def sortish_sampler_indices(data: List, bs: int, shuffle=True) -> np.array:
    "Go through the text data by order of src length with a bit of randomness. From fastai repo."
    if not shuffle:
        return np.argsort(np.array(data) * -1)

    def key_fn(i):
        return data[i]

    idxs = np.random.permutation(len(data))
    sz = bs * 50
    ck_idx = [idxs[i : i + sz] for i in range(0, len(idxs), sz)]
    sort_idx = np.concatenate([sorted(s, key=key_fn, reverse=True) for s in ck_idx])
    sz = bs
    ck_idx = [sort_idx[i : i + sz] for i in range(0, len(sort_idx), sz)]
    max_ck = np.argmax([key_fn(ck[0]) for ck in ck_idx])  # find the chunk with the largest key,
    ck_idx[0], ck_idx[max_ck] = ck_idx[max_ck], ck_idx[0]  # then make sure it goes first.
    sort_idx = np.concatenate(np.random.permutation(ck_idx[1:])) if len(ck_idx) > 1 else np.array([], dtype=int)
    sort_idx = np.concatenate((ck_idx[0], sort_idx))
    return sort_idx
Go through the text data by order of src length with a bit of randomness. From fastai repo.
dpkd/transformers/examples/legacy/seq2seq/utils.py