"""Torch-native waveform to log-mel feature extraction.

The frontend intentionally avoids torchaudio so the deployable student has one
fewer binary dependency.  Exported production models normally accept log-mel
features; keeping this implementation in the repository gives training, demo,
and parity tests one canonical preprocessing contract.
"""

from __future__ import annotations

from collections.abc import Mapping
from dataclasses import dataclass

import torch
import torch.nn.functional as F
from torch import Tensor, nn


@dataclass(frozen=True)
class LogMelConfig:
    sample_rate: int = 16_000
    n_fft: int = 400
    hop_length: int = 160
    win_length: int = 400
    n_mels: int = 80
    f_min: float = 0.0
    f_max: float | None = 8_000.0
    log_floor: float = 1e-10
    normalize: bool = True
    mel_scale: str = "htk"
    log_scale: str = "standard"
    center: bool = False
    drop_last_frame: bool = False
    pad_side: str = "right"

    @classmethod
    def from_mapping(cls, values: Mapping[str, object]) -> LogMelConfig:
        known = {field.name for field in cls.__dataclass_fields__.values()}
        return cls(**{key: value for key, value in values.items() if key in known})  # type: ignore[arg-type]


def _hz_to_mel(freq: Tensor) -> Tensor:
    # HTK mel convention.  It is stable, simple, and matches common speech
    # frontends closely enough for a model trained with the same extractor.
    return 2595.0 * torch.log10(1.0 + freq / 700.0)


def _mel_to_hz(mels: Tensor) -> Tensor:
    return 700.0 * (torch.pow(10.0, mels / 2595.0) - 1.0)


def _hz_to_slaney_mel(freq: Tensor) -> Tensor:
    linear_spacing = 200.0 / 3.0
    min_log_hz = 1_000.0
    min_log_mel = min_log_hz / linear_spacing
    log_step = torch.log(torch.tensor(6.4, dtype=freq.dtype, device=freq.device)) / 27.0
    linear = freq / linear_spacing
    logarithmic = min_log_mel + torch.log((freq / min_log_hz).clamp_min(1e-12)) / log_step
    return torch.where(freq >= min_log_hz, logarithmic, linear)


def _slaney_mel_to_hz(mels: Tensor) -> Tensor:
    linear_spacing = 200.0 / 3.0
    min_log_hz = 1_000.0
    min_log_mel = min_log_hz / linear_spacing
    log_step = torch.log(torch.tensor(6.4, dtype=mels.dtype, device=mels.device)) / 27.0
    linear = mels * linear_spacing
    logarithmic = min_log_hz * torch.exp(log_step * (mels - min_log_mel))
    return torch.where(mels >= min_log_mel, logarithmic, linear)


def make_mel_filterbank(config: LogMelConfig) -> Tensor:
    """Create a ``[n_mels, n_fft // 2 + 1]`` triangular filterbank."""

    max_hz = float(config.sample_rate / 2 if config.f_max is None else config.f_max)
    if not 0.0 <= config.f_min < max_hz <= config.sample_rate / 2:
        raise ValueError("expected 0 <= f_min < f_max <= sample_rate / 2")

    fft_freqs = torch.linspace(0.0, config.sample_rate / 2, config.n_fft // 2 + 1)
    if config.mel_scale == "htk":
        to_mel, to_hz = _hz_to_mel, _mel_to_hz
    elif config.mel_scale == "slaney":
        to_mel, to_hz = _hz_to_slaney_mel, _slaney_mel_to_hz
    else:
        raise ValueError("mel_scale must be 'htk' or 'slaney'")
    mel_edges = torch.linspace(
        to_mel(torch.tensor(float(config.f_min))),
        to_mel(torch.tensor(max_hz)),
        config.n_mels + 2,
    )
    hz_edges = to_hz(mel_edges)
    lower = hz_edges[:-2, None]
    center = hz_edges[1:-1, None]
    upper = hz_edges[2:, None]
    rising = (fft_freqs[None, :] - lower) / (center - lower).clamp_min(1e-12)
    falling = (upper - fft_freqs[None, :]) / (upper - center).clamp_min(1e-12)
    filters = torch.minimum(rising, falling).clamp_min(0.0)

    # Area normalization reduces sensitivity to mel-band width.
    enorm = 2.0 / (upper - lower).clamp_min(1e-12)
    return filters * enorm


class LogMelFrontend(nn.Module):
    """Convert padded mono waveforms to normalized log-mel features.

