assignment1_ns3783.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from os import listdir\n",
    "import random\n",
    "\n",
    "# load doc into memory\n",
    "def load_doc(filename):\n",
    "    # open the file as read only\n",
    "    file = open(filename, 'r')\n",
    "    # read all text\n",
    "    text = file.read()\n",
    "    # close the file\n",
    "    file.close()\n",
    "    return text\n",
    "\n",
    "# load all docs in a directory\n",
    "def process_docs(directory):\n",
    "    # walk through all files in the folder\n",
    "    full_dataset = []\n",
    "    for filename in listdir(directory):\n",
    "        # skip files that do not have the right extension\n",
    "        if not filename.endswith(\".txt\"):\n",
    "            continue\n",
    "        # create the full path of the file to open\n",
    "        path = directory + '/' + filename\n",
    "        # load document\n",
    "        doc = load_doc(path)\n",
    "        full_dataset.append(doc)\n",
    "    return full_dataset\n",
    "\n",
    "directory_neg = 'aclImdb/train/neg'\n",
    "train = process_docs(directory_neg)\n",
    "directory_pos = 'aclImdb/train/pos'\n",
    "train.extend(process_docs(directory_pos))\n",
    "train_target = [0] * 12500 + [1] * 12500\n",
    "\n",
    "#shuffle training data\n",
    "combined = list(zip(train, train_target))\n",
    "random.shuffle(combined)\n",
    "train[:], train_target[:] = zip(*combined)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dataset size is 20000\n",
      "Val dataset size is 5000\n",
      "Test dataset size is 25000\n"
     ]
    }
   ],
   "source": [
    "# split training/validation data\n",
    "train_split = 20000\n",
    "train_data = train[:train_split]\n",
    "train_targets = train_target[:train_split]\n",
    "\n",
    "val_data = train[train_split:]\n",
    "val_targets = train_target[train_split:]\n",
    "\n",
    "# load test data\n",
    "directory_neg = 'aclImdb/test/neg'\n",
    "test_data = process_docs(directory_neg)\n",
    "directory_pos = 'aclImdb/test/pos'\n",
    "test_data.extend(process_docs(directory_pos))\n",
    "test_targets = [0] * 12500 + [1] * 12500\n",
    "\n",
    "print (\"Train dataset size is {}\".format(len(train_data)))\n",
    "print (\"Val dataset size is {}\".format(len(val_data)))\n",
    "print (\"Test dataset size is {}\".format(len(test_data)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'is', 'looking', 'at', 'buying', 'u.k.', 'startup', 'for', '1', 'billion']\n",
      "['apple', 'looking', 'buying', 'u.k', 'startup', 'billion']\n"
     ]
    }
   ],
   "source": [
    "# Let's write the tokenization function \n",
    "import spacy\n",
    "import string\n",
    "\n",
    "# Load English tokenizer, tagger, parser, NER and word vectors\n",
    "tokenizer = spacy.load('en_core_web_sm')\n",
    "punctuations = string.punctuation\n",
    "\n",
    "def tokenize(sent):\n",
    "    tokens = tokenizer(sent)\n",
    "    return [token.text.lower() for token in tokens if (token.text not in punctuations)]\n",
    "\n",
    "# Try tokenize thoroughly\n",
    "def tokenize_indetails(sent):\n",
    "    tokens = tokenizer(sent)\n",
    "    # lowercase and remove punctuation\n",
    "    tokens = [token.text.lower() for token in tokens if (token.text not in punctuations)]\n",
    "    # remove line break symbol\n",
    "    tokens = [token for token in tokens if token != '/><br']\n",
    "    for i in range(len(tokens)):\n",
    "        if tokens[i].startswith('/>'):\n",
    "            tokens[i] = tokens[i][2:]\n",
    "        if tokens[i].endswith('<br'):\n",
    "            tokens[i] = tokens[i][:-3]\n",
    "        if tokens[i].endswith('.<br'):\n",
    "            tokens[i] = tokens[i][:-4]\n",
    "    tokens = tokenizer(' '.join(tokens))\n",
    "    # get rid of stopword\n",
    "    tokenizer.vocab[\"'s'\"].is_stop = True\n",
    "    tokenizer.vocab[\"'d'\"].is_stop = True\n",
    "    tokenizer.vocab[\"n't\"].is_stop = True\n",
    "    tokens = [token for token in tokens if not token.is_stop] \n",
    "    # remove character\n",
    "    tokens = [str(token) for token in tokens if len(token) > 1] \n",
    "    return tokens\n",
    "    \n",
    "# Example\n",
    "tokens = tokenize(u'Apple is looking at buying U.K. startup for $1 billion')\n",
    "tokens2 = tokenize_indetails(u'Apple is looking at buying U.K. startup for $1 billion')\n",
    "print (tokens)\n",
    "print (tokens2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle as pkl\n",
    "\n",
    "#create n-gram datasets\n",
    "def tokenize_dataset(dataset, n_gram=1, tokenize_scheme='simple'):\n",
    "    token_dataset = []\n",
    "        # we are keeping track of all tokens in dataset \n",
    "        # in order to create vocabulary later\n",
    "    all_tokens = []\n",
    "    for sample in dataset:\n",
    "        if tokenize_scheme == 'simple':\n",
    "            tokens = tokenize(sample)\n",
    "        if tokenize_scheme == 'detailed':\n",
    "            tokens = tokenize_indetails(sample)\n",
    "        n_tokens = []\n",
    "        for i in range(len(tokens)-n_gram+1): \n",
    "            n_token = ' '.join(tokens[i:i+n_gram])\n",
    "            n_tokens.append(n_token)\n",
    "        token_dataset.append(n_tokens)\n",
    "        all_tokens += n_tokens\n",
    "    return token_dataset, all_tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "def build_vocab(all_tokens, max_vocab_size = 10000):\n",
    "    # Returns:\n",
    "    # id2token: list of tokens, where id2token[i] returns token that corresponds to token i\n",
    "    # token2id: dictionary where keys represent tokens and corresponding values represent indices\n",
    "    # save index 0 for unk and 1 for pad\n",
    "    PAD_IDX = 0\n",
    "    UNK_IDX = 1\n",
    "    token_counter = Counter(all_tokens)\n",
    "    vocab, count = zip(*token_counter.most_common(max_vocab_size))\n",
    "    id2token = list(vocab)\n",
    "    token2id = dict(zip(vocab, range(2,2+len(vocab)))) \n",
    "    id2token = ['<pad>', '<unk>'] + id2token\n",
    "    token2id['<pad>'] = PAD_IDX \n",
    "    token2id['<unk>'] = UNK_IDX\n",
    "    return token2id, id2token\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dataset size is 20000\n",
      "Val dataset size is 5000\n",
      "Test dataset size is 25000\n"
     ]
    }
   ],
   "source": [
    "# convert token to id in the dataset\n",
    "def token2index_dataset(tokens_data):\n",
    "    indices_data = []\n",
    "    for tokens in tokens_data:\n",
    "        index_list = [token2id[token] if token in token2id else UNK_IDX for token in tokens]\n",
    "        indices_data.append(index_list)\n",
    "    return indices_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[   1, 1974,  292,  ...,    0,    0,    0],\n",
      "        [  55,  736, 3223,  ...,   36,  640,  446],\n",
      "        [   2, 1987,    9,  ...,    0,    0,    0],\n",
      "        ...,\n",
      "        [1888,  314,  152,  ...,    0,    0,    0],\n",
      "        [  61,   46,   25,  ...,    0,    0,    0],\n",
      "        [  10,  240,   25,  ...,   16,    1,   42]])\n",
      "32 200\n",
      "tensor([156, 200, 173, 116, 200, 200, 113, 117, 151, 200, 100,  77, 200, 132,\n",
      "        200,  82, 107, 108, 142, 200, 200, 200, 143, 200, 200, 129,  33, 200,\n",
      "        135,  70, 180, 200])\n",
      "tensor([0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1,\n",
      "        1, 1, 1, 0, 0, 1, 1, 1])\n"
     ]
    }
   ],
   "source": [
    "MAX_SENTENCE_LENGTH = 200\n",
    "import numpy as np\n",
    "import torch\n",
    "from torch.utils.data import Dataset\n",
    "\n",
    "class NewsGroupDataset(Dataset):\n",
    "    \"\"\"\n",
    "    Class that represents a train/validation/test dataset that's readable for PyTorch\n",
    "    Note that this class inherits torch.utils.data.Dataset\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, data_list, target_list):\n",
    "        \"\"\"\n",
    "        @param data_list: list of newsgroup tokens \n",
    "        @param target_list: list of newsgroup targets \n",
    "\n",
    "        \"\"\"\n",
    "        self.data_list = data_list\n",
    "        self.target_list = target_list\n",
    "        assert (len(self.data_list) == len(self.target_list))\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data_list)\n",
    "        \n",
    "    def __getitem__(self, key):\n",
    "        \"\"\"\n",
    "        Triggered when you call dataset[i]\n",
    "        \"\"\"\n",
    "        \n",
    "        token_idx = self.data_list[key][:MAX_SENTENCE_LENGTH]\n",
    "        label = self.target_list[key]\n",
    "        return [token_idx, len(token_idx), label]\n",
    "\n",
    "def newsgroup_collate_func(batch):\n",
    "    \"\"\"\n",
    "    Customized function for DataLoader that dynamically pads the batch so that all \n",
    "    data have the same length\n",
    "    \"\"\"\n",
    "    data_list = []\n",
    "    label_list = []\n",
    "    length_list = []\n",
    "    #print(\"collate batch: \", batch[0][0])\n",
    "    #batch[0][0] = batch[0][0][:MAX_SENTENCE_LENGTH]\n",
    "    for datum in batch:\n",
    "        label_list.append(datum[2])\n",
    "        length_list.append(datum[1])\n",
    "    # padding\n",
    "    for datum in batch:\n",
    "        padded_vec = np.pad(np.array(datum[0]), \n",
    "                                pad_width=((0,MAX_SENTENCE_LENGTH-datum[1])), \n",
    "                                mode=\"constant\", constant_values=0)\n",
    "        data_list.append(padded_vec)\n",
    "    return [torch.from_numpy(np.array(data_list)), torch.LongTensor(length_list), torch.LongTensor(label_list)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First import torch related libraries\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class BagOfWords(nn.Module):\n",
    "    \"\"\"\n",
    "    BagOfWords classification model\n",
    "    \"\"\"\n",
    "    def __init__(self, vocab_size, emb_dim):\n",
    "        \"\"\"\n",
    "        @param vocab_size: size of the vocabulary. \n",
    "        @param emb_dim: size of the word embedding\n",
    "        \"\"\"\n",
    "        super(BagOfWords, self).__init__()\n",
    "        # pay attention to padding_idx \n",
    "        self.embed = nn.Embedding(vocab_size, emb_dim, padding_idx=0)\n",
    "        self.linear = nn.Linear(emb_dim,20)\n",
    "    \n",
    "    def forward(self, data, length):\n",
    "        \"\"\"\n",
    "        \n",
    "        @param data: matrix of size (batch_size, max_sentence_length). Each row in data represents a \n",
    "            review that is represented using n-gram index. Note that they are padded to have same length.\n",
    "        @param length: an int tensor of size (batch_size), which represents the non-trivial (excludes padding)\n",
    "            length of each sentences in the data.\n",
    "        \"\"\"\n",
    "        out = self.embed(data)\n",
    "        out = torch.sum(out, dim=1)\n",
    "        out /= length.view(length.size()[0],1).expand_as(out).float()\n",
    "     \n",
    "        # return logits\n",
    "        out = self.linear(out.float())\n",
    "        return out\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function for testing the model\n",
    "def test_model(loader, model):\n",
    "    \"\"\"\n",
    "    Help function that tests the model's performance on a dataset\n",
    "    @param: loader - data loader for the dataset to test against\n",
    "    \"\"\"\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    model.eval()\n",
    "    for data, lengths, labels in loader:\n",
    "        data_batch, length_batch, label_batch = data, lengths, labels\n",
    "        outputs = F.softmax(model(data_batch, length_batch), dim=1)\n",
    "        predicted = outputs.max(1, keepdim=True)[1]\n",
    "        \n",
    "        total += labels.size(0)\n",
    "        correct += predicted.eq(labels.view_as(predicted)).sum().item()\n",
    "    return (100 * correct / total)\n",
    "\n",
    "# Function to train the model\n",
    "def train_model(emb_dim = 100, vocab_size = len(id2token), learning_rate = 0.01, annealing_rate = 0, step = 10,\n",
    "                num_epochs = 5, optim = 'Adam'):\n",
    "    \"\"\"\n",
    "    Help function that tests the model's performance on a dataset\n",
    "    @param: emb_dim - embedding size\n",
    "            vocab_size - vocabulary size\n",
    "            learning_rate - learning rate of the optimizer, if annealing_rate is not 0, this is a start learning\n",
    "                rate\n",
    "            annealing_rate - a factor that shrinks the learning rate every step size\n",
    "            step - step size (epochs)\n",
    "            optim - optimizer, can choose from 'Adam' or 'SGD'\n",
    "            \n",
    "    \"\"\"\n",
    "    model = BagOfWords(vocab_size, emb_dim)\n",
    "    criterion = torch.nn.CrossEntropyLoss()  \n",
    "    if optim == 'Adam':\n",
    "        optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
    "    if optim == 'SGD':\n",
    "        optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
    "    # set the scheduler for learning rate\n",
    "    scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=step, gamma=annealing_rate)\n",
    "    epoch_train_loss = []\n",
    "    epoch_val_acc = []\n",
    "    for epoch in range(num_epochs):\n",
    "        # only decay the learning rate when annealing rate is given\n",
    "        if annealing_rate!=0:\n",
    "            scheduler.step()\n",
    "        for i, (data, lengths, labels) in enumerate(train_loader):\n",
    "            model.train()\n",
    "            data_batch, length_batch, label_batch = data, lengths, labels\n",
    "            optimizer.zero_grad()\n",
    "            outputs = model(data_batch, length_batch)\n",
    "            loss = criterion(outputs, label_batch)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "                \n",
    "        epoch_train_loss.append(loss.item())\n",
    "        val_acc = test_model(val_loader, model)\n",
    "        print('Epoch: [{}/{}], Validation Acc: {}'.format( \n",
    "                       epoch+1, num_epochs, val_acc))\n",
    "        epoch_val_acc.append(val_acc)\n",
    "    return epoch_train_loss, epoch_val_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = 10, 8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tokenization schemes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### run code below to tokenize simply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # val set tokens\n",
    "# print (\"Tokenizing val data\")\n",
    "# val_data_tokens, _ = tokenize_dataset(val_data)\n",
    "# pkl.dump(val_data_tokens, open(\"val_data_tokens.p\", \"wb\"))\n",
    "\n",
    "# # test set tokens\n",
    "# print (\"Tokenizing test data\")\n",
    "# test_data_tokens, _ = tokenize_dataset(test_data)\n",
    "# pkl.dump(test_data_tokens, open(\"test_data_tokens.p\", \"wb\"))\n",
    "\n",
    "# # train set tokens\n",
    "# print (\"Tokenizing train data\")\n",
    "# train_data_tokens, all_train_tokens = tokenize_dataset(train_data)\n",
    "# pkl.dump(train_data_tokens, open(\"train_data_tokens.p\", \"wb\"))\n",
    "# pkl.dump(all_train_tokens, open(\"all_train_tokens.p\", \"wb\"))\n",
    "# Then, load preprocessed train, val and test datasets\n",
    "\n",
    "\n",
    "# run code above if these files do not exist\n",
    "train_data_tokens = pkl.load(open(\"train_data_tokens.p\", \"rb\"))\n",
    "all_train_tokens = pkl.load(open(\"all_train_tokens.p\", \"rb\"))\n",
    "\n",
    "val_data_tokens = pkl.load(open(\"val_data_tokens.p\", \"rb\"))\n",
    "test_data_tokens = pkl.load(open(\"test_data_tokens.p\", \"rb\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [],
   "source": [
    "token2id, id2token = build_vocab(all_train_tokens, max_vocab_size=10000)\n",
    "train_data_indices = token2index_dataset(train_data_tokens)\n",
    "val_data_indices = token2index_dataset(val_data_tokens)\n",
    "test_data_indices = token2index_dataset(test_data_tokens)\n",
    "\n",
    "# create pytorch dataloader\n",
    "#train_loader = NewsGroupDataset(train_data_indices, train_targets)\n",
    "#val_loader = NewsGroupDataset(val_data_indices, val_targets)\n",
    "#test_loader = NewsGroupDataset(test_data_indices, test_targets)\n",
    "\n",
    "BATCH_SIZE = 32\n",
    "train_dataset = NewsGroupDataset(train_data_indices, train_targets)\n",
    "train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n",
    "                                           batch_size=BATCH_SIZE,\n",
    "                                           collate_fn=newsgroup_collate_func,\n",
    "                                           shuffle=True)\n",
    "\n",
    "val_dataset = NewsGroupDataset(val_data_indices, val_targets)\n",
    "val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n",
    "                                           batch_size=BATCH_SIZE,\n",
    "                                           collate_fn=newsgroup_collate_func,\n",
    "                                           shuffle=True)\n",
    "# no need for test data right now\n",
    "# test_dataset = NewsGroupDataset(test_data_indices, test_targets)\n",
    "# test_loader = torch.utils.data.DataLoader(dataset=test_dataset, \n",
    "#                                            batch_size=BATCH_SIZE,\n",
    "#                                            collate_fn=newsgroup_collate_func,\n",
    "#                                            shuffle=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### train "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: [1/30], Validation Acc: 85.12\n",
      "Epoch: [2/30], Validation Acc: 86.26\n",
      "Epoch: [3/30], Validation Acc: 85.3\n",
      "Epoch: [4/30], Validation Acc: 84.8\n",
      "Epoch: [5/30], Validation Acc: 84.18\n",
      "Epoch: [6/30], Validation Acc: 83.54\n",
      "Epoch: [7/30], Validation Acc: 83.42\n",
      "Epoch: [8/30], Validation Acc: 82.88\n",
      "Epoch: [9/30], Validation Acc: 82.3\n",
      "Epoch: [10/30], Validation Acc: 82.06\n",
      "Epoch: [11/30], Validation Acc: 82.42\n",
      "Epoch: [12/30], Validation Acc: 82.22\n",
      "Epoch: [13/30], Validation Acc: 81.5\n",
      "Epoch: [14/30], Validation Acc: 81.8\n",
      "Epoch: [15/30], Validation Acc: 81.6\n",
      "Epoch: [16/30], Validation Acc: 81.84\n",
      "Epoch: [17/30], Validation Acc: 81.84\n",
      "Epoch: [18/30], Validation Acc: 81.46\n",
      "Epoch: [19/30], Validation Acc: 81.58\n",
      "Epoch: [20/30], Validation Acc: 81.94\n",
      "Epoch: [21/30], Validation Acc: 81.54\n",
      "Epoch: [22/30], Validation Acc: 81.76\n",
      "Epoch: [23/30], Validation Acc: 81.2\n",
      "Epoch: [24/30], Validation Acc: 81.38\n",
      "Epoch: [25/30], Validation Acc: 81.42\n",
      "Epoch: [26/30], Validation Acc: 81.34\n",
      "Epoch: [27/30], Validation Acc: 81.64\n",
      "Epoch: [28/30], Validation Acc: 81.4\n",
      "Epoch: [29/30], Validation Acc: 81.3\n",
      "Epoch: [30/30], Validation Acc: 81.66\n"
     ]
    }
   ],
   "source": [
    "train_loss_simple, val_acc_simple, _ = train_model(vocab_size = len(id2token), num_epochs = 30, \n",
    "                                            optim = 'Adam', learning_rate = 0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### run code below to tokenize in details\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tokenizing test data\n"
     ]
    }
   ],
   "source": [
    "# # val set tokens\n",
    "# print (\"Tokenizing val data\")\n",
    "# val_data_tokens, _ = tokenize_dataset(val_data, tokenize_scheme='detailed')\n",
    "# pkl.dump(val_data_tokens, open(\"val_data_tokens_detailed.p\", \"wb\"))\n",
    "\n",
    "# # test set tokens\n",
    "# print (\"Tokenizing test data\")\n",
    "# test_data_tokens, _ = tokenize_dataset(test_data, tokenize_scheme='detailed')\n",
    "# pkl.dump(test_data_tokens, open(\"test_data_tokens_detailed.p\", \"wb\"))\n",
    "\n",
    "# # train set tokens\n",
    "# print (\"Tokenizing train data\")\n",
    "# train_data_tokens, all_train_tokens = tokenize_dataset(train_data, tokenize_scheme='detailed')\n",
    "# pkl.dump(train_data_tokens, open(\"train_data_tokens_detailed.p\", \"wb\"))\n",
    "# pkl.dump(all_train_tokens, open(\"all_train_tokens_detailed.p\", \"wb\"))\n",
    "\n",
    "# run code above if these files do not exist\n",
    "train_data_tokens = pkl.load(open(\"train_data_tokens_detailed.p\", \"rb\"))\n",
    "all_train_tokens = pkl.load(open(\"all_train_tokens_detailed.p\", \"rb\"))\n",
    "\n",
    "val_data_tokens = pkl.load(open(\"val_data_tokens_detailed.p\", \"rb\"))\n",
    "test_data_tokens = pkl.load(open(\"test_data_tokens_detailed.p\", \"rb\"))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [],
   "source": [
    "token2id, id2token = build_vocab(all_train_tokens, max_vocab_size=10000)\n",
    "train_data_indices = token2index_dataset(train_data_tokens)\n",
    "val_data_indices = token2index_dataset(val_data_tokens)\n",
    "test_data_indices = token2index_dataset(test_data_tokens)\n",
    "\n",
    "BATCH_SIZE = 32\n",
    "train_dataset = NewsGroupDataset(train_data_indices, train_targets)\n",
    "train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n",
    "                                           batch_size=BATCH_SIZE,\n",
    "                                           collate_fn=newsgroup_collate_func,\n",
    "                                           shuffle=True)\n",
    "\n",
    "val_dataset = NewsGroupDataset(val_data_indices, val_targets)\n",
    "val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n",
    "                                           batch_size=BATCH_SIZE,\n",
    "                                           collate_fn=newsgroup_collate_func,\n",
    "                                           shuffle=True)\n",
    "# no need for test data right now\n",
    "# test_dataset = NewsGroupDataset(test_data_indices, test_targets)\n",
    "# test_loader = torch.utils.data.DataLoader(dataset=test_dataset, \n",
    "#                                            batch_size=BATCH_SIZE,\n",
    "#                                            collate_fn=newsgroup_collate_func,\n",
    "#                                            shuffle=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: [1/30], Validation Acc: 88.1\n",
      "Epoch: [2/30], Validation Acc: 87.68\n",
      "Epoch: [3/30], Validation Acc: 86.92\n",
      "Epoch: [4/30], Validation Acc: 86.42\n",
      "Epoch: [5/30], Validation Acc: 85.82\n",
      "Epoch: [6/30], Validation Acc: 85.62\n",
      "Epoch: [7/30], Validation Acc: 85.12\n",
      "Epoch: [8/30], Validation Acc: 85.26\n",
      "Epoch: [9/30], Validation Acc: 85.08\n",
      "Epoch: [10/30], Validation Acc: 84.98\n",
      "Epoch: [11/30], Validation Acc: 84.94\n",
      "Epoch: [12/30], Validation Acc: 84.74\n",
      "Epoch: [13/30], Validation Acc: 84.62\n",
      "Epoch: [14/30], Validation Acc: 84.76\n",
      "Epoch: [15/30], Validation Acc: 84.96\n",
      "Epoch: [16/30], Validation Acc: 85.04\n",
      "Epoch: [17/30], Validation Acc: 85.2\n",
      "Epoch: [18/30], Validation Acc: 85.24\n",
      "Epoch: [19/30], Validation Acc: 85.24\n",
      "Epoch: [20/30], Validation Acc: 84.76\n",
      "Epoch: [21/30], Validation Acc: 84.7\n",
      "Epoch: [22/30], Validation Acc: 84.72\n",
      "Epoch: [23/30], Validation Acc: 84.38\n",
      "Epoch: [24/30], Validation Acc: 85.1\n",
      "Epoch: [25/30], Validation Acc: 84.6\n",
      "Epoch: [26/30], Validation Acc: 84.56\n",
      "Epoch: [27/30], Validation Acc: 84.86\n",
      "Epoch: [28/30], Validation Acc: 84.9\n",
      "Epoch: [29/30], Validation Acc: 84.96\n",
      "Epoch: [30/30], Validation Acc: 84.98\n"
     ]
    }
   ],
   "source": [
    "train_loss_detailed, val_acc_detailed, _ = train_model(vocab_size = len(id2token), num_epochs = 30, \n",
    "                                            optim = 'Adam', learning_rate = 0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### compare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'validation accuracy')"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(train_loss_simple, label = 'tokenize simply')\n",
    "plt.plot(train_loss_detailed, label = 'tokenize in details')\n",
    "plt.legend()\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('training loss')\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(val_acc_simple, label = 'tokenize simply')\n",
    "plt.plot(val_acc_detailed, label = 'tokenize in details')\n",
    "plt.legend()\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('validation accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After training for 30 epochs\n",
      "The training losses are 0.0004957094788551331, 4.859268665313721e-05\n",
      "The validation accuracy are 81.66, 84.98\n",
      "The Maxmimum Val Acc is 84.98 with detailed tokenization\n"
     ]
    }
   ],
   "source": [
    "print (\"After training for 30 epochs\")\n",
    "results = [val_acc_simple[-1], val_acc_detailed[-1]]\n",
    "max_val_acc = max(results)\n",
    "max_val_acc_ind = results.index(max_val_acc)\n",
    "print(\"The training losses are {}, {}\".format(train_loss_simple[-1], train_loss_detailed[-1]))\n",
    "print(\"The validation accuracy are {}, {}\".format(val_acc_simple[-1], val_acc_detailed[-1]))\n",
    "\n",
    "print (\"The Maxmimum Val Acc is {} with {} tokenization\".format(max_val_acc,\n",
    "                                ['simple','detailed'][max_val_acc_ind]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optimization hyperparameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adam optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### learning rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: [1/30], Validation Acc: 80.34\n",
      "Epoch: [2/30], Validation Acc: 85.94\n",
      "Epoch: [3/30], Validation Acc: 87.28\n",
      "Epoch: [4/30], Validation Acc: 87.76\n",
      "Epoch: [5/30], Validation Acc: 88.2\n",
      "Epoch: [6/30], Validation Acc: 88.48\n",
      "Epoch: [7/30], Validation Acc: 88.2\n",
      "Epoch: [8/30], Validation Acc: 88.38\n",
      "Epoch: [9/30], Validation Acc: 87.96\n",
      "Epoch: [10/30], Validation Acc: 88.12\n",
      "Epoch: [11/30], Validation Acc: 87.92\n",
      "Epoch: [12/30], Validation Acc: 87.84\n",
      "Epoch: [13/30], Validation Acc: 87.54\n",
      "Epoch: [14/30], Validation Acc: 87.48\n",
      "Epoch: [15/30], Validation Acc: 87.52\n",
      "Epoch: [16/30], Validation Acc: 87.42\n",
      "Epoch: [17/30], Validation Acc: 87.2\n",
      "Epoch: [18/30], Validation Acc: 87.1\n",
      "Epoch: [19/30], Validation Acc: 87.08\n",
      "Epoch: [20/30], Validation Acc: 86.96\n",
      "Epoch: [21/30], Validation Acc: 86.86\n",
      "Epoch: [22/30], Validation Acc: 86.58\n",
      "Epoch: [23/30], Validation Acc: 86.56\n",
      "Epoch: [24/30], Validation Acc: 86.56\n",
      "Epoch: [25/30], Validation Acc: 86.3\n",
      "Epoch: [26/30], Validation Acc: 86.06\n",
      "Epoch: [27/30], Validation Acc: 86.04\n",
      "Epoch: [28/30], Validation Acc: 86.0\n",
      "Epoch: [29/30], Validation Acc: 86.0\n",
      "Epoch: [30/30], Validation Acc: 85.88\n",
      "Epoch: [1/30], Validation Acc: 87.94\n",
      "Epoch: [2/30], Validation Acc: 87.36\n",
      "Epoch: [3/30], Validation Acc: 86.6\n",
      "Epoch: [4/30], Validation Acc: 86.28\n",
      "Epoch: [5/30], Validation Acc: 85.92\n",
      "Epoch: [6/30], Validation Acc: 85.32\n",
      "Epoch: [7/30], Validation Acc: 85.22\n",
      "Epoch: [8/30], Validation Acc: 85.28\n",
      "Epoch: [9/30], Validation Acc: 84.66\n",
      "Epoch: [10/30], Validation Acc: 84.58\n",
      "Epoch: [11/30], Validation Acc: 85.28\n",
      "Epoch: [12/30], Validation Acc: 84.7\n",
      "Epoch: [13/30], Validation Acc: 85.26\n",
      "Epoch: [14/30], Validation Acc: 84.7\n",
      "Epoch: [15/30], Validation Acc: 84.76\n",
      "Epoch: [16/30], Validation Acc: 84.74\n",
      "Epoch: [17/30], Validation Acc: 84.6\n",
      "Epoch: [18/30], Validation Acc: 84.88\n",
      "Epoch: [19/30], Validation Acc: 84.92\n",
      "Epoch: [20/30], Validation Acc: 84.88\n",
      "Epoch: [21/30], Validation Acc: 84.94\n",
      "Epoch: [22/30], Validation Acc: 84.82\n",
      "Epoch: [23/30], Validation Acc: 84.88\n",
      "Epoch: [24/30], Validation Acc: 84.9\n",
      "Epoch: [25/30], Validation Acc: 84.94\n",
      "Epoch: [26/30], Validation Acc: 85.02\n",
      "Epoch: [27/30], Validation Acc: 84.96\n",
      "Epoch: [28/30], Validation Acc: 85.0\n",
      "Epoch: [29/30], Validation Acc: 84.94\n",
      "Epoch: [30/30], Validation Acc: 85.0\n",
      "Epoch: [1/30], Validation Acc: 86.32\n",
      "Epoch: [2/30], Validation Acc: 84.2\n",
      "Epoch: [3/30], Validation Acc: 81.64\n",
      "Epoch: [4/30], Validation Acc: 83.8\n",
