def test_negative_scalar_index(adata, index: int, obs: bool):
    pos_index = index + (adata.n_obs if obs else adata.n_vars)

    if obs:
        adata_pos_subset = adata[pos_index]
        adata_neg_subset = adata[index]
    else:
        adata_pos_subset = adata[:, pos_index]
        adata_neg_subset = adata[:, index]

    np.testing.assert_array_equal(
        adata_pos_subset.obs_names, adata_neg_subset.obs_names
    )
    np.testing.assert_array_equal(
        adata_pos_subset.var_names, adata_neg_subset.var_names
    )
def verify(opt):
    if opt['datatype'] == 'train':
        logging.warning("changing datatype from train to train:ordered")
        opt['datatype'] = 'train:ordered'
    opt.log()
    # create repeat label agent and assign it to the specified task
    agent = RepeatLabelAgent(opt)
    world = create_task(opt, agent)

    log_every_n_secs = opt.get('log_every_n_secs', -1)
    if log_every_n_secs <= 0:
        log_every_n_secs = float('inf')
    log_time = TimeLogger()

    counts = {}
    counts['missing_text'] = 0
    counts['missing_labels'] = 0
    counts['missing_label_candidates'] = 0
    counts['empty_string_label_candidates'] = 0
    counts['label_candidates_with_missing_label'] = 0
    counts['did_not_return_message'] = 0

    # Show some example dialogs.
    while not world.epoch_done():
        world.parley()

        act = world.acts[0]

        if not isinstance(act, Message):
            counts['did_not_return_message'] += 1

        if 'text' not in act and 'image' not in act:
            warn("warning: missing text field:\n", act, opt)
            counts['missing_text'] += 1

        if 'labels' not in act and 'eval_labels' not in act:
            warn("warning: missing labels/eval_labels field:\n", act, opt)
            counts['missing_labels'] += 1
        else:
            if 'label_candidates' not in act:
                counts['missing_label_candidates'] += 1
            else:
                labels = act.get('labels', act.get('eval_labels'))
                is_label_cand = {}
                for l in labels:
                    is_label_cand[l] = False
                for c in act['label_candidates']:
                    if c == '':
                        warn("warning: empty string label_candidate:\n", act, opt)
                        counts['empty_string_label_candidates'] += 1
                    if c in is_label_cand:
                        if is_label_cand[c] is True:
                            warn(
                                "warning: label mentioned twice in candidate_labels:\n",
                                act,
                                opt,
                            )
                        is_label_cand[c] = True
                for _, has in is_label_cand.items():
                    if has is False:
                        warn("warning: label missing in candidate_labels:\n", act, opt)
                        counts['label_candidates_with_missing_label'] += 1

        if log_time.time() > log_every_n_secs:
            text, log = report(world, counts, log_time)
            print(text)

    try:
        # print dataset size if available
        logging.info(
            f'Loaded {world.num_episodes()} episodes with a '
            f'total of {world.num_examples()} examples'
        )
    except AttributeError:
        pass

    counts['exs'] = int(world.report()['exs'])
    return counts