From 583b16c56b3f05ff42ca9c4908ee8995fd7557ef Mon Sep 17 00:00:00 2001 From: Karina Date: Tue, 15 Oct 2024 17:56:29 +0300 Subject: [PATCH] this year programme --- LICENSE | 21 - ...uage Processing exam program MSAI 21f .pdf | Bin 30585 -> 0 bytes .../README.md | 2 - .../assignment01_three_headed_network.ipynb | 906 --- .../network.py | 50 - .../assignment02_attention_scores/README.md | 1 - ...P_par1_Embedding_based_MT-checkpoint.ipynb | 753 --- .../Lab1_NLP_part2_NMT-checkpoint.ipynb | 941 ---- .../Lab1_NLP_part2_NMT_old-checkpoint.ipynb | 900 --- ..._part1_embedding_based_mt-checkpoint.ipynb | 753 --- .../lab1_02_nlp_part2_nmt-checkpoint.ipynb | 941 ---- homeworks/lab01_nlp/README.md | 6 - ...lab1_01_nlp_part1_embedding_based_mt.ipynb | 753 --- .../lab01_nlp/lab1_02_nlp_part2_nmt.ipynb | 941 ---- homeworks/lab01_nlp/my_network.py | 182 - homeworks/lab01_nlp/utils.py | 33 - homeworks/lab02_qa/LICENSE | 21 - homeworks/lab02_qa/README.md | 40 - ..._preprocessing_and_problem_statement.ipynb | 360 -- homeworks/lab02_qa/args.py | 247 - 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-Copyright (c) 2019 ml-mipt - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/Natural Language Processing exam program MSAI 21f .pdf b/Natural Language Processing exam program MSAI 21f .pdf deleted file mode 100644 index ff5d6ac009e8b699cd4e4eef96fd41f5a2f5ac9d..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 30585 zcmaHSb8sh3+iq;zw(VqNCmUP8*tTukwr$%^HpvDXI~yA(&-Xs(d(U5|s;j#Do?F-5 zQ+>_!)Q~HPiqkVQu)&ZoouBT)FcUEm*%?~G@bNLKSU6jo5K+kkoLw9N)B%)HbvH&oC z#~`xd<|dM`vorhlDQairM5JV5Yi#0Z;z%tZ0P{WkCn)QGgBt;em|+-&<%k&N?Hp|Y z*8f#v|F4p`g|+i{3`TKlfU}9HiIJVL2@IpGiLIHlIT14(7qfr>k(0Bd3BU%%J^NBy z*A|Zx&UZ(78oV-`gj`xsvEOx{uk9LsgF-NXkFDbe(&bgPo2gsx`mQJerNv&qyc!bq zrw<8rCXarfZ^5ga%*p3#`U~R+t=%zuG{-q8Dbab-}bj_k2d|eWS(1Q 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z=5_klH_-V=+wMXv6it(o1WTBdQw9%XNNUAYfx)_eModFo)u85GFk*7EnI~DK57P@N z?Gad$=2@geRcBk58lSbJQ0!8=g*81#D#|e1t)!~Y5|c)kN@nD8!%I&*AIEQw<^bY! z&t?OP<+Z=PKU%x5ss1O^iT<6W^gkd-@8~5Ef=J)a)W*Tu?!Qn>Qo5EP+!OC#7ZtCa zsjh{j0w~8q*VrB(gdpOz*9Vc7@Yw)Nvxm}1#y;iZA1)BjZGZzS?EDl1^^JmToMH#kxcb@t={Q{Aac`tG<<&{pJ+fI zu+cIx0a#e^X@Crjv@8G?01%%6007|A13)daGO+5h={0bZ}?u|Kl?53sORvPKfkEJdx>|L69cF_q5=xm!lD9_ zx;7xhl>w;xrVg%undM#WL46msGO~VWTFDt2n}WbvuK47i4SDP2eRNZGc(h&u`z(k0$K5a zY#^K!11&u>3mXeQBPfu6a+&D?pin?_iqG<|RyFY%S@6~Wsh{=l;Qp!P@52Aq&_A|+ z%Kcv(`a2W=J{u#DmX(o(nOPH`nH8Uz?cch^0*aFfbbtI4$UndRJsSV_+<$A}y#|KA z-~ZheO?=Q;gF5|Bm8_tF|7RNha{JeS{9CDa`#-(=D?9)|6LjMWDE~Fl{|)>CP3hkY z^Z$$ZVq^Jtz?WvsM5I4HV*eQ@#K37b(#-0uj)Yo^BCm%)6Xjf?uJx~; zdl3lro7x#!A0MZ6u#cAiEKU4S^|fs3)2c>E|Iwb=C|!6Kj$s~kall%~^<3(m6L_kC zg}S(R6j5Kqm8h}*^X4>orHLYT47 zf)a`5uve+FgUMM@ujjZ4Vo;pEd{Z{}q}qPgL*T!Q|M3(&W&nnAi$}PSEc2g2_>P(T z4;_48h5x it's count }\n", - "from collections import Counter\n", - "from tqdm import tqdm as tqdm\n", - "\n", - "token_counts = Counter()# \n", - "for _, row in tqdm(data[text_columns].iterrows()):\n", - " for string in row:\n", - " token_counts.update(string.split())\n", - "\n", - "# hint: you may or may not want to use collections.Counter" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2598827" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "token_counts.most_common(1)[0][1]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 215 - }, - "colab_type": "code", - "id": "GiOWbc15ycOb", - "outputId": "1e807140-5513-4af0-d9a9-9f029059a553" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total unique tokens : 201127\n", - "('and', 2598827)\n", - "('.', 2471477)\n", - "(',', 2266256)\n", - "('the', 2036428)\n", - "('to', 1977039)\n", - "...\n", - "('dbms_stats', 1)\n", - "('dbms_output', 1)\n", - "('dbms_job', 1)\n", - "Correct!\n", - "Vocabulary size: 33795\n", - "Correct!\n", - "Correct!\n" - ] - } - ], - "source": [ - "print(\"Total unique tokens :\", len(token_counts))\n", - "print('\\n'.join(map(str, token_counts.most_common(n=5))))\n", - "print('...')\n", - "print('\\n'.join(map(str, token_counts.most_common()[-3:])))\n", - "\n", - "assert token_counts.most_common(1)[0][1] in range(2500000, 2700000)\n", - "assert len(token_counts) in range(200000, 210000)\n", - "print('Correct!')\n", - "\n", - "min_count = 10\n", - "\n", - "# tokens from token_counts keys that had at least min_count occurrences throughout the dataset\n", - "tokens = [token for token, count in token_counts.items() if count >= min_count]# \n", - "# Add a special tokens for unknown and empty words\n", - "UNK, PAD = \"UNK\", \"PAD\"\n", - "tokens = [UNK, PAD] + sorted(tokens)\n", - "print(\"Vocabulary size:\", len(tokens))\n", - "\n", - "assert type(tokens) == list\n", - "assert len(tokens) in range(32000, 35000)\n", - "assert 'me' in tokens\n", - "assert UNK in tokens\n", - "print(\"Correct!\")\n", - "\n", - "token_to_id = {token: idx for idx, token in enumerate(tokens)}\n", - "assert isinstance(token_to_id, dict)\n", - "assert len(token_to_id) == len(tokens)\n", - "for tok in tokens:\n", - " assert tokens[token_to_id[tok]] == tok\n", - "\n", - "print(\"Correct!\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "JEsLeBjVycOw" - }, - "outputs": [], - "source": [ - "UNK_IX, PAD_IX = map(token_to_id.get, [UNK, PAD])\n", - "\n", - "def as_matrix(sequences, max_len=None):\n", - " \"\"\" Convert a list of tokens into a matrix with padding \"\"\"\n", - " if isinstance(sequences[0], str):\n", - " sequences = list(map(str.split, sequences))\n", - " \n", - " max_len = min(max(map(len, sequences)), max_len or float('inf'))\n", - " \n", - " matrix = np.full((len(sequences), max_len), np.int32(PAD_IX))\n", - " for i,seq in enumerate(sequences):\n", - " row_ix = [token_to_id.get(word, UNK_IX) for word in seq[:max_len]]\n", - " matrix[i, :len(row_ix)] = row_ix\n", - " \n", - " return matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 179 - }, - "colab_type": "code", - "id": "JiBlPkdKycOy", - "outputId": "3866b444-1e2d-4d79-d429-fecc6d8e02a8" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Lines:\n", - "engineering systems analyst\n", - "hr assistant\n", - "senior ec & i engineer\n", - "\n", - "Matrix:\n", - "[[10705 29830 2143 1 1]\n", - " [14875 2817 1 1 1]\n", - " [27345 10107 15 15069 10702]]\n" - ] - } - ], - "source": [ - "print(\"Lines:\")\n", - "print('\\n'.join(data[\"Title\"][::100000].values), end='\\n\\n')\n", - "print(\"Matrix:\")\n", - "print(as_matrix(data[\"Title\"][::100000]))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 53 - }, - "colab_type": "code", - "id": "DpOlBp7ZycO6", - "outputId": "30a911f2-7d35-4cb5-8991-60457b1e8bac" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "DictVectorizer(dtype=, separator='=', sort=True,\n", - " sparse=False)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.feature_extraction import DictVectorizer\n", - "\n", - "# we only consider top-1k most frequent companies to minimize memory usage\n", - "top_companies, top_counts = zip(*Counter(data['Company']).most_common(1000))\n", - "recognized_companies = set(top_companies)\n", - "data[\"Company\"] = data[\"Company\"].apply(lambda comp: comp if comp in recognized_companies else \"Other\")\n", - "\n", - "categorical_vectorizer = DictVectorizer(dtype=np.float32, sparse=False)\n", - "categorical_vectorizer.fit(data[categorical_columns].apply(dict, axis=1))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "yk4jmtAYycO8" - }, - "source": [ - "### The deep learning part\n", - "\n", - "Once we've learned to tokenize the data, let's design a machine learning experiment.\n", - "\n", - "As before, we won't focus too much on validation, opting for a simple train-test split.\n", - "\n", - "__To be completely rigorous,__ we've comitted a small crime here: we used the whole data for tokenization and vocabulary building. A more strict way would be to do that part on training set only. You may want to do that and measure the magnitude of changes.\n", - "\n", - "\n", - "#### Here comes the simple one-headed network from the seminar. " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 53 - }, - "colab_type": "code", - "id": "TngLcWA0ycO_", - "outputId": "6731b28c-07b1-41dc-9574-f76b01785bba" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train size = 191814\n", - "Validation size = 47954\n" - ] - } - ], - "source": [ - "from sklearn.model_selection import train_test_split\n", - "\n", - "data_train, data_val = train_test_split(data, test_size=0.2, random_state=42)\n", - "data_train.index = range(len(data_train))\n", - "data_val.index = range(len(data_val))\n", - "\n", - "print(\"Train size = \", len(data_train))\n", - "print(\"Validation size = \", len(data_val))" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "2PXuKgOSycPB" - }, - "outputs": [], - "source": [ - "def make_batch(data, max_len=None, word_dropout=0):\n", - " \"\"\"\n", - " Creates a keras-friendly dict from the batch data.\n", - " :param word_dropout: replaces token index with UNK_IX with this probability\n", - " :returns: a dict with {'title' : int64[batch, title_max_len]\n", - " \"\"\"\n", - " batch = {}\n", - " batch[\"Title\"] = as_matrix(data[\"Title\"].values, max_len)\n", - " batch[\"FullDescription\"] = as_matrix(data[\"FullDescription\"].values, max_len)\n", - " batch['Categorical'] = categorical_vectorizer.transform(data[categorical_columns].apply(dict, axis=1))\n", - " \n", - " if word_dropout != 0:\n", - " batch[\"FullDescription\"] = apply_word_dropout(batch[\"FullDescription\"], 1. - word_dropout)\n", - " \n", - " if target_column in data.columns:\n", - " batch[target_column] = data[target_column].values\n", - " \n", - " return batch\n", - "\n", - "def apply_word_dropout(matrix, keep_prop, replace_with=UNK_IX, pad_ix=PAD_IX,):\n", - " dropout_mask = np.random.choice(2, np.shape(matrix), p=[keep_prop, 1 - keep_prop])\n", - " dropout_mask &= matrix != pad_ix\n", - " return np.choose(dropout_mask, [matrix, np.full_like(matrix, replace_with)])" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 251 - }, - "colab_type": "code", - "id": "I6LpEQf0ycPD", - "outputId": "e3520cae-fba1-46cc-a216-56287b6e4929" - }, - "outputs": [], - "source": [ - "a = make_batch(data_train[:3], max_len=10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But to start with let's build the simple model using only the part of the data. Let's create the baseline solution using only the description part (so it should definetely fit into the Sequential model)." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "from torch import nn\n", - "import torch.nn.functional as F" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# You will need these to make it simple\n", - "\n", - "class Flatten(nn.Module):\n", - " def forward(self, input):\n", - " return input.view(input.size(0), -1)\n", - "\n", - "class Reorder(nn.Module):\n", - " def forward(self, input):\n", - " return input.permute((0, 2, 1))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To generate minibatches we will use simple pyton generator." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "def iterate_minibatches(data, batch_size=256, shuffle=True, cycle=False, **kwargs):\n", - " \"\"\" iterates minibatches of data in random order \"\"\"\n", - " while True:\n", - " indices = np.arange(len(data))\n", - " if shuffle:\n", - " indices = np.random.permutation(indices)\n", - "\n", - " for start in range(0, len(indices), batch_size):\n", - " batch = make_batch(data.iloc[indices[start : start + batch_size]], **kwargs)\n", - " target = batch.pop(target_column)\n", - " yield batch, target\n", - " \n", - " if not cycle: break" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "iterator = iterate_minibatches(data_train, 3)\n", - "batch, target = next(iterator)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Here is some startup code:\n", - "n_tokens=len(tokens)\n", - "n_cat_features=len(categorical_vectorizer.vocabulary_)\n", - "hid_size=64\n", - "simple_model = nn.Sequential()\n", - "\n", - "simple_model.add_module('emb', nn.Embedding(num_embeddings=n_tokens, embedding_dim=hid_size))\n", - "simple_model.add_module('reorder', Reorder())\n", - "simple_model.add_module('conv1', nn.Conv1d(\n", - " in_channels=hid_size,\n", - " out_channels=hid_size,\n", - " kernel_size=2)\n", - " )\n", - "simple_model.add_module('relu1', nn.ReLU())\n", - "simple_model.add_module('adapt_avg_pool', nn.AdaptiveAvgPool1d(output_size=1))\n", - "simple_model.add_module('flatten1', Flatten())\n", - "simple_model.add_module('linear1', nn.Linear(in_features=hid_size, out_features=1))\n", - "# " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Title': array([[11439, 1467, 1, 1, 1, 1, 1, 1, 1,\n", - " 1, 1],\n", - " [18664, 7252, 195, 24093, 18670, 12351, 13242, 195, 12724,\n", - " 195, 10720],\n", - " [26688, 10702, 1, 1, 1, 1, 1, 1, 1,\n", - " 1, 1]], dtype=int32),\n", - " 'FullDescription': array([[30411, 26324, 33079, ..., 1, 1, 1],\n", - " [18664, 7252, 195, ..., 195, 0, 80],\n", - " [26688, 10702, 10364, ..., 1, 1, 1]], dtype=int32),\n", - " 'Categorical': array([[1., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)}" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "batch" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "__Remember!__ We are working with regression problem and predicting only one number." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[0.0493],\n", - " [0.1251],\n", - " [0.0742]], grad_fn=)" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Try this to check your model. `torch.long` tensors are required for nn.Embedding layers.\n", - "simple_model(torch.tensor(batch['FullDescription'], dtype=torch.long))" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(3, 653)" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "batch['FullDescription'].shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And now simple training pipeline (it's commented because we've already done that in class. No need to do it again)." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# from IPython.display import clear_output\n", - "# from random import sample\n", - "\n", - "# epochs = 1\n", - "\n", - "# model = simple_model\n", - "# opt = torch.optim.Adam(model.parameters())\n", - "# loss_func = nn.MSELoss()\n", - "\n", - "# history = []\n", - "# for epoch_num in range(epochs):\n", - "# for idx, (batch, target) in enumerate(iterate_minibatches(data_train)):\n", - "# # Preprocessing the batch data and target\n", - "# batch = torch.tensor(batch['FullDescription'], dtype=torch.long)\n", - "\n", - "# target = torch.tensor(target)\n", - "\n", - "\n", - "# predictions = model(batch)\n", - "# predictions = predictions.view(predictions.size(0))\n", - "\n", - "# loss = loss_func(predictions, target)# \n", - "\n", - "# # train with backprop\n", - "# loss.backward()\n", - "# opt.step()\n", - "# opt.zero_grad()\n", - "# # \n", - "\n", - "# history.append(loss.data.numpy())\n", - "# if (idx+1)%10==0:\n", - "# clear_output(True)\n", - "# plt.plot(history,label='loss')\n", - "# plt.legend()\n", - "# plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Actual homework starts here\n", - "__Your ultimate task is to code the three headed network described on the picture below.__ \n", - "To make it closer to the real world, please store the network code in file `network.py` in this directory. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "0eI5h9UMycPF" - }, - "source": [ - "#### Architecture\n", - "\n", - "Our main model consists of three branches:\n", - "* Title encoder\n", - "* Description encoder\n", - "* Categorical features encoder\n", - "\n", - "We will then feed all 3 branches into one common network that predicts salary.\n", - "\n", - "\n", - "\n", - "This clearly doesn't fit into PyTorch __Sequential__ interface. To build such a network, one will have to use [__PyTorch nn.Module API__](https://pytorch.org/docs/stable/nn.html#torch.nn.Module)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import network" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Re-run this cell if you updated the file with network source code\n", - "import imp\n", - "imp.reload(network)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model = network.ThreeInputsNet(\n", - " n_tokens=len(tokens),\n", - " n_cat_features=len(categorical_vectorizer.vocabulary_),\n", - "\n", - " # this parameter defines the number of the inputs in the layer,\n", - " # which stands after the concatenation. In should be found out by you.\n", - " concat_number_of_features= \n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "testing_batch, _ = next(iterate_minibatches(data_train, 3))\n", - "testing_batch = [\n", - " torch.tensor(testing_batch['Title'], dtype=torch.long),\n", - " torch.tensor(testing_batch['FullDescription'], dtype=torch.long),\n", - " torch.tensor(testing_batch['Categorical'])\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "assert model(testing_batch).shape == torch.Size([3, 1])\n", - "assert model(testing_batch).dtype == torch.float32\n", - "print('Seems fine!')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now train the network for a while (100 batches would be fine)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Training pipeline comes here (almost the same as for the simple_model)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, to evaluate the model it can be switched to `eval` state." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.eval()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def generate_submission(model, data, batch_size=256, name=\"\", three_inputs_mode=True, **kw):\n", - " squared_error = abs_error = num_samples = 0.0\n", - " output_list = []\n", - " for batch_x, batch_y in tqdm(iterate_minibatches(data, batch_size=batch_size, shuffle=False, **kw)):\n", - " if three_inputs_mode:\n", - " batch = [\n", - " torch.tensor(batch_x['Title'], dtype=torch.long),\n", - " torch.tensor(batch_x['FullDescription'], dtype=torch.long),\n", - " torch.tensor(batch_x['Categorical'])\n", - " ]\n", - " else:\n", - " batch = torch.tensor(batch_x['FullDescription'], dtype=torch.long)\n", - "\n", - " batch_pred = model(batch)[:, 0].detach().numpy()\n", - " \n", - " output_list.append((list(batch_pred), list(batch_y)))\n", - " \n", - " squared_error += np.sum(np.square(batch_pred - batch_y))\n", - " abs_error += np.sum(np.abs(batch_pred - batch_y))\n", - " num_samples += len(batch_y)\n", - " print(\"%s results:\" % (name or \"\"))\n", - " print(\"Mean square error: %.5f\" % (squared_error / num_samples))\n", - " print(\"Mean absolute error: %.5f\" % (abs_error / num_samples))\n", - " \n", - "\n", - " batch_pred = [c for x in output_list for c in x[0]]\n", - " batch_y = [c for x in output_list for c in x[1]]\n", - " output_df = pd.DataFrame(list(zip(batch_pred, batch_y)), columns=['batch_pred', 'batch_y'])\n", - " output_df.to_csv('submission.csv', index=False)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "generate_submission(model, data_for_autotest, name='Submission')\n", - "print('Submission file generated')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "__Both the notebook and the `.py` file are required to submit this homework.__" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "name": "CNN_for_texts.ipynb", - "provenance": [], - "version": "0.3.2" - }, - "kernelspec": { - "display_name": "Py3 research env", - "language": "python", - "name": "py3_research" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/homeworks/assignment01_three_headed_network/network.py b/homeworks/assignment01_three_headed_network/network.py deleted file mode 100644 index 07decef..0000000 --- a/homeworks/assignment01_three_headed_network/network.py +++ /dev/null @@ -1,50 +0,0 @@ - -import numpy as np -import pandas as pd - -import torch -from torch import nn -import torch.nn.functional as F - -import tqdm - - -class ThreeInputsNet(nn.Module): - def __init__(self, n_tokens, n_cat_features, concat_number_of_features, hid_size=64): - super(ThreeInputsNet, self).__init__() - self.title_emb = nn.Embedding(n_tokens, embedding_dim=hid_size) - # - - self.full_emb = nn.Embedding(num_embeddings=n_tokens, embedding_dim=hid_size) - # - - self.category_out = # - - - # Example for the final layers (after the concatenation) - self.inter_dense = nn.Linear(in_features=concat_number_of_features, out_features=hid_size*2) - self.final_dense = nn.Linear(in_features=hid_size*2, out_features=1) - - - - def forward(self, whole_input): - input1, input2, input3 = whole_input - title_beg = self.title_emb(input1).permute((0, 2, 1)) - title = # - - full_beg = self.full_emb(input2).permute((0, 2, 1)) - full = # - - category = # - - concatenated = torch.cat( - [ - title.view(title.size(0), -1), - full.view(full.size(0), -1), - category.view(category.size(0), -1) - ], - dim=1) - - out = # - - return out \ No newline at end of file diff --git a/homeworks/assignment02_attention_scores/README.md b/homeworks/assignment02_attention_scores/README.md deleted file mode 100644 index 3d9f15b..0000000 --- a/homeworks/assignment02_attention_scores/README.md +++ /dev/null @@ -1 +0,0 @@ -Please, refer to week04 attention notebook and finish the concat and general attention scores. diff --git a/homeworks/lab01_nlp/.ipynb_checkpoints/Lab1_NLP_par1_Embedding_based_MT-checkpoint.ipynb b/homeworks/lab01_nlp/.ipynb_checkpoints/Lab1_NLP_par1_Embedding_based_MT-checkpoint.ipynb deleted file mode 100644 index 0f42882..0000000 --- a/homeworks/lab01_nlp/.ipynb_checkpoints/Lab1_NLP_par1_Embedding_based_MT-checkpoint.ipynb +++ /dev/null @@ -1,753 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "eulvfJWl7ueY" - }, - "source": [ - "# Lab 1\n", - "\n", - "\n", - "## Part 1: Bilingual dictionary induction and unsupervised embedding-based MT (30%)\n", - "*Note: this homework is based on materials from yandexdataschool [NLP course](https://github.com/yandexdataschool/nlp_course/). Feel free to check this awesome course if you wish to dig deeper.*\n", - "\n", - "*Refined by [Nikolay Karpachev](https://www.linkedin.com/in/nikolay-karpachev-b0146a104/)*" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "fV4rIjxa7uei" - }, - "source": [ - "**In this homework** **YOU** will make machine translation system without using parallel corpora, alignment, attention, 100500 depth super-cool recurrent neural network and all that kind superstuff.\n", - "\n", - "But even without parallel corpora this system can be good enough (hopefully), in particular for similar languages, e.g. Ukrainian and Russian. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "idSYq2GU7uew" - }, - "source": [ - "### Frament of the Swadesh list for some slavic languages\n", - "\n", - "The Swadesh list is a lexicostatistical stuff. It's named after American linguist Morris Swadesh and contains basic lexis. This list are used to define subgroupings of languages, its relatedness.\n", - "\n", - "So we can see some kind of word invariance for different Slavic languages.\n", - "\n", - "\n", - "| Russian | Belorussian | Ukrainian | Polish | Czech | Bulgarian |\n", - "|-----------------|--------------------------|-------------------------|--------------------|-------------------------------|-----------------------|\n", - "| женщина | жанчына, кабета, баба | жінка | kobieta | žena | жена |\n", - "| мужчина | мужчына | чоловік, мужчина | mężczyzna | muž | мъж |\n", - "| человек | чалавек | людина, чоловік | człowiek | člověk | човек |\n", - "| ребёнок, дитя | дзіця, дзіцёнак, немаўля | дитина, дитя | dziecko | dítě | дете |\n", - "| жена | жонка | дружина, жінка | żona | žena, manželka, choť | съпруга, жена |\n", - "| муж | муж, гаспадар | чоловiк, муж | mąż | muž, manžel, choť | съпруг, мъж |\n", - "| мать, мама | маці, матка | мати, матір, неня, мама | matka | matka, máma, 'стар.' mateř | майка |\n", - "| отец, тятя | бацька, тата | батько, тато, татусь | ojciec | otec | баща, татко |\n", - "| много | шмат, багата | багато | wiele | mnoho, hodně | много |\n", - "| несколько | некалькі, колькі | декілька, кілька | kilka | několik, pár, trocha | няколко |\n", - "| другой, иной | іншы | інший | inny | druhý, jiný | друг |\n", - "| зверь, животное | жывёла, звер, істота | тварина, звір | zwierzę | zvíře | животно |\n", - "| рыба | рыба | риба | ryba | ryba | риба |\n", - "| птица | птушка | птах, птиця | ptak | pták | птица |\n", - "| собака, пёс | сабака | собака, пес | pies | pes | куче, пес |\n", - "| вошь | вош | воша | wesz | veš | въшка |\n", - "| змея, гад | змяя | змія, гад | wąż | had | змия |\n", - "| червь, червяк | чарвяк | хробак, черв'як | robak | červ | червей |\n", - "| дерево | дрэва | дерево | drzewo | strom, dřevo | дърво |\n", - "| лес | лес | ліс | las | les | гора, лес |\n", - "| палка | кій, палка | палиця | patyk, pręt, pałka | hůl, klacek, prut, kůl, pálka | палка, пръчка, бастун |" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "cNM3_fjr7ue2" - }, - "source": [ - "But the context distribution of these languages demonstrates even more invariance. And we can use this fact for our for our purposes." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "YLppwa527ue6" - }, - "source": [ - "## Data" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "lYBGKAUn7ue_" - }, - "outputs": [], - "source": [ - "import gensim\n", - "import numpy as np\n", - "from gensim.models import KeyedVectors" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "MwGoVhRA7ufP" - }, - "source": [ - "In this notebook we're going to use pretrained word vectors - FastText (original paper - https://arxiv.org/abs/1607.04606).\n", - "\n", - "You can download them from the official [website](https://fasttext.cc/docs/en/crawl-vectors.html). We're going to need embeddings for Russian and Ukrainian languages. Please use word2vec-compatible format (.text)." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "u1JjQv_97ufT" - }, - "outputs": [], - "source": [ - "uk_emb = KeyedVectors.load_word2vec_format(\"cc.uk.300.vec\")" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "ffzuept_7ufd" - }, - "outputs": [], - "source": [ - "ru_emb = KeyedVectors.load_word2vec_format(\"cc.ru.300.vec\")" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "nTkXfT0W7ufk" - }, - "outputs": [], - "source": [ - "ru_emb.most_similar([ru_emb[\"август\"]], topn=10)" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "vdBA8lcg7ufs" - }, - "outputs": [], - "source": [ - "uk_emb.most_similar([uk_emb[\"серпень\"]])" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "_yJvcKXO7uf0" - }, - "outputs": [], - "source": [ - "ru_emb.most_similar([uk_emb[\"серпень\"]])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "pNdYAR1q7uf6" - }, - "source": [ - "Load small dictionaries for correspoinding words pairs as trainset and testset." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "35d_DAK67uf8" - }, - "outputs": [], - "source": [ - "def load_word_pairs(filename):\n", - " uk_ru_pairs = []\n", - " uk_vectors = []\n", - " ru_vectors = []\n", - " with open(filename, \"r\") as inpf:\n", - " for line in inpf:\n", - " uk, ru = line.rstrip().split(\"\\t\")\n", - " if uk not in uk_emb or ru not in ru_emb:\n", - " continue\n", - " uk_ru_pairs.append((uk, ru))\n", - " uk_vectors.append(uk_emb[uk])\n", - " ru_vectors.append(ru_emb[ru])\n", - " return uk_ru_pairs, np.array(uk_vectors), np.array(ru_vectors)" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "wkNL602WHJyO" - }, - "outputs": [], - "source": [ - "!wget -O ukr_rus.train.txt http://tiny.cc/jfgecz" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "uoclU6JcHCcn" - }, - "outputs": [], - "source": [ - "!wget -O ukr_rus.test.txt http://tiny.cc/6zoeez" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "05BqsdSK7ugD" - }, - "outputs": [], - "source": [ - "uk_ru_train, X_train, Y_train = load_word_pairs(\"ukr_rus.train.txt\")" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "zQOZw51r7ugL" - }, - "outputs": [], - "source": [ - "uk_ru_test, X_test, Y_test = load_word_pairs(\"ukr_rus.test.txt\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "-ZBBNvpz7ugQ" - }, - "source": [ - "## Embedding space mapping (0.3 pts)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "x_Dhk5gL7ugS" - }, - "source": [ - "Let $x_i \\in \\mathrm{R}^d$ be the distributed representation of word $i$ in the source language, and $y_i \\in \\mathrm{R}^d$ is the vector representation of its translation. Our purpose is to learn such linear transform $W$ that minimizes euclidian distance between $Wx_i$ and $y_i$ for some subset of word embeddings. Thus we can formulate so-called Procrustes problem:\n", - "\n", - "$$W^*= \\arg\\min_W \\sum_{i=1}^n||Wx_i - y_i||_2$$\n", - "or\n", - "$$W^*= \\arg\\min_W ||WX - Y||_F$$\n", - "\n", - "where $||*||_F$ - Frobenius norm." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "acOjDdtL7ugY" - }, - "source": [ - "$W^*= \\arg\\min_W \\sum_{i=1}^n||Wx_i - y_i||_2$ looks like simple multiple linear regression (without intercept fit). So let's code." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "Lb-KN1be7uga" - }, - "outputs": [], - "source": [ - "from sklearn.linear_model import LinearRegression\n", - "\n", - "# YOUR CODE HERE\n", - "# mapping = ...\n", - "# -------" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "X7tqJwoY7ugf" - }, - "source": [ - "Let's take a look at neigbours of the vector of word _\"серпень\"_ (_\"август\"_ in Russian) after linear transform." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "31SrFSbn7ugi" - }, - "outputs": [], - "source": [ - "august = mapping.predict(uk_emb[\"серпень\"].reshape(1, -1))\n", - "ru_emb.most_similar(august)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "okSkjk597ugo" - }, - "source": [ - "We can see that neighbourhood of this embedding cosists of different months, but right variant is on the ninth place." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "o2uY6Y9B7ugt" - }, - "source": [ - "As quality measure we will use precision top-1, top-5 and top-10 (for each transformed Ukrainian embedding we count how many right target pairs are found in top N nearest neighbours in Russian embedding space)." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "zptuho8LAfIE" - }, - "outputs": [], - "source": [ - "def precision(pairs, mapped_vectors, topn=1):\n", - " \"\"\"\n", - " :args:\n", - " pairs = list of right word pairs [(uk_word_0, ru_word_0), ...]\n", - " mapped_vectors = list of embeddings after mapping from source embedding space to destination embedding space\n", - " topn = the number of nearest neighbours in destination embedding space to choose from\n", - " :returns:\n", - " precision_val, float number, total number of words for those we can find right translation at top K.\n", - " \"\"\"\n", - " assert len(pairs) == len(mapped_vectors)\n", - " num_matches = 0\n", - " for i, (_, ru) in enumerate(pairs):\n", - " # YOUR CODE HERE\n", - " precision_val = num_matches / len(pairs)\n", - " return precision_val" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "duhj9hpv7ugy" - }, - "outputs": [], - "source": [ - "assert precision([(\"серпень\", \"август\")], august, topn=5) == 0.0\n", - "assert precision([(\"серпень\", \"август\")], august, topn=9) == 1.0\n", - "assert precision([(\"серпень\", \"август\")], august, topn=10) == 1.0" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "0-iyd5gP7ug5" - }, - "outputs": [], - "source": [ - "assert precision(uk_ru_test, X_test) == 0.0\n", - "assert precision(uk_ru_test, Y_test) == 1.0" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "U-ssEJ3x7uhA" - }, - "outputs": [], - "source": [ - "precision_top1 = precision(uk_ru_test, mapping.predict(X_test), 1)\n", - "precision_top5 = precision(uk_ru_test, mapping.predict(X_test), 5)" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "7K-hy7a6Ksn2" - }, - "outputs": [], - "source": [ - "print(precision_top1)\n", - "print(precision_top5)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "hf6Ou8bx7uhH" - }, - "source": [ - "## Making it better (orthogonal Procrustean problem) (0.3 pts)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "4oLs-drN7uhK" - }, - "source": [ - "It can be shown (see original paper) that a self-consistent linear mapping between semantic spaces should be orthogonal. \n", - "We can restrict transform $W$ to be orthogonal. Then we will solve next problem:\n", - "\n", - "$$W^*= \\arg\\min_W ||WX - Y||_F \\text{, where: } W^TW = I$$\n", - "\n", - "$$I \\text{- identity matrix}$$\n", - "\n", - "Instead of making yet another regression problem we can find optimal orthogonal transformation using singular value decomposition. It turns out that optimal transformation $W^*$ can be expressed via SVD components:\n", - "$$X^TY=U\\Sigma V^T\\text{, singular value decompostion}$$\n", - "$$W^*=UV^T$$" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "_KSaRJFGMFiJ" - }, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "DdFQ7qti7uhL" - }, - "outputs": [], - "source": [ - "def learn_transform(X_train, Y_train):\n", - " \"\"\" \n", - " :returns: W* : float matrix[emb_dim x emb_dim] as defined in formulae above\n", - " \"\"\"\n", - " # YOUR CODE GOES HERE\n", - " # compute orthogonal embedding space mapping\n", - " # mapping = ...\n", - "\n", - " return mapping" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "7X7QfYDd7uhQ" - }, - "outputs": [], - "source": [ - "W = learn_transform(X_train, Y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "OVOFYYa37uhX" - }, - "outputs": [], - "source": [ - "ru_emb.most_similar([np.matmul(uk_emb[\"серпень\"], W)])" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "r297sYP37uhb" - }, - "outputs": [], - "source": [ - "print(precision(uk_ru_test, np.matmul(X_test, W)))\n", - "print(precision(uk_ru_test, np.matmul(X_test, W), 5))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "hvUZ72U5AfJg" - }, - "source": [ - "## Unsupervised embedding-based MT (0.4 pts)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "LLyuVfHBLrJn" - }, - "source": [ - "Now, let's build our word embeddings-based translator!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "tPAURW1CMuP7" - }, - "source": [ - "Firstly, download OPUS Tatoeba corpus." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "F80kUKzQMsDu" - }, - "outputs": [], - "source": [ - "!wget https://object.pouta.csc.fi/OPUS-Tatoeba/v20190709/mono/uk.txt.gz" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "0CGFZoxCUVf1" - }, - "outputs": [], - "source": [ - "!gzip -d ./uk.txt.gz" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "2MV3VvoVUX5U" - }, - "outputs": [], - "source": [ - "with open('./uk.txt', 'r') as f:\n", - " uk_corpus = f.readlines()" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "tU7nPVf0UhbI" - }, - "outputs": [], - "source": [ - "# To save your time and CPU, feel free to use first 1000 sentences of the corpus\n", - "uk_corpus = uk_corpus[:1000]" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "FLN8dBOXAfJ1" - }, - "outputs": [], - "source": [ - "# Any necessary preprocessing if needed\n", - "# YOUR CODE HERE" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "FGksC7l_NMi9" - }, - "outputs": [], - "source": [ - "def translate(sentence):\n", - " \"\"\"\n", - " :args:\n", - " sentence - sentence in Ukrainian (str)\n", - " :returns:\n", - " translation - sentence in Russian (str)\n", - "\n", - " * find ukrainian embedding for each word in sentence\n", - " * transform ukrainian embedding vector\n", - " * find nearest russian word and replace\n", - " \"\"\"\n", - " # YOUR CODE GOES HERE\n", - "\n", - " return \" \".join(translated)" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "4hbbMy-tNxlf" - }, - "outputs": [], - "source": [ - "assert translate(\".\") == \".\"\n", - "assert translate(\"1 , 3\") == \"1 , 3\"\n", - "assert translate(\"кіт зловив мишу\") == \"кот поймал мышку\"" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "ia6I2ce7O_HI" - }, - "source": [ - "Now you can play with your model and try to get as accurate translations as possible. **Note**: one big issue is out-of-vocabulary words. Try to think of various ways of handling it (you can start with translating each of them to a special **UNK** token and then move to more sophisticated approaches). Good luck!" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "ap1W7ZCeOAVU" - }, - "outputs": [], - "source": [ - "for sent in uk_corpus[::10]:\n", - " print(translate(sent))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Great! \n", - "See second notebook for the Neural Machine Translation assignment." - ] - } - ], - "metadata": { - "anaconda-cloud": {}, - "colab": { - "collapsed_sections": [], - "machine_shape": "hm", - "name": "homework.ipynb", - "provenance": [] - }, - "kernelspec": { - "display_name": "Py3 research env", - "language": "python", - "name": "py3_research" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/homeworks/lab01_nlp/.ipynb_checkpoints/Lab1_NLP_part2_NMT-checkpoint.ipynb b/homeworks/lab01_nlp/.ipynb_checkpoints/Lab1_NLP_part2_NMT-checkpoint.ipynb deleted file mode 100644 index cae6998..0000000 --- a/homeworks/lab01_nlp/.ipynb_checkpoints/Lab1_NLP_part2_NMT-checkpoint.ipynb +++ /dev/null @@ -1,941 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Lab 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Part 2: Neural Machine Translation in the wild\n", - "In the third homework you are supposed to get the best translation you can for the EN-RU translation task.\n", - "\n", - "Basic approach using RNNs as encoder and decoder is implemented for you. \n", - "\n", - "Your ultimate task is to use the techniques we've covered, e.g.\n", - "\n", - "* Optimization enhancements (e.g. learning rate decay)\n", - "\n", - "* CNN encoder (with or without positional encoding)\n", - "\n", - "* attention/self-attention mechanism\n", - "\n", - "* pretraining the language model\n", - "\n", - "* [Byte Pair Encoding](https://github.com/rsennrich/subword-nmt)\n", - "\n", - "* or just fine-tunning BERT ;)\n", - "\n", - "to improve the translation quality. \n", - "\n", - "__Please use at least three different approaches/models and compare them (translation quality/complexity/training and evaluation time).__\n", - "\n", - "Write down some summary on your experiments and illustrate it with convergence plots/metrics and your thoughts. Just like you would approach a real problem." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# You might need to install the libraries below. Do it in the desired environment\n", - "# if you are working locally.\n", - "\n", - "# ! pip install subword-nmt\n", - "# ! pip install nltk\n", - "# ! pip install torchtext" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Thanks to YSDA NLP course team for the data\n", - "# (who thanks tilda and deephack teams for the data in their turn)\n", - "\n", - "import os\n", - "path_do_data = '../../datasets/Machine_translation_EN_RU/data.txt'\n", - "if not os.path.exists(path_do_data):\n", - " print(\"Dataset not found locally. Downloading from github. Loading special files as well\")\n", - " !wget https://raw.githubusercontent.com/girafe-ai/ml-mipt/advanced_f20/datasets/Machine_translation_EN_RU/data.txt -nc\n", - " path_do_data = './data.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if not os.path.exists('./utils.py'):\n", - " print(\"utils file not found locally. Downloading from github.\")\n", - " !wget https://raw.githubusercontent.com/girafe-ai/ml-mipt/advanced_f20/homeworks_advanced/Lab1_NLP/utils.py -nc\n", - "\n", - "if not os.path.exists('./my_network.py'):\n", - " print(\"network file not found locally. Downloading from github.\")\n", - " !wget https://raw.githubusercontent.com/girafe-ai/ml-mipt/advanced_f20/homeworks_advanced/Lab1_NLP/my_network.py -nc" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", - "\n", - "import torchtext\n", - "from torchtext.datasets import TranslationDataset, Multi30k\n", - "from torchtext.data import Field, BucketIterator\n", - "\n", - "import spacy\n", - "\n", - "import random\n", - "import math\n", - "import time\n", - "\n", - "import matplotlib\n", - "matplotlib.rcParams.update({'figure.figsize': (16, 12), 'font.size': 14})\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "from IPython.display import clear_output\n", - "\n", - "from nltk.tokenize import WordPunctTokenizer\n", - "from subword_nmt.learn_bpe import learn_bpe\n", - "from subword_nmt.apply_bpe import BPE\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Main part\n", - "__Here comes the preprocessing. Do not hesitate to use BPE or more complex preprocessing ;)__" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "tokenizer_W = WordPunctTokenizer()\n", - "def tokenize(x, tokenizer=tokenizer_W):\n", - " return tokenizer.tokenize(x.lower())" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "SRC = Field(tokenize=tokenize,\n", - " init_token = '', \n", - " eos_token = '', \n", - " lower = True)\n", - "\n", - "TRG = Field(tokenize=tokenize,\n", - " init_token = '', \n", - " eos_token = '', \n", - " lower = True)\n", - "\n", - "dataset = torchtext.data.TabularDataset(\n", - " path=path_do_data,\n", - " format='tsv',\n", - " fields=[('trg', TRG), ('src', SRC)]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "train_data, valid_data, test_data = dataset.split(split_ratio=[0.8, 0.15, 0.05])" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of training examples: 40000\n", - "Number of validation examples: 2500\n", - "Number of testing examples: 7500\n" - ] - } - ], - "source": [ - "print(f\"Number of training examples: {len(train_data.examples)}\")\n", - "print(f\"Number of validation examples: {len(valid_data.examples)}\")\n", - "print(f\"Number of testing examples: {len(test_data.examples)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "SRC.build_vocab(train_data, min_freq = 3)\n", - "TRG.build_vocab(train_data, min_freq = 3)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Unique tokens in source (ru) vocabulary: 9267\n", - "Unique tokens in target (en) vocabulary: 6699\n" - ] - } - ], - "source": [ - "print(f\"Unique tokens in source (ru) vocabulary: {len(SRC.vocab)}\")\n", - "print(f\"Unique tokens in target (en) vocabulary: {len(TRG.vocab)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here are tokens from original (RU) corpus:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['',\n", - " '29',\n", - " 'соль',\n", - " 'комо',\n", - " '―',\n", - " 'электрическая',\n", - " 'ming',\n", - " 'утренний',\n", - " 'детском',\n", - " 'таунус']" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "SRC.vocab.itos[::1000]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And from target (EN) corpus:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['', 'king', 'buffets', 'catch', 'media', 'schedule', 'maraunenhof']" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "TRG.vocab.itos[::1000]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And here is example from train dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'trg': ['laundry', 'service', 'is', 'provided', '.'], 'src': ['помимо', 'этого', ',', 'гостям', 'предоставляются', 'услуги', 'прачечной', '.']}\n" - ] - } - ], - "source": [ - "print(vars(train_data.examples[9]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's check the length distributions:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Length distribution in Train data\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "src_length = map(len, [vars(x)['src'] for x in train_data.examples])\n", - "trg_length = map(len, [vars(x)['trg'] for x in train_data.examples])\n", - "\n", - "print('Length distribution in Train data')\n", - "plt.figure(figsize=[8, 4])\n", - "plt.subplot(1, 2, 1)\n", - "plt.title(\"source length\")\n", - "plt.hist(list(src_length), bins=20);\n", - "\n", - "plt.subplot(1, 2, 2)\n", - "plt.title(\"translation length\")\n", - "plt.hist(list(trg_length), bins=20);" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Length distribution in Test data\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "src_length = map(len, [vars(x)['src'] for x in test_data.examples])\n", - "trg_length = map(len, [vars(x)['trg'] for x in test_data.examples])\n", - "\n", - "print('Length distribution in Test data')\n", - "plt.figure(figsize=[8, 4])\n", - "plt.subplot(1, 2, 1)\n", - "plt.title(\"source length\")\n", - "plt.hist(list(src_length), bins=20);\n", - "\n", - "plt.subplot(1, 2, 2)\n", - "plt.title(\"translation length\")\n", - "plt.hist(list(trg_length), bins=20);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Model side\n", - "__Here comes simple pipeline of NMT model learning. It almost copies the week03 practice__" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "device(type='cuda', index=1)" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "device" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "def _len_sort_key(x):\n", - " return len(x.src)\n", - "\n", - "BATCH_SIZE = 128\n", - "\n", - "train_iterator, valid_iterator, test_iterator = BucketIterator.splits(\n", - " (train_data, valid_data, test_data), \n", - " batch_size = BATCH_SIZE, \n", - " device = device,\n", - " sort_key=_len_sort_key\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "[torchtext.data.batch.Batch of size 128]\n", - "\t[.trg]:[torch.cuda.LongTensor of size 55x128 (GPU 1)]\n", - "\t[.src]:[torch.cuda.LongTensor of size 59x128 (GPU 1)]\n", - "torch.Size([59, 128]) torch.Size([55, 128])\n" - ] - } - ], - "source": [ - "for x in train_iterator:\n", - " break\n", - "print(x)\n", - "print(x.src.shape, x.trg.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "import my_network\n", - "Encoder = my_network.Encoder\n", - "Decoder = my_network.Decoder\n", - "Seq2Seq = my_network.Seq2Seq" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "INPUT_DIM = len(SRC.vocab)\n", - "OUTPUT_DIM = len(TRG.vocab)\n", - "ENC_EMB_DIM = 256\n", - "DEC_EMB_DIM = 256\n", - "HID_DIM = 512\n", - "N_LAYERS = 2\n", - "ENC_DROPOUT = 0.5\n", - "DEC_DROPOUT = 0.5\n", - "\n", - "enc = Encoder(INPUT_DIM, ENC_EMB_DIM, HID_DIM, N_LAYERS, ENC_DROPOUT)\n", - "dec = Decoder(OUTPUT_DIM, DEC_EMB_DIM, HID_DIM, N_LAYERS, DEC_DROPOUT)\n", - "\n", - "# dont forget to put the model to the right device\n", - "model = Seq2Seq(enc, dec, device).to(device)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Seq2Seq(\n", - " (encoder): Encoder(\n", - " (embedding): Embedding(9267, 256)\n", - " (rnn): LSTM(256, 512, num_layers=2, dropout=0.5)\n", - " (dropout): Dropout(p=0.5, inplace=False)\n", - " )\n", - " (decoder): Decoder(\n", - " (embedding): Embedding(6699, 256)\n", - " (rnn): LSTM(256, 512, num_layers=2, dropout=0.5)\n", - " (out): Linear(in_features=512, out_features=6699, bias=True)\n", - " (dropout): Dropout(p=0.5, inplace=False)\n", - " )\n", - ")" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def init_weights(m):\n", - " # \n", - " for name, param in m.named_parameters():\n", - " nn.init.uniform_(param, -0.08, 0.08)\n", - " \n", - "model.apply(init_weights)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The model has 14,880,299 trainable parameters\n" - ] - } - ], - "source": [ - "def count_parameters(model):\n", - " return sum(p.numel() for p in model.parameters() if p.requires_grad)\n", - "\n", - "print(f'The model has {count_parameters(model):,} trainable parameters')" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "PAD_IDX = TRG.vocab.stoi['']\n", - "optimizer = optim.Adam(model.parameters())\n", - "criterion = nn.CrossEntropyLoss(ignore_index = PAD_IDX)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "def train(model, iterator, optimizer, criterion, clip, train_history=None, valid_history=None):\n", - " model.train()\n", - " \n", - " epoch_loss = 0\n", - " history = []\n", - " for i, batch in enumerate(iterator):\n", - " \n", - " src = batch.src\n", - " trg = batch.trg\n", - " \n", - " optimizer.zero_grad()\n", - " \n", - " output = model(src, trg)\n", - " \n", - " #trg = [trg sent len, batch size]\n", - " #output = [trg sent len, batch size, output dim]\n", - " \n", - " output = output[1:].view(-1, output.shape[-1])\n", - " trg = trg[1:].view(-1)\n", - " \n", - " #trg = [(trg sent len - 1) * batch size]\n", - " #output = [(trg sent len - 1) * batch size, output dim]\n", - " \n", - " loss = criterion(output, trg)\n", - " \n", - " loss.backward()\n", - " \n", - " # Let's clip the gradient\n", - " torch.nn.utils.clip_grad_norm_(model.parameters(), clip)\n", - " \n", - " optimizer.step()\n", - " \n", - " epoch_loss += loss.item()\n", - " \n", - " history.append(loss.cpu().data.numpy())\n", - " if (i+1)%10==0:\n", - " fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(12, 8))\n", - "\n", - " clear_output(True)\n", - " ax[0].plot(history, label='train loss')\n", - " ax[0].set_xlabel('Batch')\n", - " ax[0].set_title('Train loss')\n", - " if train_history is not None:\n", - " ax[1].plot(train_history, label='general train history')\n", - " ax[1].set_xlabel('Epoch')\n", - " if valid_history is not None:\n", - " ax[1].plot(valid_history, label='general valid history')\n", - " plt.legend()\n", - " \n", - " plt.show()\n", - "\n", - " \n", - " return epoch_loss / len(iterator)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "def evaluate(model, iterator, criterion):\n", - " \n", - " model.eval()\n", - " \n", - " epoch_loss = 0\n", - " \n", - " history = []\n", - " \n", - " with torch.no_grad():\n", - " \n", - " for i, batch in enumerate(iterator):\n", - "\n", - " src = batch.src\n", - " trg = batch.trg\n", - "\n", - " output = model(src, trg, 0) #turn off teacher forcing\n", - "\n", - " #trg = [trg sent len, batch size]\n", - " #output = [trg sent len, batch size, output dim]\n", - "\n", - " output = output[1:].view(-1, output.shape[-1])\n", - " trg = trg[1:].view(-1)\n", - "\n", - " #trg = [(trg sent len - 1) * batch size]\n", - " #output = [(trg sent len - 1) * batch size, output dim]\n", - "\n", - " loss = criterion(output, trg)\n", - " \n", - " epoch_loss += loss.item()\n", - " \n", - " return epoch_loss / len(iterator)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "def epoch_time(start_time, end_time):\n", - " elapsed_time = end_time - start_time\n", - " elapsed_mins = int(elapsed_time / 60)\n", - " elapsed_secs = int(elapsed_time - (elapsed_mins * 60))\n", - " return elapsed_mins, elapsed_secs" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "train_history = []\n", - "valid_history = []\n", - "\n", - "N_EPOCHS = 10\n", - "CLIP = 1\n", - "\n", - "best_valid_loss = float('inf')" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch: 10 | Time: 1m 10s\n", - "\tTrain Loss: 2.998 | Train PPL: 20.040\n", - "\t Val. Loss: 4.710 | Val. PPL: 111.007\n" - ] - } - ], - "source": [ - "for epoch in range(N_EPOCHS):\n", - " \n", - " start_time = time.time()\n", - " \n", - " train_loss = train(model, train_iterator, optimizer, criterion, CLIP, train_history, valid_history)\n", - " valid_loss = evaluate(model, valid_iterator, criterion)\n", - " \n", - " end_time = time.time()\n", - " \n", - " epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n", - " \n", - " if valid_loss < best_valid_loss:\n", - " best_valid_loss = valid_loss\n", - " torch.save(model.state_dict(), 'tut1-model.pt')\n", - " \n", - " train_history.append(train_loss)\n", - " valid_history.append(valid_loss)\n", - " print(f'Epoch: {epoch+1:02} | Time: {epoch_mins}m {epoch_secs}s')\n", - " print(f'\\tTrain Loss: {train_loss:.3f} | Train PPL: {math.exp(train_loss):7.3f}')\n", - " print(f'\\t Val. Loss: {valid_loss:.3f} | Val. PPL: {math.exp(valid_loss):7.3f}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "__Let's take a look at our network quality__:" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": {}, - "outputs": [], - "source": [ - "del utils" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": {}, - "outputs": [], - "source": [ - "import utils\n", - "import imp\n", - "imp.reload(utils)\n", - "generate_translation = utils.generate_translation\n", - "remove_tech_tokens = utils.remove_tech_tokens\n", - "get_text = utils.get_text\n", - "flatten = utils.flatten" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": {}, - "outputs": [], - "source": [ - "batch = next(iter(test_iterator))" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Original: there is a 24 - hour front desk at the property .\n", - "Generated: the property offers a 24 - hour front desk . .\n", - "\n", - "Original: this property also features free wifi .\n", - "Generated: free wifi access . . . .\n", - "\n" - ] - } - ], - "source": [ - "for idx in [1,2]:\n", - " src = batch.src[:, idx:idx+1]\n", - " trg = batch.trg[:, idx:idx+1]\n", - " generate_translation(src, trg, model, TRG.vocab)" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [], - "source": [ - "from nltk.translate.bleu_score import corpus_bleu\n", - "\n", - "# \"\"\" Estimates corpora-level BLEU score of model's translations given inp and reference out \"\"\"\n", - "# translations, _ = model.translate_lines(inp_lines, **flags)\n", - "# # Note: if you experience out-of-memory error, split input lines into batches and translate separately\n", - "# return corpus_bleu([[ref] for ref in out_lines], translations) * 100" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": {}, - "outputs": [], - "source": [ - "import tqdm" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "59it [00:03, 18.87it/s]\n" - ] - } - ], - "source": [ - "original_text = []\n", - "generated_text = []\n", - "model.eval()\n", - "with torch.no_grad():\n", - "\n", - " for i, batch in tqdm.tqdm(enumerate(test_iterator)):\n", - "\n", - " src = batch.src\n", - " trg = batch.trg\n", - "\n", - " output = model(src, trg, 0) #turn off teacher forcing\n", - "\n", - " #trg = [trg sent len, batch size]\n", - " #output = [trg sent len, batch size, output dim]\n", - "\n", - " output = output.argmax(dim=-1)\n", - " \n", - " original_text.extend([get_text(x, TRG.vocab) for x in trg.cpu().numpy().T])\n", - " generated_text.extend([get_text(x, TRG.vocab) for x in output[1:].detach().cpu().numpy().T])\n", - "\n", - "# original_text = flatten(original_text)\n", - "# generated_text = flatten(generated_text)" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "14.139920232081806" - ] - }, - "execution_count": 111, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "corpus_bleu([[text] for text in original_text], generated_text) * 100" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Baseline solution BLEU score is quite low. Try to achieve at least __24__ BLEU on the test set. \n", - "The checkpoints are:\n", - "\n", - "* __22__ - minimal score to submit the homework, 30% of points\n", - "\n", - "* __27__ - good score, 70% of points\n", - "\n", - "* __29__ - excellent score, 100% of points" - ] - } - ], - "metadata": { - "anaconda-cloud": {}, - "colab": { - "collapsed_sections": [], - "machine_shape": "hm", - "name": "homework.ipynb", - "provenance": [] - }, - "kernelspec": { - "display_name": "Py3 research env", - "language": "python", - "name": "py3_research" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/homeworks/lab01_nlp/.ipynb_checkpoints/Lab1_NLP_part2_NMT_old-checkpoint.ipynb b/homeworks/lab01_nlp/.ipynb_checkpoints/Lab1_NLP_part2_NMT_old-checkpoint.ipynb deleted file mode 100644 index 4e586b1..0000000 --- a/homeworks/lab01_nlp/.ipynb_checkpoints/Lab1_NLP_part2_NMT_old-checkpoint.ipynb +++ /dev/null @@ -1,900 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "eulvfJWl7ueY" - }, - "source": [ - "# Lab 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Part 2: Neural Machine Translation in the wild\n", - "In the second part of the homework you are supposed to get the best translation you can for the EN-RU translation task.\n", - "\n", - "Basic approach using RNNs as encoder and decoder is implemented for you. \n", - "\n", - "Your ultimate task is to use the techniques we've covered, e.g.\n", - "* [Byte Pair Encoding](https://github.com/rsennrich/subword-nmt)\n", - "\n", - "* CNN encoder (with or without positional encoding)\n", - "\n", - "* attention/self-attention mechanism\n", - "\n", - "* pretraining the language model\n", - "\n", - "* or just fine-tunning BERT)\n", - "\n", - "to improve the translation quality. \n", - "\n", - "__Please use at least three different approaches/models and compare them (translation quality/complexity/training and evaluation time).__\n", - "Write down some summary on your experiments and illustrate it with convergence plots/metrics and your thoughts. Just like you would approach a real problem." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# ! pip install subword-nmt\n", - "# ! pip install nltk\n", - "# ! pip install torchtext\n", - "# ! wget https://raw.githubusercontent.com/girafe-ai/ml-mipt/advanced/homeworks/Lab1_NLP/data.txt\n", - "\n", - "# Thanks to YSDA NLP course team for the data\n", - "# (who thanks tilda and deephack teams for the data in their turn)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", - "\n", - "import torchtext\n", - "from torchtext.datasets import TranslationDataset, Multi30k\n", - "from torchtext.data import Field, BucketIterator\n", - "\n", - "import spacy\n", - "\n", - "import random\n", - "import math\n", - "import time\n", - "\n", - "import matplotlib\n", - "matplotlib.rcParams.update({'figure.figsize': (16, 12), 'font.size': 14})\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "from IPython.display import clear_output\n", - "\n", - "from nltk.tokenize import WordPunctTokenizer\n", - "from subword_nmt.learn_bpe import learn_bpe\n", - "from subword_nmt.apply_bpe import BPE\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Main part\n", - "__Here comes the preprocessing. Do not hesitate to use BPE or more complex preprocessing ;)__" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "tokenizer_W = WordPunctTokenizer()\n", - "def tokenize(x, tokenizer=tokenizer_W):\n", - " return tokenizer.tokenize(x.lower())" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [], - "source": [ - "SRC = Field(tokenize=tokenize,\n", - " init_token = '', \n", - " eos_token = '', \n", - " lower = True)\n", - "\n", - "TRG = Field(tokenize=tokenize,\n", - " init_token = '', \n", - " eos_token = '', \n", - " lower = True)\n", - "\n", - "dataset = torchtext.data.TabularDataset(\n", - " path='data.txt',\n", - " format='tsv',\n", - " fields=[('trg', TRG), ('src', SRC)]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": {}, - "outputs": [], - "source": [ - "train_data, valid_data, test_data = dataset.split(split_ratio=[0.8, 0.15, 0.05])" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of training examples: 40000\n", - "Number of validation examples: 2500\n", - "Number of testing examples: 7500\n" - ] - } - ], - "source": [ - "print(f\"Number of training examples: {len(train_data.examples)}\")\n", - "print(f\"Number of validation examples: {len(valid_data.examples)}\")\n", - "print(f\"Number of testing examples: {len(test_data.examples)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": {}, - "outputs": [], - "source": [ - "SRC.build_vocab(train_data, min_freq = 3)\n", - "TRG.build_vocab(train_data, min_freq = 3)" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Unique tokens in source (ru) vocabulary: 9285\n", - "Unique tokens in target (en) vocabulary: 6770\n" - ] - } - ], - "source": [ - "print(f\"Unique tokens in source (ru) vocabulary: {len(SRC.vocab)}\")\n", - "print(f\"Unique tokens in target (en) vocabulary: {len(TRG.vocab)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here are tokens from original (RU) corpus:" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['',\n", - " 'общими',\n", - " 'ferienwohnung',\n", - " 'закат',\n", - " 'campo',\n", - " 'шампанское',\n", - " 'louis',\n", - " 'уэверли',\n", - " 'диннер',\n", - " 'стеклянными']" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "SRC.vocab.itos[::1000]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And from target (EN) corpus:" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['', '46', 'cheeses', 'columbia', 'macerata', 'rouge', 'mactan']" - ] - }, - "execution_count": 98, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "TRG.vocab.itos[::1000]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And here is example from train dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'trg': ['you', 'can', 'find', 'a', 'restaurant', '1', 'km', 'from', 'the', 'grain', 'bauernhof', ',', 'while', 'a', 'supermarket', 'is', '5', 'km', 'away', '.'], 'src': ['расстояние', 'от', 'фермерского', 'дома', 'grain', 'bauernhof', 'до', 'ресторана', 'составляет', '1', 'км', ',', 'до', 'супермаркета', '—', '5', 'км', '.']}\n" - ] - } - ], - "source": [ - "print(vars(train_data.examples[9]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's check the length distributions:" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Length distribution in Train data\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "src_length = map(len, [vars(x)['src'] for x in train_data.examples])\n", - "trg_length = map(len, [vars(x)['trg'] for x in train_data.examples])\n", - "\n", - "print('Length distribution in Train data')\n", - "plt.figure(figsize=[8, 4])\n", - "plt.subplot(1, 2, 1)\n", - "plt.title(\"source length\")\n", - "plt.hist(list(src_length), bins=20);\n", - "\n", - "plt.subplot(1, 2, 2)\n", - "plt.title(\"translation length\")\n", - "plt.hist(list(trg_length), bins=20);" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Length distribution in Test data\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "src_length = map(len, [vars(x)['src'] for x in test_data.examples])\n", - "trg_length = map(len, [vars(x)['trg'] for x in test_data.examples])\n", - "\n", - "print('Length distribution in Test data')\n", - "plt.figure(figsize=[8, 4])\n", - "plt.subplot(1, 2, 1)\n", - "plt.title(\"source length\")\n", - "plt.hist(list(src_length), bins=20);\n", - "\n", - "plt.subplot(1, 2, 2)\n", - "plt.title(\"translation length\")\n", - "plt.hist(list(trg_length), bins=20);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Model side\n", - "__Here comes simple pipeline of NMT model learning. It almost copies the week03 practice__" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [], - "source": [ - "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "device(type='cuda')" - ] - }, - "execution_count": 103, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "device" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": {}, - "outputs": [], - "source": [ - "def _len_sort_key(x):\n", - " return len(x.src)\n", - "\n", - "BATCH_SIZE = 128\n", - "\n", - "train_iterator, valid_iterator, test_iterator = BucketIterator.splits(\n", - " (train_data, valid_data, test_data), \n", - " batch_size = BATCH_SIZE, \n", - " device = device,\n", - " sort_key=_len_sort_key\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "[torchtext.data.batch.Batch of size 128]\n", - "\t[.trg]:[torch.cuda.LongTensor of size 47x128 (GPU 0)]\n", - "\t[.src]:[torch.cuda.LongTensor of size 47x128 (GPU 0)]\n", - "torch.Size([47, 128]) torch.Size([47, 128])\n" - ] - } - ], - "source": [ - "for x in train_iterator:\n", - " break\n", - "print(x)\n", - "print(x.src.shape, x.trg.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": {}, - "outputs": [], - "source": [ - "import my_network\n", - "Encoder = my_network.Encoder\n", - "Decoder = my_network.Decoder\n", - "Seq2Seq = my_network.Seq2Seq" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [], - "source": [ - "INPUT_DIM = len(SRC.vocab)\n", - "OUTPUT_DIM = len(TRG.vocab)\n", - "ENC_EMB_DIM = 256\n", - "DEC_EMB_DIM = 256\n", - "HID_DIM = 512\n", - "N_LAYERS = 2\n", - "ENC_DROPOUT = 0.5\n", - "DEC_DROPOUT = 0.5\n", - "\n", - "enc = Encoder(INPUT_DIM, ENC_EMB_DIM, HID_DIM, N_LAYERS, ENC_DROPOUT)\n", - "dec = Decoder(OUTPUT_DIM, DEC_EMB_DIM, HID_DIM, N_LAYERS, DEC_DROPOUT)\n", - "\n", - "# dont forget to put the model to the right device\n", - "model = Seq2Seq(enc, dec, device).to(device)" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Seq2Seq(\n", - " (encoder): Encoder(\n", - " (embedding): Embedding(9285, 256)\n", - " (rnn): LSTM(256, 512, num_layers=2, dropout=0.5)\n", - " (dropout): Dropout(p=0.5, inplace=False)\n", - " )\n", - " (decoder): Decoder(\n", - " (embedding): Embedding(6770, 256)\n", - " (rnn): LSTM(256, 512, num_layers=2, dropout=0.5)\n", - " (out): Linear(in_features=512, out_features=6770, bias=True)\n", - " (dropout): Dropout(p=0.5, inplace=False)\n", - " )\n", - ")" - ] - }, - "execution_count": 108, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def init_weights(m):\n", - " # \n", - " for name, param in m.named_parameters():\n", - " nn.init.uniform_(param, -0.08, 0.08)\n", - " \n", - "model.apply(init_weights)" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The model has 14,939,506 trainable parameters\n" - ] - } - ], - "source": [ - "def count_parameters(model):\n", - " return sum(p.numel() for p in model.parameters() if p.requires_grad)\n", - "\n", - "print(f'The model has {count_parameters(model):,} trainable parameters')" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "metadata": {}, - "outputs": [], - "source": [ - "PAD_IDX = TRG.vocab.stoi['']\n", - "optimizer = optim.Adam(model.parameters())\n", - "criterion = nn.CrossEntropyLoss(ignore_index = PAD_IDX)" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "metadata": {}, - "outputs": [], - "source": [ - "def train(model, iterator, optimizer, criterion, clip, train_history=None, valid_history=None):\n", - " model.train()\n", - " \n", - " epoch_loss = 0\n", - " history = []\n", - " for i, batch in enumerate(iterator):\n", - " \n", - " src = batch.src\n", - " trg = batch.trg\n", - " \n", - " optimizer.zero_grad()\n", - " \n", - " output = model(src, trg)\n", - " \n", - " #trg = [trg sent len, batch size]\n", - " #output = [trg sent len, batch size, output dim]\n", - " \n", - " output = output[1:].view(-1, output.shape[-1])\n", - " trg = trg[1:].view(-1)\n", - " \n", - " #trg = [(trg sent len - 1) * batch size]\n", - " #output = [(trg sent len - 1) * batch size, output dim]\n", - " \n", - " loss = criterion(output, trg)\n", - " \n", - " loss.backward()\n", - " \n", - " # Let's clip the gradient\n", - " torch.nn.utils.clip_grad_norm_(model.parameters(), clip)\n", - " \n", - " optimizer.step()\n", - " \n", - " epoch_loss += loss.item()\n", - " \n", - " history.append(loss.cpu().data.numpy())\n", - " if (i+1)%10==0:\n", - " fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(12, 8))\n", - "\n", - " clear_output(True)\n", - " ax[0].plot(history, label='train loss')\n", - " ax[0].set_xlabel('Batch')\n", - " ax[0].set_title('Train loss')\n", - " if train_history is not None:\n", - " ax[1].plot(train_history, label='general train history')\n", - " ax[1].set_xlabel('Epoch')\n", - " if valid_history is not None:\n", - " ax[1].plot(valid_history, label='general valid history')\n", - " plt.legend()\n", - " \n", - " plt.show()\n", - "\n", - " \n", - " return epoch_loss / len(iterator)" - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "metadata": {}, - "outputs": [], - "source": [ - "def evaluate(model, iterator, criterion):\n", - " \n", - " model.eval()\n", - " \n", - " epoch_loss = 0\n", - " \n", - " history = []\n", - " \n", - " with torch.no_grad():\n", - " \n", - " for i, batch in enumerate(iterator):\n", - "\n", - " src = batch.src\n", - " trg = batch.trg\n", - "\n", - " output = model(src, trg, 0) #turn off teacher forcing\n", - "\n", - " #trg = [trg sent len, batch size]\n", - " #output = [trg sent len, batch size, output dim]\n", - "\n", - " output = output[1:].view(-1, output.shape[-1])\n", - " trg = trg[1:].view(-1)\n", - "\n", - " #trg = [(trg sent len - 1) * batch size]\n", - " #output = [(trg sent len - 1) * batch size, output dim]\n", - "\n", - " loss = criterion(output, trg)\n", - " \n", - " epoch_loss += loss.item()\n", - " \n", - " return epoch_loss / len(iterator)" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "metadata": {}, - "outputs": [], - "source": [ - "def epoch_time(start_time, end_time):\n", - " elapsed_time = end_time - start_time\n", - " elapsed_mins = int(elapsed_time / 60)\n", - " elapsed_secs = int(elapsed_time - (elapsed_mins * 60))\n", - " return elapsed_mins, elapsed_secs" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "metadata": {}, - "outputs": [], - "source": [ - "train_history = []\n", - "valid_history = []\n", - "\n", - "N_EPOCHS = 10\n", - "CLIP = 1\n", - "\n", - "best_valid_loss = float('inf')" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch: 10 | Time: 1m 15s\n", - "\tTrain Loss: 3.074 | Train PPL: 21.637\n", - "\t Val. Loss: 4.600 | Val. PPL: 99.469\n" - ] - } - ], - "source": [ - "for epoch in range(N_EPOCHS):\n", - " \n", - " start_time = time.time()\n", - " \n", - " train_loss = train(model, train_iterator, optimizer, criterion, CLIP, train_history, valid_history)\n", - " valid_loss = evaluate(model, valid_iterator, criterion)\n", - " \n", - " end_time = time.time()\n", - " \n", - " epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n", - " \n", - " if valid_loss < best_valid_loss:\n", - " best_valid_loss = valid_loss\n", - " torch.save(model.state_dict(), 'tut1-model.pt')\n", - " \n", - " train_history.append(train_loss)\n", - " valid_history.append(valid_loss)\n", - " print(f'Epoch: {epoch+1:02} | Time: {epoch_mins}m {epoch_secs}s')\n", - " print(f'\\tTrain Loss: {train_loss:.3f} | Train PPL: {math.exp(train_loss):7.3f}')\n", - " print(f'\\t Val. Loss: {valid_loss:.3f} | Val. PPL: {math.exp(valid_loss):7.3f}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "__Let's take a look at our network quality__:" - ] - }, - { - "cell_type": "code", - "execution_count": 136, - "metadata": {}, - "outputs": [], - "source": [ - "import utils\n", - "import imp\n", - "imp.reload(utils)\n", - "generate_translation = utils.generate_translation\n", - "remove_tech_tokens = utils.remove_tech_tokens\n", - "get_text = utils.get_text\n", - "flatten = utils.flatten" - ] - }, - { - "cell_type": "code", - "execution_count": 137, - "metadata": {}, - "outputs": [], - "source": [ - "batch = next(iter(test_iterator))" - ] - }, - { - "cell_type": "code", - "execution_count": 138, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Original: each room has a tv .\n", - "Generated: each room is equipped with a tv . .\n", - "\n", - "Original: you will find a 24 - hour front desk at the property .\n", - "Generated: the hotel offers a 24 - hour front desk . property .