pytorch_translation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GMu08_zMAY7m"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from tqdm.notebook import tqdm\n",
    "import os\n",
    "\n",
    "import torch\n",
    "from torch import nn, Tensor\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.nn.utils.rnn import pad_sequence\n",
    "\n",
    "import math\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "try:\n",
    "    import youtokentome as yttm\n",
    "except:\n",
    "    # Install YouTokenToMe for tokenization\n",
    "    !pip install youtokentome\n",
    "    import youtokentome as yttm\n",
    "\n",
    "vocab_size = 2 ** 15 # TODO: maybe use power of two?\n",
    "PADDING_TOKEN = 0\n",
    "UNK_TOKEN = 1\n",
    "BOS_TOKEN = 2\n",
    "EOS_TOKEN = 3\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "BATCH_SIZE = 16\n",
    "\n",
    "# Choose dataset\n",
    "data_path = 'data'\n",
    "# path = f'{data_path}/paracrawl-clean'\n",
    "path = f'{data_path}/yandex'\n",
    "# Tokenizer model path.\n",
    "tokenizer_path = f'{path}_v{vocab_size}.tokenizer'\n",
    "\n",
    "# Model save path.\n",
    "save_path = 'models'\n",
    "sub_name = '_1'\n",
    "save_model_name = f'test_model{sub_name}.pth'\n",
    "model_save_path = os.path.join(save_path, save_model_name)\n",
    "\n",
    "if not os.path.exists(save_path):\n",
    "    os.makedirs(save_path)\n",
    "\n",
    "def load_files(path):\n",
    "    res = ([], [])\n",
    "    for i, ext in enumerate(['.en', '.ru']):\n",
    "        with open(path + ext, encoding='utf8') as in_file:\n",
    "            res[i].extend(in_file.readlines())\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 32"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vaMjWUZtg38R"
   },
   "source": [
    "## Preparation\n",
    "Update/Install packages and download datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "vmfrqBmzyNBY",
    "outputId": "88e898ca-1af8-4cc3-f60a-f6c2b279a55e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000000, 2)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_en, data_ru = load_files(path)\n",
    "\n",
    "raw_data = {'English' : [line for line in data_en], 'Russian': [line for line in data_ru]}\n",
    "\n",
    "df = pd.DataFrame(raw_data, columns=list(raw_data.keys()))\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "loAavSAPym44",
    "outputId": "f5e943c0-b0b6-4bc8-a253-4d7d4644b768"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000000, 2)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['en_len'] = df['English'].str.count(' ')\n",
    "df['ru_len'] = df['Russian'].str.count(' ')\n",
    "# df = df.query('ru_len < 30 & en_len < 30')\n",
    "# df = df.query('ru_len < en_len * 1.5 & ru_len * 1.5 > en_len')\n",
    "# df.sort_values(['ru_len', 'en_len'], ascending=[True, True], inplace=True)\n",
    "del df['en_len'], df['ru_len']\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "bRQEFSLt3zKx",
    "outputId": "868a427e-339d-4812-b1bd-551645add33b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200000, 2)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[:200000]\n",
    "# import nltk\n",
    "# df['English'] = df.English.apply(lambda x: ' '.join(nltk.word_tokenize(x)))\n",
    "# df['Russian'] = df.Russian.apply(lambda x: ' '.join(nltk.word_tokenize(x)))\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yYYvRotrzRq9"
   },
   "outputs": [],
   "source": [
    "# Create train, test, val sets.\n",
    "train, test = train_test_split(df, test_size=0.2)\n",
    "test, val = train_test_split(test, test_size=0.5)\n",
    "train.to_csv(os.path.join(data_path, 'train.csv'), index=False)\n",
    "test.to_csv(os.path.join(data_path, 'test.csv'), index=False)\n",
    "val.to_csv(os.path.join(data_path, 'val.csv'), index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "s2CY1bO1hMVj"
   },
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IZN-aua3JPTa"
   },
   "outputs": [],
   "source": [
    "if os.path.exists(tokenizer_path):\n",
    "    tokenizer = yttm.BPE(model=tokenizer_path)\n",
    "else:\n",
    "    # Create temp file with data to train tokenizer.\n",
    "    data_en, data_ru = load_files(path)\n",
    "    temp_file_path = 'tokenizer_text.temp'\n",
    "    with open(temp_file_path, 'w', encoding='utf8') as out_file:\n",
    "        out_file.write('\\n'.join(map(str.lower, data_en)))\n",
    "        out_file.write('\\n'.join(map(str.lower, data_ru)))\n",
    "    # Train tokenizer.\n",
    "    tokenizer = yttm.BPE.train(data=temp_file_path, vocab_size=vocab_size, model=tokenizer_path)\n",
    "    # Delete temp file.\n",
    "    os.remove(temp_file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "6UnqEDMXsd-C",
    "outputId": "8744bcd2-41b3-498f-9f9d-966b14cff2e1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: 133329 \n",
      "Val: 16692 \n",
      "Test: 16736\n"
     ]
    }
   ],
   "source": [
    "class TextDataset(torch.utils.data.Dataset):\n",
    "\n",
    "    __output_types = { 'id': yttm.OutputType.ID,\n",
    "                       'subword':yttm.OutputType.SUBWORD }\n",
    "\n",
    "    def __init__(self, csv_file, tokenizer, max_len=50, max_len_ratio=1.5):\n",
    "        self.tokenizer = tokenizer\n",
    "        df = pd.read_csv(csv_file)\n",
    "        # Tokenize sentences using tokenizer.\n",
    "        # TODO: Improve code by rewriting lambdas to smth else.\n",
    "        tokenize_lambda = lambda x: self.tokenize(x.lower().strip(), 'subword')\n",
    "        df['eng_enc'] = df.English.apply(tokenize_lambda)\n",
    "        df['rus_enc'] = df.Russian.apply(tokenize_lambda)\n",
    "        # Delete sentences that exceed the max length and max length ratio.\n",
    "        df['en_len'] = df['eng_enc'].str.len()\n",
    "        df['ru_len'] = df['rus_enc'].str.len()\n",
    "        df.query(f'ru_len < {max_len} & en_len < {max_len}', inplace=True)\n",
    "        df.query(f'ru_len < en_len * {max_len_ratio} & ru_len * {max_len_ratio} > en_len', inplace=True)\n",
    "        # Sort the values for less padding in batching.\n",
    "        df.sort_values(['ru_len', 'en_len'], ascending=[False, False], inplace=True)\n",
    "        # TODO: better unpacking\n",
