


'''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	(						lowercase__						,  unittest.TestCase							):
							# FIXME: add fast tests
							pass





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





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





							@property
							def 		a__   (  self		:List[str]			):
									snake_case_		:							List[Any]			       =						ort.SessionOptions()
									snake_case_		:							Optional[Any]			       =						False
									return options





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

									snake_case_		:							Tuple			       =						"""A red cat sitting on a park bench"""

									snake_case_		:							List[str]			       =						np.random.RandomState(0			)
									snake_case_		:							int			       =						pipe(
									    prompt=_UpperCamelCase  ,image=_UpperCamelCase  ,mask_image=_UpperCamelCase  ,guidance_scale=7.5  ,num_inference_steps=1_0  ,generator=_UpperCamelCase  ,output_type="""np"""  ,)
									snake_case_		:							int			       =						output.images
									snake_case_		:							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)
									snake_case_		:							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 		a__   (  self		:str			):
									snake_case_		:							Optional[Any]			       =						load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/in_paint/overture-creations-5sI6fQgYIuo.png"""			)
									snake_case_		:							Tuple			       =						load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""			)
									snake_case_		:							Any			       =						LMSDiscreteScheduler.from_pretrained(
									    """runwayml/stable-diffusion-inpainting"""  ,subfolder="""scheduler"""  ,revision="""onnx"""			)
									snake_case_		:							Optional[int]			       =						OnnxStableDiffusionInpaintPipeline.from_pretrained(
									    """runwayml/stable-diffusion-inpainting"""  ,revision="""onnx"""  ,scheduler=_UpperCamelCase  ,safety_checker=_UpperCamelCase  ,feature_extractor=_UpperCamelCase  ,provider=self.gpu_provider  ,sess_options=self.gpu_options  ,)
									pipe.set_progress_bar_config(disable=_UpperCamelCase			)

									snake_case_		:							str			       =						"""A red cat sitting on a park bench"""

									snake_case_		:							str			       =						np.random.RandomState(0			)
									snake_case_		:							List[Any]			       =						pipe(
									    prompt=_UpperCamelCase  ,image=_UpperCamelCase  ,mask_image=_UpperCamelCase  ,guidance_scale=7.5  ,num_inference_steps=2_0  ,generator=_UpperCamelCase  ,output_type="""np"""  ,)
									snake_case_		:							Dict			       =						output.images
									snake_case_		:							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)
									snake_case_		:							str			       =						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



'''simple docstring'''






from abc import ABC, abstractmethod
from argparse import ArgumentParser
class 						__UpperCamelCase	(						lowercase__							):





							@staticmethod
							@abstractmethod
							def 		a__   (  _UpperCamelCase		:ArgumentParser			):
									raise NotImplementedError()





							@abstractmethod
							def 		a__   (  self		:Optional[Any]			):
									raise NotImplementedError()