NMT_Mashalov.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/NMashalov/2021-DGM-MIPT-course/blob/main/NMT_Mashalov.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qHX7MZ-tYyr-"
      },
      "source": [
        "# Lab assignment 02"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-1OX-4jiYysC"
      },
      "source": [
        "### 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",
        "* Transformer/CNN/<whatever you select> encoder (with or without positional encoding)\n",
        "\n",
        "* attention/self-attention mechanism\n",
        "\n",
        "* pretraining the language models (for decoder and encoder)\n",
        "\n",
        "* or just fine-tunning BART/ELECTRA/... ;)\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": null,
      "metadata": {
        "id": "yBws38K8YysC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "deca8cac-c9ec-402d-a51d-5fa48bda91c4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.2/64.2 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m44.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "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 torchtext==0.6.0 nltk -q"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yUxgx2RWYysD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "96451eb1-583d-4533-bba4-e87a6b65d116"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset not found locally. Downloading from github.\n",
            "--2023-05-21 19:06:42--  https://raw.githubusercontent.com/neychev/made_nlp_course/master/datasets/Machine_translation_EN_RU/data.txt\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 12905334 (12M) [text/plain]\n",
            "Saving to: ‘data.txt’\n",
            "\n",
            "\rdata.txt              0%[                    ]       0  --.-KB/s               \rdata.txt            100%[===================>]  12.31M  --.-KB/s    in 0.07s   \n",
            "\n",
            "2023-05-21 19:06:42 (184 MB/s) - ‘data.txt’ saved [12905334/12905334]\n",
            "\n"
          ]
        }
      ],
      "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 = 'data.txt'\n",
        "if not os.path.exists(path_do_data):\n",
        "    print(\"Dataset not found locally. Downloading from github.\")\n",
        "    !wget https://raw.githubusercontent.com/neychev/made_nlp_course/master/datasets/Machine_translation_EN_RU/data.txt -nc\n",
        "    path_do_data = './data.txt'"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Baseline solution BLEU score is quite low. Try to achieve at least __21__ BLEU on the test set. \n",
        "The checkpoints are:\n",
        "\n",
        "* __21__ - minimal score to submit the homework, 30% of points\n",
        "\n",
        "* __25__ - good score, 70% of points\n",
        "\n",
        "* __27__ - excellent score, 100% of points"
      ],
      "metadata": {
        "id": "CPc7No7qZhe5"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L0ivhaMsYysE"
      },
      "source": [
        "### Warning! The code below is deeeeeeeply deprecated and is is provided only as simple guide.\n",
        "We suggest you to stick to most recent pipelines here, e.g. by Huggingface: \n",
        "* Example notebook: [link](https://github.com/huggingface/notebooks/blob/main/examples/translation.ipynb)\n",
        "* Converting your own dataset to specific format: [link](https://discuss.huggingface.co/t/correct-way-to-create-a-dataset-from-a-csv-file/15686/15)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FfAUkvESYysH"
      },
      "outputs": [],
      "source": [
        "# old deprecated code\n",
        "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": {
        "id": "3EQGOJgiYysH"
      },
      "source": [
        "### Main part\n",
        "__Here comes the preprocessing. Do not hesitate to use BPE or more complex preprocessing ;)__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w4KaKNrOYysI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "dbf12b56-9942-49f1-923f-1b583440332b"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-50-512e54127605>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtokenizer_W\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mWordPunctTokenizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtokenize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtokenizer_W\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtokenize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m SRC = Field(tokenize=tokenize,\n",
            "\u001b[0;31mNameError\u001b[0m: name 'WordPunctTokenizer' is not defined"
          ]
        }
      ],
      "source": [
        "tokenizer_W = WordPunctTokenizer()\n",
        "def tokenize(x, tokenizer=tokenizer_W):\n",
        "    return tokenizer.tokenize(x.lower())\n",
        "\n",
        "SRC = Field(tokenize=tokenize,\n",
        "            init_token = '<sos>', \n",
        "            eos_token = '<eos>', \n",
        "            lower = True)\n",
        "\n",
        "TRG = Field(tokenize=tokenize,\n",
        "            init_token = '<sos>', \n",
        "            eos_token = '<eos>', \n",
        "            lower = True)\n",
        "\n",
        "dataset = torchtext.data.TabularDataset(\n",
        "    path=path_do_data,\n",
        "    format='tsv',\n",
        "    fields=[('trg', TRG), ('src', SRC)]\n",
        ")\n",
        "\n",
        "train_data, valid_data, test_data = dataset.split(split_ratio=[0.8, 0.15, 0.05])\n",
        "\n",
        "SRC.build_vocab(train_data, min_freq = 3)\n",
        "TRG.build_vocab(train_data, min_freq = 3)\n",
        "\n",
        "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": {
        "id": "N-V6wrMWYysJ"
      },
      "source": [
        "Here are tokens from original (RU) corpus:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RVItjKmjYysJ",
        "outputId": "2d579b53-f579-4246-ee12-73d3e9140307",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['<unk>',\n",
              " 'ле',\n",
              " 'las',\n",
              " 'канала',\n",
              " '59',\n",
              " 'ana',\n",
              " 'ming',\n",
              " 'фортепианную',\n",
              " 'доривала',\n",
              " 'трамвай']"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "SRC.vocab.itos[::1000]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JjnoDImoYysK"
      },
      "source": [
        "Let's check the length distributions:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jPjwaScOYysK",
        "outputId": "e42d5333-7c64-4a92-871c-a0b8b4153951",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Length distribution in Train data\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "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": null,
      "metadata": {
        "id": "ON_BIRUhYysK",
        "outputId": "cd7d860e-e5b6-44a3-b1b0-62feeb73f315",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Length distribution in Test data\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "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": {
        "id": "gEqTjomEYysL"
      },
      "source": [
        "### Model side\n",
        "__Here comes simple pipeline of NMT model learning. It almost copies the week03 practice__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OrmsNpPGYysL"
      },
      "outputs": [],
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mPyQVA26YysL",
        "outputId": "520687a2-f1b3-4e72-8749-7a20a9348ee9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "device"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gPC9UnnIYysL"
      },
      "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",
        ")\n",
        "\n",
        "for x in train_iterator:\n",
        "    break\n",
        "print(x)\n",
        "print(x.src.shape, x.trg.shape)"
      ]
    },
    {
      "cell_type": "code",
      "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 random\n",
        "import math\n",
        "import time\n",
        "\n",
        "\n",
        "class Encoder(nn.Module):\n",
        "    def __init__(self, input_dim, emb_dim, hid_dim, n_layers, dropout):\n",
        "        super().__init__()\n",
        "        \n",
        "        self.input_dim = input_dim\n",
        "        self.emb_dim = emb_dim\n",
        "        self.hid_dim = hid_dim\n",
        "        self.n_layers = n_layers\n",
        "#         self.dropout = dropout\n",
        "        \n",
        "        self.embedding = nn.Embedding(\n",
        "            num_embeddings=input_dim,\n",
        "            embedding_dim=emb_dim\n",
        "        )\n",
        "            # <YOUR CODE HERE>\n",
        "        \n",
        "        self.rnn = nn.LSTM(\n",
        "            input_size=emb_dim,\n",
        "            hidden_size=hid_dim,\n",
        "            num_layers=n_layers,\n",
        "            dropout=dropout\n",
        "        )\n",
        "            # <YOUR CODE HERE>\n",
        "        \n",
        "        self.dropout = nn.Dropout(p=dropout)# <YOUR CODE HERE>\n",
        "        \n",
        "    def forward(self, src):\n",
        "        \n",
        "        #src = [src sent len, batch size]\n",
        "        \n",
        "        # Compute an embedding from the src data and apply dropout to it\n",
        "        embedded = self.embedding(src)# <YOUR CODE HERE>\n",
        "        \n",
        "        embedded = self.dropout(embedded)\n",
        "        \n",
        "        output, (hidden, cell) = self.rnn(embedded)\n",
        "        #embedded = [src sent len, batch size, emb dim]\n",
        "        \n",
        "        # Compute the RNN output values of the encoder RNN. \n",
        "        # outputs, hidden and cell should be initialized here. Refer to nn.LSTM docs ;)\n",
        "        \n",
        "        # <YOUR CODE HERE> \n",
        "        \n",
        "        #outputs = [src sent len, batch size, hid dim * n directions]\n",
        "        #hidden = [n layers * n directions, batch size, hid dim]\n",
        "        #cell = [n layers * n directions, batch size, hid dim]\n",
        "        \n",
        "        #outputs are always from the top hidden layer\n",
