miniproject2_ChatBot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Miniproject 2: Chatbot\n",
    "\n",
    "## Introduction\n",
    "\n",
    "### Description\n",
    "\n",
    "Developing a model employing ANN on real-world data requires going through several major steps, each of which with \n",
    "important design choices that directly impact the final results. \n",
    "In this project, we guide you through these choices starting from a large database of \n",
    "[conversations](http://parl.ai/downloads/personachat/personachat.tgz) to a functional chatbot. \n",
    "\n",
    "### Prerequisites\n",
    "\n",
    "- You should have a running installation of [tensorflow](https://www.tensorflow.org/install/) and [keras](https://keras.io/).\n",
    "- You should know the concepts \"recurrent neural networks\", \"LSTM\", \"training and validation data\", \"overfitting\" and \"early stopping\".\n",
    "\n",
    "### What you will learn\n",
    "\n",
    "- You will be guided through a data processing procedure and understand the importance of design choices in ANN modeling\n",
    "- You will learn how to define recurrent neural networks in keras and fit them to data.\n",
    "- You will be guided through a prototyping procedure for the application of deep learning to a specific domain.\n",
    "- You will get in contact with concepts discussed in the lecture, like \"overfitting\", \"LSTM network\", and \"Generative model\".\n",
    "- You will learn to be more patient :) Some fits may take your computer quite a bit of time; run them over night and make sure you save (and load) your data and models.\n",
    "\n",
    "### Evaluation criteria\n",
    "\n",
    "The evaluation is (mostly) based on the figures you submit and your answer sentences. \n",
    "We will only do random tests of your code and not re-run the full notebook. \n",
    "Please ensure that your notebook is fully executed before handing it in. \n",
    "\n",
    "### Submission \n",
    "\n",
    "You should submit your notebook through the Moodle page submission tool. You should work in teams of two people and each member should submit the same notebook to Moodle.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions and imports\n",
    "\n",
    "For your convenience we import some libraries and provide some functions below. Fill in your names, sciper numbers and run the following cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "names = {'student_1': \"Fredrik Bixo\",\n",
    "        'student_2': \"Pierre Armel Mbanga Ndjock\"}\n",
    "\n",
    "sciper = {'student_1': 300685, \n",
    "          'student_2': 229047}\n",
    "\n",
    "seed = sciper['student_1']+sciper['student_2']\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import numpy.ma as ma\n",
    "import matplotlib.pyplot as plt\n",
    "import os, sys\n",
    "import copy\n",
    "\n",
    "plt.rcParams['font.size'] = 28\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams[\"axes.grid\"] = False\n",
    "c = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
    "\n",
    "import keras\n",
    "from keras.models import Model, load_model\n",
    "from keras.layers import Input, Masking, TimeDistributed, Dense, Concatenate, Dropout, LSTM, GRU, SimpleRNN, Bidirectional, Embedding, BatchNormalization\n",
    "from keras.optimizers import Adam\n",
    "from keras.utils import np_utils\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "\n",
    "\n",
    "def getRawDataFromFile(datapath=\"personachat/\", file=\"train_both_revised.txt\"):\n",
    "    \n",
    "    f = open(datapath+file)\n",
    "\n",
    "    conversations = []\n",
    "    current_conversation = []\n",
    "    \n",
    "    for l, line in enumerate(f):\n",
    "        #print(l, line)\n",
    "        if \"persona:\" in line:\n",
    "            if len(current_conversation) > 1:\n",
    "                conversations.append(current_conversation)\n",
    "            current_conversation = [] \n",
    "            continue\n",
    "\n",
    "        #remove numberings\n",
    "        processed_line = line.split(' ')\n",
    "        processed_line = \" \".join(processed_line[1:])\n",
    "        line = processed_line\n",
    "        #print(line)\n",
    "\n",
    "        conv = line.split('\\t')    \n",
    "        q = conv[0]\n",
    "        a = conv[1]\n",
    "        current_conversation.append(q)\n",
    "        current_conversation.append(a)\n",
    "    \n",
    "    return conversations \n",
    "\n",
    "\n",
    "\n",
    "# Plotting\n",
    "\n",
    "def prepare_standardplot(title, xlabel):\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "    fig.suptitle(title)\n",
    "    ax1.set_ylabel('categorical cross entropy')\n",
    "    ax1.set_xlabel(xlabel)\n",
    "    ax1.set_yscale('log')\n",
    "    ax2.set_ylabel('accuracy [% correct]')\n",
    "    ax2.set_xlabel(xlabel)\n",
    "    return fig, ax1, ax2\n",
    "\n",
    "def finalize_standardplot(fig, ax1, ax2):\n",
    "    ax1handles, ax1labels = ax1.get_legend_handles_labels()\n",
    "    if len(ax1labels) > 0:\n",
    "        ax1.legend(ax1handles, ax1labels)\n",
    "    ax2handles, ax2labels = ax2.get_legend_handles_labels()\n",
    "    if len(ax2labels) > 0:\n",
    "        ax2.legend(ax2handles, ax2labels)\n",
    "    fig.tight_layout()\n",
    "    plt.subplots_adjust(top=0.9)\n",
    "\n",
    "def plot_history(history, title):\n",
    "    fig, ax1, ax2 = prepare_standardplot(title, 'epoch')\n",
    "    ax1.plot(history['loss'], label = \"training\")\n",
    "    ax1.plot(history['val_loss'], label = \"validation\")\n",
    "    ax2.plot(history['acc'], label = \"training\")\n",
    "    ax2.plot(history['val_acc'], label = \"validation\")\n",
    "    finalize_standardplot(fig, ax1, ax2)\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Data visualization and preprocessing\n",
    "\n",
    "Here we will process and visualize the data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parse raw data \n",
    "\n",
    "Download the dataset on http://parl.ai/downloads/personachat/personachat.tgz. Unpack it and add it to your project folder. Read and run the getRawDataFromFile function (if needed, modify the default path). It extracts the conversations.\n",
    "\n",
    "**Output** Display two randomly selected conversations. [1 pt]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "conversations = getRawDataFromFile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8938"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(conversations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hi , how are you doing ? i am getting ready to do some cheetah chasing to stay in shape .',\n",
       " 'you must be very fast . hunting is one of my favorite hobbies .',\n",
       " 'i am ! for my hobby i like to do canning or some whittling .',\n",
       " 'i also remodel homes when i am not out bow hunting .',\n",
       " 'that is neat . when i was in high school i placed 6th in 100m dash !',\n",
       " 'that is awesome . do you have a favorite season or time of year ?',\n",
       " 'i do not . but i do have a favorite meat since that is all i eat exclusively .',\n",
       " 'what is your favorite meat to eat ?',\n",
       " 'i would have to say its prime rib . do you have any favorite foods ?',\n",
       " 'i like chicken or macaroni and cheese .',\n",
       " 'do you have anything planned for today ? i think i am going to do some canning .',\n",
       " 'i am going to watch football . what are you canning ?',\n",
       " 'i think i will can some jam . do you also play footfall for fun ?',\n",
       " 'if i have time outside of hunting and remodeling homes . which is not much !']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conversations[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentences = [sentence for conv in conversations for sentence in conv]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "separator = ' '"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract word tokens\n",
    "\n",
    "Let's start looking at our data. \n",
    "\n",
    "**Code** Compute the set of unique words (dictionary) in all sentences along with the number of occurences of each of these words. HINT: each word is separated by a space character, use the python string.split(' ') function to separate words. Consider punctuations as 'words'. [1 pt]\n",
    "\n",
    "**Figure** In a bar plot, show the first 75 most frequent words (x-axis) and their number of occurences (y-axis). [1 pt]\n",
    "\n",
    "**Figure** In another bar plot, show the 75 least frequent words (x-axis) and their number of occurences (y-axis). [1 pt] \n",
    "\n",
    "**Figure** In a log-log scale, plot the sorted word index (x-axis) vs their respective count (y-axis). [1 pt]\n",
    "\n",
    "**Question** Relate the sorted word count distribution with Zipf's law.\n",
    "Argue using the log-log plot. [1 pt]\n",
    "\n",
    "**Answer**\n",
    "\n",
    "**Question** How many words appear only once in the entire dataset? [1 pt]\n",
    "\n",
    "**Answer**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import OrderedDict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "word_dic = {} \n",
    "\n",
    "for conv in conversations:\n",
    "    for sentence in conv:\n",
    "        words = sentence.split(separator)\n",
    "        for w in words:\n",
    "            if w not in word_dic:\n",
    "                word_dic[w] = 0\n",
    "            word_dic[w] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "word                 frequency\n",
      "-------------------- ------\n",
      "hi                   004485\n",
      ",                    039499\n",
      "how                  010903\n",
      "are                  017934\n",
      "you                  048295\n",
      "doing                004250\n",
      "?                    043768\n",
      "i                    103476\n",
      "am                   024797\n",
      "getting              000823\n",
      "ready                000412\n"
     ]
    }
   ],
   "source": [
    "print('{:20} {:>06s}'.format('word', 'frequency'))\n",
    "print('{:20} {:>06s}'.format('-'*20, '-'*6))\n",
    "first_n = 10\n",
    "for i, w in enumerate(word_dic.keys()):\n",
    "    print('{:20} {:>06d}'.format(w, word_dic[w]))\n",
    "    if not i < first_n:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cut = 75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "word                 frequency\n",
      "-------------------- ------\n",
      "i                    103476\n",
      ".                    101587\n",
      "you                  048295\n",
      "?                    043768\n",
      ",                    039499\n",
      "a                    034213\n",
      "to                   032102\n",
      "do                   030574\n",
      "is                   028573\n",
      "my                   026950\n",
      "am                   024797\n",
      "!                    023246\n",
      "the                  021139\n",
      "that                 020133\n",
      "have                 018422\n",
      "are                  017934\n",
      "and                  016756\n",
      "like                 016708\n",
      "it                   014512\n",
      "not                  014116\n",
      "in                   013501\n",
      "what                 013188\n",
      "of                   012253\n",
      "for                  011337\n",
      "how                  010903\n",
      "love                 010748\n",
      "but                  008145\n",
      "good                 008036\n",
      "me                   007958\n",
      "just                 007810\n",
      "so                   007655\n",
      "with                 007228\n",
      "work                 006373\n",
      "your                 006014\n",
      "on                   005876\n",
      "too                  005872\n",
      "at                   005854\n",
      "be                   005686\n",
      "about                005523\n",
      "no                   005048\n",
      "oh                   004930\n",
      "well                 004701\n",
      "yes                  004569\n",
      "would                004548\n",
      "great                004511\n",
      "hi                   004485\n",
      "was                  004338\n",
      "doing                004250\n",
      "cool                 004196\n",
      "can                  004161\n",
      "favorite             004121\n",
      "nice                 004078\n",
      "from                 004005\n",
      "there                003970\n",
      "they                 003891\n",
      "time                 003859\n",
      "go                   003852\n",
      "really               003805\n",
      "all                  003687\n",
      "get                  003569\n",
      "as                   003552\n",
      "any                  003490\n",
      "today                003278\n",
      "will                 003255\n",
      "its                  003240\n",
      "fun                  003238\n",
      "hello                003200\n",
      "we                   003159\n",
      "sounds               003056\n",
      "out                  003029\n",
      "an                   002985\n",
      "very                 002976\n",
      "one                  002959\n",
      "music                002906\n",
      "up                   002865\n"
     ]
    }
   ],
   "source": [
    "# top 75 words\n",
    "print('{:20} {:>06s}'.format('word', 'frequency'))\n",
    "print('{:20} {:>06s}'.format('-'*20, '-'*6))\n",
    "\n",
    "top = OrderedDict(sorted(word_dic.items(), key=lambda kv: kv[1], reverse=True)[:cut])\n",
    "for w in top.keys():\n",
    "    print('{:20} {:>06d}'.format(w, top[w]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "word                 frequency\n",
      "-------------------- ------\n",
      "100m                 000001\n",
      "footfall             000001\n",
      "mojitos              000001\n",
      "asians               000001\n",
      "taos                 000001\n",
      "braids               000001\n",
      "clot                 000001\n",
      "breaker              000001\n",
      "ventured             000001\n",
      "smash                000001\n",
      "whatsis              000001\n",
      "whenso               000001\n",
      "1960                 000001\n",
      "secured              000001\n",
      "michener             000001\n",
      "churchill            000001\n",
      "colombia             000001\n",
      "hillbilly            000001\n",
      "reel                 000001\n",
      "ido                  000001\n",
      "negatively           000001\n",
      "gravity              000001\n",
      "futurama             000001\n",
      "forestry             000001\n",
      "shibamotos           000001\n",
      "providence           000001\n",
      "omega                000001\n",
      "togther              000001\n",
      "jobless              000001\n",
      "greenway             000001\n",
      "yessir               000001\n",
      "lantern              000001\n",
      "laguna               000001\n",
      "wanders              000001\n",
      "gish                 000001\n",
      "colonial             000001\n",
      "wade                 000001\n",
      "isaac                000001\n",
      "asimov               000001\n",
      "thickens             000001\n",
      "fences               000001\n",
      "sn                   000001\n",
      "piggies              000001\n",
      "hortons              000001\n",
      "dunkin               000001\n",
      "ap                   000001\n",
      "corpus               000001\n",
      "christi              000001\n",
      "victoria             000001\n",
      "bomber               000001\n",
      "redecorating         000001\n",
      "curtains             000001\n",
      "spikes               000001\n",
      "stainless            000001\n",
      "prolific             000001\n",
      "anaconda             000001\n",
      "kenyan               000001\n",
      "pollutants           000001\n",
      "verizon              000001\n",
      "betterment           000001\n",
      "initial              000001\n",
      "streaked             000001\n",
      "scavenge             000001\n",
      "crawlers             000001\n",
      "minos                000001\n",
      "unclear              000001\n",
      "refering             000001\n",
      "rector               000001\n",
      "parrish              000001\n",
      "gotch                000001\n",
