# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization classes for RoFormer.""" import json from typing import List, Optional, Tuple from tokenizers import normalizers from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_roformer import RoFormerTokenizer from .tokenization_utils import JiebaPreTokenizer logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"} class RoFormerTokenizerFast(PreTrainedTokenizerFast): r""" Construct a "fast" RoFormer tokenizer (backed by HuggingFace's *tokenizers* library). [`RoFormerTokenizerFast`] is almost identical to [`BertTokenizerFast`] and runs end-to-end tokenization: punctuation splitting and wordpiece. There are some difference between them when tokenizing Chinese. This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Example: ```python >>> from transformers import RoFormerTokenizerFast >>> tokenizer = RoFormerTokenizerFast.from_pretrained("junnyu/roformer_chinese_base") >>> tokenizer.tokenize("今天天气非常好。") ['今', '天', '天', '气', '非常', '好', '。'] ```""" vocab_files_names = VOCAB_FILES_NAMES slow_tokenizer_class = RoFormerTokenizer def __init__( self, vocab_file=None, tokenizer_file=None, do_lower_case=True, unk_token="[UNK]", sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]", mask_token="[MASK]", tokenize_chinese_chars=True, strip_accents=None, **kwargs, ): super().__init__( vocab_file, tokenizer_file=tokenizer_file, do_lower_case=do_lower_case, unk_token=unk_token, sep_token=sep_token, pad_token=pad_token, cls_token=cls_token, mask_token=mask_token, tokenize_chinese_chars=tokenize_chinese_chars, strip_accents=strip_accents, **kwargs, ) normalizer_state = json.loads(self.backend_tokenizer.normalizer.__getstate__()) if ( normalizer_state.get("lowercase", do_lower_case) != do_lower_case or normalizer_state.get("strip_accents", strip_accents) != strip_accents ): normalizer_class = getattr(normalizers, normalizer_state.pop("type")) normalizer_state["lowercase"] = do_lower_case normalizer_state["strip_accents"] = strip_accents self.backend_tokenizer.normalizer = normalizer_class(**normalizer_state) # Make sure we correctly set the custom PreTokenizer vocab = self.backend_tokenizer.get_vocab() self.backend_tokenizer.pre_tokenizer = PreTokenizer.custom(JiebaPreTokenizer(vocab)) self.do_lower_case = do_lower_case def __getstate__(self): state = self.__dict__.copy() state["_tokenizer"].pre_tokenizer = BertPreTokenizer() return state def __setstate__(self, d): self.__dict__ = d vocab = self.__dict__["_tokenizer"].get_vocab() self.__dict__["_tokenizer"].pre_tokenizer = PreTokenizer.custom(JiebaPreTokenizer(vocab)) def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A RoFormer sequence has the following format: - single sequence: `[CLS] X [SEP]` - pair of sequences: `[CLS] A [SEP] B [SEP]` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ output = [self.cls_token_id] + token_ids_0 + [self.sep_token_id] if token_ids_1 is not None: output += token_ids_1 + [self.sep_token_id] return output def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. A RoFormer sequence pair mask has the following format: ``` 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence | ``` If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s). Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s). """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: files = self._tokenizer.model.save(save_directory, name=filename_prefix) return tuple(files) def save_pretrained( self, save_directory, legacy_format=None, filename_prefix=None, push_to_hub=False, **kwargs, ): self.backend_tokenizer.pre_tokenizer = BertPreTokenizer() return super().save_pretrained(save_directory, legacy_format, filename_prefix, push_to_hub, **kwargs)