import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
from sklearn.utils import shuffle
import input_data

random_numer = 42

np.random.seed(random_numer)


def ReLu(x):
    mask = (x > 0) * 1.0
    return mask * x


def d_ReLu(x):
    mask = (x > 0) * 1.0
    return mask


def arctan(x):
    return np.arctan(x)


def d_arctan(x):
    return 1 / (1 + x ** 2)


def log(x):
    return 1 / (1 + np.exp(-1 * x))


def d_log(x):
    return log(x) * (1 - log(x))


def tanh(x):
    return np.tanh(x)


def d_tanh(x):
    return 1 - np.tanh(x) ** 2


def plot(samples):
    fig = plt.figure(figsize=(4, 4))
    gs = gridspec.GridSpec(4, 4)
    gs.update(wspace=0.05, hspace=0.05)

    for i, sample in enumerate(samples):
        ax = plt.subplot(gs[i])
        plt.axis("off")
        ax.set_xticklabels([])
        ax.set_yticklabels([])
        ax.set_aspect("equal")
        plt.imshow(sample.reshape(28, 28), cmap="Greys_r")

    return fig


# 1. Load Data and declare hyper
print("--------- Load Data ----------")
mnist = input_data.read_data_sets("MNIST_data", one_hot=False)
temp = mnist.test
images, labels = temp.images, temp.labels
images, labels = shuffle(np.asarray(images), np.asarray(labels))
num_epoch = 10
learing_rate = 0.00009
G_input = 100
hidden_input, hidden_input2, hidden_input3 = 128, 256, 346
hidden_input4, hidden_input5, hidden_input6 = 480, 560, 686


print("--------- Declare Hyper Parameters ----------")
# 2. Declare Weights
D_W1 = (
    np.random.normal(size=(784, hidden_input), scale=(1.0 / np.sqrt(784 / 2.0))) * 0.002
)
# D_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))       *0.002
D_b1 = np.zeros(hidden_input)

D_W2 = (
    np.random.normal(size=(hidden_input, 1), scale=(1.0 / np.sqrt(hidden_input / 2.0)))
    * 0.002
)
# D_b2 = np.random.normal(size=(1),scale=(1. / np.sqrt(1 / 2.)))           *0.002
D_b2 = np.zeros(1)


G_W1 = (
    np.random.normal(size=(G_input, hidden_input), scale=(1.0 / np.sqrt(G_input / 2.0)))
    * 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002
G_b1 = np.zeros(hidden_input)

G_W2 = (
    np.random.normal(
        size=(hidden_input, hidden_input2), scale=(1.0 / np.sqrt(hidden_input / 2.0))
    )
    * 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002
G_b2 = np.zeros(hidden_input2)

G_W3 = (
    np.random.normal(
        size=(hidden_input2, hidden_input3), scale=(1.0 / np.sqrt(hidden_input2 / 2.0))
    )
    * 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002
G_b3 = np.zeros(hidden_input3)

G_W4 = (
    np.random.normal(
        size=(hidden_input3, hidden_input4), scale=(1.0 / np.sqrt(hidden_input3 / 2.0))
    )
    * 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002
G_b4 = np.zeros(hidden_input4)

G_W5 = (
    np.random.normal(
        size=(hidden_input4, hidden_input5), scale=(1.0 / np.sqrt(hidden_input4 / 2.0))
    )
    * 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002
G_b5 = np.zeros(hidden_input5)

G_W6 = (
    np.random.normal(
        size=(hidden_input5, hidden_input6), scale=(1.0 / np.sqrt(hidden_input5 / 2.0))
    )
    * 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002
G_b6 = np.zeros(hidden_input6)

G_W7 = (
    np.random.normal(
        size=(hidden_input6, 784), scale=(1.0 / np.sqrt(hidden_input6 / 2.0))
    )
    * 0.002
)
# G_b2 = np.random.normal(size=(784),scale=(1. / np.sqrt(784 / 2.)))      *0.002
G_b7 = np.zeros(784)

