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 __getitem__(self, index):

        img1 = self._read_img(self._image_list[index][0])
        img2 = self._read_img(self._image_list[index][1])

        if self._flow_list:  # it will be empty for some dataset when split="test"
            flow = self._read_flow(self._flow_list[index])
            if self._has_builtin_flow_mask:
                flow, valid_flow_mask = flow
            else:
                valid_flow_mask = None
        else:
            flow = valid_flow_mask = None

        if self.transforms is not None:
            img1, img2, flow, valid_flow_mask = self.transforms(img1, img2, flow, valid_flow_mask)

        if self._has_builtin_flow_mask or valid_flow_mask is not None:
            # The `or valid_flow_mask is not None` part is here because the mask can be generated within a transform
            return img1, img2, flow, valid_flow_mask
        else:
            return img1, img2, flow