# -*- coding: UTF-8 -*-
#build Neural Style Transfer
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

# import the necessary packages
import numpy as np
import tensorflow as tf
def compute_content_cost(a_C, a_G):
    """
    Computes the content cost

    Arguments:
    a_C -- tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing content of the image C
    a_G -- tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing content of the image G

    Returns:
    J_content -- scalar that you compute using equation 1 above.
    """

    # Retrieve dimensions from a_G (≈1 line)
    m, n_H, n_W, n_C = a_G.get_shape().as_list()

    # Reshape a_C and a_G (≈2 lines)
    a_C_unrolled = tf.reshape(tf.transpose(a_C,perm=[0,3,1,2]),[n_C, n_H * n_W])
    a_G_unrolled = tf.reshape(tf.transpose(a_G,perm=[0,3,1,2]),[n_C, n_H * n_W])

    # compute the cost with tensorflow (≈1 line)
    J_content =  (1 / (4 * n_H * n_W * n_C))*tf.reduce_sum(tf.square(tf.subtract(a_C_unrolled,a_G_unrolled)))

    return J_content
"""
#validate compute_content_cost
tf.reset_default_graph()

with tf.Session() as test:
    tf.set_random_seed(1)
    a_C = tf.random_normal([1, 4, 4, 3], mean=1, stddev=4)
    a_G = tf.random_normal([1, 4, 4, 3], mean=1, stddev=4)
    J_content = compute_content_cost(a_C, a_G)
    print("J_content = " + str(J_content.eval()))

#J_content = 6.76559
"""

def gram_matrix(A):
    """
    Argument:
    A -- matrix of shape (n_C, n_H*n_W)

    Returns:
    GA -- Gram matrix of A, of shape (n_C, n_C)
    """

    GA = tf.matmul(A,tf.transpose(A))

    return GA
"""
#validate gram_matrix
tf.reset_default_graph()

with tf.Session() as test:
    tf.set_random_seed(1)
    A = tf.random_normal([3, 2*1], mean=1, stddev=4)
    GA = gram_matrix(A)

    print("GA = " + str(GA.eval()))

#GA = [[  6.42230511  -4.42912197  -2.09668207]
# [ -4.42912197  19.46583748  19.56387138]
# [ -2.09668207  19.56387138  20.6864624 ]]
"""

def compute_layer_style_cost(a_S, a_G):
    """
    Arguments:
    a_S -- tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing style of the image S
    a_G -- tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing style of the image G

    Returns:
    J_style_layer -- tensor representing a scalar value, style cost defined above by equation (2)
    """

    # Retrieve dimensions from a_G
    m, n_H, n_W, n_C = a_G.get_shape().as_list()

    # Reshape the images to have them of shape (n_C, n_H*n_W) (≈2 lines)
    a_S = tf.reshape(tf.transpose(a_S,perm=[0,3,1,2]),[n_C, n_H * n_W])
    a_G = tf.reshape(tf.transpose(a_G,perm=[0,3,1,2]),[n_C, n_H * n_W])

    # Computing gram_matrices for both images S and G
    GS = gram_matrix(a_S)
    GG = gram_matrix(a_G)
    # Computing the loss
    J_style_layer = (1 / (4 * (n_C * n_H * n_W)**2))*tf.reduce_sum(tf.square(tf.subtract(GS,GG)))

    return J_style_layer

"""
#validate compute_layer_style_cost
tf.reset_default_graph()

with tf.Session() as test:
    tf.set_random_seed(1)
    a_S = tf.random_normal([1, 4, 4, 3], mean=1, stddev=4)
    a_G = tf.random_normal([1, 4, 4, 3], mean=1, stddev=4)
    J_style_layer = compute_layer_style_cost(a_S, a_G)

    print("J_style_layer = " + str(J_style_layer.eval()))
#J_style_layer = 9.19028
"""

STYLE_LAYERS = [
    ('block1_conv1', 0.2),
    ('block2_conv1', 0.2),
    ('block3_conv1', 0.2),
    ('block4_conv1', 0.2),
    ('block5_conv1', 0.2)]

def compute_style_cost(model,style_wave, STYLE_LAYERS):
    """
    Computes the overall style cost from several chosen layers

