# coding=utf-8 # Copyright 2018 The Google AI Language 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. """Tokenization classes.""" import collections import copy import os import unicodedata from typing import Optional from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer, load_vocab from .utils import logging logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "cl-tohoku/bert-base-japanese": "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese/vocab.txt", "cl-tohoku/bert-base-japanese-whole-word-masking": "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-whole-word-masking/vocab.txt", "cl-tohoku/bert-base-japanese-char": "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-char/vocab.txt", "cl-tohoku/bert-base-japanese-char-whole-word-masking": "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-char-whole-word-masking/vocab.txt", } } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "cl-tohoku/bert-base-japanese": 512, "cl-tohoku/bert-base-japanese-whole-word-masking": 512, "cl-tohoku/bert-base-japanese-char": 512, "cl-tohoku/bert-base-japanese-char-whole-word-masking": 512, } PRETRAINED_INIT_CONFIGURATION = { "cl-tohoku/bert-base-japanese": { "do_lower_case": False, "word_tokenizer_type": "mecab", "subword_tokenizer_type": "wordpiece", }, "cl-tohoku/bert-base-japanese-whole-word-masking": { "do_lower_case": False, "word_tokenizer_type": "mecab", "subword_tokenizer_type": "wordpiece", }, "cl-tohoku/bert-base-japanese-char": { "do_lower_case": False, "word_tokenizer_type": "mecab", "subword_tokenizer_type": "character", }, "cl-tohoku/bert-base-japanese-char-whole-word-masking": { "do_lower_case": False, "word_tokenizer_type": "mecab", "subword_tokenizer_type": "character", }, } class BertJapaneseTokenizer(BertTokenizer): """BERT tokenizer for Japanese text""" vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES def __init__( self, vocab_file, do_lower_case=False, do_word_tokenize=True, do_subword_tokenize=True, word_tokenizer_type="basic", subword_tokenizer_type="wordpiece", never_split=None, unk_token="[UNK]", sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]", mask_token="[MASK]", mecab_kwargs=None, **kwargs ): """ Constructs a MecabBertTokenizer. Args: **vocab_file**: Path to a one-wordpiece-per-line vocabulary file. **do_lower_case**: (`optional`) boolean (default True) Whether to lower case the input. Only has an effect when do_basic_tokenize=True. **do_word_tokenize**: (`optional`) boolean (default True) Whether to do word tokenization. **do_subword_tokenize**: (`optional`) boolean (default True) Whether to do subword tokenization. **word_tokenizer_type**: (`optional`) string (default "basic") Type of word tokenizer. **subword_tokenizer_type**: (`optional`) string (default "wordpiece") Type of subword tokenizer. **mecab_kwargs**: (`optional`) dict passed to `MecabTokenizer` constructor (default None) """ super(BertTokenizer, self).__init__( unk_token=unk_token, sep_token=sep_token, pad_token=pad_token, cls_token=cls_token, mask_token=mask_token, do_lower_case=do_lower_case, do_word_tokenize=do_word_tokenize, do_subword_tokenize=do_subword_tokenize, word_tokenizer_type=word_tokenizer_type, subword_tokenizer_type=subword_tokenizer_type, never_split=never_split, mecab_kwargs=mecab_kwargs, **kwargs, ) # ^^ We call the grandparent's init, not the parent's. if not os.path.isfile(vocab_file): raise ValueError( "Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained " "model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file) ) self.vocab = load_vocab(vocab_file) self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()]) self.do_word_tokenize = do_word_tokenize self.word_tokenizer_type = word_tokenizer_type self.lower_case = do_lower_case self.never_split = never_split self.mecab_kwargs = copy.deepcopy(mecab_kwargs) if do_word_tokenize: if word_tokenizer_type == "basic": self.word_tokenizer = BasicTokenizer( do_lower_case=do_lower_case, never_split=never_split, tokenize_chinese_chars=False ) elif word_tokenizer_type == "mecab": self.word_tokenizer = MecabTokenizer( do_lower_case=do_lower_case, never_split=never_split, **(mecab_kwargs or {}) ) else: raise ValueError("Invalid word_tokenizer_type '{}' is specified.".format(word_tokenizer_type)) self.do_subword_tokenize = do_subword_tokenize self.subword_tokenizer_type = subword_tokenizer_type if do_subword_tokenize: if subword_tokenizer_type == "wordpiece": self.subword_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=self.unk_token) elif subword_tokenizer_type == "character": self.subword_tokenizer = CharacterTokenizer(vocab=self.vocab, unk_token=self.unk_token) else: raise ValueError("Invalid subword_tokenizer_type '{}' is specified.".format(subword_tokenizer_type)) @property def do_lower_case(self): return self.lower_case def __getstate__(self): state = dict(self.