    "        raw_src, raw_tgt = zip(df[['Russian', 'English']].T.values)\n",
    "        src, tgt = zip(df[['rus_enc', 'eng_enc']].T.values)\n",
    "        self.tgt, self.src = tgt[0], src[0]\n",
    "        self.raw_src, self.raw_tgt = raw_src[0], raw_tgt[0]\n",
    "        \n",
    "\n",
    "    def tokenize(self, s, output_type='id'):\n",
    "        \"\"\"Tokenize the sentence.\n",
    "        :param s: the sentence to tokenize\n",
    "        :param output_type: either 'id' or 'subword' for corresponding output\n",
    "        :return: tokenized sentence\"\"\"\n",
    "        return self.tokenizer.encode(s, output_type=self.__output_types[output_type],\n",
    "                                bos=True, eos=True)\n",
    "    def decode(self, tokens):\n",
    "        return self.tokenizer.id_to_subword(tokens)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.src)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        if torch.is_tensor(idx):\n",
    "            idx = idx.tolist()\n",
    "        src = self.src[idx]\n",
    "        src = [self.tokenizer.subword_to_id(token) for token in src]\n",
    "        tgt = self.tgt[idx]\n",
    "        tgt = [self.tokenizer.subword_to_id(token) for token in tgt]\n",
    "        return src, tgt\n",
    "\n",
    "def load_datasets(path, tokenizer, ext='.csv'):\n",
    "    res = []\n",
    "    for name in  ['train', 'val', 'test']:\n",
    "        dataset_path = os.path.join(path, name + ext)\n",
    "        res.append(TextDataset(dataset_path, tokenizer))\n",
    "    return res\n",
    "\n",
    "train_data, val_data, test_data = load_datasets(data_path, tokenizer)\n",
    "print('Train:', len(train_data),\n",
    "      '\\nVal:', len(val_data),\n",
    "      '\\nTest:', len(test_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "b_Ysl0QUn2c3"
   },
   "source": [
    "## Dataloaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qNztDWcjn2c5"
   },
   "outputs": [],
   "source": [
    "def my_collate(batch):\n",
    "    src, tgt = zip(*batch)\n",
    "    src = [Tensor(s) for s in src]\n",
    "    tgt = [Tensor(t) for t in tgt]\n",
    "    # TODO: Generalize padding value\n",
    "    src = pad_sequence(src, batch_first=True, padding_value=PADDING_TOKEN).long()\n",
    "    tgt = pad_sequence(tgt, batch_first=True, padding_value=PADDING_TOKEN).long()\n",
    "    return src.t(), tgt.t()\n",
    "\n",
    "def make_dataloaders(datasets, batch_size, num_workers=0):\n",
    "    res = []\n",
    "    for dataset in datasets:\n",
    "        res.append(DataLoader(dataset, batch_size=batch_size,\n",
    "                        shuffle=False, num_workers=num_workers, collate_fn=my_collate))\n",
    "    return res\n",
    "\n",
    "(train_iterator,\n",
    " val_iterator,\n",
    " test_iterator) = make_dataloaders([train_data, val_data, test_data],\n",
    "                                   batch_size=BATCH_SIZE,\n",
    "                                   num_workers=0)\n",
    "\n",
    "data_iterators = {\n",
    "    'train': train_iterator,\n",
    "    'val': val_iterator,\n",
    "    'test': test_iterator,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 141
    },
    "colab_type": "code",
    "id": "NQwdXgmPB36j",
    "outputId": "0f83abc0-01c2-42bf-b548-f3157ec38a11"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raw input: Она неутомимо и страстно атакует соперницу, стараясь решительно сокрушить ее (из 47 побед, 31 Кристи завершила нокаутом) - Кристи называет себя \"бойцом концентрированного действия\".\n",
      " She tirelessly and passionately batters her opponent trying to decidedly trounce her (she ended her fights with 31 knockouts out of 47 wins) - Christy called herself an \"action-packed fighter\".\n",
      "\n",
      "Tokenized input: ['<BOS>', '▁она', '▁неу', 'то', 'ми', 'мо', '▁и', '▁стра', 'стно', '▁ата', 'ку', 'ет', '▁сопер', 'ницу', ',', '▁ста', 'раясь', '▁решительно', '▁со', 'кру', 'шить', '▁ее', '▁(из', '▁47', '▁побе', 'д,', '▁31', '▁кри', 'сти', '▁заверши', 'ла', '▁но', 'ка', 'у', 'том', ')', '▁-', '▁кри', 'сти', '▁называет', '▁себя', '▁\"', 'бой', 'цом', '▁концентри', 'рованного', '▁действия', '\".', '<EOS>'] ['<BOS>', '▁she', '▁t', 'ire', 'lessly', '▁and', '▁passion', 'ately', '▁bat', 'ters', '▁her', '▁opponent', '▁trying', '▁to', '▁decided', 'ly', '▁tr', 'ounce', '▁her', '▁(s', 'he', '▁ended', '▁her', '▁fight', 's', '▁with', '▁31', '▁knock', 'out', 's', '▁out', '▁of', '▁47', '▁win', 's)', '▁-', '▁christ', 'y', '▁called', '▁herself', '▁an', '▁\"', 'action', '-p', 'ack', 'ed', '▁fighter', '\".', '<EOS>']\n",
      "src shape: torch.Size([49, 32])\n",
      "tgt shape: torch.Size([49, 32])\n",
      "['<BOS>', '▁она', '▁неу', 'то', 'ми', 'мо', '▁и', '▁стра', 'стно', '▁ата', 'ку', 'ет', '▁сопер', 'ницу', ',', '▁ста', 'раясь', '▁решительно', '▁со', 'кру', 'шить', '▁ее', '▁(из', '▁47', '▁побе', 'д,', '▁31', '▁кри', 'сти', '▁заверши', 'ла', '▁но', 'ка', 'у', 'том', ')', '▁-', '▁кри', 'сти', '▁называет', '▁себя', '▁\"', 'бой', 'цом', '▁концентри', 'рованного', '▁действия', '\".', '<EOS>']\n",
      "['<BOS>', '▁she', '▁t', 'ire', 'lessly', '▁and', '▁passion', 'ately', '▁bat', 'ters', '▁her', '▁opponent', '▁trying', '▁to', '▁decided', 'ly', '▁tr', 'ounce', '▁her', '▁(s', 'he', '▁ended', '▁her', '▁fight', 's', '▁with', '▁31', '▁knock', 'out', 's', '▁out', '▁of', '▁47', '▁win', 's)', '▁-', '▁christ', 'y', '▁called', '▁herself', '▁an', '▁\"', 'action', '-p', 'ack', 'ed', '▁fighter', '\".', '<EOS>']\n",
      "['<BOS>', '▁на', '▁самом', '▁деле,', '▁одна', '▁из', '▁причин,', '▁по', '▁которой', '▁я', '▁выбра', 'ла', '▁небольшое', '▁бу', 'нга', 'ло,', '▁в', '▁котором', '▁живу', '▁сейчас', '▁на', '▁га', 'вай', 'ях', '▁-', '▁это', '▁была', '▁кро', 'ше', 'чная', '▁бесполез', 'ная', '▁комната,', '▁присоеди', 'ня', 'ющаяся', '▁к', '▁другой', '▁кро', 'ше', 'чной', '▁бесполез', 'ной', '▁комнате', '▁с', '▁ра', 'кови', 'ной.', '<EOS>']\n",
      "['<BOS>', '▁actual', 'ly,', '▁one', '▁of', '▁the', '▁reasons', '▁i', '▁chose', '▁the', '▁little', '▁b', 'ung', 'al', 'ow', '▁where', '▁i', '▁live', '▁in', '▁haw', 'ai', 'i', '▁is', '▁because', '▁there', '▁was', '▁this', '▁tiny', '▁little', '▁us', 'eless', '▁room', '▁that', '▁att', 'aches', '▁to', '▁another', '▁tiny', '▁little', '▁us', 'eless', '▁room', '▁with', '▁a', '▁s', 'ink', '▁in', '▁it.', '<EOS>']\n"
