import torch as th
import torch.nn as nn

from torchy_baselines.common.policies import BasePolicy, register_policy


class BaseNetwork(nn.Module):
    """docstring for BaseNetwork."""

    def __init__(self, device='cpu'):
        super(BaseNetwork, self).__init__()

    def load_from_vector(self, vector):
        """
        Load parameters from a 1D vector.

        :param vector: (np.ndarray)
        """
        device = next(self.parameters()).device
        th.nn.utils.vector_to_parameters(th.FloatTensor(vector).to(device), self.parameters())

    def parameters_to_vector(self):
        """
        Convert the parameters to a 1D vector.

        :return: (np.ndarray)
        """
        return th.nn.utils.parameters_to_vector(self.parameters()).detach().cpu().numpy()


class Actor(BaseNetwork):
    def __init__(self, state_dim, action_dim, net_arch=None, activation_fn=nn.ReLU):
        super(Actor, self).__init__()

        if net_arch is None:
            net_arch = [400, 300]

        # TODO: orthogonal initialization?

        self.actor_net = nn.Sequential(
            nn.Linear(state_dim, net_arch[0]),
            activation_fn(),
            nn.Linear(net_arch[0], net_arch[1]),
            activation_fn(),
            nn.Linear(net_arch[1], action_dim),
            nn.Tanh(),
        )

    def forward(self, x):
        return self.actor_net(x)


class Critic(BaseNetwork):
    def __init__(self, state_dim, action_dim, net_arch=None, activation_fn=nn.ReLU):
        super(Critic, self).__init__()

        if net_arch is None:
            net_arch = [400, 300]

        self.q1_net = nn.Sequential(
            nn.Linear(state_dim + action_dim, net_arch[0]),
            activation_fn(),
            nn.Linear(net_arch[0], net_arch[1]),
            activation_fn(),
            nn.Linear(net_arch[1], 1),
        )

        self.q2_net = nn.Sequential(
            nn.Linear(state_dim + action_dim, net_arch[0]),
            activation_fn(),
            nn.Linear(net_arch[0], net_arch[1]),
            activation_fn(),
            nn.Linear(net_arch[1], 1),
        )

    def forward(self, obs, action):
        qvalue_input = th.cat([obs, action], dim=1)
        return self.q1_net(qvalue_input), self.q2_net(qvalue_input)

    def q1_forward(self, obs, action):
        return self.q1_net(th.cat([obs, action], dim=1))


class TD3Policy(BasePolicy):
    def __init__(self, observation_space, action_space,
                 learning_rate=1e-3, net_arch=None, device='cpu',
                 activation_fn=nn.ReLU):
        super(TD3Policy, self).__init__(observation_space, action_space, device)
        self.state_dim = self.observation_space.shape[0]
        self.action_dim = self.action_space.shape[0]
        self.net_arch = net_arch
        self.activation_fn = activation_fn
        self.net_args = {
            'state_dim': self.state_dim,
            'action_dim': self.action_dim,
            'net_arch': self.net_arch,
            'activation_fn': self.activation_fn
        }
        self.actor, self.actor_target = None, None
        self.critic, self.critic_target = None, None
        self._build(learning_rate)

    def _build(self, learning_rate):
        self.actor = self.make_actor()
        self.actor_target = self.make_actor()
        self.actor_target.load_state_dict(self.actor.state_dict())
        self.actor.optimizer = th.optim.Adam(self.actor.parameters(), lr=learning_rate)

        self.critic = self.make_critic()
        self.critic_target = self.make_critic()
        self.critic_target.load_state_dict(self.critic.state_dict())
        self.critic.optimizer = th.optim.Adam(self.critic.parameters(), lr=learning_rate)

    def make_actor(self):
        return Actor(**self.net_args).to(self.device)

    def make_critic(self):
        return Critic(**self.net_args).to(self.device)

MlpPolicy = TD3Policy

register_policy("MlpPolicy", MlpPolicy)