text: import os
  from unittest.mock import patch
  
  import pytest
  import sklearn
  import torch
  from sklearn.metrics import cohen_kappa_score
  
  import ignite.distributed as idist
  from ignite.contrib.metrics import CohenKappa
  from ignite.engine import Engine
  from ignite.exceptions import NotComputableError
  
  torch.manual_seed(12)
  
  
  @pytest.fixture()
  def mock_no_sklearn():
      with patch.dict("sys.modules", {"sklearn.metrics": None}):
          yield sklearn
  
  
  def test_no_sklearn(mock_no_sklearn):
      with pytest.raises(RuntimeError, match=r"This contrib module requires sklearn to be installed."):
          CohenKappa()
  
  
  def test_no_update():
      ck = CohenKappa()
  
      with pytest.raises(
          NotComputableError, match=r"EpochMetric must have at least one example before it can be computed"
      ):
          ck.compute()
  
  
  def test_input_types():
      ck = CohenKappa()
      ck.reset()
      output1 = (torch.rand(4, 3), torch.randint(0, 2, size=(4, 3), dtype=torch.long))
      ck.update(output1)
  
      with pytest.raises(ValueError, match=r"Incoherent types between input y_pred and stored predictions"):
          ck.update((torch.randint(0, 5, size=(4, 3)), torch.randint(0, 2, size=(4, 3))))
  
      with pytest.raises(ValueError, match=r"Incoherent types between input y and stored targets"):
          ck.update((torch.rand(4, 3), torch.randint(0, 2, size=(4, 3)).to(torch.int32)))
  
      with pytest.raises(ValueError, match=r"Incoherent types between input y_pred and stored predictions"):
          ck.update((torch.randint(0, 2, size=(10,)).long(), torch.randint(0, 2, size=(10, 5)).long()))
  
  
  def test_check_shape():
      ck = CohenKappa()
  
      with pytest.raises(ValueError, match=r"Predictions should be of shape"):
          ck._check_shape((torch.tensor(0), torch.tensor(0)))
  
      with pytest.raises(ValueError, match=r"Predictions should be of shape"):
          ck._check_shape((torch.rand(4, 3, 1), torch.rand(4, 3)))
  
      with pytest.raises(ValueError, match=r"Targets should be of shape"):
          ck._check_shape((torch.rand(4, 3), torch.rand(4, 3, 1)))
  
  
  def test_cohen_kappa_wrong_weights_type():
      with pytest.raises(ValueError, match=r"Kappa Weighting type must be"):
          ck = CohenKappa(weights=7)
  
      with pytest.raises(ValueError, match=r"Kappa Weighting type must be"):
          ck = CohenKappa(weights="dd")
  
  
  @pytest.mark.parametrize("weights", [None, "linear", "quadratic"])
  def test_binary_input_N(weights):
  
      ck = CohenKappa(weights)
  
      def _test(y_pred, y, n_iters):
          ck.reset()
          ck.update((y_pred, y))
  
          np_y = y.numpy()
          np_y_pred = y_pred.numpy()
  
          if n_iters > 1:
              batch_size = y.shape[0] // n_iters + 1
              for i in range(n_iters):
                  idx = i * batch_size
                  ck.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size]))
  
          res = ck.compute()
          assert isinstance(res, float)
          assert cohen_kappa_score(np_y, np_y_pred, weights=weights) == pytest.approx(res)
  
      def get_test_cases():
          test_cases = [
              (torch.randint(0, 2, size=(10,)).long(), torch.randint(0, 2, size=(10,)).long(), 1),
              (torch.randint(0, 2, size=(10, 1)).long(), torch.randint(0, 2, size=(10, 1)).long(), 1),
              # updated batches
              (torch.randint(0, 2, size=(50,)).long(), torch.randint(0, 2, size=(50,)).long(), 16),
              (torch.randint(0, 2, size=(50, 1)).long(), torch.randint(0, 2, size=(50, 1)).long(), 16),
          ]
          return test_cases
  
      for _ in range(5):
          # check multiple random inputs as random exact occurencies are rare
          test_cases = get_test_cases()
          for y_pred, y, n_iters in test_cases:
              _test(y_pred, y, n_iters)
  
