





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
     _snake_case  :   Optional[Any]						   =  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=512,
         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   a_   (			lowerCAmelCase_				:      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)

     _snake_case  :   List[str]						   =  parser.parse_args()

     _snake_case  :   int						   =  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)








class     _UpperCAmelCase   :

  """simple docstring"""


  def __init__(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		List[str] ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		Dict					)							->						List[str]:
     __lowerCAmelCase							     =     name
     __lowerCAmelCase							     =     value
     __lowerCAmelCase							     =     weight






  def __repr__(    self			:		Union[str, Any]					)							->						List[str]:
     return f"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""






  def     lowercase		(    self			:		int					)							->						Optional[int]:
     return self.value






  def     lowercase		(    self			:		Optional[Any]					)							->						Union[str, Any]:
     return self.name






  def     lowercase		(    self			:		List[Any]					)							->						Tuple:
     return self.weight






  def     lowercase		(    self			:		int					)							->						Dict:
     return self.value / self.weight





def   a_   (			lowerCAmelCase_				:      Dict,     lowerCAmelCase_				:      int,     lowerCAmelCase_				:      Optional[int]     ):
   __lowerCAmelCase							     =     []
   for i in range(len(lowerCAmelCase_     )     ):
      menu.append(Things(name[i],     value[i],     weight[i]     )     )
   return menu

def   a_   (			lowerCAmelCase_				:      Optional[int],     lowerCAmelCase_				:      List[Any],     lowerCAmelCase_				:      Optional[Any]     ):
   __lowerCAmelCase							     =     sorted(lowerCAmelCase_,     key=lowerCAmelCase_,     reverse=lowerCAmelCase_     )
   __lowerCAmelCase							     =     []
   __lowerCAmelCase     ,     __lowerCAmelCase							     =     0.0, 0.0
   for i in range(len(lowerCAmelCase_     )     ):
      if (total_cost + items_copy[i].get_weight()) <= max_cost:
         result.append(items_copy[i]     )
         total_cost += items_copy[i].get_weight()
         total_value += items_copy[i].get_value()
   return (result, total_value)

def   a_   (			):
   pass


if __name__ == "__main__":
     import doctest

     doctest.testmod()

