"""Numerical contract tests for block-concatenated Core ML LoRA slots."""

import torch


def test_block_concatenation_matches_weighted_adapter_sum() -> None:
    generator = torch.Generator().manual_seed(20260813)
    batch, pixels, input_width, output_width, rank = 2, 11, 7, 9, 4
    hidden = torch.randn(batch, pixels, input_width, generator=generator)
    downs = [
        torch.randn(rank, input_width, generator=generator)
        for _ in range(3)
    ]
    ups = [
        torch.randn(output_width, rank, generator=generator)
        for _ in range(3)
    ]
    strengths = [0.25, 1.0, 1.35]

    expected = sum(
        strength * ((hidden @ down.T) @ up.T)
        for strength, down, up in zip(strengths, downs, ups)
    )

    state_down = torch.cat(downs, dim=0)
    state_up = torch.cat(
        [strength * up for strength, up in zip(strengths, ups)],
        dim=1,
    )
    actual = (hidden @ state_down.T) @ state_up.T

    torch.testing.assert_close(actual, expected, rtol=1e-5, atol=1e-5)


def test_unused_slots_are_exactly_zero() -> None:
    generator = torch.Generator().manual_seed(20260813)
    hidden = torch.randn(1, 5, 6, generator=generator)
    down = torch.randn(3, 6, generator=generator)
    up = torch.randn(8, 3, generator=generator)

    state_down = torch.zeros(9, 6)
    state_up = torch.zeros(8, 9)
    state_down[:3] = down
    state_up[:, :3] = up

    torch.testing.assert_close(
        (hidden @ state_down.T) @ state_up.T,
        (hidden @ down.T) @ up.T,
    )