# %%
import os

os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"

import tensorflow as tf

tf.random.set_seed(777)  # for reproducibility

import gc
from datetime import date

import numpy as np
from keras import backend as K
from keras.datasets import mnist
from keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D
from keras.models import Sequential
from tensorflow import keras
from tqdm import tqdm

from pso import Optimizer


def get_data():
    (x_train, y_train), (x_test, y_test) = mnist.load_data()

    x_train, x_test = x_train / 255.0, x_test / 255.0
    x_train = x_train.reshape((60000, 28, 28, 1))
    x_test = x_test.reshape((10000, 28, 28, 1))

    print(f"x_train : {x_train[0].shape} | y_train : {y_train[0].shape}")
    print(f"x_test : {x_test[0].shape} | y_test : {y_test[0].shape}")
    return x_train, y_train, x_test, y_test


def get_data_test():
    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    x_test = x_test.reshape((10000, 28, 28, 1))

    return x_test, y_test


def make_model():
    model = Sequential()
    model.add(
        Conv2D(32, kernel_size=(5, 5), activation="relu", input_shape=(28, 28, 1))
    )
    model.add(MaxPooling2D(pool_size=(3, 3)))
    model.add(Conv2D(64, kernel_size=(3, 3), activation="relu"))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(128, activation="relu"))
    model.add(Dense(10, activation="softmax"))

    return model


# %%
model = make_model()
x_test, y_test = get_data_test()
# loss = 'binary_crossentropy'
# loss = 'categorical_crossentropy'
# loss = 'sparse_categorical_crossentropy'
# loss = 'kullback_leibler_divergence'
# loss = 'poisson'
# loss = 'cosine_similarity'
# loss = 'log_cosh'
# loss = 'huber_loss'
# loss = 'mean_absolute_error'
# loss = 'mean_absolute_percentage_error'
# loss = 'mean_squared_error'

loss = [
    "mse",
    "categorical_crossentropy",
    "binary_crossentropy",
    "kullback_leibler_divergence",
    "poisson",
    "cosine_similarity",
    "log_cosh",
    "huber_loss",
    "mean_absolute_error",
    "mean_absolute_percentage_error",
]
n_particles = [50, 75, 100]
c0 = [0.25, 0.35, 0.45, 0.55]
c1 = [0.5, 0.6, 0.7, 0.8, 0.9]
w_min = [0.5, 0.6, 0.7]
w_max = [1.1, 1.2, 1.3]
negative_swarm = [0.25, 0.3, 0.5]
eb = [True, False]
dispersion = [True, False]

if __name__ == "__main__":
    try:
        for loss_ in loss:
            for n in n_particles:
                for c_0 in c0:
                    for c_1 in c1:
                        for w_m in w_min:
                            for w_M in w_max:
                                for n_s in negative_swarm:
                                    pso_mnist = Optimizer(
                                        model,
                                        loss=loss_,
                                        n_particles=n,
                                        c0=c_0,
                                        c1=c_1,
                                        w_min=w_m,
                                        w_max=w_M,
                                        negative_swarm=n_s,
                                    )

                                    best_score = pso_mnist.fit(
                                        x_test,
                                        y_test,
                                        epochs=200,
                                        save=True,
                                        save_path="./result/mnist",
                                        renewal="acc",
                                        empirical_balance=False,
                                        Dispersion=False,
                                        check_point=25,
                                    )

                                    del pso_mnist
                                    gc.collect()
                                    tf.keras.backend.clear_session()

    except KeyboardInterrupt:
        print("KeyboardInterrupt")
    finally:
        print("Finish")