# Copyright (c) 2024 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

from typing import Optional, Tuple

import numpy as np
import scipy
import torch
from torch import nn, view_as_real, view_as_complex
from torch import nn
from torch.nn.utils import weight_norm, remove_weight_norm
from torchaudio.functional.functional import _hz_to_mel, _mel_to_hz
import librosa


def safe_log(x: torch.Tensor, clip_val: float = 1e-7) -> torch.Tensor:
    """
    Computes the element-wise logarithm of the input tensor with clipping to avoid near-zero values.

    Args:
        x (Tensor): Input tensor.
        clip_val (float, optional): Minimum value to clip the input tensor. Defaults to 1e-7.

    Returns:
        Tensor: Element-wise logarithm of the input tensor with clipping applied.
    """
    return torch.log(torch.clip(x, min=clip_val))


def symlog(x: torch.Tensor) -> torch.Tensor:
    return torch.sign(x) * torch.log1p(x.abs())


def symexp(x: torch.Tensor) -> torch.Tensor:
    return torch.sign(x) * (torch.exp(x.abs()) - 1)


class STFT(nn.Module):
    def __init__(
        self,
        n_fft: int,
        hop_length: int,
        win_length: int,
        center=True,
    ):
        super().__init__()
        self.center = center
        self.n_fft = n_fft
        self.hop_length = hop_length
        self.win_length = win_length
        window = torch.hann_window(win_length)
        self.register_buffer("window", window)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        # x: (B, T * hop_length)

        if not self.center:
            pad = self.win_length - self.hop_length
            x = torch.nn.functional.pad(x, (pad // 2, pad // 2), mode="reflect")

        stft_spec = torch.stft(
            x,
            self.n_fft,
            hop_length=self.hop_length,
            win_length=self.win_length,
            window=self.window,
            center=self.center,
            return_complex=False,
        )  # (B, n_fft // 2 + 1, T, 2)

        rea = stft_spec[:, :, :, 0]  # (B, n_fft // 2 + 1, T, 2)
        imag = stft_spec[:, :, :, 1]  # (B, n_fft // 2 + 1, T, 2)

        log_mag = torch.log(
            torch.abs(torch.sqrt(torch.pow(rea, 2) + torch.pow(imag, 2))) + 1e-5
        )  # (B, n_fft // 2 + 1, T)
        phase = torch.atan2(imag, rea)  # (B, n_fft // 2 + 1, T)

        return log_mag, phase


class ISTFT(nn.Module):
    """
    Custom implementation of ISTFT since torch.istft doesn't allow custom padding (other than `center=True`) with
    windowing. This is because the NOLA (Nonzero Overlap Add) check fails at the edges.
    See issue: https://github.com/pytorch/pytorch/issues/62323
    Specifically, in the context of neural vocoding we are interested in "same" padding analogous to CNNs.
    The NOLA constraint is met as we trim padded samples anyway.

    Args:
        n_fft (int): Size of Fourier transform.
        hop_length (int): The distance between neighboring sliding window frames.
        win_length (int): The size of window frame and STFT filter.
        padding (str, optional): Type of padding. Options are "center" or "same". Defaults to "same".
    """

    def __init__(
        self, n_fft: int, hop_length: int, win_length: int, padding: str = "same"
    ):
        super().__init__()
        if padding not in ["center", "same"]:
            raise ValueError("Padding must be 'center' or 'same'.")
        self.padding = padding
        self.n_fft = n_fft
        self.hop_length = hop_length
        self.win_length = win_length
        window = torch.hann_window(win_length)
        self.register_buffer("window", window)

    def forward(self, spec: torch.Tensor) -> torch.Tensor:
        """
        Compute the Inverse Short Time Fourier Transform (ISTFT) of a complex spectrogram.

        Args:
            spec (Tensor): Input complex spectrogram of shape (B, N, T), where B is the batch size,
                            N is the number of frequency bins, and T is the number of time frames.

