





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 			UpperCamelCase_		(  unittest.TestCase					):




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




					@property
					def 	lowerCAmelCase			(		self  )		->							Optional[int]:
										_snake_case									=				ort.SessionOptions()
										_snake_case									=				False
										return options






					def 	lowerCAmelCase			(		self  )		->							List[str]:
										_snake_case									=				load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
										    '/in_paint/overture-creations-5sI6fQgYIuo.png'  )
										_snake_case									=				load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
										    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'  )
										_snake_case									=				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
										_snake_case									=				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_  )

										_snake_case									=				'A red cat sitting on a park bench'

										_snake_case									=				np.random.RandomState(0  )
										_snake_case									=				pipe(
										    prompt=lowerCAmelCase_				,						image=lowerCAmelCase_				,						mask_image=lowerCAmelCase_				,						strength=0.75				,						guidance_scale=7.5				,						num_inference_steps=15				,						generator=lowerCAmelCase_				,						output_type='np'				,						)

										_snake_case									=				output.images[0]

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










def 						lowerCamelCase__     (     UpperCamelCase__			:       list[int]       ,    UpperCamelCase__			:       int						)			-> bool:



					'''simple docstring'''




					_snake_case									=				len(UpperCamelCase__						)
					_snake_case									=				[[False] * (required_sum + 1) for _ in range(arr_len + 1						)]

					# for each arr value, a sum of zero(0) can be formed by not taking any element
					# hence True/1
					for i in range(arr_len + 1						):
										_snake_case									=				True

					# sum is not zero and set is empty then false
					for i in range(1       ,    required_sum + 1						):
										_snake_case									=				False

					for i in range(1       ,    arr_len + 1						):
										for j in range(1       ,    required_sum + 1						):
															if arr[i - 1] > j:
																				_snake_case									=				subset[i - 1][j]
															if arr[i - 1] <= j:
																				_snake_case									=				subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

					return subset[arr_len][required_sum]


if __name__ == "__main__":
					import doctest

					doctest.testmod()



