




"""simple docstring"""





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class  __lowercase    (     snake_case_		):


				'''simple docstring'''


				def __init__(				self				:       int ,SCREAMING_SNAKE_CASE_				:       Dict ,SCREAMING_SNAKE_CASE_				:       Dict ):
										super().__init__()
										self.register_modules(unet=SCREAMING_SNAKE_CASE_ ,scheduler=SCREAMING_SNAKE_CASE_ )





				@torch.no_grad()
				def __call__(				self				:       int ,SCREAMING_SNAKE_CASE_				:       int = 1 ,SCREAMING_SNAKE_CASE_				:       Optional[torch.Generator] = None ,SCREAMING_SNAKE_CASE_				:       int = 5_0 ,SCREAMING_SNAKE_CASE_				:       Optional[str] = "pil" ,SCREAMING_SNAKE_CASE_				:       bool = True ,**SCREAMING_SNAKE_CASE_				:       Tuple ,):
										snake_case__    :   List[str]							     =   torch.randn(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,generator=SCREAMING_SNAKE_CASE_ ,)
										snake_case__    :   Tuple							     =   image.to(self.device )

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

										for t in self.progress_bar(self.scheduler.timesteps ):
																# 1. predict noise model_output
																snake_case__    :   Optional[Any]							     =   self.unet(SCREAMING_SNAKE_CASE_ ,SCREAMING_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__    :   Dict							     =   self.scheduler.step(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ).prev_sample

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

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

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








"""simple docstring"""





def 						lowerCAmelCase							(	lowerCamelCase						):
						'''simple docstring'''


						return int(lowerCamelCase						) if x - int(lowerCamelCase						) <= 0 else int(lowerCamelCase						) + 1


if __name__ == "__main__":
							import doctest

							doctest.testmod()


