# models/bert_model.py

import torch
import torch.nn as nn
from transformers import BertModel
from config import DROPOUT_RATE, BERT_MODEL_NAME # Import BERT_MODEL_NAME from config

class BertMultiOutputModel(nn.Module):
    """
    BERT-based model for multi-output classification.
    It uses a pre-trained BERT model as its backbone and adds a dropout layer
    followed by separate linear classification heads for each target label.
    """
    # Statically set tokenizer name for easy access in main.py
    tokenizer_name = BERT_MODEL_NAME

    def __init__(self, num_labels):
        """
        Initializes the BertMultiOutputModel.

        Args:
            num_labels (list): A list where each element is the number of classes
                                for a corresponding label column.
        """
        super(BertMultiOutputModel, self).__init__()
        # Load the pre-trained BERT model.
        # BertModel provides contextual embeddings and a pooled output for classification.
        self.bert = BertModel.from_pretrained(BERT_MODEL_NAME)
        self.dropout = nn.Dropout(DROPOUT_RATE) # Dropout layer for regularization

        # Create a list of classification heads, one for each label column.
        # Each head is a linear layer mapping BERT's pooled output size to the number of classes for that label.
        self.classifiers = nn.ModuleList([
            nn.Linear(self.bert.config.hidden_size, n_classes) for n_classes in num_labels
        ])

    def forward(self, input_ids, attention_mask):
        """
        Performs the forward pass of the model.

        Args:
            input_ids (torch.Tensor): Tensor of token IDs (from tokenizer).
            attention_mask (torch.Tensor): Tensor indicating attention (from tokenizer).

        Returns:
            list: A list of logit tensors, one for each classification head.
                  Each tensor has shape (batch_size, num_classes_for_that_label).
        """
        # Pass input_ids and attention_mask through BERT.
        # .pooler_output typically represents the hidden state of the [CLS] token,
        # processed through a linear layer and tanh activation, often used for classification.
        pooled_output = self.bert(input_ids=input_ids, attention_mask=attention_mask).pooler_output

        # Apply dropout for regularization
        pooled_output = self.dropout(pooled_output)

        # Pass the pooled output through each classification head.
        # The result is a list of logits (raw scores before softmax/sigmoid) for each label.
        return [classifier(pooled_output) for classifier in self.classifiers]