text: import json
  import os
  
  import pytest
  import torch
  
  import ignite.distributed as idist
  from ignite.engine import Engine
  from ignite.metrics.classification_report import ClassificationReport
  
  
  def _test_integration_multiclass(device, output_dict):
  
      rank = idist.get_rank()
      torch.manual_seed(12)
  
      def _test(metric_device, n_classes, labels=None):
  
          classification_report = ClassificationReport(device=metric_device, output_dict=output_dict, labels=labels)
          n_iters = 80
          s = 16
          offset = n_iters * s
          y_true = torch.randint(0, n_classes, size=(offset * idist.get_world_size(),)).to(device)
          y_preds = torch.rand(offset * idist.get_world_size(), n_classes).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)
  
          classification_report.attach(engine, "cr")
  
          data = list(range(n_iters))
          engine.run(data=data)
  
          assert "cr" in engine.state.metrics
          res = engine.state.metrics["cr"]
          res2 = classification_report.compute()
          assert res == res2
  
          assert isinstance(res, dict if output_dict else str)
          if not output_dict:
              res = json.loads(res)
  
          from sklearn.metrics import classification_report as sklearn_classification_report
  
          sklearn_result = sklearn_classification_report(
              y_true.cpu().numpy(), torch.argmax(y_preds, dim=1).cpu().numpy(), output_dict=True
          )
  
          for i in range(n_classes):
              label_i = labels[i] if labels else str(i)
              assert pytest.approx(res[label_i]["precision"] == sklearn_result[str(i)]["precision"])
              assert pytest.approx(res[label_i]["f1-score"] == sklearn_result[str(i)]["f1-score"])
              assert pytest.approx(res[label_i]["recall"] == sklearn_result[str(i)]["recall"])
          assert pytest.approx(res["macro avg"]["precision"] == sklearn_result["macro avg"]["precision"])
          assert pytest.approx(res["macro avg"]["recall"] == sklearn_result["macro avg"]["recall"])
          assert pytest.approx(res["macro avg"]["f1-score"] == sklearn_result["macro avg"]["f1-score"])
  
      for _ in range(5):
          # check multiple random inputs as random exact occurencies are rare
          metric_devices = ["cpu"]
          if device.type != "xla":
              metric_devices.append(idist.device())
          for metric_device in metric_devices:
              _test(metric_device, 2, ["label0", "label1"])
              _test(metric_device, 2)
              _test(metric_device, 3, ["label0", "label1", "label2"])
              _test(metric_device, 3)
              _test(metric_device, 4, ["label0", "label1", "label2", "label3"])
              _test(metric_device, 4)
  
  
  def _test_integration_multilabel(device, output_dict):
  
      rank = idist.get_rank()
      torch.manual_seed(12)
  
      def _test(metric_device, n_epochs, labels=None):
  
          classification_report = ClassificationReport(device=metric_device, output_dict=output_dict, is_multilabel=True)
  
          n_iters = 10
          s = 16
          n_classes = 7
  
          offset = n_iters * s
          y_true = torch.randint(0, 2, size=(offset * idist.get_world_size(), n_classes, 6, 8)).to(device)
          y_preds = torch.randint(0, 2, size=(offset * idist.get_world_size(), n_classes, 6, 8)).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)
  
          classification_report.attach(engine, "cr")
  
          data = list(range(n_iters))
          engine.run(data=data, max_epochs=n_epochs)
  
          assert "cr" in engine.state.metrics
          res = engine.state.metrics["cr"]
          res2 = classification_report.compute()
          assert res == res2
  
          assert isinstance(res, dict if output_dict else str)
          if not output_dict:
              res = json.loads(res)
  
          np_y_preds = to_numpy_multilabel(y_preds)
          np_y_true = to_numpy_multilabel(y_true)
  
          from sklearn.metrics import classification_report as sklearn_classification_report
  
          sklearn_result = sklearn_classification_report(np_y_true, np_y_preds, output_dict=True)
  
          for i in range(n_classes):
              label_i = labels[i] if labels else str(i)
              assert pytest.approx(res[label_i]["precision"] == sklearn_result[str(i)]["precision"])
              assert pytest.approx(res[label_i]["f1-score"] == sklearn_result[str(i)]["f1-score"])
              assert pytest.approx(res[label_i]["recall"] == sklearn_result[str(i)]["recall"])
          assert pytest.approx(res["macro avg"]["precision"] == sklearn_result["macro avg"]["precision"])
          assert pytest.approx(res["macro avg"]["recall"] == sklearn_result["macro avg"]["recall"])
          assert pytest.approx(res["macro avg"]["f1-score"] == sklearn_result["macro avg"]["f1-score"])
  
      for _ in range(3):
          # check multiple random inputs as random exact occurencies are rare
          metric_devices = ["cpu"]
          if device.type != "xla":
              metric_devices.append(idist.device())
          for metric_device in metric_devices:
              _test(metric_device, 1)
              _test(metric_device, 2)
              _test(metric_device, 1, ["0", "1", "2", "3", "4", "5", "6"])
              _test(metric_device, 2, ["0", "1", "2", "3", "4", "5", "6"])
  
  
  @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_nccl_gpu(distributed_context_single_node_nccl):
  
      device = idist.device()
      _test_integration_multiclass(device, True)
      _test_integration_multiclass(device, False)
      _test_integration_multilabel(device, True)
      _test_integration_multilabel(device, False)
  
  
  @pytest.mark.distributed
  @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support")
  def test_distrib_gloo_cpu_or_gpu(local_rank, distributed_context_single_node_gloo):
  
      device = idist.device()
      _test_integration_multiclass(device, True)
      _test_integration_multiclass(device, False)
      _test_integration_multilabel(device, True)
      _test_integration_multilabel(device, False)
  
  
  @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_integration_multiclass, (device, True), np=nproc, do_init=True)
      gloo_hvd_executor(_test_integration_multiclass, (device, False), np=nproc, do_init=True)
      gloo_hvd_executor(_test_integration_multilabel, (device, True), np=nproc, do_init=True)
      gloo_hvd_executor(_test_integration_multilabel, (device, False), np=nproc, do_init=True)
  
  
  def _test_distrib_xla_nprocs(index):
  
      device = idist.device()
      _test_integration_multiclass(device, True)
      _test_integration_multiclass(device, False)
      _test_integration_multilabel(device, True)
      _test_integration_multilabel(device, False)
  
  
  @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)
  
  
  def to_numpy_multilabel(y):
      # reshapes input array to (N x ..., C)
      y = y.transpose(1, 0).cpu().numpy()
      num_classes = y.shape[0]
      y = y.reshape((num_classes, -1)).transpose(1, 0)
      return y
  
  
  @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_gloo_cpu_or_gpu(distributed_context_multi_node_gloo):
  
      device = idist.device()
      _test_integration_multiclass(device, True)
      _test_integration_multiclass(device, False)
      _test_integration_multilabel(device, True)
      _test_integration_multilabel(device, False)
  
  
  @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_nccl_gpu(distributed_context_multi_node_nccl):
  
      device = idist.device()
      _test_integration_multiclass(device, True)
      _test_integration_multiclass(device, False)
      _test_integration_multilabel(device, True)
      _test_integration_multilabel(device, False)
  