



# 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							( snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       Queue()
					_lowerCAmelCase         =       [False] * len(snake_case     )
					_lowerCAmelCase         =       [-1] * len(snake_case     )

					def bfs():
										while not queue.empty():
															_lowerCAmelCase         =       queue.get()
															_lowerCAmelCase         =       True

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

																				if color[neighbour] == -1:
																									_lowerCAmelCase         =       1 - color[u]
																									queue.put(snake_case     )

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

										return True

					for i in range(len(snake_case     )     ):
										if not visited[i]:
															queue.put(snake_case     )
															_lowerCAmelCase         =       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]}))








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, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import EfficientNetImageProcessor





class    __lowerCAmelCase					(	unittest.TestCase			):




				def __init__(				self		,							_snake_case		,							_snake_case=13		,							_snake_case=3		,							_snake_case=18		,							_snake_case=30		,							_snake_case=400		,							_snake_case=True		,							_snake_case=None		,							_snake_case=True		,							_snake_case=[0.5, 0.5, 0.5]		,							_snake_case=[0.5, 0.5, 0.5]		,							):



									"""simple docstring"""





									_lowerCAmelCase         =       size if size is not None else {"""height""": 18, """width""": 18}
									_lowerCAmelCase         =       parent
									_lowerCAmelCase         =       batch_size
									_lowerCAmelCase         =       num_channels
									_lowerCAmelCase         =       image_size
									_lowerCAmelCase         =       min_resolution
									_lowerCAmelCase         =       max_resolution
									_lowerCAmelCase         =       do_resize
									_lowerCAmelCase         =       size
									_lowerCAmelCase         =       do_normalize
									_lowerCAmelCase         =       image_mean
									_lowerCAmelCase         =       image_std




				def 				snake_case  (				self						):



									"""simple docstring"""





									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									}





@require_torch
@require_vision
class    __lowerCAmelCase					(	lowerCamelCase__			,		unittest.TestCase			):
				__lowerCamelCase					=							EfficientNetImageProcessor if is_vision_available() else None




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       EfficientNetImageProcessorTester(self						)




				@property
				def 				snake_case  (				self						):



									"""simple docstring"""





									return self.image_processor_tester.prepare_image_processor_dict()




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.image_processing_class(**self.image_processor_dict						)
									self.assertTrue(hasattr(_snake_case		,							"""image_mean"""						)						)
									self.assertTrue(hasattr(_snake_case		,							"""image_std"""						)						)
									self.assertTrue(hasattr(_snake_case		,							"""do_normalize"""						)						)
									self.assertTrue(hasattr(_snake_case		,							"""do_resize"""						)						)
									self.assertTrue(hasattr(_snake_case		,							"""size"""						)						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.image_processing_class.from_dict(self.image_processor_dict						)
									self.assertEqual(image_processor.size		,							{"""height""": 18, """width""": 18}						)

									_lowerCAmelCase         =       self.image_processing_class.from_dict(self.image_processor_dict		,							size=42						)
									self.assertEqual(image_processor.size		,							{"""height""": 42, """width""": 42}						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.image_processing_class(**self.image_processor_dict						)
									# create random PIL images
									_lowerCAmelCase         =       prepare_image_inputs(self.image_processor_tester		,							equal_resolution=_snake_case						)
									for image in image_inputs:
														self.assertIsInstance(_snake_case		,							Image.Image						)

									# Test not batched input
									_lowerCAmelCase         =       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
									_lowerCAmelCase         =       image_processing(_snake_case		,							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 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.image_processing_class(**self.image_processor_dict						)
									# create random numpy tensors
									_lowerCAmelCase         =       prepare_image_inputs(self.image_processor_tester		,							equal_resolution=_snake_case		,							numpify=_snake_case						)
									for image in image_inputs:
														self.assertIsInstance(_snake_case		,							np.ndarray						)

									# Test not batched input
									_lowerCAmelCase         =       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
									_lowerCAmelCase         =       image_processing(_snake_case		,							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 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.image_processing_class(**self.image_processor_dict						)
									# create random PyTorch tensors
									_lowerCAmelCase         =       prepare_image_inputs(self.image_processor_tester		,							equal_resolution=_snake_case		,							torchify=_snake_case						)
									for image in image_inputs:
														self.assertIsInstance(_snake_case		,							torch.Tensor						)

									# Test not batched input
									_lowerCAmelCase         =       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
									_lowerCAmelCase         =       image_processing(_snake_case		,							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"""],
									    )		,							)



