import keras
import numpy as np
#from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Lambda,Dense,Input
import tensorflow.keras.backend as K

def VAE():
    m=50
    #Dimension of latent variables(n_z) 
    n_z=2
    epochs=100

    inputs=Input(shape=(784,))
    h1=Dense(512,activation='relu')(inputs)
    mean=Dense(n_z,activation='linear')(h1)
    variance=Dense(n_z,activation='linear')(h1)
    mean
    inputs

    def sampling_z(args):
        u,log_sigma=args
        eps = K.random_normal(shape=(m, n_z,),mean=0,stddev=1)
        print(eps)
        return u+K.exp(log_sigma)*eps

    z=Lambda(sampling_z)([mean,variance])
    #sampling_z([mean,variance])

    

    decoder_hidden=Dense(512,activation='relu')
    decoder_out=Dense(784,activation='sigmoid')

    h_p = decoder_hidden(z)
    outputs = decoder_out(h_p)

    #The three models for implementation of VAEs in Keras

    #Training Model
    vae=Model(inputs,outputs)

    #Encoder
    encoder=Model(inputs,mean)



    #Decoder
    d_in = Input(shape=(n_z,))
    d_h = decoder_hidden(d_in)
    d_out=decoder_out(d_h)
    decoder=Model(d_in,d_out)



    def vae_loss(y_true, y_pred):
        """ Calculate loss = reconstruction loss + KL loss for each data in minibatch """
        # E[log P(X|z)]
        #print(y_true.shape,y_pred.shape)
        recon = K.sum(K.square(y_pred-y_true), axis=1)
        # D_KL(Q(z|X) || P(z|X)); calculate in closed form as both dist. are Gaussian
        kl = 0.5 * K.sum(K.exp(variance) + K.square(mean) - 1. - variance, axis=1)
        #print(kl)
        #recon= K.sum(K.square(y_pred-y_true))
            # compute the KL loss
        #kl= - 0.5 * K.sum(1 + variance - K.square(mean) - K.exp(variance), axis=-1)
        return K.mean(recon + kl)

    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
    x_train[0].shape
    x_train=x_train.reshape(60000,784)
    x_train[50].shape

    vae.compile(optimizer='adam',loss=vae_loss,metrics=['accuracy'])
    vae.fit(x_train, x_train, batch_size=m, epochs=20)

    x_test=x_test.reshape(10000,784)

    pred=vae.predict(x_train,batch_size=m)


    import matplotlib.pyplot as plt
    pred=pred.reshape(60000,28,28)
    x_train=x_train.reshape(60000,28,28)
    



    
    n = 30
    digit_size = 28
    figure = np.zeros((digit_size * n, digit_size * n))
    grid_x = np.linspace(-4, 4, n)
    grid_y = np.linspace(-4, 4, n)[::-1]

    for i, yi in enumerate(grid_y):
        for j, xi in enumerate(grid_x):
            z_sample = np.array([[xi, yi]])
            x_decoded = decoder.predict(z_sample)
            digit = x_decoded[0].reshape(digit_size, digit_size)
            figure[i * digit_size: (i + 1) * digit_size,
                   j * digit_size: (j + 1) * digit_size] = digit

    plt.figure(figsize=(10, 10))
    start_range = digit_size // 2
    end_range = n * digit_size + start_range + 1
    pixel_range = np.arange(start_range, end_range, digit_size)
    sample_range_x = np.round(grid_x, 1)
    sample_range_y = np.round(grid_y, 1)
    plt.xticks(pixel_range, sample_range_x)
    plt.yticks(pixel_range, sample_range_y)
    plt.xlabel("z[0]")
    plt.ylabel("z[1]")
    plt.imshow(figure, cmap='Greys_r')

    x_test_encoded = encoder.predict(x_test, batch_size=m)
    plt.figure(figsize=(6, 6))
    plt.scatter(x_test_encoded[: ,0], x_test_encoded[: ,1], c=y_test)
    plt.colorbar()
    plt.show()

    n = 15  # figure with 15x15 digits
    digit_size = 28
    figure = np.zeros((digit_size * n, digit_size * n))
    # we will sample n points within [-15, 15] standard deviations
    grid_x = np.linspace(-15, 15, n)
    grid_y = np.linspace(-15, 15, n)

    for i, yi in enumerate(grid_x):
        for j, xi in enumerate(grid_y):
            z_sample = np.array([[xi, yi]]) * 1
            x_decoded = decoder.predict(z_sample)
            digit = x_decoded[0].reshape(digit_size, digit_size)
            figure[i * digit_size: (i + 1) * digit_size,
                   j * digit_size: (j + 1) * digit_size] = digit

    plt.figure(figsize=(10, 10))
    plt.imshow(figure,cmap='gray')
    plt.show()
