{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "text_sum_no_generator.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python2",
      "display_name": "Python 2"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/gmihaila/deep_learning_toolbox/blob/master/text_sum_no_generator.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "SaVEJaZd4X-Y",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Download Data"
      ]
    },
    {
      "metadata": {
        "id": "UtjoTtBS4pbz",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "link for data: http://research.signalmedia.co/newsir16/signalmedia-1m.jsonl.gz"
      ]
    },
    {
      "metadata": {
        "id": "c6_3xtVm4bqk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        },
        "outputId": "d1510ce7-c7e9-4d88-9e4e-c6a97d0b6715"
      },
      "cell_type": "code",
      "source": [
        "!wget http://research.signalmedia.co/newsir16/signalmedia-1m.jsonl.gz\n",
        "\n",
        "\n",
        "print(\"\\nFinished!\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2018-05-28 15:52:22--  http://research.signalmedia.co/newsir16/signalmedia-1m.jsonl.gz\n",
            "Resolving research.signalmedia.co (research.signalmedia.co)... 52.218.105.28\n",
            "Connecting to research.signalmedia.co (research.signalmedia.co)|52.218.105.28|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1066404716 (1017M) [application/x-gzip]\n",
            "Saving to: ‘signalmedia-1m.jsonl.gz’\n",
            "\n",
            "signalmedia-1m.json  87%[================>   ] 890.29M  25.7MB/s    eta 8s     "
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "signalmedia-1m.json 100%[===================>]   1017M  29.7MB/s    in 53s     \n",
            "\n",
            "2018-05-28 15:53:16 (19.0 MB/s) - ‘signalmedia-1m.jsonl.gz’ saved [1066404716/1066404716]\n",
            "\n",
            "\n",
            "Finished!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "eQp3ngapCJUF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Parse json and create indexes"
      ]
    },
    {
      "metadata": {
        "id": "IbVJ__gmEbxW",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Imports"
      ]
    },
    {
      "metadata": {
        "id": "qnoxcyTPdCls",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!pip install gensim\n",
        "!apt-get -qq install -y graphviz && pip install -q pydot"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "uSd5QG5H1qoR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e14e4fc2-20b2-4b03-c35f-21f5052a1041"
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "import sys\n",
        "import json\n",
        "import gzip\n",
        "import string\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.text import text_to_word_sequence\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.utils import to_categorical\n",
        "from keras.utils.vis_utils import plot_model\n",
        "from keras import callbacks\n",
        "\n",
        "from keras.layers import Embedding\n",
        "from keras.layers import LSTM\n",
        "from keras.layers import RepeatVector\n",
        "from keras.layers import TimeDistributed\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Input\n",
        "from keras.models import Sequential\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "\n",
        "\n",
        "import numpy as np\n",
        "import cPickle as pickle\n",
        "\n",
        "import gensim\n",
        "from gensim.models import KeyedVectors\n",
        "\n",
        "from unicodedata import normalize\n",
        "\n",
        "import pydot\n",
        "from IPython.display import Image\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from nltk.translate.bleu_score import corpus_bleu\n",
        "\n",
        "import random\n",
        "\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "HTSj-0aUFHMd",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Functions"
      ]
    },
    {
      "metadata": {
        "id": "ZbZLqG3tiumL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# clean text strings\n",
        "def CleanText(text):\n",
        "  # normalize unicode characters\n",
        "#   junk_words = ['\\r', '\\t', '-', '/', '!','\"','#','$','%','&','(',')','*', '+']\n",
        "  junk_words = list(string.punctuation) + ['\\r', '\\t', 'ht', '2']\n",
