






"""simple docstring"""



import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel




def      _lowerCAmelCase		(							UpperCAmelCase__       :					str,						UpperCAmelCase__       :					Any,						UpperCAmelCase__       :					Optional[Any]      )		->List[Any]:
						A__						:  Dict		  =  OmegaConf.load(UpperCAmelCase__      )
						A__						:  Optional[int]		  =  torch.load(UpperCAmelCase__,						map_location="""cpu"""      )["""model"""]
						A__						:  str		  =  list(state_dict.keys()      )

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

    # extract state_dict for UNetLDM
						A__						:  Dict		  =  {}
						A__						:  Optional[int]		  =  """model.diffusion_model."""
						for key in keys:
												if key.startswith(UpperCAmelCase__      ):
																		A__						:  List[Any]		  =  state_dict[key]

						A__						:  Optional[Any]		  =  config.model.params.first_stage_config.params
						A__						:  Dict		  =  config.model.params.unet_config.params

						A__						:  Optional[Any]		  =  VQModel(**UpperCAmelCase__      ).eval()
						vqvae.load_state_dict(UpperCAmelCase__      )

						A__						:  Tuple		  =  UNetLDMModel(**UpperCAmelCase__      ).eval()
						unet.load_state_dict(UpperCAmelCase__      )

						A__						:  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=UpperCAmelCase__,						)

						A__						:  Tuple		  =  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)








"""simple docstring"""



from timeit import timeit




def      _lowerCAmelCase		(							UpperCAmelCase__       :					int      )		->int:
						if number < 0:
												raise ValueError("""the value of input must not be negative"""      )
						A__						:  Optional[int]		  =  0
						while number:
												number &= number - 1
												result += 1
						return result




def      _lowerCAmelCase		(							UpperCAmelCase__       :					int      )		->int:
						if number < 0:
												raise ValueError("""the value of input must not be negative"""      )
						A__						:  Tuple		  =  0
						while number:
												if number % 2 == 1:
																		result += 1
												number >>= 1
						return result




def      _lowerCAmelCase		(							)		->None:

						def do_benchmark(UpperCAmelCase__       :					int      ) -> None:
												A__						:  Optional[int]		  =  """import __main__ as z"""
												print(f'Benchmark when {number = }:'      )
												print(f'{get_set_bits_count_using_modulo_operator(UpperCAmelCase__      ) = }'      )
												A__						:  Any		  =  timeit("""z.get_set_bits_count_using_modulo_operator(25)""",						setup=UpperCAmelCase__      )
												print(f'timeit() runs in {timing} seconds'      )
												print(f'{get_set_bits_count_using_brian_kernighans_algorithm(UpperCAmelCase__      ) = }'      )
												A__						:  List[str]		  =  timeit(
												    """z.get_set_bits_count_using_brian_kernighans_algorithm(25)""",						setup=UpperCAmelCase__,						)
												print(f'timeit() runs in {timing} seconds'      )

						for number in (2_5, 3_7, 5_8, 0):
												do_benchmark(UpperCAmelCase__      )
												print()


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						benchmark()






