{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b5d51386a14e407ca2bdd18926ef997c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating 2023_math split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a3015bdd603e44c789b8347863bb94c8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating 2024_math split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset=load_dataset(\"json\",data_files={\"2023_math\":\"./data/json/2023/math.json\",\n",
    "                                        \"2024_math\":\"./data/json/2024/math.json\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "221c4bba0e78418b9686c1ba0a0fa668",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Uploading the dataset shards:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1763d5a9fc5c4c8099d0deb716f06c73",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Creating parquet from Arrow format:   0%|          | 0/1 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "04f7bfc0e76c438ab84048301cb0e10e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Uploading the dataset shards:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8707c527afaf47d0928b5167c509d5f7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Creating parquet from Arrow format:   0%|          | 0/1 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/datasets/cfpark00/KoreanSAT/commit/8fdf990d118abc5511f7fe828a4ae482e4b67d07', commit_message='Upload dataset', commit_description='', oid='8fdf990d118abc5511f7fe828a4ae482e4b67d07', pr_url=None, repo_url=RepoUrl('https://huggingface.co/datasets/cfpark00/KoreanSAT', endpoint='https://huggingface.co', repo_type='dataset', repo_id='cfpark00/KoreanSAT'), pr_revision=None, pr_num=None)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#save as parquet\n",
    "dataset.push_to_hub(\"cfpark00/KoreanSAT\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': 1,\n",
       " 'name': '1',\n",
       " 'problem': '1. \\\\left( \\\\frac{4}{2^{\\\\sqrt{2}}} \\\\right)^{2 + \\\\sqrt{2}} \\\\text{의 값은? [2점]}\\n\\n\\\\begin{itemize}\\n    \\\\item[1] $\\\\frac{1}{4}$\\n    \\\\item[2] $\\\\frac{1}{2}$\\n    \\\\item[3] $1$\\n    \\\\item[4] $2$\\n    \\\\item[5] $4$\\n\\\\end{itemize}\\n',\n",
       " 'answer': -1,\n",
       " 'score': -1}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"2023_math\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ed6870fcaa5d4641ae911c05f8d5e1a3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Creating json from Arrow format:   0%|          | 0/1 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "33057"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "ds = load_dataset(\"cfpark00/KoreanSAT\")\n",
    "ds[\"2024_math\"].to_json(\"./data/json/2024/math.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    2024_math: Dataset({\n",
       "        features: ['id', 'name', 'problem', 'answer', 'score'],\n",
       "        num_rows: 46\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "56b0357800c24f42af02e56bcd9f9133",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Creating json from Arrow format:   0%|          | 0/1 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "33057"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"2024_math\"].to_json(\"./data/json/2024/math.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "jsons=[]\n",
    "for line in open(\"./data/json/2024/math.json\"):\n",
    "    jsons.append(json.loads(line))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16. 방정식\n",
      "\\[\n",
      "\\log_2{(3x+2)} = 2 + \\log_2{(x-2)}\n",
      "\\]\n",
      "\\text{를 만족시키는 실수 } \\( x \\) \\text{의 값을 구하시오. [3점]}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(jsons[15][\"problem\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': 16,\n",
       " 'name': '16',\n",
       " 'problem': '16. 방정식\\n\\\\[\\n\\\\log_2{(3x+2)} = 2 + \\\\log_2{(x-2)}\\n\\\\]\\n\\\\text{를 만족시키는 실수 } \\\\( x \\\\) \\\\text{의 값을 구하시오. [3점]}\\n',\n",
       " 'answer': '2',\n",
       " 'score': 3}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jsons[15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
   "codemirror_mode": {
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