#!/usr/bin/env python3
"""Apply the frozen PickupLoc clean-feasibility gate."""

import argparse
import json
from pathlib import Path


ROOT = Path(__file__).resolve().parents[1]
DEFAULT_RESULTS = ROOT / "results" / "babyai_shared" / "pickup_p0"
DEFAULT_OUT = ROOT / "results" / "babyai_shared" / "pickup_p0_analysis.json"


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--results", type=Path, default=DEFAULT_RESULTS)
    parser.add_argument("--out", type=Path, default=DEFAULT_OUT)
    args = parser.parse_args()
    candidates = []
    for depth in (2, 4):
        for learning_rate in (0.01, 0.03):
            records = {}
            sources = []
            for condition in ("bp", "clean_kp"):
                path = args.results / (
                    f"d{depth}_lr{learning_rate}_{condition}.json")
                with open(path, encoding="utf-8") as handle:
                    records[condition] = json.load(handle)
                sources.append(str(path.relative_to(ROOT)))
            bp, kp = records["bp"], records["clean_kp"]
            bp_success = 100.0 * bp["rollout"]["success"]
            kp_success = 100.0 * kp["rollout"]["success"]
            lesion_success = 100.0 * bp["mission_lesion_rollout"]["success"]
            checks = {
                "both_finite": bool(bp["finite"] and kp["finite"]),
                "bp_success_at_least_70": bp_success >= 70.0,
                "clean_kp_success_at_least_60": kp_success >= 60.0,
                "bp_mission_lesion_drop_at_least_20": (
                    bp_success - lesion_success >= 20.0),
            }
            candidates.append({
                "hidden_layers": depth,
                "learning_rate": learning_rate,
                "bp_rollout_success_percent": bp_success,
                "clean_kp_rollout_success_percent": kp_success,
                "bp_mission_lesion_success_percent": lesion_success,
                "bp_mission_lesion_drop_points": bp_success - lesion_success,
                "bp_action_accuracy_percent": (
                    100.0 * bp["validation"]["accuracy"]),
                "clean_kp_action_accuracy_percent": (
                    100.0 * kp["validation"]["accuracy"]),
                "eligible": all(checks.values()),
                "checks": checks,
                "source_files": sources,
            })
    eligible = [row for row in candidates if row["eligible"]]
    selected = max(eligible, key=lambda row: (
        row["clean_kp_rollout_success_percent"],
        row["clean_kp_action_accuracy_percent"],
        -row["hidden_layers"], -row["learning_rate"])) if eligible else None
    best_clean = max(candidates, key=lambda row: (
        row["clean_kp_rollout_success_percent"],
        row["clean_kp_action_accuracy_percent"]))
    report = {
        "stage": "babyai_pickup_p0_clean_feasibility",
        "gate": "pass" if selected is not None else "fail",
        "candidates": candidates,
        "selected": selected,
        "best_clean_candidate_for_diagnosis": best_clean,
        "raw_or_sdil_results_run_or_read": False,
        "test_split_generated_or_read": False,
        "decision": (
            "Add task memory and repeat a clean-only gate; do not run raw or "
            "SDIL on the present feedforward policy."
            if selected is None else "Open the frozen shared-feedback endpoint."),
    }
    args.out.parent.mkdir(parents=True, exist_ok=True)
    with open(args.out, "w", encoding="utf-8") as handle:
        json.dump(report, handle, indent=2, sort_keys=True)
        handle.write("\n")
    print(json.dumps(report, indent=2, sort_keys=True))


if __name__ == "__main__":
    main()