        Returns:
            Tensor: Reconstructed time-domain signal of shape (B, L), where L is the length of the output signal.
        """
        if self.padding == "center":
            # Fallback to pytorch native implementation
            return torch.istft(
                spec,
                self.n_fft,
                self.hop_length,
                self.win_length,
                self.window,
                center=True,
            )
        elif self.padding == "same":
            pad = (self.win_length - self.hop_length) // 2
        else:
            raise ValueError("Padding must be 'center' or 'same'.")

        assert spec.dim() == 3, "Expected a 3D tensor as input"
        B, N, T = spec.shape

        # Inverse FFT
        ifft = torch.fft.irfft(spec, self.n_fft, dim=1, norm="backward")
        ifft = ifft * self.window[None, :, None]

        # Overlap and Add
        output_size = (T - 1) * self.hop_length + self.win_length
        y = torch.nn.functional.fold(
            ifft,
            output_size=(1, output_size),
            kernel_size=(1, self.win_length),
            stride=(1, self.hop_length),
        )[:, 0, 0, pad:-pad]

        # Window envelope
        window_sq = self.window.square().expand(1, T, -1).transpose(1, 2)
        window_envelope = torch.nn.functional.fold(
            window_sq,
            output_size=(1, output_size),
            kernel_size=(1, self.win_length),
            stride=(1, self.hop_length),
        ).squeeze()[pad:-pad]

        # Normalize
        assert (window_envelope > 1e-11).all()
        y = y / window_envelope

        return y


class MDCT(nn.Module):
    """
    Modified Discrete Cosine Transform (MDCT) module.

    Args:
        frame_len (int): Length of the MDCT frame.
        padding (str, optional): Type of padding. Options are "center" or "same". Defaults to "same".
    """

    def __init__(self, frame_len: int, padding: str = "same"):
        super().__init__()
        if padding not in ["center", "same"]:
            raise ValueError("Padding must be 'center' or 'same'.")
        self.padding = padding
        self.frame_len = frame_len
        N = frame_len // 2
        n0 = (N + 1) / 2
        window = torch.from_numpy(scipy.signal.cosine(frame_len)).float()
        self.register_buffer("window", window)

        pre_twiddle = torch.exp(-1j * torch.pi * torch.arange(frame_len) / frame_len)
        post_twiddle = torch.exp(-1j * torch.pi * n0 * (torch.arange(N) + 0.5) / N)
        # view_as_real: NCCL Backend does not support ComplexFloat data type
        # https://github.com/pytorch/pytorch/issues/71613
        self.register_buffer("pre_twiddle", view_as_real(pre_twiddle))
        self.register_buffer("post_twiddle", view_as_real(post_twiddle))

    def forward(self, audio: torch.Tensor) -> torch.Tensor:
        """
        Apply the Modified Discrete Cosine Transform (MDCT) to the input audio.

        Args:
            audio (Tensor): Input audio waveform of shape (B, T), where B is the batch size
                and T is the length of the audio.

        Returns:
            Tensor: MDCT coefficients of shape (B, L, N), where L is the number of output frames
                and N is the number of frequency bins.
        """
        if self.padding == "center":
            audio = torch.nn.functional.pad(
                audio, (self.frame_len // 2, self.frame_len // 2)
            )
        elif self.padding == "same":
            # hop_length is 1/2 frame_len
            audio = torch.nn.functional.pad(
                audio, (self.frame_len // 4, self.frame_len // 4)
            )
        else:
            raise ValueError("Padding must be 'center' or 'same'.")

        x = audio.unfold(-1, self.frame_len, self.frame_len // 2)
        N = self.frame_len // 2
        x = x * self.window.expand(x.shape)
        X = torch.fft.fft(
            x * view_as_complex(self.pre_twiddle).expand(x.shape), dim=-1
        )[..., :N]
        res = X * view_as_complex(self.post_twiddle).expand(X.shape) * np.sqrt(1 / N)
        return torch.real(res) * np.sqrt(2)


class IMDCT(nn.Module):
    """
    Inverse Modified Discrete Cosine Transform (IMDCT) module.

