# Copyright 2019 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 official.nlp.bert.export_tfhub."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

from absl.testing import parameterized
import numpy as np
import tensorflow as tf
import tensorflow_hub as hub

from official.nlp.bert import configs
from official.nlp.bert import export_tfhub


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

  @parameterized.parameters("model", "encoder")
  def test_export_tfhub(self, ckpt_key_name):
    # Exports a savedmodel for TF-Hub
    hidden_size = 16
    bert_config = configs.BertConfig(
        vocab_size=100,
        hidden_size=hidden_size,
        intermediate_size=32,
        max_position_embeddings=128,
        num_attention_heads=2,
        num_hidden_layers=1)
    bert_model, encoder = export_tfhub.create_bert_model(bert_config)
    model_checkpoint_dir = os.path.join(self.get_temp_dir(), "checkpoint")
    checkpoint = tf.train.Checkpoint(**{ckpt_key_name: encoder})
    checkpoint.save(os.path.join(model_checkpoint_dir, "test"))
    model_checkpoint_path = tf.train.latest_checkpoint(model_checkpoint_dir)

    vocab_file = os.path.join(self.get_temp_dir(), "uncased_vocab.txt")
    with tf.io.gfile.GFile(vocab_file, "w") as f:
      f.write("dummy content")

    hub_destination = os.path.join(self.get_temp_dir(), "hub")
    export_tfhub.export_bert_tfhub(bert_config, model_checkpoint_path,
                                   hub_destination, vocab_file)

    # Restores a hub KerasLayer.
    hub_layer = hub.KerasLayer(hub_destination, trainable=True)

    if hasattr(hub_layer, "resolved_object"):
      # Checks meta attributes.
      self.assertTrue(hub_layer.resolved_object.do_lower_case.numpy())
      with tf.io.gfile.GFile(
          hub_layer.resolved_object.vocab_file.asset_path.numpy()) as f:
        self.assertEqual("dummy content", f.read())
    # Checks the hub KerasLayer.
    for source_weight, hub_weight in zip(bert_model.trainable_weights,
                                         hub_layer.trainable_weights):
      self.assertAllClose(source_weight.numpy(), hub_weight.numpy())

    seq_length = 10
    dummy_ids = np.zeros((2, seq_length), dtype=np.int32)
    hub_outputs = hub_layer([dummy_ids, dummy_ids, dummy_ids])
    source_outputs = bert_model([dummy_ids, dummy_ids, dummy_ids])

    # The outputs of hub module are "pooled_output" and "sequence_output",
    # while the outputs of encoder is in reversed order, i.e.,
    # "sequence_output" and "pooled_output".
    encoder_outputs = reversed(encoder([dummy_ids, dummy_ids, dummy_ids]))
    self.assertEqual(hub_outputs[0].shape, (2, hidden_size))
    self.assertEqual(hub_outputs[1].shape, (2, seq_length, hidden_size))
    for source_output, hub_output, encoder_output in zip(
        source_outputs, hub_outputs, encoder_outputs):
      self.assertAllClose(source_output.numpy(), hub_output.numpy())
      self.assertAllClose(source_output.numpy(), encoder_output.numpy())

    # Test that training=True makes a difference (activates dropout).
    def _dropout_mean_stddev(training, num_runs=20):
      input_ids = np.array([[14, 12, 42, 95, 99]], np.int32)
      inputs = [input_ids, np.ones_like(input_ids), np.zeros_like(input_ids)]
      outputs = np.concatenate(
          [hub_layer(inputs, training=training)[0] for _ in range(num_runs)])
      return np.mean(np.std(outputs, axis=0))

    self.assertLess(_dropout_mean_stddev(training=False), 1e-6)
    self.assertGreater(_dropout_mean_stddev(training=True), 1e-3)

    # Test propagation of seq_length in shape inference.
    input_word_ids = tf.keras.layers.Input(shape=(seq_length,), dtype=tf.int32)
    input_mask = tf.keras.layers.Input(shape=(seq_length,), dtype=tf.int32)
    input_type_ids = tf.keras.layers.Input(shape=(seq_length,), dtype=tf.int32)
    pooled_output, sequence_output = hub_layer(
        [input_word_ids, input_mask, input_type_ids])
    self.assertEqual(pooled_output.shape.as_list(), [None, hidden_size])
    self.assertEqual(sequence_output.shape.as_list(),
                     [None, seq_length, hidden_size])


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