from __future__ import annotations

import pytest
import torch

from kernels.triton.row_softmax import triton_row_softmax
from reference.torch_row_softmax import torch_row_softmax


def _run_impl_or_skip(fn, *args):
    try:
        return fn(*args)
    except NotImplementedError:
        pytest.skip("implementation is still TODO")
    except RuntimeError as exc:
        pytest.skip(str(exc))


@pytest.mark.reference
def test_row_softmax_reference_matches_torch():
    x = torch.randn(8, 17)
    out = torch_row_softmax(x)
    torch.testing.assert_close(out, torch.softmax(x, dim=1))


@pytest.mark.reference
def test_row_softmax_reference_is_numerically_stable():
    x = torch.tensor([[1000.0, 1001.0, 1002.0], [-1000.0, -999.0, -998.0]])
    out = torch_row_softmax(x)
    torch.testing.assert_close(out.sum(dim=1), torch.ones(2), atol=1e-6, rtol=1e-6)


@pytest.mark.triton_required
@pytest.mark.skeleton
def test_triton_row_softmax_if_available():
    if not torch.cuda.is_available():
        pytest.skip("CUDA is not available")
    x = torch.randn(16, 63, device="cuda")
    out = _run_impl_or_skip(triton_row_softmax, x)
    torch.testing.assert_close(out, torch.softmax(x, dim=1), atol=1e-4, rtol=1e-4)