import re

def clean_code(code):
    """
    Basic code cleaning: removes empty lines and trailing whitespace.
    """
    lines = code.split('\n')
    cleaned_lines = [line.rstrip() for line in lines if line.strip()]
    return '\n'.join(cleaned_lines)

def create_pairs(data):
    """
    Extracts vulnerable and secure code pairs.
    Returns a list of tuples: (vulnerable_code, secure_code)
    """
    pairs = []
    for item in data:
        vuln = item.get('vulnerable_code', '')
        secure = item.get('secure_code', '')
        if vuln and secure:
            pairs.append((vuln, secure))
    return pairs

def prepare_training_data(data):
    """
    Formats data for LLM Fine-tuning (Prompt-Response format).
    """
    training_samples = []
    
    for item in data:
        vuln_code = item.get('vulnerable_code', '')
        secure_code = item.get('secure_code', '')
        
        prompt = f"### Instruction:\nFix the following vulnerable code securely:\n\n### Input:\n{vuln_code}\n\n### Response:\n"
        completion = f"{secure_code}<|endoftext|>"
        
        training_samples.append({
            "prompt": prompt,
            "completion": completion
        })
        
    return training_samples

import hashlib

def check_data_leakage(train_set, test_set):
    """
    Checks if any code snippets from the train set appear in the test set.
    """
    train_hashes = {hashlib.md5(item['code'].strip().encode()).hexdigest() for item in train_set}
    leaked_count = 0
    
    for item in test_set:
        test_hash = hashlib.md5(item['code'].strip().encode()).hexdigest()
        if test_hash in train_hashes:
            leaked_count += 1
            
    return leaked_count == 0, leaked_count

def tokenize_data(tokenizer, data_samples, max_length=512):
    """
    Tokenizes data for model training. 
    (Requires a tokenizer from transformers)
    """
    # Placeholder for actual tokenization logic
    pass