#!/usr/bin/env python3
# -*- encoding: utf-8 -*-
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
#  MIT License  (https://opensource.org/licenses/MIT)

import time
import torch
import logging
from torch.cuda.amp import autocast
from typing import Union, Dict, List, Tuple, Optional

from funasr.register import tables
from funasr.models.ctc.ctc import CTC
from funasr.utils import postprocess_utils
from funasr.metrics.compute_acc import th_accuracy
from funasr.utils.datadir_writer import DatadirWriter
from funasr.models.paraformer.search import Hypothesis
from funasr.models.paraformer.cif_predictor import mae_loss
from funasr.train_utils.device_funcs import force_gatherable
from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
from funasr.models.transformer.utils.nets_utils import make_pad_mask, pad_list
from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank


@tables.register("model_classes", "UniASR")
class UniASR(torch.nn.Module):
    """
    Author: Speech Lab of DAMO Academy, Alibaba Group
    """

    def __init__(
        self,
        specaug: Optional[str] = None,
        specaug_conf: Optional[Dict] = None,
        normalize: str = None,
        normalize_conf: Optional[Dict] = None,
        encoder: str = None,
        encoder_conf: Optional[Dict] = None,
        decoder: str = None,
        decoder_conf: Optional[Dict] = None,
        ctc: str = None,
        ctc_conf: Optional[Dict] = None,
        predictor: str = None,
        predictor_conf: Optional[Dict] = None,
        ctc_weight: float = 0.5,
        input_size: int = 80,
        vocab_size: int = -1,
        ignore_id: int = -1,
        blank_id: int = 0,
        sos: int = 1,
        eos: int = 2,
        lsm_weight: float = 0.0,
        length_normalized_loss: bool = False,
        # report_cer: bool = True,
        # report_wer: bool = True,
        # sym_space: str = "<space>",
        # sym_blank: str = "<blank>",
        # extract_feats_in_collect_stats: bool = True,
        # predictor=None,
        predictor_weight: float = 0.0,
        predictor_bias: int = 0,
        sampling_ratio: float = 0.2,
        share_embedding: bool = False,
        # preencoder: Optional[AbsPreEncoder] = None,
        # postencoder: Optional[AbsPostEncoder] = None,
        use_1st_decoder_loss: bool = False,
        encoder1_encoder2_joint_training: bool = True,
        **kwargs,
        
    ):
        assert 0.0 <= ctc_weight <= 1.0, ctc_weight
        assert 0.0 <= interctc_weight < 1.0, interctc_weight

        super().__init__()
        self.blank_id = 0
        self.sos = 1
        self.eos = 2
        self.vocab_size = vocab_size
        self.ignore_id = ignore_id
        self.ctc_weight = ctc_weight
        self.interctc_weight = interctc_weight
        self.token_list = token_list.copy()

        self.frontend = frontend
        self.specaug = specaug
        self.normalize = normalize
        self.preencoder = preencoder
        self.postencoder = postencoder
        self.encoder = encoder

        if not hasattr(self.encoder, "interctc_use_conditioning"):
            self.encoder.interctc_use_conditioning = False
        if self.encoder.interctc_use_conditioning:
            self.encoder.conditioning_layer = torch.nn.Linear(
                vocab_size, self.encoder.output_size()
            )

        self.error_calculator = None

        # we set self.decoder = None in the CTC mode since
        # self.decoder parameters were never used and PyTorch complained
        # and threw an Exception in the multi-GPU experiment.
        # thanks Jeff Farris for pointing out the issue.
        if ctc_weight == 1.0:
            self.decoder = None
        else:
            self.decoder = decoder

        self.criterion_att = LabelSmoothingLoss(
            size=vocab_size,
            padding_idx=ignore_id,
            smoothing=lsm_weight,
            normalize_length=length_normalized_loss,
        )

        if report_cer or report_wer:
            self.error_calculator = ErrorCalculator(
                token_list, sym_space, sym_blank, report_cer, report_wer
            )

        if ctc_weight == 0.0:
            self.ctc = None
        else:
            self.ctc = ctc

        self.extract_feats_in_collect_stats = extract_feats_in_collect_stats
        self.predictor = predictor
        self.predictor_weight = predictor_weight
        self.criterion_pre = mae_loss(normalize_length=length_normalized_loss)
        self.step_cur = 0
        if self.encoder.overlap_chunk_cls is not None:
            from funasr.models.scama.chunk_utilis import build_scama_mask_for_cross_attention_decoder
            self.build_scama_mask_for_cross_attention_decoder_fn = build_scama_mask_for_cross_attention_decoder
            self.decoder_attention_chunk_type = decoder_attention_chunk_type

        self.encoder2 = encoder2
        self.decoder2 = decoder2
        self.ctc_weight2 = ctc_weight2
        if ctc_weight2 == 0.0:
            self.ctc2 = None
        else:
            self.ctc2 = ctc2
        self.interctc_weight2 = interctc_weight2
        self.predictor2 = predictor2
        self.predictor_weight2 = predictor_weight2
        self.decoder_attention_chunk_type2 = decoder_attention_chunk_type2
        self.stride_conv = stride_conv
        self.loss_weight_model1 = loss_weight_model1
        if self.encoder2.overlap_chunk_cls is not None:
            from funasr.models.scama.chunk_utilis import build_scama_mask_for_cross_attention_decoder
            self.build_scama_mask_for_cross_attention_decoder_fn2 = build_scama_mask_for_cross_attention_decoder
            self.decoder_attention_chunk_type2 = decoder_attention_chunk_type2