    Arguments:
    model -- our tensorflow model
    STYLE_LAYERS -- A python list containing:
                        - the names of the layers we would like to extract style from
                        - a coefficient for each of them

    Returns:
    J_style -- tensor representing a scalar value, style cost defined above by equation (2)
    """

    # initialize the overall style cost
    J_style = 0

    for layer_name, coeff in STYLE_LAYERS:

        # Select the output tensor of the currently selected layer
        #out = model[layer_name]
        out = model.get_layer(layer_name).output
        # Set a_S to be the hidden layer activation from the layer we have selected, by running the session on out
        a_S = Model(input=model.input, output=model.get_layer(layer_name).output).predict(style_wave)

        # Set a_G to be the hidden layer activation from same layer. Here, a_G references model[layer_name]
        # and isn't evaluated yet. Later in the code, we'll assign the image G as the model input, so that
        # when we run the session, this will be the activations drawn from the appropriate layer, with G as input.
        a_G = out

        # Compute style_cost for the current layer
        J_style_layer = compute_layer_style_cost(a_S, a_G)

        # Add coeff * J_style_layer of this layer to overall style cost
        J_style += coeff * J_style_layer

    return J_style

def total_cost(J_content, J_style, alpha = 10, beta = 40):
    """
    Computes the total cost function

    Arguments:
    J_content -- content cost coded above
    J_style -- style cost coded above
    alpha -- hyperparameter weighting the importance of the content cost
    beta -- hyperparameter weighting the importance of the style cost

    Returns:
    J -- total cost as defined by the formula above.
    """

    J = alpha * J_content + beta * J_style

    return J

"""
#validate total_cost
tf.reset_default_graph()

with tf.Session() as test:
    np.random.seed(3)
    J_content = np.random.randn()
    J_style = np.random.randn()
    J = total_cost(J_content, J_style)
    print("J = " + str(J))
#J = 35.34667875478276
"""




# Reset the graph
tf.reset_default_graph()

# Start interactive session
sess = tf.InteractiveSession()

'''
a_C = Model(input=base_model.input, output=base_model.get_layer('block4_pool').output).predict(content_wave)
a_G = out
J_content = compute_content_cost(a_C, a_G)

J_style = compute_style_cost(base_model, style_wave,STYLE_LAYERS)

J = total_cost(J_content, J_style, alpha = 10, beta = 40)

optimizer = tf.train.AdamOptimizer(2.0)
train_step = optimizer.minimize(J)

def model_nn(sess, input_wave, num_iterations = 200):

    # Initialize global variables (you need to run the session on the initializer)
    sess.run(tf.global_variables_initializer())
    base_model.load_weights()
    # Run the noisy input image (initial generated image) through the model. Use assign().
    sess.run(base_model,{input:input_wave})
    for i in range(num_iterations):

        # Run the session on the train_step to minimize the total cost
        sess.run(train_step)

        # Compute the generated image by running the session on the current model['input']
        generated_wave = Model(input=base_model.input, output=base_model.get_layer('block4_pool').output).predict(input_wave)

        # Print every 20 iteration.
        if i%20 == 0:
            Jt, Jc, Js = sess.run([J, J_content, J_style])
            print("Iteration " + str(i) + " :")
            print("total cost = " + str(Jt))
            print("content cost = " + str(Jc))
            print("style cost = " + str(Js))

            # save current generated image in the "/output" directory
            #save_image("output/" + str(i) + ".png", generated_image)
            wave_path = os.path.dirname(__file__)+os.path.sep+'output'+os.path.sep+'generated'+i+'.wav'
            generated_de_wave = wave_utils.deprocess_wave(generated_wave)
            wave_utils.writeWav(generated_de_wave,params,wave_path)
    # save last generated image
    wave_path = os.path.dirname(__file__)+os.path.sep+'output'+os.path.sep+'generated.wav'
    generated_de_wave = wave_utils.deprocess_wave(generated_wave)
    wave_utils.writeWav(generated_de_wave,params,wave_path)
    # save_image('output/generated_image.jpg', generated_image)

    return generated_image

model_nn(sess, content_image)
# -*- coding: UTF-8 -*-
#build Neural Style Transfer
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

