"""Dependency-light audio normalization and log-mel feature extraction.

The frontend configuration is serialized next to every exported model. Keeping
one implementation for export validation and inference prevents a very common
failure mode: a correct ONNX graph fed subtly different features in production.
"""

from __future__ import annotations

from dataclasses import asdict, dataclass
from functools import lru_cache
from typing import Any


def _numpy() -> Any:
    try:
        import numpy as np
    except ImportError as exc:  # pragma: no cover - exercised in minimal installs
        raise RuntimeError("Audio inference requires numpy; install the base package") from exc
    return np


@dataclass(frozen=True, slots=True)
class FrontendConfig:
    sample_rate: int = 16_000
    max_seconds: float = 8.0
    n_fft: int = 400
    win_length: int = 400
    hop_length: int = 160
    n_mels: int = 80
    f_min: float = 0.0
    f_max: float = 8_000.0
    normalization: str = "whisper"
    pad_side: str = "left"

    def __post_init__(self) -> None:
        if self.sample_rate <= 0 or self.max_seconds <= 0:
            raise ValueError("sample_rate and max_seconds must be positive")
        if self.n_fft <= 0 or self.win_length <= 0 or self.hop_length <= 0:
            raise ValueError("FFT and window sizes must be positive")
        if self.win_length > self.n_fft:
            raise ValueError("win_length cannot exceed n_fft")
        if self.n_mels <= 0:
            raise ValueError("n_mels must be positive")
        if not 0.0 <= self.f_min < self.f_max <= self.sample_rate / 2:
            raise ValueError("mel frequency bounds must lie inside Nyquist")
        if self.normalization not in {"whisper", "log10", "none"}:
            raise ValueError("unsupported normalization")
        if self.pad_side not in {"left", "right"}:
            raise ValueError("pad_side must be 'left' or 'right'")

    @property
    def max_samples(self) -> int:
        return round(self.sample_rate * self.max_seconds)

    @property
    def target_frames(self) -> int:
        return self.max_samples // self.hop_length

    def to_dict(self) -> dict[str, Any]:
        return asdict(self)


def normalize_waveform(audio: Any) -> Any:
    """Convert mono/stereo integer/float audio to finite mono float32 in [-1, 1]."""

    np = _numpy()
    samples = np.asarray(audio)
    if samples.size == 0:
        raise ValueError("audio cannot be empty")
    original_dtype = samples.dtype
    if samples.ndim == 2:
        # Gradio commonly returns [samples, channels]; accept [channels, samples] too.
        channel_axis = 1 if samples.shape[1] <= 8 else 0
        samples = samples.astype(np.float32).mean(axis=channel_axis)
    elif samples.ndim != 1:
        raise ValueError(f"expected mono/stereo audio, got shape {samples.shape}")
    if np.issubdtype(original_dtype, np.integer):
        info = np.iinfo(original_dtype)
        scale = float(max(abs(info.min), info.max))
        samples = samples.astype(np.float32) / scale
    else:
        samples = samples.astype(np.float32, copy=False)
    samples = np.nan_to_num(samples, nan=0.0, posinf=1.0, neginf=-1.0)
    peak = float(np.max(np.abs(samples)))
    if peak > 1.0:
        samples = samples / peak
    return np.clip(samples, -1.0, 1.0)


def resample_waveform(audio: Any, source_rate: int, target_rate: int) -> Any:
    """Resample with the same deterministic linear rule used during training.

    Optional packages must not change model inputs, so this deliberately avoids
    a SciPy-dependent branch.  Higher-quality telephony resampling can happen
    upstream, but training and serving always agree at this boundary.
    """

    np = _numpy()
    if source_rate <= 0 or target_rate <= 0:
        raise ValueError("sample rates must be positive")
    samples = normalize_waveform(audio)
    if source_rate == target_rate:
        return samples
    output_length = max(1, round(len(samples) * target_rate / source_rate))
    old_x = np.linspace(0.0, 1.0, len(samples), endpoint=False)
    new_x = np.linspace(0.0, 1.0, output_length, endpoint=False)
    return np.interp(new_x, old_x, samples).astype(np.float32)


def pad_or_trim(audio: Any, config: FrontendConfig) -> tuple[Any, int]:
    """Return fixed-length audio and number of genuine (non-padding) samples."""

