# 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 transformer block layer."""

from __future__ import absolute_import
from __future__ import division
# from __future__ import google_type_annotations
from __future__ import print_function

import tensorflow as tf

from official.nlp.modeling.layers import attention
from official.nlp.modeling.layers import dense_einsum
from official.nlp.modeling.layers.util import tf_function_if_eager


@tf.keras.utils.register_keras_serializable(package="Text")
class Transformer(tf.keras.layers.Layer):
  """Transformer layer.

  This layer implements the Transformer from "Attention Is All You Need".
  (https://arxiv.org/abs/1706.03762).

  Attributes:
    num_attention_heads: Number of attention heads.
    intermediate_size: Size of the intermediate layer.
    intermediate_activation: Activation for the intermediate layer.
    dropout_rate: Dropout probability for the post-attention and output dropout.
    attention_dropout_rate: Dropout probability for within the attention layer.
    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_attention_heads,
               intermediate_size,
               intermediate_activation,
               dropout_rate=0.0,
               attention_dropout_rate=0.0,
               kernel_initializer="glorot_uniform",
               bias_initializer="zeros",
               kernel_regularizer=None,
               bias_regularizer=None,
               activity_regularizer=None,
               kernel_constraint=None,
               bias_constraint=None,
               **kwargs):
    super(Transformer, self).__init__(**kwargs)

    self._num_heads = num_attention_heads
    self._intermediate_size = intermediate_size
    self._intermediate_activation = intermediate_activation
    self._attention_dropout_rate = attention_dropout_rate
    self._dropout_rate = dropout_rate
    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)

  def build(self, input_shape):
    input_tensor = input_shape[0] if len(input_shape) == 2 else input_shape
    input_tensor_shape = tf.TensorShape(input_tensor)
    if len(input_tensor_shape) != 3:
      raise ValueError("TransformerLayer expects a three-dimensional input of "
                       "shape [batch, sequence, width].")
    batch_size, sequence_length, hidden_size = input_tensor_shape

    if len(input_shape) == 2:
      mask_tensor_shape = tf.TensorShape(input_shape[1])
      expected_mask_tensor_shape = tf.TensorShape(
          [batch_size, sequence_length, sequence_length])
      if not expected_mask_tensor_shape.is_compatible_with(mask_tensor_shape):
        raise ValueError("When passing a mask tensor to TransformerLayer, the "
                         "mask tensor must be of shape [batch, "
                         "sequence_length, sequence_length] (here %s). Got a "
                         "mask tensor of shape %s." %
                         (expected_mask_tensor_shape, mask_tensor_shape))
    if hidden_size % self._num_heads != 0:
      raise ValueError(
          "The input size (%d) is not a multiple of the number of attention "
          "heads (%d)" % (hidden_size, self._num_heads))
    self._attention_head_size = int(hidden_size // self._num_heads)

    self._attention_layer = attention.Attention(
        num_heads=self._num_heads,
        head_size=self._attention_head_size,
        dropout_rate=self._attention_dropout_rate,
        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,
        name="self_attention")
    self._attention_output_dense = dense_einsum.DenseEinsum(
        output_shape=hidden_size,
        num_summed_dimensions=2,
        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,
        name="self_attention_output")
    self._attention_dropout = tf.keras.layers.Dropout(rate=self._dropout_rate)
    # Use float32 in layernorm for numeric stability.
    # It is probably safe in mixed_float16, but we haven't validated this yet.
    self._attention_layer_norm = (
        tf.keras.layers.LayerNormalization(
            name="self_attention_layer_norm",
            axis=-1,
            epsilon=1e-12,
            dtype=tf.float32))
    self._intermediate_dense = dense_einsum.DenseEinsum(
        output_shape=self._intermediate_size,
        activation=None,
        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,
        name="intermediate")
    # Use float32 in intermediate gelu activation for numeric stability.
    # TODO(b/149117297): investigate gelu numeric stability.
    self._intermediate_activation_layer = tf.keras.layers.Activation(
        self._intermediate_activation, dtype=tf.float32)
    self._output_dense = dense_einsum.DenseEinsum(
        output_shape=hidden_size,
        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,
        name="output")
    self._output_dropout = tf.keras.layers.Dropout(rate=self._dropout_rate)
    # Use float32 in layernorm for numeric stability.
    self._output_layer_norm = tf.keras.layers.LayerNormalization(
        name="output_layer_norm", axis=-1, epsilon=1e-12, dtype=tf.float32)

    super(Transformer, self).build(input_shape)

  def get_config(self):
    config = {
        "num_attention_heads":
            self._num_heads,
        "intermediate_size":
            self._intermediate_size,
        "intermediate_activation":
            self._intermediate_activation,
        "dropout_rate":
            self._dropout_rate,
        "attention_dropout_rate":
            self._attention_dropout_rate,
        "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(Transformer, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))

  @tf_function_if_eager(experimental_compile=True)
  def call(self, inputs):
    if isinstance(inputs, (list, tuple)) and len(inputs) == 2:
      input_tensor, attention_mask = inputs
    else:
      input_tensor, attention_mask = (inputs, None)

    attention_inputs = [input_tensor, input_tensor]

    if attention_mask is not None:
      attention_inputs.append(attention_mask)

    with tf.name_scope(self.name):
      attention_output = self._attention_layer(attention_inputs)
      attention_output = self._attention_output_dense(attention_output)
      attention_output = self._attention_dropout(attention_output)
      attention_output = self._attention_layer_norm(input_tensor +
                                                    attention_output)
      intermediate_output = self._intermediate_dense(attention_output)
      intermediate_output = self._intermediate_activation_layer(
          intermediate_output)
      layer_output = self._output_dense(intermediate_output)
      layer_output = self._output_dropout(layer_output)
      # During mixed precision training, attention_output is from layer norm and
      # is always fp32 for now. Cast layer_output to fp32 for the subsequent
      # add.
      layer_output = tf.cast(layer_output, tf.float32)
      layer_output = self._output_layer_norm(layer_output + attention_output)

      return layer_output