code: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
  
  
  __snake_case             =	{
      '''configuration_flaubert''': ['''FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FlaubertConfig''', '''FlaubertOnnxConfig'''],
      '''tokenization_flaubert''': ['''FlaubertTokenizer'''],
  }
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      __snake_case             =	[
          '''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:
      __snake_case             =	[
          '''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
  
      __snake_case             =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
style_context: 
  
  
  from ..utils import DummyObject, requires_backends
  
  
  
  class   __lowerCamelCase (metaclass=_a	):
         _lowercase       							=    ["""torch""", """transformers""", """k_diffusion"""]
  
  
  
  
  
  
  
         def __init__( self:   Tuple,*A_:   Union[str, Any],**A_:   Tuple				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              requires_backends(self,['torch', 'transformers', 'k_diffusion']				)
  
  
  
  
  
  
  
         @classmethod
         def 		snake_case_  ( cls:   int,*A_:   Optional[Any],**A_:   Tuple				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              requires_backends(cls,['torch', 'transformers', 'k_diffusion']				)
  
  
  
  
  
  
  
         @classmethod
         def 		snake_case_  ( cls:   int,*A_:   Tuple,**A_:   Tuple				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              requires_backends(cls,['torch', 'transformers', 'k_diffusion']				)
  
  
  