        "  text = normalize('NFD', text).encode('ascii', 'ignore')\n",
        "  text.replace('\\r', '')\n",
        "  \n",
        "  # split a loaded document into sentences\n",
        "  text = text.strip().split('\\n')\n",
        "  text = \" \".join([line for line in text])\n",
        "  \n",
        "  # remove junk words\n",
        "  for junk_word in junk_words:\n",
        "    text = text.translate(None, junk_word)\n",
        "  # lower case\n",
        "  text = text.lower()\n",
        "  # split on white space\n",
        "  text = text.split(' ')\n",
        "  # remove null strings\n",
        "  text = filter(None, text)\n",
        "  text = [word for word in text if len(word)< 25]\n",
        "  return text\n",
        "\n",
        "\n",
        "# fit a tokenizer\n",
        "def MyTokenizer(data, n_words):\n",
        "  # fit tokenizer\n",
        "  tokenizer = Tokenizer(num_words=n_words+1, filters=string.punctuation, lower=True, split=' ', char_level=False, oov_token='<unk>')\n",
        "  # feed each sequence\n",
        "  [tokenizer.fit_on_texts([x[0]+x[1]]) for x in data]\n",
        "  \n",
        "  tokenizer.word_index = {e:i for e,i in tokenizer.word_index.items() if i <= n_words}\n",
        "  tokenizer.word_index[tokenizer.oov_token] = n_words + 1\n",
        "\n",
        "  return tokenizer\n",
        "\n",
        "\n",
        "# encode and pad sequences\n",
        "def EncSeq(data, tokenizer, n_words, n_input, n_output, start_f=False, end_f=False):\n",
        "  # input and output\n",
        "  x, y = [], []\n",
        "  s_flag, e_flag = 0, 0\n",
        "  \n",
        "  if start_f:\n",
        "    s_flag = 1\n",
        "  if end_f:\n",
        "    e_flag = 1\n",
        "    \n",
        "  for content,title in data:\n",
        "    sequence = tokenizer.texts_to_sequences([content])\n",
        "    x.append(sequence[0])\n",
        "    \n",
        "    sequence = tokenizer.texts_to_sequences([title])\n",
        "    y.append([n_words+2] * s_flag + sequence[0] + [n_words+2] * e_flag)\n",
        "    del sequence\n",
        "    \n",
        "  x = pad_sequences(x, maxlen=n_input, padding='post')\n",
        "  y = pad_sequences(y, maxlen=(n_output + s_flag + e_flag), padding='post')\n",
        "  \n",
        "  return x,y\n",
        "\n",
        "\n",
        "# one hot encode target sequence\n",
        "def EncOutput(sequences, n_words):\n",
        "  output = []\n",
        "  for sequence in sequences:\n",
        "    seq_output = []\n",
        "    \n",
        "    for index in sequence:\n",
        "      encoded = to_categorical(index, num_classes=n_words+3).astype(int)\n",
        "      seq_output.append(encoded)\n",
        "      \n",
        "    output.append(seq_output)\n",
        "    \n",
        "  return np.array(output)\n",
        "\n",
        "\n",
        "# index word dictionary\n",
        "def IndexWord(tokenizer):\n",
        "  index_word = {}\n",
        "  for word, index in tokenizer.word_index.items():\n",
        "    index_word[index] = word\n",
        "    \n",
        "  return index_word\n",
        "\n",
        "# define model\n",
        "def define_model(n_words, n_input, n_output, n_units):\n",
        "  model = Sequential()\n",
        "  model.add(Embedding(input_dim=(n_words + 2), output_dim=n_embed, input_length=n_input, mask_zero=True, name='EMBEDDING'))\n",
        "  model.add(LSTM(units=n_units, dropout=0.0, recurrent_dropout=0.0,return_sequences=False, return_state=False, name='ENCODER_LSTM'))\n",
        "  model.add(RepeatVector(n=n_output, name='REPEAT_VECT'))\n",
        "  model.add(LSTM(units=n_units, dropout=0.0, recurrent_dropout=0.0,return_sequences=True, return_state=False, name='DECODER_LSTM'))\n",
        "  model.add(TimeDistributed(Dense(units=(n_words + 3), activation='softmax', name='DECODER_OUT')))\n",
        "  \n",
        "  return model\n",
        "\n",
        "\n",
        "# generate target given source sequence\n",
        "def Predictions(model, test_data, index_word):\n",
        "  pred_sequence = model.predict(test_data, verbose=0)\n",
        "  word_predictions = []\n",
        "  for sequence in pred_sequence:\n",
        "    word_seq = []\n",
        "    for prediction in sequence:\n",
        "      index = np.argmax(prediction)\n",
