'''simple docstring'''




import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
  __UpperCAmelCase     							=argparse.ArgumentParser()

  parser.add_argument(
      "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
  )
  parser.add_argument(
      "--original_config_file",
      type=str,
      required=True,
      help="The YAML config file corresponding to the original architecture.",
  )
  parser.add_argument(
      "--num_in_channels",
      default=None,
      type=int,
      help="The number of input channels. If `None` number of input channels will be automatically inferred.",
  )
  parser.add_argument(
      "--image_size",
      default=5_1_2,
      type=int,
      help=(
          "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"
          " Base. Use 768 for Stable Diffusion v2."
      ),
  )
  parser.add_argument(
      "--extract_ema",
      action="store_true",
      help=(
          "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
          " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
          " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
      ),
  )
  parser.add_argument(
      "--upcast_attention",
      action="store_true",
      help=(
          "Whether the attention computation should always be upcasted. This is necessary when running stable"
          " diffusion 2.1."
      ),
  )
  parser.add_argument(
      "--from_safetensors",
      action="store_true",
      help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.",
  )
  parser.add_argument(
      "--to_safetensors",
      action="store_true",
      help="Whether to store pipeline in safetensors format or not.",
  )
  parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
  parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")






  def __lowerCAmelCase (							UpperCamelCase__				)	->    Tuple:
      if string == "True":
          return True
      elif string == "False":
          return False
      else:
          raise ValueError(f"""could not parse string as bool {string}"""				)

  parser.add_argument(
      "--use_linear_projection", help="Override for use linear projection", required=False, type=parse_bool
  )

  parser.add_argument("--cross_attention_dim", help="Override for cross attention_dim", required=False, type=int)

  __UpperCAmelCase     							=parser.parse_args()

  __UpperCAmelCase     							=download_controlnet_from_original_ckpt(
      checkpoint_path=args.checkpoint_path,
      original_config_file=args.original_config_file,
      image_size=args.image_size,
      extract_ema=args.extract_ema,
      num_in_channels=args.num_in_channels,
      upcast_attention=args.upcast_attention,
      from_safetensors=args.from_safetensors,
      device=args.device,
      use_linear_projection=args.use_linear_projection,
      cross_attention_dim=args.cross_attention_dim,
  )



  controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)





'''simple docstring'''




import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    '''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
    ''' StableDiffusionInpaintPipeline` instead.'''
)