import torch.nn as nn

class BERT_Arch(nn.Module):

    def __init__(self, bert):
      
      super(BERT_Arch, self).__init__()

      self.bert = bert 
      
      # dropout layer
      self.dropout = nn.Dropout(0.1)
      
      # relu activation function
      self.relu =  nn.ReLU()

      # dense layer 1
      self.fc1 = nn.Linear(2,512)
      
      # dense layer 2 (Output layer)
      self.fc2 = nn.Linear(512,2)

      #softmax activation function
      self.softmax = nn.LogSoftmax(dim=1)

    #define the forward pass
    def forward(self, sent_id, mask):

      #pass the inputs to the model  
      senence_classifier_output = self.bert(sent_id, attention_mask=mask)
      x = senence_classifier_output.logits.float()
      x = self.fc1(x)

      x = self.relu(x)

      x = self.dropout(x)

      # output layer
      x = self.fc2(x)
      
      # apply softmax activation
      x = self.softmax(x)

      return x