      "revelation           000001\n",
      "screws               000001\n",
      "breasts              000001\n",
      "batteries            000001\n",
      "bribery              000001\n"
     ]
    }
   ],
   "source": [
    "# last 75 words\n",
    "print('{:20} {:>06s}'.format('word', 'frequency'))\n",
    "print('{:20} {:>06s}'.format('-'*20, '-'*6))\n",
    "\n",
    "bottom = OrderedDict(sorted(word_dic.items(), key=lambda kv: kv[1], reverse=False)[:cut])\n",
    "for w in bottom.keys():\n",
    "    print('{:20} {:>06d}'.format(w, bottom[w]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('classic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1480x840 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = np.arange(len(top))\n",
    "ys = top.values()\n",
    "\n",
    "bar_width = .5\n",
    "opacity = 0.7\n",
    "error_config = {'ecolor': '0.3'}\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(18.5, 10.5)\n",
    "fig.set_facecolor(color='white')\n",
    "\n",
    "\n",
    "rects = ax.bar(xs, ys, width=bar_width,\n",
    "               align='edge',\n",
    "               alpha=opacity, color='b',\n",
    "               error_kw=error_config,\n",
    "               label='Frequency')\n",
    "\n",
    "ax.set_xlabel('Words')\n",
    "ax.set_title('Top 75 words')\n",
    "ax.set_xticks(xs + bar_width / 2)\n",
    "ax.set_xticklabels(top.keys())\n",
    "ax.legend()\n",
    "\n",
    "plt.xticks(rotation=90)\n",
    "fig.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1480x840 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = np.arange(len(bottom))\n",
    "ys = bottom.values()\n",
    "\n",
    "bar_width = .5\n",
    "opacity = 0.7\n",
    "error_config = {'ecolor': '0.3'}\n",
    "\n",
    "#plt.style.use('classic')\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(18.5, 10.5)\n",
    "fig.set_facecolor(color='white')\n",
    "\n",
    "\n",
    "rects = ax.bar(xs, ys, width=bar_width,\n",
    "               align='edge',\n",
    "               alpha=opacity, color='b',\n",
    "               error_kw=error_config,\n",
    "               label='Frequency')\n",
    "\n",
    "ax.set_xlabel('Words')\n",
    "ax.set_title('bottom 75 words')\n",
    "ax.set_xticks(xs + bar_width / 2)\n",
    "ax.set_xticklabels(bottom.keys())\n",
    "ax.legend()\n",
    "\n",
    "plt.xticks(rotation=90)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1480x840 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort = OrderedDict(sorted(word_dic.items(), key=lambda kv: kv[1], reverse=True))\n",
    "\n",
    "xs = np.arange(len(sort.keys()))\n",
    "ys = sort.values()\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(18.5, 10.5)\n",
    "fig.set_facecolor(color='white')\n",
    "\n",
    "rects = ax.loglog(xs, ys, \n",
    "                alpha=opacity,\n",
    "                color='b',\n",
    "                label='log(frequency)')\n",
    "\n",
    "ax.set_xlabel('Words')\n",
    "ax.set_title('bottom 75 words')\n",
    "\n",
    "ax.legend()\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the graph is almost linear we can see that the word count follows a zipf distrubition. In other words, the most common word frequency is inversy proportional. So the most common word is multiplicative of the second most common word and so on.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 7080 words appearing only once in the entire dataset.\n"
     ]
    }
   ],
   "source": [
    "# How many words appear only once in the entire dataset ?\n",
    "v = 1\n",
    "values = np.array(list(word_dic.values()))\n",
    "indices = np.where(values == v)\n",
    "num = len(np.squeeze(indices))\n",
    "print('there are {:d} words appearing only once in the entire dataset.'.format(num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering\n",
    "\n",
    "We suggest to filter your data by removing sentences containing rare words. \n",
    "\n",
    "\n",
    "**Code** To achieve that, you should create a new dataset where sentences containing rare words (words that occur less than N times in the dataset) are removed. Keep at least 50'000 sentences (depending on your computing power, you can keep more). \n",
    "HINT: Start by updating the dictionary accordingly and then remove any sentence that contains at least a single word that is not in the dictionary of words. [2 pts]\n",
    "\n",
    "**Question**: How much did you reduce the number of unique words with your rare event suppression procedure? [1 pt]\n",
    "    \n",
    "**Answer**: 18673 - 1293 = 17'380 words have been removed (93.08%)\n",
    "\n",
    "**Question**: How many sentences are in your filtered and original dataset? [1 pt]\n",
    "\n",
    "**Answer**: Original: 131424, Filtered: 58826\n",
    "\n",
    "**Question**: What is the impact on learning and generalization of removing sentences with rare words from your dataset? [2 pt]\n",
    "\n",
    "**Answer**: The generalization will be better because it will exclude rare words that probably was just occur in the training set and not in the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We keep only sentences where all word occurences in the\n",
    "# dataset are above the predefined threshold\n",
    "threshold = 70"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Update the vocabulary accordingly\n",
    "dtype = np.dtype([('word','<U34'), ('frequency', int)])\n",
    "word_array = np.array(list(word_dic.items()), dtype=dtype)\n",
    "indices = np.squeeze(np.where(word_array['frequency'] >= threshold))\n",
    "\n",
    "# new dictionary\n",
    "freqword_dic = dict(word_array[indices])\n",
    "# new array\n",
    "freqword_array = word_array[indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "freqword_set = set(freqword_dic.keys())\n",
    "freqsentences = [s for s in sentences if set(s.split(separator)) <=  freqword_set]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               |      Original       |      Filtered      \n",
      "------------------------------ |-------------------- |--------------------\n",
      "# words                        |       18673         |        1293        \n",
      "# sentences                    |       131424        |       58826        \n",
      "\n",
      "\n",
      "                               |     percentage      |         #          \n",
      "------------------------------ |-------------------- |--------------------\n",
      "suppresed words                |       93.08         |       17380        \n",
      "suppresed sentences            |       55.24         |       72598        \n"
     ]
    }
   ],
   "source": [
    "print('{:^30s} |{:^20s} |{:^20s}'.format('', 'Original', 'Filtered'))\n",
    "print('{:^30s} |{:^20s} |{:^20s}'.format('-'*30, '-'*20, '-'*20))\n",
    "print('{:30s} |{:^20d} |{:^20d}'.format('# words', len(word_dic.keys()), len(freqword_dic.keys())))\n",
    "print('{:30s} |{:^20d} |{:^20d}'.format('# sentences', len(sentences), len(freqsentences)))\n",
    "print('\\n')\n",
    "\n",
    "\n",
    "w_prop = 100 * (1 - len(freqword_dic.keys()) / len(word_dic.keys()))\n",
    "s_prop = 100 * (1 - len(freqsentences) / len(sentences))\n",
    "print('{:^30s} |{:^20s} |{:^20s}'.format('', 'percentage', '#'))\n",
    "print('{:^30s} |{:^20s} |{:^20s}'.format('-'*30, '-'*20, '-'*20))\n",
    "print('{:30s} |{:^20.2f} |{:^20d}'.format('suppresed words', w_prop, len(word_dic.keys()) - len(freqword_dic.keys())))\n",
    "print('{:30s} |{:^20.2f} |{:^20d}'.format('suppresed sentences', s_prop, len(sentences) - len(freqsentences)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TODO:\n",
    "\n",
    "By removing rare sentances with rare words, you may get better generalization before data points that are extreme and unusual are not considered. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tokenization and padding\n",
    "\n",
    "Now you will transform our filtered data into a format that is understandable by an ANN. To achieve that, you should transform words to integers, where single integers in the range [1,size of the dictionary] are mapped to single words in your dictionary. This process is commonly named 'tokenization'. In addition, we will keep the value 0 to a specific artificial word 'PADD' that will be used to account for the variable length of sentences and add to each sentence a 'START' and an 'END' word. \n",
    "\n",
    "**Code** Start by adding the three artificial words to your dictionary (list of possible tokens) and then translate every sentences to a list of integers. \n",
    "HINT: use the Python List index() method. [2 pts]\n",
    "\n",
    "**Figure** Use the violinplot to show the density of tokenized sentences length. [1pt]\n",
    "\n",
    "**Code** From this figure, select a maximum number (=maxlen) of tokens for which most of the sentences have less. Padd (and eventually truncate) all sentences with the 'PADD' token (value 0 in the integer representation) until all tokenized sentences have the same length (maxlen).\n",
    "HINT: use the pad_sequences function from keras.preprocessing.sequence [2 pts]\n",
    "\n",
    "**Code** Check that you can recover the original sentence. Randomly select two sentences from your integer and padded representation and translate them back using your dictionary. [1 pt]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "word_dic, sentences = freqword_dic, freqsentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add the three artificial words\n",
    "tokens = ['PADD'] + ['START'] + ['END'] + list(word_dic.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fixing random state for reproducibility\n",
    "np.random.seed(19680801)\n",
    "# lengths of sentences\n",
    "xs = [len(s.split(separator)) for s in sentences]\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(9, 9))\n",
    "ax.set_title('Violin plot')\n",
    "ax.set_xticks([1])\n",
    "ax.set_xticklabels(['x1'])\n",
    "ax.legend()\n",
    "\n",
    "ax.violinplot(xs,\n",
    "              showmeans=False,\n",
    "              showmedians=True)\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dictionary of mappings\n",
    "dic = dict(zip(tokens, range(len(tokens))))\n",
    "# translate every sentence to a list of integer\n",
    "isentences = []\n",
    "for s in sentences:\n",
    "    ints = [tokens.index('START')]\n",
    "    for word in s.split(separator):\n",
    "        ints += [tokens.index(word)]\n",
    "    isentences += [ints + [tokens.index('END')]] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# padd sequences of integers\n",
    "#isentences = pad_sequences(isentences, \n",
    "#                           maxlen=max(xs), \n",
    " #                          dtype='int32', \n",
    "  #                         padding='post', \n",
    "   #                        truncating='post', \n",
    "    #                       value=0)\n",
    "# padd sequences of strings\n",
    "psentences = pad_sequences([s.split(separator) for s in sentences], \n",
    "                              maxlen=max(xs), \n",
    "                              dtype='<U34', \n",
    "                              padding='post', \n",
    "                              truncating='post', \n",
    "                              value='PADD')\n",
    "psentences = np.apply_along_axis(lambda s: separator.join(s), 1, psentences).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 7, 21, 22, 23, 24, 20, 25, 26, 27, 28, 29, 30, 31, 20, 2],\n",
       " [1, 41, 26, 46, 20, 15, 7, 47, 48, 30, 49, 36, 50, 28, 51, 9, 2]]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isentences[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['you must be very fast . hunting is one of my favorite hobbies . PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD',\n",
       " 'that is awesome . do you have a favorite season or time of year ? PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD PADD']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psentences[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_flags_old(isentences):\n",
    "    # mask padd values\n",
    "    mask = ma.masked_where(isentences != 0, isentences)\n",
    "    if np.any(np.logical_not(mask.mask)):\n",
    "        # remove padd values\n",
    "        isentences = isentences[mask.mask]\n",
    "        # split on <START> => value = 1 (to recover original shape) \n",
    "        indices = np.where(isentences == 1)[0]\n",
    "        # w/o padd\n",
    "        isentences = np.split(isentences, indices)[1:]\n",
    "    \n",
    "    # remove <START>, <END> => [1, 2] values\n",
    "    for i, s in enumerate(isentences):\n",
    "        mask = np.isin(s, [1, 2])\n",
    "        if np.any(mask):\n",
    "            mask = np.logical_not(mask)\n",
    "            isentences[i] = s[mask]\n",
    "    return isentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_flags(isentences):\n",
    "    # remove <PADD>, <START>, <END> => [0, 1, 2] values\n",
    "    res = [0]*len(isentences)\n",
    "    for i, s in enumerate(isentences):\n",
    "        mask = np.isin(s, [0, 1, 2])\n",
    "        res[i] = s\n",
    "        if np.any(mask):   \n",
    "            res[i] = s[np.logical_not(mask)]\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recover(isentences):\n",
    "    # faster lookup\n",
    "    dic_array = np.array(list(dic.items()), dtype=[('word','<U34'), ('id', int)])    \n",
    "    # remove padding\n",
    "    isentences = remove_flags(isentences)\n",
    "    sentences = []\n",
    "    for idx in isentences:\n",
    "        sentences += [separator.join(dic_array['word'][idx])]\n",
    "    return sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Check that you can recover the original sentence.\n",
    "n = len(isentences)\n",
    "size = 2\n",
    "selection = np.random.randint(n, size=size)\n",
    "\n",
    "#print(isentences[selection])\n",
    "#recover(isentences[selection])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving\n",
    "\n",
    "Now is a good time to save your data (end of processing). An example code using the pickle library is shown below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "from pathlib import Path\n",
    "from os.path import join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save(filename, obj, save_path=Path('./save/')):\n",
    "    pickle_out = open(join(save_path, Path(filename)), 'wb+')\n",
    "    pickle.dump(obj, pickle_out, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "    pickle_out.close()\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load(filename, save_path=Path('./save/')):\n",
    "    pickle_in = open(join(save_path, Path(filename)), 'rb')\n",
    "    obj = pickle.load(pickle_in)\n",
    "    pickle_in.close()\n",
    "    return obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# name space\n",
    "name_frequencies = 'data/train_both_revised/word_dic.pkl' # dictionary {word: frequency}\n",
    "name_psentences = 'data/train_both_revised/psentences.pkl'\n",
    "name_isentences = 'data/train_both_revised/isentences.pkl' \n",
    "name_dictionary = 'data/train_both_revised/dic.pkl' # dictionary {token: id}\n",
    "name_sentences = 'data/train_both_revised/sentences.pkl'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# save data\n",
    "save(name_frequencies, word_dic)\n",
    "save(name_psentences, psentences)\n",
    "save(name_isentences, isentences)\n",
    "save(name_dictionary, dic)\n",
    "save(name_sentences, sentences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data\n",
    "word_dic = load(name_frequencies)\n",
    "psentences = load(name_psentences)\n",
    "isentences = load(name_isentences)\n",
    "dic = load(name_dictionary)\n",
    "sentences = load(name_sentences)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building and training generative models of language"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RNN vs LSTM vs GRU \n",
    "\n",
    "Build, train and compare generative models of language based on RNNs with different recurrent units (SimpleRNN, GRU and LSTM). \n",
    "\n",
    "The target of the network will be to approximate the word transition probabilities Pr(word[n+1]|H[n]) with H[n]=f(word[:n]) being the hidden state of the network.  \n",
    "\n",
    "**code** You should complete the proposed model (using the Keras API rather than the Sequential model for more flexibility). Be sure to understand each line. The embedding layer allows to transform an integer to a dense vector. That would be our input to the recurrent network - each sentence is mapped to a sequence of vectors, each representing a single word. You can then design your own readout(s) and output layers. By default, use the proposed meta parameters. You can adapt them if you have more or less computing power (32 epochs should take around 30 minutes). [2 pts]\n",
