# Copyright 2025 Xiaomi Corporation.
import typing as tp

import torch
import torch.distributed as dist
import torch.nn.functional as F
from einops import rearrange, repeat
from torch import nn


def rank():
    if dist.is_initialized():
        return dist.get_rank()
    else:
        return 0


def world_size():
    if dist.is_initialized():
        return dist.get_world_size()
    else:
        return 1


def default(val: tp.Any, d: tp.Any) -> tp.Any:
    return val if val is not None else d


def ema_inplace(moving_avg, new, decay: float):
    if dist.is_initialized():
        dist.all_reduce(new, op=dist.ReduceOp.SUM)
    moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay))


def laplace_smoothing(x, n_categories: int, epsilon: float = 1e-5):
    return (x + epsilon) / (x.sum() + n_categories * epsilon)


def uniform_init(*shape: int):
    t = torch.empty(shape)
    nn.init.kaiming_uniform_(t)
    return t


def sample_vectors(samples, num: int):
    num_samples, device = samples.shape[0], samples.device

    if num_samples >= num:
        indices = torch.randperm(num_samples, device=device)[:num]
    else:
        indices = torch.randint(0, num_samples, (num,), device=device)

    selected_samples = samples[indices]

    if dist.is_initialized():
        dist.broadcast(selected_samples, src=0)

    return selected_samples


def kmeans(samples, num_clusters: int, num_iters: int = 10):
    dim, dtype = samples.shape[-1], samples.dtype

    means = sample_vectors(samples, num_clusters)

    for _ in range(num_iters):
        dists = -(samples.pow(2).sum(1, keepdim=True) - 2 * samples @ means.t() + means.t().pow(2).sum(0, keepdim=True))

        buckets = dists.max(dim=-1).indices
        bins = torch.bincount(buckets, minlength=num_clusters)

        new_means = buckets.new_zeros(num_clusters, dim, dtype=dtype)
        new_means = new_means.scatter_add_(0, repeat(buckets, "n -> n d", d=dim), samples)

        if dist.is_initialized():
            dist.all_reduce(bins, op=dist.ReduceOp.SUM)
            dist.all_reduce(new_means, op=dist.ReduceOp.SUM)

        zero_mask = bins == 0
        bins_min_clamped = bins.masked_fill(zero_mask, 1)

        new_means = new_means / bins_min_clamped[..., None]

        means = torch.where(zero_mask[..., None], means, new_means)

    return means, bins


class EuclideanCodebook(nn.Module):
    """Codebook with Euclidean distance.
    Args:
        dim (int): Dimension.
        codebook_size (int): Codebook size.
        kmeans_init (bool): Whether to use k-means to initialize the codebooks.
            If set to true, run the k-means algorithm on the first training batch and use
            the learned centroids as initialization.
        kmeans_iters (int): Number of iterations used for k-means algorithm at initialization.
        decay (float): Decay for exponential moving average over the codebooks.
        epsilon (float): Epsilon value for numerical stability.
        threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
            that have an exponential moving average cluster size less than the specified threshold with
            randomly selected vector from the current batch.
    """

    def __init__(
        self,
        dim: int,
        codebook_size: int,
        kmeans_init: int = False,
        kmeans_iters: int = 10,
        decay: float = 0.99,
        epsilon: float = 1e-5,
        threshold_ema_dead_code: int = 2,
    ):
        super().__init__()
        self.decay = decay
        init_fn: tp.Callable[..., torch.Tensor] | tp.Any = uniform_init if not kmeans_init else torch.zeros
        embed = init_fn(codebook_size, dim)

        self.codebook_size = codebook_size

        self.kmeans_iters = kmeans_iters
        self.epsilon = epsilon
        self.threshold_ema_dead_code = threshold_ema_dead_code

        self.register_buffer("inited", torch.Tensor([not kmeans_init]))
        self.register_buffer("cluster_size", torch.zeros(codebook_size))
        self.register_buffer("embed", embed)
        self.register_buffer("embed_avg", embed.clone())

    @torch.jit.ignore
    def init_embed_(self, data):
        if self.inited:
            return

        embed, cluster_size = kmeans(data, self.codebook_size, self.kmeans_iters)
        self.embed.data.copy_(embed)
        self.embed_avg.data.copy_(embed.clone())
        self.cluster_size.data.copy_(cluster_size)
        self.inited.data.copy_(torch.Tensor([True]))

    def replace_(self, samples, mask):
        # modified_codebook = torch.where(
        #     mask[..., None], sample_vectors(samples, self.codebook_size), self.embed
        # )
        replace_num = mask.sum()
        modified_codebook = self.embed.clone()
        modified_codebook[mask] = sample_vectors(samples, replace_num)
        self.embed.data.copy_(modified_codebook)

    def expire_codes_(self, batch_samples):
        if self.threshold_ema_dead_code == 0:
            return

        expired_codes = self.cluster_size < self.threshold_ema_dead_code
        if not torch.any(expired_codes):
            return

        batch_samples = rearrange(batch_samples, "... d -> (...) d")
        self.replace_(batch_samples, mask=expired_codes)

