text: # 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.
  
  """Bert encoder network."""
  # pylint: disable=g-classes-have-attributes
  
  import collections
  from absl import logging
  import tensorflow as tf
  
  from official.nlp.keras_nlp import layers
  
  
  @tf.keras.utils.register_keras_serializable(package='keras_nlp')
  class BertEncoder(tf.keras.Model):
    """Bi-directional Transformer-based encoder network.
  
    This network implements a bi-directional Transformer-based encoder as
    described in "BERT: Pre-training of Deep Bidirectional Transformers for
    Language Understanding" (https://arxiv.org/abs/1810.04805). It includes the
    embedding lookups and transformer layers, but not the masked language model
    or classification task networks.
  
    The default values for this object are taken from the BERT-Base implementation
    in "BERT: Pre-training of Deep Bidirectional Transformers for Language
    Understanding".
  
    *Note* that the network is constructed by
    [Keras Functional API](https://keras.io/guides/functional_api/).
  
    Args:
      vocab_size: The size of the token vocabulary.
      hidden_size: The size of the transformer hidden layers.
      num_layers: The number of transformer layers.
      num_attention_heads: The number of attention heads for each transformer. The
        hidden size must be divisible by the number of attention heads.
      max_sequence_length: The maximum sequence length that this encoder can
        consume. If None, max_sequence_length uses the value from sequence length.
        This determines the variable shape for positional embeddings.
      type_vocab_size: The number of types that the 'type_ids' input can take.
      inner_dim: The output dimension of the first Dense layer in a two-layer
          feedforward network for each transformer.
      inner_activation: The activation for the first Dense layer in a two-layer
          feedforward network for each transformer.
      output_dropout: Dropout probability for the post-attention and output
          dropout.
      attention_dropout: The dropout rate to use for the attention layers
        within the transformer layers.
      initializer: The initialzer to use for all weights in this encoder.
      output_range: The sequence output range, [0, output_range), by slicing the
        target sequence of the last transformer layer. `None` means the entire
        target sequence will attend to the source sequence, which yields the full
        output.
      embedding_width: The width of the word embeddings. If the embedding width is
        not equal to hidden size, embedding parameters will be factorized into two
        matrices in the shape of ['vocab_size', 'embedding_width'] and
        ['embedding_width', 'hidden_size'] ('embedding_width' is usually much
        smaller than 'hidden_size').
      embedding_layer: An optional Layer instance which will be called to
       generate embeddings for the input word IDs.
    """
  
    def __init__(
        self,
        vocab_size,
        hidden_size=768,
        num_layers=12,
        num_attention_heads=12,
        max_sequence_length=512,
        type_vocab_size=16,
        inner_dim=3072,
        inner_activation=lambda x: tf.keras.activations.gelu(x, approximate=True),
        output_dropout=0.1,
        attention_dropout=0.1,
        initializer=tf.keras.initializers.TruncatedNormal(stddev=0.02),
        output_range=None,
        embedding_width=None,
        embedding_layer=None,
        **kwargs):
      activation = tf.keras.activations.get(inner_activation)
      initializer = tf.keras.initializers.get(initializer)
  
      word_ids = tf.keras.layers.Input(
          shape=(None,), dtype=tf.int32, name='input_word_ids')
      mask = tf.keras.layers.Input(
          shape=(None,), dtype=tf.int32, name='input_mask')
      type_ids = tf.keras.layers.Input(
          shape=(None,), dtype=tf.int32, name='input_type_ids')
  
      if embedding_width is None:
        embedding_width = hidden_size
  
      if embedding_layer is None:
        embedding_layer_inst = layers.OnDeviceEmbedding(
            vocab_size=vocab_size,
            embedding_width=embedding_width,
            initializer=initializer,
            name='word_embeddings')
      else:
        embedding_layer_inst = embedding_layer
      word_embeddings = embedding_layer_inst(word_ids)
  
      # Always uses dynamic slicing for simplicity.
      position_embedding_layer = layers.PositionEmbedding(
          initializer=initializer,
          max_length=max_sequence_length,
          name='position_embedding')
      position_embeddings = position_embedding_layer(word_embeddings)
      type_embedding_layer = layers.OnDeviceEmbedding(
          vocab_size=type_vocab_size,
          embedding_width=embedding_width,
          initializer=initializer,
          use_one_hot=True,
          name='type_embeddings')
      type_embeddings = type_embedding_layer(type_ids)
  
      embeddings = tf.keras.layers.Add()(
          [word_embeddings, position_embeddings, type_embeddings])
  
      embedding_norm_layer = tf.keras.layers.LayerNormalization(
          name='embeddings/layer_norm', axis=-1, epsilon=1e-12, dtype=tf.float32)
  
      embeddings = embedding_norm_layer(embeddings)
      embeddings = (tf.keras.layers.Dropout(rate=output_dropout)(embeddings))
  
