#!/usr/bin/env python3
"""Re-evaluate existing GE LLM predictions with updated metrics.

Reads predictions.jsonl from run directories, recomputes metrics using
the latest evaluation code, and writes updated results.json (backing up
the original).

L3 runs are skipped — L3 evaluation requires the judge script:
    python scripts_depmap/run_ge_l3_judge.py

Usage:
    PYTHONPATH=src python scripts_depmap/reeval_ge_llm.py
    PYTHONPATH=src python scripts_depmap/reeval_ge_llm.py --task ge-l2
    PYTHONPATH=src python scripts_depmap/reeval_ge_llm.py --run-dir results/ge_llm/ge-l4_gpt-4o-mini_3-shot_fs0
"""

from __future__ import annotations

import argparse
import json
import re
import shutil
import sys
from pathlib import Path

PROJECT_ROOT = Path(__file__).resolve().parent.parent
RESULTS_DIR = PROJECT_ROOT / "results" / "ge_llm"
EXPORTS_DIR = PROJECT_ROOT / "exports" / "ge_llm"

TASK_DATASET = {
    "ge-l1": "ge_l1_dataset.jsonl",
    "ge-l2": "ge_l2_dataset.jsonl",
    "ge-l3": "ge_l3_dataset.jsonl",  # loaded but runs are skipped
    "ge-l4": "ge_l4_dataset.jsonl",
}


def load_jsonl(path: Path) -> list[dict]:
    records = []
    with open(path) as f:
        for line in f:
            records.append(json.loads(line))
    return records


def reeval_run(run_dir: Path, gold_records: dict[str, dict], task: str) -> bool:
    """Re-evaluate a single run directory. Returns True if successful."""
    # L3 requires judge evaluation — skip
    if task == "ge-l3":
        print(f"  SKIP {run_dir.name}: L3 requires judge evaluation "
              "(run scripts_depmap/run_ge_l3_judge.py)")
        return False

    pred_path = run_dir / "predictions.jsonl"
    if not pred_path.exists():
        print(f"  SKIP {run_dir.name}: no predictions.jsonl")
        return False

    preds = load_jsonl(pred_path)
    if not preds:
        print(f"  SKIP {run_dir.name}: empty predictions")
        return False

    pred_texts = []
    gold_list = []
    for p in preds:
        qid = p.get("question_id", "")
        if qid not in gold_records:
            continue
        pred_texts.append(str(p.get("prediction", "")))
        gold_list.append(gold_records[qid])

    if not pred_texts:
        print(f"  SKIP {run_dir.name}: no matching question_ids")
        return False

    from negbiodb_depmap.llm_eval import compute_all_ge_llm_metrics

    try:
        metrics = compute_all_ge_llm_metrics(task, pred_texts, gold_list)
    except Exception as e:
        print(f"  ERROR {run_dir.name}: {e}")
        return False

    results_path = run_dir / "results.json"
    if results_path.exists():
        backup_path = run_dir / "results_original.json"
        if not backup_path.exists():
            shutil.copy2(results_path, backup_path)

    with open(results_path, "w") as f:
        json.dump(metrics, f, indent=2)

    print(f"  OK {run_dir.name}: {len(pred_texts)} predictions re-evaluated")
    return True


def main(argv: list[str] | None = None) -> int:
    parser = argparse.ArgumentParser(description="Re-evaluate GE LLM predictions")
    parser.add_argument("--task", type=str, help="Task filter (e.g. ge-l2)")
    parser.add_argument("--run-dir", type=Path, help="Single run directory")
    parser.add_argument("--results-dir", type=Path, default=RESULTS_DIR)
    parser.add_argument("--exports-dir", type=Path, default=EXPORTS_DIR)
    args = parser.parse_args(argv)

    if args.run_dir:
        run_dirs = [args.run_dir]
        task_match = re.match(r"(ge-l\d)", args.run_dir.name)
        tasks = {task_match.group(1)} if task_match else set()
    else:
        run_dirs = sorted(
            d for d in args.results_dir.iterdir()
            if d.is_dir() and not d.name.endswith("_judged")
        )
        if args.task:
            run_dirs = [d for d in run_dirs if d.name.startswith(args.task + "_")]
            tasks = {args.task}
        else:
            tasks = set()
            for d in run_dirs:
                m = re.match(r"(ge-l\d)", d.name)
                if m:
                    tasks.add(m.group(1))

    gold_by_task: dict[str, dict[str, dict]] = {}
    for task in tasks:
        dataset_file = args.exports_dir / TASK_DATASET.get(task, "")
        if not dataset_file.exists():
            print(f"WARNING: Gold dataset not found: {dataset_file}")
            continue
        records = load_jsonl(dataset_file)
        gold_by_task[task] = {
            r["question_id"]: r for r in records if r.get("split") in ("test", "val")
        }
        print(f"Loaded {len(gold_by_task[task])} gold records for {task}")

    n_ok = 0
    n_skip = 0
    for run_dir in run_dirs:
        task_match = re.match(r"(ge-l\d)", run_dir.name)
        if not task_match:
            continue
        task = task_match.group(1)
        if task not in gold_by_task:
            n_skip += 1
            continue
        if reeval_run(run_dir, gold_by_task[task], task):
            n_ok += 1
        else:
            n_skip += 1

    print(f"\nDone: {n_ok} re-evaluated, {n_skip} skipped")
    return 0


if __name__ == "__main__":
    sys.exit(main())