def testFoldr_Simple(self):
    elems = constant_op.constant([1, 2, 3, 4, 5, 6], name="data")

    r = functional_ops.foldr(
        lambda a, x: math_ops.multiply(math_ops.add(a, x), 2),
        elems)
    self.assertAllEqual(450, self.evaluate(r))

    r = functional_ops.foldr(
        lambda a, x: math_ops.multiply(math_ops.add(a, x), 2),
        elems,
        initializer=10)
    self.assertAllEqual(1282, self.evaluate(r))

  def testFoldr_Simple(self):
    with self.test_session():
      elems = constant_op.constant([1, 2, 3, 4, 5, 6], name="data")

      r = functional_ops.foldr(lambda a, x: math_ops.mul(math_ops.add(a, x), 2),
                               elems)
      self.assertAllEqual(450, r.eval())

      r = functional_ops.foldr(
          lambda a, x: math_ops.mul(math_ops.add(a, x), 2),
          elems,
          initializer=10)
      self.assertAllEqual(1282, r.eval())

 def testFoldr_MultiInputSingleOutput(self):
   with self.test_session():
     elems = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
     initializer = np.array(1.0)
     r = functional_ops.foldr(lambda a, x: a + x[0] + x[1], (elems, -elems),
                              initializer)
     self.assertAllEqual(1, self.evaluate(r))

  def testFoldr_SingleInputMultiOutput(self):
    elems = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
    initializer = np.array([1, -1.0])
    r = functional_ops.foldr(lambda a, x: a + x, elems, initializer)
    r_value = self.evaluate(r)

    self.assertAllEqual(22, r_value[0])
    self.assertAllEqual(20, r_value[1])

  def testFoldr_Scoped(self):
    with self.cached_session() as sess:
      with variable_scope.variable_scope("root") as varscope:
        elems = constant_op.constant([1, 2, 3, 4, 5, 6], name="data")

        r = functional_ops.foldr(simple_scoped_fn, elems)
        # Check that we have the one variable we asked for here.
        self.assertEqual(len(variables.trainable_variables()), 1)
        self.assertEqual(variables.trainable_variables()[0].name,
                         "root/body/two:0")
        sess.run([variables.global_variables_initializer()])
        self.assertAllEqual(450, self.evaluate(r))

        # Now let's reuse our single variable.
        varscope.reuse_variables()
        r = functional_ops.foldr(simple_scoped_fn, elems, initializer=10)
        self.assertEqual(len(variables.trainable_variables()), 1)
        self.assertAllEqual(1282, self.evaluate(r))

  def testFold_Grad(self):
    with self.cached_session():
      elems = constant_op.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], name="data")
      v = constant_op.constant(2.0, name="v")
      r = functional_ops.foldl(
          lambda a, x: math_ops.multiply(a, x), elems, initializer=v)
      r = gradients_impl.gradients(r, v)[0]
      self.assertAllEqual(720.0, self.evaluate(r))

      r = functional_ops.foldr(
          lambda a, x: math_ops.multiply(a, x), elems, initializer=v)
      r = gradients_impl.gradients(r, v)[0]
      self.assertAllEqual(720.0, self.evaluate(r))