hard_output_eval_results.json

{
  "date": "2025-01-16 15:29",
  "eval": {
    "BigCodeBench/123": [
      {
        "task_id": "BigCodeBench/123",
        "solution": "import pandas as pd\nimport os\nimport glob\ndef task_func(my_list, file_dir='./data_files/', file_ext='.csv'):\n\n    pass\nimport pandas as pd\nimport os\nimport glob\n\ndef task_func(my_list, file_dir='./data_files/', file_ext='.csv'):\n    # Step 1: Type check for my_list\n    if not isinstance(my_list, list):\n        raise TypeError(\"my_list must be a list.\")\n    \n    # Step 2: Modify my_list by adding '12'\n    my_list.append(12)\n    \n    # Step 3: Calculate the sum of the numbers in my_list\n    total_files_to_read = sum(my_list)\n    \n    # Step 4: Verify if there are files in the given directory\n    all_files = glob.glob(os.path.join(file_dir, f'*{file_ext}'))\n    if not all_files:\n        raise FileNotFoundError(f\"No files found in directory: {file_dir}\")\n    \n    # Ensure we do not attempt to read more files than exist\n    total_files_to_read = min(total_files_to_read, len(all_files))\n    \n    # Step 5: Read and concatenate the specified number of CSV files\n    csv_files_to_read = all_files[:total_files_to_read]  # Limit to the number of files determined\n    dataframes = [pd.read_csv(file) for file in csv_files_to_read]\n    \n    # Concatenate all DataFrames\n    result_df = pd.concat(dataframes, ignore_index=True)\n    \n    return result_df",
        "status": "pass",
        "details": {}
      }
    ],
    "BigCodeBench/17": [
      {
        "task_id": "BigCodeBench/17",
        "solution": "import subprocess\nimport psutil\nimport time\ndef task_func(process_name: str) -> str:\n\n    pass\nimport subprocess\nimport psutil\n\ndef task_func(process_name: str) -> str:\n    # Check if the process is running\n    process_found = False\n    for proc in psutil.process_iter(['name']):\n        if proc.info['name'] == process_name:\n            process_found = True\n            break\n    \n    # If the process is found, restart it\n    if process_found:\n        for proc in psutil.process_iter():\n            if proc.name() == process_name:\n                proc.terminate()  # Terminate the process\n                try:\n                    proc.wait(timeout=5)  # Wait for the process to be terminated\n                except psutil.TimeoutExpired:\n                    proc.kill()  # Ensure it is killed if it doesn't terminate in time\n                break\n        subprocess.Popen([process_name])  # Restart the process\n        return f\"Process found. Restarting {process_name}.\"\n    \n    # If the process is not found, start it\n    subprocess.Popen([process_name])  # Start the process\n    return f\"Process not found. Starting {process_name}.\"",
        "status": "fail",
        "details": {
          "test_process_found_restarts_process": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 53, in test_process_found_restarts_process\nAssertionError: 'Process not found. Starting notepad.' != 'Process found. Restarting notepad.'\n- Process not found. Starting notepad.\n?         ----       ^\n+ Process found. Restarting notepad.\n?                ^^^\n\n",
          "test_process_not_found_starts_process": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 44, in test_process_not_found_starts_process\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 956, in assert_called_once_with\n    return self.assert_called_with(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 944, in assert_called_with\n    raise AssertionError(_error_message()) from cause\nAssertionError: expected call not found.\nExpected: Popen('random_non_existent_process')\n  Actual: Popen(['random_non_existent_process'])\n",
          "test_process_terminates_and_restarts_multiple_instances": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 67, in test_process_terminates_and_restarts_multiple_instances\nAssertionError: 'Process not found. Starting multi_instance.' != 'Process found. Restarting multi_instance.'\n- Process not found. Starting multi_instance.\n?         ----       ^\n+ Process found. Restarting multi_instance.\n?                ^^^\n\n"
        }
      }
    ],
    "BigCodeBench/120": [
      {
        "task_id": "BigCodeBench/120",
        "solution": "import pandas as pd\nfrom datetime import datetime, timedelta\nfrom random import randint, seed as random_seed\ndef task_func(start_date=datetime(2020, 1, 1), end_date=datetime(2020, 12, 31), seed=42):\n\n    pass\nimport pandas as pd\nfrom datetime import datetime, timedelta\nfrom random import seed as random_seed, sample\n\ndef task_func(start_date=datetime(2020, 1, 1), end_date=datetime(2020, 12, 31), seed=42):\n    # Step 1: Validate the input parameters\n    if not isinstance(start_date, datetime) or not isinstance(end_date, datetime):\n        raise ValueError(\"Both 'start_date' and 'end_date' must be datetime.datetime instances.\")\n    if start_date > end_date:\n        raise ValueError(\"'start_date' cannot be later than 'end_date'.\")\n\n    # Step 2: Calculate the number of days in the date range\n    delta = (end_date - start_date).days + 1  # Adding 1 to include both dates\n\n    # Step 3: Generate random dates\n    random_seed(seed)  # Set the seed for reproducibility\n    date_range = [start_date + timedelta(days=i) for i in range(delta)]\n    random_dates = sample(date_range, len(date_range))  # Randomly select dates without replacement\n\n    # Step 4: Create a pandas Series from these dates\n    random_dates_series = pd.Series(random_dates)\n\n    # Step 5: Return the result\n    return random_dates_series",
        "status": "fail",
        "details": {
          "test_reproducibility_with_seed": "Traceback (most recent call last):\n  File \"__test__.py\", line 45, in test_reproducibility_with_seed\nAssertionError: Lists differ: ['202[337 chars]-12-22', '2020-08-02', '2020-04-22', '2020-08-[4731 chars]-21'] != ['202[337 chars]-12-25', '2020-10-06', '2020-08-02', '2020-04-[4717 chars]-10']\n\nFirst differing element 24:\n'2020-12-22'\n'2020-12-25'\n\nFirst list contains 1 additional elements.\nFirst extra element 365:\n'2020-10-21'\n\nDiff is 11523 characters long. Set self.maxDiff to None to see it. : DataFrame contents should match the expected output\n",
          "test_series_length": "Traceback (most recent call last):\n  File \"__test__.py\", line 51, in test_series_length\nAssertionError: 10 != 9\n"
        }
      }
    ],
    "BigCodeBench/19": [
      {
        "task_id": "BigCodeBench/19",
        "solution": "import os\nimport glob\nimport zipfile\ndef task_func(directory):\n\n    pass\nimport os\nimport glob\nimport zipfile\n\ndef task_func(directory):\n    # Step 1: Check if the directory exists\n    if not os.path.exists(directory):\n        raise FileNotFoundError(f\"The specified directory does not exist: {directory}\")\n    \n    # Step 2: Gather all files in the directory (non-recursively)\n    file_pattern = os.path.join(directory, '*')  # Match all files in the directory\n    files = glob.glob(file_pattern)\n    \n    # Step 3: Check if there are any files\n    if not files:\n        return None\n    \n    # Step 4: Create the zip file\n    zip_file_path = os.path.join(directory, 'files.zip')\n    with zipfile.ZipFile(zip_file_path, 'w') as zip_file:\n        for file in files:\n            if os.path.isfile(file):  # Ensure we are only adding files\n                zip_file.write(file, os.path.basename(file))  # Add file to zip\n    \n    # Step 5: Return the path to the zip file\n    return zip_file_path",
        "status": "pass",
        "details": {}
      }
    ],
    "BigCodeBench/129": [
      {
        "task_id": "BigCodeBench/129",
