


def 						lowercase_(    SCREAMING_SNAKE_CASE_ = 1000000      ):


  '''simple docstring'''

  lowerCamelCase					:   Union[str, Any]       =							set(range(3 ,		_lowercase ,		2      )      )
  primes.add(2      )
  for p in range(3 ,		_lowercase ,		2      ):
    if p not in primes:
      continue
    primes.difference_update(set(range(p * p ,		_lowercase ,		_lowercase      )      )      )

  lowerCamelCase					:   Tuple       =							[float(_lowercase      ) for n in range(limit + 1      )]

  for p in primes:
    for n in range(_lowercase ,		limit + 1 ,		_lowercase      ):
      phi[n] *= 1 - 1 / p

  return int(sum(phi[2:]      )      )


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







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)


