#Import the libraries we will need.
import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
import matplotlib.pyplot as plt
import tensorflow.contrib.slim as slim
import os
import scipy.misc
import scipy

"""
Explanation: InfoGAN Tutorial
This tutorials walks through an implementation of InfoGAN as described in InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets.
To learn more about InfoGAN, see this Medium post on them. To lean more about GANs generally, see this one.
End of explanation
"""


mnist = input_data.read_data_sets("MNIST_data/", one_hot=False)

"""
Explanation: Load the MNIST dataset.
End of explanation
"""


#This function performns a leaky relu activation, which is needed for the discriminator network.
def lrelu(x, leak=0.2, name="lrelu"):
     with tf.variable_scope(name):
         f1 = 0.5 * (1 + leak)
         f2 = 0.5 * (1 - leak)
         return f1 * x + f2 * abs(x)
    
#The below functions are taken from carpdem20's implementation https://github.com/carpedm20/DCGAN-tensorflow
#They allow for saving sample images from the generator to follow progress
def save_images(images, size, image_path):
    return imsave(inverse_transform(images), size, image_path)

def imsave(images, size, path):
    return scipy.misc.imsave(path, merge(images, size))

def inverse_transform(images):
    return (images+1.)/2.

def merge(images, size):
    h, w = images.shape[1], images.shape[2]
    img = np.zeros((h * size[0], w * size[1]))

    for idx, image in enumerate(images):
        i = idx % size[1]
        j = idx // size[1]
        img[j*h:j*h+h, i*w:i*w+w] = image

    return img

"""
Explanation: Helper Functions
End of explanation
"""


def generator(z):
    
    zP = slim.fully_connected(z,4*4*256,normalizer_fn=slim.batch_norm,\
        activation_fn=tf.nn.relu,scope='g_project',weights_initializer=initializer)
    zCon = tf.reshape(zP,[-1,4,4,256])
    
    gen1 = slim.convolution2d(\
        zCon,num_outputs=128,kernel_size=[3,3],\
        padding="SAME",normalizer_fn=slim.batch_norm,\
        activation_fn=tf.nn.relu,scope='g_conv1', weights_initializer=initializer)
    gen1 = tf.depth_to_space(gen1,2)
    
    gen2 = slim.convolution2d(\
        gen1,num_outputs=64,kernel_size=[3,3],\
        padding="SAME",normalizer_fn=slim.batch_norm,\
        activation_fn=tf.nn.relu,scope='g_conv2', weights_initializer=initializer)
    gen2 = tf.depth_to_space(gen2,2)
    
    gen3 = slim.convolution2d(\
        gen2,num_outputs=32,kernel_size=[3,3],\
        padding="SAME",normalizer_fn=slim.batch_norm,\
        activation_fn=tf.nn.relu,scope='g_conv3', weights_initializer=initializer)
    gen3 = tf.depth_to_space(gen3,2)
    
    g_out = slim.convolution2d(\
        gen3,num_outputs=1,kernel_size=[32,32],padding="SAME",\
        biases_initializer=None,activation_fn=tf.nn.tanh,\
        scope='g_out', weights_initializer=initializer)
    
    return g_out

"""
Explanation: Defining the Adversarial Networks
Generator Network
The generator takes a vector of random numbers and transforms it into a 32x32 image. Each layer in the network involves a strided  transpose convolution, batch normalization, and rectified nonlinearity. Tensorflow's slim library allows us to easily define each of these layers.
End of explanation
"""


def discriminator(bottom, cat_list,conts, reuse=False):
    
    dis1 = slim.convolution2d(bottom,32,[3,3],padding="SAME",\
        biases_initializer=None,activation_fn=lrelu,\
        reuse=reuse,scope='d_conv1',weights_initializer=initializer)
    dis1 = tf.space_to_depth(dis1,2)
    
    dis2 = slim.convolution2d(dis1,64,[3,3],padding="SAME",\
        normalizer_fn=slim.batch_norm,activation_fn=lrelu,\
        reuse=reuse,scope='d_conv2', weights_initializer=initializer)
    dis2 = tf.space_to_depth(dis2,2)
    
    dis3 = slim.convolution2d(dis2,128,[3,3],padding="SAME",\
        normalizer_fn=slim.batch_norm,activation_fn=lrelu,\
        reuse=reuse,scope='d_conv3',weights_initializer=initializer)
    dis3 = tf.space_to_depth(dis3,2)
        
    dis4 = slim.fully_connected(slim.flatten(dis3),1024,activation_fn=lrelu,\
        reuse=reuse,scope='d_fc1', weights_initializer=initializer)
        
    d_out = slim.fully_connected(dis4,1,activation_fn=tf.nn.sigmoid,\
        reuse=reuse,scope='d_out', weights_initializer=initializer)
    
    q_a = slim.fully_connected(dis4,128,normalizer_fn=slim.batch_norm,\
        reuse=reuse,scope='q_fc1', weights_initializer=initializer)
    
