code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  import unittest
  
  import numpy as np
  
  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
  
  
  if is_torch_available():
  				import torch
  
  if is_vision_available():
  				from PIL import Image
  
  				from transformers import TvltImageProcessor
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase=10       ,			UpperCAmelCase=10       ,			UpperCAmelCase=False       ,			UpperCAmelCase=False    )      ->Union[str, Any]:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	A							  =      []
  	for i in range(image_processor_tester.num_frames    ):
  		video.append(np.random.randint(255       ,			size=(image_processor_tester.num_channels, width, height)       ,			dtype=np.uinta    )    )
  
  	if not numpify and not torchify:
  		# PIL expects the channel dimension as last dimension
  		A							  =      [Image.fromarray(np.moveaxis(__lowerCamelCase       ,			0       ,			-1    )    ) for frame in video]
  
  	if torchify:
  		A							  =      [torch.from_numpy(__lowerCamelCase    ) for frame in video]
  
  	return video
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase=False       ,			UpperCAmelCase=False       ,			UpperCAmelCase=False    )      ->List[Any]:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
  	A							  =      []
  	for i in range(image_processor_tester.batch_size    ):
  		if equal_resolution:
  			A							  =      A							  =      image_processor_tester.max_resolution
  		else:
  			A  ,    A							  =      np.random.choice(
  			    np.arange(image_processor_tester.min_resolution       ,			image_processor_tester.max_resolution    )       ,			2    )
  			A							  =      prepare_video(
  			    image_processor_tester=__lowerCamelCase       ,			width=__lowerCamelCase       ,			height=__lowerCamelCase       ,			numpify=__lowerCamelCase       ,			torchify=__lowerCamelCase       ,			)
  		video_inputs.append(__lowerCamelCase    )
  
  	return video_inputs
  
  
  
  class 							__UpperCAmelCase  (       unittest.TestCase						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(self						:      Any      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      Dict=7      ,      _lowerCAmelCase						:      Optional[Any]=3      ,      _lowerCAmelCase						:      Optional[int]=4      ,      _lowerCAmelCase						:      Optional[Any]=18      ,      _lowerCAmelCase						:      Optional[int]=30      ,      _lowerCAmelCase						:      Optional[int]=400      ,      _lowerCAmelCase						:      Any=True      ,      _lowerCAmelCase						:      int=None      ,      _lowerCAmelCase						:      Optional[Any]=True      ,      _lowerCAmelCase						:      Union[str, Any]=[0.5, 0.5, 0.5]      ,      _lowerCAmelCase						:      int=[0.5, 0.5, 0.5]      ,      _lowerCAmelCase						:      Union[str, Any]=True      ,      _lowerCAmelCase						:      List[Any]=None      ,      ):
  			A							  =      size if size is not None else {"""shortest_edge""": 18}
  			A							  =      crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
  
  			A							  =      parent
  			A							  =      batch_size
  			A							  =      num_channels
  			A							  =      num_frames
  			A							  =      image_size
  			A							  =      min_resolution
  			A							  =      max_resolution
  			A							  =      do_resize
  			A							  =      size
  			A							  =      do_normalize
  			A							  =      image_mean
  			A							  =      image_std
  			A							  =      do_center_crop
  			A							  =      crop_size
  
  
  
  
  
  
  
  		def        A     (self						:      str      ):
  			return {
  			    "image_mean": self.image_mean,
  			    "image_std": self.image_std,
  			    "do_normalize": self.do_normalize,
  			    "do_resize": self.do_resize,
  			    "size": self.size,
  			    "do_center_crop": self.do_center_crop,
  			    "crop_size": self.crop_size,
  			}
  
  
  
  
  
  
  @require_torch
  @require_vision
  class 							__UpperCAmelCase  (       snake_case_		,					unittest.TestCase						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowerCAmelCase 				=    TvltImageProcessor if is_vision_available() else None
  
  
  
  
  
  
  
  		def        A     (self						:      Any      ):
  			A							  =      TvltImageProcessorTester(self      )
  
  
  
  
  
  
  
  		@property
  		def        A     (self						:      Dict      ):
  			return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
  
  
  
  		def        A     (self						:      List[str]      ):
  			A							  =      self.image_processing_class(**self.image_processor_dict      )
  			self.assertTrue(hasattr(_lowerCAmelCase      ,      """image_mean"""      )      )
  			self.assertTrue(hasattr(_lowerCAmelCase      ,      """image_std"""      )      )
  			self.assertTrue(hasattr(_lowerCAmelCase      ,      """do_normalize"""      )      )
  			self.assertTrue(hasattr(_lowerCAmelCase      ,      """do_resize"""      )      )
  			self.assertTrue(hasattr(_lowerCAmelCase      ,      """do_center_crop"""      )      )
  			self.assertTrue(hasattr(_lowerCAmelCase      ,      """size"""      )      )
  
