code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_torch_available,
      is_vision_available,
  )
  
  
  UpperCAmelCase__      =							{
      '''configuration_segformer''': ['''SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SegformerConfig''', '''SegformerOnnxConfig''']
  }
  
  try:
  		if not is_vision_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		UpperCAmelCase__      =							['''SegformerFeatureExtractor''']
  		UpperCAmelCase__      =							['''SegformerImageProcessor''']
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		UpperCAmelCase__      =							[
  		    '''SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
  		    '''SegformerDecodeHead''',
  		    '''SegformerForImageClassification''',
  		    '''SegformerForSemanticSegmentation''',
  		    '''SegformerLayer''',
  		    '''SegformerModel''',
  		    '''SegformerPreTrainedModel''',
  		]
  
  try:
  		if not is_tf_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		UpperCAmelCase__      =							[
  		    '''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
  
  		UpperCAmelCase__      =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  
  UpperCAmelCase__      =							[[0, 1, 1], [0, 0, 1], [1, 0, 0]]
  class 	a__				:
  
  
  
  
  
  			'''simple docstring'''
  
  			def __init__(     self       : Any ,						lowerCAmelCase_       : Optional[int]  )			->		Tuple:
  						__A=  name
  						__A=  []
  						__A=  []
  			def 				lowerCAmelCase    (     self       : Any ,						lowerCAmelCase_       : Dict  )			->		Any:
  						self.inbound.append(lowerCAmelCase_  )
  			def 				lowerCAmelCase    (     self       : str ,						lowerCAmelCase_       : Any  )			->		List[Any]:
  						self.outbound.append(lowerCAmelCase_  )
  
  			def __repr__(     self       : int  )			->		Union[str, Any]:
  						return F"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Any,_SCREAMING_SNAKE_CASE				:   Optional[int]=3,_SCREAMING_SNAKE_CASE				:   Dict=0.85   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  {}
  			for node in nodes:
  						__A=  1
  
  			__A=  {}
  			for node in nodes:
  						__A=  len(node.outbound   )
  
  			for i in range(_SCREAMING_SNAKE_CASE   ):
  						print(f"""======= Iteration {i + 1} ======="""   )
  						for _, node in enumerate(_SCREAMING_SNAKE_CASE   ):
  									__A=  (1 - d) + d * sum(
  									    ranks[ib] / outbounds[ib] for ib in node.inbound   )
  						print(_SCREAMING_SNAKE_CASE   )
  
  
  
  
  
  
  def 	UpperCAmelCase__(							):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  list(input('Enter Names of the Nodes: '   ).split()   )
  
  			__A=  [Node(_SCREAMING_SNAKE_CASE   ) for name in names]
  
  			for ri, row in enumerate(_SCREAMING_SNAKE_CASE   ):
  						for ci, col in enumerate(_SCREAMING_SNAKE_CASE   ):
  									if col == 1:
  												nodes[ci].add_inbound(names[ri]   )
  												nodes[ri].add_outbound(names[ci]   )
  
  			print('======= Nodes ======='   )
  			for node in nodes:
  						print(_SCREAMING_SNAKE_CASE   )
  
  			page_rank(_SCREAMING_SNAKE_CASE   )
  
  
  if __name__ == "__main__":
  					main()
  
  
  
  