code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  import unittest
  
  import numpy as np
  from datasets import load_dataset
  
  from transformers.testing_utils import require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  
  from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
  
  
  if is_torch_available():
  					import torch
  
  if is_vision_available():
  					from PIL import Image
  
  					from transformers import SegformerImageProcessor
  class 	a__				(				unittest.TestCase      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			def __init__(     self       : Optional[Any] ,						lowerCAmelCase_       : Any ,						lowerCAmelCase_       : Tuple=7 ,						lowerCAmelCase_       : int=3 ,						lowerCAmelCase_       : Optional[int]=30 ,						lowerCAmelCase_       : str=400 ,						lowerCAmelCase_       : Tuple=True ,						lowerCAmelCase_       : List[Any]=None ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : Dict=[0.5, 0.5, 0.5] ,						lowerCAmelCase_       : Tuple=[0.5, 0.5, 0.5] ,						lowerCAmelCase_       : List[str]=False ,						)			->		str:
  						__A=  size if size is not None else {'height': 30, 'width': 30}
  						__A=  parent
  						__A=  batch_size
  						__A=  num_channels
  						__A=  min_resolution
  						__A=  max_resolution
  						__A=  do_resize
  						__A=  size
  						__A=  do_normalize
  						__A=  image_mean
  						__A=  image_std
  						__A=  do_reduce_labels
  
  			def 				lowerCAmelCase    (     self       : List[str]  )			->		Optional[int]:
  						return {
  						    "do_resize": self.do_resize,
  						    "size": self.size,
  						    "do_normalize": self.do_normalize,
  						    "image_mean": self.image_mean,
  						    "image_std": self.image_std,
  						    "do_reduce_labels": self.do_reduce_labels,
  						}
  
  
  
  
  
  
  def 	UpperCAmelCase__(							):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  load_dataset('hf-internal-testing/fixtures_ade20k',split='test'   )
  
  			__A=  Image.open(dataset[0]['file']   )
  			__A=  Image.open(dataset[1]['file']   )
  
  			return image, map
  
  
  
  
  
  
  def 	UpperCAmelCase__(							):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  load_dataset('hf-internal-testing/fixtures_ade20k',split='test'   )
  
  			__A=  Image.open(dataset[0]['file']   )
  			__A=  Image.open(dataset[1]['file']   )
  			__A=  Image.open(dataset[2]['file']   )
  			__A=  Image.open(dataset[3]['file']   )
  
  			return [imagea, imagea], [mapa, mapa]
  
  
  
  
  
  
  @require_torch
  @require_vision
  class 	a__				(				a_			,     unittest.TestCase      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							Any							= SegformerImageProcessor if is_vision_available() else None
  			def 				lowerCAmelCase    (     self       : str  )			->		List[Any]:
  						__A=  SegformerImageProcessingTester(self  )
  			@property
  			def 				lowerCAmelCase    (     self       : Any  )			->		List[Any]:
  						return self.image_processor_tester.prepare_image_processor_dict()
  			def 				lowerCAmelCase    (     self       : List[str]  )			->		Tuple:
  						__A=  self.image_processing_class(**self.image_processor_dict  )
  						self.assertTrue(hasattr(lowerCAmelCase_ ,						'do_resize'  )  )
  						self.assertTrue(hasattr(lowerCAmelCase_ ,						'size'  )  )
  						self.assertTrue(hasattr(lowerCAmelCase_ ,						'do_normalize'  )  )
  						self.assertTrue(hasattr(lowerCAmelCase_ ,						'image_mean'  )  )
  						self.assertTrue(hasattr(lowerCAmelCase_ ,						'image_std'  )  )
  						self.assertTrue(hasattr(lowerCAmelCase_ ,						'do_reduce_labels'  )  )
  			def 				lowerCAmelCase    (     self       : List[Any]  )			->		List[Any]:
  						__A=  self.image_processing_class.from_dict(self.image_processor_dict  )
  						self.assertEqual(image_processor.size ,						{'height': 30, 'width': 30}  )
  						self.assertEqual(image_processor.do_reduce_labels ,						lowerCAmelCase_  )
  