# 3. For Adam Optimzier
v1, m1 = 0, 0
v2, m2 = 0, 0
v3, m3 = 0, 0
v4, m4 = 0, 0

v5, m5 = 0, 0
v6, m6 = 0, 0
v7, m7 = 0, 0
v8, m8 = 0, 0
v9, m9 = 0, 0
v10, m10 = 0, 0
v11, m11 = 0, 0
v12, m12 = 0, 0

v13, m13 = 0, 0
v14, m14 = 0, 0

v15, m15 = 0, 0
v16, m16 = 0, 0

v17, m17 = 0, 0
v18, m18 = 0, 0


beta_1, beta_2, eps = 0.9, 0.999, 0.00000001

print("--------- Started Training ----------")
for iter in range(num_epoch):

    random_int = np.random.randint(len(images) - 5)
    current_image = np.expand_dims(images[random_int], axis=0)

    # Func: Generate The first Fake Data
    Z = np.random.uniform(-1.0, 1.0, size=[1, G_input])
    Gl1 = Z.dot(G_W1) + G_b1
    Gl1A = arctan(Gl1)
    Gl2 = Gl1A.dot(G_W2) + G_b2
    Gl2A = ReLu(Gl2)
    Gl3 = Gl2A.dot(G_W3) + G_b3
    Gl3A = arctan(Gl3)

    Gl4 = Gl3A.dot(G_W4) + G_b4
    Gl4A = ReLu(Gl4)
    Gl5 = Gl4A.dot(G_W5) + G_b5
    Gl5A = tanh(Gl5)
    Gl6 = Gl5A.dot(G_W6) + G_b6
    Gl6A = ReLu(Gl6)
    Gl7 = Gl6A.dot(G_W7) + G_b7

    current_fake_data = log(Gl7)

    # Func: Forward Feed for Real data
    Dl1_r = current_image.dot(D_W1) + D_b1
    Dl1_rA = ReLu(Dl1_r)
    Dl2_r = Dl1_rA.dot(D_W2) + D_b2
    Dl2_rA = log(Dl2_r)

    # Func: Forward Feed for Fake Data
    Dl1_f = current_fake_data.dot(D_W1) + D_b1
    Dl1_fA = ReLu(Dl1_f)
    Dl2_f = Dl1_fA.dot(D_W2) + D_b2
    Dl2_fA = log(Dl2_f)

    # Func: Cost D
    D_cost = -np.log(Dl2_rA) + np.log(1.0 - Dl2_fA)

    # Func: Gradient
    grad_f_w2_part_1 = 1 / (1.0 - Dl2_fA)
    grad_f_w2_part_2 = d_log(Dl2_f)
    grad_f_w2_part_3 = Dl1_fA
    grad_f_w2 = grad_f_w2_part_3.T.dot(grad_f_w2_part_1 * grad_f_w2_part_2)
    grad_f_b2 = grad_f_w2_part_1 * grad_f_w2_part_2

    grad_f_w1_part_1 = (grad_f_w2_part_1 * grad_f_w2_part_2).dot(D_W2.T)
    grad_f_w1_part_2 = d_ReLu(Dl1_f)
    grad_f_w1_part_3 = current_fake_data
    grad_f_w1 = grad_f_w1_part_3.T.dot(grad_f_w1_part_1 * grad_f_w1_part_2)
    grad_f_b1 = grad_f_w1_part_1 * grad_f_w1_part_2

    grad_r_w2_part_1 = -1 / Dl2_rA
    grad_r_w2_part_2 = d_log(Dl2_r)
    grad_r_w2_part_3 = Dl1_rA
    grad_r_w2 = grad_r_w2_part_3.T.dot(grad_r_w2_part_1 * grad_r_w2_part_2)
    grad_r_b2 = grad_r_w2_part_1 * grad_r_w2_part_2

    grad_r_w1_part_1 = (grad_r_w2_part_1 * grad_r_w2_part_2).dot(D_W2.T)
    grad_r_w1_part_2 = d_ReLu(Dl1_r)
    grad_r_w1_part_3 = current_image
    grad_r_w1 = grad_r_w1_part_3.T.dot(grad_r_w1_part_1 * grad_r_w1_part_2)
    grad_r_b1 = grad_r_w1_part_1 * grad_r_w1_part_2

    grad_w1 = grad_f_w1 + grad_r_w1
    grad_b1 = grad_f_b1 + grad_r_b1

    grad_w2 = grad_f_w2 + grad_r_w2
    grad_b2 = grad_f_b2 + grad_r_b2

    # ---- Update Gradient ----
    m1 = beta_1 * m1 + (1 - beta_1) * grad_w1
    v1 = beta_2 * v1 + (1 - beta_2) * grad_w1 ** 2

