#!/usr/bin/env python3
"""
41.3.3 Active Defense: Adversarial Unlearning

Source: Chapter_41_Industry_Best_Practices
Category: compliance
"""

import argparse
import sys

# Conceptual snippet for Adversarial Unlearning (PyTorch)
def unlearn_concept(model, tokenizer, harmful_prompts):
    optimizer = torch.optim.AdamW(model.parameters(), lr=1e-5)

    for prompt in harmful_prompts:
        inputs = tokenizer(prompt, return_tensors='pt')
        outputs = model(**inputs, labels=inputs["input_ids"])

        # We want to MAXIMIZE the loss (Gradient Ascent)
        # so the model becomes "bad" at generating this specific harmful text
        loss = -outputs.loss

        loss.backward()
        optimizer.step()
        optimizer.zero_grad()


def main():
    """Command-line interface."""
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--verbose", "-v", action="store_true", help="Verbose output")
    args = parser.parse_args()
    
    # TODO: Add main execution logic
    pass

if __name__ == "__main__":
    main()