content: # --------------------------------------------------------
  # Based on BEiT, timm, DINO and DeiT code bases
  # https://github.com/microsoft/unilm/tree/master/beit
  # https://github.com/rwightman/pytorch-image-models/tree/master/timm
  # https://github.com/facebookresearch/deit
  # https://github.com/facebookresearch/dino
  # --------------------------------------------------------'
  import math
  import os
  import sys
  from typing import Iterable, Optional
  
  import numpy as np
  import torch
  from scipy.special import softmax
  from timm.data import Mixup
  from timm.utils import ModelEma, accuracy
  
  import utils
  
  
  def train_class_batch(model, samples, target, criterion):
      outputs = model(samples)
      loss = criterion(outputs, target)
      return loss, outputs
  
  
  def get_loss_scale_for_deepspeed(model):
      optimizer = model.optimizer
      return optimizer.loss_scale if hasattr(
          optimizer, "loss_scale") else optimizer.cur_scale
  
  
  def train_one_epoch(model: torch.nn.Module,
                      criterion: torch.nn.Module,
                      data_loader: Iterable,
                      optimizer: torch.optim.Optimizer,
                      device: torch.device,
                      epoch: int,
                      loss_scaler,
                      args,
                      max_norm: float = 0,
                      model_ema: Optional[ModelEma] = None,
                      mixup_fn: Optional[Mixup] = None,
                      log_writer=None,
                      start_steps=None,
                      lr_schedule_values=None,
                      wd_schedule_values=None,
                      num_training_steps_per_epoch=None,
                      update_freq=None):
      model.train(True)
      metric_logger = utils.MetricLogger(delimiter="  ")
      metric_logger.add_meter(
          'lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
      metric_logger.add_meter(
          'min_lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
      header = 'Epoch: [{}]'.format(epoch)
      print_freq = 10
  
      if loss_scaler is None:
          model.zero_grad()
          model.micro_steps = 0
      else:
          optimizer.zero_grad()
  
      for data_iter_step, (samples, targets, _, _) in enumerate(
              metric_logger.log_every(data_loader, print_freq, header)):
          step = data_iter_step // update_freq
          if step >= num_training_steps_per_epoch:
              continue
          it = start_steps + step  # global training iteration
          # Update LR & WD for the first acc
          if lr_schedule_values is not None or wd_schedule_values is not None and data_iter_step % update_freq == 0:
              for i, param_group in enumerate(optimizer.param_groups):
                  if lr_schedule_values is not None:
                      param_group["lr"] = lr_schedule_values[it] * param_group[
                          "lr_scale"]
                  if wd_schedule_values is not None and param_group[
                          "weight_decay"] > 0:
                      param_group["weight_decay"] = wd_schedule_values[it]
  
          samples = samples.to(device, non_blocking=True)
          targets = targets.to(device, non_blocking=True)
  
          if args.num_segment > 1:
              samples = samples.view((-1, ) + samples.size()[2:])
  
          if mixup_fn is not None:
              B, C, T, H, W = samples.shape
              samples = samples.view(B, C * T, H, W)
              samples, targets = mixup_fn(samples, targets)
              samples = samples.view(B, C, T, H, W)
  
          if loss_scaler is None:
              samples = samples.half()
              loss, output = train_class_batch(model, samples, targets,
                                               criterion)
          else:
              with torch.cuda.amp.autocast():
                  loss, output = train_class_batch(model, samples, targets,
                                                   criterion)
  
          loss_value = loss.item()
  
          if not math.isfinite(loss_value):
              print("Loss is {}, stopping training".format(loss_value))
              sys.exit(1)
  
          if loss_scaler is None:
              loss /= update_freq
              model.backward(loss)
              grad_norm = model.get_global_grad_norm()
  
