




import math





def        __UpperCAmelCase						(							lowerCamelCase_ :     Optional[int]  ,							lowerCamelCase_ :     Optional[Any] = 0  ,							lowerCamelCase_ :     int = 0  )       ->  Tuple:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : List[str]					  =      end or len(_a  )
			for i in range(_a  ,							_a  ):
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      i
						SCREAMING_SNAKE_CASE_   : Any					  =      array[i]
						while temp_index != start and temp_index_value < array[temp_index - 1]:
									SCREAMING_SNAKE_CASE_   : Tuple					  =      array[temp_index - 1]
									temp_index -= 1
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      temp_index_value
			return array





def        __UpperCAmelCase						(							lowerCamelCase_ :     Optional[int]  ,							lowerCamelCase_ :     List[Any]  ,							lowerCamelCase_ :     Optional[Any]  )       ->  Optional[int]:  # Max Heap







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      index
			SCREAMING_SNAKE_CASE_   : str					  =      2 * index + 1  # Left Node
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      2 * index + 2  # Right Node

			if left_index < heap_size and array[largest] < array[left_index]:
						SCREAMING_SNAKE_CASE_   : Tuple					  =      left_index

			if right_index < heap_size and array[largest] < array[right_index]:
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      right_index

			if largest != index:
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      array[largest], array[index]
						heapify(_a  ,							_a  ,							_a  )





def        __UpperCAmelCase						(							lowerCamelCase_ :     Optional[Any]  )       ->  Dict:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      len(_a  )

			for i in range(n // 2  ,							-1  ,							-1  ):
						heapify(_a  ,							_a  ,							_a  )

			for i in range(n - 1  ,							0  ,							-1  ):
						SCREAMING_SNAKE_CASE_   : int					  =      array[0], array[i]
						heapify(_a  ,							0  ,							_a  )

			return array





def        __UpperCAmelCase						(							lowerCamelCase_ :     Dict  ,							lowerCamelCase_ :     Union[str, Any]  ,							lowerCamelCase_ :     str  ,							lowerCamelCase_ :     Tuple  )       ->  Dict:







			"""simple docstring"""





			if (array[first_index] > array[middle_index]) != (
			    array[first_index] > array[last_index]
			):
						return array[first_index]
			elif (array[middle_index] > array[first_index]) != (
			    array[middle_index] > array[last_index]
			):
						return array[middle_index]
			else:
						return array[last_index]





def        __UpperCAmelCase						(							lowerCamelCase_ :     List[str]  ,							lowerCamelCase_ :     Optional[Any]  ,							lowerCamelCase_ :     Union[str, Any]  ,							lowerCamelCase_ :     Optional[int]  )       ->  Any:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      low
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      high
			while True:
						while array[i] < pivot:
									i += 1
						j -= 1
						while pivot < array[j]:
									j -= 1
						if i >= j:
									return i
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      array[j], array[i]
						i += 1





def        __UpperCAmelCase						(							lowerCamelCase_ :     Union[str, Any]  )       ->  Optional[Any]:







			"""simple docstring"""





			if len(_a  ) == 0:
						return array
			SCREAMING_SNAKE_CASE_   : int					  =      2 * math.ceil(math.loga(len(_a  )  )  )
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      16
			return intro_sort(_a  ,							0  ,							len(_a  )  ,							_a  ,							_a  )





def        __UpperCAmelCase						(							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     Tuple  ,							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     Any  ,							lowerCamelCase_ :     Optional[int]  )       ->  List[Any]:







			"""simple docstring"""





			while end - start > size_threshold:
						if max_depth == 0:
									return heap_sort(_a  )
						max_depth -= 1
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      median_of_a(_a  ,							_a  ,							start + ((end - start) // 2) + 1  ,							end - 1  )
						SCREAMING_SNAKE_CASE_   : List[str]					  =      partition(_a  ,							_a  ,							_a  ,							_a  )
						intro_sort(_a  ,							_a  ,							_a  ,							_a  ,							_a  )
						SCREAMING_SNAKE_CASE_   : int					  =      p
			return insertion_sort(_a  ,							_a  ,							_a  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						UpperCamelCase__ :   List[Any]						=							input('''Enter numbers separated by a comma : ''').strip()
						UpperCamelCase__ :   Any						=							[float(item) for item in user_input.split(''',''')]
						print(sort(unsorted))






import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			@property
			def 				__UpperCamelCase			(      self				:			List[str]						)				->							Union[str, Any]:

				"""simple docstring"""

				torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   str					  =							UNetaDModel(
				    block_out_channels=(32, 64)   ,   layers_per_block=2   ,   sample_size=32   ,   in_channels=3   ,   out_channels=3   ,   down_block_types=("DownBlock2D", "AttnDownBlock2D")   ,   up_block_types=("AttnUpBlock2D", "UpBlock2D")   ,   )
				return model






			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							self.dummy_uncond_unet
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							KarrasVeScheduler()

				SCREAMING_SNAKE_CASE :   Any					  =							KarrasVePipeline(unet=a   ,   scheduler=a						)
				pipe.to(a						)
				pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   Any					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							pipe(num_inference_steps=2   ,   generator=a   ,   output_type="numpy"						).images

				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							pipe(num_inference_steps=2   ,   generator=a   ,   output_type="numpy"   ,   return_dict=a						)[0]

				SCREAMING_SNAKE_CASE :   List[Any]					  =							image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE :   Any					  =							image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				SCREAMING_SNAKE_CASE :   str					  =							np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2






@slow
@require_torch
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			def 				__UpperCamelCase			(      self				:			int						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							"google/ncsnpp-celebahq-256"
				SCREAMING_SNAKE_CASE :   List[Any]					  =							UNetaDModel.from_pretrained(a						)
				SCREAMING_SNAKE_CASE :   Any					  =							KarrasVeScheduler()

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							KarrasVePipeline(unet=a   ,   scheduler=a						)
				pipe.to(a						)
				pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Any					  =							pipe(num_inference_steps=20   ,   generator=a   ,   output_type="numpy"						).images

				SCREAMING_SNAKE_CASE :   List[str]					  =							image[0, -3:, -3:, -1]
				assert image.shape == (1, 256, 256, 3)
				SCREAMING_SNAKE_CASE :   str					  =							np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2