e3t7Jycm9ik49LXervr6ew+Hs2rWrpxUx9SKAInCFEgCgE52+19Iv2NvbR0VFRUVF0aM2dqWtrS0jI6O5uTkgIEC5AfRdy/KIjIx0dXUNCQlR/a4BBjMklABqsGzZMn19fQaDUVBQoO5YCCHk+PHjtra20g+0sVgsMzOzt95668svv3z+/Lm6A4SeiYiIWLhwYUBAQKdv59Byc3OPHz+enZ3d1Zw6vdZ3LXcrLi6uoKDgzJkzShxoEwDkgYQSQA2++eabgwcPqjuK//Pee++Vl5fb2dkZGhpSFCUWi2tra9PT021sbMLDw52dnX/99Vd1x6g6n3322aFDh168eGFjY3Ps2DF1h9NLMTExISEh27Zt62qD6dOnHz58WDIpuRL1XcuyZWZmvnr1Kjc318jISMW7BgCMQwkA7TEYjKFDh7711ltvvfWWr6+vv7+/r6/vgwcP2o32N1DFxsbGxsaqOwolePvtt99++211R6E68+bNmzdvnrqjABikcIUSQD0YDIa6Q5DL+++/HxgYWFtbu3//fnXHAgAAGgoJJYCKUBT15ZdfOjo6stlsQ0PDf/7zn9KlbW1tn3/+ubW1tY6Ozvjx4+kXTvfu3aurq8vlcjMzM999910DAwMrK6uUlBRJrZ9//vmNN97gcrkGBgYuLi5NTU1dNUUIOXv2bO8GRKTHX8zOzlZZqAAA0L8goQRQkc2bN4eHh69YsaKmpubp06fthmLesGHDzp074+Pjf//99zlz5nz44Ye//vprcHDwmjVr+Hy+vr5+WlpaWVmZra3t8uXL6WlaWltb586d+/777zc0NJSUlDg4ONAzznXaFPnjtWWxWNzTyOlB/iRzk6ggVAAA6GfUPW4RwEBAuhsvkMfjcbncGTNmSNbQV+9u3bpFURSfz+dyuQEBAZKN2Wx2cHAwRVEbN24khPD5fLooKSmJEFJaWkpR1G+//UYIOXXqlPSOZDTVLclLOR3RT1VqSKiDZ7zAbj9XoESD53MF0BfwUg6AKpSWlvJ4vOnTp3daev/+fR6PN27cOHpRR0dn+PDh9+7d67gli8UihNCX/Wxtbc3MzJYsWRIaGhoYGDh69OgeNSW/1tZWiqIMDAw0KtT09HRFOtVfXL16Vd0hDBY41AAKUXdGCzAQkO6uJJ05c4YQIj1xiPQVyl9++aXjd9PNzY3qcNmPHmzo7t279OJvv/02e/ZsJpPJYDD8/f15PJ6MprrV1RVKekbmt99+W0NCxaOW0Hfk+aYAQEd4hhJAFTgcDiHk1atXnZaampoSQuLj46W/nPJcL3F2ds7Kyqqurg4PD09LS9u1a1evm5Lh7NmzhJB3331Xo0Ltu9Oi5iC45a1C+EMFQBFIKAFUYdy4cUOGDPn55587LR05ciSHw+nprDnV1dV37twhhJiamm7btm3SpEl37tzpXVMyPH36ND4+3srKaunSpRoeKgAAqAsSSgBVMDU1fe+9944dO5acnNzU1FRUVHTgwAFJKYfD+fTTT1NSUvbu3dvU1NTW1vb48ePff/9ddpvV1dVBQUH37t0TCAS3bt2qrKx0c3OT0VR2dna3wwZRFNXS0iIWiymKqqurS0tL8/T01NLSysjIoJ+hVE2oAADQz6j7JgPAQEDkuDXZ3Ny8bNkyExMTPT09Ly+vzz//nBBiZWVVWFhIUdSrV6/Cw8Otra2ZTCadfRYXFyclJdGzIY8ZM6asrOzAgQN0Vjdq1KgHDx5UVFR4eHgYGRlpaWmNGDFi48aNIpGoq6Yoijpz5oy+vn50dHTH2E6ePDl+/Hgul8tisYYMGUL+mCznjTfeiIqKevbsmfTGKghVtsHzNq48nytQlsHzuQLoCwyKotSVywIMGAwGIy0tbdGiReoOZFBIT0/39/cfDOcufK5UafB8rgD6Am55AwAAAIBCkFACAAAAgEKQUAIADATnz5+PiIgQi8Xz58+3trbmcDiWlpbz5s0rKiqSvxGxWBwfH+/h4dFufVRUlJOTk4GBAZvNtre3X79+fUtLi5xtCoXCzz//3NbWlsViWVpa/uMf/+Dz+ZLS6Ohoxp9JxrpXJKqTJ0/u2LGDnm4UAFQACSUAQL/3xRdfJCYmfvbZZ2Kx+NKlS0eOHGloaLh8+TKfz586dWp1dbU8jZSUlEydOnXt2rU8Hq9dUU5OzurVqysqKurr62NjYxMSEhYuXChnbGFhYV9++WVsbOyzZ88OHz588ODBZcuWyd+13kU1d+5cDoczffr0xsZG+fcFAL2GhBIAoD0+n9/xepjam+rK9u3bU1NT09PT9fX1CSHu7u5eXl5cLtfGxiYmJubFixf//ve/u22ksLBww4YNK1eudHV17Viqp6e3YsUKY2NjfX39RYsWzZ8//+zZs48ePeq22fLy8v3793/88ccBAQH6+vpvvfVWSEjIkSNH7t69K9nm+++/l35XlJ75XfGoQkNDJ0yYMGvWLJFI1G2cAKAgJJQAAO0lJyfX1tZqWlOdKi0t3bx585YtW+jZmJhMZlZWlqTU1taWEFJWVtZtOxMmTDh+/PjixYvZbHbH0lOnTmlpaUkWhw0bRgjpeMmwo/z8fLFY/Oabb0rWzJw5kxBy7ty5busqHlVkZGRBQUFCQoI8+wIARSChBICBiaKouLi4sWPHstlsIyMjPz+/e/fu0UUhISEsFmv48OH04qpVq3R1dRkMRn19PSEkLCxs3bp1ZWVlDAbD3t4+MTGRw+GYmZkFBQVZWFhwOBwPD49r1671oilCyNmzZ7sdXr5HEhMTKYqaO3dup6X004r0mKBK9OTJEx0dHRsbm263pIc11dHRkawZM2YMIUT6CmXfRWVkZOTj45OQkIDBgAD6GhJKABiYIiMjIyIiNm7cWFtbe/HixUePHnl7e9fU1BBCEhMTpQd3TEpK2rJli2QxISFhzpw5dnZ2FEWVlpaGhIQEBgbyeLzQ0NCKioqbN2+KRKIZM2bQt1Z71BQhhH5NRCwWK6ubp0+fdnR0pEeV7+j69euEEC8vL2XtjhDC4/FycnKWL1/OYrG63fi1114jf04fTUxMCCF1dXWSNREREUZGRiwWy8bGxs/PL///tXd/IU29YRzAz2iaroFLamO2JEsKzCNaFhtLhgQmeWFEswWRXQSxLlSoC4uiWLUgZDdhCDJ2UzjL1uyiIYQd6qLJIjJZRAlpqZWJ/zZxbbrzu3hh7GfTtp0zT83v527nvHvO4+HgHt73vO/r8fCYVVlZ2ejoaH9/fxIxASB+KCgBIA3Nz89bLJajR4+ePHkyJyeHpum2traJiYnoHS8TIhaLSWdnUVHR3bt3fT6fzWZLIk5NTc3s7OyVK1eSS2OJubm5z58/79ix4/dTP378sNvtjY2NGo1muf7L5JjNZqVSeePGjXga0zRdXV3d2tra29sbCAS+f//ucDhEIlEoFCIN6uvrnzx58vXrV7/f39HR8eXLF51O5/V6+cqKdIgODAwkGhAAEoKCEgDSkNfr9fv95eXlkSP79u3LzMyMDFVzUV5eLpFIIgPoAhofH2dZNmb3pEajaWxsPHLkiMvlysjI4OuKDofjwYMHPT09ZAJQPOx2u16vP3XqVG5urlarffz4McuypJ+SoqitW7eWlZVJpdLMzEy1Wm2z2ebn51tbW/nKitwc0jMNAKkjFjoBAAD+kcVipFJp9EGZTObz+XiJv379+uhBW6EEAgGSzO+n5HK51WrdvXs3j5ez2+0Wi4VhmLy8vPi/lZOT09bWFvn47du3jo6O5SLQNL1u3bqPHz/ylRV5fZPcKABIHRSUAJCGZDIZRVFLysfp6WmVSsU9eCgU4isUR6Rairl89+bNm8lN4MudO3d6enp6e3uXlOmJIq9IVlZWxjwbDofD4XDMEjm5rILBIPX/WUEAkAooKAEgDRUXF0ul0tevX0eO9PX1BYPBvXv3ko9isTjyGl+iGIZhWVatVnMPxZFcLheJRDMzM7+fil48iCOWZZubm6emppxOp1jM9Vejvb29oKBAp9ORj4cOHYpeQsjj8bAsq9Fo+MqK3ByFQsExbQBYGd6hBIA0lJWVdf78eYfDce/evdnZ2YGBAaPRqFQqz549SxoUFhZOTk46nc5QKPTz58/h4eHor+fm5o6NjQ0NDfl8PlIshsPhqamphYWFd+/eNTU15efnnz59OolQLpeLx2WDJBLJ9u3bR0ZGlhwfHBxUKBTHjx+PPmgwGBQKxZs3bxK9yvv372/fvt3e3p6RkRG9R2JLS0s8kffv3z88PLywsDA0NHThwoVnz55ZrdbIXOzR0VG73T49PR0KhV69enXmzJn8/Hyj0cg9K4LcHJqmE/2rASAhKCgBID1dvXrVbDabTKZNmzbpdLpt27YxDLNhwwZy9ty5c5WVlSdOnNi1a9f169fJkKhGoyGLARmNRrlcXlRUdPjw4cnJSYqiAoEATdPZ2dkVFRU7d+58/vx5ZFg20VD8qqmp8Xq90btjUxQVc9nFYDA4Pj7e3d0dM47b7T5w4EBeXl5fX19/f79SqdRqtS9evFguWvyRZTJZaWlpdnb2nj17Pnz48PLly+jx7urq6suXL6tUKolEUldXp9Vq3W53ZMoOl6wIj8ezZcuWkpKSeBoDQPJYAOCMoqjOzk6hs1grOjs7V/l/F9nfbzWvSMTzXH369EksFi/ZvTCmxcXFiooKq9XKU3arEZmjiYmJrKyslpaWeBqv/nMFkE7QQwkA8GcxJ778DQoLC00mk8lk8vv9KzRbXFx0Op0+n89gMPCbQOoic3ft2rXS0tKGhgahEwFIfygoAQD+bRcvXtTr9QaDIebsHIJhmEePHrlcruX21Ela6iJzZLFY3r59+/TpUx6X4QSA5aCgBABYyaVLl2w228zMTEFBQVdXl9DpxHbz5s2GhoZbt24t1+DgwYP379+P7DnOo9RF5qK7u/vXr18Mw2zcuFHoXADWBCwbBACwErPZbDabhc7iz6qqqqqqqoTO4m9RW1tbW1srdBYAawh6KAEAAACAExSUAAAAAMAJCkoAAAAA4AQFJQAAAABwImLj22wAAFYgEonUarVKpRI6kTVhZGTE7XYfO3ZM6ERSrqurC8/VqiHPFX4TAZKDghKAB3q9XugUAIAHDx8+FDoFgH8SCkoAAAAA4ATvUAIAAAAAJygoAQAAAIATFJQAAAAAwAkKSgAAAADg5D8u3sgjd36crQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NUVA948AqBoc",
        "colab_type": "text"
      },
      "source": [