     ]
    }
   ],
   "source": [
    "# Test dataset\n",
    "print('Raw input:', train_data.raw_src[0], train_data.raw_tgt[0])\n",
    "print('Tokenized input:', train_data.src[0], train_data.tgt[0])\n",
    "# Test iterator\n",
    "for _, (src, tgt) in enumerate(train_iterator):\n",
    "    print('src shape:', src.shape)\n",
    "    print('tgt shape:', tgt.shape)\n",
    "    for s, t in zip(src.t()[:2], tgt.t()[:2]):\n",
    "        print([tokenizer.id_to_subword(token) for token in s])\n",
    "        print([tokenizer.id_to_subword(token) for token in t])\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jYV4b6icn2dB"
   },
   "source": [
    "Defining our ``nn.Module`` and ``Optimizer``\n",
    "----------------\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_3y5Drcrn2dD"
   },
   "outputs": [],
   "source": [
    "class PositionalEncoding(nn.Module):\n",
    "    # From https://pytorch.org/tutorials/beginner/transformer_tutorial.html\n",
    "\n",
    "    def __init__(self, d_model, dropout=0.1):\n",
    "        super(PositionalEncoding, self).__init__()\n",
    "        self.dropout = nn.Dropout(p=dropout)\n",
    "        self.d_model = d_model\n",
    "    \n",
    "    def create_pe(self, seq_len):\n",
    "        pe = torch.zeros(seq_len, self.d_model)\n",
    "        position = torch.arange(0, seq_len, dtype=torch.float).unsqueeze(1)\n",
    "        div_term = torch.exp(torch.arange(0, self.d_model, 2).float() * (-math.log(10000.0) / self.d_model))\n",
    "        pe[:, 0::2] = torch.sin(position * div_term)\n",
    "        pe[:, 1::2] = torch.cos(position * div_term)\n",
    "        pe = pe.unsqueeze(0).transpose(0, 1)\n",
    "        return pe\n",
    "\n",
    "    def forward(self, x):\n",
    "        pe = self.create_pe(x.size(0))\n",
    "        x = x + pe.to(x.device)\n",
    "        return self.dropout(x)\n",
    "\n",
    "\n",
    "class TransformerModel(nn.Module):\n",
    "\n",
    "    def __init__(self, ntokens_src, ntokens_tgt, ninp, nhead, dim_feedforward, nlayers, pad_token, dropout=0.5):\n",
    "        super(TransformerModel, self).__init__()\n",
    "        from torch.nn import Transformer\n",
    "        self.model_type = 'Transformer'\n",
    "        self.ninp = ninp\n",
    "        self.pad_token = pad_token\n",
    "        self.masks = {\n",
    "            'src': None,\n",
    "            'tgt': None,\n",
    "            'memory': None,\n",
    "        }\n",
    "        # Token Encoders\n",
    "        self.src_encoder = nn.Embedding(ntokens_src, ninp)\n",
    "        self.tgt_encoder = nn.Embedding(ntokens_tgt, ninp)\n",
    "        # Positional Encoding\n",
    "        self.pos_encoder = PositionalEncoding(ninp, dropout)\n",
    "        # Transformer\n",
    "        self.transformer = Transformer(\n",
    "            d_model=ninp,\n",
    "            nhead=nhead,\n",
    "            num_encoder_layers=nlayers,\n",
    "            num_decoder_layers=nlayers,\n",
    "            dropout=dropout,\n",
    "            dim_feedforward=dim_feedforward,\n",
    "        )\n",
    "        self.out = nn.Linear(ninp, ntokens_tgt)\n",
    "\n",
    "        self.init_weights()\n",
    "\n",
    "    def _generate_square_subsequent_mask(self, sx, sy=None):\n",
    "        \"\"\"Generate matrix for seqential reveal of tokens.\"\"\"\n",
    "        sy = sy or sx\n",
    "        mask = (torch.triu(torch.ones((sx, sy))) == 1).transpose(0, 1)\n",
    "        mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))\n",
    "        return mask\n",
    "\n",
    "    def init_weights(self):\n",
    "        self.transformer._reset_parameters()\n",
    "    \n",
    "    def preprocess(self, x, x_type):\n",
    "        # Create masks\n",
    "        padding_mask = (x == self.pad_token).bool().t()\n",
    "        if self.masks[x_type] is None or self.masks[x_type].size(0) != len(x):\n",
    "            self.masks[x_type] = self._generate_square_subsequent_mask(len(x), len(x)).to(x.device)\n",
    "        \n",
    "        x_enc = self.src_encoder(x) if x_type == 'src' else self.tgt_encoder(x)\n",
    "        x_enc *= math.sqrt(self.ninp) # TODO: * or / or remove?\n",
    "        x_enc = self.pos_encoder(x_enc)\n",
    "        \n",
    "        return x_enc, self.masks[x_type], padding_mask\n",
    "        \n",
    "    def forward(self, src, tgt):\n",
    "\n",
    "        # TODO: Do we need memory mask?\n",
    "        if (    self.masks['memory'] is None or\n",
    "                self.masks['src'].size(0) != len(src) or\n",
    "                self.masks['tgt'].size(0) != len(tgt)):\n",
    "            self.masks['memory'] = self._generate_square_subsequent_mask(len(src), len(tgt)).to(src.device)\n",
    "        \n",
    "        src_enc, _, src_key_padding_mask = self.preprocess(src, 'src')\n",
    "        tgt_enc, _, tgt_key_padding_mask = self.preprocess(tgt, 'tgt')\n",
    "        memory_key_padding_mask = src_key_padding_mask.clone().detach()\n",
    "        \n",
    "        output = self.transformer(src_enc, tgt_enc,\n",
    "#                                   src_mask=self.masks['src'],\n",
    "                                  tgt_mask=self.masks['tgt'],\n",
    "#                                   memory_mask=self.masks['memory'],\n",
    "                                  src_key_padding_mask=src_key_padding_mask,\n",
    "                                  tgt_key_padding_mask=tgt_key_padding_mask,\n",
    "                                  memory_key_padding_mask=memory_key_padding_mask,\n",
    "                                  )\n",
    "        output = self.out(output)\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_model(model, criterion, optimizer, data_iterator, is_train_phase, n_words=1, desc=''):\n",
    "    \"\"\"Run one epoch of a model with given data.\n",
    "    \n",
    "    :param model: model to run on\n",
    "    :param criterion: critetion to use\n",
    "    :param optimizer: optimizer to use\n",
    "    :param data_iterator: iterator of (x, y) data tuples\n",
    "    :param is_train_phase: True if you want to train\n",
    "    :param n_words: number of words to predict, the bigger the longer it takes to run\n",
