# 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 google3.research.vale.object_detection.minibatch_sampler."""

import numpy as np
import tensorflow as tf

from avod.core import minibatch_sampler


class MinibatchSamplerTest(tf.test.TestCase):
    def test_subsample_indicator_when_more_true_elements_than_num_samples(
            self):
        np_indicator = [True, False, True, False, True, True, False]
        indicator = tf.constant(np_indicator)
        samples = minibatch_sampler.MinibatchSampler.subsample_indicator(
            indicator, 3)
        with self.test_session() as sess:
            samples_out = sess.run(samples)
            self.assertTrue(np.sum(samples_out), 3)
            self.assertAllEqual(samples_out,
                                np.logical_and(samples_out, np_indicator))

    def test_subsample_when_more_true_elements_than_num_samples_no_shape(self):
        np_indicator = [True, False, True, False, True, True, False]
        indicator = tf.placeholder(tf.bool)
        feed_dict = {indicator: np_indicator}

        samples = minibatch_sampler.MinibatchSampler.subsample_indicator(
            indicator, 3)
        with self.test_session() as sess:
            samples_out = sess.run(samples, feed_dict=feed_dict)
            self.assertTrue(np.sum(samples_out), 3)
            self.assertAllEqual(samples_out,
                                np.logical_and(samples_out, np_indicator))

    def test_subsample_indicator_when_less_true_elements_than_num_samples(
            self):
        np_indicator = [True, False, True, False, True, True, False]
        indicator = tf.constant(np_indicator)
        samples = minibatch_sampler.MinibatchSampler.subsample_indicator(
            indicator, 5)
        with self.test_session() as sess:
            samples_out = sess.run(samples)
            self.assertTrue(np.sum(samples_out), 4)
            self.assertAllEqual(samples_out,
                                np.logical_and(samples_out, np_indicator))

    def test_subsample_indicator_when_num_samples_is_zero(self):
        np_indicator = [True, False, True, False, True, True, False]
        indicator = tf.constant(np_indicator)
        samples_none = minibatch_sampler.MinibatchSampler.subsample_indicator(
            indicator, 0)
        with self.test_session() as sess:
            samples_none_out = sess.run(samples_none)
            self.assertAllEqual(
                np.zeros_like(samples_none_out, dtype=bool),
                samples_none_out)

    def test_subsample_indicator_when_indicator_all_false(self):
        indicator_empty = tf.zeros([0], dtype=tf.bool)
        samples_empty = minibatch_sampler.MinibatchSampler.subsample_indicator(
            indicator_empty, 4)
        with self.test_session() as sess:
            samples_empty_out = sess.run(samples_empty)
            self.assertEqual(0, samples_empty_out.size)


if __name__ == '__main__':
    tf.test.main()
