import sys
import os

# Concat top Level dir to system environmental variables
sys.path += os.path.join('..', '.')

from typing import Tuple

from util import *
from experiment import *
from network import *
from soup import *

import keras.backend


def generate_counters():
    """
    Initial build of the counter dict, to store counts.

    :rtype: dict
    :return: dictionary holding counter for: 'divergent', 'fix_zero', 'fix_sec', 'other'
    """
    return {'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 0}


def count(counters, soup, notable_nets=[]):
    """
    Count the occurences ot the types of weight trajectories.

    :param counters:      A counter dictionary.
    :param soup:          A Soup
    :param notable_nets:  A list to store and save intersting candidates

    :rtype      Tuple[dict, list]
    :return:    Both the counter dictionary and the list of interessting nets.
    """

    for net in soup.particles:
        if net.is_diverged():
            counters['divergent'] += 1
        elif net.is_fixpoint():
            if net.is_zero():
                counters['fix_zero'] += 1
            else:
                counters['fix_other'] += 1
                # notable_nets += [net]
        # elif net.is_fixpoint(2):
        #     counters['fix_sec'] += 1
        #     notable_nets += [net]
        else:
            counters['other'] += 1
    return counters, notable_nets

if __name__ == '__main__':

    with Experiment('mixed-soup') as exp:
        exp.trials = 10
        exp.soup_size = 10
        exp.soup_life = 5
        exp.trains_per_selfattack_values = [10 * i for i in range(11)]
        exp.epsilon = 1e-4
        net_generators = []
        for activation in ['linear']:  # ['linear', 'sigmoid', 'relu']:
            for use_bias in [False]:
                net_generators += [lambda activation=activation, use_bias=use_bias: WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
                net_generators += [lambda activation=activation, use_bias=use_bias: AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
                # net_generators += [lambda activation=activation, use_bias=use_bias: RecurrentNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]

        all_names = []
        all_data = []
        for net_generator_id, net_generator in enumerate(net_generators):
            xs = []
            ys = []
            zs = []
            for trains_per_selfattack in exp.trains_per_selfattack_values:
                counters = generate_counters()
                notable_nets = []
                for soup_idx in tqdm(range(exp.trials)):
                    soup = Soup(exp.soup_size,
                                lambda net_generator=net_generator, exp=exp: TrainingNeuralNetworkDecorator(
                                    net_generator()).with_params(epsilon=exp.epsilon))
                    soup.with_params(attacking_rate=0.1, learn_from_rate=-1, train=trains_per_selfattack,
                                     learn_from_severity=-1)
                    soup.seed()
                    name = str(soup.particles[0].net.__class__.__name__) + " activiation='" + str(
                        soup.particles[0].get_keras_params().get('activation')) + "' use_bias=" + str(
                        soup.particles[0].get_keras_params().get('use_bias'))
                    for _ in range(exp.soup_life):
                        soup.evolve()
                    count(counters, soup, notable_nets)
                    keras.backend.clear_session()

                xs += [trains_per_selfattack]
                ys += [float(counters['fix_zero']) / float(exp.trials)]
                zs += [float(counters['fix_other']) / float(exp.trials)]
            all_names += [name]
            # xs: how many trains per self-attack from exp.trains_per_selfattack_values
            # ys: average amount of zero-fixpoints found
            # zs: average amount of non-zero fixpoints
            all_data += [{'xs': xs, 'ys': ys, 'zs': zs}]

        exp.save(all_names=all_names)
        exp.save(all_data=all_data)
        for exp_id, name in enumerate(all_names):
            exp.log(all_names[exp_id])
            exp.log(all_data[exp_id])
            exp.log('\n')