"""SUMO SAC training entrypoint."""
import os
import copy
import yaml
import numpy as np
import matplotlib

matplotlib.use("Agg")
from tqdm import tqdm

from envs.edge_vsl_env import SUMOEdgeVSLEnvironment
from envs.reward_system import REWARD_COMPONENT_COLUMNS, average_reward_components, init_reward_component_totals
from agents.sac_agent import SACAgent
from utils.config import get_agent_config, get_training_config
from utils.episode_artifacts import save_training_episode_artifacts
from utils.logger import TrainingLogger
from utils.plot import plot_training_curves
from utils.run_dirs import resolve_run_dirs, write_shared_run_config


def train_sumo_sac(log_dir=None, checkpoint_dir=None, run_timestamp=None):
    """Train SAC under the SUMO+TraCI VSL environment."""
    with open("config_sumo_vsl.yaml", "r", encoding="utf-8") as f:
        config = yaml.safe_load(f)

    agent_config = get_agent_config(config, "sac")
    train_config = get_training_config(config)

    _, checkpoint_dir, log_dir = resolve_run_dirs(
        "sac",
        log_dir=log_dir,
        checkpoint_dir=checkpoint_dir,
        run_timestamp=run_timestamp,
    )
    os.makedirs(checkpoint_dir, exist_ok=True)
    os.makedirs(log_dir, exist_ok=True)
    runtime_config = copy.deepcopy(config)
    runtime_config.setdefault("runtime", {})["output_dir"] = log_dir
    runtime_config["runtime"]["evaluation_mode"] = False
    write_shared_run_config(
        runtime_config,
        log_dir=log_dir,
        checkpoint_dir=checkpoint_dir,
        run_timestamp=run_timestamp,
    )

    logger = TrainingLogger(log_dir, "sac")
    env = SUMOEdgeVSLEnvironment(runtime_config)

    state_dim = env.state_dim
    action_dims = [env.action_dim] * env.num_controlled_edges

    print("=" * 70)
    print("SAC training - SUMO+TraCI VSL environment")
    print("=" * 70)
    print(f"  State dim: {state_dim}")
    print(f"  Action space: {action_dims}")
    print(f"  Episode steps: {env.episode_length}")
    print(f"  Control interval: {env.control_interval}s")
    print(f"  Learning rate: {agent_config.get('learning_rate', 3e-4)}")
    print(f"  Device: {agent_config.get('device', 'cuda')}")
    print()

    agent = SACAgent(
        state_dim=state_dim,
        action_dims=action_dims,
        learning_rate=agent_config.get("learning_rate", 3e-4),
        buffer_size=agent_config.get("buffer_size", 100000),
        learning_starts=agent_config.get("learning_starts", 1000),
        batch_size=agent_config.get("batch_size", 256),
        tau=agent_config.get("tau", 0.005),
        gamma=agent_config.get("gamma", 0.99),
        ent_coef=agent_config.get("ent_coef", "auto"),
        target_entropy=agent_config.get("target_entropy", "auto"),
        target_update_interval=agent_config.get("target_update_interval", 1),
        log_std_init=agent_config.get("log_std_init", -3.0),
        device=agent_config.get("device", "cuda"),
        actor_hidden_dims=agent_config.get("actor_hidden_dims"),
        critic_hidden_dims=agent_config.get("critic_hidden_dims"),
        activation_fn=agent_config.get("activation_fn", "relu"),
    )

    num_episodes = train_config["num_episodes"]
    save_freq = train_config.get("save_freq", 50)
    log_freq = train_config.get("log_freq", 10)
    base_seed = train_config.get("random_seed", 42)

    episode_rewards = []
    episode_throughputs = []
    episode_mean_speeds = []
    episode_speed_variance_norms = []
    episode_hard_brakes = []
    best_reward = -float("inf")

    print("Starting training...\n")

    try:
        for episode in range(1, num_episodes + 1):
            seed = base_seed + episode
            state = env.reset(seed=seed)
            episode_reward = 0.0
            episode_throughput = 0.0
            episode_speed = 0.0
            episode_speed_variance_norm = 0.0
            episode_reward_components = init_reward_component_totals()
            episode_brakes = 0
            done = False
            step = 0

