import torch
from transformers import BertModel

class Ensembler(torch.nn.Module):
    def __init__(self, specialists):
        super().__init__()

        self.specialists = specialists

    def forward(self, input_ids, attention_mask):
        outputs = torch.cat([specialist(input_ids, attention_mask)
                             for specialist in self.specialists], dim=1)

        return torch.mean(outputs, dim=1).unsqueeze(1)
        

class LanguageIdentifier(torch.nn.Module):
    def __init__(self):
        super().__init__()
        
        self.portuguese_bert = BertModel.from_pretrained("neuralmind/bert-large-portuguese-cased")
        
        self.linear_layer = torch.nn.Sequential(
            torch.nn.Dropout(p=0.2),
            torch.nn.Linear(self.portuguese_bert.config.hidden_size, 1),
        )

    def forward(self, input_ids, attention_mask):
        
        #(Batch_Size,Sequence Length, Hidden_Size)
        outputs = self.portuguese_bert(input_ids=input_ids, attention_mask=attention_mask).last_hidden_state[:, 0, :]
        
        outputs = self.linear_layer(outputs)

        return outputs