
import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase__       =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')
lowercase__       =parser.parse_args()


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

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

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

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

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

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

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


def    __UpperCamelCase     (				lowerCAmelCase__							:				int = 5_0_0_0_0_0_0_0  ):
 __a :  int						=  set()
 __a :  str						=  int((limit - 2_4) ** (1 / 2)  )

 __a :  int						=  set(range(3  ,   prime_square_limit + 1  ,   2  )  )
 primes.add(2  )
 for p in range(3  ,   prime_square_limit + 1  ,   2  ):
  if p not in primes:
   continue
  primes.difference_update(set(range(p * p  ,   prime_square_limit + 1  ,   lowerCAmelCase__  )  )  )

 for primea in primes:
  __a :  Union[str, Any]						=  primea * primea
  for primea in primes:
   __a :  Union[str, Any]						=  primea * primea * primea
   if square + cube >= limit - 1_6:
    break
   for primea in primes:
    __a :  int						=  primea * primea * primea * primea
    __a :  Union[str, Any]						=  square + cube + tetr
    if total >= limit:
     break
    ret.add(lowerCAmelCase__  )

 return len(lowerCAmelCase__  )


if __name__ == "__main__":
      print(F"""{solution() = }""")