        self.enable_maas_finetune = enable_maas_finetune
        self.freeze_encoder2 = freeze_encoder2
        self.encoder1_encoder2_joint_training = encoder1_encoder2_joint_training
        self.length_normalized_loss = length_normalized_loss

    def forward(
        self,
        speech: torch.Tensor,
        speech_lengths: torch.Tensor,
        text: torch.Tensor,
        text_lengths: torch.Tensor,
        decoding_ind: int = None,
    ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
        """Frontend + Encoder + Decoder + Calc loss
        Args:
                        speech: (Batch, Length, ...)
                        speech_lengths: (Batch, )
                        text: (Batch, Length)
                        text_lengths: (Batch,)
        """
        assert text_lengths.dim() == 1, text_lengths.shape
        # Check that batch_size is unified
        assert (
            speech.shape[0]
            == speech_lengths.shape[0]
            == text.shape[0]
            == text_lengths.shape[0]
        ), (speech.shape, speech_lengths.shape, text.shape, text_lengths.shape)
        batch_size = speech.shape[0]

        # for data-parallel
        text = text[:, : text_lengths.max()]
        speech = speech[:, :speech_lengths.max()]

        ind = self.encoder.overlap_chunk_cls.random_choice(self.training, decoding_ind)
        # 1. Encoder
        if self.enable_maas_finetune:
            with torch.no_grad():
                speech_raw, encoder_out, encoder_out_lens = self.encode(speech, speech_lengths, ind=ind)
        else:
            speech_raw, encoder_out, encoder_out_lens = self.encode(speech, speech_lengths, ind=ind)

        intermediate_outs = None
        if isinstance(encoder_out, tuple):
            intermediate_outs = encoder_out[1]
            encoder_out = encoder_out[0]

        loss_att, acc_att, cer_att, wer_att = None, None, None, None
        loss_ctc, cer_ctc = None, None
        stats = dict()
        loss_pre = None
        loss, loss1, loss2 = 0.0, 0.0, 0.0

        if self.loss_weight_model1 > 0.0:
            ## model1
            # 1. CTC branch
            if self.enable_maas_finetune:
                with torch.no_grad():
                    if self.ctc_weight != 0.0:
                        if self.encoder.overlap_chunk_cls is not None:
                            encoder_out_ctc, encoder_out_lens_ctc = self.encoder.overlap_chunk_cls.remove_chunk(encoder_out,
                                                                                                                encoder_out_lens,
                                                                                                                chunk_outs=None)
                        loss_ctc, cer_ctc = self._calc_ctc_loss(
                            encoder_out_ctc, encoder_out_lens_ctc, text, text_lengths
                        )

                        # Collect CTC branch stats
                        stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None
                        stats["cer_ctc"] = cer_ctc

                    # Intermediate CTC (optional)
                    loss_interctc = 0.0
                    if self.interctc_weight != 0.0 and intermediate_outs is not None:
                        for layer_idx, intermediate_out in intermediate_outs:
                            # we assume intermediate_out has the same length & padding
                            # as those of encoder_out
                            if self.encoder.overlap_chunk_cls is not None:
                                encoder_out_ctc, encoder_out_lens_ctc = \
                                    self.encoder.overlap_chunk_cls.remove_chunk(
                                        intermediate_out,
                                        encoder_out_lens,
                                        chunk_outs=None)
                            loss_ic, cer_ic = self._calc_ctc_loss(
                                encoder_out_ctc, encoder_out_lens_ctc, text, text_lengths
                            )
                            loss_interctc = loss_interctc + loss_ic

                            # Collect Intermedaite CTC stats
                            stats["loss_interctc_layer{}".format(layer_idx)] = (
                                loss_ic.detach() if loss_ic is not None else None
                            )
                            stats["cer_interctc_layer{}".format(layer_idx)] = cer_ic

                        loss_interctc = loss_interctc / len(intermediate_outs)

                        # calculate whole encoder loss
                        loss_ctc = (
                                    1 - self.interctc_weight
                                ) * loss_ctc + self.interctc_weight * loss_interctc

                    # 2b. Attention decoder branch
                    if self.ctc_weight != 1.0:
                        loss_att, acc_att, cer_att, wer_att, loss_pre = self._calc_att_predictor_loss(
                            encoder_out, encoder_out_lens, text, text_lengths
                        )

                    # 3. CTC-Att loss definition
                    if self.ctc_weight == 0.0:
                        loss = loss_att + loss_pre * self.predictor_weight
                    elif self.ctc_weight == 1.0:
                        loss = loss_ctc
                    else:
                        loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att + loss_pre * self.predictor_weight

                    # Collect Attn branch stats
                    stats["loss_att"] = loss_att.detach() if loss_att is not None else None
                    stats["acc"] = acc_att
                    stats["cer"] = cer_att
                    stats["wer"] = wer_att
                    stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None
            else:
                if self.ctc_weight != 0.0:
                    if self.encoder.overlap_chunk_cls is not None:
                        encoder_out_ctc, encoder_out_lens_ctc = self.encoder.overlap_chunk_cls.remove_chunk(encoder_out,
                                                                                                            encoder_out_lens,
                                                                                                            chunk_outs=None)
                    loss_ctc, cer_ctc = self._calc_ctc_loss(
                        encoder_out_ctc, encoder_out_lens_ctc, text, text_lengths
                    )

                    # Collect CTC branch stats
                    stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None
                    stats["cer_ctc"] = cer_ctc

