


from typing import List, Optional, Tuple, Union

import torch

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


class 	a__						(    lowerCAmelCase_     ):

       def __init__( self   :  List[Any]   ,				lowerCamelCase_   :  Tuple   ,				lowerCamelCase_   :  Any     ):
            super().__init__()
            self.register_modules(unet=lowerCamelCase_   ,				scheduler=lowerCamelCase_     )

       @torch.no_grad()
       def __call__( self   :  int   ,				lowerCamelCase_   :  int = 1   ,				lowerCamelCase_   :  Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,				lowerCamelCase_   :  int = 1_0_0_0   ,				lowerCamelCase_   :  Optional[str] = "pil"   ,				lowerCamelCase_   :  bool = True   ,				):
            # Sample gaussian noise to begin loop
            if isinstance(self.unet.config.sample_size   ,				lowerCamelCase_     ):
                 a_     :  Dict				      =     (
                     batch_size,
                     self.unet.config.in_channels,
                     self.unet.config.sample_size,
                     self.unet.config.sample_size,
                 )
            else:
                 a_     :  int				      =     (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

            if self.device.type == "mps":
                 # randn does not work reproducibly on mps
                 a_     :  Tuple				      =     randn_tensor(lowerCamelCase_   ,				generator=lowerCamelCase_     )
                 a_     :  Dict				      =     image.to(self.device     )
            else:
                 a_     :  Dict				      =     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
                 a_     :  int				      =     self.unet(lowerCamelCase_   ,				lowerCamelCase_     ).sample

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

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

            if not return_dict:
                 return (image,)

            return ImagePipelineOutput(images=lowerCamelCase_     )




"""simple docstring"""







def       A__    (	lowercase_     :	list						)  ->							list:

   for insert_index, insert_value in enumerate(collection[1:]						):
      __lowerCamelCase    		=					insert_index
      while insert_index >= 0 and insert_value < collection[insert_index]:
         __lowerCamelCase    		=					collection[insert_index]
         insert_index -= 1
      if insert_index != temp_index:
         __lowerCamelCase    		=					insert_value
   return collection


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

     testmod()

     _SCREAMING_SNAKE_CASE	:		List[str]   =   input('''Enter numbers separated by a comma:\n''').strip()
     _SCREAMING_SNAKE_CASE	:		Optional[int]   =   [int(item) for item in user_input.split(''',''')]
     print(F"{insertion_sort(unsorted) = }")