    m2 = beta_1 * m2 + (1 - beta_1) * grad_b1
    v2 = beta_2 * v2 + (1 - beta_2) * grad_b1 ** 2

    m3 = beta_1 * m3 + (1 - beta_1) * grad_w2
    v3 = beta_2 * v3 + (1 - beta_2) * grad_w2 ** 2

    m4 = beta_1 * m4 + (1 - beta_1) * grad_b2
    v4 = beta_2 * v4 + (1 - beta_2) * grad_b2 ** 2

    D_W1 = D_W1 - (learing_rate / (np.sqrt(v1 / (1 - beta_2)) + eps)) * (
        m1 / (1 - beta_1)
    )
    D_b1 = D_b1 - (learing_rate / (np.sqrt(v2 / (1 - beta_2)) + eps)) * (
        m2 / (1 - beta_1)
    )

    D_W2 = D_W2 - (learing_rate / (np.sqrt(v3 / (1 - beta_2)) + eps)) * (
        m3 / (1 - beta_1)
    )
    D_b2 = D_b2 - (learing_rate / (np.sqrt(v4 / (1 - beta_2)) + eps)) * (
        m4 / (1 - beta_1)
    )

    # Func: Forward Feed for G
    Z = np.random.uniform(-1.0, 1.0, size=[1, G_input])
    Gl1 = Z.dot(G_W1) + G_b1
    Gl1A = arctan(Gl1)
    Gl2 = Gl1A.dot(G_W2) + G_b2
    Gl2A = ReLu(Gl2)
    Gl3 = Gl2A.dot(G_W3) + G_b3
    Gl3A = arctan(Gl3)

    Gl4 = Gl3A.dot(G_W4) + G_b4
    Gl4A = ReLu(Gl4)
    Gl5 = Gl4A.dot(G_W5) + G_b5
    Gl5A = tanh(Gl5)
    Gl6 = Gl5A.dot(G_W6) + G_b6
    Gl6A = ReLu(Gl6)
    Gl7 = Gl6A.dot(G_W7) + G_b7

    current_fake_data = log(Gl7)

    Dl1 = current_fake_data.dot(D_W1) + D_b1
    Dl1_A = ReLu(Dl1)
    Dl2 = Dl1_A.dot(D_W2) + D_b2
    Dl2_A = log(Dl2)

    # Func: Cost G
    G_cost = -np.log(Dl2_A)

    # Func: Gradient
    grad_G_w7_part_1 = ((-1 / Dl2_A) * d_log(Dl2).dot(D_W2.T) * (d_ReLu(Dl1))).dot(
        D_W1.T
    )
    grad_G_w7_part_2 = d_log(Gl7)
    grad_G_w7_part_3 = Gl6A
    grad_G_w7 = grad_G_w7_part_3.T.dot(grad_G_w7_part_1 * grad_G_w7_part_1)
    grad_G_b7 = grad_G_w7_part_1 * grad_G_w7_part_2

    grad_G_w6_part_1 = (grad_G_w7_part_1 * grad_G_w7_part_2).dot(G_W7.T)
    grad_G_w6_part_2 = d_ReLu(Gl6)
    grad_G_w6_part_3 = Gl5A
    grad_G_w6 = grad_G_w6_part_3.T.dot(grad_G_w6_part_1 * grad_G_w6_part_2)
    grad_G_b6 = grad_G_w6_part_1 * grad_G_w6_part_2

    grad_G_w5_part_1 = (grad_G_w6_part_1 * grad_G_w6_part_2).dot(G_W6.T)
    grad_G_w5_part_2 = d_tanh(Gl5)
    grad_G_w5_part_3 = Gl4A
    grad_G_w5 = grad_G_w5_part_3.T.dot(grad_G_w5_part_1 * grad_G_w5_part_2)
    grad_G_b5 = grad_G_w5_part_1 * grad_G_w5_part_2

    grad_G_w4_part_1 = (grad_G_w5_part_1 * grad_G_w5_part_2).dot(G_W5.T)
    grad_G_w4_part_2 = d_ReLu(Gl4)
    grad_G_w4_part_3 = Gl3A
    grad_G_w4 = grad_G_w4_part_3.T.dot(grad_G_w4_part_1 * grad_G_w4_part_2)
    grad_G_b4 = grad_G_w4_part_1 * grad_G_w4_part_2

