




# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class     A(		UpperCamelCase  ):



 '''simple docstring'''


 def __init__(							self   :    Optional[int]		,				A_   :    Union[str, Any]		,				A_   :    Union[str, Any]    )  ->      Tuple:

       """simple docstring"""






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





 @torch.no_grad()
 def __call__(							self   :    Optional[Any]		,				A_   :    int = 1		,				A_   :    Optional[torch.Generator] = None		,				A_   :    int = 50		,				A_   :    Optional[str] = "pil"		,				A_   :    bool = True		,				**A_   :    List[Any]		,				)  ->      Union[ImagePipelineOutput, Tuple]:

       """simple docstring"""






       lowerCamelCase_							=       torch.randn(
           (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,				generator=A_		,				)
       lowerCamelCase_							=       image.to(self.device    )

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

       for t in self.progress_bar(self.scheduler.timesteps    ):
             # 1. predict noise model_output
             lowerCamelCase_							=       self.unet(A_		,				A_    ).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
             lowerCamelCase_							=       self.scheduler.step(A_		,				A_		,				A_    ).prev_sample

       lowerCamelCase_							=       (image / 2 + 0.5).clamp(0		,				1    )
       lowerCamelCase_							=       image.cpu().permute(0		,				2		,				3		,				1    ).numpy()
       if output_type == "pil":
             lowerCamelCase_							=       self.numpy_to_pil(A_    )

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

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







def 							__lowerCamelCase		(					UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						int		):




      """simple docstring"""





      while b:
            a				,							a		:int									=			b, a % b
      return a
def 							__lowerCamelCase		(					UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						int		):




      """simple docstring"""





      return a if b == 0 else euclidean_gcd_recursive(UpperCAmelCase_					,    a % b		)
def 							__lowerCamelCase		(					):




      """simple docstring"""





      print(F'''euclidean_gcd(3, 5) = {euclidean_gcd(3					,    5		)}'''		)
      print(F'''euclidean_gcd(5, 3) = {euclidean_gcd(5					,    3		)}'''		)
      print(F'''euclidean_gcd(1, 3) = {euclidean_gcd(1					,    3		)}'''		)
      print(F'''euclidean_gcd(3, 6) = {euclidean_gcd(3					,    6		)}'''		)
      print(F'''euclidean_gcd(6, 3) = {euclidean_gcd(6					,    3		)}'''		)

      print(F'''euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3					,    5		)}'''		)
      print(F'''euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5					,    3		)}'''		)
      print(F'''euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1					,    3		)}'''		)
      print(F'''euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3					,    6		)}'''		)
      print(F'''euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6					,    3		)}'''		)


if __name__ == "__main__":
     main()





