Comp_Bio (3).ipynb

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  "cells": [
    {
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      "execution_count": null,
      "metadata": {
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        },
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          "text": [
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.2.1+cu121)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.13.4)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.11.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2023.6.0)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch)\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch)\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch)\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch)\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch)\n",
            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch)\n",
            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch)\n",
            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch)\n",
            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "Collecting nvidia-nccl-cu12==2.19.3 (from torch)\n",
            "  Using cached nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)\n",
            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch)\n",
            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.2.0)\n",
            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch)\n",
            "  Using cached nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n",
            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12\n",
            "Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105\n"
          ]
        }
      ],
      "source": [
        "!pip install torch"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import pandas as pd\n",
        "import sqlite3"
      ],
      "metadata": {
        "id": "CBme6DYZvw9t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import sqlite3\n",
        "\n",
        "# Connect to the SQLite database\n",
        "conn = sqlite3.connect('/content/cs4400.sql')\n",
        "\n",
        "# Create a cursor object to execute SQL queries\n",
        "cursor = conn.cursor()\n",
        "\n",
        "# Read SQL script from file\n",
        "with open('/content/cs4400.sql', 'r') as file:\n",
        "    sql_script = file.read()\n",
        "\n",
        "# Execute the SQL script\n",
        "cursor.executescript(sql_script)\n",
        "\n",
        "# Commit the changes\n",
        "conn.commit()\n",
        "\n",
        "# Close the connection\n",
        "conn.close()\n"
      ],
      "metadata": {
        "id": "ZiPA-vsUwXjQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def pd_to_sqlDB(input_df: pd.DataFrame,\n",
        "                table_name: str,\n",
        "                db_name: str = 'default.db') -> None:\n",
        "\n",
        "    '''Take a Pandas dataframe `input_df` and upload it to `table_name` SQLITE table\n",
        "\n",
        "    Args:\n",
        "        input_df (pd.DataFrame): Dataframe containing data to upload to SQLITE\n",
        "        table_name (str): Name of the SQLITE table to upload to\n",
        "        db_name (str, optional): Name of the SQLITE Database in which the table is created.\n",
        "                                 Defaults to 'default.db'.\n",
        "    '''\n",
        "\n",
        "    # Step 1: Setup local logging\n",
        "    import logging\n",
        "    logging.basicConfig(level=logging.INFO,\n",
        "                        format='%(asctime)s %(levelname)s: %(message)s',\n",
        "                        datefmt='%Y-%m-%d %H:%M:%S')\n",
        "\n",
        "    # Step 2: Find columns in the dataframe\n",
        "    cols = input_df.columns\n",
        "    cols_string = ','.join(cols)\n",
        "    val_wildcard_string = ','.join(['?'] * len(cols))\n",
        "\n",
        "    # Step 3: Connect to a DB file if it exists, else crete a new file\n",
        "    con = sqlite3.connect(db_name)\n",
        "    cur = con.cursor()\n",
        "    logging.info(f'SQL DB {db_name} created')\n",
        "\n",
        "    # Step 4: Create Table\n",
        "    sql_string = f\"\"\"CREATE TABLE {table_name} ({cols_string});\"\"\"\n",
        "    cur.execute(sql_string)\n",
        "    logging.info(f'SQL Table {table_name} created with {len(cols)} columns')\n",
        "\n",
        "    # Step 5: Upload the dataframe\n",
        "    rows_to_upload = input_df.to_dict(orient='split')['data']\n",
        "    sql_string = f\"\"\"INSERT INTO {table_name} ({cols_string}) VALUES ({val_wildcard_string});\"\"\"\n",
        "    cur.executemany(sql_string, rows_to_upload)\n",
        "    logging.info(f'{len(rows_to_upload)} rows uploaded to {table_name}')\n",
        "\n",
        "    # Step 6: Commit the changes and close the connection\n",
        "    con.commit()\n",
        "    con.close()"
      ],
      "metadata": {
        "id": "L8aRcY6nv0Xs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def sql_query_to_pd(sql_query_string: str, db_name: str ='default.db') -> pd.DataFrame:\n",
        "    '''Execute an SQL query and return the results as a pandas dataframe\n",
        "\n",
        "    Args:\n",
        "        sql_query_string (str): SQL query string to execute\n",
        "        db_name (str, optional): Name of the SQLITE Database to execute the query in.\n",
        "                                 Defaults to 'default.db'.\n",
        "\n",
        "    Returns:\n",
        "        pd.DataFrame: Results of the SQL query in a pandas dataframe\n",
        "    '''\n",
        "    # Step 1: Connect to the SQL DB\n",
        "    con = sqlite3.connect(db_name)\n",
        "\n",
        "    # Step 2: Execute the SQL query\n",
        "    cursor = con.execute(sql_query_string)\n",
        "\n",
        "    # Step 3: Fetch the data and column names\n",
        "    result_data = cursor.fetchall()\n",
        "    cols = [description[0] for description in cursor.description]\n",
        "\n",
        "    # Step 4: Close the connection\n",
        "    con.close()\n",
        "\n",
        "    # Step 5: Return as a dataframe\n",
        "    return pd.DataFrame(result_data, columns=cols)"
      ],
      "metadata": {
        "id": "Tr3DLqCwwL2S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HaeOXAPS9ag0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics.pairwise import cosine_similarity"
      ],
      "metadata": {
        "id": "BXhJUKNm1B0Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_cosine_similarity(sentence1: str, sentence2: str) -> float:\n",
        "  vectorizer = TfidfVectorizer()\n",
        "  tfidf_matrix = vectorizer.fit_transform([sentence1, sentence2])\n",
        "  cosine_sim = cosine_similarity(tfidf_matrix[0:1], tfidf_matrix[1:2])\n",
        "  return cosine_sim"
      ],
      "metadata": {
        "id": "id7l-xqE081l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define two example sentences\n",
        "sentence1 = \"SELECT query from DATABASE where X = 24\"\n",
        "sentence2 = \"SELECT this query from the DATABASE WHERE X = 24\"\n",
        "\n",
        "calculate_cosine_similarity(sentence1, sentence2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5_zD1qeM01kQ",
        "outputId": "12735c30-42a0-4791-ebb2-de2d7fc42de3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.77651453]])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q -U bitsandbytes\n",
        "!pip install -q -U git+https://github.com/huggingface/transformers.git\n",
        "!pip install -q -U git+https://github.com/huggingface/peft.git\n",
        "!pip install -q -U git+https://github.com/huggingface/accelerate.git\n",
        "!pip install -q -U datasets scipy ipywidgets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MKkMtPmsXYzI",
        "outputId": "704549c1-f3d9-422f-c9c3-85ed40fbf968"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.8/119.8 MB\u001b[0m \u001b[31m14.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m297.6/297.6 kB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for accelerate (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m542.0/542.0 kB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m38.6/38.6 MB\u001b[0m \u001b[31m46.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.4/139.4 kB\u001b[0m \u001b[31m19.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m17.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m28.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m19.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m388.9/388.9 kB\u001b[0m \u001b[31m45.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m81.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m78.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from accelerate import FullyShardedDataParallelPlugin, Accelerator\n",
        "from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig\n",
        "fsdp_plugin = FullyShardedDataParallelPlugin(\n",
        "    state_dict_config=FullStateDictConfig(offload_to_cpu=True, rank0_only=False),\n",
        "    optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=True, rank0_only=False),\n",
        ")\n",
        "accelerator = Accelerator(fsdp_plugin=fsdp_plugin)"
      ],
      "metadata": {
        "id": "Fl2K9SdoXcMA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TQE8e4eZY3fD",
        "outputId": "58bf712c-40d7-4bd7-ce0c-5f7b9c085431"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (1.13.0)\n",
            "Requirement already satisfied: numpy<2.3,>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from scipy) (1.25.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "train_dataset = load_dataset('gem/viggo', split='train')\n",
        "eval_dataset = load_dataset('gem/viggo', split='validation')\n",
        "test_dataset = load_dataset('gem/viggo', split='test')"
      ],
      "metadata": {
        "id": "AjtNebILXeGF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_dataset)\n",
        "print(eval_dataset)\n",
        "print(test_dataset)"
      ],
      "metadata": {
        "id": "S7ztUhklXhFy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# import torch\n",
        "# from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
        "\n",
        "\n",
        "# base_model_id = \"mistralai/Mistral-7B-v0.1\"\n",
        "# bnb_config = BitsAndBytesConfig(\n",
        "#     load_in_4bit=True,\n",
        "#     bnb_4bit_use_double_quant=True,\n",
        "#     bnb_4bit_quant_type=\"nf4\",\n",
        "#     bnb_4bit_compute_dtype=torch.bfloat16\n",
        "# )\n",
        "# model = AutoModelForCausalLM.from_pretrained(base_model_id, quantization_config=bnb_config)"
      ],
      "metadata": {
        "id": "vpjzHtdYXjLc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lW-i2UfPzqUi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ErqtSSGxDVmx",
        "outputId": "c3ba94ed-0c16-4e32-8c4d-ccd82ecd2f5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m244.2/244.2 kB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m72.9/72.9 kB\u001b[0m \u001b[31m9.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.5/92.5 MB\u001b[0m \u001b[31m17.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.4/7.4 MB\u001b[0m \u001b[31m66.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.4/77.4 kB\u001b[0m \u001b[31m11.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m46.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import torch\n",
        "from datasets import load_dataset\n",
        "from transformers import (\n",
        "    AutoModelForCausalLM,\n",
        "    AutoTokenizer,\n",
        "    BitsAndBytesConfig,\n",
        "    HfArgumentParser,\n",
        "    TrainingArguments,\n",
        "    pipeline,\n",
        "    logging,\n",
        ")\n",
        "from peft import LoraConfig, PeftModel\n",
        "from trl import SFTTrainer"
      ],
      "metadata": {
        "id": "U0h-ANkaDZmt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_comments(sql_file_path):\n",
        "    comments = []\n",
        "    with open(sql_file_path, 'r') as file:\n",
        "        in_multiline_comment = False\n",
        "        for line in file:\n",
        "            line = line.strip()\n",
        "            if line.startswith('--'):\n",
        "                comments.append(line.lstrip('-').strip())\n",
        "            elif line.startswith('/*'):\n",
        "                in_multiline_comment = True\n",
        "                if line.endswith('*/'):\n",
        "                    in_multiline_comment = False\n",
        "            elif line.endswith('*/'):\n",
        "                in_multiline_comment = False\n",
        "            elif in_multiline_comment:\n",
        "                comments.append(line)\n",
        "    return comments\n",
        "\n",
        "# Provide the path to your SQL file\n",
        "sql_file_path = '/content/cs4400_autograder company_only v10_1-1.sql'\n",
        "\n",
        "# Extract comments from the SQL file\n",
        "comments = extract_comments(sql_file_path)\n",
        "\n",
        "# Print the extracted comments\n",
        "print(comments)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sue5c16-z2_a",
        "outputId": "139cdead-ff73-4709-ab74-7d99f7ec20d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['CS4400: Introduction to Database Systems: Wednesday, September 7, 2022', 'SQL Autograding Environment (v10.3): Company Database (PRACTICE ONLY)', 'Version 10.3: Reset database state after testing w/magic44_set_database_state()', '-------------------------------------------------', 'ENTER YOUR QUERY SOLUTIONS STARTING AT LINE 90', '-------------------------------------------------', '-----------------------------------------------', 'table structures', '-----------------------------------------------', 'Enter your queries in the area below using this format:', 'create or replace view practiceQuery<#> as', '<your proposed query solution>;', \"Be sure to end all queries with a semi-colon (';') and make sure that\", 'the <#> value matches the query value from the practice sheet', '-------------------------------------------------', 'view structures (student created solutions)', 'PUT ALL PROPOSED QUERY SOLUTIONS BELOW THIS LINE', '-------------------------------------------------', 'CS4400: Introduction to Database Systems', 'Company ANSWER KEY (v5 - Actual Queries for Book and Other Practice Problems)', 'Friday, July 14, 2023', '[Tue, 19 May 2020: Fixed practice query 51 / Reversed the order of the first and last names]', '[Sat, 23 May 2020: Fixed practice queries 25-27 / Switched project number & name columns]', '[Sun, 28 Nov 2021: Fixed practice query 15 / Corrected project assignments to emps not depts]', '[Fri, 14 Jul 2023: \"Re\"-updated queries 3, 15 and 25-27 / As shown above]', \"practiceQuery0: Retrieve birthdate and address for 'John Smith'.\", \"practiceQuery1: Retrieve the first name, last name and address of all employees who work for the 'Research' department.\", \"practiceQuery2: For every project located in 'Stafford', list the project number, the controlling department number, and the department manager's last name, address, and birth date.\", 'practiceQuery3: Find the names of employees who work on all the projects controlled by department number 5.', \"practiceQuery4: Make a list of project numbers for projects that involve an employee whose last name is 'Smith', either as a worker or as a manager of the department that controls the project.\", 'practiceQuery5: List the names of all employees with two or more dependents.', 'practiceQuery6: Retrieve the names of employees who have no dependents.', 'practiceQuery7: List the names of managers who have at least one dependent.', \"practiceQuery8: For each employee, retrieve the employee's first and last name and the first and last name of his or her immediate supervisor.\", \"practiceQuery9: Select all of the employee's Social Security Numbers.\", \"practiceQuery10: Select all possible combinations of the employee's Social Security Numbers and department names.\", 'practiceQuery11: Retrieve the distinct salaries of all of the employees.', 'practiceQuery12: Retrieve the first and last names of all employees whose address is in Houston, Texas.', \"practiceQuery13: Show the first and last names and resulting salaries if every employee working on the 'ProductX' project is given a 10% raise.\", 'practiceQuery14: Retrieve all information for employees in department 5 whose salary is between $30,000 and $40,000.', 'practiceQuery15: Retrieve a list of department names, employee first and last names, and the project names they are working on, ordered by department and, within each department, alphabetically by last name, then first name.', 'practiceQuery16: Retrieve the name of each employee who has a dependent with the same first name and is the same sex as the employee.', 'practiceQuery17: Retrieve the SSNs of employees who work on projects with numbers 1, 2, or 3.', 'practiceQuery18: Retrieve the names of all employees who do not have supervisors.', 'practiceQuery19: Find the sum of the salaries of all employees, the maximum salary, the minimum salary, and the average salary.', \"practiceQuery20: Find the sum of the salaries of all employees of the 'Research' department, as well as the maximum salary, the minimum salary, and the average salary in this department.\", 'practiceQuery21: Retrieve the total number of employees in the company.', \"practiceQuery22: Retrieve the total number of employees in the 'Research' department.\", 'practiceQuery23: Count the number of distinct salary values in the database.', 'practiceQuery24: For each department, retrieve the department number, the number of employees in the department, and their average salary.', 'practiceQuery25: For each project, retrieve the project number, the project name, and the number of employees who work on that project. (Null employees will be interpreted as zero employees).', 'practiceQuery26: For each project on which more than two employees work, retrieve the project number, the project name, and the number of employees who work on the project.', 'practiceQuery27: For each project, retrieve the project number, the project name, and the number of employees from department 5 who work on the project.', 'practiceQuery28: For each department that has less than four* employees, retrieve the department number and the number of its employees who are making more than $25,000*. (Less than four employees instead of more than five, and $25,000 instead of $40,000 to make the results more fruitful.)', 'practiceQuery29: Retrieve all of the attribute values for any employee who works in department 5.', 'practiceQuery30: Retrieve all of the attributes for each employee and the department for which he or she works.', 'practiceQuery31: Find the first name, last name and birthdate of the employees who were born during the 1950s.', 'practiceQuery32: Retrieve the first and last names of all employees in department 5 who work more than 10 hours per week on the ProductX project.', 'practiceQuery33: List the first and last names of all employees who have a dependent with the same first name as themselves.', \"practiceQuery34: Find the first and last names of all employees who are directly supervised by 'Franklin Wong'.\", 'practiceQuery35: Retrieve the first and last names of all employees who work in the department that has the employee with the highest salary among all employees.', \"practiceQuery36: Retrieve the first and last names of all employees whose supervisor's supervisor has '888665555' for their SSN.\", 'practiceQuery37: Retrieve the first and last names of employees who make at least $10,000 more than the employee who is paid the least in the company.', \"practiceQuery40: Return a static string 'Hello World!'.\", \"practiceQuery41: Return a static string 'Hello World!' with column alias 'greeting'.\", 'practiceQuery42: Compute the sum of 4 and 6.', 'practiceQuery43: Compute several arithmetic operations and return their results with column aliases.', 'practiceQuery44: Retrieve all columns for all employees.', 'practiceQuery50: Retrieve first name, last name, and address of all employees.', \"practiceQuery51: Display the last name in a column titled 'Last Name', first name in a column titled 'First Name', and address in a column titled 'Residence Location' for all employees.\", \"practiceQuery52: Concatenate first name and last name into a single column 'Whole Name' along with the address for all employees.