

import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def        __lowerCAmelCase    (       _UpperCamelCase		:  List[str]		,							_UpperCamelCase		:  Union[str, Any]		,							_UpperCamelCase		:  Tuple	)		->							Union[str, Any]:
 '''simple docstring'''


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

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

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

 SCREAMING_SNAKE_CASE								=			config.model.params.first_stage_config.params
 SCREAMING_SNAKE_CASE								=			config.model.params.unet_config.params

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

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

 SCREAMING_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=_UpperCamelCase		,							)

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


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

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







def        __lowerCAmelCase    (       _UpperCamelCase		:  int = 10_00	)		->							int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			2**power
 SCREAMING_SNAKE_CASE								=			str(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			list(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			0

 for i in list_num:
  sum_of_num += int(_UpperCamelCase	)

 return sum_of_num


if __name__ == "__main__":
      a_    :						List[str]    		=      int(input("Enter the power of 2: ").strip())
      print("2 ^ ", power, " = ", 2**power)
      a_    :						int    		=      solution(power)
      print("Sum of the digits is: ", result)

