from __future__ import annotations

from synaptopus.hopfield import HopfieldNetwork, HopfieldParams


def test_hopfield_zero_matrix_runs_on_arbitrary_grid_shape() -> None:
    inputs = (
        (0.8, 0.2),
        (0.1, 0.9),
        (0.4, 0.3),
    )
    size = len(inputs) * len(inputs[0])
    weights = tuple(tuple(0.0 for _ in range(size)) for _ in range(size))

    result = HopfieldNetwork(weight_matrix=weights).run(inputs)

    assert result.iterations > 0
    assert len(result.state.outputs) == 3
    assert len(result.state.outputs[0]) == 2


def test_hopfield_respects_initial_activation_shape() -> None:
    inputs = (
        (0.5, 0.5),
        (0.5, 0.5),
    )
    weights = tuple(tuple(0.0 for _ in range(4)) for _ in range(4))
    network = HopfieldNetwork(weight_matrix=weights, params=HopfieldParams())

    result = network.run(
        inputs,
        initial_activations=(
            (0.1, 0.2),
            (0.3, 0.4),
        ),
    )

    assert len(result.state.activations) == 2
    assert len(result.state.activations[0]) == 2


def test_hopfield_round_trips_through_dict() -> None:
    weights = tuple(tuple(float(i == j) for j in range(4)) for i in range(4))
    network = HopfieldNetwork(
        weight_matrix=weights,
        params=HopfieldParams(epsilon=0.01, weight_scale=0.5),
    )

    restored = HopfieldNetwork.from_dict(network.to_dict())

    assert restored.weight_matrix == network.weight_matrix
    assert restored.params == network.params