def test_view_mixin_copies_data(adata, array_type: type, attr):
    N = 100
    adata = ad.AnnData(
        obs=pd.DataFrame(index=np.arange(N)), var=pd.DataFrame(index=np.arange(N))
    )

    X = array_type(sparse.eye(N, N).multiply(np.arange(1, N + 1)))
    if attr == "X":
        adata.X = X
    else:
        getattr(adata, attr)["arr"] = X

    view = adata[:50]

    if attr == "X":
        arr_view = view.X
    else:
        arr_view = getattr(view, attr)["arr"]

    arr_view_copy = arr_view.copy()

    if sparse.issparse(X):
        assert not np.shares_memory(arr_view.indices, arr_view_copy.indices)
        assert not np.shares_memory(arr_view.indptr, arr_view_copy.indptr)
        assert not np.shares_memory(arr_view.data, arr_view_copy.data)

        arr_view_copy.data[0] = -5
        assert not np.array_equal(arr_view_copy.data, arr_view.data)
    else:
        assert not np.shares_memory(arr_view, arr_view_copy)

        arr_view_copy[0, 0] = -5
        assert not np.array_equal(arr_view_copy, arr_view)
def testWrapperV2Caller(self, wrapper):
    """Tests that wrapper V2 is using the LayerRNNCell's caller."""

    with legacy_base_layer.keras_style_scope():
      base_cell = rnn_cell_impl.MultiRNNCell(
          [rnn_cell_impl.BasicRNNCell(1) for _ in range(2)])
    rnn_cell = wrapper(base_cell)
    inputs = ops.convert_to_tensor_v2_with_dispatch([[1]], dtype=dtypes.float32)
    state = ops.convert_to_tensor_v2_with_dispatch([[1]], dtype=dtypes.float32)
    _ = rnn_cell(inputs, [state, state])
    weights = base_cell._cells[0].weights
    self.assertLen(weights, expected_len=2)
    self.assertTrue(all("_wrapper" in v.name for v in weights))