content: import argparse
  import datetime
  import numpy as np
  import time
  import torch
  import torch.backends.cudnn as cudnn
  import json
  import os
  from functools import partial
  
  from pathlib import Path
  from timm.models import create_model
  from optim_factory import (
      create_optimizer,
      get_parameter_groups,
  )
  from datasets import build_pretraining_dataset, build_multi_pretraining_dataset
  from engines.engine_for_distill import train_one_epoch
  from utils import NativeScalerWithGradNormCount as NativeScaler
  from utils import multiple_pretrain_samples_collate
  import utils
  from models import *
  
  
  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('--update_freq', default=1, type=int)
      parser.add_argument('--save_ckpt_freq', default=50, type=int)
      parser.add_argument('--steps_per_print', default=1, type=int)
      parser.add_argument('--use_ceph_checkpoint', action='store_true',
                          help="whether use ceph to save and load checkpoint, may be some bug now")
      parser.set_defaults(use_ceph_checkpoint=False)
      parser.add_argument('--ceph_checkpoint_prefix', default='', type=str,
                          help='prefix for checkpoint in ceph')
      parser.add_argument('--ckpt_path_split', default='/exp/', type=str,
                          help='string for splitting the ckpt_path')
  
      # 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', 'attention'],
                          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: 0.0)')
      parser.add_argument('--normlize_target', default=True, type=bool,
                          help='normalized the target patch pixels')
      parser.add_argument('--tubelet_size', default=1, type=int,
                          help='temporal tube size for the patch embedding')
      parser.add_argument('--layer_scale_init_value', default=1e-5, type=float, 
                          help="0.1 for base, 1e-5 for large. set 0 to disable LayerScale")
      parser.add_argument('--layerscale_no_force_fp32', action='store_true',
                          help="Not force fp32 for LayerScale")
      parser.set_defaults(layerscale_no_force_fp32=False)
      parser.add_argument('--sep_pos_embed', action='store_true',
                          help="whether use seperable position embedding")
      parser.set_defaults(sep_pos_embed=False)
  
      # CLIP decpder parameters
      parser.add_argument('--clip_teacher', default='internvl_clip_6b', type=str,
                          help='Name of CLIP teacher')
      parser.add_argument('--clip_input_resolution', default=224, type=int,
                          help='input resolution of CLIP decoder')
      parser.add_argument('--clip_teacher_embed_dim', default=3200, type=int,
                          help='output dimension of CLIP decoder in the intermediate layers')
      parser.add_argument('--clip_teacher_final_dim', default=768, type=int,
                          help='output dimension of CLIP decoder in the final layer, 0 means w/o alignment')
      parser.add_argument('--clip_loss_ratio', default=[1, 1], type=float, nargs='+', metavar='BETA',
                          help='Loss ratio for middle features and final features (default: [1, 0.5])')
      parser.add_argument('--clip_norm_type', default='l2', type=str,
                          help='type of feature normalization')
      parser.add_argument('--clip_return_attn', action='store_true',
                          help="whether return CLIP attention")
      parser.set_defaults(clip_return_attn=False)
      parser.add_argument('--clip_return_layer', default=1, type=int,
                          help='number of CLIP return layers')
      parser.add_argument('--clip_teacher_return_interval', default=1, type=float,
                          help='interval of CLIP teacher return layers')
      parser.add_argument('--clip_student_return_interval', default=1, type=float,
                          help='interval of CLIP student return layers')
      parser.add_argument('--clip_teacher_return_index', default=None, type=int, nargs='+', metavar='BETA',
                          help='index of CLIP teacher return layers (default: None)')
      parser.add_argument('--clip_student_return_index', default=None, type=int, nargs='+', metavar='BETA',
                          help='index of CLIP student return layers (default: None)')
      parser.add_argument('--clip_student_decoder', default='Linear_Decoder', type=str,
                          help='type of student decoder')
      
