    def finalize_hypos(
        self,
        step: int,
        bbsz_idx,
        eos_scores,
        tokens,
        scores,
        finalized: List[List[Dict[str, Tensor]]],
        finished: List[bool],
        beam_size: int,
        attn: Optional[Tensor],
        src_lengths,
        max_len: int,
    ):
        """Finalize hypothesis, store finalized information in `finalized`, and change `finished` accordingly.
        A sentence is finalized when {beam_size} finished items have been collected for it.

        Returns number of sentences (not beam items) being finalized.
        These will be removed from the batch and not processed further.
        Args:
            bbsz_idx (Tensor):
        """
        assert bbsz_idx.numel() == eos_scores.size(0)

        # clone relevant token and attention tensors.
        # tokens is (batch * beam, max_len). So the index_select
        # gets the newly EOS rows, then selects cols 1..{step + 2}
        tokens_clone = tokens.index_select(0, bbsz_idx)[
            :, 1 : step + 2
        ]  # skip the first index, which is EOS

        tokens_clone[:, step] = self.eos
        attn_clone = (
            attn.index_select(0, bbsz_idx)[:, :, 1 : step + 2]
            if attn is not None
            else None
        )

        # compute scores per token position
        pos_scores = scores.index_select(0, bbsz_idx)[:, : step + 1]
        pos_scores[:, step, :] = eos_scores
        # convert from cumulative to per-position scores
        pos_scores[:, 1:] = pos_scores[:, 1:] - pos_scores[:, :-1]

        # normalize sentence-level scores
        if self.normalize_scores:
            eos_scores /= (step + 1) ** self.len_penalty

        # cum_unfin records which sentences in the batch are finished.
        # It helps match indexing between (a) the original sentences
        # in the batch and (b) the current, possibly-reduced set of
        # sentences.
        cum_unfin: List[int] = []
        prev = 0
        for f in finished:
            if f:
                prev += 1
            else:
                cum_unfin.append(prev)

        # The keys here are of the form "{sent}_{unfin_idx}", where
        # "unfin_idx" is the index in the current (possibly reduced)
        # list of sentences, and "sent" is the index in the original,
        # unreduced batch
        # set() is not supported in script export
        sents_seen: Dict[str, Optional[Tensor]] = {}

        # For every finished beam item
        for i in range(bbsz_idx.size()[0]):
            idx = bbsz_idx[i]
            score = eos_scores[i].sum()
            # sentence index in the current (possibly reduced) batch
            unfin_idx = idx // beam_size
            # sentence index in the original (unreduced) batch
            sent = unfin_idx + cum_unfin[unfin_idx]
            # Cannot create dict for key type '(int, int)' in torchscript.
            # The workaround is to cast int to string
            seen = str(sent.item()) + "_" + str(unfin_idx.item())
            if seen not in sents_seen:
                sents_seen[seen] = None

            if self.match_source_len and step > src_lengths[unfin_idx]:
                score = torch.tensor(-math.inf).to(score)

            # An input sentence (among those in a batch) is finished when
            # beam_size hypotheses have been collected for it
            if len(finalized[sent]) < beam_size:
                if attn_clone is not None:
                    # remove padding tokens from attn scores
                    hypo_attn = attn_clone[i]
                else:
                    hypo_attn = torch.empty(0)

                finalized[sent].append(
                    {
                        "tokens": tokens_clone[i],
                        "score": score,
                        "attention": hypo_attn,  # src_len x tgt_len
                        "alignment": torch.empty(0),
                        "positional_scores": pos_scores[i],
                    }
                )

        newly_finished: List[int] = []

        for seen in sents_seen.keys():
            # check termination conditions for this sentence
            sent: int = int(float(seen.split("_")[0]))
            unfin_idx: int = int(float(seen.split("_")[1]))

            if not finished[sent] and self.is_finished(
                step, unfin_idx, max_len, len(finalized[sent]), beam_size
            ):
                finished[sent] = True
                newly_finished.append(unfin_idx)

        return newly_finished
Finalize hypothesis, store finalized information in `finalized`, and change `finished` accordingly.
A sentence is finalized when {beam_size} finished items have been collected for it.

Returns number of sentences (not beam items) being finalized.
These will be removed from the batch and not processed further.
Args:
bbsz_idx (Tensor):
fairseq/models/speech_dlm/sequence_generator/multichannel_sequence_generator.py