Eval_Near_Pretrained.ipynb

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          "text": [
            "Collecting datasets\n",
            "  Downloading datasets-2.19.2-py3-none-any.whl (542 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m542.1/542.1 kB\u001b[0m \u001b[31m6.4 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) (3.14.0)\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[31m7.6 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",
            "Collecting requests>=2.32.1 (from datasets)\n",
            "  Downloading requests-2.32.3-py3-none-any.whl (64 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.9/64.9 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.4)\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[31m7.9 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[31m8.1 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",
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            "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, requests, dill, multiprocess, datasets\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.31.0\n",
            "    Uninstalling requests-2.31.0:\n",
            "      Successfully uninstalled requests-2.31.0\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",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed datasets-2.19.2 dill-0.3.8 multiprocess-0.70.16 requests-2.32.3 xxhash-3.4.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
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        },
        "outputId": "cc5bfc3e-92c0-42f0-b698-032815bbb66e"
      },
      "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",
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            ],
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            }
          },
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          "metadata": {}
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        {
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            ],
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          },
          "metadata": {}
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        {
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            ],
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            ],
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:515: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.7` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
            "  warnings.warn(\n",
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prompt: from typing import List\n",
            "\n",
            "\n",
            "def has_close_elements(numbers: List[float], threshold: float) -> bool:\n",
            "    \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n",
            "    given threshold.\n",
            "    >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n",
            "    False\n",
            "    >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n",
            "    True\n",
            "    \"\"\"\n",
            "\n",
            "Expected Output:     for idx, elem in enumerate(numbers):\n",
            "        for idx2, elem2 in enumerate(numbers):\n",
            "            if idx != idx2:\n",
            "                distance = abs(elem - elem2)\n",
            "                if distance < threshold:\n",
            "                    return True\n",
            "\n",
            "    return False\n",
            "\n",
            "Generated Output: from typing import List\n",
            "\n",
            "\n",
            "def has_close_elements(numbers: List[float], threshold: float) -> bool:\n",
            "    \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n",
            "    given threshold.\n",
            "    >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n",
            "    False\n",
            "    >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n",
            "    True\n",
            "    \"\"\"\n",
            "\n",
            "def has_close_elements_v2(numbers: List[float], threshold: float) -> bool:\n",
            "    \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n",
            "    given threshold.\n",
            "    >>> has_close_elements_v2([1.0, 2.0, 3.0], 0.5)\n",
            "    False\n",
            "    >>> has_close_elements_v2([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n",
            "    True\n",
            "    \"\"\"\n",
            "\n",
            "def has_close_elements_v3(numbers: List[float], threshold: float) -> bool:\n",
            "    \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n",
            "    given threshold.\n",
            "    >>> has_close_elements_v3([1.0, 2.0, 3.0], 0.5)\n",
            "    False\n",
            "    >>> has_close_elements_v3([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n",
            "    True\n",
            "    \"\"\"\n",
            "\n",
