from __future__ import annotations

import pytest
import torch

from tokenspeed.runtime.layers.moe import topk as topk_module
from tokenspeed.runtime.layers.moe.topk import TopKConfig, select_experts


@pytest.mark.parametrize("renormalize", [False, True])
def test_correction_bias_route_forwards_renormalize(
    monkeypatch: pytest.MonkeyPatch,
    renormalize: bool,
) -> None:
    calls: list[bool] = []

    def fake_cuda_routing_flash(
        _router_logits: torch.Tensor,
        _correction_bias: torch.Tensor,
        topk_ids: torch.Tensor,
        topk_weights: torch.Tensor,
        _num_real_experts: int,
        _routed_scaling_factor: float,
        renorm: bool,
    ) -> None:
        calls.append(renorm)
        topk_ids.fill_(0)
        topk_weights.fill_(1.0)

    monkeypatch.setattr(
        topk_module,
        "cuda_routing_flash",
        fake_cuda_routing_flash,
    )

    select_experts(
        hidden_states=torch.empty((1, 4), dtype=torch.float32),
        router_logits=torch.empty((1, 8), dtype=torch.float32),
        topk_config=TopKConfig(
            top_k=2,
            renormalize=renormalize,
            correction_bias=torch.zeros((8,), dtype=torch.float32),
            routed_scaling_factor=1.0,
        ),
    )

    assert calls == [renormalize]