# coding=utf-8 # Copyright 2021 VinAI Research 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 """ Tokenization classes for BARTpho-syllable model.""" import os from collections import defaultdict from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple, Union from transformers.tokenization_utils import AddedToken from transformers.tokenization_utils_base import EncodingFast from transformers.tokenization_utils_fast import PreTrainedTokenizerFast from transformers.utils import is_sentencepiece_available, logging if is_sentencepiece_available(): from .tokenization_bartpho import BartphoTokenizer else: BartphoTokenizer = None logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = { "vocab_file": "sentencepiece.bpe.model", "monolingual_vocab_file": "dict.txt", "tokenizer_file": "tokenizer.json", } PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "vinai/bartpho-syllable": "https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model", }, "monolingual_vocab_file": { "vinai/bartpho-syllable": "https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt", }, "tokenizer_file": { "vinai/bartpho-syllable": "https://huggingface.co/vinai/bartpho-syllable/resolve/main/tokenizer.json", }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"vinai/bartpho-syllable": 1024} class BartphoTokenizerFast(PreTrainedTokenizerFast): """ Construct a "fast" BARTpho tokenizer (backed by HuggingFace's *tokenizers* library). Adapted from [`XLMRobertaTokenizerFast`]. Based on [SentencePiece](https://github.com/google/sentencepiece). 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. bos_token (`str`, *optional*, defaults to `""`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. 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`. eos_token (`str`, *optional*, defaults to `""`): The end of sequence token. 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`. 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. cls_token (`str`, *optional*, defaults to `""`): 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 `""`): 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 `""`): The token used for padding, for example when batching sequences of different lengths. mask_token (`str`, *optional*, defaults to `""`): 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. additional_special_tokens (`List[str]`, *optional*, defaults to `["NOTUSED", "NOTUSED"]`): Additional special tokens used by the tokenizer. """ 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 = BartphoTokenizer def __init__( self, vocab_file=None, monolingual_vocab_file=None, tokenizer_file=None, bos_token="", eos_token="", sep_token="", cls_token="", unk_token="", pad_token="", mask_token="", **kwargs ): # Mask token behave like a normal word, i.e. include the space before it mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token super().__init__( vocab_file, monolingual_vocab_file, tokenizer_file=tokenizer_file, 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, **kwargs, ) self.vocab_file = vocab_file self.monolingual_vocab_file = monolingual_vocab_file self.can_save_slow_tokenizer = False if not self.vocab_file else True def get_added_vocab_hacking(self): """ Returns the added tokens in the vocabulary as a dictionary of token to index. Returns: `Dict[str, int], Dict[int, int]`: The added tokens, and their original and new ids """ base_vocab_size = self._tokenizer.get_vocab_size(with_added_tokens=False) full_vocab_size = self._tokenizer.get_vocab_size(with_added_tokens=True) if full_vocab_size == base_vocab_size: return {}, {} # Tokens in added_vocab should have ids that are equal to or larger than the size of base_vocab added_vocab = dict( (self._tokenizer.id_to_token(index), index + 1 - base_vocab_size + self.mask_token_id) for index in range(base_vocab_size, full_vocab_size) ) id_mapping = dict((index, self._tokenizer.token_to_id(tok)) for tok, index in added_vocab.items()) return added_vocab, id_mapping def _decode( self, token_ids: Union[int, List[int]], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True, **kwargs ) -> str: self._decode_use_source_tokenizer = kwargs.pop("use_source_tokenizer", False) if isinstance(token_ids, int): token_ids = [token_ids] # Mapping ids into their original values _, id_mapping = self.get_added_vocab_hacking() if len(id_mapping) > 0: token_ids = [id_mapping[id] if id in id_mapping else id for id in token_ids] text = self._tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens) if clean_up_tokenization_spaces: clean_text = self.clean_up_tokenization(text) return clean_text else: return text def _convert_encoding( self, encoding: EncodingFast, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, ) -> Tuple[Dict[str, Any], List[EncodingFast]]: """ Convert the encoding representation (from low-level HuggingFace tokenizer output) to a python Dict and a list of encodings, take care of building a batch from overflowing tokens. Overflowing tokens are converted to additional examples (like batches) so the output values of the dict are lists (overflows) of lists (tokens). Output shape: (overflows, sequence length) """ if return_token_type_ids is None: return_token_type_ids = "token_type_ids" in self.model_input_names if return_attention_mask is None: return_attention_mask = "attention_mask" in self.model_input_names if return_overflowing_tokens and encoding.overflowing is not None: encodings = [encoding] + encoding.overflowing else: encodings = [encoding] encoding_dict = defaultdict(list) added_vocab, _ = self.get_added_vocab_hacking() for e in encodings: # encoding_dict["input_ids"].append(e.ids) # Reassign ids of tokens due to the hacking strategy ids = [] for id, token in zip(e.ids, e.tokens): if id <= self.mask_token_id: ids.append(id) else: if token.strip() in added_vocab: ids.append(added_vocab[token.strip()]) else: ids.append(self.unk_token_id) encoding_dict["input_ids"].append(ids) if return_token_type_ids: encoding_dict["token_type_ids"].append(e.type_ids) if return_attention_mask: encoding_dict["attention_mask"].append(e.attention_mask) if return_special_tokens_mask: encoding_dict["special_tokens_mask"].append(e.special_tokens_mask) if return_offsets_mapping: encoding_dict["offset_mapping"].append(e.offsets) if return_length: # encoding_dict["length"].append(len(e.ids)) encoding_dict["length"].append(len(ids)) return encoding_dict, encodings def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A BARTpho sequence has the following format: - single sequence: ` X ` - pair of sequences: ` A B ` 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. """ if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] cls = [self.cls_token_id] sep = [self.sep_token_id] return cls + token_ids_0 + sep + sep + token_ids_1 + sep 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. BARTpho 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] def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not self.can_save_slow_tokenizer: raise ValueError( "Your fast tokenizer does not have the necessary information to save the vocabulary for a " "slow tokenizer." ) 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"], ) out_monolingual_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["monolingual_vocab_file"], ) if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file): copyfile(self.vocab_file, out_vocab_file) if os.path.abspath(self.monolingual_vocab_file) != os.path.abspath(out_monolingual_vocab_file): copyfile(self.monolingual_vocab_file, out_monolingual_vocab_file) return (out_vocab_file, out_monolingual_vocab_file)