code: # coding=utf-8
  # Copyright 2018 The Google AI Language Team Authors.
  #
  # 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.
  """The main BERT model and related functions."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import collections
  import copy
  import json
  import math
  import re
  import six
  import tensorflow as tf
  
  
  class BertConfig(object):
    """Configuration for `BertModel`."""
  
    def __init__(self,
                 vocab_size,
                 hidden_size=768,
                 num_hidden_layers=12,
                 num_attention_heads=12,
                 intermediate_size=3072,
                 hidden_act="gelu",
                 hidden_dropout_prob=0.1,
                 attention_probs_dropout_prob=0.1,
                 max_position_embeddings=512,
                 type_vocab_size=16,
                 initializer_range=0.02):
      """Constructs BertConfig.
  
      Args:
        vocab_size: Vocabulary size of `inputs_ids` in `BertModel`.
        hidden_size: Size of the encoder layers and the pooler layer.
        num_hidden_layers: Number of hidden layers in the Transformer encoder.
        num_attention_heads: Number of attention heads for each attention layer in
          the Transformer encoder.
        intermediate_size: The size of the "intermediate" (i.e., feed-forward)
          layer in the Transformer encoder.
        hidden_act: The non-linear activation function (function or string) in the
          encoder and pooler.
        hidden_dropout_prob: The dropout probability for all fully connected
          layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob: The dropout ratio for the attention
          probabilities.
        max_position_embeddings: The maximum sequence length that this model might
          ever be used with. Typically set this to something large just in case
          (e.g., 512 or 1024 or 2048).
        type_vocab_size: The vocabulary size of the `token_type_ids` passed into
          `BertModel`.
        initializer_range: The stdev of the truncated_normal_initializer for
          initializing all weight matrices.
      """
      self.vocab_size = vocab_size
      self.hidden_size = hidden_size
      self.num_hidden_layers = num_hidden_layers
      self.num_attention_heads = num_attention_heads
      self.hidden_act = hidden_act
      self.intermediate_size = intermediate_size
      self.hidden_dropout_prob = hidden_dropout_prob
      self.attention_probs_dropout_prob = attention_probs_dropout_prob
      self.max_position_embeddings = max_position_embeddings
      self.type_vocab_size = type_vocab_size
      self.initializer_range = initializer_range
  
    @classmethod
    def from_dict(cls, json_object):
      """Constructs a `BertConfig` from a Python dictionary of parameters."""
      config = BertConfig(vocab_size=None)
      for (key, value) in six.iteritems(json_object):
        config.__dict__[key] = value
      return config
  
    @classmethod
    def from_json_file(cls, json_file):
      """Constructs a `BertConfig` from a json file of parameters."""
      with tf.gfile.GFile(json_file, "r") as reader:
        text = reader.read()
      return cls.from_dict(json.loads(text))
  
    def to_dict(self):
      """Serializes this instance to a Python dictionary."""
      output = copy.deepcopy(self.__dict__)
      return output
  
    def to_json_string(self):
      """Serializes this instance to a JSON string."""
      return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
  
  
  class BertModel(object):
    """BERT model ("Bidirectional Encoder Representations from Transformers").
  
    Example usage:
  
    ```python
    # Already been converted into WordPiece token ids
    input_ids = tf.constant([[31, 51, 99], [15, 5, 0]])
    input_mask = tf.constant([[1, 1, 1], [1, 1, 0]])
    token_type_ids = tf.constant([[0, 0, 1], [0, 2, 0]])
  
    config = modeling.BertConfig(vocab_size=32000, hidden_size=512,
      num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024)
  
    model = modeling.BertModel(config=config, is_training=True,
      input_ids=input_ids, input_mask=input_mask, token_type_ids=token_type_ids)
  
    label_embeddings = tf.get_variable(...)
    pooled_output = model.get_pooled_output()
    logits = tf.matmul(pooled_output, label_embeddings)
    ...
    ```
    """
  
    def __init__(self,
                 config,
                 is_training,
                 input_ids,
                 input_mask=None,
                 token_type_ids=None,
                 use_one_hot_embeddings=True,
                 scope=None):
      """Constructor for BertModel.
  
      Args:
        config: `BertConfig` instance.
        is_training: bool. true for training model, false for eval model. Controls
          whether dropout will be applied.
        input_ids: int32 Tensor of shape [batch_size, seq_length].
        input_mask: (optional) int32 Tensor of shape [batch_size, seq_length].
        token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length].
        use_one_hot_embeddings: (optional) bool. Whether to use one-hot word
          embeddings or tf.embedding_lookup() for the word embeddings. On the TPU,
          it is much faster if this is True, on the CPU or GPU, it is faster if
          this is False.
        scope: (optional) variable scope. Defaults to "bert".
  
