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- # 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")
- 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") 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)
- @staticmethod
- 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
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