        "solution": "import requests\nfrom bs4 import BeautifulSoup\nimport pandas as pd\ndef task_func(url='http://example.com'):\n\n    pass\nimport requests  \nfrom bs4 import BeautifulSoup  \nimport pandas as pd  \n  \ndef task_func(url='http://example.com'):  \n    # Step 1: Make an HTTP Request  \n    try:  \n        response = requests.get(url)  \n        response.raise_for_status()  # Raises HTTPError for bad responses (4xx or 5xx)  \n    except requests.ConnectionError:  \n        raise ConnectionError(f\"Failed to connect to {url}\")  \n    except requests.HTTPError as e:  \n        raise requests.HTTPError(f\"HTTP error occurred: {e}\")  \n  \n    # Step 2: Parse HTML Content  \n    try:  \n        soup = BeautifulSoup(response.content, 'html.parser')  \n    except Exception:  \n        raise ValueError(\"Failed to parse HTML content\")  \n  \n    # Step 3: Locate the First Table  \n    table = soup.find('table')  \n    if not table:  \n        raise ValueError(\"No table found on the page\")  \n  \n    # Step 4: Extract Header and Rows Data  \n    headers = []  \n    if table.find('thead'):  \n        headers = [th.get_text(strip=True) for th in table.find('thead').find_all('th')]  \n    else:  \n        headers = [f\"Column {i}\" for i in range(len(table.find_all('tr')[0].find_all('td')))]  \n  \n    # Extracting rows  \n    rows_data = []  \n    for row in table.find_all('tr')[1:]:  # Skip the header row  \n        cols = row.find_all('td')  \n        if cols:  \n            rows_data.append([col.get_text(strip=True) for col in cols])  \n  \n    # Step 5: Construct DataFrame  \n    if not rows_data:  \n        raise ValueError(\"No table data found on the page\")  \n  \n    df = pd.DataFrame(rows_data, columns=headers)  \n    return df",
        "status": "fail",
        "details": {
          "test_return_type": "Traceback (most recent call last):\n  File \"__test__.py\", line 23, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bs4/__init__.py\", line 319, in __init__\n    for (self.markup, self.original_encoding, self.declared_html_encoding,\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bs4/builder/_htmlparser.py\", line 325, in prepare_markup\n    dammit = UnicodeDammit(markup, try_encodings, is_html=True,\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bs4/dammit.py\", line 445, in __init__\n    for encoding in self.detector.encodings:\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bs4/dammit.py\", line 301, in encodings\n    self.declared_encoding = self.find_declared_encoding(\n                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bs4/dammit.py\", line 378, in find_declared_encoding\n    declared_encoding_match = xml_re.search(markup, endpos=xml_endpos)\n                              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nTypeError: expected string or bytes-like object, got 'MagicMock'\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 62, in test_return_type\n  File \"__test__.py\", line 25, in task_func\nValueError: Failed to parse HTML content\n",
          "test_return_type_with_complex_data": "Traceback (most recent call last):\n  File \"__test__.py\", line 23, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bs4/__init__.py\", line 319, in __init__\n    for (self.markup, self.original_encoding, self.declared_html_encoding,\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bs4/builder/_htmlparser.py\", line 325, in prepare_markup\n    dammit = UnicodeDammit(markup, try_encodings, is_html=True,\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bs4/dammit.py\", line 445, in __init__\n    for encoding in self.detector.encodings:\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bs4/dammit.py\", line 301, in encodings\n    self.declared_encoding = self.find_declared_encoding(\n                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bs4/dammit.py\", line 378, in find_declared_encoding\n    declared_encoding_match = xml_re.search(markup, endpos=xml_endpos)\n                              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nTypeError: expected string or bytes-like object, got 'MagicMock'\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 110, in test_return_type_with_complex_data\n  File \"__test__.py\", line 25, in task_func\nValueError: Failed to parse HTML content\n"
        }
      }
    ],
    "BigCodeBench/161": [
      {
        "task_id": "BigCodeBench/161",
        "solution": "import re\nimport pandas as pd\nfrom datetime import datetime\ndef task_func(log_file):\n\n    pass\nimport re\nimport pandas as pd\nfrom datetime import datetime\n\ndef task_func(log_file, output_csv='structured_log_data.csv'):\n    # Step 1: Read the log file\n    with open(log_file, 'r') as file:\n        lines = file.readlines()\n\n    # Step 2: Parse log entries\n    log_entries = []\n    log_pattern = re.compile(r'^(.*): \\[(\\d{4}-\\d{2}-\\d{2} \\d{2}:\\d{2}:\\d{2})\\] - (.+)$')\n\n    for line in lines:\n        match = log_pattern.match(line.strip())\n        if match:\n            message_type, timestamp, message = match.groups()\n            # Step 3: Validate the timestamp\n            try:\n                datetime.strptime(timestamp, '%Y-%m-%d %H:%M:%S')\n                log_entries.append((message_type, timestamp, message))\n            except ValueError:\n                continue  # Skip invalid timestamps\n\n    # Step 4: Collect valid entries\n    if not log_entries:\n        raise ValueError(\"No valid log entries found\")\n\n    # Step 5: Save to CSV\n    df = pd.DataFrame(log_entries, columns=['Message Type', 'Timestamp', 'Message'])\n    df.to_csv(output_csv, index=False)\n\n    return output_csv",
        "status": "fail",
        "details": {
          "test_log_to_csv_content": "Traceback (most recent call last):\n  File \"__test__.py\", line 65, in test_log_to_csv_content\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/_testing/asserters.py\", line 1185, in assert_frame_equal\n    assert_index_equal(\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/_testing/asserters.py\", line 322, in assert_index_equal\n    _testing.assert_almost_equal(\n  File \"pandas/_libs/testing.pyx\", line 52, in pandas._libs.testing.assert_almost_equal\n  File \"pandas/_libs/testing.pyx\", line 172, in pandas._libs.testing.assert_almost_equal\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/_testing/asserters.py\", line 599, in raise_assert_detail\n    raise AssertionError(msg)\nAssertionError: DataFrame.columns are different\n\nDataFrame.columns values are different (33.33333 %)\n[left]:  Index(['Type', 'Timestamp', 'Message'], dtype='object')\n[right]: Index(['Message Type', 'Timestamp', 'Message'], dtype='object')\nAt positional index 0, first diff: Type != Message Type\n"
        }
      }
    ],
    "BigCodeBench/100": [
      {
        "task_id": "BigCodeBench/100",
        "solution": "import matplotlib.pyplot as plt\nimport pandas as pd\nimport random\nfrom datetime import datetime\ndef task_func(seed=42):\n\n    pass\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport random\nfrom datetime import datetime\n\ndef task_func(seed=42):\n    try:\n        # Step 1: Date Generation\n        dates = pd.date_range(end=datetime.today(), periods=30).to_pydatetime()\n\n        # Step 2: Random Value Generation\n        random.seed(seed)  \n        values = [random.uniform(0, 100) for _ in range(30)]  # Random values between 0 and 100\n\n        # Step 3: Plotting\n        fig, ax = plt.subplots()\n        ax.plot(dates, values, marker='o')\n        ax.set_xlabel('Date')\n        ax.set_ylabel('Value')\n        ax.set_title('Random Time Series Data')\n        plt.xticks(rotation=45, ha='right')\n        \n        # Set the font to Arial\n        plt.rc('font', family='Arial')\n\n        # Step 4: Returning the Axes object\n        return ax\n\n    except Exception as e:\n        raise ValueError(\"Error in generating data or plot: \" + str(e))",
        "status": "fail",
        "details": {
          "test_value": "Traceback (most recent call last):\n  File \"__test__.py\", line 74, in test_value\nAssertionError: Lists differ: [81, 14, 3, 94, 35, 31, 28, 17, 94, 13, 86[69 chars], 69] != [63.942679845788376, 2.5010755222666936, 2[531 chars]8911]\n\nFirst differing element 0:\n81\n63.942679845788376\n\nDiff is 874 characters long. Set self.maxDiff to None to see it. : DataFrame contents should match the expected output\n"
        }
      }
    ],
    "BigCodeBench/187": [
      {
        "task_id": "BigCodeBench/187",