      Raises:
        ValueError: The config is invalid or one of the input tensor shapes
          is invalid.
      """
      config = copy.deepcopy(config)
      if not is_training:
        config.hidden_dropout_prob = 0.0
        config.attention_probs_dropout_prob = 0.0
  
      input_shape = get_shape_list(input_ids, expected_rank=2)
      batch_size = input_shape[0]
      seq_length = input_shape[1]
  
      if input_mask is None:
        input_mask = tf.ones(shape=[batch_size, seq_length], dtype=tf.int32)
  
      if token_type_ids is None:
        token_type_ids = tf.zeros(shape=[batch_size, seq_length], dtype=tf.int32)
  
      with tf.variable_scope(scope, default_name="bert"):
        with tf.variable_scope("embeddings"):
          # Perform embedding lookup on the word ids.
          (self.embedding_output, self.embedding_table) = embedding_lookup(
              input_ids=input_ids,
              vocab_size=config.vocab_size,
              embedding_size=config.hidden_size,
              initializer_range=config.initializer_range,
              word_embedding_name="word_embeddings",
              use_one_hot_embeddings=use_one_hot_embeddings)
  
          # Add positional embeddings and token type embeddings, then layer
          # normalize and perform dropout.
          self.embedding_output = embedding_postprocessor(
              input_tensor=self.embedding_output,
              use_token_type=True,
              token_type_ids=token_type_ids,
              token_type_vocab_size=config.type_vocab_size,
              token_type_embedding_name="token_type_embeddings",
              use_position_embeddings=True,
              position_embedding_name="position_embeddings",
              initializer_range=config.initializer_range,
              max_position_embeddings=config.max_position_embeddings,
              dropout_prob=config.hidden_dropout_prob)
  
        with tf.variable_scope("encoder"):
          # This converts a 2D mask of shape [batch_size, seq_length] to a 3D
          # mask of shape [batch_size, seq_length, seq_length] which is used
          # for the attention scores.
          attention_mask = create_attention_mask_from_input_mask(
              input_ids, input_mask)
  
          # Run the stacked transformer.
          # `sequence_output` shape = [batch_size, seq_length, hidden_size].
          self.all_encoder_layers = transformer_model(
              input_tensor=self.embedding_output,
              attention_mask=attention_mask,
              hidden_size=config.hidden_size,
              num_hidden_layers=config.num_hidden_layers,
              num_attention_heads=config.num_attention_heads,
              intermediate_size=config.intermediate_size,
              intermediate_act_fn=get_activation(config.hidden_act),
              hidden_dropout_prob=config.hidden_dropout_prob,
              attention_probs_dropout_prob=config.attention_probs_dropout_prob,
              initializer_range=config.initializer_range,
              do_return_all_layers=True)
  
        self.sequence_output = self.all_encoder_layers[-1]
        # The "pooler" converts the encoded sequence tensor of shape
        # [batch_size, seq_length, hidden_size] to a tensor of shape
        # [batch_size, hidden_size]. This is necessary for segment-level
        # (or segment-pair-level) classification tasks where we need a fixed
        # dimensional representation of the segment.
        with tf.variable_scope("pooler"):
          # We "pool" the model by simply taking the hidden state corresponding
          # to the first token. We assume that this has been pre-trained
          first_token_tensor = tf.squeeze(self.sequence_output[:, 0:1, :], axis=1)
          self.pooled_output = tf.layers.dense(
              first_token_tensor,
              config.hidden_size,
              activation=tf.tanh,
              kernel_initializer=create_initializer(config.initializer_range))
  
    def get_pooled_output(self):
      return self.pooled_output
  
    def get_sequence_output(self):
      """Gets final hidden layer of encoder.
  
      Returns:
        float Tensor of shape [batch_size, seq_length, hidden_size] corresponding
        to the final hidden of the transformer encoder.
      """
      return self.sequence_output
  
    def get_all_encoder_layers(self):
      return self.all_encoder_layers
  
    def get_embedding_output(self):
      """Gets output of the embedding lookup (i.e., input to the transformer).
  
      Returns:
        float Tensor of shape [batch_size, seq_length, hidden_size] corresponding
        to the output of the embedding layer, after summing the word
        embeddings with the positional embeddings and the token type embeddings,
        then performing layer normalization. This is the input to the transformer.
      """
      return self.embedding_output
  
    def get_embedding_table(self):
      return self.embedding_table
  
  
  def gelu(input_tensor):
    """Gaussian Error Linear Unit.
  
