#!/usr/bin/env python3
"""Live training status for the ICML geometric-memory Claim 1 run.

Usage:
  # one-shot pretty print
  python repro/scripts/training_status.py

  # continuous terminal watch (Ctrl+C to stop viewer only)
  python repro/scripts/training_status.py --watch --interval 5

  # also rewrite the Claim 1 logbook cell + status files
  python repro/scripts/training_status.py --logbook

  # background-friendly: watch + logbook updates
  python repro/scripts/training_status.py --watch --interval 30 --logbook
"""

from __future__ import annotations

import argparse
import json
import os
import re
import signal
import subprocess
import sys
import time
from datetime import datetime, timezone
from pathlib import Path

ROOT = Path(__file__).resolve().parents[2]
# Prefer active symlink, then newest claim1 log.
def _default_log() -> Path:
    active = ROOT / "logs_claim1_active.log"
    if active.exists():
        return active.resolve() if active.is_symlink() else active
    candidates = sorted(
        ROOT.glob("logs_claim1*.log"),
        key=lambda p: p.stat().st_mtime if p.exists() else 0,
        reverse=True,
    )
    return candidates[0] if candidates else ROOT / "logs_claim1_medium.log"


DEFAULT_LOG = _default_log()
STATUS_JSON = ROOT / "repro" / "outputs" / "training_status.json"
STATUS_MD = ROOT / "repro" / "outputs" / "training_status.md"
STATUS_HTML = ROOT / "repro" / "outputs" / "training_status.html"
CLAIM1_PAGE = (
    ROOT
    / ".trackio"
    / "logbook"
    / "pages"
    / "claim-1-path-star-near-perfect-accuracy"
    / "page.md"
)

LIVE_BEGIN = "<!-- LIVE-TRAINING-STATUS-BEGIN -->"
LIVE_END = "<!-- LIVE-TRAINING-STATUS-END -->"

TRAIN_CMDS = (
    "train_in_weights.py",
    "geometry_and_spectral_repro.py",
)


def _now() -> str:
    return datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S UTC")


def find_training_processes() -> list[dict]:
    """Return running training-related python processes."""
    try:
        out = subprocess.check_output(
            ["ps", "-eo", "pid,etime,pcpu,pmem,args"],
            text=True,
            stderr=subprocess.DEVNULL,
        )
    except Exception:
        return []
    procs = []
    for line in out.splitlines()[1:]:
        if not any(cmd in line for cmd in TRAIN_CMDS):
            continue
        if "training_status.py" in line:
            continue
        parts = line.strip().split(None, 4)
        if len(parts) < 5:
            continue
        pid, etime, pcpu, pmem, args = parts
        procs.append(
            {
                "pid": int(pid),
                "etime": etime,
                "pcpu": pcpu,
                "pmem": pmem,
                "cmd": args[:200],
            }
        )
    return procs


def gpu_snapshot() -> dict | None:
    try:
        out = subprocess.check_output(
            [
                "nvidia-smi",
                "--query-gpu=name,utilization.gpu,memory.used,memory.total",
                "--format=csv,noheader,nounits",
            ],
            text=True,
            stderr=subprocess.DEVNULL,
        )
        name, util, used, total = [x.strip() for x in out.strip().splitlines()[0].split(",")]
        return {
            "name": name,
            "util_pct": float(util),
            "mem_used_mib": float(used),
            "mem_total_mib": float(total),
        }
    except Exception:
        return None


def _last_match(pattern: str, text: str):
    ms = list(re.finditer(pattern, text))
    return ms[-1] if ms else None


def parse_log(log_path: Path) -> dict:
    status: dict = {
        "log_path": str(log_path),
        "log_exists": log_path.exists(),
        "log_bytes": log_path.stat().st_size if log_path.exists() else 0,
        "log_mtime": (
            datetime.fromtimestamp(log_path.stat().st_mtime, tz=timezone.utc).isoformat()
            if log_path.exists()
            else None
        ),
        "stage": "unknown",
        "finished": False,
        "edge": None,
        "path": None,
        "last_test_acc": None,
        "best_test_acc": None,
        "forced_acc": None,
        "run_dir": None,
        "model_params": None,
        "device": None,
        "graph": None,
        "recent_lines": [],
    }
    if not log_path.exists():
        return status

