"""ESPnet2 ASR Transducer model."""

import logging
from contextlib import contextmanager
from typing import Dict, List, Optional, Tuple, Union

import torch
from packaging.version import parse as V
from typeguard import check_argument_types

from espnet2.asr.frontend.abs_frontend import AbsFrontend
from espnet2.asr.specaug.abs_specaug import AbsSpecAug
from espnet2.asr_transducer.decoder.abs_decoder import AbsDecoder
from espnet2.asr_transducer.encoder.encoder import Encoder
from espnet2.asr_transducer.joint_network import JointNetwork
from espnet2.asr_transducer.utils import get_transducer_task_io
from espnet2.layers.abs_normalize import AbsNormalize
from espnet2.torch_utils.device_funcs import force_gatherable
from espnet2.train.abs_espnet_model import AbsESPnetModel

if V(torch.__version__) >= V("1.6.0"):
    from torch.cuda.amp import autocast
else:

    @contextmanager
    def autocast(enabled=True):
        yield


class ESPnetASRTransducerModel(AbsESPnetModel):
    """ESPnet2ASRTransducerModel module definition.

    Args:
        vocab_size: Size of complete vocabulary (w/ SOS/EOS and blank included).
        token_list: List of tokens in vocabulary (minus reserved tokens).
        frontend: Frontend module.
        specaug: SpecAugment module.
        normalize: Normalization module.
        encoder: Encoder module.
        decoder: Decoder module.
        joint_network: Joint Network module.
        transducer_weight: Weight of the Transducer loss.
        fastemit_lambda: FastEmit lambda value.
        auxiliary_ctc_weight: Weight of auxiliary CTC loss.
        auxiliary_ctc_dropout_rate: Dropout rate for auxiliary CTC loss inputs.
        auxiliary_lm_loss_weight: Weight of auxiliary LM loss.
        auxiliary_lm_loss_smoothing: Smoothing rate for LM loss' label smoothing.
        ignore_id: Initial padding ID.
        sym_space: Space symbol.
        sym_blank: Blank Symbol.
        report_cer: Whether to report Character Error Rate during validation.
        report_wer: Whether to report Word Error Rate during validation.
        extract_feats_in_collect_stats: Whether to use extract_feats stats collection.

    """

    def __init__(
        self,
        vocab_size: int,
        token_list: Union[Tuple[str, ...], List[str]],
        frontend: Optional[AbsFrontend],
        specaug: Optional[AbsSpecAug],
        normalize: Optional[AbsNormalize],
        encoder: Encoder,
        decoder: AbsDecoder,
        joint_network: JointNetwork,
        transducer_weight: float = 1.0,
        fastemit_lambda: float = 0.0,
        auxiliary_ctc_weight: float = 0.0,
        auxiliary_ctc_dropout_rate: float = 0.0,
        auxiliary_lm_loss_weight: float = 0.0,
        auxiliary_lm_loss_smoothing: float = 0.05,
        ignore_id: int = -1,
        sym_space: str = "<space>",
        sym_blank: str = "<blank>",
        report_cer: bool = False,
        report_wer: bool = False,
        extract_feats_in_collect_stats: bool = True,
    ) -> None:
        """Construct an ESPnetASRTransducerModel object."""
        super().__init__()

        assert check_argument_types()

        # The following labels ID are reserved:
        #    - 0: Blank symbol.
        #    - 1: Unknown symbol.
        #    - vocab_size - 1: SOS/EOS symbol.
        self.vocab_size = vocab_size
        self.ignore_id = ignore_id
        self.token_list = token_list.copy()

        self.sym_space = sym_space
        self.sym_blank = sym_blank

        self.frontend = frontend
        self.specaug = specaug
        self.normalize = normalize

        self.encoder = encoder
        self.decoder = decoder
        self.joint_network = joint_network

        self.criterion_transducer = None
        self.error_calculator = None

        self.use_auxiliary_ctc = auxiliary_ctc_weight > 0
        self.use_auxiliary_lm_loss = auxiliary_lm_loss_weight > 0

        if self.use_auxiliary_ctc:
            self.ctc_lin = torch.nn.Linear(encoder.output_size, vocab_size)
            self.ctc_dropout_rate = auxiliary_ctc_dropout_rate

        if self.use_auxiliary_lm_loss:
            self.lm_lin = torch.nn.Linear(decoder.output_size, vocab_size)

            eps = auxiliary_lm_loss_smoothing / (vocab_size - 1)

