import sys
import turtle



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			):
    '''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(SCREAMING_SNAKE_CASE  ,			get_mid(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)  ,			get_mid(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)  ,			depth - 1	)
    triangle(SCREAMING_SNAKE_CASE  ,			get_mid(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)  ,			get_mid(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)  ,			depth - 1	)
    triangle(SCREAMING_SNAKE_CASE  ,			get_mid(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)  ,			get_mid(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)  ,			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>'''
        )
    __lowercase							 =					turtle.Turtle()
    my_pen.ht()
    my_pen.speed(5)
    my_pen.pencolor('''red''')

    __lowercase							 =					[(-175, -125), (0, 175), (175, -125)]  # vertices of triangle
    triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))











"""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 					lowercase(     unittest.TestCase ):







 '''simple docstring'''





 @property
 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''simple docstring'''



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


 @property
 def  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



   _snake_case							:		Any								=  ort.SessionOptions()
   _snake_case							:		Union[str, Any]								=  False
   return options


 def  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		Any								=  load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/in_paint/overture-creations-5sI6fQgYIuo.png"""		)
   _snake_case							:		Union[str, Any]								=  load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""		)
   _snake_case							:		Union[str, Any]								=  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							:		Optional[Any]								=  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_		)

   _snake_case							:		Optional[Any]								=  """A red cat sitting on a park bench"""

   _snake_case							:		Optional[int]								=  np.random.RandomState(0		)
   _snake_case							:		Any								=  pipe(
       prompt=a_,      image=a_,      mask_image=a_,      strength=0.75,      guidance_scale=7.5,      num_inference_steps=15,      generator=a_,      output_type="""np""",      )

   _snake_case							:		Dict								=  output.images[0]

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


