"""simple docstring"""




from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = 1 / sqrt(2				)				):






							"""simple docstring"""





							A__	     =							tau * frequency / samplerate
							A__	     =							sin(UpperCamelCase__				)
							A__	     =							cos(UpperCamelCase__				)
							A__	     =							_sin / (2 * q_factor)

							A__	     =							(1 - _cos) / 2
							A__	     =							1 - _cos

							A__	     =							1 + alpha
							A__	     =							-2 * _cos
							A__	     =							1 - alpha

							A__	     =							IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]       ,    [ba, ba, ba]				)
							return filt






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = 1 / sqrt(2				)				):






							"""simple docstring"""





							A__	     =							tau * frequency / samplerate
							A__	     =							sin(UpperCamelCase__				)
							A__	     =							cos(UpperCamelCase__				)
							A__	     =							_sin / (2 * q_factor)

							A__	     =							(1 + _cos) / 2
							A__	     =							-1 - _cos

							A__	     =							1 + alpha
							A__	     =							-2 * _cos
							A__	     =							1 - alpha

							A__	     =							IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]       ,    [ba, ba, ba]				)
							return filt






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = 1 / sqrt(2				)				):






							"""simple docstring"""





							A__	     =							tau * frequency / samplerate
							A__	     =							sin(UpperCamelCase__				)
							A__	     =							cos(UpperCamelCase__				)
							A__	     =							_sin / (2 * q_factor)

							A__	     =							_sin / 2
							A__	     =							0
							A__	     =							-ba

							A__	     =							1 + alpha
							A__	     =							-2 * _cos
							A__	     =							1 - alpha

							A__	     =							IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]       ,    [ba, ba, ba]				)
							return filt






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = 1 / sqrt(2				)				):






							"""simple docstring"""





							A__	     =							tau * frequency / samplerate
							A__	     =							sin(UpperCamelCase__				)
							A__	     =							cos(UpperCamelCase__				)
							A__	     =							_sin / (2 * q_factor)

							A__	     =							1 - alpha
							A__	     =							-2 * _cos
							A__	     =							1 + alpha

							A__	     =							IIRFilter(2				)
							filt.set_coefficients([ba, ba, ba]       ,    [ba, ba, ba]				)
							return filt






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = 1 / sqrt(2				)       ,    ):






							"""simple docstring"""





							A__	     =							tau * frequency / samplerate
							A__	     =							sin(UpperCamelCase__				)
							A__	     =							cos(UpperCamelCase__				)
							A__	     =							_sin / (2 * q_factor)
							A__	     =							10 ** (gain_db / 40)

							A__	     =							1 + alpha * big_a
							A__	     =							-2 * _cos
							A__	     =							1 - alpha * big_a
							A__	     =							1 + alpha / big_a
							A__	     =							-2 * _cos
							A__	     =							1 - alpha / big_a

							A__	     =							IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]       ,    [ba, ba, ba]				)
							return filt






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = 1 / sqrt(2				)       ,    ):






							"""simple docstring"""





							A__	     =							tau * frequency / samplerate
							A__	     =							sin(UpperCamelCase__				)
							A__	     =							cos(UpperCamelCase__				)
							A__	     =							_sin / (2 * q_factor)
							A__	     =							10 ** (gain_db / 40)
							A__	     =							(big_a + 1) - (big_a - 1) * _cos
							A__	     =							(big_a + 1) + (big_a - 1) * _cos
							A__	     =							(big_a - 1) - (big_a + 1) * _cos
							A__	     =							(big_a - 1) + (big_a + 1) * _cos
							A__	     =							2 * sqrt(UpperCamelCase__				) * alpha

							A__	     =							big_a * (pmc + aaa)
							A__	     =							2 * big_a * mpc
							A__	     =							big_a * (pmc - aaa)
							A__	     =							ppmc + aaa
							A__	     =							-2 * pmpc
							A__	     =							ppmc - aaa

							A__	     =							IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]       ,    [ba, ba, ba]				)
							return filt






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = 1 / sqrt(2				)       ,    ):






							"""simple docstring"""





							A__	     =							tau * frequency / samplerate
							A__	     =							sin(UpperCamelCase__				)
							A__	     =							cos(UpperCamelCase__				)
							A__	     =							_sin / (2 * q_factor)
							A__	     =							10 ** (gain_db / 40)
							A__	     =							(big_a + 1) - (big_a - 1) * _cos
							A__	     =							(big_a + 1) + (big_a - 1) * _cos
							A__	     =							(big_a - 1) - (big_a + 1) * _cos
							A__	     =							(big_a - 1) + (big_a + 1) * _cos
							A__	     =							2 * sqrt(UpperCamelCase__				) * alpha

							A__	     =							big_a * (ppmc + aaa)
							A__	     =							-2 * big_a * pmpc
							A__	     =							big_a * (ppmc - aaa)
							A__	     =							pmc + aaa
							A__	     =							2 * mpc
							A__	     =							pmc - aaa

							A__	     =							IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]       ,    [ba, ba, ba]				)
							return filt






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





						@property
						def 				snake_case__	(       self     )					->				Union[str, Any]:
													A__	     =							ort.SessionOptions()
													A__	     =							False
													return options





						def 				snake_case__	(       self     )					->				str:
													A__	     =							load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/in_paint/overture-creations-5sI6fQgYIuo.png'     )
													A__	     =							load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'     )
													A__	     =							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
													A__	     =							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     )

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

													A__	     =							np.random.RandomState(0     )
													A__	     =							pipe(
													    prompt=__UpperCAmelCase		,image=__UpperCAmelCase		,mask_image=__UpperCAmelCase		,strength=0.7_5		,guidance_scale=7.5		,num_inference_steps=15		,generator=__UpperCAmelCase		,output_type='np'		,)

													A__	     =							output.images[0]

													assert image.shape == (5_12, 5_12, 3)
													assert np.abs(expected_image - image     ).max() < 1e-2