\n", - "\n" - ] - } - ], - "source": [ - "for idx in [1,2]:\n", - " src = batch.src[:, idx:idx+1]\n", - " trg = batch.trg[:, idx:idx+1]\n", - " generate_translation(src, trg, model, TRG.vocab)" - ] - }, - { - "cell_type": "code", - "execution_count": 139, - "metadata": {}, - "outputs": [], - "source": [ - "from nltk.translate.bleu_score import corpus_bleu\n", - "\n", - "# \"\"\" Estimates corpora-level BLEU score of model's translations given inp and reference out \"\"\"\n", - "# translations, _ = model.translate_lines(inp_lines, **flags)\n", - "# # Note: if you experience out-of-memory error, split input lines into batches and translate separately\n", - "# return corpus_bleu([[ref] for ref in out_lines], translations) * 100" - ] - }, - { - "cell_type": "code", - "execution_count": 140, - "metadata": {}, - "outputs": [], - "source": [ - "import tqdm" - ] - }, - { - "cell_type": "code", - "execution_count": 149, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "59it [00:03, 18.95it/s]\n" - ] - } - ], - "source": [ - "original_text = []\n", - "generated_text = []\n", - "model.eval()\n", - "with torch.no_grad():\n", - "\n", - " for i, batch in tqdm.tqdm(enumerate(test_iterator)):\n", - "\n", - " src = batch.src\n", - " trg = batch.trg\n", - "\n", - " output = model(src, trg, 0) #turn off teacher forcing\n", - "\n", - " #trg = [trg sent len, batch size]\n", - " #output = [trg sent len, batch size, output dim]\n", - "\n", - " output = output.argmax(dim=-1)\n", - " \n", - " original_text.extend([get_text(x, TRG.vocab) for x in trg.cpu().numpy().T])\n", - " generated_text.extend([get_text(x, TRG.vocab) for x in output.detach().cpu().numpy().T])\n", - "\n", - "# original_text = flatten(original_text)\n", - "# generated_text = flatten(generated_text)" - ] - }, - { - "cell_type": "code", - "execution_count": 150, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "14.449864542777785" - ] - }, - "execution_count": 150, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "corpus_bleu([[text] for text in original_text], generated_text) * 100" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Baseline solution BLEU score is quite low. Try to achieve at least __18__ BLEU on the test set. \n", - "The checkpoints are:\n", - "\n", - "* __18__ - minimal score to submit the homework, 30% of points\n", - "\n", - "* __20__ - good score, 70% of points\n", - "\n", - "* __25__ - excellent score, 100% of points" - ] - } - ], - "metadata": { - "anaconda-cloud": {}, - "colab": { - "collapsed_sections": [], - "machine_shape": "hm", - "name": "homework.ipynb", - "provenance": [] - }, - "kernelspec": { - "display_name": "Py3 research env", - "language": "python", - "name": "py3_research" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/homeworks/lab01_nlp/.ipynb_checkpoints/lab1_01_nlp_part1_embedding_based_mt-checkpoint.ipynb b/homeworks/lab01_nlp/.ipynb_checkpoints/lab1_01_nlp_part1_embedding_based_mt-checkpoint.ipynb deleted file mode 100644 index 2bcd322..0000000 --- a/homeworks/lab01_nlp/.ipynb_checkpoints/lab1_01_nlp_part1_embedding_based_mt-checkpoint.ipynb +++ /dev/null @@ -1,753 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "eulvfJWl7ueY" - }, - "source": [ - "# Lab 1\n", - "\n", - "\n", - "## Part 1: Bilingual dictionary induction and unsupervised embedding-based MT (30%)\n", - "*Note: this homework is based on materials from yandexdataschool [NLP course](https://github.com/yandexdataschool/nlp_course/). Feel free to check this awesome course if you wish to dig deeper.*\n", - "\n", - "*Refined by [Nikolay Karpachev](https://www.linkedin.com/in/nikolay-karpachev-b0146a104/)*" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "fV4rIjxa7uei" - }, - "source": [ - "**In this homework** **YOU** will make machine translation system without using parallel corpora, alignment, attention, 100500 depth super-cool recurrent neural network and all that kind superstuff.\n", - "\n", - "But even without parallel corpora this system can be good enough (hopefully), in particular for similar languages, e.g. Ukrainian and Russian. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "idSYq2GU7uew" - }, - "source": [ - "### Frament of the Swadesh list for some slavic languages\n", - "\n", - "The Swadesh list is a lexicostatistical stuff. It's named after American linguist Morris Swadesh and contains basic lexis. This list are used to define subgroupings of languages, its relatedness.\n", - "\n", - "So we can see some kind of word invariance for different Slavic languages.\n", - "\n", - "\n", - "| Russian | Belorussian | Ukrainian | Polish | Czech | Bulgarian |\n", - "|-----------------|--------------------------|-------------------------|--------------------|-------------------------------|-----------------------|\n", - "| женщина | жанчына, кабета, баба | жінка | kobieta | žena | жена |\n", - "| мужчина | мужчына | чоловік, мужчина | mężczyzna | muž | мъж |\n", - "| человек | чалавек | людина, чоловік | człowiek | člověk | човек |\n", - "| ребёнок, дитя | дзіця, дзіцёнак, немаўля | дитина, дитя | dziecko | dítě | дете |\n", - "| жена | жонка | дружина, жінка | żona | žena, manželka, choť | съпруга, жена |\n", - "| муж | муж, гаспадар | чоловiк, муж | mąż | muž, manžel, choť | съпруг, мъж |\n", - "| мать, мама | маці, матка | мати, матір, неня, мама | matka | matka, máma, 'стар.' mateř | майка |\n", - "| отец, тятя | бацька, тата | батько, тато, татусь | ojciec | otec | баща, татко |\n", - "| много | шмат, багата | багато | wiele | mnoho, hodně | много |\n", - "| несколько | некалькі, колькі | декілька, кілька | kilka | několik, pár, trocha | няколко |\n", - "| другой, иной | іншы | інший | inny | druhý, jiný | друг |\n", - "| зверь, животное | жывёла, звер, істота | тварина, звір | zwierzę | zvíře | животно |\n", - "| рыба | рыба | риба | ryba | ryba | риба |\n", - "| птица | птушка | птах, птиця | ptak | pták | птица |\n", - "| собака, пёс | сабака | собака, пес | pies | pes | куче, пес |\n", - "| вошь | вош | воша | wesz | veš | въшка |\n", - "| змея, гад | змяя | змія, гад | wąż | had | змия |\n", - "| червь, червяк | чарвяк | хробак, черв'як | robak | červ | червей |\n", - "| дерево | дрэва | дерево | drzewo | strom, dřevo | дърво |\n", - "| лес | лес | ліс | las | les | гора, лес |\n", - "| палка | кій, палка | палиця | patyk, pręt, pałka | hůl, klacek, prut, kůl, pálka | палка, пръчка, бастун |" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "cNM3_fjr7ue2" - }, - "source": [ - "But the context distribution of these languages demonstrates even more invariance. And we can use this fact for our for our purposes." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "YLppwa527ue6" - }, - "source": [ - "## Data" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "lYBGKAUn7ue_" - }, - "outputs": [], - "source": [ - "import gensim\n", - "import numpy as np\n", - "from gensim.models import KeyedVectors" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "MwGoVhRA7ufP" - }, - "source": [ - "In this notebook we're going to use pretrained word vectors - FastText (original paper - https://arxiv.org/abs/1607.04606).\n", - "\n", - "You can download them from the official [website](https://fasttext.cc/docs/en/crawl-vectors.html). We're going to need embeddings for Russian and Ukrainian languages. Please use word2vec-compatible format (.text)." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "u1JjQv_97ufT" - }, - "outputs": [], - "source": [ - "uk_emb = KeyedVectors.load_word2vec_format(\"cc.uk.300.vec\")" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "ffzuept_7ufd" - }, - "outputs": [], - "source": [ - "ru_emb = KeyedVectors.load_word2vec_format(\"cc.ru.300.vec\")" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "nTkXfT0W7ufk" - }, - "outputs": [], - "source": [ - "ru_emb.most_similar([ru_emb[\"август\"]], topn=10)" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "vdBA8lcg7ufs" - }, - "outputs": [], - "source": [ - "uk_emb.most_similar([uk_emb[\"серпень\"]])" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "_yJvcKXO7uf0" - }, - "outputs": [], - "source": [ - "ru_emb.most_similar([uk_emb[\"серпень\"]])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "pNdYAR1q7uf6" - }, - "source": [ - "Load small dictionaries for correspoinding words pairs as trainset and testset." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "35d_DAK67uf8" - }, - "outputs": [], - "source": [ - "def load_word_pairs(filename):\n", - " uk_ru_pairs = []\n", - " uk_vectors = []\n", - " ru_vectors = []\n", - " with open(filename, \"r\") as inpf:\n", - " for line in inpf:\n", - " uk, ru = line.rstrip().split(\"\\t\")\n", - " if uk not in uk_emb or ru not in ru_emb:\n", - " continue\n", - " uk_ru_pairs.append((uk, ru))\n", - " uk_vectors.append(uk_emb[uk])\n", - " ru_vectors.append(ru_emb[ru])\n", - " return uk_ru_pairs, np.array(uk_vectors), np.array(ru_vectors)" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "wkNL602WHJyO" - }, - "outputs": [], - "source": [ - "!wget -O ukr_rus.train.txt http://tiny.cc/jfgecz" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "uoclU6JcHCcn" - }, - "outputs": [], - "source": [ - "!wget -O ukr_rus.test.txt http://tiny.cc/6zoeez" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "05BqsdSK7ugD" - }, - "outputs": [], - "source": [ - "uk_ru_train, X_train, Y_train = load_word_pairs(\"ukr_rus.train.txt\")" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "zQOZw51r7ugL" - }, - "outputs": [], - "source": [ - "uk_ru_test, X_test, Y_test = load_word_pairs(\"ukr_rus.test.txt\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "-ZBBNvpz7ugQ" - }, - "source": [ - "## Embedding space mapping (0.3 pts)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "x_Dhk5gL7ugS" - }, - "source": [ - "Let $x_i \\in \\mathrm{R}^d$ be the distributed representation of word $i$ in the source language, and $y_i \\in \\mathrm{R}^d$ is the vector representation of its translation. Our purpose is to learn such linear transform $W$ that minimizes euclidian distance between $Wx_i$ and $y_i$ for some subset of word embeddings. Thus we can formulate so-called Procrustes problem:\n", - "\n", - "$$W^*= \\arg\\min_W \\sum_{i=1}^n||Wx_i - y_i||_2$$\n", - "or\n", - "$$W^*= \\arg\\min_W ||WX - Y||_F$$\n", - "\n", - "where $||*||_F$ - Frobenius norm." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "acOjDdtL7ugY" - }, - "source": [ - "$W^*= \\arg\\min_W \\sum_{i=1}^n||Wx_i - y_i||_2$ looks like simple multiple linear regression (without intercept fit). So let's code." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "Lb-KN1be7uga" - }, - "outputs": [], - "source": [ - "from sklearn.linear_model import LinearRegression\n", - "\n", - "# YOUR CODE HERE\n", - "# mapping = ...\n", - "# -------" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "X7tqJwoY7ugf" - }, - "source": [ - "Let's take a look at neigbours of the vector of word _\"серпень\"_ (_\"август\"_ in Russian) after linear transform." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "31SrFSbn7ugi" - }, - "outputs": [], - "source": [ - "august = mapping.predict(uk_emb[\"серпень\"].reshape(1, -1))\n", - "ru_emb.most_similar(august)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "okSkjk597ugo" - }, - "source": [ - "We can see that neighbourhood of this embedding cosists of different months, but right variant is on the ninth place." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "o2uY6Y9B7ugt" - }, - "source": [ - "As quality measure we will use precision top-1, top-5 and top-10 (for each transformed Ukrainian embedding we count how many right target pairs are found in top N nearest neighbours in Russian embedding space)." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "zptuho8LAfIE" - }, - "outputs": [], - "source": [ - "def precision(pairs, mapped_vectors, topn=1):\n", - " \"\"\"\n", - " :args:\n", - " pairs = list of right word pairs [(uk_word_0, ru_word_0), ...]\n", - " mapped_vectors = list of embeddings after mapping from source embedding space to destination embedding space\n", - " topn = the number of nearest neighbours in destination embedding space to choose from\n", - " :returns:\n", - " precision_val, float number, total number of words for those we can find right translation at top K.\n", - " \"\"\"\n", - " assert len(pairs) == len(mapped_vectors)\n", - " num_matches = 0\n", - " for i, (_, ru) in enumerate(pairs):\n", - " # YOUR CODE HERE\n", - " precision_val = num_matches / len(pairs)\n", - " return precision_val" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "duhj9hpv7ugy" - }, - "outputs": [], - "source": [ - "assert precision([(\"серпень\", \"август\")], august, topn=5) == 0.0\n", - "assert precision([(\"серпень\", \"август\")], august, topn=9) == 1.0\n", - "assert precision([(\"серпень\", \"август\")], august, topn=10) == 1.0" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "0-iyd5gP7ug5" - }, - "outputs": [], - "source": [ - "assert precision(uk_ru_test, X_test) == 0.0\n", - "assert precision(uk_ru_test, Y_test) == 1.0" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "U-ssEJ3x7uhA" - }, - "outputs": [], - "source": [ - "precision_top1 = precision(uk_ru_test, mapping.predict(X_test), 1)\n", - "precision_top5 = precision(uk_ru_test, mapping.predict(X_test), 5)" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "7K-hy7a6Ksn2" - }, - "outputs": [], - "source": [ - "print(precision_top1)\n", - "print(precision_top5)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "hf6Ou8bx7uhH" - }, - "source": [ - "## Making it better (orthogonal Procrustean problem) (0.3 pts)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "4oLs-drN7uhK" - }, - "source": [ - "It can be shown (see original paper) that a self-consistent linear mapping between semantic spaces should be orthogonal. \n", - "We can restrict transform $W$ to be orthogonal. Then we will solve next problem:\n", - "\n", - "$$W^*= \\arg\\min_W ||WX - Y||_F \\text{, where: } W^TW = I$$\n", - "\n", - "$$I \\text{- identity matrix}$$\n", - "\n", - "Instead of making yet another regression problem we can find optimal orthogonal transformation using singular value decomposition. It turns out that optimal transformation $W^*$ can be expressed via SVD components:\n", - "$$X^TY=U\\Sigma V^T\\text{, singular value decompostion}$$\n", - "$$W^*=UV^T$$" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "_KSaRJFGMFiJ" - }, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "DdFQ7qti7uhL" - }, - "outputs": [], - "source": [ - "def learn_transform(X_train, Y_train):\n", - " \"\"\" \n", - " :returns: W* : float matrix[emb_dim x emb_dim] as defined in formulae above\n", - " \"\"\"\n", - " # YOUR CODE GOES HERE\n", - " # compute orthogonal embedding space mapping\n", - " # mapping = ...\n", - "\n", - " return mapping" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "7X7QfYDd7uhQ" - }, - "outputs": [], - "source": [ - "W = learn_transform(X_train, Y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "OVOFYYa37uhX" - }, - "outputs": [], - "source": [ - "ru_emb.most_similar([np.matmul(uk_emb[\"серпень\"], W)])" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "r297sYP37uhb" - }, - "outputs": [], - "source": [ - "print(precision(uk_ru_test, np.matmul(X_test, W)))\n", - "print(precision(uk_ru_test, np.matmul(X_test, W), 5))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "hvUZ72U5AfJg" - }, - "source": [ - "## Unsupervised embedding-based MT (0.4 pts)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "LLyuVfHBLrJn" - }, - "source": [ - "Now, let's build our word embeddings-based translator!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "tPAURW1CMuP7" - }, - "source": [ - "Firstly, download OPUS Tatoeba corpus." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "F80kUKzQMsDu" - }, - "outputs": [], - "source": [ - "!wget https://object.pouta.csc.fi/OPUS-Tatoeba/v20190709/mono/uk.txt.gz" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "0CGFZoxCUVf1" - }, - "outputs": [], - "source": [ - "!gzip -d ./uk.txt.gz" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "2MV3VvoVUX5U" - }, - "outputs": [], - "source": [ - "with open('./uk.txt', 'r') as f:\n", - " uk_corpus = f.readlines()" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "tU7nPVf0UhbI" - }, - "outputs": [], - "source": [ - "# To save your time and CPU, feel free to use first 1000 sentences of the corpus\n", - "uk_corpus = uk_corpus[:1000]" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "FLN8dBOXAfJ1" - }, - "outputs": [], - "source": [ - "# Any necessary preprocessing if needed\n", - "# YOUR CODE HERE" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "FGksC7l_NMi9" - }, - "outputs": [], - "source": [ - "def translate(sentence):\n", - " \"\"\"\n", - " :args:\n", - " sentence - sentence in Ukrainian (str)\n", - " :returns:\n", - " translation - sentence in Russian (str)\n", - "\n", - " * find ukrainian embedding for each word in sentence\n", - " * transform ukrainian embedding vector\n", - " * find nearest russian word and replace\n", - " \"\"\"\n", - " # YOUR CODE GOES HERE\n", - "\n", - " return \" \".join(translated)" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "4hbbMy-tNxlf" - }, - "outputs": [], - "source": [ - "assert translate(\".\") == \".\"\n", - "assert translate(\"1 , 3\") == \"1 , 3\"\n", - "assert translate(\"кіт зловив мишу\") == \"кот поймал мышку\"" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "ia6I2ce7O_HI" - }, - "source": [ - "Now you can play with your model and try to get as accurate translations as possible. **Note**: one big issue is out-of-vocabulary words. Try to think of various ways of handling it (you can start with translating each of them to a special **UNK** token and then move to more sophisticated approaches). Good luck!" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "ap1W7ZCeOAVU" - }, - "outputs": [], - "source": [ - "for sent in uk_corpus[::10]:\n", - " print(translate(sent))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Great! \n", - "See second notebook for the Neural Machine Translation assignment." - ] - } - ], - "metadata": { - "anaconda-cloud": {}, - "colab": { - "collapsed_sections": [], - "machine_shape": "hm", - "name": "homework.ipynb", - "provenance": [] - }, - "kernelspec": { - "display_name": "Py3 Research", - "language": "python", - "name": "py3_research" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/homeworks/lab01_nlp/.ipynb_checkpoints/lab1_02_nlp_part2_nmt-checkpoint.ipynb b/homeworks/lab01_nlp/.ipynb_checkpoints/lab1_02_nlp_part2_nmt-checkpoint.ipynb deleted file mode 100644 index eb346fa..0000000 --- a/homeworks/lab01_nlp/.ipynb_checkpoints/lab1_02_nlp_part2_nmt-checkpoint.ipynb +++ /dev/null @@ -1,941 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Lab 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Part 2: Neural Machine Translation in the wild\n", - "In the third homework you are supposed to get the best translation you can for the EN-RU translation task.\n", - "\n", - "Basic approach using RNNs as encoder and decoder is implemented for you. \n", - "\n", - "Your ultimate task is to use the techniques we've covered, e.g.\n", - "\n", - "* Optimization enhancements (e.g. learning rate decay)\n", - "\n", - "* CNN encoder (with or without positional encoding)\n", - "\n", - "* attention/self-attention mechanism\n", - "\n", - "* pretraining the language model\n", - "\n", - "* [Byte Pair Encoding](https://github.com/rsennrich/subword-nmt)\n", - "\n", - "* or just fine-tunning BERT ;)\n", - "\n", - "to improve the translation quality. \n", - "\n", - "__Please use at least three different approaches/models and compare them (translation quality/complexity/training and evaluation time).__\n", - "\n", - "Write down some summary on your experiments and illustrate it with convergence plots/metrics and your thoughts. Just like you would approach a real problem." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# You might need to install the libraries below. Do it in the desired environment\n", - "# if you are working locally.\n", - "\n", - "# ! pip install subword-nmt\n", - "# ! pip install nltk\n", - "# ! pip install torchtext" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Thanks to YSDA NLP course team for the data\n", - "# (who thanks tilda and deephack teams for the data in their turn)\n", - "\n", - "import os\n", - "path_do_data = '../../datasets/Machine_translation_EN_RU/data.txt'\n", - "if not os.path.exists(path_do_data):\n", - " print(\"Dataset not found locally. Downloading from github. Loading special files as well\")\n", - " !wget https://raw.githubusercontent.com/girafe-ai/ml-mipt/master/datasets/Machine_translation_EN_RU/data.txt -nc\n", - " path_do_data = './data.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if not os.path.exists('./utils.py'):\n", - " print(\"utils file not found locally. Downloading from github.\")\n", - " !wget https://raw.githubusercontent.com/girafe-ai/ml-mipt/master/homeworks_advanced/Lab1_NLP/utils.py -nc\n", - "\n", - "if not os.path.exists('./my_network.py'):\n", - " print(\"network file not found locally. Downloading from github.\")\n", - " !wget https://raw.githubusercontent.com/girafe-ai/ml-mipt/master/homeworks_advanced/Lab1_NLP/my_network.py -nc" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", - "\n", - "import torchtext\n", - "from torchtext.legacy.datasets import TranslationDataset, Multi30k\n", - "from torchtext.legacy.data import Field, BucketIterator, TabularDataset\n", - "\n", - "import spacy\n", - "\n", - "import random\n", - "import math\n", - "import time\n", - "\n", - "import matplotlib\n", - "matplotlib.rcParams.update({'figure.figsize': (16, 12), 'font.size': 14})\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "from IPython.display import clear_output\n", - "\n", - "from nltk.tokenize import WordPunctTokenizer\n", - "from subword_nmt.learn_bpe import learn_bpe\n", - "from subword_nmt.apply_bpe import BPE" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Main part\n", - "__Here comes the preprocessing. Do not hesitate to use BPE or more complex preprocessing ;)__" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "tokenizer_W = WordPunctTokenizer()\n", - "def tokenize(x, tokenizer=tokenizer_W):\n", - " return tokenizer.tokenize(x.lower())" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "SRC = Field(tokenize=tokenize,\n", - " init_token = '', \n", - " eos_token = '', \n", - " lower = True)\n", - "\n", - "TRG = Field(tokenize=tokenize,\n", - " init_token = '', \n", - " eos_token = '', \n", - " lower = True)\n", - "\n", - "dataset = TabularDataset(\n", - " path=path_do_data,\n", - " format='tsv',\n", - " fields=[('trg', TRG), ('src', SRC)]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "train_data, valid_data, test_data = dataset.split(split_ratio=[0.8, 0.15, 0.05])" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of training examples: 40000\n", - "Number of validation examples: 2500\n", - "Number of testing examples: 7500\n" - ] - } - ], - "source": [ - "print(f\"Number of training examples: {len(train_data.examples)}\")\n", - "print(f\"Number of validation examples: {len(valid_data.examples)}\")\n", - "print(f\"Number of testing examples: {len(test_data.examples)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "SRC.build_vocab(train_data, min_freq = 3)\n", - "TRG.build_vocab(train_data, min_freq = 3)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Unique tokens in source (ru) vocabulary: 9267\n", - "Unique tokens in target (en) vocabulary: 6699\n" - ] - } - ], - "source": [ - "print(f\"Unique tokens in source (ru) vocabulary: {len(SRC.vocab)}\")\n", - "print(f\"Unique tokens in target (en) vocabulary: {len(TRG.vocab)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here are tokens from original (RU) corpus:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['',\n", - " '29',\n", - " 'соль',\n", - " 'комо',\n", - " '―',\n", - " 'электрическая',\n", - " 'ming',\n", - " 'утренний',\n", - " 'детском',\n", - " 'таунус']" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "SRC.vocab.itos[::1000]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And from target (EN) corpus:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['', 'king', 'buffets', 'catch', 'media', 'schedule', 'maraunenhof']" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "TRG.vocab.itos[::1000]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And here is example from train dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'trg': ['laundry', 'service', 'is', 'provided', '.'], 'src': ['помимо', 'этого', ',', 'гостям', 'предоставляются', 'услуги', 'прачечной', '.']}\n" - ] - } - ], - "source": [ - "print(vars(train_data.examples[9]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's check the length distributions:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Length distribution in Train data\n" - ] - }, - { - "data": { - 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "src_length = map(len, [vars(x)['src'] for x in train_data.examples])\n", - "trg_length = map(len, [vars(x)['trg'] for x in train_data.examples])\n", - "\n", - "print('Length distribution in Train data')\n", - "plt.figure(figsize=[8, 4])\n", - "plt.subplot(1, 2, 1)\n", - "plt.title(\"source length\")\n", - "plt.hist(list(src_length), bins=20);\n", - "\n", - "plt.subplot(1, 2, 2)\n", - "plt.title(\"translation length\")\n", - "plt.hist(list(trg_length), bins=20);" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Length distribution in Test data\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "src_length = map(len, [vars(x)['src'] for x in test_data.examples])\n", - "trg_length = map(len, [vars(x)['trg'] for x in test_data.examples])\n", - "\n", - "print('Length distribution in Test data')\n", - "plt.figure(figsize=[8, 4])\n", - "plt.subplot(1, 2, 1)\n", - "plt.title(\"source length\")\n", - "plt.hist(list(src_length), bins=20);\n", - "\n", - "plt.subplot(1, 2, 2)\n", - "plt.title(\"translation length\")\n", - "plt.hist(list(trg_length), bins=20);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Model side\n", - "__Here comes simple pipeline of NMT model learning. It almost copies the week03 practice__" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "device(type='cuda', index=1)" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "device" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "def _len_sort_key(x):\n", - " return len(x.src)\n", - "\n", - "BATCH_SIZE = 128\n", - "\n", - "train_iterator, valid_iterator, test_iterator = BucketIterator.splits(\n", - " (train_data, valid_data, test_data), \n", - " batch_size = BATCH_SIZE, \n", - " device = device,\n", - " sort_key=_len_sort_key\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "[torchtext.data.batch.Batch of size 128]\n", - "\t[.trg]:[torch.cuda.LongTensor of size 55x128 (GPU 1)]\n", - "\t[.src]:[torch.cuda.LongTensor of size 59x128 (GPU 1)]\n", - "torch.Size([59, 128]) torch.Size([55, 128])\n" - ] - } - ], - "source": [ - "for x in train_iterator:\n", - " break\n", - "print(x)\n", - "print(x.src.shape, x.trg.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "import my_network\n", - "Encoder = my_network.Encoder\n", - "Decoder = my_network.Decoder\n", - "Seq2Seq = my_network.Seq2Seq" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "INPUT_DIM = len(SRC.vocab)\n", - "OUTPUT_DIM = len(TRG.vocab)\n", - "ENC_EMB_DIM = 256\n", - "DEC_EMB_DIM = 256\n", - "HID_DIM = 512\n", - "N_LAYERS = 2\n", - "ENC_DROPOUT = 0.5\n", - "DEC_DROPOUT = 0.5\n", - "\n", - "enc = Encoder(INPUT_DIM, ENC_EMB_DIM, HID_DIM, N_LAYERS, ENC_DROPOUT)\n", - "dec = Decoder(OUTPUT_DIM, DEC_EMB_DIM, HID_DIM, N_LAYERS, DEC_DROPOUT)\n", - "\n", - "# dont forget to put the model to the right device\n", - "model = Seq2Seq(enc, dec, device).to(device)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Seq2Seq(\n", - " (encoder): Encoder(\n", - " (embedding): Embedding(9267, 256)\n", - " (rnn): LSTM(256, 512, num_layers=2, dropout=0.5)\n", - " (dropout): Dropout(p=0.5, inplace=False)\n", - " )\n", - " (decoder): Decoder(\n", - " (embedding): Embedding(6699, 256)\n", - " (rnn): LSTM(256, 512, num_layers=2, dropout=0.5)\n", - " (out): Linear(in_features=512, out_features=6699, bias=True)\n", - " (dropout): Dropout(p=0.5, inplace=False)\n", - " )\n", - ")" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def init_weights(m):\n", - " # \n", - " for name, param in m.named_parameters():\n", - " nn.init.uniform_(param, -0.08, 0.08)\n", - " \n", - "model.apply(init_weights)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The model has 14,880,299 trainable parameters\n" - ] - } - ], - "source": [ - "def count_parameters(model):\n", - " return sum(p.numel() for p in model.parameters() if p.requires_grad)\n", - "\n", - "print(f'The model has {count_parameters(model):,} trainable parameters')" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "PAD_IDX = TRG.vocab.stoi['']\n", - "optimizer = optim.Adam(model.parameters())\n", - "criterion = nn.CrossEntropyLoss(ignore_index = PAD_IDX)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "def train(model, iterator, optimizer, criterion, clip, train_history=None, valid_history=None):\n", - " model.train()\n", - " \n", - " epoch_loss = 0\n", - " history = []\n", - " for i, batch in enumerate(iterator):\n", - " \n", - " src = batch.src\n", - " trg = batch.trg\n", - " \n", - " optimizer.zero_grad()\n", - " \n", - " output = model(src, trg)\n", - " \n", - " #trg = [trg sent len, batch size]\n", - " #output = [trg sent len, batch size, output dim]\n", - " \n", - " output = output[1:].view(-1, output.shape[-1])\n", - " trg = trg[1:].view(-1)\n", - " \n", - " #trg = [(trg sent len - 1) * batch size]\n", - " #output = [(trg sent len - 1) * batch size, output dim]\n", - " \n", - " loss = criterion(output, trg)\n", - " \n", - " loss.backward()\n", - " \n", - " # Let's clip the gradient\n", - " torch.nn.utils.clip_grad_norm_(model.parameters(), clip)\n", - " \n", - " optimizer.step()\n", - " \n", - " epoch_loss += loss.item()\n", - " \n", - " history.append(loss.cpu().data.numpy())\n", - " if (i+1)%10==0:\n", - " fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(12, 8))\n", - "\n", - " clear_output(True)\n", - " ax[0].plot(history, label='train loss')\n", - " ax[0].set_xlabel('Batch')\n", - " ax[0].set_title('Train loss')\n", - " if train_history is not None:\n", - " ax[1].plot(train_history, label='general train history')\n", - " ax[1].set_xlabel('Epoch')\n", - " if valid_history is not None:\n", - " ax[1].plot(valid_history, label='general valid history')\n", - " plt.legend()\n", - " \n", - " plt.show()\n", - "\n", - " \n", - " return epoch_loss / len(iterator)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "def evaluate(model, iterator, criterion):\n", - " \n", - " model.eval()\n", - " \n", - " epoch_loss = 0\n", - " \n", - " history = []\n", - " \n", - " with torch.no_grad():\n", - " \n", - " for i, batch in enumerate(iterator):\n", - "\n", - " src = batch.src\n", - " trg = batch.trg\n", - "\n", - " output = model(src, trg, 0) #turn off teacher forcing\n", - "\n", - " #trg = [trg sent len, batch size]\n", - " #output = [trg sent len, batch size, output dim]\n", - "\n", - " output = output[1:].view(-1, output.shape[-1])\n", - " trg = trg[1:].view(-1)\n", - "\n", - " #trg = [(trg sent len - 1) * batch size]\n", - " #output = [(trg sent len - 1) * batch size, output dim]\n", - "\n", - " loss = criterion(output, trg)\n", - " \n", - " epoch_loss += loss.item()\n", - " \n", - " return epoch_loss / len(iterator)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "def epoch_time(start_time, end_time):\n", - " elapsed_time = end_time - start_time\n", - " elapsed_mins = int(elapsed_time / 60)\n", - " elapsed_secs = int(elapsed_time - (elapsed_mins * 60))\n", - " return elapsed_mins, elapsed_secs" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "train_history = []\n", - "valid_history = []\n", - "\n", - "N_EPOCHS = 10\n", - "CLIP = 1\n", - "\n", - "best_valid_loss = float('inf')" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch: 10 | Time: 1m 10s\n", - "\tTrain Loss: 2.998 | Train PPL: 20.040\n", - "\t Val. Loss: 4.710 | Val. PPL: 111.007\n" - ] - } - ], - "source": [ - "for epoch in range(N_EPOCHS):\n", - " \n", - " start_time = time.time()\n", - " \n", - " train_loss = train(model, train_iterator, optimizer, criterion, CLIP, train_history, valid_history)\n", - " valid_loss = evaluate(model, valid_iterator, criterion)\n", - " \n", - " end_time = time.time()\n", - " \n", - " epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n", - " \n", - " if valid_loss < best_valid_loss:\n", - " best_valid_loss = valid_loss\n", - " torch.save(model.state_dict(), 'tut1-model.pt')\n", - " \n", - " train_history.append(train_loss)\n", - " valid_history.append(valid_loss)\n", - " print(f'Epoch: {epoch+1:02} | Time: {epoch_mins}m {epoch_secs}s')\n", - " print(f'\\tTrain Loss: {train_loss:.3f} | Train PPL: {math.exp(train_loss):7.3f}')\n", - " print(f'\\t Val. Loss: {valid_loss:.3f} | Val. PPL: {math.exp(valid_loss):7.3f}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "__Let's take a look at our network quality__:" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": {}, - "outputs": [], - "source": [ - "del utils" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": {}, - "outputs": [], - "source": [ - "import utils\n", - "import imp\n", - "imp.reload(utils)\n", - "generate_translation = utils.generate_translation\n", - "remove_tech_tokens = utils.remove_tech_tokens\n", - "get_text = utils.get_text\n", - "flatten = utils.flatten" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": {}, - "outputs": [], - "source": [ - "batch = next(iter(test_iterator))" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Original: there is a 24 - hour front desk at the property .\n", - "Generated: the property offers a 24 - hour front desk . .\n", - "\n", - "Original: this property also features free wifi .\n", - "Generated: free wifi access . . . .\n", - "\n" - ] - } - ], - "source": [ - "for idx in [1,2]:\n", - " src = batch.src[:, idx:idx+1]\n", - " trg = batch.trg[:, idx:idx+1]\n", - " generate_translation(src, trg, model, TRG.vocab)" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [], - "source": [ - "from nltk.translate.bleu_score import corpus_bleu\n", - "\n", - "# \"\"\" Estimates corpora-level BLEU score of model's translations given inp and reference out \"\"\"\n", - "# translations, _ = model.translate_lines(inp_lines, **flags)\n", - "# # Note: if you experience out-of-memory error, split input lines into batches and translate separately\n", - "# return corpus_bleu([[ref] for ref in out_lines], translations) * 100" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": {}, - "outputs": [], - "source": [ - "import tqdm" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "59it [00:03, 18.87it/s]\n" - ] - } - ], - "source": [ - "original_text = []\n", - "generated_text = []\n", - "model.eval()\n", - "with torch.no_grad():\n", - "\n", - " for i, batch in tqdm.tqdm(enumerate(test_iterator)):\n", - "\n", - " src = batch.src\n", - " trg = batch.trg\n", - "\n", - " output = model(src, trg, 0) #turn off teacher forcing\n", - "\n", - " #trg = [trg sent len, batch size]\n", - " #output = [trg sent len, batch size, output dim]\n", - "\n", - " output = output.argmax(dim=-1)\n", - " \n", - " original_text.extend([get_text(x, TRG.vocab) for x in trg.cpu().numpy().T])\n", - " generated_text.extend([get_text(x, TRG.vocab) for x in output[1:].detach().cpu().numpy().T])\n", - "\n", - "# original_text = flatten(original_text)\n", - "# generated_text = flatten(generated_text)" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "14.139920232081806" - ] - }, - "execution_count": 111, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "corpus_bleu([[text] for text in original_text], generated_text) * 100" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Baseline solution BLEU score is quite low. Try to achieve at least __24__ BLEU on the test set. \n", - "The checkpoints are:\n", - "\n", - "* __22__ - minimal score to submit the homework, 30% of points\n", - "\n", - "* __27__ - good score, 70% of points\n", - "\n", - "* __29__ - excellent score, 100% of points" - ] - } - ], - "metadata": { - "anaconda-cloud": {}, - "colab": { - "collapsed_sections": [], - "machine_shape": "hm", - "name": "homework.ipynb", - "provenance": [] - }, - "kernelspec": { - "display_name": "Py3 Research", - "language": "python", - "name": "py3_research" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/homeworks/lab01_nlp/README.md b/homeworks/lab01_nlp/README.md deleted file mode 100644 index 3b94c95..0000000 --- a/homeworks/lab01_nlp/README.md +++ /dev/null @@ -1,6 +0,0 @@ -Lab assignment #1 - -* Part 1: Embedding-based Machine Translation: -[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/girafe-ai/natural-language-processing/blob/master/homeworks/lab01_nlp/lab1_01_nlp_part1_embedding_based_mt.ipynb) - -* Part 2: NMT: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/girafe-ai/natural-language-processing/blob/master/homeworks/lab01_nlp/lab1_02_nlp_part2_nmt.ipynb) diff --git a/homeworks/lab01_nlp/lab1_01_nlp_part1_embedding_based_mt.ipynb b/homeworks/lab01_nlp/lab1_01_nlp_part1_embedding_based_mt.ipynb deleted file mode 100644 index 2bcd322..0000000 --- a/homeworks/lab01_nlp/lab1_01_nlp_part1_embedding_based_mt.ipynb +++ /dev/null @@ -1,753 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "eulvfJWl7ueY" - }, - "source": [ - "# Lab 1\n", - "\n", - "\n", - "## Part 1: Bilingual dictionary induction and unsupervised embedding-based MT (30%)\n", - "*Note: this homework is based on materials from yandexdataschool [NLP course](https://github.com/yandexdataschool/nlp_course/). Feel free to check this awesome course if you wish to dig deeper.*\n", - "\n", - "*Refined by [Nikolay Karpachev](https://www.linkedin.com/in/nikolay-karpachev-b0146a104/)*" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "fV4rIjxa7uei" - }, - "source": [ - "**In this homework** **YOU** will make machine translation system without using parallel corpora, alignment, attention, 100500 depth super-cool recurrent neural network and all that kind superstuff.\n", - "\n", - "But even without parallel corpora this system can be good enough (hopefully), in particular for similar languages, e.g. Ukrainian and Russian. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "idSYq2GU7uew" - }, - "source": [ - "### Frament of the Swadesh list for some slavic languages\n", - "\n", - "The Swadesh list is a lexicostatistical stuff. It's named after American linguist Morris Swadesh and contains basic lexis. This list are used to define subgroupings of languages, its relatedness.\n", - "\n", - "So we can see some kind of word invariance for different Slavic languages.\n", - "\n", - "\n", - "| Russian | Belorussian | Ukrainian | Polish | Czech | Bulgarian |\n", - "|-----------------|--------------------------|-------------------------|--------------------|-------------------------------|-----------------------|\n", - "| женщина | жанчына, кабета, баба | жінка | kobieta | žena | жена |\n", - "| мужчина | мужчына | чоловік, мужчина | mężczyzna | muž | мъж |\n", - "| человек | чалавек | людина, чоловік | człowiek | člověk | човек |\n", - "| ребёнок, дитя | дзіця, дзіцёнак, немаўля | дитина, дитя | dziecko | dítě | дете |\n", - "| жена | жонка | дружина, жінка | żona | žena, manželka, choť | съпруга, жена |\n", - "| муж | муж, гаспадар | чоловiк, муж | mąż | muž, manžel, choť | съпруг, мъж |\n", - "| мать, мама | маці, матка | мати, матір, неня, мама | matka | matka, máma, 'стар.' mateř | майка |\n", - "| отец, тятя | бацька, тата | батько, тато, татусь | ojciec | otec | баща, татко |\n", - "| много | шмат, багата | багато | wiele | mnoho, hodně | много |\n", - "| несколько | некалькі, колькі | декілька, кілька | kilka | několik, pár, trocha | няколко |\n", - "| другой, иной | іншы | інший | inny | druhý, jiný | друг |\n", - "| зверь, животное | жывёла, звер, істота | тварина, звір | zwierzę | zvíře | животно |\n", - "| рыба | рыба | риба | ryba | ryba | риба |\n", - "| птица | птушка | птах, птиця | ptak | pták | птица |\n", - "| собака, пёс | сабака | собака, пес | pies | pes | куче, пес |\n", - "| вошь | вош | воша | wesz | veš | въшка |\n", - "| змея, гад | змяя | змія, гад | wąż | had | змия |\n", - "| червь, червяк | чарвяк | хробак, черв'як | robak | červ | червей |\n", - "| дерево | дрэва | дерево | drzewo | strom, dřevo | дърво |\n", - "| лес | лес | ліс | las | les | гора, лес |\n", - "| палка | кій, палка | палиця | patyk, pręt, pałka | hůl, klacek, prut, kůl, pálka | палка, пръчка, бастун |" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "cNM3_fjr7ue2" - }, - "source": [ - "But the context distribution of these languages demonstrates even more invariance. And we can use this fact for our for our purposes." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "YLppwa527ue6" - }, - "source": [ - "## Data" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "lYBGKAUn7ue_" - }, - "outputs": [], - "source": [ - "import gensim\n", - "import numpy as np\n", - "from gensim.models import KeyedVectors" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "MwGoVhRA7ufP" - }, - "source": [ - "In this notebook we're going to use pretrained word vectors - FastText (original paper - https://arxiv.org/abs/1607.04606).\n", - "\n", - "You can download them from the official [website](https://fasttext.cc/docs/en/crawl-vectors.html). We're going to need embeddings for Russian and Ukrainian languages. Please use word2vec-compatible format (.text)." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "u1JjQv_97ufT" - }, - "outputs": [], - "source": [ - "uk_emb = KeyedVectors.load_word2vec_format(\"cc.uk.300.vec\")" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "ffzuept_7ufd" - }, - "outputs": [], - "source": [ - "ru_emb = KeyedVectors.load_word2vec_format(\"cc.ru.300.vec\")" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "nTkXfT0W7ufk" - }, - "outputs": [], - "source": [ - "ru_emb.most_similar([ru_emb[\"август\"]], topn=10)" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "vdBA8lcg7ufs" - }, - "outputs": [], - "source": [ - "uk_emb.most_similar([uk_emb[\"серпень\"]])" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "_yJvcKXO7uf0" - }, - "outputs": [], - "source": [ - "ru_emb.most_similar([uk_emb[\"серпень\"]])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "pNdYAR1q7uf6" - }, - "source": [ - "Load small dictionaries for correspoinding words pairs as trainset and testset." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "35d_DAK67uf8" - }, - "outputs": [], - "source": [ - "def load_word_pairs(filename):\n", - " uk_ru_pairs = []\n", - " uk_vectors = []\n", - " ru_vectors = []\n", - " with open(filename, \"r\") as inpf:\n", - " for line in inpf:\n", - " uk, ru = line.rstrip().split(\"\\t\")\n", - " if uk not in uk_emb or ru not in ru_emb:\n", - " continue\n", - " uk_ru_pairs.append((uk, ru))\n", - " uk_vectors.append(uk_emb[uk])\n", - " ru_vectors.append(ru_emb[ru])\n", - " return uk_ru_pairs, np.array(uk_vectors), np.array(ru_vectors)" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "wkNL602WHJyO" - }, - "outputs": [], - "source": [ - "!wget -O ukr_rus.train.txt http://tiny.cc/jfgecz" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "uoclU6JcHCcn" - }, - "outputs": [], - "source": [ - "!wget -O ukr_rus.test.txt http://tiny.cc/6zoeez" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "05BqsdSK7ugD" - }, - "outputs": [], - "source": [ - "uk_ru_train, X_train, Y_train = load_word_pairs(\"ukr_rus.train.txt\")" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "zQOZw51r7ugL" - }, - "outputs": [], - "source": [ - "uk_ru_test, X_test, Y_test = load_word_pairs(\"ukr_rus.test.txt\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "-ZBBNvpz7ugQ" - }, - "source": [ - "## Embedding space mapping (0.3 pts)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "x_Dhk5gL7ugS" - }, - "source": [ - "Let $x_i \\in \\mathrm{R}^d$ be the distributed representation of word $i$ in the source language, and $y_i \\in \\mathrm{R}^d$ is the vector representation of its translation. Our purpose is to learn such linear transform $W$ that minimizes euclidian distance between $Wx_i$ and $y_i$ for some subset of word embeddings. Thus we can formulate so-called Procrustes problem:\n", - "\n", - "$$W^*= \\arg\\min_W \\sum_{i=1}^n||Wx_i - y_i||_2$$\n", - "or\n", - "$$W^*= \\arg\\min_W ||WX - Y||_F$$\n", - "\n", - "where $||*||_F$ - Frobenius norm." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "acOjDdtL7ugY" - }, - "source": [ - "$W^*= \\arg\\min_W \\sum_{i=1}^n||Wx_i - y_i||_2$ looks like simple multiple linear regression (without intercept fit). So let's code." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "Lb-KN1be7uga" - }, - "outputs": [], - "source": [ - "from sklearn.linear_model import LinearRegression\n", - "\n", - "# YOUR CODE HERE\n", - "# mapping = ...\n", - "# -------" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "X7tqJwoY7ugf" - }, - "source": [ - "Let's take a look at neigbours of the vector of word _\"серпень\"_ (_\"август\"_ in Russian) after linear transform." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "31SrFSbn7ugi" - }, - "outputs": [], - "source": [ - "august = mapping.predict(uk_emb[\"серпень\"].reshape(1, -1))\n", - "ru_emb.most_similar(august)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "okSkjk597ugo" - }, - "source": [ - "We can see that neighbourhood of this embedding cosists of different months, but right variant is on the ninth place." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "o2uY6Y9B7ugt" - }, - "source": [ - "As quality measure we will use precision top-1, top-5 and top-10 (for each transformed Ukrainian embedding we count how many right target pairs are found in top N nearest neighbours in Russian embedding space)." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "zptuho8LAfIE" - }, - "outputs": [], - "source": [ - "def precision(pairs, mapped_vectors, topn=1):\n", - " \"\"\"\n", - " :args:\n", - " pairs = list of right word pairs [(uk_word_0, ru_word_0), ...]\n", - " mapped_vectors = list of embeddings after mapping from source embedding space to destination embedding space\n", - " topn = the number of nearest neighbours in destination embedding space to choose from\n", - " :returns:\n", - " precision_val, float number, total number of words for those we can find right translation at top K.\n", - " \"\"\"\n", - " assert len(pairs) == len(mapped_vectors)\n", - " num_matches = 0\n", - " for i, (_, ru) in enumerate(pairs):\n", - " # YOUR CODE HERE\n", - " precision_val = num_matches / len(pairs)\n", - " return precision_val" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "duhj9hpv7ugy" - }, - "outputs": [], - "source": [ - "assert precision([(\"серпень\", \"август\")], august, topn=5) == 0.0\n", - "assert precision([(\"серпень\", \"август\")], august, topn=9) == 1.0\n", - "assert precision([(\"серпень\", \"август\")], august, topn=10) == 1.0" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "0-iyd5gP7ug5" - }, - "outputs": [], - "source": [ - "assert precision(uk_ru_test, X_test) == 0.0\n", - "assert precision(uk_ru_test, Y_test) == 1.0" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "U-ssEJ3x7uhA" - }, - "outputs": [], - "source": [ - "precision_top1 = precision(uk_ru_test, mapping.predict(X_test), 1)\n", - "precision_top5 = precision(uk_ru_test, mapping.predict(X_test), 5)" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "7K-hy7a6Ksn2" - }, - "outputs": [], - "source": [ - "print(precision_top1)\n", - "print(precision_top5)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "hf6Ou8bx7uhH" - }, - "source": [ - "## Making it better (orthogonal Procrustean problem) (0.3 pts)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "4oLs-drN7uhK" - }, - "source": [ - "It can be shown (see original paper) that a self-consistent linear mapping between semantic spaces should be orthogonal. \n", - "We can restrict transform $W$ to be orthogonal. Then we will solve next problem:\n", - "\n", - "$$W^*= \\arg\\min_W ||WX - Y||_F \\text{, where: } W^TW = I$$\n", - "\n", - "$$I \\text{- identity matrix}$$\n", - "\n", - "Instead of making yet another regression problem we can find optimal orthogonal transformation using singular value decomposition. It turns out that optimal transformation $W^*$ can be expressed via SVD components:\n", - "$$X^TY=U\\Sigma V^T\\text{, singular value decompostion}$$\n", - "$$W^*=UV^T$$" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "_KSaRJFGMFiJ" - }, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "DdFQ7qti7uhL" - }, - "outputs": [], - "source": [ - "def learn_transform(X_train, Y_train):\n", - " \"\"\" \n", - " :returns: W* : float matrix[emb_dim x emb_dim] as defined in formulae above\n", - " \"\"\"\n", - " # YOUR CODE GOES HERE\n", - " # compute orthogonal embedding space mapping\n", - " # mapping = ...\n", - "\n", - " return mapping" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "7X7QfYDd7uhQ" - }, - "outputs": [], - "source": [ - "W = learn_transform(X_train, Y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "OVOFYYa37uhX" - }, - "outputs": [], - "source": [ - "ru_emb.most_similar([np.matmul(uk_emb[\"серпень\"], W)])" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "r297sYP37uhb" - }, - "outputs": [], - "source": [ - "print(precision(uk_ru_test, np.matmul(X_test, W)))\n", - "print(precision(uk_ru_test, np.matmul(X_test, W), 5))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "hvUZ72U5AfJg" - }, - "source": [ - "## Unsupervised embedding-based MT (0.4 pts)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "LLyuVfHBLrJn" - }, - "source": [ - "Now, let's build our word embeddings-based translator!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "tPAURW1CMuP7" - }, - "source": [ - "Firstly, download OPUS Tatoeba corpus." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "F80kUKzQMsDu" - }, - "outputs": [], - "source": [ - "!wget https://object.pouta.csc.fi/OPUS-Tatoeba/v20190709/mono/uk.txt.gz" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "0CGFZoxCUVf1" - }, - "outputs": [], - "source": [ - "!gzip -d ./uk.txt.gz" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "2MV3VvoVUX5U" - }, - "outputs": [], - "source": [ - "with open('./uk.txt', 'r') as f:\n", - " uk_corpus = f.readlines()" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "tU7nPVf0UhbI" - }, - "outputs": [], - "source": [ - "# To save your time and CPU, feel free to use first 1000 sentences of the corpus\n", - "uk_corpus = uk_corpus[:1000]" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "FLN8dBOXAfJ1" - }, - "outputs": [], - "source": [ - "# Any necessary preprocessing if needed\n", - "# YOUR CODE HERE" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "FGksC7l_NMi9" - }, - "outputs": [], - "source": [ - "def translate(sentence):\n", - " \"\"\"\n", - " :args:\n", - " sentence - sentence in Ukrainian (str)\n", - " :returns:\n", - " translation - sentence in Russian (str)\n", - "\n", - " * find ukrainian embedding for each word in sentence\n", - " * transform ukrainian embedding vector\n", - " * find nearest russian word and replace\n", - " \"\"\"\n", - " # YOUR CODE GOES HERE\n", - "\n", - " return \" \".join(translated)" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "4hbbMy-tNxlf" - }, - "outputs": [], - "source": [ - "assert translate(\".\") == \".\"\n", - "assert translate(\"1 , 3\") == \"1 , 3\"\n", - "assert translate(\"кіт зловив мишу\") == \"кот поймал мышку\"" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "ia6I2ce7O_HI" - }, - "source": [ - "Now you can play with your model and try to get as accurate translations as possible. **Note**: one big issue is out-of-vocabulary words. Try to think of various ways of handling it (you can start with translating each of them to a special **UNK** token and then move to more sophisticated approaches). Good luck!" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "ap1W7ZCeOAVU" - }, - "outputs": [], - "source": [ - "for sent in uk_corpus[::10]:\n", - " print(translate(sent))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Great! \n", - "See second notebook for the Neural Machine Translation assignment." - ] - } - ], - "metadata": { - "anaconda-cloud": {}, - "colab": { - "collapsed_sections": [], - "machine_shape": "hm", - "name": "homework.ipynb", - "provenance": [] - }, - "kernelspec": { - "display_name": "Py3 Research", - "language": "python", - "name": "py3_research" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/homeworks/lab01_nlp/lab1_02_nlp_part2_nmt.ipynb b/homeworks/lab01_nlp/lab1_02_nlp_part2_nmt.ipynb deleted file mode 100644 index eb346fa..0000000 --- a/homeworks/lab01_nlp/lab1_02_nlp_part2_nmt.ipynb +++ /dev/null @@ -1,941 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Lab 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Part 2: Neural Machine Translation in the wild\n", - "In the third homework you are supposed to get the best translation you can for the EN-RU translation task.\n", - "\n", - "Basic approach using RNNs as encoder and decoder is implemented for you. \n", - "\n", - "Your ultimate task is to use the techniques we've covered, e.g.\n", - "\n", - "* Optimization enhancements (e.g. learning rate decay)\n", - "\n", - "* CNN encoder (with or without positional encoding)\n", - "\n", - "* attention/self-attention mechanism\n", - "\n", - "* pretraining the language model\n", - "\n", - "* [Byte Pair Encoding](https://github.com/rsennrich/subword-nmt)\n", - "\n", - "* or just fine-tunning BERT ;)\n", - "\n", - "to improve the translation quality. \n", - "\n", - "__Please use at least three different approaches/models and compare them (translation quality/complexity/training and evaluation time).__\n", - "\n", - "Write down some summary on your experiments and illustrate it with convergence plots/metrics and your thoughts. Just like you would approach a real problem." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# You might need to install the libraries below. Do it in the desired environment\n", - "# if you are working locally.\n", - "\n", - "# ! pip install subword-nmt\n", - "# ! pip install nltk\n", - "# ! pip install torchtext" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Thanks to YSDA NLP course team for the data\n", - "# (who thanks tilda and deephack teams for the data in their turn)\n", - "\n", - "import os\n", - "path_do_data = '../../datasets/Machine_translation_EN_RU/data.txt'\n", - "if not os.path.exists(path_do_data):\n", - " print(\"Dataset not found locally. Downloading from github. Loading special files as well\")\n", - " !wget https://raw.githubusercontent.com/girafe-ai/ml-mipt/master/datasets/Machine_translation_EN_RU/data.txt -nc\n", - " path_do_data = './data.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if not os.path.exists('./utils.py'):\n", - " print(\"utils file not found locally. Downloading from github.\")\n", - " !wget https://raw.githubusercontent.com/girafe-ai/ml-mipt/master/homeworks_advanced/Lab1_NLP/utils.py -nc\n", - "\n", - "if not os.path.exists('./my_network.py'):\n", - " print(\"network file not found locally. Downloading from github.\")\n", - " !wget https://raw.githubusercontent.com/girafe-ai/ml-mipt/master/homeworks_advanced/Lab1_NLP/my_network.py -nc" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", - "\n", - "import torchtext\n", - "from torchtext.legacy.datasets import TranslationDataset, Multi30k\n", - "from torchtext.legacy.data import Field, BucketIterator, TabularDataset\n", - "\n", - "import spacy\n", - "\n", - "import random\n", - "import math\n", - "import time\n", - "\n", - "import matplotlib\n", - "matplotlib.rcParams.update({'figure.figsize': (16, 12), 'font.size': 14})\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "from IPython.display import clear_output\n", - "\n", - "from nltk.tokenize import WordPunctTokenizer\n", - "from subword_nmt.learn_bpe import learn_bpe\n", - "from subword_nmt.apply_bpe import BPE" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Main part\n", - "__Here comes the preprocessing. Do not hesitate to use BPE or more complex preprocessing ;)__" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "tokenizer_W = WordPunctTokenizer()\n", - "def tokenize(x, tokenizer=tokenizer_W):\n", - " return tokenizer.tokenize(x.lower())" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "SRC = Field(tokenize=tokenize,\n", - " init_token = '', \n", - " eos_token = '', \n", - " lower = True)\n", - "\n", - "TRG = Field(tokenize=tokenize,\n", - " init_token = '', \n", - " eos_token = '', \n", - " lower = True)\n", - "\n", - "dataset = TabularDataset(\n", - " path=path_do_data,\n", - " format='tsv',\n", - " fields=[('trg', TRG), ('src', SRC)]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "train_data, valid_data, test_data = dataset.split(split_ratio=[0.8, 0.15, 0.05])" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of training examples: 40000\n", - "Number of validation examples: 2500\n", - "Number of testing examples: 7500\n" - ] - } - ], - "source": [ - "print(f\"Number of training examples: {len(train_data.examples)}\")\n", - "print(f\"Number of validation examples: {len(valid_data.examples)}\")\n", - "print(f\"Number of testing examples: {len(test_data.examples)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "SRC.build_vocab(train_data, min_freq = 3)\n", - "TRG.build_vocab(train_data, min_freq = 3)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Unique tokens in source (ru) vocabulary: 9267\n", - "Unique tokens in target (en) vocabulary: 6699\n" - ] - } - ], - "source": [ - "print(f\"Unique tokens in source (ru) vocabulary: {len(SRC.vocab)}\")\n", - "print(f\"Unique tokens in target (en) vocabulary: {len(TRG.vocab)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here are tokens from original (RU) corpus:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['',\n", - " '29',\n", - " 'соль',\n", - " 'комо',\n", - " '―',\n", - " 'электрическая',\n", - " 'ming',\n", - " 'утренний',\n", - " 'детском',\n", - " 'таунус']" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "SRC.vocab.itos[::1000]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And from target (EN) corpus:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['', 'king', 'buffets', 'catch', 'media', 'schedule', 'maraunenhof']" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "TRG.vocab.itos[::1000]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And here is example from train dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'trg': ['laundry', 'service', 'is', 'provided', '.'], 'src': ['помимо', 'этого', ',', 'гостям', 'предоставляются', 'услуги', 'прачечной', '.']}\n" - ] - } - ], - "source": [ - "print(vars(train_data.examples[9]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's check the length distributions:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Length distribution in Train data\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "src_length = map(len, [vars(x)['src'] for x in train_data.examples])\n", - "trg_length = map(len, [vars(x)['trg'] for x in train_data.examples])\n", - "\n", - "print('Length distribution in Train data')\n", - "plt.figure(figsize=[8, 4])\n", - "plt.subplot(1, 2, 1)\n", - "plt.title(\"source length\")\n", - "plt.hist(list(src_length), bins=20);\n", - "\n", - "plt.subplot(1, 2, 2)\n", - "plt.title(\"translation length\")\n", - "plt.hist(list(trg_length), bins=20);" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Length distribution in Test data\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "src_length = map(len, [vars(x)['src'] for x in test_data.examples])\n", - "trg_length = map(len, [vars(x)['trg'] for x in test_data.examples])\n", - "\n", - "print('Length distribution in Test data')\n", - "plt.figure(figsize=[8, 4])\n", - "plt.subplot(1, 2, 1)\n", - "plt.title(\"source length\")\n", - "plt.hist(list(src_length), bins=20);\n", - "\n", - "plt.subplot(1, 2, 2)\n", - "plt.title(\"translation length\")\n", - "plt.hist(list(trg_length), bins=20);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Model side\n", - "__Here comes simple pipeline of NMT model learning. It almost copies the week03 practice__" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "device(type='cuda', index=1)" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "device" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "def _len_sort_key(x):\n", - " return len(x.src)\n", - "\n", - "BATCH_SIZE = 128\n", - "\n", - "train_iterator, valid_iterator, test_iterator = BucketIterator.splits(\n", - " (train_data, valid_data, test_data), \n", - " batch_size = BATCH_SIZE, \n", - " device = device,\n", - " sort_key=_len_sort_key\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "[torchtext.data.batch.Batch of size 128]\n", - "\t[.trg]:[torch.cuda.LongTensor of size 55x128 (GPU 1)]\n", - "\t[.src]:[torch.cuda.LongTensor of size 59x128 (GPU 1)]\n", - "torch.Size([59, 128]) torch.Size([55, 128])\n" - ] - } - ], - "source": [ - "for x in train_iterator:\n", - " break\n", - "print(x)\n", - "print(x.src.shape, x.trg.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "import my_network\n", - "Encoder = my_network.Encoder\n", - "Decoder = my_network.Decoder\n", - "Seq2Seq = my_network.Seq2Seq" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "INPUT_DIM = len(SRC.vocab)\n", - "OUTPUT_DIM = len(TRG.vocab)\n", - "ENC_EMB_DIM = 256\n", - "DEC_EMB_DIM = 256\n", - "HID_DIM = 512\n", - "N_LAYERS = 2\n", - "ENC_DROPOUT = 0.5\n", - "DEC_DROPOUT = 0.5\n", - "\n", - "enc = Encoder(INPUT_DIM, ENC_EMB_DIM, HID_DIM, N_LAYERS, ENC_DROPOUT)\n", - "dec = Decoder(OUTPUT_DIM, DEC_EMB_DIM, HID_DIM, N_LAYERS, DEC_DROPOUT)\n", - "\n", - "# dont forget to put the model to the right device\n", - "model = Seq2Seq(enc, dec, device).to(device)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Seq2Seq(\n", - " (encoder): Encoder(\n", - " (embedding): Embedding(9267, 256)\n", - " (rnn): LSTM(256, 512, num_layers=2, dropout=0.5)\n", - " (dropout): Dropout(p=0.5, inplace=False)\n", - " )\n", - " (decoder): Decoder(\n", - " (embedding): Embedding(6699, 256)\n", - " (rnn): LSTM(256, 512, num_layers=2, dropout=0.5)\n", - " (out): Linear(in_features=512, out_features=6699, bias=True)\n", - " (dropout): Dropout(p=0.5, inplace=False)\n", - " )\n", - ")" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def init_weights(m):\n", - " # \n", - " for name, param in m.named_parameters():\n", - " nn.init.uniform_(param, -0.08, 0.08)\n", - " \n", - "model.apply(init_weights)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The model has 14,880,299 trainable parameters\n" - ] - } - ], - "source": [ - "def count_parameters(model):\n", - " return sum(p.numel() for p in model.parameters() if p.requires_grad)\n", - "\n", - "print(f'The model has {count_parameters(model):,} trainable parameters')" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "PAD_IDX = TRG.vocab.stoi['']\n", - "optimizer = optim.Adam(model.parameters())\n", - "criterion = nn.CrossEntropyLoss(ignore_index = PAD_IDX)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "def train(model, iterator, optimizer, criterion, clip, train_history=None, valid_history=None):\n", - " model.train()\n", - " \n", - " epoch_loss = 0\n", - " history = []\n", - " for i, batch in enumerate(iterator):\n", - " \n", - " src = batch.src\n", - " trg = batch.trg\n", - " \n", - " optimizer.zero_grad()\n", - " \n", - " output = model(src, trg)\n", - " \n", - " #trg = [trg sent len, batch size]\n", - " #output = [trg sent len, batch size, output dim]\n", - " \n", - " output = output[1:].view(-1, output.shape[-1])\n", - " trg = trg[1:].view(-1)\n", - " \n", - " #trg = [(trg sent len - 1) * batch size]\n", - " #output = [(trg sent len - 1) * batch size, output dim]\n", - " \n", - " loss = criterion(output, trg)\n", - " \n", - " loss.backward()\n", - " \n", - " # Let's clip the gradient\n", - " torch.nn.utils.clip_grad_norm_(model.parameters(), clip)\n", - " \n", - " optimizer.step()\n", - " \n", - " epoch_loss += loss.item()\n", - " \n", - " history.append(loss.cpu().data.numpy())\n", - " if (i+1)%10==0:\n", - " fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(12, 8))\n", - "\n", - " clear_output(True)\n", - " ax[0].plot(history, label='train loss')\n", - " ax[0].set_xlabel('Batch')\n", - " ax[0].set_title('Train loss')\n", - " if train_history is not None:\n", - " ax[1].plot(train_history, label='general train history')\n", - " ax[1].set_xlabel('Epoch')\n", - " if valid_history is not None:\n", - " ax[1].plot(valid_history, label='general valid history')\n", - " plt.legend()\n", - " \n", - " plt.show()\n", - "\n", - " \n", - " return epoch_loss / len(iterator)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "def evaluate(model, iterator, criterion):\n", - " \n", - " model.eval()\n", - " \n", - " epoch_loss = 0\n", - " \n", - " history = []\n", - " \n", - " with torch.no_grad():\n", - " \n", - " for i, batch in enumerate(iterator):\n", - "\n", - " src = batch.src\n", - " trg = batch.trg\n", - "\n", - " output = model(src, trg, 0) #turn off teacher forcing\n", - "\n", - " #trg = [trg sent len, batch size]\n", - " #output = [trg sent len, batch size, output dim]\n", - "\n", - " output = output[1:].view(-1, output.shape[-1])\n", - " trg = trg[1:].view(-1)\n", - "\n", - " #trg = [(trg sent len - 1) * batch size]\n", - " #output = [(trg sent len - 1) * batch size, output dim]\n", - "\n", - " loss = criterion(output, trg)\n", - " \n", - " epoch_loss += loss.item()\n", - " \n", - " return epoch_loss / len(iterator)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "def epoch_time(start_time, end_time):\n", - " elapsed_time = end_time - start_time\n", - " elapsed_mins = int(elapsed_time / 60)\n", - " elapsed_secs = int(elapsed_time - (elapsed_mins * 60))\n", - " return elapsed_mins, elapsed_secs" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "train_history = []\n", - "valid_history = []\n", - "\n", - "N_EPOCHS = 10\n", - "CLIP = 1\n", - "\n", - "best_valid_loss = float('inf')" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch: 10 | Time: 1m 10s\n", - "\tTrain Loss: 2.998 | Train PPL: 20.040\n", - "\t Val. Loss: 4.710 | Val. PPL: 111.007\n" - ] - } - ], - "source": [ - "for epoch in range(N_EPOCHS):\n", - " \n", - " start_time = time.time()\n", - " \n", - " train_loss = train(model, train_iterator, optimizer, criterion, CLIP, train_history, valid_history)\n", - " valid_loss = evaluate(model, valid_iterator, criterion)\n", - " \n", - " end_time = time.time()\n", - " \n", - " epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n", - " \n", - " if valid_loss < best_valid_loss:\n", - " best_valid_loss = valid_loss\n", - " torch.save(model.state_dict(), 'tut1-model.pt')\n", - " \n", - " train_history.append(train_loss)\n", - " valid_history.append(valid_loss)\n", - " print(f'Epoch: {epoch+1:02} | Time: {epoch_mins}m {epoch_secs}s')\n", - " print(f'\\tTrain Loss: {train_loss:.3f} | Train PPL: {math.exp(train_loss):7.3f}')\n", - " print(f'\\t Val. Loss: {valid_loss:.3f} | Val. PPL: {math.exp(valid_loss):7.3f}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "__Let's take a look at our network quality__:" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": {}, - "outputs": [], - "source": [ - "del utils" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": {}, - "outputs": [], - "source": [ - "import utils\n", - "import imp\n", - "imp.reload(utils)\n", - "generate_translation = utils.generate_translation\n", - "remove_tech_tokens = utils.remove_tech_tokens\n", - "get_text = utils.get_text\n", - "flatten = utils.flatten" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": {}, - "outputs": [], - "source": [ - "batch = next(iter(test_iterator))" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Original: there is a 24 - hour front desk at the property .\n", - "Generated: the property offers a 24 - hour front desk . .\n", - "\n", - "Original: this property also features free wifi .\n", - "Generated: free wifi access . . . .\n", - "\n" - ] - } - ], - "source": [ - "for idx in [1,2]:\n", - " src = batch.src[:, idx:idx+1]\n", - " trg = batch.trg[:, idx:idx+1]\n", - " generate_translation(src, trg, model, TRG.vocab)" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [], - "source": [ - "from nltk.translate.bleu_score import corpus_bleu\n", - "\n", - "# \"\"\" Estimates corpora-level BLEU score of model's translations given inp and reference out \"\"\"\n", - "# translations, _ = model.translate_lines(inp_lines, **flags)\n", - "# # Note: if you experience out-of-memory error, split input lines into batches and translate separately\n", - "# return corpus_bleu([[ref] for ref in out_lines], translations) * 100" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": {}, - "outputs": [], - "source": [ - "import tqdm" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "59it [00:03, 18.87it/s]\n" - ] - } - ], - "source": [ - "original_text = []\n", - "generated_text = []\n", - "model.eval()\n", - "with torch.no_grad():\n", - "\n", - " for i, batch in tqdm.tqdm(enumerate(test_iterator)):\n", - "\n", - " src = batch.src\n", - " trg = batch.trg\n", - "\n", - " output = model(src, trg, 0) #turn off teacher forcing\n", - "\n", - " #trg = [trg sent len, batch size]\n", - " #output = [trg sent len, batch size, output dim]\n", - "\n", - " output = output.argmax(dim=-1)\n", - " \n", - " original_text.extend([get_text(x, TRG.vocab) for x in trg.cpu().numpy().T])\n", - " generated_text.extend([get_text(x, TRG.vocab) for x in output[1:].detach().cpu().numpy().T])\n", - "\n", - "# original_text = flatten(original_text)\n", - "# generated_text = flatten(generated_text)" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "14.139920232081806" - ] - }, - "execution_count": 111, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "corpus_bleu([[text] for text in original_text], generated_text) * 100" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Baseline solution BLEU score is quite low. Try to achieve at least __24__ BLEU on the test set. \n", - "The checkpoints are:\n", - "\n", - "* __22__ - minimal score to submit the homework, 30% of points\n", - "\n", - "* __27__ - good score, 70% of points\n", - "\n", - "* __29__ - excellent score, 100% of points" - ] - } - ], - "metadata": { - "anaconda-cloud": {}, - "colab": { - "collapsed_sections": [], - "machine_shape": "hm", - "name": "homework.ipynb", - "provenance": [] - }, - "kernelspec": { - "display_name": "Py3 Research", - "language": "python", - "name": "py3_research" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/homeworks/lab01_nlp/my_network.py b/homeworks/lab01_nlp/my_network.py deleted file mode 100644 index 966416d..0000000 --- a/homeworks/lab01_nlp/my_network.py +++ /dev/null @@ -1,182 +0,0 @@ -import torch -import torch.nn as nn -import torch.optim as optim - -import torchtext -from torchtext.datasets import TranslationDataset, Multi30k -from torchtext.data import Field, BucketIterator - -import random -import math -import time - - -class Encoder(nn.Module): - def __init__(self, input_dim, emb_dim, hid_dim, n_layers, dropout): - super().__init__() - - self.input_dim = input_dim - self.emb_dim = emb_dim - self.hid_dim = hid_dim - self.n_layers = n_layers -# self.dropout = dropout - - self.embedding = nn.Embedding( - num_embeddings=input_dim, - embedding_dim=emb_dim - ) - # - - self.rnn = nn.LSTM( - input_size=emb_dim, - hidden_size=hid_dim, - num_layers=n_layers, - dropout=dropout - ) - # - - self.dropout = nn.Dropout(p=dropout)# - - def forward(self, src): - - #src = [src sent len, batch size] - - # Compute an embedding from the src data and apply dropout to it - embedded = self.embedding(src)# - - embedded = self.dropout(embedded) - - output, (hidden, cell) = self.rnn(embedded) - #embedded = [src sent len, batch size, emb dim] - - # Compute the RNN output values of the encoder RNN. - # outputs, hidden and cell should be initialized here. Refer to nn.LSTM docs ;) - - # - - #outputs = [src sent len, batch size, hid dim * n directions] - #hidden = [n layers * n directions, batch size, hid dim] - #cell = [n layers * n directions, batch size, hid dim] - - #outputs are always from the top hidden layer - - return hidden, cell - - -class Decoder(nn.Module): - def __init__(self, output_dim, emb_dim, hid_dim, n_layers, dropout): - super().