





'''simple docstring'''



def 					snake_case_   (		SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__							):







  '''simple docstring'''



  if number < 0 or shift_amount < 0:
    raise ValueError("both inputs must be positive integers"							)

  _snake_case  		=					str(bin(__a							)							)
  binary_number += "0" * shift_amount
  return binary_number




def 					snake_case_   (		SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__							):







  '''simple docstring'''



  if number < 0 or shift_amount < 0:
    raise ValueError("both inputs must be positive integers"							)

  _snake_case  		=					str(bin(__a							)							)[2:]
  if shift_amount >= len(__a							):
    return "0b0"
  _snake_case  		=					binary_number[: len(__a							) - shift_amount]
  return "0b" + shifted_binary_number




def 					snake_case_   (		SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__							):







  '''simple docstring'''



  if number >= 0:  # Get binary representation of positive number
    _snake_case  		=					"0" + str(bin(__a							)							).strip("-"							)[2:]
  else:  # Get binary (2's complement) representation of negative number
    _snake_case  		=					len(bin(__a							)[3:]							)  # Find 2's complement of number
    _snake_case  		=					bin(abs(__a							) - (1 << binary_number_length)							)[3:]
    _snake_case  		=					(
        "1" + "0" * (binary_number_length - len(__a							)) + binary_number
    )

  if shift_amount >= len(__a							):
    return "0b" + binary_number[0] * len(__a							)
  return (
      "0b"
      + binary_number[0] * shift_amount
      + binary_number[: len(__a							) - shift_amount]
  )


if __name__ == "__main__":
 import doctest

 doctest.testmod()






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=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   _lowercase(					__a				:						Optional[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)

	_lowerCAmelCase:						str   				=       parser.parse_args()

	_lowerCAmelCase:						Tuple   				=       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)

