"""
Small helpers shared across backends (no heavy imports).
"""

from __future__ import annotations

from pathlib import Path

import numpy as np


def safe_pad_audio(audio: np.ndarray, multiple: int = 1600) -> np.ndarray:
    """
    Right-pad a 1D float32 waveform with zeros so its length is a multiple of `multiple`.

    Several model preprocessors (e.g. Moonshine's `view(B, -1, 80)` step) require the input
    waveform length to divide a small chunk size. Trailing silence is harmless for ASR but
    avoids silent crashes like ``shape '[1, -1, 80]' is invalid for input of size N``.

    Default `multiple=1600` = 100 ms at 16 kHz, divisible by all common ASR strides
    (80, 160, 320, 400, 800).
    """
    arr = np.asarray(audio, dtype=np.float32).reshape(-1)
    if multiple <= 1:
        return arr
    rem = arr.size % multiple
    if rem == 0:
        return arr
    pad = multiple - rem
    return np.concatenate([arr, np.zeros(pad, dtype=np.float32)])


def load_wav_mono(path: str | Path, sampling_rate: int = 16000) -> np.ndarray:
    """
    Load a WAV file as a 1-D float32 mono waveform at ``sampling_rate`` Hz.

    Uses ``soundfile`` only (no torchcodec / FFmpeg). Eval samples and the
    custom ``evaluate(Path)`` hook are written as 16 kHz PCM WAVs.
    """
    import soundfile as sf

    audio, sr = sf.read(str(path), dtype="float32", always_2d=True)
    audio = audio.mean(axis=1)
    if int(sr) != int(sampling_rate):
        try:
            import librosa

            audio = librosa.resample(
                audio, orig_sr=int(sr), target_sr=int(sampling_rate)
            )
        except Exception as exc:
            raise RuntimeError(
                f"Audio is {sr} Hz but {sampling_rate} Hz was requested; "
                "install librosa for resampling."
            ) from exc
    return np.asarray(audio, dtype=np.float32).reshape(-1)