"""Audit and apply the frozen C2 calibration-quality development rule."""
import glob
import json
import math
import os
import statistics


ROOT = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "results")
PREFIX = "c2_calibdev_v1_"
DIRECTIONS = (4, 16)
DEPTHS = (1, 4)
MODEL_SEEDS = (0, 1, 2)


def audit_row(path, row):
    args = row["args"]
    required = {
        "mode": "sdil", "dataset": "tentmap", "width": 8,
        "act": "relu", "residual": 1, "vectorizer_mode": "context_gated",
        "epochs": 80, "batch_size": 256, "eta": 0.03, "momentum": 0.9,
        "eta_A": 0.01, "eta_P": 0.002, "pert_sigma": 0.01,
        "pert_every": 4, "pert_mode": "layerwise", "traffic_mode": "none",
        "nuis_rho": 0.0, "use_residual": 1, "learn_A": 1, "learn_P": 1,
        "p_neutral": 1, "task_train_examples": 10000,
        "task_test_examples": 5000, "task_levels": 2, "task_n_in": 1,
        "task_seed": 0, "val_examples": 2000, "split_seed": 2027,
        "eval_split": "validation", "eval_every": 0,
        "diagnostics": "alignment", "diagnostics_schedule": "final",
        "probe_bs": 512,
    }
    mismatches = {key: (args.get(key), expected) for key, expected in required.items()
                  if args.get(key) != expected}
    expected_lesion = 1.0 / 3.0 if args["depth"] == 4 else 0.0
    if abs(args.get("residual_lesion_fraction", 0.0) - expected_lesion) > 1e-12:
        mismatches["residual_lesion_fraction"] = (
            args.get("residual_lesion_fraction"), expected_lesion)
    if args.get("pert_ndirs") not in DIRECTIONS:
        mismatches["pert_ndirs"] = (args.get("pert_ndirs"), DIRECTIONS)
    if mismatches:
        raise RuntimeError(f"protocol mismatch {path}: {mismatches}")
    if row["final"].get("eval_split") != "validation":
        raise RuntimeError(f"test-contaminated development row: {path}")
    if any("eval_acc" in step or "cos_r_negg" in step for step in row.get("steps", [])):
        raise RuntimeError(f"intermediate held-out metric/diagnostic: {path}")
    split = row.get("split", {})
    if (not split.get("split_from_training_only")
            or split.get("validation_examples") != 2000
            or split.get("evaluation_split") != "validation"):
        raise RuntimeError(f"invalid validation split {path}: {split}")
    if row.get("provenance", {}).get("git_dirty") is not False:
        raise RuntimeError(f"dirty or unknown source provenance: {path}")
    if args["depth"] == 4:
        lesion = row["final"].get("residual_lesion")
        if not lesion or lesion.get("lesioned_layers") != [3]:
            raise RuntimeError(f"incorrect d4 lesion: {path}")


def mean_sd(values):
    return statistics.mean(values), statistics.stdev(values)


def main():
    paths = sorted(glob.glob(os.path.join(ROOT, PREFIX + "*.json")))
    rows = {}
    commits = set()
    split_hashes = set()
    for path in paths:
        with open(path) as handle:
            row = json.load(handle)
        audit_row(path, row)
        args = row["args"]
        key = (args["pert_ndirs"], args["depth"], args["seed"])
        if key in rows:
            raise RuntimeError(f"duplicate row: {key}")
        rows[key] = row
        commits.add(row["provenance"]["git_commit"])
        split_hashes.add(row["split"]["validation_index_sha256"])
    expected = {(directions, depth, seed) for directions in DIRECTIONS
                for depth in DEPTHS for seed in MODEL_SEEDS}
    if set(rows) != expected or len(commits) != 1 or len(split_hashes) != 1:
        raise RuntimeError(f"incomplete/mixed pilot: rows={len(rows)}, "
                           f"missing={expected - set(rows)}, extra={set(rows) - expected}, "
                           f"commits={commits}, split_hashes={split_hashes}")

    summaries = {}
    print(f"commit={next(iter(commits))} rows={len(rows)} task_seed=0")
    print("| K | depth | validation (%) | depth gain | d4 lesion | d4 work/ordinary |")
    print("|---:|---:|---:|---:|---:|---:|")
    for directions in DIRECTIONS:
        accs = {depth: [100 * rows[(directions, depth, seed)]["final"]["val_acc"]
                        for seed in MODEL_SEEDS] for depth in DEPTHS}
        gains = [deep - shallow for shallow, deep in zip(accs[1], accs[4])]
        nonfinite = [seed for depth in DEPTHS for seed in MODEL_SEEDS
                     if not math.isfinite(rows[(directions, depth, seed)]["final"]
                                          .get("val_loss", math.nan))]
        lesions = [100 * rows[(directions, 4, seed)]["final"]["residual_lesion"]
                   ["lesion_acc_drop"] for seed in MODEL_SEEDS]
        work_ratios = [rows[(directions, 4, seed)]["cost"]
                       ["training_forward_equivalent_examples"]
                       / rows[(directions, 4, seed)]["cost"]
                       ["ordinary_training_forward_examples"] for seed in MODEL_SEEDS]
        summaries[directions] = {
            "d4_mean": statistics.mean(accs[4]),
            "finite": not nonfinite,
        }
        for depth in DEPTHS:
            mean, sd = mean_sd(accs[depth])
            gain_text = "--" if depth == 1 else f"{statistics.mean(gains):+.3f}"
            lesion_text = "--" if depth == 1 else f"{statistics.mean(lesions):+.3f}"
            work_text = "--" if depth == 1 else f"{statistics.mean(work_ratios):.1f}x"
            print(f"| {directions} | {depth} | {mean:.3f} +/- {sd:.3f} | "
                  f"{gain_text} | {lesion_text} | {work_text} |")
        print(f"K={directions} positive gains={sum(value > 0 for value in gains)}/3; "
              f"positive lesions={sum(value > 0 for value in lesions)}/3; "
              f"nonfinite={nonfinite}")

    eligible = [directions for directions in DIRECTIONS if summaries[directions]["finite"]]
    if not eligible:
        raise RuntimeError("no finite calibration candidate")
    best_mean = max(summaries[directions]["d4_mean"] for directions in eligible)
    selected = min(directions for directions in eligible
                   if summaries[directions]["d4_mean"] >= best_mean - 1.0)
    print(f"C2 calibration-quality development selection: K={selected} layerwise/e4 "
          f"(best d4={best_mean:.3f}%, selected d4={summaries[selected]['d4_mean']:.3f}%)")


if __name__ == "__main__":
    main()