"""Tests for Liquid Neural Networks module."""

from __future__ import annotations

from fusionagi.reasoning.liquid_networks import LiquidCell, LiquidNetwork, LiquidNetworkConfig


class TestLiquidCell:
    def test_init_defaults(self) -> None:
        cell = LiquidCell(input_dim=4, hidden_dim=3)
        assert len(cell.w_in) == 3
        assert len(cell.w_in[0]) == 4
        assert len(cell.state) == 3

    def test_step_changes_state(self) -> None:
        cell = LiquidCell(input_dim=2, hidden_dim=2)
        initial = list(cell.state)
        cell.step([1.0, 0.5])
        assert cell.state != initial

    def test_reset_zeros_state(self) -> None:
        cell = LiquidCell(input_dim=2, hidden_dim=2)
        cell.step([1.0, 0.5])
        cell.reset()
        assert all(s == 0.0 for s in cell.state)

    def test_multiple_steps_evolve(self) -> None:
        cell = LiquidCell(input_dim=3, hidden_dim=4)
        states = []
        for _ in range(5):
            states.append(list(cell.step([0.5, -0.3, 0.8])))
        assert states[0] != states[4]


class TestLiquidNetwork:
    def test_init_default_config(self) -> None:
        net = LiquidNetwork()
        assert net.config.input_dim == 64

    def test_forward_output_shape(self) -> None:
        cfg = LiquidNetworkConfig(input_dim=8, hidden_dim=4, output_dim=3, num_layers=1)
        net = LiquidNetwork(cfg)
        out = net.forward([1.0] * 8)
        assert len(out) == 3

    def test_forward_padding(self) -> None:
        cfg = LiquidNetworkConfig(input_dim=8, hidden_dim=4, output_dim=2)
        net = LiquidNetwork(cfg)
        out = net.forward([1.0, 2.0])  # Shorter than input_dim
        assert len(out) == 2

    def test_forward_truncation(self) -> None:
        cfg = LiquidNetworkConfig(input_dim=4, hidden_dim=2, output_dim=2)
        net = LiquidNetwork(cfg)
        out = net.forward([1.0] * 10)  # Longer than input_dim
        assert len(out) == 2

    def test_forward_sequence(self) -> None:
        cfg = LiquidNetworkConfig(input_dim=4, hidden_dim=3, output_dim=2, num_layers=1)
        net = LiquidNetwork(cfg)
        inputs = [[float(i)] * 4 for i in range(5)]
        outputs = net.forward_sequence(inputs)
        assert len(outputs) == 5
        assert all(len(o) == 2 for o in outputs)

    def test_reset_clears_state(self) -> None:
        cfg = LiquidNetworkConfig(input_dim=4, hidden_dim=3, output_dim=2)
        net = LiquidNetwork(cfg)
        net.forward([1.0] * 4)
        net.reset()
        for layer in net._layers:
            assert all(s == 0.0 for s in layer.state)

    def test_adapt_weights(self) -> None:
        cfg = LiquidNetworkConfig(input_dim=4, hidden_dim=3, output_dim=2, num_layers=1)
        net = LiquidNetwork(cfg)
        inputs = [[0.1, 0.2, 0.3, 0.4], [0.5, 0.6, 0.7, 0.8]]
        targets = [[0.5, -0.5], [0.3, 0.3]]
        result = net.adapt_weights(inputs, targets, epochs=5)
        assert "final_loss" in result
        assert result["epochs_run"] <= 5

    def test_get_summary(self) -> None:
        net = LiquidNetwork()
        summary = net.get_summary()
        assert summary["type"] == "LiquidNetwork"
        assert "total_parameters" in summary

    def test_output_bounded(self) -> None:
        cfg = LiquidNetworkConfig(input_dim=4, hidden_dim=4, output_dim=3)
        net = LiquidNetwork(cfg)
        out = net.forward([10.0, -10.0, 5.0, -5.0])
        for val in out:
            assert -1.0 <= val <= 1.0