# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
# This implementation is inspired from
# https://github.com/lucidrains/vector-quantize-pytorch
# which is released under MIT License. Hereafter, the original license:
# MIT License
#
# Copyright (c) 2020 Phil Wang
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
"""Core vector quantization implementation."""

import torch.nn.functional as F
from einops import rearrange
from einops import repeat
from torch import nn

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""Torch distributed utilities."""
import typing as tp

import torch


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


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


def is_distributed():
    return world_size() > 1


def all_reduce(tensor: torch.Tensor, op=torch.distributed.ReduceOp.SUM):
    if is_distributed():
        return torch.distributed.all_reduce(tensor, op)


def _is_complex_or_float(tensor):
    return torch.is_floating_point(tensor) or torch.is_complex(tensor)


def _check_number_of_params(params: tp.List[torch.Tensor]):
    # utility function to check that the number of params in all workers is the same,
    # and thus avoid a deadlock with distributed all reduce.
    if not is_distributed() or not params:
        return
    # print('params[0].device ', params[0].device)
    tensor = torch.tensor(
        [len(params)], device=params[0].device, dtype=torch.long)
    all_reduce(tensor)
    if tensor.item() != len(params) * world_size():
        # If not all the workers have the same number, for at least one of them,
        # this inequality will be verified.
        raise RuntimeError(
            f"Mismatch in number of params: ours is {len(params)}, "
            "at least one worker has a different one.")


def broadcast_tensors(tensors: tp.Iterable[torch.Tensor], src: int = 0):
    """Broadcast the tensors from the given parameters to all workers.
    This can be used to ensure that all workers have the same model to start with.
    """
    if not is_distributed():
        return
    tensors = [tensor for tensor in tensors if _is_complex_or_float(tensor)]
    _check_number_of_params(tensors)
    handles = []
    for tensor in tensors:
        # src = int(rank()) # added code
        handle = torch.distributed.broadcast(
            tensor.data, src=src, async_op=True)
        handles.append(handle)
    for handle in handles:
        handle.wait()


def sync_buffer(buffers, average=True):
    """
    Sync grad for buffers. If average is False, broadcast instead of averaging.
    """
    if not is_distributed():
        return
    handles = []
    for buffer in buffers:
        if torch.is_floating_point(buffer.data):
            if average:
                handle = torch.distributed.all_reduce(
                    buffer.data,
                    op=torch.distributed.ReduceOp.SUM,
                    async_op=True)
            else:
                handle = torch.distributed.broadcast(
                    buffer.data, src=0, async_op=True)
            handles.append((buffer, handle))
    for buffer, handle in handles:
        handle.wait()
        if average:
            buffer.data /= world_size


def sync_grad(params):
    """
    Simpler alternative to DistributedDataParallel, that doesn't rely
    on any black magic. For simple models it can also be as fast.
    Just call this on your model parameters after the call to backward!
    """
    if not is_distributed():
        return
    handles = []
    for p in params:
        if p.grad is not None:
            handle = torch.distributed.all_reduce(
                p.grad.data, op=torch.distributed.ReduceOp.SUM, async_op=True)
            handles.append((p, handle))
    for p, handle in handles:
        handle.wait()
        p.grad.data /= world_size()


def average_metrics(metrics: tp.Dict[str, float], count=1.):
    """Average a dictionary of metrics across all workers, using the optional
    `count` as unormalized weight.
    """
    if not is_distributed():
        return metrics
    keys, values = zip(*metrics.items())
    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    tensor = torch.tensor(
        list(values) + [1], device=device, dtype=torch.float32)
    tensor *= count
    all_reduce(tensor)
    averaged = (tensor[:-1] / tensor[-1]).cpu().tolist()
    return dict(zip(keys, averaged))


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):
    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)

    return samples[indices]


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):
        diffs = rearrange(samples, "n d -> n () d") - rearrange(means,
                                                                "c d -> () c d")
        dists = -(diffs ** 2).sum(dim=-1)

        buckets = dists.max(dim=-1).indices
        bins = torch.bincount(buckets, minlength=num_clusters)
        zero_mask = bins == 0
        bins_min_clamped = bins.masked_fill(zero_mask, 1)

        new_means = buckets.new_zeros(num_clusters, dim, dtype=dtype)
        new_means.scatter_add_(0, repeat(buckets, "n -> n d", d=dim), samples)
        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.Union[
            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]))
        # Make sure all buffers across workers are in sync after initialization
        broadcast_tensors(self.buffers())

    def replace_(self, samples, mask):
        modified_codebook = torch.where(
            mask[..., None],
            sample_vectors(samples, self.codebook_size), self.embed)
        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)
        broadcast_tensors(self.buffers())

