def test_layers_view():
    X = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
    L = np.array([[10, 11, 12], [13, 14, 15], [16, 17, 18]])
    real_adata = ad.AnnData(X)
    real_adata.layers["L"] = L
    view_adata = real_adata[1:, 1:]
    real_hash = joblib.hash(real_adata)
    view_hash = joblib.hash(view_adata)

    assert view_adata.is_view

    with pytest.raises(ValueError):
        view_adata.layers["L2"] = L + 2

    assert view_adata.is_view  # Failing to set layer item makes adata not view
    assert real_hash == joblib.hash(real_adata)
    assert view_hash == joblib.hash(view_adata)

    view_adata.layers["L2"] = L[1:, 1:] + 2

    assert not view_adata.is_view
    assert real_hash == joblib.hash(real_adata)
    assert view_hash != joblib.hash(view_adata)
def testWrapperWeights(self, wrapper):
    """Tests that wrapper weights contain wrapped cells weights."""
    base_cell = layers.SimpleRNNCell(1, name="basic_rnn_cell")
    rnn_cell = wrapper(base_cell)
    rnn_layer = layers.RNN(rnn_cell)
    inputs = ops.convert_to_tensor_v2_with_dispatch([[[1]]],
                                                    dtype=dtypes.float32)
    rnn_layer(inputs)

    wrapper_name = generic_utils.to_snake_case(wrapper.__name__)
    expected_weights = ["rnn/" + wrapper_name + "/" + var for var in
                        ("kernel:0", "recurrent_kernel:0", "bias:0")]
    self.assertLen(rnn_cell.weights, 3)
    self.assertCountEqual([v.name for v in rnn_cell.weights], expected_weights)
    self.assertCountEqual([v.name for v in rnn_cell.trainable_variables],
                          expected_weights)
    self.assertCountEqual([v.name for v in rnn_cell.non_trainable_variables],
                          [])
    self.assertCountEqual([v.name for v in rnn_cell.cell.weights],
                          expected_weights)