    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.numel()

        # 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)
        cum_fin_tensor = torch.tensor(cum_unfin, dtype=torch.int).to(bbsz_idx)

        unfin_idx = torch.div(bbsz_idx, beam_size, rounding_mode="trunc")
        sent = unfin_idx + torch.index_select(cum_fin_tensor, 0, unfin_idx)

        # Create a set of "{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
        # For every finished beam item
        # sentence index in the current (possibly reduced) batch
        seen = (sent << 32) + unfin_idx
        unique_seen: List[int] = torch.unique(seen).tolist()

        if self.match_source_len:
            condition = step > torch.index_select(src_lengths, 0, unfin_idx)
            eos_scores = torch.where(condition, torch.tensor(-math.inf), eos_scores)
        sent_list: List[int] = sent.tolist()
        for i in range(bbsz_idx.size()[0]):
            # An input sentence (among those in a batch) is finished when
            # beam_size hypotheses have been collected for it
            if len(finalized[sent_list[i]]) < 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_list[i]].append(
                    {
                        "tokens": tokens_clone[i],
                        "score": eos_scores[i],
                        "attention": hypo_attn,  # src_len x tgt_len
                        "alignment": torch.empty(0),
                        "positional_scores": pos_scores[i],
                    }
                )

        newly_finished: List[int] = []
        for unique_s in unique_seen:
            # check termination conditions for this sentence
            unique_sent: int = unique_s >> 32
            unique_unfin_idx: int = unique_s - (unique_sent << 32)

            if not finished[unique_sent] and self.is_finished(
                step, unique_unfin_idx, max_len, len(finalized[unique_sent]), beam_size
            ):
                finished[unique_sent] = True
                newly_finished.append(unique_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/sequence_generator.py