      "Epoch: [5/30], Validation Acc: 83.14\n",
      "Epoch: [6/30], Validation Acc: 84.54\n",
      "Epoch: [7/30], Validation Acc: 83.38\n",
      "Epoch: [8/30], Validation Acc: 84.48\n",
      "Epoch: [9/30], Validation Acc: 84.48\n",
      "Epoch: [10/30], Validation Acc: 85.08\n",
      "Epoch: [11/30], Validation Acc: 82.76\n",
      "Epoch: [12/30], Validation Acc: 83.78\n",
      "Epoch: [13/30], Validation Acc: 84.4\n",
      "Epoch: [14/30], Validation Acc: 84.46\n",
      "Epoch: [15/30], Validation Acc: 84.18\n",
      "Epoch: [16/30], Validation Acc: 84.72\n",
      "Epoch: [17/30], Validation Acc: 84.72\n",
      "Epoch: [18/30], Validation Acc: 84.2\n",
      "Epoch: [19/30], Validation Acc: 84.38\n",
      "Epoch: [20/30], Validation Acc: 84.72\n",
      "Epoch: [21/30], Validation Acc: 84.74\n",
      "Epoch: [22/30], Validation Acc: 84.7\n",
      "Epoch: [23/30], Validation Acc: 84.42\n",
      "Epoch: [24/30], Validation Acc: 84.22\n",
      "Epoch: [25/30], Validation Acc: 84.6\n",
      "Epoch: [26/30], Validation Acc: 84.76\n",
      "Epoch: [27/30], Validation Acc: 84.24\n",
      "Epoch: [28/30], Validation Acc: 84.46\n",
      "Epoch: [29/30], Validation Acc: 84.56\n",
      "Epoch: [30/30], Validation Acc: 84.24\n"
     ]
    }
   ],
   "source": [
    "learning_rates = [0.001, 0.01, 0.1]\n",
    "train_loss_adam_lrs = []\n",
    "val_acc_adam_lrs = []\n",
    "for rate in learning_rates:\n",
    "    train_loss_adam_lr, val_acc_adam_lr, _ = train_model(vocab_size = len(id2token), num_epochs = 30, \n",
    "                                                optim = 'Adam', learning_rate = rate)\n",
    "    train_loss_adam_lrs.append(train_loss_adam_lr)\n",
    "    val_acc_adam_lrs.append(val_acc_adam_lr)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### annealing learning rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: [1/30], Validation Acc: 87.78\n",
      "Epoch: [2/30], Validation Acc: 87.76\n",
      "Epoch: [3/30], Validation Acc: 87.4\n",
      "Epoch: [4/30], Validation Acc: 86.06\n",
      "Epoch: [5/30], Validation Acc: 85.64\n",
      "Epoch: [6/30], Validation Acc: 85.58\n",
      "Epoch: [7/30], Validation Acc: 85.02\n",
      "Epoch: [8/30], Validation Acc: 84.88\n",
      "Epoch: [9/30], Validation Acc: 85.1\n",
      "Epoch: [10/30], Validation Acc: 84.74\n",
      "Epoch: [11/30], Validation Acc: 85.2\n",
      "Epoch: [12/30], Validation Acc: 84.88\n",
      "Epoch: [13/30], Validation Acc: 85.26\n",
      "Epoch: [14/30], Validation Acc: 84.6\n",
      "Epoch: [15/30], Validation Acc: 84.34\n",
      "Epoch: [16/30], Validation Acc: 85.0\n",
      "Epoch: [17/30], Validation Acc: 85.1\n",
      "Epoch: [18/30], Validation Acc: 85.16\n",
      "Epoch: [19/30], Validation Acc: 85.14\n",
      "Epoch: [20/30], Validation Acc: 85.1\n",
      "Epoch: [21/30], Validation Acc: 85.18\n",
      "Epoch: [22/30], Validation Acc: 85.22\n",
      "Epoch: [23/30], Validation Acc: 85.22\n",
      "Epoch: [24/30], Validation Acc: 85.12\n",
      "Epoch: [25/30], Validation Acc: 85.06\n",
      "Epoch: [26/30], Validation Acc: 85.04\n",
      "Epoch: [27/30], Validation Acc: 85.04\n",
      "Epoch: [28/30], Validation Acc: 85.08\n",
      "Epoch: [29/30], Validation Acc: 85.1\n",
      "Epoch: [30/30], Validation Acc: 85.08\n"
     ]
    }
   ],
   "source": [
    "train_loss_adam_lr, val_acc_adam_lr, _ = train_model(vocab_size = len(id2token), num_epochs = 30, \n",
    "                                                optim = 'Adam', learning_rate = 0.01, annealing_rate=0.1, step=15)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Adam')"
      ]
     },
     "execution_count": 387,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAHwCAYAAAAIDnN0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzs3Xt81NWd//HXyf0eIIiJhq5orIBclatF1Fq8VREFFK0Xlnoplmrrrxd7YVu3dlfFtrvoapcqRV22rS4UsK01dbtAUVADRbQkmlqtCeVmQpiZ3CbJnN8fkxkSM0kmkMn3O5P38/HgQfKdM9/vyZAMn3zO5/s5xlqLiIiIiDgnyekJiIiIiAx2CshEREREHKaATERERMRhCshEREREHKaATERERMRhCshEREREHKaATEQGHWPMYmPMNqfnISISooBMRBKKMWazMeaIMSbd6bmIiERLAZmIJAxjzGnA+YAF5jo6GRGRPlBAJiKJ5BZgB7AGuDV00BhTYIzZZIzxGGNeB87o+CRjzL8bY6raH99pjDm/w2PfM8Y8b4z5L2OM1xjzljHmk8aYbxpjDrU/75IB+vpEJEEpIBORRHILsLb9z6XGmJPbj/8H0AQUAUva/3T0BjAJGAb8N/C8MSajw+NXAc8CQ4E/AS8RfP88Ffhn4D9j8cWIyOBhtJeliCQCY8ws4P+AImvtR8aYCoKB0kqCwdh4a21F+9h/AWZba2d1c64jwIXW2jeNMd8DPmWtndP+2FXAz4F8a22bMSYX8ABDrbV1sf0qRSRRKUMmIoniVqDUWvtR++f/3X7sJCAFqOow9m8dn2iM+X/GmHJjzFFjTB2QDwzvMORgh48bgY+stW0dPgfI6Z8vQ0QGoxSnJyAicqKMMZnAdUCyMeZA++F0YAhwMtAKjAQq2h/7RIfnng98A7gY+LO1NtCeITMDNH0REQVkIpIQ5gFtwHjA3+H4cwTrytYD3zPGLAFOI5g5+6B9TC7BgO0wkGKMuQ/IG5BZi4i005KliCSCW4GfWWs/tNYeCP0BHgM+BywjuKR4gOAdmD/r8NyXgBeBdwkuZTbReXlTRCTmVNQvIiIi4jBlyEREREQcFrOAzBizur1p4ts9jLnQGLPbGPNnY8yWWM1FRERExM1itmRpjJkN+IBnrLXjIjw+BHgVuMxa+6ExZoS19lBMJiMiIiLiYjHLkFlrtwK1PQy5EVhvrf2wfbyCMRERERmUnKwh+yQw1BizuX3vuFscnIuIiIiIY5zsQ5YCnEuwGWMmsN0Ys8Na++7HBxpj7gDuAMjOzj539OjRAzpRERERkeOxc+fOj6y1J/U2zsmArJrg9iP1QL0xZiswkWAvoE6stauAVQBTpkyxZWVlAzpRERERkeNhjPlb76OcXbLcCJxvjEkxxmQB04FyB+cjIiIi4oiYZciMMT8HLgSGG2Oqge8CqQDW2p9Ya8uNMb8D9gAB4ElrbbctMkREREQSVcwCMmvtDVGMWQGsiNUcREREROJBQmwu3tLSQnV1NU1NTU5PRURcKCMjg+LiYlJTU52eiohIRAkRkFVXV5Obm8tpp52GMcbp6YiIi1hrqampobq6mlGjRjk9HRGRiBJiL8umpiYKCgoUjIlIF8YYCgoKlEEXEVdLiIAMUDAmIt3S+4OIuF3CBGQiIiIi8UoBWT/JycmJ+TU2bdrEgw8+GPPrRLJhwwb27t3bp+dYa7n77rspKSlhwoQJ7Nq1K+K4nTt3Mn78eEpKSrj77rsJbXhfW1vLnDlzOPPMM5kzZw5HjhwBoKKigpkzZ5Kens4jjzxyYl+YiIiICyggc5m2trZuH5s7dy733XefI9c+noDsxRdfpLKyksrKSlatWsXSpUsjjlu6dCmrVq0Kj/3d734HwIMPPsjFF19MZWUlF198cTgYHTZsGCtXruSrX/1qn+YjIiLiVglxl2VH97/wZ/b+3dOv5xx7Sh7fversqMevWLGC5557jubmZq655hruv/9+AObNm0dVVRVNTU3cc8893HHHHUAwu3bvvffy0ksv8cMf/pCbbrqJW2+9lRdeeIGWlhaef/55Ro8ezZo1aygrK+Oxxx5j8eLF5OXlUVZWxoEDB3j44YdZsGABgUCAZcuWsWXLFkaNGkUgEGDJkiUsWLAg4lxPO+00lixZQmlpKcuWLcPr9bJq1Sr8fj8lJSU8++yz7N69m02bNrFlyxYeeOAB1q1bB8AXv/hFDh8+TFZWFj/96U/5+B6jGzdu5JZbbsEYw4wZM6irq2P//v0UFRWFx+zfvx+Px8PMmTMBuOWWW9iwYQOXX345GzduZPPmzQDceuutXHjhhTz00EOMGDGCESNG8Jvf/CbqfxMRERE3U4asn5WWllJZWcnrr7/O7t272blzJ1u3bgVg9erV7Ny5k7KyMlauXElNTQ0A9fX1jBs3jtdee41Zs2YBMHz4cHbt2sXSpUu7XZbbv38/27Zt49e//nU4c7Z+/Xo++OAD3nrrLZ588km2b9/e65wzMjLYtm0bixYt4tprr+WNN97gzTffZMyYMTz11FOcd955zJ07lxUrVrB7927OOOMM7rjjDh599FF27tzJI488wl133dXlvPv27WPkyJHhz4uLi9m3b1+XMcXFxRHHHDx4MBy8FRUVcejQoV6/FhERkXiUcBmyvmSyYqG0tJTS0lImT54MgM/no7KyktmzZ7Ny5Up+9atfAVBVVUVlZSUFBQUkJyczf/78Tue59tprATj33HNZv359xGvNmzePpKQkxo4dy8GDBwHYtm0bCxcuJCkpicLCQi666KJe53z99deHP3777bf5zne+Q11dHT6fj0svvbTLeJ/Px6uvvsrChQvDx5qbm7uMC9WCdfTxu92iGSMiIpLoEi4gc5q1lm9+85vceeednY5v3ryZl19+me3bt5OVlcWFF14Y7ouUkZFBcnJyp/Hp6ekAJCcn09raGvFaoTGh63b8uy+ys7PDHy9evJgNGzYwceJE1qxZE14y7CgQCDBkyBB2797d43mLi4upqqoKf15dXc0pp5zSZUx1dXXEMSeffHJ4iXP//v2MGDGiz1+biIhIPNCSZT+79NJLWb16NT6fDwguyR06dIijR48ydOhQsrKyqKioYMeOHTG5/qxZs1i3bh2BQICDBw9GDKh64vV6KSoqoqWlhbVr14aP5+bm4vV6AcjLy2PUqFE8//zzQDAIfPPNN7uca+7cuTzzzDNYa9mxYwf5+fmd6scguBSZm5vLjh07sNbyzDPPcPXVV4ef//TTTwPw9NNPh4+LiIgkGgVk/eySSy7hxhtvZObMmYwfP54FCxbg9Xq57LLLaG1tZcKECSxfvpwZM2bE5Prz58+nuLiYcePGceeddzJ9+nTy8/Ojfv73v/99pk+fzpw5czoV6S9atIgVK1YwefJk3nvvPdauXctTTz3FxIkTOfvss9m4cWOXc11xxRWcfvrplJSUcPvtt/P444+HH5s0aVL44yeeeILbbruNkpISzjjjDC6//HIA7rvvPn7/+99z5pln8vvf/z5cJ3fgwAGKi4v50Y9+xAMPPEBxcTEeT//eyCEiIjKQzPEscTlpypQptqysrNOx8vJyxowZ49CM3Mfn85GTk0NNTQ3Tpk3jlVdeobCw0OlpiThK7xMi4gRjzE5r7ZTexqmGLAFdeeWV1NXV4ff7Wb58uYIxkQRzzx/u4dTcU/n61K87PRUR6ScKyBJQpLqxa665hvfff7/TsYceeijiXZQi4m4VtRV81PSR09MQkX6kgGyQCLXbEJH45/F7aA1EvvtaROKTAjIRkTjSFmjD1+KjvqWelrYWUpNTnZ6SiPQD3WUpIhJHfC3BljoWy8GGgw7PRkT6iwIyEZE44vEfa/Gyv36/gzMRkf6kgExEJI50DMgO1B9wcCYi0p8UkPWTnJycmF9j06ZNPPjggzG/TiQbNmxg7969fXqOtZa7776bkpISJkyYwK5duyKO27lzJ+PHj6ekpIS77747vP3T888/z9lnn01SUhIf7z0nMlh5mpUhE0lECshcpq2trdvH5s6dG+5WP9DXPp6A7MUXX6SyspLKykpWrVrF0qVLI45bunQpq1atCo/93e9+B8C4ceNYv349s2fP7tN1RRKZ1+8Nf6yATCRxJN5dli/eBwfe6t9zFo6Hy6PPTK1YsYLnnnuO5uZmrrnmGu6//34A5s2bR1VVFU1NTdxzzz3ccccdQDC7du+99/LSSy/xwx/+kJtuuolbb72VF154gZaWFp5//nlGjx7NmjVrKCsr47HHHmPx4sXk5eVRVlbGgQMHePjhh1mwYAGBQIBly5axZcsWRo0aRSAQYMmSJSxYsCDiXE877TSWLFlCaWkpy5Ytw+v1smrVKvx+PyUlJTz77LPs3r2bTZs2sWXLFh544AHWrVsHwBe/+EUOHz5MVlYWP/3pTztttQSwceNGbrnlFowxzJgxg7q6uvBm4SH79+/H4/Ewc+ZMAG655RY2bNjA5Zdfrq7qIhGEliwLMgoUkIkkEGXI+llpaSmVlZW8/vrr7N69m507d7J161YAVq9ezc6dOykrK2PlypXU1NQAUF9fz7hx43jttdeYNWsWAMOHD2fXrl0sXbqURx55JOK19u/fz7Zt2/j1r38dzpytX7+eDz74gLfeeosnn3yS7du39zrnjIwMtm3bxqJFi7j22mt54403ePPNNxkzZgxPPfUU5513HnPnzmXFihXs3r2bM844gzvuuINHH32UnTt38sgjj3DXXXd1Oe++ffsYOXJk+PPi4mL27dvXZUxxcXGPY0TkmFCG7KxhZ3HApxoykUSReBmyPmSyYqG0tJTS0lImT54MBPeVrKysZPbs2axcuTLcoLWqqorKykoKCgpITk5m/vz5nc5z7bXXAnDuueeyfv36iNeaN28eSUlJjB07loMHg7e/b9u2jYULF5KUlERhYSEXXXRRr3O+/vrrwx+//fbbfOc736Gurg6fzxexk7/P5+PVV19l4cKF4WPNzc1dxkXaJ9UY0+cxInKMx+8hxaQwKn8Uuw/txlqrnxmRBJB4AZnDrLV885vf5M477+x0fPPmzbz88sts376drKwsLrzwQpqamoBghio5ObnT+PT0dACSk5NpbY3ckTs0JnTdjn/3RXZ2dvjjxYsXs2HDBiZOnMiaNWsibsMUCAQYMmQIu3fv7vG8xcXFVFVVhT+vrq7mlFNO6TKmurq6xzEicozX7yUvPY+i7CIaWhvw+D3kp+c7PS0ROUFasuxnl156KatXr8bnCzZv3LdvH4cOHeLo0aMMHTqUrKwsKioq2LFjR0yuP2vWLNatW0cgEODgwYMRA6qeeL1eioqKaGlpYe3ateHjubm5eL3BpZK8vDxGjRrF888/DwSDwDfffLPLuebOncszzzyDtZYdO3aQn5/fqX4MoKioiNzcXHbs2IG1lmeeeYarr766j1+1yODhafaQm5ZLUXbwZ0mtL0QSgwKyfnbJJZdw4403MnPmTMaPH8+CBQvwer1cdtlltLa2MmHCBJYvX86MGTNicv358+dTXFzMuHHjuPPOO5k+fTr5+dH/9vz973+f6dOnM2fOnE5F+osWLWLFihVMnjyZ9957j7Vr1/LUU08xceJEzj77bDZu3NjlXFdccQWnn346JSUl3H777Tz++OPhxyZNmhT++IknnuC2226jpKSEM844g8svvxwI7r9ZXFzM9u3b+exnP6uN0EUILlnmpeWFAzIV9oskBnM8S1xOmjJliv14T6ry8nLdkdeBz+cjJyeHmpoapk2bxiuvvEJhYaHT0xJxVKK8T3zuN58jJy2HH8z6ARc9dxHfmv4tbhh9g9PTEpFuGGN2Wmun9DZONWQJ6Morr6Surg6/38/y5csVjIkkEI/fwyk5pzAsYxipSanKkIkkCAVkCShS3dg111zD+++/3+nYQw89pGVAkTgTWrJMMkkUZheq9YVIglBANkiE2m2ISPyy1uLxB4v6AYqyi5QhE0kQKuoXEYkTTW1NtAZayUvPAxSQiSQSBWQiInEitLF4OEOWU8ThxsO0BFqcnJaI9AMFZCIicSK0j2Ve2rEMWcAGONxw2MlpiUg/UEAmIhInQvtYhgKywuzgHdRathSJfwrI+klOTk7Mr7Fp0yYefNCZvTo3bNjA3r17+/Qcay133303JSUlTJgwgV27dkUc9+1vf5uRI0cOyGsoEs8iZchAAZlIIlBA5jJtbW3dPjZ37lzuu+8+R659PAHZiy++SGVlJZWVlaxatYqlS5dGHHfVVVfx+uuv9+ncIoNRdxkybZ8kEv8Sru3FQ68/REVtRb+ec/Sw0Xxj2jeiHr9ixQqee+45mpubueaaa7j//vsBmDdvHlVVVTQ1NXHPPfdwxx13AMHs2r333stLL73ED3/4Q2666SZuvfVWXnjhBVpaWnj++ecZPXo0a9asoaysjMcee4zFixeTl5dHWVkZBw4c4OGHH2bBggUEAgGWLVvGli1bGDVqFIFAgCVLlrBgwYKIcz3ttNNYsmQJpaWlLFu2DK/Xy6pVq/D7/ZSUlPDss8+ye/duNm3axJYtW3jggQdYt24dAF/84hc5fPgwWVlZ/PSnP+201RLAxo0bueWWWzDGMGPGDOrq6ti/f3+X/SxjtY2USKIJZchCRf2ZKZkMTR/Kfp8yZCLxThmyflZaWkplZSWvv/46u3fvZufOnWzduhWA1atXs3PnTsrKyli5ciU1NTUA1NfXM27cOF577TVmzZoFwPDhw9m1axdLly7lkUceiXit/fv3s23bNn7961+HM2fr16/ngw8+4K233uLJJ59k+/btvc45IyODbdu2sWjRIq699lreeOMN3nzzTcaMGcNTTz3Feeedx9y5c1mxYgW7d+/mjDPO4I477uDRRx9l586dPPLII9x1111dzrtv3z5GjhwZ/ry4uJh9+/b17QUVkbDQXZY5aceW9wuzC7VkKZIAEi5D1pdMViyUlpZSWlrK5MmTgeC+kpWVlcyePZuVK1eGG7RWVVVRWVlJQUEBycnJzJ8/v9N5rr32WgDOPfdc1q9fH/Fa8+bNIykpibFjx3Lw4EEAtm3bxsKFC0lKSqKwsJCLLrqo1zlff/314Y/ffvttvvOd71BXV4fP54vYyd/n8/Hqq6+ycOHC8LHm5uYu4yLtk2qM6XU+IhKZx+8hOzWblKRjb91F2UV86P3QwVmJSH9IuIDMadZavvnNb3LnnXd2Or5582Zefvlltm/fTlZWFhdeeCFNTU1AMEOVnJzcaXx6ejoAycnJtLa2RrxWaEzouh3/7ovs7Ozwx4sXL2bDhg1MnDiRNWvWRNyGKRAIMGTIEHbv3t3jeYuLi6mqqgp/Xl1dzSmnnNLn+YlIUGjbpI6Kcop4/YBqMEXiXcyWLI0xq40xh4wxb/cybqoxps0YE7nIKc5ceumlrF69Gp/PBwSX7Q4dOsTRo0cZOnQoWVlZVFRUsGPHjphcf9asWaxbt45AIMDBgwcjBlQ98Xq9FBUV0dLSwtq1a8PHc3Nz8XrbC4rz8hg1ahTPP/88EAwC33zzzS7nmjt3Ls888wzWWnbs2EF+fn6X+jERiZ7X7+0akGUX4WvxhQv+RSQ+xbKGbA1wWU8DjDHJwEPASzGcx4C65JJLuPHGG5k5cybjx49nwYIFeL1eLrvsMlpbW5kwYQLLly+PWSH7/PnzKS4uZty4cdx5551Mnz6d/Pz8qJ///e9/n+nTpzNnzpxORfqLFi1ixYoVTJ48mffee4+1a9fy1FNPMXHiRM4++2w2btzY5VxXXHEFp59+OiUlJdx+++08/vjj4ccmTZoU/vjrX/86xcXFNDQ0UFxczPe+973j++JFElzHfSxD1ItMJDGY41niivrkxpwG/NpaO66bx78MtABT28f9T2/nnDJlii0rK+t0rLy8nDFjxpzwfBOFz+cjJyeHmpoapk2bxiuvvEJhYaHT0xJxVCK8T8zfNJ/inGL+/dP/Hj625/AePvfbz/EfF/8Hs4tnOzg7EYnEGLPTWjult3GO1ZAZY04FrgE+TTAg62nsHcAdAJ/4xCdiP7k4d+WVV1JXV4ff72f58uUKxkQSRKQMWbg5rFpfiMQ1J4v6/w34hrW2rbc776y1q4BVEMyQDcDc4lqkurFrrrmG999/v9Oxhx56KOJdlCLiTp5mD3npnWvICjILSElK0ZKlSJxzMiCbAvyiPRgbDlxhjGm11m5wcE4JK9RuQ0TiU2uglYbWhi4ZsiSTRGGWepGJxDvHAjJr7ajQx8aYNQRryBSMiYhE8PFtkzoqyinS9kkicS5mAZkx5ufAhcBwY0w18F0gFcBa+5NYXVdEJBH1GJBlF/HGgTcGekoi0o9iFpBZa2/ow9jFsZqHiEgiCO1jGSkgK8wu5FDDIVoDrZ26+ItI/NBelv0kJyen90EnaNOmTTz44IMxv05Hmzdv5tVXXz2hc1x44YV8vFWJkxYvXsz//E/XDisnMk9rLXfffTclJSVMmDCBXbt2RRz37W9/m5EjRw7I98vxuuyyyxgyZAhXXnllp+Pvv/8+06dP58wzz+T666/H7/cP+Ny6+7cbDMIBWXrkDFmbbeOjxo8Geloi0k8UkLlMW1tbt4/NnTs3vIl4f+puaybon4BsMHjxxReprKyksrKSVatWsXTp0ojjrrrqKl5/feC3uenp3/jjvva1r/Hss892Of6Nb3yDr3zlK1RWVjJ06FCeeuqp/pyi9CIUkOWm5nZ5LNz6QoX9InFLAVkMrFixgqlTpzJhwgS++93vho/PmzePc889l7PPPptVq1aFj+fk5PBP//RPTJ8+ne3bt3Paaafx3e9+l3POOYfx48dTUVEBwJo1a1i2bBkQzBTcfffdnHfeeZx++unhrEEgEOCuu+7i7LPP5sorr+SKK67oNhv0rW99iwsuuIB///d/54UXXmD69OlMnjyZz3zmMxw8eJAPPviAn/zkJ/z4xz9m0qRJ/PGPf+Tw4cPMnz+fqVOnMnXqVF555ZUu525sbGTRokVMmDCB66+/nsbGxvBjpaWlzJw5k3POOYeFCxeGt5h64403OO+885g4cSLTpk3D6/XywQcfcP7553POOedwzjnnhAPDm2++udPOAJ/73OfYtGkTbW1tfO1rXwu/9v/5n/8JBLNXy5YtY+zYsXz2s5/l0KFD3f7b/dd//RfnnXce48aN4/XXXycQCHDmmWdy+PDh8OtbUlLCRx91zkRs3LiRW265BWMMM2bMoK6ujv37u/7nOGPGjF63j6qvr2fJkiVMnTqVyZMnh7/W6dOn8+c//7nTv+HOnTu7Hb9mzRoWLlzIVVddxSWXXNLt6/ZxF198Mbm5nf/Tt9byhz/8gQULgjuc3XrrrWzY0PUenJ7mcvXVV3PZZZdx1llncf/994ef86Mf/Yhx48Yxbtw4/u3f/i18/JlnnmHChAlMnDiRm2++OXx869atXb7v9+/fz+zZs5k0aRLjxo3jj3/8Y4+vcTzyNPecIQP1IhOJZwlXbHDgX/6F5vKKfj1n+pjRFH7rW1GNLS0tpbKyktdffx1rLXPnzmXr1q3Mnj2b1atXM2zYMBobG5k6dSrz58+noKCA+vp6xo0bxz//8z+HzzN8+HB27drF448/ziOPPMKTTz7Z5Vr79+9n27ZtVFRUMHfuXBYsWMD69ev54IMPeOuttzh06BBjxoxhyZIlEedaV1fHli1bADhy5Ag7duzAGMOTTz7Jww8/zA9/+EO+8IUvkJOTw1e/+lUAbrzxRr7yla8wa9YsPvzwQy699FLKy8s7nfeJJ54gKyuLPXv2sGfPHs455xwAPvroIx544AFefvllsrOzeeihh/jRj37Efffdx/XXX88vf/lLpk6disfjITMzkxEjRvD73/+ejIwMKisrueGGGygrK+O2227jxz/+MVdffTVHjx7l1Vdf5emnn+app54iPz+fN954g+bmZj71qU9xySWX8Kc//Yl33nmHt956i4MHDzJ27NhuX5P6+npeffVVtm7dypIlS3j77be56aabWLt2LV/+8pd5+eWXmThxIsOHD+/0vH379jFy5Mjw58XFxezbt++49u78wQ9+wKc//WlWr15NXV0d06ZN4zOf+QyLFi3iueee4/7772f//v38/e9/59xzz+Vb3/pWxPEA27dvZ8+ePQwbNowtW7ZEfN2iUVNTw5AhQ0hJSen09UU7d4DXX3+dt99+m6ysLKZOncpnP/tZjDH87Gc/47XXXsNay/Tp07ngggtIS0vjBz/4Aa+88grDhw+ntrY2fI1I3/f//d//zaWXXsq3v/1t2traaGho6PPr7nahov6Pt70AbZ8kkggSLiBzWmlpKaWlpUyePBkIbmNUWVnJ7NmzWblyZbgfWFVVFZWVlRQUFJCcnMz8+fM7nefaa68F4Nxzz2X9+vURrzVv3jySkpIYO3YsBw8eBGDbtm0sXLiQpKQkCgsLueiii7qd6/XXXx/+uLq6muuvv579+/fj9/sZNWpUxOe8/PLL7N27N/y5x+PB6/V2yqhs3bqVu+++G4AJEyYwYcIEAHbs2MHevXv51Kc+BYDf72fmzJm88847FBUVMXVqcMOGvLxgBqC+vp5ly5axe/dukpOTeffddwG44IIL+OIXv8ihQ4dYv3498+fPJyUlhdLSUvbs2RPOmhw9epTKykq2bt3KDTfcQHJyMqeccgqf/vSnu31NbrgheC/K7Nmz8Xg81NXVsWTJEq6++mq+/OUvs3r1av7xH/+xy/MibUHWW8Pj7pSWlrJp0yYeeeQRAJqamvjwww+57rrrmDNnDvfffz/PPfccCxcu7HE8wJw5cxg2bFiPr1s0ov36eptLQUEBEPz+3rZtG8YYrrnmGrKzs8PH//jHP2KMYcGCBeHAN/Q1QOTv+6lTp7JkyRJaWlqYN29ep71SE4XH7yE1KZWM5Iwuj2WlZpGfnq+ATCSOJVxAFm0mK1astXzzm9/kzjvv7HR88+bNvPzyy2zfvp2srCwuvPBCmpqaAMjIyCA5ObnT+PT0dACSk5O7rf8JjQldt+Pf0Qj9JwjwpS99iXvvvZe5c+eyefPmbjf4DgQCbN++nczMzB7PHek/a2tqkUpKAAAgAElEQVQtc+bM4ec//3mn43v27Ik4/sc//jEnn3wyb775JoFAgIyMY/8R3Xzzzaxdu5Zf/OIXrF69Onz+Rx99tMvuA7/97W+jDo4+Ps4Yw8iRIzn55JP5wx/+wGuvvcbatWu7PK+4uJiqqqrw59XV1ZxyyilRXfPjrLWsW7eOs846q8tjBQUF7Nmzh1/+8pedlmQjjX/ttdc6/RtD5NctGsOHD6euro7W1lZSUlK6/fp6mkuk17a771drbbf/ZpG+72fPns3WrVv5zW9+w80338zXvvY1brnllqi/vnjg9XvJS8vr9nUpylYvMpF4phqyfnbppZeyevXqcG3Uvn37OHToEEePHmXo0KFkZWVRUVHBjh07YnL9WbNmsW7dOgKBAAcPHoy4jVIkR48e5dRTTwXotIyVm5uL1+sNf37JJZfw2GOPhT/fvXt3l3PNnj07HLS8/fbb7NmzBwjWT73yyiv85S9/AaChoYF3332X0aNH8/e//5033gj2UfJ6vbS2tnL06FGKiopISkri2Wef7XTDw+LFi8P1RmeffTYQfO2feOIJWlpaAHj33Xepr69n9uzZ/OIXv6CtrY39+/fzf//3f92+Dr/85S+BYKYxPz+f/Px8AG677TZuuukmrrvuui7BMwRvuHjmmWew1rJjxw7y8/OPa7ky9HU8+uij4WDjT3/6U/ixRYsW8fDDD3P06FHGjx/f6/iPi/S6RcMYw0UXXRTOPj799NNcffXVfZr773//e2pra2lsbGTDhg186lOfYvbs2WzYsIGGhgbq6+v51a9+xfnnn8/FF1/Mc889R01NDUCnJctI/va3vzFixAhuv/12Pv/5z3d7l2s8i7SPZUeF2erWLxLPFJD1s0suuYQbb7yRmTNnMn78eBYsWIDX6+Wyyy6jtbWVCRMmsHz5cmbMmBGT68+fP5/i4mLGjRvHnXfeyfTp08NBRU++973vsXDhQs4///xO9VFXXXUVv/rVr8JF/StXrqSsrIwJEyYwduxYfvKTrj1+ly5dis/nY8KECTz88MNMmzYNgJNOOok1a9Zwww03MGHCBGbMmEFFRQVpaWn88pe/5Etf+hITJ05kzpw5NDU1cdddd/H0008zY8YM3n333U7ZnpNPPpkxY8Z0Wj687bbbGDt2LOecc074629tbeWaa67hzDPPZPz48SxdupQLLrig29dh6NChnHfeeXzhC1/odBfh3Llz8fl8EZcrAa644gpOP/10SkpKuP3223n88cfDj3VcPvv6179OcXExDQ0NFBcXR8xELl++nJaWFiZMmMC4ceNYvnx5+LEFCxbwi1/8guuuuy6q8R8X6XX7uPPPP5+FCxfyv//7vxQXF/PSSy8BhGv+SkpKqKmp4fOf/3yf5j5r1ixuvvlmJk2axPz585kyZQrnnHMOixcvZtq0aUyfPp3bbruNyZMnc/bZZ/Ptb3+bCy64gIkTJ3Lvvfd2O18IZqAnTZrE5MmTWbduHffcc0+P4+NRpH0sOyrKLlJAJhLHTF+WuNxgypQp9uO9osrLyxkzZoxDM3Ifn89HTk4ONTU1TJs2jVdeeYXCwkKnp9WvGhoaGD9+PLt27Yoq4DxRZWVlfOUrX4n7u/cG+nULWbNmDWVlZZ2yqwMt3t8nbvj1DeRn5POTz0Te6ORnb/+MH+38Edtv2E5Omnv73IkMNsaYndbaKb2NU4YsAV155ZVMmjSJ888/n+XLlydcMPbyyy8zevRovvSlLw1IUPHggw8yf/58/vVf/zXm14qlgX7dpH95/J6IXfpD1ItMJL4lXFG/EHXdWLz6zGc+E75zbyDcd999MWnIO9AG+nXraPHixSxevNiRayeKUFF/dzq2vjhz6JkDNS0R6SfKkImIuJy1NuoMme60FIlPCROQxVstnIgMnHh/f2hsbaTNtvUYkA3PHE6KSdGSpUicSoiALCMjg5qamrh/0xWR/metpaamplMfu3gT3seyh7YXyUnJnJx9sgIykTiVEDVkxcXFVFdXh/cbFBHpKCMjg+LiYqencdyONh8FIu9j2VFhdqH2sxSJUwkRkKWmpna71Y+ISLzraR/Ljoqyi9h1MPGa4ooMBgmxZCkikshCS5Y91ZBBMCA72HCQtkBbj+NExH0UkImIuFwoQ9ZbQFaYXUibbeNwo8o3ROKNAjIREZeLpqgf1PpCJJ4pIBMRcTmv34vBRB2Q6U5LkfijgExExOU8fg85qTkkmZ7fsjt26xeR+KKATETE5TzNnl5bXgDkpOWQm5ar1hcicUgBmYiIy3n93l6XK0OKsotUQyYShxSQiYi4XG/7WHZUlF2kJUuROKSATETE5foSkBVmFyogE4lDCshERFzO4/f0acnS4/dQ31If41mJSH9SQCYi4nJev7dPS5agXmQi8UYBmYiIi7W0tdDY2hh9hixHvchE4pECMhERFwvvYxlF2wtQc1iReKWATETExUL7WEabIRueOZxkk6xeZCJxRgGZiIiLhTNkUdaQpSSlMCJrhGrIROKMAjIRERcLZciiDchAvchE4pECMhERF+trhgzUi0wkHikgExFxMU9z34r6IZghO9hwkLZAW6ymJSL9TAGZiIiLeVv6VtQPwYCsNdBKTVNNrKYlIv1MAZmIiIt5mj2kJ6eTnpwe9XPUi0wk/iggExFxsb5smxRSmF0IKCATiScKyEREXKwvG4uHhLdP8qn1hUi8UEAmIuJifdnHMiQ3LZec1BxlyETiiAIyEREXO54lS1DrC5F4o4BMRMTFPM2ePrW8CCnKLlK3fpE4ooBMRMTFvC1eclP7niFTt36R+KKATETEpQI2EKwhO54MWU4Rdc11NLQ0xGBmItLfYhaQGWNWG2MOGWPe7ubxzxlj9rT/edUYMzFWcxERiUcNLQ0EbKDPRf1wrPXFgQYtW4rEg1hmyNYAl/Xw+PvABdbaCcD3gVUxnIuISNw5nn0sQ9T6QiS+pMTqxNbarcaY03p4/NUOn+4AimM1FxGReNQfAZnqyETig1tqyD4PvNjdg8aYO4wxZcaYssOHDw/gtEREnOP1930fy5CTsk4iySQpIBOJE44HZMaYiwgGZN/oboy1dpW1doq1dspJJ500cJMTEXGQp7k9Q3YcRf2pSamclHmSAjKROBGzJctoGGMmAE8Cl1tra5yci4iI24SWLI8nQwbqRSYSTxzLkBljPgGsB2621r7r1DxERNzqRGrIQL3IROJJzDJkxpifAxcCw40x1cB3gVQAa+1PgH8CCoDHjTEArdbaKbGaj4hIvPH6vSSZJLJTs4/r+YU5hbz84csEbIAk43iFioj0IJZ3Wd7Qy+O3AbfF6voiIvHO4/eQk5pz3MFUUXYRLYEWaptqGZ45vJ9nJyL9Sb8yiYi4lMfvOe7lSujQ+sKnZUsRt1NAJiLiUl6/97gL+kG9yETiiQIyERGX8jR7jqvlRUho+yQFZCLup4BMRMSlvH7vCS1Z5qXlkZWSpdYXInFAAZmIiEudaA2ZMUatL0TihAIyERGXOtEMGQRbXyggE3E/BWQiIi7kb/PT1NZ0QkX9oG79IvFCAZmIiAudaJf+kKLsImqbamlqbeqPaYlIjCggExFxoRPdxzIk1PpCWTIRd1NAJiLiQp7m9gzZCbS9ALW+EIkXCshERFzI6/cCypCJDBYKyEREXKi/ashOzjoZg1GGTMTlFJDJoPXE7id4fPfjTk9DJKL+CshSk1M5KfMkBWQiLqeATAat9X9Zz+aqzU5PQySi0JLliQZkoF5kIvFAAZkMSkeajnCg/gC+Fp/TUxGJyNPsITMlk9Tk1BM+l3qRibifAjIZlMpry4FjWQgRt/G2eMlNPbGC/pCi7CL2+/Zjre2X84lI/1NAJoNSeU0wIPP5ffpPSlzJ0+w54ZYXIYXZhfgDfmqbavvlfCLS/xSQyaAUypC12laa2tTBXNynP/axDFHrCxH3U0Amg1JFbUX4Yy1biht5/J4T7kEWEgrIVNgv4l4KyGTQ8fl9/M3zN0qGlIQ/F3Ebj9/T7xkyBWQi7qWATAadd468A8C0wmnAsX5PIm7Snxmy/PR8MlMyFZCJuJgCMhl0QgX9oYBMrS/EbQI2gM/v67eifmMMhdmFqiETcTEFZDLolNeWc1LmSZyWfxqgGjJxH1+LD4vtt7YXcKz1hYi4kwIyGXTKa8sZPWx0eDlIAZm4jae5fdukfsqQQXtApiVLEddSQCaDSlNrE3+t+ytjCsaQk5oDKCAT9+nPbZNCCrMLqWmqobmtud/OKSL9RwGZDCqVRypps22MGTaGzJRMUkyKasjEdUI3mvRXUT8cu9PyYP3BfjuniPQfBWQyqIQawo4pGIMxhpy0HGXIxHVCAVl/ZsjU+kLE3RSQyaBSXltOXloep2SfAgQzEArIxG1isWSpgEzE3RSQyaBSXlPOmGHB7BhATqoyZOI+sSjqPzn7ZEABmYhbKSCTQaMl0ELlkUrGFIwJH8tLy1MNmbiOx+8h2SSTlZLVb+dMS05jeOZw9SITcSkFZDJo/LXur/gDfsYMOxaQqYZM3CjUpT+Uye0v6kUm4l4KyGTQCG0oPrpgdPiYasjEjfpzH8uOCrMLtWQp4lIKyGTQKK8tJzMlk3/I/YfwMdWQiRt5/d5+bXkRUpRdxIH6A1hr+/3cInJiFJDJoFFeU85ZQ88iOSk5fCwvLY+G1gZaA60Ozkyks1hlyIqyi2hqa6Kuua7fzy0iJ0YBmQwKARugoraiU0E/BGvIAOpb6p2YlkhEscyQge60FHEjBWQyKHzo+ZCG1oZOBf2A9rMUV/I0e/q15UVIYU4hoIBMxI0UkMmg0LFDf0e5qQrIxH1inSFT6wsR91FAJoNCeW05qUmpnJF/Rqfjof/01ItM3KKptQl/wB+TGrKh6UNJT05X6wsRF1JAJoNCeU05Zw49k9Tk1E7HQzVkob0DRZwWi30sQ4wxwV5kWrIUcR0FZJLwrLXBgv6P1Y9BhwyZXxkycYdY7GPZUWF2oZYsRVxIAZkkvAP1B6hrrosckKmGTFwmlhkyQBkyEZdSQCYJb2/tXqBzh/6Q0JKlt0UBmbhD6JeDWBT1QzAgO9x4GH+bPybnF5Hjo4BMEl5FbQVJJolPDv1kl8dSklLITMlUhkxc42jzUYCYtL2A4JIlwMGGgzE5v4gcn5gFZMaY1caYQ8aYt7t53BhjVhpj/mKM2WOMOSdWc5HBrbymnNPzTyczJTPi47lpuaohE9eIdQ1ZUY5aX4i4USwzZGuAy3p4/HLgzPY/dwBPxHAuMoiV15QzeljX5cqQ3FRtMC7uEaohCy2n9zd16xdxp5gFZNbarUBtD0OuBp6xQTuAIcaYoljNRwanjxo/4lDjoYgF/SG5abmqIRPX8Pg9ZKVkkZqU2vvg43By1skA6kUm4jJO1pCdClR1+Ly6/VgXxpg7jDFlxpiyw4cPD8jkJDFU1FYAXTv0d5STlqMMmbhGrLr0h2SkZDAsY5gyZCIu42RAZiIcs5EGWmtXWWunWGunnHTSSTGeliSS8prglkk9LlmqhkxcJFb7WHZUlF2kGjIRl3EyIKsGRnb4vBj4u0NzkQRVXlvOyNyRPWYcVEMmbuJt8Yb748WKepGJuI+TAdkm4Jb2uy1nAEettXqHcCmv38vXt3ydmsYap6fSJ70V9MOxGjJrIyZoRQbUQGTICrML2V+/X9/zIi4Sy7YXPwe2A2cZY6qNMZ83xnzBGPOF9iG/Bf4K/AX4KXBXrOYiJ27XwV28+MGL/OnQn5yeStQ8fg/VvmrGFoztcVxOWg6tgVaa2poGaGYi3fP4PTFreRFSlF1EY2uj9nAVcZGUWJ3YWntDL49b4Iuxur70r2pfNXCsaWU8eKf2HYAe77CEY/2efH5ft73KRAaK1++NfUCWc6z1RX56fkyvJSLRUad+iUqVN3hD7FF//ARke2vat0zqZckyJ7V9+yTVkYnD2gJt+Fp8A5IhA7W+EHETBWQSlWpvMENW11zn8EyiV1FbwYisERRkFvQ4LlTwr15k4jRfS/Bu31i2vYBj2yepsF/EPRSQSVRCAZmnOX5qTspryhk7rOf6MegQkClDJg4L/XzFuqh/WMYw0pLS1PpCxEUUkEmvAjYQdzVkja2NvO95n9EFPS9XwrGATL3IxGmelmBAFuu2F0kmKXynpYi4gwIy6dVHjR/R3NYMxE8N2btH3iVgA70W9MOxGjLdcSZOG6gMGagXmYjbKCCTXoWWKzNTMuOmhizUoT+agCycIWtRhkycFfqlINZF/YAyZCIuo4BMehW6w3LMsDFxs2RZXlvOkPQh4eLlnmSmZJJsklVDJo4LfQ/Guqgfgq0vDjccpiXQEvNriUjvFJBJr6p91RgMYwrGxE1Rf3lNOWOGjcGYSFumdmaMCXbrV0AmDhvIDFlRdhEWy6GGQzG/loj0TgGZ9KraW01hdiHDM4fT1NZEU6u7O9q3tLVQWVcZVUF/SE5qjgIycZzX7yXFpAxIg+LhmcMBqG2sjfm1RKR3CsikV1XeKkbmjgz/1u724vf3jr5Ha6A1qpYXIblpuaohE8eF9rGMJrN7okId+uPlRh2RRKeATHpV7a2mOLeYIelDAPc3hw0X9Bf0XtAfoiVLcQOv3zsg9WMA+WnBgMztP88ig4UCMulRQ0sDNU01FOcUH/uN2uWF/Xtr9pKdms3I3JFRP0cBmbjBQGwsHhIvP88ig4UCMulRqCHsyNyR4Tdwtxf2V9RWcNbQs0gy0X97q4ZM3GAgA7JQJs7tP88ig4UCMulRqAdZcW5xeInDzTUnbYE23jnyTp+WK0E1ZOIOA7lkmZKUQm5qrqt/nkUGkxSnJyDuFg7IcopJS04D3F1z8jfP32hsbYyqIWxHuWm51LfU0xZoIzkpOUazE+nZQGbIILhsqSVLEXdQhkx6VOWtIic1h/z0fDJTMklNSnX1G3h5bd8L+kHd+sV51lo8fs+AZcggGJC5+RcskcFEAZn0qNpXzcjckRhjMMa4/jfq8ppy0pLSGJU/qk/PC+1nqToycUpjayOtgdYB2ccyJD89XzVkIi6hgEx6FGp5EZKflu/qPmQVtRV8cugnSU1K7dPzQstEypCJUwayS39Iflq+ashEXEIBmXSrLdDGPt8+inM6BGQuzpBZa9lbu7fPy5UAOWnKkImzBnIfy5C89DzX/jyLDDYKyKRbhxuDGw93ypC5uOZkn28fXr+X0cOi3zIpJPSfoAIycYoTGbIh6UPw+D0EbGDArikikSkgk25VeasAugRkbv2NuqK2AoCxBdFvmRSSm6qATJwV+t4b6LssAzag73sRF1BAJt0KtbwYmXOs472ba8j21uwl2SRz5tAz+/xc3WUpTnOkhixOmj2LDAYKyKRbVd4qkk0yhTmF4WP56fk0tjbS3Nbs4MwiK68t5/Qhp5OenN7n52anZQPKkIlznKghi4dmzyKDhQIy6Va1r5rC7MJOdyy6+TfqitqKPjeEDUlNSiUzJVMBmTgm9DM10H3IQPtZiriBAjLp1sdbXsCxN3C3FfYfbjjMR40fHXdABsE6Mi1ZilM8fg85qTkDulOEAjIR91BAJt2q9gabwnbk1jfw4+3Q31FOmjYYF+cMdJd+cO8vWCKDkQIyicjn93Gk+UinHmTg3pqT8ppgQHY8LS9CctNyFZCJYwZ6H0s4dgOB236eRQajXgMyY8zDxpg8Y0yqMeZ/jTEfGWNuGojJiXOqfe2binezZOm2GrLy2nL+Ie8fyE7NPu5zKEMmTvL6vQOeIUtJSiEnNcd1P88ig1E0GbJLrLUe4EqgGvgk8LWYzkocF2558bElyyHpQwD3LXGcSEF/SF5qnmrIxDFOZMjA3b0FRQaTaAKy0C12VwA/t9bWxnA+4hKhgOzjGbLMlExSklJc9QZ+tPko+3z7Tmi5EpQhE2c5kSGD9oBMS5YijkuJYswLxpgKoBG4yxhzEtAU22mJ06q8VeSl5XX5jd0Y47oNifujoB+O1ZBZazHG9MfURKLmafaQl+5AhizNvduhiQwmvWbIrLX3ATOBKdbaFqAeuDrWExNnVfu63mEZ4rYljoqa4JZJJ7pkmZuWS0ugxZVNbyWxtQRaaGhtcGzJUjVkIs6Lpqh/IdBqrW0zxnwH+C/glJjPTBwVqQdZiNvewPfW7qUwu5ChGUNP6Dyh/SxVRyYDzecPfs85tmTpol+wRAaraGrIlltrvcaYWcClwNPAE7GdljipLdDG331/79LyIiQ/3V1LHOU15SecHYNgDRng2r06JXE5sY9lSF5aHkf9RwnYwIBfW0SOiSYga2v/+7PAE9bajUBa7KYkTjvQcIBW29r9kqWLasgaWhr4m+dvJ1w/Bh02GPcrQyYDK3QziRMB2ZD0IQRsgPqW+gG/togcE01Ats8Y85/AdcBvjTHpUT5P4lR3d1iGuGmJ450j72Cx/ZIhCwVkutNSBlqoBMCRon516xdxhWgCq+uAl4DLrLV1wDDUhyyhRROQNbY24m/zD+S0Igp16O+XgKy9hszbooBMBlZoyTL0PTiQ3NrsWWSwieYuywbgPeBSY8wyYIS1tjTmMxPHVHmrSDEpFGYVRnw8tH2SG2qtymvLGZYxjBFZI074XKEaMmXIZKCFa8gczJC5JestMlhFc5flPcBaYET7n/8yxnwp1hMT51T7qjkl5xSSk5IjPp6f0b7E0eT8EkeooL8/+oaF6ndUQyYDzcmifrfuTysy2ETTGPbzwHRrbT2AMeYhYDvwaCwnJs7pqeUFuOcN3N/m57269zi/+Px+OV9mSibJJlkZMhlwXr+X1KRU0pPTB/zaypCJuEM0NWSGY3da0v6x2pgnsCpvVbd3WIJ73sAr6yppta0nvGVSiDFG2yeJI0L7WDqxQ0RomVRF/SLOiiZD9jPgNWPMr9o/nwc8FbspiZOONh/F4/d024MM3BOQhQr6xw4b22/nzEnNUVG/DDin9rEESE1KJTs12/GfZ5HBrteAzFr7I2PMZmAWwczYP1pr/xTriYkz9vn2Ad3fYQnBvkXgfEBWUVtBbmpuj3Ptq7y0PNWQyYBzah/LkPy0fFfcpCMymHW7ZGmMGRb6A3xAcMukZ4G/tR/rlTHmMmPMO8aYvxhj7ovw+CeMMf9njPmTMWaPMeaK4/w6pJ9UeasAelyyzErJIsWkOF5DVl5TzlnDzurXZR4tWYoTQkuWTnFTb0GRwaqnDNlOwHKsXsy2/23aPz69pxMbY5KB/wDmANXAG8aYTdbavR2GfQd4zlr7hDFmLPBb4LS+fhHSf0I9yE7NObXbMcYY8tLzHH0Dbw208s6Rd7jurOv69by5qblU+ar69ZwivfH6vXwi7xOOXV8BmYjzug3IrLWjTvDc04C/WGv/CmCM+QVwNdAxILNA6NfCfODvJ3hNOUHVvmqGpg8N9+TqjtNv4B8c/YDmtuZ+aQjbkTJk4gQ3ZMgO1B9w7PoiEtstkE4FOqYaqtuPdfQ94CZjTDXB7FjE/mbGmDuMMWXGmLLDhw/HYq7SrspbFVVNltP7WZbX9l+H/o5UQyYDzVqL1+91NiBTDZmI42IZkEUq7LEf+/wGYI21thi4AnjWGNNlTtbaVdbaKdbaKSeddFIMpiohvfUgCxmSPsTRDFl5bTkZyRmcln9av543Jy0HX4uPtkBb74NF+kFDawNtts3xDNnR5qNY+/G3aBEZKLEMyKqBjpXhxXRdkvw88ByAtXY7kAEMj+GcpActgRYO1B/oseVFiNM1ZOU15Xxy6CdJSYqmc0v0QnsJ1rfW9+t5RboTWiJ3qu0FBAOyNttGfYu+70WcEs3WScMi/EmN4txvAGcaY0YZY9KARcCmj435ELi4/TpjCAZkWpN0yAHfAdpsW493WIY4WUMWsAEqaisYU9C/y5Vw7D9F1ZHJQAn9HDna9iLdHbtviAxm0WTIdhEMkt4FKts/ft8Ys8sYc253T7LWtgLLgJeAcoJ3U/7ZGPPPxpi57cP+H3C7MeZN4OfAYqucuWNCdxdGW0PW0NpAS1tLrKfVxT7vPnwtvn6vH4NjAZnqyGSgOLmPZUhoOzR16xdxTjTrPb8DfmWtfQnAGHMJcBnBpcbHgendPdFa+1uCxfodj/1Th4/3Ap/q+7QlFkItL6LJkIWbw/qPMjxzYFeZP/R+CMCo/BO9Ebir0N2lKnCWgeKWJUtwvtmzyGAWTYZsSigYA7DWlgKzrbU7gIHfCVdiptpbTWpSKidl9n7jhJNv4B81fgQQk0BQGTJ3+dOhP7G3Zm/vA+OYKzJk7T/Pnmb9IiLilGgCslpjzDeMMf/Q/ufrwJH2xq+BGM9PBlC1r5pTc04lOSm517GhehcnArKaphoACjIL+v3coaJ+7WfpDv/y2r/wbzv/zelpxJQyZCIC0QVkNxK8Q3IDsBH4RPuxZKB/26SLo6JteQHOvoHXNNaQkZxBVkpWv59bRf3ucrjhMB81feT0NGLK4/dgMM4GZGkq6hdxWjSbi39ENw1bgb/073TEKdZaqrxVTDxpYlTjnXwDr2mqoSCzoF/3sAwJ1ZApIHNeW6CNI81HsF3aFyYWT7OHnNQckrq2YBwwqcmpZKVkqahfxEG9BmTGmE8CXyW4x2R4vLX207Gblgy0o81H8bX4oirohw5F/Q7VkMViuRIgNSmVzJRM1ZC5QF1zHQEboK65jrZAW1RL6fHI6/c62vIixOnt0EQGu2jusnwe+AnwJKD25Qmq2he8wzLaJcvs1GySTbJjS5bRBo7HIyc1RzVkLhCqFQzYAEf9RxmWMczhGcWG0/tYhuSn56uoX8RB0QRkrdbaJ2I+E3FUlTf6HmQAxhjHfqOubapl0ohJMTt/blqulixdoKaxJvxxbWNtwgZkXr/X0fqxEKf3pxUZ7KIpWnjBGHOXMaaoY7f+mM9MBlSoB1k02yaF5KXlDfgbeGuglSNNRyjIiM2SJQTryBSQOS+UIdqY1ggAACAASURBVPv4x4nGLRkyp7dDExnsosmQ3dr+99c6HLPA6f0/HXFKta+agowCslKjv3PRiQzZkaZgkXcsm9HmpuVytEn/MTmtU4asqdbBmcSWx+9xRYZsSPoQFfWLOCiauyz7vx26uE6Vtyrq5cqQIelDONRwKEYziiyWPchCclNzwxlDcU7HrFgiB2Rev9cVGbJQDZm1NiZ3MItIz7oNyIwxn7bW/sEYc22kx62162M3LRlo1d5qzj25261JI8pPz6fySGWMZhRZKGsSyyVL1ZC5Q01jDSOyRlDbWNspW5ZIWtpaaGxtdEWGLD8tn1bbSkNrA9mp2U5PR2TQ6SlDdgHwB+CqCI9ZQAFZgmhpa+FA/YE+Z8icqCEbiAyZasjcoaaphuGZw7HWJmyGLLxtkkvaXkCwlY0CMpGB121AZq39bvvf/zhw0xEn7PPtw2L7VNAPwTfw+pZ6WgItpCalxmh2ncVyH8uQvLQ8WgItNLc1k56s7VqdUttYy/DM4bQF2hK2qN8N+1iGdNwO7ZScUxyejcjgE01j2HRgPl0bw/5z7KYlAynUg6yvvb1CzWE9zZ6YZqw6iuW2SSE5qce69adnKiBzSk1jDWcNO4s220ZtY2JmyNywj2VI6OdZhf0izoim7cVG4GqgFajv8EcSRLjlRR+XLJ3YzzKW2yaFaD9L5wVsgNqmWgoyChiWMUwZsgGg/SxFnBVN24tia+1lMZ+JOKbKW0V6cnqflwGdeAOvaayJaUE/KCBzA0+zh1bbSkFmAS2BloStIQt9j7kiIGv/BUvd+kWcEU2G7FVjzPiYz0QcU+2tpjinuM+bGzuZIYulUECm/SydEwrACjIKKMgsoLG1kYaWBodn1f9CwY8bivo71pCJyMCL5n/gWcBOY8w7xpg9xpi3jDF7Yj0xGTjVvuo+L1eCM2/gNY2xD8hCNWSeFmUKnBJaohyWOSy8ZVIiZslCS5ZuqCFLT04nMyVTAZmIQ6JZsrw85rMQx1hrqfJWMbVwap+fO9BFwAOxbRIoQ+YGHfvN+dv8QDAgO55fHNzM6/eSnpzumrt589PzVdQv4pCeGsPmWWs9gAppElhtUy2NrY19vsMSgpmkZJM8YL9R1zXXYbEDtmSpGjLndOw35w8cC8gSjVv2sQzRBuMizukpQ/bfwJXAToKNYDve1qa9LBNEqOVFX3uQARhjyEvLCy+7xFooaxLLHmQAWSlZJJkkBWQOqmmsIdkkMyR9SDhDlojd+t2yj2VIaPskERl4PTWGvbL9b+1lmcCqvFVA31tehAzkBuOhprCxXrI0xpCTqm79TqppqmFoxlCSTBJDM4YCypANhPz0fP5a91enpyEyKEVTQ4YxZihwJpAROmat3RqrScnACfUgOzXn1ON6/kAGZAOxbVJIblouvhbVkDmlY3uT9OR0clJzEjMga/bEPOPbF05shyYiQb3eZWmMuQ3YCrwE3N/+9/diOy0J+XPNn7HWxuz81d5qRmSOICMlo/fBEQxkEfBAbCweog3GnfXxu2kLMgsScsnS6/e6ouVFyJD0IcFazRi+54hIZNG0vbgHmAr8zVp7ETAZOBzTWQkAbx1+i0W/XsSL778Ys2tUeatO6M61/LT8Aa0hy0jOGJCNjxWQOaumqXMD4GEZwxIzQ+bCJcvWQCuNrY1OT0Vk0IkmIGuy1jZBcF9La20FcFZspyUAbx5+EyCmAdnx9iALGdAasqaPYr5tUkhOag7eFgVkTrDWdsmQJeL2SQEbwNfic11RP6g5rIgTognIqo0xQ4ANwO+NMRuBv8d2WgLB5UqAV/7+SkyyUM1tzRxqOHRCAVleeh6+Fh8tgZZ+nFlkA7FtUkhuWq76kDnE1+LDH/B3+rcuyChIuAxZfUs9ARtwV4ZM+1mKOKbXgMxae421ts5a+z1gOfAUMC/WE5NgQFaUXURLoIXNVZv7/fz7vPuA42t5ERJqDjsQy3s1TTUMyxwW8+uAAjInhWsFO2bIModxpOkIbYE2p6bV79y0j2WItk8ScU6PAZkxJskY83boc2vtlv/P3nmHx3WWaf/3Tpc0TRpbstwkSy5xSdwdOwkphMQJpBAH0uh1Q1lgl/1YPtgLWHbZb3dhKQsBlrawCyExqQ4hpNkpRLZjOy6J7DhuapbVRtLMSJqRppzvj/EZybbKlHPOjKT3d126bM+cOee1Jc8853nu974VRdmmKMqQ/kub3vQN9dEQaGDLoi1UllTydMPTml9D9SDLxhRWRb2jNkLY7w/7DduRpu6yTCgJQ64nGSa1m/Y8DZmCMqVc5NWudyEVZEanb0gkkmHGLcgURUkAB4UQ8w1aj+QsR7qPoKCw3LeczdWbqWut0/yuNVcPMhjWnOhtJmlUbJKK0+pEQaE/2m/I9STDpILFi84dWY58biqg/p+RGjKJRALpacgqgXohxPNCiG3ql94Lm+4c9h8GYJlvGZurNxNLxNjRvEPTa7SEWiiyFOVU5Bj1Bm5UbJKK2rWQOy2NZ9SR5dmA8akk7E+NLAvI9kL9uTdq57REIhkmHWPYf9R9FZILqO9K6sd8RT7KHGXMcc7h6YanefdC7eR7LaEW5jjn5LRr0SgRsJEeZABOmxOQBVk+8Ef8CERqfAaktIPd4SnUISvAkaXD4sBhdsgOmUSSB9LpkL3zrHYs9QW8U++FTXfq/fUs9y0HklE+11dfz67WXZq+Ubb0teSkHwPwOM5qyCL6ak6MyrFUkQHj+cMf9uO1e7GYhu8Xp+TIcqjwRpaQ7NjJgkwiMZ50CrLrRnnsRq0XIhkmMBigKdTE8hnLU49trt5MTImxvWm7JtdQFIWWUG4eZJDUWpmESfcOWVfkbI6lQSNLlzX5ISnjk4znfA8ySHaRLMIypUaWwaEgJmEyxOg4E1S3folEYixjFmRCiE8JIV4HlgghDo34OgUcMm6J048j3UeApH5MZVnZMuY65/Lnhj9rco2ucBeReCQnywsAkzAl8+90vqOWI8vpw/ku/ZDsEk81t/7QUCh1Q1NIGGn2LJFIhhnvneB+4GZg29lf1a+1iqK834C1TVvqu5KGsOrIEpIfSJurN7P7zG56Ij05X0MLywsVj92j+y5Lf9iP3Ww3rJsgR5b5wx8e3W+urKhsymnICkk/pmJkHJpEIhlmzIJMUZSAoigNiqLcrShK44ivqfOOWKDU++uZ65yb2sGocsOCG4grcZ5vej7na2hheaHisXv0F/VHkh5kRsQmwfDIUhZkxjNahwyS3dEpNbIcDBacfgxkh0wiyReF1SuXAEnLi5H6MZUlpUuocldpYhLbEmpBIJjjnJPzuTw2j+6aEyNjkwCsZisOs0NqyAxmIDpAOBYeVSs4FUeWhWR5oaKK+hVFyfdSJJJphSzICoyeSA+n+06fM65UEUJwfdX1vNr2as4fTC2hFipKKrCZbTmdB4y5o+6KdBkWm6TitDllh8xgRnPpV5lqBVmhjiy9di9DiSHCsXC+lyKRTCtkQVZgqIawoxVkkNxtmVASPNf4XE7XaQ415yzoVzFKQ2ZkhwySOjJZkBnLaKawKmVFZYRjYQaiA0YvSxdCQ6GCLMhUb0GpI5NIjEUWZAWGWpAt9S0d9fnFpYupdlfzTMMzOV2npS93ywsVj81DKBoilohpcr7ziSfi9A72GmZ5oeKyyoLMaFIdslG+12pBPlV0ZIXaIZPxSRJJfpAFWYFR76+n2l09pthX3W25p30PXeGurK4RjoXpCndpssMSht/A9SpeegZ7SCgJw0xhVdSAcYlxjGdvosYnTYWx5WB8kMH4YMGK+kEWZBKJ0ehakAkhbhBCHBVCHBdCfHmMY+4QQhwWQtQLIe7Xcz2TgXp//Tn+Y6Ohji2fb8xut2VLKGl5oeXIEtBN2G+0B5mK1JAZTypYfLSCbArFJ6VyLAuwQ6auSe+d0xKJ5Fx0K8iEEGbgPpKu/suAu4UQy847ZhHwf4HLFUVZDnxBr/VMBrrCXbT1t42pH1NZ6F1Ijacma5PYVEGm1chS5zvq8XRFeiI1ZMbjD/tx29xYzdYLnptKI0tVc1mIHTI1Q1S69UskxqJnh2wDcFxRlJOKogwBDwC3nnfMJ4D7FEXpAVAUpUPH9RQ8qn5sog6ZOrbc176PzoHOjK+jpSks6C8CHm/nnZ5IDZnx+CP+1GjyfKbSyDIVLF6AthdyZCmR5Ac9C7I5QPOIP7ecfWwki4HFQohXhBC7hBA36LiegqfeX49AjCnoH8nm6s0oKDzb+GzG12kONVNiLUndCeeKUR2yfGjIhhJDDMYHDb3udGa0HEsVm9mGy+qaWgVZAY4sHRYHdrNd953TEonkXPQsyEazVD/fadACLAKuBu4GfiGEuKBKEEJ8UgixVwixt7Mz847QZOFw12EWeBakFQ9U661loXdhViaxLaEW5jrnauZ6r7eGrCvcZWhskorMszSe7kj3uJ1QX5EvVaBPZtSfqUIcWUKy6y01ZBKJsehZkLUAI2dic4HWUY55XFGUqKIop4CjJAu0c1AU5WeKoqxTFGXdzJkzdVtwvqn310+oHxvJ5urN7O/YT3t/e0bXaelr0WxcCckPFYHQr0N2NkrHqNgkFfXDsm9I7rQ0ivE6ZDB1zGELuUMGw279EonEOPQsyPYAi4QQC4QQNuAukkHlI3kMuAZACDGD5AjzpI5rKlg6BjroDHeOGpk0FtdXX4+CwnNN6ZvEJpQEp0OnNRP0A5iESdc38Ik+pPVC5lkay2B8kFA0NG6HbMoUZIOFXZB57V4p6pdIDEa3gkxRlBjwWeBp4AiwVVGUeiHEN4UQt5w97GnAL4Q4DOwA/o+iKJN/HpEF9V31wNgO/aNR46lhcenijMaWHQMdDCWGNLO8UNFzxOGP5KkgO9shC0VlQWYEqp3FRB2yqTKyLLIUjbqbtBCQAeMSifFY9Dy5oih/Av503mNfG/F7Bfjbs1/Tmnp/PSZhYknZkoxet7l6Mz/c/0Pa+tuYVTJrwuNVywstR5aQvKPWSwTcFe7i4hkX63Lu8ZAaMmNJZzetr8hH72AvsUQMi0nXty9dCQ4FC1Y/BsbEoUkkknORTv0FQr2/nlpvLUWWooxed33V9QBp77ZsDiU3vmo5soSk5kSPEUe+YpNgeJwkNWTGkI7fXJmjDAVl0o/TCjXHUkWK+iUS45EFWQGgKAqH/YczGleqVHuquajsorRNYlv6WjAJE5UllRlfazz0GnGosUlGe5ABOK2yQ2Yk4+VYqkwVL7JCzbFUcdvdDMYHicQi+V6KRDJtkAVZAdDW30Z3pDurggySY8tDnYdo7Tt/E+uFtIRaqCyp1Fy7otcddb48yACKrcWYhElqyAwinYgstVib7DqyQh9ZSrd+icR4ZEFWANT7Mxf0j2Rz1WYgvbFlS1+L5oJ+SHbIQkMh4om4pufNV2wSJHePllhLZIfMIPwRPyXWEhwWx5jHTJUOWcGPLKVbv0RiOLIgKwAO+w9jERYWly3O6vXz3PNY5luW1m7LllCL5voxGH4D17p4yVdskorb5pYaMoPwh/0Tfp+nSkEWHCzsDpnecWgSieRCZEFWANT761lUugi72Z71OTZXb+b1rtc53Xd6zGP6o/10R7p1Lci0HnHks0MGSR2Z7JAZQ3eke8Lvs9vmxmKyTOqRZUJJ0BftK8gcSxXZIZNIjEcWZKNwujdMeEjb0dtYKIpCvb9+wkDxiVB3Wz7T8MyYx6iWF7oUZGfvqLXWkfkjfuxme0pgbzQum0tqyAwinQ6ZEGLSm8OGhkIoKHJkKZFIzkEWZOext6Gby/91O68c7zLkeqf7ThMYDGTk0D8ac11zWeFbMe7YMuVB5tTWgwz0ewNXP6SNjk1Scdpkh8wo/BF/aiQ5Hj6Hb9IXZFC4OZagfz6tRCK5EFmQnceKOR5sFhO7ThozEslV0D+SzdWbqffXp7zGzqelT78OmborS+uCrCvclbdxJUgNmVFEE9G0/eYmu1t/oedYAjjMDmwmm/Qik0gMRBZk5+Gwmlkz38tOAwsyq8nKIu8FmeoZc331+GPL5lAzLpsrdferJbp1yCITj7H0RGrIjKEn0gOkt3nDVzS5O2RqQVbIHTIhhHTrl0gMRhZko7CxxsfhM0ECA1Hdr3W46zCLSxdr4gs22zmbS2ZcMubYsiWkj+UFJAsXgdBeQ5anYHEVl81FX7SPhJLI2xqmA5ls3lA1ZMnktcmHWuAXcocMZJ6lRGI0siAbhU01PhQFXm3Q9y48oSSydugfi+urr+dI9xEag40XPNfSp4/lBYDZZMZlc2n6Bh5PxOkZ7Ml7Qaag0B/tz9sapgPpuPSrlDnKiMQjDMQG9F6WLqhdp0IvyNw2txxZSiQGIguyUVg5z4vdYmLnCX3Hls2hZkLRUM6C/pFsrk6axJ4/town4pzuO615qPhItL6jzmdskoo6VpI6Mn1Jx6VfRS3ausOTc2yZ6pAVsO0FJHWhUtQvkRiHLMhGIakjK9Vd2F/fpZ2gX2VWySxWzVx1wdiyfaCdWCKmW4cMkm/gWt5R59uDDIbzLKVBpr5k2iEb+ZrJRnAoiFmYKbYU53sp4yJHlhKJsciCbAw21fo40hakd2BIt2vU++uxm+3UeGs0Pe/m6s0c7TnKqcCp1GMpDzKdNGSQvOMPRDQsyCL5y7FUSXXIorJDpif+sB+H2ZFWkTLZ3frVHMt8WbmkixT1SyTGIguyMdh4Vke2+5R+b/r1/nqWlC3BatI26Pu6quuAc8eWquWFriNLjQPGMxlj6YVakMmdlvrijyQ3b6RTpKg/D5O5Q1bo+jFIFmSReIRILJLvpUgk0wJZkI3Bynke7Dr6kcUTcY74j2g6rlSpKKlgTfkanm4cHls2h5oxCzOzSmZpfj0VrUcchTCylAWZMaTj0q+S6pBNUg2Z2iErdNSiUY7rJRJjkAXZGNgtZtZVl7LrpD5v+o3BRgZiA7oUZJDcbXms5xgne08CyZFlZUklFpNFl+tBsiALDYWIJ7SJnfJH/NhMtrzFJsGwhkwWZPrij/gpK5rYpR/Aarbisrkm7cgyNBSaFB0y1exZCvslEmOQBdk4bFzg48iZID392uvItHToH43rqq5DIFJdspZQi67jSki+gSsomumtVA+yfGptpIbMGLrD3RmNpn0O3+QdWQ5Ojg6ZzLOUSIxFFmTjsLE2+QGhh46s3l9PkaWIBZ4Fmp8boLy4nDUVa1I6sua+Zl13WMLwiEOrO2p/xJ9XQT+AzWzDbrbLDpmOZOM3N5kDxkNDoYK3vIDhgkwK+yUSY5AF2TisnOvFYdVHR3bYf5ilZUsxm8yan1tlc/Vmjvce50DHAQKDAd0LMq3vqLvCXXkV9Ku4bC5ZkOlI72Bvxn5zviLfpNSQKYoyeUT9trP/n6U5rERiCLIgGwebxcS6qjLNC7JYIsab3W+yzLdM0/Oez3VV12ESJn75+i8BfXdYgvYFWb5jk1RknqW+qKPHdDVkcDZgfBKOLCPxCNFEVI4sJRLJBciCbAI21pTxZluIbg11ZKcCpwjHwpo69I/GjKIZrKtYxwstLwD6epDBsAhYiztqdYyl7qjLJ26bW2rIdCQbexOfw0fvYC+xREyvZenCZMmxBCiyFGExWaSoXyIxCFmQTcCmszqyV09pdzeut6B/JGqUEqD/yNKm3R21OsbKt4YMwGmTHTI9ycSlX0Ut1CdbsTBZciwBhBDJ9A3ZIZNIDEE/D4QpwsVzvBRZzew84eeGFZWanLO+q54SawlV7ipNzjce186/lm/t/hZum1v3MYl6fi3ewLvCXUB+PchUXDYXrX2t+V7GlCWbDpk63vSHjdv4sfXoVupa6yiyFKW+HBZH6vfFluJznkt9WYd/r3p6TYaCDJI3WdKHTCIxBlmQTYDNYtLcj0wV9JuE/g1KX5GPK+ZcwWB8UPdrmU1mXDaXJgVZqmtSAKJ+qSHTF3/Ej9VkzahIyYdb/y9f/yWhaNJDLBwLE46FicQiKCgZn0vVZxU6Ms9SIjEOWZClwcYaH99++ij+vkF8TntO54omorzZ/SZ3X3S3RqubmG9f+W3DrqVVfFIhuPSrTBYNWcdAB/+y+1/4h43/UBCj3nTxh/2UOcoy8pszOs9yKD7Emf4z3LvyXj696tOpxxVFYTA+mCrQzv8aiA0QiUXOecxutrOkbIkh684Vt93Nmb4z+V6GRDItkAVZGmysGfYje+fFuY0tT/SeYCgxpLugfyTF1okDm7VCK82J+kFbCIWF0+ZkMD7IUHwIm9mW7+WMyZ9P/Znnm55nSekSPrXqU/leTtqoOZaZoI4sjbK+ON13GgXlgp3KQggcFgcOi4NSSg1Zi5F4bB6ODB7J9zIkkmmBFPWnwSVzPRTbzJrYX9R3GSfozwdajTj84fzHJqlMljzLujN1ADx6/FHN4quMIFOXfgCX1YXVZDVsZNkcagb0t44pNLx2r9SQSSQGIQuyUTjafRRFGdaFWM0m1lVr40dW76/HZXVN2Td2t92tmag/37FJKpMhz3IwPsi+tn3Mc83jTP8Zdp3Zle8lpU02fnNCCEPd+tWCbL57viHXKxQ8dg/hWNgQDapEMt2RBdl57G3by3ueeA9/PPnHcx7fWFPGW+19dPXl9sZU769n2YxlBVFo6IFmGrKIvyAE/TC8I66QdWSvtb9GJB7hi2u/SKm9lIePPZzvJaVFQknQHcm8QwbGxic1BZsosZZQap96Y8nxkPFJEolxyILsPNZUrOGSGZfwnb3fOadVn9KR5bDbcig+xFs9b03ZcSWA1+ElOBgkoSRyOo+RdgYT4bQlO2SFPLrZ2boTi8nCptmbuLn2ZnY070htjChkQkMhYkosq80bZUVlhv0dm0JNzHfNn7I3UmOhZm7KnZYSif7Iguw8TMLEVzd+ld7BXu7bf1/q8YvneCjJUUd2rOcYsURsShdkHpsHBSXn8V42Qm+9UDVkfUOF2yGra61jTfkaiq3FbFm0hVgidkGXtxDJxoNMxefwGdYhawm16G6sXIjIPEuJxDhkQTYKy3zLuGPxHTxw9AHe7H4TGNaR7cyhIEs59Bu4w9JotMi/iyfidEe6CyI2CZICcijckWVXuIujPUfZNHsTALXeWlbNXMXDxx4+RwtZiGTj0q+iFmR6/x3jiTgtfS3Md00v/RgMx6FNtkSEXOgb6mPr0a0F/39HMvWQBdkYfHb1Z/HavXxr17dS47eNNT6Od/TRGcpOR1bvr8dr9zK7ZLaWSy0otCjI1NikQuuQFaqof2frTgAum31Z6rEti7ZwKnCKA50H8rWstMilQ1bmKGMwPkh/tF/rZZ1D20AbsURs2gn6YXpqyJ48+ST/tOufONx9ON9LkUwzZEE2Bh67h79Z+zcc6DzAthPbgOFcy91Z5loe9h9muW/5lNahqAL4XEYcatekUDRkxdZiBKJgC7K61jrKHGVcVHZR6rHN1ZspthTz8FuFLe5Xv9eqr1gmqAW73mPLpmATMP0sL0CbG6zJRkOwAUh6RkokRiILsnG4pfYWVs1cxff2fY/AYIAVs92U2JK5lpkSiUU43nOcZb5lOqy0cFBHHLm8gefSNdEDkzAVbMB4Qkmws3UnGys3nhPFVWwt5sYFN/JM4zMFuW4Vf9iPWZhTPzeZYJRb/3T1IAMothRjEZZppSGTBZkkX8iCbBxGCvx/uP+HWMwm1i/Izo/srZ63iCmxKa0fg+E76lw0J4UULK7isroKUkP2Vs9b+CP+c8aVKrcvup1wLMxTp57Kw8rSwx/xU+oozSrXVS3I9DaHbQ41YzPZKC8u1/U6hYgQQjNvwclCQ6ABkAWZxHhkQTYBF5VdxN0X3c3Wo1up99ezqcbHic5+OkKRjM6TEvRP4R2WMKy3ykVzonY8Cqogs7kK0vairjXpzj9aQbZixgoWlS7ikWOPGL2stPGHs/ebSxVkOltfNIeameeal1XROBXw2r3TRtQ/FB+itb8VkAWZxHim5ztMhnx61acpc5TxL7v+hQ0LksaQuzL0I6vvqsfn8FFRXKHHEgsGi8mCy+rKTUN2NjZJ3d1YCDhtzoK0vahrrWNR6SJmFs+84DkhBLcvup16f31qt3ChkY1Lv4pRI8umUNO0HFeqeOyeaSPqbwm1kFASVLmrON13mnAsnO8lSaYRsiBLA7fNzRfXfZFDXYc4Ht6O027JeGxZ769nmW/qOvSPJNcRh+pBVkj/Vi6bq+C0WAPRAV5rf43LKi/sjqncVHMTNpOtYLtkuSQyWM1W3Da3rgWZoii0hFqY557GBZlG6RuTgVPBUwC8ff7bUVA4FTiV5xVJphOyIEuTm2puYk35Gv5z/w9Ys8CWUUE2EB3gZODklNePqXjt3pwKsq5wV8EI+lUKUUO2r30f0UR01HGlisfu4dqqa/njyT8SiWU2ZtcbRVFy6pBBskum58iyK9xFOBae1h2y6aQhaww2AnDt/GsBObaUGIssyNJECMFXN36V0FCIqPtJTnb20x5M7wPuaM9REkpiyuvHVDx2T867LAtJPwaFqSGra63DbrazpmLNuMfdvuh2QkMhnmt6zqCVpUdftI+hxFBOxbevSF+3/qZQ0vJiOprCquT6/3ky0RhsxOfwscy3DIvJwvHe4/lekmQaoWtBJoS4QQhxVAhxXAjx5XGOe48QQhFCrNNzPbmyuHQx9yy9h/rQM5gczWl3yeq7koL+qW55oZLriMMfKZwcSxWnzUl/tD/njE4t2dm6k7UVa3FYHOMet37WeuY65/LosUcNWll6pOxNcuyQ6VmQTWfLCxWv3ctAbIBoPJrvpehOQ6CBKncVVpOVanc1J3tP5ntJkmmEbgWZEMIM3AfcCCwD7hZCXFCRCCFcwOeA3XqtRUs+vfLT+Ip8lFQ+zs4TXWm9pt5fT3lR+bTZNp/LiCOeiNMT6SmY2CQVt81NQkkwEB3I91IAaOtv40TgxLjjShWTMLFl0RZePpZ8EQAAIABJREFUbXs1ZXJaCKRik3LokJU5ynS1vWgKNmEWZiqdlbpdo9CZTnmWDcEGqj3VANR4amSHTGIoenbINgDHFUU5qSjKEPAAcOsox/0T8O9AYQlcxsBpc/J36/4OHC28eCa98OZ6fz3LZkyP7hgk76iDQ8Gsukm9g73ElXjBjSydVidQOHmWalySml85EbcuvBWTMPHo8cLpkmlhb+Ir8hEYDBBN6NO9aQm1UFlSidVk1eX8k4Hp4tYfHArSHemm2l0NwELvQrnTUmIoehZkc4DmEX9uOftYCiHEamCeoijpVTYFwjsXvJM5jhWEirZxtKNt3GP7hvpoCDRMG/0YJN/AE0oiq+Ill7BpPXHakgVZoejI6lrrmFk0k0XeRWkdX15czpVzruSx448RS8R0Xl16aDGyVLtrvRF9fLKaQk3TMsNyJG772Ti0KV6QNQaSgv4qdxUANd4audNSYih6FmSjeRYoqSeFMAHfA7444YmE+KQQYq8QYm9nZ6eGS8wOIQSfX/UlMEf4193fHffYI91HUFCmXUEGEIhk/gZeaLFJKqrhbSF4kcUTcXae2cmm2ZsysgbZsmgLXeEuXm55WcfVpY8/4kcgsopNUtHbi0w1hZ3OTJcOmRqZNLJDBnKnpcQ49CzIWoCR72RzgdYRf3YBK4AXhBANwEZg22jCfkVRfqYoyjpFUdbNnHmhAWY+uH7RKkTgbezt/jOHOg+Nedxh/2Fg+gj6ITfNSaEFi6uoJrWF4EV2pPsIgcFAWvqxkbxt7tuYWTSzYDzJ/GE/XrsXi8mS9Tn0dOsPDAYIDgWnfUGmFsxT3a2/MdiISZhS3+/57vlYhEUWZBLD0LMg2wMsEkIsEELYgLuAbeqTiqIEFEWZoShKtaIo1cAu4BZFUfbquKaJiQRgzy9gYPw7brNJsM57ByLu5p93/TPxRHzU4+q76qksqSy4EZye5HJHrcUYSw/UDlkomv+CTI1LSlc/pmIxWbh14a28dPol2vvb9VhaRmhhb6K+Xg9hv7rDcjpbXsDwDVahjOv1oiHYwBznHKzmpF7QarJS5a7iREAWZBJj0K0gUxQlBnwWeBo4AmxVFKVeCPFNIcQtel03Z3oa4MkvwhsPT3jo5TVzGTjzTo50H+Ghtx4a9ZjD3Yen1bgSci/IrCZrQcUmwbCGrBA6ZHWtdSwtW5rVTtTbFt5GQkmw7cS2iQ/WmVxc+lX0HFmqO1Kne4esxFqCWZin/MiyMdiY0o+p1HprZYdMYhi6+pApivInRVEWK4pSqyjKt84+9jVFUS74NFAU5eq8d8eAoVgZLXuqiL782wmP3VjjIxa6hAXOlfxg/w8u+FAIDgVpDDZOG4d+FbUgy2bEUYixSVA4GrK+oT4OdhzMeFypMt89nw2zNvDIsUfy7qnmD/spK8rN3sRpdWI1WXXtkM11zdX83JMJIcSUN4dVFIXGYGNKP6ZS662lJdQid1pKDEE69Z+HMJnoa4zT+fwp6Dw67rFLK924HVbmxd9POBbm+/u+f87zR/xHgOmlH4OkZxdkqSEL+5nhKCz9GIDdbMdmsuW9Q7anbQ8xJZZ1QQZJcX9LXwt72vZouLLM0aJDJoRIuvWHdeiQhZooLy6f0Hh3OuC2uae0D1n7QDvhWHjUgkxBoSHQkJd1SaYXsiA7D+ucOXjfu4XAqWIGn/7puMeaTYJLa3y80Wjng8s+yKPHH+VAx4HU8/X+pEP/dBtZWkwWnFYnwcHMNSdqh6wQcdlcedeQ1bXWUWQpYlX5qqzP8Y6qd+C2uXn42MRjeb0YiA4QjoU1+V7r5dbfHGqe9voxFa/dO6VF/WqGZZXnvJGlpxZAGsRKDEEWZKMw47NfwGQ10/m7p2AMsb7Kxhofjf4Bbqn6IBXFFXxr97dSPk/1XfXMdc5NjfCmE9mOOLrCXYVdkOW5Q1bXWsf6WeuxmW1Zn8NutnNTzU081/icbv5dE6GFS7+KXm790vJiGI/dk9UN1mRBLcjO75BVuauwCAsnAzJCSaI/siAbBYvPR9mtVxM6JQg/85txj91Yk9TAHGwK86X1X+LN7jfZenQrcNahf5qNK1U89szzLBNKgp5IT8F5kKm4bK68asiaQ800hZpyGleqbFm0hWgiypOnntRgZZmj5W5an0P7gPGB6ABd4a5pbwqrMtU1ZKcCpyiyFF0Qb2c1J3dayg6ZxAhkQTYGZV/8Jma7QuePfjbucUtnufEUWdl10s91VdexqXITP9r/I070nuB03+lpJ+hX8dg8GY84CjU2ScVpdea1Q5ZpXNJ4LClbwgrfCh566yEURZn4BRqjZSJDWVEZ3eFuTf8eUtB/LlNdQ9YYbGS+az4mceFHYo23RoaMSwxBFmRjYPb68G1eSv/xAP0vPT/mcSaT4NIFZew86UcIwVcu/QrheJgv7PgCMP30YyrZjDgK1YNMJd8asp2tO6ksqWSBe4Em59uyeAvHe4/zRtcbmpwvE7RMZPA5fAwlhjTNGZUeZOfisXvoj/brlhmabxqDjalQ8fNZ6F1Ic6iZSGxSxC1LJjGyIBuH0k99CUtRnI5/+9a4d98ba3w0d4dp6Rmg2lPNR5Z/JBXDsdS31KDVFhbZjDi6wl1A4cUmqeRTQxZLxNh9ZjeXzb5MM0uQG6tvpMhSlBdxfypYXCMN2chzaoFakEkNWRLVrX8qji2j8Sin+05f4EGmomZaqu/pEoleyIJsHEzVG5m5qZjIiTP0PT92l2xTbfJDZffJ5AfCxy/+OJUllVS7q1MWENMNVUOWiddVoQaLq+RTQ/ZG1xuEoiFNxpUqTpuTzdWbeerUUwxEBzQ7bzr4w37cNnfKFT0X1KJOy4KsKdREqb005T833VE3Jk1FYX9zXzNxJX6BoF9loSeZaSl1ZBK9kQXZeAiB584PYnNF6fjOv6PER99xuaTChbfYys6TyYKi2FrMz677Gd+56jtGrrag8Ng8JJRERmMkdYxVaDmWKk6rk0g8QjRu/NimrrUOkzCxsXKjpufdsmgLA7EBnm54WtPzToSW9iaquayWXmTNoWbmuWV3TCWXfNpCpzEw+g5LFXWnpXTsl+iNLMgmQKy+m5mX9DHU0EzgiSdGPUbVke06Obz1vtpTzZKyJUYts+DIJj7JHynM2CSVfOZZ1rXWscK3QnMLlVUzV7HAs8DwsaU/nLsprEoqYFxD64vmoLS8GEkucWiFjjqKHGtHrdVsZb57vizIJLojC7KJcFfiuvoyHDOg6z9/SGJoaNTDNtX4aOkJ09xt7OinUMlmxKGGTRdabJJKqiAzWEcWGAzwetfrmo4rVYQQ3L7odg52HuR4j3Ejme5Id1ZZnKNR6igFtCvIhuJDtA20SUH/CNz2s+kbU7Agaww2UuYoG/dmR2ZaSoxAFmRpIFbdw8wVfqKtrfQ+uHXUYzae1ZGN7JJNZ7LqkGnYNdGDfOVZvtr2KgkloYn/2GjcVHMTFpOFR44/osv5R0MtvrXAarLisXs0G1me7jtNQknIDtkIVFH/VHTrbwg2jDmuVKn11tLS1yJ3Wkp0RRZk6XDRuyiZ76C4xkPXT35Cor//gkMWl7soLbay66T2ES7ZoCgKkWicrr5BGrr6iUTHTxzQmlRBloHmxB/xF6x+DJIaMkiGxhtJXWsdTquTi2derMv5fUU+rpl3DU+ceIKh+OgdYC0ZjA8SioY0Lb61dOuXOywvxGl1YhbmKdkhawg0jLnDUqXWW0tCScidlhJdseR7AZMCaxHi4tso73iIhj+76f6f/2HGpz51ziEmk2BjjU/TDpmiKBxtD9EVGqJvMEooEqNvMEbf2V9DI37fFzn758Fo6rFofNiqY463iK33bmKOt0iz9Y2HKgLO5I7aH/YXdLJBqkOmod/VRCiKQt3pOjbM2oDVlPuOxLG4fdHtPNv4LNubt3ND9Q26XQeGxfda7qbV0q1fFmQXIoTAbXMbfjOiN6GhEP6If+KC7Gym5YneE1xUdpERS5NMQ2RBli6r3kfRvl/jXLcO/y9/hfeuu7CUlp5zyMYaH0+90UZz9wDzyopzulxz9wBf31bP9jc7Rn3ebjHhclhw2i04z/46x1uEy+E65zGXw4JJCP7tz2/yvp/vYuu9myh3OXJaWzpkqjlJKAm6I92TYmRppIasMdhIa38rH13xUV2vs7FyI5UllTzy1iO6F2Ra5liqlDnKONZ7TJNzNQWbKLGWaKZxmypMxfikpmATwJimsCrV7mrMwix1ZBJdkQVZusxdD2W1lF/Sz8l9/fh//gsqvvR/zjlkY03yA2bnSX/WBdlgLM7PXzrJD7cfx2ISfPnGi1hbVZosss4WWCV2C1ZzZtPmpZUu3v+LV/nAL17lwb/aiLc4+3DqdLCarJRYS9J+Ay/02CTIT0FW11oHoJt+TMVsMnPbwtv48cEf0xJq0TUySI9EhjJHWeq8uaKGihfq5pJ84ba7p1xBdip4Chjb8kJF7rSUGIHUkKWLELDqHuwDe/DceC09v/sd0ba2cw5ZXOGkrMSW9diy7ngXN/7gZb7zzFtcu7Sc5754FfdeVcv66jKWVrqZV1aMt9iWcTEGsLaqjJ9/cB2nuvr50K9eJRTR30vLa/emPeIo9NgkgBJrCQJhaEG2s3Un81zzDPHEevfCdyMQPHb8MV2vo4cBcFlRGcGhoCYecWpBJjkXr9075UT9jcFGTMKU1vd7oXchJwKyIJPohyzIMmHlXYBgxmVulESCrvt+fM7TQgg21pSx64Q/o6DjjlCELzywn3t+sZtYXOG/P7KeH79vLZUebfVeVyyawX3vW8MbrUE+9pu9hIf0Ffq7benfUesxxtIakzDhtDoN05BF41FebXtV9+6YSqWzksvmXMajxx8lntDvZ0PLHEsV9Vw9gz05nSeeiNPS1yItL0bBY/NMOQ1ZY6CR2SWzsZknnhjUeGpoDjUzGB80YGWS6YgsyDLBMxdqrsLWso3SO++k95FHGDx16pxDNtb4aA1EaO4OT3i6eELhf3Y2cO1/vMifXm/jc29fyDN/cyXXLCnX6S8A1y2r4Lt3rGRPQzef+t0+hmLpRxtlisfuSfuOOpVjWcAdMkjGDRnVITvQeYCB2IAu/mNjcfui2+kY6OCV1ld0u4Y/4qfEWoLDop2WUav4pLaBNmKJmOyQjcJU1JA1BBuo8owv6FdZ6F2Y3GkZaNB3UZJpiyzIMmXlPdDbxIybViPsdjr/8z/PeXpTTXp+ZIdaerntx6/wtcfruWSuhz9/4W387fVLcFjNui1d5dZVc/iX2y7mhaOdfP6B/cTi+hRlmbyB69E10QMjA8Z3tu7ELMxsmLXBkOsBXD33asocZTx+/HHdrtEd1n7zhhqflKuOTN1hOZZr+3TGbXfTF+0jmjA+OkwPFEVJy4NMpdab3GkpMy0leiELskxZehPYXFga/ojvwx8i9NSfCdfXp55eWO5khnNsHVkgHOVrj7/Brfe9wplAhP+8ezW//dil1Mx0GvU3AODuDfP5h3ct5ak32vj7h18nkUh/xJoumYw41NikQg9jd1qN65C90voKK2euNDTg2mq2csWcK3i17dWMxu6ZoGWOpYpWHTJ1153skF2IamVjdFKFXnSGOwnHwmkXZFXuKrnTUqIrsiDLFFsJLL8V6h+j7J47MHs8dH7/B6mnhRBcWuNj58lzdWSKovDY/tNc+x8v8ttdjXxoUzXPf/Eqblk5O2+7uT7+thq+8I5FPPxaC//4RL3mH8Bqhyyd8xZ6bJKK2+Y2REPWHenmiP+IoeNKlbUVa+kd7OVU4NTEB2eBHokMqkVFrgVZS6gFm8lGebF+soHJylRz61dHjxN5kKnYzDa501KiK7Igy4ZV74NoP+aWHfj+6q/of/ll+l99NfX0xhofZwIRms7mWh7v6ON9v9jNFx48wJzSIrZ99gq+ccty3A79jD7T5fPXLuITb1vAb3Y28u2nj2p6bo/dQ1yJp1XA+COFHZukYpSGbPeZ3Sgohgn6R7KmfA0A+zr26XJ+PTpkJdYSbCZbziPLplATc11zMQn51ng+2eTTFjKq6366HTJIGsSeDJzUZ0GSaY9818mG+ZugtBoO3k/pPXdjqaig87vfS3WCNtUk79Z3vNnBt59+kxt/8BJvnA7wrdtW8MinLmPFnLFDbI1GCMFX3rmUuzfM58cvnOC+HdrpIzLJs9Qy21BPjNKQ1bXW4ba5We5brvu1zqfKXUWZo4z97fs1P3c0EaV3sFfz4lsIga/Il3N8UnOoWe6wHINs8mkLmcZgIw6zg4qSirRfU+utpSnUJHdaSnRBFmTZIERS3H/qJUyRDmZ85tOEDxygb8cOAGpnOpnhtPONJw5z344T3LxyNtv/7mred2kVZlPhjeSEEPzzu1dw66rZfPvpo/ymrkGT86qak3TyLAs9WFxFtb3QS18Fw3FJGys3Yjbpv8njfIQQrClfw2sdr2l+7p5I0pZCDxf8MkdZTiNLRVFoDjXraoo7mcnk//NkoCHYwHz3/Iy6oalMS7nTUqIDsiDLlpV3JX89+CDeLVuwVVfT+b3vo8TjCCHYsmYOy2e7eeCTG/nuHauY4bTnd70TYDYJvvPelVy3rIKvb6vnD3ubcz6n15HUnEx0R63GJhVysLiK2+YmoSQYiA3odo0TvSfoCHfkZVypsqZiDaf7TtPW3zbxwRmgpwFwrm79XeEuwrGw3GE5BpnGoRU6jcHGtPVjKupOS6kjk+iBLMiypbQKqt8GB+9HmM3M/PznGDx2jOAf/wjAV965lCc/97ZUnNJkwGo28cO7V3PFwhn8/cOH+NPrZ3I6n3pHPZHmJDAYKPjYJBWnLbkbVs+xpVFxSeOh6sj2d2g7ttTDpV8l1w6ZDBUfH5fNhUmYpoSoP5qI0hJqyUg/BsOZltL6QqIHsiDLhZV3Q/dJaN6Na/NmHMuW0fnDH6EMDeV7ZVnjsJr52QfXsmZ+KZ9/YD87xgg3Twf1jnrkG/hoo76UKewkGFkakWdZd6aOBZ4FVDordbvGRCwpW0KxpZh97doK+/X0m/MV+eiOdGc9Tm4KJS0vpIZsdEzClFH6RiZEE1F2ndml+XnHoiXUQlyJTxgqfj42s415rnlS2C/RBVmQ5cKyW8FaAgd+hzCZmPk3f0O0pYWeP/wh3yvLiWKbhV99ZD1LZrm497f7ss7mLDYni5e6U81884nD3PlfO7nkH5/h47/Zc85xenZNtMZl1bcgG4wPsq9tX167YwAWk4WVM1dOug5ZNBElFM3ue9McasYszHkthAsdj92jyy7Lbce38YlnPsEbXW9ofu7RaAw2AulbXoxkoXehHFlKdEEWZLlgd8KyW6D+MRgaoOSKyynesIGun/yUxIB+GiMjcDus/OYjG5hXVszHfr2HA83jjyn6BmPsbejmN3UNfOmhg9z0w5dZ9Y/bUeI2nj5ykvtfbWQwluDiOR6eO9LBvsbh0dJkCBZXUTtkenmRvdb+GpF4JO8FGcDqitUc6zmmaX6hP+zHYXZQbCnW7JwqKS+ycHZjy+ZgM5UllVhN+bejKVQ8No8uov7dbbsBeLXt1QmO1Aa1IMt0ZAlQ462hKdTEUHzyTkIkhYksyHJl1T0wGIQ3n0QIQfnf/g3xri66/+d/872ynPE57fzu45fic9r50K9e5ciZ5AdzRyjCjqMd3LfjOJ/53Wtc850XuPgbT/Oen+7k69vqee5IB6XFNj5yeTWlDi+bL3FT/4838NhnLucXH1pHabGVH+8YvsOcLLFJMKwh0ytkeWfrTiwmC+sq1uly/kxYW74WBYUDHQc0O6fqQaaHAbBa0GerI2sONUtB/wS47dqPLBVFYV9bcjS+t22vpucei1OBU5TaS1NWHpmgZlrqZZwsmb5Y8r2ASU/VFeCZDwfvh0veS9GqVTivvRb/L39J6V13YvZ6873CnKhwO/jdxy/lvT/dyZ3/tRObxUxX37AHz/yyYpZVutmyeg7LZrtZPttDhdue+sDd/4QPRQyk7D6KbRY+evkC/uPZtzjcGmTZbDddka5JEZsEIzpkQ/p0yF5pfYU15WsotmrfQcqUi2dejEVYeK39Na6ce6Um59TT3iTX+KSmUBM3zrhRyyVNObx2r+aFSFOoiY5wBy6ri9c6XiOWiGEx6fvRlM0OS5UaTw0AJwMnWVK2RMtlSaY5skOWKyZT0gLjxA4InAZg5uc/R6Kvj/Z//Tfigcm/RXxeWTG//filrK8u46rFM/naTct44JMbOfj163npS9fw0w+s5a+vXcS1SyuY5XGc0/1w290X7Mr64KZqnHYLP34huVPJH/ZT5igr+Ngk0FfU3znQyVs9b+UlLmk0iixFLPMt09SPzB/xp4LAtUYdWWZjfREYDBAcCsodlhOgh4ZM7Yq9f9n76Y/2c7Rb28SQ0WgMNmYs6FdZ4FmASZjkTkuJ5siCTAtW3gUocOhBAByLF1P6gfcTeOwxjl15Fa3/9yuEDx7U1UxUbxaWO/nlh9fzH3es5KNXLGBjjQ9P0cRam9E0J55iK+/fWMWTr5/hZGcf/oh/UniQAdjNdmwmW9bC8fFQd5kVgn5MZU3FGt7oekMzZ/LucLduHbJSR2nyGll0yKTlRXp4bB5C0RCxREyzc+5t34vP4eM9i9+T+rOe9A310RnuzLpDZjPbmO+az8leudNSoi2yINMCX20yTunA/XC26Jr1la+w4LFH8dz2bkJPP03DnXdx6vbb6XlwK4n+/jwv2DjUgPHz+dgVC7CZTfzXiyeTH9KTQNCvoleeZV1rHWWOMi4qu0jzc2fLmvI1RBNRTXa/xRNxegZ7dPteW0wWvHZvVvFJakEmLS/GR7Wy0ernX1EU9rTtYd2sdZQXl1PlrmJP256JX5gDjaHsBf0qtd5a2SGTaI4syLRi5d3gPwanh32bHBddROU3vsHCl15i1je+DvEEbV//OseuvIq2b36TyNG38rhgY/DavQQHgxd0B2e67Ny1fh6P7G+hvb8zP4L+rR+CBz+Q8cvcNrfmGrKEkqCuNRmXVEjB1qvLVwPJ3Z+50jvYS0JJ6Pq9ztYctimY9CCTsUnjo3WeZUtfC+0D7alNLOsq1vFa+2vEE3FNzj8ajQFtCrLmULPcaSnRlMJ555/sLH83WIrgwO8ueMrsLKH0rrtY8NijVD/we1zveAe9Dz3MqVtvpeHuewg8/jiJwakZVuuxe4gpMfqjF3YFP3FlDYqSoGcwDx2yYCsc2QZvPgkDmX2AO63ad8je6nmL7kh3QY0rIRl/VeupZV9H7gaxRvjNZRuf1BRqory4HIfFocOqpg5ee3KTklZu/ap+bP2s9QCsm7WOUDTE0R79dGQNwQYEgnnu7MfTtZ5a4kqchmCDdguTTHtkQaYVDg8svQneeBiikVEPEUJQtGoVs//tX1n00ouUf/nviff00Pr3X+b4lVfR/m//zlBDg7Hr1hl15+Ro3kVzS4t55yovCgkcJoN3ox56EJQEKHE4+qeMXuqyuTTXkKlxSYUi6B/Jmoo1HOw4mHPXwgh7E9WtP1NaQi1SP5YGqTg0jWxf9rbvpcxRltq5qHbK9LS/aAg2MNs5G7s5+3xhmWkp0QNZkGnJqnsgEkjrA97s9eL78IepeepPzP/1f1O8aRPd//u/nLjhRpo++lGCTz+DEo0asGh9mWjE8e61yYLtYIN2IuEJURQ48HuYd2nSsuTwtoxe7rQ5NR9ZvnL6FRZ6F1JeXK7pebVgdflq+qJ9HOs9ltN51A6ZXrss4WyHLAsNWVOoSerH0kDrkeXetr2srVib2mE9q2QW81zzdBX2NwYbcxpXAlR7qjEJkyzIJJoifci0ZMFV4JoNB38PK7ak9RIhBCUbN1KycSPRjg4CjzxCz9atnP7857HMnInnttuw19Zg8ngwezyYPV7MXg9mtxthKfxvnzriGOsNvLg4mWjw8pEIwRuiuB0GuKS3vgZdR+Gm70PXMdjzc4gEwZGeD5rb5tZ0ZBkYDLCvfR8fXv5hzc6pJWsr1gKwr31fThsOjOiQlTnKCA2FiMajWM3p/SwNRAfoCndJU9g00LIgO913mtb+Vj60/EPnPL6uYh3PNz1PQklorqdUFIWGQAOrFq7K6Tx2s535rvmyIJNoSuF/ok8mTOakBcYr34dQG7hmZfRya3k5M+69F98nPkHfSy/R+8CD+H/+89TOzQsu53SeLdI8mL2e84o2b+pxs8eDfeFCzJ7MXalzJfUGPkbcivoh3R8u5n93NvKZaxbqv6gDvwezHZbfBp1vwq774NgzcPF70nq50+rUNDrppZaXiCtx3j7/7ZqdU0sqSyqpKK5gf8d+3rf0fVmfxx/x624APNKtv6KkIq3XqDsspaB/YpxWJwKhSXySOpZcN+vcVIp1s9bx6PFHOdZzTHPj1a5wFwOxgawtL0ZS46nhREAWZBLtkAWZ1qy6B/7yXTi0FS7/XFanEGYzrmuuwXXNNcT7+oj7/cQDgeRX79lfA73EAwESIx6LnmlLHUf8XL2P/aKLWPDoI4abr6YKssj4BdllC6r51V9O8dHLF1BkM+u3oNgQvPEQXPQuKPLC3A3grIDDj6ddkLlsLsKxMNFEVJPcwx3NO5hZNJMVM1bkfC49EEKwpmINe9v2oihK1j9DRhgAp/IsMyjIWkItgLS8SAezyYzL5qI3kruof2/7Xjx2Dwu9596EpXRk7Xs1L8hUEX62prAjqfXW8mLLiwzFh7CZbTmfTyKRBZnWzFgEc9cnPcku+2vI8cPH7HRidjozeo2iKCT6+88War30Pb+drh//mPD+/RSvWZPTejJlPFE/DHdNPnflJdzxX7t4YE8TH7l8gX4LOvY0hHuShTMkkxYuuik5Zh4aANvEkUVqnmXfUF/KjDRbBuOD/OX0X7ip5qaCsrs4n7Xla3nq1FO09GUvfldzLPVEHYdmoiNrCiUtL6SoPz28dq8mHbI9bXtYV7Hugp/RoOk1AAAgAElEQVT72c7ZzHHOYU/bnpw6sqORKshy1JBBMtMyrsRpDDayqHRRzueTSHT9BBBC3CCEOCqEOC6E+PIoz/+tEOKwEOKQEOJ5IUTufeRCYOXd0HkEzmgXypwJQgjMTie2uXMoWr4c38c+iqmkhN4HHzR8LTazjSJL0ZiaE7VrsmGBjw3VZfzspZMMxRL6LejA75MdsZprhh9bdgtEB+DE82mdQi0ytdCR7T6zm3AsXLDjSpXVFbn7kenp0q+STZ5lc6iZUntpKhZLMj5axCed6TvD6b7TqW7Y+aytWMu+9n0kFG3fCxoDjdjNdmaVZCYnGQ2501KiNboVZEIIM3AfcCOwDLhbCLHsvMP2A+sURbkEeAj4d73WYygrtiQ1Sgfuz/dKADCVlOC+5WaCT/2ZeK82/kGZ4LV7xyzIuiJdqa7JZ96+kDOBCI/tP63PQvq7kh2yi98L5hHN4aoroKg07d2WTmuyQ6aF9cX2pu2UWEvYMGtDzufSk4Xehbht7pxyLf1h/Ttk6g7O7nD6BVlTqEl2xzLAbXfnLOpXd1Gq/mPns37WenoHezUvdhqDjcx3z9ekG63utJSO/RKt0LNDtgE4rijKSUVRhoAHgFtHHqAoyg5FUQbO/nEXMDVUtUWlsPRmOPhAcvdeAVB6550oQ0MEHn/c8Gt77BfmWap0h7tTOZZXLprBijlufvLiCeIJHXI/X38IErHhcaWK2QJL3gVv/RliExv0ahUwHk/E2dG8gyvmXFHwGhSTMLG6fHXWHbKEkqA7on+HrNhSjN1sz2hk2RJqyckkdLoxWj5tpuxt34vb5h5z1Kd2zrSOUWoINmgyroTkTst5rnmcDMhMS4k26FmQzQGaR/y55exjY/Ex4Ckd12Msmz4Dg0HY9+t8rwRIxjgVrVxJz4NbDQ8599iG8yz7XnyR0HPPpZ7zh/2pD2khBJ+5eiGnuvr50+tntF/Iwfth1iVQsfzC55bdkvx+nXppwtOoBVmuXmSvd71Od6Sbt88r7HGlyury1TQEG7Jywg8NhYgpMd07ZEKIjOKThuJDnOk/IztkGeCxe3J26t/btpc1FWvG7FTNcc6hsqRSUz+yaCJKS6hFkx2WKrUemWkp0Q49C7LR1OyjVgJCiPcD64Bvj/H8J4UQe4UQezs7OzVcoo7MWQMLroRdP07u7CsAvHfeydDJkwzs0Te893zUEUdiYIDWL/09Z776DyhDQ8NdkxEf0puXz6J2Zgn37TiubeHYfhjOHLywO6ZSczXY3cndlhOgVYdse/N2LMLCFXOvyOk8RqH6ke3v2J/xa43wIFPxOXxpd8hO950moSTkDssM8Nq9hIZCWSc3tPe30xRqYn3F6ONKSBbW6yrWsa99n2bvA619rcSUmGYdMkjqyJqCTUTjk9/EW5J/9CzIWoCRt51zgdbzDxJCvAP4KnCLoiijzosURfmZoijrFEVZN3PmTF0WqwuXfx5CZ+D1P+R7JQC4b7wBk8tF74NbDb2uqiHrfejhlC1H31/+QmAwkOyajPiQNpkEn7p6IW+2hdhxtEO7RRz8PZgsSf3YaFjssHhzMtsyPn5qgLrLMteCbEfTDtbPWq+rL5eWLPMtw262Z6UjMyLHUqWsqCxtDZnqQSY7ZOmjWtlk+/Ovdr3O9x87n3Wz1tEd6dZsJNgYTIaKa9oh88pMS4l26FmQ7QEWCSEWCCFswF3AOappIcRq4L9IFmMafvoWCLXXQsUKqPtPSOi4czBNTEVFeN79bkLPPEOsp8ew63rsHvrCvfj/+78pWrUKc1kZgW1PpLomqoZM5dZVs5njLeJH2zXqksVjSV+4RddDyYyxj1t6M4S7oalu3NOp5pi5iPpPBk7SEGzgmvnXTHxwgWAz21gxY0VWOjIjO2SZxCfJgixzJrKymYi97XtxWV0sKR3fY0ztoGmVa3kqcArQxvJCJbXTUhrESjRAt4JMUZQY8FngaeAIsFVRlHohxDeFELecPezbgBP4gxDigBAis1DBQkeIZJes882kE3wBUHrnHSjRKIFHHjXsmh6bhw31UWJnzuD75Cdxv/Od9O3Ygb8r+WF4ftfEajZx71U1vNbUy+5TmQdFX8DJF6CvLWlHMh4L3wGWogl3W5qEiRJrSU4asu1N2wG4Zt7kKcgA1pSv4c3uNxmIDkx88AiM7JD5HMmA8XSK+eZQMyXWkpShrGRico1PUvVjZtP4BtBzXXMpLy5nT7s2EovGYCNeuxevw6vJ+QAWeBbITEuJZujqQ6Yoyp8URVmsKEqtoijfOvvY1xRF2Xb29+9QFKVCUZRVZ79uGf+Mk5Dlt4FnHrzyg3yvBAD7woUUrV1Lz9YHUQzq2nlsbm7ZlcBUU4Xz6qvw3HwTyuAgg8+/CIzeNXnvunnMcNq5b4cGgtmD9yd3vi7ePP5xthJY9A448sSEHU2XzUVwKPsdtDuadrDct1wTPyQjWVuxlrgS52DnwYxe5w/7MQtz6sNcT8ocZcQSsbQ6mE3BpOWF0QkWkxn1e5iNsL9zoJOGYMOY/mMjUXVkakJErjQGGzUdV8LwTktZkEm0oHCtwacKZmtyx2VTHTS/mu/VAMkuWbSxiYHduw253sxDLVR1QuKumxEmE45LLsFaNR/787uA0bsmDquZj79tAS8f6+Jgcw47uiKBpC5sxXuSOrGJWHpLspt2evwxidPmzLpD1jnQyaGuQ5p3x462hfjgr16lyZ9Z9yoTVs5ciUmYMtaR+SN+Sh2lhqQRqF5k6ewGbQ41y3FlhnjtyQ5TNh2yfe37gIn1YyrrZ63HH/FrotFqCDRoXpDB2UxLWZBJNEAWZEaw+gPg8BZMl8y1eTNmj4ceg8T93odewO+CwNUrgeSdr+fmW3C90Uh5n3lMUfv7Lp2P22Hhxy/k0CWrfxRikYnHlSqLN4PJOuFuS5fVlbWGbEfzDgBN3flj8QR/94eDvPRWJ9999qhm5z0fp83JktIlGevIRtqb6E26bv3xRDynKKjpiseW7JBl0yHe07aHEmsJF5VdlNbxWvmRDUQH6Ah3sMCjfSzbQu9CudNSogmyIDMCuxM2fCLZqek6lu/VYLLb8dx2G6HnniPW1aXrtcKHDmE+cIQn15sIJPpTj3tuvgmhwDveso85LnI5rHz48gU8Xd/OsfYsBfQHfg8zFidtSNLB4YHaa+DINhhnTOKyubLukO1o3sFc59wLQpVz4Zd/OcXrpwOsnOfl8YOt2f97pcGaijUc6jxENJH+B5ARLv0qIwPGx6N9oJ1YIiYtLzJEtX3JpkO2t30vq8tXYzGlF6Nc5a5iZtHMnP3I9NhhqVLjrSGmxFLXkEiyRRZkRrHhr8BsS+64LAC8d9wBsRi9Dz+i63X8v/glwuXkuVXinDdwW1UVbdUuNr4+vkfbRy6rpthm5icvZDES8J+A5l3J7lgmGqGlN0NvE7QdGvMQp82ZVYegP9rP7jO7efv8t2umWzrV1c93n32L65ZV8N8fXk+x1cz3n9Ov8F9dvppIPMIR/5G0X+OPGNghO1v4TTSylKHi2WE2mXHZXBkXZF3hLk4GTo4ZlzQaKT+yttz8yNSRpx4FmXpjdTwgDWIluSELMqNwzoTV70vGKYXa8r0a7DULKN6wgd4//EE3cf/gqVOEnn0Wz913ErGLC97A961yMqs1QuToW2Oeo7TExj0b5vP4wVaauzPURh16EBBwyZ2ZvW7Ju0CYx91t6bK66Itm3iH7y+m/EE1ENRtXJhIKX374EDaLiX9+9wrKSmx89IoFPPn6GQ636hPbpRrEpju2VBTF0A6Z1+5FICbskKmWF/PdskOWKR5b5m79Kf1YGoL+kaybtY6OcEeqgM6GhmADAqFLN7Tancy0PNkrI5QkuSELMiPZ9NlkluLun+Z7JQB477yDaEsL/a+M77uVLd3//WuE1crMD36YIkvRBQXZy0sSJEyC4B+fGPc8n7iyBrMQ/PTFDLpkiUTSDLbmavCMl9g1CiU+qL48ObYcA3Vkmeld+/am7ZTaS1k1c1VmaxqDB/Y0s/tUN19951Iq3A4APn5FDS6Hhe89N3ahmwszimYw3zU/bWF/X7SPocSQYR0yi8mC1+6duCALNmMz2SgvLjdkXVMJr92bsQ/Z3ra9FFmKWOpbmtHr1A0AufiRNQYbqSypxGFxZH2OsXBYHMx1zpURSpKckQWZkfhqk7v49vyqIELHXdddh7msjN6tD058cPvh5KaEzvQE47HOTgKPPYbnttuwzJhxQcB4QknQZA7gXzmPwB+fHLdLV+F28J51c/nD3hY6gpG0rk9TXXLsOFZU0kQsvQW63hrz7+uyuYgrccKxcNqnjCaivNzyMlfNu2pCD6Z0OBMI8//+dIRNNT7uXD88dvMUW/nE22p49nA7h1pyyxwci9Xlq9nfsZ+EMnF3NWUKa1CHDM6aw6YxspzrmmvIzs+phsfuITiY2XvY3va9rClfg9Vkzeh1C9wL8Dl8OfmR6bXDUqXWWys7ZJKcke9ERnP552AwAK/9Jt8rwWSz4d1yG6HtO4i2jxKUEI8lx3a/vgl+sgme/Rrcdyk8/PEJNyd0/+9vUaJRfB/5MHBuwDhAcDBITIkRunoNsTNnGNg7/t3vvVfWEksk+MVfTqX3lzvwe7C54KKb0jv+fNTXjTG2VOOTMtGR7W3bSyga0sTuQlEU/uHRN4gmEvzr7RdfoEf7yOXVeIutfPdZfbpkayvW0jvYS0OgYcJj1U6VUR0yOBuflMbIUurHskPNp02XnkgPx3uPp213MRIhBGsr1mbtR6YoCo3BRqo91Rm/Nl1qvbU0BhvlTktJTsiCzGjmrIXqt8HOwggd9773vRCP0/vwQ8MP9vvh5f+AH6yErR+AnkZ4xz/CZ/fCZX+d3C163wZ45JPQdWGbPt7XR8/vf4/r+uuxVVcDyTvqkW/gXeHk7k7zlRsxFRcTfGL8seV8XzG3rJzNb3c10jswwb/bUD8cfgyW3wq24vT+Ic7HXQnzLoUjo9tfqDvNMtlpub1pOw6zg02zN2W3phE8cegMz7/Zwd9dv4QqX8mF63NY+asra3nhaCf7GjVIOziPNRXJXav7OvZNeKyRLv0qqlv/WCiKIguyHPDYPBmNLLPVj6msn7We9oF2WvpaMn6tP+KnL9qne4dM7rSU5IosyPLB5V+AUCu88dDEx+qMraqKkss20fuHh1Ca98Gjn4LvLoXnv5kcsd51P3z+AFzxBZixCK7/J/j8oaTZ7eFtcN96ePTe5I7Gs/Ru/QOJUAjfxz+Weuz8gkz9kC7zVuK67jqCf36axOCo2fIpPnX1QgaG4vy6rmH8v9SRP8JQH6zMclypsvRmaHsdui/syrmsyYIsXS8yRVHY0byDy2ZfRpGlKKdldfcP8Y1t9ayc5+Ujl4/tq/Shy6qY4bTxH89o3yWb75pPmaMsLWF/IY4s/RE/4VhYCvqzRB1ZxhPxtI7f07aHIksRy33Ls7qeWshloyNTu7haZlieT61HZlpKckcWZPlg4bVQvhxeKYDQ8dgQ3k1VxM6coe+fbkwaoq5+P3x6N3xoG1z0Ljhf7+ScCdf/M3zhEGz8dNJ89Ufr4dFPobQfpfs3v6H40kspuvji1EvO15CNDBZ333IziVCIvhdeHHepS2a5ktYOrzTQNxgb+8CD94O3Cubn2IlaenPy1yMXdu/UDlloKL2C7HD3YdoH2jUJE//mE/WEIlH+/fZLMJvGts4otlm496pa6k742XkivbDtdFHHSPs79k94rD/iRyBSDu9GUOYoIxQNMRQfvZvaFJSWF7ngtXtRUNLeaby3fS8rZ67Eas5MP6ZS662l1F6alR+Z2rXSc2S5wLMAgZCO/ZKckAVZPkiFjh+B48/mZw2hNtjx/+D7K3A1fxdzMfQG18EXj8BN34XyNJy0neWw+VvJjtmlfwX1jxD40jXE2tvx3fmucw5VNWSqBmTkGKtk40bMM2dMuNsS4DPXLCQQjnL/7jFGA4HTcPJFWHkXmHL88S6thsqVo+62VDVk6RZk25u2YxImrpp7VU5L2v5mO48daOXTVy9kySzXhMe/f2MVFW473332qCZ5gCNZXb6a032naesf38bFH07GJqVrBqoFajdurLFlyvJCmsJmRSYB44HBAMd6jmU9roSzfmSz1mXVIWsMNmIz2ZhVrF9urMPiYK5rrizIJDkhC7J8sWILuOcaG6ekKMk8zYc+Bt9bDi/+K8y6BPGBh/C+/5P0HWok2pNFDqKrAm74fyh/vR9/wxzspTFK9twL/7+98w6Pqvj+8Dtb0zsJKdQkdJGOUqRJk/JFaXbFDnb8WbAiVlQEewPsDQUFbCCC0qSEXqUGUkhCEtLblvn9cXdDEtLbIs77PHnu3bltdvZm72fPOXPO0nu0+DO0L3CL3VI8KzEtPw2DzoCPyQeh1+N7xShy/vwLW0blswK7NPOjX1QQH607TkJGOTMcd38LSE2Q1Qftx0D8VshKLNXsLPdU3RiyNXFr6BrcFX83/1p3JbvAwhM/7KVNiBfTBkVW6xg3o567B0WxNfYM6w7Xb1UGZxxZVW7LtPy04uz5jUVV2fpPZp9EL/SEeoU2ZrcuGGoiyGKSY5DIGiWELY/uId1JzE0kISehRscdzzpOc5/m9TKzuTIi/SKVIFPUCSXIXIWz6PiJDRBXtzptVWIpgB1fwocDYMFQOLwSet0B926H67+H6KFa5n4pyfiu9nFtOdv+oSg5h8AHn0H0vBV2L4K3usGy+/C1ai5G5xd4an4qAW4BxbMDfcaOQVosZK1YWeV17hsSTXpuEX1fXs3ot9bx5h+HOXAqS0udsetrzVUZ0LrW76MU7f+nLQ/8VKrZy+iwkFUjhiwuO47DZw4zuFndksHO/u0gSVkFzB7fGbOh+g+XyT2bEe7nzpzfD9Wrlaytf1s8DB5V5iNrzCz9TpyCrKI4srjsOEI9Q2ucgkGh4fxBUp3A/pikGMx6M52COtXpmk5BV1Mr2YmsEw0aP+Yk0tcx07IGJcUUipIoQeZKut3oKDo+r+GukZ0M84fA0mlgLYRRc2D6ARjxkha078AUEY5n/35kfP890lpJfFYlpM2fjzEsDJ+rroYrXtEmA3SfAru+xvf3mQBkpml5vdIK0ghyDyo+1q1DB0yRkWQurzgZq5NerQJYNX0Aj41sh9mgZ+6qQ4x8Yx13zv4QUg9xNHwsVls9xeY1aQNN2p3jtjTrzRh1xmq5LNec1IqJ1yV+bPOxNL7YdJJb+raia/OaWdnMBj33Do5iV1wGqw+Wk96klhh0Bi5ucnHVgiw/jQD3xrWQVVVgPC5LzbCsC04LWXWy9Tvjx0x6U52uGeUXha/Zt0aFxq12K3HZcQ06w9KJc6alMz5RoagpSpC5ErMX9Lyt4YqOZ8TBxyMh/RhM/hKmbdKuZ/Yqd3f/yZOxpqSQ81flwfXlkbd9O/nbtxMwZQrC4IgV8gmDUa/BfTvwjRoGQOa318LvT2uldEpYTYQQ+I4ZQ37MNiwJVbskWgV5cteASBZP7cPmx4fw0lUXcbVpAwXSyLg1Tej5wioeWrSLFfuSyC+q3kywCmk/RrNk5p51+Qkh8DZ5V0uQrY5bTbR/dK0FQIHFxmNL9tAswJ2HhrWp1TnGd4+geYAHr1fHSiZlpYXVS9ItpBtHzhyp1HXlCgtZlTFkOXFqhmUdcE7QqMplmVmYyT/p/9Qq/1hZdEJH9+DuNQrsT8xJxGq3Noogc9a0VG5LRW1RgszV9L7LUXT8rfo9b9pRWDhCExE3/AjtR1dZYNtrwAAMISGc+aYamfvLXm7+AvR+fviNv+rcjb4R+A58HIDMVn1hwxukpR8m0OH2cOIzWkvGmvnTzzW6drC3G9d0C2GwZS36jmOZfV0/BrYN5vf9Sdz5+Ta6PreS2z6NYVFMHGk5lafWKJf2Y0HaNeFcAmf5pMo4U3CGHSk76uSunLfqMMdTc3n5qs54mGoXGG/U67h/SDT7ErNYsa+SIHxrEXz2P/jupmqJsu4h3ZFIdp3eVe72PEse+db8Rk15AeBucMdN71auyzKzMJPMwkxlIasDzlnGVWXr35GyA4msU0B/SXo27UlCTgKnck5Va39nUfFWvhWnh6kvWvq2VDMtFXVCCTJXU1x0/GvNvVgfJO3VxJg1H25eDs17V+swYTDgN348uevXUxRf/cDZwiNHyFm9Gv/rrkPnUX4i1mIXR+eJ2K+YQ7q0EPjP75oVz4EpIhz37t3JXLas5rFOh36DggyM3a7liotCmTu5C9ueGspXt/Xm6p7N2Z+YySPf76bnC6uY9P7fzF93jBNpudU7d9OLtBmXZdyWXkYvsiyVP5D+iv8Lu7TX2l25NyGTj9YdY3KPZvSNCqr6gEoY1zWc1k08mfv7Yez2CsZ35ZNw/C8t/UkltTyddArqhEFnKE78WZbi2bSNbCETQhDgVn62/vhsLbmoEmS1x6Az4G30rjKGbGvSVkw6E52bdK6X6xbXtaymlcyZ8qIxLGTuBncivFVNS0XtUYLsfODSe8BmqZ+i4/Hb4JNRoDPAlN+0tA01wG/iBBCCjO++q/YxaQs/Rri54X/9dRXu4xRkWUVZZHWegFUIgvIz4KPBkHD2Ye47ZgxFR49SeOBAjfrNzq/BOxRanxU+Rr2OPlFBzBzbkQ2PDeane/txz+BosgutPP/zAQa8+ifXfrSJxPJma5ZECM1teewvyD8bM1MdC9nqk6sJ8QihQ0CHmr0fwGKz88j3uwn0NPH4qJoVZC4PvU7wwOVt+Cc5m5/2lGNh2LsYtnwAve6EkE7w2wyt6kEluBvc6RDYocKZlq5ICusk0L38bP0ns1UOsvqgOuWTYpJj6NykM2a9uV6u2ca/DT4mn2oLstjMWHxMPo2WAy/SN5JjmaqmpaJ2KEF2PhAYCR3GwtYFUFi9vFblcnwdfDYW3P3gll+1gPQaYgwNxWvAADKWLEZaqp4tZElKInP5cvzGj8fgX3GwuVlvxt3gTmZh5lmryWUzwOgGH4/SLDKAz4jhYDSSuazqnGTF5JzW8rl1nnRuElsHQgg6hfsyfWgbfr2/P+seGcRjI9uxKy6DkW+s47e9VbhA2v8P7BY4tKK4qaoYsnxrPn8n/s3g5oPPqTVZHT5ce4z9p7J4blwnfN3rZzbg6ItCaRvizbzfD5We+HD6ECy7TysXNfwFuOI1yEqAta9Wec7uwd3Zm7aXAuu5hd9dUTbJSYBbQPH1S+IMuo7wjmjsLl1Q+Jp9Kw3qzy7K5mD6wXqJH3OiEzq6hXSr9kxLZw3L2vz/1YZIv0his2LVTEtFrVCC7Hyh7/1a0fFttSw6fmglfDkBfCM0y5h/y1p3xW/yJGynU8levabKfdM/+xzsdgKmTKlyXx+T9ou62GoS0hluWw1NO8GiG2Hd6+h9ffG67DKyfv4ZaatmMP7e78FuhYuvqd7+QLMAD+4aEMnP9/WnRaAHd32xnRlLdpNXVMEM0/Du4B1Wyo1XlYXs78S/KbAV1KqY+JGUHN744zCjLgpleMf6S2ip0wkeHBrNsdRcftzpyK1WlKuNv8EMEz7WUrK0uFQrPbXx7SonnHQN7orVbmVv6t5ztrmisLiTALcA0vPPtZDFZccR7B5c5xJW/3X8zH6VxpDtSNmBXdrpGVK3/GNl6RHSg5PZJ0nOrTrEIzYrtlFSXjiJ9IvUZnZmxVW9s0JRBiXIzhecRcc31aLo+L4f4JtroElbuPkXrTB2HfDq3x9DWCgZ335T6X62rCwyvv0WnxEjMEWEV3leZz1LZ2HxQPdALYbupuXQ8Sr441lYeg++o0ZiPX2avM2bq9fhnV9BaBcIrrlbr2WQJ9/f1YepAyP5ZmscY95az77EctwwOp02MeLIqmI3npfRq9I8ZGvi1uBt9K6xhcBulzy2eDfuRj0zx9au9l9lDO/YlI5hPrz5x2EsVhv89CCcPgjj54Nvic9x6LNg9IBfHq40wL9rcFeActNfFItvFwgyp8uybDxiXHYczXyUuzIlq4CvNp+kwFK7WchVFRiPSYrBqDPWW/yYk+J8ZFW4LfMseSTnJTdK/JiTSD8tlZCKI1PUBiXIzif63q+5ifYurv4xO76A72+BiJ6asPGs+4NP6PX4TZhA7sa/KTpRQYki4Mw332LPzS1VRLwynC4OpxupOA+Z0R3GL4DLHoGdX+CV+A46L8/quS2T90HSbuhS+0LiJoOOR0e044tbe5NTaOXKdzYyf92xcwPf248FawEc1spdeZu8ybfml+uesNqt/Bn3J/0j+tc4+egXm08Qc+IMT4/uQBPv+om9KYkQgulD23AyPY8dP87TqhsMnAGR2kxQabNpIsYrGAY/CcfWFLuUy8PPzY8ov6gKBZmPyafWNQzrQoBbAFZpJauotBUnLjvuP10ySUrJ0p0JDJ27lsd/2MPNH28hu6DmLraqYsi2Jm3loqCLcDO41aW759DWvy3eRu8qBZkzVrAxLWTFNS1VkXFFLVCC7Hwi6nII7qCVU6rOLMNN78PSu6H1QLh+Cbj51ltX/MZPAL2+wuB+e2Eh6Z99hmffvri1r55lytfkS1ZRVqmyScXodDD4CbjyA3SntuLdrIDsFSuw51cRcL/zK9AZodOE6r61CukbFcSv91/GZW2a8PzPB5jyyVZOZ5dIk9GiD3gEFbstnVP/y3Nb7kzZSUZhBoObVy/dhZQSKSXxZ/KY/etBLmvThKu6VW11rC2D2wUzvulpuux9EXvrwXDZwwDYCwo4ce11nLjueuy5udDjFm2W6YrHobBi92zX4K7sStmFzV7a2pJWkOaS+DEoka2/RBxZniWP0/mn/7MB/ak5hUz7cjv3f7OT1k08