Anton Bacaj abacaj

## train.py
import tqdm
import numpy as np
import torch
import torch.distributed as dist
import transformers

def extract_xml_answer(text: str) -> str:
    answer = text.split("<final_answer>")[-1]
    answer = answer.split("</final_answer>")[0]
    return answer.strip()

## humaneval_m7x8.jsonl
{"task_id": "HumanEval/0", "prompt": "from typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n    \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n    given threshold.\n    >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n    False\n    >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n    True\n    \"\"\"\n", "canonical_solution": "    for idx, elem in enumerate(numbers):\n        for idx2, elem2 in enumerate(numbers):\n            if idx != idx2:\n                distance = abs(elem - elem2)\n                if distance < threshold:\n                    return True\n\n    return False\n", "test": "\n\nMETADATA = {\n    'author': 'jt',\n    'dataset': 'test'\n}\n\n\ndef check(candidate):\n    assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3) == True\n    assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05) == False\n    assert candidate([1.0, 2.0, 5.9, 4.0, 5.0], 0.95) == True\n    assert candidate([1.0, 2.0,

## fused_false.jsonl
{"completion": "# Write a python function to loop to 1000\n\ndef loop_to_1000():\n    for i in range(1000):\n        print(i)\n\n\nloop_to_1000()\n"}
{"completion": "# Write a python function to loop to 1000\n\ndef loop_to_1000():\n    for i in range(1000):\n        print(i)\n\n\nloop_to_1000()\n"}
{"completion": "# Write a python function to loop to 1000\n\ndef loop_to_1000():\n    for i in range(1000):\n        print(i)\n\n\nloop_to_1000()\n"}
{"completion": "# Write a python function to loop to 1000\n\ndef loop_to_1000():\n    for i in range(1000):\n        print(i)\n\n\nloop_to_1000()\n"}
{"completion": "# Write a python function to loop to 1000\n\ndef loop_to_1000():\n    for i in range(1000):\n        print(i)\n\n\nloop_to_1000()\n"}

## streaming_tokens.py
from queue import Queue
from threading import Thread
import transformers
import torch

class TextIteratorStreamer:
    def __init__(
        self, tokenizer
    ):
        self.tokenizer = tokenizer

## long_gpt.py
import time
from contextlib import suppress

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cuda as cuda
from torch.utils.data import DataLoader, IterableDataset

## index.html
<!DOCTYPE html>
<html class="no-js" lang="">
  <head>
    <meta charset="utf-8" />
    <title></title>
    <meta name="description" content="" />
    <meta name="viewport" content="width=device-width, initial-scale=1" />

    <meta property="og:title" content="" />
    <meta property="og:type" content="" />

## main.dart
import 'package:flutter_slidable/flutter_slidable.dart';
import 'package:flutter/material.dart';

void main() {
  runApp(MaterialApp(
    title: 'Navigation Basics',
    home: FirstScreen(items: List<String>.generate(40, (i) => "Item $i")),
  ));
}

## js-observables-binding.md

      
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                abacaj
                / js-observables-binding.md
            
            
              Created
              January 30, 2017 15:58
                — forked from austinhyde/js-observables-binding.md
            
              
                Vanilla JavaScript Data Binding
              
          
    Observables

You don't really need a framework or fancy cutting-edge JavaScript features to do two-way data binding. Let's start basic - first and foremost, you need a way to tell when data changes. Traditionally, this is done via an Observer pattern, but a full-blown implementation of that is a little clunky for nice, lightweight JavaScript. So, if native getters/setters are out, the only mechanism we have are accessors:
var n = 5;
function getN() { return n; }
function setN(newN) { n = newN; }

console.log(getN()); // 5

setN(10);

  
## move_files.cs
    class Program
    {
        static void Main(string[] args)
        {
            Console.WriteLine("Enter base directory:");
            var basePath = Console.ReadLine();
            Console.WriteLine("Enter destination directory (this will be created for you, make sure it doesn't exist):");
            var destination = Console.ReadLine();
            var directories = System.IO.Directory.EnumerateDirectories(basePath);

## fizzbuzz.js
// Write a program that prints the numbers from 1 to 100. But for multiples of three print “Fizz” instead of the number and for the multiples of five print “Buzz”. For numbers which are multiples of both three and five print “FizzBuzz”.

function fizzbuzz() {
  for (var i = 1; i <= 100; i++) {
    var str = "";

    str += (i % 3 === 0) ? "fizz" : "";
    str += (i % 5 === 0) ? "buzz" : "";
    str = (str) ? str : i;
	import tqdm
	import numpy as np
	import torch
	import torch.distributed as dist
	import transformers

	def extract_xml_answer(text: str) -> str:
	answer = text.split("<final_answer>")[-1]
	answer = answer.split("</final_answer>")[0]
	return answer.strip()
	{"completion": "# Write a python function to loop to 1000\n\ndef loop_to_1000():\n for i in range(1000):\n print(i)\n\n\nloop_to_1000()\n"}
	{"completion": "# Write a python function to loop to 1000\n\ndef loop_to_1000():\n for i in range(1000):\n print(i)\n\n\nloop_to_1000()\n"}
	{"completion": "# Write a python function to loop to 1000\n\ndef loop_to_1000():\n for i in range(1000):\n print(i)\n\n\nloop_to_1000()\n"}
	{"completion": "# Write a python function to loop to 1000\n\ndef loop_to_1000():\n for i in range(1000):\n print(i)\n\n\nloop_to_1000()\n"}
	{"completion": "# Write a python function to loop to 1000\n\ndef loop_to_1000():\n for i in range(1000):\n print(i)\n\n\nloop_to_1000()\n"}
	from queue import Queue
	from threading import Thread
	import transformers
	import torch

	class TextIteratorStreamer:
	def __init__(
	self, tokenizer
	):
	self.tokenizer = tokenizer
	import time
	from contextlib import suppress

	import torch
	import torch.nn as nn
	import torch.optim as optim
	import torch.nn.functional as F
	import torch.backends.cuda as cuda
	from torch.utils.data import DataLoader, IterableDataset
	<!DOCTYPE html>
	<html class="no-js" lang="">
	<head>
	<meta charset="utf-8" />
	<title></title>
	<meta name="description" content="" />
	<meta name="viewport" content="width=device-width, initial-scale=1" />

	<meta property="og:title" content="" />
	<meta property="og:type" content="" />
	import 'package:flutter_slidable/flutter_slidable.dart';
	import 'package:flutter/material.dart';

	void main() {
	runApp(MaterialApp(
	title: 'Navigation Basics',
	home: FirstScreen(items: List<String>.generate(40, (i) => "Item $i")),
	));
	}
	class Program
	{
	static void Main(string[] args)
	{
	Console.WriteLine("Enter base directory:");
	var basePath = Console.ReadLine();
	Console.WriteLine("Enter destination directory (this will be created for you, make sure it doesn't exist):");
	var destination = Console.ReadLine();
	var directories = System.IO.Directory.EnumerateDirectories(basePath);
	// Write a program that prints the numbers from 1 to 100. But for multiples of three print “Fizz” instead of the number and for the multiples of five print “Buzz”. For numbers which are multiples of both three and five print “FizzBuzz”.

	function fizzbuzz() {
	for (var i = 1; i <= 100; i++) {
	var str = "";

	str += (i % 3 === 0) ? "fizz" : "";
	str += (i % 5 === 0) ? "buzz" : "";
	str = (str) ? str : i;