text: # Lint as: python3
  # 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.
  # ==============================================================================
  """Keras-based attention layer."""
  # pylint: disable=g-classes-have-attributes
  from __future__ import absolute_import
  from __future__ import division
  # from __future__ import google_type_annotations
  from __future__ import print_function
  
  import collections
  import math
  import string
  
  import numpy as np
  import tensorflow as tf
  
  from official.nlp.modeling.layers import masked_softmax
  
  EinsumDense = tf.keras.layers.experimental.EinsumDense
  _CHR_IDX = string.ascii_lowercase
  
  
  def _build_attention_equation(qkv_rank, attn_axes):
    """Builds einsum equations for the attention computation.
  
    Query, key, value inputs after projection are expected to have the shape as:
    (bs, <non-attention dims>, <attention dims>, num_heads, channels).
    bs and <non-attention dims> are treated as <batch dims>.
    The attention operations can be generalized:
    (1) Query-key dot product:
    (<batch dims>, <query attention dims>, num_heads, channels), (<batch dims>,
    <key attention dims>, num_heads, channels) -> (<batch dims>,
    num_heads, <query attention dims>, <key attention dims>)
    (2) Combination:
    (<batch dims>, num_heads, <query attention dims>, <key attention dims>),
    (<batch dims>, <value attention dims>, num_heads, channels) -> (<batch dims>,
    <query attention dims>, num_heads, channels)
  
    Args:
      qkv_rank: the rank of query, key, value tensors.
      attn_axes: a list/tuple of axes, [1, rank), that will do attention.
  
    Returns:
      Einsum equations.
    """
    target_notation = _CHR_IDX[:qkv_rank]
    # `batch_dims` includes the head dim.
    batch_dims = tuple(np.delete(range(qkv_rank), attn_axes + (qkv_rank - 1,)))
    letter_offset = qkv_rank
    source_notation = ""
    for i in range(qkv_rank):
      if i in batch_dims or i == qkv_rank - 1:
        source_notation += target_notation[i]
      else:
        source_notation += _CHR_IDX[letter_offset]
        letter_offset += 1
  
    product_notation = "".join([target_notation[i] for i in batch_dims] +
                               [target_notation[i] for i in attn_axes] +
                               [source_notation[i] for i in attn_axes])
    dot_product_equation = "%s,%s->%s" % (source_notation, target_notation,
                                          product_notation)
    attn_scores_rank = len(product_notation)
    combine_equation = "%s,%s->%s" % (product_notation, source_notation,
                                      target_notation)
    return dot_product_equation, combine_equation, attn_scores_rank
  
  
  def _build_proj_equation(free_dims, bound_dims, output_dims):
    """Builds an einsum equation for projections inside multi-head attention."""
    input_str = ""
    kernel_str = ""
    output_str = ""
    bias_axes = ""
    letter_offset = 0
    for i in range(free_dims):
      char = _CHR_IDX[i + letter_offset]
      input_str += char
      output_str += char
  
    letter_offset += free_dims
    for i in range(bound_dims):
      char = _CHR_IDX[i + letter_offset]
      input_str += char
      kernel_str += char
  
    letter_offset += bound_dims
    for i in range(output_dims):
      char = _CHR_IDX[i + letter_offset]
      kernel_str += char
      output_str += char
      bias_axes += char
    equation = "%s,%s->%s" % (input_str, kernel_str, output_str)
  
    return equation, bias_axes, len(output_str)
  
  
  def _get_output_shape(output_rank, known_last_dims):
    return [None] * (output_rank - len(known_last_dims)) + list(known_last_dims)
  
  
  @tf.keras.utils.register_keras_serializable(package="Text")
  class MultiHeadAttention(tf.keras.layers.Layer):
    """MultiHeadAttention layer.
  
    This is an implementation of multi-headed attention based on "Attention
    is all you Need". If `query`, `key,` `value` are the same, then
    this is self-attention. Each timestep in `query` attends to the
    corresponding sequence in `key`, and returns a fixed-width vector.
  
