content: # coding=utf-8
  # Copyright 2018 The Open AI 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 for OpenAI GPT."""
  from __future__ import (absolute_import, division, print_function,
                          unicode_literals)
  
  import json
  import logging
  import os
  import re
  import sys
  from io import open
  
  from tqdm import tqdm
  
  from .file_utils import cached_path
  from .tokenization import BasicTokenizer
  
  logger = logging.getLogger(__name__)
  
  PRETRAINED_VOCAB_ARCHIVE_MAP = {
      'openai-gpt': "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-vocab.json",
  }
  PRETRAINED_MERGES_ARCHIVE_MAP = {
      'openai-gpt': "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-merges.txt",
  }
  PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = {
      'openai-gpt': 512,
  }
  VOCAB_NAME = 'vocab.json'
  MERGES_NAME = 'merges.txt'
  SPECIAL_TOKENS_NAME = 'special_tokens.txt'
  
  def get_pairs(word):
      """
      Return set of symbol pairs in a word.
      word is represented as tuple of symbols (symbols being variable-length strings)
      """
      pairs = set()
      prev_char = word[0]
      for char in word[1:]:
          pairs.add((prev_char, char))
          prev_char = char
      return pairs
  
  def text_standardize(text):
      """
      fixes some issues the spacy tokenizer had on books corpus
      also does some whitespace standardization
      """
      text = text.replace('—', '-')
      text = text.replace('–', '-')
      text = text.replace('―', '-')
      text = text.replace('…', '...')
      text = text.replace('´', "'")
      text = re.sub(r'''(-+|~+|!+|"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)''', r' \1 ', text)
      text = re.sub(r'\s*\n\s*', ' \n ', text)
      text = re.sub(r'[^\S\n]+', ' ', text)
      return text.strip()
  
  class OpenAIGPTTokenizer(object):
      """
      BPE tokenizer. Peculiarities:
          - lower case all inputs
          - uses SpaCy tokenizer and ftfy for pre-BPE tokenization if they are installed, fallback to BERT's BasicTokenizer if not.
          - argument special_tokens and function set_special_tokens:
              can be used to add additional symbols (ex: "__classify__") to a vocabulary.
      """
      @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]
              merges_file = PRETRAINED_MERGES_ARCHIVE_MAP[pretrained_model_name_or_path]
              special_tokens_file = None
          else:
              vocab_file = os.path.join(pretrained_model_name_or_path, VOCAB_NAME)
              merges_file = os.path.join(pretrained_model_name_or_path, MERGES_NAME)
              special_tokens_file = os.path.join(pretrained_model_name_or_path, SPECIAL_TOKENS_NAME)
              if not os.path.exists(special_tokens_file):
                  special_tokens_file = None
              else:
                  logger.info("loading special tokens file {}".format(special_tokens_file))
          # redirect to the cache, if necessary
          try:
              resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir)
              resolved_merges_file = cached_path(merges_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 files {} and {} "
                  "at this path or url.".format(
                      pretrained_model_name_or_path,
                      ', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()),
                      pretrained_model_name_or_path,
                      vocab_file, merges_file))
              return None
          if resolved_vocab_file == vocab_file and resolved_merges_file == merges_file:
              logger.info("loading vocabulary file {}".format(vocab_file))
              logger.info("loading merges file {}".format(merges_file))
          else:
              logger.info("loading vocabulary file {} from cache at {}".format(
                  vocab_file, resolved_vocab_file))
              logger.info("loading merges file {} from cache at {}".format(
                  merges_file, resolved_merges_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.
          if special_tokens_file and 'special_tokens' not in kwargs:
              special_tokens = open(special_tokens_file, encoding='utf-8').read().split('\n')[:-1]
          else:
              special_tokens = kwargs.pop('special_tokens', [])
          tokenizer = cls(resolved_vocab_file, resolved_merges_file, special_tokens=special_tokens, *inputs, **kwargs)
          return tokenizer
  
      def __init__(self, vocab_file, merges_file, special_tokens=None, max_len=None):
          try:
              import ftfy
              import spacy
              self.nlp = spacy.load('en', disable=['parser', 'tagger', 'ner', 'textcat'])
              self.fix_text = ftfy.fix_text
          except ImportError:
              logger.warning("ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.")
              self.nlp = BasicTokenizer(do_lower_case=True,
                                        never_split=special_tokens if special_tokens is not None else [])
              self.fix_text = None
  
          self.max_len = max_len if max_len is not None else int(1e12)
          self.encoder = json.load(open(vocab_file, encoding="utf-8"))
          self.decoder = {v:k for k,v in self.encoder.items()}
          merges = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
          merges = [tuple(merge.split()) for merge in merges]
          self.bpe_ranks = dict(zip(merges, range(len(merges))))
          self.cache = {}
          self.special_tokens = {}
          self.special_tokens_decoder = {}
          self.set_special_tokens(special_tokens)
  
      def __len__(self):
          return len(self.encoder) + len(self.special_tokens)
  
      def set_special_tokens(self, special_tokens):
          """ Add a list of additional tokens to the encoder.
              The additional tokens are indexed starting from the last index of the
              current vocabulary in the order of the `special_tokens` list.
          """
          if not special_tokens:
              self.special_tokens = {}
              self.special_tokens_decoder = {}
              return
          self.special_tokens = dict((tok, len(self.encoder) + i) for i, tok in enumerate(special_tokens))
          self.special_tokens_decoder = {v:k for k, v in self.special_tokens.items()}
          if self.fix_text is None:
              # Using BERT's BasicTokenizer: we can update the tokenizer
              self.nlp.never_split = special_tokens
          logger.info("Special tokens {}".format(self.special_tokens))
  
