"""Character-level RNA tokenizer for UTR-LM."""

import json
import os
from typing import Dict, List, Optional, Tuple

from transformers import PreTrainedTokenizer

# Canonical vocab - fixed; never changes across checkpoints.
_VOCAB: Dict[str, int] = {
    "<pad>": 0,
    "<eos>": 1,
    "<unk>": 2,
    "A": 3,
    "G": 4,
    "C": 5,
    "T": 6,
    "<cls>": 7,
    "<mask>": 8,
    "<sep>": 9,
}
_IDS_TO_TOKENS: Dict[int, str] = {v: k for k, v in _VOCAB.items()}


class UtrLmTokenizer(PreTrainedTokenizer):
    """
    Character-level tokenizer for UTR-LM RNA sequences.

    Each nucleotide (A / G / C / T) maps to a single token.
    Sequences are automatically wrapped with [CLS] ... [EOS] on encoding.

    Example::

        tok = UtrLmTokenizer()
        enc = tok("ATGCATG", return_tensors="pt")
        # enc.input_ids: [[7, 3, 6, 4, 5, 3, 6, 1]]
        #                  CLS A T G C A T  EOS
    """

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

    def __init__(
        self,
        vocab_file: Optional[str] = None,
        cls_token: str = "<cls>",
        pad_token: str = "<pad>",
        mask_token: str = "<mask>",
        eos_token: str = "<eos>",
        unk_token: str = "<unk>",
        sep_token: str = "<sep>",
        **kwargs,
    ):
        # Build vocab from file if provided (allows future extension), else use default
        if vocab_file is not None and os.path.isfile(vocab_file):
            with open(vocab_file) as f:
                self._vocab = json.load(f)
        else:
            self._vocab = dict(_VOCAB)
        self._ids_to_tokens = {v: k for k, v in self._vocab.items()}

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

    # ------------------------------------------------------------------
    # Required overrides
    # ------------------------------------------------------------------

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

    def get_vocab(self) -> Dict[str, int]:
        return dict(self._vocab)

    def _tokenize(self, text: str) -> List[str]:
        """Split sequence into individual characters."""
        return list(text)

    def _convert_token_to_id(self, token: str) -> int:
        return self._vocab.get(token, self._vocab["<unk>"])

    def _convert_id_to_token(self, index: int) -> str:
        return self._ids_to_tokens.get(index, "<unk>")

    def save_vocabulary(
        self, save_directory: str, filename_prefix: Optional[str] = None
    ) -> Tuple[str]:
        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,)

    # ------------------------------------------------------------------
    # Special-token wrapping: prepend [CLS], append [EOS]
    # ------------------------------------------------------------------

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

    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 += [1] + [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] + token_ids_0 + [0]
        return [0] + token_ids_0 + [0, 0] + token_ids_1 + [0]