






from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class        __snake_case(					__UpperCamelCase      ):



 def __init__(	self   :   Dict ,		UpperCAmelCase__   :   Tuple ,		UpperCAmelCase__   :   int	):

    """simple docstring"""






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







 @torch.no_grad()
 def __call__(	self   :   Dict ,		UpperCAmelCase__   :   int = 1 ,		UpperCAmelCase__   :   Optional[Union[torch.Generator, List[torch.Generator]]] = None ,		UpperCAmelCase__   :   int = 1_000 ,		UpperCAmelCase__   :   Optional[str] = "pil" ,		UpperCAmelCase__   :   bool = True ,		):

    """simple docstring"""






    if isinstance(self.unet.config.sample_size ,		UpperCAmelCase__	):
       lowercase__						:	Optional[Any]         = (
           batch_size,
           self.unet.config.in_channels,
           self.unet.config.sample_size,
           self.unet.config.sample_size,
       )
    else:
       lowercase__						:	Dict         = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

    if self.device.type == "mps":
       # randn does not work reproducibly on mps
       lowercase__						:	Optional[int]         = randn_tensor(UpperCAmelCase__ ,		generator=UpperCAmelCase__	)
       lowercase__						:	Dict         = image.to(self.device	)
    else:
       lowercase__						:	str         = randn_tensor(UpperCAmelCase__ ,		generator=UpperCAmelCase__ ,		device=self.device	)

    # set step values
    self.scheduler.set_timesteps(UpperCAmelCase__	)

    for t in self.progress_bar(self.scheduler.timesteps	):
       # 1. predict noise model_output
       lowercase__						:	List[str]         = self.unet(UpperCAmelCase__ ,		UpperCAmelCase__	).sample

       # 2. compute previous image: x_t -> x_t-1
       lowercase__						:	str         = self.scheduler.step(UpperCAmelCase__ ,		UpperCAmelCase__ ,		UpperCAmelCase__ ,		generator=UpperCAmelCase__	).prev_sample

    lowercase__						:	Tuple         = (image / 2 + 0.5).clamp(0 ,		1	)
    lowercase__						:	List[str]         = image.cpu().permute(0 ,		2 ,		3 ,		1	).numpy()
    if output_type == "pil":
       lowercase__						:	List[str]         = self.numpy_to_pil(UpperCAmelCase__	)

    if not return_dict:
       return (image,)

    return ImagePipelineOutput(images=UpperCAmelCase__	)










def       a__						(  A					):

   if not isinstance(A		,				A					) or number < 0:
      raise ValueError("""Input must be a non-negative integer"""					)

   lowercase__						:	str         = number & ~(number - 1)
   lowercase__						:	Optional[int]         = 0
   while intermediate:
      intermediate >>= 1
      index += 1
   return index - 1


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)


