text: # 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 logging
  import os
  import json
  import six
  from io import open
  
  from .file_utils import cached_path
  
  logger = logging.getLogger(__name__)
  
  SPECIAL_TOKENS_MAP_FILE = 'special_tokens_map.json'
  ADDED_TOKENS_FILE = 'added_tokens.json'
  
  class PreTrainedTokenizer(object):
      """ Base class for all tokenizers.
      Handle all the shared methods for tokenization and special tokens as well as methods dowloading/caching/loading pretrained tokenizers as well as adding tokens to the vocabulary.
  
      This class also contain the added tokens in a unified way on top of all tokenizers so we don't have to handle the specific vocabulary augmentation methods of the various underlying dictionary structures (BPE, sentencepiece...).
  
      Class attributes (overridden by derived classes):
  
          - ``vocab_files_names``: a python ``dict`` with, as keys, the ``__init__`` keyword name of each vocabulary file required by the model, and as associated values, the filename for saving the associated file (string).
          - ``pretrained_vocab_files_map``: a python ``dict of dict`` the high-level keys being the ``__init__`` keyword name of each vocabulary file required by the model, the low-level being the `short-cut-names` (string) of the pretrained models with, as associated values, the `url` (string) to the associated pretrained vocabulary file.
          - ``max_model_input_sizes``: a python ``dict`` with, as keys, the `short-cut-names` (string) of the pretrained models, and as associated values, the maximum length of the sequence inputs of this model, or None if the model has no maximum input size.
  
      Parameters:
  
          - ``bos_token``: (`Optional`) string: a beginning of sentence token. Will be associated to ``self.bos_token``
  
          - ``eos_token``: (`Optional`) string: an end of sentence token. Will be associated to ``self.eos_token``
  
          - ``unk_token``: (`Optional`) string: an unknown token. Will be associated to ``self.unk_token``
  
          - ``sep_token``: (`Optional`) string: a separation token (e.g. to separate context and query in an input sequence). Will be associated to ``self.sep_token``
  
          - ``pad_token``: (`Optional`) string: a padding token. Will be associated to ``self.pad_token``
  
          - ``cls_token``: (`Optional`) string: a classification token (e.g. to extract a summary of an input sequence leveraging self-attention along the full depth of the model). Will be associated to ``self.cls_token``
  
          - ``mask_token``: (`Optional`) string: a masking token (e.g. when training a model with masked-language modeling). Will be associated to ``self.mask_token``
  
          - ``additional_special_tokens``: (`Optional`) list: a list of additional special tokens. Adding all special tokens here ensure they won't be split by the tokenization process. Will be associated to ``self.additional_special_tokens``
      """
      vocab_files_names = {}
      pretrained_vocab_files_map = {}
      max_model_input_sizes = {}
  
      SPECIAL_TOKENS_ATTRIBUTES = ["bos_token", "eos_token", "unk_token", "sep_token",
                                   "pad_token", "cls_token", "mask_token",
                                   "additional_special_tokens"]
  
      @property
      def bos_token(self):
          """ Beginning of sentence token (string). Log an error if used while not having been set. """
          if self._bos_token is None:
              logger.error("Using bos_token, but it is not set yet.")
          return self._bos_token
  
      @property
      def eos_token(self):
          """ End of sentence token (string). Log an error if used while not having been set. """
          if self._eos_token is None:
              logger.error("Using eos_token, but it is not set yet.")
          return self._eos_token
  
      @property
      def unk_token(self):
          """ Unknown token (string). Log an error if used while not having been set. """
          if self._unk_token is None:
              logger.error("Using unk_token, but it is not set yet.")
          return self._unk_token
  
      @property
      def sep_token(self):
          """ Separation token (string). E.g. separate context and query in an input sequence. Log an error if used while not having been set. """
          if self._sep_token is None:
              logger.error("Using sep_token, but it is not set yet.")
          return self._sep_token
  
      @property
      def pad_token(self):
          """ Padding token (string). Log an error if used while not having been set. """
          if self._pad_token is None:
              logger.error("Using pad_token, but it is not set yet.")
          return self._pad_token
  
      @property
      def cls_token(self):
          """ Classification token (string). E.g. to extract a summary of an input sequence leveraging self-attention along the full depth of the model. Log an error if used while not having been set. """
          if self._cls_token is None:
              logger.error("Using cls_token, but it is not set yet.")
          return self._cls_token
  
