content: # coding=utf-8
  # Copyright 2018 The Google AI Language Team Authors and The HugginFace 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
  from __future__ import division
  from __future__ import print_function
  
  import collections
  import unicodedata
  import six
  '''
  import spacy
  from spacy.tokens import Doc
  from typing import List
  
  class SpacyParser(object):
      def __init__(self,do_lower_case=True):
          self.nlp =spacy.load('en')
          ent_labels = ['','PERSON', 'NORP', 'FAC', 'ORG', 'GPE', 'LOC', 'PRODUCT', 'EVENT', 'WORK_OF_ART',
                        'LAW', 'LANGUAGE', 'DATE', 'TIME', 'PERCENT', 'MONEY', 'QUANTITY', 'ORDINAL', 'CARDINAL']
          self.ent2id =  {v:k for k,v in enumerate(ent_labels)}
          self.id2ent =  {k:v for k,v in enumerate(ent_labels)}
          pos_labels = ['<PAD>','X','ADJ','ADP','ADV','AUX','CONJ','CCONJ','DET','INTJ','NOUN','NUM','PART','PRON','PROPN',
                        'PUNCT','SCONJ','SYM','VERB','SPACE']
          self.pos2id = {v:k for k,v in enumerate(pos_labels)}
          self.id2pos = {k:v for k,v in enumerate(pos_labels)}
  
      def parse(self,words):
          ent_tokens = [] # List of int
          pos_tokens = [] # List of int
          spaces = [True] * len(words)
          spaces[-1] =  False
          doc = Doc(self.nlp.vocab, words=words, spaces=spaces)
          for name,proc in self.nlp.pipeline:
              doc = proc(doc)
          for token in doc:
              ent_tokens.append(self.ent2id[token.ent_type_])
              pos_tokens.append(self.pos2id[token.pos_])
          return ent_tokens, pos_tokens
  
  spacy_parser = SpacyParser()
  '''
  
  
  
  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",encoding='utf-8') 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_tokens_to_ids(vocab, tokens):
      """Converts a sequence of tokens into ids using the vocab."""
      ids = []
      for token in tokens:
          ids.append(vocab[token])
      return ids
  
  
  def whitespace_tokenize(text):
      """Runs basic whitespace cleaning and splitting on a peice 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.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
          self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
  
      def tokenize(self, text, do_parse=False):
          split_tokens = []
          split_ent = []
          split_pos = []
          if False: #do_parse:
              tokens = self.basic_tokenizer.tokenize(text)
              ent, pos = spacy_parser.parse(tokens)
              for i,token in enumerate(tokens):
                  for sub_token in self.wordpiece_tokenizer.tokenize(token):
                      split_tokens.append(sub_token)
                      split_ent.append(ent[i])
                      split_pos.append(pos[i])
  
              return split_tokens, split_ent, split_pos
  
          else:
              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_tokens_to_ids(self.vocab, tokens)
  
  
  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)
          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 _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.
          """
  
          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.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
  