"""
Exp 22: Parallel DummyVecEnv — generated_road + generated_track, warm-started.

Purpose:
  - Keep the generated_road champion warm-start idea.
  - Use the full termination stack so wedged cars and circular exploits end fast.
  - Use the v6 reward wrapper, which explicitly kills no-progress / low-efficiency
    behaviour instead of merely giving it weak reward.

Setup:
  - Sim 1: generated_road  on port 9091
  - Sim 2: generated_track on port 9093
  - Warm-start from agent/models/champion/model.zip
"""
import os
import sys
import time
from datetime import datetime

sys.path.insert(0, '/home/paulh/projects/donkeycar-rl-autoresearch/agent')

import gymnasium as gym
import numpy as np
from stable_baselines3 import PPO
from stable_baselines3.common.utils import get_schedule_fn
from stable_baselines3.common.vec_env import DummyVecEnv, VecTransposeImage

from donkeycar_sb3_runner import ThrottleClampWrapper
from multitrack_runner import StuckTerminationWrapper
from reward_wrapper import SpeedRewardWrapper


HOST = 'localhost'
THROTTLE_MIN = 0.2
LR = 0.000225
TOTAL_STEPS = 150_000
CHECKPOINT_EVERY = 10_000
SAVE_DIR = '/home/paulh/projects/donkeycar-rl-autoresearch/agent/models/exp22-generated-pair-warm-v6'
WARM_PATH = '/home/paulh/projects/donkeycar-rl-autoresearch/agent/models/champion/model.zip'
os.makedirs(SAVE_DIR, exist_ok=True)

EFFICIENCY_WINDOW = 60
MIN_EFFICIENCY = 0.15
MIN_LAP_TIME = 12.0
MAX_CTE_TERMINATE = 2.5
CTE_PATIENCE = 3
PROGRESS_PATIENCE = 100
EFFICIENCY_PATIENCE = 12
LOW_SPEED_PATIENCE = 10
LOW_SPEED_THRESHOLD = 0.25
LOW_SPEED_MIN_DISPLACEMENT = 0.20
LOW_SPEED_GRACE_STEPS = 15
MAX_STUCK_SECONDS = 3.0
MAX_EPISODE_SECONDS = 18.0


def log(msg):
    print(f'[{datetime.now().strftime("%H:%M:%S")}] {msg}', flush=True)


def make_env(track_id, port):
    def _init():
        raw = gym.make(track_id, conf={'host': HOST, 'port': port})
        env = ThrottleClampWrapper(raw, throttle_min=THROTTLE_MIN)
        env = StuckTerminationWrapper(
            env,
            stuck_steps=40,
            min_displacement=0.5,
            max_stuck_seconds=MAX_STUCK_SECONDS,
            max_episode_seconds=MAX_EPISODE_SECONDS,
        )
        env = SpeedRewardWrapper(
            env,
            window_size=EFFICIENCY_WINDOW,
            min_efficiency=MIN_EFFICIENCY,
            min_lap_time=MIN_LAP_TIME,
            max_cte_terminate=MAX_CTE_TERMINATE,
            cte_patience=CTE_PATIENCE,
            progress_patience=PROGRESS_PATIENCE,
            efficiency_patience=EFFICIENCY_PATIENCE,
            low_speed_patience=LOW_SPEED_PATIENCE,
            low_speed_threshold=LOW_SPEED_THRESHOLD,
            low_speed_min_displacement=LOW_SPEED_MIN_DISPLACEMENT,
            low_speed_grace_steps=LOW_SPEED_GRACE_STEPS,
        )
        return env
    return _init


def make_eval_env(track_id, port):
    inner = make_env(track_id, port)()
    return VecTransposeImage(DummyVecEnv([lambda e=inner: e]))


log('=' * 60)
log('Exp 22: generated_road + generated_track, warm-started, v6 reward')
log(f'  Warm start: {WARM_PATH}')
log(f'  Sim 1: {HOST}:9091 -> generated_road')
log(f'  Sim 2: {HOST}:9093 -> generated_track')
log(f'  throttle_min={THROTTLE_MIN}, lr={LR}, total={TOTAL_STEPS:,}')
log('  Reward: v6 (speed x CTE with progress/efficiency exploit termination)')
log(f'  Stuck timeout: {MAX_STUCK_SECONDS:.1f}s, hard cap: {MAX_EPISODE_SECONDS:.1f}s')
log(f'  Progress patience: {PROGRESS_PATIENCE} steps')
log(f'  Checkpoints: every {CHECKPOINT_EVERY:,} steps')
log('=' * 60)

log('Creating DummyVecEnv with the two road tracks...')
env = DummyVecEnv([
    make_env('donkey-generated-roads-v0', 9091),
    make_env('donkey-generated-track-v0', 9093),
])
env = VecTransposeImage(env)
log(f'  VecEnv num_envs={env.num_envs}, obs={env.observation_space.shape}')

if not os.path.exists(WARM_PATH):
    raise FileNotFoundError(WARM_PATH)

model = PPO.load(WARM_PATH, env=env, device='cpu')
model.learning_rate = LR
try:
    model.lr_schedule = get_schedule_fn(LR)
except Exception:
    model.lr_schedule = None
try:
    for pg in model.policy.optimizer.param_groups:
        pg['lr'] = LR
except Exception:
    pass

log('Warm-start model attached. Starting training...')

