


'''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 							__UpperCAmelCase      ( self       :      Tuple    )				->  Union[str, Any]:
        return (
            "CUDAExecutionProvider",
            {
                "gpu_mem_limit": "15000000000",  # 15GB
                "arena_extend_strategy": "kSameAsRequested",
            },
        )





     @property
     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  str:
        lowerCAmelCase     =			ort.SessionOptions()
        lowerCAmelCase     =			False
        return options





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

        lowerCAmelCase     =			'''A red cat sitting on a park bench'''

        lowerCAmelCase     =			np.random.RandomState(0    )
        lowerCAmelCase     =			pipe(
            prompt=UpperCAmelCase__    ,      image=UpperCAmelCase__    ,      mask_image=UpperCAmelCase__    ,      strength=0.75    ,      guidance_scale=7.5    ,      num_inference_steps=1_5    ,      generator=UpperCAmelCase__    ,      output_type='np'    ,      )

        lowerCAmelCase     =			output.images[0]

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












def 		lowerCAmelCase_    (  __UpperCAmelCase:			float		,   __UpperCAmelCase:			int      )      ->	float:
    if digit_amount > 0:
        return round(number - int(__UpperCAmelCase      )		,   __UpperCAmelCase      )
    return number - int(__UpperCAmelCase      )


if __name__ == "__main__":
  print(decimal_isolate(1.53, 0))
  print(decimal_isolate(35.345, 1))
  print(decimal_isolate(35.345, 2))
  print(decimal_isolate(35.345, 3))
  print(decimal_isolate(-14.789, 3))
  print(decimal_isolate(0, 2))
  print(decimal_isolate(-14.123, 1))
  print(decimal_isolate(-14.123, 2))
  print(decimal_isolate(-14.123, 3))

