# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""U2 ASR Model
Unified Streaming and Non-streaming Two-pass End-to-end Model for Speech Recognition
(https://arxiv.org/pdf/2012.05481.pdf)
"""
import time
from typing import Dict
from typing import List
from typing import Optional
from typing import Tuple

import paddle
from paddle import jit
from paddle import nn

from paddlespeech.s2t.frontend.utility import IGNORE_ID
from paddlespeech.s2t.frontend.utility import load_cmvn
from paddlespeech.s2t.modules.cmvn import GlobalCMVN
from paddlespeech.s2t.modules.ctc import CTCDecoderBase
from paddlespeech.s2t.modules.decoder import TransformerDecoder
from paddlespeech.s2t.modules.encoder import ConformerEncoder
from paddlespeech.s2t.modules.encoder import TransformerEncoder
from paddlespeech.s2t.modules.loss import LabelSmoothingLoss
from paddlespeech.s2t.modules.mask import subsequent_mask
from paddlespeech.s2t.utils import checkpoint
from paddlespeech.s2t.utils import layer_tools
from paddlespeech.s2t.utils.log import Log
from paddlespeech.s2t.utils.tensor_utils import add_sos_eos
from paddlespeech.s2t.utils.tensor_utils import th_accuracy
from paddlespeech.s2t.utils.utility import UpdateConfig

__all__ = ["U2STModel", "U2STInferModel"]

logger = Log(__name__).getlog()


class U2STBaseModel(nn.Layer):
    """CTC-Attention hybrid Encoder-Decoder model"""

    def __init__(self,
                 vocab_size: int,
                 encoder: TransformerEncoder,
                 st_decoder: TransformerDecoder,
                 decoder: TransformerDecoder=None,
                 ctc: CTCDecoderBase=None,
                 ctc_weight: float=0.0,
                 asr_weight: float=0.0,
                 ignore_id: int=IGNORE_ID,
                 lsm_weight: float=0.0,
                 length_normalized_loss: bool=False,
                 **kwargs):
        assert 0.0 <= ctc_weight <= 1.0, ctc_weight

        super().__init__()
        # note that eos is the same as sos (equivalent ID)
        self.sos = vocab_size - 1
        self.eos = vocab_size - 1
        self.vocab_size = vocab_size
        self.ignore_id = ignore_id
        self.ctc_weight = ctc_weight
        self.asr_weight = asr_weight

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

    def forward(
            self,
            speech: paddle.Tensor,
            speech_lengths: paddle.Tensor,
            text: paddle.Tensor,
            text_lengths: paddle.Tensor,
            asr_text: paddle.Tensor=None,
            asr_text_lengths: paddle.Tensor=None,
    ) -> Tuple[Optional[paddle.Tensor], Optional[paddle.Tensor], Optional[
            paddle.Tensor]]:
        """Frontend + Encoder + Decoder + Calc loss
        Args:
            speech: (Batch, Length, ...)
            speech_lengths: (Batch, )
            text: (Batch, Length)
            text_lengths: (Batch,)
        Returns:
            total_loss, attention_loss, ctc_loss
        """
        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)
        # 1. Encoder
        start = time.time()
        encoder_out, encoder_mask = self.encoder(speech, speech_lengths)
        encoder_time = time.time() - start
        #logger.debug(f"encoder time: {encoder_time}")
        #TODO(Hui Zhang): sum not support bool type
        #encoder_out_lens = encoder_mask.squeeze(1).sum(1)  #[B, 1, T] -> [B]
        encoder_out_lens = encoder_mask.squeeze(1).cast(paddle.int64).sum(
            1)  #[B, 1, T] -> [B]

        # 2a. ST-decoder branch
        start = time.time()
        loss_st, acc_st = self._calc_st_loss(encoder_out, encoder_mask, text,
                                             text_lengths)
        decoder_time = time.time() - start

        loss_asr_att = None
        loss_asr_ctc = None
        # 2b. ASR Attention-decoder branch
        if self.asr_weight > 0.:
            if self.ctc_weight != 1.0:
                start = time.time()
                loss_asr_att, acc_att = self._calc_att_loss(
                    encoder_out, encoder_mask, asr_text, asr_text_lengths)
                decoder_time = time.time() - start

            # 2c. CTC branch
            if self.ctc_weight != 0.0:
                start = time.time()
                loss_asr_ctc = self.ctc(encoder_out, encoder_out_lens, asr_text,
                                        asr_text_lengths)
                ctc_time = time.time() - start

            if loss_asr_ctc is None:
                loss_asr = loss_asr_att
            elif loss_asr_att is None:
                loss_asr = loss_asr_ctc
            else:
                loss_asr = self.ctc_weight * loss_asr_ctc + (1 - self.ctc_weight
                                                             ) * loss_asr_att
            loss = self.asr_weight * loss_asr + (1 - self.asr_weight) * loss_st
        else:
            loss = loss_st
        return loss, loss_st, loss_asr_att, loss_asr_ctc

    def _calc_st_loss(
            self,
            encoder_out: paddle.Tensor,
            encoder_mask: paddle.Tensor,
            ys_pad: paddle.Tensor,
            ys_pad_lens: paddle.Tensor, ) -> Tuple[paddle.Tensor, float]:
        """Calc attention loss.

