#!/usr/bin/env python3
import argparse
import concurrent.futures
import json
import sys
from datetime import datetime
from pathlib import Path

PROJECT_ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(PROJECT_ROOT))

from src.evaluation.metrics import compute_error_taxonomy, format_results_table
from src.evaluation.scorer import score_batch

from EGP.api_client import OpenAICompatClient
from EGP.benchmark import collect_episode_paths, load_resume_state
from EGP.pipeline import evaluate_episode_with_egp

BENCHMARK_DIR = PROJECT_ROOT / "data" / "benchmark"
RESULTS_DIR = PROJECT_ROOT / "results"


def _load_episode(path: Path) -> dict:
    with open(path, "r", encoding="utf-8") as f:
        return json.load(f)


def main() -> None:
    parser = argparse.ArgumentParser(description="SafeHome Evidence-Guided Pipeline runner")
    parser.add_argument("--model", default="", help="Model name for OpenAI-compatible API")
    parser.add_argument("--api-base", default="", help="API base URL; can be omitted in preview-only mode")
    parser.add_argument("--api-key", default="", help="Optional API key; leave empty for local vLLM")
    parser.add_argument("--sq", nargs="*", default=None)
    parser.add_argument("--variant", nargs="*", default=None)
    parser.add_argument("--episode-id", nargs="*", default=None, help="Run specific episode IDs only")
    parser.add_argument("--max-episodes", type=int, default=None)
    parser.add_argument("--workers", type=int, default=1)
    parser.add_argument("--temperature", type=float, default=0.0)
    parser.add_argument("--max-tokens", type=int, default=2048)
    parser.add_argument("--timeout", type=int, default=300)
    parser.add_argument("--thinking", action="store_true")
    parser.add_argument("--no-thinking", "--no_thinking", dest="no_thinking", action="store_true")
    parser.add_argument("--extra-json", default=None)
    parser.add_argument("--preview-only", action="store_true", help="Build EGP artifacts without API calls")
    parser.add_argument("--output-dir", default=None)
    parser.add_argument("--no-resume", action="store_true")
    parser.add_argument("--salient-events", type=int, default=60)
    parser.add_argument("--focus-events", type=int, default=120)
    args = parser.parse_args()

    if not args.preview_only and (not args.model or not args.api_base):
        raise SystemExit("--model and --api-base are required unless --preview-only is used")

    episode_paths = collect_episode_paths(
        BENCHMARK_DIR,
        sq_types=args.sq,
        variants=args.variant,
        max_episodes=args.max_episodes,
        episode_ids=args.episode_id,
    )

    model_safe = (args.model or "preview").replace("/", "_").replace(":", "_")
    output_dir = Path(args.output_dir) if args.output_dir else RESULTS_DIR / f"{model_safe}_EGP"
    output_dir.mkdir(parents=True, exist_ok=True)
    results_file = output_dir / "results.jsonl"

    results = []
    completed = set()
    if not args.no_resume:
        results, completed = load_resume_state(results_file)
    remaining = [p for p in episode_paths if p.stem not in completed]

    client = None
    if not args.preview_only:
        client = OpenAICompatClient(
            model=args.model,
            api_base=args.api_base,
            api_key=args.api_key,
            temperature=args.temperature,
            max_tokens=args.max_tokens,
            timeout=args.timeout,
            thinking=args.thinking,
            no_thinking=args.no_thinking,
            extra_json=args.extra_json,
        )
        print("Testing API connectivity...")
        print((client.test_connection() or "<empty-response>")[:80])

    print(f"EGP run: {len(remaining)} / {len(episode_paths)} episodes pending")
    with open(results_file, "a", encoding="utf-8") as out_f:
        if args.workers <= 1:
            for idx, path in enumerate(remaining, 1):
                episode = _load_episode(path)
                result = evaluate_episode_with_egp(
                    episode,
                    str(path),
                    client,
                    max_salient_events=args.salient_events,
                    max_focus_events=args.focus_events,
                    preview_only=args.preview_only,
                )
                results.append(result)
                out_f.write(json.dumps(result, ensure_ascii=False) + "\n")
                out_f.flush()
                print(f"[{idx}/{len(remaining)}] {result['episode_id']}: {result['scores'].get('detection_label')}")
        else:
            with concurrent.futures.ThreadPoolExecutor(max_workers=args.workers) as executor:
                futures = {
                    executor.submit(
                        evaluate_episode_with_egp,
                        _load_episode(path),
                        str(path),
                        client,
                        args.salient_events,
                        args.focus_events,
                        args.preview_only,
                    ): path
                    for path in remaining
                }
                done = 0
                for future in concurrent.futures.as_completed(futures):
                    result = future.result()
                    results.append(result)
                    out_f.write(json.dumps(result, ensure_ascii=False) + "\n")
                    out_f.flush()
                    done += 1
                    print(f"[{done}/{len(remaining)}] {result['episode_id']}: {result['scores'].get('detection_label')}")

    summary = score_batch(results)
    errors = compute_error_taxonomy(results)
    summary_data = {
        "model": args.model or "preview-only",
        "api_base": args.api_base,
        "timestamp": datetime.now().isoformat(),
        "total_evaluated": len(results),
        "summary": summary,
        "errors": errors,
        "pipeline": "EGP",
    }
    with open(output_dir / "summary.json", "w", encoding="utf-8") as f:
        json.dump(summary_data, f, ensure_ascii=False, indent=2)

    print()
    print(format_results_table(summary, f"{args.model or 'preview-only'} + EGP"))
    print(f"\nResults saved to: {output_dir}")


if __name__ == "__main__":
    main()