import tensorflow as tf
import numpy as np
import sys
from tensorflow.examples.tutorials.mnist import input_data

sys.path.append("/data")


def test_1():
    sess = tf.Session()
    hell = tf.constant('Hello!!!')
    sess.run(hell)


def simple_mnist():
    mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
    x = tf.placeholder("float", [None, 784])
    W = tf.Variable(tf.zeros([784, 10]))
    b = tf.Variable(tf.zeros([10]))
    y = tf.nn.softmax(tf.matmul(x, W) + b)
    y_ = tf.placeholder("float", [None, 10])
    cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
    train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
    init = tf.global_variables_initializer()
    with tf.Session() as sess:
        sess.run(init)
        for i in range(1000):
            batch_xs, batch_ys = mnist.train.next_batch(100)
            sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
        #
        correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
        print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))


def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)


def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)


def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')


def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')


def deep_mnist():
    sess = tf.InteractiveSession()

    mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
    x = tf.placeholder("float", [None, 784])
    x_image = tf.reshape(x, [-1, 28, 28, 1])

    W_conv1 = weight_variable([5, 5, 1, 32])
    b_conv1 = bias_variable([32])
    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
    h_pool1 = max_pool_2x2(h_conv1)

    W_conv2 = weight_variable([5, 5, 32, 64])
    b_conv2 = bias_variable([64])
    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
    h_pool2 = max_pool_2x2(h_conv2)

    W_fc1 = weight_variable([7 * 7 * 64, 1024])
    b_fc1 = bias_variable([1024])

    h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

    keep_prob = tf.placeholder('float')
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

    W_fc2 = weight_variable([1024, 10])
    b_fc2 = bias_variable([10])
    y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

    y_ = tf.placeholder('float', [None, 10])
    obj_var = y_conv
    cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))
    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
    correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))

    sess.run(tf.global_variables_initializer())

    """insert code"""
    from scripts.utils.tf_utils import GradientSearcher
    gradient_search = GradientSearcher(name="pytest_grist")
    obj_function = tf.reduce_min(tf.abs(obj_var))
    obj_grads = tf.gradients(obj_function, x)[0]
    batch = mnist.train.next_batch(50)
    batch_xs, batch_ys = batch[0], batch[1]
    max_val, min_val = np.max(batch_xs), np.min(batch_xs)
    gradient_search.build(batch_size=50, min_val=min_val, max_val=max_val)
    """insert code"""

    while True:
        """inserted code"""
        monitor_vars = {'loss': cross_entropy, 'obj_function': obj_function, 'obj_grad': obj_grads}
        feed_dict = {x: batch_xs, y_: batch_ys, keep_prob: 0.5}
        batch_xs, scores_rank = gradient_search.update_batch_data(session=sess, monitor_var=monitor_vars,
                                                                  feed_dict=feed_dict, input_data=batch_xs, )
        """inserted code"""

        _, loss = sess.run([train_step, cross_entropy], feed_dict=feed_dict)

        """inserted code"""
        new_batch = mnist.train.next_batch(50)
        new_batch_xs, new_batch_ys = new_batch[0], new_batch[1]
        new_data_dict = {'x': new_batch_xs, 'y': new_batch_ys}
        old_data_dict = {'x': batch_xs, 'y': batch_ys}
        batch_xs, batch_ys = gradient_search.switch_new_data(new_data_dict=new_data_dict,
                                                             old_data_dict=old_data_dict,
                                                             scores_rank=scores_rank)
        gradient_search.check_time()
        """inserted code"""


if __name__ == "__main__":
    # simple_mnist()
    deep_mnist()