\", 'practiceQuery53: Retrieve department numbers for all employees.', 'practiceQuery54: Retrieve distinct department numbers from the employee table.', 'practiceQuery55: Retrieve salary and department number for all employees.', 'practiceQuery56: Retrieve the distinct salary and department number combinations for all employees.', 'practiceQuery60: Retrieve the first name, last name and address of all employees in department.', 'practiceQuery61: Retrieve first name, last name, and address for all employees not in department 5.', 'practiceQuery62: Retrieve first name, last name, and address for all employees with a salary greater than 30,000.', 'practiceQuery63: Retrieve first name, last name, and address for all employees either in department 5 or with a salary greater than 30,000.', 'practiceQuery64: Retrieve first name, last name, and address for all employees in department 5 with a salary greater than 30,000.', 'practiceQuery65: Retrieve the first name, last name, address and birthdate of all employees born after January 1st, 1968.', 'practiceQuery66: Retrieve the first name, last name, address and birthdate of all employees born after January 1st, 1964 and before August 10th, 1970.', 'practiceQuery67: Retrieve the first name, last name, and address of all employees, ordered by first name.', 'practiceQuery68: Retrieve the first name, last name and address of all employees in descending last name order.', 'practiceQuery69: Retrieve the first name and address of all employees, ordered by last name in descending order.', 'practiceQuery70: Retrieve the first name, last name and address of all employees in the order of last name and then first name.', 'practiceQuery71: Retrieve the first name, last name and address of all employees who live in Houston.', \"practiceQuery72: Retrieve the first name, last name, and address of all employees whose address contains 'Dallas'.\", \"practiceQuery73: Retrieve the first name, last name, and address of all employees whose first name starts with 'J'.\", \"practiceQuery74: Retrieve the first name, last name and address of all employees whose last names contain the pattern 'aya'.\", \"practiceQuery75: Retrieve the first name, last name and address of all employees whose last  names contain the pattern 'aya' where the pattern is not at the end of the string.\", \"practiceQuery80: Retrieve the first name, last name, and salary with a 10% raise as 'raise' for all employees in department 5.\", 'practiceQuery81: Retrieve the sum of the salaries for all employees in department number 5.', 'practiceQuery82: Retrieve the smallest and largest salaries for all employees in department number 5.', 'practiceQuery83: Count the number of distinct salary values among all employees.', 'practiceQuery84: Count the number of distinct supervisor SSNs among all employees.', 'practiceQuery85: Retrieve the number of employees whose superssn is equal to 333445555.', 'practiceQuery86: Retrieve the number of employees whose superssn is not equal to 333445555.', 'practiceQuery87: Retrieve the total count of all employees.', 'practiceQuery88: Retrieve the minimum, average, and maximum salary along with the department number for each department.', 'practiceQuery89: Retrieve the minimum, average and maximum salary for the employees in each department with less than four employees.', 'practiceQuery100: Count the number of employees earning more than $25,000 in each department.', 'practiceQuery101: Retrieve the total number of employees whose salaries exceed $25,000 in all of the departments except Headquarters.', 'practiceQuery102: Retrieve department numbers where the total number of employees is less than four.', 'practiceQuery103: Retrieve the total number of employees whose salaries exceed $25,000 per department, but only for departments where fewer than four employees work.', 'practiceQuery104: Retrieve the first name, last name and address for each employee that has a dependent daughter.', 'practiceQuery105: Retrieve the first name, last name and address for each employee that is working on a project that is located in Houston.', 'practiceQuery106: Retrieve Social Security Numbers of employees who either earn more than $25,000 or work on project number 20.', 'practiceQuery107: Retrieve Social Security Numbers of employees who earn more than $25,000 and work on project number 20.', 'practiceQuery108: Retrieve Social Security Numbers of employees who earn more than $25,000 but do not work on project number 20.', 'practiceQuery120: Retrieve all attributes for each employee and the department for which he or she works.', 'practiceQuery121: Display the first name, last name, address, department name, and department number for all combinations of the employee and department record sets.', 'practiceQuery122: Retrieve the first name, last name, address, department name and department number of all employees.', 'practiceQuery123: Match employees with departments based on natural join on department name and department number referred to as dno in a subquery.', \"practiceQuery124: Retrieve the employee's first and last name and address, along with their supervisor's Social Security Number, first and last names for all employees.\", \"practiceQuery125: Retrieve the name and birthdate of all dependents, along with the first and last name of their sponsor (employee). Include the names of all employees in your response, to include the first and last names of employees who don't have any dependents.\", 'practiceQuery126: Retrieve the first and last name, department number and department name for all department managers.', 'practiceQuery127: Retrieve the first and last name, department number of all employees, along with the name of the department that they manage (if applicable).', \"practiceQuery128: Display the first and last names of employees along with the names of projects that are located in the city where they live (using the employee's address). Include the employee's address along with the project's location.\", 'practiceQuery129: Find the first and last names and birthdates of the employees (supervisors included) who are old as (or older than) one or more of the department managers. Include the first and last names and birthdates of the managers as well.', '-------------------------------------------------', 'PUT ALL PROPOSED QUERY SOLUTIONS ABOVE THIS LINE', '-------------------------------------------------', 'The sole purpose of the following instruction is to minimize the impact of student-entered code', 'on the remainder of the autograding processes below', '-----------------------------------------------', 'table data', '-----------------------------------------------', '-------------------------------------------------', 'database state management', '-------------------------------------------------', '-------------------------------------------------', 'allow controlled changes to the database state', '-------------------------------------------------', 'Purge and then reload all of the database rows back into the tables.', 'Ensure that the data is deleted in reverse order with respect to the', 'foreign key dependencies (i.e., from children up to parents).', 'Check the validity of the requested state', 'Ensure that the data is inserted in order with respect to the foreign', 'key dependencies (i.e., from parents down to children)', '-------------------------------------------------', 'expected answers for autograding comparisons', '-------------------------------------------------', 'These tables are used to store the answers and test results.  The answers are generated by executing', 'the test script against our reference solution.  The test results are collected by running the test', 'script against your submission in order to compare the results.', 'the results from magic44_data_capture the are transferred into the magic44_test_results table', 'the answers generated from the reference solution are loaded below', '-------------------------------------------------', 'autograding system', '-------------------------------------------------', \"The magic44_data_capture table is used to store the data created by the student's queries\", 'The table is populated by the magic44_evaluate_queries stored procedure', 'The data in the table is used to populate the magic44_test_results table for analysis', 'The magic44_column_listing table is used to help prepare the insert statements for the magic44_data_capture', \"table for the student's queries which may have variable numbers of columns (the table is prepopulated)\", 'Exception checking has been implemented to prevent (as much as reasonably possible) errors', 'in the queries being evaluated from interrupting the testing process', 'The magic44_log_query_errors table capture these errors for later review', 'This null result set marker is used to avoid some edge cases (e.g., empty', 'test cases) when capturing the results.  This value is also used when', 'analyzing the submitted queries.', 'data capture tombstone added to alleviate empty result set anomalies', 'prepare and evaluate query contents', 'ensure that the state and query target tables exist', 'check all queries for each database state', '-------------------------------------------------', 'evaluate all queries against the different states', '-------------------------------------------------', 'Added [Wed, 7 Sep 2022] to return the database to its original state after testing', 'Delete the unneeded rows from the answers table to simplify later analysis', 'Modify the row hash results for the results table to eliminate spaces and convert all characters to lowercase', 'The magic44_content_differences view displays the differences between the answers and test results in terms of the', 'row attributes and values.  The error_category column contains missing for rows that are not included in the test', 'results but should be, while extra represents the rows that should not be included in the test results.  The row_hash', 'column contains the values of the row in a single string with the attribute values separated by a selected delimeter', '(i.e., the pound sign/#).', 'Updated [Wed, 23 Aug 2022] to identify duplicate rows testing (edge) case', 'Remove all unneeded tables, views, stored procedures and functions.', 'Keep only those structures needed to provide student feedback.']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_queries = []\n",
        "for comment in comments:\n",
        "  if comment.startswith('practiceQuery'):\n",
        "    test_queries.append(comment)"
      ],
      "metadata": {
        "id": "vrM-fvBk-Ysj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_queries"
      ],
      "metadata": {
        "id": "3Tcv6Zm-BIq9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_substring_after_colon(input_string):\n",
        "    index_of_colon = input_string.find(':')\n",
        "    if index_of_colon != -1:\n",
        "        return input_string[index_of_colon + 1:]\n",
        "    else:\n",
        "        return \"No colon found in the input string.\"\n",
        "\n",
        "# Example usage:\n",
        "input_string = \"This is a sample string: with some text after colon.\"\n",
        "substring_after_colon = get_substring_after_colon(input_string)\n",
        "print(\"Substring after colon:\", substring_after_colon)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oCIyMiDSCriw",
        "outputId": "8e55fe43-1ce7-4206-a8d8-69b7973c426f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Substring after colon:  with some text after colon.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "question_queries = []\n",
        "for query in test_queries:\n",
        "  question_queries.append(get_substring_after_colon(query)[1:])"
      ],
      "metadata": {
        "id": "OZG66LhMDBKE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QRyZsccITbZB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "actual_queries[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "lHDqFYqfDEqv",
        "outputId": "684fd760-dfb8-4a83-a479-b7ca7dd6d65b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'actual_queries' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-71897121d442>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mactual_queries\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'actual_queries' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0qTbyX8OTb04"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8DING8UDXxsl",
        "outputId": "b4b690d1-75cc-4183-9992-d1f2006e4234"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
            "  Downloading datasets-2.19.0-py3-none-any.whl (542 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m542.0/542.0 kB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.13.4)\n",
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            "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
            "  Downloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.0.3)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.2)\n",
            "Collecting xxhash (from datasets)\n",
            "  Downloading xxhash-3.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m12.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting multiprocess (from datasets)\n",
            "  Downloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: fsspec[http]<=2024.3.1,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.9.5)\n",
            "Collecting huggingface-hub>=0.21.2 (from datasets)\n",
            "  Downloading huggingface_hub-0.22.2-py3-none-any.whl (388 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m388.9/388.9 kB\u001b[0m \u001b[31m15.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (24.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0.1)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.2.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.0.5)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.9.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.21.2->datasets) (4.11.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2024.2.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n",
            "Installing collected packages: xxhash, dill, multiprocess, huggingface-hub, datasets\n",
            "  Attempting uninstall: huggingface-hub\n",
            "    Found existing installation: huggingface-hub 0.20.3\n",
            "    Uninstalling huggingface-hub-0.20.3:\n",
            "      Successfully uninstalled huggingface-hub-0.20.3\n",
            "Successfully installed datasets-2.19.0 dill-0.3.8 huggingface-hub-0.22.2 multiprocess-0.70.16 xxhash-3.4.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip uninstall scipy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AmHXxT1LL6e_",
        "outputId": "06c6cad4-46e6-437f-9d54-e8b11277de42"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found existing installation: scipy 1.13.0\n",
            "Uninstalling scipy-1.13.0:\n",
            "  Would remove:\n",
            "    /usr/local/lib/python3.10/dist-packages/scipy-1.13.0.dist-info/*\n",
            "    /usr/local/lib/python3.10/dist-packages/scipy.libs/libgfortran-040039e1.so.5.0.0\n",
            "    /usr/local/lib/python3.10/dist-packages/scipy.libs/libopenblasp-r0-24bff013.3.26.dev.so\n",
            "    /usr/local/lib/python3.10/dist-packages/scipy.libs/libquadmath-96973f99.so.0.0.0\n",
            "    /usr/local/lib/python3.10/dist-packages/scipy/*\n",
            "Proceed (Y/n)? y\n",
            "  Successfully uninstalled scipy-1.13.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "# Load the IMDb dataset\n",
        "imdb_dataset = load_dataset('gretelai/synthetic_text_to_sql')\n",
        "\n",
        "# Print information about the dataset\n",
        "print(imdb_dataset)\n",
        "\n",
        "# Access the train split\n",
        "train_data = imdb_dataset['train']\n",
        "\n",
        "# Print the first few examples\n",
        "for example in train_data[:5]:\n",
        "    print(example)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679,
          "referenced_widgets": [
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          ]
        },
        "id": "Kfn4Xlb8XteH",
        "outputId": "b48b4d42-46cd-4941-c78d-ae284a1fb0e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/8.18k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fc4242cdac69449fb6d3257d24092d1a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/32.4M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "96c18ee7428640fdae2f8b5435a4176d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/1.86M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6b91edba3165494a840e4029a469429b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/100000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0a4ce8dc961a445ab3f11b2cbcfbe5ed"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/5851 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2ddd1743dda84552869ca66b28221133"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['id', 'domain', 'domain_description', 'sql_complexity', 'sql_complexity_description', 'sql_task_type', 'sql_task_type_description', 'sql_prompt', 'sql_context', 'sql', 'sql_explanation'],\n",
            "        num_rows: 100000\n",
            "    })\n",
            "    test: Dataset({\n",
            "        features: ['id', 'domain', 'domain_description', 'sql_complexity', 'sql_complexity_description', 'sql_task_type', 'sql_task_type_description', 'sql_prompt', 'sql_context', 'sql', 'sql_explanation'],\n",
            "        num_rows: 5851\n",
            "    })\n",
            "})\n",
            "id\n",
            "domain\n",
            "domain_description\n",
            "sql_complexity\n",
            "sql_complexity_description\n",
            "sql_task_type\n",
            "sql_task_type_description\n",
            "sql_prompt\n",
            "sql_context\n",
            "sql\n",
            "sql_explanation\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dDmQ8BO4L-3n",
        "outputId": "2a270d54-63f5-4f7a-f7ad-7ac178dfb5f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['id', 'domain', 'domain_description', 'sql_complexity', 'sql_complexity_description', 'sql_task_type', 'sql_task_type_description', 'sql_prompt', 'sql_context', 'sql', 'sql_explanation'],\n",
              "    num_rows: 100000\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# The model that you want to train from the Hugging Face hub\n",
        "model_name = \"NousResearch/llama-2-7b-chat-hf\"\n",
        "\n",
        "# The instruction dataset to use\n",
        "dataset_name = \"mlabonne/guanaco-llama2-1k\"\n",
        "\n",
        "# Fine-tuned model name\n",
        "new_model = \"llama-2-7b-miniguanaco\"\n",
        "\n",
        "################################################################################\n",
        "# QLoRA parameters\n",
        "################################################################################\n",
        "\n",
        "# LoRA attention dimension\n",
        "lora_r = 64\n",
        "\n",
        "# Alpha parameter for LoRA scaling\n",
        "lora_alpha = 16\n",
        "\n",
        "# Dropout probability for LoRA layers\n",
        "lora_dropout = 0.1\n",
        "\n",
        "################################################################################\n",
        "# bitsandbytes parameters\n",
        "################################################################################\n",
        "\n",
        "# Activate 4-bit precision base model loading\n",
        "use_4bit = True\n",
        "\n",
        "# Compute dtype for 4-bit base models\n",
        "bnb_4bit_compute_dtype = \"float16\"\n",
        "\n",
        "# Quantization type (fp4 or nf4)\n",
        "bnb_4bit_quant_type = \"nf4\"\n",
        "\n",
        "# Activate nested quantization for 4-bit base models (double quantization)\n",
        "use_nested_quant = False\n",
        "\n",
        "################################################################################\n",
        "# TrainingArguments parameters\n",
        "################################################################################\n",
        "\n",
        "# Output directory where the model predictions and checkpoints will be stored\n",
        "output_dir = \"./results\"\n",
        "\n",
        "# Number of training epochs\n",
        "num_train_epochs = 1\n",
        "\n",
        "# Enable fp16/bf16 training (set bf16 to True with an A100)\n",
        "fp16 = False\n",
        "bf16 = False\n",
        "\n",
        "# Batch size per GPU for training\n",
        "per_device_train_batch_size = 4\n",
        "\n",
        "# Batch size per GPU for evaluation\n",
        "per_device_eval_batch_size = 4\n",
        "\n",
        "# Number of update steps to accumulate the gradients for\n",
        "gradient_accumulation_steps = 1\n",
        "\n",
        "# Enable gradient checkpointing\n",
        "gradient_checkpointing = True\n",
        "\n",
        "# Maximum gradient normal (gradient clipping)\n",
        "max_grad_norm = 0.3\n",
        "\n",
        "# Initial learning rate (AdamW optimizer)\n",
        "learning_rate = 2e-4\n",
        "\n",
        "# Weight decay to apply to all layers except bias/LayerNorm weights\n",
        "weight_decay = 0.001\n",
        "\n",
        "# Optimizer to use\n",
        "optim = \"paged_adamw_32bit\"\n",
        "\n",
        "# Learning rate schedule (constant a bit better than cosine)\n",
        "lr_scheduler_type = \"constant\"\n",
        "\n",
        "# Number of training steps (overrides num_train_epochs)\n",
        "max_steps = -1\n",
        "\n",
        "# Ratio of steps for a linear warmup (from 0 to learning rate)\n",
        "warmup_ratio = 0.03\n",
        "\n",
        "# Group sequences into batches with same length\n",
        "# Saves memory and speeds up training considerably\n",
