{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "x = open(\"./mbpp.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": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'text': 'Write a function to find the minimum cost path to reach (m, n) from (0, 0) for the given cost matrix cost[][] and a position (m, n) in cost[][].', 'code': 'R = 3\\r\\nC = 3\\r\\ndef min_cost(cost, m, n): \\r\\n\\ttc = [[0 for x in range(C)] for x in range(R)] \\r\\n\\ttc[0][0] = cost[0][0] \\r\\n\\tfor i in range(1, m+1): \\r\\n\\t\\ttc[i][0] = tc[i-1][0] + cost[i][0] \\r\\n\\tfor j in range(1, n+1): \\r\\n\\t\\ttc[0][j] = tc[0][j-1] + cost[0][j] \\r\\n\\tfor i in range(1, m+1): \\r\\n\\t\\tfor j in range(1, n+1): \\r\\n\\t\\t\\ttc[i][j] = min(tc[i-1][j-1], tc[i-1][j], tc[i][j-1]) + cost[i][j] \\r\\n\\treturn tc[m][n]', 'task_id': 1, 'test_setup_code': '', 'test_list': ['assert min_cost([[1, 2, 3], [4, 8, 2], [1, 5, 3]], 2, 2) == 8', 'assert min_cost([[2, 3, 4], [5, 9, 3], [2, 6, 4]], 2, 2) == 12', 'assert min_cost([[3, 4, 5], [6, 10, 4], [3, 7, 5]], 2, 2) == 16'], 'challenge_test_list': []}\n"
     ]
    }
   ],
   "source": [
    "print(entries[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\"source\": [], \"target\": [], \"program_id\": []}\n",
    "\n",
    "for i,entry in enumerate(entries):\n",
    "    data[\"source\"].append(entry[\"text\"])\n",
    "    data[\"target\"].append(entry[\"code\"])\n",
    "    data[\"program_id\"].append(\"MBPP_\"+str(i))\n",
    "\n",
    "pd.DataFrame(data=data).to_csv(\"./mbpp.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
}