    "    :param desc: description for tqdm bar\n",
    "    :return: epoch loss\n",
    "    \"\"\"\n",
    "    if is_train_phase:\n",
    "        model.train() # Turn on the train mode\n",
    "    else:\n",
    "        model.eval()\n",
    "    total_loss = 0.0\n",
    "    pbar = tqdm(total=len(data_iterator), desc=desc, position=0, leave=True)\n",
    "    for i, (src, tgt) in enumerate(data_iterator):\n",
    "        src, tgt = src.to(device), tgt.to(device)\n",
    "        \n",
    "        tgt_losses = 0.0\n",
    "        # Predict `n_words` last words.\n",
    "        for j in range(max(1, len(tgt) - n_words), len(tgt)):\n",
    "            optimizer.zero_grad()\n",
    "            tgt_in = tgt[:j, :]\n",
    "            tgt_out = tgt[1:j+1, :]\n",
    "            \n",
    "            with torch.set_grad_enabled(is_train_phase):\n",
    "                output = model(src, tgt_in).transpose(1, 2)\n",
    "                loss = criterion(output, tgt_out)\n",
    "\n",
    "                if is_train_phase:\n",
    "                    loss.backward()\n",
    "                    # Clip gradient to deal with gradient explosion\n",
    "                    torch.nn.utils.clip_grad_norm_(model.parameters(), 1.)\n",
    "                    optimizer.step()\n",
    "            tgt_losses += loss.item()\n",
    "        total_loss += tgt_losses / j\n",
    "        pbar.update(1)\n",
    "        pbar.set_description(desc + f'- loss: {total_loss / (i+1):7.4}')\n",
    "        if i % 2000 == 0:\n",
    "            translate(model, 'Машинное обучение это здорово!', verbose=True)\n",
    "            rand_ind = np.random.randint(0, len(test_data))\n",
    "            translate(model, test_data.raw_src[rand_ind], verbose=True)\n",
    "    return total_loss / (i+1)\n",
    "\n",
    "def train_model(model, n_epochs, data_iterators,\n",
    "                criterion, optimizer, n_words=1, scheduler=None, model_save_path=None):\n",
    "    stats = {'train':{'loss':[]},\n",
    "             'val':{'loss':[]}}\n",
    "    best_loss = None\n",
    "    \n",
    "    for epoch in range(n_epochs):\n",
    "        lr = optimizer.state_dict()['param_groups'][0]['lr']\n",
    "        print(f'------------ Epoch {epoch}; lr: {lr:.5f} ------------')\n",
    "        for phase in ['train', 'val']:\n",
    "            desc = f'{phase.title()} Epoch #{epoch} '\n",
    "            epoch_loss = run_model(model, criterion, optimizer,\n",
    "                                   data_iterators[phase], phase == 'train',\n",
    "                                   n_words, desc)\n",
    "            stats[phase]['loss'].append(epoch_loss)\n",
    "            print_hist = lambda l: ' -> '.join(map(lambda x:f\"{x:.4}\", l[-2:]))\n",
    "            tqdm.write(f'{phase.title()} Loss: ' + print_hist(stats[phase]['loss']))\n",
    "        if best_loss == None or stats['val']['loss'][-1] < best_loss:\n",
    "            best_loss = stats['val']['loss'][-1]\n",
    "            print('Smallest val loss')\n",
    "            print('Saving model...')\n",
    "            if model_save_path:\n",
    "                try:\n",
    "                    torch.save(model, model_save_path)\n",
    "                    print('Saved successfully')\n",
    "                except FileNotFoundError:\n",
    "                    print('Error during saving!')\n",
    "        try:\n",
    "            translate(model, 'Машинное обучение это здорово!', verbose=True)\n",
    "            rand_ind = np.random.randint(0, len(test_data))\n",
    "            translate(model, test_data.raw_src[rand_ind], verbose=True)\n",
    "        except:\n",
    "            print('Error while translation.')\n",
    "        if scheduler:\n",
    "            scheduler.step()\n",
    "    return stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OOgM82TGthyA"
   },
   "outputs": [],
   "source": [
    "ntokens_src = tokenizer.vocab_size() # the size of vocabulary\n",
    "ntokens_tgt = tokenizer.vocab_size() # the size of vocabulary\n",
    "pad_token = PADDING_TOKEN\n",
    "emsize = 256 # embedding dimension\n",
    "nhid = 256 # the dimension of the feedforward network model in nn.TransformerEncoder\n",
    "nlayers = 4 # the number of nn.TransformerEncoderLayer in nn.TransformerEncoder\n",
    "nhead = 4 # the number of heads in the multiheadattention models\n",
    "dropout = 0.1 # the dropout value\n",
    "# model = TransformerModel(ntokens_src, ntokens_tgt, emsize, nhead, nhid, nlayers, pad_token, dropout).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model save path.\n",
    "save_path = 'models'\n",
    "sub_name = '_1_2'\n",
    "save_model_name = f'test_model{sub_name}.pth'\n",
    "model_save_path = os.path.join(save_path, save_model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# torch.save(model, model_save_path)\n",
    "model = torch.load(model_save_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lNl9Nrjon2dO"
   },
   "outputs": [],
   "source": [
    "# Ignore padding index during the loss computation.\n",
    "PAD_IDX = PADDING_TOKEN\n",
    "criterion = nn.CrossEntropyLoss(ignore_index=PAD_IDX, reduction='mean')\n",
    "lr = 0.1 # learning rate\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=lr)\n",
    "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "vXr1J0fQ_TP9",
    "outputId": "e86488c3-ba8e-4f80-89f2-4c9b7c8582fd",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------ Epoch 0; lr: 0.10000 ------------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2945356db5864064a435f8d1ebf835be",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Train Epoch #0 ', max=4167.0, style=ProgressStyle(descrip…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------ Translation ------------\n",
      "Input: Машинное обучение это здорово!\n",
      "Output weights:\n",
      "  0 {'▁the': 0.11004267632961273, '▁this': 0.07904434949159622, '▁it': 0.059231411665678024}\n",
      "  1 {'▁way': 0.01204590406268835, '▁d': 0.0060770632699131966, '▁second': 0.005667828489094973}\n",
      "  2 {'▁to': 0.1039687916636467, '▁of': 0.09857677668333054, '▁it': 0.071696437895298}\n",
      "translation: <BOS> the way to make it a great way to make it a great way to make it a great way to understand the way!<EOS>\n",
