from typing import Optional
import io
import warnings

import librosa
import librosa.display
import matplotlib.pyplot as plt
import numpy as np
import pyloudnorm as pyln
from matplotlib.patches import Patch
from PIL import Image

from constants import TARGET_LOUDNESS, TARGET_TP, VAD_OFF, VAD_ON

_WER_NORMALIZERS: dict[str, object] = {}


def get_vad_labels(vad_timestamps: list[list[float]], length: float) -> list[dict]:
    subtitles = []
    cur = 0.0

    for start, end in vad_timestamps:
        if start > cur:
            subtitles.append(
                {
                    "text": f"Voice Detection: {VAD_OFF}",
                    "timestamp": [cur, start],
                }
            )

        subtitles.append(
            {
                "text": f"Voice Detection: {VAD_ON}",
                "timestamp": [start, end],
            }
        )
        cur = end

    if cur < length:
        subtitles.append(
            {
                "text": f"Voice Detection: {VAD_OFF}",
                "timestamp": [cur, length],
            }
        )

    return subtitles


def to_gradio_audio(x: np.ndarray, sr: int) -> tuple[int, np.ndarray]:
    """Return (sample_rate, int16 array) for Gradio Audio."""
    x = np.asarray(x)
    x = np.squeeze(x)

    if x.ndim == 2 and x.shape[0] in (1, 2) and x.shape[1] > x.shape[0]:
        x = x.T

    if x.ndim == 2 and x.shape[1] == 1:
        x = x[:, 0]

    x = x.astype(np.float32)
    x = np.clip(x, -1.0, 1.0)
    x = (x * 32767).astype(np.int16)

    return sr, x


def _merge_vad_segments(
    vad_timestamps: list[list[float]],
    gap_tolerance: float = 0.05,
) -> list[tuple[float, float]]:
    if not vad_timestamps:
        return []

    segments = sorted((float(start), float(end)) for start, end in vad_timestamps)
    merged: list[tuple[float, float]] = [segments[0]]

    for start, end in segments[1:]:
        last_start, last_end = merged[-1]
        if start <= last_end + gap_tolerance:
            merged[-1] = (last_start, max(last_end, end))
        else:
            merged.append((start, end))

    return merged


def spec_image(
    audio_array: np.ndarray,
    sr: int,
    n_fft: int = 2048,
    hop_length: int = 512,
    n_mels: int = 128,
    fmax: Optional[float] = None,
    vad_timestamps: Optional[list[list[float]]] = None,
) -> Image.Image:
    y = np.asarray(audio_array, dtype=np.float32).flatten()

    S = librosa.feature.melspectrogram(
        y=y,
        sr=sr,
        n_fft=n_fft,
        hop_length=hop_length,
        n_mels=n_mels,
        fmax=fmax or sr // 2,
    )
    S_db = librosa.power_to_db(S, ref=np.max)

    fig, ax = plt.subplots(figsize=(8, 3), dpi=150)

    img = librosa.display.specshow(
        S_db,
        sr=sr,
        hop_length=hop_length,
        x_axis="time",
        y_axis="mel",
        cmap="magma",
        ax=ax,
    )

    if vad_timestamps:
        vad_color = "#22C55E"  # softer, cleaner green
        merged_segments = _merge_vad_segments(vad_timestamps, gap_tolerance=0.05)

        # Draw VAD bar as a fixed portion of the figure height (e.g., 4% of axes height)
        bar_height_axes = 0.05  # 2% of axes height
        bar_bottom_axes = 0.0  # 0% above the bottom

        for start, end in merged_segments:
            ax.fill_between(
                [start, end],
                [bar_bottom_axes, bar_bottom_axes],
                [bar_bottom_axes + bar_height_axes, bar_bottom_axes + bar_height_axes],
                color=vad_color,
                alpha=0.95,
                linewidth=0,
                zorder=5,
                transform=ax.get_xaxis_transform(), 
            )

        vad_patch = Patch(
            facecolor=vad_color,
            edgecolor=vad_color,
            label="Voice Activity",
        )
        ax.legend(
            handles=[vad_patch],
            loc="upper right",
            fontsize=8,
            frameon=True,
            framealpha=0.9,
        )