    # For large tqdm logs, only need the tail for most metrics, but epoch regexes
    # are densest at the end. Read last ~400KB + full scan for rare markers.
    raw = log_path.read_bytes()
    tail = raw[-400_000:].decode("utf-8", errors="ignore")
    head = raw[:20_000].decode("utf-8", errors="ignore")
    text = tail if len(raw) > 400_000 else raw.decode("utf-8", errors="ignore")

    m = re.search(r"Device: (\S+)", head + "\n" + text)
    if m:
        status["device"] = m.group(1)
    m = re.search(r"Graph setup: ([^\n]+)", head + "\n" + text)
    if m:
        status["graph"] = m.group(1).strip()
    m = re.search(r"Model parameters: ([0-9,]+)", head + "\n" + text)
    if m:
        status["model_params"] = m.group(1)
    m = re.search(r"Run directory: ([^\n]+)", head + "\n" + text)
    if m:
        status["run_dir"] = m.group(1).strip()

    if "Training finished" in text or "Final checkpoint saved" in text:
        status["finished"] = True
        status["stage"] = "finished"

    m = _last_match(
        r"Edge Epoch (\d+)/(\d+):\s*.*?acc=([0-9.]+)%,\s*loss=([0-9.]+)",
        text,
    )
    if m:
        status["edge"] = {
            "epoch": int(m.group(1)),
            "total": int(m.group(2)),
            "acc_pct": float(m.group(3)),
            "loss": float(m.group(4)),
            "frac": int(m.group(1)) / max(int(m.group(2)), 1),
        }
        if not status["finished"]:
            status["stage"] = "edge_memorization"

    m = _last_match(
        r"Path Epoch (\d+)/(\d+):\s*.*?acc=([0-9.]+)%,\s*loss=([0-9.]+)",
        text,
    )
    if m:
        status["path"] = {
            "epoch": int(m.group(1)),
            "total": int(m.group(2)),
            "acc_pct": float(m.group(3)),
            "loss": float(m.group(4)),
            "frac": int(m.group(1)) / max(int(m.group(2)), 1),
        }
        if not status["finished"]:
            status["stage"] = "path_finetuning"

    # mixed_full_path recipe logs "Joint Epoch"
    m = _last_match(
        r"Joint Epoch (\d+)/(\d+):\s*.*?acc=([0-9.]+)%,\s*loss=([0-9.]+)",
        text,
    )
    if m:
        status["path"] = {
            "epoch": int(m.group(1)),
            "total": int(m.group(2)),
            "acc_pct": float(m.group(3)),
            "loss": float(m.group(4)),
            "frac": int(m.group(1)) / max(int(m.group(2)), 1),
            "kind": "joint_mixed",
        }
        if not status["finished"]:
            status["stage"] = "joint_mixed_training"

    m = _last_match(r"Epoch (\d+) \| Test Acc: ([0-9.]+)%", text)
    if m:
        status["last_test_acc"] = {"epoch": int(m.group(1)), "acc_pct": float(m.group(2))}

    m = _last_match(r"Forced Acc: ([0-9.]+)", text)
    if m:
        try:
            status["forced_acc"] = float(m.group(1))
        except ValueError:
            pass

    m = _last_match(r"Best test accuracy:\s*([0-9.]+)%", text)
    if m:
        status["best_test_acc"] = float(m.group(1))

    if "Starting path" in text or "PATH FINETUNING" in text.upper() or "Path finetuning" in text:
        if status["stage"] == "edge_memorization" and status.get("path"):
            status["stage"] = "path_finetuning"
        elif status["stage"] == "unknown" and not status["finished"]:
            status["stage"] = "path_finetuning"

    if "EDGE MEMORIZATION TRAINING" in text and status["stage"] == "unknown":
        status["stage"] = "edge_memorization"