            self.lm_loss_smooth_neg = eps
            self.lm_loss_smooth_pos = (1 - auxiliary_lm_loss_smoothing) + eps

        self.transducer_weight = transducer_weight
        self.fastemit_lambda = fastemit_lambda

        self.auxiliary_ctc_weight = auxiliary_ctc_weight
        self.auxiliary_lm_loss_weight = auxiliary_lm_loss_weight

        self.report_cer = report_cer
        self.report_wer = report_wer

        self.extract_feats_in_collect_stats = extract_feats_in_collect_stats

    def forward(
        self,
        speech: torch.Tensor,
        speech_lengths: torch.Tensor,
        text: torch.Tensor,
        text_lengths: torch.Tensor,
        **kwargs,
    ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
        """Forward architecture and compute loss(es).

        Args:
            speech: Speech sequences. (B, S)
            speech_lengths: Speech sequences lengths. (B,)
            text: Label ID sequences. (B, L)
            text_lengths: Label ID sequences lengths. (B,)
            kwargs: Contains "utts_id".

        Return:
            loss: Main loss value.
            stats: Task statistics.
            weight: Task weights.

        """
        assert text_lengths.dim() == 1, text_lengths.shape
        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]
        text = text[:, : text_lengths.max()]

        # 1. Encoder
        encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)

        # 2. Transducer-related I/O preparation
        decoder_in, target, t_len, u_len = get_transducer_task_io(
            text,
            encoder_out_lens,
            ignore_id=self.ignore_id,
        )

        # 3. Decoder
        self.decoder.set_device(encoder_out.device)
        decoder_out = self.decoder(decoder_in)

        # 4. Joint Network
        joint_out = self.joint_network(
            encoder_out.unsqueeze(2), decoder_out.unsqueeze(1)
        )

        # 5. Losses
        loss_trans, cer_trans, wer_trans = self._calc_transducer_loss(
            encoder_out,
            joint_out,
            target,
            t_len,
            u_len,
        )

        loss_ctc, loss_lm = 0.0, 0.0

        if self.use_auxiliary_ctc:
            loss_ctc = self._calc_ctc_loss(
                encoder_out,
                target,
                t_len,
                u_len,
            )

        if self.use_auxiliary_lm_loss:
            loss_lm = self._calc_lm_loss(decoder_out, target)

        loss = (
            self.transducer_weight * loss_trans
            + self.auxiliary_ctc_weight * loss_ctc
            + self.auxiliary_lm_loss_weight * loss_lm
        )

        stats = dict(
            loss=loss.detach(),
            loss_transducer=loss_trans.detach(),
            loss_aux_ctc=loss_ctc.detach() if loss_ctc > 0.0 else None,
            loss_aux_lm=loss_lm.detach() if loss_lm > 0.0 else None,
            cer_transducer=cer_trans,
            wer_transducer=wer_trans,
        )

        # force_gatherable: to-device and to-tensor if scalar for DataParallel
        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,
        **kwargs,
    ) -> Dict[str, torch.Tensor]:
        """Collect features sequences and features lengths sequences.

        Args:
            speech: Speech sequences. (B, S)
            speech_lengths: Speech sequences lengths. (B,)
            text: Label ID sequences. (B, L)
            text_lengths: Label ID sequences lengths. (B,)
            kwargs: Contains "utts_id".

        Return:
            {}: "feats": Features sequences. (B, T, D_feats),
                "feats_lengths": Features sequences lengths. (B,)

        """
        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,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """Encoder speech sequences.

        Args:
            speech: Speech sequences. (B, S)
            speech_lengths: Speech sequences lengths. (B,)

        Return:
            encoder_out: Encoder outputs. (B, T, D_enc)
            encoder_out_lens: Encoder outputs lengths. (B,)

        """
        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)

        # 4. Forward encoder
        encoder_out, encoder_out_lens = self.encoder(feats, feats_lengths)

        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(),
        )

        return encoder_out, encoder_out_lens

    def _extract_feats(
        self, speech: torch.Tensor, speech_lengths: torch.Tensor
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """Extract features sequences and features sequences lengths.