              # if utils.is_main_process():
              #     import json
              #     all_grad = {}
              #     for name, param in model.module.named_parameters():
              #         print(name)
              #         if hasattr(param, 'grad') and param.grad is not None:
              #             all_grad[name] = torch.norm(param.grad.detach(), 2).item()
              #     with open('pfs_work_dir/grad_test/temp.json', 'w') as f:
              #         json.dump(all_grad, f)
              # torch.distributed.barrier()
              # sys.exit(1)
  
              model.step()
  
              if (data_iter_step + 1) % update_freq == 0:
                  # model.zero_grad()
                  # Deepspeed will call step() & model.zero_grad() automatic
                  if model_ema is not None:
                      model_ema.update(model)
              loss_scale_value = get_loss_scale_for_deepspeed(model)
          else:
              # this attribute is added by timm on one optimizer (adahessian)
              is_second_order = hasattr(
                  optimizer, 'is_second_order') and optimizer.is_second_order
              loss /= update_freq
              grad_norm = loss_scaler(loss,
                                      optimizer,
                                      clip_grad=max_norm,
                                      parameters=model.parameters(),
                                      create_graph=is_second_order,
                                      update_grad=(data_iter_step + 1) %
                                      update_freq == 0)
              if (data_iter_step + 1) % update_freq == 0:
                  optimizer.zero_grad()
                  if model_ema is not None:
                      model_ema.update(model)
              loss_scale_value = loss_scaler.state_dict()["scale"]
  
          torch.cuda.synchronize()
  
          if mixup_fn is None:
              class_acc = (output.max(-1)[-1] == targets).float().mean()
          else:
              class_acc = None
          metric_logger.update(loss=loss_value)
          metric_logger.update(class_acc=class_acc)
          metric_logger.update(loss_scale=loss_scale_value)
          min_lr = 10.
          max_lr = 0.
          for group in optimizer.param_groups:
              min_lr = min(min_lr, group["lr"])
              max_lr = max(max_lr, group["lr"])
  
          metric_logger.update(lr=max_lr)
          metric_logger.update(min_lr=min_lr)
          weight_decay_value = None
          for group in optimizer.param_groups:
              if group["weight_decay"] > 0:
                  weight_decay_value = group["weight_decay"]
          metric_logger.update(weight_decay=weight_decay_value)
          metric_logger.update(grad_norm=grad_norm)
  
          if log_writer is not None:
              log_writer.update(loss=loss_value, head="loss")
              log_writer.update(class_acc=class_acc, head="loss")
              log_writer.update(loss_scale=loss_scale_value, head="opt")
              log_writer.update(lr=max_lr, head="opt")
              log_writer.update(min_lr=min_lr, head="opt")
              log_writer.update(weight_decay=weight_decay_value, head="opt")
              log_writer.update(grad_norm=grad_norm, head="opt")
  
              log_writer.set_step()
  
      # gather the stats from all processes
      metric_logger.synchronize_between_processes()
      print("Averaged stats:", metric_logger)
      return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
  
  
  @torch.no_grad()
  def validation_one_epoch(data_loader, model, device):
      criterion = torch.nn.CrossEntropyLoss()
  
      metric_logger = utils.MetricLogger(delimiter="  ")
      header = 'Val:'
  
      # switch to evaluation mode
      model.eval()
  
      for batch in metric_logger.log_every(data_loader, 10, header):
          images = batch[0]
          target = batch[1]
          images = images.to(device, non_blocking=True)
          target = target.to(device, non_blocking=True)
  
          # compute output
          with torch.cuda.amp.autocast():
              output = model(images)
              loss = criterion(output, target)
  
          acc1, acc5 = accuracy(output, target, topk=(1, 5))
  
          batch_size = images.shape[0]
          metric_logger.update(loss=loss.item())
          metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
          metric_logger.meters['acc5'].update(acc5.item(), n=batch_size)
      # gather the stats from all processes
      metric_logger.synchronize_between_processes()
      print(
          '* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}'
          .format(top1=metric_logger.acc1,
                  top5=metric_logger.acc5,
                  losses=metric_logger.loss))
  
      return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
  
  
  @torch.no_grad()
  def final_test(data_loader, model, device, file):
      criterion = torch.nn.CrossEntropyLoss()
  
      metric_logger = utils.MetricLogger(delimiter="  ")
      header = 'Test:'
  
