



import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
     a_						:     str	     =		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     __lowercase(	UpperCAmelCase__ ):


           """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_						:     Union[str, Any]	     =		parser.parse_args()

     a_						:     Union[str, Any]	     =		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)




a_						:     List[str]	     =		'\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

a_						:     Any	     =		[{'type': 'code', 'content': INSTALL_CONTENT}]
a_						:     str	     =		{
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}