




'''simple docstring'''



import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel







def       _UpperCamelCase			(_lowerCamelCase				:  str ,						_lowerCamelCase				:  Optional[int] ,						_lowerCamelCase				:  Optional[Any]						)-> Union[str, Any]:




     '''simple docstring'''





     __snake_case            =	OmegaConf.load(_lowerCamelCase						)
     __snake_case            =	torch.load(_lowerCamelCase ,						map_location='''cpu'''						)['''model''']
     __snake_case            =	list(state_dict.keys()						)

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

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

     __snake_case            =	config.model.params.first_stage_config.params
     __snake_case            =	config.model.params.unet_config.params

     __snake_case            =	VQModel(**_lowerCamelCase						).eval()
     vqvae.load_state_dict(_lowerCamelCase						)

     __snake_case            =	UNetLDMModel(**_lowerCamelCase						).eval()
     unet.load_state_dict(_lowerCamelCase						)

     __snake_case            =	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 ,						)

     __snake_case            =	LDMPipeline(_lowerCamelCase ,						_lowerCamelCase ,						_lowerCamelCase						)
     pipeline.save_pretrained(_lowerCamelCase						)


if __name__ == "__main__":
     UpperCAmelCase_							:	Optional[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)
     UpperCAmelCase_							:	List[str]	     =		parser.parse_args()

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












'''simple docstring'''



def       _UpperCamelCase			(_lowerCamelCase				:  int						)-> int:




     '''simple docstring'''





     __snake_case            =	abs(_lowerCamelCase						)
     __snake_case            =	0
     while n > 0:
          res += n % 10
          n //= 10
     return res







def       _UpperCamelCase			(_lowerCamelCase				:  int						)-> int:




     '''simple docstring'''





     __snake_case            =	abs(_lowerCamelCase						)
     return n if n < 10 else n % 10 + sum_of_digits(n // 10						)







def       _UpperCamelCase			(_lowerCamelCase				:  int						)-> int:




     '''simple docstring'''





     return sum(int(_lowerCamelCase						) for c in str(abs(_lowerCamelCase						)						)						)







def       _UpperCamelCase			()-> None:




     '''simple docstring'''





     from collections.abc import Callable
     from timeit import timeit

     def benchmark_a_function(_lowerCamelCase				:  Callable ,						_lowerCamelCase				:  int						) -> None:
          __snake_case            =	f'''{func.__name__}({value})'''
          __snake_case            =	timeit(f'''__main__.{call}''' ,						setup='''import __main__'''						)
          print(f'''{call:56} = {func(_lowerCamelCase						)} -- {timing:.4f} seconds'''						)

     for value in (26_21_44, 11_25_89_99_06_84_26_24, 1_26_76_50_60_02_28_22_94_01_49_67_03_20_53_76):
          for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
               benchmark_a_function(_lowerCamelCase ,						_lowerCamelCase						)
          print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()






