"""Custom SentencePiece tokenizer for Jeeves model.

Wraps SentencePiece directly for exact token ID match with training.

Usage:
    from transformers import AutoTokenizer
    tokenizer = AutoTokenizer.from_pretrained("REPO_ID", trust_remote_code=True)
"""

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

import sentencepiece as spm
from transformers import PreTrainedTokenizer


class JeevesTokenizer(PreTrainedTokenizer):
    """SentencePiece BPE tokenizer for Jeeves models."""

    vocab_files_names = {"vocab_file": "tokenizer.model"}
    model_input_names = ["input_ids", "attention_mask"]

    def __init__(
        self,
        vocab_file: str,
        bos_token: str = "<s>",
        eos_token: str = "</s>",
        unk_token: str = "<unk>",
        pad_token: str = "<pad>",
        chat_template: Optional[str] = None,
        additional_special_tokens: Optional[List[str]] = None,
        **kwargs,
    ):
        self.vocab_file = vocab_file
        self.sp_model = spm.SentencePieceProcessor()
        self.sp_model.Load(vocab_file)

        if additional_special_tokens is None:
            additional_special_tokens = [
                "<|im_start|>", "<|im_end|>",
                "<|tool_call|>", "<|tool_result|>",
                "<|system|>", "<|user|>", "<|assistant|>",
            ]

        super().__init__(
            bos_token=bos_token, eos_token=eos_token,
            unk_token=unk_token, pad_token=pad_token,
            additional_special_tokens=additional_special_tokens,
            chat_template=chat_template, **kwargs,
        )

    @property
    def vocab_size(self) -> int:
        return self.sp_model.GetPieceSize()

    def get_vocab(self) -> Dict[str, int]:
        vocab = {self.sp_model.IdToPiece(i): i for i in range(self.vocab_size)}
        vocab.update(self.added_tokens_encoder)
        return vocab

    def _tokenize(self, text: str) -> List[str]:
        return self.sp_model.EncodeAsPieces(text)

    def _convert_token_to_id(self, token: str) -> int:
        return self.sp_model.PieceToId(token)

    def _convert_id_to_token(self, index: int) -> str:
        if index < 0 or index >= self.vocab_size:
            return self.unk_token
        return self.sp_model.IdToPiece(index)

    def convert_tokens_to_string(self, tokens: List[str]) -> str:
        return self.sp_model.DecodePieces(tokens)

    def save_vocabulary(
        self, save_directory: str, filename_prefix: Optional[str] = None
    ) -> Tuple[str]:
        if not os.path.isdir(save_directory):
            os.makedirs(save_directory, exist_ok=True)
        out_path = os.path.join(
            save_directory,
            (filename_prefix + "-" if filename_prefix else "") + "tokenizer.model",
        )
        if os.path.abspath(self.vocab_file) != os.path.abspath(out_path):
            import shutil
            shutil.copyfile(self.vocab_file, out_path)
        return (out_path,)

    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
        if token_ids_1 is None:
            return token_ids_0
        return token_ids_0 + token_ids_1

    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_0, token_ids_1=token_ids_1,
                already_has_special_tokens=True,
            )
        n = len(token_ids_0) + (len(token_ids_1) if token_ids_1 else 0)
        return [0] * n

    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)
        return [0] * len(token_ids_0) + [1] * len(token_ids_1)