{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import unicode_literals, print_function, division\n",
    "from io import open\n",
    "import unicodedata\n",
    "import re\n",
    "import random\n",
    "import time\n",
    "import math\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch import optim\n",
    "import torch.nn.functional as F\n",
    "\n",
    "import numpy as np\n",
    "from torch.utils.data import TensorDataset, DataLoader, RandomSampler\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "import pandas as pd\n",
    "from nltk.translate.bleu_score import corpus_bleu, SmoothingFunction\n",
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading lines...\n",
      "Read 49943 sentence pairs\n",
      "Trimmed to 3613 sentence pairs\n",
      "Counting words...\n",
      "Counted words:\n",
      "pol 3070\n",
      "en 1969\n",
      "['jestes sumienny', 'you re conscientious']\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "SOS_token = 0\n",
    "EOS_token = 1\n",
    "\n",
    "class Lang:\n",
    "    def __init__(self, name):\n",
    "        self.name = name\n",
    "        self.word2index = {}\n",
    "        self.word2count = {}\n",
    "        self.index2word = {0: \"SOS\", 1: \"EOS\"}\n",
    "        self.n_words = 2\n",
    "\n",
    "    def addSentence(self, sentence):\n",
    "        for word in sentence.split(' '):\n",
    "            self.addWord(word)\n",
    "\n",
    "    def addWord(self, word):\n",
    "        if word not in self.word2index:\n",
    "            self.word2index[word] = self.n_words\n",
    "            self.word2count[word] = 1\n",
    "            self.index2word[self.n_words] = word\n",
    "            self.n_words += 1\n",
    "        else:\n",
    "            self.word2count[word] += 1\n",
    "\n",
    "def unicodeToAscii(s):\n",
    "    return ''.join(\n",
    "        c for c in unicodedata.normalize('NFD', s)\n",
    "        if unicodedata.category(c) != 'Mn'\n",
    "    )\n",
    "\n",
    "def normalizeString(s):\n",
    "    s = unicodeToAscii(s.lower().strip())\n",
    "    s = re.sub(r\"([.!?])\", r\" \\1\", s)\n",
    "    s = re.sub(r\"[^a-zA-Z!?]+\", r\" \", s)\n",
    "    return s.strip()\n",
    "\n",
    "def readLangs(reverse=False):\n",
    "    print(\"Reading lines...\")\n",
    "    lang1=\"en\"\n",
    "    lang2=\"pol\"\n",
    "    # Read the file and split into lines\n",
    "    lines = open('pol.txt', encoding='utf-8').\\\n",
    "        read().strip().split('\\n')\n",
    "\n",
    "    # Split every line into pairs and normalize\n",
    "    pairs = [[normalizeString(s) for s in l.split('\\t')[:-1]] for l in lines]\n",
    "\n",
    "    # Reverse pairs, make Lang instances\n",
    "    if reverse:\n",
    "        pairs = [list(reversed(p)) for p in pairs]\n",
    "        input_lang = Lang(lang2)\n",
    "        output_lang = Lang(lang1)\n",
    "    else:\n",
    "        input_lang = Lang(lang1)\n",
    "        output_lang = Lang(lang2)\n",
    "\n",
    "    return input_lang, output_lang, pairs\n",
    "\n",
    "MAX_LENGTH = 10\n",
    "\n",
    "eng_prefixes = (\n",
    "    \"i am \", \"i m \",\n",
    "    \"he is\", \"he s \",\n",
    "    \"she is\", \"she s \",\n",
    "    \"you are\", \"you re \",\n",
    "    \"we are\", \"we re \",\n",
    "    \"they are\", \"they re \"\n",
    ")\n",
    "\n",
    "def filterPair(p):\n",
    "    return len(p[0].split(' ')) < MAX_LENGTH and \\\n",
