import torch
import torch.nn as nn
import torch.nn.functional as F
# from audiotools import AudioSignal
# from audiotools import ml
# from audiotools import STFTParams
from einops import rearrange
from torch.nn.utils import weight_norm

from collections import namedtuple

STFTParams = namedtuple(
    "STFTParams",
    ["window_length", "hop_length", "window_type", "match_stride", "padding_type"],
)

STFTParams.__new__.__defaults__ = (None, None, None, None, None)


def WNConv1d(*args, **kwargs):
    act = kwargs.pop("act", True)
    conv = weight_norm(nn.Conv1d(*args, **kwargs))
    if not act:
        return conv
    return nn.Sequential(conv, nn.LeakyReLU(0.1))


def WNConv2d(*args, **kwargs):
    act = kwargs.pop("act", True)
    conv = weight_norm(nn.Conv2d(*args, **kwargs))
    if not act:
        return conv
    return nn.Sequential(conv, nn.LeakyReLU(0.1))


class MPD(nn.Module):
    def __init__(self, period):
        super().__init__()
        self.period = period
        self.convs = nn.ModuleList(
            [
                WNConv2d(1, 32, (5, 1), (3, 1), padding=(2, 0)),
                WNConv2d(32, 128, (5, 1), (3, 1), padding=(2, 0)),
                WNConv2d(128, 512, (5, 1), (3, 1), padding=(2, 0)),
                WNConv2d(512, 1024, (5, 1), (3, 1), padding=(2, 0)),
                WNConv2d(1024, 1024, (5, 1), 1, padding=(2, 0)),
            ]
        )
        self.conv_post = WNConv2d(
            1024, 1, kernel_size=(3, 1), padding=(1, 0), act=False
        )

    def pad_to_period(self, x):
        t = x.shape[-1]
        x = F.pad(x, (0, self.period - t % self.period), mode="reflect")
        return x

    def forward(self, x):
        fmap = []

        x = self.pad_to_period(x)
        x = rearrange(x, "b c (l p) -> b c l p", p=self.period)

        for layer in self.convs:
            x = layer(x)
            fmap.append(x)

        x = self.conv_post(x)
        fmap.append(x)

        return fmap


class MSD(nn.Module):
    def __init__(self, rate: int = 1, sample_rate: int = 48000):
        super().__init__()
        self.convs = nn.ModuleList(
            [
                WNConv1d(1, 16, 15, 1, padding=7),
                WNConv1d(16, 64, 41, 4, groups=4, padding=20),
                WNConv1d(64, 256, 41, 4, groups=16, padding=20),
                WNConv1d(256, 1024, 41, 4, groups=64, padding=20),
                WNConv1d(1024, 1024, 41, 4, groups=256, padding=20),
                WNConv1d(1024, 1024, 5, 1, padding=2),
            ]
        )
        self.conv_post = WNConv1d(1024, 1, 3, 1, padding=1, act=False)
        self.sample_rate = sample_rate
        self.rate = rate

    def forward(self, x):
        # x = AudioSignal(x, self.sample_rate)
        # x.resample(self.sample_rate // self.rate)
        # x = x.audio_data

        fmap = []

        for l in self.convs:
            x = l(x)
            fmap.append(x)
        x = self.conv_post(x)
        fmap.append(x)

        return fmap


BANDS = [(0.0, 0.1), (0.1, 0.25), (0.25, 0.5), (0.5, 0.75), (0.75, 1.0)]


class MRD(nn.Module):
    def __init__(
        self,
        window_length: int,
        hop_factor: float = 0.25,
        sample_rate: int = 24000,
        bands: list = BANDS,
    ):
        """Complex multi-band spectrogram discriminator.
        Parameters
        ----------
        window_length : int
            Window length of STFT.
        hop_factor : float, optional
            Hop factor of the STFT, defaults to ``0.25 * window_length``.
        sample_rate : int, optional
            Sampling rate of audio in Hz, by default 24000
        bands : list, optional
            Bands to run discriminator over.
        """
        super().__init__()

        self.window_length = window_length
        self.hop_factor = hop_factor
        self.sample_rate = sample_rate
        self.stft_params = STFTParams(
            window_length=window_length,
            hop_length=int(window_length * hop_factor),
            match_stride=True,
        )

