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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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"""Tests for ALBERT transformer-based text encoder network."""

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

from official.nlp.modeling.networks import albert_encoder


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

  def tearDown(self):
    super(AlbertEncoderTest, self).tearDown()
    tf_keras.mixed_precision.set_global_policy("float32")

  @parameterized.named_parameters(
      dict(testcase_name="default", expected_dtype=tf.float32),
      dict(testcase_name="with_float16_dtype", expected_dtype=tf.float16),
  )
  def test_network_creation(self, expected_dtype):
    hidden_size = 32
    sequence_length = 21

    kwargs = dict(
        vocab_size=100,
        hidden_size=hidden_size,
        num_attention_heads=2,
        num_layers=3)
    if expected_dtype == tf.float16:
      tf_keras.mixed_precision.set_global_policy("mixed_float16")

    # Create a small TransformerEncoder for testing.
    test_network = albert_encoder.AlbertEncoder(**kwargs)

    # Create the inputs (note that the first dimension is implicit).
    word_ids = tf_keras.Input(shape=(sequence_length,), dtype=tf.int32)
    mask = tf_keras.Input(shape=(sequence_length,), dtype=tf.int32)
    type_ids = tf_keras.Input(shape=(sequence_length,), dtype=tf.int32)
    data, pooled = test_network([word_ids, mask, type_ids])

    expected_data_shape = [None, sequence_length, hidden_size]
    expected_pooled_shape = [None, hidden_size]
    self.assertAllEqual(expected_data_shape, data.shape.as_list())
    self.assertAllEqual(expected_pooled_shape, pooled.shape.as_list())

    # If float_dtype is set to float16, the data output is float32 (from a layer
    # norm) and pool output should be float16.
    self.assertEqual(tf.float32, data.dtype)
    self.assertEqual(expected_dtype, pooled.dtype)

    # ALBERT has additonal 'embedding_hidden_mapping_in' weights and
    # it shares transformer weights.
    self.assertNotEmpty(
        [x for x in test_network.weights if "embedding_projection/" in x.name])
    self.assertNotEmpty(
        [x for x in test_network.weights if "transformer/" in x.name])
    self.assertEmpty(
        [x for x in test_network.weights if "transformer/layer" in x.name])

  def test_network_invocation(self):
    hidden_size = 32
    sequence_length = 21
    vocab_size = 57
    num_types = 7
    num_layers = 3
    # Create a small TransformerEncoder for testing.
    test_network = albert_encoder.AlbertEncoder(
        vocab_size=vocab_size,
        embedding_width=8,
        hidden_size=hidden_size,
        num_attention_heads=2,
        num_layers=num_layers,
        type_vocab_size=num_types)
    # Create the inputs (note that the first dimension is implicit).
    word_ids = tf_keras.Input(shape=(sequence_length,), dtype=tf.int32)
    mask = tf_keras.Input(shape=(sequence_length,), dtype=tf.int32)
    type_ids = tf_keras.Input(shape=(sequence_length,), dtype=tf.int32)
    data, pooled = test_network([word_ids, mask, type_ids])

    # Create a model based off of this network:
    model = tf_keras.Model([word_ids, mask, type_ids], [data, pooled])

    # Invoke the model. We can't validate the output data here (the model is too
    # complex) but this will catch structural runtime errors.
    batch_size = 3
    word_id_data = np.random.randint(
        vocab_size, size=(batch_size, sequence_length))
    mask_data = np.random.randint(2, size=(batch_size, sequence_length))
    type_id_data = np.random.randint(
        num_types, size=(batch_size, sequence_length))
    list_outputs = model.predict([word_id_data, mask_data, type_id_data])

    # Creates a TransformerEncoder with max_sequence_length != sequence_length
    max_sequence_length = 128
    test_network = albert_encoder.AlbertEncoder(
        vocab_size=vocab_size,
        embedding_width=8,
        hidden_size=hidden_size,
        max_sequence_length=max_sequence_length,
        num_attention_heads=2,
        num_layers=num_layers,
        type_vocab_size=num_types)
    model = tf_keras.Model([word_ids, mask, type_ids], [data, pooled])
    _ = model.predict([word_id_data, mask_data, type_id_data])

    # Tests dictionary outputs.
    test_network_dict = albert_encoder.AlbertEncoder(
        vocab_size=vocab_size,
        embedding_width=8,
        hidden_size=hidden_size,
        max_sequence_length=max_sequence_length,
        num_attention_heads=2,
        num_layers=num_layers,
        type_vocab_size=num_types,
        dict_outputs=True)
    _ = test_network_dict([word_ids, mask, type_ids])
    test_network_dict.set_weights(test_network.get_weights())
    list_outputs = test_network([word_id_data, mask_data, type_id_data])
    dict_outputs = test_network_dict(
        dict(
            input_word_ids=word_id_data,
            input_mask=mask_data,
            input_type_ids=type_id_data))
    self.assertAllEqual(list_outputs[0], dict_outputs["sequence_output"])
    self.assertAllEqual(list_outputs[1], dict_outputs["pooled_output"])
    self.assertLen(dict_outputs["pooled_output"], num_layers)

  def test_serialize_deserialize(self):
    tf_keras.mixed_precision.set_global_policy("mixed_float16")
    # Create a network object that sets all of its config options.
    kwargs = dict(
        vocab_size=100,
        embedding_width=8,
        hidden_size=32,
        num_layers=3,
        num_attention_heads=2,
        max_sequence_length=21,
        type_vocab_size=12,
        intermediate_size=1223,
        activation="relu",
        dropout_rate=0.05,
        attention_dropout_rate=0.22,
        initializer="glorot_uniform")
    network = albert_encoder.AlbertEncoder(**kwargs)

    expected_config = dict(kwargs)
    expected_config["activation"] = tf_keras.activations.serialize(
        tf_keras.activations.get(expected_config["activation"]))
    expected_config["initializer"] = tf_keras.initializers.serialize(
        tf_keras.initializers.get(expected_config["initializer"]))
    self.assertEqual(network.get_config(), expected_config)

    # Create another network object from the first object's config.
    new_network = (
        albert_encoder.AlbertEncoder.from_config(
            network.get_config()))

    # Validate that the config can be forced to JSON.
    _ = new_network.to_json()

    # If the serialization was successful, the new config should match the old.
    self.assertAllEqual(network.get_config(), new_network.get_config())


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