


"""simple docstring"""






import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


_lowerCamelCase     =  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')
_lowerCamelCase     =  parser.parse_args()


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

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

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

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

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

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

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




"""simple docstring"""






def __lowercase     (      lowerCamelCase_  :      List[Any]  ):
				SCREAMING_SNAKE_CASE__     							=				[]
				SCREAMING_SNAKE_CASE__     							=				set({"(", "[", "{"}  )
				SCREAMING_SNAKE_CASE__     							=				set({")", "]", "}"}  )
				SCREAMING_SNAKE_CASE__     							=				{"{": "}", "[": "]", "(": ")"}

				for i in range(len(lowerCamelCase_  )  ):
								if s[i] in open_brackets:
												stack.append(s[i]  )

								elif s[i] in closed_brackets and (
								    len(lowerCamelCase_  ) == 0 or (len(lowerCamelCase_  ) > 0 and open_to_closed[stack.pop()] != s[i])
								):
												return False

				return len(lowerCamelCase_  ) == 0


def __lowercase     (      ):
				SCREAMING_SNAKE_CASE__     							=				input("Enter sequence of brackets: "  )
				if is_balanced(lowerCamelCase_  ):
								print(lowerCamelCase_						,					"is balanced"  )
				else:
								print(lowerCamelCase_						,					"is not balanced"  )


if __name__ == "__main__":
	main()
