






"""simple docstring"""





def   a    (     __UpperCAmelCase      :    int = 2_0_0_0_0_0_0  )    ->							int:
						__magic_name__:	str							=       [0 for i in range(n + 1  )]
						__magic_name__:	Tuple							=       1
						__magic_name__:	Dict							=       1

						for i in range(2      ,   int(n**0.5  ) + 1  ):
												if primality_list[i] == 0:
																		for j in range(i * i      ,   n + 1      ,   __UpperCAmelCase  ):
																								__magic_name__:	Optional[int]							=       1
						__magic_name__:	Any							=       0
						for i in range(__UpperCAmelCase  ):
												if primality_list[i] == 0:
																		sum_of_primes += i
						return sum_of_primes


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







'''simple docstring'''
import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
		__snake_case				 =	argparse.ArgumentParser()

		parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

		parser.add_argument(
		    '''--txt2img_unclip''',
		    default='''kakaobrain/karlo-v1-alpha''',
		    type=str,
		    required=False,
		    help='''The pretrained txt2img unclip.''',
		)

		__snake_case				 =	parser.parse_args()

		__snake_case				 =	UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

		__snake_case				 =	CLIPImageProcessor()
		__snake_case				 =	CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

		__snake_case				 =	UnCLIPImageVariationPipeline(
		    decoder=txtaimg.decoder,
		    text_encoder=txtaimg.text_encoder,
		    tokenizer=txtaimg.tokenizer,
		    text_proj=txtaimg.text_proj,
		    feature_extractor=feature_extractor,
		    image_encoder=image_encoder,
		    super_res_first=txtaimg.super_res_first,
		    super_res_last=txtaimg.super_res_last,
		    decoder_scheduler=txtaimg.decoder_scheduler,
		    super_res_scheduler=txtaimg.super_res_scheduler,
		)

		imgaimg.save_pretrained(args.dump_path)