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     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	UNetaDModel
  SCREAMING_SNAKE_CASE_	:	ScoreSdeVeScheduler






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      Dict:





       '''simple docstring'''





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






  @torch.no_grad()
  def __call__(self   ,				SCREAMING_SNAKE_CASE_ = 1   ,				SCREAMING_SNAKE_CASE_ = 2000   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = "pil"   ,				SCREAMING_SNAKE_CASE_ = True   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      Union[ImagePipelineOutput, Tuple]:





       '''simple docstring'''





       UpperCamelCase__										=      self.unet.config.sample_size
       UpperCamelCase__										=      (batch_size, 3, img_size, img_size)

       UpperCamelCase__										=      self.unet

       UpperCamelCase__										=      randn_tensor(SCREAMING_SNAKE_CASE_   ,				generator=SCREAMING_SNAKE_CASE_	) * self.scheduler.init_noise_sigma
       UpperCamelCase__										=      sample.to(self.device	)

       self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_	)
       self.scheduler.set_sigmas(SCREAMING_SNAKE_CASE_	)

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

            # correction step
            for _ in range(self.scheduler.config.correct_steps	):
                 UpperCamelCase__										=      self.unet(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	).sample
                 UpperCamelCase__										=      self.scheduler.step_correct(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				generator=SCREAMING_SNAKE_CASE_	).prev_sample

            # prediction step
            UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	).sample
            UpperCamelCase__										=      self.scheduler.step_pred(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				generator=SCREAMING_SNAKE_CASE_	)

            UpperCamelCase__						,       UpperCamelCase__										=      output.prev_sample, output.prev_sample_mean

       UpperCamelCase__										=      sample_mean.clamp(0   ,				1	)
       UpperCamelCase__										=      sample.cpu().permute(0   ,				2   ,				3   ,				1	).numpy()
       if output_type == "pil":
            UpperCamelCase__										=      self.numpy_to_pil(SCREAMING_SNAKE_CASE_	)

       if not return_dict:
            return (sample,)

       return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_	)




def  __UpperCamelCase    (						A = 600851475143							):

     try:
          UpperCamelCase__										=      int(A							)
     except (TypeError, ValueError):
          raise TypeError('''Parameter n must be int or castable to int.'''							)
     if n <= 0:
          raise ValueError('''Parameter n must be greater than or equal to one.'''							)
     UpperCamelCase__										=      1
     UpperCamelCase__										=      2
     while i * i <= n:
          while n % i == 0:
               UpperCamelCase__										=      i
               n //= i
          i += 1
     if n > 1:
          UpperCamelCase__										=      n
     return int(A							)


if __name__ == "__main__":
     print(f"""{solution() = }""")