    This is a smoother version of the RELU.
    Original paper: https://arxiv.org/abs/1606.08415
  
    Args:
      input_tensor: float Tensor to perform activation.
  
    Returns:
      `input_tensor` with the GELU activation applied.
    """
    cdf = 0.5 * (1.0 + tf.erf(input_tensor / tf.sqrt(2.0)))
    return input_tensor * cdf
  
  
  def get_activation(activation_string):
    """Maps a string to a Python function, e.g., "relu" => `tf.nn.relu`.
  
    Args:
      activation_string: String name of the activation function.
  
    Returns:
      A Python function corresponding to the activation function. If
      `activation_string` is None, empty, or "linear", this will return None.
      If `activation_string` is not a string, it will return `activation_string`.
  
    Raises:
      ValueError: The `activation_string` does not correspond to a known
        activation.
    """
  
    # We assume that anything that"s not a string is already an activation
    # function, so we just return it.
    if not isinstance(activation_string, six.string_types):
      return activation_string
  
    if not activation_string:
      return None
  
    act = activation_string.lower()
    if act == "linear":
      return None
    elif act == "relu":
      return tf.nn.relu
    elif act == "gelu":
      return gelu
    elif act == "tanh":
      return tf.tanh
    else:
      raise ValueError("Unsupported activation: %s" % act)
  
  
  def get_assignment_map_from_checkpoint(tvars, init_checkpoint):
    """Compute the union of the current variables and checkpoint variables."""
    assignment_map = {}
    initialized_variable_names = {}
  
    name_to_variable = collections.OrderedDict()
    for var in tvars:
      name = var.name
      m = re.match("^(.*):\\d+$", name)
      if m is not None:
        name = m.group(1)
      name_to_variable[name] = var
  
    init_vars = tf.train.list_variables(init_checkpoint)
  
    assignment_map = collections.OrderedDict()
    for x in init_vars:
      (name, var) = (x[0], x[1])
      if name not in name_to_variable:
        continue
      assignment_map[name] = name
      initialized_variable_names[name] = 1
      initialized_variable_names[name + ":0"] = 1
  
    return (assignment_map, initialized_variable_names)
  
  
  def dropout(input_tensor, dropout_prob):
    """Perform dropout.
  
    Args:
      input_tensor: float Tensor.
      dropout_prob: Python float. The probability of dropping out a value (NOT of
        *keeping* a dimension as in `tf.nn.dropout`).
  
    Returns:
      A version of `input_tensor` with dropout applied.
    """
    if dropout_prob is None or dropout_prob == 0.0:
      return input_tensor
  
    output = tf.nn.dropout(input_tensor, 1.0 - dropout_prob)
    return output
  
  
  def layer_norm(input_tensor, name=None):
    """Run layer normalization on the last dimension of the tensor."""
    return tf.contrib.layers.layer_norm(
        inputs=input_tensor, begin_norm_axis=-1, begin_params_axis=-1, scope=name)
  
  
  def layer_norm_and_dropout(input_tensor, dropout_prob, name=None):
    """Runs layer normalization followed by dropout."""
    output_tensor = layer_norm(input_tensor, name)
    output_tensor = dropout(output_tensor, dropout_prob)
    return output_tensor
  
  
  def create_initializer(initializer_range=0.02):
    """Creates a `truncated_normal_initializer` with the given range."""
    return tf.truncated_normal_initializer(stddev=initializer_range)
  
  
  def embedding_lookup(input_ids,
                       vocab_size,
                       embedding_size=128,
                       initializer_range=0.02,
                       word_embedding_name="word_embeddings",
                       use_one_hot_embeddings=False):
    """Looks up words embeddings for id tensor.
  
    Args:
      input_ids: int32 Tensor of shape [batch_size, seq_length] containing word
        ids.
      vocab_size: int. Size of the embedding vocabulary.
      embedding_size: int. Width of the word embeddings.
      initializer_range: float. Embedding initialization range.
      word_embedding_name: string. Name of the embedding table.
      use_one_hot_embeddings: bool. If True, use one-hot method for word
        embeddings. If False, use `tf.nn.embedding_lookup()`. One hot is better
        for TPUs.
  
