





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       ):
			'''simple docstring'''




			@property
			def   a_			(     self  :       str  )      ->      str:







					"""simple docstring"""






					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   a_			(     self  :       Tuple  )      ->      Optional[Any]:







					"""simple docstring"""






					A__     =  self.dummy_uncond_unet
					A__     =  ScoreSdeVeScheduler()

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

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

					A__     =  torch.manual_seed(0  )
					A__     =  sde_ve(num_inference_steps=2				,      output_type="""numpy"""				,      generator=__lowerCAmelCase				,      return_dict=__lowerCAmelCase  )[
					    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       ):
			'''simple docstring'''




			def   a_			(     self  :       str  )      ->      Dict:







					"""simple docstring"""






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

					A__     =  ScoreSdeVeScheduler.from_pretrained(__lowerCAmelCase  )

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

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










class     A     :
			'''simple docstring'''




			def __init__(     self  :       Optional[int]  )      ->      Dict:







					"""simple docstring"""






					A__     =  {}






			def   a_			(     self  :       Any  )      ->      None:







					"""simple docstring"""






					print(self.vertex  )
					for i in self.vertex:
							print(__lowerCAmelCase				,      """ -> """				,      """ -> """.join([str(__lowerCAmelCase  ) for j in self.vertex[i]]  )  )






			def   a_			(     self  :       Optional[int]				,      __lowerCAmelCase  :       int				,      __lowerCAmelCase  :       int  )      ->      None:







					"""simple docstring"""






					if from_vertex in self.vertex:
							self.vertex[from_vertex].append(__lowerCAmelCase  )
					else:
							# else make a new vertex
							A__     =  [to_vertex]






			def   a_			(     self  :       List[str]  )      ->      None:







					"""simple docstring"""






					A__     =  [False] * len(self.vertex  )

					# call the recursive helper function
					for i in range(len(self.vertex  )  ):
							if not visited[i]:
									self.dfs_recursive(__lowerCAmelCase				,      __lowerCAmelCase  )







			def   a_			(     self  :       List[str]				,      __lowerCAmelCase  :       int				,      __lowerCAmelCase  :       list  )      ->      None:







					"""simple docstring"""






					A__     =  True

					print(__lowerCAmelCase				,      end=""" """  )

					# Recur for all the vertices that are adjacent to this node
					for i in self.vertex:
							if not visited[i]:
									self.dfs_recursive(__lowerCAmelCase				,      __lowerCAmelCase  )


if __name__ == "__main__":
					A       :   Tuple					= Graph()
					g.add_edge(0, 1)
					g.add_edge(0, 2)
					g.add_edge(1, 2)
					g.add_edge(2, 0)
					g.add_edge(2, 3)
					g.add_edge(3, 3)

					g.print_graph()
					print('''DFS:''')
					g.dfs()

					# OUTPUT:
					# 0  ->  1 -> 2
					# 1  ->  2
					# 2  ->  0 -> 3
					# 3  ->  3
					# DFS:
					#  0 1 2 3



