text: from distutils.version import LooseVersion
  from typing import Optional
  from typing import Tuple
  from typing import Union
  
  import torch
  from torch_complex.tensor import ComplexTensor
  from typeguard import check_argument_types
  
  from muskit.torch_utils.nets_utils import make_pad_mask
  from muskit.layers.inversible_interface import InversibleInterface
  
  
  class Stft(torch.nn.Module, InversibleInterface):
      def __init__(
          self,
          n_fft: int = 512,
          win_length: int = None,
          hop_length: int = 128,
          window: Optional[str] = "hann",
          center: bool = True,
          normalized: bool = False,
          onesided: bool = True,
      ):
          assert check_argument_types()
          super().__init__()
          self.n_fft = n_fft
          if win_length is None:
              self.win_length = n_fft
          else:
              self.win_length = win_length
          self.hop_length = hop_length
          self.center = center
          self.normalized = normalized
          self.onesided = onesided
          if window is not None and not hasattr(torch, f"{window}_window"):
              raise ValueError(f"{window} window is not implemented")
          self.window = window
  
      def extra_repr(self):
          return (
              f"n_fft={self.n_fft}, "
              f"win_length={self.win_length}, "
              f"hop_length={self.hop_length}, "
              f"center={self.center}, "
              f"normalized={self.normalized}, "
              f"onesided={self.onesided}"
          )
  
      def forward(
          self, input: torch.Tensor, ilens: torch.Tensor = None
      ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
          """STFT forward function.
  
          Args:
              input: (Batch, Nsamples) or (Batch, Nsample, Channels)
              ilens: (Batch)
          Returns:
              output: (Batch, Frames, Freq, 2) or (Batch, Frames, Channels, Freq, 2)
  
          """
          bs = input.size(0)
          if input.dim() == 3:
              multi_channel = True
              # input: (Batch, Nsample, Channels) -> (Batch * Channels, Nsample)
              input = input.transpose(1, 2).reshape(-1, input.size(1))
          else:
              multi_channel = False
  
          # NOTE(kamo):
          #   The default behaviour of torch.stft is compatible with librosa.stft
          #   about padding and scaling.
          #   Note that it's different from scipy.signal.stft
  
          # output: (Batch, Freq, Frames, 2=real_imag)
          # or (Batch, Channel, Freq, Frames, 2=real_imag)
          if self.window is not None:
              window_func = getattr(torch, f"{self.window}_window")
              window = window_func(
                  self.win_length, dtype=input.dtype, device=input.device
              )
          else:
              window = None
          output = torch.stft(
              input,
              n_fft=self.n_fft,
              win_length=self.win_length,
              hop_length=self.hop_length,
              center=self.center,
              window=window,
              normalized=self.normalized,
              onesided=self.onesided,
          )
          # output: (Batch, Freq, Frames, 2=real_imag)
          # -> (Batch, Frames, Freq, 2=real_imag)
          output = output.transpose(1, 2)
          if multi_channel:
              # output: (Batch * Channel, Frames, Freq, 2=real_imag)
              # -> (Batch, Frame, Channel, Freq, 2=real_imag)
              output = output.view(bs, -1, output.size(1), output.size(2), 2).transpose(
                  1, 2
              )
  
          if ilens is not None:
              if self.center:
                  pad = self.win_length // 2
                  ilens = ilens + 2 * pad
  
              olens = (ilens - self.win_length) // self.hop_length + 1
              output.masked_fill_(make_pad_mask(olens, output, 1), 0.0)
          else:
              olens = None
  
          return output, olens
  
      def inverse(
          self, input: Union[torch.Tensor, ComplexTensor], ilens: torch.Tensor = None
      ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
          """Inverse STFT.
  
          Args:
              input: Tensor(batch, T, F, 2) or ComplexTensor(batch, T, F)
              ilens: (batch,)
          Returns:
              wavs: (batch, samples)
              ilens: (batch,)
          """
          if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
              istft = torch.functional.istft
          else:
              try:
                  import torchaudio
              except ImportError:
                  raise ImportError(
                      "Please install torchaudio>=0.3.0 or use torch>=1.6.0"
                  )
  
              if not hasattr(torchaudio.functional, "istft"):
                  raise ImportError(
                      "Please install torchaudio>=0.3.0 or use torch>=1.6.0"
                  )
              istft = torchaudio.functional.istft
  
          if self.window is not None:
              window_func = getattr(torch, f"{self.window}_window")
              window = window_func(
                  self.win_length, dtype=input.dtype, device=input.device
              )
          else:
              window = None
  
          if isinstance(input, ComplexTensor):
              input = torch.stack([input.real, input.imag], dim=-1)
          assert input.shape[-1] == 2
          input = input.transpose(1, 2)
  
          wavs = istft(
              input,
              n_fft=self.n_fft,
              hop_length=self.hop_length,
              win_length=self.win_length,
              window=window,
              center=self.center,
              normalized=self.normalized,
              onesided=self.onesided,
              length=ilens.max() if ilens is not None else ilens,
          )
  
          return wavs, ilens
  