






"""simple docstring"""






import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


UpperCAmelCase_     :				Optional[int]         =				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""")
UpperCAmelCase_     :				List[Any]         =				parser.parse_args()


UpperCAmelCase_     :				Union[str, Any]         =				"""cpu"""
UpperCAmelCase_     :				Tuple         =				"""a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"""

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

# to channels last
UpperCAmelCase_     :				Tuple         =				pipe.unet.to(memory_format=torch.channels_last)
UpperCAmelCase_     :				Tuple         =				pipe.vae.to(memory_format=torch.channels_last)
UpperCAmelCase_     :				List[Any]         =				pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
  UpperCAmelCase_     :				Optional[Any]         =				pipe.safety_checker.to(memory_format=torch.channels_last)

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

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

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

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










"""simple docstring"""






from math import isqrt



def       _A    (__a							)				-> list[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	[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							):
                SCREAMING_SNAKE_CASE_   :						Tuple							=	False

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



def       _A    (__a = 10**8							)				-> int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Any							=	calculate_prime_numbers(max_number // 2							)

    SCREAMING_SNAKE_CASE_   :						List[str]							=	0
    SCREAMING_SNAKE_CASE_   :						Any							=	0
    SCREAMING_SNAKE_CASE_   :						List[str]							=	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() = }''')