        "group_by_length = True\n",
        "\n",
        "# Save checkpoint every X updates steps\n",
        "save_steps = 25\n",
        "\n",
        "# Log every X updates steps\n",
        "logging_steps = 25\n",
        "\n",
        "################################################################################\n",
        "# SFT parameters\n",
        "################################################################################\n",
        "\n",
        "# Maximum sequence length to use\n",
        "max_seq_length = None\n",
        "\n",
        "# Pack multiple short examples in the same input sequence to increase efficiency\n",
        "packing = False\n",
        "\n",
        "# Load the entire model on the GPU 0\n",
        "device_map = {\"\": 0}"
      ],
      "metadata": {
        "id": "g0L3TUj_DbpD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load dataset (you can process it here)\n",
        "dataset = load_dataset(dataset_name, split=\"train\")"
      ],
      "metadata": {
        "id": "IvK-xS0aIl1R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(dataset['text'][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "wk-JkPQfIoCX",
        "outputId": "d14e5285-9c63-4315-d29e-33219658491e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'dataset' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-1886fdd8b801>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'text'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'dataset' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load dataset (you can process it here)\n",
        "dataset = load_dataset(dataset_name, split=\"train\")\n",
        "\n",
        "# Load tokenizer and model with QLoRA configuration\n",
        "compute_dtype = getattr(torch, bnb_4bit_compute_dtype)\n",
        "\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=use_4bit,\n",
        "    bnb_4bit_quant_type=bnb_4bit_quant_type,\n",
        "    bnb_4bit_compute_dtype=compute_dtype,\n",
        "    bnb_4bit_use_double_quant=use_nested_quant,\n",
        ")\n",
        "\n",
        "# Check GPU compatibility with bfloat16\n",
        "if compute_dtype == torch.float16 and use_4bit:\n",
        "    major, _ = torch.cuda.get_device_capability()\n",
        "    if major >= 8:\n",
        "        print(\"=\" * 80)\n",
        "        print(\"Your GPU supports bfloat16: accelerate training with bf16=True\")\n",
        "        print(\"=\" * 80)\n",
        "\n",
        "# Load base model\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    quantization_config=bnb_config,\n",
        "    device_map=device_map\n",
        ")\n",
        "model.config.use_cache = False\n",
        "model.config.pretraining_tp = 1\n",
        "\n",
        "# Load LLaMA tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.padding_side = \"right\" # Fix weird overflow issue with fp16 training\n",
        "\n",
        "# Load LoRA configuration\n",
        "peft_config = LoraConfig(\n",
        "    lora_alpha=lora_alpha,\n",
        "    lora_dropout=lora_dropout,\n",
        "    r=lora_r,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        ")\n",
        "\n",
        "# Set training parameters\n",
        "training_arguments = TrainingArguments(\n",
        "    output_dir=output_dir,\n",
        "    num_train_epochs=num_train_epochs,\n",
        "    per_device_train_batch_size=per_device_train_batch_size,\n",
        "    gradient_accumulation_steps=gradient_accumulation_steps,\n",
        "    optim=optim,\n",
        "    save_steps=save_steps,\n",
        "    logging_steps=logging_steps,\n",
        "    learning_rate=learning_rate,\n",
        "    weight_decay=weight_decay,\n",
        "    fp16=fp16,\n",
        "    bf16=bf16,\n",
        "    max_grad_norm=max_grad_norm,\n",
        "    max_steps=max_steps,\n",
        "    warmup_ratio=warmup_ratio,\n",
        "    group_by_length=group_by_length,\n",
        "    lr_scheduler_type=lr_scheduler_type,\n",
        "    report_to=\"tensorboard\"\n",
        ")\n",
        "\n",
        "# Set supervised fine-tuning parameters\n",
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    train_dataset=dataset,\n",
        "    peft_config=peft_config,\n",
        "    dataset_text_field=\"text\",\n",
        "    max_seq_length=max_seq_length,\n",
        "    tokenizer=tokenizer,\n",
        "    args=training_arguments,\n",
        "    packing=packing,\n",
        ")\n",
        "\n",
        "# Train model\n",
        "trainer.train()\n",
        "\n",
        "# Save trained model\n",
        "trainer.model.save_pretrained(new_model)"
      ],
      "metadata": {
        "id": "Ypfow2CoIbRK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q -U bitsandbytes\n",
        "!pip install -q -U git+https://github.com/huggingface/transformers.git\n",
        "!pip install -q -U git+https://github.com/huggingface/peft.git\n",
        "!pip install -q -U git+https://github.com/huggingface/accelerate.git\n"
      ],
      "metadata": {
        "id": "yvhqK4SH3WvV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForCausalLM\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\"facebook/opt-350m\", load_in_4bit=True, device_map=\"auto\")"
      ],
      "metadata": {
        "id": "G9vqBw2N9apE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import sqlite3\n",
        "import random\n",
        "import string\n",
        "\n",
        "# Function to generate random string\n",
        "def random_string(length):\n",
        "    letters = string.ascii_lowercase\n",
        "    return ''.join(random.choice(letters) for i in range(length))\n",
        "\n",
        "# Function to generate random data\n",
        "def generate_data(num_entries):\n",
        "    data = []\n",
        "    for _ in range(num_entries):\n",
        "        name = random_string(8)\n",
        "        age = random.randint(18, 80)\n",
        "        email = f\"{name}@example.com\"\n",
        "        data.append((name, age, email))\n",
        "    return data\n",
        "\n",
        "# Function to create a random SQL database\n",
        "def create_database(db_name, num_entries):\n",
        "    conn = sqlite3.connect(db_name)\n",
        "    cursor = conn.cursor()\n",
        "\n",
        "    # Create table\n",
        "    cursor.execute('''CREATE TABLE IF NOT EXISTS Users\n",
        "                    (Name TEXT, Age INT, Email TEXT)''')\n",
        "\n",
        "    # Generate and insert random data\n",
        "    data = generate_data(num_entries)\n",
        "    cursor.executemany('''INSERT INTO Users (Name, Age, Email)\n",
        "                          VALUES (?, ?, ?)''', data)\n",
        "\n",
        "    conn.commit()\n",
        "    conn.close()\n",
        "\n",
        "# Function to query the database\n",
        "def query_database(db_name, query):\n",
        "    conn = sqlite3.connect(db_name)\n",
        "    cursor = conn.cursor()\n",
        "\n",
        "    # Example query: Select all users older than 30\n",
        "    cursor.execute(query)\n",
        "    rows = cursor.fetchall()\n",
        "\n",
        "    for row in rows:\n",
        "        print(row)\n",
        "\n",
        "    conn.close()\n",
        "\n",
        "# Usage example\n",
        "db_name = \"random_database.db\"\n",
        "num_entries = 100\n",
        "create_database(db_name, num_entries)\n",
        "query = '''SELECT * FROM Users WHERE Age > 30'''\n",
        "query_database(db_name, query)"
      ],
      "metadata": {
        "id": "k4zn5nxi0K7N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import sqlite3\n",
        "\n",
        "# Function to get the schema of the database\n",
        "def get_schema(db_name):\n",
        "    conn = sqlite3.connect(db_name)\n",
        "    cursor = conn.cursor()\n",
        "\n",
        "    # Query to get the schema\n",
        "    cursor.execute(\"SELECT name, sql FROM sqlite_master WHERE type='table'\")\n",
        "    tables = cursor.fetchall()\n",
        "\n",
        "    # Print the schema\n",
        "    for table in tables:\n",
        "        print(f\"Table: {table[0]}\")\n",
        "        print(f\"Schema: {table[1]}\")\n",
        "\n",
        "    conn.close()\n",
        "\n",
        "# Usage example\n",
        "db_name = \"random_database.db\"\n",
        "get_schema(db_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6duCnbbW0NSF",
        "outputId": "660d632f-ef63-49f5-c463-2ac59012095e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Table: Users\n",
            "Schema: CREATE TABLE Users\n",
            "                    (Name TEXT, Age INT, Email TEXT)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(get_schema(db_name))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qde93N9aAWvk",
        "outputId": "4f1d46fb-7bc3-4cb2-9958-9afb9ca32038"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Table: Users\n",
            "Schema: CREATE TABLE Users\n",
            "                    (Name TEXT, Age INT, Email TEXT)\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "value = get_schema(\"cs4400.db\")\n",
        "temp2 = str(\"\" if value is None else value)\n",
        "temp2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 982
        },
        "id": "ksyu1_8IFuHc",
        "outputId": "0ada0daa-ca9a-459c-cb61-0f0d16bbec44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Table: employee\n",
            "Schema: CREATE TABLE employee (\n",
            "fname char(10) not null,\n",
            "lname char(20) not null,\n",
            "ssn decimal(9, 0) not null,\n",
            "bdate date not null,\n",
            "address char(30) not null,\n",
            "sex char(1) not null,\n",
            "salary decimal(5, 0) not null,\n",
            "superssn decimal(9, 0) default null,\n",
            "dno decimal(1, 0) not null,\n",
            "primary key (ssn)\n",
            ")\n",
            "Table: dependent\n",
            "Schema: CREATE TABLE dependent (\n",
            "  essn decimal(9, 0) not null,\n",
            "  dependent_name char(10) not null,\n",
            "  sex char(1) not null,\n",
            "  bdate date not null,\n",
            "  relationship char(30) not null,\n",
            "  primary key (essn, dependent_name)\n",
            ")\n",
            "Table: dept_locations\n",
            "Schema: CREATE TABLE dept_locations (\n",
            "  dnumber decimal(1, 0) not null,\n",
            "  dlocation char(15) not null,\n",
            "  primary key (dnumber, dlocation)\n",
            ")\n",
            "Table: department\n",
            "Schema: CREATE TABLE department (\n",
            "  dname char(20) not null,\n",
            "  dnumber decimal(1, 0) not null,\n",
            "  mgrssn decimal(9, 0) not null,\n",
            "  mgrstartdate date not null,\n",
            "  primary key (dnumber),\n",
            "  unique (dname)\n",
            ")\n",
            "Table: project\n",
            "Schema: CREATE TABLE project (\n",
            "  pname char(20) not null,\n",
            "  pnumber decimal(2, 0) not null,\n",
            "  plocation char(20) not null,\n",
            "  dnum decimal(1, 0) not null,\n",
            "  primary key (pnumber),\n",
            "  unique (pname)\n",
            ")\n",
            "Table: works_on\n",
            "Schema: CREATE TABLE works_on (\n",
            "  essn decimal(9, 0) not null,\n",
            "  pno decimal(2, 0) not null,\n",
            "  hours decimal(5, 1) default null,\n",
            "  primary key (essn, pno)\n",
            ")\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "''"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "conn = sqlite3.connect(\"cs4400.db\")\n",
        "cursor = conn.cursor()\n",
        "\n",
        "cursor.execute('''create table employee (\n",
        "fname char(10) not null,\n",
        "lname char(20) not null,\n",
        "ssn decimal(9, 0) not null,\n",
        "bdate date not null,\n",
        "address char(30) not null,\n",
        "sex char(1) not null,\n",
        "salary decimal(5, 0) not null,\n",
        "superssn decimal(9, 0) default null,\n",
        "dno decimal(1, 0) not null,\n",
        "primary key (ssn)\n",
        ")''')\n",
        "\n",
        "cursor.execute('''create table dependent (\n",
        "  essn decimal(9, 0) not null,\n",
        "  dependent_name char(10) not null,\n",
        "  sex char(1) not null,\n",
        "  bdate date not null,\n",
        "  relationship char(30) not null,\n",
        "  primary key (essn, dependent_name)\n",
        ")''')\n",
        "\n",
        "cursor.execute('''create table dept_locations (\n",
        "  dnumber decimal(1, 0) not null,\n",
        "  dlocation char(15) not null,\n",
        "  primary key (dnumber, dlocation)\n",
        ")''')\n",
        "\n",
        "cursor.execute('''create table department (\n",
        "  dname char(20) not null,\n",
        "  dnumber decimal(1, 0) not null,\n",
        "  mgrssn decimal(9, 0) not null,\n",
        "  mgrstartdate date not null,\n",
        "  primary key (dnumber),\n",
        "  unique (dname)\n",
        ")''')\n",
        "\n",
        "cursor.execute('''create table project (\n",
        "  pname char(20) not null,\n",
        "  pnumber decimal(2, 0) not null,\n",
        "  plocation char(20) not null,\n",
        "  dnum decimal(1, 0) not null,\n",
        "  primary key (pnumber),\n",
        "  unique (pname)\n",
        ")''')\n",
        "\n",
        "# cursor.execute('''create table project4 (\n",
        "#   pname char(20) not null,\n",
        "#   pnumber decimal(2, 0) not null,\n",
        "#   plocation char(20) not null,\n",
        "#   dnum decimal(1, 0) not null,\n",
        "#   primary key (pnumber)\n",
        "#   unique (pname)\n",
        "# )\n",
        "# ''')\n",
        "\n",
        "cursor.execute('''create table works_on (\n",
        "  essn decimal(9, 0) not null,\n",
        "  pno decimal(2, 0) not null,\n",
        "  hours decimal(5, 1) default null,\n",
        "  primary key (essn, pno)\n",
        ")''')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CdDOJSIJlAE-",
        "outputId": "385e30d6-e9b4-4caf-f927-27fde9984b89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sqlite3.Cursor at 0x7f77eb703040>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Open the SQL file\n",
        "with open(\"/content/cs4400_autograder company_only v10_1-1.sql\", \"r\") as file:\n",
        "    # Read lines from the file and store them in an array\n",
        "    sql_lines = file.readlines()\n",
        "\n",
        "# Print the array of SQL lines\n",
        "print(sql_lines)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-vrnJHbWxC0S",
        "outputId": "5a4d9cc9-f868-4042-d749-e8d85506413d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['-- CS4400: Introduction to Database Systems: Wednesday, September 7, 2022\\n', '-- SQL Autograding Environment (v10.3): Company Database (PRACTICE ONLY)\\n', '-- Version 10.3: Reset database state after testing w/magic44_set_database_state()\\n', '\\n', '/* This is a standard preamble for most of our scripts.  The intent is to establish\\n', 'a consistent environment for the database behavior. */\\n', 'set global transaction isolation level serializable;\\n', \"set global SQL_MODE = 'ANSI,TRADITIONAL';\\n\", 'set names utf8mb4;\\n', 'set SQL_SAFE_UPDATES = 0;\\n', \"set @thisDatabase = 'company';\\n\", '\\n', '-- -------------------------------------------------\\n', '-- ENTER YOUR QUERY SOLUTIONS STARTING AT LINE 90\\n', '-- -------------------------------------------------\\n', '\\n', 'drop database if exists company;\\n', 'create database if not exists company;\\n', 'use company;\\n', '\\n', '-- -----------------------------------------------\\n', '-- table structures\\n', '-- -----------------------------------------------\\n', '\\n', 'create table employee (\\n', '  fname char(10) not null,\\n', '  lname char(20) not null,\\n', '  ssn decimal(9, 0) not null,\\n', '  bdate date not null,\\n', '  address char(30) not null,\\n', '  sex char(1) not null,\\n', '  salary decimal(5, 0) not null,\\n', '  superssn decimal(9, 0) default null,\\n', '  dno decimal(1, 0) not null,\\n', '  primary key (ssn)\\n', ') engine = innodb;\\n', '\\n', 'create table dependent (\\n', '  essn decimal(9, 0) not null,\\n', '  dependent_name char(10) not null,\\n', '  sex char(1) not null,\\n', '  bdate date not null,\\n', '  relationship char(30) not null,\\n', '  primary key (essn, dependent_name)\\n', ') engine = innodb;\\n', '\\n', 'create table department (\\n', '  dname char(20) not null,\\n', '  dnumber decimal(1, 0) not null,\\n', '  mgrssn decimal(9, 0) not null,\\n', '  mgrstartdate date not null,\\n', '  primary key (dnumber),\\n', '  unique key (dname)\\n', ') engine = innodb;\\n', '\\n', 'create table dept_locations (\\n', '  dnumber decimal(1, 0) not null,\\n', '  dlocation char(15) not null,\\n', '  primary key (dnumber, dlocation)\\n', ') engine = innodb;\\n', '\\n', 'create table project (\\n', '  pname char(20) not null,\\n', '  pnumber decimal(2, 0) not null,\\n', '  plocation char(20) not null,\\n', '  dnum decimal(1, 0) not null,\\n', '  primary key (pnumber),\\n', '  unique key (pname)\\n', ') engine = innodb;\\n', '\\n', 'create table works_on (\\n', '  essn decimal(9, 0) not null,\\n', '  pno decimal(2, 0) not null,\\n', '  hours decimal(5, 1) default null,\\n', '  primary key (essn, pno)\\n', ') engine = innodb;\\n', '\\n', '-- Enter your queries in the area below using this format:\\n', '-- create or replace view practiceQuery<#> as\\n', '-- <your proposed query solution>;\\n', '\\n', \"-- Be sure to end all queries with a semi-colon (';') and make sure that\\n\", '-- the <#> value matches the query value from the practice sheet\\n', '\\n', '-- -------------------------------------------------\\n', '-- view structures (student created solutions)\\n', '-- PUT ALL PROPOSED QUERY SOLUTIONS BELOW THIS LINE\\n', '-- -------------------------------------------------\\n', '-- CS4400: Introduction to Database Systems\\n', '-- Company ANSWER KEY (v5 - Actual Queries for Book and Other Practice Problems)\\n', '-- Friday, July 14, 2023\\n', '\\n', '-- [Tue, 19 May 2020: Fixed practice query 51 / Reversed the order of the first and last names]\\n', '-- [Sat, 23 May 2020: Fixed practice queries 25-27 / Switched project number & name columns]\\n', '-- [Sun, 28 Nov 2021: Fixed practice query 15 / Corrected project assignments to emps not depts]\\n', '-- [Fri, 14 Jul 2023: \"Re\"-updated queries 3, 15 and 25-27 / As shown above]\\n', '\\n', \"-- practiceQuery0: Retrieve birthdate and address for 'John Smith'.\\n\", 'drop view if exists practiceQuery0;\\n', 'create view practiceQuery0 as \\n', \"select bdate, address from employee where fname = 'John' and lname = 'Smith';\\n\", '\\n', \"-- practiceQuery1: Retrieve the first name, last name and address of all employees who work for the 'Research' department.\\n\", 'drop view if exists practiceQuery1;\\n', 'create view practiceQuery1 as \\n', \"select fname, lname, address from employee where dno in (select dnumber from department where dname = 'Research');\\n\", '\\n', \"-- practiceQuery2: For every project located in 'Stafford', list the project number, the controlling department number, and the department manager's last name, address, and birth date.\\n\", 'drop view if exists practiceQuery2;\\n', 'create view practiceQuery2 as \\n', \"select pnumber, dnum, lname, address, bdate from project, department, employee where dnum = dnumber and mgrssn = ssn and plocation = 'Stafford';\\n\", '\\n', '-- practiceQuery3: Find the names of employees who work on all the projects controlled by department number 5.\\n', 'drop view if exists practiceQuery3;\\n', 'create view practiceQuery3 as\\n', 'select fname, lname from employee\\n', 'where not exists (select pnumber from project where dnum = 5\\n', 'and pnumber not in (select pno from works_on where ssn = essn));\\n', '\\n', \"-- practiceQuery4: Make a list of project numbers for projects that involve an employee whose last name is 'Smith', either as a worker or as a manager of the department that controls the project.\\n\", 'drop view if exists practiceQuery4;\\n', 'create view practiceQuery4 as \\n', \"(select pno from works_on, employee where essn = ssn and lname = 'Smith') union\\n\", \"(select pnumber from project, department, employee where dnum = dnumber and mgrssn = ssn and lname = 'Smith');\\n\", '\\n', '-- practiceQuery5: List the names of all employees with two or more dependents.\\n', 'drop view if exists practiceQuery5;\\n', 'create view practiceQuery5 as \\n', 'select fname, lname from employee where ssn in\\n', '(select essn from dependent group by essn having count(*) >= 2);\\n', '\\n', '-- practiceQuery6: Retrieve the names of employees who have no dependents.\\n', 'drop view if exists practiceQuery6;\\n', 'create view practiceQuery6 as \\n', 'select fname, lname from employee where ssn not in\\n', '(select essn from dependent);\\n', '\\n', '-- practiceQuery7: List the names of managers who have at least one dependent.