



"""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       _a  ( unittest.TestCase):



			@property
			def 				__lowercase					(			self							:				List[str]		)							->					str:
									return (
									    "CUDAExecutionProvider",
									    {
									        "gpu_mem_limit": "15000000000",  # 15GB
									        "arena_extend_strategy": "kSameAsRequested",
									    },
									)



			@property
			def 				__lowercase					(			self							:				Union[str, Any]		)							->					Dict:
									snake_case   :			Dict						    = ort.SessionOptions()
									snake_case   :			Optional[Any]						    = False
									return options



			def 				__lowercase					(			self							:				Tuple		)							->					List[Any]:
									snake_case   :			Dict						    = load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/in_paint/overture-creations-5sI6fQgYIuo.png"		)
									snake_case   :			str						    = 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   :			Dict						    = 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   :			Tuple						    = "A red cat sitting on a park bench"

									snake_case   :			Optional[Any]						    = np.random.RandomState(0		)
									snake_case   :			Union[str, Any]						    = 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   :			Optional[int]						    = output.images[0]

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






from math import factorial, pi

def 			lowerCamelCase__			(       _a				,      _a = 30):

	if not isinstance(_a				,      (int, float)):
		raise ValueError("maclaurin_sin() requires either an int or float for theta")

	if not isinstance(_a				,      _a) or accuracy <= 0:
		raise ValueError("maclaurin_sin() requires a positive int for accuracy")

	SCREAMING_SNAKE_CASE :   int					  =							float(_a)
	SCREAMING_SNAKE_CASE :   Dict					  =							theta // (2 * pi)
	theta -= 2 * div * pi
	return sum(
	    (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1) for r in range(_a))

def 			lowerCamelCase__			(       _a				,      _a = 30):

	if not isinstance(_a				,      (int, float)):
		raise ValueError("maclaurin_cos() requires either an int or float for theta")

	if not isinstance(_a				,      _a) or accuracy <= 0:
		raise ValueError("maclaurin_cos() requires a positive int for accuracy")

	SCREAMING_SNAKE_CASE :   str					  =							float(_a)
	SCREAMING_SNAKE_CASE :   Any					  =							theta // (2 * pi)
	theta -= 2 * div * pi
	return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r) for r in range(_a))


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			print(maclaurin_sin(10))
			print(maclaurin_sin(-10))
			print(maclaurin_sin(10, 15))
			print(maclaurin_sin(-10, 15))

			print(maclaurin_cos(5))
			print(maclaurin_cos(-5))
			print(maclaurin_cos(10, 15))
			print(maclaurin_cos(-10, 15))