hallucination / SupplementaryMaterial_Code /transformers-4.37.2 /models /bart /tokenization_bart_fast.py
| # coding=utf-8 | |
| # Copyright 2020 The Facebook AI Research Team Authors and The HuggingFace Inc. team. | |
| # | |
| # 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. | |
| import json | |
| from typing import List, Optional, Tuple | |
| from tokenizers import pre_tokenizers, processors | |
| from ...tokenization_utils_base import AddedToken, BatchEncoding | |
| from ...tokenization_utils_fast import PreTrainedTokenizerFast | |
| from ...utils import logging | |
| from .tokenization_bart import BartTokenizer | |
| logger = logging.get_logger(__name__) | |
| VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"} | |
| # See all BART models at https://huggingface.co/models?filter=bart | |
| PRETRAINED_VOCAB_FILES_MAP = { | |
| "vocab_file": { | |
| "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json", | |
| "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json", | |
| "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json", | |
| "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json", | |
| "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json", | |
| "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json", | |
| }, | |
| "merges_file": { | |
| "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt", | |
| "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt", | |
| "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt", | |
| "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt", | |
| "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt", | |
| "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt", | |
| }, | |
| "tokenizer_file": { | |
| "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json", | |
| "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json", | |
| "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json", | |
| "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json", | |
| "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json", | |
| "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json", | |
| }, | |
| } | |
| PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { | |
| "facebook/bart-base": 1024, | |
| "facebook/bart-large": 1024, | |
| "facebook/bart-large-mnli": 1024, | |
| "facebook/bart-large-cnn": 1024, | |
| "facebook/bart-large-xsum": 1024, | |
| "yjernite/bart_eli5": 1024, | |
| } | |
| class BartTokenizerFast(PreTrainedTokenizerFast): | |
| r""" | |
| Construct a "fast" BART tokenizer (backed by HuggingFace's *tokenizers* library), derived from the GPT-2 tokenizer, | |
| using byte-level Byte-Pair-Encoding. | |
| This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will | |
| be encoded differently whether it is at the beginning of the sentence (without space) or not: | |
| ```python | |
| >>> from transformers import BartTokenizerFast | |
| >>> tokenizer = BartTokenizerFast.from_pretrained("facebook/bart-base") | |
| >>> tokenizer("Hello world")["input_ids"] | |
| [0, 31414, 232, 2] | |
| >>> tokenizer(" Hello world")["input_ids"] | |
| [0, 20920, 232, 2] | |
| ``` | |
| You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you | |
| call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance. | |
| <Tip> | |
| When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`. | |
| </Tip> | |
| This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should | |
| refer to this superclass for more information regarding those methods. | |
| Args: | |
| vocab_file (`str`): | |
| Path to the vocabulary file. | |
| merges_file (`str`): | |
| Path to the merges file. | |
| errors (`str`, *optional*, defaults to `"replace"`): | |
| Paradigm to follow when decoding bytes to UTF-8. See | |
| [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information. | |
| bos_token (`str`, *optional*, defaults to `"<s>"`): | |
| The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. | |
| <Tip> | |
| When building a sequence using special tokens, this is not the token that is used for the beginning of | |
| sequence. The token used is the `cls_token`. | |
| </Tip> | |
| eos_token (`str`, *optional*, defaults to `"</s>"`): | |
| The end of sequence token. | |
| <Tip> | |
| When building a sequence using special tokens, this is not the token that is used for the end of sequence. | |
| The token used is the `sep_token`. | |
| </Tip> | |
| sep_token (`str`, *optional*, defaults to `"</s>"`): | |
| The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for | |
| sequence classification or for a text and a question for question answering. It is also used as the last | |
| token of a sequence built with special tokens. | |
| cls_token (`str`, *optional*, defaults to `"<s>"`): | |
| The classifier token which is used when doing sequence classification (classification of the whole sequence | |
| instead of per-token classification). It is the first token of the sequence when built with special tokens. | |
| unk_token (`str`, *optional*, defaults to `"<unk>"`): | |
| The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this | |
| token instead. | |
| pad_token (`str`, *optional*, defaults to `"<pad>"`): | |
| The token used for padding, for example when batching sequences of different lengths. | |
| mask_token (`str`, *optional*, defaults to `"<mask>"`): | |
| The token used for masking values. This is the token used when training this model with masked language | |
| modeling. This is the token which the model will try to predict. | |
| add_prefix_space (`bool`, *optional*, defaults to `False`): | |
