





'''simple docstring'''

import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Union[str, Any]					     =     OmegaConf.load(SCREAMING_SNAKE_CASE_	)
      _snake_case		:	List[str]					     =     torch.load(SCREAMING_SNAKE_CASE_			,      map_location='''cpu'''	)['model']
      _snake_case		:	str					     =     list(state_dict.keys()	)

      # extract state_dict for VQVAE
      _snake_case		:	Dict					     =     {}
      _snake_case		:	Any					     =     'first_stage_model.'
      for key in keys:
            if key.startswith(SCREAMING_SNAKE_CASE_	):
                  _snake_case		:	Dict					     =     state_dict[key]

    # extract state_dict for UNetLDM
      _snake_case		:	str					     =     {}
      _snake_case		:	Optional[int]					     =     'model.diffusion_model.'
      for key in keys:
            if key.startswith(SCREAMING_SNAKE_CASE_	):
                  _snake_case		:	Union[str, Any]					     =     state_dict[key]

      _snake_case		:	Tuple					     =     config.model.params.first_stage_config.params
      _snake_case		:	str					     =     config.model.params.unet_config.params

      _snake_case		:	int					     =     VQModel(**SCREAMING_SNAKE_CASE_	).eval()
      vqvae.load_state_dict(SCREAMING_SNAKE_CASE_	)

      _snake_case		:	str					     =     UNetLDMModel(**SCREAMING_SNAKE_CASE_	).eval()
      unet.load_state_dict(SCREAMING_SNAKE_CASE_	)

      _snake_case		:	Optional[Any]					     =     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=SCREAMING_SNAKE_CASE_			,      )

      _snake_case		:	Tuple					     =     LDMPipeline(SCREAMING_SNAKE_CASE_			,      SCREAMING_SNAKE_CASE_			,      SCREAMING_SNAKE_CASE_	)
      pipeline.save_pretrained(SCREAMING_SNAKE_CASE_	)


if __name__ == "__main__":
   UpperCAmelCase					:		str		=							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[Any]		=							parser.parse_args()

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









'''simple docstring'''

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
   from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline


