import torch
import torch.nn as nn
import torch.nn.functional as F

class Policy(nn.Module):
    def __init__(self, s_size, a_size, h_size):
        super(Policy, self).__init__()
        self.fc1 = nn.Linear(s_size, h_size)
        self.fc2 = nn.Linear(h_size, a_size)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return F.softmax(x, dim=1)
    
    def act(self, state):
        state = torch.from_numpy(state).float().unsqueeze(0).to(next(self.parameters()).device)
        probs = self(state).cpu()
        m = torch.distributions.Categorical(probs)
        action = m.sample()
        return action.item(), m.log_prob(action)