        Args:
            speech: Speech sequences. (B, S)
            speech_lengths: Speech sequences lengths. (B,)

        Return:
            feats: Features sequences. (B, T, D_feats)
            feats_lengths: Features sequences lengths. (B,)

        """
        assert speech_lengths.dim() == 1, speech_lengths.shape

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

        if self.frontend is not None:
            feats, feats_lengths = self.frontend(speech, speech_lengths)
        else:
            feats, feats_lengths = speech, speech_lengths

        return feats, feats_lengths

    def _calc_transducer_loss(
        self,
        encoder_out: torch.Tensor,
        joint_out: torch.Tensor,
        target: torch.Tensor,
        t_len: torch.Tensor,
        u_len: torch.Tensor,
    ) -> Tuple[torch.Tensor, Optional[float], Optional[float]]:
        """Compute Transducer loss.

        Args:
            encoder_out: Encoder output sequences. (B, T, D_enc)
            joint_out: Joint Network output sequences (B, T, U, D_joint)
            target: Target label ID sequences. (B, L)
            t_len: Encoder output sequences lengths. (B,)
            u_len: Target label ID sequences lengths. (B,)

        Return:
            loss_transducer: Transducer loss value.
            cer_transducer: Character error rate for Transducer.
            wer_transducer: Word Error Rate for Transducer.

        """
        if self.criterion_transducer is None:
            try:
                from warprnnt_pytorch import RNNTLoss

                self.criterion_transducer = RNNTLoss(
                    reduction="mean",
                    fastemit_lambda=self.fastemit_lambda,
                )
            except ImportError:
                logging.error(
                    "warp-rnnt was not installed."
                    "Please consult the installation documentation."
                )
                exit(1)

        loss_transducer = self.criterion_transducer(
            joint_out,
            target,
            t_len,
            u_len,
        )

        if not self.training and (self.report_cer or self.report_wer):
            if self.error_calculator is None:
                from espnet2.asr_transducer.error_calculator import ErrorCalculator

                self.error_calculator = ErrorCalculator(
                    self.decoder,
                    self.joint_network,
                    self.token_list,
                    self.sym_space,
                    self.sym_blank,
                    report_cer=self.report_cer,
                    report_wer=self.report_wer,
                )

            cer_transducer, wer_transducer = self.error_calculator(
                encoder_out, target, t_len
            )

            return loss_transducer, cer_transducer, wer_transducer

        return loss_transducer, None, None

    def _calc_ctc_loss(
        self,
        encoder_out: torch.Tensor,
        target: torch.Tensor,
        t_len: torch.Tensor,
        u_len: torch.Tensor,
    ) -> torch.Tensor:
        """Compute CTC loss.

        Args:
            encoder_out: Encoder output sequences. (B, T, D_enc)
            target: Target label ID sequences. (B, L)
            t_len: Encoder output sequences lengths. (B,)
            u_len: Target label ID sequences lengths. (B,)

        Return:
            loss_ctc: CTC loss value.

        """
        ctc_in = self.ctc_lin(
            torch.nn.functional.dropout(encoder_out, p=self.ctc_dropout_rate)
        )
        ctc_in = torch.log_softmax(ctc_in.transpose(0, 1), dim=-1)

        target_mask = target != 0
        ctc_target = target[target_mask].cpu()

        with torch.backends.cudnn.flags(deterministic=True):
            loss_ctc = torch.nn.functional.ctc_loss(
                ctc_in,
                ctc_target,
                t_len,
                u_len,
                zero_infinity=True,
                reduction="sum",
            )
        loss_ctc /= target.size(0)

        return loss_ctc

    def _calc_lm_loss(
        self,
        decoder_out: torch.Tensor,
        target: torch.Tensor,
    ) -> torch.Tensor:
        """Compute LM loss (i.e.: Cross-entropy with smoothing).

        Args:
            decoder_out: Decoder output sequences. (B, U, D_dec)
            target: Target label ID sequences. (B, L)

        Return:
            loss_lm: LM loss value.

        """
        batch_size = decoder_out.size(0)

        logp = torch.log_softmax(
            self.lm_lin(decoder_out[:, :-1, :]).view(-1, self.vocab_size),
            dim=1,
        )
        target = target.view(-1).type(torch.int64)
        ignore = (target == 0).unsqueeze(1)

        with torch.no_grad():
            true_dist = logp.clone().fill_(self.lm_loss_smooth_neg)

            true_dist.scatter_(1, target.unsqueeze(1), self.lm_loss_smooth_pos)

        loss_lm = torch.nn.functional.kl_div(logp, true_dist, reduction="none")
        loss_lm = loss_lm.masked_fill(ignore, 0).sum() / batch_size

        return loss_lm
