from pathlib import Path
from typing import List, Dict

def diarize_with_resemblyzer(wav_path: Path, window_s: float = 1.5, hop_s: float = 0.75, distance_threshold: float = 0.6) -> List[Dict]:
    """Lightweight embedding-based diarization using resemblyzer + sklearn.

    Returns list of segments: {'start': float, 'end': float, 'speaker': 'spk_N'}
    """
    try:
        import numpy as np
        import librosa
        from resemblyzer import VoiceEncoder
        from sklearn.cluster import AgglomerativeClustering
    except Exception as e:
        raise RuntimeError(f"Missing dependency for embedding diarization: {e}")

    sr = 16000
    wav, sr_loaded = librosa.load(str(wav_path), sr=sr)
    n = wav.shape[0]
    win = int(window_s * sr)
    hop = int(hop_s * sr)
    if n <= win:
        # short file: embed whole
        encoder = VoiceEncoder()
        emb = encoder.embed_utterance(wav)
        labels = [0]
        centers = [ (0.0 + n / sr) / 2.0 ]
        windows = [(0.0, n / sr)]
    else:
        encoder = VoiceEncoder()
        embeddings = []
        centers = []
        windows = []
        for start in range(0, n - win + 1, hop):
            chunk = wav[start:start+win]
            try:
                e = encoder.embed_utterance(chunk)
            except Exception:
                # fallback: mean pooling
                e = np.mean(chunk)
            embeddings.append(e)
            t0 = start / sr
            t1 = (start + win) / sr
            centers.append((t0 + t1) / 2.0)
            windows.append((t0, t1))

        X = np.vstack(embeddings)
        # Agglomerative clustering with distance threshold
        model = AgglomerativeClustering(n_clusters=None, distance_threshold=distance_threshold, affinity='euclidean', linkage='average')
        labels = model.fit_predict(X)

    # merge consecutive windows with same label into segments
    segs = []
    if len(labels) == 0:
        return segs
    cur_label = labels[0]
    cur_start = windows[0][0]
    cur_end = windows[0][1]
    for i, lab in enumerate(labels[1:], start=1):
        if lab == cur_label:
            cur_end = windows[i][1]
        else:
            segs.append({"start": cur_start, "end": cur_end, "speaker": f"spk_{int(cur_label)+1}"})
            cur_label = lab
            cur_start = windows[i][0]
            cur_end = windows[i][1]
    segs.append({"start": cur_start, "end": cur_end, "speaker": f"spk_{int(cur_label)+1}"})

    return segs