    "\n",
    "**Question** How will your networks deal with the artificial word 'PADD' that you added at the end of each sentences  [2 pts]\n",
    "\n",
    "**Answer**\n",
    "\n",
    "Todo\n",
    "\n",
    "The embedding layer will mask the 0 and they will therefore be excludes from the network.\n",
    "\n",
    "**code** Then train three different networks with the same architecture but using different recurrent units (simpleRNN, GRU and LSTM). Save the learning history (training/validation loss and accuracy for each epoch) as well as the models. [1 pt]\n",
    "\n",
    "**Question** How can you use this network to approximate the word transition probabilities? What will be the inputs and targets of the network at each batch? Give the input/output tensor dimensions. [2 pts]\n",
    "\n",
    "Todo\n",
    "\n",
    "**Answer**\n",
    "\n",
    "**Figure** Show the learning curves (training and validation loss) for the different recurrent units. [1 pt]\n",
    "\n",
    "Done\n",
    "\n",
    "**Figure** Show the learning curves (training and validation accuracy) for the different recurrent units. [1 pt]\n",
    "\n",
    "Done\n",
    "\n",
    "**Question:** Which recurrent unit yields the best validation accuracy? Which is the fastest learner? [1 pt]\n",
    "\n",
    "GRU seems to have the best validationa accuracy. However, SimpleRNN requires less time to learn and is the fastest learner.\n",
    "\n",
    "**Answer**: \n",
    "\n",
    "**Question:** Do you observe an overfitting effect? Where and for which case? Give a possible explanation. [1 pt] \n",
    "\n",
    "Yes, there is an overfitting effect. After around 5 epochs the validation accuracy and the accuracy starts to diverge. One reason that the data set starts to overfit, is because the training set is not very big compared to the total number possible sentances. Therefore, the network also starts to overfit.\n",
    "\n",
    "**Answer**: \n",
    "\n",
    "**Question:** Suggest one option modifying your dataset to decrease overfitting. [1 pt]\n",
    "\n",
    "One way to reduce the overfitting effect would be to either decrease the number of \"rare\" words. In this case the network will not try to model the outliers as much. Another way would be to increase the size of the dataset through data augumenation.\n",
    "\n",
    "**Answer**: \n",
    "\n",
    "**Question:** Suggest one possible option modifying your network to decrease overfitting. [1 pt]\n",
    "\n",
    "One way to modify the network to decrease overfitting would be to reduce the number of neurons in each layer. This will make the flexibility of the network decrease. This could be done either in the readout layer or the recurrent layer.\n",
    "\n",
    "**Answer**: \n",
    "\n",
    "**Question:** Suggest one possible option modifying the training modalities to counter overfitting. [1 pt]   \n",
    "\n",
    "**Answer**: We could use dropout or any other network or l2 regularization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils import plot_model, to_categorical\n",
    "from math import ceil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Meta-parameters\n",
    "maxlen = 41\n",
    "embedding_size = 128\n",
    "hidden_size = 64\n",
    "dropout = 0.2\n",
    "recurrent_dropout = 0.2\n",
    "\n",
    "batch_size = 128\n",
    "epochs = 32\n",
    "validation_split = 0.2\n",
    "\n",
    "dataset_cut = int(.1 * len(isentences))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "I = {}\n",
    "E = {}\n",
    "H = {}\n",
    "R = {}\n",
    "Y = {}\n",
    "models = {}\n",
    "logs = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def batch_iter(x, batch_size, num_batches=1):\n",
    "    \"\"\"\n",
    "    Generate a minibatch iterator for a dataset.\n",
    "    Takes as input one iterable (here the file names of the articles)\n",
    "    Outputs an iterator which gives mini-batches of `batch_size` matching elements from `x`.\n",
    "    Example of use :\n",
    "    for minibatch_filenames, in batch_iter(x, 2):\n",
    "        <DO-SOMETHING>\n",
    "    \"\"\"\n",
    "    data_size = len(x)\n",
    "\n",
    "    for batch_num in range(num_batches):\n",
    "        start_index = batch_num * batch_size\n",
    "        end_index = min((batch_num + 1) * batch_size, data_size)\n",
    "        if start_index != end_index:\n",
    "            yield x[start_index:end_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_data(isentences, split=.1):\n",
    "    n = len(isentences)\n",
    "    cut = int(split * n)\n",
    "    \n",
    "    x = []\n",
    "    y = []\n",
    "    \n",
    "    # padd sequences of integers\n",
    "    x2 = pad_sequences(isentences,   maxlen=maxlen, \n",
    "                           dtype='int32', \n",
    "                           padding='post', \n",
    "                           truncating='post', \n",
    "                           value=0)\n",
    "    \n",
    "    for s in x2:\n",
    "        x.append(s[:-1])\n",
    "        #One hot encode\n",
    "        y2 = to_categorical(s[1:], len(tokens))\n",
    "        y.append(y2)\n",
    "\n",
    "    # convert to np array\n",
    "    x = np.array(x)\n",
    "    y = np.array(y)\n",
    "    \n",
    "    x_train, x_test = x[cut:], x[:cut]\n",
    "    \n",
    "    # convert to one hot encoding\n",
    "    y_train, y_test = y[cut:], y[:cut]\n",
    "\n",
    "    return (x_train, y_train), (x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get data\n",
    "(x_train, y_train), (x_test, y_test) = prepare_data(isentences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# LSTM\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = prepare_data(isentences)\n",
    "\n",
    "# Input\n",
    "I['LSTM'] = Input(shape=(maxlen-1,), name=\"input\")\n",
    "# Transform every character into a 128-dimensional vector:\n",
    "E['LSTM'] = Embedding(len(dic), embedding_size, mask_zero=True, name=\"embedding\")(I['LSTM'])\n",
    "# The RNN layer with dropout\n",
    "H['LSTM'] = LSTM(hidden_size, activation='tanh', dropout=dropout, recurrent_dropout=recurrent_dropout)(E['LSTM'])\n",
    "\n",
    "# The readout layer\n",
    "R['LSTM'] = Dense(embedding_size, activation='sigmoid')(H['LSTM'])\n",
    "\n",
    "# The output layer \n",
    "Y['LSTM'] = Dense(len(dic), activation='softmax')(R['LSTM'])\n",
    "\n",
    "models['LSTM'] = Model(inputs = I['LSTM'], outputs = Y['LSTM'])\n",
    "models['LSTM'].compile(\n",
    "    loss='categorical_crossentropy', \n",
    "    optimizer=Adam(),\n",
    "    metrics=['acc'])\n",
    "models['LSTM'].summary()\n",
    "\n",
    "\n",
    "\n",
    "# plot_model(models['RNN'], to_file='recurrent_neural_network.png')\n",
    "\n",
    "logs['LSTM'] = models['LSTM'].fit(x_train, y_train, \n",
    "                                epochs=epochs, \n",
    "                                validation_split=validation_split, \n",
    "                                batch_size=batch_size).history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SimpleRNN\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = prepare_data(isentences)\n",
    "\n",
    "I['RNN'] = Input(shape=(maxlen-1,), name=\"input\")\n",
    "# Transform every character into a 128-dimensional vector:\n",
    "E['RNN'] = Embedding(len(dic), embedding_size, mask_zero=True, name=\"embedding\")(I['RNN'])\n",
    "# The RNN layer with dropout\n",
    "H['RNN'] = SimpleRNN(hidden_size, activation='tanh', dropout=dropout, recurrent_dropout=recurrent_dropout)(E['RNN'])\n",
    "\n",
    "# The readout layer\n",
    "R['RNN'] = Dense(embedding_size, activation='sigmoid')(H['RNN'])\n",
    "\n",
    "# The output layer \n",
    "Y['RNN'] = Dense(len(dic), activation='softmax')(R['RNN'])\n",
    "\n",
    "models['RNN'] = Model(inputs = I['RNN'], outputs = Y['RNN'])\n",
    "models['RNN'].compile(\n",
    "    loss='categorical_crossentropy', \n",
    "    optimizer=Adam(),\n",
    "    metrics=['acc'])\n",
    "models['RNN'].summary()\n",
    "\n",
    "# plot_model(models['RNN'], to_file='recurrent_neural_network.png')\n",
    "\n",
    "logs['RNN'] = models['RNN'].fit(x_train, y_train, \n",
    "                                epochs=epochs, \n",
    "                                validation_split=validation_split, \n",
    "                                batch_size=batch_size).history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRU\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = prepare_data(isentences)\n",
    "\n",
    "I['GRU'] = Input(shape=(maxlen-1,), name=\"input\")\n",
    "# Transform every character into a 128-dimensional vector:\n",
    "E['GRU'] = Embedding(len(dic), embedding_size, mask_zero=True, name=\"embedding\")\n",
    "\n",
    "# The RNN layer with dropout\n",
    "H['GRU'] = GRU(hidden_size, activation='tanh', dropout=dropout, recurrent_dropout=recurrent_dropout, return_sequences=True)(E['GRU'](I['GRU']))\n",
    "\n",
    "# The readout layer\n",
    "R['GRU'] = TimeDistributed(Dense(embedding_size, activation='sigmoid'))(H['GRU'])\n",
    "\n",
    "# The output layer \n",
    "Y['GRU'] = TimeDistributed(Dense(len(dic), activation='softmax'))(R['GRU'])\n",
    "\n",
    "models['GRU'] = Model(inputs = I['GRU'], outputs = Y['GRU'])\n",
    "models['GRU'].compile(\n",
    "    loss='categorical_crossentropy', \n",
    "    optimizer=Adam(),\n",
    "    metrics=['acc'])\n",
    "models['GRU'].summary()\n",
    "\n",
    "# plot_model(models['RNN'], to_file='recurrent_neural_network.png')\n",
    "\n",
    "logs['GRU'] = models['GRU'].fit(x_train, y_train, \n",
    "                                epochs=epochs, \n",
    "                                validation_split=validation_split, \n",
    "                                batch_size=batch_size).history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save models and layers\n",
    "save('models/dict.pkl', [I,E,H,R,Y,models,logs])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save\n",
    "#save('models/rnn.pkl', [models['RNN'], logs['RNN'], x_train, y_train, x_test, y_test])\n",
    "\n",
    "save('models/gru2.pkl', [models['GRU'], logs['GRU']])\n",
    "\n",
    "#save('models/lstm.pkl', [models['LSTM'], logs['LSTM'], x_train, y_train, x_test, y_test])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load\n",
    "gru_model, gru_history = load('models/gru2.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rnn_model, rnn_history = load('models/rnn.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lstm_model, lstm_history = load('models/lstm.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def viz(ix, iy, iyHat):\n",
    "    # recover string representation\n",
    "    \n",
    "    syHat = recover(iyHat[:, None])\n",
    "    sy = recover(iy[:, None])\n",
    "    sx = recover(ix)\n",
    "    \n",
    "    print('{:^75s} |{:^15s} |{:^15s}'.format('input', 'target', 'prediction'))\n",
    "    print('{:^75s} |{:^15s} |{:^15s}'.format('-'*75, '-'*15, '-'*15))\n",
    "    \n",
    "    for i, v in enumerate(zip(sx, sy, syHat)):\n",
    "        input_, target, pred = v\n",
    "        print('{:75s} |{:^15s} |{:^15s}'.format(input_, target, pred))\n",
    "        if i == 100: \n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy(model, ix, hy, show=True):\n",
    "    # target: change one hot encoding back to integer\n",
    "    iy = np.apply_along_axis(np.argmax, 1, hy)\n",
    "    # compute predictions\n",
    "    hyHat = model.predict(ix)\n",
    "    # prediction: change one hot encoding back to integer\n",
    "    iyHat = np.apply_along_axis(np.argmax, 1, hyHat)\n",
    "    \n",
    "    n_samples = len(iy)\n",
    "    n_errors = len(np.nonzero(iy - iyHat)[0])\n",
    "    # simple viz\n",
    "    viz(ix, iy, iyHat) if show else None\n",
    "        \n",
    "    return 1 - n_errors / n_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy(rnn_model, x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot GRU history\n",
    "\n",
    "plot = plot_history(gru_history, \"GRU History\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plot LSTM history\n",
    "\n",
    "plot = plot_history(lstm_history, \"LSTM History\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plot SimpleRNN history\n",
    "\n",
    "plot = plot_history(rnn_history, \"SimpleRNN History\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimal network size\n",
    "\n",
    "Compare the learning curves for three networks with 64 (previous exercise), 128 and 256 GRUs (single layer) and one with two hidden layers of 64 GRUs. \n",
    "\n",
    "**Code** Build and train the networks. Apply EarlyStopping (monitor='val_acc', min_delta=0.001, patience=2). Use transfer learning, do not train from scratch your embedding layer, rather re-use the embedding layer from your best performing network in the last exercise. [4 pts]\n",
    "\n",
    "**Figure** Show the learning curves (training and validation loss) for the four models. [1 pt]\n",
    "\n",
    "**Figure** Show the learning curves (training and validation accuracy) for the four models. [1 pt]\n",
    "\n",
    "**Question** List and briefly explain the differences in the learning curves for the different models? [2 pts]\n",
    "\n",
    "**Answer**\n",
    "\n",
    "**Question** What effect had EarlyStopping? Give one advantage and one drawback. [2 pts]\n",
    "\n",
    "**Answer**\n",
    "\n",
    "**Question** What is your best model? Why? [1 pt]\n",
    "\n",
    "**Answer**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the patience for EarlyStopping\n",
    "patience = 2\n",
    "# Define Early Stopping Callback\n",
    "es = EarlyStopping(monitor='val_loss', verbose=1, patience=patience, min_delta=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1 Layer, 64 Neurons GRU\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = prepare_data(isentences)\n",
    "\n",
    "I['GRU64'] = Input(shape=(maxlen-1,), name=\"input\")\n",
    "# Transform every character into a 128-dimensional vector:\n",
    "#E['LSTM'] = Embedding(len(dic), embedding_size, mask_zero=True, name=\"embedding\")(I['LSTM'])\n",
    "print(E['GRU'])\n",
    "E['GRU'].trainable = False\n",
    "# The RNN layer with dropout\n",
    "# Use the embedding layer from previous models (Transfer learning)\n",
    "H['GRU64'] = GRU(64, activation='tanh', dropout=dropout, recurrent_dropout=recurrent_dropout, return_sequences=True)(E['GRU'](I['GRU64']))\n",
    "\n",
    "# The readout layer\n",
    "R['GRU64'] = TimeDistributed(Dense(embedding_size, activation='relu'))(H['GRU64'])\n",
    "\n",
    "# The output layer \n",
    "Y['GRU64'] = TimeDistributed(Dense(len(dic), activation='softmax'))(R['GRU64'])\n",
    "\n",
    "models['GRU64'] = Model(inputs = I['GRU64'], outputs = Y['GRU64'])\n",
    "models['GRU64'].compile(\n",
    "    loss='categorical_crossentropy', \n",
    "    optimizer=Adam(),\n",
    "    metrics=['acc'])\n",
    "models['GRU64'].summary()\n",
    "\n",
    "# plot_model(models['RNN'], to_file='recurrent_neural_network.png')\n",
    "\n",
    "logs['GRU64'] = models['GRU64'].fit(x_train, y_train, \n",
    "                                epochs=epochs, \n",
    "                                validation_split=validation_split, \n",
    "                                batch_size=batch_size, callbacks=[es]).history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1 Layer, 128 Neurons GRU\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = prepare_data(isentences)\n",
    "\n",
    "I['GRU128'] = Input(shape=(maxlen-1,), name=\"input\")\n",
    "# Transform every character into a 128-dimensional vector:\n",
    "#E['LSTM'] = Embedding(len(dic), embedding_size, mask_zero=True, name=\"embedding\")(I['LSTM'])\n",
    "print(E['GRU'])\n",
    "E['GRU'].trainable = False\n",
    "# The RNN layer with dropout\n",
    "# Use the embedding layer from previous models (Transfer learning)\n",
    "H['GRU128'] = GRU(128, activation='tanh', dropout=dropout, recurrent_dropout=recurrent_dropout, return_sequences=True)(E['GRU'](I['GRU128']))\n",
    "\n",
    "# The readout layer\n",
    "R['GRU128'] = TimeDistributed(Dense(embedding_size, activation='relu'))(H['GRU128'])\n",
    "\n",
    "# The output layer \n",
    "Y['GRU128'] = TimeDistributed(Dense(len(dic), activation='softmax'))(R['GRU128'])\n",
    "\n",
    "models['GRU128'] = Model(inputs = I['GRU128'], outputs = Y['GRU128'])\n",
