"""simple docstring"""
from __future__ import annotations

import math



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_ )      ->str:

    if num <= 0:
        _lowerCamelCase	: Optional[Any]       =       F'''{num}: Invalid input, please enter a positive integer.'''
        raise ValueError(A__ )

    _lowerCamelCase	: Dict       =       [True] * (num + 1)
    _lowerCamelCase	: Dict       =       []
    _lowerCamelCase	: List[str]       =       2
    _lowerCamelCase	: List[str]       =       int(math.sqrt(A__ ) )

    while start <= end:
        # If start is a prime
        if sieve[start] is True:
            prime.append(A__ )

            # Set multiples of start be False
            for i in range(start * start						,	num + 1						,	A__ ):
                if sieve[i] is True:
                    _lowerCamelCase	: List[str]       =       False

        start += 1

    for j in range(end + 1						,	num + 1 ):
        if sieve[j] is True:
            prime.append(A__ )

    return prime


if __name__ == "__main__":
  print(prime_sieve(int(input('Enter a positive integer: ').strip())))




'''simple docstring'''
import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
		lowercase__											=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.',
		)

		lowercase__											=parser.parse_args()

		lowercase__											=UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

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

		lowercase__											=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)
