


def 	a__ (							A__							):
				if num < 0:
								return -1
				return 1 if num else 0






def 	a__ (							):
				assert signum(5							) == 1
				assert signum(-5							) == -1
				assert signum(0							) == 0


if __name__ == "__main__":
				print(signum(12))
				print(signum(-12))
				print(signum(0))









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   __lowercase						(__SCREAMING_SNAKE_CASE     ):





							"""simple docstring"""







							_UpperCAmelCase						     =	42
							_UpperCAmelCase						     =	42






							def __init__(			self ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										super().__init__()

										SCREAMING_SNAKE_CASE_		:    str 		=				PNDMScheduler.from_config(scheduler.config					)

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






							@torch.no_grad()
							def __call__(			self ,	lowerCAmelCase__ = 1 ,	lowerCAmelCase__ = 5_0 ,	lowerCAmelCase__ = None ,	lowerCAmelCase__ = "pil" ,	lowerCAmelCase__ = True ,	**lowerCAmelCase__ ,	):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Tuple 		=				randn_tensor(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,	generator=lowerCAmelCase__ ,	device=self.device ,	)

										self.scheduler.set_timesteps(lowerCAmelCase__					)
										for t in self.progress_bar(self.scheduler.timesteps					):
													SCREAMING_SNAKE_CASE_		:    Dict 		=				self.unet(lowerCAmelCase__ ,	lowerCAmelCase__					).sample

													SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				self.scheduler.step(lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					).prev_sample

										SCREAMING_SNAKE_CASE_		:    Dict 		=				(image / 2 + 0.5).clamp(0 ,	1					)
										SCREAMING_SNAKE_CASE_		:    Any 		=				image.cpu().permute(0 ,	2 ,	3 ,	1					).numpy()
										if output_type == "pil":
													SCREAMING_SNAKE_CASE_		:    str 		=				self.numpy_to_pil(lowerCAmelCase__					)

										if not return_dict:
													return (image,)

										return ImagePipelineOutput(images=lowerCAmelCase__					)