    Parameters
    ----------
    waveforms:
        Float tensor shaped ``[batch, samples]`` (or ``[samples]``).
    lengths:
        Optional valid sample counts.  The returned mask is ``[batch, frames]``.
    """

    def __init__(self, config: LogMelConfig | None = None) -> None:
        super().__init__()
        config = config or LogMelConfig()
        self.config = config
        if config.pad_side not in {"left", "right"}:
            raise ValueError("pad_side must be 'left' or 'right'")
        # numpy.hanning in the dependency-light runtime uses a symmetric window.
        self.register_buffer(
            "window", torch.hann_window(config.win_length, periodic=False), persistent=False
        )
        self.register_buffer("mel_filters", make_mel_filterbank(config), persistent=True)

    def forward(self, waveforms: Tensor, lengths: Tensor | None = None) -> tuple[Tensor, Tensor]:
        if waveforms.ndim == 1:
            waveforms = waveforms.unsqueeze(0)
        if waveforms.ndim != 2:
            raise ValueError("waveforms must have shape [batch, samples]")

        batch, original_samples = waveforms.shape
        if lengths is None:
            lengths = torch.full(
                (batch,), original_samples, dtype=torch.long, device=waveforms.device
            )
        else:
            lengths = lengths.to(device=waveforms.device, dtype=torch.long).clamp(
                min=0, max=original_samples
            )
        if original_samples < self.config.n_fft:
            waveforms = F.pad(waveforms, (0, self.config.n_fft - original_samples))

        spectrum = torch.stft(
            waveforms,
            n_fft=self.config.n_fft,
            hop_length=self.config.hop_length,
            win_length=self.config.win_length,
            window=self.window.to(dtype=waveforms.dtype),
            center=self.config.center,
            return_complex=True,
        )
        if self.config.drop_last_frame:
            spectrum = spectrum[..., :-1]
        power = spectrum.abs().square()
        mel = torch.matmul(self.mel_filters.to(dtype=power.dtype), power)
        log_mel = torch.log10(mel.clamp_min(self.config.log_floor))
        if self.config.log_scale == "whisper":
            dynamic_floor = log_mel.amax(dim=(-2, -1), keepdim=True) - 8.0
            log_mel = torch.maximum(log_mel, dynamic_floor)
            log_mel = (log_mel + 4.0) / 4.0
        elif self.config.log_scale != "standard":
            raise ValueError("log_scale must be 'standard' or 'whisper'")

        if self.config.center:
            if self.config.drop_last_frame:
                frame_lengths = torch.div(
                    lengths + self.config.hop_length - 1,
                    self.config.hop_length,
                    rounding_mode="floor",
                )
            else:
                frame_lengths = 1 + torch.div(
                    lengths, self.config.hop_length, rounding_mode="floor"
                )
        else:
            padded_lengths = lengths.clamp_min(self.config.n_fft)
            frame_lengths = 1 + torch.div(
                padded_lengths - self.config.n_fft,
                self.config.hop_length,
                rounding_mode="floor",
            )
        if self.config.drop_last_frame and not self.config.center:
            frame_lengths = (frame_lengths - 1).clamp_min(0)
        frame_lengths = torch.where(lengths > 0, frame_lengths, torch.zeros_like(frame_lengths))
        frame_lengths = frame_lengths.clamp(max=log_mel.shape[-1])
        positions = torch.arange(log_mel.shape[-1], device=waveforms.device)
        if self.config.pad_side == "left":
            mask = positions.unsqueeze(0) >= (log_mel.shape[-1] - frame_lengths).unsqueeze(1)
        else:
            mask = positions.unsqueeze(0) < frame_lengths.unsqueeze(1)

        if self.config.normalize:
            valid = mask.unsqueeze(1).to(log_mel.dtype)
            denominator = valid.sum(dim=-1, keepdim=True).clamp_min(1.0)
            mean = (log_mel * valid).sum(dim=-1, keepdim=True) / denominator
            variance = ((log_mel - mean).square() * valid).sum(dim=-1, keepdim=True)
            variance = variance / denominator
            log_mel = (log_mel - mean) * torch.rsqrt(variance + 1e-5)
            log_mel = log_mel * valid

        return log_mel, mask