




import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
       lowerCamelCase	:	int				=   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 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Dict:
          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)

       lowerCamelCase	:	Tuple				=   parser.parse_args()

       lowerCamelCase	:	str				=   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)











import warnings
from functools import wraps
from typing import Callable




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Callable:

   @wraps(lowercase					)
   def _inner_fn(*lowercase						,**lowercase					):
      warnings.warn(
          (f"""'{fn.__name__}' is experimental and might be subject to breaking changes in the future.""")						,lowercase						,)
      return fn(*lowercase						,**lowercase					)

   return _inner_fn





