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       "model_id": "70bdd4dd77d446399c8da752cc93667d",
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     },
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    },
    {
     "data": {
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       "model_id": "4d06625d7aba4fe695fcb25689b3254d",
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      ]
     },
     "metadata": {},
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    },
    {
     "data": {
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       "model_id": "66d31a7060014dfe85e9e0865d9e6529",
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      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "from datasets import load_dataset, concatenate_datasets, DatasetDict\n",
    "import os\n",
    "\n",
    "\n",
    "lst = ['ar', 'de', 'en', 'es', 'fr', 'ja', 'pt', 'zh']\n",
    "\n",
    "lst_train_ds = []\n",
    "lst_test_ds = []\n",
    "lst_val_ds = []\n",
    "\n",
    "for i in lst:\n",
    "\tds = load_dataset(\"dstc12/bot_adversarial_dialogue\", i)\n",
    "\tds2 = load_dataset(\"dstc12/dialogue_safety\", i)\n",
    "\tds3 = load_dataset(\"dstc12/ProsocialDialog\", i)\n",
    "\tif i == 'pt':\n",
    "\t\tds['train'] = concatenate_datasets([ds2['train'], ds3['train']]) # Remove pt training bot_adversarial_dialogue\n",
    "\telse:\n",
    "\t\tds['train'] = concatenate_datasets([ds['train'], ds2['train'], ds3['train']])\n",
    "\tds['test'] = concatenate_datasets([ds['test'], ds2['test'], ds3['test']])\n",
    "\tds['val'] = concatenate_datasets([ds['val'], ds2['val'], ds3['val']])\n",
    "\tlst_train_ds.append(ds['train'])\n",
    "\tlst_test_ds.append(ds['test'])\n",
    "\tlst_val_ds.append(ds['val'])\n",
    "\n",
    "concated_train_ds = concatenate_datasets(lst_train_ds)\n",
    "concated_test_ds = concatenate_datasets(lst_test_ds)\n",
    "concated_val_ds = concatenate_datasets(lst_val_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "576bced2-76db-41fa-8775-ca0d57eaf1e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_ds = DatasetDict({\n",
    "    'train': concated_train_ds,\n",
    "    'test': concated_test_ds,\n",
    "    'val': concated_val_ds\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7b6e0708-e02a-4a8c-bbfa-4db3984f787e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['context', 'response', 'safety_label', 'metadata'],\n",
       "        num_rows: 1191130\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['context', 'response', 'safety_label', 'metadata'],\n",
       "        num_rows: 236048\n",
       "    })\n",
       "    val: Dataset({\n",
       "        features: ['context', 'response', 'safety_label', 'metadata'],\n",
       "        num_rows: 233176\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3bf964cc-0509-4377-a51c-a99445982535",
   "metadata": {},
   "outputs": [
    {
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       "model_id": "76cf712010134411898a0858a18012fa",
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      "text/plain": [
       "Map (num_proc=64):   0%|          | 0/233176 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['context', 'response', 'safety_label', 'metadata', 'instruction', 'input', 'output', 'system', 'history'],\n",
       "        num_rows: 1191130\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['context', 'response', 'safety_label', 'metadata', 'instruction', 'input', 'output', 'system', 'history'],\n",
       "        num_rows: 236048\n",
       "    })\n",
       "    val: Dataset({\n",
       "        features: ['context', 'response', 'safety_label', 'metadata', 'instruction', 'input', 'output', 'system', 'history'],\n",
       "        num_rows: 233176\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def process_data(data):\n",
    "\tres = {}\n",
    "\tif len(data['context']) % 2 == 0:\n",
    "\t\tres['instruction'] = \"Check if there is unsafe content in 'user' messages in the conversation.\"\n",
    "\t\tres['input'] = data['response']\n",
    "\t\tres['output'] = 'safe' if data['safety_label'] == 0 else 'unsafe'\n",
    "\t\tres['system'] = \"\"\n",
    "\t\tres['history'] = [data['context'][i:i+2] for i in range(0, len(data['context']), 2)]\n",
    "\telse:\n",
    "\t\tres['instruction'] = \"Check if there is unsafe content in 'assistant' messages in the conversation.\"\n",
    "\t\tres['input'] = \"\"\n",
    "\t\tdata['context'].append(data['response'])\n",
    "\t\tres['output'] = 'safe' if data['safety_label'] == 0 else 'unsafe'\n",
    "\t\tres['system'] = \"\"\n",
    "\t\tres['history'] = [data['context'][i:i+2] for i in range(0, len(data['context']), 2)]\n",
    "\treturn res\n",
    "\n",
    "processed_ds = final_ds.map(function=process_data, num_proc=64)\n",