    Returns:
      float Tensor of shape [batch_size, seq_length, embedding_size].
    """
    # This function assumes that the input is of shape [batch_size, seq_length,
    # num_inputs].
    #
    # If the input is a 2D tensor of shape [batch_size, seq_length], we
    # reshape to [batch_size, seq_length, 1].
    if input_ids.shape.ndims == 2:
      input_ids = tf.expand_dims(input_ids, axis=[-1])
  
    embedding_table = tf.get_variable(
        name=word_embedding_name,
        shape=[vocab_size, embedding_size],
        initializer=create_initializer(initializer_range))
  
    if use_one_hot_embeddings:
      flat_input_ids = tf.reshape(input_ids, [-1])
      one_hot_input_ids = tf.one_hot(flat_input_ids, depth=vocab_size)
      output = tf.matmul(one_hot_input_ids, embedding_table)
    else:
      output = tf.nn.embedding_lookup(embedding_table, input_ids)
  
    input_shape = get_shape_list(input_ids)
  
    output = tf.reshape(output,
                        input_shape[0:-1] + [input_shape[-1] * embedding_size])
    return (output, embedding_table)
  
  
  def embedding_postprocessor(input_tensor,
                              use_token_type=False,
                              token_type_ids=None,
                              token_type_vocab_size=16,
                              token_type_embedding_name="token_type_embeddings",
                              use_position_embeddings=True,
                              position_embedding_name="position_embeddings",
                              initializer_range=0.02,
                              max_position_embeddings=512,
                              dropout_prob=0.1):
    """Performs various post-processing on a word embedding tensor.
  
    Args:
      input_tensor: float Tensor of shape [batch_size, seq_length,
        embedding_size].
      use_token_type: bool. Whether to add embeddings for `token_type_ids`.
      token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length].
        Must be specified if `use_token_type` is True.
      token_type_vocab_size: int. The vocabulary size of `token_type_ids`.
      token_type_embedding_name: string. The name of the embedding table variable
        for token type ids.
      use_position_embeddings: bool. Whether to add position embeddings for the
        position of each token in the sequence.
      position_embedding_name: string. The name of the embedding table variable
        for positional embeddings.
      initializer_range: float. Range of the weight initialization.
      max_position_embeddings: int. Maximum sequence length that might ever be
        used with this model. This can be longer than the sequence length of
        input_tensor, but cannot be shorter.
      dropout_prob: float. Dropout probability applied to the final output tensor.
  
    Returns:
      float tensor with same shape as `input_tensor`.
  
    Raises:
      ValueError: One of the tensor shapes or input values is invalid.
    """
    input_shape = get_shape_list(input_tensor, expected_rank=3)
    batch_size = input_shape[0]
    seq_length = input_shape[1]
    width = input_shape[2]
  
    output = input_tensor
  
    if use_token_type:
      if token_type_ids is None:
        raise ValueError("`token_type_ids` must be specified if"
                         "`use_token_type` is True.")
      token_type_table = tf.get_variable(
          name=token_type_embedding_name,
          shape=[token_type_vocab_size, width],
          initializer=create_initializer(initializer_range))
      # This vocab will be small so we always do one-hot here, since it is always
      # faster for a small vocabulary.
      flat_token_type_ids = tf.reshape(token_type_ids, [-1])
      one_hot_ids = tf.one_hot(flat_token_type_ids, depth=token_type_vocab_size)
      token_type_embeddings = tf.matmul(one_hot_ids, token_type_table)
      token_type_embeddings = tf.reshape(token_type_embeddings,
                                         [batch_size, seq_length, width])
      output += token_type_embeddings
  
    if use_position_embeddings:
      assert_op = tf.assert_less_equal(seq_length, max_position_embeddings)
      with tf.control_dependencies([assert_op]):
        full_position_embeddings = tf.get_variable(
            name=position_embedding_name,
            shape=[max_position_embeddings, width],
            initializer=create_initializer(initializer_range))
        # Since the position embedding table is a learned variable, we create it
        # using a (long) sequence length `max_position_embeddings`. The actual
        # sequence length might be shorter than this, for faster training of
        # tasks that do not have long sequences.
        #
        # So `full_position_embeddings` is effectively an embedding table
        # for position [0, 1, 2, ..., max_position_embeddings-1], and the current
        # sequence has positions [0, 1, 2, ... seq_length-1], so we can just
        # perform a slice.
        position_embeddings = tf.slice(full_position_embeddings, [0, 0],
                                       [seq_length, -1])
        num_dims = len(output.shape.as_list())
  
        # Only the last two dimensions are relevant (`seq_length` and `width`), so
        # we broadcast among the first dimensions, which is typically just
        # the batch size.
        position_broadcast_shape = []
        for _ in range(num_dims - 2):
          position_broadcast_shape.append(1)
        position_broadcast_shape.extend([seq_length, width])
        position_embeddings = tf.reshape(position_embeddings,
                                         position_broadcast_shape)
        output += position_embeddings
  
    output = layer_norm_and_dropout(output, dropout_prob)
    return output
  
  
  def create_attention_mask_from_input_mask(from_tensor, to_mask):
    """Create 3D attention mask from a 2D tensor mask.
  
