"""
TD3 Agent using Stable-Baselines3
Adapted for MultiDiscrete VSL control.
"""
import numpy as np
import torch
from stable_baselines3 import TD3
from stable_baselines3.common.noise import NormalActionNoise
import gymnasium as gym
from gymnasium import spaces


class MultiDiscreteWrapper(gym.Env):
    """Wrap a MultiDiscrete action space as a continuous Box for TD3."""

    def __init__(self, state_dim, action_dims):
        super().__init__()
        self.state_dim = state_dim
        self.action_dims = action_dims
        self.num_zones = len(action_dims)

        self.observation_space = spaces.Box(
            low=-np.inf, high=np.inf, shape=(state_dim,), dtype=np.float32
        )
        self.action_space = spaces.Box(
            low=0.0, high=1.0, shape=(self.num_zones,), dtype=np.float32
        )

    def reset(self, seed=None, options=None):
        return np.zeros(self.state_dim, dtype=np.float32), {}

    def step(self, action):
        return np.zeros(self.state_dim, dtype=np.float32), 0.0, False, False, {}


class TD3Agent:
    """TD3 agent wrapper."""

    def __init__(
        self,
        state_dim: int,
        action_dims: list,
        learning_rate: float = 3e-4,
        buffer_size: int = 100000,
        learning_starts: int = 100,
        batch_size: int = 64,
        tau: float = 0.005,
        gamma: float = 0.99,
        policy_delay: int = 2,
        exploration_sigma: float = 0.1,
        device: str = "cuda",
    ):
        self.state_dim = state_dim
        self.action_dims = action_dims
        self.num_zones = len(action_dims)
        self.device = device
        self.learning_starts = learning_starts
        self.total_steps = 0
        self.exploration_sigma = exploration_sigma

        dummy_env = MultiDiscreteWrapper(state_dim, action_dims)

        action_noise = NormalActionNoise(
            mean=np.zeros(self.num_zones),
            sigma=0.1 * np.ones(self.num_zones),
        )

        self.model = TD3(
            "MlpPolicy",
            env=dummy_env,
            learning_rate=learning_rate,
            buffer_size=buffer_size,
            learning_starts=learning_starts,
            batch_size=batch_size,
            tau=tau,
            gamma=gamma,
            policy_delay=policy_delay,
            action_noise=action_noise,
            device=device,
            verbose=0,
        )

    def select_action(self, state: np.ndarray, deterministic: bool = False):
        if not deterministic and self.total_steps < self.learning_starts:
            discrete_action = np.array(
                [np.random.randint(self.action_dims[i]) for i in range(self.num_zones)],
                dtype=np.int64,
            )
            return discrete_action, 0.0, 0.0

        continuous_action, _ = self.model.predict(state, deterministic=deterministic)
        if not deterministic:
            noise = np.random.normal(0.0, self.exploration_sigma, size=self.num_zones)
            continuous_action = np.clip(continuous_action + noise, 0.0, 1.0)

        discrete_action = np.array(
            [int(cont * (self.action_dims[i] - 1) + 0.5) for i, cont in enumerate(continuous_action)]
        )
        discrete_action = np.clip(discrete_action, 0, [d - 1 for d in self.action_dims])

        return discrete_action, 0.0, 0.0

    def store_transition(self, state, action, reward, next_state, done):
        self.total_steps += 1
        continuous_action = np.array(
            [action[i] / (self.action_dims[i] - 1) for i in range(self.num_zones)],
            dtype=np.float32,
        )

        self.model.replay_buffer.add(
            state, next_state, continuous_action, reward, done, [{}]
        )

    def update(self):
        if self.model.replay_buffer.size() < self.model.batch_size:
            return {}
        self.model.learn(total_timesteps=1, reset_num_timesteps=False, log_interval=None)
        return {"actor_loss": 0.0, "critic_loss": 0.0}

    def save(self, path: str):
        self.model.save(path)

    def load(self, path: str):
        self.model = TD3.load(path, device=self.device)