    def preprocess(self, x):
        x = rearrange(x, "... d -> (...) d")
        return x

    def quantize(self, x):
        embed = self.embed.t()
        dist = -(x.pow(2).sum(1, keepdim=True) - 2 * x @ embed + embed.pow(2).sum(0, keepdim=True))
        embed_ind = dist.max(dim=-1).indices
        return embed_ind

    def postprocess_emb(self, embed_ind, shape):
        return embed_ind.view(*shape[:-1])

    def dequantize(self, embed_ind):
        quantize = F.embedding(embed_ind, self.embed)
        return quantize

    def encode(self, x):
        shape = x.shape
        # pre-process
        x = self.preprocess(x)
        # quantize
        embed_ind = self.quantize(x)
        # post-process
        embed_ind = self.postprocess_emb(embed_ind, shape)
        return embed_ind

    def decode(self, embed_ind):
        quantize = self.dequantize(embed_ind)
        return quantize

    def forward(self, x):
        shape, dtype = x.shape, x.dtype
        x = self.preprocess(x)

        self.init_embed_(x)

        embed_ind = self.quantize(x)
        embed_onehot = F.one_hot(embed_ind, self.codebook_size).type(dtype)
        embed_ind = self.postprocess_emb(embed_ind, shape)
        quantize = self.dequantize(embed_ind)

        if self.training:
            # We do the expiry of code at that point as buffers are in sync
            # and all the workers will take the same decision.
            self.expire_codes_(x)
            ema_inplace(self.cluster_size, embed_onehot.sum(0), self.decay)
            embed_sum = x.t() @ embed_onehot
            ema_inplace(self.embed_avg, embed_sum.t().contiguous(), self.decay)
            cluster_size = (
                laplace_smoothing(self.cluster_size, self.codebook_size, self.epsilon) * self.cluster_size.sum()
            )
            embed_normalized = self.embed_avg / cluster_size.unsqueeze(1)
            self.embed.data.copy_(embed_normalized)

        return quantize, embed_ind


class VectorQuantization(nn.Module):
    """Vector quantization implementation.
    Currently supports only euclidean distance.
    Args:
        dim (int): Dimension
        codebook_size (int): Codebook size
        codebook_dim (int): Codebook dimension. If not defined, uses the specified dimension in dim.
        decay (float): Decay for exponential moving average over the codebooks.
        epsilon (float): Epsilon value for numerical stability.
        kmeans_init (bool): Whether to use kmeans to initialize the codebooks.
        kmeans_iters (int): Number of iterations used for kmeans initialization.
        threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
            that have an exponential moving average cluster size less than the specified threshold with
            randomly selected vector from the current batch.
        commitment_weight (float): Weight for commitment loss.
    """

    def __init__(
        self,
        dim: int,
        codebook_size: int,
        codebook_dim: int | None = None,
        decay: float = 0.99,
        epsilon: float = 1e-5,
        kmeans_init: bool = True,
        kmeans_iters: int = 50,
        threshold_ema_dead_code: int = 2,
        commitment_weight: float = 1.0,
    ):
        super().__init__()
        _codebook_dim: int = default(codebook_dim, dim)

        requires_projection = _codebook_dim != dim
        self.project_in = nn.Linear(dim, _codebook_dim) if requires_projection else nn.Identity()
        self.project_out = nn.Linear(_codebook_dim, dim) if requires_projection else nn.Identity()

        self.epsilon = epsilon
        self.commitment_weight = commitment_weight

        self._codebook = EuclideanCodebook(
            dim=_codebook_dim,
            codebook_size=codebook_size,
            kmeans_init=kmeans_init,
            kmeans_iters=kmeans_iters,
            decay=decay,
            epsilon=epsilon,
            threshold_ema_dead_code=threshold_ema_dead_code,
        )
        self.codebook_size = codebook_size

    @property
    def codebook(self):
        return self._codebook.embed

    def encode(self, x):
        # x = rearrange(x, "b d n -> b n d")
        x = self.project_in(x)
        embed_in = self._codebook.encode(x)
        return embed_in

    def decode(self, embed_ind):
        quantize = self._codebook.decode(embed_ind)
        quantize = self.project_out(quantize)
        # quantize = rearrange(quantize, "b n d -> b d n")
        return quantize

    def forward(self, x):
        device = x.device
        x = self.project_in(x)

        quantize, embed_ind = self._codebook(x)

        if self.training:
            quantize = x + (quantize - x).detach()

        loss = torch.tensor([0.0], device=device, requires_grad=self.training)

        if self.training:
            if self.commitment_weight > 0:
                commit_loss = F.mse_loss(quantize.detach(), x)
                loss = loss + commit_loss * self.commitment_weight

        quantize = self.project_out(quantize)
        # quantize = rearrange(quantize, "b n d -> b d n")
        return quantize, embed_ind, loss


class ResidualVectorQuantization(nn.Module):
    """Residual vector quantization implementation.
    Follows Algorithm 1. in https://arxiv.org/pdf/2107.03312.pdf
    """

