






import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
   __A					=	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_12,
       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       lowercase__	(					A_:						List[Any]		)	->				Union[str, Any]:







          """simple docstring"""






          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)

   __A					=	parser.parse_args()

   __A					=	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)








def 							UpperCamelCase_( lowerCamelCase_					)       ->		int:

    if not isinstance(lowerCamelCase_				,					lowerCamelCase_					):
        raise ValueError('multiplicative_persistence() only accepts integral values'					)
    if num < 0:
        raise ValueError('multiplicative_persistence() does not accept negative values'					)

    _lowercase			:				List[str] 						=						0
    _lowercase			:				Optional[int] 						=						str(lowerCamelCase_					)

    while len(lowerCamelCase_					) != 1:
        _lowercase			:				Any 						=						[int(lowerCamelCase_					) for i in num_string]

        _lowercase			:				List[Any] 						=						1
        for i in range(0				,					len(lowerCamelCase_					)					):
            total *= numbers[i]

        _lowercase			:				Optional[Any] 						=						str(lowerCamelCase_					)

        steps += 1
    return steps



def 							UpperCamelCase_( lowerCamelCase_					)       ->		int:

    if not isinstance(lowerCamelCase_				,					lowerCamelCase_					):
        raise ValueError('additive_persistence() only accepts integral values'					)
    if num < 0:
        raise ValueError('additive_persistence() does not accept negative values'					)

    _lowercase			:				Optional[int] 						=						0
    _lowercase			:				str 						=						str(lowerCamelCase_					)

    while len(lowerCamelCase_					) != 1:
        _lowercase			:				Dict 						=						[int(lowerCamelCase_					) for i in num_string]

        _lowercase			:				Any 						=						0
        for i in range(0				,					len(lowerCamelCase_					)					):
            total += numbers[i]

        _lowercase			:				Dict 						=						str(lowerCamelCase_					)

        steps += 1
    return steps


if __name__ == "__main__":
 import doctest

 doctest.testmod()