    "        len(p[1].split(' ')) < MAX_LENGTH and \\\n",
    "        p[1].startswith(eng_prefixes)\n",
    "\n",
    "\n",
    "def filterPairs(pairs):\n",
    "    return [pair for pair in pairs if filterPair(pair)]\n",
    "\n",
    "def prepareData(reverse=False):\n",
    "    input_lang, output_lang, pairs = readLangs(reverse)\n",
    "    print(\"Read %s sentence pairs\" % len(pairs))\n",
    "    pairs = filterPairs(pairs)\n",
    "    print(\"Trimmed to %s sentence pairs\" % len(pairs))\n",
    "    print(\"Counting words...\")\n",
    "    for pair in pairs:\n",
    "        input_lang.addSentence(pair[0])\n",
    "        output_lang.addSentence(pair[1])\n",
    "    print(\"Counted words:\")\n",
    "    print(input_lang.name, input_lang.n_words)\n",
    "    print(output_lang.name, output_lang.n_words)\n",
    "    return input_lang, output_lang, pairs\n",
    "\n",
    "input_lang, output_lang, pairs = prepareData(True)\n",
    "print(random.choice(pairs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EncoderRNN(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, dropout_p=0.1):\n",
    "        super(EncoderRNN, self).__init__()\n",
    "        self.hidden_size = hidden_size\n",
    "\n",
    "        self.embedding = nn.Embedding(input_size, hidden_size)\n",
    "        self.gru = nn.GRU(hidden_size, hidden_size, batch_first=True)\n",
    "        self.dropout = nn.Dropout(dropout_p)\n",
    "\n",
    "    def forward(self, input):\n",
    "        embedded = self.dropout(self.embedding(input))\n",
    "        output, hidden = self.gru(embedded)\n",
    "        return output, hidden\n",
    "    \n",
    "class DecoderRNN(nn.Module):\n",
    "    def __init__(self, hidden_size, output_size):\n",
    "        super(DecoderRNN, self).__init__()\n",
    "        self.embedding = nn.Embedding(output_size, hidden_size)\n",
    "        self.gru = nn.GRU(hidden_size, hidden_size, batch_first=True)\n",
    "        self.out = nn.Linear(hidden_size, output_size)\n",
    "\n",
    "    def forward(self, encoder_outputs, encoder_hidden, target_tensor=None):\n",
    "        batch_size = encoder_outputs.size(0)\n",
    "        decoder_input = torch.empty(batch_size, 1, dtype=torch.long, device=device).fill_(SOS_token)\n",
    "        decoder_hidden = encoder_hidden\n",
    "        decoder_outputs = []\n",
    "\n",
    "        for i in range(MAX_LENGTH):\n",
    "            decoder_output, decoder_hidden  = self.forward_step(decoder_input, decoder_hidden)\n",
    "            decoder_outputs.append(decoder_output)\n",
    "\n",
    "            if target_tensor is not None:\n",
    "                # Teacher forcing: Feed the target as the next input\n",
    "                decoder_input = target_tensor[:, i].unsqueeze(1) # Teacher forcing\n",
    "            else:\n",
    "                # Without teacher forcing: use its own predictions as the next input\n",
    "                _, topi = decoder_output.topk(1)\n",
    "                decoder_input = topi.squeeze(-1).detach()  # detach from history as input\n",
    "\n",
    "        decoder_outputs = torch.cat(decoder_outputs, dim=1)\n",
    "        decoder_outputs = F.log_softmax(decoder_outputs, dim=-1)\n",
    "        return decoder_outputs, decoder_hidden, None # We return `None` for consistency in the training loop\n",
    "\n",
    "    def forward_step(self, input, hidden):\n",
    "        output = self.embedding(input)\n",
    "        output = F.relu(output)\n",
