
"""simple docstring"""





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput






class    _UpperCAmelCase(   lowerCamelCase     ):



  def __init__( self    ,		__a    ,		__a)	->	List[Any]:






       '''simple docstring'''
       super().__init__()
       self.register_modules(unet=_lowercase    ,		scheduler=_lowercase)




  @torch.no_grad()
  def __call__( self    ,		__a = 1    ,		__a = None    ,		__a = 50    ,		__a = "pil"    ,		__a = True    ,		**__a    ,		)	->	Union[ImagePipelineOutput, Tuple]:






       '''simple docstring'''
       _UpperCamelCase										=  torch.randn(
           (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    ,		generator=_lowercase    ,		)
       _UpperCamelCase										=  image.to(self.device)

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

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

            # 2. predict previous mean of image x_t-1 and add variance depending on eta
            # eta corresponds to η in paper and should be between [0, 1]
            # do x_t -> x_t-1
            _UpperCamelCase										=  self.scheduler.step(_lowercase    ,		_lowercase    ,		_lowercase).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(_lowercase)

       if not return_dict:
            return (image,), "This is a local test"

       return ImagePipelineOutput(images=_lowercase), "This is a local test"








"""simple docstring"""





from __future__ import annotations



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Optional[int]:


					"""simple docstring"""





					if len(__snake_case     ) <= 1 or n <= 1:
										return

					insert_next(__snake_case,					n - 1     )
					rec_insertion_sort(__snake_case,					n - 1     )



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Dict:


					"""simple docstring"""





					if index >= len(__snake_case     ) or collection[index - 1] <= collection[index]:
										return

					# Swaps adjacent elements since they are not in ascending order
					_UpperCamelCase			,  _UpperCamelCase										=  (
					    collection[index],
					    collection[index - 1],
					)

					insert_next(__snake_case,					index + 1     )


if __name__ == "__main__":
				_a														=		input("""Enter integers separated by spaces: """)
				_a														=		[int(num) for num in numbers.split()]
				rec_insertion_sort(number_list, len(number_list))
				print(number_list)






