# ---------------------------
# Model
# ---------------------------
import torch.nn as nn
from transformers import AutoModel

class BertEmotionClassifier(nn.Module):
    def __init__(self, model_name: str = "roberta-base", num_labels: int = 5, dropout: float = 0.3):
        super().__init__()
        self.encoder = AutoModel.from_pretrained(model_name)
        self.dropout = nn.Dropout(dropout)
        self.classifier = nn.Linear(self.encoder.config.hidden_size, 128)
        self.classifier1 = nn.Linear(128, num_labels)

    def forward(self, input_ids, attention_mask):
        out = self.encoder(input_ids=input_ids, attention_mask=attention_mask)
        cls = out.last_hidden_state[:, 0, :]
        cls = self.classifier(cls)
        cls = self.dropout(cls)
        logits = self.classifier1(cls)
        return logits