__init__() - - self.emb_dim = emb_dim - self.hid_dim = hid_dim - self.output_dim = output_dim - self.n_layers = n_layers - self.dropout = dropout - - self.embedding = nn.Embedding( - num_embeddings=output_dim, - embedding_dim=emb_dim - ) - # - - self.rnn = nn.LSTM( - input_size=emb_dim, - hidden_size=hid_dim, - num_layers=n_layers, - dropout=dropout - ) - # - - self.out = nn.Linear( - in_features=hid_dim, - out_features=output_dim - ) - # - - self.dropout = nn.Dropout(p=dropout)# - - def forward(self, input, hidden, cell): - - #input = [batch size] - #hidden = [n layers * n directions, batch size, hid dim] - #cell = [n layers * n directions, batch size, hid dim] - - #n directions in the decoder will both always be 1, therefore: - #hidden = [n layers, batch size, hid dim] - #context = [n layers, batch size, hid dim] - - input = input.unsqueeze(0) - - #input = [1, batch size] - - # Compute an embedding from the input data and apply dropout to it - embedded = self.dropout(self.embedding(input))# - - #embedded = [1, batch size, emb dim] - - # Compute the RNN output values of the encoder RNN. - # outputs, hidden and cell should be initialized here. Refer to nn.LSTM docs ;) - # - - - #output = [sent len, batch size, hid dim * n directions] - #hidden = [n layers * n directions, batch size, hid dim] - #cell = [n layers * n directions, batch size, hid dim] - - #sent len and n directions will always be 1 in the decoder, therefore: - #output = [1, batch size, hid dim] - #hidden = [n layers, batch size, hid dim] - #cell = [n layers, batch size, hid dim] - - - output, (hidden, cell) = self.rnn(embedded, (hidden, cell)) - prediction = self.out(output.squeeze(0)) - - #prediction = [batch size, output dim] - - return prediction, hidden, cell - - -class Seq2Seq(nn.Module): - def __init__(self, encoder, decoder, device): - super().__init__() - - self.encoder = encoder - self.decoder = decoder - self.device = device - - assert encoder.hid_dim == decoder.hid_dim, \ - "Hidden dimensions of encoder and decoder must be equal!" - assert encoder.n_layers == decoder.n_layers, \ - "Encoder and decoder must have equal number of layers!" - - def forward(self, src, trg, teacher_forcing_ratio = 0.5): - - #src = [src sent len, batch size] - #trg = [trg sent len, batch size] - #teacher_forcing_ratio is probability to use teacher forcing - #e.g. if teacher_forcing_ratio is 0.75 we use ground-truth inputs 75% of the time - - # Again, now batch is the first dimention instead of zero - batch_size = trg.shape[1] - max_len = trg.shape[0] - trg_vocab_size = self.decoder.output_dim - - #tensor to store decoder outputs - outputs = torch.zeros(max_len, batch_size, trg_vocab_size).to(self.device) - - #last hidden state of the encoder is used as the initial hidden state of the decoder - hidden, cell = self.encoder(src) - - #first input to the decoder is the tokens - input = trg[0,:] - - for t in range(1, max_len): - - output, hidden, cell = self.decoder(input, hidden, cell) - outputs[t] = output - teacher_force = random.random() < teacher_forcing_ratio - top1 = output.max(1)[1] - input = (trg[t] if teacher_force else top1) - - return outputs diff --git a/homeworks/lab01_nlp/utils.py b/homeworks/lab01_nlp/utils.py deleted file mode 100644 index f3691d2..0000000 --- a/homeworks/lab01_nlp/utils.py +++ /dev/null @@ -1,33 +0,0 @@ - -def flatten(l): - return [item for sublist in l for item in sublist] - -def remove_tech_tokens(mystr, tokens_to_remove=['', '', '', '']): - return [x for x in mystr if x not in tokens_to_remove] - - -def get_text(x, TRG_vocab): - text = [TRG_vocab.itos[token] for token in x] - try: - end_idx = text.index('') - text = text[:end_idx] - except ValueError: - pass - text = remove_tech_tokens(text) - if len(text) < 1: - text = [] - return text - - -def generate_translation(src, trg, model, TRG_vocab): - model.eval() - - output = model(src, trg, 0) #turn off teacher forcing - output = output.argmax(dim=-1).cpu().numpy() - - original = get_text(list(trg[:,0].cpu().numpy()), TRG_vocab) - generated = get_text(list(output[1:, 0]), TRG_vocab) - - print('Original: {}'.format(' '.join(original))) - print('Generated: {}'.format(' '.join(generated))) - print() diff --git a/homeworks/lab02_qa/LICENSE b/homeworks/lab02_qa/LICENSE deleted file mode 100644 index e1b9ab0..0000000 --- a/homeworks/lab02_qa/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -The MIT License - -Copyright (c) 2019 Christopher Chute http://chrischute.com - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in -all copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN -THE SOFTWARE. diff --git a/homeworks/lab02_qa/README.md b/homeworks/lab02_qa/README.md deleted file mode 100644 index de29ad3..0000000 --- a/homeworks/lab02_qa/README.md +++ /dev/null @@ -1,40 +0,0 @@ -#### Lab02: QA system - -In this homework your goal is to build the QA system for specific language. The default code is available for English and Russian languages. Russian example using the [SberQuAD dataset](https://arxiv.org/pdf/1912.09723.pdf). The preprocessing code and baseline solution (BiDAF) are the slightly adapted version of the [Stanford CS224n Starter code](https://github.com/chrischute/squad) for the SQuAD dataset. - -**To use any other language, please, refer to [this post](https://medium.com/deepset-ai/going-beyond-squad-part-1-question-answering-in-different-languages-8eac6cf56f21) or to the Table 2 in the paper [Deep learning based question answering systemin Bengali](https://www.researchgate.net/publication/346129818_Deep_learning_based_question_answering_system_in_Bengali), where the authors provide an overview of available datasets.** - -The available languages are (but not limited to): Korean, Arabic, French, Spanish, Italian, Russian, English, Hindi and Chinese. - -The starting point of this assighnment is the `SberQuAD_preprocessing_and_problem_statement.ipynb` notebook. -[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github//natural-language-processing/tree/master/homeworks/lab02_qa/SberQuAD_preprocessing_and_problem_statement.ipynb) - - -You may choose either this assignment or the `homework05` on the Image Captioning. Or do both ;) - -Next comes the original instructions from the https://github.com/chrischute/squad repository. - -P.s. Downgrading PyTorch is not required, starter code works fine on PyTorch 1.4 -P.p.s. If you are running in Colab, mount your Google Drive and store the checkpoints/word vectors there. [Official instruction (en)](https://colab.research.google.com/notebooks/io.ipynb), [Habr post (ru)](https://habr.com/ru/post/348058/). Restarting the kernel after you finished the preprocessing (and saved the data to your disk) might be a good idea to release the memory. - -#### Setup - -1. Make sure you have [Miniconda](https://docs.conda.io/en/latest/miniconda.html) installed - 1. Conda is a package manager that sandboxes your project’s dependencies in a virtual environment - 2. Miniconda contains Conda and its dependencies with no extra packages by default (as opposed to Anaconda, which installs some extra packages) - -2. cd into src, run `conda env create -f environment.yml` - 1. This creates a Conda environment called `squad` - -3. Run `source activate squad` - 1. This activates the `squad` environment - 2. Do this each time you want to write/test your code - -4. Run `python setup.py` - 1. This downloads SQuAD 2.0 training and dev sets, as well as the GloVe 300-dimensional word vectors (840B) - 2. This also pre-processes the dataset for efficient data loading - 3. For a MacBook Pro on the Stanford network, `setup.py` takes around 30 minutes total - -5. Browse the code in `train.py` - 1. The `train.py` script is the entry point for training a model. It reads command-line arguments, loads the SQuAD dataset, and trains a model. - 2. You may find it helpful to browse the arguments provided by the starter code. Either look directly at the `parser.add_argument` lines in the source code, or run `python train.py -h`. diff --git a/homeworks/lab02_qa/SberQuAD_preprocessing_and_problem_statement.ipynb b/homeworks/lab02_qa/SberQuAD_preprocessing_and_problem_statement.ipynb deleted file mode 100644 index 7dafe1f..0000000 --- a/homeworks/lab02_qa/SberQuAD_preprocessing_and_problem_statement.ipynb +++ /dev/null @@ -1,360 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Credits: the provided initial code is an adaptation of the [Starter code for Stanford CS224n default final project on SQuAD 2.0](https://github.com/chrischute/squad) which is shared under MIT License. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook does initial preprocessing for the SberQuAD dataset and will give you the starting point in this assignment. If it looks too complex and/or time/resourse-expensive, you may stick to homework05 as well." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1. Preprocessing\n", - "This code is a bit changed version of the code from `setup.py`. If you want to work with the SQuAD dataset, stick to the original instructions from the https://github.com/chrischute/squad repository." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# If running on Colab, uncomment the following lines \n", - "\n", - "# !wget https://raw.githubusercontent.com/girafe-ai/natural-language-processing/master/homeworks/lab02_qa/args.py -nc\n", - "# !wget https://raw.githubusercontent.com/girafe-ai/natural-language-processing/master/homeworks/lab02_qa/layers.py -nc\n", - "# !wget https://raw.githubusercontent.com/girafe-ai/natural-language-processing/master/homeworks/lab02_qa/models.py -nc\n", - "# !wget https://raw.githubusercontent.com/girafe-ai/natural-language-processing/master/homeworks/lab02_qa/setup.py -nc\n", - "# !wget https://raw.githubusercontent.com/girafe-ai/natural-language-processing/master/homeworks/lab02_qa/test.py -nc\n", - "# !wget https://raw.githubusercontent.com/girafe-ai/natural-language-processing/master/homeworks/lab02_qa/train.py -nc\n", - "# !wget https://raw.githubusercontent.com/girafe-ai/natural-language-processing/master/homeworks/lab02_qa/util.py -nc" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# If running on Colab, uncomment the following lines \n", - "\n", - "# !pip install ujson\n", - "# !pip install tensorboardX\n", - "# !pip install pymorphy2==0.8" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"Train a model on SQuAD.\n", - "\n", - "Author:\n", - " Chris Chute (chute@stanford.edu)\n", - "\"\"\"\n", - "\n", - "import numpy as np\n", - "import random\n", - "import torch\n", - "import torch.nn as nn\n", - "import torch.nn.functional as F\n", - "import torch.optim as optim\n", - "import torch.optim.lr_scheduler as sched\n", - "import torch.utils.data as data\n", - "import util\n", - "\n", - "from args import get_train_args\n", - "from collections import OrderedDict\n", - "from json import dumps\n", - "from models import BiDAF\n", - "from tensorboardX import SummaryWriter\n", - "from tqdm import tqdm\n", - "from ujson import load as json_load\n", - "from util import collate_fn, SQuAD" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "Path(\"./data\").mkdir(parents=True, exist_ok=True)\n", - "Path(\"./save\").mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Downloading the SberQuAD data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!wget http://files.deeppavlov.ai/datasets/sber_squad_clean-v1.1.tar.gz -nc -O ./data/sber_squad_clean-v1.1.tar.gz" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "! tar -xzvf ./data/sber_squad_clean-v1.1.tar.gz\n", - "! mv train-v1.1.json data\n", - "! mv dev-v1.1.json data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Downloading the word vectors (this may take a while)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "! wget http://files.deeppavlov.ai/embeddings/ft_native_300_ru_wiki_lenta_nltk_wordpunct_tokenize/ft_native_300_ru_wiki_lenta_nltk_wordpunct_tokenize.vec -nc -O ./data/ft_native_300_ru_wiki_lenta_nltk_wordpunct_tokenize.vec" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And finally the preprocessing for the SberQuAD dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "train_file = './data/train-v1.1.json'\n", - "dev_file = './data/dev-v1.1.json'\n", - "glove_file = './data/ft_native_300_ru_wiki_lenta_nltk_wordpunct_tokenize.vec'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from setup import *" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Uncomment this cell if needed\n", - "# !pip install pymorphy2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "nlp = spacy.blank(\"ru\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following cell may take a while (usually 10 minutes or less)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Process training set and use it to decide on the word/character vocabularies\n", - "word_counter, char_counter = Counter(), Counter()\n", - "train_examples, train_eval = process_file(train_file, \"train\", word_counter, char_counter, nlp)\n", - "word_emb_mat, word2idx_dict = get_embedding(\n", - " word_counter, 'word', emb_file=glove_file, vec_size=300, num_vectors=1560132)\n", - "char_emb_mat, char2idx_dict = get_embedding(\n", - " char_counter, 'char', emb_file=None, vec_size=64)\n", - "\n", - "\n", - "dev_examples, dev_eval = process_file(dev_file, \"dev\", word_counter, char_counter, nlp)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we have the preprocessed data:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "train_record_file = './data/train.npz'\n", - "dev_record_file = './data/dev.npz'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from args import add_common_args, get_setup_args" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Retreiving the default arguments for the preprocessing script\n", - "_args = get_setup_args(bypass=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "_args" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "build_features(_args, train_examples, \"train\", train_record_file, word2idx_dict, char2idx_dict)\n", - "dev_meta = build_features(_args, dev_examples, \"dev\", dev_record_file, word2idx_dict, char2idx_dict)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "save(_args.word_emb_file, word_emb_mat, message=\"word embedding\")\n", - "save(_args.char_emb_file, char_emb_mat, message=\"char embedding\")\n", - "save(_args.train_eval_file, train_eval, message=\"train eval\")\n", - "save(_args.dev_eval_file, dev_eval, message=\"dev eval\")\n", - "save(_args.word2idx_file, word2idx_dict, message=\"word dictionary\")\n", - "save(_args.char2idx_file, char2idx_dict, message=\"char dictionary\")\n", - "save(_args.dev_meta_file, dev_meta, message=\"dev meta\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2. The experiment\n", - "\n", - "Now you are almost ready to go. You may follow these steps to begin (or just start your experiments here).\n", - "\n", - "1. Try running the `train.py` script from the console (or via `!`) (default command-line arguments are ok for the start). If will run the BiDAF model on the preprocessed data. Set `--use_squad_v2` flag to False (SberQuAD is similar to SQuAD v1.1).\n", - "\n", - "Example code (be careful with the path and the names of the variables):\n", - "```\n", - "python train.py --name first_run_on_sberquad --use_squad_v2 False\n", - "```\n", - "\n", - "2. After if finishes (might take an 1-2-3 hours depending on the hardware), evaluate your model on the `dev` set and measure the quality.\n", - "Example code (be careful with the path and the names of the variables):\n", - "```\n", - " python test.py --split dev --load_path ./save/train/first_run_on_sberquad-02/best.pth.tar --name best_evaluation_experiment\n", - "```\n", - "The result should be similar to the following:\n", - "```\n", - ">>> Dev NLL: 02.47, F1: 75.62, EM: 55.73, AvNA: 99.42\n", - "```\n", - "\n", - "The [DeepPavlov's RuBERT](http://docs.deeppavlov.ai/en/master/features/models/squad.html) achieves $F1 = 84.60\\pm0.11$ and $EM = 66.30\\pm0.24$" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Here comes your quest: try to improve the quality of this QA system. \n", - "\n", - "This is a very creative assignment. It is all about experimenting, trying different approaches (and a lot of computations). But if you wish to stick to some numbers, try to increase F1 at least by $5$ points.\n", - "\n", - "Here are some ideas that might help you on your way:\n", - "* Try adapting the optimization hyperparameters/network structure to Russian language (the baseline is designed for English SQuAD dataset).\n", - "* Incorporating the additional information about the data (like PoS tags) might be a good idea.\n", - "* __Distilling the knowledge from a pre-trained RuBERT__ (e.g. try to use the predictions of the model we've discussed on `week10` as soft targets).\n", - "* Or anything else.\n", - "\n", - "\n", - "And, first of all, read the initial code carefully.\n", - "\n", - "\n", - "Good luck! Feel free to share your results :)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Py3 Research", - "language": "python", - "name": "py3_research_kernel" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/homeworks/lab02_qa/args.py b/homeworks/lab02_qa/args.py deleted file mode 100644 index 47a59d1..0000000 --- a/homeworks/lab02_qa/args.py +++ /dev/null @@ -1,247 +0,0 @@ -"""Command-line arguments for setup.py, train.py, test.py. - -Author: - Chris Chute (chute@stanford.edu) -""" - -import argparse - - -def get_setup_args(bypass=False): - """Get arguments needed in setup.py.""" - parser = argparse.ArgumentParser('Download and pre-process SQuAD') - - add_common_args(parser) - - parser.add_argument('--train_url', - type=str, - default='https://github.com/chrischute/squad/data/train-v2.0.json') - parser.add_argument('--dev_url', - type=str, - default='https://github.com/chrischute/squad/data/dev-v2.0.json') - parser.add_argument('--test_url', - type=str, - default='https://github.com/chrischute/squad/data/test-v2.0.json') - parser.add_argument('--glove_url', - type=str, - default='http://nlp.stanford.edu/data/glove.840B.300d.zip') - parser.add_argument('--dev_meta_file', - type=str, - default='./data/dev_meta.json') - parser.add_argument('--test_meta_file', - type=str, - default='./data/test_meta.json') - parser.add_argument('--word2idx_file', - type=str, - default='./data/word2idx.json') - parser.add_argument('--char2idx_file', - type=str, - default='./data/char2idx.json') - parser.add_argument('--answer_file', - type=str, - default='./data/answer.json') - parser.add_argument('--para_limit', - type=int, - default=400, - help='Max number of words in a paragraph') - parser.add_argument('--ques_limit', - type=int, - default=50, - help='Max number of words to keep from a question') - parser.add_argument('--test_para_limit', - type=int, - default=1000, - help='Max number of words in a paragraph at test time') - parser.add_argument('--test_ques_limit', - type=int, - default=100, - help='Max number of words in a question at test time') - parser.add_argument('--char_dim', - type=int, - default=64, - help='Size of char vectors (char-level embeddings)') - parser.add_argument('--glove_dim', - type=int, - default=300, - help='Size of GloVe word vectors to use') - parser.add_argument('--glove_num_vecs', - type=int, - default=2196017, - help='Number of GloVe vectors') - parser.add_argument('--ans_limit', - type=int, - default=30, - help='Max number of words in a training example answer') - parser.add_argument('--char_limit', - type=int, - default=16, - help='Max number of chars to keep from a word') - parser.add_argument('--include_test_examples', - type=lambda s: s.lower().startswith('t'), - default=True, - help='Process examples from the test set') - - if bypass: - args = parser.parse_args('') - else: - args = parser.parse_args() - - return args - - -def get_train_args(): - """Get arguments needed in train.py.""" - parser = argparse.ArgumentParser('Train a model on SQuAD') - - add_common_args(parser) - add_train_test_args(parser) - - parser.add_argument('--eval_steps', - type=int, - default=50000, - help='Number of steps between successive evaluations.') - parser.add_argument('--lr', - type=float, - default=0.5, - help='Learning rate.') - parser.add_argument('--l2_wd', - type=float, - default=0, - help='L2 weight decay.') - parser.add_argument('--num_epochs', - type=int, - default=30, - help='Number of epochs for which to train. Negative means forever.') - parser.add_argument('--drop_prob', - type=float, - default=0.2, - help='Probability of zeroing an activation in dropout layers.') - parser.add_argument('--metric_name', - type=str, - default='F1', - choices=('NLL', 'EM', 'F1'), - help='Name of dev metric to determine best checkpoint.') - parser.add_argument('--max_checkpoints', - type=int, - default=5, - help='Maximum number of checkpoints to keep on disk.') - parser.add_argument('--max_grad_norm', - type=float, - default=5.0, - help='Maximum gradient norm for gradient clipping.') - parser.add_argument('--seed', - type=int, - default=224, - help='Random seed for reproducibility.') - parser.add_argument('--ema_decay', - type=float, - default=0.999, - help='Decay rate for exponential moving average of parameters.') - - args = parser.parse_args() - - if args.metric_name == 'NLL': - # Best checkpoint is the one that minimizes negative log-likelihood - args.maximize_metric = False - elif args.metric_name in ('EM', 'F1'): - # Best checkpoint is the one that maximizes EM or F1 - args.maximize_metric = True - else: - raise ValueError(f'Unrecognized metric name: "{args.metric_name}"') - - return args - - -def get_test_args(): - """Get arguments needed in test.py.""" - parser = argparse.ArgumentParser('Test a trained model on SQuAD') - - add_common_args(parser) - add_train_test_args(parser) - - parser.add_argument('--split', - type=str, - default='dev', - choices=('train', 'dev', 'test'), - help='Split to use for testing.') - parser.add_argument('--sub_file', - type=str, - default='submission.csv', - help='Name for submission file.') - - # Require load_path for test.py - args = parser.parse_args() - if not args.load_path: - raise argparse.ArgumentError('Missing required argument --load_path') - - return args - - -def add_common_args(parser): - """Add arguments common to all 3 scripts: setup.py, train.py, test.py""" - parser.add_argument('--train_record_file', - type=str, - default='./data/train.npz') - parser.add_argument('--dev_record_file', - type=str, - default='./data/dev.npz') - parser.add_argument('--test_record_file', - type=str, - default='./data/test.npz') - parser.add_argument('--word_emb_file', - type=str, - default='./data/word_emb.json') - parser.add_argument('--char_emb_file', - type=str, - default='./data/char_emb.json') - parser.add_argument('--train_eval_file', - type=str, - default='./data/train_eval.json') - parser.add_argument('--dev_eval_file', - type=str, - default='./data/dev_eval.json') - parser.add_argument('--test_eval_file', - type=str, - default='./data/test_eval.json') - - -def add_train_test_args(parser): - """Add arguments common to train.py and test.py""" - parser.add_argument('--name', - '-n', - type=str, - required=True, - help='Name to identify training or test run.') - parser.add_argument('--max_ans_len', - type=int, - default=15, - help='Maximum length of a predicted answer.') - parser.add_argument('--num_workers', - type=int, - default=4, - help='Number of sub-processes to use per data loader.') - parser.add_argument('--save_dir', - type=str, - default='./save/', - help='Base directory for saving information.') - parser.add_argument('--batch_size', - type=int, - default=64, - help='Batch size per GPU. Scales automatically when \ - multiple GPUs are available.') - parser.add_argument('--use_squad_v2', - type=lambda s: s.lower().startswith('t'), - default=True, - help='Whether to use SQuAD 2.0 (unanswerable) questions.') - parser.add_argument('--hidden_size', - type=int, - default=100, - help='Number of features in encoder hidden layers.') - parser.add_argument('--num_visuals', - type=int, - default=10, - help='Number of examples to visualize in TensorBoard.') - parser.add_argument('--load_path', - type=str, - default=None, - help='Path to load as a model checkpoint.') diff --git a/homeworks/lab02_qa/layers.py b/homeworks/lab02_qa/layers.py deleted file mode 100644 index 6859e4d..0000000 --- a/homeworks/lab02_qa/layers.py +++ /dev/null @@ -1,222 +0,0 @@ -"""Assortment of layers for use in models.py. - -Author: - Chris Chute (chute@stanford.edu) -""" - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence -from util import masked_softmax - - -class Embedding(nn.Module): - """Embedding layer used by BiDAF, without the character-level component. - - Word-level embeddings are further refined using a 2-layer Highway Encoder - (see `HighwayEncoder` class for details). - - Args: - word_vectors (torch.Tensor): Pre-trained word vectors. - hidden_size (int): Size of hidden activations. - drop_prob (float): Probability of zero-ing out activations - """ - def __init__(self, word_vectors, hidden_size, drop_prob): - super(Embedding, self).__init__() - self.drop_prob = drop_prob - self.embed = nn.Embedding.from_pretrained(word_vectors) - self.proj = nn.Linear(word_vectors.size(1), hidden_size, bias=False) - self.hwy = HighwayEncoder(2, hidden_size) - - def forward(self, x): - emb = self.embed(x) # (batch_size, seq_len, embed_size) - emb = F.dropout(emb, self.drop_prob, self.training) - emb = self.proj(emb) # (batch_size, seq_len, hidden_size) - emb = self.hwy(emb) # (batch_size, seq_len, hidden_size) - - return emb - - -class HighwayEncoder(nn.Module): - """Encode an input sequence using a highway network. - - Based on the paper: - "Highway Networks" - by Rupesh Kumar Srivastava, Klaus Greff, Jürgen Schmidhuber - (https://arxiv.org/abs/1505.00387). - - Args: - num_layers (int): Number of layers in the highway encoder. - hidden_size (int): Size of hidden activations. - """ - def __init__(self, num_layers, hidden_size): - super(HighwayEncoder, self).__init__() - self.transforms = nn.ModuleList([nn.Linear(hidden_size, hidden_size) - for _ in range(num_layers)]) - self.gates = nn.ModuleList([nn.Linear(hidden_size, hidden_size) - for _ in range(num_layers)]) - - def forward(self, x): - for gate, transform in zip(self.gates, self.transforms): - # Shapes of g, t, and x are all (batch_size, seq_len, hidden_size) - g = torch.sigmoid(gate(x)) - t = F.relu(transform(x)) - x = g * t + (1 - g) * x - - return x - - -class RNNEncoder(nn.Module): - """General-purpose layer for encoding a sequence using a bidirectional RNN. - - Encoded output is the RNN's hidden state at each position, which - has shape `(batch_size, seq_len, hidden_size * 2)`. - - Args: - input_size (int): Size of a single timestep in the input. - hidden_size (int): Size of the RNN hidden state. - num_layers (int): Number of layers of RNN cells to use. - drop_prob (float): Probability of zero-ing out activations. - """ - def __init__(self, - input_size, - hidden_size, - num_layers, - drop_prob=0.): - super(RNNEncoder, self).__init__() - self.drop_prob = drop_prob - self.rnn = nn.LSTM(input_size, hidden_size, num_layers, - batch_first=True, - bidirectional=True, - dropout=drop_prob if num_layers > 1 else 0.) - - def forward(self, x, lengths): - # Save original padded length for use by pad_packed_sequence - orig_len = x.size(1) - - # Sort by length and pack sequence for RNN - lengths, sort_idx = lengths.sort(0, descending=True) - x = x[sort_idx] # (batch_size, seq_len, input_size) - x = pack_padded_sequence(x, lengths, batch_first=True) - - # Apply RNN - x, _ = self.rnn(x) # (batch_size, seq_len, 2 * hidden_size) - - # Unpack and reverse sort - x, _ = pad_packed_sequence(x, batch_first=True, total_length=orig_len) - _, unsort_idx = sort_idx.sort(0) - x = x[unsort_idx] # (batch_size, seq_len, 2 * hidden_size) - - # Apply dropout (RNN applies dropout after all but the last layer) - x = F.dropout(x, self.drop_prob, self.training) - - return x - - -class BiDAFAttention(nn.Module): - """Bidirectional attention originally used by BiDAF. - - Bidirectional attention computes attention in two directions: - The context attends to the query and the query attends to the context. - The output of this layer is the concatenation of [context, c2q_attention, - context * c2q_attention, context * q2c_attention]. This concatenation allows - the attention vector at each timestep, along with the embeddings from - previous layers, to flow through the attention layer to the modeling layer. - The output has shape (batch_size, context_len, 8 * hidden_size). - - Args: - hidden_size (int): Size of hidden activations. - drop_prob (float): Probability of zero-ing out activations. - """ - def __init__(self, hidden_size, drop_prob=0.1): - super(BiDAFAttention, self).__init__() - self.drop_prob = drop_prob - self.c_weight = nn.Parameter(torch.zeros(hidden_size, 1)) - self.q_weight = nn.Parameter(torch.zeros(hidden_size, 1)) - self.cq_weight = nn.Parameter(torch.zeros(1, 1, hidden_size)) - for weight in (self.c_weight, self.q_weight, self.cq_weight): - nn.init.xavier_uniform_(weight) - self.bias = nn.Parameter(torch.zeros(1)) - - def forward(self, c, q, c_mask, q_mask): - batch_size, c_len, _ = c.size() - q_len = q.size(1) - s = self.get_similarity_matrix(c, q) # (batch_size, c_len, q_len) - c_mask = c_mask.view(batch_size, c_len, 1) # (batch_size, c_len, 1) - q_mask = q_mask.view(batch_size, 1, q_len) # (batch_size, 1, q_len) - s1 = masked_softmax(s, q_mask, dim=2) # (batch_size, c_len, q_len) - s2 = masked_softmax(s, c_mask, dim=1) # (batch_size, c_len, q_len) - - # (bs, c_len, q_len) x (bs, q_len, hid_size) => (bs, c_len, hid_size) - a = torch.bmm(s1, q) - # (bs, c_len, c_len) x (bs, c_len, hid_size) => (bs, c_len, hid_size) - b = torch.bmm(torch.bmm(s1, s2.transpose(1, 2)), c) - - x = torch.cat([c, a, c * a, c * b], dim=2) # (bs, c_len, 4 * hid_size) - - return x - - def get_similarity_matrix(self, c, q): - """Get the "similarity matrix" between context and query (using the - terminology of the BiDAF paper). - - A naive implementation as described in BiDAF would concatenate the - three vectors then project the result with a single weight matrix. This - method is a more memory-efficient implementation of the same operation. - - See Also: - Equation 1 in https://arxiv.org/abs/1611.01603 - """ - c_len, q_len = c.size(1), q.size(1) - c = F.dropout(c, self.drop_prob, self.training) # (bs, c_len, hid_size) - q = F.dropout(q, self.drop_prob, self.training) # (bs, q_len, hid_size) - - # Shapes: (batch_size, c_len, q_len) - s0 = torch.matmul(c, self.c_weight).expand([-1, -1, q_len]) - s1 = torch.matmul(q, self.q_weight).transpose(1, 2)\ - .expand([-1, c_len, -1]) - s2 = torch.matmul(c * self.cq_weight, q.transpose(1, 2)) - s = s0 + s1 + s2 + self.bias - - return s - - -class BiDAFOutput(nn.Module): - """Output layer used by BiDAF for question answering. - - Computes a linear transformation of the attention and modeling - outputs, then takes the softmax of the result to get the start pointer. - A bidirectional LSTM is then applied the modeling output to produce `mod_2`. - A second linear+softmax of the attention output and `mod_2` is used - to get the end pointer. - - Args: - hidden_size (int): Hidden size used in the BiDAF model. - drop_prob (float): Probability of zero-ing out activations. - """ - def __init__(self, hidden_size, drop_prob): - super(BiDAFOutput, self).__init__() - self.att_linear_1 = nn.Linear(8 * hidden_size, 1) - self.mod_linear_1 = nn.Linear(2 * hidden_size, 1) - - self.rnn = RNNEncoder(input_size=2 * hidden_size, - hidden_size=hidden_size, - num_layers=1, - drop_prob=drop_prob) - - self.att_linear_2 = nn.Linear(8 * hidden_size, 1) - self.mod_linear_2 = nn.Linear(2 * hidden_size, 1) - - def forward(self, att, mod, mask): - # Shapes: (batch_size, seq_len, 1) - logits_1 = self.att_linear_1(att) + self.mod_linear_1(mod) - mod_2 = self.rnn(mod, mask.sum(-1)) - logits_2 = self.att_linear_2(att) + self.mod_linear_2(mod_2) - - # Shapes: (batch_size, seq_len) - log_p1 = masked_softmax(logits_1.squeeze(), mask, log_softmax=True) - log_p2 = masked_softmax(logits_2.squeeze(), mask, log_softmax=True) - - return log_p1, log_p2 diff --git a/homeworks/lab02_qa/models.py b/homeworks/lab02_qa/models.py deleted file mode 100644 index 3487ea2..0000000 --- a/homeworks/lab02_qa/models.py +++ /dev/null @@ -1,72 +0,0 @@ -"""Top-level model classes. - -Author: - Chris Chute (chute@stanford.edu) -""" - -import layers -import torch -import torch.nn as nn - - -class BiDAF(nn.Module): - """Baseline BiDAF model for SQuAD. - - Based on the paper: - "Bidirectional Attention Flow for Machine Comprehension" - by Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, Hannaneh Hajishirzi - (https://arxiv.org/abs/1611.01603). - - Follows a high-level structure commonly found in SQuAD models: - - Embedding layer: Embed word indices to get word vectors. - - Encoder layer: Encode the embedded sequence. - - Attention layer: Apply an attention mechanism to the encoded sequence. - - Model encoder layer: Encode the sequence again. - - Output layer: Simple layer (e.g., fc + softmax) to get final outputs. - - Args: - word_vectors (torch.Tensor): Pre-trained word vectors. - hidden_size (int): Number of features in the hidden state at each layer. - drop_prob (float): Dropout probability. - """ - def __init__(self, word_vectors, hidden_size, drop_prob=0.): - super(BiDAF, self).__init__() - self.emb = layers.Embedding(word_vectors=word_vectors, - hidden_size=hidden_size, - drop_prob=drop_prob) - - self.enc = layers.RNNEncoder(input_size=hidden_size, - hidden_size=hidden_size, - num_layers=1, - drop_prob=drop_prob) - - self.att = layers.BiDAFAttention(hidden_size=2 * hidden_size, - drop_prob=drop_prob) - - self.mod = layers.RNNEncoder(input_size=8 * hidden_size, - hidden_size=hidden_size, - num_layers=2, - drop_prob=drop_prob) - - self.out = layers.BiDAFOutput(hidden_size=hidden_size, - drop_prob=drop_prob) - - def forward(self, cw_idxs, qw_idxs): - c_mask = torch.zeros_like(cw_idxs) != cw_idxs - q_mask = torch.zeros_like(qw_idxs) != qw_idxs - c_len, q_len = c_mask.sum(-1), q_mask.sum(-1) - - c_emb = self.emb(cw_idxs) # (batch_size, c_len, hidden_size) - q_emb = self.emb(qw_idxs) # (batch_size, q_len, hidden_size) - - c_enc = self.enc(c_emb, c_len) # (batch_size, c_len, 2 * hidden_size) - q_enc = self.enc(q_emb, q_len) # (batch_size, q_len, 2 * hidden_size) - - att = self.att(c_enc, q_enc, - c_mask, q_mask) # (batch_size, c_len, 8 * hidden_size) - - mod = self.mod(att, c_len) # (batch_size, c_len, 2 * hidden_size) - - out = self.out(att, mod, c_mask) # 2 tensors, each (batch_size, c_len) - - return out diff --git a/homeworks/lab02_qa/setup.py b/homeworks/lab02_qa/setup.py deleted file mode 100644 index c270cdf..0000000 --- a/homeworks/lab02_qa/setup.py +++ /dev/null @@ -1,396 +0,0 @@ -"""Download and pre-process SQuAD and GloVe. - -Usage: - > source activate squad - > python setup.py - -Pre-processing code adapted from: - > https://github.com/HKUST-KnowComp/R-Net/blob/master/prepro.py - -Author: - Chris Chute (chute@stanford.edu) -""" - -import numpy as np -import os -import spacy -import ujson as json -import urllib.request - -from args import get_setup_args -from codecs import open -from collections import Counter -from subprocess import run -from tqdm import tqdm -from zipfile import ZipFile - - -def download_url(url, output_path, show_progress=True): - class DownloadProgressBar(tqdm): - def update_to(self, b=1, bsize=1, tsize=None): - if tsize is not None: - self.total = tsize - self.update(b * bsize - self.n) - - if show_progress: - # Download with a progress bar - with DownloadProgressBar(unit='B', unit_scale=True, - miniters=1, desc=url.split('/')[-1]) as t: - urllib.request.urlretrieve(url, - filename=output_path, - reporthook=t.update_to) - else: - # Simple download with no progress bar - urllib.request.urlretrieve(url, output_path) - - -def url_to_data_path(url): - return os.path.join('./data/', url.split('/')[-1]) - - -def download(args): - downloads = [ - # Can add other downloads here (e.g., other word vectors) - ('GloVe word vectors', args.glove_url), - ] - - for name, url in downloads: - output_path = url_to_data_path(url) - if not os.path.exists(output_path): - print(f'Downloading {name}...') - download_url(url, output_path) - - if os.path.exists(output_path) and output_path.endswith('.zip'): - extracted_path = output_path.replace('.zip', '') - if not os.path.exists(extracted_path): - print(f'Unzipping {name}...') - with ZipFile(output_path, 'r') as zip_fh: - zip_fh.extractall(extracted_path) - - print('Downloading spacy language model...') - run(['python', '-m', 'spacy', 'download', 'en']) - -def word_tokenize(sent, nlp): - doc = nlp(sent) - return [token.text for token in doc] - - -def convert_idx(text, tokens): - current = 0 - spans = [] - for token in tokens: - current = text.find(token, current) - if current < 0: - print(f"Token {token} cannot be found") - raise Exception() - spans.append((current, current + len(token))) - current += len(token) - return spans - - -def process_file(filename, data_type, word_counter, char_counter, nlp): - print(f"Pre-processing {data_type} examples...") - examples = [] - eval_examples = {} - total = 0 - with open(filename, "r") as fh: - source = json.load(fh) - for article in tqdm(source["data"]): - for para in article["paragraphs"]: - context = para["context"].replace( - "''", '" ').replace("``", '" ') - context_tokens = word_tokenize(context, nlp) - context_chars = [list(token) for token in context_tokens] - spans = convert_idx(context, context_tokens) - for token in context_tokens: - word_counter[token] += len(para["qas"]) - for char in token: - char_counter[char] += len(para["qas"]) - for qa in para["qas"]: - total += 1 - ques = qa["question"].replace( - "''", '" ').replace("``", '" ') - ques_tokens = word_tokenize(ques, nlp) - ques_chars = [list(token) for token in ques_tokens] - for token in ques_tokens: - word_counter[token] += 1 - for char in token: - char_counter[char] += 1 - y1s, y2s = [], [] - answer_texts = [] - for answer in qa["answers"]: - answer_text = answer["text"] - answer_start = answer['answer_start'] - answer_end = answer_start + len(answer_text) - answer_texts.append(answer_text) - answer_span = [] - for idx, span in enumerate(spans): - if not (answer_end <= span[0] or answer_start >= span[1]): - answer_span.append(idx) - y1, y2 = answer_span[0], answer_span[-1] - y1s.append(y1) - y2s.append(y2) - example = {"context_tokens": context_tokens, - "context_chars": context_chars, - "ques_tokens": ques_tokens, - "ques_chars": ques_chars, - "y1s": y1s, - "y2s": y2s, - "id": total} - examples.append(example) - eval_examples[str(total)] = {"context": context, - "question": ques, - "spans": spans, - "answers": answer_texts, - "uuid": qa["id"]} - print(f"{len(examples)} questions in total") - return examples, eval_examples - - -def get_embedding(counter, data_type, limit=-1, emb_file=None, vec_size=None, num_vectors=None): - print(f"Pre-processing {data_type} vectors...") - embedding_dict = {} - filtered_elements = [k for k, v in counter.items() if v > limit] - if emb_file is not None: - assert vec_size is not None - with open(emb_file, "r", encoding="utf-8") as fh: - for line in tqdm(fh, total=num_vectors): - array = line.split() - word = "".join(array[0:-vec_size]) - vector = list(map(float, array[-vec_size:])) - if word in counter and counter[word] > limit: - embedding_dict[word] = vector - print(f"{len(embedding_dict)} / {len(filtered_elements)} tokens have corresponding {data_type} embedding vector") - else: - assert vec_size is not None - for token in filtered_elements: - embedding_dict[token] = [np.random.normal( - scale=0.1) for _ in range(vec_size)] - print(f"{len(filtered_elements)} tokens have corresponding {data_type} embedding vector") - - NULL = "--NULL--" - OOV = "--OOV--" - token2idx_dict = {token: idx for idx, token in enumerate(embedding_dict.keys(), 2)} - token2idx_dict[NULL] = 0 - token2idx_dict[OOV] = 1 - embedding_dict[NULL] = [0. for _ in range(vec_size)] - embedding_dict[OOV] = [0. for _ in range(vec_size)] - idx2emb_dict = {idx: embedding_dict[token] - for token, idx in token2idx_dict.items()} - emb_mat = [idx2emb_dict[idx] for idx in range(len(idx2emb_dict))] - return emb_mat, token2idx_dict - - -def convert_to_features(args, data, word2idx_dict, char2idx_dict, is_test): - example = {} - context, question = data - context = context.replace("''", '" ').replace("``", '" ') - question = question.replace("''", '" ').replace("``", '" ') - example['context_tokens'] = word_tokenize(context) - example['ques_tokens'] = word_tokenize(question) - example['context_chars'] = [list(token) for token in example['context_tokens']] - example['ques_chars'] = [list(token) for token in example['ques_tokens']] - - para_limit = args.test_para_limit if is_test else args.para_limit - ques_limit = args.test_ques_limit if is_test else args.ques_limit - char_limit = args.char_limit - - def filter_func(example): - return len(example["context_tokens"]) > para_limit or \ - len(example["ques_tokens"]) > ques_limit - - if filter_func(example): - raise ValueError("Context/Questions lengths are over the limit") - - context_idxs = np.zeros([para_limit], dtype=np.int32) - context_char_idxs = np.zeros([para_limit, char_limit], dtype=np.int32) - ques_idxs = np.zeros([ques_limit], dtype=np.int32) - ques_char_idxs = np.zeros([ques_limit, char_limit], dtype=np.int32) - - def _get_word(word): - for each in (word, word.lower(), word.capitalize(), word.upper()): - if each in word2idx_dict: - return word2idx_dict[each] - return 1 - - def _get_char(char): - if char in char2idx_dict: - return char2idx_dict[char] - return 1 - - for i, token in enumerate(example["context_tokens"]): - context_idxs[i] = _get_word(token) - - for i, token in enumerate(example["ques_tokens"]): - ques_idxs[i] = _get_word(token) - - for i, token in enumerate(example["context_chars"]): - for j, char in enumerate(token): - if j == char_limit: - break - context_char_idxs[i, j] = _get_char(char) - - for i, token in enumerate(example["ques_chars"]): - for j, char in enumerate(token): - if j == char_limit: - break - ques_char_idxs[i, j] = _get_char(char) - - return context_idxs, context_char_idxs, ques_idxs, ques_char_idxs - - -def is_answerable(example): - return len(example['y2s']) > 0 and len(example['y1s']) > 0 - - -def build_features(args, examples, data_type, out_file, word2idx_dict, char2idx_dict, is_test=False): - para_limit = args.test_para_limit if is_test else args.para_limit - ques_limit = args.test_ques_limit if is_test else args.ques_limit - ans_limit = args.ans_limit - char_limit = args.char_limit - - def drop_example(ex, is_test_=False): - if is_test_: - drop = False - else: - drop = len(ex["context_tokens"]) > para_limit or \ - len(ex["ques_tokens"]) > ques_limit or \ - (is_answerable(ex) and - ex["y2s"][0] - ex["y1s"][0] > ans_limit) - - return drop - - print(f"Converting {data_type} examples to indices...") - total = 0 - total_ = 0 - meta = {} - context_idxs = [] - context_char_idxs = [] - ques_idxs = [] - ques_char_idxs = [] - y1s = [] - y2s = [] - ids = [] - for n, example in tqdm(enumerate(examples)): - total_ += 1 - - if drop_example(example, is_test): - continue - - total += 1 - - def _get_word(word): - for each in (word, word.lower(), word.capitalize(), word.upper()): - if each in word2idx_dict: - return word2idx_dict[each] - return 1 - - def _get_char(char): - if char in char2idx_dict: - return char2idx_dict[char] - return 1 - - context_idx = np.zeros([para_limit], dtype=np.int32) - context_char_idx = np.zeros([para_limit, char_limit], dtype=np.int32) - ques_idx = np.zeros([ques_limit], dtype=np.int32) - ques_char_idx = np.zeros([ques_limit, char_limit], dtype=np.int32) - - for i, token in enumerate(example["context_tokens"]): - context_idx[i] = _get_word(token) - context_idxs.append(context_idx) - - for i, token in enumerate(example["ques_tokens"]): - ques_idx[i] = _get_word(token) - ques_idxs.append(ques_idx) - - for i, token in enumerate(example["context_chars"]): - for j, char in enumerate(token): - if j == char_limit: - break - context_char_idx[i, j] = _get_char(char) - context_char_idxs.append(context_char_idx) - - for i, token in enumerate(example["ques_chars"]): - for j, char in enumerate(token): - if j == char_limit: - break - ques_char_idx[i, j] = _get_char(char) - ques_char_idxs.append(ques_char_idx) - - if is_answerable(example): - start, end = example["y1s"][-1], example["y2s"][-1] - else: - start, end = -1, -1 - - y1s.append(start) - y2s.append(end) - ids.append(example["id"]) - - np.savez(out_file, - context_idxs=np.array(context_idxs), - context_char_idxs=np.array(context_char_idxs), - ques_idxs=np.array(ques_idxs), - ques_char_idxs=np.array(ques_char_idxs), - y1s=np.array(y1s), - y2s=np.array(y2s), - ids=np.array(ids)) - print(f"Built {total} / {total_} instances of features in total") - meta["total"] = total - return meta - - -def save(filename, obj, message=None): - if message is not None: - print(f"Saving {message}...") - with open(filename, "w") as fh: - json.dump(obj, fh) - - -def pre_process(args): - # Process training set and use it to decide on the word/character vocabularies - word_counter, char_counter = Counter(), Counter() - train_examples, train_eval = process_file(args.train_file, "train", word_counter, char_counter) - word_emb_mat, word2idx_dict = get_embedding( - word_counter, 'word', emb_file=args.glove_file, vec_size=args.glove_dim, num_vectors=args.glove_num_vecs) - char_emb_mat, char2idx_dict = get_embedding( - char_counter, 'char', emb_file=None, vec_size=args.char_dim) - - # Process dev and test sets - dev_examples, dev_eval = process_file(args.dev_file, "dev", word_counter, char_counter) - build_features(args, train_examples, "train", args.train_record_file, word2idx_dict, char2idx_dict) - dev_meta = build_features(args, dev_examples, "dev", args.dev_record_file, word2idx_dict, char2idx_dict) - if args.include_test_examples: - test_examples, test_eval = process_file(args.test_file, "test", word_counter, char_counter) - save(args.test_eval_file, test_eval, message="test eval") - test_meta = build_features(args, test_examples, "test", - args.test_record_file, word2idx_dict, char2idx_dict, is_test=True) - save(args.test_meta_file, test_meta, message="test meta") - - save(args.word_emb_file, word_emb_mat, message="word embedding") - save(args.char_emb_file, char_emb_mat, message="char embedding") - save(args.train_eval_file, train_eval, message="train eval") - save(args.dev_eval_file, dev_eval, message="dev eval") - save(args.word2idx_file, word2idx_dict, message="word dictionary") - save(args.char2idx_file, char2idx_dict, message="char dictionary") - save(args.dev_meta_file, dev_meta, message="dev meta") - - -if __name__ == '__main__': - # Get command-line args - args_ = get_setup_args() - - # Download resources - download(args_) - - # Import spacy language model - nlp = spacy.blank("en") - - # Preprocess dataset - args_.train_file = url_to_data_path(args_.train_url) - args_.dev_file = url_to_data_path(args_.dev_url) - if args_.include_test_examples: - args_.test_file = url_to_data_path(args_.test_url) - glove_dir = url_to_data_path(args_.glove_url.replace('.zip', '')) - glove_ext = f'.txt' if glove_dir.endswith('d') else f'.{args_.glove_dim}d.txt' - args_.glove_file = os.path.join(glove_dir, os.path.basename(glove_dir) + glove_ext) - pre_process(args_) diff --git a/homeworks/lab02_qa/test.py b/homeworks/lab02_qa/test.py deleted file mode 100644 index 745fa36..0000000 --- a/homeworks/lab02_qa/test.py +++ /dev/null @@ -1,138 +0,0 @@ -"""Test a model and generate submission CSV. - -Usage: - > python test.py --split SPLIT --load_path PATH --name NAME - where - > SPLIT is either "dev" or "test" - > PATH is a path to a checkpoint (e.g., save/train/model-01/best.pth.tar) - > NAME is a name to identify the test run - -Author: - Chris Chute (chute@stanford.edu) -""" - -import csv -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.utils.data as data -import util - -from args import get_test_args -from collections import OrderedDict -from json import dumps -from models import BiDAF -from os.path import join -from tensorboardX import SummaryWriter -from tqdm import tqdm -from ujson import load as json_load -from util import collate_fn, SQuAD - - -def main(args): - # Set up logging - args.save_dir = util.get_save_dir(args.save_dir, args.name, training=False) - log = util.get_logger(args.save_dir, args.name) - log.info(f'Args: {dumps(vars(args), indent=4, sort_keys=True)}') - device, gpu_ids = util.get_available_devices() - args.batch_size *= max(1, len(gpu_ids)) - - # Get embeddings - log.info('Loading embeddings...') - word_vectors = util.torch_from_json(args.word_emb_file) - - # Get model - log.info('Building model...') - model = BiDAF(word_vectors=word_vectors, - hidden_size=args.hidden_size) - model = nn.DataParallel(model, gpu_ids) - log.info(f'Loading checkpoint from {args.load_path}...') - model = util.load_model(model, args.load_path, gpu_ids, return_step=False) - model = model.to(device) - model.eval() - - # Get data loader - log.info('Building dataset...') - record_file = vars(args)[f'{args.split}_record_file'] - dataset = SQuAD(record_file, args.use_squad_v2) - data_loader = data.DataLoader(dataset, - batch_size=args.batch_size, - shuffle=False, - num_workers=args.num_workers, - collate_fn=collate_fn) - - # Evaluate - log.info(f'Evaluating on {args.split} split...') - nll_meter = util.AverageMeter() - pred_dict = {} # Predictions for TensorBoard - sub_dict = {} # Predictions for submission - eval_file = vars(args)[f'{args.split}_eval_file'] - with open(eval_file, 'r') as fh: - gold_dict = json_load(fh) - with torch.no_grad(), \ - tqdm(total=len(dataset)) as progress_bar: - for cw_idxs, cc_idxs, qw_idxs, qc_idxs, y1, y2, ids in data_loader: - # Setup for forward - cw_idxs = cw_idxs.to(device) - qw_idxs = qw_idxs.to(device) - batch_size = cw_idxs.size(0) - - # Forward - log_p1, log_p2 = model(cw_idxs, qw_idxs) - y1, y2 = y1.to(device), y2.to(device) - loss = F.nll_loss(log_p1, y1) + F.nll_loss(log_p2, y2) - nll_meter.update(loss.item(), batch_size) - - # Get F1 and EM scores - p1, p2 = log_p1.exp(), log_p2.exp() - starts, ends = util.discretize(p1, p2, args.max_ans_len, args.use_squad_v2) - - # Log info - progress_bar.update(batch_size) - if args.split != 'test': - # No labels for the test set, so NLL would be invalid - progress_bar.set_postfix(NLL=nll_meter.avg) - - idx2pred, uuid2pred = util.convert_tokens(gold_dict, - ids.tolist(), - starts.tolist(), - ends.tolist(), - args.use_squad_v2) - pred_dict.update(idx2pred) - sub_dict.update(uuid2pred) - - # Log results (except for test set, since it does not come with labels) - if args.split != 'test': - results = util.eval_dicts(gold_dict, pred_dict, args.use_squad_v2) - results_list = [('NLL', nll_meter.avg), - ('F1', results['F1']), - ('EM', results['EM'])] - if args.use_squad_v2: - results_list.append(('AvNA', results['AvNA'])) - results = OrderedDict(results_list) - - # Log to console - results_str = ', '.join(f'{k}: {v:05.2f}' for k, v in results.items()) - log.info(f'{args.split.title()} {results_str}') - - # Log to TensorBoard - tbx = SummaryWriter(args.save_dir) - util.visualize(tbx, - pred_dict=pred_dict, - eval_path=eval_file, - step=0, - split=args.split, - num_visuals=args.num_visuals) - - # Write submission file - sub_path = join(args.save_dir, args.split + '_' + args.sub_file) - log.info(f'Writing submission file to {sub_path}...') - with open(sub_path, 'w', newline='', encoding='utf-8') as csv_fh: - csv_writer = csv.writer(csv_fh, delimiter=',') - csv_writer.writerow(['Id', 'Predicted']) - for uuid in sorted(sub_dict): - csv_writer.writerow([uuid, sub_dict[uuid]]) - - -if __name__ == '__main__': - main(get_test_args()) diff --git a/homeworks/lab02_qa/train.py b/homeworks/lab02_qa/train.py deleted file mode 100644 index 42e4265..0000000 --- a/homeworks/lab02_qa/train.py +++ /dev/null @@ -1,212 +0,0 @@ -"""Train a model on SQuAD. - -Author: - Chris Chute (chute@stanford.edu) -""" - -import numpy as np -import random -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.optim as optim -import torch.optim.lr_scheduler as sched -import torch.utils.data as data -import util - -from args import get_train_args -from collections import OrderedDict -from json import dumps -from models import BiDAF -from tensorboardX import SummaryWriter -from tqdm import tqdm -from ujson import load as json_load -from util import collate_fn, SQuAD - - -def main(args): - # Set up logging and devices - args.save_dir = util.get_save_dir(args.save_dir, args.name, training=True) - log = util.get_logger(args.save_dir, args.name) - tbx = SummaryWriter(args.save_dir) - - import warnings - warnings.filterwarnings('ignore') - - device, args.gpu_ids = util.get_available_devices() - log.info(f'Args: {dumps(vars(args), indent=4, sort_keys=True)}') - args.batch_size *= max(1, len(args.gpu_ids)) - - # Set random seed - log.info(f'Using random seed {args.seed}...') - random.seed(args.seed) - np.random.seed(args.seed) - torch.manual_seed(args.seed) - torch.cuda.manual_seed_all(args.seed) - - # Get embeddings - log.info('Loading embeddings...') - word_vectors = util.torch_from_json(args.word_emb_file) - - # Get model - log.info('Building model...') - model = BiDAF(word_vectors=word_vectors, - hidden_size=args.hidden_size, - drop_prob=args.drop_prob) - model = nn.DataParallel(model, args.gpu_ids) - if args.load_path: - log.info(f'Loading checkpoint from {args.load_path}...') - model, step = util.load_model(model, args.load_path, args.gpu_ids) - else: - step = 0 - model = model.to(device) - model.train() - ema = util.EMA(model, args.ema_decay) - - # Get saver - saver = util.CheckpointSaver(args.save_dir, - max_checkpoints=args.max_checkpoints, - metric_name=args.metric_name, - maximize_metric=args.maximize_metric, - log=log) - - # Get optimizer and scheduler - optimizer = optim.Adadelta(model.parameters(), args.lr, - weight_decay=args.l2_wd) - scheduler = sched.LambdaLR(optimizer, lambda s: 1.) # Constant LR - - # Get data loader - log.info('Building dataset...') - train_dataset = SQuAD(args.train_record_file, args.use_squad_v2) - train_loader = data.DataLoader(train_dataset, - batch_size=args.batch_size, - shuffle=True, - num_workers=args.num_workers, - collate_fn=collate_fn) - dev_dataset = SQuAD(args.dev_record_file, args.use_squad_v2) - dev_loader = data.DataLoader(dev_dataset, - batch_size=args.batch_size, - shuffle=False, - num_workers=args.num_workers, - collate_fn=collate_fn) - - # Train - log.info('Training...') - steps_till_eval = args.eval_steps - epoch = step // len(train_dataset) - while epoch != args.num_epochs: - epoch += 1 - log.info(f'Starting epoch {epoch}...') - with torch.enable_grad(), \ - tqdm(total=len(train_loader.dataset)) as progress_bar: - for cw_idxs, cc_idxs, qw_idxs, qc_idxs, y1, y2, ids in train_loader: - # Setup for forward - cw_idxs = cw_idxs.to(device) - qw_idxs = qw_idxs.to(device) - batch_size = cw_idxs.size(0) - optimizer.zero_grad() - - # Forward - log_p1, log_p2 = model(cw_idxs, qw_idxs) - y1, y2 = y1.to(device), y2.to(device) - loss = F.nll_loss(log_p1, y1) + F.nll_loss(log_p2, y2) - loss_val = loss.item() - - # Backward - loss.backward() - nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm) - optimizer.step() - scheduler.step(step // batch_size) - ema(model, step // batch_size) - - # Log info - step += batch_size - progress_bar.update(batch_size) - progress_bar.set_postfix(epoch=epoch, - NLL=loss_val) - tbx.add_scalar('train/NLL', loss_val, step) - tbx.add_scalar('train/LR', - optimizer.param_groups[0]['lr'], - step) - - steps_till_eval -= batch_size - if steps_till_eval <= 0: - steps_till_eval = args.eval_steps - - # Evaluate and save checkpoint - log.info(f'Evaluating at step {step}...') - ema.assign(model) - results, pred_dict = evaluate(model, dev_loader, device, - args.dev_eval_file, - args.max_ans_len, - args.use_squad_v2) - saver.save(step, model, results[args.metric_name], device) - ema.resume(model) - - # Log to console - results_str = ', '.join(f'{k}: {v:05.2f}' for k, v in results.items()) - log.info(f'Dev {results_str}') - - # Log to TensorBoard - log.info('Visualizing in TensorBoard...') - for k, v in results.items(): - tbx.add_scalar(f'dev/{k}', v, step) - util.visualize(tbx, - pred_dict=pred_dict, - eval_path=args.dev_eval_file, - step=step, - split='dev', - num_visuals=args.num_visuals) - - -def evaluate(model, data_loader, device, eval_file, max_len, use_squad_v2): - nll_meter = util.AverageMeter() - - model.eval() - pred_dict = {} - with open(eval_file, 'r') as fh: - gold_dict = json_load(fh) - with torch.no_grad(), \ - tqdm(total=len(data_loader.dataset)) as progress_bar: - for cw_idxs, cc_idxs, qw_idxs, qc_idxs, y1, y2, ids in data_loader: - # Setup for forward - cw_idxs = cw_idxs.to(device) - qw_idxs = qw_idxs.to(device) - batch_size = cw_idxs.size(0) - - # Forward - log_p1, log_p2 = model(cw_idxs, qw_idxs) - y1, y2 = y1.to(device), y2.to(device) - loss = F.nll_loss(log_p1, y1) + F.nll_loss(log_p2, y2) - nll_meter.update(loss.item(), batch_size) - - # Get F1 and EM scores - p1, p2 = log_p1.exp(), log_p2.exp() - starts, ends = util.discretize(p1, p2, max_len, use_squad_v2) - - # Log info - progress_bar.update(batch_size) - progress_bar.set_postfix(NLL=nll_meter.avg) - - preds, _ = util.convert_tokens(gold_dict, - ids.tolist(), - starts.tolist(), - ends.tolist(), - use_squad_v2) - pred_dict.update(preds) - - model.train() - - results = util.eval_dicts(gold_dict, pred_dict, use_squad_v2) - results_list = [('NLL', nll_meter.avg), - ('F1', results['F1']), - ('EM', results['EM'])] - if use_squad_v2: - results_list.append(('AvNA', results['AvNA'])) - results = OrderedDict(results_list) - - return results, pred_dict - - -if __name__ == '__main__': - main(get_train_args()) diff --git a/homeworks/lab02_qa/util.py b/homeworks/lab02_qa/util.py deleted file mode 100644 index 1cad0bb..0000000 --- a/homeworks/lab02_qa/util.py +++ /dev/null @@ -1,725 +0,0 @@ -"""Utility classes and methods. - -Author: - Chris Chute (chute@stanford.edu) -""" -import logging -import os -import queue -import re -import shutil -import string -import torch -import torch.nn.functional as F -import torch.utils.data as data -import tqdm -import numpy as np -import ujson as json - -from collections import Counter - - -class SQuAD(data.Dataset): - """Stanford Question Answering Dataset (SQuAD). - - Each item in the dataset is a tuple with the following entries (in order): - - context_idxs: Indices of the words in the context. - Shape (context_len,). - - context_char_idxs: Indices of the characters in the context. - Shape (context_len, max_word_len). - - question_idxs: Indices of the words in the question. - Shape (question_len,). - - question_char_idxs: Indices of the characters in the question. - Shape (question_len, max_word_len). - - y1: Index of word in the context where the answer begins. - -1 if no answer. - - y2: Index of word in the context where the answer ends. - -1 if no answer. - - id: ID of the example. - - Args: - data_path (str): Path to .npz file containing pre-processed dataset. - use_v2 (bool): Whether to use SQuAD 2.0 questions. Otherwise only use SQuAD 1.1. - """ - def __init__(self, data_path, use_v2=True): - super(SQuAD, self).__init__() - - dataset = np.load(data_path) - self.context_idxs = torch.from_numpy(dataset['context_idxs']).long() - self.context_char_idxs = torch.from_numpy(dataset['context_char_idxs']).long() - self.question_idxs = torch.from_numpy(dataset['ques_idxs']).long() - self.question_char_idxs = torch.from_numpy(dataset['ques_char_idxs']).long() - self.y1s = torch.from_numpy(dataset['y1s']).long() - self.y2s = torch.from_numpy(dataset['y2s']).long() - - if use_v2: - # SQuAD 2.0: Use index 0 for no-answer token (token 1 = OOV) - batch_size, c_len, w_len = self.context_char_idxs.size() - ones = torch.ones((batch_size, 1), dtype=torch.int64) - self.context_idxs = torch.cat((ones, self.context_idxs), dim=1) - self.question_idxs = torch.cat((ones, self.question_idxs), dim=1) - - ones = torch.ones((batch_size, 1, w_len), dtype=torch.int64) - self.context_char_idxs = torch.cat((ones, self.context_char_idxs), dim=1) - self.question_char_idxs = torch.cat((ones, self.question_char_idxs), dim=1) - - self.y1s += 1 - self.y2s += 1 - - # SQuAD 1.1: Ignore no-answer examples - self.ids = torch.from_numpy(dataset['ids']).long() - self.valid_idxs = [idx for idx in range(len(self.ids)) - if use_v2 or self.y1s[idx].item() >= 0] - - def __getitem__(self, idx): - idx = self.valid_idxs[idx] - example = (self.context_idxs[idx], - self.context_char_idxs[idx], - self.question_idxs[idx], - self.question_char_idxs[idx], - self.y1s[idx], - self.y2s[idx], - self.ids[idx]) - - return example - - def __len__(self): - return len(self.valid_idxs) - - -def collate_fn(examples): - """Create batch tensors from a list of individual examples returned - by `SQuAD.__getitem__`. Merge examples of different length by padding - all examples to the maximum length in the batch. - - Args: - examples (list): List of tuples of the form (context_idxs, context_char_idxs, - question_idxs, question_char_idxs, y1s, y2s, ids). - - Returns: - examples (tuple): Tuple of tensors (context_idxs, context_char_idxs, question_idxs, - question_char_idxs, y1s, y2s, ids). All of shape (batch_size, ...), where - the remaining dimensions are the maximum length of examples in the input. - - Adapted from: - https://github.com/yunjey/seq2seq-dataloader - """ - def merge_0d(scalars, dtype=torch.int64): - return torch.tensor(scalars, dtype=dtype) - - def merge_1d(arrays, dtype=torch.int64, pad_value=0): - lengths = [(a != pad_value).sum() for a in arrays] - padded = torch.zeros(len(arrays), max(lengths), dtype=dtype) - for i, seq in enumerate(arrays): - end = lengths[i] - padded[i, :end] = seq[:end] - return padded - - def merge_2d(matrices, dtype=torch.int64, pad_value=0): - heights = [(m.sum(1) != pad_value).sum() for m in matrices] - widths = [(m.sum(0) != pad_value).sum() for m in matrices] - padded = torch.zeros(len(matrices), max(heights), max(widths), dtype=dtype) - for i, seq in enumerate(matrices): - height, width = heights[i], widths[i] - padded[i, :height, :width] = seq[:height, :width] - return padded - - # Group by tensor type - context_idxs, context_char_idxs, \ - question_idxs, question_char_idxs, \ - y1s, y2s, ids = zip(*examples) - - # Merge into batch tensors - context_idxs = merge_1d(context_idxs) - context_char_idxs = merge_2d(context_char_idxs) - question_idxs = merge_1d(question_idxs) - question_char_idxs = merge_2d(question_char_idxs) - y1s = merge_0d(y1s) - y2s = merge_0d(y2s) - ids = merge_0d(ids) - - return (context_idxs, context_char_idxs, - question_idxs, question_char_idxs, - y1s, y2s, ids) - - -class AverageMeter: - """Keep track of average values over time. - - Adapted from: - > https://github.com/pytorch/examples/blob/master/imagenet/main.py - """ - def __init__(self): - self.avg = 0 - self.sum = 0 - self.count = 0 - - def reset(self): - """Reset meter.""" - self.__init__() - - def update(self, val, num_samples=1): - """Update meter with new value `val`, the average of `num` samples. - - Args: - val (float): Average value to update the meter with. - num_samples (int): Number of samples that were averaged to - produce `val`. - """ - self.count += num_samples - self.sum += val * num_samples - self.avg = self.sum / self.count - - -class EMA: - """Exponential moving average of model parameters. - Args: - model (torch.nn.Module): Model with parameters whose EMA will be kept. - decay (float): Decay rate for exponential moving average. - """ - def __init__(self, model, decay): - self.decay = decay - self.shadow = {} - self.original = {} - - # Register model parameters - for name, param in model.named_parameters(): - if param.requires_grad: - self.shadow[name] = param.data.clone() - - def __call__(self, model, num_updates): - decay = min(self.decay, (1.0 + num_updates) / (10.0 + num_updates)) - for name, param in model.named_parameters(): - if param.requires_grad: - assert name in self.shadow - new_average = \ - (1.0 - decay) * param.data + decay * self.shadow[name] - self.shadow[name] = new_average.clone() - - def assign(self, model): - """Assign exponential moving average of parameter values to the - respective parameters. - Args: - model (torch.nn.Module): Model to assign parameter values. - """ - for name, param in model.named_parameters(): - if param.requires_grad: - assert name in self.shadow - self.original[name] = param.data.clone() - param.data = self.shadow[name] - - def resume(self, model): - """Restore original parameters to a model. That is, put back - the values that were in each parameter at the last call to `assign`. - Args: - model (torch.nn.Module): Model to assign parameter values. - """ - for name, param in model.named_parameters(): - if param.requires_grad: - assert name in self.shadow - param.data = self.original[name] - - -class CheckpointSaver: - """Class to save and load model checkpoints. - - Save the best checkpoints as measured by a metric value passed into the - `save` method. Overwrite checkpoints with better checkpoints once - `max_checkpoints` have been saved. - - Args: - save_dir (str): Directory to save checkpoints. - max_checkpoints (int): Maximum number of checkpoints to keep before - overwriting old ones. - metric_name (str): Name of metric used to determine best model. - maximize_metric (bool): If true, best checkpoint is that which maximizes - the metric value passed in via `save`. Otherwise, best checkpoint - minimizes the metric. - log (logging.Logger): Optional logger for printing information. - """ - def __init__(self, save_dir, max_checkpoints, metric_name, - maximize_metric=False, log=None): - super(CheckpointSaver, self).__init__() - - self.save_dir = save_dir - self.max_checkpoints = max_checkpoints - self.metric_name = metric_name - self.maximize_metric = maximize_metric - self.best_val = None - self.ckpt_paths = queue.PriorityQueue() - self.log = log - self._print(f"Saver will {'max' if maximize_metric else 'min'}imize {metric_name}...") - - def is_best(self, metric_val): - """Check whether `metric_val` is the best seen so far. - - Args: - metric_val (float): Metric value to compare to prior checkpoints. - """ - if metric_val is None: - # No metric reported - return False - - if self.best_val is None: - # No checkpoint saved yet - return True - - return ((self.maximize_metric and self.best_val < metric_val) - or (not self.maximize_metric and self.best_val > metric_val)) - - def _print(self, message): - """Print a message if logging is enabled.""" - if self.log is not None: - self.log.info(message) - - def save(self, step, model, metric_val, device): - """Save model parameters to disk. - - Args: - step (int): Total number of examples seen during training so far. - model (torch.nn.DataParallel): Model to save. - metric_val (float): Determines whether checkpoint is best so far. - device (torch.device): Device where model resides. - """ - ckpt_dict = { - 'model_name': model.__class__.__name__, - 'model_state': model.cpu().state_dict(), - 'step': step - } - model.to(device) - - checkpoint_path = os.path.join(self.save_dir, - f'step_{step}.pth.tar') - torch.save(ckpt_dict, checkpoint_path) - self._print(f'Saved checkpoint: {checkpoint_path}') - - if self.is_best(metric_val): - # Save the best model - self.best_val = metric_val - best_path = os.path.join(self.save_dir, 'best.pth.tar') - shutil.copy(checkpoint_path, best_path) - self._print(f'New best checkpoint at step {step}...') - - # Add checkpoint path to priority queue (lowest priority removed first) - if self.maximize_metric: - priority_order = metric_val - else: - priority_order = -metric_val - - self.ckpt_paths.put((priority_order, checkpoint_path)) - - # Remove a checkpoint if more than max_checkpoints have been saved - if self.ckpt_paths.qsize() > self.max_checkpoints: - _, worst_ckpt = self.ckpt_paths.get() - try: - os.remove(worst_ckpt) - self._print(f'Removed checkpoint: {worst_ckpt}') - except OSError: - # Avoid crashing if checkpoint has been removed or protected - pass - - -def load_model(model, checkpoint_path, gpu_ids, return_step=True): - """Load model parameters from disk. - - Args: - model (torch.nn.DataParallel): Load parameters into this model. - checkpoint_path (str): Path to checkpoint to load. - gpu_ids (list): GPU IDs for DataParallel. - return_step (bool): Also return the step at which checkpoint was saved. - - Returns: - model (torch.nn.DataParallel): Model loaded from checkpoint. - step (int): Step at which checkpoint was saved. Only if `return_step`. - """ - device = f"cuda:{gpu_ids[0] if gpu_ids else 'cpu'}" - ckpt_dict = torch.load(checkpoint_path, map_location=device) - - # Build model, load parameters - model.load_state_dict(ckpt_dict['model_state']) - - if return_step: - step = ckpt_dict['step'] - return model, step - - return model - - -def get_available_devices(): - """Get IDs of all available GPUs. - - Returns: - device (torch.device): Main device (GPU 0 or CPU). - gpu_ids (list): List of IDs of all GPUs that are available. - """ - gpu_ids = [] - if torch.cuda.is_available(): - gpu_ids += [gpu_id for gpu_id in range(torch.cuda.device_count())] - device = torch.device(f'cuda:{gpu_ids[0]}') - torch.cuda.set_device(device) - else: - device = torch.device('cpu') - - return device, gpu_ids - - -def masked_softmax(logits, mask, dim=-1, log_softmax=False): - """Take the softmax of `logits` over given dimension, and set - entries to 0 wherever `mask` is 0. - - Args: - logits (torch.Tensor): Inputs to the softmax function. - mask (torch.Tensor): Same shape as `logits`, with 0 indicating - positions that should be assigned 0 probability in the output. - dim (int): Dimension over which to take softmax. - log_softmax (bool): Take log-softmax rather than regular softmax. - E.g., some PyTorch functions such as `F.nll_loss` expect log-softmax. - - Returns: - probs (torch.Tensor): Result of taking masked softmax over the logits. - """ - mask = mask.type(torch.float32) - masked_logits = mask * logits + (1 - mask) * -1e30 - softmax_fn = F.log_softmax if log_softmax else F.softmax - probs = softmax_fn(masked_logits, dim) - - return probs - - -def visualize(tbx, pred_dict, eval_path, step, split, num_visuals): - """Visualize text examples to TensorBoard. - - Args: - tbx (tensorboardX.SummaryWriter): Summary writer. - pred_dict (dict): dict of predictions of the form id -> pred. - eval_path (str): Path to eval JSON file. - step (int): Number of examples seen so far during training. - split (str): Name of data split being visualized. - num_visuals (int): Number of visuals to select at random from preds. - """ - if num_visuals <= 0: - return - if num_visuals > len(pred_dict): - num_visuals = len(pred_dict) - - visual_ids = np.random.choice(list(pred_dict), size=num_visuals, replace=False) - - with open(eval_path, 'r') as eval_file: - eval_dict = json.load(eval_file) - for i, id_ in enumerate(visual_ids): - pred = pred_dict[id_] or 'N/A' - example = eval_dict[str(id_)] - question = example['question'] - context = example['context'] - answers = example['answers'] - - gold = answers[0] if answers else 'N/A' - tbl_fmt = (f'- **Question:** {question}\n' - + f'- **Context:** {context}\n' - + f'- **Answer:** {gold}\n' - + f'- **Prediction:** {pred}') - tbx.add_text(tag=f'{split}/{i+1}_of_{num_visuals}', - text_string=tbl_fmt, - global_step=step) - - -def save_preds(preds, save_dir, file_name='predictions.csv'): - """Save predictions `preds` to a CSV file named `file_name` in `save_dir`. - - Args: - preds (list): List of predictions each of the form (id, start, end), - where id is an example ID, and start/end are indices in the context. - save_dir (str): Directory in which to save the predictions file. - file_name (str): File name for the CSV file. - - Returns: - save_path (str): Path where CSV file was saved. - """ - # Validate format - if (not isinstance(preds, list) - or any(not isinstance(p, tuple) or len(p) != 3 for p in preds)): - raise ValueError('preds must be a list of tuples (id, start, end)') - - # Make sure predictions are sorted by ID - preds = sorted(preds, key=lambda p: p[0]) - - # Save to a CSV file - save_path = os.path.join(save_dir, file_name) - np.savetxt(save_path, np.array(preds), delimiter=',', fmt='%d') - - return save_path - - -def get_save_dir(base_dir, name, training, id_max=100): - """Get a unique save directory by appending the smallest positive integer - `id < id_max` that is not already taken (i.e., no dir exists with that id). - - Args: - base_dir (str): Base directory in which to make save directories. - name (str): Name to identify this training run. Need not be unique. - training (bool): Save dir. is for training (determines subdirectory). - id_max (int): Maximum ID number before raising an exception. - - Returns: - save_dir (str): Path to a new directory with a unique name. - """ - for uid in range(1, id_max): - subdir = 'train' if training else 'test' - save_dir = os.path.join(base_dir, subdir, f'{name}-{uid:02d}') - if not os.path.exists(save_dir): - os.makedirs(save_dir) - return save_dir - - raise RuntimeError('Too many save directories created with the same name. \ - Delete old save directories or use another name.') - - -def get_logger(log_dir, name): - """Get a `logging.Logger` instance that prints to the console - and an auxiliary file. - - Args: - log_dir (str): Directory in which to create the log file. - name (str): Name to identify the logs. - - Returns: - logger (logging.Logger): Logger instance for logging events. - """ - class StreamHandlerWithTQDM(logging.Handler): - """Let `logging` print without breaking `tqdm` progress bars. - - See Also: - > https://stackoverflow.com/questions/38543506 - """ - def emit(self, record): - try: - msg = self.format(record) - tqdm.tqdm.write(msg) - self.flush() - except (KeyboardInterrupt, SystemExit): - raise - except: - self.handleError(record) - - # Create logger - logger = logging.getLogger(name) - logger.setLevel(logging.DEBUG) - - # Log everything (i.e., DEBUG level and above) to a file - log_path = os.path.join(log_dir, 'log.txt') - file_handler = logging.FileHandler(log_path) - file_handler.setLevel(logging.DEBUG) - - # Log everything except DEBUG level (i.e., INFO level and above) to console - console_handler = StreamHandlerWithTQDM() - console_handler.setLevel(logging.INFO) - - # Create format for the logs - file_formatter = logging.Formatter('[%(asctime)s] %(message)s', - datefmt='%m.