"""simple docstring"""




from __future__ import annotations






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							[]
							A__	     =							input_list[low:mid], input_list[mid : high + 1]
							while left and right:
														result.append((left if left[0] <= right[0] else right).pop(0				)				)
							A__	     =							result + left + right
							return input_list






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							if len(__UpperCamelCase				) <= 1:
														return input_list
							A__	     =							list(__UpperCamelCase				)

							# iteration for two-way merging
							A__	     =							2
							while p <= len(__UpperCamelCase				):
														# getting low, high and middle value for merge-sort of single list
														for i in range(0       ,    len(__UpperCamelCase				)       ,    __UpperCamelCase				):
																					A__	     =							i
																					A__	     =							i + p - 1
																					A__	     =							(low + high + 1) // 2
																					A__	     =							merge(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase				)
														# final merge of last two parts
														if p * 2 >= len(__UpperCamelCase				):
																					A__	     =							i
																					A__	     =							merge(__UpperCamelCase       ,    0       ,    __UpperCamelCase       ,    len(__UpperCamelCase				) - 1				)
																					break
														p *= 2

							return input_list


if __name__ == "__main__":
				__lowerCamelCase											=  input("Enter numbers separated by a comma:\n").strip()
				if user_input == "":
								__lowerCamelCase											=  []
				else:
								__lowerCamelCase											=  [int(item.strip()) for item in user_input.split(",")]
				print(iter_merge_sort(unsorted))






"""simple docstring"""




import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
				import onnxruntime as ort
@nightly
@require_onnxruntime
@require_torch_gpu
class     UpperCamelCase__(			unittest.TestCase ):





						@property
						def 				snake_case__	(       self     )					->				int:
													return (
													    "CUDAExecutionProvider",
													    {
													        "gpu_mem_limit": "15000000000",  # 15GB
													        "arena_extend_strategy": "kSameAsRequested",
													    },
													)





						@property
						def 				snake_case__	(       self     )					->				Union[str, Any]:
													A__	     =							ort.SessionOptions()
													A__	     =							False
													return options





						def 				snake_case__	(       self     )					->				str:
													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__	     =							load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'     )

													# using the PNDM scheduler by default
													A__	     =							OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
													    'CompVis/stable-diffusion-v1-4'		,revision='onnx'		,safety_checker=__UpperCAmelCase		,feature_extractor=__UpperCAmelCase		,provider=self.gpu_provider		,sess_options=self.gpu_options		,)
													pipe.set_progress_bar_config(disable=__UpperCAmelCase     )

													A__	     =							'A red cat sitting on a park bench'

													A__	     =							np.random.RandomState(0     )
													A__	     =							pipe(
													    prompt=__UpperCAmelCase		,image=__UpperCAmelCase		,mask_image=__UpperCAmelCase		,strength=0.7_5		,guidance_scale=7.5		,num_inference_steps=15		,generator=__UpperCAmelCase		,output_type='np'		,)

													A__	     =							output.images[0]

													assert image.shape == (5_12, 5_12, 3)
													assert np.abs(expected_image - image     ).max() < 1e-2





