
'''simple docstring'''



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class      A							(    __UpperCAmelCase			):



			def __init__(   self    ,       lowerCamelCase__    ,       lowerCamelCase__    )     ->						List[str]:




									'''simple docstring'''



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



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




									'''simple docstring'''



									lowercase__								=    torch.randn(
									    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    ,       generator=lowerCamelCase__    ,       )
									lowercase__								=    image.to(self.device    )

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

									for t in self.progress_bar(self.scheduler.timesteps    ):
															# 1. predict noise model_output
															lowercase__								=    self.unet(lowerCamelCase__    ,       lowerCamelCase__    ).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
															lowercase__								=    self.scheduler.step(lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__    ).prev_sample

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

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

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








'''simple docstring'''



def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						if right <= left:
												return
						lowercase__								=    lowercase__								=    left
						lowercase__								=    right
						lowercase__								=    sorting[left]
						while i <= b:
												if sorting[i] < pivot:
																		lowercase__	,  lowercase__								=    sorting[i], sorting[a]
																		a += 1
																		i += 1
												elif sorting[i] > pivot:
																		lowercase__	,  lowercase__								=    sorting[i], sorting[b]
																		b -= 1
												else:
																		i += 1
						quick_sort_apartition(lowercase__						,						lowercase__						,						a - 1		)
						quick_sort_apartition(lowercase__						,						b + 1						,						lowercase__		)





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						if left < right:
												lowercase__								=    lomuto_partition(lowercase__						,						lowercase__						,						lowercase__		)
												quick_sort_lomuto_partition(lowercase__						,						lowercase__						,						pivot_index - 1		)
												quick_sort_lomuto_partition(lowercase__						,						pivot_index + 1						,						lowercase__		)





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						lowercase__								=    sorting[right]
						lowercase__								=    left
						for i in range(lowercase__						,						lowercase__		):
												if sorting[i] < pivot:
																		lowercase__	,  lowercase__								=    sorting[i], sorting[store_index]
																		store_index += 1
						lowercase__	,  lowercase__								=    sorting[store_index], sorting[right]
						return store_index





def 	_A						(							lowercase__		):
						if len(lowercase__		) <= 1:
												return sorting
						return (
						    three_way_radix_quicksort([i for i in sorting if i < sorting[0]]		)
						    + [i for i in sorting if i == sorting[0]]
						    + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]		)
						)


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)

				__A       =      input("Enter numbers separated by a comma:\n").strip()
				__A       =      [int(item) for item in user_input.split(",")]
				quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
				print(unsorted)






