



import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def 		a__      (		_UpperCamelCase      :			List[str]			,_UpperCamelCase      :			str			,_UpperCamelCase      :			Any				):
   __lowerCamelCase    				=     OmegaConf.load(_UpperCamelCase				)
   __lowerCamelCase    				=     torch.load(_UpperCamelCase			,map_location='''cpu'''				)['''model''']
   __lowerCamelCase    				=     list(state_dict.keys()				)

   # extract state_dict for VQVAE
   __lowerCamelCase    				=     {}
   __lowerCamelCase    				=     '''first_stage_model.'''
   for key in keys:
      if key.startswith(_UpperCamelCase				):
         __lowerCamelCase    				=     state_dict[key]

    # extract state_dict for UNetLDM
   __lowerCamelCase    				=     {}
   __lowerCamelCase    				=     '''model.diffusion_model.'''
   for key in keys:
      if key.startswith(_UpperCamelCase				):
         __lowerCamelCase    				=     state_dict[key]

   __lowerCamelCase    				=     config.model.params.first_stage_config.params
   __lowerCamelCase    				=     config.model.params.unet_config.params

   __lowerCamelCase    				=     VQModel(**_UpperCamelCase				).eval()
   vqvae.load_state_dict(_UpperCamelCase				)

   __lowerCamelCase    				=     UNetLDMModel(**_UpperCamelCase				).eval()
   unet.load_state_dict(_UpperCamelCase				)

   __lowerCamelCase    				=     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=_UpperCamelCase			,)

   __lowerCamelCase    				=     LDMPipeline(_UpperCamelCase			,_UpperCamelCase			,_UpperCamelCase				)
   pipeline.save_pretrained(_UpperCamelCase				)


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

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











def 		a__      (		_UpperCamelCase      :			int				):
   __lowerCamelCase    				=     int(_UpperCamelCase				)
   if decimal in (0, 1):  # Exit cases for the recursion
      return str(_UpperCamelCase				)
   __lowerCamelCase				,__lowerCamelCase    				=     divmod(_UpperCamelCase			,2				)
   return binary_recursive(_UpperCamelCase				) + str(_UpperCamelCase				)





def 		a__      (		_UpperCamelCase      :			str				):
   __lowerCamelCase    				=     str(_UpperCamelCase				).strip()
   if not number:
      raise ValueError('''No input value was provided'''				)
   __lowerCamelCase    				=     '''-''' if number.startswith('''-'''				) else ''''''
   __lowerCamelCase    				=     number.lstrip('''-'''				)
   if not number.isnumeric():
      raise ValueError('''Input value is not an integer'''				)
   return F"""{negative}0b{binary_recursive(int(_UpperCamelCase				)				)}"""


if __name__ == "__main__":
  from doctest import testmod

  testmod()