  
  def test_multilabel_inputs():
      ck = CohenKappa()
  
      with pytest.raises(ValueError, match=r"multilabel-indicator is not supported"):
          ck.reset()
          ck.update((torch.randint(0, 2, size=(10, 4)).long(), torch.randint(0, 2, size=(10, 4)).long()))
          ck.compute()
  
      with pytest.raises(ValueError, match=r"multilabel-indicator is not supported"):
          ck.reset()
          ck.update((torch.randint(0, 2, size=(10, 6)).long(), torch.randint(0, 2, size=(10, 6)).long()))
          ck.compute()
  
      with pytest.raises(ValueError, match=r"multilabel-indicator is not supported"):
          ck.reset()
          ck.update((torch.randint(0, 2, size=(10, 8)).long(), torch.randint(0, 2, size=(10, 8)).long()))
          ck.compute()
  
  
  @pytest.mark.parametrize("weights", [None, "linear", "quadratic"])
  def test_integration_binary_input(weights):
      def _test(y_pred, y, batch_size):
          def update_fn(engine, batch):
              idx = (engine.state.iteration - 1) * batch_size
              y_true_batch = np_y[idx : idx + batch_size]
              y_pred_batch = np_y_pred[idx : idx + batch_size]
              return torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch)
  
          engine = Engine(update_fn)
  
          ck_metric = CohenKappa(weights=weights)
          ck_metric.attach(engine, "ck")
  
          np_y = y.numpy()
          np_y_pred = y_pred.numpy()
  
          np_ck = cohen_kappa_score(np_y, np_y_pred, weights=weights)
  
          data = list(range(y_pred.shape[0] // batch_size))
          ck = engine.run(data, max_epochs=1).metrics["ck"]
  
          assert isinstance(ck, float)
          assert np_ck == pytest.approx(ck)
  
      def get_test_cases():
          test_cases = [
              (torch.randint(0, 2, size=(50,)).long(), torch.randint(0, 2, size=(50,)).long(), 10),
              (torch.randint(0, 2, size=(50, 1)).long(), torch.randint(0, 2, size=(50, 1)).long(), 10),
          ]
          return test_cases
  
      for _ in range(5):
          # check multiple random inputs as random exact occurencies are rare
          test_cases = get_test_cases()
          for y_pred, y, batch_size in test_cases:
              _test(y_pred, y, batch_size)
  
  
  def _test_distrib_binary_input_N(device):
  
      rank = idist.get_rank()
      torch.manual_seed(12)
  
      def _test(y_pred, y, n_iters, metric_device):
  
          metric_device = torch.device(metric_device)
          ck = CohenKappa(device=metric_device)
  
          torch.manual_seed(10 + rank)
  
          ck.reset()
          ck.update((y_pred, y))
  
          if n_iters > 1:
              batch_size = y.shape[0] // n_iters + 1
              for i in range(n_iters):
                  idx = i * batch_size
                  ck.update((y_pred[idx : idx + batch_size], y[idx : idx + batch_size]))
  
          # gather y_pred, y
          y_pred = idist.all_gather(y_pred)
          y = idist.all_gather(y)
  
          np_y = y.cpu().numpy()
          np_y_pred = y_pred.cpu().numpy()
  
          res = ck.compute()
          assert isinstance(res, float)
          assert cohen_kappa_score(np_y, np_y_pred) == pytest.approx(res)
  
      def get_test_cases():
          test_cases = [
              (torch.randint(0, 2, size=(10,)).long(), torch.randint(0, 2, size=(10,)).long(), 1),
              (torch.randint(0, 2, size=(10, 1)).long(), torch.randint(0, 2, size=(10, 1)).long(), 1),
              # updated batches
              (torch.randint(0, 2, size=(50,)).long(), torch.randint(0, 2, size=(50,)).long(), 16),
              (torch.randint(0, 2, size=(50, 1)).long(), torch.randint(0, 2, size=(50, 1)).long(), 16),
          ]
          return test_cases
  
