import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


__a      :       Optional[Any]				  =   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      :       Optional[int]				  =   parser.parse_args()


__a      :       Dict				  =   "cpu"
__a      :       Optional[int]				  =   "a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"

__a      :       Tuple				  =   "path-to-your-trained-model"
__a      :       Optional[int]				  =   StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
  __a      :       Dict				  =   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
__a      :       str				  =   pipe.to(device)

# to channels last
__a      :       List[Any]				  =   pipe.unet.to(memory_format=torch.channels_last)
__a      :       str				  =   pipe.vae.to(memory_format=torch.channels_last)
__a      :       Dict				  =   pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
  __a      :       Union[str, Any]				  =   pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
__a      :       Tuple				  =   torch.randn(2, 4, 64, 64)
__a      :       str				  =   torch.rand(1) * 999
__a      :       Dict				  =   torch.randn(2, 77, 768)
__a      :       Dict				  =   (sample, timestep, encoder_hidden_status)
try:
  __a      :       List[Any]				  =   ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
  __a      :       str				  =   ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
__a      :       Any				  =   ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
__a      :       Optional[int]				  =   ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
  __a      :       str				  =   ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
__a      :       List[str]				  =   666
__a      :       Dict				  =   torch.Generator(device).manual_seed(seed)
__a      :       int				  =   {"generator": generator}
if args.steps is not None:
  __a      :       Tuple				  =   args.steps

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

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












def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[Any]			,				__lowercase   :			Dict			,				__lowercase   :			str    )						->      Dict:





    """simple docstring"""

    if n == 0:
        return 1

    elif n % 2 == 1:
        return (binary_exponentiation(__lowercase			,				n - 1			,				__lowercase    ) * a) % mod

    else:
        __A         =	binary_exponentiation(__lowercase			,				n / 2			,				__lowercase    )
        return (b * b) % mod


# a prime number
__a      :       Union[str, Any]				  =   701

__a      :       Dict				  =   1000000000
__a      :       Union[str, Any]				  =   10

# using binary exponentiation function, O(log(p)):
print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p)






print((a / b) % p == (a * b ** (p - 2)) % p)




