



"""simple docstring"""





import math
from collections.abc import Iterator
from itertools import takewhile
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int    ):


						'''simple docstring'''






						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5					, int(math.sqrt(__snake_case    ) + 1    )					, 6    ):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True
def 	__lowerCAmelCase						(		):


						'''simple docstring'''






						snake_case_   : str						=			2
						while True:
												if is_prime(__snake_case    ):
																		yield num
												num += 1
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int = 2_0_0_0_0_0_0    ):


						'''simple docstring'''






						return sum(takewhile(lambda __UpperCamelCase    : x < n					, prime_generator()    )    )


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






"""simple docstring"""





import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


__lowerCAmelCase       :					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''')
__lowerCAmelCase       :					Optional[Any]      					=					parser.parse_args()


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

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

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

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

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

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

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