    cbar = fig.colorbar(img, ax=ax, format="%+2.0f dB")
    cbar.set_label("dB")

    ax.set_title("Mel-spectrogram")
    ax.set_xlabel("Time in s")
    ax.set_ylabel("Frequency in Hz")

    fig.tight_layout(pad=0.2)

    buf = io.BytesIO()
    fig.savefig(buf, format="png", bbox_inches="tight", pad_inches=0)
    plt.close(fig)

    buf.seek(0)
    return Image.open(buf).convert("RGB")


def _normalize_wer_text(text: str, normalizer: Optional[str] = "basic") -> str:
    """Normalize transcript text before WER tokenization."""
    if normalizer is None:
        return text.strip()

    if normalizer not in {"basic", "english"}:
        raise ValueError(
            f"Unsupported normalizer '{normalizer}'. Use 'basic', 'english', or None."
        )

    if normalizer == "english":
        if normalizer not in _WER_NORMALIZERS:
            from whisper_normalizer.english import EnglishTextNormalizer

            _WER_NORMALIZERS[normalizer] = EnglishTextNormalizer()
    else:
        if normalizer not in _WER_NORMALIZERS:
            from whisper_normalizer.basic import BasicTextNormalizer

            _WER_NORMALIZERS[normalizer] = BasicTextNormalizer()

    return _WER_NORMALIZERS[normalizer](text)


def compute_wer(
    reference: str, hypothesis: str, normalizer: Optional[str] = "english"
) -> float:
    """
    Compute Word Error Rate (WER) between reference and hypothesis transcripts.

    Text is normalized before tokenization. Supported normalizers are
    ``"basic"``, ``"english"``, or ``None`` to disable normalization.
    """
    ref_words = _normalize_wer_text(reference, normalizer=normalizer).split()
    hyp_words = _normalize_wer_text(hypothesis, normalizer=normalizer).split()

    d = np.zeros((len(ref_words) + 1, len(hyp_words) + 1), dtype=np.uint16)

    for i in range(len(ref_words) + 1):
        d[i][0] = i
    for j in range(len(hyp_words) + 1):
        d[0][j] = j

    for i in range(1, len(ref_words) + 1):
        for j in range(1, len(hyp_words) + 1):
            cost = 0 if ref_words[i - 1] == hyp_words[j - 1] else 1
            d[i][j] = min(
                d[i - 1][j] + 1,
                d[i][j - 1] + 1,
                d[i - 1][j - 1] + cost,
            )

    return d[len(ref_words)][len(hyp_words)] / max(len(ref_words), 1)


def measure_loudness(x: np.ndarray, sr: int) -> float:
    meter = pyln.Meter(sr)
    return float(meter.integrated_loudness(x))


def true_peak_limiter(
    x: np.ndarray,
    sr: int,
    max_true_peak: float = TARGET_TP,
) -> np.ndarray:
    upsampled_sr = 192000
    x_upsampled = librosa.resample(x, orig_sr=sr, target_sr=upsampled_sr)
    true_peak = np.max(np.abs(x_upsampled))

    if true_peak > 0:
        true_peak_db = 20 * np.log10(true_peak)
        if true_peak_db > max_true_peak:
            gain_db = max_true_peak - true_peak_db
            gain = 10 ** (gain_db / 20)
            x_upsampled = x_upsampled * gain

    x_limited = librosa.resample(x_upsampled, orig_sr=upsampled_sr, target_sr=sr)
    x_limited = librosa.util.fix_length(x_limited, size=x.shape[-1])
    return x_limited.astype(np.float32)


def normalize_lufs(x: np.ndarray, sr: int) -> np.ndarray:
    """
    Normalize audio to a fixed integrated loudness target and limit true peak.
    """
    try:
        current_lufs = measure_loudness(x, sr)

        if not np.isfinite(current_lufs):
            return x.astype(np.float32)

        gain_db = TARGET_LOUDNESS - current_lufs
        gain = 10 ** (gain_db / 20)

        y = x * gain
        y = true_peak_limiter(y, sr, max_true_peak=TARGET_TP)

        return y.astype(np.float32)
    except Exception as e:
        warnings.warn(f"LUFS normalization failed, returning input unchanged: {e}")
        return x.astype(np.float32)