    "        output, hidden = self.gru(output, hidden)\n",
    "        output = self.out(output)\n",
    "        return output, hidden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BahdanauAttention(nn.Module):\n",
    "    def __init__(self, hidden_size):\n",
    "        super(BahdanauAttention, self).__init__()\n",
    "        self.Wa = nn.Linear(hidden_size, hidden_size)\n",
    "        self.Ua = nn.Linear(hidden_size, hidden_size)\n",
    "        self.Va = nn.Linear(hidden_size, 1)\n",
    "\n",
    "    def forward(self, query, keys):\n",
    "        scores = self.Va(torch.tanh(self.Wa(query) + self.Ua(keys)))\n",
    "        scores = scores.squeeze(2).unsqueeze(1)\n",
    "\n",
    "        weights = F.softmax(scores, dim=-1)\n",
    "        context = torch.bmm(weights, keys)\n",
    "\n",
    "        return context, weights\n",
    "\n",
    "class AttnDecoderRNN(nn.Module):\n",
    "    def __init__(self, hidden_size, output_size, dropout_p=0.1):\n",
    "        super(AttnDecoderRNN, self).__init__()\n",
    "        self.embedding = nn.Embedding(output_size, hidden_size)\n",
    "        self.attention = BahdanauAttention(hidden_size)\n",
    "        self.gru = nn.GRU(2 * hidden_size, hidden_size, batch_first=True)\n",
    "        self.out = nn.Linear(hidden_size, output_size)\n",
    "        self.dropout = nn.Dropout(dropout_p)\n",
    "\n",
    "    def forward(self, encoder_outputs, encoder_hidden, target_tensor=None):\n",
    "        batch_size = encoder_outputs.size(0)\n",
    "        decoder_input = torch.empty(batch_size, 1, dtype=torch.long, device=device).fill_(SOS_token)\n",
    "        decoder_hidden = encoder_hidden\n",
    "        decoder_outputs = []\n",
    "        attentions = []\n",
    "\n",
    "        for i in range(MAX_LENGTH):\n",
    "            decoder_output, decoder_hidden, attn_weights = self.forward_step(\n",
    "                decoder_input, decoder_hidden, encoder_outputs\n",
    "            )\n",
    "            decoder_outputs.append(decoder_output)\n",
    "            attentions.append(attn_weights)\n",
    "\n",
    "            if target_tensor is not None:\n",
    "                decoder_input = target_tensor[:, i].unsqueeze(1)\n",
    "            else:\n",
    "                _, topi = decoder_output.topk(1)\n",
    "                decoder_input = topi.squeeze(-1).detach()\n",
    "\n",
    "        decoder_outputs = torch.cat(decoder_outputs, dim=1)\n",
    "        decoder_outputs = F.log_softmax(decoder_outputs, dim=-1)\n",
    "        attentions = torch.cat(attentions, dim=1)\n",
    "\n",
    "        return decoder_outputs, decoder_hidden, attentions\n",
    "\n",
    "    def forward_step(self, input, hidden, encoder_outputs):\n",
    "        embedded =  self.dropout(self.embedding(input))\n",
    "\n",
    "        query = hidden.permute(1, 0, 2)\n",
    "        context, attn_weights = self.attention(query, encoder_outputs)\n",
    "        input_gru = torch.cat((embedded, context), dim=2)\n",
    "\n",
    "        output, hidden = self.gru(input_gru, hidden)\n",
    "        output = self.out(output)\n",
    "\n",
    "        return output, hidden, attn_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def indexesFromSentence(lang, sentence):\n",
    "    return [lang.word2index[word] for word in sentence.split(' ')]\n",
    "\n",
    "def tensorFromSentence(lang, sentence):\n",
    "    indexes = indexesFromSentence(lang, sentence)\n",
    "    indexes.append(EOS_token)\n",