      # Optimizer parameters
      parser.add_argument('--opt', default='adamw', type=str, metavar='OPTIMIZER',
                          help='Optimizer (default: "adamw"')
      parser.add_argument('--opt_eps', default=1e-6, type=float, metavar='EPSILON',
                          help='Optimizer Epsilon (default: 1e-6)')
      parser.add_argument('--opt_betas', default=[0.9, 0.98], type=float, nargs='+', metavar='BETA',
                          help='Optimizer Betas (default: [0.9, 0.98])')
      parser.add_argument('--clip_grad', type=float, default=3.0, metavar='NORM',
                          help='Clip gradient norm (default: 3.0)')
      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')
      parser.add_argument('--use_checkpoint', action='store_true')
      parser.set_defaults(use_checkpoint=False)
      parser.add_argument('--checkpoint_num', type=int, default=0)
  
      # Augmentation parameters
      parser.add_argument('--num_sample', type=int, default=1, help='Repeated_aug (default: 1)')
      parser.add_argument('--color_jitter', type=float, default=0.0, metavar='PCT',
                          help='Color jitter factor (default: 0.0)')
      parser.add_argument('--train_interpolation', type=str, default='bicubic',
                          help='Training interpolation (random, bilinear, bicubic default: "bicubic")')
      parser.add_argument('--flip', default=False, action='store_true',
                          help='whether flip the video in pretraining')
  
      # Dataset parameters
      parser.add_argument('--prefix', default='', type=str, help='prefix for data')
      parser.add_argument('--split', default=' ', type=str, help='split for metadata')
      parser.add_argument('--data_path', default='you_data_path', type=str,
                          help='dataset path')
      parser.add_argument('--imagenet_default_mean_and_std', default=True, action='store_true')
      parser.add_argument('--use_decord', action='store_true',
                          help='whether use decord to load video, otherwise load image')
      parser.add_argument('--no_use_decord', action='store_false', dest='use_decord')
      parser.set_defaults(use_decord=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('--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('--test_best', action='store_true',
                          help='Whether test the best model')
      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')
      
      parser.add_argument('--enable_deepspeed',
                          action='store_true', default=False)
      parser.add_argument('--bf16', default=False, action='store_true')
      parser.add_argument('--zero_stage', default=0, type=int,
                          help='ZeRO optimizer stage (default: 0)')
      
      known_args, _ = parser.parse_known_args()
  
      if known_args.enable_deepspeed:
          try:
              import deepspeed
              parser = deepspeed.add_config_arguments(parser)
              ds_init = deepspeed.initialize
          except:
              print("Please install DeepSpeed")
              exit(0)
      else:
          ds_init = None
  
      return parser.parse_args(), ds_init
  
  
  def get_model(args):
      print(f"Creating model: {args.model}")
      model = create_model(
          args.model,
          pretrained=False,
          drop_path_rate=args.drop_path,
          num_frames=args.num_frames//args.tubelet_size,
          tubelet_size=args.tubelet_size,
          sep_pos_embed=args.sep_pos_embed,
          use_checkpoint=args.use_checkpoint,
          checkpoint_num=args.checkpoint_num,
          init_values=args.layer_scale_init_value,
          layerscale_no_force_fp32=args.layerscale_no_force_fp32,
          clip_teacher_embed_dim=args.clip_teacher_embed_dim,
          clip_teacher_final_dim=args.clip_teacher_final_dim,
          clip_norm_type=args.clip_norm_type,
          clip_return_layer=args.clip_return_layer,
          clip_student_return_interval=args.clip_student_return_interval,
          clip_student_return_index=args.clip_student_return_index,
          clip_student_decoder=args.clip_student_decoder,
      )
      return model
  
  
  def main(args, ds_init):
      utils.init_distributed_mode(args)
  
      if ds_init is not None:
          utils.create_internvideo2_ds_config(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.patch_embed.patch_size
      print("Patch size = %s" % str(patch_size))
      print("Tubelet size = %s" % str(args.tubelet_size))
      args.window_size = (args.num_frames // args.tubelet_size, args.input_size // patch_size[0], args.input_size // patch_size[1])
      args.patch_size = patch_size
  
