




'''simple docstring'''



import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
      __UpperCAmelCase    :Optional[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.",
      )

      __UpperCAmelCase    :Optional[int]				       =     parser.parse_args()

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

      __UpperCAmelCase    :List[str]				       =     CLIPImageProcessor()
      __UpperCAmelCase    :str				       =     CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")

      __UpperCAmelCase    :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'''



from __future__ import annotations




def 			_a			(		_lowercase      :			list						):





     '''simple docstring'''






     if not nums:
          raise ValueError('''List is empty'''						)
     return sum(_lowercase						) / len(_lowercase						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()