    Args:
        frame_len (int): Length of the MDCT frame.
        padding (str, optional): Type of padding. Options are "center" or "same". Defaults to "same".
    """

    def __init__(self, frame_len: int, padding: str = "same"):
        super().__init__()
        if padding not in ["center", "same"]:
            raise ValueError("Padding must be 'center' or 'same'.")
        self.padding = padding
        self.frame_len = frame_len
        N = frame_len // 2
        n0 = (N + 1) / 2
        window = torch.from_numpy(scipy.signal.cosine(frame_len)).float()
        self.register_buffer("window", window)

        pre_twiddle = torch.exp(1j * torch.pi * n0 * torch.arange(N * 2) / N)
        post_twiddle = torch.exp(1j * torch.pi * (torch.arange(N * 2) + n0) / (N * 2))
        self.register_buffer("pre_twiddle", view_as_real(pre_twiddle))
        self.register_buffer("post_twiddle", view_as_real(post_twiddle))

    def forward(self, X: torch.Tensor) -> torch.Tensor:
        """
        Apply the Inverse Modified Discrete Cosine Transform (IMDCT) to the input MDCT coefficients.

        Args:
            X (Tensor): Input MDCT coefficients of shape (B, L, N), where B is the batch size,
                L is the number of frames, and N is the number of frequency bins.

        Returns:
            Tensor: Reconstructed audio waveform of shape (B, T), where T is the length of the audio.
        """
        B, L, N = X.shape
        Y = torch.zeros((B, L, N * 2), dtype=X.dtype, device=X.device)
        Y[..., :N] = X
        Y[..., N:] = -1 * torch.conj(torch.flip(X, dims=(-1,)))
        y = torch.fft.ifft(
            Y * view_as_complex(self.pre_twiddle).expand(Y.shape), dim=-1
        )
        y = (
            torch.real(y * view_as_complex(self.post_twiddle).expand(y.shape))
            * np.sqrt(N)
            * np.sqrt(2)
        )
        result = y * self.window.expand(y.shape)
        output_size = (1, (L + 1) * N)
        audio = torch.nn.functional.fold(
            result.transpose(1, 2),
            output_size=output_size,
            kernel_size=(1, self.frame_len),
            stride=(1, self.frame_len // 2),
        )[:, 0, 0, :]

        if self.padding == "center":
            pad = self.frame_len // 2
        elif self.padding == "same":
            pad = self.frame_len // 4
        else:
            raise ValueError("Padding must be 'center' or 'same'.")

        audio = audio[:, pad:-pad]
        return audio


class FourierHead(nn.Module):
    """Base class for inverse fourier modules."""

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Args:
            x (Tensor): Input tensor of shape (B, L, H), where B is the batch size,
                        L is the sequence length, and H denotes the model dimension.

        Returns:
            Tensor: Reconstructed time-domain audio signal of shape (B, T), where T is the length of the output signal.
        """
        raise NotImplementedError("Subclasses must implement the forward method.")


class ISTFTHead(FourierHead):
    """
    ISTFT Head module for predicting STFT complex coefficients.

    Args:
        dim (int): Hidden dimension of the model.
        n_fft (int): Size of Fourier transform.
        hop_length (int): The distance between neighboring sliding window frames, which should align with
                          the resolution of the input features.
        padding (str, optional): Type of padding. Options are "center" or "same". Defaults to "same".
    """

    def __init__(self, dim: int, n_fft: int, hop_length: int, padding: str = "same"):
        super().__init__()
        out_dim = n_fft + 2
        self.out = torch.nn.Linear(dim, out_dim)
        self.istft = ISTFT(
            n_fft=n_fft, hop_length=hop_length, win_length=n_fft, padding=padding
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Forward pass of the ISTFTHead module.

        Args:
            x (Tensor): Input tensor of shape (B, L, H), where B is the batch size,
                        L is the sequence length, and H denotes the model dimension.

        Returns:
            Tensor: Reconstructed time-domain audio signal of shape (B, T), where T is the length of the output signal.
        """
        x = self.out(x).transpose(1, 2)
        mag, p = x.chunk(2, dim=1)
        mag = torch.exp(mag)
        mag = torch.clip(
            mag, max=1e2
        )  # safeguard to prevent excessively large magnitudes
        # wrapping happens here. These two lines produce real and imaginary value
        x = torch.cos(p)
        y = torch.sin(p)
        # recalculating phase here does not produce anything new
        # only costs time
        # phase = torch.atan2(y, x)
        # S = mag * torch.exp(phase * 1j)
        # better directly produce the complex value
        S = mag * (x + 1j * y)
        audio = self.istft(S)
        return audio


class IMDCTSymExpHead(FourierHead):
    """
    IMDCT Head module for predicting MDCT coefficients with symmetric exponential function

