import math
from typing import List, Union
import numpy as np
import torch
from torch import nn
from torch.nn.utils import weight_norm
import torch.nn.functional as F
from einops import rearrange

def WNConv1d(*args, **kwargs):
    return weight_norm(nn.Conv1d(*args, **kwargs))


def WNConvTranspose1d(*args, **kwargs):
    return weight_norm(nn.ConvTranspose1d(*args, **kwargs))


# Scripting this brings model speed up 1.4x
@torch.jit.script
def snake(x, alpha):
    shape = x.shape
    x = x.reshape(shape[0], shape[1], -1)
    x = x + (alpha + 1e-9).reciprocal() * torch.sin(alpha * x).pow(2)
    x = x.reshape(shape)
    return x


class Snake1d(nn.Module):
    def __init__(self, channels):
        super().__init__()
        self.alpha = nn.Parameter(torch.ones(1, channels, 1))

    def forward(self, x):
        return snake(x, self.alpha)


class VectorQuantize(nn.Module):
    """
    Implementation of VQ similar to Karpathy's repo:
    https://github.com/karpathy/deep-vector-quantization
    Additionally uses following tricks from Improved VQGAN
    (https://arxiv.org/pdf/2110.04627.pdf):
        1. Factorized codes: Perform nearest neighbor lookup in low-dimensional space
            for improved codebook usage
        2. l2-normalized codes: Converts euclidean distance to cosine similarity which
            improves training stability
    """

    def __init__(self, input_dim: int, codebook_size: int, codebook_dim: int):
        super().__init__()
        self.codebook_size = codebook_size
        self.codebook_dim = codebook_dim

        self.in_proj = WNConv1d(input_dim, codebook_dim, kernel_size=1)
        self.out_proj = WNConv1d(codebook_dim, input_dim, kernel_size=1)
        self.codebook = nn.Embedding(codebook_size, codebook_dim)

    def forward(self, z):
        """Quantized the input tensor using a fixed codebook and returns
        the corresponding codebook vectors

        Parameters
        ----------
        z : Tensor[B x D x T]

        Returns
        -------
        Tensor[B x D x T]
            Quantized continuous representation of input
        Tensor[1]
            Commitment loss to train encoder to predict vectors closer to codebook
            entries
        Tensor[1]
            Codebook loss to update the codebook
        Tensor[B x T]
            Codebook indices (quantized discrete representation of input)
        Tensor[B x D x T]
            Projected latents (continuous representation of input before quantization)
        """

        # Factorized codes (ViT-VQGAN) Project input into low-dimensional space
        z_e = self.in_proj(z)  # z_e : (B x D x T)
        z_q, indices = self.decode_latents(z_e)

        commitment_loss = F.mse_loss(z_e, z_q.detach(), reduction="none").mean([1, 2])
        codebook_loss = F.mse_loss(z_q, z_e.detach(), reduction="none").mean([1, 2])

        z_q = (
            z_e + (z_q - z_e).detach()
        )  # noop in forward pass, straight-through gradient estimator in backward pass

        z_q = self.out_proj(z_q)

        return z_q, commitment_loss, codebook_loss, indices, z_e

    def embed_code(self, embed_id):
        return F.embedding(embed_id, self.codebook.weight)

    def decode_code(self, embed_id):
        return self.embed_code(embed_id).transpose(1, 2)

    def decode_latents(self, latents):
        encodings = rearrange(latents, "b d t -> (b t) d")
        codebook = self.codebook.weight  # codebook: (N x D)

        # L2 normalize encodings and codebook (ViT-VQGAN)
        encodings = F.normalize(encodings)
        codebook = F.normalize(codebook)

