FoobarProtocol

Getting straight to the point here, check out json file number one (formatted like a legitimate EIP-712 hash):

    {
      "types": {
        "EIP712Domain": [
          {
            "name": "name",
            "type": "string"
          },

FoobarProtocol

// Decompiled by library.dedaub.com
// 2025.04.18 18:07 UTC

object "contract" {
    code { }
    object "runtime" {
        code {
            function func_0x311e(_0, _1, _2)  -> ret_val_0, ret_val_1 {
                    let _3 := mload(0x40)                    
                    mstore(_3, _0)

FoobarProtocol

// SPDX-License-Identifier: LGPL-3.0-only
pragma solidity 0.7.6;

import "./GnosisSafe.sol";

contract GnosisSafeTest is GnosisSafe {
    constructor() {
        // Initialize the Gnosis Safe with some owners and a threshold
        address[] memory owners = new address[](3);
        owners[0] = address(0x1);

FoobarProtocol

// SPDX-License-Identifier: LGPL-3.0-only
pragma solidity 0.7.6;

// src/common/Enum.sol

/// @title Enum - Collection of enums
/// @author Richard Meissner - <[email protected]>
contract Enum {
    enum Operation {
        Call,

FoobarProtocol

import { expect } from "chai";
import hre, { deployments, waffle } from "hardhat";
import "@nomiclabs/hardhat-ethers";
import { getSafeWithOwners } from "../utils/setup";
import { executeContractCallWithSigners, calculateSafeMessageHash } from "../../src/utils/execution";
import { chainId } from "../utils/encoding";

describe("SignMessageLib", async () => {
    const [user1, user2] = waffle.provider.getWallets();

FoobarProtocol

#!/usr/bin/env python3

import json
import openai
import time
import os
import logging
from openai.error import InvalidRequestError, RateLimitError
from concurrent.futures import ThreadPoolExecutor

FoobarProtocol

import argparse
import os

import torch
from accelerate import Accelerator
from datasets import load_dataset
from peft import LoraConfig, get_peft_model, prepare_model_for_int8_training, set_peft_model_state_dict
from torch.utils.data import IterableDataset
from tqdm import tqdm
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments, logging, set_seed

FoobarProtocol

import argparse
import csv
import os

# Function to calculate total rows in CSV
def get_total_rows(csv_file):
    with open(csv_file, 'r') as f:
        return sum(1 for row in csv.reader(f)) - 1  # Exclude header

# Function to split CSV files

FoobarProtocol

# Importing necessary libraries for data preprocessing and visualization
import matplotlib.pyplot as plt
import numpy as np
from tqdm.notebook import trange
import pandas as pd
import random
import torch
import re
from datasets import load_dataset
from simplet5 import SimpleT5

FoobarProtocol

import os
import logging
import csv
import argparse
from tqdm import tqdm
import sys
from dotenv import load_dotenv
import openai

logging.basicConfig(level=logging.INFO,