#!/usr/bin/env python3
"""Summarize the frozen PickupLoc shared-feedback endpoint."""

import argparse
import json
from pathlib import Path
import statistics


ROOT = Path(__file__).resolve().parents[1]
DEFAULT_RESULTS = ROOT / "results" / "babyai_shared" / "pickup_p2"
DEFAULT_OUT = ROOT / "results" / "babyai_shared" / "pickup_p2_analysis.json"
SEEDS = (4101, 4102, 4103)
CONDITIONS = ("bp", "clean_kp", "raw_shared", "sdil")


def summarize(values):
    return {
        "values": values,
        "mean": statistics.mean(values),
        "sample_std": statistics.stdev(values),
    }


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()
    records = {}
    for seed in SEEDS:
        for condition in CONDITIONS:
            path = args.results / f"seed{seed}_{condition}.json"
            with open(path, encoding="utf-8") as handle:
                records[(seed, condition)] = json.load(handle)

    summaries = {}
    for condition in CONDITIONS:
        rows = [records[(seed, condition)] for seed in SEEDS]
        summaries[condition] = {
            "rollout_success_percent": summarize([
                100.0 * row["rollout"]["success"] for row in rows]),
            "expert_action_accuracy_percent": summarize([
                100.0 * row["validation"]["accuracy"] for row in rows]),
            "mission_lesion_rollout_success_percent": summarize([
                100.0 * row["mission_lesion_rollout"]["success"]
                for row in rows]),
            "all_finite": all(row["finite"] for row in rows),
        }
    sdil_minus_raw = [100.0 * (
        records[(seed, "sdil")]["rollout"]["success"]
        - records[(seed, "raw_shared")]["rollout"]["success"])
                      for seed in SEEDS]
    clean_minus_raw = [100.0 * (
        records[(seed, "clean_kp")]["rollout"]["success"]
        - records[(seed, "raw_shared")]["rollout"]["success"])
                       for seed in SEEDS]
    predictor_r2 = [statistics.mean(
        row["mean_per_cell_r2"]
        for row in records[(seed, "sdil")]["predictor"])
                    for seed in SEEDS]
    residual_ratios = [max(
        row["residual_context_rms_ratio"]
        for row in records[(seed, "sdil")]["predictor"])
                       for seed in SEEDS]
    checks = {
        "all_runs_finite": all(row["finite"] for row in records.values()),
        "clean_kp_success_at_least_60_every_seed": all(
            records[(seed, "clean_kp")]["rollout"]["success"] >= 0.6
            for seed in SEEDS),
        "raw_below_clean_kp_every_seed": all(
            value > 0 for value in clean_minus_raw),
        "sdil_above_raw_every_seed": all(
            value > 0 for value in sdil_minus_raw),
        "sdil_nonzero_success_every_seed": all(
            records[(seed, "sdil")]["rollout"]["success"] > 0
            for seed in SEEDS),
    }
    report = {
        "stage": "babyai_pickup_p2_analysis",
        "gate": "pass" if all(checks.values()) else "fail",
        "checks": checks,
        "conditions": summaries,
        "paired_clean_kp_minus_raw_rollout_points": summarize(clean_minus_raw),
        "paired_sdil_minus_raw_rollout_points": summarize(sdil_minus_raw),
        "sdil_predictor": {
            "mean_per_cell_r2": summarize(predictor_r2),
            "maximum_layer_residual_context_rms_ratio": summarize(
                residual_ratios),
            "action_or_teaching_observations": 0,
        },
        "test_split_generated_or_read": False,
        "decision": (
            "Close the diagonal per-cell predictor on PickupLoc. Raw shared "
            "feedback fails as hypothesized, but this SDIL implementation does "
            "not recover it. Screen the originally specified population "
            "predictor using neutral prediction only before another 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()