    grad_G_w3_part_1 = (grad_G_w4_part_1 * grad_G_w4_part_2).dot(G_W4.T)
    grad_G_w3_part_2 = d_arctan(Gl3)
    grad_G_w3_part_3 = Gl2A
    grad_G_w3 = grad_G_w3_part_3.T.dot(grad_G_w3_part_1 * grad_G_w3_part_2)
    grad_G_b3 = grad_G_w3_part_1 * grad_G_w3_part_2

    grad_G_w2_part_1 = (grad_G_w3_part_1 * grad_G_w3_part_2).dot(G_W3.T)
    grad_G_w2_part_2 = d_ReLu(Gl2)
    grad_G_w2_part_3 = Gl1A
    grad_G_w2 = grad_G_w2_part_3.T.dot(grad_G_w2_part_1 * grad_G_w2_part_2)
    grad_G_b2 = grad_G_w2_part_1 * grad_G_w2_part_2

    grad_G_w1_part_1 = (grad_G_w2_part_1 * grad_G_w2_part_2).dot(G_W2.T)
    grad_G_w1_part_2 = d_arctan(Gl1)
    grad_G_w1_part_3 = Z
    grad_G_w1 = grad_G_w1_part_3.T.dot(grad_G_w1_part_1 * grad_G_w1_part_2)
    grad_G_b1 = grad_G_w1_part_1 * grad_G_w1_part_2

    # ---- Update Gradient ----
    m5 = beta_1 * m5 + (1 - beta_1) * grad_G_w1
    v5 = beta_2 * v5 + (1 - beta_2) * grad_G_w1 ** 2

    m6 = beta_1 * m6 + (1 - beta_1) * grad_G_b1
    v6 = beta_2 * v6 + (1 - beta_2) * grad_G_b1 ** 2

    m7 = beta_1 * m7 + (1 - beta_1) * grad_G_w2
    v7 = beta_2 * v7 + (1 - beta_2) * grad_G_w2 ** 2

    m8 = beta_1 * m8 + (1 - beta_1) * grad_G_b2
    v8 = beta_2 * v8 + (1 - beta_2) * grad_G_b2 ** 2

    m9 = beta_1 * m9 + (1 - beta_1) * grad_G_w3
    v9 = beta_2 * v9 + (1 - beta_2) * grad_G_w3 ** 2

    m10 = beta_1 * m10 + (1 - beta_1) * grad_G_b3
    v10 = beta_2 * v10 + (1 - beta_2) * grad_G_b3 ** 2

    m11 = beta_1 * m11 + (1 - beta_1) * grad_G_w4
    v11 = beta_2 * v11 + (1 - beta_2) * grad_G_w4 ** 2

    m12 = beta_1 * m12 + (1 - beta_1) * grad_G_b4
    v12 = beta_2 * v12 + (1 - beta_2) * grad_G_b4 ** 2

    m13 = beta_1 * m13 + (1 - beta_1) * grad_G_w5
    v13 = beta_2 * v13 + (1 - beta_2) * grad_G_w5 ** 2

    m14 = beta_1 * m14 + (1 - beta_1) * grad_G_b5
    v14 = beta_2 * v14 + (1 - beta_2) * grad_G_b5 ** 2

    m15 = beta_1 * m15 + (1 - beta_1) * grad_G_w6
    v15 = beta_2 * v15 + (1 - beta_2) * grad_G_w6 ** 2

    m16 = beta_1 * m16 + (1 - beta_1) * grad_G_b6
    v16 = beta_2 * v16 + (1 - beta_2) * grad_G_b6 ** 2

    m17 = beta_1 * m17 + (1 - beta_1) * grad_G_w7
    v17 = beta_2 * v17 + (1 - beta_2) * grad_G_w7 ** 2

    m18 = beta_1 * m18 + (1 - beta_1) * grad_G_b7
    v18 = beta_2 * v18 + (1 - beta_2) * grad_G_b7 ** 2

    G_W1 = G_W1 - (learing_rate / (np.sqrt(v5 / (1 - beta_2)) + eps)) * (
        m5 / (1 - beta_1)
    )
    G_b1 = G_b1 - (learing_rate / (np.sqrt(v6 / (1 - beta_2)) + eps)) * (
        m6 / (1 - beta_1)
    )