      "------------ Translation ------------\n",
      "Input: У меня уже есть трудовой опыт с немцами и итальянцами и могу констатировать, что с чехами у меня большее взаимопонимание.\n",
      "\n",
      "Output weights:\n",
      "  0 {'▁the': 0.11565890163183212, '▁it': 0.05007937550544739, '▁i': 0.029847433790564537}\n",
      "  1 {'▁second': 0.011151408776640892, '▁idea': 0.009973309934139252, '▁decision': 0.008864009752869606}\n",
      "  2 {'▁reason': 0.037126556038856506, '▁thing': 0.02684709243476391, '▁stage': 0.013499141670763493}\n",
      "translation: <BOS> the second reason for the purpose of the health care and the problem of the problem is that the problem is to be able to get the problem of the environment.<EOS>\n",
      "------------ Translation ------------\n",
      "Input: Машинное обучение это здорово!\n",
      "Output weights:\n",
      "  0 {'▁the': 0.042829856276512146, '▁let': 0.022413402795791626, '▁dear': 0.017622051760554314}\n",
      "  1 {'▁d': 0.007004592102020979, '▁l': 0.005607934202998877, '▁g': 0.005372087005525827}\n",
      "  2 {'ive': 0.02298927679657936, 'ess': 0.018639933317899704, 'amp': 0.01809670589864254}\n",
      "translation: <BOS> the dive of the sunbard is a great deal of the holy ghost of the holy ghost.<EOS>\n",
      "------------ Translation ------------\n",
      "Input: Был ли это пророческий взгляд на изменяющий мир эффект, который это знание окажет на каждого человека, как только подавленной истине, наконец, позволят увидеть дневной свет?\n",
      "\n",
      "Output weights:\n",
      "  0 {'▁the': 0.2639942765235901, '▁a': 0.03171581029891968, '▁an': 0.010662046261131763}\n",
      "  1 {'▁main': 0.006451605353504419, '▁second': 0.005227240733802319, '▁best': 0.00504894508048892}\n",
      "  2 {'▁thing': 0.061098065227270126, '▁reason': 0.021293748170137405, '▁body': 0.010806511156260967}\n",
      "translation: <BOS> the main thing is that the right of the universe is the right of the universe of the universe of the universe of the universe.<EOS>\n",
      "------------ Translation ------------\n",
      "Input: Машинное обучение это здорово!\n",
      "Output weights:\n",
      "  0 {'▁let': 0.06464803218841553, '▁the': 0.05594506114721298, '▁you': 0.022700974717736244}\n",
      "  1 {'▁us': 0.37356752157211304, '▁me': 0.2183918058872223, '▁the': 0.0705682560801506}\n",
      "  2 {'▁know': 0.0402064174413681, '▁get': 0.037184007465839386, '▁make': 0.037175849080085754}\n",
      "translation: <BOS> let us know the way to do it.<EOS>\n",
      "------------ Translation ------------\n",
      "Input: В случаях чрезвычайных рыночных или экономических ситуаций средства пенсионного плана кратковременно можно держать до 100% активов пенсионного плана в виде денежных средств в банке-держателе и во вложениях в кредитных учреждениях.\n",
      "\n",
      "Output weights:\n",
      "  0 {'▁the': 0.3256310224533081, '▁a': 0.01942128874361515, '▁there': 0.017632512375712395}\n",
      "  1 {'▁main': 0.02255082130432129, '▁third': 0.01019490510225296, '▁state': 0.009901966899633408}\n",
      "  2 {'▁element': 0.012425464577972889, '▁form': 0.011839941143989563, '▁factor': 0.010558940470218658}\n",
      "translation: <BOS> the main element is to be found.<EOS>\n",
      "Train Loss: 0.2361\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c95537c437214fe894e8ab8f1fb77c45",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Val Epoch #0 ', max=522.0, style=ProgressStyle(descriptio…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------ Translation ------------\n",
      "Input: Машинное обучение это здорово!\n",
      "Output weights:\n",
      "  0 {'▁let': 0.04806356132030487, \"▁it's\": 0.040228333324193954, '▁the': 0.024042978882789612}\n",
      "  1 {'▁us': 0.4976542294025421, '▁me': 0.18254466354846954, '▁the': 0.0315854549407959}\n",
      "  2 {'▁know': 0.037218738347291946, '▁see': 0.034714680165052414, '▁understand': 0.031841401010751724}\n",
      "translation: <BOS> let us know<EOS>\n",
      "------------ Translation ------------\n",
      "Input: ArcSight ESM контролирует все события по всему предприятию и применяет мощные инструменты для анализа и корреляции с целью выявления деловых и технологических угроз.\n",
      "\n",
      "Output weights:\n",
      "  0 {'▁the': 0.059592097997665405, '▁a': 0.010768134146928787, '▁special': 0.007886426523327827}\n",
      "  1 {'▁main': 0.01938093639910221, '▁best': 0.012697727419435978, '▁key': 0.00845651887357235}\n",
      "  2 {'▁features': 0.04411599412560463, '▁thing': 0.024239061400294304, '▁feature': 0.021930957213044167}\n",
      "translation: <BOS> the main features<EOS>\n",
      "Val Loss: 0.4337\n",
      "Smallest val loss\n",
      "Saving model...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kzvda\\Anaconda3\\envs\\tabnine\\lib\\site-packages\\torch\\serialization.py:256: UserWarning: Couldn't retrieve source code for container of type TransformerModel. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "C:\\Users\\kzvda\\Anaconda3\\envs\\tabnine\\lib\\site-packages\\torch\\serialization.py:256: UserWarning: Couldn't retrieve source code for container of type PositionalEncoding. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved successfully\n",
      "------------ Translation ------------\n",
      "Input: Машинное обучение это здорово!\n",
      "Output weights:\n",
      "  0 {'▁let': 0.04806356132030487, \"▁it's\": 0.040228333324193954, '▁the': 0.024042978882789612}\n",
      "  1 {'▁us': 0.4976542294025421, '▁me': 0.18254466354846954, '▁the': 0.0315854549407959}\n",
      "  2 {'▁know': 0.037218738347291946, '▁see': 0.034714680165052414, '▁understand': 0.031841401010751724}\n",
      "translation: <BOS> let us know<EOS>\n",
      "------------ Translation ------------\n",
      "Input: Учитывая глобальный характер Интернета, пользование Сайтом для сбора и обработки личной информации неизбежно подразумевает передачу данных на международной основе.\n",
      "\n",
      "Output weights:\n",
      "  0 {'▁the': 0.04498930275440216, '▁a': 0.01855502650141716, '▁special': 0.00816279835999012}\n",
      "  1 {'▁main': 0.012413336895406246, '▁author': 0.00936434231698513, '▁current': 0.00710848206654191}\n",
