"""openwakeword wrapper for the Pi client.

Loads the stock `alexa` model. Each call to `predict()` takes a 1280-sample
16 kHz mono int16 frame (produced by `client.audio.resample_24k_to_16k`).
Returns True at most once per cooldown window.
"""
import time

import numpy as np

from client.log import get_logger

_log = get_logger("wakeword")

WAKEWORD = "alexa"
DEFAULT_THRESHOLD = 0.5
DEFAULT_COOLDOWN_S = 1.5


class WakewordDetector:
    def __init__(self, *, threshold: float = DEFAULT_THRESHOLD,
                 cooldown_s: float = DEFAULT_COOLDOWN_S) -> None:
        # Lazy-import openwakeword to keep the test suite importable without it.
        import openwakeword.utils
        from openwakeword.model import Model

        _log.info("downloading openwakeword models (idempotent)")
        # No-arg form dodges the None-handling crash documented in findings.md §4.
        openwakeword.utils.download_models()
        _log.info("loading openwakeword model %r", WAKEWORD)
        t0 = time.monotonic()
        self._model = Model(wakeword_models=[WAKEWORD], inference_framework="onnx")
        _log.info("openwakeword model loaded in %.2fs", time.monotonic() - t0)

        self._threshold = threshold
        self._cooldown_s = cooldown_s
        self._last_fired = 0.0

    def predict(self, frame_16k_int16: np.ndarray) -> bool:
        scores = self._model.predict(frame_16k_int16)
        score = float(scores.get(WAKEWORD, 0.0))
        rms = float(np.sqrt(np.mean(frame_16k_int16.astype(np.float32) ** 2)))
        now = time.monotonic()
        # Diagnostic: log any non-trivial score so we can see what's getting
        # close to firing.
        if score >= 0.1:
            _log.info("score=%.3f rms=%.0f", score, rms)
        if score >= self._threshold and (now - self._last_fired) >= self._cooldown_s:
            self._last_fired = now
            _log.info("wakeword fired score=%.3f rms=%.0f", score, rms)
            return True
        return False