        "        \n",
        "        return hidden, cell\n",
        "    \n",
        "\n",
        "class Decoder(nn.Module):\n",
        "    def __init__(self, output_dim, emb_dim, hid_dim, n_layers, dropout):\n",
        "        super().__init__()\n",
        "\n",
        "        self.emb_dim = emb_dim\n",
        "        self.hid_dim = hid_dim\n",
        "        self.output_dim = output_dim\n",
        "        self.n_layers = n_layers\n",
        "        self.dropout = dropout\n",
        "        \n",
        "        self.embedding = nn.Embedding(\n",
        "            num_embeddings=output_dim,\n",
        "            embedding_dim=emb_dim\n",
        "        )\n",
        "            # <YOUR CODE HERE>\n",
        "        \n",
        "        self.rnn = nn.LSTM(\n",
        "            input_size=emb_dim,\n",
        "            hidden_size=hid_dim,\n",
        "            num_layers=n_layers,\n",
        "            dropout=dropout\n",
        "        )\n",
        "            # <YOUR CODE HERE>\n",
        "        \n",
        "        self.out = nn.Linear(\n",
        "            in_features=hid_dim,\n",
        "            out_features=output_dim\n",
        "        )\n",
        "            # <YOUR CODE HERE>\n",
        "        \n",
        "        self.dropout = nn.Dropout(p=dropout)# <YOUR CODE HERE>\n",
        "        \n",
        "    def forward(self, input, hidden, cell):\n",
        "        \n",
        "        #input = [batch size]\n",
        "        #hidden = [n layers * n directions, batch size, hid dim]\n",
        "        #cell = [n layers * n directions, batch size, hid dim]\n",
        "        \n",
        "        #n directions in the decoder will both always be 1, therefore:\n",
        "        #hidden = [n layers, batch size, hid dim]\n",
        "        #context = [n layers, batch size, hid dim]\n",
        "        \n",
        "        input = input.unsqueeze(0)\n",
        "        \n",
        "        #input = [1, batch size]\n",
        "        \n",
        "        # Compute an embedding from the input data and apply dropout to it\n",
        "        embedded = self.dropout(self.embedding(input))# <YOUR CODE HERE>\n",
        "        \n",
        "        #embedded = [1, batch size, emb dim]\n",
        "        \n",
        "        # Compute the RNN output values of the encoder RNN. \n",
        "        # outputs, hidden and cell should be initialized here. Refer to nn.LSTM docs ;)\n",
        "        # <YOUR CODE HERE>\n",
        "        \n",
        "        \n",
        "        #output = [sent len, batch size, hid dim * n directions]\n",
        "        #hidden = [n layers * n directions, batch size, hid dim]\n",
        "        #cell = [n layers * n directions, batch size, hid dim]\n",
        "        \n",
        "        #sent len and n directions will always be 1 in the decoder, therefore:\n",
        "        #output = [1, batch size, hid dim]\n",
        "        #hidden = [n layers, batch size, hid dim]\n",
        "        #cell = [n layers, batch size, hid dim]\n",
        "        \n",
        "        \n",
        "        output, (hidden, cell) = self.rnn(embedded, (hidden, cell))\n",
        "        prediction = self.out(output.squeeze(0))\n",
        "        \n",
        "        #prediction = [batch size, output dim]\n",
        "        \n",
        "        return prediction, hidden, cell\n",
        "\n",
        "\n",
        "class Seq2Seq(nn.Module):\n",
        "    def __init__(self, encoder, decoder, device):\n",
        "        super().__init__()\n",
        "        \n",
        "        self.encoder = encoder\n",
        "        self.decoder = decoder\n",
        "        self.device = device\n",
        "        \n",
        "        assert encoder.hid_dim == decoder.hid_dim, \\\n",
        "            \"Hidden dimensions of encoder and decoder must be equal!\"\n",
        "        assert encoder.n_layers == decoder.n_layers, \\\n",
        "            \"Encoder and decoder must have equal number of layers!\"\n",
        "        \n",
        "    def forward(self, src, trg, teacher_forcing_ratio = 0.5):\n",
        "        \n",
        "        #src = [src sent len, batch size]\n",
        "        #trg = [trg sent len, batch size]\n",
        "        #teacher_forcing_ratio is probability to use teacher forcing\n",
        "        #e.g. if teacher_forcing_ratio is 0.75 we use ground-truth inputs 75% of the time\n",
        "        \n",
        "        # Again, now batch is the first dimention instead of zero\n",
        "        batch_size = trg.shape[1]\n",
        "        max_len = trg.shape[0]\n",
        "        trg_vocab_size = self.decoder.output_dim\n",
        "        \n",
        "        #tensor to store decoder outputs\n",
        "        outputs = torch.zeros(max_len, batch_size, trg_vocab_size).to(self.device)\n",
        "        \n",
        "        #last hidden state of the encoder is used as the initial hidden state of the decoder\n",
        "        hidden, cell = self.encoder(src)\n",
        "        \n",
        "        #first input to the decoder is the <sos> tokens\n",
        "        input = trg[0,:]\n",
        "        \n",
        "        for t in range(1, max_len):\n",
        "            \n",
        "            output, hidden, cell = self.decoder(input, hidden, cell)\n",
        "            outputs[t] = output\n",
        "            teacher_force = random.random() < teacher_forcing_ratio\n",
        "            top1 = output.max(1)[1]\n",
        "            input = (trg[t] if teacher_force else top1)\n",
        "        \n",
        "        return outputs\n"
      ],
      "metadata": {
        "id": "QTK-QksyTMul"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zFBiLQq7YysL"
      },
      "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": null,
      "metadata": {
        "id": "J_3XtT0nYysL"
      },
      "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": null,
      "metadata": {
        "id": "PXhYY4GgYysM",
        "outputId": "8e4b2a29-8e54-4cee-b5e2-2cc5fc36e943",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Seq2Seq(\n",
              "  (encoder): Encoder(\n",
              "    (embedding): Embedding(9252, 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(6688, 256)\n",
              "    (rnn): LSTM(256, 512, num_layers=2, dropout=0.5)\n",
              "    (out): Linear(in_features=512, out_features=6688, bias=True)\n",
              "    (dropout): Dropout(p=0.5, inplace=False)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "def init_weights(m):\n",
        "    # <YOUR CODE HERE>\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": null,
      "metadata": {
        "id": "ro9618sGYysM",
        "outputId": "29bac9f7-a87c-47c7-be79-867ec4bd6770",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The model has 14,868,000 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": null,
      "metadata": {
        "id": "JkALWaOqYysM"
      },
      "outputs": [],
      "source": [
        "PAD_IDX = TRG.vocab.stoi['<pad>']\n",
        "optimizer = optim.Adam(model.parameters())\n",
        "criterion = nn.CrossEntropyLoss(ignore_index = PAD_IDX)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_Bp2FkHKYysM"
      },
      "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": null,
      "metadata": {
        "id": "o-hvqlZHYysM"
      },
      "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": null,
      "metadata": {
        "id": "ecYLy6sQYysR"
      },
      "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": null,
      "metadata": {
        "id": "68D_vf9qYysR"
      },
      "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": null,
      "metadata": {
        "id": "bTQEfSjJYysS",
        "outputId": "70146d4c-79ae-4ea5-d3be-2c8533d0fcd0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 784
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 10 | Time: 2m 1s\n",
            "\tTrain Loss: 2.911 | Train PPL:  18.376\n",
            "\t Val. Loss: 4.659 |  Val. PPL: 105.539\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": {
        "id": "T0JBE1vNYysS"
      },
      "source": [
        "__Let's take a look at our network quality__:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KbNrn87aYysS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "outputId": "91d18254-dfef-4b5e-9f5b-ecc0d456240d"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-34-f9dab4e8f560>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mdel\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'utils' is not defined"
          ]
        }
      ],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3PTCOWZQYysS"
      },
      "outputs": [],
      "source": [
        "generate_translation = generate_translation\n",
        "remove_tech_tokens = remove_tech_tokens\n",
        "get_text = get_text\n",
        "flatten = flatten"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CToiY8zaYysT"
      },
      "outputs": [],
      "source": [
        "batch = next(iter(test_iterator))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q6vSoBePYysT",
        "outputId": "5ec00780-38bb-4c39-bf89-f4174cb5b53d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original: a dishwasher is also available .\n",
            "Generated: the kitchen is equipped with a dishwasher and a .\n",
            "\n",
            "Original: the reception desk is open 24 hours a day .\n",
            "Generated: a 24 - hour front desk .\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": null,
      "metadata": {
        "id": "PwfPfUGtYysT"
      },
      "outputs": [],
      "source": [
        "from nltk.translate.bleu_score import corpus_bleu\n",
        "\n",
        "def generate_translation(src, trg, model, TRG_vocab):\n",
        "    model.eval()\n",
        "\n",
        "    output = model(src, trg, 0) #turn off teacher forcing\n",
        "    output = output.argmax(dim=-1).cpu().numpy()\n",
        "\n",
        "    original = get_text(list(trg[:,0].cpu().numpy()), TRG_vocab)\n",
        "    generated = get_text(list(output[1:, 0]), TRG_vocab)\n",
        "    \n",
