#!/usr/bin/env python3
import argparse
import json
import sys
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple

import torch
import torchaudio
from torchaudio import load as ta_load
from torchaudio.functional import resample as ta_resample
from zcodec.models import WavVAE

# -------------------------
# Data structures
# -------------------------


@dataclass
class WavVaeSpec:
    name: str
    wavvae_dir: str


# -------------------------
# Utilities
# -------------------------


def load_json_if_exists(path: Path) -> Optional[Dict[str, Any]]:
    if path.is_file():
        try:
            return json.load(path.open("r", encoding="utf-8"))
        except Exception:
            return None
    return None


def read_config_any(checkpoint_dir: str) -> Dict[str, Any]:
    cand = [
        Path(checkpoint_dir) / "config.json",
        Path(checkpoint_dir) / "model_config.json",
        Path(checkpoint_dir) / "config.yaml",  # shown as path only
    ]
    for p in cand:
        if p.exists():
            if p.suffix == ".json":
                j = load_json_if_exists(p)
                if j is not None:
                    return j
            else:
                return {"_config_file": str(p)}
    return {}


def sanitize_name(s: str) -> str:
    return "".join(c if c.isalnum() or c in "-_." else "_" for c in s)


def ensure_mono_and_resample(
    wav: torch.Tensor, sr: int, target_sr: int
) -> Tuple[torch.Tensor, int]:
    if wav.ndim != 2:
        raise ValueError(f"Expected (C,T), got {tuple(wav.shape)}")
    if wav.size(0) > 1:
        wav = wav.mean(dim=0, keepdim=True)
    if sr != target_sr:
        wav = ta_resample(wav, sr, target_sr)
        sr = target_sr
    return wav.to(torch.float32), sr


def save_wav(path: Path, wav: torch.Tensor, sr: int):
    path.parent.mkdir(parents=True, exist_ok=True)
    if wav.ndim == 1:
        wav = wav.unsqueeze(0)
    wav = wav.clamp(-1, 1).contiguous().cpu()
    torchaudio.save(
        str(path), wav, sample_rate=sr, encoding="PCM_S", bits_per_sample=16
    )


def read_audio_manifest(txt_path: str) -> List[Path]:
    lines = Path(txt_path).read_text(encoding="utf-8").splitlines()
    files = [
        Path(l.strip()) for l in lines if l.strip() and not l.strip().startswith("#")
    ]
    return files


def html_escape(s: str) -> str:
    return (
        s.replace("&", "&")
        .replace("<", "&lt;")
        .replace(">", "&gt;")
        .replace('"', "&quot;")
        .replace("'", "&#39;")
    )


def make_html(
    output_dir: Path,
    audio_files: List[Path],
    specs: List[WavVaeSpec],
    sr_by_model: Dict[str, int],
    wavvae_cfg: Dict[str, Dict[str, Any]],
) -> str:
    def player(src_rel: str) -> str:
        return f'<audio controls preload="none" src="{html_escape(src_rel)}"></audio>'

    # cards
    cards = []
    for s in specs:
        cfg = wavvae_cfg.get(s.name, {})
        cfg_short = json.dumps(cfg if cfg else {"_": "no JSON config found"}, indent=2)[
            :1200
        ]
        card = f"""
        <div class="model-card">
          <h3>{html_escape(s.name)}</h3>
          <p><b>Sample rate</b>: {sr_by_model.get(s.name, "N/A")} Hz</p>
          <details><summary>WavVAE config</summary><pre>{html_escape(cfg_short)}</pre></details>
        </div>
        """
        cards.append(card)

    css = """
    body { font-family: system-ui, -apple-system, Segoe UI, Roboto, Helvetica, Arial, sans-serif; padding: 20px; }
    .cards { display: grid; grid-template-columns: repeat(auto-fill, minmax(320px, 1fr)); gap: 12px; margin-bottom: 18px; }
    .model-card { border: 1px solid #ddd; border-radius: 12px; padding: 12px; }
    table { border-collapse: collapse; width: 100%; }
    th, td { border: 1px solid #eee; padding: 8px; vertical-align: top; }
    th { background: #fafafa; position: sticky; top: 0; }
    audio { width: 260px; }
    """

    th = "<th>Input</th><th>Original</th>" + "".join(
        f"<th>{html_escape(s.name)}</th>" for s in specs
    )
    rows = []
    for af in audio_files:
        base = af.stem
        orig_rel = f"original/{html_escape(af.name)}"
        tds = [f"<td>{html_escape(base)}</td>", f"<td>{player(orig_rel)}</td>"]
        for s in specs:
            rec_rel = f"recon/{html_escape(s.name)}/{html_escape(base)}.wav"
            tds.append(f"<td>{player(rec_rel)}</td>")
        rows.append("<tr>" + "".join(tds) + "</tr>")

