"""Canonical waveform preprocessing, extracted without numerical changes."""
import numpy as np
import torch
from scipy import signal

def prepare_staqa_waveform(
    wav: np.ndarray,
    sample_rate: int,
    *,
    target_sample_rate: int = 32_000,
    target_length: int = 320_000,
    normalize: bool = True,
) -> torch.Tensor:
    """Return the canonical two-channel, fixed-length StAQA waveform."""

    if (
        isinstance(sample_rate, bool)
        or not isinstance(sample_rate, int)
        or sample_rate <= 0
    ):
        raise ValueError("sample_rate must be a positive integer")
    if target_sample_rate <= 0:
        raise ValueError("target_sample_rate must be positive")
    if target_length <= 0:
        raise ValueError("target_length must be positive")

    waveform = np.asarray(wav).T.astype(np.float32)
    if waveform.ndim != 2:
        raise ValueError(
            "StAQA waveform must be [samples, channels], "
            f"got shape {tuple(np.asarray(wav).shape)}"
        )
    if waveform.shape[0] == 1:
        waveform = np.repeat(waveform, 2, axis=0)
    elif waveform.shape[0] > 2:
        waveform = waveform[:2]

    if int(sample_rate) != int(target_sample_rate):
        waveform = signal.resample_poly(
            waveform,
            int(target_sample_rate),
            int(sample_rate),
            axis=-1,
        ).astype(np.float32)

    if normalize:
        for channel in range(waveform.shape[0]):
            rms = float(np.sqrt(np.mean(waveform[channel] ** 2)))
            if rms > 0:
                waveform[channel] = waveform[channel] * 10 ** (
                    (-14.0 - 20 * np.log10(rms)) / 20
                )

    if waveform.shape[1] >= target_length:
        waveform = waveform[:, :target_length]
    else:
        pad = np.zeros(
            (waveform.shape[0], target_length - waveform.shape[1]),
            dtype=np.float32,
        )
        waveform = np.concatenate([waveform, pad], axis=1)
    return torch.from_numpy(waveform)