    # clean recent non-tqdm-ish lines from absolute end
    lines = [ln.strip() for ln in text.splitlines() if ln.strip()]
    interesting = [
        ln
        for ln in lines
        if any(
            k in ln
            for k in (
                "Edge Epoch",
                "Path Epoch",
                "Test Acc",
                "Best test",
                "Final checkpoint",
                "Training finished",
                "INFO",
                "ERROR",
            )
        )
    ]
    status["recent_lines"] = interesting[-8:]
    return status


def progress_bar(frac: float, width: int = 28) -> str:
    frac = max(0.0, min(1.0, frac))
    filled = int(round(frac * width))
    return "[" + "#" * filled + "-" * (width - filled) + f"] {frac*100:5.1f}%"


def build_snapshot(log_path: Path) -> dict:
    procs = find_training_processes()
    log_status = parse_log(log_path)
    snap = {
        "updated_at": _now(),
        "running": bool(procs) and not log_status.get("finished"),
        "processes": procs,
        "gpu": gpu_snapshot(),
        "log": log_status,
    }
    return snap


def format_text(snap: dict) -> str:
    log = snap["log"]
    lines = []
    lines.append("=" * 60)
    lines.append("ICML Repro — Claim 1 training status")
    lines.append(f"Updated: {snap['updated_at']}")
    lines.append("=" * 60)

    if snap["processes"]:
        for p in snap["processes"]:
            lines.append(
                f"PID {p['pid']}  elapsed={p['etime']}  cpu={p['pcpu']}%  "
                f"mem={p['pmem']}%  running"
            )
            lines.append(f"  {p['cmd']}")
    else:
        lines.append("No train_in_weights.py process found.")

    if snap.get("gpu"):
        g = snap["gpu"]
        lines.append(
            f"GPU: {g['name']}  util={g['util_pct']:.0f}%  "
            f"mem={g['mem_used_mib']:.0f}/{g['mem_total_mib']:.0f} MiB"
        )

    lines.append(f"Stage: {log.get('stage')}")
    lines.append(f"Finished: {log.get('finished')}")
    if log.get("graph"):
        lines.append(f"Graph: {log['graph']}")
    if log.get("model_params"):
        lines.append(f"Params: {log['model_params']}")
    if log.get("device"):
        lines.append(f"Device: {log['device']}")

    if log.get("edge"):
        e = log["edge"]
        lines.append(
            f"Edge: epoch {e['epoch']}/{e['total']}  "
            f"acc={e['acc_pct']:.2f}%  loss={e['loss']:.4f}"
        )
        lines.append("      " + progress_bar(e["frac"]))
    if log.get("path"):
        p = log["path"]
        lines.append(
            f"Path: epoch {p['epoch']}/{p['total']}  "
            f"acc={p['acc_pct']:.2f}%  loss={p['loss']:.4f}"
        )
        lines.append("      " + progress_bar(p["frac"]))
    if log.get("last_test_acc") is not None:
        t = log["last_test_acc"]
        lines.append(f"Last test acc: {t['acc_pct']:.2f}% (epoch {t['epoch']})")
    if log.get("best_test_acc") is not None:
        lines.append(f"Best test acc: {log['best_test_acc']:.2f}%")

    lines.append(f"Log: {log.get('log_path')}  ({log.get('log_bytes', 0)} bytes)")
    if log.get("run_dir"):
        lines.append(f"Run dir: {log['run_dir']}")
    lines.append("-" * 60)
    lines.append("Tip: tail -f logs_claim1_medium.log")
    lines.append("     python repro/scripts/training_status.py --watch")
    lines.append("Logbook UI: http://localhost:7861/")
    lines.append("=" * 60)
    return "\n".join(lines)


