from abc import ABC
from abc import abstractmethod

from .bert_tokenization import FullTokenizer as FullBertTokenizer


def build_tokenizer(args):
    """Initialize tokenizer."""
    print('> building {} tokenizer ...'.format(args.tokenizer_type), flush=True)

    # Select and instantiate the tokenizer.
    assert args.vocab_file is not None
    if args.tokenizer_type == 'BertWordPieceLowerCase':
        tokenizer = _BertWordPieceTokenizer(vocab_file=args.vocab_file,
                                            lower_case=True,
                                            vocab_extra_ids=args.vocab_extra_ids)
    elif args.tokenizer_type == 'BertWordPieceCase':
        tokenizer = _BertWordPieceTokenizer(vocab_file=args.vocab_file,
                                            lower_case=False,
                                            vocab_extra_ids=args.vocab_extra_ids)
    else:
        raise NotImplementedError('{} tokenizer is not '
                                  'implemented.'.format(args.tokenizer_type))

    # Add vocab size.
    args.padded_vocab_size = _vocab_size_with_padding(tokenizer.vocab_size,
                                                      args)

    return tokenizer


def _vocab_size_with_padding(orig_vocab_size, args):
    """Pad vocab size so it is divisible by model parallel size and
    still having GPU friendly size."""

    after = orig_vocab_size
    # multiple = args.make_vocab_size_divisible_by * args.tensor_model_parallel_size
    multiple = args.make_vocab_size_divisible_by
    while (after % multiple) != 0:
        after += 1

    print(' > padded vocab (size: {}) with {} dummy tokens (new size: {})'
          .format(orig_vocab_size, after - orig_vocab_size, after), flush=True)
    return after


class AbstractTokenizer(ABC):
    """Abstract class for tokenizer."""

    def __init__(self, name):
        self.name = name
        super().__init__()

    @property
    @abstractmethod
    def vocab_size(self):
        pass

    @property
    @abstractmethod
    def vocab(self):
        """Dictionary from vocab text token to id token."""
        pass

    @property
    @abstractmethod
    def inv_vocab(self):
        """Dictionary from vocab id token to text token."""
        pass

    @abstractmethod
    def tokenize(self, text):
        pass

    def detokenize(self, token_ids):
        raise NotImplementedError('detokenizer is not implemented for {} '
                                  'tokenizer'.format(self.name))

    @property
    def cls(self):
        raise NotImplementedError('CLS is not provided for {} '
                                  'tokenizer'.format(self.name))

    @property
    def sep(self):
        raise NotImplementedError('SEP is not provided for {} '
                                  'tokenizer'.format(self.name))

    @property
    def pad(self):
        raise NotImplementedError('PAD is not provided for {} '
                                  'tokenizer'.format(self.name))

    @property
    def eod(self):
        raise NotImplementedError('EOD is not provided for {} '
                                  'tokenizer'.format(self.name))

    @property
    def mask(self):
        raise NotImplementedError('MASK is not provided for {} '
                                  'tokenizer'.format(self.name))


class _BertWordPieceTokenizer(AbstractTokenizer):
    """Original BERT wordpiece tokenizer."""

    def __init__(self, vocab_file, lower_case=True, vocab_extra_ids=0):
        if lower_case:
            name = 'BERT Lower Case'
        else:
            name = 'BERT Upper Case'
        super().__init__(name)
        self.tokenizer = FullBertTokenizer(vocab_file, do_lower_case=lower_case)
        self.cls_id = self.tokenizer.vocab['[CLS]']
        self.sep_id = self.tokenizer.vocab['[SEP]']
        self.pad_id = self.tokenizer.vocab['[PAD]']
        self.mask_id = self.tokenizer.vocab['[MASK]']
        self._additional_special_tokens = []

        # (dsachan) Add BOS and EOS tokens
        SPECIAL_TOKENS = {'eos_token': '[EOS]',
                          'bos_token': '[BOS]'}
        self._bos_token = '[BOS]'
        self.add_token(self._bos_token)
        self._bos_token_id = self.vocab.get(self._bos_token)

        self._eos_token = '[EOS]'
        self.add_token(self._eos_token)
        self._eos_token_id = self.vocab.get(self._eos_token)

        # (dsachan) Add additional special tokens
        # These can be used as sentinel tokens in T5 model inputs
        additional_special_tokens = []
        additional_special_tokens.extend(
            ["<extra_id_{}>".format(i) for i in range(vocab_extra_ids)])
        self.add_additional_special_tokens(additional_special_tokens)

    def add_token(self, token):
        if token not in self.vocab:
            self.inv_vocab[self.vocab_size] = token
            # self.vocab_size comes from len(vocab)
            # and it will increase as we add elements
            self.vocab[token] = self.vocab_size

    def add_additional_special_tokens(self, tokens_list):
        setattr(self, "additional_special_tokens", tokens_list)
        for value in tokens_list:
            self.add_token(value)

    @property
    def vocab_size(self):
        return self.tokenizer.vocab_size()

    @property
    def vocab(self):
        return self.tokenizer.vocab

    @property
    def inv_vocab(self):
        return self.tokenizer.inv_vocab

    def tokenize(self, text):
        text_tokens = self.tokenizer.tokenize(text)
        return self.tokenizer.convert_tokens_to_ids(text_tokens)

    def decode(self, ids):
        tokens = self.tokenizer.convert_ids_to_tokens(ids)
        return self.tokenizer.convert_tokens_to_string(tokens)

    def decode_token_ids(self, token_ids):
        tokens = self.tokenizer.convert_ids_to_tokens(token_ids)
        exclude_list = ['[PAD]', '[CLS]']
        non_pads = [t for t in tokens if t not in exclude_list]

        result = ""
        for s in non_pads:
            if s.startswith("##"):
                result += s[2:]
            else:
                result += " " + s

        return result

    @property
    def cls(self):
        return self.cls_id

    @property
    def sep(self):
        return self.sep_id

    @property
    def pad(self):
        return self.pad_id

    @property
    def mask(self):
        return self.mask_id

    @property
    def bos_token(self):
        """ Beginning of sentence token id """
        return self._bos_token

    @property
    def eos_token(self):
        """ End of sentence token id """
        return self._eos_token

    @property
    def additional_special_tokens(self):
        """ All the additional special tokens you may want to use (list of strings)."""
        return self._additional_special_tokens

    @property
    def bos_token_id(self):
        """ Id of the beginning of sentence token in the vocabulary."""
        return self._bos_token_id

    @property
    def eos_token_id(self):
        """ Id of the end of sentence token in the vocabulary."""
        return self._eos_token_id

    @property
    def additional_special_tokens_ids(self):
        """ Ids of all the additional special tokens in the vocabulary (list of integers)."""
        return [self.vocab.get(token) for token in self._additional_special_tokens]

    @additional_special_tokens.setter
    def additional_special_tokens(self, value):
        self._additional_special_tokens = value