

'''simple docstring'''





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

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







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase	)       ->			list[int]:
					lowerCamelCase_  				=      True
					lowerCamelCase_  				=      []

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

					order.append(__UpperCamelCase	)

					return order







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase	)       ->			list[int]:
					lowerCamelCase_  				=      True
					lowerCamelCase_  				=      [vert]

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

					return component







def   _UpperCamelCase	(		__UpperCamelCase	)       ->			list[list[int]]:
					lowerCamelCase_  				=      len(__UpperCamelCase	) * [False]
					lowerCamelCase_  				=      {vert: [] for vert in range(len(__UpperCamelCase	)	)}

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

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

					lowerCamelCase_  				=      []
					lowerCamelCase_  				=      len(__UpperCamelCase	) * [False]

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

					return components_list








import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, 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				):







											"""simple docstring"""



											torch.manual_seed(0				)
											a__	:							str      						= 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				):







											"""simple docstring"""



											a__	:							Dict      						= self.dummy_uncond_unet
											a__	:							Optional[Any]      						= PNDMScheduler()

											a__	:							Union[str, Any]      						= PNDMPipeline(unet=__UpperCAmelCase ,					scheduler=__UpperCAmelCase				)
											pndm.to(__UpperCAmelCase				)
											pndm.set_progress_bar_config(disable=__UpperCAmelCase				)

											a__	:							Tuple      						= torch.manual_seed(0				)
											a__	:							Tuple      						= pndm(generator=__UpperCAmelCase ,					num_inference_steps=20 ,					output_type="numpy"				).images

											a__	:							Optional[int]      						= torch.manual_seed(0				)
											a__	:							Tuple      						= pndm(generator=__UpperCAmelCase ,					num_inference_steps=20 ,					output_type="numpy" ,					return_dict=__UpperCAmelCase				)[0]

											a__	:							str      						= image[0, -3:, -3:, -1]
											a__	:							int      						= image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											a__	:							int      						= np.array([1.0, 1.0, 0.0, 1.0, 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				):







											"""simple docstring"""



											a__	:							Dict      						= "google/ddpm-cifar10-32"

											a__	:							List[Any]      						= UNetaDModel.from_pretrained(__UpperCAmelCase				)
											a__	:							Optional[Any]      						= PNDMScheduler()

											a__	:							int      						= PNDMPipeline(unet=__UpperCAmelCase ,					scheduler=__UpperCAmelCase				)
											pndm.to(__UpperCAmelCase				)
											pndm.set_progress_bar_config(disable=__UpperCAmelCase				)
											a__	:							Union[str, Any]      						= torch.manual_seed(0				)
											a__	:							Union[str, Any]      						= pndm(generator=__UpperCAmelCase ,					output_type="numpy"				).images

											a__	:							List[Any]      						= image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											a__	:							Dict      						= np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]				)

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


