






"""simple docstring"""





import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel



def   a    (     __UpperCAmelCase      :    List[str]      ,   __UpperCAmelCase      :    List[str]      ,   __UpperCAmelCase      :    Dict  )    ->							int:
						__magic_name__:	Optional[Any]							=       OmegaConf.load(__lowerCamelCase  )
						__magic_name__:	Any							=       torch.load(__lowerCamelCase      ,   map_location="""cpu"""  )["""model"""]
						__magic_name__:	Tuple							=       list(state_dict.keys()  )

						# extract state_dict for VQVAE
						__magic_name__:	Union[str, Any]							=       {}
						__magic_name__:	Optional[Any]							=       """first_stage_model."""
						for key in keys:
												if key.startswith(__lowerCamelCase  ):
																		__magic_name__:	Tuple							=       state_dict[key]

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

						__magic_name__:	List[str]							=       config.model.params.first_stage_config.params
						__magic_name__:	Optional[int]							=       config.model.params.unet_config.params

						__magic_name__:	Optional[Any]							=       VQModel(**__lowerCamelCase  ).eval()
						vqvae.load_state_dict(__lowerCamelCase  )

						__magic_name__:	str							=       UNetLDMModel(**__lowerCamelCase  ).eval()
						unet.load_state_dict(__lowerCamelCase  )

						__magic_name__:	List[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      ,   )

						__magic_name__:	int							=       LDMPipeline(__lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase  )
						pipeline.save_pretrained(__lowerCamelCase  )


if __name__ == "__main__":
							__lowerCamelCase							=  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)
							__lowerCamelCase							=  parser.parse_args()

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









from bisect import bisect
from itertools import accumulate





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Dict:
	a									=				sorted(zip(__lowerCamelCase				,     __lowerCamelCase       )				,     key=lambda __lowerCamelCase       : x[0] / x[1]				,     reverse=__lowerCamelCase       )
	a							,					a									=				[i[0] for i in r], [i[1] for i in r]
	a									=				list(accumulate(__lowerCamelCase       )       )
	a									=				bisect(__lowerCamelCase				,     __lowerCamelCase       )
	return (
	    0
	    if k == 0
	    else sum(vl[:k]       ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
	    if k != n
	    else sum(vl[:k]       )
	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()

