



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 	lowerCamelCase_    ( unittest.TestCase						):






						'''simple docstring'''







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







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				int:
									A							:					Optional[Any]              =						ort.SessionOptions()
									A							:					int              =						False
									return options







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

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

									A							:					Optional[Any]              =						np.random.RandomState(0 )
									A							:					List[str]              =						pipe(
									    prompt=__lowerCamelCase	,	image=__lowerCamelCase	,	mask_image=__lowerCamelCase	,	strength=0.75	,	guidance_scale=7.5	,	num_inference_steps=15	,	generator=__lowerCamelCase	,	output_type="np"	,	)

									A							:					Tuple              =						output.images[0]

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




from math import sqrt




def 	UpperCAmelCase   (						_lowerCamelCase = 100_0000    ):
			A							:					int              =						0
			A							:					int              =						0
			A							:					int

			while num_cuboids <= limit:
						max_cuboid_size += 1
						for sum_shortest_sides in range(2 ,    2 * max_cuboid_size + 1    ):
									if sqrt(sum_shortest_sides**2 + max_cuboid_size**2    ).is_integer():
												num_cuboids += (
												    min(_lowerCamelCase ,    sum_shortest_sides // 2    )
												    - max(1 ,    sum_shortest_sides - max_cuboid_size    )
												    + 1
												)

			return max_cuboid_size


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