            "def has_close_elements_v4(numbers: List[float], threshold: float) -> bool:\n",
            "    \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n",
            "    given threshold.\n",
            "    >>> has_close_elements_v4([1.0, 2.0, 3.0], 0.5)\n",
            "    False\n",
            "    >>> has_close_elements_v4([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n",
            "    True\n",
            "    \"\"\"\n",
            "\n",
            "def has_close_elements_v5(numbers: List[float], threshold: float) -> bool:\n",
            "    \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n",
            "    given threshold.\n",
            "    >>> has_close_elements_v5([1.0, 2.0, 3.0], 0.5)\n",
            "    False\n",
            "    >>> has_close_elements_v5([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n",
            "    True\n",
            "    \"\"\"\n",
            "\n",
            "def has_close_elements_v6(numbers: List[float], threshold: float) -> bool:\n",
            "    \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n",
            "    given threshold.\n",
            "    >>> has_close_elements_v6([1.0, 2.0, 3.0], 0.5)\n",
            "    False\n",
            "    >>> has_close_elements_v6([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n",
            "    True\n",
            "    \"\"\"\n",
            "\n",
            "def has_close_elements_v7(numbers: List[float], threshold: float) -> bool:\n",
            "    \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n",
            "    given threshold.\n",
            "    >>> has_close_elements_v7([1.0, 2.0, 3.0], 0.5)\n",
            "    False\n",
            "    >>> has_close_elements_v7([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n",
            "    True\n",
            "    \"\"\"\n",
            "\n",
            "def has_close_elements_v8(numbers: List[float], threshold: float) -> bool:\n",
            "    \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n",
            "    given threshold.\n",
            "    >>> has_close_elements_v8([1.0, 2.0, 3.0], 0.5)\n",
            "    False\n",
            "    >>> has_close_elements_v8([1.0, 2.8, 3\n",
            "----------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prompt: from typing import List\n",
            "\n",
            "\n",
            "def separate_paren_groups(paren_string: str) -> List[str]:\n",
            "    \"\"\" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to\n",
            "    separate those group into separate strings and return the list of those.\n",
            "    Separate groups are balanced (each open brace is properly closed) and not nested within each other\n",
            "    Ignore any spaces in the input string.\n",
            "    >>> separate_paren_groups('( ) (( )) (( )( ))')\n",
            "    ['()', '(())', '(()())']\n",
            "    \"\"\"\n",
            "\n",
            "Expected Output:     result = []\n",
            "    current_string = []\n",
            "    current_depth = 0\n",
            "\n",
            "    for c in paren_string:\n",
            "        if c == '(':\n",
            "            current_depth += 1\n",
            "            current_string.append(c)\n",
            "        elif c == ')':\n",
            "            current_depth -= 1\n",
            "            current_string.append(c)\n",
            "\n",
            "            if current_depth == 0:\n",
            "                result.append(''.join(current_string))\n",
            "                current_string.clear()\n",
            "\n",
            "    return result\n",
            "\n",
            "Generated Output: from typing import List\n",
            "\n",
            "\n",
            "def separate_paren_groups(paren_string: str) -> List[str]:\n",
            "    \"\"\" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to\n",
            "    separate those group into separate strings and return the list of those.\n",
            "    Separate groups are balanced (each open brace is properly closed) and not nested within each other\n",
            "    Ignore any spaces in the input string.\n",
            "    >>> separate_paren_groups('( ) (( )) (( )( ))')\n",
            "    ['()', '(())', '(()())']\n",
            "    \"\"\"\n",
            "\n",
            "def separate_paren_groups_recursive(paren_string: str) -> List[str]:\n",
            "    \"\"\" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to\n",
            "    separate those group into separate strings and return the list of those.\n",
            "    Separate groups are balanced (each open brace is properly closed) and not nested within each other\n",
            "    Ignore any spaces in the input string.\n",
            "    >>> separate_paren_groups_recursive('( ) (( )) (( )( ))')\n",
            "    ['()', '(())', '(()())']\n",
            "    \"\"\"\n",
            "\n",
