| # coding=utf-8 |
| # 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 |
| from __future__ import division |
| from __future__ import 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") |
| 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 |
| 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, "rb") as reader: |
| while True: |
| token = reader.readline() |
| token = token.decode("utf-8", "ignore") |
| 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) |
| |
| |
| 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 |