    
    ## Here we define the unique layers used for the q-network. The number of outputs depends on the number of 
    ## latent variables we choose to define.
    q_cat_outs = []
    for idx,var in enumerate(cat_list):
        q_outA = slim.fully_connected(q_a,var,activation_fn=tf.nn.softmax,\
            reuse=reuse,scope='q_out_cat_'+str(idx), weights_initializer=initializer)
        q_cat_outs.append(q_outA)
    
    q_cont_outs = None
    if conts > 0:
        q_cont_outs = slim.fully_connected(q_a,conts,activation_fn=tf.nn.tanh,\
            reuse=reuse,scope='q_out_cont_'+str(conts), weights_initializer=initializer)
    
    return d_out,q_cat_outs,q_cont_outs

"""
Explanation: Discriminator Network
The discriminator network takes as input a 32x32 image and transforms it into a single valued probability of being generated from real-world data. Again we use tf.slim to define the convolutional layers, batch normalization, and weight initialization.
End of explanation
"""


tf.reset_default_graph()

z_size = 64 #Size of initial z vector used for generator.

# Define latent variables.
categorical_list = [10] # Each entry in this list defines a categorical variable of a specific size.
number_continuous = 2 # The number of continous variables.

#This initializaer is used to initialize all the weights of the network.
initializer = tf.truncated_normal_initializer(stddev=0.02)

#These placeholders are used for input into the generator and discriminator, respectively.
z_in = tf.placeholder(shape=[None,z_size],dtype=tf.float32) #Random vector
real_in = tf.placeholder(shape=[None,32,32,1],dtype=tf.float32) #Real images

#These placeholders load the latent variables.
latent_cat_in = tf.placeholder(shape=[None,len(categorical_list)],dtype=tf.int32)
latent_cat_list = tf.split(latent_cat_in,len(categorical_list),1)
latent_cont_in = tf.placeholder(shape=[None,number_continuous],dtype=tf.float32)

oh_list = []
for idx,var in enumerate(categorical_list):
    latent_oh = tf.one_hot(tf.reshape(latent_cat_list[idx],[-1]),var)
    oh_list.append(latent_oh)

#Concatenate all c and z variables.
z_lats = oh_list[:]
z_lats.append(z_in)
z_lats.append(latent_cont_in)
z_lat = tf.concat(z_lats,1)


Gz = generator(z_lat) #Generates images from random z vectors
Dx,_,_ = discriminator(real_in,categorical_list,number_continuous) #Produces probabilities for real images
Dg,QgCat,QgCont = discriminator(Gz,categorical_list,number_continuous,reuse=True) #Produces probabilities for generator images

#These functions together define the optimization objective of the GAN.
d_loss = -tf.reduce_mean(tf.log(Dx) + tf.log(1.-Dg)) #This optimizes the discriminator.
g_loss = -tf.reduce_mean(tf.log((Dg/(1-Dg)))) #KL Divergence optimizer

#Combine losses for each of the categorical variables.
cat_losses = []
for idx,latent_var in enumerate(oh_list):
    cat_loss = -tf.reduce_sum(latent_var*tf.log(QgCat[idx]),axis=1)
    cat_losses.append(cat_loss)
    
#Combine losses for each of the continous variables.
if number_continuous > 0:
    q_cont_loss = tf.reduce_sum(0.5 * tf.square(latent_cont_in - QgCont),axis=1)
else:
    q_cont_loss = tf.constant(0.0)

q_cont_loss = tf.reduce_mean(q_cont_loss)
q_cat_loss = tf.reduce_mean(cat_losses)
q_loss = tf.add(q_cat_loss,q_cont_loss)
tvars = tf.trainable_variables()

#The below code is responsible for applying gradient descent to update the GAN.
trainerD = tf.train.AdamOptimizer(learning_rate=0.0002,beta1=0.5)
trainerG = tf.train.AdamOptimizer(learning_rate=0.002,beta1=0.5)
trainerQ = tf.train.AdamOptimizer(learning_rate=0.0002,beta1=0.5)
d_grads = trainerD.compute_gradients(d_loss,tvars[9:-2-((number_continuous>0)*2)-(len(categorical_list)*2)]) #Only update the weights for the discriminator network.
g_grads = trainerG.compute_gradients(g_loss, tvars[0:9]) #Only update the weights for the generator network.
q_grads = trainerQ.compute_gradients(q_loss, tvars) 

update_D = trainerD.apply_gradients(d_grads)
update_G = trainerG.apply_gradients(g_grads)
update_Q = trainerQ.apply_gradients(q_grads)