            "def separate_paren_groups_iterative(paren_string: str) -> List[str]:\n",
            "    \"\"\" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to\n",
            "    separate those group into separate strings and return the list of those.\n",
            "    Separate groups are balanced (each open brace is properly closed) and not nested within each other\n",
            "    Ignore any spaces in the input string.\n",
            "    >>> separate_paren_groups_iterative('( ) (( )) (( )( ))')\n",
            "    ['()', '(())', '(()())']\n",
            "    \"\"\"\n",
            "\n",
            "def separate_paren_groups_iterative_recursive(paren_string: str) -> List[str]:\n",
            "    \"\"\" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to\n",
            "    separate those group into separate strings and return the list of those.\n",
            "    Separate groups are balanced (each open brace is properly closed) and not nested within each other\n",
            "    Ignore any spaces in the input string.\n",
            "    >>> separate_paren_groups_iterative_recursive('( ) (( )) (( )( ))')\n",
            "    ['()', '(())', '(()())']\n",
            "    \"\"\"\n",
            "\n",
            "def separate_paren_groups_iterative_recursive_2(paren_string: str) -> List[str]:\n",
            "    \"\"\" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to\n",
            "    separate those group into separate strings and return the list of those.\n",
            "    Separate groups are balanced (each open brace is properly closed) and not nested within each other\n",
            "    Ignore any spaces in the input string.\n",
            "    >>> separate_paren_groups_iterative_recursive_2('( ) (( )) (( )( ))')\n",
            "    ['()', '(())', '(()())']\n",
            "    \"\"\"\n",
            "\n",
            "def separate_paren_groups_iterative_recursive_3(paren_string: str) -> List[str]:\n",
            "    \"\"\" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to\n",
            "    separate those group into separate strings and return the list of those.\n",
            "    Separate groups are balanced (each open brace is properly closed) and not nested within each other\n",
            "    Ignore any spaces in the input string.\n",
            "    >>> separate_paren_groups_iterative_recursive_3('( ) (( )) (( )( ))')\n",
            "    ['()', '(())', '(()())']\n",
            "    \"\"\"\n",
            "\n",
            "def separate_paren_groups_iterative_recursive_4(paren_string: str) -> List[str]:\n",
            "    \"\"\" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to\n",
            "    separate those group into separate strings and return the list of those.\n",
            "    Separate groups are balanced (each open brace is properly closed) and not nested within each other\n",
            "    Ignore any spaces in the input string.\n",
            "    >>> separate_paren_groups_iterative_recursive_4('( ) (( )) (( )( ))')\n",
            "    ['()', '(())', '(()())']\n",
            "    \"\"\"\n",
            "\n",
            "def separate_paren_groups_iterative_recursive_5(paren_string: str) -> List[str]:\n",
            "    \"\"\" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to\n",
            "    separate those group into separate strings and return the list of those.\n",
            "    Separate groups are balanced (each open brace is properly closed) and not nested within each other\n",
            "    Ignore any spaces in the input string.\n",
            "    >>> separate_paren_groups_iterative_recursive_5('( ) (( )) (( )( ))')\n",
            "    ['()', '(())', '(()())']\n",
            "    \"\"\"\n",
            "\n",
            "def separate_paren_groups_iterative_recursive_6(\n",
            "----------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prompt: \n",
            "\n",
            "def truncate_number(number: float) -> float:\n",
            "    \"\"\" Given a positive floating point number, it can be decomposed into\n",
            "    and integer part (largest integer smaller than given number) and decimals\n",
            "    (leftover part always smaller than 1).\n",
            "\n",
            "    Return the decimal part of the number.\n",
            "    >>> truncate_number(3.5)\n",
            "    0.5\n",
            "    \"\"\"\n",
            "\n",
            "Expected Output:     return number % 1.0\n",
            "\n",
            "Generated Output: \n",
            "\n",
            "def truncate_number(number: float) -> float:\n",
            "    \"\"\" Given a positive floating point number, it can be decomposed into\n",
            "    and integer part (largest integer smaller than given number) and decimals\n",
            "    (leftover part always smaller than 1).\n",
            "\n",
            "    Return the decimal part of the number.\n",
            "    >>> truncate_number(3.5)\n",