        # Compute euclidean distance with codebook
        dist = (
            encodings.pow(2).sum(1, keepdim=True)
            - 2 * encodings @ codebook.t()
            + codebook.pow(2).sum(1, keepdim=True).t()
        )
        indices = rearrange((-dist).max(1)[1], "(b t) -> b t", b=latents.size(0))
        z_q = self.decode_code(indices)
        return z_q, indices


class ResidualVectorQuantize(nn.Module):
    """
    Introduced in SoundStream: An end2end neural audio codec
    https://arxiv.org/abs/2107.03312
    """

    def __init__(
        self,
        input_dim: int = 512,
        n_codebooks: int = 9,
        codebook_size: int = 1024,
        codebook_dim: Union[int, list] = 8,
        quantizer_dropout: float = 0.0,
    ):
        super().__init__()
        if isinstance(codebook_dim, int):
            codebook_dim = [codebook_dim for _ in range(n_codebooks)]

        self.n_codebooks = n_codebooks
        self.codebook_dim = codebook_dim
        self.codebook_size = codebook_size

        self.quantizers = nn.ModuleList(
            [
                VectorQuantize(input_dim, codebook_size, codebook_dim[i])
                for i in range(n_codebooks)
            ]
        )
        self.quantizer_dropout = quantizer_dropout

    def forward(self, z, n_quantizers: int = None):
        """Quantized the input tensor using a fixed set of `n` codebooks and returns
        the corresponding codebook vectors
        Parameters
        ----------
        z : Tensor[B x D x T]
        n_quantizers : int, optional
            No. of quantizers to use
            (n_quantizers < self.n_codebooks ex: for quantizer dropout)
            Note: if `self.quantizer_dropout` is True, this argument is ignored
                when in training mode, and a random number of quantizers is used.
        Returns
        -------
        dict
            A dictionary with the following keys:

            "z" : Tensor[B x D x T]
                Quantized continuous representation of input
            "codes" : Tensor[B x N x T]
                Codebook indices for each codebook
                (quantized discrete representation of input)
            "latents" : Tensor[B x N*D x T]
                Projected latents (continuous representation of input before quantization)
            "vq/commitment_loss" : Tensor[1]
                Commitment loss to train encoder to predict vectors closer to codebook
                entries
            "vq/codebook_loss" : Tensor[1]
                Codebook loss to update the codebook
        """
        z_q = 0
        residual = z
        commitment_loss = 0
        codebook_loss = 0

        codebook_indices = []
        latents = []

        if n_quantizers is None:
            n_quantizers = self.n_codebooks
        if self.training:
            n_quantizers = torch.ones((z.shape[0],)) * self.n_codebooks + 1
            dropout = torch.randint(1, self.n_codebooks + 1, (z.shape[0],))
            n_dropout = int(z.shape[0] * self.quantizer_dropout)
            n_quantizers[:n_dropout] = dropout[:n_dropout]
            n_quantizers = n_quantizers.to(z.device)

        for i, quantizer in enumerate(self.quantizers):
            if self.training is False and i >= n_quantizers:
                break

            z_q_i, commitment_loss_i, codebook_loss_i, indices_i, z_e_i = quantizer(
                residual
            )

            # Create mask to apply quantizer dropout
            mask = (
                torch.full((z.shape[0],), fill_value=i, device=z.device) < n_quantizers
            )
            z_q = z_q + z_q_i * mask[:, None, None]
            residual = residual - z_q_i

            # Sum losses
            commitment_loss += (commitment_loss_i * mask).mean()
            codebook_loss += (codebook_loss_i * mask).mean()

            codebook_indices.append(indices_i)
            latents.append(z_e_i)

        codes = torch.stack(codebook_indices, dim=1)
        latents = torch.cat(latents, dim=1)

        return z_q, codes, latents, commitment_loss, codebook_loss

    def from_codes(self, codes: torch.Tensor):
        """Given the quantized codes, reconstruct the continuous representation
        Parameters
        ----------
        codes : Tensor[B x N x T]
            Quantized discrete representation of input
        Returns
        -------
        Tensor[B x D x T]
            Quantized continuous representation of input
        """
        z_q = 0.0
        z_p = []
        n_codebooks = codes.shape[1]
        for i in range(n_codebooks):
            z_p_i = self.quantizers[i].decode_code(codes[:, i, :])
            z_p.append(z_p_i)

            z_q_i = self.quantizers[i].out_proj(z_p_i)
            z_q = z_q + z_q_i
        return z_q, torch.cat(z_p, dim=1), codes

    def from_latents(self, latents: torch.Tensor):
        """Given the unquantized latents, reconstruct the
        continuous representation after quantization.