      # switch to evaluation mode
      model.eval()
      final_result = []
  
      for batch in metric_logger.log_every(data_loader, 10, header):
          images = batch[0]
          target = batch[1]
          ids = batch[2]
          chunk_nb = batch[3]
          split_nb = batch[4]
          images = images.to(device, non_blocking=True)
          target = target.to(device, non_blocking=True)
  
          # compute output
          with torch.cuda.amp.autocast():
              output = model(images)
              loss = criterion(output, target)
  
          for i in range(output.size(0)):
              string = "{} {} {} {} {}\n".format(ids[i], \
                                                  str(output.data[i].cpu().numpy().tolist()), \
                                                  str(int(target[i].cpu().numpy())), \
                                                  str(int(chunk_nb[i].cpu().numpy())), \
                                                  str(int(split_nb[i].cpu().numpy())))
              final_result.append(string)
  
          acc1, acc5 = accuracy(output, target, topk=(1, 5))
  
          batch_size = images.shape[0]
          metric_logger.update(loss=loss.item())
          metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
          metric_logger.meters['acc5'].update(acc5.item(), n=batch_size)
  
      if not os.path.exists(file):
          os.mknod(file)
      with open(file, 'w') as f:
          f.write("{}, {}\n".format(acc1, acc5))
          for line in final_result:
              f.write(line)
      # gather the stats from all processes
      metric_logger.synchronize_between_processes()
      print(
          '* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}'
          .format(top1=metric_logger.acc1,
                  top5=metric_logger.acc5,
                  losses=metric_logger.loss))
  
      return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
  
  
  def merge(eval_path, num_tasks, method='prob'):
      assert method in ['prob', 'score']
      dict_feats = {}
      dict_label = {}
      dict_pos = {}
      print("Reading individual output files")
  
      for x in range(num_tasks):
          file = os.path.join(eval_path, str(x) + '.txt')
          lines = open(file, 'r').readlines()[1:]
          for line in lines:
              line = line.strip()
              name = line.split('[')[0]
              label = line.split(']')[1].split(' ')[1]
              chunk_nb = line.split(']')[1].split(' ')[2]
              split_nb = line.split(']')[1].split(' ')[3]
              data = np.fromstring(line.split('[')[1].split(']')[0],
                                   dtype=np.float,
                                   sep=',')
              if not name in dict_feats:
                  dict_feats[name] = []
                  dict_label[name] = 0
                  dict_pos[name] = []
              if chunk_nb + split_nb in dict_pos[name]:
                  continue
              if method == 'prob':
                  dict_feats[name].append(softmax(data))
              else:
                  dict_feats[name].append(data)
              dict_pos[name].append(chunk_nb + split_nb)
              dict_label[name] = label
      print("Computing final results")
  
      input_lst = []
      print(len(dict_feats))
      for i, item in enumerate(dict_feats):
          input_lst.append([i, item, dict_feats[item], dict_label[item]])
      from multiprocessing import Pool
      p = Pool(64)
      ans = p.map(compute_video, input_lst)
      top1 = [x[1] for x in ans]
      top5 = [x[2] for x in ans]
      pred = [x[0] for x in ans]
      label = [x[3] for x in ans]
      final_top1, final_top5 = np.mean(top1), np.mean(top5)
  
      # print(final_top1*100 ,final_top5*100)
      return final_top1 * 100, final_top5 * 100
  
  
  def compute_video(lst):
      i, video_id, data, label = lst
      feat = [x for x in data]
      feat = np.mean(feat, axis=0)
      pred = np.argmax(feat)
      top1 = (int(pred) == int(label)) * 1.0
      top5 = (int(label) in np.argsort(-feat)[:5]) * 1.0
      return [pred, top1, top5, int(label)]
  