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


import os

from six.moves import xrange
import tensorflow as tf

IMAGE_SIZE = 24

NUM_CLASSES = 10
NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 50000
NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = 10000


def read_cifar10(filename_queue):
    """Reads and parses examples from CIFAR10 data files.
    Recommendation: if you want N-way read parallelism, call this function
    N times.  This will give you N independent Readers reading different
    files & positions within those files, which will give better mixing of
    examples.
    Args:
      filename_queue: A queue of strings with the filenames to read from.
    Returns:
      An object representing a single example, with the following fields:
        height: number of rows in the result (32)
        width: number of columns in the result (32)
        depth: number of color channels in the result (3)
        key: a scalar string Tensor describing the filename & record number
          for this example.
        label: an int32 Tensor with the label in the range 0..9.
        uint8image: a [height, width, depth] uint8 Tensor with the image data
    """
    class CIFAR10Record(object):
        pass
    result = CIFAR10Record()

    label_bytes = 1
    result.height = 32
    result.width = 32
    result.depth = 3
    image_bytes = result.height * result.width * result.depth
    record_bytes = label_bytes + image_bytes

    reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)
    result.key, value = reader.read(filename_queue)

    record_bytes = tf.decode_raw(value, tf.uint8)

    result.label = tf.cast(
        tf.strided_slice(record_bytes, [0], [label_bytes]), tf.int32
    )

    depth_major = tf.reshape(
        tf.strided_slice(record_bytes, [label_bytes], [label_bytes + image_bytes]),
        [result.depth, result.height, result.width]
    )
    result.uint8image = tf.transpose(depth_major, [1, 2, 0])

    return result


def _generate_image_and_label_batch(image, label, min_queue_examples,
                                    batch_size, shuffle):
    """Construct a queued batch of images and labels.
    Args:
      image: 3-D Tensor of [height, width, 3] of type.float32.
      label: 1-D Tensor of type.int32
      min_queue_examples: int32, minimum number of samples to retain
        in the queue that provides of batches of examples.
      batch_size: Number of images per batch.
      shuffle: boolean indicating whether to use a shuffling queue.
    Returns:
      images: Images. 4D tensor of [batch_size, height, width, 3] size.
      labels: Labels. 1D tensor of [batch_size] size.
    """
    num_preprocess_threads = 16
    if shuffle:
        images, label_batch = tf.train.shuffle_batch(
            [image, label],
            batch_size=batch_size,
            num_threads=num_preprocess_threads,
            capacity=min_queue_examples + 3 * batch_size,
            min_after_dequeue=min_queue_examples
        )
    else:
        images, label_batch = tf.train.batch(
            [image, label],
            batch_size=batch_size,
            num_threads=num_preprocess_threads,
            capacity=min_queue_examples + 3 * batch_size
        )
    tf.summary.image('images', images)
    return images, tf.reshape(label_batch, [batch_size])


def distorted_inputs(data_dir, batch_size):
    """Construct distorted input for CIFAR training using the Reader ops.
    Args:
      data_dir: Path to the CIFAR-10 data directory.
      batch_size: Number of images per batch.
    Returns:
      images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
      labels: Labels. 1D tensor of [batch_size] size.
    """
    filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i)
                 for i in xrange(1, 6)]
    for f in filenames:
        if not tf.gfile.Exists(f):
            raise ValueError('Failed to find file: ' + f)

    filename_queue = tf.train.string_input_producer(filenames)
    read_input = read_cifar10(filename_queue)
    reshaped_image = tf.cast(read_input.uint8image, tf.float32)

    height = IMAGE_SIZE
    width = IMAGE_SIZE

    distorted_image = tf.random_crop(reshaped_image, [height, width, 3])
    distorted_image = tf.image.random_brightness(distorted_image,
                                                 max_delta=63)
    distorted_image = tf.image.random_contrast(distorted_image,
                                               lower=0.2,
                                               upper=1.8)

    float_image = tf.image.per_image_standardization(distorted_image)
    float_image.set_shape([height, width, 3])
    read_input.label.set_shape([1])

    min_fraction_of_examples_in_queue = 0.4
    min_queue_examples = int(NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN *
                             min_fraction_of_examples_in_queue)
    print('Filling queue with %d CIFAR images before starting to train. '
          'This will take a few minutes.' % min_queue_examples)
    return _generate_image_and_label_batch(float_image, read_input.label,
                                           min_queue_examples, batch_size,
                                           shuffle=True)


def inputs(eval_data, data_dir, batch_size):
    """Construct input for CIFAR evaluation using the Reader ops.
    Args:
      eval_data: bool, indicating if one should use the train or eval data set.
      data_dir: Path to the CIFAR-10 data directory.
      batch_size: Number of images per batch.
    Returns:
      images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
      labels: Labels. 1D tensor of [batch_size] size.
    """
    if not eval_data:
        filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i)
                     for i in xrange(1, 6)]
        num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN
    else:
        filenames = [os.path.join(data_dir, 'test_batch.bin')]
        num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_EVAL

    for f in filenames:
        if not tf.gfile.Exists(f):
            raise ValueError('Failed to find file: ' + f)

    filename_queue = tf.train.string_input_producer(filenames)

    read_input = read_cifar10(filename_queue)
    reshaped_image = tf.cast(read_input.uint8image, tf.float32)

    height = IMAGE_SIZE
    width = IMAGE_SIZE

    resized_image = tf.image.resize_image_with_crop_or_pad(reshaped_image,
                                                           height, width)
    float_image = tf.image.per_image_standardization(resized_image)
    float_image.set_shape([height, width, 3])
    read_input.label.set_shape([1])

    min_fraction_of_examples_in_queue = 0.4
    min_queue_examples = int(num_examples_per_epoch * min_fraction_of_examples_in_queue)

    return _generate_image_and_label_batch(float_image, read_input.label,
                                           min_queue_examples, batch_size,
                                           shuffle=False)