tokenizer_hanforge.py

from __future__ import annotations

import shutil
from pathlib import Path

import sentencepiece as spm
from transformers import PreTrainedTokenizer


class HanForgeTokenizer(PreTrainedTokenizer):
    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>",
        additional_special_tokens: list[str] | None = None,
        **kwargs,
    ):
        self.vocab_file = vocab_file
        self.sp_model = spm.SentencePieceProcessor(model_file=vocab_file)
        super().__init__(
            bos_token=bos_token,
            eos_token=eos_token,
            unk_token=unk_token,
            pad_token=pad_token,
            additional_special_tokens=additional_special_tokens or [],
            **kwargs,
        )

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

    def get_vocab(self) -> dict[str, int]:
        vocab = {self.sp_model.id_to_piece(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 list(self.sp_model.encode(text, out_type=str))

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

    def _convert_id_to_token(self, index: int) -> str:
        return str(self.sp_model.id_to_piece(index))

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

    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
        output = [self.bos_token_id] + list(token_ids_0)
        if token_ids_1 is not None:
            output += list(token_ids_1)
        output += [self.eos_token_id]
        return output

    def save_vocabulary(self, save_directory: str, filename_prefix: str | None = None):
        save_dir = Path(save_directory)
        save_dir.mkdir(parents=True, exist_ok=True)
        out_name = f"{filename_prefix + '-' if filename_prefix else ''}tokenizer.model"
        out_path = save_dir / out_name
        if Path(self.vocab_file).resolve() != out_path.resolve():
            shutil.copy2(self.vocab_file, out_path)
        vocab_src = Path(self.vocab_file).with_suffix(".vocab")
        if vocab_src.exists():
            vocab_out = save_dir / f"{filename_prefix + '-' if filename_prefix else ''}tokenizer.vocab"
            if vocab_src.resolve() != vocab_out.resolve():
                shutil.copy2(vocab_src, vocab_out)
        return (str(out_path),)