






def        _a  (     UpperCamelCase_					:				list[int]   ) ->   list[int]:
						"""simple docstring"""







						lowerCAmelCase__       =						len(UpperCamelCase_   )
						for i in range(UpperCamelCase_   ):
												for j in range(i + 1    ,   UpperCamelCase_   ):
																		if numbers[j] < numbers[i]:
																								lowerCAmelCase__      ,							lowerCAmelCase__       =						numbers[j], numbers[i]
						return numbers


if __name__ == "__main__":
	a_							=    input('''Enter numbers separated by a comma:\n''').strip()
	a_							=    [int(item) for item in user_input.split(''',''')]
	print(exchange_sort(unsorted))














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)