        "    print('Original: {}'.format(' '.join(original)))\n",
        "    print('Generated: {}'.format(' '.join(generated)))\n",
        "    print()\n",
        "\n",
        "def flatten(l):\n",
        "    return [item for sublist in l for item in sublist]\n",
        "\n",
        "def remove_tech_tokens(mystr, tokens_to_remove=['<eos>', '<sos>', '<unk>', '<pad>']):\n",
        "    return [x for x in mystr if x not in tokens_to_remove]\n",
        "\n",
        "\n",
        "def get_text(x, TRG_vocab):\n",
        "    text = [TRG_vocab.itos[token] for token in x]\n",
        "    try:\n",
        "        end_idx = text.index('<eos>')\n",
        "        text = text[:end_idx]\n",
        "    except ValueError:\n",
        "        pass\n",
        "    text = remove_tech_tokens(text)\n",
        "    if len(text) < 1:\n",
        "        text = []\n",
        "    return text\n",
        "\n",
        "\n",
        "\n",
        "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)\n",
        "\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": null,
      "metadata": {
        "id": "NgA2WuG5YysT"
      },
      "outputs": [],
      "source": [
        "import tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RgvU97aMYysT",
        "outputId": "e33bc15b-0dcc-4221-9b5c-4510d6b6a5af",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "59it [00:06,  8.43it/s]\n"
          ]
        }
      ],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IrtG0gj2YysT",
        "outputId": "ab8b28ee-7900-4335-8df3-f8cd4921baab",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14.354085903946906"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "corpus_bleu([[text] for text in original_text], generated_text) * 100"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "GRU Attention"
      ],
      "metadata": {
        "id": "WrROLjUqO08C"
      }
    },
    {
      "cell_type": "code",
      "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 torchtext==0.6.0 nltk -q"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "29bkxU4Kt6cI",
        "outputId": "52201748-7d02-4e21-9dd6-128473e00a3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.2/64.2 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m16.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.tokenize import WordPunctTokenizer\n",
        "import torchtext\n",
        "from torchtext.datasets import TranslationDataset, Multi30k\n",
        "from torchtext.data import Field, BucketIterator\n",
        "import torch\n",
        "\n",
        "import os\n",
        "path_do_data = 'data.txt'\n",
        "if not os.path.exists(path_do_data):\n",
        "    print(\"Dataset not found locally. Downloading from github.\")\n",
        "    !wget https://raw.githubusercontent.com/neychev/made_nlp_course/master/datasets/Machine_translation_EN_RU/data.txt -nc\n",
        "    path_do_data = './data.txt'\n",
        "    \n",
        "tokenizer_W = WordPunctTokenizer()\n",
        "def tokenize(x, tokenizer=tokenizer_W):\n",
        "    return tokenizer.tokenize(x.lower())\n",
        "\n",
        "SRC = Field(tokenize=tokenize,\n",
        "            init_token = '<sos>', \n",
        "            eos_token = '<eos>', \n",
        "            lower = True,\n",
        "            include_lengths = True\n",
        "            )\n",
        "\n",
        "TRG = Field(tokenize=tokenize,\n",
        "            init_token = '<sos>', \n",
        "            eos_token = '<eos>', \n",
        "            lower = True)\n",
        "\n",
        "dataset = torchtext.data.TabularDataset(\n",
        "    path=path_do_data,\n",
        "    format='tsv',\n",
        "    fields=[('trg', TRG), ('src', SRC)]\n",
        ")\n",
        "\n",
        "train_data, valid_data, test_data = dataset.split(split_ratio=[0.8, 0.15, 0.05])\n",
        "\n",
        "SRC.build_vocab(train_data, min_freq = 3)\n",
        "TRG.build_vocab(train_data, min_freq = 3)\n",
        "\n",
        "print(f\"Unique tokens in source (ru) vocabulary: {len(SRC.vocab)}\")\n",
        "print(f\"Unique tokens in target (en) vocabulary: {len(TRG.vocab)}\")\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "metadata": {
        "id": "EIdITkh4O0TJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5d49b23e-4577-4cf2-caab-dc939965465c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset not found locally. Downloading from github.\n",
            "--2023-05-23 16:28:40--  https://raw.githubusercontent.com/neychev/made_nlp_course/master/datasets/Machine_translation_EN_RU/data.txt\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 12905334 (12M) [text/plain]\n",
            "Saving to: ‘data.txt’\n",
            "\n",
            "data.txt            100%[===================>]  12.31M  --.-KB/s    in 0.1s    \n",
            "\n",
            "2023-05-23 16:28:41 (90.0 MB/s) - ‘data.txt’ saved [12905334/12905334]\n",
            "\n",
            "Unique tokens in source (ru) vocabulary: 9270\n",
            "Unique tokens in target (en) vocabulary: 6686\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "\n",
        "class Encoder(nn.Module):\n",
        "    def __init__(self, vocab_size, embedding_dim, enc_units,dropout):\n",
        "        super(Encoder, self).__init__()\n",
        "        self.enc_units = enc_units\n",
        "        self.vocab_size = vocab_size\n",
        "        self.embedding_dim = embedding_dim\n",
        "        self.embedding = nn.Embedding(self.vocab_size, self.embedding_dim)\n",
        "        self.gru = nn.GRU(self.embedding_dim, self.enc_units,num_layers=2)\n",
        "        self.dropout = nn.Dropout(p=dropout)\n",
        "        \n",
        "    def forward(self, x, lens):\n",
        "        \n",
        "        # x: max_length, batch_size, embedding_dim\n",
        "        x = self.dropout(self.embedding(x)) \n",
        "                \n",
        "        # x transformed = max_len X batch_size X embedding_dim\n",
        "        # x = x.permute(1,0,2)\n",
        "        x = pack_padded_sequence(x, lens) # unpad\n",
        "        \n",
        "        # output: max_length, batch_size, enc_units\n",
        "        # self.hidden: 1, batch_size, enc_units\n",
        "        output, hidden = self.gru(x) # gru returns hidden state of all timesteps as well as hidden state at last timestep\n",
        "        \n",
        "        # pad the sequence to the max length in the batch\n",
        "        output, _ = pad_packed_sequence(output)\n",
        "        \n",
        "        return output, hidden\n",
        "\n",
        "class Decoder(nn.Module):\n",
        "    def __init__(self, vocab_size, embedding_dim, dec_units, enc_units,dropout):\n",
        "        super(Decoder, self).__init__()\n",
        "        self.dec_units = dec_units\n",
        "        self.enc_units = enc_units\n",
        "        self.vocab_size = vocab_size\n",
        "        self.embedding_dim = embedding_dim\n",
        "        self.embedding = nn.Embedding(self.vocab_size, self.embedding_dim)\n",
        "        self.gru = nn.GRU(self.embedding_dim + self.enc_units, \n",
        "                          self.dec_units,\n",
        "                          batch_first=True,\n",
        "                          num_layers=2)\n",
        "        \n",
        "        self.bn1 = nn.BatchNorm1d(self.enc_units)\n",
        "        self.fc1 = nn.Linear(self.enc_units, self.vocab_size//2)\n",
        "        self.fc2 = nn.Linear(self.vocab_size//2, self.vocab_size)\n",
        "\n",
        "        # used for attention\n",
        "        self.W1 = nn.Linear(self.enc_units, self.dec_units)\n",
        "        self.W2 = nn.Linear(self.enc_units, self.dec_units)\n",
        "        self.V = nn.Linear(self.enc_units, 1)\n",
        "        self.dropout = nn.Dropout(p=dropout)\n",
        "    \n",
        "    def forward(self, x, hidden, enc_output):\n",
        "        # enc_output original: (max_length, batch_size, enc_units)\n",
        "        # enc_output converted == (batch_size, max_length, hidden_size)\n",
        "        enc_output = enc_output.permute(1,0,2)\n",
        "        # hidden shape == (batch_size, hidden size)\n",
        "        # hidden_with_time_axis shape == (batch_size, 1, hidden size)\n",
        "        # we are doing this to perform addition to calculate the score\n",
        "        \n",
        "        # hidden shape == (batch_size, hidden size)\n",
        "        # hidden_with_time_axis shape == (batch_size, 1, hidden size)\n",
        "        hidden_with_time_axis = hidden[1].unsqueeze(0).permute(1, 0, 2)\n",
        "        \n",
        "        # score: (batch_size, max_length, hidden_size) # Bahdanaus's\n",
        "        # we get 1 at the last axis because we are applying tanh(FC(EO) + FC(H)) to self.V\n",
        "        # It doesn't matter which FC we pick for each of the inputs\n",
        "        score = torch.tanh(self.W1(enc_output) + self.W2(hidden_with_time_axis))\n",
        "        \n",
        "        #score = torch.tanh(self.W2(hidden_with_time_axis) + self.W1(enc_output))\n",
        "          \n",
        "        # attention_weights shape == (batch_size, max_length, 1)\n",
        "        # we get 1 at the last axis because we are applying score to self.V\n",
        "        attention_weights = torch.softmax(self.V(score), dim=1)\n",
        "        \n",
        "        # context_vector shape after sum == (batch_size, hidden_size)\n",
        "        context_vector = attention_weights * enc_output\n",
        "        context_vector = torch.sum(context_vector, dim=1)\n",
        "        \n",
        "        # x shape after passing through embedding == (batch_size, 1, embedding_dim)\n",
        "        # takes case of the right portion of the model above (illustrated in red)\n",
        "        x = self.dropout(self.embedding(x))\n",
        "    \n",
        "        # x shape after concatenation == (batch_size, 1, embedding_dim + hidden_size)\n",
        "        #x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1)\n",
        "        # ? Looks like attention vector in diagram of source\n",