    This layer first projects `query`, `key` and `value`. These are
    (effectively) a list of tensors of length `num_attention_heads`, where the
    corresponding shapes are [batch_size, <query dimensions>, key_size],
    [batch_size, <key/value dimensions>, key_size],
    [batch_size, <key/value dimensions>, value_size].
  
    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.
  
    Finally, the result tensor with the last dimension as value_size can take an
    linear projection and return.
  
    Examples:
  
    Performs 1D cross-attention over two sequence inputs with an attention mask.
    Returns the additional attention weights over heads.
  
    >>> layer = MultiHeadAttention(num_heads=2, key_size=2,
    ...                            return_attention_scores=True)
    >>> target = tf.keras.Input(shape=[8, 16])
    >>> source = tf.keras.Input(shape=[4, 16])
    >>> mask_tensor = tf.keras.Input(shape=[8, 4])
    >>> output_tensor, weights = layer([target, source])
    >>> print(output_tensor.shape), print(weights.shape)
    (None, 8, 16)  (None, 2, 8, 4)
  
    Performs 2D self-attention over a 5D input tensor on axes 2 and 3.
  
    >>> layer = MultiHeadAttention(num_heads=2, key_size=2, attention_axes=(2, 3))
    >>> input_tensor = tf.keras.Input(shape=[5, 3, 4, 16])
    >>> output_tensor = layer([input_tensor, input_tensor])
    >>> print(output_tensor.shape)
    (None, 5, 3, 4, 16)
  
    Arguments:
      num_heads: Number of attention heads.
      key_size: Size of each attention head for query and key.
      value_size:  Size of each attention head for value.
      dropout: Dropout probability.
      use_bias: Boolean, whether the dense layers use bias vectors/matrices.
      output_shape: The expected shape of an output tensor, besides the batch and
        sequence dims. If not specified, projects back to the key feature dim.
      attention_axes: axes over which the attention is applied. `None` means
        attention over all axes, but batch, heads, and features.
      return_attention_scores: bool, if `True`, returns the multi-head
        attention scores as an additional output argument.
      kernel_initializer: Initializer for dense layer kernels.
      bias_initializer: Initializer for dense layer biases.
      kernel_regularizer: Regularizer for dense layer kernels.
      bias_regularizer: Regularizer for dense layer biases.
      activity_regularizer: Regularizer for dense layer activity.
      kernel_constraint: Constraint for dense layer kernels.
      bias_constraint: Constraint for dense layer kernels.
    """
  
    def __init__(self,
                 num_heads,
                 key_size,
                 value_size=None,
                 dropout=0.0,
                 use_bias=True,
                 output_shape=None,
                 attention_axes=None,
                 return_attention_scores=False,
                 kernel_initializer="glorot_uniform",
                 bias_initializer="zeros",
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 **kwargs):
      super(MultiHeadAttention, self).__init__(**kwargs)
      self._num_heads = num_heads
      self._key_size = key_size
      self._value_size = value_size if value_size else key_size
      self._dropout = dropout
      self._use_bias = use_bias
      self._output_shape = output_shape
      self._return_attention_scores = return_attention_scores
      self._kernel_initializer = tf.keras.initializers.get(kernel_initializer)
      self._bias_initializer = tf.keras.initializers.get(bias_initializer)
      self._kernel_regularizer = tf.keras.regularizers.get(kernel_regularizer)
      self._bias_regularizer = tf.keras.regularizers.get(bias_regularizer)
      self._kernel_constraint = tf.keras.constraints.get(kernel_constraint)
      self._bias_constraint = tf.keras.constraints.get(bias_constraint)
      if attention_axes is not None and not isinstance(attention_axes,
                                                       collections.abc.Sized):
        self._attention_axes = (attention_axes,)
      else:
        self._attention_axes = attention_axes
  