    Args:
        dim (int): Hidden dimension of the model.
        mdct_frame_len (int): Length of the MDCT frame.
        padding (str, optional): Type of padding. Options are "center" or "same". Defaults to "same".
        sample_rate (int, optional): The sample rate of the audio. If provided, the last layer will be initialized
                                     based on perceptual scaling. Defaults to None.
        clip_audio (bool, optional): Whether to clip the audio output within the range of [-1.0, 1.0]. Defaults to False.
    """

    def __init__(
        self,
        dim: int,
        mdct_frame_len: int,
        padding: str = "same",
        sample_rate: Optional[int] = None,
        clip_audio: bool = False,
    ):
        super().__init__()
        out_dim = mdct_frame_len // 2
        self.out = nn.Linear(dim, out_dim)
        self.imdct = IMDCT(frame_len=mdct_frame_len, padding=padding)
        self.clip_audio = clip_audio

        if sample_rate is not None:
            # optionally init the last layer following mel-scale
            m_max = _hz_to_mel(sample_rate // 2)
            m_pts = torch.linspace(0, m_max, out_dim)
            f_pts = _mel_to_hz(m_pts)
            scale = 1 - (f_pts / f_pts.max())

            with torch.no_grad():
                self.out.weight.mul_(scale.view(-1, 1))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Forward pass of the IMDCTSymExpHead module.

        Args:
            x (Tensor): Input tensor of shape (B, L, H), where B is the batch size,
                        L is the sequence length, and H denotes the model dimension.

        Returns:
            Tensor: Reconstructed time-domain audio signal of shape (B, T), where T is the length of the output signal.
        """
        x = self.out(x)
        x = symexp(x)
        x = torch.clip(
            x, min=-1e2, max=1e2
        )  # safeguard to prevent excessively large magnitudes
        audio = self.imdct(x)
        if self.clip_audio:
            audio = torch.clip(x, min=-1.0, max=1.0)

        return audio


class IMDCTCosHead(FourierHead):
    """
    IMDCT Head module for predicting MDCT coefficients with parametrizing MDCT = exp(m) · cos(p)

    Args:
        dim (int): Hidden dimension of the model.
        mdct_frame_len (int): Length of the MDCT frame.
        padding (str, optional): Type of padding. Options are "center" or "same". Defaults to "same".
        clip_audio (bool, optional): Whether to clip the audio output within the range of [-1.0, 1.0]. Defaults to False.
    """

    def __init__(
        self,
        dim: int,
        mdct_frame_len: int,
        padding: str = "same",
        clip_audio: bool = False,
    ):
        super().__init__()
        self.clip_audio = clip_audio
        self.out = nn.Linear(dim, mdct_frame_len)
        self.imdct = IMDCT(frame_len=mdct_frame_len, padding=padding)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Forward pass of the IMDCTCosHead module.

        Args:
            x (Tensor): Input tensor of shape (B, L, H), where B is the batch size,
                        L is the sequence length, and H denotes the model dimension.

        Returns:
            Tensor: Reconstructed time-domain audio signal of shape (B, T), where T is the length of the output signal.
        """
        x = self.out(x)
        m, p = x.chunk(2, dim=2)
        m = torch.exp(m).clip(
            max=1e2
        )  # safeguard to prevent excessively large magnitudes
        audio = self.imdct(m * torch.cos(p))
        if self.clip_audio:
            audio = torch.clip(x, min=-1.0, max=1.0)
        return audio


class ConvNeXtBlock(nn.Module):
    """ConvNeXt Block adapted from https://github.com/facebookresearch/ConvNeXt to 1D audio signal.

