
import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


__magic_name__  							=	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''')
__magic_name__  							=	parser.parse_args()


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

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

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

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

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

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

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





import math





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase				):

    if initial_intensity < 0:
        raise ValueError("The value of intensity cannot be negative"				)
        # handling of negative values of initial intensity
    if angle < 0 or angle > 360:
        raise ValueError("In Malus Law, the angle is in the range 0-360 degrees"				)
        # handling of values out of allowed range
    return initial_intensity * (math.cos(math.radians(__lowerCAmelCase				)				) ** 2)


if __name__ == "__main__":
     import doctest

     doctest.testmod(name='''malus_law''')



