



"""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    __lowercase     (					unittest.TestCase):







						"""simple docstring"""







						@property
						def 						__UpperCamelCase							(self       ):
											torch.manual_seed(0       )
											snake_case_					:		Optional[Any] 					=					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       ):
											snake_case_					:		Dict 					=					self.dummy_uncond_unet
											snake_case_					:		Dict 					=					ScoreSdeVeScheduler()

											snake_case_					:		Any 					=					ScoreSdeVePipeline(unet=UpperCAmelCase__   ,		scheduler=UpperCAmelCase__       )
											sde_ve.to(UpperCAmelCase__       )
											sde_ve.set_progress_bar_config(disable=UpperCAmelCase__       )

											snake_case_					:		List[Any] 					=					torch.manual_seed(0       )
											snake_case_					:		Any 					=					sde_ve(num_inference_steps=2   ,		output_type="""numpy"""   ,		generator=UpperCAmelCase__       ).images

											snake_case_					:		Any 					=					torch.manual_seed(0       )
											snake_case_					:		Tuple 					=					sde_ve(num_inference_steps=2   ,		output_type="""numpy"""   ,		generator=UpperCAmelCase__   ,		return_dict=UpperCAmelCase__       )[
											    0
											]

											snake_case_					:		List[Any] 					=					image[0, -3:, -3:, -1]
											snake_case_					:		Tuple 					=					image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											snake_case_					:		Optional[int] 					=					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    __lowercase     (					unittest.TestCase):







						"""simple docstring"""







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Any 					=					"""google/ncsnpp-church-256"""
											snake_case_					:		List[Any] 					=					UNetaDModel.from_pretrained(UpperCAmelCase__       )

											snake_case_					:		List[str] 					=					ScoreSdeVeScheduler.from_pretrained(UpperCAmelCase__       )

											snake_case_					:		Dict 					=					ScoreSdeVePipeline(unet=UpperCAmelCase__   ,		scheduler=UpperCAmelCase__       )
											sde_ve.to(UpperCAmelCase__       )
											sde_ve.set_progress_bar_config(disable=UpperCAmelCase__       )

											snake_case_					:		str 					=					torch.manual_seed(0       )
											snake_case_					:		Dict 					=					sde_ve(num_inference_steps=10   ,		output_type="""numpy"""   ,		generator=UpperCAmelCase__       ).images

											snake_case_					:		Optional[int] 					=					image[0, -3:, -3:, -1]

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

											snake_case_					:		Dict 					=					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"""

import random







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  float				,    SCREAMING_SNAKE_CASE__ :  bool = False			):



					"""simple docstring"""





					snake_case_					:		dict 					=					{i: [] for i in range(SCREAMING_SNAKE_CASE__			)}

					# if probability is greater or equal than 1, then generate a complete graph
					if probability >= 1:
										return complete_graph(SCREAMING_SNAKE_CASE__			)
					# if probability is lower or equal than 0, then return a graph without edges
					if probability <= 0:
										return graph

					# for each couple of nodes, add an edge from u to v
					# if the number randomly generated is greater than probability probability
					for i in range(SCREAMING_SNAKE_CASE__			):
										for j in range(i + 1				,    SCREAMING_SNAKE_CASE__			):
															if random.random() < probability:
																				graph[i].append(SCREAMING_SNAKE_CASE__			)
																				if not directed:
																									# if the graph is undirected, add an edge in from j to i, either
																									graph[j].append(SCREAMING_SNAKE_CASE__			)
					return graph







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int			):



					"""simple docstring"""





					return {
					    i: [j for j in range(SCREAMING_SNAKE_CASE__			) if i != j] for i in range(SCREAMING_SNAKE_CASE__			)
					}


if __name__ == "__main__":
			import doctest

			doctest.testmod()