        "solution": "import numpy as np\nimport geopandas as gpd\nfrom shapely.geometry import Point\ndef task_func(dic={'Lon': (-180, 180), 'Lat': (-90, 90)}, cities=['New York', 'London', 'Beijing', 'Tokyo', 'Sydney']):\n\n    pass\nimport numpy as np\nimport geopandas as gpd\nfrom shapely.geometry import Point\n\ndef task_func(dic={'Lon': (-180, 180), 'Lat': (-90, 90)}, cities=['New York', 'London', 'Beijing', 'Tokyo', 'Sydney']):\n    # Step 1: Input validation\n    if 'Lon' not in dic or 'Lat' not in dic:\n        raise ValueError(\"Both 'Lon' and 'Lat' keys must be present in the dictionary.\")\n    if not isinstance(dic['Lon'], tuple) or not isinstance(dic['Lat'], tuple):\n        raise ValueError(\"'Lon' and 'Lat' values must be tuples.\")\n    \n    # Step 2: Coordinate generation\n    lon_range = dic['Lon']\n    lat_range = dic['Lat']\n    \n    coordinates = []\n    for city in cities:\n        lon = np.random.uniform(*lon_range)\n        lat = np.random.uniform(*lat_range)\n        coordinates.append(Point(lon, lat))\n    \n    # Step 3: Creating the GeoDataFrame\n    gdf = gpd.GeoDataFrame({'City': cities, 'Coordinates': coordinates}, geometry='Coordinates')\n    \n    return gdf\n\n# Example usage\nresult = task_func()\nprint(result)",
        "status": "pass",
        "details": {}
      }
    ],
    "BigCodeBench/89": [
      {
        "task_id": "BigCodeBench/89",
        "solution": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy import stats\nfrom sklearn.preprocessing import StandardScaler\ndef task_func(data, column, outlier_z_score):\n\n    pass\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy import stats\nfrom sklearn.preprocessing import StandardScaler\n\ndef task_func(data, column, outlier_z_score):\n    # Step 1: Standardize the specified column\n    scaler = StandardScaler()\n    data[column] = scaler.fit_transform(data[[column]])\n    \n    # Step 2: Calculate Z-scores\n    z_scores = stats.zscore(data[column])\n    \n    # Step 3: Identify outliers\n    outliers_mask = np.abs(z_scores) > outlier_z_score\n    \n    # Step 4: Remove outliers from the dataset\n    data_without_outliers = data[~outliers_mask]\n    \n    # Store outlier indices\n    outlier_indices = np.where(outliers_mask)[0]\n    \n    # Step 5: Visualize the data\n    plt.figure(figsize=(12, 6))\n    \n    plt.subplot(1, 2, 1)\n    plt.scatter(range(len(data)), data[column], color='blue', label='Original Data')\n    plt.title('Data with Outliers')\n    plt.xlabel('Index')\n    plt.ylabel(column)\n    plt.axhline(y=outlier_z_score, color='r', linestyle='--', label='Z-threshold')\n    plt.axhline(y=-outlier_z_score, color='r', linestyle='--')\n    plt.legend()\n\n    plt.subplot(1, 2, 2)\n    plt.scatter(range(len(data_without_outliers)), data_without_outliers[column], color='green', label='Data without Outliers')\n    plt.title('Data without Outliers')\n    plt.xlabel('Index')\n    plt.ylabel(column)\n    \n    plt.tight_layout()\n    plt.show()\n    \n    # Step 6: Return results\n    return (data, data_without_outliers, outlier_indices)",
        "status": "fail",
        "details": {
          "test_data_without_outliers": "Traceback (most recent call last):\n  File \"__test__.py\", line 68, in test_data_without_outliers\n  File \"__test__.py\", line 25, in task_func\nIndexError: boolean index did not match indexed array along dimension 0; dimension is 4 but corresponding boolean dimension is 2\n",
          "test_no_change_in_data_dimension": "Traceback (most recent call last):\n  File \"__test__.py\", line 84, in test_no_change_in_data_dimension\n  File \"__test__.py\", line 25, in task_func\nIndexError: boolean index did not match indexed array along dimension 0; dimension is 4 but corresponding boolean dimension is 2\n",
          "test_no_plotting": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 80, in test_no_plotting\n  File \"__test__.py\", line 25, in task_func\nIndexError: boolean index did not match indexed array along dimension 0; dimension is 4 but corresponding boolean dimension is 2\n",
          "test_original_data_unchanged": "Traceback (most recent call last):\n  File \"__test__.py\", line 64, in test_original_data_unchanged\n  File \"__test__.py\", line 25, in task_func\nIndexError: boolean index did not match indexed array along dimension 0; dimension is 4 but corresponding boolean dimension is 2\n",
          "test_plot_titles": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 89, in test_plot_titles\n  File \"__test__.py\", line 25, in task_func\nIndexError: boolean index did not match indexed array along dimension 0; dimension is 4 but corresponding boolean dimension is 2\n",
          "test_return_type": "Traceback (most recent call last):\n  File \"__test__.py\", line 72, in test_return_type\n  File \"__test__.py\", line 25, in task_func\nIndexError: boolean index did not match indexed array along dimension 0; dimension is 4 but corresponding boolean dimension is 2\n"
        }
      }
    ],
    "BigCodeBench/139": [
      {
        "task_id": "BigCodeBench/139",
        "solution": "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\ndef task_func(df):\n\n    pass\nimport pandas as pd  \nimport numpy as np  \nimport matplotlib.pyplot as plt  \n  \ndef task_func(df):  \n    # Step 1: Input validation  \n    if not isinstance(df, pd.DataFrame) or df.empty:  \n        raise ValueError(\"Input must be a non-empty DataFrame\")  \n    \n    # Step 2: Identify numeric columns  \n    numeric_cols = df.select_dtypes(include=[np.number]).columns  \n    if len(numeric_cols) == 0:  \n        raise ValueError(\"No numeric columns found in the DataFrame\")  \n  \n    # Step 3: Create histograms and collect Axes objects  \n    axes_list = []  \n    for col in numeric_cols:  \n        fig, ax = plt.subplots()  \n        ax.hist(df[col], bins=30, edgecolor='black')  \n        ax.set_title(col)  \n        ax.set_xlabel('Value')  \n        ax.set_ylabel('Frequency')  \n        axes_list.append(ax)  \n  \n    plt.show()  # Optional: Display the plots immediately  \n    # Return the list of Axes objects  \n    return axes_list",
        "status": "pass",
        "details": {}
      }
    ],
    "BigCodeBench/34": [
      {
        "task_id": "BigCodeBench/34",
        "solution": "import re\nfrom wordcloud import WordCloud\nimport matplotlib.pyplot as plt\ndef task_func(text):\n\n    pass\nimport re\nfrom wordcloud import WordCloud\nimport matplotlib.pyplot as plt\n\ndef task_func(text):\n    # Step 1: Remove URLs from the text\n    cleaned_text = re.sub(r'http[s]?://\\\\S+|www\\\\.\\\\S+', '', text)\n\n    # Step 2: Check if there are words available\n    if not cleaned_text.strip() or not re.search(r'\\w+', cleaned_text):\n        raise ValueError(\"No words available to generate a word cloud after removing URLs.\")\n    \n    # Step 3: Generate the word cloud\n    wordcloud = WordCloud(width=800, height=400, background_color='white').generate(cleaned_text)\n\n    # Step 4: Plot the word cloud\n    plt.figure(figsize=(10, 5))\n    plt.imshow(wordcloud, interpolation='bilinear')\n    plt.axis('off')  # Turn off axis\n    plt.show()\n\n    return wordcloud",
        "status": "fail",
        "details": {
          "test_case_4": "Traceback (most recent call last):\n  File \"__test__.py\", line 50, in test_case_4\nAssertionError: ValueError not raised\n"
        }
      }
    ],
    "BigCodeBench/211": [
      {
        "task_id": "BigCodeBench/211",