eeKK9sTEnuHqDzeVvterga9Z+3+2y3OTMOdacjmQfqBe48ec6HV6uoZ0rTKOzJnyojEtZO4Gd8K9wlXqC0WtUILsfKJk0fHDlRQdl1ILttnbATIAACAASURBVP7tUW323zXfgKn8dBO1xRgSjPfgQWQs+QF70bku1MylS7GlphJ4+23VPqfTZZlWkFaqbFIpLr4ablyKb4t87Pn55Cz6oOIT2qxaeaY2w+vFMggQ4Gnioxu789y4Tmw6lsbIN9by5z+O7PY6PbS7QgvstxRUKsjWxK3BqDPSL7xflde0FxZy8uYpnLz1Np76ficSePHKTg0aiCwKMnje8hqp0ofvWjxdXIg96fnnyd+1i/ydO4m/9z6kzQ5XzKkywL9bSDdyLDkcztDizQosNjYeSSU5N9Ul7kooUc+yRBxZfI4j5cV/0GX5655TDJu7lj8OpPDYyHZ8f1cfbr+sNR/d1INjp3OZ8P5GTqZVv5atr8kXu7SXO7FlR8oObNJWb/nHytIzpCexWbGczjtdqr3AYiuerBKbGQvQ6AmAo/yilCBT1AolyM43Oo2vvOi4lLDqGVj9PHS+GiZ8ogVjNwB+kyZjS08n+/fS4lDabKQvWIhbx4549K5ejjMoHUNW6UO6RR88nvgNg5cg89M3YVcFsWxHV0NuSo2C+auDEIIbLmnB8nv7EeRl5uaPtzJr+X4KrTZttmVRDhz7s7ieZdlcZFJKVp9cTe/Q3ngaPSu9lpSSpKefJm/zZvI2bqTj4vk8OqIdEf71K7BLYbfDD1Nxyz/F24FPMmd9GgUWGxmLl5D5/WIC77yT0OefJ3fjRhIfewwZ0RO6XAd/vw2n/yn3lN2DuwMQk7SNH3ckMGTOX1w7fzM7E+I4mCB5Z80R9iVm1mstzaooL1u/M9j6v2QhO5NbxH1f72Dql9sJ93Pnp/v6cdeASPQ6TfAPahvMl7f3JjPfwlXvbSw/hrIc/Ny0VBLlBfZvTdqKQWiltRqC8vKRrdqfTN+XVzPwtT9ZujOB2MwThHqGNvrkjdZ+rdVMS0WtUILsfENvhEunwYn1EF/GJG+3w8/TNbHW8zYY9x7oa5e5vTp49u2DMSKCjG8XlWrP/uMPik6cIPD222pkxfE1+1JkLyIhJ6FUHcvyEE2i8Z1wLTlJZqxfT9UEqL2Ma2TX1+AeANHDqt2HmtAmxJsf7+7LzX1asnDDcca9s5EjXl3B7AsHllVoITuScYT4nPhquSvTFywgc+kyPO6cxrL2QxgV+zdjTlZzMkNt2fgmHPoVMewFxowaR0p2IT8sWk3SrFl4XHIJTe67F7+rriT44f8j65dfSX7+BeSQmWD0rDDAP9QrFH9zMG9tXMkD3+7E193Im9d0xc0tD2Hz5tUV/zDqzfVc8tIfPLZ4Nyv2JZFTWLuaqdWlPJdlXPZ/S5Ct2p/MsHlr+XXvKR4a2oYl0/rQJsT7nP26NffnuzsvxagXXP3BJjYdK78oe0l8TVqIRHmB/THJMXQK6oSHsWF+WLQLaIen0ZOYpBjyiqw8/sMebvsshmAfN7zdjNz/zU5+P7IXX0NYg1y/MqL8otRMS0WtUILsfKTbTecWHbdZ4ce7IGYh9HtQyxOla9iPT+h0+E2aRN6WLRQeOw5oFp20+QswNm+O99ChNTqf8wv8ZNbJasUV+Vw5CeyQJS/T3GWLbwWLI6YsP0MrZXTRRDDUrWhxZbg5ZjouvLkHyVkFjH53C8cD+yMP/oy3XvvlXdZls/rkagAGRgys8LwWm519i38mec7rxHbuw83WTnzcfiSi1yUkv/ACedsrL9hda2LXwx+zoMM46H0nl0YGMijcndC5s9D5+hI+5zWEXsvlFnjrrQTcegtnvvqK1M++gyFPaVn89/1Q6pSHk7O55ZOtpKSEka87wpyJnfnp3n4M7xRIkczjpt6d2PLEEF6Z0JnuLfz5efcpraTVrJVcP38z89cd49jpyhPs1gY/sx8CUcpCdjL7JH5mv9LxixcgmfkWHlq0i9s+iyHQ08TSu/tx75BojPqKvzOiQ7xZPLUPwT5mbly4hd/2VlJii7PJnssG9udZ8tifur9B4secGHQGugZ3ZUP8Fka/tZ6vt5zkjsta8+Pdffj53n7MmdiZIpHM7uNGbly4hf2JlVfUqE9a+7UGUIH9ihqjBNn5iLPo+IGfIPUIWAu12oK7v4UhT8PlM6usS1lf+F11JRgMZCzSrGR5W7dSsHs3gbdMKX5wVxfnF7hN2qoVV+TWtg3mNm3IinWHy5/VhMCnYyAnRVu3FUKX+nVXVsTgdiH8dn9/erYM4KXYaERBBvr4fUA5gixuNZ2bdKaJRxMArDY7B5OyWBQTx1M/7mXcOxsY+cAnFDzzBId9w3myzThC/dx5/ZpuRL81D2NYKPH33Y8lqfIHYo3JTtZm5Aa0grFvgRBIKZm+41sCc9PZetPDGAJLfy7B//d/+F55Jalvvc2ZIx7QtDOseAIKc0jJKmDGkt0Mn7eWrbHpXN7qEqQ+i17RWr4zZ+xWoHsgwd5uTOrRjHev6872p4fy9e2XMKVvK5KyCnj+5wMMnvMXA19dw8xl+1h76HStUzGUxKAz4O/mXyqG7L8ww/LPf1IYPnctP+5M4L7BUSy7px8dwqonQMP83Pn+rj50CPVh2pfb+GbLyQr39TFr5ywryHam7MQqrQ0WPwZgs0ssua1IyIsl15LBl7f25vEr2mM26NHpBAM7eIAun8ujOrErLoNRb61j+qKdJFRVlaMeaO3bGoFQqS8UNabh/F2KutH7Tq3g+NpXNAFybA2MfBV639Go3TAEBeF9+eVk/vADTR58gLT589EHBuI7blyNz+UUZFD9zO2+Y8eQ8tocipq/gmlyJCy+HT4aAiZPaNJeyz/WSAT7uPHplF588pc3eX++y5afF0Gz0oIsIfsU+9P2c3nIFGYu28fu+Az2n8qiwKK5W73MBnoGCJ7c9ikmL086L/yAre1aodOdFdjN3n6b2MlXE3/vfbT44nN05nqIEbRZNQtjQRbc8AO4aQ/T9IULMWxcy6rB17IgwcTYAgvebmdTVAghCH1uFraMDJKeewH9E1PxSXqSrZ/N4Ka4UVhsdm7q05J7B0eTVtSav5a9z7aUbTTzaVZhHUujXselkYFcGhnI41e0Jy49jzX/pLDmYApfbznJJxtjcTfq6RsVxKB2TRjRsSmBXrUbg7LZ+uOy4xosrsnVZBdYePGXA3y9JY7oYC8+vLE7nSNqXjLI39PEV7f35q4vtvPYkj2k5RYxbWDkOeEJTot32Wz9Mckx6IW+OD9dfRN/Jo/pi3YRc8obz1bw2JVG+pSp8+qsYXlNt+68OKQ37/55hI83xvLT7lNM6dOSaQOj8PVomFQszpmWxzJUCSVFzVAWsvMVr2Att9bubzU30bj3Gl2MOfGfPAlbZiapb71F7tp1BNxwPTq3mucWKinIqoohc+IzahQIQeby5dqM0im/gK1Im4na5ZpGsxQ60ekEtwzqgKX1EIbZtBi/n/YeY9by/Ux8fyPDPngXgKUb/VkUE4dBp+O63i2YN7kLfzw0gF0zBjIz5gu8cjKI+uBdWnVoXUqMAZijowl7ZTYFe/aQ9MzM+gmEX/MCxK6D0XMhREs2m7d1Kymvz8V7xAgGzLiHjDwLC9fHnnOoMBgIn/s6bl27EvfSh/ye3Jsu8V9ydat8Vk0fwDNjOhLgaSLSLxIfkw87UnYA1a9j2SzAgxsvbcnHU3qx8+lhfHxzTyZ0j+DAqSye+GEvg177k0Vb42o1DiULjFtsFk7lnmr0WXeNwcYjqYyYt45vt8Zx14BIlt/br1ZizImHycD8G3vwvy5hvLriH55dvv+cvHzFLssyMWQxyTF0DOzYIPFjS3cmMPKNdexLyOSl0SNxN7iz/8zOc/YrmfLC18PIjCvas+b/BjK6cygfrjvGZa+u4cO1R+vFElsekX6RykKmqDHKQnY+0/d+SNwO/R+CDv9zWTc8evfG2KI5afMXIDw88L+mdm5C5y9qqH7mdmNoKB69epG1bDlB06YhwrvB7X9osXTdb65VP+oD327j4fgvGAliV2ISu9NP0DHMl6ahR7HrI/jhngm0CvIqnskGjhmVz8wkb+tWwl59BfeLK7bUeF9+OUF3303qO+/g1qE9ATfeWPvO/vMbrH9di010uHgtKSnET5+OqVkzQp9/jggvL4Z2CGH++mPc3KdlKeuBlJLVxzJ546LrmXrkVcLXJ1M41JundQsh4KylVCd0dAvuVlxovDZ1LN1Nega1C2ZQu2BmScn+U1k8u2w/jyzezbJdibx45UU0D6z+gz7QLRCPlZs4Mnc44r4p2KX9ggrozy20Mvu3g3z29wlaB3ny3V196N6i4pqyNcFk0DF3UhcCPc0s3HCc9NwiXpt4MSaD9jveoDPgZfQqNcsy35rPntQ93NihDvdrOWTmW3h66V6W7kykW3M/5k3uSvNAD1af6VpugtjYrFiMOiNhnmeD+sP93Hl9Uhdu69ea2b8d5MVfDvLpxhM8NKwN47qEn/PDqC5E+kWyIXEDVrsVg049ZhXVQ1nIzmcCWsGda10qxkAL7vefNBkA/4kT0fvWLgFtbVyWAL5jRlN04gQFe/Y4GiK0WLp6TIRbY9oMB72JQJ2OsV0D2DtzOB/f2ol02wHGRA8lKti7lBgDOPPlV2QsWkTgHXfgO2ZMlZcIunsaXkOGkDz7FXI3bapdP8/Ewg93aLFfI18BQFqtJE5/CHtOLuFvvoHeS0vfMX1oG7ILrHy07qyrZXd8Bld/uInbPoshx+iOcc5beIYEk7gukMJdG2HfklKX6xrSldisWFLzU+tcNkkIQccwX7654xKeG9eJnXEZDJ+3lgXrj2MrW0WhHKTdTt/lx5m8+DS21FQKH5nF8G32CyaGbMvxdEa+sY7PN53glr6t+Pm+/vUmxpzodIKnRrfnkRFtWbYrkVs/3UpuidmxzlQ2Tnad3oXVXr/xY5uPpXHFG+v4afcppg9tw6I7Ly0W5T2b9uRIxhHOFJwpdcyJzBM0926OXndunGuHMB8+vaUXX97WG39PI9MX7WLUW+tZe+j0OfvWFmdNS2e1AIWiOihBpqgWfpMm4n/ttQTecXutz+FmcMNNr7k6a/KQ9h42DGEykbn8p1pfu94xe0PkYLyK8imy52LQ61gXvw6rtDK42bnpLnI2bCD5pZfwGjyYJg/cX61LCJ2OsNkvY2rZkoQHHqQoPr5mfbQWwqKbQAKTPgWjNvYpc+eSFxND6LMzcWvTpnj39qE+jLoolI83HGd3fAb3fb2DsW9v4EhKDrP+15GVD17G0P4dab5gPpjcObmuKZbFT0Dh2Ri6bsHdAC2wO60gDU+jZ51L5+h0Wl64lQ9exiWtA3jup/2Mf28jh5IrriNqLygg4cHptPlpL793FTRfvYLM7tHcutKO30dLkWVTqJyn5BfZOJGWy5bj6Szblcj8dcd44ef93Pl5DJM//BuAb26/hKfHdMDdVLNJNtVFCMG0gVHMHn8RG46kcu38zaTnasmifUw+pVyWW5O2ohO6eokfK7LaeeW3g1z90SYMesH3d13KfUOiMZSYKeoUftuSt5U6NjYrtsoM/X2jglh2dz/euLoL2QUWbly4hevnb2ZvQvXysFWGs6aliiNT1ARlS1VUC723N02ffqrO5/Ex+2DNtxbP0KrWtX188Bo0iKyffybkkYcRxsavi1gu7cfis2U7OTmnAC07f6Bb4DnFlAuPHyfhwemYW7cm7JVXEDVIV6L38qLZO29zfOIk4u+5l5ZffYnOo5ouu99mwKmdcPVXEKBNxc9etYr0BQvxu+ZqfMeOPeeQBy6P5pe9pxj79gbcjDruGRTFnQNalwr0N7VoQfOPPuLE9ddx8udsWnZ+Dv2VmvWtY2BHzHoz25K3kZ6fXq9Z+sP83Fl4c0+W7Upk5rJ9jHpzHfcMimbqwMhiNxqANTWVuGl3azF4t4zgo+DfmWS08tfdvfF4J5ahn39DQnI6Ya/MrlUsZH1gsdk5nV1IUlYBKVkFJGcVklxqqf1lFZybq81s0NHU142bLm3Jw8Pb4mlunK/xyT2b4+dh4t6vdzDh/Y18fmvv4vq0TmKSYugQ0AEvk1edrnX0dA4PfLOTPQmZTO7RjKfHdCj3fXYM7Ii7wZ2tSVu5vMXlANjsNk5mn2Rgs4FVXkenE/yvSzgjOjXli00neXv1YUa/tZ7/dQnjnkFRRJeTs606tPJpBWg5CZ39UiiqQgkyRaPidFvqRM2Ms75jx5C9YgW5f/+N12WXNUTXak7bkXhtmsnpnFMU2YpYF7+Oka1Glnpvtqws4qfdjdDriXjvXfRelWfuLw9Ty5aEvz6HuDvu5NSTTxI2Z07VCXl3L4KYBdDnPmg3CoCiEydIfGwGbhddRMiMGeUeFh3izYOXtyEpq4B7B0cR6lt+lnO3Dh2IeO8D4m6dQtxrS2je7Vp0Lbpg1Bu5KOgitqdsx8voVe91LIXQHqD9ooJ4dvl+5q46xC97TvHKhM5c3MyPgkOHiL9rKtYzZ4h4602S2uhhzSrSCtKIy00gZWI01w0ZTcrsVzh5cwoR776DISCgzv0qsNhIzy0iPbeIM3lFxesl29Jyzi7Tcs8tR2bQCYK9zYT4uhHZxIs+kYEE+7gR4uNGUx83QnzMBPu44eNmaNCyWpUxvGNTPr+lF7d9FsP4dzfSpbsnWYVaepYCawF7UvdwXfvran1+KSVfbTnJ8z8dwGzU8f713RjRKbTC/Y16Ixc3ubhUHFlibiJWu7VGRcXNBj239mvFxB4RvP/nURZuOM7SnYkM6xDCtEFRdGlWswkSHkYPov2j+WTfJwS5BzE+erzLPjPFvwclyBSNir/ZH6Ou5hYur/790fv6krls+fkjyDwC8PYI4lhBBltObSbPmlcqO7+0WkmY/hBF8fG0WLgAU0RErS/l1b8/TaY/yOk5r2Nu156gylzHKQdh+f3QvI8Wa4fmwou//wFNGM6bi85UcTLd+4ZEV6tPnr17EfbScyQ88jjxU2+h2Y+bECYT3UK6MX/PfEI8QugY2LFG77O6BHqZefOaroy9OIwnf9zLle9uYEZQBv2/nofe3Z0Wn3+Oe6eOBJzeBWgzPk9mnSTaP5rAsTdjDA0j8ZFHiL36Gpp/+AGmli0rvJaUkmOpuWw8kkpsWl65IiuvSJut504BwSKDYDIIFhmE6M4Qbcwm1JBFiMggiAzMegt7Ol7L6TbXEOzrSbCPmRAfNwI8TPUaWN5Q9G4dyKI7L+XGhVvYcCgf70Atfmv36d1Y7JZax4+l5RTy6OI9rDqQTP/oIF6beDEhPlVbMHuE9ODtnW+TUZCBn5tfcQ3L2hQV93Ez8siIdtzWvzWfbDjOJxtjWbk/mT6RgUwbGEXfqMBqC6u3Br/FMxue4dm/n2VF7Aqe7fMsYV6NXzlA8e9BCTJFo3J/t/ux2mteMkeYTHiPGEHmsmXYc3PRedbc0tQQePm3Jjt1B6tXPoi7XkfvXUshdjv4hJPy9Vpy16+n6cyn8OjZs87XCrztNgoPHOD03Lm4tWtbvjAtzIFFN2h52iYs1EpxAUmznqPwn39o9sH7GMPD69wXJz5jrsJ28C+SFqwk8e4bCPvga7oFd8Mu7ZzKPcVlEQ0rni/vEEKv1gEsnvkmPecv5KR/GN6vvUF0pw7A2fJJqfmpJOQkFAtmn+HDMIYEEzd1GrFXX0PEu+/g0a1b8XmTswrYcCSVDUfS+PtwMgXZaQSKLCIMWbR0y6azMZtQfSbBpjMEGM7ga0vHy5KK0Zp7bieFEdxDwDsEvNpD7mkGH50NOT/DiJchrH+DjlFD0D7UhyVT+zD+m1/JKsrimaV7OFjwKyD4JcaNFTF7sNslVrvEbpfYpMRml9gdS5sd7LLEdrvkUHI22QVWnhrdgSl9WlZbnPZsqv1vbUvZxpDmQ4pzkLX0bVnr9xfgaWL6sLbcMSCSrzafYP6641y/YDOdI3yZOiCS4R2bVtm/cK9wPhr2Ed8d+o45MXO4cumVPNTjISa0mVBjD4Hiv4ESZIpGpWx8VU3wHTuGjG+/JfuPP8qNf3IFPk27kHNmD39ipZ8FzLsXQWEWGUc9SN/qh3+bHPwPToVXngKfsDJ/4WeX3qFahQYpwW4DuxWkTVuXNrDbEdJG6KP3UHj4HxKmT6fl/LmYI0JL77fudUg7AjcuBR/N1ZPx/fdkLllC0LSpDWJd9H/odWwHe3N63W70LzxH50emoxM67NJerzFk5SFtNvLnvc4lP36CpWcf5rSbwD8/HOeaBCszrmhffP39afux2C1aygspoSgX9+a+tJz3OHEzZnPixhvRje/FmWAdeWeSMBem00FkMUCXhT/Z6NxKTAKwOP6Mng6R1RS8uzmWIeDl+PNuqrW5+5cucyYl7F8KK5+CT0dD+7Ew7Dnwb9mgY1XfNAvw4OZLOvDenpUs2n4EfegOhC6MlXsy0euy0AmBXifQCYFBL9ALgU5XYqmjVFvX5n48NKwt7UNrVtaqU1AnzHozMUkxDGk+hNisWLxN3vib6z7j1Mts4I7LIrmpT0uWbE/gg7+OMvXL7bRu4sldAyIZ1yW8VPxiWYQQTGo7iX7h/Xhm4zM8t+k5VsauZGafmUR4195irrgwUYJM8a/BvWtXjOHhZC5bft4IMi+PQGxITmNj8OCXoPVo8jb+xak77sGzWzQh08dBbhJkJWp/mQkQtwVKlPM5i0CbElkxOiCirZ7YE0HE3zGFlkNT0RvLHDP4KWilCa+C/ftJmvUcnn36EHT33fXyns/tlJ7Ame9ju+dK0r/8BkOTprQNb8uB9AP1HkNWEnteHgkPP0LOH3/gf8MNhDz2KD/aYN6qQ3y07hirD6bw4ugo3IWRXYeWAtB8xbOw6B6waiV0TECL3oL4dQHkf/s3zTvnIS7yQAQ0wcOvA95BYQjPJuDZBDyDtITNTuFlrl3AN0JAx3Fa6pSNb2s54g6tgD73Qv/pmnXzX0KYj/b5/vzgxUxcHsfkdpN5pOewRu2DSW8qFUcWmxVLS5+W9RqzZTbouaZXcyb1aMYve07x7p9HeeT73cz9/RC392/N1b2a4WGq+HEa5hXGh0M/ZMnhJbwa8ypXLbuKB7s/yOS2k5W1TFGMEmSKfw1Cp8NnzGjSPvwI6+nTGJo0cXWX8DZpD2W90NM/vD9F8QnEPzQDU0QE4e9/gvCp4Ne+Jf+sSMs+BZnxYMkDnQGEXrOoCD3o9CWWOtDpMQk94V3iOPniVySeGEjEw9ciDEZtP48AaNEXAFtmJvH3P4A+IICw116tce3RmiCa9SB4yjis7y/n9Lx5XDnlUg40rX0OsqqwJKcQP3UqBQcPEvLkkwRcrwWSu+thxhXtGR+ezj8/v03PxWsIaObPIYMBBBQZ2/KLey/2ZZpItnlzBl/8m4bTdnozev/yPZnr/8Svw2RC7n0SYWjgr0ejOwx4WKvIseoZWPca7PwKhj4LF01s9CoUtcHPrAW7r0tYR5G9iJ4hdXfN14YeTXvw3s73yCzM5ETWiQbrh14nGHNxGKM7h/LXodO8++dRZv20n7dWH+bmPq24qU8L/DzKj88UQjC+zXj6hPXh2b+f5cXNL7IydiWz+syimc+Fk6xYUXuUIFP8q/AdM4a09z8g65dfCLjpJld3B2+jJsh6NO2Bl8XAiWnTkDabNqOyIjEG2sM4MFL7qwWe3SHE3oLkF18k9a9kmtx3b6nt0m4n8bEZWJKSaPn5Z/Uyk7AqxNCZhB1Yjk3nzsWfbqLXOEHT0U3r/ToFBw4Qd9dU7NnZNHvvXbwGDNA2FGbD3sWw7RPaJO4gWm/mn5DBpFtTsBtPI+0Gro+9ibYhvvTpGcjIqCB6tQooTukhxw3i9OuvkzZ/AdZTSYS/PqdxYhV9w2H8fOh5G/z6KCy5HbZ8BCNnQ3i3qo93Ic5Z03+c/AOBoFuIa/rbI6QHEsnfiX+TlJtUq4D+miCEYGDbYAa2DWbbiXTeXXOUuasO8eHao1zbuzm39W9d4YSEUK9Q3rv8PX488iOvbH2F8cvHc3+3+7mm3TUutZZJKSm02skrspFXZCW/yEZekY18i6143SYlYb5uNAvwoImX+V8xCeXfhBJkin8V5shI3Dp0IHP5T8WCTEqJLCzEnp2NLScHe07O2fXsHOw5Z9dtOdmONm1d5+6BOSoKc1SkYxmF3q/6U9ydFrJB4QNJfPRRCo8epdmHH2Bu1apB3n9J/G+4noIDB0h9913M7dvhM3Ro8ba0+QvIWbOGkCeewL1LIxVg9wxEDH2GiNwHOWnsxUNLE/E8+TIpXbvi3rUL7l261FkYZv/5JwnTH0Lv60uLr7/SEtsmbINtn8CexWDJheAOMGI2ovMk2nkE0OnXqWxNOU2weyirnxhKsHf5D0qh0xH8f/+HMSKCpFnPEXvDDTR7732MIcF16nO1aX4J3L4Gdn4JfzwLHw2CLtdrM2W9QxqnDzXEWQ5tZ8pO2vi3KVWNozHp3KQzJp2JxYcXA9DCt2EFWUm6twhgwc0BHEzKcqTMiOXTjSe4qls4Yy4Oo1WQJ0193EqJFyEEV0ZfyaVhlzLr71m8vOVlzVrWd1a9iEkpJadzCjmSklP8l55bVCys8iw2Cops5FlKC6+alIs16XWE+7sT4e9OhL8HEf7uNAtwLP09CPIyqVQfNUTUS+Hiik4uxAjgDUAPzJdSvlxmuxn4DOgOpAGTpZSxlZ2zR48eMibm3Npliv8OaZ98QsrLszE2a+YQVjlgsVR5nHB3R+/lhc7bG523F3pPL2zZ2RQePYrMyyveT98kCHNkVLFAc4q18oRariWXedvmceN6PTnzPyXkiScIuOH6en2/lWEvLOTEDTdSdOQILb/9BnN0NLmbNnPyllvwGTGCsDmvNe6Xot0G84dgO32KVHE9eTv2UHDgAFi1mbWmFi1w79IF965dce/aFXNUZLVdqemffU7yyy/j1r49EXNfxpi0BrZ9Csl7wOgBHa/S6ptG9Cjl7ntm4zMsObyEAREDeHvI29W6Vs5ffxH/4HT0eRTy9AAADw1JREFUvr40++D9UhUNGoWCLFj7Kmx6DwxucNn/wSVTwWBu3H5UQWp+KoMWDQLg+vbX82ivR13Wlym/TSmOI/t+zPe0DWjrkn6cTMvjw3VHWRQTT5FVmwxiNuhoGehJqyBPWgZ50tqxbBnkQZCnieXHljN7y2wsdgv3dr2X69pfV27Zp7JIKUnMLOBwcnax8DrsWGbmn/1O9DIbCPY2427S42HS424y4G7U4WEyaG1Grd2teN1QYl897o42ISAhI5/4M/nEn8kjPt2xPJN/Tm49s0FXSqRF+HvQzCHcfN2NmAw6TAYdRr0Os0GHSa+7YC1uQohtUsoq88E0mCATQuiBQ8BQIB7YClwjpdxfYp9pQGcp5V1CiKuBK6WUkys7rxJkCltGBknPPQ9CaMLKywudl0NkeXuj8/RC7+0QXl7O7V4VxgRJux3rqVMUHjlC4ZGjjuWR8oVaVNRZsRYdhTkykpx160l8+GH8Jk2i6bMzG/1XoSU5meMTJqBz96DZO29z4uYp6H19afXdItekB0nYBh8NgUvvhuEvYC8ooGDvXvJ27CB/5y7yd+zAlq5NatB5euJ+8cVnRdrFnc9x9UqrleSXXubMl1/i3bcbYcM80R1epgXmN+0M3W/SYq4qqG36xvY3mL9nfo0FQ/6+fcTfNRV7fj4Rb72J56WX1n5MakvaUVjxOBz6Tau2MPxFaDPivIkvs9gsdPtCc1POGziPIS2GuKwv7+x8h/d3vQ/A5ms342GsfhH6hiAjr4j9p7I4nppLbGouxx1/J9PzsNjOPne9zAZaBnkQFlhEvO5z4gq3Ee3TiZl9nqVziPZDwGaXxKXncTglh8MpZ8XX0ZQcch058EBL1xEV7EVUsBfRwV5EB3sTFexFiI+5wb+XcgutJGTkE5eeVyzY4tLzic/QXmfkVf2jWa8TmPRlhJpDrBkNZbfp8TRrQtHTpMfDbMDL+bq43YCHWa8tTXo8zdo2k17XqN/T54MguxSYKaUc7ng9A0BK+VKJfVY49vlbCGEAkoAmspJOKUGmaCyqK9QAPHr2pPmC+YhKEq42JHnbd3DC4cIVRiOtFn2LOSrKJX0BtMS02z4Fv2bg5gfufsVLafbFkmMgPy6bvGOp5B9OoPBEItglCIE5MrLYgubWoT0pr84md8MmArqZCI6ORZi94KIJmjUsrOqaiV/s/4LZW2czo9cMrm1/bY3ehiUxkbg776TweCyhzz2H35XjajkgdeTwKlgxA1IPQeRgGP4SBLer+ji7HewWsBWBzbksuW7RJos4Jowg9JrYc04iEfoS23Rl9tW2X7JoILnWXNZNWoufe/0WN68JW05t4daVtxLiEcKqiatc1o+qsNrsJGYUcCw156xQS8vjeGoOCWfy0HnvxK3pMhAW9BkjCbRfTvyZwmJrG0BTH7di4eUUX1HBXgR6nV8W1JJkFVhIOKNZ13IKLRRZ7RRZ7RRa7VhsUntts1HkeF3o2F5ks2NxLItKtBVYNDdrbqEW85ZXQphWhUEnigXahO4RPDSsYa2p54MgmwCMkFLe5nh9A9BbSnlPiX32OvaJd7w+6tgntaLzKkGmcDVlhZotK4uAm2/C4O+6hxHAme++I+nZWYS9/DK+o0e5tC8UZML6eZCVAPkZkH8GCjK09YIMTQyUwGYRFKQZyUs1kZ9mIj/NjL3I8QtWSJr2yMT/sg6aNazjVVrOtmryy7FfeHTdo7x3+Xv0C+9X47diy84m/r77yPt7E+7du2u1VEt+bzrXy2mTzjQm1f2areBHu3CmRMk+BRlxYLchTZ6aiLXbkVKClEjHayTFbUihdcfxJ6U420WpXVMIQEjH8tzXxeucu22Huxm9lHQuLCrubdk/WaqdEu0l37AosRBnjYBllsWWjRLtArAJ2KKz4CMFHTGWGXPHe3a+lCWOP+ezcYy1c5yQ5X9+5dwCFX7OJceuZKfLbJNoH2mhtHNSZyVDZ8cswSS18RCOO6HsfSKqur+qcf/V1F5UzvCW3l5eWzmHlOq7LHEq57ossX8l78N5veKPosSJJQIpJOV9TLkdmjNx3q8Vn7geqK4ga8ig/vI+37LDWZ19EELcAdwB0Lx587r3TKGoA0KnwxgejjE8/OwMv/MA/4kT8R01qvrFxxsSN1+4/Jnyt0mppf0oIdD0+Rl4FmTg6Xgtc89QFJdI/rFkzJGtcR93HzTtVKuu9Gzak+Eth9OlSe0mN+i9vWn+wQekvD6X/D17kM54xWJdUOJrzPnEdSSCFSXbqnKRVPTjuGS7b4SWdDbjJMJaADqhFavXaalShE4Heh3C8Rq93rGu12L1dHqtTW8AvcPKJSXSZnNY0+xIm10TfM7XdjvYHKLPVqLNsQy15GIUOqSn0zrjVHrOtyyLJZlzuzjn0Si1J3PJZqeoLbXr2X2k87VdWxdAS2nHJAT2yuTFOZtkmU1OBXBWRlJm8mOpj1KUyR9YvLECMe7o99n35Vixa9fSAe4S2tr0nLEKUrGXEDcVK5LyBFBNtlePkkpWozwxKGT5w+yU4fLsP885fbOXWJcODV9yuyxxXCmxVqYvpddlcZ/KCkBvfYmkzy5GuSwVCoVCoVAoGojqWsgaMunJViBaCNFKCGECrgaWldlnGeBMJjUBWF2ZGFMoFAqFQqG4EGkwl6WU0iqEuAdYgZb2YqGUcp8QYhYQI6VcBiwAPhdCHAHS0USbQqFQKBQKxX+KBk0MK6X8BfilTNvTJdYLgIkN2QeFQqFQKBSK8x1V1VShUCgUCoXCxShBplAoFAqFQuFilCBTKBQKhUKhcDFKkCkUCoVCoVC4GCXIFAqFQqFQKFyMEmQKhUKhUCgULkYJMoVCoVAoFAoXowSZQqFQKBQKhYtRgkyhUCgUCoXCxShBplAoFAqFQuFilCBTKBQKhUKhcDFKkCkUCoVCoVC4GCXIFAqFQqFQKFyMEmQKhUKhUCgULkYJMoVCoVAoFAoXI6SUru5DjRBCnAZONMKlgoDURrjOfxE1tg2HGtuGRY1vw6HGtmFR49twVDW2LaSUTao6yb9OkDUWQogYKWUPV/fjQkSNbcOhxrZhUePbcKixbVjU+DYc9TW2ymWpUCgUCoVC4WKUIFMoFAqFQqFwMUqQVcyHru7ABYwa24ZDjW3Dosa34VBj27Co8W046mVsVQyZQqFQKBQKhYtRFjKFQqFQKBQKF6MEWRmEECOEEP8IIY4IIR5zdX8uNIQQsUKIPUKInUKIGFf359+MEGKhECJFCLG3RFuAEOJ3IcRhx9LflX38N1PB+M4UQiQ47t+dQogrXNnHfytCiGZCiDVCiANCiH1CiPsd7er+rSOVjK26d+sBIYSbEGKLEGKXY3yfdbS3EkJsdty73wohTDU+t3JZnkUIoQcOAUOBeGArcI2Ucr9LO3YBIYSIBXpIKVU+nDoihLgMyAE+k1J2crS9AqRLKV92/KDwl1I+6sp+/lupYHxnAjlSytdc2bd/O0KIUCBUSrldCOENbAPGATej7t86UcnYTkLdu3VGCCEATylljhDCCKwH7gemA0uklN8IId4Hdkkp36vJuZWFrDS9gCNSymNSyiLgG+B/Lu6TQlEuUsq1QHqZ5v8BnzrWP0X7IlbUggrGV1EPSClPSSm3O9azgQNAOOr+rTOVjK2iHpAaOY6XRsefBAYD3zvaa3XvKkFWmnAgrsTreNSNXN9IYKUQYpsQ4g5Xd+YCJERKeQq0L2Yg2MX9uRC5Rwix2+HSVC61OiKEaAl0BTaj7t96pczYgrp36wUhhF4IsRNIAX4HjgIZUkqrY5daaQclyEojymlTPt36pa+UshswErjb4RZSKP4tvAdEAl2AU8Ac13bn340QwgtYDDwgpcxydX8uJMoZW3Xv1hNSSpuUsgsQgeZZa1/ebjU9rxJkpYkHmpV4HQEkuqgvFyRSykTHMgX4Ae1mVtQfyY4YEmcsSYqL+3NBIaVMdnwZ24GPUPdvrXHE3ywGvpRSLnE0q/u3HihvbNW9W/9IKTOAP4FLAD8hhMGxqVbaQQmy0mwFoh2zJUzA1cAyF/fpgkEI4ekIMkUI4QkMA/ZWfpSihiwDbnKs3wQsdWFfLjicYsHBlaj7t1Y4AqMXAAeklK+X2KTu3zpS0diqe7d+EEI0EUL4OdbdgcvR4vTWABMcu9Xq3lWzLMvgmAo8D9ADC6WUL7i4SxcMQojWaFYxAAPwlRrf2iOE+BoYCAQBycAzwI/AIqA5cBKYKKVUgem1oILxHYjm8pFALHCnM+ZJUX2EEP2AdcAewO5ofhwt1kndv3WgkrG9BnXv1hkhRGe0oH09mlFrkZRyluP59g0QAOwArpdSFtbo3EqQKRQKhUKhULgW5bJUKBQKhUKhcDFKkCkUCoVCoVC4GCXIFAqFQqFQKFyMEmQKhUKhUCgULkYJMoVCoVAoFAoXowSZQqFQVBMhxEAhxE+u7odCobjwUIJMoVAoFAqFwsUoQaZQKC44hBDXCyG2CCF2CiE+cBQDzhFCzBFCbBdC/CGEaOLYt4sQYpOj6PIPzqLLQogoIcQqIcQuxzGRjtN7CSG+F0IcFEJ86ciMrlAoFHVCCTKFQnFBIYRoD0xGK2TfBbAB1wGewHZHcfu/0DLvA3wGPCql7IyW3dzZ/iXwjpTyYqAPWkFmgK7AA0AHoDXQt8HflEKhuOAxVL2LQqFQ/KsYAnQHtjqMV+5oRartwLeOfb4AlgghfAE/KeVfjvZPge8cNVfDpZQ/AEgpCwAc59sipYx3vN4JtATWN/zbUigUFzJKkCkUigsNAXwqpZxRqlGIp8rsV1nduMrckCXr09lQ36MKhaIeUC5LhUJxofEHMEEIEQwghAgQQrRA+76b4NjnWmC9lDITOCOE6O9ovwH4S0qZBcQLIcY5zmEWQng06rtQKBT/KdQvO4VCcUEhpdwvhHgSWCmE0AEW4G4gF+gohNgGZKLFmQHcBLzvEFzHgCmO9huAD4QQsxznmNiIb0OhUPzHEFJWZrVXKBSKCwMhRI6U0svV/VAoFIryUC5LhUKhUCgUChejLGQKhUKhUCgULkZZyBQKhUKhUChcjBJkCoVCoVAoFC5GCTKFQqFQKBQKF6MEmUKhUCgUCoWLUYJMoVAoFAqFwsUoQaZQKBQKhULhYv4fI4t9uaMuxHgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rates = [0.001, 0.01, 0.1]\n",
    "plt.figure()\n",
    "for i in range(len(learning_rates)):\n",
    "    plt.plot(range(30),train_loss_adam_lrs[i], label='learning_rate {:g}'.format(learning_rates[i]))\n",
    "    \n",
    "plt.plot(range(30), train_loss_adam_lr, label = 'learning rate decayed by 0.1 every 10 epochs')\n",
    "plt.legend()\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('training loss')\n",
    "plt.title('Adam')\n",
    "    \n",
    "plt.figure()\n",
    "for i in range(len(learning_rates)):\n",
    "    plt.plot(range(30),val_acc_adam_lrs[i], label='learning_rate {:g}'.format(learning_rates[i]))\n",
    "plt.plot(range(30),val_acc_adam_lr, label = 'learning rate decayed by 0.1 every 10 epochs')\n",
    "plt.legend()\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('validation accuracy')\n",
    "plt.title('Adam')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SGD optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### learning rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: [1/200], Validation Acc: 61.56\n",
      "Epoch: [2/200], Validation Acc: 63.88\n",
      "Epoch: [3/200], Validation Acc: 64.88\n",
      "Epoch: [4/200], Validation Acc: 65.42\n",
      "Epoch: [5/200], Validation Acc: 65.58\n",
      "Epoch: [6/200], Validation Acc: 66.32\n",
      "Epoch: [7/200], Validation Acc: 66.62\n",
      "Epoch: [8/200], Validation Acc: 67.28\n",
      "Epoch: [9/200], Validation Acc: 67.7\n",
      "Epoch: [10/200], Validation Acc: 68.02\n",
      "Epoch: [11/200], Validation Acc: 68.58\n",
      "Epoch: [12/200], Validation Acc: 68.98\n",
      "Epoch: [13/200], Validation Acc: 69.56\n",
      "Epoch: [14/200], Validation Acc: 69.92\n",
      "Epoch: [15/200], Validation Acc: 70.12\n",
      "Epoch: [16/200], Validation Acc: 70.2\n",
      "Epoch: [17/200], Validation Acc: 70.7\n",
      "Epoch: [18/200], Validation Acc: 70.96\n",
      "Epoch: [19/200], Validation Acc: 71.28\n",
      "Epoch: [20/200], Validation Acc: 71.56\n",
      "Epoch: [21/200], Validation Acc: 71.78\n",
      "Epoch: [22/200], Validation Acc: 72.14\n",
      "Epoch: [23/200], Validation Acc: 72.4\n",
      "Epoch: [24/200], Validation Acc: 72.6\n",
      "Epoch: [25/200], Validation Acc: 72.86\n",
      "Epoch: [26/200], Validation Acc: 73.02\n",
      "Epoch: [27/200], Validation Acc: 73.22\n",
      "Epoch: [28/200], Validation Acc: 73.54\n",
      "Epoch: [29/200], Validation Acc: 73.78\n",
      "Epoch: [30/200], Validation Acc: 74.1\n",
      "Epoch: [31/200], Validation Acc: 74.36\n",
      "Epoch: [32/200], Validation Acc: 74.62\n",
      "Epoch: [33/200], Validation Acc: 74.76\n",
      "Epoch: [34/200], Validation Acc: 74.94\n",
      "Epoch: [35/200], Validation Acc: 75.08\n",
      "Epoch: [36/200], Validation Acc: 75.3\n",
      "Epoch: [37/200], Validation Acc: 75.4\n",
      "Epoch: [38/200], Validation Acc: 75.44\n",
      "Epoch: [39/200], Validation Acc: 75.64\n",
      "Epoch: [40/200], Validation Acc: 75.66\n",
      "Epoch: [41/200], Validation Acc: 75.88\n",
      "Epoch: [42/200], Validation Acc: 76.16\n",
      "Epoch: [43/200], Validation Acc: 76.24\n",
      "Epoch: [44/200], Validation Acc: 76.38\n",
      "Epoch: [45/200], Validation Acc: 76.44\n",
      "Epoch: [46/200], Validation Acc: 76.74\n",
      "Epoch: [47/200], Validation Acc: 76.78\n",
      "Epoch: [48/200], Validation Acc: 76.9\n",
      "Epoch: [49/200], Validation Acc: 77.14\n",
      "Epoch: [50/200], Validation Acc: 76.86\n",
      "Epoch: [51/200], Validation Acc: 77.28\n",
      "Epoch: [52/200], Validation Acc: 77.36\n",
      "Epoch: [53/200], Validation Acc: 77.4\n",
      "Epoch: [54/200], Validation Acc: 77.62\n",
      "Epoch: [55/200], Validation Acc: 77.68\n",
      "Epoch: [56/200], Validation Acc: 77.76\n",
      "Epoch: [57/200], Validation Acc: 77.96\n",
      "Epoch: [58/200], Validation Acc: 78.06\n",
      "Epoch: [59/200], Validation Acc: 78.3\n",
      "Epoch: [60/200], Validation Acc: 78.04\n",
      "Epoch: [61/200], Validation Acc: 78.2\n",
      "Epoch: [62/200], Validation Acc: 78.56\n",
      "Epoch: [63/200], Validation Acc: 78.46\n",
      "Epoch: [64/200], Validation Acc: 78.58\n",
      "Epoch: [65/200], Validation Acc: 78.72\n",
      "Epoch: [66/200], Validation Acc: 78.7\n",
      "Epoch: [67/200], Validation Acc: 78.96\n",
      "Epoch: [68/200], Validation Acc: 78.86\n",
      "Epoch: [69/200], Validation Acc: 78.72\n",
      "Epoch: [70/200], Validation Acc: 79.28\n",
      "Epoch: [71/200], Validation Acc: 79.14\n",
      "Epoch: [72/200], Validation Acc: 79.16\n",
      "Epoch: [73/200], Validation Acc: 79.5\n",
      "Epoch: [74/200], Validation Acc: 79.5\n",
      "Epoch: [75/200], Validation Acc: 79.3\n",
      "Epoch: [76/200], Validation Acc: 79.66\n",
      "Epoch: [77/200], Validation Acc: 79.78\n",
      "Epoch: [78/200], Validation Acc: 79.64\n",
      "Epoch: [79/200], Validation Acc: 79.6\n",
      "Epoch: [80/200], Validation Acc: 79.78\n",
      "Epoch: [81/200], Validation Acc: 79.72\n",
      "Epoch: [82/200], Validation Acc: 80.26\n",
      "Epoch: [83/200], Validation Acc: 79.9\n",
      "Epoch: [84/200], Validation Acc: 80.36\n",
      "Epoch: [85/200], Validation Acc: 80.06\n",
      "Epoch: [86/200], Validation Acc: 80.08\n",
      "Epoch: [87/200], Validation Acc: 80.46\n",
      "Epoch: [88/200], Validation Acc: 80.28\n",
      "Epoch: [89/200], Validation Acc: 80.46\n",
      "Epoch: [90/200], Validation Acc: 80.48\n",
      "Epoch: [91/200], Validation Acc: 80.58\n",
      "Epoch: [92/200], Validation Acc: 80.58\n",
      "Epoch: [93/200], Validation Acc: 80.58\n",
      "Epoch: [94/200], Validation Acc: 80.74\n",
      "Epoch: [95/200], Validation Acc: 80.84\n",
      "Epoch: [96/200], Validation Acc: 80.86\n",
      "Epoch: [97/200], Validation Acc: 80.84\n",
      "Epoch: [98/200], Validation Acc: 80.9\n",
      "Epoch: [99/200], Validation Acc: 80.92\n",
      "Epoch: [100/200], Validation Acc: 80.94\n",
      "Epoch: [101/200], Validation Acc: 81.1\n",
      "Epoch: [102/200], Validation Acc: 80.94\n",
      "Epoch: [103/200], Validation Acc: 81.06\n",
      "Epoch: [104/200], Validation Acc: 81.06\n",
      "Epoch: [105/200], Validation Acc: 81.1\n",
      "Epoch: [106/200], Validation Acc: 81.14\n",
      "Epoch: [107/200], Validation Acc: 81.4\n",
      "Epoch: [108/200], Validation Acc: 81.54\n",
      "Epoch: [109/200], Validation Acc: 81.38\n",
      "Epoch: [110/200], Validation Acc: 81.56\n",
      "Epoch: [111/200], Validation Acc: 81.58\n",
      "Epoch: [112/200], Validation Acc: 81.26\n",
      "Epoch: [113/200], Validation Acc: 81.76\n",
      "Epoch: [114/200], Validation Acc: 81.74\n",
      "Epoch: [115/200], Validation Acc: 81.82\n",
      "Epoch: [116/200], Validation Acc: 81.84\n",
      "Epoch: [117/200], Validation Acc: 81.74\n",
      "Epoch: [118/200], Validation Acc: 81.78\n",
      "Epoch: [119/200], Validation Acc: 81.46\n",
      "Epoch: [120/200], Validation Acc: 81.98\n",
      "Epoch: [121/200], Validation Acc: 81.96\n",
      "Epoch: [122/200], Validation Acc: 81.76\n",
      "Epoch: [123/200], Validation Acc: 82.08\n",
      "Epoch: [124/200], Validation Acc: 82.02\n",
      "Epoch: [125/200], Validation Acc: 82.24\n",
      "Epoch: [126/200], Validation Acc: 82.16\n",
      "Epoch: [127/200], Validation Acc: 82.18\n",
      "Epoch: [128/200], Validation Acc: 82.22\n",
      "Epoch: [129/200], Validation Acc: 82.22\n",
      "Epoch: [130/200], Validation Acc: 82.26\n",
      "Epoch: [131/200], Validation Acc: 82.24\n",
      "Epoch: [132/200], Validation Acc: 82.22\n",
      "Epoch: [133/200], Validation Acc: 82.26\n",
      "Epoch: [134/200], Validation Acc: 82.34\n",
      "Epoch: [135/200], Validation Acc: 82.42\n",
      "Epoch: [136/200], Validation Acc: 82.32\n",
      "Epoch: [137/200], Validation Acc: 82.54\n",
      "Epoch: [138/200], Validation Acc: 82.5\n",
      "Epoch: [139/200], Validation Acc: 82.44\n",
      "Epoch: [140/200], Validation Acc: 82.42\n",
      "Epoch: [141/200], Validation Acc: 82.52\n",
      "Epoch: [142/200], Validation Acc: 82.56\n",
      "Epoch: [143/200], Validation Acc: 82.52\n",
      "Epoch: [144/200], Validation Acc: 82.5\n",
      "Epoch: [145/200], Validation Acc: 82.46\n",
      "Epoch: [146/200], Validation Acc: 82.66\n",
      "Epoch: [147/200], Validation Acc: 82.78\n",
      "Epoch: [148/200], Validation Acc: 82.72\n",
      "Epoch: [149/200], Validation Acc: 82.72\n",
      "Epoch: [150/200], Validation Acc: 82.84\n",
      "Epoch: [151/200], Validation Acc: 82.84\n",
      "Epoch: [152/200], Validation Acc: 82.8\n",
      "Epoch: [153/200], Validation Acc: 82.76\n",
      "Epoch: [154/200], Validation Acc: 82.82\n",
      "Epoch: [155/200], Validation Acc: 82.98\n",
      "Epoch: [156/200], Validation Acc: 83.04\n",
      "Epoch: [157/200], Validation Acc: 83.06\n",
      "Epoch: [158/200], Validation Acc: 83.1\n",
      "Epoch: [159/200], Validation Acc: 83.14\n",
      "Epoch: [160/200], Validation Acc: 83.2\n",
      "Epoch: [161/200], Validation Acc: 83.06\n",
      "Epoch: [162/200], Validation Acc: 83.2\n",
      "Epoch: [163/200], Validation Acc: 83.28\n",
      "Epoch: [164/200], Validation Acc: 83.24\n",
      "Epoch: [165/200], Validation Acc: 83.18\n",
      "Epoch: [166/200], Validation Acc: 83.26\n",
      "Epoch: [167/200], Validation Acc: 83.26\n",
      "Epoch: [168/200], Validation Acc: 83.42\n",
      "Epoch: [169/200], Validation Acc: 83.22\n",
      "Epoch: [170/200], Validation Acc: 83.3\n",
      "Epoch: [171/200], Validation Acc: 83.38\n",
      "Epoch: [172/200], Validation Acc: 83.42\n",
      "Epoch: [173/200], Validation Acc: 83.42\n",
      "Epoch: [174/200], Validation Acc: 83.48\n",
      "Epoch: [175/200], Validation Acc: 83.52\n",
      "Epoch: [176/200], Validation Acc: 83.48\n",
      "Epoch: [177/200], Validation Acc: 83.6\n",
      "Epoch: [178/200], Validation Acc: 83.54\n",
      "Epoch: [179/200], Validation Acc: 83.54\n",
      "Epoch: [180/200], Validation Acc: 83.6\n",
      "Epoch: [181/200], Validation Acc: 83.64\n",
      "Epoch: [182/200], Validation Acc: 83.68\n",
      "Epoch: [183/200], Validation Acc: 83.74\n",
      "Epoch: [184/200], Validation Acc: 83.48\n",
      "Epoch: [185/200], Validation Acc: 83.58\n",
      "Epoch: [186/200], Validation Acc: 83.6\n",
      "Epoch: [187/200], Validation Acc: 83.82\n",
      "Epoch: [188/200], Validation Acc: 83.64\n",
      "Epoch: [189/200], Validation Acc: 83.58\n",
      "Epoch: [190/200], Validation Acc: 83.62\n",
      "Epoch: [191/200], Validation Acc: 83.8\n",
      "Epoch: [192/200], Validation Acc: 83.82\n",
      "Epoch: [193/200], Validation Acc: 83.72\n",
      "Epoch: [194/200], Validation Acc: 83.72\n",
      "Epoch: [195/200], Validation Acc: 83.82\n",
      "Epoch: [196/200], Validation Acc: 83.8\n",
      "Epoch: [197/200], Validation Acc: 84.04\n",
      "Epoch: [198/200], Validation Acc: 83.82\n",
      "Epoch: [199/200], Validation Acc: 84.0\n",
      "Epoch: [200/200], Validation Acc: 83.8\n",
      "Epoch: [1/200], Validation Acc: 65.56\n",
      "Epoch: [2/200], Validation Acc: 66.38\n",
      "Epoch: [3/200], Validation Acc: 66.72\n",
      "Epoch: [4/200], Validation Acc: 67.78\n",
      "Epoch: [5/200], Validation Acc: 68.32\n",
      "Epoch: [6/200], Validation Acc: 68.92\n",
      "Epoch: [7/200], Validation Acc: 69.66\n",
      "Epoch: [8/200], Validation Acc: 69.9\n",
      "Epoch: [9/200], Validation Acc: 70.62\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: [10/200], Validation Acc: 71.04\n",
      "Epoch: [11/200], Validation Acc: 71.42\n",
      "Epoch: [12/200], Validation Acc: 72.02\n",
      "Epoch: [13/200], Validation Acc: 72.4\n",
      "Epoch: [14/200], Validation Acc: 73.06\n",
      "Epoch: [15/200], Validation Acc: 73.68\n",
      "Epoch: [16/200], Validation Acc: 73.8\n",
      "Epoch: [17/200], Validation Acc: 73.94\n",
      "Epoch: [18/200], Validation Acc: 74.28\n",
      "Epoch: [19/200], Validation Acc: 74.66\n",
      "Epoch: [20/200], Validation Acc: 75.3\n",
      "Epoch: [21/200], Validation Acc: 75.38\n",
      "Epoch: [22/200], Validation Acc: 75.66\n",
      "Epoch: [23/200], Validation Acc: 75.98\n",
      "Epoch: [24/200], Validation Acc: 75.96\n",
      "Epoch: [25/200], Validation Acc: 76.3\n",
      "Epoch: [26/200], Validation Acc: 76.2\n",
      "Epoch: [27/200], Validation Acc: 76.78\n",
      "Epoch: [28/200], Validation Acc: 77.06\n",
      "Epoch: [29/200], Validation Acc: 77.0\n",
      "Epoch: [30/200], Validation Acc: 77.4\n",
      "Epoch: [31/200], Validation Acc: 77.58\n",
      "Epoch: [32/200], Validation Acc: 77.68\n",
      "Epoch: [33/200], Validation Acc: 78.08\n",
      "Epoch: [34/200], Validation Acc: 77.98\n",
      "Epoch: [35/200], Validation Acc: 78.08\n",
      "Epoch: [36/200], Validation Acc: 78.78\n",
      "Epoch: [37/200], Validation Acc: 78.42\n",