        "        x = torch.cat((context_vector.unsqueeze(1), x), -1)\n",
        "        \n",
        "        # passing the concatenated vector to the GRU\n",
        "        # output: (batch_size, 1, hidden_size)\n",
        "        output, state = self.gru(x,hidden)\n",
        "        \n",
        "        # output shape == (batch_size * 1, hidden_size)\n",
        "        output =  self.bn1(output.view(-1, output.size(2)))\n",
        "        \n",
        "        # output shape == (batch_size * 1, vocab)\n",
        "        x = F.relu(self.fc1(output))\n",
        "        x = self.fc2(x)\n",
        "\n",
        "        return x, state, attention_weights\n",
        "    \n",
        "\n"
      ],
      "metadata": {
        "id": "mZMvIp2IP11G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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 = 256\n",
        "BATCH_SIZE = 100\n",
        "ENC_DROPOUT = 0.5\n",
        "DEC_DROPOUT = 0.5\n",
        "teacher_forcing_ratio = 0.5\n",
        "\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "## TODO: Combine the encoder and decoder into one class\n",
        "encoder = Encoder(INPUT_DIM, ENC_EMB_DIM, HID_DIM,ENC_DROPOUT)\n",
        "decoder = Decoder(INPUT_DIM, DEC_EMB_DIM, HID_DIM, HID_DIM,DEC_DROPOUT)\n",
        "\n",
        "encoder.to(device)\n",
        "decoder.to(device)\n",
        "\n",
        "parametrs = list(encoder.parameters()) + list(decoder.parameters())\n",
        "\n",
        "optimizer = torch.optim.Adam(parametrs, \n",
        "                       lr=0.001)\n",
        "scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)\n",
        "\n",
        "PAD_IDX = TRG.vocab.stoi['<pad>']\n",
        "START_IDX = TRG.vocab.stoi['<sos>']\n",
        "criterion = nn.CrossEntropyLoss(ignore_index = PAD_IDX)\n",
        "\n",
        "def loss_function(real, pred):\n",
        "    \"\"\" Only consider non-zero inputs in the loss; mask needed \"\"\"\n",
        "    #mask = 1 - np.equal(real, 0) # assign 0 to all above 0 and 1 to all 0s\n",
        "    #print(mask)\n",
        "    mask = real.ge(1).type(torch.cuda.FloatTensor)\n",
        "    \n",
        "    loss_ = criterion(pred, real) * mask \n",
        "    return torch.mean(loss_)\n",
        "\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=False, \n",
        ")\n",
        "\n",
        "def sort_batch(X, y, lengths):\n",
        "    lengths, indx = lengths.sort(dim=0, descending=True)\n",
        "    X = X[:,indx]\n",
        "    y = y[:,indx]\n",
        "    return X, y, lengths "
      ],
      "metadata": {
        "id": "yOkroGTevFA4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.cuda.empty_cache()"
      ],
      "metadata": {
        "id": "vVO_0XZD2RrN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import time \n",
        "from IPython.display import clear_output\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "EPOCHS = 20\n",
        "\n",
        "history=[]\n",
        "\n",
        "encoder.train()\n",
        "decoder.train()\n",
        "\n",
        "for epoch in range(EPOCHS):\n",
        "    start = time.time()\n",
        "    total_loss = 0\n",
        "    \n",
        "    for batch, data in enumerate(train_iterator):\n",
        "        loss = 0\n",
        "        inp, inp_len = data.src\n",
        "        targ = data.trg\n",
        "        xs, ys, lens = sort_batch(inp, targ, inp_len)\n",
        "        enc_output, dec_hidden = encoder(xs, lens.cpu())\n",
        "\n",
        "        # use teacher forcing - feeding the target as the next input (via dec_input)\n",
        "        dec_input = torch.tensor([[START_IDX]] * int(ys.size(1)),device=device)\n",
        "        \n",
        "        # run code below for every timestep in the ys batch\n",
        "        for t in range(1, ys.size(0)):\n",
        "            predictions, dec_hidden, _ = decoder(dec_input, \n",
        "                                         dec_hidden, \n",
        "                                         enc_output)\n",
        "            loss += torch.mean(criterion(predictions,ys[t]))\n",
        "            teacher_force = random.random() < teacher_forcing_ratio\n",
        "            top1 = predictions.max(1)[1].unsqueeze(1)\n",
        "            dec_input = ys[t].unsqueeze(1) if teacher_force else top1\n",
        "            \n",
        "        loss.backward()\n",
        "        torch.nn.utils.clip_grad_norm_(parametrs, 1)\n",
        "        optimizer.step()\n",
        "        optimizer.zero_grad()\n",
        "        batch_loss = (loss / int(ys.size(1)))\n",
        "        total_loss += batch_loss\n",
        "    \n",
        "        history.append(batch_loss.detach().item())\n",
        "            \n",
        "        if (batch+1) % 10 == 0:\n",
        "            clear_output(True)\n",
        "            plt.plot(history[::10])\n",
        "            plt.yscale('log')\n",
        "            plt.show()\n",
        "    scheduler.step()\n",
        "        \n",
        "    ### TODO: Save checkpoint for model\n",
        "\n",
        "    torch.save(encoder.state_dict(), f'encoder-model-{epoch + 1}.pt')\n",
        "    torch.save(decoder.state_dict(), f'decoder-model-{epoch + 1}.pt')\n",
        "    print('Epoch {} Loss {:.4f}'.format(epoch + 1,\n",
        "                                        total_loss / len(train_iterator)))\n",
        "    print('Time taken for 1 epoch {} sec\\n'.format(time.time() - start))\n",
        "    "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 793
        },
        "id": "Gu8KMLV3vhic",
        "outputId": "2b31f009-93dc-41f2-e36a-dc90e0dcff0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-106-efae74edd10a>\u001b[0m in \u001b[0;36m<cell line: 12>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0mtarg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0mxs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msort_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minp_len\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m         \u001b[0menc_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdec_hidden\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mencoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlens\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0;31m# use teacher forcing - feeding the target as the next input (via dec_input)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-82-51c3814864fb>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x, lens)\u001b[0m\n\u001b[1;32m     25\u001b[0m         \u001b[0;31m# output: max_length, batch_size, enc_units\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0;31m# self.hidden: 1, batch_size, enc_units\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m         \u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhidden\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgru\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# gru returns hidden state of all timesteps as well as hidden state at last timestep\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m         \u001b[0;31m# pad the sequence to the max length in the batch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/rnn.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input, hx)\u001b[0m\n\u001b[1;32m    999\u001b[0m                              self.dropout, self.training, self.bidirectional, self.batch_first)\n\u001b[1;32m   1000\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1001\u001b[0;31m             result = _VF.gru(input, batch_sizes, hx, self._flat_weights, self.bias,\n\u001b[0m\u001b[1;32m   1002\u001b[0m                              self.num_layers, self.dropout, self.training, self.bidirectional)\n\u001b[1;32m   1003\u001b[0m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(encoder.state_dict(), f'encoder-model-final.pt')\n",
        "torch.save(decoder.state_dict(), f'decoder-model-final.pt')"
      ],
      "metadata": {
        "id": "ATInEgZfIniR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "encoder.load_state_dict(torch.load('encoder-model-final.pt'))\n",
        "decoder.load_state_dict(torch.load('decoder-model-final.pt'))\n",
        "\n",
        "encoder.eval()\n",
        "decoder.eval()\n",
        "\n",
        "total_loss = 0\n",
        "\n",
        "with torch.no_grad():\n",
        "    for batch, data in enumerate(valid_iterator):\n",
        "        loss=0\n",
        "        inp, inp_len = data.src\n",
        "        targ = data.trg\n",
        "        xs, ys, lens = sort_batch(inp, targ, inp_len)\n",
        "        enc_output, enc_hidden = encoder(xs, lens.cpu())\n",
        "        dec_hidden = enc_hidden\n",
        "        \n",
        "        # use teacher forcing - feeding the target as the next input (via dec_input)\n",
        "        dec_input = torch.tensor([[START_IDX]] * int(ys.size(1)),device=device)\n",
        "        \n",
        "        # run code below for every timestep in the ys batch\n",
        "        for t in range(1, ys.size(0)):\n",
        "            predictions, dec_hidden, _ = decoder(dec_input, \n",
        "                                            dec_hidden, \n",
        "                                            enc_output)\n",
        "            loss += criterion(predictions,ys[t])\n",
        "            dec_input = predictions.max(1)[1].unsqueeze(1)\n",
        "        batch_loss = (loss / int(ys.size(1)))\n",
        "        total_loss += batch_loss\n",
        "\n",
        "print(total_loss/ len(valid_iterator))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BiSvvlrWFYN3",
        "outputId": "6a161386-b318-498a-8010-a009f5f82184"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(2.9743, device='cuda:0')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_iterator,test_iterator = BucketIterator.splits(\n",
        "    (train_data, test_data), \n",
        "    batch_size = 5, \n",
        "    device = device,\n",
        "    sort_key=lambda x: x.src[1],\n",
        "    shuffle=True\n",
        ")\n",
        "\n",
        "encoder.eval()\n",
        "decoder.eval()\n",
        "\n",
        "test_examples = next(iter(train_iterator))\n",