        Parameters
        ----------
        latents : Tensor[B x N x T]
            Continuous representation of input after projection

        Returns
        -------
        Tensor[B x D x T]
            Quantized representation of full-projected space
        Tensor[B x D x T]
            Quantized representation of latent space
        """
        z_q = 0
        z_p = []
        codes = []
        dims = np.cumsum([0] + [q.codebook_dim for q in self.quantizers])

        n_codebooks = np.where(dims <= latents.shape[1])[0].max(axis=0, keepdims=True)[
            0
        ]
        for i in range(n_codebooks):
            j, k = dims[i], dims[i + 1]
            z_p_i, codes_i = self.quantizers[i].decode_latents(latents[:, j:k, :])
            z_p.append(z_p_i)
            codes.append(codes_i)

            z_q_i = self.quantizers[i].out_proj(z_p_i)
            z_q = z_q + z_q_i

        return z_q, torch.cat(z_p, dim=1), torch.stack(codes, dim=1)


class AbstractDistribution:
    def sample(self):
        raise NotImplementedError()

    def mode(self):
        raise NotImplementedError()


class DiracDistribution(AbstractDistribution):
    def __init__(self, value):
        self.value = value

    def sample(self):
        return self.value

    def mode(self):
        return self.value


class DiagonalGaussianDistribution(object):
    def __init__(self, parameters, deterministic=False):
        self.parameters = parameters
        self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
        self.deterministic = deterministic
        self.std = torch.exp(0.5 * self.logvar)
        self.var = torch.exp(self.logvar)
        if self.deterministic:
            self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)

    def sample(self):
        x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
        return x

    def kl(self, other=None):
        if self.deterministic:
            return torch.Tensor([0.0])
        else:
            if other is None:
                return 0.5 * torch.mean(
                    torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,
                    dim=[1, 2],
                )
            else:
                return 0.5 * torch.mean(
                    torch.pow(self.mean - other.mean, 2) / other.var
                    + self.var / other.var
                    - 1.0
                    - self.logvar
                    + other.logvar,
                    dim=[1, 2],
                )

    def nll(self, sample, dims=[1, 2]):
        if self.deterministic:
            return torch.Tensor([0.0])
        logtwopi = np.log(2.0 * np.pi)
        return 0.5 * torch.sum(
            logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
            dim=dims,
        )

    def mode(self):
        return self.mean


def normal_kl(mean1, logvar1, mean2, logvar2):
    """
    source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
    Compute the KL divergence between two gaussians.
    Shapes are automatically broadcasted, so batches can be compared to
    scalars, among other use cases.
    """
    tensor = None
    for obj in (mean1, logvar1, mean2, logvar2):
        if isinstance(obj, torch.Tensor):
            tensor = obj
            break
    assert tensor is not None, "at least one argument must be a Tensor"

    # Force variances to be Tensors. Broadcasting helps convert scalars to
    # Tensors, but it does not work for torch.exp().
    logvar1, logvar2 = [x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) for x in (logvar1, logvar2)]

    return 0.5 * (
        -1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2) + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
    )