                    # Intermediate CTC (optional)
                loss_interctc = 0.0
                if self.interctc_weight != 0.0 and intermediate_outs is not None:
                    for layer_idx, intermediate_out in intermediate_outs:
                        # we assume intermediate_out has the same length & padding
                        # as those of encoder_out
                        if self.encoder.overlap_chunk_cls is not None:
                            encoder_out_ctc, encoder_out_lens_ctc = \
                                self.encoder.overlap_chunk_cls.remove_chunk(
                                    intermediate_out,
                                    encoder_out_lens,
                                    chunk_outs=None)
                        loss_ic, cer_ic = self._calc_ctc_loss(
                            encoder_out_ctc, encoder_out_lens_ctc, text, text_lengths
                        )
                        loss_interctc = loss_interctc + loss_ic

                        # Collect Intermedaite CTC stats
                        stats["loss_interctc_layer{}".format(layer_idx)] = (
                            loss_ic.detach() if loss_ic is not None else None
                        )
                        stats["cer_interctc_layer{}".format(layer_idx)] = cer_ic

                    loss_interctc = loss_interctc / len(intermediate_outs)

                    # calculate whole encoder loss
                    loss_ctc = (
                                1 - self.interctc_weight
                            ) * loss_ctc + self.interctc_weight * loss_interctc

                # 2b. Attention decoder branch
                if self.ctc_weight != 1.0:
                    loss_att, acc_att, cer_att, wer_att, loss_pre = self._calc_att_predictor_loss(
                        encoder_out, encoder_out_lens, text, text_lengths
                    )

                # 3. CTC-Att loss definition
                if self.ctc_weight == 0.0:
                    loss = loss_att + loss_pre * self.predictor_weight
                elif self.ctc_weight == 1.0:
                    loss = loss_ctc
                else:
                    loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att + loss_pre * self.predictor_weight

                # Collect Attn branch stats
                stats["loss_att"] = loss_att.detach() if loss_att is not None else None
                stats["acc"] = acc_att
                stats["cer"] = cer_att
                stats["wer"] = wer_att
                stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None

        loss1 = loss

        if self.loss_weight_model1 < 1.0:
            ## model2

            # encoder2
            if self.freeze_encoder2:
                with torch.no_grad():
                    encoder_out, encoder_out_lens = self.encode2(encoder_out, encoder_out_lens, speech_raw, speech_lengths, ind=ind)
            else:
                encoder_out, encoder_out_lens = self.encode2(encoder_out, encoder_out_lens, speech_raw, speech_lengths, ind=ind)

            intermediate_outs = None
            if isinstance(encoder_out, tuple):
                intermediate_outs = encoder_out[1]
                encoder_out = encoder_out[0]
            # CTC2
            if self.ctc_weight2 != 0.0:
                if self.encoder2.overlap_chunk_cls is not None:
                    encoder_out_ctc, encoder_out_lens_ctc = \
                        self.encoder2.overlap_chunk_cls.remove_chunk(
                            encoder_out,
                            encoder_out_lens,
                            chunk_outs=None,
                        )
                loss_ctc, cer_ctc = self._calc_ctc_loss2(
                    encoder_out_ctc, encoder_out_lens_ctc, text, text_lengths
                )

                # Collect CTC branch stats
                stats["loss_ctc2"] = loss_ctc.detach() if loss_ctc is not None else None
                stats["cer_ctc2"] = cer_ctc

            # Intermediate CTC (optional)
            loss_interctc = 0.0
            if self.interctc_weight2 != 0.0 and intermediate_outs is not None:
                for layer_idx, intermediate_out in intermediate_outs:
                    # we assume intermediate_out has the same length & padding
                    # as those of encoder_out
                    if self.encoder2.overlap_chunk_cls is not None:
                        encoder_out_ctc, encoder_out_lens_ctc = \
                            self.encoder2.overlap_chunk_cls.remove_chunk(
                                intermediate_out,
                                encoder_out_lens,
                                chunk_outs=None)
                    loss_ic, cer_ic = self._calc_ctc_loss2(
                        encoder_out_ctc, encoder_out_lens_ctc, text, text_lengths
                    )
                    loss_interctc = loss_interctc + loss_ic

                    # Collect Intermedaite CTC stats
                    stats["loss_interctc_layer{}2".format(layer_idx)] = (
                        loss_ic.detach() if loss_ic is not None else None
                    )
                    stats["cer_interctc_layer{}2".format(layer_idx)] = cer_ic

                loss_interctc = loss_interctc / len(intermediate_outs)

                # calculate whole encoder loss
                loss_ctc = (
                               1 - self.interctc_weight2
                           ) * loss_ctc + self.interctc_weight2 * loss_interctc

            # 2b. Attention decoder branch
            if self.ctc_weight2 != 1.0:
                loss_att, acc_att, cer_att, wer_att, loss_pre = self._calc_att_predictor_loss2(
                    encoder_out, encoder_out_lens, text, text_lengths
                )

            # 3. CTC-Att loss definition
            if self.ctc_weight2 == 0.0:
                loss = loss_att + loss_pre * self.predictor_weight2
            elif self.ctc_weight2 == 1.0:
                loss = loss_ctc
            else:
                loss = self.ctc_weight2 * loss_ctc + (
                    1 - self.ctc_weight2) * loss_att + loss_pre * self.predictor_weight2