    html = f"""<!doctype html>
<html>
  <head><meta charset="utf-8"/><title>WavVAE Comparison</title><style>{css}</style></head>
  <body>
    <h1>WavVAE Comparison</h1>
    <div class="cards">{"".join(cards)}</div>
    <table>
      <thead><tr>{th}</tr></thead>
      <tbody>{"".join(rows)}</tbody>
    </table>
  </body>
</html>
"""
    out = output_dir / "index.html"
    out.write_text(html, encoding="utf-8")
    return str(out)


# -------------------------
# Core
# -------------------------


@torch.inference_mode()
def reconstruct_wavvae(
    wav_mono: torch.Tensor, wavvae: WavVAE, device: str
) -> torch.Tensor:
    x = wav_mono.to(device)  # (1,T)
    z = wavvae.encode(x)
    wav_hat = wavvae.decode(z)  # (1,1,T)
    return wav_hat.squeeze(0).squeeze(0).detach()


def parse_models_manifest(path: str) -> List[WavVaeSpec]:
    """
    JSON list of:
    {"name": "...", "wavvae": "/path/to/WavVAE_dir"}
    """
    raw = json.loads(Path(path).read_text(encoding="utf-8"))
    specs = []
    for it in raw:
        specs.append(WavVaeSpec(name=it["name"], wavvae_dir=it["wavvae"]))
    return specs


def main():
    ap = argparse.ArgumentParser(
        description="Compare WavVAE checkpoints and generate a static HTML page."
    )
    ap.add_argument("--models", required=True, help="JSON manifest of WavVAE models.")
    ap.add_argument(
        "--audio_manifest", required=True, help="TXT file: one audio path per line."
    )
    ap.add_argument("--out", default="compare_wavvae_out")
    ap.add_argument("--device", default="cuda")
    ap.add_argument("--force", action="store_true")
    args = ap.parse_args()

    device = "cuda" if args.device == "cuda" and torch.cuda.is_available() else "cpu"
    out_dir = Path(args.out)
    (out_dir / "original").mkdir(parents=True, exist_ok=True)
    recon_dir = out_dir / "recon"
    recon_dir.mkdir(parents=True, exist_ok=True)

    specs = parse_models_manifest(args.models)
    if not specs:
        print("No models.", file=sys.stderr)
        sys.exit(1)

    # load models
    wavvae_by_name: Dict[str, WavVAE] = {}
    sr_by_model: Dict[str, int] = {}
    wavvae_cfg: Dict[str, Dict[str, Any]] = {}
    for s in specs:
        print(f"[Load] {s.name}")
        w = WavVAE.from_pretrained_local(s.wavvae_dir).to(device)
        wavvae_by_name[s.name] = w
        sr_by_model[s.name] = int(getattr(w, "sampling_rate", 24000))
        wavvae_cfg[s.name] = read_config_any(s.wavvae_dir)

    audio_paths = read_audio_manifest(args.audio_manifest)
    # normalize originals to wav+mono (browser-friendly); keep native sr for original column
    actual_audio = []
    for ap in audio_paths:
        if not ap.exists():
            print(f"[Skip missing] {ap}", file=sys.stderr)
            continue
        wav, sr = ta_load(str(ap))
        wav_mono, sr = ensure_mono_and_resample(wav, sr, sr)
        out_orig = out_dir / "original" / (ap.stem + ".wav")
        if args.force or not out_orig.exists():
            save_wav(out_orig, wav_mono, sr)
        actual_audio.append(out_orig)

    # recon per model
    for out_orig in actual_audio:
        wav0, sr0 = ta_load(str(out_orig))
        if wav0.size(0) > 1:
            wav0 = wav0.mean(dim=0, keepdim=True)
        for s in specs:
            target_sr = sr_by_model[s.name]
            wav_in = ta_resample(wav0, sr0, target_sr) if sr0 != target_sr else wav0
            out_path = recon_dir / s.name / f"{sanitize_name(out_orig.stem)}.wav"
            if args.force or not out_path.exists():
                print(f"[Reconstruct] {s.name} ← {out_orig.name}")
                wav_hat = reconstruct_wavvae(wav_in, wavvae_by_name[s.name], device)
                save_wav(out_path, wav_hat, target_sr)

    html_path = make_html(out_dir, actual_audio, specs, sr_by_model, wavvae_cfg)
    print(f"Done. Open: {html_path}")


if __name__ == "__main__":
    main()