    Args:
      from_tensor: 2D or 3D Tensor of shape [batch_size, from_seq_length, ...].
      to_mask: int32 Tensor of shape [batch_size, to_seq_length].
  
    Returns:
      float Tensor of shape [batch_size, from_seq_length, to_seq_length].
    """
    from_shape = get_shape_list(from_tensor, expected_rank=[2, 3])
    batch_size = from_shape[0]
    from_seq_length = from_shape[1]
  
    to_shape = get_shape_list(to_mask, expected_rank=2)
    to_seq_length = to_shape[1]
  
    to_mask = tf.cast(
        tf.reshape(to_mask, [batch_size, 1, to_seq_length]), tf.float32)
  
    # We don't assume that `from_tensor` is a mask (although it could be). We
    # don't actually care if we attend *from* padding tokens (only *to* padding)
    # tokens so we create a tensor of all ones.
    #
    # `broadcast_ones` = [batch_size, from_seq_length, 1]
    broadcast_ones = tf.ones(
        shape=[batch_size, from_seq_length, 1], dtype=tf.float32)
  
    # Here we broadcast along two dimensions to create the mask.
    mask = broadcast_ones * to_mask
  
    return mask
  
  
  def attention_layer(from_tensor,
                      to_tensor,
                      attention_mask=None,
                      num_attention_heads=1,
                      size_per_head=512,
                      query_act=None,
                      key_act=None,
                      value_act=None,
                      attention_probs_dropout_prob=0.0,
                      initializer_range=0.02,
                      do_return_2d_tensor=False,
                      batch_size=None,
                      from_seq_length=None,
                      to_seq_length=None):
    """Performs multi-headed attention from `from_tensor` to `to_tensor`.
  
    This is an implementation of multi-headed attention based on "Attention
    is all you Need". If `from_tensor` and `to_tensor` are the same, then
    this is self-attention. Each timestep in `from_tensor` attends to the
    corresponding sequence in `to_tensor`, and returns a fixed-with vector.
  
    This function first projects `from_tensor` into a "query" tensor and
    `to_tensor` into "key" and "value" tensors. These are (effectively) a list
    of tensors of length `num_attention_heads`, where each tensor is of shape
    [batch_size, seq_length, size_per_head].
  
    Then, the query and key tensors are dot-producted and scaled. These are
    softmaxed to obtain attention probabilities. The value tensors are then
    interpolated by these probabilities, then concatenated back to a single
    tensor and returned.
  
    In practice, the multi-headed attention are done with transposes and
    reshapes rather than actual separate tensors.
  
    Args:
      from_tensor: float Tensor of shape [batch_size, from_seq_length,
        from_width].
      to_tensor: float Tensor of shape [batch_size, to_seq_length, to_width].
      attention_mask: (optional) int32 Tensor of shape [batch_size,
        from_seq_length, to_seq_length]. The values should be 1 or 0. The
        attention scores will effectively be set to -infinity for any positions in
        the mask that are 0, and will be unchanged for positions that are 1.
      num_attention_heads: int. Number of attention heads.
      size_per_head: int. Size of each attention head.
      query_act: (optional) Activation function for the query transform.
      key_act: (optional) Activation function for the key transform.
      value_act: (optional) Activation function for the value transform.
      attention_probs_dropout_prob: (optional) float. Dropout probability of the
        attention probabilities.
      initializer_range: float. Range of the weight initializer.
      do_return_2d_tensor: bool. If True, the output will be of shape [batch_size
        * from_seq_length, num_attention_heads * size_per_head]. If False, the
        output will be of shape [batch_size, from_seq_length, num_attention_heads
        * size_per_head].
      batch_size: (Optional) int. If the input is 2D, this might be the batch size
        of the 3D version of the `from_tensor` and `to_tensor`.
      from_seq_length: (Optional) If the input is 2D, this might be the seq length
        of the 3D version of the `from_tensor`.
      to_seq_length: (Optional) If the input is 2D, this might be the seq length
        of the 3D version of the `to_tensor`.
  
    Returns:
      float Tensor of shape [batch_size, from_seq_length,
        num_attention_heads * size_per_head]. (If `do_return_2d_tensor` is
        true, this will be of shape [batch_size * from_seq_length,
        num_attention_heads * size_per_head]).
  
