# Most of the tensorflow code is adapted from Tensorflow's tutorial on using
# CNNs to train MNIST
# https://www.tensorflow.org/versions/r0.9/tutorials/mnist/pros/index.html#build-a-multilayer-convolutional-network.  # noqa: E501

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import ray
import tensorflow as tf


def get_batch(data, batch_index, batch_size):
  # This method currently drops data when num_data is not divisible by
  # batch_size.
  num_data = data.shape[0]
  num_batches = num_data // batch_size
  batch_index %= num_batches
  return data[(batch_index * batch_size):((batch_index + 1) * batch_size)]


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


def bias(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 cnn_setup(x, y, keep_prob, lr, stddev):
  first_hidden = 32
  second_hidden = 64
  fc_hidden = 1024
  W_conv1 = weight([5, 5, 1, first_hidden], stddev)
  B_conv1 = bias([first_hidden])
  x_image = tf.reshape(x, [-1, 28, 28, 1])
  h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + B_conv1)
  h_pool1 = max_pool_2x2(h_conv1)
  W_conv2 = weight([5, 5, first_hidden, second_hidden], stddev)
  b_conv2 = bias([second_hidden])
  h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
  h_pool2 = max_pool_2x2(h_conv2)
  W_fc1 = weight([7 * 7 * second_hidden, fc_hidden], stddev)
  b_fc1 = bias([fc_hidden])
  h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * second_hidden])
  h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
  h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
  W_fc2 = weight([fc_hidden, 10], stddev)
  b_fc2 = bias([10])
  y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
  cross_entropy = tf.reduce_mean(-tf.reduce_sum(y * tf.log(y_conv),
                                 reduction_indices=[1]))
  correct_pred = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y, 1))
  return (tf.train.AdamOptimizer(lr).minimize(cross_entropy),
          tf.reduce_mean(tf.cast(correct_pred, tf.float32)), cross_entropy)


# Define a remote function that takes a set of hyperparameters as well as the
# data, consructs and trains a network, and returns the validation accuracy.
@ray.remote
def train_cnn_and_compute_accuracy(params, steps, train_images, train_labels,
                                   validation_images, validation_labels,
                                   weights=None):
  # Extract the hyperparameters from the params dictionary.
  learning_rate = params["learning_rate"]
  batch_size = params["batch_size"]
  keep = 1 - params["dropout"]
  stddev = params["stddev"]
  # Create the network and related variables.
  with tf.Graph().as_default():
    # Create the input placeholders for the network.
    x = tf.placeholder(tf.float32, shape=[None, 784])
    y = tf.placeholder(tf.float32, shape=[None, 10])
    keep_prob = tf.placeholder(tf.float32)
    # Create the network.
    train_step, accuracy, loss = cnn_setup(x, y, keep_prob, learning_rate,
                                           stddev)
    # Do the training and evaluation.
    with tf.Session() as sess:
      # Use the TensorFlowVariables utility. This is only necessary if we want
      # to set and get the weights.
      variables = ray.experimental.TensorFlowVariables(loss, sess)
      # Initialize the network weights.
      sess.run(tf.global_variables_initializer())
      # If some network weights were passed in, set those.
      if weights is not None:
        variables.set_weights(weights)
      # Do some steps of training.
      for i in range(1, steps + 1):
        # Fetch the next batch of data.
        image_batch = get_batch(train_images, i, batch_size)
        label_batch = get_batch(train_labels, i, batch_size)
        # Do one step of training.
        sess.run(train_step, feed_dict={x: image_batch, y: label_batch,
                                        keep_prob: keep})
      # Training is done, so compute the validation accuracy and the current
      # weights and return.
      totalacc = accuracy.eval(feed_dict={x: validation_images,
                                          y: validation_labels,
                                          keep_prob: 1.0})
      new_weights = variables.get_weights()
  return float(totalacc), new_weights