    "    return torch.tensor(indexes, dtype=torch.long, device=device).view(1, -1)\n",
    "\n",
    "def tensorsFromPair(pair):\n",
    "    input_tensor = tensorFromSentence(input_lang, pair[0])\n",
    "    target_tensor = tensorFromSentence(output_lang, pair[1])\n",
    "    return (input_tensor, target_tensor)\n",
    "\n",
    "def get_dataloader(batch_size):\n",
    "    input_lang, output_lang, pairs = prepareData(True)\n",
    "\n",
    "    n = len(pairs)\n",
    "    input_ids = np.zeros((n, MAX_LENGTH), dtype=np.int32)\n",
    "    target_ids = np.zeros((n, MAX_LENGTH), dtype=np.int32)\n",
    "\n",
    "    for idx, (inp, tgt) in enumerate(pairs):\n",
    "        inp_ids = indexesFromSentence(input_lang, inp)\n",
    "        tgt_ids = indexesFromSentence(output_lang, tgt)\n",
    "        inp_ids.append(EOS_token)\n",
    "        tgt_ids.append(EOS_token)\n",
    "        input_ids[idx, :len(inp_ids)] = inp_ids\n",
    "        target_ids[idx, :len(tgt_ids)] = tgt_ids\n",
    "\n",
    "    train_data = TensorDataset(torch.LongTensor(input_ids).to(device),\n",
    "                               torch.LongTensor(target_ids).to(device))\n",
    "\n",
    "    train_sampler = RandomSampler(train_data)\n",
    "    train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size)\n",
    "    return input_lang, output_lang, train_dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_epoch(dataloader, encoder, decoder, encoder_optimizer,\n",
    "          decoder_optimizer, criterion):\n",
    "\n",
    "    total_loss = 0\n",
    "    for data in dataloader:\n",
    "        input_tensor, target_tensor = data\n",
    "\n",
    "        encoder_optimizer.zero_grad()\n",
    "        decoder_optimizer.zero_grad()\n",
    "\n",
    "        encoder_outputs, encoder_hidden = encoder(input_tensor)\n",
    "        decoder_outputs, _, _ = decoder(encoder_outputs, encoder_hidden, target_tensor)\n",
    "\n",
    "        loss = criterion(\n",
    "            decoder_outputs.view(-1, decoder_outputs.size(-1)),\n",
    "            target_tensor.view(-1)\n",
    "        )\n",
    "        loss.backward()\n",
    "\n",
    "        encoder_optimizer.step()\n",
    "        decoder_optimizer.step()\n",
    "\n",
    "        total_loss += loss.item()\n",
    "\n",
    "    return total_loss / len(dataloader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def asMinutes(s):\n",
    "    m = math.floor(s / 60)\n",
    "    s -= m * 60\n",
    "    return '%dm %ds' % (m, s)\n",
    "\n",
    "def timeSince(since, percent):\n",
    "    now = time.time()\n",
    "    s = now - since\n",
    "    es = s / (percent)\n",
    "    rs = es - s\n",
    "    return '%s (- %s)' % (asMinutes(s), asMinutes(rs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(train_dataloader, encoder, decoder, n_epochs, learning_rate=0.001,\n",
    "               print_every=100, plot_every=100):\n",
    "    start = time.time()\n",
    "    plot_losses = []\n",
    "    print_loss_total = 0\n",
    "    plot_loss_total = 0\n",
    "\n",
    "    encoder_optimizer = optim.Adam(encoder.parameters(), lr=learning_rate)\n",
    "    decoder_optimizer = optim.Adam(decoder.parameters(), lr=learning_rate)\n",
    "    criterion = nn.NLLLoss()\n",
    "\n",
    "    for epoch in range(1, n_epochs + 1):\n",
    "        loss = train_epoch(train_dataloader, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion)\n",
    "        print_loss_total += loss\n",
