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# Licensed under the Apache License, Version 2.0 (the "License");
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"""Tests for official.nlp.configs.encoders."""
import os

import tensorflow as tf, tf_keras

from official.modeling import hyperparams
from official.nlp.configs import encoders
from official.nlp.modeling import networks
from official.projects.teams import teams


class EncodersTest(tf.test.TestCase):

  def test_encoder_from_yaml(self):
    config = encoders.EncoderConfig(
        type="bert", bert=encoders.BertEncoderConfig(num_layers=1))
    encoder = encoders.build_encoder(config)
    ckpt = tf.train.Checkpoint(encoder=encoder)
    ckpt_path = ckpt.save(self.get_temp_dir() + "/ckpt")
    params_save_path = os.path.join(self.get_temp_dir(), "params.yaml")
    hyperparams.save_params_dict_to_yaml(config, params_save_path)

    retored_cfg = encoders.EncoderConfig.from_yaml(params_save_path)
    retored_encoder = encoders.build_encoder(retored_cfg)
    status = tf.train.Checkpoint(encoder=retored_encoder).restore(ckpt_path)
    status.assert_consumed()

  def test_build_teams(self):
    config = encoders.EncoderConfig(
        type="any", any=teams.TeamsEncoderConfig(num_layers=1))
    encoder = encoders.build_encoder(config)
    self.assertIsInstance(encoder, networks.EncoderScaffold)
    self.assertIsInstance(encoder.embedding_network,
                          networks.PackedSequenceEmbedding)


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