        "solution": "import requests\nimport os\nimport zipfile\ndef task_func(url, destination_directory, headers=None):\n\n    pass\nimport requests\nimport os\nimport zipfile\n\ndef task_func(url, destination_directory, headers=None):\n    # Ensure the destination directory exists\n    os.makedirs(destination_directory, exist_ok=True)\n\n    # 1. Downloading the Zip File\n    try:\n        response = requests.get(url, headers=headers, stream=True)\n        response.raise_for_status()  # Raise an error for bad responses\n    except requests.exceptions.RequestException as e:\n        print(f\"Error downloading the file: {e}\")\n        return []\n\n    # 2. Saving the Zip File\n    zip_file_path = os.path.join(destination_directory, 'downloaded.zip')\n    with open(zip_file_path, 'wb') as zip_file:\n        zip_file.write(response.content)\n\n    # 3. Extracting the Zip File\n    extracted_files = []\n    try:\n        with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:\n            zip_ref.extractall(destination_directory)  # Extract to specified directory\n            extracted_files = zip_ref.namelist()  # Get the list of filenames\n    except zipfile.BadZipFile as e:\n        print(f\"Error extracting the zip file: {e}\")\n    except Exception as e:\n        print(f\"An unexpected error occurred: {e}\")\n\n    # Clean-up: remove the downloaded zip file\n    os.remove(zip_file_path)\n\n    # 4. Listing Extracted Files\n    return extracted_files",
        "status": "fail",
        "details": {
          "test_2": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 97, in test_2\n  File \"__test__.py\", line 13, in task_func\n  File \"<frozen os>\", line 215, in makedirs\n  File \"<frozen os>\", line 215, in makedirs\n  File \"<frozen os>\", line 225, in makedirs\nPermissionError: [Errno 13] Permission denied: '/path'\n",
          "test_3": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 121, in test_3\n  File \"__test__.py\", line 13, in task_func\n  File \"<frozen os>\", line 215, in makedirs\n  File \"<frozen os>\", line 215, in makedirs\n  File \"<frozen os>\", line 225, in makedirs\nPermissionError: [Errno 13] Permission denied: '/path'\n",
          "test_4": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 145, in test_4\n  File \"__test__.py\", line 13, in task_func\n  File \"<frozen os>\", line 215, in makedirs\n  File \"<frozen os>\", line 215, in makedirs\n  File \"<frozen os>\", line 225, in makedirs\nPermissionError: [Errno 13] Permission denied: '/path'\n",
          "test_5": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 169, in test_5\n  File \"__test__.py\", line 13, in task_func\n  File \"<frozen os>\", line 215, in makedirs\n  File \"<frozen os>\", line 215, in makedirs\n  File \"<frozen os>\", line 225, in makedirs\nPermissionError: [Errno 13] Permission denied: '/path'\n",
          "test_download_and_extract": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 73, in test_download_and_extract\n  File \"__test__.py\", line 13, in task_func\n  File \"<frozen os>\", line 215, in makedirs\n  File \"<frozen os>\", line 215, in makedirs\n  File \"<frozen os>\", line 225, in makedirs\nPermissionError: [Errno 13] Permission denied: '/path'\n"
        }
      }
    ],
    "BigCodeBench/208": [
      {
        "task_id": "BigCodeBench/208",
        "solution": "import numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\ndef task_func(elements, seed=0):\n\n    pass\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\ndef task_func(elements, seed=0):\n    \"\"\"\n    Generate a random walk based on the number of steps specified.\n    \n    Parameters:\n      elements (int): The number of steps for the random walk (must be a positive integer).\n      seed (int): Seed for random number generator for reproducibility (default is 0).\n    \n    Returns:\n      dict: A dictionary containing the descriptive statistics of the random walk.\n      matplotlib.axes.Axes: The Axes object with the plotted random walk.\n    \"\"\"\n    # 1. Input Validation\n    if not isinstance(elements, int) or elements <= 0:\n        raise ValueError(\"elements must be a positive integer.\")\n\n    # 2. Random Walk Generation\n    np.random.seed(seed)  # Set the random seed for reproducibility\n    steps = np.random.choice([-1, 1], size=elements)  # Generate random steps\n    random_walk = np.cumsum(steps)  # Cumulative sum to get the positions\n\n    # 3. Descriptive Statistics Calculation\n    stats = {\n        'count': len(random_walk),\n        'mean': np.mean(random_walk),\n        'std': np.std(random_walk),\n        'min': np.min(random_walk),\n        '5th_percentile': np.percentile(random_walk, 5),\n        '25th_percentile': np.percentile(random_walk, 25),\n        'median': np.median(random_walk),\n        '75th_percentile': np.percentile(random_walk, 75),\n        '95th_percentile': np.percentile(random_walk, 95),\n        'max': np.max(random_walk)\n    }\n\n    # 4. Plotting the Random Walk\n    fig, ax = plt.subplots()\n    ax.plot(random_walk, marker='o')\n    ax.set(title='Random Walk', xlabel='Step', ylabel='Position')\n    ax.grid(True)\n\n    # 5. Combining Results\n    return stats, ax",
        "status": "fail",
        "details": {
          "test_case_1": "Traceback (most recent call last):\n  File \"__test__.py\", line 75, in test_case_1\nAssertionError: 3.9280529527998986 != 3.94784 within 5 places (0.01978704720010116 difference)\n"
        }
      }
    ],
    "BigCodeBench/124": [
      {
        "task_id": "BigCodeBench/124",
        "solution": "from random import randint,seed as random_seed\nimport time\nimport matplotlib.pyplot as plt\ndef task_func(my_list, size=100, seed=100):\n\n    pass\nfrom random import randint, seed as random_seed\nimport time\nimport matplotlib.pyplot as plt\n\ndef task_func(my_list, size=100, seed=100):\n    # Input Validation\n    if not isinstance(my_list, list):\n        raise TypeError(\"Input must be a list.\")\n    if not all(isinstance(x, (int, float)) for x in my_list):\n        raise ValueError(\"All elements in the list must be numeric.\")\n\n    # Appending to the List\n    my_list.append(12)\n\n    # Sum Calculation\n    total_sum = sum(my_list)\n\n    # Limiting the size of generated random integers list\n    num_elements = min(max(total_sum, 0), size)  # Ensure we do not exceed the specified `size` and handle potential zero\n\n    # Random Integer Generation\n    random_random_numbers = []\n    random_seed(seed)  # Set the seed for reproducibility\n    start_time = time.time()  # Start time measurement\n    random_random_numbers = [randint(1, 100) for _ in range(num_elements)]\n    end_time = time.time()  # End time measurement\n\n    # Timing the Process\n    time_taken = end_time - start_time\n\n    # Histogram Plotting\n    fig, ax = plt.subplots()\n    ax.hist(random_random_numbers, bins=range(1, 102), align='left', edgecolor='black')\n    ax.set_xlabel('Number')\n    ax.set_ylabel('Frequency')\n    ax.set_title('Histogram of Random Numbers')\n\n    # Return Values\n    return (time_taken, ax)",
        "status": "fail",
        "details": {
          "test_output_types": "Traceback (most recent call last):\n  File \"__test__.py\", line 56, in test_output_types\nAssertionError: Lists differ: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0[453 chars] 0.0] != [2.0, 2.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0[53 chars] 2.0]\n\nFirst differing element 0:\n0.0\n2.0\n\nFirst list contains 80 additional elements.\nFirst extra element 20:\n0.0\n\nDiff is 928 characters long. Set self.maxDiff to None to see it. : DataFrame contents should match the expected output\n"
        }
      }
    ],
    "BigCodeBench/162": [
      {
        "task_id": "BigCodeBench/162",
        "solution": "import re\nimport matplotlib.pyplot as plt\nimport numpy as np\ndef task_func(text, rwidth=0.8):\n\n    pass\nimport re\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ndef task_func(text, rwidth=0.8):\n    # Step 1: Text Preprocessing\n    # Extract words using regex\n    words = re.findall(r'\\b\\w+\\b', text)\n    \n    # Step 2: Word Length Calculation\n    word_lengths = [len(word) for word in words]\n    \n    # Step 3: Histogram Generation\n    if not word_lengths:  # Edge case where there are no words\n        fig, ax = plt.subplots()\n        ax.hist([], bins=0)  # Creating an empty histogram\n        ax.set_title('Word Length Distribution (No Data)')\n        ax.set_xlabel('Word Length')\n        ax.set_ylabel('Frequency')\n        return ax\n    \n    # Creating histogram of word lengths\n    fig, ax = plt.subplots()\n    ax.hist(word_lengths, bins=np.arange(1, max(word_lengths) + 2) - 0.5, rwidth=rwidth)\n    \n    # Step 4: Output the Plot\n    ax.set_title('Word Length Distribution')\n    ax.set_xlabel('Word Length')\n    ax.set_ylabel('Frequency')\n    \n    return ax",