        "From the output, you can see that we have two types of input. input_1 is the input placeholder for the encoder, which is embedded and passed through lstm_1 layer, which basically is the encoder LSTM. There are three outputs from the lstm_1 layer: the output, the hidden layer and the cell state. However, only the cell state and the hidden state are passed to the decoder.\n",
        "\n",
        "Here the lstm_2 layer is the decoder LSTM. The input_2 contains the output sentences with <sos> token appended at the start. The input_2 is also passed through an embedding layer and is used as input to the decoder LSTM, lstm_2. Finally, the output from the decoder LSTM is passed through the dense layer to make predictions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vZzdglYorCE9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.callbacks import EarlyStopping\n",
        "es = EarlyStopping(monitor='val_loss', mode='min', verbose=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a8U38-qbo3P9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "outputId": "c3ad54ca-4c3a-48a6-b85c-865d1955e927"
      },
      "source": [
        "history = model.fit([encoder_input_sequences, decoder_input_sequences], decoder_targets_one_hot,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    epochs=20,\n",
        "    callbacks=[es],\n",
        "    validation_split=0.1,\n",
        ")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "282/282 [==============================] - 19s 67ms/step - loss: 2.1474 - accuracy: 0.7019 - val_loss: 2.1597 - val_accuracy: 0.6942\n",
            "Epoch 2/20\n",
            "282/282 [==============================] - 18s 65ms/step - loss: 1.5282 - accuracy: 0.7733 - val_loss: 1.9416 - val_accuracy: 0.7264\n",
            "Epoch 3/20\n",
            "282/282 [==============================] - 18s 64ms/step - loss: 1.3091 - accuracy: 0.8045 - val_loss: 1.7569 - val_accuracy: 0.7488\n",
            "Epoch 4/20\n",
            "282/282 [==============================] - 18s 64ms/step - loss: 1.1767 - accuracy: 0.8205 - val_loss: 1.6797 - val_accuracy: 0.7580\n",
            "Epoch 5/20\n",
            "282/282 [==============================] - 18s 64ms/step - loss: 1.0790 - accuracy: 0.8317 - val_loss: 1.6264 - val_accuracy: 0.7616\n",
            "Epoch 6/20\n",
            "282/282 [==============================] - 18s 64ms/step - loss: 0.9990 - accuracy: 0.8418 - val_loss: 1.5946 - val_accuracy: 0.7695\n",
            "Epoch 7/20\n",
            "282/282 [==============================] - 18s 65ms/step - loss: 0.9342 - accuracy: 0.8504 - val_loss: 1.5834 - val_accuracy: 0.7692\n",
            "Epoch 8/20\n",
            "282/282 [==============================] - 18s 65ms/step - loss: 0.8795 - accuracy: 0.8586 - val_loss: 1.5615 - val_accuracy: 0.7731\n",
            "Epoch 9/20\n",
