code: 
  
  
  '''simple docstring'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_torch_available,
      is_vision_available,
  )
  
  
  __lowercase			:		Dict =			{
      'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
  }
  
  try:
  	if not is_vision_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	__lowercase			:		List[Any] =			['SegformerFeatureExtractor']
  	__lowercase			:		Optional[Any] =			['SegformerImageProcessor']
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	__lowercase			:		List[Any] =			[
  	    'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
  	    'SegformerDecodeHead',
  	    'SegformerForImageClassification',
  	    'SegformerForSemanticSegmentation',
  	    'SegformerLayer',
  	    'SegformerModel',
  	    'SegformerPreTrainedModel',
  	]
  
  try:
  	if not is_tf_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	__lowercase			:		str =			[
  	    'TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
  	    'TFSegformerDecodeHead',
  	    'TFSegformerForImageClassification',
  	    'TFSegformerForSemanticSegmentation',
  	    'TFSegformerModel',
  	    'TFSegformerPreTrainedModel',
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig
  
  	try:
  		if not is_vision_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .feature_extraction_segformer import SegformerFeatureExtractor
  		from .image_processing_segformer import SegformerImageProcessor
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_segformer import (
  		    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    SegformerDecodeHead,
  		    SegformerForImageClassification,
  		    SegformerForSemanticSegmentation,
  		    SegformerLayer,
  		    SegformerModel,
  		    SegformerPreTrainedModel,
  		)
  	try:
  		if not is_tf_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_tf_segformer import (
  		    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    TFSegformerDecodeHead,
  		    TFSegformerForImageClassification,
  		    TFSegformerForSemanticSegmentation,
  		    TFSegformerModel,
  		    TFSegformerPreTrainedModel,
  		)
  
  else:
  	import sys
  
  	__lowercase			:		Optional[Any] =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     set		,   _SCREAMING_SNAKE_CASE   :     set     ):
  					assert isinstance(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     ), F"""The input value of [set_a={set_a}] is not a set"""
  					assert isinstance(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     ), F"""The input value of [set_b={set_b}] is not a set"""
  
  					return {a + b for a in set_a for b in set_b}
  
  
  if __name__ == "__main__":
  	from doctest import testmod
  
  	testmod()
  
  
  
  