import torch

class Model(torch.nn.Module):
    
    def __init__(self):
        super(Model, self).__init__()
        # in: 4 słowa kontekstu przed i 1 słowo kontekstu po 
        """
        5 in features
        150 out features
        """
        self.dense1 = torch.nn.Linear(6, 150, bias=False)
        self.tanh1 = torch.nn.Tanh()
        """
        150 in features
        300 hidden values
        2 num layers
        """
        self.lstm = torch.nn.LSTM(150, 300, 2)
        self.dense2 = torch.nn.Linear(300, 7)
        self.softmax = torch.nn.Softmax(dim=1)

    def forward(self, data, hidden_state, cell_state):
        data = self.dense1(data.T)
        data = self.tanh1(data)
        data, (hidden_state, cell_state) = self.lstm(data.unsqueeze(1), (hidden_state, cell_state))
        data = self.dense2(data)
        data = self.softmax(data)
        return data, (hidden_state, cell_state)