"""
Shared waveform cache — avoids re-reading and re-decoding large WAV files
when the same file is loaded multiple times (e.g. sync_audio_test → verify).
"""

from __future__ import annotations
import os
import numpy as np

_cache: dict[tuple[str, int], np.ndarray] = {}


def cached_load_wav(path: str, target_sr: int = 48000) -> tuple[np.ndarray, int]:
    """Load a WAV file with caching. Returns (audio_array, sample_rate)."""
    real_path = os.path.realpath(path)
    key = (real_path, target_sr)
    if key in _cache:
        print(f"  (cached: {os.path.basename(path)})")
        return _cache[key].copy(), target_sr

    # Import here to avoid circular imports
    from sync_audio import load_wav
    data, sr = load_wav(path, target_sr=target_sr)
    _cache[key] = data
    return data, sr


def clear_cache():
    _cache.clear()