text: # Copyright 2017 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.
  # ==============================================================================
  
  """Zoneout Wrapper"""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow as tf
  
  
  class ZoneoutWrapper(tf.contrib.rnn.RNNCell):
      """Add Zoneout to a RNN cell."""
  
      def __init__(self, cell, zoneout_drop_prob, is_training=True):
          self._cell = cell
          self._zoneout_prob = zoneout_drop_prob
          self._is_training = is_training
  
      @property
      def state_size(self):
          return self._cell.state_size
  
      @property
      def output_size(self):
          return self._cell.output_size
  
      def __call__(self, inputs, state, scope=None):
          output, new_state = self._cell(inputs, state, scope)
          if not isinstance(self._cell.state_size, tuple):
              new_state = tf.split(value=new_state, num_or_size_splits=2, axis=1)
              state = tf.split(value=state, num_or_size_splits=2, axis=1)
          final_new_state = [new_state[0], new_state[1]]
          if self._is_training:
              for i, state_element in enumerate(state):
                  random_tensor = 1 - self._zoneout_prob  # keep probability
                  random_tensor += tf.random_uniform(tf.shape(state_element))
                  # 0. if [zoneout_prob, 1.0) and 1. if [1.0, 1.0 + zoneout_prob)
                  binary_tensor = tf.floor(random_tensor)
                  final_new_state[
                      i] = (new_state[i] - state_element) * binary_tensor + state_element
          else:
              for i, state_element in enumerate(state):
                  final_new_state[
                      i] = state_element * self._zoneout_prob + new_state[i] * (
                          1 - self._zoneout_prob)
          if isinstance(self._cell.state_size, tuple):
              return output, tf.contrib.rnn.LSTMStateTuple(
                  final_new_state[0], final_new_state[1])
  
          return output, tf.concat([final_new_state[0], final_new_state[1]], 1)
  