# 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 positional embedding layer."""
# pylint: disable=g-classes-have-attributes

import math

import tensorflow as tf

from official.modeling import tf_utils


@tf.keras.utils.register_keras_serializable(package="Text")
class PositionEmbedding(tf.keras.layers.Layer):
  """Creates a positional embedding.

  This layer creates a positional embedding as described in "BERT: Pre-training
  of Deep Bidirectional Transformers for Language Understanding"
  (https://arxiv.org/abs/1810.04805).

  This layer can be set up to either create a statically shaped slice or a
  dynamically shaped slice. If `use_dynamic_slicing` is True, the input tensor
  can have a dynamic 1st dimension, while if `use_dynamic_slicing` is False the
  input size must be fixed.

  Arguments:
    use_dynamic_slicing: Whether to use the dynamic slicing path.
    max_sequence_length: The maximum size of the dynamic sequence. Only
      applicable if `use_dynamic_slicing` is True.
    initializer: The initializer to use for the embedding weights. Defaults to
      "glorot_uniform".
  """

  def __init__(self,
               initializer="glorot_uniform",
               use_dynamic_slicing=False,
               max_sequence_length=None,
               **kwargs):
    # We need to have a default dtype of float32, since the inputs (which Keras
    # usually uses to infer the dtype) will always be int32.
    if "dtype" not in kwargs:
      kwargs["dtype"] = "float32"

    super(PositionEmbedding, self).__init__(**kwargs)
    if use_dynamic_slicing and max_sequence_length is None:
      raise ValueError(
          "If `use_dynamic_slicing` is True, `max_sequence_length` must be set."
      )
    self._max_sequence_length = max_sequence_length
    self._initializer = tf.keras.initializers.get(initializer)
    self._use_dynamic_slicing = use_dynamic_slicing

  def get_config(self):
    config = {
        "max_sequence_length": self._max_sequence_length,
        "initializer": tf.keras.initializers.serialize(self._initializer),
        "use_dynamic_slicing": self._use_dynamic_slicing,
    }
    base_config = super(PositionEmbedding, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))

  def build(self, input_shape):
    """Implements build() for the layer."""
    dimension_list = input_shape.as_list()

    if len(dimension_list) != 3:
      raise ValueError("PositionEmbedding expects a 3-dimensional input tensor "
                       "of shape [batch, sequence, width]")
    seq_length = dimension_list[1]
    width = dimension_list[2]

    # If we are not using dynamic slicing, we must assume that the sequence
    # length is fixed and max_sequence_length should not be specified.
    if not self._use_dynamic_slicing:
      if seq_length is None:
        raise ValueError(
            "PositionEmbedding must have `use_dynamic_slicing` set "
            "to True (and max_sequence_length set) when the "
            "sequence (1st) dimension of the input is None.")
      if self._max_sequence_length is not None:
        raise ValueError(
            "When `use_dynamic_slicing` is False, max_sequence_length should "
            "not be specified and we ought to use seq_length to get the "
            "variable shape.")

    if self._max_sequence_length is not None:
      weight_sequence_length = self._max_sequence_length
    else:
      weight_sequence_length = seq_length

    self._position_embeddings = self.add_weight(
        "embeddings",
        shape=[weight_sequence_length, width],
        initializer=self._initializer)

    super(PositionEmbedding, self).build(input_shape)

  def call(self, inputs):
    """Implements call() for the layer."""
    input_shape = tf_utils.get_shape_list(inputs, expected_rank=3)
    if self._use_dynamic_slicing:
      position_embeddings = self._position_embeddings[:input_shape[1], :]
    else:
      position_embeddings = self._position_embeddings

    return tf.broadcast_to(position_embeddings, input_shape)


@tf.keras.utils.register_keras_serializable(package="Text")
class RelativePositionEmbedding(tf.keras.layers.Layer):
  """Creates a positional embedding.

  This layer calculates the position encoding as a mix of sine and cosine
  functions with geometrically increasing wavelengths. Defined and formulized in
   "Attention is All You Need", section 3.5.
  (https://arxiv.org/abs/1706.03762).

  Arguments:
    hidden_size: Size of the hidden layer.
    min_timescale: Minimum scale that will be applied at each position
    max_timescale: Maximum scale that will be applied at each position.
  """

  def __init__(self,
               hidden_size,
               min_timescale=1.0,
               max_timescale=1.0e4,
               **kwargs):
    # We need to have a default dtype of float32, since the inputs (which Keras
    # usually uses to infer the dtype) will always be int32.
    # We compute the positional encoding in float32 even if the model uses
    # float16, as many of the ops used, like log and exp, are numerically
    # unstable in float16.
    if "dtype" not in kwargs:
      kwargs["dtype"] = "float32"

    super(RelativePositionEmbedding, self).__init__(**kwargs)
    self._hidden_size = hidden_size
    self._min_timescale = min_timescale
    self._max_timescale = max_timescale

  def get_config(self):
    config = {
        "hidden_size": self._hidden_size,
        "min_timescale": self._min_timescale,
        "max_timescale": self._max_timescale,
    }
    base_config = super(RelativePositionEmbedding, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))

  def call(self, inputs, length=None):
    """Implements call() for the layer.

    Args:
      inputs: An tensor whose second dimension will be used as `length`. If
        `None`, the other `length` argument must be specified.
      length: An optional integer specifying the number of positions. If both
        `inputs` and `length` are spcified, `length` must be equal to the second
        dimension of `inputs`.

    Returns:
      A tensor in shape of [length, hidden_size].
    """
    if inputs is None and length is None:
      raise ValueError("If inputs is None, `length` must be set in "
                       "RelativePositionEmbedding().")
    if inputs is not None:
      input_shape = tf_utils.get_shape_list(inputs)
      if length is not None and length != input_shape[1]:
        raise ValueError(
            "If inputs is not None, `length` must equal to input_shape[1].")
      length = input_shape[1]
    position = tf.cast(tf.range(length), tf.float32)
    num_timescales = self._hidden_size // 2
    min_timescale, max_timescale = self._min_timescale, self._max_timescale
    log_timescale_increment = (
        math.log(float(max_timescale) / float(min_timescale)) /
        (tf.cast(num_timescales, tf.float32) - 1))
    inv_timescales = min_timescale * tf.exp(
        tf.cast(tf.range(num_timescales), tf.float32) *
        -log_timescale_increment)
    scaled_time = tf.expand_dims(position, 1) * tf.expand_dims(
        inv_timescales, 0)
    position_embeddings = tf.concat(
        [tf.sin(scaled_time), tf.cos(scaled_time)], axis=1)
    return position_embeddings