    def get_config(self):
      config = {
          "num_heads":
              self._num_heads,
          "key_size":
              self._key_size,
          "value_size":
              self._value_size,
          "dropout":
              self._dropout,
          "use_bias":
              self._use_bias,
          "output_shape":
              self._output_shape,
          "attention_axes":
              self._attention_axes,
          "return_attention_scores":
              self._return_attention_scores,
          "kernel_initializer":
              tf.keras.initializers.serialize(self._kernel_initializer),
          "bias_initializer":
              tf.keras.initializers.serialize(self._bias_initializer),
          "kernel_regularizer":
              tf.keras.regularizers.serialize(self._kernel_regularizer),
          "bias_regularizer":
              tf.keras.regularizers.serialize(self._bias_regularizer),
          "activity_regularizer":
              tf.keras.regularizers.serialize(self._activity_regularizer),
          "kernel_constraint":
              tf.keras.constraints.serialize(self._kernel_constraint),
          "bias_constraint":
              tf.keras.constraints.serialize(self._bias_constraint)
      }
      base_config = super(MultiHeadAttention, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
    def build(self, input_shape):
      inputs_len = len(input_shape)
      if inputs_len > 3 or inputs_len < 2:
        raise ValueError(
            "Expects inputs list of length 2 or 3, namely [query, value] or "
            "[query, value, key]. "
            "Given length: %d" % inputs_len)
      tensor_shapes = tf.nest.map_structure(tf.TensorShape, input_shape)
      query_shape = tensor_shapes[0]
      value_shape = tensor_shapes[1]
      key_shape = tensor_shapes[2] if inputs_len == 3 else value_shape
  
      common_kwargs = dict(
          kernel_initializer=self._kernel_initializer,
          bias_initializer=self._bias_initializer,
          kernel_regularizer=self._kernel_regularizer,
          bias_regularizer=self._bias_regularizer,
          activity_regularizer=self._activity_regularizer,
          kernel_constraint=self._kernel_constraint,
          bias_constraint=self._bias_constraint)
  
      free_dims = query_shape.rank - 1
      einsum_equation, bias_axes, output_rank = _build_proj_equation(
          free_dims, bound_dims=1, output_dims=2)
      self._query_dense = EinsumDense(
          einsum_equation,
          output_shape=_get_output_shape(output_rank - 1,
                                         [self._num_heads, self._key_size]),
          bias_axes=bias_axes if self._use_bias else None,
          name="query",
          **common_kwargs)
      einsum_equation, bias_axes, output_rank = _build_proj_equation(
          key_shape.rank - 1, bound_dims=1, output_dims=2)
      self._key_dense = EinsumDense(
          einsum_equation,
          output_shape=_get_output_shape(output_rank - 1,
                                         [self._num_heads, self._key_size]),
          bias_axes=bias_axes if self._use_bias else None,
          name="key",
          **common_kwargs)
      einsum_equation, bias_axes, output_rank = _build_proj_equation(
          value_shape.rank - 1, bound_dims=1, output_dims=2)
      self._value_dense = EinsumDense(
          einsum_equation,
          output_shape=_get_output_shape(output_rank - 1,
                                         [self._num_heads, self._value_size]),
          bias_axes=bias_axes if self._use_bias else None,
          name="value",
          **common_kwargs)
  
      # Builds the attention computations for multi-head dot product attention.
      # These computations could be wrapped into the keras attention layer once it
      # support mult-head einsum computations.
      self._build_attention(output_rank)
      if self._output_shape:
        if not isinstance(self._output_shape, collections.abc.Sized):
          output_shape = [self._output_shape]
        else:
          output_shape = self._output_shape
      else:
        output_shape = [query_shape[-1]]
      einsum_equation, bias_axes, output_rank = _build_proj_equation(
          free_dims, bound_dims=2, output_dims=len(output_shape))
      self._output_dense = EinsumDense(
          einsum_equation,
          output_shape=_get_output_shape(output_rank - 1, output_shape),
          bias_axes=bias_axes if self._use_bias else None,
          name="attention_output",
          **common_kwargs)
      super(MultiHeadAttention, self).build(input_shape)
  
    def _build_attention(self, qkv_rank):
      """Builds multi-head dot-product attention computations.
  