        Args:
            encoder_out (paddle.Tensor): [B, Tmax, D]
            encoder_mask (paddle.Tensor): [B, 1, Tmax]
            ys_pad (paddle.Tensor): [B, Umax]
            ys_pad_lens (paddle.Tensor): [B]

        Returns:
            Tuple[paddle.Tensor, float]: attention_loss, accuracy rate
        """
        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.st_decoder(encoder_out, encoder_mask, 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, )
        return loss_att, acc_att

    def _calc_att_loss(
            self,
            encoder_out: paddle.Tensor,
            encoder_mask: paddle.Tensor,
            ys_pad: paddle.Tensor,
            ys_pad_lens: paddle.Tensor, ) -> Tuple[paddle.Tensor, float]:
        """Calc attention loss.

        Args:
            encoder_out (paddle.Tensor): [B, Tmax, D]
            encoder_mask (paddle.Tensor): [B, 1, Tmax]
            ys_pad (paddle.Tensor): [B, Umax]
            ys_pad_lens (paddle.Tensor): [B]

        Returns:
            Tuple[paddle.Tensor, float]: attention_loss, accuracy rate
        """
        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_mask, 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, )
        return loss_att, acc_att

    def _forward_encoder(
            self,
            speech: paddle.Tensor,
            speech_lengths: paddle.Tensor,
            decoding_chunk_size: int=-1,
            num_decoding_left_chunks: int=-1,
            simulate_streaming: bool=False,
    ) -> Tuple[paddle.Tensor, paddle.Tensor]:
        """Encoder pass.

        Args:
            speech (paddle.Tensor): [B, Tmax, D]
            speech_lengths (paddle.Tensor): [B]
            decoding_chunk_size (int, optional): chuck size. Defaults to -1.
            num_decoding_left_chunks (int, optional): nums chunks. Defaults to -1.
            simulate_streaming (bool, optional): streaming or not. Defaults to False.

        Returns:
            Tuple[paddle.Tensor, paddle.Tensor]:
                encoder hiddens (B, Tmax, D),
                encoder hiddens mask (B, 1, Tmax).
        """
        # Let's assume B = batch_size
        # 1. Encoder
        if simulate_streaming and decoding_chunk_size > 0:
            encoder_out, encoder_mask = self.encoder.forward_chunk_by_chunk(
                speech,
                decoding_chunk_size=decoding_chunk_size,
                num_decoding_left_chunks=num_decoding_left_chunks
            )  # (B, maxlen, encoder_dim)
        else:
            encoder_out, encoder_mask = self.encoder(
                speech,
                speech_lengths,
                decoding_chunk_size=decoding_chunk_size,
                num_decoding_left_chunks=num_decoding_left_chunks
            )  # (B, maxlen, encoder_dim)
        return encoder_out, encoder_mask

    def translate(
            self,
            speech: paddle.Tensor,
            speech_lengths: paddle.Tensor,
            beam_size: int=10,
            word_reward: float=0.0,
            maxlenratio: float=0.5,
            decoding_chunk_size: int=-1,
            num_decoding_left_chunks: int=-1,
            simulate_streaming: bool=False, ) -> paddle.Tensor:
        """ Apply beam search on attention decoder with length penalty
        Args:
            speech (paddle.Tensor): (batch, max_len, feat_dim)
            speech_length (paddle.Tensor): (batch, )
            beam_size (int): beam size for beam search
            word_reward (float): word reward used in beam search
            maxlenratio (float): max length ratio to bound the length of translated text
            decoding_chunk_size (int): decoding chunk for dynamic chunk
                trained model.
                <0: for decoding, use full chunk.
                >0: for decoding, use fixed chunk size as set.
                0: used for training, it's prohibited here
            simulate_streaming (bool): whether do encoder forward in a
                streaming fashion
        Returns:
            paddle.Tensor: decoding result, (batch, max_result_len)
        """
        assert speech.shape[0] == speech_lengths.shape[0]
        assert decoding_chunk_size != 0
        assert speech.shape[0] == 1
        device = speech.place

