text: # Copyright 2016 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.
  # ==============================================================================
  """Chain Tests."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  
  from tensorflow.contrib.distributions.python.ops.bijectors.bijector_test_util import assert_scalar_congruency
  from tensorflow.contrib.distributions.python.ops.bijectors.chain import Chain
  from tensorflow.contrib.distributions.python.ops.bijectors.exp import Exp
  from tensorflow.contrib.distributions.python.ops.bijectors.softmax_centered import SoftmaxCentered
  from tensorflow.contrib.distributions.python.ops.bijectors.softplus import Softplus
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.platform import test
  
  
  class ChainBijectorTest(test.TestCase):
    """Tests the correctness of the Y = Chain(bij1, bij2, bij3) transformation."""
  
    def testBijector(self):
      with self.test_session():
        chain = Chain((Exp(event_ndims=1), Softplus(event_ndims=1)))
        self.assertEqual("chain_of_exp_of_softplus", chain.name)
        x = np.asarray([[[1., 2.],
                         [2., 3.]]])
        self.assertAllClose(1. + np.exp(x), chain.forward(x).eval())
        self.assertAllClose(np.log(x - 1.), chain.inverse(x).eval())
        self.assertAllClose(
            -np.sum(np.log(x - 1.), axis=2),
            chain.inverse_log_det_jacobian(x).eval())
        self.assertAllClose(
            np.sum(x, axis=2), chain.forward_log_det_jacobian(x).eval())
  
    def testBijectorIdentity(self):
      with self.test_session():
        chain = Chain()
        self.assertEqual("identity", chain.name)
        x = np.asarray([[[1., 2.],
                         [2., 3.]]])
        self.assertAllClose(x, chain.forward(x).eval())
        self.assertAllClose(x, chain.inverse(x).eval())
        self.assertAllClose(0., chain.inverse_log_det_jacobian(x).eval())
        self.assertAllClose(0., chain.forward_log_det_jacobian(x).eval())
  
    def testScalarCongruency(self):
      with self.test_session():
        bijector = Chain((Exp(), Softplus()))
        assert_scalar_congruency(
            bijector, lower_x=1e-3, upper_x=1.5, rtol=0.05)
  
    def testShapeGetters(self):
      with self.test_session():
        bijector = Chain([
            SoftmaxCentered(
                event_ndims=1, validate_args=True),
            SoftmaxCentered(
                event_ndims=0, validate_args=True)
        ])
        x = tensor_shape.TensorShape([])
        y = tensor_shape.TensorShape([2 + 1])
        self.assertAllEqual(y, bijector.forward_event_shape(x))
        self.assertAllEqual(
            y.as_list(),
            bijector.forward_event_shape_tensor(x.as_list()).eval())
        self.assertAllEqual(x, bijector.inverse_event_shape(y))
        self.assertAllEqual(
            x.as_list(),
            bijector.inverse_event_shape_tensor(y.as_list()).eval())
  
  
  if __name__ == "__main__":
    test.main()
  