      def bpe(self, token):
          word = tuple(token[:-1]) + (token[-1] + '</w>',)
          if token in self.cache:
              return self.cache[token]
          pairs = get_pairs(word)
  
          if not pairs:
              return token+'</w>'
  
          while True:
              bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf')))
              if bigram not in self.bpe_ranks:
                  break
              first, second = bigram
              new_word = []
              i = 0
              while i < len(word):
                  try:
                      j = word.index(first, i)
                      new_word.extend(word[i:j])
                      i = j
                  except:
                      new_word.extend(word[i:])
                      break
  
                  if word[i] == first and i < len(word)-1 and word[i+1] == second:
                      new_word.append(first+second)
                      i += 2
                  else:
                      new_word.append(word[i])
                      i += 1
              new_word = tuple(new_word)
              word = new_word
              if len(word) == 1:
                  break
              else:
                  pairs = get_pairs(word)
          word = ' '.join(word)
          if word == '\n  </w>':
              word = '\n</w>'
          self.cache[token] = word
          return word
  
      def tokenize(self, text):
          """ Tokenize a string. """
          split_tokens = []
          if self.fix_text is None:
              # Using BERT's BasicTokenizer
              text = self.nlp.tokenize(text)
              for token in text:
                  split_tokens.extend([t for t in self.bpe(token).split(' ')])
          else:
              # Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
              text = self.nlp(text_standardize(self.fix_text(text)))
              for token in text:
                  split_tokens.extend([t for t in self.bpe(token.text.lower()).split(' ')])
          return split_tokens
  
      def convert_tokens_to_ids(self, tokens):
          """ Converts a sequence of tokens into ids using the vocab. """
          ids = []
          if isinstance(tokens, str) or (sys.version_info[0] == 2 and isinstance(tokens, unicode)):
              if tokens in self.special_tokens:
                  return self.special_tokens[tokens]
              else:
                  return self.encoder.get(tokens, 0)
          for token in tokens:
              if token in self.special_tokens:
                  ids.append(self.special_tokens[token])
              else:
                  ids.append(self.encoder.get(token, 0))
          if len(ids) > self.max_len:
              logger.warning(
                  "Token indices sequence length is longer than the specified maximum "
                  " sequence length for this OpenAI GPT model ({} > {}). Running this"
                  " sequence through the model will result in indexing errors".format(len(ids), self.max_len)
              )
          return ids
  
      def convert_ids_to_tokens(self, ids, skip_special_tokens=False):
          """Converts a sequence of ids in BPE tokens using the vocab."""
          tokens = []
          for i in ids:
              if i in self.special_tokens_decoder:
                  if not skip_special_tokens:
                      tokens.append(self.special_tokens_decoder[i])
              else:
                  tokens.append(self.decoder[i])
          return tokens
  
      def encode(self, text):
          return self.convert_tokens_to_ids(self.tokenize(text))
  
      def decode(self, ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
          """Converts a sequence of ids in a string."""
          tokens = self.convert_ids_to_tokens(ids, skip_special_tokens=skip_special_tokens)
          out_string = ''.join(tokens).replace('</w>', ' ').strip()
          if clean_up_tokenization_spaces:
              out_string = out_string.replace('<unk>', '')
              out_string = out_string.replace(' .', '.').replace(' ?', '?').replace(' !', '!').replace(' ,', ',').replace(' ,', ','
                      ).replace(" ' ", "'").replace(" n't", "n't").replace(" 'm", "'m").replace(" do not", " don't"
                      ).replace(" 's", "'s").replace(" 've", "'ve").replace(" 're", "'re")
          return out_string
  
      def save_vocabulary(self, vocab_path):
          """Save the tokenizer vocabulary and merge files to a directory."""
          if not os.path.isdir(vocab_path):
              logger.error("Vocabulary path ({}) should be a directory".format(vocab_path))
              return
          vocab_file = os.path.join(vocab_path, VOCAB_NAME)
          merge_file = os.path.join(vocab_path, MERGES_NAME)
          special_tokens_file = os.path.join(vocab_path, SPECIAL_TOKENS_NAME)
  
          with open(vocab_file, 'w', encoding='utf-8') as f:
              f.write(json.dumps(self.encoder, ensure_ascii=False))
  
          index = 0
          with open(merge_file, "w", encoding="utf-8") as writer:
              writer.write(u'#version: 0.2\n')
              for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
                  if index != token_index:
                      logger.warning("Saving vocabulary to {}: BPE merge indices are not consecutive."
                                     " Please check that the tokenizer is not corrupted!".format(merge_file))
                      index = token_index
                  writer.write(' '.join(bpe_tokens) + u'\n')
                  index += 1
  
          index = len(self.encoder)
          with open(special_tokens_file, 'w', encoding='utf-8') as writer:
              for token, token_index in sorted(self.special_tokens.items(), key=lambda kv: kv[1]):
                  if index != token_index:
                      logger.warning("Saving special tokens vocabulary to {}: BPE indices are not consecutive."
                                     " Please check that the tokenizer is not corrupted!".format(special_tokens_file))
                      index = token_index
                  writer.write(token + u'\n')
                  index += 1
  
          return vocab_file, merge_file, special_tokens_file
  