| from __future__ import annotations |
|
|
| import shutil |
| from pathlib import Path |
|
|
| import sentencepiece as spm |
| from transformers import PreTrainedTokenizer |
|
|
|
|
| class TinyGPTTokenizer(PreTrainedTokenizer): |
| vocab_files_names = {"vocab_file": "tokenizer.model"} |
| model_input_names = ["input_ids", "attention_mask"] |
|
|
| def __init__( |
| self, |
| vocab_file: str, |
| unk_token: str = "<unk>", |
| bos_token: str = "<s>", |
| eos_token: str = "</s>", |
| pad_token: str = "<pad>", |
| **kwargs, |
| ) -> None: |
| self.vocab_file = vocab_file |
| self.sp_model = spm.SentencePieceProcessor(model_file=vocab_file) |
| super().__init__( |
| unk_token=unk_token, |
| bos_token=bos_token, |
| eos_token=eos_token, |
| pad_token=pad_token, |
| **kwargs, |
| ) |
|
|
| @property |
| def vocab_size(self) -> int: |
| return self.sp_model.get_piece_size() |
|
|
| def get_vocab(self) -> dict[str, int]: |
| vocab = {self._convert_id_to_token(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 self.sp_model.id_to_piece(int(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: list[int], |
| token_ids_1: list[int] | None = None, |
| ) -> list[int]: |
| 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: list[int] | None = None, |
| already_has_special_tokens: bool = False, |
| ) -> list[int]: |
| if already_has_special_tokens: |
| return [0] * (len(token_ids_0) + (len(token_ids_1) if token_ids_1 else 0)) |
| return [0] * (len(token_ids_0) + (len(token_ids_1) if token_ids_1 else 0)) |
|
|
| def save_vocabulary(self, save_directory: str, filename_prefix: str | None = None) -> tuple[str]: |
| if not self.vocab_file: |
| raise ValueError("No SentencePiece model file to save") |
|
|
| save_dir = Path(save_directory) |
| save_dir.mkdir(parents=True, exist_ok=True) |
| filename = "tokenizer.model" |
| out_path = save_dir / filename |
| shutil.copy2(self.vocab_file, out_path) |
| return (str(out_path),) |
|
|
|
|
| TinyGPTTokenizer.register_for_auto_class() |
|
|
