


import inspect
import unittest



class 	__lowerCamelCase			(unittest.TestCase						):







       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            try:
                 import diffusers  # noqa: F401
            except ImportError:
                 assert False







       def 		snake_case_	(			self: Optional[Any]					):






            '''simple docstring'''




            import diffusers
            from diffusers.dependency_versions_table import deps

            __UpperCamelCase =			inspect.getmembers(A_,inspect.isclass					)

            for cls_name, cls_module in all_classes:
                 if "dummy_" in cls_module.__module__:
                      for backend in cls_module._backends:
                           if backend == "k_diffusion":
                                __UpperCamelCase =			'k-diffusion'
                           elif backend == "invisible_watermark":
                                __UpperCamelCase =			'invisible-watermark'
                           assert backend in deps, F'''{backend} is not in the deps table!'''






import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


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

    __snake_case   							=						parser.parse_args()

    __snake_case   							=						UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

    __snake_case   							=						CLIPImageProcessor()
    __snake_case   							=						CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

    __snake_case   							=						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)