        "\n",
        "inp, inp_len = test_examples.src\n",
        "targ = test_examples.trg\n",
        "xs, ys, lens = sort_batch(inp, targ, inp_len)\n",
        "enc_output, dec_hidden = encoder(xs, lens.cpu())\n",
        "# use teacher forcing - feeding the target as the next input (via dec_input)\n",
        "dec_input = torch.tensor([[START_IDX]] * int(ys.size(1)),device=device)\n",
        "translation = []\n",
        "translation.append(dec_input)\n",
        "\n",
        "\n",
        "for t in range(1, ys.size(0)):\n",
        "    predictions, dec_hidden, _ = decoder(dec_input, \n",
        "                                    dec_hidden, \n",
        "                                    enc_output)\n",
        "    dec_input = predictions.max(1)[1].unsqueeze(1)\n",
        "    translation.append(dec_input)\n",
        "\n",
        "inps = xs.T.detach().cpu().numpy()\n",
        "results = torch.concatenate(translation,axis=1).detach().cpu().numpy()\n",
        "reals = ys.T.detach().cpu().numpy()"
      ],
      "metadata": {
        "id": "THTgExWhILDC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokens_to_remove = ['<eos>', '<sos>', '<unk>', '<pad>']\n",
        "\n",
        "for inp, real, result in zip(inps,reals,results):\n",
        "    print('Исходный текст:',' '.join([SRC.vocab.itos[token] for token in inp]))\n",
        "    print('Мой перевод:',' '.join([TRG.vocab.itos[token] for token in result if token not in tokens_to_remove]))\n",
        "    print('Правильный перевод:',' '.join([TRG.vocab.itos[token] for token in real]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xbw1i2V9ULIs",
        "outputId": "4d25f801-d9b4-43ef-e511-6c4cc8f31868"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Исходный текст: <sos> в отеле и конференц - центре howard johnson plaza гости могут посещать крытый бассейн , сезонный открытый бассейн и фитнес - центр , а также пользоваться прачечной самообслуживания . <eos>\n",
            "Мой перевод: <sos> a fitness centre , fitness centre , fitness centre , fitness centre , fitness centre , fitness centre and fitness centre , fitness centre and fitness centre . <eos> fitness centre . <eos> fitness centre . <eos>\n",
            "Правильный перевод: <sos> an indoor pool and an outdoor seasonal pool are featured at kansas city howard johnson plaza hotel and conference center . guests can exercise in the fitness centre or make use of the launderette facilities . <eos>\n",
            "Исходный текст: <sos> к услугам гостей номера с бесплатным доступом в интернет и кабельным телевидением , а также кафе , бар и бесплатная парковка . <eos> <pad> <pad> <pad> <pad> <pad> <pad> <pad>\n",
            "Мой перевод: <sos> the hotel offers free parking , it features free private parking on site . <eos> on - site restaurant and free parking . <eos> on site . <eos> <eos> <eos> <eos> . <eos> . <eos> . <eos>\n",
            "Правильный перевод: <sos> it offers rooms with free internet and cable tv , a coffee shop , bar and free parking . <eos> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad>\n",
            "Исходный текст: <sos> в числе удобств балкон , терраса и бесплатный wi - fi . <eos> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad>\n",
            "Мой перевод: <sos> the accommodation features free wifi and a balcony . <eos> <eos> <eos> . <eos> <eos> . <eos> . <eos> . <eos> . <eos> . <eos> . <eos> . <eos> . <eos> . <eos> . <eos> . <eos>\n",
            "Правильный перевод: <sos> guests benefit from balcony and a terrace . free wifi is offered throughout the property . <eos> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad>\n",
            "Исходный текст: <sos> на полностью оборудованной мини - кухне установлена посудомоечная машина и духовка . <eos> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad>\n",
            "Мой перевод: <sos> the kitchenette is a dishwasher and an oven . <eos> <eos> . <eos> oven . <eos> . <eos> . <eos> . <eos> . <eos> . <eos> . <eos> . <eos> . <eos> . <eos> . <eos> .\n",
            "Правильный перевод: <sos> there is a full kitchenette with a dishwasher and oven . <eos> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad>\n",
            "Исходный текст: <sos> в числе прочих удобств — телевизор . <eos> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad>\n",
            "Мой перевод: <sos> a tv is offered . <eos> <eos> . <eos> <eos> <eos> <eos> <eos> <eos> . <eos> <eos> . <eos> . <eos> . <eos> . <eos> . <eos> . <eos> . <eos> . <eos> . <eos> . <eos>\n",
            "Правильный перевод: <sos> a tv is offered . <eos> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_tech_tokens(mystr, tokens_to_remove=['<eos>', '<sos>', '<unk>', '<pad>']):\n",
        "    return [x for x in mystr if x not in tokens_to_remove]\n",
        "\n",
        "\n",
        "def get_text(x, TRG_vocab):\n",
        "    text = [TRG_vocab.itos[token] for token in x]\n",
        "    try:\n",
        "        end_idx = text.index('<eos>')\n",
        "        text = text[:end_idx]\n",
        "    except ValueError:\n",
        "        pass\n",
        "    text = remove_tech_tokens(text)\n",
        "    if len(text) < 1:\n",
        "        text = []\n",
        "    return text\n",
        "\n",
        "original_text = []\n",
        "generated_text = []\n",
        "encoder.eval()\n",
        "decoder.eval()\n",
        "with torch.no_grad():\n",
        "    for i, test_examples in enumerate(test_iterator):\n",
        "        inp, inp_len = test_examples.src\n",
        "        targ = test_examples.trg\n",
        "        xs, ys, lens = sort_batch(inp, targ, inp_len)\n",
        "        enc_output, dec_hidden = encoder(xs, lens.cpu())\n",
        "        # use teacher forcing - feeding the target as the next input (via dec_input)\n",
        "        dec_input = torch.tensor([[START_IDX]] * int(ys.size(1)),device=device)\n",
        "        translation = []\n",
        "        translation.append(dec_input)\n",
        "        for t in range(1, ys.size(0)):\n",
        "            predictions, dec_hidden, _ = decoder(dec_input, \n",
        "                                            dec_hidden, \n",
        "                                            enc_output)\n",
        "            dec_input = predictions.max(1)[1].unsqueeze(1)\n",
        "            translation.append(dec_input)\n",
        "        results = torch.concatenate(translation,axis=1)\n",
        "        \n",
        "        original_text.extend([get_text(x, TRG.vocab) for x in targ.cpu().numpy().T])\n",
        "        generated_text.extend([get_text(x, TRG.vocab) for x in results.detach().cpu().numpy().T])"
      ],
      "metadata": {
        "id": "TG6-pLzgCMlc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(original_text), len(original_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WD-LIJggGPQE",
        "outputId": "3524d98d-00d1-40a7-eeea-637cdb456e57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7500, 7500)"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.translate.bleu_score import corpus_bleu\n",
        "corpus_bleu(original_text, generated_text) * 100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 183
        },
        "id": "E0kpYraBGEyV",
        "outputId": "51609bff-fcf3-4c14-aac7-f23066ab0fb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-ea6f74a06330>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnltk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranslate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbleu_score\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcorpus_bleu\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcorpus_bleu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moriginal_text\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgenerated_text\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'original_text' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "generated_text[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E1dDx7lWFOuc",
        "outputId": "83d0cf38-d5be-426e-db30-54c43df3b4a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[],\n",
              " ['offering', 'a', 'with', 'a', 'the'],\n",
              " ['a', 'a', 'a', 'breakfast', 'guests'],\n",
              " ['10', 'breakfast', 'the'],\n",
              " ['is', '-', 'breakfast', 'shops'],\n",
              " ['a', 'minute', 'is', 'and'],\n",
              " ['quiet', 'drive', 'available', 'restaurants'],\n",
              " ['-', 'from', 'daily', 'and'],\n",
              " ['winning', 'the', '.', 'restaurants'],\n",
              " ['hotel', 'centre', 'is']]"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Transformer"
      ],
      "metadata": {
        "id": "U85MEVBGkKpO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.tokenize import WordPunctTokenizer\n",
        "\n",
        "import os\n",
        "path_do_data = 'data.txt'\n",
        "if not os.path.exists(path_do_data):\n",
        "    print(\"Dataset not found locally. Downloading from github.\")\n",
        "    !wget https://raw.githubusercontent.com/neychev/made_nlp_course/master/datasets/Machine_translation_EN_RU/data.txt -nc\n",
        "    path_do_data = './data.txt'\n",
        "\n",
        "MAX_LENGTH = 128\n",
        "\n",
        "tokenizer_W = WordPunctTokenizer()\n",
        "def tokenize(x, tokenizer=tokenizer_W):\n",
        "    return tokenizer.tokenize(x.lower())\n",
        "\n",
        "SRC = Field(tokenize=tokenize,\n",
        "            init_token = '<sos>', \n",
        "            eos_token = '<eos>', \n",
        "            lower = True,\n",
        "            )\n",
        "\n",
        "TRG = Field(tokenize=tokenize,\n",
        "            init_token = '<sos>', \n",
        "            eos_token = '<eos>', \n",
        "            lower = True)\n",
        "\n",
        "dataset = torchtext.data.TabularDataset(\n",
        "    path=path_do_data,\n",
        "    format='tsv',\n",
        "    fields=[('trg', TRG), ('src', SRC)]\n",
        ")\n",
        "\n",
        "train_data, valid_data, test_data = dataset.split(split_ratio=[0.8, 0.15, 0.05])\n",
        "\n",
        "SRC.build_vocab(train_data, min_freq = 3)\n",
