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 __snake_case       (		lowerCamelCase_	):




   def __init__(					self    :       Optional[int] ,			_lowercase    :       VQModel ,			_lowercase    :       UNetaDModel ,			_lowercase    :       DDIMScheduler   ):



     """simple docstring"""






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




   @torch.no_grad()
   def __call__(					self    :       List[Any] ,			_lowercase    :       int = 1 ,			_lowercase    :       Optional[Union[torch.Generator, List[torch.Generator]]] = None ,			_lowercase    :       float = 0.0 ,			_lowercase    :       int = 50 ,			_lowercase    :       Optional[str] = "pil" ,			_lowercase    :       bool = True ,			**_lowercase    :       Optional[Any] ,			):



     """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=_lowercase ,			)
     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(_lowercase   )

     # 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(_lowercase ,			_lowercase   )
       # predict the noise residual
       SCREAMING_SNAKE_CASE__    							=   self.unet(_lowercase ,			_lowercase   ).sample
       # compute the previous noisy sample x_t -> x_t-1
       SCREAMING_SNAKE_CASE__    							=   self.scheduler.step(_lowercase ,			_lowercase ,			_lowercase ,			**_lowercase   ).prev_sample

     # decode the image latents with the VAE
     SCREAMING_SNAKE_CASE__    							=   self.vqvae.decode(_lowercase   ).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(_lowercase   )

     if not return_dict:
       return (image,)

     return ImagePipelineOutput(images=_lowercase   )

def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int			)							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   [1]

  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   0, 0, 0
  SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 2
  SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 3
  SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 5

  for _ in range(1				,  __UpperCamelCase			):
    SCREAMING_SNAKE_CASE__    							=   min(__UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase			)
    ugly_nums.append(__UpperCamelCase			)
    if next_num == next_a:
      ia += 1
      SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 2
    if next_num == next_a:
      ia += 1
      SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 3
    if next_num == next_a:
      ia += 1
      SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 5
  return ugly_nums[-1]


if __name__ == "__main__":
   from doctest import testmod

   testmod(verbose=True)
   print(F"""{ugly_numbers(200) = }""")
