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| # Copyright 2018 The Google AI Language Team Authors. | |
| # | |
| # 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.""" | |
| from __future__ import absolute_import, division, print_function | |
| import collections | |
| import re | |
| import unicodedata | |
| import six | |
| def validate_case_matches_checkpoint(do_lower_case, init_checkpoint): | |
| """Checks whether the casing config is consistent with the checkpoint name.""" | |
| # The casing has to be passed in by the user and there is no explicit check | |
| # as to whether it matches the checkpoint. The casing information probably | |
| # should have been stored in the bert_config.json file, but it's not, so | |
| # we have to heuristically detect it to validate. | |
| if not init_checkpoint: | |
| return | |
| m = re.match('^.*?([A-Za-z0-9_-]+)/bert_model.ckpt', init_checkpoint) | |
| if m is None: | |
| return | |
| model_name = m.group(1) | |
| lower_models = [ | |
| 'uncased_L-24_H-1024_A-16', 'uncased_L-12_H-768_A-12', | |
| 'multilingual_L-12_H-768_A-12', 'chinese_L-12_H-768_A-12' | |
| ] | |
| cased_models = [ | |
| 'cased_L-12_H-768_A-12', 'cased_L-24_H-1024_A-16', | |
| 'multi_cased_L-12_H-768_A-12' | |
| ] | |
| is_bad_config = False | |
| if model_name in lower_models and not do_lower_case: | |
| is_bad_config = True | |
| actual_flag = 'False' | |
| case_name = 'lowercased' | |
| opposite_flag = 'True' | |
| if model_name in cased_models and do_lower_case: | |
| is_bad_config = True | |
| actual_flag = 'True' | |
| case_name = 'cased' | |
| opposite_flag = 'False' | |
| if is_bad_config: | |
| raise ValueError( | |
| 'You passed in `--do_lower_case=%s` with `--init_checkpoint=%s`. ' | |
| 'However, `%s` seems to be a %s model, so you ' | |
| 'should pass in `--do_lower_case=%s` so that the fine-tuning matches ' | |
| 'how the model was pre-training. If this error is wrong, please ' | |
| 'just comment out this check.' % | |
| (actual_flag, init_checkpoint, model_name, case_name, | |
| opposite_flag)) | |
| def convert_to_unicode(text): | |
| """Converts `text` to Unicode (if it's not already), assuming utf-8 input.""" | |
| if six.PY3: | |
| if isinstance(text, str): | |
| return text | |
| elif isinstance(text, bytes): | |
| return text.decode('utf-8', 'ignore') | |
| else: | |
| raise ValueError('Unsupported string type: %s' % (type(text))) | |
| elif six.PY2: | |
| if isinstance(text, str): | |
| return text.decode('utf-8', 'ignore') | |
| elif isinstance(text, unicode): | |
| return text | |
| else: | |
| raise ValueError('Unsupported string type: %s' % (type(text))) | |
| else: | |
| raise ValueError('Not running on Python2 or Python 3?') | |
| def printable_text(text): | |
| """Returns text encoded in a way suitable for print or `tf.logging`.""" | |
| # These functions want `str` for both Python2 and Python3, but in one case | |
| # it's a Unicode string and in the other it's a byte string. | |
| if six.PY3: | |
| if isinstance(text, str): | |
| return text | |
| elif isinstance(text, bytes): | |
| return text.decode('utf-8', 'ignore') | |
| else: | |
| raise ValueError('Unsupported string type: %s' % (type(text))) | |
| elif six.PY2: | |
| if isinstance(text, str): | |
| return text | |
| elif isinstance(text, unicode): | |
| return text.encode('utf-8') | |
| else: | |
| raise ValueError('Unsupported string type: %s' % (type(text))) | |
| else: | |
| raise ValueError('Not running on Python2 or Python 3?') | |
| def load_vocab(vocab_file): | |
| """Loads a vocabulary file into a dictionary.""" | |
| vocab = collections.OrderedDict() | |
| index = 0 | |
| with open(vocab_file, 'r', encoding='utf-8') as reader: | |
| while True: | |
| token = convert_to_unicode(reader.readline()) | |
| if not token: | |
| break | |
| token = token.strip() | |
| vocab[token] = index | |
| index += 1 | |
| return vocab | |
| def convert_by_vocab(vocab, items): | |
| """Converts a sequence of [tokens|ids] using the vocab.""" | |
| output = [] | |
| for item in items: | |
| output.append(vocab[item]) | |
| return output | |
| def convert_tokens_to_ids(vocab, tokens): | |
| return convert_by_vocab(vocab, tokens) | |
| def convert_ids_to_tokens(inv_vocab, ids): | |
| return convert_by_vocab(inv_vocab, ids) | |
| def whitespace_tokenize(text): | |