        "TRG.build_vocab(train_data, min_freq = 3)\n",
        "\n",
        "print(f\"Unique tokens in source (ru) vocabulary: {len(SRC.vocab)}\")\n",
        "print(f\"Unique tokens in target (en) vocabulary: {len(TRG.vocab)}\")\n",
        "\n",
        "def _len_sort_key(x):\n",
        "    return len(x.src)\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "BATCH_SIZE = 1\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",
        ")\n",
        "\n",
        "for x in train_iterator:\n",
        "    break\n",
        "print(x.src.squeeze())\n",
        "print(x.src.shape, x.trg.shape)"
      ],
      "metadata": {
        "id": "X1MHQVWSlr5z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torch import Tensor\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.nn import Transformer\n",
        "import math\n",
        "DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "# helper Module that adds positional encoding to the token embedding to introduce a notion of word order.\n",
        "class PositionalEncoding(nn.Module):\n",
        "    def __init__(self,\n",
        "                 emb_size: int,\n",
        "                 dropout: float,\n",
        "                 maxlen: int = 5000):\n",
        "        super(PositionalEncoding, self).__init__()\n",
        "        den = torch.exp(- torch.arange(0, emb_size, 2)* math.log(10000) / emb_size)\n",
        "        pos = torch.arange(0, maxlen).reshape(maxlen, 1)\n",
        "        pos_embedding = torch.zeros((maxlen, emb_size))\n",
        "        pos_embedding[:, 0::2] = torch.sin(pos * den)\n",
        "        pos_embedding[:, 1::2] = torch.cos(pos * den)\n",
        "        pos_embedding = pos_embedding.unsqueeze(-2)\n",
        "\n",
        "        self.dropout = nn.Dropout(dropout)\n",
        "        self.register_buffer('pos_embedding', pos_embedding)\n",
        "\n",
        "    def forward(self, token_embedding: Tensor):\n",
        "        return self.dropout(token_embedding + self.pos_embedding[:token_embedding.size(0), :])\n",
        "\n",
        "# helper Module to convert tensor of input indices into corresponding tensor of token embeddings\n",
        "class TokenEmbedding(nn.Module):\n",
        "    def __init__(self, vocab_size: int, emb_size):\n",
        "        super(TokenEmbedding, self).__init__()\n",
        "        self.embedding = nn.Embedding(vocab_size, emb_size)\n",
        "        self.emb_size = emb_size\n",
        "\n",
        "    def forward(self, tokens: Tensor):\n",
        "        return self.embedding(tokens.long()) * math.sqrt(self.emb_size)\n",
        "\n",
        "# Seq2Seq Network\n",
        "class Seq2SeqTransformer(nn.Module):\n",
        "    def __init__(self,\n",
        "                 num_encoder_layers: int,\n",
        "                 num_decoder_layers: int,\n",
        "                 emb_size: int,\n",
        "                 nhead: int,\n",
        "                 src_vocab_size: int,\n",
        "                 tgt_vocab_size: int,\n",
        "                 dim_feedforward: int = 512,\n",
        "                 dropout: float = 0.1):\n",
        "        super(Seq2SeqTransformer, self).__init__()\n",
        "        self.transformer = Transformer(d_model=emb_size,\n",
        "                                       nhead=nhead,\n",
        "                                       num_encoder_layers=num_encoder_layers,\n",
        "                                       num_decoder_layers=num_decoder_layers,\n",
        "                                       dim_feedforward=dim_feedforward,\n",
        "                                       dropout=dropout)\n",
        "        self.generator = nn.Linear(emb_size, tgt_vocab_size)\n",
        "        self.src_tok_emb = TokenEmbedding(src_vocab_size, emb_size)\n",
        "        self.tgt_tok_emb = TokenEmbedding(tgt_vocab_size, emb_size)\n",
        "        self.positional_encoding = PositionalEncoding(\n",
        "            emb_size, dropout=dropout)\n",
        "\n",
        "    def forward(self,\n",
        "                src: Tensor,\n",
        "                trg: Tensor,\n",
        "                src_mask: Tensor,\n",
        "                tgt_mask: Tensor,\n",
        "                src_padding_mask: Tensor,\n",
        "                tgt_padding_mask: Tensor,\n",
        "                memory_key_padding_mask: Tensor):\n",
        "        src_emb = self.positional_encoding(self.src_tok_emb(src))\n",
        "        tgt_emb = self.positional_encoding(self.tgt_tok_emb(trg))\n",
        "        outs = self.transformer(src_emb, tgt_emb, src_mask, tgt_mask, None,\n",
        "                                src_padding_mask, tgt_padding_mask, memory_key_padding_mask)\n",
        "        return self.generator(outs)\n",
        "\n",
        "    def encode(self, src: Tensor, src_mask: Tensor):\n",
        "        return self.transformer.encoder(self.positional_encoding(\n",
        "                            self.src_tok_emb(src)), src_mask)\n",
        "\n",
        "    def decode(self, tgt: Tensor, memory: Tensor, tgt_mask: Tensor):\n",
        "        return self.transformer.decoder(self.positional_encoding(\n",
        "                          self.tgt_tok_emb(tgt)), memory,\n",
        "                          tgt_mask)"
      ],
      "metadata": {
        "id": "phYabl6bkKLS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_square_subsequent_mask(sz):\n",
        "    mask = (torch.triu(torch.ones((sz, sz), device=DEVICE)) == 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",
        "\n",
        "def create_mask(src, tgt):\n",
        "    src_seq_len = src.shape[0]\n",
        "    tgt_seq_len = tgt.shape[0]\n",
        "\n",
        "    tgt_mask = generate_square_subsequent_mask(tgt_seq_len)\n",
        "    src_mask = torch.zeros((src_seq_len, src_seq_len),device=DEVICE).type(torch.bool)\n",
        "\n",
        "    src_padding_mask = (src == PAD_IDX).transpose(0, 1)\n",
        "    tgt_padding_mask = (tgt == PAD_IDX).transpose(0, 1)\n",
        "    return src_mask, tgt_mask, src_padding_mask, tgt_padding_mask"
      ],
      "metadata": {
        "id": "Z4EH9LmckJ89"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(0)\n",
        "\n",
        "SRC_VOCAB_SIZE = len(vocab_transform[SRC_LANGUAGE])\n",
        "TGT_VOCAB_SIZE = len(vocab_transform[TGT_LANGUAGE])\n",
        "EMB_SIZE = 512\n",
        "NHEAD = 8\n",
        "FFN_HID_DIM = 512\n",
        "BATCH_SIZE = 128\n",
        "NUM_ENCODER_LAYERS = 3\n",
        "NUM_DECODER_LAYERS = 3\n",
        "\n",
        "transformer = Seq2SeqTransformer(NUM_ENCODER_LAYERS, NUM_DECODER_LAYERS, EMB_SIZE,\n",
        "                                 NHEAD, SRC_VOCAB_SIZE, TGT_VOCAB_SIZE, FFN_HID_DIM)\n",
        "\n",
        "for p in transformer.parameters():\n",
        "    if p.dim() > 1:\n",
        "        nn.init.xavier_uniform_(p)\n",
        "\n",
        "transformer = transformer.to(DEVICE)\n",
        "\n",
        "loss_fn = torch.nn.CrossEntropyLoss(ignore_index=PAD_IDX)\n",
        "\n",
        "optimizer = torch.optim.Adam(transformer.parameters(), lr=0.0001, betas=(0.9, 0.98), eps=1e-9)"
      ],
      "metadata": {
        "id": "A8GYBuutkp16"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.nn.utils.rnn import pad_sequence\n",
        "\n",
        "# helper function to club together sequential operations\n",
        "def sequential_transforms(*transforms):\n",
        "    def func(txt_input):\n",
        "        for transform in transforms:\n",
        "            txt_input = transform(txt_input)\n",
        "        return txt_input\n",
        "    return func\n",
        "\n",
        "# function to add BOS/EOS and create tensor for input sequence indices\n",
        "def tensor_transform(token_ids: List[int]):\n",
        "    return torch.cat((torch.tensor([BOS_IDX]),\n",
        "                      torch.tensor(token_ids),\n",
        "                      torch.tensor([EOS_IDX])))\n",
        "\n",
        "# ``src`` and ``tgt`` language text transforms to convert raw strings into tensors indices\n",
        "text_transform = {}\n",
        "for ln in [SRC_LANGUAGE, TGT_LANGUAGE]:\n",
        "    text_transform[ln] = sequential_transforms(token_transform[ln], #Tokenization\n",
        "                                               vocab_transform[ln], #Numericalization\n",
        "                                               tensor_transform) # Add BOS/EOS and create tensor\n",
        "\n",
        "\n",
        "# function to collate data samples into batch tensors\n",
        "def collate_fn(batch):\n",
        "    src_batch, tgt_batch = [], []\n",
        "    for src_sample, tgt_sample in batch:\n",
        "        src_batch.append(text_transform[SRC_LANGUAGE](src_sample.rstrip(\"\\n\")))\n",
        "        tgt_batch.append(text_transform[TGT_LANGUAGE](tgt_sample.rstrip(\"\\n\")))\n",
        "\n",
        "    src_batch = pad_sequence(src_batch, padding_value=PAD_IDX)\n",
        "    tgt_batch = pad_sequence(tgt_batch, padding_value=PAD_IDX)\n",
        "    return src_batch, tgt_batch"
      ],
      "metadata": {
        "id": "8DSWXZW5ksik"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from timeit import default_timer as timer\n",
        "NUM_EPOCHS = 18\n",
        "\n",
        "for epoch in range(1, NUM_EPOCHS+1):\n",
        "    start_time = timer()\n",
        "    train_loss = train_epoch(transformer, optimizer)\n",
        "    end_time = timer()\n",
        "    val_loss = evaluate(transformer)\n",
        "    print((f\"Epoch: {epoch}, Train loss: {train_loss:.3f}, Val loss: {val_loss:.3f}, \"f\"Epoch time = {(end_time - start_time):.3f}s\"))\n",
        "\n",
        "\n",
        "# function to generate output sequence using greedy algorithm\n",
        "def greedy_decode(model, src, src_mask, max_len, start_symbol):\n",
        "    src = src.to(DEVICE)\n",
        "    src_mask = src_mask.to(DEVICE)\n",
        "\n",
        "    memory = model.encode(src, src_mask)\n",
        "    ys = torch.ones(1, 1).fill_(start_symbol).type(torch.long).to(DEVICE)\n",
        "    for i in range(max_len-1):\n",
