import os
from collections import Counter
from nltk.tokenize import RegexpTokenizer
from source.config import Config


class Vocab:
    """
    Offers word2index and index2word functionality after counting words in input sentences.
    Allows choosing the size of the vocabulary by taking the most common words. Explicitly reserves four indices:
    <pad>, <sos>, <eos> and <unk>.
    """
    def __init__(self, sentence_splitter=None):
        """
        Args:
        sentence_splitter: tokenizing function
        """
        self.config = Config()

        self.counter = Counter()
        self.word2index = dict()
        self.index2word = dict()
        self.size = 0

        # predefined tokens
        self.PADDING_INDEX = 0
        self.SOS = 1
        self.EOS = 2
        self.UNKNOWN_WORD_INDEX = 3

        if sentence_splitter is None:
            # matches sequences of characters including ones between < >
            word_regex = r'(?:\w+|<\w+>)'
            # tokenize the string into words
            sentence_splitter = RegexpTokenizer(word_regex).tokenize
        self.splitter = sentence_splitter

    def add_sentence(self, sentence: str):
        """
        Update word counts from sentence after tokenizing it into words
        """
        self.counter.update(self.splitter(sentence))

    def build_vocab(self, vocab_size: int, file_name: str):
        """ Build vocabulary dictionaries word2index and index2word from a text file at config.ROOT path

        Args:

            vocab_size (int): size of vocabulary (including 4 predefined tokens: <pad>, <sos>, <eos>, <unk>)

            file_name (str): name of the text file from which the vocabulary will be built.
                Note: the lines in file are assumed to be in form: 'word SPACE index' and
                it asssumes a header line (for example: 'captions.txt')
        """

        filepath = os.path.join(self.config.ROOT, file_name)

        try:
            with open(filepath, 'r', encoding='utf-8') as file:
                for i, line in enumerate(file):
                    # ignore header line
                    if i == 0:
                        continue
                    caption = line.strip().lower().split(",", 1)[1]  # id=0, caption=1
                    self.add_sentence(caption)
        except Exception as e:
            print(f"Error processing file {filepath}: {e}")
            return

        # adding predefined tokens in the vocabulary
        self._add_predefined_tokens()

        words = self.counter.most_common(vocab_size - 4)
        # (index + 4) because first 4 tokens are the predefined ones
        for index, (word, _) in enumerate(words, start=4):
            self.word2index[word] = index
            self.index2word[index] = word

        self.size = len(self.word2index)

        # adding predefined tokens in the vocabulary
        self.index2word[self.PADDING_INDEX] = '<pad>'
        self.word2index['<pad>'] = self.PADDING_INDEX
        self.index2word[self.SOS] = '<sos>'
        self.word2index['<sos>'] = self.SOS
        self.index2word[self.EOS] = '<eos>'
        self.word2index['<eos>'] = self.EOS
        self.index2word[self.UNKNOWN_WORD_INDEX] = '<unk>'
        self.word2index['<unk>'] = self.UNKNOWN_WORD_INDEX

    def word_to_index(self, word: str) -> int:
        """ Map word to index from word2index dictionary in vocabulary

        Args:
            word (str): word to be mapped

        Returns:
            int: index matched to the word
        """
        try:
            return self.word2index[word]
        except KeyError:
            return self.UNKNOWN_WORD_INDEX

    def index_to_word(self, index: int) -> str:
        """ Map word to index from index2word dictionary in vocabulary

        Args:
            word (str): index to be mapped

        Returns:
            str: word matched to the index
        """
        try:
            return self.index2word[index]
        except KeyError:
            return self.index2word[self.UNKNOWN_WORD_INDEX]

    def load_vocab(self, file_name: str):
        """ Load the word2index and index2word dictionaries from a text file at config.ROOT path

        Args:
            file_name (str): name of the text file where the vocabulary is saved (i.e 'word2index.txt')
                Note: the lines in file are assumed to be in form: 'word SPACE index' and it asssumes a header line
        """

        filepath = os.path.join(self.config.ROOT, file_name)

        self.word2index = dict()
        self.index2word = dict()

        try:
            with open(filepath) as file:
                for line in file:
                    line = line.strip().split(' ')
                    word, index = line[0], line[1]
                    self.word2index[word] = int(index)
                    self.index2word[int(index)] = word
        except Exception as e:
            print(f"Error loading vocabulary from file {filepath}: {e}")