






'''simple docstring'''



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'''



from math import isqrt


def 				lowercase_					(     __A			:							int      )							->   list[int]:



 """simple docstring"""




 lowercase       : Dict						=[True] * max_number
 for i in range(2       ,   isqrt(max_number - 1      ) + 1      ):
  if is_prime[i]:
   for j in range(i**2       ,   __A       ,   __A      ):
    lowercase       : str						=False

 return [i for i in range(2       ,   __A      ) if is_prime[i]]


def 				lowercase_					(     __A			:							int = 1_0**8      )							->   int:



 """simple docstring"""




 lowercase       : Dict						=calculate_prime_numbers(max_number // 2      )

 lowercase       : str						=0
 lowercase       : Optional[Any]						=0
 lowercase       : Union[str, Any]						=len(__A      ) - 1
 while left <= right:
  while prime_numbers[left] * prime_numbers[right] >= max_number:
   right -= 1
  semiprimes_count += right - left + 1
  left += 1

 return semiprimes_count


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



