
"""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       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase=10					,							__UpperCamelCase=10					,							__UpperCamelCase=False					,							__UpperCamelCase=False					):
			__lowercase	:       List[str]				=   []
			for i in range(image_processor_tester.num_frames					):
						video.append(np.random.randint(2_55					,							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
						__lowercase	:       Optional[int]				=   [Image.fromarray(np.moveaxis(A__					,							0					,							-1					)					) for frame in video]

			if torchify:
						__lowercase	:       Any				=   [torch.from_numpy(A__					) for frame in video]

			return video







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase=False					,							__UpperCamelCase=False					,							__UpperCamelCase=False					):
			assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

			__lowercase	:       str				=   []
			for i in range(image_processor_tester.batch_size					):
						if equal_resolution:
									__lowercase	:       Optional[int]				=   image_processor_tester.max_resolution
						else:
									__lowercase	:       Any				=   np.random.choice(
									    np.arange(image_processor_tester.min_resolution					,							image_processor_tester.max_resolution					)					,							2					)
									__lowercase	:       Any				=   prepare_video(
									    image_processor_tester=A__					,							width=A__					,							height=A__					,							numpify=A__					,							torchify=A__					,							)
						video_inputs.append(A__					)

			return video_inputs

class        UpperCAmelCase_  (	unittest.TestCase						):
		def __init__(		self							,		UpperCamelCase_							,		UpperCamelCase_=7							,		UpperCamelCase_=3							,		UpperCamelCase_=4							,		UpperCamelCase_=18							,		UpperCamelCase_=30							,		UpperCamelCase_=4_00							,		UpperCamelCase_=True							,		UpperCamelCase_=None							,		UpperCamelCase_=True							,		UpperCamelCase_=[0.5, 0.5, 0.5]							,		UpperCamelCase_=[0.5, 0.5, 0.5]							,		UpperCamelCase_=True							,		UpperCamelCase_=None							,		)		->   int:
					__lowercase	:       Tuple				=   size if size is not None else {'shortest_edge': 18}
					__lowercase	:       str				=   crop_size if crop_size is not None else {'height': 18, 'width': 18}

					__lowercase	:       List[str]				=   parent
					__lowercase	:       int				=   batch_size
					__lowercase	:       Any				=   num_channels
					__lowercase	:       Tuple				=   num_frames
					__lowercase	:       Optional[int]				=   image_size
					__lowercase	:       List[str]				=   min_resolution
					__lowercase	:       Union[str, Any]				=   max_resolution
					__lowercase	:       List[Any]				=   do_resize
					__lowercase	:       Dict				=   size
					__lowercase	:       Optional[int]				=   do_normalize
					__lowercase	:       Any				=   image_mean
					__lowercase	:       Any				=   image_std
					__lowercase	:       List[Any]				=   do_center_crop
					__lowercase	:       int				=   crop_size
		def 					_lowerCamelCase    (		self   )		->   Tuple:
					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_  (	_lowerCamelCase ,			unittest.TestCase						):
		UpperCamelCase									=TvltImageProcessor if is_vision_available() else None
		def 					_lowerCamelCase    (		self   )		->   Dict:
					__lowercase	:       Tuple				=   TvltImageProcessorTester(self   )
		@property
		def 					_lowerCamelCase    (		self   )		->   Optional[int]:
					return self.image_processor_tester.prepare_image_processor_dict()
		def 					_lowerCamelCase    (		self   )		->   Union[str, Any]:
					__lowercase	:       Tuple				=   self.image_processing_class(**self.image_processor_dict   )
					self.assertTrue(hasattr(__UpperCamelCase							,		'''image_mean'''   )   )
					self.assertTrue(hasattr(__UpperCamelCase							,		'''image_std'''   )   )
					self.assertTrue(hasattr(__UpperCamelCase							,		'''do_normalize'''   )   )
					self.assertTrue(hasattr(__UpperCamelCase							,		'''do_resize'''   )   )
					self.assertTrue(hasattr(__UpperCamelCase							,		'''do_center_crop'''   )   )
					self.assertTrue(hasattr(__UpperCamelCase							,		'''size'''   )   )
		def 					_lowerCamelCase    (		self   )		->   List[str]:
					__lowercase	:       Tuple				=   self.image_processing_class(**self.image_processor_dict   )
					# create random PIL videos
					__lowercase	:       Optional[Any]				=   prepare_video_inputs(self.image_processor_tester							,		equal_resolution=__UpperCamelCase   )
					for video in video_inputs:
								self.assertIsInstance(__UpperCamelCase							,		__UpperCamelCase   )
								self.assertIsInstance(video[0]							,		Image.Image   )

					# Test not batched input
					__lowercase	:       Tuple				=   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
					__lowercase	:       int				=   image_processor(__UpperCamelCase							,		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 					_lowerCamelCase    (		self   )		->   List[Any]:
					__lowercase	:       str				=   self.image_processing_class(**self.image_processor_dict   )
					# create random numpy tensors
					__lowercase	:       Optional[Any]				=   prepare_video_inputs(self.image_processor_tester							,		equal_resolution=__UpperCamelCase							,		numpify=__UpperCamelCase   )
					for video in video_inputs:
								self.assertIsInstance(__UpperCamelCase							,		__UpperCamelCase   )
								self.assertIsInstance(video[0]							,		np.ndarray   )

					# Test not batched input
					__lowercase	:       Dict				=   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
					__lowercase	:       List[Any]				=   image_processor(__UpperCamelCase							,		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 					_lowerCamelCase    (		self   )		->   int:
					__lowercase	:       Any				=   self.image_processing_class(**self.image_processor_dict   )
					# create random PyTorch tensors
					__lowercase	:       Optional[Any]				=   prepare_video_inputs(self.image_processor_tester							,		equal_resolution=__UpperCamelCase							,		torchify=__UpperCamelCase   )
					for video in video_inputs:
								self.assertIsInstance(__UpperCamelCase							,		__UpperCamelCase   )
								self.assertIsInstance(video[0]							,		torch.Tensor   )

					# Test not batched input
					__lowercase	:       Any				=   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
					__lowercase	:       List[Any]				=   image_processor(__UpperCamelCase							,		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"""




# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
from queue import Queue







def       __UpperCAmelCase      (				__UpperCamelCase					):
			__lowercase	:       int				=   Queue()
			__lowercase	:       str				=   [False] * len(__UpperCamelCase					)
			__lowercase	:       str				=   [-1] * len(__UpperCamelCase					)

			def bfs():
						while not queue.empty():
									__lowercase	:       int				=   queue.get()
									__lowercase	:       List[str]				=   True

									for neighbour in graph[u]:
												if neighbour == u:
															return False

												if color[neighbour] == -1:
															__lowercase	:       str				=   1 - color[u]
															queue.put(__UpperCamelCase					)

												elif color[neighbour] == color[u]:
															return False

						return True

			for i in range(len(__UpperCamelCase					)					):
						if not visited[i]:
									queue.put(__UpperCamelCase					)
									__lowercase	:       int				=   0
									if bfs() is False:
												return False

			return True


if __name__ == "__main__":
			# Adjacency List of graph
			print(check_bipartite({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}))



