

'''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 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):







				@property
				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										return (
										    "CUDAExecutionProvider",
										    {
										        "gpu_mem_limit": "15000000000",  # 15GB
										        "arena_extend_strategy": "kSameAsRequested",
										    },
										)







				@property
				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										lowercase :							List[str]    		=ort.SessionOptions()
										lowercase :							List[str]    		=False
										return options







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

										lowercase :							List[Any]    		="""A red cat sitting on a park bench"""

										lowercase :							Dict    		=np.random.RandomState(0   )
										lowercase :							str    		=pipe(
										    prompt=__A		,					image=__A		,					mask_image=__A		,					strength=0.75		,					guidance_scale=7.5		,					num_inference_steps=15		,					generator=__A		,					output_type='''np'''		,					)

										lowercase :							List[Any]    		=output.images[0]

										assert image.shape == (512, 512, 3)
										assert np.abs(expected_image - image   ).max() < 1E-2








from __future__ import annotations
def 							__SCREAMING_SNAKE_CASE     (   a__    :   List[str]			,a__    :   Dict			,a__    :   Union[str, Any]			,a__    :   Any       )					->				Optional[int]:  # noqa: E741
	while r - l > 1:
		__A :			Any		    =			(l + r) // 2
		if v[m] >= key:
			__A :			Optional[int]		    =			m
		else:
			__A :			List[Any]		    =			m  # noqa: E741
	return r
def 							__SCREAMING_SNAKE_CASE     (   a__    :   list[int]       )					->				int:
	if len(a__       ) == 0:
		return 0

	__A :			str		    =			[0] * len(a__       )
	__A :			List[str]		    =			1

	__A :			List[Any]		    =			v[0]

	for i in range(1			,len(a__       )       ):
		if v[i] < tail[0]:
			__A :			int		    =			v[i]
		elif v[i] > tail[length - 1]:
			__A :			Union[str, Any]		    =			v[i]
			length += 1
		else:
			__A :			Any		    =			v[i]

	return length


if __name__ == "__main__":
			import doctest

			doctest.testmod()





