| """Hugging Face tokenizer for CFRD SentencePiece models.""" |
|
|
| from __future__ import annotations |
|
|
| import os |
| import shutil |
| import unicodedata |
| from pathlib import Path |
|
|
| import sentencepiece as spm |
| from transformers import PreTrainedTokenizer |
|
|
|
|
| VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"} |
|
|
|
|
| class CFRDTokenizer(PreTrainedTokenizer): |
| """SentencePiece tokenizer with lossless newline preservation.""" |
|
|
| vocab_files_names = VOCAB_FILES_NAMES |
| model_input_names = ["input_ids", "attention_mask"] |
|
|
| def __init__( |
| self, |
| vocab_file: str, |
| newline_token: str = "<|nl|>", |
| newline_escape_token: str = "<|literal_nl|>", |
| add_bos_token: bool = True, |
| add_eos_token: bool = False, |
| **kwargs, |
| ) -> None: |
| self.vocab_file = vocab_file |
| self.newline_token = newline_token |
| self.newline_escape_token = newline_escape_token |
| self.add_bos_token = add_bos_token |
| self.add_eos_token = add_eos_token |
| self.sp_model = spm.SentencePieceProcessor(model_file=str(vocab_file)) |
| super().__init__(**kwargs) |
|
|
| @property |
| def vocab_size(self) -> int: |
| return int(self.sp_model.vocab_size()) |
|
|
| def get_vocab(self) -> dict[str, int]: |
| vocabulary = {self.convert_ids_to_tokens(index): index for index in range(self.vocab_size)} |
| vocabulary.update(self.get_added_vocab()) |
| return vocabulary |
|
|
| def _tokenize(self, text: str) -> list[str]: |
| text = unicodedata.normalize("NFC", text) |
| text = text.replace(self.newline_token, self.newline_escape_token) |
| text = text.replace("\n", self.newline_token) |
| 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: |
| text = self.sp_model.decode(tokens) |
| text = text.replace(self.newline_token, "\n") |
| return text.replace(self.newline_escape_token, self.newline_token) |
|
|
| 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 not None: |
| raise ValueError("CFRDTokenizer does not support paired sequences") |
| output = list(token_ids_0) |
| if self.add_bos_token: |
| output.insert(0, self.bos_token_id) |
| if self.add_eos_token: |
| output.append(self.eos_token_id) |
| return output |
|
|
| 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: |
| special_ids = set(self.all_special_ids) |
| return [1 if token_id in special_ids else 0 for token_id in token_ids_0] |
| if token_ids_1 is not None: |
| raise ValueError("CFRDTokenizer does not support paired sequences") |
| return ([1] if self.add_bos_token else []) + [0] * len(token_ids_0) + ( |
| [1] if self.add_eos_token else [] |
| ) |
|
|
| def create_token_type_ids_from_sequences( |
| self, |
| token_ids_0: list[int], |
| token_ids_1: list[int] | None = None, |
| ) -> list[int]: |
| return [0] * len(self.build_inputs_with_special_tokens(token_ids_0, token_ids_1)) |
|
|
| def save_vocabulary(self, save_directory: str, filename_prefix: str | None = None): |
| directory = Path(save_directory) |
| directory.mkdir(parents=True, exist_ok=True) |
| filename = ((filename_prefix + "-") if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] |
| destination = directory / filename |
| if os.path.abspath(self.vocab_file) != os.path.abspath(destination): |
| shutil.copyfile(self.vocab_file, destination) |
| return (str(destination),) |
|
|
|
|
| CFRDTokenizer.register_for_auto_class("AutoTokenizer") |
|
|
