from pathlib import Path

from composer_ans.backprop import BackpropNetwork
from composer_ans.io.legacy_files import load_salieri_config, load_salieri_weights
from composer_ans.salieri import SalieriCritic


THES = Path(__file__).resolve().parents[1] / "THES"


def test_generic_backprop_predict_and_train_step() -> None:
    network = BackpropNetwork.random(
        n_input=2,
        n_hidden=2,
        n_output=1,
        learning_rate=0.5,
        alpha=0.1,
    )

    predicted = network.predict((0.0, 1.0))
    trained = network.train_step((0.0, 1.0), (1.0,))

    assert len(predicted.outputs) == 1
    assert len(trained.outputs) == 1
    assert 0.0 <= trained.outputs[0] <= 1.0
    assert trained.error >= 0.0
    assert any(state.delta != 0.0 for state in trained.node_states if state.node_type != "input")


def test_backprop_loads_legacy_salieri_network() -> None:
    config = load_salieri_config(THES / "S61.DAT")
    weights = load_salieri_weights(THES / "S61.WT")
    network = BackpropNetwork.from_legacy(config=config, legacy_weights=weights)

    result = network.predict(tuple(0.0 for _ in range(config.n_input)))

    assert network.node_count == 61
    assert len(result.outputs) == 1
    assert 0.0 <= result.outputs[0] <= 1.0


def test_salieri_wrapper_runs_on_thesis_sequence_window() -> None:
    critic = SalieriCritic.from_legacy_paths(THES)

    result = critic.evaluate_and_train((1, 4, 5, 1, 0))

    assert result.target in (0, 1)
    assert 0.0 <= result.raw_output <= 1.0
    assert isinstance(result.is_classical, bool)