from keras.models import Sequential
from keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D
from keras.datasets import mnist
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split

# from tensorflow.data.Dataset import from_tensor_slices
import tensorflow as tf
import os

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


gpus = tf.config.experimental.list_physical_devices("GPU")
if gpus:
    try:
        tf.config.experimental.set_memory_growth(gpus[0], True)
    except Exception as e:
        print(e)
    finally:
        del gpus


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

class _batch_generator_:
    def __init__(self, x, y, batch_size: int = None):
        self.index = 0
        self.x = x
        self.y = y
        self.setBatchSize(batch_size)

    def next(self):
        self.index += 1
        if self.index >= self.max_index:
            self.index = 0
            self.__getBatchSlice(self.batch_size)
        return self.dataset[self.index][0], self.dataset[self.index][1]

    def getMaxIndex(self):
        return self.max_index

    def getIndex(self):
        return self.index

    def setIndex(self, index):
        self.index = index

    def getBatchSize(self):
        return self.batch_size

    def setBatchSize(self, batch_size: int = None):
        if batch_size is None:
            batch_size = len(self.x) // 10
        elif batch_size > len(self.x):
            batch_size = len(self.x)
        self.batch_size = batch_size
        print(f"batch size : {self.batch_size}")
        self.dataset = self.__getBatchSlice(self.batch_size)
        self.max_index = len(self.dataset)

    def __getBatchSlice(self, batch_size):
        return list(
            tf.data.Dataset.from_tensor_slices((self.x, self.y))
            .shuffle(len(self.x))
            .batch(batch_size)
        )

    def getDataset(self):
        return self.dataset


def make_model():
    model = Sequential()
    model.add(
        Conv2D(64, kernel_size=(5, 5), activation="relu", input_shape=(28, 28, 1))
    )
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.5))
    model.add(Conv2D(128, kernel_size=(3, 3), activation="relu"))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Flatten())
    model.add(Dropout(0.5))
    model.add(Dense(2048, activation="relu"))
    model.add(Dropout(0.8))
    model.add(Dense(1024, activation="relu"))
    model.add(Dropout(0.8))
    model.add(Dense(10, activation="softmax"))

    return model


model = make_model()
x_train, y_train, x_test, y_test = get_data()
y_train = tf.one_hot(y_train, 10)
y_test = tf.one_hot(y_test, 10)

batch = 64
dataset = _batch_generator_(x_train, y_train, batch)

model.compile(
    optimizer="adam",
    loss="categorical_crossentropy",
    metrics=["accuracy", "mse"],
)

count = 0
print(f"batch size : {batch}")
print("iter " + str(dataset.getMaxIndex()))
print("Training model...")
# while count < dataset.getMaxIndex():
# x_batch, y_batch = dataset.next()
# count += 1
# print(f"iter {count}/{dataset.getMaxIndex()}")
model.fit(x_train, y_train, epochs=1000, batch_size=batch, verbose=1)

print(count)

print("Evaluating model...")
model.evaluate(x_test, y_test, verbose=1)

weights = model.get_weights()
