

a		=0  # The first color of the flag.
a		=1  # The second color of the flag.
a		=2  # The third color of the flag.
a		=(red, white, blue)






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					list:
	if not sequence:
		return []
	if len(lowerCamelCase__      ) == 1:
		return list(lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			0
	__lowerCamelCase    :    Tuple 	=			len(lowerCamelCase__      ) - 1
	__lowerCamelCase    :    Optional[Any] 	=			0
	while mid <= high:
		if sequence[mid] == colors[0]:
			__lowerCamelCase    :    Union[str, Any] 	=			sequence[mid], sequence[low]
			low += 1
			mid += 1
		elif sequence[mid] == colors[1]:
			mid += 1
		elif sequence[mid] == colors[2]:
			__lowerCamelCase    :    Dict 	=			sequence[high], sequence[mid]
			high -= 1
		else:
			__lowerCamelCase    :    int 	=			F"The elements inside the sequence must contains only {colors} values"
			raise ValueError(lowerCamelCase__      )
	return sequence


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				a		=input("""Enter numbers separated by commas:\n""").strip()
				a		=[int(item.strip()) for item in user_input.split(""",""")]
				print(F"""{dutch_national_flag_sort(unsorted)}""")







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 	A_		(	unittest.TestCase    ):
							@property
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								return (
								    "CUDAExecutionProvider",
								    {
								        "gpu_mem_limit": "15000000000",  # 15GB
								        "arena_extend_strategy": "kSameAsRequested",
								    },
								)
							@property
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    Optional[int] 	=			ort.SessionOptions()
								__lowerCamelCase    :    Tuple 	=			False
								return options


							def    lowerCAmelCase					(		self		:      Any):
								__lowerCamelCase    :    Tuple 	=			load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/overture-creations-5sI6fQgYIuo.png')
								__lowerCamelCase    :    List[str] 	=			load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png')
								__lowerCamelCase    :    Optional[int] 	=			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
								__lowerCamelCase    :    Dict 	=			OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
								    'CompVis/stable-diffusion-v1-4'       ,revision='onnx'       ,safety_checker=SCREAMING_SNAKE_CASE__       ,feature_extractor=SCREAMING_SNAKE_CASE__       ,provider=self.gpu_provider       ,sess_options=self.gpu_options       ,)
								pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Union[str, Any] 	=			'A red cat sitting on a park bench'

								__lowerCamelCase    :    Any 	=			np.random.RandomState(0)
								__lowerCamelCase    :    List[Any] 	=			pipe(
								    prompt=SCREAMING_SNAKE_CASE__       ,image=SCREAMING_SNAKE_CASE__       ,mask_image=SCREAMING_SNAKE_CASE__       ,strength=0.75       ,guidance_scale=7.5       ,num_inference_steps=1_5       ,generator=SCREAMING_SNAKE_CASE__       ,output_type='np'       ,)

								__lowerCamelCase    :    Union[str, Any] 	=			output.images[0]

								assert image.shape == (5_1_2, 5_1_2, 3)
								assert np.abs(expected_image - image).max() < 1E-2




