# %%
import gym
from options.options import gail_ppo, Buffer
from core.value import SetValue
from core.policy import SetDiscretePolicy
from core.discriminator import DeepsetDiscriminator
import torch.optim
from intersim.envs import IntersimpleLidarFlatRandom
from intersim.envs.intersimple import speed_reward
import functools
from util.wrappers import CollisionPenaltyWrapper, TransformObservation, Setobs
import numpy as np
from options.options import OptionsEnv
from torch.utils.tensorboard import SummaryWriter

obs_min = np.array([
    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
    [0, -np.pi, -20, -20, -np.pi, -1e-1],
    [0, -np.pi, -20, -20, -np.pi, -1e-1],
    [0, -np.pi, -20, -20, -np.pi, -1e-1],
    [0, -np.pi, -20, -20, -np.pi, -1e-1],
    [0, -np.pi, -20, -20, -np.pi, -1e-1],
]).reshape(-1)

obs_max = np.array([
    [1000, 1000, 20, np.pi, 1e-1, 0.],
    [50, np.pi, 20, 20, np.pi, 1e-1],
    [50, np.pi, 20, 20, np.pi, 1e-1],
    [50, np.pi, 20, 20, np.pi, 1e-1],
    [50, np.pi, 20, 20, np.pi, 1e-1],
    [50, np.pi, 20, 20, np.pi, 1e-1],
]).reshape(-1)

envs = [OptionsEnv(Setobs(
    TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlatRandom(
        n_rays=5,
        reward=functools.partial(
            speed_reward,
            collision_penalty=0
        ),
        stop_on_collision=False,
    ), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))
), options=[(0, 5), (1, 5), (2, 5), (4, 5), (6, 5), (8, 5)]) for _ in range(60)]

env_fn = lambda i: envs[i]

policy = SetDiscretePolicy(env_fn(0).action_space.n)
pi_opt = torch.optim.Adam(policy.parameters(), lr=3e-4)

value = SetValue()
v_opt = torch.optim.Adam(value.parameters(), lr=1e-3)

discriminator = DeepsetDiscriminator()
disc_opt = torch.optim.Adam(discriminator.parameters(), lr=1e-3, weight_decay=1e-4)

expert_data = torch.load('intersimple-expert-data-setobs2.pt')
expert_data = Buffer(*expert_data)

# %%
def callback(epoch, value, policy):
    if not epoch % 10:
        torch.save(policy.state_dict(), f'gail-ppo-options-setobs2-{epoch}.pt')
        torch.save(value.state_dict(), f'gail-ppo-options-setobs2-value-{epoch}.pt')

value, policy = gail_ppo(
    env_fn=env_fn,
    expert_data=expert_data,
    discriminator=discriminator,
    disc_opt=disc_opt,
    disc_iters=100,
    policy=policy,
    value=value,
    v_opt=v_opt,
    v_iters=1000,
    epochs=200,
    rollout_episodes=60,
    rollout_steps=60,
    gamma=0.99,
    gae_lambda=0.9,
    clip_ratio=0.2,
    pi_opt=pi_opt,
    pi_iters=100,
    logger=SummaryWriter(comment='gail-ppo-options-setobs2'),
    callback=callback,
)

torch.save(policy.state_dict(), 'gail-ppo-options-setobs2.pt')

# %%
policy = SetDiscretePolicy(env_fn(0).action_space.n)
policy(torch.zeros(env_fn(0).observation_space.shape))
policy.load_state_dict(torch.load('gail-ppo-options-setobs2.pt'))

env = env_fn(0)
obs = env.reset()
env.render(mode='post')
for i in range(300):
    action = policy.sample(policy(torch.tensor(obs, dtype=torch.float32)))
    obs, reward, done, _ = env.step(action, render_mode='post')
    print('step', i, 'reward', reward)
    if done:
        break
env.close()