






'''simple docstring'''
class    _snake_case   :

					"""simple docstring"""





					def __init__(     self   , UpperCAmelCase__     )      ->	None:
										a_									=   data
										a_									=   42
										a_									=   42



def 							a	(   _UpperCAmelCase )				->				None:






					"""simple docstring"""







					a_									=   0
					a_									=   x

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase )				->				None:






					"""simple docstring"""







					a_							,					a_									=   find_set(_UpperCAmelCase ), find_set(_UpperCAmelCase )
					if x == y:
										return

					elif x.rank > y.rank:
										a_									=   x
					else:
										a_									=   y
										if x.rank == y.rank:
															y.rank += 1

def 							a	(   _UpperCAmelCase )				->				Node:






					"""simple docstring"""







					if x != x.parent:
										a_									=   find_set(x.parent )
					return x.parent

def 							a	(   _UpperCAmelCase )				->				set:






					"""simple docstring"""







					a_									=   ({0, 1, 2}, {3, 4, 5})
					for s in sets:
										if node.data in s:
															return s
					a_									=   F'''{node.data} is not in {sets}'''
					raise ValueError(_UpperCAmelCase )

def 							a	(   )				->				None:






					"""simple docstring"""







					a_									=   [Node(_UpperCAmelCase ) for i in range(6 )]
					for v in vertex:
										make_set(_UpperCAmelCase )

					union_set(vertex[0]					,  vertex[1] )
					union_set(vertex[1]					,  vertex[2] )
					union_set(vertex[3]					,  vertex[4] )
					union_set(vertex[3]					,  vertex[5] )

					for nodea in vertex:
										for nodea in vertex:
															if find_python_set(_UpperCAmelCase ).isdisjoint(find_python_set(_UpperCAmelCase ) ):
																				assert find_set(_UpperCAmelCase ) != find_set(_UpperCAmelCase )
															else:
																				assert find_set(_UpperCAmelCase ) == find_set(_UpperCAmelCase )


if __name__ == "__main__":
						test_disjoint_set()









'''simple docstring'''
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class    _snake_case   (				snake_case					):

					"""simple docstring"""





					_UpperCamelCase		 =      42
					_UpperCamelCase		 =      42




					def __init__(     self   , UpperCAmelCase__   , UpperCAmelCase__     )      ->	int:
										super().__init__()

										a_									=   PNDMScheduler.from_config(scheduler.config     )

										self.register_modules(unet=UpperCAmelCase__   , scheduler=UpperCAmelCase__     )




					@torch.no_grad()
					def __call__(     self   , UpperCAmelCase__ = 1   , UpperCAmelCase__ = 50   , UpperCAmelCase__ = None   , UpperCAmelCase__ = "pil"   , UpperCAmelCase__ = True   , **UpperCAmelCase__   , )      ->	Union[ImagePipelineOutput, Tuple]:
										a_									=   randn_tensor(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   , generator=UpperCAmelCase__   , device=self.device   , )

										self.scheduler.set_timesteps(UpperCAmelCase__     )
										for t in self.progress_bar(self.scheduler.timesteps     ):
															a_									=   self.unet(UpperCAmelCase__   , UpperCAmelCase__     ).sample

															a_									=   self.scheduler.step(UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     ).prev_sample

										a_									=   (image / 2 + 0.5).clamp(0   , 1     )
										a_									=   image.cpu().permute(0   , 2   , 3   , 1     ).numpy()
										if output_type == "pil":
															a_									=   self.numpy_to_pil(UpperCAmelCase__     )

										if not return_dict:
															return (image,)

										return ImagePipelineOutput(images=UpperCAmelCase__     )

