# %%
import os

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

import tensorflow as tf

tf.random.set_seed(777)  # for reproducibility

import numpy as np

np.random.seed(777)

import gc
from datetime import date

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 pso_tf import PSO
from pso import Optimizer
from tensorflow import keras
from tqdm import tqdm

# 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'))

    return model

# %%
model = make_model()
x_test, y_test = get_data_test()

loss = ['mse', 'categorical_crossentropy', 'binary_crossentropy', 'kullback_leibler_divergence', 'poisson', 'cosine_similarity', 'log_cosh', 'huber_loss', 'mean_absolute_error', 'mean_absolute_percentage_error']

if __name__ == "__main__":
    try:
        pso_mnist = Optimizer(
            model,
            loss=loss[0], 
            n_particles=75,
            c0=0.3, 
            c1=0.7, 
            w_min=0.6,
            w_max=0.9,
            negative_swarm=0.25,
            momentun_swarm=0,
            )

        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
            )
    except Exception as e:
        print(e)
    finally:
        gc.collect()