"""validate_engine_audit_v1.py — ENGINE_AUDIT_V1 산출물 검증기

검증 항목 (프롬프트 §3.10 / §7)
- §3.10 필수 섹션 존재(final_json_schema_valid)
- decision.decision_source == "rule_engine"
- llm_control.final_decision_from_llm == false / llm_generated_decision_field_count == 0
- imputed_data_exposure 불변식: 대체데이터 감지 시 게이트 실제 발동
  (fundamental_core_factor_coverage < min → fundamental_claim_allowed == false 등)
- 게이트 산식 재현(weighted_coverage / imputed_field_ratio / effective_confidence_honest)

기본 모드: 산출물 무결성만 검증(엔진 status=failed 여도 PASS 가능).
--strict : 추가로 final_verdict.status == "passed" 를 요구(엔진 투자준비 게이트).

종료코드: 검증 실패 시 1 (repo validator 컨벤션).
"""
from __future__ import annotations

import argparse
import json
import sys
from pathlib import Path
from typing import Any

ROOT = Path(__file__).resolve().parents[1]
DEFAULT_JSON = ROOT / "Temp" / "engine_audit_v1.json"

REQUIRED_SECTIONS = [
    "meta", "data_quality", "routing", "scores", "decision",
    "sell_plan", "evidence", "risk", "llm_control", "audit",
    "imputed_data_exposure", "final_verdict",
]
EPS = 0.01


def _emit(failures: list[str], cond: bool, msg: str) -> None:
    if not cond:
        failures.append(msg)


def main() -> int:
    ap = argparse.ArgumentParser(description="ENGINE_AUDIT_V1 validator")
    ap.add_argument("--json", default=str(DEFAULT_JSON))
    ap.add_argument("--strict", action="store_true",
                    help="final_verdict.status == passed 까지 요구")
    args = ap.parse_args()

    path = Path(args.json)
    if not path.is_absolute():
        path = ROOT / path
    if not path.exists():
        print(f"FAIL: file not found: {path}")
        return 1
    try:
        d = json.loads(path.read_text(encoding="utf-8"))
    except Exception as exc:  # noqa: BLE001
        print(f"FAIL: cannot parse JSON: {exc}")
        return 1

    failures: list[str] = []

    # 1) 필수 섹션
    for sec in REQUIRED_SECTIONS:
        _emit(failures, sec in d, f"missing required section: {sec}")
    if failures:
        for f in failures:
            print("FAIL:", f)
        return 1

    fv = d["final_verdict"]
    dec = d["decision"]
    llm = d["llm_control"]
    exp = d["imputed_data_exposure"]

    # 2) 스키마/판단 출처 불변식
    _emit(failures, fv.get("final_json_schema_valid") is True, "final_json_schema_valid != true")
    _emit(failures, dec.get("decision_source") == "rule_engine", "decision.decision_source != rule_engine")
    _emit(failures, llm.get("final_decision_from_llm") is False, "llm_control.final_decision_from_llm != false")
    _emit(failures, llm.get("llm_generated_decision_field_count") == 0, "llm_generated_decision_field_count != 0")
    _emit(failures, fv.get("llm_generated_decision_field_count") == 0, "final_verdict.llm_generated_decision_field_count != 0")

    # 3) 게이트 불변식: 대체데이터 감지 시 발동
    fcc = exp.get("fundamental_core_factor_coverage")
    minc = (exp.get("thresholds") or {}).get("fund_factor_min_coverage", 0.5)
    if isinstance(fcc, (int, float)) and fcc < minc:
        _emit(failures, exp.get("fundamental_claim_allowed") is False,
              "fundamental coverage < min but fundamental_claim_allowed != false")
    ifr = exp.get("imputed_field_ratio")
    block = (exp.get("thresholds") or {}).get("block_ratio", 0.5)
    if isinstance(ifr, (int, float)) and ifr >= block:
        _emit(failures, exp.get("gate_status") == "IMPUTED_DATA_BLOCK",
              f"imputed_field_ratio>={block} but gate_status != IMPUTED_DATA_BLOCK")

    # 4) 게이트 산식 재현 (weighted_coverage / imputed_field_ratio / honest cap)
    dc = exp.get("domain_coverage") or {}
    dw = exp.get("domain_weights") or {}
    if dc and dw:
        wc = sum(dw.get(k, 0) * v for k, v in dc.items())
        _emit(failures, abs(wc - (exp.get("weighted_coverage") or -1)) < EPS,
              f"weighted_coverage mismatch: recomputed={wc:.4f} stored={exp.get('weighted_coverage')}")
        _emit(failures, abs((1.0 - wc) - (exp.get("imputed_field_ratio") or -1)) < EPS,
              "imputed_field_ratio mismatch (expected 1 - weighted_coverage)")
        raw = exp.get("raw_confidence_cap_basis")
        ech = exp.get("effective_confidence_honest")
        if isinstance(raw, (int, float)) and isinstance(ech, (int, float)):
            expect = raw * (0.4 + 0.6 * wc)
            _emit(failures, abs(expect - ech) < 0.2,
                  f"effective_confidence_honest mismatch: expected={expect:.1f} stored={ech}")

    # 5) failed_metrics ↔ status 정합
    fm = fv.get("failed_metrics") or []
    _emit(failures, (fv.get("status") == "failed") == (len(fm) > 0),
          "status/failed_metrics inconsistent")
    _emit(failures, fv.get("investment_decision_allowed") == (fv.get("status") == "passed"),
          "investment_decision_allowed inconsistent with status")

    if failures:
        for f in failures:
            print("FAIL:", f)
        print(f"VALIDATE_ENGINE_AUDIT_V1: FAIL ({len(failures)} issue(s))")
        return 1

    print(f"VALIDATE_ENGINE_AUDIT_V1: OK | engine_status={fv.get('status')} "
          f"gate={exp.get('gate_status')} imputed_field_ratio={exp.get('imputed_field_ratio')} "
          f"honest_cap={exp.get('effective_confidence_honest')} (raw={exp.get('raw_confidence_cap_basis')})")

    if args.strict and fv.get("status") != "passed":
        print("STRICT_FAIL: final_verdict.status != passed "
              f"-> failed_metrics={fm}")
        return 1
    return 0


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