# Copyright 2018 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.
# ==============================================================================
"""Implementation of multiheaded attention and self-attention layers."""
import math

import tensorflow as tf
from official.nlp.modeling import layers


class Attention(tf.keras.layers.Layer):
  """Multi-headed attention layer."""

  def __init__(self, hidden_size, num_heads, attention_dropout):
    """Initialize Attention.

    Args:
      hidden_size: int, output dim of hidden layer.
      num_heads: int, number of heads to repeat the same attention structure.
      attention_dropout: float, dropout rate inside attention for training.
    """
    if hidden_size % num_heads:
      raise ValueError(
          "Hidden size ({}) must be divisible by the number of heads ({})."
          .format(hidden_size, num_heads))

    super(Attention, self).__init__()
    self.hidden_size = hidden_size
    self.num_heads = num_heads
    self.attention_dropout = attention_dropout

  def build(self, input_shape):
    """Builds the layer."""
    # Layers for linearly projecting the queries, keys, and values.
    size_per_head = self.hidden_size // self.num_heads

    def _glorot_initializer(fan_in, fan_out):
      limit = math.sqrt(6.0 / (fan_in + fan_out))
      return tf.keras.initializers.RandomUniform(minval=-limit, maxval=limit)

    attention_initializer = _glorot_initializer(input_shape.as_list()[-1],
                                                self.hidden_size)
    self.query_dense_layer = layers.DenseEinsum(
        output_shape=(self.num_heads, size_per_head),
        kernel_initializer=attention_initializer,
        use_bias=False,
        name="query")
    self.key_dense_layer = layers.DenseEinsum(
        output_shape=(self.num_heads, size_per_head),
        kernel_initializer=attention_initializer,
        use_bias=False,
        name="key")
    self.value_dense_layer = layers.DenseEinsum(
        output_shape=(self.num_heads, size_per_head),
        kernel_initializer=attention_initializer,
        use_bias=False,
        name="value")

    output_initializer = _glorot_initializer(self.hidden_size, self.hidden_size)
    self.output_dense_layer = layers.DenseEinsum(
        output_shape=self.hidden_size,
        num_summed_dimensions=2,
        kernel_initializer=output_initializer,
        use_bias=False,
        name="output_transform")
    super(Attention, self).build(input_shape)

  def get_config(self):
    return {
        "hidden_size": self.hidden_size,
        "num_heads": self.num_heads,
        "attention_dropout": self.attention_dropout,
    }

  def call(self,
           query_input,
           source_input,
           bias,
           training,
           cache=None,
           decode_loop_step=None):
    """Apply attention mechanism to query_input and source_input.

    Args:
      query_input: A tensor with shape [batch_size, length_query, hidden_size].
      source_input: A tensor with shape [batch_size, length_source,
        hidden_size].
      bias: A tensor with shape [batch_size, 1, length_query, length_source],
        the attention bias that will be added to the result of the dot product.
      training: A bool, whether in training mode or not.
      cache: (Used during prediction) A dictionary with tensors containing
        results of previous attentions. The dictionary must have the items:
            {"k": tensor with shape [batch_size, i, heads, dim_per_head],
             "v": tensor with shape [batch_size, i, heads, dim_per_head]} where
               i is the current decoded length for non-padded decode, or max
               sequence length for padded decode.
      decode_loop_step: An integer, step number of the decoding loop. Used only
        for autoregressive inference on TPU.

    Returns:
      Attention layer output with shape [batch_size, length_query, hidden_size]
    """
    # Linearly project the query, key and value using different learned
    # projections. Splitting heads is automatically done during the linear
    # projections --> [batch_size, length, num_heads, dim_per_head].
    query = self.query_dense_layer(query_input)
    key = self.key_dense_layer(source_input)
    value = self.value_dense_layer(source_input)

    if cache is not None:
      # Combine cached keys and values with new keys and values.
      if decode_loop_step is not None:
        cache_k_shape = cache["k"].shape.as_list()
        indices = tf.reshape(
            tf.one_hot(decode_loop_step, cache_k_shape[1], dtype=key.dtype),
            [1, cache_k_shape[1], 1, 1])
        key = cache["k"] + key * indices
        cache_v_shape = cache["v"].shape.as_list()
        indices = tf.reshape(
            tf.one_hot(decode_loop_step, cache_v_shape[1], dtype=value.dtype),
            [1, cache_v_shape[1], 1, 1])
        value = cache["v"] + value * indices
      else:
        key = tf.concat([tf.cast(cache["k"], key.dtype), key], axis=1)
        value = tf.concat([tf.cast(cache["v"], value.dtype), value], axis=1)

      # Update cache
      cache["k"] = key
      cache["v"] = value

    # Scale query to prevent the dot product between query and key from growing
    # too large.
    depth = (self.hidden_size // self.num_heads)
    query *= depth**-0.5

    # Calculate dot product attention
    logits = tf.einsum("BTNH,BFNH->BNFT", key, query)
    logits += bias
    # Note that softmax internally performs math operations using float32
    # for numeric stability. When training with float16, we keep the input
    # and output in float16 for better performance.
    weights = tf.nn.softmax(logits, name="attention_weights")
    if training:
      weights = tf.nn.dropout(weights, rate=self.attention_dropout)
    attention_output = tf.einsum("BNFT,BTNH->BFNH", weights, value)

    # Run the outputs through another linear projection layer. Recombining heads
    # is automatically done --> [batch_size, length, hidden_size]
    attention_output = self.output_dense_layer(attention_output)
    return attention_output


class SelfAttention(Attention):
  """Multiheaded self-attention layer."""

  def call(self,
           query_input,
           bias,
           training,
           cache=None,
           decode_loop_step=None):
    return super(SelfAttention, self).call(query_input, query_input, bias,
                                           training, cache, decode_loop_step)
