{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "collapsed": true,
        "id": "bAOmtZk6dzWU"
      },
      "outputs": [],
      "source": [
        "import itertools\n",
        "import lzma\n",
        "import numpy as np\n",
        "import regex as re\n",
        "import torch\n",
        "from torch import nn\n",
        "from torch.utils.data import IterableDataset, DataLoader\n",
        "from torchtext.vocab import build_vocab_from_iterator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "N35v7jfAdzWV"
      },
      "outputs": [],
      "source": [
        "vocab_size = 10000\n",
        "embed_size = 200\n",
        "hidden_size = 100\n",
        "batch_size = 1000\n",
        "device = 'cuda'\n",
        "path_to_train = 'train/in.tsv.xz'\n",
        "path_to_model = 'model1.bin'\n",
        "\n",
        "def clean_line(line: str):\n",
        "    # Preprocessing\n",
        "    separated = line.split('\\t')\n",
        "    prefix = separated[6].replace(r'\\n', ' ')\n",
        "    suffix = separated[7].replace(r'\\n', ' ')\n",
        "    return prefix + ' ' + suffix\n",
        "\n",
        "def get_words_from_line(line):\n",
        "    line = clean_line(line)\n",
        "    for word in line.split():\n",
        "        yield word\n",
        "\n",
        "def get_word_lines_from_file(file_name):\n",
        "    with lzma.open(file_name, mode='rt', encoding='utf-8') as fid:\n",
        "        for line in fid:\n",
        "            yield get_words_from_line(line)\n",
        "\n",
        "def double_look_ahead_iterator(gen):\n",
        "    prev_prev = None\n",
        "    prev = None\n",
        "    for item in gen:\n",
        "        if prev_prev is not None:\n",
        "            yield np.asarray((prev_prev, prev, item))\n",
        "        prev_prev = prev\n",
        "        prev = item\n",
        "\n",
        "def prediction(words, model, top) -> str:\n",
        "    words_tensor = [train_dataset.vocab.forward([word]) for word in words]\n",
        "    ixs = torch.tensor(words_tensor).view(-1).to(device)\n",
        "    out = model(ixs)\n",
        "    top = torch.topk(out[0], top)\n",
        "    top_indices = top.indices.tolist()\n",
        "    top_probs = top.values.tolist()\n",
        "    top_words = vocab.lookup_tokens(top_indices)\n",
        "    zipped = list(zip(top_words, top_probs))\n",
        "    for index, element in enumerate(zipped):\n",
        "        unk = None\n",
        "        if '<unk>' in element:\n",
        "            unk = zipped.pop(index)\n",
        "            zipped.append(('', unk[1]))\n",
        "            break\n",
        "    if unk is None:\n",
        "        zipped[-1] = ('', zipped[-1][1])\n",
        "    return ' '.join([f'{x[0]}:{x[1]:.5f}' for x in zipped])\n",
        "\n",
        "def create_outputs(folder_name, model, top):\n",
        "    print(f'Creating outputs in {folder_name}')\n",
        "    with lzma.open(f'{folder_name}/in.tsv.xz', mode='rt', encoding='utf-8') as fid:\n",
        "        with open(f'{folder_name}/out-top={top}.tsv', 'w', encoding='utf-8', newline='\\n') as f:\n",
        "            for line in fid:\n",
        "                separated = line.split('\\t')\n",
        "                prefix = separated[6].replace(r'\\n', ' ').split()[-2:]\n",
        "                output_line = prediction(prefix, model, top)\n",
        "                f.write(output_line + '\\n')\n",
        "\n",
        "def train_model(lr):\n",
        "    model = SimpleTrigramNeuralLanguageModel(vocab_size, embed_size, hidden_size).to(device)\n",
        "    data = DataLoader(train_dataset, batch_size=batch_size)\n",
        "    optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
        "    criterion = torch.nn.NLLLoss()\n",
        "\n",
        "    model.train()\n",
        "    step = 0\n",
        "    for batch in data:\n",
        "        x = batch[:, :2]\n",
        "        y = batch[:, 2]\n",
        "        x = x.to(device)\n",
        "        y = y.to(device)\n",
        "        optimizer.zero_grad()\n",
        "        ypredicted = model(x)\n",
        "        loss = criterion(torch.log(ypredicted), y)\n",
        "        if step % 100 == 0:\n",
        "            print(step, loss)\n",
        "        step += 1\n",
        "        loss.backward()\n",
        "\n",
        "        torch.nn.utils.clip_grad_norm_(model.parameters(), 10)\n",
        "\n",
        "\n",
        "        optimizer.step()\n",
        "\n",
        "    torch.save(model.state_dict(), path_to_model)\n",
        "\n",
        "def with_hyperparams():\n",
        "      train_model(lr=0.0001)\n",
        "      model = SimpleTrigramNeuralLanguageModel(vocab_size, embed_size, hidden_size).to(device)\n",
        "      model.load_state_dict(torch.load(path_to_model))\n",
        "      model.eval()\n",
        "      for top in [200, 400, 600]:\n",
        "          create_outputs('dev-0', model, top)\n",
        "          create_outputs('test-A', model, top)\n",
        "\n",
        "class Trigrams(IterableDataset):\n",
        "    def __init__(self, text_file, vocabulary_size):\n",
