

'''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					(							snake_case__					):






						'''simple docstring'''






						def __init__(					self :		List[Any]     ,					snake_case :		Optional[Any]     ,					snake_case :		Tuple   ):



										"""simple docstring"""





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




						@torch.no_grad()
						def __call__(					self :		Union[str, Any]     ,					snake_case :		List[str] = 1     ,					snake_case :		List[str] = None     ,					snake_case :		Tuple = 50     ,					snake_case :		int = "pil"     ,					snake_case :		Tuple = True     ,					**snake_case :		int     ,					):



										"""simple docstring"""





										_snake_case : Any												=		torch.randn(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     ,					generator=__snake_case     ,					)
										_snake_case : Optional[Any]												=		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
														_snake_case : int												=		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
														_snake_case : int												=		self.scheduler.step(__snake_case     ,					__snake_case     ,					__snake_case   ).prev_sample

										_snake_case : Union[str, Any]												=		(image / 2 + 0.5).clamp(0     ,					1   )
										_snake_case : Dict												=		image.cpu().permute(0     ,					2     ,					3     ,					1   ).numpy()
										if output_type == "pil":
														_snake_case : Dict												=		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__       (      a__			,      a__)		->    int:



    """simple docstring"""







    _snake_case : Tuple												=		0
    for ele in arr:
        total_sum += product_sum(a__			,      depth + 1) if isinstance(a__			,      a__) else ele
    return total_sum * depth

def   lowerCamelCase__       (      a__)		->    int:



    """simple docstring"""







    return product_sum(a__			,      1)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



