{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "58e6167e-1e30-4bbf-93a1-af6ab5cb4673",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def preprocess_test(test_solutions_path: str, test_tasks_path: str, test_tests_path: str, save_path: str) -> None:\n",
    "    def read_files(*paths):\n",
    "        return (pd.read_excel(path) for path in paths)\n",
    "\n",
    "    solutions_df, tasks_df, tests_df = read_files(test_solutions_path, test_tasks_path, test_tests_path)\n",
    "\n",
    "    test_dataset = pd.merge(pd.merge(solutions_df, tasks_df, left_on='task_id', right_on='id', how='inner'), \n",
    "                            tests_df, on='task_id', how='inner')\n",
    "\n",
    "    test_dataset['input_output'] = test_dataset.apply(\n",
    "        lambda row: f\"{row['input']}->{row['output']}\" if pd.notna(row['input']) or pd.notna(row['output']) else \"\", \n",
    "        axis=1\n",
    "    )\n",
    "\n",
    "    test_dataset = test_dataset[['id', 'student_solution', 'description', 'author_solution', 'input_output']]\n",
    "    test_dataset.to_excel(save_path, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e561a852-007c-4b78-a7e1-697f80272169",
   "metadata": {},
   "outputs": [],
   "source": [
    "base = 'data/raw/test/{}'\n",
    "\n",
    "preprocess_test(base.format('solutions.xlsx'), base.format('tasks.xlsx'), base.format('tests.xlsx'), './test.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2c15dbb7-1034-41a7-96be-69e2418bf98e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d923d6c4-f962-4646-a041-999862bb3950",
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocess_test(base.format('solutions.xlsx'), base.format('tasks.xlsx'), base.format('tests.xlsx'), './test2.xlsx')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:.conda-poetry]",
   "language": "python",
   "name": "conda-env-.conda-poetry-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}