






'''simple docstring'''




import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
		__lowerCamelCase   :					List[Any]   							=					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.''',
		)

		__lowerCamelCase   :					Optional[Any]   							=					parser.parse_args()

		__lowerCamelCase   :					Tuple   							=					UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

		__lowerCamelCase   :					Union[str, Any]   							=					CLIPImageProcessor()
		__lowerCamelCase   :					Optional[Any]   							=					CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

		__lowerCamelCase   :					List[Any]   							=					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)












'''simple docstring'''




def 	__UpperCAmelCase       ( __magic_name__						)->    list[int]:



			"""simple docstring"""



			if length <= 0 or not isinstance(__magic_name__     ,__magic_name__						):
						raise ValueError("Length must be a positive integer."						)
			return [n * (2 * n - 1) for n in range(__magic_name__						)]


if __name__ == "__main__":
		print(hexagonal_numbers(length=5))
		print(hexagonal_numbers(length=10))