# import the necessary packages
import numpy as np
import tensorflow as tf
def compute_content_cost(a_C, a_G):
    """
    Computes the content cost

    Arguments:
    a_C -- tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing content of the image C
    a_G -- tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing content of the image G

    Returns:
    J_content -- scalar that you compute using equation 1 above.
    """

    # Retrieve dimensions from a_G (≈1 line)
    m, n_H, n_W, n_C = a_G.get_shape().as_list()

    # Reshape a_C and a_G (≈2 lines)
    a_C_unrolled = tf.reshape(tf.transpose(a_C,perm=[0,3,1,2]),[n_C, n_H * n_W])
    a_G_unrolled = tf.reshape(tf.transpose(a_G,perm=[0,3,1,2]),[n_C, n_H * n_W])

    # compute the cost with tensorflow (≈1 line)
    J_content =  (1 / (4 * n_H * n_W * n_C))*tf.reduce_sum(tf.square(tf.subtract(a_C_unrolled,a_G_unrolled)))

    return J_content
"""
#validate compute_content_cost
tf.reset_default_graph()

with tf.Session() as test:
    tf.set_random_seed(1)
    a_C = tf.random_normal([1, 4, 4, 3], mean=1, stddev=4)
    a_G = tf.random_normal([1, 4, 4, 3], mean=1, stddev=4)
    J_content = compute_content_cost(a_C, a_G)
    print("J_content = " + str(J_content.eval()))

#J_content = 6.76559
"""

def gram_matrix(A):
    """
    Argument:
    A -- matrix of shape (n_C, n_H*n_W)

    Returns:
    GA -- Gram matrix of A, of shape (n_C, n_C)
    """

    GA = tf.matmul(A,tf.transpose(A))

    return GA
"""
#validate gram_matrix
tf.reset_default_graph()

with tf.Session() as test:
    tf.set_random_seed(1)
    A = tf.random_normal([3, 2*1], mean=1, stddev=4)
    GA = gram_matrix(A)

    print("GA = " + str(GA.eval()))

#GA = [[  6.42230511  -4.42912197  -2.09668207]
# [ -4.42912197  19.46583748  19.56387138]
# [ -2.09668207  19.56387138  20.6864624 ]]
"""

def compute_layer_style_cost(a_S, a_G):
    """
    Arguments:
    a_S -- tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing style of the image S
    a_G -- tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing style of the image G

    Returns:
    J_style_layer -- tensor representing a scalar value, style cost defined above by equation (2)
    """

    # Retrieve dimensions from a_G
    m, n_H, n_W, n_C = a_G.get_shape().as_list()

    # Reshape the images to have them of shape (n_C, n_H*n_W) (≈2 lines)
    a_S = tf.reshape(tf.transpose(a_S,perm=[0,3,1,2]),[n_C, n_H * n_W])
    a_G = tf.reshape(tf.transpose(a_G,perm=[0,3,1,2]),[n_C, n_H * n_W])

    # Computing gram_matrices for both images S and G
    GS = gram_matrix(a_S)
    GG = gram_matrix(a_G)
    # Computing the loss
    J_style_layer = (1 / (4 * (n_C * n_H * n_W)**2))*tf.reduce_sum(tf.square(tf.subtract(GS,GG)))

    return J_style_layer

"""
#validate compute_layer_style_cost
tf.reset_default_graph()

with tf.Session() as test:
    tf.set_random_seed(1)
    a_S = tf.random_normal([1, 4, 4, 3], mean=1, stddev=4)
    a_G = tf.random_normal([1, 4, 4, 3], mean=1, stddev=4)
    J_style_layer = compute_layer_style_cost(a_S, a_G)

    print("J_style_layer = " + str(J_style_layer.eval()))
#J_style_layer = 9.19028
"""

STYLE_LAYERS = [
    ('block1_conv1', 0.2),
    ('block2_conv1', 0.2),
    ('block3_conv1', 0.2),
    ('block4_conv1', 0.2),
    ('block5_conv1', 0.2)]

def compute_style_cost(model,style_wave, STYLE_LAYERS):
    """
    Computes the overall style cost from several chosen layers

