text: import os.path
  from typing import Optional, Union
  
  import torch
  
  
  _TEST_DIR_PATH = os.path.realpath(os.path.join(os.path.dirname(__file__), ".."))
  
  
  def get_asset_path(*paths):
      """Return full path of a test asset"""
      return os.path.join(_TEST_DIR_PATH, "assets", *paths)
  
  
  def convert_tensor_encoding(
      tensor: torch.tensor,
      dtype: torch.dtype,
  ):
      """Convert input tensor with values between -1 and 1 to integer encoding
      Args:
          tensor: input tensor, assumed between -1 and 1
          dtype: desired output tensor dtype
      Returns:
          Tensor: shape of (n_channels, sample_rate * duration)
      """
      if dtype == torch.int32:
          tensor *= (tensor > 0) * 2147483647 + (tensor < 0) * 2147483648
      if dtype == torch.int16:
          tensor *= (tensor > 0) * 32767 + (tensor < 0) * 32768
      if dtype == torch.uint8:
          tensor *= (tensor > 0) * 127 + (tensor < 0) * 128
          tensor += 128
      tensor = tensor.to(dtype)
      return tensor
  
  
  def get_whitenoise(
      *,
      sample_rate: int = 16000,
      duration: float = 1,  # seconds
      n_channels: int = 1,
      seed: int = 0,
      dtype: Union[str, torch.dtype] = "float32",
      device: Union[str, torch.device] = "cpu",
      channels_first=True,
      scale_factor: float = 1,
  ):
      """Generate pseudo audio data with whitenoise
      Args:
          sample_rate: Sampling rate
          duration: Length of the resulting Tensor in seconds.
          n_channels: Number of channels
          seed: Seed value used for random number generation.
              Note that this function does not modify global random generator state.
          dtype: Torch dtype
          device: device
          channels_first: whether first dimension is n_channels
          scale_factor: scale the Tensor before clamping and quantization
      Returns:
          Tensor: shape of (n_channels, sample_rate * duration)
      """
      if isinstance(dtype, str):
          dtype = getattr(torch, dtype)
      if dtype not in [torch.float64, torch.float32, torch.int32, torch.int16, torch.uint8]:
          raise NotImplementedError(f"dtype {dtype} is not supported.")
      # According to the doc, folking rng on all CUDA devices is slow when there are many CUDA devices,
      # so we only fork on CPU, generate values and move the data to the given device
      with torch.random.fork_rng([]):
          torch.random.manual_seed(seed)
          tensor = torch.randn([n_channels, int(sample_rate * duration)], dtype=torch.float32, device="cpu")
      tensor /= 2.0
      tensor *= scale_factor
      tensor.clamp_(-1.0, 1.0)
      if not channels_first:
          tensor = tensor.t()
  
      tensor = tensor.to(device)
  
      return convert_tensor_encoding(tensor, dtype)
  
  
  def get_sinusoid(
      *,
      frequency: float = 300,
      sample_rate: int = 16000,
      duration: float = 1,  # seconds
      n_channels: int = 1,
      dtype: Union[str, torch.dtype] = "float32",
      device: Union[str, torch.device] = "cpu",
      channels_first: bool = True,
  ):
      """Generate pseudo audio data with sine wave.
  
      Args:
          frequency: Frequency of sine wave
          sample_rate: Sampling rate
          duration: Length of the resulting Tensor in seconds.
          n_channels: Number of channels
          dtype: Torch dtype
          device: device
  
      Returns:
          Tensor: shape of (n_channels, sample_rate * duration)
      """
      if isinstance(dtype, str):
          dtype = getattr(torch, dtype)
      pie2 = 2 * 3.141592653589793
      end = pie2 * frequency * duration
      num_frames = int(sample_rate * duration)
      # Randomize the initial phase. (except the first channel)
      theta0 = pie2 * torch.randn(n_channels, 1, dtype=torch.float32, device=device)
      theta0[0, :] = 0
      theta = torch.linspace(0, end, num_frames, dtype=torch.float32, device=device)
      theta = theta0 + theta
      tensor = torch.sin(theta, out=None)
      if not channels_first:
          tensor = tensor.t()
      return convert_tensor_encoding(tensor, dtype)
  
  
  def get_spectrogram(
      waveform,
      *,
      n_fft: int = 2048,
      hop_length: Optional[int] = None,
      win_length: Optional[int] = None,
      window: Optional[torch.Tensor] = None,
      center: bool = True,
      pad_mode: str = "reflect",
      power: Optional[float] = None,
  ):
      """Generate a spectrogram of the given Tensor
  
      Args:
          n_fft: The number of FFT bins.
          hop_length: Stride for sliding window. default: ``n_fft // 4``.
          win_length: The size of window frame and STFT filter. default: ``n_fft``.
          winwdow: Window function. default: Hann window
          center: Pad the input sequence if True. See ``torch.stft`` for the detail.
          pad_mode: Padding method used when center is True. Default: "reflect".
          power: If ``None``, raw spectrogram with complex values are returned,
              otherwise the norm of the spectrogram is returned.
      """
      hop_length = hop_length or n_fft // 4
      win_length = win_length or n_fft
      window = torch.hann_window(win_length, device=waveform.device) if window is None else window
      spec = torch.stft(
          waveform,
          n_fft=n_fft,
          hop_length=hop_length,
          win_length=win_length,
          center=center,
          window=window,
          pad_mode=pad_mode,
          return_complex=True,
      )
      if power is not None:
          spec = spec.abs() ** power
      return spec
  