# Copyright 2016 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.
# ==============================================================================
"""Contains utilities for downloading and converting datasets."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import sys
import tarfile

from six.moves import urllib
import tensorflow as tf

LABELS_FILENAME = 'labels.txt'


def int64_feature(value):
    """Wrapper for inserting int64 features into Example proto.
    """
    if not isinstance(value, list):
        value = [value]
    return tf.train.Feature(int64_list=tf.train.Int64List(value=value))


def float_feature(value):
    """Wrapper for inserting float features into Example proto.
    """
    if not isinstance(value, list):
        value = [value]
    return tf.train.Feature(float_list=tf.train.FloatList(value=value))


def bytes_feature(value):
    """Wrapper for inserting bytes features into Example proto.
    """
    if not isinstance(value, list):
        value = [value]
    return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))


def image_to_tfexample(image_data, image_format, height, width, class_id):
    return tf.train.Example(features=tf.train.Features(feature={
      'image/encoded': bytes_feature(image_data),
      'image/format': bytes_feature(image_format),
      'image/class/label': int64_feature(class_id),
      'image/height': int64_feature(height),
      'image/width': int64_feature(width),
    }))


def download_and_uncompress_tarball(tarball_url, dataset_dir):
    """Downloads the `tarball_url` and uncompresses it locally.

    Args:
    tarball_url: The URL of a tarball file.
    dataset_dir: The directory where the temporary files are stored.
    """
    filename = tarball_url.split('/')[-1]
    filepath = os.path.join(dataset_dir, filename)

    def _progress(count, block_size, total_size):
        sys.stdout.write('\r>> Downloading %s %.1f%%' % (
            filename, float(count * block_size) / float(total_size) * 100.0))
        sys.stdout.flush()
    filepath, _ = urllib.request.urlretrieve(tarball_url, filepath, _progress)
    print()
    statinfo = os.stat(filepath)
    print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
    tarfile.open(filepath, 'r:gz').extractall(dataset_dir)


def write_label_file(labels_to_class_names, dataset_dir,
                     filename=LABELS_FILENAME):
    """Writes a file with the list of class names.

    Args:
    labels_to_class_names: A map of (integer) labels to class names.
    dataset_dir: The directory in which the labels file should be written.
    filename: The filename where the class names are written.
    """
    labels_filename = os.path.join(dataset_dir, filename)
    with tf.gfile.Open(labels_filename, 'w') as f:
        for label in labels_to_class_names:
            class_name = labels_to_class_names[label]
            f.write('%d:%s\n' % (label, class_name))


def has_labels(dataset_dir, filename=LABELS_FILENAME):
    """Specifies whether or not the dataset directory contains a label map file.

    Args:
    dataset_dir: The directory in which the labels file is found.
    filename: The filename where the class names are written.

    Returns:
    `True` if the labels file exists and `False` otherwise.
    """
    return tf.gfile.Exists(os.path.join(dataset_dir, filename))


def read_label_file(dataset_dir, filename=LABELS_FILENAME):
    """Reads the labels file and returns a mapping from ID to class name.

    Args:
    dataset_dir: The directory in which the labels file is found.
    filename: The filename where the class names are written.

    Returns:
    A map from a label (integer) to class name.
    """
    labels_filename = os.path.join(dataset_dir, filename)
    with tf.gfile.Open(labels_filename, 'rb') as f:
        lines = f.read()
    lines = lines.split(b'\n')
    lines = filter(None, lines)

    labels_to_class_names = {}
    for line in lines:
        index = line.index(b':')
        labels_to_class_names[int(line[:index])] = line[index+1:]
    return labels_to_class_names