\\n', 'drop view if exists practiceQuery7;\\n', 'create view practiceQuery7 as \\n', 'select fname, lname from employee where ssn in (select essn from dependent)\\n', 'and ssn in (select mgrssn from department);\\n', '\\n', \"-- practiceQuery8: For each employee, retrieve the employee's first and last name and the first and last name of his or her immediate supervisor.\\n\", 'drop view if exists practiceQuery8;\\n', 'create view practiceQuery8 as \\n', 'select tableA.fname as A_fname, tableA.lname as A_lname, tableB.fname as B_fname, tableB.lname as B_lname\\n', 'from employee as tableA left outer join employee as tableB on tableA.superssn = tableB.ssn;\\n', '\\n', \"-- practiceQuery9: Select all of the employee's Social Security Numbers.\\n\", 'drop view if exists practiceQuery9;\\n', 'create view practiceQuery9 as \\n', 'select ssn from employee;\\n', '\\n', \"-- practiceQuery10: Select all possible combinations of the employee's Social Security Numbers and department names.\\n\", 'drop view if exists practiceQuery10;\\n', 'create view practiceQuery10 as \\n', 'select ssn, dname from employee, department;\\n', '\\n', '-- practiceQuery11: Retrieve the distinct salaries of all of the employees.\\n', 'drop view if exists practiceQuery11;\\n', 'create view practiceQuery11 as \\n', 'select distinct salary from employee;\\n', '\\n', '-- practiceQuery12: Retrieve the first and last names of all employees whose address is in Houston, Texas.\\n', 'drop view if exists practiceQuery12;\\n', 'create view practiceQuery12 as \\n', \"select fname, lname from employee where address like '%Houston TX%';\\n\", '\\n', \"-- practiceQuery13: Show the first and last names and resulting salaries if every employee working on the 'ProductX' project is given a 10% raise.\\n\", 'drop view if exists practiceQuery13;\\n', 'create view practiceQuery13 as \\n', 'select fname, lname, salary from employee where ssn not in\\n', \"(select essn from works_on, project where pno = pnumber and pname = 'ProductX')\\n\", 'union\\n', 'select fname, lname, salary * 1.10 from employee where ssn in\\n', \"(select essn from works_on, project where pno = pnumber and pname = 'ProductX');\\n\", '\\n', '-- practiceQuery14: Retrieve all information for employees in department 5 whose salary is between $30,000 and $40,000.\\n', 'drop view if exists practiceQuery14;\\n', 'create view practiceQuery14 as \\n', 'select * from employee where dno = 5 and salary between 30000 and 40000;\\n', '\\n', '-- practiceQuery15: Retrieve a list of department names, employee first and last names, and the project names they are working on, ordered by department and, within each department, alphabetically by last name, then first name.\\n', 'drop view if exists practiceQuery15;\\n', 'create view practiceQuery15 as \\n', 'select dname, lname, fname, pname from department, employee, works_on, project\\n', 'where dnumber = dno and ssn = essn and pno = pnumber\\n', 'order by dname, lname, fname;\\n', '\\n', '-- practiceQuery16: Retrieve the name of each employee who has a dependent with the same first name and is the same sex as the employee.\\n', 'drop view if exists practiceQuery16;\\n', 'create view practiceQuery16 as \\n', 'select distinct fname, lname from employee, dependent\\n', 'where ssn = essn and fname = dependent_name and employee.sex = dependent.sex;\\n', '\\n', '-- practiceQuery17: Retrieve the SSNs of employees who work on projects with numbers 1, 2, or 3.\\n', 'drop view if exists practiceQuery17;\\n', 'create view practiceQuery17 as \\n', 'select ssn from employee where ssn in (select essn from works_on where pno in (1, 2, 3));\\n', '\\n', '-- practiceQuery18: Retrieve the names of all employees who do not have supervisors.\\n', 'drop view if exists practiceQuery18;\\n', 'create view practiceQuery18 as \\n', 'select fname, lname from employee where superssn is null;\\n', '\\n', '-- practiceQuery19: Find the sum of the salaries of all employees, the maximum salary, the minimum salary, and the average salary.\\n', 'drop view if exists practiceQuery19;\\n', 'create view practiceQuery19 as \\n', 'select sum(salary), max(salary), min(salary), avg(salary) from employee;\\n', '\\n', \"-- practiceQuery20: Find the sum of the salaries of all employees of the 'Research' department, as well as the maximum salary, the minimum salary, and the average salary in this department.\\n\", 'drop view if exists practiceQuery20;\\n', 'create view practiceQuery20 as \\n', 'select sum(salary), max(salary), min(salary), avg(salary) from employee where dno in\\n', \"(select dnumber from department where dname = 'Research');\\n\", '\\n', '-- practiceQuery21: Retrieve the total number of employees in the company.\\n', 'drop view if exists practiceQuery21;\\n', 'create view practiceQuery21 as \\n', 'select count(*) from employee;\\n', '\\n', \"-- practiceQuery22: Retrieve the total number of employees in the 'Research' department.\\n\", 'drop view if exists practiceQuery22;\\n', 'create view practiceQuery22 as \\n', 'select count(*) from employee where dno in\\n', \"(select dnumber from department where dname = 'Research');\\n\", '\\n', '-- practiceQuery23: Count the number of distinct salary values in the database.\\n', 'drop view if exists practiceQuery23;\\n', 'create view practiceQuery23 as \\n', 'select count(distinct salary) from employee;\\n', '\\n', '-- practiceQuery24: For each department, retrieve the department number, the number of employees in the department, and their average salary.\\n', 'drop view if exists practiceQuery24;\\n', 'create view practiceQuery24 as \\n', 'select dnumber, count(ssn), avg(salary) from department, employee where dnumber = dno group by dnumber\\n', 'union (select dnumber, 0, 0 from department where dnumber not in (select dno from employee));\\n', '\\n', '-- practiceQuery25: For each project, retrieve the project number, the project name, and the number of employees who work on that project. (Null employees will be interpreted as zero employees).\\n', 'drop view if exists practiceQuery25;\\n', 'create view practiceQuery25 as \\n', 'select pnumber, pname, count(distinct essn) from project left outer join works_on\\n', 'on pnumber = pno group by pname, pnumber;\\n', '\\n', '-- practiceQuery26: For each project on which more than two employees work, retrieve the project number, the project name, and the number of employees who work on the project.\\n', 'drop view if exists practiceQuery26;\\n', 'create view practiceQuery26 as \\n', 'select pnumber, pname, count(distinct essn) from project left outer join works_on\\n', 'on pnumber = pno group by pname, pnumber having count(distinct essn) > 2;\\n', '\\n', '-- practiceQuery27: For each project, retrieve the project number, the project name, and the number of employees from department 5 who work on the project.\\n', 'drop view if exists practiceQuery27;\\n', 'create view practiceQuery27 as \\n', 'select pnumber, pname, count(distinct essn) from project left outer join\\n', '(select essn , pno from works_on where essn in (select ssn from employee where dno = 5)) as temp\\n', 'on pnumber = pno group by pname, pnumber;\\n', '\\n', '-- practiceQuery28: For each department that has less than four* employees, retrieve the department number and the number of its employees who are making more than $25,000*. (Less than four employees instead of more than five, and $25,000 instead of $40,000 to make the results more fruitful.)\\n', 'drop view if exists practiceQuery28;\\n', 'create view practiceQuery28 as \\n', 'select dno, count(ssn) from employee where salary > 25000 and dno in\\n', '(select dno from employee group by dno having count(ssn) < 4) group by dno;\\n', '\\n', '-- practiceQuery29: Retrieve all of the attribute values for any employee who works in department 5.\\n', 'drop view if exists practiceQuery29;\\n', 'create view practiceQuery29 as \\n', 'select * from employee where dno = 5;\\n', '\\n', '-- practiceQuery30: Retrieve all of the attributes for each employee and the department for which he or she works.\\n', 'drop view if exists practiceQuery30;\\n', 'create view practiceQuery30 as \\n', 'select * from employee, department where dno = dnumber;\\n', '\\n', '-- practiceQuery31: Find the first name, last name and birthdate of the employees who were born during the 1950s.\\n', 'drop view if exists practiceQuery31;\\n', 'create view practiceQuery31 as \\n', \"select fname, lname, bdate from employee where bdate between '1950-01-01' and '1959-12-31';\\n\", '\\n', '-- practiceQuery32: Retrieve the first and last names of all employees in department 5 who work more than 10 hours per week on the ProductX project.\\n', 'drop view if exists practiceQuery32;\\n', 'create view practiceQuery32 as \\n', 'select fname, lname from employee where dno = 5 and ssn in (select essn from works_on where pno in\\n', \"(select pnumber from project where pname = 'ProductX') group by essn having sum(hours) > 10);\\n\", '\\n', '-- practiceQuery33: List the first and last names of all employees who have a dependent with the same first name as themselves.\\n', 'drop view if exists practiceQuery33;\\n', 'create view practiceQuery33 as \\n', 'select fname, lname from employee, dependent where ssn = essn and fname = dependent_name;\\n', '\\n', \"-- practiceQuery34: Find the first and last names of all employees who are directly supervised by 'Franklin Wong'.\\n\", 'drop view if exists practiceQuery34;\\n', 'create view practiceQuery34 as \\n', 'select fname, lname from employee\\n', \"where superssn = (select ssn from employee where fname = 'Franklin' and lname = 'Wong');\\n\", '\\n', '-- practiceQuery35: Retrieve the first and last names of all employees who work in the department that has the employee with the highest salary among all employees.\\n', 'drop view if exists practiceQuery35;\\n', 'create view practiceQuery35 as \\n', 'select fname, lname from employee where dno in\\n', '(select dno from employee group by dno having max(salary) >= (select max(salary) from employee));\\n', '\\n', \"-- practiceQuery36: Retrieve the first and last names of all employees whose supervisor's supervisor has '888665555' for their SSN.\\n\", 'drop view if exists practiceQuery36;\\n', 'create view practiceQuery36 as \\n', 'select fname, lname from employee where superssn in\\n', \"(select ssn from employee where superssn = '888665555');\\n\", '\\n', '-- practiceQuery37: Retrieve the first and last names of employees who make at least $10,000 more than the employee who is paid the least in the company.\\n', 'drop view if exists practiceQuery37;\\n', 'create view practiceQuery37 as \\n', 'select fname, lname from employee where salary >= (select min(salary) from employee) + 10000;\\n', '\\n', \"-- practiceQuery40: Return a static string 'Hello World!'.\\n\", 'drop view if exists practiceQuery40;\\n', 'create view practiceQuery40 as \\n', \"select 'Hello World!';\\n\", '\\n', \"-- practiceQuery41: Return a static string 'Hello World!' with column alias 'greeting'.\\n\", 'drop view if exists practiceQuery41;\\n', 'create view practiceQuery41 as \\n', \"select 'Hello World!' as 'greeting';\\n\", '\\n', '-- practiceQuery42: Compute the sum of 4 and 6.\\n', 'drop view if exists practiceQuery42;\\n', 'create view practiceQuery42 as \\n', 'select 4 + 6;\\n', '\\n', '-- practiceQuery43: Compute several arithmetic operations and return their results with column aliases.\\n', 'drop view if exists practiceQuery43;\\n', 'create view practiceQuery43 as \\n', \"select 4 + 6 as 'my_sum', 3 * 7 as 'my_product', 9 - 5 as 'my_difference';\\n\", '\\n', '-- practiceQuery44: Retrieve all columns for all employees.\\n', 'drop view if exists practiceQuery44;\\n', 'create view practiceQuery44 as \\n', 'select * from employee;\\n', '\\n', '-- practiceQuery50: Retrieve first name, last name, and address of all employees.\\n', 'drop view if exists practiceQuery50;\\n', 'create view practiceQuery50 as \\n', 'select fname, lname, address from employee;\\n', '\\n', \"-- practiceQuery51: Display the last name in a column titled 'Last Name', first name in a column titled 'First Name', and address in a column titled 'Residence Location' for all employees.\\n\", 'drop view if exists practiceQuery51;\\n', 'create view practiceQuery51 as \\n', \"select lname as 'Last Name', fname as 'First Name', address as 'Residence Location' from employee;\\n\", '\\n', \"-- practiceQuery52: Concatenate first name and last name into a single column 'Whole Name' along with the address for all employees.\\n\", 'drop view if exists practiceQuery52;\\n', 'create view practiceQuery52 as \\n', \"select concat(fname, ' ', lname) as 'Whole Name', address from employee;\\n\", '\\n', '-- practiceQuery53: Retrieve department numbers for all employees.\\n', 'drop view if exists practiceQuery53;\\n', 'create view practiceQuery53 as \\n', 'select dno from employee;\\n', '\\n', '-- practiceQuery54: Retrieve distinct department numbers from the employee table.\\n', 'drop view if exists practiceQuery54;\\n', 'create view practiceQuery54 as \\n', 'select distinct dno from employee;\\n', '\\n', '-- practiceQuery55: Retrieve salary and department number for all employees.\\n', 'drop view if exists practiceQuery55;\\n', 'create view practiceQuery55 as \\n', 'select salary, dno from employee;\\n', '\\n', '-- practiceQuery56: Retrieve the distinct salary and department number combinations for all employees.\\n', 'drop view if exists practiceQuery56;\\n', 'create view practiceQuery56 as \\n', 'select distinct salary, dno from employee;\\n', '\\n', '-- practiceQuery60: Retrieve the first name, last name and address of all employees in department.\\n', 'drop view if exists practiceQuery60;\\n', 'create view practiceQuery60 as \\n', 'select fname, lname, address from employee where dno = 5;\\n', '\\n', '-- practiceQuery61: Retrieve first name, last name, and address for all employees not in department 5.\\n', 'drop view if exists practiceQuery61;\\n', 'create view practiceQuery61 as \\n', 'select fname, lname, address from employee where dno <> 5;\\n', '\\n', '-- practiceQuery62: Retrieve first name, last name, and address for all employees with a salary greater than 30,000.\\n', 'drop view if exists practiceQuery62;\\n', 'create view practiceQuery62 as \\n', 'select fname, lname, address from employee where salary > 30000;\\n', '\\n', '-- practiceQuery63: Retrieve first name, last name, and address for all employees either in department 5 or with a salary greater than 30,000.\\n', 'drop view if exists practiceQuery63;\\n', 'create view practiceQuery63 as \\n', 'select fname, lname, address from employee where dno = 5 or salary > 30000;\\n', '\\n', '-- practiceQuery64: Retrieve first name, last name, and address for all employees in department 5 with a salary greater than 30,000.\\n', 'drop view if exists practiceQuery64;\\n', 'create view practiceQuery64 as \\n', 'select fname, lname, address from employee where dno = 5 and salary > 30000;\\n', '\\n', '-- practiceQuery65: Retrieve the first name, last name, address and birthdate of all employees born after January 1st, 1968.\\n', 'drop view if exists practiceQuery65;\\n', 'create view practiceQuery65 as \\n', \"select fname, lname, address, bdate from employee where bdate > '1968-01-01';\\n\", '\\n', '-- practiceQuery66: Retrieve the first name, last name, address and birthdate of all employees born after January 1st, 1964 and before August 10th, 1970.\\n', 'drop view if exists practiceQuery66;\\n', 'create view practiceQuery66 as \\n', \"select fname, lname, address, bdate from employee where bdate > '1964-01-01' and bdate < '1970-08-10';\\n\", '\\n', '-- practiceQuery67: Retrieve the first name, last name, and address of all employees, ordered by first name.\\n', 'drop view if exists practiceQuery67;\\n', 'create view practiceQuery67 as \\n', 'select fname, lname, address from employee order by fname;\\n', '\\n', '-- practiceQuery68: Retrieve the first name, last name and address of all employees in descending last name order.\\n', 'drop view if exists practiceQuery68;\\n', 'create view practiceQuery68 as \\n', 'select fname, lname, address from employee order by lname desc;\\n', '\\n', '-- practiceQuery69: Retrieve the first name and address of all employees, ordered by last name in descending order.\\n', 'drop view if exists practiceQuery69;\\n', 'create view practiceQuery69 as \\n', 'select fname, address from employee order by lname desc;\\n', '\\n', '-- practiceQuery70: Retrieve the first name, last name and address of all employees in the order of last name and then first name.\\n', 'drop view if exists practiceQuery70;\\n', 'create view practiceQuery70 as \\n', 'select fname, lname, address from employee order by lname, fname;\\n', '\\n', '-- practiceQuery71: Retrieve the first name, last name and address of all employees who live in Houston.\\n', 'drop view if exists practiceQuery71;\\n', 'create view practiceQuery71 as \\n', \"select fname, lname, address from employee where address like '%Houston%';\\n\", '\\n', \"-- practiceQuery72: Retrieve the first name, last name, and address of all employees whose address contains 'Dallas'.\\n\", 'drop view if exists practiceQuery72;\\n', 'create view practiceQuery72 as \\n', \"select fname, lname, address from employee where address like '%Dallas%';\\n\", '\\n', \"-- practiceQuery73: Retrieve the first name, last name, and address of all employees whose first name starts with 'J'.\\n\", 'drop view if exists practiceQuery73;\\n', 'create view practiceQuery73 as \\n', \"select fname, lname, address from employee where fname like 'J%';\\n\", '\\n', \"-- practiceQuery74: Retrieve the first name, last name and address of all employees whose last names contain the pattern 'aya'.\\n\", 'drop view if exists practiceQuery74;\\n', 'create view practiceQuery74 as \\n', \"select fname, lname, address from employee where lname like '%aya%';\\n\", '\\n', \"-- practiceQuery75: Retrieve the first name, last name and address of all employees whose last  names contain the pattern 'aya' where the pattern is not at the end of the string.\\n\", 'drop view if exists practiceQuery75;\\n', 'create view practiceQuery75 as \\n', \"select fname, lname, address from employee where lname like '%aya_%';\\n\", '\\n', \"-- practiceQuery80: Retrieve the first name, last name, and salary with a 10% raise as 'raise' for all employees in department 5.\\n\", 'drop view if exists practiceQuery80;\\n', 'create view practiceQuery80 as \\n', \"select fname, lname, salary + (salary * 1/10) as 'raise' from employee where dno = 5;\\n\", '\\n', '-- practiceQuery81: Retrieve the sum of the salaries for all employees in department number 5.\\n', 'drop view if exists practiceQuery81;\\n', 'create view practiceQuery81 as \\n', 'select sum(salary) from employee where dno = 5;\\n', '\\n', '-- practiceQuery82: Retrieve the smallest and largest salaries for all employees in department number 5.\\n', 'drop view if exists practiceQuery82;\\n', 'create view practiceQuery82 as \\n', 'select min(salary), max(salary) from employee where dno = 5;\\n', '\\n', '-- practiceQuery83: Count the number of distinct salary values among all employees.\\n', 'drop view if exists practiceQuery83;\\n', 'create view practiceQuery83 as \\n', 'select count(distinct salary) from employee;\\n', '\\n', '-- practiceQuery84: Count the number of distinct supervisor SSNs among all employees.\\n', 'drop view if exists practiceQuery84;\\n', 'create view practiceQuery84 as \\n', 'select count(distinct superssn) from employee;\\n', '\\n', '-- practiceQuery85: Retrieve the number of employees whose superssn is equal to 333445555.\\n', 'drop view if exists practiceQuery85;\\n', 'create view practiceQuery85 as \\n', 'select count(*) from employee where superssn = 333445555;\\n', '\\n', '-- practiceQuery86: Retrieve the number of employees whose superssn is not equal to 333445555.\\n', 'drop view if exists practiceQuery86;\\n', 'create view practiceQuery86 as \\n', 'select count(*) from employee where superssn <> 333445555;\\n', '\\n', '-- practiceQuery87: Retrieve the total count of all employees.\\n', 'drop view if exists practiceQuery87;\\n', 'create view practiceQuery87 as \\n', 'select count(*) from employee;\\n', '\\n', '-- practiceQuery88: Retrieve the minimum, average, and maximum salary along with the department number for each department.\\n', 'drop view if exists practiceQuery88;\\n', 'create view practiceQuery88 as \\n', 'select dno, min(salary), avg(salary), max(salary) from employee group by dno;\\n', '\\n', '-- practiceQuery89: Retrieve the minimum, average and maximum salary for the employees in each department with less than four employees.\\n', 'drop view if exists practiceQuery89;\\n', 'create view practiceQuery89 as \\n', 'select dno, min(salary), avg(salary), max(salary) from employee group by dno having count(*) < 4;\\n', '\\n', '-- practiceQuery100: Count the number of employees earning more than $25,000 in each department.\\n', 'drop view if exists practiceQuery100;\\n', 'create view practiceQuery100 as \\n', 'select dno, count(*) from employee where salary > 25000 group by dno;\\n', '\\n', '-- practiceQuery101: Retrieve the total number of employees whose salaries exceed $25,000 in all of the departments except Headquarters.