'''simple docstring'''






import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_	   =			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_12,
        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  UpperCamelCase__					(					_lowercase    :				str     )							->	List[str]:







      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)

    SCREAMING_SNAKE_CASE_	   =			parser.parse_args()

    SCREAMING_SNAKE_CASE_	   =			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)
'''simple docstring'''






def  UpperCamelCase__					(					_lowercase    :				list     )							->	list:
  if len(_lowercase     ) <= 1:
    return lst

  __UpperCAmelCase:					List[str]		= 1

  while i < len(_lowercase     ):
    if lst[i - 1] <= lst[i]:
      i += 1
    else:
      __UpperCAmelCase, __UpperCAmelCase:					Tuple		= lst[i], lst[i - 1]
      i -= 1
      if i == 0:
        __UpperCAmelCase:					List[str]		= 1

  return lst


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_	   =			input('Enter numbers separated by a comma:\n').strip()
    SCREAMING_SNAKE_CASE_	   =			[int(item) for item in user_input.split(',')]
    print(gnome_sort(unsorted))