from functools import partial

import numpy as np
from numpy.typing import NDArray

from algorithms.neat.genome.utils import flatten_connections, set_operation_analysis


def distance(nodes1: NDArray, connections1: NDArray, nodes2: NDArray, connections2: NDArray) -> NDArray:
    """
    Calculate the distance between two genomes.
    nodes are a 2-d array with shape (N, 5), its columns are [key, bias, response, act, agg]
    connections are a 3-d array with shape (2, N, N), axis 0 means [weights, enable]
    """

    node_distance = gene_distance(nodes1, nodes2, 'node')

    # refactor connections
    keys1, keys2 = nodes1[:, 0], nodes2[:, 0]
    cons1 = flatten_connections(keys1, connections1)
    cons2 = flatten_connections(keys2, connections2)

    connection_distance = gene_distance(cons1, cons2, 'connection')
    return node_distance + connection_distance


def gene_distance(ar1, ar2, gene_type, compatibility_coe=0.5, disjoint_coe=1.):
    if gene_type == 'node':
        keys1, keys2 = ar1[:, :1], ar2[:, :1]
    else:  # connection
        keys1, keys2 = ar1[:, :2], ar2[:, :2]

    n_sorted_indices, n_intersect_mask, n_union_mask = set_operation_analysis(keys1, keys2)
    nodes = np.concatenate((ar1, ar2), axis=0)
    sorted_nodes = nodes[n_sorted_indices]

    if gene_type == 'node':
        node_exist_mask = np.any(~np.isnan(sorted_nodes[:, 1:]), axis=1)
    else:
        node_exist_mask = np.any(~np.isnan(sorted_nodes[:, 2:]), axis=1)

    n_intersect_mask = n_intersect_mask & node_exist_mask
    n_union_mask = n_union_mask & node_exist_mask

    fr_sorted_nodes, sr_sorted_nodes = sorted_nodes[:-1], sorted_nodes[1:]

    non_homologous_cnt = np.sum(n_union_mask) - np.sum(n_intersect_mask)
    if gene_type == 'node':
        node_distance = batch_homologous_node_distance(fr_sorted_nodes, sr_sorted_nodes)
    else:  # connection
        node_distance = batch_homologous_connection_distance(fr_sorted_nodes, sr_sorted_nodes)

    node_distance = np.where(np.isnan(node_distance), 0, node_distance)
    homologous_distance = np.sum(node_distance * n_intersect_mask[:-1])

    gene_cnt1 = np.sum(np.all(~np.isnan(ar1), axis=1))
    gene_cnt2 = np.sum(np.all(~np.isnan(ar2), axis=1))
    max_cnt = np.maximum(gene_cnt1, gene_cnt2)

    val = non_homologous_cnt * disjoint_coe + homologous_distance * compatibility_coe

    return np.where(max_cnt == 0, 0, val / max_cnt)  # consider the case that both genome has no gene


def batch_homologous_node_distance(b_n1, b_n2):
    res = []
    for n1, n2 in zip(b_n1, b_n2):
        d = homologous_node_distance(n1, n2)
        res.append(d)
    return np.stack(res, axis=0)


def batch_homologous_connection_distance(b_c1, b_c2):
    res = []
    for c1, c2 in zip(b_c1, b_c2):
        d = homologous_connection_distance(c1, c2)
        res.append(d)
    return np.stack(res, axis=0)


def homologous_node_distance(n1, n2):
    d = 0
    d += np.abs(n1[1] - n2[1])  # bias
    d += np.abs(n1[2] - n2[2])  # response
    d += n1[3] != n2[3]  # activation
    d += n1[4] != n2[4]
    return d


def homologous_connection_distance(c1, c2):
    d = 0
    d += np.abs(c1[2] - c2[2])  # weight
    d += c1[3] != c2[3]  # enable
    return d