        "        memory = memory.to(DEVICE)\n",
        "        tgt_mask = (generate_square_subsequent_mask(ys.size(0))\n",
        "                    .type(torch.bool)).to(DEVICE)\n",
        "        out = model.decode(ys, memory, tgt_mask)\n",
        "        out = out.transpose(0, 1)\n",
        "        prob = model.generator(out[:, -1])\n",
        "        _, next_word = torch.max(prob, dim=1)\n",
        "        next_word = next_word.item()\n",
        "\n",
        "        ys = torch.cat([ys,\n",
        "                        torch.ones(1, 1).type_as(src.data).fill_(next_word)], dim=0)\n",
        "        if next_word == EOS_IDX:\n",
        "            break\n",
        "    return ys\n",
        "\n",
        "\n",
        "# actual function to translate input sentence into target language\n",
        "def translate(model: torch.nn.Module, src_sentence: str):\n",
        "    model.eval()\n",
        "    src = text_transform[SRC_LANGUAGE](src_sentence).view(-1, 1)\n",
        "    num_tokens = src.shape[0]\n",
        "    src_mask = (torch.zeros(num_tokens, num_tokens)).type(torch.bool)\n",
        "    tgt_tokens = greedy_decode(\n",
        "        model,  src, src_mask, max_len=num_tokens + 5, start_symbol=BOS_IDX).flatten()\n",
        "    return \" \".join(vocab_transform[TGT_LANGUAGE].lookup_tokens(list(tgt_tokens.cpu().numpy()))).replace(\"<bos>\", \"\").replace(\"<eos>\", \"\")"
      ],
      "metadata": {
        "id": "JvaSl2-al3oW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5j6QXpC-YysT"
      },
      "source": [
        "# BERT finetuning"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corpus_bleu([[text] for text in original_text], generated_text) * 100\n",
        "from nltk.translate.bleu_score import corpus_bleu\n",
        "\n",
        "\n",
        "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)"
      ],
      "metadata": {
        "id": "zpBhX2Y9Rxb1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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 = 'data.txt'\n",
        "if not os.path.exists(path_do_data):\n",
        "    print(\"Dataset not found locally. Downloading from github.\")\n",
        "    !wget https://raw.githubusercontent.com/neychev/made_nlp_course/master/datasets/Machine_translation_EN_RU/data.txt -nc\n",
        "    path_do_data = './data.txt'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1l1flYXTAS7t",
        "outputId": "cd376d10-95fe-4e5a-fcf5-638e5c090920"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset not found locally. Downloading from github.\n",
            "--2023-05-20 21:13:30--  https://raw.githubusercontent.com/neychev/made_nlp_course/master/datasets/Machine_translation_EN_RU/data.txt\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 12905334 (12M) [text/plain]\n",
            "Saving to: ‘data.txt’\n",
            "\n",
            "\rdata.txt              0%[                    ]       0  --.-KB/s               \rdata.txt            100%[===================>]  12.31M  --.-KB/s    in 0.04s   \n",
            "\n",
            "2023-05-20 21:13:30 (321 MB/s) - ‘data.txt’ saved [12905334/12905334]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers accelerate datasets sentencepiece -q"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TGBikUioFk30",
        "outputId": "15c69e4c-ff21-4a28-ba3b-b0aa5b70aa71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.1/7.1 MB\u001b[0m \u001b[31m31.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m219.1/219.1 kB\u001b[0m \u001b[31m17.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m474.6/474.6 kB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m44.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m224.5/224.5 kB\u001b[0m \u001b[31m16.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m61.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.5/110.5 kB\u001b[0m \u001b[31m12.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.5/212.5 kB\u001b[0m \u001b[31m21.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.3/134.3 kB\u001b[0m \u001b[31m16.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m27.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.5/114.5 kB\u001b[0m \u001b[31m13.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m28.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.6/149.6 kB\u001b[0m \u001b[31m18.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "dataset = load_dataset(\"csv\",  data_files=\"data.txt\",delimiter='\\t',column_names=['source', 'target',], split='train').train_test_split(test_size=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164,
          "referenced_widgets": [
            "9dc20dab424141c59eece31dda373951",
            "c2cdda513b1d46edae1f08a576ebf8cc",
            "05aac7b2c56040a99480b256bdbe79ac",
            "a932c85be98d4a6d9cfd516607ee5087",
            "01a56b437b0841c2b3fccbd2ec1e0c8f",
            "efc82876e67b434d8e2bb39cd1115960",
            "f7c17f6446834b15aa746ce7013e8c43",
            "70cf4a94b5624c55818164923c07dd6e",
            "b99e747f9cf7462ebe77fa430b52c9d3",
            "139fc99353f74c8c9d71439b9c7ef227",
            "e5e3476d027a4b64bb26e527b19b5bae",
            "e5b938ccd9b549e085489e59f2d175f4",
            "0fcf7c42613d4de784ea41e383b9454e",
            "0d05d4b47a844e80a3a994298d564117",
            "438c91010ce541608859d36b7bad8339",
            "8ea38d6b8a964ddda0eeed00338e63d5",
            "61055a78d7d74d34bd5c290b303ad123",
            "db8d8bf18d6b4d99b617efec7405ca75",
            "a661f05778434bdfa8e03d041c9b8c68",
            "4e359d7a22b24c6a972cf18988d7ca48",
            "9b1d1528a31149148607e0582cf99d43",
            "b5495551645c45cb841addccf715089c",
            "642bcd463efe421d8eab1cc59869f712",
            "ded3d2ab49d247bfb754db25c01595cc",
            "df9bad78d277455bb5472666eddf67c5",
            "2558d8d0195343cabd73cfe1a530746e",
            "f4df86ab49f54ad5b3eb066ebfec132e",
            "21afe63c5e084dd68e22d887b393b26f",
            "48359a86d59b4feb8dc0653c4d3a9468",
            "a92970f2ec294f46a16bcb61515f1470",
            "fd1684192101444fb6c90283c5635acd",
            "b61ce0ea982f44b1b7db998d69d13354",
            "97cdb77b6f1443c1943985ee4b5a623e"
          ]
        },
        "id": "w6XgPJRICSey",
        "outputId": "fdb36ccf-c752-4774-d285-f9fb18a6d6e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-7bc61aea1a0f1d5e/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9dc20dab424141c59eece31dda373951"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e5b938ccd9b549e085489e59f2d175f4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "642bcd463efe421d8eab1cc59869f712"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-7bc61aea1a0f1d5e/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1. Subsequent calls will reuse this data.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-_BKliMFCxCL",
        "outputId": "a5f4eff6-17d3-4d0b-d258-e184015d54b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['source', 'target'],\n",
              "        num_rows: 40000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['source', 'target'],\n",
              "        num_rows: 10000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import MBartForConditionalGeneration, MBart50TokenizerFast\n",
        "\n",
        "tokenizer = MBart50TokenizerFast.from_pretrained(\"facebook/mbart-large-50\", src_lang=\"en_XX\", tgt_lang=\"ru_RU\")\n",
        "model = MBartForConditionalGeneration.from_pretrained(\"facebook/mbart-large-50\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377,
          "referenced_widgets": [
            "d26e611496ba4e2698100d98cf9985e6",
            "ce6b42e70aca4d7390f69e6eafbe1312",
            "fbcc4748bbd4469d825981ff80c5c68c",
            "3c9783c1f56e43b79e256b28e88959a2",
            "281fbed8cacd4415959b5eceaa97be3c",
            "d86254f2dfb5488c86c9842054fe5aa2",
            "8abf3ac33ea94d4e9eaecd5e6becf711",
            "cbd42bf32e8a4af7b37594a809510318",
            "6d4192cd44fe49b7913219a67cc24171",
            "795178fa246f41d5b3b6c37baea4b997",
            "efc0ada6ef5644469e8c367cbad449d2",
            "61ccb51a11dd4f1484f1e3f1d4fe3afe",
            "ef86068b1467429faad0ef9a789650b4",
            "074b1b929ecf4049afea017a1b3d3659",
            "4165751537bd4017bc343d203f54af8c",
            "5659020816d14aeeb41e4f45209ac68e",
            "d84a9f3d0b374a3ba8c0ec7cf30a82ce",
            "0bf0064a2f0640a4a2385ef851c60315",
            "711b26ee0a2b46f4ab0bf0d7dcb80524",
            "1531b9c17c8644c5840b723e4c7a570b",
            "e66b569e348b4f75ae29aa3c7f3dea38",
            "01f44362a29e4031a39e295c59ae929a",
            "29592aa440a9435984e5e8edf2806496",
            "9c4241e8bc9d4060802211493af3703b",
            "016ab5d50f7a4fab91bcb5c0d4e8ffa9",
            "be7cf49da3bd4370afa798b0b74e1e7a",
            "6930589625bd47c6abde55034efa1dcf",
            "9fc52f79d9794a1bafcd9afeaf653ee2",
            "bca86714bbed4bc4b6691d6b6acf123f",
            "ca23d4640f2546a9b30f3a550fb22b6e",
            "1edce7ded58d48daa70e01983b6fb962",
            "0ab914ddcf314f9ba9cdba1ddbebaa0f",
            "1dd8b6915ae347aabb66acd77c74c09a",
            "e838fadb44d942799da414b5d335f5d9",
            "40608a45458e4b5db2308b6c4aa15de5",
            "f12d5e362a734723baa88fc981fa2345",
            "febee309bad24945a3de7d34268f5b35",
            "76ced73221124edebafa210f7be6553d",
            "ab14d1551bce461090e899ad68545916",
            "c53df5f5acc244ac80a027b6063a0840",
            "4d8558d94b0942b7ad84f2b4599c1f05",
            "dcb1f993ad554e86ab2090b232d60663",
            "d6a0c4254f2046de8afb88c897d2934c",
            "a68d4c186e374626ab7612ddb9ed2a2d",
            "f903f6d6390c4b849e24805f46e04df1",
            "55e4cdd62f804884b9c44eb4f00165d5",
            "0d5da0bda60946a4bed4ebef66986bd5",
            "2788b07181024aeb884a60a7e4bc8ff1",
            "c4a340db98a34aaba2542eba0850e021",
            "a1d65b45dc494d78a5cd68fdecee85c7",
            "fe0fe6f072064db8af8156d3e0873381",
            "fa8a67000fad4945aae770a69d7a0f97",
            "44a88ff2aaf9456a82adaa1522917291",
            "99185d6620ca45a993ec2d685abc8657",
