text: from __future__ import print_function
  
  from collections import defaultdict, deque
  import datetime
  import pickle
  import subprocess
  import time
  
  import torch
  import torch.distributed as dist
  
  import os
  
  
  class SmoothedValue(object):
      """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]
  
      # serialized to a Tensor
      buffer = pickle.dumps(data)
      storage = torch.ByteStorage.from_buffer(buffer)
      tensor = torch.ByteTensor(storage).to("cuda")
  
      # obtain Tensor size of each rank
      local_size = torch.tensor([tensor.numel()], device="cuda")
      size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)]
      dist.all_gather(size_list, local_size)
      size_list = [int(size.item()) for size in size_list]
      max_size = max(size_list)
  
      # receiving Tensor from all ranks
      # we pad the tensor because torch all_gather does not support
      # gathering tensors of different shapes
      tensor_list = []
      for _ in size_list:
          tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda"))
      if local_size != max_size:
          padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda")
          tensor = torch.cat((tensor, padding), dim=0)
      dist.all_gather(tensor_list, tensor)
  
      data_list = []
      for size, tensor in zip(size_list, tensor_list):
          buffer = tensor.cpu().numpy().tobytes()[:size]
          data_list.append(pickle.loads(buffer))
  
      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.no_grad():
          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(object):
      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("'{}' object has no attribute '{}'".format(type(self).__name__, attr))
  
      def __str__(self):
          loss_str = []
          for name, meter in self.meters.items():
              loss_str.append("{}: {}".format(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(
              "{} Total time: {} ({:.4f} s / it)".format(
                  header, total_time_str, total_time / len(iterable)
              )
          )
  
  
  def get_sha():
      cwd = os.path.dirname(os.path.abspath(__file__))
  
      def _run(command):
          return subprocess.check_output(command, cwd=cwd).decode("ascii").strip()
  
      sha = "N/A"
      diff = "clean"
      branch = "N/A"
      try:
          sha = _run(["git", "rev-parse", "HEAD"])
          subprocess.check_output(["git", "diff"], cwd=cwd)
          diff = _run(["git", "diff-index", "HEAD"])
          diff = "has uncommited changes" if diff else "clean"
          branch = _run(["git", "rev-parse", "--abbrev-ref", "HEAD"])
      except Exception:
          pass
      message = f"sha: {sha}, status: {diff}, branch: {branch}"
      return message
  
  
  def collate_fn(batch):
      batch = list(zip(*batch))
      batch[0] = NestedTensor.from_tensor_list(batch[0])
      return tuple(batch)
  
  
  class NestedTensor(object):
      def __init__(self, tensors, mask):
          self.tensors = tensors
          self.mask = mask
  
      def to(self, *args, **kwargs):
          cast_tensor = self.tensors.to(*args, **kwargs)
          cast_mask = self.mask.to(*args, **kwargs) if self.mask is not None else None
          return type(self)(cast_tensor, cast_mask)
  
      def decompose(self):
          return self.tensors, self.mask
  
      @classmethod
      def from_tensor_list(cls, tensor_list):
          # TODO make this more general
          # print('---- nest tensor size ----')
          # print(tensor_list[1].shape)
          if tensor_list[0].ndim == 3:
              # TODO make it support different-sized images
              max_size = tuple(max(s) for s in zip(*[img.shape for img in tensor_list]))
              # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))
              batch_shape = (len(tensor_list),) + max_size
              b, h, w, d = batch_shape
              dtype = tensor_list[0].dtype
              device = tensor_list[0].device
              tensor = torch.zeros(batch_shape, dtype=dtype, device=device)
              mask = torch.ones((b, h, w, d), dtype=torch.bool, device=device)
              for img, pad_img, m in zip(tensor_list, tensor, mask):
                  pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
                  m[: img.shape[0], : img.shape[1], : img.shape[2]] = False
          elif tensor_list[0].ndim == 4:
              max_size = tuple(max(s) for s in zip(*[img.shape for img in tensor_list]))
              # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))
              batch_shape = (len(tensor_list),) + max_size
              b, c, h, w, d = batch_shape
              dtype = tensor_list[0].dtype
              device = tensor_list[0].device
              tensor = torch.zeros(batch_shape, dtype=dtype, device=device)
              mask = torch.ones((b, c, h, w, d), dtype=torch.bool, device=device)
              for img, pad_img, m in zip(tensor_list, tensor, mask):
                  pad_img[: img.shape[0], : img.shape[1], : img.shape[2], : img.shape[3]].copy_(img)
                  m[: img.shape[0], : img.shape[1], : img.shape[2], : img.shape[3]] = False
          else:
              raise ValueError("not supported")
          return cls(tensor, mask)
  
      def __repr__(self):
          return repr(self.tensors)
  
  
  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("| distributed init (rank {}): {}".format(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)
  
  
  def accuracy(output, target, topk=(1,)):
      """Computes the precision@k for the specified values of k"""
      if target.numel() == 0:
          return [torch.zeros([], device=output.device)]
      maxk = max(topk)
      batch_size = target.size(0)
  
      _, pred = output.topk(maxk, 1, True, True)
      pred = pred.t()
      correct = pred.eq(target.view(1, -1).expand_as(pred))
  
      res = []
      for k in topk:
          correct_k = correct[:k].view(-1).float().sum(0)
          res.append(correct_k.mul_(100.0 / batch_size))
      return res
  