        "        self.vocab = build_vocab_from_iterator(\n",
        "            get_word_lines_from_file(text_file),\n",
        "            max_tokens=vocabulary_size,\n",
        "            specials=['<unk>'])\n",
        "        self.vocab.set_default_index(self.vocab['<unk>'])\n",
        "        self.vocabulary_size = vocabulary_size\n",
        "        self.text_file = text_file\n",
        "\n",
        "    def __iter__(self):\n",
        "        return double_look_ahead_iterator(\n",
        "            (self.vocab[t] for t in itertools.chain.from_iterable(get_word_lines_from_file(self.text_file))))\n",
        "\n",
        "\n",
        "class SimpleTrigramNeuralLanguageModel(nn.Module):\n",
        "    def __init__(self, vocabulary_size, embedding_size, hidden_size):\n",
        "        super(SimpleTrigramNeuralLanguageModel, self).__init__()\n",
        "        self.embedding_size = embedding_size\n",
        "        self.embedding = nn.Embedding(vocabulary_size, embedding_size)\n",
        "        self.lin1 = nn.Linear(2 * embedding_size, hidden_size)\n",
        "        self.rel = nn.ReLU()\n",
        "        self.lin2 = nn.Linear(hidden_size, vocabulary_size)\n",
        "        self.sm = nn.Softmax()\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.embedding(x).view((-1, 2 * self.embedding_size))\n",
        "        x = self.lin1(x)\n",
        "        x = self.rel(x)\n",
        "        x = self.lin2(x)\n",
        "        return self.sm(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NkOOve5pdzWZ",
        "outputId": "35cf7a57-a1e3-4194-c76f-708d8929511f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "/content/drive/MyDrive/modelowanie_jezyka\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive',force_remount=True)\n",
        "%cd /content/drive/MyDrive/modelowanie_jezyka"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "4RYvjsWvdzWZ"
      },
      "outputs": [],
      "source": [
        "vocab = build_vocab_from_iterator(\n",
        "    get_word_lines_from_file(path_to_train),\n",
        "    max_tokens=vocab_size,\n",
        "    specials=['<unk>']\n",
        ")\n",
        "\n",
        "vocab.set_default_index(vocab['<unk>'])\n",
        "train_dataset = Trigrams(path_to_train, vocab_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9gg3bNu5dzWZ",
        "outputId": "17b973c6-0247-4fe2-85c6-a97dc02242e1"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-7-15bd6173eff4>:131: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  return self.sm(x)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "129300 tensor(4.3820, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "129400 tensor(4.8125, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "129500 tensor(4.9783, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "129600 tensor(4.5286, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "129700 tensor(4.8962, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "129800 tensor(4.6818, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "129900 tensor(5.0567, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "130000 tensor(5.1978, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "130100 tensor(5.1098, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "130200 tensor(4.6280, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "130300 tensor(4.9298, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "130400 tensor(4.6004, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "130500 tensor(4.8462, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "130600 tensor(4.7324, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "130700 tensor(4.7167, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "130800 tensor(4.4547, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "130900 tensor(4.5560, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "131000 tensor(4.0357, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "131100 tensor(4.8499, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "131200 tensor(4.9097, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "131300 tensor(4.8725, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "131400 tensor(4.8507, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "131500 tensor(4.8822, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "131600 tensor(4.8053, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "131700 tensor(4.7774, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "131800 tensor(4.6166, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "131900 tensor(5.0381, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "132000 tensor(5.1435, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "132100 tensor(4.9651, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "132200 tensor(4.3226, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "132300 tensor(4.9645, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "132400 tensor(5.1297, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "132500 tensor(4.9514, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "132600 tensor(4.4122, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "132700 tensor(4.8552, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "132800 