import numpy as np 

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


class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.fc1 = nn.Linear(256, 256)
        self.fc2 = nn.Linear(256, 256)
        self.fc3 = nn.Linear(256, 187)
        self.rnn_layer = nn.LSTM(
                input_size=187,
                hidden_size=128,
                num_layers=1,
                bidirectional=True,
                batch_first=True,
            )
    def forward(self, x):
        x,_ = self.rnn_layer(x)
        x = x.view(-1,256)
        x = F.leaky_relu(self.fc1(x))
        x = F.leaky_relu(self.fc2(x))
        x = F.dropout(x, p=0.2)
        x = self.fc3(x)
        return x.unsqueeze(1)


class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        
        self.rnn_layer = nn.LSTM(
                input_size=187,
                hidden_size=256,
                num_layers=1,
                bidirectional=True,
                batch_first=True,
            )
        self.fc1 = nn.Linear(512, 512)
        self.fc2 = nn.Linear(512, 256) 
        self.fc3 = nn.Linear(256, 1)

    def forward(self, x):
        x,_ = self.rnn_layer(x)
        x = x.view(-1, 512)
        x = F.leaky_relu(self.fc1(x))
        x = F.leaky_relu(self.fc2(x))
        x = F.dropout(x, p=0.2)
        x = torch.sigmoid(self.fc3(x))
        return x