import json
import os

from transformers import PreTrainedTokenizer

_DEFAULT_VOCAB = {
    "[UNK]": 0,
    "[CLS]": 1,
    "[SEP]": 2,
    "[PAD]": 3,
    "[MASK]": 4,
    "A": 5,
    "U": 6,
    "C": 7,
    "G": 8,
    "N": 9,
}


class StructRFMTokenizer(PreTrainedTokenizer):
    """Single-nucleotide tokenizer for structRFM.

    Matches the upstream preprocessing (`seq.upper().replace("T", "U")`,
    then one token per character) and adds [CLS] ... [SEP] when
    `add_special_tokens=True`. Characters outside the vocabulary
    (including whitespace) map to [UNK], as in the upstream tokenizer.
    """

    vocab_files_names = {"vocab_file": "vocab.json"}
    model_input_names = ["input_ids", "attention_mask"]

    def __init__(
        self,
        vocab_file=None,
        cls_token="[CLS]",
        sep_token="[SEP]",
        pad_token="[PAD]",
        mask_token="[MASK]",
        unk_token="[UNK]",
        **kwargs,
    ):
        self._vocab = dict(_DEFAULT_VOCAB)
        if vocab_file and os.path.isfile(vocab_file):
            with open(vocab_file) as f:
                self._vocab = json.load(f)
        self._ids_to_tokens = {v: k for k, v in self._vocab.items()}
        super().__init__(
            cls_token=cls_token,
            sep_token=sep_token,
            pad_token=pad_token,
            mask_token=mask_token,
            unk_token=unk_token,
            **kwargs,
        )

    @property
    def vocab_size(self):
        return len(self._vocab)

    def get_vocab(self):
        return dict(self._vocab)

    def _tokenize(self, text):
        return list(text.upper().replace("T", "U"))

    def _convert_token_to_id(self, token):
        return self._vocab.get(token, self._vocab["[UNK]"])

    def _convert_id_to_token(self, index):
        return self._ids_to_tokens.get(index, "[UNK]")

    def convert_tokens_to_string(self, tokens):
        return "".join(tokens)

    def save_vocabulary(self, save_directory, filename_prefix=None):
        os.makedirs(save_directory, exist_ok=True)
        fname = (filename_prefix + "-" if filename_prefix else "") + "vocab.json"
        path = os.path.join(save_directory, fname)
        with open(path, "w") as f:
            json.dump(self._vocab, f, indent=2)
        return (path,)

    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
        cls = [self.cls_token_id]
        sep = [self.sep_token_id]
        if token_ids_1 is None:
            return cls + token_ids_0 + sep
        return cls + token_ids_0 + sep + token_ids_1 + sep

    def get_special_tokens_mask(self, token_ids_0, token_ids_1=None,
                                already_has_special_tokens=False):
        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_0, token_ids_1, already_has_special_tokens=True
            )
        mask = [1] + [0] * len(token_ids_0) + [1]
        if token_ids_1 is not None:
            mask += [0] * len(token_ids_1) + [1]
        return mask

    def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None):
        if token_ids_1 is None:
            return [0] * (len(token_ids_0) + 2)
        return [0] * (len(token_ids_0) + 2) + [1] * (len(token_ids_1) + 1)