import pytest
import torch

from pytorch_lightning.metrics.utils import class_reduce, reduce


def test_reduce():
    start_tensor = torch.rand(50, 40, 30)

    assert torch.allclose(reduce(start_tensor, 'elementwise_mean'), torch.mean(start_tensor))
    assert torch.allclose(reduce(start_tensor, 'sum'), torch.sum(start_tensor))
    assert torch.allclose(reduce(start_tensor, 'none'), start_tensor)

    with pytest.raises(ValueError):
        reduce(start_tensor, 'error_reduction')


def test_class_reduce():
    num = torch.randint(1, 10, (100, )).float()
    denom = torch.randint(10, 20, (100, )).float()
    weights = torch.randint(1, 100, (100, )).float()

    assert torch.allclose(class_reduce(num, denom, weights, 'micro'), torch.sum(num) / torch.sum(denom))
    assert torch.allclose(class_reduce(num, denom, weights, 'macro'), torch.mean(num / denom))
    assert torch.allclose(
        class_reduce(num, denom, weights, 'weighted'), torch.sum(num / denom * (weights / torch.sum(weights)))
    )
    assert torch.allclose(class_reduce(num, denom, weights, 'none'), num / denom)

torch.rand torch.randint torch.mean torch.sum