code: import datetime
  import errno
  import os
  import time
  from collections import defaultdict, deque
  
  import torch
  import torch.distributed as dist
  
  
  class SmoothedValue:
      """Track a series of values and provide access to smoothed values over a
      window or the global series average.
      """
  
      def __init__(self, window_size=20, fmt=None):
          if fmt is None:
              fmt = "{median:.4f} ({global_avg:.4f})"
          self.deque = deque(maxlen=window_size)
          self.total = 0.0
          self.count = 0
          self.fmt = fmt
  
      def update(self, value, n=1):
          self.deque.append(value)
          self.count += n
          self.total += value * n
  
      def synchronize_between_processes(self):
          """
          Warning: does not synchronize the deque!
          """
          if not is_dist_avail_and_initialized():
              return
          t = torch.tensor([self.count, self.total], dtype=torch.float64, device="cuda")
          dist.barrier()
          dist.all_reduce(t)
          t = t.tolist()
          self.count = int(t[0])
          self.total = t[1]
  
      @property
      def median(self):
          d = torch.tensor(list(self.deque))
          return d.median().item()
  
      @property
      def avg(self):
          d = torch.tensor(list(self.deque), dtype=torch.float32)
          return d.mean().item()
  
      @property
      def global_avg(self):
          return self.total / self.count
  
      @property
      def max(self):
          return max(self.deque)
  
      @property
      def value(self):
          return self.deque[-1]
  
      def __str__(self):
          return self.fmt.format(
              median=self.median, avg=self.avg, global_avg=self.global_avg, max=self.max, value=self.value
          )
  
  
  def all_gather(data):
      """
      Run all_gather on arbitrary picklable data (not necessarily tensors)
      Args:
          data: any picklable object
      Returns:
          list[data]: list of data gathered from each rank
      """
      world_size = get_world_size()
      if world_size == 1:
          return [data]
      data_list = [None] * world_size
      dist.all_gather_object(data_list, data)
      return data_list
  
  
  def reduce_dict(input_dict, average=True):
      """
      Args:
          input_dict (dict): all the values will be reduced
          average (bool): whether to do average or sum
      Reduce the values in the dictionary from all processes so that all processes
      have the averaged results. Returns a dict with the same fields as
      input_dict, after reduction.
      """
      world_size = get_world_size()
      if world_size < 2:
          return input_dict
      with torch.inference_mode():
          names = []
          values = []
          # sort the keys so that they are consistent across processes
          for k in sorted(input_dict.keys()):
              names.append(k)
              values.append(input_dict[k])
          values = torch.stack(values, dim=0)
          dist.all_reduce(values)
          if average:
              values /= world_size
          reduced_dict = {k: v for k, v in zip(names, values)}
      return reduced_dict
  
  
  class MetricLogger:
      def __init__(self, delimiter="\t"):
          self.meters = defaultdict(SmoothedValue)
          self.delimiter = delimiter
  
      def update(self, **kwargs):
          for k, v in kwargs.items():
              if isinstance(v, torch.Tensor):
                  v = v.item()
              assert isinstance(v, (float, int))
              self.meters[k].update(v)
  
      def __getattr__(self, attr):
          if attr in self.meters:
              return self.meters[attr]
          if attr in self.__dict__:
              return self.__dict__[attr]
          raise AttributeError(f"'{type(self).__name__}' object has no attribute '{attr}'")
  
      def __str__(self):
          loss_str = []
          for name, meter in self.meters.items():
              loss_str.append(f"{name}: {str(meter)}")
          return self.delimiter.join(loss_str)
  
      def synchronize_between_processes(self):
          for meter in self.meters.values():
              meter.synchronize_between_processes()
  
