def build_training_sample(sample,
                          target_seq_length, max_seq_length,
                          vocab_id_list, vocab_id_to_token_dict,
                          cls_id, sep_id, mask_id, pad_id,
                          masked_lm_prob, np_rng, binary_head):
    """Biuld training sample.

    Arguments:
        sample: A list of sentences in which each sentence is a list token ids.
        target_seq_length: Desired sequence length.
        max_seq_length: Maximum length of the sequence. All values are padded to
            this length.
        vocab_id_list: List of vocabulary ids. Used to pick a random id.
        vocab_id_to_token_dict: A dictionary from vocab ids to text tokens.
        cls_id: Start of example id.
        sep_id: Separator id.
        mask_id: Mask token id.
        pad_id: Padding token id.
        masked_lm_prob: Probability to mask tokens.
        np_rng: Random number genenrator. Note that this rng state should be
              numpy and not python since python randint is inclusive for
              the opper bound whereas the numpy one is exclusive.
    """

    if binary_head:
        # We assume that we have at least two sentences in the sample
        assert len(sample) > 1
    assert target_seq_length <= max_seq_length

    # Divide sample into two segments (A and B).
    if binary_head:
        tokens_a, tokens_b, is_next_random = get_a_and_b_segments(sample,
                                                                  np_rng)
    else:
        tokens_a = []
        for j in range(len(sample)):
            tokens_a.extend(sample[j])
        tokens_b = []
        is_next_random = False

    # Truncate to `target_sequence_length`.
    max_num_tokens = target_seq_length
    truncated = truncate_segments(tokens_a, tokens_b, len(tokens_a),
                                  len(tokens_b), max_num_tokens, np_rng)

    # Build tokens and toketypes.
    tokens, tokentypes = create_tokens_and_tokentypes(tokens_a, tokens_b,
                                                      cls_id, sep_id)

    # Masking.
    max_predictions_per_seq = masked_lm_prob * max_num_tokens
    (tokens, masked_positions, masked_labels, _, _) = create_masked_lm_predictions(
        tokens, vocab_id_list, vocab_id_to_token_dict, masked_lm_prob,
        cls_id, sep_id, mask_id, max_predictions_per_seq, np_rng)

    # Padding.
    tokens_np, tokentypes_np, labels_np, padding_mask_np, loss_mask_np \
        = pad_and_convert_to_numpy(tokens, tokentypes, masked_positions,
                                   masked_labels, pad_id, max_seq_length)

    train_sample = {
        'text': tokens_np,
        'types': tokentypes_np,
        'labels': labels_np,
        'is_random': int(is_next_random),
        'loss_mask': loss_mask_np,
        'padding_mask': padding_mask_np,
        'truncated': int(truncated)}
    return train_sample
Biuld training sample.

    Arguments:
        sample: A list of sentences in which each sentence is a list token ids.
        target_seq_length: Desired sequence length.
        max_seq_length: Maximum length of the sequence. All values are padded to
            this length.
        vocab_id_list: List of vocabulary ids. Used to pick a random id.
        vocab_id_to_token_dict: A dictionary from vocab ids to text tokens.
        cls_id: Start of example id.
        sep_id: Separator id.
        mask_id: Mask token id.
        pad_id: Padding token id.
        masked_lm_prob: Probability to mask tokens.
        np_rng: Random number genenrator. Note that this rng state should be
              numpy and not python since python randint is inclusive for
              the opper bound whereas the numpy one is exclusive.
    
training/DeepSpeed-Domino/megatron/data/bert_dataset.py