import torch
import torch.nn as nn
from transformers import BertModel

class PersonaAssigner(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(PersonaAssigner, self).__init__()
        self.fc1 = nn.Linear(input_dim, hidden_dim)
        self.fc2 = nn.Linear(hidden_dim, output_dim)
        
    def forward(self, x):
        x = torch.relu(self.fc1(x))
        return self.fc2(x)

class PreferencePredictor(nn.Module):
    def __init__(self, input_dim):
        super(PreferencePredictor, self).__init__()
        self.fc1 = nn.Linear(input_dim, 256)
        self.fc2 = nn.Linear(256, 3)
        
    def forward(self, x):
        x = torch.relu(self.fc1(x))
        return self.fc2(x)

class BERTEncoder(nn.Module):
    def __init__(self, model_name='bert-base-uncased'):
        super(BERTEncoder, self).__init__()
        self.bert = BertModel.from_pretrained(model_name)
        
    def forward(self, input_ids, attention_mask, token_type_ids=None):
        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)
        return outputs.last_hidden_state.mean(dim=1)