



from __future__ import annotations



def 		lowerCAmelCase__    (					UpperCamelCase_				:       int ,       UpperCamelCase_				:       int	)->  list[list[int]]:
    A__		 =					[]
    create_all_state(1 ,       UpperCamelCase_ ,       UpperCamelCase_ ,       [] ,       UpperCamelCase_	)
    return result



def 		lowerCAmelCase__    (					UpperCamelCase_				:       int ,       UpperCamelCase_				:       int ,       UpperCamelCase_				:       int ,       UpperCamelCase_				:       list[int] ,       UpperCamelCase_				:       list[list[int]] ,       )->  None:
    if level == 0:
        total_list.append(current_list[:]	)
        return

    for i in range(UpperCamelCase_ ,       total_number - level + 2	):
        current_list.append(UpperCamelCase_	)
        create_all_state(i + 1 ,       UpperCamelCase_ ,       level - 1 ,       UpperCamelCase_ ,       UpperCamelCase_	)
        current_list.pop()



def 		lowerCAmelCase__    (					UpperCamelCase_				:       list[list[int]]	)->  None:
    for i in total_list:
        print(*UpperCamelCase_	)


if __name__ == "__main__":
   _lowercase						  =		4
   _lowercase						  =		2
   _lowercase						  =		generate_all_combinations(n, k)
   print_all_state(total_list)





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
   _lowercase						  =		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 		lowerCAmelCase__    (					UpperCamelCase_				:       Any	)->  Union[str, Any]:
       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)

   _lowercase						  =		parser.parse_args()

   _lowercase						  =		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)