      # CLIP teacher model
      print(f'CLIP Teacher model: {args.clip_teacher}')
      clip_teacher_model = eval(args.clip_teacher)(
          clip_norm_type=args.clip_norm_type,
          return_attn=args.clip_return_attn,
          clip_return_layer=args.clip_return_layer,
          clip_return_interval=args.clip_teacher_return_interval,
          clip_return_index=args.clip_teacher_return_index,
      )
  
      # get dataset
      # dataset_train = build_pretraining_dataset(args)
      dataset_train = build_multi_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
  
      if args.num_sample > 1:
          collate_func = partial(multiple_pretrain_samples_collate, fold=False)
      else:
          collate_func = 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,
          collate_fn=collate_func,
          worker_init_fn=utils.seed_worker,
          persistent_workers=True
      )
  
      model.to(device)
      clip_teacher_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 * args.num_sample / 256
      args.min_lr = args.min_lr * total_batch_size * args.num_sample / 256
      args.warmup_lr = args.warmup_lr * total_batch_size * args.num_sample / 256
      print("LR = %.8f" % args.lr)
      print("Batch size = %d" % total_batch_size)
      print("Repeated sample = %d" % args.num_sample)
      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))
  
      skip_weight_decay_list = model.no_weight_decay()
      print("Skip weight decay list: ", skip_weight_decay_list)
  
      if args.enable_deepspeed:
          loss_scaler = None
          optimizer_params = get_parameter_groups(
              model, args.weight_decay, skip_weight_decay_list
          )
          model, optimizer, _, _ = ds_init(
              args=args, model=model, model_parameters=optimizer_params,
              dist_init_required=not args.distributed,
          )
  
          print("model.gradient_accumulation_steps() = %d" %
                model.gradient_accumulation_steps())
          assert model.gradient_accumulation_steps() == args.update_freq
      else:
          if args.distributed:
              model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=False)
              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)))
  
      ceph_args = {
          'use_ceph_checkpoint': args.use_ceph_checkpoint,
          'ceph_checkpoint_prefix': args.ceph_checkpoint_prefix,
          'ckpt_path_split': args.ckpt_path_split,
          'local_rank': args.gpu,
      }
      if ceph_args['use_ceph_checkpoint']:
          print("Will automatically upload model on ceph")
          assert ceph_args['ceph_checkpoint_prefix'] != '', "Should set prefix for ceph checkpoint!"
  
      utils.auto_load_model(
          args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler,
          ceph_args=ceph_args,
      )
      torch.cuda.empty_cache()
      print(f"Start training for {args.epochs} epochs")
      print(f"Use bf16 {args.bf16}")
      print(f"Mask ratio: {args.mask_ratio}")
      print(f"Mask typr: {args.mask_type}")
      distill_final_features = args.clip_teacher_final_dim > 0
      print(f"Distill final (AttnPoll) features of teacher: {distill_final_features}")
      print(f"Loss ratio: {args.clip_loss_ratio}")
      
      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,
              clip_teacher_model=clip_teacher_model, 
              clip_input_resolution=args.clip_input_resolution,
              distill_final_features=distill_final_features,
              clip_loss_ratio=args.clip_loss_ratio,
              mask_type=args.mask_type,
              mask_ratio=args.mask_ratio,
              bf16=args.bf16,
          )
          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,
                      ceph_args=ceph_args,
                  )
              utils.save_model(
                  args=args, model=model, model_without_ddp=model_without_ddp, 
                  optimizer=optimizer, loss_scaler=loss_scaler, epoch=epoch,
                  model_name='latest', ceph_args=ceph_args,
              )
  
          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, ds_init = get_args()
      if opts.output_dir:
          Path(opts.output_dir).mkdir(parents=True, exist_ok=True)
      main(opts, ds_init)
  