






def        _a  (     UpperCamelCase_					:				int = 3    ,   UpperCamelCase_					:				int = 7    ,   UpperCamelCase_					:				int = 1_000_000   ) ->   Any:
						"""simple docstring"""







						lowerCAmelCase__       =						0
						lowerCAmelCase__       =						1

						for current_denominator in range(1    ,   limit + 1   ):
												lowerCAmelCase__       =						current_denominator * numerator // denominator
												if current_denominator % denominator == 0:
																		current_numerator -= 1
												if current_numerator * max_denominator > current_denominator * max_numerator:
																		lowerCAmelCase__       =						current_numerator
																		lowerCAmelCase__       =						current_denominator
						return max_numerator


if __name__ == "__main__":
					print(solution(numerator=3, denominator=7, limit=100_0000))














import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


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

	a_							=    parser.parse_args()

	a_							=    UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

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

	a_							=    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)