%d.%y %H:%M:%S') - file_handler.setFormatter(file_formatter) - console_formatter = logging.Formatter('[%(asctime)s] %(message)s', - datefmt='%m.%d.%y %H:%M:%S') - console_handler.setFormatter(console_formatter) - - # add the handlers to the logger - logger.addHandler(file_handler) - logger.addHandler(console_handler) - - return logger - - -def torch_from_json(path, dtype=torch.float32): - """Load a PyTorch Tensor from a JSON file. - - Args: - path (str): Path to the JSON file to load. - dtype (torch.dtype): Data type of loaded array. - - Returns: - tensor (torch.Tensor): Tensor loaded from JSON file. - """ - with open(path, 'r') as fh: - array = np.array(json.load(fh)) - - tensor = torch.from_numpy(array).type(dtype) - - return tensor - - -def discretize(p_start, p_end, max_len=15, no_answer=False): - """Discretize soft predictions to get start and end indices. - - Choose the pair `(i, j)` of indices that maximizes `p1[i] * p2[j]` - subject to `i <= j` and `j - i + 1 <= max_len`. - - Args: - p_start (torch.Tensor): Soft predictions for start index. - Shape (batch_size, context_len). - p_end (torch.Tensor): Soft predictions for end index. - Shape (batch_size, context_len). - max_len (int): Maximum length of the discretized prediction. - I.e., enforce that `preds[i, 1] - preds[i, 0] + 1 <= max_len`. - no_answer (bool): Treat 0-index as the no-answer prediction. Consider - a prediction no-answer if `preds[0, 0] * preds[0, 1]` is greater - than the probability assigned to the max-probability span. - - Returns: - start_idxs (torch.Tensor): Hard predictions for start index. - Shape (batch_size,) - end_idxs (torch.Tensor): Hard predictions for end index. - Shape (batch_size,) - """ - if p_start.min() < 0 or p_start.max() > 1 \ - or p_end.min() < 0 or p_end.max() > 1: - raise ValueError('Expected p_start and p_end to have values in [0, 1]') - - # Compute pairwise probabilities - p_start = p_start.unsqueeze(dim=2) - p_end = p_end.unsqueeze(dim=1) - p_joint = torch.matmul(p_start, p_end) # (batch_size, c_len, c_len) - - # Restrict to pairs (i, j) such that i <= j <= i + max_len - 1 - c_len, device = p_start.size(1), p_start.device - is_legal_pair = torch.triu(torch.ones((c_len, c_len), device=device)) - is_legal_pair -= torch.triu(torch.ones((c_len, c_len), device=device), - diagonal=max_len) - if no_answer: - # Index 0 is no-answer - p_no_answer = p_joint[:, 0, 0].clone() - is_legal_pair[0, :] = 0 - is_legal_pair[:, 0] = 0 - else: - p_no_answer = None - p_joint *= is_legal_pair - - # Take pair (i, j) that maximizes p_joint - max_in_row, _ = torch.max(p_joint, dim=2) - max_in_col, _ = torch.max(p_joint, dim=1) - start_idxs = torch.argmax(max_in_row, dim=-1) - end_idxs = torch.argmax(max_in_col, dim=-1) - - if no_answer: - # Predict no-answer whenever p_no_answer > max_prob - max_prob, _ = torch.max(max_in_col, dim=-1) - start_idxs[p_no_answer > max_prob] = 0 - end_idxs[p_no_answer > max_prob] = 0 - - return start_idxs, end_idxs - - -def convert_tokens(eval_dict, qa_id, y_start_list, y_end_list, no_answer): - """Convert predictions to tokens from the context. - - Args: - eval_dict (dict): Dictionary with eval info for the dataset. This is - used to perform the mapping from IDs and indices to actual text. - qa_id (int): List of QA example IDs. - y_start_list (list): List of start predictions. - y_end_list (list): List of end predictions. - no_answer (bool): Questions can have no answer. E.g., SQuAD 2.0. - - Returns: - pred_dict (dict): Dictionary index IDs -> predicted answer text. - sub_dict (dict): Dictionary UUIDs -> predicted answer text (submission). - """ - pred_dict = {} - sub_dict = {} - for qid, y_start, y_end in zip(qa_id, y_start_list, y_end_list): - context = eval_dict[str(qid)]["context"] - spans = eval_dict[str(qid)]["spans"] - uuid = eval_dict[str(qid)]["uuid"] - if no_answer and (y_start == 0 or y_end == 0): - pred_dict[str(qid)] = '' - sub_dict[uuid] = '' - else: - if no_answer: - y_start, y_end = y_start - 1, y_end - 1 - start_idx = spans[y_start][0] - end_idx = spans[y_end][1] - pred_dict[str(qid)] = context[start_idx: end_idx] - sub_dict[uuid] = context[start_idx: end_idx] - return pred_dict, sub_dict - - -def metric_max_over_ground_truths(metric_fn, prediction, ground_truths): - if not ground_truths: - return metric_fn(prediction, '') - scores_for_ground_truths = [] - for ground_truth in ground_truths: - score = metric_fn(prediction, ground_truth) - scores_for_ground_truths.append(score) - return max(scores_for_ground_truths) - - -def eval_dicts(gold_dict, pred_dict, no_answer): - avna = f1 = em = total = 0 - for key, value in pred_dict.items(): - total += 1 - ground_truths = gold_dict[key]['answers'] - prediction = value - em += metric_max_over_ground_truths(compute_em, prediction, ground_truths) - f1 += metric_max_over_ground_truths(compute_f1, prediction, ground_truths) - if no_answer: - avna += compute_avna(prediction, ground_truths) - - eval_dict = {'EM': 100. * em / total, - 'F1': 100. * f1 / total} - - if no_answer: - eval_dict['AvNA'] = 100. * avna / total - - return eval_dict - - -def compute_avna(prediction, ground_truths): - """Compute answer vs. no-answer accuracy.""" - return float(bool(prediction) == bool(ground_truths)) - - -# All methods below this line are from the official SQuAD 2.0 eval script -# https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/ -def normalize_answer(s): - """Convert to lowercase and remove punctuation, articles and extra whitespace.""" - - def remove_articles(text): - regex = re.compile(r'\b(a|an|the)\b', re.UNICODE) - return re.sub(regex, ' ', text) - - def white_space_fix(text): - return ' '.join(text.split()) - - def remove_punc(text): - exclude = set(string.punctuation) - return ''.join(ch for ch in text if ch not in exclude) - - def lower(text): - return text.lower() - - return white_space_fix(remove_articles(remove_punc(lower(s)))) - - -def get_tokens(s): - if not s: - return [] - return normalize_answer(s).split() - - -def compute_em(a_gold, a_pred): - return int(normalize_answer(a_gold) == normalize_answer(a_pred)) - - -def compute_f1(a_gold, a_pred): - gold_toks = get_tokens(a_gold) - pred_toks = get_tokens(a_pred) - common = Counter(gold_toks) & Counter(pred_toks) - num_same = sum(common.values()) - if len(gold_toks) == 0 or len(pred_toks) == 0: - # If either is no-answer, then F1 is 1 if they agree, 0 otherwise - return int(gold_toks == pred_toks) - 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-version = "1.0.0" -description = "Machine learning course at MIPT" -authors = ["Vladislav Goncharenko , Radoslav Neychev "] -license = "MIT License" - -[tool.poetry.dependencies] -python = "^3.8" -scikit-learn = "^0.24.1" -matplotlib = "^3.3.4" -pandas = "^1.2.2" -numpy = "^1.20.1" -scipy = "^1.6.0" -statsmodels = "^0.12.2" -seaborn = "^0.11.1" -xgboost = "^1.3.3" -opencv-python = "^4.5.1" -torch = "^1.7.1" -torchvision = "^0.8.2" -torchsummary = "^1.5.1" - -# basic -Pillow = {version = "^7.2.0", optional = true} # TODO: remove -tqdm = {version = "^4.56.2", optional = true} # TODO: remove -scikit-image = {version = "^0.18.1", optional = true} # TODO: remove week0_12 imread and resize -h5py = {version = "^3.1.0", optional = true} # parse cats and dogs dataset, maybe remove? -pydotplus = {version = "^2.0.2", optional = true} # graph visualization -eli5 = {version = "^0.11.0", optional = true} # week0_07 feature importance -PDPbox = {version = "^0.2.0", optional = true} # week0_07 feature importance -shap = {version = "^0.38.1", optional = true} # week0_07 feature importance - -# advanced -ipywidgets = "^7.6.3" # week1_15 downloading mnist via torchvision - -# nlp -nltk = "^3.5" -gensim = "^3.8.3" -spacy = "^3.1.1" -subword-nmt = "^0.3.7" - -pytorch-transformers = "^1.2.0" -torchtext = "^0.8" - -bokeh = "^2.3.0" - -# rl -gym = {version = "^0.18.0", optional = true} -graphviz = "^0.16" - -[tool.poetry.extras] -basic = ["Pillow", "tqdm", "scikit-image", "h5py", "pydotplus", "eli5", "PDPbox", "shap"] -nlp = ["nltk", "gensim", "spacy", "subword-nmt", "pytorch-transformers", "torchtext", "bokeh"] -rl = ["gym", "graphviz"] - -[tool.poetry.dev-dependencies] -pre-commit = "^2.10.1" -ipykernel = "^5.4.3" - -[tool.black] -line-length = 100 -target-version = ["py38"] - -[tool.isort] -multi_line_output = 3 -include_trailing_comma = true -force_grid_wrap = 0 -use_parentheses = true -ensure_newline_before_comments = true -line_length = 100 -lines_after_imports = 2 - -[tool.nbqa.config] -black = "pyproject.toml" -isort = "pyproject.toml" -flake8 = "setup.cfg" - -[tool.nbqa.addopts] -flake8 = ["--extend-ignore=E402"] - -[tool.nbqa.mutate] -black = 1 -isort = 1 - -[build-system] -requires = ["poetry-core>=1.0.0"] -build-backend = "poetry.core.masonry.api" diff --git a/setup.cfg b/setup.cfg deleted file mode 100644 index 8392765..0000000 --- a/setup.cfg +++ /dev/null @@ -1,5 +0,0 @@ -[flake8] -max-line-length = 100 -ignore = E203, E501, W503, B950 -max-complexity = 12 -select = B, C, E, F, W, B9 diff --git a/week05_transformer_pos_tagging/README.md b/week05_transformer_pos_tagging/README.md deleted file mode 100644 index 1443d4f..0000000 --- a/week05_transformer_pos_tagging/README.md +++ /dev/null @@ -1,24 +0,0 @@ -PoS Tagging with BiLSTM: -* Self-practice version: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/girafe-ai/natural-language-processing/blob/master/week05_transformer_pos_tagging/week05_bilstm_for_pos_tagging.ipynb) -* Completed version: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/girafe-ai/natural-language-processing/blob/master/week05_transformer_pos_tagging/week05_bilstm_for_pos_tagging__completed.ipynb) - - -Understanding the positional encoding: -[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/girafe-ai/natural-language-processing/blob/master/week05_transformer_pos_tagging/week05_positional_encoding_carriers.ipynb) - -Full Transformer architecture and training pipeline by Harvard NLP: -[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/harvardnlp/annotated-transformer/blob/master/The%20Annotated%20Transformer.ipynb) - - - -__Further readings__: -* [en] The Illustrated Transformer [blog post](https://jalammar.github.io/illustrated-transformer/) - -* [en] Harvard NLP [full implementation in PyTorch](http://nlp.seas.harvard.edu/2018/04/03/attention.html) - -* [en] OpenAI blog post [Better Language Models -and Their Implications (GPT-2)](https://openai.com/blog/better-language-models/) - -* [en] Paper describing positional encoding ["Convolutional Sequence to Sequence Learning"](https://arxiv.org/pdf/1705.03122) - -* [en] Paper presenting [Layer Normalization](https://arxiv.org/abs/1607.06450) diff --git a/week05_transformer_pos_tagging/assets/pos-bert.png b/week05_transformer_pos_tagging/assets/pos-bert.png deleted file mode 100644 index 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\ No newline at end of file diff --git a/week05_transformer_pos_tagging/week05_bilstm_for_pos_tagging.ipynb b/week05_transformer_pos_tagging/week05_bilstm_for_pos_tagging.ipynb deleted file mode 100644 index e79553d..0000000 --- a/week05_transformer_pos_tagging/week05_bilstm_for_pos_tagging.ipynb +++ /dev/null @@ -1,1533 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Practice: BiLSTM for PoS Tagging\n", - "*This notebook is based on [open-source implementation](https://github.com/bentrevett/pytorch-pos-tagging) of PoS Tagging in PyTorch.*\n", - "\n", - "### Introduction\n", - "\n", - "In this series we'll be building a machine learning model that produces an output for every element in an input sequence, using PyTorch and TorchText. Specifically, we will be inputting a sequence of text and the model will output a part-of-speech (PoS) tag for each token in the input text. This can also be used for named entity recognition (NER), where the output for each token will be what type of entity, if any, the token is.\n", - "\n", - "In this notebook, we'll be implementing a multi-layer bi-directional LSTM (BiLSTM) to predict PoS tags using the Universal Dependencies English Web Treebank (UDPOS) dataset.\n", - "\n", - "### Preparing Data\n", - "\n", - "First, let's import the necessary Python modules." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", - "\n", - "from torchtext.legacy import data\n", - "from torchtext.legacy import datasets\n", - "\n", - "import spacy\n", - "import numpy as np\n", - "\n", - "import time\n", - "import random" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we'll set the random seeds for reproducability." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "SEED = 1234\n", - "\n", - "random.seed(SEED)\n", - "np.random.seed(SEED)\n", - "torch.manual_seed(SEED)\n", - "torch.backends.cudnn.deterministic = True" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One of the key parts of TorchText is the `Field`. The `Field` handles how your dataset is processed.\n", - "\n", - "Our `TEXT` field handles how the text that we need to tag is dealt with. All we do here is set `lower = True` which lowercases all of the text.\n", - "\n", - "Next we'll define the `Fields` for the tags. This dataset actually has two different sets of tags, [universal dependency (UD) tags](https://universaldependencies.org/u/pos/) and [Penn Treebank (PTB) tags](https://www.sketchengine.eu/penn-treebank-tagset/). We'll only train our model on the UD tags, but will load the PTB tags to show how they could be used instead.\n", - "\n", - "`UD_TAGS` handles how the UD tags should be handled. Our `TEXT` vocabulary - which we'll build later - will have *unknown* tokens in it, i.e. tokens that are not within our vocabulary. However, we won't have unknown tags as we are dealing with a finite set of possible tags. TorchText `Fields` initialize a default unknown token, ``, which we remove by setting `unk_token = None`.\n", - "\n", - "`PTB_TAGS` does the same as `UD_TAGS`, but handles the PTB tags instead." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "TEXT = data.Field(lower = True)\n", - "UD_TAGS = data.Field(unk_token = None)\n", - "PTB_TAGS = data.Field(unk_token = None)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We then define `fields`, which handles passing our fields to the dataset.\n", - "\n", - "Note that order matters, if you only wanted to load the PTB tags your field would be:\n", - "\n", - "```\n", - "fields = ((\"text\", TEXT), (None, None), (\"ptbtags\", PTB_TAGS))\n", - "```\n", - "\n", - "Where `None` tells TorchText to not load those tags." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fields = ((\"text\", TEXT), (\"udtags\", UD_TAGS), (\"ptbtags\", PTB_TAGS))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we load the UDPOS dataset using our defined fields." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "train_data, valid_data, test_data = datasets.UDPOS.splits(fields)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can check how many examples are in each section of the dataset by checking their length." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(f\"Number of training examples: {len(train_data)}\")\n", - "print(f\"Number of validation examples: {len(valid_data)}\")\n", - "print(f\"Number of testing examples: {len(test_data)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's print out an example:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(vars(train_data.examples[0]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also view the text and tags separately:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(vars(train_data.examples[0])['text'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(vars(train_data.examples[0])['udtags'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(vars(train_data.examples[0])['ptbtags'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we'll build the vocabulary - a mapping of tokens to integers. \n", - "\n", - "We want some unknown tokens within our dataset in order to replicate how this model would be used in real life, so we set the `min_freq` to 2 which means only tokens that appear twice in the training set will be added to the vocabulary and the rest will be replaced by `` tokens.\n", - "\n", - "We also load the [GloVe](https://nlp.stanford.edu/projects/glove/) pre-trained token embeddings. Specifically, the 100-dimensional embeddings that have been trained on 6 billion tokens. Using pre-trained embeddings usually leads to improved performance - although admittedly the dataset used in this tutorial is too small to take advantage of the pre-trained embeddings. \n", - "\n", - "`unk_init` is used to initialize the token embeddings which are not in the pre-trained embedding vocabulary. By default this sets those embeddings to zeros, however it is better to not have them all initialized to the same value, so we initialize them from a Normal/Gaussian distribution.\n", - "\n", - "These pre-trained vectors are now loaded into our vocabulary and we will initialize our model with these values later." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "MIN_FREQ = 2\n", - "\n", - "TEXT.build_vocab(train_data, \n", - " min_freq = MIN_FREQ,\n", - " vectors = \"glove.6B.100d\",\n", - " unk_init = torch.Tensor.normal_)\n", - "\n", - "\n", - "UD_TAGS.build_vocab(train_data)\n", - "PTB_TAGS.build_vocab(train_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can check how many tokens and tags are in our vocabulary by getting their length:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(f\"Unique tokens in TEXT vocabulary: {len(TEXT.vocab)}\")\n", - "print(f\"Unique tokens in UD_TAG vocabulary: {len(UD_TAGS.vocab)}\")\n", - "print(f\"Unique tokens in PTB_TAG vocabulary: {len(PTB_TAGS.vocab)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Exploring the vocabulary, we can check the most common tokens within our texts:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(TEXT.vocab.freqs.most_common(20))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see the vocabularies for both of our tags:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(UD_TAGS.vocab.itos)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(PTB_TAGS.vocab.itos)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also see how many of each tag are in our vocabulary:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(UD_TAGS.vocab.freqs.most_common())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(PTB_TAGS.vocab.freqs.most_common())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also view how common each of the tags are within the training set:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def tag_percentage(tag_counts):\n", - " \n", - " total_count = sum([count for tag, count in tag_counts])\n", - " \n", - " tag_counts_percentages = [(tag, count, count/total_count) for tag, count in tag_counts]\n", - " \n", - " return tag_counts_percentages" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Tag\\t\\tCount\\t\\tPercentage\\n\")\n", - "\n", - "for tag, count, percent in tag_percentage(UD_TAGS.vocab.freqs.most_common()):\n", - " print(f\"{tag}\\t\\t{count}\\t\\t{percent*100:4.1f}%\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Tag\\t\\tCount\\t\\tPercentage\\n\")\n", - "\n", - "for tag, count, percent in tag_percentage(PTB_TAGS.vocab.freqs.most_common()):\n", - " print(f\"{tag}\\t\\t{count}\\t\\t{percent*100:4.1f}%\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The final part of data preparation is handling the iterator. \n", - "\n", - "This will be iterated over to return batches of data to process. Here, we set the batch size and the `device` - which is used to place the batches of tensors on our GPU, if we have one. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "BATCH_SIZE = 128\n", - "\n", - "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n", - "\n", - "train_iterator, valid_iterator, test_iterator = data.BucketIterator.splits(\n", - " (train_data, valid_data, test_data), \n", - " batch_size = BATCH_SIZE,\n", - " device = device)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Building the Model\n", - "\n", - "Next up, we define our model - a multi-layer bi-directional LSTM. The image below shows a simplified version of the model with only one LSTM layer and omitting the LSTM's cell state for clarity.\n", - "\n", - "![](assets/pos-bidirectional-lstm.png)\n", - "\n", - "The model takes in a sequence of tokens, $X = \\{x_1, x_2,...,x_T\\}$, passes them through an embedding layer, $e$, to get the token embeddings, $e(X) = \\{e(x_1), e(x_2), ..., e(x_T)\\}$.\n", - "\n", - "These embeddings are processed - one per time-step - by the forward and backward LSTMs. The forward LSTM processes the sequence from left-to-right, whilst the backward LSTM processes the sequence right-to-left, i.e. the first input to the forward LSTM is $x_1$ and the first input to the backward LSTM is $x_T$. \n", - "\n", - "The LSTMs also take in the the hidden, $h$, and cell, $c$, states from the previous time-step\n", - "\n", - "$$h^{\\rightarrow}_t = \\text{LSTM}^{\\rightarrow}(e(x^{\\rightarrow}_t), h^{\\rightarrow}_{t-1}, c^{\\rightarrow}_{t-1})$$\n", - "$$h^{\\leftarrow}_t=\\text{LSTM}^{\\leftarrow}(e(x^{\\leftarrow}_t), h^{\\leftarrow}_{t-1}, c^{\\leftarrow}_{t-1})$$\n", - "\n", - "After the whole sequence has been processed, the hidden and cell states are then passed to the next layer of the LSTM.\n", - "\n", - "The initial hidden and cell states, $h_0$ and $c_0$, for each direction and layer are initialized to a tensor full of zeros.\n", - "\n", - "We then concatenate both the forward and backward hidden states from the final layer of the LSTM, $H = \\{h_1, h_2, ... h_T\\}$, where $h_1 = [h^{\\rightarrow}_1;h^{\\leftarrow}_T]$, $h_2 = [h^{\\rightarrow}_2;h^{\\leftarrow}_{T-1}]$, etc. and pass them through a linear layer, $f$, which is used to make the prediction of which tag applies to this token, $\\hat{y}_t = f(h_t)$.\n", - "\n", - "When training the model, we will compare our predicted tags, $\\hat{Y}$ against the actual tags, $Y$, to calculate a loss, the gradients w.r.t. that loss, and then update our parameters.\n", - "\n", - "We implement the model detailed above in the `BiLSTMPOSTagger` class.\n", - "\n", - "`nn.Embedding` is an embedding layer and the input dimension should be the size of the input (text) vocabulary. We tell it what the index of the padding token is so it does not update the padding token's embedding entry.\n", - "\n", - "`nn.LSTM` is the LSTM. We apply dropout as regularization between the layers, if we are using more than one.\n", - "\n", - "`nn.Linear` defines the linear layer to make predictions using the LSTM outputs. We double the size of the input if we are using a bi-directional LSTM. The output dimensions should be the size of the tag vocabulary.\n", - "\n", - "We also define a dropout layer with `nn.Dropout`, which we use in the `forward` method to apply dropout to the embeddings and the outputs of the final layer of the LSTM." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class BiLSTMPOSTagger(nn.Module):\n", - " def __init__(self, \n", - " input_dim, \n", - " embedding_dim, \n", - " hidden_dim, \n", - " output_dim, \n", - " n_layers, \n", - " bidirectional, \n", - " dropout, \n", - " pad_idx):\n", - " \n", - " super().__init__()\n", - " \n", - " self.embedding = nn.Embedding(input_dim, embedding_dim, padding_idx = pad_idx)\n", - " \n", - " self.lstm = nn.LSTM(embedding_dim, \n", - " hidden_dim, \n", - " num_layers = n_layers, \n", - " bidirectional = bidirectional,\n", - " dropout = dropout if n_layers > 1 else 0)\n", - " \n", - " self.fc = nn.Linear(hidden_dim * 2 if bidirectional else hidden_dim, output_dim)\n", - " \n", - " self.dropout = nn.Dropout(dropout)\n", - " \n", - " def forward(self, text):\n", - "\n", - " #text = [sent len, batch size]\n", - " \n", - " #pass text through embedding layer\n", - " embedded = self.dropout(self.embedding(text))\n", - " \n", - " #embedded = [sent len, batch size, emb dim]\n", - " \n", - " #pass embeddings into LSTM\n", - " outputs, (hidden, cell) = self.lstm(embedded)\n", - " \n", - " #outputs holds the backward and forward hidden states in the final layer\n", - " #hidden and cell are the backward and forward hidden and cell states at the final time-step\n", - " \n", - " #output = [sent len, batch size, hid dim * n directions]\n", - " #hidden/cell = [n layers * n directions, batch size, hid dim]\n", - " \n", - " #we use our outputs to make a prediction of what the tag should be\n", - " predictions = self.fc(self.dropout(outputs))\n", - " \n", - " #predictions = [sent len, batch size, output dim]\n", - " \n", - " return predictions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Training the Model\n", - "\n", - "Next, we instantiate the model. We need to ensure the embedding dimensions matches that of the GloVe embeddings we loaded earlier.\n", - "\n", - "The rest of the hyperparmeters have been chosen as sensible defaults, though there may be a combination that performs better on this model and dataset.\n", - "\n", - "The input and output dimensions are taken directly from the lengths of the respective vocabularies. The padding index is obtained using the vocabulary and the `Field` of the text." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "INPUT_DIM = len(TEXT.vocab)\n", - "EMBEDDING_DIM = 100\n", - "HIDDEN_DIM = 128\n", - "OUTPUT_DIM = len(UD_TAGS.vocab)\n", - "N_LAYERS = 2\n", - "BIDIRECTIONAL = True\n", - "DROPOUT = 0.25\n", - "PAD_IDX = TEXT.vocab.stoi[TEXT.pad_token]\n", - "\n", - "model = BiLSTMPOSTagger(INPUT_DIM, \n", - " EMBEDDING_DIM, \n", - " HIDDEN_DIM, \n", - " OUTPUT_DIM, \n", - " N_LAYERS, \n", - " BIDIRECTIONAL, \n", - " DROPOUT, \n", - " PAD_IDX)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We initialize the weights from a simple Normal distribution. Again, there may be a better initialization scheme for this model and dataset." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def init_weights(m):\n", - " for name, param in m.named_parameters():\n", - " nn.init.normal_(param.data, mean = 0, std = 0.1)\n", - " \n", - "model.apply(init_weights)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, a small function to tell us how many parameters are in our model. Useful for comparing different models." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def count_parameters(model):\n", - " return sum(p.numel() for p in model.parameters() if p.requires_grad)\n", - "\n", - "print(f'The model has {count_parameters(model):,} trainable parameters')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll now initialize our model's embedding layer with the pre-trained embedding values we loaded earlier.\n", - "\n", - "This is done by getting them from the vocab's `.vectors` attribute and then performing a `.copy` to overwrite the embedding layer's current weights." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pretrained_embeddings = TEXT.vocab.vectors\n", - "\n", - "print(pretrained_embeddings.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.embedding.weight.data.copy_(pretrained_embeddings)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It's common to initialize the embedding of the pad token to all zeros. This, along with setting the `padding_idx` in the model's embedding layer, means that the embedding should always output a tensor full of zeros when a pad token is input." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.embedding.weight.data[PAD_IDX] = torch.zeros(EMBEDDING_DIM)\n", - "\n", - "print(model.embedding.weight.data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We then define our optimizer, used to update our parameters w.r.t. their gradients. We use Adam with the default learning rate." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "optimizer = optim.Adam(model.parameters())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we define our loss function, cross-entropy loss.\n", - "\n", - "Even though we have no `` tokens within our tag vocab, we still have `` tokens. This is because all sentences within a batch need to be the same size. However, we don't want to calculate the loss when the target is a `` token as we aren't training our model to recognize padding tokens.\n", - "\n", - "We handle this by setting the `ignore_index` in our loss function to the index of the padding token in our tag vocabulary." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "TAG_PAD_IDX = UD_TAGS.vocab.stoi[UD_TAGS.pad_token]\n", - "\n", - "criterion = nn.CrossEntropyLoss(ignore_index = TAG_PAD_IDX)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We then place our model and loss function on our GPU, if we have one." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model = model.to(device)\n", - "criterion = criterion.to(device)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will be using the loss value between our predicted and actual tags to train the network, but ideally we'd like a more interpretable way to see how well our model is doing - accuracy.\n", - "\n", - "The issue is that we don't want to calculate accuracy over the `` tokens as we aren't interested in predicting them.\n", - "\n", - "The function below only calculates accuracy over non-padded tokens. `non_pad_elements` is a tensor containing the indices of the non-pad tokens within an input batch. We then compare the predictions of those elements with the labels to get a count of how many predictions were correct. We then divide this by the number of non-pad elements to get our accuracy value over the batch." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def categorical_accuracy(preds, y, tag_pad_idx):\n", - " \"\"\"\n", - " Returns accuracy per batch, i.e. if you get 8/10 right, this returns 0.8, NOT 8\n", - " \"\"\"\n", - " max_preds = preds.argmax(dim = 1, keepdim = True) # get the index of the max probability\n", - " non_pad_elements = (y != tag_pad_idx).nonzero()\n", - " correct = max_preds[non_pad_elements].squeeze(1).eq(y[non_pad_elements])\n", - " return correct.sum() / torch.FloatTensor([y[non_pad_elements].shape[0]]).to(device)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next is the function that handles training our model.\n", - "\n", - "We first set the model to `train` mode to turn on dropout/batch-norm/etc. (if used). Then we iterate over our iterator, which returns a batch of examples. \n", - "\n", - "For each batch: \n", - "- we zero the gradients over the parameters from the last gradient calculation\n", - "- insert the batch of text into the model to get predictions\n", - "- as PyTorch loss functions cannot handle 3-dimensional predictions we reshape our predictions\n", - "- calculate the loss and accuracy between the predicted tags and actual tags\n", - "- call `backward` to calculate the gradients of the parameters w.r.t. the loss\n", - "- take an optimizer `step` to update the parameters\n", - "- add to the running total of loss and accuracy" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def train(model, iterator, optimizer, criterion, tag_pad_idx):\n", - " \n", - " epoch_loss = 0\n", - " epoch_acc = 0\n", - " \n", - " model.train()\n", - " \n", - " for batch in iterator:\n", - " \n", - " text = batch.text\n", - " tags = batch.udtags\n", - " \n", - " optimizer.zero_grad()\n", - " \n", - " #text = [sent len, batch size]\n", - " \n", - " predictions = model(text)\n", - " \n", - " #predictions = [sent len, batch size, output dim]\n", - " #tags = [sent len, batch size]\n", - " \n", - " predictions = predictions.view(-1, predictions.shape[-1])\n", - " tags = tags.view(-1)\n", - " \n", - " #predictions = [sent len * batch size, output dim]\n", - " #tags = [sent len * batch size]\n", - " \n", - " loss = criterion(predictions, tags)\n", - " \n", - " acc = categorical_accuracy(predictions, tags, tag_pad_idx)\n", - " \n", - " loss.backward()\n", - " \n", - " optimizer.step()\n", - " \n", - " epoch_loss += loss.item()\n", - " epoch_acc += acc.item()\n", - " \n", - " return epoch_loss / len(iterator), epoch_acc / len(iterator)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `evaluate` function is similar to the `train` function, except with changes made so we don't update the model's parameters.\n", - "\n", - "`model.eval()` is used to put the model in evaluation mode, so dropout/batch-norm/etc. are turned off. \n", - "\n", - "The iteration loop is also wrapped in `torch.no_grad` to ensure we don't calculate any gradients. We also don't need to call `optimizer.zero_grad()` and `optimizer.step()`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def evaluate(model, iterator, criterion, tag_pad_idx):\n", - " \n", - " epoch_loss = 0\n", - " epoch_acc = 0\n", - " \n", - " model.eval()\n", - " \n", - " with torch.no_grad():\n", - " \n", - " for batch in iterator:\n", - "\n", - " text = batch.text\n", - " tags = batch.udtags\n", - " \n", - " predictions = model(text)\n", - " \n", - " predictions = predictions.view(-1, predictions.shape[-1])\n", - " tags = tags.view(-1)\n", - " \n", - " loss = criterion(predictions, tags)\n", - " \n", - " acc = categorical_accuracy(predictions, tags, tag_pad_idx)\n", - "\n", - " epoch_loss += loss.item()\n", - " epoch_acc += acc.item()\n", - " \n", - " return epoch_loss / len(iterator), epoch_acc / len(iterator)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we have a small function that tells us how long an epoch takes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def epoch_time(start_time, end_time):\n", - " elapsed_time = end_time - start_time\n", - " elapsed_mins = int(elapsed_time / 60)\n", - " elapsed_secs = int(elapsed_time - (elapsed_mins * 60))\n", - " return elapsed_mins, elapsed_secs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we train our model!\n", - "\n", - "After each epoch we check if our model has achieved the best validation loss so far. If it has then we save the parameters of this model and we will use these \"best\" parameters to calculate performance over our test set." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "N_EPOCHS = 15\n", - "\n", - "best_valid_loss = float('inf')\n", - "\n", - "for epoch in range(N_EPOCHS):\n", - "\n", - " start_time = time.time()\n", - " \n", - " train_loss, train_acc = train(model, train_iterator, optimizer, criterion, TAG_PAD_IDX)\n", - " valid_loss, valid_acc = evaluate(model, valid_iterator, criterion, TAG_PAD_IDX)\n", - " \n", - " end_time = time.time()\n", - "\n", - " epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n", - " \n", - " if valid_loss < best_valid_loss:\n", - " best_valid_loss = valid_loss\n", - " torch.save(model.state_dict(), 'tut1-model.pt')\n", - " \n", - " print(f'Epoch: {epoch+1:02} | Epoch Time: {epoch_mins}m {epoch_secs}s')\n", - " print(f'\\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')\n", - " print(f'\\t Val. Loss: {valid_loss:.3f} | Val. Acc: {valid_acc*100:.2f}%')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We then load our \"best\" parameters and evaluate performance on the test set." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.load_state_dict(torch.load('tut1-model.pt'))\n", - "\n", - "test_loss, test_acc = evaluate(model, test_iterator, criterion, TAG_PAD_IDX)\n", - "\n", - "print(f'Test Loss: {test_loss:.3f} | Test Acc: {test_acc*100:.2f}%')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Inference\n", - "\n", - "88% accuracy looks pretty good, but let's see our model tag some actual sentences.\n", - "\n", - "We define a `tag_sentence` function that will:\n", - "- put the model into evaluation mode\n", - "- tokenize the sentence with spaCy if it is not a list\n", - "- lowercase the tokens if the `Field` did\n", - "- numericalize the tokens using the vocabulary\n", - "- find out which tokens are not in the vocabulary, i.e. are `` tokens\n", - "- convert the numericalized tokens into a tensor and add a batch dimension\n", - "- feed the tensor into the model\n", - "- get the predictions over the sentence\n", - "- convert the predictions into readable tags\n", - "\n", - "As well as returning the tokens and tags, it also returns which tokens were `` tokens." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def tag_sentence(model, device, sentence, text_field, tag_field):\n", - " \n", - " model.eval()\n", - " \n", - " if isinstance(sentence, str):\n", - " nlp = spacy.load('en')\n", - " tokens = [token.text for token in nlp(sentence)]\n", - " else:\n", - " tokens = [token for token in sentence]\n", - "\n", - " if text_field.lower:\n", - " tokens = [t.lower() for t in tokens]\n", - " \n", - " numericalized_tokens = [text_field.vocab.stoi[t] for t in tokens]\n", - "\n", - " unk_idx = text_field.vocab.stoi[text_field.unk_token]\n", - " \n", - " unks = [t for t, n in zip(tokens, numericalized_tokens) if n == unk_idx]\n", - " \n", - " token_tensor = torch.LongTensor(numericalized_tokens)\n", - " \n", - " token_tensor = token_tensor.unsqueeze(-1).to(device)\n", - " \n", - " predictions = model(token_tensor)\n", - " \n", - " top_predictions = predictions.argmax(-1)\n", - " \n", - " predicted_tags = [tag_field.vocab.itos[t.item()] for t in top_predictions]\n", - " \n", - " return tokens, predicted_tags, unks" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll get an already tokenized example from the training set and test our model's performance." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "example_index = 1\n", - "\n", - "sentence = vars(train_data.examples[example_index])['text']\n", - "actual_tags = vars(train_data.examples[example_index])['udtags']\n", - "\n", - "print(sentence)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can then use our `tag_sentence` function to get the tags. Notice how the tokens referring to subject of the sentence, the \"respected cleric\", are both `` tokens!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tokens, pred_tags, unks = tag_sentence(model, \n", - " device, \n", - " sentence, \n", - " TEXT, \n", - " UD_TAGS)\n", - "\n", - "print(unks)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can then check how well it did. Surprisingly, it got every token correct, including the two that were unknown tokens!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Pred. Tag\\tActual Tag\\tCorrect?\\tToken\\n\")\n", - "\n", - "for token, pred_tag, actual_tag in zip(tokens, pred_tags, actual_tags):\n", - " correct = '✔' if pred_tag == actual_tag else '✘'\n", - " print(f\"{pred_tag}\\t\\t{actual_tag}\\t\\t{correct}\\t\\t{token}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's now make up our own sentence and see how well the model does.\n", - "\n", - "Our example sentence below has every token within the model's vocabulary." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sentence = 'The Queen will deliver a speech about the conflict in North Korea at 1pm tomorrow.'\n", - "\n", - "tokens, tags, unks = tag_sentence(model, \n", - " device, \n", - " sentence, \n", - " TEXT, \n", - " UD_TAGS)\n", - "\n", - "print(unks)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Looking at the sentence it seems like it gave sensible tags to every token!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Pred. Tag\\tToken\\n\")\n", - "\n", - "for token, tag in zip(tokens, tags):\n", - " print(f\"{tag}\\t\\t{token}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We've now seen how to implement PoS tagging with PyTorch and TorchText! \n", - "\n", - "The BiLSTM isn't a state-of-the-art model, in terms of performance, but is a strong baseline for PoS tasks and is a good tool to have in your arsenal." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Going deeper\n", - "What if we could combine word-level and char-level approaches? \n", - "![title](https://i.postimg.cc/tT9hsBfj/ive-put-an-rnn-in-your-rnn-so-you-can-train-an-rnn-on-every-step-of-your-rnn-training-loop.jpg)\n", - "\n", - "\n", - "Actually, we can. Let's use LSTM or GRU to generate embedding for every word on char-level.\n", - "![title](https://guillaumegenthial.github.io/assets/char_representation.png)\n", - "*Image source: https://guillaumegenthial.github.io/sequence-tagging-with-tensorflow.html*\n", - "\n", - "![title](https://guillaumegenthial.github.io/assets/bi-lstm.png)\n", - "*Image source: https://guillaumegenthial.github.io/sequence-tagging-with-tensorflow.html*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To do that we need to make few adjustments to the code above" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Now lets try both word and character embeddings\n", - "WORD = data.Field(lower = True)\n", - "UD_TAG = data.Field(unk_token = None)\n", - "PTB_TAG = data.Field(unk_token = None)\n", - "\n", - "# We'll use NestedField to tokenize each word into list of chars\n", - "CHAR_NESTING = data.Field(tokenize=list, init_token=\"\", eos_token=\"\")\n", - "CHAR = data.NestedField(CHAR_NESTING)#, init_token=\"\", eos_token=\"\")\n", - "\n", - "fields = [(('word', 'char'), (WORD, CHAR)), ('udtag', UD_TAG), ('ptbtag', PTB_TAG)]\n", - "train_data, valid_data, test_data = datasets.UDPOS.splits(fields)\n", - "# train, val, test = datasets.UDPOS.splits(fields=fields)\n", - "\n", - "print(train_data.fields)\n", - "print(len(train_data))\n", - "print(vars(train_data[0]))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "WORD.build_vocab(\n", - " train_data,\n", - " min_freq = MIN_FREQ,\n", - " vectors=\"glove.6B.100d\",\n", - " unk_init = torch.Tensor.normal_\n", - ")\n", - "\n", - "\n", - "CHAR.build_vocab(train_data)\n", - "UD_TAG.build_vocab(train_data)\n", - "PTB_TAG.build_vocab(train_data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(f\"Unique tokens in WORD vocabulary: {len(WORD.vocab)}\")\n", - "print(f\"Unique tokens in CHAR vocabulary: {len(CHAR.vocab)}\")\n", - "print(f\"Unique tokens in UD_TAG vocabulary: {len(UD_TAG.vocab)}\")\n", - "print(f\"Unique tokens in PTB_TAG vocabulary: {len(PTB_TAG.vocab)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "BATCH_SIZE = 64\n", - "\n", - "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n", - "\n", - "train_iterator, valid_iterator, test_iterator = data.BucketIterator.splits(\n", - " (train_data, valid_data, test_data), \n", - " batch_size = BATCH_SIZE,\n", - " device = device)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "batch = next(iter(train_iterator))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "text = batch.word\n", - "chars = batch.char\n", - "tags = batch.udtag\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class BiLSTMPOSTaggerWithChars(nn.Module):\n", - " def __init__(self, \n", - " word_input_dim, \n", - " word_embedding_dim,\n", - " char_input_dim,\n", - " char_embedding_dim,\n", - " char_hidden_dim,\n", - " hidden_dim,\n", - " output_dim, \n", - " n_layers, \n", - " bidirectional, \n", - " dropout, \n", - " pad_idx):\n", - " \n", - " super().__init__()\n", - " \n", - " self.char_embedding = # YOUR CODE HERE\n", - " self.char_gru = # YOUR CODE HERE\n", - " \n", - " self.word_embedding = nn.Embedding(word_input_dim, word_embedding_dim, padding_idx = pad_idx)\n", - " self.lstm = nn.LSTM(word_embedding_dim + # YOUR CODE HERE, \n", - " hidden_dim, \n", - " num_layers = n_layers, \n", - " bidirectional = bidirectional,\n", - " dropout = dropout if n_layers > 1 else 0)\n", - " \n", - " self.fc = nn.Linear(hidden_dim * 2 if bidirectional else hidden_dim, output_dim)\n", - " \n", - " self.dropout = nn.Dropout(dropout)\n", - " \n", - " def forward(self, text, chars):\n", - "\n", - " #text = [sent len, batch size]\n", - " \n", - " #pass text through embedding layer\n", - " embedded = self.dropout(self.word_embedding(text))\n", - " #embedded = [sent len, batch size, emb dim]\n", - " \n", - " chars_embedded = # YOUR CODE HERE\n", - " hid_from_chars = # YOUR CODE HERE\n", - " \n", - " embedded_with_chars = torch.cat([embedded, hid_from_chars], dim=2)\n", - " \n", - " \n", - " #pass embeddings into LSTM\n", - " outputs, (hidden, cell) = self.lstm(embedded_with_chars)\n", - "# outputs, (hidden, cell) = self.lstm(hid)\n", - "\n", - " \n", - " #outputs holds the backward and forward hidden states in the final layer\n", - " #hidden and cell are the backward and forward hidden and cell states at the final time-step\n", - " \n", - " #output = [sent len, batch size, hid dim * n directions]\n", - " #hidden/cell = [n layers * n directions, batch size, hid dim]\n", - " \n", - " #we use our outputs to make a prediction of what the tag should be\n", - " predictions = self.fc(self.dropout(outputs))\n", - " \n", - " #predictions = [sent len, batch size, output dim]\n", - " \n", - " return predictions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "INPUT_DIM = len(WORD.vocab)\n", - "EMBEDDING_DIM = 100\n", - "HIDDEN_DIM = 160\n", - "CHAR_INPUT_DIM = 112\n", - "CHAR_EMBEDDING_DIM = 30\n", - "CHAR_HIDDEN_DIM = 30\n", - "OUTPUT_DIM = len(UD_TAGS.vocab)\n", - "N_LAYERS = 2\n", - "BIDIRECTIONAL = True\n", - "DROPOUT = 0.25\n", - "PAD_IDX = TEXT.vocab.stoi[TEXT.pad_token]\n", - "\n", - "model = BiLSTMPOSTaggerWithChars(\n", - " INPUT_DIM, \n", - " EMBEDDING_DIM,\n", - " CHAR_INPUT_DIM,\n", - " CHAR_EMBEDDING_DIM,\n", - " CHAR_HIDDEN_DIM,\n", - " HIDDEN_DIM, \n", - " OUTPUT_DIM, \n", - " N_LAYERS, \n", - " BIDIRECTIONAL, \n", - " DROPOUT, \n", - " PAD_IDX\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Congratulations, you've got LSTM which relies on GRU output on each step.**\n", - "\n", - "Now we need only to train it. Same actions, very small adjustments." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def init_weights(m):\n", - " for name, param in m.named_parameters():\n", - " nn.init.normal_(param.data, mean = 0, std = 0.1)\n", - " \n", - "model.apply(init_weights)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def count_parameters(model):\n", - " return sum(p.numel() for p in model.parameters() if p.requires_grad)\n", - "\n", - "print(f'The model has {count_parameters(model):,} trainable parameters')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pretrained_embeddings = TEXT.vocab.vectors\n", - "\n", - "print(pretrained_embeddings.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.word_embedding.weight.data.copy_(pretrained_embeddings)\n", - "model.word_embedding.weight.data[PAD_IDX] = torch.zeros(EMBEDDING_DIM)\n", - "\n", - "print(model.word_embedding.weight.data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "optimizer = optim.Adam(model.parameters())\n", - "\n", - "TAG_PAD_IDX = UD_TAGS.vocab.stoi[UD_TAGS.pad_token]\n", - "\n", - "criterion = nn.CrossEntropyLoss(ignore_index = TAG_PAD_IDX)\n", - "\n", - "model = model.to(device)\n", - "criterion = criterion.to(device)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def train(model, iterator, optimizer, criterion, tag_pad_idx):\n", - " \n", - " epoch_loss = 0\n", - " epoch_acc = 0\n", - " \n", - " model.train()\n", - " \n", - " for batch in iterator:\n", - " \n", - " text = batch.word\n", - " chars = batch.char\n", - " tags = batch.udtag\n", - " \n", - " optimizer.zero_grad()\n", - " \n", - " #text = [sent len, batch size]\n", - " \n", - " predictions = model(text, chars)\n", - " \n", - " #predictions = [sent len, batch size, output dim]\n", - " #tags = [sent len, batch size]\n", - " \n", - " predictions = predictions.view(-1, predictions.shape[-1])\n", - " tags = tags.view(-1)\n", - " \n", - " #predictions = [sent len * batch size, output dim]\n", - " #tags = [sent len * batch size]\n", - " \n", - " loss = criterion(predictions, tags)\n", - " \n", - " acc = categorical_accuracy(predictions, tags, tag_pad_idx)\n", - " \n", - " loss.backward()\n", - " \n", - " optimizer.step()\n", - " \n", - " epoch_loss += loss.item()\n", - " epoch_acc += acc.item()\n", - " \n", - " return epoch_loss / len(iterator), epoch_acc / len(iterator)\n", - "\n", - "\n", - "def evaluate(model, iterator, criterion, tag_pad_idx):\n", - " \n", - " epoch_loss = 0\n", - " epoch_acc = 0\n", - " \n", - " model.eval()\n", - " \n", - " with torch.no_grad():\n", - " \n", - " for batch in iterator:\n", - "\n", - " text = batch.word\n", - " chars = batch.char\n", - " tags = batch.udtag\n", - " \n", - " predictions = model(text, chars)\n", - " \n", - " predictions = predictions.view(-1, predictions.shape[-1])\n", - " tags = tags.view(-1)\n", - " \n", - " loss = criterion(predictions, tags)\n", - " \n", - " acc = categorical_accuracy(predictions, tags, tag_pad_idx)\n", - "\n", - " epoch_loss += loss.item()\n", - " epoch_acc += acc.item()\n", - " \n", - " return epoch_loss / len(iterator), epoch_acc / len(iterator)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "N_EPOCHS = 15\n", - "\n", - "best_valid_loss = float('inf')\n", - "\n", - "for epoch in range(N_EPOCHS):\n", - "\n", - " start_time = time.time()\n", - " \n", - " train_loss, train_acc = train(model, train_iterator, optimizer, criterion, TAG_PAD_IDX)\n", - " valid_loss, valid_acc = evaluate(model, valid_iterator, criterion, TAG_PAD_IDX)\n", - " \n", - " end_time = time.time()\n", - "\n", - " epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n", - " \n", - " if valid_loss < best_valid_loss:\n", - " best_valid_loss = valid_loss\n", - " torch.save(model.state_dict(), 'tut2-model.pt')\n", - " \n", - " print(f'Epoch: {epoch+1:02} | Epoch Time: {epoch_mins}m {epoch_secs}s')\n", - " print(f'\\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')\n", - " print(f'\\t Val. Loss: {valid_loss:.3f} | Val. Acc: {valid_acc*100:.2f}%')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Let's take a look at the model from the last epoch\n", - "test_loss, test_acc = evaluate(model, test_iterator, criterion, TAG_PAD_IDX)\n", - "\n", - "print(f'Test Loss: {test_loss:.3f} | Test Acc: {test_acc*100:.2f}%')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# And at the best checkpoint (based on validation score)\n", - "model.load_state_dict(torch.load('tut2-model.pt'))\n", - "\n", - "test_loss, test_acc = evaluate(model, test_iterator, criterion, TAG_PAD_IDX)\n", - "\n", - "print(f'Test Loss: {test_loss:.3f} | Test Acc: {test_acc*100:.2f}%')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Py3 research env", - "language": "python", - "name": "py3_research" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/week05_transformer_pos_tagging/week05_bilstm_for_pos_tagging__completed.ipynb b/week05_transformer_pos_tagging/week05_bilstm_for_pos_tagging__completed.ipynb deleted file mode 100644 index cd5e9a7..0000000 --- a/week05_transformer_pos_tagging/week05_bilstm_for_pos_tagging__completed.ipynb +++ /dev/null @@ -1,2358 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Practice: BiLSTM for PoS Tagging\n", - "*This notebook is based on [open-source implementation](https://github.com/bentrevett/pytorch-pos-tagging) of PoS Tagging in PyTorch.*\n", - "\n", - "### Introduction\n", - "\n", - "In this series we'll be building a machine learning model that produces an output for every element in an input sequence, using PyTorch and TorchText. Specifically, we will be inputting a sequence of text and the model will output a part-of-speech (PoS) tag for each token in the input text. This can also be used for named entity recognition (NER), where the output for each token will be what type of entity, if any, the token is.\n", - "\n", - "In this notebook, we'll be implementing a multi-layer bi-directional LSTM (BiLSTM) to predict PoS tags using the Universal Dependencies English Web Treebank (UDPOS) dataset.\n", - "\n", - "### Preparing Data\n", - "\n", - "First, let's import the necessary Python modules." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", - "\n", - "from torchtext.legacy import data\n", - "from torchtext.legacy import datasets\n", - "\n", - "import spacy\n", - "import numpy as np\n", - "\n", - "import time\n", - "import random" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we'll set the random seeds for reproducability." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "SEED = 1234\n", - "\n", - "random.seed(SEED)\n", - "np.random.seed(SEED)\n", - "torch.manual_seed(SEED)\n", - "torch.backends.cudnn.deterministic = True" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One of the key parts of TorchText is the `Field`. The `Field` handles how your dataset is processed.\n", - "\n", - "Our `TEXT` field handles how the text that we need to tag is dealt with. All we do here is set `lower = True` which lowercases all of the text.\n", - "\n", - "Next we'll define the `Fields` for the tags. This dataset actually has two different sets of tags, [universal dependency (UD) tags](https://universaldependencies.org/u/pos/) and [Penn Treebank (PTB) tags](https://www.sketchengine.eu/penn-treebank-tagset/). We'll only train our model on the UD tags, but will load the PTB tags to show how they could be used instead.\n", - "\n", - "`UD_TAGS` handles how the UD tags should be handled. Our `TEXT` vocabulary - which we'll build later - will have *unknown* tokens in it, i.e. tokens that are not within our vocabulary. However, we won't have unknown tags as we are dealing with a finite set of possible tags. TorchText `Fields` initialize a default unknown token, ``, which we remove by setting `unk_token = None`.\n", - "\n", - "`PTB_TAGS` does the same as `UD_TAGS`, but handles the PTB tags instead." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "TEXT = data.Field(lower = True)\n", - "UD_TAGS = data.Field(unk_token = None)\n", - "PTB_TAGS = data.Field(unk_token = None)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We then define `fields`, which handles passing our fields to the dataset.\n", - "\n", - "Note that order matters, if you only wanted to load the PTB tags your field would be:\n", - "\n", - "```\n", - "fields = ((\"text\", TEXT), (None, None), (\"ptbtags\", PTB_TAGS))\n", - "```\n", - "\n", - "Where `None` tells TorchText to not load those tags." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "fields = ((\"text\", TEXT), (\"udtags\", UD_TAGS), (\"ptbtags\", PTB_TAGS))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we load the UDPOS dataset using our defined fields." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "downloading en-ud-v2.zip\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "en-ud-v2.zip: 100%|██████████| 688k/688k [00:00<00:00, 1.02MB/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "extracting\n" - ] - } - ], - "source": [ - "train_data, valid_data, test_data = datasets.UDPOS.splits(fields)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can check how many examples are in each section of the dataset by checking their length." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of training examples: 12543\n", - "Number of validation examples: 2002\n", - "Number of testing examples: 2077\n" - ] - } - ], - "source": [ - "print(f\"Number of training examples: {len(train_data)}\")\n", - "print(f\"Number of validation examples: {len(valid_data)}\")\n", - "print(f\"Number of testing examples: {len(test_data)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's print out an example:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'text': ['i', 'will', 'never', 'return', 'there', 'again', '(', 'and', 'now', 'have', 'some', 'serious', 'doubts', 'about', 'the', 'quality', 'of', 'work', 'they', 'actually', 'performed', 'on', 'my', 'car', ')', '.'], 'udtags': ['PRON', 'AUX', 'ADV', 'VERB', 'ADV', 'ADV', 'PUNCT', 'CCONJ', 'ADV', 'VERB', 'DET', 'ADJ', 'NOUN', 'ADP', 'DET', 'NOUN', 'ADP', 'NOUN', 'PRON', 'ADV', 'VERB', 'ADP', 'PRON', 'NOUN', 'PUNCT', 'PUNCT'], 'ptbtags': ['PRP', 'MD', 'RB', 'VB', 'RB', 'RB', '-LRB-', 'CC', 'RB', 'VBP', 'DT', 'JJ', 'NNS', 'IN', 'DT', 'NN', 'IN', 'NN', 'PRP', 'RB', 'VBD', 'IN', 'PRP$', 'NN', '-RRB-', '.']}\n" - ] - } - ], - "source": [ - "print(vars(train_data.examples[-1]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also view the text and tags separately:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['i', 'will', 'never', 'return', 'there', 'again', '(', 'and', 'now', 'have', 'some', 'serious', 'doubts', 'about', 'the', 'quality', 'of', 'work', 'they', 'actually', 'performed', 'on', 'my', 'car', ')', '.']\n" - ] - } - ], - "source": [ - "print(vars(train_data.examples[-1])['text'])" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['PRON', 'AUX', 'ADV', 'VERB', 'ADV', 'ADV', 'PUNCT', 'CCONJ', 'ADV', 'VERB', 'DET', 'ADJ', 'NOUN', 'ADP', 'DET', 'NOUN', 'ADP', 'NOUN', 'PRON', 'ADV', 'VERB', 'ADP', 'PRON', 'NOUN', 'PUNCT', 'PUNCT']\n" - ] - } - ], - "source": [ - "print(vars(train_data.examples[-1])['udtags'])" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['PRP', 'MD', 'RB', 'VB', 'RB', 'RB', '-LRB-', 'CC', 'RB', 'VBP', 'DT', 'JJ', 'NNS', 'IN', 'DT', 'NN', 'IN', 'NN', 'PRP', 'RB', 'VBD', 'IN', 'PRP$', 'NN', '-RRB-', '.']\n" - ] - } - ], - "source": [ - "print(vars(train_data.examples[-1])['ptbtags'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we'll build the vocabulary - a mapping of tokens to integers. \n", - "\n", - "We want some unknown tokens within our dataset in order to replicate how this model would be used in real life, so we set the `min_freq` to 2 which means only tokens that appear twice in the training set will be added to the vocabulary and the rest will be replaced by `` tokens.\n", - "\n", - "We also load the [GloVe](https://nlp.stanford.edu/projects/glove/) pre-trained token embeddings. Specifically, the 100-dimensional embeddings that have been trained on 6 billion tokens. Using pre-trained embeddings usually leads to improved performance - although admittedly the dataset used in this tutorial is too small to take advantage of the pre-trained embeddings. \n", - "\n", - "`unk_init` is used to initialize the token embeddings which are not in the pre-trained embedding vocabulary. By default this sets those embeddings to zeros, however it is better to not have them all initialized to the same value, so we initialize them from a Normal/Gaussian distribution.\n", - "\n", - "These pre-trained vectors are now loaded into our vocabulary and we will initialize our model with these values later." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|█████████▉| 398413/400000 [00:16<00:00, 24234.96it/s]" - ] - } - ], - "source": [ - "MIN_FREQ = 2\n", - "\n", - "TEXT.build_vocab(train_data, \n", - " min_freq = MIN_FREQ,\n", - " vectors = \"glove.6B.100d\",\n", - " unk_init = torch.Tensor.normal_)\n", - "\n", - "\n", - "UD_TAGS.build_vocab(train_data)\n", - "PTB_TAGS.build_vocab(train_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can check how many tokens and tags are in our vocabulary by getting their length:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Unique tokens in TEXT vocabulary: 8866\n", - "Unique tokens in UD_TAG vocabulary: 18\n", - "Unique tokens in PTB_TAG vocabulary: 51\n" - ] - } - ], - "source": [ - "print(f\"Unique tokens in TEXT vocabulary: {len(TEXT.vocab)}\")\n", - "print(f\"Unique tokens in UD_TAG vocabulary: {len(UD_TAGS.vocab)}\")\n", - "print(f\"Unique tokens in PTB_TAG vocabulary: {len(PTB_TAGS.vocab)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Exploring the vocabulary, we can check the most common tokens within our texts:" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[('the', 9076), ('.', 8640), (',', 7021), ('to', 5137), ('and', 5002), ('a', 3782), ('of', 3622), ('i', 3379), ('in', 3112), ('is', 2239), ('you', 2156), ('that', 2036), ('it', 1850), ('for', 1842), ('-', 1426), ('have', 1359), ('\"', 1296), ('on', 1273), ('was', 1244), ('with', 1216)]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|█████████▉| 398413/400000 [00:30<00:00, 24234.96it/s]" - ] - } - ], - "source": [ - "print(TEXT.vocab.freqs.most_common(20))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see the vocabularies for both of our tags:" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['', 'NOUN', 'PUNCT', 'VERB', 'PRON', 'ADP', 'DET', 'PROPN', 'ADJ', 'AUX', 'ADV', 'CCONJ', 'PART', 'NUM', 'SCONJ', 'X', 'INTJ', 'SYM']\n" - ] - } - ], - "source": [ - "print(UD_TAGS.vocab.itos)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['', 'NN', 'IN', 'DT', 'NNP', 'PRP', 'JJ', 'RB', '.', 'VB', 'NNS', ',', 'CC', 'VBD', 'VBP', 'VBZ', 'CD', 'VBN', 'VBG', 'MD', 'TO', 'PRP$', '-RRB-', '-LRB-', 'WDT', 'WRB', ':', '``', \"''\", 'WP', 'RP', 'UH', 'POS', 'HYPH', 'JJR', 'NNPS', 'JJS', 'EX', 'NFP', 'GW', 'ADD', 'RBR', '$', 'PDT', 'RBS', 'SYM', 'LS', 'FW', 'AFX', 'WP$', 'XX']\n" - ] - } - ], - "source": [ - "print(PTB_TAGS.vocab.itos)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also see how many of each tag are in our vocabulary:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[('NOUN', 34781), ('PUNCT', 23679), ('VERB', 23081), ('PRON', 18577), ('ADP', 17638), ('DET', 16285), ('PROPN', 12946), ('ADJ', 12477), ('AUX', 12343), ('ADV', 10548), ('CCONJ', 6707), ('PART', 5567), ('NUM', 3999), ('SCONJ', 3843), ('X', 847), ('INTJ', 688), ('SYM', 599)]\n" - ] - } - ], - "source": [ - "print(UD_TAGS.vocab.freqs.most_common())" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[('NN', 26915), ('IN', 20724), ('DT', 16817), ('NNP', 12449), ('PRP', 12193), ('JJ', 11591), ('RB', 10831), ('.', 10317), ('VB', 9476), ('NNS', 8438), (',', 8062), ('CC', 6706), ('VBD', 5402), ('VBP', 5374), ('VBZ', 4578), ('CD', 3998), ('VBN', 3967), ('VBG', 3330), ('MD', 3294), ('TO', 3286), ('PRP$', 3068), ('-RRB-', 1008), ('-LRB-', 973), ('WDT', 948), ('WRB', 869), (':', 866), ('``', 813), (\"''\", 785), ('WP', 760), ('RP', 755), ('UH', 689), ('POS', 684), ('HYPH', 664), ('JJR', 503), ('NNPS', 498), ('JJS', 383), ('EX', 359), ('NFP', 338), ('GW', 294), ('ADD', 292), ('RBR', 276), ('$', 258), ('PDT', 175), ('RBS', 169), ('SYM', 156), ('LS', 117), ('FW', 93), ('AFX', 48), ('WP$', 15), ('XX', 1)]\n" - ] - } - ], - "source": [ - "print(PTB_TAGS.vocab.freqs.most_common())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also view how common each of the tags are within the training set:" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "def tag_percentage(tag_counts):\n", - " \n", - " total_count = sum([count for tag, count in tag_counts])\n", - " \n", - " tag_counts_percentages = [(tag, count, count/total_count) for tag, count in tag_counts]\n", - " \n", - " return tag_counts_percentages" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tag\t\tCount\t\tPercentage\n", - "\n", - "NOUN\t\t34781\t\t17.0%\n", - "PUNCT\t\t23679\t\t11.6%\n", - "VERB\t\t23081\t\t11.3%\n", - "PRON\t\t18577\t\t 9.1%\n", - "ADP\t\t17638\t\t 8.6%\n", - "DET\t\t16285\t\t 8.0%\n", - "PROPN\t\t12946\t\t 6.3%\n", - "ADJ\t\t12477\t\t 6.1%\n", - "AUX\t\t12343\t\t 6.0%\n", - "ADV\t\t10548\t\t 5.2%\n", - "CCONJ\t\t6707\t\t 3.3%\n", - "PART\t\t5567\t\t 2.7%\n", - "NUM\t\t3999\t\t 2.0%\n", - "SCONJ\t\t3843\t\t 1.9%\n", - "X\t\t847\t\t 0.4%\n", - "INTJ\t\t688\t\t 0.3%\n", - "SYM\t\t599\t\t 0.3%\n" - ] - } - ], - "source": [ - "print(\"Tag\\t\\tCount\\t\\tPercentage\\n\")\n", - "\n", - "for tag, count, percent in tag_percentage(UD_TAGS.vocab.freqs.most_common()):\n", - " print(f\"{tag}\\t\\t{count}\\t\\t{percent*100:4.1f}%\")" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tag\t\tCount\t\tPercentage\n", - "\n", - "NN\t\t26915\t\t13.2%\n", - "IN\t\t20724\t\t10.1%\n", - "DT\t\t16817\t\t 8.2%\n", - "NNP\t\t12449\t\t 6.1%\n", - "PRP\t\t12193\t\t 6.0%\n", - "JJ\t\t11591\t\t 5.7%\n", - "RB\t\t10831\t\t 5.3%\n", - ".\t\t10317\t\t 5.0%\n", - "VB\t\t9476\t\t 4.6%\n", - "NNS\t\t8438\t\t 4.1%\n", - ",\t\t8062\t\t 3.9%\n", - "CC\t\t6706\t\t 3.3%\n", - "VBD\t\t5402\t\t 2.6%\n", - "VBP\t\t5374\t\t 2.6%\n", - "VBZ\t\t4578\t\t 2.2%\n", - "CD\t\t3998\t\t 2.0%\n", - "VBN\t\t3967\t\t 1.9%\n", - "VBG\t\t3330\t\t 1.6%\n", - "MD\t\t3294\t\t 1.6%\n", - "TO\t\t3286\t\t 1.6%\n", - "PRP$\t\t3068\t\t 1.5%\n", - "-RRB-\t\t1008\t\t 0.5%\n", - "-LRB-\t\t973\t\t 0.5%\n", - "WDT\t\t948\t\t 0.5%\n", - "WRB\t\t869\t\t 0.4%\n", - ":\t\t866\t\t 0.4%\n", - "``\t\t813\t\t 0.4%\n", - "''\t\t785\t\t 0.4%\n", - "WP\t\t760\t\t 0.4%\n", - "RP\t\t755\t\t 0.4%\n", - "UH\t\t689\t\t 0.3%\n", - "POS\t\t684\t\t 0.3%\n", - "HYPH\t\t664\t\t 0.3%\n", - "JJR\t\t503\t\t 0.2%\n", - "NNPS\t\t498\t\t 0.2%\n", - "JJS\t\t383\t\t 0.2%\n", - "EX\t\t359\t\t 0.2%\n", - "NFP\t\t338\t\t 0.2%\n", - "GW\t\t294\t\t 0.1%\n", - "ADD\t\t292\t\t 0.1%\n", - "RBR\t\t276\t\t 0.1%\n", - "$\t\t258\t\t 0.1%\n", - "PDT\t\t175\t\t 0.1%\n", - "RBS\t\t169\t\t 0.1%\n", - "SYM\t\t156\t\t 0.1%\n", - "LS\t\t117\t\t 0.1%\n", - "FW\t\t93\t\t 0.0%\n", - "AFX\t\t48\t\t 0.0%\n", - "WP$\t\t15\t\t 0.0%\n", - "XX\t\t1\t\t 0.0%\n" - ] - } - ], - "source": [ - "print(\"Tag\\t\\tCount\\t\\tPercentage\\n\")\n", - "\n", - "for tag, count, percent in tag_percentage(PTB_TAGS.vocab.freqs.most_common()):\n", - " print(f\"{tag}\\t\\t{count}\\t\\t{percent*100:4.1f}%\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The final part of data preparation is handling the iterator. \n", - "\n", - "This will be iterated over to return batches of data to process. Here, we set the batch size and the `device` - which is used to place the batches of tensors on our GPU, if we have one. " - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "BATCH_SIZE = 128\n", - "\n", - "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n", - "\n", - "train_iterator, valid_iterator, test_iterator = data.BucketIterator.splits(\n", - " (train_data, valid_data, test_data), \n", - " batch_size = BATCH_SIZE,\n", - " device = device)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Building the Model\n", - "\n", - "Next up, we define our model - a multi-layer bi-directional LSTM. The image below shows a simplified version of the model with only one LSTM layer and omitting the LSTM's cell state for clarity.\n", - "\n", - "![](assets/pos-bidirectional-lstm.png)\n", - "\n", - "The model takes in a sequence of tokens, $X = \\{x_1, x_2,...,x_T\\}$, passes them through an embedding layer, $e$, to get the token embeddings, $e(X) = \\{e(x_1), e(x_2), ..., e(x_T)\\}$.\n", - "\n", - "These embeddings are processed - one per time-step - by the forward and backward LSTMs. The forward LSTM processes the sequence from left-to-right, whilst the backward LSTM processes the sequence right-to-left, i.e. the first input to the forward LSTM is $x_1$ and the first input to the backward LSTM is $x_T$. \n", - "\n", - "The LSTMs also take in the the hidden, $h$, and cell, $c$, states from the previous time-step\n", - "\n", - "$$h^{\\rightarrow}_t = \\text{LSTM}^{\\rightarrow}(e(x^{\\rightarrow}_t), h^{\\rightarrow}_{t-1}, c^{\\rightarrow}_{t-1})$$\n", - "$$h^{\\leftarrow}_t=\\text{LSTM}^{\\leftarrow}(e(x^{\\leftarrow}_t), h^{\\leftarrow}_{t-1}, c^{\\leftarrow}_{t-1})$$\n", - "\n", - "After the whole sequence has been processed, the hidden and cell states are then passed to the next layer of the LSTM.\n", - "\n", - "The initial hidden and cell states, $h_0$ and $c_0$, for each direction and layer are initialized to a tensor full of zeros.\n", - "\n", - "We then concatenate both the forward and backward hidden states from the final layer of the LSTM, $H = \\{h_1, h_2, ... h_T\\}$, where $h_1 = [h^{\\rightarrow}_1;h^{\\leftarrow}_T]$, $h_2 = [h^{\\rightarrow}_2;h^{\\leftarrow}_{T-1}]$, etc. and pass them through a linear layer, $f$, which is used to make the prediction of which tag applies to this token, $\\hat{y}_t = f(h_t)$.\n", - "\n", - "When training the model, we will compare our predicted tags, $\\hat{Y}$ against the actual tags, $Y$, to calculate a loss, the gradients w.r.t. that loss, and then update our parameters.\n", - "\n", - "We implement the model detailed above in the `BiLSTMPOSTagger` class.\n", - "\n", - "`nn.Embedding` is an embedding layer and the input dimension should be the size of the input (text) vocabulary. We tell it what the index of the padding token is so it does not update the padding token's embedding entry.\n", - "\n", - "`nn.LSTM` is the LSTM. We apply dropout as regularization between the layers, if we are using more than one.\n", - "\n", - "`nn.Linear` defines the linear layer to make predictions using the LSTM outputs. We double the size of the input if we are using a bi-directional LSTM. The output dimensions should be the size of the tag vocabulary.\n", - "\n", - "We also define a dropout layer with `nn.Dropout`, which we use in the `forward` method to apply dropout to the embeddings and the outputs of the final layer of the LSTM." - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "example_batch = next(iter(train_iterator))" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[1355, 50, 9, ..., 11, 368, 2],\n", - " [ 4, 23, 660, ..., 14, 107, 781],\n", - " [ 69, 79, 9, ..., 844, 48, 20],\n", - " ...,\n", - " [ 1, 1, 1, ..., 1, 1, 1],\n", - " [ 1, 1, 1, ..., 1, 1, 1],\n", - " [ 1, 1, 1, ..., 1, 1, 1]], device='cuda:0')" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "example_batch.text" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "class BiLSTMPOSTagger(nn.Module):\n", - " def __init__(self, \n", - " input_dim, \n", - " embedding_dim, \n", - " hidden_dim, \n", - " output_dim, \n", - " n_layers, \n", - " bidirectional, \n", - " dropout, \n", - " pad_idx):\n", - " \n", - " super().