        "status": "fail",
        "details": {
          "test_empty_text": "Traceback (most recent call last):\n  File \"__test__.py\", line 56, in test_empty_text\n  File \"__test__.py\", line 22, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/matplotlib/__init__.py\", line 1442, in inner\n    return func(ax, *map(sanitize_sequence, args), **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/matplotlib/axes/_axes.py\", line 6790, in hist\n    m, bins = np.histogram(x[i], bins, weights=w[i], **hist_kwargs)\n              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/numpy/lib/histograms.py\", line 780, in histogram\n    bin_edges, uniform_bins = _get_bin_edges(a, bins, range, weights)\n                              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/numpy/lib/histograms.py\", line 424, in _get_bin_edges\n    raise ValueError('`bins` must be positive, when an integer')\nValueError: `bins` must be positive, when an integer\n"
        }
      }
    ],
    "BigCodeBench/199": [
      {
        "task_id": "BigCodeBench/199",
        "solution": "import pandas as pd\nimport pytz\nfrom datetime import datetime\nfrom random import randint, seed as set_seed\ndef task_func(\n    utc_datetime,\n    cities=['New York', 'London', 'Beijing', 'Tokyo', 'Sydney'],\n    weather_conditions=['Sunny', 'Cloudy', 'Rainy', 'Snowy', 'Stormy'],\n    timezones={\n        'New York': 'America/New_York',\n        'London': 'Europe/London',\n        'Beijing': 'Asia/Shanghai',\n        'Tokyo': 'Asia/Tokyo',\n        'Sydney': 'Australia/Sydney'\n    },\n    seed=42\n):\n\n    pass\nimport pandas as pd\nimport pytz\nfrom datetime import datetime\nfrom random import randint, seed as set_seed\n\ndef task_func(\n    utc_datetime,\n    cities=['New York', 'London', 'Beijing', 'Tokyo', 'Sydney'],\n    weather_conditions=['Sunny', 'Cloudy', 'Rainy', 'Snowy', 'Stormy'],\n    timezones={\n        'New York': 'America/New_York',\n        'London': 'Europe/London',\n        'Beijing': 'Asia/Shanghai',\n        'Tokyo': 'Asia/Tokyo',\n        'Sydney': 'Australia/Sydney'\n    },\n    seed=42\n):\n    # Input Validation\n    if not isinstance(utc_datetime, datetime):\n        raise ValueError(\"utc_datetime must be a datetime object.\")\n    if not all(isinstance(city, str) for city in cities):\n        raise ValueError(\"Cities must be a list of strings.\")\n    if not all(city in timezones for city in cities):\n        raise ValueError(\"All cities must have corresponding timezones defined.\")\n\n    # Setting the random seed for consistency\n    set_seed(seed)\n\n    # Data for the final DataFrame\n    data = []\n\n    for city in cities:\n        # Local timezone of the city\n        local_tz = pytz.timezone(timezones[city])\n        \n        # Convert UTC datetime to local time\n        local_time = utc_datetime.astimezone(local_tz)\n        \n        # Format local time string\n        local_time_str = local_time.strftime('%Y-%m-%d %H:%M:%S %Z')\n        \n        # Randomly select weather condition\n        weather_condition = weather_conditions[randint(0, len(weather_conditions) - 1)]\n        \n        # Append data for the city\n        data.append({\n            'City': city,\n            'Local Time': local_time_str,\n            'Weather Condition': weather_condition\n        })\n\n    # Create DataFrame\n    weather_report_df = pd.DataFrame(data)\n    \n    return weather_report_df",
        "status": "pass",
        "details": {}
      }
    ],
    "BigCodeBench/308": [
      {
        "task_id": "BigCodeBench/308",
        "solution": "import pandas as pd\nfrom statistics import mean\nimport random\n# Constants for generating the report data\nFIELDS = ['Physics', 'Math', 'Chemistry', 'Biology', 'English', 'History']\nSTUDENTS = ['Student_' + str(i) for i in range(1, 101)]\ndef task_func(additional_fields = []):\n\n    pass\nimport pandas as pd\nfrom statistics import mean\nimport random\n\n# Constants for generating the report data\nFIELDS = ['Physics', 'Math', 'Chemistry', 'Biology', 'English', 'History']\nSTUDENTS = ['Student_' + str(i) for i in range(1, 101)]\n\ndef task_func(additional_fields=[]):\n    # Step 1: Data Generation\n    # Generate random grades for 100 students across the defined subjects\n    data = {student: [random.randint(0, 100) for _ in FIELDS] for student in STUDENTS}\n    \n    # Step 2: Data Structuring\n    # Create a DataFrame from the grade data\n    grades_df = pd.DataFrame(data, index=FIELDS).T\n    \n    # Step 3: Average Calculation\n    # Calculate average grade for each student (row-wise)\n    grades_df['Average Grade'] = grades_df.mean(axis=1)\n\n    # Calculate average grade for each subject (column-wise)\n    average_row = grades_df.mean(axis=0).rename('Average')\n    \n    # Step 4: Data Output\n    # Append the average row to the DataFrame using concat\n    grades_df = pd.concat([grades_df, average_row.to_frame().T])\n    grades_df.index.name = 'Students'\n    \n    return grades_df\n\n# Generating the report and printing it\nreport = task_func()\nprint(report)",
        "status": "fail",
        "details": {
          "test_additional_fields": "Traceback (most recent call last):\n  File \"__test__.py\", line 61, in test_additional_fields\nAssertionError: Lists differ: ['Phy[47 chars]', 'History', 'Average Grade'] != ['Phy[47 chars]', 'History', 'Computer Science', 'Geography', 'Average Grade']\n\nFirst differing element 6:\n'Average Grade'\n'Computer Science'\n\nSecond list contains 2 additional elements.\nFirst extra element 7:\n'Geography'\n\n  ['Physics',\n   'Math',\n   'Chemistry',\n   'Biology',\n   'English',\n   'History',\n+  'Computer Science',\n+  'Geography',\n   'Average Grade']\n"
        }
      }
    ],
    "BigCodeBench/241": [
      {
        "task_id": "BigCodeBench/241",
        "solution": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import preprocessing\ndef task_func(original):\n\n    pass\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import preprocessing\n\ndef task_func(original):\n    # Step 1: Input Conversion\n    original_array = np.array(original)\n    \n    # Step 2: Normalization\n    normalized_array = preprocessing.MinMaxScaler().fit_transform(original_array.reshape(-1, 1)).flatten()\n    \n    # Step 3: Plotting\n    fig, ax = plt.subplots()\n    ax.plot(original_array, label='Original Data', marker='o')\n    ax.plot(normalized_array, label='Normalized Data', marker='o')\n    ax.set_title('Original vs. Normalized Data')\n    ax.set_xlabel('Index')\n    ax.set_ylabel('Value')\n    ax.legend()\n    \n    # Step 4: Return Values\n    return original_array, normalized_array, ax\n\n# Example usage:\noriginal_list = [10, 20, 15, 30, 25]\noriginal_data, normalized_data, axes = task_func(original_list)\nplt.show()  # This will display the plot",
        "status": "fail",
        "details": {
          "test_case_1": "Traceback (most recent call last):\n  File \"__test__.py\", line 40, in test_case_1\n  File \"__test__.py\", line 16, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/utils/_set_output.py\", line 157, in wrapped\n    data_to_wrap = f(self, X, *args, **kwargs)\n                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/base.py\", line 916, in fit_transform\n    return self.fit(X, **fit_params).transform(X)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/preprocessing/_data.py\", line 435, in fit\n    return self.partial_fit(X, y)\n           ^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/base.py\", line 1152, in wrapper\n    return fit_method(estimator, *args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/preprocessing/_data.py\", line 473, in partial_fit\n    X = self._validate_data(\n        ^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/base.py\", line 605, in _validate_data\n    out = check_array(X, input_name=\"X\", **check_params)\n          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 915, in check_array\n    array = _asarray_with_order(array, order=order, dtype=dtype, xp=xp)\n            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/utils/_array_api.py\", line 380, in _asarray_with_order\n    array = numpy.asarray(array, order=order, dtype=dtype)\n            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nValueError: could not convert string to float: 'a'\n",
