content: import argparse
  import datetime
  import numpy as np
  import time
  import torch
  import torch.backends.cudnn as cudnn
  import json
  import os
  from pathlib import Path
  from timm.models import create_model
  from optim_factory import create_optimizer
  from datasets import build_pretraining_dataset
  from engine_for_pretraining import train_one_epoch
  from utils import NativeScalerWithGradNormCount as NativeScaler
  import utils
  import modeling_pretrain
  
  
  def get_args():
      parser = argparse.ArgumentParser('VideoMAE pre-training script', add_help=False)
      parser.add_argument('--batch_size', default=64, type=int)
      parser.add_argument('--epochs', default=800, type=int)
      parser.add_argument('--save_ckpt_freq', default=50, type=int)
  
      # Model parameters
      parser.add_argument('--model', default='pretrain_videomae_base_patch16_224', type=str, metavar='MODEL',
                          help='Name of model to train')
  
      parser.add_argument('--decoder_depth', default=4, type=int,
                          help='depth of decoder')
  
      parser.add_argument('--mask_type', default='tube', choices=['random', 'tube'],
                          type=str, help='masked strategy of video tokens/patches')
  
      parser.add_argument('--mask_ratio', default=0.75, type=float,
                          help='ratio of the visual tokens/patches need be masked')
  
      parser.add_argument('--input_size', default=224, type=int,
                          help='videos input size for backbone')
  
      parser.add_argument('--drop_path', type=float, default=0.0, metavar='PCT',
                          help='Drop path rate (default: 0.1)')
  
      parser.add_argument('--normlize_target', default=True, type=bool,
                          help='normalized the target patch pixels')
  
      # 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. 
          (Set the same value with args.weight_decay to keep weight decay no change)""")
  
      parser.add_argument('--lr', type=float, default=1.5e-4, metavar='LR',
                          help='learning rate (default: 1.5e-4)')
      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-5, metavar='LR',
                          help='lower lr bound for cyclic schedulers that hit 0 (1e-5)')
  
      parser.add_argument('--warmup_epochs', type=int, default=40, metavar='N',
                          help='epochs to warmup LR, if scheduler supports')
      parser.add_argument('--warmup_steps', type=int, default=-1, metavar='N',
                          help='epochs to warmup LR, if scheduler supports')
  
      # Augmentation parameters
      parser.add_argument('--color_jitter', type=float, default=0.0, metavar='PCT',
                          help='Color jitter factor (default: 0.4)')
      parser.add_argument('--train_interpolation', type=str, default='bicubic',
                          help='Training interpolation (random, bilinear, bicubic default: "bicubic")')
  
      # Dataset parameters
      parser.add_argument('--data_path', default='/path/to/list_kinetics-400', type=str,
                          help='dataset path')
      parser.add_argument('--imagenet_default_mean_and_std', default=True, action='store_true')
      parser.add_argument('--num_frames', type=int, default=16)
      parser.add_argument('--sampling_rate', type=int, default=4)
      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('--start_epoch', default=0, type=int, metavar='N',
                          help='start epoch')
      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',
                          help='')
      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')
  
      return parser.parse_args()
  
  
  def get_model(args):
      print(f"Creating model: {args.model}")
      model = create_model(
          args.model,
          pretrained=False,
          drop_path_rate=args.drop_path,
          drop_block_rate=None,
          decoder_depth=args.decoder_depth
      )
      return model
  
  
  def main(args):
      utils.init_distributed_mode(args)
  
      print(args)
  
      device = torch.device(args.device)
  
      # fix the seed for reproducibility
      seed = args.seed + utils.get_rank()
      torch.manual_seed(seed)
      np.random.seed(seed)
  
      cudnn.benchmark = True
  
      model = get_model(args)
      patch_size = model.encoder.patch_embed.patch_size
      print("Patch size = %s" % str(patch_size))
      args.window_size = (args.num_frames // 2, args.input_size // patch_size[0], args.input_size // patch_size[1])
      args.patch_size = patch_size
  