      "Epoch: [38/200], Validation Acc: 78.92\n",
      "Epoch: [39/200], Validation Acc: 78.96\n",
      "Epoch: [40/200], Validation Acc: 79.22\n",
      "Epoch: [41/200], Validation Acc: 79.16\n",
      "Epoch: [42/200], Validation Acc: 79.32\n",
      "Epoch: [43/200], Validation Acc: 79.4\n",
      "Epoch: [44/200], Validation Acc: 79.4\n",
      "Epoch: [45/200], Validation Acc: 79.6\n",
      "Epoch: [46/200], Validation Acc: 79.7\n",
      "Epoch: [47/200], Validation Acc: 79.86\n",
      "Epoch: [48/200], Validation Acc: 79.58\n",
      "Epoch: [49/200], Validation Acc: 80.02\n",
      "Epoch: [50/200], Validation Acc: 80.08\n",
      "Epoch: [51/200], Validation Acc: 80.14\n",
      "Epoch: [52/200], Validation Acc: 80.12\n",
      "Epoch: [53/200], Validation Acc: 80.3\n",
      "Epoch: [54/200], Validation Acc: 80.42\n",
      "Epoch: [55/200], Validation Acc: 80.52\n",
      "Epoch: [56/200], Validation Acc: 80.56\n",
      "Epoch: [57/200], Validation Acc: 80.68\n",
      "Epoch: [58/200], Validation Acc: 80.84\n",
      "Epoch: [59/200], Validation Acc: 80.76\n",
      "Epoch: [60/200], Validation Acc: 81.22\n",
      "Epoch: [61/200], Validation Acc: 81.04\n",
      "Epoch: [62/200], Validation Acc: 81.24\n",
      "Epoch: [63/200], Validation Acc: 81.28\n",
      "Epoch: [64/200], Validation Acc: 81.36\n",
      "Epoch: [65/200], Validation Acc: 81.52\n",
      "Epoch: [66/200], Validation Acc: 81.62\n",
      "Epoch: [67/200], Validation Acc: 81.76\n",
      "Epoch: [68/200], Validation Acc: 81.7\n",
      "Epoch: [69/200], Validation Acc: 81.78\n",
      "Epoch: [70/200], Validation Acc: 81.8\n",
      "Epoch: [71/200], Validation Acc: 81.86\n",
      "Epoch: [72/200], Validation Acc: 82.04\n",
      "Epoch: [73/200], Validation Acc: 82.12\n",
      "Epoch: [74/200], Validation Acc: 81.94\n",
      "Epoch: [75/200], Validation Acc: 82.34\n",
      "Epoch: [76/200], Validation Acc: 82.26\n",
      "Epoch: [77/200], Validation Acc: 82.38\n",
      "Epoch: [78/200], Validation Acc: 82.36\n",
      "Epoch: [79/200], Validation Acc: 82.4\n",
      "Epoch: [80/200], Validation Acc: 82.56\n",
      "Epoch: [81/200], Validation Acc: 82.44\n",
      "Epoch: [82/200], Validation Acc: 82.74\n",
      "Epoch: [83/200], Validation Acc: 82.86\n",
      "Epoch: [84/200], Validation Acc: 82.86\n",
      "Epoch: [85/200], Validation Acc: 82.92\n",
      "Epoch: [86/200], Validation Acc: 82.84\n",
      "Epoch: [87/200], Validation Acc: 82.64\n",
      "Epoch: [88/200], Validation Acc: 82.8\n",
      "Epoch: [89/200], Validation Acc: 83.02\n",
      "Epoch: [90/200], Validation Acc: 83.08\n",
      "Epoch: [91/200], Validation Acc: 83.02\n",
      "Epoch: [92/200], Validation Acc: 83.1\n",
      "Epoch: [93/200], Validation Acc: 82.92\n",
      "Epoch: [94/200], Validation Acc: 83.12\n",
      "Epoch: [95/200], Validation Acc: 83.02\n",
      "Epoch: [96/200], Validation Acc: 82.98\n",
      "Epoch: [97/200], Validation Acc: 82.92\n",
      "Epoch: [98/200], Validation Acc: 83.18\n",
      "Epoch: [99/200], Validation Acc: 83.14\n",
      "Epoch: [100/200], Validation Acc: 83.16\n",
      "Epoch: [101/200], Validation Acc: 83.18\n",
      "Epoch: [102/200], Validation Acc: 83.3\n",
      "Epoch: [103/200], Validation Acc: 83.28\n",
      "Epoch: [104/200], Validation Acc: 83.2\n",
      "Epoch: [105/200], Validation Acc: 83.16\n",
      "Epoch: [106/200], Validation Acc: 83.24\n",
      "Epoch: [107/200], Validation Acc: 83.26\n",
      "Epoch: [108/200], Validation Acc: 83.3\n",
      "Epoch: [109/200], Validation Acc: 83.32\n",
      "Epoch: [110/200], Validation Acc: 83.3\n",
      "Epoch: [111/200], Validation Acc: 83.38\n",
      "Epoch: [112/200], Validation Acc: 83.44\n",
      "Epoch: [113/200], Validation Acc: 83.32\n",
      "Epoch: [114/200], Validation Acc: 83.42\n",
      "Epoch: [115/200], Validation Acc: 83.48\n",
      "Epoch: [116/200], Validation Acc: 83.46\n",
      "Epoch: [117/200], Validation Acc: 83.5\n",
      "Epoch: [118/200], Validation Acc: 83.54\n",
      "Epoch: [119/200], Validation Acc: 83.6\n",
      "Epoch: [120/200], Validation Acc: 83.64\n",
      "Epoch: [121/200], Validation Acc: 83.74\n",
      "Epoch: [122/200], Validation Acc: 83.58\n",
      "Epoch: [123/200], Validation Acc: 83.72\n",
      "Epoch: [124/200], Validation Acc: 83.76\n",
      "Epoch: [125/200], Validation Acc: 83.66\n",
      "Epoch: [126/200], Validation Acc: 83.72\n",
      "Epoch: [127/200], Validation Acc: 83.7\n",
      "Epoch: [128/200], Validation Acc: 83.78\n",
      "Epoch: [129/200], Validation Acc: 83.78\n",
      "Epoch: [130/200], Validation Acc: 83.8\n",
      "Epoch: [131/200], Validation Acc: 83.72\n",
      "Epoch: [132/200], Validation Acc: 83.78\n",
      "Epoch: [133/200], Validation Acc: 83.76\n",
      "Epoch: [134/200], Validation Acc: 83.78\n",
      "Epoch: [135/200], Validation Acc: 83.86\n",
      "Epoch: [136/200], Validation Acc: 83.88\n",
      "Epoch: [137/200], Validation Acc: 83.94\n",
      "Epoch: [138/200], Validation Acc: 83.92\n",
      "Epoch: [139/200], Validation Acc: 83.92\n",
      "Epoch: [140/200], Validation Acc: 83.94\n",
      "Epoch: [141/200], Validation Acc: 83.86\n",
      "Epoch: [142/200], Validation Acc: 83.96\n",
      "Epoch: [143/200], Validation Acc: 83.96\n",
      "Epoch: [144/200], Validation Acc: 83.86\n",
      "Epoch: [145/200], Validation Acc: 83.9\n",
      "Epoch: [146/200], Validation Acc: 83.94\n",
      "Epoch: [147/200], Validation Acc: 83.98\n",
      "Epoch: [148/200], Validation Acc: 84.0\n",
      "Epoch: [149/200], Validation Acc: 84.06\n",
      "Epoch: [150/200], Validation Acc: 84.08\n",
      "Epoch: [151/200], Validation Acc: 84.16\n",
      "Epoch: [152/200], Validation Acc: 84.2\n",
      "Epoch: [153/200], Validation Acc: 84.1\n",
      "Epoch: [154/200], Validation Acc: 84.24\n",
      "Epoch: [155/200], Validation Acc: 84.06\n",
      "Epoch: [156/200], Validation Acc: 84.12\n",
      "Epoch: [157/200], Validation Acc: 84.2\n",
      "Epoch: [158/200], Validation Acc: 84.12\n",
      "Epoch: [159/200], Validation Acc: 84.08\n",
      "Epoch: [160/200], Validation Acc: 84.14\n",
      "Epoch: [161/200], Validation Acc: 84.34\n",
      "Epoch: [162/200], Validation Acc: 84.16\n",
      "Epoch: [163/200], Validation Acc: 84.34\n",
      "Epoch: [164/200], Validation Acc: 84.2\n",
      "Epoch: [165/200], Validation Acc: 84.34\n",
      "Epoch: [166/200], Validation Acc: 84.28\n",
      "Epoch: [167/200], Validation Acc: 84.34\n",
      "Epoch: [168/200], Validation Acc: 84.26\n",
      "Epoch: [169/200], Validation Acc: 84.4\n",
      "Epoch: [170/200], Validation Acc: 84.3\n",
      "Epoch: [171/200], Validation Acc: 84.3\n",
      "Epoch: [172/200], Validation Acc: 84.3\n",
      "Epoch: [173/200], Validation Acc: 84.32\n",
      "Epoch: [174/200], Validation Acc: 84.42\n",
      "Epoch: [175/200], Validation Acc: 84.32\n",
      "Epoch: [176/200], Validation Acc: 84.38\n",
      "Epoch: [177/200], Validation Acc: 84.48\n",
      "Epoch: [178/200], Validation Acc: 84.4\n",
      "Epoch: [179/200], Validation Acc: 84.48\n",
      "Epoch: [180/200], Validation Acc: 84.5\n",
      "Epoch: [181/200], Validation Acc: 84.36\n",
      "Epoch: [182/200], Validation Acc: 84.3\n",
      "Epoch: [183/200], Validation Acc: 84.38\n",
      "Epoch: [184/200], Validation Acc: 84.48\n",
      "Epoch: [185/200], Validation Acc: 84.4\n",
      "Epoch: [186/200], Validation Acc: 84.46\n",
      "Epoch: [187/200], Validation Acc: 84.4\n",
      "Epoch: [188/200], Validation Acc: 84.5\n",
      "Epoch: [189/200], Validation Acc: 84.46\n",
      "Epoch: [190/200], Validation Acc: 84.42\n",
      "Epoch: [191/200], Validation Acc: 84.46\n",
      "Epoch: [192/200], Validation Acc: 84.56\n",
      "Epoch: [193/200], Validation Acc: 84.52\n",
      "Epoch: [194/200], Validation Acc: 84.54\n",
      "Epoch: [195/200], Validation Acc: 84.5\n",
      "Epoch: [196/200], Validation Acc: 84.52\n",
      "Epoch: [197/200], Validation Acc: 84.6\n",
      "Epoch: [198/200], Validation Acc: 84.62\n",
      "Epoch: [199/200], Validation Acc: 84.56\n",
      "Epoch: [200/200], Validation Acc: 84.56\n",
      "Epoch: [1/200], Validation Acc: 67.34\n",
      "Epoch: [2/200], Validation Acc: 69.66\n",
      "Epoch: [3/200], Validation Acc: 73.16\n",
      "Epoch: [4/200], Validation Acc: 74.9\n",
      "Epoch: [5/200], Validation Acc: 76.42\n",
      "Epoch: [6/200], Validation Acc: 78.06\n",
      "Epoch: [7/200], Validation Acc: 78.4\n",
      "Epoch: [8/200], Validation Acc: 79.58\n",
      "Epoch: [9/200], Validation Acc: 79.92\n",
      "Epoch: [10/200], Validation Acc: 80.68\n",
      "Epoch: [11/200], Validation Acc: 80.98\n",
      "Epoch: [12/200], Validation Acc: 81.36\n",
      "Epoch: [13/200], Validation Acc: 81.62\n",
      "Epoch: [14/200], Validation Acc: 82.06\n",
      "Epoch: [15/200], Validation Acc: 81.98\n",
      "Epoch: [16/200], Validation Acc: 82.42\n",
      "Epoch: [17/200], Validation Acc: 82.56\n",
      "Epoch: [18/200], Validation Acc: 82.86\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: [19/200], Validation Acc: 83.06\n",
      "Epoch: [20/200], Validation Acc: 83.18\n",
      "Epoch: [21/200], Validation Acc: 83.36\n",
      "Epoch: [22/200], Validation Acc: 83.46\n",
      "Epoch: [23/200], Validation Acc: 83.5\n",
      "Epoch: [24/200], Validation Acc: 83.5\n",
      "Epoch: [25/200], Validation Acc: 83.54\n",
      "Epoch: [26/200], Validation Acc: 83.6\n",
      "Epoch: [27/200], Validation Acc: 84.04\n",
      "Epoch: [28/200], Validation Acc: 83.62\n",
      "Epoch: [29/200], Validation Acc: 84.1\n",
      "Epoch: [30/200], Validation Acc: 83.94\n",
      "Epoch: [31/200], Validation Acc: 84.38\n",
      "Epoch: [32/200], Validation Acc: 84.36\n",
      "Epoch: [33/200], Validation Acc: 84.16\n",
      "Epoch: [34/200], Validation Acc: 84.38\n",
      "Epoch: [35/200], Validation Acc: 84.24\n",
      "Epoch: [36/200], Validation Acc: 84.3\n",
      "Epoch: [37/200], Validation Acc: 84.4\n",
      "Epoch: [38/200], Validation Acc: 84.46\n",
      "Epoch: [39/200], Validation Acc: 84.64\n",
      "Epoch: [40/200], Validation Acc: 84.78\n",
      "Epoch: [41/200], Validation Acc: 84.7\n",
      "Epoch: [42/200], Validation Acc: 84.64\n",
      "Epoch: [43/200], Validation Acc: 84.8\n",
      "Epoch: [44/200], Validation Acc: 84.78\n",
      "Epoch: [45/200], Validation Acc: 84.92\n",
      "Epoch: [46/200], Validation Acc: 84.9\n",
      "Epoch: [47/200], Validation Acc: 84.9\n",
      "Epoch: [48/200], Validation Acc: 84.84\n",
      "Epoch: [49/200], Validation Acc: 84.94\n",
      "Epoch: [50/200], Validation Acc: 84.9\n",
      "Epoch: [51/200], Validation Acc: 84.7\n",
      "Epoch: [52/200], Validation Acc: 84.84\n",
      "Epoch: [53/200], Validation Acc: 85.04\n",
      "Epoch: [54/200], Validation Acc: 84.98\n",
      "Epoch: [55/200], Validation Acc: 84.84\n",
      "Epoch: [56/200], Validation Acc: 85.0\n",
      "Epoch: [57/200], Validation Acc: 85.12\n",
      "Epoch: [58/200], Validation Acc: 85.1\n",
      "Epoch: [59/200], Validation Acc: 84.98\n",
      "Epoch: [60/200], Validation Acc: 85.22\n",
      "Epoch: [61/200], Validation Acc: 85.16\n",
      "Epoch: [62/200], Validation Acc: 85.3\n",
      "Epoch: [63/200], Validation Acc: 85.1\n",
      "Epoch: [64/200], Validation Acc: 85.28\n",
      "Epoch: [65/200], Validation Acc: 84.9\n",
      "Epoch: [66/200], Validation Acc: 85.26\n",
      "Epoch: [67/200], Validation Acc: 85.46\n",
      "Epoch: [68/200], Validation Acc: 85.44\n",
      "Epoch: [69/200], Validation Acc: 85.36\n",
      "Epoch: [70/200], Validation Acc: 85.46\n",
      "Epoch: [71/200], Validation Acc: 85.38\n",
      "Epoch: [72/200], Validation Acc: 85.12\n",
      "Epoch: [73/200], Validation Acc: 85.3\n",
      "Epoch: [74/200], Validation Acc: 85.36\n",
      "Epoch: [75/200], Validation Acc: 85.2\n",
      "Epoch: [76/200], Validation Acc: 85.42\n",
      "Epoch: [77/200], Validation Acc: 85.44\n",
      "Epoch: [78/200], Validation Acc: 85.34\n",
      "Epoch: [79/200], Validation Acc: 85.26\n",
      "Epoch: [80/200], Validation Acc: 85.28\n",
      "Epoch: [81/200], Validation Acc: 85.16\n",
      "Epoch: [82/200], Validation Acc: 85.16\n",
      "Epoch: [83/200], Validation Acc: 85.28\n",
      "Epoch: [84/200], Validation Acc: 85.02\n",
      "Epoch: [85/200], Validation Acc: 85.16\n",
      "Epoch: [86/200], Validation Acc: 85.1\n",
      "Epoch: [87/200], Validation Acc: 85.16\n",
      "Epoch: [88/200], Validation Acc: 85.1\n",
      "Epoch: [89/200], Validation Acc: 85.02\n",
      "Epoch: [90/200], Validation Acc: 84.9\n",
      "Epoch: [91/200], Validation Acc: 84.9\n",
      "Epoch: [92/200], Validation Acc: 84.88\n",
      "Epoch: [93/200], Validation Acc: 85.08\n",
      "Epoch: [94/200], Validation Acc: 84.9\n",
      "Epoch: [95/200], Validation Acc: 84.94\n",
      "Epoch: [96/200], Validation Acc: 84.92\n",
      "Epoch: [97/200], Validation Acc: 84.98\n",
      "Epoch: [98/200], Validation Acc: 84.94\n",
      "Epoch: [99/200], Validation Acc: 84.78\n",
      "Epoch: [100/200], Validation Acc: 84.86\n",
      "Epoch: [101/200], Validation Acc: 84.84\n",
      "Epoch: [102/200], Validation Acc: 85.04\n",
      "Epoch: [103/200], Validation Acc: 84.68\n",
      "Epoch: [104/200], Validation Acc: 84.88\n",
      "Epoch: [105/200], Validation Acc: 84.96\n",
      "Epoch: [106/200], Validation Acc: 84.9\n",
      "Epoch: [107/200], Validation Acc: 84.8\n",
      "Epoch: [108/200], Validation Acc: 84.7\n",
      "Epoch: [109/200], Validation Acc: 84.8\n",
      "Epoch: [110/200], Validation Acc: 84.8\n",
      "Epoch: [111/200], Validation Acc: 84.86\n",
      "Epoch: [112/200], Validation Acc: 84.74\n",
      "Epoch: [113/200], Validation Acc: 84.72\n",
      "Epoch: [114/200], Validation Acc: 84.66\n",
      "Epoch: [115/200], Validation Acc: 84.7\n",
      "Epoch: [116/200], Validation Acc: 84.62\n",
      "Epoch: [117/200], Validation Acc: 84.66\n",
      "Epoch: [118/200], Validation Acc: 84.64\n",
      "Epoch: [119/200], Validation Acc: 84.62\n",
      "Epoch: [120/200], Validation Acc: 84.66\n",
      "Epoch: [121/200], Validation Acc: 84.48\n",
      "Epoch: [122/200], Validation Acc: 84.64\n",
      "Epoch: [123/200], Validation Acc: 84.64\n",
      "Epoch: [124/200], Validation Acc: 84.58\n",
      "Epoch: [125/200], Validation Acc: 84.58\n",
      "Epoch: [126/200], Validation Acc: 84.58\n",
      "Epoch: [127/200], Validation Acc: 84.54\n",
      "Epoch: [128/200], Validation Acc: 84.62\n",
      "Epoch: [129/200], Validation Acc: 84.52\n",
      "Epoch: [130/200], Validation Acc: 84.6\n",
      "Epoch: [131/200], Validation Acc: 84.5\n",
      "Epoch: [132/200], Validation Acc: 84.46\n",
      "Epoch: [133/200], Validation Acc: 84.54\n",
      "Epoch: [134/200], Validation Acc: 84.46\n",
      "Epoch: [135/200], Validation Acc: 84.64\n",
      "Epoch: [136/200], Validation Acc: 84.54\n",
      "Epoch: [137/200], Validation Acc: 84.62\n",
      "Epoch: [138/200], Validation Acc: 84.68\n",
      "Epoch: [139/200], Validation Acc: 84.7\n",
      "Epoch: [140/200], Validation Acc: 84.6\n",
      "Epoch: [141/200], Validation Acc: 84.7\n",
      "Epoch: [142/200], Validation Acc: 84.66\n",
      "Epoch: [143/200], Validation Acc: 84.78\n",
      "Epoch: [144/200], Validation Acc: 84.7\n",
      "Epoch: [145/200], Validation Acc: 84.68\n",
      "Epoch: [146/200], Validation Acc: 84.66\n",
      "Epoch: [147/200], Validation Acc: 84.66\n",
      "Epoch: [148/200], Validation Acc: 84.66\n",
      "Epoch: [149/200], Validation Acc: 84.64\n",
      "Epoch: [150/200], Validation Acc: 84.74\n",
      "Epoch: [151/200], Validation Acc: 84.66\n",
      "Epoch: [152/200], Validation Acc: 84.68\n",
      "Epoch: [153/200], Validation Acc: 84.68\n",
      "Epoch: [154/200], Validation Acc: 84.64\n",
      "Epoch: [155/200], Validation Acc: 84.72\n",
      "Epoch: [156/200], Validation Acc: 84.7\n",
      "Epoch: [157/200], Validation Acc: 84.68\n",
      "Epoch: [158/200], Validation Acc: 84.7\n",
      "Epoch: [159/200], Validation Acc: 84.68\n",
      "Epoch: [160/200], Validation Acc: 84.7\n",
      "Epoch: [161/200], Validation Acc: 84.74\n",
      "Epoch: [162/200], Validation Acc: 84.72\n",
      "Epoch: [163/200], Validation Acc: 84.76\n",
      "Epoch: [164/200], Validation Acc: 84.72\n",
      "Epoch: [165/200], Validation Acc: 84.74\n",
      "Epoch: [166/200], Validation Acc: 84.8\n",
      "Epoch: [167/200], Validation Acc: 84.76\n",
      "Epoch: [168/200], Validation Acc: 84.7\n",
      "Epoch: [169/200], Validation Acc: 84.72\n",
      "Epoch: [170/200], Validation Acc: 84.72\n",
      "Epoch: [171/200], Validation Acc: 84.78\n",
      "Epoch: [172/200], Validation Acc: 84.78\n",
      "Epoch: [173/200], Validation Acc: 84.82\n",
      "Epoch: [174/200], Validation Acc: 84.8\n",
      "Epoch: [175/200], Validation Acc: 84.76\n",
      "Epoch: [176/200], Validation Acc: 84.84\n",
      "Epoch: [177/200], Validation Acc: 84.82\n",
      "Epoch: [178/200], Validation Acc: 84.84\n",
      "Epoch: [179/200], Validation Acc: 84.86\n",
      "Epoch: [180/200], Validation Acc: 84.78\n",
      "Epoch: [181/200], Validation Acc: 84.84\n",
      "Epoch: [182/200], Validation Acc: 84.86\n",
      "Epoch: [183/200], Validation Acc: 84.86\n",
      "Epoch: [184/200], Validation Acc: 84.88\n",
      "Epoch: [185/200], Validation Acc: 84.88\n",
      "Epoch: [186/200], Validation Acc: 84.92\n",
      "Epoch: [187/200], Validation Acc: 84.92\n",
      "Epoch: [188/200], Validation Acc: 84.88\n",
      "Epoch: [189/200], Validation Acc: 84.86\n",
      "Epoch: [190/200], Validation Acc: 84.9\n",
      "Epoch: [191/200], Validation Acc: 84.86\n",
      "Epoch: [192/200], Validation Acc: 84.9\n",
      "Epoch: [193/200], Validation Acc: 85.0\n",
      "Epoch: [194/200], Validation Acc: 84.94\n",
      "Epoch: [195/200], Validation Acc: 84.9\n",
      "Epoch: [196/200], Validation Acc: 84.94\n",
      "Epoch: [197/200], Validation Acc: 84.9\n",
      "Epoch: [198/200], Validation Acc: 84.96\n",
      "Epoch: [199/200], Validation Acc: 84.84\n",
      "Epoch: [200/200], Validation Acc: 84.96\n"
     ]
    }
   ],
   "source": [
    "learning_rates = [0.05, 0.1, 0.5]\n",
    "train_loss_sgd_lrs = []\n",
    "val_acc_sgd_lrs = []\n",
    "for rate in learning_rates:\n",
    "    train_loss_sgd_lr, val_acc_sgd_lr, _ = train_model(vocab_size = len(id2token), num_epochs = 200, \n",
    "                                                optim = 'SGD', learning_rate = rate)\n",
    "    train_loss_sgd_lrs.append(train_loss_sgd_lr)\n",
    "    val_acc_sgd_lrs.append(val_acc_sgd_lr)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### annealing learning rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: [1/100], Validation Acc: 70.24\n",
      "Epoch: [2/100], Validation Acc: 72.04\n",
      "Epoch: [3/100], Validation Acc: 74.22\n",
      "Epoch: [4/100], Validation Acc: 76.06\n",
      "Epoch: [5/100], Validation Acc: 77.08\n",
      "Epoch: [6/100], Validation Acc: 78.12\n",
      "Epoch: [7/100], Validation Acc: 78.98\n",
      "Epoch: [8/100], Validation Acc: 79.76\n",
      "Epoch: [9/100], Validation Acc: 80.12\n",
      "Epoch: [10/100], Validation Acc: 80.5\n",
      "Epoch: [11/100], Validation Acc: 80.92\n",
      "Epoch: [12/100], Validation Acc: 81.34\n",
      "Epoch: [13/100], Validation Acc: 81.96\n",
      "Epoch: [14/100], Validation Acc: 82.42\n",
      "Epoch: [15/100], Validation Acc: 82.44\n",
      "Epoch: [16/100], Validation Acc: 82.8\n",
      "Epoch: [17/100], Validation Acc: 82.86\n",
      "Epoch: [18/100], Validation Acc: 83.22\n",
      "Epoch: [19/100], Validation Acc: 83.48\n",
      "Epoch: [20/100], Validation Acc: 83.7\n",
      "Epoch: [21/100], Validation Acc: 83.82\n",
      "Epoch: [22/100], Validation Acc: 83.64\n",
      "Epoch: [23/100], Validation Acc: 83.78\n",
      "Epoch: [24/100], Validation Acc: 84.1\n",
      "Epoch: [25/100], Validation Acc: 83.92\n",
      "Epoch: [26/100], Validation Acc: 84.1\n",
      "Epoch: [27/100], Validation Acc: 84.02\n",
      "Epoch: [28/100], Validation Acc: 84.1\n",
      "Epoch: [29/100], Validation Acc: 84.02\n",
      "Epoch: [30/100], Validation Acc: 84.2\n",
      "Epoch: [31/100], Validation Acc: 84.18\n",
      "Epoch: [32/100], Validation Acc: 84.08\n",
      "Epoch: [33/100], Validation Acc: 84.02\n",
      "Epoch: [34/100], Validation Acc: 84.06\n",
      "Epoch: [35/100], Validation Acc: 84.12\n",
      "Epoch: [36/100], Validation Acc: 84.12\n",
      "Epoch: [37/100], Validation Acc: 84.08\n",
      "Epoch: [38/100], Validation Acc: 84.12\n",
      "Epoch: [39/100], Validation Acc: 83.98\n",
      "Epoch: [40/100], Validation Acc: 84.04\n",
      "Epoch: [41/100], Validation Acc: 84.04\n",
      "Epoch: [42/100], Validation Acc: 84.2\n",
      "Epoch: [43/100], Validation Acc: 84.1\n",
      "Epoch: [44/100], Validation Acc: 84.16\n",
      "Epoch: [45/100], Validation Acc: 84.16\n",
      "Epoch: [46/100], Validation Acc: 84.22\n",
      "Epoch: [47/100], Validation Acc: 84.08\n",
      "Epoch: [48/100], Validation Acc: 84.18\n",
      "Epoch: [49/100], Validation Acc: 84.26\n",
      "Epoch: [50/100], Validation Acc: 84.3\n",
      "Epoch: [51/100], Validation Acc: 84.22\n",
      "Epoch: [52/100], Validation Acc: 84.24\n",
      "Epoch: [53/100], Validation Acc: 84.22\n",
      "Epoch: [54/100], Validation Acc: 84.2\n",
      "Epoch: [55/100], Validation Acc: 84.22\n",
      "Epoch: [56/100], Validation Acc: 84.24\n",
      "Epoch: [57/100], Validation Acc: 84.26\n",
      "Epoch: [58/100], Validation Acc: 84.22\n",
      "Epoch: [59/100], Validation Acc: 84.26\n",
      "Epoch: [60/100], Validation Acc: 84.2\n",
      "Epoch: [61/100], Validation Acc: 84.18\n",
      "Epoch: [62/100], Validation Acc: 84.24\n",
      "Epoch: [63/100], Validation Acc: 84.22\n",
      "Epoch: [64/100], Validation Acc: 84.26\n",
      "Epoch: [65/100], Validation Acc: 84.16\n",
      "Epoch: [66/100], Validation Acc: 84.18\n",
      "Epoch: [67/100], Validation Acc: 84.14\n",
      "Epoch: [68/100], Validation Acc: 84.22\n",
      "Epoch: [69/100], Validation Acc: 84.18\n",
      "Epoch: [70/100], Validation Acc: 84.16\n",
      "Epoch: [71/100], Validation Acc: 84.2\n",
      "Epoch: [72/100], Validation Acc: 84.18\n",
      "Epoch: [73/100], Validation Acc: 84.22\n",
      "Epoch: [74/100], Validation Acc: 84.22\n",
      "Epoch: [75/100], Validation Acc: 84.2\n",
      "Epoch: [76/100], Validation Acc: 84.2\n",
      "Epoch: [77/100], Validation Acc: 84.2\n",
      "Epoch: [78/100], Validation Acc: 84.2\n",
      "Epoch: [79/100], Validation Acc: 84.18\n",
      "Epoch: [80/100], Validation Acc: 84.2\n",
      "Epoch: [81/100], Validation Acc: 84.2\n",
      "Epoch: [82/100], Validation Acc: 84.18\n",
      "Epoch: [83/100], Validation Acc: 84.2\n",
      "Epoch: [84/100], Validation Acc: 84.16\n",
      "Epoch: [85/100], Validation Acc: 84.2\n",
      "Epoch: [86/100], Validation Acc: 84.22\n",
      "Epoch: [87/100], Validation Acc: 84.2\n",
      "Epoch: [88/100], Validation Acc: 84.2\n",
      "Epoch: [89/100], Validation Acc: 84.22\n",
      "Epoch: [90/100], Validation Acc: 84.2\n",
      "Epoch: [91/100], Validation Acc: 84.24\n",
      "Epoch: [92/100], Validation Acc: 84.2\n",
      "Epoch: [93/100], Validation Acc: 84.22\n",
      "Epoch: [94/100], Validation Acc: 84.2\n",
      "Epoch: [95/100], Validation Acc: 84.24\n",
      "Epoch: [96/100], Validation Acc: 84.22\n",
      "Epoch: [97/100], Validation Acc: 84.22\n",
      "Epoch: [98/100], Validation Acc: 84.2\n",
      "Epoch: [99/100], Validation Acc: 84.22\n",
      "Epoch: [100/100], Validation Acc: 84.2\n"
     ]
    }
   ],
   "source": [
    "train_loss_sgd_lr, val_acc_sgd_lr, _ = train_model(vocab_size = len(id2token), num_epochs = 100, \n",
    "                                                optim = 'SGD', learning_rate = 0.5, annealing_rate=0.2, step=25)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'SGD')"
      ]
     },
     "execution_count": 412,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rates = [0.05, 0.1, 0.5]\n",
    "\n",
    "plt.figure()\n",
    "for i in range(len(learning_rates)):\n",
    "    plt.plot(range(200),train_loss_sgd_lrs[i], label='learning_rate {:g}'.format(learning_rates[i]))\n",
    "    \n",
    "plt.plot(range(100),train_loss_sgd_lr, label = 'learning rate decayed by 0.2 every 25 epochs')\n",
    "plt.legend()\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('training loss')\n",
    "plt.title('SGD')\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "for i in range(len(learning_rates)):\n",
    "    plt.plot(range(200),val_acc_sgd_lrs[i], label='learning_rate {:g}'.format(learning_rates[i]))\n",
    "\n",
    "plt.plot(range(100),val_acc_sgd_lr, label = 'learning rate decayed by 0.2 every 25 epochs')\n",
    "plt.legend()\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('validation accuracy')\n",
    "plt.title('SGD')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model hyperparameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### embedding size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: [1/30], Validation Acc: 87.96\n",
      "Epoch: [2/30], Validation Acc: 87.92\n",
      "Epoch: [3/30], Validation Acc: 87.16\n",
      "Epoch: [4/30], Validation Acc: 86.34\n",
      "Epoch: [5/30], Validation Acc: 86.1\n",
      "Epoch: [6/30], Validation Acc: 85.74\n",
      "Epoch: [7/30], Validation Acc: 85.48\n",
      "Epoch: [8/30], Validation Acc: 85.56\n",
      "Epoch: [9/30], Validation Acc: 85.34\n",
      "Epoch: [10/30], Validation Acc: 85.44\n",
      "Epoch: [11/30], Validation Acc: 84.96\n",
      "Epoch: [12/30], Validation Acc: 85.44\n",
      "Epoch: [13/30], Validation Acc: 85.18\n",
      "Epoch: [14/30], Validation Acc: 85.04\n",
      "Epoch: [15/30], Validation Acc: 84.92\n",
      "Epoch: [16/30], Validation Acc: 85.04\n",
      "Epoch: [17/30], Validation Acc: 85.16\n",
      "Epoch: [18/30], Validation Acc: 85.06\n",
      "Epoch: [19/30], Validation Acc: 85.06\n",
      "Epoch: [20/30], Validation Acc: 85.02\n",
      "Epoch: [21/30], Validation Acc: 85.12\n",
      "Epoch: [22/30], Validation Acc: 85.08\n",
      "Epoch: [23/30], Validation Acc: 85.22\n",
      "Epoch: [24/30], Validation Acc: 85.2\n",
      "Epoch: [25/30], Validation Acc: 85.2\n",
      "Epoch: [26/30], Validation Acc: 85.2\n",
      "Epoch: [27/30], Validation Acc: 85.26\n",
      "Epoch: [28/30], Validation Acc: 85.22\n",
      "Epoch: [29/30], Validation Acc: 85.28\n",
      "Epoch: [30/30], Validation Acc: 85.3\n",
      "Epoch: [1/30], Validation Acc: 88.1\n",
      "Epoch: [2/30], Validation Acc: 87.06\n",
      "Epoch: [3/30], Validation Acc: 87.06\n",
      "Epoch: [4/30], Validation Acc: 86.22\n",
      "Epoch: [5/30], Validation Acc: 85.66\n",
      "Epoch: [6/30], Validation Acc: 85.4\n",
      "Epoch: [7/30], Validation Acc: 85.12\n",
      "Epoch: [8/30], Validation Acc: 84.84\n",
      "Epoch: [9/30], Validation Acc: 84.72\n",
      "Epoch: [10/30], Validation Acc: 84.8\n",
      "Epoch: [11/30], Validation Acc: 85.4\n",
      "Epoch: [12/30], Validation Acc: 84.5\n",
      "Epoch: [13/30], Validation Acc: 85.4\n",
      "Epoch: [14/30], Validation Acc: 85.04\n",
      "Epoch: [15/30], Validation Acc: 84.86\n",
      "Epoch: [16/30], Validation Acc: 84.82\n",
      "Epoch: [17/30], Validation Acc: 84.78\n",
      "Epoch: [18/30], Validation Acc: 84.58\n",
      "Epoch: [19/30], Validation Acc: 84.66\n",
      "Epoch: [20/30], Validation Acc: 85.02\n",
      "Epoch: [21/30], Validation Acc: 84.7\n",
      "Epoch: [22/30], Validation Acc: 84.62\n",
      "Epoch: [23/30], Validation Acc: 84.86\n",
      "Epoch: [24/30], Validation Acc: 85.14\n",
      "Epoch: [25/30], Validation Acc: 84.72\n",
      "Epoch: [26/30], Validation Acc: 85.24\n",
      "Epoch: [27/30], Validation Acc: 85.0\n",
      "Epoch: [28/30], Validation Acc: 84.96\n",
      "Epoch: [29/30], Validation Acc: 85.0\n",
      "Epoch: [30/30], Validation Acc: 85.0\n",
      "Epoch: [1/30], Validation Acc: 88.08\n",
      "Epoch: [2/30], Validation Acc: 87.98\n",
      "Epoch: [3/30], Validation Acc: 86.98\n",
      "Epoch: [4/30], Validation Acc: 86.04\n",
      "Epoch: [5/30], Validation Acc: 85.42\n",
      "Epoch: [6/30], Validation Acc: 85.82\n",
      "Epoch: [7/30], Validation Acc: 85.04\n",
      "Epoch: [8/30], Validation Acc: 85.12\n",
      "Epoch: [9/30], Validation Acc: 84.88\n",
      "Epoch: [10/30], Validation Acc: 85.02\n",
      "Epoch: [11/30], Validation Acc: 84.54\n",
      "Epoch: [12/30], Validation Acc: 84.74\n",
      "Epoch: [13/30], Validation Acc: 84.16\n",
      "Epoch: [14/30], Validation Acc: 84.66\n",
      "Epoch: [15/30], Validation Acc: 84.56\n",
      "Epoch: [16/30], Validation Acc: 85.02\n",
      "Epoch: [17/30], Validation Acc: 85.14\n",
      "Epoch: [18/30], Validation Acc: 84.88\n",
      "Epoch: [19/30], Validation Acc: 84.64\n",
