{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import islice\n",
    "import regex as re\n",
    "import sys\n",
    "from torchtext.vocab import build_vocab_from_iterator\n",
    "from torch import nn\n",
    "import torch\n",
    "from torch.utils.data import IterableDataset\n",
    "import itertools\n",
    "import pandas as pd\n",
    "from torch.utils.data import DataLoader\n",
    "import csv\n",
    "\n",
    "def data_preprocessing(text):\n",
    "    return re.sub(r'\\p{P}', '', text.lower().replace('-\\\\n', '').replace('\\\\n', ' ').replace(\"'ll\", \" will\").replace(\"-\", \"\").replace(\"'ve\", \" have\").replace(\"'s\", \" is\"))\n",
    "\n",
    "def get_words_from_line(line):\n",
    "  line = line.rstrip()\n",
    "  yield '<s>'\n",
    "  for m in re.finditer(r'[\\p{L}0-9\\*]+|\\p{P}+', line):\n",
    "     yield m.group(0).lower()\n",
    "  yield '</s>'\n",
    "\n",
    "\n",
    "def get_word_lines_from_file(data):\n",
    "  for line in data:\n",
    "        yield get_words_from_line(line)\n",
    "\n",
    "\n",
    "class SimpleBigramNeuralLanguageModel(nn.Module):\n",
    "  def __init__(self, vocabulary_size, embedding_size):\n",
    "      super(SimpleBigramNeuralLanguageModel, self).__init__()\n",
    "      self.model = nn.Sequential(\n",
    "          nn.Embedding(vocabulary_size, embedding_size),\n",
    "          nn.Linear(embedding_size, vocabulary_size),\n",
    "          nn.Softmax()\n",
    "      )\n",
    "\n",
    "  def forward(self, x):\n",
    "      return self.model(x)\n",
    "\n",
    "\n",
    "def look_ahead_iterator(gen):\n",
    "   prev = None\n",
    "   for item in gen:\n",
    "      if prev is not None:\n",
    "         yield (prev, item)\n",
    "      prev = item\n",
    "\n",
    "class Bigrams(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 look_ahead_iterator(\n",
    "         (self.vocab[t] for t in itertools.chain.from_iterable(get_word_lines_from_file(self.text_file))))\n",
    "\n",
    "in_file = 'train/in.tsv.xz'\n",
    "out_file = 'train/expected.tsv'\n",
    "\n",
    "train_set = pd.read_csv(\n",
    "    'train/in.tsv.xz',\n",
    "    sep='\\t',\n",
    "    header=None,\n",
    "    quoting=csv.QUOTE_NONE,\n",
    "    nrows=35000)\n",
    "\n",
    "train_labels = pd.read_csv(\n",
    "    'train/expected.tsv',\n",
    "    sep='\\t',\n",
    "    header=None,\n",
    "    quoting=csv.QUOTE_NONE,\n",
    "    nrows=35000)\n",
    "\n",
    "data = pd.concat([train_set, train_labels], axis=1)\n",
    "data = train_set[6] + train_set[0] + train_set[7]\n",
    "data = data.apply(data_preprocessing)\n",
    "\n",
    "vocab_size = 30000\n",
    "embed_size = 150\n",
    "\n",
    "\n",
    "bigram_data = Bigrams(data, vocab_size)\n",
    "\n",
    "device = 'cpu'\n",
    "model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n",
    "data = DataLoader(bigram_data, batch_size=5000)\n",
    "optimizer = torch.optim.Adam(model.parameters())\n",
    "criterion = torch.nn.NLLLoss()\n",
    "\n",
    "model.train()\n",
    "step = 0\n",
    "for x, y in data:\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",
    "    optimizer.step()\n",
    "\n",
    "torch.save(model.state_dict(), 'model1.bin')\n",
    "\n",
    "vocab = bigram_data.vocab\n",
    "prediction = 'the:0.03 be:0.03 to:0.03 of:0.025 and:0.025 a:0.025 in:0.020 that:0.020 have:0.015 I:0.010 it:0.010 for:0.010 not:0.010 on:0.010 with:0.010 he:0.010 as:0.010 you:0.010 do:0.010 at:0.010 :0.77'\n",
    "\n",
    "def predict_word(w):\n",
    "    ixs = torch.tensor(vocab.forward(w)).to(device)\n",
    "    out = model(ixs)\n",
    "    top = torch.topk(out[0], 8)\n",
    "    top_indices = top.indices.tolist()\n",
    "    top_probs = top.values.tolist()\n",
    "    top_words = vocab.lookup_tokens(top_indices)\n",
    "    pred_str = \"\"\n",
    "    for word, prob in list(zip(top_words, top_probs)):\n",
    "                pred_str += f\"{word}:{prob} \"\n",
    "    return pred_str\n",
    "\n",
    "\n",
    "def predict(f):\n",
    "    x = pd.read_csv(f'{f}/in.tsv.xz', sep='\\t', header=None, quoting=csv.QUOTE_NONE,  on_bad_lines='skip', encoding=\"UTF-8\")[6]\n",
    "    x = x.apply(data_preprocessing)\n",
    "\n",
    "    with open(f'{f}/out.tsv', \"w+\", encoding=\"UTF-8\") as f:\n",
    "        for row in x:\n",
    "            result = {}\n",
    "            before = None\n",
    "            for before in get_words_from_line(data_preprocessing(str(row)), False):\n",
    "                pass\n",
    "            before = [before]\n",
    "            if(len(before) < 1):\n",
    "                pred_str = prediction\n",
    "            else:\n",
    "                pred_str = predict_word(before)\n",
    "\n",
    "            pred_str = pred_str.strip()\n",
    "            f.write(pred_str + \"\\n\")\n",
    "\n",
    "prediction(\"dev-0/\")\n",
    "prediction(\"test-A/\")"
   ]
  }
 ],
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  "language_info": {
   "name": "python"
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