      @property
      def mask_token(self):
          """ Mask token (string). E.g. when training a model with masked-language modeling. Log an error if used while not having been set. """
          if self._mask_token is None:
              logger.error("Using mask_token, but it is not set yet.")
          return self._mask_token
  
      @property
      def additional_special_tokens(self):
          """ All the additional special tokens you may want to use (list of strings). Log an error if used while not having been set. """
          if self._additional_special_tokens is None:
              logger.error("Using additional_special_tokens, but it is not set yet.")
          return self._additional_special_tokens
  
      @bos_token.setter
      def bos_token(self, value):
          self._bos_token = value
  
      @eos_token.setter
      def eos_token(self, value):
          self._eos_token = value
  
      @unk_token.setter
      def unk_token(self, value):
          self._unk_token = value
  
      @sep_token.setter
      def sep_token(self, value):
          self._sep_token = value
  
      @pad_token.setter
      def pad_token(self, value):
          self._pad_token = value
  
      @cls_token.setter
      def cls_token(self, value):
          self._cls_token = value
  
      @mask_token.setter
      def mask_token(self, value):
          self._mask_token = value
  
      @additional_special_tokens.setter
      def additional_special_tokens(self, value):
          self._additional_special_tokens = value
  
      def __init__(self, max_len=None, **kwargs):
          self._bos_token = None
          self._eos_token = None
          self._unk_token = None
          self._sep_token = None
          self._pad_token = None
          self._cls_token = None
          self._mask_token = None
          self._additional_special_tokens = []
  
          self.max_len = max_len if max_len is not None else int(1e12)
          self.added_tokens_encoder = {}
          self.added_tokens_decoder = {}
  
          for key, value in kwargs.items():
              if key in self.SPECIAL_TOKENS_ATTRIBUTES:
                  if key == 'additional_special_tokens':
                      assert isinstance(value, (list, tuple)) and all(isinstance(t, str) or (six.PY2 and isinstance(t, unicode)) for t in value)
                  else:
                      assert isinstance(value, str) or (six.PY2 and isinstance(value, unicode))
                  setattr(self, key, value)
  
  
      @classmethod
      def from_pretrained(cls, *inputs, **kwargs):
          r"""
          Instantiate a :class:`~pytorch_transformers.PreTrainedTokenizer` (or a derived class) from a predefined tokenizer.
  
          Args:
              pretrained_model_name_or_path: either:
  
                  - a string with the `shortcut name` of a predefined tokenizer to load from cache or download, e.g.: ``bert-base-uncased``.
                  - a path to a `directory` containing vocabulary files required by the tokenizer, for instance saved using the :func:`~pytorch_transformers.PreTrainedTokenizer.save_pretrained` method, e.g.: ``./my_model_directory/``.
                  - (not applicable to all derived classes) a path or url to a single saved vocabulary file if and only if the tokenizer only requires a single vocabulary file (e.g. Bert, XLNet), e.g.: ``./my_model_directory/vocab.txt``.
  
              cache_dir: (`optional`) string:
                  Path to a directory in which a downloaded predefined tokenizer vocabulary files should be cached if the standard cache should not be used.
  
              inputs: (`optional`) positional arguments: will be passed to the Tokenizer ``__init__`` method.
  
              kwargs: (`optional`) keyword arguments: will be passed to the Tokenizer ``__init__`` method. Can be used to set special tokens like ``bos_token``, ``eos_token``, ``unk_token``, ``sep_token``, ``pad_token``, ``cls_token``, ``mask_token``, ``additional_special_tokens``. See parameters in the doc string of :class:`~pytorch_transformers.PreTrainedTokenizer` for details.
  
          Examples::
  
              # We can't instantiate directly the base class `PreTrainedTokenizer` so let's show our examples on a derived class: BertTokenizer
  
              # Download vocabulary from S3 and cache.
              tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
  
              # If vocabulary files are in a directory (e.g. tokenizer was saved using `save_pretrained('./test/saved_model/')`)
              tokenizer = BertTokenizer.from_pretrained('./test/saved_model/')
  
              # If the tokenizer uses a single vocabulary file, you can point directly to this file
              tokenizer = BertTokenizer.from_pretrained('./test/saved_model/my_vocab.txt')
  
              # You can link tokens to special vocabulary when instantiating
              tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', unk_token='<unk>')
              # You should be sure '<unk>' is in the vocabulary when doing that.
              # Otherwise use tokenizer.add_special_tokens({'unk_token': '<unk>'}) instead)
              assert tokenizer.unk_token == '<unk>'
  
