# adopted from https://github.com/jik876/hifi-gan/blob/master/models.py
import torch
from torch import nn
from torch.nn import functional as F

LRELU_SLOPE = 0.1


class DiscriminatorP(torch.nn.Module):
    """HiFiGAN Periodic Discriminator

    Takes every Pth value from the input waveform and applied a stack of convoluations.

    Note:
        if `period` is 2
        `waveform = [1, 2, 3, 4, 5, 6 ...] --> [1, 3, 5 ... ] --> convs -> score, feat`

    Args:
        x (Tensor): input waveform.

    Returns:
        [Tensor]: discriminator scores per sample in the batch.
        [List[Tensor]]: list of features from each convolutional layer.

    Shapes:
        x: [B, 1, T]
    """

    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
        super().__init__()
        self.period = period
        get_padding = lambda k, d: int((k * d - d) / 2)
        norm_f = nn.utils.spectral_norm if use_spectral_norm else nn.utils.parametrizations.weight_norm
        self.convs = nn.ModuleList(
            [
                norm_f(nn.Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
                norm_f(nn.Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
                norm_f(nn.Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
                norm_f(nn.Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
                norm_f(nn.Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
            ]
        )
        self.conv_post = norm_f(nn.Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))

    def forward(self, x):
        """
        Args:
            x (Tensor): input waveform.

        Returns:
            [Tensor]: discriminator scores per sample in the batch.
            [List[Tensor]]: list of features from each convolutional layer.

        Shapes:
            x: [B, 1, T]
        """
        feat = []

        # 1d to 2d
        b, c, t = x.shape
        if t % self.period != 0:  # pad first
            n_pad = self.period - (t % self.period)
            x = F.pad(x, (0, n_pad), "reflect")
            t = t + n_pad
        x = x.view(b, c, t // self.period, self.period)

        for l in self.convs:
            x = l(x)
            x = F.leaky_relu(x, LRELU_SLOPE)
            feat.append(x)
        x = self.conv_post(x)
        feat.append(x)
        x = torch.flatten(x, 1, -1)

        return x, feat


class MultiPeriodDiscriminator(torch.nn.Module):
    """HiFiGAN Multi-Period Discriminator (MPD)
    Wrapper for the `PeriodDiscriminator` to apply it in different periods.
    Periods are suggested to be prime numbers to reduce the overlap between each discriminator.
    """

    def __init__(self, use_spectral_norm=False):
        super().__init__()
        self.discriminators = nn.ModuleList(
            [
                DiscriminatorP(2, use_spectral_norm=use_spectral_norm),
                DiscriminatorP(3, use_spectral_norm=use_spectral_norm),
                DiscriminatorP(5, use_spectral_norm=use_spectral_norm),
                DiscriminatorP(7, use_spectral_norm=use_spectral_norm),
                DiscriminatorP(11, use_spectral_norm=use_spectral_norm),
            ]
        )

    def forward(self, x):
        """
        Args:
            x (Tensor): input waveform.

        Returns:
        [List[Tensor]]: list of scores from each discriminator.
            [List[List[Tensor]]]: list of list of features from each discriminator's each convolutional layer.

        Shapes:
            x: [B, 1, T]
        """
        scores = []
        feats = []
        for _, d in enumerate(self.discriminators):
            score, feat = d(x)
            scores.append(score)
            feats.append(feat)
        return scores, feats


class DiscriminatorS(torch.nn.Module):
    """HiFiGAN Scale Discriminator.
    It is similar to `MelganDiscriminator` but with a specific architecture explained in the paper.

    Args:
        use_spectral_norm (bool): if `True` swith to spectral norm instead of weight norm.

    """

    def __init__(self, use_spectral_norm=False):
        super().__init__()
        norm_f = nn.utils.spectral_norm if use_spectral_norm else nn.utils.parametrizations.weight_norm
        self.convs = nn.ModuleList(
            [
                norm_f(nn.Conv1d(1, 128, 15, 1, padding=7)),
                norm_f(nn.Conv1d(128, 128, 41, 2, groups=4, padding=20)),
                norm_f(nn.Conv1d(128, 256, 41, 2, groups=16, padding=20)),
                norm_f(nn.Conv1d(256, 512, 41, 4, groups=16, padding=20)),
                norm_f(nn.Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
                norm_f(nn.Conv1d(1024, 1024, 41, 1, groups=16, padding=20)),
                norm_f(nn.Conv1d(1024, 1024, 5, 1, padding=2)),
            ]
        )
        self.conv_post = norm_f(nn.Conv1d(1024, 1, 3, 1, padding=1))

    def forward(self, x):
        """
        Args:
            x (Tensor): input waveform.

        Returns:
            Tensor: discriminator scores.
            List[Tensor]: list of features from the convolutiona layers.
        """
        feat = []
        for l in self.convs:
            x = l(x)
            x = F.leaky_relu(x, LRELU_SLOPE)
            feat.append(x)
        x = self.conv_post(x)
        feat.append(x)
        x = torch.flatten(x, 1, -1)
        return x, feat


class MultiScaleDiscriminator(torch.nn.Module):
    """HiFiGAN Multi-Scale Discriminator.
    It is similar to `MultiScaleMelganDiscriminator` but specially tailored for HiFiGAN as in the paper.
    """

    def __init__(self):
        super().__init__()
        self.discriminators = nn.ModuleList(
            [
                DiscriminatorS(use_spectral_norm=True),
                DiscriminatorS(),
                DiscriminatorS(),
            ]
        )
        self.meanpools = nn.ModuleList([nn.AvgPool1d(4, 2, padding=2), nn.AvgPool1d(4, 2, padding=2)])

    def forward(self, x):
        """
        Args:
            x (Tensor): input waveform.

        Returns:
            List[Tensor]: discriminator scores.
            List[List[Tensor]]: list of list of features from each layers of each discriminator.
        """
        scores = []
        feats = []
        for i, d in enumerate(self.discriminators):
            if i != 0:
                x = self.meanpools[i - 1](x)
            score, feat = d(x)
            scores.append(score)
            feats.append(feat)
        return scores, feats


class HifiganDiscriminator(nn.Module):
    """HiFiGAN discriminator wrapping MPD and MSD."""

    def __init__(self):
        super().__init__()
        self.mpd = MultiPeriodDiscriminator()
        self.msd = MultiScaleDiscriminator()

    def forward(self, x):
        """
        Args:
            x (Tensor): input waveform.

        Returns:
            List[Tensor]: discriminator scores.
            List[List[Tensor]]: list of list of features from each layers of each discriminator.
        """
        scores, feats = self.mpd(x)
        scores_, feats_ = self.msd(x)
        return scores + scores_, feats + feats_