import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


SCREAMING_SNAKE_CASE									=						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')
SCREAMING_SNAKE_CASE									=						parser.parse_args()


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

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

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

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

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

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

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




"""simple docstring"""







def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Any							=							len(__UpperCAmelCase			)
	_lowercase :    Any							=							len(__UpperCAmelCase			)
	_lowercase :    List[Any]							=							[[False for _ in range(m + 1			)] for _ in range(n + 1			)]
	_lowercase :    Union[str, Any]							=							True
	for i in range(__UpperCAmelCase			):
		for j in range(m + 1			):
			if dp[i][j]:
				if j < m and a[i].upper() == b[j]:
					_lowercase :    Optional[int]							=							True
				if a[i].islower():
					_lowercase :    Union[str, Any]							=							True
	return dp[n][m]


if __name__ == "__main__":
						import doctest

						doctest.testmod()



