{
 "cells": [
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "import json"
=======
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset"
>>>>>>> 41af016e94a20b80de932855d2fd5110dfdd4df6
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "for year in [2022,2023]:\n",
    "    for subject in [\"math\"]:\n",
    "        file_orig=f\"./data/json/{year}/{subject}_v1.json\"\n",
    "        file_dest=f\"./data/json/{year}/{subject}.json\"\n",
    "        with open(file_dest,\"w\") as f:\n",
    "            for line in open(file_orig,\"r\"):\n",
    "                data = json.loads(line)\n",
    "                if \"incomplete\" not in data:\n",
    "                    data[\"incomplete\"] = False\n",
    "                linewrite=json.dumps(data,ensure_ascii=False)\n",
    "                f.write(linewrite+\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
=======
   "execution_count": 4,
>>>>>>> 41af016e94a20b80de932855d2fd5110dfdd4df6
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
<<<<<<< HEAD
       "model_id": "462231fd51404fd290bffe90a8c0b158",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "baa7471b21ee486fb4b4ab7c2a3c7ece",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "49b01ff66b3f4c3ab7927cdd94c24212",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating 2022_math split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3598e1ab98304d5a98a1cd7c322c7fa6",
=======
       "model_id": "b5d51386a14e407ca2bdd18926ef997c",
>>>>>>> 41af016e94a20b80de932855d2fd5110dfdd4df6
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating 2023_math split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
<<<<<<< HEAD
    }
   ],
   "source": [
    "dataset=load_dataset(\"json\",data_files={\"2022_math\":\"./data/json/2022/math.json\",\n",
    "                                        \"2023_math\":\"./data/json/2023/math.json\"})"
=======
    },
    {
     "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\"})"
>>>>>>> 41af016e94a20b80de932855d2fd5110dfdd4df6
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 25,
=======
   "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,
>>>>>>> 41af016e94a20b80de932855d2fd5110dfdd4df6
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': 1,\n",
       " 'name': '1',\n",
<<<<<<< HEAD
       " 'problem': '1. $\\\\left(2^{\\\\sqrt{3}} \\\\times 4\\\\right)^{\\\\sqrt{3} - 2}$ 의 값은? [2점] \\\\begin{itemize} \\\\item[1] \\\\frac{1}{4} \\\\item[2] \\\\frac{1}{2} \\\\item[3] 1 \\\\item[4] 2 \\\\item[5] 4 \\\\end{itemize}',\n",
       " 'answer': 2,\n",
       " 'score': 2,\n",
       " 'review': None,\n",
       " 'incomplete': False}"
      ]
     },
     "execution_count": 25,
=======
       " '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,
>>>>>>> 41af016e94a20b80de932855d2fd5110dfdd4df6
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
<<<<<<< HEAD
    "dataset[\"2022_math\"][0]"
=======
    "dataset[\"2023_math\"][0]"
>>>>>>> 41af016e94a20b80de932855d2fd5110dfdd4df6
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 24,
=======
   "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,
>>>>>>> 41af016e94a20b80de932855d2fd5110dfdd4df6
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
<<<<<<< HEAD
       "model_id": "f612f040825040c6affa3b897f2633ef",
=======
       "model_id": "ed6870fcaa5d4641ae911c05f8d5e1a3",
>>>>>>> 41af016e94a20b80de932855d2fd5110dfdd4df6
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
<<<<<<< HEAD
       "Pushing dataset shards to the dataset hub:   0%|          | 0/1 [00:00<?, ?it/s]"
=======
       "Creating json from Arrow format:   0%|          | 0/1 [00:00<?, ?ba/s]"
>>>>>>> 41af016e94a20b80de932855d2fd5110dfdd4df6
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
<<<<<<< HEAD
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f3cb1129210b4586bbb81b52357c424e",
       "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": "0ebfe1b984304baf85c7ff3fdd836835",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Pushing dataset shards to the dataset hub:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5177d4e1287d4b86a8a79865c1bdff83",
       "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": "50d7d7a030f64ad4ad13d6f6f57bb583",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Deleting unused files from dataset repository:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d0e94febebca4af989c1517727feb56a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading metadata:   0%|          | 0.00/1.21k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#save as parquet\n",
    "dataset.push_to_hub(\"cfpark00/KoreanSAT\")"
=======
      "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\")"
>>>>>>> 41af016e94a20b80de932855d2fd5110dfdd4df6
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
=======
   "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",
>>>>>>> 41af016e94a20b80de932855d2fd5110dfdd4df6
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv1",
   "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",
<<<<<<< HEAD
   "version": "3.10.9"
=======
   "version": "3.8.9"
>>>>>>> 41af016e94a20b80de932855d2fd5110dfdd4df6
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}