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 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)