    np = _numpy()
    samples = normalize_waveform(audio)
    if len(samples) >= config.max_samples:
        return samples[-config.max_samples :].copy(), config.max_samples
    pad = config.max_samples - len(samples)
    widths = (pad, 0) if config.pad_side == "left" else (0, pad)
    return np.pad(samples, widths).astype(np.float32), len(samples)


def _hz_to_mel(value: Any) -> Any:
    np = _numpy()
    return 2595.0 * np.log10(1.0 + np.asarray(value) / 700.0)


def _mel_to_hz(value: Any) -> Any:
    np = _numpy()
    return 700.0 * (10.0 ** (np.asarray(value) / 2595.0) - 1.0)


@lru_cache(maxsize=16)
def mel_filterbank(config: FrontendConfig) -> Any:
    """Create a deterministic triangular mel filter bank."""

    np = _numpy()
    mel_points = np.linspace(_hz_to_mel(config.f_min), _hz_to_mel(config.f_max), config.n_mels + 2)
    hz_points = _mel_to_hz(mel_points)
    fft_hz = np.linspace(0.0, config.sample_rate / 2, config.n_fft // 2 + 1)
    filters = np.zeros((config.n_mels, len(fft_hz)), dtype=np.float32)
    for index in range(config.n_mels):
        left, center, right = hz_points[index : index + 3]
        filters[index] = np.maximum(
            0.0,
            np.minimum(
                (fft_hz - left) / max(center - left, 1e-12),
                (right - fft_hz) / max(right - center, 1e-12),
            ),
        )
    # Area normalization reduces frequency-dependent scale drift.
    enorm = 2.0 / np.maximum(hz_points[2 : config.n_mels + 2] - hz_points[: config.n_mels], 1e-12)
    result = filters * enorm[:, None]
    result.flags.writeable = False
    return result


@lru_cache(maxsize=16)
def _hann_window(length: int) -> Any:
    np = _numpy()
    window = np.hanning(length).astype(np.float32)
    window.flags.writeable = False
    return window


def log_mel_spectrogram(
    audio: Any,
    source_rate: int,
    config: FrontendConfig | None = None,
) -> tuple[Any, Any]:
    """Return ``[n_mels, frames]`` features and a valid-frame mask.

    The implementation follows Whisper's centered-STFT and dynamic-range
    normalization convention closely, while remaining free of torch/librosa at
    inference time. Export parity tests compare it against the training path.
    """

    np = _numpy()
    cfg = config or FrontendConfig()
    if source_rate <= 0:
        raise ValueError("source sample rate must be positive")
    # Bound work before resampling: an uploaded meeting can be hours long, while
    # endpoint intent uses only the configured suffix. Training uses the same rule.
    normalized = normalize_waveform(audio)
    source_suffix_samples = max(1, round(cfg.max_seconds * source_rate))
    normalized = normalized[-source_suffix_samples:]
    resampled = resample_waveform(normalized, source_rate, cfg.sample_rate)
    fixed, valid_samples = pad_or_trim(resampled, cfg)

    pad = cfg.n_fft // 2
    padded = np.pad(fixed, (pad, pad), mode="reflect")
    frames = np.lib.stride_tricks.sliding_window_view(padded, cfg.win_length)[:: cfg.hop_length]
    frames = frames[: cfg.target_frames]
    window = _hann_window(cfg.win_length)
    spectrum = np.fft.rfft(frames * window[None, :], n=cfg.n_fft, axis=1)
    power = (spectrum.real**2 + spectrum.imag**2).astype(np.float32)
    mel = np.maximum(mel_filterbank(cfg) @ power.T, 1e-10)
    features = np.log10(mel)
    if cfg.normalization == "whisper":
        features = np.maximum(features, float(features.max()) - 8.0)
        features = (features + 4.0) / 4.0
    elif cfg.normalization == "none":
        features = mel

    frame_mask = np.zeros(cfg.target_frames, dtype=np.float32)
    valid_frames = min(
        cfg.target_frames, max(1, (valid_samples + cfg.hop_length - 1) // cfg.hop_length)
    )
    if cfg.pad_side == "left":
        frame_mask[-valid_frames:] = 1.0
    else:
        frame_mask[:valid_frames] = 1.0
    return features.astype(np.float32), frame_mask