

"""simple docstring"""




import colorsys

from PIL import Image  # type: ignore






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					float:
	__lowerCamelCase    :    List[Any] 	=			x
	__lowerCamelCase    :    Any 	=			y
	for step in range(UpperCAmelCase__      ):  # noqa: B007
		__lowerCamelCase    :    Dict 	=			a * a - b * b + x
		__lowerCamelCase    :    str 	=			2 * a * b + y
		__lowerCamelCase    :    Optional[Any] 	=			a_new

		# divergence happens for all complex number with an absolute value
		# greater than 4
		if a * a + b * b > 4:
			break
	return step / (max_step - 1)






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					tuple:
	if distance == 1:
		return (0, 0, 0)
	else:
		return (2_5_5, 2_5_5, 2_5_5)






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					tuple:
	if distance == 1:
		return (0, 0, 0)
	else:
		return tuple(round(i * 2_5_5      ) for i in colorsys.hsv_to_rgb(UpperCAmelCase__     ,			1     ,			1      )      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__ = 8_0_0     ,			lowerCamelCase__ = 6_0_0     ,			lowerCamelCase__ = -0.6     ,			lowerCamelCase__ = 0     ,			lowerCamelCase__ = 3.2     ,			lowerCamelCase__ = 5_0     ,			lowerCamelCase__ = True     ,			)  ->					Image.Image:
	__lowerCamelCase    :    Union[str, Any] 	=			Image.new('RGB'     ,			(image_width, image_height)      )
	__lowerCamelCase    :    Tuple 	=			img.load()

	# loop through the image-coordinates
	for image_x in range(UpperCAmelCase__      ):
		for image_y in range(UpperCAmelCase__      ):
			# determine the figure-coordinates based on the image-coordinates
			__lowerCamelCase    :    Any 	=			figure_width / image_width * image_height
			__lowerCamelCase    :    Tuple 	=			figure_center_x + (image_x / image_width - 0.5) * figure_width
			__lowerCamelCase    :    Union[str, Any] 	=			figure_center_y + (image_y / image_height - 0.5) * figure_height

			__lowerCamelCase    :    str 	=			get_distance(UpperCAmelCase__     ,			UpperCAmelCase__     ,			UpperCAmelCase__      )

			# color the corresponding pixel based on the selected coloring-function
			if use_distance_color_coding:
				__lowerCamelCase    :    List[Any] 	=			get_color_coded_rgb(UpperCAmelCase__      )
			else:
				__lowerCamelCase    :    Dict 	=			get_black_and_white_rgb(UpperCAmelCase__      )

	return img


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					# colored version, full figure
					a		=get_image()

					# uncomment for colored version, different section, zoomed in
					# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
					# figure_width = 0.8)

					# uncomment for black and white version, full figure
					# img = get_image(use_distance_color_coding = False)

					# uncomment to save the image
					# img.save("mandelbrot.png")

					img.show()







import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


a		=argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.add_argument("""--dpm""", action="""store_true""", help="""Enable DPMSolver or not""")
parser.add_argument("""--steps""", default=None, type=int, help="""Num inference steps""")
a		=parser.parse_args()


a		="""cpu"""
a		="""a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"""

a		="""path-to-your-trained-model"""
a		=StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
					a		=DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
a		=pipe.to(device)

# to channels last
a		=pipe.unet.to(memory_format=torch.channels_last)
a		=pipe.vae.to(memory_format=torch.channels_last)
a		=pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
					a		=pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
a		=torch.randn(2, 4, 64, 64)
a		=torch.rand(1) * 999
a		=torch.randn(2, 77, 768)
a		=(sample, timestep, encoder_hidden_status)
try:
					a		=ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
					a		=ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
a		=ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
a		=ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
					a		=ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
a		=666
a		=torch.Generator(device).manual_seed(seed)
a		={"""generator""": generator}
if args.steps is not None:
					a		=args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
					a		=pipe(prompt, **generate_kwargs).images[0]

# save image
image.save("""generated.png""")