        # Let's assume B = batch_size and N = beam_size
        # 1. Encoder and init hypothesis
        encoder_out, encoder_mask = self._forward_encoder(
            speech, speech_lengths, decoding_chunk_size,
            num_decoding_left_chunks,
            simulate_streaming)  # (B, maxlen, encoder_dim)

        maxlen = max(int(encoder_out.shape[1] * maxlenratio), 5)

        hyp = {"score": 0.0, "yseq": [self.sos], "cache": None}
        hyps = [hyp]
        ended_hyps = []
        cur_best_score = -float("inf")
        cache = None

        # 2. Decoder forward step by step
        for i in range(1, maxlen + 1):
            ys = paddle.ones((len(hyps), i), dtype=paddle.long)

            if hyps[0]["cache"] is not None:
                cache = [
                    paddle.ones(
                        (len(hyps), i - 1, hyp_cache.shape[-1]),
                        dtype=paddle.float32) for hyp_cache in hyps[0]["cache"]
                ]
            for j, hyp in enumerate(hyps):
                ys[j, :] = paddle.to_tensor(hyp["yseq"])
                if hyps[0]["cache"] is not None:
                    for k in range(len(cache)):
                        cache[k][j] = hyps[j]["cache"][k]
            ys_mask = subsequent_mask(i).unsqueeze(0).to(device)

            logp, cache = self.st_decoder.forward_one_step(
                encoder_out.repeat(len(hyps), 1, 1),
                encoder_mask.repeat(len(hyps), 1, 1), ys, ys_mask, cache)

            hyps_best_kept = []
            for j, hyp in enumerate(hyps):
                top_k_logp, top_k_index = logp[j:j + 1].topk(beam_size)

                for b in range(beam_size):
                    new_hyp = {}
                    new_hyp["score"] = hyp["score"] + float(top_k_logp[0, b])
                    new_hyp["yseq"] = [0] * (1 + len(hyp["yseq"]))
                    new_hyp["yseq"][:len(hyp["yseq"])] = hyp["yseq"]
                    new_hyp["yseq"][len(hyp["yseq"])] = int(top_k_index[0, b])
                    new_hyp["cache"] = [cache_[j] for cache_ in cache]
                    # will be (2 x beam) hyps at most
                    hyps_best_kept.append(new_hyp)

                hyps_best_kept = sorted(
                    hyps_best_kept, key=lambda x: -x["score"])[:beam_size]

            # sort and get nbest
            hyps = hyps_best_kept
            if i == maxlen:
                for hyp in hyps:
                    hyp["yseq"].append(self.eos)

            # finalize the ended hypotheses with word reward (by length)
            remained_hyps = []
            for hyp in hyps:
                if hyp["yseq"][-1] == self.eos:
                    hyp["score"] += (i - 1) * word_reward
                    cur_best_score = max(cur_best_score, hyp["score"])
                    ended_hyps.append(hyp)
                else:
                    # stop while guarantee the optimality
                    if hyp["score"] + maxlen * word_reward > cur_best_score:
                        remained_hyps.append(hyp)

            # stop predition when there is no unended hypothesis
            if not remained_hyps:
                break
            hyps = remained_hyps

        # 3. Select best of best
        best_hyp = max(ended_hyps, key=lambda x: x["score"])

        return paddle.to_tensor([best_hyp["yseq"][1:]])

    # @jit.to_static
    def subsampling_rate(self) -> int:
        """ Export interface for c++ call, return subsampling_rate of the
            model
        """
        return self.encoder.embed.subsampling_rate

    # @jit.to_static
    def right_context(self) -> int:
        """ Export interface for c++ call, return right_context of the model
        """
        return self.encoder.embed.right_context

    # @jit.to_static
    def sos_symbol(self) -> int:
        """ Export interface for c++ call, return sos symbol id of the model
        """
        return self.sos

    # @jit.to_static
    def eos_symbol(self) -> int:
        """ Export interface for c++ call, return eos symbol id of the model
        """
        return self.eos

