


"""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 			lowerCamelCase_		(			unittest.TestCase   ):





				"""simple docstring"""







				@property
				def   lowerCAmelCase__		(						self     ):
								return (
								    "CUDAExecutionProvider",
								    {
								        "gpu_mem_limit": "15000000000",  # 15GB
								        "arena_extend_strategy": "kSameAsRequested",
								    },
								)



				@property
				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				ort.SessionOptions()
								SCREAMING_SNAKE_CASE__     							=				False
								return options






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

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

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

								SCREAMING_SNAKE_CASE__     							=				output.images[0]

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




"""simple docstring"""






from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors


def __lowercase     (      lowerCamelCase_  :      int  ):
				SCREAMING_SNAKE_CASE__     							=				prime_factors(lowerCamelCase_  )
				if is_square_free(lowerCamelCase_  ):
								return -1 if len(lowerCamelCase_  ) % 2 else 1
				return 0


if __name__ == "__main__":
	import doctest

	doctest.testmod()
