elyase

```
You are a powerful agentic AI coding assistant powered by Cursor. You operate exclusively in Cursor, the world's best IDE.

You are pair programming with a USER to solve their coding task.
Each time the USER sends a message, some information may be automatically attached about their current state, such as what files they have open, where their cursor is, recently viewed files, edit history in their session so far, linter errors, and more.
This information may or may not be relevant to the coding task, it is up for you to decide.
Your main goal is to follow the USER's instructions at each message.

<communication>
1. Format your responses in markdown. Use backticks to format file, directory, function, and class names.

elyase

import argparse
import random
import sys

from transformers import AutoModelForCausalLM, AutoTokenizer, DynamicCache
import torch

parser = argparse.ArgumentParser()
parser.add_argument("question", type=str)
parser.add_argument(

elyase

import asyncio
import base64
import json
import os
import pyaudio
from websockets.asyncio.client import connect


class SimpleGeminiVoice:
    def __init__(self):

elyase

Python Development Assistant Prompt System

Version Control

version: 1.0.0
last_updated: 2024-03-19
sections:
  - core_requirements
  - python_version
 - code_style

elyase

[package]
name = "playground"
version = "0.1.0"
edition = "2021"

[dependencies]
alloy = { git = "https://github.com/alloy-rs/alloy.git", features = ["contract", "provider-ws", "rpc-types-eth"] }
anyhow = "1.0.82"
tokio = { version = "1.37.0", features = ["full"] }
tracing = "0.1.40"

elyase

import json

MIN_ERC20_ABI = json.loads(
    """
    [{"constant": true, "inputs": [], "name": "name", "outputs": [ { "name": "", "type": "string" } ], "payable": false, "stateMutability": "view", "type": "function" }, { "constant": false, "inputs": [ { "name": "_spender", "type": "address" }, { "name": "_value", "type": "uint256" } ], "name": "approve", "outputs": [ { "name": "", "type": "bool" } ], "payable": false, "stateMutability": "nonpayable", "type": "function" }, { "constant": true, "inputs": [], "name": "totalSupply", "outputs": [ { "name": "", "type": "uint256" } ], "payable": false, "stateMutability": "view", "type": "function" }, { "constant": false, "inputs": [ { "name": "_from", "type": "address" }, { "name": "_to", "type": "address" }, { "name": "_value", "type": "uint256" } ], "name": "transferFrom", "outputs": [ { "name": "", "type": "bool" } ], "payable": false, "stateMutability": "nonpayable", "type": "function" }, { "constant": true, "inputs": [], "name": "decimals", "outputs": [ { "name"

elyase

elyase

#!/bin/bash
#
# convert a mysql database to sqlite3
#
#see https://stackoverflow.com/questions/5164033/
#    export-a-mysql-database-to-sqlite-database

mysql_host=localhost
mysql_user=george
#mysql_passwd=****************

elyase

"""
preprocess-twitter.py

python preprocess-twitter.py "Some random text with #hashtags, @mentions and http://t.co/kdjfkdjf (links). :)"

Script for preprocessing tweets by Romain Paulus
with small modifications by Jeffrey Pennington
with translation to Python by Motoki Wu

Translation of Ruby script to create features for GloVe vectors for Twitter data.

elyase

function svmStruct = best_svm_classifer_rbf(cdata,labels)


%Write a function called crossfun to calculate the predicted classification yfit from a test vector 
%xtest, when the SVM is trained on a sample xtrain that has classification ytrain.

function yfit = crossfun(xtrain,ytrain,xtest, rbf_sigma, boxconstraint) 

% Train the model on xtrain, ytrain, 
% and get predictions of class of xtest and output it as yfit