import numpy as np
import torch
import torch.nn as nn


class ActorPPO(nn.Module):
    def __init__(self, mid_dim, state_dim, action_dim):
        super().__init__()
        self.net = nn.Sequential(nn.Linear(state_dim, mid_dim), nn.ReLU(),
                                 nn.Linear(mid_dim, mid_dim), nn.ReLU(),
                                 nn.Linear(mid_dim, mid_dim), nn.Hardswish(),
                                 nn.Linear(mid_dim, action_dim))
        # the logarithm (log) of standard deviation (std) of action, it is a trainable parameter
        self.a_logstd = nn.Parameter(torch.zeros((1, action_dim)) - 0.5, requires_grad=True)
        self.sqrt_2pi_log = np.log(np.sqrt(2 * np.pi))

    def forward(self, state):
        return self.net(state).tanh()  # action.tanh() limit the data output of action

    def get_action(self, state):
        a_avg = self.forward(state)  # too big for the action
        a_std = self.a_logstd.exp()

        noise = torch.randn_like(a_avg)
        action = a_avg + noise * a_std
        return action, noise

    def get_logprob_entropy(self, state, action):
        a_avg = self.forward(state)
        a_std = self.a_logstd.exp()
        delta = ((a_avg - action) / a_std).pow(2) * 0.5
        logprob = -(self.a_logstd + self.sqrt_2pi_log + delta).sum(1)  # new_logprob

        dist_entropy = (logprob.exp() * logprob).mean()  # policy entropy
        return logprob, dist_entropy

    def get_old_logprob(self, _action, noise):  # noise = action - a_noise
        delta = noise.pow(2) * 0.5
        return -(self.a_logstd + self.sqrt_2pi_log + delta).sum(1)  # old_logprob


class CriticAdv(nn.Module):
    def __init__(self, mid_dim, state_dim, _action_dim):
        super().__init__()
        self.net = nn.Sequential(nn.Linear(state_dim, mid_dim), nn.ReLU(),
                                 nn.Linear(mid_dim, mid_dim), nn.ReLU(),
                                 nn.Linear(mid_dim, mid_dim), nn.Hardswish(),
                                 nn.Linear(mid_dim, 1))

    def forward(self, state):
        return self.net(state)  # Advantage value