



"""simple docstring"""







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__			(   _lowerCAmelCase       ,		unittest.TestCase      ):

			"""simple docstring"""


			pass






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

			"""simple docstring"""


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


			@property
			def   snake_case__	(      self		)      ->	Any:
				A__											=					ort.SessionOptions()
				A__											=					False
				return options


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

				A__											=					"A red cat sitting on a park bench"

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

				assert images.shape == (1, 512, 512, 3)
				A__											=					np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4]		)

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







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

				A__											=					"A red cat sitting on a park bench"

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

				assert images.shape == (1, 512, 512, 3)
				A__											=					np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5]		)

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











from ..utils import DummyObject, requires_backends




class 			__UpperCamelCase    (							metaclass=A__    ):
     __A	:  str							  =   ["""torch""", """scipy"""]





     def __init__(     self				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        requires_backends(self				,							['''torch''', '''scipy''']					)





     @classmethod
     def 		UpperCamelCase(     cls				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        requires_backends(cls				,							['''torch''', '''scipy''']					)





     @classmethod
     def 		UpperCamelCase(     cls				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        requires_backends(cls				,							['''torch''', '''scipy''']					)