# Copyright 2017 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.
# ==============================================================================
"""Tests for the experimental input pipeline ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections

import numpy as np

from tensorflow.contrib.data.python.ops import dataset_ops
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.ops import array_ops
from tensorflow.python.platform import test


class ShuffleDatasetTest(test.TestCase):

  def testShuffleDataset(self):
    components = (
        np.array([1, 2, 3, 4]), np.array([5, 6, 7, 8]),
        np.array([9.0, 10.0, 11.0, 12.0])
    )
    count_placeholder = array_ops.placeholder_with_default(
        constant_op.constant(5, dtypes.int64), shape=[])
    buffer_size_placeholder = array_ops.placeholder(dtypes.int64, shape=[])
    seed_placeholder = array_ops.placeholder(dtypes.int64, shape=[])

    repeat_dataset = (dataset_ops.Dataset.from_tensor_slices(components)
                      .repeat(count_placeholder))

    shuffle_dataset = repeat_dataset.shuffle(buffer_size_placeholder,
                                             seed_placeholder)

    self.assertEqual(tuple([c.shape[1:] for c in components]),
                     shuffle_dataset.output_shapes)

    # Create initialization ops for iterators without and with
    # shuffling, respectively.
    iterator = dataset_ops.Iterator.from_structure(
        shuffle_dataset.output_types, shuffle_dataset.output_shapes)
    init_fifo_op = iterator.make_initializer(repeat_dataset)
    init_shuffle_op = iterator.make_initializer(shuffle_dataset)

    get_next = iterator.get_next()

    with self.test_session() as sess:
      # First run without shuffling to collect the "ground truth".
      sess.run(init_fifo_op)
      unshuffled_elements = []
      for _ in range(20):
        unshuffled_elements.append(sess.run(get_next))
      with self.assertRaises(errors.OutOfRangeError):
        sess.run(get_next)

      # Assert that the shuffled dataset has the same elements as the
      # "ground truth".
      sess.run(
          init_shuffle_op,
          feed_dict={buffer_size_placeholder: 100,
                     seed_placeholder: 37})
      shuffled_elements = []
      for _ in range(20):
        shuffled_elements.append(sess.run(get_next))
      with self.assertRaises(errors.OutOfRangeError):
        sess.run(get_next)
      self.assertAllEqual(
          sorted(unshuffled_elements), sorted(shuffled_elements))

      # Assert that shuffling twice with the same seeds gives the same sequence.
      sess.run(
          init_shuffle_op,
          feed_dict={buffer_size_placeholder: 100,
                     seed_placeholder: 37})
      reshuffled_elements_same_seed = []
      for _ in range(20):
        reshuffled_elements_same_seed.append(sess.run(get_next))
      with self.assertRaises(errors.OutOfRangeError):
        sess.run(get_next)
      self.assertEqual(shuffled_elements, reshuffled_elements_same_seed)

      # Assert that shuffling twice with a different seed gives a different
      # permutation of the same elements.
      sess.run(
          init_shuffle_op,
          feed_dict={buffer_size_placeholder: 100,
                     seed_placeholder: 1037})
      reshuffled_elements_different_seed = []
      for _ in range(20):
        reshuffled_elements_different_seed.append(sess.run(get_next))
      with self.assertRaises(errors.OutOfRangeError):
        sess.run(get_next)
      self.assertNotEqual(shuffled_elements, reshuffled_elements_different_seed)
      self.assertAllEqual(
          sorted(shuffled_elements), sorted(reshuffled_elements_different_seed))

      # Assert that the shuffled dataset has the same elements as the
      # "ground truth" when the buffer size is smaller than the input
      # dataset.
      sess.run(
          init_shuffle_op,
          feed_dict={buffer_size_placeholder: 2,
                     seed_placeholder: 37})
      reshuffled_elements_small_buffer = []
      for _ in range(20):
        reshuffled_elements_small_buffer.append(sess.run(get_next))
      with self.assertRaises(errors.OutOfRangeError):
        sess.run(get_next)
      self.assertAllEqual(
          sorted(unshuffled_elements), sorted(reshuffled_elements_small_buffer))

      # Test the case of shuffling an empty dataset.
      sess.run(init_shuffle_op, feed_dict={buffer_size_placeholder: 2,
                                           seed_placeholder: 37,
                                           count_placeholder: 0})
      with self.assertRaises(errors.OutOfRangeError):
        sess.run(get_next)

  def testDefaultArguments(self):
    components = [0, 1, 2, 3, 4]
    iterator = (dataset_ops.Dataset.from_tensor_slices(components).shuffle(5)
                .repeat().make_one_shot_iterator())

    get_next = iterator.get_next()

    with self.test_session() as sess:
      counts = collections.defaultdict(lambda: 0)
      for _ in range(10):
        for _ in range(5):
          counts[sess.run(get_next)] += 1

    for i in range(5):
      self.assertEqual(10, counts[i])


if __name__ == "__main__":
  test.main()