  						__A=  self.image_processing_class.from_dict(self.image_processor_dict ,						size=42 ,						reduce_labels=lowerCAmelCase_  )
  						self.assertEqual(image_processor.size ,						{'height': 42, 'width': 42}  )
  						self.assertEqual(image_processor.do_reduce_labels ,						lowerCAmelCase_  )
  			def 				lowerCAmelCase    (     self       : List[Any]  )			->		Optional[int]:
  						pass
  			def 				lowerCAmelCase    (     self       : List[Any]  )			->		List[str]:
  						# Initialize image_processing
  						__A=  self.image_processing_class(**self.image_processor_dict  )
  						# create random PIL images
  						__A=  prepare_image_inputs(self.image_processor_tester ,						equal_resolution=lowerCAmelCase_  )
  						for image in image_inputs:
  									self.assertIsInstance(lowerCAmelCase_ ,						Image.Image  )
  
  						# Test not batched input
  						__A=  image_processing(image_inputs[0] ,						return_tensors='pt'  ).pixel_values
  						self.assertEqual(
  						    encoded_images.shape ,						(
  						        1,
  						        self.image_processor_tester.num_channels,
  						        self.image_processor_tester.size['height'],
  						        self.image_processor_tester.size['width'],
  						    ) ,						)
  
  						# Test batched
  						__A=  image_processing(lowerCAmelCase_ ,						return_tensors='pt'  ).pixel_values
  						self.assertEqual(
  						    encoded_images.shape ,						(
  						        self.image_processor_tester.batch_size,
  						        self.image_processor_tester.num_channels,
  						        self.image_processor_tester.size['height'],
  						        self.image_processor_tester.size['width'],
  						    ) ,						)
  			def 				lowerCAmelCase    (     self       : List[Any]  )			->		int:
  						# Initialize image_processing
  						__A=  self.image_processing_class(**self.image_processor_dict  )
  						# create random numpy tensors
  						__A=  prepare_image_inputs(self.image_processor_tester ,						equal_resolution=lowerCAmelCase_ ,						numpify=lowerCAmelCase_  )
  						for image in image_inputs:
  									self.assertIsInstance(lowerCAmelCase_ ,						np.ndarray  )
  
  						# Test not batched input
  						__A=  image_processing(image_inputs[0] ,						return_tensors='pt'  ).pixel_values
  						self.assertEqual(
  						    encoded_images.shape ,						(
  						        1,
  						        self.image_processor_tester.num_channels,
  						        self.image_processor_tester.size['height'],
  						        self.image_processor_tester.size['width'],
  						    ) ,						)
  
  						# Test batched
  						__A=  image_processing(lowerCAmelCase_ ,						return_tensors='pt'  ).pixel_values
  						self.assertEqual(
  						    encoded_images.shape ,						(
  						        self.image_processor_tester.batch_size,
  						        self.image_processor_tester.num_channels,
  						        self.image_processor_tester.size['height'],
  						        self.image_processor_tester.size['width'],
  						    ) ,						)
  			def 				lowerCAmelCase    (     self       : Dict  )			->		Optional[int]:
  						# Initialize image_processing
  						__A=  self.image_processing_class(**self.image_processor_dict  )
  						# create random PyTorch tensors
  						__A=  prepare_image_inputs(self.image_processor_tester ,						equal_resolution=lowerCAmelCase_ ,						torchify=lowerCAmelCase_  )
  						for image in image_inputs:
  									self.assertIsInstance(lowerCAmelCase_ ,						torch.Tensor  )
  
  						# Test not batched input
  						__A=  image_processing(image_inputs[0] ,						return_tensors='pt'  ).pixel_values
  						self.assertEqual(
  						    encoded_images.shape ,						(
  						        1,
  						        self.image_processor_tester.num_channels,
  						        self.image_processor_tester.size['height'],
  						        self.image_processor_tester.size['width'],
  						    ) ,						)
  
  						# Test batched
  						__A=  image_processing(lowerCAmelCase_ ,						return_tensors='pt'  ).pixel_values
  						self.assertEqual(
  						    encoded_images.shape ,						(
  						        self.image_processor_tester.batch_size,
  						        self.image_processor_tester.num_channels,
  						        self.image_processor_tester.size['height'],
  						        self.image_processor_tester.size['width'],
  						    ) ,						)
  			def 				lowerCAmelCase    (     self       : str  )			->		str:
  						# Initialize image_processing
  						__A=  self.image_processing_class(**self.image_processor_dict  )
  						# create random PyTorch tensors
  						__A=  prepare_image_inputs(self.image_processor_tester ,						equal_resolution=lowerCAmelCase_ ,						torchify=lowerCAmelCase_  )
  						__A=  []
  						for image in image_inputs:
  									self.assertIsInstance(lowerCAmelCase_ ,						torch.Tensor  )
  									maps.append(torch.zeros(image.shape[-2:]  ).long()  )
  