    G_W2 = G_W2 - (learing_rate / (np.sqrt(v7 / (1 - beta_2)) + eps)) * (
        m7 / (1 - beta_1)
    )
    G_b2 = G_b2 - (learing_rate / (np.sqrt(v8 / (1 - beta_2)) + eps)) * (
        m8 / (1 - beta_1)
    )

    G_W3 = G_W3 - (learing_rate / (np.sqrt(v9 / (1 - beta_2)) + eps)) * (
        m9 / (1 - beta_1)
    )
    G_b3 = G_b3 - (learing_rate / (np.sqrt(v10 / (1 - beta_2)) + eps)) * (
        m10 / (1 - beta_1)
    )

    G_W4 = G_W4 - (learing_rate / (np.sqrt(v11 / (1 - beta_2)) + eps)) * (
        m11 / (1 - beta_1)
    )
    G_b4 = G_b4 - (learing_rate / (np.sqrt(v12 / (1 - beta_2)) + eps)) * (
        m12 / (1 - beta_1)
    )

    G_W5 = G_W5 - (learing_rate / (np.sqrt(v13 / (1 - beta_2)) + eps)) * (
        m13 / (1 - beta_1)
    )
    G_b5 = G_b5 - (learing_rate / (np.sqrt(v14 / (1 - beta_2)) + eps)) * (
        m14 / (1 - beta_1)
    )

    G_W6 = G_W6 - (learing_rate / (np.sqrt(v15 / (1 - beta_2)) + eps)) * (
        m15 / (1 - beta_1)
    )
    G_b6 = G_b6 - (learing_rate / (np.sqrt(v16 / (1 - beta_2)) + eps)) * (
        m16 / (1 - beta_1)
    )

    G_W7 = G_W7 - (learing_rate / (np.sqrt(v17 / (1 - beta_2)) + eps)) * (
        m17 / (1 - beta_1)
    )
    G_b7 = G_b7 - (learing_rate / (np.sqrt(v18 / (1 - beta_2)) + eps)) * (
        m18 / (1 - beta_1)
    )

    # --- Print Error ----
    # print("Current Iter: ",iter, " Current D cost:",D_cost, " Current G cost: ", G_cost,end='\r')

    if iter == 0:
        learing_rate = learing_rate * 0.01
    if iter == 40:
        learing_rate = learing_rate * 0.01

    # ---- Print to Out put ----
    if iter % 10 == 0:

        print(
            "Current Iter: ",
            iter,
            " Current D cost:",
            D_cost,
            " Current G cost: ",
            G_cost,
            end="\r",
        )
        print("--------- Show Example Result See Tab Above ----------")
        print("--------- Wait for the image to load ---------")
        Z = np.random.uniform(-1.0, 1.0, size=[16, G_input])

        Gl1 = Z.dot(G_W1) + G_b1
        Gl1A = arctan(Gl1)
        Gl2 = Gl1A.dot(G_W2) + G_b2
        Gl2A = ReLu(Gl2)
        Gl3 = Gl2A.dot(G_W3) + G_b3
        Gl3A = arctan(Gl3)

        Gl4 = Gl3A.dot(G_W4) + G_b4
        Gl4A = ReLu(Gl4)
        Gl5 = Gl4A.dot(G_W5) + G_b5
        Gl5A = tanh(Gl5)
        Gl6 = Gl5A.dot(G_W6) + G_b6
        Gl6A = ReLu(Gl6)
        Gl7 = Gl6A.dot(G_W7) + G_b7

        current_fake_data = log(Gl7)

        fig = plot(current_fake_data)
        fig.savefig(
            "Click_Me_{}.png".format(
                str(iter).zfill(3)
                + "_Ginput_"
                + str(G_input)
                + "_hiddenone"
                + str(hidden_input)
                + "_hiddentwo"
                + str(hidden_input2)
                + "_LR_"
                + str(learing_rate)
            ),
            bbox_inches="tight",
        )
# for complete explanation visit https://towardsdatascience.com/only-numpy-implementing-gan-general-adversarial-networks-and-adam-optimizer-using-numpy-with-2a7e4e032021
# -- end code --