      This function builds attributes necessary for `_compute_attention` to
      costomize attention computation to replace the default dot-product
      attention.
  
      Args:
        qkv_rank: the rank of query, key, value tensors.
      """
      if self._attention_axes is None:
        self._attention_axes = tuple(range(1, qkv_rank - 2))
      else:
        self._attention_axes = tuple(self._attention_axes)
      self._dot_product_equation, self._combine_equation, attn_scores_rank = (
          _build_attention_equation(qkv_rank, attn_axes=self._attention_axes))
      norm_axes = tuple(
          range(attn_scores_rank - len(self._attention_axes), attn_scores_rank))
      self._masked_softmax = masked_softmax.MaskedSoftmax(
          mask_expansion_axes=[1], normalization_axes=norm_axes)
      self._dropout_layer = tf.keras.layers.Dropout(rate=self._dropout)
  
    def _compute_attention(self,
                           query_tensor,
                           key_tensor,
                           value_tensor,
                           attention_mask=None):
      """Applies Dot-product attention with query, key, value tensors.
  
      This function defines the computation inside `call` with projected
      multi-head Q, K, V inputs. Users can override this function for customized
      attention implementation.
  
      Args:
        query_tensor: Projected query `Tensor` of shape `[B, T, N, key_size]`.
        key_tensor: Projected key `Tensor` of shape `[B, T, N, key_size]`.
        value_tensor: Projected value `Tensor` of shape `[B, T, N, value_size]`.
        attention_mask: a boolean mask of shape `[B, T, S]`, that prevents
          attention to certain positions.
  
      Returns:
        attention_output: Multi-headed outputs of attention computation.
        attention_scores: Multi-headed attention weights.
      """
      # Take the dot product between "query" and "key" to get the raw
      # attention scores.
      attention_scores = tf.einsum(self._dot_product_equation, key_tensor,
                                   query_tensor)
      attention_scores = tf.multiply(attention_scores,
                                     1.0 / math.sqrt(float(self._key_size)))
  
      # Normalize the attention scores to probabilities.
      # `attention_scores` = [B, N, T, S]
      attention_scores = self._masked_softmax(attention_scores, attention_mask)
  
      # 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_scores_dropout = self._dropout_layer(attention_scores)
  
      # `context_layer` = [B, T, N, H]
      attention_output = tf.einsum(self._combine_equation,
                                   attention_scores_dropout, value_tensor)
      return attention_output, attention_scores
  
    def call(self, inputs, attention_mask=None):
      """Implements the forward pass.
  
      Size glossary:
        * Number of heads (H): the number of attention heads.
        * Value size (V): the size of each value embedding per head.
        * Key size (K): the size of each key embedding per head. Equally, the size
            of each query embedding per head. Typically K <= V.
        * Batch dimensions (B).
        * Query (target) attention axes shape (T).
        * Value (source) attention axes shape (S), the rank must match the target.
  
      Args:
        inputs: List of the following tensors:
          * query: Query `Tensor` of shape `[B, T, dim]`.
          * value: Value `Tensor` of shape `[B, S, dim]`.
          * key: Optional key `Tensor` of shape `[B, S, dim]`. If not given, will
            use `value` for both `key` and `value`, which is the most common case.
        attention_mask: a boolean mask of shape `[B, T, S]`, that prevents
          attention to certain positions.
  