      "  2 {'▁features': 0.032591190189123154, '▁element': 0.025745755061507225, '▁topic': 0.023528005927801132}\n",
      "translation: <BOS> the main features<EOS>\n",
      "------------ Epoch 1; lr: 0.09000 ------------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "add1750cbbfa41c998345586bd861e53",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Train Epoch #1 ', max=4167.0, style=ProgressStyle(descrip…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------ Translation ------------\n",
      "Input: Машинное обучение это здорово!\n",
      "Output weights:\n",
      "  0 {'▁let': 0.04936174303293228, \"▁it's\": 0.04003230854868889, '▁the': 0.024674812331795692}\n",
      "  1 {'▁us': 0.4916578233242035, '▁me': 0.18118657171726227, '▁the': 0.03361755982041359}\n",
      "  2 {'▁know': 0.037714261561632156, '▁see': 0.03622036799788475, '▁understand': 0.03315741941332817}\n",
      "translation: <BOS> let us know<EOS>\n",
      "------------ Translation ------------\n",
      "Input: 310 на первом этапе Принципиальное соглашение с МФ и 316 на втором о поэтапном (два этапа) отказе от этапеc) БМ к концу 2008 года 127 203?\n",
      "\n",
      "Output weights:\n",
      "  0 {'▁the': 0.07105135917663574, '▁a': 0.015260101296007633, '▁special': 0.008429670706391335}\n",
      "  1 {'▁main': 0.017750665545463562, '▁author': 0.01018123421818018, '▁current': 0.009392685256898403}\n",
      "  2 {'▁thing': 0.024097789078950882, '▁topic': 0.019400086253881454, '▁event': 0.014818182215094566}\n",
      "translation: <BOS> the main thing<EOS>\n"
     ]
    }
   ],
   "source": [
    "torch.cuda.empty_cache()\n",
    "n_epochs = 3\n",
    "n_words = 1\n",
    "stats = train_model(model, n_epochs, data_iterators,\n",
    "                    criterion, optimizer, n_words, scheduler, model_save_path)\n",
    "\n",
    "n_epochs = 1\n",
    "n_words = 2\n",
    "stats = train_model(model, n_epochs, data_iterators,\n",
    "                    criterion, optimizer, n_words, scheduler, model_save_path)\n",
    "\n",
    "n_epochs = 5\n",
    "n_words = 1\n",
    "stats = train_model(model, n_epochs, data_iterators,\n",
    "                    criterion, optimizer, n_words, scheduler, model_save_path)\n",
    "# 1.444"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NtY1RCJnLQA_"
   },
   "outputs": [],
   "source": [
    "def subword_to_str(tokens):\n",
    "    return ''.join(tokens).replace('▁', ' ')\n",
    "\n",
    "def tokens_to_str(tokens):\n",
    "    return subword_to_str([tokenizer.id_to_subword(ix) for ix in tokens])\n",
    "\n",
    "def translate(model, text, max_len=80, verbose=False):\n",
    "    model.eval()\n",
    "    \n",
    "    if verbose:\n",
    "        print('------------ Translation ------------')\n",
    "        print('Input:', text)\n",
    "    # Prepare text\n",
    "    src = tokenizer.encode(text, output_type=yttm.OutputType.ID,\n",
    "                           bos=True, eos=True)\n",
    "    src = Tensor(src).long().to(device)\n",
    "    # Run encoder\n",
    "    src_enc, src_mask, _ = model.preprocess(src, 'src')\n",
    "    e_outputs = model.transformer.encoder(src_enc, \n",
    "                                          src_mask,\n",
    "                                          )\n",
    "    \n",
    "    # Prepare tensor for answers\n",
    "    outputs = torch.zeros(max_len).type_as(src.data)\n",
    "    # Set the first token as '<sos>'\n",
    "    outputs[0] = torch.LongTensor([BOS_TOKEN])\n",
    "    vals = []\n",
    "    for i in range(1, max_len):\n",
    "        outputs_enc, tgt_mask, _ = model.preprocess(outputs[:i].unsqueeze(1), 'tgt')\n",
    "#         memory_mask = model._generate_square_subsequent_mask(len(src), i+1).to(src.device)\n",
    "        d_out = model.transformer.decoder(outputs_enc, e_outputs,\n",
    "                                          tgt_mask=tgt_mask,\n",
    "#                                           memory_mask=memory_mask,\n",
    "                                          )\n",
    "        out = model.out(d_out)\n",
    "        out = F.softmax(out, dim=-1)\n",
    "        val, ix = out.data.topk(3, dim=-1)\n",
    "        outputs[i] = ix[-1][0][0]\n",
    "        if outputs[i] == EOS_TOKEN:\n",
    "            break\n",
    "    result = tokens_to_str(outputs[:i+1])\n",
    "    if verbose:\n",
    "        print('Output weights:')\n",
    "        for j in range(min(3, i)):\n",
    "            print(f'  {j}', {tokenizer.id_to_subword(k):v.item()\n",
    "                             for k, v in zip(ix[j][0], val[j][0])})\n",
    "        print('translation:', result)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------ Translation ------------\n",
      "Input: Машинное обучение это здорово!\n",
      "Output weights:\n",
      "  0 {'▁the': 0.10451338440179825, '▁this': 0.08264591544866562, '▁it': 0.06017918884754181}\n",
      "  1 {'▁way': 0.013550149276852608, '▁best': 0.00585717149078846, '▁d': 0.005779571365565062}\n",
      "  2 {'▁to': 0.10261761397123337, '▁of': 0.08953908085823059, '▁it': 0.07421953231096268}\n",
      "translation: <BOS> the way to make it easy to understand the way of the brain is to understand the way of the universe!<EOS>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'<BOS> the way to make it easy to understand the way of the brain is to understand the way of the universe!<EOS>'"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "translate(model, 'Машинное обучение это здорово!', verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------ Translation ------------\n",
      "Input: При участии астронавтки Хайди Стефанишин-Пайпер и всемирно известного писателя Пауло Коэльо состоялось награждение победителей стипендиальной программы Фонда Виктора Пинчука \"Завтра.\n",
      "\n",
      "Output weights:\n",
      "  0 {'▁the': 0.16819918155670166, '▁a': 0.019818376749753952, '▁8.': 0.009165264666080475}\n",
      "  1 {'▁main': 0.007805072236806154, '▁author': 0.0066763185895979404, '▁second': 0.006010976620018482}\n",
      "  2 {'▁idea': 0.004995526280254126, '▁award': 0.00440758652985096, '▁material': 0.004048016853630543}\n",
      "translation: <BOS> the main idea of the kel's office is to be the main part of the kitten.<EOS>\n",
