import math
import numpy as np
import h5py
import matplotlib.pyplot as plt
import tensorflow as tf
#from tensorflow.python.framework import ops
from tf_utils import load_dataset, random_mini_batches, convert_to_one_hot, predict
import os
#import tensorflow.python.util.deprecation as deprecation
#deprecation._PRINT_DEPRECATION_WARNINGS = False
#import tensorflow as tf
if type(tf.contrib) != type(tf): tf.contrib._warning = None
'''np.random.seed(1)

y_hat = tf.constant(36, name='y_hat')
y = tf.constant(39, name='y')

loss = tf.Variable((y-y_hat)**2, name='loss')
init = tf.global_variables_initializer()

with tf.Session() as session:
    session.run(init)
    print(session.run(loss))
'''
'''session = tf.Session()
w = tf.Variable([.3], tf.float32, name='w')
b = tf.Variable([-.3], tf.float32, name='b')
x = tf.placeholder(tf.float32, name='x')
linear = w*x+b

init = tf.global_variables_initializer()

session.run(init)
print(session.run(linear, feed_dict={x:[1,2,3,4]}))
'''


def linear_function():
    np.random.seed(1)
    X = tf.constant(np.random.randn(3, 1), name='X')
    W = tf.constant(np.random.randn(4, 3), name='W')
    b = tf.constant(np.random.randn(4, 1), name='b')
    Y = tf.add(tf.matmul(W, X), b)
    sess = tf.Session()
    result = sess.run(Y)

    sess.close()

    return result


def sigmoid(z):
    x = tf.placeholder(tf.float32, name='x')
    sigmoid = tf.sigmoid(x)
    with tf.Session() as sess:
        result = sess.run(sigmoid, feed_dict={x: z})

    return result


def cost(logits, labels):
    z = tf.placeholder(tf.float32, name='logits')
    y = tf.placeholder(tf.float32, name='labels')
    cost = tf.nn.sigmoid_cross_entropy_with_logits(logits=z, labels=y)
    sess = tf.Session()
    cost = sess.run(cost, feed_dict={z: logits, y: labels})
    sess.close()
    return cost


def one_hot_matrix(lables, C):
    C = tf.constant(C, name='C')
    one_hot_matrix = tf.one_hot(labels, C, axis=0)
    sess = tf.Session()
    one_hot = sess.run(one_hot_matrix)
    sess.close()

    return one_hot


def ones(shape):
    ones = tf.ones(shape)
    sess = tf.Session()
    ones = sess.run(ones)
    sess.close()
    return ones


X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()
# Flatten the training and test images
X_train_flatten = X_train_orig.reshape(X_train_orig.shape[0], -1).T
X_test_flatten = X_test_orig.reshape(X_test_orig.shape[0], -1).T
# Normalize image vectors
X_train = X_train_flatten / 255.
X_test = X_test_flatten / 255.
# Convert training and test labels to one hot matrices
Y_train = convert_to_one_hot(Y_train_orig, 6)
Y_test = convert_to_one_hot(Y_test_orig, 6)


def create_placeholders(n_x, n_y):
    X = tf.placeholder(tf.float32, [n_x, None], name='X')
    Y = tf.placeholder(tf.float32, [n_y, None], name='Y')

    return X, Y


def initialize_parameters():
    tf.set_random_seed(1)
    W1 = tf.get_variable(
        "W1", [25, 12288],
        initializer=tf.contrib.layers.xavier_initializer(seed=1))
    b1 = tf.get_variable("b1", [25, 1], initializer=tf.zeros_initializer())
    W2 = tf.get_variable(
        "W2", [12, 25],
        initializer=tf.contrib.layers.xavier_initializer(seed=1))
    b2 = tf.get_variable("b2", [12, 1], initializer=tf.zeros_initializer())
    W3 = tf.get_variable(
        "W3", [6, 12],
        initializer=tf.contrib.layers.xavier_initializer(seed=1))
    b3 = tf.get_variable("b3", [6, 1], initializer=tf.zeros_initializer())
    parameters = {"W1": W1, "b1": b1, "W2": W2, "b2": b2, "W3": W3, "b3": b3}

    return parameters


def forward_propagation(X, parameters):
    W1 = parameters['W1']
    b1 = parameters['b1']
    W2 = parameters['W2']
    b2 = parameters['b2']
    W3 = parameters['W3']
    b3 = parameters['b3']

    Z1 = tf.add(tf.matmul(W1, X), b1)
    A1 = tf.nn.relu(Z1)
    Z2 = tf.add(tf.matmul(W2, A1), b2)
    A2 = tf.nn.relu(Z2)
    Z3 = tf.add(tf.matmul(W3, A2), b3)

    return Z3


def compute_cost(Z3, Y):
    logits = tf.transpose(Z3)
    labels = tf.transpose(Y)

    cost = tf.reduce_mean(
        tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels))

    return cost


def model(X_train,
          Y_train,
          X_test,
          Y_test,
          learning_rate=0.0001,
          num_epochs=1500,
          minibatch_size=32,
          print_cost=True):
    #ops.reset_default_graph()
    tf.set_random_seed(1)
    seed = 3
    (n_x, m) = X_train.shape
    n_y = Y_train.shape[0]
    costs = []

    X, Y = create_placeholders(n_x, n_y)
    parameters = initialize_parameters()
    Z3 = forward_propagation(X, parameters)
    cost = compute_cost(Z3, Y)
    optimizer = tf.train.AdamOptimizer(
        learning_rate=learning_rate).minimize(cost)

    init = tf.global_variables_initializer()
    with tf.Session() as sess:
        sess.run(init)
        for epoch in range(num_epochs):
            epoch_cost = 0
            num_minibatches = int(
                m / minibatch_size
            )  # number of minibatches of size minibatch_size in the train set
            seed = seed + 1
            minibatches = random_mini_batches(X_train, Y_train, minibatch_size,
                                              seed)
            for minibatch in minibatches:

                # Select a minibatch
                (minibatch_X, minibatch_Y) = minibatch

                # IMPORTANT: The line that runs the graph on a minibatch.
                # Run the session to execute the "optimizer" and the "cost", the feedict should contain a minibatch for (X,Y).
                ### START CODE HERE ### (1 line)
                _, minibatch_cost = sess.run([optimizer, cost],
                                             feed_dict={
                                                 X: minibatch_X,
                                                 Y: minibatch_Y
                                             })

                ### END CODE HERE ###

                epoch_cost += minibatch_cost / num_minibatches
            if print_cost == True and epoch % 100 == 0:
                print("Cost after epoch %i: %f" % (epoch, epoch_cost))
            if print_cost == True and epoch % 5 == 0:
                costs.append(epoch_cost)
            # plot the cost
        '''plt.plot(np.squeeze(costs))
        plt.ylabel('cost')
        plt.xlabel('iterations (per tens)')
        plt.title("Learning rate =" + str(learning_rate))
        plt.show()'''

        # lets save the parameters in a variable
        parameters = sess.run(parameters)
        print("Parameters have been trained!")

        # Calculate the correct predictions
        correct_prediction = tf.equal(tf.argmax(Z3), tf.argmax(Y))

        # Calculate accuracy on the test set
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

        print("Train Accuracy:", accuracy.eval({X: X_train, Y: Y_train}))
        print("Test Accuracy:", accuracy.eval({X: X_test, Y: Y_test}))

        return parameters


parameters = model(X_train, Y_train, X_test, Y_test)
