#!/usr/bin/env python3
"""Audit and select the frozen convolutional-HFA short screen."""
import argparse
import glob
import json
import math
import os


RATES = (0.01, 0.03, 0.1)


def read(path):
    with open(path) as handle:
        record = json.load(handle)
    args = record["args"]
    expected = {
        "mode": "hfa", "depth": 20, "width": 16, "seed": 0,
        "loader_seed": 0, "epochs": 20, "train_limit": 10000,
        "val_examples": 5000, "split_seed": 2027,
        "eval_split": "validation", "eval_every": 0,
        "augment_train": 1, "lr_schedule": "cosine", "warmup_epochs": 0,
        "momentum": 0.9, "weight_decay": 1e-4,
        "normalization": "batchnorm", "output_lr": 0.1,
        "a_scale": 1.0, "alignment_probe": 32,
    }
    for key, value in expected.items():
        if args.get(key) != value:
            raise ValueError(f"{path}: {key} drift")
    if float(args["lr"]) not in RATES:
        raise ValueError(f"{path}: unregistered learning rate")
    if record["provenance"]["git_tracked_dirty"]:
        raise ValueError(f"tracked-dirty result: {path}")
    protocol = record["evaluation_protocol"]
    if protocol["test_evaluations"] or protocol["test_used_for_selection"]:
        raise ValueError(f"short screen touched test: {path}")
    accuracy = float(record["final"]["accuracy"])
    loss = float(record["final"]["loss"])
    diagnostics = record.get("diagnostics") or {}
    early = float(diagnostics.get("early_third_mean", float("nan")))
    finite = (bool(record["final"]["finite"])
              and math.isfinite(accuracy + loss + early))
    return {
        "path": path, "lr": float(args["lr"]), "accuracy": accuracy,
        "loss": loss, "early_third_alignment": early, "finite": finite,
        "total_macs": int(record["work"]["total_macs_estimate"]),
        "fixed_feedback_parameters": int(
            record["architecture"]["fixed_feedback_parameters"]),
        "logical_batch_loss_queries": int(
            record["work"]["logical_batch_loss_queries"]),
        "peak_memory_allocated_bytes": int(
            record["hardware"]["peak_memory_allocated_bytes"]),
        "wall_s": float(record["timing"]["total_timed_wall_s"]),
        "source_commit": record["provenance"]["git_commit"],
    }


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--input", default="results/oral_a_hfa_short")
    parser.add_argument(
        "--out", default="results/oral_a_hfa_short_selection.json")
    args = parser.parse_args()
    rows = [read(path) for path in sorted(
        glob.glob(os.path.join(args.input, "*.json")))]
    if len(rows) != len(RATES):
        raise ValueError(f"expected {len(RATES)} HFA-S1 runs, found {len(rows)}")
    if {row["lr"] for row in rows} != set(RATES):
        raise ValueError("HFA-S1 learning-rate grid is incomplete")
    if len({row["source_commit"] for row in rows}) != 1:
        raise ValueError("HFA-S1 source commits differ")
    finite = [row for row in rows if row["finite"]]
    if not finite:
        selected = None
        status = "failed_no_finite_candidate"
    else:
        selected = sorted(
            finite, key=lambda row: (
                -row["accuracy"], row["total_macs"], row["lr"]))[0]
        status = ("selected_full_open" if selected["accuracy"] >= 0.50
                  else "selected_full_closed")
    output = {
        "protocol": "convolutional_hfa_S1_v1", "status": status,
        "rows": rows, "selected": selected,
        "comparators": {
            "matched_short_dfa_accuracy": 0.3716,
            "matched_short_failed_v1_sdil_accuracy": 0.4198,
            "matched_short_bp_accuracy": 0.7494,
        },
        "full_validation_threshold": 0.50,
        "confirmation_test_seeds_touched": False,
    }
    os.makedirs(os.path.dirname(os.path.abspath(args.out)), exist_ok=True)
    with open(args.out, "w") as handle:
        json.dump(output, handle, indent=2, sort_keys=True)
        handle.write("\n")
    print(json.dumps({
        "status": status, "selected": selected,
        "rows": [{key: row[key] for key in (
            "lr", "accuracy", "early_third_alignment", "finite")}
                 for row in rows],
    }, indent=2))


if __name__ == "__main__":
    main()