code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
  
  
  _lowerCamelCase							:		Union[str, Any]										=						{
      'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
      'tokenization_flaubert': ['FlaubertTokenizer'],
  }
  
  try:
  						if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  						pass
  else:
  						_lowerCamelCase							:		Optional[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:
  						_lowerCamelCase							:		str										=						[
  						    '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
  
  						_lowerCamelCase							:		Tuple										=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import warnings
  
  from ...utils import logging
  from .image_processing_vilt import ViltImageProcessor
  
  
  _lowerCamelCase							:		Optional[Any]										=						logging.get_logger(__name__)
  
  
  
  
  
  class   lowerCamelCase_							( UpperCAmelCase_							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def __init__(					self,						*__UpperCamelCase,						**__UpperCamelCase				):
  							warnings.warn(
  							    "The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
  							    " use ViltImageProcessor instead.",						__UpperCamelCase,						)
  							super().__init__(*__UpperCamelCase,						**__UpperCamelCase				)
  
  
  
  
  
  