import math
import random

import numpy as np
import torch.nn as nn

from Preprocessing.Codec.seanet import SEANetDecoder
from Preprocessing.Codec.seanet import SEANetEncoder
from Preprocessing.Codec.vq import ResidualVectorQuantizer


# Generator
class EnCodec(nn.Module):
    def __init__(self,
                 n_filters,
                 D,
                 target_bandwidths=[1, 1.5, 2, 4, 6, 12],
                 ratios=[8, 5, 4, 2],
                 sample_rate=16000,
                 bins=1024,
                 normalize=False):
        super().__init__()
        self.hop_length = np.prod(ratios)  # 计算乘积
        self.encoder = SEANetEncoder(n_filters=n_filters, dimension=D, ratios=ratios)
        n_q = int(1000 * target_bandwidths[-1] // (math.ceil(sample_rate / self.hop_length) * 10))
        self.frame_rate = math.ceil(sample_rate / np.prod(ratios))  # 50
        self.bits_per_codebook = int(math.log2(bins))
        self.target_bandwidths = target_bandwidths
        self.quantizer = ResidualVectorQuantizer(dimension=D, n_q=n_q, bins=bins)
        self.decoder = SEANetDecoder(n_filters=n_filters, dimension=D, ratios=ratios)

    def get_last_layer(self):
        return self.decoder.layers[-1].weight

    def forward(self, x):
        e = self.encoder(x)
        max_idx = len(self.target_bandwidths) - 1
        bw = self.target_bandwidths[random.randint(0, max_idx)]
        quantized, codes, bandwidth, commit_loss = self.quantizer(e, self.frame_rate, bw)
        o = self.decoder(quantized)
        return o, commit_loss, None

    def encode(self, x, target_bw=None, st=None):
        e = self.encoder(x)
        if target_bw is None:
            bw = self.target_bandwidths[-1]
        else:
            bw = target_bw
        if st is None:
            st = 0
        codes = self.quantizer.encode(e, self.frame_rate, bw, st)
        return codes

    def decode(self, codes):
        quantized = self.quantizer.decode(codes)
        o = self.decoder(quantized)
        return o