


"""simple docstring"""







import math




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_ = 0  ,						lowerCAmelCase_ = 0       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    end or len(lowerCAmelCase_       )
					for i in range(lowerCAmelCase_  ,						lowerCAmelCase_       ):
										__SCREAMING_SNAKE_CASE						       =    i
										__SCREAMING_SNAKE_CASE						       =    array[i]
										while temp_index != start and temp_index_value < array[temp_index - 1]:
															__SCREAMING_SNAKE_CASE						       =    array[temp_index - 1]
															temp_index -= 1
										__SCREAMING_SNAKE_CASE						       =    temp_index_value
					return array




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):  # Max Heap




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    index
					__SCREAMING_SNAKE_CASE						       =    2 * index + 1  # Left Node
					__SCREAMING_SNAKE_CASE						       =    2 * index + 2  # Right Node

					if left_index < heap_size and array[largest] < array[left_index]:
										__SCREAMING_SNAKE_CASE						       =    left_index

					if right_index < heap_size and array[largest] < array[right_index]:
										__SCREAMING_SNAKE_CASE						       =    right_index

					if largest != index:
										__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    array[largest], array[index]
										heapify(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       )




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    len(lowerCAmelCase_       )

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

					for i in range(n - 1  ,						0  ,						-1       ):
										__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    array[0], array[i]
										heapify(lowerCAmelCase_  ,						0  ,						lowerCAmelCase_       )

					return array




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''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_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    low
					__SCREAMING_SNAKE_CASE						       =    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       ,  __SCREAMING_SNAKE_CASE						       =    array[j], array[i]
										i += 1




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					if len(lowerCAmelCase_       ) == 0:
										return array
					__SCREAMING_SNAKE_CASE						       =    2 * math.ceil(math.loga(len(lowerCAmelCase_       )       )       )
					__SCREAMING_SNAKE_CASE						       =    16
					return intro_sort(lowerCAmelCase_  ,						0  ,						len(lowerCAmelCase_       )  ,						lowerCAmelCase_  ,						lowerCAmelCase_       )




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					while end - start > size_threshold:
										if max_depth == 0:
															return heap_sort(lowerCAmelCase_       )
										max_depth -= 1
										__SCREAMING_SNAKE_CASE						       =    median_of_a(lowerCAmelCase_  ,						lowerCAmelCase_  ,						start + ((end - start) // 2) + 1  ,						end - 1       )
										__SCREAMING_SNAKE_CASE						       =    partition(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       )
										intro_sort(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       )
										__SCREAMING_SNAKE_CASE						       =    p
					return insertion_sort(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()

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




"""simple docstring"""







import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin




@skip_mps
class 			UpperCamelCase_     (			UpperCamelCase      ,  UpperCamelCase      ,  unittest.TestCase):

	"""simple docstring"""





	snake_case__    :  str    =      IFInpaintingSuperResolutionPipeline
	snake_case__    :  Optional[Any]    =      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
	snake_case__    :  Union[str, Any]    =      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"})
	snake_case__    :  Dict    =      PipelineTesterMixin.required_optional_params - {"latents"}




	def   UpperCAmelCase_				(			self      :    List[Any]       )	->     Optional[int]:
						return self._get_superresolution_dummy_components()




	def   UpperCAmelCase_				(			self      :    List[str]      ,      UpperCAmelCase__      :    List[Any]      ,      UpperCAmelCase__      :    List[str]=0       )	->     List[str]:
						if str(UpperCAmelCase__       ).startswith("mps"       ):
											__SCREAMING_SNAKE_CASE						       =    torch.manual_seed(UpperCAmelCase__       )
						else:
											__SCREAMING_SNAKE_CASE						       =    torch.Generator(device=UpperCAmelCase__       ).manual_seed(UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    floats_tensor((1, 3, 1_6, 1_6)      ,      rng=random.Random(UpperCAmelCase__       )       ).to(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    floats_tensor((1, 3, 3_2, 3_2)      ,      rng=random.Random(UpperCAmelCase__       )       ).to(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    floats_tensor((1, 3, 3_2, 3_2)      ,      rng=random.Random(UpperCAmelCase__       )       ).to(UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    {
						    "prompt": "A painting of a squirrel eating a burger",
						    "image": image,
						    "original_image": original_image,
						    "mask_image": mask_image,
						    "generator": generator,
						    "num_inference_steps": 2,
						    "output_type": "numpy",
						}

						return inputs




	@unittest.skipIf(
	    torch_device != "cuda" or not is_xformers_available()      ,      reason="XFormers attention is only available with CUDA and `xformers` installed"      ,      )
	def   UpperCAmelCase_				(			self      :    str       )	->     Dict:
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3       )




	def   UpperCAmelCase_				(			self      :    str       )	->     Tuple:
						self._test_save_load_optional_components()




	@unittest.skipIf(torch_device != "cuda"      ,      reason="float16 requires CUDA"       )
	def   UpperCAmelCase_				(			self      :    Tuple       )	->     Tuple:
						# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
						super().test_save_load_floataa(expected_max_diff=1E-1       )




	def   UpperCAmelCase_				(			self      :    Optional[Any]       )	->     List[Any]:
						self._test_attention_slicing_forward_pass(expected_max_diff=1E-2       )




	def   UpperCAmelCase_				(			self      :    Any       )	->     Union[str, Any]:
						self._test_save_load_local()




	def   UpperCAmelCase_				(			self      :    Union[str, Any]       )	->     Optional[Any]:
						self._test_inference_batch_single_identical(
						    expected_max_diff=1E-2      ,      )
