Flatten 1_raw_dataset submodules into plain tracked files

FocalLoRA, Should-It-Be-Executed-Or-Processed, and topicattack were
nested git repos (with an inner FocalLoRA/data/FocalLoRA/.git as well).
Drop their .git history and track the contents directly in this repo
instead of as submodules/gitlinks.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

Codes/1_raw_dataset/topicattack/run_baselines.py

@@ -0,0 +1,74 @@
+import argparse
+import copy
+
+import tqdm
+import torch
+torch.backends.cudnn.deterministic = True
+from utils import jload, Logger, set_seeds
+from chatbot import HuggingfaceChatbot, GPTChatbot, OpensourceAPIChatbot
+from shield import BaselineProcessor
+from attack_defense_tools import *
+
+def load_model(model_path, system_path):
+    if "gpt" in model_path.lower():
+        model = GPTChatbot(model_path, system_path)
+    elif any([t in model_path.lower() for t in ["70b","72b","405b","27b"]]):
+        model = OpensourceAPIChatbot(model_path, system_path)
+    else:
+        model = HuggingfaceChatbot(model_path, system_path)
+
+    return model
+def run_defense(args):
+    logger = Logger(args.log_file)
+    logger.log(str(args))
+    # data = jload(args.data_path)
+    victim_model = load_model(args.victim_model_path, args.victim_system_path)
+    attack_model = None
+    if args.attack_model_path is not None:
+        attack_model = load_model(args.attack_model_path, args.attack_system_path)
+
+
+    shield_processor = BaselineProcessor(args.input_template_path)
+
+    for attack in args.attacks:
+        for defense in args.defenses:
+            acc_items = []
+            asr_items = []
+            data = jload(args.data_path)
+            # random.shuffle(data)
+            for d_item in tqdm.tqdm(data):
+                output = d_item["output"]
+                injection_output =  d_item["injection_output"]
+                user_input = shield_processor.construct_input(copy.deepcopy(d_item), eval(attack), eval(defense), model=attack_model, side=args.side)
+                response = victim_model.respond(user_input)
+                logger.log("*****************")
+                logger.log(user_input)
+                logger.log(response)
+                acc_items.append(output.lower() in response.lower())
+                asr_items.append(injection_output.lower() in response.lower())
+
+            acc = sum(acc_items) / len(acc_items) * 100
+            asr = sum(asr_items) / len(asr_items) * 100
+            logger.log(f"############ attack:{attack}, defense:{defense} ############")
+            logger.log(f"Accuracy: {acc:.2f}%")
+            logger.log(f"ASR: {asr:.2f}%")
+            logger.log(f"##################################")
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--victim_model_path", type=str, default="gpt-4o")
+    parser.add_argument("--attack_model_path", type=str, default=None)
+    parser.add_argument("--data_path", type=str, default='data/crafted_instruction_data_squad_injection_qa.json')
+    parser.add_argument("--victim_system_path", type=str, default="prompts/generator_system_prompt.txt")
+    parser.add_argument("--attack_system_path", type=str, default="prompts/generator_system_prompt.txt")
+    parser.add_argument("--input_template_path", type=str, default="prompts/victim_instruction_data_template.txt")
+    parser.add_argument("--attacks", type=str, nargs='+', default=["naive"])
+    parser.add_argument("--defenses", type=str, nargs='+', default=["none"])
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument("--side", type=str, default="end")
+    parser.add_argument("--log_file", type=str, default="logs/log.txt")
+
+    args = parser.parse_args()
+    set_seeds(args)
+    run_defense(args)