






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  _UpperCAmelCase   (			A__						,unittest.TestCase      ):

				"""simple docstring"""




				pass



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

				"""simple docstring"""




				@property
				def 							lowercase__  (	self		:       Dict    ):

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




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

								'''simple docstring'''
								lowercase__  					=     ort.SessionOptions()
								lowercase__  					=     False
								return options




				def 							lowercase__  (	self		:       int    ):

								'''simple docstring'''
								lowercase__  					=     load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/in_paint/overture-creations-5sI6fQgYIuo.png'''    )
								lowercase__  					=     load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''    )
								lowercase__  					=     OnnxStableDiffusionInpaintPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-inpainting''',    revision='''onnx''',    safety_checker=lowerCamelCase,    feature_extractor=lowerCamelCase,    provider=self.gpu_provider,    sess_options=self.gpu_options,    )
								pipe.set_progress_bar_config(disable=lowerCamelCase    )

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

								lowercase__  					=     np.random.RandomState(0    )
								lowercase__  					=     pipe(
								    prompt=lowerCamelCase,    image=lowerCamelCase,    mask_image=lowerCamelCase,    guidance_scale=7.5,    num_inference_steps=10,    generator=lowerCamelCase,    output_type='''np''',    )
								lowercase__  					=     output.images
								lowercase__  					=     images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								lowercase__  					=     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 							lowercase__  (	self		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/in_paint/overture-creations-5sI6fQgYIuo.png'''    )
								lowercase__  					=     load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''    )
								lowercase__  					=     LMSDiscreteScheduler.from_pretrained(
								    '''runwayml/stable-diffusion-inpainting''',    subfolder='''scheduler''',    revision='''onnx'''    )
								lowercase__  					=     OnnxStableDiffusionInpaintPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-inpainting''',    revision='''onnx''',    scheduler=lowerCamelCase,    safety_checker=lowerCamelCase,    feature_extractor=lowerCamelCase,    provider=self.gpu_provider,    sess_options=self.gpu_options,    )
								pipe.set_progress_bar_config(disable=lowerCamelCase    )

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

								lowercase__  					=     np.random.RandomState(0    )
								lowercase__  					=     pipe(
								    prompt=lowerCamelCase,    image=lowerCamelCase,    mask_image=lowerCamelCase,    guidance_scale=7.5,    num_inference_steps=20,    generator=lowerCamelCase,    output_type='''np''',    )
								lowercase__  					=     output.images
								lowercase__  					=     images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								lowercase__  					=     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












def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     len(lowerCamelCase_							)
				lowercase__  					=     [[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							):
								lowercase__  					=     True

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

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