        "      word_seq.append(index_word[index])\n",
        "    word_predictions.append(word_seq)  \n",
        "\n",
        "  return word_predictions\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ZQBz7Ktj-Yi1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Parse Data"
      ]
    },
    {
      "metadata": {
        "id": "mTMERv2t9OgC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "outputId": "9d2ca4b6-976d-4d90-c453-d7cd9693110f"
      },
      "cell_type": "code",
      "source": [
        "dataset = []    # whole data\n",
        "\n",
        "n_input = 10    # number of tokens of article\n",
        "n_output = 5    # number of tokens in headline\n",
        "\n",
        "n_articles = 100# number of articles (data length)\n",
        "\n",
        "n_words = 20    # number of top most frequent words to use\n",
        "\n",
        "# open zip and start reading\n",
        "with gzip.open(\"signalmedia-1m.jsonl.gz\", \"rb\") as f:\n",
        "  print ('Open JSON to read...\\n')\n",
        "  \n",
        "  for index, line in enumerate(f):\n",
        "    \n",
        "    if (index+1)%100000 == 0:\n",
        "      print('Another 100,000 articles.\\n')\n",
        "      \n",
        "    # convert from JSON\n",
        "    entry = json.loads(line.decode(\"ascii\"))\n",
        "    \n",
        "    ID = entry['id']\n",
        "    content = entry['content']\n",
        "    title = entry['title']\n",
        "    media = entry['media-type']\n",
        "    source = entry['source']\n",
        "    published = entry['published']\n",
        "    \n",
        "    # clear entry variable\n",
        "    del entry\n",
        "    \n",
        "    # clean data\n",
        "    content = CleanText(content)\n",
        "    title = CleanText(title)\n",
        "    \n",
        "    # append data\n",
        "    dataset.append([content[:n_input], title[:n_output]])\n",
        "    \n",
        "    # clean memory\n",
        "    del content, title, media, source, published\n",
        "    \n",
        "    if index+1 == n_articles:\n",
        "      break\n",
        "    \n",
        "print('Dataset: %s'%len(dataset))\n",
        "print ('Articles: %s'%n_articles)\n",
        "  \n",
        "print ('\\nFinsihed!')\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Open JSON to read...\n",
            "\n",
            "Dataset: 100\n",
            "Articles: 100\n",
            "\n",
            "Finsihed!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "LLCJo0V_IOiQ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Tokenizing Data"
      ]
    },
    {
      "metadata": {
        "id": "l4tyjwk3IRbR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "1461cb5e-c645-4888-edac-253e6a4b4828"
      },
      "cell_type": "code",
      "source": [
        "# random shuffle\n",
        "np.random.shuffle(dataset)\n",
        "\n",
        "# split into train/test\n",
        "train_data, test_data = dataset[:9], dataset[9:]\n",
        "print('Train size: %s'%len(train_data))\n",
        "print('Test size: %s'%len(test_data))\n",
        "\n",
        "# create the tokenizer for words\n",
        "tokenizer = MyTokenizer(data=dataset, n_words=n_words)\n",
        "\n",
        "# create training sequences\n",
        "x_train, y_train = EncSeq(data=train_data, tokenizer=tokenizer, n_words=n_words, n_input=n_input, n_output=n_output, start_f=False, end_f=False)\n",
        "y_train = EncOutput(sequences=y_train, n_words=n_words)\n",
        "\n",
        "# create testing sequences\n",
        "x_test, y_test = EncSeq(data=test_data, tokenizer=tokenizer, n_words=n_words, n_input=n_input, n_output=n_output, start_f=False, end_f=False)\n",
        "y_test = [data[1] for data in test_data]\n",
        "\n",
        "\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train size: 9\n",
            "Test size: 91\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "A7AMzF0ybrx6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Model"
      ]
    },
    {
      "metadata": {
        "id": "qX9Ovtdsbtcw",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# parameters\n",
        "n_embed = 32\n",
        "enc_lstm = 1\n",
        "n_units = 32\n",
        "n_epochs = 2\n",
        "n_batch = 32\n",
        "\n",
        "# define model\n",