            # Collect Attn branch stats
            stats["loss_att2"] = loss_att.detach() if loss_att is not None else None
            stats["acc2"] = acc_att
            stats["cer2"] = cer_att
            stats["wer2"] = wer_att
            stats["loss_pre2"] = loss_pre.detach().cpu() if loss_pre is not None else None
        loss2 = loss

        loss = loss1 * self.loss_weight_model1 + loss2 * (1 - self.loss_weight_model1)
        stats["loss1"] = torch.clone(loss1.detach())
        stats["loss2"] = torch.clone(loss2.detach())
        stats["loss"] = torch.clone(loss.detach())
        # force_gatherable: to-device and to-tensor if scalar for DataParallel
        if self.length_normalized_loss:
            batch_size = int((text_lengths + 1).sum())


        loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
        return loss, stats, weight

    def collect_feats(
        self,
        speech: torch.Tensor,
        speech_lengths: torch.Tensor,
        text: torch.Tensor,
        text_lengths: torch.Tensor,
    ) -> Dict[str, torch.Tensor]:
        if self.extract_feats_in_collect_stats:
            feats, feats_lengths = self._extract_feats(speech, speech_lengths)
        else:
            # Generate dummy stats if extract_feats_in_collect_stats is False
            logging.warning(
                "Generating dummy stats for feats and feats_lengths, "
                "because encoder_conf.extract_feats_in_collect_stats is "
                f"{self.extract_feats_in_collect_stats}"
            )
            feats, feats_lengths = speech, speech_lengths
        return {"feats": feats, "feats_lengths": feats_lengths}

    def encode(
        self, speech: torch.Tensor, speech_lengths: torch.Tensor, ind: int = 0,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """Frontend + Encoder. Note that this method is used by asr_inference.py
        Args:
                        speech: (Batch, Length, ...)
                        speech_lengths: (Batch, )
        """
        with autocast(False):
            # 1. Extract feats
            feats, feats_lengths = self._extract_feats(speech, speech_lengths)

            # 2. Data augmentation
            if self.specaug is not None and self.training:
                feats, feats_lengths = self.specaug(feats, feats_lengths)

            # 3. Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
            if self.normalize is not None:
                feats, feats_lengths = self.normalize(feats, feats_lengths)
        speech_raw = feats.clone().to(feats.device)
        # Pre-encoder, e.g. used for raw input data
        if self.preencoder is not None:
            feats, feats_lengths = self.preencoder(feats, feats_lengths)

        # 4. Forward encoder
        # feats: (Batch, Length, Dim)
        # -> encoder_out: (Batch, Length2, Dim2)
        if self.encoder.interctc_use_conditioning:
            encoder_out, encoder_out_lens, _ = self.encoder(
                feats, feats_lengths, ctc=self.ctc, ind=ind
            )
        else:
            encoder_out, encoder_out_lens, _ = self.encoder(feats, feats_lengths, ind=ind)
        intermediate_outs = None
        if isinstance(encoder_out, tuple):
            intermediate_outs = encoder_out[1]
            encoder_out = encoder_out[0]

        # Post-encoder, e.g. NLU
        if self.postencoder is not None:
            encoder_out, encoder_out_lens = self.postencoder(
                encoder_out, encoder_out_lens
            )

        assert encoder_out.size(0) == speech.size(0), (
            encoder_out.size(),
            speech.size(0),
        )
        assert encoder_out.size(1) <= encoder_out_lens.max(), (
            encoder_out.size(),
            encoder_out_lens.max(),
        )

        if intermediate_outs is not None:
            return (encoder_out, intermediate_outs), encoder_out_lens

        return speech_raw, encoder_out, encoder_out_lens

    def encode2(
        self,
        encoder_out: torch.Tensor,
        encoder_out_lens: torch.Tensor,
        speech: torch.Tensor,
        speech_lengths: torch.Tensor,
        ind: int = 0,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """Frontend + Encoder. Note that this method is used by asr_inference.py
        Args:
                        speech: (Batch, Length, ...)
                        speech_lengths: (Batch, )
        """
        # with autocast(False):
        #     # 1. Extract feats
        #     feats, feats_lengths = self._extract_feats(speech, speech_lengths)
        #
        #     # 2. Data augmentation
        #     if self.specaug is not None and self.training:
        #         feats, feats_lengths = self.specaug(feats, feats_lengths)
        #
        #     # 3. Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
        #     if self.normalize is not None:
        #         feats, feats_lengths = self.normalize(feats, feats_lengths)

        # Pre-encoder, e.g. used for raw input data
        # if self.preencoder is not None:
        #     feats, feats_lengths = self.preencoder(feats, feats_lengths)
        encoder_out_rm, encoder_out_lens_rm = self.encoder.overlap_chunk_cls.remove_chunk(
            encoder_out,
            encoder_out_lens,
            chunk_outs=None,
        )
        # residual_input
        encoder_out = torch.cat((speech, encoder_out_rm), dim=-1)
        encoder_out_lens = encoder_out_lens_rm
        if self.stride_conv is not None:
            speech, speech_lengths = self.stride_conv(encoder_out, encoder_out_lens)
        if not self.encoder1_encoder2_joint_training:
            speech = speech.detach()
            speech_lengths = speech_lengths.detach()
        # 4. Forward encoder
        # feats: (Batch, Length, Dim)
        # -> encoder_out: (Batch, Length2, Dim2)
        if self.encoder2.interctc_use_conditioning:
            encoder_out, encoder_out_lens, _ = self.encoder2(
                speech, speech_lengths, ctc=self.ctc2, ind=ind
            )
        else:
            encoder_out, encoder_out_lens, _ = self.encoder2(speech, speech_lengths, ind=ind)
        intermediate_outs = None
        if isinstance(encoder_out, tuple):
            intermediate_outs = encoder_out[1]
            encoder_out = encoder_out[0]

