
'''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 						__lowercase					(	unittest.TestCase     ):

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

			@property
			def 							UpperCAmelCase__			(self   ):
								lowerCamelCase_       :   Optional[int]  =		ort.SessionOptions()
								lowerCamelCase_       :   Tuple  =		False
								return options






			def 							UpperCAmelCase__			(self   ):
								lowerCamelCase_       :   List[Any]  =		load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/in_paint/overture-creations-5sI6fQgYIuo.png'''   )
								lowerCamelCase_       :   Any  =		load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''   )
								lowerCamelCase_       :   Any  =		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
								lowerCamelCase_       :   Union[str, Any]  =		OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''      , revision='''onnx'''      , safety_checker=__lowercase      , feature_extractor=__lowercase      , provider=self.gpu_provider      , sess_options=self.gpu_options      , )
								pipe.set_progress_bar_config(disable=__lowercase   )

								lowerCamelCase_       :   Dict  =		'''A red cat sitting on a park bench'''

								lowerCamelCase_       :   int  =		np.random.RandomState(0   )
								lowerCamelCase_       :   Optional[int]  =		pipe(
								    prompt=__lowercase      , image=__lowercase      , mask_image=__lowercase      , strength=0.75      , guidance_scale=7.5      , num_inference_steps=1_5      , generator=__lowercase      , output_type='''np'''      , )

								lowerCamelCase_       :   Union[str, Any]  =		output.images[0]

								assert image.shape == (5_1_2, 5_1_2, 3)
								assert np.abs(expected_image - image   ).max() < 1E-2









"""simple docstring"""





from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
							import accelerate



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str ):


							'''simple docstring'''







							if not is_accelerate_available():
														return method
							__lowerCamelCase   :   Dict       =version.parse(accelerate.__version__ ).base_version
							if version.parse(SCREAMING_SNAKE_CASE ) < version.parse('''0.17.0''' ):
														return method

							def wrapper(self							:				List[Any]     ,    *SCREAMING_SNAKE_CASE							:				Any     ,    **SCREAMING_SNAKE_CASE							:				List[str] ):
														if hasattr(self     ,    '''_hf_hook''' ) and hasattr(self._hf_hook     ,    '''pre_forward''' ):
																					self._hf_hook.pre_forward(self )
														return method(self     ,    *SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE )

							return wrapper




