# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

# This code is modified from https://github.com/jaywalnut310/vits/blob/main/models.py
import math
import torch
from torch import nn
from torch.nn import functional as F

from utils.util import *
from modules.flow.modules import *
from modules.base.base_module import *
from modules.transformer.attentions import Encoder
from modules.duration_predictor.standard_duration_predictor import DurationPredictor
from modules.duration_predictor.stochastic_duration_predictor import (
    StochasticDurationPredictor,
)
from models.vocoders.gan.generator.hifigan import HiFiGAN_vits as Generator

try:
    from modules import monotonic_align
except ImportError:
    print("Monotonic align not found. Please make sure you have compiled it.")


class TextEncoder(nn.Module):
    def __init__(
        self,
        n_vocab,
        out_channels,
        hidden_channels,
        filter_channels,
        n_heads,
        n_layers,
        kernel_size,
        p_dropout,
    ):
        super().__init__()
        self.n_vocab = n_vocab
        self.out_channels = out_channels
        self.hidden_channels = hidden_channels
        self.filter_channels = filter_channels
        self.n_heads = n_heads
        self.n_layers = n_layers
        self.kernel_size = kernel_size
        self.p_dropout = p_dropout

        self.emb = nn.Embedding(n_vocab, hidden_channels)
        nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)

        self.encoder = Encoder(
            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
        )
        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)

    def forward(self, x, x_lengths):
        x = self.emb(x) * math.sqrt(self.hidden_channels)  # [b, t, h]
        x = torch.transpose(x, 1, -1)  # [b, h, t]
        x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)

        x = self.encoder(x * x_mask, x_mask)
        stats = self.proj(x) * x_mask

        m, logs = torch.split(stats, self.out_channels, dim=1)
        return x, m, logs, x_mask


class ResidualCouplingBlock(nn.Module):
    def __init__(
        self,
        channels,
        hidden_channels,
        kernel_size,
        dilation_rate,
        n_layers,
        n_flows=4,
        gin_channels=0,
    ):
        super().__init__()
        self.channels = channels
        self.hidden_channels = hidden_channels
        self.kernel_size = kernel_size
        self.dilation_rate = dilation_rate
        self.n_layers = n_layers
        self.n_flows = n_flows
        self.gin_channels = gin_channels

        self.flows = nn.ModuleList()
        for i in range(n_flows):
            self.flows.append(
                ResidualCouplingLayer(
                    channels,
                    hidden_channels,
                    kernel_size,
                    dilation_rate,
                    n_layers,
                    gin_channels=gin_channels,
                    mean_only=True,
                )
            )
            self.flows.append(Flip())

    def forward(self, x, x_mask, g=None, reverse=False):
        if not reverse:
            for flow in self.flows:
                x, _ = flow(x, x_mask, g=g, reverse=reverse)
        else:
            for flow in reversed(self.flows):
                x = flow(x, x_mask, g=g, reverse=reverse)
        return x


class PosteriorEncoder(nn.Module):
    def __init__(
        self,
        in_channels,
        out_channels,
        hidden_channels,
        kernel_size,
        dilation_rate,
        n_layers,
        gin_channels=0,
    ):
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.hidden_channels = hidden_channels
        self.kernel_size = kernel_size
        self.dilation_rate = dilation_rate
        self.n_layers = n_layers
        self.gin_channels = gin_channels

        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
        self.enc = WN(
            hidden_channels,
            kernel_size,
            dilation_rate,
            n_layers,
            gin_channels=gin_channels,
        )
        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)

    def forward(self, x, x_lengths, g=None):
        x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
        x = self.pre(x) * x_mask
        x = self.enc(x, x_mask, g=g)
        stats = self.proj(x) * x_mask
        m, logs = torch.split(stats, self.out_channels, dim=1)
        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
        return z, m, logs, x_mask


class SynthesizerTrn(nn.Module):
    """
    Synthesizer for Training
    """

    def __init__(
        self,
        n_vocab,
        spec_channels,
        segment_size,
        inter_channels,
        hidden_channels,
        filter_channels,
        n_heads,
        n_layers,
        kernel_size,
        p_dropout,
        resblock,
        resblock_kernel_sizes,
        resblock_dilation_sizes,
        upsample_rates,
        upsample_initial_channel,
        upsample_kernel_sizes,
        n_speakers=0,
        gin_channels=0,
        use_sdp=True,
        **kwargs,
    ):
        super().__init__()
        self.n_vocab = n_vocab
        self.spec_channels = spec_channels
        self.inter_channels = inter_channels
        self.hidden_channels = hidden_channels
        self.filter_channels = filter_channels
        self.n_heads = n_heads
        self.n_layers = n_layers
        self.kernel_size = kernel_size
        self.p_dropout = p_dropout
        self.resblock = resblock
        self.resblock_kernel_sizes = resblock_kernel_sizes
        self.resblock_dilation_sizes = resblock_dilation_sizes
        self.upsample_rates = upsample_rates
        self.upsample_initial_channel = upsample_initial_channel
        self.upsample_kernel_sizes = upsample_kernel_sizes
        self.segment_size = segment_size
        self.n_speakers = n_speakers
        self.gin_channels = gin_channels