          """
          return cls._from_pretrained(*inputs, **kwargs)
  
  
      @classmethod
      def _from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
          cache_dir = kwargs.pop('cache_dir', None)
  
          s3_models = list(cls.max_model_input_sizes.keys())
          vocab_files = {}
          if pretrained_model_name_or_path in s3_models:
              # Get the vocabulary from AWS S3 bucket
              for file_id, map_list in cls.pretrained_vocab_files_map.items():
                  vocab_files[file_id] = map_list[pretrained_model_name_or_path]
          else:
              # Get the vocabulary from local files
              logger.info(
                  "Model name '{}' not found in model shortcut name list ({}). "
                  "Assuming '{}' is a path or url to a directory containing tokenizer files.".format(
                      pretrained_model_name_or_path, ', '.join(s3_models),
                      pretrained_model_name_or_path))
  
              # Look for the tokenizer main vocabulary files
              for file_id, file_name in cls.vocab_files_names.items():
                  if os.path.isdir(pretrained_model_name_or_path):
                      # If a directory is provided we look for the standard filenames
                      full_file_name = os.path.join(pretrained_model_name_or_path, file_name)
                  else:
                      # If a path to a file is provided we use it (will only work for non-BPE tokenizer using a single vocabulary file)
                      full_file_name = pretrained_model_name_or_path
                  if not os.path.exists(full_file_name):
                      logger.info("Didn't find file {}. We won't load it.".format(full_file_name))
                      full_file_name = None
                  vocab_files[file_id] = full_file_name
  
              # Look for the additional tokens files
              all_vocab_files_names = {'added_tokens_file': ADDED_TOKENS_FILE,
                                       'special_tokens_map_file': SPECIAL_TOKENS_MAP_FILE}
  
              # If a path to a file was provided, get the parent directory
              saved_directory = pretrained_model_name_or_path
              if os.path.exists(saved_directory) and not os.path.isdir(saved_directory):
                  saved_directory = os.path.dirname(saved_directory)
  
              for file_id, file_name in all_vocab_files_names.items():
                  full_file_name = os.path.join(saved_directory, file_name)
                  if not os.path.exists(full_file_name):
                      logger.info("Didn't find file {}. We won't load it.".format(full_file_name))
                      full_file_name = None
                  vocab_files[file_id] = full_file_name
  
              if all(full_file_name is None for full_file_name in vocab_files.values()):
                  logger.error(
                      "Model name '{}' was not found in model name list ({}). "
                      "We assumed '{}' was a path or url but couldn't find tokenizer files"
                      "at this path or url.".format(
                          pretrained_model_name_or_path, ', '.join(s3_models),
                          pretrained_model_name_or_path, ))
                  return None
  
          # Get files from url, cache, or disk depending on the case
          try:
              resolved_vocab_files = {}
              for file_id, file_path in vocab_files.items():
                  if file_path is None:
                      resolved_vocab_files[file_id] = None
                  else:
                      resolved_vocab_files[file_id] = cached_path(file_path, cache_dir=cache_dir)
          except EnvironmentError:
              if pretrained_model_name_or_path in s3_models:
                  logger.error("Couldn't reach server to download vocabulary.")
              else:
                  logger.error(
                      "Model name '{}' was not found in model name list ({}). "
                      "We assumed '{}' was a path or url but couldn't find files {} "
                      "at this path or url.".format(
                          pretrained_model_name_or_path, ', '.join(s3_models),
                          pretrained_model_name_or_path, str(vocab_files.keys())))
              return None
  
          for file_id, file_path in vocab_files.items():
              if file_path == resolved_vocab_files[file_id]:
                  logger.info("loading file {}".format(file_path))
              else:
                  logger.info("loading file {} from cache at {}".format(
                      file_path, resolved_vocab_files[file_id]))
  
          # Set max length if needed
          if pretrained_model_name_or_path in cls.max_model_input_sizes:
              # if we're using a pretrained model, ensure the tokenizer
              # wont index sequences longer than the number of positional embeddings
              max_len = cls.max_model_input_sizes[pretrained_model_name_or_path]
              if max_len is not None and isinstance(max_len, (int, float)):
                  kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
  
