#!/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 torch

from funasr_detach.register import tables
from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask


class mae_loss(torch.nn.Module):

    def __init__(self, normalize_length=False):
        super(mae_loss, 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, alphas, threshold):
    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(
            [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


def cif_wo_hidden(alphas, threshold):
    batch_size, len_time = alphas.size()

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

    for t in range(len_time):
        alpha = alphas[:, t]

        integrate += alpha
        list_fires.append(integrate)

        fire_place = integrate >= threshold
        integrate = torch.where(
            fire_place,
            integrate - torch.ones([batch_size], device=alphas.device) * threshold,
            integrate,
        )

    fires = torch.stack(list_fires, 1)
    return fires


@tables.register("predictor_classes", "CifPredictorV3")
class CifPredictorV3(torch.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=0.0,
        tf2torch_tensor_name_prefix_torch="predictor",
        tf2torch_tensor_name_prefix_tf="seq2seq/cif",
        smooth_factor2=1.0,
        noise_threshold2=0,
        upsample_times=5,
        upsample_type="cnn",
        use_cif1_cnn=True,
        tail_mask=True,
    ):
        super(CifPredictorV3, self).__init__()

        self.pad = torch.nn.ConstantPad1d((l_order, r_order), 0)
        self.cif_conv1d = torch.nn.Conv1d(idim, idim, l_order + r_order + 1)
        self.cif_output = torch.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
        self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
        self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf

        self.upsample_times = upsample_times
        self.upsample_type = upsample_type
        self.use_cif1_cnn = use_cif1_cnn
        if self.upsample_type == "cnn":
            self.upsample_cnn = torch.nn.ConvTranspose1d(
                idim, idim, self.upsample_times, self.upsample_times
            )
            self.cif_output2 = torch.nn.Linear(idim, 1)
        elif self.upsample_type == "cnn_blstm":
            self.upsample_cnn = torch.nn.ConvTranspose1d(
                idim, idim, self.upsample_times, self.upsample_times
            )
            self.blstm = torch.nn.LSTM(
                idim,
                idim,
                1,
                bias=True,
                batch_first=True,
                dropout=0.0,
                bidirectional=True,
            )
            self.cif_output2 = torch.nn.Linear(idim * 2, 1)
        elif self.upsample_type == "cnn_attn":
            self.upsample_cnn = torch.nn.ConvTranspose1d(
                idim, idim, self.upsample_times, self.upsample_times
            )
            from funasr_detach.models.transformer.encoder import (
                EncoderLayer as TransformerEncoderLayer,
            )
            from funasr_detach.models.transformer.attention import MultiHeadedAttention
            from funasr_detach.models.transformer.positionwise_feed_forward import (
                PositionwiseFeedForward,
            )

            positionwise_layer_args = (
                idim,
                idim * 2,
                0.1,
            )
            self.self_attn = TransformerEncoderLayer(
                idim,
                MultiHeadedAttention(4, idim, 0.1),
                PositionwiseFeedForward(*positionwise_layer_args),
                0.1,
                True,  # normalize_before,
                False,  # concat_after,
            )
            self.cif_output2 = torch.nn.Linear(idim, 1)
        self.smooth_factor2 = smooth_factor2
        self.noise_threshold2 = noise_threshold2

    def forward(
        self,
        hidden,
        target_label=None,
        mask=None,
        ignore_id=-1,
        mask_chunk_predictor=None,
        target_label_length=None,
    ):
        h = hidden
        context = h.transpose(1, 2)
        queries = self.pad(context)
        output = torch.relu(self.cif_conv1d(queries))

        # alphas2 is an extra head for timestamp prediction
        if not self.use_cif1_cnn:
            _output = context
        else:
            _output = output
        if self.upsample_type == "cnn":
            output2 = self.upsample_cnn(_output)
            output2 = output2.transpose(1, 2)
        elif self.upsample_type == "cnn_blstm":
            output2 = self.upsample_cnn(_output)
            output2 = output2.transpose(1, 2)
            output2, (_, _) = self.blstm(output2)
        elif self.upsample_type == "cnn_attn":
            output2 = self.upsample_cnn(_output)
            output2 = output2.transpose(1, 2)
            output2, _ = self.self_attn(output2, mask)
        # import pdb; pdb.set_trace()
        alphas2 = torch.sigmoid(self.cif_output2(output2))
        alphas2 = torch.nn.functional.relu(
            alphas2 * self.smooth_factor2 - self.noise_threshold2
        )
        # repeat the mask in T demension to match the upsampled length
        if mask is not None:
            mask2 = (
                mask.repeat(1, self.upsample_times, 1)
                .transpose(-1, -2)
                .reshape(alphas2.shape[0], -1)
            )
            mask2 = mask2.unsqueeze(-1)
            alphas2 = alphas2 * mask2
        alphas2 = alphas2.squeeze(-1)
        token_num2 = alphas2.sum(-1)

        output = output.transpose(1, 2)

        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, token_num2

    def get_upsample_timestamp(self, hidden, mask=None, token_num=None):
        h = hidden
        b = hidden.shape[0]
        context = h.transpose(1, 2)
        queries = self.pad(context)
        output = torch.relu(self.cif_conv1d(queries))

        # alphas2 is an extra head for timestamp prediction
        if not self.use_cif1_cnn:
            _output = context
        else:
            _output = output
        if self.upsample_type == "cnn":
            output2 = self.upsample_cnn(_output)
            output2 = output2.transpose(1, 2)
        elif self.upsample_type == "cnn_blstm":
            output2 = self.upsample_cnn(_output)
            output2 = output2.transpose(1, 2)
            output2, (_, _) = self.blstm(output2)
        elif self.upsample_type == "cnn_attn":
            output2 = self.upsample_cnn(_output)
            output2 = output2.transpose(1, 2)
            output2, _ = self.self_attn(output2, mask)
        alphas2 = torch.sigmoid(self.cif_output2(output2))
        alphas2 = torch.nn.functional.relu(
            alphas2 * self.smooth_factor2 - self.noise_threshold2
        )
        # repeat the mask in T demension to match the upsampled length
        if mask is not None:
            mask2 = (
                mask.repeat(1, self.upsample_times, 1)
                .transpose(-1, -2)
                .reshape(alphas2.shape[0], -1)
            )
            mask2 = mask2.unsqueeze(-1)
            alphas2 = alphas2 * mask2
        alphas2 = alphas2.squeeze(-1)
        _token_num = alphas2.sum(-1)
        if token_num is not None:
            alphas2 *= (token_num / _token_num)[:, None].repeat(1, alphas2.size(1))
        # re-downsample
        ds_alphas = alphas2.reshape(b, -1, self.upsample_times).sum(-1)
        ds_cif_peak = cif_wo_hidden(ds_alphas, self.threshold - 1e-4)
        # upsampled alphas and cif_peak
        us_alphas = alphas2
        us_cif_peak = cif_wo_hidden(us_alphas, self.threshold - 1e-4)
        return ds_alphas, ds_cif_peak, us_alphas, us_cif_peak

    def tail_process_fn(self, hidden, alphas, token_num=None, mask=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 = torch.tensor([tail_threshold], dtype=alphas.dtype).to(
                alphas.device
            )
            tail_threshold = torch.reshape(tail_threshold, (1, 1))
            alphas = torch.cat([alphas, tail_threshold], 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, maxlen=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, maxlen=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()