"""Physical and runtime audit for the refined conditional DENIM v2."""

from __future__ import annotations

import json
import time
from collections import defaultdict

import torch

from src.t2_denim_conditional import ConditionalDENIM, rollout
from src.train_t2_denim_conditional_v2 import ARTIFACT_DIR, MODEL_DIR, load_all_trajectories


def run() -> dict[str, object]:
    checkpoint = torch.load(
        MODEL_DIR / "denim_conditional_v2_refined.pt",
        map_location="cpu",
        weights_only=False,
    )
    model = ConditionalDENIM()
    model.load_state_dict(checkpoint["state_dict"])
    model.eval()
    trajectories = load_all_trajectories()
    groups: dict[str, list] = defaultdict(list)
    for trajectory in trajectories:
        groups[trajectory.evaluation].append(trajectory)
    selected = (
        groups["hidden_frozen_test"][:8]
        + groups["test_path_ood"][:8]
        + groups["test_long_horizon"][:8]
    )
    strain = torch.stack([item.strain for item in selected])
    started = time.perf_counter()
    with torch.no_grad():
        result = rollout(
            strain,
            torch.tensor([item.young for item in selected]),
            torch.tensor([item.poisson for item in selected]),
            torch.tensor([item.yield_stress for item in selected]),
            torch.stack([item.descriptor for item in selected]),
            model,
            bisection_iterations=24,
        )
    seconds = time.perf_counter() - started
    reference = torch.stack([item.stress for item in selected])
    error = result["stress"] - reference
    active = result["plastic_increment"] > 1.0e-11
    report = {
        "model": "material-conditioned DENIM v2 refined",
        "parameter_count": sum(parameter.numel() for parameter in model.parameters()),
        "audit_trajectory_count": len(selected),
        "audit_state_point_count": int(strain.shape[0] * strain.shape[1]),
        "stress_rmse_mpa": float(torch.sqrt(error.square().mean()) / 1.0e6),
        "maximum_yield_residual_pa": float(result["yield_residual"][active].abs().max()),
        "minimum_peeq_increment": float(torch.diff(result["peeq"], dim=1).min()),
        "maximum_plastic_strain_trace": float(
            result["plastic_strain"][..., :3].sum(dim=-1).abs().max()
        ),
        "maximum_memory_trace_pa": float(
            result["memories"][..., :3].sum(dim=-1).abs().max()
        ),
        "all_finite": all(torch.isfinite(value).all() for value in result.values()),
        "inference_seconds": seconds,
        "state_points_per_second": float(strain.shape[0] * strain.shape[1] / seconds),
        "descriptor_hidden_hardening_entries_zero": bool(
            torch.count_nonzero(torch.stack([x.descriptor[3:10] for x in selected])) == 0
        ),
        "scope": "material-point audit; conditional v2 is not yet an AgentFEM global provider bundle",
    }
    ARTIFACT_DIR.mkdir(parents=True, exist_ok=True)
    (ARTIFACT_DIR / "physical_runtime_validation.json").write_text(
        json.dumps(report, indent=2) + "\n", encoding="utf-8"
    )
    print(json.dumps(report, indent=2))
    return report


if __name__ == "__main__":
    run()