"""
Label video pairs using Claude CLI in parallel.

Usage:
  # Test 20 samples with 10 workers
  python3 label_parallel.py --test 20 --workers 10

  # Full run
  python3 label_parallel.py --max-samples 0 --workers 15
"""

import argparse
import json
import os
import subprocess
import time
import glob
import re
from pathlib import Path
from concurrent.futures import ProcessPoolExecutor, as_completed

import pyarrow.parquet as pq

# ── Config ──
SAVE_INTERVAL = 100
MAX_RETRIES = 3
MODEL = "claude-haiku-4-5-20251001"

SYSTEM_PROMPT = """You are an expert at determining whether two TikTok videos are thematically similar.
Given metadata for two videos (video captions, keywords, category tags), determine:
1. Whether they are similar (label: 1) or not (label: 0)
2. The type of thematic similarity
3. Which elements are similar

Respond ONLY with a JSON object:
{"similar_theme": "<theme_type>", "similar_elements": <elements_list>, "label": <0_or_1>}

similar_theme values:
- "Fine-grained thematic similarity": Very specific thematic overlap (label=1)
- "General thematic similarity": Broad category overlap, label=1 only if meaningful shared elements
- "Irrelevant": Not similar (label=0)

similar_elements (pick from):
- "Subject of shooting", "How the subject acts", "Art style presentation", "Music", "Sentence and copywriting", "None of the above are similar"

If label=0, similar_elements=["None of the above are similar"].
Output ONLY the JSON."""


def build_user_prompt(msgs):
    texts = []
    for item in msgs[0]["content"]:
        if item["type"] == "text":
            texts.append(item["text"])
    if len(texts) == 2:
        return f"Video 1 metadata:\n{texts[0]}\n\nVideo 2 metadata:\n{texts[1]}"
    elif len(texts) == 1:
        return f"Video pair metadata:\n{texts[0]}"
    return "\n\n".join(f"Metadata {i+1}:\n{t}" for i, t in enumerate(texts))


def call_claude_single(args_tuple):
    """Worker function for ProcessPoolExecutor. Takes (key, prompt, gt) tuple."""
    key, prompt, gt = args_tuple
    full_prompt = f"{SYSTEM_PROMPT}\n\n{prompt}"

    for attempt in range(MAX_RETRIES):
        try:
            result = subprocess.run(
                ["claude", "-p", "--model", MODEL, "--max-turns", "1"],
                input=full_prompt,
                capture_output=True,
                text=True,
                timeout=90,
            )
            text = result.stdout.strip()
            if not text:
                if attempt < MAX_RETRIES - 1:
                    time.sleep(2)
                    continue
                return {"key": key, "error": f"empty response", "gt": gt, "est_tokens": 0}

            # Parse JSON
            clean = text
            if "```" in clean:
                m = re.search(r"```(?:json)?\s*([\s\S]+?)```", clean)
                if m:
                    clean = m.group(1).strip()

            # Find JSON object
            for s in range(len(clean)):
                if clean[s] == '{':
                    for e in range(len(clean), s, -1):
                        if clean[e-1] == '}':
                            try:
                                parsed = json.loads(clean[s:e])
                                est_tokens = (len(full_prompt) + len(text)) // 4
                                pred_label = parsed.get("label")
                                gt_label = gt.get("label") if gt else None
                                match = (pred_label == gt_label) if (pred_label is not None and gt_label is not None) else None
                                return {
                                    "key": key, "pred": parsed, "gt": gt,
                                    "match": match, "est_tokens": est_tokens,
                                }
                            except json.JSONDecodeError:
                                continue

            return {"key": key, "error": f"no JSON: {text[:150]}", "gt": gt, "est_tokens": 0}

        except subprocess.TimeoutExpired:
            if attempt < MAX_RETRIES - 1:
                time.sleep(2)
                continue
            return {"key": key, "error": "timeout", "gt": gt, "est_tokens": 0}
        except Exception as e:
            if attempt < MAX_RETRIES - 1:
                time.sleep(1)
                continue
            return {"key": key, "error": str(e), "gt": gt, "est_tokens": 0}

    return {"key": key, "error": "max retries", "gt": gt, "est_tokens": 0}


def load_samples(data_dir, max_samples=0):
    all_files = sorted(glob.glob(f"{data_dir}/*.parquet"))
    if not all_files:
        raise FileNotFoundError(f"No parquet files in {data_dir}")
    samples = []
    for pf in all_files:
        table = pq.read_table(pf, columns=["messages", "extra_info"])
        fname = Path(pf).stem
        for i in range(len(table)):
            row = table.slice(i, 1).to_pydict()
            msgs = json.loads(row["messages"][0])
            key = f"{fname}:{i}"
            samples.append((key, msgs))
            if max_samples > 0 and len(samples) >= max_samples:
                return samples
    return samples


def extract_gt(msgs):
    try:
        return json.loads(msgs[1]["content"][0]["text"])
    except:
        return None


def save_results(path, results, stats):
    path.write_text(json.dumps({
        "model": MODEL,
        "total_samples": len(results),
        "stats": stats,
        "results": results,
    }, ensure_ascii=False, indent=2))


