





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	):
														return (
														    "CUDAExecutionProvider",
														    {
														        "gpu_mem_limit": "15000000000",  # 15GB
														        "arena_extend_strategy": "kSameAsRequested",
														    },
														)






							@property
							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      ort.SessionOptions()
														UpperCamelCase__					  =      False
														return options







							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/in_paint/overture-creations-5sI6fQgYIuo.png"""	)
														UpperCamelCase__					  =      load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""	)
														UpperCamelCase__					  =      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
														UpperCamelCase__					  =      OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
														    """CompVis/stable-diffusion-v1-4"""	,			revision="""onnx"""	,			safety_checker=__lowerCAmelCase	,			feature_extractor=__lowerCAmelCase	,			provider=self.gpu_provider	,			sess_options=self.gpu_options	,			)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														UpperCamelCase__					  =      """A red cat sitting on a park bench"""

														UpperCamelCase__					  =      np.random.RandomState(0	)
														UpperCamelCase__					  =      pipe(
														    prompt=__lowerCAmelCase	,			image=__lowerCAmelCase	,			mask_image=__lowerCAmelCase	,			strength=0.75	,			guidance_scale=7.5	,			num_inference_steps=15	,			generator=__lowerCAmelCase	,			output_type="""np"""	,			)

														UpperCamelCase__					  =      output.images[0]

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











from __future__ import annotations

from math import pi





def      _UpperCamelCase  (a__ :float ,      a__ :float ,      a__ :float							):






							"""simple docstring"""





							if (inductance, frequency, reactance).count(0							) != 1:
														raise ValueError("""One and only one argument must be 0"""							)
							if inductance < 0:
														raise ValueError("""Inductance cannot be negative"""							)
							if frequency < 0:
														raise ValueError("""Frequency cannot be negative"""							)
							if reactance < 0:
														raise ValueError("""Inductive reactance cannot be negative"""							)
							if inductance == 0:
														return {"inductance": reactance / (2 * pi * frequency)}
							elif frequency == 0:
														return {"frequency": reactance / (2 * pi * inductance)}
							elif reactance == 0:
														return {"reactance": 2 * pi * frequency * inductance}
							else:
														raise ValueError("""Exactly one argument must be 0"""							)


if __name__ == "__main__":
					import doctest

					doctest.testmod()




