# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

"""Converts MNIST data to TFRecords file format with Example protos."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import os
import sys

import tensorflow as tf

from tensorflow.contrib.learn.python.learn.datasets import mnist

FLAGS = None


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(data_set, name):
    """Converts a dataset to tfrecords."""
    images = data_set.images
    labels = data_set.labels
    num_examples = data_set.num_examples

    if images.shape[0] != num_examples:
        raise ValueError('Images size %d does not match label size %d.' %
                         (images.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)
    with tf.python_io.TFRecordWriter(filename) as writer:
        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(unused_argv):
    # Get the data.
    data_sets = mnist.read_data_sets(FLAGS.directory,
                                   dtype=tf.uint8,
                                   reshape=False,
                                   validation_size=FLAGS.validation_size)

    # Convert to Examples and write the result to TFRecords.
    convert_to(data_sets.train, 'train')
    convert_to(data_sets.validation, 'validation')
    convert_to(data_sets.test, 'test')


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument(
      '--directory',
      type=str,
      default='/tmp/data',
      help='Directory to download data files and write the converted result'
    )
    parser.add_argument(
      '--validation_size',
      type=int,
      default=5000,
      help="""\
      Number of examples to separate from the training data for the validation
      set.\
      """
    )
    FLAGS, unparsed = parser.parse_known_args()
    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)