


"""simple docstring"""


import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel



def   snake_case						(				_a:     Optional[Any]		,   _a:     Dict		,   _a:     Optional[Any]							)->		List[Any]:
      '''simple docstring'''


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

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

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

      lowerCamelCase__           =		config.model.params.first_stage_config.params
      lowerCamelCase__           =		config.model.params.unet_config.params

      lowerCamelCase__           =		VQModel(**_a							).eval()
      vqvae.load_state_dict(_a							)

      lowerCamelCase__           =		UNetLDMModel(**_a							).eval()
      unet.load_state_dict(_a							)

      lowerCamelCase__           =		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=_a		,   )

      lowerCamelCase__           =		LDMPipeline(_a		,   _a		,   _a							)
      pipeline.save_pretrained(_a							)


if __name__ == "__main__":
   _snake_case          =							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)
   _snake_case          =							parser.parse_args()

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







"""simple docstring"""


import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    "The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
    " StableDiffusionImg2ImgPipeline` instead."
)



