Update install stuff

hacking.py

@@ -8,17 +8,16 @@ import argparse
 from typing import List, Dict, Tuple, Any, Optional
 from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
 from huggingface_hub import login
-from llm_attacks.minimal_gcg.opt_utils import get_filtered_cands
 from words import words
 from utils import (
     minimize_tokens,
     sample_control,
     count_tokens,
-    get_combined_score,
     get_random_words,
     token_gradients_combined,
     find_best_word_to_add,
     words_db,
+    get_filtered_cands,
 )
 
 # check if cuda is available

installnotes.bash

@@ -3,22 +3,15 @@ conda init
 conda create --name prompt-guard python=3.12 -c conda-forge
 conda activate prompt-guard
 
-curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
-rustup install 1.82
-rustup default 1.82
-
-pip install fschat
 #cuda
-pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128
+pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126
 
 #or cpu
 pip install torch --index-url https://download.pytorch.org/whl/cpu
 
 pip install -U "huggingface_hub[cli]"
 # install transformers
-pip install transformers
+pip install -U transformers
-export RUSTFLAGS="-A invalid_reference_casting"
+pip install -U tiktoken
-pip install git+https://github.com/llm-attacks/llm-attacks
 
 huggingface-cli login
-pip install -U transformers

utils.py

@@ -644,3 +644,20 @@ def get_combined_score(
                 best_idx = idx
 
     return best_idx
+
+def get_filtered_cands(tokenizer, control_cand, filter_cand=True, curr_control=None):
+    cands, count = [], 0
+    for i in range(control_cand.shape[0]):
+        decoded_str = tokenizer.decode(control_cand[i], skip_special_tokens=True)
+        if filter_cand:
+            if decoded_str != curr_control and len(tokenizer(decoded_str, add_special_tokens=False).input_ids) == len(control_cand[i]):
+                cands.append(decoded_str)
+            else:
+                count += 1
+        else:
+            cands.append(decoded_str)
+
+    if filter_cand:
+        cands = cands + [cands[-1]] * (len(control_cand) - len(cands))
+        # print(f"Warning: {round(count / len(control_cand), 2)} control candidates were not valid")
+    return cands