    "models['GRU128'].compile(\n",
    "    loss='categorical_crossentropy', \n",
    "    optimizer=Adam(),\n",
    "    metrics=['acc'])\n",
    "models['GRU128'].summary()\n",
    "\n",
    "# plot_model(models['RNN'], to_file='recurrent_neural_network.png')\n",
    "\n",
    "logs['GRU128'] = models['GRU128'].fit(x_train, y_train, \n",
    "                                epochs=epochs, \n",
    "                                validation_split=validation_split, \n",
    "                                batch_size=batch_size, callbacks=[es]).history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1 Layer, 256 Neurons GRU\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = prepare_data(isentences)\n",
    "\n",
    "I['GRU256'] = Input(shape=(maxlen-1,), name=\"input\")\n",
    "# Transform every character into a 128-dimensional vector:\n",
    "#E['LSTM'] = Embedding(len(dic), embedding_size, mask_zero=True, name=\"embedding\")(I['LSTM'])\n",
    "print(E['GRU'])\n",
    "E['GRU'].trainable = False\n",
    "# The RNN layer with dropout\n",
    "# Use the embedding layer from previous models (Transfer learning)\n",
    "H['GRU256'] = GRU(256, activation='tanh', dropout=dropout, recurrent_dropout=recurrent_dropout, return_sequences=True)(E['GRU'](I['GRU256']))\n",
    "\n",
    "# The readout layer\n",
    "R['GRU256'] = TimeDistributed(Dense(embedding_size, activation='relu'))(H['GRU256'])\n",
    "\n",
    "# The output layer \n",
    "Y['GRU256'] = TimeDistributed(Dense(len(dic), activation='softmax'))(R['GRU256'])\n",
    "\n",
    "models['GRU256'] = Model(inputs = I['GRU256'], outputs = Y['GRU256'])\n",
    "models['GRU256'].compile(\n",
    "    loss='categorical_crossentropy', \n",
    "    optimizer=Adam(),\n",
    "    metrics=['acc'])\n",
    "models['GRU256'].summary()\n",
    "\n",
    "# plot_model(models['RNN'], to_file='recurrent_neural_network.png')\n",
    "\n",
    "logs['GRU256'] = models['GRU256'].fit(x_train, y_train, \n",
    "                                epochs=epochs, \n",
    "                                validation_split=validation_split, \n",
    "                                batch_size=batch_size, callbacks=[es]).history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2 Layers, 64 Neurons GRU\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = prepare_data(isentences)\n",
    "\n",
    "I['GRU2x64'] = Input(shape=(maxlen-1,), name=\"input\")\n",
    "# Transform every character into a 128-dimensional vector:\n",
    "#E['LSTM'] = Embedding(len(dic), embedding_size, mask_zero=True, name=\"embedding\")(I['LSTM'])\n",
    "print(E['GRU'])\n",
    "E['GRU'].trainable = False\n",
    "# The RNN layer with dropout\n",
    "# Use the embedding layer from previous models (Transfer learning)\n",
    "H['GRU2x64'] = GRU(64, activation='tanh', dropout=dropout, recurrent_dropout=recurrent_dropout, return_sequences=True)(E['GRU'](I['GRU2x64']))\n",
    "\n",
    "H2 = GRU(64, activation='tanh', dropout=dropout, recurrent_dropout=recurrent_dropout, return_sequences=True)(H['GRU2x64'])\n",
    "# The readout layer\n",
    "R['GRU2x64'] = TimeDistributed(Dense(embedding_size, activation='relu'))(H2)\n",
    "\n",
    "# The output layer \n",
    "Y['GRU2x64'] = TimeDistributed(Dense(len(dic), activation='softmax'))(R['GRU2x64'])\n",
    "\n",
    "models['GRU2x64'] = Model(inputs = I['GRU2x64'], outputs = Y['GRU2x64'])\n",
    "models['GRU2x64'].compile(\n",
    "    loss='categorical_crossentropy', \n",
    "    optimizer=Adam(),\n",
    "    metrics=['acc'])\n",
    "models['GRU2x64'].summary()\n",
    "\n",
    "# plot_model(models['RNN'], to_file='recurrent_neural_network.png')\n",
    "\n",
    "logs['GRU2x64'] = models['GRU2x64'].fit(x_train, y_train, \n",
    "                                epochs=epochs, \n",
    "                                validation_split=validation_split, \n",
    "                                batch_size=batch_size, callbacks=[es]).history"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate sentences\n",
    "\n",
    "Now you will generate new sentences from your best performing model.\n",
    "\n",
    "**Code** To achieve that, use the provided sample function below to generate new sentences from your model. You should start by constructing a sentence that starts with the 'START' artificial word and all other words being the 'PADD' artificial word. Then sample the first word from the corresponding probabilities given by your model. Add this word to the sentence and continue like this until you sample the 'END' artificial word or the maximum sentence length. [2 pts]\n",
    "\n",
    "**Code** Generate 10 sentences for different sampling temperature in [0., 0.25, 0.5, 0.75, 1., 1.5., 2.]. [1 pt]\n",
    "\n",
    "**7 Figures** For each temperature, use matplotlib imshow to plot the probablities of every word in one generated sentence (and only these words) at each time step. y-axis should be the words that are present in the sentence. x-axis the timesteps and the imshow value the probabilities given by the model for all words in the sentence at each timestep. Use the a colormap where 0 is white, e.g. cmap='Greys'. [2 pts]\n",
    "\n",
    "**Code** Finally, seed your model with two different beginnings of max 4 words and let it generate 10 possible continuations (use sampling temperature of 1.). [2 pts]\n",
    "\n",
    "**Question** What is the effect of sampling temperature on the generated sentences? [1 pt]\n",
    "\n",
    "**Answer** Tempratures determines how randomly the next word in the sentance should be chosen.\n",
    "\n",
    "**Question** In terms of sampling a probability distribution, what does a sampling temperature of 0 corresponds to? [1 pt] \n",
    "\n",
    "If the sampling temprature is 0, there is no randomness involved, and the the word will highest probability will be selected.\n",
    "\n",
    "**Answer**\n",
    "\n",
    "**Question** In terms of sampling a probability distribution, what does a sampling temperature of 1. corresponds to? [1 pt] \n",
    "\n",
    "If the sampling temprature is 1, then the probabilities will not change because exp(ln(p)/1) = p. Therefore, the each word will be selected randomply with probability p.\n",
    "\n",
    "**Answer**\n",
    "\n",
    "**Question** In terms of sampling a probability distribution, what does a very high sampling temperature corresponds to? [1 pt]\n",
    "\n",
    "**Answer** There is a higher chance that the words with low probability is chosen. I.e the words are chosen more or less uniformly.\n",
    "\n",
    "**Question** Based on the plotted word probabilities, explain how a sentence is generated. [2 pts]\n",
    "\n",
    "**Answer**\n",
    "\n",
    "**Question** Do you observe timesteps with more than one word with non-zero probability? How do these probable words relate in terms of language? [1 pt]\n",
    "\n",
    "**Answer**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def plotLines(lines, labels):\n",
    "    \n",
    "    for i,l in zip(lines,labels):  \n",
    "        plt.plot(x,i,label='l')\n",
    "        plt.legend(labels)    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample(preds, temperature=1.):\n",
    "    # helper function to sample an index from a probability array\n",
    "    if temperature == 0.:\n",
    "        return np.argmax(preds)\n",
    "    preds = np.asarray(preds).astype('float64')\n",
    "    preds = np.log(preds) / temperature\n",
    "    exp_preds = np.exp(preds)\n",
    "    preds = exp_preds / np.sum(exp_preds)\n",
    "    probas = np.random.multinomial(1, preds)\n",
    "    return np.argmax(probas)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_sentance(temperature=1., sentance = []):\n",
    "    \n",
    "    unique, counts = np.unique(sentance, return_counts=True)\n",
    "    counts = dict(zip(unique, counts))\n",
    "    \n",
    "    for x in range(counts[0]):\n",
    "        # Predict the probabilties next possible words\n",
    "        yHat = gru_model.predict(sentance)\n",
    "        # Randomly sample a token\n",
    "        sampled_token = sample(yHat[0][x],1)\n",
    "        # Stop generating a sentance if sampling the END token\n",
    "        if (sampled_token == 2):\n",
    "            #print(\"END OF SENTANCE\")\n",
    "            break\n",
    "        sentance[0][x+(len(sentance[0])-counts[0])] = sampled_token\n",
    "    return sentance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "temp = 0.1\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x8000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['blue , you should get to know , like us and shrimp love it']\n",
      "['which ones are you made ?']\n",
      "['good evening , tacos and look better than me but no south do .']\n",
      "['oh you have a wonderful day ! what happened ? dance ?']\n",
      "['i am good thank you ?']\n",
      "['yes she is . i like cleaning videos']\n",
      "['hi how are you today ?']\n",
      "['that is pretty bad . mine is nothing else about that so i am not very late , more bread .']\n",
      "['it is pretty family very much about both . how about you ?']\n",
      "['i am so happy to hear !']\n",
      "temp = 0.25\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x8000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['hi , i am doing well . i hope you been to high school .']\n",
      "['i wish i lived on my parents and watched']\n",
      "['it could be good for me . you should look of painting .']\n",
      "['yeah it is blue . i like animals']\n",
      "['what are they studying ? i have no have not had a wedding driver']\n",
      "['i hate my hair , i wish my mom died']\n",
      "['i bet you must be very relaxing .']\n",
      "['that would be a only hobby lately , are you sick ?']\n",
      "['yoga and i am going there which tea is sweet .']\n",
      "['yeah i play hockey . do you cook much ?']\n",
      "temp = 0.5\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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1CSPi6RrttNUhjHZqVE5t61yGu1IltlKp1IRUvGRZFXDEAEIIcYPS2iRcIkZeEVbdy9bTWJVU07pOppHb3WHd4g4LL3l0jWq6LG+0Lrq0A793AVznXdd5pQfoIqy2cVnL5XItwiqPxSoQf7ciImv6QUoIIeQ1KK1NxiSvqriKadNLXATVXsde+1X3yWirGVd5CCKDYeMn2gmENyar6+34RiXVa/umeb/TunXySyeo5XJ5iqzK0jo5OYlMJjPlOOQfnKqwdurfHCGE2KC0xoRrrp5JVsU0AKO4qvuI8tZsu9EO0S6vKHGYImnap03C1GVB9t+IsLp8Vv3b0wmrLKuysJbLZaTT6Sl/f3InLJFCIKKtunoRQgh5DUprwrClCKh5dfLFTSeu6sUyrFuz7YJrdFJdnjT5DzPa6ec4/EQ8XbfdiMj6kVTXsq7CWiqVau9CWMV6efvZbLYWXa1UKtr0AEIIIXoorQlCd5HXRVp10grUi6ttqKuoomztjCo2SRNXtR66+Si/dy9hNe3P7/6D3vr3iq6axNYUaZVfQlRlYS2VSigWi8hms7XtyekAYtxWeWgsQgghdjpGWjdu3Fi7gORyOeRyuZhrZMcU8dFdSNUojctDBfwKTFKELGpcI11xtUejkbigt+D9jtdq+3yUbWeSV1tZr0i17m9OnlY7Y8npAV1dXUilUrXlcscseTvqfgghpBPI5/PI5/MAgGKx6Fm+a3h4uK3/Q46NjWH58uUYGRnB4OBg0/brR37UC6HuwmiKtIqIjYjeyJEcObKjWw6YO+R4jUbQDgRNDwCiaze/Muj6eVNqQ9D6+k0NsG3fpU667TYadTUtVz8jR1V1OaxylFWNvKbTaWSzWWQymbr3dDqNdDqNTCaDTCaDdDqNVCpV92L0lRDSSRQKBcycORO7d+9Gf3+/tkzHRFqTHMHQ1csUEZUvZEJadZKq25a8ryB5ru0UbQ1yLniNpxnm0FBByvsRxUZRx4v1mjfVw2+9dHcR/E6b6mqKJou/r2q1WouUiml5G6IcgNqPyVQqVSesqpzqflwSQgjR0zY/5b/whS/gt7/9bdzV0GK6te8i0qZoqbgg2qKqtougum9dXZIq+Y3SyHE1U1hdz5WgP8iiEFl1Xl4WtZCZ9qWb1tVNXWf7u5NfImqqi6iqkVVZWsX21LsfhBBC9LRNpHXz5s1xV6GGi0C4SobpQqZ2ugoarfHKN+zE6KopStiMuvhdHpRmfqdRpqDYIry6uxW2adNnZGnV/YAwya0YzkpEWFVpVSOtQduAEEI6hbaR1rhx7fShW66LlHndvlfL2SJHprq4XLB1n221i2rUAuq3PVxzUaOot1ddG/1u1XMqqv2o23JNVXCRWrV+QkTVXv6myLIsrXI0Vkiq+m5L7SGEEPI6lNYQiDJqZhJJOQ9PFlfxri6z1cNPRKqVaDQNoFniFWanpkbLhYWp/Wz1aKSOOlEFvM9xl3Xy4P+mOxzy0FUiwirSd9R0AjU9gMJKCCFudIy0NrMjlp+omamnM2DuIKIu93pX96d+vh0EVSWM79oWNQxLFm2Cqlunbs81Eh9GXXXlXYQ67HPKJsSudyxc02DkH4ZytFVNCZCfbCXeRaRVja7ahJUCSwghZjpGWptB0Nu+JlkV0zp0QuuaF+clrmq5sPE6trC2HxbNEgiTsJqmXaTLlSiPMYqUBK9Irh95VetgE1o5r1V8ThZaeTg6tbOV+hKfUwWYEEKIHkprSAS5xRskTcC2zM+tV5u4RiWwLpHDRmlWNN0FP1FWr2m/chVG/Vy30WgaRhSo7aIuN7WnrU7iWCcnJwG8LrBiuZgXj24VUVfdKAHqtLxfiishhOihtIaAH2F1lVevi64fTJ9xkdSwxNKWHhFVbmgS8fr+VUk1TbvceneNvDeKKWXBpWwY+3XNPbZFqV3qJtpaN4qAPK9GY3XRVN1DPhhpJYQQOx0jrc3KafXKSTStsxFVNFKW0rAlVbc/2/p2ulgHPRabsKrCleQ85GbXxfQDz1ZOLmvKZZXXiTbX/Wgwyau8X91LXUcIIcROSzxcYOnSpRgfH4+7Gp7YomPqRU0to8N0sRORGttDBfzU0fQeBq7banSfSYiy+pEPl7b2+n6CRO3bHT9/D7ZyXn936u19taOV6bGsLo9a7qTvixBC/NAxkdao8duJxparqM67rFO3bVquq7dr+kDUEb1mRAxdbwW7EGZdXQXV9J0kLdqaNFx/yNnK6SLc6udNkXHdu2kZIYQQPYmU1ocffhjbt2/H9OnTccoppxjLjY+PY+vWrXj22WcBAGeccQZWrlzZrGrWcBVTdVrdhstFTV2nS0FQbxu7RCGjkJ8kRD9lTGLvSrOE2iuiGuS7cr2FbqtX0ONvZN/NwOu45L8hW0qG7W/Ra3+UVkII8SZx0nrgwAFcd911+OpXv4r58+fjm9/8prHszp07US6Xcdttt2FiYgKXXHIJ3vjGN+KMM85oWn0bEVYv6VDz3tRl4t0WQbVJsvx5U55ksyN4QfbnKuVB9xXV8btE6kwRedecVpsgBZHXIJJsWx6mxEZ5nqptRskkhJDmkzhpfeaZZ3Dcccdh/vz5AICzzjoLt956q7bs448/josvvhjd3d3o7e3FsmXL8Pjjj2ul9bLLLkM2mwUALFu2DLlcLrJjiAKbhIaRm+onXcFr3jW1odPRfZ+m71hdHkc7NiqsUdKsfYaxnyRGmwkhJA7y+Tzy+TwAoFgsepZPnLQ2+g/ddFG5+uqrMTg42NC2TftTo5bytCkyZqur7Xawuh+1vMu0vA2viLDXNsS72onEtMx23F7rbJ/xOm+SIH0m1Gi3bZnt3bTMa78u5ZJM0utHCCFETy6XqwURC4UCbrzxRmv5xI0ecOKJJ+J3v/sdXnzxRQDAfffdZyy7aNEi3HfffahWqxgfH8eDDz6IRYsWNauqnrhGH00IqaxWq7Wn7Ihp9ek78rQso2I7rvuoVCqoVCool8solUoolUooFouYmJjA4cOHMT4+jkOHDmFsbAzj4+OYmJjAxMQEisVirXy5XK5tR7zUOsVBkE4vcUYN/eQ4J0nc4v6eCSGEtCeJi7TOmjUL69evx2WXXYbBwUGcfvrpxrLnnXcetm7dilWrVgF4rSPWkiVLmlVVLbqInksnDrHcRlQyYBNi8apUKlPEWB7iRze0j/wSTwfym38ZNkmSO8A+Vq4pr7gVxNWFMM7nVjtmQgghwekaHh5u67DI2NgYli9fjv3790eSHiDQ3dK3zbuWcSkbtDeyTlLVCKm6rFwuo1wuAwDS6XRtPErTtDyGpXilUqlA9XU5nrAJW4pcOl3Z1gWNvtqOI6ofQ83YZytKKyPRhBAylUKhgJkzZ2L37t3o7+/XlklcpLVVMeVM6vJXdZFXHS4yI2/HNYKp7ldETmUpladFqoCcMlCtVpHJZGqvbDaLTCaDdDpdk10hr6Z6eC2Pm2Z27vHKafXKW01iGwa9o+BKEo+ZEEJIdFBaI0YnpjqBVdF1klIlU2zLJDfqett+dJIq8lnV3NaJiQlMTk5i2rRp6O3tRSaTQblcRiaTwbRp06Yca1dX15TnsavtEAauHYtctxUFXnW0deYz1a9ReQ2z3VR0HRAJIYSQIFBaQ8R2cTaJqu4zqrDa5tXe+q7iqtZHpAcIYRWdq+ROWIcPH8ahQ4cwPj6OSqWCvr4+9Pb2oq+vD+VyuSasqpzKOa26/XcqphEhdLnPJtn3O+1Sl7BphbQNQgghyYfSGgFekVSXSKtY5/WSo5hCXNX9eEVadaMGqCMGjI2N1Y0aUC6XcfjwYfT396NYLKJcLtd10pIjrOVyWRtpjYJG5avZMmQSV9069XO2+UbrkmQorIQQ0plQWiPGJotivS7KanqpPfjlKKocyTTVQcikLtIqUgOEsI6Pj2N0dBRjY2O1dyGtajRWrpe8HzGigC4CGxVBb0cnQYa8cqFdZLWR40j6rfwkfEeNkNR2JYSQVoDSGhNqFNQ0uoAsqyL3VJ5XO+XoxNXWAUqNtAoZnZiYqEVWDx48iIMHD9bJq8hzVcdhFfLc3d1d64iVyWTqhLZZuAhYEiTIlgLgkhag21YUdYqTJHxPhBBC4oXSGhGuF1mbrMrTQljVhwoISRW338W0XA+XjlhyPmuxWKxFWYWwFgqF2vzo6OgUaRXIY7eK0QQymUysDxhImvCYfqR4iapte0HWuaB+Pq4fHoQQQkjHSKt6Szwp6IY7AvRPqlIH+jd1eBLTLsMmAZgSaS2VSjh06BAOHTqE0dHROnk9ePAg/vKXv9RGEFBzWEV0NZvNIpvN1j0dKylRuyRgyx91yXmO61x2zccOcz+EEEII0CRp3bVrF/74xz9i/fr1AIDR0VGce+65uOOOO6YM+P/b3/4WW7duxeHDh5HNZnHRRRdhwYIFU7a5efNmHH/88fjQhz4EALjpppvQ29uL888/P/LjCYppzEqv9AD5Ua3yoP9qRFbejy7nVVcPOU9WCKsYJUBNDygUCnj11Vexf/9+HD58GKVSqU6SRUqAGPpKFlexD/I6UaYvNEP6WqXjFiGEkPZA33MnZJYvX45HHnkEo6OjAID7778fixcvniKspVIJ//Iv/4KVK1fitttuw0UXXYQrrrgC4+Pjzahm6MidnlxzEL06YKlPrjI9dtWr45NOXEWkdXx8vCauovPV6OgoDhw4gH379mHfvn149dVX8corr2Dfvn21aKwYYUAMi2XKeyX1qOeJ7tVI+ajr3grbJIQQ0vo0JdI6MDCA0047DXv27MHZZ5+Ne+65B1dcccWUci+++CLS6TTe9a53AQAWLFiAmTNn4ve//z1OPPHEhupw2WWXIZvNAgCWLVuGXC7X0Pa8cM0z9JI4VVjVeXW7cpTVBTUFQR3yqlgs1oa9klMERCS8r68PfX19mDZtGnp6emqduMRn1aiwHAEm7rC9CCGEtBv5fB75fB4AUCwWPcs3Laf17LPPxuWXX4558+Zh1qxZOO6446aUMcmMblkqlarrAFQsFtHb22vc/9VXXz0lshslXp2fvJCjZnInK+C1YxdlRKcncXtevGxROhlRXnScEk+5Eg8NEE/EEsNhCQGdmJjAEUccgdmzZ2PGjBno7++vfW7atGmYNm0aMplMrS7iuIUg2447yLp2Ikg0ulPahhBCSPuQy+VqQcRCoYAbb7zRWr4p6QEAMH/+fBxzzDG4/vrra3moujKlUglPPPEEAOCpp57CX/7yFxx77LFTys6dOxe/+c1vAAAjIyN47LHHoqt8QFzGJlXXqaIpS6U8nUqlkE6na9M6eTXdMlanZWnNZDK16Gl/fz8GBgYwMDCAwcFBzJw5E3PmzMHRRx+No446CnPmzMERRxxRK9Pf34+enh709fWhp6cHmUwGqVRKK62mVAZbakM7phboUkHi3E4QKMzusK0IISQ4TR094IMf/CC2bt2KJUuWaNdnMhlceeWV+OpXv4qhoSFks1lcccUV2gjqWWedhSuuuAKrVq3C3Llz8da3vjXq6jeEH3EF6uVVjriqn1MjrEJcvXId5WWyBItoa39//5QIqyCVSmF8fBwzZ87E9OnTMX369Jq0ikhrNputbVeVVjnaahuSS+7o044Xe5fUEBtebcI0DEIIIe1EU6X1iSeewIoVK5BOm3d7wgknYGhoyHNbg4OD2LJlS5jVSwyqyJkeGKBKqyysphQBXbqAEFYxVFWlUkFfX19dPqqohxghYGBgAH19fZg+fXotIiuis0Jc0+l0XdQXeD1HV86/9RpL1rVcK2ESUq8fN6Z8aFsqSju0VzvBURcIISQYTZHWV199FevWrcPg4CDWrFnTjF0mFq9b3qYhqtQHBghsuaymSKsugivEVUhrT09PrXOWQE5DKJVKNUmVUwlEaoBIXZDrLacGiDxdP8La7piGPTPN6wQ2rnaiiBFCCImapkjrkUceiZ07dzZjV0aiHAqo0du8KqbOU7oHBqiy6tUJy5YiID+9Sn4YgCgjR2NLpVItf1V02hKvnp4eZLPZKeLqlRogT7dzaoBLrq6LrOrap1MEv9Wh5BNCiH865olYUWK6ALncApY7zei2I48eoA4ZJaTVK7pqE3Y10lqpVJDNZuvqIT9SwybKAAAgAElEQVQ0QOS8lkqlupEGent70dPToxVWU06rTqKTGklsFn6EVif7uvlmEZaIUbwJIYTooLQ2EdfOWLYLthADNQpqSgNweckjEmQymbp9CWEVEdZp06bVnoYlxmYVy4XUyi+5IxYAVCoVT9E2HXc7ov6A0U2r8zpZ9RpirVnt104RxKh/KKkRc0IIIXYorRHhFTEL0jNcTQ0Qy3SSqps37UfIqSysYp0qtD09PTh8+DDK5TIymUxtlAAhqEJw5Uirmh4gi6uuk5lLW7QbLvKqywF2mW42YchYnJF11zzisGgn0SeEkCihtDYB1wgaYM7l1MmKXEaVVd0yU06rnH4gRnZQ0wZEHmupVEJvby/K5fKU8WHVaVlYRZ1FWoBIbZD3pbaD2kbtJq9eKSVi2iSugD0vOG5aTV5d6hlV++rOd0IIIfVQWiPANWpmwpTbaeqwpCunE1i1nFxWHZ1AfYiByHUtl8u1jlqmTmBiW+rwW7pOWKKcGFFA1z5JFLIwscmr+m6SVF3bJKW9bHVwFbSoj