from __future__ import annotations

import pytest
import torch

from kernels.triton.flash_attention_fwd import triton_flash_attention_fwd
from reference.torch_attention import torch_attention


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


@pytest.mark.reference
def test_attention_reference_small_shape():
    q = torch.randn(1, 2, 8, 16)
    k = torch.randn(1, 2, 8, 16)
    v = torch.randn(1, 2, 8, 16)
    out = torch_attention(q, k, v, causal=False)
    expected = torch.nn.functional.scaled_dot_product_attention(q, k, v, is_causal=False)
    torch.testing.assert_close(out, expected, atol=1e-5, rtol=1e-5)


@pytest.mark.reference
def test_attention_reference_causal_small_shape():
    q = torch.randn(1, 1, 8, 16)
    k = torch.randn(1, 1, 8, 16)
    v = torch.randn(1, 1, 8, 16)
    out = torch_attention(q, k, v, causal=True)
    expected = torch.nn.functional.scaled_dot_product_attention(q, k, v, is_causal=True)
    torch.testing.assert_close(out, expected, atol=1e-5, rtol=1e-5)


@pytest.mark.triton_required
@pytest.mark.skeleton
def test_triton_flash_attention_if_available():
    if not torch.cuda.is_available():
        pytest.skip("CUDA is not available")
    q = torch.randn(1, 2, 16, 32, device="cuda")
    k = torch.randn(1, 2, 16, 32, device="cuda")
    v = torch.randn(1, 2, 16, 32, device="cuda")
    out = _run_impl_or_skip(triton_flash_attention_fwd, q, k, v, causal=False)
    expected = torch_attention(q, k, v, causal=False)
    torch.testing.assert_close(out, expected, atol=2e-3, rtol=2e-3)