# 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.
# ==============================================================================
"""CIFAR100 small image classification dataset.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

import numpy as np

from tensorflow.contrib.keras.python.keras import backend as K
from tensorflow.contrib.keras.python.keras.datasets.cifar import load_batch
from tensorflow.contrib.keras.python.keras.utils.data_utils import get_file


def load_data(label_mode='fine'):
  """Loads CIFAR100 dataset.

  Arguments:
      label_mode: one of "fine", "coarse".

  Returns:
      Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.

  Raises:
      ValueError: in case of invalid `label_mode`.
  """
  if label_mode not in ['fine', 'coarse']:
    raise ValueError('label_mode must be one of "fine" "coarse".')

  dirname = 'cifar-100-python'
  origin = 'http://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz'
  path = get_file(dirname, origin=origin, untar=True)

  fpath = os.path.join(path, 'train')
  x_train, y_train = load_batch(fpath, label_key=label_mode + '_labels')

  fpath = os.path.join(path, 'test')
  x_test, y_test = load_batch(fpath, label_key=label_mode + '_labels')

  y_train = np.reshape(y_train, (len(y_train), 1))
  y_test = np.reshape(y_test, (len(y_test), 1))

  if K.image_data_format() == 'channels_last':
    x_train = x_train.transpose(0, 2, 3, 1)
    x_test = x_test.transpose(0, 2, 3, 1)

  return (x_train, y_train), (x_test, y_test)
