content: import os
  import time
  import numpy as np
  import math
  import sys
  from typing import Iterable, Optional
  import torch
  from datasets.mixup import Mixup
  from timm.utils import accuracy, ModelEma
  import utils
  from scipy.special import softmax
  
  
  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, 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,
          bf16=False,
      ):
      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 = 1
  
      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 mixup_fn is not None:
              samples, targets = mixup_fn(samples, targets)
  
          if loss_scaler is None:
              samples = samples.bfloat16() if bf16 else 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)
              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)
              grad_norm = None
              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, ds=False, bf16=False):
      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):
          videos = batch[0]
          target = batch[1]
          videos = videos.to(device, non_blocking=True)
          target = target.to(device, non_blocking=True)
  
          # compute output
          if ds:
              videos = videos.bfloat16() if bf16 else videos.half()
              output = model(videos)
              loss = criterion(output, target)
          else:
              with torch.cuda.amp.autocast():
                  output = model(videos)
                  loss = criterion(output, target)
  
          acc1, acc5 = accuracy(output, target, topk=(1, 5))
  
          batch_size = videos.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, ds=False, bf16=False):
      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):
          videos = batch[0]
          target = batch[1]
          ids = batch[2]
          chunk_nb = batch[3]
          split_nb = batch[4]
          videos = videos.to(device, non_blocking=True)
          target = target.to(device, non_blocking=True)
  
          # compute output
          if ds:
              videos = videos.bfloat16() if bf16 else videos.half()
              output = model(videos)
              loss = criterion(output, target)
          else:
              with torch.cuda.amp.autocast():
                  output = model(videos)
                  loss = criterion(output, target)
  
          for i in range(output.size(0)):
              string = "{} {} {} {} {}\n".format(ids[i], \
                                                  str(output.data[i].float().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 = videos.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):
      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(' '.join(line.split(' ')[1:]).split('[')[1].split(']')[0], dtype=np.float32, sep=',')
              data = softmax(data)
              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
              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)
      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)]
  