code: # 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
  import tensorflow as tf
  
  
  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 tf.gfile.GFile(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 = []
    #print("items:",items) #['[CLS]', '日', '##期', '，', '但', '被', '##告', '金', '##东', '##福', '载', '##明', '[MASK]', 'U', '##N', '##K', ']', '保', '##证', '本', '##月', '1', '##4', '[MASK]', '到', '##位', '，', '2', '##0', '##1', '##5', '年', '6', '[MASK]', '1', '##1', '日', '[', 'U', '##N', '##K', ']', '，', '原', '##告', '[MASK]', '认', '##可', '于', '2', '##0', '##1', '##5', '[MASK]', '6', '月', '[MASK]', '[MASK]', '日', '##向', '被', '##告', '主', '##张', '权', '##利', '。', '而', '[MASK]', '[MASK]', '自', '[MASK]', '[MASK]', '[MASK]', '[MASK]', '年', '6', '月', '1', '##1', '日', '[SEP]', '原', '##告', '于', '2', '##0', '##1', '##6', '[MASK]', '6', '[MASK]', '2', '##4', '日', '起', '##诉', '，', '主', '##张', '保', '##证', '责', '##任', '，', '已', '超', '##过', '保', '##证', '期', '##限', '[MASK]', '保', '##证', '人', '依', '##法', '不', '##再', '承', '##担', '保', '##证', '[MASK]', '[MASK]', '[MASK]', '[SEP]']
    for i,item in enumerate(items):
      #print(i,"item:",item) #  ##期
      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
  
apis: tensorflow.gfile.GFile