Foobar Protocol FoobarProtocol

🤐

Stealth

Pioneering in Smart Contracts. An organic creation.

7 followers · 3 following

Librehash
Baltimore City
07:57 (UTC -03:00)
https://librehash.org

View GitHub Profile

Recently created

Least recently created

Recently updated

Least recently updated

FoobarProtocol / echidna_example.sol

Last active September 14, 2024 02:05

This is the test echidna file that we created to go with the tutorial. There are errors in here on purpose. We're going to refine this smart contract and the finalized version will be submitted in an entirely separate contract

	// 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 / gnosis_safe_flattened.sol

Created September 14, 2024 01:42

Gnosis Safe Implementation proxy contract (flattened)

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

	// src/common/Enum.sol

	/// @title Enum - Collection of enums
	/// @author Richard Meissner - <richard@gnosis.pm>
	contract Enum {
	enum Operation {
	Call,

FoobarProtocol / SignMessageLib.spec.ts.js

Created September 8, 2024 20:25

This is the framework owners interact with to sign individual messages. Likely one of the sources of compromise for the WazirX team at this point. Can be found in this GitHub directory by Safe & it serves as one of the de-facto tests for <1.3.0 signing mechanisms

	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 / GPT_iteration.py

Created December 29, 2023 05:08

OpenAI Skeleton for Those Looking to Iteratively Employ ChatGPT

	#!/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 / CodeT5_fine_tune_iteration.py

Created October 23, 2023 21:51

This is one iteration of the fine-tuning script for CodeT5+; warning I don't think that this script is complete

	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 / Split_CSV_Files.py

Created October 21, 2023 23:48

This is a really robust self-created script that partitions CSV files contingent on user input so let's get to using it shall we?

	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 / DataPreprocessing.py

Created October 21, 2023 23:47

Very comprehensive dataset preprocessing for solidity smart contracts. Immaculately commented too. You're welcome if you've stumbled upon this #givingbacktothecommunity ; explains the logic behind all decisions that I've made when it comes to that too.

	# 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 / Datagen_Evolved_Seeds.py

Created October 21, 2023 23:46

This is some really high quality code here from 'Peyton Cleveland' (guy from GitHub here - https://github.com/PeytonCleveland/Fair-Use/blob/main/Darwin-JSTS/main.py)

	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,

FoobarProtocol / Convert Alpaca to Evol Dataset.py

Created October 21, 2023 23:46

This brief piece of code outlines how to convert an alpca

	def convert_alpaca_to_evol(
	file_path: str,
	lines: bool = False,
	output_file: str = "converted_alpaca.json"
	):
	"""Convert the Instruction/Input/Output format of Alpaca Instruct datasets
	to the Evol-Instruct format of Instruction/Output. Inputs are appended to the
	instructions.

	Args:

FoobarProtocol / main_evol_instruct.py

Created October 21, 2023 23:45

This is the `main.py` from the Evol_Instruct repo that got removed by the researchers (this makes calls to the different transformative prompts; this is meant to be separated from the Auroboros technique)

	import json
	import random

	from openai_access import call_chatgpt
	from depth import createConstraintsPrompt, createDeepenPrompt, createConcretizingPrompt, createReasoningPrompt
	from breadth import createBreadthPrompt


	fr = open('alpaca_data.json','r')

NewerOlder