hallucination / SupplementaryMaterial_Code /transformers-4.37.2 /models /bert_generation /tokenization_bert_generation.py
| # coding=utf-8 | |
| # Copyright (c) 2020, NVIDIA CORPORATION. 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 class for model BertGeneration.""" | |
| import os | |
| from shutil import copyfile | |
| from typing import Any, Dict, List, Optional, Tuple | |
| import sentencepiece as spm | |
| from ...tokenization_utils import PreTrainedTokenizer | |
| from ...utils import logging | |
| logger = logging.get_logger(__name__) | |
| VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"} | |
| PRETRAINED_VOCAB_FILES_MAP = { | |
| "vocab_file": { | |
| "bert_for_seq_generation": ( | |
| "https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model" | |
| ), | |
| } | |
| } | |
| PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"bert_for_seq_generation": 512} | |
| class BertGenerationTokenizer(PreTrainedTokenizer): | |
| """ | |
| Construct a BertGeneration tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). | |
| This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to | |
| this superclass for more information regarding those methods. | |
| Args: | |
| vocab_file (`str`): | |
| [SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that | |
| contains the vocabulary necessary to instantiate a tokenizer. | |
| bos_token (`str`, *optional*, defaults to `"<s>"`): | |
| The begin of sequence token. | |
| eos_token (`str`, *optional*, defaults to `"</s>"`): | |
| The end of sequence token. | |
| 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. | |
| sep_token (`str`, *optional*, defaults to `"<::::>"`): | |
| 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. | |
| sp_model_kwargs (`dict`, *optional*): | |
| Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for | |
| SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, | |
| to set: | |
| - `enable_sampling`: Enable subword regularization. | |
| - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. | |
| - `nbest_size = {0,1}`: No sampling is performed. | |
| - `nbest_size > 1`: samples from the nbest_size results. | |
| - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) | |
| using forward-filtering-and-backward-sampling algorithm. | |
| - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for | |
| BPE-dropout. | |
| """ | |
| vocab_files_names = VOCAB_FILES_NAMES | |
| pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP | |
| max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES | |
| prefix_tokens: List[int] = [] | |
| model_input_names = ["input_ids", "attention_mask"] | |
| def __init__( | |
| self, | |
| vocab_file, | |
| bos_token="<s>", | |
| eos_token="</s>", | |
| unk_token="<unk>", | |
| pad_token="<pad>", | |
| sep_token="<::::>", | |
| sp_model_kwargs: Optional[Dict[str, Any]] = None, | |
| **kwargs, | |
| ) -> None: | |
| self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs | |
| self.vocab_file = vocab_file | |
| self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) | |
| self.sp_model.Load(vocab_file) | |
| # Add extra_ids to the special token list | |
| super().__init__( | |
| bos_token=bos_token, | |
| eos_token=eos_token, | |
| unk_token=unk_token, | |
| pad_token=pad_token, | |
| sep_token=sep_token, | |
| sp_model_kwargs=self.sp_model_kwargs, | |
| **kwargs, | |
| ) | |
| def vocab_size(self): | |
| return self.sp_model.get_piece_size() | |
| def get_vocab(self): | |
| vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} | |
| vocab.update(self.added_tokens_encoder) | |
| return vocab | |
| def __getstate__(self): | |
| state = self.__dict__.copy() | |
| state["sp_model"] = None | |
| return state | |
| def __setstate__(self, d): | |
| self.__dict__ = d | |
| # for backward compatibility | |
| if not hasattr(self, "sp_model_kwargs"): | |
| self.sp_model_kwargs = {} | |
| self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) | |
| self.sp_model.Load(self.vocab_file) | |
| def _tokenize(self, text: str) -> List[str]: | |
| """Take as input a string and return a list of strings (tokens) for words/sub-words""" | |
| return self.sp_model.encode(text, out_type=str) | |
| def _convert_token_to_id(self, token): | |
| """Converts a token (str) in an id using the vocab.""" | |
| return self.sp_model.piece_to_id(token) | |
| def _convert_id_to_token(self, index): | |
| """Converts an index (integer) in a token (str) using the vocab.""" | |
| token = self.sp_model.IdToPiece(index) | |
| return token | |
| def convert_tokens_to_string(self, tokens): | |
| """Converts a sequence of tokens (string) in a single string.""" | |
| current_sub_tokens = [] | |
| out_string = "" | |
| for token in tokens: | |
| # make sure that special tokens are not decoded using sentencepiece model | |
| if token in self.all_special_tokens: | |
| out_string += self.sp_model.decode(current_sub_tokens) + token | |
| current_sub_tokens = [] | |
| else: | |
| current_sub_tokens.append(token) | |
| out_string += self.sp_model.decode(current_sub_tokens) | |
| return out_string.strip() | |
| def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: | |
| if not os.path.isdir(save_directory): | |
| logger.error(f"Vocabulary path ({save_directory}) should be a directory") | |
| return | |
| out_vocab_file = os.path.join( | |
| save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] | |
| ) | |
| if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file): | |
| copyfile(self.vocab_file, out_vocab_file) | |
| elif not os.path.isfile(self.vocab_file): | |
| with open(out_vocab_file, "wb") as fi: | |
| content_spiece_model = self.sp_model.serialized_model_proto() | |
| fi.write(content_spiece_model) | |
| return (out_vocab_file,) | |