    Raises:
      ValueError: Any of the arguments or tensor shapes are invalid.
    """
  
    def transpose_for_scores(input_tensor, batch_size, num_attention_heads,
                             seq_length, width):
      output_tensor = tf.reshape(
          input_tensor, [batch_size, seq_length, num_attention_heads, width])
  
      output_tensor = tf.transpose(output_tensor, [0, 2, 1, 3])
      return output_tensor
  
    from_shape = get_shape_list(from_tensor, expected_rank=[2, 3])
    to_shape = get_shape_list(to_tensor, expected_rank=[2, 3])
  
    if len(from_shape) != len(to_shape):
      raise ValueError(
          "The rank of `from_tensor` must match the rank of `to_tensor`.")
  
    if len(from_shape) == 3:
      batch_size = from_shape[0]
      from_seq_length = from_shape[1]
      to_seq_length = to_shape[1]
    elif len(from_shape) == 2:
      if (batch_size is None or from_seq_length is None or to_seq_length is None):
        raise ValueError(
            "When passing in rank 2 tensors to attention_layer, the values "
            "for `batch_size`, `from_seq_length`, and `to_seq_length` "
            "must all be specified.")
  
    # Scalar dimensions referenced here:
    #   B = batch size (number of sequences)
    #   F = `from_tensor` sequence length
    #   T = `to_tensor` sequence length
    #   N = `num_attention_heads`
    #   H = `size_per_head`
  
    from_tensor_2d = reshape_to_matrix(from_tensor)
    to_tensor_2d = reshape_to_matrix(to_tensor)
  
    # `query_layer` = [B*F, N*H]
    query_layer = tf.layers.dense(
        from_tensor_2d,
        num_attention_heads * size_per_head,
        activation=query_act,
        name="query",
        kernel_initializer=create_initializer(initializer_range))
  
    # `key_layer` = [B*T, N*H]
    key_layer = tf.layers.dense(
        to_tensor_2d,
        num_attention_heads * size_per_head,
        activation=key_act,
        name="key",
        kernel_initializer=create_initializer(initializer_range))
  
    # `value_layer` = [B*T, N*H]
    value_layer = tf.layers.dense(
        to_tensor_2d,
        num_attention_heads * size_per_head,
        activation=value_act,
        name="value",
        kernel_initializer=create_initializer(initializer_range))
  
    # `query_layer` = [B, N, F, H]
    query_layer = transpose_for_scores(query_layer, batch_size,
                                       num_attention_heads, from_seq_length,
                                       size_per_head)
  
    # `key_layer` = [B, N, T, H]
    key_layer = transpose_for_scores(key_layer, batch_size, num_attention_heads,
                                     to_seq_length, size_per_head)
  
    # Take the dot product between "query" and "key" to get the raw
    # attention scores.
    # `attention_scores` = [B, N, F, T]
    attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
    attention_scores = tf.multiply(attention_scores,
                                   1.0 / math.sqrt(float(size_per_head)))
  
    if attention_mask is not None:
      # `attention_mask` = [B, 1, F, T]
      attention_mask = tf.expand_dims(attention_mask, axis=[1])
  
      # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
      # masked positions, this operation will create a tensor which is 0.0 for
      # positions we want to attend and -10000.0 for masked positions.
      adder = (1.0 - tf.cast(attention_mask, tf.float32)) * -10000.0
  
      # Since we are adding it to the raw scores before the softmax, this is
      # effectively the same as removing these entirely.
      attention_scores += adder
  
    # Normalize the attention scores to probabilities.
    # `attention_probs` = [B, N, F, T]
    attention_probs = tf.nn.softmax(attention_scores)
  
    # This is actually dropping out entire tokens to attend to, which might
    # seem a bit unusual, but is taken from the original Transformer paper.
    attention_probs = dropout(attention_probs, attention_probs_dropout_prob)
  
    # `value_layer` = [B, T, N, H]
    value_layer = tf.reshape(
        value_layer,
        [batch_size, to_seq_length, num_attention_heads, size_per_head])
  
    # `value_layer` = [B, N, T, H]
    value_layer = tf.transpose(value_layer, [0, 2, 1, 3])
  
    # `context_layer` = [B, N, F, H]
    context_layer = tf.matmul(attention_probs, value_layer)
  
    # `context_layer` = [B, F, N, H]
    context_layer = tf.transpose(context_layer, [0, 2, 1, 3])
  
    if do_return_2d_tensor:
      # `context_layer` = [B*F, N*H]
      context_layer = tf.reshape(
          context_layer,
          [batch_size * from_seq_length, num_attention_heads * size_per_head])
    else:
      # `context_layer` = [B, F, N*H]
      context_layer = tf.reshape(
          context_layer,
          [batch_size, from_seq_length, num_attention_heads * size_per_head])
  
    return context_layer
  
  
  def transformer_model(input_tensor,
                        attention_mask=None,
                        hidden_size=768,
                        num_hidden_layers=12,
                        num_attention_heads=12,
                        intermediate_size=3072,
                        intermediate_act_fn=gelu,
                        hidden_dropout_prob=0.1,
                        attention_probs_dropout_prob=0.1,
                        initializer_range=0.02,
                        do_return_all_layers=False):
    """Multi-headed, multi-layer Transformer from "Attention is All You Need".
  
