






a_							=    0  # The first color of the flag.
a_							=    1  # The second color of the flag.
a_							=    2  # The third color of the flag.
a_							=    (red, white, blue)


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







						if not sequence:
												return []
						if len(UpperCamelCase_   ) == 1:
												return list(UpperCamelCase_   )
						lowerCAmelCase__       =						0
						lowerCAmelCase__       =						len(UpperCamelCase_   ) - 1
						lowerCAmelCase__       =						0
						while mid <= high:
												if sequence[mid] == colors[0]:
																		lowerCAmelCase__      ,							lowerCAmelCase__       =						sequence[mid], sequence[low]
																		low += 1
																		mid += 1
												elif sequence[mid] == colors[1]:
																		mid += 1
												elif sequence[mid] == colors[2]:
																		lowerCAmelCase__      ,							lowerCAmelCase__       =						sequence[high], sequence[mid]
																		high -= 1
												else:
																		lowerCAmelCase__       =						F"The elements inside the sequence must contains only {colors} values"
																		raise ValueError(UpperCamelCase_   )
						return sequence


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	a_							=    input('''Enter numbers separated by commas:\n''').strip()
	a_							=    [int(item.strip()) for item in user_input.split(''',''')]
	print(F"{dutch_national_flag_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)