        # # Post-encoder, e.g. NLU
        # if self.postencoder is not None:
        #     encoder_out, encoder_out_lens = self.postencoder(
        #         encoder_out, encoder_out_lens
        #     )

        assert encoder_out.size(0) == speech.size(0), (
            encoder_out.size(),
            speech.size(0),
        )
        assert encoder_out.size(1) <= encoder_out_lens.max(), (
            encoder_out.size(),
            encoder_out_lens.max(),
        )

        if intermediate_outs is not None:
            return (encoder_out, intermediate_outs), encoder_out_lens

        return encoder_out, encoder_out_lens

    def _extract_feats(
        self, speech: torch.Tensor, speech_lengths: torch.Tensor
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        assert speech_lengths.dim() == 1, speech_lengths.shape

        # for data-parallel
        speech = speech[:, : speech_lengths.max()]

        if self.frontend is not None:
            # Frontend
            #  e.g. STFT and Feature extract
            #       data_loader may send time-domain signal in this case
            # speech (Batch, NSamples) -> feats: (Batch, NFrames, Dim)
            feats, feats_lengths = self.frontend(speech, speech_lengths)
        else:
            # No frontend and no feature extract
            feats, feats_lengths = speech, speech_lengths
        return feats, feats_lengths

    def nll(
        self,
        encoder_out: torch.Tensor,
        encoder_out_lens: torch.Tensor,
        ys_pad: torch.Tensor,
        ys_pad_lens: torch.Tensor,
    ) -> torch.Tensor:
        """Compute negative log likelihood(nll) from transformer-decoder
        Normally, this function is called in batchify_nll.
        Args:
                        encoder_out: (Batch, Length, Dim)
                        encoder_out_lens: (Batch,)
                        ys_pad: (Batch, Length)
                        ys_pad_lens: (Batch,)
        """
        ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
        ys_in_lens = ys_pad_lens + 1

        # 1. Forward decoder
        decoder_out, _ = self.decoder(
            encoder_out, encoder_out_lens, ys_in_pad, ys_in_lens
        )  # [batch, seqlen, dim]
        batch_size = decoder_out.size(0)
        decoder_num_class = decoder_out.size(2)
        # nll: negative log-likelihood
        nll = torch.nn.functional.cross_entropy(
            decoder_out.view(-1, decoder_num_class),
            ys_out_pad.view(-1),
            ignore_index=self.ignore_id,
            reduction="none",
        )
        nll = nll.view(batch_size, -1)
        nll = nll.sum(dim=1)
        assert nll.size(0) == batch_size
        return nll

    def batchify_nll(
        self,
        encoder_out: torch.Tensor,
        encoder_out_lens: torch.Tensor,
        ys_pad: torch.Tensor,
        ys_pad_lens: torch.Tensor,
        batch_size: int = 100,
    ):
        """Compute negative log likelihood(nll) from transformer-decoder
        To avoid OOM, this fuction seperate the input into batches.
        Then call nll for each batch and combine and return results.
        Args:
                        encoder_out: (Batch, Length, Dim)
                        encoder_out_lens: (Batch,)
                        ys_pad: (Batch, Length)
                        ys_pad_lens: (Batch,)
                        batch_size: int, samples each batch contain when computing nll,
                                                                        you may change this to avoid OOM or increase
                                                                        GPU memory usage
        """
        total_num = encoder_out.size(0)
        if total_num <= batch_size:
            nll = self.nll(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens)
        else:
            nll = []
            start_idx = 0
            while True:
                end_idx = min(start_idx + batch_size, total_num)
                batch_encoder_out = encoder_out[start_idx:end_idx, :, :]
                batch_encoder_out_lens = encoder_out_lens[start_idx:end_idx]
                batch_ys_pad = ys_pad[start_idx:end_idx, :]
                batch_ys_pad_lens = ys_pad_lens[start_idx:end_idx]
                batch_nll = self.nll(
                    batch_encoder_out,
                    batch_encoder_out_lens,
                    batch_ys_pad,
                    batch_ys_pad_lens,
                )
                nll.append(batch_nll)
                start_idx = end_idx
                if start_idx == total_num:
                    break
            nll = torch.cat(nll)
        assert nll.size(0) == total_num
        return nll

    def _calc_att_loss(
        self,
        encoder_out: torch.Tensor,
        encoder_out_lens: torch.Tensor,
        ys_pad: torch.Tensor,
        ys_pad_lens: torch.Tensor,
    ):
        ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
        ys_in_lens = ys_pad_lens + 1

        # 1. Forward decoder
        decoder_out, _ = self.decoder(
            encoder_out, encoder_out_lens, ys_in_pad, ys_in_lens
        )

        # 2. Compute attention loss
        loss_att = self.criterion_att(decoder_out, ys_out_pad)
        acc_att = th_accuracy(
            decoder_out.view(-1, self.vocab_size),
            ys_out_pad,
            ignore_label=self.ignore_id,
        )