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


class ResidualUnit(nn.Module):
    def __init__(self, dim: int = 16, dilation: int = 1):
        super().__init__()
        pad = ((7 - 1) * dilation) // 2
        self.block = nn.Sequential(
            Snake1d(dim),
            WNConv1d(dim, dim, kernel_size=7, dilation=dilation, padding=pad),
            Snake1d(dim),
            WNConv1d(dim, dim, kernel_size=1),
        )

    def forward(self, x):
        y = self.block(x)
        pad = (x.shape[-1] - y.shape[-1]) // 2
        if pad > 0:
            x = x[..., pad:-pad]
        return x + y


class EncoderBlock(nn.Module):
    def __init__(self, dim: int = 16, stride: int = 1):
        super().__init__()
        self.block = nn.Sequential(
            ResidualUnit(dim // 2, dilation=1),
            ResidualUnit(dim // 2, dilation=3),
            ResidualUnit(dim // 2, dilation=9),
            Snake1d(dim // 2),
            WNConv1d(
                dim // 2,
                dim,
                kernel_size=2 * stride,
                stride=stride,
                padding=math.ceil(stride / 2),
            ),
        )

    def forward(self, x):
        return self.block(x)


class Encoder(nn.Module):
    def __init__(
        self,
        d_model: int = 64,
        strides: list = [2, 4, 8, 8],
        d_latent: int = 64,
    ):
        super().__init__()
        # Create first convolution
        self.block = [WNConv1d(1, d_model, kernel_size=7, padding=3)]

        # Create EncoderBlocks that double channels as they downsample by `stride`
        for stride in strides:
            d_model *= 2
            self.block += [EncoderBlock(d_model, stride=stride)]

        # Create last convolution
        self.block += [
            Snake1d(d_model),
            WNConv1d(d_model, d_latent, kernel_size=3, padding=1),
        ]

        # Wrap black into nn.Sequential
        self.block = nn.Sequential(*self.block)
        self.enc_dim = d_model

    def forward(self, x):
        return self.block(x)


class DecoderBlock(nn.Module):
    def __init__(self, input_dim: int = 16, output_dim: int = 8, stride: int = 1):
        super().__init__()
        self.block = nn.Sequential(
            Snake1d(input_dim),
            WNConvTranspose1d(
                input_dim,
                output_dim,
                kernel_size=2 * stride,
                stride=stride,
                padding=math.ceil(stride / 2),
                output_padding=stride % 2,
            ),
            ResidualUnit(output_dim, dilation=1),
            ResidualUnit(output_dim, dilation=3),
            ResidualUnit(output_dim, dilation=9),
        )

    def forward(self, x):
        return self.block(x)


class Decoder(nn.Module):
    def __init__(
        self,
        input_channel,
        channels,
        rates,
        d_out: int = 1,
    ):
        super().__init__()

        # Add first conv layer
        layers = [WNConv1d(input_channel, channels, kernel_size=7, padding=3)]

        # Add upsampling + MRF blocks
        for i, stride in enumerate(rates):
            input_dim = channels // 2**i
            output_dim = channels // 2 ** (i + 1)
            layers += [DecoderBlock(input_dim, output_dim, stride)]

        # Add final conv layer
        layers += [
            Snake1d(output_dim),
            WNConv1d(output_dim, d_out, kernel_size=7, padding=3),
            nn.Tanh(),
        ]

        self.model = nn.Sequential(*layers)

    def forward(self, x):
        return self.model(x)


class DacVAE(nn.Module):

    def __init__(
        self,
        encoder_dim: int = 128,
        encoder_rates: List[int] = [2, 3, 4, 5, 8],
        latent_dim: int = 128,
        decoder_dim: int = 2048,
        decoder_rates: List[int] = [8, 5, 4, 3, 2],
        n_codebooks: int = 9,
        codebook_size: int = 1024,
        codebook_dim: Union[int, list] = 8,
        quantizer_dropout: bool = False,
        sample_rate: int = 48000,
        continuous: bool = True,
        use_weight_norm: bool = False,
    ):
        super().__init__()

        self.encoder_dim = encoder_dim
        self.encoder_rates = encoder_rates
        self.decoder_dim = decoder_dim
        self.decoder_rates = decoder_rates
        self.sample_rate = sample_rate
        self.continuous = continuous
        self.use_weight_norm = use_weight_norm

        if latent_dim is None:
            latent_dim = encoder_dim * (2 ** len(encoder_rates))

        self.latent_dim = latent_dim

        self.hop_length = np.prod(encoder_rates)
        self.encoder = Encoder(encoder_dim, encoder_rates, latent_dim)

