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# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""Customized Swish activation."""

import tensorflow as tf


@tf.keras.utils.register_keras_serializable(package='Text')
def simple_swish(features):
  """Computes the Swish activation function.

  The tf.nn.swish operation uses a custom gradient to reduce memory usage.
  Since saving custom gradients in SavedModel is currently not supported, and
  one would not be able to use an exported TF-Hub module for fine-tuning, we
  provide this wrapper that can allow to select whether to use the native
  TensorFlow swish operation, or whether to use a customized operation that
  has uses default TensorFlow gradient computation.

  Args:
    features: A `Tensor` representing preactivation values.

  Returns:
    The activation value.
  """
  features = tf.convert_to_tensor(features)
  return features * tf.nn.sigmoid(features)


@tf.keras.utils.register_keras_serializable(package='Text')
def hard_swish(features):
  """Computes a hard version of the swish function.

  This operation can be used to reduce computational cost and improve
  quantization for edge devices.

  Args:
    features: A `Tensor` representing preactivation values.

  Returns:
    The activation value.
  """
  features = tf.convert_to_tensor(features)
  fdtype = features.dtype
  return features * tf.nn.relu6(features + tf.cast(3., fdtype)) * (1. / 6.)


@tf.keras.utils.register_keras_serializable(package='Text')
def identity(features):
  """Computes the identity function.

  Useful for helping in quantization.

  Args:
    features: A `Tensor` representing preactivation values.

  Returns:
    The activation value.
  """
  features = tf.convert_to_tensor(features)
  return tf.identity(features)