    @jit.to_static
    def forward_encoder_chunk(
            self,
            xs: paddle.Tensor,
            offset: int,
            required_cache_size: int,
            subsampling_cache: Optional[paddle.Tensor]=None,
            elayers_output_cache: Optional[List[paddle.Tensor]]=None,
            conformer_cnn_cache: Optional[List[paddle.Tensor]]=None,
    ) -> Tuple[paddle.Tensor, paddle.Tensor, List[paddle.Tensor], List[
            paddle.Tensor]]:
        """ Export interface for c++ call, give input chunk xs, and return
            output from time 0 to current chunk.
        Args:
            xs (paddle.Tensor): chunk input
            subsampling_cache (Optional[paddle.Tensor]): subsampling cache
            elayers_output_cache (Optional[List[paddle.Tensor]]):
                transformer/conformer encoder layers output cache
            conformer_cnn_cache (Optional[List[paddle.Tensor]]): conformer
                cnn cache
        Returns:
            paddle.Tensor: output, it ranges from time 0 to current chunk.
            paddle.Tensor: subsampling cache
            List[paddle.Tensor]: attention cache
            List[paddle.Tensor]: conformer cnn cache
        """
        return self.encoder.forward_chunk(
            xs, offset, required_cache_size, subsampling_cache,
            elayers_output_cache, conformer_cnn_cache)

    # @jit.to_static
    def ctc_activation(self, xs: paddle.Tensor) -> paddle.Tensor:
        """ Export interface for c++ call, apply linear transform and log
            softmax before ctc
        Args:
            xs (paddle.Tensor): encoder output
        Returns:
            paddle.Tensor: activation before ctc
        """
        return self.ctc.log_softmax(xs)

    @jit.to_static
    def forward_attention_decoder(
            self,
            hyps: paddle.Tensor,
            hyps_lens: paddle.Tensor,
            encoder_out: paddle.Tensor, ) -> paddle.Tensor:
        """ Export interface for c++ call, forward decoder with multiple
            hypothesis from ctc prefix beam search and one encoder output
        Args:
            hyps (paddle.Tensor): hyps from ctc prefix beam search, already
                pad sos at the begining, (B, T)
            hyps_lens (paddle.Tensor): length of each hyp in hyps, (B)
            encoder_out (paddle.Tensor): corresponding encoder output, (B=1, T, D)
        Returns:
            paddle.Tensor: decoder output, (B, L)
        """
        assert encoder_out.shape[0] == 1
        num_hyps = hyps.shape[0]
        assert hyps_lens.shape[0] == num_hyps
        encoder_out = encoder_out.repeat(num_hyps, 1, 1)
        # (B, 1, T)
        encoder_mask = paddle.ones(
            [num_hyps, 1, encoder_out.shape[1]], dtype=paddle.bool)
        # (num_hyps, max_hyps_len, vocab_size)
        decoder_out, _ = self.decoder(encoder_out, encoder_mask, hyps,
                                      hyps_lens)
        decoder_out = paddle.nn.functional.log_softmax(decoder_out, dim=-1)
        return decoder_out

    @paddle.no_grad()
    def decode(self,
               feats: paddle.Tensor,
               feats_lengths: paddle.Tensor,
               text_feature: Dict[str, int],
               decoding_method: str,
               beam_size: int,
               word_reward: float=0.0,
               maxlenratio: float=0.5,
               decoding_chunk_size: int=-1,
               num_decoding_left_chunks: int=-1,
               simulate_streaming: bool=False):
        """u2 decoding.

        Args:
            feats (Tensor): audio features, (B, T, D)
            feats_lengths (Tensor): (B)
            text_feature (TextFeaturizer): text feature object.
            decoding_method (str): decoding mode, e.g.
                    'fullsentence',
                    'simultaneous'
            beam_size (int): beam size for search
            decoding_chunk_size (int, optional): decoding chunk size. Defaults to -1.
                    <0: for decoding, use full chunk.
                    >0: for decoding, use fixed chunk size as set.
                    0: used for training, it's prohibited here.
            num_decoding_left_chunks (int, optional):
                    number of left chunks for decoding. Defaults to -1.
            simulate_streaming (bool, optional): simulate streaming inference. Defaults to False.

        Raises:
            ValueError: when not support decoding_method.

        Returns:
            List[List[int]]: transcripts.
        """
        batch_size = feats.shape[0]

        if decoding_method == 'fullsentence':
            hyps = self.translate(
                feats,
                feats_lengths,
                beam_size=beam_size,
                word_reward=word_reward,
                maxlenratio=maxlenratio,
                decoding_chunk_size=decoding_chunk_size,
                num_decoding_left_chunks=num_decoding_left_chunks,
                simulate_streaming=simulate_streaming)
            hyps = [hyp.tolist() for hyp in hyps]
        else:
            raise ValueError(f"Not support decoding method: {decoding_method}")

        res = [text_feature.defeaturize(hyp) for hyp in hyps]
        return res


class U2STModel(U2STBaseModel):
    def __init__(self, configs: dict):
        vocab_size, encoder, decoder = U2STModel._init_from_config(configs)

        if isinstance(decoder, Tuple):
            st_decoder, asr_decoder, ctc = decoder
            super().__init__(
                vocab_size=vocab_size,
                encoder=encoder,
                st_decoder=st_decoder,
                decoder=asr_decoder,
                ctc=ctc,
                **configs['model_conf'])
        else:
            super().__init__(
                vocab_size=vocab_size,
                encoder=encoder,
                st_decoder=decoder,
                **configs['model_conf'])

    @classmethod
    def _init_from_config(cls, configs: dict):
        """init sub module for model.

