content: # coding=utf-8
  # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
  #
  # 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, unicode_literals
  
  import collections
  import logging
  import os
  import unicodedata
  from io import open
  
  from .file_utils import cached_path
  
  logger = logging.getLogger(__name__)
  
  PRETRAINED_VOCAB_ARCHIVE_MAP = {
      'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt",
      'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt",
      'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-vocab.txt",
      'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-vocab.txt",
      'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-vocab.txt",
      'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt",
      'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt",
  }
  PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = {
      'bert-base-uncased': 512,
      'bert-large-uncased': 512,
      'bert-base-cased': 512,
      'bert-large-cased': 512,
      'bert-base-multilingual-uncased': 512,
      'bert-base-multilingual-cased': 512,
      'bert-base-chinese': 512,
  }
  VOCAB_NAME = 'vocab.txt'
  
  
  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 = reader.readline()
              if not token:
                  break
              token = token.strip()
              vocab[token] = index
              index += 1
      return vocab
  
  
  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 BertTokenizer(object):
      """Runs end-to-end tokenization: punctuation splitting + wordpiece"""
  
      def __init__(self, vocab_file, do_lower_case=True, max_len=None, do_basic_tokenize=True,
                   never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
          """Constructs a BertTokenizer.
  
          Args:
            vocab_file: Path to a one-wordpiece-per-line vocabulary file
            do_lower_case: Whether to lower case the input
                           Only has an effect when do_wordpiece_only=False
            do_basic_tokenize: Whether to do basic tokenization before wordpiece.
            max_len: An artificial maximum length to truncate tokenized sequences to;
                           Effective maximum length is always the minimum of this
                           value (if specified) and the underlying BERT model's
                           sequence length.
            never_split: List of tokens which will never be split during tokenization.
                           Only has an effect when do_wordpiece_only=False
          """
          if not os.path.isfile(vocab_file):
              raise ValueError(
                  "Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained "
                  "model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file))
          self.vocab = load_vocab(vocab_file)
          self.ids_to_tokens = collections.OrderedDict(
              [(ids, tok) for tok, ids in self.vocab.items()])
          self.do_basic_tokenize = do_basic_tokenize
          if do_basic_tokenize:
            self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case,
                                                  never_split=never_split)
          self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
          self.max_len = max_len if max_len is not None else int(1e12)
  
      def tokenize(self, text):
          split_tokens = []
          if self.do_basic_tokenize:
              for token in self.basic_tokenizer.tokenize(text):
                  for sub_token in self.wordpiece_tokenizer.tokenize(token):
                      split_tokens.append(sub_token)
          else:
              split_tokens = self.wordpiece_tokenizer.tokenize(text)
          return split_tokens
  
      def convert_tokens_to_ids(self, tokens):
          """Converts a sequence of tokens into ids using the vocab."""
          ids = []
          for token in tokens:
              ids.append(self.vocab[token])
          if len(ids) > self.max_len:
              logger.warning(
                  "Token indices sequence length is longer than the specified maximum "
                  " sequence length for this BERT model ({} > {}). Running this"
                  " sequence through BERT will result in indexing errors".format(len(ids), self.max_len)
              )
          return ids
  
      def convert_ids_to_tokens(self, ids):
          """Converts a sequence of ids in wordpiece tokens using the vocab."""
          tokens = []
          for i in ids:
              tokens.append(self.ids_to_tokens[i])
          return tokens
  
      def save_vocabulary(self, vocab_path):
          """Save the tokenizer vocabulary to a directory or file."""
          index = 0
          if os.path.isdir(vocab_path):
              vocab_file = os.path.join(vocab_path, VOCAB_NAME)
          with open(vocab_file, "w", encoding="utf-8") as writer:
              for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
                  if index != token_index:
                      logger.warning("Saving vocabulary to {}: vocabulary indices are not consecutive."
                                     " Please check that the vocabulary is not corrupted!".format(vocab_file))
                      index = token_index
                  writer.write(token + u'\n')
                  index += 1
          return vocab_file
  
      @classmethod
      def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
          """
          Instantiate a PreTrainedBertModel from a pre-trained model file.
          Download and cache the pre-trained model file if needed.
          """
          if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP:
              vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name_or_path]
              if '-cased' in pretrained_model_name_or_path and kwargs.get('do_lower_case', True):
                  logger.warning("The pre-trained model you are loading is a cased model but you have not set "
                                 "`do_lower_case` to False. We are setting `do_lower_case=False` for you but "
                                 "you may want to check this behavior.")
                  kwargs['do_lower_case'] = False
              elif '-cased' not in pretrained_model_name_or_path and not kwargs.get('do_lower_case', True):
                  logger.warning("The pre-trained model you are loading is an uncased model but you have set "
                                 "`do_lower_case` to False. We are setting `do_lower_case=True` for you "
                                 "but you may want to check this behavior.")
                  kwargs['do_lower_case'] = True
          else:
              vocab_file = pretrained_model_name_or_path
          if os.path.isdir(vocab_file):
              vocab_file = os.path.join(vocab_file, VOCAB_NAME)
          # redirect to the cache, if necessary
          try:
              resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir)
          except EnvironmentError:
              logger.error(
                  "Model name '{}' was not found in model name list ({}). "
                  "We assumed '{}' was a path or url but couldn't find any file "
                  "associated to this path or url.".format(
                      pretrained_model_name_or_path,
                      ', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()),
                      vocab_file))
              return None
          if resolved_vocab_file == vocab_file:
              logger.info("loading vocabulary file {}".format(vocab_file))
          else:
              logger.info("loading vocabulary file {} from cache at {}".format(
                  vocab_file, resolved_vocab_file))
          if pretrained_model_name_or_path in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP:
              # if we're using a pretrained model, ensure the tokenizer wont index sequences longer
              # than the number of positional embeddings
              max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[pretrained_model_name_or_path]
              kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
          # Instantiate tokenizer.
          tokenizer = cls(resolved_vocab_file, *inputs, **kwargs)
          return tokenizer
  
  
  class BasicTokenizer(object):
      """Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
  
      def __init__(self,
                   do_lower_case=True,
                   never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
          """Constructs a BasicTokenizer.
  
          Args:
            do_lower_case: Whether to lower case the input.
          """
          self.do_lower_case = do_lower_case
          self.never_split = never_split
  
      def tokenize(self, text):
          """Tokenizes a piece of 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 and token not in self.never_split:
                  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."""
          if text in self.never_split:
              return [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 tokenization."""
  
      def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=100):
          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.
          """
  
          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.startswith("C"):
          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
  