        # Compute cer/wer using attention-decoder
        if self.training or self.error_calculator is None:
            cer_att, wer_att = None, None
        else:
            ys_hat = decoder_out.argmax(dim=-1)
            cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu())

        return loss_att, acc_att, cer_att, wer_att

    def _calc_att_predictor_loss(
        self,
        encoder_out: torch.Tensor,
        encoder_out_lens: torch.Tensor,
        ys_pad: torch.Tensor,
        ys_pad_lens: torch.Tensor,
    ):
        ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
        ys_in_lens = ys_pad_lens + 1

        encoder_out_mask = sequence_mask(encoder_out_lens, maxlen=encoder_out.size(1), dtype=encoder_out.dtype,
                                         device=encoder_out.device)[:, None, :]
        mask_chunk_predictor = None
        if self.encoder.overlap_chunk_cls is not None:
            mask_chunk_predictor = self.encoder.overlap_chunk_cls.get_mask_chunk_predictor(None,
                                                                                           device=encoder_out.device,
                                                                                           batch_size=encoder_out.size(
                                                                                               0))
            mask_shfit_chunk = self.encoder.overlap_chunk_cls.get_mask_shfit_chunk(None, device=encoder_out.device,
                                                                                   batch_size=encoder_out.size(0))
            encoder_out = encoder_out * mask_shfit_chunk
        pre_acoustic_embeds, pre_token_length, pre_alphas, _ = self.predictor(encoder_out,
                                                                              ys_out_pad,
                                                                              encoder_out_mask,
                                                                              ignore_id=self.ignore_id,
                                                                              mask_chunk_predictor=mask_chunk_predictor,
                                                                              target_label_length=ys_in_lens,
                                                                              )
        predictor_alignments, predictor_alignments_len = self.predictor.gen_frame_alignments(pre_alphas,
                                                                                             encoder_out_lens)

        scama_mask = None
        if self.encoder.overlap_chunk_cls is not None and self.decoder_attention_chunk_type == 'chunk':
            encoder_chunk_size = self.encoder.overlap_chunk_cls.chunk_size_pad_shift_cur
            attention_chunk_center_bias = 0
            attention_chunk_size = encoder_chunk_size
            decoder_att_look_back_factor = self.encoder.overlap_chunk_cls.decoder_att_look_back_factor_cur
            mask_shift_att_chunk_decoder = self.encoder.overlap_chunk_cls.get_mask_shift_att_chunk_decoder(None,
                                                                                                           device=encoder_out.device,
                                                                                                           batch_size=encoder_out.size(
                                                                                                               0))
            scama_mask = self.build_scama_mask_for_cross_attention_decoder_fn(
                predictor_alignments=predictor_alignments,
                encoder_sequence_length=encoder_out_lens,
                chunk_size=1,
                encoder_chunk_size=encoder_chunk_size,
                attention_chunk_center_bias=attention_chunk_center_bias,
                attention_chunk_size=attention_chunk_size,
                attention_chunk_type=self.decoder_attention_chunk_type,
                step=None,
                predictor_mask_chunk_hopping=mask_chunk_predictor,
                decoder_att_look_back_factor=decoder_att_look_back_factor,
                mask_shift_att_chunk_decoder=mask_shift_att_chunk_decoder,
                target_length=ys_in_lens,
                is_training=self.training,
            )
        elif self.encoder.overlap_chunk_cls is not None:
            encoder_out, encoder_out_lens = self.encoder.overlap_chunk_cls.remove_chunk(encoder_out, encoder_out_lens,
                                                                                        chunk_outs=None)
        # try:
        # 1. Forward decoder
        decoder_out, _ = self.decoder(
            encoder_out,
            encoder_out_lens,
            ys_in_pad,
            ys_in_lens,
            chunk_mask=scama_mask,
            pre_acoustic_embeds=pre_acoustic_embeds,

        )

        # 2. Compute attention loss
        loss_att = self.criterion_att(decoder_out, ys_out_pad)
        acc_att = th_accuracy(
            decoder_out.view(-1, self.vocab_size),
            ys_out_pad,
            ignore_label=self.ignore_id,
        )
        # predictor loss
        loss_pre = self.criterion_pre(ys_in_lens.type_as(pre_token_length), pre_token_length)
        # Compute cer/wer using attention-decoder
        if self.training or self.error_calculator is None:
            cer_att, wer_att = None, None
        else:
            ys_hat = decoder_out.argmax(dim=-1)
            cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu())

        return loss_att, acc_att, cer_att, wer_att, loss_pre

    def _calc_att_predictor_loss2(
        self,
        encoder_out: torch.Tensor,
        encoder_out_lens: torch.Tensor,
        ys_pad: torch.Tensor,
        ys_pad_lens: torch.Tensor,
    ):
        ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
        ys_in_lens = ys_pad_lens + 1

        encoder_out_mask = sequence_mask(encoder_out_lens, maxlen=encoder_out.size(1), dtype=encoder_out.dtype,
                                         device=encoder_out.device)[:, None, :]
        mask_chunk_predictor = None
        if self.encoder2.overlap_chunk_cls is not None:
            mask_chunk_predictor = self.encoder2.overlap_chunk_cls.get_mask_chunk_predictor(None,
                                                                                            device=encoder_out.device,
                                                                                            batch_size=encoder_out.size(
                                                                                                0))
            mask_shfit_chunk = self.encoder2.overlap_chunk_cls.get_mask_shfit_chunk(None, device=encoder_out.device,
                                                                                    batch_size=encoder_out.size(0))
            encoder_out = encoder_out * mask_shfit_chunk
        pre_acoustic_embeds, pre_token_length, pre_alphas, _ = self.predictor2(encoder_out,
                                                                               ys_out_pad,
                                                                               encoder_out_mask,
                                                                               ignore_id=self.ignore_id,
                                                                               mask_chunk_predictor=mask_chunk_predictor,
                                                                               target_label_length=ys_in_lens,
                                                                               )
        predictor_alignments, predictor_alignments_len = self.predictor2.gen_frame_alignments(pre_alphas,
                                                                                              encoder_out_lens)