        if not continuous:
            self.n_codebooks = n_codebooks
            self.codebook_size = codebook_size
            self.codebook_dim = codebook_dim
            self.quantizer = ResidualVectorQuantize(
                input_dim=latent_dim,
                n_codebooks=n_codebooks,
                codebook_size=codebook_size,
                codebook_dim=codebook_dim,
                quantizer_dropout=quantizer_dropout,
            )
        else:
            self.quant_conv = torch.nn.Conv1d(latent_dim, 2 * latent_dim, 1)
            self.post_quant_conv = torch.nn.Conv1d(latent_dim, latent_dim, 1)

        self.decoder = Decoder(
            latent_dim,
            decoder_dim,
            decoder_rates,
        )
        self.sample_rate = sample_rate
        self.apply(init_weights)

        self.delay = self.get_delay()

        if not self.use_weight_norm:
            self.remove_weight_norm()

    def get_delay(self):
        # Any number works here, delay is invariant to input length
        l_out = self.get_output_length(0)
        L = l_out

        layers = []
        for layer in self.modules():
            if isinstance(layer, (nn.Conv1d, nn.ConvTranspose1d)):
                layers.append(layer)

        for layer in reversed(layers):
            d = layer.dilation[0]
            k = layer.kernel_size[0]
            s = layer.stride[0]

            if isinstance(layer, nn.ConvTranspose1d):
                L = ((L - d * (k - 1) - 1) / s) + 1
            elif isinstance(layer, nn.Conv1d):
                L = (L - 1) * s + d * (k - 1) + 1

            L = math.ceil(L)

        l_in = L

        return (l_in - l_out) // 2

    def get_output_length(self, input_length):
        L = input_length
        # Calculate output length
        for layer in self.modules():
            if isinstance(layer, (nn.Conv1d, nn.ConvTranspose1d)):
                d = layer.dilation[0]
                k = layer.kernel_size[0]
                s = layer.stride[0]

                if isinstance(layer, nn.Conv1d):
                    L = ((L - d * (k - 1) - 1) / s) + 1
                elif isinstance(layer, nn.ConvTranspose1d):
                    L = (L - 1) * s + d * (k - 1) + 1

                L = math.floor(L)
        return L

    @property
    def dtype(self):
        """Get the dtype of the model parameters."""
        # Return the dtype of the first parameter found
        for param in self.parameters():
            return param.dtype
        return torch.float32  # fallback

    @property
    def device(self):
        """Get the device of the model parameters."""
        # Return the device of the first parameter found
        for param in self.parameters():
            return param.device
        return torch.device('cpu')  # fallback

    def preprocess(self, audio_data, sample_rate):
        if sample_rate is None:
            sample_rate = self.sample_rate
        assert sample_rate == self.sample_rate

        length = audio_data.shape[-1]
        right_pad = math.ceil(length / self.hop_length) * self.hop_length - length
        audio_data = nn.functional.pad(audio_data, (0, right_pad))

        return audio_data

    def encode(
        self,
        audio_data: torch.Tensor,
        n_quantizers: int = None,
    ):
        """Encode given audio data and return quantized latent codes

        Parameters
        ----------
        audio_data : Tensor[B x 1 x T]
            Audio data to encode
        n_quantizers : int, optional
            Number of quantizers to use, by default None
            If None, all quantizers are used.