  
  
  
  
  
  
  		def        A     (self						:      int      ):
  			# Initialize image_processor
  			A							  =      self.image_processing_class(**self.image_processor_dict      )
  			# create random PIL videos
  			A							  =      prepare_video_inputs(self.image_processor_tester      ,      equal_resolution=_lowerCAmelCase      )
  			for video in video_inputs:
  				self.assertIsInstance(_lowerCAmelCase      ,      _lowerCAmelCase      )
  				self.assertIsInstance(video[0]      ,      Image.Image      )
  
  			# Test not batched input
  			A							  =      image_processor(video_inputs[0]      ,      return_tensors="""pt"""      ).pixel_values
  			self.assertEqual(
  			    encoded_videos.shape      ,      (
  			        1,
  			        self.image_processor_tester.num_frames,
  			        self.image_processor_tester.num_channels,
  			        self.image_processor_tester.crop_size["""height"""],
  			        self.image_processor_tester.crop_size["""width"""],
  			    )      ,      )
  
  			# Test batched
  			A							  =      image_processor(_lowerCAmelCase      ,      return_tensors="""pt"""      ).pixel_values
  			self.assertEqual(
  			    encoded_videos.shape      ,      (
  			        self.image_processor_tester.batch_size,
  			        self.image_processor_tester.num_frames,
  			        self.image_processor_tester.num_channels,
  			        self.image_processor_tester.crop_size["""height"""],
  			        self.image_processor_tester.crop_size["""width"""],
  			    )      ,      )
  
  
  
  
  
  
  
  		def        A     (self						:      Union[str, Any]      ):
  			# Initialize image_processor
  			A							  =      self.image_processing_class(**self.image_processor_dict      )
  			# create random numpy tensors
  			A							  =      prepare_video_inputs(self.image_processor_tester      ,      equal_resolution=_lowerCAmelCase      ,      numpify=_lowerCAmelCase      )
  			for video in video_inputs:
  				self.assertIsInstance(_lowerCAmelCase      ,      _lowerCAmelCase      )
  				self.assertIsInstance(video[0]      ,      np.ndarray      )
  
  			# Test not batched input
  			A							  =      image_processor(video_inputs[0]      ,      return_tensors="""pt"""      ).pixel_values
  			self.assertEqual(
  			    encoded_videos.shape      ,      (
  			        1,
  			        self.image_processor_tester.num_frames,
  			        self.image_processor_tester.num_channels,
  			        self.image_processor_tester.crop_size["""height"""],
  			        self.image_processor_tester.crop_size["""width"""],
  			    )      ,      )
  
  			# Test batched
  			A							  =      image_processor(_lowerCAmelCase      ,      return_tensors="""pt"""      ).pixel_values
  			self.assertEqual(
  			    encoded_videos.shape      ,      (
  			        self.image_processor_tester.batch_size,
  			        self.image_processor_tester.num_frames,
  			        self.image_processor_tester.num_channels,
  			        self.image_processor_tester.crop_size["""height"""],
  			        self.image_processor_tester.crop_size["""width"""],
  			    )      ,      )
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[int]      ):
  			# Initialize image_processor
  			A							  =      self.image_processing_class(**self.image_processor_dict      )
  			# create random PyTorch tensors
  			A							  =      prepare_video_inputs(self.image_processor_tester      ,      equal_resolution=_lowerCAmelCase      ,      torchify=_lowerCAmelCase      )
  			for video in video_inputs:
  				self.assertIsInstance(_lowerCAmelCase      ,      _lowerCAmelCase      )
  				self.assertIsInstance(video[0]      ,      torch.Tensor      )
  
  			# Test not batched input
  			A							  =      image_processor(video_inputs[0]      ,      return_tensors="""pt"""      ).pixel_values
  			self.assertEqual(
  			    encoded_videos.shape      ,      (
  			        1,
  			        self.image_processor_tester.num_frames,
  			        self.image_processor_tester.num_channels,
  			        self.image_processor_tester.crop_size["""height"""],
  			        self.image_processor_tester.crop_size["""width"""],
  			    )      ,      )
  
  			# Test batched
  			A							  =      image_processor(_lowerCAmelCase      ,      return_tensors="""pt"""      ).pixel_values
  			self.assertEqual(
  			    encoded_videos.shape      ,      (
  			        self.image_processor_tester.batch_size,
  			        self.image_processor_tester.num_frames,
  			        self.image_processor_tester.num_channels,
  			        self.image_processor_tester.crop_size["""height"""],
  			        self.image_processor_tester.crop_size["""width"""],
  			    )      ,      )
  
  
  
  
  
  
  
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()
  
  