from __future__ import print_function

import glob, os
import json
import numpy as np

from utils import load_emoji_data

import sklearn
from sklearn import preprocessing, model_selection
import tensorflow as tf

emojis = load_emoji_data("noissy_data")
emojis = np.array(zip(*sorted(zip(emojis["images"], emojis["labels"]), key=lambda tup: int(tup[1])))[0])
emojis = emojis.reshape((len(emojis), -1))

with open('noissy_data/labels.json') as labels_file:    
    labels = list(map(str, json.load(labels_file)))

le = preprocessing.LabelBinarizer()
le.fit(labels)
labels = le.transform(labels)

X_train, X_test, y_train, y_test = model_selection.train_test_split(emojis, labels, test_size=0.33)

import argparse

parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument('-t', dest="train", action='store_true', help="run training")
options = parser.parse_args()

# Parameters
learning_rate = 0.01
training_iters = 1000000
batch_size = 8192
display_step = 10

# Network Parameters
n_input = 28 * 28 # Emoji data input
n_classes = len(le.classes_) # Emoji total classes (0-9 digits)
dropout = 0.75 # Dropout, probability to keep units

trained_network_path = "trained_network/trained.ckpt"

print("Total {0} classes".format(n_classes))

# tf Graph input
x = tf.placeholder(tf.float32, [None, n_input])
y = tf.placeholder(tf.float32, [None, n_classes])
keep_prob = tf.placeholder(tf.float32) #dropout (keep probability)


# Create some wrappers for simplicity
def conv2d(x, W, b, strides=1):
    # Conv2D wrapper, with bias and relu activation
    x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')
    x = tf.nn.bias_add(x, b)
    return tf.nn.relu(x)


def maxpool2d(x, k=2):
    # MaxPool2D wrapper
    return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],
                          padding='SAME')


# Create model
def conv_net(x, weights, biases, dropout):
    # Reshape input picture
    x = tf.reshape(x, shape=[-1, 28, 28, 1])

    # Convolution Layer
    conv1 = conv2d(x, weights['wc1'], biases['bc1'])
    # Max Pooling (down-sampling)
    conv1 = maxpool2d(conv1, k=2)

    # Convolution Layer
    conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
    # Max Pooling (down-sampling)
    conv2 = maxpool2d(conv2, k=2)

    # Fully connected layer
    # Reshape conv2 output to fit fully connected layer input
    fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])
    fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
    fc1 = tf.nn.relu(fc1)
    # Apply Dropout
    fc1 = tf.nn.dropout(fc1, dropout)

    # Output, class prediction
    out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])
    return out

# Store layers weight & bias
weights = {
    # 5x5 conv, 1 input, 32 outputs
    'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),
    # 5x5 conv, 32 inputs, 64 outputs
    'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),
    # fully connected, 7*7*64 inputs, 1024 outputs
    'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])),
    # 1024 inputs, 10 outputs (class prediction)
    'out': tf.Variable(tf.random_normal([1024, n_classes]))
}

biases = {
    'bc1': tf.Variable(tf.random_normal([32])),
    'bc2': tf.Variable(tf.random_normal([64])),
    'bd1': tf.Variable(tf.random_normal([1024])),
    'out': tf.Variable(tf.random_normal([n_classes]))
}

# Construct model
pred = conv_net(x, weights, biases, keep_prob)

# Define loss and optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

# Evaluate model
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

# Initializing the variables
init = tf.initialize_all_variables()

# Add ops to save and restore all the variables.
saver = tf.train.Saver()

# Launch the graph
sess = tf.Session()
sess.run(init)

if options.train:
    step = 1
    # Keep training until reach max iterations
    while step * batch_size < training_iters:
        batch_x, batch_y = sklearn.utils.resample(X_train, y_train, replace=True, n_samples=batch_size)
        # Run optimization op (backprop)
        sess.run(optimizer, feed_dict={x: batch_x, y: batch_y,
                                       keep_prob: dropout})
        if step % display_step == 0:
            # Calculate batch loss and accuracy
            loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x,
                                                              y: batch_y,
                                                              keep_prob: 1.})
            print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \
                  "{:.6f}".format(loss) + ", Training Accuracy= " + \
                  "{:.5f}".format(acc))
        step += 1
    print("Optimization Finished!")
    print("Saving model")
    saver.save(sess, trained_network_path)
else:
    saver.restore(sess, trained_network_path)

# Calculate accuracy for 256 mnist test images
print("Testing Accuracy:", \
    sess.run(accuracy, feed_dict={x: X_test,
                                  y: y_test,
                                  keep_prob: 1.}))

def predict_image(image_path):
    prediction = sess.run(pred, feed_dict={x: x})
    predicted_emoji_code = le.inverse_transform(prediction)

