from __future__ import absolute_import, division, print_function, unicode_literals

import tensorflow as tf

from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model

from trains import Task


task = Task.init(project_name='examples',
                 task_name='Tensorflow v2 mnist with summaries')


# Load and prepare the MNIST dataset.
mnist = tf.keras.datasets.mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

# Add a channels dimension
x_train = x_train[..., tf.newaxis].astype('float32')
x_test = x_test[..., tf.newaxis].astype('float32')

# Use tf.data to batch and shuffle the dataset
train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(10000).batch(32)
test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)


# Build the tf.keras model using the Keras model subclassing API
class MyModel(Model):
    def __init__(self):
        super(MyModel, self).__init__()
        self.conv1 = Conv2D(32, 3, activation='relu', dtype=tf.float32)
        self.flatten = Flatten()
        self.d1 = Dense(128, activation='relu', dtype=tf.float32)
        self.d2 = Dense(10, activation='softmax', dtype=tf.float32)

    def call(self, x):
        x = self.conv1(x)
        x = self.flatten(x)
        x = self.d1(x)
        return self.d2(x)


# Create an instance of the model
model = MyModel()

# Choose an optimizer and loss function for training
loss_object = tf.keras.losses.SparseCategoricalCrossentropy()
optimizer = tf.keras.optimizers.Adam()

# Select metrics to measure the loss and the accuracy of the model.
# These metrics accumulate the values over epochs and then print the overall result.
train_loss = tf.keras.metrics.Mean(name='train_loss', dtype=tf.float32)
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')

test_loss = tf.keras.metrics.Mean(name='test_loss', dtype=tf.float32)
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')

# Use tf.GradientTape to train the model
@tf.function
def train_step(images, labels):
    with tf.GradientTape() as tape:
        predictions = model(images)
        loss = loss_object(labels, predictions)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))

    train_loss(loss)
    train_accuracy(labels, predictions)


# Test the model
@tf.function
def test_step(images, labels):
    predictions = model(images)
    t_loss = loss_object(labels, predictions)

    test_loss(t_loss)
    test_accuracy(labels, predictions)


# Set up summary writers to write the summaries to disk in a different logs directory
train_log_dir = '/tmp/logs/gradient_tape/train'
test_log_dir = '/tmp/logs/gradient_tape/test'
train_summary_writer = tf.summary.create_file_writer(train_log_dir)
test_summary_writer = tf.summary.create_file_writer(test_log_dir)

# Set up checkpoints manager
ckpt = tf.train.Checkpoint(step=tf.Variable(1), optimizer=optimizer, net=model)
manager = tf.train.CheckpointManager(ckpt, '/tmp/tf_ckpts', max_to_keep=3)
ckpt.restore(manager.latest_checkpoint)
if manager.latest_checkpoint:
    print("Restored from {}".format(manager.latest_checkpoint))
else:
    print("Initializing from scratch.")

# Start training
EPOCHS = 5
for epoch in range(EPOCHS):
    for images, labels in train_ds:
        train_step(images, labels)
        with train_summary_writer.as_default():
            tf.summary.scalar('loss', train_loss.result(), step=epoch)
            tf.summary.scalar('accuracy', train_accuracy.result(), step=epoch)

    ckpt.step.assign_add(1)
    if int(ckpt.step) % 1 == 0:
        save_path = manager.save()
        print("Saved checkpoint for step {}: {}".format(int(ckpt.step), save_path))

    for test_images, test_labels in test_ds:
        test_step(test_images, test_labels)
        with test_summary_writer.as_default():
            tf.summary.scalar('loss', test_loss.result(), step=epoch)
            tf.summary.scalar('accuracy', test_accuracy.result(), step=epoch)

    template = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}'
    print(template.format(epoch+1,
                          train_loss.result(),
                          train_accuracy.result()*100,
                          test_loss.result(),
                          test_accuracy.result()*100))

    # Reset the metrics for the next epoch
    train_loss.reset_states()
    train_accuracy.reset_states()
    test_loss.reset_states()
    test_accuracy.reset_states()