# %%
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

import tensorflow as tf
tf.random.set_seed(777)  # for reproducibility

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

# from pso_tf import PSO
from pso import Optimizer
# from optimizer import Optimizer

import numpy as np

from datetime import date
from tqdm import tqdm

import gc

# print(tf.__version__)
# print(tf.config.list_physical_devices())
# print(f"Num GPUs Available: {len(tf.config.list_physical_devices('GPU'))}")


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'))

    # model.summary()

    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'


pso_mnist = Optimizer(model, loss=loss, n_particles=50, c0=0.4, c1=0.8, w_min=0.4, w_max=0.95, negative_swarm=0.3)
weight, score = pso_mnist.fit(
    x_test, y_test, epochs=500, save=True, save_path="./result/mnist", renewal="acc", empirical_balance=True, Dispersion=False, check_point=10)
# pso_mnist.model_save("./result/mnist")
# pso_mnist.save_info("./result/mnist")

gc.collect()