

from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):







				def __init__(					self    :		List[Any]		,				UpperCAmelCase__    :		Dict		,				UpperCAmelCase__    :		Tuple      ):
										'''simple docstring'''
										super().__init__()
										self.register_modules(unet=UpperCAmelCase__		,				scheduler=UpperCAmelCase__      )







				@torch.no_grad()
				def __call__(					self    :		int		,				UpperCAmelCase__    :		int = 1		,				UpperCAmelCase__    :		Optional[torch.Generator] = None		,				UpperCAmelCase__    :		int = 50		,				UpperCAmelCase__    :		Optional[str] = "pil"		,				UpperCAmelCase__    :		bool = True		,				**UpperCAmelCase__    :		str		,				):
										'''simple docstring'''
										lowercase   :       str								=torch.randn(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,				generator=UpperCAmelCase__		,				)
										lowercase   :       Dict								=image.to(self.device      )

										# set step values
										self.scheduler.set_timesteps(UpperCAmelCase__      )

										for t in self.progress_bar(self.scheduler.timesteps      ):
																# 1. predict noise model_output
																lowercase   :       Tuple								=self.unet(UpperCAmelCase__		,				UpperCAmelCase__      ).sample

																# 2. predict previous mean of image x_t-1 and add variance depending on eta
																# eta corresponds to η in paper and should be between [0, 1]
																# do x_t -> x_t-1
																lowercase   :       Union[str, Any]								=self.scheduler.step(UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__      ).prev_sample

										lowercase   :       List[Any]								=(image / 2 + 0.5).clamp(0		,				1      )
										lowercase   :       Union[str, Any]								=image.cpu().permute(0		,				2		,				3		,				1      ).numpy()
										if output_type == "pil":
																lowercase   :       Optional[Any]								=self.numpy_to_pil(UpperCAmelCase__      )

										if not return_dict:
																return (image,), "This is a local test"

										return ImagePipelineOutput(images=UpperCAmelCase__      ), "This is a local test"








'''simple docstring'''





def 							_lowerCAmelCase (      __magic_name__	:    int = 1000000	)							->     int:
						lowercase   :       Dict								=set(range(3      ,   __magic_name__      ,   2	)	)
						primes.add(2	)
						for p in range(3      ,   __magic_name__      ,   2	):
												if p not in primes:
																		continue
												primes.difference_update(set(range(p * p      ,   __magic_name__      ,   __magic_name__	)	)	)

						lowercase   :       List[Any]								=[float(__magic_name__	) for n in range(limit + 1	)]

						for p in primes:
												for n in range(__magic_name__      ,   limit + 1      ,   __magic_name__	):
																		phi[n] *= 1 - 1 / p

						return int(sum(phi[2:]	)	)


if __name__ == "__main__":
							print(f'''{solution() = }''')





