"""
Custom SentencePiece tokenizer for Jeeves model.

This tokenizer wraps SentencePiece directly, bypassing HuggingFace's
broken SentencePieceExtractor conversion (transformers >= 5.x).
It ensures token IDs match training exactly.

Usage:
    from transformers import AutoTokenizer
    tokenizer = AutoTokenizer.from_pretrained("Anurich/Jeeves-Small-100M", 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.

    Wraps the SentencePiece model directly to guarantee exact token ID
    correspondence with the training tokenizer. This avoids the broken
    HF slow->fast converter in transformers >= 5.x.
    """

    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]:
        """Tokenize text into subword pieces using SentencePiece."""
        return self.sp_model.EncodeAsPieces(text)

    def _convert_token_to_id(self, token: str) -> int:
        """Convert a token string to its ID."""
        return self.sp_model.PieceToId(token)

    def _convert_id_to_token(self, index: int) -> str:
        """Convert an ID to its token string."""
        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:
        """Convert a sequence of tokens to a single string."""
        return self.sp_model.DecodePieces(tokens)

    def save_vocabulary(
        self, save_directory: str, filename_prefix: Optional[str] = None
    ) -> Tuple[str]:
        """Save the SentencePiece model file."""
        if not os.path.isdir(save_directory):
            os.makedirs(save_directory, exist_ok=True)

        out_vocab_file = os.path.join(
            save_directory,
            (filename_prefix + "-" if filename_prefix else "") + "tokenizer.model",
        )

        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
            import shutil
            shutil.copyfile(self.vocab_file, out_vocab_file)

        return (out_vocab_file,)

    def build_inputs_with_special_tokens(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
    ) -> List[int]:
        """Build model inputs — no BOS/EOS added by default (matching training)."""
        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: List[int],
        token_ids_1: Optional[List[int]] = None,
        already_has_special_tokens: bool = False,
    ) -> List[int]:
        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,
            )
        if token_ids_1 is None:
            return [0] * len(token_ids_0)
        return [0] * len(token_ids_0) + [0] * len(token_ids_1)

    def create_token_type_ids_from_sequences(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
    ) -> List[int]:
        if token_ids_1 is None:
            return [0] * len(token_ids_0)
        return [0] * len(token_ids_0) + [1] * len(token_ids_1)