| Whether or not to add an initial space to the input. This allows to treat the leading word just as any | |
| other word. (BART tokenizer detect beginning of words by the preceding space). | |
| trim_offsets (`bool`, *optional*, defaults to `True`): | |
| Whether the post processing step should trim offsets to avoid including whitespaces. | |
| """ | |
| vocab_files_names = VOCAB_FILES_NAMES | |
| pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP | |
| max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES | |
| model_input_names = ["input_ids", "attention_mask"] | |
| slow_tokenizer_class = BartTokenizer | |
| def __init__( | |
| self, | |
| vocab_file=None, | |
| merges_file=None, | |
| tokenizer_file=None, | |
| errors="replace", | |
| bos_token="<s>", | |
| eos_token="</s>", | |
| sep_token="</s>", | |
| cls_token="<s>", | |
| unk_token="<unk>", | |
| pad_token="<pad>", | |
| mask_token="<mask>", | |
| add_prefix_space=False, | |
| trim_offsets=True, | |
| **kwargs, | |
| ): | |
| # we have to specify that this tokens is special otherwise adding it will reset the normalized flag to `False` in `add_special_tokens` | |
| mask_token = ( | |
| AddedToken(mask_token, lstrip=True, normalized=True, special=True) | |
| if isinstance(mask_token, str) | |
| else mask_token | |
| ) | |
| super().__init__( | |
| vocab_file, | |
| merges_file, | |
| tokenizer_file=tokenizer_file, | |
| errors=errors, | |
| bos_token=bos_token, | |
| eos_token=eos_token, | |
| sep_token=sep_token, | |
| cls_token=cls_token, | |
| unk_token=unk_token, | |
| pad_token=pad_token, | |
| mask_token=mask_token, | |
| add_prefix_space=add_prefix_space, | |
| trim_offsets=trim_offsets, | |
| **kwargs, | |
| ) | |
| pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()) | |
| if pre_tok_state.get("add_prefix_space", add_prefix_space) != add_prefix_space: | |
| pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop("type")) | |
| pre_tok_state["add_prefix_space"] = add_prefix_space | |
| self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state) | |
| self.add_prefix_space = add_prefix_space | |
| # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__` | |
| tokenizer_component = "post_processor" | |
| tokenizer_component_instance = getattr(self.backend_tokenizer, tokenizer_component, None) | |
| if tokenizer_component_instance: | |
| state = json.loads(tokenizer_component_instance.__getstate__()) | |
| # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class` | |
| if "sep" in state: | |
| state["sep"] = tuple(state["sep"]) | |
| if "cls" in state: | |
| state["cls"] = tuple(state["cls"]) | |
| changes_to_apply = False | |
| if state.get("add_prefix_space", add_prefix_space) != add_prefix_space: | |
| state["add_prefix_space"] = add_prefix_space | |
| changes_to_apply = True | |
| if state.get("trim_offsets", trim_offsets) != trim_offsets: | |
| state["trim_offsets"] = trim_offsets | |
| changes_to_apply = True | |
| if changes_to_apply: | |
| component_class = getattr(processors, state.pop("type")) | |
| new_value = component_class(**state) | |
| setattr(self.backend_tokenizer, tokenizer_component, new_value) | |
| def mask_token(self) -> str: | |
| """ | |
| `str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while not | |
| having been set. | |
| BART tokenizer has a special mask token to be usable in the fill-mask pipeline. The mask token will greedily | |
| comprise the space before the *<mask>*. | |
| """ | |
| if self._mask_token is None: | |
| if self.verbose: | |
| logger.error("Using mask_token, but it is not set yet.") | |
| return None | |
| return str(self._mask_token) | |
| def mask_token(self, value): | |
| """ | |
| Overriding the default behavior of the mask token to have it eat the space before it. | |
| This is needed to preserve backward compatibility with all the previously used models based on Bart. | |
| """ | |
| # Mask token behave like a normal word, i.e. include the space before it | |
| # So we set lstrip to True | |
| value = AddedToken(value, lstrip=True, rstrip=False) if isinstance(value, str) else value | |
| self._mask_token = value | |
| def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding: | |
| is_split_into_words = kwargs.get("is_split_into_words", False) | |
| if is_split_into_words and not self.add_prefix_space: | |
| raise ValueError( | |
| f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " | |
| "to use it with pretokenized inputs." | |
| ) | |
| return super()._batch_encode_plus(*args, **kwargs) | |
| def _encode_plus(self, *args, **kwargs) -> BatchEncoding: | |
| is_split_into_words = kwargs.get("is_split_into_words", False) | |
| if is_split_into_words and not self.add_prefix_space: | |
| raise ValueError( | |
| f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " | |
| "to use it with pretokenized inputs." | |
| ) | |
| return super()._encode_plus(*args, **kwargs) | |
| 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 build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): | |
| output = [self.bos_token_id] + token_ids_0 + [self.eos_token_id] | |
| if token_ids_1 is None: | |
| return output | |
| return output + [self.eos_token_id] + token_ids_1 + [self.eos_token_id] | |
| 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. BART does not | |
| make use of token type ids, therefore a list of zeros is returned. | |
| 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 zeros. | |
| """ | |
| 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 + sep + token_ids_1 + sep) * [0] | |