8WPKEZdlyR8Z4QQ0mxc/vdF/kSspUuXYnx8fMryRx55BDfffLP2M5deeikeffTRqKsWKl6NbYqgyfmp6vZsOaiyMPoZ9kq3D3mcV3FrX3S46unpqXusqxjaanBwsDbEleiEJR4qIOezqiIq0gPE0FfyWLCmp2F1SuTJJKpe0VeXdUnFK31FJqrjCrLdVmpjQghpF2KLtC5evBiLFy9u2v78XBz94Nq5Sl3mFWm1oZNP07xpWsyLZSJFQEwLgUylUnU9/sW7/IQsVTR10ml6yahjt5qii+2El6jaIq22KGur4ZrXGfaxNpq60OrtTgghrURs0rpnzx48+uijuPLKK63lfvzjH+OOO+7AlVdeiXnz5tWt279/P7Zu3Yo//elPKBaLWLx4MVatWhVltafgcrG1iUgQ4dRdKE0XT9tysX9ZEoU4qp2n1HqbXkJoVckVn5Wjqbp6yu3ZTlKm4vJjxyuntV0F1ouwjjGMaGkntDchhCSFROe0futb38IjjzyCLVu2YHBwcMr6a665Bh/96Edx8skno1KpYMOGDfjJT36C0047LYba2nERV688VVMZE34itrp8SNMxiGld9FWelm/9d3V11aYrlYo2r1aNsIrpdpcCl1v+YtpVVNu93Ro9vjBv77d7WxNCSFJIrLTefvvtOPLII3Httdcim81OWT8+Po4nn3wSBw4cqFv2wgsvaLe3cePG2nZyuRxyuVw0FTdgkg2vz5jew7pI+o3QCmShVFMFdGW8XrrPqMt0Edogx+YX0z7D/A78SpRaXvfjxzZtWuYnih+0roQQQggA5PN55PN5AECxWPQsn1hpPfHEE/Hzn/8ce/fuxfz586esFxfpm2++uTZMk41NmzZpo7Vx4iei6ifCGhdqyoFtDFax3kVi5Qis7kla6nblz+qW2/AjWLqyjXw/uh806rStDq511/34sb3bIolxCWlSoqyEEEKCIwcRC4UChoaGrOUjHz0gKO985zvxmc98Bhs2bMBzzz03ZX1fXx8WLFiAu+66q7bs1VdfxSuvvNLMampxEQEXYbWRpAuvLYIqj0ygvstjueoEV00TkPNh5XQE20vdlgm/UdxGt+Pyo8R2PujyiMVQZJVKBeVyGeVyuTa2rniJ5XJZ8dLlI5uOJaz2CkIShTVJf4+EENKuJFZaAeDkk0/GF7/4RXzpS1/C008/PWX95Zdfjv/7v//DqlWrsGrVKnzpS1/CyMhIDDV9DdvF1CQlNjFJelTVttxFXMW0XE6XmynLqyqrqlzZZNUmYC74EbVGJcYms7r2kMVVSKksrsVisU5a5Ze6DRdhdcHrh0SQNkny3wQhhJBo6RoeHm7rEMHY2BiWL1+OkZGRyNIDvCTJa71XdM1PjmIzMY2vqluuSpEqtS55r0FSKLyi2C4S1UhOq+2Wvu38MK2zvUztLecJqz8g5Pa3PSzClHbh2lZ+2idIGReaEQ2N+2+SEEJalUKhgBkzZmD37t3o7+/XlklsTms74ZKjmCQZ9Yuor3pcYrxXMa3Kp/zkLPE5m1TqOmi5RLeD4pJSEMZ+dOeHmkuq63il/kBQR23Q/VCQH72rRr7l+qhj5prqoWsP03KbwEd9zjfr9r3XOUkIISQ4lNYQsEmpTVa95NVrXdzIxyTmVYT8qPIql7dFQdV9yMvFOp0omERQ3Y4JP5LjJWW2VArbuWI7Z8S0aFs1N1U3Lz/1TLyLHxbpdLq2bfGdmdrW1ga2ZUHbqRGYa0oIIe0DpTViXGXVZT6JuNZRFSHb53XRVPl2uSx1sryKz+ra209bughYFOJlE21bmoDaoUrubCXnropH9FYqFaTTaUxOTtaeeAYA6XS67geEn9xV27ypfFSpAXGLKqOthBASDZTWCNBFIHWiZrt4t6LACkzHE7QzjinS6rX/ILeeXYQ16LZ12M4VUyqJXAe1A5aQVXW0gGKxiHQ6jUwmg0wmM6WzlSyrfr4rL2G1/UDxm+7hQtzCKqC4EkJI+FBaI8JLVOVl6udcScqF0asOJhHzc0tZnteJTqNt6Vof3Xov8VKXufyo0cmqGnlVo6zqMFdixIBSqYSJiQlkMpma1JpGCFA7YvlJoXCdD1tUTfuLm6T8fRJCSLtAaQ0J3e1UL1F1idZ5XfTivjB63Y43rdflq+pSAeR39bOqrNo6C4Vxm9n2PTYqYrb8YJPsyZ2whLjKklosFlEsFjExMYHx8XFkMhn09vZqz1MhqSJdQBZaFxm0tZWf9on7fA6bdjseQgiJk0SP03r77bejVCrFXQ1nbB1uTJFAv5/REeSWe9iot5fVlzp6gPxQAZ2ACmRx0g3jpHvpCBoVdJ33i5/zQV2utokcaRXCKl5jY2M4ePAgDh48iNHRUYyNjWF8fLy2Xh6/VY7aBk3lkOun1lW3XJ0OStznv40k140QQlqJREvrjh07UC6X466GL7yipqYIoWmdH5p9cZRFVbzrRFUdB9Qmq7rjlwVKHtLJJq464XJtH9fP2cTLZV9eP1hMkWSxfXWYK/nJV8ViEYcOHcKhQ4cwNjaG0dHRmrQKcRWR2GKxWJcPa0odcJFOk7B6lW13OulYCSEkKhKbHrBlyxYAwNq1a9HV1YVrr70Ws2bNqivzwAMPYNeuXejq6sKcOXOwfv16zJkzJ47q1qGLinmVa3V0qQFiue72sJ+IqJy/qX5WHl9Ud3u90duzOskK89a2LaXBq4yc06o+/Wp8fLwmrOI9m83WJFf+4dDd3V0bBkseJstvpNUmrHLb6NrJaz0hhBCSWGldt24d7r33Xmzbtg29vb1T1v/hD3/AzTffjFtuuQVz5szBnXfeieuuuw6bN2+OobZmXHJSw6TZF3xVqGwdouQycuRUxiQ+6lO2ZIS4qtuXp21tEiSCGLZgudTPltMqd8CamJjA4cOH6yKtY2NjKJVKdbmr4j2VSiGTydSGw9I9GterbrplOsm2yWq700nHSgghUZBYafXiySefxLvf/e5aZHXFihW44447jBeGjRs3IpvNAgByuRxyuVxT62vCT96qK3FcHF32p5MuMbi9TjjlsvLtcHUZAOsTnKJoC9M+otifLRoroq3ysFfFYhGHDx+uyevhw4cxPj6OarVa92CBTCaDbDaLdDpdlxog5w274iW2avtQ4AghhOTzeeTzeQBAsVj0LN+y0qq7DW1j06ZNGBwcjLpaiaAVZUD3XYp3OQVAYMqPNW0rjPrp0g+i2p+pDvIximOXJbRcLmPatGno7e2d0mktm82it7cXfX196OvrQ09PD6ZNm1aLssqPdpWfXOZaN1VcdXKq+44IIYR0JnIQsVAoYGhoyFo+0dLa19eH0dFRbXrAwoULsWvXLuzfvx+zZ8/G97//fSxcuLBtL4Y6KTCVaxVkqdEJoSpogmq1OkVY1Y5dJol1rZM6rdZXravf/TSC3NFNCGulUkEmk6mLlsq3+Lu6upDJZDAwMFCT1t7eXmSz2doDBxqRVrV+plQN+V2dDkqrdHJqpb9NQghJIomW1nPOOQfr169HNpud0hHr2GOPxerVq/HZz34WAGodsdoZL3Ft5YuiLddRpBDIuaumCKu6Hde0BVO7ukZYvY4nLGQh10Va1Vv88mey2Sz6+/u1wioe8Sq2p5N/U31sucSNtkW7pBG0wzEQQkjcJFpaV65ciZUrVxrXJyk3tVmYBKtVL4qmfE31eGRxFdKqCpwp0hqkTroIq61+zWx/NdIqpFWNsMpi293djUwmU4uyZrNZTJs2DdlstpbXKrYlS6vANZrpFalWy5rWucAIKyGEdBaJllaiJ6woVpJwOQ4hrELITMJqI4h8qfNewtqM70QV18nJyZq4yvVU0whEhFWWVhFlFeLqN7XCJVLtp410UfdWol3+JgkhJGlQWluUdr4weh2bLqKqSxPwE3U1RVNtkUKXurqWcUWVdSGsuhxWOcoqpFROCVDzWeWhsBqNVAPmYdDijFKHSavWmxBCWhVKK4kV3YVfJztCIHWRVq+8Vtu+XOpniyKGsY8gyMedSqWmDOKvSquItMpDXYnxWcU6tROWn2OxpVC4jOEbBZRKQghpLyitJDZcIpmmcjpJdZVX1/qo9fJKyWhmWoCQUl195REV5IcHiLxWORVAFlbREUt81u8xqe2kLvdaFhaUVUIIaU8orSQRmHIfgfoe5LrOO7p3PzmZunqY1tk+5/czQVHFVV0ni2elUqk9nlWWWN1LN3xY0PoB+lxUlzQNQgghRAellcSC2rEpyJOSbNKqe/dTN8CtA1CzJUtNiTBJqxhtoVKp1IbBkqOvssDaHtJgkleb3Mtl/B5bo1B6CSGkfaG0kljRCatuAH/1IQS2iKtpWZC6CVxueds+HzayTMqyKWRVdNCSx21V81xVUdVNNwvKJiGEEC+mPh+zSXzhC1/AH//4R89yv/zlL7FmzZom1IjEhS6yp4soytKlEy15G/J21emgdbRFHk3HFCbqMaryqctRFUNbTZs2rfbYVjFqgDo2q05kvVIibC8/5QkhhBAvmhJpFXl1Mps3b27GrkmLIN+St+WHBhmCqp0ihur2RZvIL+D1MW0BTBkKSyf2rmkBjdSVEEIIaYTIpHXp0qX4xCc+gZ/97Gc44YQT8PGPf7xu/Yc//GFcc801OPbYY6d89rbbbsOPfvQjHHnkkTjhhBOM+9i/fz+2bt2KP/3pTygWi1i8eDFWrVoV+rGQaNDljobVUacZwhSXlMlyqaZNAKiTVfld/by6Ldv6dnuYBSGEkNYj0khrsVjEV77yFV+f+dnPfoaf/exn2L59O7LZLP7lX/7FWPaaa67BRz/6UZx88smoVCrYsGEDfvKTn+C0005rtOqkiXjdgo7riUhJFDRdpFUlKU+QMrVfUupHCCGktYhUWj/wgQ/4/syTTz6JpUuXore3FwDwd3/3d7jjjjumlBsfH8eTTz6JAwcO1C174YUXtNvduHEjstksACCXyyGXy/muG4kHv/IYlhSF3Ts+TLxE3wWXdgrSlq7R8rDlNYk/MgghhJjJ5/PI5/MAXgt0ehGptArxjAJxW/Tmm29GOu19GJs2bcLg4GBk9SHu+BnWKsnYpKsdjg+YOjQZIYQQEhZyELFQKGBoaMhaPrbRA0wsXLgQw8PDGB8fR6VSwZ49e7Tl+vr6sGDBAtx11121Za+++ipeeeWVZlWVOKB2ElI7B+nWNUqjcmWqm586h3k8UeEnP5jCSgghJG4SN07ru9/9bjz99NNYvXo1jjzySJx88slGEb388ssxNDRU63zV29uLT3/605gzZ04zq0w0BBU2P52ywsRWX68nO9nq3C5R5SgIM1+ZbUwIIe1P1/DwcLLDQQ0yNjaG5cuXY2RkhOkBTSCK6GIQIfFTD11Z2zKXzlAuZZJCnLmllFZCCCHAa+kBM2bMwO7du9Hf368tk7j0ANK6uAqI31vnUXQGMm1brZsuDcBrPmid2wG/8hiGbFJYCSGkM6C0klBw7YnuJXuNbF+lkVEHbPKqG7RfV0fbuiTRytIXZd3DzrUmhBDSGInLaSXth9dFP8re6abcU3Xez7Tao16dbkUJDCO/NOhxB913VO3slS7Sit8vIYS0A4y0kobx24kpaNkwpMpLOExPltKNeqBbZ9pGK9CIjIXxyFc/24hCHF2jqozAEkJIPFBaSWQEGQ4qDhGwRdb8yqluXSsRRB7DFEiv7UU1/FYYo10QQgiJFqYHkEhw6ZFvupVuu8Ue5e13k3i6iGgrpwbosD21qhnHmJThzvx8vl2+e0IISSotL6379+/H5s2bsW/fPkxMTOAjH/lIoMfHkmD4HS5Kt9yvuEaBX2EV9dPVU13XyjLbqvV2JcxIaSt/z4QQ0gq0vLRWKhVccMEFeOtb34o///nPOO+88/C+970PPT09cVeN/H+CdsRqhgS4pgbI73IUsl06YnUiUdza5/dPCCHR0fLSOmfOnNoTsEqlErq6utDdzVTduHDtyAIEj+JFJQZeOarqsamSapomySPKXFR+94QQEg1tY3cHDhzAZZddhosuugjZbDbu6pD/j5+xS/2mFYSF1xBXtrFZdctadfQAEh783gkhJHxaPtIquOGGG3D66afjrLPO0q7fuHFjTWZzuRxyuVwzq9eRuEqpOpZqHFEqVTRdRggA9OPAmqKtjMAlg2YJJb9vQgixk8/nkc/nAQDFYtGzfNtI6+9+9zt87GMfM66/6qqrMDg4WJufnJyMtD5ew/YEwc+t9yCf1dVLXeZ3OCRV8HS32INuPyzUnvK6nFV1mUtbyaj5r34+S1oXiishhJiRg4iFQgFDQ0PW8m0jrZ/85Cdx9NFHG9dXKhVUKpVI9i1flEwCpK5zuZC5RoRsUUHbel3d1He5XJCORiZxdZU+076iEgFTxyq1HXTvJlzazBS9Ja0PUwUIIcQbl/+VbSOt3//+9/HGN74RAwMD2vWTk5ORRle9RMbWaUeHH2G19Xb3ElpVXOX6q+tchnByeSSn3yhlI2VdtqVKqjztV1xdv1OXcpRXQggh5HXaRlo3b95sXR+ltKqy4yJ2JoJGV4WgenUeksuJuupEVV0nH6dfmbINVC+vd13eKKY0BVdh9YPp8y5i2om3loOmsRBCCGl/2kZavYhaWuV3r9vfjciIqTOT+jItN0mr+vI6VhumaKtLTquf/USBTVhtEWoXdN97p0dVg9w6ZyoFIYR0Jh0jrVHltOoik16ddkwXWtsF3FVWTa/Jycm66cnJSXR3d6Orq2vKu5i2Ha9Lu9jENagYR5Ea4OfdVhc1kqr7kWKSVNvydhWzMHI9/QpsK+SXtkIdCSEkbDoqp9WLqCOtOtmzRef84PVFukqqPD05OYmuri6kUilUq1V0d3fX3mUR1x2PLmXA1C4mcTURt6B5CatXFN2EKqWdKKmCqKSs1duOskoIIXY6SlqjGj1AjlQKGZQl1uv2MuBvqCpdpyqdsMqCKo5flVYRcU2lUkilUrXjEct1eZ5+sEmtrmyz0d3+dxXWRnJcTfOkMVqxPSmrhBDiRsdIa7lcRrlcDn27QmxERFKNTuqESF5uw4+windVWIWoysIqpkW9hKzK00JYZXkNmpMbtCNW0HJBMYkrEEyGXFMBOklkmyFordJ+lFVCCPFHx0hrlOO0ikilTl7V6KrrBdVLWOVlutQAIafyS10m6ixeQljVY1O3r8vldLkANyISUUqITlblfdq+M5fjVsuYcl1t+2gFCSPuuKT8EEJIJ8GcVokopVWHGqn06tTjhfplyhKpzuuEVX2Vy+W6vFd5H3J6g269Dj9pAH6JQth06RoueacmATWt12GS1UYju+1K0L+XIN9NM7DVIyl1JISQJNJW0rp69WoMDQ1h2rRpU9ZFmR4A6GVGvrUui5/XxTTIvCmHVZZU8S5eItIqOqil02lUKpW6jliyuHqJadji2mxpc5FVr+/O1gadLqh+zw1dKkw7txOFlRBC7LSVtG7fvt24Tghb2AgpVW+ty+kBptvqLrgKq05cddHVUqlUk1YRedZ1MNJJt98UgCAX4WZKidfoBmokVkaXTmBClS+brHaKwHrhIv4u20haW9qOixBCiJ22ktalS5fi/vvvR29v75R1UaUHTE5O1gmrKd9Tdxtfh+utQ9M2q9XqlBxWWVbld7Xe6kseDSHpAhoUWycxlxxTdVp8zpR/bOrgRYF9HZcfAe3ULqZzhRBCOgnmtEpEnR4gpoUY6yKUfi+2utuj6rS8zBRpFcdeKpVq0losFpFO13/9qqzKnbXU/bQbfjpamTpuuXzeJqlqmU7E9dxq5G8pLlzlNAl1JYSQJNIx0nrDDTcgk8kAABYvXozFixeHsl05/zOp2MYWFaLq9blORdcGpvzeKDujEUIIIe3GAw88gAceeAAAUCwWPct3jLSuX78eAwMDoW+3q6urNjC/eOkeiyrK+t22LqKn6/kuR0nlPFsRGRXCLpNKpZBOp5HJZJDJZJBOp7XHoct3Ja9jE1jdqATqtG57xE4jf0tx4fpDJwl1JYSQZrBs2TIsW7YMAFAoFHDTTTdZy3eMtApBCxtZWoUwink1R1SU94pumi5YJgGStyv2D9gH8xf11gmrOA7xoqjq8RIOnbiK5ep0J7Svi4yFKWym/PI48SOuhBDSSbj83+soadUNnt8oQv5kWVVlrxHp0wmPLcoqR1h1IxuoEVkhrOLldQy8mOrxiorr8li92pJtraeRdkmCuOpIar0IISRJtJW0Dg8PG9cJKYsCWVpl2QtL+kziqtu2eCKX2oFKXt/V1VUbp1WIqpoaoKYHUFi9cRFXuazXdBKwReujImqBi1sQTfsXbUp5JYQQPW0lrTaiirQCmCKsJtlzvRXscgvRFnFV0wS6urqmDPelpjKosqoeA9HjJ51D1+M9icLqIk2mNJWocd2faxQ7LkF0PW8IIYS8DqW1QeRb7boIa9CcUD+5b2q+rIi2imXiKVeyzFYqFaO4mnJaKbCv4Uc4VHGVl6ufs803gyCi5FdeXYWsWXmeYcurWkfbdr3OI0II6SSY0yoRVXqAGt00pQOEJX3yRVaXLynGWBXiKvYpxFWM4arKtp9cVl5Q63GJggNmwUtCezYqbVGMLdvMiGNQeQ1y1ySM/RJCSCfSMdIq3y4PGxdhDbpdwD7AvW4fsrjKoiQ/eEDUV5fWENaQXZ2El6jKy9TPuSyL8oEDYQlTlHX0s91G6iB/tpk9+imvhBDiTUdJq3zbPCxM0VTTsiARV1O0xnTrGah/Ipcaia1Wq3XpATrp9srLJVOx3dJ2FaBOaF8/EdQ42yOsfQc5XsorIYRMpWOkNcqOWDqp85Ma4HJRM0WAvLYpv+Rlpsiw14u8hu2HBBAsShdH+7abHCX1HPWb6pDU4yCEkKhw+b8XfujRB7fffjtKpVJT9qV2Lgr75RWlDDNSaYvkqvWxjRJg64BFcfXG1hau7eWnXVtBMFshghoX/PshhJDGiDXSumPHDvzTP/2T9hGjKpVKpaFIaVTpAYC+V7hOUsO+xS7f9pfn1WnTu62+OjEmU/EbJW+UKPNGW51WaRdb3iwhhBAzsUVat2zZAgBYu3YtVq9ejQMHDtSt37t3L1asWIEdO3bgkksuwXe/+9269bt27cJ1111Xmx8dHcWKFStQKBS0+4s60ur6UAHb7fwgF11dxNVUN/UhCLqHIpiORd0XeZ1mR6HD7DjlWi4KueJ5ZM55J4QQMpXYIq3r1q3Dvffei23btqG3t1dbplAoYN68eVi5cuWUdcuXL8d5552HNWvWYGBgAPfffz8WL16MwcFB7baizGk10YiEuqJ70pKpc5bL0DtBlpHXCfL9BaFZEVe5fkmO8ia1XkFpt+MhhBAvXP7vJbojVjabxZlnnqldNzAwgNNOOw179uzB2WefjXvuuQdXXHGFcVuXX345stksACCXyyGXy0VR5aZDsWxtbN9VEm8dhy2ufjsoEUIIaR/y+Tzy+TwAoFgsepZPtLT29PRYL5Bnn302Lr/8csybNw+zZs3CcccdZyy7adMmYxSWxI+fW9UCP/LUiiJvEzr5lr0aDVXLmbbttSxom7kO+0UIIaSzkYOIhUIBQ0ND1vKxSmtfXx9GR0eN6QFezJ8/H8cccwyuv/56rFmzJuTakWagExm/y1wEzG+EMCnDE+nEVRVW07TuXc2ZNHW8U+ugm3YlalltxR8khBBC/BPrkFfnnHMO1q9fr+2I5coHP/hBVCoVLFmyJOTakahxkbFqtYrJycm6aXleXa8TONP+dPsO0uko6OdcUXubq20xOTmJSqVSN12pVFAul1Eul1Eqlaa8yuVyrZxuG+rxsIMQIYSQuIk10rpy5UptJysAOOaYY3DPPfd4buOJJ57AihUrkE4nOtOBWNDd3nZZZoqwqsN+eclWmLJpi/42gjqEmfpSxV4ntuo4vrrxhXX7dTneIMdDCCGE+KFlTe/VV1/FunXrMDg4yNSANsHvbW6bkMrrbeWC5NKK7XuVj0LM5HYwyakuAlupVNDV1aUd7kwIq3hSmiyvYbSd12cpsIQQQlxoWWk98sgjsXPnzrirQRrES1RtD0YQ87ocTBdh9VM/23Kb1EUlrvKrUqnUxLVcLte9ixSASqVS92S0yclJdHd3Y3JyEqlUqiasAnl8Xt1xhNlxLqroNCGEkPaiZaWVtDamHu9+OhaJ29o6gVKF1a94+YkiekUjw5YxU16ryFOV81XFdKlUqglrOp1GOp2uyevk5GRdeo1oO/EKa+xZLzmNSvIJIYS0B5RWkghsHal0naRc5MYmrF518Vre6OgEQVHbRBZW8RKSWqlUUCwWa52xhLBms9maqOrSHkT+a5Db/y6fCSuySsElhJDOgtJKYsPU694ksOoy3SNyTVFXP3VyXRanuMrCKkdVZVktlUq1dyGtQlgzmcyUUQLk9jSlYweV9OcAACAASURBVHjVyzYv9iGvd2nDJA2zRVEmhJD4oLSSRKATU7FcN6SVrQNWI3VwWaau8xKxMFHbSUirHGkVslosFjExMYFSqYTx8fGaqIrOWbrIsTzCQNjCKpZH1V5RiarrPii0hBASLZTWFkR3S7cdUCOpqqzKL93wTF6osuRSH9O8TbyiFDN5m2pHK1lYJyYmUCwWMT4+jsOHD+PQoUPIZrOoVCro7e2d8uNAHvZK7qjVaDvJ86Z0jTDapxnCGqQO7fK3SQghSSDWhwt4cfvtt6NUKsVdjUThNxrYCuiiq6bUAHVIJ53Qis/p9mOrQyPzzUSXHqCLso6Pj2N8fBxjY2MYHR3F6OgoDh06hLGxsVoZ+UEDunYNUje1nqZ1LrS69LX63yYhhCSJREvrjh07UC6X465GYvCSrla8QOpExhRd1T21yfQ0LHk7jdRJN2+ru9dnGkVtEznSKkdZdcI6NjaGsbGx2jpVXGV5dU3HMB1zWNPtQLsdDyGExEVi0wO2bNkCAFi7di26urpw7bXXYtasWXVlHnjgAezatQtdXV2YM2cO1q9fjzlz5sRR3chxvfBFnVMZJkGFVf6cegs7SJ6pawTWa9vNyGeVp9VIqxplFZIqIqylUqnWniIdQH7IgDwEltzOfiLUunqKd107tdL52gidcpyEEBIliZXWdevW4d5778W2bdvQ29s7Zf0f/vAH3HzzzbjlllswZ84c3HnnnbjuuuuwefPmGGpLGsFLXnUpASoix1XIgZ8IoVdddNNxCogpyirEVURSRSqAeC+Xy3W5q93d3chkMrV3sS1TG3vVyWtZJ8qqTCceMyGEhElipdWLJ598Eu9+97trkdUVK1bgjjvuMF4YNm7ciGw2CwDI5XLI5XJNrS/xT9Bbxp0qByZxtOX76lIpwr6d3anfByGEEDv5fB75fB4AUCwWPcu3rLSqF0Kvi+KmTZswODgYdbVIANTIqHz7GsCUkQKq1WrttrY8tqjuFUZddNNxoru1n06nkclkUC6X0dvbW4uYViqV2uemTZuGvr4+9Pf3o7e3F729vchms8hkMnVPyZIf4eqnTqrsqk8l8/P32o504jETQogNOYhYKBQwNDRkLZ/ojlh9fX0YHR3Vrlu4cCEee+wx7N+/HwDw/e9/HwsXLmzbC4PrcbXS8eskxiSfQs7kd/k2t01WvdrEtl6to23bUbe9ui85F1VIZyaTQTabrUlpf38/BgYGMDAwgP7+/rpXT08Pstls7aVrX6/j0q0zfa+29e1OpxwnIYRESaIjreeccw7Wr1+PbDY7pSPWsccei9WrV+Ozn/0sANQ6YrUzXo/WbMULoynKKuZl5LxVU5RV/lwY0VbdvLxcN62bDwuTzMuPZwXqb/eLtspms+jr60Nvb29NWEWUVchvKpXStqlLyoBLZNrvdDvQbsdDCCFxkWhpXblyJVauXGlc34m5qabbsK2MKka2CJ8YLcCWGmD6rFfE0Caquvm4UIV1cnIS6XQak5OTtbxt3QgBcgRWjrDK4iq2GVZ6haiLPK9O22j1fNhWrjshhCSNREsr0SPLQDtdFNXjMh2bVx6rTWblbXhFD11FtVlRVnUfQjArlUot2ip3uhL1EDKayWTqUgJEKoEabVV/ELjUxY/gR9FeLt9n1LTT3yIhhCQRSmsL004XSfVYZHHV9YAXn7GJaZA6mDoTudTZz7Kg6NID0unX/ozVMViFfAqhTaVSNUmVXyIfVo62+u2MFSSlQjffCPK2ohLYdvqbI4SQVoPSSmJFlzPpGt3T5a42KrGu4hq1nHohpFSMpCALvaiHkE8hpCLaKkQ1k8nUCa0aZfV7TKYIq+mOgOsyPz8aXNYRQghpTSitJDZMnXVMg9DrpEiVK11uq1+BcZVUP+vDQhdprVartWirmu8q0gdkMZWjqvIyNcoapvTb5oOSlCHICCGENAdKK4kFVVj9CojaqUcXGTRN6+rgZ52tLn7WBUUnrgCQTqfrlsk5r2KdLKryS5T3Gj7MtX4CPxF0yichhBAblFYSK0IOTTmtts/5eQ9aN7k+pnUunw8TVVh1EitGFZAfMiCkVR3zVt6OLp/Vr/Cr5cIoQwghhMT6cIHbb78dpVIpziqQBKG7xS/LlCpVrsNehREJ9Rt9jFJYxbvaJrqHDEybNq323tPTU5vXjRjgd+SARn8QBI3kEkII6UxildYdO3agXC47lZUfR0naC10Oqu4lC5XXwwX8RFrDFKeoRMw0aoIsrmrOqpBXIazyyAG6vFbbDwCv4/X7IoQQQvwSm7Ru2bIFALB27VqsXr0aBw4cqFu/d+9erFixAjt27MAll1yC7373u3Xrd+3aheuuu642Pzo6ihUrVqBQKERfeRIKqryY5FWVVVVYddFBnRx5RVyDSlXUMmYTVjnKKk+rUVfdEFe6fFZTRyx1uLEkkdR6EUIICZfYclrXrVuHe++9F9u2bUNvb6+2TKFQwLx587RPxVq+fDnOO+88rFmzBgMDA7j//vuxePFiDA4ORl11EiJCjLw67LiW89pPVOWjwuXxsXIZtXwjY8zqygR5oIXuuyOEEEL8kuiOWNlsFmeeeaZ23cDAAE477TTs2bMHZ599Nu655x5cccUVxm1t3Lix9ojLTnz8a9Lxk1vaKdgkT9dRyrVtXOWxEcl0keIw9kMIIaR1yefzyOfzAIBisehZPtHS2tPTY70Qn3322bj88ssxb948zJo1C8cdd5yx7KZNmxiFJS2FGkXVrY9iu7ZyYf9ooLASQkjnIgcRC4UChoaGrOVj7YjV19eH0dHRwJ+fP38+jjnmGFx//fX40Ic+FGLNCEkGcUeWdRFdF1xkNCxhjbuNCCGENIdYpfWcc87B+vXrtR2xXPngBz+ISqWCJUuWhFw7QpJB3NHNoJ3MbPtphwir6Jzm+iKEENIYsaYHrFy5UtvJCgCOOeYY3HPPPZ7beOKJJ7BixYraYywJCZMobs8HwfWWvo04xEl9JG+rEWado061IISQdqdlTe/VV1/FunXrMDg4iDVr1sRdHdJGBOmo1AwBUffRKhLYCvWMS+gByishhLjSstJ65JFHYufOnXFXg7QZQeWFAtJ6JEWmgwwjRgghnUisOa2EJIkwJCYpIkT0NDPH1M8+eN4QQog3LRtpJSRMws5dZOSsOYQ9Nm0j6PbBCDwhhIQHpZV0PIxyJZtGhS+q79dvJNXrOPhjhxBC7FBaO5BmdyBqJ1zFggISDmGPDdsojeyDUVdCCGkM5rR2ELpcPkYZ3ZDbjm3WHJIkrGHmwbb7+LWEEBIVbSOte/fuxYoVK+KuRmLhhVJP0Cc3NfOJT3ET9OECje4zCQSVVa/Ptcu5QQghzYTpAR2Aq2AlRRSShJd4tEqbBb01rXuMq1/haqV2Ahof9ky3rJWOnxBCkkrTpPXqq6/GCy+8gHK5jKOOOgqf+9znMGvWrCnlLr30Urz1rW/F008/jX379mHRokVYt26ddpt33303vv3tb+OII47AySefHPUhtCRhdxbpJNpF9uXj8FPfMI5LTqlIejsBwYQ16HnSKm1CCCFJoWnSunbtWsyYMQMAcNddd2Hnzp341Kc+pS378ssv4/rrr0e5XMb555+Pp59+GieeeGJdmeeffx7//u//jltvvRWzZ8/G9ddfH/kxtBpBL8C8kPrD1mZxt6cp+udVJ9t610fK6vKnXdoijvaKSlZdtsG/N0IIcaNp0vrggw/iwQcfRKlUwsTEBGbPnm0s+773vQ+pVAqpVApvectb8PLLL0+R1l/+8pc49dRTa9s566yz8NBDDxm3uXHjRmSzWQBALpdDLpdr/KBIy+IlHJ2ec+gl4lFsV92+6ygNrQJHniCEkHry+Tzy+TwAoFgsepZvirT++te/xve+9z1s27YNM2fOxCOPPGJ9BKuQSwDo7u5GpVKZUsbvxWrTpk0YHBz09ZlWxzUipn6m1QljWCI/eEUl4yTIOSCIWwibuf8g+bp+P6M7F1yXEUJIOyIHEQuFAoaGhqzlmzJ6wMGDB9HX14fp06ejVCrh3nvvbXib73jHO/DYY4/hwIEDAID777+/4W22I34ugK18sXR9PKdcTvfySyu0mVrHZtU5ikht1ARpG5fRFSinhBDSOE2JtJ5yyil48MEHsXLlSsyZMwcnnngifv7znze0zTe/+c34yEc+grVr12L27Nk49dRTQ6pt++ESbWvlC2hcQwu1UpvFVVdd5yP5PYxthk3QURLkenHUAEIICZ+u4eHh5IY9QmBsbAzLly/HyMhIx6UHqJguwq18YfUzhmqYxx93h6IoZbyR291hbdsPUZ+/za5zK/89EkJIUAqFAmbMmIHdu3ejv79fW4bjtHYQughSK18gXYQ1SC/3RnrWtyJh5byG2S66OsU1QoMughrW9vysI4SQTofS2oG064XRS1hdRcgmQc0c49REmJG/sLflIvx+I+G69XGNeRo0dUD9PCGEEP9QWknTiSLfTx1A32udKh8uEpQE4YijE5PfIajCShuwRXJdB+tvVDJNmHJ1XcsHLUMIIZ0MpZU0jaCDzYexP5PIylITVl2iOKYoZNXvWLVRpZW4pnQ08p1FJa/q9mU43iohhIRLU4a8IsQmIo2KhE5OTcKq7s8mtkFoFWH12l+zHr7gktLhEjl3rZfL8FRh0SnDzRFCSLOgtJLICdIZKkh5m7CqY7Gq07rt+oksRiFDYQh9kH261iPsunn9uAjzx4b4vpopsba6EEII8aYtpPW5557D8PBw3NUgGvwIRRD5cBmj1fRuWuaHqKQnKQPwe8lhI/V0kdCoI+OCOCQ2CcJMCCGtRNtI60MPPRR3NYhCM8XLS1B17y5Sq5KkCJ0NtZ6unahs82KZ3+/VTzu5iqztc2Gga78wvvdWOHcIISSpxNIR69lnn8Wtt96KsbExVKtVnHvuuViyZMmUcj/84Q/xne98B+VyGdVqFatXr57y5KsDBw7gG9/4BsbGxrB69Wq87W1vw7p165p1KMRAUIkIo/OKq7gmVRwaETCvMUC9OjnZ5qMaJ9Ul1UPUXe2Epdt3MzpAJfXcIYSQdqbp0jo6OootW7bgmmuuwRFHHIGRkRGsWbMGCxYswOzZs+vKvvOd78SZZ56Jrq4u7N27F2vXrsWuXbuQTr9e7VmzZuGCCy7Ao48+iiuvvLLZh0MiwK902CKkOmHVDXdlk6BmEpWwBq2DOh3FaAvqvkxpHF7fT9zfHSGEkGhpurQ+9dRTePnll/H5z3++tqxareKFF16YIq179+7F1VdfjVdeeQWpVAojIyP405/+hHnz5vne78aNG5HNZgEAuVwOuVyusQMhRuLMx/TqXCXmGxnzs5MxiatJGMUyW5TXtB/5XffDIik/NAghhAQjn88jn88DAIrFomf5pktrtVrFm9/8Ztxwww2eZb/85S/jE5/4BN773vcCAP7+7//e6aB0bNq0CYODg4E+S/zhV1Ci3re6zEVGW02C4hjGyTQd1n5UGZUFWPdOCCGktZCDiIVCAUNDQ9byTe+IddJJJ+Gll17CE088UVv23HPPoVQqTSk7OjqKY445BgDw4IMP4uDBg9pt9vX1YWxsLJoKk6bjV0K8Iny6d9N03ALUzP27CGizJFXeptd35fp5Qggh7UXTpXX69OnYtGkT7rjjDlx44YU4//zzceutt2ojc2vXrsUXv/hFXHzxxXj++edx9NFHa7e5cOFCHD58GBdeeCG2bNkS9SEQB+IQBy9R9ZLUVo3kRSmQYtqrg1cj+/D60eHyHRFCCGl/uoaHh5MxIGREjI2NYfny5RgZGWF6QAxENTySn7FXdWX8yGocguTabkHq5JXva5v3Sq2wzXvlG9vW+4mQRx0RJoQQEj6FQgEzZszA7t270d/fry3TFuO0kuQSR66lusxPxDUpETw/t8PD3I9tPqyOaX4FNKiQxv0dEkIICRdKK4kcV3kIIhlenahsUuoiqnGKj+22fJj1chHXZnVeM6UFqNtnZJUQQjoPSitpCl45kWFHDU3iqspq0E4/zcRPfmkU+3RZ51diXSO46nfm9VlCCCHtC6WVNA2dfDRDVk379ppOElHLqkk6Xb6vZqQpmOoTdj0IIYQkl1ge40o6m6jloqura0oHHq8OPS7z7YLcPupywLvzles6r/3q5uX9+0mNaNfvihBCyOsw0kpaEr95mKa0AN28y/bbGddOYFFEWG3bjjLSSwghJPkw0kraBl2k0E+k1bSsHTFFW+X1Aq/Ip9fnXcv6ifB6re+U75EQQjoJp0jr7bffrn1iFQDs3bsXK1asCLVShDSCKXKqvrw+I69rR1yPy29UNahMmr6bRutDCCGkPXCS1h07dqBcLkddF0J84So3LhIbdB+tjqso+tmWSzk/9XL9rvzUgRBCSOvhmR4gHou6du1adHV14dprr8WsWbOmlPv617+O//qv/8LY2BguvvhinHrqqVPKjI+PY+vWrXj22WcBAGeccQZWrlw5pdxLL72EzZs3Y3x8HNVqFYsXL8aFF144pdyzzz6LW2+9FWNjY6hWqzj33HOxZMkS76MmbYPXbW61bJjl2g3dcds6bIW1L79PTbNtixBCSPviKa3r1q3Dvffei23btqG3t1dbplAo4K//+q+xatUq/Pd//ze2bdumldadO3eiXC7jtttuw8TEBC655BK88Y1vxBlnnFFX7u6778app56Kj370o7Xtq4yOjmLLli245pprcMQRR2BkZARr1qzBggULMHv2bJdjJ20CpSU6mtG2/P4IIYS4EEpHrJ6eHrz3ve8FALztbW/DH//4R225xx9/HBdffDG6u7vR29uLZcuW4fHHH58irW9/+9txyy234PDhwzj55JOxaNGiKdt66qmn8PLLL+Pzn/98bVm1WsULL7ygldaNGzcim80CAHK5HHK5XNDDJYQQQgghDZLP55HP5wEAxWLRs3wo0ipkEABSqRQmJyedP6uLsixZsgQnnXQSfvGLX+B73/sevvOd72Dz5s11ZarVKt785jfjhhtucNrPpk2bMDg46FwvQgghhBASHXIQsVAoYGhoyFreqSNWX18fRkdHG67cokWLcN9996FarWJ8fBwPPvigNor60ksvYebMmcjlclizZg2eeeaZKWVOOukkvPTSS3jiiSdqy5577jnjKAeEEEIIIaR1cYq0nnPOOVi/fj2y2ayxI5YL5513HrZu3YpVq1YBeK0jlq7j1EMPPYQf/vCHyGQyqFar+PSnPz2lzPTp07Fp0ybcfPPNGBoaQqVSwVFHHYWrrroqUN1I8vE7jichfnEdu5YQQkjz6RoeHm6s627CGRsbw/LlyzEyMsL0gBaGMkGiIsjoBTznCCEkXAqFAmbMmIHdu3ejv79fW4ZPxCKJxkUoqtUqJYL4ppGhtoI8JYwQQkhjOOW0EpJ0Gh3rk3QWYZ0vPO8IIaR5UFpJYvErBBQI4kLY5wnPO0IIaQ6UVpJIKAIkCqL6IcTzlRBCoofSShJHGLmGhDRCtVqtnUs8pwghJBlQWgkhREInqa4dAgkhhERHqNL605/+FCtXrsTq1avx+9//PsxNO7F06VKMj483fb8kWVAeiI5GxVOOvhJCCGk+oQ55de+99+KCCy7AGWecEeZmSQdBKSBxwXOPEEKSTWjSunXrVvzqV7/Ciy++iG9/+9vYtm1b3frx8XFs3boVzz77LIDXnoa1cuVK7ba++c1vIp/Po7u7G29605tw6aWXYmBgYEq5hx9+GNu3b8f06dNxyimnhHUopA3g2K3ED36E1XZu8bwjhJDoCC094JJLLsHxxx+Piy++eIqwAsDOnTtRLpdx22234cYbb8RPf/pTPPTQQ1PKPfbYY9izZw+2bduGr3/96+jp6cH27dunlDtw4ACuu+46XHXVVRgaGkImkwnrUEhMMNJF4sB03tnSAXiuEkJI82naE7Eef/xxXHzxxeju7kZvby+WLVuGxx9/fEoqweOPP473v//9tcjqihUr8K//+q9TtvfMM8/guOOOw/z58wEAZ511Fm699Vbj/jdu3IhsNgsAyOVyyOVyIR0ZIaTdkKWU0VNCCImGfD6PfD4PACgWi57lY32Mq+5C4HqB8Bvp2LRpEwYHB319Jk7CjuTwoks6FdfRALw6Yfkpr2L6+9Mt598qIaRTkIOIhUIBQ0ND1vJNG/Jq0aJFuO+++1CtVjE+Po4HH3wQixYtmlLub/7mb/CjH/0Ihw4dAgDs3r1bW+7EE0/E7373O7z44osAgPvuuy/aA4gYcStSvSWpLq9Wq5icnKx76cqoF1TbuqTAi3V74XVORr0v29+BbZlpO+rfnXhVKhVfL/nvVv37FfsnhBAylaZFWs877zxs3boVq1atAvBaR6wlS5ZMKXfKKafg97//PT75yU+iq6ur1hFLZdasWVi/fj0uu+wyDA4O4vTTT4/8GKJCdxE1zdsiPjbp6zQhDPt4de2e1DYNIwIY1T7DbEe/nae8Puvn7851HfD68eneu7q6ptxd0i0jhBACdA0PD7f1z/qxsTEsX74cIyMjiU0P0F0ATRdFdb16sfMzbVsWJ2FEmsI4pjjkrxEabbcgxxBHGksjguznB6I8b/u7tL0LMVVFVZ42lUnCOUUIIc2iUChgxowZ2L17N/r7+7VlYs1pJfW4XATldzEtLm666IyI2sjTahmXSG0r0ehxBBGxONswLHH0ewxR3Mb2ijB67dM1L9Xv3Q3d36DLtE5MbULaLn+DhBASBZTWhGATVJu0inmvi53LLcekyKss2kE+G5QwJKzZt3XjEEfX/QZNBQjShq6yqs67rFP/FnU5qLr8V3Ec3d3d6O7ursmqOl2tVtHdXd+9IO6/QUIISSKU1pgxXSRdpNXr4i6LqldEVq1T3BfNIOIat7DK22pG+8UljmFEO12k2HRHwHV/YeSomsRU1wFS7Vw1OTlZJ62pVKpOUuV0gMnJyZq4MqeVEEL0UFoThu62pFf0Vb74qRdjv8Iq7zvui6YfcY27ripJaD8vmQs7h9WP+IcV1dft02tZkGmdqIr5SqVSm5eFVYhoKpWqfUaIKwB0d3fXyuhENYooOiGEJBWX/3mU1gSgi/p43YIUy9SLnNwjWaDmtfqpV9ziJcu2V5mgBIlYxi3+Ycij1znhp/5Bo6uNtJGLsDYiq2JeFVbT0Fe6lyymQl5TqVRt+yLyqv6dx/13RwghSYTSmhBUKbUJq7xO/rxu2Bx53lbeVq8kXEB18tqsetnkKAltI2OLroYhp3736bIfl+U2GbXtL+i0PK9GV9UxWnVjtqrSKouq7W4Io6uEEGKG0ppwTMIqR2RMvZFtF8GkyZYrYdfbSxJc1ielLb3kMUjEM+jx+U1NCFOcXeTVT3mdtMpyqnuvVCool8tIpVLIZDJ120in06hUKrV2ELmvFFdCCLFDaU0YXpKq3qpUex2rhNHhKkli1kz83GaPKorZKKaIZFgi6hrxDFNSXSOqpnWu0VXx7iWs6qtcLtfe0+k0qtUqMplMbR9qByxVXBttH0IIaVcorQlBd8tfXq4Kq3gHYBVXr3xWXhz1+I12NbMdg8qjuq5RkfRzi95lf0HaMKiwupY1RVp1wloqleqktVQq1f3AFMjSKg97ZaobIYSQ16C0JgDb7Uh5mdozWb0Q6sZ6lEUgqluzrYrXLWzT8lZoq6DRzqgioK7bDkNk1X27CqtJgMXfn5yrqkZW1ZcQ2MnJydp25FQekesqXuJvlcJKCCFmKK0JQncxteXUyeVEz2SBGmFV5dWvrHSq2ApUiUlS1NpFFsV7VD9YvOpg2l8j0V0/067CakoPUH8w6iKsxWKxNl8ul1EsFuvSAoDXf/CIYa66u7tRqVQ803wIIYR0kLRu3LgR2WwWAJDL5ZDL5WKukR2v3Do5KpNKpWoRHXnMR8D8gAHTPjtZTIFw8lgbKRs1QW/Buw70r1vnJaphto9NWE1lbWIrL5Mjo2JeRFPL5XLtXQhrsVhEtVqtPVxACKroiCX/0NTdYSGEkHYnn88jn88DAIrFomf5jpHWTZs2YXBwMO5qEEIIIYQQ1AcRC4UCbrzxRmt53pMihBBCCCGJp22k9Qtf+AJ++9vfxl0NQgghhBASAW0jrZs3b8bxxx8f6LMin4L4g+3mH7ZZMNhu/mGbBYPt5h+2WTDYbv5pG2ltBJ44wWC7+YdtFgy2m3/YZsFgu/mHbRYMtpt/KK2EEEIIISTxtP3oAWL4mEKhYCxTLBat66NEHQNSNy6k+kABMcyOGE4nlUrVDVouhr0S8+qg5roHEQhsQw+p6+JstzDQDS3k54EDurYytZ9YHkabuY7LappX6+g1L5aZxkJV510eKmCb160T7eY6HqvXMt16XXl5XFYxnJX6qNZSqVQbq1VMl0ql2jitYnmxWEQ2m0WpVEI6nUYmk6m9UqkU0uk0UqlUbbrRsVtb/e8zLthu/mGbBYPtVo9oC9uwf13Dw8NtPSjgK6+8gnPOOSfuahBCCCGEEA++9a1vYc6cOdp1bS+tk5OT2LdvH3p7exMzwDshhBBCCHmdarWK8fFxHHHEEcY7TW2fHtDd3W00dkIIIYQQkgwGBgas69kRixBCCCGEJB5KKyGEEEIISTyUVkIIIYQQknjaPqfVi7vuugvf/e53MTo6ikWLFmH9+vWYPXt23NVKJLfffjt27NhRt2zx4sW46qqrYqpRMnn44Yfxve99D//7v/+LsbEx/PCHP0Qqlaqtf/HFF7FlyxY888wzmDVrFs477zx84AMfiLHGycCr3ZYuXTrlM1/72tfwlre8pZnVTBR33nknHn74Ybz44ovo6+vDu971LqxZswYzZ86sleH5Vo9Lm/Fcm8pdd92FPXv24M9//jOmTZuGk046CR//+Mfxhje8AQDPMx1ebcbzzD8dLa0/+MEPcMcdd2DDhg2YO3cutm3bhiuvvBI33HBD3FVLLCecj0crRAAABa1JREFUcAKuvvrq2nw2m42xNslkYmICCxcuxKJFi7B9+/a6deVyGRs2bMBb3vIW3HzzzXjmmWewZcsWHH300Vi0aFFMNU4GtnYTfOlLX8Lb3/722vyMGTOaVb1E8tRTT+Ef//Efcfzxx2NsbAxbt27Fl7/8ZWzZsgUAzzcdXm0m4LlWz9y5c/GpT30Kc+fOxdjYGHbs2IENGzbgzjvv5HlmwNZmAp5n/uhoab377rtx9tln4/TTTwcAfO5zn8O5556L5557jr90DKTTaUaiPfjbv/1bAMAvf/nLKesee+wx/PnPf8att96Kvr4+HHvssfif//kf3H333R39zx2wt5tg+vTpPP8kNm/eXDe/du1arF27FqOjoxgYGOD5psGrzQQ81+o544wz6uYvuOACXHjhhdi/fz9+85vf8DzTYGszcW7xPPNHx+a0FotFPP/883jHO95RWzZ37lwcc8wxeOaZZ2KsWbJ5/vnn8Q//8A/42Mc+hq985Ss4ePBg3FVqKZ599lmccMIJ6Ovrqy1buHAhfvOb38RYq9Zh8+bN+NCHPoRLLrkEjz76aNzVSRwjIyPIZrPo7e0FwPPNBbXNBDzXzExMTGDPnj14wxvegJkzZ/I8c0BtMwHPM390bKS1UChgcnISs2bNqls+c+ZM/OUvf4mpVsnmbW97GzZs2IB58+Zh7969+NrXvobLL78cX/nKV/jgBkcOHDhQ9w8L4DnnyoUXXoiFCxcilUrhpz/9KS677DJce+21HR3JkSkWi9i5cydyuVwtF5jnmx1dmwE810w8+uij+PKXv4yJiQn81V/9Ff7t3/4N3d3dPM8smNoM4HkWhI6VVtuzbYmed73rXbXpN73pTXjjG9/4/9q7f5f02jCO45+nE0UQSVlQGC2BZj/EpZb8D1raMyKa2puDEAorCiJCoTWE6A+IhhoapJbCoSiKQCizwUyCIAifIR55/HpSvi3nyHm/JtHl4uKDXOfc97mPwuGwbm9v5fP5LKwMThAOh0uffT6fstmsDg4O+IOX9PX1peXlZUnS/Py8xdXUh2o9I2vmgsGgdnd3lcvltL+/r0gkoq2tLavLsrWfetbY2EjOfsGx2wNcLlfpCvH/8vl8xRUjzHk8HrW2tiqTyVhdSt1ob2+vuPtA5n7H6/WSPX2/qjoajSqdTmt1dbVsmZu8mavWMzNk7VtLS4s8Ho9GRka0uLioh4cHnZ2dkbMqfuqZGXJWm2OH1qamJvX395c99JHJZPT8/KzBwUELK6sf2WxW7+/v6u7utrqUujEwMKCbmxt9fHyUvru4uJDf77ewqvp0f3/v+OwVi0Wtra3p6upK6+vramtrK/udvFWq1TMzZM1csViUYRjk7C/81zMz5Kw2x24PkKTJyUltb2/L6/Wqp6dHOzs7CgQCnBzwg1gspvHxcXV1dSmTySgWi2loaEher9fq0mylUCjo5eVFj4+PkqS7uzsZhiGPx6OxsTF1dnYqGo1qZmZG19fXOj4+rnii2Ymq9e3y8lL5fF5+v1+GYej09FRHR0el5V2n2tjYUDKZ1MrKiiQpl8tJ+l5JMgyDvJmo1bNkMknWTMTjcYVCIbndbr2+viqRSMjlcml4eFjNzc3kzES1npGz3/nn5OTE0Zs79/b2yl4usLCwwPETP1haWlIqlVKhUJDb7dbo6Kjm5uZYAvrD4eGhotFoxfebm5sKBoNKp9OlQ7g7Ojo0PT2tiYkJCyq1l2p9+/z8VDwe19PTkxoaGtTX16epqSmFQiELKrUPs8PJJSmRSJTu2JC3crV6dn5+TtZMRCIRpVIpvb29yeVyKRAIaHZ2Vr29vZLImZlqPSNnv+P4oRUAAAD259g9rQAAAKgfDK0AAACwPYZWAAAA2B5DKwAAAGyPoRUAAAC2x9AKAAAA22NoBQAAgO0xtAIAAMD2GFoBAABge/8Cj87bf0O95z8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x8000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['i bet . do you like to ride ? i try to try and eat those food .']\n",
      "['oh that is great . what are you into ?']\n",
      "['hi , just got back single . how are you doing ?']\n",
      "['i bet you are good to way to get into your life']\n",
      "['summer of course is nice but think about men']\n",
      "['chocolate is my favorite are great .']\n",
      "['my dad would love to happen in mountains .']\n",
      "['i take care by fun , unfortunately , bacon a nice place to watch because i like to stay up late to date in the park .']\n",
      "['i will sometimes you took books playing my bike too .']\n",
      "['yes i guess . cooking for my dog .']\n",
      "temp = 0.75\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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VXTvZRreinukxIiIiolwIhUJobGxEZ2cn6urqUp4veKW4mP3yl7/E7373O8TjcTQ0NOCb3/xmQcejq+bmgtP3s+sflsemtk2owVeuLGc7FiIiIqJcYii2cfnll+Pyyy8v9DBSDHc5ZfW1difPORmLTD2Zz7QCn3gtwzEREREVA4biEpVNMLZbcENXBXZSGTY9Jx4zTf8mTuazOxmPiIiIaKQwFFcI3cwTulkndJysmiffFtVi8Tq32520YIjLdWR6bDG7hTo+hmMiIiIaaQzFJWw4bRS63t90wVi9b7qIUJxIJKxqsHwt9iffVnuSGYyJiIhoJDEUl7hs2yh0U7I5CaS6pZ7FinfytbitrrAniHmQ5RYKBmIiIiIqFIbiCqCrxMq3TcFYbZsQTMtAi9vifjweh9vtNs7dLAKxLpAzHBMREdFIchV6AMVo7ty56O/vT3l8y5Yt+N73vleAEdnLZsEQ0zzBdhd1n2o4jsfj1iUWiyEWiyEajVq35W1EcDZdiIiIiEYSK8UVxFQFdnqtkqvDIujKoTcej1t9w4LH47HG4mSFOyIiIqKRUJKV4qeeego/+tGPAAC7du3C3Llz8cc//hEAcP/992PTpk3a1z366KO48sorsXTpUnz729/Gvn37RmzMxSabSrE6W4XaQqFWiuUqcSwWSwnMpmoxERER0UgryVB8xhlnoLu7GwCwfft2TJs2Ddu3bwcAdHd3o6mpKeU1v/71r7Fnzx60t7fjnnvuwbnnnos77rhjRMedT07DpGk5ZrtwrNuX7sQ6NRDLoVgOzCIQq6E4089CRERElCsl2T4xfvx4AMC7776L7u5uLFmyBOvXr0c4HMY777yDyZMnp7zm+eefx5tvvolly5YB+Hi6sFxqaWmBz+cDAAQCAQQCgZy+f67YzT6h3pa3Ec/J1/LJdSIYy9VheYU7OWjLr5FPuGMgJiIiolwJBoMIBoMAgEgkYrttSYZiAJgxYwa2bduGd999F6effjoSiQSee+45nHrqqUlTf8kuv/xynH/++XkbU2trKxoaGvL2/sNhmpdYd1+38p3dqnTy9Gputzupgux2u63H5cU71Eq0ek1EREQ0XHKRMhQKoaOjw7htSbZPAEdC8aOPPmpVhT/zmc/ggQce0LZOAMDnP/95PPnkkwiFQgCAWCyGv/zlLyM23nzLR5i06y1W5x+WL16vFx6PBz6fD16v17ovnpdfqwvIRERERCOtZCvFTU1N2LdvnxWCm5qa8OijjxpD8bx58xAKhfCNb3wDABCPx3H++efjlFNOGbExF5t01WPTa+TV6ETLg646L5Z4lgOxfJEDsdhevlbHRkRERJQvVV1dXWziHKZwOIzm5mYcOnSoYO0TTkKjaVo1u8ecLNChu8hTsukCsalqrFaQs/mcRERERKpQKITGxkZ0dnairq4u5fmSrRTTx4YTFHXVYbW/WDc7hW6ZZvn5RCJh3TaF4HSzXOiofc9EREREucBQXEFM7RGmYAwkh1AxK4UIxHIbhQi28Xg86VruO9bdziYYi3ExGBMREVGuMBSXuFwFQ7t+Yt2Kc2q1WFdNloOz0yoxgzEREREVAkMxWexaKdTwqbZPyBVldQ5iuWKsm5JtOLNPMBgTERFRLjAUl7BswmC6GSaczPwgt1Goi26ozwEwhmCeTEdERETFgqG4ROX65DrT++u2U0+ukx8Xr1Grx3aVYVO12GkVmNViIiIiGq6iWLxj48aNiEaj2ufef/99XHTRRSM8oo+tXr0ajz/+eMH2r5OLAOj0PewCrbqQh9o3bDf1mtP2CS79TERERCOhKELxAw88gFgsVuhhlIRcVkQzeS+7MCy3QqgBWbfMc7qqsU66YMzgTERERMNR8PaJtWvXAgCuueYaVFVVYc2aNRgzZkzKdvfffz9+//vfIxwO49prr8XnPve5lG26u7tx//33IxKJIBaL4bLLLsM//uM/pmy3Y8cOtLe3Y9q0aXj11VcRj8exYsUKTJo0yXasr7zyCtauXeto21zLV3uA2q5g2kY3+4R4TO4nNr2PaYaJbGeeICIiIsqlgofiG264AU8//TTa29vh9/u124RCIUycOBGLFy/GSy+9hPb2dm0onjhxItra2uB2uxEKhbB06VLMnDkTRx99dMq2f/vb3/Dv//7v+MY3voGnnnoKGzZswJo1a4zj/M1vfoOf/exnWL16NcaNG6fdJtsZFIqFqX3B9JwaltUwnG6hjUwfZ+8wERER5UvBQ7ETNTU1mD17NgBg6tSp2Lt3r3a7UCiENWvWYM+ePXC73ejp6cHf/vY3bSg+7rjjrGrv1KlT8eijjxr3/+yzz8Lj8WDdunWor6/PwScqHelOfNPNTJFPDMZERESUDyURin0+n3Xb7XYjkUhot1u7di3OOussrFq1ClVVVVi6dCkikYij94zH48b9n3zyyXjllVfw17/+FdOnTzdu19LSYr1vIBBAIBCw/VylxkkYtdvGFJgzDdLDfZ9MK9SVzMkxtWu7scPjTURE+RYMBhEMBgHAmAmFogjFtbW16O3tNbZPOHX48GGMGzcOVVVVePnll7Fr166cjG/ixIn48pe/jJtuugnXX389zjjjDO12t9xyCxoaGqz7uayalnOAsJsiTp0DWb2WX6fraVZP4DNNB6e7XQqcfMcy/Sx2x1u9dnrMTdeldryJiKi0yEXKUCiEjo4O47ZFEYovu+wyLF++HD6fz3iinRNLly7F7bffjocffhgnnHACpkyZkrMx/t3f/R1uvfVWrFixAkuXLrXaOUZKuv7cUuckGMshLJFIpDwuLuI53ep5djNgiHmWi/kYZ/NDS7dKoZPXmC52z2c6H7XcDlPMx52IiMpfVVdXF+eyGqZwOIzm5mYcOnQoqVKcT6UaIDKdWk0sFy2HXflaXOLxeMrtqqoq49RwdtPJmVbaK6R89GqbPqP8g0P98aF7Tr4GkDL9nnobgPF5IiKifAmFQmhsbERnZyfq6upSni+KSjFlrlwrx2rFWA1kcvBVL7FYLOm22+2Gx+NJWkREvS+HNLXiWQychuF00+CZXmM6UTLdjxDTjxH1h4j8I0T827pcLiQSiaQVEIvleBMRUeViKC5x5RgodMFYDcOxWCzpEo1GrWtx8Xg88Hq98Hg88Pl88Hg82ou671KRSdU9k2nuTD9ETD9G1B8idhfxvrofI6V07ImIqPwwFJeBUgoUdr3Duu3kHmIRyuTgG4vFEIlEEI1GEYlErMvg4CC8Xi+qq6vh9XoRjUbh8/ng9Xrh8/mS3lvucx3ustLZ9O86fS8nz5m+C3bfEafBWP4xot4W/xYul8v6ISKq8h6Px6o2ix8huTjWREREucRQTEVPBCoRxEQgFuFXXCKRCPr6+jAwMIC+vj74fD7U1taipqYGfr8fsVgM1dXVSX3K6ol2uTiRze654f54cTIdnam1xmnLjSkQi+BrqsxHIpGU6rzX60UikbDC8NDQELxeb85+hBAREeUKQ3GZKMdqMYCkcCaH4kgkgoGBASsQ9/X1IRwOIxwOo6+vzwrF/f39qKurQzQaRV1dnRXyxDjERcx/Lf6s73RsmcokmGb7WLp9OfmuqKFYblXRVeaj0SgGBgasSrzP50tqu5DHqc4AwlBMRETFgKGYCsJJGFJP+pL/ZC/CcH9/P/r6+hAKhaxQLC4+n8+qIIs/86sVYnFCWDweh9vtTtpvunENRy5OlLQLyelWItS9Tl2u2xSI5RaVSCSC/v5+DAwMoL+/H16vF7W1tUkn4MmL7Zgq8wzGRERUaAzFOZTPPwU7nVu2VKrFgPNgLIfieDyeFMpEID58+DAOHz6M3t5ehMNh9Pb2orq6OikMyxViMSOC6HsVf+YfiUCse79MK8dO7tsF43TfFd1xl6v08o8SUZ3v7++Hz+ezArSub1scd7kyz0BMRETFgKG4RJRa4HVKrU7qyOFM9K+qgVgOxiIcq6FYDmhqKI7H40m9tKZx5Eu6E+EyuW96z0zaJsRFHDtdH7cIw729vVb7ijjm8nzGAFICcTweh8fjYU8xEREVDYbiElKuwRhI/XO/TD7JTv4TvmiTEEE4FApZ4fjgwYNWD7EazOQp2kT/qxqcVSMR3MS/byYn7ulOsAOSf2yYgnG6IK5Owyb3EYu2ld7eXisU9/b2YnBw0PqBIchhWMxGoR5vBmMiIiq0sgnFP/vZz7B3714sX74cANDb24v58+fjwQcfTFll7o033kBbWxsGBgbg8/lw1VVXYfr06SnvuXr1akyaNAkXX3wxAODuu++G3+/HV7/61bx/HhMnf/Yu9eCshji1UixmmRChuLe31wrFPT09OHDgAD788EOMGjUqJRSLUCbCcCQSQXV1tRX8Ch3OMtm/KRCL+07Cr+l95eMuzzghArE47uIi7ovp7oSqqip4PJ6UUKxWi4mIiArN+an2Ra65uRkvvPACent7AQCbN2/GrFmzUgJxNBrFd7/7XSxcuBD33XcfrrrqKqxcuRL9/f2FGHZWKi1E6KqVYto1tVosAvH+/fvxwQcfYP/+/Vb1WIQ38Wf/gYEBqy1Arlw6bU8YCabgm6vbdvvV9ROLWSbkYy//MFFPdlSPtdrjXWnfZSIiKl5lUymur6/HF77wBWzZsgWXXnopnnzySaxcuTJluz179sDj8WDmzJkAgOnTp2P06NHYvXs3pk2bNqwx3HjjjfD5fACAefPmIRAIDOv9CCkVS7mfWAQzUa08ePCgFYj379+P+vp6a/ov+YQ6MVVbX18famtrtX3H8v6LmS7sZlIl1j2vTscm93KLQCyOnxyIDx8+jGg0ar2PqAp7vV5rIZVIJJLSrlLsx5iIiEpXMBhEMBgEAEQiEdttyyYUA8Cll16Km266CRMmTMCYMWNwyimnpGxjCgm6x8SfeIVIJAK/32/c/y233JJSmabsiX8Teeo0EWzl+XCrq6tRXV2Nuro6jBo1Cn19faivr0+6iO1EL7EIbCI0y0QgTDeufMu00qvrR852rOrUaeL4q8d9cHAQPp8PNTU1iEaj1r+FWElQbOv1epNObJSPe6m3+xARUfEKBAJWkTIUCqGjo8O4bVmF4uOPPx7jxo3DunXrsGzZMuM20WgU27dvx4wZM/Dqq6/i0KFDOPHEE1O2HT9+PHbu3AkA6OnpwbZt2zBv3ry8fgYnKilEyIFY9AJXV1ejtrYW/f39GBwctHpe5WnXXC4X6uvrccwxx2D06NGor69HXV0damtr4ff74ff7UV1dnRLQ0s1A4WS8Tjh9/3TbqUFY3n+mt+XH1B8iYqo1sUqgvMSz/EOzurra+iFSV1dnHfOamhpUV1dboVhcMlkshYiIKJ/KKhQDwAUXXIC2tjacc8452ue9Xi9WrVqFO++8Ex0dHfD5fFi5cqW2AnzhhRdi5cqVWLx4McaPH48pU6bke/hpVVIgBlIDmvhzfG1tLQYGBhCJRJJOkpMX5qivr7cC8ahRo1BXV5cUiOWqsXpynzynsToeUwi1k2nIzmQWjFxWiOXXq7NGiMqv6DGW2x/UUCwCsTjeolrs9XqTArH4tyIiIiq0sgvF27dvx0UXXQSPx/zRJk+ebFs+FxoaGrB27dpcDm9YnISHcgwYYtYIOZiJdgm1QizPMCFXLUeNGmXdFtViEdB0leJEIpEUNk0VVSeLbwx3QRAngdpuLOkqw7r3kpe/lhc3MfUCy+0VIhCLcCx+gMihWARutlAQEVGxKJtQvH//ftxwww1oaGgwtk6UskoNDaZKsQjFdks3izYLOaCJP+eLP+WLvmIR0EQgFiutmY672FbXj+x09cFMHnciXa+8XVhWH1NDsdy+In6E1NXVWdvK8z+LCrG4lkOxPCUbK8VERFRMyiYUH3PMMdi0aVNBx1Do/8CXa7hQA7EczOQeYvVEvOrqavj9fisYy6FYDmlut9val6mnOJuALF6X7rF8zL6QLgDbfVfUE+w8Hk/S8VaXyxY/LsS/jajEyyfdiRPv5GNe6P+9EBERycomFFP5kf+ML1ctxQIRugqxvDCH1+uF3+9HTU0NamtrrYtcufR4PCnhTG2fEM+b+nYzWRwj3YIb6Y5HOk5nVnH6Ovm4ywttmGam8Hq9qKmpsf4NRKuLONZyPzFrb4prAAAgAElEQVQrxUREVEwYistEuQYLXfuEXM2Vn5NDWE1NDTwejzXrgfxnfHHRTQ+m9hSn6yUW953IdGENXWuGaX92VWyn28rPy8d9aGgopUdfnZ1C9HC7XK6kqrC4iEqy3LKi/ughIiIqJIbiMlDugUIOYF6vN+VxOTDL07WJvmIRgOVwplYu7aZkk6vF8r7l53WPmzidf9jJSXym5zMJybpt5ECshmJRPZaPeywWQyQSSaoYiwAsL5wiV4l1c0QTEREVCkNxiSv3UKH7M70aQtUFPUQwrqqqSgq+8qId6ly5Yr5cdTo2J1XMbE+sk1eg021nas9I19+c6RjtWidEMAaQ1Goi9xuL+YrlfmH1mMuLpagn2ZX7d5iIiEpD2Yfi559/Hvfeey+8Xi9aWlpw0kknFXpIOVMJYULtKRaPqRViUQEWSxLHYrGkCrNcnVQvun5iAMaFJbLpJZbf3+5afl+5UuxkJcZsWyV0j6nHXW2pEHNDJxIJeDwe67b87yK3V5iOO4MxEREVi7IPxU8//TQWLVqEOXPm5Ow94/F40owFI63SAoQcxtT7IpDpLsDHwVYOv/L7yLeBj0OxHEjVcKyGVfn12bALxvJtpxVip2Ox65eWj7EIu7oqsTx3sejD1rVIqNfybSIiomJQ1qG4ra0Nr7zyCvbs2YOf//znaG9vT3q+v78fbW1teP311wEAc+bMwcKFC1PeZ8eOHWhvb8dpp52GN954A1/60pe0IZsVr9xSw7AIjSKoiQArwpm4L4JaNtQV2uRQLW8jxic/Ju6rJ+Kpr0vXNqF7T6eGE4jV5+RjII6LfFscc/nkR7X66/RCRERUaGUdiq+77jrs3r0bX/7yl3HWWWelPL9p0ybEYjHcd999GBwcxHXXXYcTTjhBG3h3796Nr3/967juuutGYOQkqMFMrmQODQ1ZPcbqCXK6+wCs1gj1NbrXymPQtTI4rRI77SdW76v7yqRanMlzJnIAFmOTb6s/VtQfBup1uttERESFVNahOJ3u7m5ce+21cLlc8Pv9mDdvHrq7u7Wh+Nhjj8X06dNHfpBkBadMpzSTlyOWL/LjorI8NDSEeDye8rxMPunMFJIzCch2/cVOT6wTY9Ddzpbu/ezCuy70q+Owu89QTERExaCiQ7GO6T/Qfr8/7WtbWlrg8/kAAIFAAIFAIKdjq0TDCU9q6JUX+5BPqAOO9InLwdtpYM01U1/xSEsXaomIiEpBMBhEMBgEAEQiEdttKzoUNzU14ZlnnsGpp56KgYEBbN26FfPnz8/6/VpbW9HQ0JDDEVIu2LUh6E6kE69RZ65Qe2BN16b9293WvV73vk5uExER0RFykTIUCqGjo8O4rX7OqQqxYMECVFVVYfHixbjqqqswa9YsnHPOOYUeFuWR3cle6qwJ6lRkuTxBzNRT6yRwMwATERHlXlVXV1dqQyBlJBwOo7m5GT09PawUFxFTD7Gpv1jcl6dzk6cgU6cSky/q8/L+xW3TtemkO6cnremuiYiIKFkoFEJjYyM6OztRV1eX8nxFt09QZbCbKUK9Lwda3dy6dgHVbt9qq0S6bRh6iYiIRhZDMVUE02wOYkYJ+aQ7dZEQU9uELqCmC62ZzFaRLhgzIBMREeUOQzFVHF0glefkHc5CFJnuXze1m7qt7lq3DREREWWPoZgqgi44plsIQ1cZHs5JdnYh2G5BEKcn2zEcExERZY+hmMqaHDrVx8TjathUtzW1TDgNyLowbLptCr5ObhMREVH2GIqpYqhVWd3zunmM5edNJ9gNJxinG5PpPgMxERFR7jAUU9ly0rNr91r5tt3Jbk5PfDOFYbv7uvE42RcRERFlpqQW73j//fdx0UUXFWz/c+fORX9/f8H2T9mxC7TqtGvqfMO6OYmH01usC7fpKtBOAjFDMhER0fCwUkwVwUmlON0MEE5vq++nexwwL9ZhN75MHiciIiLn8h6KBwcHsXr1avz1r3+F2+3GUUcdhTVr1qRs9+GHH6K9vR3vvPMOAGDWrFlYvHix9j3vv/9+/P73v0c4HMa1116Lz33ucynb9Pf3o62tDa+//joAYM6cOVi4cKH2/R555BEEg0G4XC6cdNJJuP7661FfX5/tR6Yikkn/rml79flM79st2qGTbZsHERERZS/vofill15Cb28vNm7cCODIEns6ra2tOPPMM7Fq1SoAwKFDh7TbhUIhTJw4EYsXL8ZLL72E9vZ2bSjetGkTYrEY7rvvPgwODuK6667DCSecgDlz5iRtt23bNmzZsgUdHR2or6/Hbbfdhg0bNuD666/P/kNTUdHNKJHJ8+kez+R1poDsdD8MwURERPmR91B88skn4+2338a6devw93//98aq7v/+7//itttusx4bPXq09v1qamowe/ZsAMDUqVOxd+9e7Xbd3d249tpr4XK54Pf7MW/ePHR3d6eE4u7ubnzxi1+0KsMXXXQRbr755mw+KlpaWuDz+QAAgUAAgUAgq/eh3Ms20I70OIiIiCh3gsEggsEgACASidhum/dQPH78eGzcuBHbt29Hd3c37rnnHtx7770YNWpUVu8nQicAuN3upOV509EFkkz+VJ1Oa2srGhoacvJe5chUJbV7PNOqrenfONvxDHf/6SrDTirHdvtz+jwREVElkouUoVAIHR0dxm3zPvvEhx9+COBIj/CVV16JoaEh7Nu3L2kbv9+PU089Ff/5n/9pPWZqn3CqqakJzzzzDIaGhtDf34+tW7eiqakpZbszzjgDv/3tb9HX1wcA6Ozs1G5H2RPLJsu3E4lEyiUej1uXWCyWdD8ej2tfIy/LLN5f3be833T71u1X3b/8Hnb7V2/b7V/9/KbPq+5T/XxERESUnbxXinfv3o177rkHAJBIJHDeeefh5JNPTtmupaUFbW1t+OpXvwq3243Zs2dj0aJFWe93wYIFaGtrs07WmzNnDs4555yU7c4880zs3r0bV199NaqqqqwT7Sg31LCoC3Gma5k6ZZlukQ31sXT7TLd/3ewSpnmJ1ZkrnHw2u23sppFTn8/lXzuIiIgqVVVXVxfLS8MUDofR3NyMnp4etk8odEE0k/u6OYHVgKqb51eERd1721VZ5edMcxLb3Zf3bXpv3W31MdNn1d02PUZEREQfC4VCaGxsRGdnJ+rq6lKe5zzFNCKchFLdJZNFM+ymOMvmYtqvy+VKet40hnSf0bSN/