# %%
import json
import os
import sys

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

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

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))

    y_train, y_test = tf.one_hot(y_train, 10), tf.one_hot(y_test, 10)

    x_train, x_test = tf.convert_to_tensor(x_train), tf.convert_to_tensor(x_test)
    y_train, y_test = tf.convert_to_tensor(y_train), tf.convert_to_tensor(y_test)

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

    y_train, y_test = tf.one_hot(y_train, 10), tf.one_hot(y_test, 10)

    x_train, x_test = tf.convert_to_tensor(x_train), tf.convert_to_tensor(x_test)
    y_train, y_test = tf.convert_to_tensor(y_train), tf.convert_to_tensor(y_test)

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

    return x_test, y_test


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

    return model


def random_state():
    with open(
        "result/mnist/20230723-061626/mean_squared_error_[0.6384999752044678, 0.0723000094294548].json",
        "r",
    ) as f:
        json_ = json.load(f)
        rs = (
            json_["random_state_0"],
            np.array(json_["random_state_1"]),
            json_["random_state_2"],
            json_["random_state_3"],
            json_["random_state_4"],
        )

    return rs


# %%
model = make_model()
x_train, y_train = get_data_test()

loss = [
    "mean_squared_error",
    "categorical_crossentropy",
    "sparse_categorical_crossentropy",
    "binary_crossentropy",
    "kullback_leibler_divergence",
    "poisson",
    "cosine_similarity",
    "log_cosh",
    "huber_loss",
    "mean_absolute_error",
    "mean_absolute_percentage_error",
]

# rs = random_state()

pso_mnist = optimizer(
    model,
    loss="mean_squared_error",
    n_particles=2000,
    c0=0.2,
    c1=0.4,
    w_min=0.3,
    w_max=0.7,
    negative_swarm=0.1,
    mutation_swarm=0.3,
    particle_min=-4,
    particle_max=4,
)

best_score = pso_mnist.fit(
    x_train,
    y_train,
    epochs=300,
    save_info=True,
    log=1,
    log_name="mnist",
    save_path="./logs/mnist",
    renewal="acc",
    check_point=25,
    empirical_balance=False,
    dispersion=False,

)

print("Done!")

sys.exit(0)