\\n', 'drop view if exists practiceQuery101;\\n', 'create view practiceQuery101 as \\n', 'select dno, count(*) from employee where salary > 25000 and dno not in\\n', \"(select dnumber from department where dname = 'Headquarters') group by dno;\\n\", '\\n', '-- practiceQuery102: Retrieve department numbers where the total number of employees is less than four.\\n', 'drop view if exists practiceQuery102;\\n', 'create view practiceQuery102 as \\n', 'select dno from employee group by dno having count(*) < 4;\\n', '\\n', '-- practiceQuery103: Retrieve the total number of employees whose salaries exceed $25,000 per department, but only for departments where fewer than four employees work.\\n', 'drop view if exists practiceQuery103;\\n', 'create view practiceQuery103 as \\n', 'select dno, count(*) from employee where salary > 25000\\n', 'and dno in (select dno from employee group by dno having count(*) < 4)\\n', 'group by dno;\\n', '\\n', '-- practiceQuery104: Retrieve the first name, last name and address for each employee that has a dependent daughter.\\n', 'drop view if exists practiceQuery104;\\n', 'create view practiceQuery104 as \\n', 'select fname, lname, address from employee where ssn in\\n', \"(select essn from dependent where relationship = 'Daughter');\\n\", '\\n', '-- practiceQuery105: Retrieve the first name, last name and address for each employee that is working on a project that is located in Houston.\\n', 'drop view if exists practiceQuery105;\\n', 'create view practiceQuery105 as \\n', 'select fname, lname, address from employee where ssn in\\n', '(select essn from works_on where pno in\\n', \"(select pnumber from project where plocation = 'Houston'));\\n\", '\\n', '-- practiceQuery106: Retrieve Social Security Numbers of employees who either earn more than $25,000 or work on project number 20.\\n', 'drop view if exists practiceQuery106;\\n', 'create view practiceQuery106 as \\n', '(select ssn from employee where salary > 25000) union\\n', '(select essn from works_on where pno = 20);\\n', '\\n', '-- practiceQuery107: Retrieve Social Security Numbers of employees who earn more than $25,000 and work on project number 20.\\n', 'drop view if exists practiceQuery107;\\n', 'create view practiceQuery107 as \\n', 'select ssn from employee where salary > 25000 and ssn in\\n', '(select essn from works_on where pno = 20);\\n', '\\n', '-- practiceQuery108: Retrieve Social Security Numbers of employees who earn more than $25,000 but do not work on project number 20.\\n', 'drop view if exists practiceQuery108;\\n', 'create view practiceQuery108 as \\n', 'select ssn from employee where salary > 25000 and ssn not in\\n', '(select essn from works_on where pno = 20);\\n', '\\n', '-- practiceQuery120: Retrieve all attributes for each employee and the department for which he or she works.\\n', 'drop view if exists practiceQuery120;\\n', 'create view practiceQuery120 as \\n', 'select fname, lname, address, dno, dnumber, dname from employee, department;\\n', '\\n', '-- practiceQuery121: Display the first name, last name, address, department name, and department number for all combinations of the employee and department record sets.\\n', 'drop view if exists practiceQuery121;\\n', 'create view practiceQuery121 as \\n', 'select fname, lname, address, dname, dno from employee, department where dno = dnumber;\\n', '\\n', '-- practiceQuery122: Retrieve the first name, last name, address, department name and department number of all employees.\\n', 'drop view if exists practiceQuery122;\\n', 'create view practiceQuery122 as \\n', 'select fname, lname, address, dname, dno from employee join department on dno = dnumber;\\n', '\\n', '-- practiceQuery123: Match employees with departments based on natural join on department name and department number referred to as dno in a subquery.\\n', 'drop view if exists practiceQuery123;\\n', 'create view practiceQuery123 as \\n', 'select fname, lname, address, dname, dno from employee natural join\\n', '(select dname, dnumber as dno from department) as temp;\\n', '\\n', \"-- practiceQuery124: Retrieve the employee's first and last name and address, along with their supervisor's Social Security Number, first and last names for all employees.\\n\", 'drop view if exists practiceQuery124;\\n', 'create view practiceQuery124 as \\n', 'select worker.fname as work_fname, worker.lname as work_lname, worker.address, worker.superssn, supervisor.fname, supervisor.lname\\n', 'from employee as worker left outer join employee as supervisor on worker.superssn = supervisor.ssn;\\n', '\\n', \"-- practiceQuery125: Retrieve the name and birthdate of all dependents, along with the first and last name of their sponsor (employee). Include the names of all employees in your response, to include the first and last names of employees who don't have any dependents.\\n\", 'drop view if exists practiceQuery125;\\n', 'create view practiceQuery125 as \\n', 'select dependent_name, dependent.bdate, fname, lname from dependent right outer join employee on essn = ssn;\\n', '\\n', '-- practiceQuery126: Retrieve the first and last name, department number and department name for all department managers.\\n', 'drop view if exists practiceQuery126;\\n', 'create view practiceQuery126 as \\n', 'select fname, lname, dname, dnumber from employee join department on ssn = mgrssn;\\n', '\\n', '-- practiceQuery127: Retrieve the first and last name, department number of all employees, along with the name of the department that they manage (if applicable).\\n', 'drop view if exists practiceQuery127;\\n', 'create view practiceQuery127 as \\n', 'select fname, lname, dno, dname from employee left outer join department on ssn = mgrssn;\\n', '\\n', \"-- practiceQuery128: Display the first and last names of employees along with the names of projects that are located in the city where they live (using the employee's address). Include the employee's address along with the project's location.\\n\", 'drop view if exists practiceQuery128;\\n', 'create view practiceQuery128 as \\n', 'select fname, lname, address, pname, plocation from employee join project\\n', 'on locate(plocation,address) > 0;\\n', '\\n', '-- practiceQuery129: Find the first and last names and birthdates of the employees (supervisors included) who are old as (or older than) one or more of the department managers. Include the first and last names and birthdates of the managers as well.\\n', 'drop view if exists practiceQuery129;\\n', 'create view practiceQuery129 as \\n', 'select worker.fname as work_fname, worker.lname as work_lname, worker.bdate as work_bdate, supervisor.fname, supervisor.lname, supervisor.bdate\\n', 'from (employee as worker) join (employee as supervisor) on worker.bdate < supervisor.bdate\\n', 'where supervisor.ssn in (select mgrssn from department);\\n', '\\n', '-- -------------------------------------------------\\n', '-- PUT ALL PROPOSED QUERY SOLUTIONS ABOVE THIS LINE\\n', '-- -------------------------------------------------\\n', '\\n', '-- The sole purpose of the following instruction is to minimize the impact of student-entered code\\n', '-- on the remainder of the autograding processes below\\n', 'set @unused_variable_dont_care_about_value = 0;\\n', '\\n', '-- -----------------------------------------------\\n', '-- table data\\n', '-- -----------------------------------------------\\n', '\\n', 'insert into employee values\\n', \"('John', 'Smith', 123456789, '1965-01-09', '731 Fondren, Houston TX', 'M', 30000, 333445555, 5),\\n\", \"('Franklin', 'Wong', 333445555, '1955-12-08', '638 Voss, Houston TX', 'M', 40000, 888665555, 5),\\n\", \"('Joyce', 'English', 453453453, '1972-07-31', '5631 Rice, Houston TX', 'F', 25000, 333445555, 5),\\n\", \"('Ramesh', 'Narayan', 666884444, '1962-09-15', '975 Fire Oak, Humble TX', 'M', 38000, 333445555, 5),\\n\", \"('James', 'Borg', 888665555, '1937-11-10', '450 Stone, Houston TX', 'M', 55000, null, 1),\\n\", \"('Jennifer', 'Wallace', 987654321, '1941-06-20', '291 Berry, Bellaire TX', 'F', 43000, 888665555, 4),\\n\", \"('Ahmad', 'Jabbar', 987987987, '1969-03-29', '980 Dallas, Houston TX', 'M', 25000, 987654321, 4),\\n\", \"('Alicia', 'Zelaya', 999887777, '1968-01-19', '3321 Castle, Spring TX', 'F', 25000, 987654321, 4);\\n\", '\\n', 'insert into dependent values\\n', \"(123456789, 'Alice', 'F', '1988-12-30', 'Daughter'),\\n\", \"(123456789, 'Elizabeth', 'F', '1967-05-05', 'Spouse'),\\n\", \"(123456789, 'Michael', 'M', '1988-01-04', 'Son'),\\n\", \"(333445555, 'Alice', 'F', '1986-04-04', 'Daughter'),\\n\", \"(333445555, 'Joy', 'F', '1958-05-03', 'Spouse'),\\n\", \"(333445555, 'Theodore', 'M', '1983-10-25', 'Son'),\\n\", \"(987654321, 'Abner', 'M', '1942-02-28', 'Spouse');\\n\", '\\n', 'insert into department values\\n', \"('Headquarters', 1, 888665555, '1981-06-19'),\\n\", \"('Administration', 4, 987654321, '1995-01-01'),\\n\", \"('Research', 5, 333445555, '1988-05-22');\\n\", '\\n', 'insert into dept_locations values\\n', \"(1, 'Houston'),\\n\", \"(4, 'Stafford'),\\n\", \"(5, 'Bellaire'),\\n\", \"(5, 'Houston'),\\n\", \"(5, 'Sugarland');\\n\", '\\n', 'insert into project values\\n', \"('ProductX', 1, 'Bellaire', 5),\\n\", \"('ProductY', 2, 'Sugarland', 5),\\n\", \"('ProductZ', 3, 'Houston', 5),\\n\", \"('Computerization', 10, 'Stafford', 4),\\n\", \"('Reorganization', 20, 'Houston', 1),\\n\", \"('Newbenefits', 30, 'Stafford', 4);\\n\", '\\n', 'insert into works_on values\\n', '(123456789, 1, 32.5),\\n', '(123456789, 2, 7.5),\\n', '(333445555, 2, 10.0),\\n', '(333445555, 3, 10.0),\\n', '(333445555, 10, 10.0),\\n', '(333445555, 20, 10.0),\\n', '(453453453, 1, 20.0),\\n', '(453453453, 2, 20.0),\\n', '(666884444, 3, 40.0),\\n', '(888665555, 20, null),\\n', '(987654321, 20, 15.0),\\n', '(987654321, 30, 20.0),\\n', '(987987987, 10, 35.0),\\n', '(987987987, 30, 5.0),\\n', '(999887777, 10, 10.0),\\n', '(999887777, 30, 30.0);\\n', '\\n', '-- -------------------------------------------------\\n', '-- database state management\\n', '-- -------------------------------------------------\\n', '\\n', 'create table magic44_database_state (\\n', '  state_id integer not null,\\n', '  state_description varchar(2000) default null,\\n', '  primary key (state_id)\\n', ') engine = innodb;\\n', '\\n', 'insert into magic44_database_state values\\n', \"(0,'initial/original state'),\\n\", \"(1,'deleted department 4'),\\n\", \"(2,'deleted department 5'),\\n\", \"(3,'add new department 9'),\\n\", \"(4,'random value perturbations');\\n\", '\\n', 'create table magic44_state_holds_rows (\\n', '  state_id integer not null,\\n', '  row_id varchar(100) not null,\\n', '  primary key (state_id, row_id)\\n', ') engine = innodb;\\n', '\\n', 'insert into magic44_state_holds_rows values\\n', \"(0,'department_1'),\\n\", \"(0,'department_2'),\\n\", \"(0,'department_3'),\\n\", \"(0,'dependent_1'),\\n\", \"(0,'dependent_2'),\\n\", \"(0,'dependent_3'),\\n\", \"(0,'dependent_4'),\\n\", \"(0,'dependent_5'),\\n\", \"(0,'dependent_6'),\\n\", \"(0,'dependent_7'),\\n\", \"(0,'dept_locations_1'),\\n\", \"(0,'dept_locations_2'),\\n\", \"(0,'dept_locations_3'),\\n\", \"(0,'dept_locations_4'),\\n\", \"(0,'dept_locations_5'),\\n\", \"(0,'employee_1'),\\n\", \"(0,'employee_2'),\\n\", \"(0,'employee_3'),\\n\", \"(0,'employee_4'),\\n\", \"(0,'employee_5'),\\n\", \"(0,'employee_6'),\\n\", \"(0,'employee_7'),\\n\", \"(0,'employee_8'),\\n\", \"(0,'project_1'),\\n\", \"(0,'project_2'),\\n\", \"(0,'project_3'),\\n\", \"(0,'project_4'),\\n\", \"(0,'project_5'),\\n\", \"(0,'project_6'),\\n\", \"(0,'works_on_1'),\\n\", \"(0,'works_on_10'),\\n\", \"(0,'works_on_11'),\\n\", \"(0,'works_on_12'),\\n\", \"(0,'works_on_13'),\\n\", \"(0,'works_on_14'),\\n\", \"(0,'works_on_15'),\\n\", \"(0,'works_on_16'),\\n\", \"(0,'works_on_2'),\\n\", \"(0,'works_on_3'),\\n\", \"(0,'works_on_4'),\\n\", \"(0,'works_on_5'),\\n\", \"(0,'works_on_6'),\\n\", \"(0,'works_on_7'),\\n\", \"(0,'works_on_8'),\\n\", \"(0,'works_on_9'),\\n\", \"(1,'department_1'),\\n\", \"(1,'department_3'),\\n\", \"(1,'dependent_1'),\\n\", \"(1,'dependent_2'),\\n\", \"(1,'dependent_3'),\\n\", \"(1,'dependent_4'),\\n\", \"(1,'dependent_5'),\\n\", \"(1,'dependent_6'),\\n\", \"(1,'dept_locations_1'),\\n\", \"(1,'dept_locations_3'),\\n\", \"(1,'dept_locations_4'),\\n\", \"(1,'dept_locations_5'),\\n\", \"(1,'employee_1'),\\n\", \"(1,'employee_2'),\\n\", \"(1,'employee_3'),\\n\", \"(1,'employee_4'),\\n\", \"(1,'employee_5'),\\n\", \"(1,'project_1'),\\n\", \"(1,'project_2'),\\n\", \"(1,'project_3'),\\n\", \"(1,'project_5'),\\n\", \"(1,'works_on_1'),\\n\", \"(1,'works_on_10'),\\n\", \"(1,'works_on_2'),\\n\", \"(1,'works_on_3'),\\n\", \"(1,'works_on_4'),\\n\", \"(1,'works_on_6'),\\n\", \"(1,'works_on_7'),\\n\", \"(1,'works_on_8'),\\n\", \"(1,'works_on_9'),\\n\", \"(2,'department_1'),\\n\", \"(2,'department_2'),\\n\", \"(2,'dependent_7'),\\n\", \"(2,'dept_locations_1'),\\n\", \"(2,'dept_locations_2'),\\n\", \"(2,'employee_5'),\\n\", \"(2,'employee_6'),\\n\", \"(2,'employee_7'),\\n\", \"(2,'employee_8'),\\n\", \"(2,'project_4'),\\n\", \"(2,'project_5'),\\n\", \"(2,'project_6'),\\n\", \"(2,'works_on_10'),\\n\", \"(2,'works_on_11'),\\n\", \"(2,'works_on_12'),\\n\", \"(2,'works_on_13'),\\n\", \"(2,'works_on_14'),\\n\", \"(2,'works_on_15'),\\n\", \"(2,'works_on_16'),\\n\", \"(3,'department_1'),\\n\", \"(3,'department_2'),\\n\", \"(3,'department_3'),\\n\", \"(3,'department_4'),\\n\", \"(3,'dependent_1'),\\n\", \"(3,'dependent_10'),\\n\", \"(3,'dependent_2'),\\n\", \"(3,'dependent_3'),\\n\", \"(3,'dependent_4'),\\n\", \"(3,'dependent_5'),\\n\", \"(3,'dependent_6'),\\n\", \"(3,'dependent_7'),\\n\", \"(3,'dependent_9'),\\n\", \"(3,'dept_location_6'),\\n\", \"(3,'dept_location_7'),\\n\", \"(3,'dept_locations_1'),\\n\", \"(3,'dept_locations_2'),\\n\", \"(3,'dept_locations_3'),\\n\", \"(3,'dept_locations_4'),\\n\", \"(3,'dept_locations_5'),\\n\", \"(3,'employee_1'),\\n\", \"(3,'employee_10'),\\n\", \"(3,'employee_11'),\\n\", \"(3,'employee_12'),\\n\", \"(3,'employee_13'),\\n\", \"(3,'employee_2'),\\n\", \"(3,'employee_3'),\\n\", \"(3,'employee_4'),\\n\", \"(3,'employee_5'),\\n\", \"(3,'employee_6'),\\n\", \"(3,'employee_7'),\\n\", \"(3,'employee_8'),\\n\", \"(3,'employee_9'),\\n\", \"(3,'project_1'),\\n\", \"(3,'project_12'),\\n\", \"(3,'project_2'),\\n\", \"(3,'project_3'),\\n\", \"(3,'project_4'),\\n\", \"(3,'project_5'),\\n\", \"(3,'project_6'),\\n\", \"(3,'works_on_1'),\\n\", \"(3,'works_on_10'),\\n\", \"(3,'works_on_11'),\\n\", \"(3,'works_on_12'),\\n\", \"(3,'works_on_13'),\\n\", \"(3,'works_on_14'),\\n\", \"(3,'works_on_15'),\\n\", \"(3,'works_on_16'),\\n\", \"(3,'works_on_2'),\\n\", \"(3,'works_on_3'),\\n\", \"(3,'works_on_36'),\\n\", \"(3,'works_on_37'),\\n\", \"(3,'works_on_38'),\\n\", \"(3,'works_on_4'),\\n\", \"(3,'works_on_5'),\\n\", \"(3,'works_on_6'),\\n\", \"(3,'works_on_7'),\\n\", \"(3,'works_on_8'),\\n\", \"(3,'works_on_9'),\\n\", \"(4,'department_1'),\\n\", \"(4,'department_2'),\\n\", \"(4,'department_3'),\\n\", \"(4,'dependent_1'),\\n\", \"(4,'dependent_2'),\\n\", \"(4,'dependent_3'),\\n\", \"(4,'dependent_4'),\\n\", \"(4,'dependent_5'),\\n\", \"(4,'dependent_6'),\\n\", \"(4,'dependent_7'),\\n\", \"(4,'dept_locations_1'),\\n\", \"(4,'dept_locations_2'),\\n\", \"(4,'dept_locations_3'),\\n\", \"(4,'dept_locations_4'),\\n\", \"(4,'dept_locations_5'),\\n\", \"(4,'employee_16'),\\n\", \"(4,'employee_17'),\\n\", \"(4,'employee_18'),\\n\", \"(4,'employee_19'),\\n\", \"(4,'employee_20'),\\n\", \"(4,'employee_3'),\\n\", \"(4,'employee_4'),\\n\", \"(4,'employee_5'),\\n\", \"(4,'project_1'),\\n\", \"(4,'project_2'),\\n\", \"(4,'project_3'),\\n\", \"(4,'project_4'),\\n\", \"(4,'project_5'),\\n\", \"(4,'project_6'),\\n\", \"(4,'works_on_10'),\\n\", \"(4,'works_on_14'),\\n\", \"(4,'works_on_15'),\\n\", \"(4,'works_on_39'),\\n\", \"(4,'works_on_40'),\\n\", \"(4,'works_on_41'),\\n\", \"(4,'works_on_42'),\\n\", \"(4,'works_on_43'),\\n\", \"(4,'works_on_44'),\\n\", \"(4,'works_on_45'),\\n\", \"(4,'works_on_46'),\\n\", \"(4,'works_on_47'),\\n\", \"(4,'works_on_48'),\\n\", \"(4,'works_on_49'),\\n\", \"(4,'works_on_50'),\\n\", \"(4,'works_on_6');\\n\", '\\n', 'create table magic44_employee_datastore (\\n', '  fname char(10) not null,\\n', '  lname char(20) not null,\\n', '  ssn decimal(9, 0) not null,\\n', '  bdate date not null,\\n', '  address char(30) not null,\\n', '  sex char(1) not null,\\n', '  salary decimal(5, 0) not null,\\n', '  superssn decimal(9, 0) default null,\\n', '  dno decimal(1, 0) not null,\\n', '  row_id varchar(100) default null,\\n', '  auto_id integer not null auto_increment,\\n', '  primary key (auto_id)\\n', ') engine = innodb;\\n', '\\n', 'insert into magic44_employee_datastore values\\n', \"('John','Smith',123456789,'1965-01-09','731 Fondren, Houston TX','M',30000,333445555,5,'employee_1',1),\\n\", \"('Franklin','Wong',333445555,'1955-12-08','638 Voss, Houston TX','M',40000,888665555,5,'employee_2',2),\\n\", \"('Joyce','English',453453453,'1972-07-31','5631 Rice, Houston TX','F',25000,333445555,5,'employee_3',3),\\n\", \"('Ramesh','Narayan',666884444,'1962-09-15','975 Fire Oak, Humble TX','M',38000,333445555,5,'employee_4',4),\\n\", \"('James','Borg',888665555,'1937-11-10','450 Stone, Houston TX','M',55000,null,1,'employee_5',5),\\n\", \"('Jennifer','Wallace',987654321,'1941-06-20','291 Berry, Bellaire TX','F',43000,888665555,4,'employee_6',6),\\n\", \"('Ahmad','Jabbar',987987987,'1969-03-29','980 Dallas, Houston TX','M',25000,987654321,4,'employee_7',7),\\n\", \"('Alicia','Zelaya',999887777,'1968-01-19','3321 Castle, Spring TX','F',25000,987654321,4,'employee_8',8),\\n\", \"('Camila','Jackson',163479608,'1975-04-20','3830 Stellar Fruit, Tulsa OK','F',37000,235711131,9,'employee_9',9),\\n\", \"('Hector','Cuevas',235711131,'1970-11-06','107 Five Finger Way, Dallas TX','M',31000,888665555,9,'employee_10',10),\\n\", \"('Heike','Weiss',378990405,'1966-11-13','219 Zoo Palast, Norman OK','F',41000,235711131,9,'employee_11',11),\\n\", \"('Hiroto','Watanabe',510176317,'1961-11-17','606 Spring Tail, Houston TX','M',38000,235711131,9,'employee_12',12),\\n\", \"('Alicia','Smith',701294005,'1967-03-19','2 Teleport, Dallas TX','F',51000,235711131,9,'employee_13',13),\\n\", \"('John','Smith',123456789,'1965-01-09','731 Fondren, Houston TX','M',36000,333445555,5,'employee_16',16),\\n\", \"('Franklin','Wong',333445555,'1955-12-08','638 Voss, Houston TX','M',37000,888665555,5,'employee_17',17),\\n\", \"('Jennifer','Wallace',987654321,'1941-06-20','291 Berry, Bellaire TX','F',46000,888665555,4,'employee_18',18),\\n\", \"('Ahmad','Jabbar',987987987,'1969-03-29','980 Dallas, Houston TX','M',34000,987654321,4,'employee_19',19),\\n\", \"('Alicia','Zelaya',999887777,'1968-01-19','3321 Castle, Spring TX','F',31000,987654321,4,'employee_20',20);\\n\", '\\n', 'create table magic44_dependent_datastore (\\n', '  essn decimal(9, 0) not null,\\n', '  dependent_name char(10) not null,\\n', '  sex char(1) not null,\\n', '  bdate date not null,\\n', '  relationship char(30) not null,\\n', '  row_id varchar(100) default null,\\n', '  auto_id integer not null auto_increment,\\n', '  primary key (auto_id)\\n', ') engine = innodb;\\n', '\\n', 'insert into magic44_dependent_datastore values\\n', \"(123456789,'Alice','F','1988-12-30','Daughter','dependent_1',1),\\n\", \"(123456789,'Elizabeth','F','1967-05-05','Spouse','dependent_2',2),\\n\", \"(123456789,'Michael','M','1988-01-04','Son','dependent_3',3),\\n\", \"(333445555,'Alice','F','1986-04-04','Daughter','dependent_4',4),\\n\", \"(333445555,'Joy','F','1958-05-03','Spouse','dependent_5',5),\\n\", \"(333445555,'Theodore','M','1983-10-25','Son','dependent_6',6),\\n\", \"(987654321,'Abner','M','1942-02-28','Spouse','dependent_7',7),\\n\", \"(999887777,'Aurora','F','2010-01-01','Daughter','dependent_8',8),\\n\", \"(378990405,'Ariel','M','1989-05-25','Son','dependent_9',9),\\n\", \"(378990405,'Florence','F','1966-01-25','Spouse','dependent_10',10);\\n\", '\\n', 'create table magic44_department_datastore (\\n', '  dname char(20) not null,\\n', '  dnumber decimal(1, 0) not null,\\n', '  mgrssn decimal(9, 0) not null,\\n', '  mgrstartdate date not null,\\n', '  row_id varchar(100) default null,\\n', '  auto_id integer not null auto_increment,\\n', '  primary key (auto_id)\\n', ') engine = innodb;\\n', '\\n', 'insert into magic44_department_datastore values\\n', \"('Headquarters',1,888665555,'1981-06-19','department_1',1),\\n\", \"('Administration',4,987654321,'1995-01-01','department_2',2),\\n\", \"('Research',5,333445555,'1988-05-22','department_3',3),\\n\", \"('Reverse Engineering',9,235711131,'2002-06-22','department_4',4);\\n\", '\\n', 'create table magic44_dept_locations_datastore (\\n', '  dnumber decimal(1, 0) not null,\\n', '  dlocation char(15) not null,\\n', '  row_id varchar(100) default null,\\n', '  auto_id integer not null auto_increment,\\n', '  primary key (auto_id)\\n', ') engine = innodb;\\n', '\\n', 'insert into magic44_dept_locations_datastore values\\n', \"(1,'Houston','dept_locations_1',1),\\n\", \"(4,'Stafford','dept_locations_2',2),\\n\", \"(5,'Bellaire','dept_locations_3',3),\\n\", \"(5,'Houston','dept_locations_4',4),\\n\", \"(5,'Sugarland','dept_locations_5',5),\\n\", \"(9,'Dallas','dept_location_6',6),\\n\", \"(9,'Sugarland','dept_location_7',7);\\n\", '\\n', 'create table magic44_project_datastore (\\n', '  pname char(20) not null,\\n', '  pnumber decimal(2, 0) not null,\\n', '  plocation char(20) not null,\\n', '  dnum decimal(1, 0) not null,\\n', '  row_id varchar(100) default null,\\n', '  auto_id integer not null auto_increment,\\n', '  primary key (auto_id)\\n', ') engine = innodb;\\n', '\\n', 'insert into magic44_project_datastore values\\n', \"('ProductX',1,'Bellaire',5,'project_1',1),\\n\", \"('ProductY',2,'Sugarland',5,'project_2',2),\\n\", \"('ProductZ',3,'Houston',5,'project_3',3),\\n\", \"('Computerization',10,'Stafford',4,'project_4',4),\\n\", \"('Reorganization',20,'Houston',1,'project_5',5),\\n\", \"('Newbenefits',30,'Stafford',4,'project_6',6),\\n\", \"('Special',41,'Stafford',4,'project_8',8),\\n\", \"('Community',43,'Amarillo',5,'project_9',9),\\n\", \"('WrongPlace',55,'Bellaire',4,'project_10',10),\\n\", \"('WrongDept',66,'Amarillo',1,'project_11',11),\\n\", \"('Hindsight',55,'Dallas',9,'project_12',12);\\n\", '\\n', 'create table magic44_works_on_datastore (\\n', '  essn decimal(9, 0) not null,\\n', '  pno decimal(2, 0) not null,\\n', '  hours decimal(5, 1) default null,\\n', '  row_id varchar(100) default null,\\n', '  auto_id integer not null auto_increment,\\n', '  primary key (auto_id)\\n', ') engine = innodb;\\n', '\\n', 'insert into magic44_works_on_datastore values\\n', \"(123456789,1,32.5,'works_on_1',1),\\n\", \"(123456789,2,7.5,'works_on_2',2),\\n\", \"(333445555,2,10.0,'works_on_3',3),\\n\", \"(333445555,3,10.0,'works_on_4',4),\\n\", \"(333445555,10,10.0,'works_on_5',5),\\n\", \"(333445555,20,10.0,'works_on_6',6),\\n\", \"(453453453,1,20.0,'works_on_7',7),\\n\", \"(453453453,2,20.0,'works_on_8',8),\\n\", \"(666884444,3,40.0,'works_on_9',9),\\n\", \"(888665555,20,null,'works_on_10',10),\\n\", \"(987654321,20,15.0,'works_on_11',11),\\n\", \"(987654321,30,20.0,'works_on_12',12),\\n\", \"(987987987,10,35.0,'works_on_13',13),\\n\", \"(987987987,30,5.0,'works_on_14',14),\\n\", \"(999887777,10,10.0,'works_on_15',15),\\n\", \"(999887777,30,30.0,'works_on_16',16),\\n\", \"(123456789,41,99.0,'works_on_32',32),\\n\", \"(333445555,41,99.0,'works_on_33',33),\\n\", \"(333445555,43,99.0,'works_on_34',34),\\n\", \"(987654321,41,99.0,'works_on_35',35),\\n\", \"(163479608,55,35.0,'works_on_36',36),\\n\", \"(235711131,55,10.0,'works_on_37',37),\\n\", \"(701294005,55,20.0,'works_on_38',38),\\n\", \"(123456789,1,44.5,'works_on_39',39),\\n\", \"(123456789,2,9.0,'works_on_40',40),\\n\", \"(333445555,2,11.5,'works_on_41',41),\\n\", \"(333445555,3,14.5,'works_on_42',42),\\n\", \"(333445555,10,13.0,'works_on_43',43),\\n\", \"(453453453,1,30.5,'works_on_44',44),\\n\", \"(453453453,2,27.5,'works_on_45',45),\\n\", \"(666884444,3,49.0,'works_on_46',46),\\n\", \"(987654321,20,27.0,'works_on_47',47),\\n\", \"(987654321,30,24.5,'works_on_48',48),\\n\", \"(987987987,10,41.0,'works_on_49',49),\\n\", \"(999887777,30,31.5,'works_on_50',50);\\n\", '\\n', '-- -------------------------------------------------\\n', '-- allow controlled changes to the database state\\n', '-- -------------------------------------------------\\n', '\\n', 'drop procedure if exists magic44_set_database_state;\\n', 'delimiter //\\n', 'create procedure magic44_set_database_state (in requestedState integer)\\n', 'begin\\n', '\\t-- Purge and then reload all of the database rows back into the tables.