    Args:
        dim (int): Number of input channels.
        intermediate_dim (int): Dimensionality of the intermediate layer.
        layer_scale_init_value (float, optional): Initial value for the layer scale. None means no scaling.
            Defaults to None.
        adanorm_num_embeddings (int, optional): Number of embeddings for AdaLayerNorm.
            None means non-conditional LayerNorm. Defaults to None.
    """

    def __init__(
        self,
        dim: int,
        intermediate_dim: int,
        layer_scale_init_value: float,
        adanorm_num_embeddings: Optional[int] = None,
    ):
        super().__init__()
        self.dwconv = nn.Conv1d(
            dim, dim, kernel_size=7, padding=3, groups=dim
        )  # depthwise conv
        self.adanorm = adanorm_num_embeddings is not None
        if adanorm_num_embeddings:
            self.norm = AdaLayerNorm(adanorm_num_embeddings, dim, eps=1e-6)
        else:
            self.norm = nn.LayerNorm(dim, eps=1e-6)
        self.pwconv1 = nn.Linear(
            dim, intermediate_dim
        )  # pointwise/1x1 convs, implemented with linear layers
        self.act = nn.GELU()
        self.pwconv2 = nn.Linear(intermediate_dim, dim)
        self.gamma = (
            nn.Parameter(layer_scale_init_value * torch.ones(dim), requires_grad=True)
            if layer_scale_init_value > 0
            else None
        )

    def forward(
        self, x: torch.Tensor, cond_embedding_id: Optional[torch.Tensor] = None
    ) -> torch.Tensor:
        residual = x
        x = self.dwconv(x)
        x = x.transpose(1, 2)  # (B, C, T) -> (B, T, C)
        if self.adanorm:
            assert cond_embedding_id is not None
            x = self.norm(x, cond_embedding_id)
        else:
            x = self.norm(x)
        x = self.pwconv1(x)
        x = self.act(x)
        x = self.pwconv2(x)
        if self.gamma is not None:
            x = self.gamma * x
        x = x.transpose(1, 2)  # (B, T, C) -> (B, C, T)

        x = residual + x
        return x


class AdaLayerNorm(nn.Module):
    """
    Adaptive Layer Normalization module with learnable embeddings per `num_embeddings` classes

    Args:
        num_embeddings (int): Number of embeddings.
        embedding_dim (int): Dimension of the embeddings.
    """

    def __init__(self, num_embeddings: int, embedding_dim: int, eps: float = 1e-6):
        super().__init__()
        self.eps = eps
        self.dim = embedding_dim
        self.scale = nn.Embedding(
            num_embeddings=num_embeddings, embedding_dim=embedding_dim
        )
        self.shift = nn.Embedding(
            num_embeddings=num_embeddings, embedding_dim=embedding_dim
        )
        torch.nn.init.ones_(self.scale.weight)
        torch.nn.init.zeros_(self.shift.weight)

    def forward(self, x: torch.Tensor, cond_embedding_id: torch.Tensor) -> torch.Tensor:
        scale = self.scale(cond_embedding_id)
        shift = self.shift(cond_embedding_id)
        x = nn.functional.layer_norm(x, (self.dim,), eps=self.eps)
        x = x * scale + shift
        return x


class ResBlock1(nn.Module):
    """
    ResBlock adapted from HiFi-GAN V1 (https://github.com/jik876/hifi-gan) with dilated 1D convolutions,
    but without upsampling layers.

    Args:
        dim (int): Number of input channels.
        kernel_size (int, optional): Size of the convolutional kernel. Defaults to 3.
        dilation (tuple[int], optional): Dilation factors for the dilated convolutions.
            Defaults to (1, 3, 5).
        lrelu_slope (float, optional): Negative slope of the LeakyReLU activation function.
            Defaults to 0.1.
        layer_scale_init_value (float, optional): Initial value for the layer scale. None means no scaling.
            Defaults to None.
    """

    def __init__(
        self,
        dim: int,
        kernel_size: int = 3,
        dilation: Tuple[int, int, int] = (1, 3, 5),
        lrelu_slope: float = 0.1,
        layer_scale_init_value: Optional[float] = None,
    ):
        super().__init__()
        self.lrelu_slope = lrelu_slope
        self.convs1 = nn.ModuleList(
            [
                weight_norm(
                    nn.Conv1d(
                        dim,
                        dim,
                        kernel_size,
                        1,
                        dilation=dilation[0],
                        padding=self.get_padding(kernel_size, dilation[0]),
                    )
                ),
                weight_norm(
                    nn.Conv1d(
                        dim,
                        dim,
                        kernel_size,
                        1,
                        dilation=dilation[1],
                        padding=self.get_padding(kernel_size, dilation[1]),
                    )
                ),
                weight_norm(
                    nn.Conv1d(
                        dim,
                        dim,
                        kernel_size,
                        1,
                        dilation=dilation[2],
                        padding=self.get_padding(kernel_size, dilation[2]),
                    )
                ),
            ]
        )