            "282/282 [==============================] - 18s 65ms/step - loss: 0.8348 - accuracy: 0.8665 - val_loss: 1.5742 - val_accuracy: 0.7729\n",
            "Epoch 00009: early stopping\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qx1MYN2nxzEd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save('seq2seq_eng-fra.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4VJuggcCqthT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "ae847efe-6103-46d4-d1b2-7127ff5b6cfe"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "# %matplotlib inline\n",
        "plt.title('Model Loss')\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vLkAJ63xxD5q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "516a1322-8ef7-4b26-d910-7c7ac9cf45a0"
      },
      "source": [
        " "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xOMYDe0JyitN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "encoder_model = Model(encoder_inputs, encoder_states)\n",
        "model.compile(optimizer='rmsprop', loss='categorical_crossentropy')\n",
        "model.load_weights('seq2seq_eng-fra.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kIFayPLDwfTl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "decoder_state_input_h = Input(shape=(LSTM_NODES,))\n",
        "decoder_state_input_c = Input(shape=(LSTM_NODES,))\n",
        "decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]\n",
        "\n",
        "decoder_inputs_single = Input(shape=(1,))\n",
        "decoder_inputs_single_x = decoder_embedding(decoder_inputs_single)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MgCeNWUOwpjW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "decoder_outputs, h, c = decoder_lstm(decoder_inputs_single_x, initial_state=decoder_states_inputs)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3_5YdURtwqMf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "decoder_states = [h, c]\n",
        "decoder_outputs = decoder_dense(decoder_outputs)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ZhPgdzkwxNL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "decoder_model = Model(\n",
        "    [decoder_inputs_single] + decoder_states_inputs,\n",
        "    [decoder_outputs] + decoder_states\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sKUBy8F-wxwM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.utils import plot_model\n",
        "plot_model(decoder_model, to_file='model_plot_dec.png', show_shapes=True, show_layer_names=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yLK0AsmV9Isl",
        "colab_type": "text"
      },
      "source": [
        "# Making Predictions\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WjdGe0_DAfj-",
        "colab_type": "text"
      },
      "source": [