\\n', '    -- Ensure that the data is deleted in reverse order with respect to the\\n', '    -- foreign key dependencies (i.e., from children up to parents).\\n', '\\tdelete from dept_locations;\\n', '\\tdelete from works_on;\\n', '\\tdelete from dependent;\\n', '\\tdelete from project;\\n', '\\tdelete from department;\\n', '\\tdelete from employee;\\n', '    \\n', '    -- Check the validity of the requested state\\n', '    if exists(select * from magic44_state_holds_rows where state_id = requestedState) then\\n', '\\t\\tset @trueState = requestedState;\\n', '\\telse\\n', '\\t\\tset @trueState = 0;\\n', '\\tend if;\\n', '\\n', '    -- Ensure that the data is inserted in order with respect to the foreign\\n', '    -- key dependencies (i.e., from parents down to children)\\n', '    insert into employee (select fname, lname, ssn, bdate, address, sex, salary,\\n', '\\t\\tsuperssn, dno from magic44_employee_datastore where row_id in\\n', '        (select row_id from magic44_state_holds_rows where state_id = @trueState));\\n', '\\tinsert into dependent (select essn, dependent_name, sex, bdate, relationship\\n', '\\t\\tfrom magic44_dependent_datastore where row_id in\\n', '        (select row_id from magic44_state_holds_rows where state_id = @trueState));\\n', '\\tinsert into department (select dname, dnumber, mgrssn, mgrstartdate\\n', '\\t\\tfrom magic44_department_datastore where row_id in\\n', '        (select row_id from magic44_state_holds_rows where state_id = @trueState));\\n', '\\tinsert into dept_locations (select dnumber, dlocation\\n', '\\t\\tfrom magic44_dept_locations_datastore where row_id in\\n', '        (select row_id from magic44_state_holds_rows where state_id = @trueState));\\n', '\\tinsert into project (select pname, pnumber, plocation, dnum\\n', '\\t\\tfrom magic44_project_datastore where row_id in\\n', '        (select row_id from magic44_state_holds_rows where state_id = @trueState));\\n', '\\tinsert into works_on (select essn, pno, hours\\n', '\\t\\tfrom magic44_works_on_datastore where row_id in\\n', '        (select row_id from magic44_state_holds_rows where state_id = @trueState));\\n', 'end //\\n', 'delimiter ;\\n', '\\n', '-- -------------------------------------------------\\n', '-- expected answers for autograding comparisons\\n', '-- -------------------------------------------------\\n', '\\n', '-- These tables are used to store the answers and test results.  The answers are generated by executing\\n', '-- the test script against our reference solution.  The test results are collected by running the test\\n', '-- script against your submission in order to compare the results.\\n', '\\n', '-- the results from magic44_data_capture the are transferred into the magic44_test_results table\\n', 'drop table if exists magic44_test_results;\\n', 'create table magic44_test_results (\\n', '\\tstate_id integer not null,\\n', '    query_id integer,\\n', '\\trow_hash varchar(2000) not null\\n', ');\\n', '\\n', '-- the answers generated from the reference solution are loaded below\\n', 'drop table if exists magic44_expected_results;\\n', 'create table magic44_expected_results (\\n', '\\tstate_id integer not null,\\n', '    query_id integer,\\n', '\\trow_hash varchar(2000) not null\\n', ');\\n', '\\n', 'insert into magic44_expected_results values\\n', \"(0,0,'result#set#exists############'),\\n\", \"(0,1,'result#set#exists############'),\\n\", \"(0,2,'result#set#exists############'),\\n\", \"(0,3,'result#set#exists############'),\\n\", \"(0,4,'result#set#exists############'),\\n\", \"(0,5,'result#set#exists############'),\\n\", \"(0,6,'result#set#exists############'),\\n\", \"(0,7,'result#set#exists############'),\\n\", \"(0,8,'result#set#exists############'),\\n\", \"(0,9,'result#set#exists############'),\\n\", \"(0,10,'result#set#exists############'),\\n\", \"(0,11,'result#set#exists############'),\\n\", \"(0,12,'result#set#exists############'),\\n\", \"(0,13,'result#set#exists############'),\\n\", \"(0,14,'result#set#exists############'),\\n\", \"(0,15,'result#set#exists############'),\\n\", \"(0,16,'result#set#exists############'),\\n\", \"(0,17,'result#set#exists############'),\\n\", \"(0,18,'result#set#exists############'),\\n\", \"(0,19,'result#set#exists############'),\\n\", \"(0,20,'result#set#exists############'),\\n\", \"(0,21,'result#set#exists############'),\\n\", \"(0,22,'result#set#exists############'),\\n\", \"(0,23,'result#set#exists############'),\\n\", \"(0,24,'result#set#exists############'),\\n\", \"(0,25,'result#set#exists############'),\\n\", \"(0,26,'result#set#exists############'),\\n\", \"(0,27,'result#set#exists############'),\\n\", \"(0,28,'result#set#exists############'),\\n\", \"(0,29,'result#set#exists############'),\\n\", \"(0,30,'result#set#exists############'),\\n\", \"(0,31,'result#set#exists############'),\\n\", \"(0,32,'result#set#exists############'),\\n\", \"(0,33,'result#set#exists############'),\\n\", \"(0,34,'result#set#exists############'),\\n\", \"(0,35,'result#set#exists############'),\\n\", \"(0,36,'result#set#exists############'),\\n\", \"(0,37,'result#set#exists############'),\\n\", \"(0,40,'result#set#exists############'),\\n\", \"(0,41,'result#set#exists############'),\\n\", \"(0,42,'result#set#exists############'),\\n\", \"(0,43,'result#set#exists############'),\\n\", \"(0,44,'result#set#exists############'),\\n\", \"(0,50,'result#set#exists############'),\\n\", 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\"(4,106,'123456789##############'),\\n\", \"(4,106,'333445555##############'),\\n\", \"(4,106,'666884444##############'),\\n\", \"(4,106,'888665555##############'),\\n\", \"(4,106,'987654321##############'),\\n\", \"(4,106,'987987987##############'),\\n\", \"(4,106,'999887777##############'),\\n\", \"(4,107,'333445555##############'),\\n\", \"(4,107,'888665555##############'),\\n\", \"(4,107,'987654321##############'),\\n\", \"(4,108,'123456789##############'),\\n\", \"(4,108,'666884444##############'),\\n\", \"(4,108,'987987987##############'),\\n\", \"(4,108,'999887777##############'),\\n\", \"(4,120,'john#smith#731fondren,houstontx#5#5#research#########'),\\n\", \"(4,120,'john#smith#731fondren,houstontx#5#1#headquarters#########'),\\n\", \"(4,120,'john#smith#731fondren,houstontx#5#4#administration#########'),\\n\", \"(4,120,'franklin#wong#638voss,houstontx#5#5#research#########'),\\n\", \"(4,120,'franklin#wong#638voss,houstontx#5#1#headquarters#########'),\\n\", 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\"(4,121,'ahmad#jabbar#980dallas,houstontx#administration#4##########'),\\n\", \"(4,121,'alicia#zelaya#3321castle,springtx#administration#4##########'),\\n\", \"(4,122,'john#smith#731fondren,houstontx#research#5##########'),\\n\", \"(4,122,'franklin#wong#638voss,houstontx#research#5##########'),\\n\", \"(4,122,'joyce#english#5631rice,houstontx#research#5##########'),\\n\", \"(4,122,'ramesh#narayan#975fireoak,humbletx#research#5##########'),\\n\", \"(4,122,'james#borg#450stone,houstontx#headquarters#1##########'),\\n\", \"(4,122,'jennifer#wallace#291berry,bellairetx#administration#4##########'),\\n\", \"(4,122,'ahmad#jabbar#980dallas,houstontx#administration#4##########'),\\n\", \"(4,122,'alicia#zelaya#3321castle,springtx#administration#4##########'),\\n\", \"(4,123,'john#smith#731fondren,houstontx#research#5##########'),\\n\", \"(4,123,'franklin#wong#638voss,houstontx#research#5##########'),\\n\", \"(4,123,'joyce#english#5631rice,houstontx#research#5##########'),\\n\", \"(4,123,'ramesh#narayan#975fireoak,humbletx#research#5##########'),\\n\", \"(4,123,'james#borg#450stone,houstontx#headquarters#1##########'),\\n\", \"(4,123,'jennifer#wallace#291berry,bellairetx#administration#4##########'),\\n\", \"(4,123,'ahmad#jabbar#980dallas,houstontx#administration#4##########'),\\n\", \"(4,123,'alicia#zelaya#3321castle,springtx#administration#4##########'),\\n\", \"(4,124,'john#smith#731fondren,houstontx#333445555#franklin#wong#########'),\\n\", \"(4,124,'franklin#wong#638voss,houstontx#888665555#james#borg#########'),\\n\", \"(4,124,'joyce#english#5631rice,houstontx#333445555#franklin#wong#########'),\\n\", \"(4,124,'ramesh#narayan#975fireoak,humbletx#333445555#franklin#wong#########'),\\n\", \"(4,124,'james#borg#450stone,houstontx############'),\\n\", \"(4,124,'jennifer#wallace#291berry,bellairetx#888665555#james#borg#########'),\\n\", \"(4,124,'ahmad#jabbar#980dallas,houstontx#987654321#jennifer#wallace#########'),\\n\", \"(4,124,'alicia#zelaya#3321castle,springtx#987654321#jennifer#wallace#########'),\\n\", \"(4,125,'alice#1988-12-30#john#smith###########'),\\n\", \"(4,125,'elizabeth#1967-05-05#john#smith###########'),\\n\", \"(4,125,'michael#1988-01-04#john#smith###########'),\\n\", \"(4,125,'alice#1986-04-04#franklin#wong###########'),\\n\", \"(4,125,'joy#1958-05-03#franklin#wong###########'),\\n\", \"(4,125,'theodore#1983-10-25#franklin#wong###########'),\\n\", \"(4,125,'##joyce#english###########'),\\n\", \"(4,125,'##ramesh#narayan###########'),\\n\", \"(4,125,'##james#borg###########'),\\n\", \"(4,125,'abner#1942-02-28#jennifer#wallace###########'),\\n\", \"(4,125,'##ahmad#jabbar###########'),\\n\", \"(4,125,'##alicia#zelaya###########'),\\n\", \"(4,126,'james#borg#headquarters#1###########'),\\n\", \"(4,126,'jennifer#wallace#administration#4###########'),\\n\", \"(4,126,'franklin#wong#research#5###########'),\\n\", \"(4,127,'john#smith#5############'),\\n\", \"(4,127,'franklin#wong#5#research###########'),\\n\", \"(4,127,'joyce#english#5############'),\\n\", \"(4,127,'ramesh#narayan#5############'),\\n\", \"(4,127,'james#borg#1#headquarters###########'),\\n\", \"(4,127,'jennifer#wallace#4#administration###########'),\\n\", \"(4,127,'ahmad#jabbar#4############'),\\n\", \"(4,127,'alicia#zelaya#4############'),\\n\", \"(4,128,'john#smith#731fondren,houstontx#reorganization#houston##########'),\\n\", \"(4,128,'john#smith#731fondren,houstontx#productz#houston##########'),\\n\", \"(4,128,'franklin#wong#638voss,houstontx#reorganization#houston##########'),\\n\", \"(4,128,'franklin#wong#638voss,houstontx#productz#houston##########'),\\n\", \"(4,128,'joyce#english#5631rice,houstontx#reorganization#houston##########'),\\n\", \"(4,128,'joyce#english#5631rice,houstontx#productz#houston##########'),\\n\", \"(4,128,'james#borg#450stone,houstontx#reorganization#houston##########'),\\n\", \"(4,128,'james#borg#450stone,houstontx#productz#houston##########'),\\n\", \"(4,128,'jennifer#wallace#291berry,bellairetx#productx#bellaire##########'),\\n\", \"(4,128,'ahmad#jabbar#980dallas,houstontx#reorganization#houston##########'),\\n\", \"(4,128,'ahmad#jabbar#980dallas,houstontx#productz#houston##########'),\\n\", \"(4,129,'james#borg#1937-11-10#franklin#wong#1955-12-08#########'),\\n\", \"(4,129,'james#borg#1937-11-10#jennifer#wallace#1941-06-20#########'),\\n\", \"(4,129,'jennifer#wallace#1941-06-20#franklin#wong#1955-12-08#########');\\n\", '\\n', '-- -------------------------------------------------\\n', '-- autograding system\\n', '-- -------------------------------------------------\\n', \"-- The magic44_data_capture table is used to store the data created by the student's queries\\n\", '-- The table is populated by the magic44_evaluate_queries stored procedure\\n', '-- The data in the table is used to populate the magic44_test_results table for analysis\\n', '\\n', 'drop table if exists magic44_data_capture;\\n', 'create table magic44_data_capture (\\n', '\\tstateID integer, queryID integer,\\n', '    columnDump0 varchar(1000), columnDump1 varchar(1000), columnDump2 varchar(1000), columnDump3 varchar(1000), columnDump4 varchar(1000),\\n', '    columnDump5 varchar(1000), columnDump6 varchar(1000), columnDump7 varchar(1000), columnDump8 varchar(1000), columnDump9 varchar(1000),\\n', '\\tcolumnDump10 varchar(1000), columnDump11 varchar(1000), columnDump12 varchar(1000), columnDump13 varchar(1000), columnDump14 varchar(1000)\\n', ');\\n', '\\n', '-- The magic44_column_listing table is used to help prepare the insert statements for the magic44_data_capture\\n', \"-- table for the student's queries which may have variable numbers of columns (the table is prepopulated)\\n\", '\\n', 'drop table if exists magic44_column_listing;\\n', 'create table magic44_column_listing (\\n', '\\tcolumnPosition integer,\\n', '    simpleColumnName varchar(50),\\n', '    nullColumnName varchar(50)\\n', ');\\n', '\\n', 'insert into magic44_column_listing (columnPosition, simpleColumnName) values\\n', \"(0, 'columnDump0'), (1, 'columnDump1'), (2, 'columnDump2'), (3, 'columnDump3'), (4, 'columnDump4'),\\n\", \"(5, 'columnDump5'), (6, 'columnDump6'), (7, 'columnDump7'), (8, 'columnDump8'), (9, 'columnDump9'),\\n\", \"(10, 'columnDump10'), (11, 'columnDump11'), (12, 'columnDump12'), (13, 'columnDump13'), (14, 'columnDump14');\\n\", '\\n', 'drop function if exists magic44_gen_simple_template;\\n', 'delimiter //\\n', 'create function magic44_gen_simple_template(numberOfColumns integer)\\n', '\\treturns varchar(1000) deterministic\\n', 'begin\\n', \"return (select group_concat(simpleColumnName separator ', ') from magic44_column_listing\\n\", '\\twhere columnPosition < numberOfColumns);\\n', 'end //\\n', 'delimiter ;\\n', '\\n', 'drop function if exists magic44_query_exists;\\n', 'delimiter //\\n', 'create function magic44_query_exists(thisQuery integer)\\n', '\\treturns integer deterministic\\n', 'begin\\n', '\\treturn (select exists (select * from information_schema.views\\n', '\\t\\twhere table_schema = @thisDatabase\\n', \"        and table_name like concat('practiceQuery', thisQuery)));\\n\", 'end //\\n', 'delimiter ;\\n', '\\n', '-- Exception checking has been implemented to prevent (as much as reasonably possible) errors\\n', '-- in the queries being evaluated from interrupting the testing process\\n', '-- The magic44_log_query_errors table capture these errors for later review\\n', '\\n', 'drop table if exists magic44_log_query_errors;\\n', 'create table magic44_log_query_errors (\\n', '\\tstate_id integer,\\n', '    query_id integer,\\n', '    error_code char(5),\\n', '    error_message text\\t\\n', ');\\n', '\\n', 'drop function if exists magic44_query_capture;\\n', 'delimiter //\\n', 'create function magic44_query_capture(thisQuery integer, thisState integer)\\n', '\\treturns varchar(1000) reads sql data\\n', 'begin\\n', '\\tset @numberOfColumns = (select count(*) from information_schema.columns\\n', '\\t\\twhere table_schema = @thisDatabase\\n', \"        and table_name = concat('practiceQuery', thisQuery));\\n\", '\\n', \"\\tset @buildQuery = 'insert into magic44_data_capture (stateID, queryID, ';\\n\", '    set @buildQuery = concat(@buildQuery, magic44_gen_simple_template(@numberOfColumns));\\n', \"    set @buildQuery = concat(@buildQuery, ') select ');\\n\", \"    set @buildQuery = concat(@buildQuery, thisState, ', ');\\n\", \"    set @buildQuery = concat(@buildQuery, thisQuery, ', practiceQuery');\\n\", \"    set @buildQuery = concat(@buildQuery, thisQuery, '.* from practiceQuery');\\n\", \"    set @buildQuery = concat(@buildQuery, thisQuery, ';');\\n\", '    \\n', 'return @buildQuery;\\n', 'end //\\n', 'delimiter ;\\n', '\\n', '-- This null result set marker is used to avoid some edge cases (e.g., empty\\n', '-- test cases) when capturing the results.  This value is also used when\\n', '-- analyzing the submitted queries.\\n', \"set @null_result_set_marker = 'result#set#exists############';\\n\", 'drop procedure if exists magic44_evaluate_query;\\n', 'delimiter //\\n', 'create procedure magic44_evaluate_query(in thisQuery integer, in thisState integer)\\n', 'begin\\n', \"\\tdeclare err_code char(5) default '00000';\\n\", '    declare err_msg text;\\n', '    \\n', '\\tdeclare continue handler for SQLEXCEPTION\\n', '    begin\\n', '\\t\\tget diagnostics condition 1\\n', '\\t\\t\\terr_code = RETURNED_SQLSTATE, err_msg = MESSAGE_TEXT;\\n', '\\tend;\\n', '\\n', '\\tif magic44_query_exists(thisQuery) then\\n', '\\t\\t-- data capture tombstone added to alleviate empty result set anomalies\\n', '\\t\\tinsert into magic44_test_results values (thisState, thisQuery, @null_result_set_marker);\\n', '        \\n', '\\t\\t-- prepare and evaluate query contents\\n', '\\t\\tset @sql_text = magic44_query_capture(thisQuery, thisState);\\n', '\\t\\tprepare statement from @sql_text;\\n', '        execute statement;\\n', \"        if err_code <> '00000' then\\n\", '\\t\\t\\tinsert into magic44_log_query_errors values (thisState, thisQuery, err_code, err_msg);\\n', '\\t\\tend if;\\n', '        deallocate prepare statement;\\n', '\\tend if;\\n', 'end //\\n', 'delimiter ;\\n', '\\n', 'drop procedure if exists magic44_evaluate_solutions;\\n', 'delimiter //\\n', 'create procedure magic44_evaluate_solutions()\\n', 'sp_main: begin\\n', '\\t-- ensure that the state and query target tables exist\\n', '\\tif not exists (select * from magic44_state_holds_rows) then leave sp_main; end if;\\n', '\\tif not exists (select * from magic44_expected_results) then leave sp_main; end if;\\n', '    \\n', '\\tset @startingState = (select min(state_id) from magic44_state_holds_rows); \\n', '\\tset @endingState = (select max(state_id) from magic44_state_holds_rows); \\n', '\\tset @startingQuery = (select min(query_id) from magic44_expected_results); \\n', '\\tset @endingQuery = (select max(query_id) from magic44_expected_results); \\n', '\\n', '    -- check all queries for each database state\\n', '    set @stateCounter = @startingState;\\n', '    check_next_state: while (@stateCounter <= @endingState) do\\n', '\\t\\tif @stateCounter not in (select state_id from magic44_state_holds_rows) then\\n', '\\t\\t\\tset @stateCounter = @stateCounter + 1;\\n', '\\t\\t\\titerate check_next_state;\\n', '\\t\\tend if;\\n', '        call magic44_set_database_state(@stateCounter);\\n', '        \\n', '\\t\\tset @queryCounter = @startingQuery;\\n', '        check_next_query: while (@queryCounter <= @endingQuery) do\\n', '\\t\\t\\tif not magic44_query_exists(@queryCounter) then\\n', '\\t\\t\\t\\tset @queryCounter = @queryCounter + 1;\\n', '\\t\\t\\t\\titerate check_next_query;\\n', '\\t\\t\\tend if;\\n', '        \\n', '\\t\\t\\tcall magic44_evaluate_query(@queryCounter, @stateCounter);\\n', '\\t\\t\\tset @queryCounter = @queryCounter + 1;\\n', '\\t\\tend while;\\n', '        \\n', '\\t\\tset @stateCounter = @stateCounter + 1;\\n', '\\tend while;\\n', 'end //\\n', 'delimiter ;\\n', '\\n', '-- -------------------------------------------------\\n', '-- evaluate all queries against the different states\\n', '-- -------------------------------------------------\\n', '\\n', 'call magic44_evaluate_solutions();\\n', '-- Added [Wed, 7 Sep 2022] to return the database to its original state after testing\\n', 'call magic44_set_database_state(0);\\n', '\\n', 'insert into magic44_test_results\\n', \"select stateID, queryID, concat_ws('#', ifnull(columndump0, ''), ifnull(columndump1, ''), ifnull(columndump2, ''), ifnull(columndump3, ''),\\n\", \"ifnull(columndump4, ''), ifnull(columndump5, ''), ifnull(columndump6, ''), ifnull(columndump7, ''), ifnull(columndump8, ''), ifnull(columndump9, ''),\\n\", \"ifnull(columndump10, ''), ifnull(columndump11, ''), ifnull(columndump12, ''), ifnull(columndump13, ''), ifnull(columndump14, ''))\\n\", 'from magic44_data_capture;\\n', '\\n', '-- Delete the unneeded rows from the answers table to simplify later analysis\\n', 'delete from magic44_expected_results where not magic44_query_exists(query_id);\\n', '\\n', '-- Modify the row hash results for the results table to eliminate spaces and convert all characters to lowercase\\n', \"update magic44_test_results set row_hash = lower(replace(row_hash, ' ', ''));\\n\", '\\n', '/*\\n', 'The magic44_content_differences view displays the differences between the answers and test results in terms of the\\n', 'row attributes and values.  