"""TensorFlow 2.0 implementation of vanilla Autoencoder."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

__version__ = '0.0.1'
__author__ = 'Abien Fred Agarap'

import numpy as np
import tensorflow as tf

tf.random.set_seed(1)
batch_size = 128
epochs = 10
learning_rate = 1e-3
momentum = 9e-1
intermediate_dim = 64
original_dim = 784

(training_features, _), _ = tf.keras.datasets.mnist.load_data()
training_features = training_features / np.max(training_features)
training_features = training_features.reshape(training_features.shape[0],
                                              training_features.shape[1] * training_features.shape[2]).astype(np.float32)
training_dataset = tf.data.Dataset.from_tensor_slices(training_features).batch(batch_size)


class Encoder(tf.keras.layers.Layer):
  def __init__(self, intermediate_dim):
    super(Encoder, self).__init__()
    self.hidden_layer = tf.keras.layers.Dense(units=intermediate_dim, activation=tf.nn.relu)
    self.output_layer = tf.keras.layers.Dense(units=intermediate_dim, activation=tf.nn.relu)

  def call(self, input_features):
    activation = self.hidden_layer(input_features)
    return self.output_layer(activation)

class Decoder(tf.keras.layers.Layer):
  def __init__(self, intermediate_dim, original_dim):
    super(Decoder, self).__init__()
    self.hidden_layer = tf.keras.layers.Dense(units=intermediate_dim, activation=tf.nn.relu)
    self.output_layer = tf.keras.layers.Dense(units=original_dim, activation=tf.nn.relu)

  def call(self, code):
    activation = self.hidden_layer(code)
    return self.output_layer(activation)

class Autoencoder(tf.keras.Model):
  def __init__(self, intermediate_dim, original_dim):
    super(Autoencoder, self).__init__()
    self.encoder = Encoder(intermediate_dim=intermediate_dim)
    self.decoder = Decoder(intermediate_dim=intermediate_dim, original_dim=original_dim)

  def call(self, input_features):
    code = self.encoder(input_features)
    reconstructed = self.decoder(code)
    return reconstructed

autoencoder = Autoencoder(intermediate_dim=intermediate_dim, original_dim=original_dim)
opt = tf.optimizers.SGD(learning_rate=learning_rate, momentum=momentum)

def loss(model, original):
  reconstruction_error = tf.reduce_mean(tf.square(tf.subtract(model(original), original)))
  return reconstruction_error

def train(loss, model, opt, original):
  with tf.GradientTape() as tape:
    gradients = tape.gradient(loss(model, original), model.trainable_variables)
    gradient_variables = zip(gradients, model.trainable_variables)
    opt.apply_gradients(gradient_variables)

writer = tf.summary.create_file_writer('tmp')

with writer.as_default():
  with tf.summary.record_if(True):
    for epoch in range(epochs):
      for step, batch_features in enumerate(training_dataset):
        train(loss, autoencoder, opt, batch_features)
        loss_values = loss(autoencoder, batch_features)
        original = tf.reshape(batch_features, (batch_features.shape[0], 28, 28, 1))
        reconstructed = tf.reshape(autoencoder(tf.constant(batch_features)), (batch_features.shape[0], 28, 28, 1))
        tf.summary.scalar('loss', loss_values, step=step)
        tf.summary.image('original', original, max_outputs=10, step=step)
        tf.summary.image('reconstructed', reconstructed, max_outputs=10, step=step)