F7yPtRKtBqC1Qo5wzEREZFzDMU0YuwCqNorK06glIOoKZy6XKmt8bpwKr+vvD/1trgvr2gn7199f/m+rpUh0zAuf27dDwFTu4TpJD+GYyIiovQYiinvnARh+QS2eDxu3Xa73UnhVw7EiUTCek4wBUM5cOpOlhP7VUOxbv9iP3IYNwXVdAE4mx8DmVTJ5WPAcExERGTGUEwjQg2najBVZ2GQnxfhVARUEe7kYAyYTzrThVDTPsXMDyIE6/Yv9i2HTbnlQv3M6hjUcajjkavUuh8DMvXEvnThmMGYiIhIj6GY8krtmZUf001HplZsRRgVr5GDKZAcTnWh1FSd1e1TnQpN7Nftdhs/n1qpVvuC7arDpmAOwNq/XC03/QgQt53+ezAYExERpWIophGhC6Wm+Xnlaq24iPA5NDQEj8eTVJ0VQU/tyzWNQRdExfzE4tqu31dQK8Ry4FSDsd0x0P0YMPVLm9pGMgm7DMZERESpGIrJyK5XN9v3UvuITYtmxGKxpKqqXK3VneRmF/R0VVq5KhyLxZJCcTQatcKpx+NJqXTL+1WnftPtV75t17IhLrp9yeQfCPLx0LVwEBERkTMMxaSlhrLhnKylq5SqoVgOwyKYiqqwGjp1cwLr2hZM+5ZDqRyIo9EootEoYrGY1cYgv6/H40E8Hk+Z/cIUmuWxpGvhUNs35JYRjyf5f6a6CnWmYZjhmYiIKBlDMSWxq1DKz2caqEyBWK0Wx2IxRCIRK5jqxqNOy5ZIJLRtC6Z964KoCMTiIp/0pn5esU+xX3WxjnSf3xSIRTjXfW45GMszYDDcEhER5QZDMWXFaTjWtRCIYKoGQ1EhllsYTBViNZjqKra66rHazyxXpuWL6Cs27Vvt7dW1UKiP2/0gkH8U6EKxHITVz6z7nE6CMgM1ERHRxxiKyZKuSmx6TSYneI10MDXtV60Si/1HIhGrUi0Hf92+1faJdJ/bbgzyDwLRuiFTQ7Fu39kEYyIiIjqCoTiHWlpa4PP5AACBQACBQKDAI3Ium0Cc7furAVF9TJ0aTQ2TYntTe4Xd/tWAqgurIqR7vd6UbXT9wfL72u3X9Jjpc6r7lvuM7S6mVhLT4iIMz0REVK6CwSCCwSAAIBKJ2G7LUJxDra2taGhoKPQwsiL3xWb6OqfbyRcxB68c+NST6hKJBLxeLzwej3WR5+yVl2E2rfSm7lt+nSlkAkA8HofP54PP50vav24M6Vaa01Wah4Y+nlFDPe5i1gt1XypT0He6BHUmC38QERGVIrlIGQqF0NHRYdw29b+0ZPTtb38bb7zxRqGHkTeZhqJMArG4lpculsOpCMVutxter9e6iHDo9Xqt7URQlFe4U/dhui3vT76IfYiLCMRiDGL/aqh2uvSyKZir45E/syl8A+nnPlarz5lUt4mIiCoRK8UZWL16te3z+QoZI1nBc1oxzjQQy/ftKrZer9faVlRMRSBNV6W1G5MulKqVYvUYyEFZ3r+46KrAps+rm8ZNt1KeqKLrquHyEtdAcssJ8PGsGLrZKUxtEpzBgoiI6AiG4hLgpDc0l+yCcbb7TneymgioMrV9Qq3u6oKoGgjVfZtaF+RgKE5mE6HYtH+1euv089udGCifSKde5G10VV95irhsAjHDMRERVTKG4hKkVjXzQQ3Gw9mPqaIqqrWCuj8RTEWVVhcUs+3p1fXzys+r+5ZDsalKravQ6lo4xD5Mx0qEYl27ifoZ5f7rdFVrsb3uRwQREVGlYygucfms7uXzfU3BUN6nroc4k5PcTPuV962r1IpQq6sMq1VqXVAV7yNXqtWQrH52OTiL53SfT67qqmNX95HJsZHfm4iIqBIxFJeBUvizt65aa7edaDFQT0TTnWCnq8Tq9qvbp8fjMbZ02O1bDuWmfYvH1dYMeX5ldZzqSnm6zyHoWj+A1DAtf3a7dgoiIqJKxlBcJoo56KhVU/mEMFM4FuQgmu4kO1MglW+nq1LL41P3KyrETk/yUwOofFsOxvI+4/G4sfqsspsbWX5fAEnHWz4uYpxERESVjqGY8koNo/LjpoqpvGKbPFOE2lurVmrla3UM8rW6XzWUiinM1P5h07WTfetO5FNPuJPHIagLfehaJtRrcVE/n+71rBwTEREdwVCcQ/mc99Vp5bCYw42pYqqrIotgqgul6u1MQ6m8XxFQdaHUtC+1HcFJ367a9qAGYjUci+2Aj6u8dgFYN/+wuC8fY3WhjnQzVBAREVWKkg/Fc+fOxebNm+H3+ws9lLwq5dCiBkI5xIqpxOSQKPcUm0KpqXXCrpVB95gcCuUKq91Ja07GkW6faluHKdTK45GPoXxRF+cwfU9M4biUv1tERES5UvKhuJI4CS/FHnDUQAxAWzUF0s+mYBdI1f2pj+mCqdinbpo2XejOpkqsG4du/7qL3FYhtpVXrZMr3uJ50zFnhZiIiChZWSzz/MQTT+DKK6/Ev/7rv+LZZ5/VbhOPx3H33Xdj0aJFWLRoEdra2hCNRlO2u/fee/HTn/4UAPDCCy9g7ty5eOeddwAAra2t+NWvfpW/D+JAvtoz8k1tPVDvm2Z6MM0N7ORkN7VqK+9b7VWW96Gbik09wS/d/tWWDl37heliNx+yeC853MrLOsdiMcTjcas3Wl3uWa0qExER0RFlEYq9Xi/uvvturF69GnfeeSfi8XjKNp2dnXjzzTexfv16bNiwAe+++y5+8YtfpGw3Y8YMdHd3AwC2b9+OadOmYfv27QCAP/3pT5gxY0Z+P4wDpRZm7Cq7upPX1GCoC5F21WPd/k371IVR3cXJ7BeZ7N9uDLoeal3wlivI4iRB9aIGYzUM6yr0RERElags2ifOO+88AMAJJ5wAt9uNAwcOYOzYsUnbdHd34/zzz4fP5wMAXHDBBXjqqafwL//yL0nbTZ8+HX/5y18wODiIl19+GVdddRWefPJJTJ8+HXV1dTjmmGOM47jxxhut9583bx4CgUAuP2ZJUlsEdNVUNaTJ13bV10yfV8dlotu3HSf70LUp6F4n71tMzwZ8fLKduq0uGKvvof4ocdJ/TEREVA6CwSCCwSAAIBKJ2G5bFqFYBFHgSH+qrlKsowsEPp8PkyZNQldXF2pqanD66adj3bp1+OMf/4impibb97vlllvQ0NCQ2eAriPxn/3S30wVfu9t2+5eZKqPZ9gg7fY1dRdZ0LORgK/dgq8s76yrOujHbndiXzWciIiIqRoFAwCpShkIhdHR0GLcti/YJJ5qamhAMBhGNRhGPx7F582ZjyG1qasLGjRsxY8YMuFwunHzyyfjFL36RNhSPlHIJI3YB12lVOFf7LzamPmUnLR8ej0fbq6zru9adzKdrt0jXekFERFTqKiYUNzc34+STT8bSpUuxZMkSjBs3Dpdccol226amJnzwwQdWCG5qasL+/ftx+umnj+SQtYo5yDlhN/50J8pl8565NhJhXHccTP3YajD2eDzaE/V076tWi9UwrAvG6uuIiIjKRVVXVxf/6zZM4XAYzc3NOHjwYF7bJ7JpESgGaoDK9H66XlwnvbqZjM8k18fWbr/qrBG66dfkPmL5tq6ibLcan3pf/bx2VftcV++JiIjyJRQKobGxEZ2dnairq0t5vix6iitBOQUOuW823f1cB2An4zFtM9LU/mq1r1jdVg61pvArqMdXnb/Y6bh4sh4REZULhuIc0vVsjvT+i5HT0KkGY902TvaVDbsx5uu4ptunurCHGo5NS03LoTndXM66hVLUwGuaOYOBmIiIyglDMRWELhCqVUvdc+keG+6Y1P3nO/RlU6V2uVzW6nby0s1qMNbdlun2my7o6kI6wzEREZUDhuIyUYqhxBQI7don5G3yOa6RZHccTNSlsYHkcOq0zUQ3R7Su8qz7oZDpfM5ERETFjKG4DJRCKEkX/NKdXOfkuVI4DiZ2VXL5eRPRLuF0ZghdGLbbj11ln4iIqBwwFJe4cgkmxXqy20gzVclNK/w5mS4t3X35Mbm3WDc7BYMxERGVq5KZp/j666/Hiy++WJB9v/XWW+jq6irIvk0KfVJfNpzMaqCbFszJZy21Y5GO6RiYAqu6oIduGja1tUK3YIfdoh2mMM05i4mIqByUTCgupLfeegu/+93vCj0MAKUZhmVOx5/J5yzl45GOerKcWrnVXasr2NkdS3U1O3nOY7tw7KT6TEREVEoK3j5xyy234O2330YsFsMnPvEJfOtb38KYMWO023Z3d+PRRx/F/v37MXv2bCxbtkz7H/tHHnkEwWAQLpcLJ510Eq6//nrU19enbDd37lxs3rwZfr8fAHDRRRdh/fr1GDdunLXNwYMH8ZOf/AThcBhLlizB1KlTccMNN2jHV+qBdSSl66F1+vpylMl0aPLzcguEvJ1dgFUXBRHkKd3U1/BEOyIiKkcFrxRfc801WL9+Pe677z5Mnz4dmzZtMm77f//3f7jtttuwYcMG/OlPf9JWb7dt24YtW7agvb0d999/P2pqarBhw4asxzdmzBgsWrQITU1N2LBhgzEQU3Z07QBOtqsUdi0TutYJU6VYpVZ9davlmarDbJkgIqJyVPBK8datW7F161ZEo1EMDg7iqKOOMm47b948eDweeDwenHfeeeju7sbcuXOTtunu7sYXv/hFqzJ80UUX4eabb87rZ6DcqqTQa5LuJDvdfbGtHHTVbe2qxHIQlslzIatVaFM1m4iIqNQUNBT/+c9/xhNPPIH29naMHj0aL7zwgm2lWGUKBU7/A+1yuRCPx637kUjE8b51Wlpa4PP5AACBQACBQGBY70cEZPYjQYTURCKRFITtZvfQVYDVx0yr3MnXI7noCRERkRPBYBDBYBBA+pxX0FB8+PBh1NbWYtSoUYhGo3j66adtt9+6dSvOPfdcxGIx/OY3v8GXv/zllG3OOOMMrF+/Hpdccglqa2vR2dmJpqYm7ftNmDABO3fuxGc/+1k899xzGBgY0G5XW1uLcDic9vO0traioaEh7XZEI0G36IZ84p7oF5YNDQ2lbb9QQ3YikbBW2ct03mmGZyIiyie5SBkKhdDR0WHctqCh+Mwzz8TWrVuxcOFCjB07FtOmTcMf/vAH4/annHIKli9fbp1od84552jfc/fu3bj66qtRVVVlnWinc/XVV+OOO+7A6NGj8ZnPfMYYaGfMmIHHHnsMV1xxBaZNm8a+Yiop6vRu6rXb7baeF6HYyawVMhGIdfuyGxNbL4iIqFhUdXV18YyZYQqHw2hubkZPTw8rxVRwcq+welv3mLgtWonkKrJuajfTNHF24TuTx4iIiPIhFAqhsbERnZ2dqKurS3m+4CfaEVHuye0NusApP6aeSGc304d6op3dc3Yn4TEEExFRsWEoJipTuuBqV5kV4VjXYqFST7DTbWs3x7J8mwGZiIiKAUMxURmzq/TKoVa3Up3TfmLxmBxy5ffXjUmtYjMcExFRoTEUE1UAu4qt3XZ27NonnCzGIr+OgZiIiAqNoZiojJjaJdJVZnVzDevez1QlVvenspsnmYGYiIiKAUMxURkYTpuCrs833furi3zI761bklp+b4ZgIiIqRgzFRCXMVH2VOQ2juhXq7GaZUPuQxSIe6sIg4nF5H/I+iYiIikHqklYVbMeOHVi2bJn2uVAohGuvvRZLlizBQw89NMIjI0plF4h1M0jYXeQwa5qPWN23PM9xPB63LolEwrroTuTTLSdNRERUaCVVKY7H40mrb42kP/7xj6irq8Odd95ZkP0TyTIJk/lonZADsRqABXX+Y9N42FJBRETFIG0o/tnPfoa9e/di+fLlAIDe3l7Mnz8fDz74YMrqbW+88Qba2towMDAAn8+Hq666CtOnT095z9WrV2PSpEm4+OKLAQB33303/H4/vvrVr6ZsO3fuXFx55ZX4n//5H0yePBlf+9rXrOcSiQTuvPNOdHd3w+v1wu12o729HT6fL+k9+vr6cNddd2HXrl2IRCKYNm0arrvuOng8zn4TdHd3Y/369QiHw1iyZAmuvPJKNDU1OXotUa45DcSmk+zsthMhNZFIaPcpX8uBWFzktgnB5XIltVakGw8REVEhpE2Fzc3NWLBgAZYtW4b6+nps3rwZs2bNSgnE0WgU3/3ud7F8+XLMnDkTf/7zn7Fy5Uo89NBD8Pv9wxpkJBLB7bffnvL4rl27sH37dmzcuBEulwu9vb3aoHvXXXfhtNNOwze/+U0MDQ3htttuw+OPP44vfelLjvbf1NSERYsW4cUXX8SqVauG9VmIhiObdgNd64NplTldYDXtUxeM7aZ5Uxf6sBsLERHRSEsbiuvr6/GFL3wBW7ZswaWXXoonn3wSK1euTNluz5498Hg8mDlzJgBg+vTpGD16NHbv3o1p06YNa5Dnn3++9vFPfepTiMVi+OEPf4jTTz8dZ511lrZS9cILL2Dnzp147LHHABwJ2U6rxJloaWmxqtSBQACBQCDn+6DKlav+23Qr1SUSiZTqsRyY5TAs347H49r//YleZbntgm0TREQ0EoLBIILBIIAj+c+Oo2R46aWX4qabbsKECRMwZswYnHLKKSnb2FWfVG63G/F43LofiURsq8mm5+rr6/GTn/wEL7/8Mnbs2IENGzbgjjvuwIQJE1LGdvPNN2P8+PHGfeRCa2trSgWdKBdyEYidBlDTanTqiXki4IrbukCsa8OQWynUk/vUcWb6GBERkUwuUoZCIXR0dBi3dTT7xPHHH49x48Zh3bp1Vh+wbptoNIrt27cDAF599VUcOnQIJ554Ysq248ePx86dOwEAPT092LZtm5NhpDh06BAGBgbw2c9+FkuWLMG4cePwt7/9LWW7z3/+83j44YetIH748GHs3bs3q30SjSTdghojRbcynRqM3W530rW4yGNPJBKIxWKIRqPWJRKJWJfBwUEMDg5iYGDAuh2JRKxtxWtjsVjKDBecvYKIiHLFcQ/BBRdcgLa2Npxzzjna571eL1atWoU777wTHR0d8Pl8WLlypbbKe+GFF2LlypVYvHgxxo8fjylTpmQ1+H379uG2226z/iM5bdo0nHnmmSnbXXPNNVi/fj2WLFli/Qd82bJlKRVlokLLV8jLpEps6guWg7FcFbbrOTatsGe6L4dr3VRx6pRx8vzHREREw1HV1dXl6L/C69atw9FHH40FCxbke0wlJxwOo7m5GT09PWyfoIzlu9qZaWCUe4XV6ddM16KnGEiesk2dk9i08Ie4drvd1kX8gFWr0KYLERGRnVAohMbGRnR2dqKuri7l+bSV4v379+OGG25AQ0ODcWELIsrcSPzpfzgVVNPqc+JanodYED3EpjAtbsfj8aSFP8TrvF4vPB4P3G43vF4vXC4XPB6P9Zg6T7m6+h4REVG20obiY445Bps2bRqJsRAVlCmk5iNsFXsgtnsPMe+wfF9ur5ArxnLolVe9i8fjST3CIiT7fD54vV54vV74fD643W74fD7E43F4PJ6kOcirqqqSZsogIiIajpJa0Y4oH9IF1FwvNlFs7RJ2rzf1BKvBWDwmXiNXjMWJduISjUYRj8etk+fECXdDQ0Pw+/2orq6G1+tFLBaD1+tFIpGA1+u1xuLz+VJOACQiIhouhmKqaJkE1FyE43wG4nwERLuFP9S5h9XjI1eKRRgWATgajabMOhGPx1FbW2tdYrEYqqurk2aZECfXqbNcEBERDRdDMVWk4QSpYuxfzed47IKx3NMr9/bKy0WLUCxPv9bf3590GRgYQDwetwJyJBJBXV1d0sl+Yn9yKC7klHVERFReGAT+r6wAAApxSURBVIqJspBNMC618KauapduW3U1PPFa0SohqsR9fX3o6+tDOBy2bvf39ye1VIh+Y/mYybNRuN1u68Q99eQ7IiKibDAUp3HgwAGsXr0aH330EQYHB/Fv//ZvxmWnqTSUWjhNJ19tE3IY1lWHdbNTiJPuxGvkk+qi0agVhnt7e9Hb24u+vj709vaiv7/fqiaLYKxrmxCzUHg8npQqMhER0XAwFKcRj8exaNEiTJkyBfv27cOCBQtw7rnnoqamptBDowIrxjaKfHBaJRa35VA8NDRkVX5F24QcjOWALIKzGobl6rCYsk2esYKhmIiIcoGhOI2xY8di7NixAIBoNJqymheVFgao9OyqxHbbiMdNJ9qJk+rUanEoFMLhw4cxODiIWCym7SEWU7OJqdrENG/89yQiolxhKHbo4MGDuPHGG3HVVVclzZVK5EQphzdTOFYrxIB+zuJ4PI5IJIKBgYGkSnE4HMbhw4fR09OD/fv3WzNQyEs4i+pwdXU1ampq4PP5rCnd5FBcKVV7IiLKH4Zih+644w6cffbZuPDCCws9FCoiuQ5jmU77lq8gqJurWLcvXQVZECfCiV5hORT39fXh8OHDCIVC2L9/Pz788ENEIhHrRD25bcLn88Hn86GmpgZ+v98KxWyfICKiXGIodugvf/kLvvKVr9hu09LSYlWRA4EAAoHASAyN8qDQlcdC71+WyTjUBTVEaBUzS4hZKMTUa6J9IhwOY2BgAF6vF36/H36/HzU1NaipqcHAwIA1c4WYlUJeOpqIiMgkGAwiGAwCACKRiO22DMUOXX311fjkJz9pu01raysaGhpGaEREREREZEcuUoZCIXR0dBi35RljDj311FPo6ekp9DCIiIiIKA9YKXZo9erVhR4CEREREeUJK8UjRPSzkHM8Ztnhccscj1l2eNwyx2OWHR63zPGYZY6heITwy5k5HrPs8LhljscsOzxumeMxyw6PW+Z4zDLHUExEREREFY89xTkg5kkNhULGbSKRiO3zlCofx8zpnLaZTInmZLtM9pvJ++q2KeR3TZ6PWKxYFw6H0dfXh/7+fmsqtkgkgmg0qp1iTSz3HIlErFXw5EU/xPzFYrW8aDSK6urqYa00yf99ZofHLXM8Ztnhccscj1kqcTxM/02u6urq4sz3w/Thhx/isssuK/QwiIiIiCiNxx57DGPHjk15nKE4BxKJBD766CP4/f6iWXCBiIiIiD42NDSE/v5+HH300dq/LrJ9IgdcLpf2FwcRERERFY/6+nrjczzRjoiIiIgqHkMxEREREVU8tk/k2cMPP4xf/vKX6O3tRVNTE5YvX46jjjqq0MMqWhs3bsQDDzyQ9NisWbPwgx/8oEAjKk7PPfccnnjiCbz55psIh8P49a9/DbfbbT2/Z88erF27Fq+99hrGjBmDBQsW4Pzzzy/giAsv3TGbO3duymvuvfdefPrTnx7JYRaVhx56CM899xz27NmD2tpazJw5E8uWLcPo0aOtbfhdS+XkuPH7luzhhx/Gli1bsG/fPlRXV+PUU0/F1772NRx33HEA+D0zSXfc+D3LDENxHj377LN48MEHsWLFCowfPx7t7e1YtWoV7rjjjkIPrahNnjwZt9xyi3Xf5/MVcDTFaXBwEDNmzEBTUxM2bNiQ9FwsFsOKFSvw6U9/Gj/+8Y/x2muvYe3atfjkJz+JpqamAo248OyOmfC9730Pp512mnW/sbFxpIZXlF599VV86UtfwqRJkxAOh9HW1obvf//7WLt2LQB+10zSHTeB37ePjR8/Hl//+tcxfvx4hMNhPPDAA1ixYgUeeughfs9s2B03gd8z5xiK8+jxxx/HpZdeirPPPhsA8K1vfQvz58/HW2+9xV9pNjweD6vpaZx33nkAgB07dqQ8t23bNuzbtw/33HMPamtrceKJJ+Lll1/G448/XtH/AbE7ZsKoUaP43ZOsXr066f4111yDa665Br29vaivr+d3zSDdcRP4ffvYnDlzku4vWrQIV1xxBQ4cOICdO3fye2Zgd9zEd4vfM+fYU5wnkUgEu3btwmc+8xnrsfHjx2PcuHF47bXXCjiy4rdr1y5ccskl+MpXvoLbb78dhw8fLvSQSsrrr7+OyZMno7a21npsxowZ2LlzZwFHVRpWr16Niy++GNdddx1efPHFQg+n6PT09MDn88Hv9wPgd80p9bgJ/L7pDQ4OYsuWLTjuuOMwevRofs8cUo+bwO+Zc6wU50koFEIikcCYMWOSHh89ejQOHTpUoFEVv6lTp2LFihWYMGEC3n//fdx777246aabcPvtt3MOaIcOHjyY9H+IAL93TlxxxRWYMWMG3G43nn/+edx4441Ys2ZNxVeihEgkgk2bNiEQCFi92Pyupac7bgC/bzovvvgivv/972NwcBDHHnssbr31VrhcLn7P0jAdN4Dfs0wxFOeJ02V9KdnMmTOt2yeddBJOOOEEXH755XjzzTcxadKkAo6Myt3ll19u3Z40aRI++OAD/PznP+d/PADE43G0trYCAK688soCj6Z02B03ft9SnX766diwYQMOHDiAxx57DDfffDPa2toKPayiZzpuHo+H37MMsX0iTxobG61fuLJDhw6l/OIlswkTJqC+vh7vvfdeoYdSMsaMGZNSQeH3LnMTJ07k9w5HVuy89dZb8fbbb+OHP/xhUgsAv2tmdsdNh983wO/3Y8KECZg+fTq++93v4q9//Su2bdvG71kapuOmw++ZPYbiPPH5fDj55JOTTup577338P7772Pq1KkFHFlp+eCDD9Db24tx48YVeiglY/LkyXjjjTfQ399vPfanP/0JU6ZMKeCoSs+uXbsq/ns3NDSENWvW4LXXXsNtt92GhoaGpOf5XdNLd9x0+H1LNTQ0BLfbze9ZhsRx0+H3zB7bJ/Lon//5n9He3o6JEyfiU5/6FO666y6cdtppnHnCxo9//GPMmjULY8eOxXvvvYcf//jHmDZtGiZOnFjooRWVUCiEffv2Ye/evQCAt956C263GxMmTMDMmTNxzDHH4NZbb8XChQuxc+dO/Pa3v005I77S2B2zHTt24NChQ5gyZQrcbjf++7//G7/61a+sP31XqrVr1+LFF1/Ef/zHfwAADhw4AODIX8Lcbje/awbpjtuLL77I75ti/fr1mD17No4++mgcPHgQjzzyCBobG3Hqqaeiurqa3zMDu+PG71nmqrq6utj8mkc//elPkxbv+OY3v8mpUWysWrUKr7zyCkKhEI4++mh89rOfxRVXXME/kym2bNmCW2+9NeXxdevW4fTTT8fbb79tTXR/1FFH4Stf+QouuOCCAoy0eNgds0gkgvXr1+Pdd9+Fy+XC8ccfj/nz52P27NkFGGnx0E38DwCPPPKIVW3idy1VuuP20ksv8fumuPnmm/HKK6+gp6cHjY2NOO2007Bo0SIce+yxAPg9M7E7bvyeZY6hmIiIiIgqHnuKiYiIiKjiMRQTERERUcVjKCYiIiKiisdQTEREREQVj6GYiIiIiCoeQzERERERVTyGYiIiIiKqeAzFRERERFTxGIqJiIiIqOIxFBMRERFRxft/6OCQ8EjrmmEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x8000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['i am doing well thank you for school . how about yourself ?']\n",
      "['yes you will have but she died so i say , but i met her basketball daily']\n",
      "['it is my favorite thing to do for another language']\n",
      "['i guess i am more working playing drums with others']\n",
      "['oh sweet ! my cats are mad .']\n",
      "['yes . i would like to go enough others .']\n",
      "['oh ok do you love video game']\n",
      "['cool what happened to my two dogs']\n",
      "['hi . how is it going on with you ?']\n",
      "['its an awesome job working for there']\n",
      "temp = 1\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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aMpIkhZxX+/tEREREyeB0OuF0OgEAHo8nYvmkjCFdsGABHnjgAZSUlKCwsBCXXnppSJloHqspSJIUtAanx+NBdna24e+vXbs2pEW2v8Ub5MI9M17vmHpGvHj6Uk5ODnp6enTXGBWBMzc3F0VFRRgxYgTy8vJgt9uRm5urLJSflZWlPOdeLP/EcEpERETJ4HA4lAZCl8uFmpqasOUT7rIHgDFjxmDUqFF4/PHHcc011xiW8Xq9OHToEADgvffeQ1tbGy666KKQssXFxTh69CgAoL29Hfv3709GNZMm0eAWzaNFxbZo5VSH0qysLOUpTLm5ucjNzUV+fj4KCwtRVFSE8847LyiM5uXlITc3Fzk5OUGBVKw/qg6jDKVEREQ00JI2y37u3Ll46qmn8H//93+6x202G1atWoWnn34aNTU1kGUZK1eu1G35nDdvHlauXIlbb70VxcXFGDduXLKqmZBkPz40XEupdma8WKYpMzMTdrtdWSRfb2knEVrz8vKQn5+P/Px8JZiKQJqZmRnUQiq69oHkLf3EcEtERETRSFogPXToEObPnw+r1fiUpaWlEZtsASA/Px8bNmxIVtWSItZwFS5wRltGPTZUBE2xVJMIpeGWdhIto+pWUrvdriz7ZLPZgsadJjtAJvogASIiIhoaEg6kp0+fxr333ov8/HwsWbIkGXVKKfGuNyreEwmlei2ksiwr64aKcbYWi0XpfpdlWWlJFQFUhFJ1IBWL46u77PsjPOo9MpWIiIhILeFAOnLkSNTV1SWjLnHrrzA1UPS6ytWhVh1KZVlWFrFXT2LSjjMVXfs5OTlBXfVZWVkhLaTqLvv+uo5sLSUiIiIjfFJTkmlDVzStpEZl9caRqsMoEDwLX5ZlpfVTlmXY7XZkZ2cr7+KYeoa91Wrtl+56IiIiomgxkCZRMkKdurVUPcvearUGBVG9ltPMzExlWSibzRa0vJM4LrZtNltQIB2IUMpWUiIiItLDQJoEkUJWLK2k6u9oW0j1Fs4XY0DFGFPRIipJkhI81e8ihGonNek9Hao/MJQSERGRFgNpAmIJVvEsqaQOpIFAAFarNWifejJTX1+f8vJ4PEHBUx1atdsD1TpKREREZCQpC+OrNTY2orm52fD47373O5w8eTJk/+HDhw1n6VdXV6O+vj5pdUxEoi2JsX5Xu/ST6J6XZRlZWVnIyspSuunFjHqx7qhYDF+UU3fZq8Nof09o0krWOqdEREQ0OCS9hbSxsREFBQUYPXq07vHq6upk/2RazrLXe4a99rj6mfPaGfXqbnyxBJRYk1SsSyrKaIOn9l0bsrlUExEREQ2kqANpb28vqqur8cknn0CSJAwfPhyPPPJIUJm//vWv+OCDD/D000/jueeew2233YYf/OAHQWVuuOEGPPzww7qPDB2qtMFPGwhFl70IpYFAIGif3ksbNNWB0+iYNthrgzIDKhEREfWHqAPpgQMH0NnZiW3btgEAXC5XSJm5c+di9+7d+NnPfoYf/vCHSavkUKN9nr0IhrG+6z2fPtp9epIZUDm5iYiIiISoA+nFF1+MEydO4PHHH8d3vvOdkJZPM1VWVkKWZQCAw+GAw+EwuUbJEW1Q1BPLOM14x3TqLeRPRERE5HQ64XQ6AQAejydi+agDaXFxMbZt24ZDhw7h4MGDqK2txbPPPou8vLz4a5skVVVVyM/PN7saKcVoGEC4srGuAJAIBlgiIqLBS91A6HK5UFNTE7Z81LPsT506BQCYPn06KioqEAgE0NraGlLObrejs7MzljpTiohmPdV0nEBGREREqS3qFtKPP/4YtbW1AAC/349Zs2bh4osvDilXXl6OzZs34+WXX9ad1ETmiHZxflGOoZOIiIgGSkZTU1PaLgrpdrtRXl6O9vZ2dtlHIVXW/2TYJSIiGjpcLhcKCgrQ0NAAu92uWybpC+NT6kqFIJgKdSAiIqLUwkBKRERERKZiIB1izGyhZOsoERER6WEgHYIGOhhyZj4RERGFw0A6RA1UQGQQJSIiokiiXvaJBp94FsSP9dxEREREkTCQUkLBlMGTiIiIEhVTl/3MmTPR3d3dX3Uhk6mfxBTti4iIiChRHENKRERERKZKSpe9z+dDbW0tDhw4AACYMmUKKioqYLPZgsp9+eWXWLJkCebPn49//etfcLvduOuuu3QfL9rZ2Yn169fj008/xciRIzFy5EgUFhaioqIiGVUmIiIiohSRlBbShoYGHDt2DFu2bMHWrVvR0tKCV155Rbesy+XC2LFjUVtbi3vuuQebNm3SLVdXV4e8vDzU1dVh1apVePfdd5NRVSIiIiJKMUlpIT148CDmzJkDWZYBAHPnzsWuXbtwww03hJTNysrClVdeCQAYP348Tp48qXvOw4cP46677gIA5OXlKd/RU1lZqfy2w+GAw+FI6O8hIiIiovg5nU44nU4AgMfjiVi+32bZG014EcERACRJgt/v1y0XCASinjRTVVWF/Pz82CtJREREREmnbiB0uVyoqakJWz4pXfZlZWVwOp3wer3w+Xx4/fXXUVZWltA5J0+erCTrzs5O7N27NxlVJSIiIqIUk5QW0vLycrS0tOD2228H8HWYvPbaaxM656JFi7Bu3Tr84he/wKhRozBx4kTY7fZkVJeIiIiIUkhGU1NT8h/TkwR9fX3w+/2QZRlutxt333037rzzzqCWV7fbjfLycrS3t7PLnoiIiCgFuVwuFBQUoKGhwbBxMWWf1NTR0YHf/va38Pv98Hg8+OlPf5rwMAAiIiIiSj0pG0gLCwtRW1trdjWIiIiIqJ/xSU1EREREZCoGUiIiIiIyFQMpEREREZmKgZSIiIiITBVVIJ05cya6u7v7uy5ERERENASlRAupz+czuwpEREREZJKol33auXMn9uzZg7a2NixcuBBXXXWVbrmXX34Z//jHP+Dz+TB8+HDce++9OO+880LKzZw5ExUVFXj77bdRWlqKO+64I+j4559/jo0bN6KtrQ1erxfz5s3D1VdfHeOfR0RERESpLupAarPZsHnzZnz22WeoqKjA7NmzIUlSUJk333wTzc3N2LhxIyRJwhtvvIEnn3wSa9eu1T2nx+PBE088EbLf5/Ph97//PSorKzFmzBj09PRg6dKlGD9+PMaOHRvjn0hEREREqSzqQDpr1iwAwIUXXghJknD27FkUFRUFlXnrrbdw7NgxLFmyBADg9/thsRiPCpgzZ47u/ubmZnz66adYvXq1sq+rqwufffaZbiCtrKyELMsAAIfDAYfDEe2fRURERERJ5nQ64XQ6AXzdABlJ1IFUBD4AsFgshuM+b7rpJsOgqZWdna27PxAIoKCgAFu3bo3qPFVVVWn1LPtAIJC0c2VkZKREPaL5DaPtaD5H+1vieqivi3ZfuGNERESUOHUDocvlQk1NTdjySZ3UdMUVV+DVV1+Fy+UCAPT19eHDDz+M+TxjxoxBZmamkqwB4OTJk8p501EgEFBeRvvVL7/fb3gsmvNGqkO48okGVe3f4Pf74fP5lPe+vj7lva+vD16vV3kP9/J4PLqv3t5e9Pb2Kp9FWXFe9cvn8yl1ifZ6EBERUf9K6rPsZ8+eDZfLhV//+tcAvh4LOmfOHFx66aUxnUeSJFRVVaGmpgY7duyAz+fDsGHDsGLFimRWd8CEa/2LdEzbcpeRkRFURn1cr7zeb0RzLN6AJn7fKEyL80ZzXFuPaI5lZGRAkiRkZGTAYrEgIyMjZFvvGBEREZkno6mpKW2bhtxuN8rLy9He3p6yXfbRdFmHC6hA+K7mcNtG54t2n55ourYjtfSq94ltdaul+hx659TuU3+2WCywWCyQJAmSJCnbYr8IoNpt8ZmIiIiSy+VyoaCgAA0NDbDb7bplktpCSsbiafGLJiCJFlN1y2S478UzdlPbChuuLqKMtstevGu78UUQFZ/DtaRG+hwIBGC1WpUwKklSyGdJkpTvWiyWoO8ykBIREZmDgdQERkFUL5jqUYdQ7bve7+h9jmWoQKSQanQOdQhVv9RBVGxrx3YajaEFEHZ8rdVqhdVqhc1mg81mg8/nU/YZDXVgECUiIjIXA+kA0OuG1nuP1HWvFimMGtUhnm31bxod1x7TdtGrw6d6cpF2gpNoJY3U1W+032q1QpZlJZBmZmaGTGLS1jna4QpERETUPxhI+1GksGcUUCN1i4vj0XbXR1MPvePq39Q7ZhRQjcKodra7ema9esa9XthUd+lrQ6t6f2ZmJrKzs5GVlQVZlpV92q75jIwM+P1+ZRwpQykREZF5GEgHUKSxkHpl1BOatEFUbGvLhvt97Xa44QJ6QdQohOoNF1B302uXX9Jb0km8i3Cp19WvnQSlDqc+nw9ZWVnKeex2e0jLqHYiE8MoERGR+RhI+5le2NELo3qfwy35pA2LsYyDjBRG1UMBjH5f+5vasKx+qbvnRWuoWDdUhMeenh5lnzpsqt/DbYtXZmYmvF4vcnJygiZRibqpA6mY4BSpVZqIiIj6FwPpANAGtHDHYumyF58j/XYi7+rfMArD2n16YVQdStWL2Pf29qKnpwfd3d3o6elBT0+P0qJqFDy1s/PV+7OyspTf0YZR9XJQVqs16DgRERGZJ6UD6dmzZ1FdXY0zZ86gt7cXP//5