          "test_case_2": "Traceback (most recent call last):\n  File \"__test__.py\", line 53, in test_case_2\n  File \"__test__.py\", line 16, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/utils/_set_output.py\", line 157, in wrapped\n    data_to_wrap = f(self, X, *args, **kwargs)\n                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/base.py\", line 916, in fit_transform\n    return self.fit(X, **fit_params).transform(X)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/preprocessing/_data.py\", line 435, in fit\n    return self.partial_fit(X, y)\n           ^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/base.py\", line 1152, in wrapper\n    return fit_method(estimator, *args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/preprocessing/_data.py\", line 473, in partial_fit\n    X = self._validate_data(\n        ^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/base.py\", line 605, in _validate_data\n    out = check_array(X, input_name=\"X\", **check_params)\n          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 915, in check_array\n    array = _asarray_with_order(array, order=order, dtype=dtype, xp=xp)\n            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/utils/_array_api.py\", line 380, in _asarray_with_order\n    array = numpy.asarray(array, order=order, dtype=dtype)\n            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nValueError: could not convert string to float: 'a'\n",
          "test_case_3": "Traceback (most recent call last):\n  File \"__test__.py\", line 69, in test_case_3\n  File \"__test__.py\", line 16, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/utils/_set_output.py\", line 157, in wrapped\n    data_to_wrap = f(self, X, *args, **kwargs)\n                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/base.py\", line 916, in fit_transform\n    return self.fit(X, **fit_params).transform(X)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/preprocessing/_data.py\", line 435, in fit\n    return self.partial_fit(X, y)\n           ^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/base.py\", line 1152, in wrapper\n    return fit_method(estimator, *args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/preprocessing/_data.py\", line 473, in partial_fit\n    X = self._validate_data(\n        ^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/base.py\", line 605, in _validate_data\n    out = check_array(X, input_name=\"X\", **check_params)\n          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 915, in check_array\n    array = _asarray_with_order(array, order=order, dtype=dtype, xp=xp)\n            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/utils/_array_api.py\", line 380, in _asarray_with_order\n    array = numpy.asarray(array, order=order, dtype=dtype)\n            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nValueError: could not convert string to float: 'a'\n",
          "test_case_4": "Traceback (most recent call last):\n  File \"__test__.py\", line 82, in test_case_4\n  File \"__test__.py\", line 16, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/utils/_set_output.py\", line 157, in wrapped\n    data_to_wrap = f(self, X, *args, **kwargs)\n                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/base.py\", line 916, in fit_transform\n    return self.fit(X, **fit_params).transform(X)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/preprocessing/_data.py\", line 435, in fit\n    return self.partial_fit(X, y)\n           ^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/base.py\", line 1152, in wrapper\n    return fit_method(estimator, *args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/preprocessing/_data.py\", line 473, in partial_fit\n    X = self._validate_data(\n        ^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/base.py\", line 605, in _validate_data\n    out = check_array(X, input_name=\"X\", **check_params)\n          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 915, in check_array\n    array = _asarray_with_order(array, order=order, dtype=dtype, xp=xp)\n            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/utils/_array_api.py\", line 380, in _asarray_with_order\n    array = numpy.asarray(array, order=order, dtype=dtype)\n            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nValueError: could not convert string to float: 'a'\n",
          "test_case_5": "Traceback (most recent call last):\n  File \"__test__.py\", line 99, in test_case_5\n  File \"__test__.py\", line 16, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/utils/_set_output.py\", line 157, in wrapped\n    data_to_wrap = f(self, X, *args, **kwargs)\n                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/base.py\", line 916, in fit_transform\n    return self.fit(X, **fit_params).transform(X)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/preprocessing/_data.py\", line 435, in fit\n    return self.partial_fit(X, y)\n           ^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/base.py\", line 1152, in wrapper\n    return fit_method(estimator, *args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/preprocessing/_data.py\", line 473, in partial_fit\n    X = self._validate_data(\n        ^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/base.py\", line 605, in _validate_data\n    out = check_array(X, input_name=\"X\", **check_params)\n          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 967, in check_array\n    raise ValueError(\nValueError: Found array with 0 sample(s) (shape=(0, 1)) while a minimum of 1 is required by MinMaxScaler.\n"
        }
      }
    ],
    "BigCodeBench/108": [
      {
        "task_id": "BigCodeBench/108",
        "solution": "import pandas as pd\nimport matplotlib.pyplot as plt\nfrom statsmodels.tsa.seasonal import seasonal_decompose\ndef task_func(df, freq='D', decomposition_model='multiplicative'):\n\n    pass\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom statsmodels.tsa.seasonal import seasonal_decompose\n\ndef task_func(df, freq='D', decomposition_model='multiplicative'):\n    # Input Validation\n    if not isinstance(df, pd.DataFrame):\n        raise ValueError(\"Input 'df' must be a pandas DataFrame.\")\n    if 'value' not in df.columns:\n        raise ValueError(\"DataFrame must contain a column named 'value'.\")\n    if not pd.api.types.is_numeric_dtype(df['value']):\n        raise ValueError(\"Column 'value' must contain numeric data types.\")\n    \n    # Frequency Validation\n    if not isinstance(freq, str) or freq not in ['D', 'W', 'M', 'Q', 'A']:\n        raise ValueError(\"Frequency 'freq' must be a valid frequency string (e.g., 'D', 'W', 'M').\")\n    \n    # Decomposition Model Validation\n    if decomposition_model not in ['additive', 'multiplicative']:\n        raise ValueError(\"Parameter 'decomposition_model' must be 'additive' or 'multiplicative'.\")\n\n    # Check if the index is a datetime index\n    if not pd.api.types.is_datetime64_any_dtype(df.index):\n        raise ValueError(\"DataFrame index must be a datetime type for time series decomposition.\")\n\n    # Perform Decomposition\n    decomposition = seasonal_decompose(df['value'], model=decomposition_model, period=pd.infer_freq(df.index))\n\n    # Visualization\n    fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(10, 8), sharex=True)\n    decomposition.trend.plot(ax=axes[0], title='Trend')\n    decomposition.seasonal.plot(ax=axes[1], title='Seasonality')\n    decomposition.resid.plot(ax=axes[2], title='Residuals')\n    plt.tight_layout()\n\n    # Return results\n    return decomposition, axes",
        "status": "fail",
        "details": {