"""
Explanation: Connecting them together
End of explanation
"""


batch_size = 64 #Size of image batch to apply at each iteration.
iterations = 500000 #Total number of iterations to use.
sample_directory = './figsTut' #Directory to save sample images from generator in.
model_directory = './models' #Directory to save trained model to.

init = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as sess:  
    sess.run(init)
    for i in range(iterations):
        zs = np.random.uniform(-1.0,1.0,size=[batch_size,z_size]).astype(np.float32) #Generate a random z batch
        lcat = np.random.randint(0,10,[batch_size,len(categorical_list)]) #Generate random c batch
        lcont = np.random.uniform(-1,1,[batch_size,number_continuous]) #
        
        xs,_ = mnist.train.next_batch(batch_size) #Draw a sample batch from MNIST dataset.
        xs = (np.reshape(xs,[batch_size,28,28,1]) - 0.5) * 2.0 #Transform it to be between -1 and 1
        xs = np.lib.pad(xs, ((0,0),(2,2),(2,2),(0,0)),'constant', constant_values=(-1, -1)) #Pad the images so the are 32x32
        
        _,dLoss = sess.run([update_D,d_loss],feed_dict={z_in:zs,real_in:xs,latent_cat_in:lcat,latent_cont_in:lcont}) #Update the discriminator
        _,gLoss = sess.run([update_G,g_loss],feed_dict={z_in:zs,latent_cat_in:lcat,latent_cont_in:lcont}) #Update the generator, twice for good measure.
        _,qLoss,qK,qC = sess.run([update_Q,q_loss,q_cont_loss,q_cat_loss],feed_dict={z_in:zs,latent_cat_in:lcat,latent_cont_in:lcont}) #Update to optimize mutual information.
        if i % 100 == 0:
            print "Gen Loss: " + str(gLoss) + " Disc Loss: " + str(dLoss) + " Q Losses: " + str([qK,qC])
            z_sample = np.random.uniform(-1.0,1.0,size=[100,z_size]).astype(np.float32) #Generate another z batch
            lcat_sample = np.reshape(np.array([e for e in range(10) for _ in range(10)]),[100,1])
            a = a = np.reshape(np.array([[(e/4.5 - 1.)] for e in range(10) for _ in range(10)]),[10,10]).T
            b = np.reshape(a,[100,1])
            c = np.zeros_like(b)
            lcont_sample = np.hstack([b,c])
            samples = sess.run(Gz,feed_dict={z_in:z_sample,latent_cat_in:lcat_sample,latent_cont_in:lcont_sample}) #Use new z to get sample images from generator.
            if not os.path.exists(sample_directory):
                os.makedirs(sample_directory)
            #Save sample generator images for viewing training progress.
            save_images(np.reshape(samples[0:100],[100,32,32]),[10,10],sample_directory+'/fig'+str(i)+'.png')
        if i % 1000 == 0 and i != 0:
            if not os.path.exists(model_directory):
                os.makedirs(model_directory)
            saver.save(sess,model_directory+'/model-'+str(i)+'.cptk')
            print "Saved Model"

"""
Explanation: Training the network
Now that we have fully defined our network, it is time to train it!
End of explanation
"""


sample_directory = './figsTut' #Directory to save sample images from generator in.
model_directory = './models' #Directory to load trained model from.

init = tf.initialize_all_variables()
saver = tf.train.Saver()
with tf.Session() as sess:  
    sess.run(init)
    #Reload the model.
    print 'Loading Model...'
    ckpt = tf.train.get_checkpoint_state(model_directory)
    saver.restore(sess,ckpt.model_checkpoint_path)
    
    z_sample = np.random.uniform(-1.0,1.0,size=[100,z_size]).astype(np.float32) #Generate another z batch
    lcat_sample = np.reshape(np.array([e for e in range(10) for _ in range(10)]),[100,1])
    a = a = np.reshape(np.array([[(e/4.5 - 1.)] for e in range(10) for _ in range(10)]),[10,10]).T
    b = np.reshape(a,[100,1])
    c = np.zeros_like(b)
    lcont_sample = np.hstack([b,c])
    samples = sess.run(Gz,feed_dict={z_in:z_sample,latent_cat_in:lcat_sample,latent_cont_in:lcont_sample}) #Use new z to get sample images from generator.
    if not os.path.exists(sample_directory):
        os.makedirs(sample_directory)
    #Save sample generator images for viewing training progress.
    save_images(np.reshape(samples[0:100],[100,32,32]),[10,10],sample_directory+'/fig_test'+'.png')

"""
Explanation: Using a trained network
Once we have a trained model saved, we may want to use it to generate new images, and explore the representation it has learned.
End of explanation
"""


