import jax.numpy as jnp
import jax.random

from utils import mutate_float
from . import BaseConnGene


class DefaultConnGene(BaseConnGene):
    "Default connection gene, with the same behavior as in NEAT-python."

    custom_attrs = ["weight"]

    def __init__(
        self,
        weight_init_mean: float = 0.0,
        weight_init_std: float = 1.0,
        weight_mutate_power: float = 0.5,
        weight_mutate_rate: float = 0.8,
        weight_replace_rate: float = 0.1,
    ):
        super().__init__()
        self.weight_init_mean = weight_init_mean
        self.weight_init_std = weight_init_std
        self.weight_mutate_power = weight_mutate_power
        self.weight_mutate_rate = weight_mutate_rate
        self.weight_replace_rate = weight_replace_rate

    def new_custom_attrs(self, state):
        return jnp.array([self.weight_init_mean])

    def new_random_attrs(self, state, randkey):
        weight = (
            jax.random.normal(randkey, ()) * self.weight_init_std
            + self.weight_init_mean
        )
        return jnp.array([weight])

    def mutate(self, state, randkey, conn):
        input_index = conn[0]
        output_index = conn[1]
        weight = mutate_float(
            randkey,
            conn[2],
            self.weight_init_mean,
            self.weight_init_std,
            self.weight_mutate_power,
            self.weight_mutate_rate,
            self.weight_replace_rate,
        )

        return jnp.array([input_index, output_index, weight])

    def distance(self, state, attrs1, attrs2):
        return jnp.abs(attrs1[0] - attrs2[0])

    def forward(self, state, attrs, inputs):
        weight = attrs[0]
        return inputs * weight