from algorithm.neat import *
from utils import Act, Agg, State

import jax, jax.numpy as jnp


def test_default():

    # index, bias, response, activation, aggregation
    nodes = jnp.array(
        [
            [0, 0, 1, 0, 0],  # in[0]
            [1, 0, 1, 0, 0],  # in[1]
            [2, 0.5, 1, 0, 0],  # out[0],
            [3, 1, 1, 0, 0],  # hidden[0],
            [4, -1, 1, 0, 0],  # hidden[1],
        ]
    )

    # in_node, out_node, enable, weight
    conns = jnp.array(
        [
            [0, 3, 1, 0.5],  # in[0] -> hidden[0]
            [1, 4, 1, 0.5],  # in[1] -> hidden[1]
            [3, 2, 1, 0.5],  # hidden[0] -> out[0]
            [4, 2, 1, 0.5],  # hidden[1] -> out[0]
        ]
    )

    genome = DefaultGenome(
        num_inputs=2,
        num_outputs=1,
        max_nodes=5,
        max_conns=4,
        node_gene=DefaultNodeGene(
            activation_default=Act.identity,
            activation_options=(Act.identity,),
            aggregation_default=Agg.sum,
            aggregation_options=(Agg.sum,),
        ),
    )

    state = genome.setup(State(randkey=jax.random.key(0)))

    transformed = genome.transform(state, nodes, conns)
    print(*transformed, sep="\n")

    inputs = jnp.array([[0, 0], [0, 1], [1, 0], [1, 1]])
    outputs = jax.jit(jax.vmap(genome.forward, in_axes=(None, 0, None)))(
        state, inputs, transformed
    )
    print(outputs)
    assert jnp.allclose(outputs, jnp.array([[0.5], [0.75], [0.75], [1]]))
    # expected: [[0.5], [0.75], [0.75], [1]]

    print("\n-------------------------------------------------------\n")

    conns = conns.at[0, 2].set(False)  # disable in[0] -> hidden[0]
    print(conns)

    transformed = genome.transform(state, nodes, conns)
    print(*transformed, sep="\n")

    inputs = jnp.array([[0, 0], [0, 1], [1, 0], [1, 1]])
    outputs = jax.vmap(genome.forward, in_axes=(None, 0, None))(
        state, inputs, transformed
    )
    print(outputs)
    assert jnp.allclose(outputs, jnp.array([[0], [0.25], [0], [0.25]]))
    # expected: [[0.5], [0.75], [0.5], [0.75]]


def test_recurrent():

    # index, bias, response, activation, aggregation
    nodes = jnp.array(
        [
            [0, 0, 1, 0, 0],  # in[0]
            [1, 0, 1, 0, 0],  # in[1]
            [2, 0.5, 1, 0, 0],  # out[0],
            [3, 1, 1, 0, 0],  # hidden[0],
            [4, -1, 1, 0, 0],  # hidden[1],
        ]
    )

    # in_node, out_node, enable, weight
    conns = jnp.array(
        [
            [0, 3, 1, 0.5],  # in[0] -> hidden[0]
            [1, 4, 1, 0.5],  # in[1] -> hidden[1]
            [3, 2, 1, 0.5],  # hidden[0] -> out[0]
            [4, 2, 1, 0.5],  # hidden[1] -> out[0]
        ]
    )

    genome = RecurrentGenome(
        num_inputs=2,
        num_outputs=1,
        max_nodes=5,
        max_conns=4,
        node_gene=DefaultNodeGene(
            activation_default=Act.identity,
            activation_options=(Act.identity,),
            aggregation_default=Agg.sum,
            aggregation_options=(Agg.sum,),
        ),
        activate_time=3,
    )

    state = genome.setup(State(randkey=jax.random.key(0)))

    transformed = genome.transform(state, nodes, conns)
    print(*transformed, sep="\n")

    inputs = jnp.array([[0, 0], [0, 1], [1, 0], [1, 1]])
    outputs = jax.jit(jax.vmap(genome.forward, in_axes=(None, 0, None)))(
        state, inputs, transformed
    )
    print(outputs)
    assert jnp.allclose(outputs, jnp.array([[0.5], [0.75], [0.75], [1]]))
    # expected: [[0.5], [0.75], [0.75], [1]]

    print("\n-------------------------------------------------------\n")

    conns = conns.at[0, 2].set(False)  # disable in[0] -> hidden[0]
    print(conns)

    transformed = genome.transform(state, nodes, conns)
    print(*transformed, sep="\n")

    inputs = jnp.array([[0, 0], [0, 1], [1, 0], [1, 1]])
    outputs = jax.vmap(genome.forward, in_axes=(None, 0, None))(
        state, inputs, transformed
    )
    print(outputs)
    assert jnp.allclose(outputs, jnp.array([[0], [0.25], [0], [0.25]]))
    # expected: [[0.5], [0.75], [0.5], [0.75]]