| """Runs basic whitespace cleaning and splitting on a piece of text.""" | |
| text = text.strip() | |
| if not text: | |
| return [] | |
| tokens = text.split() | |
| return tokens | |
| class FullTokenizer(object): | |
| """Runs end-to-end tokenziation.""" | |
| def __init__(self, vocab_file, do_lower_case=True): | |
| self.vocab = load_vocab(vocab_file) | |
| self.inv_vocab = {v: k for k, v in self.vocab.items()} | |
| self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case) | |
| self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab) | |
| def tokenize(self, text): | |
| split_tokens = [] | |
| for token in self.basic_tokenizer.tokenize(text): | |
| for sub_token in self.wordpiece_tokenizer.tokenize(token): | |
| split_tokens.append(sub_token) | |
| return split_tokens | |
| def convert_tokens_to_ids(self, tokens): | |
| return convert_by_vocab(self.vocab, tokens) | |
| def convert_ids_to_tokens(self, ids): | |
| return convert_by_vocab(self.inv_vocab, ids) | |
| def convert_tokens_to_string(tokens, clean_up_tokenization_spaces=True): | |
| """ Converts a sequence of tokens (string) in a single string. """ | |
| def clean_up_tokenization(out_string): | |
| """ Clean up a list of simple English tokenization artifacts | |
| like spaces before punctuations and abreviated forms. | |
| """ | |
| out_string = ( | |
| out_string.replace(' .', '.').replace(' ?', '?').replace( | |
| ' !', '!').replace(' ,', ',').replace(" ' ", "'").replace( | |
| " n't", "n't").replace(" 'm", "'m").replace( | |
| " 's", "'s").replace(" 've", | |
| "'ve").replace(" 're", "'re")) | |
| return out_string | |
| text = ' '.join(tokens).replace(' ##', '').strip() | |
| if clean_up_tokenization_spaces: | |
| clean_text = clean_up_tokenization(text) | |
| return clean_text | |
| else: | |
| return text | |
| def vocab_size(self): | |
| return len(self.vocab) | |
| class BasicTokenizer(object): | |
| """Runs basic tokenization (punctuation splitting, lower casing, etc.).""" | |
| def __init__(self, do_lower_case=True): | |
| """Constructs a BasicTokenizer. | |
| Args: | |
| do_lower_case: Whether to lower case the input. | |
| """ | |
| self.do_lower_case = do_lower_case | |
| def tokenize(self, text): | |
| """Tokenizes a piece of text.""" | |
| text = convert_to_unicode(text) | |
| text = self._clean_text(text) | |
| # This was added on November 1st, 2018 for the multilingual and Chinese | |
| # models. This is also applied to the English models now, but it doesn't | |
| # matter since the English models were not trained on any Chinese data | |
| # and generally don't have any Chinese data in them (there are Chinese | |
| # characters in the vocabulary because Wikipedia does have some Chinese | |
| # words in the English Wikipedia.). | |
| text = self._tokenize_chinese_chars(text) | |
| orig_tokens = whitespace_tokenize(text) | |
| split_tokens = [] | |
| for token in orig_tokens: | |
| if self.do_lower_case: | |
| token = token.lower() | |
| token = self._run_strip_accents(token) | |
| split_tokens.extend(self._run_split_on_punc(token)) | |
| output_tokens = whitespace_tokenize(' '.join(split_tokens)) | |
| return output_tokens | |
| def _run_strip_accents(self, text): | |
| """Strips accents from a piece of text.""" | |
| text = unicodedata.normalize('NFD', text) | |
| output = [] | |
| for char in text: | |
| cat = unicodedata.category(char) | |
| if cat == 'Mn': | |
| continue | |
| output.append(char) | |
| return ''.join(output) | |
| def _run_split_on_punc(self, text): | |
| """Splits punctuation on a piece of text.""" | |
| chars = list(text) | |
| i = 0 | |
| start_new_word = True | |
| output = [] | |
| while i < len(chars): | |
| char = chars[i] | |
| if _is_punctuation(char): | |
| output.append([char]) | |
| start_new_word = True | |
| else: | |
| if start_new_word: | |
| output.append([]) | |
| start_new_word = False | |
| output[-1].append(char) | |
| i += 1 | |
| return [''.join(x) for x in output] | |
| def _tokenize_chinese_chars(self, text): | |
| """Adds whitespace around any CJK character.""" | |
| output = [] | |
| for char in text: | |
| cp = ord(char) | |
| if self._is_chinese_char(cp): | |
| output.append(' ') | |
| output.append(char) | |
| output.append(' ') | |
| else: | |
| output.append(char) | |
| return ''.join(output) | |
