import math
import typing as tp
from dataclasses import dataclass, field
import typing as tp
import torch
from torch import nn
from einops import rearrange
import torch.nn.functional as F

@dataclass
class QuantizedResult:
    x: 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 EuclideanCodebook(nn.Module):
    def __init__(
        self,
        dim,
        codebook_size,
        kmeans_init=False,
        kmeans_iters=10,
        decay=0.8,
        epsilon=1e-5,
    ):
        super().__init__()
        self.decay=decay
        init_fn=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.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
        # flashy.distrib.broadcast_tensors(self.buffers())   # brodcast param values to all GPUS



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

    def dequantize(self, embed_ind):
        # embed_ind[0] = 2048
        # print('MAX MAX MAX', embed_ind.shape)
        quantize = F.embedding(embed_ind, self.embed)
        # print('\n\nDE QUANT\n\n', quantize.shape)  # (1, 35, 128) -> also arrives here for special_token
        return quantize

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



class VectorQuantization(nn.Module):
    
    def __init__(
        self,
        dim,
        codebook_size,
        codebook_dim=None,
        decay=0.8,
        epsilon=1e-5,
        kmeans_init=False,
        kmeans_iters=10,
        channels_last=False,
    ):
        super().__init__()
        # _codebook_dim: int = default(codebook_dim, dim)
        _codebook_dim = codebook_dim if codebook_dim is not None else 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._codebook = EuclideanCodebook(dim=_codebook_dim, 
                                           codebook_size=codebook_size,
                                           kmeans_init=kmeans_init, 
                                           kmeans_iters=kmeans_iters,
                                           decay=decay,
                                           epsilon=epsilon)
        self.codebook_size = codebook_size

        self.channels_last = channels_last

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

    @property
    def inited(self):
        return self._codebook.inited

    def _postprocess(self, quantize):
        if not self.channels_last:
            quantize = rearrange(quantize, "b n d -> b d n")
        return quantize

    def decode(self, embed_ind):
        quantize = self._codebook.decode(embed_ind)
        quantize = self.project_out(quantize)
        quantize = self._postprocess(quantize)
        return quantize




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 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

    
    
    
# ------------------------------------- END core_vq.py    


class ResidualVectorQuantizer(nn.Module):
    """Residual Vector Quantizer.

    Args:
        dimension (int): Dimension of the codebooks.
        n_q (int): Number of residual vector quantizers used.
        q_dropout (bool): Random quantizer drop out at train time.
        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.
        orthogonal_reg_weight (float): Orthogonal regularization weights.
        orthogonal_reg_active_codes_only (bool): Apply orthogonal regularization only on active codes.
        orthogonal_reg_max_codes (optional int): Maximum number of codes to consider.
            for orthogonal regularization.
    """
    def __init__(
        self,
        dimension: int = 256,
        n_q: int = 8,
        q_dropout: bool = False,
        bins: int = 1024,
        decay: float = 0.99,
        kmeans_init: bool = True,
        kmeans_iters: int = 10,
        threshold_ema_dead_code: int = 2,
        orthogonal_reg_weight: float = 0.0,
        orthogonal_reg_active_codes_only: bool = False,
        orthogonal_reg_max_codes: tp.Optional[int] = None,
    ):
        super().__init__()
        self.max_n_q = n_q
        self.n_q = n_q
        self.q_dropout = q_dropout
        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.orthogonal_reg_weight = orthogonal_reg_weight
        self.orthogonal_reg_active_codes_only = orthogonal_reg_active_codes_only
        self.orthogonal_reg_max_codes = orthogonal_reg_max_codes
        print(f'         {kmeans_init=}\n\n\n\n')
        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,
            channels_last=False
        )

    def forward(self, x: torch.Tensor, frame_rate: int):
        n_q = self.n_q
        if self.training and self.q_dropout:
            n_q = int(torch.randint(1, self.n_q + 1, (1,)).item())
        bw_per_q = math.log2(self.bins) * frame_rate / 1000
        quantized, codes, commit_loss = self.vq(x, n_q=n_q)
        codes = codes.transpose(0, 1)
        # codes is [B, K, T], with T frames, K nb of codebooks.
        bw = torch.tensor(n_q * bw_per_q).to(x)
        return QuantizedResult(quantized, codes, bw, penalty=torch.mean(commit_loss))

    def encode(self, x: torch.Tensor) -> torch.Tensor:
        """Encode a given input tensor with the specified frame 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.n_q
        codes = self.vq.encode(x, n_q=n_q)
        codes = codes.transpose(0, 1)
        # codes is [B, K, T], with T frames, K nb of codebooks.
        return codes

    def decode(self, codes: torch.Tensor) -> torch.Tensor:
        """Decode the given codes to the quantized representation."""
        # codes is [B, K, T], with T frames, K nb of codebooks, vq.decode expects [K, B, T].
        codes = codes.transpose(0, 1)
        quantized = self.vq.decode(codes)
        return quantized

    @property
    def total_codebooks(self):
        return self.max_n_q

    @property
    def num_codebooks(self):
        return self.n_q

    def set_num_codebooks(self, n: int):
        assert n > 0 and n <= self.max_n_q
        self.n_q = n