    def __init__(self, *, num_quantizers, codebook_size, **kwargs):
        super().__init__()
        if isinstance(codebook_size, int):
            codebook_size = [codebook_size] * num_quantizers
        elif len(codebook_size) < num_quantizers:
            codebook_size += [codebook_size[-1]] * (num_quantizers - len(codebook_size))
        self.layers = nn.ModuleList(
            [VectorQuantization(codebook_size=codebook_size[i], **kwargs) for i in range(num_quantizers)]
        )

    def forward(self, x, n_q: int | None = None, layers: list | None = None):
        quantized_out = 0.0
        residual = x

        all_losses = []
        all_indices = []
        out_quantized = []

        n_q = n_q or len(self.layers)

        for i, layer in enumerate(self.layers[:n_q]):
            quantized, indices, loss = layer(residual)
            residual = residual - quantized
            quantized_out = quantized_out + quantized

            all_indices.append(indices)
            all_losses.append(loss)
            if layers and i in layers:
                out_quantized.append(quantized_out)

        out_losses, out_indices = map(torch.stack, (all_losses, all_indices))
        return quantized_out, out_indices, out_losses, out_quantized

    def encode(self, x: torch.Tensor, n_q: int | None = None, st: int | None = None) -> torch.Tensor:
        residual = x
        all_indices = []
        n_q = len(self.layers) if n_q is None else n_q
        st = 0 if st is None else st
        for layer in self.layers[st:n_q]:
            indices = layer.encode(residual)
            quantized = layer.decode(indices)
            residual = residual - quantized
            all_indices.append(indices)
        out_indices = torch.stack(all_indices)
        return out_indices

    def decode(self, q_indices: torch.Tensor, st: int = 0) -> torch.Tensor:
        quantized_out = torch.tensor(0.0, device=q_indices.device)
        for i, indices in enumerate(q_indices):
            layer = self.layers[st + i]
            quantized = layer.decode(indices)
            quantized_out = quantized_out + quantized
        return quantized_out


class ResidualVectorQuantizer(nn.Module):
    """Residual Vector Quantizer.
    Args:
        dimension (int): Dimension of the codebooks.
        n_q (int): Number of residual vector quantizers used.
        bins (int): Codebook size.
        decay (float): Decay for exponential moving average over the codebooks.
        kmeans_init (bool): Whether to use kmeans to initialize the codebooks.
        kmeans_iters (int): Number of iterations used for kmeans initialization.
        threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
            that have an exponential moving average cluster size less than the specified threshold with
            randomly selected vector from the current batch.
    """

    def __init__(
        self,
        dimension: int = 256,
        n_q: int = 8,
        bins: int | list = 1024,
        decay: float = 0.99,
        kmeans_init: bool = True,
        kmeans_iters: int = 50,
        threshold_ema_dead_code: int = 2,
    ):
        super().__init__()
        self.n_q = n_q
        self.dimension = dimension
        self.bins = bins
        self.decay = decay
        self.kmeans_init = kmeans_init
        self.kmeans_iters = kmeans_iters
        self.threshold_ema_dead_code = threshold_ema_dead_code
        self.vq = ResidualVectorQuantization(
            dim=self.dimension,
            codebook_size=self.bins,
            num_quantizers=self.n_q,
            decay=self.decay,
            kmeans_init=self.kmeans_init,
            kmeans_iters=self.kmeans_iters,
            threshold_ema_dead_code=self.threshold_ema_dead_code,
        )

    def forward(
        self,
        x: torch.Tensor,
        n_q: int | None = None,
        layers: list | None = None,
    ):
        """Residual vector quantization on the given input tensor.
        Args:
            x (torch.Tensor): Input tensor.
            n_q (int): Number of quantizer used to quantize. Default: All quantizers.
            layers (list): Layer that need to return quantized. Default: None.
        Returns:
            QuantizedResult:
                The quantized (or approximately quantized) representation with
                the associated number quantizers and layer quantized required to return.
        """
        n_q = n_q if n_q else self.n_q
        quantized, codes, commit_loss, quantized_list = self.vq(x, n_q=n_q, layers=layers)
        return quantized, codes, torch.mean(commit_loss), quantized_list

    def encode(self, x: torch.Tensor, n_q: int | None = None, st: int | None = None) -> torch.Tensor:
        """Encode a given input tensor with the specified sample rate at the given bandwidth.
        The RVQ encode method sets the appropriate number of quantizer to use
        and returns indices for each quantizer.
        Args:
            x (torch.Tensor): Input tensor.
            n_q (int): Number of quantizer used to quantize. Default: All quantizers.
            st (int): Start to encode input from which layers. Default: 0.
        """
        n_q = n_q if n_q else self.n_q
        st = st or 0
        codes = self.vq.encode(x, n_q=n_q, st=st)
        return codes

    def decode(self, codes: torch.Tensor, st: int = 0) -> torch.Tensor:
        """Decode the given codes to the quantized representation.
        Args:
            codes (torch.Tensor): Input indices for each quantizer.
            st (int): Start to decode input codes from which layers. Default: 0.
        """
        quantized = self.vq.decode(codes, st=st)
        return quantized