"""Uçtan uca: örnek belgeler -> veritabanları -> test kümesi -> değerlendirme -> rapor.

Kullanım:
  python scripts/run_all.py                    # kural tabanlı sistemler (anahtar gerekmez)
  python scripts/run_all.py --claude           # + Claude (ANTHROPIC_API_KEY gerekli)
  python scripts/run_all.py --openai gpt-4o-mini
  python scripts/run_all.py --ollama qwen2.5-coder:7b
  python scripts/run_all.py --hizli            # yalnızca TR kipi, kural tabanlı
"""
from __future__ import annotations

import argparse
import json
import subprocess
import sys
import warnings
from pathlib import Path

ROOT = Path(__file__).resolve().parent.parent
sys.path.insert(0, str(ROOT))
warnings.filterwarnings("ignore")

from t2s.errors import ErrorLog  # noqa: E402
from t2s.evaluate import SystemSpec, evaluate_system, gold_on_naive_db, load_benchmark  # noqa: E402
from t2s.pipeline import (DOCS_DIR, build_database, build_reference_db, extraction_accuracy,  # noqa: E402
                          number_format_stats)
from t2s.report import write_report  # noqa: E402

OUT = ROOT / "outputs"
DB = OUT / "db"


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--claude", nargs="?", const="auto", default=None,
                    help="Claude model adı (boş/auto: API'deki en yeni model)")
    ap.add_argument("--openai", nargs="?", const="auto", default=None, help="OpenAI uyumlu model adı (auto: ilk model)")
    ap.add_argument("--ollama", nargs="?", const="auto", default=None, help="Ollama model adı (auto: kurulu ilk model)")
    ap.add_argument("--hizli", action="store_true")
    args = ap.parse_args()

    print("1) Örnek belgeler")
    if not any(DOCS_DIR.glob("*.pdf")):
        subprocess.run([sys.executable, str(ROOT / "data" / "generate_samples.py")], check=True)
    print("   ", ", ".join(sorted(p.name for p in DOCS_DIR.iterdir())))

    print("2) Veritabanları (referans + 3 sayı kipi)")
    build_reference_db(DB / "referans.db")
    modes = ["tr"] if args.hizli else ["tr", "en", "text"]
    db_paths = {}
    for m in modes:
        db_paths[m] = DB / f"sistem_{m}.db"
        build_database(db_paths[m], mode=m, verbose=(m == "tr"))

    print("3) Tablo çıkarma doğruluğu")
    extraction = {}
    for meth in ["pdfplumber", "camelot-lattice", "camelot-stream", "auto"]:
        extraction[meth] = extraction_accuracy(meth)
        acc = sum(r["hucre_dogrulugu"] for r in extraction[meth]) / len(extraction[meth])
        print(f"   {meth:16} ort. hücre doğruluğu: %{100 * acc:.1f}")

    print("4) Test kümesi")
    subprocess.run([sys.executable, str(ROOT / "benchmark" / "build_benchmark.py")], check=True)
    items = load_benchmark()

    print("5) Sistemler")
    specs = [SystemSpec("kural_tr", "kural", "tr", True),
             SystemSpec("kural_tr_duzeltmesiz", "kural", "tr", False)]
    if not args.hizli:
        specs += [SystemSpec("kural_en_naif", "kural", "en", True),
                  SystemSpec("kural_metin_naif", "kural", "text", True)]
    llms = []
    from t2s.llm import list_models, make_backend
    for key, model in (("claude", args.claude), ("openai", args.openai), ("ollama", args.ollama)):
        if not model:
            continue
        if model == "auto":  # model senkronizasyonu: sağlayıcının listesinden seç
            found = list_models(key)
            if not found:
                raise SystemExit(f"{key}: kullanılabilir model bulunamadı")
            model = found[0].id
            print(f"   {key}: model otomatik seçildi -> {model}")
        llms.append((key, make_backend(key, model)))
    for key, be in llms:
        specs += [SystemSpec(f"{key}_tr", be, "tr", True), SystemSpec(f"{key}_tr_duzeltmesiz", be, "tr", False)]
        if not args.hizli:
            specs += [SystemSpec(f"{key}_en_naif", be, "en", True), SystemSpec(f"{key}_metin_naif", be, "text", True)]

    log_path = OUT / "hata_gunlugu.jsonl"
    if log_path.exists():
        log_path.unlink()
    elog = ErrorLog(log_path)
    summaries = []
    for spec in specs:
        if spec.db_mode not in db_paths:
            continue
        s = evaluate_system(spec, items, db_paths, DB / "referans.db", OUT / "sonuclar", elog)
        summaries.append(s)
        print(f"   {spec.name:26} EX=%{s['EX']:5}  test=%{s['bolum'].get('test')}  "
              f"ilk deneme=%{s['EX_ilk_deneme']}  cevapsız doğru red=%{s['cevapsiz_dogru_red']}")

    print("6) Sayı biçimi ablasyonu (altın SQL, naif veritabanları)")
    naive = gold_on_naive_db(items, db_paths, DB / "referans.db")
    for r in naive:
        print(f"   {r['kip']:5} altın SQL EX=%{r['EX']}  (bozulan {len(r['bozulan'])}/{r['n']})")
    nstats = number_format_stats(db_paths)

    ozet = {"sistemler": summaries, "cikarim": extraction, "sayi_ablasyonu": naive,
            "sayi_istatistik": nstats, "soru_sayisi": len(items)}
    (OUT / "ozet.json").write_text(json.dumps(ozet, ensure_ascii=False, indent=1), encoding="utf-8")
    rp = write_report(ozet, OUT / "basarim_raporu.html")
    print("7) Rapor ->", rp)


if __name__ == "__main__":
    main()