






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   lowerCamelCase				(_snake_case			,  unittest.TestCase      ):
		pass





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



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



		@property
		def  __UpperCAmelCase      (							self       )     ->							int:
							UpperCAmelCase_   :				Union[str, Any]						=						ort.SessionOptions()
							UpperCAmelCase_   :				Any						=						False
							return options



		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							UpperCAmelCase_   :				Any						=						load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/in_paint/overture-creations-5sI6fQgYIuo.png'       )
							UpperCAmelCase_   :				Union[str, Any]						=						load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'       )
							UpperCAmelCase_   :				Optional[Any]						=						OnnxStableDiffusionInpaintPipeline.from_pretrained(
							    'runwayml/stable-diffusion-inpainting'    ,		revision='onnx'    ,		safety_checker=_a    ,		feature_extractor=_a    ,		provider=self.gpu_provider    ,		sess_options=self.gpu_options    ,		)
							pipe.set_progress_bar_config(disable=_a       )

							UpperCAmelCase_   :				Optional[int]						=						'A red cat sitting on a park bench'

							UpperCAmelCase_   :				str						=						np.random.RandomState(0       )
							UpperCAmelCase_   :				List[str]						=						pipe(
							    prompt=_a    ,		image=_a    ,		mask_image=_a    ,		guidance_scale=7.5    ,		num_inference_steps=1_0    ,		generator=_a    ,		output_type='np'    ,		)
							UpperCAmelCase_   :				Dict						=						output.images
							UpperCAmelCase_   :				int						=						images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

							assert images.shape == (1, 5_1_2, 5_1_2, 3)
							UpperCAmelCase_   :				Optional[Any]						=						np.array([0.25_14, 0.30_07, 0.35_17, 0.17_90, 0.23_82, 0.31_67, 0.19_44, 0.22_73, 0.24_64]       )

							assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3



		def  __UpperCAmelCase      (							self       )     ->							Optional[Any]:
							UpperCAmelCase_   :				int						=						load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/in_paint/overture-creations-5sI6fQgYIuo.png'       )
							UpperCAmelCase_   :				Tuple						=						load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'       )
							UpperCAmelCase_   :				Optional[Any]						=						LMSDiscreteScheduler.from_pretrained(
							    'runwayml/stable-diffusion-inpainting'    ,		subfolder='scheduler'    ,		revision='onnx'       )
							UpperCAmelCase_   :				Union[str, Any]						=						OnnxStableDiffusionInpaintPipeline.from_pretrained(
							    'runwayml/stable-diffusion-inpainting'    ,		revision='onnx'    ,		scheduler=_a    ,		safety_checker=_a    ,		feature_extractor=_a    ,		provider=self.gpu_provider    ,		sess_options=self.gpu_options    ,		)
							pipe.set_progress_bar_config(disable=_a       )

							UpperCAmelCase_   :				Optional[int]						=						'A red cat sitting on a park bench'

							UpperCAmelCase_   :				int						=						np.random.RandomState(0       )
							UpperCAmelCase_   :				List[str]						=						pipe(
							    prompt=_a    ,		image=_a    ,		mask_image=_a    ,		guidance_scale=7.5    ,		num_inference_steps=2_0    ,		generator=_a    ,		output_type='np'    ,		)
							UpperCAmelCase_   :				Tuple						=						output.images
							UpperCAmelCase_   :				Union[str, Any]						=						images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

							assert images.shape == (1, 5_1_2, 5_1_2, 3)
							UpperCAmelCase_   :				Any						=						np.array([0.00_86, 0.00_77, 0.00_83, 0.00_93, 0.01_07, 0.01_39, 0.00_94, 0.00_97, 0.01_25]       )

							assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3














def 					lowercase__				(					__snake_case   :							List[str]					,	__snake_case   :							List[str]					,	__snake_case   :							Union[str, Any]					,	__snake_case   :							Optional[int]					,	__snake_case   :							str					,	__snake_case   :							Optional[Any]  ):


					'''simple docstring'''


					if index == r:
										for j in range(__snake_case  ):
															print(data[j]					,	end=' '  )
										print(' '  )
										return
					#  When no more elements are there to put in data[]
					if i >= n:
										return
					# current is included, put next at next location
					UpperCAmelCase_   :				int						=						arr[i]
					combination_util(__snake_case					,	__snake_case					,	__snake_case					,	index + 1					,	__snake_case					,	i + 1  )
					# current is excluded, replace it with
					# next (Note that i+1 is passed, but
					# index is not changed)
					combination_util(__snake_case					,	__snake_case					,	__snake_case					,	__snake_case					,	__snake_case					,	i + 1  )
					# The main function that prints all combinations
					# of size r in arr[] of size n. This function
					# mainly uses combinationUtil()




def 					lowercase__				(					__snake_case   :							List[Any]					,	__snake_case   :							List[Any]					,	__snake_case   :							str  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Union[str, Any]						=						[0] * r
					# Print all combination using temporary array 'data[]'
					combination_util(__snake_case					,	__snake_case					,	__snake_case					,	0					,	__snake_case					,	0  )


if __name__ == "__main__":
		# Driver code to check the function above
		__UpperCAmelCase     							=						[10, 20, 30, 40, 50]
		print_combination(arr, len(arr), 3)
		# This code is contributed by Ambuj sahu






