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 PoolingEncoder.
  """
  
  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 ConvEncoder
  
  
  class ConvEncoderTest(tf.test.TestCase):
    """
    Tests the ConvEncoder class.
    """
  
    def setUp(self):
      super(ConvEncoderTest, self).setUp()
      self.batch_size = 4
      self.sequence_length = 16
      self.input_depth = 10
      self.mode = tf.contrib.learn.ModeKeys.TRAIN
  
    def _test_with_params(self, params):
      """Tests the encoder with a given parameter configuration"""
      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 = ConvEncoder(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)
  
      att_value_units = encode_fn.params["attention_cnn.units"]
      output_units = encode_fn.params["output_cnn.units"]
  
      np.testing.assert_array_equal(
          encoder_output_.outputs.shape,
          [self.batch_size, self.sequence_length, att_value_units])
      np.testing.assert_array_equal(
          encoder_output_.attention_values.shape,
          [self.batch_size, self.sequence_length, output_units])
      np.testing.assert_array_equal(
          encoder_output_.final_state.shape,
          [self.batch_size, output_units])
  
    def test_encode_with_pos(self):
      self._test_with_params({
          "position_embeddings.enable": True,
          "position_embeddings.num_positions": self.sequence_length,
          "attention_cnn.units": 5,
          "output_cnn.units": 6
      })
  
  if __name__ == "__main__":
    tf.test.main()
  