



"""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  _UpperCAmelCase (    unittest.TestCase):

 @property
 def 		_snake_case      (  self	: Optional[Any]       ):
     return (
         "CUDAExecutionProvider",
         {
             "gpu_mem_limit": "15000000000",  # 15GB
             "arena_extend_strategy": "kSameAsRequested",
         },
     )

 @property
 def 		_snake_case      (  self	: Tuple       ):
     snake_case_				:  Optional[int]       =			ort.SessionOptions()
     snake_case_				:  Optional[Any]       =			False
     return options






 def 		_snake_case      (  self	: str       ):
     snake_case_				:  Union[str, Any]       =			load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/in_paint/overture-creations-5sI6fQgYIuo.png'''       )
     snake_case_				:  Optional[int]       =			load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''       )
     snake_case_				:  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
     snake_case_				:  Tuple       =			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_       )

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

     snake_case_				:  List[Any]       =			np.random.RandomState(0       )
     snake_case_				:  Dict       =			pipe(
         prompt=lowercase_						,  image=lowercase_						,  mask_image=lowercase_						,  strength=0.75						,  guidance_scale=7.5						,  num_inference_steps=15						,  generator=lowercase_						,  output_type='''np'''						,  )

     snake_case_				:  Dict       =			output.images[0]

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










"""simple docstring"""

def __lowercase    (							_a					):
    snake_case_				:  Optional[Any]       =			int(_a					)
    if decimal in (0, 1):  # Exit cases for the recursion
        return str(_a					)
    snake_case_,			snake_case_				:  Optional[int]       =			divmod(_a	,    2					)
    return binary_recursive(_a					) + str(_a					)




def __lowercase    (							_a					):
    snake_case_				:  Dict       =			str(_a					).strip()
    if not number:
        raise ValueError('''No input value was provided'''					)
    snake_case_				:  List[str]       =			'''-''' if number.startswith('''-'''					) else ''''''
    snake_case_				:  Optional[Any]       =			number.lstrip('''-'''					)
    if not number.isnumeric():
        raise ValueError('''Input value is not an integer'''					)
    return f"{negative}0b{binary_recursive(int(_a					)					)}"


if __name__ == "__main__":
     from doctest import testmod

     testmod()