    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(), 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: tp.Optional[int] = 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., ):
        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)
        if len(quantize.size()) < 3:
            quantize = quantize.unsqueeze(0)
        quantize = rearrange(quantize, "b n d -> b d n")
        return quantize

    def forward(self, x):
        device = x.device
        x = rearrange(x, "b d n -> b n d")
        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, **kwargs):
        super().__init__()
        self.layers = nn.ModuleList(
            [VectorQuantization(**kwargs) for _ in range(num_quantizers)])

    def forward(self, x, n_q: tp.Optional[int] = None):
        quantized_out = 0.0
        residual = x

        all_losses = []
        all_indices = []

        n_q = n_q or len(self.layers)

        for layer in 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)

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

    def encode(self,
               x: torch.Tensor,
               n_q: tp.Optional[int] = None,
               st: tp.Optional[int] = None) -> torch.Tensor:
        residual = x
        all_indices = []
        n_q = n_q or len(self.layers)
        st = st or 0
        for layer in self.layers[st:n_q]:  # 设置解码的起止layer
            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) -> torch.Tensor:
        quantized_out = torch.tensor(0.0, device=q_indices.device)
        for i, indices in enumerate(q_indices):
            layer = self.layers[i]
            quantized = layer.decode(indices)
            quantized_out = quantized_out + quantized
        return quantized_out


# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

"""Residual vector quantizer implementation."""

from dataclasses import dataclass, field
import math
import typing as tp

import torch
from torch import nn


@dataclass
class QuantizedResult:
    quantized: torch.Tensor
    codes: torch.Tensor
    bandwidth: torch.Tensor  # bandwidth in kb/s used, per batch item.
    penalty: tp.Optional[torch.Tensor] = None
    metrics: dict = field(default_factory=dict)


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 = 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, sample_rate: int, bandwidth: tp.Optional[float] = None) -> QuantizedResult:
        """Residual vector quantization on the given input tensor.
        Args:
            x (torch.Tensor): Input tensor.
            sample_rate (int): Sample rate of the input tensor.
            bandwidth (float): Target bandwidth.
        Returns:
            QuantizedResult:
                The quantized (or approximately quantized) representation with
                the associated bandwidth and any penalty term for the loss.
        """
        bw_per_q = self.get_bandwidth_per_quantizer(sample_rate)
        n_q = self.get_num_quantizers_for_bandwidth(sample_rate, bandwidth)
        quantized, codes, commit_loss = self.vq(x, n_q=n_q)
        bw = torch.tensor(n_q * bw_per_q).to(x)
        return quantized, codes, bw, torch.mean(commit_loss)
        # return QuantizedResult(quantized, codes, bw, penalty=torch.mean(commit_loss))

    def get_num_quantizers_for_bandwidth(self, sample_rate: int, bandwidth: tp.Optional[float] = None) -> int:
        """Return n_q based on specified target bandwidth.
        """
        bw_per_q = self.get_bandwidth_per_quantizer(sample_rate)
        n_q = self.n_q
        if bandwidth and bandwidth > 0.:
            n_q = int(max(1, math.floor(bandwidth / bw_per_q)))
        return n_q

    def get_bandwidth_per_quantizer(self, sample_rate: int):
        """Return bandwidth per quantizer for a given input sample rate.
        """
        return math.log2(self.bins) * sample_rate / 1000

    def encode(self, x: torch.Tensor, sample_rate: int, bandwidth: tp.Optional[float] = None, st: tp.Optional[int] = 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.
        """
        n_q = self.get_num_quantizers_for_bandwidth(sample_rate, bandwidth)
        st = st or 0
        codes = self.vq.encode(x, n_q=n_q, st=st)
        return codes

    def decode(self, codes: torch.Tensor) -> torch.Tensor:
        """Decode the given codes to the quantized representation.
        """
        quantized = self.vq.decode(codes)
        return quantized