text: # Copyright (c) OpenMMLab. All rights reserved.
  import pytest
  import torch
  
  from mmcls.models.losses.utils import convert_to_one_hot
  
  
  def ori_convert_to_one_hot(targets: torch.Tensor, classes) -> torch.Tensor:
      assert (torch.max(targets).item() <
              classes), 'Class Index must be less than number of classes'
      one_hot_targets = torch.zeros((targets.shape[0], classes),
                                    dtype=torch.long,
                                    device=targets.device)
      one_hot_targets.scatter_(1, targets.long(), 1)
      return one_hot_targets
  
  
  def test_convert_to_one_hot():
      # label should smaller than classes
      targets = torch.tensor([1, 2, 3, 8, 5])
      classes = 5
      with pytest.raises(AssertionError):
          _ = convert_to_one_hot(targets, classes)
  
      # test with original impl
      classes = 10
      targets = torch.randint(high=classes, size=(10, 1))
      ori_one_hot_targets = torch.zeros((targets.shape[0], classes),
                                        dtype=torch.long,
                                        device=targets.device)
      ori_one_hot_targets.scatter_(1, targets.long(), 1)
      one_hot_targets = convert_to_one_hot(targets, classes)
      assert torch.equal(ori_one_hot_targets, one_hot_targets)
  
  
  # test cuda version
  @pytest.mark.skipif(
      not torch.cuda.is_available(), reason='requires CUDA support')
  def test_convert_to_one_hot_cuda():
      # test with original impl
      classes = 10
      targets = torch.randint(high=classes, size=(10, 1)).cuda()
      ori_one_hot_targets = torch.zeros((targets.shape[0], classes),
                                        dtype=torch.long,
                                        device=targets.device)
      ori_one_hot_targets.scatter_(1, targets.long(), 1)
      one_hot_targets = convert_to_one_hot(targets, classes)
      assert torch.equal(ori_one_hot_targets, one_hot_targets)
      assert ori_one_hot_targets.device == one_hot_targets.device
  