content: import argparse
  
  def get_args():
      parser = argparse.ArgumentParser('VideoMAE fine-tuning and evaluation script for video classification', add_help=False)
      parser.add_argument('--batch_size', default=64, type=int)
      parser.add_argument('--epochs', default=30, type=int)
      parser.add_argument('--update_freq', default=1, type=int)
      parser.add_argument('--save_ckpt_freq', default=100, type=int)
  
      # Model parameters
      parser.add_argument('--model', default='vit_base_patch16_224', type=str, metavar='MODEL',
                          help='Name of model to train')
      parser.add_argument('--tubelet_size', type=int, default= 2)
      parser.add_argument('--input_size', default=224, type=int,
                          help='videos input size')
  
      parser.add_argument('--drop', type=float, default=0.0, metavar='PCT',
                          help='Dropout rate (default: 0.)')
      parser.add_argument('--attn_drop_rate', type=float, default=0.0, metavar='PCT',
                          help='Attention dropout rate (default: 0.)')
      parser.add_argument('--drop_path', type=float, default=0.1, metavar='PCT',
                          help='Drop path rate (default: 0.1)')
  
      parser.add_argument('--disable_eval_during_finetuning', action='store_true', default=False)
      parser.add_argument('--model_ema', action='store_true', default=False)
      parser.add_argument('--model_ema_decay', type=float, default=0.9999, help='')
      parser.add_argument('--model_ema_force_cpu', action='store_true', default=False, help='')
  
      # Optimizer parameters
      parser.add_argument('--opt', default='adamw', type=str, metavar='OPTIMIZER',
                          help='Optimizer (default: "adamw"')
      parser.add_argument('--opt_eps', default=1e-8, type=float, metavar='EPSILON',
                          help='Optimizer Epsilon (default: 1e-8)')
      parser.add_argument('--opt_betas', default=None, type=float, nargs='+', metavar='BETA',
                          help='Optimizer Betas (default: None, use opt default)')
      parser.add_argument('--clip_grad', type=float, default=None, metavar='NORM',
                          help='Clip gradient norm (default: None, no clipping)')
      parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
                          help='SGD momentum (default: 0.9)')
      parser.add_argument('--weight_decay', type=float, default=0.05,
                          help='weight decay (default: 0.05)')
      parser.add_argument('--weight_decay_end', type=float, default=None, help="""Final value of the
          weight decay. We use a cosine schedule for WD and using a larger decay by
          the end of training improves performance for ViTs.""")
  
      parser.add_argument('--lr', type=float, default=1e-3, metavar='LR',
                          help='learning rate (default: 1e-3)')
      parser.add_argument('--layer_decay', type=float, default=0.75)
  
      parser.add_argument('--warmup_lr', type=float, default=1e-6, metavar='LR',
                          help='warmup learning rate (default: 1e-6)')
      parser.add_argument('--min_lr', type=float, default=1e-6, metavar='LR',
                          help='lower lr bound for cyclic schedulers that hit 0 (1e-5)')
  
      parser.add_argument('--warmup_epochs', type=int, default=5, metavar='N',
                          help='epochs to warmup LR, if scheduler supports')
      parser.add_argument('--warmup_steps', type=int, default=-1, metavar='N',
                          help='num of steps to warmup LR, will overload warmup_epochs if set > 0')
  
      # Augmentation parameters
      parser.add_argument('--color_jitter', type=float, default=0.4, metavar='PCT',
                          help='Color jitter factor (default: 0.4)')
      parser.add_argument('--num_sample', type=int, default=2,
                          help='Repeated_aug (default: 2)')
      parser.add_argument('--aa', type=str, default='rand-m7-n4-mstd0.5-inc1', metavar='NAME',
                          help='Use AutoAugment policy. "v0" or "original". " + "(default: rand-m7-n4-mstd0.5-inc1)'),
      parser.add_argument('--smoothing', type=float, default=0.1,
                          help='Label smoothing (default: 0.1)')
      parser.add_argument('--train_interpolation', type=str, default='bicubic',
                          help='Training interpolation (random, bilinear, bicubic default: "bicubic")')
  
      # Evaluation parameters
      parser.add_argument('--crop_pct', type=float, default=None)
      parser.add_argument('--short_side_size', type=int, default=224)
      parser.add_argument('--test_num_segment', type=int, default=5)
      parser.add_argument('--test_num_crop', type=int, default=3)
      
      # Random Erase params
      parser.add_argument('--reprob', type=float, default=0.25, metavar='PCT',
                          help='Random erase prob (default: 0.25)')
      parser.add_argument('--remode', type=str, default='pixel',
                          help='Random erase mode (default: "pixel")')
      parser.add_argument('--recount', type=int, default=1,
                          help='Random erase count (default: 1)')
      parser.add_argument('--resplit', action='store_true', default=False,
                          help='Do not random erase first (clean) augmentation split')
  
