text: # Copyright (c) OpenMMLab. All rights reserved.
  import numpy as np
  import torch
  import torch.distributed as dist
  from mmcv.runner import get_dist_info
  
  
  def check_dist_init():
      return dist.is_available() and dist.is_initialized()
  
  
  def sync_random_seed(seed=None, device='cuda'):
      """Make sure different ranks share the same seed.
  
      All workers must call
      this function, otherwise it will deadlock. This method is generally used in
      `DistributedSampler`, because the seed should be identical across all
      processes in the distributed group.
      In distributed sampling, different ranks should sample non-overlapped
      data in the dataset. Therefore, this function is used to make sure that
      each rank shuffles the data indices in the same order based
      on the same seed. Then different ranks could use different indices
      to select non-overlapped data from the same data list.
      Args:
          seed (int, Optional): The seed. Default to None.
          device (str): The device where the seed will be put on.
              Default to 'cuda'.
      Returns:
          int: Seed to be used.
      """
      if seed is None:
          seed = np.random.randint(2**31)
      assert isinstance(seed, int)
  
      rank, world_size = get_dist_info()
  
      if world_size == 1:
          return seed
  
      if rank == 0:
          random_num = torch.tensor(seed, dtype=torch.int32, device=device)
      else:
          random_num = torch.tensor(0, dtype=torch.int32, device=device)
      dist.broadcast(random_num, src=0)
      return random_num.item()
  