import math


def 				lowerCAmelCase__       (			a__    ,     a__ = 0    ,     a__ = 0 )      ->Any:




  '''simple docstring'''

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


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__ )      ->Tuple:  # Max Heap




  '''simple docstring'''

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

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

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

  if largest != index:
    _UpperCamelCase      ,						_UpperCamelCase           =    array[largest], array[index]
    heapify(__a    ,     __a    ,     __a )


def 				lowerCAmelCase__       (			a__ )      ->Tuple:




  '''simple docstring'''

  _UpperCamelCase           =    len(__a )

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

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

  return array


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__    ,     a__ )      ->str:




  '''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 				lowerCAmelCase__       (			a__    ,     a__    ,     a__    ,     a__ )      ->Any:




  '''simple docstring'''

  _UpperCamelCase           =    low
  _UpperCamelCase           =    high
  while True:
    while array[i] < pivot:
      i += 1
    j -= 1
    while pivot < array[j]:
      j -= 1
    if i >= j:
      return i
    _UpperCamelCase      ,						_UpperCamelCase           =    array[j], array[i]
    i += 1


def 				lowerCAmelCase__       (			a__ )      ->Union[str, Any]:




  '''simple docstring'''

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


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__    ,     a__    ,     a__ )      ->Union[str, Any]:




  '''simple docstring'''

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


if __name__ == "__main__":
   import doctest

   doctest.testmod()

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






"""simple docstring"""





import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
		import onnxruntime as ort



class __lowerCAmelCase					(       _lowercase  ,					unittest.TestCase   ):




			"""simple docstring"""







			pass





@nightly
@require_onnxruntime
@require_torch_gpu
class __lowerCAmelCase					(       unittest.TestCase   ):




			"""simple docstring"""







			@property
			def   lowerCamelCase__  (					self    :       Union[str, Any]						)   ->			Dict:







								"""simple docstring"""
								return (
								    "CUDAExecutionProvider",
								    {
								        "gpu_mem_limit": "15000000000",  # 15GB
								        "arena_extend_strategy": "kSameAsRequested",
								    },
								)




			@property
			def   lowerCamelCase__  (					self    :       List[str]						)   ->			List[Any]:







								"""simple docstring"""
								A_								=    ort.SessionOptions()
								A_								=    False
								return options




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







								"""simple docstring"""
								A_								=    load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo.png"						)
								A_								=    load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"						)
								A_								=    OnnxStableDiffusionInpaintPipeline.from_pretrained(
								    "runwayml/stable-diffusion-inpainting"							, revision="onnx"							, safety_checker=_snake_case							, feature_extractor=_snake_case							, provider=self.gpu_provider							, sess_options=self.gpu_options							, )
								pipe.set_progress_bar_config(disable=_snake_case						)

								A_								=    "A red cat sitting on a park bench"

								A_								=    np.random.RandomState(0						)
								A_								=    pipe(
								    prompt=_snake_case							, image=_snake_case							, mask_image=_snake_case							, guidance_scale=7.5							, num_inference_steps=10							, generator=_snake_case							, output_type="np"							, )
								A_								=    output.images
								A_								=    images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								A_								=    np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4]						)

								assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-3






			def   lowerCamelCase__  (					self    :       Optional[int]						)   ->			Tuple:







								"""simple docstring"""
								A_								=    load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo.png"						)
								A_								=    load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"						)
								A_								=    LMSDiscreteScheduler.from_pretrained(
								    "runwayml/stable-diffusion-inpainting"							, subfolder="scheduler"							, revision="onnx"						)
								A_								=    OnnxStableDiffusionInpaintPipeline.from_pretrained(
								    "runwayml/stable-diffusion-inpainting"							, revision="onnx"							, scheduler=_snake_case							, safety_checker=_snake_case							, feature_extractor=_snake_case							, provider=self.gpu_provider							, sess_options=self.gpu_options							, )
								pipe.set_progress_bar_config(disable=_snake_case						)

								A_								=    "A red cat sitting on a park bench"

								A_								=    np.random.RandomState(0						)
								A_								=    pipe(
								    prompt=_snake_case							, image=_snake_case							, mask_image=_snake_case							, guidance_scale=7.5							, num_inference_steps=20							, generator=_snake_case							, output_type="np"							, )
								A_								=    output.images
								A_								=    images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								A_								=    np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5]						)

								assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-3