    Arguments:
    model -- our tensorflow model
    STYLE_LAYERS -- A python list containing:
                        - the names of the layers we would like to extract style from
                        - a coefficient for each of them

    Returns:
    J_style -- tensor representing a scalar value, style cost defined above by equation (2)
    """

    # initialize the overall style cost
    J_style = 0

    for layer_name, coeff in STYLE_LAYERS:

        # Select the output tensor of the currently selected layer
        #out = model[layer_name]
        out = model.get_layer(layer_name).output
        # Set a_S to be the hidden layer activation from the layer we have selected, by running the session on out
        a_S = Model(input=model.input, output=model.get_layer(layer_name).output).predict(style_wave)

        # Set a_G to be the hidden layer activation from same layer. Here, a_G references model[layer_name]
        # and isn't evaluated yet. Later in the code, we'll assign the image G as the model input, so that
        # when we run the session, this will be the activations drawn from the appropriate layer, with G as input.
        a_G = out

        # Compute style_cost for the current layer
        J_style_layer = compute_layer_style_cost(a_S, a_G)

        # Add coeff * J_style_layer of this layer to overall style cost
        J_style += coeff * J_style_layer

    return J_style

def total_cost(J_content, J_style, alpha = 10, beta = 40):
    """
    Computes the total cost function

    Arguments:
    J_content -- content cost coded above
    J_style -- style cost coded above
    alpha -- hyperparameter weighting the importance of the content cost
    beta -- hyperparameter weighting the importance of the style cost

    Returns:
    J -- total cost as defined by the formula above.
    """

    J = alpha * J_content + beta * J_style

    return J

"""
#validate total_cost
tf.reset_default_graph()

with tf.Session() as test:
    np.random.seed(3)
    J_content = np.random.randn()
    J_style = np.random.randn()
    J = total_cost(J_content, J_style)
    print("J = " + str(J))
#J = 35.34667875478276
"""




# Reset the graph
tf.reset_default_graph()

# Start interactive session
sess = tf.InteractiveSession()

'''
a_C = Model(input=base_model.input, output=base_model.get_layer('block4_pool').output).predict(content_wave)
a_G = out
J_content = compute_content_cost(a_C, a_G)

J_style = compute_style_cost(base_model, style_wave,STYLE_LAYERS)

J = total_cost(J_content, J_style, alpha = 10, beta = 40)

optimizer = tf.train.AdamOptimizer(2.0)
train_step = optimizer.minimize(J)

def model_nn(sess, input_wave, num_iterations = 200):

    # Initialize global variables (you need to run the session on the initializer)
    sess.run(tf.global_variables_initializer())
    base_model.load_weights()
    # Run the noisy input image (initial generated image) through the model. Use assign().
    sess.run(base_model,{input:input_wave})
    for i in range(num_iterations):

        # Run the session on the train_step to minimize the total cost
        sess.run(train_step)

        # Compute the generated image by running the session on the current model['input']
        generated_wave = Model(input=base_model.input, output=base_model.get_layer('block4_pool').output).predict(input_wave)

        # Print every 20 iteration.
        if i%20 == 0:
            Jt, Jc, Js = sess.run([J, J_content, J_style])
            print("Iteration " + str(i) + " :")
            print("total cost = " + str(Jt))
            print("content cost = " + str(Jc))
            print("style cost = " + str(Js))

            # save current generated image in the "/output" directory
            #save_image("output/" + str(i) + ".png", generated_image)
            wave_path = os.path.dirname(__file__)+os.path.sep+'output'+os.path.sep+'generated'+i+'.wav'
            generated_de_wave = wave_utils.deprocess_wave(generated_wave)
            wave_utils.writeWav(generated_de_wave,params,wave_path)
    # save last generated image
    wave_path = os.path.dirname(__file__)+os.path.sep+'output'+os.path.sep+'generated.wav'
    generated_de_wave = wave_utils.deprocess_wave(generated_wave)
    wave_utils.writeWav(generated_de_wave,params,wave_path)
    # save_image('output/generated_image.jpg', generated_image)