def format_markdown(snap: dict) -> str:
    log = snap["log"]
    running = "🟢 **running**" if snap["running"] else (
        "✅ **finished**" if log.get("finished") else "⚪ **idle / unknown**"
    )
    parts = [
        f"### Live training status",
        f"_Auto-updated: {snap['updated_at']}_ · {running}",
        "",
    ]
    if snap["processes"]:
        p = snap["processes"][0]
        parts.append(f"- **PID:** `{p['pid']}` · elapsed `{p['etime']}` · CPU `{p['pcpu']}%`")
    if snap.get("gpu"):
        g = snap["gpu"]
        parts.append(
            f"- **GPU:** {g['name']} · util `{g['util_pct']:.0f}%` · "
            f"mem `{g['mem_used_mib']:.0f}/{g['mem_total_mib']:.0f}` MiB"
        )
    parts.append(f"- **Stage:** `{log.get('stage')}`")
    if log.get("edge"):
        e = log["edge"]
        parts.append(
            f"- **Edge memorization:** epoch **{e['epoch']}/{e['total']}** · "
            f"acc **{e['acc_pct']:.2f}%** · loss `{e['loss']:.4f}`  \n"
            f"  `{progress_bar(e['frac'])}`"
        )
    if log.get("path"):
        p = log["path"]
        parts.append(
            f"- **Path finetuning:** epoch **{p['epoch']}/{p['total']}** · "
            f"acc **{p['acc_pct']:.2f}%** · loss `{p['loss']:.4f}`  \n"
            f"  `{progress_bar(p['frac'])}`"
        )
    if log.get("last_test_acc"):
        t = log["last_test_acc"]
        parts.append(f"- **Last held-out test acc:** **{t['acc_pct']:.2f}%** (epoch {t['epoch']})")
    if log.get("best_test_acc") is not None:
        parts.append(f"- **Best test acc:** **{log['best_test_acc']:.2f}%**")
    if log.get("graph"):
        parts.append(f"- **Graph:** `{log['graph']}`")
    parts.append(f"- **Log file:** `logs_claim1_medium.log`")
    parts.append("")
    parts.append(
        "Watch in terminal: `python repro/scripts/training_status.py --watch` · "
        "or `tail -f logs_claim1_medium.log`"
    )
    return "\n".join(parts)


def format_html(snap: dict) -> str:
    mdish = format_markdown(snap).replace("\n", "<br>\n")
    # simple HTML, auto-refresh every 10s if opened in browser
    return f"""<!doctype html>
<html><head>
<meta charset="utf-8"/>
<meta http-equiv="refresh" content="10"/>
<title>Claim 1 training status</title>
<style>
 body {{ font-family: ui-sans-serif, system-ui, sans-serif; margin: 1.5rem; max-width: 720px; }}
 code {{ background: #f4f4f5; padding: 0.1rem 0.3rem; border-radius: 4px; }}
 .box {{ border: 1px solid #e4e4e7; border-radius: 12px; padding: 1rem 1.25rem; }}
 h1 {{ font-size: 1.25rem; }}
</style>
</head>
<body>
<h1>Claim 1 — training status</h1>
<p>Auto-refreshes every 10s. Generated {_now()}.</p>
<div class="box">{mdish}</div>
<p><a href="http://localhost:7861/">Open Trackio logbook</a></p>
</body></html>
"""


def write_status_files(snap: dict) -> None:
    STATUS_JSON.parent.mkdir(parents=True, exist_ok=True)
    STATUS_JSON.write_text(json.dumps(snap, indent=2))
    STATUS_MD.write_text(format_markdown(snap) + "\n")
    STATUS_HTML.write_text(format_html(snap))