__init__()\n", - " \n", - " self.embedding = nn.Embedding(input_dim, embedding_dim, padding_idx = pad_idx)\n", - " \n", - " self.lstm = nn.LSTM(embedding_dim, \n", - " hidden_dim, \n", - " num_layers = n_layers, \n", - " bidirectional = bidirectional,\n", - " dropout = dropout if n_layers > 1 else 0)\n", - " \n", - " self.fc = nn.Linear(hidden_dim * 2 if bidirectional else hidden_dim, output_dim)\n", - " \n", - " self.dropout = nn.Dropout(dropout)\n", - " \n", - " def forward(self, text):\n", - "\n", - " #text = [sent len, batch size]\n", - " \n", - " #pass text through embedding layer\n", - " embedded = self.dropout(self.embedding(text))\n", - " \n", - " #embedded = [sent len, batch size, emb dim]\n", - " \n", - " #pass embeddings into LSTM\n", - " outputs, (hidden, cell) = self.lstm(embedded)\n", - " \n", - " #outputs holds the backward and forward hidden states in the final layer\n", - " #hidden and cell are the backward and forward hidden and cell states at the final time-step\n", - " \n", - " #output = [sent len, batch size, hid dim * n directions]\n", - " #hidden/cell = [n layers * n directions, batch size, hid dim]\n", - " \n", - " #we use our outputs to make a prediction of what the tag should be\n", - " predictions = self.fc(self.dropout(outputs))\n", - " \n", - " #predictions = [sent len, batch size, output dim]\n", - " \n", - " return predictions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Training the Model\n", - "\n", - "Next, we instantiate the model. We need to ensure the embedding dimensions matches that of the GloVe embeddings we loaded earlier.\n", - "\n", - "The rest of the hyperparmeters have been chosen as sensible defaults, though there may be a combination that performs better on this model and dataset.\n", - "\n", - "The input and output dimensions are taken directly from the lengths of the respective vocabularies. The padding index is obtained using the vocabulary and the `Field` of the text." - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [], - "source": [ - "INPUT_DIM = len(TEXT.vocab)\n", - "EMBEDDING_DIM = 100\n", - "HIDDEN_DIM = 128\n", - "OUTPUT_DIM = len(UD_TAGS.vocab)\n", - "N_LAYERS = 2\n", - "BIDIRECTIONAL = True\n", - "DROPOUT = 0.25\n", - "PAD_IDX = TEXT.vocab.stoi[TEXT.pad_token]\n", - "\n", - "model = BiLSTMPOSTagger(INPUT_DIM, \n", - " EMBEDDING_DIM, \n", - " HIDDEN_DIM, \n", - " OUTPUT_DIM, \n", - " N_LAYERS, \n", - " BIDIRECTIONAL, \n", - " DROPOUT, \n", - " PAD_IDX)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We initialize the weights from a simple Normal distribution. Again, there may be a better initialization scheme for this model and dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "BiLSTMPOSTagger(\n", - " (embedding): Embedding(8866, 100, padding_idx=1)\n", - " (lstm): LSTM(100, 128, num_layers=2, dropout=0.25, bidirectional=True)\n", - " (fc): Linear(in_features=256, out_features=18, bias=True)\n", - " (dropout): Dropout(p=0.25, inplace=False)\n", - ")" - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def init_weights(m):\n", - " for name, param in m.named_parameters():\n", - " nn.init.normal_(param.data, mean = 0, std = 0.1)\n", - " \n", - "model.apply(init_weights)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, a small function to tell us how many parameters are in our model. Useful for comparing different models." - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The model has 1,522,010 trainable parameters\n" - ] - } - ], - "source": [ - "def count_parameters(model):\n", - " return sum(p.numel() for p in model.parameters() if p.requires_grad)\n", - "\n", - "print(f'The model has {count_parameters(model):,} trainable parameters')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll now initialize our model's embedding layer with the pre-trained embedding values we loaded earlier.\n", - "\n", - "This is done by getting them from the vocab's `.vectors` attribute and then performing a `.copy` to overwrite the embedding layer's current weights." - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([8866, 100])\n" - ] - } - ], - "source": [ - "pretrained_embeddings = TEXT.vocab.vectors\n", - "\n", - "print(pretrained_embeddings.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [], - "source": [ - "a = next(model.embedding.parameters())" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([8866, 100])" - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[-0.1117, -0.4966, 0.1631, ..., 1.2647, -0.2753, -0.1325],\n", - " [-0.8555, -0.7208, 1.3755, ..., 0.0825, -1.1314, 0.3997],\n", - " [-0.0382, -0.2449, 0.7281, ..., -0.1459, 0.8278, 0.2706],\n", - " ...,\n", - " [ 0.9261, 2.3049, 0.5502, ..., -0.3492, -0.5298, -0.1577],\n", - " [-0.5972, 0.0471, -0.2406, ..., -0.9446, -0.1126, -0.2260],\n", - " [-0.4809, 2.5629, 0.9530, ..., 0.5278, -0.4588, 0.7294]])" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.embedding.weight.data.copy_(pretrained_embeddings)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It's common to initialize the embedding of the pad token to all zeros. This, along with setting the `padding_idx` in the model's embedding layer, means that the embedding should always output a tensor full of zeros when a pad token is input." - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([[-0.1117, -0.4966, 0.1631, ..., 1.2647, -0.2753, -0.1325],\n", - " [ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],\n", - " [-0.0382, -0.2449, 0.7281, ..., -0.1459, 0.8278, 0.2706],\n", - " ...,\n", - " [ 0.9261, 2.3049, 0.5502, ..., -0.3492, -0.5298, -0.1577],\n", - " [-0.5972, 0.0471, -0.2406, ..., -0.9446, -0.1126, -0.2260],\n", - " [-0.4809, 2.5629, 0.9530, ..., 0.5278, -0.4588, 0.7294]])\n" - ] - } - ], - "source": [ - "model.embedding.weight.data[PAD_IDX] = torch.zeros(EMBEDDING_DIM)\n", - "\n", - "print(model.embedding.weight.data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We then define our optimizer, used to update our parameters w.r.t. their gradients. We use Adam with the default learning rate." - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [], - "source": [ - "optimizer = optim.Adam(model.parameters())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we define our loss function, cross-entropy loss.\n", - "\n", - "Even though we have no `` tokens within our tag vocab, we still have `` tokens. This is because all sentences within a batch need to be the same size. However, we don't want to calculate the loss when the target is a `` token as we aren't training our model to recognize padding tokens.\n", - "\n", - "We handle this by setting the `ignore_index` in our loss function to the index of the padding token in our tag vocabulary." - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [], - "source": [ - "TAG_PAD_IDX = UD_TAGS.vocab.stoi[UD_TAGS.pad_token]\n", - "\n", - "criterion = nn.CrossEntropyLoss(ignore_index = TAG_PAD_IDX)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We then place our model and loss function on our GPU, if we have one." - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": {}, - "outputs": [], - "source": [ - "model = model.to(device)\n", - "criterion = criterion.to(device)" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": {}, - "outputs": [], - "source": [ - "model.embedding.weight.requires_grad = True" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.embedding.weight.requires_grad" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will be using the loss value between our predicted and actual tags to train the network, but ideally we'd like a more interpretable way to see how well our model is doing - accuracy.\n", - "\n", - "The issue is that we don't want to calculate accuracy over the `` tokens as we aren't interested in predicting them.\n", - "\n", - "The function below only calculates accuracy over non-padded tokens. `non_pad_elements` is a tensor containing the indices of the non-pad tokens within an input batch. We then compare the predictions of those elements with the labels to get a count of how many predictions were correct. We then divide this by the number of non-pad elements to get our accuracy value over the batch." - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [], - "source": [ - "def categorical_accuracy(preds, y, tag_pad_idx):\n", - " \"\"\"\n", - " Returns accuracy per batch, i.e. if you get 8/10 right, this returns 0.8, NOT 8\n", - " \"\"\"\n", - " max_preds = preds.argmax(dim = 1, keepdim = True) # get the index of the max probability\n", - " non_pad_elements = (y != tag_pad_idx).nonzero()\n", - " correct = max_preds[non_pad_elements].squeeze(1).eq(y[non_pad_elements])\n", - " return correct.sum() / torch.FloatTensor([y[non_pad_elements].shape[0]])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next is the function that handles training our model.\n", - "\n", - "We first set the model to `train` mode to turn on dropout/batch-norm/etc. (if used). Then we iterate over our iterator, which returns a batch of examples. \n", - "\n", - "For each batch: \n", - "- we zero the gradients over the parameters from the last gradient calculation\n", - "- insert the batch of text into the model to get predictions\n", - "- as PyTorch loss functions cannot handle 3-dimensional predictions we reshape our predictions\n", - "- calculate the loss and accuracy between the predicted tags and actual tags\n", - "- call `backward` to calculate the gradients of the parameters w.r.t. the loss\n", - "- take an optimizer `step` to update the parameters\n", - "- add to the running total of loss and accuracy" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": {}, - "outputs": [], - "source": [ - "def train(model, iterator, optimizer, criterion, tag_pad_idx):\n", - " \n", - " epoch_loss = 0\n", - " epoch_acc = 0\n", - " \n", - " model.train()\n", - " \n", - " for batch in iterator:\n", - " \n", - " text = batch.text\n", - " tags = batch.udtags\n", - " \n", - " optimizer.zero_grad()\n", - " \n", - " #text = [sent len, batch size]\n", - " \n", - " predictions = model(text)\n", - " \n", - " #predictions = [sent len, batch size, output dim]\n", - " #tags = [sent len, batch size]\n", - " \n", - " predictions = predictions.view(-1, predictions.shape[-1])\n", - " tags = tags.view(-1)\n", - " \n", - " #predictions = [sent len * batch size, output dim]\n", - " #tags = [sent len * batch size]\n", - " \n", - " loss = criterion(predictions, tags)\n", - " \n", - " acc = categorical_accuracy(predictions, tags, tag_pad_idx)\n", - " \n", - " loss.backward()\n", - " \n", - " optimizer.step()\n", - " \n", - " epoch_loss += loss.item()\n", - " epoch_acc += acc.item()\n", - " \n", - " return epoch_loss / len(iterator), epoch_acc / len(iterator)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `evaluate` function is similar to the `train` function, except with changes made so we don't update the model's parameters.\n", - "\n", - "`model.eval()` is used to put the model in evaluation mode, so dropout/batch-norm/etc. are turned off. \n", - "\n", - "The iteration loop is also wrapped in `torch.no_grad` to ensure we don't calculate any gradients. We also don't need to call `optimizer.zero_grad()` and `optimizer.step()`." - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": {}, - "outputs": [], - "source": [ - "def evaluate(model, iterator, criterion, tag_pad_idx):\n", - " \n", - " epoch_loss = 0\n", - " epoch_acc = 0\n", - " \n", - " model.eval()\n", - " \n", - " with torch.no_grad():\n", - " \n", - " for batch in iterator:\n", - "\n", - " text = batch.text\n", - " tags = batch.udtags\n", - " \n", - " predictions = model(text)\n", - " \n", - " predictions = predictions.view(-1, predictions.shape[-1])\n", - " tags = tags.view(-1)\n", - " \n", - " loss = criterion(predictions, tags)\n", - " \n", - " acc = categorical_accuracy(predictions, tags, tag_pad_idx)\n", - "\n", - " epoch_loss += loss.item()\n", - " epoch_acc += acc.item()\n", - " \n", - " return epoch_loss / len(iterator), epoch_acc / len(iterator)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we have a small function that tells us how long an epoch takes." - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": {}, - "outputs": [], - "source": [ - "def epoch_time(start_time, end_time):\n", - " elapsed_time = end_time - start_time\n", - " elapsed_mins = int(elapsed_time / 60)\n", - " elapsed_secs = int(elapsed_time - (elapsed_mins * 60))\n", - " return elapsed_mins, elapsed_secs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we train our model!\n", - "\n", - "After each epoch we check if our model has achieved the best validation loss so far. If it has then we save the parameters of this model and we will use these \"best\" parameters to calculate performance over our test set." - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch: 01 | Epoch Time: 0m 2s\n", - "\tTrain Loss: 1.343 | Train Acc: 58.15%\n", - "\t Val. Loss: 0.684 | Val. Acc: 78.59%\n", - "Epoch: 02 | Epoch Time: 0m 2s\n", - "\tTrain Loss: 0.477 | Train Acc: 85.02%\n", - "\t Val. Loss: 0.499 | Val. Acc: 83.89%\n", - "Epoch: 03 | Epoch Time: 0m 2s\n", - "\tTrain Loss: 0.347 | Train Acc: 89.12%\n", - "\t Val. Loss: 0.446 | Val. Acc: 85.16%\n", - "Epoch: 04 | Epoch Time: 0m 2s\n", - "\tTrain Loss: 0.287 | Train Acc: 90.97%\n", - "\t Val. Loss: 0.406 | Val. Acc: 86.60%\n", - "Epoch: 05 | Epoch Time: 0m 2s\n", - "\tTrain Loss: 0.250 | Train Acc: 92.03%\n", - "\t Val. Loss: 0.397 | Val. Acc: 86.90%\n", - "Epoch: 06 | Epoch Time: 0m 2s\n", - "\tTrain Loss: 0.223 | Train Acc: 92.95%\n", - "\t Val. Loss: 0.384 | Val. Acc: 87.23%\n", - "Epoch: 07 | Epoch Time: 0m 2s\n", - "\tTrain Loss: 0.203 | Train Acc: 93.52%\n", - "\t Val. Loss: 0.366 | Val. Acc: 87.35%\n", - "Epoch: 08 | Epoch Time: 0m 2s\n", - "\tTrain Loss: 0.189 | Train Acc: 93.96%\n", - "\t Val. Loss: 0.360 | Val. Acc: 87.65%\n", - "Epoch: 09 | Epoch Time: 0m 2s\n", - "\tTrain Loss: 0.175 | Train Acc: 94.35%\n", - "\t Val. Loss: 0.356 | Val. Acc: 87.71%\n", - "Epoch: 10 | Epoch Time: 0m 2s\n", - "\tTrain Loss: 0.165 | Train Acc: 94.64%\n", - "\t Val. Loss: 0.366 | Val. Acc: 87.55%\n", - "Epoch: 11 | Epoch Time: 0m 2s\n", - "\tTrain Loss: 0.154 | Train Acc: 95.05%\n", - "\t Val. Loss: 0.367 | Val. Acc: 87.82%\n", - "Epoch: 12 | Epoch Time: 0m 2s\n", - "\tTrain Loss: 0.146 | Train Acc: 95.27%\n", - "\t Val. Loss: 0.349 | Val. Acc: 88.14%\n", - "Epoch: 13 | Epoch Time: 0m 2s\n", - "\tTrain Loss: 0.139 | Train Acc: 95.43%\n", - "\t Val. Loss: 0.340 | Val. Acc: 88.35%\n", - "Epoch: 14 | Epoch Time: 0m 2s\n", - "\tTrain Loss: 0.130 | Train Acc: 95.76%\n", - "\t Val. Loss: 0.338 | Val. Acc: 88.56%\n", - "Epoch: 15 | Epoch Time: 0m 2s\n", - "\tTrain Loss: 0.124 | Train Acc: 95.91%\n", - "\t Val. Loss: 0.336 | Val. Acc: 88.58%\n" - ] - } - ], - "source": [ - "N_EPOCHS = 15\n", - "\n", - "best_valid_loss = float('inf')\n", - "\n", - "for epoch in range(N_EPOCHS):\n", - "\n", - " start_time = time.time()\n", - " \n", - " train_loss, train_acc = train(model, train_iterator, optimizer, criterion, TAG_PAD_IDX)\n", - " valid_loss, valid_acc = evaluate(model, valid_iterator, criterion, TAG_PAD_IDX)\n", - " \n", - " end_time = time.time()\n", - "\n", - " epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n", - " \n", - " if valid_loss < best_valid_loss:\n", - " best_valid_loss = valid_loss\n", - " torch.save(model.state_dict(), 'tut1-model.pt')\n", - " \n", - " print(f'Epoch: {epoch+1:02} | Epoch Time: {epoch_mins}m {epoch_secs}s')\n", - " print(f'\\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')\n", - " print(f'\\t Val. Loss: {valid_loss:.3f} | Val. Acc: {valid_acc*100:.2f}%')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We then load our \"best\" parameters and evaluate performance on the test set." - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test Loss: 0.349 | Test Acc: 88.66%\n" - ] - } - ], - "source": [ - "model.load_state_dict(torch.load('tut1-model.pt'))\n", - "\n", - "test_loss, test_acc = evaluate(model, test_iterator, criterion, TAG_PAD_IDX)\n", - "\n", - "print(f'Test Loss: {test_loss:.3f} | Test Acc: {test_acc*100:.2f}%')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Inference\n", - "\n", - "88% accuracy looks pretty good, but let's see our model tag some actual sentences.\n", - "\n", - "We define a `tag_sentence` function that will:\n", - "- put the model into evaluation mode\n", - "- tokenize the sentence with spaCy if it is not a list\n", - "- lowercase the tokens if the `Field` did\n", - "- numericalize the tokens using the vocabulary\n", - "- find out which tokens are not in the vocabulary, i.e. are `` tokens\n", - "- convert the numericalized tokens into a tensor and add a batch dimension\n", - "- feed the tensor into the model\n", - "- get the predictions over the sentence\n", - "- convert the predictions into readable tags\n", - "\n", - "As well as returning the tokens and tags, it also returns which tokens were `` tokens." - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": {}, - "outputs": [], - "source": [ - "def tag_sentence(model, device, sentence, text_field, tag_field):\n", - " \n", - " model.eval()\n", - " \n", - " if isinstance(sentence, str):\n", - " nlp = spacy.load('en')\n", - " tokens = [token.text for token in nlp(sentence)]\n", - " else:\n", - " tokens = [token for token in sentence]\n", - "\n", - " if text_field.lower:\n", - " tokens = [t.lower() for t in tokens]\n", - " \n", - " numericalized_tokens = [text_field.vocab.stoi[t] for t in tokens]\n", - "\n", - " unk_idx = text_field.vocab.stoi[text_field.unk_token]\n", - " \n", - " unks = [t for t, n in zip(tokens, numericalized_tokens) if n == unk_idx]\n", - " \n", - " token_tensor = torch.LongTensor(numericalized_tokens)\n", - " \n", - " token_tensor = token_tensor.unsqueeze(-1).to(device)\n", - " \n", - " predictions = model(token_tensor)\n", - " \n", - " top_predictions = predictions.argmax(-1)\n", - " \n", - " predicted_tags = [tag_field.vocab.itos[t.item()] for t in top_predictions]\n", - " \n", - " return tokens, predicted_tags, unks" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll get an already tokenized example from the training set and test our model's performance." - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['[', 'this', 'killing', 'of', 'a', 'respected', 'cleric', 'will', 'be', 'causing', 'us', 'trouble', 'for', 'years', 'to', 'come', '.', ']']\n" - ] - } - ], - "source": [ - "example_index = 1\n", - "\n", - "sentence = vars(train_data.examples[example_index])['text']\n", - "actual_tags = vars(train_data.examples[example_index])['udtags']\n", - "\n", - "print(sentence)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can then use our `tag_sentence` function to get the tags. Notice how the tokens referring to subject of the sentence, the \"respected cleric\", are both `` tokens!" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['respected', 'cleric']\n" - ] - } - ], - "source": [ - "tokens, pred_tags, unks = tag_sentence(model, \n", - " device, \n", - " sentence, \n", - " TEXT, \n", - " UD_TAGS)\n", - "\n", - "print(unks)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can then check how well it did. Surprisingly, it got every token correct, including the two that were unknown tokens!" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pred. Tag\tActual Tag\tCorrect?\tToken\n", - "\n", - "PUNCT\t\tPUNCT\t\t✔\t\tthe\n", - "DET\t\tDET\t\t✔\t\tqueen\n", - "NOUN\t\tNOUN\t\t✔\t\twill\n", - "ADP\t\tADP\t\t✔\t\tdeliver\n", - "DET\t\tDET\t\t✔\t\ta\n", - "ADJ\t\tADJ\t\t✔\t\tspeech\n", - "NOUN\t\tNOUN\t\t✔\t\tabout\n", - "AUX\t\tAUX\t\t✔\t\tthe\n", - "AUX\t\tAUX\t\t✔\t\tconflict\n", - "VERB\t\tVERB\t\t✔\t\tin\n", - "PRON\t\tPRON\t\t✔\t\tnorth\n", - "NOUN\t\tNOUN\t\t✔\t\tkorea\n", - "ADP\t\tADP\t\t✔\t\tat\n", - "NOUN\t\tNOUN\t\t✔\t\t1\n", - "PART\t\tPART\t\t✔\t\tpm\n", - "VERB\t\tVERB\t\t✔\t\ttomorrow\n", - "PUNCT\t\tPUNCT\t\t✔\t\t.\n" - ] - } - ], - "source": [ - "print(\"Pred. Tag\\tActual Tag\\tCorrect?\\tToken\\n\")\n", - "\n", - "for token, pred_tag, actual_tag in zip(tokens, pred_tags, actual_tags):\n", - " correct = '✔' if pred_tag == actual_tag else '✘'\n", - " print(f\"{pred_tag}\\t\\t{actual_tag}\\t\\t{correct}\\t\\t{token}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's now make up our own sentence and see how well the model does.\n", - "\n", - "Our example sentence below has every token within the model's vocabulary." - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[]\n" - ] - } - ], - "source": [ - "sentence = 'The Queen will deliver a speech about the conflict in North Korea at 1pm tomorrow.'\n", - "\n", - "tokens, tags, unks = tag_sentence(model, \n", - " device, \n", - " sentence, \n", - " TEXT, \n", - " UD_TAGS)\n", - "\n", - "print(unks)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Looking at the sentence it seems like it gave sensible tags to every token!" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pred. Tag\tToken\n", - "\n", - "DET\t\tthe\n", - "NOUN\t\tqueen\n", - "AUX\t\twill\n", - "VERB\t\tdeliver\n", - "DET\t\ta\n", - "NOUN\t\tspeech\n", - "ADP\t\tabout\n", - "DET\t\tthe\n", - "NOUN\t\tconflict\n", - "ADP\t\tin\n", - "PROPN\t\tnorth\n", - "PROPN\t\tkorea\n", - "ADP\t\tat\n", - "NUM\t\t1\n", - "NOUN\t\tpm\n", - "NOUN\t\ttomorrow\n", - "PUNCT\t\t.\n" - ] - } - ], - "source": [ - "print(\"Pred. Tag\\tToken\\n\")\n", - "\n", - "for token, tag in zip(tokens, tags):\n", - " print(f\"{tag}\\t\\t{token}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We've now seen how to implement PoS tagging with PyTorch and TorchText! \n", - "\n", - "The BiLSTM isn't a state-of-the-art model, in terms of performance, but is a strong baseline for PoS tasks and is a good tool to have in your arsenal." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Going deeper\n", - "What if we could combine word-level and char-level approaches? \n", - "![title](https://i.postimg.cc/tT9hsBfj/ive-put-an-rnn-in-your-rnn-so-you-can-train-an-rnn-on-every-step-of-your-rnn-training-loop.jpg)\n", - "\n", - "\n", - "Actually, we can. Let's use LSTM or GRU to generate embedding for every word on char-level.\n", - "![title](https://guillaumegenthial.github.io/assets/char_representation.png)\n", - "*Image source: https://guillaumegenthial.github.io/sequence-tagging-with-tensorflow.html*\n", - "\n", - "![title](https://guillaumegenthial.github.io/assets/bi-lstm.png)\n", - "*Image source: https://guillaumegenthial.github.io/sequence-tagging-with-tensorflow.html*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To do that we need to make few adjustments to the code above" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'udtag': , 'ptbtag': , 'word': , 'char': }\n", - "12543\n", - "{'word': ['i', 'will', 'never', 'return', 'there', 'again', '(', 'and', 'now', 'have', 'some', 'serious', 'doubts', 'about', 'the', 'quality', 'of', 'work', 'they', 'actually', 'performed', 'on', 'my', 'car', ')', '.'], 'char': [['I'], ['w', 'i', 'l', 'l'], ['n', 'e', 'v', 'e', 'r'], ['r', 'e', 't', 'u', 'r', 'n'], ['t', 'h', 'e', 'r', 'e'], ['a', 'g', 'a', 'i', 'n'], ['('], ['a', 'n', 'd'], ['n', 'o', 'w'], ['h', 'a', 'v', 'e'], ['s', 'o', 'm', 'e'], ['s', 'e', 'r', 'i', 'o', 'u', 's'], ['d', 'o', 'u', 'b', 't', 's'], ['a', 'b', 'o', 'u', 't'], ['t', 'h', 'e'], ['q', 'u', 'a', 'l', 'i', 't', 'y'], ['o', 'f'], ['w', 'o', 'r', 'k'], ['t', 'h', 'e', 'y'], ['a', 'c', 't', 'u', 'a', 'l', 'l', 'y'], ['p', 'e', 'r', 'f', 'o', 'r', 'm', 'e', 'd'], ['o', 'n'], ['m', 'y'], ['c', 'a', 'r'], [')'], ['.']], 'udtag': ['PRON', 'AUX', 'ADV', 'VERB', 'ADV', 'ADV', 'PUNCT', 'CCONJ', 'ADV', 'VERB', 'DET', 'ADJ', 'NOUN', 'ADP', 'DET', 'NOUN', 'ADP', 'NOUN', 'PRON', 'ADV', 'VERB', 'ADP', 'PRON', 'NOUN', 'PUNCT', 'PUNCT'], 'ptbtag': ['PRP', 'MD', 'RB', 'VB', 'RB', 'RB', '-LRB-', 'CC', 'RB', 'VBP', 'DT', 'JJ', 'NNS', 'IN', 'DT', 'NN', 'IN', 'NN', 'PRP', 'RB', 'VBD', 'IN', 'PRP$', 'NN', '-RRB-', '.']}\n" - ] - } - ], - "source": [ - "# Now lets try both word and character embeddings\n", - "WORD = data.Field(lower = True)\n", - "UD_TAG = data.Field(unk_token = None)\n", - "PTB_TAG = data.Field(unk_token = None)\n", - "\n", - "# We'll use NestedField to tokenize each word into list of chars\n", - "CHAR_NESTING = data.Field(tokenize=list, init_token=\"\", eos_token=\"\")\n", - "CHAR = data.NestedField(CHAR_NESTING)#, init_token=\"\", eos_token=\"\")\n", - "\n", - "fields = [(('word', 'char'), (WORD, CHAR)), ('udtag', UD_TAG), ('ptbtag', PTB_TAG)]\n", - "train_data, valid_data, test_data = datasets.UDPOS.splits(fields)\n", - "# train, val, test = datasets.UDPOS.splits(fields=fields)\n", - "\n", - "print(train_data.fields)\n", - "print(len(train_data))\n", - "print(vars(train_data[-1]))" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": {}, - "outputs": [], - "source": [ - "WORD.build_vocab(\n", - " train_data,\n", - " min_freq = MIN_FREQ,\n", - " vectors=\"glove.6B.100d\",\n", - " unk_init = torch.Tensor.normal_\n", - ")\n", - "\n", - "\n", - "CHAR.build_vocab(train_data)\n", - "UD_TAG.build_vocab(train_data)\n", - "PTB_TAG.build_vocab(train_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Unique tokens in WORD vocabulary: 8866\n", - "Unique tokens in CHAR vocabulary: 112\n", - "Unique tokens in UD_TAG vocabulary: 18\n", - "Unique tokens in PTB_TAG vocabulary: 51\n" - ] - } - ], - "source": [ - "print(f\"Unique tokens in WORD vocabulary: {len(WORD.vocab)}\")\n", - "print(f\"Unique tokens in CHAR vocabulary: {len(CHAR.vocab)}\")\n", - "print(f\"Unique tokens in UD_TAG vocabulary: {len(UD_TAG.vocab)}\")\n", - "print(f\"Unique tokens in PTB_TAG vocabulary: {len(PTB_TAG.vocab)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "metadata": {}, - "outputs": [], - "source": [ - "BATCH_SIZE = 64\n", - "\n", - "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n", - "\n", - "train_iterator, valid_iterator, test_iterator = data.BucketIterator.splits(\n", - " (train_data, valid_data, test_data), \n", - " batch_size = BATCH_SIZE,\n", - " device = device)" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "metadata": {}, - "outputs": [], - "source": [ - "batch = next(iter(train_iterator))" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "metadata": {}, - "outputs": [], - "source": [ - "text = batch.word\n", - "chars = batch.char\n", - "tags = batch.udtag\n" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([46, 64])" - ] - }, - "execution_count": 128, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# seq len, batch_size\n", - "text.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 129, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([64, 46, 19])" - ] - }, - "execution_count": 129, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# not another_seq_len, batch_size\n", - "chars.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "metadata": {}, - "outputs": [], - "source": [ - "# new seq_len, batch_size_1, batch_size_2\n", - "chars = chars.permute(2, 0, 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 135, - "metadata": {}, - "outputs": [], - "source": [ - "# new seq_len, new batch_size\n", - "\n", - "chars_new = chars.view(chars.shape[0], -1)" - ] - }, - { - "cell_type": "code", - "execution_count": 139, - "metadata": {}, - "outputs": [], - "source": [ - "emb_test = nn.Embedding(112, 64).to(device)" - ] - }, - { - "cell_type": "code", - "execution_count": 143, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([19, 2944, 64])" - ] - }, - "execution_count": 143, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a = emb_test(chars_new)\n", - "a.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 144, - "metadata": {}, - "outputs": [], - "source": [ - "lstm_test = nn.LSTM(64, 32).to(device)" - ] - }, - { - "cell_type": "code", - "execution_count": 146, - "metadata": {}, - "outputs": [], - "source": [ - "b = lstm_test(a)" - ] - }, - { - "cell_type": "code", - "execution_count": 156, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([46, 64, 32])" - ] - }, - "execution_count": 156, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "b[1][0].permute(1, 2, 0).reshape(32, -1).reshape(*text.shape, -1).shape" - ] - }, - { - "cell_type": "code", - "execution_count": 158, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([46, 64])" - ] - }, - "execution_count": 158, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "text.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([46, 64])" - ] - }, - "execution_count": 120, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tags.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 195, - "metadata": {}, - "outputs": [], - "source": [ - "class BiLSTMPOSTaggerWithChars(nn.Module):\n", - " def __init__(self, \n", - " word_input_dim, \n", - " word_embedding_dim,\n", - " char_input_dim,\n", - " char_embedding_dim,\n", - " char_hidden_dim,\n", - " hidden_dim,\n", - " output_dim, \n", - " n_layers, \n", - " bidirectional, \n", - " dropout, \n", - " pad_idx):\n", - " \n", - " super().__init__()\n", - " \n", - " self.word_embedding = nn.Embedding(word_input_dim, word_embedding_dim, padding_idx = pad_idx)\n", - " self.char_embedding = nn.Embedding(char_input_dim, char_embedding_dim, padding_idx = pad_idx)\n", - " self.char_lstm = nn.LSTM(char_embedding_dim, char_hidden_dim, bidirectional=True)\n", - " \n", - " self.lstm = nn.LSTM(word_embedding_dim + char_hidden_dim*2, \n", - " hidden_dim, \n", - " num_layers = n_layers, \n", - " bidirectional = bidirectional,\n", - " dropout = dropout if n_layers > 1 else 0)\n", - " \n", - " self.fc = nn.Linear(hidden_dim * 2 if bidirectional else hidden_dim, output_dim)\n", - " \n", - " self.dropout = nn.Dropout(dropout)\n", - " \n", - " def forward(self, text, chars):\n", - "\n", - " #text = [sent len, batch size]\n", - " \n", - " #pass text through embedding layer\n", - " embedded = self.dropout(self.word_embedding(text))\n", - " #embedded = [sent len, batch size, emb dim]\n", - " \n", - " chars = chars.permute(2, 0, 1)\n", - " chars = chars.view(chars.shape[0], -1)\n", - "\n", - " chars_embedded = self.char_embedding(chars)\n", - " _, (hid, _) = self.char_lstm(chars_embedded)\n", - " hid = hid.permute(1, 2, 0)\n", - " hid = hid.reshape(hid.shape[0], -1)\n", - " hid = hid.reshape(*text.shape, -1)\n", - " \n", - " embedded_with_chars = torch.cat([embedded, hid], dim=2)\n", - " \n", - " \n", - " #pass embeddings into LSTM\n", - " outputs, (hidden, cell) = self.lstm(self.dropout(embedded_with_chars))\n", - " \n", - " #outputs holds the backward and forward hidden states in the final layer\n", - " #hidden and cell are the backward and forward hidden and cell states at the final time-step\n", - " \n", - " #output = [sent len, batch size, hid dim * n directions]\n", - " #hidden/cell = [n layers * n directions, batch size, hid dim]\n", - " \n", - " #we use our outputs to make a prediction of what the tag should be\n", - " predictions = self.fc(self.dropout(outputs))\n", - " \n", - " #predictions = [sent len, batch size, output dim]\n", - " \n", - " return predictions" - ] - }, - { - "cell_type": "code", - "execution_count": 196, - "metadata": {}, - "outputs": [], - "source": [ - "INPUT_DIM = len(WORD.vocab)\n", - "EMBEDDING_DIM = 100\n", - "HIDDEN_DIM = 160\n", - "CHAR_INPUT_DIM = 112\n", - "CHAR_EMBEDDING_DIM = 30\n", - "CHAR_HIDDEN_DIM = 30\n", - "OUTPUT_DIM = len(UD_TAGS.vocab)\n", - "N_LAYERS = 2\n", - "BIDIRECTIONAL = True\n", - "DROPOUT = 0.25\n", - "PAD_IDX = TEXT.vocab.stoi[TEXT.pad_token]\n", - "\n", - "model = BiLSTMPOSTaggerWithChars(\n", - " INPUT_DIM, \n", - " EMBEDDING_DIM,\n", - " CHAR_INPUT_DIM,\n", - " CHAR_EMBEDDING_DIM,\n", - " CHAR_HIDDEN_DIM,\n", - " HIDDEN_DIM, \n", - " OUTPUT_DIM, \n", - " N_LAYERS, \n", - " BIDIRECTIONAL, \n", - " DROPOUT, \n", - " PAD_IDX\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Congratulations, you've got LSTM which relies on GRU output on each step.**\n", - "\n", - "Now we need only to train it. Same actions, very small adjustments." - ] - }, - { - "cell_type": "code", - "execution_count": 197, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "BiLSTMPOSTaggerWithChars(\n", - " (word_embedding): Embedding(8866, 100, padding_idx=1)\n", - " (char_embedding): Embedding(112, 30, padding_idx=1)\n", - " (char_lstm): LSTM(30, 30, bidirectional=True)\n", - " (lstm): LSTM(160, 160, num_layers=2, dropout=0.25, bidirectional=True)\n", - " (fc): Linear(in_features=320, out_features=18, bias=True)\n", - " (dropout): Dropout(p=0.25, inplace=False)\n", - ")" - ] - }, - "execution_count": 197, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def init_weights(m):\n", - " for name, param in m.named_parameters():\n", - " nn.init.normal_(param.data, mean = 0, std = 0.1)\n", - " \n", - "model.apply(init_weights)" - ] - }, - { - "cell_type": "code", - "execution_count": 198, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The model has 1,939,738 trainable parameters\n" - ] - } - ], - "source": [ - "def count_parameters(model):\n", - " return sum(p.numel() for p in model.parameters() if p.requires_grad)\n", - "\n", - "print(f'The model has {count_parameters(model):,} trainable parameters')" - ] - }, - { - "cell_type": "code", - "execution_count": 199, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([8866, 100])\n" - ] - } - ], - "source": [ - "pretrained_embeddings = TEXT.vocab.vectors\n", - "\n", - "print(pretrained_embeddings.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 200, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([[-0.1117, -0.4966, 0.1631, ..., 1.2647, -0.2753, -0.1325],\n", - " [ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],\n", - " [-0.0382, -0.2449, 0.7281, ..., -0.1459, 0.8278, 0.2706],\n", - " ...,\n", - " [ 0.9261, 2.3049, 0.5502, ..., -0.3492, -0.5298, -0.1577],\n", - " [-0.5972, 0.0471, -0.2406, ..., -0.9446, -0.1126, -0.2260],\n", - " [-0.4809, 2.5629, 0.9530, ..., 0.5278, -0.4588, 0.7294]])\n" - ] - } - ], - "source": [ - "model.word_embedding.weight.data.copy_(pretrained_embeddings)\n", - "model.word_embedding.weight.data[PAD_IDX] = torch.zeros(EMBEDDING_DIM)\n", - "\n", - "print(model.word_embedding.weight.data)" - ] - }, - { - "cell_type": "code", - "execution_count": 201, - "metadata": {}, - "outputs": [], - "source": [ - "optimizer = optim.Adam(model.parameters())\n", - "\n", - "TAG_PAD_IDX = UD_TAGS.vocab.stoi[UD_TAGS.pad_token]\n", - "\n", - "criterion = nn.CrossEntropyLoss(ignore_index = TAG_PAD_IDX)\n", - "\n", - "model = model.to(device)\n", - "criterion = criterion.to(device)" - ] - }, - { - "cell_type": "code", - "execution_count": 202, - "metadata": {}, - "outputs": [], - "source": [ - "def train(model, iterator, optimizer, criterion, tag_pad_idx):\n", - " \n", - " epoch_loss = 0\n", - " epoch_acc = 0\n", - " \n", - " model.train()\n", - " \n", - " for batch in iterator:\n", - " \n", - " text = batch.word\n", - " chars = batch.char\n", - " tags = batch.udtag\n", - " \n", - " optimizer.zero_grad()\n", - " \n", - " #text = [sent len, batch size]\n", - " \n", - " predictions = model(text, chars)\n", - " \n", - " #predictions = [sent len, batch size, output dim]\n", - " #tags = [sent len, batch size]\n", - " \n", - " predictions = predictions.view(-1, predictions.shape[-1])\n", - " tags = tags.view(-1)\n", - " \n", - " #predictions = [sent len * batch size, output dim]\n", - " #tags = [sent len * batch size]\n", - " \n", - " loss = criterion(predictions, tags)\n", - " \n", - " acc = categorical_accuracy(predictions, tags, tag_pad_idx)\n", - " \n", - " loss.backward()\n", - " \n", - " optimizer.step()\n", - " \n", - " epoch_loss += loss.item()\n", - " epoch_acc += acc.item()\n", - " \n", - " return epoch_loss / len(iterator), epoch_acc / len(iterator)\n", - "\n", - "\n", - "def evaluate(model, iterator, criterion, tag_pad_idx):\n", - " \n", - " epoch_loss = 0\n", - " epoch_acc = 0\n", - " \n", - " model.eval()\n", - " \n", - " with torch.no_grad():\n", - " \n", - " for batch in iterator:\n", - "\n", - " text = batch.word\n", - " chars = batch.char\n", - " tags = batch.udtag\n", - " \n", - " predictions = model(text, chars)\n", - " \n", - " predictions = predictions.view(-1, predictions.shape[-1])\n", - " tags = tags.view(-1)\n", - " \n", - " loss = criterion(predictions, tags)\n", - " \n", - " acc = categorical_accuracy(predictions, tags, tag_pad_idx)\n", - "\n", - " epoch_loss += loss.item()\n", - " epoch_acc += acc.item()\n", - " \n", - " return epoch_loss / len(iterator), epoch_acc / len(iterator)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 203, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch: 01 | Epoch Time: 0m 12s\n", - "\tTrain Loss: 1.029 | Train Acc: 67.43%\n", - "\t Val. Loss: 0.560 | Val. Acc: 81.96%\n", - "Epoch: 02 | Epoch Time: 0m 12s\n", - "\tTrain Loss: 0.453 | Train Acc: 85.30%\n", - "\t Val. Loss: 0.465 | Val. Acc: 84.58%\n", - "Epoch: 03 | Epoch Time: 0m 13s\n", - "\tTrain Loss: 0.345 | Train Acc: 88.82%\n", - "\t Val. Loss: 0.431 | Val. Acc: 85.22%\n", - "Epoch: 04 | Epoch Time: 0m 12s\n", - "\tTrain Loss: 0.289 | Train Acc: 90.62%\n", - "\t Val. Loss: 0.402 | Val. Acc: 86.37%\n", - "Epoch: 05 | Epoch Time: 0m 12s\n", - "\tTrain Loss: 0.253 | Train Acc: 91.79%\n", - "\t Val. Loss: 0.379 | Val. Acc: 87.08%\n", - "Epoch: 06 | Epoch Time: 0m 12s\n", - "\tTrain Loss: 0.228 | Train Acc: 92.57%\n", - "\t Val. Loss: 0.368 | Val. Acc: 86.99%\n", - "Epoch: 07 | Epoch Time: 0m 12s\n", - "\tTrain Loss: 0.207 | Train Acc: 93.28%\n", - "\t Val. Loss: 0.357 | Val. Acc: 87.62%\n", - "Epoch: 08 | Epoch Time: 0m 12s\n", - "\tTrain Loss: 0.192 | Train Acc: 93.71%\n", - "\t Val. Loss: 0.353 | Val. Acc: 89.27%\n", - "Epoch: 09 | Epoch Time: 0m 12s\n", - "\tTrain Loss: 0.179 | Train Acc: 94.13%\n", - "\t Val. Loss: 0.343 | Val. Acc: 89.94%\n", - "Epoch: 10 | Epoch Time: 0m 13s\n", - "\tTrain Loss: 0.168 | Train Acc: 94.47%\n", - "\t Val. Loss: 0.343 | Val. Acc: 89.78%\n", - "Epoch: 11 | Epoch Time: 0m 12s\n", - "\tTrain Loss: 0.158 | Train Acc: 94.82%\n", - "\t Val. Loss: 0.336 | Val. Acc: 90.07%\n", - "Epoch: 12 | Epoch Time: 0m 12s\n", - "\tTrain Loss: 0.150 | Train Acc: 95.05%\n", - "\t Val. Loss: 0.337 | Val. Acc: 90.19%\n", - "Epoch: 13 | Epoch Time: 0m 12s\n", - "\tTrain Loss: 0.143 | Train Acc: 95.29%\n", - "\t Val. Loss: 0.329 | Val. Acc: 90.39%\n", - "Epoch: 14 | Epoch Time: 0m 12s\n", - "\tTrain Loss: 0.137 | Train Acc: 95.46%\n", - "\t Val. Loss: 0.344 | Val. Acc: 89.93%\n", - "Epoch: 15 | Epoch Time: 0m 12s\n", - "\tTrain Loss: 0.129 | Train Acc: 95.74%\n", - "\t Val. Loss: 0.330 | Val. Acc: 90.45%\n" - ] - } - ], - "source": [ - "N_EPOCHS = 15\n", - "\n", - "best_valid_loss = float('inf')\n", - "\n", - "for epoch in range(N_EPOCHS):\n", - "\n", - " start_time = time.time()\n", - " \n", - " train_loss, train_acc = train(model, train_iterator, optimizer, criterion, TAG_PAD_IDX)\n", - " valid_loss, valid_acc = evaluate(model, valid_iterator, criterion, TAG_PAD_IDX)\n", - " \n", - " end_time = time.time()\n", - "\n", - " epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n", - " \n", - " if valid_loss < best_valid_loss:\n", - " best_valid_loss = valid_loss\n", - " torch.save(model.state_dict(), 'tut2-model.pt')\n", - " \n", - " print(f'Epoch: {epoch+1:02} | Epoch Time: {epoch_mins}m {epoch_secs}s')\n", - " print(f'\\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')\n", - " print(f'\\t Val. Loss: {valid_loss:.3f} | Val. Acc: {valid_acc*100:.2f}%')" - ] - }, - { - "cell_type": "code", - "execution_count": 206, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test Loss: 0.342 | Test Acc: 89.85%\n" - ] - } - ], - "source": [ - "# Let's take a look at the model from the last epoch\n", - "test_loss, test_acc = evaluate(model, test_iterator, criterion, TAG_PAD_IDX)\n", - "\n", - "print(f'Test Loss: {test_loss:.3f} | Test Acc: {test_acc*100:.2f}%')" - ] - }, - { - "cell_type": "code", - "execution_count": 207, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test Loss: 0.342 | Test Acc: 89.85%\n" - ] - } - ], - "source": [ - "# And at the best checkpoint (based on validation score)\n", - "model.load_state_dict(torch.load('tut2-model.pt'))\n", - "\n", - "test_loss, test_acc = evaluate(model, test_iterator, criterion, TAG_PAD_IDX)\n", - "\n", - "print(f'Test Loss: {test_loss:.3f} | Test Acc: {test_acc*100:.2f}%')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "py3_research env", - "language": "python", - "name": "py3_research" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/week05_transformer_pos_tagging/week05_positional_encoding_carriers.ipynb b/week05_transformer_pos_tagging/week05_positional_encoding_carriers.ipynb deleted file mode 100644 index 160ef2c..0000000 --- a/week05_transformer_pos_tagging/week05_positional_encoding_carriers.ipynb +++ /dev/null @@ -1,2438 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## week04: understanding the positional encoding\n", - "\n", - "_This notebook is brought to you by [Vladislav Goncharenko](https://www.linkedin.com/in/vladislav-goncharenko/)_" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# If using Colab, uncomment this cell\n", - "#! pip install plotly --upgrade" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "ExecuteTime": { - "end_time": "2019-09-26T22:51:09.014457Z", - "start_time": "2019-09-26T22:51:08.160758Z" - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "\n", - "import plotly.express as px" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "ExecuteTime": { - "end_time": "2019-09-26T22:51:09.019768Z", - "start_time": "2019-09-26T22:51:09.016810Z" - } - }, - "outputs": [], - "source": [ - "plt.rcParams.update({'font.size': 14})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Positional Encoding matrix components proposed in the article\n", - "\n", - "$$\n", - "PE_{(pos,2i)} = \n", - "\\sin \\left( \\frac{pos}{10000^{2i/d_{\\text{model}}}} \\right) \\sim\n", - "$$\n", - "\n", - "$$\n", - "\\sim \\sin \\left( \\exp \\left( -\\frac{2i}{d_{\\text{model}}} \\right) \\cdot \\text{pos} \\right) =\n", - "\\sin(\\omega \\cdot t)\n", - "$$\n", - "\n", - "$$ \\\\ $$\n", - "\n", - "$$\n", - "PE_{(pos,2i+1)} =\n", - "\\cos(\\dots) \\sim \\cos (\\omega \\cdot t)\n", - "$$\n", - "\n", - "Let's treat $\\text{pos}$ as time and number of embedding component as carrier frequency of our signal.\n", - "\n", - "Note that carrier frequencies decrease exponentionally." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "ExecuteTime": { - "end_time": "2019-09-26T22:51:10.035945Z", - "start_time": "2019-09-26T22:51:10.032742Z" - } - }, - "outputs": [], - "source": [ - "def make_carriers(d_mod, denom):\n", - " return 1 / np.power(denom, np.arange(d_mod) / d_mod)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "ExecuteTime": { - "end_time": "2019-09-26T22:51:10.770645Z", - "start_time": "2019-09-26T22:51:10.473266Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "carriers = {i: make_carriers(10, 10**i) for i in (8, 4, 2)}\n", - "\n", - "plt.figure(figsize=(15, 7))\n", - "for i, carrier in carriers.items():\n", - " plt.plot(carrier, label=f'denominator: 10^{i}')\n", - "plt.legend()\n", - "plt.xlabel('Number of embedding component')\n", - "plt.ylabel('Carrier frequency')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In fact different frequencies correspond to different \"clocks\" that we have to measure $\\text{pos}$ as time.\n", - "\n", - "Model doesn't know $\\text{pos}$ value directly but it sees all the \"times\" (in fact phases) of differend \"clocks\" ($\\sin$s and $\\cos$s of different frequencies)\n", - "\n", - "Having representative sutie of \"clocks\" we can definetly say _what time is it now ($\\text{pos}$ value)_ for every given \"moment\"" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "ExecuteTime": { - "end_time": "2019-09-26T22:51:11.741823Z", - "start_time": "2019-09-26T22:51:11.736491Z" - } - }, - "outputs": [], - "source": [ - "def make_pa_matrix(n_pos, d_mod, denom):\n", - " res= np.empty((n_pos, d_mod))\n", - " carriers = make_carriers(d_mod, denom)\n", - "\n", - " for pos in range(n_pos):\n", - " if pos % 2:\n", - " funct = np.sin\n", - " else:\n", - " funct = np.cos\n", - "\n", - " for i in range(d_mod):\n", - " res[pos, i] = funct((pos // 2) * carriers[i])\n", - " return res" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2d case" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "ExecuteTime": { - "end_time": "2019-09-26T22:51:13.233975Z", - "start_time": "2019-09-26T22:51:12.672375Z" - }, - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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U09fu4pNZa/Lurc0HYrn/6DQmPnojsSdSiI1P5YomtejfpRnmYkbGL5bnPpnNweMJeHQDl1tHN4GhKj7X0aPrrNx5mGu7XTgzwZHtRC3imZA/E+B8rCtkIJ1DeNcg3btO2p0ufl+0nSdvL9/f96z1u/ly3nqy7E4sZhNjB3TmwaHdi3SsxR9LJCs92+d1t8vD2llb/BpJdqe7WDW6/ursti7exU9vzijgfD0QeZS37/yCjxa/csEx8yOE4LWbP2Hvhui89WTON0u9z/9N73AoLtEb3VPwcXgCXuPY6mtMp0UE8sHOPWj7NHTd4NuFm7muR2u2xSX4XkdFwV2vKtn7U0o0d4kEKo+RVOYIISYDk8GbbldRxz0Qm0BSRo7vA1ZV8ObEFHpdeD1ZO9cfJHZTLC6Hm5XTNzH1/bl8sfI1olPSeG3KYpIzszEM6Ni0DhPGDiO8ShDpRWxYXS43bocLW5CNji3qsv3giYL2Wb6/BFjN9GrXON90BCcOxfsdN/bgqSLPe8fRk8zfvB/dMBjauSXdWzYoctHfvf4g79w9MW/RzMl0MPPLRTiynTz8nu+Cf3aeBWQqi/gXDbCa6disTpHzBLiyV0uWTVmDvZBxqGoKLbtcOJr367JtPht/j26QlGXHrHujI6siD3PweAJ/vn9PuURGhBB8PKWQ91sB74UpeN0dLg+7jp4qtpHUqlszdD/GtTXQQu9R3Us0z3o1QjFpaoFIUu4ZoBT6NzSE4ExKyYwwgLSkDD5+5Ht2rNwLQN1mtXj26/tp0anxBb7pS47TxZiP/uBkcgZ2lxuTpvL76h18/MBIerVuVOLxPEUY9gKvsVRSruncnD+W78BdODrshwCrmc6t6pX4GAe3HvFrIDhynCz/bd1FGUke3SDH6SLYZkUtwhAvDv83bgrxx5Pyfqf2LJ34mAS+eWEqz3/3cInGCgyw+DeIDMCPfWG1aNQpYVpWvRa1cTt9fwsms4lmHUv2O/XoBl/OWe+zBjlcHh75dAaBLhWH00OAzczkGev57o07CC2BQVcajp9O4fCJpAL3uShiPXJ5dJL8bPr9UadZLQJCAnBkF3QGmq0m+t7co1hjBNssmDXVm6J9Htweg8gDccUaUwjBjpX72LhgB0GhgQy8oyf1mvmvhTUMwdY9x4ncF0diTjYLdx/Ki/S7dRc/LN2Koig8ONT/2moNtPhE0s4SGOzfydL9igbouv+H5Nl1V1UU2jfzrVmc8eXCAgYSeOvADkYeJeFEMhH1wv2O64+D246yb+OhAg4Xt9ND4okU1s3eStN61Qmwmslxuf1mhhgCbMk55P/XNywamf2b5NWnAeiGhxlro4qeiAJ9brmq6PclkiKoLHkt8UDhPIGa+d4732f87+gvEZujYop+0xBeQ+ksuX/WPAJTTEqel9GR7STpZAqfvPknj06cxamUDJxuHbeus+3wCR76amae2IA/atSthjW3sPjFOwcQFGDFYvZ92gdYzHRsUZfurc957hVFKTKkXjUi1O/rX8xdx6MTZzFr425mb9rLM9/O463flxXpyZry7mzfSFWOi7+/X+nzMDxLvYgw6keE5Rleec/ffJhNKhFhwQzq0sLvGGfpPao74XWrYbae8xFYAyy06dmSll0vbCSl+/M6kzuh3DXeoxskp2WzcVfMBccrDdl2FymFowRFGI4Wk0bjWtWKPXZgSACPfDwGa6Al73pbAy3UaVqTofddOL0lPzf3bYepkCdYVbwP6MLzFQLaNLmw6EbB7wheGP4BO1buxePW8bh1ju8/yQsjPshTkzwfC9fs5YanJtPrrk+45b/f886UpcQlpeV5nj26gcPl4ZWfF6EbJTdqet3QDZOfe6/xlfUJCi15lPHea7sRUTUYW26qkklTsVlMtG9aB5vl3O/ZZjHRpnEturYqeRTdZDYVee9abBa/r18IwxBM/HsDfV78P65+ZRID/jeJOZv3lmosIQTr527zMeQ9bp21s7eWeDyr2cSIXq2xFkojtGkagVYz5tzf79k6jhdHX1Nix0etRhF0HdoBS6EaCrPVxKinhpVorJTMnCKNZDcGjlxjzO5wE5+YweQZGy44ZlpiBgt+Ws2cycuJP55YovkApKTn+N7nRdwuZk2jfZPzO7LyxlBVnv/pCayB1rz7yBpopXrdcG4dd32xxhjWrVWRAg9KvsuoKAr1i1FbahgG4++eyFt3fcW8b1cw/fOFPNbrdZb+tt7ns26PzpMTpvPiZ3P5dd4W5kbuzzOQzuJwefhpWWSRam7htavSrGNjn4iaNdDKdY8N9vudmtVCuH94d2wWU0G7Q5w7V5vVxP0jfQ2z1Hj/mRiaWSM90deJFZOQyo/LtvLT8khOJKUVeC962zEMP+umI9vJvs2HGdK9FQFWs/d5AAX2RzaLicdH9SSsSmCBc3fVC0Xxsz5516xCe6zcMc1ug+seHOj3vP71CNAv4X+VncoSSdoIvK8oik0IcXYHOgg4BcTk+8wg4MN83xsEXPgJUIGYNQ3VAEMt5BURgppulZoNq7P7pFeSUjEgUNXQUzOxHi+4uOTUCGZ+VhqeQNUnfep0Sga7Y+J56L07eW7QeFx2V96mxhpo4bFPx+ZtbhvXCWfWO/cyc/UuDhxPINBmISPLgUBwbY8rGNStpY9H9/ZxI33C7dZAC3e8cJ3P+R5PSGXqqu0FipvtLjdzNu4lcssx+rVrwpjruxMRfs7wOnHYv12rqArJ8WnUbeo/r/7DJ6/jkfenk5njBCFwGzotGkWQnuPE5dEZ1KUF91/bDcsFaibMFhNfrHub3yb8xeppGzFZNIbedw03PzPivN87S8/WjZi7aa9vLUjuGn0Wl1vn2Klkends4jOGYRgs+Gk1cyevICfLwVXDOnDn8yMJq1E8D7XNavaJ0ChnNTUL7clNmsqoPu2KNe5ZRjw8iCbtGzLv6yWkJqTT6/quDB7b36dQ9kLUqxHG50/cwOs/LiY1y44hBG0a1iQ5MZOk1Oy8zZ7NYuKqto1oWq9kBaT7Nh/mTGwSHp8Ns4eFP6/hrheL3kjNXbmbT39ekeeRPxGfxhF7Boaf6IHL4+HwqWRa1itZAfO9b97K9mVRpCdl4sh2Yg2wYLKYeO7bkkU7zlIlyMafb4zh7037iDwQR90aYYzq25aa1UKYv34fs9fuxjAMRvRsww1925YqWtOiSxNsgVbsmQWdAbYgK9feXzIj+SwTF2xgyqrtedc6JcvOu9NWUCXARp82jTmVkEZwoI1qxTQcRZF1WKV76j47+mqcbp2lmw9iMqkYQnDv8O6M7NOGqUu3s/VgLHXCQxkzpAttm9Qu1TFemvIUP736Jwu+W44920HrHi144vN7SyzaEBpkK7KCQCm0H3XrBss3RzPungFFjrd2TiQfPvI9iqogDMH3r8/gjmeHc8dzxVsPAZo3qOFT56nooAoFRVPzHAw2i4mOzerSrgTXsMvg9kza+SHzvlnCmZhEOg1sx8C7+55X2S0/daqH8ubYwbzxy5I849bhdKN5FIx8qR1Ws8adwy5cP7554S62rdib59DT3Tq6W+fLZ36h5/COBZwfc1ftZu/h03m/e0PxjawBOF0eHG43gVb/6+urvz/NuMHjCyj89b+lB0Pv7V/kPB8Y0Z2ureozZ/1eHC43EaFBbN4dQ0qmnQ7N6/DoqN7Ur+krHtN1cDtOHjmDp3CatICGVxQ0br9esIHJS7Zg5O4/Pp+7lqev6809A7y1PxH1w9HMGhRKM7UGWKjVyBtF+ul/o3l/ynI27IkBBapWCaR98zqMHtiJDs3qMmRZY96+43OORsWiagqW8GBcNjNOP9E1xeCcB1XJzd5B4cUxg7AWsyZXIslPuajbKYoSDDTL/esG4D1gLpAihIhVFGUC0E0IMSD386HAQWAVMB5oAfwEvCmEOCsB3hOvRPj/gNnAjcBbQO8LSYBXpLpdzMlk7njpZxyBFFgLNUPht5fvpEXDCE4kpDFvzR6SM3Lo3LwuX934Kbrr3APGsJrIvKYFzlANw+q7oAbZLLw+ehCDO7bg6O5YpoyfyeGdMdRrXps7X76RNj1bXtQ5CCH4/f05TPv4b3SPjmbWuP25kdw2bqRPCt3UVdv5fM46b3Hu2bcEIAQmOwQ6FQIDLPz6/hgiqnkNpVdv+oStS3b5OHxsgVamHf/qvBtx3TDYduAEyenZtG1am3qXQFXuTGomt787hRynG5dHP6u/gckhUPM9VwJtZt5+bLhfI+nTJ39i1awteYaoyaxRJTyYB966hejtMVSvU5UBt11F1YiijabPflvFrBVRBVLurBaNuo3CORLvra1qVLMab4wZROuGJYvQlDVCCE4nZxBgNVM1JJDMbAc/ztvM8q2HsFg0RvVvxy0DO/p4oy/Est83MPG/v2D3E4Hsd3N3XvrhkSLnM+Kxb3yicY4QBWH2vedsZhPTXr6bBjVK/ntz2l2smbGJ/VsOU69FbQbe2cevIlVlInrbUV4a8T66bmB4DIQQDB7Tlyc+G1tiURK3rtPnhf/D7qc2rXZoMMS7cLl1PLpBhyvq8vaTIwi9gCLfG7d9xuZFuwqkIWkmlauGd+LVqU+WaH75ych2kJyeTe3qVfKidZWRD6av4q8Nuwum3Akw5Qi0Qpc5PDSQvyf6vw8yU7O4s/U4n1opa4CFjxe9SLN2xY9E/jRvCz/O3ZxXN2c2qVQJsnH94PYs23EIVVW4oeeV3NyvHWatYuqk8pPjcLE1Og6TqnJFw5q898My1kcdQ1UVggMsvHTPIPp2unAmwYT7vmH1zC0+rweG2Hj26/vpNfKc8M/9r/3G3iOn8/7uDFEQJt/7JzwkkGXvPnTee0sIwd4NB0k6mUrLLk2o3aR8RDrSEjJ4pPsrZKZl5xlK1kALj7x/J9fmyyQ4cjqJm9779awdkjtJ759fHt6PUde0RxGCsW2eJeV0WgEHRmCVAH7a/VGBrJWze9GirkHy6VScOS6Ca4cy8KXJvtFUITBlA4ZAqAKhKZg0lZv6tmPcXddUuPz35aJu17adWcy5hOp2TevHV+rrVF5GUn9gpZ+3fhZC3KMoyk9AfyFEo3zfaQtMBLoBqcA3wFsi3wQVRbkZrxHVBDgCvCKEmHWh+VSkkfT7J3/z3cyNZDYMQ5gUhAaKW9CpahiTvnzA73deHPkBUesO5j3wnY2q4biiFu4gFXeggtDyFb8aYNNUZr0yttTF5MXF7fKQkZxFaPXgIhWtZq7fzbvTl+PxF+LOEpjt3kjGDQPa8ey9Xm/moR3HeHbIuz6RqlufGc5dL5VcWvdSkJyRzdQVO4iMjqN2tSrsioojI92eF10yaSp1aoTyx/tjfVJzzsQm80C3V3xqFBRVRTNreFweLDYzqqYyftpTXHmV//RBj0fno19X8ve6vXnHGDuyG/eM7IbD7cGjG8VWtLtcidl/kqf7v+WTvmkLtHDvm7dw/cP+UyzcHp1+Yz/zMdQ9FnAHqQVEzTRFoXGtasx8ZUxZT79S47S72LJwJxkpWbTvdwX1mpcsgpJtd4EQuAyDq1/5puBmCvIir4FJ5wwdk6bSqklNvn3Lf23iWZJOpfBU/7fIycjBnuXEFmQlOCyIL1a/TnityiHHX554dIPPZq9h5trdGEIQaDUTbrZxOi6tQITbYta4dXBHnrjDfx3Zsj828NVzU33SnBVVoUWnxqQkpKOoCgNv68ltTw+9YCR53Y6jTF0YSWqmnV7tG3PXtV3KVbzmYsnKcZJldxJRNaTYUdePH/2eZb+v91k7AkNsvPD9w3Qf0j7vtYfe/J2o6HO1vLoJ3MFKgSwTm8XEK7cNYGT31hd3MmVIWmIGM79cxLZlu6lepyo3PTWM9n3PCW1E7j7Oi78sJNFp9w2MCYHNqVBbC+Lb8aPRM+28d+/XHN4RAwrUbhzB8989QvOOjUo9v5lrohj/x3Lyp26oTjDlGFhjU9HS7ZhDrDz57HWMvPnS1CJJI6l4/CuNpMpGRRpJtzV/hvTkLDxVrLhqe6MAltOZhOiCWbFf+vVmJJ9O5bkhE0hLysTQDXIaViO7aTi6pmCvnvv5vJxdqBUWzJI3HqyQ8zkfGVl2/lqzm0+W+OZiIwSWNIEpd+/asE41/vj43ry39246xLcdV2oAACAASURBVOSXfudI1HHCqlfhtudGMOKBivf2lBVJaVl89MsK1m4/iqoqXN21Oc/edbVfj/i6edv45PEfycn0p6hV8PzDa4cxZc8H570u2XYXyWnZRIQHV2rvd3nxxu2fs2PFXpy53nDNpBJWowrfRr5LYBERCSEEwx/9htSMgpEkAYgaJuxnm4kAFlXj56dvo80llFe+nDidmMH4iQvZffAUAmjasDo77EV0vReCwMSCzyCrxcTPE+6mYZ3z19G5HC7Wzt7K8f2naNS6Lr2v71LqmqnLFZfbQ6bdq7SXlJrNg2/+QVaOE7dHx6SpNKkXzlcv3UKAzf+6sGTqOv7v+d99VFJRFVSTlue4s9jMNG1bn08WvHDZrtEXy+mYRKLWR5N6Jp3f3p+Ly1HQMRNYJYA/Dn+GJZ8a2/zVe/jo5+V5dWLgNZSMKhqKWaF6SBDP3tCXQR3PX0dbmViwei8ffbeMlCAd4SfTBSHQHILATIVenZrw/jiv4zM9KROPWye8dtk4MV7+ah4rdh3BYxioblB1AR6DKuuOojo8qIrCt5vfLjJ9v7yRRlLxqOxGUmWpSfrHkJXhlSU1ZTgxZZwrgHUo3nx5TfMTaq9dle92vMeOlXs5E5tEYL2qvPnbajJNHu+uLb+HS4GkzGy27I+l2xUVJ21emBkLt/PVlDW4ghQIwK86lGFWwOXdAEUUkuVt06M5n698rQJmWjFUDwvmvad8a7b8UaNuNf+1E342H9npdmIPnqZhq6ILnYMCLASVsFbockUIwfZV+1n911Y0k8qAW6/ilV8eZ/pnC1j442qcDhc9hnXgntdvLtJAAm9KxwM39+TLqasKbGC0QA2nWYH8vZcVwSfz1vD947eU56n9I3B7dB559XeS07LzfuPRMQkQofpdI/wV15hNGkmpWRc0kiw2CwNu71UGs758sZhNhOdG+WuGhzDrk/vYsOsYpxIzaN6gBp2uqHdeo6broLYY+lTfNxSlQCqjy+Hm2L6T7Fp7kA59W5X5eVRmhBBMemUaC39dh6IqqKqKYjFjMgSaSfFGoAS88ftTBQwkgGF9WrNh5zE27DyKbhhoJpWMYAOTTcNjGKS5HHw4bw1tG9emVljJexBVNB7d4ItfVuFweTCZwG3B73NLc3n3Oxu2H0UIcV5BqNIyfGBbojNTOR6fipHpwRyfReDBRFSn7p2TqrB46nrue21UmR73n4eCXlS/A4k0ksqalh0bsW/LEZ/XG7Wu57cHUHamnZkTl7Jm7jasNgvD7+lL3wHtuC0lk+/WRvrVH/R4BM98OIsPHh9Jr84la0BbFuw/Es/EqWtxuXXcLsCm+F0oz+Yj2Kwm7hrxz2viJoTA7vJgM5tKVCDfomMjajeqQezBUxeUgRZCYLbI2xS81+LTp39h7ZxtOHJcKIrCyplbuf6Bq7n31RsZ/XzxjNSzjBrYHk1T+G7GBpJSs6lVvQqmOjYOnikY9XDrBlExp/l1/lbWbzqMosCIa9oytG/rUjUv/SezLvII2TnOAk4AYXiVzvwJYpjd+Tpb5+Jye2jWsGQCGf9Glkcd5uslmziTnsmV9Wvx9PBetKobQd/OzS785VyqRoTyyITb+eblP7z1Z7qBoql+nThup5vonTH/OiNp06JdLJ663qduq2qNUO567loCqwTQfWh7AvzIcWuqyrtPj+TAsTNs3x/H2mPH2RpzEkeuwt1Z9czX/1jCpEduqpDzuRgSkzPz+hWa7eAOhjyRBPA+8wWYzrawKqeo45cL1jNlzXZvnaMGSjAQrhGcL4XfMARzv1vJ2FdukOu0pNTI3VcZ88iE23l+5Ie4nB7cJgURbCbAKXj8Q98ce5fDzTPDPiA+NimvPmXyazPYvfEQL3x9H6c9Oczdvj9POeYcAt1h8ObnC5j//aMXVHMra+Ysi8KVqyZmcoDLr7aAQojQ0GwqT9zZl27tGhU5nsvp5pf35rLw13U4c1y06dGMx9697bzRk0vN8qhDvDd7NYkZWVhNJu7s05HHh11VLGlgRVGY8Nd/ee+ByezddBhFUzBbzLic7gJ1SorijTrVbiw3jAAHIo+xZs62vFo2IQTOHBezJ69g8OiepUqruP7qdlx/dbs8b+dNH/zi93Me3eDbGevRs72/++hjCazdepgJ467/16Yf+eNUQnre2nAWBbCkGXhqmDCEwBACs6ZiM5sIdIJdc+WpNNqsJm4f1vmi+voIIUg5k441wEJwKWTWLwf+3LCLj+asydtsrz8Qw7ajJ/j1ydtpWbdk68W19/ajQ79WrJq5BZfTA4rC7EnLfVLwLFYzNeoWv5XAP4UFP6/FkePbfNyR46Rph4a0LEY/tlaNa9KqcU2+eXUrLr3g/WEIwcaDsQy6+3NaN6vN43f3o0U5iTJcLFWCbXn9lxQBAckCR5iCMHlfU91gTff2CtRUhd6dmpT5+hifmsnPq7blNXIHECYVd3UbjnohBMSdkyl3Od24HW60oH92be7FIPB2p5H4RxpJZUyLjo34aMmL/GfSbM6objRFQTNrbM5O5cpCn10zdxuJJ1MKbIyddhcbF+wk7lA8j4/oxZI9h8hx5vNgCa+CmuoGTLA3+jQd29SvkHM7S1a285wSjQGWVIGrKigoWCwaKPDfEX3p0rguDWpXvaARN+Gh79i+an+epy5qfTT/HfEh36x5jRp1fCVKLzVbDsXx4tRFeRuUHJebX9dsx+nxMO76fsUaI6xGFd6b8xwZKVnYs5yE1wljwv2TiVy2ByEEmknDbDXx6i+PlvghoxsGu2JOk+Ny07FRHYL+IXUam5dE4bL7blYAIpfvvajc87PX+KqWDYlJSPVpPKnrAk+OkZeU4HB62BoVy97o01zZsvIa8xVNy8YRmM2aT/PgENXMfUN7cTA5mWNnUujYtC539euIYsDPszezYecxQoMDuGN4ZwZeVXp1zp3rDvLpf34lLTEDQwg69G7Fc1+OJTS8cqsJFoeMTDtpmXYiwkP4/O/1eesPeDc6DpeHLxeu56sHSi5+U6dJTUaPGwl4m3vP/3EViv1cyxlFUbAEWOg1vGNZnMplRWFRmLMoqoLL4b9ZdFEULVEvyHa4idwdy6Ov/sG3E0bTpEHlc44FBVrp3705q7ccwuXWUXUITBaYrSrBATacDjcOw4PNZqJKcADPPeArnBN/PInUpEwat6qDrRTGy4Kt+/H4ycAQZg1H/eACRlJ4rTCsgf+M55/k0iCNpHLg+517iLcJhEfBg1eF7Julm6lTLZRhHc9tAHatO+jXQ6WoCge2HWPQ7Vfxw+O38ODn08n05EpF28GWKs7JTpsqPozcv0dzNu44hv2s1KsTTGcESqDCcw9czcCOzakSWLweFqdiEgsYSGdxOd3M/W4V9792Y5nPv7TEJ2aQkJzJl4sLblAAHG4P0zZE8cSwngSUQDyhSrXgPDnoV39+lMNRsezbfJiqEaF0H9rOJ8f9Qhw8lcij3/5FttOFqih4dIOXbryaUd0Lm+iXH7ZAC5pJ8+mJpKoKtsCy8RTee00X5kfuJ9PuzDOUTKqKlurJ61R/Frfbw459cdJIykenNg1oVDecI7GJ56LNJo0a4SHcOqADJpNvzt2z9w7g2TI49smjCbxx99cFNrU71h7g1dFf8cXiF8vgCJcGu8PFO18tYn3kEUyaijApOGr4aaYJ7I49c9HHCwyx8dH853n/4e84ccjb065R67q8OOkBLEUIQJyP9BwH6w/GoCoKvVo2uuwUN6++qRvRO48XUGMFQEDLzo1KNNbg9i2YvXVvQSeCEKhO8tYXp8vDD9M2Mv65kqUPlwWZqdnsizxKSFgQrTo38tuE96WHB6PrBuu2HcGkaQgED9zSk1uGdWL99iMci0umYd1q9OnctMD9npGSxZv3TOJwVCwms4buMRj74khufLjoHl6Fyci088u0jRjBRsFabQBDeOuRcrEGWHh0wm0y0i+5KKSRVMbkOF0s2XWoQCgYvJvo71dsLWAkRdQLx2zRcLsKpaeoCtVqhQLQun5NJtw6hDe+WIDD6SlQXme1mmjdrHSNDS+G/t1b8NeSXRw4cga70+3t3m3WePCmXozq1bZEY504FI/JrPkYSR6XzqGo42U57VKTY3fx6sdz2bEnDrNZ43Q13adhK3i9rcmZOdQLDy31sZq1a1Ci3iT58egGD02aSUqWvcDrE/5aSZv6NWlZp/J5JktC/1Hd+P2ThVDISBJC0HN4hzI5RnhIEDPG3c2PKyJZfyCGiNBgmoeEsWjhHhwUNIzNZhNhlVjeuCSkJmawa300gcE2OvZtVeo6OFVV+Or1W/lhxkYWrtmLYQgG9mzFg7f19GsglSVzf1iFp5DzQnfrxB6K58ieOJpeWbER97Li7c8XsGnHMdxuHbdbRyjgrqb5FcKoXbVsiuMbtarD16tfIzUhA0VVCCtl0f38bft5Y/rSvJoQwxC8e8dQBrVrXibzrAgG3n4Vy6dv5sjuOBzZTjSzhsmk8uzEe0rsxHpqeC+2HI4jKTPbmyFiAAICUs4ZTUII9h/x33C9PJn+1RKmfLQAk0VDGIKQqkG8++eT1G1SsNmxzWpm/DMjScvIISU9h7o1Q7HmOgb7dW1Ov67+/23HP/AtB3fEoLv1vOf9z+/No16zWnQd0KZYc1ywai9KjgFBvu+ZTRo9atYkobaLgCtr0nxoG9yNQvHoRon77/3bkMINRSONpDImw+5ELcJzcSYts8Dfh97Zk1lfLy1gJKmqQlCVQDr0OVcc26drM4b2ac2iNfu88pqaiqIqfPBC2RckHtlzgsN74qhVP5y2VzXz60kyaSqfv3oLKzdFs3JTNMGBVq4f2I42JeyjAlC3aU2fyAB4m6s2qySbmgkTF7F9Txxut47LraMFq3hsis8mRVEgItTP6l1BbDkch9PPtXR5dH5fu5MhzZtitZhod0W9y/KhUathdZ7+5C4+/+8UNJOKgoJhCF767gFCqpbdda9eJYhxN/RjHN7UyYxMO4sX7/X5nKrA1T0uH+neovjzi8VM/WQBJpOGoihoJpXxvz9Biw4NSzVegM3M43f15fG7/PfmKS9OHknwK4SiaSoJJ1IuSyMpNT2HjbkG0lkUAZYsA0+ISv6ztZlNPDq4R5ke/3zNrC/E6dQM3pi+1NtoPJ/T8KXfF9GpcV3CQyrWwRCblMbq/UcxqSoD2zajRpXipWCaLSbe/+sZNi2OInLZHkKrhzB4dC/qlKJWNCwogL+eH8OKPYeJOnaaWfN2oGQaPlHqehXc62vnuoNM/WQhLqcbV26GiCPHxf9GT+SHjW/4jcaEVQkstpMo8WQKB7d5DaT8OO0uZn69rNhGUkxcMi6XTmAC2CPy97NTuK5dK8bdcTX3fzOD3WdS2LE/mr+iDxMWGMCvT95W7H9viSQ/0kgqY2pUCcKsaT7pWAiBJ92FYYg8JbQadavx5pTH+eCxH8lKz0EYBg1a1OaV7x8qYPwoisLzDw/ithGd2LYnjirBNnp3aYqtkBcr8VSq1xscYqNL/9YlSo1wOd28ff+37N50BEXxHrNazVA+mPk01fw8KE2ayqBerRjU6+KUjuo2iaB975bsWnewQDTJbDVx3QNXn+ebFUOO3cW6rUcKdPe2pRlk1VQLNgQ0m3hoYDcspkt3S2XYHfjTVDaEYP6qPWyctx8hvA0mP3xlFFeUwqgtLYfjk1h38DhBVjOD2jYnLKh0hfnX3NKdboPbsmPVflRNpfPVrUuV114SqoQE8PHLo/jfx/NwON0IINBm4d1x1xF8mRcE79l8mN8/W4Tb6SlQG/nqnRP5bdcEtHKO/pQl7Xo2Z/fGQz5RabfLc1kaSACp6dmYTVoBIwnAliLQAszkBOQqiJpNPDuyD/3aNLkU0/TL4l3RfkSHvMzetIcrqoZTvVowzRtHlHtK1LcrtvDN0k0IvM+2j+av4fWbB3Jd5+I1cNVMGr2GdyyTmiyzSWNIh5YM6dAS44yLxWv24XSdu/esFhP33lKxDVDn/bDap/ZKCEFaUiaHo+Jo3v7i2o2kJ2dhsmh5Blh+UhPSiz1Oq2a1WLb+AHanm6CTBroVUCBImLi5Zzu+WrSB6NNJeZk8Lo+Ow+3h1T+X8M2DUgrcHwIZSTof0kgqYzRV5crgamzMOeWNNChKnixmQBps2HWUM247bl2nX6vGtOvVgl93vsupY4lYrKbzqgc1rBtOw7rhft/79cO/mf71MjRNQ1UVFFXhnd8eo2Uxu1pP/79lRG08XGCDER+bxMf/+ZV3fnu8JJegxPzv+4f4/q1ZLPl9A067myu6NObx9+8got6lV1LKynGiFMp91lwQdMar1qUEaFQPCeLBgd24sXvxvGHlRZcm9XwEBwAwQM0yyM7xPjhy7PDMWzOY8/2jWMtZXlwIwbuzVzJr614Mw8Ckqrw/dzWfjRlJ71aNSjVmcGggfa7vXLYTvQDtr6jH7EkPcygmEUWB5o0iSiT77o+s9Bx2rovGZNbo2Kcl1kvQ62rRlPU+TTHBm+4atfEQHftcPnLPw+7uw+zJK9E9el5EyRpgod8NXSrFWlIa6taqir+G7yZVYVir5jzz4ADScxyEhwQWS1mzInG4POj+pMTdHr79Yz1hDg1dF9SrHcYnr99M1bDyicJHn05i0rLN3ohWPt6csYxeLRsRHnzpUmaffWAANquJOUuj0HWDqqGB/Oe+a+jQumKN+sy0HL+vq6pCdqbd73sloUGL2gg/vwWTWaPrgOLXyw7ucwU/Tt+Ay+39bZmcXqdfq2Y1ad28Fo9Mne1T6qAbgs2H4nC6PVgrWAlYcvkjfzHlQKhuJuiMgSNURZi93adtaQJ3iMqT0+Z7i28FfDhvNU8O6ck9/br45P2WhF3ro5k5aYXXG5yvbuK1Md/w2453iuUNXjR1g48HVvcYRG04hD3bSUA5eswtNjOPvnsbj757W54Uc3HJcblZfzAGp9vDVS0alvkDr3rVYEICrSS7CkYGLW6FYU2a8fp/RpTp8S6G6lWCeGhgd75bsQVH7nxNqgpOHXN2wQeUYQg27zhG3+7lWxuw8VAssyP34cyNrJ414v7763zWvPEItsvooaVpKq3KqHv7kmmbmPjy9Ly0QYHgte8fpEOvik3fy8l2UoSz37dQvZITEhbIV8te4tcP/2bL0igCgmxc90B/RtxTsWl/ZYnVYuLBO3oz+be1eU2PVVXBZrMw9uYeWM0mIkIrZxpR39aN+X7lVp+sCsMANVMnOzc6FhOXzOsfz+eLt28r0+NnZTuJO5XC3D0HC2QCnEVVFFbuPcLN3UtWR1uWmEwaT997DY/f3Q+7001woLXYz7/sTDsHtsUQEGylVSf/IgvFpdfw9kTvjMFp990DtOrUqNTjnsViM/Pgmzcx6dUZeRErk8VEcGgANz3mq4BXFIEBFr7/4G6++nkV67cdxWxSGda/DQ/e3htFUfwa5eDNppAq15LScPnsUC4j+nZvzo69cZjOnHs4GCqkVhWgiwLe/i+XbKRni0a0qF291Mdb9NsGvzKlHpfO7k2H6dD7wpK6blfRUqYelwcqKK2oJAbSpkOxPPXzXBRFQQiBxzB4bkRfRvcsmyJ+8G5Ixj0yiNc/nY/L5fEqCmoqATYzD97Ru8yOU1Y8PKg7nRrXYdrGKDLtTvQUN/u2xPkE04UhyC7UB6WsEEKQlJKFzWpmTuQ+7C7fFAtFUdh8OJZ+V1Se9KCK4sTRBCa+PN3HKfHmvZOZun08gX6aUpYXfUd2Ysfq/T4qmx6PTturLp/i+rNUqxnK0x+NBnz70pWWpMxs9p1MIKJKMK0ugfjJrSM6U6dmKFNmbSEpNYuOV9bn3luuonZE6QViKoLW9WpyY7c2zN6y12soKd6WEeZ0Ay3fT9+jG+w5eIqUtGyqlUE0SQjB5ClrmTZvG2aTSkqQjh7m+1wRnE+Su2IxmTRCSpDaunDqeia9PgvN7BVZCKpiY/yUx2jYsnQp1EPu6MWiqRs4HZOE0+5CURUsVjMPv3VzmSmHDrurN3UbRzDzm+UknU6lc//WjHpkwAVFQbLSczgVk0RE3aqEVQ+hetVg3ijCOdmjSX2W7jlcsF5YCMxuBZNUuSsSQ8hrUxTSSCoHBvdrzV+LdnL8VArOs3n+oRpmk4rbKJgO5fHoLNh5gBa1S7/h9icjfpbCG7Gi6Dm0HUumbfYprKzXtGaZFsWXFTlOF0/9PJecQhvwT/5eS5fG9S7K6CxM767NmPjW7UydvYUT8Wl0aF2P0dd3pUZ42ShJlTVdm9WnazNvusbaLYd5Kyoee+EooWHQqe3F5Zn7Y/vuWCZ8sZCU9ByEIVCbFR3ZqywblIpmxcyt6H4824qisGnJbq4Z1bXC5tJnZEcWTV3PwZ3HcWQ7UTUFs9nEQ2/dRFCV0jd0/ScghOCD+av5Y1MUFpOGbggaVg9j8v2jih2xzs6wYxiCkLCLi3D37tqM3l2bXdQYl4KXbriawe1asGDHAVRVYdPSaNJTsn0+p6kKOXZXmRhJc5dEMWP+NlwuDy4XaDpQRfORjBZC0L/15eekORQVy6Q3ZuF0uCF3XbdnO3n5jon8svWtUok52QItfPb3OJbP2MzGRVGEVQ9hxL39aFlK8ZaiaNerBe3OEy3XDYNjCSkEWi3UDgvhu/FzmP/zWkwWE26Xh15D2/PMx6OxWP1vXWt7bKgegaHh/fc2BIqA0BSFyF3H6dH58vv3llxapJFUDlgtJr5+9w4WrtrLyg0HqRIcgK1uANN2+ipkGULg1n03TCWh73Wd2Omn55Lu0bmyR/EerGOeH0Hkqv1kpmbjyHFhsZrRzCrPfnbXRc2tvFi9/xj+HENuj87cbft4bkTZpti0alaLty9B34qLpWfnJrRpUZs9B0/jyC2atVnN3DqyEzWrl165yh8nT6fywviZeWlBAPqJbNRaKkbhlhaGQffmZW+kXQ7kZDn8qrAZhnFeh0d5oJk03vnjCTYuimL9wp2EhAUxdHRPmrSpV6HzqIzM27Gf6Zt34/LoeXUOh+OTeXbq3/z08C3n/e6ZuGQ+eOpXonfFAtD4ijqM+/xu6jcrm3TNywVFUejStB5dmnp/T5+nKMxevNOnGWhAgIU6NctG0e2P2VsKrEEmJ1jTBM4wUDUVVVHQNJVxI/pW2lTF87Hg1/UFRFbOYs9xsnfzEdr1LBgBdus6hiEuWI9jDbBw7d19uPbuPmU63+Kyct8R/jd9CS6Pjm4Y1DBZMf11FMPpwZV7vhsWRxESFshj42/2O4Y9y0VIrIE7WMFjFWhusGQKNKtGRpajIk/nskEKN5wfaSSVE1armRuGdOCGIR1wu3VufXwyehXfBmhWs4nBbS+uDqHvyI4snbaJ/ZHHcOS40DQVzazx+ITbip26E1Y9hMmrXmHlrEj2RR6lXrOaDLn9qlL3xyhvnB4P/gIRuhDMXrKTLXMOcO2AK7ljVDesFhNnTqQw+a2/iFx1ALPFxKBbujH2+eHYLkGxfEWiaSofvXozK9YfZNm6/QRYzVw3uD2dyyGKNHPBDp/Nj5plYMlWEWEaLo+OKVdY5N3bhxJYgqa7/yR6DG7L4t83+hhEhiHo3P+KCp+PZtLoPaIjvUdcvHLXP4lf1u7AXqiexmMY7Io9TVJmNtVD/Ec93C4P/73hM9KSMvOipYd3x/HsjZ/x86Y3yrW+s7Iz5uburNoYTWaWA6fLg6oqmM0aLz4+5KKFUM6SnuErNBCYbBCYo3LTnZ0JCrQypF0LGlSvWJntsiItOavIKHxm+jkBhtRsO6/PWsrq/ccwhKB9g9q8ddMgmkRUPhGTI2eSee63BQXq1064sjH1Cqf2/JN5W3iXw83iPzfx8Bs3+q217tmlKeu2HMae5caSde51XTdoLx0/klIgjaQK4Lep60k7k0WAU8FeQ83Ll1WEQrd6dWnf8OKkmDWTxttTHmPr8r1sXOT1tAy6vQcNW5RsXFuglWF39WLYXb0uaj4VQa8WDdENf0puAj3RRXyOkykzN7N9dyzvPn8dT4/8hIzUbIQhcDnc/D1lPUf2nuCDaU9W/OQrGJOmMrjvFQzuW74b8BOnUgt2ksf7Uw9PUxg9sjuZmk6wzcqwDi2peRl6cMuK9j2b03VAG7Yu34sjx4WiKFhsZm56+BpqFkOFLTo+ic8WriMqLp4aVYJ4+JpuDG134bpDScnIdPj3PGuKwsJVe9gVGYvVambkkHZ079Q4r55y09I92LOdBTayQniNp9VztzP0joqVd65MVA0L4pcv7mHekigidx2nds1Qbh7eicYNyi49ul3remyIPOIjSFIzKIinh/cud7nx8qbnsHb+M0fcOld2bwp4HS5jJk0jNikNT+5zcufxU9z59R8sGncfoYEVV/dYHH7fuMtXXENV0AM0XOFWrMnn6mc9bh2X00OAHyOp/1XNmTF/G0eOJ+ZFE21WM6Ou7VjmmROSfwfSSCpnVs3dxm/frkAPDyQgDSw5Os4QBRQFS5ZOty61yuQ4mqbSY3Bbegz2Veo5lZbBlPU72H8qkTZ1I7irV0dqhVbOCFFxqVElmCeH9GTiko04PV5BBcUAc5bAlON9OrpcHg4cjuenb5bjyHEWkCB1Oz1E74rj8O44mrW9PHuoXApiDp5m2Ywt5GQ5uWpwWzr3a5mnqtTxyvps3x1boOcHgMdjMKRLK2pV8kLzikJRFF6cOJbIlftZNWcbFquZQbd2p03XC+fLHz6TxJ3/9wd2l7dfU2qOnVemLyEhI4sxvStWFv2fTv8rmvLnxl0+daS62+CXXzbk1Ztu3RHDDdd24LF7+wPeVDt/6VCOHBfxscnlPu/KTkiQjdE3dmP0jd3KZfxHxvRjx544nE43uiFQFLBYTPz3kUGXvYEE0O+6Tsz/aS3Ho+O9IgsKWGwWRj89hNBqXufTlqNxxKdl5hlI4E2rcnl05mzbx5g+nS7R7P1zKi0DvQiZTU+ghjXfklRgMQAAIABJREFUbVOzXrUio7Emk8YX429j4Yq9LFt3gCCbheuHtucqWYtUJAIFncrVPqAyIY2kckTXDb5+/S8MXYeqAaApaC4ITBaAIDDAQqMG/vselRX7TyUwZtI03LqOWzfYHnOS3zbu5M6eHbi5azsaXqYpBwD39utCt6b1mbttH9v3xBK3JxFzjiiQXat7DPbsO+EjbQreFlbHDpyWRlIx+XvKeiaPn4PHrWPoBivnbKNTn5a88vU9qKrKiEHt+HNuJB7dQM+NKNmsJq7uJQ2kwqiqSrcBbehWzE7zZ/lq6UbsbncBOVuH28NXSzdxe4/2l7SZ8T+Nh67pxpLdh0jPseP06GiqgqoohJw0zgnyAA6nm1nzt3PjtR2pXTOUZm3rY7Ka8BTyjAcEWWneTq41ZUVKQga7Nh4iMNhGx94t84r5G9UP58dPx/LrzE3sPXia+nWqctdN3Su0eXZ5YrGa+XDW06ycFcmaedsJDg1i+JhetMtVo8yxu1i366jfTAuH28PhhMpnqPds1pDNh+N85OKFpqBXD8VIcKG5dCw2E4+/c/56QIvZxPVD2nP9kPblOWXJvwT5RC1HkuPTcTpcaHY3isdAKMq5miQhCK0SQO9u5atYNH7OigIKcB7DwGPAj2u2MXXDTh7s341HB/Qo1zmUJ23q1aRNvZr8Zd7B17tW4aDgIms2azRoWIPT1qN+u33Xa1Lxkr6XIxmp2Ux+e3ZeAS14PePb1x5ky4p99Bh4JSHBNr7/ZAw//rGB9VsPExhgYdS1nbhxmKx1uRiEEBiGQNNUouLi/fY1EkLw/+ydd3gU5fbHPzM7W9IrkEIPJfTQe+9NaUpRVLCiXPV6vZaf7dr16rVXsKAginQVFFA6hF5CKIGQBAikkd62zczvj0Ag7AYQdlPn8zw+Puzu7ByW3Zn3vOec7/dcdj5WuWTouWVIHZfNeNRWgrw9WfnEdH7ecYjo+FOEB/hhO13EttjjDq8VRZG9B08xZlh7OvRqTqMWISQcOVtaUdIbJOqEBdBj6PUbZ2qUz4+frOPHT9YhSToEoUSQ4bXvHqRlh5JZy7AQf55+ZEQlR+k+DEY9w6f2ZPgVrZvLV+3n8282InsJWENUriwQeBgk2tWveuIh47u2Yf62faRk51+qKCkqxmwVHTqUlvXoWz+YKbOHXvemZmFeMcVFFoLq+dWICqI70STAy0dLktyIl68HilxS2fA4cR5LuC+yX4msrh8CX/z3DqS/4Y3wd1FVlYNnUpw/B1jsMl9t2s2AVk1pFXbjZrZVgSH9WjF3/ha4LEkSBAEPk54HZg9l37K92Ky20gWmpNdRP6KuS4zyagMHt59Ap9fBFW1E5iIrm387QI8hJYu/oABvnpw1jCdnDauMMGsUdlnhmx+2svy3/RSbrTRqEIRfUyNpFDi81irLzPxmCbnFFgTA06Dnvamj6dJEG1a+GXw9TNw3sCv3DSyRZZ/z/WZ26kSH2TtRFPC5IJIjCAJvLZrNjx+u4a8lu1EUhf63dubOf464LmNvjasTuyuBRZ/9VWKeftn16MUZc1m46z+19jOOPXqWL77diMVqR7WCzk/E7nFpY1YnCviYjIzuWPHiMNfCy2jg53/cwdin55KtsyEqKqYsFUNeyQ1bMUk88vZkAq5DIj4/p5B3nljI/q3HEUUBvyBv/vn2FDr2qVijbo2agdaI6Ea8fEz0HtEOg1FCkBVMp3PwOpRCUHwmTz4w5Lp+8DeKXVZYve4QghNtg8ux2mVWHTjmtjgqCh9vEx+/OYWmjYLR63XoJR2tmofw6VvTCK7rx3vLH6N1l6YIooCk19FnVBRvLnxY22G6TvRGyalIqCAKmDxrtkJgZfHeZ2tZ8steioqtqCoknc4k80AmxisWgQZJByKk5hZQbLVRZLVxvqCIh75bQVZBUTnvrnEjjBrSzqkPjSgK9Ohyae7B5GFgxjNjWbDnFRbue40HXxpf632nrpeMlBw+eOZnpvd6lUdGv8f6FXtRLyuf/v5jNFazE/N0m52YnScrMtQqxbLf9pXOgwqA3ykFU5aKKINR0tEyrA6KAL1f+5w7v1zEoeTUyg34Cvw8TdQrMhCQIOOXpGDMu6x1/m9Y6r0wYy77tx4vFXjIOJfDyw98TfLJdHeErVHD0SpJbuaxtyZjNdvZvekoer2ELMtMmT2E/mPd14KkqirPv7ac/QdPYwiSMQeKDtLjF1FUlaQzmVgsNozG6i3JHNG4LvM+mkFWTiGiKODve8nAsWHzEN5d+iiyXUYQhVKxAY0S8rIL2bByH+nncmjduTE9BrcusyPbqU9LcJImGYwSw27rXoGR1g7y8otZu+EItivMncVcO+31dYg3FlBkKVkoRjUOIyY5FdsV1Q1ZUVh18BjTe1etIe3qTP2wAJ59fCRvffRHSTujWuKL9/aLEzEatNvpzZKdkc/sMe9TmFeMLCucT83l4+eWcup4KjOeGg2UGKeWM+PvdPa0tpCVU1TmcxFU8E5T8MqXaDs6gr9OJpRK2u87dY575i7mx1lTaRHiOmXBG0FRVDbGJbDq4DGUliZUayFC7qXrniBAk4ZB17WpnHjsHElxqdivuG7arTIr523hkVcnujz+6o7mk3R1tKu6m5H0ElMfG86khwZjMEqENwnG5Olen4yYw8nsjzmN2WLDKwVkg4DNm5KrzRW/BUFRObI5iYnrP+f9NybTPKLq9Sv/Xa7m2l5bWzGuxvGYMzx75xfY7QpWs43VC6MJb1KHdxc9XPpdNZj0/Ofr+3hp5lwQQFVUZLvC1NnDiOzoWld2DUhNz0Ov1zkkSaoKcqqFzZ8+SGZhET4mIz/tPMjeU+cc3sNil8nIL8Rqs5Ofb8bPzxPJSRVE4xJHz6VzNiePVqF1CQ9wLhk8qE8kvbpGEHv0LAaDRJuWYU6rSxp/nxXztlBUaC4VfgEwF1tZ8e0WJj0wEB9/T/qNiWL/tuMOEth2u0K7brVXxaxP92YciTtXRlQEwCIrrIs/ieUKIRGLXeaz9Tv4YNqYigyzDKqq8uSi1Ww6nkix1YYACA0EvLwlTOfsmIx6DAaJF568vhjTz2ajkxx/i7KskJyoVZI0/j5akuRGtq09xAfPLEa+oPYV3jiYl764x+1J0oFDJfKnULKb5JckIxugOFiHrU5JkiArKqgqxvMKco5CPjaee205i755UGtBq0Woqspbjy2gqOCSD4W5yMqZ+DSWfrWJOx69NFvUrnsEC/e8wu4NRzAXWenUtyVB9TTVOncQFuLvoI4GJW1dzZvWRRQF6lwwM+3SOBxJFLgin8LToCfteBZjvvwI9ULF4/67+3LraE1I40pyisw8MG8ZJ9Oz0IkCNllmeNsWvD5xGDonVWeTUU+XqMYVH2gNJyY6HrvV8XuvN0gkxqXQvnsEfUd1YM2inRw7cApzkbXEkNYg8eCL42p1S+PoYe345Y8DpKbllbbdmYwSYyd05LsThx2SJEVVOXquchOHXYnJbIpLpNhWsl5RAVUAc7DItF5diKxfl/69WmC6zi6XiDbh2KyO8vsGo0T7Hu4VydKomWhJkps4HZ/GO0/8iMV8qfx/6ngqz0z/kq//etqt7V5+vh4YDFKZHSWdFYKzRKaN6sGS6BhSMvIw5ilIl/kl5uUVk5CUQUSTqinioCgqX2/Zw/fb95FnNtM2PIRnR/enbfjNe01ZLTaWzN3EuqW7URSVAWOjmDJrcLl+DDWF9LPZZKblOTxutdhZv2JfmSQJSmYt+o6Kqqjwai3eXkbGDu/AqrUxpaaIAAa9jum3l1W0als/hD4tGrP1eFJpO41JLxEgGtm/4WTpdcBqtfPpVxvw8/NkQB/NfPZynlu6hrjUjDIti2sPnyAytA73aP5TFUZIwyDiYs6U8bQDsNlkgi9syOgkHa99/yA71sUSvfYQ3n6eDL+9O01ahVVGyFUGD5OBL9+bzm9rYtgcfQJ/Xw8mjO1Es+b1+OqNGKfHFKQX8e7Ha5g6qRvhoQEVHDFsOpaA2ebYIimKAnWbBzK8x9+zSAgO8WfIhC6sX7G3tPVSpxPx9DYx+o5eLom55iEgq1olvDy0JMlN/LZgO7YrNP8VRSU3q5ANW45w2JZHZkERfVs0ZmBkU6e7lTfKoH6RfPHNRofHBUFgwtAoojefIC89x/FAQcBuv4bSQyXy5uqNLN0bW+qlsP/0Oe7+ajE/z5pGRN0b95tSVZXnZ3xFXMwZrBeS2uXfbGHPxmN8tOKxGt2iJ+pEymvw11qIKpfZ9w0iONCbn1fuIb/ATMtmIfzj/kE0aeQ4Q/De1NGs2HeEJXtikRWFWzpE8v27mxxbbyx25v2wTUuSLqPQYmXriSSHmS6zzc7CHQfcniRZzDai18WSfi6HFu0b0KFHRK2t5k+4tz/Ra2PLbC5Keh0t2zcgrPGl771OJ9J7RHt6j2hfGWFWWTxMBm67tQu33dqlzOPjOrXml/1Hy/oQKSpqQjGrj8bw54YjfPLuHTRrWrEbpHpRhygIDkayOlHE6wZnpGe/NommrcNY+e0WigosdB3Yiun/HIFvgPuEsjRqLlqS5CbOp+aiyI6Lz/wQPU/89ReqUKJAtyrmGK1C6/D1jEklKlUuwNfHg7dfnsSLb6zEarWjUlJ2f+258fh4mxg+uA0JpzIcFlBGg67CL5LXS26xmSV7Djntq56zaRdv3zbyht/78J5ETsQmlyZIADarnZQzmezccJReNdjbpE6oP+FN6pAUl1pGQcpo0jNisnsFGVRVJel8Nga9RLi/8/mP2owoCkyb1J1pk67976ATRSZ2acvELiXf1ZzcIuYpG5y+NuN8vkvjrO5Y7HaEcgaXL/eYcwfJiRk8OeUzrBY7VrMNvVGiaWQob3z3AEZT9RbSuRGat6vPUx/cwcfPLaG4yIIiq3Ts05x/vzetskOr1jw/dhA+JiMLdxyk2GJDtKj4nJXRF6nIQLFs49O563n/zSkVFtOfG47w61e7UJoLDjrLKjCoVYTDMaqqUlRgxuRpLHcTTxRFxtzZhzF39nFD1DUPFVA0oety0ZIkN9Glf0v2bTteRm1HFeFclDfqZU7YRVYbh8+ls2L/YW7v6rpdsQ5tG7Bs/sMcP5mGIEDzpvVKLyqjh7dn/eZjnDiZRrHZhl6vQycKvPT0LVW2epCclYtep3PaV33kJvuq4w6ecVDDASgutHJs/6kanSQB/N8n03ly8mdYzTbsVjs6SUebrk245W733WR2JSbz78WryTdbUFSVxkEBfDh1LI2C/N12ztqEr48HXp5GcnIdJcBbNKv+4iw3g9ls4+vvt/DHulisNjvdOjch2NuTc7llk0edINCvRRO3xvL2P38gL7uodINCLrISf/gsS+Zu5I5/DHXruasqvYa1pceQ1qSfzcbLxwMff89rH6RxVSSdyL9G9GX2wJ4Mm/g+KI4buLFHzlZYPKlpufz3gz9QrHa8TwnkN9QhqIAAnh4GPr7zFrxNZVvd1y7Zzbfv/k5BbjEGo8TE+/oz5eFBmlKthlvRkiQ3MWhcZ5Z/u4X0s9lYL1Rs1FBP9Hod1itE/802O78dPObSJAlKWhJatQh1eNygl/jgzSns2pvI3oNJBAV4M2xQG4ICvV16fldgt8tsjT7BoRPnMDsZyBQFgeb1bk7CtG6YP3qD5JAoGT301KsfeFPvXR2o37Qu3299np1/HeZ8ai6RHRsRGdXQbS0/aXkFPDR/RemwLsCJ9Ezu+vpn/vrXfZoCmwsQRYGHZvbn/c/WlakYG40SD8zoX4mRVT7PvLSEI8fOYb0gELAtOh6prhFTYwm7rGBXFIyShLfRwGNDe7stjpzMAk6dSCtTwYWSecB1y/bU2iQJSqoBIQ1uvIX6etiRcJrvtu8jI7+I/i2aML1nR/w9TW4959/BarWzZXUMOzceJTDYh1FTutPwJjc4DAYdRr3OoYsEwKsC52/XbzqKcmGz2CNbxZhrx+YjYNBLPD6lFz0iGpZ5/bY1h/jslRWlm852u8zPc0oq5dNmD6mwuDVqH1qS5CZMHgY+WPoov3y/lS2/x+DlY6L9uHZ8GHcQq5MWDpO+YlsrdDqRnt0i6NnNsaRdVcjLL+bhx+eTmVVIcbEVY1M9sr/A5TOGBknHA/273dR5ug9ug9G0AnOxtczAsKTXMWBs7RApMBgl+o7qUCHnWrq3ZG7mchRVpdBqY9vJU/R38+69u8nJLCAvu5CwRsFI+sqbZxs5tB2+vh7M+2Ebael5NG9al/vu6ed046S2cDw+lWNxKaUJEpR894QcOw9FdiPLx05iRjadG4dze7f2bl00K8qFrXMnlOcDpOEaFuzYz//Wbi2d0YlPP8+yfbGsmD0dP4/KT5QsZhtPTvmc5KSMEgU/ncAfP+/iibduo99NXKcFQWDM8A789sfBUgU8KNk8mXRrxQmUFBVby8i8iwoYc1X0egW71XEuev6Hax08sCzFNpZ+tYnJswaV6YBRFJVDZ1MpsFiJahCKl1EzO78Wmk9S+WhJkhvx8jEx9ZEhTH2kZKdDUVS+fveoQ5+7oEBjURsqvJIvvtpIalpuqZiEZ6INNVyHpZ6EIqhE1A3ihbGDiAytc1PnMRgl/rfoEd56/AeSjqcAAqGNgnj6vWl4+dReSVl3kZKbj1V2bG9UFIX0vIJKiMg1FOQV8/Y/F3JwZwKSJCJKOma9MJbBFbj4uJLe3ZvRu7smfXuRhMTzTiukFoud9NM5PP9UxXnGBNbxIbxxMKdOpJZJigxGiUG3aDLt7qLIaiuTIEHJbGt2UTHzo/cze1DPqxxdMfy+aCdnEtJLBSwUWcUi2/jguaX0GNIGw00YFz90b3+ysgvYtjMevV7CZrUzdGBrpl7H7KOr6NW9GUuW78VsuWItJAj06OrodZV+zonQFCXVNnORpfQ+fTIjk/u/X05esRlBELArCs+NGsCkzu1c/5fQqBVoSVIFIooCn08fx51fLMJstZU03QlgylDYdDiWTiFhjLjO+ZfkhAxWzt9GcuJ52nZtzNhpPWucesumrXFl1PYEFbyTZXxTVH5b+hieJtftEIU1DuajFY+Rk1mAIisE1tWEBNxF9yYNWB0TR9EV0q+KCv6Kni/mbgABBg9oTfNqND/z+j8WELsnEbtNxnbB5/LjF5ZTLzyQtl2qd3WsptCgfgDOijQGg0STxjfXtnsjPP3+NP497XPsVhlzsRWTp4HwxsFMfmhQhcdys6iqyroj8czbvpfsIjP9WjTmgb7dCPKuWjNFx1LSkZzMsVjsMhvjEqtEkrTl95gyCn8XEQQ4cSiZNp0b3/B7G/QS/3n2Vs5n5nMuNZcG4QEEXMWA3R20ahnKwP6RbNh8DLPZhiCA0aBn3NiONHDS4t6oeT2OHTjt8LiXj6nUpkNWFGbOW0pGfmGZ3/jrqzfSKrQubcKqz72kIlFVTQL8amhJUgXTKrQu4XEqGdhRJAF9voJkAQsy387fcl1J0sEdJ3npoXnYrTKyrHBkXxK/Lojm4+WPUifEPeaehQVmzqfmUjfUv8K8g8qbiRHA6U3OFfgHVb25rGuRkJFFck4uLeoGE+LnU9nhXJNhbZozZ/MuTmfllApxeOglGhq8+d9rq7Ba7QiCwIpf9jHltu7cM73qqxSlnc3myL4kh7k2i7mkJURLkqoGrSPDaBAeSOKpjNINGEEAvV7H6OEVLyfduEUI3218li2/x5B+NpsW7RvQpX9klRXQuRqfbdzB11v3lHp1nd2Vy+pDcfwy+y4CPKtORT7AyxO74tzqoo6PJ8VmKyajvlJl2Mu7xyqKisnDNZuDwUE+BAdVzv1CEASeenwEg/pF8tfGo4g6geGD29KhXQOnr5/571G8cO/XZRJHo4eeGU+OLBVu2HvqLIUWm8MmiNUu89OuGF4dV3tn/DRuHC1JqmBkWSEvp5iSS2DZn/P5zGu3Gqmqyvv/t6RMf67VYke2K8z/cC1PvHmba+O1y3zx+q+sXboHnaRDlhXG3dWbe54Y7vabyOABrVj9Rwy2yxTtRFGgU1Sjm2o3qCkUWKw8vHAlMcmpSDoRq11mVNuWvD5uqEt9t1yNQdLx4wNTmLdtH6sPHcMoSQxu2oRfPt9xSeREVbFY7Pz4804GD2ztdHexKpF9Ph9JryuN/3LSU5y3imhUPIIg8N5bk3n/k7Vs3nYcRVFpHRnGk48Ox9+vcioent4mht92c3OVlU1usZm5W3aXUR+1yQp5Zgs/7DhQJaozF2kSHEBEnUCOpWYgXzaDqhdF4tclMfaHD/D2NjHjrj7cOrZTpcQ4ZlpPDu9JwlxsLX1MEEo28Zq2qhkzhYIg0LVzE7p2vvYGUrtuTXn163v55p3VnDqRRp1QP6Y/Npw+Iy610eUWW3C2JFFUlawiR5VPDY3rQVtpugG7rLDy4BEW74tFVVUmRLVhQqc26HU6dDqRenV9SUvPczguPOzajtfZ5wvIynD0OZFlhd2b4lwS/+XM/2gd65btLVn8XVgArpy/jYBgb8a5USIa4P4Z/TkUm0xKWg5Wq4zBIOHtbeTfj49w63mrCy/9+icHzqSUzPdcWJv/ceQ4zesGcW+fLlc/uJLxMhp4ZFAPHhnUA4AFC7c7lWFXVYXoHfE0mFS1F5ENI+o5NWKW9DqietacmaDCfDPrlu8hdk8S9ZvUYdTk7tQNq16y7T7eJl585hZkWUFVVaQabBZdUcSlZji1aLDaZbadPFWlkiSAz+68lUcW/EJ8RiaSKGKx2fE5LWNPLdl8zM0r5vO5G9DrJUZVgmFt90GtGHNHT1Z+v61U/MXDy8grc2bUWqPhdt2a8v7i2eU+37lRGFa74z3EQy8xpFXNuQa7A0UTbigXLUlyMaqq8uiiX4lOOF3adnA87TzrjsYzd/p4BEHgofsG8Nb/VpeV5jVIzLpv4DXf3+ihd5CMvYint2vb4FRV5ZcF0Q690ZZiG0u+3uz2JMnby8hXn81g994ETiZmEB4WQK/uzdBXomLY1VBVlYTzWQiCQJOgALfezCw2O2uPnMAml12Ym212Fuw8UOWTpCuR9DpEUbig+HUJQRArVSHuevH0NnLH7MEs/PSv0iqvThLx9DYycWa/So7ONWRl5PPopI8pyDNjMdvQG3SsnL+dN76eSauOjSo7vL9NdWxpq6rU8fFy2sImCBDmX/VagOv6eLN41jQSz2eTU1TMK88sJSfTXOY1FoudeQu2VkqSJAgC9z41ilvv6k3snkT8Arxo3yOi0r6ziqJQmG/B08uAropuKnhJesILDCQZikoUcAUBQYbwIF9Gt2tZ2eFpVFO0JMnFHEhOITrhTGmCBFBss7PvzDl2JSXTvUkDBvZrhUEvMXfeZlJSc6gfHsgDM/vTvYujqsuVeHmb6NK3JXu2xJXZeTd66Ll1ei+X/l3sNhlzkdXpc3nZFVO+FkWB7l0j6N616kqVAxxMTuHxxavIKTIDKkHeXnx8+xhahdZ1y/ksdnu5MsEFFotbzulOBvSN5NvvtwKOC61+fVpUfEA3wO0PDKR+k7os+Woj2ecL6NynOVNmDa4xIiDff7iWnKxC5AsVM5tVxobMe88tZe7qJyo5Oo2KZsOmo3z73VbS0nOpHx5IeEsfTuXllkmWjJLE3T0rT93xWjQJDkBV/cnJdH4/y7yOFnh3Ehzix4AxlWtD8fviXcx7fy1FBWb0BomJM/oyddbAKmfiunjpbuSjhfibZIrqiag6AWOWgn+qBYOkLXXLQwVkqta/ZVVC++a4mN1Jydhkx7mEYquN3ReSJIDePZvTu2fzGzrHE29N4sX7vyUxLhWdJGKzygwYHcWYO1zb0qA3SIQ1CuJs0nmH55q1CXPpuaozucVmZn6/lMLLpN2Ts3O5+7slbHzifjwNrvPAysjI41BsMn5+njQI8CMxM7vM86Ig0LtZ9dvVDwnx49GHh/DRZ+tKb76KovLk4yMqbbj4Rug1tA29hrap7DDcws4NR0sTpMtJO5tFTlYB/lXQjLq2kZFfyJJ9hzh5Pouo+mGM69AKb5PrhXb+WHuIDz5eW9oNkZCYgZQu0WxAHRJyc5BEEUkUeXHMINrXD3H5+V2JIAiEhviRkprr8FxYaPVqJXU1m3+P4cs3fivtJrHbZBZ/vQlRFJg66+oKjKqqcizmDId2JeLj70nf4W3x9nWfgMeadbFYrXaMVjDmXdpAzjDkk5qWS0g994haadRstCTJxQR5eWLQSdiVsi1qRr1EkJdrBoN9/Dx5/+dHSIxLJf1cNk1bhblN1W7WC7fw6iPzSy+SgiBgMEo88GzF+YlUdVbHxqE4KevYFYW1R04wLqr1TZ9DVVXmzN3AshV70Us6EEAIkDBFSNgVGbuiYpB0eOj1PDm0702frzIYPbIDvXo2Y/uOeARBoHePZvhV0jC9hiNGDwNQ6PC4qpZsqGhULkdT0rnz28XYZRmLLPPXsZN8uWUXSx+cRl0f1yWwqqoy95tNZdrFAeyFdgKP2PnynZnkFJtpEhyAXlc1W7Ou5MH7BvDmO6vKtsAbJR66/9ot8DWZ+R//6bzd/pvNTH5wQLnVJFlWePOJH9mz9Tg2qx29QWLO26t49ct7aHsT8uXloapque3tKuUr5WpoXAvtzuZihrduzhu/b3R4XBQERrZ1bV9sk5YhNGnp3l26zn1a8Nb397Pw0784czKdiNbhTHtkEE0jtUrSRTLyC8u0V17EYrdzvsBxUXkjbI8+wcpf92OzydgutFkKxVaaib60n9CGk+ez6NQglDu6RxHsXX39sgL8vRg94sYd5TXcx+ip3fnhk7/KLJokSSSqRwRe3ia3nVeWFVYu2M5vC3dgLrbSY1Brps8eTEBw9akwVgT/t3IthdZL7dHFNjtWu8z/1m3l7QmuE7ux2WRycpy3pyWfzaKurzd1fatXVbF/30j0eom532wiJTWH8LAA7p/Znx7dqnabt7s576S6BiXWBpZiW7kKd2DRAAAgAElEQVRS5ZtWHWTv1hOl85kX///6Yz+wYNOzLpmtssoy7/+1lUV7D1FssxHW0hsKJci+dC8WhJJqYL0a0vLsHjSfpKuhJUkuRrWphMVDQoiKogNUEBWY3qwV/p7uW0i4k8gODXllzgyXvNfpk+kUFpiJiAzFYHRdG1pl0qlhOJ4GPUXWsjtuBp1E+7AQMjML8Pf3vKkbw8pf92O+YkdPVaEoo5g7WrUhIkIzytNwLxPu7sOJ2LPs3HAUnaRDVVVC6gfyr7dcaztwJe8+s5jov46UJmdrl+1h18ajfPnrP/HyqZ7XVFdTYLFyIj3T4XFZVdlwPMGl59LrdXh5GcnPNzs8V7dO9V2M9urRjF493KOClpyTy/7T5wj08qRHkwZV2qLhcho2q8vxQ8kOj/v6e2LyLN+vac2yPWXkyy9isdg5EXuWyA7O/ZD+Dv9e+jsbTyRisV/w5TIXoGulIzRWgEI7BoOEJOl46flbb/pcGrUXLUlyMctX7MWWWkzd0zJ2r5ISr1Sosvbgfh6a1BcPFxnBVTfSzmXz0sPzSTmThU4SURWV2S/ewuCxHSs7tJumV9OGtAmty6FzaZgvVJRMeok6qpEXHlmEAHh46HnowUEMH97u6m9WDkXlCGiIokCxE2d2DQ1Xo5N0/N/700hOzCD+yDnqhQcQ2aGBW1tZzp3KZPufh8v4T8l2hYI8M+uW72XcXb3ddu7qxNXMtY0uHloXBIHp03rxzbwtmC2XmXsaJWbeXT1bfd2Fqqq89vsGFu+LRdKJCAh4Gw18d/ckGgdd2/Kjsrn3yZG8+OC8siauJj0z/zXyqr/78kSFAFQHu9e/T0puPhtPJDhIzgs6aH1LBD2N9QgO8qFvnxaYTDVjM9ZdqICiCTeUi/bJuJjo6BNYrTICoC9U0ReqCJTIzZ48mX7N4+MOJTPnv6uZ89/VHI913MGpjqiqyrP3fcvpk+lYzDaKCiwUF1n56D8rOXHkbGWHd9OIosDX0yfwz0G9iawXTOuQunTWBSFG52Kz2rFa7eTmFvPBh2vYtevGdnUH9o/EaHS+2GnRvGoPRldlTmXl8PLq9dwx72feWruJ1DxHDzKNstRvUocBozvQKqqh23v9Txw561Ry2GK2EbM70a3nrk6Y9BJ9mzV2SJaMksSkjm3Yvv0ES5fuJubg6XItJP4OkyZ04d4ZffH1NSGKAoGBXjz+j2EMHNDqpt+7JvHHkeMsO3AYqyxTZLVRaLWSnl/Awz+tdMm/g7tp360pr86ZQWRUQzy9jDRuEcJT70xmyLirm+wOm9AZk4djcqI36GjRtv5Nx5WUmY3BybybXVHJsBUzdXIPhg5poyVIGjeNVklyMUHlKDzJsoKv39WVXb55fw0rF0RjtdgAgVWLdjFuek/ufmwYYjUePIw7lEx2Rr6DB47NaueXH3bwr9cnVlJkrsMgSdzdsxN39+xEcbGV8RM+xGotu8tlsdiZP38b3bpdW+r9SsaMjmLNuliSk7Mwm23odAKSpOPJJ0Zi0Ibmb4gDySnMmL8Uq2zHrqgcPJvK4v2x/HzvVCKCAys7PA2gXliA08WkpNcR3jioEiKqurxx6zDu/m4xyTklRuWKqtIhNITor/fzZ54Fm01GpxNp2rQu77w79aYWkIIgMGlCVyaO74LVWtLapA3HO7Jw90GHeVWVkkpIwvksIupU/e9wu65NeP/HWX/rmIFjoti29jAHdsRjMdswGPUIAjz3wTSXzCM1CQ4oMVG/AkkUae0m2w2N2om2unIxEyd2Ze++U1gua0MQRYGGDYNo2KD8C2JSfBorLzNutZsEUruYeCvnMO+8dpR+zRrz8ugh1KtmA7EAuVmFCKLjDVRRVDLT8yohIveSk1N0QfXH8SKemuZ8EPZaGI16PvlwOhs3HWPHzniCAr0ZMzqKRo2CbzLamomqqmxYdZCfvtpITmYhraIaMvPx4TSKuHQDfWnVnxTZLv1ObbKMXZZ5a+0m5k4bXxlha1xBy/b1CakfyJmE9DLy45IkMmZKj0qMrOoR4OXBylnT2Xf6HMk5uUTWq8Pcd9ZxML2gdIPKZpOJj0/j+++28sCDN6/cJggCxhoyW+oOCi3OW6FFQXAq9lNT0OlEXvzkTg7vO0XMrgR8AzzpN6I9vv6uUSsN8fVhcMsI1sclYL4wk4QKggq3ta+ZFgzuRFa1DY7y0NrtXEzHjo144P7+GI0SXl5GjEaJiIi6vPH61Yebd244VmoOq4pwbogfRaEGEAVkVWVzfBK3f/0jVnvVv7CqqkrMuVTWHz9JRkEhkR0aYLM6JgxGk57u/WueE3adOj6ITpJCQYDIyNAbfl+DQWLY0La8+Pw4Hnl4iJYgXYVFX23iw1dWcPpkBnk5RezadIzHpn3OmcQMAKx2O8edDLqrwK6kmtHmWhMQBIE3v5lJVPcIJL0OvUEitGEgr86ZQb3wqj/TUdEIgkDnRuHc2qE1Df38iIk541DBt1rtrF17qJIirLkoisKZxAwyUnOAkoR0ROvmmJzMhEmiSGRInYoOsUIRBIG2nRszbdYgxkzp4bIE6SJvjx9BN/8QRLsKqoohVyb4gIWXn1xCfn6xS8+lUXvRKkluYPz4LowY0Z6TJ9Px9fO4agXpIpJeh6gTkWWFojADikGAyxbasqqSb7HwZ9xJRrWpuolFWl4BM35YSkpuPqIoYLXL3Nm1A7fd25dl87ZiviAFajBKBIf4MXxCl0qO2PVIko6ZM/sxd+6m0oqiIJRUg2bcow02uxtzsZUf52wsM2ysqmA12/jxyw089dbtSDodep3oMPgL4GWsneIqVRX/QG9emzuDgrxirBY7AcHeWmvXdXBlcnQ5suJoCqxx4+zbHs87zy2muNCCDcDPE4usIBpEPHp6Ieglim32UpPdN8cNv6rYhsa1sVnsJP+WROgVfl05hkJWLN/L9Lv6VFJkGjUJLUlyEx4eBtr+jQHFfsPb8t1H6wCw+ehQJcdFQJHVRsL5bJfF6A5mL/6VpMxs5MvmCH7ce4g3xg7l2XZTWLkgmvzcYvoMbc2YqT2uKiNanZkwvgtBgd4s+GE7GRn5tIoM5b77BtC0qdYv7W5Sz2YjOul7lxWVAwdOsXNHPK1ah3Nr+9asjDlSJlEySRJ3dGlfkeHWamRFYcOJRLbEJxLo5cmEDq1pEODv9LXevlef6dQoi5eXkebN6xEXl1JGbUySRPr3j6y8wGoYKWeyePmxBVjMNlRRwO7vCReuKbJFxnd7IQ3aB9KgRxj1fL25vVO7aqFsV9WJj09HJ4lgKfu41SqzY2e8liRdJyoCstZUVi5aklRFqBPqzz9evJWPX1mJl1kg1w7KFa3engY9LepW3UHPc7l5xKVnlEmQAIptNr7ftZ+fZkyhey26OffvH6ktRiqBoDo+pa2rF1FFAbufBxlWmddfW4nNpjD1rp70aNyAHUnJGHQiVllmWKtmPNi3eyVFXruwyTIzf1hGbEoaRVYbep3IN9F7eXf8CIZGNq/s8GoETz09hscenY/NJmM22/Dw0BMQ4M3Mmf0rO7Qaw+olu0rn5WSTHq7Y37RbZQoO5fDIrFu0TTIX4u/vWWZOsRRVRbVplVIN16AlSVWIoeM60bVfS9atOsjbCXspxM7Fn7okigR5eTKwxd9XRqso8s3WCyZ5ji1MeWaL4wEaGm7Ax8+TvkPbsvXPWKwWOypg9/UAUUBRVQoLSzynFi3YwauvTeKFkQM5lZVLRHAgoX4+lRt8LWLloaMcOpdaOsBukxVsKDy9ci39mzXB4GJ/n9pIo0bBLPhhFuvXHyE5OYvIlqH4eRr49LVfKCww0294OwaOao9er33WN0ra2RzsF6vROrGkt/oKdDqBs8nZNTpJOpySxiebdxCXdp6IOoHM7teDDuE3PoN7LRo2DCKknh+nTp13+MxPxZwhJ6sQ/0Avt52/JqGoWiWpPLRPpoqRkJjOvB+2EbizCFOKHUFW0akwqnULFt07Fb0Tb4CqQkSdQKfxGXQ6hka6x8lcQ8MZj78ynkFjojAYJSSTHiTHxYvZbGP58j00CPCnT0QjLUGqYH45dNSpwpcgwIGzqZUQUc3E29vELbd04uGHh5B2MoNXHl/Ipj8OsWfrCT5941eevvcbh8qrxvUT1b1pqSeQYJedOqnaZYXGTWqW0I6iqphtdlRVZc/ps9zx3c9sOJ7A2dw8Nscncdf8JWxPOOXWGLq1qY8gKyWfuVIi4CAWWJFUiN5w1K3n1qgdaElSFUK2K7z68grMZhtykUzgURvhm8w0jrbTT6pHoGfV7smXRJHXxwzBpJfQXViQGnU6jKpIzI9H+M+LSzl29FwlR1k5JJ1I44VH5jOpz+vcd+uHrF25r1qYCVZXDAaJx/8znsVbn+fZd6fi6WV0+rq8XE0FqbJwpvoFJeqYzowiNW6O7MwCFs7ZiKX4kqCJpdhGQlwqW9bFVmJk1ZtBY6IIquuL3qBDNNtKJDIvu7YbDBJdOjehwXUIOFUHZEXh/Q3b6PzOp0T99xMGffINz/yyhmJbSdX+ImabndfWbHRrLAaDDn2uBSm7GF2eGSmzCJ3FDioostZyp3HzaEmSm1AUhZzMAqzW65fsPnEi9VLZ/jIsZhtr11QPydahkc1ZPHMqkzq2pWv9cPxO2vHfVMDp4xls3RLHE48vYMvmY5UdZoVyJjGDx++aw55txynIN5OcdJ5P3/iNH+duquzQajxGk57O3Zo47V03GiX69qu6SpHVGUVVOZySxqFzqeUqqd3eqT0eekePHU+DgfbhIe4OsdZxaE8ikuR4yzcXW4neULuuya7E5GHgw4WzmHh3H+o3DCYi2IdmEXUxGiX8/DyYdFtXXvxPzfFde/vPzXy7cx+FVhuKqnI2N4/TObk42/I7eT4LxY2bgX2GtMVglBAUFdGulI6DqYpKN+3afl2ogIxYaf9VdbRGZDewduU+vnpvDcVFFkRRZPRtXbn38WHopKvvjoo6sdzqgl5ffXZWW9QN5pXRQ3jz9V9IS7SUStGqKlgsdj58fw29+7R06iVUE1k4ZyNWs61MF4bFbGPR15uZML0XJo+aqfBXVfDwMPDI7CF8+sk6rFY7qlqSIIWE+DN6TFRlh1fjOHg2hUcW/0qB1YpAScXoo4lj6NqorNrn4BZNmRjVmsX7YxEFAVEQkESRTyaMYe4X61mzOgar1U7nrk14ePZQ6oX4Vc5fqIbg5WNyOi8jigK+/lW7S6Gq4+3rwT2PDuOeR4dVdihupchq48d9h7Bcp1+jj9GI6Ea5/hZtwhl9e1dW/bwbq8WOKAroJJF7Hh1KHe16oeECtCTJxezcFMenb/x2mUeLzKrFu1EUlYeeGnXVY5s1q4eXl4ni4rIu3SaTnlGjq95i7mxuHtsSTuFlNDCwWVM8DWV3hfftS3Lq1VFUZCEjI4969WrHRexYTLLTz0HUCaSezaZxs3pXPf7UyXRysgpoFhlWstDR+NuMHhNF06Z1WLliH1nZhfTq3ZwRI9pjMjlWMjRunAKLhRk/LKPAai19rNBq4/6fVrDhH/cScFnLsCAIvDBiEHd168SuU2fwM5no37wJLz69mEMxZ0qr8Nu3Hic2Jpl5PzyIj4+2mL9Roro1xWCUKC4sK6KjN0iMnNi1kqLSqE6cLygsbaV3QIDLy0kmSWJKVFu3x/TAk6MYMLID2/48XCJvP7I9DWuwQIarURGQ1dqxYX0jaEmSi1nwxfoyJpZQUjX4fekeZj42FIOx/EWZKAq8+voknvzXQhRZxW6XEUWBXr2bM2hwG3eH/rf4cNN2voregygKpTtFcyaPo2vDS7vFfn4eZGUWOByrKCre3rVnsR/WMJCU5CyHx2WbQlBd33KPyzqfz4v/WMCZxPPoJBG7TWbq/f2Zep8m33sjtGodTqvW4ZUdRo1mzdF4p+01sqrw2+E4pnd13OxpFOhPo8ASb6SEk+nEHjpTpk1ZVcFstvLHqoPcNqWH+4Kv4egkHW9+eQ/Pz/qe4mILgiAg2xVmPTOaiEj3qZBp1Bzq+Xo7fVwA6vv7cT6/EAGwWO0YEi2s+zOaxKUn+b8XbiW8fqDb4mrRJpwWbbRru4br0ZIkF5OekuP0cVVVyc8tJqju1XeuW7QMZdHif7B92wlycgqJimpERLN6KKrK5pNJbDmZhL+nifHtWhPmV/4C253sPp3MNzv3YpHlMmrfD/38C9GPP1Aq3Tt5Sk8+eO93zJcljXq9jp69muNVziB9TWTq/f2J3XeqTPJsMEr0G9YWn6sYZL7yxI8kHk9DvmwA9aevNtO0RQjdtX5rjSpIVlERVtnJXKVdJquw6JrHJyZkIIqOfeoWi52jtVT0xZU0bRnK/HX/5ujB05iLrLTu2KhcURNnpJ7NZv/OBDy9DHTv11JrFa5lmAut3NKkGSsSTmC+rOXOKEl8OHE0oZ5e3HnvHKzZZgR7yfLgeFwqj83+noU/z8Zg0JacGtUL7RvrYpq1CmPv9niHxw0G6bo1+z08DAwecqlyZFcU7v9pOfuTUyiylZgufrltNx+MH8WgFhEui/16WXIgFrMT6V4VleikM/Rv1gSAIUPbcOZMJosX7USv12Gz2Ynq2Jh/Pz26okOuVNp2asy/35jI52+tIi+7CEEUGHprRx78d/ntl6lns0mISy2TIEFJVXLZ/O1akqRRJenWqAF6nYj9CrEGT72e7o0bXPP48PoBTucyDQapRnvMVCQ6nUjbTo3/9nHfffInS+dvRxCE0nnSVz+dTtuOjVwcoUZVQ1VVvvpyPcsW70ZvkPAJVhGaGVCMAq3r1eWZof1oG1qP1b8dQJcvI9jLHmsx29m29TgDB7WuvL+ERrko1UBAobLQkiQXc8+jQx2qBkaTnnseHXpN4Yby+DX2GPuSUyi2lbznRdPFf638g53/fLDCTRfNdtmpkg0qZXaRBUFg5r39uX1yd06fyiS4jg91r9JeVpPpM7gNvQe1Ji+nCA8v4zV31PLziku+LxbHZDQnq9BdYWqUg6KqrIuLZ/mhIwjA+PZtGNoiAsGNQ8nVhVNJGRw7eo7gOr50iGpIn6aN2ZqQVOqB5KGX6NwgjO5XCDc4o2VkKI0aB5NwMh3bZd49kl7H6LFVby6ztnBwdyLLfojGesX16D+P/sCP65/SzGhrOH+ujWXlsr3YbDI2m4xHIfictdO5a1Nef2Zy6etSU3PKdI5cxGq1k56a6/Y4MzPy2fB7DLnZRXTs3pSO3Ztq12iNm0K7srmY5q3CeOebe/n2o3WcOHKO4Hq+3PnQQPoMufGZopWxR0sTpMsRgP1nU+je6No7tK5kdOuWbIxPdIjJpij0dLJb7O1torXWL4wgCPgFXF81sVGE811zvV5H9/5aFakiUVWVp375gz+Pn6Townc+OukMQ1tG8M4tIys5uspDtiu8/spydkbHIwgCgijg5+fBOx/cSXSLpiw+EIuiqkzs0Ibx7Vtf12JFEATe/t9UPn5/DZs2HkVRVFq2CuOf/xpJQKDzeQgN97Nmxd4y/koXURSVg7sT6dKreSVEpVFRLP5ph0PyY7cr7NuTSF5eMb4X2sZbRobh4aF3EJ8yGHS0dPPc297oeF5+4icURcFmlfll0S7adWrIyx9Mu+EN6tqAqoKsapWk8tCSJDfQok04b355j8verzxjRRUVyUn/vrtQVZUjsWfJ2J9KhIcvJ8ml2GZHEkUkUeQ/Iwbhbaw9s0buxGCQePjpUXzyxm9YLSXy4QaDhG+AJ5Pu6l3Z4dUqYlLSWHc8vrQyAlBks7HmWDzTu6TSPqx2evqsXL6HndHxWC6rLlgsNt56dSUffnY3Ezrc2MaQt7eJZ1+4laf+byyKolYr+4OaypUVpMuxWR1n0Ko7RYUW1v6yn5i9SYQ3CGL0bV0ICQtwy7l2JJ1hzo7dpOTm061hfR7s1bXS5o3LIz/Puem2KIoUFlhKk6QePZoRGhbAmdOZpZVgg0GiSdO6dHBjW6bdJvPG04vLdPCYi60c2neK9asPMfQWrQqtcWNoSVI14Paoduw8lexQuTFJeqLCK0aVSJYVXn5uCfv3JGG12tEbJIKDRNrd0oKGYUGMa9+KpkHuU6+pjQy9pSP1GwezbP52zqfn0bV3c8ZO6X5VsQcN17M98RQWJybPNllmW+LpWpsk/bpyX5kECUCRVY7HpZCdXUjAdVZNy0OnEylnf0ijghkwoh17tp3AfEWFQJYVOnRtUklRuYecrEJmT/uCvNxiLGYbkl7HL4t28ton02nXybUL/WUxh3l5zfrSDZhT2dmsOhrHipl3UN+/6lhkdO0ewZrfYxxmZD09DWX8y3SSyAefTGfh/O38ua7E/2zYiHZMvbOXW9vejsU6t9kwF9tY99sBLUnSuGG0JKkaMLhFU8a1a8WymMMljtKqgCIr3OrRkIy0PEJC/d0ew7o/DrF/T1Jpyd1itiGehZSfE3lv+dhaYwxb0bRq34Dn3pl87RfWQMzFVn5fsZdt64/i4+vBLZO707Fb0wqPw8doRK8THRIlvU6Hr6n2Vk7Lqy4IolBGwluj+tNzYCs6dGvKwV2JmIut6CQRnU7k0efH/i11vOrAgi83kJ1ZgN1ekhDYbTJ2m8y7Ly5j3q+Pu2yxb1cU3vhzU5kKtV1RKbRY+XTrDt4cM9wl53EFd83oy7YtxykqsmCzyQiCgMGg4/EnRzrc+z09jdz34EDue3CgW2LJKTbz8ZZo/og7gUGnY3JUO3p51HMq+AIgSVor2dURUNDWb+WhJUnVAEEQeHnkYKZ3juK5D1eQmpSFdMbKJvEgWxfH8PQLt9B3QCu3xrBm1QGnA5nFRVZOnkileUvNZ0PDdVjMNh6/5yvOJWeVtlDs23GSaff1Z/KMvhUay6hWLfjv+i2OTwgwslWLCo2lKtFvQCTLl+3BbiubPAYGetdagZaaik4n8tL7U9m/I4Hojcfw9jUxZGwU9RsFV3ZoLmf7xmOlCdLlZJ0vIDMjn2AXfbeTc3Kxy47nkVWV7UlnXHIOVxFcx5evvruf5Ut2c2D/KULD/Jl0e/cKv++bbXYmzFtIan4+tguf3afbdrK3YX2MJj3FRdYyrzd56BkxvnOFxqhRs3BbkiQIwsPAv4FQ4DDwuKqqTlYaIAjCPOBuJ08VqarqdeE1A4ANTl7TSlXVY66IuaJJPJHGT99uJjE+nWaRoUyZ0ZeGTeqU+/rTB1Mp2pmJ4cKi0a7I2O3w39d/pVvPZhivYlR7s5SzSXPN5zQ0boR1vx0okyABmM02FszZyMjxnfH196ywWAK9PPls0i08uvy3Ekf5C5tuH08YgzmrmK/mbScjPY8u3SPoP7h1rfECmXZXb7ZvPU5mZgFmsw29XockiTzz/C2aopQLUVWVhftjmLtjD9lFxXQMD+XpQf1oVa/8e4U7EEWRzr2a0blXswo9b0VTnveToqgYjK77bft7eDhI5V+krvfNtaq6g4BAb2Y+4J7q0PWy6mgcmYVFpQkSgNluZ8fpZN76z0i+evZXVFXFbpMRdSJ9Brem7xBNdvxqqGjCDVfDLXdzQRAmAx8CDwNbL/z/d0EQWquqetrJIY8Bz1zx2DZgs5PXtgGyLvtzxs1HXPHE7j/F/82ej81qR1FUziRmsG39Ef775QxalqME99faWKfVHFEUOHTwDF3c2Io0fHQHTp5Iczi/yUNPsxa1cyZDw31EbzpWJkG6iKTXcSTmDD0q2CeqT9NG7HjsQfacKTE07dIwnAO7Enng+bnIdhm7XWH71uMs/jGaD7+YgYdnzTfZ9PHxYM68+9nw1xEO7k8iNDyQUaM7EFxHqyK5kv9t2sb3e/aXtmVtSzrNlPmLWDZjGhHaHKjLGXt7V779+K8y1x+dTqRtx4b4+rluc8bfw8SAZk3YGJ9YxjrDQy/xQM+uLjtPTWLPmbOlCqOXIwAFPvDDmn+xbf1R8vOK6dClCREttbWJxs3hri3PJ4B5qqrOvfDnfwiCMAKYBTx75YtVVc0FSkX0BUHoDTQFpjt573RVVc+7PuSK5ZO3V5W5CCuKirnYxufv/s4H397n9JhyVZ5U0LtZ4nLYiPZs3XiMmAOnsVhsGIwSgiDw4qsTXT6PlJtTyM/fbSN6Uxw+vibGT+1B/2FtK213+uJO7hfRu8gsKqJlnWCeHdSfbg2v7fuicWMEBHojCIJDn7mqqvj4VY5whUGS6NWkIVAif/32qyuvUFOycTY5m5VLdjOlligQGo16RozqwIhRHSo7lBpJgcXKvN37sdjLzniZ7XY+27aT/9ViCXp3ccvk7sQdOsu2DUfR6URUoG6IH0+/PtHl53przHD+tXI12xJPo9fpUFSFCY1bsHdRLLvkGIYMb0e3ns20yuwFGgX6Y9TpsMhlW3xFUSDM1xdPLyNDNT81DRfi8iRJEAQD0Bl494qn1gK9rvNt7gcOq6q63clzewRBMAJHgNdUVXXWglelURSFpPg0p88dP3K23ONGjo1i764Eh2qOJOlo2969Xkk6SeS1dyYTc+A0MQdO4+/vSf/BrUulP11FYYGZR+74kuyswtJZh/df/YX4uBTue3SYS891vXwRvZvPo3eW7uTGpqZz78/L+X7qJDpWkLpgbWPs7d3Y/OfhMkmIIAj4+HnSql3lJ6eJCWXNTi9itdjZ8NfhWpMkabiXMzm56EURyxWPK6rKoRTn9xCNm0OnE3nmzUmcPZ3JiaMp1KnnS+sODdySqHgbDXx5+zgyCgrJKCxk7cK9/PXFpfnfHdtO0LtfS55+4VYtUQImtW/DF9t3lUmSdIJAgIcHvZx4NGpcHzJau115uOOTCQZ0wJVX8DTgmrVPQRD8gNuBuVc8lUJJJWoiMAGIA/4SBMHpFLcgCA8IgrBHEIQ9GRkV35F3MjOLe35aSuv/fkin9z/lzfWbSncDBUEot+/Z28dU7nt26xHByLFRGAwSBqOEh4cBT08Dr/73dnQVoLfZ9SAAACAASURBVOAiCAIdOjZi+oy+jB3f2eUJEsDqpXvIzSkqMwxuNttY8dMucrIKXH6+a2Gx2/kielcZBSIo2cn9YIuzHF7DFbRsE86sJ0diNOnx9DJi8jAQEu7PW5/dhViB3mDlYTLpUWTnw3jl/bY1KgdFUfhj5T4evuMLZk74iG8++ZOCfOe+L1WNUF+fMq1YFxGAJoHuVzWtzYQ3DGLA8La0iWro9gSljrcXXkUCf/56sMwmqLnYxrZNcRw9XP7maW3CKIvc5d8CH7sOHQJ6UaRT/TAW3nk7uipwX9CoeVTFCeM7KUne5l/+oKqqcZQkRheJFgShMSXiEA6CEKqqzgHmAHTp0qVCpQUyCgq57fsfKbBYUQGbxcoP+w6SmJnNnNvGIQgCY2/ryspFO7GYLy2+jSY946b2KPd9BUHg4ceGccuELuzfk4iXt4mefZrjUYMWZXt2nHQqLWww6Dh+5Bzd+lSsmtj5wiJUnH99jmdU+67PKs3I8Z0ZOKIdx2LP4uVtpFlkaJXZTa3fMIiQUD9OnzpfRrjEZNJz64QulReYhgMfvP4rm9Zcmudc9kM0m/88zBc/zcJkqtrXTn8PE6Nbt+T3o8cxX9ZyZ5QkZvXqXomRabia3TtPOvX6sVhs7NweT+u2lV9Br0zOZ+Tz8IyvKCq0EGSxE+Qpohd1/N87PQj19ans8KotKgKKWjXuq1URdyRJ5wEZqHfF4/WA1Os4/n5gqaqqWdd8JewEpvy98NzPgn0HsNjlMktri10m+tQZErOyaRIYwN2zBpOTXcSGPw6hN+iwW2WGjOnA5HuuLW9cv0Eg9RvUzIHdeqH+iKLgcLOQZYWgOhV/IQz28qScHIkmge5xYHc15/LyePXPjWxOSELS6RjXJpKnBvTFy1C1F4hQUpWJqqJmlS+/fTtPzp5PYaEFVVVRZJXBw9sxcGibyg5N4wKpZ7PZ8PuhMr5NNptM1vkC/loVw+iJVS+hjT+eyqoV+8jJKaRX35a8NGQgPgYDPx+MxabIhPr48tKwgRVmJK5RMXh6GtDpRIc2XknS4e1ds7yoDh88w4/fbCb5dCaRbcKZdl9/Gja+upz8vDkbycstQr5YwS9SsKHw7uu/8u2ih6vMBppGzcLlSZKqqlZBEPYCQ4HFlz01FFh6tWMFQegGdAAev87TRVHShlelOJSS5rRFQhJF4s9n0iQwAEmv418vjePeR4eSejabsPqBFSprXFUZN7U7G9fGXqEsJBBWP5CmlaCiZ5Qk7urSsURd6rKdXJMk8VifnhUez98l32Jh/Hc/kl1cjKKqWGSZxTGHOZyWzuI7p2g3lpsgvH4gC5b+gwN7k8jKKqBtuwaEhlePxLm2cCw2uaQVuax9Chazjf27E6pckrRm1UE+fvd3bDYZRVHZszOBFYt38/7nd/PskP6YbXa8DHrtd1sD6TOgFZ99uM7hcUEUGDCk5my87Ngcxxv/twTLhY6RtHM5RG+O472vZhJxlXv8zu0nLiVIl5GelkdOdhEBgVVPNl2j+uOudrv3gPmCIOyiRMr7ISAM+AJAEITvAVRVveuK4x4ATqiquvHKNxQE4XEgiRLPJQMlbXnjKJlRqlK0rleXnaeTHRIlu6I4VB/8A7zwD9B+3Bdp2jyEp14Zzwev/YrdJiPLCs1ahfLC27dX6MIgIT6NNb8eID/fTM9+LTD16Mo3u/dRYLHQwN+P54YMoHujqj8ouvLwUYpsVpTLesKsskxcRib7z6XQKTysEqOr/uh0Ip3dKL2vcXOUV32WJJF6oVVrpqe42MrH//ujdPEIJTMpp5LOs+6PGEbf2glvY9Wv/l6LmJRU3ly/mdi0NAI9PHige1emdWxf6xM/X18PXnpjEq8+txRBgP9n77zDoji7PnzP9qU3ETuoIKKigr232KMmdqNJTO/9TeKb3uubGFO+9J5oYhI1lkRj7LEXrFhARVEQ6W3bzM73xyIRWRSVZZdl7uvaS5n2HJbdmec855zfQQBJknnimTE0cFFz5sL8Un79cROb1x0mKNiX62/oQXcXprTLsswHby6r8Bk/p+z7xfsrefX96VWeazDqIK/UyTVBX4P9q+ojinBD1bjkkyXL8k+CIIQCT+NoJrsPGCnLclrZIc0vPEcQBH8cqXMvVnFZHfAW0BQw4XCWRsmyvKyGzb9qpid24vudSRWcJL1aTULTxrQOC3WjZXWDPoPi6NmvDSfTcvD109OgYWCtjr9s4U4+mr0csWw1d8OaZNp1aMbW/92JLIBW7Vq59ZpkX2ZWJdEJAGSZI9k5ipOk4NW069ScoBBfLGZbhRRetUbNaA+LIiXvS0etrjxZsZhtrFl5gFFjE9xgVc1y6Gw2N8ydX35POmUr4vXV6zhbUspDfT0/Mu9qunZvxfylD5O04ziSJNM5MdJlPdeKCk3cM/0T8nNLylP8kvelM+2WfkyZ2cclY5YUW8jLcS7AlLw3/aLnjr2+C19/tqaCg6XRqEjsFoWPr3elIyp4Di5zH2VZ/kiW5UhZlvWyLCfKsrzuvH0DZFkecMHxRbIs+8my/GYV13tTluVoWZaNsiyHyLLc1xMdJIAIfz/mTZ9Mx4gIBECDQPfgRrw/ZpS7TaszqDVqIluF17qDVFxk5qPZy7FaxPJJldlkY//ek/yz5mCdcpAAYsPDMGgqr4UIgkDLOlJTpVBznEzL5t1XFvPgrV/y8bvLycosuPRJdRiVSsWbH99MdNvGaHUaDEYtIWF+PPf2FBo19ay6TqOPHtlJ4T6An793TALnbNiE+YJFG5Mo8sXW7ZicNAmtj+j1Wrr3iqZX3xiXNqVePH8bBXklFWqgLGYbP3yxlpJis0vGNBi1qJwsBAAEBV+83OC6yd3o3T8WnU6Dj68Og0FLZMtwHn9mTIXjckpKeXr5Srp/8DH9Pv6c/9u0FZuT8gcFheqgxChdhDZfQvz+JDGihMUictaYyiN/f8V7n83E169qmW8F97J753E0GnUlhT2zycbavw/Qv47lhl/XPo73/9mCRRTL9Se0KhUtgoPo0rSJW21TqF327z7Bk/f/gM0mYpdkjiSf5s/fk5jz5S00j2rgbvNcRnhEIHO+uZ2zZwqwmG00bhbiETLyF9KmbWP8AgyYzdZKionXeoli4v4zWU51cFQqgVMFhUqmRS2ybWMKVquT2mmtmiMHM+jUpeYFczQaNcPHJrB80c4KESG9Qcukmy4evVKrVcx6fhwZp/JIOXKGhhGBRLeJqJCmWWK1MvbbH8guKUW02wH4cNMWdmdk8vH1Y6q6dL1GBuyy590PPQXlnXERrz+/gNISC1aLiIBjkp1xKo8fvtrgbtMqYLNJ/LV0Ny88/jPvvbaU1MPVESD0XvQGrdPtgoBLV/VcRaDBwK8zptCjeTNUgqOvxIjYGL6bMp59u08w79sNrFiSRGnJhe0qFbyN2a8tdaSdlRU/i6IdU6mFT2ZXLhb3Rho0DKRpizCPdJDA4Si89s5UgkP88PFx9MDT6tRMntGLBBdMWN1BVYqgot1OQ3+/GhsnJSeHB39fysBPvmTm/N/Ynq70GbqQsPAAnJWBSaKd4JCa+1tcyB0PDaXfNe3KIkJ69AYtE6b3ZPjYztU6v1GTYPoOiCXGSUuIBfsOUGA2lztI4OhpuOF4GoezlZYdCpePEklyAbk5xZxOz6u03WaTWLNyH3fcP8QNVlXGahF55I6vOHE8G7PJhkolsPKPPdz72HCGj6neDcvb6JQY6bQuQKfXMnJM3awJiAwJ5rupE5DsdlSCgCTZeebReezffQKbTUSr0/B/s5fz1oc30rqNIivsjVgtIiePV54kyDLs2ZXm5AwFd9AiqgE/LnyAPbvSKCo006FTM5dOWGub+3v1YNvJUxV6Phk0Gq5rH4e/vmZSCpOzzjL5h58wiyJ2WeZkQQHbTp7i3dEjuCamdY2M4Q1cN7U7WzYcrqQk2ywyjBYtXRdZ1mrVPPbcOO54aBi5Z4uIaBJUYw24t6efdlqDqxIE9p/JIibs4jLj9RMBifotmnIxPHNJrY6jVquqaq2DRuP6mpbM0/n88OU6Pp3zF7u2H0OWnVuzYkkSacccDhI4VGYsZhsfvv0nJpPV6Tnejkaj5uV3puLnb8DHR4fRqEWrUzPtpj606+j5anYXQ61SIQgCSxfsZN/uE5jNNiTJoSxUUmzhxVnzq/ysVBeTzcbXO3Yyde5P3LVgERuOKxNwT0CtUVV57/H1sh4sdR21WkXnLlH0G9TWqxwkgISmjXl/3CiaBgagFgSMWg0zEjrx9KD+bN+Syoqlu0k/kXNVY7y5Zj2lNlsFRU+zKPLC36uv+v7mTcTFN+O+x0fg46vDx1eHTq8hpm0TXnp3Wq2MHxBoJLJ1eI05SAAtQ4PRV1E33DTANQqBCt6NEklyAYFBPkS3ieDQgdMVFJV0eg3Dr+3k0rHXrTrAWy8sRLLbEW12lizYTmK3Vjzz2kRUqoqrBev+PlBhFekcarWK5L3pJNRTaeO27Zowb8nD7NiSSmmplU6JkYSGeU9H7z8X73T6d8/PLeFkWs4lm/pVhdlmY8L3c0nLzy9fKf7neBr39OzO3T26X5XNCleHWq1iyMh4Vi7bU6Gxqt6gZeykbm60TKG+MbBVSwa0jMIsiujUarIyCrhl/AcUF5sdTZntMn0HxfGfZ8ZWemZVh6QM560Tc0pKKbRYCDQoNcHnGHptZwYM60Baahb+AUYi6nift8nxHfh86w44T6hBoxJoFOCv1OAqXBFKJMlFzHrxeoJD/TD66NDpNBgMWhp2acj+CBPPrFjpkhxpk8nKWy8uwmIREW2OnFyzycaOran8s+ZgpeP9/J0/LGRZrpP1NzWJTqehZ982DB7WwascJICLLqZexUrrwgPJnCjIr5BKYxJFPti4mdxS0xVftz5htYisXbmfRfO3kXqkZusD735kGAndW6LTafD106PVqel/TRyTZvSq0XEUFC6FIAgYtVrUKhUvzvqZ7LNFmEqtmE02rBaRDauTWbE06YquHeLjXCVNrVJh1DqvOa3P6HQaots2rvMOEkBDfz++nzKBViEhaAQVGkGge7Nm/DBlQr3vw1UV54Qb3PXydJRIkouIaBTEd7/dz7ZNKWRlFrKeMyxPP4p57z4AFh44wJT4eJ4aNKDGxty7K81pPY3ZZGPV8j30HdS2wvbR47uwfVMq5guiCn7+BtrEKasu3srQUZ346uO/sZgr5m4HBPnQ7AqjSAArj6Q6zQfXqtXsPH2aIa1bXfG16wPHUs7w2L3fIYoSkmhHUAn07BPDky9cd0Ur6heiN2h58X9TyDydT8apPJpHhXndAoBC3eJMRj4n03IqpcFZzDYW/7KN4ddefm3sXd278uLfqyvciwwaDRM7tENXx1o4KFw+vkUQujAXjcWCCoECMYXDEemE9W/jbtMU6iCe78bVYTQaNT37tqHtgCj+TD+KqUyGWQZMNpG5u/dw6OzZGhvPmYNUbou2sj+c0K0lk27sjVancdTf+OgICfPj1fduqJFJmTs4mV/AVzt28u3OXWQWFbnbHI/k2vFdiI1rgrEsF1xv0OLjq+OZV69utS3U1weVk/NlWSZYSXG5KLIs89wTP1NUaMJUasVqFbGYbWzecJiVf+yp0bEiGgfRuWuU4iApuB2LRXR6zwAqLd5diuIiM9lZhYxvH8cd3bpi1Gjw1WnRq9WMahvDrEH9a8Jkj0SWZQ4fzGDrphQK8kvdbY7bsFpFHr/nO7KzirAVWLEUWCgtsfDaM7+ScaqymJaCA6lMvMEdL09HiSTVAquPHq0gSXkOmySx+ugx2jSoGSWZ+IRIp5KeBoOWYVXUQk2/rR+jrk9k7640/AOMxCe0uKiz5cl8tnUb7/6zEXCkc7y+dh0vDBnMxA7t3WyZZ6HVqnnzwxvZte0Y+/ecJDjUjwFD2lWZflldZnTuxNKDhyqk2wlAoNFA5yaNr9Jq7ybt2Fnyc0sqbTebbSxduJOhozq6wSoFBdfStHkoPr76Sg6RTqdhwDXVu28X5JfyxnMLSNp+HJUgEBTiw6PPjOW2++8ivaCQcD9fr65DyjpTwKwHf+RsVgFqlQqrTWTyjN7ceJv3OoVVsW1jCpKTxrGSaGf54iRuvmugG6xSqMvUzdlwHcOg0aB24r2oVSoMmprzU7VaNS+8ORmjUYfR6FCr0ek1jBibQOJFRBiCQ3zpNziOzl2j6qyDlJKTw+yNm7BIEhZJwiyKWCSJ51b+zZniYneb53EIgkBCt5bMuK0/o69LvGoHCaB9REOeGzwQo0aDn06Hj1ZLs6BAvp00ocrVYgUH59LrnGFzksKo4F5KrTbWHD3GxhMnsDmZlClUD5VK4Innx2EwaNFoHalwBqOWRk2CGT+1xyXPl2WZ/z7wA7u2HUO0SVitIlmZhTz76DxyMwqJDgv1agcJ4LnHf+ZUeo5DpbTEgs0q8csPm9i0/rC7TbsqZFnmbFYhebnVf34XFpjK+8CdjyjanS5CKShcCiWSVAsMbxPDm+vWV9ouCDCiTUyNjhWfEMncJQ+zcd0hSkssJHRrSdPm3t/FfNmhw04nK4IgsDIllRs6KSvxNc2pk7n839t/kLT9GHq9lmFjOnPz3YMYFRvLnsxM/PQ62oWHKwWz1SCyVTh6vRZTaUXpfb1eyzUjlM+uJ7E4+SCzlq9AXdaUVqNS8dn140horERLr4TOXVvy+bx7WLZoJ1mZBXTuGkX/IY5mo5ci9XAmJ9OykcSKmRqiTWLhz1u597ERrjLbIzh1Mof0tOxKjoHZbGPhz1vp2bdm5xe1xaEDp3nt+QWczSpClmVaRTfkqZeuJ6JR0EXP65jYooKi8DkMRi1deyk9spwhy0KdEFBwF8o7Uws09PPjrRHDMZTlSPvqtOg1Gt4YPoyGfjXfB8PHV8+QEfGMmdC1XjhI4Fh1qkqXTXKS6qhwdRTkl/LgzZ+zfVMqNqtEcZGZxb9s44XH5uGj09KjeTPaN2yoOEjVRK1W8dTL12MwOPpyARiMOlrFNGTUdXWzibE3cjwvjyeXr8AkihRbrRRbreSbzcz85TdMtsuroVH4l/CIQG6+cyCPPzeOa0Z2rJaDBJCVWYhKVXkaI0l2Tp3MrWkzPY7iIgtqjfNpXGFB3axNysst4bEHv+d4bj5mScRmkzh8MIOH7/6mkjN8IY2bhjB8bGcMxn9VDA0GLa1jGtGjjjqMCu5FiSTVApJox76/iF5Jes76iXTo3IIHJg8kzN/X3aZ5DcNjovl02/YK9TDgcJ6GtFZWkGqapb9ux2K2VVClslpE9u5KI+3oWZd2bPdWOiZE8vUv97Hyjz3kZBfRKTGSbr2i62wKrDfyy779TutLzWYbQ298hwZ5akaO6cyNt/Wv9kRf4cppHRuBzVY5g0Cn19AxIbL2DaplolqH42x1UKdT03tAbO0bVAO89MsKDg5WIwtqEMDvhEhYkqPh+fYtqXTvHX3R8+99bDidu0axbMEOzGYbg4Z3YOjoTsp99CJISiSpSpS7uIuRZZkX/jOPpO3Hyxt4Ju3O57UtZ3nrk5vrrIqcp9GmQQNu69KFz7dvxyZJCIKAWqXi8X59aBygqHjVNIcOnK7QlPQcao2K46lZipN0hYSE+jFputK3yFMptFicOkmS3Y5VsFNUaGPBz9s4cTybF9+c7AYL6xfhDQMZPKIDq5fvK3++qtUqfP0MjKwHEVidTsP9/xnJ7NeXYLVKyLKMXq8hJMyf6+pgk+iVKaksLTyOXfvvvKi4mQZkaHrATlZW4SWvIQgCvQfE1lknUcGzUJwkF3Nw3yl2n+cggUP2NOVQJju3pNKlpxLlqCke7tOLUbExrDiSgizZ0Rwzs+aNzfxRupbo6AhuuXsgLaMj3G2mV9AqpiE7Nqdgs1ZcxZUkmaYt6keKp0L9Y2DLKBbsP0Dphal1AhizHM6T1SqyY9sx0k/m0rRZiBusrF88NGs00W0asfCnLZSWWOnRN4bpt/fDP8DobtNqhcHDO9AsMoyFP2/lWF4uJ1rJ7JCKGfzd10zv1Im7undD4yQl0RP5YPNmRKFiaEzWCBS30GA/LBIbp9T9KdQuipPkYvbvPoEoVk4HMJus7NmZpjhJNUxMWBgtg4J54I6vSD1ypryIc8vmFLZvSeWeB4cypg6usHkao8d3ZcHcLRWcJK1OTXRsI1rFKI6ognfSPyqKLk2asP3UKYejJIMgyQSkimhL/53caTRq0o6eVZykWkClErh2QheundDF3aa4jZjYRtz48CCGf/MNxRaroxejKPJ/W7dyPD+Pt0fUDQGLKnsbytCmcxOi2zSqXYPqATJgrwP9itxF3VheqMMEh/ih0WocH0SVgF0tIAuOnOHQBkoamCvYsOYgaceyK6rcCAKSLPPJ7BXk5ylSoFdLSJgf73w+k3YdmyEIDgdp0LAOvDR7mrtN8yp2nT7N/UuWMHHuXN7ftIkCs9ndJtVrVILA59eP49Vh1zCoVUtiNYE02WYjdH/FyJIoSjRtrjhICrXH1zt3YRalCiVKZlFk2aHDZNSRxuqdGjVyOl03aDW89rKSvlofEQRBLQjCS4IgHBMEwVz278uCIGjOO0YQBOF5QRBOC4JgEgRhjSAI7S64TrAgCN8JglBQ9vpOEISLyyWiRJJcTu+Bscx5cymytswfFQRklYwV6DOwrVtt81Z2bD3qtF4GAJXAzs2pDBoRX7tGeSFRrRvyzue3IEl2VCpBUbKrYX7bv59n/v4biygiA/uzspi3dy9LZswg2Fg/Uok8EbVKxbWxsVwbG8vZrEJuXfoxpguOsVlEdmxKoUWUUpunUDskZWQ4bYOhU6s5kpNDI3/PX5R9pE9v/jlxApPNVu7sGTUanh44EINOe9FzFa4UwdOFG54A7gVuAvYC8cA3gAV4qeyYx4FHgZuBQ8CzwF+CILSRZfncCsGPQHNgeNnPnwPfAddebHCPfme8Aa1Og8qgdTRFOjeJFATUOjUb1h106dhp+fl8vn07X+zYwcmCApeO5UmENvCnqvm6I+qhrA3UKDLs2XWCbZtTMZmslz5e4ZJYRJEXVq/GXOYgAVgkiVyTic+3b3erbQr/0iA8gDfem4ZKAGTZ8ZLsYBX5+qNVHEk+7W4TFeoJMWFhaJw8+Kx2Oy2CLrlg7hHEhIXx2w3TGBrdmnBfX+LDG/JS74FMjItzt2kK7qMXsFiW5cWyLB+XZfl34HegOziiSMBDwOuyLP8qy/I+HA6VPzCt7Ji2OJyjO2RZ3iTL8ibgTmC0IAhtLja44iS5mKMpZ5xq+9usEiv/3OuycT/fvp0R33zD/zZs4O0NGxj29dd8u2uXy8bzJIaP7lTevb2cMqlqNShN5WqQ5P2nmDR2Ns8++TMvP/cbE6+dzaq/9rnbrDpPSm5uBXn1c1gliVVHj7rBIoWqKMgpwUejRrBJCDYJleTI8LdZRf5ctNPd5inUE2YmJqBVV3zu6dVqujdtWmecJIDo0FBmDxvB+MxwbB8d5asHFzHpmjdZrnyXvJUwQRC2n/e644L9G4CBgiDEAgiCEAcMApaV7Y8CIoAV506QZdkErMPhYAH0BIqBjedd9x+g5LxjnKI4SS5GrVY5neyc2+cKjuXl8c7GjVgkCavdjlWSsEgSr69bR/p5ESWTzcaujNOk5ee7xA53EdEoiOdfnYheryl3jgRBwKASeO7tKRiMOjdb6B1YLDZmPTqXwgITpaVWSkusWMw23nl9Ken1oJGjKwkyGJxKTQOE+PjUsjUKF8NssiLLMgJUqKew22VKS5TIam0iSXbM5vrZ1DcqOJhvJ06gTVgYKkFAp1Yzpm1bPhxz0Wwij+S9VxazdsU+bFYRi9lGcaGZD99cxraNR9xtmtchA3ZZcNsLyJZluct5r08vMPENHGlxBwRBsAH7gW9kWf6obP85pagzF5x35rx9EcBZ+bzJeNn/s847xilK3pGLiWoVTkCgD2ZzxXQ3g0HLyDGdXTLmiiNHsDuZYMnAipQUbklM5LukJF5fvw61SoVotxMbFsYnY8bSwNc7Gtx269maRSseZ+e2oxw5eJpGjYPp0TcGo4/e3aZ5DVs3pSLbKy8AiJKd5ct2c+udA91glXfQJCCAuPBw9mZmIp63yGLUaLglwfv7v9QlOnVr6TRbwGDU0XewkiZUG9hsEp9+uJI/Fidhs0lENA7igUeHk9i1pbtNcznb/jnC95+uIfNUHq3aRDD73sE0jw5Ho1bXGenv8ykpMrP2r32V2ktYzDbmfrGOrr0u3kxWweuYDNyII3VuP9AJeE8QhGOyLH/h6sHr3jeojiEIAi+8PhE/fwNGHx1anRq9XkOP3tEMGe4a8QAZp024y/dtPHGC19evwySKFFutmEWRfWfOcOfvi1xij7tQa1R07dmaaTP7MXBYB8VBqmFKis0VFQTLkEQ7xYUXlrIrXC4fjxlDbHg4Ro0Gf50OvVrNPd27M7hVK3ebpnAeQcG+3HL/EPQGbXlzcINRS3xiC3r0i3GzdfWDd15fwh+Lk7BYROx2mdPpeTw36xeOHMpwt2kuZdWfe3jp8Z84uC+d/LwSdmxO5bHbv+L4oTN10kECyM8rQX1B2uA5sjLrT211bSKhcturGrwFvC3L8jxZlvfKsvwd8A4wq2x/Ztm/DS84r+F5+zKBBsJ56lJl/w8/7xinKJGkWqB1TATzFj3IxvWHycstIb5Tc1q7sJfMsOho5mzeXCldRwCGtm7N86tXYRIrqr+JsszB7GyO5+cRGRTsMtsUvIdOiZFOnSSDUUuP3spq3+UgyzJ7dhxn7Yp9aLUahozqSHRcYxbdcANHcnLILimhXXg4AQbDZV331/37mbN5E2dKSmgVHMyT/frTt0ULF/0W9ZfrpvagfefmLF+0i9ISC30GxdGjXwyqOjpRrUvk55WwdnVypciD1WJj7ncbefbl8W6yzLXIssyn7yyv0KgeJdeZGAAAIABJREFUHBGXL+b8xVufznSTZVdHeKNABCdfG5VKIC6+2SXPlyQ7K/7cw9LfdyFJdoYM68C1YxPQKYJNdRUf4ELZRol/gzzHcDg61wDbAARBMAB9gf+UHbMJ8MNRm3SuLqkn4EvFOqVKKJ+aWkKv1zJwSLtLH1gDRAUH81DPnszeuBF7WaqOSqXiP3360CwwkKwS532CtCoVuaUmxUlSqBYRjYIYN7ELv/+6o7wOwGDU0q59U7r2UMQxqossy7z74u+sXb63bMIj8MdvO5hya1+m3daf6NBQokNDL/u651Jqzy2IJGdnc+fvi/hi3Dh6Nmtew7+FQnRsY6JjG7vbjHpH1plCtFpNJSdJliHteLabrHI9xUVmigqcR+xTDtbdCJpWq2HmPYP54v2V5Q6gIAjoDVpmVCOF++XnF7BtS2r5M+lEWjbr1yTzvzkzXFYHruBSFgNPCoJwDEe6XWfgEeBbcNQWCYIwG/ivIAgHgcPA0ziEGn4sOyZZEIQ/gU/OE4b4BFgiy/Khiw2uOEleSFZGPp3MgXw+aDR7TTkgCAyPji5XuBkYGcWRnBysF/RUEO12YhsofT0Uqs/tdw8moUsUfyxOwmy2MWhIO/oPiitPO1K4NAd2n2Ttn3vPKziXHfn3n61jyKiOhDe6fGUqyW7n3U0bK0WMzaLIWxs28NtUpemvgmeQn1/CujUHMZVa6dq9JS1bXZg1c3EaNwlGtFXuD6RSC8S29V6n1cdHh0arQhQr/+6XalSfUVTE93uSSD6bTYeGDZke39Gj6pHHTulBWHggc79cR87ZQuI6NuemuwfRLDLsoucdPpTB1i2pFaJrFotIakoW27cepXtPZfHuQmTKBRQ8lftx9EP6CEd6XAbwGfDiece8CRiBD4FgYAsw9LweSeCoaXofWF728+/AfZcaXHGSvAhJsvPO8wtZu2I/Wp0a0SYR26EpL8yeho/vv/U4MxMSmH9gP3kmU7mjZNRoeLxPX3y0SsM2hcsjsWvLelEg7So2rk7GbKmsyCWoBLZuOMLoiV0v+5pFVgslNucqXym5ivKggmewZVMKLz77KwCiaOfbr9YxdEQ8Dzw8vNrNqf38DYy5PpHFC3dWmBzrdBqmzrioum+dRq1Rc+2kbvz+09YKv7feoOWG2wdUed6BrCwmz/8Zm13CKklsOnmCb5J2sWDKNKKCPSeLpPegtvQe1Payztm35yR2qbKIislkJWlXmuIk1UHKHJ2Hyl5VHSMDz5e9qjomD5h+ueMrsUcv4tfvNrJ+5QFsVpHSYgtWi0jynpPMeXlxheOCjUaWTZ/BHYldaBcezoDIKD4dO46bOrtGbU/B8zCVWkk7no2pVJEodjd6vRa1k9oVQRDQ669s0cJfp0dfRfFzs8DAK7qmgkJNYjbbePn537BYRCwWEUmyY7GI/PXnXnZsO3ZZ17r9nsHcescAGoT7ozdo6ZQYyeyPbqJp88tPU61LzLx3CKPGd0Gv16A3aPH103PLfUMYOLxDlec89fdKSmzW8gVSiyRRZLHw0to1tWT1vxRZLCxPOcLK1FRMVSzqXA7BIb5oNJXvezqdhrAwv6u+vkL9Q4kkeRGL5m2pVMRps0qs//sAj1jFCoWLwUYjj/TuzSO9e7N62R4+vesXXsksoGHjIGY+cA39hravbfMVagG7XebT//ub3xfuRK1RIYl2xlyXyB13DVJS5NzEwJHxzP/2H6QLVkBlWabHgIs2A68StUrF3V278cGWzRVS7gwaDY/06n1V9ioo1ARJO48jOLnnmM02/lq+hy7dqh+dVqkErpvUjesmdatJEz0etVrFnY8MZ+a9gyksMBEU7Fu5kfp5iHY7e85UFvOSgU0nT7jQ0sosPnSQJ/5aUa7CZ5dlPhw1mv6RUVd8zV59Ypjzzp+VtqvUAoOuUeY0VWFX4iVVorwzXkRpicXpdtkuY7OITvf9vSSJ2S8u4szpfOx2mYz0PP737ALWrdjnSlMV3MTcHzay+PddWK0iplIrVqvIkkU7+WnuJnebVm9pFhnGXY8NR6fTYDDqMPro0Bu0PPXmJPwDjFd83bu6duXBnj0J1OsRgEZ+/rw1dBiDWyqpkeCYlElVNOxVcD2yLFfZq6KK/usKVaDTawkLD7iogwSgEgS0VUSYjbWYan+qsJDHV6zAXNaGpNhqpdRm454li8k3X3n7CL1ey//mTKdRoyAMBi1Go46QEF9ee3MKwcGeU3OlUHdQIkkuQpZlVq3cz/yftlBUaKJrj1ZMv7EPYWEXL6i8GhJ7tOKfVcmVZJkbNw/B19+5dPBX5ynInMNitvHVnL+UaJIX8uvPWyv9vc1mG/N/2sLUG7w3f9/TGTWhK70HxbF9YwoajYqufaLx9bs8ue8LEQSBO7p05fbELtjsdnRlkyObTWTtn/vYuDqZoGBfRk7sSuvYRjXxa9QJCi1mnlu9imVHDiPJMt0aN+HlwUNoGRzibtPqFZ0SqmghYNAyRHn2uASVIDAuti0LDyZXEG7Sq9VMaV91il5Ns/jQQexy5QUKQRBYnpLC5KuwpWWrhnw77x5OpGUjSXYio8KVLImLIMsgebZwg1tRnCQX8fknq1m04F9p5D+W7GbDukN8/vXtBAW5ZkXjtoeGkrT1GGazDZtVRK1WodWqefjZsU6Pl2WZ7DOFTvedych3iY0K7qWoyPkqXVGhuZYtUbiQoBBfhozuWOPXFQSh3EGyWmw8OvMLTh47i9lkQ6US+Hvpbu5+YiTDr0us8bE9DVmWueHXXzick42tLIq05VQ643+ay+qbbyHIcOWRO4XLw2jUMeuZsbz64kJkGURRQqvTMGBQHF27Kw2TXcUz/QdwoqCApMwMNCoVot1Or2bNebBHz1qzodhqrdTHERzpgKU1UJskCAItIhWlXoWrR3GSXEBBQSkLft2G9by+DZJkp6TYwsJft3Pzrf1dMm5Ek2A+++0+fp+3hQN7TtIsqgHXTetBkyqKVwVBILSBPzlniyrtC4+4fNlhV5BdWsp3u5NIysygTVgYN3XsTJOAgFoZu7DQxNo1yRQXmemcGEmsF/RAiYpqwNGjZytvb6k8UOoDfy1O4sTRs+XRRLvdITf+f28so/+w9hh99Je4Qt1mR8ZpjuXnlTtI4Mj4skgSv+zfz22JXdxnXD2kd982fDv3HtasSsZkstC1eyvaeMF91pPx1en4ccJEDudkcywvj+jQMHxKYPemozRpHkqTFq4XuxgY1ZKvdu3CJFZ0iARBoH+LSJePr6BQXRQnyQUcTc1Cq9VUcJIAbDaJXTvTuPlW140dHOrHTfcOrvbxN947mI9eX1pJQvSm+6p/DVdxPD+P6+b9iFkUsUgSm9NP8uPePfw4fiLxDSNcOvauXcd5etZ8ZFlGFCW+++4feveOZtZTY+t06P6eB4by1BM/YbWKyDIIgkP5594HrnG3aQq1wPoV+yulWwKoNSqSd58kwcslco/l5TlqYS7ALIokZ3tv41FPJjTMn/H1THDBE4gJDSMqIIg3nvyFresPodE62obEd4ni6f9NwWDUuWzshEaNGN66NctTUyi12RBwiMrcEN+RliFK2mtt4+F9ktyKItzgAsLC/J02eBMEgYhG/8rvHs3L5e+jqaTluy+1bdi4BO6dNYqwcEd0pkFEIA88cy0DR8S7zaZzvLJuLUVWK5ay3GlbWSj+v3+vdOm4oijx/LO/YTbbyqRpHavtG/85wvp1B106tqvp1LkF78yZQfeerYmICKR7z9a8+/4MOnZq4W7TFGoBvwDndU6yXcbHz7ujSAAxoaFA5QmBUaOhQ8PLa2LqaqxWkbNZhdicNEpVUKgJfvxkDVvXH8Jq+bdtyJ7tx/j0f5UV4moSQRB4e9hwPhg5mrGxsVwfF8fnY8fx336uybJRULhSlEiSC2jWPJRWrRty+FBmBWdJp1MzYVJ3zKKNu5f+zpZT6WhUKmySnX4tWjBn+Gj0mtr/kwwdm8DQsQnIslztBn61wab0k9idrPoezD6LRRRd9l4d2H/KaUM6s9nGn3/sof+Ay2tw52m0iW3Ey69NcrcZCm5g9KRubPvnCBbTv9EkQQC/ACMx7Zq40bLaIb5hBHENGrA360x54bpKEPDRarm+bZybrXMgyzLffLWe+T9vQZZlVCqBKVN7csOM3h51f1ao+yz5eSvWC5RvrRaRlb/v4v6nrnXJ5y0nq5AvZq9g89qDaHUaho7tzPS7BqE3KI3s3YGMgF1W4iVVobwzLuLl1yfROaEFWq0ag0FLUJAP/31mLNExEby6fi2b00+Wy19aJJF1acd5d/NGt9rsaQ9gYxVOkEalKu+t4AqcpeNUZ5+CgqfTqVtLJs/si1anwcdXj9FXR3CoHy9/OAOVC79TnoIgCHxz3XimtO+An06HXq1mcFRLFk65gQC9Z0TS5v+8hfk/bymPZJtMNub+uImFv+1wt2kKXoa5imbiNqvkdKHwaiktsfDAtI9Z++deSostFOSWsOjHzTx3//c1PpaCQk2gRJJcRECAkdfemkJBfiklJRYaRgSiVquQZZlfkveXp5CdwyJJzNu3hyf79HOTxZ7HtA4d+WzndsznNcPUqdWMjm6D2oUTurh2TZ3WHRkMWoZ7QBqit1JoMfPWxg0sPXIIARgdE8ujPft4zOTVW5h2xwBGTujCvp1p+AUY6ZAYiVrtnQ6SLMvs2XaM7f8cxi/AyKBRHWkQEcTzAwbx/IBB7jbPKfN+3FSuinoOs9nGjz/8w3XjFWEJhZojvksUOzenVOpLFR3XGLXm4j2XroRVS3dTXGyu0DjbahFJ3nOSIwdOER138Wi2KEqsWL6PFSv2otWoGTW6E/0HxHrcAq+C96A4SS4mMMiHwCCf8p9lwCI6b+x6odJLfee+bt05kpvN6mPH0alV2Ox2OjaM4PmBrp3caLVqnn3+ep59+hdkWcZqFdHrtXTv0Yp+/et2qp2nItrtTJg/j7T8fGx2xwLCT/v3suVUOkunznCpU1wfCQrxo8+Qdu42w6VIkp1XHp3Lzk2pWMxW1Bo1P36yhiffmETPgZ75PZZlmYIC5zL9+fmltWyNgrdz1xMjeXD6J9gsIjabhEarRqNVc//T17pkvIN70yuk+p5DEODY4TMXdZLsdpknn/iJ5AOnyxcRDhw4xdatqTz+xGiX2FtfkJzUaSo4UJykWkYlCHSKaMSuzIwK2wWgW5Om7jHKwzjXv0mr0/DRqDGk5edzOCebyKBgokNdL08KkNglih/m3cOa1ckUFZlJTIyibVxjZcXKRaw+dpSMosJyBwnAKkmcKixgbdpxBkW1dKN1CnWRf1buZ+emFMxlkzLRJiHaJN6cNZ95a2Z5ZA2EIAg0bRZC+sncSvtaRIa5waL6g0UU+SP1CCm5OUSHhDK8VXSt1wiLokRS0gmsFpH4js3wu8qG0peiWVQDPv3tARbN3cTh/adoGRPB2Gk9iWgS7JLxmrdsgE6vqVQHJQgCEU0vPua2bUc5mHy6QpTVbLaxZnUyEyd1JypKaWOhUPMoTpIbeHngECb98hNWScJml9Cp1Og0ap7t55npH7XJgd0neGvWL+ScLUS2y0S3a8KsNydxTavalyYOCvJl3HVKekttkJx9lhInTQRNNpHk7LOKk6Rw2fy9JKncQTofQRDYt/M4ib2i3WDVpbn3vmt4/tlfsZw3kdTrNdx9zxA3WuXdnCkuZtz8Hyi0WCi12fDRanlj43oWTJxGQz+/WrEh+cAp/jtrfrnYkyjaeeDBoYwYWfMNps8nrGEAtz40zKVjnGPYuAR++nxtBSdJrVER3iiIDomRFz13547jmJx8n2UZdielKU7SFSKjSIBfDCWHxQ20bRDO8uk3cXOnzvRu1pxbExL5a/rMWouSeCo5WYX8986vyUjPxVoW/j+4N53HZn6O3Ul3bgXvoXlgED7ayiv7Rq2G5oGBTs5QULg4movUVHhyDVa37q147Y3JdIhvRlCQDx07NueNt6aS2CXK3aZ5Lc+u/ZuzJSWUli3UlNpsZJUU8/y6VbUyvtUq8uQTP1FYaKK01EppqRWrVeT9OSs4fqxy8++6SmCwL//7+nbatG+KWq1Co1HRvV8b3vzilktmaQQF+aDVVv5Oq9UqAgKM5T/bZZmzpSVVljUoKFwOSiTJTTT2D+DhxJ4s/207Gz7bzycBaYye0sPrmzlejOULdiCJFZ0hu2SnML+UpK1HSehRf98bb2d469a8umENZlEsl31XCwK+Oh1DW3re372w0MSXX65l7dqDqASBoUM7cONNfTC6sAGjwuUxdFwCOzamYDZVVPBSqVW0T4h0j1HVpGOnFsyeM8PdZtQbVh0/inSBeoEky/x9LLVWxt+29Sh2e2XlVJtNYtmy3dxzr/dEESOjG/LeD3diNllRqVXodNWbhg65pj3ffbuh0naVSqBX7xgAFh1K5qX1ayi2WgCY0LYdz/YbhE5d8yIUCvUDxUlyExazjYenf8LpEzlYynJsd2xKYdodA5h82wD3GucmTp/MxWatvPoj22WyMwvcYJFCbWHQaPl14jSeWPkn206fAhw1em8OGe6W3mEXw2aTuO++bziTWVieGrNgwXb27DnBBx/epNSteQjd+rVh8JhO/LVwJ8ig0qgQgOdmT0PjZEVaof4iVFG4Xlvf5dJSi9P2Ena7THGxuVZsqG0Ml7mg1KCBPy+8OJ6XX1qE3W5HlsHHR8dLr0zAYNDyz8k0Zq1agem8CNKvBw9gtdt5c3DtpBPWTZQ+SRfDs2Yf9YiVi3dVcJAALCYbP3y8mhETuhFwniLe+ZhMVtavP0Rubgkd2jclrl0Tr5mUxXeJZMNf+yrVEcgyxLRXRC28nWaBgfw4fjLmMpVHg8aRfme32zm0Nx2rVaRtfDN0evcW3G/YcIjcnOIKjaJtNom0tGySkk7QuXMLN1qncA5BELj/qTGMndqDnZtS8fXT02twHL4uLoavL5wuKuTbPUkczDlL54aNmR7fkVCj8+eWpzO8VWv+SD2CeF5at1alYnir2qlb65wQWUEW+xwGo5Y+fdrUig11ga7dWvLrggc4dDADtUZFTEyj8nYd72/bXMFBAjCLIr8fSubpPgOUVhIKV4TiJLmJzauTKzhI59Bo1ezfleZUojYl5QyPPvIDkmTHapXQatXEd2zOSy+Nv2j+fV2h//B45n62luzMAmw2xwRUb9CS2DuayNYN3WydQm1xzjkCSD14mmfu+RZTiQVBJSDbZR55aTx9h7Z3m32HD2U6LSC22eykppxRnCQPo3nLcJq3DHe3GV7F3qwzTPntJ2yShM1uZ3P6Sb7evZOFk2+gRWCQu827bJ7rP4i9WWcctSyShF6tJtzXl+f6DayV8cPC/Jl2Qy/mzt2M1WJDlh19+dq3a0r3Hq1qxYa6gkajpp2TRdOTBc6zTdQqFTmmUsVJugh2RQK8ShQnyU0EhfqVT/rOR7bLTqNIsizz/HO/UlxsKd8mSXZ2J6WxdGkSY8cmutzm87HbZacNV68GvUHLnLl3M+/TNaz/ax86vZaRE7sydmqPGh2nLmMRRQ7n5hBsMNI0IMDd5rgUq1Xkydu/ouiC/jBvP/ULrWIb0bi5e4ROmjQNxmDQVmr4qdWpiWhU9yaICgqXy6xVK8pFDsDRDN1mt/PKhjV8OmqcGy27MkKNPvw1fSZr0o6VS4APaBFVq/3ZZtzYh44dm7Ns6W5MJisDBralX/9YjxYZ8SQ6RTQiM7W4vKb1HALQ2N/fPUYp1HkUJ8lNXDulO+v+3FshmiQIAv6BPrTt2KzS8SdP5pKXV7mZoMUi8sey3bXmJC1fvpcvvlxLdnYxISG+zJzZj1E1KFHqH2Dk9sdGcPtjI2rsmt7CvP17eXn9agRBwCbZ6RAezsejxtbZFJdLsWPDYSSbVGm7JEosX7CdmQ+6J8980KA4vvh8LZayFV9wFA/7+erpoaz6Kng5FtEhy38hdlnmnxMn3GBRzaBRqRgS1YohUY7vcGZ6Lj/832r2bj9GWMMAJt3Wn279XJv6Ft+xOfEdm7t0DG/loe69WJN2DJPNxjk3yajR8GC3nujVylRX4cpQlijcREy7ptz95Gj0Bi0+fnoMPjoaNgnitc9monKyeuVM+aZ8n5OCT1fw18r9zH5vOdnZxQDk5pbwwQcrWfbH7loZvz6z5VQ6L65bRYnNRrHVikUSSTqTye1LFrrbNJdRVGBy+rkXRTv5OSVusMiBj4+eOXNmEBvbGLVahVqtomPH5sx5/0avSHtVULgYapUKjeB86mDUesdkNDM9l3vHf8jfv+8iMz2PfTvSeOXhuSyZt8XdpilUQXRIKL9OmMrAyCiCDQZiQkJ5Y/Awbk/o6m7TPBpZBkkW3PbydLzjjuZhFJjNfL1nF6uOp9LAx5dbOyXSs2nl1aHh47swYEQ8h/al4+Orp3Vc4ypFGFq0CMXf31ApxUev1zBsWLxLfo8L+eqrdRUaHAJYLDa++mo9I0e4tuFdfeeLXdsrFaWKdjvJ2Wc5np9HZJBrOqS7k/iuUU77YxmMOrq6eEX3UjRrHsoHH95EaakFQRDqvfR3auoZ1qw9iCAI9O8XS6tWSg2Qt6JRqRgV3YalKYewSv9Geg0aDTe0947nwI8fr8ZkslRYpLGYbXz5znKGXZ+Itpqy1Qq1S0v/IO7wj0M0tqFT91b4+isiLQpXh/JNr2EKzGZGzvuWbFNp+QNkY/oJnujZl5s6JlQ63uCjo2O3lpe8riAIPPf8dfznsXnY7XYsFhGjUUt0dATXXtu5xn8PZ2RlFTrdnpNTjCzLXqOy54lklhQ73a5VqcguLfVKJymiaQijJ3dn2fyt5YqHeqOW1nGN6Tkg1s3WOfDxUYqBv/56PT/9vKVcbGX+/K1MndKDG2/s42bLFFzFiwMGc6qogL1ZZ1CrVIiSnX7NI7m3q3fUj+7Zdgy7VDmKLcsymel5NGvZwA1WKVyMpM2pvHT/9wDIOHos3vfsWIaMqzzvUlCoLoqTVMN8vWdXBQcJwCSKvLFpPRPatsdXd+Urzm3bNmHuvHtYvSqZ7Jwi4uObk5AQWeMCClXRKCKIU6fzKm0PDw9QHKQqSCvIJ6OoiDahYQQbjZc+oQr6t4jkUE52hc8VgM1uJzbMex/Ytz82go7dWpY7SgNHdWTwtZ1QK2ltHkFaWjbzftqC9bz+ZhaLyI9zNzNwYBzNmoW40ToFV+Gn0/HT+CkcysnmeH4ebULDvGqhJqxhAJnplZ91oigRGOLrBos8B4e6rojBoPWY535piYUX7v2uUuPo919cSFznFjRu4R6Rn7qC0iepahQnqYZZdTy10kQWHHncyTln6dKoyVVd39/fyJix7lkZue22/rz+xpIKKXd6vYbbbu3vFns8mUKLhTuXLSIpMwOtWoVVkrgxvjOzevW7ogfLzI4J/Lx/H/lmM1a74/Nl1Gh4uEdv/K7C8fZ0BEGge/9Yuvf3jMiRQkU2bkxx2t/Fbrfzz8YjTJnc3Q1WKdQ0x49k8s3svzi09yQNGgUx7e5BdB8QS5vQMNqEhrnbvBpn8u39ObJ/bgVhJa1OTff+sVX2MPR2JMnOZ5+v4fclu7DZJEJD/bj/3iH07hXjbtPYvCoZZ49VSbTz9++7mHH/kNo3SsErUNzHGqaBj/NVJtFuJ9hw5ZEET6B//1hmPTmapk2DUatVNG4cxOP/GcmQIe3cbZrH8djKP9iZcRqzJFJktWKRJL7fu5tfD+6/ouuFGH1YNu1GbumcQJvQMPo0a87/jRzD7Z271LDlCgrVR60WnEayBUFAo1EeL97AscOZPDz1Y7asOUhedjGH96bz2iNz+fOXbe42zWV07duG2/8zAqOvHqOPDq1OTbf+sTz26gR3m+Y25nywgkWLd2I225AkO1lZhbz86u/s2XvS3aZhLrU4FfmRRDslxWY3WFR3kBGwy+57eTpKJKmGubVTIhvTT1QoslcLAq2DQ2gVXPdTT/r1i6VfP2VV/2IUWiysPXG8POJzDpNo47Nd25nQtnqNUK1WkaRNKVhMNuK7tyQ02JcnevXjiV79XGG2gsJl069/LF9+tb7SdkEQ6NfXveIaCg72n83i411bSc3LJSGiMXd27kqzgMBqn//te39hMVk5X0TVYrbxxdt/cs24BK9NfR09pTtDr0/kTHoegSG+9TaCBFBSYmH58r1YL2jJYLGIfPf9P7z1xpTybYWFJv5auY/TGfm0b9eUPr1j0Gpd+xlJ6B2N7ETl12DU0XNQW5eOreDdKE5SDdOzaXOe6NmXNzatR6NSIdrttAoO4YvR17nbNIVaothqQVVFSl2+pXqrWslJJ3j2zq+x2+0OiU5RYuYjwxl3Y++aNFVB4aqIaBjIffcO4YMPV5aluwjIsswDD1xDeLh3NzuuC6w/eZzbly3CKknYZZnDOdksPJTMwgnTaB1SvTqNg3tO4qzLhM0qkptdTIOI6jtcdQ2dTqOINAA5ucUOZ9hJ37r09Nzy/x9JyeThR39EkhziUn8u38M3323ggzkz8PN1ndJcRNMQrr+5Dwu+/Qer2dG/zuCjo2vfGOKrIYyloFAVipPkAm7qmMDEuA4cyM4i2GD0igiSQvWJ8PMnQKfHfIFkt1oQ6Ncs8pLnW60iz975FcWFFR2qr99dTrvESKLbXV1dm4JCTTJ6dCd69mzNxo1HAOjdO5qQEL+ruqbVKpb3oFK4MmRZ5qk1Kyvch0RZpsRm5bWN66q9cNcgIpD8nMrqmrIs4x9Yt1PIFapHw/AAp+lsggAxMRHlP7/62mJKS/8VTzCZbGRk5PPDD5u4846BLrPvaH4u7Se3J65HFOt/34PNKtJ/ZDzd+rfxGHEJT8aO8h5VheIkuYBjhzP5/v2VHNmXTkSzEKbdM5hOPVq52yyFWkIlCLwy8BruX76kfAVXq1Lhq9PxUPdelzw/aWOK0weSzSqy/JdtipOk4HGEhvrYSdFEAAAgAElEQVTVSCuC5IOneee95aQePYtWq2LokPbce9dgDAZtDVhZvyiyWjldXFRpuwxszUiv9nWm3jWQN/7zUwURA71Bw+AxCRjqeX+w+oJer2Xq5O7M+2kLZst5nwOdlpvKpP5zc4s5nZFf6VybTWLVmgMucZKySoq5delCjuTloC3L3PnvlP7M6NCpxsdSqJ8oTlINk3LgNI9N/xirWUSWZc5mFvD83d/w6OsT6Tusg7vNU6glhkS14pfxU/hs13bSCvLp0aQZt3RMpIHvpeVjzRfk/5/DbpcpKba4wFoFBfdzOiOfRx6fV94w22qVWLFyP1lZhbzx6iQ3W1f3MGg0qAUB0cm+QH31U596Do7j9idG8tU7yxFtErIsM2hMAnf9d3TNGavg8cyY3pvQUD9+nLeZ/LwSYmIiuOuOQbSMcjSOVqtVTp9bABoXRYRvWbqA5OyzSLLMubyLVzeuJToklB5NmrlkTG9DhjohoOAuFCephvny7T+wmGwVtlnMNj5+ZTF9hravV6FfWZbZf+AUO3el4e9vYGD/tgTVo+LXdg0aMnvoqArbigtNJO86gV+gkdiOzZx+HuK7t0QSK+d+G3x09BlaPdEHBYW6xi+/bStvSHsOq1Ukac9JTp3Ko0kT7+nDUxvo1GrGxbRl4eFkLOe1pTBqNNzWKfGyrjVqcneGXd+FnKxCAoN9MfgoEaSqMIs25ifvY9nRwwTqDdzYoTO9mjR3t1lXjSAIjBrZiVEjnUdpAgN9iI5uyKFDGRUyIfR6DSNHdKxxe1LyckjNy0W6wDMziSJfJO1QnCSFGkFxkmqYw/ucpzEU5pdSXGDCv544CXa7zEuvLmLz1qNYLDZ0Wg2ffL6GV14YT2JCpLvNcwsLvt7A1+/8iUarRrbL+Af58MqXt9I0qmJhcFCIHzc/NIxv3luBzSpit8sYfHS0S2hBD0WpR8FLOXrsrNOeS1qtmlOnFSfpSnih3yAKLGZWpx1Dp1ZjlSSmxsVzU4fLT43UaNU0VP4GF8Usioz/bS5H83PLFW7XnjjG/V16ck+C9/cMe+a/Y3jg4e8pKbEiSRIqlYq4tk2YOKFbjY+VazKhUTmPUGWVltT4eAr1E8VJqmGCw/woKaqsYKZWq+rV6tuadQfZvPVoeeqMxep4YDz/8kIW/Hw/Gi+Vja2KfduP8827y7FaRKxlzXjNJhtP3/olX/39eKWI0nU396Fdl0j+nL8NU4mFPkPb02NwnFLIfpXkmEopslpo5h+IuooHrIJ7iG3TiP0HTiNeEEW1WUVatPC+hqW1gUGj5eMRY8ksLuJUcRGtgoIJquP9+jyZRYeTKzhI4IhsvLdtI1Pj4ut8r8RLERERxI/f3c2WrUfJyioktk0jYmMbuSSDpl1YODZ75UUVvVrNoEhF0e5ysMvKs7AqFCephpl850A+eGFhhZQ7vUHLiEnd0Oqq93YnHzzNR5+s4nBKJgH+RqZM6s71YxPrVKre8r/2ljtI52O3y+w7cIpO8XU//eByWPLDRqyWiu+HLMsU5pVwaM9JYjtWfj9i2jclpn3T2jLRq8kzm3jgryVsyUhHLagwajS81v8ahrV0f7d4BQfjr+vCkmW7kSSpvLZBr9fQu2c0DRU58asiws+fCD//8p8P703nly/WcTotm/Zdohh/az+vlvKuLZYfO1LBQTqHVqVmW8Yphka1doNVtYtGo6Z3r2iXj+Or0/F4j768tXl9+XuuV6sJNfpw8xVEShUUnKE4STXM4DGdyTtbyI//txpwdHweNKYztz42olrnpx7N4pHH52IuizZk5xTz+Zdryc0t5vZbBrjI6ppHUCQlK1CQV+q0qFVQCZQUKh3BXc0ty35j39kzZSuPEibRxoN/L+MX/0DaN2jobvMUgAZh/nz03gw+/GQVSbtP4OOjY+zozkyf1tPdpnkVm1cd4PWH52K1OMSFjh8+w8oFO3h/wf00ala93kkKzgk1+qASBOwX3OxlIOgyxDIUqsctHRNoExLK57t3kF1ayuDIlsyMTyDQoLzX1UYWFOGGi+AyJ0kQhHuA/wCNgP3AQ7IsV27N7jh2ALDaya62siwfPO+48cBLQCsgFXhKluUFNWz6VSEIAhNvG8DYGb3JziwgKMwfH199tc//9seNFGtEClsKSHrwyZKRM0R+XbiD6VN7YawjkqvDh3Vg996TlaJJapWK9nH1T8K699B2JCelVRL1EG0SsZ3rV1SttknJy+FgztlKqRlWUeKLPTt4d/BIN1mmcCHNm4fyxisT3W2G12K323n/uQUV5LwlUaK0xM63s1fwxP+mutG6us+M9p1YknKwQjRJAAJ0ero0qn/Pvdqgd7MW9G7WgrMZ+ZSWWPDT1o05kkLdwCWJiIIgTAbeA14FOgMbgT8EQbjUbLAdDqfq3OvIedfsCfwE/AB0Kvt3viAIHlkNqdNradwi7LIcJICt2adIH6iioLVAcaSKswkqMvqqEDQCmWcKXGRtzdOvTxt694zGoNegVgvo9RoMBi0vPDuu3tUjAVxzfRcaNw9Fb3T0exEE0Bu13Pqfkfj6KateruRMSTEaVeXPnB2Z1bsOU+SkhlBBwRvJO1tMcYGp0nbZLpO0KdUNFnkX8eERPNtnEAaNBn+dDl+tlib+AXw/ZiKqOpQuX5fIzizgoYkfcNvQt3howgdM6/USW1Ynu9ssBS/BVZGkR4CvZVn+rOzn+wVBGA7cDcy6yHlZsixnV7HvIWC1LMuvlP38iiAIA8u2e8Xyl02SSIm2IKv+vZnKGgFrgExOYzsNGtSdvHyVSuDpWddy8FAGO3el4eOrZ1D/WAICvLtwtSr0Bi3v/nwvKxfs4J+/9hEY4se103oSl9DC3aZ5PW1DG2CVnHSLkWRU6TZmf7CCZ2aNqX3DFBRqGR8/PbKTRtUAAfVEedXVTI2LZ0x0LLsyM/DX6YgPj6hT9cR1CVmWmXXzZ5xOy8FepoxpLrXy2oM/MOe3+2neWkmlvhQyYFfKI6qkxp0kQRB0QCLw9gW7VgC9LnH6dkEQ9MAB4GVZls9PwesJvH/B8cuB+6qw4w7gDoDmzetGOtPes2fQalXYLpDBlTUCqrZG/C4zKuUJZBhNfCbv59ipPAJ//Yfb47twT0L3ermqpjdoGTW1B6Om9nC3KfWKEKMPMzsk8sn2rcjn7niSjNoGfqn/z959h0dRbg8c/87ObElCeoAQeu+9gzRpgg0p0uxdvJZrvd7r1Wv74VWxixd7RaVJEaT33kKHAKETAikkJNk+M78/AjFlg6C7mWTzfp6Hx4fZMgcJu3PmPe85GqsPJaFpOiZT5fuZFCqXkDAr3Qe2ZMPSfXjcv984sIaYGX5PLwMjq7g0TWPr6oOsW7yb0DAbg0Z0IiYhij2LjrB2wyHCwqyMuqUz/fs2F8mSnyXtOkl6anZBgnSJ16Py69QNTHhxmEGRCcEiECtJcYAMnC12/CwwoJTXnCF/lWkLYAFuB5ZJktSn0D6m+FLeM97XG+q6/inwKUCnTp1KmQNdvlgVBUwSlJwjSoNaFa8F7uYzp3h48VycF+uzs11OPk7ciN3r5tmuvQ2OTqhMnuvWiznfbCajro5mhpCzOlFJOrIbNJOOrusg7qYJlcATr43Anuti16ZkFLOC1+Plptt6MGhEJ6NDq3A0TePVR75lx4ZknHY3JpPE/Gmb0BvG4XR78VxsZ//2+wtJOniGRx7sb3DEwSXj7AWfN7dUVePsqfMGRFQxicYNpSsX3e10XU8Ckgod2iBJUj3yGz/4bPYQjFrEViXGFoLdU3Rzf6hi5rZWvqdcl2fvbllXkCBd4vB6+Wr3dh7r2B2bYjYoMqEy0C92mJIkCUmSGJjQkHWrDxWZBi9J0KpFTTF/qpzJdbtZeuwweV4PvWvVo3aEaE/tLyFhVl797G7OpWSRdiaLOo2qER4pSu3+jM0rDhQkSJA/4sJlMeHNdebf8LzI6fQw+9dExozqSmxMFaPCDTpN29TG7VbRzPl7TiWPikR+1UaHawLfhlwIfoFIktLJXwspXgxaHUi9ivfZBIwp9PtUP7xnuSZJEl8MuYWxc6fhVlVUXUfTNYY3bcGQCjjPJTkrs5RHJNLsdnHhIwTE+fQcPv7PbDYu2ws6dO7bjL+9fAt/e6g/e/aewuHw4HR5sFoVLGaFpx6/zuiQhULWnz7BfQt/QQJUPX+V74G2nXmqyzVGhxZUqiVEUS0hyugwKrTVv+0qSJAu0ULMRRKkSyxmmaSDqfToFvyzksrKuaw8vA1ifu/WqGmEZTiIig5j4PDOxgYnBAW/J0m6rrslSdoGDASmF3poIDDzKt6qHflleJdsuPgebxV7z/V/MtRyqWlsVTbe8RCrTx4j02mnS41a1IuMNjqsP6VpTBzn7HkljktA1dCwsg9ICHpej8qTt35M2pksVG9+nfrmlft5YuRpvlj6HN99+QCLlu4h6eAZGtSrynWD2xBZSZuJlEdOr4f7F84usZr++a6t9K5dj841xHBlofywhVqQTFKRZhiSVwNdz1+mLkTVdGJjxSqSv+TkOnn6nz/jcHt/T0pNMu6ECN74/D5Cq1S8PdxG0BHldpcTqHK7d4DvJEnaDKwDHgISgP8BSJL0LYCu63dc/P0TwDHy5ylZgNuAYcCIQu/5PrBakqR/ALOBW4B+QFDdXjy05xTJ+05TvVYM13ZriclUccuA/t65J1tSTxcpuQtRFB5s1xmbUi4qPYUgs2nFfrIz8woSJABN1cnLcbJ+8W763tie4Td3NDBC4XLWnjrhc2eY0+tletIekSRdpSPH0liweBe5OS6u6dGY7l0aitJSPxo8ojPLZ28vMndKyXahhhe9QJdliRrxkTSpBN3WnF4vX+3exsyDezFJErc2bc0drdpjkf07+mPVmgNFSqcvkc0y23ae4PoaFfPmslC+BORKVdf1nyVJigVeIH/e0R5gqK7rxy8+pXi7OQv5K0S1AAf5ydL1uq4vKPSe6yVJGgO8BrxC/jDZ0bqubwrEn6GsuV0eXrr/K/bvOA46mGQTMdXCeWvqw0THhRsd3p/SoXoCXw0ZzqvrV3LwfDqxthAebt+VO1u1Nzo0IUidPpqGy+kucdyR5+Jk8jkDIhKuhlfz0bWG/LudLq/vxwTf5i/axQeTl+Lxqmiazqq1SbRsUZM3XhmJIhIlv2jatjbjHx3Ad+8vQTGbAAlJgjvHX8PPv27H7faiahqNG8Xzyr+GBX13O03XGffrNPaln8N5cezCpK1rWXHyCN9fP8qvf/7M83ZcLk+J4263l8zzJStYhNKJlaTSBex2vq7rk4HJpTzWt9jv3wTevIL3nAHM8Ed85c1Pk5exb/sx3K7fV11ST3p457lpvPrFvQZG9td0r1mHBaPuMDoMoZKo06gaVpsFR56ryPGQMAt1Ggf/XdyKrnvNOng1rcTxUMXMjY2aGRBRxZSb5+L9yUtxF2rz7XB62LPvNKvXJnFtn+YGRhdcRt3fl/7DOpK47hC2EAud+jTFajMzenwPTp3OJDTUStUKeqOzOIfHw5Hs81QLDfNZMr/65DEOZKQVJEiQ36wp8ewZtqSeposfV4LbtKqF1WrG6SyaKFksCm1b1/bbeYTKTdQ8lROLZmwpkiABqF6NxPWHcDrc2EIsBkXmP6qqsWTGFhZO24Tq1eh/S0eGju2OxRpcP4ZeVePbH9bxy7xE8uwumjWpwWMP96dZkxpGhxb0OvdpRkzVcFJdnoKSO1kxER4VRo+BrQyOTvgjkVYbr/UawAtrluLVNFRdI0Qx07dOfa6t28Do8CqMnbtPoCgm3MUWVZ1OD8tXHRBJkp/FVA2n/7AORY7Jsom6dSre6I7SfJK4mQ+2r0eWTHg0lT616/PetUMJNf9+bbL97GnsXh+rO6qX7WdT/JoktW1dm9Yta7F7z0mcF6+dbFYzbVrVonVLUZYr+EdwXZ1WYB536aUkapCUmUx87Du2rk7C5cj/ED2ZfI61C3fz5tSHKvTeq+Lefm8hK9YcwHXxg3vfgRSeeO4nPvvwTmrXijE4uuAmKzKTfn6EKa/PZd2i3QB0H9CSB1+4CbNFfNxVBCObtaJjfE1+ObiXXI+bAfUa0T2hdtCXKvmT1eJ7vIIkQWioGL0gXJ35yUl8sH09jkL7i1edPMpzqxbx4YAbC45VDwsnRFGKPA/AoihUD/Vv0wpJkpj48gjmL9zJgsW7kYAhg9tw/eA24rPiKuhIotzuMsRVQ4Bkns9j556TVAmz0r5t3T+sAe8+oCXLZm8rsuEcoF6TeMLCK373rUN7ThVJkABcTg/J+06zdXUSXfoGx53NzPN5LFu1H4+naGLrdnv5ccYmnn1iiEGRVR6RMWE8O2ksTBprdChBz62qLDx6kG1nT1M7PIoRTVoSbfvrn1f1o6J50kfLb5fTQ+a5C8RWj8BiFRf7pWnXprbPBg1Wi8L1g9saEJFQkX2yY3OJxMelqiw6epgLLhcR1vxGFTc0bMobG1eVeL3FJHNdff/PLVIUmZtv6MDNN3T44ycLwp8gkqQA+O6nDXz743oURQYp/4tp0uu30rB+tVJfc/fTQ0hcd5CcbAdOuxuLTUFRFJ767+gyjDxw9mw5iqaW3GvgtLvZtTE5aJKk0ynnsZiVEkmSpukcOnzWoKgEwf8uuF2MmPMDp3MuYPd6sMkK721bx483jKZ11Xi/nkvTNL6ZtJA536xFMkmgw/B7e3Pb44PEXWMfFEXmv6+M5NkXpqNdnDWlejXG3dpV7NcQrlqaj1EeALJJItvlKEiSIq02frxxNI8sncfZvFwAaoZHMHngjYSYxU0NoeIRSZKfJe46wfc/b8TtUXFfvFC229088+/pTP/m4VLbr0bFVuHThc+wYl4iSTtPULN+VQYO70RUkMxViI6rgmJWSpQVWqwKsdUiDIrK/xJqROH2eEscN5kkGjYoPUkWhIpmcuJGjmdn4b7Ykc6pekGFJ5bPZ9lo/zabmTZlJXO+XVuk1fLML1ZRJTKEW+7u7ddzBYsWzRKYNfURNm87it3upkO7OsTFBkcDAaFsdUuoxa/JSah60ZbbVlkhoUrR7+9WVauzcsy9nLiQjUmC2hFiYHF5p/kcvCCASJL8bu6CHTh9tKV0ODzsPZBCm8tsKLSFWhgyuitDRncNZIh/2uGsDCZuXMXm1FNEWW3c17oTd7Rsf0V3crsNaMnH//mlxHGTbKLfzcGzVB4bU4W+vZqyet3Bgj1JABazwthRf/3v1atqLF25jyXL92G1KtwwuA3duzQUd9OFMjc3+UBBglTYqdxsUvNyiA/z3wX5rM9XFSnVBXA5PEyfslIkSZdhsShc093/ZU5C5fJkp2tYfuIodo+7IFEKURT+0/NaZB/7iSVJom5kFE67iyUzt5CReoGmbWvTrmdj8V0lVCgiSfKz3GKthy+RJHA4Ss5vqShO5mQz7JfvyfO40YEct4s3Nq/iRE4W/+5+7R++3hZi4b/fP8SrE77hfHpu/gbiKlaef/+2oFktu+S5vw8hLjacufMTsTvcNGkUz+MTBlC3duxfel9N03n239PZeyCloO3pth3HuX5wGx57sL8/QheEK2YupdmKroPix0Ysuq6Tk233+dgFMQ9FEAKubmQUC0beweTETWxKOUWt8AgmtO9Kt4TSSzePH0zlmTGT8bi9uJwerDYz9ZslMPH7B7HaROlduaGLOUmXI5IkP+vXqxm79pwqsZrkVTVat6jpl3M4vR52p58lwmKlSXRcmdyZmbJjM06vl8KL7Q6vl+/37eDR9t2JuoLN2g2aJ/Dl8n9w4vA5NFWlbpP4oOpqd4miyDx4Tx8evKcPuq777e9n09Yj7CuUIEF+S995v+1kxE0dqCkmjAtlaEyzNry/bX2RmSgmSaJFbDXiQkrOUPmzJEmiVoOqnDqSVuKxuk38u/dJ+HPcHi9mRRarBEGsdngkE3sPuuLnT3zse3Kz7Vyq0HPa3STvO8WsL1Yx9pEBAYpSEPxLJEl+NrBfC35dtJPko2k4nR5MJgmzWeaxh/oTGmr9y+8/PWk3L61fhkmSUHWdmlUi+Grw8IDX/W4/dxqvXrLxgsUkk5ydSUfblSWAkiRRtxIN9fTnRcPGLck4nCVLOSUpf0VJJElCWbq3dSfWp5xga+ppNF3HbDIRZrbwYf8b/H6uh/59M68+/E2RPUlWm5kH/3WT388lXLkNm5P5YMpSUs9dwGY1M+Kmjtw9vmepe2+FiiUn287Pnyxn/cLd2EIt3HhHTwbf2uUPb26mp2Zz5ng6xbYw4XZ6WTpzq0iShApDJEl+ZjbLfPDfsaxcm8Sa9YeIjAzhxuva0rjhX08MdqWl8uL6pUVacR7JyuS236az8tb7AnoXr2FULAcy0tEo+qnn1lRqVgmexgvlWWREKIpswlusS6BsMhERBG3ihYrFIst8O2QkO9NS2ZV2hhphEfStUx+zSfb7uTr2asrr39zP9+8v5sThc9RrEs/tTwymWbs6fj+XcGV27jnJS2/MKdh7aXe4mTZ7Cw6nm0cfEOW/FZ3T7uKxm94j/Ww23osNl6a8OocDiSf4+39vvexr9eLZ0RU+JpQ9HVFudzkiSQoARZEZ0LcFA/q28Ov7frN3O65ig2U1dNLseexIO0P7agl+PV9hD7XtwpLjh3EWStCssky/2g38ukFbKN11A1rx08zNJZIkk0mie+cGBkVVuaXm5TD94B7O5OXQM6Eug+o1CkiSUF7kZOWRk+2geq0YZNmEJEm0q1aDdtVqBPzcLTvWZ+K3Dwb8PMKV+XrquiLNaQBcLi9zf9vJfXf0IsRmMSgywR+W/rKN8+m5BQkS5DdLWTF3O2P/1p/4y+yxrVojivg6sZw4VHTshcWqMGB4p4DFDJCekcOO3ScJr2KjY7u6+aNYBOFPEklSBZJmzyuxkgP5+wCynM6AnrtlXHU+HTSMf61ZQmpeDiZJ4uZGLXilp7hjWFYSakTxr6evZ+I7CzCZJHQdrFaFif8ZgVUM1ixz61NOcO+iWaiahltTmX1oH//bGcO0G8cSogTX30feBQdvPzWVbauTMMkmrDYzj7w6gt7XtzM6NMEgJ0+f93lcNklkZOZRK0EkSRXZjnWHcPloNqUoMgd2nLhskgTw/Ae38cyYyXg9Kk6HG1uIhXpN4xlxf98ARQxffr+WqTM2Yb6YGFksMu+8PvqyMyoFsZJ0OSJJqkCurduQLamncKhF7955NLVM7uT2rlWf1WPu54LbhU1RsMrix6es9bmmKd26NGTvvtOYzTItmiWI+n8DaLrOY8vn4fD+vkfG7vVw6HwG3+zdzkNty2cb/z/r9Ue+Yc/m5II5Zy6Hm3ee+ZGqNaJo3qGescEJhmjUoBrpGTkl9p3oQNUg61haGcXXiUUxy3iLDUZH14mrHvmHr6/XtAbfrn2BNQt2kp6aTdN2dWjfs3HAmjVtTTzGz7O24PGoBcPc7Q545sUZzPjmYUwmkQgIV09cXVUgtzZpRY0qEdgKJSchiplH23cn+gq6y/0Zuq5zIPE4Mz9byfLZ23A5PURabSJBMpDVotChXV1at6wlEiSDHDyfjt1TsomGU/Uy+/B+AyIKnHOnz7N3y5ESg6DdTg8zPl1hUFSC0e4Z3xOLpej3gM2qMHZElwq3sn3mbDYff7GCp1+czldT13E+y5jW8g6vh/9uWU2XqZ/Q6YfJvLxhORfcvseKBNrQsd2QlaLfLybZRHS1CFp2rn9F7xESZmXQqC6Me3QgHXs1DWg32zmlzqh0s+9ASsDOKwQ3caVbgYSaLcwddjvf79/BomMHibKGcFfLDvSpfWUfWFdL9aq89vDXJK47hOpVMVsUPnlpFm/8OIGGfmpnLggVkcUko/sofYX8vXrBJONcNopFwV1s/4muQ+rJTIOiEozWpFE877w+msmfr+BQ8lmiokIZP6obNw+tWCWY+5JSePKFaXi8Kl6vxs49J5k5bzufvns7CfGB7RpbmK7rjJ3/M/syz+FS829IfL9/B6tPH2Xh8LvKfK9jQt04XppyN28//RN5OQ40TadRi5o8/9Ht5bLVu72UOZSSBA4fyZOQT0cS5XaXIZKkCqaKxcJDbbvwUNsuAT/XommbSFx3sGDS/aVl91cf/IqvVv+rXH5QCkJZqB8ZTY2wCI5mZxZJlUIUM+ObV6yLxD9Sp1E8Xre3xHHFLNO2eyMDIhLKi1bNazJ50m1Gh/GXvPXhoiKjFdweFa+q8clXq3j1+ZvLLI6NZ06SdD69IEGC/O6xZ3JzWHL8MEPrNy2zWC5pf00Tvlv/AqknMrGFWoipZnwnW7vHzbELWcSHVSHGFlpw/Nrezdiz73SJ1SRV1WnVPHBNrYTgJpKkCsrt9LDgx/Ws+GUbFquZIeN70Pem9n5dzl7486aCBKmwrIxcTiafo06jyjPvSAis/QfP8MGny0g6fJbwKlZG3dyJcSO6lts6ckmS+HTQMMbM+wmn6sWr5XccHFS3ESObtDI4Ov8KC7dx68P9mTFlBc6Ld2tNskRImDWgm7AFIdAcTjfHTmaUOK5pOlsTj5VpLLvTz+LR1BLH87wedqWlGpIkAZhMJhLqxRly7sJ0XeeDxA1M3rkJxWTCo6kMqtuYt3sPwaYoDOrXkgWLd3G48IxKRebJRwaKTot/QBcrSaUSSVIFpHpVnhv7MUf3pxQMVzy89xQ71h3kybfG+u08mlpyeCzkL1+X9pggXK2jx9N54p8/4bxYzpWV7eDbnzeQkZnL4w+W36GDjaJiWT/uIVaePEKaI4/O1WvRJMb4i4lAGPfYIGo1rMbMKSvIysyl/TVNGP/YYGKvYAO3IJRXZkXGZJLQtJKlsyG2st1XVSs8Aous4NGKlo2FKAp1AjwsviKYdWgvn+zahFP1wsVccsnxw7y4filv9r4Os1nm/TfGsnr9QdZuPERURCg3XNeWBvWqGhu4UKGJJMkguq5z6KT/fBQAACAASURBVMg57A43zRvHX9VG141L93Is6UyR6fNOu5tV8xIZ+eC1flvhGTC8E6eSzxU5D0BYeAh1GotVJME/vpu+AXexDkoul5dfF+3invHXEF7FZlBkf8wiywyq17jEcV3XcTk9WKxKQDcrlxVJkuhzQ3v63NDe6FCCXrbLyfyjB8h2ubimZl1ax8UbHVLQUhSZvj2bsmrdQTyFZhBaLQo3Dy3bn/UBdRpRxWzB4fWgXWwZKAFWWeHGBs3KNJbyaPKuTTi8Rct+naqX2cn7eKVHf2yKGUWRubZ3c67t3dygKIVgI5IkA5w8nckzL8/kfFZewV2sJx8eyOB+La/o9YlrknDaS25SlCTYsynZb0nS0PE9WbdoN4f3nsKZ58ZiMyPLJv758R1BceEnlA+Hk8/5vJNrNsucSc0mvFH5TZJ8WTVvO5+9PpfzaTmEhFoY+eC13Dqhv/g3I/yh9SnHuXfJLHTAo6q8n7iOIfWb8k7voWIPaIA8OWEgaRk5HDiUiiKb8HhVenRpyPiRgd/3W5hFlpl54zj+vnI+O9LOANAspirv9rmecIu1TGMpjzIcDp/HdSDH7cYWZLPpypKG+GwpjUiSypiqajzxws+kZ+YWmS/x9uTFNKxXlUZXMPQspnokZouCp9hmapPJRKQf51NYrAr//XEC29cksWfTEaKrRdDv5g5ERIf57RyCUL9uVU6cPo9ebOCK26MSX934jcJXY/Pyvbz7zE8Fq695OU5++ngJXq/KbU9cZ3B0Qnnm0VQeXDYbe6HZW15VY+Gxgwyq25gh9ZoYGF3wCgu18sHEsRw9nk5KahYN6sZRowy72hVWOzySGTeO44Lbha7rRFor1g2iQOpUvSZLTxwu0VM0ymIjNiTU52sE4a8StzbL2K59p8izu0sM4PN4VOb8tuOK3mPgyM6Y5JKZv9mi0LlfC3+EWcBkMtGpT3PuevZ6br6rlyEJktPl4bdle/j4yxUsXL4HV4Dbeeq6zqJjhxi34CdunPMtn+zcRJ7Hd3tR4a+7fXQ3LJai7W2tVoVB/VoQER6Y+V+B8u2k30qUp7ocHmZ9trLkUEZBKGTr2dMFZVaF2b0eph/cbUBElUv9unH07NrIsASpsAiLtSBBSjuTxYbFuzm480SJG0mVyXOdexNqtiAXWlENURRe6dEfk1hl/dN0HTRdMuxXeSdWksrYhRynz+OappNx/soG2FVNiObfU+7hzce/w+NW0XWdqNgqvPT5fViswfVXejbtAg8+/T0OhxuH00OIzcxn361hytu3Exegqe4Tt6zi2/2JOC7e0T14Pp1Zh/cy7+bbxZJ+ADSqX423/jOS96Ys48ixNEJDLAy/oT13j7/G6NCu2tlTvucGeb0quRccRAXoZ1ao+C53AVzaTC4heOm6zscvzGDxtE2YLQqaqhFfO5b/mzqB6KrhRodX5hpHx7Jg2B18uGMD286mUC8iikfadaNzfC2jQxOCWHBdUZdDuq6j6Tryxf0IrZvXxOsteUfZZjXTs8uVzxzp2LsZU7e8wpH9KZgtCnWbxAdlzfo7nywhK8tecIfV4fTgcnt5/7NlvPoP/8+wOJuXw9f7thWZVeFUvZzMzWJ28n7GNG3j93MK0LZVbb768C40TS+3bb+vRJ3G8ezberTEcavNTHiUKAkRStexek2fOwNCFTMjGwdXW3nhjy3+eRNLZ2zB4/Liudj582TyWd742zf89+e/GRydMepFRjOpz1CjwxAqEVFuFyCarvPRjg20/eEDGnz9Nr2nf8qyk8nERIcxdngXbIW62VmtCrUSohjQ5+o6ssiKTOPWtanXtEZQJki6rrNp+9ESJSiaprN+S3JAzrn1XIrPyeYOr5flJwNzTuF3FTlBArj72euxFmsdbA2xcPuTQ5Bl8XFbnuxLSuG5V2cy7qHPefnteT7n5ZQlq6ww+dqbCZEVrLKCRH6C1LdWA4bUM2ZGjmCcOV+uxuUoWuatejX2bztGVnqOQVEZ58zxdL55az4f/ONnNizejSrGkPiNrkuG/SrvxEpSgEzavoYv9m4taFl5PCeLCcvn8PWgkdw7/hpaNkvgl/mJ5Oa56HdNU24Y1AarRfx1FFfaDItAXUzH2kJ8FrbIkkR8qCiVEi6vVZeGvPL1A3zxf3M5djCV2OqR3PbEYK69pZPRoQmFbNx2hH+/MQfXxeY3KWezWLclmY8mjqVJA+PGG/SuVZ81ox/k1yMHyHY56VWzHh2qJQTlTTDh8uy5vkvzTbKEPddFVFzlKblbt3AXbz72HaqqoXpUVszeRuM2tXn9+4cxi+smIYDET1cAOL3eIglSwXHVyzvb1zL9+nF069iAbh0bGBRhxSBJEr26NWb1hkNF7hopiol+PQNzZ7VLfG2iLDYcHg9aoXTJbJK5rXlwzYjJdjnZnXGWqiGhNI0WA/f8pU23Rrw/90mjwxBKoes6705ZWpAgQf7qtNPp4eMvV/L+a6MNjA6qhoRxd8uOhsYgGK/boFbM/25diYYvYREhxNeJMSgq/9M0Hacrf7+xr5sBbqeHd578AXexuZAHd55k+S9bGTy6W1mGG4QqRgMFo4gkKQAynXZK22d7JNv3xm7Bt78/NIDDR86RcT4Pj0dFMZuoXjWCR++7NiDnM0kSPw4dzT2LZ3I6L6egk86b11xHk+i4gJzTCO8nruPjXZuwmEx4dZ0GEdF8PWgk1cRqmRDknC4P59Iu+Hxs/6EzZRaH16My9+vVLJy6Aa/HS+8bOzBqwgDCwkXbZwHGPDqIdb/tJOe8HZfTgyybUCwyT749Nihmrum6zrQ5W/l22gbsTg9Vwqzcf1svbhrctsjzDuw4Dj5267kcblb8sk0kSUJAiSQpAOJCwkptSdk0iC60y0JURCjffnwPW3Yc48SpTOrVjqVTu3oB3btSNyKapSPu5VBWBnavhxYx1bDIJfcpVVRLThzik92bcaleXBdvUh44n8aDy2fzyw23GRucIASYxaygmGVUl7fEY1Fl2HL+9Qe/JHFNUkHL+FmfrWDD4t189NszooRIICq2Cv9b8g8W/rSRnesOEl8nlhvv7EVtPw2LN9qMedv4YupanBf/HWZfcPDhF8uxWRUG9W1Z8DyzRSm186PFJrrNCoElPokDwCLLTGjTjY92bSxoIw1gkxWe6tjLwMgqJlk2lUl5oiPPBUBImBVJkoJq5aiwz/dsLfJzCaDqOvsyznEqN5taVSINikwQAk+WTdw0uC1zF+4sUnJns5oZO7xLmcRweM8pEtcmFZmp5XF5OXc6k3W/7aLvzR3KJI7yItvlZN7R/Zy159KxWk1616wvZt+QX1o34oF+jHign1/fNzMrj4PJZ4mNDqNR/WplvudN13W+nbaxIEG6xOXy8uXUdUWSpKbt6hASZi34fr7EFmph6LgeZRJvsKsIDRSMIpKkAHmkbTciLFY+3rWRdKedplFx/LvrtXSsVtPo0IRiUk9kMOnJH9i/Lb91c7MO9XjqnfHUqBucSVKG0+7zuGIykeVyiiRJCHoP39mHPLubpav2oSgyqqox4oYODBvSrkzOn5R4rMRAcQBnnps9Gw9XqiRpd3oqYxf+hKprOLxewhQzTaOrMvW6MdgUcYniT7quM/mbVcyavx2zWUZVdWrViGLSf0YRE1V2g+JVVeNCrsPnY+cyinbuM5lMvPzV/Tw/7hNUVUVXdTRNY/DobnQd2NLnewiCv4hPoACRJIk7WnTgjhaV58uuInI7PTw57F2yM3ILuujt33qUJ4e9y9frX8IaYjE4Qv8bWKcRxy9k4dZKzutqEhWciaEgFKYoMv949Doeubsv59IvUKN6FKFl+G89rkaUz5bwFqtC9TqxZRaH0XRd55GVc8j1/N7qOs/rYV/mOb7et42H2nQ1MLrgs2zNAWb/tgO3R8V9sSHE0ZMZ/PvNuXz8f2PLLA5FkYmLCScto2Qr89oJJZtSNGpdmx+2vMyWFfvIybLTpntjEuqJ7yoh8Cr+7r9yzu3ykLgmie2rDxTpziKUD+sX7sJpdxdpM65pOi6Hh3W/7TQwssB5oFUXYkNCsV7cZyWRXwr6WveBQbX3qrI7mXKeb6dv5Muf1nHo6DmjwymXwqvYaFivWpkmSACd+jYnNNyGVGxvpazIDBjZuUxjMdKJnCzO2fNKHHeqXmYc3mNARMHt57lbcbqKlVqrGgcOnSE9M7dMY3nozj5YrUXv01stChPu7uvz+RabmZ5D2nLd2O6XTZB0XWfekf2M+e1Hhv36HV/v24bTW3L/oZBPBzRdMuxXeSdWkgIocU0Srz/4BbpG/pWoDs9/ched+rUwOjThojMnMnAWG9gH4LC7SD0RnJ0Io20hLBp2N9/u386q00dJCIvgnpadaFe1htGhCX7yy2+JfPT1KlRVQ9d0pv6yheFD2zPhzj5GhyaQnwy9NeMxJk74mmNJZ5AkiZhqETz74R1EV40wOrwyc7l9R2JPkv/l5vmevSTLJnLzXMTFlF1304F9mmOzKnz2/RpSz2VTOyGGB+/sTZf29f/S+/5z/WLmHNmH/eK+26TMNOYc2c/0oeNQgqAroFC2RJIUIBfO5/HKPZ+VuAB/7YEv+Gr9S5Xqi7A8a9iyJrYQS4lNoSGhVhq0SDAoqsCLtNp4tF0PHm0nNr4Gm/TMXD76amVBOQ2Ay+1l1m+JXNuzKc0axRsYnXBJjbpxfDD/aTJSs/F6vFSrFVPphsbWqhJJQlgERy4UvSFlkxVubdzaoKiCV88ujZg5fzter1bkuMWsUDshuszj6dWtMb26Nfbb+x3NzmRW8l5c6u8rRw7VS9L5NJacOMSQeoGZr1ih6fjcHynkE2l1gKydvwPdx7AkXYfVcxMNiOh3mqazZPV+/vbCTzz03A/MXrgDj6fk/pTKoGPf5sTXiS3SclexyFSrGU3n/pVrU6jH7WXV3O18NXEeS6Ztwml3/fGLhHJn/dYjPlvku91elq9LMiAi4XJi4yOpXju20iVIkL93d3K/m4my2AhTzCiSiVDFTKdqNbmj+ZXt53WpXv5vywraTH2PJt++zR2LfxbzCEsxfnhXoiNDsV78vjOZJKxWhWcfGexzj1xFs+nsSZ8rkHavh9Wnj5V9QEKFJ1aSAiQ3215iUjbkX4jm5fju6lJWJn74Gys3HCqoTU4+nsbSNQd4/5Vbg+KD8mrIsom3Zj7Od28vYOXsrehA32EdueOpoZXq/0V2Zi5P3DCJ7IxcHHkubKEWvvy/ubw790niK9FG8mAgmyR8DV+UJAlZlJsI5UyzmKpsGP0wC48fLGgB3qlazStOGh9eMZt1Z44XrB6sSTnGsPnfsuyW+6kaUnYd2yqC6MhQvnn/buYs2snWnceIrxbJyBs60KheNaND84sYa2jBAPjCzCZZ/CwIf4pIkgKkQ+9mTH1vIWrxZW2rmfa9jFvyPXI8jRXrDxaZD+J0eTl45CybEo/So1NDw2IzSli4jYdeHs5DLw83OhTDfP7qbNJSzhf8vDrtbtxOD+898yNv/Py3Ul+nahpHL2QSaraQECZKSMuDHp0b8u5ny0ocNysyA3o3MyAiQbi8EMXMLQ2vfuU+OTuD9YUSJMjfiO7yevnuwHaebC/mEhYXXsXGbSO6ctuI4Osc2LdWA8ymks2HFJPEKFG+WSrNx001IZ9IkgKkUeva9L6xA2t+TcRpz9+XZAu10G1gK5p1qFcmMWzffYIPv17JsZPpxESFcdeo7rjcHh9FgOBweti683ilTJIEWP/brhIJvabp7N54GI/bW6Qc8ZIVp5J5eu0CHF4Pqq7TPLoqn/QbRg2RLBkqOjKU5x4ZzBsfLUSSpIJp9Xfe2p2GdasaHJ0g/DG3y8PinzeyYtZWrDYzQ2+/hp5D25ZYXTqUlZG/Gb9Y0YZLU9mVnlqGEQvlgUWW+WnIGO5bOosMpx2TJCFLJt7rfT21w8X8P+HqiSQpgP4+aRw9rmvD4p83ouswYFQXug9uXSa15zv3n+KZ12cVrBidTc/h/S+X06drYxTZRPF+bhazXKbD5ITypXgr4oLjEj5/XpOzM5iwYjaOQndwd2ekMn7Rzyy75b5Kub+iPBnYuzkdWtdh9aZDqF6NHp0bkFA9yuiwBOEPqV6V52/9kOS9p3A58kvC9287yo61Sfxt4ugiz20QEY1X00q8h8Uk0yImOErIhKvTNLoqq0c+wP7zabhUL61iq/tcXRLy6YBeAVpxG0UUqAeQJEl0G9SaF7+4nyf/dwfnm1uYd+wAWa7A70n69Ie1RUrqIL+sbu2WZHxtSzCZJK7rV7kaFQi/63tzBxRL0S8SWTbRsU9zFHPJL5hv928vMYxW1XXO2nPYnpYS0FgrI7fHy4nTmeSU0sLXl9joMG65rh0jb+ggEiShwti4eDdH9qUUJEiQX/675OdNnD5SdN5Xk+iqdKiagLXYRbBZlrmjmRjkXllJkkSLmGq0r5ogEiThLxErSWVg0fGDPL5mXv6maR28usbE7oMZ3rBVwM559GS6z+NeVeON54fxxkeLyMl1IkkSimLiP0/eUKYzEoTy5e7nb2Lf1qOknsjA7fJisSqER4fx2H/H+Hz+6bwLqD76hkqSxDl72Q4lDHbTf93OZ1PXoJM/+LFv9yY8N2FwQYcqQQgm21bu99lZ02SS2LMpmZoNiq4Qfd5/BK9uWc6s5L24VS8dqtXktW6DiA8LL6uQhXJE9apMn7yUeV+txp7rpE2Pxtz/4nBqNRQri8LVE9+yAZbhtPP4mnk4VW+Ruul/blhEl+q1qVUlMHWyteKj2JdTsibbZJJo16I2Mz59gMPH0vB4VJo0rI5SiTq5CSWFRYTw0aJnSVyTxNH9KdSsX5Uu/VsiK77vwl2TUI91KceKlNsBeDRVDKX1o1UbDzHlh9U4Xd4ix2STxL8eG2pgZIIQGFFVw1HMconusJJsIjw6tMTzQ80WJva4jv/rPhhN10UHx0ru3ad+YO2vO3A581cityzbx97Nyfxv+b+IqyFW1EuS0ES5XanEp0mALTx+EMlH5xBV15h3dH/Aznv/2GtK3Gm2WRXG3NQJs1lGkiQa169GiyY1gjZB0nWdDakn+M+mpbyxbSUHz6cZHVK5ZjLll9eNfKg/3Qe3KTVBAhjVqDVxIWFYCpUyhChmRjduKxo3+NG3MzcWSZAgfzDssnVJ2B3FdxYKQsU3aHQ3n+MXFLNMp34tgPwV1bTT57Hn/l5+Klrc/+7UmfMsXXuAPUkpBY1bKoO0lPOsnpdYkCBB/nWA2+lhzperDIxMqKjESlKAOVUPql5yY6mqaTi8Hh+v8I/O7erx4hND+fCrlZxNv0CVUCtjh3XmtluCr+2nL7qu8+Ta+Sw6cRCH14NJkvh6/zae79SXO5t1NDq8Ci/MbOHXG+9iyp5N/HY8iXCzlbtadOSWBmJfmz+lZ/ouXTSZJC7kOAgNsZRxRIIQWPF14nj+f/fw1mPfoms6mq4THhnKy988iMVqZvXcbUz+5zQceS50Tafn9e14/O1x2EKtRoduOFXVePWDBazedBhFkdB1iK8awfv/ubVSNGY6cSgVi0XBU+zGksetcmDbUYOiKv8qUR591USSFGD9ajbkze2rSxy3ygoDajcK6Ln7dGtCn25N8HpVZNlUqTqOrTtznEUnDmK/mIiquo6qevm/rSu4vm4z4sRgub8s0mrj2Y59eLZjH6NDCVptmtVk5cZDJe4GW8wKcbFiz4UQnLoObMVPuyZycMdxLDYzDVvVQpIk9mw6zDtPfFekqcP6BTtwOz38+8sHDIy4fJixYDtrNh/G7fHivvi/6ERKJq++v4B3XxplbHBloEadODzFyjQBZMVEvWYJBkQkVHRibTrAGkTGcG/zToTIChIgkV+WNKJhK9rElc3eDUWRK1WCBLDgeFJBglSYIplYnSLuKP0ZTruLrcv3smv9QVRvyS8iwf/uH3cNITYzpkIt2m1WhUfu6hu0ZbIVka7r/HY8ibGLf+TG+d8wZc8m7B5RDvlXKGaZFp0b0Kh17YLvr2kfLi6SIAG4XV62LN/L+bQLRoRZrvyycEeJrraqqrNj30ku5F55Z0wjpWXkMHX2Zv73wxoS9568qnLBhPpVad2tEWZr0fv/ZovCsPv6+jlSoTIQK0ll4NmOfehfuxG/HNmLqmvcVL8F3arXNjqsoGaVZUxAyUJHCYssWoJereWzNvPB0z8iKyZ0HSxWhZe/n0DTdnWNDi2o1akZw+dv3cZXP69n94EUqleN4M6R3ejSrp7RoQmFvLZ1OVMP7SwooT6Ulc6sI3uZc/0d2GTxNesvqSd8d201mxUyUrOJrlq590M6XL5L+CVJwu3x+nysPFm7JZmX3pmXv4/IozJj/na6tq/Hq0/dVORG0eW88Nm9fPLvGayYtRVV1ajTuDqPvjGmRFdE4XdiTlLpxKd3GelYrSYdq9U0OoxKY0TDVvx4cGd+V8FCdHT61WxoUFQV06nDZ3n/qam4C22GdeTCC2M+5IcdE7HYzAZGF/zqJMTw0t9vMDoMoRQpeRf4/mAiLvX31VWn6uVkbhbzju5nVKPWBkYXXFp2bUTKkTRUtejtL9WrUrNBVYOiKj96dW7EvGW7S/z/iYupQmw535Pkcnt55b1fi6yEOV0eNiUeY+XGg1zbo+kVvY8t1MrfJ43nsf+OweNRsYl9m8JfIOo1ykB2Ri6/fr2a6R8t5uj+00aHUym0io3n8bY9scoyIbKZMMVMiGLmk77DCDOLD82rsfinDT7L61RVZ8vyvQZEJAjGyMzKY8oPq3nk3z/x5pTFHD+dwdZzp1CkkqvTdq+HlaeTDYgyeI15bDDWUEvR8tNQC6MfH0xImM3AyMqHe8f0ICYqFNvFcjOzIhNiM/OvR4eU+5L7XftPFcSoo5NbR+PMAJXkAU6e27uInelnrur9ZEUWCdIV0PX8lSSjfpV3YiUpwDYv3cP/3f85oOP1aPwwaT4Dbu3OI2+MLvcfWhXdw627cUuDlqxKOYJNNnNtrYaEW0QHpKt14Xweqrdk4aKuadhzHAZEJAhlL+VsFvc+9z0OpwePV2VX0mkWrd7HbY/0wNdHuSxJVA8VzTX8qXrtWD5c9BzfvTWfnesOEh0XzqhHB9F3WCejQysXoiPD+OH9e5i/fA+Je09Qp0YMwwa3Jb5aYOYx+pOpUPv2nMY6OU109ItXqGmKgzGLpjJjyG20jKluUIRCZSSSpABy2t1MfPALXIXmmahelWXTN9J9SBs69m1hYHSVQ3xYOKMbtzU6jAqt68BWrJq9Fae96EZ0VdVo2/PKSiAEoaL739S15Oa50C5uJNc0HafLy4KpO6nS10qex03hLeZmk8y4Ju2MCTaIJdSvxnOT7zY6jHLFrapccDuJtoYQGmJh1PUdGHV9B6PDuiptmtVEMknopqIJ0iVO1ct7O9by2bUjjAlQqJREkhRAO9cm+dxs6LS7WTZ9k0iS/MjucPPtrI0sWrMfCYkhfVtw+y1dsVl/3y+jqhobF+5k9dzt2EItXDe+J807NTAw6oqhy8DWNOtYnwPbjhYkStYQCzff15dqtWIMjk4QysaWnccKEqTCzqXn8GnPsfx906+k5OUgmyRMSLzVYyiNImMNiFSoLFRN460dq/hm/3Y0NEJkM8926Fshk3OzWWbic8N44r0ZQMnOkDqwJ/NsmcdVGWgVoOzNKCJJCiAdHUrpXqlrYnqXv6iqxoR//8jx05m4L85ImDpnC1t2HWfK6+OQJAlN03j5jk/YveEwTrsLSZJYNXsbY/8+hNGPDTb4T1C+ybKJ16Y+wqo521j5y1ZsoVaG3NaT9r2bGR2aIJSZsFCrzzbKEhJNY+JYevN9HM7OIM/rpmVMdcwm0UVTCKxJO1bzzf7tONT8pjouVeXVLcuIttoYUrfifT63b1mb6W/fR885/8PjozdtvYhoA6ISKjPRuCGA2vVsWqLLjA5YQy1cO7KrMUEFoQ2JRzmVmlWQIAG4PSpHTqSzbfcJIH9v2KUECfLnmrgcbn6YNJ/Ms9mGxF2RyIrMtSO68Mr3E/jnp/eKBCkAPJrK9rTT7M5I9bliIRhr1ND2BRviLzGbZXp3aYTVakaSJBpHxdEuLkEkSELAeTSVrw9sK0iQLnGoHt7budagqP66apHhjG3ajpBirfNDZIXH2/Q0KCqhshIrSQFkC7Py7OS7+O/DX6Ghk5sQiTs+ghzZxIcLtvJUQiRtm9cq87h0XQPtHEjhSKby3Rb0ShxITsXhLDkfwu32sj85lU5t6rJ+wc6CBKkwRZFJXH2A/qNE0ioYZ+XpIzy+Zi6qrqHpOhEWG5/3G0Gr2HijQxMuGjW0I8dOZbBw1T7MZhmvV6Nlkxo899CgUl+TuGo/0z5aTPrp87Tu2YQxj18nSlQFv8h1u/FqJVdbAFLycso4Gv96sXN/QhSF75LyW+tXD63CS5370y2+jtGhBSVxT650IkkKsB5D2vHVpld49rWZXDiXBRfL7JJPpPPk6zP5bOJ4GtSOK7N4NMcSyPkPaBcAHd02GCniVSRTaJnF4G/V4yIIsZpLDNKzWhTiLw4XDAu3YZIlNLXYp4EkEVJFdLwTjJOSd4GHV87CUWiml93rYfySn9g06m9iGGk5YTJJPPfQYO65tSdHTqRTo1oEdRJKT3gWTV3HJ/+cVtC458zxNNbM2cZHy/5J9dpir5Lw10RabYSbrWS47CUeaxFTsQenKiYTz3fsx7Pt++BUvYQqZtENWDCEKLcrA7pFITnjAt5i+5DcHpUfZm/x+/l2ZZzhp0M7WZ96vEjZju7eAdlPgZYGuAA3OBejZz/p9xjK0rU9mmI2y0Xa8EoSWCwKvbs0BmDQuB4o5pIXm5IEnfq1LKtQK5XUtAucPHMeXdymuqyZyXtQffw/8moay04eNiAi4XKqxlSha7t6l02QvB6VT1+aWayzqYY918mP7ywoizCFIGeSJP7Rsa/PZSm1lQAAIABJREFUsrTnOvQ1Jig/k00mwswWkSAFmJiTVDpxi7IMpJzLwmyWi+yZgfwWsskn0vx2HqfXw93Lp7Pj4tA1kyQRHxrOz4PGERcShp73KfnJUWEucK1DV88iyRVz/kBYiIXJr47h5ffnc+xUJgAN68bxnyduwGrJ/xGv37wmD74ykikvzkBW8hMqk0ni5e8nYLGZL/f2wlU6lZrFC5Pmcvx0JiaTRGSVEF56fKghpaUVQbojD7fmY1ivrpHp4y7xH9G9J0BNAXNTJJPY6GyEM8fS0NSSpVCaqrFjbZIBEQnBaFSjNkRabLy3cy0peRdoHlONZ9v3pX3VBKND84us9By+eHkW63/bgWSS6HtLZ+5+YRhh4SFGhyZUEiJJKgO14qPxeEpeBJlMEs0a+C8x+WDXOranp+AqVLZzPOc8z238jS/6jQT1BD7b7UlmUFOhgiZJAPVrx/H123dyPjsPkIiOLFk+OPSOXvS6qSO71iVhsVlo16spZov4J+BPXq/KhBd/IjPLXrCC5HTl8NTrs/jpw3uIi65icITlT++a9ZmevAu7t+S+uu7Vr7wGX9dy0M9PAM/O/H/Tuhs99Hak8GfEndgyFhFTBdXHZz5AbHz5H+wpVByD6jRhUJ0mRofhd26Xh78P/S9pp7NQvfn/lhZNXc/+rUf4cMnzRYbPCkKgiJ+yMhAdGcp1fVoUrGpcYrUojB/WxW/nmZa8q0iCBODVNValHMGpesHcCV95sa57mHPSyevbljMzeTdOHxdrFUV0ZJjPBOmS8KhQel7fns79W4oEKQA27TyGw+kpUWKnahoLVuw1KKryrW9CA1rHxhcpmwlVzNxcvwWNoq58v6Ke/Sx4tgNO0HMAF9h/AMcv/g9auKzI2Cr5nzHFuuFZQyyM+psYOVBWMrLyWLctmQNHzgZd2a8914nDR0v6YLF+/g6y03MLEiQAr9vLmaNp7FgjVmP9Rce4UjtRbicUePq+AcTHRTB9wXZy7S5aNE7gibv7UruG/8ph3KrvO5fo+RepUpX70Z3zQM+DizMINGx8daIN7yavxe71EKqYeStxFXOG3kn10HC/xSZUDumZeSXa3kP+/rvU9AsGRFT+ySYT3w0Yw/TDu/jlyF6ssszYJu24/irmnOhaDrjWAMVvcDjQ7V8ghQ73a8zCH3vqo7t48+Ev2b5qP4pZQdd17vzHTXQb3Mbo0IKerut8+N0qZi3agVmR0TSdGtUieO+FkRV+NftU8lkm/e0bDu04DkDLrg158sM7r6gZiK7lobtWAi4kSy8kuWpgg/0LkvecxJFXsiOtx+3l6L7TdOjT3ICohMpGJEllRJZN3DmiG3eO6Bawcwys3Zg5R/fh1X+/SJWAFjHVCTNbgJoQOws9513wbAJTDF8fb8vEpAi0ixdXdq8Hl+rlpS1L+F8fcWElXJ2WTWr4PB5iM9O+hdiTVBqLLDO+aXvGN23/595Az6XUwgBNzAEzQmgVG//5bgLnz10gK+0CCQ2qYQ2xGB1WpbBk3QFmL9mJ26MW7AU+fjqTf02ax5TXxhoc3Z/nyHXy1NC3yDmfV7AytmfjYZ66/i2+2vraZasjNNc6tKyHyb8q0EFXkcKfQg67p2yCv0q1GsVjC7XgtLuLHDdbFWrWL7/JXUUUXGus/hWwcjtJkiZIknRUkiSnJEnbJEnqdZnnDpckabEkSWmSJOVIkrRJkqSbij3nLkmSdB+/bIH6M1Q0z3XoS1xIGKFKfiMCm6xQxWzlre5DC54jKXUxRb+HqdoGpNhfmbg/qsRca1XXWX4quQwjF4JFo7pV6d6hfpGhmxazTI2qkfTtGnx1839W3gUHB3cc5/w5P62umaqDydfKrwzWa4occaleph3eyT0rpvH0+l/ZkZ7inxgEn6KrRVC/ZS2RIJWhaQu243QVLT1XNZ2ko2dJy6y4M4RWz96G2+kuUjqoqRr2HAebFu0q9XW6lpefIOn2/EoS3Q640HPeQffsK4PIr16fmztiDbVgMv1ekiUrJiJiqtB5QKvLvvbY6QxmLEzkt9X7yHO4L/tcQbicgKwkSZI0GngfmACsvfjf3yRJaqHr+gkfL+kDLAdeADKB8cAvkiT11XV9TaHn2YGGhV+o63rwFuVepWohVVh+0/38cnQvO9JTaBwZx8iGrYmxlb5HxyRB8dFB+cfLf62oUD698sQNzFm6i9mL8+/kDrymGWNu7ITZLBsdmuF0Xefr12cze8pyFLOMx+2l6+A2PPPx3X+py6IkmSDiNfSsxwE3+eW0FpBCkao8VvA8l+pl5KJvSb6QicPrwYTEguP7eb7jtdzepONf/vMJQnmQ46NMC/IrOnLtbqpW0Hm+Z46llVhZAfA4vaSeSC/1dbp7FfkrSMW50Ry/IJtb+C/Iq6TrDnTnb+jeI0hKMyTbQCTJii3Myrvzn+X9p75n94bDSBJ07NuCx9+5DVnx/V2i6zqTvlzG/BV70XUdWTbx9udLmfT8cNqJ7qrCnxCocrsnga91Xf/s4u8flSTpOuBh4PniT9Z1/fFih16WJOl6YBiwpuhT9dRABBwsQs0Wxjdpz/gmf1y2I0kS19VuysKTSXgKTe42m0wMvYr9EIJQmCybGD64HcMHtzM6lHJn4XdrmfPZCtxOD25nfonr5sW7mfyPn3jivdv/0ntLtn4Q+xN63pegHgdLV6TQO4vsO5iZvJvk7IyCwbUaOg7Vy/9tW86weq0It4jBykLF17tzQ6YtSMTjLbpP12JWqJNQcdviN2xTm5Awa4m9OopVoWGr2qW/UHfhu6hKA93h1xivhq6eRs0YCVoeYEeXQiH3beTYmUimWGrUq8obM/+Ox+1FkiSUP7jRtj7xKAtW7sPlvriKeLHU8h9vzeHXTx9CKSW5qtR0KkQDBaP4vdxOkiQL0BFYXOyhxUCPq3ircOB8sWMhkiQdlyTplCRJv0qS9CcL+IOPpmnsXn+QtfO2kXn2yvcgvNxlILWrRBGmWDCbZMIUM/XCo3mxU/8ARisIldOMjxbjKnYn2O30sHzGJtyuv95VUjK3wBT1NqbY6ZjCny6xMXvhyaSCBKkws0lme/rpv3x+Qbgcu9PN7CU7efuLpcxZuhO7MzClULfd3IWYqNCCjrKyScJqUfjnw4OQK3Dr6O5D2hGXEI1i+f1i32xVqN2oOm17NS31dZKlF+i+GjuFkOLtzmNr59B/7hQeWjWT3RlnAhC5b2r2v0DLIL9IiPwyQDUV7cLEIs8zW5Q/TJAAfl2+G6ePz1GvqrHzgPh8E65eIFaS4gAZOFvs+FlgwJW8gSRJjwC1gO8KHU4C7gF2kp9APQ6skySpra7rh3y8xwPAAwB16lz5rJGKKOXIOf4x/B1ysvKQJAmP28uICQO561+3/OFrY2yhLLnxPlafOUpydgaNo+LoVaO+KLcThAC4kJnn87iu6TjzXFisgR1sHGmxXdq2XYSm64SbK++eGc29GdU+HXQHJtsNmGwDkSRx19mfzqRd4L5//YDD6cHp8mCzmvls2no+/7/xxMdF+PVckeEhfP/2ncxZtpvNu44RHxfBqCEdaFjnylvql0eKWWbSgmf47o15rJ69FUmS6H9rV8Y/c8Nl5wZJchxS+DPoOW/zezluKFlSdwYv3odLU9F0naMXMlmVcoTP+46iZ416Af2z6LoX3BugxK5oL7qr+D32K1N85fASifwOv0IpROeGUpW77naSJI0A3gJG67p+/NJxXdc3ABsKPW89sAN4FHis+Pvouv4p8ClAp06dgvZHQNd1Xhz3IWmnzxfZzDl7yjKad2pI1ytoNyubTPSr2ZB+NRv+4XMFQfjzWnZrxObFuyg+siW6WgTh0WEBP/9tTTqw7NRhHOrvd1sl8pOndnE1A37+8sib8z5q7qeAE9DRnCsxWXugRP8vf6+X4BeTvlhKdo4DTbs0ZNqD2+3lnS+X8+azw/x+vrBQK+Nu7MS4Gzv5/b2NFB4VxoQ3xjDhjTFX9To57C50S1c0xy+gO5Bsg3l45REc6u8rLDrgVL28tGUxS296wM+RFyfhe58Ulzl+edf1bkHi3lM4iq0mabpO22aV8/NN+GsC8Q2QDqhA9WLHqwOX3U8kSdJI8leP7tB1fd7lnqvrugpsBRr/+VArvuMHUkhPKZogATjtbuZ+vvyq38/lcLNy1mZmfbKE/VuPBN0Avsomz+7ix1+38vjrM5g4ZRGHj6cZHVKlds+Lt2ALsyEr+R+9kpQ/YPRvb45DKoPV267V6/BEm2uwygpVzBbCFAvVQ8P5pv/oSrl6rKunUXM/ARz8fjvVjuZej+5ac5lXCldD13U27TxWkCBdouk6G3ccNSiqykcyN0eO+Cdy5KuYrNewK8P3JdmRCxl4tFLmLv5FaY5cnl7//+ydd3hU1daH333alPRCCr13QaSrgCBNBRFRxIa9ole9tmtBr+VTr71f9KrYFSuIYsFCESlK7713EtIm0075/pgQCDMIhEwm5bzPMw9kz5x91kwy5+y111q/9S2dvniZmTkNMKzDl6EqwjmkXHP37dGSLic1xFUigqMqEg5N4aFbzsKhRTdKb1MzqfBIkmVZASHEfGAA8PkhTw0AvjzScUKIkcB7wBWWZX1xtPOI0IqiA6H0u1pLcaEPSY7s6xblFx/XXJtX7+Cuoc+gB3T0gIGsSJx0akse/uBmFDX2QUczsBCjaByWsRWhdUOJvwEhR+7LYwMFRV6u/NeH7M8vxh/QkSTBT7+v4qFbzqKvLccdExq2zOa13x7gs5d+YOVfG6nXLIORtw2mVafGlWbD9e16MLJ5R/7cs40kh5MuderXSgcJQr1jEHJ4uolVjOGbiuTsExO7aiKyLGFEWHjLR7h/2USfZIeLPd6isHGXoqJEIYrqCQY49/t32evzYFgm96zowedd9pGmBXDJBggHyPWQEu4t1/yyJPHU3cOYv3wrsxduIDHexeBebcis4HTOmoYt3HBkorXyfR74QAgxD5gF3AjUBcYBCCHeB7Asa3TJz6MIRZDuAmYIIbJK5glYlpVb8pqHgTnAWiCRUIpdB0KKebWW5h0bhqXuAGguld7ndT2uuR6/6g2K9ntK5wsGYMmsNXz37nSGXRdbIQfD+z163p1ASKXH0tcR8E5CS5+MUGxpz0h8PPkvcvZ7SvO0TdPCH9B56o2f6NWlOYq9OIkJ2Y3rcNsLJ6Zkd6IkO1wMaFB7gvCGaSIJER6tE/FETqhQjtB3yqY8CCHo16MVP89eha4foqSqSJzZ096wiRXXtunGC4tnlkm/dcoKl7fsHJXI9sSNy8gP+jBKGt7vC7jpP/sCBtbZwdiTm5GZ2Bmh9TqhNFchBF3aN6RL+5pdi16bEEJkA08BZxPSJNgA3GRZ1vSS5wXwMCEdghRgLjDGsqzlh8yRArwMHOjB+g1wq2VZeX937qiskizLmgDcTqjv0SLgdODsQ2qMGpY8DnAjIYftRWDnIY+vDnlNMqEao5WElPLqAb0ty5oXjfdwouzJLeS58b8w6s7x3Pr4Z8xdsikq59EcKrc9fxkOl4okhy5qTrdG3cZ1OOeK3sc8z85Ne9m9ZV+Yw+X3Bvjhg98r0uTjxrIM9IKxHKgZCKGDVYhe+EIMLavazPhzXcRCVsMw2bQtJwYW2VRldm3ey0dPT2bcfZ+y4LcVNSLVdlnuLob98C4tPvkPbSc8y4Nzf8CnH1wQSs6+RzhSQXZfUDlG1hJuv7IvjbJTcTlVNE3B7VRpVC+N26440u/AJtpc06Ybl7Q4uTT91iHJDGvcjjtPPva1w/GwYN92vHrZeiHDkpiW24TfC/ojOfrYdYAxwLJi9zgaQohkQsEWAZwDtCGkRbDnkJfdA9xZMt615LmpQohDd7o+Bk4BBpc8TqGsOFxEopZDZVnW68DrR3jujL/7+QjH3AHcURG2RZs9uYWMvvd9PN4AumGyeUcuy9bu5NZL+3D+wJMxAvPRC5/D1NcgyU1QEv6J7OhZ7vOdcX43GrWuy7fjp5O7K59uA0+i3wXdj6vDu2mYR9w5OnTnLyYYO8GM1MvBxAz8UenmVBcS4pwRx3XDJM5t98OxOciMSX/x7E3vYBometDghw9/p+PprXjowzHVNh1qW1EeF039kOKSRZnP0Pli41K2F+czvu9FAAjhQk19h2DutZRuwFg6ctKjSIotZFORJMY7ef+Z0cxfvpXN23NoXC+NU9o1qJRaPJvISELwYJf+3NahF1uL8qgbl0iywxW18zVLTMchK/gPa0MgAQ3ik6N2XptqzT3AzgOZZyWUFjKWRJFuB56yLOvLkrErCDlKlwBvCCHaEHKMTi8RgUMIcQMwUwjRyrKs1Uc6eewLTWogH0yaV+ogHcAX0HntkxmcfZoHq+BaQlERMM0cArlXo6W8iuwsf0pbk7b1ufWZS8t9fN2mGSSlJ7Bna9kIg+ZU6T+yR7nnLQ+WZbHHW4RDVkIXbCmRkBZIOEKqpq3TK4GRZ5/C2s178PkP3pAkSdC8UR2y69g52jYhfB4/z40ZX9rc9sDY4pmrmfXNfHoPP7603arC+FV/ETDKXjf8hs6c3VvYWJBLk8TQtUPSuqJlzsP0zwb8SFpPhGR/P6KBnQpVNUnQHLRNPVxrq+IZ2bwD/13xB/5DvpaKkMh2J9I942+a4drUZs4DfhBCTAD6AjuAt4DXrFC6QxMgi0N6s1qW5RVCzCDUm/UNoCdQBBy6qz4L8JS85ohOUvXcIqzi/LlsSxkH6SACf96jHHCQDuIjWPBYJVh2ZIQQPPD29bjinThcIRUYZ5yDJm3rcd4NlVePtHDfds78dhxnfPM63b9+mVFTP2C3TyA5+wGHRcaECzn+hkqz7fcF67nivg8YeO2r3PTIpyxeXbWb0/Xr0ZIRgzqhqTJxLg2XQ6VR3VSevHNYrE2zqUIsm702YrTIV+zn1y/mxsCiimFl3h50K/w6rEoymwpzy4wJ4UB2noHsHGQ7SDa1ltzd+bz/5CTGjnyJ8Y99xb4d+yt0/nRnHBMGXEa7lEwUIaEIid7ZTfhkwKV2RDFGWISEG2L1ANKFEH8d8jhce74pcDOhOqRBwEuE6pPGlDx/QMMgUm/WrENes9c6JIe85P97DnlNROxIUhSokxLH5h25YeO6YaBKGyIeYxlbsCwdIWL3K2l1ShPeW/AEv305j73bc2nfowVdB5xUaek2u4sLufzXj0vTYwDm79vGqJ8/5Jdz/oNl3ooVmAtCAyuAHHcdknNopdj206yVPPHmT/gDoajMolXbue2JL3jxvhGc3LpqCkcIIRhzaW8uPqczK9fvIi0lnlZNMqrMzciyfAQLnsXwfgaWD0k7FTXpISSlaaxNq1XIf9PJXo1yc9to0iEtm/l7txE4TFEtYBo0T6reTUVtbCqarWt3cfvAJwj4ggT9OotmrOKb//3Gc9/fS9N2FXePa5uSybdnX01h0I8qJJxK9b3G2FQI+yzL+rtmZhLwl2VZ95X8vFAI0YKQk/RqtI2zI0lR4NKh3XBqZZ0dVZHp1KYBQop8czaJB2Lf4T0xNZ5h1/Xj2n9fQI/BHSu1HmHC+kXoh3XFNiyLHJ+HuXtz0NLeRcv4BTX1PbTMeSgJt1fKgt+yLF75cHqpg3QAf0DntY9nRP38J0pqchyndW5G66aZVcZBAgjk3oBR/CFYhUAQMzAD/77zsYx9sTatVnHSqS0ithFwujUGXXpaDCyqGK5s1QVNlsu0pXTKCn3rNrPrH2xsDmPcfZ9SXOAjWJKeHQzoeIt8vHrnh1E5X4LqsB2kqoAFWCJ2j6OzE1hx2NhKDoq/HWj29Xe9WXcBdcQhC6CS/2dwlP6ttpMUBXp0bMwtl/bG5VRxuzQ0VeaUtg147B/noMTfApQtjPQaCq9vaMPZ37/Fdk9+bIyuAmwq3B+26wshJ2VncQEAQq6LpJ2MqER53mJfkP2FkYQjYN0We0FfHszgWszAPEKS7gewwPKhF38cK7NqJYqq8O+Pb8EV78QV70BzqWhOlbOu6E3nfu1ibV65yXIn8PWgKzg9uwmqJJOsObm6dVdeOq16ppv+vmA9I//5Dqde+hxDbh7Hl1MX1QgFwlhjWUEss7DWf5aLf18d8TNY+ecGTPPExZvycwp597GvuLn3Izx4wYss+G350Q+ysQnVDrU6bKwlcEAteyMhR2fAgSeFEE6gFwdrkGYD8YRqkw7QE4ijbJ1SGHa6XZQYMbATQ844ic07cklNcpOeEg+AZV0CVgGBwlfxGwEs4L0tJzFucweE2Mdlv3zMr0NvrFI7/pVF94yG/LhtdZhEqInFSamxaxrrdCg4NIVibyDsuTolv1eb48PS14JQwpt44scMLomFSbWa9j1a8PGKZ5jz42I8+V5OOaMtdZtmxNqsE6Z5Ujrv9xsVazNOmLlLNvHgy9+WRrNz8jy8+vF0AkGdi8/+u0wVmyNhWQECBY+jF08AdISUiZb0GMoJCChVZxxOFf2wbAkA1aGc8HqkILeIm3s9QkFuEUG/zga2svSPNVz98PkMu6H/Cc1tU+N5AfhDCPEAMAHoRKhP6v0Qqi0SQrwI3C+EWAWsIdR+qIiQ7DeWZa0UQvxASOnuQM3TG8C3f6dsB3YkKao4NIWWjTNKHSQI1Ymo8Tfx5NYnOXfeSE6dOZpXN3XBQmBaFvt8Hhbuq9qCANHi3MbtSHO4UaWDaYdOWaF3dlNaJteJmV2yJHHJOZ1xOsruKTg1hWsuKL90e21GKE3ACr8hgwNJbVtmRDdN/ti1iV+3r6Mo6I9wjE1F4Ip30ndEd4ZcfUaNcJBqEuMm/B6W7uvz64z/ag5GBezy10YC+f8qcZB8gI5lbse/fwxGYEGsTYsJgy8/Hc1ZNv1NdSj0H9XzhJ2kr/87lYKcotJUPgj1YHznka/wecp3TfcFguQXeWt9BLAiqMp9kizL+pOQwt1IYBnwf8BYyrYYepqQM/Ua8BeQDQy0LKvwkNdcAiwGfix5LAaO2tXdjiTFiB3FXnb44sLGBZDjL658g6oALkVl0uCreHXZLL7fsgqHrHBpi05c2apbrE3jquE9MQyLT7+fj2GYODSFG0aexqDT2sTatGqJpLZB0jpiBhYCh0TohIrivqz0x8U5O7h6+gSChgEIdMvgsS6DGdG0Q6XbbFO1WT5nLR88MZEtq3fSqHVdLn/gPNp2ax5rsyqErbsiq4z5AjpFxX6S4qPX26YmYpl56N7JlLn2AOAjWPQKcur4WJgVU654YDhb1u5i8YxVKKqMoRu06dqM6x8becJzz/tpKcEIUSpZkdmwbCttux/799TjDfD021P5bd5aLMsiMz2R+64dQOd2B2XlTWMnWDpCrl8rs3JqGpZlfQd89zfPW8C/Sx5Hes1+4LIjPX8kbCcpRvSt24y5u7fgNcqmlgVNg5PT6sbIqtiT4nAztvMAxnYecPQXVyKSJLh+5GlcfX4PCjx+khKcyJIdiD0RtJS3CRY8juH9GgggqV1Qkx5DyKEoht/QueK3TykIlpXMH/vXD3RIy6ZFUuyiizZViwW/LueRS17BX5ISm7srjxVz1/HIhNs4uU/138ion5XC6o2HK9yGshXi7cbQx41p7ChVSS2LhalvjHhMTUdzqjz26T/YunYXW1bvoH7zLBq1rpi1SFpWMuuXbAkb13Wd5DrHV1/8r+cnsXj1doJ6qH55++487nr2a8Y/fhkNM/Px7b8ZS98ACISchSP5FWTN3lSzKR/2Ki9GjGjagSx3Ag75oJ/qklWubt2NOi67zqWqoigyqUlu20GqAIQUh5b8JM6slTiz1uFI/wxJPVifOWPnBswIfW6CpsHnG+y6JZuDjPvXx6UO0gH83gBv3PdJjCyqWG666HQchymmOh0KVw3vbl+LyoEkNzpCuq+MpHYs/WlN/l4mrF/Er9vXhSmvVhQFHh8bt+XgCwSP/uJKoEGLLE4bckqFOUgAI24ZiMNdts+hrEg0bdeAuk2PvYnt1l37Wbp2R6mDdIBg0OCzH+bgzbkAS19FSBDIh2Vswpd7MZZZsf2eahxWDB9VHDuSFCPcisbEwVfx/uq/+H7rKpI0J1e07MKA+i1jbVqlYlkW3/zvV7546XsKcoto3bkp1/3fRTTv2CjWptlUEqF0iPCUiKKgHzPCVdSwLPL8kdUGbWoflmWxZfXOiM9tXlkz6ju7d2jMY7cO4ZWPprFtdx6pSXFcNbwH5/fvePSDKwnLMjH1FWDpSGr7mPb8OxpCikONu5ag523gkGuJcKAl3Iphmtw+exK/bF+LACQhEa9qfHrm5TRKSKkQGwJBnSffmsovc1ejyDKmZXH1eT24fGjXGpci1rFXa6579ELeeugLJFmgBw2atm/Avz++5bjm2bk3H1WWOLyKyTAt4tVZYPkIW3lbOsHiiWjxV53Qe7CpnVTdq1gtIEF1MKb9aYxpX317kZwo7zz8Bd/87xf8xaFd4MUzV3HX4Kd4edpYGraqvWmHNtAjs1HEonS3ota6zQSbIyOEIDE1noLcorDnEtMqr1VAtOnVuRm9OjeLtRkRMQJLKc69FssqadWAhiv1VRRHrxhbdmTUhLsQcjZBzxtYZg6S2gkt8QEkpTkfrV3Ar9vX4TMORpu8epCbf/+S7866tkLO//z7v/HrvDUEggaBYCgy8s7EOWSkJTC4Bta6DrmmL/0vPpVNK7aTlJ5AduPjT5duWj+dgB7eJkRVJNo0FmBFisb5sMyasVkSHQTWsfUrqpXYcfoqgmVZGEbtUikqLvQy6Y2fSx2kAwT8QT559tsYWWVTVch2J3JD25645IOKSy5FpVNaPfrVrdyC/EKPj0WrtrFtd16lntfm2LjgtsFh6TwOt8bIO86KkUW1B8v04sm5GMvcCZYHLA+WtZ/i3GsxjfA6qqqCEAI17jLcGTOJy1qBK+0j5BJlzY/WLQirFzax2FCQWyG9DP0BnSkzl0dQLAzy3qS5JzwhqosEAAAgAElEQVR/VcXpdtC6S9NyOUgA6SnxnNWrLc5DUk8lIXA6VDq0OxsLOfwgEYes2TL5NuXDjiTFmGBA591HvuC7d6bhL/bTpF0Dxjx/Oe16tIi1aVFn16a9KKpMwHfYzcgwWbOgdhbPHg+WZWEE52MZuchaJyS55gkZ3H5Sb7pnNGTC+kV49ABDGrbjnIZtKq0Ow7Is3vpyNh98Ow9VkdF1kzbNMnn6n+eRGOesFBtsjs4F/xhMcYGXr/87FVGSunn+LQMZfvPAGFtW89H9U4Hw3X0sk2Dx1zgSbqx0m06UgBHh/RBakPuNSLVMx4cnQs+9A+Tke054/prMPVcPoEn9NCZ8v4CiYj9d2zfi5lG9mF6wmURvOicn7sQlh35HuqWiKY2RHXYvJpvyYTtJMeb5m99m1uQFBEoumhuWbeX+857l5WkP0ah1vRhbF13q1EslGAi/GQkBDVvGrnlsdcDUt5bs3uYAoTQDLf56nIl3x9q0CqdnZmN6ZjaOybl/nrOaj6b8VSYlZvm6nTz06ne8eO+ImNhkE44kSVz50Aguvnso+3fnk5qVHNbzxSY6WEbOEUQQ/Fjm3kq3pyIY2qgt41b8gd8se39K0pw0SUg94fmTE1zEux3k5pdt9yGA9s3te9/fIUmCiwZ35qLBnUvH9vk8PDj9RwxrEJfUXcb52atQhMkPe1sxqOUztK3C9XFVgmogoBAr7HS7GJK7O5/fJ/1V6iAdIOjX+fyFKWXGLMti6dodTPtzLXv3h+feV0cSUuPpe2F3HK6yixnNqTHqrqExsqp6UJx7FZaxrSS9pQjwE/C8RdA3NdamVRp+b4C8fQVRbSb48ZT5+PyHyfTrJgtWbGV/Qe3sZ1aVcbg0shrXsR2kCsZv6HyzeTmPzP+R8avnlRFOkR09iCS8gnAjO6pnve21rbvTKCEVtxL6O9IkGbei8uKpwypEVEGSBP8c3S9i2tiYUb1PeP7axm871iEJgW7JvL+9I+f9dRFD/ryY1zd15tstm2Jtnk01xnavo4Rp5KD7p4FQUB39EFJ4AfGuTXtQHWqZLtShY002Lt9W+vPunEJuefJz9uV5kIQgqBuM6N+Rf1zSp9qr4PzjxdHEJbmZ8s40ggGdzIbpjHn2Mlp1bhJr06osRnAtprEVOKyGzfISKBqP6qxaPaYqGp/Hz8u3v8fMr/8EIDkjkdtevpIu/U+q8HPlFUZ2hGRZotDjIyXRXeHntLGpShQEfJw/9V32eAvx6EGcssJLy2bySb/LaJOSiay2QXGdhe77EayS74twIasdUBxnxNT28hKnanwz6Gq+37qKOXs2U8+dxAUlbTsqiv49WpGa5Gb813PYvief9i2yuWZ4TxrVPfFIVW3jyBtlVkSFVJtDsLCFG/4G20mKAn7Ph3jz/w0ooQ02yyQu9XVUZ9m82LrNMsMcJABJlspIYN/zwiS278nHNA9+2b/+dSntmmXTv0ersOOrE4qqcMMTo7j2sZEE/UGcdmPEo2JZRRCpQBWwrBMvKq7q/N8Vr7No+srSDu57t+Xy2KWv8sLPD9D0pIZHOfr46NmhCZOmLQ0TVdFUmboZyRV6LhubqsjLy2ay3ZNPoCT1zGfo+Aydf875hu/Pug4AV/IL6N5vCBR/DFYQxTUCLW4kQlTfZBVNlhnWuB3DGreL2jlOadOAU9o0iNr8tYV+9Zrz7wU/hY1rssI5DWueUqBN5VF9r2BVFENfjzf/EULNzEJKP+DFs/9mTLOsMlZyeiJnjjoVh6usKpPmVBl5x9kA7NiTz6btuWUcJAip4Hz248IovpNjY+2WvUz6bSlzl26KKNd8rMiyZDtIh7EkZwcP/jWF22dP5Mdtq0o/3wMKTOE4UZ3nVJ6BMWD3ln0snrGS4OEpcP4gn7/0fYWf76rhPUiMc6AqIadUCHBqCvdc3R9Fti+fNjWfKVtXljpIh7KxMJccX0hkQAgJ1X0ecemfEVfnaxzxlyGEFnZMdccwTLat3cX+3TV/M6o6ke6M59+nDMQhK6iSjCwknLLClS27clKqXeNlU37sSFIFEyieCEQoYrUEQd9PONwjywzf+uJoMhqkMmncz3jyi2nVpRk3/udi6jXPAkIqOLIsIIL8f1Hx4S3VKg9dN7j/5W+Zu2wzQoTyqZPiXfz3wZFkpSfGzK5YYxq7Cfp+AEtHcQ5AVsoX2Xhr1RxeWDadgGFgYvHL9jV0rdOQ//UaiSw5cCY9iS//HrAChJSlXAi5Llrc6Ap9P1WNvdtyUTUlXBHRtNi+bleFn69OSjwfPXUlE36Yz5/LtpBdJ5FLzulCu2b2jdemdiD/TTRIqSSVyarA7O8W8MKYd/AXBzAMg7bdW3D/uzeTnFF773dViZHNTua0rCZ8v3UVQdPgzHotaJlU8xRfo4KdkXhEbCeporG8RJRDxSzpBl0WWZa45J5zueSecyNO16R+GnKEHWtNlenbLXYy4RN+WsjcZZvL9HnwBXTGvvYd/3v44pjZFUv8xV/hzbuHkhxLKHgSZ8IdOBPGHNc8+3wenl86rYyyUrER5M99W/h151oG1GuF5h6GrLYg4Hkf09iJ4jgTzX0hQnJV7JuqYjRsVTdiiqqiyrTtHp3vQ2qSm5su6sVNF0VlehubKs2FTTvyxorZ+MyD3ztZCDqmZpOk1ezrzQE2LNvCk1f+F/8hIkvLZq/hgfOf5bXfH42hZTaHUi8uiWtbd4+1GTY1iNqzDVRJqM5BWETqn2KhOvod93yKLPHgdYNwaAqyFCquc2oKGakJXHxW56McHT0m/rokrBGeaVqs2rinVqp+mca+EgfJD/hK/vXjK3wRI7jquOaavWcTihRec1SsB/lh68G5ZLUtruSniEt7D0f86BrvIAEkpsVz9jVnlGkcKiSBw6Ux4h+DY2iZzeEEgjo/z13NB9/9yfyVW6OqQmgTPW5o05NO6fVwyyoOSSFO0chwxvNCz2GxNq3SmPja1NIayAMYQYNta3axYdmWGFllczh7t+eyYu46PPm1bw1yYogYPqo2diSpgtkRaMbve5pwRtoanJKOBQRNhSL1CpKV+uWas0+X5ox/9BK+mLqYXTkFnNqxCef0aocrhjK3wWDkZntCHPm5mkzQNxWEFCFsHSTgnYxLbX3Mc7llNeKlQxaCBNWu27rhyYup3yKLr175kcI8Dx17teHqf19AnXq2KlRVYdvuPK577FN8/iCBoIGqyLRoWIdX/jUCp2bLc0cTyzII+n8hGFiALNdFcw1DkpLKPZ9DVvig7yUsytnB0tyd1I9Lond2s1qVardry15MI7zmVlYkcnbm0bR9xQrG2Bwf3iIf/3fFayyevhJFU9ADOuffMogrH76g2isA28QW20mqYJ5e+hs/be9Fp93N6J++Dt2U+W5PS/xSGlPPKv+8Teunc89VZ1acoSdIv24t+XzqIoJ6WYcoIzWBOqnxMbIqlhhHyOu1iJx+eWROz2oa8cKuSjIXNj25XNbVJIQQDLmmH0OuOf7IrE3lMPb1Kewv8JZGj3TDZNWm3bw/+U+uH3FqjK2ruVhmMfk5IzD0DSHRIOGiuOBJEtM/R1Hbl3teIQSd0uvRKb1mNzg/Ep3OaMvKuevCaiEDfp0WHRvHxiibUl685R0WTQsJ+hz4HU18/SfqNc9i4GW9YmydTXWm9mwFVRJ/7N6AacH8/Lr8Z31vntt4Gqs8ddhalEd+wHv0CaoJV53Xnaz0BFyOkmZ7qozLqfLvmwbXyp0b1XkmYX2LAHCUKs4VBny8tmIm5/70P0ZP+5Bfd6yNOJdDVnin9ygSVAfxika8oqFJMvd06Ee7lKzovQkbmwogv9DL2i17wtLrAkGD72Yuj5FVtQNv0X8xgmtKVFUBy4tlFVKYe3x1kYX7Pfw1dQlrF2600ySBIdeeSUJKHIp6MA3a6dY494Yza61ww6YdubwzaQ5vfT2b9Vv3xcwOb5GPWd/MD1M89RUH+PzFKTGyqpphxfBRxbEjSRVMvOqgIBiuOieEwCnXnDSThDgnHz4xmp/nrGbhqm3Uz0pmaO/2pCXHxdq0mCDJ2TgTx+IreJyQuqEFqDjirkLRTsITDHDez2+zq7gQf0kB9MKc7VzXqif/aB/eYf2U9PrMHXY7f+zeSLEe5NTMxqQ47MalNlUf07I4Uq65aS+4o4rf+xWhesiymMZ2DH07snL0SNCE5ybz4RMTUTQF0zBJy07hiW/uJqtR7VUKS0iJ4/VZj/HJs5OZM2UhCSlxnH/LIPqO7Blr02LCR1P+4o0v/kA3QlkS70/+k9FDu3Lt8Mr9PEwzn+KC97nmsWVsWBLHzInZeD0Hl7UFOUWVao9NzcN2kiqY0c278eLy6fiMg7samiQzqH5rHHLN+rgdmsI5vdtxTu/oNdurTjjjr0B19ibg/RYsHc01GFkNNbL7fOMidnsPOkgAXiPIuFWzuKxFF1IjOEAOWaFv3dgpGNrYlIeURDeN66aybsveMhuFmiIzqOex1+bZlIfITabBCtVMHoX5vyzjo6cmEfAdTFvauWE3D414njf+fKJWZgkcIDkjkZuevpSbnr401qbElO178hj3xSwCh9QeG6bO+5P/pF+3ljStl1Ypdhj6JvL3DgV89B7updsgieG3bGTsiK7s2+FCkgQnn3GknoI2ZbD3ro6InW5XwVzVshvnNGiLJskkqA6cssIpafV5rPPZsTbNphKQlSa4Em7FlXhHqYME8OuOtfiMcOlqTVJYkrsDgM0rtzPt8zmsWbDBTnGxqdY8etNZxMc5cDpCG0Nup0rD7BSuGtYjxpbVbBzuURCmriqQlWbI8sHeXnP2bGL09A/pO+VV/jn3azYU5gChOg5/caDM0aZpsXvzPjav2B5l622qAzMWbIi4qA7qOo//70de/nQ667dFP/3Ok3cflpUPhMoYnG6T+KQgV4xdg6LKuBJcXDH2/KjbYVOzqVmhjSqALEn8p9tQbm/fhzX5e2kQl0zTxMrZWbGpumS5E5AQmIfdXUzLJFFojB3xPIumr0BRJEzTolHrejzxzd3E19L0RZvqTdP66Ux64TqmzlnFjr0FtGuWxWknN0WJ0PPNpuJwxV9D0D8NPbgIrCAIDSEcJKT+t/Q1325Zzn1/TS7dtNnhyeeXHWv5ot9VFOQWRpxXUiQK8zyV8h5qEpblx++diN83FUlKxxU3GkWt3tENWRIRs2lNYPmGXazatJsvpi7m1lG9uHBAp6jYYFkWwcAsDq8DlhXo2DuXs6/uy4V3nE1GfXvtZXNi2E5SlMh2J5Ltrp0FnTbhjG7elSlbV5SJJklCkOVOZNG4OSyatjyU4lLy3PqlW3j5tne5/73jK7i2sakqxLk0zuvbIdZm1CqEcJCYNgE98Cd6cCGSnI3mHIgQoeiSaVk8vujHMtchEwuvHuDZZb9y5tDObFiyJUzFzTRMWnRqXJlvpdpjWV7y9p6HbmwAqxiQ8Xm/ID7pP7jcI2JtXrnp06U5r3w644jPG6aFYeq8/OkM+ndvRUpitGppFSAQPqo5GfPc5VE6Zw3EAqzam0Z7NOxtvShgWRbL/1jDlLd/Zenvq+zUKRvap2bzf13OIa5Erc4lq7RKzODdPpfw47szwhYlekBn1jfz0YPhKXo2NjWBPbmFjPtiFve+9A0fTvmLAo8v1ibVCIQQqI5uuOJvwOE6t9RBAsjxeygKhi8sLWDhvu0Mue5MMhqk4XBppXM5XBo3PnMZTrfdo+148Ho+QdfXlzhIEGoT4aUo/19YVvVVus1MTeCu0f3QVBmHpiBJRIwsKbLEnKWbomKDEALNNRQoK4YVMGUWF3VmjzdyRNTG5nixI0kVjKfAy7/OfpItq3ZgmRaSLFG3aQZP/3i/nTpVyxnW6CQG12/DqrzdJGpOmiSEUgH8vvBFC4R2b/WggaLaX9NoEPDPwut5D8vMR3MOwRV3YZkFpU30WLlxNzc/+RlB3SCom8xeuokPv/uT9x69jMy0hFibV2P5u2bUdZxxuBNcvPr7o/zw3nRmf7eAtKxkht00kNZdm1WilTUDv+9bDtTLHIpAJhhYiOaovv3Chp1xEj07NGb6/PX8Mm81i9fsiKBcKdCieO+KS3oUQ1+N178WwzKwgI3Fqdy1vBWOVW/xw6AbSdJcUTt/TcLexz8ydiSpgvnffR+zcdlWfB4/fm8Ab5GPzat28PqdH8TaNJsqgENW6JhWr9RBAujS/yQkKXwrrlmHhvbubZTwFL5Mfu4VBHzfEQz8jqfwEfL2DceywuWTbSqe/3v7J4p9QYJ6qKbAH9