code: 
  
  
  '''simple docstring'''
  
  
  
  from typing import Optional, Tuple
  
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_openai import OpenAIGPTTokenizer
  
  
  A__	:      Tuple				=						logging.get_logger(__name__)
  
  A__	:      str				=						{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}
  
  A__	:      Union[str, Any]				=						{
      """vocab_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/vocab.json"""},
      """merges_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/merges.txt"""},
      """tokenizer_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/tokenizer.json"""},
  }
  
  A__	:      Dict				=						{
      """openai-gpt""": 512,
  }
  
  
  
  
  
  class      UpperCAmelCase_						(_UpperCAmelCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      List[Any]							  =    VOCAB_FILES_NAMES
      lowerCamelCase						:      Optional[int]							  =    PRETRAINED_VOCAB_FILES_MAP
      lowerCamelCase						:      Tuple							  =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCamelCase						:      Optional[Any]							  =    ['input_ids', 'attention_mask']
      lowerCamelCase						:      Dict							  =    OpenAIGPTTokenizer
  
  
  
  
  
  
  
      def __init__(							self    ,  SCREAMING_SNAKE_CASE_=None    ,  SCREAMING_SNAKE_CASE_=None    ,  SCREAMING_SNAKE_CASE_=None    ,  SCREAMING_SNAKE_CASE_="<unk>"    ,  **SCREAMING_SNAKE_CASE_			)       ->  Dict:
           super().__init__(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  tokenizer_file=SCREAMING_SNAKE_CASE_    ,  unk_token=SCREAMING_SNAKE_CASE_    ,  **SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      @property
      def    lowercase_       (							self			)       ->  List[str]:
           return True
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_ = None			)       ->  Tuple[str]:
           __lowerCamelCase     :			List[str]              =	self._tokenizer.model.save(SCREAMING_SNAKE_CASE_    ,  name=SCREAMING_SNAKE_CASE_			)
           return tuple(SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
  
  
  A__	:      List[str]				=						{
      """configuration_flaubert""": ["""FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FlaubertConfig""", """FlaubertOnnxConfig"""],
      """tokenization_flaubert""": ["""FlaubertTokenizer"""],
  }
  
  try:
        if not is_torch_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        A__	:      int				=						[
            """FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
            """FlaubertForMultipleChoice""",
            """FlaubertForQuestionAnswering""",
            """FlaubertForQuestionAnsweringSimple""",
            """FlaubertForSequenceClassification""",
            """FlaubertForTokenClassification""",
            """FlaubertModel""",
            """FlaubertWithLMHeadModel""",
            """FlaubertPreTrainedModel""",
        ]
  
  try:
        if not is_tf_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        A__	:      int				=						[
            """TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
            """TFFlaubertForMultipleChoice""",
            """TFFlaubertForQuestionAnsweringSimple""",
            """TFFlaubertForSequenceClassification""",
            """TFFlaubertForTokenClassification""",
            """TFFlaubertModel""",
            """TFFlaubertPreTrainedModel""",
            """TFFlaubertWithLMHeadModel""",
        ]
  
  
  if TYPE_CHECKING:
        from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
        from .tokenization_flaubert import FlaubertTokenizer
  
        try:
              if not is_torch_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_flaubert import (
                  FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FlaubertForMultipleChoice,
                  FlaubertForQuestionAnswering,
                  FlaubertForQuestionAnsweringSimple,
                  FlaubertForSequenceClassification,
                  FlaubertForTokenClassification,
                  FlaubertModel,
                  FlaubertPreTrainedModel,
                  FlaubertWithLMHeadModel,
              )
  
        try:
              if not is_tf_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_tf_flaubert import (
                  TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFFlaubertForMultipleChoice,
                  TFFlaubertForQuestionAnsweringSimple,
                  TFFlaubertForSequenceClassification,
                  TFFlaubertForTokenClassification,
                  TFFlaubertModel,
                  TFFlaubertPreTrainedModel,
                  TFFlaubertWithLMHeadModel,
              )
  
  else:
        import sys
  
        A__	:      Tuple				=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  