code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  import unittest
  from dataclasses import dataclass
  from typing import List, Union
  
  import numpy as np
  import PIL.Image
  
  from diffusers.utils.outputs import BaseOutput
  
  
  
  @dataclass
  class 							__UpperCAmelCase  (       A__						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowerCAmelCase 				=    42
  
  
  
  
  
  
  class 							__UpperCAmelCase  (       unittest.TestCase						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def        A     (self						:      List[str]      ):
  			A							  =      CustomOutput(images=np.random.rand(1      ,      3      ,      4      ,      4      )      )
  
  			# check every way of getting the attribute
  			assert isinstance(outputs.images      ,      np.ndarray      )
  			assert outputs.images.shape == (1, 3, 4, 4)
  			assert isinstance(outputs["""images"""]      ,      np.ndarray      )
  			assert outputs["images"].shape == (1, 3, 4, 4)
  			assert isinstance(outputs[0]      ,      np.ndarray      )
  			assert outputs[0].shape == (1, 3, 4, 4)
  
  			# test with a non-tensor attribute
  			A							  =      CustomOutput(images=[PIL.Image.new("""RGB"""      ,      (4, 4)      )]      )
  
  			# check every way of getting the attribute
  			assert isinstance(outputs.images      ,      _lowerCAmelCase      )
  			assert isinstance(outputs.images[0]      ,      PIL.Image.Image      )
  			assert isinstance(outputs["""images"""]      ,      _lowerCAmelCase      )
  			assert isinstance(outputs["""images"""][0]      ,      PIL.Image.Image      )
  			assert isinstance(outputs[0]      ,      _lowerCAmelCase      )
  			assert isinstance(outputs[0][0]      ,      PIL.Image.Image      )
  
  
  
  
  
  
  
  		def        A     (self						:      List[str]      ):
  			# test output reinitialization with a `dict` for compatibility with `accelerate`
  			A							  =      CustomOutput({"""images""": np.random.rand(1      ,      3      ,      4      ,      4      )}      )
  
  			# check every way of getting the attribute
  			assert isinstance(outputs.images      ,      np.ndarray      )
  			assert outputs.images.shape == (1, 3, 4, 4)
  			assert isinstance(outputs["""images"""]      ,      np.ndarray      )
  			assert outputs["images"].shape == (1, 3, 4, 4)
  			assert isinstance(outputs[0]      ,      np.ndarray      )
  			assert outputs[0].shape == (1, 3, 4, 4)
  
  			# test with a non-tensor attribute
  			A							  =      CustomOutput({"""images""": [PIL.Image.new("""RGB"""      ,      (4, 4)      )]}      )
  
  			# check every way of getting the attribute
  			assert isinstance(outputs.images      ,      _lowerCAmelCase      )
  			assert isinstance(outputs.images[0]      ,      PIL.Image.Image      )
  			assert isinstance(outputs["""images"""]      ,      _lowerCAmelCase      )
  			assert isinstance(outputs["""images"""][0]      ,      PIL.Image.Image      )
  			assert isinstance(outputs[0]      ,      _lowerCAmelCase      )
  			assert isinstance(outputs[0][0]      ,      PIL.Image.Image      )
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  _lowerCamelCase				:	List[str]				    =       [[0, 1, 1], [0, 0, 1], [1, 0, 0]]
  
  
  
  class 							__UpperCAmelCase  :
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(self						:      List[Any]      ,      _lowerCAmelCase						:      List[Any]      ):
  			A							  =      name
  			A							  =      []
  			A							  =      []
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      str      ):
  			self.inbound.append(_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      Any      ):
  			self.outbound.append(_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def __repr__(self						:      Tuple      ):
  			return F"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""
  
  
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase=3       ,			UpperCAmelCase=0.85    )      ->Tuple:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	A							  =      {}
  	for node in nodes:
  		A							  =      1
  
  	A							  =      {}
  	for node in nodes:
  		A							  =      len(node.outbound    )
  
  	for i in range(UpperCAmelCase    ):
  		print(f"""======= Iteration {i + 1} ======="""    )
  		for _, node in enumerate(UpperCAmelCase    ):
  			A							  =      (1 - d) + d * sum(
  			    ranks[ib] / outbounds[ib] for ib in node.inbound    )
  		print(UpperCAmelCase    )
  
  
  
  
  def    __a (     )      ->Any:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	A							  =      list(input("""Enter Names of the Nodes: """    ).split()    )
  
  	A							  =      [Node(UpperCAmelCase    ) for name in names]
  
  	for ri, row in enumerate(UpperCAmelCase    ):
  		for ci, col in enumerate(UpperCAmelCase    ):
  			if col == 1:
  				nodes[ci].add_inbound(names[ri]    )
  				nodes[ri].add_outbound(names[ci]    )
  
  	print("""======= Nodes ======="""    )
  	for node in nodes:
  		print(UpperCAmelCase    )
  
  	page_rank(UpperCAmelCase    )
  
  
  if __name__ == "__main__":
  				main()
  
  