from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 			__snake_case   (     lowerCamelCase__   ):
 def __init__(  self		,   snake_case__		,   snake_case__						)	->							int:
       '''simple docstring'''





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






 @torch.no_grad()
 def __call__(  self		,   snake_case__ = 1		,   snake_case__ = None		,   snake_case__ = 1000		,   snake_case__ = "pil"		,   snake_case__ = True		,   )	->							Union[ImagePipelineOutput, Tuple]:
       '''simple docstring'''





       if isinstance(self.unet.config.sample_size		,   snake_case__						):
             UpperCAmelCase    : int      =(
                 batch_size,
                 self.unet.config.in_channels,
                 self.unet.config.sample_size,
                 self.unet.config.sample_size,
             )
       else:
             UpperCAmelCase    : Optional[Any]      =(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

       if self.device.type == "mps":
             # randn does not work reproducibly on mps
             UpperCAmelCase    : int      =randn_tensor(snake_case__		,   generator=snake_case__						)
             UpperCAmelCase    : str      =image.to(self.device						)
       else:
             UpperCAmelCase    : str      =randn_tensor(snake_case__		,   generator=snake_case__		,   device=self.device						)

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

       for t in self.progress_bar(self.scheduler.timesteps						):
             # 1. predict noise model_output
             UpperCAmelCase    : Optional[int]      =self.unet(snake_case__		,   snake_case__						).sample

             # 2. compute previous image: x_t -> x_t-1
             UpperCAmelCase    : Optional[int]      =self.scheduler.step(snake_case__		,   snake_case__		,   snake_case__		,   generator=snake_case__						).prev_sample

       UpperCAmelCase    : Optional[int]      =(image / 2 + 0.5).clamp(0		,   1						)
       UpperCAmelCase    : List[Any]      =image.cpu().permute(0		,   2		,   3		,   1						).numpy()
       if output_type == "pil":
             UpperCAmelCase    : str      =self.numpy_to_pil(snake_case__						)

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=snake_case__						)

import math





def   lowerCAmelCase_ (				__lowerCAmelCase						)->  list[int]:






      '''simple docstring'''





      if not isinstance(__lowerCAmelCase		,					__lowerCAmelCase						):
            UpperCAmelCase    : Union[str, Any]      =f'''Input value of [number={number}] must be an integer'''
            raise TypeError(__lowerCAmelCase						)
      if number < 1:
            UpperCAmelCase    : Dict      =f'''Input value of [number={number}] must be a positive integer'''
            raise ValueError(__lowerCAmelCase						)
      UpperCAmelCase    : Dict      =[number]
      while number != 1:
            if number % 2 == 0:
                  UpperCAmelCase    : Optional[Any]      =math.floor(math.sqrt(__lowerCAmelCase						)						)
            else:
                  UpperCAmelCase    : List[str]      =math.floor(
                      math.sqrt(__lowerCAmelCase						) * math.sqrt(__lowerCAmelCase						) * math.sqrt(__lowerCAmelCase						)						)
            sequence.append(__lowerCAmelCase						)
      return sequence


if __name__ == "__main__":
     import doctest

     doctest.testmod()