The error_category column contains missing for rows that are not included in the test\\n', 'results but should be, while extra represents the rows that should not be included in the test results.  The row_hash\\n', 'column contains the values of the row in a single string with the attribute values separated by a selected delimeter\\n', '(i.e., the pound sign/#).\\n', '*/\\n', '\\n', 'create or replace view magic44_scoring_content_differences as\\n', \"select query_id, state_id, 'missing' as category, row_hash\\n\", 'from magic44_expected_results where row(state_id, query_id, row_hash) not in\\n', '\\t(select state_id, query_id, row_hash from magic44_test_results)\\n', 'union\\n', \"select query_id, state_id, 'extra' as category, row_hash\\n\", 'from magic44_test_results where row(state_id, query_id, row_hash) not in\\n', '\\t(select state_id, query_id, row_hash from magic44_expected_results)\\n', 'order by query_id, state_id, row_hash;\\n', '\\n', 'drop table if exists magic44_autograding_content_errors;\\n', 'create table magic44_autograding_content_errors (\\n', '\\tquery_id integer,\\n', '    state_id integer,\\n', '    extra_or_missing char(20),\\n', '    row_hash varchar(15000)\\n', ');\\n', '\\n', 'insert into magic44_autograding_content_errors\\n', 'select * from magic44_scoring_content_differences order by query_id, state_id, row_hash;\\n', '\\n', 'create or replace view magic44_tally_row_count as\\n', 'select * from (select state_id, query_id, count(*) - 1 as actual_row_count from magic44_test_results\\n', '\\tgroup by state_id, query_id) as actual_dump\\n', 'natural join\\n', '(select state_id, query_id, count(*) - 1 as expected_row_count from magic44_expected_results\\n', '\\tgroup by state_id, query_id) as expected_dump;\\n', '\\n', 'drop function if exists magic44_valid_row;\\n', 'delimiter //\\n', 'create function magic44_valid_row(ip_row_hash varchar(15000))\\n', '\\treturns boolean reads sql data\\n', 'begin\\n', '\\treturn (ip_row_hash <> @null_result_set_marker);\\n', 'end //\\n', 'delimiter ;\\n', '\\n', 'create or replace view magic44_tally_correct as\\n', 'select state_id, query_id, count(*) as match_total\\n', 'from magic44_test_results where row(state_id, query_id, row_hash)\\n', '\\tin (select state_id, query_id, row_hash from magic44_expected_results)\\n', '    and magic44_valid_row(row_hash) group by state_id, query_id;\\n', '\\n', 'create or replace view magic44_tally_missing as\\n', 'select state_id, query_id, count(*) as missing_total\\n', 'from magic44_expected_results where row(state_id, query_id, row_hash)\\n', '    not in (select state_id, query_id, row_hash from magic44_test_results)\\n', '    and magic44_valid_row(row_hash) group by state_id, query_id;\\n', '\\n', 'create or replace view magic44_tally_excess as\\n', 'select state_id, query_id, count(*) as excess_total\\n', 'from magic44_test_results where row(state_id, query_id, row_hash)\\n', '    not in (select state_id, query_id, row_hash from magic44_expected_results)\\n', '    and magic44_valid_row(row_hash) group by state_id, query_id;\\n', '\\n', '-- Updated [Wed, 23 Aug 2022] to identify duplicate rows testing (edge) case\\n', 'drop function if exists magic44_category_logic;\\n', 'delimiter //\\n', 'create function magic44_category_logic(actual integer, expected integer, matching integer,\\n', '\\tmissing integer, excess integer) returns varchar(50) deterministic\\n', 'begin\\n', \"\\tif (actual = expected and expected = matching) then return 'all_correct';\\n\", \"    elseif (actual <> expected and excess = 0) then return 'likely_duplicate_rows';\\n\", \"\\telseif (actual = expected) then return 'columns_values_incorrect';\\n\", \"\\telseif (missing > 0 and excess > 0) then return 'missing_and_excess_rows';\\n\", \"\\telseif (missing > 0) then return 'missing_rows';\\n\", \"\\telseif (excess > 0) then return 'excess_rows';\\n\", \"\\telse return 'undefined_status';\\n\", '    end if;\\n', 'end //\\n', 'delimiter ;\\n', '\\n', 'drop function if exists magic44_scoring_logic;\\n', 'delimiter //\\n', 'create function magic44_scoring_logic(actual integer, expected integer, matching integer,\\n', '\\tmissing integer, excess integer) returns decimal(5, 2) deterministic\\n', 'begin\\n', '\\tset @line_score = 0.00;\\n', '\\tif (actual = expected and expected = matching and missing = 0 and excess = 0) then\\n', '\\t\\tset @line_score = @line_score + 1.00; end if;\\n', '    return @line_score + least(1.00, round((matching + 1.00) / (expected + 1.00), 2));\\n', 'end //\\n', 'delimiter ;\\n', '\\n', 'create or replace view magic44_scoring_details as\\n', 'select query_id, state_id, actual_row_count, expected_row_count, ifnull(match_total, 0) as match_count,\\n', '\\tifnull(missing_total, 0) as missing_count, ifnull(excess_total, 0) as excess_count,\\n', '    magic44_category_logic(actual_row_count, expected_row_count, ifnull(match_total, 0),\\n', '\\t\\tifnull(missing_total, 0), ifnull(excess_total, 0)) as category,\\n', '    magic44_scoring_logic(actual_row_count, expected_row_count, ifnull(match_total, 0),\\n', '\\t\\tifnull(missing_total, 0), ifnull(excess_total, 0)) as score\\n', 'from ((magic44_tally_row_count natural left outer join magic44_tally_correct)\\n', '    natural left outer join magic44_tally_missing) natural left outer join magic44_tally_excess\\n', 'order by query_id, state_id, category;\\n', '\\n', 'drop table if exists magic44_autograding_scoring_details;\\n', 'create table magic44_autograding_scoring_details (\\n', '\\tquery_id integer,\\n', '    state_id integer,\\n', '    actual_row_count integer,\\n', '    expected_row_count integer,\\n', '    match_total integer,\\n', '\\tmissing_total integer,\\n', '    excess_total integer,\\n', '    category varchar(50),\\n', '    score decimal(5, 2)\\n', ');\\n', '\\n', 'insert into magic44_autograding_scoring_details\\n', 'select * from magic44_scoring_details order by query_id, state_id, category;\\n', '\\n', 'create or replace view magic44_scoring_summary as\\n', '(select query_id, category, group_concat(state_id) as states_affected,\\n', '\\tsum(score) as score_subtotals from magic44_scoring_details\\n', '\\tgroup by query_id, category\\n', 'union\\n', \"select null, '{overall_score}', null, sum(score) from magic44_scoring_details);\\n\", '\\n', 'drop table if exists magic44_autograding_scoring_summary;\\n', 'create table magic44_autograding_scoring_summary (\\n', '\\tquery_id integer,\\n', '    category varchar(50),\\n', '    state_listing varchar(50),\\n', '    score decimal(5, 2)\\n', ');\\n', '\\n', 'insert into magic44_autograding_scoring_summary\\n', 'select * from magic44_scoring_summary;\\n', '\\n', '-- Remove all unneeded tables, views, stored procedures and functions.\\n', '-- Keep only those structures needed to provide student feedback.\\n', 'drop view if exists magic44_scoring_content_differences;\\n', 'drop view if exists magic44_scoring_details;\\n', 'drop view if exists magic44_scoring_summary;\\n', 'drop view if exists magic44_tally_correct;\\n', 'drop view if exists magic44_tally_excess;\\n', 'drop view if exists magic44_tally_missing;\\n', 'drop view if exists magic44_tally_row_count;\\n', 'drop procedure if exists magic44_evaluate_query;\\n', 'drop procedure if exists magic44_evaluate_solutions;\\n', 'drop function if exists magic44_category_logic;\\n', 'drop function if exists magic44_gen_simple_template;\\n', 'drop function if exists magic44_query_capture;\\n', 'drop function if exists magic44_query_exists;\\n', 'drop function if exists magic44_scoring_logic;\\n', 'drop function if exists magic44_valid_row;\\n', 'drop table if exists magic44_column_listing;\\n', 'drop table if exists magic44_data_capture;\\n']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "actual_queries = []\n",
        "for line in sql_lines:\n",
        "  if line.startswith(\"select\"):\n",
        "    actual_queries.append (line)\n",
        "  #actual_queries.append(line)"
      ],
      "metadata": {
        "id": "Q3COx1HFxJ-5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(actual_queries)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N3_DcCrOxN64",
        "outputId": "3e148740-4606-4b68-e964-d9f0108a8957"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "106"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "actual_queries"
      ],
      "metadata": {
        "id": "dMZ3-br9GYxT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def check_query(query1, query2):\n",
        "  normalized_sentence1 = sentence1.replace(\" \", \"\").lower()\n",
        "  normalized_sentence2 = sentence2.replace(\" \", \"\").lower()\n",
        "\n",
        "  return (normalized_sentence1 == normalized_sentence2)\n",
        "\n",
        "# # Example usage\n",
        "# sentence1 = \"This is a test sentence.\"\n",
        "# sentence2 = \"Thiss is a test sentence.\"\n",
        "# result = check_query(sentence1, sentence2)\n",
        "# print(result)"
      ],
      "metadata": {
        "id": "4B6C4Weu044s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_valid_query_accuracy(queries, db_name):\n",
        "  num_queries = len(queries)\n",
        "  failed_queries = 0\n",
        "  for i in range(num_queries):\n",
        "    try:\n",
        "      query_database(db_name, queries[i])\n",
        "    except Exception as e:\n",
        "      failed_queries += 1\n",
        "  return (1 - (failed_queries / num_queries))"
      ],
      "metadata": {
        "id": "O80NW2dy1ZAa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "try:\n",
        "    x = int(input(\"Enter a number: \"))\n",
        "    y = 10 / x\n",
        "except ValueError:\n",
        "    print(\"Error: Please enter a valid integer.\")\n",
        "except ZeroDivisionError:\n",
        "    print(\"Error: Division by zero!\")\n",
        "else:\n",
        "    print(f\"The result is: {y}\")\n",
        "finally:\n",
        "    print(\"End of program.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jjwc1R-y0hAy",
        "outputId": "dc15ff7d-4e9d-402e-9b9c-d1d918832eeb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter a number: 0\n",
            "Error: Division by zero!\n",
            "End of program.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# pip install transformers accelerate\n",
        "\n",
        "from transformers import AutoTokenizer\n",
        "import transformers\n",
        "import torch\n",
        "\n",
        "model = \"mlabonne/llama-2-7b-miniguanaco\"\n",
        "prompt = \"What is a large language model?\"\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model)\n",
        "pipeline = transformers.pipeline(\n",
        "    \"text-generation\",\n",
        "    model=model,\n",
        "    torch_dtype=torch.float16,\n",
        "    device_map=\"auto\",\n",
        ")\n",
        "\n",
        "sequences = pipeline(\n",
        "    f'<s>[INST] {prompt} [/INST]',\n",
        "    do_sample=True,\n",
        "    top_k=10,\n",
        "    num_return_sequences=1,\n",
        "    eos_token_id=tokenizer.eos_token_id,\n",
        "    max_length=200,\n",
        ")\n",
        "for seq in sequences:\n",
        "    print(f\"Result: {seq['generated_text']}\")\n"
      ],
      "metadata": {
        "id": "hwzDTDj_4oSR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "LLAMA-7B INSTRUCTION TUNED\n"
      ],
      "metadata": {
        "id": "BjFSztD7IHAZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7 guardrail-ml==0.0.12 tensorboard\n",
        "!apt-get -qq install poppler-utils tesseract-ocr\n",
        "!pip install -q unstructured[\"local-inference\"]==0.7.4 pillow"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xgbh_cjrIK5d",
        "outputId": "8d5087ff-20f1-4061-f6f4-8bd124294406"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m232.6/232.6 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25hSelecting previously unselected package poppler-utils.\n",
            "(Reading database ... 121752 files and directories currently installed.)\n",
            "Preparing to unpack .../poppler-utils_22.02.0-2ubuntu0.3_amd64.deb ...\n",
            "Unpacking poppler-utils (22.02.0-2ubuntu0.3) ...\n",
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            "Preparing to unpack .../tesseract-ocr-eng_1%3a4.00~git30-7274cfa-1.1_all.deb ...\n",
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            "Preparing to unpack .../tesseract-ocr-osd_1%3a4.00~git30-7274cfa-1.1_all.deb ...\n",
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            "Selecting previously unselected package tesseract-ocr.\n",
            "Preparing to unpack .../tesseract-ocr_4.1.1-2.1build1_amd64.deb ...\n",
            "Unpacking tesseract-ocr (4.1.1-2.1build1) ...\n",
            "Setting up tesseract-ocr-eng (1:4.00~git30-7274cfa-1.1) ...\n",
            "Setting up tesseract-ocr-osd (1:4.00~git30-7274cfa-1.1) ...\n",
            "Setting up poppler-utils (22.02.0-2ubuntu0.3) ...\n",
            "Setting up tesseract-ocr (4.1.1-2.1build1) ...\n",
            "Processing triggers for man-db (2.10.2-1) ...\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m10.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m420.8/420.8 kB\u001b[0m \u001b[31m17.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m101.8/101.8 kB\u001b[0m \u001b[31m14.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.6/5.6 MB\u001b[0m \u001b[31m40.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.8/6.8 MB\u001b[0m \u001b[31m92.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.9/75.9 kB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m17.1/17.1 MB\u001b[0m \u001b[31m80.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.6/114.6 kB\u001b[0m \u001b[31m16.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m159.9/159.9 kB\u001b[0m \u001b[31m20.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.9/77.9 kB\u001b[0m \u001b[31m11.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.2/42.2 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.4/56.4 kB\u001b[0m \u001b[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m112.5/112.5 kB\u001b[0m \u001b[31m14.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m10.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m92.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.5/79.5 kB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.8/2.8 MB\u001b[0m \u001b[31m77.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m117.0/117.0 kB\u001b[0m \u001b[31m15.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for iopath (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "chex 0.1.86 requires numpy>=1.24.1, but you have numpy 1.23.5 which is incompatible.\n",
            "pandas-stubs 2.0.3.230814 requires numpy>=1.25.0; python_version >= \"3.9\", but you have numpy 1.23.5 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OHJa9uCHIf0s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import torch\n",
        "from datasets import load_dataset\n",
        "from transformers import (\n",
        "    AutoModelForCausalLM,\n",
        "    AutoTokenizer,\n",
        "    BitsAndBytesConfig,\n",
        "    HfArgumentParser,\n",
        "    TrainingArguments,\n",
        "    pipeline,\n",
        "    logging,\n",
        ")\n",
        "from peft import LoraConfig, PeftModel, get_peft_model\n",
        "# from trl import SFTTrainer\n",
        "from guardrail.client import (\n",
        "    run_metrics,\n",
        "    run_simple_metrics,\n",
        "    create_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "85Ap1KvwINRj",
        "outputId": "47d2cdfb-6469-4bfe-adff-0365b157b916"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Used for multi-gpu\n",
        "local_rank = -1\n",
        "per_device_train_batch_size = 4\n",
        "per_device_eval_batch_size = 4\n",
        "gradient_accumulation_steps = 1\n",
        "learning_rate = 2e-4\n",
        "max_grad_norm = 0.3\n",
        "weight_decay = 0.001\n",
        "lora_alpha = 16\n",
        "lora_dropout = 0.1\n",
        "lora_r = 64\n",
        "max_seq_length = None\n",
        "\n",
        "# The model that you want to train from the Hugging Face hub\n",
        "model_name = \"guardrail/llama-2-7b-guanaco-instruct-sharded\"\n",
        "\n",
        "# Fine-tuned model name\n",
        "new_model = \"llama-2-7b-guanaco-dolly-mini\"\n",
        "\n",
        "# The instruction dataset to use\n",
        "dataset_name = \"databricks/databricks-dolly-15k\"\n",
        "\n",
        "# Activate 4-bit precision base model loading\n",
        "use_4bit = True\n",
        "\n",
        "# Activate nested quantization for 4-bit base models\n",
        "use_nested_quant = False\n",
        "\n",
        "# Compute dtype for 4-bit base models\n",
        "bnb_4bit_compute_dtype = \"float16\"\n",
        "\n",
        "# Quantization type (fp4 or nf4)\n",
        "bnb_4bit_quant_type = \"nf4\"\n",
        "\n",
        "# Number of training epochs\n",
        "num_train_epochs = 2\n",
        "\n",
        "# Enable fp16 training, (bf16 to True with an A100)\n",
        "fp16 = False\n",
        "\n",
        "# Enable bf16 training\n",
        "bf16 = False\n",
        "\n",
        "# Use packing dataset creating\n",
        "packing = False\n",
        "\n",
        "# Enable gradient checkpointing\n",
        "gradient_checkpointing = True\n",
        "\n",
        "# Optimizer to use, original is paged_adamw_32bit\n",
        "optim = \"paged_adamw_32bit\"\n",
        "\n",
        "# Learning rate schedule (constant a bit better than cosine, and has advantage for analysis)\n",
        "lr_scheduler_type = \"cosine\"\n",
        "\n",
        "# Number of optimizer update steps, 10K original, 20 for demo purposes\n",
        "max_steps = -1\n",
        "\n",
        "# Fraction of steps to do a warmup for\n",
        "warmup_ratio = 0.03\n",
        "\n",
        "# Group sequences into batches with same length (saves memory and speeds up training considerably)\n",
        "group_by_length = True\n",
        "\n",
        "# Save checkpoint every X updates steps\n",
        "save_steps = 10\n",
        "\n",
        "# Log every X updates steps\n",
        "logging_steps = 1\n",
        "\n",
        "# The output directory where the model predictions and checkpoints will be written\n",
        "output_dir = \"./results\"\n",
        "\n",
        "# Load the entire model on the GPU 0\n",
        "device_map = {\"\": 0}\n",
        "\n",
        "# Visualize training\n",
        "report_to = \"tensorboard\"\n",
        "\n",
        "# Tensorboard logs\n",
        "tb_log_dir = \"./results/logs\""
      ],
      "metadata": {
        "id": "KauFybClIkfT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def load_model(model_name):\n",
        "    # Load tokenizer and model with QLoRA configuration\n",
        "    compute_dtype = getattr(torch, bnb_4bit_compute_dtype)\n",
        "\n",
        "    bnb_config = BitsAndBytesConfig(\n",
        "        load_in_4bit=use_4bit,\n",
        "        bnb_4bit_quant_type=bnb_4bit_quant_type,\n",
        "        bnb_4bit_compute_dtype=compute_dtype,\n",
        "        bnb_4bit_use_double_quant=use_nested_quant,\n",
        "    )\n",
        "\n",
        "    if compute_dtype == torch.float16 and use_4bit:\n",
        "        major, _ = torch.cuda.get_device_capability()\n",
        "        if major >= 8:\n",
        "            print(\"=\" * 80)\n",
        "            print(\"Your GPU supports bfloat16, you can accelerate training with the argument --bf16\")\n",
        "            print(\"=\" * 80)\n",
        "\n",
        "    model = AutoModelForCausalLM.from_pretrained(\n",
        "        model_name,\n",
        "        device_map=device_map,\n",
        "        quantization_config=bnb_config\n",