        Returns
        -------
        dict
            A dictionary with the following keys:
            "z" : Tensor[B x D x T]
                Quantized continuous representation of input
            "codes" : Tensor[B x N x T]
                Codebook indices for each codebook
                (quantized discrete representation of input)
            "latents" : Tensor[B x N*D x T]
                Projected latents (continuous representation of input before quantization)
            "vq/commitment_loss" : Tensor[1]
                Commitment loss to train encoder to predict vectors closer to codebook
                entries
            "vq/codebook_loss" : Tensor[1]
                Codebook loss to update the codebook
            "length" : int
                Number of samples in input audio
        """
        z = self.encoder(audio_data)  # [B x D x T]
        if not self.continuous:
            z, codes, latents, commitment_loss, codebook_loss = self.quantizer(z, n_quantizers)
        else:
            z = self.quant_conv(z)  # [B x 2D x T]
            z = DiagonalGaussianDistribution(z)
            codes, latents, commitment_loss, codebook_loss = None, None, 0, 0

        return z, codes, latents, commitment_loss, codebook_loss

    def decode(self, z: torch.Tensor):
        """Decode given latent codes and return audio data

        Parameters
        ----------
        z : Tensor[B x D x T]
            Quantized continuous representation of input
        length : int, optional
            Number of samples in output audio, by default None

        Returns
        -------
        dict
            A dictionary with the following keys:
            "audio" : Tensor[B x 1 x length]
                Decoded audio data.
        """
        if not self.continuous:
            audio = self.decoder(z)
        else:
            z = self.post_quant_conv(z)
            audio = self.decoder(z)

        return audio

    def forward(
        self,
        audio_data: torch.Tensor,
        sample_rate: int = None,
        n_quantizers: int = None,
    ):
        """Model forward pass

        Parameters
        ----------
        audio_data : Tensor[B x 1 x T]
            Audio data to encode
        sample_rate : int, optional
            Sample rate of audio data in Hz, by default None
            If None, defaults to `self.sample_rate`
        n_quantizers : int, optional
            Number of quantizers to use, by default None.
            If None, all quantizers are used.

        Returns
        -------
        dict
            A dictionary with the following keys:
            "z" : Tensor[B x D x T]
                Quantized continuous representation of input
            "codes" : Tensor[B x N x T]
                Codebook indices for each codebook
                (quantized discrete representation of input)
            "latents" : Tensor[B x N*D x T]
                Projected latents (continuous representation of input before quantization)
            "vq/commitment_loss" : Tensor[1]
                Commitment loss to train encoder to predict vectors closer to codebook
                entries
            "vq/codebook_loss" : Tensor[1]
                Codebook loss to update the codebook
            "length" : int
                Number of samples in input audio
            "audio" : Tensor[B x 1 x length]
                Decoded audio data.
        """
        length = audio_data.shape[-1]
        audio_data = self.preprocess(audio_data, sample_rate)
        if not self.continuous:
            z, codes, latents, commitment_loss, codebook_loss = self.encode(audio_data, n_quantizers)

            x = self.decode(z)
            return {
                "audio": x[..., :length],
                "z": z,
                "codes": codes,
                "latents": latents,
                "vq/commitment_loss": commitment_loss,
                "vq/codebook_loss": codebook_loss,
            }
        else:
            posterior, _, _, _, _ = self.encode(audio_data, n_quantizers)
            z = posterior.sample()
            x = self.decode(z)

            kl_loss = posterior.kl()
            kl_loss = kl_loss.mean()

            return {
                "audio": x[..., :length],
                "z": z,
                "kl_loss": kl_loss,
            }

    def remove_weight_norm(self):
        """
        Remove weight_norm from all modules in the model.
        This fuses the weight_g and weight_v parameters into a single weight parameter.
        Should be called before inference for better performance.
        Returns:
            self: The model with weight_norm removed
        """
        from torch.nn.utils import remove_weight_norm
        num_removed = 0
        for name, module in list(self.named_modules()):
            if hasattr(module, "_forward_pre_hooks"):
                for hook_id, hook in list(module._forward_pre_hooks.items()):
                    if "WeightNorm" in str(type(hook)):
                        try:
                            remove_weight_norm(module)
                            num_removed += 1
                            # print(f"Removed weight_norm from: {name}")
                        except ValueError as e:
                            print(f"Failed to remove weight_norm from {name}: {e}")
        if num_removed > 0:
            # print(f"Successfully removed weight_norm from {num_removed} modules")
            self.use_weight_norm = False
        else:
            print("No weight_norm found in the model")
        return self