# Copyright 2021 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 for transformer-based bert encoder network."""

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

from official.nlp.modeling.networks import funnel_transformer


class SingleLayerModel(tf.keras.Model):

  def __init__(self, layer):
    super().__init__()
    self.layer = layer

  def call(self, inputs):
    return self.layer(inputs)


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

  def tearDown(self):
    super(FunnelTransformerEncoderTest, self).tearDown()
    tf.keras.mixed_precision.set_global_policy("float32")

  @parameterized.named_parameters(("mix", "mixed_float16", tf.float16),
                                  ("float32", "float32", tf.float32))
  def test_network_creation(self, policy, pooled_dtype):
    tf.keras.mixed_precision.set_global_policy(policy)

    hidden_size = 32
    sequence_length = 21
    pool_stride = 2
    num_layers = 3
    # Create a small FunnelTransformerEncoder for testing.
    test_network = funnel_transformer.FunnelTransformerEncoder(
        vocab_size=100,
        hidden_size=hidden_size,
        num_attention_heads=2,
        num_layers=num_layers,
        pool_stride=pool_stride,
        unpool_length=0)
    # 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)
    dict_outputs = test_network([word_ids, mask, type_ids])
    data = dict_outputs["sequence_output"]
    pooled = dict_outputs["pooled_output"]

    self.assertIsInstance(test_network.transformer_layers, list)
    self.assertLen(test_network.transformer_layers, num_layers)
    self.assertIsInstance(test_network.pooler_layer, tf.keras.layers.Dense)

    # Stride=2 compresses sequence length to half the size at each layer.
    # This configuration gives each layer of seq length: 21->11->6->3.
    expected_data_shape = [None, 3, 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())

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

  @parameterized.named_parameters(
      ("no_stride_no_unpool", 1, 0),
      ("large_stride_with_unpool", 3, 1),
      ("large_stride_with_large_unpool", 5, 10),
      ("no_stride_with_unpool", 1, 1),
  )
  def test_all_encoder_outputs_network_creation(self, pool_stride,
                                                unpool_length):
    hidden_size = 32
    sequence_length = 21
    num_layers = 3
    # Create a small FunnelTransformerEncoder for testing.
    test_network = funnel_transformer.FunnelTransformerEncoder(
        vocab_size=100,
        hidden_size=hidden_size,
        num_attention_heads=2,
        num_layers=num_layers,
        pool_stride=pool_stride,
        unpool_length=unpool_length)
    # 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)
    dict_outputs = test_network([word_ids, mask, type_ids])
    all_encoder_outputs = dict_outputs["encoder_outputs"]
    pooled = dict_outputs["pooled_output"]

    expected_data_shape = [None, sequence_length, hidden_size]
    expected_pooled_shape = [None, hidden_size]
    self.assertLen(all_encoder_outputs, num_layers)
    for data in all_encoder_outputs:
      expected_data_shape[1] = unpool_length + (expected_data_shape[1] +
                                                pool_stride - 1 -
                                                unpool_length) // pool_stride
      print("shapes:", expected_data_shape, data.shape.as_list())
      self.assertAllEqual(expected_data_shape, data.shape.as_list())
    self.assertAllEqual(expected_pooled_shape, pooled.shape.as_list())

    # The default output dtype is float32.
    self.assertAllEqual(tf.float32, all_encoder_outputs[-1].dtype)
    self.assertAllEqual(tf.float32, pooled.dtype)

  @parameterized.named_parameters(
      ("all_sequence", None, 3, 0),
      ("output_range", 1, 1, 0),
      ("all_sequence_wit_unpool", None, 4, 1),
      ("output_range_with_unpool", 1, 1, 1),
      ("output_range_with_large_unpool", 1, 1, 2),
  )
  def test_network_invocation(self, output_range, out_seq_len, unpool_length):
    hidden_size = 32
    sequence_length = 21
    vocab_size = 57
    num_types = 7
    pool_stride = 2
    # Create a small FunnelTransformerEncoder for testing.
    test_network = funnel_transformer.FunnelTransformerEncoder(
        vocab_size=vocab_size,
        hidden_size=hidden_size,
        num_attention_heads=2,
        num_layers=3,
        type_vocab_size=num_types,
        output_range=output_range,
        pool_stride=pool_stride,
        unpool_length=unpool_length)
    # 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)
    dict_outputs = test_network([word_ids, mask, type_ids])
    data = dict_outputs["sequence_output"]
    pooled = dict_outputs["pooled_output"]

    # 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))
    outputs = model.predict([word_id_data, mask_data, type_id_data])
    self.assertEqual(outputs[0].shape[1], out_seq_len)  # output_range

    # Creates a FunnelTransformerEncoder with max_sequence_length !=
    # sequence_length
    max_sequence_length = 128
    test_network = funnel_transformer.FunnelTransformerEncoder(
        vocab_size=vocab_size,
        hidden_size=hidden_size,
        max_sequence_length=max_sequence_length,
        num_attention_heads=2,
        num_layers=3,
        type_vocab_size=num_types,
        pool_stride=pool_stride)
    dict_outputs = test_network([word_ids, mask, type_ids])
    data = dict_outputs["sequence_output"]
    pooled = dict_outputs["pooled_output"]
    model = tf.keras.Model([word_ids, mask, type_ids], [data, pooled])
    outputs = model.predict([word_id_data, mask_data, type_id_data])
    self.assertEqual(outputs[0].shape[1], 3)

    # Creates a FunnelTransformerEncoder with embedding_width != hidden_size
    test_network = funnel_transformer.FunnelTransformerEncoder(
        vocab_size=vocab_size,
        hidden_size=hidden_size,
        max_sequence_length=max_sequence_length,
        num_attention_heads=2,
        num_layers=3,
        type_vocab_size=num_types,
        embedding_width=16,
        pool_stride=pool_stride)
    dict_outputs = test_network([word_ids, mask, type_ids])
    data = dict_outputs["sequence_output"]
    pooled = dict_outputs["pooled_output"]
    model = tf.keras.Model([word_ids, mask, type_ids], [data, pooled])
    outputs = model.predict([word_id_data, mask_data, type_id_data])
    self.assertEqual(outputs[0].shape[-1], hidden_size)
    self.assertTrue(hasattr(test_network, "_embedding_projection"))

  def test_serialize_deserialize(self):
    # Create a network object that sets all of its config options.
    kwargs = dict(
        vocab_size=100,
        hidden_size=32,
        num_layers=3,
        num_attention_heads=2,
        max_sequence_length=21,
        type_vocab_size=12,
        inner_dim=1223,
        inner_activation="relu",
        output_dropout=0.05,
        attention_dropout=0.22,
        initializer="glorot_uniform",
        output_range=-1,
        embedding_width=16,
        embedding_layer=None,
        norm_first=False,
        pool_stride=2,
        unpool_length=0)
    network = funnel_transformer.FunnelTransformerEncoder(**kwargs)
    expected_config = dict(kwargs)
    expected_config["inner_activation"] = tf.keras.activations.serialize(
        tf.keras.activations.get(expected_config["inner_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 = funnel_transformer.FunnelTransformerEncoder.from_config(
        network.get_config())

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

    # Tests model saving/loading.
    model_path = self.get_temp_dir() + "/model"
    network_wrapper = SingleLayerModel(network)
    # One forward-path to ensure input_shape.
    batch_size = 3
    sequence_length = 21
    vocab_size = 100
    num_types = 12
    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))

    _ = network_wrapper.predict([word_id_data, mask_data, type_id_data])
    network_wrapper.save(model_path)
    _ = tf.keras.models.load_model(model_path)


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