"""
Directional Corridor MAPPO training script for SUMO + TraCI VSL.
"""
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.dcmappo_agent import DCMAPPOAgent
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
from utils.seeding import derive_seed, resolve_base_seed, set_global_seed


def train_sumo_dcmappo(log_dir=None, checkpoint_dir=None, run_timestamp=None):
    with open("config_sumo_vsl.yaml", "r", encoding="utf-8") as f:
        config = yaml.safe_load(f)

    agent_config = get_agent_config(config, "dcmappo")
    train_config = get_training_config(config)
    base_seed = resolve_base_seed(train_config)
    set_global_seed(base_seed)

    resolved_run_timestamp, checkpoint_dir, log_dir = resolve_run_dirs(
        "dcmappo",
        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
    runtime_config["runtime"]["run_timestamp"] = resolved_run_timestamp
    write_shared_run_config(
        runtime_config,
        log_dir=log_dir,
        checkpoint_dir=checkpoint_dir,
        run_timestamp=run_timestamp,
    )

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

    print("=" * 70)
    print("DC-MAPPO training - SUMO+TraCI VSL environment")
    print("=" * 70)
    print(f"  State dim: {env.state_dim}")
    print(f"  Agents: {env.num_edges}")
    print(f"  Actions per agent: {env.action_dim}")
    print(f"  Episode steps: {env.episode_length}")
    print(f"  Control interval: {env.control_interval}s")
    print(f"  Hidden dim: {agent_config.get('hidden_dim', 256)}")
    print(f"  Corridor blocks: {agent_config.get('num_corridor_blocks', 2)}")
    print(f"  LR: {agent_config.get('learning_rate', 3e-4)}")
    print(f"  Device: {agent_config.get('device', 'cuda')}")
    print(f"  Global seed: {base_seed if base_seed is not None else 'None (random)'}")
    print()

    agent = DCMAPPOAgent(
        state_dim=env.state_dim,
        num_agents=env.num_controlled_edges,
        num_actions=env.action_dim,
        edge_feature_dim=env.features_per_edge,
        total_edge_count=env.num_edges,
        controlled_start_index=env.controlled_edge_start_index,
        hidden_dim=agent_config.get("hidden_dim", 256),
        critic_hidden_dim=agent_config.get("critic_hidden_dim", 256),
        num_corridor_blocks=agent_config.get("num_corridor_blocks", 2),
        corridor_kernel_size=agent_config.get("corridor_kernel_size", 3),
        corridor_dropout=agent_config.get("corridor_dropout", 0.05),
        learning_rate=agent_config.get("learning_rate", 3e-4),
        gamma=agent_config.get("gamma", 0.99),
        gae_lambda=agent_config.get("gae_lambda", 0.95),
        clip_epsilon=agent_config.get("clip_epsilon", 0.2),
        value_coef=agent_config.get("value_coef", 0.5),
        entropy_coef=agent_config.get("entropy_coef", 0.01),
        max_grad_norm=agent_config.get("max_grad_norm", 0.5),
        ppo_epochs=agent_config.get("ppo_epochs", 4),
        minibatch_size=agent_config.get("batch_size", 15),
        device=agent_config.get("device", "cuda"),
        lr_schedule=agent_config.get("lr_schedule", "cosine"),
        total_episodes=train_config["num_episodes"],
    )

    num_episodes = train_config["num_episodes"]
    save_freq = train_config.get("save_freq", 50)
    log_freq = train_config.get("log_freq", 10)
    episode_rewards = []
    episode_throughputs = []
    episode_mean_speeds = []
    episode_speed_variance_norms = []
    episode_ttc_risks = []
    policy_losses = []
    value_losses = []
    entropies = []
    best_reward = -float("inf")

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

    try:
        for episode in range(1, num_episodes + 1):
            seed = derive_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_ttc_risk = 0.0
            done = False
            step = 0

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

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

                agent.store_transition(state, action, reward, value, log_probs, done)

                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_ttc_risk += float(info.get("ttc_risk_rate", 0.0))
                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()

            next_value = 0.0 if done else agent.get_value(next_state)
            train_stats = agent.update(next_value)

            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_ttc_risks.append(episode_ttc_risk)

            if train_stats:
                policy_losses.append(train_stats["policy_loss"])
                value_losses.append(train_stats["value_loss"])
                entropies.append(train_stats["entropy"])
                logger.log(
                    episode,
                    episode_reward,
                    avg_tp,
                    avg_speed,
                    speed_variance_norm=avg_speed_variance_norm,
                    reward_components=avg_reward_components,
                    ttc_risk=episode_ttc_risk,
                    policy_loss=train_stats["policy_loss"],
                    value_loss=train_stats["value_loss"],
                    entropy=train_stats["entropy"],
                )
            else:
                logger.log(
                    episode,
                    episode_reward,
                    avg_tp,
                    avg_speed,
                    speed_variance_norm=avg_speed_variance_norm,
                    reward_components=avg_reward_components,
                    ttc_risk=episode_ttc_risk,
                )

            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),
                "ttc_risk": float(episode_ttc_risk),
            }
            for column, value in avg_reward_components.items():
                episode_summary[f"avg_{column}"] = float(value)
            if train_stats:
                episode_summary.update(
                    policy_loss=float(train_stats["policy_loss"]),
                    value_loss=float(train_stats["value_loss"]),
                    entropy=float(train_stats["entropy"]),
                )
            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.pt"))

            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 train_stats:
                    print(f"  Policy Loss: {train_stats['policy_loss']:.4f}")
                    print(f"  Value Loss: {train_stats['value_loss']:.4f}")
                    print(f"  Entropy: {train_stats['entropy']:.4f}")

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

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

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

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

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