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--data-dir", default="/mnt/hdfs/byte_tt_data_cu_vagcp/haogeng.liu/new_policy7w_v2_reformat")
    parser.add_argument("--output", default="/mnt/bn/bohanzhainas1/jiashuo/playground/claude_label/results.json")
    parser.add_argument("--test", type=int, default=0)
    parser.add_argument("--max-samples", type=int, default=0)
    parser.add_argument("--workers", type=int, default=15)
    args = parser.parse_args()

    n = args.test if args.test > 0 else args.max_samples

    print(f"Loading samples from {args.data_dir}...")
    samples = load_samples(args.data_dir, max_samples=n if n > 0 else 0)
    print(f"Loaded {len(samples)} samples")

    out_path = Path(args.output)
    out_path.parent.mkdir(parents=True, exist_ok=True)

    # Resume
    done_keys = set()
    results = []
    if out_path.exists():
        try:
            saved = json.loads(out_path.read_text())
            results = saved.get("results", [])
            done_keys = {r["key"] for r in results if "key" in r}
            print(f"Resuming: {len(done_keys)} already done")
        except:
            pass

    # Build work items
    work = []
    for key, msgs in samples:
        if key not in done_keys:
            prompt = build_user_prompt(msgs)
            gt = extract_gt(msgs)
            work.append((key, prompt, gt))

    print(f"To process: {len(work)} with {args.workers} workers")
    if not work:
        print("Nothing to do!")
        return

    correct = sum(1 for r in results if r.get("match") is True)
    evaluated = sum(1 for r in results if r.get("match") is not None)
    total_est_tokens = sum(r.get("est_tokens", 0) for r in results)
    errors = 0
    t0 = time.time()
    processed = 0
    last_save = time.time()

    with ProcessPoolExecutor(max_workers=args.workers) as executor:
        futures = {executor.submit(call_claude_single, w): w[0] for w in work}

        for future in as_completed(futures):
            result = future.result()
            results.append(result)
            done_keys.add(result["key"])
            processed += 1
            total_est_tokens += result.get("est_tokens", 0)

            if result.get("match") is not None:
                evaluated += 1
                if result["match"]:
                    correct += 1
            if "error" in result:
                errors += 1

            # Progress every 10
            if processed % 10 == 0 or processed == len(work):
                elapsed = time.time() - t0
                speed = processed / elapsed
                acc = correct / evaluated if evaluated > 0 else 0
                remaining = len(work) - processed
                eta_h = remaining / speed / 3600 if speed > 0 else 0
                print(
                    f"[{processed}/{len(work)}] acc={acc:.3f} "
                    f"{speed:.1f}/s err={errors} "
                    f"~{total_est_tokens//1000}k tok "
                    f"ETA={eta_h:.1f}h"
                )

            # Save periodically
            if time.time() - last_save > 60 or processed % SAVE_INTERVAL == 0:
                acc = correct / evaluated if evaluated > 0 else 0
                stats = {
                    "accuracy": acc, "correct": correct, "evaluated": evaluated,
                    "errors": errors, "est_total_tokens": total_est_tokens,
                    "processed": len(results),
                }
                save_results(out_path, results, stats)
                last_save = time.time()

    # Final save
    elapsed = time.time() - t0
    acc = correct / evaluated if evaluated > 0 else 0
    stats = {
        "accuracy": acc, "correct": correct, "evaluated": evaluated,
        "errors": errors, "est_total_tokens": total_est_tokens,
        "processed": len(results), "elapsed_s": elapsed,
        "speed": processed / elapsed if elapsed > 0 else 0,
    }
    save_results(out_path, results, stats)

    print(f"\n{'='*60}")
    print(f"DONE: {len(results)} samples")
    print(f"Accuracy: {acc:.4f} ({correct}/{evaluated}), errors: {errors}")
    print(f"Est tokens: ~{total_est_tokens:,}")
    print(f"Time: {elapsed/3600:.1f}h ({processed/elapsed:.1f} samples/s)")
    print(f"Saved: {out_path}")

    if args.test > 0 and processed > 0:
        avg_tok = total_est_tokens / processed
        total_all = 48512
        speed = processed / elapsed
        print(f"\n--- Extrapolation for {total_all} samples ---")
        print(f"Avg ~{avg_tok:.0f} tokens/sample")
        print(f"Est total: ~{avg_tok * total_all / 1e6:.1f}M tokens")
        print(f"Est time @ {speed:.1f}/s with {args.workers} workers: {total_all / speed / 3600:.1f}h")


if __name__ == "__main__":
    main()