            "    0.5\n",
            "    \"\"\"\n",
            "\n",
            "def truncate_number_to_n_digits(number: float, n: int) -> float:\n",
            "    \"\"\" Given a positive floating point number, it can be decomposed into\n",
            "    and integer part (largest integer smaller than given number) and decimals\n",
            "    (leftover part always smaller than 1).\n",
            "\n",
            "    Return the decimal part of the number truncated to n digits.\n",
            "    >>> truncate_number_to_n_digits(3.5, 1)\n",
            "    0.5\n",
            "    >>> truncate_number_to_n_digits(3.5, 2)\n",
            "    0.50\n",
            "    \"\"\"\n",
            "\n",
            "def truncate_number_to_n_digits_with_rounding(number: float, n: int) -> float:\n",
            "    \"\"\" Given a positive floating point number, it can be decomposed into\n",
            "    and integer part (largest integer smaller than given number) and decimals\n",
            "    (leftover part always smaller than 1).\n",
            "\n",
            "    Return the decimal part of the number truncated to n digits,\n",
            "    rounding the result if necessary.\n",
            "    >>> truncate_number_to_n_digits_with_rounding(3.5, 1)\n",
            "    0.5\n",
            "    >>> truncate_number_to_n_digits_with_rounding(3.5, 2)\n",
            "    0.50\n",
            "    >>> truncate_number_to_n_digits_with_rounding(3.5, 3)\n",
            "    0.500\n",
            "    \"\"\"\n",
            "\n",
            "def truncate_number_to_n_digits_with_rounding_up(number: float, n: int) -> float:\n",
            "    \"\"\" Given a positive floating point number, it can be decomposed into\n",
            "    and integer part (largest integer smaller than given number) and decimals\n",
            "    (leftover part always smaller than 1).\n",
            "\n",
            "    Return the decimal part of the number truncated to n digits,\n",
            "    rounding the result if necessary.\n",
            "    >>> truncate_number_to_n_digits_with_rounding_up(3.5, 1)\n",
            "    4.0\n",
            "    >>> truncate_number_to_n_digits_with_rounding_up(3.5, 2)\n",
            "    4.00\n",
            "    >>> truncate_number_to_n_digits_with_rounding_up(3.5, 3)\n",
            "    4.000\n",
            "    \"\"\"\n",
            "\n",
            "def truncate_number_to_n_digits_with_rounding_down(number: float, n: int) -> float:\n",
            "    \"\"\" Given a positive floating point number, it can be decomposed into\n",
            "    and integer part (largest integer smaller than given number) and decimals\n",
            "    (leftover part always smaller than 1).\n",
            "\n",
            "    Return the decimal part of the number truncated to n digits,\n",
            "    rounding the result if necessary.\n",
            "    >>> truncate_number_to_n_digits_with_rounding_down(3.5, 1)\n",
            "    3.0\n",
            "    >>> truncate_number_to_n_digits_with_rounding_down(3.5, 2)\n",
            "    3.00\n",
            "    >>> truncate_number_to_n_digits_with_rounding_down(3.5, 3)\n",
            "    3.000\n",
            "    \"\"\"\n",
            "\n",
            "def truncate_number_to_n_digits_with_rounding_half_up(number: float, n: int) -> float:\n",
            "    \"\"\" Given a positive floating point number, it can be decomposed into\n",
            "    and integer part (largest integer smaller than given number) and decimals\n",
            "    (leftover part always smaller than 1).\n",
            "\n",
            "    Return the decimal part of the number truncated to n digits,\n",
            "    rounding the result if necessary.\n",
            "    >>> truncate_number_to_n_digits_with_rounding_half_up(3.5, 1)\n",
            "    4.0\n",
            "    >>> truncate_number_to_n_digits_with_rounding_half_up(3.5, 2)\n",
            "    3.50\n",
            "    >>> truncate_number_to_n_digits_with_rounding_half_up(3.5, 3)\n",
            "    3.500\n",
            "    \"\"\"\n",
            "\n",
            "def truncate_number_to_n_digits_with_rounding_half_down(number: float, n: int) -> float:\n",
            "    \"\"\" Given a positive floating point number, it can be decomposed into\n",
            "    and integer part (largest integer smaller\n",
            "----------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prompt: from typing import List\n",
            "\n",
            "\n",
            "def below_zero(operations: List[int]) -> bool:\n",
            "    \"\"\" You're given a list of deposit and withdrawal operations on a bank account that starts with\n",
            "    zero balance. Your task is to detect if at any point the balance of account fallls below zero, and\n",
            "    at that point function should return True. Otherwise it should return False.\n",
            "    >>> below_zero([1, 2, 3])\n",
