




"""simple docstring"""




def 					a__				(       snake_case__       )		->      List[str]:
			return [
			    {
			        0: [1, 2],
			        1: [0, 2],
			        2: [0, 1, 3, 5],
			        3: [2, 4],
			        4: [3],
			        5: [2, 6, 8],
			        6: [5, 7],
			        7: [6, 8],
			        8: [5, 7],
			    },
			    {
			        0: [6],
			        1: [9],
			        2: [4, 5],
			        3: [4],
			        4: [2, 3],
			        5: [2],
			        6: [0, 7],
			        7: [6],
			        8: [],
			        9: [1],
			    },
			    {
			        0: [4],
			        1: [6],
			        2: [],
			        3: [5, 6, 7],
			        4: [0, 6],
			        5: [3, 8, 9],
			        6: [1, 3, 4, 7],
			        7: [3, 6, 8, 9],
			        8: [5, 7],
			        9: [5, 7],
			    },
			    {
			        0: [1, 3],
			        1: [0, 2, 4],
			        2: [1, 3, 4],
			        3: [0, 2, 4],
			        4: [1, 2, 3],
			    },
			][index]



def 					a__				(       snake_case__       )		->      list[tuple[int, int]]:
			lowerCamelCase       =      0
			lowerCamelCase       =      len(UpperCamelCase__       )  # No of vertices in graph
			lowerCamelCase       =      [0] * n
			lowerCamelCase       =      [False] * n

			def dfs(snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__       ):
						lowerCamelCase       =      True
						lowerCamelCase       =      id_
						id_ += 1
						for to in graph[at]:
									if to == parent:
												pass
									elif not visited[to]:
												dfs(UpperCamelCase__ ,				UpperCamelCase__ ,				UpperCamelCase__ ,				id_       )
												lowerCamelCase       =      min(low[at] ,				low[to]       )
												if id_ <= low[to]:
															bridges.append((at, to) if at < to else (to, at)       )
									else:
												# This edge is a back edge and cannot be a bridge
												lowerCamelCase       =      min(low[at] ,				low[to]       )

			lowerCamelCase       =      []
			for i in range(UpperCamelCase__       ):
						if not visited[i]:
									dfs(UpperCamelCase__ ,				-1 ,				UpperCamelCase__ ,				id_       )
			return bridges


if __name__ == "__main__":
		import doctest

		doctest.testmod()









"""simple docstring"""




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 					__magic_name__							( unittest.TestCase       ):






				'''simple docstring'''





				@property
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							torch.manual_seed(0				)
							lowerCamelCase       =      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      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.dummy_uncond_unet
							lowerCamelCase       =      ScoreSdeVeScheduler()

							lowerCamelCase       =      ScoreSdeVePipeline(unet=_a	,				scheduler=_a				)
							sde_ve.to(_a				)
							sde_ve.set_progress_bar_config(disable=_a				)

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

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

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

							assert image.shape == (1, 32, 32, 3)
							lowerCamelCase       =      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 					__magic_name__							( unittest.TestCase       ):






				'''simple docstring'''





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      """google/ncsnpp-church-256"""
							lowerCamelCase       =      UNetaDModel.from_pretrained(_a				)

							lowerCamelCase       =      ScoreSdeVeScheduler.from_pretrained(_a				)

							lowerCamelCase       =      ScoreSdeVePipeline(unet=_a	,				scheduler=_a				)
							sde_ve.to(_a				)
							sde_ve.set_progress_bar_config(disable=_a				)

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

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

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

							lowerCamelCase       =      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



