import os

import numpy as np
import pytest
import torch
from sklearn.metrics import DistanceMetric

import ignite.distributed as idist
from ignite.contrib.metrics.regression import CanberraMetric
from ignite.engine import Engine


def test_wrong_input_shapes():
    m = CanberraMetric()

    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_compute():
    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 = CanberraMetric()

    canberra = DistanceMetric.get_metric("canberra")

    m.update((torch.from_numpy(a), torch.from_numpy(ground_truth)))
    np_sum = (np.abs(ground_truth - a) / (np.abs(a) + np.abs(ground_truth))).sum()
    assert m.compute() == pytest.approx(np_sum)
    assert canberra.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)) / (np.abs(b) + np.abs(ground_truth))).sum()
    assert m.compute() == pytest.approx(np_sum)
    v1 = np.hstack([a, b])
    v2 = np.hstack([ground_truth, ground_truth])
    assert canberra.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)) / (np.abs(c) + np.abs(ground_truth))).sum()
    assert m.compute() == pytest.approx(np_sum)
    v1 = np.hstack([v1, c])
    v2 = np.hstack([v2, ground_truth])
    assert canberra.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) / (np.abs(d) + np.abs(ground_truth))).sum()
    assert m.compute() == pytest.approx(np_sum)
    v1 = np.hstack([v1, d])
    v2 = np.hstack([v2, ground_truth])
    assert canberra.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 = CanberraMetric()
        m.attach(engine, "cm")

        np_y = y.numpy().ravel()
        np_y_pred = y_pred.numpy().ravel()

        canberra = DistanceMetric.get_metric("canberra")

        data = list(range(y_pred.shape[0] // batch_size))
        cm = engine.run(data, max_epochs=1).metrics["cm"]

        assert canberra.pairwise([np_y_pred, np_y])[0][1] == pytest.approx(cm)

    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 = CanberraMetric()
    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()

    canberra = DistanceMetric.get_metric("canberra")

    def _test(metric_device):
        metric_device = torch.device(metric_device)
        m = CanberraMetric(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 canberra.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)
    canberra = DistanceMetric.get_metric("canberra")

    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 = CanberraMetric(device=metric_device)
        m.attach(engine, "cm")

        data = list(range(n_iters))
        engine.run(data=data, max_epochs=n_epochs)

        assert "cm" in engine.state.metrics

        res = engine.state.metrics["cm"]
        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) == canberra.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_nccl_gpu(distributed_context_single_node_nccl):

    device = idist.device()
    _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_gloo_cpu_or_gpu(distributed_context_single_node_gloo):

    device = idist.device()
    _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_gloo_cpu_or_gpu(distributed_context_multi_node_gloo):

    device = idist.device()
    _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_nccl_gpu(distributed_context_multi_node_nccl):

    device = idist.device()
    _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)
