# This module is from [WeNet](https://github.com/wenet-e2e/wenet).

# ## Citations

# ```bibtex
# @inproceedings{yao2021wenet,
#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
#   booktitle={Proc. Interspeech},
#   year={2021},
#   address={Brno, Czech Republic },
#   organization={IEEE}
# }

# @article{zhang2022wenet,
#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
#   journal={arXiv preprint arXiv:2203.15455},
#   year={2022}
# }
#

from typing import Optional

import torch
from torch import nn
from modules.wenet_extractor.utils.mask import make_pad_mask


class Predictor(nn.Module):
    def __init__(
        self,
        idim,
        l_order,
        r_order,
        threshold=1.0,
        dropout=0.1,
        smooth_factor=1.0,
        noise_threshold=0,
        tail_threshold=0.45,
    ):
        super().__init__()

        self.pad = nn.ConstantPad1d((l_order, r_order), 0.0)
        self.cif_conv1d = nn.Conv1d(idim, idim, l_order + r_order + 1, groups=idim)
        self.cif_output = nn.Linear(idim, 1)
        self.dropout = torch.nn.Dropout(p=dropout)
        self.threshold = threshold
        self.smooth_factor = smooth_factor
        self.noise_threshold = noise_threshold
        self.tail_threshold = tail_threshold

    def forward(
        self,
        hidden,
        target_label: Optional[torch.Tensor] = None,
        mask: torch.Tensor = torch.tensor(0),
        ignore_id: int = -1,
        mask_chunk_predictor: Optional[torch.Tensor] = None,
        target_label_length: Optional[torch.Tensor] = None,
    ):
        h = hidden
        context = h.transpose(1, 2)
        queries = self.pad(context)
        memory = self.cif_conv1d(queries)
        output = memory + context
        output = self.dropout(output)
        output = output.transpose(1, 2)
        output = torch.relu(output)
        output = self.cif_output(output)
        alphas = torch.sigmoid(output)
        alphas = torch.nn.functional.relu(
            alphas * self.smooth_factor - self.noise_threshold
        )
        if mask is not None:
            mask = mask.transpose(-1, -2).float()
            alphas = alphas * mask
        if mask_chunk_predictor is not None:
            alphas = alphas * mask_chunk_predictor
        alphas = alphas.squeeze(-1)
        mask = mask.squeeze(-1)
        if target_label_length is not None:
            target_length = target_label_length
        elif target_label is not None:
            target_length = (target_label != ignore_id).float().sum(-1)
        else:
            target_length = None
        token_num = alphas.sum(-1)
        if target_length is not None:
            alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1))
        elif self.tail_threshold > 0.0:
            hidden, alphas, token_num = self.tail_process_fn(
                hidden, alphas, token_num, mask=mask
            )

        acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)

        if target_length is None and self.tail_threshold > 0.0:
            token_num_int = torch.max(token_num).type(torch.int32).item()
            acoustic_embeds = acoustic_embeds[:, :token_num_int, :]

        return acoustic_embeds, token_num, alphas, cif_peak

    def tail_process_fn(
        self,
        hidden,
        alphas,
        token_num: Optional[torch.Tensor] = None,
        mask: Optional[torch.Tensor] = None,
    ):
        b, t, d = hidden.size()
        tail_threshold = self.tail_threshold
        if mask is not None:
            zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device)
            ones_t = torch.ones_like(zeros_t)
            mask_1 = torch.cat([mask, zeros_t], dim=1)
            mask_2 = torch.cat([ones_t, mask], dim=1)
            mask = mask_2 - mask_1
            tail_threshold = mask * tail_threshold
            alphas = torch.cat([alphas, zeros_t], dim=1)
            alphas = torch.add(alphas, tail_threshold)
        else:
            tail_threshold_tensor = torch.tensor(
                [tail_threshold], dtype=alphas.dtype
            ).to(alphas.device)
            tail_threshold_tensor = torch.reshape(tail_threshold_tensor, (1, 1))
            alphas = torch.cat([alphas, tail_threshold_tensor], dim=1)
        zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device)
        hidden = torch.cat([hidden, zeros], dim=1)
        token_num = alphas.sum(dim=-1)
        token_num_floor = torch.floor(token_num)

        return hidden, alphas, token_num_floor

    def gen_frame_alignments(
        self, alphas: torch.Tensor = None, encoder_sequence_length: torch.Tensor = None
    ):
        batch_size, maximum_length = alphas.size()
        int_type = torch.int32

        is_training = self.training
        if is_training:
            token_num = torch.round(torch.sum(alphas, dim=1)).type(int_type)
        else:
            token_num = torch.floor(torch.sum(alphas, dim=1)).type(int_type)

        max_token_num = torch.max(token_num).item()