    return generated_image

model_nn(sess, content_image)
# -*- coding: UTF-8 -*- #build Neural Style Transfer # import the necessary packages Computes the content cost      Arguments:     a_C -- tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing content of the image C     a_G -- tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing content of the image G      Returns:     J_content -- scalar that you compute using equation 1 above. # Retrieve dimensions from a_G (≈1 line) # Reshape a_C and a_G (≈2 lines) # compute the cost with tensorflow (≈1 line) #validate compute_content_cost tf.reset_default_graph()  with tf.Session() as test:     tf.set_random_seed(1)     a_C = tf.random_normal([1, 4, 4, 3], mean=1, stddev=4)     a_G = tf.random_normal([1, 4, 4, 3], mean=1, stddev=4)     J_content = compute_content_cost(a_C, a_G)     print("J_content = " + str(J_content.eval()))  #J_content = 6.76559 Argument:     A -- matrix of shape (n_C, n_H*n_W)      Returns:     GA -- Gram matrix of A, of shape (n_C, n_C) #validate gram_matrix tf.reset_default_graph()  with tf.Session() as test:     tf.set_random_seed(1)     A = tf.random_normal([3, 2*1], mean=1, stddev=4)     GA = gram_matrix(A)      print("GA = " + str(GA.eval()))  #GA = [[  6.42230511  -4.42912197  -2.09668207] # [ -4.42912197  19.46583748  19.56387138] # [ -2.09668207  19.56387138  20.6864624 ]] Arguments:     a_S -- tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing style of the image S     a_G -- tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing style of the image G      Returns:     J_style_layer -- tensor representing a scalar value, style cost defined above by equation (2) # Retrieve dimensions from a_G # Reshape the images to have them of shape (n_C, n_H*n_W) (≈2 lines) # Computing gram_matrices for both images S and G # Computing the loss #validate compute_layer_style_cost tf.reset_default_graph()  with tf.Session() as test:     tf.set_random_seed(1)     a_S = tf.random_normal([1, 4, 4, 3], mean=1, stddev=4)     a_G = tf.random_normal([1, 4, 4, 3], mean=1, stddev=4)     J_style_layer = compute_layer_style_cost(a_S, a_G)      print("J_style_layer = " + str(J_style_layer.eval())) #J_style_layer = 9.19028 Computes the overall style cost from several chosen layers      Arguments:     model -- our tensorflow model     STYLE_LAYERS -- A python list containing:                         - the names of the layers we would like to extract style from                         - a coefficient for each of them      Returns:     J_style -- tensor representing a scalar value, style cost defined above by equation (2) # initialize the overall style cost # Select the output tensor of the currently selected layer #out = model[layer_name] # Set a_S to be the hidden layer activation from the layer we have selected, by running the session on out # Set a_G to be the hidden layer activation from same layer. Here, a_G references model[layer_name] # and isn't evaluated yet. Later in the code, we'll assign the image G as the model input, so that # when we run the session, this will be the activations drawn from the appropriate layer, with G as input. # Compute style_cost for the current layer # Add coeff * J_style_layer of this layer to overall style cost Computes the total cost function      Arguments:     J_content -- content cost coded above     J_style -- style cost coded above     alpha -- hyperparameter weighting the importance of the content cost     beta -- hyperparameter weighting the importance of the style cost      Returns:     J -- total cost as defined by the formula above. #validate total_cost tf.reset_default_graph()  with tf.Session() as test:     np.random.seed(3)     J_content = np.random.randn()     J_style = np.random.randn()     J = total_cost(J_content, J_style)     print("J = " + str(J)) #J = 35.34667875478276 # Reset the graph # Start interactive session # Initialize global variables (you need to run the session on the initializer) # Run the noisy input image (initial generated image) through the model. Use assign(). # Run the session on the train_step to minimize the total cost # Compute the generated image by running the session on the current model['input'] # Print every 20 iteration. # save current generated image in the "/output" directory #save_image("output/" + str(i) + ".png", generated_image) # save last generated image # save_image('output/generated_image.jpg', generated_image)