      # get dataset
      dataset_train = build_pretraining_dataset(args)
  
      num_tasks = utils.get_world_size()
      global_rank = utils.get_rank()
      sampler_rank = global_rank
      num_training_steps_per_epoch = len(dataset_train) // args.batch_size // num_tasks
  
      sampler_train = torch.utils.data.DistributedSampler(
          dataset_train, num_replicas=num_tasks, rank=sampler_rank, shuffle=True
      )
      print("Sampler_train = %s" % str(sampler_train))
  
      if global_rank == 0 and args.log_dir is not None:
          os.makedirs(args.log_dir, exist_ok=True)
          log_writer = utils.TensorboardLogger(log_dir=args.log_dir)
      else:
          log_writer = None
  
      data_loader_train = torch.utils.data.DataLoader(
          dataset_train, sampler=sampler_train,
          batch_size=args.batch_size,
          num_workers=args.num_workers,
          pin_memory=args.pin_mem,
          drop_last=True,
          worker_init_fn=utils.seed_worker
      )
  
      model.to(device)
      model_without_ddp = model
      n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
  
      print("Model = %s" % str(model_without_ddp))
      print('number of params: {} M'.format(n_parameters / 1e6))
  
      total_batch_size = args.batch_size * utils.get_world_size()
  
      args.lr = args.lr * total_batch_size / 256
      args.min_lr = args.min_lr * total_batch_size / 256
      args.warmup_lr = args.warmup_lr * total_batch_size / 256
      print("LR = %.8f" % args.lr)
      print("Batch size = %d" % total_batch_size)
      print("Number of training steps = %d" % num_training_steps_per_epoch)
      print("Number of training examples per epoch = %d" % (total_batch_size * num_training_steps_per_epoch))
  
      if args.distributed:
          model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True)
          model_without_ddp = model.module
  
      optimizer = create_optimizer(
          args, model_without_ddp)
      loss_scaler = NativeScaler()
  
      print("Use step level LR & WD scheduler!")
      lr_schedule_values = utils.cosine_scheduler(
          args.lr, args.min_lr, args.epochs, num_training_steps_per_epoch,
          warmup_epochs=args.warmup_epochs, warmup_steps=args.warmup_steps,
      )
      if args.weight_decay_end is None:
          args.weight_decay_end = args.weight_decay
      wd_schedule_values = utils.cosine_scheduler(
          args.weight_decay, args.weight_decay_end, args.epochs, num_training_steps_per_epoch)
      print("Max WD = %.7f, Min WD = %.7f" % (max(wd_schedule_values), min(wd_schedule_values)))
  
      # resume
      utils.auto_load_model(
          args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler)
      torch.cuda.empty_cache()
      print(f"Start training for {args.epochs} epochs")
      start_time = time.time()
      for epoch in range(args.start_epoch, args.epochs):
          if args.distributed:
              data_loader_train.sampler.set_epoch(epoch)
          if log_writer is not None:
              log_writer.set_step(epoch * num_training_steps_per_epoch)
          train_stats = train_one_epoch(
              model, data_loader_train,
              optimizer, device, epoch, loss_scaler,
              args.clip_grad, log_writer=log_writer,
              start_steps=epoch * num_training_steps_per_epoch,
              lr_schedule_values=lr_schedule_values,
              wd_schedule_values=wd_schedule_values,
              patch_size=patch_size[0],
              normlize_target=args.normlize_target,
          )
          if args.output_dir:
              if (epoch + 1) % args.save_ckpt_freq == 0 or epoch + 1 == args.epochs:
                  utils.save_model(
                      args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
                      loss_scaler=loss_scaler, epoch=epoch)
  
          log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
                       'epoch': epoch, 'n_parameters': n_parameters}
  
          if args.output_dir and utils.is_main_process():
              if log_writer is not None:
                  log_writer.flush()
              with open(os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8") as f:
                  f.write(json.dumps(log_stats) + "\n")
  
      total_time = time.time() - start_time
      total_time_str = str(datetime.timedelta(seconds=int(total_time)))
      print('Training time {}'.format(total_time_str))
  
  
  if __name__ == '__main__':
      opts = get_args()
      if opts.output_dir:
          Path(opts.output_dir).mkdir(parents=True, exist_ok=True)
      main(opts)
  