






import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class 							__UpperCamelCase     (  unittest.TestCase ):







	@property
	def 			_a (	self   :				Optional[Any]      )		->			Optional[Any]:
		"""simple docstring"""







		torch.manual_seed(0      )
		__lowercase   							=	UNetaDModel(
		    block_out_channels=(32, 64)							,					layers_per_block=2							,					sample_size=32							,					in_channels=3							,					out_channels=3							,					down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")							,					up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")							,					)
		return model







	def 			_a (	self   :				Any      )		->			List[str]:
		"""simple docstring"""







		__lowercase   							=	self.dummy_uncond_unet
		__lowercase   							=	ScoreSdeVeScheduler()

		__lowercase   							=	ScoreSdeVePipeline(unet=__lowerCAmelCase							,					scheduler=__lowerCAmelCase      )
		sde_ve.to(__lowerCAmelCase      )
		sde_ve.set_progress_bar_config(disable=__lowerCAmelCase      )

		__lowercase   							=	torch.manual_seed(0      )
		__lowercase   							=	sde_ve(num_inference_steps=2							,					output_type="""numpy"""							,					generator=__lowerCAmelCase      ).images

		__lowercase   							=	torch.manual_seed(0      )
		__lowercase   							=	sde_ve(num_inference_steps=2							,					output_type="""numpy"""							,					generator=__lowerCAmelCase							,					return_dict=__lowerCAmelCase      )[
		    0
		]

		__lowercase   							=	image[0, -3:, -3:, -1]
		__lowercase   							=	image_from_tuple[0, -3:, -3:, -1]

		assert image.shape == (1, 32, 32, 3)
		__lowercase   							=	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]      )

		assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
		assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2


@slow
@require_torch
class 							__UpperCamelCase     (  unittest.TestCase ):







	def 			_a (	self   :				str      )		->			List[Any]:
		"""simple docstring"""







		__lowercase   							=	"""google/ncsnpp-church-256"""
		__lowercase   							=	UNetaDModel.from_pretrained(__lowerCAmelCase      )

		__lowercase   							=	ScoreSdeVeScheduler.from_pretrained(__lowerCAmelCase      )

		__lowercase   							=	ScoreSdeVePipeline(unet=__lowerCAmelCase							,					scheduler=__lowerCAmelCase      )
		sde_ve.to(__lowerCAmelCase      )
		sde_ve.set_progress_bar_config(disable=__lowerCAmelCase      )

		__lowercase   							=	torch.manual_seed(0      )
		__lowercase   							=	sde_ve(num_inference_steps=10							,					output_type="""numpy"""							,					generator=__lowerCAmelCase      ).images

		__lowercase   							=	image[0, -3:, -3:, -1]

		assert image.shape == (1, 256, 256, 3)

		__lowercase   							=	np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]      )

		assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2


'''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.






def 							snake_case__				(     _A:   Dict					)       ->      str:

		'''simple docstring'''


		lowerCAmelCase     =    [False] * len(_A					)
		lowerCAmelCase     =    [-1] * len(_A					)

		def dfs(_A:   Dict ,  _A:   Tuple					):
				lowerCAmelCase     =    True
				lowerCAmelCase     =    c
				for u in graph[v]:
						if not visited[u]:
								dfs(_A ,  1 - c					)

		for i in range(len(_A					)					):
				if not visited[i]:
						dfs(_A ,  0					)

		for i in range(len(_A					)					):
				for j in graph[i]:
						if color[i] == color[j]:
								return False

		return True


# Adjacency list of graph
__lowercase        =    {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}




print(check_bipartite_dfs(graph))




