text: import os
  
  import numpy as np
  import pytest
  import torch
  from sklearn.neighbors import DistanceMetric
  
  import ignite.distributed as idist
  from ignite.contrib.metrics.regression import ManhattanDistance
  from ignite.engine import Engine
  
  
  def test_wrong_input_shapes():
      m = ManhattanDistance()
  
      with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"):
          m.update((torch.rand(4), torch.rand(4, 1)))
  
      with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"):
          m.update((torch.rand(4, 1), torch.rand(4,)))
  
  
  def test_mahattan_distance():
      a = np.random.randn(4)
      b = np.random.randn(4)
      c = np.random.randn(4)
      d = np.random.randn(4)
      ground_truth = np.random.randn(4)
  
      m = ManhattanDistance()
  
      manhattan = DistanceMetric.get_metric("manhattan")
  
      m.update((torch.from_numpy(a), torch.from_numpy(ground_truth)))
      np_sum = np.abs(ground_truth - a).sum()
      assert m.compute() == pytest.approx(np_sum)
      assert manhattan.pairwise([a, ground_truth])[0][1] == pytest.approx(np_sum)
  
      m.update((torch.from_numpy(b), torch.from_numpy(ground_truth)))
      np_sum += np.abs(ground_truth - b).sum()
      assert m.compute() == pytest.approx(np_sum)
      v1 = np.hstack([a, b])
      v2 = np.hstack([ground_truth, ground_truth])
      assert manhattan.pairwise([v1, v2])[0][1] == pytest.approx(np_sum)
  
      m.update((torch.from_numpy(c), torch.from_numpy(ground_truth)))
      np_sum += np.abs(ground_truth - c).sum()
      assert m.compute() == pytest.approx(np_sum)
      v1 = np.hstack([v1, c])
      v2 = np.hstack([v2, ground_truth])
      assert manhattan.pairwise([v1, v2])[0][1] == pytest.approx(np_sum)
  
      m.update((torch.from_numpy(d), torch.from_numpy(ground_truth)))
      np_sum += np.abs(ground_truth - d).sum()
      assert m.compute() == pytest.approx(np_sum)
      v1 = np.hstack([v1, d])
      v2 = np.hstack([v2, ground_truth])
      assert manhattan.pairwise([v1, v2])[0][1] == pytest.approx(np_sum)
  
  
  def test_integration():
      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)
  
          m = ManhattanDistance()
          m.attach(engine, "md")
  
          np_y = y.numpy().ravel()
          np_y_pred = y_pred.numpy().ravel()
  
          manhattan = DistanceMetric.get_metric("manhattan")
  
          data = list(range(y_pred.shape[0] // batch_size))
          md = engine.run(data, max_epochs=1).metrics["md"]
  
          assert manhattan.pairwise([np_y_pred, np_y])[0][1] == pytest.approx(md)
  
      def get_test_cases():
          test_cases = [
              (torch.rand(size=(100,)), torch.rand(size=(100,)), 10),
              (torch.rand(size=(100, 1)), torch.rand(size=(100, 1)), 20),
          ]
          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_error_is_not_nan():
      m = ManhattanDistance()
      m.update((torch.zeros(4), torch.zeros(4)))
      assert not (torch.isnan(m._sum_of_errors).any() or torch.isinf(m._sum_of_errors).any()), m._sum_of_errors
  
  
  def _test_distrib_compute(device):
      rank = idist.get_rank()
  
      manhattan = DistanceMetric.get_metric("manhattan")
  
      def _test(metric_device):
          metric_device = torch.device(metric_device)
          m = ManhattanDistance(device=metric_device)
          torch.manual_seed(10 + rank)
  
          y_pred = torch.randint(0, 10, size=(10,), device=device).float()
          y = torch.randint(0, 10, size=(10,), device=device).float()
  
          m.update((y_pred, y))
  
          # gather y_pred, y
          y_pred = idist.all_gather(y_pred)
          y = idist.all_gather(y)
  
          np_y_pred = y_pred.cpu().numpy()
          np_y = y.cpu().numpy()
          res = m.compute()
          assert manhattan.pairwise([np_y_pred, np_y])[0][1] == pytest.approx(res)
  
      for _ in range(3):
          _test("cpu")
          if device.type != "xla":
              _test(idist.device())
  
  
  def _test_distrib_integration(device):
  
      rank = idist.get_rank()
      torch.manual_seed(12)
  
      manhattan = DistanceMetric.get_metric("manhattan")
  
      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.rand(size=(offset * idist.get_world_size(),)).to(device)
          y_preds = torch.rand(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)
  
          m = ManhattanDistance(device=metric_device)
          m.attach(engine, "md")
  
          data = list(range(n_iters))
          engine.run(data=data, max_epochs=n_epochs)
  
          assert "md" in engine.state.metrics
  
          res = engine.state.metrics["md"]
          if isinstance(res, torch.Tensor):
              res = res.cpu().numpy()
  
          np_y_true = y_true.cpu().numpy()
          np_y_preds = y_preds.cpu().numpy()
  
          assert pytest.approx(res) == manhattan.pairwise([np_y_preds, np_y_true])[0][1]
  
      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_compute(device)
      _test_distrib_integration(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_compute(device)
      _test_distrib_integration(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_compute, (device,), np=nproc, do_init=True)
      gloo_hvd_executor(_test_distrib_integration, (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_compute(device)
      _test_distrib_integration(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_compute(device)
      _test_distrib_integration(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_compute(device)
      _test_distrib_integration(device)
  
  
  def _test_distrib_xla_nprocs(index):
      device = idist.device()
      _test_distrib_compute(device)
      _test_distrib_integration(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)
  