      "--------------\n",
      "real: The astronaut Heidi Stefanyshyn-Piper and the world famous writer Paulo Coelho attended the ceremony of rewarding winners of the scholarship programme of Victor Pinchuk Fund ZAVTRA.UA\n",
      "\n"
     ]
    }
   ],
   "source": [
    "index = np.random.randint(0, 50)\n",
    "translate(model, train_data.raw_src[index], verbose=True)\n",
    "print('--------------')\n",
    "print('real:', train_data.raw_tgt[index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "def translate_beam(model, text, max_len=10, beam_capacity=3, verbose=False):\n",
    "    \"\"\"\n",
    "    Algorithm: https://www.youtube.com/watch?v=RLWuzLLSIgw\n",
    "    \"\"\"\n",
    "    model.eval()\n",
    "    if verbose:\n",
    "        print('------------ Translation ------------')\n",
    "        print('Input:', text)\n",
    "    # Prepare text\n",
    "    src = tokenizer.encode(text, output_type=yttm.OutputType.ID,\n",
    "                           bos=True, eos=True)\n",
    "    src = Tensor(src).long().to(device)\n",
    "    # Run encoder\n",
    "    src_enc, src_mask, _ = model.preprocess(src, 'src')\n",
    "    e_outputs = model.transformer.encoder(src_enc, \n",
    "#                                           src_mask,\n",
    "                                          )\n",
    "\n",
    "    # Prepare tensor for answers\n",
    "    basic_vec = torch.zeros(max_len).type_as(src.data)\n",
    "    basic_vec[0] = torch.LongTensor([BOS_TOKEN])\n",
    "\n",
    "    beam_pool = [(basic_vec, 1.0)]\n",
    "\n",
    "    def beam_filter(pool, top_k=beam_capacity):\n",
    "        return sorted(pool, key=lambda x: x[1], reverse=True)[:top_k]\n",
    "\n",
    "    for i in range(1, max_len):\n",
    "        if verbose:\n",
    "            print(\"Beam epoch: \", i)\n",
    "        new_pool = []\n",
    "        # For each candidate path:\n",
    "        for beam, old_prob in beam_pool:\n",
    "            outputs_enc, tgt_mask, _ = model.preprocess(beam[:i].unsqueeze(1), 'tgt')\n",
    "            d_out = model.transformer.decoder(outputs_enc, e_outputs,\n",
    "                                              tgt_mask=tgt_mask,\n",
    "                                              )\n",
    "            out = model.out(d_out).cpu().detach()\n",
    "            out = F.softmax(out, dim=-1)\n",
    "            probs, ixs = out[-1, :].topk(beam_capacity)\n",
    "            for prob, token_id in zip(probs.squeeze(), ixs.squeeze()):\n",
    "                tmp_beam = beam.clone()\n",
    "                tmp_beam[i] = token_id.item()\n",
    "                new_pool.append((tmp_beam, prob * old_prob))\n",
    "        beam_pool = beam_filter(new_pool)\n",
    "        if verbose:\n",
    "            for beam, old_prob in beam_pool:\n",
    "                print(\"Candidate '{}' with prob: {:.7f}\".format(\n",
    "                    tokens_to_str(beam[1:i + 1]), prob * old_prob\n",
    "                ))\n",
    "        # Stop if EOS_TOKEN\n",
    "        if beam_pool[0][0][i] == EOS_TOKEN:\n",
    "            break\n",
    "    the_best = beam_filter(beam_pool, 1)[0][0]\n",
    "    # Cut by EOS_TOKEN\n",
    "#     if EOS_TOKEN in the_best:\n",
    "#         i = (the_best == EOS_TOKEN).nonzero()[0]\n",
    "    result = tokens_to_str(the_best[:i+1])\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<BOS> the author of the kings of the russian federation of the ussr, and the general assembly of the ussr.<EOS>'"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "translate_beam(model, train_data.raw_src[index], verbose=False, beam_capacity=4, max_len=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test BLEU score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b02489582f7c49f791ae332cadda28df",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Test BLEU score', max=16736.0, style=ProgressStyle(descri…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8f9d9e69d9f94e00a9d4d2dc276376c5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=16736.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test BLEU score - 0.2212\n"
     ]
    }
   ],
   "source": [
    "from nltk.translate.bleu_score import corpus_bleu\n",
    "references = []\n",
    "candidates = []\n",
    "pbar = tqdm(total=len(test_data), desc='Test BLEU score')\n",
    "# TODO: batch translation.\n",
    "for raw_src, raw_tgt in tqdm(zip(test_data.raw_src[:30], test_data.raw_tgt), total=len(test_data)):\n",
    "    references.append([raw_tgt])\n",
    "    candidate = translate_beam(model, raw_src, beam_capacity=4, max_len=50)\n",
    "    candidate = candidate.replace('<BOS>', '').replace('<EOS>', '')\n",
    "    candidates.append(candidate)\n",
    "    pbar.update(1)\n",
    "score = corpus_bleu(references, candidates, weights=(0.5, 0.5, 0, 0))\n",
    "print(f'Test BLEU score - {score:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test BLEU score - 0.2564\n"
     ]
    }
   ],
   "source": [
    "score = corpus_bleu(references, candidates, weights=(0.5, 0.5, 0, 0))\n",
    "print(f'Test BLEU score - {score:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data/truth.txt', encoding='utf8') as in_file:\n",
    "    sentences = in_file.readlines()\n",
    "    sentences = list(map(lambda s: s.lower().strip(), sentences))\n",
    "#     encode = lambda s: tokenizer.encode(s, output_type=yttm.OutputType.ID, bos=True, eos=True)\n",