    "        plot_loss_total += loss\n",
    "\n",
    "        if epoch % print_every == 0:\n",
    "            print_loss_avg = print_loss_total / print_every\n",
    "            print_loss_total = 0\n",
    "            print('%s (%d %d%%) %.4f' % (timeSince(start, epoch / n_epochs),\n",
    "                                        epoch, epoch / n_epochs * 100, print_loss_avg))\n",
    "\n",
    "        if epoch % plot_every == 0:\n",
    "            plot_loss_avg = plot_loss_total / plot_every\n",
    "            plot_losses.append(plot_loss_avg)\n",
    "            plot_loss_total = 0\n",
    "\n",
    "    showPlot(plot_losses)\n",
    "\n",
    "plt.switch_backend('agg')\n",
    "\n",
    "def showPlot(points):\n",
    "    plt.figure()\n",
    "    fig, ax = plt.subplots()\n",
    "    loc = ticker.MultipleLocator(base=0.2)\n",
    "    ax.yaxis.set_major_locator(loc)\n",
    "    plt.plot(points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(encoder, decoder, sentence, input_lang, output_lang):\n",
    "    with torch.no_grad():\n",
    "        input_tensor = tensorFromSentence(input_lang, sentence)\n",
    "\n",
    "        encoder_outputs, encoder_hidden = encoder(input_tensor)\n",
    "        decoder_outputs, decoder_hidden, decoder_attn = decoder(encoder_outputs, encoder_hidden)\n",
    "\n",
    "        _, topi = decoder_outputs.topk(1)\n",
    "        decoded_ids = topi.squeeze()\n",
    "\n",
    "        decoded_words = []\n",
    "        for idx in decoded_ids:\n",
    "            if idx.item() == EOS_token:\n",
    "                decoded_words.append('<EOS>')\n",
    "                break\n",
    "            decoded_words.append(output_lang.index2word[idx.item()])\n",
    "    return decoded_words, decoder_attn\n",
    "\n",
    "def evaluateRandomly(encoder, decoder, n=10):\n",
    "    for i in range(n):\n",
    "        pair = random.choice(pairs)\n",
    "        print('>', pair[0])\n",
    "        print('=', pair[1])\n",
    "        output_words, _ = evaluate(encoder, decoder, pair[0], input_lang, output_lang)\n",
    "        output_sentence = ' '.join(output_words)\n",
    "        print('<', output_sentence)\n",
    "        print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading lines...\n",
      "Read 49943 sentence pairs\n",
      "Trimmed to 3613 sentence pairs\n",
      "Counting words...\n",
      "Counted words:\n",
      "pol 3070\n",
      "en 1969\n",
      "0m 44s (- 11m 8s) (5 6%) 2.0979\n",
      "1m 26s (- 10m 5s) (10 12%) 1.2611\n",
      "2m 7s (- 9m 14s) (15 18%) 0.8754\n",
      "2m 48s (- 8m 26s) (20 25%) 0.5951\n",
      "3m 29s (- 7m 41s) (25 31%) 0.3932\n",
      "4m 10s (- 6m 57s) (30 37%) 0.2515\n",
      "4m 51s (- 6m 14s) (35 43%) 0.1600\n",
      "5m 32s (- 5m 32s) (40 50%) 0.1037\n",
      "6m 15s (- 4m 51s) (45 56%) 0.0701\n",
      "6m 55s (- 4m 9s) (50 62%) 0.0530\n",
      "7m 36s (- 3m 27s) (55 68%) 0.0424\n",
      "8m 16s (- 2m 45s) (60 75%) 0.0374\n",
      "8m 58s (- 2m 4s) (65 81%) 0.0318\n",
      "9m 39s (- 1m 22s) (70 87%) 0.0287\n",
      "10m 20s (- 0m 41s) (75 93%) 0.0279\n",
      "11m 1s (- 0m 0s) (80 100%) 0.0246\n"
     ]
    }
   ],
   "source": [
    "hidden_size = 128\n",
    "batch_size = 32\n",
    "\n",
    "input_lang, output_lang, train_dataloader = get_dataloader(batch_size)\n",
    "\n",
    "encoder = EncoderRNN(input_lang.n_words, hidden_size).to(device)\n",
    "decoder = AttnDecoderRNN(hidden_size, output_lang.n_words).to(device)\n",
    "\n",
