"""Tokenization classes for FM4Bio
Currently supports only protein
"""

import os
from typing import List, Optional

from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.utils import logging


logger = logging.get_logger(__name__)

VOCAB_FILES_NAMES = {"vocab_file": "vocab_protein.txt"}

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "fm4bio/proteinmoe": "https://huggingface.co/fm4bio/proteinmoe/resolve/main/vocab.txt",
    },
}

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"proteinmoe": 2048, "rnabert": 1024}


def load_vocab_file(vocab_file):
    with open(vocab_file, "r") as f:
        lines = f.read().splitlines()
        return [l.strip() for l in lines]


class FM4BioTokenizer(PreTrainedTokenizer):
    """
    Constructs an FM4Bio tokenizer.
    """

    vocab_files_names = VOCAB_FILES_NAMES
    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    model_input_names = ["input_ids", "attention_mask"]

    def __init__(
        self,
        vocab_file,
        biotype="protein",
        unk_token="-",
        pad_token="[PAD]",
        mask_token="[MASK]",
        sep_token="[SEP]",
        cls_token=None,
        bos_token=None,
        eos_token=None,
        **kwargs,
    ):
        """
        Args:
            biotype: str, could be protein/rna/dna
            the input is like ...[SEP]
        """
        self.biotype = biotype
        if self.biotype != "protein":
            raise NotImplementedError

        self.all_tokens = load_vocab_file(vocab_file)
        self._id_to_token = dict(enumerate(self.all_tokens))
        self._token_to_id = {tok: ind for ind, tok in enumerate(self.all_tokens)}

        super().__init__(
            unk_token=unk_token,
            cls_token=cls_token,
            pad_token=pad_token,
            mask_token=mask_token,
            sep_token=sep_token,
            bos_token=bos_token,
            eos_token=eos_token,
            **kwargs,
        )

        # TODO, all the tokens are added? But they are also part of the vocab... bit strange.
        # none of them are special, but they all need special splitting.

        self.unique_no_split_tokens = self.all_tokens
        self._update_trie(self.unique_no_split_tokens)

    def _convert_id_to_token(self, index: int) -> str:
        return self._id_to_token.get(index, self.unk_token)

    def _convert_token_to_id(self, token: str) -> int:
        return self._token_to_id.get(token, self._token_to_id.get(self.unk_token))

    def _tokenize(self, text, **kwargs):
        """
        Hack for multiple chains (seperated by |)
        Args:
            text: str, eg. CALVSGGNYKPTF|CASSWGGAPLF|ELAGIGILTV
        """
        return text.replace("|", self.sep_token).split()

    def get_vocab(self):
        base_vocab = self._token_to_id.copy()
        base_vocab.update(self.added_tokens_encoder)
        return base_vocab

    def token_to_id(self, token: str) -> int:
        return self._token_to_id.get(token, self._token_to_id.get(self.unk_token))

    def id_to_token(self, index: int) -> str:
        return self._id_to_token.get(index, self.unk_token)

    def build_inputs_with_special_tokens(
        self,
        token_ids_0: List[int],
        token_ids_1: Optional[List[int]] = None,
    ) -> List[int]:
        if self.biotype == "protein":
            sep = [self.sep_token_id]
            if token_ids_1 is None:
                return token_ids_0 + sep
            else:
                return token_ids_0 + sep + token_ids_1 + sep
        else:
            raise NotImplementedError

    def get_special_tokens_mask(
        self,
        token_ids_0: List,
        token_ids_1: Optional[List] = None,
        already_has_special_tokens: bool = False,
    ) -> List[int]:
        """
        Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
        special tokens using the tokenizer `prepare_for_model` or `encode_plus` methods.

        Args:
            token_ids_0 (`List[int]`):
                List of ids of the first sequence.
            token_ids_1 (`List[int]`, *optional*):
                List of ids of the second sequence.
            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
                Whether or not the token list is already formatted with special tokens for the model.

        Returns:
            A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
        """
        if already_has_special_tokens:
            if token_ids_1 is not None:
                raise ValueError(
                    "You should not supply a second sequence if the provided sequence of "
                    "ids is already formatted with special tokens for the model."
                )

            return [1 if token in self.all_special_ids else 0 for token in token_ids_0]
        mask = ([0] * len(token_ids_0)) + [1]
        if token_ids_1 is not None:
            mask += [0] * len(token_ids_1) + [1]
        return mask

    def save_vocabulary(self, save_directory, filename_prefix):
        vocab_file = os.path.join(
            save_directory,
            (filename_prefix + "-" if filename_prefix else "") + "vocab.txt",
        )
        with open(vocab_file, "w") as f:
            f.write("\n".join(self.all_tokens))
        return (vocab_file,)

    @property
    def vocab_size(self) -> int:
        return len(self.all_tokens)