tensor(4.4157, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "132900 tensor(4.9525, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "133000 tensor(4.7483, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "133100 tensor(4.6578, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "133200 tensor(5.0746, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "133300 tensor(4.8121, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "133400 tensor(4.8101, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "133500 tensor(4.6605, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "133600 tensor(4.7754, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "133700 tensor(4.4397, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "133800 tensor(4.8784, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "133900 tensor(4.6728, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "134000 tensor(4.7126, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "134100 tensor(4.5749, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "134200 tensor(4.4029, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "134300 tensor(4.7075, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "134400 tensor(5.0396, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "134500 tensor(4.6848, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "134600 tensor(5.0197, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "134700 tensor(4.7742, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "134800 tensor(4.8104, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "134900 tensor(4.8825, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "135000 tensor(4.7189, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "135100 tensor(4.6685, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "135200 tensor(4.7691, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "135300 tensor(4.7166, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "135400 tensor(4.6312, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "135500 tensor(4.9367, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "135600 tensor(4.8343, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "135700 tensor(4.8131, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "135800 tensor(4.7636, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "135900 tensor(4.6484, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "136000 tensor(4.7523, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "136100 tensor(4.8950, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "136200 tensor(4.9203, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "136300 tensor(4.8050, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "136400 tensor(4.8176, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "136500 tensor(4.4287, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "136600 tensor(4.5731, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "136700 tensor(4.7796, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "136800 tensor(5.1640, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "136900 tensor(4.5927, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "137000 tensor(4.5682, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "137100 tensor(5.0697, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "137200 tensor(4.8193, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "137300 tensor(4.8367, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "137400 tensor(4.9668, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "137500 tensor(4.6632, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "137600 tensor(5.2432, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "137700 tensor(4.6309, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "137800 tensor(4.7814, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "137900 tensor(4.4679, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "138000 tensor(4.9990, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "138100 tensor(4.6476, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "138200 tensor(4.9484, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "138300 tensor(4.8118, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "138400 tensor(4.8842, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "138500 tensor(4.7052, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "138600 tensor(3.9411, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "138700 tensor(5.0011, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "138800 tensor(4.8827, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "138900 tensor(4.8508, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "139000 tensor(4.6841, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
            "Creating outputs in dev-0\n",
            "Creating outputs in test-A\n",
            "Creating outputs in dev-0\n",
            "Creating outputs in test-A\n",
            "Creating outputs in dev-0\n",
            "Creating outputs in test-A\n"
          ]
        }
      ],
      "source": [
        "with_hyperparams()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.12"
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  "nbformat": 4,
  "nbformat_minor": 0
}