        n_fft = window_length // 2 + 1
        bands = [(int(b[0] * n_fft), int(b[1] * n_fft)) for b in bands]
        self.bands = bands
        self.n_fft = window_length

        ch = 32
        convs = lambda: nn.ModuleList(
            [
                WNConv2d(2, ch, (3, 9), (1, 1), padding=(1, 4)),
                WNConv2d(ch, ch, (3, 9), (1, 2), padding=(1, 4)),
                WNConv2d(ch, ch, (3, 9), (1, 2), padding=(1, 4)),
                WNConv2d(ch, ch, (3, 9), (1, 2), padding=(1, 4)),
                WNConv2d(ch, ch, (3, 3), (1, 1), padding=(1, 1)),
            ]
        )
        self.band_convs = nn.ModuleList([convs() for _ in range(len(self.bands))])
        self.conv_post = WNConv2d(ch, 1, (3, 3), (1, 1), padding=(1, 1), act=False)

    def spectrogram(self, x):
        # x = AudioSignal(x, self.sample_rate, stft_params=self.stft_params)
        # x = torch.view_as_real(x.stft())

        # x.squeeze(0).stft(n_fft=1024,win_length=1024,return_complex=True).size()
        # breakpoint()
        if x.size(0)==1:
            # x = torch.view_as_real(x.squeeze(0).stft(n_fft=self.window_length,return_complex=True).unsqueeze(0))
            x = torch.view_as_real(x.squeeze(0).stft(n_fft=self.n_fft,return_complex=True).unsqueeze(0))
        else:
            # x = torch.view_as_real(x.squeeze(1).stft(n_fft=self.window_length,return_complex=True).unsqueeze(1))
            x = torch.view_as_real(x.squeeze(1).stft(n_fft=self.n_fft,return_complex=True).unsqueeze(1))
        x = rearrange(x, "b 1 f t c -> (b 1) c t f")
        # Split into bands
        x_bands = [x[..., b[0] : b[1]] for b in self.bands]
        return x_bands

    def forward(self, x):
        x_bands = self.spectrogram(x)
        fmap = []

        x = []
        for band, stack in zip(x_bands, self.band_convs):
            for layer in stack:
                band = layer(band)
                fmap.append(band)
            x.append(band)

        x = torch.cat(x, dim=-1)
        x = self.conv_post(x)
        fmap.append(x)

        return fmap


# class DACDiscriminator(ml.BaseModel):
class DACDiscriminator(nn.Module):
    def __init__(
        self,
        rates: list = [],
        periods: list = [2, 3, 5, 7, 11],
        fft_sizes: list = [2048, 1024, 512],
        sample_rate: int = 24000,
        bands: list = BANDS,
    ):
        """Discriminator that combines multiple discriminators.

        Parameters
        ----------
        rates : list, optional
            sampling rates (in Hz) to run MSD at, by default []
            If empty, MSD is not used.
        periods : list, optional
            periods (of samples) to run MPD at, by default [2, 3, 5, 7, 11]
        fft_sizes : list, optional
            Window sizes of the FFT to run MRD at, by default [2048, 1024, 512]
        sample_rate : int, optional
            Sampling rate of audio in Hz, by default 24000
        bands : list, optional
            Bands to run MRD at, by default `BANDS`
        """
        super().__init__()
        discs = []
        discs += [MPD(p) for p in periods]
        discs += [MSD(r, sample_rate=sample_rate) for r in rates]
        discs += [MRD(f, sample_rate=sample_rate, bands=bands) for f in fft_sizes]
        self.discriminators = nn.ModuleList(discs)

    def preprocess(self, y):
        # Remove DC offset
        y = y - y.mean(dim=-1, keepdims=True)
        # Peak normalize the volume of input audio
        y = 0.8 * y / (y.abs().max(dim=-1, keepdim=True)[0] + 1e-9)
        return y

    def forward(self, x):
        x = self.preprocess(x)
        fmaps = [d(x) for d in self.discriminators]
        return fmaps


if __name__ == "__main__":
    disc = DACDiscriminator()
    x = torch.zeros(1, 1, 24000)
    results = disc(x)
    breakpoint()
    for i, result in enumerate(results):
        print(f"disc{i}")
        for i, r in enumerate(result):
            print(r.shape, r.mean(), r.min(), r.max())
        print("00")
