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 sklearn_result[str(i)]["precision"] == pytest.approx(res[label_i]["precision"])
            assert sklearn_result[str(i)]["f1-score"] == pytest.approx(res[label_i]["f1-score"])
            assert sklearn_result[str(i)]["recall"] == pytest.approx(res[label_i]["recall"])
        assert sklearn_result["macro avg"]["precision"] == pytest.approx(res["macro avg"]["precision"])
        assert sklearn_result["macro avg"]["recall"] == pytest.approx(res["macro avg"]["recall"])
        assert sklearn_result["macro avg"]["f1-score"] == pytest.approx(res["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 sklearn_result[str(i)]["precision"] == pytest.approx(res[label_i]["precision"])
            assert sklearn_result[str(i)]["f1-score"] == pytest.approx(res[label_i]["f1-score"])
            assert sklearn_result[str(i)]["recall"] == pytest.approx(res[label_i]["recall"])
        assert sklearn_result["macro avg"]["precision"] == pytest.approx(res["macro avg"]["precision"])
        assert sklearn_result["macro avg"]["recall"] == pytest.approx(res["macro avg"]["recall"])
        assert sklearn_result["macro avg"]["f1-score"] == pytest.approx(res["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.xfail
@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.xfail
@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.xfail
@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.xfail
@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.xfail
@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.xfail
@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)
