# -*- coding: utf-8 -*-
"""
Created on Tue Jan 15 14:59:08 2019

@author: 13236
"""

from __future__ import absolute_import, division, print_function

# Import TensorFlow >= 1.10 and enable eager execution
import tensorflow as tf
tf.enable_eager_execution()

import os
import time
import numpy as np
import glob
import matplotlib.pyplot as plt
import PIL
import imageio
from IPython import display
from plot import plot

imagenames = ['cloud', 'envelope']

LABEL_NUM = len(imagenames)
ONE_LABEL_SAMPLE = 100000 // LABEL_NUM

train_images = []
train_labels = []

for i in range(len(imagenames)):
    timg = np.load('data/' + imagenames[i] + '.npy')[:ONE_LABEL_SAMPLE]
    tlabel = [0] * LABEL_NUM
    tlabel[i] = 1
    tlabel = [tlabel] * len(timg)
    train_images.append(timg)
    train_labels += tlabel

train_images = np.vstack(train_images)
train_labels = np.array(train_labels)

#train_labels = tf.one_hot(train_labels, LABEL_NUM, 1, 0)

#train_images = np.load('data/' + 'cloud.npy')
num_total = train_images.shape[0]
train_images = train_images.reshape((num_total,28,28))

#(train_images, train_labels), (_, _) = tf.keras.datasets.mnist.load_data()

train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32')
# We are normalizing the images to the range of [-1, 1]
train_images = (train_images - 127.5) / 127.5

BUFFER_SIZE = len(train_images)
BATCH_SIZE = 256

train_dataset = tf.data.Dataset.from_tensor_slices({'img': train_images, 'label': train_labels}).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)

label_dim = 4

class Generator(tf.keras.Model):
  def __init__(self):
    super(Generator, self).__init__()
    self.fc1 = tf.keras.layers.Dense(7*7*64, use_bias=False)
    self.fc_label = tf.keras.layers.Dense(7*7*label_dim, use_bias=False)
    self.batchnorm1 = tf.keras.layers.BatchNormalization()
    
    self.conv1 = tf.keras.layers.Conv2DTranspose(64, (5, 5), strides=(1, 1), padding='same', use_bias=False)
    self.convlabel = tf.keras.layers.Conv2DTranspose(label_dim, (5, 5), strides=(1, 1), padding='same', use_bias=False)
    self.batchnorm2 = tf.keras.layers.BatchNormalization()
    
    self.conv2 = tf.keras.layers.Conv2DTranspose(32, (5, 5), strides=(2, 2), padding='same', use_bias=False)
    self.batchnorm3 = tf.keras.layers.BatchNormalization()
    
    self.conv3 = tf.keras.layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False)

  def call(self, x, label, training=True):
    label = tf.cast(label, dtype=tf.float32)
    #print(x.shape, label.shape) 
    x = self.fc1(x) #(256, 100)

    label = self.fc_label(label)
    #print(x.shape)
    x = self.batchnorm1(x, training=training) 
    #label = self.batchnorm1(label, training=training) 
    #print(x.shape)
    x = tf.nn.relu(x)
    #print(x.shape)

    x = tf.reshape(x, shape=(-1, 7, 7, 64))
    label = tf.reshape(label, shape=(-1, 7, 7, label_dim))
    #print(x.shape)

    x = self.conv1(x)
    label = self.convlabel(label)
    #print(x.shape)
    x = self.batchnorm2(x, training=training)
    #print(x.shape)
    x = tf.nn.relu(x)
    #print(x.shape)
    x = tf.concat([x, label], 3) #[256,7,7,64*2]

    x = self.conv2(x)
    #label = self.conv2(label)
    #print(x.shape)
    x = self.batchnorm3(x, training=training)
    #print(x.shape)
    x = tf.nn.relu(x)
    #print(x.shape)

    x = tf.nn.tanh(self.conv3(x))  
    #print(x.shape)
    
    #raise(Exception('pass'))
    return x

class Discriminator(tf.keras.Model):
  def __init__(self):
    super(Discriminator, self).__init__()
    self.conv1 = tf.keras.layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same')
    self.convlabel = tf.keras.layers.Conv2D(label_dim, (5, 5), strides=(2, 2), padding='same')
    self.conv2 = tf.keras.layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same')
    self.dropout = tf.keras.layers.Dropout(0.3)
    self.flatten = tf.keras.layers.Flatten()
    self.fc1 = tf.keras.layers.Dense(1)
    self.fc2 = tf.keras.layers.Dense(28*28)

  def call(self, x, label, training=True):
    label = tf.cast(label, dtype = tf.float32)
    label = self.fc2(label)
    label = tf.reshape(label, shape=(-1, 28, 28, 1))

    x = tf.nn.leaky_relu(self.conv1(x))
    label = self.convlabel(label)
    x = tf.concat([x,label], 3)
    
    x = self.dropout(x, training=training)
    x = tf.nn.leaky_relu(self.conv2(x))
    x = self.dropout(x, training=training)
    x = self.flatten(x)
    x = self.fc1(x)
    return x

generator = Generator()
discriminator = Discriminator()

# Defun gives 10 secs/epoch performance boost
generator.call = tf.contrib.eager.defun(generator.call)
discriminator.call = tf.contrib.eager.defun(discriminator.call)

def discriminator_loss(real_output, generated_output):
    # [1,1,...,1] with real output since it is true and we want
    # our generated examples to look like it
    real_loss = tf.losses.sigmoid_cross_entropy(multi_class_labels=tf.ones_like(real_output), logits=real_output)
    #print(real_output)
    #print(real_output.shape)
    #raise Exception('pass')
    
