





'''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 __a (   _snake_case			):





   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 = 50				,lowerCamelCase				:			Optional[str] = "pil"				,lowerCamelCase				:			bool = True				,**lowerCamelCase				:			int				,):

      '''simple docstring'''

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

      # scale the initial noise by the standard deviation required by the scheduler
      __SCREAMING_SNAKE_CASE      							=       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
      __SCREAMING_SNAKE_CASE      							=       """eta""" in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )

      __SCREAMING_SNAKE_CASE      							=       {}
      if accepts_eta:
         __SCREAMING_SNAKE_CASE      							=       eta

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

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

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

      if not return_dict:
         return (image,)

      return ImagePipelineOutput(images=__A    )







'''simple docstring'''

def 						__magic_name__       (							__UpperCAmelCase = 1							,	__UpperCAmelCase = 1000      )						->					int:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       1
   __SCREAMING_SNAKE_CASE      							=       0

   for divide_by_number in range(__UpperCAmelCase							,	digit + 1      ):
      __SCREAMING_SNAKE_CASE      							=       []
      __SCREAMING_SNAKE_CASE      							=       numerator
      for _ in range(1							,	digit + 1      ):
         if now_divide in has_been_divided:
            if longest_list_length < len(__UpperCAmelCase      ):
               __SCREAMING_SNAKE_CASE      							=       len(__UpperCAmelCase      )
               __SCREAMING_SNAKE_CASE      							=       divide_by_number
         else:
            has_been_divided.append(__UpperCAmelCase      )
            __SCREAMING_SNAKE_CASE      							=       now_divide * 10 % divide_by_number

   return the_digit


# Tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()