            "    False\n",
            "    >>> below_zero([1, 2, -4, 5])\n",
            "    True\n",
            "    \"\"\"\n",
            "\n",
            "Expected Output:     balance = 0\n",
            "\n",
            "    for op in operations:\n",
            "        balance += op\n",
            "        if balance < 0:\n",
            "            return True\n",
            "\n",
            "    return False\n",
            "\n",
            "Generated Output: from typing import List\n",
            "\n",
            "\n",
            "def below_zero(operations: List[int]) -> bool:\n",
            "    \"\"\" You're given a list of deposit and withdrawal operations on a bank account that starts with\n",
            "    zero balance. Your task is to detect if at any point the balance of account fallls below zero, and\n",
            "    at that point function should return True. Otherwise it should return False.\n",
            "    >>> below_zero([1, 2, 3])\n",
            "    False\n",
            "    >>> below_zero([1, 2, -4, 5])\n",
            "    True\n",
            "    \"\"\"\n",
            "\n",
            "def below_zero_v2(operations: List[int]) -> bool:\n",
            "    \"\"\" You're given a list of deposit and withdrawal operations on a bank account that starts with\n",
            "    zero balance. Your task is to detect if at any point the balance of account fallls below zero, and\n",
            "    at that point function should return True. Otherwise it should return False.\n",
            "    >>> below_zero_v2([1, 2, 3])\n",
            "    False\n",
            "    >>> below_zero_v2([1, 2, -4, 5])\n",
            "    True\n",
            "    \"\"\"\n",
            "\n",
            "def below_zero_v3(operations: List[int]) -> bool:\n",
            "    \"\"\" You're given a list of deposit and withdrawal operations on a bank account that starts with\n",
            "    zero balance. Your task is to detect if at any point the balance of account fallls below zero, and\n",
            "    at that point function should return True. Otherwise it should return False.\n",
            "    >>> below_zero_v3([1, 2, 3])\n",
            "    False\n",
            "    >>> below_zero_v3([1, 2, -4, 5])\n",
            "    True\n",
            "    \"\"\"\n",
            "\n",
            "def below_zero_v4(operations: List[int]) -> bool:\n",
            "    \"\"\" You're given a list of deposit and withdrawal operations on a bank account that starts with\n",
            "    zero balance. Your task is to detect if at any point the balance of account fallls below zero, and\n",
            "    at that point function should return True. Otherwise it should return False.\n",
            "    >>> below_zero_v4([1, 2, 3])\n",
            "    False\n",
            "    >>> below_zero_v4([1, 2, -4, 5])\n",
            "    True\n",
            "    \"\"\"\n",
            "\n",
            "def below_zero_v5(operations: List[int]) -> bool:\n",
            "    \"\"\" You're given a list of deposit and withdrawal operations on a bank account that starts with\n",
            "    zero balance. Your task is to detect if at any point the balance of account fallls below zero, and\n",
            "    at that point function should return True. Otherwise it should return False.\n",
            "    >>> below_zero_v5([1, 2, 3])\n",
            "    False\n",
            "    >>> below_zero_v5([1, 2, -4, 5])\n",
            "    True\n",
            "    \"\"\"\n",
            "\n",
            "def below_zero_v6(operations: List[int]) -> bool:\n",
            "    \"\"\" You're given a list of deposit and withdrawal operations on a bank account that starts with\n",
            "    zero balance. Your task is to detect if at any point the balance of account fallls below zero, and\n",
            "    at that point function should return True. Otherwise it should return False.\n",
            "    >>> below_zero_v6([1, 2, 3])\n",
            "    False\n",
            "    >>> below_zero_v6([1, 2, -4, 5])\n",
            "    True\n",
            "    \"\"\"\n",
            "\n",
            "def below_zero_v7(operations: List[int]) -> bool:\n",
            "    \"\"\" You're given a list of deposit and withdrawal operations on a bank account that starts with\n",
            "    zero balance. Your task is to detect if at any point the balance of account fallls below zero, and\n",
            "    at that point function should return True. Otherwise it should return False.\n",
            "    >>> below_zero_v7([1, 2, 3])\n",
            "    False\n",
            "    >>> below_zero_v7([1, 2, -4, 5])\n",
            "    True\n",
            "    \"\"\"\n",
            "\n",
            "def below_zero_v8(operations: List[int]) -> bool:\n",
            "    \"\"\" You're given a list of deposit and withdrawal operations on a bank account that starts with\n",