          # Merge resolved_vocab_files arguments in kwargs.
          added_tokens_file = resolved_vocab_files.pop('added_tokens_file', None)
          special_tokens_map_file = resolved_vocab_files.pop('special_tokens_map_file', None)
          for args_name, file_path in resolved_vocab_files.items():
              if args_name not in kwargs:
                  kwargs[args_name] = file_path
          if special_tokens_map_file is not None:
              special_tokens_map = json.load(open(special_tokens_map_file, encoding="utf-8"))
              for key, value in special_tokens_map.items():
                  if key not in kwargs:
                      kwargs[key] = value
  
          # Instantiate tokenizer.
          tokenizer = cls(*inputs, **kwargs)
  
          # Add supplementary tokens.
          if added_tokens_file is not None:
              added_tok_encoder = json.load(open(added_tokens_file, encoding="utf-8"))
              added_tok_decoder = {v:k for k, v in added_tok_encoder.items()}
              tokenizer.added_tokens_encoder.update(added_tok_encoder)
              tokenizer.added_tokens_decoder.update(added_tok_decoder)
  
          return tokenizer
  
  
      def save_pretrained(self, save_directory):
          """ Save the tokenizer vocabulary files (with added tokens) and the
              special-tokens-to-class-attributes-mapping to a directory.
  
              This method make sure the full tokenizer can then be re-loaded using the :func:`~pytorch_transformers.PreTrainedTokenizer.from_pretrained` class method.
          """
          if not os.path.isdir(save_directory):
              logger.error("Saving directory ({}) should be a directory".format(save_directory))
              return
  
          special_tokens_map_file = os.path.join(save_directory, SPECIAL_TOKENS_MAP_FILE)
          added_tokens_file = os.path.join(save_directory, ADDED_TOKENS_FILE)
  
          with open(special_tokens_map_file, 'w', encoding='utf-8') as f:
              f.write(json.dumps(self.special_tokens_map, ensure_ascii=False))
  
          with open(added_tokens_file, 'w', encoding='utf-8') as f:
              if self.added_tokens_encoder:
                  out_str = json.dumps(self.added_tokens_encoder, ensure_ascii=False)
              else:
                  out_str = u"{}"
              f.write(out_str)
  
          vocab_files = self.save_vocabulary(save_directory)
  
          return vocab_files + (special_tokens_map_file, added_tokens_file)
  
  
      def save_vocabulary(self, save_directory):
          """ Save the tokenizer vocabulary to a directory. This method does *NOT* save added tokens
              and special token mappings.
  
              Please use :func:`~pytorch_transformers.PreTrainedTokenizer.save_pretrained` `()` to save the full Tokenizer state if you want to reload it using the :func:`~pytorch_transformers.PreTrainedTokenizer.from_pretrained` class method.
          """
          raise NotImplementedError
  
  
      def vocab_size(self):
          """ Size of the base vocabulary (without the added tokens) """
          raise NotImplementedError
  
  
      def __len__(self):
          """ Size of the full vocabulary with the added tokens """
          return self.vocab_size + len(self.added_tokens_encoder)
  
  
      def add_tokens(self, new_tokens):
          """
          Add a list of new tokens to the tokenizer class. If the new tokens are not in the
          vocabulary, they are added to it with indices starting from length of the current vocabulary.
  
          Args:
              new_tokens: list of string. Each string is a token to add. Tokens are only added if they are not already in the vocabulary (tested by checking if the tokenizer assign the index of the ``unk_token`` to them).
  
          Returns:
              Number of tokens added to the vocabulary.
  
          Examples::
  
              # Let's see how to increase the vocabulary of Bert model and tokenizer
              tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
              model = BertModel.from_pretrained('bert-base-uncased')
  
              num_added_toks = tokenizer.add_tokens(['new_tok1', 'my_new-tok2'])
              print('We have added', num_added_toks, 'tokens')
              model.resize_token_embeddings(len(tokenizer))  # Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e. the length of the tokenizer.
          """
          if not new_tokens:
              return 0
  
          to_add_tokens = []
          for token in new_tokens:
              assert isinstance(token, str) or (six.PY2 and isinstance(token, unicode))
              if token != self.unk_token and \
                      self.convert_tokens_to_ids(token) == self.convert_tokens_to_ids(self.unk_token):
                  to_add_tokens.append(token)
                  logger.info("Adding %s to the vocabulary", token)
  
          added_tok_encoder = dict((tok, len(self) + i) for i, tok in enumerate(to_add_tokens))
          added_tok_decoder = {v:k for k, v in added_tok_encoder.items()}
          self.added_tokens_encoder.update(added_tok_encoder)
          self.added_tokens_decoder.update(added_tok_decoder)
  
          return len(to_add_tokens)
  
  
      def add_special_tokens(self, special_tokens_dict):
          """
          Add a dictionary of special tokens (eos, pad, cls...) to the encoder and link them
          to class attributes. If special tokens are NOT in the vocabulary, they are added
          to it (indexed starting from the last index of the current vocabulary).
  