        "    )\n",
        "\n",
        "    model.config.use_cache = False\n",
        "    model.config.pretraining_tp = 1\n",
        "\n",
        "    # Load LoRA configuration\n",
        "    peft_config = LoraConfig(\n",
        "        lora_alpha=lora_alpha,\n",
        "        lora_dropout=lora_dropout,\n",
        "        r=lora_r,\n",
        "        bias=\"none\",\n",
        "        task_type=\"CAUSAL_LM\",\n",
        "    )\n",
        "\n",
        "    # Load Tokenizer\n",
        "    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
        "    tokenizer.pad_token = tokenizer.eos_token\n",
        "    tokenizer.padding_side = \"right\"\n",
        "\n",
        "    return model, tokenizer, peft_config"
      ],
      "metadata": {
        "id": "yPdoT5CxIm6t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# GPU llama-cpp-python\n",
        "!CMAKE_ARGS=\"-DLLAMA_CUBLAS=on\" FORCE_CMAKE=1 pip install llama-cpp-python==0.1.78 numpy==1.23.4 --force-reinstall --upgrade --no-cache-dir --verbose\n",
        "!pip install huggingface_hub\n",
        "!pip install llama-cpp-python==0.1.78\n",
        "!pip install numpy==1.23.4"
      ],
      "metadata": {
        "id": "PjcN6qGwIpjo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def count_words(sentence):\n",
        "    # Split the sentence into words based on whitespace characters\n",
        "    words = sentence.split()\n",
        "    # Return the number of words\n",
        "    return len(words)"
      ],
      "metadata": {
        "id": "2VW903I3YeHj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_penalty(query1, query2):\n",
        "  try:\n",
        "    temp = query_database(query1)\n",
        "    temp2 = query_database(query2)\n",
        "  except Exception as e:\n",
        "    #print(e)\n",
        "    return 0\n",
        "\n",
        "  if temp != temp2:\n",
        "    return 0\n",
        "\n",
        "  else:\n",
        "    #^-(1 + (Length of Generated Query)/(Length of Ground-Truth Query))\n",
        "    return exp(1 + (count_words(query2)/(count_words(query1))))\n",
        "\n"
      ],
      "metadata": {
        "id": "rfevDpq7YDqc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q transformers accelerate langchain\n",
        "!huggingface-cli login"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vp1X3NW0NVVn",
        "outputId": "0c848e0d-5ee1-4096-e56b-0650b8d39c88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m297.6/297.6 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m817.7/817.7 kB\u001b[0m \u001b[31m14.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.9/1.9 MB\u001b[0m \u001b[31m26.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m299.3/299.3 kB\u001b[0m \u001b[31m23.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.0/116.0 kB\u001b[0m \u001b[31m15.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.4/49.4 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.0/53.0 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m141.1/141.1 kB\u001b[0m \u001b[31m15.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Enter your token (input will not be visible): \n",
            "Add token as git credential? (Y/n) n\n",
            "Token is valid (permission: read).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "import transformers\n",
        "import torch\n",
        "import accelerate\n",
        "\n",
        "model = \"meta-llama/Llama-2-7b-chat-hf\"\n",
        "\n",
        "tokenizer=AutoTokenizer.from_pretrained(model)\n",
        "pipeline=transformers.pipeline(\n",
        "    \"text-generation\",\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    torch_dtype=torch.bfloat16,\n",
        "    trust_remote_code=True,\n",
        "    device_map=\"auto\",\n",
        "    max_length=1000,\n",
        "    do_sample=True,\n",
        "    top_k=10,\n",
        "    num_return_sequences=1,\n",
        "    eos_token_id=tokenizer.eos_token_id\n",
        "    )\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369,
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        "id": "wKAze0cqOmBD",
        "outputId": "c3e3c282-8080-49b6-fba2-3b8145dbc15a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.62k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2aaeac028eac46d1bc5af7d2f063fc86"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e304e86cc5fa46748ee0f364fd982076"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "95f78b513a824164b8e51b6e65e87ad3"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/414 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "85d8986d0edf445eae470fb28b346301"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/614 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3c3c8a8f959743c8b65bce87bd216bfe"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors.index.json:   0%|          | 0.00/26.8k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "abaffe62df594df482dcb214b3a44662"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a3eedb9b18034d2c805d7174e361f7fa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00001-of-00002.safetensors:   0%|          | 0.00/9.98G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bb762408aa334f14b5cd7446386e2b1e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00002-of-00002.safetensors:   0%|          | 0.00/3.50G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c700e4e5869d46698f61f4efa51e8628"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "66cc15c4090042dc897a405d71aa0ea2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "becf3169f9524c018701e1c60ddaca0c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sequences = pipeline(\n",
        "    'Hi! I like cooking. Can you suggest some recipes?\\n')\n",
        "# for seq in sequences:\n",
        "#     print(f\"Result: {seq['generated_text']}\")\n",
        "print(len(sequences))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aogT9bDYPASg",
        "outputId": "b4d9463a-76ba-49de-d35d-97d48d070b31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "value = get_schema(\"cs4400.db\")\n",
        "temp2 = str(\"\" if value is None else value)"
      ],
      "metadata": {
        "id": "klG51vnXHcnL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "temp2 = \"\"\"\n",
        "Table: employee\n",
        "Schema: CREATE TABLE employee (\n",
        "fname char(10) not null,\n",
        "lname char(20) not null,\n",
        "ssn decimal(9, 0) not null,\n",
        "bdate date not null,\n",
        "address char(30) not null,\n",
        "sex char(1) not null,\n",
        "salary decimal(5, 0) not null,\n",
        "superssn decimal(9, 0) default null,\n",
        "dno decimal(1, 0) not null,\n",
        "primary key (ssn)\n",
        ")\n",
        "Table: dependent\n",
        "Schema: CREATE TABLE dependent (\n",
        "  essn decimal(9, 0) not null,\n",
        "  dependent_name char(10) not null,\n",
        "  sex char(1) not null,\n",
        "  bdate date not null,\n",
        "  relationship char(30) not null,\n",
        "  primary key (essn, dependent_name)\n",
        ")\n",
        "Table: dept_locations\n",
        "Schema: CREATE TABLE dept_locations (\n",
        "  dnumber decimal(1, 0) not null,\n",
        "  dlocation char(15) not null,\n",
        "  primary key (dnumber, dlocation)\n",
        ")\n",
        "Table: department\n",
        "Schema: CREATE TABLE department (\n",
        "  dname char(20) not null,\n",
        "  dnumber decimal(1, 0) not null,\n",
        "  mgrssn decimal(9, 0) not null,\n",
        "  mgrstartdate date not null,\n",
        "  primary key (dnumber),\n",
        "  unique (dname)\n",
        ")\n",
        "Table: project\n",
        "Schema: CREATE TABLE project (\n",
        "  pname char(20) not null,\n",
        "  pnumber decimal(2, 0) not null,\n",
        "  plocation char(20) not null,\n",
        "  dnum decimal(1, 0) not null,\n",
        "  primary key (pnumber),\n",
        "  unique (pname)\n",
        ")\n",
        "Table: works_on\n",
        "Schema: CREATE TABLE works_on (\n",
        "  essn decimal(9, 0) not null,\n",
        "  pno decimal(2, 0) not null,\n",
        "  hours decimal(5, 1) default null,\n",
        "  primary key (essn, pno)\n",
        ")\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "WvifNMnpIgZf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = \"\"\"\n",
        "The following context is provided to answer the question. The question is a SQL query and the following context is the particular schema.\n",
        "Context:\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "BOqd_hnEIKAu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prompt += temp2"
      ],
      "metadata": {
        "id": "eFuEIVbiIYyV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prompt += \"Question:\""
      ],
      "metadata": {
        "id": "q5PPO-K3Icda"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prompt\n",
        "llama7_prompts = []"
      ],
      "metadata": {
        "id": "WQ47jEDgJW57"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = {\"prompt\": question_queries, \"response\": [\"\"] * len(question_queries)}"
      ],
      "metadata": {
        "id": "-SWPACnrMIFv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import Dataset\n",
        "\n",
        "hf_dataset = Dataset.from_dict(data)\n",
        "hf_dataset.save_to_disk(\"my_dataset\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "260b47c05213466ba8a6f59ee1425f4f",
            "b286ffd6f65847fabc9497c6434e2f98",
            "9678a60c9d4e491880ef65781c290397",
            "e55d8d91b4c7494a87b089971eb3e024",
            "beb4839aa9c24294b1cbfa7647c17b67",
            "9139756cde7444718a8d46fdd3b69ff4",
            "7361a3ec3e4b4bb1b6178caf832aed86",
            "197c6d36353e4047a6562c48dae68d95",
            "54ade959465b4cfbb5d9f242ccb20c37",
            "861e3258cf3c4e77b1f114821bb1a5ec",
            "fa69070b54d54e589c03d81d48f94f84"
          ]
        },
        "id": "DzeR7uxCMM1H",
        "outputId": "e60d23c1-d44e-4272-c75f-d504ebf59464"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Saving the dataset (0/1 shards):   0%|          | 0/95 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "260b47c05213466ba8a6f59ee1425f4f"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for query in question_queries:\n",
        "  temp = prompt\n",
        "  temp += query + \"\\n\"\n",
        "  temp += \"Query:\"\n",
        "  sequences = pipeline(query)\n",
        "  llo = 1\n",
        "  for seq in sequences:\n",
        "    llama7_prompts.append(seq['generated_text'])\n",
        "    llo += 1\n",
        "    print(llo)"
      ],
      "metadata": {
        "id": "cuaDa0dMHtOV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "i = 0\n",
        "for prompt in llama7_prompts:\n",
        "  calculate_cosine_similarity(prompt, )\n",
        "  i += 1"
      ],
      "metadata": {
        "id": "0Y73JExmM_8x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Sample data\n",
        "categories = ['Prompt-Eng.', 'Instruction-Tuned', 'Data-Tuned', 'Multi-Tuned']\n",
        "values = [0.0642, 0.2713, 0.3612, 0.4631]\n",
        "\n",
        "# Create bar graph\n",
        "plt.bar(categories, values)\n",
        "\n",
        "# Add title and labels\n",
        "plt.title('Accuracy')\n",
        "plt.xlabel('Approaches')\n",
        "plt.ylabel('Accuracy')\n",
        "\n",
        "plt.figure(figsize=(30, 6))\n",
        "# plt.xticks(fontsize=10)\n",
        "\n",
        "# Display the graph\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "cRu6sXLu5SEx",
        "outputId": "30ffcf1b-3c36-4079-e896-5dbcbefeaf48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3000x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Sample data\n",
        "categories = ['Prompt-Eng.', 'Instruction-Tuned', 'Data-Tuned', 'Multi-Tuned']\n",
        "values = [0.083, 0.197, 0.262, 0.411]\n",
        "\n",
        "# Create line graph\n",
        "plt.plot(categories, values, marker='o', linestyle='-')\n",
        "\n",
        "# Add title and labels\n",
        "plt.title('Average Distance Penalization')\n",
        "plt.xlabel('Approaches')\n",
        "plt.ylabel('Average Distance Penalization')\n",
        "\n",
        "# Set x-axis tick positions and labels\n",
        "plt.xticks(categories)\n",
        "\n",
        "# Display the graph\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "YNpRloiY9sz4",
        "outputId": "aecf8048-63cc-4e38-eafd-cd330065ef2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch import cuda, bfloat16\n",
        "import transformers\n",
        "\n",
        "model_id = 'meta-llama/Llama-2-13b-chat-hf'\n",
        "\n",
        "device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'\n",
        "\n",
        "# set quantization configuration to load large model with less GPU memory\n",
        "# this requires the `bitsandbytes` library\n",
        "bnb_config = transformers.BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type='nf4',\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_compute_dtype=bfloat16\n",
        ")\n",
        "\n",
        "# begin initializing HF items, need auth token for these\n",
        "hf_auth = 'HF_AUTH_TOKEN'\n",
        "model_config = transformers.AutoConfig.from_pretrained(\n",
        "    model_id,\n",
        "    use_auth_token=hf_auth\n",
        ")\n",
        "\n",
        "model = transformers.AutoModelForCausalLM.from_pretrained(\n",
        "    model_id,\n",
        "    trust_remote_code=True,\n",
        "    config=model_config,\n",
        "    quantization_config=bnb_config,\n",
        "    device_map='auto',\n",
        "    use_auth_token=hf_auth\n",
        ")\n",
        "model.eval()\n",
        "print(f\"Model loaded on {device}\")"
      ],
      "metadata": {
        "id": "m6HbhE9C_K7f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from abc import ABC, abstractmethod\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "\n",
        "class InstructDataset(ABC):\n",
        "    \"\"\"\n",
        "    Abstract class for creating Instruct Datasets\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, dataset_path: str):\n",
        "        \"\"\"\n",
        "        Initialize the dataset\n",
        "        :param dataset_path: The path to the dataset\n",
        "        \"\"\"\n",
        "        self.dataset = None\n",
        "        self.load_dataset(dataset_path)\n",
        "\n",
        "    def load_dataset(self, dataset_path: str) -> None:\n",
        "        \"\"\"\n",
        "        Load the dataset from the given path\n",
        "        :param dataset_path: The path to the dataset\n",
        "        :return: None\n",
        "        \"\"\"\n",
        "        self.dataset = pd.read_csv(dataset_path)\n",
        "\n",
        "    def rename_columns(self, columns: dict[str, str]) -> None:\n",
        "        \"\"\"\n",
        "        Rename the columns of the dataset\n",
        "        :param columns: A dictionary of the form {old_name: new_name}\n",
        "        :return: None\n",
        "        \"\"\"\n",
        "        self.dataset = self.dataset.rename(columns=columns)\n",
        "\n",
        "    def drop_columns(self, columns: list[str]) -> None:\n",
        "        \"\"\"\n",
        "        Drop the columns from the dataset\n",
        "        :param columns: A list of column names to drop\n",
        "        :return: None\n",
        "        \"\"\"\n",
        "        drop_columns = [col for col in columns if col in self.dataset.columns]\n",
        "        self.dataset = self.dataset.drop(columns=drop_columns)\n",
        "\n",
        "    def drop_bad_rows(self, columns: list[str]) -> None:\n",
        "        \"\"\"\n",
        "        Drop the rows which have bad values in the columns\n",
        "        :param columns: A list of columns to check for bad values\n",
        "        :return: None\n",
        "        \"\"\"\n",
        "        self.dataset = self.dataset.dropna(subset=columns)\n",
        "        self.dataset = self.dataset.drop_duplicates(subset=columns)\n",
        "\n",
        "    def create_instruction(self, instruction: str) -> None:\n",
        "        \"\"\"\n",
        "        Create an instruction column in the dataset\n",
        "        :param instruction: The instruction to add to the dataset\n",
        "        :return: None\n",
        "        \"\"\"\n",
        "        self.dataset[\"instruction\"] = instruction\n",
        "\n",
        "    @abstractmethod\n",
        "    def create_prompt(self) -> None:\n",
        "        \"\"\"\n",
        "        Create the prompt column in the dataset\n",
        "        :return: None\n",
        "        \"\"\"\n",
        "        pass\n",
        "\n",
        "    def get_dataset(self) -> pd.DataFrame:\n",
        "        \"\"\"\n",
        "        Get the dataset\n",
        "        :return: The dataset\n",
        "        \"\"\"\n",
        "        return self.dataset\n",
        "\n",
        "\n",
        "class Llama2InstructDataset(InstructDataset):\n",
        "\n",
        "    def create_prompt(self):\n",
        "        \"\"\"\n",
        "        Create the prompt column in the dataset which will be used for\n",
        "        \"\"\"\n",
        "        prompts = []\n",
        "        for index, row in self.dataset.iterrows():\n",
        "            prompt = f\"\"\"<|start_header_id|>system<|end_header_id|> {row['instruction']}<|eot_id|><|start_header_id|>user<|end_header_id|> This is the question: {row['input']}<|eot_id|><|start_header_id|>assistant<|end_header_id|> {row['output']}<|eot_id|>\"\"\"\n",
        "            prompts.append(prompt)\n",
        "        self.dataset[\"prompt\"] = prompts"
      ],
      "metadata": {
        "id": "Q9uOEkpibV_K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Used for multi-gpu\n",
        "local_rank = -1\n",
        "per_device_train_batch_size = 4\n",
        "per_device_eval_batch_size = 4\n",
        "gradient_accumulation_steps = 1\n",
        "learning_rate = 2e-4\n",
        "max_grad_norm = 0.3\n",
        "weight_decay = 0.001\n",
        "lora_alpha = 16\n",
        "lora_dropout = 0.1\n",
        "lora_r = 64\n",
        "max_seq_length = None\n",
        "\n",
        "# The model that you want to train from the Hugging Face hub\n",
        "model_name = \"guardrail/llama-2-7b-guanaco-instruct-sharded\"\n",
        "\n",
        "# Fine-tuned model name\n",
        "new_model = \"llama-2-7b-guanaco-dolly-mini\"\n",
        "\n",
        "# The instruction dataset to use\n",
        "dataset_name = \"databricks/databricks-dolly-15k\"\n",
        "\n",
        "# Activate 4-bit precision base model loading\n",
        "use_4bit = True\n",
        "\n",
        "# Activate nested quantization for 4-bit base models\n",
        "use_nested_quant = False\n",
        "\n",
        "# Compute dtype for 4-bit base models\n",
        "bnb_4bit_compute_dtype = \"float16\"\n",
        "\n",
        "# Quantization type (fp4 or nf4)\n",
        "bnb_4bit_quant_type = \"nf4\"\n",
        "\n",
        "# Number of training epochs\n",
        "num_train_epochs = 2\n",
        "\n",
        "# Enable fp16 training, (bf16 to True with an A100)\n",
        "fp16 = False\n",
        "\n",
        "# Enable bf16 training\n",
        "bf16 = False\n",
        "\n",
        "# Use packing dataset creating\n",
        "packing = False\n",
        "\n",
        "# Enable gradient checkpointing\n",
        "gradient_checkpointing = True\n",
        "\n",
        "# Optimizer to use, original is paged_adamw_32bit\n",
        "optim = \"paged_adamw_32bit\"\n",
        "\n",
        "# Learning rate schedule (constant a bit better than cosine, and has advantage for analysis)\n",
        "lr_scheduler_type = \"cosine\"\n",
        "\n",
        "# Number of optimizer update steps, 10K original, 20 for demo purposes\n",
        "max_steps = -1\n",
        "\n",
        "# Fraction of steps to do a warmup for\n",
        "warmup_ratio = 0.03\n",
        "\n",
        "# Group sequences into batches with same length (saves memory and speeds up training considerably)\n",
        "group_by_length = True\n",
        "\n",
        "# Save checkpoint every X updates steps\n",
        "save_steps = 10\n",
        "\n",
        "# Log every X updates steps\n",
        "logging_steps = 1\n",
        "\n",
        "# The output directory where the model predictions and checkpoints will be written\n",
        "output_dir = \"./results\"\n",
        "\n",
        "# Load the entire model on the GPU 0\n",
        "device_map = {\"\": 0}\n",
        "\n",
        "# Visualize training\n",
        "report_to = \"tensorboard\"\n",
        "\n",
        "# Tensorboard logs\n",
        "tb_log_dir = \"./results/logs\""
      ],
      "metadata": {
        "id": "trCrU6fhbsR_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.save(pth)"
      ],
      "metadata": {
        "id": "ahCL8j4CcM89"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import huggingface_hub"
      ],
      "metadata": {
        "id": "I5n04U-acNqY"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}