        scama_mask = None
        if self.encoder2.overlap_chunk_cls is not None and self.decoder_attention_chunk_type2 == 'chunk':
            encoder_chunk_size = self.encoder2.overlap_chunk_cls.chunk_size_pad_shift_cur
            attention_chunk_center_bias = 0
            attention_chunk_size = encoder_chunk_size
            decoder_att_look_back_factor = self.encoder2.overlap_chunk_cls.decoder_att_look_back_factor_cur
            mask_shift_att_chunk_decoder = self.encoder2.overlap_chunk_cls.get_mask_shift_att_chunk_decoder(None,
                                                                                                            device=encoder_out.device,
                                                                                                            batch_size=encoder_out.size(
                                                                                                                0))
            scama_mask = self.build_scama_mask_for_cross_attention_decoder_fn2(
                predictor_alignments=predictor_alignments,
                encoder_sequence_length=encoder_out_lens,
                chunk_size=1,
                encoder_chunk_size=encoder_chunk_size,
                attention_chunk_center_bias=attention_chunk_center_bias,
                attention_chunk_size=attention_chunk_size,
                attention_chunk_type=self.decoder_attention_chunk_type2,
                step=None,
                predictor_mask_chunk_hopping=mask_chunk_predictor,
                decoder_att_look_back_factor=decoder_att_look_back_factor,
                mask_shift_att_chunk_decoder=mask_shift_att_chunk_decoder,
                target_length=ys_in_lens,
                is_training=self.training,
            )
        elif self.encoder2.overlap_chunk_cls is not None:
            encoder_out, encoder_out_lens = self.encoder2.overlap_chunk_cls.remove_chunk(encoder_out, encoder_out_lens,
                                                                                         chunk_outs=None)
        # try:
        # 1. Forward decoder
        decoder_out, _ = self.decoder2(
            encoder_out,
            encoder_out_lens,
            ys_in_pad,
            ys_in_lens,
            chunk_mask=scama_mask,
            pre_acoustic_embeds=pre_acoustic_embeds,
        )

        # 2. Compute attention loss
        loss_att = self.criterion_att(decoder_out, ys_out_pad)
        acc_att = th_accuracy(
            decoder_out.view(-1, self.vocab_size),
            ys_out_pad,
            ignore_label=self.ignore_id,
        )
        # predictor loss
        loss_pre = self.criterion_pre(ys_in_lens.type_as(pre_token_length), pre_token_length)
        # Compute cer/wer using attention-decoder
        if self.training or self.error_calculator is None:
            cer_att, wer_att = None, None
        else:
            ys_hat = decoder_out.argmax(dim=-1)
            cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu())

        return loss_att, acc_att, cer_att, wer_att, loss_pre

    def calc_predictor_mask(
        self,
        encoder_out: torch.Tensor,
        encoder_out_lens: torch.Tensor,
        ys_pad: torch.Tensor = None,
        ys_pad_lens: torch.Tensor = None,
    ):
        # ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
        # ys_in_lens = ys_pad_lens + 1
        ys_out_pad, ys_in_lens = None, None

        encoder_out_mask = sequence_mask(encoder_out_lens, maxlen=encoder_out.size(1), dtype=encoder_out.dtype,
                                         device=encoder_out.device)[:, None, :]
        mask_chunk_predictor = None
        if self.encoder.overlap_chunk_cls is not None:
            mask_chunk_predictor = self.encoder.overlap_chunk_cls.get_mask_chunk_predictor(None,
                                                                                           device=encoder_out.device,
                                                                                           batch_size=encoder_out.size(
                                                                                               0))
            mask_shfit_chunk = self.encoder.overlap_chunk_cls.get_mask_shfit_chunk(None, device=encoder_out.device,
                                                                                   batch_size=encoder_out.size(0))
            encoder_out = encoder_out * mask_shfit_chunk
        pre_acoustic_embeds, pre_token_length, pre_alphas, _ = self.predictor(encoder_out,
                                                                              ys_out_pad,
                                                                              encoder_out_mask,
                                                                              ignore_id=self.ignore_id,
                                                                              mask_chunk_predictor=mask_chunk_predictor,
                                                                              target_label_length=ys_in_lens,
                                                                              )
        predictor_alignments, predictor_alignments_len = self.predictor.gen_frame_alignments(pre_alphas,
                                                                                             encoder_out_lens)

        scama_mask = None
        if self.encoder.overlap_chunk_cls is not None and self.decoder_attention_chunk_type == 'chunk':
            encoder_chunk_size = self.encoder.overlap_chunk_cls.chunk_size_pad_shift_cur
            attention_chunk_center_bias = 0
            attention_chunk_size = encoder_chunk_size
            decoder_att_look_back_factor = self.encoder.overlap_chunk_cls.decoder_att_look_back_factor_cur
            mask_shift_att_chunk_decoder = self.encoder.overlap_chunk_cls.get_mask_shift_att_chunk_decoder(None,
                                                                                                           device=encoder_out.device,
                                                                                                           batch_size=encoder_out.size(
                                                                                                               0))
            scama_mask = self.build_scama_mask_for_cross_attention_decoder_fn(
                predictor_alignments=predictor_alignments,
                encoder_sequence_length=encoder_out_lens,
                chunk_size=1,
                encoder_chunk_size=encoder_chunk_size,
                attention_chunk_center_bias=attention_chunk_center_bias,
                attention_chunk_size=attention_chunk_size,
                attention_chunk_type=self.decoder_attention_chunk_type,
                step=None,
                predictor_mask_chunk_hopping=mask_chunk_predictor,
                decoder_att_look_back_factor=decoder_att_look_back_factor,
                mask_shift_att_chunk_decoder=mask_shift_att_chunk_decoder,
                target_length=ys_in_lens,
                is_training=self.training,
            )
        elif self.encoder.overlap_chunk_cls is not None:
            encoder_out, encoder_out_lens = self.encoder.overlap_chunk_cls.remove_chunk(encoder_out, encoder_out_lens,
                                                                                        chunk_outs=None)