          Args:
              special_tokens_dict: dict of string. Keys should be in the list of predefined special attributes:
                  [``bos_token``, ``eos_token``, ``unk_token``, ``sep_token``, ``pad_token``, ``cls_token``, ``mask_token``,
                  ``additional_special_tokens``].
  
                  Tokens are only added if they are not already in the vocabulary (tested by checking if the tokenizer assign the index of the ``unk_token`` to them).
  
          Returns:
              Number of tokens added to the vocabulary.
  
          Examples::
  
              # Let's see how to add a new classification token to GPT-2
              tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
              model = GPT2Model.from_pretrained('gpt2')
  
              special_tokens_dict = {'cls_token': '<CLS>'}
  
              num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)
              print('We have added', num_added_toks, 'tokens')
              model.resize_token_embeddings(len(tokenizer))  # Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e. the length of the tokenizer.
  
              assert tokenizer.cls_token == '<CLS>'
          """
          if not special_tokens_dict:
              return 0
  
          added_tokens = 0
          for key, value in special_tokens_dict.items():
              assert key in self.SPECIAL_TOKENS_ATTRIBUTES
              if key == 'additional_special_tokens':
                  assert isinstance(value, (list, tuple)) and all(isinstance(t, str) or (six.PY2 and isinstance(t, unicode)) for t in value)
                  added_tokens += self.add_tokens(value)
              else:
                  assert isinstance(value, str) or (six.PY2 and isinstance(value, unicode))
                  added_tokens += self.add_tokens([value])
              logger.info("Assigning %s to the %s key of the tokenizer", value, key)
              setattr(self, key, value)
  
          return added_tokens
  
      def tokenize(self, text, **kwargs):
          """ Converts a string in a sequence of tokens (string), using the tokenizer.
              Split in words for word-based vocabulary or sub-words for sub-word-based
              vocabularies (BPE/SentencePieces/WordPieces).
  
              Take care of added tokens.
          """
          def split_on_tokens(tok_list, text):
              if not text:
                  return []
              if not tok_list:
                  return self._tokenize(text, **kwargs)
              tok = tok_list[0]
              split_text = text.split(tok)
              return sum((split_on_tokens(tok_list[1:], sub_text.strip()) + [tok] \
                          for sub_text in split_text), [])[:-1]
  
          added_tokens = list(self.added_tokens_encoder.keys()) + self.all_special_tokens
          tokenized_text = split_on_tokens(added_tokens, text)
          return tokenized_text
  
      def _tokenize(self, text, **kwargs):
          """ Converts a string in a sequence of tokens (string), using the tokenizer.
              Split in words for word-based vocabulary or sub-words for sub-word-based
              vocabularies (BPE/SentencePieces/WordPieces).
  
              Do NOT take care of added tokens.
          """
          raise NotImplementedError
  
      def convert_tokens_to_ids(self, tokens):
          """ Converts a single token, or a sequence of tokens, (str/unicode) in a single integer id
              (resp. a sequence of ids), using the vocabulary.
          """
          if isinstance(tokens, str) or (six.PY2 and isinstance(tokens, unicode)):
              return self._convert_token_to_id_with_added_voc(tokens)
  
          ids = []
          for token in tokens:
              ids.append(self._convert_token_to_id_with_added_voc(token))
          if len(ids) > self.max_len:
              logger.warning("Token indices sequence length is longer than the specified maximum sequence length "
                             "for this model ({} > {}). Running this sequence through the model will result in "
                             "indexing errors".format(len(ids), self.max_len))
          return ids
  
      def _convert_token_to_id_with_added_voc(self, token):
          if token in self.added_tokens_encoder:
              return self.added_tokens_encoder[token]
          return self._convert_token_to_id(token)
  
      def _convert_token_to_id(self, token):
          raise NotImplementedError
  
      def encode(self, text, text_pair=None, add_special_tokens=False):
          """
          Converts a string in a sequence of ids (integer), using the tokenizer and vocabulary.
          
          Same as doing ``self.convert_tokens_to_ids(self.tokenize(text))``.
  
