

'''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 lowerCamelCase			(  unittest.TestCase						):



			'''simple docstring'''






			@property
			def lowercase__						(				self	:						Any			)      ->		Tuple:





									'''simple docstring'''
									return (
									    "CUDAExecutionProvider",
									    {
									        "gpu_mem_limit": "15000000000",  # 15GB
									        "arena_extend_strategy": "kSameAsRequested",
									    },
									)


			@property
			def lowercase__						(				self	:						Optional[Any]			)      ->		Optional[Any]:





									'''simple docstring'''
									A__					:       Any     =ort.SessionOptions()
									A__					:       List[Any]     =False
									return options



			def lowercase__						(				self	:						int			)      ->		Optional[int]:





									'''simple docstring'''
									A__					:       int     =load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/in_paint/overture-creations-5sI6fQgYIuo.png"""			)
									A__					:       List[str]     =load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""			)
									A__					:       List[str]     =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__					:       Optional[int]     =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__					:       Optional[int]     ="""A red cat sitting on a park bench"""

									A__					:       Dict     =np.random.RandomState(0			)
									A__					:       int     =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__					:       List[str]     =output.images[0]

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




'''simple docstring'''



def 						__lowerCamelCase	(       __snake_case					:		int	)	->							bool:






						"""simple docstring"""





						if p < 2:
												raise ValueError("""p should not be less than 2!"""	)
						elif p == 2:
												return True

						A__					:       Any     =4
						A__					:       int     =(1 << p) - 1
						for _ in range(p - 2	):
												A__					:       Dict     =((s * s) - 2) % m
						return s == 0


if __name__ == "__main__":
		print(lucas_lehmer_test(7))
		print(lucas_lehmer_test(11))

