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 sys
  from typing import Iterable
  
  import torch
  import torch.nn as nn
  from einops import rearrange
  from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  
  import utils
  
  
  def train_one_epoch(model: torch.nn.Module,
                      data_loader: Iterable,
                      optimizer: torch.optim.Optimizer,
                      device: torch.device,
                      epoch: int,
                      loss_scaler,
                      max_norm: float = 0,
                      patch_size: int = 16,
                      normlize_target: bool = True,
                      log_writer=None,
                      lr_scheduler=None,
                      start_steps=None,
                      lr_schedule_values=None,
                      wd_schedule_values=None):
      model.train()
      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
  
      loss_func = nn.MSELoss()
  
      data_loader.dataset.transform.masked_position_generator.update_state(epoch)
  
      for step, batch in enumerate(
              metric_logger.log_every(data_loader, print_freq, header)):
          # assign learning rate & weight decay for each step
          it = start_steps + step  # global training iteration
          if lr_schedule_values is not None or wd_schedule_values is not None:
              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]
  
          images, bool_masked_pos = batch
          images = images.to(device, non_blocking=True)
          bool_masked_pos = bool_masked_pos.to(
              device, non_blocking=True).flatten(1).to(torch.bool)
          # print(images.shape)
          # import pdb; pdb.set_trace()
          with torch.no_grad():
              # calculate the predict label
              mean = torch.as_tensor(IMAGENET_DEFAULT_MEAN).to(device)[None, :,
                                                                       None, None,
                                                                       None]
              std = torch.as_tensor(IMAGENET_DEFAULT_STD).to(device)[None, :,
                                                                     None, None,
                                                                     None]
              unnorm_images = images * std + mean  # in [0, 1]
  
              if normlize_target:
                  images_squeeze = rearrange(
                      unnorm_images,
                      'b c (t p0) (h p1) (w p2) -> b (t h w) (p0 p1 p2) c',
                      p0=2,
                      p1=patch_size,
                      p2=patch_size)
                  images_norm = (images_squeeze - images_squeeze.mean(
                      dim=-2, keepdim=True)) / (
                          images_squeeze.var(dim=-2, unbiased=True,
                                             keepdim=True).sqrt() + 1e-6)
                  # we find that the mean is about 0.48 and standard deviation is about 0.08.
                  images_patch = rearrange(images_norm, 'b n p c -> b n (p c)')
              else:
                  images_patch = rearrange(
                      unnorm_images,
                      'b c (t p0) (h p1) (w p2) -> b (t h w) (p0 p1 p2 c)',
                      p0=2,
                      p1=patch_size,
                      p2=patch_size)
  
              B, N, C = images_patch.shape
              labels = images_patch[bool_masked_pos].reshape(B, -1, C)
  
          if loss_scaler is None:
              outputs = model(images, bool_masked_pos)
              loss = loss_func(input=outputs, target=labels)
          else:
              with torch.cuda.amp.autocast():
                  outputs = model(images, bool_masked_pos)
                  loss = loss_func(input=outputs, target=labels)
  
          loss_value = loss.item()
  
          if not math.isfinite(loss_value):
              print("Loss is {}, stopping training".format(loss_value))
              sys.exit(2)
  
          optimizer.zero_grad()
  
          if loss_scaler is None:
              loss.backward()
              if max_norm is None:
                  grad_norm = utils.get_grad_norm_(model.parameters())
              else:
                  grad_norm = torch.nn.utils.clip_grad_norm_(
                      model.parameters(), max_norm)
              optimizer.step()
              loss_scale_value = 0
          else:
              # this attribute is added by timm on one optimizer (adahessian)
              is_second_order = hasattr(
                  optimizer, 'is_second_order') and optimizer.is_second_order
              grad_norm = loss_scaler(
                  loss,
                  optimizer,
                  clip_grad=max_norm,
                  parameters=model.parameters(),
                  create_graph=is_second_order)
              loss_scale_value = loss_scaler.state_dict()["scale"]
  
          torch.cuda.synchronize()
  
          metric_logger.update(loss=loss_value)
          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(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()
  
          if lr_scheduler is not None:
              lr_scheduler.step_update(start_steps + 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()}
  