






"""simple docstring"""



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 				__SCREAMING_SNAKE_CASE    (					UpperCamelCase		):


							def __init__(      self   :    List[str]      ,     snake_case   :    str      ,     snake_case   :    str				):
													'''simple docstring'''





													super().__init__()
													self.register_modules(unet=snake_case      ,     scheduler=snake_case				)


							@torch.no_grad()
							def __call__(      self   :    Tuple      ,     snake_case   :    int = 1      ,     snake_case   :    Optional[torch.Generator] = None      ,     snake_case   :    int = 50      ,     snake_case   :    Optional[str] = "pil"      ,     snake_case   :    bool = True      ,     **snake_case   :    List[str]      ,     ):
													'''simple docstring'''





													A__    :	int			=	torch.randn(
													    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)      ,     generator=snake_case      ,     )
													A__    :	int			=	image.to(self.device				)

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

													for t in self.progress_bar(self.scheduler.timesteps				):
																			# 1. predict noise model_output
																			A__    :	List[Any]			=	self.unet(snake_case      ,     snake_case				).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
																			A__    :	int			=	self.scheduler.step(snake_case      ,     snake_case      ,     snake_case				).prev_sample

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

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

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














"""simple docstring"""



def   _lowerCAmelCase		(				UpperCAmelCase__							:       int = 4_0_0_0_0_0_0   )		->int:
						A__    :	int			=	1
						A__    :	List[str]			=	2
						A__    :	Union[str, Any]			=	0
						while j <= n:
												if j % 2 == 0:
																		total += j
												A__	,	A__    :	str			=	j, i + j

						return total


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