    "processed_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8f1cc915-7221-44c5-a7b9-7a30ac238a5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'context': ['مرحبًا، سعيد بلقائك!',\n",
       "  'وأنت أيضًا! أنا آسف لسماع عن والدك. فقدت ولدي عندما كنت صغيرًا أيضًا.'],\n",
       " 'response': 'لم أفقد والدي. كلا والدي على قيد الحياة.',\n",
       " 'safety_label': 0,\n",
       " 'metadata': \"episode_done:True\\tspeaker_to_eval:human\\tbot_persona:your persona: i've a lot of allergies.\\\\nyour persona: my dad died when i was in high school.\\n\",\n",
       " 'instruction': \"Check if there is unsafe content in 'user' messages in the conversation.\",\n",
       " 'input': 'لم أفقد والدي. كلا والدي على قيد الحياة.',\n",
       " 'output': 'safe',\n",
       " 'system': '',\n",
       " 'history': [['مرحبًا، سعيد بلقائك!',\n",
       "   'وأنت أيضًا! أنا آسف لسماع عن والدك. فقدت ولدي عندما كنت صغيرًا أيضًا.']]}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed_ds['train'][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7bccc0aa-d6df-41b5-a321-f0967c19c4b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2eb0f0d0ffda4fe8a386cc246db29efc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/233176 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tqdm.notebook import tqdm\n",
    "\n",
    "lst_train = []\n",
    "lst_test = []\n",
    "lst_val = []\n",
    "\n",
    "for i in tqdm(processed_ds['train']):\n",
    "    lst_train.append(\n",
    "        {\n",
    "            \"instruction\": i['instruction'],\n",
    "            \"input\": i['input'],\n",
    "            \"output\": i['output'],\n",
    "            \"system\": i['system'],\n",
    "            \"history\": i['history']\n",
    "        }\n",
    "    )\n",
    "\n",
    "for i in tqdm(processed_ds['test']):\n",
    "    lst_test.append(\n",
    "        {\n",
    "            \"instruction\": i['instruction'],\n",
    "            \"input\": i['input'],\n",
    "            \"output\": i['output'],\n",
    "            \"system\": i['system'],\n",
    "            \"history\": i['history']\n",
    "        }\n",
    "    )\n",
    "\n",
    "for i in tqdm(processed_ds['val']):\n",
    "    lst_val.append(\n",
    "        {\n",
    "            \"instruction\": i['instruction'],\n",
    "            \"input\": i['input'],\n",
    "            \"output\": i['output'],\n",
    "            \"system\": i['system'],\n",
    "            \"history\": i['history']\n",
    "        }\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "de36eff9-6f6d-4f7d-b00b-12c57ab191ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1191130, 236048, 233176)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lst_train), len(lst_test), len(lst_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6e6dd601-d6b1-46ee-80a9-92ed387cc976",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "def dump_json_to_file(data, filename):\n",
    "    with open(filename, 'w', encoding='utf-8') as f:\n",
    "        json.dump(data, f, ensure_ascii=False, indent=4)\n",
    "\n",
    "dump_json_to_file(lst_train, \"train.json\")\n",
    "dump_json_to_file(lst_test, \"test.json\")\n",
    "dump_json_to_file(lst_val, \"val.json\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cce586ad-e315-45d9-9b6a-89a675ec680a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "random.shuffle(lst_train)\n",
    "random.shuffle(lst_test)\n",
    "random.shuffle(lst_val)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2bb17f1d-734b-4216-a8e3-c155bc1e7d9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dump_json_to_file(lst_train[:1000], \"train_sample.json\")\n",
    "dump_json_to_file(lst_test[:1000], \"test_sample.json\")\n",
    "dump_json_to_file(lst_val[:1000], \"val_sample.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad821bb9-fb36-47ce-aea9-39a053f080ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8b4e2c1fb8ee4cc7b8d4e76a9dacacf3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train.json:   0%|          | 0.00/887M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from huggingface_hub import HfApi\n",
    "\n",
    "# Replace with your Hugging Face repo (public or private)\n",
    "repo_id = \"minhbui/test\"\n",
    "\n",
    "# File to upload\n",
    "file_path = \"train.json\"  # Change this to the actual file\n",
    "\n",
    "\n",
    "# Upload the file\n",
    "api = HfApi()\n",
    "api.upload_file(\n",
    "    path_or_fileobj=file_path,\n",
    "    path_in_repo=\"./\"+file_path,\n",
    "    repo_id=repo_id,\n",
    "    repo_type=\"dataset\",  # Use \"model\" if it's a model repo\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d56d93e1-4c01-4057-ba1f-84bfbc37a0c9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "doan",
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   "name": "doan"
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
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