        alphas_cumsum = torch.cumsum(alphas, dim=1)
        alphas_cumsum = torch.floor(alphas_cumsum).type(int_type)
        alphas_cumsum = alphas_cumsum[:, None, :].repeat(1, max_token_num, 1)

        index = torch.ones([batch_size, max_token_num], dtype=int_type)
        index = torch.cumsum(index, dim=1)
        index = index[:, :, None].repeat(1, 1, maximum_length).to(alphas_cumsum.device)

        index_div = torch.floor(torch.true_divide(alphas_cumsum, index)).type(int_type)
        index_div_bool_zeros = index_div.eq(0)
        index_div_bool_zeros_count = torch.sum(index_div_bool_zeros, dim=-1) + 1
        index_div_bool_zeros_count = torch.clamp(
            index_div_bool_zeros_count, 0, encoder_sequence_length.max()
        )
        token_num_mask = (~make_pad_mask(token_num, max_len=max_token_num)).to(
            token_num.device
        )
        index_div_bool_zeros_count *= token_num_mask

        index_div_bool_zeros_count_tile = index_div_bool_zeros_count[:, :, None].repeat(
            1, 1, maximum_length
        )
        ones = torch.ones_like(index_div_bool_zeros_count_tile)
        zeros = torch.zeros_like(index_div_bool_zeros_count_tile)
        ones = torch.cumsum(ones, dim=2)
        cond = index_div_bool_zeros_count_tile == ones
        index_div_bool_zeros_count_tile = torch.where(cond, zeros, ones)

        index_div_bool_zeros_count_tile_bool = index_div_bool_zeros_count_tile.type(
            torch.bool
        )
        index_div_bool_zeros_count_tile = 1 - index_div_bool_zeros_count_tile_bool.type(
            int_type
        )
        index_div_bool_zeros_count_tile_out = torch.sum(
            index_div_bool_zeros_count_tile, dim=1
        )
        index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out.type(
            int_type
        )
        predictor_mask = (
            (
                ~make_pad_mask(
                    encoder_sequence_length, max_len=encoder_sequence_length.max()
                )
            )
            .type(int_type)
            .to(encoder_sequence_length.device)
        )
        index_div_bool_zeros_count_tile_out = (
            index_div_bool_zeros_count_tile_out * predictor_mask
        )

        predictor_alignments = index_div_bool_zeros_count_tile_out
        predictor_alignments_length = predictor_alignments.sum(-1).type(
            encoder_sequence_length.dtype
        )
        return predictor_alignments.detach(), predictor_alignments_length.detach()


class MAELoss(nn.Module):
    def __init__(self, normalize_length=False):
        super(MAELoss, self).__init__()
        self.normalize_length = normalize_length
        self.criterion = torch.nn.L1Loss(reduction="sum")

    def forward(self, token_length, pre_token_length):
        loss_token_normalizer = token_length.size(0)
        if self.normalize_length:
            loss_token_normalizer = token_length.sum().type(torch.float32)
        loss = self.criterion(token_length, pre_token_length)
        loss = loss / loss_token_normalizer
        return loss


def cif(hidden: torch.Tensor, alphas: torch.Tensor, threshold: float):
    batch_size, len_time, hidden_size = hidden.size()

    # loop varss
    integrate = torch.zeros([batch_size], device=hidden.device)
    frame = torch.zeros([batch_size, hidden_size], device=hidden.device)
    # intermediate vars along time
    list_fires = []
    list_frames = []

    for t in range(len_time):
        alpha = alphas[:, t]
        distribution_completion = (
            torch.ones([batch_size], device=hidden.device) - integrate
        )

        integrate += alpha
        list_fires.append(integrate)

        fire_place = integrate >= threshold
        integrate = torch.where(
            fire_place,
            integrate - torch.ones([batch_size], device=hidden.device),
            integrate,
        )
        cur = torch.where(fire_place, distribution_completion, alpha)
        remainds = alpha - cur

        frame += cur[:, None] * hidden[:, t, :]
        list_frames.append(frame)
        frame = torch.where(
            fire_place[:, None].repeat(1, hidden_size),
            remainds[:, None] * hidden[:, t, :],
            frame,
        )

    fires = torch.stack(list_fires, 1)
    frames = torch.stack(list_frames, 1)
    list_ls = []
    len_labels = torch.round(alphas.sum(-1)).int()
    max_label_len = len_labels.max()
    for b in range(batch_size):
        fire = fires[b, :]
        l = torch.index_select(
            frames[b, :, :], 0, torch.nonzero(fire >= threshold).squeeze()
        )
        pad_l = torch.zeros(
            [int(max_label_len - l.size(0)), hidden_size], device=hidden.device
        )
        list_ls.append(torch.cat([l, pad_l], 0))
    return torch.stack(list_ls, 0), fires