




from ..utils import DummyObject, requires_backends





class 			lowerCamelCase_				( metaclass=_SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       ["transformers", "torch", "note_seq"]



 def __init__(				self    :						Tuple							,							*_lowerCAmelCase    :						List[str]							,							**_lowerCAmelCase    :						Optional[int]		):
       requires_backends(self							,							['transformers', 'torch', 'note_seq']		)



 @classmethod
 def  lowerCAmelCase_    (				cls    :						List[Any]							,							*_lowerCAmelCase    :						Any							,							**_lowerCAmelCase    :						Union[str, Any]		):
       requires_backends(cls							,							['transformers', 'torch', 'note_seq']		)




 @classmethod
 def  lowerCAmelCase_    (				cls    :						List[str]							,							*_lowerCAmelCase    :						List[str]							,							**_lowerCAmelCase    :						Optional[Any]		):
       requires_backends(cls							,							['transformers', 'torch', 'note_seq']		)






import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
					A       :   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.''',
					)

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

					A       :   int					= UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

					A       :   Tuple					= CLIPImageProcessor()
					A       :   int					= CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

					A       :   Tuple					= 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)