          "test_return_type": "Traceback (most recent call last):\n  File \"__test__.py\", line 58, in test_return_type\n  File \"__test__.py\", line 30, in task_func\nValueError: DataFrame index must be a datetime type for time series decomposition.\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n  File \"__test__.py\", line 61, in test_return_type\nAssertionError: Unexpected ValueError raised: DataFrame index must be a datetime type for time series decomposition.\n",
          "test_additive_model": "Traceback (most recent call last):\n  File \"__test__.py\", line 97, in test_additive_model\n  File \"__test__.py\", line 30, in task_func\nValueError: DataFrame index must be a datetime type for time series decomposition.\n",
          "test_component_shapes": "Traceback (most recent call last):\n  File \"__test__.py\", line 91, in test_component_shapes\n  File \"__test__.py\", line 30, in task_func\nValueError: DataFrame index must be a datetime type for time series decomposition.\n",
          "test_components_existence": "Traceback (most recent call last):\n  File \"__test__.py\", line 85, in test_components_existence\n  File \"__test__.py\", line 30, in task_func\nValueError: DataFrame index must be a datetime type for time series decomposition.\n"
        }
      }
    ],
    "BigCodeBench/177": [
      {
        "task_id": "BigCodeBench/177",
        "solution": "import re\nimport nltk\nfrom string import punctuation\ndef task_func(df):\n\n    pass\nimport re\nimport pandas as pd\nfrom string import punctuation\nfrom collections import Counter\n\ndef task_func(df):\n    # Step 1: Input Validation\n    if df.empty:\n        raise ValueError(\"The DataFrame is empty.\")\n    if not all(col in df.columns for col in ['Title', 'Content']):\n        raise ValueError(\"The DataFrame must contain 'Title' and 'Content' columns.\")\n\n    # Step 2: Filtering Articles\n    keywords = ['like', 'what']\n    filtered_articles = df[df['Title'].str.contains('|'.join(keywords), case=False, na=False)]\n\n    # Step 3: Cleaning Up Article Content\n    content_combined = ' '.join(filtered_articles['Content'].tolist())\n    content_cleaned = re.sub(rf'[{punctuation}]', '', content_combined).lower()\n\n    # Step 4: Word Frequency Analysis\n    words = content_cleaned.split()\n    frequency_dict = Counter(words)\n\n    # Step 5: Output the Results\n    return dict(frequency_dict)",
        "status": "fail",
        "details": {
          "test_case_sensitive_handling": "Traceback (most recent call last):\n  File \"__test__.py\", line 100, in test_case_sensitive_handling\nAssertionError: {'technological': 1, 'growth': 1, 'is': 1, [85 chars]': 1} != {'Technological': 1, 'growth': 1, 'is': 1, [85 chars]': 1}\n+ {'Technological': 1,\n+  'These': 1,\n- {'advancements': 1,\n? ^\n\n+  'advancements': 1,\n? ^\n\n   'are': 1,\n   'exponential': 1,\n   'growth': 1,\n   'is': 1,\n   'like': 1,\n   'no': 1,\n-  'other': 1,\n?            ^\n\n+  'other': 1}\n?            ^\n\n-  'technological': 1,\n-  'these': 1} : Case sensitivity handling is faulty.\n",
          "test_word_frequencies": "Traceback (most recent call last):\n  File \"__test__.py\", line 63, in test_word_frequencies\nAssertionError: {'data': 2, 'science': 1, 'is': 2, 'about':[132 chars]': 1} != {'Data': 1, 'Science': 1, 'is': 2, 'about':[154 chars]': 1}\n+ {'Data': 1,\n+  'Like': 1,\n+  'Python': 1,\n+  'Science': 1,\n+  'What': 1,\n- {'about': 1,\n? ^\n\n+  'about': 1,\n? ^\n\n   'among': 1,\n   'analysis': 1,\n-  'data': 2,\n?          ^\n\n+  'data': 1,\n?          ^\n\n   'developers': 1,\n   'do': 1,\n   'is': 2,\n-  'like': 1,\n   'popular': 1,\n-  'python': 1,\n-  'science': 1,\n   'see': 1,\n   'think': 1,\n-  'what': 2,\n?          ^\n\n+  'what': 1,\n?          ^\n\n   'you': 2} : The word frequencies do not match the expected output.\n",
          "test_empty_dataframe": "Traceback (most recent call last):\n  File \"__test__.py\", line 82, in test_empty_dataframe\n  File \"__test__.py\", line 15, in task_func\nValueError: The DataFrame is empty.\n"
        }
      }
    ],
    "BigCodeBench/227": [
      {
        "task_id": "BigCodeBench/227",
        "solution": "import numpy as np\nimport os\nimport soundfile as sf\nimport librosa\nimport matplotlib.pyplot as plt\ndef task_func(L, M, N, audio_file):\n\n    pass\nimport numpy as np\nimport os\nimport soundfile as sf\nimport matplotlib.pyplot as plt\n\ndef task_func(L, M, N, audio_file):\n    # Step 1: Create the MxN matrix from the list L\n    if len(L) != M * N:\n        raise ValueError(\"List length does not match MxN dimensions.\")\n    \n    matrix = np.array(L).reshape(M, N)\n    \n    # Step 2: Read audio file and compute SPL\n    if not os.path.exists(audio_file):\n        raise FileNotFoundError(f\"The audio file '{audio_file}' does not exist.\")\n    \n    data, _ = sf.read(audio_file)\n    \n    # If stereo, take just one channel for SPL calculation\n    if len(data.shape) > 1:\n        data = data[:, 0]  # Use the first channel\n    \n    SPL = 20 * np.log10(np.sqrt(np.mean(data**2))) if np.mean(data**2) > 0 else -np.inf\n    \n    # Step 3: Normalize the matrix based on SPL\n    matrix_max = np.max(np.abs(matrix))\n    normalized_matrix = (matrix / matrix_max) * (10 ** (SPL / 20)) if matrix_max != 0 else matrix\n    \n    # Step 4: Generate the spectrogram\n    plt.figure(figsize=(10, 4))\n    plt.imshow(normalized_matrix, aspect='auto', cmap='hot', origin='lower', \n               extent=[0, N, 0, M])\n    \n    plt.colorbar(label='Amplitude (scaled by SPL)')\n    plt.title('Spectrogram')\n    plt.xlabel('Time (frames)')\n    plt.ylabel('Frequency (bins)')\n    plt.yscale('log')\n    \n    # Step 5: Return the outputs\n    return normalized_matrix, plt.gcf()",
        "status": "fail",
        "details": {
          "test_docstring_examples": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 83, in test_docstring_examples\n  File \"__test__.py\", line 23, in task_func\nFileNotFoundError: The audio file 'audio.wav' does not exist.\n",
          "test_spl_calculation": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 93, in test_spl_calculation\n  File \"__test__.py\", line 23, in task_func\nFileNotFoundError: The audio file 'audio.wav' does not exist.\n",
          "test_successful_matrix_creation": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 71, in test_successful_matrix_creation\n  File \"__test__.py\", line 23, in task_func\nFileNotFoundError: The audio file 'audio.wav' does not exist.\n"
        }
      }
    ],
    "BigCodeBench/82": [
      {
        "task_id": "BigCodeBench/82",
        "solution": "from flask import Flask, render_template, redirect, url_for\nfrom flask_login import LoginManager, UserMixin, login_user, login_required, logout_user, current_user\nfrom flask_wtf import FlaskForm\nfrom wtforms import StringField, PasswordField, SubmitField\nfrom wtforms.validators import DataRequired, Length\nfrom werkzeug.security import generate_password_hash, check_password_hash\nclass LoginForm(FlaskForm):\n    username = StringField('Username', validators=[DataRequired(), Length(min=4, max=25)])\n    password = PasswordField('Password', validators=[DataRequired(), Length(min=8, max=80)])\n    submit = SubmitField('Log In')\nlogin_manager = LoginManager()\ndef task_func(secret_key, template_folder):\n\n    pass\nfrom flask import Flask, render_template, redirect, url_for, flash\nfrom flask_login import LoginManager, UserMixin, login_user, login_required, logout_user, current_user\nfrom flask_wtf import FlaskForm\nfrom wtforms import StringField, PasswordField, SubmitField\nfrom wtforms.validators import DataRequired, Length\nfrom werkzeug.security import generate_password_hash, check_password_hash\n\n# Flask application setup\ndef task_func(secret_key, template_folder):\n    app = Flask(__name__, template_folder=template_folder)\n    app.secret_key = secret_key\n    \n    # User model\n    class User(UserMixin):\n        def __init__(self, id, username, password):\n            self.id = id\n            self.username = username\n            self.password_hash = generate_password_hash(password)\n        \n        def verify_password(self, password):\n            return check_password_hash(self.password_hash, password)\n\n    users = {\n        