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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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"""Unit tests for data_pipeline."""

from absl.testing import parameterized
import tensorflow as tf

from official.recommendation.ranking.configs import config
from official.recommendation.ranking.data import data_pipeline


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

  @parameterized.named_parameters(('Train', True),
                                  ('Eval', False))
  def testSyntheticDataPipeline(self, is_training):
    task = config.Task(
        model=config.ModelConfig(
            embedding_dim=4,
            num_dense_features=8,
            vocab_sizes=[40, 12, 11, 13, 2, 5],
            bottom_mlp=[64, 32, 4],
            top_mlp=[64, 32, 1]),
        train_data=config.DataConfig(global_batch_size=16),
        validation_data=config.DataConfig(global_batch_size=16),
        use_synthetic_data=True)

    num_dense_features = task.model.num_dense_features
    num_sparse_features = len(task.model.vocab_sizes)
    batch_size = task.train_data.global_batch_size

    if is_training:
      dataset = data_pipeline.train_input_fn(task)
    else:
      dataset = data_pipeline.eval_input_fn(task)

    dataset_iter = iter(dataset(ctx=None))

    # Consume full batches and validate shapes.
    for _ in range(10):
      features, label = next(dataset_iter)
      dense_features = features['dense_features']
      sparse_features = features['sparse_features']
      self.assertEqual(dense_features.shape, [batch_size, num_dense_features])
      self.assertLen(sparse_features, num_sparse_features)
      for _, val in sparse_features.items():
        self.assertEqual(val.shape, [batch_size])
      self.assertEqual(label.shape, [batch_size])

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