





'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class        __lowerCAmelCase  (					__magic_name__				):






       """simple docstring"""





       def __init__(							self     :	Optional[int] ,				lowerCAmelCase__     :	List[Any] ,				lowerCAmelCase__     :	List[str]  )					->  str:

            '''simple docstring'''




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







       @torch.no_grad()
       def __call__(							self     :	List[str] ,				lowerCAmelCase__     :	int = 1 ,				lowerCAmelCase__     :	Optional[Union[torch.Generator, List[torch.Generator]]] = None ,				lowerCAmelCase__     :	int = 1000 ,				lowerCAmelCase__     :	Optional[str] = "pil" ,				lowerCAmelCase__     :	bool = True ,				)					->  Union[ImagePipelineOutput, Tuple]:

            '''simple docstring'''




            if isinstance(self.unet.config.sample_size ,				lowerCAmelCase__  ):
                 _UpperCamelCase       					= (
                     batch_size,
                     self.unet.config.in_channels,
                     self.unet.config.sample_size,
                     self.unet.config.sample_size,
                 )
            else:
                 _UpperCamelCase       					= (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       					= randn_tensor(lowerCAmelCase__ ,				generator=lowerCAmelCase__  )
                 _UpperCamelCase       					= image.to(self.device  )
            else:
                 _UpperCamelCase       					= randn_tensor(lowerCAmelCase__ ,				generator=lowerCAmelCase__ ,				device=self.device  )

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

            for t in self.progress_bar(self.scheduler.timesteps  ):
                 # 1. predict noise model_output
                 _UpperCamelCase       					= self.unet(lowerCAmelCase__ ,				lowerCAmelCase__  ).sample

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

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

            if not return_dict:
                 return (image,)

            return ImagePipelineOutput(images=lowerCAmelCase__  )










'''simple docstring'''

from __future__ import annotations







def       a__       (       lowercase      :  list[int | float]   )	->	int | float:





     """simple docstring"""

     if len(lowercase   ) == 0:
          raise ValueError('''find_min() arg is an empty sequence'''   )
     _UpperCamelCase       					= nums[0]
     for num in nums:
          _UpperCamelCase       					= min(lowercase,      lowercase   )
     return min_num


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)