best_total_steps = float('-inf')
best_total_reward = float('-inf')
steps_done = 0

while steps_done < TOTAL_STEPS:
    seg_steps = min(CHECKPOINT_EVERY, TOTAL_STEPS - steps_done)
    model.learn(total_timesteps=seg_steps, reset_num_timesteps=False)
    steps_done += seg_steps

    ckpt = os.path.join(SAVE_DIR, f'checkpoint_{steps_done:07d}')
    model.save(ckpt)
    model.save(os.path.join(SAVE_DIR, 'model'))
    log(f'[{steps_done:,}/{TOTAL_STEPS:,}] Checkpoint saved: {ckpt}.zip')

    try:
        obs = env.reset()
        ep_rewards = np.zeros(env.num_envs)
        ep_steps = np.zeros(env.num_envs)
        done_mask = np.zeros(env.num_envs, dtype=bool)

        for _ in range(2000):
            action, _ = model.predict(obs, deterministic=True)
            obs, rewards, dones, infos = env.step(action)
            for i in range(env.num_envs):
                if not done_mask[i]:
                    ep_rewards[i] += rewards[i]
                    ep_steps[i] += 1
                    if dones[i]:
                        done_mask[i] = True
            if done_mask.all():
                break

        status0 = '✅' if ep_steps[0] >= 2000 else f'❌@{int(ep_steps[0])}'
        status1 = '✅' if ep_steps[1] >= 2000 else f'❌@{int(ep_steps[1])}'
        log(
            f'  Eval: gen_road={ep_rewards[0]:.1f}r/{int(ep_steps[0])}s {status0}  '
            f'gen_track={ep_rewards[1]:.1f}r/{int(ep_steps[1])}s {status1}'
        )

        total_steps_eval = ep_steps.sum()
        total_reward = ep_rewards.sum()
        if (
            total_steps_eval > best_total_steps
            or (total_steps_eval == best_total_steps and total_reward > best_total_reward)
        ):
            best_total_steps = total_steps_eval
            best_total_reward = total_reward
            model.save(os.path.join(SAVE_DIR, 'best_model'))
            log(
                f'  NEW BEST: combined steps={int(best_total_steps)} '
                f'reward={best_total_reward:.1f}'
            )
    except Exception as e:
        log(f'  Eval error: {e}')
        import traceback
        traceback.print_exc()

model.save(os.path.join(SAVE_DIR, 'model'))
log(f'\nTraining complete. Best combined steps: {int(best_total_steps)}')

env.close()
time.sleep(5)

log('\n' + '=' * 60)
log('FINAL EVALUATION: best_model on generated_road, generated_track, mini_monaco')
log('=' * 60)

EVAL_TRACKS = [
    ('donkey-generated-roads-v0', 'generated_road'),
    ('donkey-generated-track-v0', 'generated_track'),
    ('donkey-minimonaco-track-v0', 'mini_monaco'),
]
EVAL_PORT = 9091
EVAL_SETS = 3
EVAL_MAX_STEPS = 2000

best_model_path = os.path.join(SAVE_DIR, 'best_model.zip')
results_by_track = {}

for track_id, track_name in EVAL_TRACKS:
    log(f'\n--- {track_name} ---')
    steps_list = []

    for s in range(1, EVAL_SETS + 1):
        try:
            eval_env = make_eval_env(track_id, EVAL_PORT)
            eval_model = PPO.load(best_model_path, env=eval_env, device='cpu')

            obs = eval_env.reset()
            total_r, total_s, done = 0.0, 0, False
            while not done and total_s < EVAL_MAX_STEPS:
                action, _ = eval_model.predict(obs, deterministic=True)
                result = eval_env.step(action)
                if len(result) == 4:
                    obs, r, d, info = result
                    done = bool(d[0])
                else:
                    obs, r, t, tr, info = result
                    done = bool(t[0] or tr[0])
                total_r += float(r[0])
                total_s += 1

            status = '✅' if total_s >= EVAL_MAX_STEPS else f'❌@{total_s}'
            log(f'  Set {s}: {total_r:.1f}r / {total_s}s {status}')
            steps_list.append(total_s)

            eval_env.close()
            time.sleep(3)
        except Exception as e:
            log(f'  Set {s}: ERROR — {e}')
            steps_list.append(0)
            time.sleep(3)

    mean_steps = np.mean(steps_list) if steps_list else 0
    results_by_track[track_name] = steps_list
    log(f'  Mean: {mean_steps:.0f} steps')

log('\n' + '=' * 60)
log('SUMMARY')
log('=' * 60)
for track_name, steps_list in results_by_track.items():
    steps_str = '/'.join(str(s) for s in steps_list)
    mean = np.mean(steps_list)
    verdict = '✅' if mean >= 1500 else '⚠️' if mean >= 500 else '❌'
    log(f'  {verdict} {track_name:20s}: {steps_str}  mean={mean:.0f}')

log('\n=== Exp 22 COMPLETE ===')