



import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


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

			UpperCamelCase__ =						parser.parse_args()

			UpperCamelCase__ =						UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

			UpperCamelCase__ =						CLIPImageProcessor()
			UpperCamelCase__ =						CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")

			UpperCamelCase__ =						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__    (    lowercase__						,       lowercase__   ) ->      int:
						if len(lowercase__   ) != len(lowercase__   ):
												raise ValueError("""String lengths must match!"""   )

						__lowercase					=				0

						for chara, chara in zip(lowercase__						,       lowercase__   ):
												if chara != chara:
																		count += 1

						return count


if __name__ == "__main__":
			import doctest

			doctest.testmod()





