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#
# Licensed under the Apache License, Version 2.0 (the "License");
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"""Tests for dataloader utils functions."""

from absl.testing import parameterized
import tensorflow as tf, tf_keras

from official.vision.dataloaders import utils


class UtilsTest(tf.test.TestCase, parameterized.TestCase):

  def test_process_empty_source_id(self):
    source_id = tf.constant([], dtype=tf.int64)
    source_id = tf.strings.as_string(source_id)
    self.assertEqual(-1, utils.process_source_id(source_id=source_id))

  @parameterized.parameters(
      ([128, 256], [128, 256]),
      ([128, 32, 16], [128, 32, 16]),
  )
  def test_process_source_id(self, source_id, expected_result):
    source_id = tf.constant(source_id, dtype=tf.int64)
    source_id = tf.strings.as_string(source_id)
    self.assertSequenceAlmostEqual(expected_result,
                                   utils.process_source_id(source_id=source_id))

  @parameterized.parameters(
      ([[10, 20, 30, 40]], [[100]], [[0]], 10, None),
      ([[0.1, 0.2, 0.5, 0.6]], [[0.5]], [[1]], 2, [[1.0, 2.0]]),
  )
  def test_pad_groundtruths_to_fixed_size(self, boxes, area, classes, size,
                                          attributes):
    groundtruths = {}
    groundtruths['boxes'] = tf.constant(boxes)
    groundtruths['is_crowds'] = tf.constant([[0]])
    groundtruths['areas'] = tf.constant(area)
    groundtruths['classes'] = tf.constant(classes)
    if attributes:
      groundtruths['attributes'] = {'depth': tf.constant(attributes)}

    actual_result = utils.pad_groundtruths_to_fixed_size(
        groundtruths=groundtruths, size=size)

    # Check that the first dimension is padded to the expected size.
    for key in actual_result:
      if key == 'attributes':
        for _, v in actual_result[key].items():
          pad_shape = v.shape[0]
          self.assertEqual(size, pad_shape)
      else:
        pad_shape = actual_result[key].shape[0]
        self.assertEqual(size, pad_shape)


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