      Returns:
        attention_output: The result of the computation, of shape [B, T, E],
          where `T` is for target sequence shapes and `E` is the query input last
          dimension if `output_shape` is `None`. Otherwise, the multi-head outputs
          are project to the shape specified by `output_shape`.
        attention_scores: [Optional] multi-head attention coeffients over
        attention
          axes.
      """
      inputs_len = len(inputs)
      if inputs_len > 3 or inputs_len < 2:
        raise ValueError(
            "Expects inputs list of length 2 or 3, namely [query, value] or "
            "[query, value, key]. "
            "Given length: %d" % inputs_len)
      query = inputs[0]
      value = inputs[1]
      key = inputs[2] if inputs_len == 3 else value
  
      #   N = `num_attention_heads`
      #   H = `size_per_head`
      # `query_tensor` = [B, T, N ,H]
      query_tensor = self._query_dense(query)
  
      # `key_tensor` = [B, S, N, H]
      key_tensor = self._key_dense(key)
  
      # `value_tensor` = [B, S, N, H]
      value_tensor = self._value_dense(value)
  
      attention_output, attention_scores = self._compute_attention(
          query_tensor, key_tensor, value_tensor, attention_mask)
      attention_output = self._output_dense(attention_output)
  
      if self._return_attention_scores:
        return attention_output, attention_scores
      return attention_output
  
  
  @tf.keras.utils.register_keras_serializable(package="Text")
  class CachedAttention(MultiHeadAttention):
    """Attention layer with cache used for auto-agressive decoding.
  
    Arguments are the same as `MultiHeadAttention` layer.
    """
  
    def _update_cache(self, key_tensor, value_tensor, cache, decode_loop_step):
      """Updates cache states and gets full-length key/value tensors."""
      # Combines cached keys and values with new keys and values.
      if decode_loop_step is not None:
        # TPU special case.
        key_seq_dim = cache["key"].shape.as_list()[1]
        indices = tf.reshape(
            tf.one_hot(decode_loop_step, key_seq_dim, dtype=key_tensor.dtype),
            [1, key_seq_dim, 1, 1])
        key_tensor = cache["key"] + key_tensor * indices
        value_seq_dim = cache["value"].shape.as_list()[1]
        indices = tf.reshape(
            tf.one_hot(decode_loop_step, value_seq_dim, dtype=value_tensor.dtype),
            [1, value_seq_dim, 1, 1])
        value_tensor = cache["value"] + value_tensor * indices
      else:
        key_tensor = tf.concat(
            [tf.cast(cache["key"], key_tensor.dtype), key_tensor], axis=1)
        value_tensor = tf.concat(
            [tf.cast(cache["value"], value_tensor.dtype), value_tensor], axis=1)
  
      # Update cache
      cache["key"] = key_tensor
      cache["value"] = value_tensor
  
      return key_tensor, value_tensor
  
    def call(self,
             inputs,
             attention_mask=None,
             cache=None,
             decode_loop_step=None):
      from_tensor = inputs[0]
      to_tensor = inputs[1]
  
      # 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`
      # `query_tensor` = [B, F, N ,H]
      query_tensor = self._query_dense(from_tensor)
  
      # `key_tensor` = [B, T, N, H]
      key_tensor = self._key_dense(to_tensor)
  
      # `value_tensor` = [B, T, N, H]
      value_tensor = self._value_dense(to_tensor)
  
      if cache:
        key_tensor, value_tensor = self._update_cache(key_tensor, value_tensor,
                                                      cache, decode_loop_step)
  
      # Take the dot product between "query" and "key" to get the raw
      # attention scores.
      attention_scores = tf.einsum(self._dot_product_equation, key_tensor,
                                   query_tensor)
      attention_scores = tf.multiply(attention_scores,
                                     1.0 / math.sqrt(float(self._key_size)))
  
      # Normalize the attention scores to probabilities.
      # `attention_scores` = [B, N, F, T]
      attention_scores = self._masked_softmax(attention_scores, attention_mask)
  
      # 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_scores = self._dropout_layer(attention_scores)
      # `context_layer` = [B, F, N, H]
      attention_output = tf.einsum(self._combine_equation, attention_scores,
                                   value_tensor)
      attention_output = self._output_dense(attention_output)
      if self._return_attention_scores:
        return attention_output, attention_scores, cache
      return attention_output, cache
  