        return pre_acoustic_embeds, pre_token_length, predictor_alignments, predictor_alignments_len, scama_mask

    def calc_predictor_mask2(
        self,
        encoder_out: torch.Tensor,
        encoder_out_lens: torch.Tensor,
        ys_pad: torch.Tensor = None,
        ys_pad_lens: torch.Tensor = None,
    ):
        # ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
        # ys_in_lens = ys_pad_lens + 1
        ys_out_pad, ys_in_lens = None, None

        encoder_out_mask = sequence_mask(encoder_out_lens, maxlen=encoder_out.size(1), dtype=encoder_out.dtype,
                                         device=encoder_out.device)[:, None, :]
        mask_chunk_predictor = None
        if self.encoder2.overlap_chunk_cls is not None:
            mask_chunk_predictor = self.encoder2.overlap_chunk_cls.get_mask_chunk_predictor(None,
                                                                                            device=encoder_out.device,
                                                                                            batch_size=encoder_out.size(
                                                                                                0))
            mask_shfit_chunk = self.encoder2.overlap_chunk_cls.get_mask_shfit_chunk(None, device=encoder_out.device,
                                                                                    batch_size=encoder_out.size(0))
            encoder_out = encoder_out * mask_shfit_chunk
        pre_acoustic_embeds, pre_token_length, pre_alphas, _ = self.predictor2(encoder_out,
                                                                               ys_out_pad,
                                                                               encoder_out_mask,
                                                                               ignore_id=self.ignore_id,
                                                                               mask_chunk_predictor=mask_chunk_predictor,
                                                                               target_label_length=ys_in_lens,
                                                                               )
        predictor_alignments, predictor_alignments_len = self.predictor2.gen_frame_alignments(pre_alphas,
                                                                                              encoder_out_lens)

        scama_mask = None
        if self.encoder2.overlap_chunk_cls is not None and self.decoder_attention_chunk_type2 == 'chunk':
            encoder_chunk_size = self.encoder2.overlap_chunk_cls.chunk_size_pad_shift_cur
            attention_chunk_center_bias = 0
            attention_chunk_size = encoder_chunk_size
            decoder_att_look_back_factor = self.encoder2.overlap_chunk_cls.decoder_att_look_back_factor_cur
            mask_shift_att_chunk_decoder = self.encoder2.overlap_chunk_cls.get_mask_shift_att_chunk_decoder(None,
                                                                                                            device=encoder_out.device,
                                                                                                            batch_size=encoder_out.size(
                                                                                                                0))
            scama_mask = self.build_scama_mask_for_cross_attention_decoder_fn2(
                predictor_alignments=predictor_alignments,
                encoder_sequence_length=encoder_out_lens,
                chunk_size=1,
                encoder_chunk_size=encoder_chunk_size,
                attention_chunk_center_bias=attention_chunk_center_bias,
                attention_chunk_size=attention_chunk_size,
                attention_chunk_type=self.decoder_attention_chunk_type2,
                step=None,
                predictor_mask_chunk_hopping=mask_chunk_predictor,
                decoder_att_look_back_factor=decoder_att_look_back_factor,
                mask_shift_att_chunk_decoder=mask_shift_att_chunk_decoder,
                target_length=ys_in_lens,
                is_training=self.training,
            )
        elif self.encoder2.overlap_chunk_cls is not None:
            encoder_out, encoder_out_lens = self.encoder2.overlap_chunk_cls.remove_chunk(encoder_out, encoder_out_lens,
                                                                                         chunk_outs=None)

        return pre_acoustic_embeds, pre_token_length, predictor_alignments, predictor_alignments_len, scama_mask

    def _calc_ctc_loss(
        self,
        encoder_out: torch.Tensor,
        encoder_out_lens: torch.Tensor,
        ys_pad: torch.Tensor,
        ys_pad_lens: torch.Tensor,
    ):
        # Calc CTC loss
        loss_ctc = self.ctc(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens)

        # Calc CER using CTC
        cer_ctc = None
        if not self.training and self.error_calculator is not None:
            ys_hat = self.ctc.argmax(encoder_out).data
            cer_ctc = self.error_calculator(ys_hat.cpu(), ys_pad.cpu(), is_ctc=True)
        return loss_ctc, cer_ctc

    def _calc_ctc_loss2(
        self,
        encoder_out: torch.Tensor,
        encoder_out_lens: torch.Tensor,
        ys_pad: torch.Tensor,
        ys_pad_lens: torch.Tensor,
    ):
        # Calc CTC loss
        loss_ctc = self.ctc2(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens)

        # Calc CER using CTC
        cer_ctc = None
        if not self.training and self.error_calculator is not None:
            ys_hat = self.ctc2.argmax(encoder_out).data
            cer_ctc = self.error_calculator(ys_hat.cpu(), ys_pad.cpu(), is_ctc=True)
        return loss_ctc, cer_ctc