


'''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  )







'''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()