def update_logbook_page(snap: dict) -> bool:
    """Rewrite the live-status block inside the Claim 1 page markdown."""
    if not CLAIM1_PAGE.exists():
        return False
    body = format_markdown(snap)
    block = f"{LIVE_BEGIN}\n\n{body}\n\n{LIVE_END}"
    text = CLAIM1_PAGE.read_text(encoding="utf-8")

    if LIVE_BEGIN in text and LIVE_END in text:
        pre, rest = text.split(LIVE_BEGIN, 1)
        _, post = rest.split(LIVE_END, 1)
        new_text = pre + block + post
    else:
        # Insert a trackio-style markdown cell near the top (after title)
        cell = (
            "\n\n---\n"
            "<!-- trackio-cell\n"
            '{"type": "markdown", "id": "cell_live_training_status", '
            f'"created_at": "{datetime.now(timezone.utc).isoformat()}", '
            '"title": "Live training status"}\n'
            "-->\n"
            f"{block}\n"
        )
        # after first heading block
        if "\n\n" in text:
            head, tail = text.split("\n\n", 1)
            new_text = head + "\n\n" + cell + "\n" + tail
        else:
            new_text = text + cell

    CLAIM1_PAGE.write_text(new_text, encoding="utf-8")

    # bump logbook.json updated_at so the UI notices
    lb = ROOT / ".trackio" / "logbook" / "logbook.json"
    if lb.exists():
        try:
            data = json.loads(lb.read_text())
            data["updated_at"] = datetime.now(timezone.utc).isoformat()
            lb.write_text(json.dumps(data, indent=2))
        except Exception:
            pass
    return True


def once(log_path: Path, logbook: bool, quiet: bool = False) -> dict:
    snap = build_snapshot(log_path)
    write_status_files(snap)
    if logbook:
        update_logbook_page(snap)
    if not quiet:
        print(format_text(snap))
        print(f"\nWrote {STATUS_JSON.relative_to(ROOT)}")
        print(f"Wrote {STATUS_MD.relative_to(ROOT)}")
        print(f"Wrote {STATUS_HTML.relative_to(ROOT)}  (open in browser; auto-refresh 10s)")
        if logbook:
            print(f"Updated logbook page: {CLAIM1_PAGE.relative_to(ROOT)}")
            print("Refresh http://localhost:7861/ → Claim 1")
    return snap


def main(argv=None) -> int:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--log", type=Path, default=DEFAULT_LOG, help="Training log path")
    parser.add_argument("--watch", action="store_true", help="Refresh continuously")
    parser.add_argument("--interval", type=float, default=5.0, help="Watch interval seconds")
    parser.add_argument(
        "--logbook",
        action="store_true",
        help="Rewrite live status block on Claim 1 logbook page",
    )
    parser.add_argument(
        "--json",
        action="store_true",
        help="Print JSON snapshot only",
    )
    args = parser.parse_args(argv)

    stop = False

    def _sig(_s, _f):
        nonlocal stop
        stop = True

    signal.signal(signal.SIGINT, _sig)
    signal.signal(signal.SIGTERM, _sig)

    if args.watch:
        while not stop:
            # clear screen for readable watch
            if not args.json and sys.stdout.isatty():
                os.system("clear" if os.name != "nt" else "cls")
            snap = once(args.log, logbook=args.logbook, quiet=args.json)
            if args.json:
                print(json.dumps(snap, indent=2))
            if snap["log"].get("finished") and not snap["running"]:
                if not args.json:
                    print("\nTraining finished — exiting watch.")
                break
            # sleep in small chunks so Ctrl+C is snappy
            end = time.time() + args.interval
            while time.time() < end and not stop:
                time.sleep(0.2)
        return 0

    snap = once(args.log, logbook=args.logbook, quiet=args.json)
    if args.json:
        print(json.dumps(snap, indent=2))
    return 0


if __name__ == "__main__":
    raise SystemExit(main())