


'''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  a     (     unittest.TestCase       ):
	@property
	def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
						torch.manual_seed(0						)
						_a			      =							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 							__UpperCAmelCase		( self						)					->							str:
						_a			      =							self.dummy_uncond_unet
						_a			      =							ScoreSdeVeScheduler()

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

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

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

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

						assert image.shape == (1, 32, 32, 3)
						_a			      =							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  a     (     unittest.TestCase       ):
	def 							__UpperCAmelCase		( self						)					->							Dict:
						_a			      =							'google/ncsnpp-church-256'
						_a			      =							UNetaDModel.from_pretrained(__magic_name__						)

						_a			      =							ScoreSdeVeScheduler.from_pretrained(__magic_name__						)

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

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

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

						assert image.shape == (1, 2_56, 2_56, 3)

						_a			      =							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'''






def 	_A				(lowerCAmelCase__  :Union[str, Any]		)   ->  List[str]:




     '''simple docstring'''



     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				(lowerCAmelCase__  :dict[int, list[int]]		)   ->  list[tuple[int, int]]:




     '''simple docstring'''



     _a			      =							0
     _a			      =							len(lowerCAmelCase__		)  # No of vertices in graph
     _a			      =							[0] * n
     _a			      =							[False] * n

     def dfs(lowerCAmelCase__  :Dict				,						lowerCAmelCase__  :str				,						lowerCAmelCase__  :Dict				,						lowerCAmelCase__  :Union[str, Any]		):
          _a			      =							True
          _a			      =							id_
          id_ += 1
          for to in graph[at]:
               if to == parent:
                    pass
               elif not visited[to]:
                    dfs(lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__				,						id_		)
                    _a			      =							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
                    _a			      =							min(low[at]				,						low[to]		)

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


if __name__ == "__main__":
 import doctest

 doctest.testmod()

