



"""simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union

import torch

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


class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):

    def __init__(				self			:				List[str] ,  lowerCAmelCase_			:				VQModel ,  lowerCAmelCase_			:				UNetaDModel ,  lowerCAmelCase_			:				DDIMScheduler):







      """simple docstring"""







      super().__init__()
      self.register_modules(vqvae=__A ,  unet=__A ,  scheduler=__A)




    @torch.no_grad()
    def __call__(				self			:				Optional[Any] ,  lowerCAmelCase_			:				int = 1 ,  lowerCAmelCase_			:				Optional[Union[torch.Generator, List[torch.Generator]]] = None ,  lowerCAmelCase_			:				float = 0.0 ,  lowerCAmelCase_			:				int = 5_0 ,  lowerCAmelCase_			:				Optional[str] = "pil" ,  lowerCAmelCase_			:				bool = True ,  **lowerCAmelCase_			:				int ,  ):







      """simple docstring"""







      lowercase_								= randn_tensor(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,  generator=__A ,  )
      lowercase_								= latents.to(self.device)

      # scale the initial noise by the standard deviation required by the scheduler
      lowercase_								= latents * self.scheduler.init_noise_sigma

      self.scheduler.set_timesteps(__A)

      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
      lowercase_								= """eta""" in set(inspect.signature(self.scheduler.step).parameters.keys())

      lowercase_								= {}
      if accepts_eta:
        lowercase_								= eta

      for t in self.progress_bar(self.scheduler.timesteps):
        lowercase_								= self.scheduler.scale_model_input(__A ,  __A)
        # predict the noise residual
        lowercase_								= self.unet(__A ,  __A).sample
        # compute the previous noisy sample x_t -> x_t-1
        lowercase_								= self.scheduler.step(__A ,  __A ,  __A ,  **__A).prev_sample

      # decode the image latents with the VAE
      lowercase_								= self.vqvae.decode(__A).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(__A)

      if not return_dict:
        return (image,)

      return ImagePipelineOutput(images=__A)






"""simple docstring"""
def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase=2_81_23							) ->						Optional[Any]:







  '''simple docstring'''


  lowercase_								= [1] * (limit + 1)

  for i in range(2 ,  int(limit**0.5							) + 1							):
    sum_divs[i * i] += i
    for k in range(i + 1 ,  limit // i + 1							):
      sum_divs[k * i] += k + i

  lowercase_								= set()
  lowercase_								= 0

  for n in range(1 ,  limit + 1							):
    if sum_divs[n] > n:
      abundants.add(__lowerCAmelCase							)

    if not any((n - a in abundants) for a in abundants							):
      res += n

  return res


if __name__ == "__main__":
       print(solution())

