import torch as th
import torch.nn as nn
from torch.distributions import Normal

from torchy_baselines.common.policies import BasePolicy, register_policy, create_mlp, BaseNetwork


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 = [64, 64]

        # TODO: orthogonal initialization?
        actor_net = create_mlp(state_dim, action_dim, net_arch, activation_fn, squash_out=True)
        self.actor_net = nn.Sequential(*actor_net)

    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]

        # TODO: solve  pytorch parameter registration
        # for _ in range(n_critics):
        #     q_net = create_mlp(state_dim + action_dim, 1, net_arch, activation_fn)
        #     self.q_net = nn.Sequential(*q_net)
        #     self.q_networks.append(self.q_net)

        q1_net = create_mlp(state_dim + action_dim, 1, net_arch, activation_fn)
        self.q1_net = nn.Sequential(*q1_net)

        q2_net = create_mlp(state_dim + action_dim, 1, net_arch, activation_fn)
        self.q2_net = nn.Sequential(*q2_net)

        self.q_networks = [self.q1_net, self.q2_net]

    def forward(self, obs, action):
        qvalue_input = th.cat([obs, action], dim=1)
        return [q_net(qvalue_input) for q_net in self.q_networks]

    def q1_forward(self, obs, action):
        return self.q_networks[0](th.cat([obs, action], dim=1))


class PPOPolicy(BasePolicy):
    def __init__(self, observation_space, action_space,
                 learning_rate=1e-3, net_arch=None, device='cpu',
                 activation_fn=nn.Tanh):
        super(PPOPolicy, self).__init__(observation_space, action_space, device)
        self.state_dim = self.observation_space.shape[0]
        self.action_dim = self.action_space.shape[0]
        if net_arch is None:
            net_arch = [64, 64]
        self.net_arch = net_arch
        self.activation_fn = activation_fn
        self.net_args = {
            'input_dim': self.state_dim,
            'output_dim': -1,
            'net_arch': self.net_arch,
            'activation_fn': self.activation_fn
        }
        self.shared_net = None
        self._build(learning_rate)

    def _build(self, learning_rate):
        shared_net = create_mlp(self.state_dim, output_dim=-1, self.net_arch, self.activation_fn)
        self.shared_net = nn.Sequential(*shared_net).to(self.device)
        self.actor_net = nn.Linear(self.net_arch[-1], self.action_dim)
        self.value_net = nn.Linear(self.net_arch[-1], 1)
        self.log_std = nn.Parameter(th.zeros(self.action_dim, 1))
        self.optimizer = th.optim.Adam(self.parameters(), lr=learning_rate)

    def forward(self, state):
        latent = self.shared_net(state)
        # TODO: initialize pi_mean weights properly
        mean_actions = self.actor_net(latent)
        action_distribution = Normal(mean_actions, self.log_std)
        # Sample from the gaussian
        action = action_distribution.rsample()
        log_prob = action_distribution.log_prob()
        # entropy = action_distribution.entropy()
        value = self.value_net(latent)
        return action, value, log_prob

    def actor_forward(self):
        latent = self.shared_net(state)
        # TODO: initialize pi_mean weights properly
        mean_actions = self.actor_net(latent)
        action_distribution = Normal(mean_actions, self.log_std)
        # Sample from the gaussian
        action = action_distribution.rsample()
        return action
        
    def value_forward(self):
        pass

MlpPolicy = PPOPolicy

register_policy("MlpPolicy", MlpPolicy)