from transformers import AutoModel, AutoTokenizer
from datasets import load_dataset
from torch.utils.data import DataLoader, Dataset
import torch.optim as optim
import torch.nn as nn

class ShellcodeDataset(Dataset):
    def __init__(self, data, tokenizer):
        self.data = data
        self.tokenizer = tokenizer

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        intent = self.data[idx]['intent']
        snippet = self.data[idx]['snippet']
        encoding = self.tokenizer(intent, return_tensors="pt", padding="max_length", truncation=True, max_length=1024)
        label = self.tokenizer(snippet, return_tensors="pt", padding="max_length", truncation=True, max_length=1024)
        return {'input_ids': encoding['input_ids'], 'labels': label['input_ids']}

# Initialize tokenizer and model
model_name = "openai-community/gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

# Add padding token to the tokenizer
tokenizer.pad_token = tokenizer.eos_token

# Load the dataset
dataset = load_dataset('SoLID/shellcode_i_a32')

# Create the dataset and dataloader
train_dataset = ShellcodeDataset(dataset['train'], tokenizer)
train_dataloader = DataLoader(train_dataset, batch_size=16)

# Define the optimizer and criterion
optimizer = optim.Adam(model.parameters())
criterion = nn.CrossEntropyLoss()

# Training loop
model.train()
for epoch in range(3):
    for batch in train_dataloader:
        optimizer.zero_grad()
        input_ids, labels = batch['input_ids'], batch['labels']
        outputs = model(input_ids)
        loss = criterion(outputs.logits, labels)
        loss.backward()
        optimizer.step()