"""Method-selection + direct reducer diagnostic (not answer persistence).

Public holdout-v5 cases only. Truth goes exclusively to the offline answer
oracle; the frozen collection, holdout, delivery and stopping gates stay live.
The bridge does not exercise option classification, owned contrast registration
or persistV9ChoiceAction. Early stop or a missing probe is not six-question
validation. This is not the accepted single-chart M2 experiment.
"""
from __future__ import annotations

import argparse
import hashlib
import json
import platform
from datetime import timedelta
from pathlib import Path

from scripts.research import varga_resolution_lib as vr
from scripts.research.varga_resolution_production_bridge import ProductionSegmentBridge
from scripts.rectification.api_service import score_candidates


def main() -> int:
    parser = argparse.ArgumentParser()
    parser.add_argument("--dataset", default=str(vr.HOLDOUT_V5))
    parser.add_argument("--output", required=True)
    parser.add_argument("--cache-dir", required=True)
    parser.add_argument("--limit", type=int)
    parser.add_argument("--radii", default="10,30,60")
    args = parser.parse_args()
    cases = vr.load_cases(Path(args.dataset))
    if args.limit:
        cases = cases[:args.limit]
    cache = Path(args.cache_dir)
    cache.mkdir(parents=True, exist_ok=True)
    bridge = ProductionSegmentBridge()
    records = []
    try:
        for case in cases:
            for radius in map(int, args.radii.split(",")):
                replay = vr.CaseReplay(case, radius, cache_dir=cache)
                key = hashlib.sha256(json.dumps(replay.request, sort_keys=True).encode()).hexdigest()
                response_file = cache / f"score-api-{key}.json"
                if response_file.exists():
                    response = json.loads(response_file.read_text(encoding="utf-8"))
                else:
                    response = score_candidates(replay.request)
                    response_file.write_text(json.dumps(response, ensure_ascii=False), encoding="utf-8")
                intervals = response["decision_receipt"]["candidate_intervals"]
                birth = case["birth"]
                origin = vr.birth_datetime(case)
                snapshot = {"birth_date": str(birth["date"]), "reported_birth_time": replay.true_time,
                            "birth_time_source": "approximate", "timezone_offset": replay.request["tz"],
                            "timezone_id": replay.request.get("timezone_id"),
                            "latitude": replay.request["lat"], "longitude": replay.request["lon"]}
                evidence = [{"id": str(event["id"]), "status": "confirmed", "domain": event["domain"],
                             "datePrecision": event.get("precision", "day"), "occurredFrom": event.get("date_start", event.get("date")),
                             "occurredTo": event.get("date_end", event.get("date")), "eventKind": event.get("event_kind", "dated_event"),
                             "summary": event.get("description", "Public calibration event")}
                            for event in replay.request["events"]]
                minutes = [{"offset": offset + radius, "date": (origin + timedelta(minutes=offset)).strftime("%Y-%m-%d"),
                            "time": (origin + timedelta(minutes=offset)).strftime("%H:%M"),
                            "signs": {chart: signs[chart] for chart in ("D1", "D9", "D10")}}
                           for offset, signs in sorted(replay.signs.items())]
                for enabled in (False, True):
                    result = bridge.call({"operation": "method_replay", "request": replay.request,
                                          "response": response, "snapshot": snapshot,
                                          "range": {"start_time": replay.request["start_time"], "end_time": replay.request["end_time"],
                                                    "candidate_intervals": intervals},
                                          "evidence": evidence, "minutes": minutes, "targets": ["D1", "D9", "D10"],
                                          "enabled": enabled, "true_time": replay.true_time, "ask_count": vr.ASK_COUNT})
                    state = result.pop("state")
                    raw = bridge.call({"operation": "weights", "rows": [{"time": row["time"], "score": row.get("raw_posterior_score", 0),
                                       "cluster_times": row.get("cluster_times")} for row in state["candidates"]],
                                       "eliminated": [row["time"] for row in state["candidates"] if row.get("raw_eliminated")]})
                    metrics = {}
                    for chart in ("D9", "D10"):
                        segments = replay.segments([chart])
                        summary = bridge.call({"operation": "summary_weights", "weights": raw, "segments": segments,
                                               "offsets": {stamp: vr.offset_of(stamp, replay.true_time) for stamp in raw}, "truth_offset": 0})
                        metrics[chart] = summary
                    records.append({"case_id": replay.case_id, "radius": radius, "strategy": "joint" if enabled else "frozen",
                                    "api_response_sha256": hashlib.sha256(response_file.read_bytes()).hexdigest(),
                                    **result, "metrics": metrics})
                print(f"method replay completed radius={radius}", flush=True)
    finally:
        bridge.close()
    aggregate = {}
    for radius in map(int, args.radii.split(",")):
        for strategy in ("frozen", "joint"):
            rows = [row for row in records if row["radius"] == radius and row["strategy"] == strategy]
            aggregate[f"{radius}:{strategy}"] = {"cases": len(rows), "asked": sum(len(row["asked"]) for row in rows),
                "D9": {"truth_kept": sum(row["metrics"]["D9"]["truth_retained"] for row in rows),
                       "top_hit": sum(row["metrics"]["D9"]["top_is_truth"] for row in rows)},
                "D10": {"truth_kept": sum(row["metrics"]["D10"]["truth_retained"] for row in rows),
                        "top_hit": sum(row["metrics"]["D10"]["top_is_truth"] for row in rows)}}
    output = {"scope": "method-selection + direct reducer diagnostic (not answer persistence); real score API/parser/inference/catalog/method/decision, default joint targets; early stop/missing probe is not six-question validation",
              "python_version": platform.python_version(), "dataset_sha256": hashlib.sha256(Path(args.dataset).read_bytes()).hexdigest(),
              "aggregate": aggregate, "records": records}
    Path(args.output).write_text(json.dumps(output, ensure_ascii=False, sort_keys=True, indent=2) + "\n", encoding="utf-8")
    return 0


if __name__ == "__main__":
    raise SystemExit(main())