import sys, os, time
sys.path.insert(0, '/home/paulh/projects/donkeycar-rl-autoresearch/agent')
from multitrack_runner import log, _send_exit_scene, StuckTerminationWrapper
from donkeycar_sb3_runner import ThrottleClampWrapper
from reward_wrapper import SpeedRewardWrapper
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import DummyVecEnv, VecTransposeImage
from stable_baselines3.common.callbacks import BaseCallback
import gymnasium as gym

THROTTLE_MIN = 0.5
LR           = 0.000725
TOTAL_STEPS  = 90000
SAVE_PATH    = '/home/paulh/projects/donkeycar-rl-autoresearch/agent/models/exp5-mountain-v5reward/model'
os.makedirs(os.path.dirname(SAVE_PATH), exist_ok=True)

def make_env(env_id):
    raw = gym.make(env_id)
    env = ThrottleClampWrapper(raw, throttle_min=THROTTLE_MIN)
    env = StuckTerminationWrapper(env, stuck_steps=80, min_displacement=0.5)
    env = SpeedRewardWrapper(env)   # v5 reward
    return env

def switch_to(current_id, next_id, name):
    log(f'  → {name}...')
    tmp = gym.make(current_id); time.sleep(2)
    _send_exit_scene(tmp, verbose=False); tmp.close(); time.sleep(5)
    env = VecTransposeImage(DummyVecEnv([lambda: make_env(next_id)]))
    log(f'  Connected to {name}'); return env

class ProgressCB(BaseCallback):
    def __init__(self, total):
        super().__init__(verbose=0); self._last=0; self._total=total
    def _on_step(self):
        if self.num_timesteps - self._last >= 10000:
            log(f'    step {self.num_timesteps:,}/{self._total:,}')
            self._last = self.num_timesteps
        return True

log('='*60)
log('Exp 5: mountain_track, v5 reward (speed×CTE), throttle_min=0.5')
log('v5 reward gives direct gradient signal for hill: slow=low reward')
log('='*60)

# Switch sim to mountain_track
log('Switching to mountain_track...')
tmp = gym.make('donkey-mountain-track-v0'); time.sleep(2)
_send_exit_scene(tmp, verbose=False); tmp.close(); time.sleep(5)
env = VecTransposeImage(DummyVecEnv([lambda: make_env('donkey-mountain-track-v0')]))
model = PPO('CnnPolicy', env, learning_rate=LR, verbose=1, device='cpu')
model.learn(total_timesteps=TOTAL_STEPS, callback=ProgressCB(TOTAL_STEPS),
            reset_num_timesteps=True)
model.save(SAVE_PATH); log(f'Saved.')
env.close(); time.sleep(3)

def eval_track(current_id, track_id, name, n=3):
    log(f'\n--- EVAL: {name} ---')
    ev = switch_to(current_id, track_id, name)
    m = PPO.load(SAVE_PATH, env=ev, device='cpu')
    for ep in range(1, n+1):
        obs = ev.reset(); total, steps, done = 0.0, 0, False
        while not done and steps < 2000:
            action, _ = m.predict(obs, deterministic=True)
            result = ev.step(action)
            if len(result)==5: obs,r,t,tr,info=result; done=bool(t[0] or tr[0])
            else: obs,r,d,info=result; done=bool(d[0])
            total+=float(r[0]); steps+=1
        status='✅ FULL' if steps>=2000 else f'❌ crash@{steps}'
        log(f'  ep{ep}: {total:.1f} reward / {steps} steps — {status}')
        time.sleep(1)
    ev.close(); time.sleep(3)
    return track_id

current = 'donkey-mountain-track-v0'
current = eval_track(current, 'donkey-mountain-track-v0', 'mountain_track (training)')
current = eval_track(current, 'donkey-generated-track-v0', 'generated_track (zero-shot)')
current = eval_track(current, 'donkey-minimonaco-track-v0', 'mini_monaco (zero-shot)')
current = eval_track(current, 'donkey-generated-roads-v0',  'generated_road (zero-shot)')
log('\n=== Exp 5 COMPLETE ===')