    # [0,0,...,0] with generated images since they are fake
    generated_loss = tf.losses.sigmoid_cross_entropy(multi_class_labels=tf.zeros_like(generated_output), logits=generated_output)

    total_loss = real_loss + generated_loss

    return real_loss, generated_loss

def generator_loss(generated_output):
    return tf.losses.sigmoid_cross_entropy(tf.ones_like(generated_output), generated_output)

discriminator_optimizer = tf.train.AdamOptimizer(1e-4)
generator_optimizer = tf.train.AdamOptimizer(1e-4)

checkpoint_dir = './training_checkpoints'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,
                                 discriminator_optimizer=discriminator_optimizer,
                                 generator=generator,
                                 discriminator=discriminator)
#-------------
# model restore
if os.path.exists('training_checkpoints'):
    
    print('restoring model')
    # restoring the latest checkpoint in checkpoint_dir
    checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))

else:
    print('first time to run')
#-------------

EPOCHS = 150000
noise_dim = 100
num_examples_to_generate = 16

# keeping the random vector constant for generation (prediction) so
# it will be easier to see the improvement of the gan.
random_vector_for_generation = tf.random_normal([num_examples_to_generate,
                                                 noise_dim])
random_label_for_generation = []
for i in range(num_examples_to_generate):
  random_label_for_generation.append(i % LABEL_NUM)
random_label_for_generation = tf.constant(random_label_for_generation)
random_label_for_generation = tf.one_hot(random_label_for_generation, LABEL_NUM, 1, 0)

def generate_and_save_images(model, epoch, test_input, test_label):
  # make sure the training parameter is set to False because we
  # don't want to train the batchnorm layer when doing inference.
  predictions = model(test_input, test_label, training=False)

  fig = plt.figure(figsize=(4,4))
  
  for i in range(predictions.shape[0]):
      plt.subplot(4, 4, i+1)
      plt.imshow(predictions[i, :, :, 0] * 127.5 + 127.5, cmap='gray')
      plt.axis('off')
        
  plt.savefig('imgs/image_at_epoch_{:04d}.png'.format(epoch))
  plt.show()

GEN_TIME = 1
DISC_TIME = 1
   
def train(dataset, epochs, noise_dim):  

  if os.path.exists('gen_loss_record.txt'): ###
      with open('gen_loss_record.txt','r') as f1:
          gen_loss_record = eval(f1.read())
      with open('disc_loss_record.txt','r') as f2:
          disc_loss_record = eval(f2.read())
  else: gen_loss_record, disc_loss_record = [], []
  
  for epoch in range(epochs):
    start = time.time()
    
    index = 0
    for onedata in dataset:
      images = onedata['img']
      labels = onedata['label']
      '''
      print(images.shape, labels.shape)
      for i in range(5):
        print(labels[i])
      '''
      
      gen_time = GEN_TIME
      disc_time = DISC_TIME

      # generating noise from a uniform distribution
      noise = tf.random_normal([labels.shape[0], noise_dim])
      for tot_time in range(999999):
        with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
          #print(images.shape, labels.shape)
          generated_images = generator(noise, labels, training=True)
          generated_output = discriminator(generated_images, labels, training=True)
          gen_loss = generator_loss(generated_output)
          gen_loss_record.append(gen_loss.numpy())
          if disc_time > 0:
            real_output = discriminator(images, labels, training=True)
            [real_loss, fake_loss] = discriminator_loss(real_output, generated_output)
            disc_loss = real_loss + fake_loss
            disc_loss_record.append((disc_loss).numpy())
        if gen_time > 0:
          gradients_of_generator = gen_tape.gradient(gen_loss, generator.variables)
          generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.variables))
          gen_time -= 1

        if disc_time > 0:
          gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.variables)
          discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.variables))
          disc_time -= 1

        if gen_time == 0 and disc_time == 0:
          break
 
      
      index += 1
      if index%20 == 0: 
          print('epoch={}, gen_loss={:.6f}, disc_loss = {:.6f}, real_loss={:.6f}, fake_loss={:.6f}'.format(epoch, gen_loss, disc_loss, real_loss, fake_loss))
      
    if epoch:
      #display.clear_output(wait=True)
      plot(epoch, gen_loss_record, 'gen_loss_record')
      plot(epoch, disc_loss_record, 'disc_loss_record')
      generate_and_save_images(generator,
                               epoch + 1,
                               random_vector_for_generation,
                               random_label_for_generation)
      
    
    # saving (checkpoint) the model every 15 epochs
    if (epoch + 1) % 15 == 0:
      checkpoint.save(file_prefix = checkpoint_prefix)
      with open('gen_loss_record.txt','w') as f1: ###
          f1.write(str(gen_loss_record))  
      with open('disc_loss_record.txt','w') as f2:
          f2.write(str(disc_loss_record))  
    
    print ('Time taken for epoch {} is {} sec'.format(epoch,
                                                      time.time()-start))
  # generating after the final epoch
  display.clear_output(wait=True)
  generate_and_save_images(generator,
                           epochs,
                           random_vector_for_generation,
                           random_label_for_generation)
  
train(train_dataset, EPOCHS, noise_dim)

def display_image(epoch_no):
  return PIL.Image.open('imgs/image_at_epoch_{:04d}.png'.format(epoch_no))

display_image(EPOCHS)

with imageio.get_writer('dcgan.gif', mode='I') as writer:
  filenames = glob.glob('imgs/image*.png')
  filenames = sorted(filenames)
  last = -1
  for i,filename in enumerate(filenames):
    frame = 2*(i**0.5)
    if round(frame) > round(last):
      last = frame
    else:
      continue
    image = imageio.imread(filename)
    writer.append_data(image)
  image = imageio.imread(filename)
  writer.append_data(image)
    
# this is a hack to display the gif inside the notebook
os.system('cp dcgan.gif dcgan.gif.png')

display.Image(filename="dcgan.gif.png")