          Args:
              text: The first sequence to be encoded.
              text_pair: Optional second sequence to be encoded.
              add_special_tokens: if set to ``True``, the sequences will be encoded with the special tokens relative
                  to their model.
          """
          if text_pair is None:
              if add_special_tokens:
                  return self.add_special_tokens_single_sentence(self.convert_tokens_to_ids(self.tokenize(text)))
              else:
                  return self.convert_tokens_to_ids(self.tokenize(text))
  
          first_sentence_tokens = [self._convert_token_to_id(token) for token in self.tokenize(text)]
          second_sentence_tokens = [self._convert_token_to_id(token) for token in self.tokenize(text_pair)]
  
          if add_special_tokens:
              return self.add_special_tokens_sentences_pair(first_sentence_tokens, second_sentence_tokens)
          else:
              return first_sentence_tokens, second_sentence_tokens
  
      def add_special_tokens_single_sentence(self, token_ids):
          raise NotImplementedError
  
      def add_special_tokens_sentences_pair(self, token_ids_0, token_ids_1):
          raise NotImplementedError
  
      def convert_ids_to_tokens(self, ids, skip_special_tokens=False):
          """ Converts a single index or a sequence of indices (integers) in a token "
              (resp.) a sequence of tokens (str/unicode), using the vocabulary and added tokens.
  
              Args:
                  skip_special_tokens: Don't decode special tokens (self.all_special_tokens). Default: False
          """
          if isinstance(ids, int):
              if ids in self.added_tokens_decoder:
                  return self.added_tokens_decoder[ids]
              else:
                  return self._convert_id_to_token(ids)
          tokens = []
          for index in ids:
              if index in self.all_special_ids and skip_special_tokens:
                  continue
              if index in self.added_tokens_decoder:
                  tokens.append(self.added_tokens_decoder[index])
              else:
                  tokens.append(self._convert_id_to_token(index))
          return tokens
  
      def _convert_id_to_token(self, index):
          raise NotImplementedError
  
      def convert_tokens_to_string(self, tokens):
          """ Converts a sequence of tokens (string) in a single string.
              The most simple way to do it is ' '.join(self.convert_ids_to_tokens(token_ids))
              but we often want to remove sub-word tokenization artifacts at the same time.
          """
          return ' '.join(self.convert_ids_to_tokens(tokens))
  
      def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
          """
          Converts a sequence of ids (integer) in a string, using the tokenizer and vocabulary
          with options to remove special tokens and clean up tokenization spaces.
          Similar to doing ``self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))``.
          """
          filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens)
          text = self.convert_tokens_to_string(filtered_tokens)
  
          if self.sep_token is not None and self.sep_token in text:
              text = text.replace(self.cls_token, self.sep_token)
              split_text = list(filter(lambda sentence: len(sentence) > 0, text.split(self.sep_token)))
              if clean_up_tokenization_spaces:
                  clean_text = [self.clean_up_tokenization(text) for text in split_text]
                  return clean_text
              else:
                  return split_text
          else:
              if clean_up_tokenization_spaces:
                  clean_text = self.clean_up_tokenization(text)
                  return clean_text
              else:
                  return text
  
      @property
      def special_tokens_map(self):
          """ A dictionary mapping special token class attribute (cls_token, unk_token...) to their
              values ('<unk>', '<cls>'...)
          """
          set_attr = {}
          for attr in self.SPECIAL_TOKENS_ATTRIBUTES:
              attr_value = getattr(self, "_" + attr)
              if attr_value:
                  set_attr[attr] = attr_value
          return set_attr
  
      @property
      def all_special_tokens(self):
          """ List all the special tokens ('<unk>', '<cls>'...) mapped to class attributes
              (cls_token, unk_token...).
          """
          all_toks = []
          set_attr = self.special_tokens_map
          for attr_value in set_attr.values():
              all_toks = all_toks + (attr_value if isinstance(attr_value, (list, tuple)) else [attr_value])
          all_toks = list(set(all_toks))
          return all_toks
  
      @property
      def all_special_ids(self):
          """ List the vocabulary indices of the special tokens ('<unk>', '<cls>'...) mapped to
              class attributes (cls_token, unk_token...).
          """
          all_toks = self.all_special_tokens
          all_ids = list(self._convert_token_to_id(t) for t in all_toks)
          return all_ids
  
      @staticmethod
      def clean_up_tokenization(out_string):
          """ Clean up a list of simple English tokenization artifacts like spaces before punctuations and abreviated forms.
          """
          out_string = out_string.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