        self.convs2 = nn.ModuleList(
            [
                weight_norm(
                    nn.Conv1d(
                        dim,
                        dim,
                        kernel_size,
                        1,
                        dilation=1,
                        padding=self.get_padding(kernel_size, 1),
                    )
                ),
                weight_norm(
                    nn.Conv1d(
                        dim,
                        dim,
                        kernel_size,
                        1,
                        dilation=1,
                        padding=self.get_padding(kernel_size, 1),
                    )
                ),
                weight_norm(
                    nn.Conv1d(
                        dim,
                        dim,
                        kernel_size,
                        1,
                        dilation=1,
                        padding=self.get_padding(kernel_size, 1),
                    )
                ),
            ]
        )

        self.gamma = nn.ParameterList(
            [
                (
                    nn.Parameter(
                        layer_scale_init_value * torch.ones(dim, 1), requires_grad=True
                    )
                    if layer_scale_init_value is not None
                    else None
                ),
                (
                    nn.Parameter(
                        layer_scale_init_value * torch.ones(dim, 1), requires_grad=True
                    )
                    if layer_scale_init_value is not None
                    else None
                ),
                (
                    nn.Parameter(
                        layer_scale_init_value * torch.ones(dim, 1), requires_grad=True
                    )
                    if layer_scale_init_value is not None
                    else None
                ),
            ]
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        for c1, c2, gamma in zip(self.convs1, self.convs2, self.gamma):
            xt = torch.nn.functional.leaky_relu(x, negative_slope=self.lrelu_slope)
            xt = c1(xt)
            xt = torch.nn.functional.leaky_relu(xt, negative_slope=self.lrelu_slope)
            xt = c2(xt)
            if gamma is not None:
                xt = gamma * xt
            x = xt + x
        return x

    def remove_weight_norm(self):
        for l in self.convs1:
            remove_weight_norm(l)
        for l in self.convs2:
            remove_weight_norm(l)

    @staticmethod
    def get_padding(kernel_size: int, dilation: int = 1) -> int:
        return int((kernel_size * dilation - dilation) / 2)


class Backbone(nn.Module):
    """Base class for the generator's backbone. It preserves the same temporal resolution across all layers."""

    def forward(self, x: torch.Tensor, **kwargs) -> torch.Tensor:
        """
        Args:
            x (Tensor): Input tensor of shape (B, C, L), where B is the batch size,
                        C denotes output features, and L is the sequence length.

        Returns:
            Tensor: Output of shape (B, L, H), where B is the batch size, L is the sequence length,
                    and H denotes the model dimension.
        """
        raise NotImplementedError("Subclasses must implement the forward method.")


class VocosBackbone(Backbone):
    """
    Vocos backbone module built with ConvNeXt blocks. Supports additional conditioning with Adaptive Layer Normalization

    Args:
        input_channels (int): Number of input features channels.
        dim (int): Hidden dimension of the model.
        intermediate_dim (int): Intermediate dimension used in ConvNeXtBlock.
        num_layers (int): Number of ConvNeXtBlock layers.
        layer_scale_init_value (float, optional): Initial value for layer scaling. Defaults to `1 / num_layers`.
        adanorm_num_embeddings (int, optional): Number of embeddings for AdaLayerNorm.
                                                None means non-conditional model. Defaults to None.
    """

    def __init__(
        self,
        input_channels: int,
        dim: int,
        intermediate_dim: int,
        num_layers: int,
        layer_scale_init_value: Optional[float] = None,
        adanorm_num_embeddings: Optional[int] = None,
    ):
        super().__init__()
        self.input_channels = input_channels
        self.embed = nn.Conv1d(input_channels, dim, kernel_size=7, padding=3)
        self.adanorm = adanorm_num_embeddings is not None
        if adanorm_num_embeddings:
            self.norm = AdaLayerNorm(adanorm_num_embeddings, dim, eps=1e-6)
        else:
            self.norm = nn.LayerNorm(dim, eps=1e-6)
        layer_scale_init_value = layer_scale_init_value or 1 / num_layers
        self.convnext = nn.ModuleList(
            [
                ConvNeXtBlock(
                    dim=dim,
                    intermediate_dim=intermediate_dim,
                    layer_scale_init_value=layer_scale_init_value,
                    adanorm_num_embeddings=adanorm_num_embeddings,
                )
                for _ in range(num_layers)
            ]
        )
        self.final_layer_norm = nn.LayerNorm(dim, eps=1e-6)
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, (nn.Conv1d, nn.Linear)):
            nn.init.trunc_normal_(m.weight, std=0.02)
            nn.init.constant_(m.bias, 0)

