{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76f1322a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17c8145d",
   "metadata": {},
   "outputs": [],
   "source": [
    "vqa_valid = json.load(open('<CT_RATE_DATASET_DIR>/labels/multiple_choice/multiple_choice_val.json'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47b5d81b",
   "metadata": {},
   "outputs": [],
   "source": [
    "vqa_dict = defaultdict(list)\n",
    "qid = 0\n",
    "\n",
    "for vqa_row in vqa_valid:\n",
    "    image_id = vqa_row['image']\n",
    "    messages = vqa_row['conversations']\n",
    "\n",
    "    for i in range(0, len(messages), 2):\n",
    "        assert messages[i]['from'] == 'human'\n",
    "        vqa_dict['image_id'].append(image_id)\n",
    "        vqa_dict['qid'].append(qid)\n",
    "        vqa_dict['question'].append(messages[i]['value'])\n",
    "        vqa_dict['answer'].append(messages[i+1]['value'])\n",
    "        qid += 1\n",
    "vqa_df = pd.DataFrame(vqa_dict)\n",
    "vqa_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b53e080",
   "metadata": {},
   "outputs": [],
   "source": [
    "vqa_df.to_csv('<CT_RATE_DATASET_DIR>/labels/multiple_choice/multiple_choice_valid.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "eval",
   "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": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}