            "    zero balance. Your task is to detect if at any point the balance of account fallls below zero, and\n",
            "    at that point function should return True. Otherwise it should return False.\n",
            "    >>> below_zero_v8([1, 2, 3])\n",
            "    False\n",
            "    >>> below_zero_v8([1, 2, -4, 5])\n",
            "    True\n",
            "    \"\"\"\n",
            "\n",
            "def below_zero_v9(operations: List[int]) -> bool:\n",
            "    \"\"\" You're given a list of deposit and withdrawal operations on a bank account that starts with\n",
            "    zero balance. Your task\n",
            "----------------------------------------------------\n",
            "Prompt: from typing import List\n",
            "\n",
            "\n",
            "def mean_absolute_deviation(numbers: List[float]) -> float:\n",
            "    \"\"\" For a given list of input numbers, calculate Mean Absolute Deviation\n",
            "    around the mean of this dataset.\n",
            "    Mean Absolute Deviation is the average absolute difference between each\n",
            "    element and a centerpoint (mean in this case):\n",
            "    MAD = average | x - x_mean |\n",
            "    >>> mean_absolute_deviation([1.0, 2.0, 3.0, 4.0])\n",
            "    1.0\n",
            "    \"\"\"\n",
            "\n",
            "Expected Output:     mean = sum(numbers) / len(numbers)\n",
            "    return sum(abs(x - mean) for x in numbers) / len(numbers)\n",
            "\n",
            "Generated Output: from typing import List\n",
            "\n",
            "\n",
            "def mean_absolute_deviation(numbers: List[float]) -> float:\n",
            "    \"\"\" For a given list of input numbers, calculate Mean Absolute Deviation\n",
            "    around the mean of this dataset.\n",
            "    Mean Absolute Deviation is the average absolute difference between each\n",
            "    element and a centerpoint (mean in this case):\n",
            "    MAD = average | x - x_mean |\n",
            "    >>> mean_absolute_deviation([1.0, 2.0, 3.0, 4.0])\n",
            "    1.0\n",
            "    \"\"\"\n",
            "\n",
            "def median_absolute_deviation(numbers: List[float]) -> float:\n",
            "    \"\"\" For a given list of input numbers, calculate Median Absolute Deviation\n",
            "    around the median of this dataset.\n",
            "    Median Absolute Deviation is the average absolute difference between each\n",
            "    element and a centerpoint (median in this case):\n",
            "    MAD = average | x - x_median |\n",
            "    >>> median_absolute_deviation([1.0, 2.0, 3.0, 4.0])\n",
            "    0.5\n",
            "    \"\"\"\n",
            "\n",
            "def standard_deviation(numbers: List[float]) -> float:\n",
            "    \"\"\" For a given list of input numbers, calculate Standard Deviation\n",
            "    around the mean of this dataset.\n",
            "    Standard Deviation is the square root of the average squared difference\n",
            "    between each element and a centerpoint (mean in this case):\n",
            "    SD = sqrt(average (x - x_mean)^2)\n",
            "    >>> standard_deviation([1.0, 2.0, 3.0, 4.0])\n",
            "    1.0\n",
            "    \"\"\"\n",
            "\n",
            "def variance(numbers: List[float]) -> float:\n",
            "    \"\"\" For a given list of input numbers, calculate Variance\n",
            "    around the mean of this dataset.\n",
            "    Variance is the average squared difference between each element and a\n",
            "    centerpoint (mean in this case):\n",
            "    Variance = average (x - x_mean)^2\n",
            "    >>> variance([1.0, 2.0, 3.0, 4.0])\n",
            "    2.0\n",
            "    \"\"\"\n",
            "\n",
            "def interquartile_range(numbers: List[float]) -> float:\n",
            "    \"\"\" For a given list of input numbers, calculate Interquartile Range\n",
            "    around the median of this dataset.\n",
            "    Interquartile Range is the difference between the 75th and 25th percentile\n",
            "    of this dataset:\n",
            "    IQR = x_75th - x_25th\n",
            "    >>> interquartile_range([1.0, 2.0, 3.0, 4.0])\n",
            "    2.0\n",
            "    \"\"\"\n",
            "\n",
            "def coefficient_of_variation(numbers: List[float]) -> float:\n",
            "    \"\"\" For a given list of input numbers, calculate Coefficient of Variation\n",
            "    around the mean of this dataset.\n",
            "    Coefficient of Variation is the ratio of the standard deviation to the\n",
            "    mean of this dataset:\n",
            "    CV = SD / mean\n",
            "    >>> coefficient_of_variation([1.0, 2.0, 3.0, 4.0])\n",
            "    0.5\n",
            "    \"\"\"\n",
            "\n",
            "def skewness(numbers: List[float]) -> float:\n",
