

import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
	import onnxruntime as ort


class 			lowerCamelCase_    (  A__	,     unittest.TestCase		):
							pass






@nightly
@require_onnxruntime
@require_torch_gpu
class 			lowerCamelCase_    (  unittest.TestCase		):


							@property
							def  lowercase   ( self						)							->   str:



								"""simple docstring"""

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


							@property
							def  lowercase   ( self						)							->   Tuple:



								"""simple docstring"""

								_UpperCamelCase			=   ort.SessionOptions()
								_UpperCamelCase			=   False
								return options


							def  lowercase   ( self						)							->   Tuple:



								"""simple docstring"""

								_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			=   OnnxStableDiffusionInpaintPipeline.from_pretrained(
								    "runwayml/stable-diffusion-inpainting"      ,				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_      ,				guidance_scale=7.5      ,				num_inference_steps=10      ,				generator=lowerCamelCase_      ,				output_type="np"      ,				)
								_UpperCamelCase			=   output.images
								_UpperCamelCase			=   images[0, 2_55:2_58, 2_55:2_58, -1]

								assert images.shape == (1, 5_12, 5_12, 3)
								_UpperCamelCase			=   np.array([0.25_14, 0.30_07, 0.35_17, 0.17_90, 0.23_82, 0.31_67, 0.19_44, 0.22_73, 0.24_64]						)

								assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3







							def  lowercase   ( self						)							->   Tuple:



								"""simple docstring"""

								_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			=   LMSDiscreteScheduler.from_pretrained(
								    "runwayml/stable-diffusion-inpainting"      ,				subfolder="scheduler"      ,				revision="onnx"						)
								_UpperCamelCase			=   OnnxStableDiffusionInpaintPipeline.from_pretrained(
								    "runwayml/stable-diffusion-inpainting"      ,				revision="onnx"      ,				scheduler=lowerCamelCase_      ,				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_      ,				guidance_scale=7.5      ,				num_inference_steps=20      ,				generator=lowerCamelCase_      ,				output_type="np"      ,				)
								_UpperCamelCase			=   output.images
								_UpperCamelCase			=   images[0, 2_55:2_58, 2_55:2_58, -1]

								assert images.shape == (1, 5_12, 5_12, 3)
								_UpperCamelCase			=   np.array([0.00_86, 0.00_77, 0.00_83, 0.00_93, 0.01_07, 0.01_39, 0.00_94, 0.00_97, 0.01_25]						)

								assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3












import sys
import turtle






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2






def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  ):






				'''simple docstring'''
				my_pen.up()
				my_pen.goto(vertexa[0]						,  vertexa[1]							)
				my_pen.down()
				my_pen.goto(vertexa[0]						,  vertexa[1]							)
				my_pen.goto(vertexa[0]						,  vertexa[1]							)
				my_pen.goto(vertexa[0]						,  vertexa[1]							)

				if depth == 0:
								return

				triangle(lowerCamelCase_						,  get_mid(lowerCamelCase_						,  lowerCamelCase_							)						,  get_mid(lowerCamelCase_						,  lowerCamelCase_							)						,  depth - 1							)
				triangle(lowerCamelCase_						,  get_mid(lowerCamelCase_						,  lowerCamelCase_							)						,  get_mid(lowerCamelCase_						,  lowerCamelCase_							)						,  depth - 1							)
				triangle(lowerCamelCase_						,  get_mid(lowerCamelCase_						,  lowerCamelCase_							)						,  get_mid(lowerCamelCase_						,  lowerCamelCase_							)						,  depth - 1							)


if __name__ == "__main__":
							if len(sys.argv) != 2:
														raise ValueError(
														    'Correct format for using this script: '
														    'python fractals.py <int:depth_for_fractal>'
														)
							A__      :     Dict       =					turtle.Turtle()
							my_pen.ht()
							my_pen.speed(5)
							my_pen.pencolor('red')

							A__      :     Union[str, Any]       =					[(-1_75, -1_25), (0, 1_75), (1_75, -1_25)]  # vertices of triangle
							triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))




