{
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   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded data for group 1:\n",
      "Train data shape: (6125, 16)\n",
      "Valid data shape: (2042, 16)\n",
      "Test data shape: (14719, 15)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "# Example usage:1\n",
    "group_number = 1  # You can change this to any group number you want to load (1, 2, 3, 4, or 5)\n",
    "\n",
    "# Select the mode for processing\n",
    "mode = 'tn_td_unit'  # Change this to 'only_td', 'tn_td', etc., as needed\n",
    "\n",
    "def load_group_data(group_number):\n",
    "    # Define the folder path based on the group number\n",
    "    group_folder = os.path.join('../../data_preprocess/dataset', str(group_number))\n",
    "    \n",
    "    # Define file paths for train, valid, and test datasets\n",
    "    train_file_path = os.path.join(group_folder, 'train.csv')\n",
    "    valid_file_path = os.path.join(group_folder, 'valid.csv')\n",
    "    test_file_path = os.path.join(group_folder, 'test.csv')\n",
    "    \n",
    "    # Check if the files exist\n",
    "    if not os.path.exists(train_file_path) or not os.path.exists(valid_file_path) or not os.path.exists(test_file_path):\n",
    "        raise FileNotFoundError(f\"One or more files for group {group_number} do not exist.\")\n",
    "    \n",
    "    # Load the CSV files into DataFrames\n",
    "    train_data = pd.read_csv(train_file_path)\n",
    "    valid_data = pd.read_csv(valid_file_path)\n",
    "    test_data = pd.read_csv(test_file_path)\n",
    "    \n",
    "    return train_data, valid_data, test_data\n",
    "\n",
    "\n",
    "try:\n",
    "    train_data, valid_data, test_data = load_group_data(group_number)\n",
    "    print(f\"Loaded data for group {group_number}:\")\n",
    "    print(f\"Train data shape: {train_data.shape}\")\n",
    "    print(f\"Valid data shape: {valid_data.shape}\")\n",
    "    print(f\"Test data shape: {test_data.shape}\")\n",
    "except FileNotFoundError as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "313f98ef12eb442bac319282e5ffe5d6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Saving the dataset (0/1 shards):   0%|          | 0/6125 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0c1834a4e7264a969085ad609320fdd6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Saving the dataset (0/1 shards):   0%|          | 0/14719 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "464f88daab334658aac93305ea6dac71",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Saving the dataset (0/1 shards):   0%|          | 0/2042 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset saved to 'combined_data'\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "from datasets import Dataset, DatasetDict\n",
    "\n",
    "# Function to process DataFrame based on mode\n",
    "def process_df(df, mode='only_td'):\n",
    "    output_list = []\n",
    "    for idx, row in df.iterrows():\n",
    "        try:\n",
    "            if mode == 'only_td':\n",
    "                input_str = f\"<TD_START>{str(row['tag_description'])}<TD_END>\"\n",
    "            elif mode == 'tn_td':\n",
    "                input_str = f\"<TN_START>{str(row['tag_name'])}<TN_END><TD_START>{str(row['tag_description'])}<TD_END>\"\n",
    "            elif mode == 'tn_td_min_max':\n",
    "                input_str = f\"<TN_START>{str(row['tag_name'])}<TN_END><TD_START>{str(row['tag_description'])}<TD_END><MIN_START>{row['min']}<MIN_END><MAX_START>{row['max']}<MAX_END>\"\n",
    "            elif mode == 'td_min_max':\n",
    "                input_str = f\"<TD_START>{str(row['tag_description'])}<TD_END><MIN_START>{row['min']}<MIN_END><MAX_START>{row['max']}<MAX_END>\"    \n",
    "            elif mode == 'td_unit':\n",
    "                input_str = f\"<TD_START>{str(row['tag_description'])}<TD_END><UNIT_START>{str(row['unit'])}<UNIT_END>\"      \n",
    "            elif mode == 'tn_td_unit':\n",
    "                input_str = f\"<TN_START>{str(row['tag_name'])}<TN_END><TD_START>{str(row['tag_description'])}<TD_END><UNIT_START>{str(row['unit'])}<UNIT_END>\"                     \n",
    "            else:\n",
    "                raise ValueError(\"Invalid mode specified\")\n",
    "            \n",
    "            output_list.append({\n",
    "                'translation': {\n",
    "                    'ships_idx': row['ships_idx'],\n",
    "                    'input': input_str,\n",
    "                    'thing_property': f\"<THING_START>{str(row['thing'])}<THING_END><PROPERTY_START>{str(row['property'])}<PROPERTY_END>\",\n",
    "                    'answer': f\"{str(row['thing'])} {str(row['property'])}\",\n",
    "                }\n",
    "            })\n",
    "        except Exception as e:\n",
    "            print(f\"Error processing row at index {idx}: {row}\")\n",
    "            print(f\"Exception: {e}\")\n",
    "    return output_list\n",
    "\n",
    "\n",
    "# Combine the mode and group information into a single dictionary\n",
    "combined_dict = {\n",
    "    \"mode\": mode,\n",
    "    \"fold_group\": group_number\n",
    "}\n",
    "\n",
    "# Save the combined dictionary to a JSON file\n",
    "with open(\"mode.json\", \"w\") as json_file:\n",
    "    json.dump(combined_dict, json_file)\n",
    "    \n",
    "try:\n",
    "    # Process the data and create a DatasetDict\n",
    "    combined_data = DatasetDict({\n",
    "        'train': Dataset.from_list(process_df(train_data, mode=mode)),\n",
    "        'test': Dataset.from_list(process_df(test_data, mode=mode)),\n",
    "        'validation': Dataset.from_list(process_df(valid_data, mode=mode)),\n",
    "    })\n",
    "    # Save the DatasetDict to disk\n",
    "    combined_data.save_to_disk(f\"combined_data/{mode}/{group_number}\")\n",
    "    print(\"Dataset saved to 'combined_data'\")\n",
    "except Exception as e:\n",
    "    print(f\"Error creating DatasetDict: {e}\")"
   ]
  }
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