




import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				List[Any]:
      _UpperCAmelCase      =			OmegaConf.load(snake_case       )
      _UpperCAmelCase      =			torch.load(snake_case			,     map_location="""cpu"""       )["""model"""]
      _UpperCAmelCase      =			list(state_dict.keys()       )

      # extract state_dict for VQVAE
      _UpperCAmelCase      =			{}
      _UpperCAmelCase      =			"""first_stage_model."""
      for key in keys:
            if key.startswith(snake_case       ):
                  _UpperCAmelCase      =			state_dict[key]

    # extract state_dict for UNetLDM
      _UpperCAmelCase      =			{}
      _UpperCAmelCase      =			"""model.diffusion_model."""
      for key in keys:
            if key.startswith(snake_case       ):
                  _UpperCAmelCase      =			state_dict[key]

      _UpperCAmelCase      =			config.model.params.first_stage_config.params
      _UpperCAmelCase      =			config.model.params.unet_config.params

      _UpperCAmelCase      =			VQModel(**snake_case       ).eval()
      vqvae.load_state_dict(snake_case       )

      _UpperCAmelCase      =			UNetLDMModel(**snake_case       ).eval()
      unet.load_state_dict(snake_case       )

      _UpperCAmelCase      =			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			,     )

      _UpperCAmelCase      =			LDMPipeline(snake_case			,     snake_case			,     snake_case       )
      pipeline.save_pretrained(snake_case       )


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)





import math




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				float:
						if (
						    not isinstance(snake_case			,     (int, float)       )
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError("""power_factor must be a valid float value between -1 and 1."""       )
						return apparent_power * power_factor




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				float:
						if (
						    not isinstance(snake_case			,     (int, float)       )
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError("""power_factor must be a valid float value between -1 and 1."""       )
						return apparent_power * math.sqrt(1 - power_factor**2       )


if __name__ == "__main__":
				import doctest

				doctest.testmod()