
class TorchVocab(object):
    """Defines a vocabulary object that will be used to numericalize a field.
    Attributes:
        freqs: A collections.Counter object holding the frequencies of tokens
            in the data used to build the Vocab.
        stoi: A collections.defaultdict instance mapping token strings to
            numerical identifiers.
        itos: A list of token strings indexed by their numerical identifiers.
    """

    def __init__(self, counter, max_size=None, min_freq=1, specials=['<pad>', '<oov>'],
                 vectors=None, unk_init=None, vectors_cache=None):
        """Create a Vocab object from a collections.Counter.
        Arguments:
            counter: collections.Counter object holding the frequencies of
                each value found in the data.
            max_size: The maximum size of the vocabulary, or None for no
                maximum. Default: None.
            min_freq: The minimum frequency needed to include a token in the
                vocabulary. Values less than 1 will be set to 1. Default: 1.
            specials: The list of special tokens (e.g., padding or eos) that
                will be prepended to the vocabulary in addition to an <unk>
                token. Default: ['<pad>']
            vectors: One of either the available pretrained vectors
                or custom pretrained vectors (see Vocab.load_vectors);
                or a list of aforementioned vectors
            unk_init (callback): by default, initialize out-of-vocabulary word vectors
                to zero vectors; can be any function that takes in a Tensor and
                returns a Tensor of the same size. Default: torch.Tensor.zero_
            vectors_cache: directory for cached vectors. Default: '.vector_cache'
        """
        self.freqs = counter
        counter = counter.copy()
        min_freq = max(min_freq, 1)

        self.itos = list(specials)
        # frequencies of special tokens are not counted when building vocabulary
        # in frequency order
        for tok in specials:
            del counter[tok]

        max_size = None if max_size is None else max_size + len(self.itos)

        # sort by frequency, then alphabetically
        words_and_frequencies = sorted(counter.items(), key=lambda tup: tup[0])
        words_and_frequencies.sort(key=lambda tup: tup[1], reverse=True)

        for word, freq in words_and_frequencies:
            if freq < min_freq or len(self.itos) == max_size:
                break
            self.itos.append(word)

        # stoi is simply a reverse dict for itos
        self.stoi = {tok: i for i, tok in enumerate(self.itos)}

        self.vectors = None
        if vectors is not None:
            self.load_vectors(vectors, unk_init=unk_init, cache=vectors_cache)
        else:
            assert unk_init is None and vectors_cache is None

    def __eq__(self, other):
        if self.freqs != other.freqs:
            return False
        if self.stoi != other.stoi:
            return False
        if self.itos != other.itos:
            return False
        if self.vectors != other.vectors:
            return False
        return True

    def __len__(self):
        return len(self.itos)

    def vocab_rerank(self):
        self.stoi = {word: i for i, word in enumerate(self.itos)}

    def extend(self, v, sort=False):
        words = sorted(v.itos) if sort else v.itos
        for w in words:
            if w not in self.stoi:
                self.itos.append(w)
                self.stoi[w] = len(self.itos) - 1
