"""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    __lowercase     (					_a					,    unittest.TestCase):







						"""simple docstring"""







						pass







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







						"""simple docstring"""







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







						@property
						def 						__UpperCamelCase							(self       ):
											snake_case_					:		str 					=					ort.SessionOptions()
											snake_case_					:		Any 					=					False
											return options







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Dict 					=					load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/in_paint/overture-creations-5sI6fQgYIuo.png"""       )
											snake_case_					:		Optional[int] 					=					load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""       )
											snake_case_					:		Dict 					=					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_       )

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

											snake_case_					:		List[str] 					=					np.random.RandomState(0       )
											snake_case_					:		Optional[int] 					=					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"""   ,		)
											snake_case_					:		Optional[int] 					=					output.images
											snake_case_					:		List[Any] 					=					images[0, 2_55:2_58, 2_55:2_58, -1]

											assert images.shape == (1, 5_12, 5_12, 3)
											snake_case_					:		Tuple 					=					np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]       )

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







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		List[Any] 					=					load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/in_paint/overture-creations-5sI6fQgYIuo.png"""       )
											snake_case_					:		List[Any] 					=					load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""       )
											snake_case_					:		List[str] 					=					LMSDiscreteScheduler.from_pretrained(
											    """runwayml/stable-diffusion-inpainting"""   ,		subfolder="""scheduler"""   ,		revision="""onnx"""       )
											snake_case_					:		List[str] 					=					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_       )

											snake_case_					:		List[Any] 					=					"""A red cat sitting on a park bench"""

											snake_case_					:		str 					=					np.random.RandomState(0       )
											snake_case_					:		Optional[int] 					=					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"""   ,		)
											snake_case_					:		Union[str, Any] 					=					output.images
											snake_case_					:		str 					=					images[0, 2_55:2_58, 2_55:2_58, -1]

											assert images.shape == (1, 5_12, 5_12, 3)
											snake_case_					:		Optional[int] 					=					np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]       )

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









"""simple docstring"""

from random import randint
from tempfile import TemporaryFile

import numpy as np







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  Optional[int]			):



					"""simple docstring"""





					snake_case_					:		Union[str, Any] 					=					0
					if start < end:
										snake_case_					:		Union[str, Any] 					=					randint(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
										snake_case_					:		List[Any] 					=					a[end]
										snake_case_					:		Dict 					=					a[pivot]
										snake_case_					:		Any 					=					temp

										snake_case_					,			snake_case_					:		Dict 					=					_in_place_partition(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
										count += _in_place_quick_sort(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    p - 1			)
										count += _in_place_quick_sort(SCREAMING_SNAKE_CASE__				,    p + 1				,    SCREAMING_SNAKE_CASE__			)
					return count







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Dict			):



					"""simple docstring"""





					snake_case_					:		Tuple 					=					0
					snake_case_					:		List[Any] 					=					randint(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					snake_case_					:		Dict 					=					a[end]
					snake_case_					:		List[Any] 					=					a[pivot]
					snake_case_					:		Optional[Any] 					=					temp
					snake_case_					:		List[str] 					=					start - 1
					for index in range(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			):
										count += 1
										if a[index] < a[end]:  # check if current val is less than pivot value
															snake_case_					:		Any 					=					new_pivot_index + 1
															snake_case_					:		Tuple 					=					a[new_pivot_index]
															snake_case_					:		Optional[int] 					=					a[index]
															snake_case_					:		Tuple 					=					temp

					snake_case_					:		Union[str, Any] 					=					a[new_pivot_index + 1]
					snake_case_					:		Union[str, Any] 					=					a[end]
					snake_case_					:		Union[str, Any] 					=					temp
					return new_pivot_index + 1, count


a_			   =					TemporaryFile()
a_			   =					100  # 1000 elements are to be sorted


a_   ,    a_			   =					0, 1  # mean and standard deviation
a_			   =					np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)


outfile.seek(0)  # using the same array
a_			   =					np.load(outfile)
a_			   =					len(M) - 1
a_			   =					_in_place_quick_sort(M, 0, r)

print(
    '''No of Comparisons for 100 elements selected from a standard normal distribution'''
    '''is :'''
)


print(z)




