class AudioGen(BaseGenModel):
    """AudioGen main model with convenient generation API.
    
    Args:
        name (str): name of the model.
        compression_model (CompressionModel): Compression model
            used to map audio to invertible discrete representations.
        lm (LMModel): Language model over discrete representations.
        max_duration (float, optional): maximum duration the model can produce,
            otherwise, inferred from the training params.
    """

def __init__(self, name: str, compression_model: CompressionModel, lm: LMModel,
             max_duration: tp.Optional[float] = None):
    super().__init__(name, compression_model, lm, max_duration)
    self.set_generation_params(duration=5)  # 기본 길이: 5초

@staticmethod
def get_pretrained(name: str = 'facebook/audiogen-medium', device=None):
    """Return pretrained model, we provide a single model for now:
    - facebook/audiogen-medium (1.5B), text to sound,
      # see: https://huggingface.co/facebook/audiogen-medium
    """

assert 'self_wav' not in lm.condition_provider.conditioners, \
    "AudioGen do not support waveform conditioning for now"

def set_generation_params(self, use_sampling: bool = True, top_k: int = 250,
                          top_p: float = 0.0, temperature: float = 1.0,
                          duration: float = 10.0, cfg_coef: float = 3.0,
                          two_step_cfg: bool = False, extend_stride: float = 2):

class CompressionModel(ABC, nn.Module):
    """Base API for all compression models that aim at being used as audio tokenizers
    with a language model.
    """

class EncodecModel(CompressionModel):
    """Encodec model operating on the raw waveform.
    
    Args:
        encoder (nn.Module): Encoder network.
        decoder (nn.Module): Decoder network.
        quantizer (qt.BaseQuantizer): Quantizer network.
        frame_rate (int): Frame rate for the latent representation.
        sample_rate (int): Audio sample rate.
        channels (int): Number of audio channels.
        causal (bool): Whether to use a causal version of the model.
        renormalize (bool): Whether to renormalize the audio before running the model.
    """

def preprocess(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
    scale: tp.Optional[torch.Tensor]
    if self.renormalize:
        mono = x.mean(dim=1, keepdim=True)
        volume = mono.pow(2).mean(dim=2, keepdim=True).sqrt()
        scale = 1e-8 + volume
        x = x / scale
        scale = scale.view(-1, 1)
    else:
        scale = None
    return x, scale

def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
    assert x.dim() == 3
    length = x.shape[-1]
    x, scale = self.preprocess(x)
    
    emb = self.encoder(x)
    q_res = self.quantizer(emb, self.frame_rate)
    out = self.decoder(q_res.x)
    
    # 인코더와 디코더에서 추가된 패딩 제거
    assert out.shape[-1] >= length, (out.shape[-1], length)
    out = out[..., :length]
    
    q_res.x = self.postprocess(out, scale)
    return q_res

class ResidualVectorQuantizer(BaseQuantizer):
    """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.
    """

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)
    
    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."""
    n_q = self.n_q
    codes = self.vq.encode(x, n_q=n_q)
    codes = codes.transpose(0, 1)
    return codes

def decode(self, codes: torch.Tensor) -> torch.Tensor:
    """Decode the given codes to the quantized representation."""
    codes = codes.transpose(0, 1)
    return self.vq.decode(codes)

@staticmethod
def get_pretrained(name: str, device: tp.Union[torch.device, str] = 'cpu') -> 'CompressionModel':
    """Instantiate a CompressionModel from a given pretrained model.
    
    Pretrained models:
        - dac_44khz (https://github.com/descriptinc/descript-audio-codec)
        - dac_24khz (same)
        - facebook/encodec_24khz (https://huggingface.co/facebook/encodec_24khz)
        - facebook/encodec_32khz (https://huggingface.co/facebook/encodec_32khz)
    """

class DAC(CompressionModel):
    def __init__(self, model_type: str = "44khz"):
        super().__init__()
        try:
            import dac.utils
        except ImportError:
            raise RuntimeError("Could not import dac, make sure it is installed, "
                               "please run `pip install descript-audio-codec`")
        self.model = dac.utils.load_model(model_type=model_type)
        self.n_quantizers = self.total_codebooks
        self.model.eval()

# AudioGen으로 짧은 효과음 생성
audiogen = AudioGen.get_pretrained('facebook/audiogen-medium')
audiogen.set_generation_params(duration=3.0, extend_stride=1.0)

descriptions = ["doorbell ringing", "car engine starting", "rain on window"]
effects = audiogen.generate(descriptions)

# EnCodec으로 오디오 압축
encodec = CompressionModel.get_pretrained('facebook/encodec_24khz')
codes, scale = encodec.encode(audio_tensor)
reconstructed = encodec.decode(codes, scale)

# 필요에 따라 코드북 수 조절
encodec.set_num_codebooks(4)  # 낮은 품질, 높은 압축률
codes_low = encodec.encode(audio)

encodec.set_num_codebooks(8)  # 높은 품질, 낮은 압축률  
codes_high = encodec.encode(audio)