{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "x = open(\"./HumanEval.jsonl\")\n",
    "entries = []\n",
    "for line in x:\n",
    "    contents = json.loads(line)\n",
    "    entries.append(contents)\n",
    "x.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'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', 'entry_point': 'has_close_elements', '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, 5.9, 4.0, 5.0], 0.8) == False\\n    assert candidate([1.0, 2.0, 3.0, 4.0, 5.0, 2.0], 0.1) == True\\n    assert candidate([1.1, 2.2, 3.1, 4.1, 5.1], 1.0) == True\\n    assert candidate([1.1, 2.2, 3.1, 4.1, 5.1], 0.5) == False\\n\\n\"}\n"
     ]
    }
   ],
   "source": [
    "print(entries[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\"source\": [], \"target\": [], \"program_id\": []}\n",
    "\n",
    "for entry in entries:\n",
    "    data[\"source\"].append(entry[\"source\"])\n",
    "    data[\"target\"].append(entry[\"target\"])\n",
    "    data[\"program_id\"].append(entry[\"program_id\"])\n",
    "\n",
    "pd.DataFrame(data=data).to_csv(\"./HumanEval.csv\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}