    def forward(self, x: torch.Tensor, **kwargs) -> torch.Tensor:
        bandwidth_id = kwargs.get("bandwidth_id", None)
        x = self.embed(x)
        if self.adanorm:
            assert bandwidth_id is not None
            x = self.norm(x.transpose(1, 2), cond_embedding_id=bandwidth_id)
        else:
            x = self.norm(x.transpose(1, 2))
        x = x.transpose(1, 2)
        for conv_block in self.convnext:
            x = conv_block(x, cond_embedding_id=bandwidth_id)
        x = self.final_layer_norm(x.transpose(1, 2))
        return x


class VocosResNetBackbone(Backbone):
    """
    Vocos backbone module built with ResBlocks.

    Args:
        input_channels (int): Number of input features channels.
        dim (int): Hidden dimension of the model.
        num_blocks (int): Number of ResBlock1 blocks.
        layer_scale_init_value (float, optional): Initial value for layer scaling. Defaults to None.
    """

    def __init__(
        self,
        input_channels,
        dim,
        num_blocks,
        layer_scale_init_value=None,
    ):
        super().__init__()
        self.input_channels = input_channels
        self.embed = weight_norm(
            nn.Conv1d(input_channels, dim, kernel_size=3, padding=1)
        )
        layer_scale_init_value = layer_scale_init_value or 1 / num_blocks / 3
        self.resnet = nn.Sequential(
            *[
                ResBlock1(dim=dim, layer_scale_init_value=layer_scale_init_value)
                for _ in range(num_blocks)
            ]
        )

    def forward(self, x: torch.Tensor, **kwargs) -> torch.Tensor:
        x = self.embed(x)
        x = self.resnet(x)
        x = x.transpose(1, 2)
        return x


class Vocos(nn.Module):
    def __init__(
        self,
        input_channels: int = 256,
        dim: int = 384,
        intermediate_dim: int = 1152,
        num_layers: int = 8,
        n_fft: int = 800,
        hop_size: int = 200,
        padding: str = "same",
        adanorm_num_embeddings=None,
        cfg=None,
    ):
        super().__init__()

        input_channels = (
            cfg.input_channels
            if cfg is not None and hasattr(cfg, "input_channels")
            else input_channels
        )
        dim = cfg.dim if cfg is not None and hasattr(cfg, "dim") else dim
        intermediate_dim = (
            cfg.intermediate_dim
            if cfg is not None and hasattr(cfg, "intermediate_dim")
            else intermediate_dim
        )
        num_layers = (
            cfg.num_layers
            if cfg is not None and hasattr(cfg, "num_layers")
            else num_layers
        )
        adanorm_num_embeddings = (
            cfg.adanorm_num_embeddings
            if cfg is not None and hasattr(cfg, "adanorm_num_embeddings")
            else adanorm_num_embeddings
        )
        n_fft = cfg.n_fft if cfg is not None and hasattr(cfg, "n_fft") else n_fft
        hop_size = (
            cfg.hop_size if cfg is not None and hasattr(cfg, "hop_size") else hop_size
        )
        padding = (
            cfg.padding if cfg is not None and hasattr(cfg, "padding") else padding
        )

        self.backbone = VocosBackbone(
            input_channels=input_channels,
            dim=dim,
            intermediate_dim=intermediate_dim,
            num_layers=num_layers,
            adanorm_num_embeddings=adanorm_num_embeddings,
        )
        self.head = ISTFTHead(dim, n_fft, hop_size, padding)

    def forward(self, x):
        x = self.backbone(x)
        x = self.head(x)

        return x[:, None, :]