        "we want our output to be a sequence of words in the French language. To do so, we need to convert the integers back to words. We will create new dictionaries for both inputs and outputs where the keys will be the integers and the corresponding values will be the words."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1jBqwVmqw0N9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "idx2word_input = {v:k for k, v in word2idx_inputs.items()}\n",
        "idx2word_target = {v:k for k, v in word2idx_outputs.items()}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dn3v4cS_AjqZ",
        "colab_type": "text"
      },
      "source": [
        "The method will accept an input-padded sequence English sentence (in the integer form) and will return the translated French sentence. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o9Wp-kQKw3Pg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def translate_sentence(input_seq):\n",
        "    states_value = encoder_model.predict(input_seq)\n",
        "    target_seq = np.zeros((1, 1))\n",
        "    target_seq[0, 0] = word2idx_outputs['<sos>']\n",
        "    eos = word2idx_outputs['<eos>']\n",
        "    output_sentence = []\n",
        "\n",
        "    for _ in range(max_out_len):\n",
        "        output_tokens, h, c = decoder_model.predict([target_seq] + states_value)\n",
        "        idx = np.argmax(output_tokens[0, 0, :])\n",
        "\n",
        "        if eos == idx:\n",
        "            break\n",
        "\n",
        "        word = ''\n",
        "\n",
        "        if idx > 0:\n",
        "            word = idx2word_target[idx]\n",
        "            output_sentence.append(word)\n",
        "\n",
        "        target_seq[0, 0] = idx\n",
        "        states_value = [h, c]\n",
        "\n",
        "    return ' '.join(output_sentence)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5gIbCMeVxcn5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "a88345a4-490d-45c4-a9fc-5b86b694367b"
      },
      "source": [
        "i = np.random.choice(len(input_sentences))\n",
        "input_seq = encoder_input_sequences[i:i+1]\n",
        "translation = translate_sentence(input_seq)\n",
        "print('Input Language : ', input_sentences[i])\n",
        "print('Actual translation : ', output_sentences[i])\n",
        "print('French translation : ', translation)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Input Language :  I was sent home.\n",
            "Actual translation :  J'ai été renvoyé à la maison. <eos>\n",
            "French translation :  j'ai été chez maison.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2YntiiVPGmqe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}