            "    \"\"\" For a given list of input numbers, calculate Skewness\n",
            "    around the mean of this dataset.\n",
            "    Skewness is the third moment of this dataset divided by the cube of the\n",
            "    standard deviation:\n",
            "    Skewness = (average (x - x_mean)^3) / (standard deviation)^3\n",
            "    >>> skewness([1.0, 2.0, 3.0, 4.0])\n",
            "    0.0\n",
            "    \"\"\"\n",
            "\n",
            "def kurtosis(numbers: List[float]) -> float:\n",
            "    \"\"\" For a given list of input numbers, calculate Kurtosis\n",
            "    around the mean of this dataset.\n",
            "    Kurtosis is the fourth moment of this dataset divided by the fourth power\n",
            "    of the standard deviation:\n",
            "    Kurtosis = (average (x - x_mean)^4) / (standard deviation)^4\n",
            "    >>> kurtosis([1.0, 2.0, 3.0, 4.0])\n",
            "    0.0\n",
            "    \"\"\"\n",
            "\n",
            "def percentile(numbers: List[float], percentile: float) -> float:\n",
            "    \"\"\" For a given list of input numbers, calculate the percentile\n",
            "    of the given percentile.\n",
            "    Percentile is the value below which a given percentage of data falls:\n",
            "    >>> percentile([1.0, 2.0, 3.0, 4.0], 50)\n",
            "    2.0\n",
            "    \"\"\"\n",
            "\n",
            "def percentile_rank(numbers: List[float], value: float) -> float:\n",
            "    \"\"\" For a\n",
            "----------------------------------------------------\n",
            "Accuracy: 0.0%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Import necessary libraries\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "from datasets import load_dataset\n",
        "import torch\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Load the model and tokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"jcarbonnell/NEAR-preTrainedStarCoder2\")\n",
        "model = AutoModelForCausalLM.from_pretrained(\"jcarbonnell/NEAR-preTrainedStarCoder2\")\n",
        "\n",
        "# Load the HumanEval dataset\n",
        "humaneval_dataset = load_dataset(\"openai_humaneval\")\n",
        "\n",
        "# Function to generate code completions\n",
        "def generate_completion(prompt, max_length=1000, temperature=0.7):\n",
        "    inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
        "    input_ids = inputs[\"input_ids\"]\n",
        "    attention_mask = inputs[\"attention_mask\"]\n",
        "\n",
        "    with torch.no_grad():\n",
        "        outputs = model.generate(input_ids=input_ids, attention_mask=attention_mask,\n",
        "                                 max_length=max_length, temperature=temperature)\n",
        "\n",
        "    completion = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
        "    return completion\n",
        "\n",
        "# Function to evaluate the model\n",
        "def evaluate_model(dataset, num_examples=5):\n",
        "    correct = 0\n",
        "    results = []\n",
        "    for i, example in enumerate(dataset[\"test\"]):\n",
        "        if i >= num_examples:\n",
        "            break\n",
        "\n",
        "        prompt = example[\"prompt\"]\n",
        "        expected_output = example[\"canonical_solution\"]\n",
        "\n",
        "        generated_output = generate_completion(prompt)\n",
        "\n",
        "        print(f\"Prompt: {prompt}\")\n",
        "        print(f\"Expected Output: {expected_output}\")\n",
        "        print(f\"Generated Output: {generated_output}\")\n",
        "        print(\"----------------------------------------------------\")\n",
        "\n",
        "        # Check if the expected output is in the generated output\n",
        "        is_correct = expected_output in generated_output\n",
        "        results.append(is_correct)\n",
        "        if is_correct:\n",
        "            correct += 1\n",
        "\n",
        "    accuracy = correct / num_examples\n",
        "    print(f\"Accuracy: {accuracy * 100}%\")\n",
        "\n",
        "    return results\n",
        "\n",
        "# Function to plot the evaluation results\n",
        "def plot_results(results):\n",
        "    labels = ['Correct', 'Incorrect']\n",
        "    counts = [sum(results), len(results) - sum(results)]\n",
        "\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    plt.bar(labels, counts, color=['green', 'red'])\n",
        "    plt.xlabel('Evaluation Result')\n",
        "    plt.ylabel('Number of Examples')\n",
        "    plt.title('Model Evaluation Results on HumanEval Dataset')\n",
        "    plt.show()\n",
        "\n",
        "# Evaluate the model on the HumanEval dataset and plot the results\n",
        "results = evaluate_model(humaneval_dataset)\n",
        "plot_results(results)\n"
      ]
    }
  ]
}