      # Mixup params
      parser.add_argument('--mixup', type=float, default=0.8,
                          help='mixup alpha, mixup enabled if > 0.')
      parser.add_argument('--cutmix', type=float, default=1.0,
                          help='cutmix alpha, cutmix enabled if > 0.')
      parser.add_argument('--cutmix_minmax', type=float, nargs='+', default=None,
                          help='cutmix min/max ratio, overrides alpha and enables cutmix if set (default: None)')
      parser.add_argument('--mixup_prob', type=float, default=1.0,
                          help='Probability of performing mixup or cutmix when either/both is enabled')
      parser.add_argument('--mixup_switch_prob', type=float, default=0.5,
                          help='Probability of switching to cutmix when both mixup and cutmix enabled')
      parser.add_argument('--mixup_mode', type=str, default='batch',
                          help='How to apply mixup/cutmix params. Per "batch", "pair", or "elem"')
  
      # Finetuning params
      parser.add_argument('--finetune', default='', help='finetune from checkpoint')
      parser.add_argument('--model_key', default='model|module', type=str)
      parser.add_argument('--model_prefix', default='', type=str)
      parser.add_argument('--init_scale', default=0.001, type=float)
      parser.add_argument('--use_mean_pooling', action='store_true')
      parser.set_defaults(use_mean_pooling=True)
      parser.add_argument('--use_cls', action='store_false', dest='use_mean_pooling')
  
      # Dataset parameters
      parser.add_argument('--data_path', default='/path/to/list_kinetics-400', type=str,
                          help='dataset path')
      parser.add_argument('--eval_data_path', default=None, type=str,
                          help='dataset path for evaluation')
      parser.add_argument('--nb_classes', default=400, type=int,
                          help='number of the classification types')
      parser.add_argument('--imagenet_default_mean_and_std', default=True, action='store_true')
      parser.add_argument('--num_segments', type=int, default= 1)
      parser.add_argument('--num_frames', type=int, default= 16)
      parser.add_argument('--sampling_rate', type=int, default= 4)
      parser.add_argument('--data_set', default='Kinetics-400', choices=['Kinetics-400', 'SSV2', 'UCF101', 'HMDB51','image_folder'],
                          type=str, help='dataset')
      parser.add_argument('--output_dir', default='',
                          help='path where to save, empty for no saving')
      parser.add_argument('--log_dir', default=None,
                          help='path where to tensorboard log')
      parser.add_argument('--device', default='cuda',
                          help='device to use for training / testing')
      parser.add_argument('--seed', default=0, type=int)
      parser.add_argument('--resume', default='',
                          help='resume from checkpoint')
      parser.add_argument('--auto_resume', action='store_true')
      parser.add_argument('--no_auto_resume', action='store_false', dest='auto_resume')
      parser.set_defaults(auto_resume=True)
  
      parser.add_argument('--save_ckpt', action='store_true')
      parser.add_argument('--no_save_ckpt', action='store_false', dest='save_ckpt')
      parser.set_defaults(save_ckpt=True)
  
      parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
                          help='start epoch')
      parser.add_argument('--eval', action='store_true',
                          help='Perform evaluation only')
      parser.add_argument('--dist_eval', action='store_true', default=False,
                          help='Enabling distributed evaluation')
      parser.add_argument('--num_workers', default=10, type=int)
      parser.add_argument('--pin_mem', action='store_true',
                          help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
      parser.add_argument('--no_pin_mem', action='store_false', dest='pin_mem')
      parser.set_defaults(pin_mem=True)
  
      # distributed training parameters
      parser.add_argument('--world_size', default=1, type=int,
                          help='number of distributed processes')
      parser.add_argument('--local_rank', default=-1, type=int)
      parser.add_argument('--dist_on_itp', action='store_true')
      parser.add_argument('--dist_url', default='env://',
                          help='url used to set up distributed training')
  
      parser.add_argument('--enable_deepspeed', action='store_true', default=False)
  
      parser.add_argument(
          "--exp_name",
          type=str,
          default="test",
          required=True,
          help="experiment id that matches to exp-id in Notion log",
      )
      parser.add_argument(
          "--run-type",
          choices=["train", "eval", "inference"],
          required=True,
          help="run type of the experiment (train, eval, inference)",
      )
      parser.add_argument(
          "--exp-config",
          type=str,
          required=True,
          help="path to config yaml containing info about experiment",
      )
      parser.add_argument(
          "opts",
          default=None,
          nargs=argparse.REMAINDER,
          help="Modify config options from command line",
      )
  
      known_args, _ = parser.parse_known_args()
  
      if known_args.enable_deepspeed:
          try:
              import deepspeed
              from deepspeed import DeepSpeedConfig
              parser = deepspeed.add_config_arguments(parser)
              ds_init = deepspeed.initialize
          except:
              print("Please 'pip install deepspeed'")
              exit(0)
      else:
          ds_init = None
  
      return parser.parse_args(), ds_init