"""Convert a5_user_data.json -> per-user txt, then plot token length distribution.

Usage:
    python /mnt/train-gui-agent/zhangxinyuan/data_mem/output/export_and_plot_lengths.py
"""

import json
import os
import glob
import statistics

# ====== Config ======
A5_PATH = "/mnt/train-gui-agent/zhangxinyuan/data_mem/output/a5_user_data.json"
USER_TEXT_DIR = "/mnt/train-gui-agent/zhangxinyuan/data_mem/output/user_text"
MODEL_PATH = "/mnt/train-gui-agent/zhangzeyu/models/Qwen3-8B"
OUT_DIR = "/mnt/train-gui-agent/zhangxinyuan/data_mem/output/eval"


def format_session(session):
    """Format a single session as plain text."""
    lines = [f"[Session: {session.get('timestamp', 'unknown')}]"]
    for turn in session.get("turns", []):
        content = turn.get("content")
        if not content:
            continue
        role = turn.get("role", "unknown")
        # Fix corrupted roles (e.g. "assistantWinvalid" -> "Assistant")
        if role.startswith("assistant"):
            role = "Assistant"
        elif role.startswith("user"):
            role = "User"
        else:
            role = role.capitalize()
        lines.append(f"{role}: {content}")
    return "\n".join(lines)


def step1_export_txt():
    """a5_user_data.json -> user_text/{user_id}.txt"""
    print(f"[Step 1] Loading {A5_PATH}")
    with open(A5_PATH, "r", encoding="utf-8") as f:
        data = json.load(f)
    print(f"  {len(data)} users")

    os.makedirs(USER_TEXT_DIR, exist_ok=True)
    count = 0
    for user in data:
        user_id = user["user_id"]
        sessions = user.get("all_sessions", [])
        if not sessions:
            continue

        sessions_sorted = sorted(sessions, key=lambda s: s.get("timestamp", ""))
        full_text = "\n\n".join(format_session(s) for s in sessions_sorted)

        out_path = os.path.join(USER_TEXT_DIR, f"{user_id}.txt")
        with open(out_path, "w", encoding="utf-8") as f:
            f.write(full_text)
        count += 1

    print(f"  Exported {count} user txt files -> {USER_TEXT_DIR}/")
    return count


def step2_token_stats():
    """Tokenize all txt files and plot distribution."""
    from transformers import AutoTokenizer
    import matplotlib
    matplotlib.use("Agg")
    import matplotlib.pyplot as plt

    print(f"\n[Step 2] Loading tokenizer from {MODEL_PATH}")
    tok = AutoTokenizer.from_pretrained(MODEL_PATH)

    files = sorted(glob.glob(os.path.join(USER_TEXT_DIR, "*.txt")))
    print(f"  Tokenizing {len(files)} files...")

    counts = []
    for f in files:
        txt = open(f, encoding="utf-8").read()
        counts.append(len(tok.encode(txt, add_special_tokens=False)))

    # Stats
    s = sorted(counts)
    n = len(s)
    def pct(p):
        return s[min(n - 1, int(p / 100 * n))]

    stats = {
        "n": n,
        "min": s[0],
        "max": s[-1],
        "mean": round(statistics.mean(s), 1),
        "median": s[n // 2],
        "p10": pct(10),
        "p25": pct(25),
        "p75": pct(75),
        "p90": pct(90),
        "p95": pct(95),
        "std": round(statistics.pstdev(s), 1),
    }

    print(f"\n  === Token Length Stats ===")
    for k, v in stats.items():
        print(f"    {k}: {v:,}" if isinstance(v, int) else f"    {k}: {v}")

    # Plot
    os.makedirs(OUT_DIR, exist_ok=True)

    fig, ax = plt.subplots(figsize=(10, 5))
    ax.hist(counts, bins=40, color="#4C72B0", edgecolor="white", alpha=0.85)
    ax.axvline(stats["median"], color="black", ls="--", lw=1.5,
               label=f"median={stats['median']:,}")
    ax.axvline(stats["mean"], color="dimgray", ls=":", lw=1.5,
               label=f"mean={stats['mean']:,.0f}")
    ax.set_title(f"User history token length (n={n}, Qwen3-8B tokens)\n"
                 f"zhangxinyuan a5_user_data")
    ax.set_xlabel("tokens per user")
    ax.set_ylabel("number of users")
    ax.legend()
    fig.tight_layout()

    out_png = os.path.join(OUT_DIR, "user_history_tokens.png")
    fig.savefig(out_png, dpi=130)
    plt.close(fig)
    print(f"\n  Plot saved -> {out_png}")

    # Save JSON
    out_json = os.path.join(OUT_DIR, "user_history_tokens.json")
    with open(out_json, "w") as f:
        json.dump({"stats": stats, "raw_counts": counts}, f, indent=2)
    print(f"  Stats saved -> {out_json}")


if __name__ == "__main__":
    step1_export_txt()
    step2_token_stats()