    "train(train_dataloader, encoder, decoder, 80, print_every=5, plot_every=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> wchodze w to\n",
      "= i m game\n",
      "< i m game <EOS>\n",
      "\n",
      "> on jest o dwa lata starszy od ciebie\n",
      "= he is two years older than you\n",
      "< he is two years older than you is questions <EOS>\n",
      "\n",
      "> wstydze sie za siebie\n",
      "= i m ashamed of myself\n",
      "< i m ashamed of myself <EOS>\n",
      "\n",
      "> nie wchodze w to\n",
      "= i am not getting involved\n",
      "< i am not getting involved <EOS>\n",
      "\n",
      "> jestes moja przyjacio ka\n",
      "= you are my friend\n",
      "< you are my friend <EOS>\n",
      "\n",
      "> jestem naga\n",
      "= i m naked\n",
      "< i m naked <EOS>\n",
      "\n",
      "> naprawde nie jestem az tak zajety\n",
      "= i m really not all that busy\n",
      "< i m really not all that busy that <EOS>\n",
      "\n",
      "> pracuje dla firmy handlowej\n",
      "= i m working for a trading firm\n",
      "< i m working for a trading firm <EOS>\n",
      "\n",
      "> jestem rysownikiem\n",
      "= i m a cartoonist\n",
      "< i m a cartoonist <EOS>\n",
      "\n",
      "> wyjezdzasz dopiero jutro prawda ?\n",
      "= you aren t leaving until tomorrow right ?\n",
      "< you aren t leaving until tomorrow right ? aren t\n",
      "\n"
     ]
    }
   ],
   "source": [
    "evaluateRandomly(encoder, decoder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BLEU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\mateu\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Unzipping tokenizers\\punkt.zip.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BLEU score: 0.7677458355439187\n"
     ]
    }
   ],
   "source": [
    "nltk.download('punkt')\n",
    "\n",
    "def filter_data(data, max_length, prefixes):\n",
    "    filtered_data = data[\n",
    "        data.apply(lambda row: len(row[\"English\"].split()) < max_length and\n",
    "                               len(row[\"Polish\"].split()) < max_length and\n",
    "                               row[\"English\"].startswith(tuple(prefixes)), axis=1)\n",
    "    ]\n",
    "    return filtered_data\n",
    "\n",
    "# Load and normalize data\n",
    "data_file = pd.read_csv(\"pol.txt\", sep='\\t', names=[\"English\", \"Polish\", \"attribution\"])\n",
    "data_file[\"English\"] = data_file[\"English\"].apply(normalizeString)\n",
    "data_file[\"Polish\"] = data_file[\"Polish\"].apply(normalizeString)\n",
    "\n",
    "# Filter data\n",
    "filtered_data = filter_data(data_file, MAX_LENGTH, eng_prefixes)\n",
    "test_section = filtered_data.sample(frac=1).head(500)\n",
    "\n",
    "# Tokenize and translate\n",
    "test_section[\"English_tokenized\"] = test_section[\"English\"].apply(nltk.word_tokenize)\n",
    "test_section[\"English_translated\"] = test_section[\"Polish\"].apply(lambda x: translate(x, tokenized=True))\n",
    "\n",
    "# Prepare corpus for BLEU calculation\n",
    "candidate_corpus = test_section[\"English_translated\"].tolist()\n",
    "references_corpus = [[ref] for ref in test_section[\"English_tokenized\"].tolist()]\n",
    "\n",
    "# Calculate BLEU score\n",
    "smooth_fn = SmoothingFunction().method4\n",
    "bleu = corpus_bleu(references_corpus, candidate_corpus, smoothing_function=smooth_fn)\n",
    "print(\"BLEU score:\", bleu)"
   ]
  }
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