__dict__) if self.word_tokenizer_type == "mecab": del state["word_tokenizer"] return state def __setstate__(self, state): self.__dict__ = state if self.word_tokenizer_type == "mecab": self.word_tokenizer = MecabTokenizer( do_lower_case=self.do_lower_case, never_split=self.never_split, **(self.mecab_kwargs or {}) ) def _tokenize(self, text): if self.do_word_tokenize: tokens = self.word_tokenizer.tokenize(text, never_split=self.all_special_tokens) else: tokens = [text] if self.do_subword_tokenize: split_tokens = [sub_token for token in tokens for sub_token in self.subword_tokenizer.tokenize(token)] else: split_tokens = tokens return split_tokens class MecabTokenizer: """Runs basic tokenization with MeCab morphological parser.""" def __init__( self, do_lower_case=False, never_split=None, normalize_text=True, mecab_dic: Optional[str] = "ipadic", mecab_option: Optional[str] = None, ): """ Constructs a MecabTokenizer. Args: **do_lower_case**: (`optional`) boolean (default True) Whether to lowercase the input. **never_split**: (`optional`) list of str Kept for backward compatibility purposes. Now implemented directly at the base class level (see :func:`PreTrainedTokenizer.tokenize`) List of tokens not to split. **normalize_text**: (`optional`) boolean (default True) Whether to apply unicode normalization to text before tokenization. **mecab_dic**: (`optional`) string (default "ipadic") Name of dictionary to be used for MeCab initialization. If you are using a system-installed dictionary, set thi option to `None` and modify `mecab_option`. **mecab_option**: (`optional`) string String passed to MeCab constructor. """ self.do_lower_case = do_lower_case self.never_split = never_split if never_split is not None else [] self.normalize_text = normalize_text try: import fugashi except ModuleNotFoundError as error: raise error.__class__( "You need to install fugashi to use MecabTokenizer." "See https://pypi.org/project/fugashi/ for installation." ) mecab_option = mecab_option or "" if mecab_dic is not None: if mecab_dic == "ipadic": try: import ipadic except ModuleNotFoundError as error: raise error.__class__( "The ipadic dictionary is not installed. " "See https://github.com/polm/ipadic-py for installation." ) dic_dir = ipadic.DICDIR elif mecab_dic == "unidic_lite": try: import unidic_lite except ModuleNotFoundError as error: raise error.__class__( "The unidic_lite dictionary is not installed. " "See https://github.com/polm/unidic-lite for installation." ) dic_dir = unidic_lite.DICDIR elif mecab_dic == "unidic": try: import unidic except ModuleNotFoundError as error: raise error.__class__( "The unidic dictionary is not installed. " "See https://github.com/polm/unidic-py for installation." ) dic_dir = unidic.DICDIR if not os.path.isdir(dic_dir): raise RuntimeError( "The unidic dictionary itself is not found." "See https://github.com/polm/unidic-py for installation." ) else: raise ValueError("Invalid mecab_dic is specified.") mecabrc = os.path.join(dic_dir, "mecabrc") mecab_option = '-d "{}" -r "{}" '.format(dic_dir, mecabrc) + mecab_option self.mecab = fugashi.GenericTagger(mecab_option) def tokenize(self, text, never_split=None, **kwargs): """Tokenizes a piece of text.""" if self.normalize_text: text = unicodedata.normalize("NFKC", text) never_split = self.never_split + (never_split if never_split is not None else []) tokens = [] for word in self.mecab(text): token = word.surface if self.do_lower_case and token not in never_split: token = token.lower() tokens.append(token) return tokens class CharacterTokenizer: """Runs Character tokenziation.""" def __init__(self, vocab, unk_token, normalize_text=True): """ Constructs a CharacterTokenizer. Args: **vocab**: Vocabulary object. **unk_token**: str A special symbol for out-of-vocabulary token. **normalize_text**: (`optional`) boolean (default True) Whether to apply unicode normalization to text before tokenization. """ self.vocab = vocab self.unk_token = unk_token self.normalize_text = normalize_text def tokenize(self, text): """ Tokenizes a piece of text into characters. For example, :obj:`input = "apple""` wil return as output :obj:`["a", "p", "p", "l", "e"]`. Args: text: A single token or whitespace separated tokens. This should have already been passed through `BasicTokenizer`. Returns: A list of characters. """ if self.normalize_text: text = unicodedata.normalize("NFKC", text) output_tokens = [] for char in text: if char not in self.vocab: output_tokens.append(self.unk_token) continue output_tokens.append(char) return output_tokens