      "Epoch: [20/30], Validation Acc: 84.72\n",
      "Epoch: [21/30], Validation Acc: 84.66\n",
      "Epoch: [22/30], Validation Acc: 84.64\n",
      "Epoch: [23/30], Validation Acc: 84.74\n",
      "Epoch: [24/30], Validation Acc: 84.98\n",
      "Epoch: [25/30], Validation Acc: 85.06\n",
      "Epoch: [26/30], Validation Acc: 84.6\n",
      "Epoch: [27/30], Validation Acc: 84.44\n",
      "Epoch: [28/30], Validation Acc: 85.0\n",
      "Epoch: [29/30], Validation Acc: 84.94\n",
      "Epoch: [30/30], Validation Acc: 84.36\n",
      "Epoch: [1/30], Validation Acc: 88.02\n",
      "Epoch: [2/30], Validation Acc: 87.58\n",
      "Epoch: [3/30], Validation Acc: 85.88\n",
      "Epoch: [4/30], Validation Acc: 86.0\n",
      "Epoch: [5/30], Validation Acc: 85.76\n",
      "Epoch: [6/30], Validation Acc: 85.26\n",
      "Epoch: [7/30], Validation Acc: 85.22\n",
      "Epoch: [8/30], Validation Acc: 85.02\n",
      "Epoch: [9/30], Validation Acc: 84.54\n",
      "Epoch: [10/30], Validation Acc: 85.2\n",
      "Epoch: [11/30], Validation Acc: 84.88\n",
      "Epoch: [12/30], Validation Acc: 85.34\n",
      "Epoch: [13/30], Validation Acc: 85.3\n",
      "Epoch: [14/30], Validation Acc: 84.84\n",
      "Epoch: [15/30], Validation Acc: 84.84\n",
      "Epoch: [16/30], Validation Acc: 85.06\n",
      "Epoch: [17/30], Validation Acc: 84.92\n",
      "Epoch: [18/30], Validation Acc: 84.82\n",
      "Epoch: [19/30], Validation Acc: 85.0\n",
      "Epoch: [20/30], Validation Acc: 84.68\n",
      "Epoch: [21/30], Validation Acc: 85.4\n",
      "Epoch: [22/30], Validation Acc: 84.94\n",
      "Epoch: [23/30], Validation Acc: 85.2\n",
      "Epoch: [24/30], Validation Acc: 84.86\n",
      "Epoch: [25/30], Validation Acc: 84.64\n",
      "Epoch: [26/30], Validation Acc: 84.68\n",
      "Epoch: [27/30], Validation Acc: 85.0\n",
      "Epoch: [28/30], Validation Acc: 85.12\n",
      "Epoch: [29/30], Validation Acc: 85.02\n",
      "Epoch: [30/30], Validation Acc: 84.48\n"
     ]
    }
   ],
   "source": [
    "emb_dims = [50, 100, 200, 400]\n",
    "train_loss_embs = []\n",
    "val_acc_embs = []\n",
    "for emb_dim in emb_dims:\n",
    "    train_loss_emb, val_acc_emb, _ = train_model(emb_dim = emb_dim, num_epochs = 30, vocab_size = len(id2token))\n",
    "    train_loss_embs.append(train_loss_emb)\n",
    "    val_acc_embs.append(val_acc_emb)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "for i in range(len(train_loss_embs)):\n",
    "    plt.plot(range(30),train_loss_embs[i], label='emb_size %d' %emb_dims[i])\n",
    "    plt.legend()\n",
    "    plt.xlabel('epoch')\n",
    "    plt.ylabel('training loss')\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "for i in range(len(val_acc_embs)):\n",
    "    plt.plot(range(30),val_acc_embs[i], label='emb_size %d' %emb_dims[i])\n",
    "    plt.legend()\n",
    "    plt.xlabel('epoch')\n",
    "    plt.ylabel('validation accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After training for 30 epochs\n",
      "The training losses are 8.344650268554688e-06, 4.9173831939697266e-06, 0.0013913214206695557, 0.008597426116466522\n",
      "The validation accuracy are 85.3, 85.0, 84.36, 84.48\n",
      "The Maxmimum Val Acc is 85.3 with Embedding Size 50\n"
     ]
    }
   ],
   "source": [
    "print (\"After training for 30 epochs\")\n",
    "results = [element[-1] for element in val_acc_embs]\n",
    "max_val_acc = max(results)\n",
    "max_val_acc_ind = results.index(max_val_acc)\n",
    "print(\"The training losses are {}, {}, {}, {}\".format(train_loss_embs[0][-1], \n",
    "                            train_loss_embs[1][-1], train_loss_embs[2][-1], train_loss_embs[3][-1]))\n",
    "print(\"The validation accuracy are {}, {}, {}, {}\".format(val_acc_embs[0][-1], \n",
    "                            val_acc_embs[1][-1], val_acc_embs[2][-1], val_acc_embs[3][-1]))\n",
    "      \n",
    "print (\"The Maxmimum Val Acc is {} with Embedding Size {}\".format(max_val_acc, emb_dims[max_val_acc_ind]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### vocabulary size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 623,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: [1/30], Validation Acc: 87.76\n",
      "Epoch: [2/30], Validation Acc: 87.4\n",
      "Epoch: [3/30], Validation Acc: 86.86\n",
      "Epoch: [4/30], Validation Acc: 86.2\n",
      "Epoch: [5/30], Validation Acc: 85.64\n",
      "Epoch: [6/30], Validation Acc: 85.16\n",
      "Epoch: [7/30], Validation Acc: 85.46\n",
      "Epoch: [8/30], Validation Acc: 85.2\n",
      "Epoch: [9/30], Validation Acc: 84.84\n",
      "Epoch: [10/30], Validation Acc: 84.72\n",
      "Epoch: [11/30], Validation Acc: 85.2\n",
      "Epoch: [12/30], Validation Acc: 84.58\n",
      "Epoch: [13/30], Validation Acc: 84.24\n",
      "Epoch: [14/30], Validation Acc: 84.36\n",
      "Epoch: [15/30], Validation Acc: 84.6\n",
      "Epoch: [16/30], Validation Acc: 84.62\n",
      "Epoch: [17/30], Validation Acc: 84.64\n",
      "Epoch: [18/30], Validation Acc: 84.54\n",
      "Epoch: [19/30], Validation Acc: 84.48\n",
      "Epoch: [20/30], Validation Acc: 84.3\n",
      "Epoch: [21/30], Validation Acc: 84.52\n",
      "Epoch: [22/30], Validation Acc: 84.26\n",
      "Epoch: [23/30], Validation Acc: 84.4\n",
      "Epoch: [24/30], Validation Acc: 84.44\n",
      "Epoch: [25/30], Validation Acc: 84.16\n",
      "Epoch: [26/30], Validation Acc: 84.5\n",
      "Epoch: [27/30], Validation Acc: 84.26\n",
      "Epoch: [28/30], Validation Acc: 83.7\n",
      "Epoch: [29/30], Validation Acc: 84.2\n",
      "Epoch: [30/30], Validation Acc: 84.58\n",
      "Epoch: [1/30], Validation Acc: 88.3\n",
      "Epoch: [2/30], Validation Acc: 87.42\n",
      "Epoch: [3/30], Validation Acc: 87.38\n",
      "Epoch: [4/30], Validation Acc: 87.06\n",
      "Epoch: [5/30], Validation Acc: 86.88\n",
      "Epoch: [6/30], Validation Acc: 86.96\n",
      "Epoch: [7/30], Validation Acc: 87.1\n",
      "Epoch: [8/30], Validation Acc: 87.06\n",
      "Epoch: [9/30], Validation Acc: 87.06\n",
      "Epoch: [10/30], Validation Acc: 87.08\n",
      "Epoch: [11/30], Validation Acc: 87.04\n",
      "Epoch: [12/30], Validation Acc: 87.06\n",
      "Epoch: [13/30], Validation Acc: 87.1\n",
      "Epoch: [14/30], Validation Acc: 87.12\n",
      "Epoch: [15/30], Validation Acc: 87.1\n",
      "Epoch: [16/30], Validation Acc: 87.16\n",
      "Epoch: [17/30], Validation Acc: 87.12\n",
      "Epoch: [18/30], Validation Acc: 87.04\n",
      "Epoch: [19/30], Validation Acc: 87.04\n",
      "Epoch: [20/30], Validation Acc: 87.06\n",
      "Epoch: [21/30], Validation Acc: 87.1\n",
      "Epoch: [22/30], Validation Acc: 87.0\n",
      "Epoch: [23/30], Validation Acc: 87.12\n",
      "Epoch: [24/30], Validation Acc: 87.14\n",
      "Epoch: [25/30], Validation Acc: 87.18\n",
      "Epoch: [26/30], Validation Acc: 87.02\n",
      "Epoch: [27/30], Validation Acc: 87.06\n",
      "Epoch: [28/30], Validation Acc: 87.02\n",
      "Epoch: [29/30], Validation Acc: 87.08\n",
      "Epoch: [30/30], Validation Acc: 87.08\n",
      "Epoch: [1/30], Validation Acc: 88.12\n",
      "Epoch: [2/30], Validation Acc: 87.52\n",
      "Epoch: [3/30], Validation Acc: 86.2\n",
      "Epoch: [4/30], Validation Acc: 86.7\n",
      "Epoch: [5/30], Validation Acc: 86.52\n",
      "Epoch: [6/30], Validation Acc: 86.56\n",
      "Epoch: [7/30], Validation Acc: 86.64\n",
      "Epoch: [8/30], Validation Acc: 86.7\n",
      "Epoch: [9/30], Validation Acc: 86.74\n",
      "Epoch: [10/30], Validation Acc: 86.74\n",
      "Epoch: [11/30], Validation Acc: 86.86\n",
      "Epoch: [12/30], Validation Acc: 86.88\n",
      "Epoch: [13/30], Validation Acc: 86.86\n",
      "Epoch: [14/30], Validation Acc: 86.98\n",
      "Epoch: [15/30], Validation Acc: 86.88\n",
      "Epoch: [16/30], Validation Acc: 86.96\n",
      "Epoch: [17/30], Validation Acc: 86.96\n",
      "Epoch: [18/30], Validation Acc: 86.94\n",
      "Epoch: [19/30], Validation Acc: 86.96\n",
      "Epoch: [20/30], Validation Acc: 86.94\n",
      "Epoch: [21/30], Validation Acc: 86.86\n",
      "Epoch: [22/30], Validation Acc: 86.86\n",
      "Epoch: [23/30], Validation Acc: 86.92\n",
      "Epoch: [24/30], Validation Acc: 86.9\n",
      "Epoch: [25/30], Validation Acc: 86.84\n",
      "Epoch: [26/30], Validation Acc: 86.88\n",
      "Epoch: [27/30], Validation Acc: 86.9\n",
      "Epoch: [28/30], Validation Acc: 86.9\n",
      "Epoch: [29/30], Validation Acc: 86.88\n",
      "Epoch: [30/30], Validation Acc: 86.9\n",
      "Epoch: [1/30], Validation Acc: 88.1\n",
      "Epoch: [2/30], Validation Acc: 87.56\n",
      "Epoch: [3/30], Validation Acc: 86.86\n",
      "Epoch: [4/30], Validation Acc: 86.9\n",
      "Epoch: [5/30], Validation Acc: 87.16\n",
      "Epoch: [6/30], Validation Acc: 87.36\n",
      "Epoch: [7/30], Validation Acc: 87.34\n",
      "Epoch: [8/30], Validation Acc: 87.3\n",
      "Epoch: [9/30], Validation Acc: 87.34\n",
      "Epoch: [10/30], Validation Acc: 87.42\n",
      "Epoch: [11/30], Validation Acc: 87.34\n",
      "Epoch: [12/30], Validation Acc: 87.32\n",
      "Epoch: [13/30], Validation Acc: 87.26\n",
      "Epoch: [14/30], Validation Acc: 87.22\n",
      "Epoch: [15/30], Validation Acc: 87.16\n",
      "Epoch: [16/30], Validation Acc: 87.16\n",
      "Epoch: [17/30], Validation Acc: 87.14\n",
      "Epoch: [18/30], Validation Acc: 87.04\n",
      "Epoch: [19/30], Validation Acc: 87.04\n",
      "Epoch: [20/30], Validation Acc: 86.96\n",
      "Epoch: [21/30], Validation Acc: 86.9\n",
      "Epoch: [22/30], Validation Acc: 86.92\n",
      "Epoch: [23/30], Validation Acc: 86.86\n",
      "Epoch: [24/30], Validation Acc: 86.84\n",
      "Epoch: [25/30], Validation Acc: 86.9\n",
      "Epoch: [26/30], Validation Acc: 86.88\n",
      "Epoch: [27/30], Validation Acc: 86.9\n",
      "Epoch: [28/30], Validation Acc: 86.76\n",
      "Epoch: [29/30], Validation Acc: 86.78\n",
      "Epoch: [30/30], Validation Acc: 86.78\n"
     ]
    }
   ],
   "source": [
    "max_vocab_sizes = [10000, 50000, 100000, 200000]\n",
    "train_loss_vocabs = []\n",
    "val_acc_vocabs = []\n",
    "for size in max_vocab_sizes:\n",
    "    token2id, id2token = build_vocab(all_train_tokens, max_vocab_size = size)\n",
    "    train_data_indices = token2index_dataset(train_data_tokens)\n",
    "    val_data_indices = token2index_dataset(val_data_tokens)\n",
    "    test_data_indices = token2index_dataset(test_data_tokens)\n",
    "    # create pytorch dataloader\n",
    "    #train_loader = NewsGroupDataset(train_data_indices, train_targets)\n",
    "    #val_loader = NewsGroupDataset(val_data_indices, val_targets)\n",
    "    #test_loader = NewsGroupDataset(test_data_indices, test_targets)\n",
    "\n",
    "    BATCH_SIZE = 32\n",
    "    train_dataset = NewsGroupDataset(train_data_indices, train_targets)\n",
    "    train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n",
    "                                               batch_size=BATCH_SIZE,\n",
    "                                               collate_fn=newsgroup_collate_func,\n",
    "                                               shuffle=True)\n",
    "\n",
    "    val_dataset = NewsGroupDataset(val_data_indices, val_targets)\n",
    "    val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n",
    "                                               batch_size=BATCH_SIZE,\n",
    "                                               collate_fn=newsgroup_collate_func,\n",
    "                                               shuffle=True) \n",
    "    # no need for test data right now\n",
    "\n",
    "#     test_dataset = NewsGroupDataset(test_data_indices, test_targets)\n",
    "#     test_loader = torch.utils.data.DataLoader(dataset=test_dataset, \n",
    "#                                                batch_size=BATCH_SIZE,\n",
    "#                                                collate_fn=newsgroup_collate_func,\n",
    "#                                                shuffle=False)\n",
    "\n",
    "    train_loss_vocab, val_acc_vocab, _ = train_model(vocab_size = len(id2token),num_epochs = 30)\n",
    "    train_loss_vocabs.append(train_loss_vocab)\n",
    "    val_acc_vocabs.append(val_acc_vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 624,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'validation accuracy')"
      ]
     },
     "execution_count": 624,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "for i in range(len(train_loss_vocabs)):\n",
    "    plt.plot(range(30),train_loss_vocabs[i], label='vocab_size %d' %max_vocab_sizes[i])\n",
    "plt.legend()\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('training loss')\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "for i in range(len(val_acc_vocabs)):\n",
    "    plt.plot(range(30),val_acc_vocabs[i], label='vocab_size %d' %max_vocab_sizes[i])\n",
    "plt.legend()\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('validation accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 625,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After training for 30 epochs\n",
      "The training losses are 0.03476259112358093, 2.9802322387695312e-08, 5.960464477539063e-08, 1.1920928955078125e-07\n",
      "The validation accuracy are 84.58, 87.08, 86.9, 86.78\n",
      "The Maxmimum Val Acc is 87.08 with Vocab Size 50000\n"
     ]
    }
   ],
   "source": [
    "print (\"After training for 30 epochs\")\n",
    "results = [element[-1] for element in val_acc_vocabs]\n",
    "max_val_acc = max(results)\n",
    "max_val_acc_ind = results.index(max_val_acc)\n",
    "print(\"The training losses are {}, {}, {}, {}\".format(train_loss_vocabs[0][-1], train_loss_vocabs[1][-1],\n",
    "                                            train_loss_vocabs[2][-1], train_loss_vocabs[3][-1]))\n",
    "print(\"The validation accuracy are {}, {}, {}, {}\".format(val_acc_vocabs[0][-1], val_acc_vocabs[1][-1],\n",
    "                                            val_acc_vocabs[2][-1], val_acc_vocabs[3][-1]))\n",
    "print (\"The Maxmimum Val Acc is {} with Vocab Size {}\".format(max_val_acc, max_vocab_sizes[max_val_acc_ind]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### N-gram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tokenizing val data\n",
      "Tokenizing test data\n",
      "Tokenizing train data\n",
      "Tokenizing val data\n",
      "Tokenizing test data\n",
      "Tokenizing train data\n",
      "Tokenizing val data\n",
      "Tokenizing test data\n",
      "Tokenizing train data\n"
     ]
    }
   ],
   "source": [
    "# # val set tokens\n",
    "# print (\"Tokenizing val data\")\n",
    "# bi_val_data_tokens, _ = tokenize_dataset(val_data, n_gram=2, tokenize_scheme = 'detailed')\n",
    "# pkl.dump(bi_val_data_tokens, open(\"bi_val_data_tokens_detailed.p\", \"wb\"))\n",
    "\n",
    "# # test set tokens\n",
    "# print (\"Tokenizing test data\")\n",
    "# bi_test_data_tokens, _ = tokenize_dataset(test_data, n_gram=2, tokenize_scheme = 'detailed')\n",
    "# pkl.dump(bi_test_data_tokens, open(\"bi_test_data_tokens_detailed.p\", \"wb\"))\n",
    "\n",
    "# # train set tokens\n",
    "# print (\"Tokenizing train data\")\n",
    "# bi_train_data_tokens, bi_all_train_tokens = tokenize_dataset(train_data, n_gram=2, tokenize_scheme = 'detailed')\n",
    "# pkl.dump(bi_train_data_tokens, open(\"bi_train_data_tokens_detailed.p\", \"wb\"))\n",
    "# pkl.dump(bi_all_train_tokens, open(\"bi_all_train_tokens_detailed.p\", \"wb\"))\n",
    "\n",
    "\n",
    "# # val set tokens\n",
    "# print (\"Tokenizing val data\")\n",
    "# tri_val_data_tokens, _ = tokenize_dataset(val_data, n_gram=3, tokenize_scheme = 'detailed')\n",
    "# pkl.dump(tri_val_data_tokens, open(\"tri_val_data_tokens_detailed.p\", \"wb\"))\n",
    "\n",
    "# # test set tokens\n",
    "# print (\"Tokenizing test data\")\n",
    "# tri_test_data_tokens, _ = tokenize_dataset(test_data, n_gram=3, tokenize_scheme = 'detailed')\n",
    "# pkl.dump(tri_test_data_tokens, open(\"tri_test_data_tokens_detailed.p\", \"wb\"))\n",
    "\n",
    "# # train set tokens\n",
    "# print (\"Tokenizing train data\")\n",
    "# tri_train_data_tokens, tri_all_train_tokens = tokenize_dataset(train_data, n_gram=3, tokenize_scheme = 'detailed')\n",
    "# pkl.dump(tri_train_data_tokens, open(\"tri_train_data_tokens_detailed.p\", \"wb\"))\n",
    "# pkl.dump(tri_all_train_tokens, open(\"tri_all_train_tokens_detailed.p\", \"wb\"))\n",
    "\n",
    "# # val set tokens\n",
    "# print (\"Tokenizing val data\")\n",
    "# four_val_data_tokens, _ = tokenize_dataset(val_data, n_gram=4, tokenize_scheme = 'detailed')\n",
    "# pkl.dump(four_val_data_tokens, open(\"four_val_data_tokens_detailed.p\", \"wb\"))\n",
    "\n",
    "# # test set tokens\n",
    "# print (\"Tokenizing test data\")\n",
    "# four_test_data_tokens, _ = tokenize_dataset(test_data, n_gram=4, tokenize_scheme = 'detailed')\n",
    "# pkl.dump(four_test_data_tokens, open(\"four_test_data_tokens_detailed.p\", \"wb\"))\n",
    "\n",
    "# # train set tokens\n",
    "# print (\"Tokenizing train data\")\n",
    "# four_train_data_tokens, four_all_train_tokens = tokenize_dataset(train_data, n_gram=4, tokenize_scheme = 'detailed')\n",
    "# pkl.dump(four_train_data_tokens, open(\"four_train_data_tokens_detailed.p\", \"wb\"))\n",
    "# pkl.dump(four_all_train_tokens, open(\"four_all_train_tokens_detailed.p\", \"wb\"))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dataset size is 20000\n",
      "Val dataset size is 5000\n",
      "Test dataset size is 25000\n",
      "Total number of tokens in train dataset is 2174614\n"
     ]
    }
   ],
   "source": [
    "# Then, load preprocessed train, val and test datasets\n",
    "train_data_tokens = pkl.load(open(\"train_data_tokens_detailed.p\", \"rb\"))\n",
    "all_train_tokens = pkl.load(open(\"all_train_tokens_detailed.p\", \"rb\"))\n",
    "\n",
    "val_data_tokens = pkl.load(open(\"val_data_tokens_detailed.p\", \"rb\"))\n",
    "test_data_tokens = pkl.load(open(\"test_data_tokens_detailed.p\", \"rb\"))\n",
    "\n",
    "# double checking\n",
    "print (\"Train dataset size is {}\".format(len(train_data_tokens)))\n",
    "print (\"Val dataset size is {}\".format(len(val_data_tokens)))\n",
    "print (\"Test dataset size is {}\".format(len(test_data_tokens)))\n",
    "\n",
    "print (\"Total number of tokens in train dataset is {}\".format(len(all_train_tokens)))\n",
    "\n",
    "token2id, id2token = build_vocab(all_train_tokens)\n",
    "train_data_indices = token2index_dataset(train_data_tokens)\n",
    "val_data_indices = token2index_dataset(val_data_tokens)\n",
    "test_data_indices = token2index_dataset(test_data_tokens)\n",
    "\n",
    "# create pytorch dataloader\n",
    "#train_loader = NewsGroupDataset(train_data_indices, train_targets)\n",
    "#val_loader = NewsGroupDataset(val_data_indices, val_targets)\n",
    "#test_loader = NewsGroupDataset(test_data_indices, test_targets)\n",
    "\n",
    "BATCH_SIZE = 32\n",
    "train_dataset = NewsGroupDataset(train_data_indices, train_targets)\n",
    "train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n",
    "                                           batch_size=BATCH_SIZE,\n",
    "                                           collate_fn=newsgroup_collate_func,\n",
    "                                           shuffle=True)\n",
    "\n",
    "val_dataset = NewsGroupDataset(val_data_indices, val_targets)\n",
    "val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n",
    "                                           batch_size=BATCH_SIZE,\n",
    "                                           collate_fn=newsgroup_collate_func,\n",
    "                                           shuffle=True)\n",
    "# no need for test data right now\n",
    "\n",
    "# test_dataset = NewsGroupDataset(test_data_indices, test_targets)\n",
    "# test_loader = torch.utils.data.DataLoader(dataset=test_dataset, \n",
    "#                                            batch_size=BATCH_SIZE,\n",
    "#                                            collate_fn=newsgroup_collate_func,\n",
    "#                                            shuffle=False)  \n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: [1/30], Validation Acc: 87.44\n",
      "Epoch: [2/30], Validation Acc: 86.76\n",
      "Epoch: [3/30], Validation Acc: 86.7\n",
      "Epoch: [4/30], Validation Acc: 86.46\n",
      "Epoch: [5/30], Validation Acc: 85.36\n",
      "Epoch: [6/30], Validation Acc: 85.54\n",
      "Epoch: [7/30], Validation Acc: 85.46\n",
      "Epoch: [8/30], Validation Acc: 84.84\n",
      "Epoch: [9/30], Validation Acc: 84.42\n",
      "Epoch: [10/30], Validation Acc: 84.86\n",
      "Epoch: [11/30], Validation Acc: 84.18\n",
      "Epoch: [12/30], Validation Acc: 84.86\n",
      "Epoch: [13/30], Validation Acc: 84.76\n",
      "Epoch: [14/30], Validation Acc: 84.84\n",
      "Epoch: [15/30], Validation Acc: 84.66\n",
      "Epoch: [16/30], Validation Acc: 84.74\n",
      "Epoch: [17/30], Validation Acc: 84.68\n",
      "Epoch: [18/30], Validation Acc: 84.9\n",
      "Epoch: [19/30], Validation Acc: 85.06\n",
      "Epoch: [20/30], Validation Acc: 84.62\n",
      "Epoch: [21/30], Validation Acc: 85.12\n",
      "Epoch: [22/30], Validation Acc: 84.54\n",
      "Epoch: [23/30], Validation Acc: 84.34\n",
      "Epoch: [24/30], Validation Acc: 84.64\n",
      "Epoch: [25/30], Validation Acc: 83.88\n",
      "Epoch: [26/30], Validation Acc: 84.2\n",
      "Epoch: [27/30], Validation Acc: 84.54\n",
      "Epoch: [28/30], Validation Acc: 84.6\n",
      "Epoch: [29/30], Validation Acc: 84.58\n",
      "Epoch: [30/30], Validation Acc: 84.54\n"
     ]
    }
   ],
   "source": [
    "train_loss_word, val_acc_word, _ = train_model(vocab_size = len(id2token), num_epochs = 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Then, load preprocessed train, val and test datasets\n",
    "train_data_tokens = pkl.load(open(\"bi_train_data_tokens_detailed.p\", \"rb\"))\n",
    "all_train_tokens = pkl.load(open(\"bi_all_train_tokens_detailed.p\", \"rb\"))\n",
    "\n",
    "val_data_tokens = pkl.load(open(\"bi_val_data_tokens_detailed.p\", \"rb\"))\n",
    "test_data_tokens = pkl.load(open(\"bi_test_data_tokens_detailed.p\", \"rb\"))\n",
    "\n",
    "\n",
    "token2id, id2token = build_vocab(all_train_tokens)\n",
    "train_data_indices = token2index_dataset(train_data_tokens)\n",
    "val_data_indices = token2index_dataset(val_data_tokens)\n",
    "test_data_indices = token2index_dataset(test_data_tokens)\n",
    "\n",
    "# create pytorch dataloader\n",
    "#train_loader = NewsGroupDataset(train_data_indices, train_targets)\n",
    "#val_loader = NewsGroupDataset(val_data_indices, val_targets)\n",
    "#test_loader = NewsGroupDataset(test_data_indices, test_targets)\n",
    "\n",
    "BATCH_SIZE = 32\n",
    "train_dataset = NewsGroupDataset(train_data_indices, train_targets)\n",
    "train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n",
    "                                           batch_size=BATCH_SIZE,\n",
    "                                           collate_fn=newsgroup_collate_func,\n",
    "                                           shuffle=True)\n",
    "\n",
    "val_dataset = NewsGroupDataset(val_data_indices, val_targets)\n",
    "val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n",
    "                                           batch_size=BATCH_SIZE,\n",
    "                                           collate_fn=newsgroup_collate_func,\n",
    "                                           shuffle=True)\n",
    "# no need for test data right now\n",
    "\n",
    "# test_dataset = NewsGroupDataset(test_data_indices, test_targets)\n",
    "# test_loader = torch.utils.data.DataLoader(dataset=test_dataset, \n",
    "#                                            batch_size=BATCH_SIZE,\n",
    "#                                            collate_fn=newsgroup_collate_func,\n",
    "#                                            shuffle=False)  \n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: [1/30], Validation Acc: 80.12\n",
      "Epoch: [2/30], Validation Acc: 79.16\n",
      "Epoch: [3/30], Validation Acc: 78.52\n",
      "Epoch: [4/30], Validation Acc: 78.42\n",
      "Epoch: [5/30], Validation Acc: 77.06\n",
      "Epoch: [6/30], Validation Acc: 77.24\n",
      "Epoch: [7/30], Validation Acc: 76.5\n",
      "Epoch: [8/30], Validation Acc: 76.1\n",
      "Epoch: [9/30], Validation Acc: 76.1\n",
      "Epoch: [10/30], Validation Acc: 76.1\n",
      "Epoch: [11/30], Validation Acc: 76.26\n",
      "Epoch: [12/30], Validation Acc: 75.9\n",
      "Epoch: [13/30], Validation Acc: 75.42\n",
      "Epoch: [14/30], Validation Acc: 75.34\n",
      "Epoch: [15/30], Validation Acc: 75.22\n",
      "Epoch: [16/30], Validation Acc: 75.34\n",
      "Epoch: [17/30], Validation Acc: 75.06\n",
      "Epoch: [18/30], Validation Acc: 74.98\n",
      "Epoch: [19/30], Validation Acc: 75.12\n",
      "Epoch: [20/30], Validation Acc: 74.62\n",
      "Epoch: [21/30], Validation Acc: 74.88\n",
      "Epoch: [22/30], Validation Acc: 74.62\n",
      "Epoch: [23/30], Validation Acc: 74.44\n",
      "Epoch: [24/30], Validation Acc: 74.58\n",
      "Epoch: [25/30], Validation Acc: 74.62\n",
      "Epoch: [26/30], Validation Acc: 74.72\n",
      "Epoch: [27/30], Validation Acc: 75.0\n",
      "Epoch: [28/30], Validation Acc: 74.54\n",
      "Epoch: [29/30], Validation Acc: 74.64\n",
      "Epoch: [30/30], Validation Acc: 74.28\n"
     ]
    }
   ],
   "source": [
    "train_loss_bigram, val_acc_bigram, _ = train_model(num_epochs = 30, vocab_size = len(id2token))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_tokens = pkl.load(open(\"tri_train_data_tokens_detailed.p\", \"rb\"))\n",
    "all_train_tokens = pkl.load(open(\"tri_all_train_tokens_detailed.p\", \"rb\"))\n",
    "\n",
    "val_data_tokens = pkl.load(open(\"tri_val_data_tokens_detailed.p\", \"rb\"))\n",
    "test_data_tokens = pkl.load(open(\"tri_test_data_tokens_detailed.p\", \"rb\"))\n",
    "\n",
    "token2id, id2token = build_vocab(all_train_tokens)\n",
    "train_data_indices = token2index_dataset(train_data_tokens)\n",
    "val_data_indices = token2index_dataset(val_data_tokens)\n",
    "test_data_indices = token2index_dataset(test_data_tokens)\n",
    "# create pytorch dataloader\n",
    "#train_loader = NewsGroupDataset(train_data_indices, train_targets)\n",
    "#val_loader = NewsGroupDataset(val_data_indices, val_targets)\n",
    "#test_loader = NewsGroupDataset(test_data_indices, test_targets)\n",
    "\n",
    "BATCH_SIZE = 32\n",
    "train_dataset = NewsGroupDataset(train_data_indices, train_targets)\n",
    "train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n",
    "                                           batch_size=BATCH_SIZE,\n",
    "                                           collate_fn=newsgroup_collate_func,\n",
    "                                           shuffle=True)\n",
    "\n",
    "val_dataset = NewsGroupDataset(val_data_indices, val_targets)\n",
    "val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n",
    "                                           batch_size=BATCH_SIZE,\n",
    "                                           collate_fn=newsgroup_collate_func,\n",
    "                                           shuffle=True)\n",
    "# no need for test data right now\n",
    "\n",
    "# test_dataset = NewsGroupDataset(test_data_indices, test_targets)\n",
    "# test_loader = torch.utils.data.DataLoader(dataset=test_dataset, \n",
    "#                                            batch_size=BATCH_SIZE,\n",
    "#                                            collate_fn=newsgroup_collate_func,\n",
    "#                                            shuffle=False)  \n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: [1/30], Validation Acc: 58.4\n",
      "Epoch: [2/30], Validation Acc: 61.92\n",
      "Epoch: [3/30], Validation Acc: 62.12\n",
      "Epoch: [4/30], Validation Acc: 62.32\n",
      "Epoch: [5/30], Validation Acc: 62.74\n",
      "Epoch: [6/30], Validation Acc: 62.86\n",
      "Epoch: [7/30], Validation Acc: 62.2\n",
      "Epoch: [8/30], Validation Acc: 60.74\n",
      "Epoch: [9/30], Validation Acc: 62.32\n",
      "Epoch: [10/30], Validation Acc: 61.36\n",
      "Epoch: [11/30], Validation Acc: 62.28\n",
      "Epoch: [12/30], Validation Acc: 61.58\n",
      "Epoch: [13/30], Validation Acc: 61.9\n",
      "Epoch: [14/30], Validation Acc: 61.88\n",
      "Epoch: [15/30], Validation Acc: 62.4\n",
      "Epoch: [16/30], Validation Acc: 62.34\n",
      "Epoch: [17/30], Validation Acc: 62.48\n",
      "Epoch: [18/30], Validation Acc: 61.68\n",
      "Epoch: [19/30], Validation Acc: 61.26\n",
      "Epoch: [20/30], Validation Acc: 61.56\n",
      "Epoch: [21/30], Validation Acc: 61.04\n",
      "Epoch: [22/30], Validation Acc: 62.58\n",
      "Epoch: [23/30], Validation Acc: 62.42\n",
      "Epoch: [24/30], Validation Acc: 61.28\n",
      "Epoch: [25/30], Validation Acc: 61.88\n",
      "Epoch: [26/30], Validation Acc: 62.46\n",
