def setUpTestData(cls):
        batch_num = 0
        section_num = 0
        voter_num = 0
        party_num = 0
        position_num = 0
        candidate_num = 0
        num_elections = 2
        voters = list()
        positions = dict()
        for i in range(num_elections):
            election = Election.objects.create(name='Election {}'.format(i))
            positions[str(election.name)] = list()

            num_batches = 2
            for j in range(num_batches):
                batch = Batch.objects.create(year=batch_num, election=election)
                batch_num += 1

                num_sections = 2 if j == 0 else 1
                for k in range(num_sections):
                    section = Section.objects.create(
                        section_name=str(section_num)
                    )
                    section_num += 1

                    num_students = 2
                    for l in range(num_students):
                        voter = User.objects.create(
                            username='user{}'.format(voter_num),
                            first_name=str(voter_num),
                            last_name=str(voter_num),
                            type=UserType.VOTER
                        )
                        voter.set_password('voter')
                        voter.save()
                        voter_num += 1

                        VoterProfile.objects.create(
                            user=voter,
                            batch=batch,
                            section=section
                        )

                        voters.append(voter)

            num_positions = 3
            for i in range(num_positions):
                position = CandidatePosition.objects.create(
                    position_name='Position {}'.format(position_num),
                    election=election
                )

                positions[str(election.name)].append(position)

                position_num += 1

            num_parties = 3
            for j in range(num_parties):
                party = CandidateParty.objects.create(
                    party_name='Party {}'.format(party_num),
                    election=election
                )
                party_num += 1

                if j != 2:  # Let every third party have no candidates.
                    num_positions = 3
                    for k in range(num_positions):
                        position = positions[str(election.name)][k]

                        candidate = Candidate.objects.create(
                            user=voters[candidate_num],
                            party=party,
                            position=position,
                            election=election
                        )

                        Vote.objects.create(
                            user=voters[candidate_num],
                            candidate=candidate,
                            election=election
                        )

                        candidate_num += 1

        # Let's give one candidate an additional vote to really make sure that
        # we all got the correct number of votes.
        Vote.objects.create(
            user=voters[0],
            # NOTE: The voter in voter[1] is a Position 1 candidate of
            #       Party 1, where the voter in voter[0] is a member.
            candidate=Candidate.objects.get(user=voters[1]),
            election=Election.objects.get(name='Election 0')
        )

        _admin = User.objects.create(username='admin', type=UserType.ADMIN)
        _admin.set_password('root')
        _admin.save()
def testDense(self, dtype, param_value):
    (learning_rate, decay, momentum, epsilon, centered, use_resource) = tuple(
        param_value)
    with self.session(use_gpu=True):
      # Initialize variables for numpy implementation.
      var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
      grads0_np = np.array([0.1, 0.2], dtype=dtype.as_numpy_dtype)
      var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
      grads1_np = np.array([0.01, 0.2], dtype=dtype.as_numpy_dtype)

      if use_resource:
        var0 = resource_variable_ops.ResourceVariable(var0_np)
        var1 = resource_variable_ops.ResourceVariable(var1_np)
      else:
        var0 = variables.Variable(var0_np)
        var1 = variables.Variable(var1_np)
      grads0 = constant_op.constant(grads0_np)
      grads1 = constant_op.constant(grads1_np)
      opt = rmsprop.RMSPropOptimizer(
          learning_rate=learning_rate,
          decay=decay,
          momentum=momentum,
          epsilon=epsilon,
          centered=centered)

      update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
      variables.global_variables_initializer().run()

      mg0 = opt.get_slot(var0, "mg")
      self.assertEqual(mg0 is not None, centered)
      mg1 = opt.get_slot(var1, "mg")
      self.assertEqual(mg1 is not None, centered)
      rms0 = opt.get_slot(var0, "rms")
      self.assertIsNotNone(rms0)
      rms1 = opt.get_slot(var1, "rms")
      self.assertIsNotNone(rms1)
      mom0 = opt.get_slot(var0, "momentum")
      self.assertIsNotNone(mom0)
      mom1 = opt.get_slot(var1, "momentum")
      self.assertIsNotNone(mom1)

      mg0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype)
      mg1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype)
      rms0_np = np.array([epsilon, epsilon], dtype=dtype.as_numpy_dtype)
      rms1_np = np.array([epsilon, epsilon], dtype=dtype.as_numpy_dtype)
      mom0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype)
      mom1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype)

      # Fetch params to validate initial values
      self.assertAllClose([1.0, 2.0], var0.eval())
      self.assertAllClose([3.0, 4.0], var1.eval())

      # Run 4 steps of RMSProp
      for _ in range(4):
        update.run()

        var0_np, mg0_np, rms0_np, mom0_np = self._rmsprop_update_numpy(
            var0_np, grads0_np, mg0_np, rms0_np, mom0_np, learning_rate,
            decay, momentum, centered)
        var1_np, mg1_np, rms1_np, mom1_np = self._rmsprop_update_numpy(
            var1_np, grads1_np, mg1_np, rms1_np, mom1_np, learning_rate,
            decay, momentum, centered)

        # Validate updated params
        if centered:
          self.assertAllCloseAccordingToType(mg0_np, mg0.eval())
          self.assertAllCloseAccordingToType(mg1_np, mg1.eval())
        self.assertAllCloseAccordingToType(rms0_np, rms0.eval())
        self.assertAllCloseAccordingToType(rms1_np, rms1.eval())
        self.assertAllCloseAccordingToType(mom0_np, mom0.eval())
        self.assertAllCloseAccordingToType(mom1_np, mom1.eval())
        # TODO(b/117393988): Reduce tolerances for float16.
        self.assertAllCloseAccordingToType(
            var0_np, var0.eval(), half_rtol=3e-3, half_atol=3e-3)
        self.assertAllCloseAccordingToType(
            var1_np, var1.eval(), half_rtol=3e-3, half_atol=3e-3)