    This is almost an exact implementation of the original Transformer encoder.
  
    See the original paper:
    https://arxiv.org/abs/1706.03762
  
    Also see:
    https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/models/transformer.py
  
    Args:
      input_tensor: float Tensor of shape [batch_size, seq_length, hidden_size].
      attention_mask: (optional) int32 Tensor of shape [batch_size, seq_length,
        seq_length], with 1 for positions that can be attended to and 0 in
        positions that should not be.
      hidden_size: int. Hidden size of the Transformer.
      num_hidden_layers: int. Number of layers (blocks) in the Transformer.
      num_attention_heads: int. Number of attention heads in the Transformer.
      intermediate_size: int. The size of the "intermediate" (a.k.a., feed
        forward) layer.
      intermediate_act_fn: function. The non-linear activation function to apply
        to the output of the intermediate/feed-forward layer.
      hidden_dropout_prob: float. Dropout probability for the hidden layers.
      attention_probs_dropout_prob: float. Dropout probability of the attention
        probabilities.
      initializer_range: float. Range of the initializer (stddev of truncated
        normal).
      do_return_all_layers: Whether to also return all layers or just the final
        layer.
  
    Returns:
      float Tensor of shape [batch_size, seq_length, hidden_size], the final
      hidden layer of the Transformer.
  
    Raises:
      ValueError: A Tensor shape or parameter is invalid.
    """
    if hidden_size % num_attention_heads != 0:
      raise ValueError(
          "The hidden size (%d) is not a multiple of the number of attention "
          "heads (%d)" % (hidden_size, num_attention_heads))
  
    attention_head_size = int(hidden_size / num_attention_heads)
    input_shape = get_shape_list(input_tensor, expected_rank=3)
    batch_size = input_shape[0]
    seq_length = input_shape[1]
    input_width = input_shape[2]
  
    # The Transformer performs sum residuals on all layers so the input needs
    # to be the same as the hidden size.
    if input_width != hidden_size:
      raise ValueError("The width of the input tensor (%d) != hidden size (%d)" %
                       (input_width, hidden_size))
  
    # We keep the representation as a 2D tensor to avoid re-shaping it back and
    # forth from a 3D tensor to a 2D tensor. Re-shapes are normally free on
    # the GPU/CPU but may not be free on the TPU, so we want to minimize them to
    # help the optimizer.
    prev_output = reshape_to_matrix(input_tensor)
  
    all_layer_outputs = []
    for layer_idx in range(num_hidden_layers):
      with tf.variable_scope("layer_%d" % layer_idx):
        layer_input = prev_output
  
        with tf.variable_scope("attention"):
          attention_heads = []
          with tf.variable_scope("self"):
            attention_head = attention_layer(
                from_tensor=layer_input,
                to_tensor=layer_input,
                attention_mask=attention_mask,
                num_attention_heads=num_attention_heads,
                size_per_head=attention_head_size,
                attention_probs_dropout_prob=attention_probs_dropout_prob,
                initializer_range=initializer_range,
                do_return_2d_tensor=True,
                batch_size=batch_size,
                from_seq_length=seq_length,
                to_seq_length=seq_length)
            attention_heads.append(attention_head)
  
          attention_output = None
          if len(attention_heads) == 1:
            attention_output = attention_heads[0]
          else:
            # In the case where we have other sequences, we just concatenate
            # them to the self-attention head before the projection.
            attention_output = tf.concat(attention_heads, axis=-1)
  
          # Run a linear projection of `hidden_size` then add a residual
          # with `layer_input`.
          with tf.variable_scope("output"):
            attention_output = tf.layers.dense(
                attention_output,
                hidden_size,
                kernel_initializer=create_initializer(initializer_range))
            attention_output = dropout(attention_output, hidden_dropout_prob)
            attention_output = layer_norm(attention_output + layer_input)
  
        # The activation is only applied to the "intermediate" hidden layer.
        with tf.variable_scope("intermediate"):
          intermediate_output = tf.layers.dense(
              attention_output,
              intermediate_size,
              activation=intermediate_act_fn,
              kernel_initializer=create_initializer(initializer_range))
  