        "model = define_model(n_words=n_words, n_input=n_input, n_output=n_output, n_units=n_units)\n",
        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NUwXLYnEoMbr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "outputId": "de628d80-2ed0-40c6-a927-b8e29e63bd33"
      },
      "cell_type": "code",
      "source": [
        "# summarize defined model\n",
        "print(model.summary())"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "EMBEDDING (Embedding)        (None, 10, 32)            704       \n",
            "_________________________________________________________________\n",
            "ENCODER_LSTM (LSTM)          (None, 32)                8320      \n",
            "_________________________________________________________________\n",
            "REPEAT_VECT (RepeatVector)   (None, 5, 32)             0         \n",
            "_________________________________________________________________\n",
            "DECODER_LSTM (LSTM)          (None, 5, 32)             8320      \n",
            "_________________________________________________________________\n",
            "time_distributed_1 (TimeDist (None, 5, 23)             759       \n",
            "=================================================================\n",
            "Total params: 18,103\n",
            "Trainable params: 18,103\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "yEMkVvJdpDnf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 643
        },
        "outputId": "ee57bd8f-feed-4ec6-9c95-263b07a03ee3"
      },
      "cell_type": "code",
      "source": [
        "# plot model\n",
        "plot_model(model, to_file='model.png', show_shapes=True)\n",
        "Image('model.png')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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EzY9FGBERUTtUWFiIwMBApKWlISkpCUVFRTh8+DAcHR0RGhqKlJQUAMCCBQsg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\n+H+VJB0nwNDPWgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "metadata": {
        "id": "1a_wXTngse35",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "outputId": "7eff1e73-ed1e-4df6-e3bf-bbcd2088411c"
      },
      "cell_type": "code",
      "source": [
        "# train with checkpoints\n",
        "checkpoint = ModelCheckpoint('model.h5', monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n",
        "history = model.fit(x_train, y_train, epochs=n_epochs, batch_size=n_batch, validation_split=0.2, callbacks=[checkpoint], verbose=2)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 7 samples, validate on 2 samples\n",
            "Epoch 1/2\n",
            " - 1s - loss: 3.1432 - acc: 0.0286 - val_loss: 3.1373 - val_acc: 0.0000e+00\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 3.13730, saving model to model.h5\n",
            "Epoch 2/2\n",
            " - 0s - loss: 3.1377 - acc: 0.0286 - val_loss: 3.1320 - val_acc: 0.3000\n",
            "\n",
            "Epoch 00002: val_loss improved from 3.13730 to 3.13200, saving model to model.h5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "cNkEp1W3wHjV",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# save model\n",
        "model.save('final_model.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SFJcnMSfwTey",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 734
        },
        "outputId": "1351faac-c47c-4d2f-9256-4201be0128bb"
      },
      "cell_type": "code",
      "source": [
        "# summarize history for loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# summarize history for accuracy\n",
        "plt.plot(history.history['acc'])\n",
        "plt.plot(history.history['val_acc'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WIQx+IiKP8frB0+3bt6OzsxP79u2b8LjzWvCOz73+xRdfxPPPP48DBw64HwsODgYAjIyM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\nIiIS4FT2IiIiAe7/AdxD8MAAI+AHAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f584245d8d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ycT0GmB1LpF1U6iLSpTQ129iQn8V7x7YBMDn6GmYPnIavl6/JyUTaT6UuIl1G\nflUBadkZFNWVEO7fk4UJyQzqEWt2LBGnUamLiMezNdt4K/8d3j32AQYGk/qN54a46Vqdi8dRqYuI\nRys4U8hrB9I5XVdML78wFibMY3BonNmxRFxCpS4iHsnmsPP6V2tZl70ZA4OJ/a7mxriZdNPqXDyY\nSl1EPE7BmULSsjM4VVtET79QFiYkM0Src+kCVOoi4jFsDjub8t9l87GtOAwHUwdNYFqfJPy8u5kd\nTaRDqNRFxCMcqz5O2oEMTtaeJswvlIXx8xg/9HJdNlS6FJW6iHRqdoedTUe3kFXwPg7Dwfg+V3Dz\noOvx8/YzO5pIh1Opi0inVVh9krTsdE7UnCK0Ww8WJswjPmyw2bFETKNSF5FOp9nRzKaC99h0dAsO\nw0Fin3HcPGgW/lqdSxenUheRTuV49UnSsjM4XnOSHt1CuC1+LsN6DjU7lohbUKmLSKfQ7Ghmc8H7\nbDy6hWajmat7j+WWwbPw9/Y3O5qI22hTqRuGgcVicXUWEZFzOlFzirTsDAqrT9CjWwi3xs9heM94\ns2OJuJ02lfqkSZO48cYbmTt3LtHR0a7OJCICfL06f+fYVt7Of5dmo5kro8YwZ/BsAny0Ohc5lzaV\n+qpVq8jKymLJkiV4e3tzyy23MG3aNHx9dbpFEXGNkzWnScvO4Fj1cUJ8g7k1fi4jeiWYHUvErVkM\nwzB+yA4FBQU8+uij5OXlkZKSwr333ku3buadrcnZJ5YIDw/WySraSTNsv648w2ZHM1uOfchb+Zux\nG81cETWauYNnE+AT8IOfqyvP0Vk0w/Zz9gzDw4PPu83a1if59NNPefTRR7nzzjsZNWoUr7/+Ot27\nd+fBBx90SkgRkVO1RTz3+d9Yd2QjAT4B3DPyRywaNv+iCl2kK2rT4fekpCT69u1LcnIyv/3tb/Hx\n8QEgLi6Od99916UBRcTzOQwHW459yIb8zdgddsZGjmLekBsIVJmL/CBtKvX//u//xjAMBgwYAMCB\nAwcYNmwYAK+//rrLwomI5ztdW8zy7Azyzxwj2DeIBUPncGn4cLNjiXRKbSr1zMxMiouLefrppwF4\n6aWX6NevH4888oj+1E1ELorDcPBe4Tb+eSQLu8POmMjLmDfkRoJ8As2OJtJptanUd+7cycqVK1tu\nv/DCCyxYsMBloUTEsxXVlbA8O4MjVQUE+wSRMuxmLou4xOxYIp1em0rdZrPR1NTU8idstbW12O12\nlwYTEc/jMBxsLfyI9Uc2YXPYGR1xKclDbiLIV6tzEWdoU6mnpKQwc+ZMRowYgcPhYO/evdx///2u\nziYiHqS4roS07FUcqTpKkE9SSQt8AAAgAElEQVQgi4alMCpipNmxRDxKm0p93rx5JCYmsnfvXiwW\nC48++ihBQUGuziYiHsBhOPjg+Mesy9uIzWHj8vBLmD/0ZoJ99TNExNnafEGXuro6wsLCADhy5AhP\nPvkkGzdudFkwEen8SurKWJ6TweHKfAJ9AkhNSGZ05KVmxxLxWG0q9SeffJLt27dTWlpKTEwMhYWF\n/PjHP3Z1NhHppByGgw+P72Bd3ts0OWxcFj6C+UNvprvv+c+EJSLt16ZS37t3Lxs3biQ1NZW0tDT2\n7dvHO++84+psItIJldaXsTx7FbmVRwj0DuC2hHmMjrhUf/4q0gHaVOrffOrdZrNhGAYjRozg97//\nvUuDiUjn4jAcfHTiE97Me5um5iZG9hpOytBbCOmm1blIR2lTqcfGxrJixQrGjBnDHXfcQWxsLNXV\nOsG/iHytrL6c5TmrOVRxmABvfxYMS2Fs5OVanYt0sDaV+m9+8xuqqqro3r07b731FmVlZdx9990X\n3G/ZsmXs2bMHi8XCkiVLGDnyX3++0tjYyOOPP05ubi6ZmZnA1ye5efDBBxk8eDAAQ4YM4bHHHruY\nr0tEOoBhGHx08hPePPwWjc1NXNIrgQVD5xDSrbvZ0US6pDaV+rJly/j1r38NwOzZs9v0xLt27aKg\noID09HTy8vJYsmQJ6enpLdv/8Ic/kJCQQG5u7ln7jRs3jj/96U9tzS8iJimrr+D1nNXkVOTi7+3P\nooT5jIsapdW5iInaVOpeXl7s2LGDUaNGtVyhDcBqPf+VW3fs2MGUKVOAr6/mVlVVRU1NTcvftz/8\n8MNUVlayfv369uQXkQ5mGAYfn9xF5uENNDQ3MqJnPAvi59CjW4jZ0US6vDaV+qpVq3j11VcxDKPl\nPovFQnZ29nn3KS0tZfjwf11pKSwsjJKSkpZSDwoKorKy8nv7HT58mHvuuYeqqiruv/9+EhMTW80W\nGhqAt7dXW76MNmvtAvTSNpph+7njDEvrynnp0+XsOZ1NgI8/945exMQBV7r16twd59jZaIbt11Ez\nbFOp7969u90v9O1fCM5nwIAB3H///cyYMYPCwkIWLVrE5s2bWz59fy4VFXXtzvZt4eHBlJToQ4Dt\noRm2n7vN0DAMdpz6lDW5G2hobmBYz6HcOnQOoX49KC2tMTveebnbHDsjzbD9nD3D1n5BaFOpv/ji\ni+e8/8EHHzzvPhEREZSWlrbcLi4uJjw8vNXXiYyMZObMmQDExMTQq1cvioqKiI6ObktMEXGBioZK\nXs9Zw4Hyg/h5+XFb/Dyu6j3GrVfnIl3V+d8U/xYvL6+W/zkcDnbu3HnBP2lLTEwkKysLgP379xMR\nEXHB88WvX7+eV155BYCSkhLKysqIjIxsS0QRcTLDMNhx8lOe2vU8B8oPkhA2hP+44qdc3WesCl3E\nTbVppf7dK7I1NzfzwAMPtLrPqFGjGD58OCkpKVgsFpYuXUpmZibBwcEkJSWxePFiTp8+TX5+Pqmp\nqSQnJzN58mQeeeQRtmzZgs1m44knnmj10LuIuEZlYxWv56xhf1kOfl7duDV+Dlf3HqcyF3Fzbb6g\ny7fZ7XaOHTt2wcc98sgjZ92Oj49v+ff5/mztv/7rvy4mkog4gWEY7Dr9Oaty11Nvryc+dDC3Jcwl\nzC/U7Ggi0gZtKvWJEyee9Rt6VVUVN998s8tCiUjHq2ys4o2cTPaVZdPNy5eUobcwvs8VWp2LdCJt\nKvXXX3+95d8Wi4WgoCC6d9cZo0Q8gWEYfFr0BasOraPOXs+Q0EEsjJ9LT/8ws6OJyA/Upg/K1dfX\ns3LlSvr27UufPn14+umnv3cmOBHpfKoaq3lp72u8emAldqOZ+UNu5oHLfqJCF+mk2lTqv/nNb5g4\ncWLL7Tlz5vDb3/7WZaFExLUMw+Cz01/w1M7n+Kp0P4N7DOTX437KhH5XYbW06ceCiLihNh1+b25u\nZsyYMS23x4wZ06aTyYiI+znTVM3Kg2+yp2QfvlYf5g25kQl9VeYinqBNpR4cHMzrr7/OFVdcgcPh\nYNu2bQQGBro6m4g4kWEYfF68h/RDa6m11TGoRywL45MJD+hpdjQRcZI2lfrTTz/Nc889xxtvvAF8\n/TfoTz/9tEuDiYjzVDfVsPLgm3xZshcfqw9zB9/AxH5Xa3Uu4mHaVOphYWHceeedDBgwAIADBw4Q\nFqYP0oh0Bp8Xf0X6wTepsdUSFzKAhQnJRAT0MjuWiLhAm0r9j3/8I8XFxS2r85deeol+/fp97+Qy\nIuI+appqST/0Jp8Xf4WP1Yc5g2dzbb9Erc5FPFibSn3nzp2sXLmy5fYLL7zAggULXBZKRNrni+K9\nrDyYSY2tloEh/VmYkExkQOsXVBKRzq9NpW6z2Whqamo5D3ttbS12u92lwUTkh6ux1ZJxcC27i/fg\nY/XmlkGzmBQ9XqtzkS6iTaWekpLCzJkzGTFiBA6Hg71793L77be7OpuI/AB7SvbxxsFMqptqiO0e\nQ2pCMpGBEWbHEpEO1KZSnzdvHgMGDKCiogKLxcLkyZP5+9//zo9+9CMXxxORC6m11bHq0Do+LfoC\nb6s3N8XN5LqYCVqdi3RBbSr1p556io8++ojS0lJiYmIoLCzkxz/+sauzicgFfFWynzcOZnKmqZr+\n3aNZlJBMVGCk2bFExCRtKvWvvvqKjRs3kpqaSlpaGvv27eOdd95xdTYROY86Wx2rctez6/TneFu8\nuDFuBtdFT8DL6mV2NBExUZtK/ZsPyNlsNgzDYMSIEfz+9793aTARObe9pQd4I2cNVU3VxAT3IzUh\nmT5BUWbHEhE30KZSj42NZcWKFYwZM4Y77riD2NhYqqurXZ1NRL6lzlbP6tz17Dy9Gy+LF7MHTicp\nZqJW5yLSok2l/pvf/Iaqqiq6d+/OW2+9RVlZGXfffbers4nI/9lflsPrOWuobKwiJrgvqQnztToX\nke9pU6lbLBZ69OgBwOzZs10aSET+pd5ez5rcDew49SleFi9mxU5jav9rtToXkXNqU6mLSMc7UHaQ\nFTmrqWysol9QHxYNm0/foN5mxxIRN6ZSF3Ez9fYG/uvTdbx3ZDtWi5XrY5OY1n+yVucickEqdRE3\nkl1+iBXZq6lorKRvUG9SE+YTHdzH7Fgi0kmo1EXcQIO9gczDb7H95E6sFitzh8/kmvDxeFv1f1ER\naTv9xBAxWU55LityVlPeUEGfwChShyUzemACJSX6s1ER+WFU6iImabA3sjbvbbad2IHVYmX6gOuY\nMeA6rc5F5KLpp4eICQ5VHGZ59irKGiroHRhJakIy/btHmx1LRDo5lbpIB2psbmJd3tt8cPxjLFiY\n2n8SM2OT8NHqXEScQD9JRDpIbkUey7NXUdpQTlRgJIu0OhcRJ1Opi7hYY3MT6/M2svX4dixYSIq5\nlutjk/Dx8jE7moh4GJW6iAsdrswnLTuD0voyIgMiSE1IJjYkxuxYIuKhVOoiLtDU3MT6I5vYWrgd\ngCkxE7k+diq+Wp2LiAup1EWcLK/yKMuzMyiuLyUioBepCfMZGNLf7Fgi0gWo1EWcpKnZxj+PbOL9\nwo8AuC56ArMGTtPqXEQ6jEpdxAmOVBWQlp1OcV0p4f49SU2YT1yPAWbHEpEuRqUu0g62Zhsb8jez\n5diHAEyKHs8NA6fj6+VrcjIR6YpU6iIXKb/qGGnZGRTVFdPLvyepCckM6hFrdiwR6cJcWurLli1j\nz549WCwWlixZwsiRI1u2NTY28vjjj5Obm0tmZmab9hFxB7ZmG2/lv8O7xz7AwODafoncEDeDblqd\ni4jJXFbqu3btoqCggPT0dPLy8liyZAnp6ekt2//whz+QkJBAbm5um/cRMVvBmUJey87gdG0RvfzC\nWJgwj8GhcWbHEhEBXFjqO3bsYMqUKQDExcVRVVVFTU0NQUFBADz88MNUVlayfv36Nu8jYhabw87G\n/Hd559hWHIaDCX2v5sa4Gfh5dzM7mohIC5eVemlpKcOHD2+5HRYWRklJSUtBBwUFUVlZ+YP2ETHD\nsTPHScvO4GTtaXr6hbIwYR5DQgeZHUtE5Hs67INyhmG4ZJ/Q0AC8vb0uJtJ5hYcHO/X5uiJPmKG9\n2c7qA2+zNjsLh+FgatwEFl56M34+fh3y+p4wQ3egObafZth+HTVDl5V6REQEpaWlLbeLi4sJDw93\n+j4VFXXtC/od4eHBlJRUO/U5uxpPmOGx6uOkHfh6dR7arQcLE+YRHzaY6kob1dhc/vqeMEN3oDm2\nn2bYfs6eYWu/IFid9irfkZiYSFZWFgD79+8nIiLigofRL2YfEWeyO+xsOLKZ//zsL5ysPU1inyv4\n9RU/JT5ssNnRREQuyGUr9VGjRjF8+HBSUlKwWCwsXbqUzMxMgoODSUpKYvHixZw+fZr8/HxSU1NJ\nTk5m9uzZ39tHpKMcrz7Ja9npnKg5RWi3HtyWMJeEsCFmxxIRaTOLcTFvdrsRZx8W0qGm9utsM2x2\nNJNV8B4bj27BYTi4uvc4bhk8C3/vjnnv/Fw62wzdlebYfpph+3Xk4XedUU66tBM1p0g7kE5hzUl6\ndAvhtvi5DOs51OxYIiIXRaUuXVKzo5nNBVvZePRdmo1mruo9ljmDZ+Hv7W92NBGRi6ZSly7nZM1p\n0rLTOVZ9ghDf7twaP4cRvRLMjiUi0m4qdekymh3NvHvsA97Ofwe70cwVUaOZO3g2AT4BZkcTEXEK\nlbp0Cadqi0g7kEFBdSEhvsHcGj9Xq3MR8TgqdfFozY5mthR+yFtHNmM3mhkXNYp5g2/Q6lxEPJJK\nXTzW6doiXsvOoOBMId19g1kw9BZGhg+/8I4iIp2USl08jsNwsOXYh2zI34zdYWds5OXMG3IjgVqd\ni4iHU6mLRymqLSYtexX5ZwoI9gliwfBbuDR8hNmxREQ6hEpdPILDcPBe4TY2HMnC5rAzOuJSkofc\nRJBvoNnRREQ6jEpdOr2iuhKWZ2dwpKqAIJ9Abh+2gMsjLjE7lohIh1OpS6flMBxsPb6d9XkbsTns\njIoYSfKQmwj21ZX9RKRrUqlLp1RcV8ry7FXkVeUT5BPIomEpjIoYaXYsERFTqdSlU3EYDj44/jHr\n8jZic9i4LPwSUoberNW5iAgqdelESuvLSMvO4HBlPoE+AaQmzGNUxKVYLBazo4mIuAWVurg9h+Fg\n24lPWHv4LZocNi4NH0HK0Jvp7nv+awqLiHRFKnVxa6X15SzPziC38giB3gHcGj+XMZGXaXUuInIO\nKnVxSw7DwUcndvJm3ls0NTcxstdwUobeQkg3rc5FRM5HpS5up6y+nOU5qzlUcZgAb38WDEthbOTl\nWp2LiFyASl3chmEYfHRyJ28e3kBjcxMjeiawIP4WenQLMTuaiEinoFIXt1DeUMGK7NXkVOTi7+3P\nooT5jIsapdW5iMgPoFIXUxmGwcendpGZu4GG5kaG94zn1vg5Wp2LiFwElbqYpqKhkhU5q8kuP4Sf\nlx8LE5K5Mmq0VuciIhdJpS4dzjAMdpz6jDW5/6ShuYFhYUO5NX4OoX49zI4mItKpqdSlQ1U2VrEi\nZzUHyg7i59WN2+LnclXvsVqdi4g4gUpdOoRhGOw8vZvVueuptzcQHzqY2xLmEuYXanY0ERGPoVIX\nl6tsrOKNnDXsK8vBz6sbtw6dw9V9xml1LiLiZCp1cRnDMNh1+nNW5a6n3l7P0NBB3BY/j57+Wp2L\niLiCSl1coqrxDG8cXMPe0mx8vXxJGXoz4/tcqdW5iIgLqdTFqQzDYNvRXbyyeyV19nqGhA5iYfxc\nevqHmR1NRMTjqdTFac40VbMyJ5M9pfvx9fJl/pCbGN/3SqwWq9nRRES6BJW6tJthGOwu+pKMQ+uo\ntdcxLHww8wfdQi//nmZHExHpUlTq0i7VTTWsPJjJlyX78LX6MG/Ijcy5bCplpbVmRxMR6XJU6nLR\ndhftIePQWmpstcSFxJKakEx4QE8dbhcRMYlKXX6w6qYa0g+t5Yvir/Cx+jB38A1M7He1ylxExGQq\ndflBPi/+ivSDb1Jjq2VgyABSE+YRERBudiwREcHFpb5s2TL27NmDxWJhyZIljBw5smXbxx9/zPPP\nP4+XlxcTJkzgvvvuY+fOnTz44IMMHjwYgCFDhvDYY4+5MqK0UU1TLRmH1rK7eA8+Vm/mDJrFtdHj\ntToXEXEjLiv1Xbt2UVBQQHp6Onl5eSxZsoT09PSW7U8++SSvvPIKkZGRLFy4kGnTpgEwbtw4/vSn\nP7kqllyEL0v2sTInk2pbDbHd+5OaMI/IwAizY4mIyHe4rNR37NjBlClTAIiLi6OqqoqamhqCgoIo\nLCwkJCSE3r17AzBx4kR27NjBkCFDXBVHLkKNrZZVh9bxWdGXeFu9uXnQ9UyOvkarcxERN+Wyn86l\npaWEhv7rHN9hYWGUlJQAUFJSQlhY2Dm3HT58mHvuuYcFCxawfft2V8WTC9hTsp8ndz7HZ0VfMqB7\nDI+OfYgpMRNV6CIibqzDPihnGMYFHzNgwADuv/9+ZsyYQWFhIYsWLWLz5s34+vqed5/Q0AC8vb2c\nGZXw8GCnPl9nUtNYyz++yGBbwS58rN7cNvJmZg+dgtX6w8q8K8/QWTRD59Ac208zbL+OmqHLSj0i\nIoLS0tKW28XFxYSHh59zW1FREREREURGRjJz5kwAYmJi6NWrF0VFRURHR5/3dSoq6pyaOzw8mJKS\naqc+Z2ext/QAr+es4UxTNf2Do0kdlkzvwEjKyn7YiWS68gydRTN0Ds2x/TTD9nP2DFv7BcFlx1IT\nExPJysoCYP/+/URERBAUFARAv379qKmp4fjx49jtdt5//30SExNZv349r7zyCvD1IfqysjIiIyNd\nFVH+T52tjtcOpPNfX/0vdbY6bhw4g5+NvpfegZq9iEhn4rKV+qhRoxg+fDgpKSlYLBaWLl1KZmYm\nwcHBJCUl8cQTT/Czn/0MgJkzZxIbG0t4eDiPPPIIW7ZswWaz8cQTT7R66F3ab19pNq/nrKGq6Qwx\nwX1JTZhPn6Aos2OJiMhFsBhtebPbjTn7sFBXOdRUZ6tnTe4/+eT0Z3hZvJgZm0RSzES8rO3/fEJX\nmaEraYbOoTm2n2bYfh15+F1nlOuC9pcd5PWc1VQ2VhEd3JfUhGT6BvU2O5aIiLSTSr0LqbfXk5m7\ngY9PfYrVYmVW7FSm9p/klNW5iIiYT6XeRWSXHWJ5zioqG6voF9SH1IRk+gX3MTuWiIg4kUrdw9Xb\nG3jz8Aa2n9yF1WJlZmwS0/tP1upcRMQDqdQ9WE55LsuzV1HRWEnfoN6kJswnWqtzERGPpVL3QA32\nBt48/BYfndyJ1WJlxoDrmD7gOryt+s8tIuLJ9FPewxwsP8zynFWUN1TQJzCK1IRkYrr3MzuWiIh0\nAJW6h2iwN7Iu720+PLEDq8XK9P6TmR47BR+tzkVEugz9xPcAhyryWJ69irKGcqICI1mUkEz/7uc/\nX76IiHgmlXon1tjcxLq8t/ng+MdYsDC1/yRmxiZpdS4i0kXpp38nlVtxhOXZGZQ2lBMVEEHqsGQG\ndI8xO5aIiJhIpd7JNDU3sT5vE1uPbwcgKeZaro9NwsfLx+RkIiJiNpV6J3K4Mp/l2RmU1JcRGRBO\nakIysSH9zY4lIiJuQldp+5aM9w7zeW4Jzc3uNRLDYscWno09NA8A7/JB+JQmYDHc86xwXl4Wt5th\nZ6MZOofm2H6aYftNGNWP2Vc67+1RXaWtE2v2L6Op9+cYvrVYmgLxPTUKr/qeZscSERE3pJX6d7jL\ntYObmm1syM/ivWPbAJgUPZ7ZA6fh6+VrcrILc5cZdmaaoXNoju2nGbafrqfexeVXFZCWnUFRXQnh\n/j1ZmJDMoB6xZscSERE3p1J3I7ZmG2/lv8O7xz4AYFK/8dwQN71TrM5FRMR8KnU3cfTMMdIOZHC6\nrphefmEsTEhmcOhAs2OJiEgnolI3mc1h5+38d3inYCsGBhP7JXJj3Ay6aXUuIiI/kErdRAVnCknL\nzuBUbRE9/cJYmDCPIaFxZscSEZFOSqVuApvDzsb8d3nn2FYchoMJfa/mxrgZ+Hl3MzuaiIh0Yir1\nDnas+jhpBzI4WXuaML9QFsbPY2jYILNjiYiIB1CpdxC7w86mo1vIKngfh+FgfN8ruTluJn7efmZH\nExERD6FS7wCF1SdJy07nRM0pQrv1YGHCPOLDBpsdS0REPIxK3YWaHc1sKniPTUe34DAcJPa5gpsH\nXY+/VuciIuICKnUXOV59krTsDI7XnCS0Ww9ui59LQs8hZscSEREPplJ3smZHM5sL3ufto+/iMBxc\n3Xsstwyehb+3v9nRRETEw6nUnehEzSnSsjMorD5Bj24h3Bo/h+E9482OJSIiXYRK3QmaHc28c2wr\nb+e/S7PRzJW9xzBn0GwCfLQ6FxGRjqNSb6eTNadJy87gWPVxQny7c2v8HEb0SjA7loiIdEEq9YvU\n7Gjm3WMf8Hb+O9iNZq6IGs3cwbMJ8AkwO5qIiHRRKvWLcKq2iLQDGRRUFxLiG8yC+Dlc0muY2bFE\nRKSLU6n/AA7DwZZjH7IhfzN2h52xkaOYN+QGArU6FxERN6BSb6PTtcUsz84g/8wxgn2DWDB0DpeG\nDzc7loiISAuV+gU4DAfvFW7jn0eysDvsjIm8jHlDbiTIJ9DsaCIiImdRqbeiqK6EtAMZ5J8pINgn\niJTht3BZ+AizY4mIiJyTS0t92bJl7NmzB4vFwpIlSxg5cmTLto8//pjnn38eLy8vJkyYwH333XfB\nfTqKw3CwtfAj1h/ZhM1hZ3TEpSQPuYkgX63ORUTEfbms1Hft2kVBQQHp6enk5eWxZMkS0tPTW7Y/\n+eSTvPLKK0RGRrJw4UKmTZtGeXl5q/t0hFPVxbz4+T84UnWUIJ9Abh+2gMsjLunQDCIiIhfDZaW+\nY8cOpkyZAkBcXBxVVVXU1NQQFBREYWEhISEh9O7dG4CJEyeyY8cOysvLz7tPR/i8+CvSstNparZx\necRI5g+5iWDfjnltERGR9nJZqZeWljJ8+L8+HR4WFkZJSQlBQUGUlJQQFhZ21rbCwkIqKirOu8/5\nhIYG4O3t5ZTMxwoK8PPuxr3jbufqmNFOec6uKjw82OwInZ5m6ByaY/tphu3XUTPssA/KGYbhkn0q\nKuouJs453RBzPf82KoWyslpKSqqd9rxdTXh4sObXTpqhc2iO7acZtp+zZ9jaLwguK/WIiAhKS0tb\nbhcXFxMeHn7ObUVFRURERODj43PefTqC1WLFarV22OuJiIg4k8saLDExkaysLAD2799PREREy2H0\nfv36UVNTw/Hjx7Hb7bz//vskJia2uo+IiIi0zmUr9VGjRjF8+HBSUlKwWCwsXbqUzMxMgoODSUpK\n4oknnuBnP/sZADNnziQ2NpbY2Njv7SMiIiJtYzEu5s1uN+Ls93r0/lH7aYbtpxk6h+bYfpph+3Xk\ne+p6A1lERMRDqNRFREQ8hEpdRETEQ6jURUREPIRKXURExEOo1EVERDyESl1ERMRDqNRFREQ8RKc/\n+YyIiIh8TSt1ERERD6FSFxER8RAqdREREQ+hUhcREfEQKnUREREPoVIXERHxEF221JctW8b8+fNJ\nSUnhq6++Omvbxx9/zNy5c5k/fz5//etfTUrYObQ2x08++YTk5GRSUlJ49NFHcTgcJqV0b63N8BvP\nPfccqampHZys82hthqdOnWLBggXMnTuXxx9/3KSE7q+1Ga5YsYL58+ezYMECnnrqKZMSdg6HDh1i\nypQpLF++/HvbOqRbjC5o586dxl133WUYhmEcPnzYSE5OPmv7jBkzjJMnTxrNzc3GggULjNzcXDNi\nur0LzTEpKck4deqUYRiG8cADDxhbt27t8Izu7kIzNAzDyM3NNebPn28sXLiwo+N1Chea4eLFi43N\nmzcbhmEYTzzxhHHixIkOz+juWpthdXW1MWnSJMNmsxmGYRh33HGH8cUXX5iS093V1tYaCxcuNP7j\nP/7DSEtL+972juiWLrlS37FjB1OmTAEgLi6OqqoqampqACgsLCQkJITevXtjtVqZOHEiO3bsMDOu\n22ptjgCZmZlERUUBEBYWRkVFhSk53dmFZgjwzDPP8PDDD5sRr1NobYYOh4Pdu3czefJkAJYuXUqf\nPn1My+quWpuhj48PPj4+1NXVYbfbqa+vJyQkxMy4bsvX15eXX36ZiIiI723rqG7pkqVeWlpKaGho\ny+2wsDBKSkoAKCkpISws7Jzb5GytzREgKCgIgOLiYrZv387EiRM7PKO7u9AMMzMzGTduHH379jUj\nXqfQ2gzLy8sJDAzk6aefZsGCBTz33HNmxXRrrc2wW7du3HfffUyZMoVJkyZx6aWXEhsba1ZUt+bt\n7Y2fn985t3VUt3TJUv8uQ2fKdYpzzbGsrIx77rmHpUuXnvVDQ87t2zOsrKwkMzOTO+64w8REnc+3\nZ2gYBkVFRSxatIjly5dz4MABtm7dal64TuLbM6ypqeHvf/87mzZtYsuWLezZs4ecnBwT00lrumSp\nR0REUFpa2nK7uLiY8PDwc24rKio656EUaX2O8PUPgzvvvJOHHnqI8ePHmxHR7bU2w08++YTy8nJu\nu+027r//fvbv38+yZcvMiuq2WpthaGgoffr0ISYmBi8vL6666ipyc3PNiuq2WpthXl4e0dHRhIWF\n4evry5gxY9i3b59ZUTutjuqWLlnqiYmJZGVlAbB//34iIiJaDhX369ePmpoajh8/jt1u5/333ycx\nMdHMuG6rtTnC1+8F33777UyYMMGsiG6vtRlOnz6dt99+m4yMDP7yl78wfPhwlixZYmZct9TaDL29\nvYmOjubo0aMt23Xo+Ptam2Hfvn3Jy8ujoaEBgH379jFgwACzonZaHdUtXfYqbc8++yyfffYZFouF\npUuXcuDAAYKDg0lKSuLTTz/l2WefBWDq1Kn827/9m8lp3df55jh+/HjGjh3L5Zdf3vLYWbNmMX/+\nfBPTuqfWvhe/cfz4cbkNe2MAAAMTSURBVB599P+3d/8sjURRGMafbGJMhGBEcEQsxCI2BjFg7FLk\nI6RUFAvBWojigogQEgwBFdQ2IDESm2kFbTSFYiMY1EYF/0RBREFFrSRbLMiCrLDFbpaZ99fNhYFz\npnnn3IE738nlclWs9P/11TO8uLhgYmKCSqVCIBBgenqab99sOc986atnWCgUME0Tp9NJd3c34+Pj\n1S73v3R4eEg6neb6+hqXy4VhGESjUVpbW/9Zttg21EVERKxGr6siIiIWoVAXERGxCIW6iIiIRSjU\nRURELEKhLiIiYhEKdRH5a0zTJB6PV7sMEdtQqIuIiFiEq9oFiEj15XI51tfXeX9/p729neHhYUZG\nRohEIh/nfM/NzWEYBltbWywtLeHxePB6vSQSCQzD4ODggFQqRU1NDfX19aTTaeDnccHxeJyzszNa\nWlpYXFzE4XBUs10Ry9KkLmJzpVKJzc1N8vk8a2tr+Hw+dnZ2uLq6IhaLsbq6SjgcJpvN8vb2xuTk\nJAsLC+RyOSKRCPPz8wCMjY2RSCRYWVmhp6eH7e1tAE5PT0kkEpimycnJCUdHR9VsV8TSNKmL2Nze\n3h6Xl5cMDg4C8Pr6yu3tLX6/n87OTgBCoRDLy8ucn5/T2NhIc3MzAOFwmEKhwMPDA09PTwQCAQCG\nhoaAn9/Ug8EgXq8XAMMweH5+/scditiHQl3E5txuN9FolKmpqY+1crlMLBb7uK5UKjgcjk/b5r+u\n/+7EaafT+ekeEfk7tP0uYnOhUIhiscjLywsA+Xyeu7s7Hh8fOT4+BmB/f5+Ojg7a2tq4v7/n5uYG\ngN3dXbq6umhoaMDv91MqlQDIZrPk8/nqNCRiY5rURWwuGAzS39/PwMAAtbW1NDU10dvbi2EYmKbJ\nzMwMlUqF2dlZPB4PyWSS0dFR3G43dXV1JJNJADKZDKlUCpfLhc/nI5PJsLGxUeXuROxFf2kTkU/K\n5TJ9fX0Ui8VqlyIif0Db7yIiIhahSV1ERMQiNKmLiIhYhEJdRETEIhTqIiIiFqFQFxERsQiFuoiI\niEUo1EVERCziB1AeBusWq7knAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f5842436e50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Uwgfs9K5ykRF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Predict"
