import random
from collections import deque

import gymnasium as gym
import torch
import torch.nn as nn
import torch.optim as optim


class DQN(nn.Module):
    def __init__(self, n_states=4, n_actions=2):
        """
        Notre modèle à deux états, et peux faire deux actions (trouver à gauche
        ou à droite)
        :param n_state:
        :param n_action:
        """
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(n_states, 128), nn.ReLU(),
            nn.Linear(128, n_actions)
        )


    def forward(self, x):
        """

        :param x:
        :return:
        """
        return self.net(x)


def epsilon_greedy(epsilon: float, s, policy_net: DQN, n_actions: int) -> int:
    if random.random() < epsilon:
        return random.randrange(n_actions)
    else:
        return torch.argmax(policy_net(s)).item()


def train_and_save(weights_path="cartpole_dqn.pth", episodes=2_000, update_target_every=20):
    env = gym.make('CartPole-v1')
    n_states, n_actions = env.observation_space.shape[0], env.action_space.n

    policy_net = DQN(n_states, n_actions)  # Q Network
    target_net = DQN(n_states, n_actions)  # Target network
    target_net.load_state_dict(policy_net.state_dict())  # same weights at start
    target_net.eval()

    optimizer = optim.Adam(policy_net.parameters(), lr=1e-3)
    gamma = 0.99  # discount factor
    epsilon = 1.0  # Fréquence d'exploration initiale
    eps_min = 0.01  # Fréquence d'exploration minimale
    eps_decay = 0.999  # Facteur de réduction d'epsilon
    memory = deque(maxlen=100_000)
    batch_size = 64

    for ep in range(episodes):
        s, _ = env.reset()
        s = torch.tensor(s, dtype=torch.float32)
        done, total_r = False, 0

        while not done:
            a = epsilon_greedy(epsilon, s, policy_net, n_actions)

            ns, r, done, _, _ = env.step(a)
            ns = torch.tensor(ns, dtype=torch.float32)

            memory.append((s, a, r, ns, done))
            s, total_r = ns, total_r + r

            if len(memory) >= batch_size:
                batch = random.sample(memory, batch_size)
                s_b, a_b, r_b, ns_b, d_b = zip(*batch)
                s_b = torch.stack(s_b)
                ns_b = torch.stack(ns_b)

                q_pred = policy_net(s_b).gather(1, torch.tensor(a_b).unsqueeze(1)).squeeze()

                with torch.no_grad():
                    q_next = target_net(ns_b).max(1)[0]
                    q_target = torch.tensor(r_b, dtype=torch.float32) + \
                        gamma * q_next * (1 - torch.tensor(d_b, dtype=torch.float32))

                loss = ((q_pred - q_target)**2).mean()
                optimizer.zero_grad(); loss.backward(); optimizer.step()

        epsilon = max(eps_min, epsilon * eps_decay)

        if (ep + 1) % update_target_every == 0:
            target_net.load_state_dict(policy_net.state_dict())

        if (ep + 1) % 20 == 0:
            print(f'Episode {ep + 1}: total reward {total_r:.1f}, epsilon {epsilon:.2f}')
    env.close()

    torch.save(policy_net.state_dict(), weights_path)
    print(f'Training finished. Weights saved to {weights_path}')
    return policy_net  # <--- trained Q-network


def show(weights_path='cartpole_dqn.pth') -> None:
    env = gym.make('CartPole-v1', render_mode='human')
    qnet = DQN()
    qnet.load_state_dict(torch.load(weights_path))
    qnet.eval()

    s, _ = env.reset()
    s = torch.tensor(s, dtype=torch.float32)
    done = False
    total_r = 0.0
    while not done:
        a = torch.argmax(qnet(s)).item()
        s_, r, done, _, _ = env.step(a)
        total_r += r
        s = torch.tensor(s_, dtype=torch.float32)
    env.close()
    print(f'Demonstration finished. {total_r:.1f}')


if __name__ == '__main__':
    #trained_model = train_and_save()
    show()