      def add_meter(self, name, meter):
          self.meters[name] = meter
  
      def log_every(self, iterable, print_freq, header=None):
          i = 0
          if not header:
              header = ""
          start_time = time.time()
          end = time.time()
          iter_time = SmoothedValue(fmt="{avg:.4f}")
          data_time = SmoothedValue(fmt="{avg:.4f}")
          space_fmt = ":" + str(len(str(len(iterable)))) + "d"
          if torch.cuda.is_available():
              log_msg = self.delimiter.join(
                  [
                      header,
                      "[{0" + space_fmt + "}/{1}]",
                      "eta: {eta}",
                      "{meters}",
                      "time: {time}",
                      "data: {data}",
                      "max mem: {memory:.0f}",
                  ]
              )
          else:
              log_msg = self.delimiter.join(
                  [header, "[{0" + space_fmt + "}/{1}]", "eta: {eta}", "{meters}", "time: {time}", "data: {data}"]
              )
          MB = 1024.0 * 1024.0
          for obj in iterable:
              data_time.update(time.time() - end)
              yield obj
              iter_time.update(time.time() - end)
              if i % print_freq == 0 or i == len(iterable) - 1:
                  eta_seconds = iter_time.global_avg * (len(iterable) - i)
                  eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
                  if torch.cuda.is_available():
                      print(
                          log_msg.format(
                              i,
                              len(iterable),
                              eta=eta_string,
                              meters=str(self),
                              time=str(iter_time),
                              data=str(data_time),
                              memory=torch.cuda.max_memory_allocated() / MB,
                          )
                      )
                  else:
                      print(
                          log_msg.format(
                              i, len(iterable), eta=eta_string, meters=str(self), time=str(iter_time), data=str(data_time)
                          )
                      )
              i += 1
              end = time.time()
          total_time = time.time() - start_time
          total_time_str = str(datetime.timedelta(seconds=int(total_time)))
          print(f"{header} Total time: {total_time_str} ({total_time / len(iterable):.4f} s / it)")
  
  
  def collate_fn(batch):
      return tuple(zip(*batch))
  
  
  def mkdir(path):
      try:
          os.makedirs(path)
      except OSError as e:
          if e.errno != errno.EEXIST:
              raise
  
  
  def setup_for_distributed(is_master):
      """
      This function disables printing when not in master process
      """
      import builtins as __builtin__
  
      builtin_print = __builtin__.print
  
      def print(*args, **kwargs):
          force = kwargs.pop("force", False)
          if is_master or force:
              builtin_print(*args, **kwargs)
  
      __builtin__.print = print
  
  
  def is_dist_avail_and_initialized():
      if not dist.is_available():
          return False
      if not dist.is_initialized():
          return False
      return True
  
  
  def get_world_size():
      if not is_dist_avail_and_initialized():
          return 1
      return dist.get_world_size()
  
  
  def get_rank():
      if not is_dist_avail_and_initialized():
          return 0
      return dist.get_rank()
  
  
  def is_main_process():
      return get_rank() == 0
  
  
  def save_on_master(*args, **kwargs):
      if is_main_process():
          torch.save(*args, **kwargs)
  
  
  def init_distributed_mode(args):
      if "RANK" in os.environ and "WORLD_SIZE" in os.environ:
          args.rank = int(os.environ["RANK"])
          args.world_size = int(os.environ["WORLD_SIZE"])
          args.gpu = int(os.environ["LOCAL_RANK"])
      elif "SLURM_PROCID" in os.environ:
          args.rank = int(os.environ["SLURM_PROCID"])
          args.gpu = args.rank % torch.cuda.device_count()
      else:
          print("Not using distributed mode")
          args.distributed = False
          return
  
      args.distributed = True
  
      torch.cuda.set_device(args.gpu)
      args.dist_backend = "nccl"
      print(f"| distributed init (rank {args.rank}): {args.dist_url}", flush=True)
      torch.distributed.init_process_group(
          backend=args.dist_backend, init_method=args.dist_url, world_size=args.world_size, rank=args.rank
      )
      torch.distributed.barrier()
      setup_for_distributed(args.rank == 0)
  
apis: torch.stack torch.distributed.get_rank torch.distributed.get_world_size torch.inference_mode torch.distributed.init_process_group torch.distributed.is_available torch.tensor torch.save torch.cuda.device_count torch.distributed.all_gather_object torch.distributed.barrier torch.distributed.is_initialized torch.cuda.is_available torch.distributed.all_reduce torch.cuda.max_memory_allocated torch.cuda.set_device