      ]
    },
    {
      "metadata": {
        "id": "NLFOZ1yUyjOQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "outputId": "10a14403-a033-462c-c556-1a575444dc20"
      },
      "cell_type": "code",
      "source": [
        "# evaluate on BLEU score\n",
        "index_word = IndexWord(tokenizer)\n",
        "\n",
        "y_hat = Predictions(model=model, test_data=x_test, index_word=index_word)\n",
        "\n",
        "print('BLEU-1: %f' % corpus_bleu(y_test, y_hat, weights=(1.0, 0, 0, 0)))\n",
        "print('BLEU-2: %f' % corpus_bleu(y_test, y_hat, weights=(0.5, 0.5, 0, 0)))\n",
        "print('BLEU-3: %f' % corpus_bleu(y_test, y_hat, weights=(0.3, 0.3, 0.3, 0)))\n",
        "print('BLEU-4: %f' % corpus_bleu(y_test, y_hat, weights=(0.25, 0.25, 0.25, 0.25)))\n",
        "print('')\n",
        "\n",
        "for index in range(1):\n",
        "  print('Original:\\t%s'%y_test[index])\n",
        "  print('----------')\n",
        "  print('Predicted:\\t%s\\n\\n'%y_hat[index])\n",
        "  "
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "BLEU-1: 0.000000\n",
            "BLEU-2: 0.000000\n",
            "BLEU-3: 0.000000\n",
            "BLEU-4: 0.000000\n",
            "\n",
            "Original:\t['police', 'ciefs', 'warn', 'minisers', 'were']\n",
            "----------\n",
            "Predicted:\t['<unk>', '<unk>', '<unk>', '015', 'for']\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "jpgYKC0XR2fj",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Word Embeddings - Not implemented yet.\n",
        "For now is creating its own embeddings for words while it trains.\n",
        "I will implement the pre-set word embeddings"
      ]
    },
    {
      "metadata": {
        "id": "LsNlmHQnR8ZQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "outputId": "dd43ad0d-35ff-456a-dcd7-b47acc763621"
      },
      "cell_type": "code",
      "source": [
        "!wget https://s3.amazonaws.com/dl4j-distribution/GoogleNews-vectors-negative300.bin.gz\n",
        "  \n",
        "!gunzip GoogleNews-vectors-negative300.bin.gz\n",
        "\n",
        "!ls"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2018-05-28 04:16:37--  https://s3.amazonaws.com/dl4j-distribution/GoogleNews-vectors-negative300.bin.gz\r\n",
            "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.225.107\n",
            "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.216.225.107|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1647046227 (1.5G) [application/x-gzip]\n",
            "Saving to: ‘GoogleNews-vectors-negative300.bin.gz’\n",
            "\n",
            "300.bin.gz           93%[=================>  ]   1.44G  46.9MB/s    eta 4s     "
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "GoogleNews-vectors- 100%[===================>]   1.53G  47.0MB/s    in 47s     \n",
            "\n",
            "2018-05-28 04:17:25 (33.2 MB/s) - ‘GoogleNews-vectors-negative300.bin.gz’ saved [1647046227/1647046227]\n",
            "\n",
            "_about.txt\t\t english-german-train.pkl\n",
            "datalab\t\t\t final_model.h5\n",
            "deu-eng.zip\t\t GoogleNews-vectors-negative300.bin\n",
            "deu.txt\t\t\t model.h5\n",
            "english-german-both.pkl  model.png\n",
            "english-german.pkl\t signalmedia-1m.jsonl.gz\n",
            "english-german-test.pkl\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "brFn0oTrcH_f",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "filename = 'GoogleNews-vectors-negative300.bin'\n",
        "model = KeyedVectors.load_word2vec_format(filename, binary=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xMhrmJ6cco3W",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "result = model.most_similar(positive=['woman', 'king'], negative=['man'], topn=1)\n",
        "print(result)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rjcCIt88dnmU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.save_word2vec_format('GoogleNews-vectors-negative300.txt', binary=False)\n"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}