      "Epoch: [27/30], Validation Acc: 62.66\n",
      "Epoch: [28/30], Validation Acc: 61.52\n",
      "Epoch: [29/30], Validation Acc: 62.38\n",
      "Epoch: [30/30], Validation Acc: 61.48\n"
     ]
    }
   ],
   "source": [
    "train_loss_trigram, val_acc_trigram, _ = train_model(num_epochs = 30, vocab_size = len(id2token))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_tokens = pkl.load(open(\"four_train_data_tokens_detailed.p\", \"rb\"))\n",
    "all_train_tokens = pkl.load(open(\"four_all_train_tokens_detailed.p\", \"rb\"))\n",
    "\n",
    "val_data_tokens = pkl.load(open(\"four_val_data_tokens_detailed.p\", \"rb\"))\n",
    "test_data_tokens = pkl.load(open(\"four_test_data_tokens_detailed.p\", \"rb\"))\n",
    "\n",
    "token2id, id2token = build_vocab(all_train_tokens)\n",
    "train_data_indices = token2index_dataset(train_data_tokens)\n",
    "val_data_indices = token2index_dataset(val_data_tokens)\n",
    "test_data_indices = token2index_dataset(test_data_tokens)\n",
    "# create pytorch dataloader\n",
    "#train_loader = NewsGroupDataset(train_data_indices, train_targets)\n",
    "#val_loader = NewsGroupDataset(val_data_indices, val_targets)\n",
    "#test_loader = NewsGroupDataset(test_data_indices, test_targets)\n",
    "\n",
    "BATCH_SIZE = 32\n",
    "train_dataset = NewsGroupDataset(train_data_indices, train_targets)\n",
    "train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n",
    "                                           batch_size=BATCH_SIZE,\n",
    "                                           collate_fn=newsgroup_collate_func,\n",
    "                                           shuffle=True)\n",
    "\n",
    "val_dataset = NewsGroupDataset(val_data_indices, val_targets)\n",
    "val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n",
    "                                           batch_size=BATCH_SIZE,\n",
    "                                           collate_fn=newsgroup_collate_func,\n",
    "                                           shuffle=True)\n",
    "# no need for test data right now\n",
    "\n",
    "# test_dataset = NewsGroupDataset(test_data_indices, test_targets)\n",
    "# test_loader = torch.utils.data.DataLoader(dataset=test_dataset, \n",
    "#                                            batch_size=BATCH_SIZE,\n",
    "#                                            collate_fn=newsgroup_collate_func,\n",
    "#                                            shuffle=False)  \n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: [1/30], Validation Acc: 49.32\n",
      "Epoch: [2/30], Validation Acc: 53.26\n",
      "Epoch: [3/30], Validation Acc: 52.06\n",
      "Epoch: [4/30], Validation Acc: 52.64\n",
      "Epoch: [5/30], Validation Acc: 54.18\n",
      "Epoch: [6/30], Validation Acc: 52.44\n",
      "Epoch: [7/30], Validation Acc: 54.12\n",
      "Epoch: [8/30], Validation Acc: 54.1\n",
      "Epoch: [9/30], Validation Acc: 52.6\n",
      "Epoch: [10/30], Validation Acc: 54.14\n",
      "Epoch: [11/30], Validation Acc: 52.52\n",
      "Epoch: [12/30], Validation Acc: 54.08\n",
      "Epoch: [13/30], Validation Acc: 52.4\n",
      "Epoch: [14/30], Validation Acc: 52.2\n",
      "Epoch: [15/30], Validation Acc: 54.1\n",
      "Epoch: [16/30], Validation Acc: 52.34\n",
      "Epoch: [17/30], Validation Acc: 52.44\n",
      "Epoch: [18/30], Validation Acc: 52.44\n",
      "Epoch: [19/30], Validation Acc: 53.96\n",
      "Epoch: [20/30], Validation Acc: 53.98\n",
      "Epoch: [21/30], Validation Acc: 52.44\n",
      "Epoch: [22/30], Validation Acc: 53.96\n",
      "Epoch: [23/30], Validation Acc: 52.48\n",
      "Epoch: [24/30], Validation Acc: 52.3\n",
      "Epoch: [25/30], Validation Acc: 54.0\n",
      "Epoch: [26/30], Validation Acc: 53.96\n",
      "Epoch: [27/30], Validation Acc: 54.06\n",
      "Epoch: [28/30], Validation Acc: 54.0\n",
      "Epoch: [29/30], Validation Acc: 52.1\n",
      "Epoch: [30/30], Validation Acc: 53.94\n"
     ]
    }
   ],
   "source": [
    "train_loss_fourgram, val_acc_fourgram, _ = train_model(num_epochs = 30, vocab_size = len(id2token))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'validation accuracy')"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAHjCAYAAABrZcgFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzs3Xl8leWd///XlfVkhySQhEBYZRECCIGgoiNaq7SCLWpx6bdu1S5qW9vOtL9O+1Vrp53x2+p02pnpqK06FltbpSqi1ipqBQUNEgirspMNspA9J2e7fn/cJxsEcoCcnJC8n4/Hedzruc/nRCRvruu6r9tYaxERERGR/hUV6QJEREREhiKFMBEREZEIUAgTERERiQCFMBEREZEIUAgTERERiQCFMBEREZEIUAgTERERiQCFMBEREZEIUAgTERERiYCYSBcQiszMTDtu3LhIlyEiIiLSq40bN1Zba0f0dt5ZEcLGjRtHUVFRpMsQERER6ZUx5kAo56k7UkRERCQCFMJEREREIkAhTERERCQCFMJEREREIkAhTERERCQCFMJEREREIkAhTERERCQCFMJEREREIkAhTERERCQCFMJEREREIkAhTERERCQCFMJEREREIkAhTERERCQCFMJEREREIkAhTERERCQCFMJEREREIkAhDGhu81Hb7Il0GSIiIjKEKIQBz20sZcFP3+Sbf9zEhr01WGsjXZKIiIgMcjGRLmAguHBSJjcW5vH8R6W8WFzOpJHJ3DA/j2vm5DIsMS7S5YmIiMggZM6GVp+CggJbVFQU9s9p9fhZtaWcZzYcpPhQHfExUXx2Zg43FeYxJ284xpiw1yAiIiJnN2PMRmttQa/nKYT1bHt5A898cIAXNpXT1OZjSlYKNy3I43Pn5ZLqiu3XWkREROTsoRDWR5rbfLy02WkdKymrJyE2miWzcrixcCyzRqepdUxERES6UQgLg5LSep754AAvFpfT4vFzbk4qNy3I4+rZuSTHa3idiIiIKISFVaPbywvFTuvYjooGkuKiWTo7l5sK85g+KlWtYyIiIkOYQlg/sNZSfKiOZzYcZNWWctzeAFEGXLHRzismCldsNPGx0STERnXuj43CFePsd7Xvj3HWE+KiSXHFkJeeyLiMJNKT4hTqREREziKhhjD1oZ0BYwzn5Q3nvLzh/PCqc1m9pYLyulbcXj9unx+3N+CsewO0+fy4vX4a3T5avf7O/cFzvf6ew3BKfAzjMpMYm+GEsnGZSYzLSGRcZhIZCmgiIiJnLYWwPpKWEMuNhXmn/X5/wAaDmZ+6Vi8Ha1rYX9PM/upm9te0UFJWz6tbK/EHOsNacnyME87ag1lHSEsiM1kBTUREZCBTCBsgoqMMSfExJMXHkJEcz8QRyced4/UHKD3a2hHODgSD2rayel7rIaAtm5PLP105VTcNiIiIDED67XwWiY2OYnxmEuMzk2BK92Nef4CyLgFtS1k9T68/wBvbD/PTZflcMmVkZIoWERGRHmlg/iC28cBRvvf8FnYfaeKaOaP50VXT9BgmERGRMAt1YL4e4D2IzR07nNXfWMjdiybxQnEZn3r477xaUhHpskRERASFsEEvPiaa714xhZfuvpCs1Hi+tuIjvvb7jRxpdEe6NBERkSFNIWyImD4qjRfuupB/vGIKb+48wuUP/53nN5ZyNnRHi4iIDEYKYUNIbHQUdy2axCvfuIhJI5P5zp83c8sTH1JW1xrp0kRERIYchbAhaNLIZP70lfO5b8m5fLCvlk8//A5Prz9AIKBWMRERkf6iEDZERUcZbr1wPK/fezHn5Q3nRy9s5frH1rOvujnSpYmIiAwJmqJCsNbyp6JD/GT1Djy+AN/59GRuu3A8MdGnn9E9vgBHGt0cbnBzuKENAyTGx5AUF01iXAxJ8c4yOT4GV2yUZvcXEZFBQ8+OlJAZY1g+L49Lpozkn/+ylZ++spPVWyp46NpZTMlOOe78RreXww1uKurdVNa7O9YPN7ipbHBTWd9GdVPbKXw+JMXFkBgXTVJ8cBkXQ2K8s2wPbFmpLq6amcOY9MS+/PoiIiIRoZYw6cZay8tbKrjvpW00ur1cM2c0Hn+gM2jVu2n2+I973/DEWLJSXeSkuchOc5GV6iI7tXPdGGhu89Pi8dHc5qe5zeese/y0tAWXwWMtHh9NbT5aPO3nOcsGtw+A+ePTuWZOLovzc0h1xfb3j0hEROSkQm0JUwiTHtU2e/jxqm28srWSzKQ4snsIV11Dlis2Ouw1ldW18sKmMp7/qJS9Vc3Ex0Tx6enZLJuTy0WTMs+o+1RERKSvDIgQZoy5F/gyYIES4FbgN8A/APXB026x1haf7DoKYZFjrR1w47WstWwprWflR6W8tLmcoy1eMpPj+dzsUSybM5pzR6VGukQRERnCIh7CjDG5wFrgXGttqzHmT8ArwCXAy9ba50K9lkKYnIjHF+DtXUd4/qNS1uw8gtdvmZqdwjVzRnP17FGMTHVFukSRiHJ7/RxpaKOqyU1VYxtRxjB/fHpEniPr9Qco2n+UNTsP8+bOIzS0+rhyRhZLZo5i3rh0oqIG1j/4RE7XQBmYHwMkGGO8QCJQHubPkyEmLtgl+enp2Rxt9vDylnKe/6iMf3llBz97dQcXnTOCZXNy+fS52STEhb/LVKQ/BAKW2hZPMFy1UdXYxpFGJ2Qd+2ps8x33fmNg+qhULpyYyQWTMpk3bjiJceH5dVDb7OGdj4/w5o4jvPNxFY1uH3HRURROSGdadizPbSzl9+sPkp3q4rMzc1gyaxSzRqcNuBZ4kXAId3fkN4F/AVqB1621NxljngTOB9qAN4HvW2uPu5XOGHMncCdAXl7e3AMHDoStThl89lQ18ZePyvjLpjLK6lpJjo/hM/nZLJszmvkD4F/crR4/JWX1TBiRRGZyfERrCQQsGw8epc0bIMUVQ2pCLCmuGFJcMcTH9F9wtdbS6vXT4vH3+2cPNI1uL4dqWzlY20Lp0RYO1bZw6GgrhxucoFXT7MHfw+TKyfExjEyJJzMlnhEp8YxIjmdkqrMcEdzX4vHz3u4a3ttTzUcHj+L1W2KjDeeNGc4FkzK4cFIms0YPIy7m9MZYWmv5+HATb+48zJodR/jo4FECFjKT47l06ggunZrFwnMySY53Ql9zm483dx5h1eZy3tlVhccfIC89kSWzcrhq5iimZqcokMlZZyB0Rw4HngeWA3XAn4HncIJXJRAHPArssdb++GTXUneknK5AwLJhXy0rPyrllZIKmj1+0hJimTcunQUT0lkwIYNpOalEhzmUef0BtpTWsW53Det2V7PpYB0ef4DYaMMV07O5sTCP8ydk9Osvm6rGNv688RB//OAQB2tbejwnLiaKVFcMKa7YjmV7QHP2dd+Oj4ly7mb1+Druem0+9k5Xj4+WNn/wDtjOO2RbvH66/nWUkRTn3AiS1uVGkC7bWakuUl0xYfuZ+QNOKIw2hviYqD4N7h5fgPI6J2QdOtrCodrWYNByAtfRFm+381NcMYwZnkh2mqszWHULWS4yU+JOuTWr1ePnw/21rNtTzXu7a9haXo+1kBgXzbxx6Vw4KYMLJmZybk7qSb+/2+vn/b01rNlxhDU7j3Q8Ci0/N41Lp47k0qkjyc9N6/VnWN/q5a/bKlm1uZz39tTgD1gmjUxm6axRXDUzhwkjkk/p+4lEykAIYdcBV1prbw9ufwlYYK39epdzLgG+a6296mTXUgiTvtDi8fHGjiO8t7ua9Xtr2F/jBI9UVwzzx2f0aSgLBCw7Kxt5b08163ZX88G+Wpo9foyBc3NSuXBSJnPyhvPBvlqe23iIBrePCZlJ3DA/j2vnjmZ4UnjG6wQClvf31vDMhoO8vr0Sr9+yYEI6N8zPIzvVRaPbR2Ob11m6fTS0emlw+2h0t+/rvt3Sw3Qlx4qOMiTFRZMcH9PjhL1J8e3zwjnHEuKiqWvxBuec65yLrqbZc9y1E+OiyU51HRfWslJdpCbE0Orxd5sGpbnN1xECuy17OOb2Brp9VnxMFK7YaFyxwWWMsx4fGx3cjiIhrnO/KzY6eCwKr892BKxDtS1UNrjp2pAVFx1F7vAExqQnMia4zEtPZMxwZ5mW2D9TsdS1eFi/t7bjz+2eKucJGsMTYzl/YgbnT8zkwokZjM9M4khjG2t2Ot2M63ZX0+r1kxAbzcJzMrls6kgWTR1J1hmMyaxuauPVrU4g+3B/LdbCjNxUlswcxWdn5jB6+MCbL3Ag3sgkkTEQQlgh8DtgHk535JNAEfCctbbCOH9SHwHc1trvn+xaCmESDhX1rWzYW8v6vTVs2Ffb8cimFFcMhePTKRyfwYIJGZw7qvdQZq3lQE2L06Kwp4b399RQGwwNEzKTuCDYonD+hIzjApbb62f1lgqe+eAgGw8cJS4mis/MyObGwrHMGze8T/5Sr2328NzGQzyz4SD7a1oYlhjLtXNGc0NhHhPPoHXB5w/Q1OYEtvpWLx5/wAlbXSbcjYvumycitPmcAeYV9c6kwIfrj50k2Fn39fIM1LjoqG4BsNsyOGFw+6TBCbHR+K3F7Q3Q5vXT6vXj9vpxewPO0ucs29r3+fy0evwdxzy+ziCXlRrfEapGd4QsJ3BlpbrC3hp7Oirr3bwX/DP93u5qyuvdAKQnxXX8+c4dlsBl05zWrgUTMsIyXU1FfSurt1SwaksFmw/VATB37HCWzMzhMzNzGJnSvzfgWGspPdrKzspGdlU2sKOykV2VjRyoaWbhpEzuuGgC50/s35btcAkELG2+9j/vnX/22/9faPOe6Fgg+P+FnzZfAGMgyhiio0zHsnMdoo0hKsp0LruuG+cfczHRUcRGRxEbbYhrX4/p3I6LaT8e5RyPMd23o53r9sd/l4iHsGARD+B0R/qATTjTVbwKjAAMUAx81VrbdLLrKIRJf6isd7NhX40TyvbWsrc9lMXHMH98OoXBlrJzc1KJiY7iSIO7oxvnvT01HV0wWanxHQOeL5iYwahhCSHXsLOygT9sOMjKj8pobPMxaWQyN87P45o5o0+5NcRapyv2mQ0HeW1rJR5/gHnjhnNjYR6LZ+T0y9xu/S0QsNQ0e6isd9PY5u32xIWkuBgS4qJPe6zT6dbT/gvobP95W2vZX9PCut3OWLJzRqZw2bSRnDMyuV/DxsGaFlZtKWfV5nJ2VjZiDIxMiWfUsARyg69RxyxTE06/27q+1cuuY8LWrspGmrrc8JCXnsiU7BRy0ly8UlJBdZOH6aNSueOiCXx2Zg6xZ8kchrXNHkrK6ikprQsu6zuC9+lobz2Oj4nC4vz/4LcWf8B2rAcCdOzrD8bAr244j6tmjgrz5wyAENZXFMIkEg43uFm/t4b1e2vZsK+GvVWdoSwzJb6j5SwtIZbzJ2Q442cmZTIhM+mMfym1eHy8vLmCFR8cZPOhOuJjovjszBxuKhzLnLxhJ71+XYuH5zaW8ocPDrKnqpkUVwzXzBnNjYV5TM46/jFUImerTw438vr2w+yvbqasrpXyulbK69x4/N27kpPjYxg1zHVcOMsd7qxnpcRjgb1VzeysbAi2cDWys6KhWwhJS4hlSnYK07JTmJKdytScFCZnpXTcZABOy/YLm8p4fO0+dh9pIifNxa0XjuP6+XkD6gkfdS1O4NpSWs/W4LL9H5IA4zOTmJGbxvjMJBK6dsPHRjld7sd0vXc9lhDntICf6jjKbiGtI6x1hjRfIIDPb/H4A3j9Abw+i8fvx+Ozznbw5fFbvL5jtv0BvL4AHn+Az87MYWp2eOeTVAgT6WNHGtys31fLhr01HG5wBwcuZ4Z9YP+28nqe2XCQFzaV0ezxMzU7hRsL8/jcebkdf6lba9l44CjPbDjIyyUVeHwBzssbxo3z87hq5ihNzyFDRiBgqW5uo7zOTXldK2VHWzsCWvvy2BsfnG4x8Pqd34cxUYZJI5OZkp3C1OxUpmanMDUnhexUV8j/wAoELG9/fITH/r6P9/fWkBwfw/J5Y7j1wnH9Pp6tvtXL1rL6jtatLWV1HKrtDFxjMxKZkZvGzNw08nPTmJ6bRlrCwAmMZyOFMJFBprnNx0uby1mx4QBbyxpIiI1myawcJmel8OeiUnYdbiQ5PobPn5fLDfPz9OQAkRNo8fiCoawzqPmtZUqWE7YmZCb3abf11rJ6Hnt3Ly9vqQDgM/k53HHReGaOHtZnn9HuaLOHbeUNbCt3QtfWsvqOm5AAxqQnkJ+bRn7uMPJz05iRmxqRiXsHO4UwkUFsS2kdz2w4yIvF5bR6/cwcncaN8/NYMmsUSfHhnoNZRE5HWV0rT67bxx8+OERTm4/C8enccdEELp068pS77tpvDthW3sD2iga2l9ezvbx792nusGDgGp0WDF5pYbvzWrpTCBMZAhrdXqqbPIzPTIp0KSISoka3l2c/PMTv1u6jvN7NhBFJfHnhBJbNye3xBg6vP8Ceqia2lTUEQ5cTuBrczs0BUQYmjkjm3FGpTB+Vyrk5aZw7KpV0Ba6IUQgTEREZwLz+AK+UVPDYu3vZWtZARlIcX1wwlgsmZrDrcCPbypxWrl2HGzumO3HFRjE1O7VL4Eplanaqxn0OMAphIiIiZwFrLev31vL4u3t5c+eRjv3DE2OZPirNCVvB0DU+M3lAzikn3Q2UB3iLiIjISRhjgk8kyGBPVRMHa1pO+W5MOTsphImIiAwQE0ckn9FTLOTscnZM4ysiIiIyyCiEiYiIiESAQpiIiIhIBCiEiYiIiESAQpiIiIhIBCiEiYiIiESAQpiIiIhIBCiEiYiIiESAQpiIiIhIBCiEiYiIiESAQpiIiIhIBCiEiYiIiESAQpiIiIhIBCiEiYiIiESAQpiIiIhIBCiEiYiIiESAQpiIiIhIBCiEiYiIiESAQpiIiIhIBCiEiYiIiESAQpiIiIhIBCiEiYiIiESAQpiIiIhIBCiEiYiIiESAQpiIiIhIBCiEiYiIiESAQpiIiIhIBCiEiYiIiESAQpiIiIhIBCiEiYiIiESAQpiIiIhIBCiEiYiIiESAQpiIiIhIBCiEiYiIiESAQpiIiIhIBIQ1hBlj7jXGbDPGbDXG/MEY4zLGjDfGbDDGfGKMedYYExfOGkLiaYEjOyJdhYiIiAwhYQthxphc4BtAgbV2BhANXA/8G/CItfYc4Chwe7hqCNn6/4T/vgBWfweaayJdjYiIiAwB4e6OjAESjDExQCJQAVwKPBc8/hTwuTDX0LuC22HeHVD0BPzHefDer8HniXRVIiIiMoiFLYRZa8uAnwMHccJXPbARqLPW+oKnlQK5Pb3fGHOnMabIGFNUVVUVrjIdienwmYfg6+/DmPnw+j/DfxXCztVgbXg/W0RERIakcHZHDgeuBsYDo4AkYHEPp/aYcqy1j1prC6y1BSNGjAhXmd2NmAJffA5ueh6iYuGPN8L/LoXKrf3z+SIiIjJkhLM78lPAPmttlbXWC6wELgCGBbsnAUYD5WGs4fSc8yn42jr4zM+hsgT+5yJ46RvQdCTSlYmIiMggEc4QdhBYYIxJNMYY4DJgO/AWcG3wnJuBF8NYw+mLjoX5d8A3NkHh16B4BfzHHFj77+Bri3R1IiIicpYL55iwDTgD8D8CSoKf9SjwPeDbxpjdQAbw23DV0CcShsOVP4Wvb4BxC+GN++DX82D7ixovJiIiIqfN2LMgSBQUFNiioqJIl+HY8xb89QdwZDuMvRCu+CmMmh3pqkRERGSAMMZstNYW9HaeZsw/VRMXwVfehasegaqd8Ogl8MJd0FgZ6cpERETkLKIQdjqiY6DgNme82AX3wJZnnfFif/9/zuz7IiIiIr1QCDsTrjT49INw1wanhWzNT+AXU5w7KQ99oDFjIiIickIxvZ8ivcqYCNevgIMbYOOTUPJn+OgpyJwMs2+EWTdASnakqxQREZEBRAPzw6GtEbb9BTatgEPrwUTDpE/BeTfB5MUQE/lnlouIiEh4hDowXyEs3Kp3O3OMbf4DNFZAQjrM/ALMvglyZka6OhEREeljCmEDTcDvTG9R/HvnmZR+D2Tnw3n/B/Kvc55fKSIiImc9hbCBrKUWSp5zAlnFZoiOgymLYfYXYeKlzt2XIiIiclZSCDtbVG51uiu3PAstNZCSA1OvguQscKVCfGqXZVrnenyqwpqIiMgApBB2tvF54JO/OoP5970D3hDmG4tN6h7UXGnd1/O/ANkzwl+7iIiIdFAIO9v5veBugLZ6Z+muh7aG4L6uy7oe9tVDax1ERcPSX8PM6yL9bURERIaMUEOY+rMGquhYSMpwXqejqQr+fDOs/DJUbobL7lf3pYiIyACiGfMHq+QR8KUXYd4d8N6vYMW1zg0BIiIiMiAohA1m0bHw2Z/D0l/BgXXw2CI4vC3SVYmIiAgKYUPDnC/BLa+A1w2PXw7bX4x0RSIiIkOeQthQMWYe3Pk2ZJ0Lf/oSvPkgBAKRrkpERGTIUggbSlJz4JbVziz97/4c/niDcyeliIiI9DuFsKEmJt4ZI/aZn8PuN+Cxy6D6k0hXJSIiMuQohA1FxsD8O+BLL0HrUXjsUtj1WqSrEhERGVIUwoaycRc648TSx8Mfrod3/h+cBZP3ioiIDAYKYUPdsDFw218h/zp46yfOoP22pkhXJSIiMugphAnEJsCyR+HT/wI7X4bfXg61e8/8up5mqN4N+9dpolgREZFj6Dk24jAGLrgbsqbDn2+BRxfBdU/AxEuPP9daaKmBhjJoqIDGcmgo77Je4Wy3dbnzMiYBZt8AhV+DEZP77WuJiIgMVHqAtxyvdh/88Sao2gHzv+I8CLyhHBornODVWAl+T/f3mChIzoKUHEgd5bxSciA1FxKGw46XYMufwN8Gkz4FC77uBDxjIvMdRUREwiTUB3grhEnP2prgxbtg+wsQ4wqGqlHOXGPd1nOdsJWc1fsDwpuroegJ+PAxaDoMI6ZC4Vdh1vVOl6iIiMggoBAmfaOtCeKS+rbFytcG2/4C7/8nVG6BhHQouNV52HhqTt99joiISAQohMnAZy0ceA/W/xfsXO10e05fBgu+BrlzIl2diIjIaQk1hGlgvkSOMc5cZeMudMahffAofPQ0lPwJxiyA878OUz7bezeniIjIWUhTVMjAkD4ervwZfHs7XPmvzk0Af/oS/Md58N6v9IxLEREZdNQdKQNTwA+7XoX1/w0H1kJcsjOh7IR/gLzzISU70hWKiIj0SN2RcnaLioZpVzmvis1OGNvyLGx8wjk+fDyMvcAJZHnnQ8ZETXchIiJnFbWEydnD73XupjzwPhwMvlpqnGNJIyFvgRPIxp4PWflDYyxZWyMEfM5cbCIiMiCoJUwGn+hYyJ3rvC6427m7svoTOPheZzDb8ZJzblwyjJnf2VI2uuDsmYvM74PmKmcutaYj0FTZZf0wNB7u3PY2O+8ZPi74sylwvmv2TIh1RfRriIjIyaklTAaX+rJgK9l6Z3l4G2AhKhZGzXZay5JGRrpKhw1A69EuQSsYspqrgR7+v3SlQXI2JI90JsdNzoKULGf8XPkmKNvoPNEAICoGsmY4gaw9nGVMgijdiyMiEm6aJ0wEnJBz6AMnkB14H8o/Ov6RS5EUFdsZppKzggGrh6CVNDK0lq2GCieMlRUFl5vA0+gci0+D3POcQJY71wloyQMkkIqIDCIKYSI98XudGfsHir5+GsGxAn6o/tgJZKVFTjg7vB2s3zmeludMjDu6AMZd5HRjqrVMROSMaEyYSE+iY53XUBEVDSOnOa/zvujs87Q4d5y2t5iVbnSeEQpOi9ukT8E5n4IJiyAxPXK1i4gMcgphIkNNXKJzB+nY8zv3NVbCnrdg999g1yuw+RkwUTB6nhPKJn0KcmarlUxEpA+pO1JEugv4nVayT/7mhLLyTc7+pBEw8TI453KYeKlayURETkBjwkSkbzRVwZ43YfcbsPtNaK11Wsly5wZbyS6HUeeplUxEJEghTET6Xvt0GO2tZGUfARYSM5xWsrwFzlQaccnOTQdxSZ3r8ckQmzQ0JtEVkSFNIUxEwq+5GvascULZnjc7n2BwMjGuYwJaD4EtYZjzFICE4ZCQ3mU9+IqJ65v6AwFoa3Ba91qPBl910BLcdtc7zykdOQ1GTIG0MXo8loj0KuJ3RxpjpgDPdtk1Afi/wDDgDqAquP8H1tpXwlWHiIRRUibM/ILzCgSgsQI8TcFXc5dXl+22xp73Nx3pfG9rXec0Gj2JSw4GsmE9B7XEdIiOc67TevSYkHW0S8iqcybNPZGYBPC1dv/czMmdoWxEl3Cm7lgROUX90hJmjIkGyoBC4FagyVr781Dfr5YwkSHGWies9RigjnbfPvZ4wHf89eJTj29NS+yhha1bmBvmTGfSUgtVO53XkZ2d602HO68fmwQjJneGspHTYMTUgRHOAgEnbDZXOS2XMfHBMXzRka1LZBCLeEvYMS4D9lhrDxg15YtIb4wBV6rzGj429PdZ67SktdQ6T0ZIGO6MUTuTueES02HsBc6rq5ZaqNrVGcqqdjpds5uf6TynPZwNH9fZ9Rqf3L3rtceu2eAyNuH47s9A8HFXLdWdwaqlGpprnO2Wamdf+/6W2uNbFRPSnbtcJ1/hjOVLGHb6Px8ZvAIBnMe+KbCHS3+FsOuBP3TZvtsY8yWgCPiOtfZoP9UhIoOZMRCf4rzCLTH9+PnWwAlIVR9D1Q4npB3ZAZUl3btgT9YF2o3pHsjaGp2WvxO935XmTCWSmAkZE52H2CeNcLqNEzOdZWstfPw6fPI6bHkWTLQTMCdfAZOvdJ4xqn8sDy2BANQf7PzzWrUr+Of3Y+fPwriLnGlpJi7Sn48+FvbuSGNMHFAOTLfWHjbGZAHtTyh+EMix1t7Ww/vuBO4EyMvLm3vgwIGw1iki0i+sBZ/75OPjjltvcp50EJ/cGbKSMp27UjtCVsaptfgF/M6jrD5+DT7+KxzZ5uxPn+CEsclXQN4FfXMThLVO9231J1DzibOs/gS8LU5YzDjHGWuXeQ4MG6s7aMMlEIC6A11CVjB0VX/s/Ldol5LTOebR74G9b0HtXudY2hgnjE28FMb/g+YLPIEBc3ekMeZq4C5r7ad7ODYOeNlaO+Nk19CYMBGRMKs76ISxj/8K+/4O/jYFLEwgAAAgAElEQVSIS4FJlzqhbNLlkDzi5Nfwup1f1jWfOL/Yq3c7y5rdzl2o7WITnfAVm+Qca6nuPBYV6wTBzHOCr8nBkDbJ6V6Wk/N5gnf8HnWCbtfu8qqPu99okjKqyxjG9htNJvf8c67d54SxPWtg79+hrR4wzrNnJwRD2eh5fXfn8lluIIWwPwJ/tdY+EdzOsdZWBNfvBQqttdef7BoKYSIi/cjT7ASx9layxgqcX7hzg4HsUvC2drZotYeuuoPdu0pTc50g1dHSNclZT83tfsNCS60Txqo/6Qxt1R87v/gD3s7zkkYEQ9mkzpazjEmQNtq54eBsZC34vU6Lk9/jrHtbnCDlbnCmSWlfbwtuH7evy3k+9/GfkZrb/W7ekdOcn9/pjgX0+6D8IyeQ7VnjtKhav9N13tF1eakTtE+n69LaYEvxMd87NhFyZjktwgPcgAhhxphE4BAwwVpbH9z3NDAbpztyP/CV9lB2IgphIiIRYi1UbulsJSvbiPPXd1BMQjAUdW25muS8zvSXpd/ndJ9Vf3x8QDt2TrrETEjNcQJHSg6kjnJe7espOc6YuTMZz+TzdLnxocqpoePGiConTPrcnWGqa7DquvS1da53DZmhiE107vZ1pXYuXWld9qV17kuf4IQuV9rpf+dQuOth37udoezoPmd/e9fluIudLmZ3fffg2BEqG5yWta7HTjRFjYlywuTouc4/CnILnFA5wG4eGBAhrK8ohImIDBBNR2D/u+Aa5oSu1NGRmYaja+tZQxk0lDuvxnJoqOjexdkuNskJainBsJaa43TJpeY4Nz4013SGrGPvMm2uCXbB9cBEO2PyEjOc68TEO+PzouOCr97Wj9kXk+AEp65Bqz1cncmdvv2ldi/sCXZd7nv3+J+biQreQNP1Ox77fbsGzOCx1qPOPwJKi5ylu865XmwSjJrthLLRBc4yNTeiNxAohImIyNDla3O6URsqnJDW03pjec/zykXFdN70kJjR5e7SEZCU0XljRPtx17DIzwc3UPl9zk0AJrozXMUln/nPy1on7LUHsrIi5y5kv8c5npwdDGRznNayUec5n99PFMJEREROJhBwWrkayp0xbu0hyzVM0zCcjXxtULnVCWTt4ax2T/Cgcbpmcwtg3u1OOAujgTZZq4iIyMASFQXJI52XnP1i4p2xYqPnQuFXnH0ttVD2UWdr2a5XYPrnI1tnFwphIiIiMjglpsM5n3Je4HRjDqAeQIUwERERGRqMGVBdzRpJKCIiIhIBCmEiIiIiEaAQJiIiIhIBCmEiIiIiEaAQJiIiIhIBCmEiIiIiEaAQJiIiIhIBCmEiIiIiEaAQJiIiIhIBCmEiIiIiEaAQJiIiIhIBvYYwY8yM/ihEREREZCgJpSXsN8aYD4wxXzfGDAt7RSIiIiJDQK8hzFq7ELgJGAMUGWOeMcZcHvbKRERERAaxkMaEWWs/AX4IfA/4B+A/jDE7jTHLwlmciIiIyGAVypiwmcaYR4AdwKXAEmvttOD6I2GuT0RERGRQignhnF8DjwE/sNa2tu+01pYbY34YtspEREREBrFQQthngFZrrR/AGBMFuKy1Ldbap8NanYiIiMggFcqYsDeAhC7bicF9IiIiInKaQglhLmttU/tGcD0xfCWJiIiIDH6hhLBmY8yc9g1jzFyg9STni4iIiEgvQhkT9i3gz8aY8uB2DrA8fCWJiIiIDH69hjBr7YfGmKnAFMAAO6213rBXJiIiIjKIhdISBk4AOxdwAecZY7DW/m/4yhIREREZ3HoNYcaY+4BLcELYK8BiYC2gECYiIiJymkIZmH8tcBlQaa29FZgFxIe1KhEREZFBLpQQ1mqtDQA+Y0wqcASYEN6yRERERAa3UMaEFRljhuE8umgj0AR8ENaqREREZEDwer2UlpbidrsjXcqA43K5GD16NLGxsaf1/pOGMGOMAX5mra0DfmOMeQ1ItdZuOa1PExERkbNKaWkpKSkpjBs3DicWCIC1lpqaGkpLSxk/fvxpXeOk3ZHWWgu80GV7vwKYiIjI0OF2u8nIyFAAO4YxhoyMjDNqIQxlTNh6Y8y80/4EEREROaspgPXsTH8uoYwJWwR8xRhzAGjGmbDVWmtnntEni4iIiAxhoYSwxWGvQkREROQEbrvtNl5++WVGjhzJ1q1bI11OnwmlO9Ke4CUiIiISdrfccguvvfbaGV/H5/P1QTV9J5SWsNU4ocvgPLZoPLALmB7GukRERGSAeWDVNraXN/TpNc8dlcp9S04eKS6++GL2799/0nMefPBBVqxYwZgxY8jMzGTu3Ll897vf5ZJLLuGCCy5g3bp1LF26lMmTJ/OTn/wEj8dDRkYGK1asICsri/vvv599+/ZRUVHBxx9/zMMPP8z69et59dVXyc3NZdWqVac9FcWJ9NoSZq3Nt9bODC7PAebjPLZIREREJOKKiop4/vnn2bRpEytXrqSoqKjb8bq6Ot555x2+853vsHDhQtavX8+mTZu4/vrreeihhzrO27NnD6tXr+bFF1/ki1/8IosWLaKkpISEhARWr17d53WH+gDvDtbaj3S3pIiIyNDTW4tVpKxdu5arr76ahIQEAJYsWdLt+PLlyzvWS0tLWb58ORUVFXg8nm5zfC1evJjY2Fjy8/Px+/1ceeWVAOTn5/faEnc6QnmA97e7bEYBc4CqPq9EREREJASHDh3qCFpf/epXcaY1PbGkpKSO9XvuuYdvf/vbLF26lLfffpv777+/41h8vPNo7KioKGJjYzumoIiKigrLeLJQBuandHnF44wRu7q3Nxljphhjiru8Gowx3zLGpBtj/maM+SS4HH5mX0FERESGkjFjxlBcXExxcTFf/epXWbhwIatWrcLtdtPU1HTSrsP6+npyc3MBeOqpp/qr5B712hJmrX3gdC5srd0FzAYwxkQDZcBfgO8Db1pr/9UY8/3g9vdO5zNERERk8Lvhhht4++23qa6uZvTo0TzwwAPcfvvtHcfnzZvH0qVLmTVrFmPHjqWgoIC0tLQer3X//fdz3XXXkZuby4IFC9i3b19/fY3jmN6a8IwxfwOuCz4/kmDL1R+ttVeE/CHGfBq4z1p7oTFmF3CJtbbCGJMDvG2tnXKy9xcUFNhjB9mJiIhI+O3YsYNp06ZFuoxeNTU1kZycTEtLCxdffDGPPvooc+bMCfvn9vTzMcZstNYW9PbeUAbmj2gPYADW2qPGmJGnWOP1wB+C61nW2orgtSpO41oiIiIi3dx5551s374dt9vNzTff3C8B7EyFEsL8xpg8a+1BAGPMWE5hslZjTBywFPj/TqUwY8ydwJ0AeXl5p/JWERERGWKeeeaZSJdwykIJYf8MrDXGvBPcvphgOArRYuAja+3h4PZhY0xOl+7IIz29yVr7KPAoON2Rp/B5IiIiIgNeKJO1voYzLcWzwJ+Audbav57CZ9xAZ1ckwEvAzcH1m4EXT+FaIiIiIoNCryHMGPN5wGutfdlauwrwGWM+F8rFjTGJwOXAyi67/xW43BjzSfDYv5562SIiIiJnt1C6I++z1v6lfcNaW2eMuQ94obc3WmtbgIxj9tUAl51qoSIiIiKDSSiTtfZ0zik/7khERETkVB06dIhFixYxbdo0pk+fzi9/+ctIl9RnQglTRcaYh4H/xLkr8h5gY1irEhEREQFiYmL4xS9+wZw5c2hsbGTu3LlcfvnlnHvuuad8LZ/PR0zMwGlHCqWSe4Af4QzMN8DrwF3hLEpEREQGoFe/D5UlfXvN7HxYfOLh4Tk5OeTk5ACQkpLCtGnTKCsrOy6EPfjgg6xYsYIxY8aQmZnJ3Llz+e53v8sll1zCBRdcwLp161i6dCmTJ0/mJz/5CR6Ph4yMDFasWEFWVhb3338/+/bto6Kigo8//piHH36Y9evX8+qrr5Kbm8uqVauIjY3t068eymOLmnEeLSQiIiISMfv372fTpk0UFhZ2219UVMTzzz/Ppk2b8Pl8zJkzh7lz53Ycr6ur4513nJm2jh49yvr16zHG8Pjjj/PQQw/xi1/8AoA9e/bw1ltvsX37ds4//3yef/55HnroIT7/+c+zevVqPve5kO5LDFmvIcwYMwL4J2A64Grfb629tE8rERERkYHtJC1W4dbU1MQ111zDv//7v5Oamtrt2Nq1a7n66qtJSEgAYMmSJd2OL1++vGO9tLSU5cuXU1FRgcfjYfz48R3HFi9eTGxsLPn5+fj9fq688koA8vPz2b9/f59/p1AG5q8AdgLjgQeA/cCHfV6JiIiISA+8Xi/XXHMNN910E8uWLePQoUPMnj2b2bNn85vf/IbenoOdlJTUsX7PPfdw9913U1JSwv/8z//gdrs7jsXHxwMQFRVFbGwsxpiObZ/P1+ffK5QQlmGt/S3OXGHvWGtvAxb0eSUiIiIix7DWcvvttzNt2jS+/e1vAzBmzBiKi4spLi7mq1/9KgsXLmTVqlW43W6amppYvXr1Ca9XX19Pbm4uAE899VS/fIcTCSWEeYPLCmPMZ40x5wGjw1iTiIiICADr1q3j6aefZs2aNR2tX6+88kq3c+bNm8fSpUuZNWsWy5Yto6CggLS0tB6vd//993Pddddx0UUXkZmZ2R9f4YRMb014xpirgHeBMcCvgFTgAWvtS+Evz1FQUGCLior66+NEREQkaMeOHUybNi3SZfSqqamJ5ORkWlpauPjii3n00UeZM2dO2D+3p5+PMWajtbagt/eGcnfky8HVemDRaVUoIiIiEkZ33nkn27dvx+12c/PNN/dLADtTA2fGMhEREZHT9Mwzz0S6hFMWypgwEREREeljCmEiIiIiERDKZK3xwDXAuK7nW2t/HL6yRERERAa3UMaEvYgzKH8j0BbeckRERESGhlBC2Ghr7ZVhr0RERETkGG63m4svvpi2tjZ8Ph/XXnstDzzwQKTL6hOhjAl7zxiTH/ZKRERERI4RHx/PmjVr2Lx5M8XFxbz22musX7/+tK7l9/v7uLozE0pL2ELgFmPMPpzuSANYa+3MsFYmIiIiA8q/ffBv7Kzd2afXnJo+le/N/94JjxtjSE5OBpxnSHq93o5nOrYLBALcfffdvPPOO4wfP55AIMBtt93Gtddey7hx47jtttt4/fXXufvuu2lsbOTRRx/F4/EwadIknn76aRITE7nllltISEhg586dHDhwgCeeeIKnnnqK999/n8LCQp588sk+/d4QWkvYYuAc4NPAEuCq4FJEREQk7Px+P7Nnz2bkyJFcfvnlFBYWdju+cuVK9u/fT0lJCY8//jjvv/9+t+Mul4u1a9dy/fXXs2zZMj788EM2b97MtGnT+O1vf9tx3tGjR1mzZg2PPPIIS5Ys4d5772Xbtm2UlJRQXFzc598rlBnzDxhjZgEXBXe9a63d3OeViIiIyIB2sharcIqOjqa4uJi6ujo+//nPs3XrVmbMmNFxfO3atVx33XVERUWRnZ3NokXdH/CzfPnyjvWtW7fywx/+kLq6Opqamrjiiis6ji1ZsgRjDPn5+WRlZZGf74zGmj59Ovv372f27Nl9+r16bQkzxnwTWAGMDL5+b4y5p0+rEBEREenFsGHDuOSSS1i9enXHw7xfeuklensOdlJSUsf6Lbfcwq9//WtKSkq47777cLvdHcfi4+MBiIqK6lhv3/b5fH38bULrjrwdKLTW/l9r7f8FFgB39HklIiIiIseoqqqirq4OgNbWVt544w2mT59OcXExxcXFLF26lIULF/L8888TCAQ4fPgwb7/99gmv19jYSE5ODl6vlxUrVvTTt+hZKAPzDdD1dgJ/cJ+IiIhIWFVUVHDzzTfj9/sJBAJ84Qtf4Kqrrup2zjXXXMObb77JjBkzmDx5MoWFhaSlpfV4vQcffJDCwkLGjh1Lfn4+jY2N/fE1emR6a8IzxnwbuBn4S3DX54AnrbX/HubaOhQUFNiioqL++jgREREJ2rFjB9OmTYt0Gb1qamoiOTmZmpoa5s+fz7p168jOzg775/b08zHGbLTWFvT23lAG5j9sjHkbZ6oKA9xqrd10mrWKiIiI9LmrrrqKuro6PB4PP/rRj/olgJ2pE4YwY0yqtbbBGJMO7A++2o+lW2trw1+eiIiISO9ONg5soDpZS9gzOHOCbQS69lma4PaEMNYlIiIiMqidMIRZa68KLsf3XzkiIiIiQ0Mo84S9Gco+EREREQndycaEuYBEINMYM5zOaSlSgVH9UJuIiIjIoHWyMWFfAb6FE7g20hnCGoD/DHNdIiIiIh38fj8FBQXk5uby8ssvR7qcPnGyMWG/BH5pjLnHWvurfqxJREREpJtf/vKXTJs2jYaGhtO+ht/vJzo6ug+rOjOhzBP2K2PMDOBcwNVl//+GszAREREZWCp/+lPaduzs02vGT5tK9g9+cNJzSktLWb16Nf/8z//Mww8/fNzxQCDA3XffzTvvvMP48eMJBALcdtttXHvttYwbN47bbruN119/nbvvvpvGxkYeffRRPB4PkyZN4umnnyYxMZFbbrmFhIQEdu7cyYEDB3jiiSd46qmneP/99yksLOTJJ5/s0+8NoQ3Mvw/4VfC1CHgIWNrnlYiIiIj04Fvf+hYPPfQQUVE9x5aVK1eyf/9+SkpKePzxx3n//fe7HXe5XKxdu5brr7+eZcuW8eGHH7J582amTZvGb3/7247zjh49ypo1a3jkkUdYsmQJ9957L9u2baOkpITi4uI+/16hPDvyWmAWsMlae6sxJgt4vM8rERERkQGttxarcHj55ZcZOXIkc+fOPeGErGvXruW6664jKiqK7OxsFi1a1O348uXLO9a3bt3KD3/4Q+rq6mhqauKKK67oOLZkyRKMMeTn55OVlUV+fj4A06dPZ//+/cyePbtPv1soIazVWhswxviMManAETRRq4iIiPSDdevW8dJLL/HKK6/gdrtpaGhg8eLFVFRUAPDjH/+Y3p6DnZSU1LF+yy238MILLzBr1iyefPLJbsEuPj4egKioqI719m2fz9eH3yp43RDOKTLGDAMew7lL8iPggz6vREREROQYP/vZzygtLWX//v388Y9/5NJLL+XVV1+luLiY4uJili5dysKFC3n++ecJBAIcPnz4pI8wamxsJCcnB6/Xy4oVK/rvi/QglIH5Xw+u/sYY8xqQaq3dEt6yREREREJzzTXX8OabbzJjxgwmT55MYWEhaWlpPZ774IMPUlhYyNixY8nPz6exsbGfq+1kTtSEZ4yZc7I3Wms/CktFPSgoKLBFRUX99XEiIiIStGPHDqZNmxbpMnrV1NREcnIyNTU1zJ8/n3Xr1pGdnR32z+3p52OM2WitLejtvSdrCftFcOkCCoDNOBO2zgQ2AAtPq1oRERGRPnbVVVdRV1eHx+PhRz/6Ub8EsDN1sslaFwEYY/4I3GmtLQluzwC+2z/liYiIiPTuZOPABqpQBuZPbQ9gANbarUDf3qMpIiIiA1Zvdx8OVWf6cwklhO0wxjxujLnEGPMPxpjHgB1n9KkiIiJyVnC5XNTU1CiIHcNaS01NDS6Xq/eTTyCUecJuBb4GfDO4/Xfgv0O5eHBqi8eBGYAFbgOuAO4AqoKn/cBa+8op1CwiIiL9ZPTo0ZSWllJVVdX7yUOMy+Vi9OjRp/3+UKaocAOPBF+n6pfAa9baa40xcUAiTgh7xFr789O4noiIiPSj2NhYxo8fH+kyBqUThjBjzJ+stV8wxpTgtGJ1Y62debILB2fXvxi4JXi+B/AYY86oYBEREZHB4GQtYe3dj1ed5rUn4HQ5PmGMmYUz2377Ne82xnwJKAK+Y609euybjTF3AncC5OXlnWYJIiIiIgPTCSdrPeMLG1MArAcutNZuMMb8EmgAfg1U47SuPQjkWGtvO9m1NFmriIiInC3OeLJWY0wjPXRD4kzYaq21qb1cuxQotdZuCG4/B3zfWnu4y2c8BrzcW5EiIiIig83JJmtNOZMLW2srjTGHjDFTrLW7gMuA7caYHGttRfC0zwNbz+RzRERERM5GoUxRAYAxZiTOI4wAsNYeDOFt9wArgndG7sWZ7uI/jDGzcVrZ9gNfOZWCRURERAaDXkOYMWYpznMkRwFHgLE4k7VO7+291tpinOdOdvV/Tr1MERERkcEllBnzHwQWAB9ba8fjdCuuC2tVIiIiIoNcKCHMa62tAaKMMVHW2rfQsyNFREREzkgoY8LqjDHJOI8rWmGMOQL4wluWiIiIyOAWSkvY1UALcC/wGrAHWBLOokREREQGu1Bawu4E/mytLQWeCnM9IiIiIkNCKC1hqcBfjTHvGmPuMsZkhbsoERERkcGu1xBmrX3AWjsduAtnmop3jDFvhL0yERERkUEslJawdkeASqAGGBmeckRERESGhl5DmDHma8aYt4E3gUzgDmvtzHAXJiIiIjKYhTIwfyzwreDs9yIiIiLSB3oNYdba7/dHISIiIiJDyamMCRMRERGRPqIQJiIiIhIBCmEiIiIiEaAQJiIiIhIBCmESNtZamjxNVDRV4Avome8iIiJdhTJFhQxx1lqavE3Ut9VT76mn3h1ctjmvurY6GjwNPW77rR+AEQkjuHrS1SybtIwxqWMi/I1EREQiTyFMuvH6vWyt2cqHlR9SVFnErqO7uoWpniTGJDIsfhhp8WmkxqcyOXFyx3ZafBoJMQm8W/ouv9v6Ox4veZz52fO55pxruGzsZcRHx/fjtxMRkVB5A16iiCI6KjrSpQxaxlob6Rp6VVBQYIuKiiJdxqDk8XvYWh0MXYeLKD5SjNvvBuCc4ecwM3Mm6a70jkCVFucsh8UPIzU+lbS4NGKjY0P6rMPNh3lxz4us/GQlZU1lpMalsmTiEpads4zJwyeH82uKyCB1oOEAvoCPzIRMUuNSMcZEuqSzRpOniYrmCufV5CzLm8upbK6kormCIy1HyEzI5P7z7+ei0RdFutyzijFmo7W2oNfzFMKGFo/fQ0l1SUfo2nxkc0fomjx8MvOy51GQVcDcrLkMdw0PSw0BG+CDyg9Y+fFK3jj4Bt6Al/zMfJads4zF4xeTFJsUls8V6U/WWho8DdS6a0mMSSQrKSvSJQ06v9v6Ox7Z+EjHdmxULJkJmYxIGEFGQgaZCZnHvdqPxUXHRbDy8PMH/FS3VneGrOYKyps6A1ZFUwWN3sZu74mJiiE7MZuc5BxyknLITspmzcE17K7bzbJzlvGPBf9IclxyhL7R2UUhTAAndG2p2sKHhz9kY+VGiquKafO3ATBl+JRuoWuYa1i/11fnrmPV3lWs/GQlu+t2kxCTwOLxi1l2zjJmZs7Uv2rllFlr2Vm7k911u4mPjscV4yI+Or7buivaRXyMs3TFuIgyvd+jFLABGj2N1LprqXXXctR99PhlW+exOncdPuvckBJlorh87OV8Of/LTE2fGu4fwaAXsAEe2fgIT257kivGXcFleZdR3VpNVWsVNa01VLdWd7yOuo9iOf73XGpcKiMSRpCZkElGQgYzMmdw/dTriY0KrWV/IGrzt7G2bC2v7XuNd0rfodXX2u14SlwKo5JGdQSsUcnOenvoynBlHNf16PF7+K/i/+KJbU+QlZjFjy/8MQtyFvTn1+qowxfwkRib2O+ffToUwoaYFm9Lx79wypvLKW8qZ0vVFjZXbabN34bBMCV9CgVZBRRkFzB3ZGRC14lYa9lSvYWVn6zk1X2v0uprZdKwSSw7ZxlLJiwZULXuPrqb9RXrWXbOsoj/hRCwAdr8bSTEJES0jkjz+r0UHS7irUNv8faht6lorjil98dGxXYEs64hLT46niZvE7WttdS11Z1wbGRKbArDXcM7XhmuDGc93tneU7eHZ3c9S5O3iYW5C/ly/peZmzW3L776kOMNeLn/vft5ac9L3DD1Br4///snDdHegJej7qPHBbSqlipq3M72kZYjlDWVMS19Gj9d+FMmDZ/Uj9/ozHgDXjZUbODVfa+y5uAamrxNDI8fzmVjL2Na+jQnbCWNIjsp+4xasTZXbeaHa3/I/ob9LJ+ynG/P/Xa//P3X6mvlT7v+xO+2/o5WXytfnPZFbp1xKylxKWH/7DOhEDaIWGupcdd09Nkf24df0VxBXVtdt/dEm2gmD59MQXYB87LmMSdrDmnxaRH6Bqem2dvMq/teZeUnKympLiE2KpZP5X2K5VOXR/wX16Yjm7jrjbto9DYyImEE35jzDZZOXBpSS0pfstbybtm7PLLxEfbV7+PSvEu5fsr1zMueN2RaDxs9jawtW8tbB99ibdlaGr2NuKJdnD/qfBaNWcSskbPwBXy0+dpw+920+du6rbf6Wo/b5/YFzwuue/weEmMTSXelk+5K7whZ6fHppCekd4SsULq2Gj2NPLvrWZ7e/jS17lrmjJzD7fm3c1HuRUPmv9mZavW18t13vsvfS//OXbPv4iszv9JnP7s3DrzBg+sfpNHTyF2z7+KW6bcM2AHp/oCfj458xKv7XuVvB/5GXVsdybHJXJZ3GYvHL2Z+zvywtOi1+lr51aZf8fvtvyc3OZefLPxJ2P5ObvO38dzHz/F4yeNUt1azIGcBafFp/HX/X0mLT+OO/Du4fur1A/bmLoWws0ibv43DzYcpby6noqmCyuZKZz0YtCqbK/EEPN3ekxiT2NmU3KU5OScph1HJo8hMyCQm6uy/+XVX7S7+svsvrNqzigZPA9+c801un3F7RH5pvVf2Ht96+1uMTBzJvXPv5Xclv2NL9RbOzTiXf5r3T/0WELdVb+P/b+++w6Oq8jeAvycFEkgCAQKEDlJCCQQISFVCiUB0rWABxQYisIrlZ1vdRQR1RewCAoooKKwoq66gIiKgojSp6T0hvU/aZGbu9/fHTEKHlJm5SXg/z5NnJlPu/c7Nzcx7zzn3zBuH3sD+jP3o7N0ZozqMwveJ36PQWIgeLXrg9j63429X/a1Rjt3IKMnArpRd2JW8CwcyD8CsmdHKoxWu7XQtQjqHYESHEZz581kAACAASURBVPW+VbDMXIatMVvx8cmPkV6Sjj6+ffBg4IOY1HVSvf3Qrw8KjYVYsHOBtUVmxPOY3me63deRV56HJX8swY6kHRjoNxBLRi9B9xbd7b6e2hARHM85ju0J2/Fj4o/IKsuCp5snxnUah8ndJ2NMxzFOG+d2MOMgXvjtBZwqPoWZ/WbikcGPwMPNwy7LNllM2Bq7FauPrUZmaSaC2wVjftB8BLe35pnw3HC8ffht/J72O9o3b495g+bhhqtuqHefdwxh9YSIoNBYWBWqMkoykFZ89vXc8tyznqOg4Ofph/Ze7av6788MWf5e/vB2976ijp7LzeX41+//wraEbbi11634x4h/OHXsxo6kHXhqz1O4qsVVWDVpFdp4toGIYFvCNrx56E1klmZiUtdJeHzo4+jk3ckhNaQYUvDu4XexPXE7fJv6Yu6guZjWexrcXd1Rbi7HD4k/YFPkJpzIPQFPN0/c0OMG3B5we4M+81REEJ0fjZ9Tfsau5F2IyIsAAHTz6YaQziEI6RKCgW0GNsjwYtJM2Ba/DR+e+BAJhQno4t0F9w+4HzdcdUOjHzReU5klmZj701wkFSXh1bGvIrRbqMPWJSLYnrAdS/9cCqPFiEeHPIoZfWc4vbW7spbo/GhsT9iO7xO/x6niU3B3ccfYjmMxpfsUXNPpGt2GRJSaSvHGoTewOWozuvl0w9IxSzHQb2Ctl2fSTPg27lt8cPQDpJWkIcgvCAsGL8Dw9sMv+Fn3Z/qfePvw2ziecxw9WvTAI0MewfjO4+vN5yJDmA6OZB3B/oz9Z5+BUpJ+3uBID1ePswZFnnu9fbP21Z724UoiInjvyHtYfWw1RnUYheXXLndKa8/WmK1YtG8RBrYZiPcnvg+fJj5n3V9mLsP6k+vx0YmPYNbMuLvf3ZgdONtuteWX52P1sdXYFLUJbsoNd/e7G/cPuP+iyz+RcwKbozZje8J2GC1GDGk7BHcE3IGJXSY6dL8qNBYioyQDLsoFrsr19KXL6d/Pu09Z5yA683eTZsKhzEPYlWwd35VWkgYFhUF+gzCu8ziEdAlBjxY9HPY6nE0TDT8n/4w1x9cgPDccbT3bYlb/Wbit9226jzmsDxIKE/DQjodQaCzEO+PfwdX+Vztlvdml2Xhx34vYnbobQ9sNxUujX0Jnb+dMNB1fGI8fEn7A9sTtSChMgKtyxYgOIzCl2xSM7zK+Xo2H2pe2D//8/Z/IKs3Cff3vw7ygeTU6iDBrZmxL2IZVR1chxZCCAa0HYMHgBRjVYdRlA5WIYGfyTrx9+G0kFiVioN9ALByyEMPaD6vry6ozhjAnSytOQ9hXYTCLtXuksluwffP21uvNO6C9l/W6b1PfepPWG6KtMVuxeN9idG/ZHSsmrED75u0dtq5Pwz/Fawdew6gOo/DmuDcv+aGYWZKJd/56B9/EfYNWHq3wyOBHcFPPm2rdSlNmLsPGiI348PiHKDWX4uaeN2Ne0Dy0bda2Ws8vNBbiv7H/xeaozUgxpKCVRyvc2utWTOs9Df5e/rWqCbC+8WWUZCAyLxKReZGIyItAVF4U0krSar3MMykoCARNXZtipP9IhHQJwTWdrkEbzzZ2WX59JSLYl74PHx7/EPsz9qNF0xaY0XcG7gq4q8GM57S3kzkn8fBPD0MphRUTV6B/6/5OXb+I4Ou4r/Hv/f+GRSx4YugTmN5nukPevzNLMvF94vf4Lv47RORFQEEhuH0wJnebjEldJzlsyiB7MFQYsOzAMmyN3YqeLXti6Zil6Ne63yWfY9Es+CHxB6w8uhKJRYno26ov5gfNxzWdrqnx9jVrZnwd+zVWHF2BrNIsjO44GguHLNT1TGSGMCdb+sdSbInegm9v/tZh3VF02r60fXj8l8fh6eaJ9ya8d9l/+JoSEaw8uhIrj67EpK6T8OrYV6t9dHci5wReO/Aa/sr6C318++CpYU9huP/waq/bolnwTdw3eO/Ie8gqzcK4TuOwcOhCXNXyqlq9Fk007Evbh02Rm7A7dTeUUhjXaRxuD7gdI/xHXLKbxayZkViYWBW0IvMiEZkfiUJjIQBrYOrq0xV9W/VFn1Z90Nm7MwQCTTRYxGK91Cxn/267vNRj+rfpj5H+I6/YlqCj2Uex9vha/JLyC5q5NcO03tNwT/97qh3AG4N9afuwcNdC+Hr44oNJH6CrT1fdaskoycA/f/sn9qXvwwj/EVg8anGdDmQqGSoM+CnpJ3wX/x32Z+yHQNC/dX+E9QjDdd2ua3B/7z2pe7Do90XIL8/H7IGzMXvg7POGjWii4aekn7Dy6ErEFsSiZ8ueWBC0AOO71L0rsdxcjs8jP8fa42tRVFGEqd2nYsHgBU5rwTwTQ5gT5ZTl4Lot1yGsRxgWj16sdzlXjJj8GMzbOQ+FxkK8fu3ruKbTNXZZriYalh1Yhg0RG3BTz5vwr5H/qvGgTxHBD0k/4M2DbyKtJA3jO4/HE8FPoItPl0s+p/KMx9iCWAS2CcTjQx+vGpBqD6eKT2FL9BZ8FfMV8srz0NWna9VA/iauTRCdH43IXGvQisyNRExBTNW8ck1cmqCXby8EtAqo+unt2/uKDUrOEJ0fjY9OfITtCdvhqlyr5vUb1n4Y+rfu32iHLXyf+D2e3fssurfojlUTV9WLMCIi+CL6C7x+8HW4Klc8Newp3NTzphoHhwpLBfam7sV3Cd9hd8puVGgV6OzdGWE9whDWPQzdWnRzzAtwkkJjIV7Z/wq+i/8OfVv1xZIxS9DbtzdEBLtSdmHFkRWIyo9CjxY98HDQwwjtGmr38XZFFUVYd2IdNoRvgFkz47bet+GhQQ85tTWdIcyJ3jj0BtafXI9vbvpG16O1K1F2aTbm75yPqPwoPDv8WdwRcEedlmfWzFj0+yJ8Hfc1Zvadif8b9n91eoMoN5djQ8QGrDm2BhVaBWYEzMCcQXPOG1d2Mucklh9ajgMZB9DZuzMeHfIoQruGOqzbusJSgR1JO7ApchOOZB+Bu4t7VSsUYJ3I8sywFdAqAN1bdK93ZyBdKVIMKfg88nP8kf4HYvJjAACebp4I8guyBrP2wRjQeoDDQpmIILM0E9H50YjOj0ZSURJ6teyFkC4hdm9l2BS5CS//+TIGtx2Mdye8e97/it5SDal44bcXcDDzIMZ2HItFoxZdNiRqouFQ5iF8F/8dfkz6EYYKA1p5tMLkbpMR1iMMgW0CG90QlZ1JO7H4j8UwVBgwo+8MHMg4gJO5J9HVpyvmDpqLKd2mOPyEmuzSbKw6ugpfxnyJJq5NcHe/u3Ff//ucMpaYIcxJCo2FCN0Sims7XYvXrn1N73KuSKWmUjy952n8kvoLZvWbhceDH69VcKqwVOCpPU9hZ/JOzAuah7kD59rtjTG7NBvv/vUu/hv7X7Rs2hILBi/ALb1uQXpJ+kXPeHSWyLxIfBv3LZq7N68KXP7N/Rvdh0JjkV+ej0OZh3Ag4wAOZB6oCmUerh4IahtU1VoW2CawVvtRiakEMfkxiCmIQXRetPUyPxqGitNfcePb1Bf5xnwAQM+WPRHSOQTju4xHv9b9an3QcuYQgHGdxmHZtcvsNu2BvWmi4fPIz/HWobfQxLUJnr36WYR1Dzvrf6byzMbv4r/DtoRtyCzNhKebJyZ0mYCwHmEY4T+i0R/UnDnlR0evjpg7aC6u73G90193UlES3vvrPXyf+D1aNm2JpWOW2q3n5GIYwpxk5ZGVWHF0BbbcsAV9WvXRu5wrlkWz4LUDr+GzyM8wsctEvDz25RrNF1VqKsXCXQuxL30fnh72NGb2m+mQOiNyI/DvA//GocxD6OzdGekl6dU645HoYvLL83E48zAOZB7AwYyDiMqPAmANZYPaDsKwdtaWssA2gWeNa7RoFiQbkhGdH42Y/Jiqy9Ti1KrHNHdvjl4te6G3b2/08rVe9vTtCZ8mPkgxpGBX8i7sStmFw1mHoYmGtp5tq85gHd5+eLXHUVo0C17Z/wo2R23GjVfdiEWjFjWIgJJYmIjnf3seR7OPYkKXCXhhxAswWozYlrAN38V/h9iCWLgpN4zqOAph3cMwrvO4K677XkSQakhFe6/2un8l1Mnck3jvr/fwzPBnHN5rxRDmBCWmEoRuCcWQtkPw7oR39S6HYD2bcdmBZQhsE4h3xr+D1p6tL/ucQmMh5u+cj+M5x/HiqBdxU8+bHFpj5WnV606sQy/fXjU645HocgrKC3Ao6xAOZhzEwcyDiMqLqjrbNMgvCO2at0NsQSziCuKqxvu5KBd08+lWFbR6teyF3q16o0PzDtVqES0oL8DeU3uxK8X67QVl5jI0d2+O0R1GI6RLCMZ2HHvRMzwrLBV4Zu8z2JG0A/cNuA+PDXmsQbXCWjQLPgn/BO/+9S7cXNyqpiQK8gtCWI8whHYLRSuPVjpXSc7GEOYEH5/4GMsPLcfGqRvrNEkd2dfOpJ14Zu8zaO3ZGismrrjknFI5ZTl4aMdDSChMwGvXvIaJXSc6sVIixys0FlZ1Xx7MPIjcslz0bNmzKnD19u2NHi172O3rX4wWI/5M/7PqezxzynLgqlwR3C4YIV1CMK7zOHT06gjAeiD76M+P4s+MP/Fk8JOY1X+WXWrQQ1xBHNYcX4PuPt0xtcdUXc7Io/qDIczBjBYjJn85GVe1vAprQ9fqXQ6d43j2cSz4eQHMmhlvhbx1wcn70orTMPvH2cguy8ZbIW9hVIdROlRK1HhpouFEzomqr5qKK4wDAPT27Y2QziHYe2ovovKisHj0Yvztqr/pXC2R/TCEOdjmyM1Y8ucSrA1d67QZnKlmUg2pmL9zPpINyXhp9Eu4vsf1VffFF8Zjzo9zUGouxYoJKxDUNkjHSomuDMlFydiVsgs/J/+MI9lH0MSlCZaPW+7wQdJEzsYQ5kAmzYTrv7oebZq1wYYpGxrU+IUrTaGxEI//8jj2Z+zHgqAFmDNwDiLzIjH3p7kAgNWTVvOECiId5Jdbz66szzPBE9VWdUNY/T/9pB7aFr8NaSVpeO7q5xjA6rkWTVtg1cRVWLRvEd478h7Cc8OxP2M/vJt4Y03oGs7rRqQThi8ihrAas2gWrD2+Fr19e7MJvYFwd3XHktFL0MmrE1YcXYFuPt2wJnSNQ79zkoiI6HIYwmpoZ/JOJBYlYtk1y9gK1oAopfBw0MMY03EMuvh0uWK/EJmIiOoPhrAaEBGsPb4WXX26YlLXSXqXQ7UQ6BeodwlEREQAAPt+a2Yj9+upXxGRF4EHBjzg8O+8IiIiosaNIayaRARrjlvHEZ051QERERFRbTg0hCmlWiqltiilIpVSEUqpkUqpVkqpHUqpGNtlgzhF5mDmQfyV9Rfu63+fU79cmYiIiBonR7eEvQ3gexEJADAIQASAZwDsFJFeAHbafq/31h5fi1YerXBLr1v0LoWIiIgaAYeFMKWUD4BrAHwIACJSISIFAG4EsN72sPUAHPttyXZwIucEfk/7Hff0uwcebh56l0NERESNgCNbwnoAyAawTin1l1JqrVKqOYB2IpIOALbLthd6slJqjlLqoFLqYHZ2tgPLvLw1x9bAu4k3bu9zu651EBERUePhyBDmBmAIgJUiMhhACWrQ9Sgiq0UkWESC/fz8HFXjZcXmx+LnlJ8xo+8MeDXx0q0OIiIialwcGcJSAaSKyJ+237fAGsoylVL+AGC7zHJgDXW29sRaeLp5YkbADL1LISIiokbEYSFMRDIApCilKr8deQKAcADfAJhlu20WgK8dVUNdpRSlYHvCdkzvPR0tPVrqXQ4RERE1Io6eMf/vADYqpZoAiAdwH6zB7z9KqQcAJAOY5uAaau2jkx/BVbliVv9Zl38wERERUQ04NISJyBEAwRe4a4Ij12sPmSWZ+Dr2a9zc82b4NdNvTBoRERE1Tpwx/yI+PvkxNNFw34D79C6FiIiIGiGGsAvIK8/DlzFfIqxHGDp5d9K7HCIiImqEGMIuYEP4BpSby/HAgAf0LoWIiIgaKYawcxgqDNgUuQkTu05Ej5Y99C6HiIiIGimGsHNsjtoMg8mABwMf1LsUIiIiasQYws5QZi7Dp+GfYnTH0ejXup/e5RAREVEjxhB2hi+jv0ReeR7mBM7RuxQiIiJq5BjCbEwWE9adXIeh7YZiSLshepdDREREjRxDmM03cd8gqzSLrWBERETkFAxhAMyaGR+e+BD9WvfDyA4j9S6HiIiIrgAMYQB+TPwRKYYUzAmcA6WU3uUQERHRFYAhDIBfMz+E9QhDSJcQvUshIiKiK4RDv8C7oRjWfhiGtR+mdxlERER0BWFLGBEREZEOGMKIiIiIdMAQRkRERKQDhjAiIiIiHTCEEREREemAIYyIiIhIBwxhRERERDpgCCMiIiLSAUMYERERkQ4YwoiIiIh0wBBGREREpAOGMCIiIiIdMIQRERER6YAhjIiIiEgHDGFEREREOmAIIyIiItIBQxgRERGRDhjCiIiIiHTAEEZERESkA4YwIiIiIh0whBERERHpgCGMiIiISAcMYUREREQ6YAgjIiIi0gFDGBEREZEOGMKIiIiIdMAQRkRERKQDhjAiIiIiHTCEEREREemAIYyIiIhIBw4NYUqpRKXUcaXUEaXUQdtti5RSp2y3HVFKTXVkDURERET1kZsT1hEiIjnn3PamiLzuhHUTERER1UvsjiQiIiLSgaNDmAD4USl1SCk154zbFyiljimlPlJK+V7oiUqpOUqpg0qpg9nZ2Q4uk4iIiMi5HB3CRovIEABTAMxXSl0DYCWAqwAEAUgHsPxCTxSR1SISLCLBfn5+Di6TiIiIyLkcGsJEJM12mQVgK4DhIpIpIhYR0QCsATDckTUQERER1UcOC2FKqeZKKe/K6wBCAZxQSvmf8bCbAZxwVA1ERERE9ZUjz45sB2CrUqpyPZ+JyPdKqU+VUkGwjhdLBPCQA2sgIiIiqpccFsJEJB7AoAvcfrej1klERETUUHCKCiIiIiIdMITRRYkItLIyvcsgIiJqlJwxYz41QFpFBdKefhrFu/eg86qVaD5c35NYK1JTUfTttxARKBdXwNXl/EtXV8DV9ezfXc6+VO7uaDZ0KFyaN9f19ViKS5D91lvwGjcOXmNG61oLAIjJBOXurncZdqOVlMCUlYWm3bvrXQoR0UUxhNF5LMUlSF2wAKV//AE3Pz+kzn0YnT9ci2aDB+tSjzE+AcmzZsFsp0l7m/bqic6rV8Pd3//yD3YAc14eUh6ai/Ljx5G/eTM6vv46fK4L1aUWACj540+k/v3v8Jl8Hdq/+CKUS8NuINdKSpA0616UR0ai0zvvwHt8iN4lNXgiAmgaYLFANA0A4OLhoXNVjZOluATGiHDAzQ3KvQmUu7v1p4n76etn/ri6XnaZYjbDYjBAKy6GZjDAUmSAVmyAxWD7vdgAzVAMi6EIWtVt1ksxm6GaNoGLhyeUR1O4NPWA8vCAS9Om1kuPplBNPc64r+npx3p4QDVtCtcWLeAZFNTg31scQYmI3jVcVnBwsBw8eFDvMq4I5txcpMyeg/KoKPgvXYLmI0ch6Z67YcnNQ5d16+AZOMCp9Rjj45E0axagCbqs+whNe/SwfghYLBCLBmgWiMUCaNrpS7PlwrdbLDClpCD9H8/DxcsLndeshkfv3k59PaZTp5D8wIMwpafDf+lS5G/ciLJjx9Dh1VfQ4oYbnFoLABh++gmnHnscLt7esOTlocUtt8B/yUsN9s1STCakzJ+Pkl9/Q5OuXWFKTUWnVSvhNVr/1ka9iQhy165F4db/Qszm04GqGpewBa8qSqHVvfei7f89qeu+YkxIQOZLL6HFrbeiRViYbnXYizk7G0n3zEJFQkL1n+TiYgtpTc4LZ1pZGSwGA6Qaw0qUpydcvbzg4u0NF28vuHr7wMXbC8rNHVJeDs1YDik3Vl1abzv7EpfIEy1uuxX+L70E24wJurAYDEh75lm0ffIJh7eSK6UOiUjw5R7HljCqUpGaiuQHHoA5Mwud3n8P3uPGAQC6fvwxkmbejeQHH0TX9R/DIyDAKfWcGcC6rv8YTXv2BADU5V/Ys39/NOnWDSmz5yBpxkx0ev89p3W1GmNikPzAg9DKy9Hlow/RbOhQeIeMQ8r8BUh76mlo5eXwnTbNKbUAQMGXXyH9hRfgGRiIzh+sQt6nG5Dz/vuAiDWIVeMIuz4REaQvWoSSPXvR/sUX4XNdKJJm3YvU+QvQZc1qNBs2TO8SdSMWCzIWv4SCzZvRbNgwuLVrB+XqAlysa/9SXf4urjDGxiBv3TpY8nLhv2SJLl3ZZceOIeWhubAUFKDk930wpaWh9YMP6vohXxfmnBwkzboXpsxMdFi2DK4tW0JMpnN+Ki5wmwkwmaBVVJz1u5jMUM084erlDRcfb+ultzdcvb3g4lUZtGy3eXnV+W8oIpCKCmsoKzdCjOXQysshRiOKtm1H3rp1cG/vD78F8+20xWpGKy9H6sPzUHr0KHzvvLP+DFUQkXr/M3ToUCHHKouMlOgxYyVy2HApOXT4vPuNKSkSfe04iRoxUspjYhxeT3lsrESNHiNRo8c4ZH0VqakSOzVMIgYESuG2bXZf/rlKDh2WyOFXS/TYa6QsMuqs+yxlZZI0e7aE9wmQ3PWfOLwWEZGcj9ZJeJ8ASbrvfrEUF1fdnvXuexLeJ0BOPf2MaGazU2qxl6x33pXwPgGS9fbbVbeZcnIkdspUiRw8REqPHtWxOv1Yysokef58Ce8TIJnL3xBN0+q8TE3TJHvlSus+NGeOWEpK7FBp9Rl275aIoMESM3GSlEdHS+pjj0t4nwBJW7RINJPJqbXYgyk7W2LDwiRi8BApOXBA73LsTtM0OfXMsxLeJ0Dyv/jC+es3mST54XkSHtBXCr79n1PWCeCgVCPf6B6wqvPDEOZYJQcOSGTwMGtAiIq66OOMCQkSNWaMRI0ZI+Xx8Q6rpzwm5nQAi4112HrM+fmScNcMCe8TIDnr1jlsPUW7dknEoCCJDb1OjCmpF3yMxWiUlAULJLxPgGSvXu2wWjRNk8zlb0h4nwBJeXShWIzG8x6T9Z4tiD31VIMJYnn/+Y+15mefOy9kVGRkSMzESRI5bLiUhYfrVKE+zAUFkjBjhoQH9JXcTz61+/LzNm2W8L79JOH2O8Scn2/35V9I/tatEt5/gMTdfLOYsrJERESzWCTz9dclvE+AJM992OmhsC5MOTkSd/31EhE0WIr//FPvchxGq6iQpAcelPB+/cXwyy/OW6+myalnn7Me5H66wWnrZQijaina+bNEDBwksddNlorUCweEM5XHxEjUyFESfc21YkxOtns95TExEjVqtMMDWCVLebmk/P0RCe8TIBmvvCqaxWLX5edv3Srh/fpL/C23iikn55KP1UwmSX3iSVtrzjt2abE4a/lms6S98E9ri8E//3XJgJX1/vsNJogV7dol4f36S9KDs0WrqLjgY4wpqRI9LsTakuuE/ao+qEhPl7jrb3B4a2/hDz9IxIBAiQ0Lk4r0dIetR9M0yVmzRsL7BEjivfeK2WA47zG5GzdKeN9+En/bNDFlZzusFnsx5eZa/0aDgqT4j8YbwCqZDcUSf/MtEhE0WEqPHXPKOjOXLat6T3UmhjC6rPwtX1oDwm3TxJSbW+3nlUVGStTwqyUmZLxUnDplt3rKo6OtAWzMGCmPi7Pbci9HM5sl/aUlEt4nQFIfe+yCrUO1UdnlZ/3AKL78E2y1nPrHP6yh8NV/2y2IaUajpDy60Nol9cab1Vpu9ooV1m3yf/9Xb4NY6bFjEhE0WOJvufWsbtULqWzJjR4zVoxJSU6qUB/lsbESPS5EIocMleJ9+xy+vuJ9f0jkkKESHRLikP9dzWKRjJdftv2PPn7J/9GinT9buyonTJTyOMe12NeVKS9P4m74mzWAOeFvVF+YsrIkZvwEiRo5yuH/hzlrPzzdTW3ng9rLYQijS6o8ojx3TFB1lR4/IZFDgyVmUqhUZGTWuZ7y6GiJGjnK6QGskqZpkrN2rTU03X2PmAsL67SsyqOvi3X5XfL5FoukL35JwvsESPqLL9a5dc5SXCxJ9z9g7Xb98KMaPbdy3E/qk/UviBkTEyVq5CiJmTCx2q0e5dHREnX1CLsfQNQnleMPo8aMcWr3a9nJk9aDqKtH2HX8ncVorBrzlfHyy9X6fyg9dsz6fjL8aik5dMhutdiLKS9P4v52o0QMHCTFv/+udzlOVx4Xb/0/DA2tUQNATeR/+ZX1PXjhQl3euxjCdJD/5Vf1vklZs1gk49V/26XVp+TwYYkcPERip0ytU9N/WVSUtYtzzFjdj1wLvvlGwgcEStz1N9Sqa0UzmarGH6QtWlTrf/4zg9ypZ56t9XLM+fmSMP12Ce/bT/K3fFmrZWSvXGXdX554st4Mejbl5EjMpFCJunpEjccnlp44IZHBwyQmNFQqMut+AFGfFO38+YzxhylOX78xMVFiJkyUiMFDxPDrr3VentlgkMR777UeQKxZU6PWDGNyssReN1kiAgdK4fbv61yLvZjy8iTupputAey33/QuRzclhw9LxMBBEj99ulhKS+267KKdP1uHKNx3n916NmqKIczJymNjJbxPQFU3kl5/+EvRKirk1FNPW1tYFr9kl/FPJfv3S0TQYIm7/gYx5eXV+Pn1KYBVKv7tN2vXyrXjpDw6utrPs5SVWc/A6RMgWe++V+fmb03Tqs5WTH3ssYuOd7qYiowM64DfwIFStGNHnWrJXvWBtY7Hn9A9iFlKSiT+tmkSMXCQlBw+/0ze6ig5fFgiBg+R2LCwWu239VHef/5zejyUg1oXqqMiM1Pi/najhA8IlIL/1f5MNFN2tsTdfLOE9+sv+Vu31m4ZeXmScMedEh7QV3I+Wuf0LqlzmfPzJe7mmyUicKAY9tY9pDZ0RTt2SHjffpI892G7va+U7N9vDXe3Tav2MBBHYAhzsvQXX5SIwIFy6jlrK0jcjTc5PIttGwAAFYFJREFUZSqH6rKUlkrynIesAeH99+36ZlT8++8SEThQ4m6+uUbdeGWRURI1YqREj73GoWdb1kZZeHjVlB3VOWPJXFh4+iy0jRvtWktlN2nyvPnVDvfGxESJGT/BNiboD7vUkf3B6qoxOXoFMc1ksu7HfftJ0U8/1WlZxX/8KREDB9V4v61vNE2rGr+X9ODsWg0vsLez/h9qcUaaMTFRYiZOkoigwWLYvbtOtVjKyiTlkUetB59LlurWrW4uKLAOSg8cKIY9e3WpoT7K++wza8/BC/+s8+dSWUSERAYPk9jJU3Q/uGIIcyJzUZFEDB4ip555VkSsTaFRI0ZKxMBBkrtxY704+qo8Gsz7/HOHrMOwe7eEDwiU+GnTL3jW0rnKIiOrApgxIcEhNdVVRWqqxE6ZetmzyyoyMyXuhr9JuAPPQsv9dIP1Q/aBBy/bdF8WEWGd4mPESCk9fsKudWSv1i+IaZomac+/IOF9Auy2Hxv27JHwAYGSMP12XY+aa0szmyX9xRdtZ7I+XePWUkeylJVJ8rz5NT7bt/TYcet4rhEj7Ta2zDqw/xXrGKEFC8RSVmaX5VaXubBQ4m+5VSIGBNY5VDZGldPmZK9YUetlGJOTrSfeXDuuXoz3ZAhzotyPP5bwPgFSeuL0B54pK8s6J0qfAEl+aO5lpydwlKouqQGBDh8XUfTTTxLef4Ak3HnXJY/GyyIjJerqEdZpLuppAKtkzs+XhDvvsh7Rf/zxefcbExKsLU6Dhzh8fEf+li0SHtDXeuLARQJD1Zxv4xxzlprI6ZM6Uh97zKlBrHLajMw33rTrcgt//FHC+/WXxLvvcfqHc12cOb1K5rJluh/sXYhmMlX1DqT969LTooiIGPb+KhGDh0jM+AkOaR3PXb9ewgP6SsL0253WZWsuLLR2nw8IdOr8WA2Jpmly6qmnrJO5fvlVjZ9vysqSmImTJGr41fWmB4ohzEk0i0ViJoVKwp13XfC+3PWfSETgQIkaPUYMe/Y4tbbyuHiJDgmxBgQnnQJduG2bhPftJ4n3zLrgB1pZRMTpAJaY6JSa6spSViYpC/5+etoI21i60hMnTh+xHzvulFoKvv2fhPfrb50c85wutKJdu6xzvk2e4vAjwcpTv1MWLnRKEMvfsuV0a48DwkbBt/+T8IC+1u68ejie81zmwkJJnHm3dQLKCxwc1Ceapknm68ut+8sjj150+xZ88411EtYbb3LoCROFP/wgEQMHSUxoqMPfg8xFRRI/bbqEDwiUop9/dui6GjrNaJSk++6X8P4DatRday4qkrgbb7LOPfbXXw6ssGYYwpykaNcuCe8TIIXffXfRx5RFRknc9TdUjUmwlJc7vK7So0clasRIiRo5yu5dUpdT8PXX1g+0Bx486w23KoBdO67BBLBKmtlcNW1E6mOPi2HPHokcMlRiQsY7fTxb4Q8/WLt+b76latxDwTffWOd8u/U2px3h53z4UdU0HI7sBjPs3m090+n+Bxy6nsqglzx/fr3q1jtXRcbp7u+6DHx3tpx1tnnzZp0/0WrlvpR4zywxFxU5vJaSw4cl6uoR1gMoB31wmw0GiZ9uC2A7GcCqw2wotp45OnhItT63LGVlkjhjpoQPCKx34+wYwpwk6f4HJHrsNZd907aUlUn6kqXWQfvX33De9wfagyk3V/I+/9y6Uwb0lZjxE3Tr7sv/4ouqrxDRjEYpCw+XqOFXWwNYA50o88wZuyv/jhUZGbrUYvjlF+ug8uuvr5rLK/GeWU4f11Q5IW3KI486JLiUHjsuEYOHWAfPO+G1VY69S338iXo3L5qISHlc3OnW7QY4vUHB119LeP8B1gOInBzrWK1XXq1qVXVmK6QxIUFiJoVKxKCgOp89fC6zwSAJt98h4f0H1PkEkitNRWamxISMl6jRYy45zYoe3wdZE9UNYcr62PotODhYDh48qHcZ5zHGxyN+ahj8Fj6KNnPnVus5xXv2IO25f0ArKkLbJ5+E790zoZSqdQ2WoiIYftqJom3bULJvH2CxoEmPHvAJmwrfO+6AW+vWtV52XeV99hkyF7+E5mPHovzYMahmzdB1/cdo0qWLbjXZQ9G2bSjevRvtnnsOri1a6FZHyR9/IOXheZCyMnhNnICOy5fDpWlTp9eR+/HHyHr13/AODUXH5a9DubvbZbkVKSlIvONOuHh4oNumz+Hm52eX5V5Ozpo1yF7+Blrcdiv8Fy+GcnGp9bJEBFJeDq28vM51VcTGIvXvjwCurui8+gN49u9f52XqoXjPHqQ+8ijc2rWFR99+MHz/PXxnzkS7556t07auDXNuLlLmzUP5seNodf998AjoC7d2beHerh3c2rWDi4dHjZdpKS5ByuzZKDt+HB3ffAM+kyY5oPLGzRgXh8S7ZsDN1xddP/8Mbr6+Z90vIkj/x/Mo/OortHv+ebSaOUOnSi9OKXVIRIIv+ziGsNrLWPwSCr74Aj1/2VWjsGPOzUX6P55H8S+/oPnYsejw8tIafcBopaUw7NqFom3bUbJnD8RkgnvnzvCZMgU+YVPRtHfvOgU7e6r8gHbz90fXT9ajSefOepfUqJQdP47S/QfQatY9UG5uutVR+Xf2DB6KZsHBcPfvAPcOHeDewR/u/v5wadasRssz5+Uh6c67YCkoQNfPP0fTHt0dVPmFZb/zLnJWrIDvjBloM/chWAzF0IoNsBgM0KquF0MzFNkuDbAU2+4zGGApLoZWVARLcTFgNtutLvcuXdBl7ZoGfyBT+tdfSJn7MLTCQvg99hhaz5mt23uWVlaGtGeeheGHH867z7VFC7i1awe39u2swaztGdfbWS9dWrSoqt1SXIKUOXNQdvSoNYCFhjr75TQapYcPI/m+++HRty+6rPsILp6eVfdlvf46ctd+iDbz5sHvkb/rWOXFMYQ5mMVgQMy14+ATGooOr75S4+eLCAo2bULmq/+GS7Nm8F+6FN7jQy76eM1oRMnevSjatg2GXb9Aysrg1rZtVfDyCAysN8HrXMW//oamvXrCvV07vUshB8r//HPkrFkDc2YWYLGcdZ9ry5Zw6+BvDWb+HeDu7386pHXoANfWrav2X62sDEn33gtjZBS6rFuHZkMGO/21iAiyXluGvHXrLvtYFy8vuHh7w7Xy0tsbLt7ecPH2gquX7bqHB1DH/0/l7gbv6647r1WgoapISYHp1Ck0HzFC71IAAJbiYpgzM2HOzIQpIxPmrEyYMjNhzsyCOSMDpqwsWHJzgXM+M1XTplWBzFKQD2N8AjouXw6fydfp9Eoaj6IdO3DqkUfhNX48Or3zNpSrK3I//AhZy5ah5Z13oP0//1lvP/cYwhws75NPkPnyK+i2ZQs8B9S+W8AYF4dTTzwJY2QkWt55B9o99VRV4heTCSV//IGi77bB8NNP0IqL4errC+/J16HF1KnwHDrU6c33RJcjZjPMWVkwpafDlJYOU1oaTOlpMKWnw5yWBtOpNGilpWc9RzVpAjf/9nDv0AFakQHl4eHo+M7bunbliAiKtm2DpbDQGqy8vKoClquXF1x8fODSvDn/B68gUlEBc3Y2TJlZMGedEdgyM2HKyoRWZECbefMYwOwob8NGZC5ZgpZ33gHPwIFIf+45eE+ZjI6vvw7l6qp3eRfFEOZAommImzIFbq1ao9vnn9V5eVpFBbLffAt569ahyVVXoc28h1G6/wAMP/wAS0EBXLy94T1pEnymTkXzEVfr2u1EVFciAs1gsIaztHRrQEtLgzk9HaZTaTDn56P17AfhO22a3qUSUT2QtXw5ctesBQA0HzUSnVatgkuTJjpXdWnVDWH8NK+Fkr17YUpKRttHH7XL8lyaNEG7p5+C19gxSHv6GaQ98SSUpye8x4+HT9hUNB8zpt7vcETVpZSCq48PXH184BEQoHc5RFTP+T32GCzFxTAlJaPjO+80qs9DtoTVQvKDs2GMjkbPnT/Z7UywSpaCApSdPIlmgwfXeDAzERER6a+6LWEczFBDxvgElPz6K3zvvMPuAQywDmD2Gj2aAYyIiKiRYwirofyNG6Hc3dFy+nS9SyEiIqIGjCGsBizFxSjcuhU+U6fqOgkqERERNXwMYTVQ+NVWaKWl8L37br1LISIiogaOIayaRNOQt3EDPIOC6jQvGBERERHAEFZtldNS+N49U+9SiIiIqBFgCKumvA0b4ebnx+8CIyIiIrtgCKsGY3wCSvbuRUsHTUtBREREVx6GsGrI/+wzKHd3+HJaCiIiIrIThrDLsBQXo/Crr+AzdQrc2rTRuxwiIiJqJBjCLqNqWoqZHJBPRERE9sMQdgmiacjfuBGegwbBMzBQ73KIiIioEWEIu4SSX39FRVISJ2clIiIiu2MIu4S8DRvg6tcGPqGT9C6FiIiIGhmGsIswJiSgZM9e+N5xB1STJnqXQ0RERI0MQ9hF5G/8DHB3h+/tt+tdChERETVCDGEXYCkuRuHWrfCZMpnTUhAREZFDuDly4UqpRAAGABYAZhEJVkq1ArAZQDcAiQCmi0i+I+uoqcKt/4VWUoJWHJBPREREDuKMlrAQEQkSkWDb788A2CkivQDstP1eb1ROS+ExaCCnpSAiIiKH0aM78kYA623X1wO4SYcaLqrkt99QkZiIVjPZCkZERESO4+gQJgB+VEodUkrNsd3WTkTSAcB22fZCT1RKzVFKHVRKHczOznZwmaflffqpdVqK60Kdtk4iIiK68jh0TBiA0SKSppRqC2CHUiqyuk8UkdUAVgNAcHCwOKrAM1UkJqJkz160WbCA01IQERGRQzm0JUxE0myXWQC2AhgOIFMp5Q8AtsssR9ZQE3lV01JM17sUIiIiauQcFsKUUs2VUt6V1wGEAjgB4BsAs2wPmwXga0fVUBOW4hIUfvUVfCZPhpufn97lEBERUSPnyO7IdgC2KqUq1/OZiHyvlDoA4D9KqQcAJAOY5sAaqq3wv5XTUszUuxQiIiK6AjgshIlIPIBBF7g9F8AER623NkTTkL9hg3VaioED9S6HiIiIrgCcMR9AyW+/26alYCsYEREROQdDGABLUSGa9u0Ln+uu07sUIiIiukI4eoqKBqFFWBh8pk6FbfwaERERkcOxJcyGAYyIiIiciSGMiIiISAcMYUREREQ6YAgjIiIi0gFDGBEREZEOGMKIiIiIdMAQRkRERKQDhjAiIiIiHTCEEREREemAIYyIiIhIBwxhRERERDpgCCMiIiLSAUMYERERkQ4YwoiIiIh0wBBGREREpAOGMCIiIiIdMIQRERER6YAhjIiIiEgHSkT0ruGylFLZAJIcvJo2AHIcvI4rGbev43DbOha3r+Nw2zoWt6/jXG7bdhURv8stpEGEMGdQSh0UkWC962isuH0dh9vWsbh9HYfb1rG4fR3HXtuW3ZFEREREOmAIIyIiItIBQ9hpq/UuoJHj9nUcblvH4vZ1HG5bx+L2dRy7bFuOCSMiIiLSAVvCiIiIiHTAEEZERESkA4YwAEqpyUqpKKVUrFLqGb3raUyUUolKqeNKqSNKqYN619PQKaU+UkplKaVOnHFbK6XUDqVUjO3SV88aG6qLbNtFSqlTtv33iFJqqp41NmRKqc5KqV1KqQil1Eml1KO227n/1tElti33XztQSnkopfYrpY7atu+Lttu7K6X+tO27m5VSTWq87Ct9TJhSyhVANIBJAFIBHABwp4iE61pYI6GUSgQQLCKcMNAOlFLXACgG8ImIDLDd9hqAPBF51XYQ4SsiT+tZZ0N0kW27CECxiLyuZ22NgVLKH4C/iBxWSnkDOATgJgD3gvtvnVxi204H9986U0opAM1FpFgp5Q7gVwCPAngcwFciskkptQrAURFZWZNlsyUMGA4gVkTiRaQCwCYAN+pcE9EFicgeAHnn3HwjgPW26+thffOlGrrItiU7EZF0ETlsu24AEAGgI7j/1tklti3ZgVgV2351t/0IgPEAtthur9W+yxBm3VFTzvg9Fdx57UkA/KiUOqSUmqN3MY1UOxFJB6xvxgDa6lxPY7NAKXXM1l3JrjI7UEp1AzAYwJ/g/mtX52xbgPuvXSilXJVSRwBkAdgBIA5AgYiYbQ+pVXZgCAPUBW67svto7Wu0iAwBMAXAfFuXD1FDsRLAVQCCAKQDWK5vOQ2fUsoLwJcAFopIkd71NCYX2Lbcf+1ERCwiEgSgE6w9aH0v9LCaLpchzJpeO5/xeycAaTrV0uiISJrtMgvAVlh3XrKvTNuYkMqxIVk619NoiEim7c1XA7AG3H/rxDae5ksAG0XkK9vN3H/t4ELblvuv/YlIAYBfAIwA0FIp5Wa7q1bZgSHMOhC/l+0shyYA7gDwjc41NQpKqea2QaJQSjUHEArgxKWfRbXwDYBZtuuzAHytYy2NSmU4sLkZ3H9rzTa4+UMAESLyxhl3cf+to4ttW+6/9qGU8lNKtbRd9wQwEdZxd7sA3GZ7WK323Sv+7EgAsJ22+xYAVwAfichSnUtqFJRSPWBt/QIANwCfcdvWjVLqcwDjALQBkAngXwD+C+A/ALoASAYwTUQ4wLyGLrJtx8HalSMAEgE8VDl+iWpGKTUGwF4AxwFotpufg3XsEvffOrjEtr0T3H/rTCk1ENaB966wNl79R0QW2z7jNgFoBeAvADNFxFijZTOEERERETkfuyOJiIiIdMAQRkRERKQDhjAiIiIiHTCEEREREemAIYyIiIhIBwxhREQXoZQap5T6n951EFHjxBBGREREpAOGMCJq8JRSM5VS+5VSR5RSH9i+bLdYKbVcKXVYKbVTKeVne2yQUuoP25cab638UmOlVE+l1E9KqaO251xlW7yXUmqLUipSKbXRNjs5EVGdMYQRUYOmlOoL4HZYvyw+CIAFwAwAzQEctn2B/G5YZ8AHgE8APC0iA2GdYbzy9o0A3heRQQBGwfqFxwAwGMBCAP0A9AAw2uEvioiuCG6XfwgRUb02AcBQAAdsjVSesH4JtAZgs+0xGwB8pZRqAaCliOy23b4ewBe27zjtKCJbAUBEygHAtrz9IpJq+/0IgG4AfnX8yyKixo4hjIgaOgVgvYg8e9aNSr1wzuMu9R1tl+piPPO74Czg+yYR2Qm7I4moodsJ4DalVFsAUEq1Ukp1hfX97TbbY+4C8KuIFALIV0qNtd1+N4DdIlIEIFUpdZNtGU2VUs2c+iqI6IrDIzoiatBEJFwp9TyAH5VSLgBMAOYDKAHQXyl1CEAhrOPGAGAWgFW2kBUP4D7b7XcD+EAptdi2jGlOfBlEdAVSIpdqoSciapiUUsUi4qV3HUREF8PuSCIiIiIdsCWMiIiISAdsCSMiIiLSAUMYERERkQ4YwoiIiIh0wBBGREREpAOGMCIiIiId/D/9U9jwuiV2iAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(train_loss_word, label = '1-gram')\n",
    "plt.plot(train_loss_bigram, label = '2-gram')\n",
    "plt.plot(train_loss_trigram, label = '3-gram')\n",
    "plt.plot(train_loss_fourgram, label = '4-gram')\n",
    "plt.legend()\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('training loss')\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(val_acc_word, label = '1-gram')\n",
    "plt.plot(val_acc_bigram, label = '2-gram')\n",
    "plt.plot(val_acc_trigram, label = '3-gram')\n",
    "plt.plot(val_acc_fourgram, label = '4-gram')\n",
    "plt.legend()\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('validation accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After training for 30 epochs\n",
      "The training losses are 2.0563602447509766e-05, 0.009786441922187805, 0.23246371746063232, 0.502927303314209\n",
      "The validation accuracy are 84.54, 74.28, 61.48, 53.94\n",
      "The Maxmimum Val Acc is 84.54 with 1-gram\n"
     ]
    }
   ],
   "source": [
    "print (\"After training for 30 epochs\")\n",
    "results = [val_acc_word[-1], val_acc_bigram[-1], val_acc_trigram[-1], val_acc_fourgram[-1]]\n",
    "max_val_acc = max(results)\n",
    "max_val_acc_ind = results.index(max_val_acc)\n",
    "print(\"The training losses are {}, {}, {}, {}\".format(train_loss_word[-1], train_loss_bigram[-1],\n",
    "                                            train_loss_trigram[-1], train_loss_fourgram[-1]))\n",
    "print(\"The validation accuracy are {}, {}, {}, {}\".format(val_acc_word[-1], val_acc_bigram[-1],\n",
    "                                            val_acc_trigram[-1], val_acc_fourgram[-1]))\n",
    "print (\"The Maxmimum Val Acc is {} with {}-gram\".format(max_val_acc, max_val_acc_ind+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train best model and report results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dataset size is 20000\n",
      "Val dataset size is 5000\n",
      "Test dataset size is 25000\n",
      "Total number of tokens in train dataset is 2174614\n"
     ]
    }
   ],
   "source": [
    "# Then, load preprocessed train, val and test datasets\n",
    "train_data_tokens = pkl.load(open(\"train_data_tokens_detailed.p\", \"rb\"))\n",
    "all_train_tokens = pkl.load(open(\"all_train_tokens_detailed.p\", \"rb\"))\n",
    "\n",
    "val_data_tokens = pkl.load(open(\"val_data_tokens_detailed.p\", \"rb\"))\n",
    "test_data_tokens = pkl.load(open(\"test_data_tokens_detailed.p\", \"rb\"))\n",
    "\n",
    "# double checking\n",
    "print (\"Train dataset size is {}\".format(len(train_data_tokens)))\n",
    "print (\"Val dataset size is {}\".format(len(val_data_tokens)))\n",
    "print (\"Test dataset size is {}\".format(len(test_data_tokens)))\n",
    "\n",
    "print (\"Total number of tokens in train dataset is {}\".format(len(all_train_tokens)))\n",
    "\n",
    "token2id, id2token = build_vocab(all_train_tokens, max_vocab_size = 100000)\n",
    "train_data_indices = token2index_dataset(train_data_tokens)\n",
    "val_data_indices = token2index_dataset(val_data_tokens)\n",
    "test_data_indices = token2index_dataset(test_data_tokens)\n",
    "\n",
    "# create pytorch dataloader\n",
    "#train_loader = NewsGroupDataset(train_data_indices, train_targets)\n",
    "#val_loader = NewsGroupDataset(val_data_indices, val_targets)\n",
    "#test_loader = NewsGroupDataset(test_data_indices, test_targets)\n",
    "\n",
    "BATCH_SIZE = 32\n",
    "train_dataset = NewsGroupDataset(train_data_indices, train_targets)\n",
    "train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n",
    "                                           batch_size=BATCH_SIZE,\n",
    "                                           collate_fn=newsgroup_collate_func,\n",
    "                                           shuffle=True)\n",
    "\n",
    "val_dataset = NewsGroupDataset(val_data_indices, val_targets)\n",
    "val_loader = torch.utils.data.DataLoader(dataset=val_dataset, \n",
    "                                           batch_size=BATCH_SIZE,\n",
    "                                           collate_fn=newsgroup_collate_func,\n",
    "                                           shuffle=False)\n",
    "\n",
    "test_dataset = NewsGroupDataset(test_data_indices, test_targets)\n",
    "test_loader = torch.utils.data.DataLoader(dataset=test_dataset, \n",
    "                                           batch_size=BATCH_SIZE,\n",
    "                                           collate_fn=newsgroup_collate_func,\n",
    "                                           shuffle=False)  \n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: [1/30], Validation Acc: 88.46\n",
      "Epoch: [2/30], Validation Acc: 87.8\n",
      "Epoch: [3/30], Validation Acc: 87.12\n",
      "Epoch: [4/30], Validation Acc: 86.7\n",
      "Epoch: [5/30], Validation Acc: 86.92\n",
      "Epoch: [6/30], Validation Acc: 86.96\n",
      "Epoch: [7/30], Validation Acc: 87.1\n",
      "Epoch: [8/30], Validation Acc: 87.08\n",
      "Epoch: [9/30], Validation Acc: 87.14\n",
      "Epoch: [10/30], Validation Acc: 86.92\n",
      "Epoch: [11/30], Validation Acc: 86.9\n",
      "Epoch: [12/30], Validation Acc: 87.0\n",
      "Epoch: [13/30], Validation Acc: 87.06\n",
      "Epoch: [14/30], Validation Acc: 87.06\n",
      "Epoch: [15/30], Validation Acc: 86.96\n",
      "Epoch: [16/30], Validation Acc: 87.0\n",
      "Epoch: [17/30], Validation Acc: 86.92\n",
      "Epoch: [18/30], Validation Acc: 86.94\n",
      "Epoch: [19/30], Validation Acc: 86.9\n",
      "Epoch: [20/30], Validation Acc: 86.9\n",
      "Epoch: [21/30], Validation Acc: 86.96\n",
      "Epoch: [22/30], Validation Acc: 86.94\n",
      "Epoch: [23/30], Validation Acc: 86.84\n",
      "Epoch: [24/30], Validation Acc: 86.84\n",
      "Epoch: [25/30], Validation Acc: 86.86\n",
      "Epoch: [26/30], Validation Acc: 86.82\n",
      "Epoch: [27/30], Validation Acc: 86.9\n",
      "Epoch: [28/30], Validation Acc: 86.84\n",
      "Epoch: [29/30], Validation Acc: 86.86\n",
      "Epoch: [30/30], Validation Acc: 86.9\n"
     ]
    }
   ],
   "source": [
    "train_loss, val_acc, model = train_model(emb_dim=50, vocab_size = len(id2token), learning_rate = 0.01,\n",
    "                                  num_epochs = 30, optim = 'Adam')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 626,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After 30 epochs\n",
      "The validation accuracy is 86.9\n",
      "Test Acc 83.192\n"
     ]
    }
   ],
   "source": [
    "print(\"After 30 epochs\")\n",
    "print(\"The validation accuracy is {}\".format(val_acc[-1]))\n",
    "print (\"Test Acc {}\".format(test_model(test_loader, model)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 582,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_example_index():\n",
    "    wrong_indices = []\n",
    "    correct_indices = []\n",
    "    n_loop = 0\n",
    "    for data, lengths, labels in val_loader:\n",
    "        data_batch, length_batch, label_batch = data, lengths, labels\n",
    "        outputs = F.softmax(model(data_batch, length_batch), dim=1)\n",
    "        predicted = outputs.max(1, keepdim=True)[1]\n",
    "        wrong = predicted.eq(labels.view_as(predicted))==0\n",
    "        correct = predicted.eq(labels.view_as(predicted))==1\n",
    "        wrong_indices_current = np.where(np.array(wrong).ravel())[0].tolist()\n",
    "        correct_indices_current = np.where(np.array(correct).ravel())[0].tolist()\n",
    "        wrong_indices.extend([index + 32*n_loop for index in wrong_indices_current])\n",
    "        correct_indices.extend([index + 32*n_loop for index in correct_indices_current])\n",
    "        n_loop += 1\n",
    "    return wrong_indices, correct_indices\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 583,
   "metadata": {},
   "outputs": [],
   "source": [
    "wrong_indices, correct_indices = find_example_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 617,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Three incorrect examples\n",
      "\n",
      "Movie review: The romance of the movie, which is also its main theme, is good and nicely presented. However, the surrounding of the love story is too lyric, graphical and unrealistic. Even worse, the psychology of the main character is weird and incomprehensible, exactly like the end of the movie. Don't hesitate to watch this movie, if it attracted your interest, but don't expect too much of it either.. \n",
      " It is originally labeled as:1, we predict it as:0\n",
      "\n",
      "\n",
      "Movie review: If you liked watching Mel Gibson in Million Dollar Hotel then you might enjoy watching Burt Reynolds in yet another film so bad it could never be distributed. I can only attest to the DVD version so maybe the VHS version is better quality wise but the movies night and dark scenes have been so poorly done that everythings seems red. I first thought my DVD players was messed up. It wasn't. If you insist on watching it I recommend you adjust the color on your TV until it is black and white. If you don't you will never be able to get through the film. If you do it will simply remind you of a poor film students attempt to revist the style of Pulp Fiction.. \n",
      " It is originally labeled as:0, we predict it as:1\n",
      "\n",
      "\n",
      "Movie review: Doctor Mordrid is one of those rare films that is completely under the radar, but is totally worthwhile. It really reminds me of the old serials from the 30s and 40s. Which is why I'd have loved to see follow-up movies... but judging by the rest of Full Moon's output there simply weren't enough tits to satisfy the typical audience. Unfortunately, thanks to a completely superfluous sacrifice scene there two too many for a family audience - which is unfortunate, because without em' this could have been a Harry Potter-style magicfest that kids would have eaten up. Both Jeffrey Combs and Yvette Nipar are great - I wasn't sure if Ms. Nipar hadn't wandered off an A-list picture onto this film, she was very believable. No, seriously! Anyway - it's a shame they didn't have the bucks to license Dr. Strange, because I think this could have been a total kiddie phenom.. \n",
      " It is originally labeled as:1, we predict it as:0\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "print(\"Three incorrect examples\\n\")\n",
    "for index in random.sample(wrong_indices,3):\n",
    "    print(\"Movie review: {}. \\n It is originally labeled as:{}, we predict it as:{}\".format(val_data[index],\n",
    "          val_targets[index], abs(val_targets[index]-1)))\n",
    "    print('\\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 622,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Three correct examples\n",
      "\n",
      "Revolutions always present opportunities for dramatic films since, in fact, most revolutions are in themselves dramatic events. Unfortunately, what this film lacks in drama is compensated for by an overabundance of boredom. One cares not who wins, loses, dies or lives--just end it as soon as possible. This is due in large measure to what seems to me to be a superficial use of background technology. Scenes of Paris and the French countryside have a cardboard quality about them. They might better be done on a bare stage and left that way. One cannot expect the amazing effects of \"The House of the Flying Daggers\" or \"The Golden Compass,\" but , after all, this is a 2002 digitally mastered production. Characters seem to enter a scene for the sake of entering a scene, so much so that one loses count of the number of times character enter and leave rooms. In my view, this film turns the French Revolution of the 1790s into the \"papier-mache\" revolution of a \"papier colle\" world.\n",
      "\n",
      "\n",
      "An angry boy who has tragically lost his parents is looked after by his grandfather. Together they find common ground in the Gaelic folk tales which have been passed down orally from generation to generation of islanders. Although tragic episodes, such as the Highland clearances, feature in the stories, there is a surprising amount of humour and gaiety in them. It's all filmed in Skye, so there is a double dose of beauty. The mountain scenery is breathtaking, and it's a rare chance to hear Scottish Gaelic spoken. I'm English, so I had to read the subtitles, but the sound of spoken Gaelic is nonetheless wonderful. The performances are just what you would expect from carefully chosen non-actors - in other words, you are watching the real thing - people who care deeply about Gaelic folklore and history. The Gaelic community, especially on Skye, worked innumerable minor miracles to make this film. Anyone who has the slightest interest in Gaelic, folk history, folk music, oral culture, Scotland, British history, multi-culturalism or social justice should go and see this film.\n",
      "\n",
      "\n",
      "I can't believe that this movie even made it to video, and that video rental stores are willing to put it on their shelves. I literary asked for a refund. Take away the fact that the movie has no historical truth it, and it is still the worse movie ever found in a video store. It is not even good enough to be called a B rated movie. Do not waste your money or your time on this movie. Just listing to the voice over and the horrible music made me sick. Anyone involved with this movie should be pulled from the union, gives the industry a black mark, but after watching most of this movie I really don't think anyone involved is a union member.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Three correct examples\\n\")\n",
    "for index in random.sample(correct_index,3):\n",
    "    print(val_data[index])\n",
    "    print('\\n')\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}