        self.use_sdp = use_sdp

        self.enc_p = TextEncoder(
            n_vocab,
            inter_channels,
            hidden_channels,
            filter_channels,
            n_heads,
            n_layers,
            kernel_size,
            p_dropout,
        )
        self.dec = Generator(
            inter_channels,
            resblock,
            resblock_kernel_sizes,
            resblock_dilation_sizes,
            upsample_rates,
            upsample_initial_channel,
            upsample_kernel_sizes,
            gin_channels=gin_channels,
        )
        self.enc_q = PosteriorEncoder(
            spec_channels,
            inter_channels,
            hidden_channels,
            5,
            1,
            16,
            gin_channels=gin_channels,
        )
        self.flow = ResidualCouplingBlock(
            inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels
        )

        if use_sdp:
            self.dp = StochasticDurationPredictor(
                hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels
            )
        else:
            self.dp = DurationPredictor(
                hidden_channels, 256, 3, 0.5, gin_channels=gin_channels
            )

        if n_speakers >= 1:
            self.emb_g = nn.Embedding(n_speakers, gin_channels)

    def forward(self, data):
        x = data["phone_seq"]
        x_lengths = data["phone_len"]
        y = data["linear"]
        y_lengths = data["target_len"]

        x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
        if self.n_speakers > 0:
            g = self.emb_g(data["spk_id"].squeeze(-1)).unsqueeze(-1)  # [b, h, 1]
        else:
            g = None

        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
        z_p = self.flow(z, y_mask, g=g)

        with torch.no_grad():
            # negative cross-entropy
            s_p_sq_r = torch.exp(-2 * logs_p)  # [b, d, t]
            neg_cent1 = torch.sum(
                -0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True
            )  # [b, 1, t_s]
            neg_cent2 = torch.matmul(
                -0.5 * (z_p**2).transpose(1, 2), s_p_sq_r
            )  # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
            neg_cent3 = torch.matmul(
                z_p.transpose(1, 2), (m_p * s_p_sq_r)
            )  # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
            neg_cent4 = torch.sum(
                -0.5 * (m_p**2) * s_p_sq_r, [1], keepdim=True
            )  # [b, 1, t_s]
            neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4

            attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
            attn = (
                monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1))
                .unsqueeze(1)
                .detach()
            )

        w = attn.sum(2)
        if self.use_sdp:
            l_length = self.dp(x, x_mask, w, g=g)
            l_length = l_length / torch.sum(x_mask)
        else:
            logw_ = torch.log(w + 1e-6) * x_mask
            logw = self.dp(x, x_mask, g=g)
            l_length = torch.sum((logw - logw_) ** 2, [1, 2]) / torch.sum(x_mask)

        # expand prior
        m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
        logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)

        z_slice, ids_slice = rand_slice_segments(z, y_lengths, self.segment_size)
        o = self.dec(z_slice, g=g)
        outputs = {
            "y_hat": o,
            "l_length": l_length,
            "attn": attn,
            "ids_slice": ids_slice,
            "x_mask": x_mask,
            "z_mask": y_mask,
            "z": z,
            "z_p": z_p,
            "m_p": m_p,
            "logs_p": logs_p,
            "m_q": m_q,
            "logs_q": logs_q,
        }
        return outputs

    def infer(
        self,
        x,
        x_lengths,
        sid=None,
        noise_scale=1,
        length_scale=1,
        noise_scale_w=1.0,
        max_len=None,
    ):
        x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
        if self.n_speakers > 0:
            sid = sid.squeeze(-1)
            g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
        else:
            g = None

        if self.use_sdp:
            logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
        else:
            logw = self.dp(x, x_mask, g=g)
        w = torch.exp(logw) * x_mask * length_scale
        w_ceil = torch.ceil(w)
        y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
        y_mask = torch.unsqueeze(sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
        attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
        attn = generate_path(w_ceil, attn_mask)

        m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(
            1, 2
        )  # [b, t', t], [b, t, d] -> [b, d, t']
        logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(
            1, 2
        )  # [b, t', t], [b, t, d] -> [b, d, t']

        z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
        z = self.flow(z_p, y_mask, g=g, reverse=True)
        o = self.dec((z * y_mask)[:, :, :max_len], g=g)

        outputs = {
            "y_hat": o,
            "attn": attn,
            "mask": y_mask,
            "z": z,
            "z_p": z_p,
            "m_p": m_p,
            "logs_p": logs_p,
        }

        return outputs

    def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
        assert self.n_speakers > 0, "n_speakers have to be larger than 0."
        g_src = self.emb_g(sid_src).unsqueeze(-1)
        g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
        z_p = self.flow(z, y_mask, g=g_src)
        z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
        o_hat = self.dec(z_hat * y_mask, g=g_tgt)
        return o_hat, y_mask, (z, z_p, z_hat)