            "87baf87317624ac2a629ccc5bc4036f1",
            "dbcf442ada5b44b39bf104195e87a7f4",
            "895f09960f21437ba893a334dd5db87f",
            "c375411aa6da4d7a9491aa6a24fd503a",
            "6aacd7e1e0354abeb216f558b96ff4f3",
            "f1fe2832c06a414899c7bedf9669bb2b",
            "1918bc1abf984607a8c14a4dc326767b",
            "ea9a490ef27446318323b2216bc886ed",
            "d173b788a00c441584b69903730f5144",
            "592c4a3f598e43118ca119075dfcfef9",
            "986e4ec37fd04c45a50d4bf0890d0335",
            "5f0078f2e40a4ba4b0d6d5ca43549b04"
          ]
        },
        "id": "MDGkU_zKzKPD",
        "outputId": "98828857-da42-4638-b7e4-97eb4b956b94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/531 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d26e611496ba4e2698100d98cf9985e6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)tencepiece.bpe.model:   0%|          | 0.00/5.07M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "61ccb51a11dd4f1484f1e3f1d4fe3afe"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/649 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "29592aa440a9435984e5e8edf2806496"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.42k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e838fadb44d942799da414b5d335f5d9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/2.44G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f903f6d6390c4b849e24805f46e04df1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/261 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dbcf442ada5b44b39bf104195e87a7f4"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Seq2SeqTrainingArguments, Seq2SeqTrainer"
      ],
      "metadata": {
        "id": "l6tuq2-b4H9V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DataCollatorForSeq2Seq\n",
        "\n",
        "data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)"
      ],
      "metadata": {
        "id": "uZV37xs29N9J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_input_length = 128\n",
        "max_target_length = 128\n",
        "\n",
        "def preprocess_function(examples):\n",
        "    model_inputs = tokenizer(examples['source'], max_length=max_input_length, truncation=True)\n",
        "\n",
        "    # Setup the tokenizer for targets\n",
        "    with tokenizer.as_target_tokenizer():\n",
        "        labels = tokenizer(examples['target'], max_length=max_target_length, truncation=True)\n",
        "\n",
        "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "    return model_inputs"
      ],
      "metadata": {
        "id": "tfCbkKeH9wix"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "args = Seq2SeqTrainingArguments(output_dir=\"./results\",\n",
        "                        do_train=True,\n",
        "                        evaluation_strategy = \"epoch\",\n",
        "                        per_device_train_batch_size=1,\n",
        "                        max_steps = 500,\n",
        "                        learning_rate=2e-5,\n",
        "                        predict_with_generate=True,\n",
        "                        fp16=True,\n",
        "                        save_total_limit=3,\n",
        "                        logging_dir=\"/logs\")"
      ],
      "metadata": {
        "id": "pfUagOoo480B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets = dataset.map(preprocess_function, batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72,
          "referenced_widgets": [
            "0bfd76f360cc4d32aea5e0aa6a42f5f8",
            "9c7f24619ee245348f5ae5d28c3030d0",
            "edd01b3f220a455a8f39f1b2b8b0488b",
            "79f6ac90159b4a86b8aec6dd6d5242f5",
            "a148de1e168b42d6a39a3b23967a7e76",
            "7c783ad3841e4d66bd3f876cc00f2ee4",
            "d7b259093f6645cc8c049fdef7c592e4",
            "29a58f847caa416db7fe99a0e592c322",
            "391478501b104f64987095e48d15460a",
            "1f6e580e1fb043ab8136ea5f0f2f5826",
            "392ca07ed86e4d45a88cf07b7154b15d",
            "7af3e1288fb644889de5380d30c088e0",
            "ac29f0c58dd7498ba132517261c0c2ba",
            "986f2262748c492cb5d4c229be586b54",
            "2db9e3aa805f485b9abad6650ae47989",
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            "05f6cf7781ce4361bc3fc00889034891",
            "09eba49cb5cf45a8b73090ece22a8087",
            "5013304c160e4227924b6a8f4c5a9c5d",
            "ff0cd2320ebc4afba0fe62e49f2efbdc"
          ]
        },
        "id": "c6prjL6LH6Lu",
        "outputId": "a35f8e04-d420-41d1-ca02-77b793afdf3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/40000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "0bfd76f360cc4d32aea5e0aa6a42f5f8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3606: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/10000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "7af3e1288fb644889de5380d30c088e0"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "trainer = Seq2SeqTrainer(model=model, \n",
        "    args=args, \n",
        "    data_collator=data_collator, \n",
        "    train_dataset=tokenized_datasets['train'],\n",
        "    eval_dataset=tokenized_datasets['test'],\n",
        ")"
      ],
      "metadata": {
        "id": "30wDTSAF6fF1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.cuda.empty_cache()"
      ],
      "metadata": {
        "id": "rl4s3P9t9uVb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "_gPSxxSvHMLK",
        "outputId": "0dbe5d05-abde-48a2-c652-fc6a41d9f696"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:407: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "You're using a MBart50TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='500' max='500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [500/500 05:35, Epoch 0/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>3.233400</td>\n",
              "      <td>1.881686</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=500, training_loss=3.23338916015625, metrics={'train_runtime': 338.4822, 'train_samples_per_second': 1.477, 'train_steps_per_second': 1.477, 'total_flos': 22837399732224.0, 'train_loss': 3.23338916015625, 'epoch': 0.01})"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "# Replace this with your own checkpoint\n",
        "model_checkpoint = \"huggingface-course/marian-finetuned-kde4-en-to-fr\"\n",
        "translator = pipeline(\"translation\", model=model_checkpoint)"
      ],
      "metadata": {
        "id": "5SKntcIvQGYu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Transformer"
      ],
      "metadata": {
        "id": "jm4acx6y4heJ"
      }
    },
    {
      "cell_type": "code",
      "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 = 'data.txt'\n",
        "if not os.path.exists(path_do_data):\n",
        "    print(\"Dataset not found locally. Downloading from github.\")\n",
        "    !wget https://raw.githubusercontent.com/neychev/made_nlp_course/master/datasets/Machine_translation_EN_RU/data.txt -nc\n",
        "    path_do_data = './data.txt'"
      ],
      "metadata": {
        "id": "iwyAQ8j7W8n2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "32b4803f-ac0e-4e82-9875-37a6b2200e06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset not found locally. Downloading from github.\n",
            "--2023-05-22 04:53:58--  https://raw.githubusercontent.com/neychev/made_nlp_course/master/datasets/Machine_translation_EN_RU/data.txt\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.110.133, 185.199.109.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 12905334 (12M) [text/plain]\n",
            "Saving to: ‘data.txt’\n",
            "\n",
            "data.txt            100%[===================>]  12.31M  --.-KB/s    in 0.1s    \n",
            "\n",
            "2023-05-22 04:54:00 (98.8 MB/s) - ‘data.txt’ saved [12905334/12905334]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install datasets -q"
      ],
      "metadata": {
        "id": "U-fUWNQf00Jm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "dataset = load_dataset(\"csv\",  data_files=\"data.txt\",delimiter='\\t',column_names=['source', 'target',], split='train').train_test_split(test_size=0.2)"
      ],
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        "colab": {
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        },
        "id": "z30vqM6pz-OY",
        "outputId": "ba91b6ca-c695-4417-a002-62731c027af8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-ddd63ac03072a3fa/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1...\n"
          ]
        },
        {
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            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
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              "version_minor": 0,
              "model_id": "2b89e41bcdc844e482ce4bb31fd1ad26"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5c1b0c9a40374c6da6f7d7974a5af3ea"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
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              "version_minor": 0,
              "model_id": "07554619a7fa45f992ab0aaf2fb0a811"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-ddd63ac03072a3fa/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1. Subsequent calls will reuse this data.\n"
          ]
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vA_G47J107EW",
        "outputId": "6dfb119a-a51c-4082-e9c6-28917e69a4e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['source', 'target'],\n",
              "        num_rows: 40000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['source', 'target'],\n",
              "        num_rows: 10000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torchtext.transforms as T\n",
        "\n",
        "max_seq_len = 50\n",
        "\n",
        "T.Truncate(max_seq_len - 2),"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VkAWffuEzqhl",
        "outputId": "4f82dcbf-a2e4-464d-b70b-b83112193998"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Truncate(),)"
            ]
          },
          "metadata": {},
          "execution_count": 2
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      ]
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