
import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   int:
       lowercase							:					List[str]	     =				OmegaConf.load(lowerCAmelCase__    )
       lowercase							:					Optional[int]	     =				torch.load(lowerCAmelCase__ ,     map_location="""cpu"""    )["""model"""]
       lowercase							:					List[str]	     =				list(state_dict.keys()    )

       # extract state_dict for VQVAE
       lowercase							:					Dict	     =				{}
       lowercase							:					Dict	     =				"""first_stage_model."""
       for key in keys:
              if key.startswith(lowerCAmelCase__    ):
                     lowercase							:					List[Any]	     =				state_dict[key]

    # extract state_dict for UNetLDM
       lowercase							:					List[str]	     =				{}
       lowercase							:					List[str]	     =				"""model.diffusion_model."""
       for key in keys:
              if key.startswith(lowerCAmelCase__    ):
                     lowercase							:					Optional[int]	     =				state_dict[key]

       lowercase							:					Dict	     =				config.model.params.first_stage_config.params
       lowercase							:					List[Any]	     =				config.model.params.unet_config.params

       lowercase							:					Optional[int]	     =				VQModel(**lowerCAmelCase__    ).eval()
       vqvae.load_state_dict(lowerCAmelCase__    )

       lowercase							:					Tuple	     =				UNetLDMModel(**lowerCAmelCase__    ).eval()
       unet.load_state_dict(lowerCAmelCase__    )

       lowercase							:					Optional[Any]	     =				DDIMScheduler(
           timesteps=config.model.params.timesteps ,     beta_schedule="""scaled_linear""" ,     beta_start=config.model.params.linear_start ,     beta_end=config.model.params.linear_end ,     clip_sample=lowerCAmelCase__ ,     )

       lowercase							:					Union[str, Any]	     =				LDMPipeline(lowerCAmelCase__ ,     lowerCAmelCase__ ,     lowerCAmelCase__    )
       pipeline.save_pretrained(lowerCAmelCase__    )


if __name__ == "__main__":
     lowercase  : Union[str, Any]						   =							argparse.ArgumentParser()
     parser.add_argument("""--checkpoint_path""", type=str, required=True)
     parser.add_argument("""--config_path""", type=str, required=True)
     parser.add_argument("""--output_path""", type=str, required=True)
     lowercase  : List[Any]						   =							parser.parse_args()

     convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)


from collections.abc import Generator







def 						_snake_case(  )			->   Generator[int, None, None]:
       lowercase     ,      lowercase							:					List[str]	     =				0, 1
       while True:
              lowercase     ,      lowercase							:					Optional[int]	     =				b, a + b
              yield b







def 						_snake_case(  SCREAMING_SNAKE_CASE__ = 1_000    )			->   int:
       lowercase							:					List[Any]	     =				1
       lowercase							:					Tuple	     =				fibonacci_generator()
       while len(str(next(SCREAMING_SNAKE_CASE__    )    )    ) < n:
              answer += 1
       return answer + 1


if __name__ == "__main__":
  print(solution(int(str(input()).strip())))