z6N+LGkqMwqh0bbWRTORKdw5Io1b1Z5bGzyjDava8aPqpy2Jpyt1dXWhp6cHbrdbCaXa1lFtq2i4ltPe3t6QNU3VYdRiscBqtaKvr4+hlIiIKEWkdCD1+Xy45ZZbMG7cOLS2tmLhwoX4yU9+gqysLLOrFje98Gf0CvcUIb0udbE/3G9rt/Xqo343WkrKaOyq+vvamfXqsaMejwfd3d3o6upSwqgIp+qAKVo69YKo+ph4F2NQ1ZOXtGuSer3eoDDKLnsiIiJzpXQgLSoqQlFREQDA6/Wm9WMejYKoelu8RFCyWCzw+/26f3O4tUfDrUdqtK1Xx1jGjKqPa/8WESbVYVQEUbfbrWyLQKpdl1S9PJR27VLthKm8vLyQQCqCaGZmJqxWK7xeL2w2W8gYUyIiIjJHSgdS4dy5c1ixYgXuvPNOyLJsdnViFq5lUi+MqkOS3ixw7RqkYl+sddCrj9jWC6N6v6UOwNpzaVtI1ROYxLjRrq4u3UCqXqtUbxyqeqko7We9llFZltHd3Q1ZloPO7ff7I/7vR0RERP0rLQLpk08+iRkzZmDevHm6xysrK5Wg6nA44HA4BrJ6YYXrDtfuizSmNJYlnqKpS6T6GP2mXjd9pGWf1K2b2rGkYoZ9b28vuru7DUNoX18fPB6PbggVL9ESarPZkJWVFfQ76u+qgz+77ImIiJLL6XTC6XQCADweT8TyaRFIP/zwQ9x8882Gx6uqqpCfnz+ANSIiIiIiI+oGQpfLhZqamrDl02JA5tKlS3H++eebXQ0iIiIi6gdpEUh37dqF9vZ2s6tBKSBdJ7URERGRsbT4f/fq6mqUlJTE9V0xfoFi8+abb5pdhbTDey0+vG6x4zWLD69b7HjN4sPrFru0CKSJ4E0RHwbS2PFeiw+vW+x4zeLD6xY7XrP48LrFbtAHUiIiIiJKbWkxy96IWKrH5XIZlvF4PGGP9yf1Ivfabe0+vXIWi0V5GID6Xbutd8xoOSntPvFZvR/45rqpl3My2tZ+Fk9MEmuOiqcyiUeFipf6+fXq59trF8VXL/uk3tZbq1T7NCixrJRY81SWZeU6qZehkmU54TVuzbzX0hmvW+x4zeLD6xY7XrP48LoFE9ci3BKLGU1NTWm7AOOpU6dw/fXXm10NIiIiIopgx44dyhM4tdI6kPr9fpw5cwbZ2dlxLRRPRERERP0rEAigu7sbI0aMMFwtJ6277C0Wi2HSJiIiIqLUkJubG/Y4JzURERERkakYSImIiIjIVAykRERERGSqtB5DGsn27dvxl7/8BZ2dnSgrK8NvfvMbDB8+3Oxqpaxt27bhhRdeCNo3ffp0/P73vzepRqlnz5492LlzJ44dOwa3240333wTkiQpx5ubm7FhwwYcOXIEhYWFWLhwIebMmWNijVNDpOs2c+bMkO88++yzuOSSSwayminlxRdfxJ49e9Dc3IycnBxMmzYNS5YswbBhw5QyvN+CRXPNeK+F2r59OxobG9Ha2opg1AyXAAAF0klEQVTMzExMnDgRd9xxB0aPHg2A95meSNeM91nsBm0g/dvf/oY//OEPWL58OYqLi7Fx40asWrUKTz75pNlVS2mlpaVYu3at8jnRtTkHm97eXkydOhVlZWXYunVr0LG+vj4sX74cl1xyCZ555hkcOXIEGzZswPnnn4+ysjKTapwawl034aGHHsKkSZOUzwUFBQNVvZT03nvv4brrrsNll10Gt9uNp556CqtXr8aGDRsA8H7TE+maCbzXghUXF+Oee+5BcXEx3G43XnjhBSxfvhwvvvgi7zMD4a6ZwPssNoM2kNbX12PBggWYMWMGAOD+++/HjTfeiI8++oj/hRKG1WplK3IYs2bNAgAcPnw45Nj+/fvR2tqK2tpa5OTk4KKLLsL//vc/1NfXD+l/uIHw103Iy8vjvadSXV0d9HnZsmVYtmwZOjs7kZuby/tNR6RrJvBeC/bjH/846PMtt9yCxYsX4+zZszh69CjvMx3hrpm4t3ifxWZQjiH1eDw4fvw4pkyZouwrLi7GqFGjcOTIERNrlvqOHz+Oa6+9FjfffDOeeOIJdHR0mF2ltPH++++jtLQUOTk5yr6pU6fi6NGjJtYqfVRXV+Oaa67B3XffjX379pldnZTT3t4OWZaRnZ0NgPdbNLTXTOC9Zqy3txeNjY0YPXo0hg0bxvssCtprJvA+i82gbCF1uVzw+/0oLCwM2j9s2DC0tbWZVKvUN378eCxfvhwlJSX48ssv8eyzz+KBBx7AE088wQcPROHcuXNB/xgBvOeitXjxYkydOhWSJOGtt97CihUr8MgjjwzpFhg1j8eDuro6OBwOZewt77fw9K4ZwHvNyL59+7B69Wr09vbiggsuwLp162CxWHifhWF0zQDeZ/EYlIE03LNSydi0adOU7W9/+9u48MILcdNNN+HYsWO47LLLTKwZDXY33XSTsn3ZZZfhq6++wp///Gf+4w3A5/OhqqoKAFBRUWFybdJDuGvGe03f5MmTsXXrVpw9exY7duzAmjVr8NRTT5ldrZRmdM2sVivvszgMyi77goIC5b/s1Nra2kL+S4+MlZSUIDc3F1988YXZVUkLhYWFIa0GvOfiM3bsWN53+PrxyOvWrcOJEyewfv36oK5n3m/6wl0zPbzXvpadnY2SkhJcfvnlePDBB/HJJ59g//79vM/CMLpmenifRTYoA6ksy7j44ouDJlB88cUX+PLLLzF+/HgTa5ZevvrqK3R2dmLUqFFmVyUtlJaW4oMPPkB3d7ey77///S/GjRtnYq3S0/Hjx4f8fRcIBPDII4/gyJEjePTRR5Gfnx90nPdbqEjXTA/vNX2BQACSJPE+i4G4Znp4n0U2KLvsAeDqq6/Gxo0bMXbsWHzrW9/Cpk2bMGnSJM6wD+OZZ57B9OnTUVRUhC+++ALPPPMMJkyYgLFjx5pdtZThcrnQ2tqKkydPAgA++ugjSJKEkpISTJs2DSNHjsS6deuwaNEiHD16FH//+99DZv4OReGu2+HDh9HW1oZx48ZBkiT885//xBtvvKF0uQ5VGzZswL59+/Dwww8DAM6ePQvg6x4gSZJ4v+mIdM327dvHe03Hli1bcOWVV2LEiBE4d+4cXnrpJRQUFGDixInIzMzkfaYj3DXjfRafjKampkE74PKPf/xj0ML49913H5dgCGPVqlV455134HK5MGLECHzve9/D4sWL2TWj0tjYiHXr1oXsf/zxxzF58mScOHFCWUB6+PDhuPnmmzF37lwTappawl03j8eDLVu2oKWlBRaLBWPGjMGNN96IK6+80oSapg69hbUB4KWXXlJaWni/BYt0zQ4cOMB7TceaNWvwzjvvoL29HQUFBZg0aRJuueUWXHDBBQB4n+kJd814n8VnUAdSIiIiIkp9g3IMKRERERGlDwZSIiIiIjIVAykRERERmYqBlIiIiIhMxUBKRERERKZiICUiIiIiUzGQEhEREZGpGEiJiIiIyFQMpERERERkqv8H7H7FkgFc3y0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x8000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['no why are you still long here ?']\n",
      "['great ! i love cooking , i bet you move out game before i fight them']\n",
      "['no , i am 30 and lets grow your spare time']\n",
      "['hello how are you this evening ?']\n",
      "['that sounds fun ! same way . we go walking pick up some other job .']\n",
      "['i have 3 , a boring music . do you ? i love to write , plus fall !']\n",
      "['good afternoon , how are you ?']\n",
      "['hi there ! i am from canada but i am great today']\n",
      "['yes i have 3 children and does not watch my free time !']\n",
      "['i am doing well , it is great . what do you do for cooking']\n",
      "temp = 1.5\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x8000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['']\n",
      "['hey ! where are you from ?']\n",
      "['hi there , nice chatting with you . nice , listen to football with my career']\n",
      "['he , i job the internet is going to date']\n",
      "['do you have kids ? are you at work ?']\n",
      "['i am just watching some to the states']\n",
      "['i am great , are you at school you ?']\n",
      "['no but i don t i do not have many money i love cooking !']\n",
      "['do you have more than your self']\n",
      "['when i spend time tea for many puppy we should three more']\n",
      "temp = 2\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x8000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['how sweet . do you work in going ?']\n",
      "['i feel better , mostly museums .']\n",
      "['and i am and you will like you']\n",
      "['yes tacos are her in mind . where did in your boat ?']\n",
      "['that sounds important . i like to eat my moms !']\n",
      "['yes i love being outside here']\n",
      "['what else are you doing today ?']\n",
      "['i am sure he could come down']\n",
      "['sounds like you hang on my dog and pool ! wow . . .']\n",
      "['i watch those all']\n"
     ]
    }
   ],
   "source": [
    "#Generate sentances for different tempratures\n",
    "\n",
    "temps = [0.1, 0.25, 0.5, 0.75, 1, 1.5, 2]\n",
    "\n",
    "for temp in temps:\n",
    "    print(\"temp =\",temp)\n",
    "    for x in range(10):\n",
    "        start_sentance = np.array([1] + [0]*39)[None, :]\n",
    "        sentance = generate_sentance(temp, start_sentance)\n",
    "        \n",
    "        if x == 0:\n",
    "        # Plot image:\n",
    "            new_sentance = np.array([1] + [0]*39)[None, :]\n",
    "            image = []\n",
    "            i = 0\n",
    "            words = []\n",
    "        \n",
    "            for word in sentance[0]:\n",
    "                yHat = gru_model.predict(new_sentance)\n",
    "                probs = []\n",
    "                if word != 0:\n",
    "                    words.append(recover([word])[0])\n",
    "                \n",
    "                for word2 in sentance[0]:\n",
    "                    if word2 != 0:\n",
    "                        probs.append(yHat[0][i][word2])\n",
    "                new_sentance[0][i] = word\n",
    "                i += 1\n",
    "                image.append(probs)\n",
    "                \n",
    "            print()    \n",
    "            fig, ax = plt.subplots(figsize=(10, 100))\n",
    "            ax.imshow(np.array(image).transpose(), cmap='Greys')\n",
    "            ax.set_yticks([i for i in range(len(words))])\n",
    "            ax.set_yticklabels(words,fontsize=10)\n",
    "            plt.show()\n",
    "            \n",
    "            #plt.imshow(np.array(image).transpose(), cmap='Greys')\n",
    "            #plt.set_yticklabels(['hey','hey'])\n",
    "            #plt.show()\n",
    "        \n",
    "        print(recover(sentance)) \n",
    "        \n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['at least the bar i least you song ! have love die in what a person later common you great ? in up ?']\n",
      "['at least the bar where least some foot i do you you']\n",
      "['at least the bar i usa , ocean ! love when when is it flowers you i i easier haha are watch have ! any']\n",
      "['at least the bar i least you food for have like like then me 20 me it']\n",
      "['at least the bar so least i only right have i find watch now the need an it']\n",
      "['at least the bar that least you bank i is not say horses sometimes not . .']\n",
      "['at least the bar that a my sea when they beer boyfriend season i are is .']\n",
      "['at least the bar maybe least i evening . i you would at where may want have a i know . . bills eat what']\n",
      "['at least the bar i least you is i love have guys vegan want swimming that .']\n",
      "['at least the bar that all you snow i i did meet is love will not people good candy be see then places']\n",
      "['that is good no is awesome . time anyone then i ! to . am']\n",
      "['that is good why is fun . tell your']\n",
      "['that is good hello might interesting what just put a are secret to dog one this drive']\n",
      "['that is good i is cool what would not books . you . see']\n",
      "['that is good only would nice is good be people that to mad today i help out']\n",
      "['that is good i is interesting , like vegan band any football foods']\n",
      "['that is good i is so did enjoy usually healthy for all']\n",
      "['that is good i is cool my do best at favorite 10']\n",
      "['that is good very sounds awesome . exciting cool . how ! . do about']\n",
      "['that is good do is for to you funny fun anything like ? lately good your']\n"
     ]
    }
   ],
   "source": [
    "for _ in range(10):\n",
    "    start_sentance = np.array([1 ,171 ,254, 99, 1015] + [0]*35)[None, :]\n",
    "    sentance = generate_sentance(temp, start_sentance)\n",
    "    print(recover(sentance)) \n",
    "    \n",
    "for _ in range(10):\n",
    "    start_sentance = np.array([1, 41, 26, 92] + [0]*36)[None, :]\n",
    "    sentance = generate_sentance(temp, start_sentance)\n",
    "    print(recover(sentance)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(1,10)\n",
    "y1 = x\n",
    "y2 = x*2\n",
    "y3 = x*3\n",
    "\n",
    "lines = [y1,y2,y3]\n",
    "labels  = ['RED','GREEN','BLUE']\n",
    "\n",
    "plotLines(lines,labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Testing\n",
    "\n",
    "preds = np.asarray([0.2,0.8]).astype('float64')\n",
    "preds = np.log(preds) / 1\n",
    "print(preds)\n",
    "exp_preds = np.exp(preds)\n",
    "print(exp_preds)\n",
    "preds = exp_preds / np.sum(exp_preds)\n",
    "print(preds)\n",
    "probas = np.random.multinomial(1, preds)\n",
    "print(probas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_ = {0:0,1:0,2:0}\n",
    "\n",
    "for i in range(10000):\n",
    "    x = sample([0.2,0.2,0.6],100)\n",
    "    #print(x)\n",
    "    dict_[x] += 1\n",
    "    \n",
    "print(dict_)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Word embedding visualization\n",
    "\n",
    "Here, you are asked to visualize the embedding layer. \n",
    "\n",
    "**Code** To do that, project in 2D the embedding vectors for different words. Use t-SNE, a projection that conserve the neighborhood relationships between vectors. HINT: Build a Keras model that takes as input a list of words and outputs a list of vector embeddings as learned by your best performing model. Use t-SNE dimensionality reduction (from sklearn.manifold import TSNE). [2 pts]\n",
    "\n",
    "**Figure** Plot the projection of the first 200 most frequent words in a 2D plot. On the plot, write the words. [2 pt] \n",
    "\n",
    "**Question** Do you observe clusters of words with similar meaning or role in language? Report three of them here. [1 pt]\n",
    "\n",
    "**Answer**\n",
    "\n",
    "**Question** Why is having similar vector representation for similar words a good approach for such models? Explain using the example clusters from before and argue in terms of prediction accuracy and/or generalization. [2 pts]\n",
    "\n",
    "**Answer**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'RNN'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-103-e1f545f7a66d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmanifold\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTSNE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0membeddings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'RNN'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mweights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'RNN'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'RNN'"
     ]
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "\n",
    "embeddings = models['RNN'].layers[1].output\n",
    "\n",
    "weights = models['RNN'].layers[1].get_weights()\n",
    "\n",
    "X = np.array(weights[0])\n",
    "X_embedded = TSNE(n_components=2).fit_transform(X)\n",
    "X_embedded\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x_ = []\n",
    "y_ = []\n",
    "\n",
    "for point in X_embedded:\n",
    "    x_.append(point[0])\n",
    "    y_.append(point[1])\n",
    "\n",
    "plt.plot(x_,y_, 'ro')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chatbot\n",
    "\n",
    "Finally, you will construct a model with which you can chat. The network will take as input a sentence and output a response.\n",
    "\n",
    "**Code** For that, you should go back to your original data and construct a new dataset containing pairs of sentences, where each pair is a sentence and its answer. Be careful to not include any pair of sentences that contains words not present in your filtered dictionary. [2 pts]\n",
    "\n",
    "**Code** You should then tokenize, padd, truncate each sentence. Only the answers need the 'START' and 'END' artificial words. [1 pt]\n",
    "\n",
    "We provide you with a possible model, you are welcome to change it. This model uses an LSTM layer to encode the first sentence (the context). The final state of this LSTM layer is transfered to initialize the state of a decoder LSTM layer from which the answer sentence will be generated. \n",
    "\n",
    "**Code** Train your chatbot model on your dataset. [1 pt]\n",
    "\n",
    "**Code** Adapt your sentence generation code from before so that you can generate an answer given a context sentence from your model. [2 pts] \n",
    "\n",
    "**Code** After training, randomly select 10 context-answers pairs from your data and show both the real answer (the one from the data) and the generated one for two different sampling temperatures (e.g. 0.5 and 1.0). [2 pts]\n",
    "\n",
    "**Question** How similar are the generated answers and the real ones? Does your model provide probable answers (given the dataset)? Report here one good and one bad example. [2 pts]\n",
    "\n",
    "**Answer**\n",
    "\n",
    "**Question** Which sampling temperature gives better answers? why? [2 pts]\n",
    "\n",
    "**Answer**\n",
    "\n",
    "**Question** Would it be good if your model was able to reproduce exactly each real answer? Why? [1 pt]\n",
    "\n",
    "**Answer**\n",
    "\n",
    "**Code** Entertain yourself with your model. Write some code to chat with your bot, let it discuss with itself, ... be creative! [2 **bonus** pts]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "context = Input(shape=(maxlen-2,), name=\"input_context\")\n",
    "shared_embedding = E['GRU']\n",
    "context_embedding = shared_embedding(context)\n",
    "\n",
    "encoder_y, encoder_h, encoder_c = LSTM(hidden_size, \n",
    "            return_sequences=False,\n",
    "            return_state=True,\n",
    "            stateful=False,\n",
    "            dropout=dropout,\n",
    "            recurrent_dropout=recurrent_dropout,\n",
    "            go_backwards=True,\n",
    "            name=\"encoder\")(context_embedding)\n",
    "\n",
    "answer = Input(shape=(maxlen-1,), name=\"input_answer\")\n",
    "answer_embedding = shared_embedding(answer)\n",
    "\n",
    "decoder_input = answer_embedding\n",
    "decoder = LSTM(hidden_size, \n",
    "            return_sequences=True,\n",
    "            stateful=False,\n",
    "            dropout=dropout,\n",
    "            recurrent_dropout=recurrent_dropout,\n",
    "            name=\"decoder\")(answer_embedding, initial_state=[encoder_h, encoder_c])\n",
    "# decoder2 = LSTM(hidden_size, \n",
    "#             return_sequences=True,\n",
    "#             stateful=False,\n",
    "#             dropout=dropout,\n",
    "#             recurrent_dropout=recurrent_dropout,\n",
    "#             name=\"decoder2\")(decoder)\n",
    "\n",
    "R = TimeDistributed(Dense(embedding_size, activation='relu'), name='readout')(decoder)\n",
    "Y = TimeDistributed(Dense(len(tokens), activation='softmax'), name='output')(R)\n",
    "\n",
    "Chatbot = Model(inputs = [context, answer], outputs = [Y])\n",
    "Chatbot.compile(\n",
    "    loss='categorical_crossentropy', \n",
    "    optimizer=Adam(),\n",
    "    metrics=['acc'])\n",
    "Chatbot.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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