
import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


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

							lowercase__		:List[Any]   =    parser.parse_args()

							lowercase__		:List[str]   =    UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

							lowercase__		:Dict   =    CLIPImageProcessor()
							lowercase__		:Dict   =    CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")

							lowercase__		: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)






def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''

					if edge <= 0 or not isinstance(lowerCAmelCase__ ,			lowerCAmelCase__		):
										raise ValueError('''Length must be a positive.'''		)
					return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''

					if edge <= 0 or not isinstance(lowerCAmelCase__ ,			lowerCAmelCase__		):
										raise ValueError('''Length must be a positive.'''		)
					return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




