






import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel






def   UpperCamelCase  (       snake_case__     :   Optional[int]	,snake_case__     :   Dict	,snake_case__     :   Union[str, Any]  ):




     '''simple docstring'''






     __snake_case			:List[Any]          =						OmegaConf.load(snake_case__  )
     __snake_case			:List[str]          =						torch.load(snake_case__	,map_location="""cpu"""  )["""model"""]
     __snake_case			:Tuple          =						list(state_dict.keys()  )

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

    # extract state_dict for UNetLDM
     __snake_case			:List[Any]          =						{}
     __snake_case			:List[Any]          =						"""model.diffusion_model."""
     for key in keys:
          if key.startswith(snake_case__  ):
               __snake_case			:Any          =						state_dict[key]

     __snake_case			:Optional[int]          =						config.model.params.first_stage_config.params
     __snake_case			:int          =						config.model.params.unet_config.params

     __snake_case			:str          =						VQModel(**snake_case__  ).eval()
     vqvae.load_state_dict(snake_case__  )

     __snake_case			:Any          =						UNetLDMModel(**snake_case__  ).eval()
     unet.load_state_dict(snake_case__  )

     __snake_case			:Dict          =						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=snake_case__	,)

     __snake_case			:Tuple          =						LDMPipeline(snake_case__	,snake_case__	,snake_case__  )
     pipeline.save_pretrained(snake_case__  )


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)










def   UpperCamelCase  (       snake_case__     :   str  ):




     '''simple docstring'''






     __snake_case			:List[Any]          =						hex_num.strip()
     if not hex_num:
          raise ValueError("""No value was passed to the function"""  )

     __snake_case			:str          =						hex_num[0] == """-"""
     if is_negative:
          __snake_case			:List[Any]          =						hex_num[1:]

     try:
          __snake_case			:Tuple          =						int(snake_case__	,16  )
     except ValueError:
          raise ValueError("""Invalid value was passed to the function"""  )

     __snake_case			:int          =						""""""
     while int_num > 0:
          __snake_case			:Optional[int]          =						str(int_num % 2  ) + bin_str
          int_num >>= 1

     return int(("""-""" + bin_str) if is_negative else bin_str  )


if __name__ == "__main__":
     import doctest

     doctest.testmod()