      for _ in range(3):
          test_cases = get_test_cases()
          for y_pred, y, batch_size in test_cases:
              _test(y_pred, y, batch_size, "cpu")
              if device.type != "xla":
                  _test(y_pred, y, batch_size, idist.device())
  
  
  def _test_distrib_integration_binary(device):
  
      rank = idist.get_rank()
      torch.manual_seed(12)
  
      def _test(n_epochs, metric_device):
          metric_device = torch.device(metric_device)
          n_iters = 80
          s = 16
          n_classes = 2
  
          offset = n_iters * s
          y_true = torch.randint(0, n_classes, size=(offset * idist.get_world_size(),)).to(device)
          y_preds = torch.randint(0, n_classes, size=(offset * idist.get_world_size(),)).to(device)
  
          def update(engine, i):
              return (
                  y_preds[i * s + rank * offset : (i + 1) * s + rank * offset],
                  y_true[i * s + rank * offset : (i + 1) * s + rank * offset],
              )
  
          engine = Engine(update)
  
          ck = CohenKappa(device=metric_device)
          ck.attach(engine, "ck")
  
          data = list(range(n_iters))
          engine.run(data=data, max_epochs=n_epochs)
  
          assert "ck" in engine.state.metrics
  
          res = engine.state.metrics["ck"]
          if isinstance(res, torch.Tensor):
              res = res.cpu().numpy()
  
          true_res = cohen_kappa_score(y_true.cpu().numpy(), y_preds.cpu().numpy())
  
          assert pytest.approx(res) == true_res
  
      metric_devices = ["cpu"]
      if device.type != "xla":
          metric_devices.append(idist.device())
      for metric_device in metric_devices:
          for _ in range(2):
              _test(n_epochs=1, metric_device=metric_device)
              _test(n_epochs=2, metric_device=metric_device)
  
  
  @pytest.mark.distributed
  @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support")
  @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU")
  def test_distrib_gpu(distributed_context_single_node_nccl):
  
      device = torch.device(f"cuda:{distributed_context_single_node_nccl['local_rank']}")
      _test_distrib_binary_input_N(device)
      _test_distrib_integration_binary(device)
  
  
  @pytest.mark.distributed
  @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support")
  def test_distrib_cpu(distributed_context_single_node_gloo):
  
      device = torch.device("cpu")
      _test_distrib_binary_input_N(device)
      _test_distrib_integration_binary(device)
  
  
  @pytest.mark.distributed
  @pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support")
  @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc")
  def test_distrib_hvd(gloo_hvd_executor):
  
      device = torch.device("cpu" if not torch.cuda.is_available() else "cuda")
      nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count()
  
      gloo_hvd_executor(
          _test_distrib_binary_input_N, (device,), np=nproc, do_init=True,
      )
      gloo_hvd_executor(
          _test_distrib_integration_binary, (device,), np=nproc, do_init=True,
      )
  
  
  @pytest.mark.multinode_distributed
  @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support")
  @pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed")
  def test_multinode_distrib_cpu(distributed_context_multi_node_gloo):
  
      device = torch.device("cpu")
      _test_distrib_binary_input_N(device)
      _test_distrib_integration_binary(device)
  
  
  @pytest.mark.multinode_distributed
  @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support")
  @pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed")
  def test_multinode_distrib_gpu(distributed_context_multi_node_nccl):
  
      device = torch.device(f"cuda:{distributed_context_multi_node_nccl['local_rank']}")
      _test_distrib_binary_input_N(device)
      _test_distrib_integration_binary(device)
  
  
  @pytest.mark.tpu
  @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars")
  @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package")
  def test_distrib_single_device_xla():
  
      device = idist.device()
      _test_distrib_binary_input_N(device)
      _test_distrib_integration_binary(device)
  
  
  def _test_distrib_xla_nprocs(index):
  
      device = idist.device()
      _test_distrib_binary_input_N(device)
      _test_distrib_integration_binary(device)
  
  
  @pytest.mark.tpu
  @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars")
  @pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package")
  def test_distrib_xla_nprocs(xmp_executor):
      n = int(os.environ["NUM_TPU_WORKERS"])
      xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n)
  