





'''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"""],
			    )      ,      )













'''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()