DJ9/h4dcKR03hsThynrDKsUXuchymtumSVG9ucFvp/vJPhYwbx9JT7uPedm2wHqZxIInK6vYWBENW/4XpGagIXDjiZ2y89A1UNV1W0LItTOzaJ2vklKYki1yfcseJsXth4GrctH8I1S4ZTaCgUBf1M2LAwaue2qT3YTlIF89uE2QT9ZVOk9IDOjC/n1oq0ux1783nh42nc/NTnvPrZTPYcoRDYJoSv2E/7ni3RXBpqiRKY6lBwJTi5/dWrY2xdzcQ0cigufBEOTXmxvOj6OvzeSbEzrJZQ7AuwYVtO2LhpWsxavDEGFtUuHuo0mMH12+CQFOIUDbeicmvbXgxt2D7WplVLdhbnc8fcrzhl4tOcOvl5Xlw+jYBp4Iq7EsThkQyBJKWhqCfFwtSo0LpxJtef3zPUUN6h4naquBwqT992bmmz+WixIn83qz0N+GZ3G5YWZnEg789n6MzbuyWq57apHdh5PBWMfkjvgEMxdDPi+BHn0bfgKXqdYGABitKSuIQxqIdISldFVm3azQ1PhFJodMNk8dodfPnrYt4ee3Gl9U6oTmxYuoW7Bz2BETRKu4WnZCQx+Mo+DL3+TNKyU2JsYc0kGJiHEFp41Mgqxu/9Aad7ZGwMqyUossSR2u04tZrTcLuq4pAVnuk2jAdPHsgeXxEN4pJrVKPzyqQg4GP4L2+R5/diYlGk+3l79WxW7N/Jm6dfjDvuJoqLXgMR+nwlEU9S6oc1rt/UZWd3ZVDPNsxZugmHpnD6yU1xO7Won7eeOwkjwuazKiSaJKRG/fw1hpq/f19u7EhSBdOl/0lIh8ncipKmZsd6YQwGV5Ozpz9ez8fowWX4vBPJ2TsEv//3aJhcYfznvV/w+oPoRsghDOoGxd4AL3z8W4wtq3pYlsWjF71E0X4P3iIfRtDACBoUFxSTnp1iO0hRREhJWBHvChKSbDvz0UZTFXp1aoqilL1OOlSF4X1rzg57VSdJc9EisY7tIJ0An29aSLEeKNPawW/qzNm7iXUFe4lLvJO0zDkkJj9HUuo7pGbOQ1Gbx9Di6FEnJZ6hvdszsEfrSnGQANomZ9E0IQ3lsEbJiiRzWfMulWKDTc3GdpIqmJufH01iWjwOd+gi4XBrJKTE8Y9XrjrmOQrzH8WyPMCBtD0TLC+5++6peIMrCMM0WbFxV9i4BSxYZTchPJxta3eRuzsvbNxfHOD7d6dVvkE1iFx/MXfPm0iHr5+kw9dPcufcr8n1H+zxomrdkUQc4ZJMGk736Eq1tbZy/zUDadGgTkl6joZDU+jWvhFXnts91qbVWjz5xUx561c+/L+vWPDLMkzz+LIfaiOLcrZHbBIuC4lV+bsBkOQMHK4haI7TECK8dsem/AghGN/7Ek7LbIIqyWiSTIO4ZP7XaxSN4u1Iks2JY6fbVTCZjdIZv+xZfvl4FmsXbaJZh4b0v+R04pKOvVdAMDCPSPFP09jMuq2bad6gUQVaXDFIQqApCv6gjhAWfTst55weC3GoOvNWtcM0r0GSqn+xakVhGuYRI4vmcaZm2hxEN01G/TaebZ48dCv0OX6/bQWLcrfxw6CbUSUZIWSS0iaQn3sJlpkPSFjoxCc+hqrZkYzKIDHOyfh/X8KqjbvZtief5g3SaWKn5MaMtQs3cs+g/8PUTXxeP063gxanNOGJb/+FFuW6kupMi8R0ftspEzDLptlblkWDODsboDJIcbh5q9fFFAb9ePUAdZzxNS6dMerYfZKOiO0kRQF3gouhN/Qv9/FCJJZEksqimzIvfjaHV++pek6SEIIhvdoxeeYybhj6Hb1OWo3TEaqzqZc+m5y9Q0jP+MGWWC6hQats4pLc+Dxl62IcLo3+l50eI6uqP7/tXMNeX1GpgwSgWya5/mJ+2bGawfVD3e4VtQWpGXPRg4uwzCJUrTNCsiX6KxMhBG2aZtGmaVasTanVWJbF4xe/RHHBQSETn8fPmr82MHncVEbcdnYMravaXNS0M+PXzi3jJKmSRJOEdDqk1I2hZbWPBNXxtxL3NjblwU63q0L8uG0lfb9/mZc3NMFrlPVf/QGFX+e3Y9Ga8JS2qsJtF/emf1cnfTquKnWQAGQ5iGlsw1f8TQytq1pIksSDH92KK96JVtK40RnvoPnJjRl6/Zkxtq76sq5gH149vO+URw+wtmBvmTEhJFTtFIqDXXntywWMeuA9bnzyM6YvWFdZ5trYxJzt63axf09+2LjfG+Cn96fHwKLqQ6YrgY/6jKZdchaykFCERL/sVrzb+1I7mlHJrF+8ifEPf8b4hz9j/eJNsTanWiGs2D2qOnYkqYowbeda7v5rIj5D5/PiDmQ7CxiSsZJgUEaTDP5a04S3pvQloQr3zXFqKvdenkrefo2D9VQhLKsYv38GrjhbOewAbXu04P1Vz/PbhNns25FLh15t6DzgJCTJ3rsoL00T0nApGp7DHKU4RaNpQnrY6ws8Pi576AP2F3oJ6qHd4BUbd3HFkG5cc26PSrG5tmNZOp6idykufh/L8uN0DiEh8R9IUlKsTasV/N1i3l7oH522Kdl83f86ivUASkldjE3l8sHjX/L5c98SDITWHV+//D0X3jmEyx8cEWPLbKo7tpNURXhxxbTSAlALwUubevHuti40UPPwTMlkf2ECTk3hogGdYmzp3yNJGUhCitDBWUWW6wHgN3RMy8Kl2LnuiWkJDLt5YKzNqDH0q9uSZM2FzwiWSsPKCBJVJwPqtgp7/Re/LCKvyFfqIAH4AjrjJ8/lwjNPJjHOTg+NNvtzb8Lv/xWrpG+Vx/M2Pt8PZGT+YqfnVgJ1m2WSmpXMzg17yow73BqDruwTI6uqH26lchTdbMqyZfUOPntuMgHvwewVvzfAZ899yxkje9KgpZ32aFN+7C3rKsKWov1hY/m6i2WebPykoikyg3q0ZvSQrjGw7tjRHKcjRCJhf1pCwSOfxzW/f0ynSf/hlG+eZtS08WwsDG8qaWNTXlRJZkLfq+iX3RJFSMhC4ozsFnzW72o0OXxPaPbSTQSC4epUqiKzevOesHGbiiUYXIXP/0upgxQigGnuxltsN/atDIQQjP3kNuKT3TjjHEiyhDPOQdvuLRly/YBYm1flME2TuVMW8sJN/+N/933MlpVHV2/dsTefR9/+kWF3vsU1j31ip/RWILMnz4/Yh9LUDWZPnh8Di6oZVowfVRw7klRFaJaQxuL9O8LGEzQHz44ZRpO6aaQlVf3CciFkUut8SV7ONej6BoSQEMJNfPLLDJn2MzuL8zFKvhkLc7Zx0bTx/Dr4VuLtgkubCiLDlcBrp47EKokk/V3KUGZqAkIQFvk0DLNafN+qO8HAIgRS2L0ylJ47B3fcRTGxq7bRrGNjPlz3CjO/mkvurjza9mjJSb1a2+l2h2EYJg8Pf5YlM1fi8/iRFYlJr//Era9cxaDRkaNuu3IKuPzhDyn2BTBMi505BYwdN4Ubzz+NSwZ3ruR3ED0sK0hh0RsUed7Hsry4nINISrwHWc6I6nkVVUaSBMZh40ISyIqd+mhzYtiRpCrCP9v3w3nYTrdLVrmt3Rl0adOwWi3YFKUR6Zk/k545jdQ631EnaxFz8+qyP1Bc6iBBaBPBb+h8u3V57Iy1qbEIIY66yBs18BQcatnvnSwJGmen0tSWpI46kpxN5NuQA0VpGDYaMA32eAsJmocviWxOFFe8k4Gj+zDqnmF06N3GdpAiMOvreaUOEoChmwS8AV65dTyeguKIx4yfPLfUQTqAL6Dzxtd/4AsEIx5THcnJvZGCwucxjK2Y5j48xRPYvWcQplkY1fP2Gt4t4t+qEIJew7tF9dw1AxGSAI/Vo4pjO0lVhJ4ZTXitx0haJmagCom6riTGnjyY0c2r75dcURqiqq0QQmJL0f6ICxuvEWRjkZ1yZ1M+DN1g2mezeeKyV3jl1ndYt3DTcR3fvlk29105gHiXFmpqqiq0a5rFC/8cHh2DbcrgcJyOJCUDZXd8hVBwx11c+rNlWby+agY9vn2G/j++Qvdvn2Hcqpml0UIbm8pg2udzwto2QCiasWT6yojHLFi9rYyDdAAhBFt2hTcUr44Eg2vx+X49LG1WxzDz8RR/FpVzFnn9vDRhOle9+BW+UV3wd6yHFu/A4dbQnCpjXrySjIbhYj02NseDnW5XheiV1YxeWc1ibUZUaJ2ciSJkAocFxd2yRvtku0+KzfGjB3XuHfwEaxdsxOfxI0mCn96fwQ3PXMaQ64+9T9lZp7ahf7eWbNyeQ2K8k6y0xChabXMoQsikpX/F/tybCAaXAhKynEFKyivI8sHrwnvr5vLm6ll4jdDOu9+Ecat/J05xcHk13kiyqV443FrE9FwA1RlZiCgzNYHNO8NrjnXDIDXx2JvMVxUCgWXk5T9KILgASUolIX4MkogHoUSoMfHi988hIf6aCrVB1w2uefxTtu7JKxXd0TrUw9GjGVe1b07PoZ1JzUqu0HPa1E7sSJJNpdAtvRHNEtPLyKMqQiLV6WZQvTYxtMymujL98zmlDhKAaVr4vQHG3fUBnvzIqS9HQlVkWjbKsB2kGKAo9amTMZnMrD/JyJxBRuZsNEdZgZo31xx0kA7gNYK8sfr3yjTVppZz1tV90Vzh9bOSLNGxT9uIx1xxTjecWtn9aFWR6d6uEenJ1SeNHiAYXMOefcPwB2ZiWR4MY3Uxc5IAACAASURBVCv5+Y/g808nchW+hiU1ZqsnciZJeZm+cD27cgrKqJIGdIM9/gD1+rS2HaTjxRZuOCK2k1QFsCyL2d/O59GLnueRC59j1qQ/a1waiRCC93tfziVNu5CiuUhUnQxv1IEv+l4TUXXMxuZozPhy7hFSXxSWzIyc+mJTdZHlOihK/bD6AsuyyPF7Ih5zpPHahq4bvD15Duf88036jXmNsW9MYXdudGtBaiMderXhwjuHoDlVnHEOXAlO4hJdPD7pblQt8n2sa9uG3H15P+LdDlwOFVWROa1DEx694exKtv7EKSh8EcvylRmz8FLs/aakxUfZzyBgCi78o4ihP4+j57fP8umGilGbW7ZhJ8X+8Hou3TBZsXFXhZzDxgbsdLsqwQs3vMm0z/4oXfDNn7qEU4d15d53x9So4tk4ReP+jgO5v6PdF8jmxIlLdB8h9cXCafc3igl+/wJy8x7CH1yCJCWTlHATifE3IET59+OEEDSNT2NDhNrFSA2CayMPvPEdsxZvwl8iZz913mrmLt/MF09eZff6qmBGjx3BWVedwcJfl+FOcNF18Mk4XH/fI2lor/ac1bMN2/cVkBTvJDneVUnWViyBwCIgXG5bCJWU5KcoLHwVn38mIMgNpvD42t7s9LuAkEPz1NKfyHIlcEZ2yxOyo26dJJyagi9Qtn2Dqkh2NkB5qFl78hWKHUmKMesWbuS3CbPK7Ij7PH5mTfyTVfPsXgo2NkfinOv6oUVYnKhOjQ69WsfAotpNILiKXfsuwB+cDwQxzb3kFTzD/vxHT3juf3UYGKb+6ZQV/tXB7uOzZdf+Mg4SgGFaFPuDTJy+NIaW1Vzq1E9j4Og+nD6821EdpAMoikyjrJRq5yAZlsn6gr3sKM5HUSLXTFtWEFVtTZ30j6iXvYKk9DlctuhClhSW3cTwGkH+u3rmCds0uEdr1MPkvSUhcDs0ep3c9ITnt7E5gB1JijELflmKHgjP1Q34Asz/eQltureIgVXHxrH0ofk79mzZx1cvTWH1/PU0PakRI24/m7rNqp+Iw67cQl78dBqzlmxEUxWGnt6OG4efilOLXMhrUzG0O7UVlz4wnA8f+wpZVRAipDL1xLf32v0xYkBewQtYVtn0R8vyUlj0HsmJdyJJCeWeu09WC9449WJeWjGNDYX7aJqQzh1t+9KtTuMTtLr6s2brXhRZ4vDsI39AZ8m68N57NjbHysxd67h3/kR8ehDDsuif0YTbGzmBQ1PunLhdw5ClFAAkKZ58PYgsJAjrXgS7vCeeBprgdvLmv0Yy9s3v2bxrP2DRulEmj91wdpjzZGNzIthOUoxxJ7hQNAVDL3sxUTWFuISqueNkmHnk7n8Qj3cyYOB09CEt5SlUpcExz7Fp+VZu7/0QAW8QPaizau46pn4wnad/Gkvrbs2jZ3wFU1Ts54pHPyKv0ItpWfgCOp//uojVm/fw33sujLV5NZ5Rdw9j0BVnsGT6StyJLjr1a4ei2pe1WBAILCVSKg5CQde3oGntTmj+HnWa0KNPkxOaoyZSr04SZoQaVlWWaVw3NQYW2dQENhXlcOvcz/EdIpjy/W4VrHO5s9lsDGMnQqjEuS8lOWlsmWOzXIkokhTmI0kIOqXWrxD7mjeowyePjSa3oBhJEtUuQlelsNPtjoidbhdjel/Qg0h/oUIIzrjo1Mo36ChYlsmuPefj8X4DBAADn38aO/echWkWHfM84+58n+ICL3pJioihG/g8fl659e3oGB4lJv++nGJfoMwiJRA0WLZhJ6s374mhZbWHlIwk+lzYg66DOtoOUpTZ5tnP0tztZRZOB9DUVkCEqLIVRFbqRd+4WkrrRhk0zk5FlcvezhVF4oK+J8fIKpvqzqcb5qMfpkhnWhbTcrLZIX9GveyV1MteQ0ryYwhRNuVQlWTuatcfl3wwm0JC4FJU/tH2jAq1MzXRbTtINlHDXlFEgSKvnxcnTOfHOavRDYPu7Rpxz2X9qJueFPbaxLQEHv7iTh4f9WLpmGVZPPDRbaRkhstYWpYJBBEiXIa0MvD5/0A3tnCgEDOEiWV6KSr+isT40cc0z9LfI6uPrVu4CUM3qk261IqNu8KKRyHk5K7dtpdWjTJiYJWNTcWS4/MwZs4EVubtQpVkTMvk3pMGclHTzqWvSUq8A69/epmGkkK4iHONQJaiI8nrLfLxzes/MuPLuSSkxHHeLYPpMaTz0Q+sQQghePWuETw+/idmLtoAQKOsFMZePZCstPKnONrUbrYX56FbESLDwF6/B0lq/LfHj2ramUxXAuNWz2SXt5BTUhtwa9s+tthKVcMCrJojEFbR2E5SBWNZFmOe+YK12/YS1EMXmNlLN3HFYx/z9VNXEx+hx0KXAR35bMebLJmxAizo0LsNmlM7bN4AOfmPU+j5EMvyoypNSU9+CpfztEp5XwcI6uuwrPA8Y4tigsFjl112xbsI+sNzk1WniiRXnwBns/rpaAtkAsHwz6RhZkoMLLKxqXhunv0py/N2olsmfjO0KfDU0p9omphO1/RGADi0DmSkvU9u3gME9bUIEUdC3NWkJN0dFZt8xX7GdL+P3Zv3EfAGAFj+x2pG3HEOVz5yUVTOWVVJjHPy9C3n4g/qBIMG8e7YbKLZ1BxOy2jK77vXh/UnC5oGHVKPLTLcN7slfU9Qyc7GJpZUn9VoNWHJ+p1s3Jlb6iBBKETt8wf5btaKIx6nOVS6DOhIl4EdwxwkgL3776Sw6IOSXVqToL6OXTmX4w8si8bbKENh0Me8fZvYWLgPVWmJEOFRHiHcqOqx1xwMvXFAmCqQ5lQZdOUZ1Ur2fFiv9miKUibJSJElGmamcFKz7Eq1xTAj7/rZ2JwIm4pyWF2wO2xX2WsEeWfN7DJjLufp1MuaTqN6W2hYdw2pyfchRHT24qa+P509W3JKHSQIKYN+/uxk9u/Jj8o5qzoOVbEdJJsK4dyGHchwJpRpAO+SVc5vdDL13HazVpvagR1JqmA27siJ2AjWF9BZvaV8NSqGsQ9P8WQsDleO8pNX+CqZaePKNe+x8Obq33l91XRUSUa3DFomZvBMq4ZYxnpCNUkAEkLEEe8efszzXvrA+exYv5vfJ85Dc6gE/UFO6d+B6/9zWVTeR7RISXTz1v0X8cR7P7Ns/U4kSdC3c3P+dXn/SnP2fluwlhcmzGDHvnwS45xceXZXLh/UpVo5m9UJywpgWsVIIqlWfMa5fg+KkIHwtNI9vshKVdFyjA5lzncL8BdHaCasKaycs5ZTz+0SdRtsbGoqbkXj877XMn7tbH7YvoJ4xcGlzboyrGGH455r16Y9fPrURJbOXEVW0wwuvvc82p9ut2moKghbuOGI2E5SBdM4KzXiwsmpKbRsUKdccwaNbSA0sA5fEJgEgqvKNeex8NvO1YxbPQO/qZem2KzI28Uj6y7kP+3W4PFOwrJ0XM5+pCX/H5IUd8xzK6rCfR/cyp6t+9i6egd1m2WR3aR61u80q5fO2/ePIqgbCCFQKjFdcM6yTYz93/eldVEFHh9vTppNIGhw7dAelWZHbcCy/OzL+zdFnk+wMFHkDNKSnyTOVbN79bRKykKPkGKrSTJ9smKnRJmWnYIkCUyz7B3eMi2S0u1aHBubEyVRc3Jbu77c1q5vuefYvm4XY7rdh8/jw9BNtqzazuJpy7n7nZvpc2HPCrTWxqbisdPtKpiOLerSMDMFVTn40QohcGgK55zWtlxzqkpjsAIRnpHJN5vx177NGEcosDwRxq+bHZaPrFsm83NzCTj/TaN6G2hcfwuZ6e+ilFO9KqNBOp37d6i2DtKhqIpcqQ4SwH8n/hEmHOEL6Lz/w5/oevjC1qb87N1/F0WeT0siukF0Yzt7cm/A558fa9OiSpyicXvbfmWUqlRJJllzc3mz7jGz69ybB6E6yvYiE5IgOSORtj3tOohYYpoWxb5AxKwKm9rFuw9NwFvoxTikBMFfHOCVW9/BMOwU8SqBFcNHFcd2kioYIQTj7rmQQd1aoykykhB0b9uQdx+8hAS3s1xzylIyCXGXIcRBmUsL8BoS9y9L58bZn9B7ynMszt1WQe8ixH5/ccRxRZLID3gjPmdTuWzdvT/iuG6YFERIRbIpH4aRS1HxN1hlmiiCZfnIK3wpRlZVHle26MHLPS7ktIxmtErM5KrmPZh05g2kONwxs6n5yY25483rcSe4cCe6cLodNGhVl//8NLZWpEFWRSzL4p3v5tLvH6/R99bXGPzPN/j2j+WxNqtaYJoedGN3zB3LgC/Ap/+ZyNVtb+eadrfz2bOTCBzeqfg4WDJ9RVi0F8BX5CNnR+6JmGpjE3XsdLsoEO928PC1g3n42sFYllUhN+y05EdRlHrkF76JYe5nWUE6b27pyQavG/DjAa6d9SEzz74Tp6webbpjondmCzZ7cgke1itBIGiWWL7UQZuKpUndNBav2xE2rikyiXHlc8ptwtGNnQihYYVFdC2C+oaY2FTZ9MpsTq/MqtXo+cxLetFrRA/WLdhIXJKbhm3q2Q5SDBn/3Tze+W5uaXQ7p6CYpz74hTinRt9TWsTYuqqJaXrYvf8uirzfIxBIUjIZKU8TH4M0XtM0uaf/o6xbuBF/iSDK+w9/xrwpC3nml4fL9d1Kzkgid1dexHPFJx97ir6NTSywI0lRpqJu2EJIJCfcRKO6C/mucDwPrhnGBm/ZfgMWFr/tXFMh5wO4puWppGjuUnUbAThllQc6nFVG8cYmdtx8/mk4tbJ7HU5N4dqhPSo99a8mE0p5DRcuABmH1ilsdJtnP1N3rGBF3s6o21bb0RwqbXu2pFHb+raDFEMM0+S97/+MmP477us/YmRV1Wdnzg14vN8DASz8GOZuduXeiC+wpNJtWfDzUjYs2VzqIAH4vQFW/7WeJdOPrM77d1x0zzCccWUVFzWnyunnd8edEJ0msP6gTl6hN+ZROZvqT9RWUUKIm4UQG4UQPiHEfCFEr6O8vk/J63xCiA1CiBtPdM6aSp7fG7HJm2GZFAQrLg0u1RHHxH43cl3L0+mQUo8BddvwzmmXc16jjhV2DpsTo3OrBjx36zCa109HkSUyUxO4c9QZXDqwdjXUjDaSFEdSwo0IcWh6mUAIJ8kJt5WOGJbJPX99ydBfXuOBBZO4fOY7jJz2JnmByKmrVZGCnEJeu+0dLml4I1e1/gdfvvgthmHXt9n8PV5fEH8w0kYC7Px/9u47PKoqfeD499w7PZNOOr33DqKA2LCtbXXtZdVVV9e+uvpbdfu6Vd1i7x3X3lBRERtKVzrSCSUhvU6fe8/vjwkhIQNoyJQk5/M8eTR37tz7BsjMnHPe875V9XGOpnMIhXfiC3yFpPUKtZR+ahoejHs86xZswNfob3M86AuydkH7JmCPPu8Izvu/M7A7bbjSnFjtViadOJabH72q3XGaMkAwvA3T9LQ67g+G+MPTczj6+gc56VePcsptTzB/ZfdY6VdiIybpdkKIc4H/AL8A5jf99wMhxHAp5fYo5/cD3geeAi4CpgEPCSEqpJSvt+eaXdmR+YN4a/tyvPsUVZASpuT079B7ZdpdXDfsKK4bdlSHXlfpOIcN78P//nBJosPo8jLTbsOiF1Hb8CCGWYXDNonsjLuwWfemoD23aQFzS9e1qgi5vm43d37zNg9OOT9RoX9vPo+fX0y6naqSGsJNKwJP3/US6xZu5K7/3Zzg6JRk5nLYSHXZqWloO1HXrzA7ARElv7BR0pTGu+/+UUkovDXu8WQXZuJIseP3tI7H5rTRoyirXdcUQnDhHWdy5g0ns3NDCdmFWWTlt7/PUnXDI1TX3wtIkAaprrPJzbwbIaz85okP+GrVVoJNRYvKahr4v0dm89ht5zC8b36776l0X7FaSfol8IyU8nEp5Top5fVAKXDNfs6/GiiRUl7fdP7jwLPArYdwzS5ret5AxmX3blVtyqlbOaffBPq42/dCpijKgQkhSHNfRO+CBfQr2kBBzovYrMNanTNr6xL8RuvZ9JA0mV+2CU84+QtpzH3+C2rL65sHSBCpRLXg3aXsWL8rgZEpyU7TBNeeOa1N+q/dauH6nyRf0ke9x095TUNCU7Js1sFR9jkCWHHaIi0cihurmL1jJUsqt2HGoIptSzPOPQLd0jaV3mLVmXbWoVWydLodDBrf/5AGSA3eN6mu/ydSepDSiyRAg/c1Kmr/SGVtI1+t3Eow1HrVOxAK8+wHSw4p9q5OyMR9JbsOX0kSQtiACcA9+zz0EXDEfp52eNPjLX0I/FQIYSWyHeaHXrPL0oTgkcMv4P2dq3l3x0psmoVz+k3gyCTbVK0o3Y03HO0DDyAgYIRJsdijP54kVn6+JmqDVl3XWL9kM72GtK/Uv3JoguFikGGslv5Jve/qjCNHkeK08chbX1Ne00jfgixuPHs6E4f2SnRozWobfNz1xPssW78DIQRZqS5+d9kJTBrWO+6x6FoGGe4rqPU8hZR7VuA0NOEkPfUq7lj2Jh/sWoOuReazs+0pPDvtUvKd6TGJJyXNxT2f/p4/n3sflTurkUBu7x789pVf4kyCQkBV9f9u8ecUIfFT752FP3AlVqvevIrU/LiE4v1UgVWUg4lFul0PQAfK9jleBhy3n+fkA3OjnG9pup74odcUQlwFXAXQu3f8X/xizaJpnNZ7NKe1o/u1oiixcWTeIGbvXImxz+x0oTOdTFviymV/X0WDCrDaLIT22XyPgJxeKmUq3gKhDZRUXknI2AEIdC2LwuxHcNqTd8/hzElDmDlpSKLDiEpKyXX/ep1NuyoJN/Xo2V3dwM33v8Ws311M77zMuMeUnX4HVkt/ahofwTRrcNqnkp1+O2/u2M2ckrWRtN2mBaSAEeLmxa/y0owrYhbPwLH9ePq7/7J7WzlCCPL7Jk8PQ8Moj3pcSoPeuVZCUXoD6ppg9ICCWIfWucnknXhJtC5b/kpK+ZiUcqKUcmJOjipXrSjJJBgKs213NQ3etpuEO7Obhh9Lhs2FXYvMP1mFhlO38ufxpyf1CsAeJ195HLq1dbqNpmtk5Wcy+sj2NcNW2seUfnaUn0kwvAkp/UjpI2zsYkfFeYSNqkSH1ymt31FB8e6a5gHSHqGwwSvzlickJiEE6e7z6Zv/Of0LV1KQ/TA2S19mbV2Mf599x4aUrKsrpcLfEPOYCvrlJdUAKWiE8chBRMuO1LV0UlPyOP+48a3SPYUAh83KT0+cHMdIla4kFitJlYAB5O1zPA/YvZ/n7N7P+eGm64l2XFNROkwoXELYKMduHYymJf+KQDJ7ae43PPTW10jAMEyOmziIuy6Zid3a+du25TnTmH3sdby6bRnLqorp5+7BBf0n0zMl/jPU7ZHbqwd/nXMXf7/kfqpLazBNybApg7lj1o2dYpDXlTT6PsKUAdq2pTeo97xBVtqViQirU9tdVY+mtf13bJiS7UmWkuUPR2/gqgkNn9H+5q6d0braUn729XMU2Qfw24HLsWlh9vw1CuGkR/ofEELjujOnUdQjnec/XEpdo49xg3ty/U+mU5QTm/REpevr8E8lUsqgEGIZMBN4tcVDM4HX9/O0BcCP9zk2E1gqpQwBtOOainLIDLOeksqf4wssRAgrEoPstFvJTut29UI6xCfLNvLAm1+16qXyybKNWHSN3116QgIj6zjpNidXDJ7GFUxLdCjtMnLqUJ7b9ACVu6qxOayk90hLdEjdkmGU0/T214qUfsKm6r/VHkP75BGKUs7ebrMwYUjy7JsCmFk4jOc3LyIkW8ebbnXQy9U5Jl06gilNrln4InUhH3WhdG7/7gzOL1zKoJQK3LYB9M++A5fjSCCyAnbmjNGcOUNtQ/jeJG3nYZRmsUq3uw+4VAhxhRBimBDiP0Ah8AiAEOI5IcRzLc5/BCgSQvy76fwrgEtpXajhgNfsTExTsqWkiu1lNarZWZIrrboWb2ABkgCmbERKH1X199Lgm5Po0Dqlp95f3KbZZCBkMGfRerz+/RQ9UOJOCEFOz2w1QIqx2qCXN4u/5ZVtS9ntq2v1mMM2ASHaVhoTIgWX/dAqjXVX+VmpnHTYsFYpWbqu4XbYOHPGqARG1taVg6eT70xrrmJrFTpO3cpfJ/y4W63qrqrZhcfY+95Q7M/ib1uO52erLuShnZc0D5AUJRZikt8ipXxZCJEN3AUUAKuBk6WUxU2n9N7n/K1CiJOBfxEp6V0C3LCnR9L3vGbSkTKML7gCISw4rKMQQuObDTv59ePv4/EHMU1JQXYq91xzKv0K1KboZBM2KvH6v4Q2jf68VNc/TKrzxMQE1olV1DZGPa5pgjqPH5fDFueIFCUxPi5Zy+3L3kATAinhr6vmcOOwY7h0YKRgq8M2Fpd9Kt7AV80VvQQO7NZBpDj2VwNJOZg7L5nJkN65vDzvW7z+INPH9OeqUw8n1ZX46m0tpducvHXMNby7YyULK7fS05XBuX0nUtRJUnc7SsAMoxF9ULjvni2lndRc/X7FbBOAlPIh4KH9PHZUlGOfA+Pbe81k0+j/gl1V1yBlGJBowo3b8SA33L8MX2DvL3bx7hquvOdVPvj7lVij9CdQEscwa0BYIUofi/1V2VEObOzAQuZ9u7nNCqrdaiE3052gqBQlvmqDXm5f9kZzw+E97l83j6m5AxiUlocQgqIeT1Lb+Dx1nllIGSYt5Swy3VdEXWFSvh9NE5xzzFjOOWZsokM5KKfFxjn9JnJOv4mJDiVhxmT2xIyScePUrfyoZ3Kt/ildT5etbpdIIaOMHZWXY5i1mLIRU3oIm2VUNlyCrreu5iWJpBt9uSr+3bU7s+1lNazaUtomdau9Sr21vLhlIS9uWUiptxYAm6UvIuqviEUt8bfTNWdMxWm3oLVIF3HYLPzynBnNvUAUpav7dPd69CgpUyHTYPbOVc3fC2ElM/Vy+ubPpV/BZ2SnXY+mOeMZqqIklF23cve4M3DoFiwi8h7h0m2MyCjk1F5q75ESW52/nFQSqvO8AVE6Y0tpMrr/er5e3bqUrmEYVNZ54hVep1ZR28jND77NlpJqLLqGaUp+ec6RnHlk+18sX9q6iHvX7O1l/K+1H3PLiBM4v99kcjP+RFntr1s0sLOiaW6y0248xJ8k/qQ08AWXYcoALtvEhHzY6leQxQt3Xsij7y5g5eZSCrJS+dkphzFleJ+4x6IoiRI2zagZLiaSkNm2sICidGfHFw1naEY+bxZ/S1XAw4z8QRyVPwRdqIm1jiBUut1+qUFSDITNSiRtu9ZbdIPM1LapW0IIxg0sjEdond6N97/Fpl2VGKZkT9biva98Tr+CLMYN6vmDr7fTU8O9az5qk/Zy75oPOTJvEEXuc7BaelHd8BCh8C5SHNPJSrsGi57fET9O3PgCy9le+VOk9BOpqG9SkHkf6SmnxD2WPvmZ/OXKk+N+X0VJFjPyBvGXVe+3Oe7QrRxfqPpRKcq+eqdkcePwYxMdhtLNqGF4DLjt0xGibS8dIXQavKNa9YNx2CxMG9WPQT1Vw9uD2VJSRXFZDYbZetojEAwza+637brm3NK1RJvTlUjmlqwFwOU4nJ45z9OvYB65mX/odAMk0/RRXHEBhlnZlP7ZgCk9lNTcSDC8LdHhKUpSafD6WfzddjaXVMbsHrnONH45fCZ2zYIuBILIAOmM3mMZm5VcpagVReniZAK/kpxaSYqBFMeRuGzjqfcvQReRFSW/YWFpXW+GHDORMQNS+GDRd1gtGmcdOZrTpo5IcMSdQ02DD4vedlwv2X/VtIMxpSRaC28piTp46owa/fOI9GJuTUqDWs+r5Kb/Kv5BKUoSevL9xTzx/kKsuk7YNOmTl8l/rz+DnPSOLypy8YApHJE7gNk7VxIyDGYWDmNMNxoghY0avMHFaCKVFPthqhiFoihJRw2SYkAIDc11P09vuompmd9hSI1PKoeyoLY/Nu1LXj3qGi49cVKiw+x0hvTOIRRuu9fLZtWZNqpfu655TMFQHlr/aZs9ZJoQHJs/rF3XTDaGWYeMMkiCEGGjKu7xKEoy+mLlFp78YBGBkEEgFPl92bSrklsefpfn/u/8mNxzQGoONw7rfilEFfWPUVb3d4SwEan+6qRfziwctq7xmqsoSteg0u1i5NOyTXxePYzfbzyVP236EV/XDkAiCEuTT0rXJTq8TsnttPOL049o1QjQZtHJSnVxbjvLufZ19+CaIUc1pb1o6ELDrlm4ZshR9HZ3jd5VKY4johYSESKFVOfeD2gBI8TsnSv455oPeK14KZ5w2311itJVvTj3mzbVMg1TsnFnJbsq6/bzLOWH8gSWUlb/z6YG3Q2YspGwWcHWiguRUhWt6K42l1Ty+YrNlKjftfhT6Xb7pVaSYiSSZd72X4Bo+lLa56LjJzCwZw9enPsN1fVejhzTn/OPGXdIjQB/Nmg6R+cP5eOStQgBxxUMp39q19kjZrP0JdN9CTWeF5HSC4AQLpy2cbgdxwBQHfBwwZePUhv04jWCOHUr//1uLi9Mu5LeKV1jsKgoB1Lb6It63KJr1Hn8FPVIj3NEXVN14/NNBWRaM6UXb2AJKY4pCYhKSZRGX4AbH3ybdcVl6JpGKGxwzLiB/PGyE6Om138fhllLg/9LBBZSHTPQtLZ7xBXl+1CDpBg5tmAY/1k3t81xXWjMLFDViw7FlOF9OrxkdP/UHH4+ZEaHXjOZ5GX8jhTHdGoaX0DKAOkpPybddUbzPoD71n5Iub+ecNOKk88IETDC/G75Wzw99WeJDF1R4uLI0f0pLqshGG67mjGwUE0UdBTDrGV/U8iGbN/eUqXzuvvFT1i9dTehFr93n63YzHMfLeXykyb/4OtVN75CSc0dTe9tkUquvbMfI9V5VIfF3JUIqUqAH4hKt4uRQlcGt4w4Abtmwabp2DQdu2bhhqHHdpk0LqXzEEKQ6jyW3jlP0yd3FhkpZyOEtfnxebvXNQ+Q9jCRLK/ZTsAIxTtcRYm7i2ZOIDPVid0aPSwVdQAAIABJREFUmTgQIlJ99LbzjsJmVfOJHSXd9SO0KNVfpQyRYp/U9P+SxZVbuHvVbO5ZM4f1dbvjHWaXs2FnBR8uWc/6HeWJDqVZMBRm3rcbWw2QAPzBMK98tuIHXy8Q2kpJ7R1I/JjS01TN1Utx1ZUYZn1Hha10I+qVP4bO7zeZGXmD+aR0HSaSY/OH0TMlM9FhKUob2n6b8gk0oRJEla4vPcXBK7+9mFc+X8H8VdvIz3Jz/jHjGd2/INGhdSnprjOoanwef2h9U/qvhhB28tPvQNfSkVJy1/I3mVu6Br8RQiB4Zdtirh1yLD8dOPWg15dS4gksIhDahN06sKlyXvd9DfMFQ9z04Nus2lqKpmmYpsmw3nncf90ZuBy2hMYWMsxo22UB8Aba9pQ8mFrvm0gZjvKIoN73EZkpP/nB11S6NzVIirFCVwYXDzg80WEoygGd0nMMrxUvIWjundHT0TgiZwBWTb1MKN1DqsvBz046jJ+ddFiiQ+myNGGjf+7r1Hnfoc77PrqWQbb7Ylz2cQAsrdrG3NI1+JpWsCUSvxnm/vWfcFLPUeQ60vZ7bcOsZ3PZOQTC25AYCDTsln4MyHsFXdv/87qy/77xJSu2lBAM7X1tX7NtN/e+9gW/uei4BEYGKQ4bvfMy2FJa3eq4JgSHtyOl3pQeorW7QJqYTftxlShk951EOBiVbqcoCjcMPZaBqXm4dBtWTSdFt5HvSuf3Y85IdGjK97CltIq3vlrNV2u2ETb2MzWrKElCEzYyU35C35yn6JV9X/MACSINvv1RUnx1IfiqfNMBr7ur5g/4QxswpQcp/ZjSiz+0gV01f+jwn6GzeHfB2lYDJIBg2OD9ReuQUXoExttvLp6J02ZtLtJgs+qkuuzccOb0H3ytNMdMNOGM8ogk1XHUoQWqdEtqilhRFFwWOy9N/zlLqrayob6M3ilZTM0dhL7fNLy2ImkuC/EFV2Gz9CbNeWyrfU9KxzNMk988PYdPl29GEwKhgdth5/FbzqZXTkaiw1OUH8yuW9AQGPsUdxAIbAdZ1a71voOk9QBLEqLW+w69s+/t8Fg7g2iFSIA2+4ASZcyAQl7+7UX879PlbCmtZkz/As6eMYastB9ekc5lP4x6ORmLMR+bFkm7C0kLS+snM7iwsKNDV7oBNUhSFAWIFHeY3KM/k3v0/8HPNUwvW8rPwx9ajynDaMKGrqUyMO9NbJaiGESrALz11Wo+W7GZQGhvHr4/EOZXj87mf3ddlMDIFKV9Tuk5lpe2LsIwW+8tMZHMyBt8wOdG348SKQrRXU0e0osF64ppuWgkgHGDipJmr1bPnAxuPeeoQ76O3wxx8+pBDHTZmJK+hbDUmF8zkJ2BnuSkLeesPhMPPdiuKPELiklLpdt1I2HD5POVm5k17xuWbdyZFEvtStdQVvdvfMG1TXnfQUzZSMgoZ3vVzYkOrUt77fOVbRqgmlJSXFZDSZWq5qR0PoPT8rhp2ExsmgWnbsOl23DqVu6dcC5uqwNPOMALW77imkXP8seVb7Oxfm/lu1THkbT9WKOR6ui67R0O5rZzjybVaW+u2mi36qQ4bfz6/GMSHFnHW1mzE13orGoo4vGd03l611Q2evPwGSHmlKxKdHhKJ6RWkuIkkoq0hIbAQixaJpmuU7Ho8UuHKa9t5LJ/vkydx0fYMNF1jUGFPXj4prNw2lRKlHJoajyvIQnsc9TAE1iCYXrQtZSExNXVtVxBakkTkfK6itIZXdj/cE4oHMlXFZuwajoz8oaQYrFTH/Rx3pcPURXw4DdD6ELjvV0r+MvYszi2YAQ9s/7Mht2nYppeJD4ETjTNRc+sPyf6R0qY3nmZvPnHy3jjy5WsKy5nSK8czpo+ul3pbMnOqVsx9zP567ba4xxN56H6JO2fGiTFgZQGWyqupCHwNab0owk7u2rvZmDOs7gd8ami9Ntn51Be24BhNv02hA2+21HOo7MXcNOZR8YlBqXrkhyoWIAqJBArx08cwjMfLmmz7yDVZadPnmo3oHRePRypnN5rXKtjz26ZT0WgobkKpyFNDMPkDyvfZkbeUGyWXgwr/JIaz5v4QmtwWkeQmfJjdC01ET9C0sh0O7tFxcaRGUWkWh14jdblw526lbP7/PDGtIqi0u3ioNrzRtMAyQuYmNKHKb1sqbwaKWO/edIXCLFsw869A6QmwbDB7IXrYn5/pfMLGmFeLV7M5Que4NrFz/FZWevKSBmuUxDsuyIpcNlGdvsPKLF08cwJ9MrJwGmP/NlbLRpOm4U/X3ZS0uw3UJSOMm/3ulZtCvYImQZbGysA0LVUeqReQq+sv9Mj9RL1+tONaELjwcmXkGVLIcVix6XbsGkWLup3BEfkDEx0eEonpFaS4qDS80rUGv2m9OMNriLFPjam9/9sxaY2A6Q9DLPrzfJ7Asup9c1BYCUr5XQcVvXieCjCpsGVC59iff1u/GZkA/Q31ds4q/ckbh1+EgD5GbfS6P+SkFGGKT0I4UQTDnpl/yuRoXd5KQ4bL95xAXO/2cji9TsoyErljKkjyctUHwyVrifV6oh63JAmKZbojyndy6C0PD4+7lcsrtpKXdDLhOy+B+ytpaAKNxyAGiQlXGxnez3+IH96cW7UxzRNcNz4QTG9f7xtr/4N1Z5XMKUf0ClreJSijDvITb000aF1WvPK1rGhoax5gATgM0K8UryY8/tOociViUXLYHDBx9R7P8QbXIXd2ocM1+nomjuBkXcPNquFkw8bxsmHDUt0KIoSUxf0O5wN9bubG80C6EJjUFoehS5V8l6JsGi6WjlSOoQaJMWYlJLS0FSc5jdYtdZlSDXhxGUbGdP7L1xXjK5Fz6p0WC1ce9rUmN4/nhoDy5oGSL6mI2GkDLOr5m4yXSdh1fMSGl9nNb98A759crwh0txxWfU2ilyRvS+asJGRcioZKafGO8RuaVNJJR9/swEpYeb4wQwq6pHokBQlpk4oGMna2l28tG0RNk3HkJJ8Zzr3Tbgg0aEpSuckVeGGA1GDpBi7d90c3they2VF+Yxwl2LVDAyp49Dt9M95FCH02AZwgH/8x4wdSIY7WnfqzqnW837TClJrQujU+T6lh/u8BETV+WXbUrAIjbBsnZqpCY0Ma9erkNQZPPHBIp6Ys5iwEdmf8dzcZVx2/ER+/qPDExxZ9+EPbaGi8WXCZjUZzmPJcM6M/et5NyeE4JfDT+SS/lNZXbuTHo5URqQnT78fRVG6FjVIiqEyXx2vFC8maBo8uP1oBrgqGJqyG7+ZwrSCqxhrnxTzGA4b1hvDaLvvyGmzcsqU4TG/fzwJzUKkFknbjb0C9eGlvc7oPZGXti1qM0iyCp3DcwYkKKruq7ishifmLG5V/tswwzz90VKOnzCEfvlZCYyue6jyvMvW6l81NS8NU+19D5dtFENyX0ATqqVCrPVwpHJUvkovVRQltlR1uxhaUbsDa3Oqm2CzN5f3KkbzSdUAvqgoiUsMbqedP116InarBZtVR9cEDquFU6YMY9KQXnGJIV4yXWcgonxAkZikO2fSEPLzwPqPOO2z+zj7i//yyraFGLLrFa7oaH1Ssvnz2LNIsdibKwblOdJ4bMplWDU1zxJvn6/cjBml4Iphmny2cnMCIupeDNPHturbkdIPRAaqpvTiDa6kyvNmYoNTlBaklCz8rpg/vzSXe177nPU7yhMdkpKMZAK/kpz6hBND2TY30fqa6WjkO9PjFsex4wcxun8BH3+zAV8gzLSRfRnSKzdu948Xl20YBek3UVr7LxACgYbEpG/WvzFI4ZKvH6TUV9NcQvbf6z9kWfU2/j5epeEdzHEFIzgydwir63Zi16wMSy9AE2qOJRE0TYuaXiQE+91/qHScxuAyRJT5RVP6qPa+S477nAREpSitSSm585k5fLZiM75gCE0IXvtyJdeeegQXHzch0eEdUL3/ayoaX0bKENkpp5HhPB6h3m+UBFCDpBgal9WbDJsLvy+E2WLIbNV0zu4d+1S7lnIy3FxwzPi43jMR8tN+QZbrdOp88xDCSobzBCx6Ju/u/IZyf12rHht+I8QX5d+xpbGc/u6uN2jsaDbdwvisvokOo9s7dtxAHnhnfpvjmhAcN05VdIo1TTiQ+5kC1YTao6ckh8XrdzQPkABMKQmEwjzwzlecOGkIOenJWXl0R81fKW98rrkAU53/M9IdRzKgx8Nq71msdIIVnURRQ/MY0oTG41MuZ0BqLg7Niku3kWpx8JdxP6F/qvpQHis2SxE5qRfTw30eFj1SeW1p1dZWZWP30IRgVc2OeIeoKO1WkJXGbWcfjd2q47BasFst2K06t5w1g8Ls+K1Qd1du2zj0KIMhTbjIdZ/f6tiHJas4+4v/cszHf+HGJc+zqaEsXmEq3dwnyzc2D5Ba0nWNBWuLExDRge2srOWh2a+ws/bJFhVqI6msdf4vaAgsSGB0SnelVpJirMiVyatHXkexpwpvOMDA1DysmioiEG+Frgysmk5on27tAqEazSmdzpnTRjFtZD8+X7kZCRw1egC5Gck5M9zVCKEzKPdp1pdf1FS4wURKg1z3JaQ5ZjSf9/yW+Ty88RP8TZMz8yvWs7R6K88fcbWaJFM6nJRhKj3vUNn4BkLoFOWOQhM6+/aRF0R6qyWTpRt2cP3Db3H4yG8ZPRQs+3xEMqWXWu8npDmOSEyASreVXL8pXViflOxEh9CtndFzIs9tmU+oReU7DUGa1cnkHqpCm9L55Ga4OfvIMYkOo1tKsY1kbNEi6nxfYJi1pDqOwG4pan48YIR4ZOO85gESRDJaAkaIRzfOU/sglQ4lpWRDxc+p9y/ElF4Ahg5Ywk9/1I+nZx/X6lxTSqaP7JeIMKOSUvLb5z/CHwzj9VsxZbSUOiu6lhr32LoL1Sdp/1S6ndIt5DnTuX/ST8l3pOPQrNg0C0PTC3liyhXoakOooij7WL+zgt88N4fL73uZh2d/TU2jr9XjmrCT6ZpJD/fZrQZIALt9dVGvaSJZXbszZjEr3VO9f0GrAVKEj0nDNtG3oBqn3YrLbsVps3LfVaeR4rAlLNZ9ldc2Ut3gAWD5pv5E23YUNE2uW76Lv6x+m4ZQ216IihIraiVJ6TbGZ/XlvaNvZZevBrtmIUel2SlJypQByhpmNafO5LjPJdd9NkKol+x4+GzlZn791PsEwwamlKwpLuO1+at46f8u/F5pjVl2937bCxS6Mjo6XKWbq/fP32eAFKFrknt+XsjaLTOwW3WmjeiHK4kGSAAOm7U5JTAQtPHAa6fxizPfjQyWLBJdmDy3awrbfTZKdy5jZe12Zk29VlVXVeJCveMq3YoQgp4u1WxTSV5SmnxXdgme4EpMGZk19dVsoM73KYNzH0twdF2faUr+NGsu/hbNeoNhgzqPn8fnLOLO84496DVSrQ5OLBjNh6UrCZh7r+PQrFwx8OiYxK10L7Kpv4gQAouehcCOJNDqHCGspLlyOTWJG8enpzgYP6CIZRt3EjZNNuzoya0PXMmIicVY+nlZ6+1BwIz0PwxJg13eahZVbubwnEEJjlzpDtQgSemUpJTMWbae/32xHF8gxPHjB3PBUeNw2ZNrlkxRfqg6/5d4gmuaB0gQ6cFT559PY2AFbrvahxRLJdV1eAPBNscN02T+6q3f+zp3jjoNm6bz7q5vkYDbYueWYSdzmNoDqRyC3b5a/rrmHb6u3ICG4Ki84dw65DiEuDdKX0ZBluuERIT5g/zlspP4+X9fY1dlPZomCBs6npQRbGlsm5oaNA02NZapQZISF2qQ1EXV+L5gZ939BMK7cNvG0DvjZly2wYkOq8P89ZV5vLtoLb5gZJa2uLyGD5auZ9ZtF2BPsso9ivJD1PsXYUpPm+NShmkILFaDpBhLcdgx9i0J1iQ9xfG9r2PVLNwx6nR+OfxkGsN+smwpKkVIOSQ+I8hPFzxCdaARE4mJ5NOytayvL+WpSQ+zpfJGaOrKqAkbg3MeQ9eSv+plVqqLV+64mDXFZZRW1zO8dx7L/Jv4x9pyfEbrCQubZqG3SxXC6lCqcMN+qU+TXVB54xtsqb6reSa62ldGrf9LRuW/RoptaIKjO3S7qup4a8EaguG9leoCIYOSqnrmLFvP6VNGJDA6RTk4U5rUh/y4LXYs+7QEsOm5COFAytYblIWwYdVy4hlmt5TpdjJxUE+WbthByNi7r8hhs3BROxpyO3QrDt3akSEq3dQnu9fgCQdaNac3pElVoIE1jflM7bWExsByBDpu+1iE6DztRoQQjOybz8i++QBkhUdx//oPCRih5p9XRyPD5mJqTteZ8FWSm5rWirPPV23m4nte4vi7HufWJ2azdXd1h15fSoNtNXe3StUBiSl9bK+9p0PvlSgrtpRi0dv+0/UFQ3y9dlv8A1KUH+CtHUs44ZO/cPKnf+WYuX/i4fUfYbbY5J+dchqCth9uhNDJdB0fz1C7rb9cehLDeufhsFlwO2zYLDrnHDmGH00elujQlG5sS0NZm5UViKSgbfNUogkbaY7JpDomdKoBUjROi41nDr+aCdn90IRAFxqH5wziqSlXtZlYUg6BjJQAT9RXslMrSXH06vwV3PvGF/ibUsTmrdjE1+u2Meu2C+ib1zHFBEJGVdQqNyBpCCxvdaTS8z476v5DMFyKyzaEPhm3k+aY2CFxxFJ2motonRQsmkZepuqloCSvebtXc+/a2fjNSP+cEAaztn2FEIKrB88EwKpnMTT3GTZWXothNgISq5bNoNxH0TVXAqPvPjLcTp699Ty27q6mrLaBwUU5ZKWqP3slsQam5uPSbXjbpKDpDHB3vQbFRa4sHpn8M0JmGIFQgyMl7tRKUpyEDIP/vP1V8wAJIk3d/MEwD723oEPuURv0UOoPRdm8GWHT85r/v6zhf2yq+hW+0EYM2UhDYBlryy+h3r+0Q2KJpYmDeuJ22tH2aahg0TV+Mm10gqJSlIN7dOPc5gHSHn4zxEvbviJs7k0fTXVMZFzRAkbkv86I/LcZU/QFKbbkrVDVVfXLz2LK0D5qgKQkhWPzR5BqdaK3+OhmETp5jnSm9BiYwMhiy6pZ1ABJSQi1khQnu6sbMIy2fTNMKVm+peSQrl0f8vGb5S+zpHozFqFzVl4/JmZsQbB3tkkTTnqmXwtESgwX1/4DU7ZujmhKP8W1/2RU/suHFE+s6ZrGEzeezU2PvcOuyjo0TWDVdf50yQn0zlE9SJJRIBTmsQ8W8ubCNQTDYY4c0Z+bzphObnrybyruSGX+6E1GQ6aB1wiSpjmbjwmh4eoCewgVRekYdt3Ks4dfzT3r3uOL8u/QEBxXMJJbhv1IFQVR2q8TpL0lihokxUmm24lhRm8umJ9xaCliv/rmBVbVbCckDUIYvFQyCpMwh2VsRxMWBBq9Mm6mR8rJAITNOgwzWkoeeIPrDymWeOmVk8Hrd15CcXkNvmCIgQU9ou5TUpLDjY+9zbebSwg09Z758Jv1LN6wg7d/c2lSdX+PtUGp+SyvKW5z3G114LbYY3LPhsBKNlX9gcbgKnQthQL3RfTJvAFNqGICitLZ5DjS+Pu48xMdhqJ0C+pTZZy4nXZOmDCkTXlqh83CFSdObvd1d3qqWFO7g5Dcm6pjoPNSySTer76dsQXvManXUgrTLmt+XNfcCBF9fGy3FLDLW83Kmu00hv1Rz0kmfXIzGdozVw2Qkth3O8tZ3mKABGCYEo8/wLuL1iYwsvi7bsiJOLTWgxOHbuW6ISfEZCbYG9rCyt0X0hhcAZgYZgMlDU+zsfLODr+XonQGUsrmRqyKoigHolaS4uiupk7tHy5bj65pWHSNG0+fxpEj+7f7mhWBeqyapVVXdwCJZLvPi8Pap81zNGGlMPVyShqebJVyJ4SD98pH8sHqf2PVdMKmyeUDjuKyAapDvNJ+63dWIETbUhu+YJhVxaWcx9gERJUYYzL78MDky3lg/YdsathNvjODqwYey9H5sSlbv7PuMUwZaHXMlH4qPLPpl3UbNr1HTO6rKMmmusHL3179lHkrNyElTB/Rl1+fcyx5Gd0r5bclUwYwZRBduKO+RivdhJoz2C81SIoju9XCny4+gdt/chR1Hj+5mW6s+qFtRhyYmk9wnwESgFXTmZy9/87uvTJuAgQlDU8hZRCLlsb82iN4v8xBSIabr/n0ls/o687l6DzVe0hpn1490olWjtBu1emX1/2aAo7J7MPjU66Ky70ag2uBtmm+mrDjC21TgySlWzBMk0v/9TIl1fWEm/YGf7lmK2vumcW7v70ch617fRQyTC+bqn5Hhec9wMRuKWJQ9p/JcB6e6NAUJamoHKUEcDvtFPVIP+QBEkCq1cnF/aa3alaoC40U3c75fafu93lCaPTOvJnDen3LpF5L6Jczl9dK01ql7QH4jRAvbP3ykONUuq9xA4ooykprkxJp0XXOPGJkgqLqHty24VF7LpkygNPSepXZkCbfVG/ly/J11AWj71lUlM7oq7XbqKz3NA+QYE/Kb5BPVmxMYGSJsa78eio87yMJIgnjDxezpvxKPMGu8WcRNkyqG7z73Qeu7CVQfZIOpHtNn3RRPx90HP3dubywdT61IQ9TegziioHHkG0/eEEIISxYRBoN4XIsQieE0eacmkBjLMJWugkhBI/fcDZ/mPUx89duRUrJkKIcfn/h8aq0coz1TL8q8mGoRe80TTjo4ToJmyWn+diWxjKuX/I0nnAAAYSkwS8GHc8F/aYlIGpF6VhbdlcTDLV9b/MGQmwurUpARInjD+2gLrAQyb5puCF21T/J4B5/i1ssYbORoFGOXS9Ab1HZs72klDzx0RKenruEkGFgt1q4+sQpXHjUOJVOqLSLGiR1AUIIji8cw/GFY9p9jV6ubCxRNo5bhMaUnEGHEp6ikOl28u+rTiMYCmOYEqddVVaLB5e1P6PzX9hb3U6kUJB6IX0yb2w+x5QmNyx5mspAfavU9Ec2fszwjJ6Mzewb97gVpSP1z8/CZtUJB1qvLLjsVgYUdK+UX394JwIb7DNIAgNvaHNcYpDSYFP13exueBUhLEhMeqZdRt+MGw9pMPP8p9/wxMeLmvtRBsMG97/3FU67lbOOGNVR4SvdiEq3UwCwaDq3DjsVh2Zt3j5iFTpui5PL+qvCDUrHsFktaoAUZ6n20YwrfJ3pfTdwRJ9v6Zd1a6vy36tqd9AY9rfZuxswQ7y2fVF8g1W6lLBhsrm0ivK6xGYjTB3el9x0d6uUX10TpDrtHDe2e00CumyD2hRzARBYSbWNY0XNNt7csYhl1VtiVgVwW+1/2d34GiYBDOnBlD521j9NScOLh3TdJz5e3DxA2sMfDPPYh+p17IBkAr+SnFpJUpqdVDSOQlcWL2z9kt3+WiZnD+CCvtO+V9qeoiidkyfsjzp7K4H6UNfbm2SYPhqCq7Fq6bisg1QaTozMXb6RP/5vLiHDIGyYjOqTzz8vP4XsBKTY6prGM788l3++/hlzv92IKSUzRg3g9p8c1aYtR1dn03uQ5z6Lcs9bLarbCjTh4F+bNNY0PI0pJZoQFDozeWjSVaTbOu7vTErJrvrnMGXrFiOm9LGj7jGK0i5q13UN06TOE71tSWW9p13XVJTu9eqgHNSYzD6MyWxbNlxRlK5pdEYfwmbb/RoOzcqx+V0rRaW04VU2Vf8ZgY7EwGEpZFTu4zisPRMdWpeybkc5dz4/B3+L3mgrtpZy7cNv8r/bLkxITBkpTu6+5CTuvuSkhNw/mQzM/gMuaz921T9D2Gwgw3EYc6snsKJuOyG59+9su6eSe9a9w5/GnNdh95aEMFq0HmkpZNS0+7q6plGUncauqvo2j/XNzWz3dbu8TlJAIVFUup2idCGhsMGyTTtZvrVEVfZRvhe31cENQ05qSrWNrKo4dCv93LmcVNh1eljVB1awqfpPmNKHIRsxpQ9vaCsryy5XzUU72IuffUMw3HrgHTZNtpZXs7GkMkFRKXsIoVGUfjmTe33BEX2+ZXjeI7y1a1erARJECrh8WrYaUx76e4mUkm+qt/DU5i8wyIl6jts+/JDuccsZR+LYZ2XQYbVw649nHNJ1le5LrSQpSc0bKqYhsBq7JZ90+3iVGnMA89du5fZn34+k+cpIH6L/XHk6o/sWJDo0Jcmd3edwhqf35LUdC6kL+jg6bwQnFI7BpnWdt4hd9c9H2YthEjDKaQyuJtXetVbNEqm0pgEzysDTomlU1HkYVKj6cyWbsGy7mgyRwi6HOoUQNg1+9e1zLK/Zht8IMsI9hHMLKrFpe+4p0ISdAZm/PqT7HDtmEPf9zMoD733Fjso6+udlcd0pU5k8uNch/gRKd9V13gGVLkVKg7WV/0eF90MEFkBit+QzLu9Z7JbcRIeXdMpqG7nlqdmt0ls8Abj64TeY+8crcdltCYxO6QxGZPRiREbX/TARNCqItlNYoB1Smo/S1pQhvVlVXEpgn7LbwbDBsF7q9TsZTc0Zwudla2k5JBIIxmf1R49S+faHmL1rGd9Wb8VvhgBY3ZhDw47pnJizgWGpkX5ufTOuO+SVJIgU6Zg6vO8hX6dbUQvp+6XS7WIkbJh8tW4b7y5ey87K2kSH0+nsbJhFhfdjTBmpfmNIL95QMWsqb0l0aEnp/aXros7cSimZtzI+ZV0VJZHqgh5e3PYFv1/1P14q/rJN0Yls59FowtHmeZIQqfb2t09Q2jp3+hjSXU6sLarJOW1WLj56PJnuQ++Ho3S8m4eeSqYtpbkxvUOzkmZ1cvvwMw752rN3LWseIO1R7M/m+ZIZZKS/yMi8hzpkgKQoHU2tJMXAtvIarrj/VbyBEBKJYZicdtgI7jz7mFbpYo3BDVT7vsaqpZGTMhOLpqrI7bGrflaLyjt7GNT5lxM0qrHpWQmJK1nVenxt9gBAZLBe541e8UdRuortngquWPQQQTNMwAzxedkant3yKU8ddh2FrshrRUHq2ZQ0vEQgXILZ1CNGE076pF+LVU/FhrixAAAgAElEQVRPZPhdTprLwcu3XchTc5fw+eotpLscXHz0eI4fNzjRoSn7ketI59XptzKn5FvWN5Qy0J3HSYXjcVvbTiz8UPvLkpfQvA9SSaBOtJIkhPg18BfgQSnldU3HBPA74CogE1gEXCulXNPieZnAf4HTmg69A1wvpTzgKoYaJHUwKSU3Pv42lQ0eWk7sz16ylgkDe3LS+CFIKVlf9Vt2e95BShMhLGyo/hOj8x4j0zEpccEnEUNGLz0shIja46G7mzKkDy/PX4kv2Hq2TgjB5EFdN4VKUQD+se6tpl5PkRddvxkiaIa577t3uGf8pQDoWgrjC1+ntOElKrwfYdWyKEq7hCzn1ARG3nVlpbq49ccz1Kb5TsRlsXNm7ykdft3TiiaxsaEUv9H6/SnFYmdQan6H30/pmoQQU4gMhFbu89BtwC3ApcB64LfAx0KIIVLKhqZzZgG9gRObvn8CeB449UD3VOl2HWxrWTVltQ3sm/nkC4Z55csVAFT65rHb8y6m9CMJYkovhvSyqvxaTBmKctWubU3dDn678iV+vvhhnt48j/qQlxzXcU17kVqzadnY9eR4UZVS8s2WXTzx8WLeWLiaRn/iBm+HDe7N+AFFOG17m4Q6bVZOGj9EbZJWujRTmnxbvaV5gNR8HMniqo2tjlk0N73Sr2R8wauMyntUDZAUJQ5OLhrPpKyBOHQbOhpOzUqKbudvYy9CO8T9Tkr3IIRIB14ELgdqWhwXwE3A36SUr0spVwM/BVKBC5rOGUZkcHSVlHKBlHIB8HPgFCHEkAPdV60kdTB/KIy2n7VlbzAIQGnD61FSySLFCur835DpPCymMSaTD0u+5a9r3yBohpFIvqvfxVs7F/Hk5Muo8H5CyKzBlH4EVoSwMCzn70lR4S5smNz45Nss27SLQCiM3apzz5uf89i1ZzGyd/wHcZom+O+Vp/PBsu94d8k6LLrGmVNGcuyYgXGP5VD5w6WUNLxNwKgg2zmVHNcMhNATHZaSpAQCXWiYUapzWbtQdT5F6ax0ofGPcRezum4731ZvJcOWwjF5ozoklU85dJ2kT9JjwGtSyk+FEL9rcbwfkA98tOeAlNInhPgCOAJ4FDgcaAS+bvG8rwBP0znr93dT9Q7SwQYX5mDRdaD1ipDdauGk8UMBkISjPJOmx6KX4eyKQmaYf373NoEWGzqDZpiaYCMv71jJzwe+R2njG9T6F+O09KEo9TycSdL08c2Fq1m6aSf+YOTv0tf0318+9S4f/u6KhAzkLLrGqZOHc+rkzrsBttI7nxXl1yOliSRIaeNbpNqGMqHgaTShKvQpbQkhmFkwho9LVxBqMVCyaRZOKhiXwMgURdlDCMGojD6MylDN6pVWegghlrb4/jEp5WMtTxBCXAkMBC6K8vw9s9Jl+xwvA4panFMhWzTEk1JKIUR5i+dHpdY5O5hF17j7ohNxWC1Ymir7OG1W+uZmcs60SAWlfPcZaMLV5rk+I8DjW4sp83ePanjbPOVRmziGpMH8iu+waCn0SruYUbn3MzDr1qQZIAG8tXhN8wCppXpvgE2lVQmIqPMzZZhVFbc2p6FCZG9afXAtu+pfT3B0SjK7echpDEotwKnbcOo2HLqVoWlFXDv45ESHFlP+YJg3F67mrhfn8MichZTVNiY6JEVROhuZwC+olFJObPG17wBpCJFCDRdIGf/9KGolKQamj+jHa/93Ma9/vZryugamDuvL8WMHY7VEUoZyXSdQ7nyfKt98DNOHITVMBC+XTmCzdyWflW/g+cNvpoc9LcE/SWylWlz7bWCXYU2JczQ/UJTBXfNDnalUTBJpCK5FyrYDT1P6KfW8Q6/08xMQldIZuK0OnjjsWtbU7aDYU05/dz7D0pNnUiUW6r1+LrhvFpX1XnzBEDaLzjPzlvLINWcytl9hosPrlPzhcnbUv0h9YCVu21B6p12E01p08CcqihIrhwM9gDUtMnR04EghxNXAiKZjecD2Fs/LA3Y3/f9uIEcIIfasJjXtZcptcU5UapAUI716ZHDTadOiPiaEzsic+9nt/ZrHN/ydxrCFlQ09aTCcgIk3HOCl4i+4fvAp8Q06zvKdGQxJK2Jt7Q4MzObjDs3KeX2j/9kli9Mnj2DT7qo2q0mpTjsD81WhhPbQsLK/WqQq1U45GCEEIzN6MzKjd6JDiYvHPlrE7poGQkbktTPSAsDgzhfmMPuuy5Ji72Zn4gluZXHJuRgygCRIjX8puxpeYWLBc6TZRxz8AoqixMJbwNJ9jj0NbCSywrSByEBnJrAEQAjhAKYDv2o6fwHgJjLg2rMv6XAghdb7lNpQg6QEEUJQFerJ/JqxeIzWVdHC0mBZdfdoAPq3MRfxy2+epthTga7phMwwF/WbwYzc5H5T+vHhI/l01Wa+3VqCPxjCbrOgC417LzsFTVMfTtrDbRuKVUvHMFqXf9eFk56p5zR/b0iTWds+55Xt82kM+xmZ3ocbh5zKwNSCeIesKAkzd8XG5gFSSxX1jZTVNpKfqfru/RDrq/9CWDayZ6JGEsaQYdZV/p7Dil5NbHBKGxtLK3n202VsK69mXP8iLp4xntx0d6LD6nz2pr0lpaY+Rq32oAghPEB1UyU7hBD/Bu4QQnxHZNB0F5FCDbOarrFOCDEHeFQIcVXTZR4FZksp91u0AWIwSBJC2IF7gPMBJ/AJ8Asp5c4DPOfXwJnAECAALAR+vecPoOmcZ4iU9WtpkZSy44v6x0kPe1qrjcZ7CKDAkRn/gBIgy57KM4ffwJbGMqoCDQxJKyTN2na/VrKx6joPXf1jlm3exdJNO8lOdXHCuMGkuVS1nvYSQjA270GW7b4MU4abiphI8lJOJC9l796Se9a9yYel3zYX/PimZjNXL3mIZ6bcRE9XdoKiV5T4slmjv31LCTarqgb5Q9X4lxDt02J9cA2mDKMJNaecLBasL+bGJ98hGDYwpWTdzgreXLiaZ26cRCMvUB/8Drd1AAMyf0G6fWSiw1Vi7x9ExhsPsreZ7PEteiRBpBz4/cCHTd+/A1x3sAvH4rf+38DpRAZJVcB9wGwhxAQp97MBBY4CHiKyVCaAPwJzhRDDpZTVLc6bC1zc4vtgB8ceV/nOTEZn9GVFzdZ9qjJZubBv92rA19+dR393XqLD+EGEEEwc2JOJA7v23od4SrUPY3qvz6n0fU7QqCbTMQm3bUDz4zXBRuaUfkPQbJ3mGDTCzNr2GbcNPyveIStKQpxzxGjuf+8r/KG9vwu6JhjeK48sd/JPNCUbXTijNirXhA1BZNAppaQh7MOuWbHr1jbnKrEnpeQPr8xt9e8+ZBgUZZSyoeFRdD0MSHzhnVT5FzI+70GynYcnLuBOoJOUAG8mpTxqn+8l8Pumr/09p4bo1fEOqEMHSU3Nnn4GXCal/Ljp2MVAMXAce0dwrUgpT9jnOhcDdcBU4N0WDwWklAfcZNXZ3D36Iv64+mUWV21A13RsmoVfDjmdkapMptJN6ZqdvJTjoz62w1uJVbO0GSQZmKyr3+9itaJ0OedNH8u3W3fx5dptaEIgBGS6nfzjp127ol+sFKWey/b6ZzGlv/mYJmwUpJyOEIIVNVv527pXKfXVIIDpuSO4bdhZuC3O732PDSWVPPDBV6zeXkZhZio/P2EK04f1i8FP03XVevxU1LWt4njq9K/R9ZbFzySm9LOu6m6m9ZwdvwCVLqWjV5ImAFZaN3XaIYRYR6RhU9RBUhSpRMqT1+xzfFpTXfNa4HPgTill+SFHnUBuq5N/jLuUuqCH+pCPAmcmFk2lSihKNIXOLEJm2wp4GoL+7vg38VWURLHoGvdediqbSitZs72M/MxUJg3sFfM9kVKabKt7muL65wmZ9WTYxzE0+3ZSbYNjet9YG5D5C7yhrVT6PkfDikmYDPsEhmT/Hzu8Fdzy7ZP4zb3JK1+Wr6Eq0MCDE6/5Xtdfv6uCS/77P/zBMBKorPdwy9OzufMnx3D65OTeg5tMHDYLRClKUpgTvfWGJ7QFKQ3VkFxpl44eJOUDBlC5z/EyDtKwaR//AZYTqUixxxzgDWAr0Bf4MzCvKY2vzRp50+asqwB6907+akfpthTSbUle9lpREqyHPY3pOcOZX7GuVRNim2bpdimqigIwsKAHAwviV1FzXdXd7Gp8s3nFpdq/gEUlF3BE0Ru4rMn/Xrs/mrAxJu8/eEM78IQ24bL2JcUaWeV5dfucNpMzIWnwXf1OtnnK6Jty8FTx/7w3v3mAtIc/FObed77glInD0DXVtvL7cNqsHDtqAPNWbW6q6BjhC9hJdfnanK+LFFRL0IPoZOl28fS9/uUIIf4shJAH+TqqIwISQtwHTAPOarmHSUr5PynlO1LKVVLKd4GTiBR6+FG060gpH9vTnConJ6cjQlOUhJLSJGjUYMa/n1pSuWvkuZxWNAmHZkUg6J+Sx73jf3bIK0lSSl788luO/t2jjLnlX5z+t2f4ct3WDopaUTq/oFHDrsbXW6WkARgywNbaJxMUVcdyWXuR4zq6eYAEUOypaNWmYg+L0Cn1Vbc5Hs3q7bujfhb1BUJUN3ijPKLsz+/OmcmEAUXYrRbcDhs2u2B1w0iCZuvVIk046JN2sSqHr7Tb911J+jfwwkHO2Q5MIdLkqQdQ0eKxPODLg91ECPEv4DzgaCnllgOdK6UsEULsBAYd7LqK0tntbHiL76rvJWw2IoSFPmkXMiTz+m6ZQmDTLNw09HRuGHIqhjSxah2zIP7UvCU8+tGi5g3BW8truOWZ2dx/xekcNqjzzpArSkfxhLahYcdsUzPJoC64OupzuoLRGX1ZVbuNoNx3NSlMf/f3az2Ql55Krcff9gEhVFXUHyjFYePRq89iZ2UtJTUNvNU4j/m1BVDfm4lpxZho6Jjo1mkMzLw20eEqndj3+nQhpaykbQpdG0KIZUCISFOnWU3HegLDOEjDJiHEf4BziQyQvvse9+oBFAGlBzu3s/P4g9R4vORlpGLVu9+H4u6uzPMpq6v+vHf2VobYVv8CYDI065cJjS2RNKGhiY5JowgZBk/MXdKqYhJE0mEe+OBrNUhSupUyfw0rareSYU1hfObA5n2yLkvPqBXgQMNtHRjfIOPozF5H8PrOrwmHDMym9SC7ZuWYvNHkOTK+1zWuOv4w7pw1p1UDcofVwhmHjcC+n3LuyoH17JFBVoaTX3/xHSFpMK96OPNrBpNu8VEfdlDg6smJvdVnpoPpbNXt4qlDfzOllHVCiCeBfzQVWNhTAnwlkfLdADQ1fHpASvlA0/cPEintfQZQI4TYkzfTKKVsFEK4iZT2e53IoKgv8FegHHizI3+GjhI2vTQEN2LXs3FZ21ciOhQ2uPv1ecxetg5d09A1wfUnT+X8aWM7OFolmW2ofbBNeosp/RTXv8TgzOvRhCpFe6jqvX5CRvQOBdvK960foyhdk5SSBze+y1u7FmBpWqV26Db+M/5q+qTkYrfkkOM6hgrvp5jsHSxpwka/jJ8lKuyYy7S5eXLyDTyy6QMWV20gxWLnrJ5TOafP9O99jZljBlFZ7+H+97/CME1MU3LqpOH86gy1l/JQeML+SB2H/2fvvqPjqK4Hjn/fzDatepcsuci9d+NuigsQMB2SUEILJUBCQsgvQHpCCElIAkkIJYEQWkLvHeOCC7Zx792WZat3bZ+Z9/tDsuy1VsZF2lV5n3N8OJrdHV0Za3fuvPvubbrQD0ob5aHGQco1QU/sAlO6hPa4ffF9wABe4vAw2W8dNSNpEI0leYfc1vTfeUed61c0JkcmMAL4FpBCY6I0H7jiqGFRHcLu2mfYXv13NGxYhEh2DGdc9iM49OO743TI796Yz3urtzZtTmz86/vLO5+TmRTPrJGqyrC78BuRF0slFiGrDqeuBqieqiS3C7uuh20EPqRPZvcY7Kwon5dv5O0DywlaBkEaVzx8ZoAfr3ua/07+MUIIRmY9yNbKP3Cg4XUsGSTe3oeh6T/v9N3tvkpuXBq/GnHVKZ3jm9NHc9mUEZTVNpAa78btVDe4TlW6M5EEWxxVwfBLQQ3B2NR+rbxKCaNWklrV5klSU6e57zb9ae054lhfR3i+Dzj7WM/pKMq8C9lR3Xjn/9A2z5rAOlaX3cWk3KeP+zy+YIi3V25ucdHmDxk88fHyDpkkefxB1u0rxu20M7JXbru3ou1KQpbB4vKN7GooJt+dwRlZo3DpDgCSHIOp9C9v8RpduHBoJ5Z4K5HZdZ0bZ07gn58ux3dUOcwdX5sSw8gUJXreLFoW1uYaGq+fqgL17G4opl9iDzThYGjGTxmSfi+WDKFraj/NibDrOnlpybEOo8vQhMZdgy7mN5v+S9AKIQGb0HDqDm7qd06sw1M6OVUI28Z21z6DKcPbUEoMagLr8BuluGxf3SoUoM4baLUjS3ldy0Fqsfb68o387vX52HQNS0oSXU4ev+Vi+udErzVtRySlpCqwBk9wH0nOgaQ4W87DqAl6uP3Lv1IVrMdnBonTHTyx8z3+Mf575MalMSjt+ywvvh7ziJI7XbgYmHpnt2zc0F5unDmBOIedf81bQVWDlz6Zqdx9welqP5LSbXjNSPuNQBMC31HJkxA6unr/UTqAGVkjeMSZzAv75nPAW8HIlAKu6nMm2S5VBaCcGpUktbGAGbm/hcBG0Kw+7iQpI8mNy24jcNRGcgGM6HV83XSiZUtRGb97fX7jpvem7tTeQIibHnuNT39xU7ed/xA0a1lWfCOeUBGH1rOTnUOZmPMPbNrhKe1P7HyXUn81hmxce/SZQQJmiD9ueYU/j72FFOcIJuY8zdbqv1AX3IpLz2FA6nfIjZ8Tix+ryxJCcNWMMVw1Y0ysQ1GUmJiZPZpdDcVhM8gABIJBiSe3t1ZRomFoci9+O/LaWIfR+UhUud0xdM+r13aUGTcNESH3FEC8o+9xn0fXNH54wQxcR3S9EQJcDjvf/drUtgi1zbyybH3EvRy+YIiVO4tiEFHHsKHifuqDezClF1P6MKWPmsAGtlX9Pex5i8o2NCdIh1hI1tbsah5gmOIayaTcfzOn9zJm5L+hEiSl09pSVMazC1fzzpeb8QaObiWtxNIFeZPoHZ+FS2ss9dXRcGp27hl6RZu12lcUReks1LteG3PY5+IxX8YhLGyahZRgSJ3sxFvQheOEznXRacPISIzniU++4GBVHSN753L7uZM7XAlbVYMPS0a6FSGo80WYC9ENSGlS7JmHJHwl0JJB9je8xbCMHzUfa62sUu3oUroSy5Lc88IHzN+4C9OysNt0Hnh9Pv+89VKG9zq1QcDKsVkyRIlnPlX+NcTZ8shPPA+n3rIUyanbeWz8HcwvXceyyq1kOJOY22MiveKzYhC1oijtTaCuNY5FJUlt7NGdi9heO4VxyXspcFdQZ7hYWVtASrWXcSfxOTNtSB+mDenT5nG2pZkj+rF02z58wfASjZBpMq5v9yzRkFjICBPaofGC5UhnZY/mg4MrCB3RAFJDMD5toLp7q3QZ763eyoJNu5tnUYXMxt+P7z39Np/+/CbV6KWdGJaHxQe+hdc4gCm9aMLF9upHmdzjKVKcQ1s8367ZmJM7jjm542IQraIoSsehyu3a2Iaa3XgsJ4uqB/GfA1N5o3QcRf40NtXuQ0Zcben8zhk9iP456cQ5Dl/Quxw2bpk9kfREdwwjix1N2El1jqTlPRqNLPe05q8Wlq1jc91eJBa6EGhC4NadZDiTuXvw5VGNWVHa0+vLN7a4kQLgCQTZcqAsBhF1Dzuqn8Jj7MOUXqBxxpohPawu/XGX/UxSoscbCPLfxWu5+9n3+Ov7Syip7nBTWRTlpKnb1G3MqTkwzJYlZg7N1mpZVWdnt+k8c8flvPPlFj5cu52kOCdfnzKK0wb0jHVoMTUq85csPngNlgxhST+6cGHT4hmW3lhq9799n/GfPZ80t9wVCOyazi39z+O8HhObp9wrSldgWpFXVgWNpXhK+zjo+QBLttz75TNL8JulxNlUqaNycirrvXzj4Rep8frwBw3sus4Ln6/hiZsvYXRBj6jE0BDax86aZ6kLbifZOYT+ydcSb8+LyvfuMtTbb6tUktTGzusxkTcPLCFoHd6L4tBsnNtjQgyjan8Om41LJ43g0kkjYh1Kh5Ho6MvMnu+xv/5N6oI7SHEOJz9xLnYtAb8Z5Jk9H4d1kZJITMtie30RF2pqNo/StVw4YShbisqay+0Oses6Q/LVnpf2EqmREABStnhMSokpLXWDRjkuj328jIo6D0bTDZCQaRIyTX7yv494957r2v3GcLV/A0uKb8aSQSQmNYEtFNW/y7QeT5PsHNyu31vpHlSS1Ma+3f9cinzlfFm1A7vQMaTJqJS+fKf/3FiHpsSAQ0+hX8p1LY4XeSvQRctqVxOLDbW7oxCZokTXBROG8tHa7azdW4wvGMJh09E1wUPXnodNV5Xf7aVX0iVsq34MSx5Z4aCR6OiPy9bYBMi0TJ7e8yFvFC3BbwbJi8vgzkEXMz5tUGyCVjqFD9dua06QjlRSU09FvYfMpIR2/f7rK34XNpdSYmBIgw2Vf2Raj6fa9Xt3JUKtJLVKJUltzKHZeGDUDRR5Kyj0lNIzPoue7sxYh6V0MOnOREJWy7bpADmutChHoyjtz67rPH7zJXyxo5AVO/aTlujmvLGDu+2+xWjpm3w1Fb4VVPlXI6WJEHZsWhzjsv/Y/Jy/7niTj4pXNq9sF/nK+en6f/OXsbcxJEkNUz5ePqOMjZWPUOJdjC6cFCRdxqDU69GEPdahtblXv9hAnTfy8GEpJU57+15eSmlRE9wS8bEq/7p2/d5K96GSpHaS784g392xWnUrHUeqI5FJGYNZXrk1rDTTqdm5svdZMYxMUdqPpgmmDOrNlEG9Yx1Kt6EJOxNz/kFNYCM1gQ24bDlku6c3X7g3GD4+LF4R9j4EELRCPLfnEx4YdWMswu50gmY9nxVdScCsAUxCwPaap6kJbGVy7p9jHV6bChkmf3pnUcStLAIY1zePpDhXO0ch0IUrbCXpEJumbrwobUMlSYoSI/cOvZKHtrzM5+Ub0YTAqdm5Y+BFjE7tH+vQuqSAWYOGjl1PjHUo3Uqt18+Db87n4/U7sCzJtMF9+MklZ5GTov4/RIsQglTXCFJdLfeMlvtrsAmd4FEz3SSwz1MapQg7v311b2BYHuBwhYApA5T6llIf3EOioyB2wbWx/ZW1rTZb0TWN3115brvHIISgd+Il7K1/FUseXtEKWRob6nMpqN3HkGR1M+a4qHK7VqkkSVFiJE538rPh1+Ax/NSHvGS6UiLuU1JOTW1gOyvLfkZ9sHGvV7prFOOz78etunq1O8uSXPvoy+yrqMZomou0aMseNj7yIu/dcwNuZ9crQ+psclxpmLLlvhKBoH+i6hJ2vCoD6zFly862GjZqg9u7VJKUlhAXcS8SwIheOWQkxUcljqHpd1IfOkixdyGmFOhIdnozWViVy/Lax/nvlJ+TYIuLSixK16SuyJQ2UVJTz7aD5YTMyPtslNbF21zkxKWpBKkdBM1aFh28kbrgdiQGEoNK/1oWHrgBSxpffQLllHyxo5DimrrmBAnAkhJPIMRH67bFMDLlkDibk0vyp+HUwhNWp2bjWwWzYxRVx3bQV8m7B5fxWekafGbjKkaivQCNlkm/xMJt61rJZkp8HNMHF+CwhXdBdNlt3Dgzep18deGgWl7BS8XTeK9sJP85OJlPKodhoWFJycKytVGLpVOTMfzTwamVpA4oZNZT4V+PXYsn3TUS0YEvnivrvdz17Dts2l+KTdfQhMZ9F5/J+eOGxDo0RaGw/t0WyZDEJGTWUupdRm789BhF1j3sKasKS5AO8QVDbC+uiEFESiQ39TuPNEcSL+2fT23Qy8DEPG4bcCH9EqIz66Yz+efOd3n9wCIEAk1oiG3wwMib6J98ObtqX8SSh8c6COwk2HuT6hwWw4jbxwNXns29L3zI4m17sesaErjza1M5fWjfqMZRE2ygKmRHkhp2PGCFqA42RDUWpetRSVIHs7P2ZdZXPozADljYtQSm9/g7yY5+sQ4totufepNtB8sxLIuA0biK9KtXP6VXRgoje+fGODqlu2sI7Y9YAmNJA69xMAYRdS8F2WnYdI2gEb7CHOewMzBXNbbpKIQQXNZrBpf1mhHrUDq01VXbefPA4hZNLn664SlenforpvV4ktXlv6Q+uBeAnPhpjMv8RZccJO92OnjkhguorPdS1eClV0ZKu3e0i2RESl+cmr15KPshTs3OyJToJmxK16OSpA6kyr+R9ZWPYMoA0LiEb5heFh28jSTfo5TX+RjVO5cBOR3j4mJ3aSW7Sitb1CYHDIPnFq3mj9ecF6PIlO7CtEwWV2xgacUGEm1uzu0xiX4Jh0tb0lwj2Vf/dosOSELopDrVamd7m9S/F7kpSRRWVBNqWlHShCDe6eDsUWoGj9K5fFC8vMXFOIAlLdbV7GJ82jBm9XyFkFmPJuzoWnt3eIu99ER3TNv4j0juy4iUvqyv2dXcwt6p2RmR0pcRySpJ+kpSzUk6FpUkdSC76l5rSpDC1flreebTZ9hXnA3AjMEF/PHqr6FrsS3Dq6j3Ng6BDIUfl7Jxj9IhphWgyDOP+uBekp39yYs/s0vOjVCiy7BMfrzuMbbX78dvBdEQfFiynNv7X8y5PSYDkJcwiy3Vj+MNlSCb/qFqwkmqcxipzpadvpS2pWmC/9x+Bb9/awEfr9uOaUmmD+nDfRefpZo2KJ3O0StIRwod8ZjqoBk9QgjuH3EjHxQv58PiFYDknNyJnJs7sUuu4CnRpZKkDiRgVhNpJ5slQQov/lDjm/CirXt4edl6vjl1dJQjDDc4L5OQ0bJRg8OmM2VQHwC8Rinziq4lZDVgSh824Wa9/jdm5j2Dy5Ye5YiVrmRB2ZrmBAnAQhKwQvx95xvMyBpDvM2FLhycmfccm6se54DnEzRho3fihQxKuYHvrh8AACAASURBVEF9gEZJstvFA988hwe+eU6sQ1GUU3JW9li+rNrWYjXJlBajU9TohlixaTpz86YwN29KrENRupiO2xGgGwmYQV7Y+zEflHgwLL3F47pmUViS1fy1P2Tw8hfroxliRElxLr498zTiHIdzbbuukex2ceW0xgRuVflv8ZuVzeVOhvTiM0pZV9m1husp0bewfG3E0heb0NlYu6v5a4eezOjMH3Nen085t/eHDE37Drrm/Mrzr9pdxC9e+YSfvPQRS7btQ0pVk6Ao3dm0zBGMSR2AS3MAoAsdp2bnh4OuIM721e8pitIhqe52rVIrSTEmpeTH6x5nR0MRhuWmhzOOVLsXu2YBgmBIZ/7KkfgD4W/AgQgrOLFw6+xJDMjN4NmFq6n2+Dh9aAHXnzGeZLcLKS1KvcuA8D1LEpMDnvmxCVjpMty6E0Gk91mJ6ziSoGP583uf898la/GHDCTw8fodnDNqIL++fLZagYqSuuBetlY/S21wJ6nOYQxOvZoEe9dqpax0LrrQ+PWI61ldvYNlFZtIsMVxdu4EesR1jH3CiqK0LZUkxdiamh3sbjhI0AoBGm+XjWZAfCn93VUMSBzIW59ms3ZnQthrHDadr43uOJueZw7vz8zhrZUatHZB2biIaUkLj+HHbXOpOUHKCflaj8ksqdjQvFn3EIdmZ3jyyQ9u3FtezQuL14TdiPAFQ3y4dhuXTRzBKNW1sd2V+9ax8OAdWDKExKQ6sJ199e8zM/9fpDgHxDo8pRvThMb4tEGMT+s4n8GKcipU44bWqavSGNtWV0jgiJIhC41tnlzeKx9GlTWXu+bcQJzD3jy0ze2w0zM9metPHx+rkI+bEBq58dMRhJcQCmzkJ8zknQOLuWLpT/nGsp9x+ZL7eKnwU1XSpBy3USn9+WavWdiFjTjdiVt3kmRz88DIm9G1lmWrx+vzrXsiVgH4QwYLNu8++YCV47aq/EFM6UfSmKhKTAzpZU2FKtNVOr/1hcXc9M/XOPP+J7nhiVdZvedArENSFCUCtZIUYxnOZJyaHd9ReytcmoNMZwpjc/N47/+u480vN3Ggqo7T+vdk9oj+OGyd43/duIx7mRfYTtCswZQBdOEkzpZFtXkG/9z9VvMqgGGavLDvY3ShcVnPs2IctdJZXNVnDufmTmJdzU7ibXGMTR2I7RQSJGic4aNrLVdAbbqmOrJFgSUNaoO7Ij5W4Y/9XkxFORUrdxXxnaffaG7EVF7n4eZ/FfPwt+YyranhUWd30LOEdRV/pT60jzhbJsNSb6Jv8gWxDktRTljnuNLuwqZnjuLxnW/it4Jhd69tms7pmY3ND7KSE7h55sTYBHiKXLYMzu31OsXexdQH95Hs6EeOewrXrXigRZlUwAry38JPuTT/TLXvQzluac4kzswe22bnmzW8Pw++taDFcU3TOlSZa1cl0NGFI+I4BLsW3+KYx/CxsXY3bt3F0OQCVbardGi/f2dBc4J0iD9k8ODbC3j3R9fFJqg2VOL9gqUlP27+/fUaJayu+COm9DMg5Yo2+z5SSmq9fhx2G26Hunl1SlQBT6tUkhRjLt3Bn8d8l99ufpYibzkIyHNlcN/Qb3WZbjmasJMXfyYccX1TGaiN+FyP4cOUFjZxaqsBinKyUuLj+NM153H38++hNV1wG5bFry6bRV5acoyj6/qEEBQkXcSeujfDEiVduBiQ/PWw575zYDFP7n4Lm9CRSOJ0J78b+R36xKt9Y0rHtKOkIuLxveXVWJZEi7CK3RFIaVET3IVAJ9lR0OqNzPWVj7a4wWFKPxurnqR/8mWINriJsWpPET9/5RMOVtcBcPqQvvz68tkkxXX94b1KdKkkqQPoHZ/DkxP+j4pALSDJcKbEOqR219OdxW7PwRbHM5zJp1wupSin6vQhfVn481tYur0Q07KYMrA3iXFd46ZFZzA643v4jQoOej9vWlUK0ithDkNSr2t+zra6Qv65+y2CVohg06BgnxngnnX/4IXJv1IrSkqHlBbvprze0+J4UpyzwyZIZb61LC65D8PyIpG49FSm5/6eNGfLlfX6YGHEc4QsDyHLi0NPiPj48SqsrOHWf72B74jVuIVbdvOdp97ghTu+eUrn7q5U44bWqU+RDiTDmdwtEiSAb/e9EKcWvkTu1Ox8u6+qW1Y6BrfTwawR/Tl71ECVIEWZLhxMzX2Q83q/zrTcPzG399uclv0ztCNWmN8rXkLQMlq81m8F2VATeU9TVxE0TD7ftpdPN+6kzuePdTjKCbjxzAnE2cPvT8fZbR22GZPfrGHBwe/jNysxpA9T+vEYxcw7cDuG5Wvx/PhW2/Tb+deeD3i9aD61oYaTjufFxWsJmeFjRUKmxbbiCrYXR16lU5STpVaSlJgYlzaIXw+/iaf3vEuht5QcVzrX9fkakzKGxzo0RVGOg2H52O9ZgM+oINM1kgzXyDbfS+i2ZeO2ZUd8rC7kQUYophcIvGbLi7euYs3eg9z2zJuYVuPPbpgm91xwBldMHBnjyJTjcdXU0dR6/TyzaBXQuLfmyqmjufGMCTGOLLJ99R8hj5p1CCClyX7PQgoSzwk7PjL9OywtuQ9THk7eTamzvr4H6xoW49TsvLDvA34/8rv0T+x5wvHsLq/CsFrGY9M0DlbXMjBXzaxS2o5KkpSYGZ06kL+m3hXrMBRFOUHVgR3MO/AdLGlgyiC6cJDhGskZPf6MJqLzsTI1YyRrqrfjP6ozqCENhif3i0oM0RYIGdz67zdo8If/zL9/ZyFjevdgQI66QOzohBDcPmcy3z5zAuX1HjIS43HZO+6lmM+oiNhExZJB/EZVi+M94qczMeuXrK18BK9RjIWb1XW5bPJkATQ3bPrD1ud4csJ9JxzP2D49+HJ3EcEj5tgBhEyTgbmZJ3y+bk+iGjccgyq3U5QT5AkEeXHJWu753wf8c/4KKhu8sQ5JUaJGSsniknsJWvUY0tc0w8hHuX8dO2pfi1ocZ2SNpU98Lk7NATSOrXZqdq7tcx5J9pZd8LqCz7ftJdIouZBh8vrKTdEPSDlpTruN/LTkDpcgBcwg/yv8mJtW/pabVz7ANo+GTcS1eJ4QNrLiRkc8R8/Emczt8zaX9/uCDyrPYJMnm6MHy5f4K1tt4HQsX588ininA+2IVWuX3cackQPpkZp0wudTlGPpWL+dSodlSYM99R+ys/ZdhNDonzSXPolzwvYIdAdldQ1c8dcXafAH8IUMnDadf81fyXPf+bpa5le6hQbjAF6jrMVxU/rZVfc2g1K+HuFVbc+u2Xho9HeZX7aKReVrSbTFc36PKQxL7huV7x8LnkAw4sBtU0oa/C3v9ivKibCkxT3rH2W35wDBphWf/+6v5GuZiSTbZHMJnS5c5Lonku4aeszzaULH1urKskQ/ieuH1Pg4Xr7zKh75cDGLt+7F7XRw5ZTRXDN9zAmfS2miVpJapZIk5StJKVlw8MeU+FY1v0lW+jez3/M5p+c+EOPooutP739OVYMXs+lCJWCYBAyTn736MS9998oYR6cobacu5MGu2YjTw5tWSGlx9F3h8Meix67ZmJMzkTk5nXOO3ImaPKAXZoT9GG6HnVnD+8cgIqUrWV29jb2eg80JEkDAMviwfCB39O1L0FyBQKdf0oX0TTr/uM55ds4kXiz8KOycGoJ+CfmkOE6u011uSiIPfuPck3qtopwIlSS1ky92FvLXj5dSWFFDv+w0vnf2VMb1aa3ry4lrvJso22TmwFcp862h1Lc6bCOmIf0c8Cylwr+JDNewdo+ho1iwZXdzgnSkLQfL8AVDxKmhdkont61uH3/Z/jzFvsZOUaNTBnHXoKtIdiQCkGjviUtPxWMUh71OF04Kks6LerzdSVZSArfOnMQTny0nYBhICXEOO+MK8pg+qCDW4Smd3Ja6PS32+AEELYsKcxBX9Lz9hM95Sf6ZrK/Zyaa63UgsdKETr7u4Z8i1bRGyorQrlSS1g/mbd3H3f99vnqr95Z4D3PzU6zx67YVM6t/rlM5tSZMNVf9mS83/CFkNJDsKmJD5Q3Ld7dc+tMS3GkO2bDNryRAl3lXdKkly6JHLA4QQ6B10xoWiHK+KQDX3rf87futw6dbamm3cu/7vPDruHoQQCCGYlvMA8w7cgcTElH5sIo4U5wAGJV8ew+i7h5vPOo0JffN5feVGPIEg54waxMxh/TrsjB2l80h3puDUHASOSpTsmp10x8kN0rZrNn478jtsq9/HjvpCMpypTEgbclKldkrbE6g5SceikqR28Pt3FzYnSIf4QwZ/fG8Rr9159Smde1X5I+yoe7t5Vac2uIf5B3/InPzHyPiK+uATUeqvZGPtLpLs8cRrSU0DHcNr3nXhwKl3j7lOh1wyYTjPLV5N4IjOOjZd4/TBBThsHevXqcbrp9rjJS81GYdNfSApX+3D4qWYMrxrlCFNSv2VbK3fy5CkxtWKdNdQLurzJvsaPsVrlJHpGk2u+7SorGwrMKZPD8b06RHrMJQu5vTMMTy1+60Wx+2aztSMUad07kGJvRmU2PuUzqEo0daxruq6ANOy2F8VuWPLztLKUzp30PSwo+5NTBl+l8eUQdZXPsVZeX86pfNDYxnfk7te54OSJehoCCFI0CWzI/YkEPROOOuUv2dnctusSWwqKmXNvoPN3XXy0pL41aWzYxzZYd5giJ++8hHzt+zGpjX+P7zr3Gl8Y9KpfcgprfOESqkK7CTBnkuqs/M2DtjvLSUkWw5oFUJQ6q9qTpIAHHoSA5IviWZ4iqK0o3hbHL8f9V1+t/kZKoI1AOS40rh3yHW4dEeMo1PajVpJapVKktqYJgTJcS5qI0xBT090n9K5vUYZAhtwdM2wpCa4+5TOfciyyvV8XLKMkGVwaJulz4Qva0cyOWUHlgwiAZvm4vTcB3HoJ7fxsrNy2m3866ZL2XKgjG0l5fRMS2Zsn7w2H6J5Kg4lSEHDJEjjqsAf31tEj5QkZgxW+xbakiVNlpU+yJ6GT9CEHUuapDkHMKvHnzrl78aw5H6srNrUPMvkEFNa9E1ouz2ViqJ0TP0T8vnXhJ9Q6q9CCEG2Ky3WISlKzKjaiDYmhODGM8YTd9Tsgzi7jVvOPO2Uzh1vz0ZiRnhEUBl08utNj/NZ6QoMK9Jzjs/7BxdH3LhZ6HMyNusJZub/ldn5f+PSgnfIiuu+E96H5GVx0bhhjCvI71AJUo3X35wgHckfMvjXgpUxiqrr2lLzCnsa5mHKICHLgyn9VPq3sqS0c3Z9nJV9GvE2N/oRHw1Ozc641CH0cufEMDKlM6vx+nllxQae+XzVKVdUKO1PCEFOXLpKkJRuT60ktYMbZozHFzT4z+ersKRE1zRuOfM0rph4akmFXXMzKOVyttW8elSnOcGiqnhqjE2sr9nBJ6XLuH/EHSe1MdJnRp61IYQgaJlkuoafdPxK+6v2eLFpWvMK0pFKautjEFHXtrXmlbDfRQCLEEWeJYQsH3at5RDGjsxti+ORsT/iuT3v8kXVBlyag3Nzp3FJfvcqq1XazpLt+/je828jAMOy+NsnS7l0/HDunXtGh7rB1FFIKSnxrWJP/adowk6/pHPJbMP9xopyNBFpQrUCqCSpXQghuGP2ZG4+8zRqvD5S4+Owt9IV7USNTb8Nl57K5uoXCJi1VIVcrK7rSY3RWMoXsILsrN/P8soNTMmIPA37WM7IGscez4EW5TYCwYDEU+vMp7S/vNTkiBceuhBM6Jsfg4i6tpDlbfUxUwaw07mSJIA0RxJ3DrqSO2MdiNLp+UMGP3jxnbBGRiHT4vVVmzhjSF+mDFAb+Y8kpWRJ6W/Z1zAfQ/oAwc669xiReg2j0q+PdXiK0u2ocrt25LDpZCUltFmCBCCExrDUq7i87/vkJz/M59VjqAwlhj3HbwX4onL9SZ3/7Nwp9Hbn4tIaN2nahI5Ts3PXoKuxayqn7ugcNp27zp2G64hyT00I4px2bj2rewzcjKYe7olEehuNt2fj1A63zC0PVHP/pie5ePH3uXTJXTy8/XkajNYTLEXpClbs2k+kwcO+YIg3V2+OfkAdXJl/PfsaPmtKkAAkpvSzofo/NISKj/lapVFJbT2/ffszLnzkWW579k1W7T0Q65A6NhnjPx2cuurtxNx6HJGqFTQ0Em3xJ3VOh2bnj6N/wNKKdays2kSaI4k5uZPJi8s6xWiVaPnGpFH0SEniXwtWUlJbz4S++dx61kR6pnevdu3RMDbjVg56l2NIH6YMItDRhZ0p2fc2r+j5zAA/XPMQNaF6JBKkycKyL9ndUMQjY36sSo6ULsuUVuuPWa0/1l3tb1iEISOVvAuKPMsYnNJ9u0lKKdlS8xobq/9HwKoj2zWSCZm3hXUTPVBdx6V/fx5vIIRhWeworeSLXfv59cWzOX/04BhGr3RWKknqxMakDsYWYd+RTdOZkzP5pM9r03RmZI1lRtbYUwlPiaEZgwtUJ7soSLDncFHvF9la+wZlvrUkOXozNOUKkhw9m5+zqHwVPtPfmCA1MaRJsa+C9bXbGZUyKBahK0q7O61vT6wIyVCcw87c0UOiGkuN14dd14l3dtxW1rrmQqAjCW/DL9CwaS6klGyr30OD4WVQYgGJ9pO7GdoZrSx/lC21bzbvAS3yLqe0cD0X9v43SY7Gzpv/mLcMjz+IecQeG3/I4IF353POiIHYdFU8pZwYlSR1YnbNxq+H384vNz1G0AohEJjS5JZ+V9A7Xg0aVJRocNlSGZ1+Q6uP72koitgx0sKkyFuqkiSlmSUNKvzb0ISNdOeATj+cN97p4IHLz+GeVz5EWpKQaeKy25g5tB+nR+kmzrr9xfzk1Y/YX1WLBKb2780Dl51NanzH2y/YN/FsNlW/iHnUrDKJxKEP4eYvf0Gd4UFDELIMvtnra1zac06Moo2egFnHltrXj5oRKTFkgA3VLzA1+/8AWLZrf1iC1Pz6kMHBmjp6qWqKiEQnKHuLFZUkdXL9E3vxn4n3s7l2NwEryNCkfrhtrliHpShKk97xPXBqDgJHJUoaOvlx2TGKSuloDnhWsKD4l1gYSClx6AnM6vEgGa7OnUTPGTGAkb1y+GDdNur9QWYM6sOoXrlRKTMtrqnnxqdewxs83IhoyY593PDUq7z+3as7XKlrsqMXp2V+nxXlf0Gg07ify+L0nPu5f8t/KA9Uh61Iv7T/Q/on9u7yN1pqg/vRhP2oJAkkJmW+Tc1fZyS4I3ZxNS1JsltdFyknTiVJXYAudEakDIh1GIrSgpSS1fsOsrW4nPy0ZKYN6I2ude674yfqjKwJvLjvfUKWgUVj6ZFN6GS70tTvrQKAJ1TOvIP3YRw52sHw8WHRnXyj71vYNGcMozt1OcmJXD9jfNS/78sr1mOY4eV+hmVRWFXL+v0ljOqVG/WYvsrA5AvplXAGBz3L0YSNvPhJ7PdWUh2sD0uQoLGb7XsHF3bpJClgBlldXUTQ9EfYgy1IdhzuunvDjPH85NWP8B3RTdGh60wf1IfkOJUkKSdOJUmKcpTqwB62132AYfnonTCDPPf4DnfHsTPwBUN8+9+vs7WkHNOysGsaKfFxPH/T18lOToh1eFETpzv50+i7eWzny6yu3oIuNKZmjOGWfpehdfJyKqVt7Kz7ECtCkwMpLQo9i+mbODMGUXV+u8urCJotZ8ZpQnCguq5DJkkALj2ZvkmHy+g8RhFaK59B9YYnWmFFnc8M8H/rHqLUX8mIhERynLXoR9SG2YSTUWnXNH99zoiB7Kuo4YkFyxvnBZomE/v25MHLz4lF+J2HKrdrlUqSFOUIm2veYHn5o1gyhMRiR92H9IyfzFm5v+z0+wOi7bH5y9l0sJSg0XiREsTEHzK477WPeOqGS2McXXRludL4xfBbkU318irpVo7kM6uxiLRvzcBv1sQgoq5hXJ88Pt++N2xOE4BhmgzpkRmjqE7cgMTeETsFOjQ7U9JPfB5iZ/H+wYWU+CoIyhCr63oxPOEAveKqEECSPYcp2XeT7hoY9ppbzjyNq6eMZk95FZmJCd3qhpzS9lSSFEU1Xj//WbyKz7buIiXOxTVTxzJraP9Yh6U08Zs1LC//e1jdsyH97Pcso8iznJ4JJ98xsDt6c/Wm5gTpEFNKVu4pwhsM4XbYYxRZ7KjkqHsq81fy5O6XWVuzGV3oTM8Yzw0Fl+K2NTYPyHOPZ3vtO0fMxzlEkBs3JvoBdxEXjxvGU4u+xDBNDKvxBoXLbmP6wD4UZKbFOLrj59KdfLvvpfxr92sErRASiVNzkO1KZ3bOlFiH124WV6whKBv3k1lorG/oyYaGfBJsNn4+9Afkx/eJ+Lp4p4Ph+TlRjLRzU40bWqeSpHa03/MFK8sfpzZURLyew3tL+rJuR0bz8v/GA6XcMH08t89UF98dwQHPl2jYMI+6o2tIP7sb5qsk6QQZx5iDEqktsKJ0RV7Dx4/W/YEGw4OFxJQWC8tXstdTxB9HNc7JyoufSIZrMBX+Lc37kmzCRZ+EM8LmwCgnJtHl5NXbr+SRT5Yyf8tu4hw2vj5xJNdPi/7+qFM1J2cqfeLzeL94ETXBeiamjeSs7Ik49Y7b0vxUxUdoQiURBC2NONWgSokClSS1k8KGJcwr/gVm02C4OmMfk8YVUeMbx459+QD4Qgb/XLSSqyaPJsXd8dqRdibl9R5MyyI7KeG47tY3GF421GzDrtkZmTIIh2ZHF6192AhsQr0hn6g5wwbw+qpNYcmSAIbkZpLg6twb0RXleC0oW07ACmKFzckyOOArY0v9LoYm9UcTOmfn/5kdte+zs+4DNGFncPKFFCSeFcPIu4bMpATuv7RrtMkemNiHgYl9Yh1G1Hwtdwbb6/eFdQYVCLKc6aozqBIVKklqJyvKH2tOkA6x20xOH7+hOUmCxs4rGw+UMm1AnyhH2DUUVtZw10vvsbO0EgT0SEnioSvOZWiP1t9APy5ZzFN7XkVvGsQrgPuG3MqgxNMiPt8mHAxI6pobP0OWjxLfBnThICduOJpou7eE78+ZytKdhVR5vHiDIeLsNuw2nd9ednabfQ9F6eh2e4patH+Hxtk3Rd4ShiY1llzrws7glAsZnHJhtENUlA5pcvpotuXs4b3iRdi0xs/rBJubnwy9RZUutyVVbtcqlSS1k9pQUcTjifE+Gv9FNv6Cm5ZFRkL3mZrdloKGydX/fIkqjw+raUP83opqrn3qVT754Y2kRJiLsN9bzFN7XiVohYDDszN+u+Ux/j3hQWbnPcDHB+5FIJBYSCxGp11HVtzQaP1YUbOj9lMWlf4RIXSQEl2zc27e78mKG9Im509xx/HOnd/io0072FhUQu/0VOaOHkySasWqdCMF8fkR52QJBPlxat+EorRGCMH1fS/hgryz2FK3mxRHIkOT+qmuoErUqCSpncTbMmkwSloc9wUcHEqQdCHomZbCoJyMKEfXNSzcthtfMNScIB1iWhbvrtvC1ZNbbnieV/oFhtWyJSzAquqNTMkYx1X93mS/5wsMy0ee+zTi7Z2nC9LxqgnuZ1HpHzBkoPkuUsiE94ru5pp+r7fZXBan3cYFo4dwwei2SbwUJRZ2lVXyyaadCAFnDx9In4zU437tGVkTeXn/B4SsUHPJnU3o9IjLYkhSv/YKWVG6jHRnCtMyx8Y6jK5JqsYNx6KSpHYyNv0GlpT9KazkzpCC3cEMsvrWU1uYysCcDP5+1Vy1bHySSmrrCZktGwD4QwZF1XURX+Mz/c0DPY8kpcRvNv6/smtu+nbxvQDbat/HlEaL4xKL/Z7lFCTOiEFUitLxPL5gOU8uWEHIMgHB4wtWcOesKVw3bdxxvT7eFscfRv2IJ3e9xNqarY3d7TLHcUPBZeq9/wj1oWI2VL9GVWA3Wa4hDEu9hHhbeqzDUhSlG1NJUjsZmHwuKyrX4At+gk0YGFJnpzeT/cE0coZW8tSF36FfRtdboYimkT1z0TUBRy0MuR12xrQyJHBi+kgWla/Eb4XvFzOlxaiU7rPa4TfrkEf/xdE4vDJgNYQdC1kh6kINJNuTmuvCFaU72FVWyRMLVhAwDt1QkJiWxSOfLmHW0P7kpyUf13myXRn8bNjtSClVYhRBuX8rbxd+H0uGsDAo9q5nU82bXNz7cVIcPWMdnqIo3ZRKktrRhgadnQ2D0ZGYCA6V2dk0nZCjDlBJ0qkYmZ/DmF49WF14sHlYoMOmk5+azFlDIpexjE4ZwsiUQayv2YrfCiIQODQ7l+TNId2ZEs3wY6p3whR21s1rMZdFYpHnbixrsKTFi4Vv837xAiQSXehcnn8uF/SYpS70lG7h0827Irayl8C8Lbu4duqJlQCp35vIFpX8Key9yCJE0DJYVvZ3zs3/fQwjU6JFSskrX27kqcUrqfb6GdMrl7vnTGdA9sltRzCsAJY0cOhqz/dXUuV2rVJJUjtKticCoilBOsyUJgk29Yt7qoQQ/OOai3h26WpeW7URw7Q4b+Qgbjr9NOx65BUPTWj8ePBNrKzawJKK1Tg1BzOzJzM4qXvNIukVP4nsuKGU+jaFzWUZnnoxifbGzeSv7P+A94sXHLHhPMRL+98jwRbPzOyuO8BQUQ7RBERKa0TTY8qpM2WIisCOCI9IDnjXRD0eJTYe/nQJzy1bg6/phufn2/eyau8BXrvtanqnH/8NTJ9Ry/ySP1DoWQFIUp29OTPn/8hyDWqnyJWuTCVJ7ej83LPYWLs9rKuRhkaeK5t8t+pq1BYcNp1vz5jAt2dMOO7XaEJjYvooJqaPasfIOjZN6Hwt/w/sqp/PzrpPsQknQ1Lmkh/f+PdoSYt3iue16MgVsIK8VvSBSpKUbmH2sAH8Y/7yiKtJs4YOiEFEXY+Gji5smDLU4jG7dnh+YMAM8kXlGg76Sugdn8+EtFHYNXUJ0xopJRsOlFLZ4GFkfi7pCe5Yh9QqTyDIf5auJmAcLgGXNM6SfGLhch645PjGRkgpebvoLqoDhVg0JluVgd287oWq0QAAIABJREFUtf8urix4Vu1xi0CgGjcci3qHaUcjUwbzzV5zebHwbWxCx5QWOa5M7h1yW6xDUxQ0YWNA0mwGJM1u8VjIMgiYLWe7AFSHIjfFUJSupk9GKj+YPZW/fLI47Pg9XzuD3JTEGEXVtQihMSBpDjvqPsGUh99zdOFkaPIFAJQHqvjJ+j/gM/34rQAuzckL9jd4YOSP0fGzq2EhljToEz+FVGevWP0oHcbBmjpufOY1yuo9aEIQNE2unzqO78+aGuvQItpbWY1d18OSJABLStYVtewS3JpS/2bqgsXNCdLh84TYXPMOEzKua4twlW5EJUntbG6PmczKnsruhkIS7Qn0cveIdUiK8pUcmp00RzIVweoWj/V258UgIkWJjW9NHcvMof35bMtOEILZQ/uTk6wSpLY0Jeu71IdKKPFtbF5V6h0/mbEZ1wLwxK4XqA3VN3cm9VsBQgGD53b/HrtYCTQ2nVlR8W9Gp17BxMwbY/azdAS3v/A2hVW1YeMxnl26mhF5OcxsZb9uLOUmJxI0WzYSEkCf9ONvt18XOkikAllThqgKFp5ChEp3pZKkKIjTXQxLHhjrMBTluAkhuK7PZfx15zNNg3cbOTQ73+pzSQwj697W7S/mmaWrKa6tY0q/3lwzaQyp8XFf/ULllOSlJnHNFDWnpb3YtTjO7/lnaoKF1AaLSHUUkORo7FBqSosNNVtbjG7QRAApl2IeuetcwrrqV+ibOJ1MV/f8zN1TUc3eyuoW8wN9IYPnl63pkElSWrybWUP6MW/L7iM6STbO2bv5BErpM5z9sSJ0bdWFg1zXsDaJtUuSqt6uNW2eJAkhnMBDwDeBOGAecJuUsugYr/kl8IujDpdKKXOOeI5oes7NQCqwHLhdSrmpTX8ARVEAmJwxBrfNxf8K36XEX07v+Dyu7HUBAxMLKPFtYWnZk1QEdhJvy+C0jG8xIOnMWIfcpb27bis/e/sTAoaBlLCluJxXVm3gjduuJiNBNYJROr8URy9SHMdXLpfuaAApWmyoMGSQHXWfddskyRMIoGtaxMdq/f4oR3P8Hrj4bB5wLuCttZuxpCQjIZ6fzT2LUT0jj/OIJM1ZgCmzMGURetO/C0tC0LLIiJvYXqErXVh7rCQ9DFxIY5JUCfwZeFcIMU5K2TLFP2wbcMYRXx/93P8Dfghc1/TcnwOfCCEGSSnr2yb0rsmyJPura4lz2MhKTIh1OEonMiplSIv5UaW+rbxZeBdG06DkYNDDvOI/4DNrGZl6USzC7PJCpslv3vusudU9QNA0qfX6+efnK7n33DNiF5yitCMpLfol9GRXwz4sJIfKqTS0pmSgZVON7mxgdkbEjowOm87sof2jHs/xctpt/OrCWfzkvDPxBkMkxzlPuGV+dbCWJVWJ5LrS6OGsRRcWFcEECn05JJcs5fqCK9op+s5NNW5oXZsmSUKIZOBG4Hop5SdNx64B9gGzgI+O8XJDShlxh17TKtL3gQellK81HbsWKAOuBJ5osx+inXiNKg561+PQE8h3j0ET0RnKuXTXPu557SMaAgFMKRmam8XDXz+f7CSVLCkn54vyp5oTpEMMGWB5+dMMT5kbtX/b3cneyuqIHdZClsXCbXtUkqR0SbsbCvntlkcwLAOERAOQ4NCc2LUCdFGBedQFnk046N+NV7UdNhu/vGAmP33zE4KGiSUlLruN7MQErp40JtbhfSWHTcdhO7nPkIO+Umyak32+TPb5wudQ7qjf0xbhKd1MW68kjQPswMeHDkgp9wshtgBTOHaS1FcIcRAI0FhKd5+UcnfTYwVAzlHn9QkhFjWdt0MnSSsrnufLyufRRONft004uLDnQ2S42nc2T2FVDbe/+HbY3ecNB0q47t+v8P73rlODDZWTUh7YGfG4KUN4jWoS7Cc3/E9pXbLLFTFJAkhTe5KULsiUJr/b8jcaDE/YcV3TuLTnucztMZvttR+xqOxhpJRILDRhY2Tqpd1+Js55IwfTNzONF75YS2ldAzMGFnDJ2GHEOx2xDq1dZTnTCVktW8lraPRUTbOUk9DWSVIOjWVyFUcdL216rDXLaSyj2wpkAT8FlgohhkkpK494bWmE80ZstSWEuJnG/Uv06hW7lqBFnrWsqnwBUwab25uG8PJO0Y+5rt9LCBG5drgt/G/Fegwz/MLKsCQHa+u56bnXGZKbzeXjhtMr7fgHtSldX9AK8UHxZywq/wKAM7Omck7OGdiaZpIk2XPwm7URX+vSk6IWZ3eSlZTA6PxcVhceDEuW4uw2rps6LoaRKUr72Fq3M+IFrylNCj1F6EJjSMq55MePZVf9QkwZoiBhKmnOPtEPtgMakpvF/RfPiXUYUZXpSmdUylDW1W4J+7dj12zM7TErhpF1YLLpjxLRcV2hCyHuF0LIr/hzxskGIaX8QEr5spRyvZTyU+D8ptiuPYVzPimlHC+lHJ+ZmfnVL2gnG2vealGaBBA0vZT4Nrfr9y6qrg27oDr0exAwTRbvKuTfS1dxwT+eY/623ZFP0AUETA/Lyp/h2d038N+9t7G55iOkVDXsrbGkxW82/4VXi96lyFdMka+Yl/a/xe+2/A3Z1AHntIxrsQln2OtswsmIlAuxaV37TmUsPfz18xmWl4XLZiPB6cBp0/n2tAnMUUNNlS4oaAUjdXMGwGcebkCQaM9mdNoVjEu/SiVICt8feCMzMiZiFzY0NPLicrhvyB3ku4+/AYSiHHK8K0kPA89/xXMKgUmADmQA5Uc8lg18frxBSSkbhBCbgEOf/of2KmU3fZ8jz3v8k8ZiIGh6Ih4XQiNo+dr1e0/u25PFO/fiCxkRHzcsC8OyuOeND1nyo1ux6e23qhULhhXkpX13UBcqaZ7mPr/0bxzwbWB27t0xjq5jWl+7hULPgbC230ErxI6GvWyp38nQpAH0SZjEGTl3saTscQJmfVOJy8VMyrwhhpF3fanxcfzvpm+yp6Ka8voGBudkkhTnavE8w7TwBIMkuU5847OidBSDEvtjRuj15NQcTMkYH4OIlM7AqTu4tf9V3NTvGxiWiVNXN+6+ilD3jVt1XEmSlLKCliV0LQghVgEhYDbwYtOxfGAIsPR4gxJCuIDBwPymQ3toTIZmAyuPeM504EfHe95o8xheNjf4iROiuR3lIZY0yHUPb9fvf8HooTy9dBWldR5Chwa1RbhmMkyLLSVljMg7VkVk57Ot7jPqQ+XNCRKAIf1sq/uMCelXkuJQNcpH21G/G7/VcuUzZIXYUb+boUmN9y0GJ89mUNJMAlYDds2NLtTItWgpyEilIKPlgEXTsnh43hKeX74OwzJJcrm45+wZzB01JMJZlPZSGzxIbaiEdGcf4m1psQ6n03Lb4rihzzd4eu//MCwDC4lTc9IvoReT01WSpBybLnR0XTURUk5Nm17ZSClrhRBPAX8QQpRxuAX4euDTQ88TQmwF/i6l/HvT1w8B79C4SpQF/AyIB/7TdF4phHgYuK/ptdtp3LfUQFMy1hE9tuvfbK4PMTjeQbweRBcSS4IubEzPvgOH1r4bruOdDl655Sr+9flKPt68g3KPJ+KqkiUlcXZ7u8YSC/u9qzFky7kQGjolvs0qSYog1ZGCU3MQsIJhxx2anVRHctgxITS1B6kDeeiTz/nfyvXNv+OVHi8/e+dTkt0uZgwoiHF0XV/I8vFe0a8o8q1HF3ZMGWRw0mzOyrlTdXw8SWdmT6VfQh/mlS2mwfAwIW0UE9JGo6u/T0VRoqA9bv9+HzCAlzg8TPZbR81IGkRjSd4h+cB/OVym9wUwSUq574jn/KHpfI9yeJjsnI46I8lreFlXswlDmmxsyCfd3kC6vYGQ1JGiH8NSzotKHCluF3efPZ27z57O+xu38ZM3Pw5LlISA7KRE+mV2vTueibYsNGxYtEwM3bY0DMtgQdkSFlUsQxMaZ2VNZ1rGRLR2bKbR0U1JH8/z+15rcVwXOqeldfz2sd1VIGTw35XrwzpZAvhDBn+bv0wlSVHwWclfKfKtw5Sh5iY92+rmkeboydj0y2McXefVKz6P6wu+HuswuoS9ldW8s34rvlCQmYP6M7ZXD1WSq6jGDcfQ5kmSlDIAfLfpT2vPEUd9/Y3jOK8Eftn0p8PzmQFEU22bRFARSqQilAhAij02F+HnDhvIyr1FvL5mE7qmIRC4HXb+ceUFXfKNcnjKeaytfgNLHpEUouHSE+kRN4IHtz7CjoY9jRuEgb2eQtZWb+B7A2+OVcgx57bF8fOhd/Hw9n9SHaoFJBmONH4w8GZcuvMrX6/ERo3P3+oH3YHquugG0w2ZMsSO+gVhpb3QOD9sbfUbKklSYu7V1Rv5zfvzMS0L07L478r1nDN0IA9cNKdDfv77zTpWV77BHs8K3HoqY9MvoXf82FiHpXQzaiNBO0lzpJBgi6c6VBN2XENjVPKwmMQkhOAX58/kxqnj+XLfAdIT3Ewu6NXlGjYckuzI5fz8X/PRwd9hWH4sLFIdPTk/75dsqtvOzoa9zQkSQMAKsqpmPXs8hRTEx65tfKz1TejFI2N+TWmgAgFkOTM65Ieoclh6vBu7TcdvtFw1HZwbu+6e3YVhBVvtmhmwWjbvqQnWUuIvJduVRapDjWBQ2le118dv3v+MgHG4oMcXMvhw8w7mjhzMlH69YxgdSCnxhwxcdhtCCPxmHc/t/g4+s6b5xkORdz1Ts65nbNrFMY1V6V5UktROhBDc3Pca/rLjiaZNpxY2YSNOd3F5zwtiGlt+ajL5qclf/cQuoHf8OG7q/zJVwf3YhZMkR2Nzik3FSwlEaFBgSYstddu7dZIEjf9+c1zq4rqzsOkad541mYc+WRxWcuey2/jBzKkxjKx7cGhukuy51ISKwo4LBD3do5u/NiyDf+55li8qV2LX7BhWiAlp47il73XNc8gU5UR4jWqqAvtJcuSQZM+K+JzFO/dh0zQChHcL9IVCvL9pe8ySJCklzyxbzeOLltMQCJLijuOuWVPpkb8mLEGCxqZLS8qeZnjKOe2+n7u7EarcrlXqXbkdjU4dwW+G38P7xZ9S4i9jSNJAzsk5i2S72uweTUJopDvDPwSS7UnYhY2QDL/zbhM6SfbEaIanKG3i6oljSHW7eXTBMkrrPQzNyeTuOdO7XNfKjkgIwczcH/DW/vswZQiJhYYNm+ZkatZNzc9748C7rKj6EkMaGGbje8+X1WtIK0rhm70ui1X4SickpcVnJX9jU+3H6MKBKUP0jh/LeXk/waaFl0bbW6kW0YTArsWukuSZZav56/ylYc1mfvP+fG6+aB2m1nKQsCZ0yv27yGvnzsCKcohKktpZL3c+t/a7LtZhKEeZmjGRV4rehqOSJE1ojE8d3cqrFKVjO2/EIM4bMSjWYXQ6O8sreXfDVkKmyazB/RnT88Q7X+a7R/HNPv9gddWrVAULyY0bypjUS0mwH+5R9Enp/LAZZNA4NHVe6UKVJCknZHXV62yu/bSpUUjjv6l9ntXML3mU2T3uCnvu9P59sGTL5QKHTeei0UOjEu/RpJQ8/vmKFh13/SGDwgqT7AiLYgHTz9sH53FhXgY94tTNnzYhgQj/NpRGKklSuqUURxL/N+gOHtnxBEErhATibW5+OPA21aBAiaraUCkrKl7igG8TKY48JqZfQW7c4FiH1W08u3w1f/50CSHTxJKSF1eu46JRQ/nFeTNP+Fxpzt7Myv1hq4/7zJYjCQB8lh8ppdr7pxy3NVVvYMjwknFTBtlSN4+zcr8XNrsu3ungkSvO53svv4uGwJISieTbU8czKj832qEDEDRM6v0tS94B1mzpyfnZFWE/n5TgNW2sqdzMyur7+emQu+mb0CdK0SrdlUqSlE4laJp4AkGS41xop3hBMTR5EI+Ne4g9nkJ0odPbna8uUpSoqg4e4Lk93yVk+ZGYVAT2sbfhS87Pu5f+iZNjHV6XV1rfwEOfLCZohm9of3PdZuaOHMLYk1hROpZ+CQXsaNjV4nhBfG/13qMcU6m/jDJ/OXnuHqQ5UvFbDRGfZ0kL0wqi6+GXdzMGFLDwrv9n77zjo7jOtn2d2dmi3ntFiI4opjcbMMYNN9zjXmInTnnzpr5JnMROb1+6UxzHSWzHdowruGEbF7DBmN5BgEAgCfUubZlyvj9WCIRWpmi1qzKXf/rhnZ05c2u1OzvPOc9zP59l1d6DeHWdeYX5Ya1Ndqg2kqIiqWntbmzikiM4P20aq6v+3jGJaeI27Bxwp2Ji4jW9/PvwMzw8/tthUG4xlLCCpEGGbvood+9CIMiKHIdNDI4msZph8Ku31/Dcph2YUhIX4eTbl8znsnG9Sy1ShMJwazbKIkysrn4czWxHdvp3S3Tp5Z3KPzI8eqZ149zHrN5/CJuigNG1oN2j6by1e3/Qg6Q78m/mR7t/hWZqmJgoKNgVlbvybwnqeSwGDx7Dyx/3/4U9zfuwKyqaqTE9aRpZEUUcbtvAqd7/8Y4MHLbIgGPFRbhYOjk87rqnIoTga4vm8tBrq7qazagqX79oHhMT8hkTu4gvbrkfXSp4za73MiVth0OsePBiGTf0jBUkDSIOt25iefmPOx8LBFdmPzgoegv8+I33eGX7nk6L45rWdr7zylskREQwq2BoO9FZDFzK2refFCCdwG000240EKUOvibP/QlVUQgUhwoheix27w3DovL4adH3ea3iTQ61HyEvMofLMy626isseuSp0mfY07wXTepohr/2aEP9Ji5Jm4VD2Ylu+jDRESjYhJ0L0/8nzIrPnKsnjcVlV/n9u2s51tRCfnICX180j7mFfqMlhy0Ckzi8Zvc01QibK9RyLYYgVpAUBhrdHp7fspMd5ZWMSkvmxikTSIoKPPNzprTpjbxc9nC3HOWXjz7MfYVPEqEOXEe9Vq+Pl7btxqd3n+19ZPXHVpBk0S+RUrKhfhOvHVtJi97ChLjxXJW1pEtfnAhbHG4jcLNXh9K7a4LF6Vk4ajgPv/Zut+0Om40lRWP65JzprlTuKbi9T8a2GFwY0uCj2o/RTzEY8pk+Vtdu5SdFj7KpbhnH3HtJcuYyJel6kp354RF7jlwybiSXjBvZ4/OLUhewsnIVPnmip6FDcbA4fWEo5A0NrJWkHrGCpBBztKGJ6x97Grem49F13ttfwuPrNvHMXTcyIjX59AP0wL7mDwj0TpdI9rWsZlLCkh6PldJkW8NKNjW8jM9wMzxmOnNSbiFKTThnPcGkrq0dVVHwndLjAaCsoSkMiiwsTs/L5St47dibeDsaFr9fvYZP6jfxswkPEWf31wJMS7yeVVWPdJncsAkHo2LmYVesmdK+Ji7Cxa+WXsI3XnwTRQhMJFJKvrxgFqPSzv16bGERDHRTx+yhSbHb8BBrT2VB+hdCrCq0XJd9JQ1aI+s7eotppsbMxGlck9XzPY2FRbCwgqQQ89OV79Pk8XbacXp1A59u8P3XVvHMXTee9Xjtejs7mnZS2rYXXXbvK2BIHa/RvTDyZN6ufISdjas6b9S2N7zJ/pZ13DP8r7hs4e8ZlBEbHXC7AMZnpoVWjEXQqWhqps3rY1hyImoYe3YEk3a9nRUVb6Cd9Jk0MHAbbt489g435l4LwPj4xTRqx9hU/wKKUDGkzrCoKVyU8eVwSR9yXDRmBO9/NYd39x3EpxvMHzmM9NjwX/csLJw2J5kRGZS5y7tsFwjGxg4Nq39VUfn88Lv5TO51VHmqSXOlWr0mLUKGFSSFmI9KSrv1K5DA1rJj6KZ5VjeJn9Rt4O+HHkdBwal4yHaBckp+vU2o5H9KTVKLVsOOxre7dLY2MfAarWxreJMZydefsZ6+wqGqfOGCmfzp/XVdeiq47Cpfmj87jMosekNVcytffG4F+6prsCkKDpuNn165mAtHDQ+3tF5z1F2OqqidNQTH0aXOruY9nY+FEMxLvZPpSddT7ztKjJpCtD0pJBq9RjsHWz9BMz0Mi55CrD0lJOftj8RHuFg6qX8UtFtYnMxdw27jl3t/22n2oQobdsXBzbk3hFtaSImzx1rBUR8gsIwbPg0rSAoxTlXFq3dPG7Mp4qwsrRt9jTxa8o/OmWqPKYnVXcSqHpSOd7xduBgRM4e0iBE9jlPpOYhN2LsESQC69HGkbVu/CJIA7pk9ldToKP6yZj01re1MyEzja4vmWSkxAxQpJXc99QKH6xowpAQM2tH46guv8/y9N/cq9bQ/kGCPRzf1gM8lO7oHQU5bVEh7I5W2beXFIw+DECBNTCQzk29kTspnQqbBwsLi9IyMKeTHRd/njWNvU+4uZ3hUARdnLCLR0T/S4S0sBjNWkBRirpk4jmc3besSKDlsNi4dN/KsgqRP6jeeskVw1JNIgqoxJiaGVFcK4+MWUxjz6SstsWoyMkDOs8BGg+blS5u/gtvwMCKmkFtybyI7MvuMNQabKyaM4YoJfVNMbRFadlRUcay5pSNAOoHPMHhqw1YevnxRmJQFh1RXCgXR+RxsLUGXJ33WFQeXZSwOozLQTA8vHf0hmvR0KWNcX/sc+VGTyYq0PmMWQ4ONR8p4bO1GKppbmD0sl7tnTiE1JnB6dzhJd6Vx17Bbwy3DYjAipf/HIiCDowBgAPHVhXM4LycTl10lymEnwm5nTHoK3790wVmN4zN9AQo6BU26i8yoy7km52FGxM45bZ+VVNdwEpxZKKfEyxLY2tRAs96CJjV2N+/hR7t/Rq239qx0WlgEorq1LeCkgCklFY0tYVAUfP535BcZGzsGVag4FSfRahT3DruTwpjwphMebtuCP8miK7r0sbPx7dALsrAIAy9t28U9T7/Ee/sPsa+qlqc+2cqVf3uSqubATVoHK6Y0WF39JL/ZewM/2305/yz5MmXte05/oIXFEMBaSQoxLrvKv267jr2VNRTX1DIsKYHxGWln3TRyUvwEXqlYjmF2Td1TFZWJ8RPOeBwhBDfk/pgV5b+krH0nAgWHEklxm0qb0XV2QTM13qhcyW15VuNDi94xITMNzeiedupS1c4eGQOdKDWKb4z+Ck1aM+16O6muFGzCFm5ZGKZGYM9XiX6SzS74P/MbGzZyqO0Q6c50ZiXPIsIWERKdFhZ9hWYY/GTl+12amGqmSYvXx18+XM9Dl10YRnWhZeWxR9jZ9F6ncVOl5wDPln6HO4b9lhRXfnjFnQNuTeOjkiMYpsnsYbnEuJzhlmQxgLGCpDAxOj2F0ennXiidHZnNgtT5vF/9QafFsENxMD/lfHIjc85qrEg1nhvzfopbb8Znuil317PzwCOAu8t+BgaHWg+fs2YLi+OkxkRz89QJ/HfTjk4zDofNRnJ0JNdOGh9mdcGlvxUc50VPxpDd66XswsXo2PM7H7fqrfxw9w9p1prxml6cipMXy1/ku2O+S0ZERiglW1gEldL6xm6pvgC6afJRSWkYFIWHdr2ZHU2rAtQka6ytfY6rsr8Z9HO26vVsa3iLBl8FuVHjGRN7AXYlOIHMmoOH+fKyV1GEQOL/e/5kySKu6KOeZ4MFy7ihZ6wgaQBzc86NTE2YwtradUhgdvJMRkb3bNJwOiLUWCKIRZdqwKJzBYWcMNYkWQwu/u+iCyjKTOff67fQ6vFy0ehC7p49lWinI9zSBjURthgWpT/Aqsq/YEoDEwO7cFEQM42C6Kmd+z1f9jz13nqMjv5kXtOLDx//OPQPHhz7YLjkW1j0mvgIF7oRuP9Qci8buw8kGrXKgMZNEpNqTwluw02jr5EkZxIOpffX5Qr3Pp4u/Q6m1DGkxt7mD/mo5lnuLPg9Eb1sN9Lk9vClZSu6OOACPPjqO0zKziQnIa5X41sMTawgaQAjhGBkzAhGxpx7YBSIJGcSRXHj2dG0s0ufF7uicmn6xUE9l8XQRQjBkvGjWTI+dK5uFn4mJlxCduQ4dja+g2a6GREzi9yoSV3SfjfWb+wMkI4jkRxqO4TH8OCy9f9mt+16M41aJQn2DCJUq/dRX9Hk9vCLt1fz+u5ipJQsHFnAdy6eT0p0VLilBSQ5OooZ+dl8fPgo2knBUoRd5Z7Z08KoLLTE29O7BUgAAoU2Q/A/W/4Hm7BhYnJZ+mVcmXnlWZcGHEdKyYryX6OZJzJUNOmhRavlw5qnuSj9/nP+PQDe3ncg4HbDNHlt114+N3dGr8Yf1FgrST1iBUkWAfl84f08e+Q5VtesQZc62RHZ3Jl/K+kR6eGWZmFhEQSSnDlckHZXj88rIrCvjxACEcD4oT9hSoM3Kh5hR9N7qMKOLjUmxC/ikozPofSDurDBhCklt/z7OQ7XNaCZ/oDjrb372VJ2jJVfuBOn2j9vM36z9DK+tOxVtpRVYLfZ0E2TL54/k0WDoE/bmRKpxlIUt4idTe921iT5UTjQ5kWTsnOi9PXK14mzxzE/df45natNb6BJq+623UBnb/OHvQ6S3D4N0wycQtnq9QU4wsLi9PTPq5dF2HEodm7Pv4Xb8j6DIQ1UxXqr9DeO1Dfy/Nad1LS2M294HheNLsRus24ALYLDnKQ5vFP9DvpJ9UsKCqNjRuO09e9i6NXVT7Oz6QMMqXXOlO9sfJcYNYF5qVYvqGDyUUkpFU3NnQESgG5KmtweVu7Zz5X9tB4k1uXi37ddR0VTMzWtbRSmJBHlGHqpvhdnPEC0msDG+uV4zTbSXIVsaWzqZtzkM328duy1sw6SjrYf5bVjKyhzHyJKCdw7ThW9f93nDc/nV6vWdNvusqssGFnQ6/EthibWna/FpyKEQBWheZtopod1tS+yo/FdACbEL2Rm8rVBK+ocTKzad5Cvvvg6ummimyZv7i7mX+s38+Tt1/fbmVuLgcXVWVdT3FpMubvcP1EiVKLUKO4Zdk+4pZ2WjfUrTpkZB0162VC/wgqSgsz+6rqADdLbNY29VTX9Nkg6TmZcLJlx/cdYJdQowsa81FuZl+rvw+Q1vHxY9wDQvWarRT+79gz7W/bz2/2/QjM1JJJMp4pL0Tg5Y08VDiYnXNqbXwGA/KQEbp9NQPIbAAAgAElEQVQ+mac2bMWj6Uggwm5n8ehCzsvO7PX4gxnLuKFnrLupPkI3TXYdq0JVFMakp55Vo9ihiJQmTx3+LtWeEvSOmd+1tc9zsHUzdwz7JaKH1J+hiM8w+NYrK/HoJ2bl2jWNfdW1PL9lJ7dMmxRGdRaDBafNyYNjHqS4tZgj7UdIcaZQFFfUL2zM3YabrY2b8RoexsaOJ9WV1vmclBKv6Q54nMdoC5XEIcOwpAScqg3d1/WmOtJupzAlKUyqLM4Vh+IgwZFAna+u23P5UflnNdYzR5/EZ55IdavyxZLlbEAV4BB2JDAsahLTkq7upWo/X79wHhcUDuOlbbvRTZMl40czb3jeOddRWVhYQVIf8OHBUv73Jf8sv0QS43Ty1xuuZFxGWrd9W/UGdjd9hNdopyB6MlmRwTVhGCiUtG2hxlvaGSCBv7lljecwh9q2URA9OYzq+he7jlUhA1RaejSdFTv3WkGSRdAQQjAqZhSjYkaFW0one5t388iB3wH+ehiQXJi2mKXZNwB+zemuAio9B7sdmxFRGEqpQ4LzC/NJjorCqzejd6TcKUIQ6bBz6diRYVZncbYIIbg171b+cvAvnQGOQGBX7NzQ8Rk7E6SUHGk/0mWbIW0c8SQRqWjcM+xOMiJGkOoaFlT90/KymZZnufBaBAdrej7IVDa38oXnl9Pk8dDm89Hu06hqaeWO/7zQpXEdwP6Wjfxh3328U/kv3q9+micOfYeXy36HDNC/YbAhpaRFa8Zr+FNiKtqL0UxPt/180kuFuzjU8vo1LlUNWKAK/vQCC4vBimb6+POB3+M1vXhNL5r0oUmNd6vfZl/L3s79Ls74HHbhRHR8xQkU7MLJ4l4Wh1t0x6YoPHPXjVw4sgBVUbAJwdyCPJbdc7N1PRqgTIqfxNdGfo2xsWNJciQxOX4yD455kILoM6/tEUIQaQtkpy4QIoGJCYuDHiBZnAMSMGX4fvo51kpSkFm+cw9GgD+8YZqsKj7I5eP8M7Ka6eWFo7/qkjevSS97mtYyNnYOI2MHrw3p7qadPFn6T5q0RgAmxU/hvPh87MKFJrsGSg7FSaw9ORwyzxqPpvPH1et4abt/qX/xqEK+unAuiZERQT3P6LQUEiIjaG/qat0aYbdz85QJQT1Xf8Ktt9CqN5DgSEcNQs8Oi4HHnubdAbf7TB9ra9cwKsZvJ58dOYY7C/4fa2uWUeU9RJqrgDnJ15Piygul3CFDUlQkf7j+CqT0r3Fb6eUDn5ExI/nGqG/0aowLUy9iZdUbXVLuHMLBorTFvZVnYRESrCApyNS2tuMzuhex6qZJg/tEnvzhtp0BbXQ16WFb46pBGySVu8v488Hfd7lobm3cRKvWhE1R0QzBCdN+gSLsjI6dExatZ4OUkruffpEdFZV4O/7+L27fzdpDR3jj83cE1UxBCMFfb7qKO558Hq+uIyUY0mTpxLFcNHrwpRPppo/l5X9kT/NabEJFIrkg5WZmp1wTbmkWIcaQ3a+tx9HNrpMGqa58rs7p3U2exdnht4e3sPBzReZVNOvNfFS7BlXYMaTO7OS5XJ5xRbilWZxM/1/QCRtWkBRkZhfk8tyWHbRrXb+wBYLpudknPe6Z4pa9bG3cxKT4KX2kMny8Xfkmutk17VCXOgfbDvKlEd9kdfU/qPdVAJDkyOLq7G/iUPp/08otZcfYXVndGSCBPzCub3fzxu5irp4wNqjnG5mazOqvfJYPD5bS4HYzLTeLnIT4oJ6jv/BaxV/Y27yui53zB9VPE+dIYVzc3DCrswglo2PHYsjurltOxcn0pFlhUNT/8Ok6r+8pZv3ho2TGx3H9xPGkx0aHW5bFEEQRCrfl3cHSrOuo9daQ7EwhSu2fDYYtLAJhBUlB5vzh+YzPSGPHsUrcHTVIEXaVS0aPYGTqibSx/KiigMX3poQ6zcPjh/7C4rTLWJK5NGTaQ0Gl5xhmAGtRv824i/sKH6FF87vqxNgHjjPS7qpqjAC1ZO2axrbyyqAHSQB2m23Q93/wmR52Nq3u1hVek14+rFlmBUlDjAhbBLfn38UThx/HlCYGBg7Fyfi4iRTFTQy3vLDT6vVy/b+e5VhTC+2ahsNm47F1G3nspquZlmsVs58tdd5jvFv1H0rbdxKlxjEv5TrGx80Lt6wBR5QaZQVHFgMSK0gKMooQPH7LUl7cuouXd+zGbrNx4+QiLhvX1R1KVRxcl/NNlh35OYbUMaSBBNymHa+pAl5WVr7KgtTFRKmDZxZwRPRIjrQf7tKgEkCTOpkR/i/xgRQcHScnPg5VEXhP2e5SVQqSEsKiaTDgMdoCpqUCtGoNXR5rpsa2xs3UeqvIisxlXOwEFMs6ftAxI2k2BdGFfFy3Fo/RzoT48xgZPcqy+QUeW7eRow1NnSnfPsMAw+BrL7/BB1+613qNzoIGXxWPHvwqPtODxKRVb+CVsj9S763k/NTrwy3PooMjDY2U1DVQkJRA7iDNpuhrrD5JPWMFSX2Aw2bjpikTuOk0RfSFMVP48qjH+FPx92j0HcNr2tHkiR4kNqFS2n6IsbFFfS05ZFyYdjFrat/HMIzOlTSH4mB20jxi7QO3od/cgjwSIyPxaM2dK0oCcKg2rprQv5sp9mei1Xgcigvd8J3yjCAn6sTrWu+r5Rd7H8ZjeNBMH3bFQbIzha+PepCIgA5LFgOZFGcqV2QGp7fKYOL1PcUBa2KbPB6ONDSRl2jdRJ4pa6qXdQZIx9GklzU1y5iZfMWASAMfzHh1nS+/+CprDx3FblPQDJ2F41wsnixw2lxMiJ9Pqis33DItBjjWNGuYiVLjSIkYQ5sR0SVAAjAxiVUHbuAQiHhHPA+O/SFTE6YTZYsmxZnKtdk3clPureGW1itsisIzd9zArPwcVEVBVRTGZ6bx7B03EuuyvkzPFUXYuDjjXuzC2blNoOBQXCxMu61z2xOHH/NbypseTEy8pocqzzFeKX8+HLItLMJCTwYxpilxquFvAjyQONK+u0uAdBxFKNR5/XWzpjQpdx+l1lv9qWMZpjkkWnuEkl+tWsPaQ0fw6jqtXi+Txu8iJnslG+pf46Pal3j04NdYX/dquGUODKQM308/x1pJ6gdcmHoxWxs3dnF8U1BIcaaSFTH4ZkKSnSl8dvgD4ZYRdFJjonn8lmtxaxqGKYl2WjbVwaAofj7RagJrapbR6KsiJ3IM56feRJIzE/DbPxe37OlW66ZLnQ3167gp9/ZwyLawCDk3nzeBX6xa3VkPC/4U8MKUJNJjY8553BpPGQ2+KtJcecQ5BkZLht4S70ij1lfebbsudWLsiexq2sa/Dv8VXfowpSTVmcb9w/+XZGdq575by4/x0Jur2FNVg8uucuOkIr6+YC6OILqdDlWWbduJV/evmqYmN5KbVYOq+r8DJCa69PF25b8YFzeXaNVaQbU4N6xPaj8gL6qAW3Lv5tmj/0ZKiSENsiJy+Nzwr1g55AMQq4Fi8BkWPZFh0T0V5vc8GxXIHMXCYrBy4+QiNhwpY1VxCUIIFAExLid/uHbJOY3nNdw8VfoTytr3YxMqhtQYFzebpdlfQhGDe2VqXsp1lLbtQjupl6Eq7BRGn0e77uHvJb/HJ09MbFZ4yvjd/p/ww3G/RREKJXX13PH0850Bq1vTeXbLDipbWvnD0nP7e1j4kVLiOWkiIDejBpst0KqfjQMtm5mUsDCU8iwGEVaQ1E+YkTSHKQkzqPCUEWmL7DIbZTE0MTrMLWzC+ph+Gg7FyfDokRxo3dclKLIJG1MTZgQ8RkrJW/sO8NSmrbR6fVw6ZiS3TplEpMMKcC0GLjZF4bfXXM6Bmjq2lh8jLSaa2cNysSnnllm/vPyvHG3fhyF19I6AYHfTx6S6cjk/ZXA5r55KXtQ4rsz6Im8c+3tHbZJkdOwsrsz6Aq9WvNitZ5dE0qa3sb91D6NixvGP9Zvw6V338eg67+0vobK5pVcre0MdIQSTszPZXOZPe1Qdeg/2PsL6/jwDLOOGnrHePf0IVVHJjcwPtwyLMNPoq+GV8kcoad0BQEH0RK7OfoA4+9BIczkX7sj/LL/Y+zA+04vX9OJUXCQ4Ergq64aA+/981Wqe3bK9c5b3QG09y3fu4YW7PhPUxr8WFuGgMCWJwpTeuYTqpsau5rWdkzXH0aSX9XWvD/ogCaAo/nzGxc2hWasnwhaFs8MEpt5Xh0HgxsbNWhMA+6pqAraFcKg2jjQ0WUFSL/nBJQu5+Yn/EhHTSExyW8B9pDQZETM1xMosBhOWcUM/xWcYvLGnmN988BEvbt+F+5TmtBaDE8308ejB/+Ng6w7Mjv8Otm7j0YP/h25a74GeSHam8pOi33Jz7p1cnnENd+bfz/fG/ixgb47K5hb+s2lbl7oNr65ztLGJV3ftC6VsC4uwopk+Pqp5lz/t/xmPl/ye4pZdnc/pUuvRbMBruEMlMewowka8I6UzQAIYGzsBh+Lstq8hDQqiRgAwPiMNVem+vuHTDfKTrBqZ3jImLYXX77udGZMbkIpCq+FASn+vSbPDE+C6nG/gstxNPx0Z5p/TIIT4thBigxCiWQhRI4RYIYQYf8o+QgjxkBCiQgjhFkK8L4QYd8o+CUKIJ4UQTR0/TwohTvtBtKZM+4AqzxHerXqOCncJyc5MFqReT27UqNMf2EFDu5vrn3iW2rY22nwakXY7v3zvQ5bdcRM58XF9qNwi3Oxu/hiv2d7FVUli4jHa2NP8CUXxc8Korn/jUBzMTDp9c9lNZRXYbbZuVsluTef9AyVcO3FcD0daWAwedFPnd8U/pNJdga+j7mZX8zYWp1/FxelX4bJFkuTMoMZb1uU4gUJh9KRwSO43TE2cxTvVr1HnrUHraHTtUJxMT5xDkjMFgLtnTOHlHXu6TG65VJVLx4wkNXrw9D4MJ5lxsdhcLWCA23TiNe04FB2JwJQuMiPO/L7Lot8yH/gzsAF/Z5UfAu8IIcZKKes79vkm8DXgTmAf8H3gbSHEKCllS8c+TwO5wCUdjx8DngSu+LSTWytJQaa8/SB/2f8tdjWto95XSXHLZh4veYh9zZvOeIxfvf8h5U3NtPn8F9d2TaPB7eY7r7/dV7KDjmGabDxazpqSUtp8p/a4sWjw1fFB9Ureq36ji31svfcYPtPTbX/N9FLnqwilxEFLUmQkgaawbEKQGmPdvFgMDTY1rKXScyJAAvCZXt489hKtuv++4qqsB7ALJwp+kwZV2HHZIlmccVvAMYcKDsXBN0c9zMXpV5LhymZYVCE359zFzTl3de6TmxDPM7ffyLTcLOyKQkKEi8/OmspPLr8ojMoHH8mOlM7/N1HwmA68ph1F2IlUrVWkgY6U8mIp5T+llDullDuA24AUYA74V5GArwA/l1K+IKXcCdwBxACf6dhnDP7g6D4p5Top5TrgfmCJEOJTI2lrJSnIvHHsX13ccMCfw/1qxWOMjDnvjNzq3txbjG52dWoxpWTDkTK8ut7vayb2VNVwz39fol3zIRAY0uShxQtZOsGaoZdSsrziST6seR0QGNLGivL/clnGdSxKX0KaKw+H4uoWKNkVJ2muvPCIHmRMz8sm1uWi3ad1CZXsNhufOa8nBz0Li8HF9saN+Exvt+2qUDnQspdJCdPIixrDF0b8lnW1K6jxlpETOYqZSZcTbbfSxVy2CC7LuIbLMq7pcZ8xaSn859bAdZEWwWFJ5nU8VvJHtJOcBh3CwYWpl1qmDWeAAMQA6Fd0EjH4F3gaOh4PA9KBt47vIKV0CyFWA7OBvwGzgFZg7UnjfAS0dezTY5699Q4KMmXtBwJub/TVoEkvDnH6xqKfFkj1d0twzTC489kXqG/vmrP+g5XvUpSRxoiUoWs+IKXkydJfs6tpPcdT1RWh4TNtvH7secbHn8eo2GnEqIk0+Kox6XC3QyXWnsSomClhVD94UITgic9cx/3LXuZYcws2oSAE/OzyxQxPTgy3vCFLq9eHZhokRESEW8qQIEqNQSC62eRLZJcZ+CRnBkuy7gu1PAuLM6IobjK35t3Li+XP0KI14VCcLEq7nEvSrwy3NIszI1kIsfGkx49KKR/9lP1/D2wF1nU8Tu/4t+qU/aqArJP2qZEnFVlKKaUQovqk4wNiBUlBJlKNpUmr6bbdJuyo4szshZeMGcXz23d1qZmwCcHsYbk4bP27N8Xaw0e62Z6CP3h6dusOvnfRgjCo6h8cbNvJvpZNnBrnOhQDj2GwtWE9l2Rcw33Df87KyifY1fQRIBgfN4eLM24f9H1JQkleYjxv3HcHB+vqafdpjE5L6fefrcFKXVs733jtTT4+fBQE5Cck8PPLFzMh41O/uyx6ybyURWyqX4OJDxOBKQUgcCouCqPHhFuehcUZMy1xNlMTZqFJH6qwowirkuSs6N5iKpTUSinPyIJQCPEbYC4wV0oZ2F4yyFhBUpBJduTR6KvpciMsJcQ7M8/4Jvfr8+eyqayCo01N+DrS62JdLn562eI+Uh08mjzegA08DSm7rS4NNXY1re9mp3scm9Axpf9KFanGsDj9Di7LuAen7fQrjxbnhhCCwuTe2SRb9A4pJbc+vYzDDY2dKcb7a+u47enneeu+O0mzasT6BN3UeLvyP7hUH7p5/JqkYBfJfGHEt62bTIsBhxACh+juOGgxOBBC/Ba4CVggpSw56anKjn/TgCMnbU876blKIEUIIY6vJnXUMqWetE9ArCApyBxsO4xP2nCc1ENBkwpl7hpMaZ7Rl0+My8nye27lw5JS9lTXkJ8Yz8LCAuwDYKZ7Rm42utF9WiLSbmfxyMIwKOo/2IUjYHoL+G1mJyVM53DbAZ4u/Su1Pv/ndmzsZG7OvZ8o1bpZtBh8bCwrp6K5pVsNpm6aPLdtJ1+aOzNMygY371e/TEnbbgypd07oCSR5UWlkRGSHV5zFgMRnGPx13Sf8d+sOPJrOBcPz+cb8eWSEqB9Uk1ZHo6+OVFcWEbburR8sBi5CiN8DN+IPkPae8vQh/IHORfgd8BBCuIB5wDc69lkHROOvTTpelzQLiKJrnVI3rCApyHhMD7pU0aQNBYnpL4tDoHfYOp/ZDJ0iBOcPz+f84fl9KTfopMVE89lZ0/jH+o2dfWgi7Cpj0lK4aNTQDpLOS7iAdXVvdikwPc75KUtw2SL4bfH3u5g27G7awp8P/ISvj/ppv69Hs7A4W8oamwNu9xkGh+rrAz5n0Xs2NqxCP+U6JJEcbt+Dx2i3estYnDX/8/JrrDl0GG9Huv3re4pZe/gob913B7GuvsuI8Bke/nPk9+xv2YYq7OhS4/yUK7g4/SbrO/MM6c/GDUKIR/A72l0NNAghjudht0opWztqi34HfEcIsRcoBh7Eb9TwNICUco8Q4k3gb0KI4wWWfwNelVJ+anNEK0gKMgVRoyhu3QmIjgDJT2ZE7pBxWvmfebOYlpPFM1u20+b1cdnYUVw5bjSqMrRTONIj8rgk/RbeqHwKgYKJgZRwZda9zExexIqKZ7ul4xkYVHuPcaS9hLyo4WFSbmHRN4xLT8UM8AUdYVeZkp0ZBkVDA1323Jja6Ej1N6TBe9Ur+LBmJR7TzYjo8VyZdSspTqtW7FS8hhtF2LArjnBL6XNq29r4xyebWFt6lIzYGO6dPoWEiAg+PFTaGSCBP8W+zedj2bad3DPjjEpOzokXyv7G/pZt6FLrfF+vqX2NJEc605KGbg30IOKBjn9XnbL9YeChjv//JRABPAIkAOuBxSf1SAK/HfgfgZUdj5cDXzzdyYfGXXsIWZp9O78t/j66qWOgo6CgKnZuyLkn3NJCyuz8XGbn54ZbRr9jTsrlTIifQ3HrVuzCwaiYyThtfjevKk95wJolgUK9r8YKkiwGHSNTkpmTn8tHh4/g0f3vfVVRiHO5uGrc2DCrG7yMi53Bhvp3Ox00j5PsyCRK9adHPXvkb2xr/Lhz5XtX8yYOtu3mW6P/H3H2hJBr7o+Utx9i2dE/U+k5ihCC0THncV3O5zpfw8FGVUsrV/zzKVq8XjTTZFdVNR8eKuXa8WOxBZgE9eg6m8uP0Vd3Pz7Dw/amj7t9b2qmlw9qlltB0pkgCdQ2sN8gpTztcmBHndFDnAiaAu3TANx6tucf2lP7fUBGRA7/N+ZXzE25iGFRI5mZtJBvjPoZ+VEjwi3Nop8QY49nSsJ8JsTP7gyQwL8KaRfdZyJNqZMVYfVIsuj/eDSd57fv4qsr3uA3qz+ivClwOt3J/PGaJTwwezqZsTEkRESwtGgsL935GaKdg39WPlwsTr+JOHsCDsVf6K4KB04lghty/BOrjb46tjau65IaLJFopo81NW+GRXN/o0Vr4K8Hf0CF5zAmBobU2du8iUcPPozsx+lLveHP6z6huSNAOo5H13l51x5Ms3stssOmUNiHbRU8phtB4HvoZs1K17XoPdZKUh+Q6Ehmafbt4ZZhMcCYmbSAd6tfxdB1zA5PTrtwMC5uMqmujDCrGxgccx9hRcWTHGrbR6QaxQUpS5ibfKnl1hUCWrxelv77GSpbW3BrOnabjX9u3Myj117FrLyeV5XtNhufnz2Dz8+eEUK1Q5soNZb/HfU7tjV+SGnbPpKdGUxNXEi0GgfAMc/RzvqOk9GlzuG24nBI7nd8XPdOwPToOl8VR9qLyYsaFSZlfceaQ4e7mawcJzM+liP1jV0CKFWxcfPkvmvQHa3GEWGLpkVv6LJdSvCYXt6vfp35qZf12fktBj/WnYPFOVHR3MzjGzbz6PoNHKyzZmyCQaQaxddH/ZQpiXOJtEUTb0/i4vRruD3/S+GWNiCo9VbyxwPfo7h1O5r00qTV88ax/7K84olwSxsS/H39RsqbmzsNWzTDwK3pfO3VNwPWHVmEF4fiZFrihVyX8wDzU6/pDJAAkp1pAVN/FWyku3JCKTPs+Ewva2vf5vFDv+D5o49S4S4FoNpb1mNtV52vOpQSQ0ZyVGBDD800eeSaK1hQWIBdUVAVhVEpyTz1metI70Mbf0UoLM3+LAq2zoyx45caTUpeP/YsXsPT4/EWANL/ooXrp59jrST1E6SUbG5Yw+ra1/EYbYyNncqFadcQrcaGW1o3nt++kx+88y5Sgiklv//oYz47fSpfmTsr3NIGPPGORG7N+3y4ZQxI3q1+Gc3s6tilSS8f173D4rTriLRs1PuU1/cWd2mAfZwWr5fShkaGJVp1LAOFFGcGw6JGUtK2r0sgoCoqF6ReGkZlocVjuPnD/u/Q4KtDk14ECpsa1nBjzufJjRzJ7qZNaNLb5RgpTTJdgzM9+rPTp/LVV9/onAgBsCsKM3KyGZ6UyCNLr8Cr6/gMgxhnaHoWjYubRpwjnTpvOeDvi2qiAAKbsHHMc9Qqd7A4Z6yVpH7C8ooneKHsMcrdh6jzVbO29i1+W/wt3EZb5z6aYfDewRKWbd/JofqGTxmt76hta+MH77yLVzfwGQa6aeLVdR77ZCO7qwbn7JlF/8CQOtsa1/FS2T95v3oFrXrXepej7Qc7bPa7ogo7Nd5joZI5ZImw2wNuN6XEpVrzcQONu4d9nUnxs7AJFQWFNGcW9xd8hxTn0En9/ah2JfW+ms5ASGKiSR/Plz3K5Ph5uGwRiJNuo+zCQUH0ONIjBqdp0UUjC/nCrBm4VJVohwOnauO8rEx+d9WJlDanqoYsQDpOsjMTAxs6Nkxs0FGnZEidmJNWSC0szhbrm6sf0Kw1sK7u7S4zdgY67XorH9etYkHqlZTU1XPLs8to1zRMCaY0WTJ6FD+7dDFKCHsBrDpQghKgUNJnGLy2t5ixaamferxuaqyvf5/NDR9hVxzMTlpEUdw0q5+BxafiMdw8cuD71Pmq8ZkeVOHg7arnuX/498iN9PffSnVmUek52q1Zry41Eh0p4ZA9pLhl8gR+8u4HXWaZFSEYmZwcsoaSFsHDaXNxS94D3JR7H7qp47T1Xa+b/sqOpo8DptRJoF6r5ssjf8HrFU+xp3kzdsXO9MRFXJi2NPRCQ8jnZk3n1vMmUVxbS0pUFDnx4Q9CFqRczv6WnV2MRmzYyIkcTpLz0+9JLED0/6y3sGEFSWHGkAZra1fiXyQ+/k71Bwya9FHcsoP5KVfw+ZeWU9vW3uX27/V9xczMy+GaEFrl9uazZEiDRw78iHL34c6L2aG2fUxPuIBrc+4OjkCLQcn71cup8R7rvGHRpQ9dwlOlv+fbo/+AEIKFaVezu3lTly9KVTgYHzeVGHt8UHRIKdlZVUVVaxtF6WmkRVspfMe5YWIRG8sqeGPffmxCIATEuyL409VLwi3NohfYhIrNNjRvFVy2qIDbTWngUiKIsydyc96XQ6wq/EQ7HZyX1X/6mBXGjOOarDt4peJJQGBInbzIQu4c9r/hlmYxwBmaV75+Qpvewp8PPEijrxZTap3rM/5ARKCgkORI4VB9AxXNLd0CFLem8/SW7SENkhYVFvCjd97rtt1hs3H56JGfeuyupk1UeEq73MT6TC8f17/HBamXkWw1KbTogS2NHwWc0W3RGmnQakh0pJIVkc/dw77Ji+X/oM5bhU2oTE9cwBWZtwVFQ3VrK3cse4HypmYUIfAZBjdPnMCDC+dbK6H4V41+veQSvjB7BlsrjpEWHc3MvJygrnTrps6WxrVsb/yEKDWa2ckXkRtp9Q+z6BvmJl/Ckfb9+MwTdUcCQaIzjVRXVhiVWZzKrOQLmZo4j0pPOdFqDAmO5HBLGjgMAAOFcGEFSWHk1YonqPdWYeAvdhb4AySBRCKwCZW5KZfS0Gz0eBPm1nrunN4XJEdF8fBFC/nB2+8i8dcb2BSFe6dNOW2q3Z7mrV2+bI6joHCwdQ9Ragy7mzaiS41RMZOIty5yFh3YROBLlUR2eW5ETBHfGv07vIYHu+IIqvX3F195lZK6eoyTvlCe27GDovQ0rrYan3YyLDGhT0wadFPjjwceotJThs/0IhBsbljLVVm3MSf5oqCfz8JiXOxU5iZdyura1zquMyMaxPAAACAASURBVJIoNZa7878Z9HMdqKujrr2dsampIa/pGSzYFQc5kcPCLcNiEGEFSWFkR9O6zgDpOMdDoUhbDDfmfp50Vw4pDhOXqtJ+SkDkUlWuGDs6RGpPcN2E8czOz+XNfQfQTINFhcMZnnT6hnExahw2bN1/Z6HQ4Kvmx7vuRwiBlCYSyUVpN7Ag7eq++jUsBhAzEhfyZuV/u6xCCgRpzizi7N3fe8Gun6hsaWFXVVWXAAn8q7n/3rzFCpJCwKaGDzsDJOhobip9vFL+JFMS5uCyBbYntrA4V4QQXJZ5M3NTLqW0vZhoNZb8yFFBXTmuaW3j3hdfoqSuHpuioJkmX5kzi89Onxa0c4QLKSV7W7aztWE9qmJneuL55EVZK78WAwcrSAojPa1wChS+N/avqIr/z2NTFH5zxaV8/sXl6FKiGQaRdjt5CfHcft6kECo+QWZsLHdPO++sjpmeNJ/3a17DkF2DJAWFNTUr/A5CJ70m71QtY2TMRLKsmaEhz9yUSzjQuosDrbuQmNiEikNxclt+aHLO23waNkWBABbXzZ7uq6MWwWdb4/qAK9E2YaOkbR9jYyd3btNMH8UtW/GY7RRGFxFnTwqlVItBRqw9nqK46X0y9v0vvcLe6pouEzB/WLuOkSnJXDBs4H73SSl5svQRdjRt7Fz5XV/3PovTr2Fxeu8mP5s9HkoaGsiMiSHVqgvtHRJE4P7AFlhBUlgZHzedbY1rMU9aWREojIqZ1BkgHWdufh4r772D57bvpLKllTl5uVw8agQOmy3Uss+ZZGcat+d/mf+UPtIRC0kibFHMS76I96pf6OYKoUuNzQ2rrSDJAptQuafgW5S1l1Davp84eyJjYif3mIYXbPIT4gOu5tptCotHFoZEw1AnoocieonEpUR0Pj7SVszjh36M7PjPlAYLUpdyYdr1oZJqYXFGlDY0UlxbG3CF+l8bN4c9SKryVPBJ3Qe4zXaK4qYyOmbCGa+iHWjd3RkgwYmV35WVLzItcR4JjrOfuJBS8ovVa3hi8xbsNhs+w2BBwTB+c9mluHpoQWBh0RusICmMXJF1B6Xt+2jTm/GaHhyKC5ctkqXZ9wXcPzM2lq/MnR1ilcFlfNxUfjT+7xxpP4hdsZMdMYzNDasDuuZJJLr0BXjGYqiSHVlAdmRByM9rUxR+funFfHn5q2iGgdHR+ycxMoL7BkFazEBgdvIidjRt6JJyCeBUIsiP8pvGGFLnn4d/isds77LP+9UvUxA9jmFRAy8tUkrJgfp6TNNkRHJySFs+WPQtjR43qhK4brK2rT3g9lCxvu4Dlh19HEMamBhsqF/DyOjx3FPw1TOq9dzWuKGHGmR4p3IZ4+OnMiJ6Iqpy5sHNM9u389SWrXgNA2/Hqv77hw7x0Kr3+Pkli894HItTsIwbesQKksJItBrHN0b/nl1NG6j0HCXFmUlR3IyzumgMRFRFpSB6VOfjkTETMWX3NCaH4qQoflYopVlY9MjC4QW8dNstPLllC2VNzczJy+WGCUWDvshaSsnBtn1sa9iAqqhMS5xDZhiaZQ6PHsOl6dfzeuVzqEJFInEqLj43/DudN20lrbsCXks06WND/aoBFyTtqqrm88uX0+B2AxDjcPDHK65gSj+yX7Y4d0anpGAGuEF12GwsHB76yaDjuI12lh19vJsTbXHrTnY0bWJi/OknhpyKEwUF86QG36owUITGtqYP2Nn8EYpQuHvY98iJPLPV+L9v2Ihb17ts8+oGy/fs4eFFC3FaTastgoz1jgozNqEyIX4WExi6wUCMPZ4lmbfzasWTmFJHIrErDibEzWJ41LhwywOgormZ2vZ2CpOSiLSW9YcsI5KT+OFFi8ItI2RIKXnmyGNsaljXUVeg8EH1W1yeeT0Xpl0Wcj0L0q5getJ8Slr3EmGLpCB6TJdZbc3saeVZ4jM8oREZJNo1jVuXLaPZ6+2y7a4XXuCDz95LQkTEpxxtMRBwqioPLpzPj1a9h0fXkYDTZiMxMpK7pp5dzW9v8Rpu9rdswcTEZ5oowtYtBd5netncsPaMgqRpSefzQc2bmB2BloKJKkyE8KfSH2/p8M9DP+G7Y/9+RqnTje7An2FTSto1zQqSLIKO9Y6y6BEpJSVtxWyuX4cQClMT55DfR840s5MvYXj0eDY3rEY3fYyPm0F+1Oiw959p8nj4wooVbC6vwG6zYZgmX5kzm3unTg2rLguLUHCwbV9ngAQgMdGkj1crnmNKwkziHad3tQw2UWoMRT3cpBVEj+1mDAP+VekJ8XP6WlpQWbl/P4bZvaLakJIVe/dy++TJAY6yGGjcMKGI4YmJ/HPTZipbWlkwfBi3Tp5EnCu4Dp2fxp7mDfy39DeIjgkHQ2oIAk8G2hXHGY2Z7srimuzbeLHsCWzChsAdcD9D6hxq3U1hzITTjjk9O5tVBw92S89PjY4iPoSv16DDyrbrkaAHSUIIJ/Br4GYgAlgFPCClLPuUYw4DeQGeel1KeXnHPg8BPzjl+SopZb/tQKqbJjurqrApCuNSUwdcLvkLZU+yru79ztnZtXXvcWHqZVye2TcF0GmubC7N+EyfjH2ufPnV19hYVo5mmp050L/7aC3DExNZUBC+dAgLi2AgpeRw225qvOWkuXLIjew6MdFjXYFQ2N28jdnJC0Ip97S4bFFclXUPy8v/gS51JCYOxUV+5GjGx80It7yzoratHV8AN0WPrlPd2hYGRRZ9xZTsLKZkh6c5bZvezH9L/58/te6km2Wb0BE4kJy4HtiFg5lJ88947DnJi5gYP529zTtYV7eCcveBgPsFusYE4pvnz+Pjo0fxaBq69Ctzqio/XLQo7BOqFoOTvlhJ+h1wFf4gqQ74DfCqEGKKlAGm+PxMA062acsANgHPnbLfPmD+SY97Gi/sfFRaypdfew3dNJFSEu1w8NerrmJCevBiumatiQ9r36PCfZS8qALmJM0nUg3sAHW2HG0/xNq697qkr2imj1VVrzEtcR6prn4bmwaN6tZWNpSVoZ0ym+vWdf62YYMVJFkMaNx6K4+VfJ96XyVSSoQQpDpzuLvgIZw2fyqXKuzd6grA389NDZGz4NkyLfFCciNHsqH+XdxGK+PiZjA65rygNhYOBdOys1BtSrfrT6Tdzoyc7DCpshhs7Gr6GOgeYNiw4VIEJq4Ol0iThWlLKIwec1bjR6uxTE2cgyokL5b9BZ/sGhAZ0qAg+sxqBYcnJfLq7bfx6IYNbK6oYFhCAvdPn05RetpZabLoirCMG3okqN9yQog44B7gLinl2x3bbgNKgUXAykDHSSlrThnnHqCZ7kGSLqWsDKbmvqCmrY37X3mlS4Fhm6Zx+wsvsPa++4JS01LhPsr/K/4hmqmjS40dTVt4u+pVvjXqhyQ5U85pzDpvBcUtm7ArTsraa9BNrds+EsnOpi0sdF3a21+h39PgdqPalM4VpJOpGYAzubppsrmiAq+uMyUry6qtGuKsKP87NZ4yDDquUxKOeQ7z5rEnuCr7fgCmJc7hg+qVnXUFxzGlZHxcaGsmzoY0Vw5LMu8It4xeMTE9ndm5eawtLe38LnGpKkVpaczJC5R4YWFx9mimt0sbkuNITOanXEZaRCEew82o2AkkOpLP+TxF8bPY2LCKI+378ZkeBAqqUFmSeTeuHuz9A5ETH8ePhlBdqEV4CfZU4BTADrx1fIOU8qgQYg8wmx6CpJMR/jXTe4CnpJSnJrEWCCEqAC+wHviOlLIkWOKDxct79gR0rDFMk7cPHOCqMWc3ExOI/xx5HLdx4uXRpA9D13mh7D/cN/wrZz3eO5X/4aPa5YBEoGBIHbviwGd2nWESKNgHufvecYYlJiICzLCpisL5w/JDrqc3bK+s5J6XXsJrGAj878WfLl7MlaNHh1vaoMSQBpsb1vNJ/Vrsip25yQsYE1PUb1JCpJTsbF53IkDqwJAaWxs/6AySMiNyuCLzBpZX/BdFKAgEpjS5a9iXgrZqbREYIQR/vvIKlu3YyXM7d2CYkmvHjePmiRMGXOq2Rf9lVOwU3qr8T7ftqnAwPn4m2ZEjgnIem7Bx97AH2d28kZ1N64m0RTEt8UIyIvKDMr6FRV8Q7CApHX8KXO0p26s6njsTLgKGAX8/Zft64E5gL5AKPAisFUKMk1LWnTqIEOI+4D6A3NzQ2tXWtbcHXH3QTZN6d+DixbNBN3UOt3XP7TUx2N+6gbcqnyQzYjhjYqefkWPMkba9rK1d0a0nUYTixmdGcOpS/KQzcLYZDDhsNh6cfwEPv/te50yuXVGIcTr53PS+6b7eF3h1nTtfeIEmb9c0h2+/9RbjU1MpSAx98f1gxpQmfz7waw62FXfm2u9u3s685IVcm31LmNX5Od5kNRCnbl+QdinnJcxkd/M2VKEyLm6yFSCFCFVRuHniBG6eePqi9oFGdWsra48eJdrhYF5enuVMFiaSnZnMTr6cdbWvo0tfh7usk4nx5wctQDqOImyMj5sx4OoDBz1Wul2PnNFVSQjxY+C7p9ktWBW8nwU2SCm3nbxRSvnGKZo+BkqAO/DXPXHK/o8CjwJMnTo1pO+A2bm5/GfbNtq1rulqihDMzMnp9fiKUFCEDUOemAVWMIlT3QhgTc2LOBQXq+xJ3Df8Fz12qj/O1sb3AzZttSsOXIpA4neNMaXBbfmfI8Ye1+vfYaBwfVEROfHx/H3DRo61tDAnL5f7pk0jJWrg3CR+cPgwegCXLN0weH7XLr45b14YVA1edjdvp+SkAAn8hcmra97hgpSLSHamhlGdH0UoFEQXUdK6A3lStbZAMCKmu2tanCOBWcnzQ6jQYjDzyPqP+eP69aiKgiIEihD8e+lSJqZnhFtav6PKU8nKyhUcbi8h05XNJelLyI4MbrrlxRm3MTp2KlsbPsCUJhMS5lEQNT6o57CwGIic6dTN74CnTrPPEWAmfgOGZODkOqM0YM3pTiKESMVv+vCF0+0rpWwVQuwCgjvVEQTm5uUxKSODLRUVnSsQEXY7iwsLGZNybvVCJ6MIhcnx09nS+ElnoBRt8yKA41kYPtNDg6+Kdyuf4fKsez91PFOaXW6UTj7P9Tl3IIlAEQrjYicNyRnkmTk5QQluw0Wz1xvQ4VOXsrNJpUXw2Nm0FW9AtybB3pZdzD0pSKrzVrCpfiVNei0joqcwLm7uGVvs9parsu7nrwf+D830oUkvduHEYXOxJPOekJzfYmiyobycP3/yCT7D6OLed9dLL7H+vvux22yfcvTQoqy9lF/t+xGaqWFicsxdzvamzXyh8GuMigluY+S8qDHkRfW+FKA/cLixgVUlJdgVhYsLR5AWHR1uSf0XCXSfQ7Xo4IyCJCllLd1T6LohhNgEaPhT5p7u2JYNjAHWnsGp7sRfb/TMGZzLBYwG3juDcUOKIgSPX3MNL+7ezYu7dqEqCjcWFbEkiPUfN+XcQY23kmOecgQSVbRxapq6IXV2NH142iBpQvxctjeuRjvFdcaUJkXxs4mwWReYgcysnJyA/VYi7XYWWg59QSfSFoUNG8YpxdCKUIi0RXY+3tf8Cc8f/TWG1DExKG7eyNral7l3+C9xKH3f8yPJmcHXRv+ZzfXvU+k5TFbEcCYlXNDpbGdh0Rf8d8cOPLrebbtumnxcVsY8y5Sik2VlT3eZcJFIfKaPp0v/xcPjfxlGZaGlWWtiRcULbG/agkNxcEHKIhakLsYmugfUj6z/mD+tX9/p2Pmz1av5yaKLWDo2uEGlxdAgqEnAUsomIcQ/gF8KIao5YQG+HXjn+H5CiL3An6SUfzppmwDuBZ6VUraeOrYQ4tfACvwrVqnA94Ao4N/B/B2Chd1m48aiIm4sKuqT8SPVKL456mFK20uocB9h5bE/dbPpBc6oUHxYVBET4s/vCJR8KNhQhMKVWZ+zAqRBQFZsLHeedx5PbN2KuyMFNNJuZ2J6uhUk9QEzk+axqvr1bk1NBYLxcf5UNkPqvFT2uy4TE5r0UO87xid1rzE35dqQaHXZopidcnlIzmXRneM3ckOJNs3XY+9Kj97dUfVsafJ4eGH3LvbU1jAuNY2lY8YS63T2etxwUNIauK9QtbeSRl8tmxtWcqB1M7FqErOSryYvavAFAh7DzU/3fJ8Wralz4ml5xfMcbivh3oKuSUd7amp45JNPutWEf/edtzk/P5/kyEgsLM6GvqiU/AqgA//lRDPZ20/pkTQKf0reyczHnzp3aw/jZuNfYTqeyvcxMFNKWRo05QMMIQT5UcPJjxrOnqZVlLbvQZ4UKKnCzqT405eKCSG4OvsBpiYuYm/zBuyKkwnx80hwWL0HBgvfnDeP2bm5PLt9O+2axhWjR7Nk1ChsysDqHTMQSHWlc3ve/Tx55O8oKIDEJlQeKPw6jo5UuirP4YCTGrr0sbPpw5AFSRbh4Wj7EZ4ufYKStgM4FAdzky9gafb1IUu1DCeXjxzFmtLSbjW7mmEyI7t3ac2HGxtY+uwzeHQdj67zenExf1r//9k77/iq6ruPv8+4O3svsoCwlyBLFHAh7lrcWldr7dDavW1tn1rbPq3aoT5qh1U7nAguRASRjewZIGTvndzkrnPO7/kjELjcBEHuTUhy3q+Xrxee+b3JzTm/7/p8N7D4plvIih14vbQu1UVLoId+YUnhucPfwaO70UWAKg5R5N7O5Rn3MiV+cMljr2tYTafmDsrM+w0/O1q2UOutIfW4mY1vHyjscQCzLEmsKCqKWNB6ICMhzDlJJyHsTpIQwgfcf+S/3o4JCZ0JIVbS00SzY/tvCouBg5TPD3uAZ4t+iM/woBl+FNlCsi2L+ak3nPI1spwFZDkLImjl2UuTvxEJiXjr4FV6m5OTw5wIlrJUtrXx3NYtbKuuYkRCIl+aOo1RSZ99rsZAZmrCTCbETeGQuxBVsjA8qiCoNMQi2xGi50Jwm2yWuw1mGn2N/Hb/r/AZXgB8ho/V9Stp8NXztZGnP75hoHHZyJG8vHsXW6ur6QwEkCUJq6Lw0Lz5Z5zx+emKFbT5fN0jODyahk/X+fmqlTx3zbXhML9PuShlAUurX8d/3FB3i2RhRFQirf7DQRL+AeHj3ernmBA7F3UQjOkwhM7GhiWsa/g38RYfXkOlXbNj0BXYUySFss7iICepp9Erp7LPxKQ3TM3NQUKcNYVvjnqawvZPaPHXke7II8919sxlOVup6CzjmcNP0ujrarlLsafypfyvkuHI7GfLBhaHm5q49j//wqtpaIbBnro63j14gGevuZbZw/pWgv9swSrbGBvTs3RzkjWTWGsyDb5KOK74yCLZODfx8j6y0CSSBAwfiqQin9A3saJ2Wcig7oAIsLdtN/W+OpLPAvXDSKLKMn//3HV8WHyYZQcPEWu3cf348YxOOjNRIyEE6yvKQxbDhhB8XFpyRtfuLy5KXUiTv5E1DatQJZWA0JgcNw2PtjdkxtlR6n3lpDsGfhn14orH2N+2EUP4UCRwygHsVo06fzQCCYEgwZoYdM7lIwv4x7ZtIT1vhhBcZJaW947pQPaK6SRFGEMYHHQfoLi1jjd21LKxvI5Ym50vnjOV2yZOCqsTo8oWxsXOCtv1Bjse3cP/Fv6aTr2ze1ulp4Lf7X+ERyf+AZsyMOvY+4Nff7yaDv+xXgNdCDyaxk9WfMCHd97dr7adjUiSxM3ZP+H54h/jNTrhyNyiKfEXMy7mvP42z+QMOOzewbtVT9Hkr0GRVKbEX8olaXd1R/fLPKUhoh4AqqxS460e9E4SgCLLXDJ8BJcMHxHW66qy3GO51UBVzJMlmRuzv8CVGddR56sh0ZpMjCWWfxT/hAZ/ecjxhtBwKtH9YGl4afJVs79tPZo4FkyQJJCEwKn48OouEqxJ5LuCxY3Hp6Zy15Qp/H3bNgK63i0v/9N580kxFe5MPgOmkxRBKj2V/OHA7/BoHjoDAaRkQWxnKuVVKTy6ZjXFLc08NDdc46VMjqfV60WWJKKPlG/oRoAa72Esso1kWw6SJPFJ00Y00YPKkgiwreUTZiaai9VTZWNlRY/N2BVtbbT7fN2/B5NjJNrSeXDUsxR37KJDayXbOYY46+BfIA9majyH+W/p/3QLcmjCz7bm9/Ho7Vw37NsA5DjzKHIfDBH20AyNdHtGn9s8WJAkiSsLRrG0cD+B49Q8rYrCNaPCpyzbH7jUKPLUYw7l7KRrqew8ECT8IqOQ4RhJrPXMx4z0NzXew8iSCuLEWZNgkwyyY8ZwV+59PQaZvzvnfK4aPZrlh4qwKDKXjywgOy6ur0w3GWSYTlKEMITB4wf+l9ZACwBHh4nnZ9fS1u6itd3Fv3ft5P7pM4l3mD0I4aKwoYHvvP8eBxobEMC0jAzun5PE2qanEQiEMIhSE7gx52c0+5uCar2PEjACtPhb+t74swAhBPvb97G/fR/RajTTE2YSY4n51PNibDbc/tCfpSJJ2FTzMdMbsqQwPGpyf5thEibW1L9K4ITB3Jrws69tHR3aPbjUOC5KvYSP61cGOUkWycK42Akk2Qb+Arc/eWjefAobGihuae5WDixITOSHF8ztb9PCSkH0NOal3MTKun+jSCq60Eiz53Fj9g/627SwEGdNQfRQAiajMCd5AZdn3HvS80cnJZ9x+eaQwiy36xVz9RIhityH8OhHB3UK7HIAq6QTkGWy0hpobXdhVRQONTVxbqbZ/xIO2nxebnz1v7T5jkXXCpsOsrz2JRTl2IKkOVDDiyU/Yn7qN7HJtpDBnxbZQp5r6NUv60LnTwef4KC7EJ/hwyJZeL3yVe4f8SBjYk4uLXv3lHP4/bq13cOTAWyKwtWjx2AdoKUuJiYnQwjBtppqWrxezklPJ87uoMFXDj3kVFXJQou/DpcaR4I1ke+N/gn/LnuBIvdBrLKN85Pncm3mor7/EIOMGJuNJbfcypbqKoqamhiZkMiU9PRB2Zt7XvJ1TE24jFpvCVFqHIm2wZOFTLePINGWQZ23FOO40lRFtjA76Zp+tMxkqGE6SRHCo3uQkJAQpFlbscrHFo/xmZ0Ul6Xi1xQyYz49Sm9yaizet4/ACfXoIzIqkKQTVcQEfsODUzFId2RS2VlO4Eha3yJZyHbmUhA9sMszPgvrG9dxwL2/O7sWEAEQ8HTRX/jD5D/2OLjvKHdOOYeSlhZe2bMbq6Lg13XmZOfw83lmOanJ4KOkpZkvLH6VZo8HSZII6DoPzphNetoI6n3lQaMYAHQRIOG4RWyWcxjfHf2jvjZ7QGEIg41NG/mo/iN0oXNe4nnMSZqDKp982SJJEtMyMpmWMfiDj3bFOShnI0mSxG25v+DNisc53LEdgFhLMldnfoM4czSJSR9iOkkRYkTUSDShEad2YpU15OMCWRZFZ964/XibryMjeuA3WZ4tlLS0BGUyAJw2H7LccyrZq7fx7YIfsLz2PdY3rkFC4rykC7g4dcGgjDx+Gusa1vRYfqgLnZKOYoZHddXEN/oq+KTpTZp8lWS7JjAl/gqcagy/uPAivjFzFkVNTWTGxJgBAJNBiRCCu958ncq2tqCc0R83refxy2djkdfiPyLvDV2KhVMSLjUHc58mzxx+hu0t27sz/WWdZWxu3sy3C76NLJnz3QYzh5ubWFK4H78+nwX5X2BMchxOJXZIvpcjjoAeRvaZHMF0kiKEU3VyfdZNrKl/IshBgq7mw7T4Fu6fPriGvvU3k9PTce7dHTSksKIxiZyURixKcIZJFxrDnOOwKTauzLiGKzPMFP7JFh7SkdkUJe7tvFr+MJrQEOhUePbySdNS7s7/I9GWJBKdThLNqeYmg5g99XXUd3aEFNV5NI1Xd1fxi4t/w/vVf6Oicz92JYpZSdcyI/GqfrF1oFLWWca2lm1BQRu/4afIXcTetr2Mjx3fj9aZRJJ/7tjGo2tXo+k6uhD8Y8dWbhw3gZ/NvfCUztcMjTcq32Bl/Up8uo/8qHxuz7mdbOfQHEVhcmaYTlIEuTD1IrY0PYPf6AjZp0gSdtWMhoWTBcNH8Nj6dVS1t3WrG1U1pBPw1+BwutGONFRbJDuT4y8l1lQSC+L8pAso7jgc0qNlla3kunIRQvB29eNBikqa8KPrOqvrX+SKjME/CNPEpN3nQ+4lot3i9ZBqz+P2vF/2sVWDi31t+zB6GLbsM3xsb9mOIqm4FCs7Wt7gQPs6JGTGxs5jfspd2JShGaTxagH+tn0riwv3oUgyN4+fwC3jJ6HKA2edUet28+s1H+E7rmzeq2m8vGcX14waw+S09E+9xrOHn2V76/ZuB/uQ+xCP7HuEX47/JcmmMEqPSKZwQ6+YTlKEGR97Adubl2MEDX6TSHeMHLIP80hhU1XeuOlmHlu/jncOHkSVJRaNHce9E7/Enrbl7Gv9GItsZ1riFYyKnt3f5p51nJswg+0t29nRug3d0FFlFQmJr414AFmScQea6NRCVf8EOkXtm/vB4sFPm8+LJElEW00J9bOFSWnp6EboAt6uqiwcUdAPFg0+otVoFEkJGdEgIbOibiVrGtaTYSvDIhscFcrY2fI+lZ37uDv/j0hDrBxPNwxuev1lChsbugepPrp2NatLS3juqs/1s3WnzqqSw0cqGoIrP7yaxjsHCz/VSWryN7GtZRsBEcAqaciSwGuoaIbGsppl3JZzWwStNxmMmE5ShJmX8gWKO7bToTXjN7xYJDuqbOGqTDPqHgni7A4enn8RD8+/KGj7zKTPMTNp4Lws+gNZkvny8K9Q0lFCYfs+otQopsZPwyJb+aRpK/vbdqP3MFcKMB3+MHOwqZFvL3+X/Q31XVL26Zn8/pLLyIg2+7z6G6fFwk8vmM8vV6/Eq2kIuhyk7Ng4bhg3ob/NGxScE38OL5a9GLJdYGAICYvUjCwdc5CgSxyjOVBFSccO8qKm9KG1/c+HJYc52NTY7SBBV/nnuooydtTWMCk1rR+tO3UUWaanJK0kSSinkBGr8dZgVwRZlnqsko6g62LVvhhKOkrCbK3JUMB0TDNj8gAAIABJREFUkiKMQ43my8OfpLB9PdWeQ8RbMxgXe0HEF5W6YbC2oozKtjYmpaYxNtksLTM5NXJdueS6cgHw6T4e3vMrarw1+Awfw+xWnIo36EVmkWxMSzB7usJFm8/L9a/+mzafr3sJuLmqgkWv/oePvnAPFlNSvd+5afxExiQl88+d22no7ODS/BF8fuw47Kqlv00b0OhC42D7emo8B7gufQbv1G6hU/cjIeEzfOgCQMImB1Ck0BIhw9Co9xUPOSdpc1VFUC/uUXRDsLW6asA4SRfnD+ehVStCtncNBB7zqeenWJNJtVSjSvqRd1TXdyTD1kqKPTbM1g4izHK7XjGdpAjj0TwsrlzK+qbNKMjMTU5mfFxkX6TV7nZueO0/NHu83XXdMzKH8X9XXGPOrDE5Ld6reZ8qT1W3RHqlN5Ysh45D1rArDjQRYGzsfM6Jv7yfLR08LC7ch1/Xg4QBdCFo8/lYVVrMJfkj+s02k2NMSkvn96fQI2Fyavj0Dl4s/iZtgXoCwosq2cixW5iX+i2iLan8bv8f6BRdswf9hoohCBFFUmQL8dbBL/19ImlR0dhVNSiTBGBRZJJdrn6y6vSJszv4/SWX8e3l7yEjYSAQQvCNGbNOaTis16jF0kPCSQISrd7QHSYmn4LpJEUQzdB4eO+vqfXWdddWL6l6hz1t+/jRmO9GTM7ywWXvUN3ejn5cdGBDZTnPbfuEr06b0b2tsLGBZ7ZupqipiXPSM/jilKlDopzHEAblnZVYZQtp9tQhLSuqC42tTW+wvfltNOFnZPRsZifdjlPtirqtb9zQ7SABGMiUeZJwKXBX7g2MjplKtCWpv8wflJS2hkrZAwQMnYr2tn6wyMQk8qypf5GWQA36keeNJnxows/25n9xR/6fGBM7hq3N2xAI2jQ7ydZ2JIzurLaEgkOJYXjUtH78FP3DtaPG8IcNa4O2SYBVUbkkb3j/GPUZWThyFDOyhrG86BB+w+DCvHwyT3Fd0qm1YpVt+I3OoO2SBFoPAlomAMLMJJ0E00mKIFuat9Pkq8cqdyAZCgGhEhABijtKOeg+REH0yLDfs8XrYVtNVZCDBF2Nj//Zs6vbSVpXXsYX33oDn65jCMHehjpe3bebxTfcSn58QtjtOlvY2bKbp4ueI2AEMBAkWRN4sODrpDsGRjlCuFla8StKOragHVGs29n8Lofdm7gz/xmssr3XAbJ+w0JelOkgRYJJqWk4LZaQ8hlVlhmX9NnKZoUQNHs9OFQLDotZEmZy9rG/9aNuB+kYgnpvCR69nRuHLWJf2/7usrtyTzJp9lacig8JmfyoqSzMeAD5JEOvBysJDicvXLuIr7/7Fs1eDwLIjI7hqcuvxqYOvGVegsPJjeMnnvZ5GY4xPXyHQJVs5EedGw7TTIYYA++vZwCxvfkNRriKEYCEoEO3UdKZhC50DrtLIuIkBQyj18yIX++KTgsh+NHK5UHR6oBhoPn9/Hrtap698tqw23U2UO9r4ImDTwbN3qj21vLIvt/x+JTf9uoQDFYafCWUdHzSLY0OYKDh0VrZ1/ohk+IvZ27yBbxS8VrQz0xCIsWeQpLNdJAiwWXDC3h84zoq2o5J2dsUhTFJyZybcfqlRB+XlfCjlcup7ehAkuCy/JE8Mv8SXFZruE036QG/EWBj42YOtBeRZk/h/OTZxFjMIeIncjJFOhmZVHsqv5rwC96rXsYhdxFp9jQuT19AuiMVkFCkob2cmZKWwZo7v0RxSzOqLJMdG9ffJvU5UZYEpiVcy9ampQREV3mdIlmJsaQwPu6iTznbxCSUof1UiSAH29fhDuxAPq651KX4yHE0Uu0bRpItMSL3TXa6GBYTS1FzU9B2iyyzcHiXPG27309lD2U7AthYWR4Ru84GVtV9jC6CpUUFAq/uY0/rPibGjafGc4C9re+jG34KYi4g2zV10Jbj1XgOdg+JPZ6A8FLRuYtJ8ZdzUep89rTuZV/7PnRhoEoqFtnC/SO+2g8WDw2sisLr19/CYxvX8fbBAyiyxKLR47h/+szT/i7ua6jny++8GRQQWXb4IC0+L89f/flwm25yAm6tg4d2/4qWQBs+w4dVsvBG5Vv8ZOx3yXWZwy2PZ3zsJWxueh1dHB+QkUl3jMKmdPXVJFjjuSXnpv4y8axHkqRTrgRp8/l4t+gAzV4PMzOHMSklbVC86y5IuYt0xyi2Ni3FZ7gZFXM+U+KvwiLb+9u0sxOBWW53EkwnKUJsbnwFQXDaV5YgSvXi1BQmx51+KvlU+cMlC7nljVfQDB2fruO0WEhyOPnGjFlAl1xtb8MQY2yD90HS5G8OcZKgy1FqCbSyqeE/bGx4CV0EEBgUtq1iePQsLsv4waB4eZxIjCW5q1j7hOejIh1rflYkhW+OeoBidwmH3EXEWWOZHDcJi2yWbEWSOLuDh+dexMNzzyz6+ey2T4IGMwL4dJ2NlRWUt7UyLMZUfIokr1UsocHf1P3c8YsAiABPFf2V30x8OOR4IYwhN+PnKLOSb6Sscwf13mIMoaFIFqyKkyszv9vfpg06tlRXcseS1zCEwK/rWBWVeTm5/GnBlacktd1XCCFYVruCt6reo01zk+3M4rbsGxgd0/s8MkmSKIg5j4KY8/rQUpPBiukkRYgOrbnH7RIKXxtxF6ocuR/9xNQ0Vn3hHl7Zu5uS1mamZ2RxxciCbnlaq6JwdcFolh7YH7SAcqgq90w+J2J29TcTYsexuWkLPsMXtN0QBsMcCSyt+HVQFDMgvBS1r6eicwfDXJP72tyIM8w5EZeSQKtRjeDYcEwZlYlxC4OOzYvKJS8qt48tNDlTiluaMHqIEloVmcr2NtNJijCbmrb0GJip8dbRFmjvLrsrdW9hZe2TNPvLsclRTE1YxPSkm4aUw2SR7dya+3vKO3dR6y0i1pLC8OjpKJIZkDker+5jR8tu/EaAiXFjibWcntiSbhh8+Z0ldBzX8+jRAqwqLWHJwf18btTYcJvcI6tKi3ls0zpK21oYlZDEd2fOYVp6cDnxaxVLeLvm/e5y7+KOUn5T+AQ/GfMdhkfl9YmdQ4LQ2dgmRxg6T+A+Js81FZnQHhe7Ymd49PiI3z/J6eQr06bzm4sW8PkxofM7fjH3Is4bloNNUYi22rApCovGjOOOSYPXSZqeMJVUezKW4166NtnK7KQZdGglyD2Wnvk42L42ZPtgQJJkbsr5HVnO8ciS2pVBsmRyfc6vibJEphzUpG+Zlp6JpYfIsF/XGZkwNH/HzV4Py4sPsbmqokcHMpyovfQ5CkR3D2RV516WVPycZn9XqbPPcLOp8d98XPdcRG07G5EkiWzXRM5N/BwFMeeZDtIJ7G7dz1e2fIeni57nb8Uvcf/WH/JudehcoZOxq74WrxYqbuDRAvx3765wmXpS3jlUyH3vLWFHXQ0tXi8bqyq4bcmrbDiu3N9v+IMcJACrpBGnNvBGxZ/waK19YqvJ0MbMJEWIGUk3U9i+Gp/eiUFXP4Aq2bgw9atnRYOpw2Lhr1d9jsr2Nira2hiRkECiI7IDbvsbVVb56dgfsrx2BRsaN2OTrVyUOo/ZiTMpbFvZY+mZhIxFGrwliFGWRG7M+R1evR3N8ONSEwZlaeHZwsbGLSytWkZboJ0JcWO5LvNKEm3xEbvfPZOn8vLe3egBf7dD4FBVbhw7YdD/vffEX7Zs5I+b12NVFAwhiLXZefGaReTHRUbRc17y+Sypfge/cWxRKiNTEDUcl9r189/Q8EK3uuRRNOFjR/NSZiXfjkV2RMQ2k4GFV/fx+8K/4D2hEuI/5W8wNqaAHNewU7qOODKQt/d9kUUIwS/XfhQy08mraTyybjVLrr8VgGZ/C9JxdqZZW8iyt3QJYYlm/lZ0C5emf5+RMRdE3miTIUv/r9YHKdGWJO7Ie5rNTa9R1rGNaEsy0xOvJ8s5ISL3aw20UewuJ84aS44z85QXupnRMac8g6CvqWhv5amtm9hUVUF2TCxfPWcGU9PPbFCgXbFxVcblXJURPPw0L2pGj28IRVIZG3fxGd1zIGBXoukh8WkSRhZXvsPiynfwHYmMflS3lk1NW/ntxJ8Rb42MElVaVDRLbryN363/mLUVZcTa7NwzeSq3jp8UkfudzawtL+XPn2zAp+vdZcadgQB3Ln2dj267JyLBgSszFlDoPkhh+yEQAllSiLa4+Mrwe7qPafSV9niuhIxbaxqSw1GHOu5AA2vr/0qJewOqbGdi/NVoIr/HYzUjwOr69dx+ik7ShJRUrIpMxwnJJIdq4YYxka9y8ekatR3uHvcVNjZ0/zvOEttdBu6Q/WTaW44TwhJowsey6kcZ5pqMXTk71zADBckUbugV00mKIFGWROan3tvr/s6An511tcTYbIxJTP5ML2khBP8qe4N3qz/EIlvQhU66PYUfjnmAOOvAfXCUtrZw5Ssv4An40YTgYHMj6yrL+N8LF3LFiFFhv59NcXFV1s9YWvEwEjICgYHGBan3kmjLCfv9TAYfHq2Vso5PUCSF7KgZWI/LAHh0L69XvB00mFfHwKt7eatqObfnXh8xu3Ji4/jzZVdF7PoDhX/u2h4ypFcADZ5OdtfXMSElNez3tMgWfjD6mxx2l3C4o4RkWxITYsciH9drlGTLw601hJwrMIhSh2ZJ5FDGp7v5d8lX8OhtCHQw2tnU8CJRlhE9Hm8gQrJLJ0OVZZ5ceDV3L30DIQQ+XcOhWpiZmcU1o8aE62P0ik1RcVkstPv9IftSXa7jjrNxccp8VtStItHShHximQddgYTD7g2Mjb00ojabDF1MJ6mf+M/enTy85kMUSUYXgvSoKP5xxedPe7bBusZPWFazioDQCByZg1TeWcVjB57h4fHfOW27dKGzonYNK+vWYgiDC5Jncmna3D5XM/vfTWvoOK5ECMCjaTz08QoWDi/oVZ3vTMiJmsqXC/5LsXszugiQ45qKUx16syZMTp/dzUv5uO4vR/oQJQQGCzMfJufIAMOKzipUWSGgB4dvNaGzp21fP1g89Gj1eXvcrkgS7f5TX2R+FvKjcsnvRfhkVvIdVHTuDCq5UyUb5yRcZ8oWD0F2t7yD3+jscpCOoAkf7YGDqFIGJ7aS22QbMxJOr5d4ZuYw1tzxRd46WHhEAjyb6RmnXoFyJkiSxL1TzuXJLRuDghYOVeWBabOCjr05+/M4VQdbGv/Z6/UMofW6z+QUMTNJvWI6Sf3A9tpqfr7mw6Ca3JLWFm5f+iqrbv30sg93oIENDX+jxL0et+Yj2RJFhS+eo3XGOgZF7hKa/C0knEYZjxCC3xc+za7Wwu5myaryN9nUtJ2fjftWUPQz0myoLO+xqbrD76fG3U5GhEoELbKDArPGuV9ZXV7Cs9s3U9fZwbxheXxp8jSSnK5PP7GfaPaVsabuSXTh53gds3crf8ZdI17GpkQRb41FM0JVzoCIzUz7rNR466nsrCHDkUq6I6W/zQkblw0vYEddTUg2STMMJqem95NVkOYo4HPZv+Kjmqdp8BVjV2KYlngD5yRc1282mfQfVZ7dIT1qAAoqF6dM5J3aAwSMAAKBTbYxOW48E2JPX5EuweHkCxOnhMPk0+ZrU2fg13X+un0LuhBYFZkHp89m0ZhxQcfJksznMq9kZkI+r5d9N+TnItC7SuVNTCKE6ST1A8/v2obvhBe1IQQNng6219Uw5SQvbJ/u5uWS+/DorQh0VAmyHT5cipfCzozu4xRJoVPrPC0n6ZC7hN3HOUjQNS2+pKOcbS07iFGa8entZLqmEG+N7CDERIeD+s6OkO0GYlDPchrq/GPXVn6zYXX3Qra4pZnXD+7l3eu/cNY6SvvblqP3EM2UkChxr2dU7CUk2RIZFT2C/e0H0Y471ipbuSpjQV+a2ysBI8BjB/7K9ua9WGQFTeiMjSngO6PuxaZY+9u8M+amseP5795dlLQ249E0ZEnCpij8/PwLcVr6V0UtyzmRW/Of7FcbTLoob2uhsKmR3Ng4RsT3fQAjwZpNCZu6BZ+OIjCYl7KA6UnXsrp+HT7dz/TEc5gUO27Aie3IksS3Z5zH/dNm0uL1kuBwoJ5kPlO6Yyzj4hayp+VdNOFHRkaWFOakfBmXWZJqEkFMJ6kfaOjs6KG6tisN3eL1nPTcvS3v4Dc6glLxiiRItHZg9/rxGl2LGVVWSXecXo191wIuNNqtSq1sqvshqiRjYEA9jIq5lLmp34zYw/krU2bwg1XLgqK+NkXh0ryRRFkH/oLNJBRPIBDkIAH4DZ1Wr5e/7tzC92eenRk+zfAFzZk6isAgcFzk85sFX+bPh/7K7tZ9yJKCKincmXszo6J77jXoa/5TtpQdLXsJiEB3WeCe1kJeKH2dL+bf1M/WnTl21cIbi25h8YG9vH/4EElOF7ePnxyRXiSTgUdA1/nGB2+zorQIq6IQ0A3OScvg2YXX4rL03TtnQvxV7Gh+M6iMTEYlzppFir0ASZLIc0UmSCmEYE3DZpZULact4GZS7Biuz76SZFtk1B+tikKK69SCX/NSv87omIspal+DIlkYFXMh8bZTE6swOQkCMMxyu94wnaR+4OK8EXxSUxlS9hHQ9ZNmkQCqPLt6TMULJKIUH37Djior3Jt/W/ccjlMlzhKLRVJPGH4omBBdiUAjcNzf0YG25WS7ziU/+vzTusepcvXI0ZS2NvPktk2oskxA17lgWC6/mW82aA5WCpsaUHoo6fQbOqvKis9aJyk/+jz2tLyFJoJ7XgSCHNf07v93qk6+N/p+2gLtuLUOUu3Jp/03Gkk+qF0TJFUNEBAaK+vWcU/ejd0BkQ6tkXW1f6SkYy0SEvlRc5md+sCAUJiyqyo3jZ3ITWMn9rcpJmcZf9qygQ9LDwepH35SXcnPP17B7y5c+Clnh48YSyrXZf+W5dX/S4u/EiTIdU3nkvTvRDxj9N/ypbxVteKYAmf9RjY37+QPk39KvPXMBk/7dD+fNO+kQ/MwMW40afbk075GmmM0aY7Rve7fUlPJ/25aw4GmRvLj4vnWuecxKzOyVS8mgxvTSeoHbhg9nhd2b6Oiva27L8mhqtw/bRZx9pPPxIi3DqMUNSQVb5EUMhwjGB+Xz8L0i8h1ZZ22XecmTObvxf8N2hateFGk0Ci5JrzsbX0rYk6SJEk8cO5s7pk8jcPNTaS6okhxRUXkXiZnB4kOJ5rR8+jvtLP4d5/hmMjw6PMpav8YTXiRkFEkC1MTbyXaEtrTE2OJJsYS3Q+Wnpzjy2yPJ2BoCAQSEprh443S++jUGruz2UXtK6n3FXJ97j+Q+9HpE0IgEH3aOzlUcfv9LCs+eKTpfxjjkwZ+Nu6lvTvw6sHvVb+h8+bB/Tw6bwHKScrBwk2aYwy35/8Vr96GIln6ZFZWh9bJksrlBI7LYBlHFDiXVC5mQoxEh9ZIlnMaOVGzkE9j3uOB9mJ+ufdPCCHQEQghWJA2hztzF4XN8VtXWcbd777evaZqrOnkrnde58lLr+LCnOFhucfgRJjCDSfBdJL6AYfFwpuLbuPfe3by3uEDxNsd3DHhHOYM+3Sp6Qnx17CreXFIKj7Bls1Xc39+Rg8cu2LjoXHf4veFT9MaaAMkYi0yVtmKLkKVoXQjdGp3uHFZrExISYv4fQYChhC0+324LNaT1m8PVIbFxDIhJZVttdVBzpJDVfnSpHP70bKTI0kSF6f/gFGxl3Co7SMUycLo2EtJPUnE82xkTMwIdrUWhmwfGZXX7XgUu1fj09uDyn0NNDoC9VR0bCY7amaf2XsUr+7j+ZLXWVW/gYChMSo6j3vzbybHZc4XigTba6u5/e1XMITAr+uosswluSN4/KIrIqI62ld0BnoOEmjCQBMGCn3/zO3L7GxZZxUW2dKtknuUGLWVTt8/2NKgYBDgQOsyEm35XDnsMVTZdtJrdgTq8BpuHtn3JJ168Bpiee1aJseNZUr8uF7OPj3+Z93K0AG1usYv1q40nSSTz4zpJPUTLouVL06exhcnTzut86ItqVyd/TtWVP+WNn81ANmuaVyU/v2wRGRyXVn8ccovqfLWYgiDdFsi/zj8efQTAg2qZKcg9pIzvl+jr4X3az6m3FPDqOh8Lk6d1T2J3uQYrxbu5tcbV9Pm82JVFL44YRrfmDZ7QC9KeuL/FlzDfcveZEddLRZZxhCCH82ey+yss7tkQpIksl3TyHad3t/z2cRdeTfy412/JWAE0ISOIilYZJUvDj/Wj9ToK0IToX2TugjQ7C8hm753kh7d/zT724q6I+D72w/z492/54nJD5FoMyX8w4khBPcuWxw04yZgGCwvKWLJwX1cW3D6KmtnC7Mzs/mw9HBIv/DYxGRsyuBfKiVa4wkYJwrQCMZHVSJLRlc/MqAJDw2+Q+xrWcqEhEU9XqsjUM8H1T+hyXcIgcT0GJ2d7ZnUB445fT7Dzwe1a8PmJB1oauxxe0lbC7ph9Gkm0GTwMPj/8vuBVp+XF/Zs46PyEjKiYrhn4lQmJp88G9Lib6Oko5IkWzxZzpMfm+4Yx235z+PRWlFka9DQynAgSRKZjmM2XJz+Y96vehhDGBgEsEgOUhxjKIg5MyepyF3GT3Y9ji50AkJja/MeFlcu53eTvk+SLf5MP8agYVnxQX665oPuHraAYfDMzs0IBN86d04/WxdeEhxOXr72ZiraW2nyeChISMSu9q/y2FBhmDOdx6f8jHerV1HkLiXXlcXl6fNJOq5pO8Gahyo5QhwlRbIQZw3/0GWP1syOphcp7ViLTY5ifPyNDI++uDsgVN5ZTWH74aASIQDN0FhWs5pbcq4Ou01Dmd31tXT0kHHxaAH+s3/XgHaSfjJ7fnevsF/XscgyFkXhV3PPPBg4EEixJzIqJp/9bUXdCpzRihdZCi3F0oWPg23v9+gkCSF4p+JB2gIV3YI2Vhkmx5SxrmUEHfoxdVpfGKtREp1OajvcIdtjbXbTQfo0zHK7XjGdpDDT5Onk8tf+SbPXg0/XkaUq3i85yG/mLuDqEaHTrIUQ/LX4Fd6vWYtFVtGETr4rmx+PvY+oT8moONQza6Q8VXKjZnFz3vMUtr6PR28m2zWdbNd0pNOo/Q8YHkrcq+jUGkixjyPNMYU/H3wxaFK4zwgQMHReKHmTb466MwKfpO8RQrCtrpri1iZGJSR/ptr9xz5ZGyLy4dE0ntu5hQemzh6UpXdZ0bFkRffN99vkGAnWOG7NubbX/fnR89jY8Ay6dkzRT0bBqSYy7DiRinDg09t5o/RuvHoLBhrtwJra39LoPcCMlK8BUOWpPSJ+ESo4UdxRHlZ7TEAXXb1pPe7rpZ9woJAXF8/ym+7mn7u2sb2umtGJSdw54Zwh9Rz6zqgv85dDz7OteQ+yJONUZKySikGoY6zIPSv+1Xv30qHVhyh+yghy7I3s7egqg7XJVuYmh++Z8fUpM3hkw0chA2rvm3z2lmqbnP2YTlKY+b8dm2n0eAgcGRxpCIFH0/jJxx+wMK8AixLc2Ly8dh0f1K4nILTuWuCSjkP85cAf+fboB1HPkonr0ZZUpiXd/pnObfIV8U751zGEhi58KJKNBFsBlT2onRsYbGnefYbWnh20+X3c9vbLHGruKgMQwISkVJ6/fBGO08iOVLrbe9yuGQZuv+9TxT4GK5ubdvF88ZtUextItsVzW85VzEk+vcnzJqeHKtv4XPZTrKl9nLKODUhI5Eafz5yUB8Mu2rC/9U18RnuQSE2XYMzrTEi4GaeaQJYz/QQ1zi4sksrwqPBntgYyQgjaA34cqopF/my/qwnJqVh6CMo4VJVFo8afqYn9TorTxXdmDK7s/OngUh18b/R9uLUOOjUvCdY4Xi65lfZAddBxqmRnTGzPWdpOrbErgHpCckKWwKl0/S3bZRujY4YzOyl8z+vbxk2mze/jyW0bMY448/dMnMqXJ4c3eGMytDCdpDCzorSo20E6Hl0YHGppYkxisOzl0qoPu+U2JQwmRlWQaW9BiN28VLScSQm3MynhjgE3LO4oQgg+rPopfuPYQl8THhp9+8mxJ3HIkxRyjrWXCNVA42drPmBfY33Q92FHfTW/3bSan82+6JSvMzohic01lSHbXVbrkB2su6lxF78r/Fu3ZHW1t54/HnwBTWjMSzFfipEkypLCZVmPII6UaETq2VTZ8Ql6D+MOZMlCo+8ATnUmmY5UxscUsKv1AAHR9V2QAItsYUFaZJQ3ByLLig/w83Uf0uDpQJVlbh49iR/OnHvazpIqy/zl0qu5593XMYTAp+s4VQtT0zK4bgCX2p0qAUPj4/otrGvYQbTFxcL0ORREDz5nPEp1EaV2zS9akPkrlpY9iIGGcSQgkRc9l5ExF/d4brJ9DIYILaOTsZHpnE6SczTT4sczKW5MWJUoJUnia+fM5EuTzqXB00GiwzkkesnCgllu1yvmNyjMxPcS1dcMg1hbqBJMh9bZ/e9xrioy7S0okgBJoAsfO5texKmmUBB7ecRsjiRurYYOrTZkuy58jHS5KfGmBg2wtUoWFqSd15cmRgRDCN46XBjiMPt0nVcP7DktJ+n7My7gtrdeCZKndagq35t+/qATbjhVni9ZHDLTx2cEeL7kzSAnqdFbyNbGp2jw7selJjMx4S5yoy/sa3MHJZEO3ERZ0pA8ckjZjhA6TvVYcOW7o+/lX6VvsqJuPX7Dz7iYkdyTf+MZz3UZLGysLucbH77d/fwIGAb/2tcld/3I+ac/d252Zjarb/kSbx7cR5PXw+zMbM7LzB6wgbxTJWAE+MHOJyjrqMZr+JGQ+Lh+C3fnf47L0wevQ55oG87tw1+jtGMDHq2JDOdk4m25vR7vsiQzKuYqDrS93T07TsaCU03gutzvYpXDK8y0p7GWv2zbQGFTHblJMvNzc1iQOdV0kEzCgvktCjP3TJjK7oY6PNqxBZwqSUxITiUjKlTOc0r8WFbVbUKgke1o6nKQjkMTXnY2vTBgnSQheq9Tj7fGkO3MoMpThyR1ZdsrM6uLAAAgAElEQVQmxY3muqwFfWhhZBBCoPfy2f36iQpCJ2daWiYvXLGIRzeuprCpgfSoaL45bTaX548Kh6kDkhpvQ4/bm/2t6EeU2Zp8B3iv4ivdL2q/v401tf+DV29mdNzn+9Jck8/AuPhFHG7/IGh4toRKrHUYibYR3dussoU78xZxZ17PSltDnT9uXR8y/8era7x2YDc/nDGXaGtw8M5vBGj0tRJvjcGu9JzVT3a6+OKkgavk+FlYVfcJpR3V3ZUfAoHPCPDXw28wL3kadsVKmfsjtjW8S01HANmYzZX5l5HZw3t/oKHIVvKjT32Y96yUb5DsGMOe5lcJGB3kRF3AxIRbwu4gbaqu4I73XkFSOsnJrqMawb/KdvBa1Vtcm3khd+SZwi2figAMM5PUG6aTFGYW5I3k3sZ6nt6xCausoAmD/LgEnrrkmh6Pvzn7Sj5p2o1muHtphwWv3hw5gyNMtCUDl5pMW6AiaLsi2RgVewWLcm/loLuUWm8Dua4shn2Kst9AQZFlpqVmsrmmIqg0W5YkLsjKO+3rnZuexWvX3hI+Awc4Sbb4Hh2lGEvUkUZ+2NrwTNACG0AXXrY2/h8Fsdec1jBEk74n0TaC+ek/4+Pa36IZPgQ6yfaxXJj+cH+bNqAoae35/aHKMnWdHd1OkhCC/5Qt49XyDwAwEFyePoe7869BMQf0srZhe7eDdDyqpLCn9RAtnr9S3rENRQ5gt4AmtvD9NSu5ueAbXJE/sGamnSmSJDEyZgEjYyIb8Hxo3Qd4tADTR5SS7mjDY1io8cWgCcHSqo+YGFfAlPih9bM3CS/mKiHMSJLEN889j7smnMPuhjqSnU5GJST3enySLZ4/nvMT3q5ahdtbiEzo0NZke3jmCPQHkiQxP/0XvFvxAIbQ0IQXVXKQYBvB2LjrkSSJguhcCqJz+9vUsPPIBZdy3eKX8Os6Xl3Doag4LBYemh1c7rW2YQcvlrxNva+ZbGcad+ZdzcS4kf1k9cDg1uwr+fOhl4IkZG2ylZuyj2VcG337COkeBjoDHjbV7mVm2sS+MNXkDMiJOp9hrtm0BSqwyq6gMjuTU2NichpV7jYEICFIcbURMBQ8gXgyo6K7j3unei2vlH8Q5Ai8W70Wh2LjttyBWckQTqJUBxKhTxSBwKftodqzA0Xueh7JMljRmZe7i5+uXcy8Yd/CZRkcvbZnC4YQFDbVcf24jYxNrKbrNyGhC5mVTaNp12FZzVrTSTI5I0wnKQJUeRp4veJDDrnLyXNlEuW4kExHSq/Hx1qiuSXnKkraY1hZ/TCG8CNJYBggsDA65u4+tD78JNoLuCHvNYrdH9Kp1ZNiH0+Gc9ppSYgPREbEJbLqpi/y8v5d7GuqZ0JSMosKJgap0X1Qs5EnD73cvdgvbC/lZ7uf5uHx95mO0km4IGUaAaHxQukSWvztxFhc3DhsIQuPa9aPUtN7ycIK7lu+nA03jzFnMA0AZEmJyAymocI3ps5mVXkxGdEVLBq7GYusIUsgk4TfuA47XYOaXyl/PyRT4jP8LK5cya05Cwd9z9GnsTB9Dhsad4bM9nEodgxjH4JQkRFdyOTF17O+qoyLc0aE7Df57MiSxIyMSkYn1KDKR0vbBYowOC/+IO81TMCrh2b+TE5EwEnaIoY6ppMUZg65K/ju9j8SMALoGBxqr2BV3RYenfR1Rn2aCo4xkZd2zWJ65l4SHB1Utsezrnw0S/ZvZem1AzebtLz0IL/95GPK2lvIi43n+9OSyHQNbgfpKAl2J5cNbyUt7hk6tTo+qIphXMJdjI69CYC/Fy8Jeen6jQB/L36Tx6Z8pz9MHjBclDqTi1JnEjA0VEkJWcRNSrybDyp/iCQd+/n6dZmt1bn4DZUVZYe5Ygj3dZkMDUYnJPPSFRexo+UrqPLxvUl1vF/xVa7LW4wsqbT4QwdxAvh0P5rQsEhDO6AwLnYEt+Rczkulb6NKKgKBXbbxi/Ffpcr9EoaQehy8GtBVs1wxQlySX41VCRZHkiVwygESLToXmCMhTM4Q00kKM08dehW73MzMuDKSLW46DQu72rN48tCrPDHl2yc99x97t1HcksDB5tlB29t8jexprGVc4ukPIu1v3inez7c+egfPkcbhfU313LdiMX+58Gouzh78kbUK90dsrPsV+lHxAKOVnY1PgzDIib6OtkBHj+eVdtT0pZkDGovc82MsyzWb2vYriLa/g1XREEhsqc5hWdEEbKqgzR8a+TU5e9jTWMuft6+nsLmBsQkp3D951klLl016R1bWYZU5USeQgNFJdedmMl2zyI/KpLC9NOTcFHsCFnloO0hHuS7rYi5OncWe1kO4VAfjYkegSDJRylUcbHsbccLQVSEkSlpSmZWR3U8WD25SnDZaekkW5bnSuCB5aImLfGZMCfBeMZ2kMFPt2cdlSbtRJANZAruiMTuuiB1tfoQQJy1ZONzaiNZD2lOWJCra2wakk/SrTR91O0hH8eoav9q0akg4STuanu52kI6iCy+7m/9OQeyN9NQzA13NwEdp8R1kX/PztPoPk2Afy9j4O4myZEXS7EHDuSmL+NoKC5LkxqtZ0EXXz9UQBnMyzRKus5WN1eXc8f4reDUNARS3NvNB+SH+tfAmzknJ6G/zBhwdWm3QUN6jCAy8etew69tyLueh3U8jTngm3ZZ7RZ/YOFCIsbiYlTQpaFuifTRTk77C5vq/4D+S2BBC4tW9F/DkxddhV4fWUqvR08mL+7ezpa6SkXGJ3DH2HLKj48J+n/zoS9nRVBYyT82qOPnRuB+hfsahySYmRxlaf7l9wOToym4H6SgW2WBSTAUGARR6b96ckTaMjdUVIXKtAUNnXGLvPU1nK0IIKtytPe4rbRu4in2nQ8cJk8qPohmddOjtGHCkofoYArqd5drOT/i4+ptHXgKCNv9hytuXc1HWc8TZzJ6lT2NeVh7npmWxuaYS/ciAQ6dq4bYxkxkWbc7ROVv56frleLRjz0EDgUfTeHjDCt68+vZ+tGxgku6cTpl7JZrwnLBHkGzvEjBZXb8TgYwh9G6BAllSWNewm/kpXRF5IQQ1nRsobl+KIQLkRC8kyzVv0PeXHo9fD3DIXYlLtZPtTO0OfI6Lv5HhMQvY3vARextbcSkTeO2qMcQOgoHfQgg+qa3k7ZL9qJLMNcPHMiGpZyXaCncrV775Tzo1Pz5dZ11VKf8q3MGLl93A1JTMsNo1Ku56Stwf0OIvxRBedCEhhMSa5lxstjUsGjY/rPczGXqYTlKYybT7epTyVmWZjkANMdbe0+63jp7E3/ZsIeDV0Y+kPx2qyuV5o8gagAs6SZJIsjtp8HaG7Et1RvWDRX1PtGUYzf4DIdutcjQWyUmX3lRw5FYANqVLlndL/aNBmSiBjiY62d7wOPMy/xJJ0wcEnZqXGm8TybY4oi2hMzhkSeJvl36et4sLWXxoL3ZV5eZREz+TDLtJMC0+D0sP76fO4+bc1CzmZOSGZbixIQSFzT3PwdrdEDqY2uTTyYm6kD3NL9IeKO+OuiuSndzoi7vfSavqt6IJARxzeAwhWNuwC0MYyJLMtobHONy2GP2Is1XTuZFS50zOS/vNkBB2WFH7CU8ceBUJCV0YpNnj+eWEL5HuSATArsQxM/UaZg68oo+T8tD6D3jl0G68WgAJeGH/dr42aSYPTJ4dcuyvN39Eq8+LceS9FhAGAc3gB2veY/l194TVLlW2syDrWb63/QGi5Cr8howqGQx3VrKu7lkKotOYGDcmrPccdJhzkk6K6SSFmWR7Hg3eHSHbFUnCoSb0ep7f0Gg32nnlyht4ascWVpQVEWWxcue4Kdwx9uxoPixta+b9soPIksyCnJFkRX264/aNKefxyOaVQVFhh6ryrXPmRNLUs4bJSV9jdfX3gsoBFMnOxMQv41BtTIkfxfbmA+gcaz61ShYuSZ2OZnhxB8p7vG6Dd2fEbT+bEULw3OG3WFz5MaokEzB0Lk6dxgMFi0JKLFRZ5prhY7hmuPmyDBdb66q4fdnLGMLAo2s4VQsTEtP454Lrz3jSvSxJRFustAdCmw1ibbYezjD5NBTZymXDnmV/y38paV+OKtspiP08+dGXdR/T2/DrowPB2/wlFLW9jnHcs0wXHmo6N1Dn2UKqc3D0f+iGl/KOD2jy7ibakkNO9BVYlRgOtVfyWOHLQUI7ZZ11fH/HUzw/48eD1kncXl/NKwd3dZfNC7pK5v+8Yz3XDh8bUka3urK420E6nsOtzbT7fSHDi8+UwvYKDnfGEiUHWJi8ExmBKgtyjQZ213+FUdFvYFPiw3pPk6GD6SSFmQkJ9xxZFB+L/iuSjZyoS7HIPWdPXi9fzd+K3wVAM3QuSJnIhvPuxar03Czr0QK8W1pIbaebKckZzEgdFvEH9NM7N/LY9rVdfVXAb7es5qfTL+S20ZNPet7tYyajCZ0ntq3D7fcTY7PxrXPmcH3BhO5j3JqHJl8bafaEXj/zQCXdOZPz037DtsY/0e4vw6GmMDHhS+TFLATgWwW38J0dT9AScKMLAxmJ4VFZ3JpzGbKkIEuWkHpr6MpEDWY0w0OF+31afYVEW/MZFr0Qi+zq3r+4YjVvVq7BbwS6W6U/rNtClMXBvcPNKeuRRAjB11a9SYd2zInp1ALsaKjmpf3buXvcmS+W7xw3led2bQ7qZ3SoKveMP/eMrz1UscgOJiTcyYSEO3vcPyNhbFfW6Dh5BxmJyfEFyJJMbeemHs/ThZfqzrWDwkny6c18UPEFfHozuvCgSHb2NP8fF2b+jSVVmwkYwaXwAkFLwM3ethLGxQ7O7PT7pQdDWgAAJCQ+LD/MnScEcZ2qpUdRHEkCqxL+HqFOzYuExNyEQqzyse+uRTYwRAd7mp7hnOTvh/2+gwpTuKFXTCcpzKQ7ZzA9+QdsbXgCTXQAEnnRlzM1+Vshx7b7i1lb+3f2NO4gUY2nwhcPSHxcvwtVUvjemJtDzilsrufGd/+F39Dx6Ro2RWVSUjr/uOTMI7i9UdTayOPb1+I74UH5y00ruDArn4yomF7PlSSJu8dN486xU/FoAZyqpduhCxgaTxx4jQ9qtqDKCkIIbsu9hJuyLxxUUbkM1ywyXLN63Jdgi+XZc3/C9uZCqr2NDI/KZHR0bvfnz4u+muL2JSGZqIK4W/rE9v7Ao9WxsuJ2Akb7kYWKg71NTzI/6wVclq6a9pcrVvYw0yXA0sq1fDH/SuQ+7JHQDYPVVcXsaqwhMyqWy3NG4RjE85cOtTbS6gsdeu3VNV49tDssTtKDU86jwdPJ64d2Y5EVAobOjQUT+crEGb2eU+NpwqP7yHalmpLLn4GvjLiOvW0ldGgevIYfu2zFplj5xsgbAFBlFzIyJ+abZFQsgyRos6vxz3i0OsQRkQtdeNGFj811P6fRN6fHDImERGugZ/n0wYBNUVAkOURUSpYkbD04PbePmcKftq8PcqwsssKCnBERWaOMjc1DlTxEqaHPJFkyqOz40HSSTD4zppMUAfJiFpITfSk+vRmLHIUqhzZulrQtZnvDo2iGn7FRggJXBaWeRJY1jsNvBFhZt42vj/wcTjX43K+tepNWv7f7Ud2pBdhWX8Xf927hvgm9LyDOhHeKCwkYPZViSLxfdpA7x0791GvIkhQycfzpQ0v4sHYrAaEROPJAfbFkOUnWWC5Mm0ilexl1nWuxq6nkxnyeKMvglFFVJJmpCT2Xgk1OfhCf0UJlx0cokhVd+MmLuZpRg9hJ2tnwe3x6I+JICaIuPOjCx7b6/2FOxlMAtAVC+9yga8aUZuhYlb5ZJHcE/Nz43r8obmuiUwvgUC38z+YPeXXhrQyPTewTG/oaCakXTUbC0pMEXSWSj85ZwA/PnUt5eyvDomN7bYCv9Tbz011/p7SjDkWSsMoWvj/mJmYljQ2LLUOFRFssf5v+Yz6q30ZRewU5rjTmp0ztfgdlRc1la/1vQs6TJJnc6IV9bW5EqOxY2e0gHUPQ7CtkRsId7GgpCgnOaEJjTExuWO3Y1VjDu6WFqJLMVXljGBmXFNbrnw5X54/hyZ0b0fTgNYAhBJflFIQcf9+EGexvquf9skNYZBldGIxNSOWR2Qsodlfj1rwURGdhC1PViEt1cHvOFXR61/a4X5HMEl2Tz47pJEUIWVJwqD0/2Px6G9sbHsUQvm4VPItkkO1oJMfeSKk3CQmJdq0zyEmqcLdS7m4NWaB4dY1XDu2MmJPURU/LohMlB06dgKHxbvXGkEGqXsPPf8reQ9F+Q6dWgS48SKiUtP2XaSm/Jc11wWe848BEkazMTnsEj9ZAR6CKaGs2NiX8UqpnEzWdH3U7SMcwqPNsQggdSVIYFT2MXa2HQ85NdyT1acnmX3au40BLA36jy95OLYBHC/Dgx2+x9Mo7+syOvmR4bAJJdiflJyhXOhSVm0ZODOu9Ym32k6qDGcLgW9ueosbT1B3l9+h+Ht79T56d/m2GOY/NVeoMVFLU+i9a/YXE2cYxPPYWHOog67A/Q+yKlQVpMyAt9F1ikaM4P+Mx1lR/ByEESCCEzoyUn+OypPeDteFHknovB7sw9VwWV22i1tuM/8h7yy5b+fywucRbw5dJ+9UnH/Li/m34dA1Jknhmzya+PeV8vjRuetjucTIMYbC7pYTmgJvxsbnkxSbw0IwLeXjjChRJRgJ0IXh87hXE2x0h56uyzJ/nX01ZewuFzfUMi4oj1iHzwLYnqPO2oEgyBgb3F3yOhemn95mK25r494EdVHe2My8znytzR2NTVK7OupR3yl7C7d/z/+ydd5hcVd3HP+eW6TvbW3azm95JDxBCDx3pPSCIgqigNF8LooiKDeRVmijii3SRJr0ESIOQENJIr5tNtu9smz63nPeP2czu7MwmMSQQMN/n4SFz99x7z7n33HN+9ftD9CroqwoXQ/znftpH8uXHwXC7fnFQSfoc0BJdhIKaEbbgUGyGe5rZFivCoegUOdKJEbr3pazYn3P81EEjuX/lh1hZ4pJPqto7GuqIFcfuJ1G43PEJYaMWm2SImcTEkiZLW27lFM+7KOK/b9q6taJ+le4vGwTZBRWBYOcXcM3Qs/j+8vuJ2waSZJ6cQ9G5bvhnuyE+v2VNSkHaCQmsa2+hPRbNKkQcyLClzdL2TWwNNVLpKeLQwlEZoWtCCB48/mwueeNpTNsmYVnoqsrhZQO5eOSEfq68f7Cqs4b2RCgjDMq0LV7a8QHXjjgLgI74WhbUfwNbJpCYtMVWsK3rWY6qeBS/Y+hn2uf+kLAsnt38CS9uWY1T1Zg1YiKnVI04oEKPS9xTOGvwW7REl2JLkxL3ZDTlizXHd4VBOWewseMp7F5FYQUqJe6p+HQ/902+kZfr3mde6wr8moezKo7i8KKx++z+nwQaeWzdsp5QNSmxLJO7ls3j9EGjGODtP7R9X6A+GuDGpQ/SZUQQgCEtzq88im+OPI2Tq4czZ8dWVEVwfOXQ3VKbV+XkUZWTlwyj//A3aYYMTVjMq78TJRHDpbqpzjmPKv95u9zb396+ke/OewnTtjGlzTs7NvHQmsU8f8pleHQHxw24izn1VxMzA93mW5tS96GMyLt0Hz6hg/hvw3+ftPk5wLRtNKVH0FCEg2zqji3BlApORedbw85E7cPSVenzU+bNoaZPjSGXqnHesHH7rL9ru7bzVsNSbGlxXOlEJuQN5roJh3Pvig+xbBshQBEKP5pyDBW7yEfaFfyahxzdQ1simPG30b5ASkHqDSlNuhIbyXPuP5Yy07b5+9qPeGLDcuKWyalVI7l+wgzynF8eQeBAR6XvFGqDL2PT42UUaAzwHpeqxzLSX8W9U27giZq32BDaQbWnlEurT2Kk/8sZkrmvYNoRpDTR1czvNmRGuf7jB6iPBjBtC13RyHP4uG/KdRQ40y3lYwtL+fDCb/PGto20RENMLa1kcvGAz1ygb0sEsxqOLGwaY22p3ytaf4Ule0I0JQamNFkV+D1HlP/lM+jprmHZNl+d/U9WtjakyCqWtNQxv76GX08/+XPuXTpUoVPm2Z9RC58fxuZ/k0BsJe3xdUhsFFScaj7TSn4OgEdzcVH1TC6qnrlf7v/Gtg0k+iFJeGf7Jr46at8x3RpWF2vb76E+9AYA5d6TuXezoDnWmRYj8sKO9xmTV81RxeM4f/h/Lmes7aqlPRFMKUgCyQVlSyjSQyQsm4QFa9r+QEt0IdPK/pi9r7bF999/NS3PKWIa1HS18+j6ZXxr3GG4tWJOGfg8LdElhM1GCpyjyT1YS3APIA96knaBg0rSfsSzmz7hzmVzaYqGKHR5uHHCUVw6YiIl7uwbjI2KIabwi0MuY2rByIy/CyG4/5izuPiNpzClTdQ08Go6w/LzMJ3NfH3R3ZS68rmk+ljG5+0Z046UNh3xT7BlnDznBB6tmctT2+YQt01A8nrDx5xSPoWbJpzLqYNG8ua2jahCcEr1CKr9e0+rKYTgumHn8Lt1T6VC7gQCp6JT4iolYmTWSZHYqGL/KivfmfsC8+q3phbjxzcsZfaOjbx15lVfymR8y7ZZ39GCQ1UZ6i88IKzWhxTdSHt8NWFjO7Y0UYSGSythYvGP09oN8pbzk7Gfb0jbuUPG8PDaJcStHm+SAEbnFx9QXqSY2cLKlp8QiCUZynyOoUwougO/c1SqzYMbX6E23Iwhk2MxLIt4zODudc/yqwlXZlzTozs4d9i+s6LvDUb7qzBl39BMcCk607rXUCktOuJrspwtCUSX7uce7hnerdvMJ4HGNDa/qGnw3JZVXDV2GkP8/ZeP+KJAGmuRkX+AWQfOGQjPLISyfz0j/ylUxcWxAx6iLf4J7fF1+PRKSt2H7TIMb19CVxQUoWREWQhIM7R+WkhpsaD+CsJGLbLbGLU9+AJH5bnYHD6c3rWyYnaCF7Yv4KjivTPEdhphlF7XG+xuoVAPo/diorNkjJboB3TE15DnzMwlXNvWjJmllk/MMnm1Zi3fGpeUqYRQKPF8NmGJB/HfgYNK0n7CS1tW89NFb6Y2vUAswh1L3kUAl46cxPTy/2Vhww1AUviX2IzLv5ILh31rl9cdW1jK+xd8m1e2rqUpEmRoXi4PbX+Ol+o2YEiLTaEGPm7bwM0jJjLc24Gu5FLqPQVHljyWzvgaljRdi2mHESjYWCxqGUPMLkm1idkJ3mhYwqkDpjE6dyDfGb/vktGPKZ1IrsPH4zVvUx9rZVROFZcPPhlNLmd5y+2pgoVJCNxaOT69ep/dvy/Wt7ekKUgAhm0TiEV4aesaLhr+2YYS7W+831DD9xa8RMw0saWk1OPjoWPP+1yThCGZ+3B85VO0xpbQldiETx9EifuwlBfpQMJ1449gfsM2tnQGiHYTNzhVjT8edUZGW9O2eLxmDs9uX0DYjDEur5rrR5zFsJz9m88hpc2HDVcQMeugO9crmFjPhw1f49iBr+PoriHybtPylIK0E5a0WRhYiyXtA5IxrtSVz6nlh/JmwxJi3Qn1DqFR6MzlpPKdLHsKinCk1ffZCVXJLED8eWBe/VYippFxXAALG2u/8EqSHX0LOr8PJAAbjOXIyJNQ9CJCSY7NlpIFDTVs6QowIq+Y6aVV+8RoI6UNdiMI3x4pZUIICl3jKXTt2/y6PcFXBo3iwVWZJAkSOHFgj1ekLhIgYZtUe4v3ismzKTKfqNmQUpCS9zDxaVGGeFrZHClJax8yM5nj9hRjcqsxZM+eWulqx6FkGjaktGmLLcuqJHk0PSuzICSNNWnXMbcgg3dB4iNQ8sDzdYTn4gPCAHgQXzwcVJL2E+5aPj/NKggQtQz+uGIBl46cRLF7GqcNepuG8FxMGaXUPR3PHia/+h1OZo1M1if6w7rnCJuxXvScklOLFpGIvcLGhEQRDta33cXk0gcocPdYWCyZYHHjVRh2V9q1Ty1aRn3sSDrMnpo0cdtkQfMqRvsH7sWT2DUm5g9jYv6wtGNSnkwgupTa0IsINAQCTfFyWNmf9utCtyLQkJWdK2IaLGra/qVSkhrCXVw15zmivQSzbcF2LnrrCT4877p9Us9CSguZWAhWI0Ifj9AzmZD6gxCCYvc0it0Hdl0cj+7g36dfzvz6raxsbaTSl8up1SNwZfE63rnueWY3Lk95Tpe1b+HbS+7nkcNupMLTY3yImc3UdD5Ce2wJHr2Kwblfx59FcNhTBGKLiFut0IcMw8ZgR/AFhuR9vft3diFESplK1D8Qcf2IcxmbO4gXdiwgYsY4pmQCF1Qdg0tNCk9CCKp8Z1EbfDEt10QRTgblnP95dTsNhS5Piuq8NzRFIX83uR8HOqQ0oetWoLegHQc7gAz9FeH/EW2xCBe8+QSNkSCmbaEpCtU5+Tx90iz8jr0fvx2fi915C9idgIVwHImSdxdC2X0h9M8Dw/KK+J/JR/P7pXMR3VmYEvj9EadS5PayPdLCLSsepT7ahiIEHtXJbeNmMbkgmVcnpQTkbg1KXYkNfYyQSWjCotgRTFOSHIrGsSV7rzDm6l4uqz6BJ7e9S8xOELKcmLaCpvSlFNdxqcUZ5ydsk+2xekr8Fg1Bg4ShsXMxcms6l4/sCUGUVh0ycD5ShpMcnFYnZtevUcxtaLk/2usxfKkhgazsxQcBB5Wk/Yb6cFfGsWJHhFkVi4kHPkLRx6B5L2fgp6RO/ahtQ1r9gjHeeoZ5WlKWGru7qO3y5us5tmp+KjGyJTIfO0uYiiIk43N2MK+9J9xPFco+o+vcEwghmFB8C8PzriAQW45TLaTYPW2fhjwkbBMFgdYr76vC6+/eltLhUFQGfYrQwgMRz25ehWVnWivjlsV7dZs5uWr3Ck0gFmFDRwuVvlwG+tI9ldKqx2qbBXY7SBuQCOexKHl/RHzJiDcUITimYgjHVAzpt01bPMjbjctI9ClGmbBMntw2l/8ZnSSciBp1LKw/P5k7hEFXYg3NkXeZUHI3JZ5j96p/EaMOMmhiwJZxQsa21O8ZRWOZ07QCq08x0UR8nWUAACAASURBVEn5w9K+kwMNQghOLJvCiWX9lyIYV3gTUbOelthHKOjYGJR6jmJUwa499/sCgXiQf26bz7L2LVR6Crmk+mhG+CvS2pw/9BAeXLWIvr4kVSgcXzmMLzSsGiCR5Q8GxN8BfsRPF7/FtmB7ai+L2xabOgP85uM5/Gb6Kf1eOpiI87tl7/HvmtVYtuSEgcP56ZSZFLt9SGM9dvu19FbOZGIBVvs30Qr/uS9HuE/xjTHTOK16JLO3b0ZXFE6sGk6hy4NpW1y75EHaE6FUzlDUSvCDFf/HY4d9m0D4fhpDryIxyXdNZXThz/E6sofde/VKVOFOy9MDUISLkJmDgsBG4lJ0St0FnF0541ON6auDT2SkfyDPb19AzCpGVbaRPicEitAp9RyTOhJObKU2tJxfr/2AxrgLXCZFDolp6IQ78zGlzYXDxnNadY+sIkMPYdkR1F4Md5qIkwg/Ct5r0LQv1z5+EPsfXy5p5QBCVU4eW7qSicNCWIzMC/D4xFdxCAsSNnZiEYnIE+iF/0TR995KnO/IoT7ak6A8IWdHVle2LU064yvJdyWtLobdSTbBSRUSt5q+oSlCMLN04l73cW/h0Svw6BW7bSetVsyu27FjbwMSxXUCmv/noCTDxnp7n2pCzfx69XOs7qxFEYIji0fzwzHnkufwMr2smkKXh1jYwOqVyKgrChcN++xDL/YnmiLBDFY2SIZXtcbCuzzXlpJfLHmbpzctx6FqGLbFtOKB/Pnoc1O1sKyO74FVT+85JuNzsCNPoHq/nNTYu8L2SCu6omUoSRY267p2pH5vbL8Hww7S89xsbBljTettFA98b69CDnvnHfWGKtzku3q8o9cOP5NVnTV0GWGiVgKX6sCtOPj+6APD2/JpoCouDi+/j5BRS9ioJUcfvEdrS29IaWMbS8BqRXFMRqhlJKwAdcGnCcZX43OMpiLnYpxajzW8MdrOlYvuIWrGMaTF2q4dzG1ezS/Gz+LI4p51v9KXy/3HnMUN819BkvTc+XQnDx9/Pq79VCT8M4PIAZlJRgCAkostJW/WbsgoVpqwLV6qWdOvkiSl5JK3n2BjZw8F/+vb1rGkeTvvnnkNeuT/yFTODDBWI83NCO3AYDXMhnKvn6+OmpR2bFFgPTErkVF4w5IWHzd+A5eoT4XPtcc+YnHDxcyofCMVTtsbZZ6ZrFb+gG3FkN1rjUDBofq4ccyveKluCa3xTg4vHM1J5VP2iZH00MJRHFqYXIvaYifzcdMPuiNZJC6tjGml/4uqOLHsGCuav0t77CNils0lZRZbo0U805g0lHqcFjNHDuDakadS6Uv3CJrxJWgiU66J2wof189lRtXZn3ocB/HfhS/46nvg4keTj+V7819COELk+mPcMmghXtVI1UUCA6SB2flTHEXP7fV9ZlUfyy9XPZWKx981ehbXQtehqcUxHS62x8rxqE4kEkva3DDibCo8Byb9tJQGicB5YDXAzirpsbeIxd5jSdwGNLaGxnL/unGYuPDndmJJM+lhlpIFLeuo/eivPH7EDShC8Mwpl/K9eS+xrLUeRQjKPTncfeRXKPV8OSrK78SM8mqe37oqax7EtJLKtN8tsS6eqJnHx21bKHflUaiW8szmT4jbFvFu4WRxcy23LHqdPx15FtJqAWMNmUp4LJmH8F+oJA1wF2DYmYKiKhSG+spSvwOxhWQzXhh2F3GrBdde1PXJc44jzzmJ9tjSFGukQENX8hjg7fFkFzhzePTwHzC3eSWbQ/VUe0s5rnQCbvXLU4zRp1ftVVFq29xOou1SpN1GskCQgXSdyZLgu1h2Akmctuh8tnf9gxz3vdRF/IzKL+Gf298jZERToYwSSdw2uHPN8xxx9Ki0fJKZlcP4+MLvsjLQgK6oHFJYts+K836eEGopUh8PxnJIK9TqRniu7CZrzh7qaWUpExGzDJpiHWzp6KAm2JZm7DGlTWcixqvb1nJWXg3ZviWEjrQaDmglKRsCiWDWshnFeisaPQpSEhIpYzR0/R9Ved9FiHQlR1WcHDngMVa03EYgtgSAQtcUJhT/Ao8+gBtH7V+W0ALXJE6oeouQsRVFaHi0gQghkHaQxpavM8BcRpEqqUcjKBUGu1uZWbCWNwPjMKTFlmhthoIE0JwoplTdgNrns9GFxZt1Xcw4SH6aHQfZ7frFQSVpP+GkqhHcdtgx/GnzczgVg6k5Tb0UpB5IY3mqQObe4KjicVw+eCb/2DobTaisDldT5e5AE703I4kiVHKdPVZjj15JVc6FbA8+m4pNVoWbXOdY7px8L4sCG7GkzWGFo8hzeDlQYcfeAbuN3puvwEKRFgWKSsCWVHhWct2oWv6w4UQM26S33GFKi4ZYO8vatzK5YAhlnhyeOeVS2mNRErZJidv3pUz4PHHgCIbnLmJ9R0uKqMKj6Zw8cAQj8nos4U3RDr668F4iZhxTWmwKNiDlOmzVCVZPwuwwfy1jcl7lw+1/osg5huqsgYuA3PsE4C8yil25HFk8hgUta9K8SbpQmTWoJ8REV3JJWJnMjkiJYm0nHnk02c79FRR9z6nwp5bdz+aOh9gefA4pDUo9MxlR8D3UPjVunKrOSeVTgP5D1/4bkWi/Gmmlhy3K2HP4UWjrZu6ySWDbCRY3/IgH1p+BKW2KC7qyKgBBM0ZLvItSV3qYqkNVmdrHSPFlgMi7B9l+FZhbQWggE+C5DFynoQjBjLJq3m/cht1LWFOFYGblMAw7adTShcojW97jH1vnoCCIWya6W4Ogk94Jc141QF1oHnbxeBRjJRneJBlHaPuvjMT+wrjc6qyqZLkzhqC3h1kyUDUYoEUQsfuIND2M7vsuuveatL3Mow9g+oCHsOyk4URVPltjiBCCHEdPiLK0u4i3nk6utQNVTcrtBWqCzaZGMxqTc2t5M5Bk2MumPAM0iovJtxfhVnvW2JilMi9QgaKVZT3nIA5iVzioJO1HREQnQsDpxZ9gQz8lMh3Ap2ONumzQ8ZxbeQQ14WbyNA+b239K1FgExKnSDEqVGIqIEGk9G3fuHSjaCIzYKwzRnRTnX8q2yAYs4gzwnU6F7wwUoXNC2aTd3vdAgDQ3Q5YEVBXwCEkA0BWbUlcXw/3NNNqZLH9SSnZEWplc0LNgH0j0zfsDmqLwz5Nm8dj6pby4dQ1OVeXSEZM4e/BYFrVuYnHrRvIcXjYE6wkbsVSeigQQEr8vTkubDgiOKtnAZUM+xKlaxC2oi9RR5rBxZUxrB8J92mc70AMIt469mD9vfI2X6xYRt02G+Mr4/qhz8Kle/rByDh+37GB68RRG5tQhe+VRCByMdJVgtF1KUuATmOG/ofuuxZHzvX7vJ6UFKAghUIWDEfnXMiL/2v0+zi8bbHML0qylr1dCwaZMlbTZvWvgwTB/IyEzDghipo2WZZe1pY33S+Sh2x2EWoQoehFprAO7GfRxCKUAKS2C8VX8fEoVF73dSMSyiJgGHk0nx6GBq42j3/45ABXuPFrind3lKZJwOBP4bAiFXbjUBN8d9Q4j/U1oipOlbQaTXAoqKinSEuFGuC9CqPuOpfWzwhBfGUcXj2V+y2pi3eQvCgrbwz6MAgtnt4BRoRoM0Mxub4oFMoQR+iNC5KF7L8647metHPUHM/wI0mpJeYGESO7jQzWTVktFE8l36FA0TizLHv4/ofwEbpp7LLcNm0eullSOl4Xz+MX2Kfzl8EM+i2F8MXHQk9QvDipJ+xEho4sx3nqm+WtotqBMJc0NbEnQveftE0+FR3ORMFUumPMUnYnBVHl9/Gz4e5Q5o90qmMQ2VhJuvQBwgDBBRnEKD6PUapaad/KzpavoSDzOyZUjuWzYFHz6gbF47gpCGwrCDTI9j8YCIrLnuQohKdJD1MfyMjx6NpJ5zetZ09HIqRUTmJC//2jGP290ROdT1/UQCauJPNeRXDHqW1w9NlljwrQtbvr4UZa11xC1EjiEiiWy5xIIIVEVG6Tg4sEf4VR75zdJNhgK45ygoAIJEB5QilG8397/gzxA4VA0rh95Jt8bcQaWtNEUle2hDk567a9ETYOEbbGk1cE5A4dxfPl6NOFMEgw4R1LIUkgrsGxhhO5Dc5+Jog1Ku48ZX0ik86fY5gYQHpyer+Hyfz+DMMOybZa0bidiJphaNJCcT8Ei9qWGDINQyGbG17IcNGyVnZ6NUEQnN8dK817rQmV60ShcqoMtwWZydBfFrgOrXtD+gtBHAcm8lM7YYta3fAcp40jggelutsdu4pP2QkblF/PP+tksbd+S8hpsjwTou1UKAR53glDYyZVD32dkbiMOxQYiGMDKuItRnvG4ZR0ouQjPlSjuCz7LIe9T3DruYl6r/4gXd3xIU7SL+i6LunAO2woKGORtxaFaVKQUpF6QUYzQPWieizATH2JE30Qobhzuc1H1z7/gqmXVY8Veg2xF5AE3ks3RAqSECnchXx18XNbr2NJmXPEalhsJvJaNBcS0Nr5a9SGbQrUcUrh/yy0cxJcPB5Wk/YSu2CImOX/MhHIbl2LSYoFHCHKVpD1SAEFbUOT7/j65X2ciyuXvPUXYTFpP2mM6Q10BFPom58eS/+3c22WYhLGBT+puZV5jkiJ8Q2czz25dwUsnfQOP5uBAgJQ2RuJDLHMbmj4W3ZEkUlBcMyFYAFacnSF3tgRTQpvds1NIBNu6CpGaQCoytdkqCBK2xZymtYDCy/VLuaR6OteNPBkpbRq6HqYx+DCW3YnPOYXq/FvxOLInwh/oaOh6jG0dv8Xu9rzFjBpawi8xccBrONRi3mxYwdL2rcSspJUyIS0UyBBMdkIVGrmOTtQsibJBKfnEKGFi3oVg7UA4DkO4T0eI/y5BPGjEUIXAo/UYHIQQaN3htXeueI9gIpbyUVgSnq2dyCedh/PYsUfh1srR4i9iBBdnubqNFXsLxffN1BHTWE2o7Yoe76oMEw8/jG0H8ObfmWq3tr2JK+c9RcQ0EEJg2BY/nXgilwyb3PcmX2h0JWLURTqp9OaRk8XoY9sdxEIPk4i9jaIW4fJejcN1TFoboY0iWxyAjUrATneXGpbCwuaeXJdo3IFbF+R4DXRFw7RNxuVWc0TRWE589zeYtoUpbcblVvK7SZdQ4PSlzpXSpjM2h87oPHS1gELveTi1/4xs4kCFYbWztvnr2L3Y1WwZpsJ5B2dNfZ9FgQbaa8L9hlX1hhACr2YztagmrUApQEzGWBcPMK3yg30+hs8KUkqWt29jY7CJgZ4CThswjTMqDmPGi/fSEQ0CcO+64zm3ainTi7agOjMjKwCk3UKk4waM2Bvd64NCPPQ3XP6f8kLTOB5av5D2eISpRQP5n/EzGZGbSce9L9CViPFy7Wp2hDs4ojDKOP1ObGsHTuxM5Y6krBSxNZ6vn0jC1IgmVBY0zWNGUQlufSSK0rOnrAy8wTBPUlHead5zKhbDvY3MCbzGOdWH7ZcxHcSXFweVpP0Ay46wrunrOJX0WOgaUyJQ8Ss2fsVAVySBponUxCfzZvslHFk+kWPKhu0yWdewmgjHFqOqeeyIDeLvW95nc7AZl+LC7pW4OdDdSUKqODOUpExowuTk4vXcvTWpJMUsk4ZIF//asoKjBwzi3ca1qELhhLIxVHoLuscYxrY70dSyfVbkM2TEeWDtAl6uXYVAcHb1IXx79JE4RScdgfOxrHpAgpTojsnkFj6KEC4chc/2YreDdqmw2ZApFiDDVmiI5LIpWIJDVSjNtVC1BAqCsGWSsCR0V6SIWQZP1nzAGZVTkLF7aQn/K6VUdMXeZ3XjeRxS/ioufdA+GfNnBcuOsa3jd6mxAEgMLLuLus6/MLjgVl6tW5ZSkHbClmRJglWZVDCEspJqVgY2offz+oVajrqLcLCdMBKrCIcewDQ2ojsm4825Fk1Lz7CVUrK6o5G6cAej88qo8n1+VK6WHaE9/Dzh+GKc2iAKfZega+kWyk3BJn624nk2dDUCMKVgEL+YeB6lfTwGC5q2ZqVP2RJMoCrj8OhuEnGNZEhu329ZSeZ39EI8eE+WvK8YRvQF7NxbUJR8TNvmirlPEoin0//+avnbHFJQzriCTGurYZus6qjDqWiMzh1wwOfpmbbN7cve4PmaFTgUFcO2mTVsCj8af0JqfbXtTjpbTsa2WoE4lglGYhGenJtx+3qowYXQ0XN/h9FxI2CQfA9uFLWcGANQ7JUINGJWjM2hQp7ZNjV1roLC4fnjueOwk9kSaqLYlUvQiHHVhw+lQqYAVnbUcv2SR3lsxneSfZMGG5qvIJxYji0jCHQauh5gaNED5LmP/wye4H+O3uGdu0Nr5GWykiogCYRfZ0d4IGaWEhVSZhptSpx+3jv3G3xc/yQyC9W4aWeW40i/ZpRY9HUsqw5dn4DDeWTanmZLSSAexqc5cWepf7YvIKVFc/BvtAT/gSXD+F3HUJH3QyyK+fbi/2NjsAm7u6BzkTOHh6dfTUei5ztP2DpP1xzG0zWH8sZhT1LhDmXcQ6gVJGJvQEoxtQCLUOfPeWDV5TTEk2vJnIZNLG6p5d8nXsWgnAIsaWNLG1359OLi2o4mZr33KKZtoxLi4vwnMM0EigADgSLVtPljSWiMu7l9w6mEbBc5WozzSx/DbwRY3ajjUBQq8m6hOOfy5BjtT9CEgVPYSClIIACBJiwqnHWfuv9fTsjkRn8QWbHPlSQhxDeBS4BJQC4wWEpZswfnnQf8EhgKbAZ+IqV8odffBXAb8E0gH1gEXCulXL2vx/Bp0Rp+EwcxvEoCG4hIlYTcaYm0yFcTvQRPgwr9I2Z4arjhwwuo8uVxzqDxHFkykAGOtUgZxe08Ck0tpKHjLlq6HkQIDUtKugzJ2rZT2BHPRQCqC5SIE9tWqI3mJenG9xCyT5p9zDJ5cusH3L/pNSxpIxA8sP4dbh5zHNNz/kVX5FUQCorwMSDvl+R5z8i4pmk1EYq+gxAaPteJqGo+UkqWtW1ndsNaHIrKVyrHM8xfgiVtZs15lM1dPXSuf9+wiA+at/K3iXOxzK30JmcwEksIB/8Xn//HCLUYPf++1N/yzTr8gV/SFl0AaGzsGsWf141BV1ROrhzFL6adTK7DzYMbZvO3zXOyPAvJwuYlDFGezth0bRmnvvNBhhT9tt9naZjbCIWfxZZBPK6TcDmnpy38TdEg85s2oSsqx5WNyCiWKKXko9ZtvLpjNQqCM6oOYXLhwF5/NwmFHiEUeRQp43jcZ+HPuQ5lF9Xko8amPsm9O8dq0BGbDySZ1jIhUFFQlGS4mGFbTMwfxK8nzMKnu4CZrGleSyD6DrbsCZVQhJuqvN3XoInH5tHediXIOGBjmuuJRV+gsPgVtO7is+3xCF+f/yRbgq2oQmDYNidWjOLOQ8/K2uetwVaWttVS5PQxo2Ro1ho/ltVGJL4QRfHicc7IYH/qD6bVxobG0zHtAFJGEThoCf6VISVP4XUm8/i6jChXfvA3QmYs5bBdEtjKlR88xMvH3ZjWZ5/upDORjcxCpKifNddpGMG7s/ZHdaXXWbOMDWSNCxM6trkDxZHPh801KbKO3kjYFk9vWcav+ihJc5rW8ZNlzyJJzk2/7ubeQy9jhL8nEVpKSXv4X7QE78G0WnE7DqEs9yd4nHteOsCWMUKR10iYNTj10fjcJ2aECMYtg5e2L+fthrX4dRcXDTqUaUWDMq51z+q5vLhtJQnbSq0nT29eSrHTx9WjpgMQCz+aUpB6BhIl0nUXTs+lKEoPo6XmPgVFG4oZeRxpNaA4j0XznMME4SaU2EDY2ERDJJfvL1nITuHTpWp4NAc/njyTXIeXSd05j7eu+FdaXg0kWdm2hJrZHGxiaE4pgfALhBPLUkYNiYGUBltar2di5ccoYvcefsOsIRqdjRAOPO7TUNVMhtItwVZWd9RT7s5lSmFVVgVnXWcTD2/4gC3BAJMLK/n68OmUe3qYxQxjPR0dP8RILAE03O6zyc37Zdrz6wvTak9bL3bClnEMu51hOdMyvm1JMudrZ4FVAKeic9PoM3DpRTi0YuJmX0FYIc91RP/9MDYRaD0bZBwpYwjhQtNGUVD0T4Ti4e26ddy+/HU6E1EkcFrlWG6fdFqastQWD7O4tQa3qjO9ZAiOXsqEaTUTiy9EUXJxO4/st0bctsD3aY++hux+3+2Rl+mKzWVu109Y19WQRvZSH23nl5+8wOSiCt5vqulzJcEjdSfxk2GvkV6814VUB4O5OePecQsm+LfS0JIMu5NA1DS4Z/VcnA6Lt+pXY0mbsXkV/Gz86ZRqC2gPPQ3Y5HkvIs97YWpcUhp0RV4lFHsXTSnB4TqPR7Zu5436VeiKQkfUIGjEAME5ZZtQhZ0Kf7eRGNjoqNg4iVsWO6I5XLf2BEKqAyHgO1VzGOJuQVMkYGFLqO/4JdKsoSZazdrAUo7Lj6Z2OgtBo+kkJHXG5O19qZWD+O/F/vAkeYC3gH8D/7snJwghpgP/JKkEPQ+cC/xLCDFDSrmou9kPgJuBrwHrgZ8BbwshRkopg/t0BJ8CHfEgNS0/pcwRQRFJy5dL2IRsm6DU0aVK0ircY0VzKDbDvAGqvfVsDsd5o/ZxJjteZ5tQcKoqSBOv5yJaw88jiSNlHAHkaHDjoLe4ef35SARCgNNpEI06aUl4ebt1CDOLtqYxvfS9N0DcUnm1OT0uWRWSZqMNu3dbCZHQLXSq9UACJFgyxo72m9C1UrzOQ1NN24OP0NJxO4hkfH4TP6I0/0/cvVHy6o5PiFkGihA8uvlDbhp7AuWuAraF2tPoXOO2SU2wkUR8DoK+Ql2cWOSf+Pw/zngHbq2CCaUPpn4fB3xrvI0iRJqXzqnqaELB6GOxVFDwKq0owokl+1omLcKJFRn33IlQ+HkCHTd3W1VNQuHHcLtOoLjgzwih8OimRdy1ajaqSFpcb5OvcPeh53F8eU9BvF+ueIPnty1PeXVeqF3BZUMP5fvjZgLQ2vZN4vE5qQ01GPoL0djrlJXMRojseWS6WpjmaewNh5qklT67cior2muI9vEmeTU3zx59E7XhFoqcfsrc6eQXI4t+w7rWHxKIvIvSrWwMyr+RIs/Mfp8TJAXrro4f9SHeMJEyTFfnLykoegyAHy95iQ2dzWnvaXb9eh7ZsIhvjJyeOmZLm1uX/pvX61an3rVbdfDoUV9jkK9HQGwP/o1A5x0g9KRpQOhUFD2Jy9HD/tgfGjvvxrCaoPtZShJImaA2cD2jyucihODVHSswpJWmqlhIOhNR3m/eyNGlPe/6iuFT+d9P5hLtpbQ4FJUTK0fi6hbEFK0Kh/82El23k/QeAdLGkXsHipqu0Kj6OGxrCxlWemmgdHvnQkYiK/OgLSXt8fRQnR2Rdn748T+J9RLSIlaCqxb+ndkn/ABHtyLXEryf5q4/peZkOL6QLS0XMLTkRdyOsbt+qIBh7mBb0xnYMoSUYYTwoqllVJe+jKok51vCMrl8wd/ZEmpJfRtzmzbwnRHHcuXwI3uGKiWPbvooQxGMWgZ/3/BhSkkyYu+QLQcCoWMZn6A404VrRR+OI/f2jOY+xwh8jhGUemH2V8bz5KZlbOhoZmJRBRcNnUieM50EpjHanlHrBkBTVFrjwW4l6fk0r29qbEjC8eXkuA7N+FtvtHfeRVfwfiQSIRTaOm6jqOA+vJ7TgaSn7fsfPcecxg0pZaTM4+eRI6+g2NUT8regaTPXfvgMCcvERrKus5EXalfw3HFXU+0rwLJaaG05i+QWLIEE0eiLmOYWikte6rd/ftfhKF0PpYXbASjCQa7rMCocgxnkLWZzqDG1J6hCJV/3ckh+JRu66qn0FPL1oTOZVJAsmDq88A7WNH+7W/myEeioiptB+f2HtHe0X4u029mpdkkZxjBWEQrdzxZzFjd/9ELaPHp9xxoiZoL7pifzmh7dvJC7V89G7zbEqELhL9MvY0JBJW2dd9MZvAe610QhXAwofgZHH1bKuLmD9sgryD45h7YdJRh9moSdLtyb0uaDlo387dBvsezdJ4hbJpaUKAgcqsrpw7+HK/88EsG7sc2tKPpoHDk3E4+8SLb9H5LRFr1hYzO3dRVgp9bdTzq289H2i5ic2wjsDNleQ1f0VaqKHkfKGDXN5xE3NyJlBNBIdD3E2sBxNESrup8vOB2CeEKn1BHqI5uAiY0pFTyes1kcOJwfLN+I6oygCINCPcQgd6BbQQKQuIWFA4PO0IN4LZWZ+VZazrGQkgFajC2GiyPKvpEx7oOgOzhn92Gt/63YN3FSvSCl/KOU8jfAgv/gtBuA96SUd0gp10op7wDmdB/f6UW6AfitlPI5KeUq4AogB5i1TwfwKfHiprsp0DpTH6oQSetXjmJh2Qoh0581h8OSggpXF7picsvQ1/CqCRxKDCnDSOKEIo93Lzw9UAR41TiD3YHUMVXtufbvNp/A/I5jQOQBGqrjUFz5f0UohSC8JAUuL9tjxTy8PZ3y16Hb9LVI+7UoE3K205dSVcooLV09npyEsZmWzl90K3SR5BhkjIa27zG3YTFRy0AClpTEbZM/rJ7N4pYaImZmqIRlJZD9Ma9kKDD9Q1OUjDDGk8vHZw1tlEgOLz4KO+v1FVz9JLradpBA+81IGSMpREukjBCNzSYam83GrmbuXv0OCdsiahlEzAQxy+Smxc/R1e1NWN3ewHPblqeekSQp3D26aRGbg60kjLXEYz0KUhIJLKuBSPTlfsfv1MrJcUxGkO4xUYSbCv/VABxfNo4Tyg7Bqeg4FA2P6sCrOblr8lfx627G5VVlKEgAquJmbMk9TB84n0nl/+KIqkVU+i/vty87IWUIy9qR7S8YiaRtJGwmmN+0JUORjVkGT2xeknbspe0rebN+DXHbJGoZhM0EgXiI6xY93XNeYjmBrt90z80Qtgxh2+3Utc5CyuxKZG90Rl+HLMqmYdZh2s0AbAu3ZoQtQpJuvj7akXbsayOm8ZXqsTgUlRzdiUvVmFRYwR3T0j1EuvdSOy6GqQAAHplJREFU3CXzcfh/isP/M9wlC9A9mQnorpzvQoai7MbhnYWiJK3/04oHYmQpJOxRdU6uTM+3+/f2jzGzfH+mtHm/ZSOQtP63dN3TZ06ClDGaOu/KODcbGttuxrJbkd0ELFKGMcxaWjruSLV5te4TtvZSkCA5D+5f/x4diV65LchUbmZfdCZ6+ijUEsiqLprJNXIvUOrJ4cbxR/Pno8/nmjHTMxQkgMOKhuHMErqUsE1G+gck+9avp0ju1usZTyynK/RAN0NiHCmjSGK0tl+HZXcC8NjmD5nbtJG4bRKxEkSsBNtCAX645PmeO0nJz5a9QswyUjTmprQJGwn+sOodACLhx5EyTvpekcA0V5NIrOy3j37nofidh6KInuejCDd5rqPxOSYihODPh17FOQMPxa+78WpOTimfwOMzvsvvJn2VF475IfdOuyqlIAHku49kYvmzlHjPJsc5iQH+rzF5wOu4+6mLZVmtmMZ6+u5zECca+RcPrltAvI+iHbdN5jRupCUWYlV7HX9c8y4J2yJsJgibCbqMGNcsfJyu6Bw6Q/el1hkpQ9h2Kw0tszIE0mhiTdb3LYkxyN2Qte8SyYi8Yl455RucN3g8I3OLObVqFM+eeAUzygajOo/EXfQ83rJluAufRHVMweE5nySbbjoUIfmgvSrjmCXNtHV3mKeFMb7t7FSQAKSMEIkvJhJfSHv4SeLmhl5yiolDMbl64BzU7qgWIUBRJIqwWRUqJWxmmcvCgeY+h1OHns2Sc2/klMrRqAj8WgxT9oisOhIHyfxiVUh8mono8y6T27tgXMG30NXMukoHcRC7w4GSkzQduLfPsTeB67r/PRgoI+mhAkBKGRVCzAOOAP7yWXRyT6BZc3CpmaEsEkjIYkbmnw2Jh0h3hSdpqjdHCpjk39FPoryNJsCQ6aFDEoFT6REavJqT6vwyBHDhkAmcPfSHaEq6Lqy7jsaMvo5t7UDVx1OScwgVm/7FjnAnqkguM8cNqGR+yzp6byD5egRTKuhZ8pzi5rbUv7siL2YVOC1bMiFnE7MD6YKYKgRBM4pH1Yn0ES6l4iEih+ATG/tcTcPhOinLc9pzDPDk85NxZ3PHqhdT1lRbSn414UKKPdV0eE6kPTI7jY5ZEU4qcrMztEXjC7prgKQflzJCKPICLzUoGFbms1NQeK9xA2dVjee9xg0ksoRC2UjmNW7kggFb0uNNUvcIE48vxOs5v9/xjiy+nw2t19EV/xgFHYlNdd4PyXMfBSQToH82/nxmDT6SjwKbydU9HFs6Jo10YFfQ1Xz0LNXd+0OSxEGFDC8hiG7vQTyLsrETfQXhp7d+lOEFk0B9pIOaUIBBvkI6U0JdH0iDSPx9vK5jd9lnZRfEEzu9eIfkVfLSjmVErPT+KUIw0p9eq0NVFH576OncMO5oNnS2UOnNZYg/u4CuqKUo3kt32T9VH4Gv8BminT/HMlYilFyc3qtw+nrmbKHLy/Xjjube1QuIdSvjblVnVF4ppw5M/zZbY6GsuSG2tGnvVkwMK7sgB5KYsWqX/U1eK04k/gGZOVcGwcjLlBUkCSfeaViT8X4h6YFZGqjl+PJk31WhMMxfxKauzHpTY/J7PG9u79UYsffoLfSBiqoORtNHZpy7r3Bh1eE8V7uY9kQk9Wxdqs6l1TPIc3gAKPbNIhRfkuFpUYUX7248nqHw89nnOCrR2Gx8nvN4auuSDEXekpIlgVq6EjH8DhcdiSjNsczcFhvJhy1bATCMNWT1xqFimptxdBPs9IUQgtElf6U5/ALNoWcRCEp8F1LsPSsV8ufRnNw8+gxuHp0Zyt0fvI6RjCz+/R637x+SbeH2rHWJHIpGY7SL57YtzbpWW1JS2/YgniyeQFuGiCc+xuWcljrm1KqRWdZA0HBoQ1GFkkZgIRCMyxuIS9UZlFPAbw87fY9GpDkm4Mq5nljwj0m2xm6v0rOtV2CL9HXYoSk4VJE2R8b4GlI03L0hZZRw7APC8XkZhpJkf2GIu5WNkZ5C2Ioi+aCtitpoLkM87b3YUV1o+gQ0R9JT6lQ1vjXiOOY1r6UulofS6404hJ0mKwnoJx/OgUvLNO4dxEHsCQ4UJakMaOpzrKn7OL3+n61NVrqf7tyobwJUVX12ZZaDlgtLClSRvrwmbJUpZT/H5z6UtqYnkDLBTrd3zFJZ3FlJXTyXoZ6Wfq8t0OgrHStItkSSLDQuReebw4/ha0OP2mUfhXChe85J/a4E3jj1GjZ2thA04ozNL2NHpI2F8zdg2T2Lc2Pcj5LFCwYqXmcPa4zsDnfIMgD0LOcLBFOKBvLWjk2IbqEtOTaBS9UpLbqPSPv53YpXDPCgKH58/lt2Oc49wekVkziqZBQftGxAEYIjikZ059rA0KK7qG3/NS2hZ7ClgUurZlDhr/A4shci7OulSRuh0ElYVtbCkhKZ2mxdqo6mKGlhh5AU/JyqjqqWkb3ilhNV3fU819U8xpY+TtxswLACuPVhqEqm0D8sp4xhOfu/8J4QOm7PBUQjz5JmNBBuvL5rAMh3eKjw5FITaks7VxWC48vTPXp9rb47oaCknq9tB8k2NyVg2+GM431R6PsqjZ139xEGVLzOqWjdit0J5WP588Z3SURNzG7hxqFojPKXMzE/+zsq8+RQ5uk/h+M/geaYSE7xi7tsc83oI5hcVMmTm5bSZcQ5feBovlI1NhU2tBMzSobzRv0nGQqfLSVTCwcl76cU9xuu4dB2T6cvupOrd4cCpxcFkeUbkhl5fbdNOoWrFzxN3EoWIlUQOFWNWyf2GFZ05zS8ubcT7vo5oIA0UfUR+Av+vtu+fBrkOjw8OeM6Htkyj/nN68h1eLh00AxOKBuXapPvPpkuz1m0Rl4ACUKoCFSGF/9tD4hyLLLmpQF0K2XZPJ2QfAsx28CPC7em9/tW/I6kB0h3TCAWe4e+Rj8pLfTdKJpCaJT6LqDU9/lQcqtqEZo2DNNcS/rzcuJ2n8eUwkq2Blux+nhSDdtisK+QkBHLup4nIwgylcskBHafchVux0g8+hgiiZXIXl5qRegcVXEL/7fjVTqNKFErgUvRcagaPzvknL4X3iO4cq7D4TkXIzYnKQe4TuSbJW6arbd4vmYlNpJil49vjT6cP657I+3cLtONKVW0PgqdEE40tQhFeLLeU0ESt3tETQE4FB1DCm5acz7fHbqB00s3IVBxei7G7bsqTdkZ5Cvmt5Mu4raVz/Ny8zTOLF2MU7EyvEY2Se9nX0VJCHA4pnMQu8BB4oZ+sUdKkhDiV8BPdtPsOCnlnE/do30EKeVfgb8CTJ069TObAXH1bAx7FWofb5KNkxzPTITQyS9+jVDXr4jF3qUjAa80j+MfDclCZyuCFWjZFAnhQVXKEFZLtztbwZQqjzccgUP1ImyLc6qmcPmQGXvd9+G9KD+H5pRwzfBj+cvGOdgpWgeNLi6iRLzQS0hUUISHEv91qXN97lNpDz2cYVVSBXwSGkxf2EhOGjCGiflV3LToRTZ2JRXF0Xml3H3Y2XhcBbhKPiAaeRrLTDKgudznIZTsi/J/Cr/u5pQBmdZZRTgZVHA71fk/w5YJVGXXBWZdriOzHhfCjc9zMSdVVPJMzdIMa7gtJUeXDQOSicH3rJmTcQ2J5OQBo3E5nSiKH8uK0FvYF0LDl6VQYDY4tXKcWiaD2ecBf94vsO124rFkgrmUCTyeWXi8VwJJy+Bvpp7JN+Y/QaKbLtmlavg0JzeMS6+VcWrFOGpCgYykeLemM9SfnNs+9+mEY+9khK4iDTzO3W+kxTlXEY5/TDA2p5sIQ6CpJVQV3ZNq41R1Hp/xLe5fP5vZjatRhcJZlZO5evixBxQr3LTiKqYV71qxPrZ0FCP8pazrakwJ1m5V58zKSVR5kx4vVfGS77uI9vAzad+8EG5K/Dfuth9COPA4ZxCJLyDdm6ST4zkr9evC6mm8XrcqTcAXJL3nkwvSx3F4ySCeOu4KHlizgI1dLYzJK+U7Y45iZG5JWjuXdxZOzzmYxhoUJR9VG8JngQKnj5tGn8ZNo7MXVxZCMKjwN5T6v0EwthBVySPPfcJu1yAAr+dMQpGnMq360sTtSjLjzSwfxbM1S1NK/E6Uu3Mp7qYhd6k6p1SM4c26NcR7GW3cqs6Vww7vvtcsQqE/I+0eox84cTinousHfqJ8XsEDBFrOThL0yAhCeFG1IXhzvsc1IxO8uj2Zg7RTGXKrOlcOPwyf7uSkAWOZ07ghI/rBtG1Kcs4nElqbuc5g4nJMoy+GljxCbeAHdEZnA+DQKqku+D0+1xieP2Y4b9avZE1nHUN8JZxWMYEcfe+LnSvqAJzeniwFJ/DLqafx00knEzET5DpcCCGY37KWJYGa1Hr6Ucdgrqj4MNsV8XvORFNLiCQ+ThuzLaHTdFMbK+huKSh0+bhx1Gk0RYNMKBjAlKKBu10Xjy4dxdszf8iWUAua/TFW4ilsYzXIVlKlP6CbXIZUukPM1sn3noV+ANSCOogvJkS/+R69GwlRBGRS46SjVvb6OoQQU4GP2AN2OyFELXCvlPLOXsf+B7hOSlkthBhCkvHuUCnlR73avAq0Simv2NX1p06dKpcsWbKrJvsMnYko9yy7novLX8eUCoqAhK1RUPA4Ff5MISxmGcxt2MSSQA3P1X6cFIaLlnFZxRJ0YaEIiRAe3M4ZlBX8ha7oa3RG3kJTiyj0XUqMKuojHQz0FuD/FAtnf6gNB3ivcR2qEJxQPpZSl5/OyL9pCd6PabXidR5Oae7/4NTThYum9lvpDD/VnZ+jIIROof8mXmqaxD3r3ksm1iOwsblr6gUcV9ZjdWyLRxBAvnPfKEGfJaKxBTQHrgBEymqb47uKgryfpGL8X9meFPQEAoeicv2Y47hyRM/ceKl2JbcufQVVURCAJW1+P+VsTq5MCh2muZ1A2zUkjNUIFBS1hML8+3A6MzffLwosqxHL3IGmDUFRCzL+vj3czhObl7A1GGBqURUXDp5EriN9vkfMBJfOe5jt4XYiVgJdUVGFwn2HXcwRJcnaNVKa1LVeQiyxLGVsEMJJof9H5Odcvcf9jSXWE0msxKFV4nUets9o8A9EJCyTF7cv5bW6FbhUnfOrpzGzbEyaYCOlSWPnbwmE/oGUBppaTHne7eR5sisBfWGYddQ2n4FlB5OsgcKNrg6gqvQlVKUnl+CZrR9x5+o30BQVW0pydBd/nX45Q3L2T02XLyKklLR1/IRQ5Olur37SC1WQ9ytyfMlwzbZ4mPPe+yudiShRy8ChqGhC5W8zLmNSLybNiJnghkXP8mFLDQ5FJWFbXDR4MreMPzn1/k1zG50dtxGPz0UIJx7Pxfhzf4gQ+34/2h+w7TCx6MvYVh26YyIO57GI7jpmW4MB7l79Lotbailwerh6xHTOqZ6AEAJL2vx/e/cepFV933H8/d1dYBdYiFwEDOHWRBNvhUi0WMFbSTI6qQEcMY1NSVtbYmKNyWhqk1Gmk9Fp2powTtKMph3G6ERb03QikZtEgg0EA06YekNbERQQBEQue9/99o/fWTl72Of+PPs8h/28Zn6z7Dnfc/vy23Oe3zm/83uWbnqU5w/vprW7Mxo4oYGvnns1fzpjFnvfuZ6Ozpei80w9ZkMZ+4H7GDVicZZ9aaXH26mvG131Gyrt3Z38YMcz/Gz387T3dDJn/Ie5/eyzaD9+Oz3RkzKzJiaPe5ARwy7B3dl/5B7ePf5INNqd0c1Q7t+5gO1HQy+Ys0dN5DsX3cDk4aV/hUOPt7HvwALaOndgtNLZU0e3GxsOncNHhh+isWEkZ4//CmeOWlyVXJrZNnefnTuyukY3jPc5zdflDqyQNUf+tabzlFcjqagVF9ZIehw4w90/GZu2Fjjk7p+LBm7YS2hI3RvNbwQOAHe4e9Z3kgaykQThTtLGfds53LKRsY3jmTNpEY15vNdxoPUoT+15gWOdbcwd18WkhmdwP8HIpmsZ0Tg/dR/EWtu3cqxlJVgDo4YvoDEa5ert1vfYuP81htU1cOWkcyrSuKumnp5jtLSupsdP0NR4BUMapr0/z93Zdmg3a/a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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pa2 = make_pa_matrix(1000, 2, 10**4)\n", - "\n", - "plt.figure(figsize=(15, 12))\n", - "plt.scatter(pa2[:, 0], pa2[:, 1], c=np.arange(len(pa2)))\n", - "plt.colorbar()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Nice and harmonic picture, isn't it?**\n", - "\n", - "That's because the curve plotted is [Lissajou's curve](https://en.wikipedia.org/wiki/Lissajous_curve):\n", - "\n", - "![](https://upload.wikimedia.org/wikipedia/commons/5/5d/Lissajous_animation.gif)\n", - "\n", - "Curve implicitly specified by harmonic coordinates\n", - "\n", - "$$\\left\\{ \\begin{align}\n", - " & x(t)=A\\sin (at+\\delta ) \\\\ \n", - " & y(t)=B\\sin (bt) \\\\ \n", - "\\end{align} \\right.\n", - "$$\n", - "\n", - "In our case $\\delta = \\pi / 2$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 3d case" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "ExecuteTime": { - "end_time": "2019-09-26T22:54:15.024781Z", - "start_time": "2019-09-26T22:54:15.019525Z" - } - }, - "outputs": [], - "source": [ - "pa3 = make_pa_matrix(250, 3, 2**3)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "ExecuteTime": { - "end_time": "2019-09-26T22:54:15.422821Z", - "start_time": "2019-09-26T22:54:15.410521Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = px.scatter_3d(pa3_df, x='x', y='y', z='z', color='c')\n", - "fig.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Py3 research env", - "language": "python", - "name": "py3_research" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/week06_bert/bert_for_text_classification.ipynb b/week06_bert/bert_for_text_classification.ipynb deleted file mode 100644 index 3412b19..0000000 --- a/week06_bert/bert_for_text_classification.ipynb +++ /dev/null @@ -1,2895 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "izA3-6kffbdT" - }, - "source": [ - "# Practice: A Visual Notebook to Using BERT for the First Time" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "izA3-6kffbdT" - }, - "source": [ - "*Credits: first part of this notebook is strongly based on Jay Alammar's [great blog post](http://jalammar.github.io/a-visual-guide-to-using-bert-for-the-first-time/). His blog is a great way to dive into the DL and NLP concepts.*\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "izA3-6kffbdT" - }, - "source": [ - "In this notebook, we will use pre-trained deep learning model to process some text. We will then use the output of that model to classify the text. The text is a list of sentences from film reviews. And we will calssify each sentence as either speaking \"positively\" about its subject of \"negatively\"." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "izA3-6kffbdT" - }, - "source": [ - "## Models: Sentence Sentiment Classification" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "izA3-6kffbdT" - }, - "source": [ - "Our goal is to create a model that takes a sentence (just like the ones in our dataset) and produces either 1 (indicating the sentence carries a positive sentiment) or a 0 (indicating the sentence carries a negative sentiment). We can think of it as looking like this:\n", - "\n", - "\n", - "\n", - "Under the hood, the model is actually made up of two model.\n", - "\n", - "* DistilBERT processes the sentence and passes along some information it extracted from it on to the next model. DistilBERT is a smaller version of BERT developed and open sourced by the team at HuggingFace. It’s a lighter and faster version of BERT that roughly matches its performance.\n", - "* The next model, a basic Logistic Regression model from scikit learn will take in the result of DistilBERT’s processing, and classify the sentence as either positive or negative (1 or 0, respectively).\n", - "\n", - "The data we pass between the two models is a vector of size 768. We can think of this of vector as an embedding for the sentence that we can use for classification.\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "izA3-6kffbdT" - }, - "source": [ - "## Dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "izA3-6kffbdT" - }, - "source": [ - "The dataset we will use in this example is [SST2](https://nlp.stanford.edu/sentiment/index.html), which contains sentences from movie reviews, each labeled as either positive (has the value 1) or negative (has the value 0):\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " sentence\n", - " \n", - " label\n", - "
\n", - " a stirring , funny and finally transporting re imagining of beauty and the beast and 1930s horror films\n", - " \n", - " 1\n", - "
\n", - " apparently reassembled from the cutting room floor of any given daytime soap\n", - " \n", - " 0\n", - "
\n", - " they presume their audience won't sit still for a sociology lesson\n", - " \n", - " 0\n", - "
\n", - " this is a visually stunning rumination on love , memory , history and the war between art and commerce\n", - " \n", - " 1\n", - "
\n", - " jonathan parker 's bartleby should have been the be all end all of the modern office anomie films\n", - " \n", - " 1\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "izA3-6kffbdT" - }, - "source": [ - "## Installing the transformers library" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "izA3-6kffbdT" - }, - "source": [ - "Let's start by installing the huggingface transformers library so we can load our deep learning NLP model." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "To9ENLU90WGl", - "outputId": "6e5be1cb-9674-40e5-cfe7-17f562537556" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "%pip install -Uqq transformers" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "zQ-42fh0hjsF" - }, - "source": [ - "## Part 1. Using BERT for text classification." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Loading pretrained BERT." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we will be using the pretrained DistilBERT model. Here is an example of it:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'sequence': \"hello i'm a role model.\",\n", - " 'score': 0.052928753197193146,\n", - " 'token': 2535,\n", - " 'token_str': 'role'},\n", - " {'sequence': \"hello i'm a fashion model.\",\n", - " 'score': 0.03968575596809387,\n", - " 'token': 4827,\n", - " 'token_str': 'fashion'},\n", - " {'sequence': \"hello i'm a business model.\",\n", - " 'score': 0.03474372997879982,\n", - " 'token': 2449,\n", - " 'token_str': 'business'},\n", - " {'sequence': \"hello i'm a model model.\",\n", - " 'score': 0.03462280333042145,\n", - " 'token': 2944,\n", - " 'token_str': 'model'},\n", - " {'sequence': \"hello i'm a modeling model.\",\n", - " 'score': 0.018145091831684113,\n", - " 'token': 11643,\n", - " 'token_str': 'modeling'}]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from transformers import pipeline\n", - "\n", - "model_name = 'distilbert-base-uncased'\n", - "unmasker = pipeline('fill-mask', model_name)\n", - "unmasker(\"Hello I'm a [MASK] model.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here is how we can use the same model to extract features from our text:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([1, 23, 768])" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import torch\n", - "\n", - "from transformers import logging; logging.set_verbosity_error() # Ignore warning on model loading.\n", - "from transformers import DistilBertModel, DistilBertTokenizer\n", - "\n", - "tokenizer = DistilBertTokenizer.from_pretrained(model_name)\n", - "model = DistilBertModel.from_pretrained(model_name)\n", - "\n", - "text = 'Lorem ipsum dolor sit amet, consectetur adipiscing elit.'\n", - "tokenized_text = tokenizer(text, return_tensors='pt')\n", - "\n", - "with torch.no_grad():\n", - " output = model(**tokenized_text)\n", - "\n", - "output.last_hidden_state.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "zQ-42fh0hjsF" - }, - "source": [ - "## Loading the dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "zQ-42fh0hjsF" - }, - "source": [ - "We'll use pandas to read the dataset and load it into a dataframe." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "cyoj29J24hPX" - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "dataset_url = 'https://github.com/clairett/pytorch-sentiment-classification/raw/master/data/SST2/train.tsv'\n", - "dataset = pd.read_csv(dataset_url, delimiter='\\t', header=None)\n", - "dataset.columns = ['text', 'label']" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dMVE3waNhuNj" - }, - "source": [ - "For performance reasons, we'll only use 2,000 sentences from the dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "gTM3hOHW4hUY" - }, - "outputs": [], - "source": [ - "dataset = dataset[:2000]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "PRc2L89hh1Tf" - }, - "source": [ - "We can ask pandas how many sentences are labeled as \"positive\" (value 1) and how many are labeled \"negative\" (having the value 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "jGvcfcCP5xpZ", - "outputId": "2679553c-f061-4254-8b9d-b005529de44d" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "1 1041\n", - "0 959\n", - "Name: label, dtype: int64" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset['label'].value_counts()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "lZDBMn3wiSX6" - }, - "source": [ - "## Preparing the Dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "lZDBMn3wiSX6" - }, - "source": [ - "Before we can hand our sentences to BERT, we need to so some minimal processing to put them in the format it requires." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "lZDBMn3wiSX6" - }, - "source": [ - "### Tokenization" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "lZDBMn3wiSX6" - }, - "source": [ - "Our first step is to tokenize the sentences -- break them up into word and subwords in the format BERT is comfortable with." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "Dg82ndBA5xlN" - }, - "outputs": [], - "source": [ - "tokenized_texts = dataset['text'].apply(lambda x: tokenizer.encode(x, add_special_tokens=True)).values" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "mHwjUwYgi-uL" - }, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "mHwjUwYgi-uL" - }, - "source": [ - "### Padding" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "mHwjUwYgi-uL" - }, - "source": [ - "After tokenization, `tokenized_texts` is a list of sentences -- each sentences is represented as a list of tokens. We want BERT to process our examples all at once (as one batch). It's just faster that way. For that reason, we need to pad all lists to the same size, so we can represent the input as one 2-d array, rather than a list of lists (of different lengths)." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "id": "URn-DWJt5xhP" - }, - "outputs": [], - "source": [ - "max_len = max(len(text) for text in tokenized_texts)\n", - "padded_texts = torch.tensor([text + [0] * (max_len - len(text)) for text in tokenized_texts])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Mdjg306wjjmL" - }, - "source": [ - "Our dataset is now in the `padded_texts` variable, we can view its dimensions below:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "jdi7uXo95xeq", - "outputId": "cc0bd2d3-921f-4dcc-d619-bcb621b2e707" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([2000, 59])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "padded_texts.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "sDZBsYSDjzDV" - }, - "source": [ - "### Masking" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "sDZBsYSDjzDV" - }, - "source": [ - "If we directly send `padded_texts` to BERT, that would slightly confuse it. We need to create another variable to tell it to ignore (mask) the padding we've added when it's processing its input. That's what `attention_mask` is for:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "4K_iGRNa_Ozc", - "outputId": "8802e284-ba3c-4bd3-bc6c-d9830c033b30" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([2000, 59])" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "attention_mask = torch.where(padded_texts > 0, 1, 0)\n", - "attention_mask.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "plt.pcolormesh(attention_mask)\n", - "plt.colorbar();" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "jK-CQB9-kN99" - }, - "source": [ - "## And Now, Deep Learning!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "jK-CQB9-kN99" - }, - "source": [ - "Now that we have our model and inputs ready, let's run our model!\n", - "\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "id": "39UVjAV56PJz" - }, - "outputs": [], - "source": [ - "with torch.no_grad():\n", - " output = model(padded_texts, attention_mask)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FoCep_WVuB3v" - }, - "source": [ - "Let's slice only the part of the output that we need. That is the output corresponding the first token of each sentence. The way BERT does sentence classification, is that it adds a token called `[CLS]` (for classification) at the beginning of every sentence. The output corresponding to that token can be thought of as an embedding for the entire sentence.\n", - "\n", - "\n", - "\n", - "We'll save those in the `features` variable, as they'll serve as the features to our logitics regression model." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "id": "C9t60At16PVs" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(2000, 768)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "features = output.last_hidden_state[:, 0, :].numpy()\n", - "features.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_VZVU66Gurr-" - }, - "source": [ - "The labels indicating which sentence is positive and negative now go into the `labels` variable" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "id": "JD3fX2yh6PTx" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(2000,)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "labels = dataset['label'].values\n", - "labels.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "iaoEvM2evRx1" - }, - "source": [ - "## Classifier training" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "iaoEvM2evRx1" - }, - "source": [ - "Let's now split our datset into a training set and testing set (even though we're using 2,000 sentences from the SST2 training set)." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "id": "ddAqbkoU6PP9" - }, - "outputs": [], - "source": [ - "from sklearn.model_selection import train_test_split\n", - "\n", - "train_features, test_features, train_labels, test_labels = train_test_split(features, labels)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "B9bhSJpcv1Bl" - }, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "B9bhSJpcv1Bl" - }, - "source": [ - "## [Extra] Grid Search for Parameters" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "B9bhSJpcv1Bl" - }, - "source": [ - "We can dive into Logistic regression directly with the Scikit Learn default parameters, but sometimes it's worth searching for the best value of the C parameter, which determines regularization strength." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "id": "cyEwr7yYD3Ci" - }, - "outputs": [], - "source": [ - "# from sklearn.model_selection import GridSearchCV\n", - "\n", - "# parameters = {'C': np.linspace(0.0001, 100, 20)}\n", - "# grid_search = GridSearchCV(LogisticRegression(), parameters)\n", - "# grid_search.fit(train_features, train_labels)\n", - "\n", - "# print('best parameters: ', grid_search.best_params_)\n", - "# print('best scrores: ', grid_search.best_score_)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "KCT9u8vAwnID" - }, - "source": [ - "We now train the LogisticRegression model. If you've chosen to do the gridsearch, you can plug the value of C into the model declaration (e.g. `LogisticRegression(C=5.2)`)." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "gG-EVWx4CzBc", - "outputId": "9ae43345-4003-4dfe-bb22-cd9c2fa82fe0" - }, - "outputs": [], - "source": [ - "import warnings; warnings.simplefilter('ignore') # Ignore warning on model fitting.\n", - "from sklearn.linear_model import LogisticRegression\n", - "\n", - "lr_clf = LogisticRegression().fit(train_features, train_labels)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3rUMKuVgwzkY" - }, - "source": [ - "\n", - "\n", - "So how well does our model do in classifying sentences? One way is to check the accuracy against the testing dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "iCoyxRJ7ECTA", - "outputId": "45b90744-a478-45db-a420-2ebbaf0b9236" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.84" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lr_clf.score(test_features, test_labels)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "75oyhr3VxHoE" - }, - "source": [ - "How good is this score? What can we compare it against? Let's first look at a dummy classifier:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "lnwgmqNG7i5l", - "outputId": "fe7730c4-446b-4a1d-f4f2-1164c45a0a31" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dummy classifier score: 0.519 (+/- 0.00)\n" - ] - } - ], - "source": [ - "from sklearn.dummy import DummyClassifier\n", - "from sklearn.model_selection import cross_val_score\n", - "\n", - "clf = DummyClassifier()\n", - "\n", - "scores = cross_val_score(clf, train_features, train_labels)\n", - "print(\"Dummy classifier score: %0.3f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "7Lg4LOpoxSOR" - }, - "source": [ - "So our model clearly does better than a dummy classifier. But how does it compare against the best models?\n", - "\n", - "For reference, the [highest accuracy score](http://nlpprogress.com/english/sentiment_analysis.html) for this dataset is currently **96.8**. DistilBERT can be trained to improve its score on this task – a process called **fine-tuning** which updates BERT’s weights to make it achieve a better performance in this sentence classification task (which we can call the downstream task). The fine-tuned DistilBERT turns out to achieve an accuracy score of **90.7**. The full size BERT model achieves **94.9**.\n", - "\n", - "And that’s it! That’s a good first contact with BERT. The next step would be to head over to the documentation and try your hand at [fine-tuning](https://huggingface.co/transformers/examples.html#glue). You can also go back and switch from distilBERT to BERT and see how that works." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 152 - }, - "id": "EJQuqV6cnWQu", - "outputId": "402d109c-01bb-485d-a510-4be8684c9c06" - }, - "source": [ - "## Part 2: Looking back." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 152 - }, - "id": "EJQuqV6cnWQu", - "outputId": "402d109c-01bb-485d-a510-4be8684c9c06" - }, - "source": [ - "__Now it is your turn to reproduce the steps above.__\n", - "\n", - "We shall revisit the first homework and see whether we could improve the results a little bit more. The average ROC-AUC on test set was around $0.9$ (using the words embeddings). \n", - "\n", - "__Let's see whether we can beat it.__" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "kz8QBEXozHJx", - "outputId": "bdf0a0d8-2ac5-4dfd-a609-72011121abda" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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should_bancomment_text
500\"Those who're in advantageous positions are th...
2501Fartsalot56 says f**k you motherclucker!!
4501Are you a fool? \\n\\nI am sorry, but you seem t...
6501I AM NOT A VANDAL!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
8500Citing sources\\n\\nCheck out the Wikipedia:Citi...
\n", - "
" - ], - "text/plain": [ - " should_ban comment_text\n", - "50 0 \"Those who're in advantageous positions are th...\n", - "250 1 Fartsalot56 says f**k you motherclucker!!\n", - "450 1 Are you a fool? \\n\\nI am sorry, but you seem t...\n", - "650 1 I AM NOT A VANDAL!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n", - "850 0 Citing sources\\n\\nCheck out the Wikipedia:Citi..." - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import os; os.environ['TOKENIZERS_PARALLELISM'] = 'false' # Ignore warning on wget.\n", - "\n", - "!wget -q -nc https://raw.githubusercontent.com/neychev/made_nlp_course/master/datasets/comments_small_dataset/comments.tsv\n", - "dataset = pd.read_csv('comments.tsv', sep='\\t')\n", - "dataset[50::200]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's prepare data for our BERT model." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "tokenized_texts = dataset['comment_text'].apply(lambda x: tokenizer.encode(x, add_special_tokens=True, max_length=512, truncation=True)).values\n", - "\n", - "max_len = max(len(text) for text in tokenized_texts)\n", - "padded_texts = torch.tensor([text + [0] * (max_len - len(text)) for text in tokenized_texts])\n", - "\n", - "attention_mask = torch.where(padded_texts > 0, 1, 0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now move the model to GPU and use it for feature extraction." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "device(type='cuda', index=2)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gpu_num = 0\n", - "device = torch.device(f'cuda:{gpu_num}' if torch.cuda.is_available() else 'cpu')\n", - "device" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1000, 768)" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "\n", - "model.to(device)\n", - "batch_size = 16\n", - "features = []\n", - "with torch.no_grad():\n", - " for i in range(0, len(padded_texts), batch_size):\n", - " texts_batch = padded_texts[i : i + batch_size].to(device)\n", - " mask_batch = attention_mask[i : i + batch_size].to(device)\n", - " output = model(texts_batch, mask_batch)\n", - " batch_features = output.last_hidden_state[:, 0, :].cpu().numpy()\n", - " features.append(batch_features)\n", - "\n", - "features = np.concatenate(features, axis=0)\n", - "features.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now it is time to split our objects into train and test and train our classifier." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "target = dataset['should_ban'].values\n", - "train_features, test_features, y_train, y_test = train_test_split(features, target, test_size=0.5, random_state=42)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.862" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lr_clf = LogisticRegression(C=0.1)\n", - "lr_clf.fit(train_features, y_train)\n", - "lr_clf.score(test_features, y_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's also plot the ROC curve and calculate the AUC metric." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "from sklearn.metrics import roc_auc_score, roc_curve\n", - "\n", - "proba = lr_clf.predict_proba(train_features)[:, 1]\n", - "auc = roc_auc_score(y_train, proba)\n", - "plt.plot(*roc_curve(y_train, proba)[:2], label='%s AUC=%.4f' % ('train', auc))\n", - "\n", - "proba = lr_clf.predict_proba(test_features)[:, 1]\n", - "auc = roc_auc_score(y_test, proba)\n", - "plt.plot(*roc_curve(y_test, proba)[:2], label='%s AUC=%.4f' % ('test', auc))\n", - "\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "cc1hBVfbzHJ7" - }, - "source": [ - "So, how does it look? Did we achieve better results? \n", - "\n", - "Here come some further ideas:\n", - "\n", - "* Try using the larger BERT (e.g. BERT-base or BERT-large) and compare the results (be careful, they require more memory).\n", - "\n", - "* Using BERT output for translation? Why not ;)" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "machine_shape": "hm", - "name": "A Visual Notebook to Using BERT for the First Time.ipynb", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.9.7" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "057e5786db794af7ae7d9cb86cf271a7": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - 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