"""Hidden-family refinement with known-family replay for conditional DENIM v2."""

from __future__ import annotations

import copy
import json
from collections import defaultdict

import torch

from src.t2_denim_conditional import ConditionalDENIM
from src.train_t2_denim_conditional_v2 import (
    ARTIFACT_DIR,
    MODEL_DIR,
    _train_stage,
    evaluate,
    load_all_trajectories,
    transitions,
)


def run(*, steps: int = 1200) -> dict[str, object]:
    trajectories = load_all_trajectories()
    hidden_name = "hidden_three_memory_tabulated_hardening"
    known_train = [x for x in trajectories if x.material != hidden_name and x.role == "train"]
    known_validation = [x for x in trajectories if x.material != hidden_name and x.role == "validation"]
    hidden_train = [x for x in trajectories if x.material == hidden_name and x.role == "train"]
    hidden_validation = [x for x in trajectories if x.material == hidden_name and x.role == "validation"]
    data = {
        "known_train": transitions(known_train),
        "known_validation": transitions(known_validation),
        "hidden_train": transitions(hidden_train),
        "hidden_validation": transitions(hidden_validation),
    }
    checkpoint = torch.load(MODEL_DIR / "denim_conditional_v2.pt", map_location="cpu", weights_only=False)
    model = ConditionalDENIM()
    model.load_state_dict(checkpoint["state_dict"])
    refined = copy.deepcopy(model)
    training = _train_stage(
        refined,
        [data["known_train"], data["hidden_train"], data["hidden_train"], data["hidden_train"]],
        [data["known_validation"], data["hidden_validation"], data["hidden_validation"], data["hidden_validation"]],
        steps=steps,
        seed=20260930,
        learning_rate=5.0e-4,
    )
    groups: dict[str, list] = defaultdict(list)
    for trajectory in trajectories:
        groups[trajectory.evaluation].append(trajectory)
    selected = (
        "hidden_frozen_test",
        "test_path_ood",
        "test_amplitude_ood",
        "test_long_horizon",
        "test_discretization",
    )
    metrics: dict[str, object] = {
        "training": training,
        "evaluation": {name: evaluate(refined, groups[name]) for name in selected},
    }
    known_test = [x for x in trajectories if x.material != hidden_name and x.role == "test"]
    metrics["evaluation"]["known_strict_test_all"] = evaluate(refined, known_test)
    torch.save(
        {
            "state_dict": refined.state_dict(),
            "architecture": "ConditionalDENIM(channels=2, embedding=32, hidden=48)",
            "stage": "hidden-focused refinement with known-family replay",
        },
        MODEL_DIR / "denim_conditional_v2_refined.pt",
    )
    ARTIFACT_DIR.mkdir(parents=True, exist_ok=True)
    (ARTIFACT_DIR / "refined_metrics.json").write_text(
        json.dumps(metrics, indent=2) + "\n", encoding="utf-8"
    )
    print(json.dumps(metrics["evaluation"], indent=2))
    return metrics


if __name__ == "__main__":
    run()