



"""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  __SCREAMING_SNAKE_CASE			(   unittest.TestCase     ):


					'''simple docstring'''





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



					@property
					def 	snake_case ( self							:						int  )->      Tuple:
								lowerCamelCase__	:	List[str]				=ort.SessionOptions()
								lowerCamelCase__	:	List[Any]				=False
								return options




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

								lowerCamelCase__	:	int				='''A red cat sitting on a park bench'''

								lowerCamelCase__	:	Any				=np.random.RandomState(0  )
								lowerCamelCase__	:	Dict				=pipe(
								    prompt=lowercase_,						image=lowercase_,						mask_image=lowercase_,						strength=0.75,						guidance_scale=7.5,						num_inference_steps=15,						generator=lowercase_,						output_type='''np''',						)

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

								assert image.shape == (512, 512, 3)
								assert np.abs(expected_image - image  ).max() < 1E-2









"""simple docstring"""






def   snake_case__     (	__lowerCamelCase       :					int = 4000000			):
			"""simple docstring"""
			lowerCamelCase__	:	Dict				=[]
			lowerCamelCase__			,     lowerCamelCase__	:	Union[str, Any]				=0, 1
			while b <= n:
						if b % 2 == 0:
									even_fibs.append(__lowerCamelCase			)
						lowerCamelCase__			,     lowerCamelCase__	:	Union[str, Any]				=b, a + b
			return sum(__lowerCamelCase			)


if __name__ == "__main__":
		print(f'{solution() = }')