  						# Test not batched input
  						__A=  image_processing(image_inputs[0] ,						maps[0] ,						return_tensors='pt'  )
  						self.assertEqual(
  						    encoding['pixel_values'].shape ,						(
  						        1,
  						        self.image_processor_tester.num_channels,
  						        self.image_processor_tester.size['height'],
  						        self.image_processor_tester.size['width'],
  						    ) ,						)
  						self.assertEqual(
  						    encoding['labels'].shape ,						(
  						        1,
  						        self.image_processor_tester.size['height'],
  						        self.image_processor_tester.size['width'],
  						    ) ,						)
  						self.assertEqual(encoding['labels'].dtype ,						torch.long  )
  						self.assertTrue(encoding['labels'].min().item() >= 0  )
  						self.assertTrue(encoding['labels'].max().item() <= 255  )
  
  						# Test batched
  						__A=  image_processing(lowerCAmelCase_ ,						lowerCAmelCase_ ,						return_tensors='pt'  )
  						self.assertEqual(
  						    encoding['pixel_values'].shape ,						(
  						        self.image_processor_tester.batch_size,
  						        self.image_processor_tester.num_channels,
  						        self.image_processor_tester.size['height'],
  						        self.image_processor_tester.size['width'],
  						    ) ,						)
  						self.assertEqual(
  						    encoding['labels'].shape ,						(
  						        self.image_processor_tester.batch_size,
  						        self.image_processor_tester.size['height'],
  						        self.image_processor_tester.size['width'],
  						    ) ,						)
  						self.assertEqual(encoding['labels'].dtype ,						torch.long  )
  						self.assertTrue(encoding['labels'].min().item() >= 0  )
  						self.assertTrue(encoding['labels'].max().item() <= 255  )
  
  						# Test not batched input (PIL images)
  						__A,  __A=  prepare_semantic_single_inputs()
  
  						__A=  image_processing(lowerCAmelCase_ ,						lowerCAmelCase_ ,						return_tensors='pt'  )
  						self.assertEqual(
  						    encoding['pixel_values'].shape ,						(
  						        1,
  						        self.image_processor_tester.num_channels,
  						        self.image_processor_tester.size['height'],
  						        self.image_processor_tester.size['width'],
  						    ) ,						)
  						self.assertEqual(
  						    encoding['labels'].shape ,						(
  						        1,
  						        self.image_processor_tester.size['height'],
  						        self.image_processor_tester.size['width'],
  						    ) ,						)
  						self.assertEqual(encoding['labels'].dtype ,						torch.long  )
  						self.assertTrue(encoding['labels'].min().item() >= 0  )
  						self.assertTrue(encoding['labels'].max().item() <= 255  )
  
  						# Test batched input (PIL images)
  						__A,  __A=  prepare_semantic_batch_inputs()
  
  						__A=  image_processing(lowerCAmelCase_ ,						lowerCAmelCase_ ,						return_tensors='pt'  )
  						self.assertEqual(
  						    encoding['pixel_values'].shape ,						(
  						        2,
  						        self.image_processor_tester.num_channels,
  						        self.image_processor_tester.size['height'],
  						        self.image_processor_tester.size['width'],
  						    ) ,						)
  						self.assertEqual(
  						    encoding['labels'].shape ,						(
  						        2,
  						        self.image_processor_tester.size['height'],
  						        self.image_processor_tester.size['width'],
  						    ) ,						)
  						self.assertEqual(encoding['labels'].dtype ,						torch.long  )
  						self.assertTrue(encoding['labels'].min().item() >= 0  )
  						self.assertTrue(encoding['labels'].max().item() <= 255  )
  
  			def 				lowerCAmelCase    (     self       : Dict  )			->		Optional[Any]:
  						# Initialize image_processing
  						__A=  self.image_processing_class(**self.image_processor_dict  )
  
  						# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
  						__A,  __A=  prepare_semantic_single_inputs()
  						__A=  image_processing(lowerCAmelCase_ ,						lowerCAmelCase_ ,						return_tensors='pt'  )
  						self.assertTrue(encoding['labels'].min().item() >= 0  )
  						self.assertTrue(encoding['labels'].max().item() <= 150  )
  
  						__A=  True
  						__A=  image_processing(lowerCAmelCase_ ,						lowerCAmelCase_ ,						return_tensors='pt'  )
  						self.assertTrue(encoding['labels'].min().item() >= 0  )
  						self.assertTrue(encoding['labels'].max().item() <= 255  )
  
  
  
  
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()
  
  
  
  