

def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       )->		list:



    """simple docstring"""






    UpperCamelCase_          =		[True for i in range(n + 1       )]
    UpperCamelCase_          =		[i - 1 for i in range(n + 1       )]
    UpperCamelCase_          =		[]
    for i in range(2       ,		n + 1       ):
        if is_prime[i]:
            primes.append(SCREAMING_SNAKE_CASE_       )
        for j in range(0       ,		len(SCREAMING_SNAKE_CASE_       )       ):
            if i * primes[j] >= n:
                break
            UpperCamelCase_          =		False

            if i % primes[j] == 0:
                UpperCamelCase_          =		totients[i] * primes[j]
                break

            UpperCamelCase_          =		totients[i] * (primes[j] - 1)

    return totients


if __name__ == "__main__":
 import doctest

 doctest.testmod()







from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 					lowerCAmelCase							(   lowercase_					):
  __lowerCamelCase		=			42
  __lowerCamelCase		=			42







  def __init__(			self  :List[Any]    ,							_lowercase  :UNetaDModel    ,							_lowercase  :PNDMScheduler					):






     '''simple docstring'''





     super().__init__()

     lowercase__    =  PNDMScheduler.from_config(scheduler.config					)

     self.register_modules(unet=_lowercase    ,							scheduler=_lowercase					)







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






     '''simple docstring'''





     lowercase__    =  randn_tensor(
         (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    ,							generator=_lowercase    ,							device=self.device    ,							)

     self.scheduler.set_timesteps(_lowercase					)
     for t in self.progress_bar(self.scheduler.timesteps					):
        lowercase__    =  self.unet(_lowercase    ,							_lowercase					).sample

        lowercase__    =  self.scheduler.step(_lowercase    ,							_lowercase    ,							_lowercase					).prev_sample

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

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=_lowercase					)






