def test_view_delitem(attr):
    adata = gen_adata((10, 10))
    getattr(adata, attr)["to_delete"] = np.ones((10, 10))
    # Shouldn’t be a subclass, should be an ndarray
    assert type(getattr(adata, attr)["to_delete"]) is np.ndarray
    view = adata[5:7, :][:, :5]
    adata_hash = joblib.hash(adata)
    view_hash = joblib.hash(view)

    getattr(view, attr).__delitem__("to_delete")

    assert not view.is_view
    assert "to_delete" not in getattr(view, attr)
    assert "to_delete" in getattr(adata, attr)
    assert adata_hash == joblib.hash(adata)
    assert view_hash != joblib.hash(view)
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)