import torch
import torch.nn as nn
from transformers import PreTrainedModel, AutoModel, PretrainedConfig

class BiLSTMAttentionBERT(PreTrainedModel):
    def __init__(self, hidden_dim, num_classes, num_layers, dropout):
        super().__init__(PretrainedConfig())
        self.bert = AutoModel.from_pretrained('dmis-lab/biobert-base-cased-v1.2')
        self.lstm = nn.LSTM(768, hidden_dim, num_layers, batch_first=True, bidirectional=True)
        self.dropout = nn.Dropout(dropout)
        self.fc = nn.Linear(hidden_dim * 2, num_classes)
        
    @classmethod
    def from_pretrained(cls, model_path, hidden_dim, num_classes, num_layers, dropout):
        model = cls(hidden_dim, num_classes, num_layers, dropout)
        state_dict = torch.load(model_path, map_location='cpu')
        model.load_state_dict(state_dict)
        return model
        
    def forward(self, input_ids, attention_mask):
        bert_output = self.bert(input_ids, attention_mask=attention_mask)[0]
        lstm_output, _ = self.lstm(bert_output)
        dropped = self.dropout(lstm_output[:, -1, :])
        output = self.fc(dropped)
        return output