"""Converts MNIST data to TFRecords file format with Example protos."""

import os
import tensorflow.python.platform

import numpy
import tensorflow as tf
from tensorflow.g3doc.tutorials.mnist import input_data


TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'  # MNIST filenames
TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'
TEST_IMAGES = 't10k-images-idx3-ubyte.gz'
TEST_LABELS = 't10k-labels-idx1-ubyte.gz'


tf.app.flags.DEFINE_string('directory', 'data',
                           'Directory to download data files and write the '
                           'converted result')
tf.app.flags.DEFINE_integer('validation_size', 5000,
                            'Number of examples to separate from the training '
                            'data for the validation set.')
FLAGS = tf.app.flags.FLAGS


def _int64_feature(value):
  return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))


def _bytes_feature(value):
  return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))


def convert_to(images, labels, name):
    num_examples = labels.shape[0]
    if images.shape[0] != num_examples:
        raise ValueError("Images size %d does not match label size %d." %
                         (dat.shape[0], num_examples))
    rows = images.shape[1]
    cols = images.shape[2]
    depth = images.shape[3]

    filename = os.path.join(FLAGS.directory, name + '.tfrecords')
    print 'Writing', filename
    writer = tf.python_io.TFRecordWriter(filename)
    for index in range(num_examples):
        image_raw = images[index].tostring()
        example = tf.train.Example(features=tf.train.Features(feature={
            'height':_int64_feature(rows),
            'width':_int64_feature(cols),
            'depth':_int64_feature(depth),
            'label':_int64_feature(int(labels[index])),
            'image_raw':_bytes_feature(image_raw)}))
        writer.write(example.SerializeToString())


def main(argv):
    # Get the data.
    train_images_filename = input_data.maybe_download(
        TRAIN_IMAGES, FLAGS.directory)
    train_labels_filename = input_data.maybe_download(
        TRAIN_LABELS, FLAGS.directory)
    test_images_filename = input_data.maybe_download(
        TEST_IMAGES, FLAGS.directory)
    test_labels_filename = input_data.maybe_download(
        TEST_LABELS, FLAGS.directory)

    # Extract it into numpy arrays.
    train_images = input_data.extract_images(train_images_filename)
    train_labels = input_data.extract_labels(train_labels_filename)
    test_images = input_data.extract_images(test_images_filename)
    test_labels = input_data.extract_labels(test_labels_filename)

    # Generate a validation set.
    validation_images = train_images[:FLAGS.validation_size, :, :, :]
    validation_labels = train_labels[:FLAGS.validation_size]
    train_images = train_images[FLAGS.validation_size:, :, :, :]
    train_labels = train_labels[FLAGS.validation_size:]

    # Convert to Examples and write the result to TFRecords.
    convert_to(train_images, train_labels, 'train')
    convert_to(validation_images, validation_labels, 'validation')
    convert_to(test_images, test_labels, 'test')


if __name__ == '__main__':
    tf.app.run()