        # Down-project back to `hidden_size` then add the residual.
        with tf.variable_scope("output"):
          layer_output = tf.layers.dense(
              intermediate_output,
              hidden_size,
              kernel_initializer=create_initializer(initializer_range))
          layer_output = dropout(layer_output, hidden_dropout_prob)
          layer_output = layer_norm(layer_output + attention_output)
          prev_output = layer_output
          all_layer_outputs.append(layer_output)
  
    if do_return_all_layers:
      final_outputs = []
      for layer_output in all_layer_outputs:
        final_output = reshape_from_matrix(layer_output, input_shape)
        final_outputs.append(final_output)
      return final_outputs
    else:
      final_output = reshape_from_matrix(prev_output, input_shape)
      return final_output
  
  
  def get_shape_list(tensor, expected_rank=None, name=None):
    """Returns a list of the shape of tensor, preferring static dimensions.
  
    Args:
      tensor: A tf.Tensor object to find the shape of.
      expected_rank: (optional) int. The expected rank of `tensor`. If this is
        specified and the `tensor` has a different rank, and exception will be
        thrown.
      name: Optional name of the tensor for the error message.
  
    Returns:
      A list of dimensions of the shape of tensor. All static dimensions will
      be returned as python integers, and dynamic dimensions will be returned
      as tf.Tensor scalars.
    """
    if name is None:
      name = tensor.name
  
    if expected_rank is not None:
      assert_rank(tensor, expected_rank, name)
  
    shape = tensor.shape.as_list()
  
    non_static_indexes = []
    for (index, dim) in enumerate(shape):
      if dim is None:
        non_static_indexes.append(index)
  
    if not non_static_indexes:
      return shape
  
    dyn_shape = tf.shape(tensor)
    for index in non_static_indexes:
      shape[index] = dyn_shape[index]
    return shape
  
  
  def reshape_to_matrix(input_tensor):
    """Reshapes a >= rank 2 tensor to a rank 2 tensor (i.e., a matrix)."""
    ndims = input_tensor.shape.ndims
    if ndims < 2:
      raise ValueError("Input tensor must have at least rank 2. Shape = %s" %
                       (input_tensor.shape))
    if ndims == 2:
      return input_tensor
  
    width = input_tensor.shape[-1]
    output_tensor = tf.reshape(input_tensor, [-1, width])
    return output_tensor
  
  
  def reshape_from_matrix(output_tensor, orig_shape_list):
    """Reshapes a rank 2 tensor back to its original rank >= 2 tensor."""
    if len(orig_shape_list) == 2:
      return output_tensor
  
    output_shape = get_shape_list(output_tensor)
  
    orig_dims = orig_shape_list[0:-1]
    width = output_shape[-1]
  
    return tf.reshape(output_tensor, orig_dims + [width])
  
  
  def assert_rank(tensor, expected_rank, name=None):
    """Raises an exception if the tensor rank is not of the expected rank.
  
    Args:
      tensor: A tf.Tensor to check the rank of.
      expected_rank: Python integer or list of integers, expected rank.
      name: Optional name of the tensor for the error message.
  
    Raises:
      ValueError: If the expected shape doesn't match the actual shape.
    """
    if name is None:
      name = tensor.name
  
    expected_rank_dict = {}
    if isinstance(expected_rank, six.integer_types):
      expected_rank_dict[expected_rank] = True
    else:
      for x in expected_rank:
        expected_rank_dict[x] = True
  
    actual_rank = tensor.shape.ndims
    if actual_rank not in expected_rank_dict:
      scope_name = tf.get_variable_scope().name
      raise ValueError(
          "For the tensor `%s` in scope `%s`, the actual rank "
          "`%d` (shape = %s) is not equal to the expected rank `%s`" %
          (name, scope_name, actual_rank, str(tensor.shape), str(expected_rank)))
  
apis: tensorflow.reshape tensorflow.ones tensorflow.variable_scope tensorflow.matmul tensorflow.squeeze tensorflow.one_hot tensorflow.slice tensorflow.get_variable_scope tensorflow.concat tensorflow.nn.softmax tensorflow.contrib.layers.layer_norm tensorflow.nn.dropout tensorflow.truncated_normal_initializer tensorflow.gfile.GFile tensorflow.transpose tensorflow.shape tensorflow.train.list_variables tensorflow.expand_dims tensorflow.cast tensorflow.control_dependencies tensorflow.nn.embedding_lookup tensorflow.zeros tensorflow.sqrt tensorflow.assert_less_equal