        Args:
            configs (dict): config dict.

        Raises:
            ValueError: raise when using not support encoder type.

        Returns:
            int, nn.Layer, nn.Layer, nn.Layer: vocab size, encoder, decoder, ctc
        """
        if configs['cmvn_file'] is not None:
            mean, istd = load_cmvn(configs['cmvn_file'],
                                   configs['cmvn_file_type'])
            global_cmvn = GlobalCMVN(
                paddle.to_tensor(mean, dtype=paddle.float),
                paddle.to_tensor(istd, dtype=paddle.float))
        else:
            global_cmvn = None

        input_dim = configs['input_dim']
        vocab_size = configs['output_dim']
        assert input_dim != 0, input_dim
        assert vocab_size != 0, vocab_size

        encoder_type = configs.get('encoder', 'transformer')
        logger.info(f"U2 Encoder type: {encoder_type}")
        if encoder_type == 'transformer':
            encoder = TransformerEncoder(
                input_dim, global_cmvn=global_cmvn, **configs['encoder_conf'])
        elif encoder_type == 'conformer':
            encoder = ConformerEncoder(
                input_dim, global_cmvn=global_cmvn, **configs['encoder_conf'])
        else:
            raise ValueError(f"not support encoder type:{encoder_type}")

        st_decoder = TransformerDecoder(vocab_size,
                                        encoder.output_size(),
                                        **configs['decoder_conf'])

        asr_weight = configs['model_conf']['asr_weight']
        logger.info(f"ASR Joint Training Weight: {asr_weight}")

        if asr_weight > 0.:
            decoder = TransformerDecoder(vocab_size,
                                         encoder.output_size(),
                                         **configs['decoder_conf'])
            # ctc decoder and ctc loss
            model_conf = configs['model_conf']
            dropout_rate = model_conf.get('ctc_dropout_rate', 0.0)
            grad_norm_type = model_conf.get('ctc_grad_norm_type', None)
            ctc = CTCDecoderBase(
                odim=vocab_size,
                enc_n_units=encoder.output_size(),
                blank_id=0,
                dropout_rate=dropout_rate,
                reduction=True,  # sum
                batch_average=True,  # sum / batch_size
                grad_norm_type=grad_norm_type)

            return vocab_size, encoder, (st_decoder, decoder, ctc)
        else:
            return vocab_size, encoder, st_decoder

    @classmethod
    def from_config(cls, configs: dict):
        """init model.

        Args:
            configs (dict): config dict.

        Raises:
            ValueError: raise when using not support encoder type.

        Returns:
            nn.Layer: U2STModel
        """
        model = cls(configs)
        return model

    @classmethod
    def from_pretrained(cls, dataloader, config, checkpoint_path):
        """Build a DeepSpeech2Model model from a pretrained model.

        Args:
            dataloader (paddle.io.DataLoader): not used.
            config (yacs.config.CfgNode):  model configs
            checkpoint_path (Path or str): the path of pretrained model checkpoint, without extension name

        Returns:
            DeepSpeech2Model: The model built from pretrained result.
        """
        with UpdateConfig(config):
            config.input_dim = dataloader.collate_fn.feature_size
            config.output_dim = dataloader.collate_fn.vocab_size

        model = cls.from_config(config)

        if checkpoint_path:
            infos = checkpoint.load_parameters(
                model, checkpoint_path=checkpoint_path)
            logger.info(f"checkpoint info: {infos}")
        layer_tools.summary(model)
        return model


class U2STInferModel(U2STModel):
    def __init__(self, configs: dict):
        super().__init__(configs)

    def forward(self,
                feats,
                feats_lengths,
                decoding_chunk_size=-1,
                num_decoding_left_chunks=-1,
                simulate_streaming=False):
        """export model function

        Args:
            feats (Tensor): [B, T, D]
            feats_lengths (Tensor): [B]

        Returns:
            List[List[int]]: best path result
        """
        return self.translate(
            feats,
            feats_lengths,
            decoding_chunk_size=decoding_chunk_size,
            num_decoding_left_chunks=num_decoding_left_chunks,
            simulate_streaming=simulate_streaming)