            pbar = tqdm(
                total=env.episode_length,
                desc=f"Ep {episode}/{num_episodes}",
                leave=False,
            )

            while not done:
                action, _, _ = agent.select_action(state, deterministic=False)
                next_state, reward, done, info = env.step(action)

                agent.store_transition(state, action, reward, next_state, done)
                agent.update()

                episode_reward += reward
                episode_throughput += info["throughput"]
                episode_speed += info["mean_speed_kmh"]
                episode_speed_variance_norm += info["speed_variance_norm"]
                for column in REWARD_COMPONENT_COLUMNS:
                    episode_reward_components[column] += float(info.get(column, 0.0))
                episode_brakes += info["num_hard_brakes"]
                state = next_state
                step += 1

                pbar.set_postfix(
                    r=f"{episode_reward:.1f}",
                    tp=f"{info['throughput']:.0f}",
                    v=f"{info['mean_speed_kmh']:.1f}",
                )
                pbar.update(1)

            pbar.close()

            avg_tp = episode_throughput / max(step, 1)
            avg_speed = episode_speed / max(step, 1)
            avg_speed_variance_norm = episode_speed_variance_norm / max(step, 1)
            avg_reward_components = average_reward_components(episode_reward_components, step)
            episode_rewards.append(episode_reward)
            episode_throughputs.append(avg_tp)
            episode_mean_speeds.append(avg_speed)
            episode_speed_variance_norms.append(avg_speed_variance_norm)
            episode_hard_brakes.append(episode_brakes)

            logger.log(
                episode,
                episode_reward,
                avg_tp,
                avg_speed,
                speed_variance_norm=avg_speed_variance_norm,
                reward_components=avg_reward_components,
                hard_brakes=episode_brakes,
            )

            episode_summary = {
                "episode": episode,
                "reward": float(episode_reward),
                "avg_throughput": float(avg_tp),
                "avg_mean_speed_kmh": float(avg_speed),
                "avg_speed_variance_norm": float(avg_speed_variance_norm),
                "hard_brakes": int(episode_brakes),
            }
            for column, value in avg_reward_components.items():
                episode_summary[f"avg_{column}"] = float(value)
            save_training_episode_artifacts(
                log_dir=log_dir,
                episode=episode,
                episode_metrics=env.episode_metrics,
                control_edges=env.control_edges,
                summary=episode_summary,
            )

            if episode_reward > best_reward:
                best_reward = episode_reward
                agent.save(os.path.join(checkpoint_dir, "model_best"))

            if episode % log_freq == 0:
                recent_rewards = episode_rewards[-log_freq:]
                print(f"\nEpisode {episode}/{num_episodes}")
                print(f"  Reward: {episode_reward:.2f} (Avg: {np.mean(recent_rewards):.2f})")
                print(f"  Throughput: {avg_tp:.1f} veh/h")
                print(f"  Mean Speed: {avg_speed:.1f} km/h")
                print(f"  Normalized Speed Variance: {avg_speed_variance_norm:.4f}")
                print(
                    "  Reward Components: "
                    + ", ".join(
                        f"{column}={avg_reward_components[column]:.3f}"
                        for column in REWARD_COMPONENT_COLUMNS
                    )
                )

            if episode % save_freq == 0:
                agent.save(os.path.join(checkpoint_dir, f"model_ep{episode}"))

    except KeyboardInterrupt:
        print("\nTraining interrupted, saving current model...")
        agent.save(os.path.join(checkpoint_dir, "model_interrupted"))
    finally:
        env.close()

    agent.save(os.path.join(checkpoint_dir, f"model_ep{num_episodes}"))

    plot_training_curves(
        episode_rewards,
        episode_throughputs,
        episode_mean_speeds,
        episode_speed_variance_norms,
        episode_hard_brakes,
        save_path=os.path.join(log_dir, "training_curves.png"),
    )

    print("=" * 70)
    print("Training complete")
    print(f"  Best reward: {best_reward:.2f}")
    print(f"  Checkpoints: {checkpoint_dir}")
    print(f"  Logs: {log_dir}")
    print("=" * 70)