      # We project the 'embedding' output to 'hidden_size' if it is not already
      # 'hidden_size'.
      if embedding_width != hidden_size:
        embedding_projection = tf.keras.layers.experimental.EinsumDense(
            '...x,xy->...y',
            output_shape=hidden_size,
            bias_axes='y',
            kernel_initializer=initializer,
            name='embedding_projection')
        embeddings = embedding_projection(embeddings)
      else:
        embedding_projection = None
  
      transformer_layers = []
      data = embeddings
      attention_mask = layers.SelfAttentionMask()(data, mask)
      encoder_outputs = []
      for i in range(num_layers):
        if i == num_layers - 1 and output_range is not None:
          transformer_output_range = output_range
        else:
          transformer_output_range = None
        layer = layers.TransformerEncoderBlock(
            num_attention_heads=num_attention_heads,
            inner_dim=inner_dim,
            inner_activation=inner_activation,
            output_dropout=output_dropout,
            attention_dropout=attention_dropout,
            output_range=transformer_output_range,
            kernel_initializer=initializer,
            name='transformer/layer_%d' % i)
        transformer_layers.append(layer)
        data = layer([data, attention_mask])
        encoder_outputs.append(data)
  
      last_encoder_output = encoder_outputs[-1]
      # Applying a tf.slice op (through subscript notation) to a Keras tensor
      # like this will create a SliceOpLambda layer. This is better than a Lambda
      # layer with Python code, because that is fundamentally less portable.
      first_token_tensor = last_encoder_output[:, 0, :]
      pooler_layer = tf.keras.layers.Dense(
          units=hidden_size,
          activation='tanh',
          kernel_initializer=initializer,
          name='pooler_transform')
      cls_output = pooler_layer(first_token_tensor)
  
      outputs = dict(
          sequence_output=encoder_outputs[-1],
          pooled_output=cls_output,
          encoder_outputs=encoder_outputs,
      )
  
      # Once we've created the network using the Functional API, we call
      # super().__init__ as though we were invoking the Functional API Model
      # constructor, resulting in this object having all the properties of a model
      # created using the Functional API. Once super().__init__ is called, we
      # can assign attributes to `self` - note that all `self` assignments are
      # below this line.
      super(BertEncoder, self).__init__(
          inputs=[word_ids, mask, type_ids], outputs=outputs, **kwargs)
  
      config_dict = {
          'vocab_size': vocab_size,
          'hidden_size': hidden_size,
          'num_layers': num_layers,
          'num_attention_heads': num_attention_heads,
          'max_sequence_length': max_sequence_length,
          'type_vocab_size': type_vocab_size,
          'inner_dim': inner_dim,
          'inner_activation': tf.keras.activations.serialize(activation),
          'output_dropout': output_dropout,
          'attention_dropout': attention_dropout,
          'initializer': tf.keras.initializers.serialize(initializer),
          'output_range': output_range,
          'embedding_width': embedding_width,
          'embedding_layer': embedding_layer,
      }
  
      # We are storing the config dict as a namedtuple here to ensure checkpoint
      # compatibility with an earlier version of this model which did not track
      # the config dict attribute. TF does not track immutable attrs which
      # do not contain Trackables, so by creating a config namedtuple instead of
      # a dict we avoid tracking it.
      config_cls = collections.namedtuple('Config', config_dict.keys())
      self._config = config_cls(**config_dict)
      self._pooler_layer = pooler_layer
      self._transformer_layers = transformer_layers
      self._embedding_norm_layer = embedding_norm_layer
      self._embedding_layer = embedding_layer_inst
      self._position_embedding_layer = position_embedding_layer
      self._type_embedding_layer = type_embedding_layer
      if embedding_projection is not None:
        self._embedding_projection = embedding_projection
  
    def get_embedding_table(self):
      return self._embedding_layer.embeddings
  
    def get_embedding_layer(self):
      return self._embedding_layer
  
    def get_config(self):
      return dict(self._config._asdict())
  
    @property
    def transformer_layers(self):
      """List of Transformer layers in the encoder."""
      return self._transformer_layers
  
    @property
    def pooler_layer(self):
      """The pooler dense layer after the transformer layers."""
      return self._pooler_layer
  
    @classmethod
    def from_config(cls, config, custom_objects=None):
      if 'embedding_layer' in config and config['embedding_layer'] is not None:
        warn_string = (
            'You are reloading a model that was saved with a '
            'potentially-shared embedding layer object. If you contine to '
            'train this model, the embedding layer will no longer be shared. '
            'To work around this, load the model outside of the Keras API.')
        print('WARNING: ' + warn_string)
        logging.warn(warn_string)
  
      return cls(**config)
  