






import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class 				__UpperCAmelCase		(unittest.TestCase   ):







			@property
			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									torch.manual_seed(0					)
									_SCREAMING_SNAKE_CASE     				=  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					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.dummy_uncond_unet
									_SCREAMING_SNAKE_CASE     				=  KarrasVeScheduler()

									_SCREAMING_SNAKE_CASE     				=  KarrasVePipeline(unet=UpperCAmelCase_				,     scheduler=UpperCAmelCase_					)
									pipe.to(UpperCAmelCase_					)
									pipe.set_progress_bar_config(disable=UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(0					)
									_SCREAMING_SNAKE_CASE     				=  pipe(num_inference_steps=2				,     generator=UpperCAmelCase_				,     output_type="""numpy"""					).images

									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(0					)
									_SCREAMING_SNAKE_CASE     				=  pipe(num_inference_steps=2				,     generator=UpperCAmelCase_				,     output_type="""numpy"""				,     return_dict=UpperCAmelCase_					)[0]

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

									assert image.shape == (1, 32, 32, 3)
									_SCREAMING_SNAKE_CASE     				=  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 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """google/ncsnpp-celebahq-256"""
									_SCREAMING_SNAKE_CASE     				=  UNetaDModel.from_pretrained(UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  KarrasVeScheduler()

									_SCREAMING_SNAKE_CASE     				=  KarrasVePipeline(unet=UpperCAmelCase_				,     scheduler=UpperCAmelCase_					)
									pipe.to(UpperCAmelCase_					)
									pipe.set_progress_bar_config(disable=UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(0					)
									_SCREAMING_SNAKE_CASE     				=  pipe(num_inference_steps=20				,     generator=UpperCAmelCase_				,     output_type="""numpy"""					).images

									_SCREAMING_SNAKE_CASE     				=  image[0, -3:, -3:, -1]
									assert image.shape == (1, 256, 256, 3)
									_SCREAMING_SNAKE_CASE     				=  np.array([0.5_78, 0.58_11, 0.59_24, 0.58_09, 0.5_87, 0.58_86, 0.58_61, 0.58_02, 0.5_86]					)

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









UpperCamelCase			    =     {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

UpperCamelCase			    =     {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}

def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__		)							->  list[int]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  True
						_SCREAMING_SNAKE_CASE     				=  []

						for neighbour in graph[vert]:
												if not visited[neighbour]:
																		order += topology_sort(snake_case__	,snake_case__	,snake_case__		)

						order.append(snake_case__		)

						return order

def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__		)							->  list[int]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  True
						_SCREAMING_SNAKE_CASE     				=  [vert]

						for neighbour in reversed_graph[vert]:
												if not visited[neighbour]:
																		component += find_components(snake_case__	,snake_case__	,snake_case__		)

						return component




def 	__lowerCamelCase      (		snake_case__		)							->  list[list[int]]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  len(snake_case__		) * [False]
						_SCREAMING_SNAKE_CASE     				=  {vert: [] for vert in range(len(snake_case__		)		)}

						for vert, neighbours in graph.items():
												for neighbour in neighbours:
																		reversed_graph[neighbour].append(snake_case__		)

						_SCREAMING_SNAKE_CASE     				=  []
						for i, was_visited in enumerate(snake_case__		):
												if not was_visited:
																		order += topology_sort(snake_case__	,snake_case__	,snake_case__		)

						_SCREAMING_SNAKE_CASE     				=  []
						_SCREAMING_SNAKE_CASE     				=  len(snake_case__		) * [False]

						for i in range(len(snake_case__		)		):
												_SCREAMING_SNAKE_CASE     				=  order[len(snake_case__		) - i - 1]
												if not visited[vert]:
																		_SCREAMING_SNAKE_CASE     				=  find_components(snake_case__	,snake_case__	,snake_case__		)
																		components_list.append(snake_case__		)

						return components_list

