text: # 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)
  
  
  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
  
  
  if __name__ == "__main__":
      test_str = "15 China's Legend Holdings will split its several business arms " \
                 "to go public on stock markets. 该集团总裁朱利安周二表示，haha中国联想控股" \
                 "将分拆其多个业务部门在股市上市。"
  
      basic_tokenizer = BasicTokenizer()
  
      out_str = basic_tokenizer.tokenize(test_str)
      print(out_str)
  
      # # test
      # import tempfile
      #
      # vocab_tokens = [
      #     "[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn",
      #     "##ing", ","
      # ]
      # with tempfile.NamedTemporaryFile(delete=False) as vocab_writer:
      #     if six.PY2:
      #         vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
      #     else:
      #         vocab_writer.write("".join(
      #             [x + "\n" for x in vocab_tokens]).encode("utf-8"))
      #
      #     vocab_file = vocab_writer.name
      #
      # wordpiece_okenizer = WordpieceTokenizer(vocab_file)
      # out_str = wordpiece_okenizer.tokenize(test_str)
      # print(out_str)