| def _is_chinese_char(self, cp): | |
| """Checks whether CP is the codepoint of a CJK character.""" | |
| # This defines a "chinese character" as anything in the CJK Unicode block: | |
| # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) | |
| # | |
| # Note that the CJK Unicode block is NOT all Japanese and Korean characters, | |
| # despite its name. The modern Korean Hangul alphabet is a different block, | |
| # as is Japanese Hiragana and Katakana. Those alphabets are used to write | |
| # space-separated words, so they are not treated specially and handled | |
| # like the all of the other languages. | |
| if ((cp >= 0x4E00 and cp <= 0x9FFF) or (cp >= 0x3400 and cp <= 0x4DBF) | |
| or (cp >= 0x20000 and cp <= 0x2A6DF) | |
| or (cp >= 0x2A700 and cp <= 0x2B73F) | |
| or (cp >= 0x2B740 and cp <= 0x2B81F) | |
| or (cp >= 0x2B820 and cp <= 0x2CEAF) | |
| or (cp >= 0xF900 and cp <= 0xFAFF) | |
| or (cp >= 0x2F800 and cp <= 0x2FA1F)): | |
| return True | |
| return False | |
| def _clean_text(self, text): | |
| """Performs invalid character removal and whitespace cleanup on text.""" | |
| output = [] | |
| for char in text: | |
| cp = ord(char) | |
| if cp == 0 or cp == 0xfffd or _is_control(char): | |
| continue | |
| if _is_whitespace(char): | |
| output.append(' ') | |
| else: | |
| output.append(char) | |
| return ''.join(output) | |
| class WordpieceTokenizer(object): | |
| """Runs WordPiece tokenziation.""" | |
| def __init__(self, vocab, unk_token='[UNK]', max_input_chars_per_word=200): | |
| self.vocab = vocab | |
| self.unk_token = unk_token | |
| self.max_input_chars_per_word = max_input_chars_per_word | |
| def tokenize(self, text): | |
| """Tokenizes a piece of text into its word pieces. | |
| This uses a greedy longest-match-first algorithm to perform tokenization | |
| using the given vocabulary. | |
| For example: | |
| input = "unaffable" | |
| output = ["un", "##aff", "##able"] | |
| Args: | |
| text: A single token or whitespace separated tokens. This should have | |
| already been passed through `BasicTokenizer. | |
| Returns: | |
| A list of wordpiece tokens. | |
| """ | |
| text = convert_to_unicode(text) | |
| output_tokens = [] | |
| for token in whitespace_tokenize(text): | |
| chars = list(token) | |
| if len(chars) > self.max_input_chars_per_word: | |
| output_tokens.append(self.unk_token) | |
| continue | |
| is_bad = False | |
| start = 0 | |
| sub_tokens = [] | |
| while start < len(chars): | |
| end = len(chars) | |
| cur_substr = None | |
| while start < end: | |
| substr = ''.join(chars[start:end]) | |
| if start > 0: | |
| substr = '##' + substr | |
| if substr in self.vocab: | |
| cur_substr = substr | |
| break | |
| end -= 1 | |
| if cur_substr is None: | |
| is_bad = True | |
| break | |
| sub_tokens.append(cur_substr) | |
| start = end | |
| if is_bad: | |
| output_tokens.append(self.unk_token) | |
| else: | |
| output_tokens.extend(sub_tokens) | |
| return output_tokens | |
| def _is_whitespace(char): | |
| """Checks whether `chars` is a whitespace character.""" | |
| # \t, \n, and \r are technically contorl characters but we treat them | |
| # as whitespace since they are generally considered as such. | |
| if char == ' ' or char == '\t' or char == '\n' or char == '\r': | |
| return True | |
| cat = unicodedata.category(char) | |
| if cat == 'Zs': | |
| return True | |
| return False | |
| def _is_control(char): | |
| """Checks whether `chars` is a control character.""" | |
| # These are technically control characters but we count them as whitespace | |
| # characters. | |
| if char == '\t' or char == '\n' or char == '\r': | |
| return False | |
| cat = unicodedata.category(char) | |
| if cat in ('Cc', 'Cf'): | |
| return True | |
| return False | |
| def _is_punctuation(char): | |
| """Checks whether `chars` is a punctuation character.""" | |
| cp = ord(char) | |
| # We treat all non-letter/number ASCII as punctuation. | |
| # Characters such as "^", "$", and "`" are not in the Unicode | |
| # Punctuation class but we treat them as punctuation anyways, for | |
| # consistency. | |
| if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) | |
| or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)): | |
| return True | |
| cat = unicodedata.category(char) | |
| if cat.startswith('P'): | |
| return True | |
| return False | |