content: # Copyright 2017 Google Inc.
  #
  # 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.
  """
  Test Cases for RNN encoders.
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  
  import tensorflow as tf
  import numpy as np
  
  from seq2seq.encoders import rnn_encoder
  
  
  class UnidirectionalRNNEncoderTest(tf.test.TestCase):
    """
    Tests the UnidirectionalRNNEncoder class.
    """
  
    def setUp(self):
      super(UnidirectionalRNNEncoderTest, self).setUp()
      tf.logging.set_verbosity(tf.logging.INFO)
      self.batch_size = 4
      self.sequence_length = 16
      self.input_depth = 10
      self.mode = tf.contrib.learn.ModeKeys.TRAIN
      self.params = rnn_encoder.UnidirectionalRNNEncoder.default_params()
      self.params["rnn_cell"]["cell_params"]["num_units"] = 32
      self.params["rnn_cell"]["cell_class"] = "BasicLSTMCell"
  
    def test_encode(self):
      inputs = tf.random_normal(
          [self.batch_size, self.sequence_length, self.input_depth])
      example_length = tf.ones(
          self.batch_size, dtype=tf.int32) * self.sequence_length
  
      encode_fn = rnn_encoder.UnidirectionalRNNEncoder(self.params, self.mode)
      encoder_output = encode_fn(inputs, example_length)
  
      with self.test_session() as sess:
        sess.run(tf.global_variables_initializer())
        encoder_output_ = sess.run(encoder_output)
  
      np.testing.assert_array_equal(encoder_output_.outputs.shape,
                                    [self.batch_size, self.sequence_length, 32])
      self.assertIsInstance(encoder_output_.final_state,
                            tf.contrib.rnn.LSTMStateTuple)
      np.testing.assert_array_equal(encoder_output_.final_state.h.shape,
                                    [self.batch_size, 32])
      np.testing.assert_array_equal(encoder_output_.final_state.c.shape,
                                    [self.batch_size, 32])
  
  
  class BidirectionalRNNEncoderTest(tf.test.TestCase):
    """
    Tests the BidirectionalRNNEncoder class.
    """
  
    def setUp(self):
      super(BidirectionalRNNEncoderTest, self).setUp()
      tf.logging.set_verbosity(tf.logging.INFO)
      self.batch_size = 4
      self.sequence_length = 16
      self.input_depth = 10
      self.params = rnn_encoder.BidirectionalRNNEncoder.default_params()
      self.params["rnn_cell"]["cell_params"]["num_units"] = 32
      self.params["rnn_cell"]["cell_class"] = "BasicLSTMCell"
      self.mode = tf.contrib.learn.ModeKeys.TRAIN
  
    def test_encode(self):
      inputs = tf.random_normal(
          [self.batch_size, self.sequence_length, self.input_depth])
      example_length = tf.ones(
          self.batch_size, dtype=tf.int32) * self.sequence_length
  
      encode_fn = rnn_encoder.BidirectionalRNNEncoder(self.params, self.mode)
      encoder_output = encode_fn(inputs, example_length)
  
      with self.test_session() as sess:
        sess.run(tf.global_variables_initializer())
        encoder_output_ = sess.run(encoder_output)
  
      np.testing.assert_array_equal(
          encoder_output_.outputs.shape,
          [self.batch_size, self.sequence_length, 32 * 2])
  
      self.assertIsInstance(encoder_output_.final_state[0],
                            tf.contrib.rnn.LSTMStateTuple)
      self.assertIsInstance(encoder_output_.final_state[1],
                            tf.contrib.rnn.LSTMStateTuple)
      np.testing.assert_array_equal(encoder_output_.final_state[0].h.shape,
                                    [self.batch_size, 32])
      np.testing.assert_array_equal(encoder_output_.final_state[0].c.shape,
                                    [self.batch_size, 32])
      np.testing.assert_array_equal(encoder_output_.final_state[1].h.shape,
                                    [self.batch_size, 32])
      np.testing.assert_array_equal(encoder_output_.final_state[1].c.shape,
                                    [self.batch_size, 32])
  
  
  class StackBidirectionalRNNEncoderTest(tf.test.TestCase):
    """
    Tests the StackBidirectionalRNNEncoder class.
    """
  
    def setUp(self):
      super(StackBidirectionalRNNEncoderTest, self).setUp()
      tf.logging.set_verbosity(tf.logging.INFO)
      self.batch_size = 4
      self.sequence_length = 16
      self.input_depth = 10
      self.mode = tf.contrib.learn.ModeKeys.TRAIN
  
    def _test_encode_with_params(self, params):
      """Tests the StackBidirectionalRNNEncoder with a specific cell"""
      inputs = tf.random_normal(
          [self.batch_size, self.sequence_length, self.input_depth])
      example_length = tf.ones(
          self.batch_size, dtype=tf.int32) * self.sequence_length
  
      encode_fn = rnn_encoder.StackBidirectionalRNNEncoder(params, self.mode)
      encoder_output = encode_fn(inputs, example_length)
  
      with self.test_session() as sess:
        sess.run(tf.global_variables_initializer())
        encoder_output_ = sess.run(encoder_output)
  
      output_size = encode_fn.params["rnn_cell"]["cell_params"]["num_units"]
  
      np.testing.assert_array_equal(
          encoder_output_.outputs.shape,
          [self.batch_size, self.sequence_length, output_size * 2])
  
      return encoder_output_
  
    def test_encode_with_single_cell(self):
      encoder_output_ = self._test_encode_with_params({
          "rnn_cell": {
              "num_layers": 1,
              "cell_params": {
                  "num_units": 32
              }
          }
      })
  
      self.assertIsInstance(encoder_output_.final_state[0][0],
                            tf.contrib.rnn.LSTMStateTuple)
      self.assertIsInstance(encoder_output_.final_state[1][0],
                            tf.contrib.rnn.LSTMStateTuple)
      np.testing.assert_array_equal(encoder_output_.final_state[0][0].h.shape,
                                    [self.batch_size, 32])
      np.testing.assert_array_equal(encoder_output_.final_state[0][0].c.shape,
                                    [self.batch_size, 32])
      np.testing.assert_array_equal(encoder_output_.final_state[1][0].h.shape,
                                    [self.batch_size, 32])
      np.testing.assert_array_equal(encoder_output_.final_state[1][0].c.shape,
                                    [self.batch_size, 32])
  
    def test_encode_with_multi_cell(self):
      encoder_output_ = self._test_encode_with_params({
          "rnn_cell": {
              "num_layers": 4,
              "cell_params": {
                  "num_units": 32
              }
          }
      })
  
      for layer_idx in range(4):
        self.assertIsInstance(encoder_output_.final_state[0][layer_idx],
                              tf.contrib.rnn.LSTMStateTuple)
        self.assertIsInstance(encoder_output_.final_state[1][layer_idx],
                              tf.contrib.rnn.LSTMStateTuple)
        np.testing.assert_array_equal(
            encoder_output_.final_state[0][layer_idx].h.shape,
            [self.batch_size, 32])
        np.testing.assert_array_equal(
            encoder_output_.final_state[0][layer_idx].c.shape,
            [self.batch_size, 32])
        np.testing.assert_array_equal(
            encoder_output_.final_state[1][layer_idx].h.shape,
            [self.batch_size, 32])
        np.testing.assert_array_equal(
            encoder_output_.final_state[1][layer_idx].c.shape,
            [self.batch_size, 32])
  
  
  if __name__ == "__main__":
    tf.test.main()
  