
"""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 			_SCREAMING_SNAKE_CASE					(			A__	, unittest.TestCase							):
			# FIXME: add fast tests
			pass




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






			@property
			def  __lowerCAmelCase (					self       )			->     List[Any]:
						return (
						    "CUDAExecutionProvider",
						    {
						        "gpu_mem_limit": "15000000000",  # 15GB
						        "arena_extend_strategy": "kSameAsRequested",
						    },
						)






			@property
			def  __lowerCAmelCase (					self       )			->     Any:
						lowerCAmelCase_    :str			     =  ort.SessionOptions()
						lowerCAmelCase_    :int			     =  False
						return options






			def  __lowerCAmelCase (					self       )			->     List[str]:
						lowerCAmelCase_    :Any			     =  load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/in_paint/overture-creations-5sI6fQgYIuo.png"""       )
						lowerCAmelCase_    :List[Any]			     =  load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""       )
						lowerCAmelCase_    :List[Any]			     =  OnnxStableDiffusionInpaintPipeline.from_pretrained(
						    """runwayml/stable-diffusion-inpainting"""       ,   revision="""onnx"""       ,   safety_checker=__A       ,   feature_extractor=__A       ,   provider=self.gpu_provider       ,   sess_options=self.gpu_options       ,   )
						pipe.set_progress_bar_config(disable=__A       )

						lowerCAmelCase_    :Dict			     =  """A red cat sitting on a park bench"""

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

						assert images.shape == (1, 512, 512, 3)
						lowerCAmelCase_    :str			     =  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       )			->     List[str]:
						lowerCAmelCase_    :int			     =  load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/in_paint/overture-creations-5sI6fQgYIuo.png"""       )
						lowerCAmelCase_    :int			     =  load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""       )
						lowerCAmelCase_    :List[str]			     =  LMSDiscreteScheduler.from_pretrained(
						    """runwayml/stable-diffusion-inpainting"""       ,   subfolder="""scheduler"""       ,   revision="""onnx"""       )
						lowerCAmelCase_    :str			     =  OnnxStableDiffusionInpaintPipeline.from_pretrained(
						    """runwayml/stable-diffusion-inpainting"""       ,   revision="""onnx"""       ,   scheduler=__A       ,   safety_checker=__A       ,   feature_extractor=__A       ,   provider=self.gpu_provider       ,   sess_options=self.gpu_options       ,   )
						pipe.set_progress_bar_config(disable=__A       )

						lowerCAmelCase_    :Optional[Any]			     =  """A red cat sitting on a park bench"""

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

						assert images.shape == (1, 512, 512, 3)
						lowerCAmelCase_    :Optional[int]			     =  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










"""simple docstring"""




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


def     lowercase			(SCREAMING_SNAKE_CASE_		:				list  )   ->						list:
						if not sequence:
												return []
						if len(SCREAMING_SNAKE_CASE_  ) == 1:
												return list(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					0
						SCREAMING_SNAKE_CASE													=					len(SCREAMING_SNAKE_CASE_  ) - 1
						SCREAMING_SNAKE_CASE													=					0
						while mid <= high:
												if sequence[mid] == colors[0]:
																		SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					sequence[mid], sequence[low]
																		low += 1
																		mid += 1
												elif sequence[mid] == colors[1]:
																		mid += 1
												elif sequence[mid] == colors[2]:
																		SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					sequence[high], sequence[mid]
																		high -= 1
												else:
																		SCREAMING_SNAKE_CASE													=					F'The elements inside the sequence must contains only {colors} values'
																		raise ValueError(SCREAMING_SNAKE_CASE_  )
						return sequence


if __name__ == "__main__":
	import doctest

	doctest.testmod()

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


