


"""simple docstring"""



def       lowerCamelCase					(a_						:int)     ->							int:
       lowercase		:Dict       							=				abs(a_)
       lowercase		:List[Any]       							=				0
       while n > 0:
              res += n % 10
              n //= 10
       return res






def       lowerCamelCase					(a_						:int)     ->							int:
       lowercase		:str       							=				abs(a_)
       return n if n < 10 else n % 10 + sum_of_digits(n // 10)






def       lowerCamelCase					(a_						:int)     ->							int:
       return sum(int(a_) for c in str(abs(a_)))






def       lowerCamelCase					()     ->							None:
       from collections.abc import Callable
       from timeit import timeit

       def benchmark_a_function(a_						:Callable				,				a_						:int) -> None:
              lowercase		:int       							=				F"""{func.__name__}({value})"""
              lowercase		:Optional[int]       							=				timeit(F"""__main__.{call}"""				,				setup='''import __main__''')
              print(F"""{call:56} = {func(a_)} -- {timing:.4f} seconds""")

       for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
              for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
                     benchmark_a_function(a_				,				a_)
              print()


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 benchmark()





"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      UNetaDModel
       __A			:      ScoreSdeVeScheduler






       def __init__(   self  :    Optional[Any]  ,     snake_case__  :    UNetaDModel  ,     snake_case__  :    ScoreSdeVeScheduler				):

              '''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__  :    int = 2_0_0_0  ,     snake_case__  :    Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,     snake_case__  :    Optional[str] = "pil"  ,     snake_case__  :    bool = True  ,     **snake_case__  :    Optional[Any]  ,     ):

              '''simple docstring'''






              lowercase		:str       							=				self.unet.config.sample_size
              lowercase		:str       							=				(batch_size, 3, img_size, img_size)

              lowercase		:Any       							=				self.unet

              lowercase		:Dict       							=				randn_tensor(snake_case__  ,     generator=snake_case__				) * self.scheduler.init_noise_sigma
              lowercase		:int       							=				sample.to(self.device				)

              self.scheduler.set_timesteps(snake_case__				)
              self.scheduler.set_sigmas(snake_case__				)

              for i, t in enumerate(self.progress_bar(self.scheduler.timesteps				)				):
                     lowercase		:Optional[int]       							=				self.scheduler.sigmas[i] * torch.ones(shape[0]  ,     device=self.device				)

                     # correction step
                     for _ in range(self.scheduler.config.correct_steps				):
                            lowercase		:str       							=				self.unet(snake_case__  ,     snake_case__				).sample
                            lowercase		:Optional[Any]       							=				self.scheduler.step_correct(snake_case__  ,     snake_case__  ,     generator=snake_case__				).prev_sample

                     # prediction step
                     lowercase		:Optional[Any]       							=				model(snake_case__  ,     snake_case__				).sample
                     lowercase		:Dict       							=				self.scheduler.step_pred(snake_case__  ,     snake_case__  ,     snake_case__  ,     generator=snake_case__				)

                     lowercase							,  lowercase		:Tuple       							=				output.prev_sample, output.prev_sample_mean

              lowercase		:str       							=				sample_mean.clamp(0  ,     1				)
              lowercase		:int       							=				sample.cpu().permute(0  ,     2  ,     3  ,     1				).numpy()
              if output_type == "pil":
                     lowercase		:str       							=				self.numpy_to_pil(snake_case__				)

              if not return_dict:
                     return (sample,)

              return ImagePipelineOutput(images=snake_case__				)