    "#     truth_data = [encode(s) for s in sentences]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'исполнительный совет'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentences[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b2741ceb427b46578fd87ab999c48449",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=7768.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-39-fad71f7ced9d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mpred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0msentence\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msentences\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mpred\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtranslate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msentence\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_len\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m150\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-13-acb131c89c16>\u001b[0m in \u001b[0;36mtranslate\u001b[1;34m(model, text, max_len, custom_string, verbose)\u001b[0m\n\u001b[0;32m     30\u001b[0m \u001b[1;31m#         memory_mask = model._generate_square_subsequent_mask(len(src), i+1).to(src.device)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     31\u001b[0m         d_out = model.transformer.decoder(outputs_enc, e_outputs,\n\u001b[1;32m---> 32\u001b[1;33m                                           \u001b[0mtgt_mask\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtgt_mask\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     33\u001b[0m \u001b[1;31m#                                           memory_mask=memory_mask,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     34\u001b[0m                                           )\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tabnine\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    545\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    546\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 547\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    548\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    549\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tabnine\\lib\\site-packages\\torch\\nn\\modules\\transformer.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, tgt, memory, tgt_mask, memory_mask, tgt_key_padding_mask, memory_key_padding_mask)\u001b[0m\n\u001b[0;32m    214\u001b[0m                                     \u001b[0mmemory_mask\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmemory_mask\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    215\u001b[0m                                     \u001b[0mtgt_key_padding_mask\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtgt_key_padding_mask\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 216\u001b[1;33m                                     memory_key_padding_mask=memory_key_padding_mask)\n\u001b[0m\u001b[0;32m    217\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    218\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tabnine\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    545\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    546\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 547\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    548\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    549\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tabnine\\lib\\site-packages\\torch\\nn\\modules\\transformer.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, tgt, memory, tgt_mask, memory_mask, tgt_key_padding_mask, memory_key_padding_mask)\u001b[0m\n\u001b[0;32m    323\u001b[0m         \"\"\"\n\u001b[0;32m    324\u001b[0m         tgt2 = self.self_attn(tgt, tgt, tgt, attn_mask=tgt_mask,\n\u001b[1;32m--> 325\u001b[1;33m                               key_padding_mask=tgt_key_padding_mask)[0]\n\u001b[0m\u001b[0;32m    326\u001b[0m         \u001b[0mtgt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtgt\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdropout1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtgt2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    327\u001b[0m         \u001b[0mtgt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnorm1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtgt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tabnine\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    545\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    546\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 547\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    548\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    549\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tabnine\\lib\\site-packages\\torch\\nn\\modules\\activation.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, query, key, value, key_padding_mask, need_weights, attn_mask)\u001b[0m\n\u001b[0;32m    781\u001b[0m                 \u001b[0mtraining\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtraining\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    782\u001b[0m                 \u001b[0mkey_padding_mask\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkey_padding_mask\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mneed_weights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mneed_weights\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 783\u001b[1;33m                 attn_mask=attn_mask)\n\u001b[0m\u001b[0;32m    784\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    785\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tabnine\\lib\\site-packages\\torch\\nn\\functional.py\u001b[0m in \u001b[0;36mmulti_head_attention_forward\u001b[1;34m(query, key, value, embed_dim_to_check, num_heads, in_proj_weight, in_proj_bias, bias_k, bias_v, add_zero_attn, dropout_p, out_proj_weight, out_proj_bias, training, key_padding_mask, need_weights, attn_mask, use_separate_proj_weight, q_proj_weight, k_proj_weight, v_proj_weight, static_k, static_v)\u001b[0m\n\u001b[0;32m   3096\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3097\u001b[0m     \u001b[0mqkv_same\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mequal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mequal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3098\u001b[1;33m     \u001b[0mkv_same\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mequal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3099\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3100\u001b[0m     \u001b[0mtgt_len\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbsz\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0membed_dim\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mquery\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "pred = []\n",
    "for sentence in tqdm(sentences):\n",
    "    pred.append(translate(model, sentence, max_len=150))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('answer.txt', 'w') as out_file:\n",
    "    out_file.writelines('\\n'.join(pred))"
   ]
  }
 ],
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