'user1': User('1', 'user1', 'password123'),  # Example user\n    }  # Simple in-memory user storage\n\n    # Flask-WTF login form\n    class LoginForm(FlaskForm):\n        username = StringField('Username', validators=[DataRequired(), Length(min=4, max=25)])\n        password = PasswordField('Password', validators=[DataRequired(), Length(min=8, max=80)])\n        submit = SubmitField('Log In')\n\n    # Initialize Flask-Login\n    login_manager = LoginManager()\n    login_manager.init_app(app)\n    login_manager.login_view = \"login\"\n\n    @login_manager.user_loader\n    def load_user(user_id):\n        return users.get(user_id)\n\n    # Routes\n    @app.route('/login', methods=['GET', 'POST'])\n    def login():\n        form = LoginForm()\n        if form.validate_on_submit():\n            user = users.get(form.username.data)\n            if user and user.verify_password(form.password.data):\n                login_user(user)\n                return redirect(url_for('protected'))\n            else:\n                flash('Invalid username or password')  # Adding flash message for errors\n        return render_template('login.html', form=form)\n    \n    @app.route('/logout')\n    def logout():\n        logout_user()\n        return redirect(url_for('login'))\n\n    @app.route('/protected')\n    @login_required\n    def protected():\n        return f'Hello, {current_user.username}'\n\n    return app",
        "status": "fail",
        "details": {
          "test_logout_route_redirects_to_login": "Traceback (most recent call last):\n  File \"__test__.py\", line 163, in test_logout_route_redirects_to_login\nAssertionError: 'Logged in as: testuser' not found in \"Hello, <MagicMock name='_get_user().username' id='131500896354176'>\"\n"
        }
      }
    ],
    "BigCodeBench/184": [
      {
        "task_id": "BigCodeBench/184",
        "solution": "import pandas as pd\nimport re\nfrom sklearn.feature_extraction.text import CountVectorizer\n# Constants\nSTOPWORDS = ['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', 'your', 'yours', 'yourself',\n             'yourselves', 'he', 'him', 'his', 'himself', 'she', 'her', 'hers', 'herself', 'it', 'its', 'itself',\n             'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', 'these',\n             'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do',\n             'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while',\n             'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before',\n             'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again',\n             'further', 'then', 'once']\ndef task_func(dataframe, text_column):\n\n    pass\nimport pandas as pd  \nimport re  \nfrom sklearn.feature_extraction.text import CountVectorizer  \n  \n# Constants  \nSTOPWORDS = ['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', 'your', 'yours', 'yourself',  \n             'yourselves', 'he', 'him', 'his', 'himself', 'she', 'her', 'hers', 'herself', 'it', 'its', 'itself',  \n             'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', 'these',  \n             'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do',  \n             'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while',  \n             'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before',  \n             'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again',  \n             'further', 'then', 'once']  \n  \ndef task_func(dataframe, text_column):  \n    # Step 1: Extract text data  \n    text_data = dataframe[text_column].astype(str).fillna('')  # Ensure that all data is string and fill NaN  \n  \n    # Step 2: Text preprocessing  \n    def preprocess_text(text):  \n        text = text.lower()  # Convert to lowercase  \n        text = re.sub(r'\\d+', '', text)  # Remove numbers  \n        text = re.sub(r'[^\\w\\s]', '', text)  # Remove punctuation  \n        text = ' '.join([word for word in text.split() if word not in STOPWORDS])  # Remove stopwords  \n        return text  \n  \n    cleaned_data = text_data.apply(preprocess_text)  \n  \n    # Step 3: Vectorization  \n    vectorizer = CountVectorizer()  \n    word_count_matrix = vectorizer.fit_transform(cleaned_data)  \n  \n    # Step 4: Convert to DataFrame  \n    word_count_df = pd.DataFrame(word_count_matrix.toarray(), columns=vectorizer.get_feature_names_out())  \n  \n    # Step 5: Return the DataFrame  \n    return word_count_df",
        "status": "pass",
        "details": {}
      }
    ],
    "BigCodeBench/267": [
      {
        "task_id": "BigCodeBench/267",
        "solution": "import numpy as np\nfrom scipy import fftpack\nimport matplotlib.pyplot as plt\ndef task_func(data, sample_rate=8000):\n\n    pass\nimport numpy as np\nfrom scipy import fftpack\nimport matplotlib.pyplot as plt\n\ndef task_func(data, sample_rate=8000):\n    # Step 1: Add a new key \"a\" with the value 1 to the dictionary\n    data['a'] = 1\n    \n    # Step 2: Generate a signal based on values in the data\n    # Assuming the signal length is equal to the number of values in data\n    signal_length = len(data)\n    signal = np.array(list(data.values()[:signal_length]))\n    \n    # Step 3: Run Fast Fourier Transform on the signal\n    fft_result = fftpack.fft(signal)\n    \n    # Step 4: Plot the FFT result\n    plt.figure()\n    plt.plot(np.abs(fft_result))\n    plt.title('FFT of the signal')\n    plt.xlabel('Frequency Bin')\n    plt.ylabel('Magnitude')\n    plt.grid()\n    \n    # Step 5: Return the FFT results as a tuple\n    return fft_result, plt.gca()  # plt.gca() gets the current Axes",
        "status": "fail",
        "details": {
          "test_case_1": "Traceback (most recent call last):\n  File \"__test__.py\", line 38, in test_case_1\n  File \"__test__.py\", line 18, in task_func\nTypeError: 'dict_values' object is not subscriptable\n",
          "test_case_2": "Traceback (most recent call last):\n  File \"__test__.py\", line 52, in test_case_2\n  File \"__test__.py\", line 18, in task_func\nTypeError: 'dict_values' object is not subscriptable\n",
          "test_case_3": "Traceback (most recent call last):\n  File \"__test__.py\", line 66, in test_case_3\n  File \"__test__.py\", line 18, in task_func\nTypeError: 'dict_values' object is not subscriptable\n",
          "test_case_4": "Traceback (most recent call last):\n  File \"__test__.py\", line 81, in test_case_4\n  File \"__test__.py\", line 18, in task_func\nTypeError: 'dict_values' object is not subscriptable\n",
          "test_case_5": "Traceback (most recent call last):\n  File \"__test__.py\", line 96, in test_case_5\n  File \"__test__.py\", line 18, in task_func\nTypeError: 'dict_values' object is not subscriptable\n"
        }
      }
    ],
    "BigCodeBench/239": [
      {
        "task_id": "BigCodeBench/239",
        "solution": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy import stats\ndef task_func(original):\n\n    pass\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy import stats\n\ndef task_func(original):\n    # Step 1: Extract numeric values from the list of tuples\n    extracted_values = []\n    for tup in original:\n        for value in tup:\n            if isinstance(value, (int, float)):  # Only consider numeric types\n                extracted_values.append(value)\n    \n    extracted_array = np.array(extracted_values)\n    \n    # Handle case with no extracted values\n    if extracted_array.size == 0:\n        return extracted_array, {'mean': None, 'std_dev': None, 'min': None, 'max': None}, None\n\n    # Step 2: Compute basic statistics\n    stats_dict = {\n        'mean': np.mean(extracted_array),\n        'std_dev': np.std(extracted_array),\n        'min': np.min(extracted_array),\n        'max': np.max(extracted_array)\n    }\n\n    # Step 3: Generate histogram and PDF\n    fig, ax = plt.subplots()\n    ax.hist(extracted_array, bins='auto', density=True, alpha=0.6, color='g')\n\n    # Overlay the PDF\n    xmin, xmax = ax.get_xlim()\n    x = np.linspace(xmin, xmax, 100)\n    p = stats.gaussian_kde(extracted_array)\n    ax.plot(x, p(x), 'k', linewidth=2)\n\n    # Step 4: Return results\n    return extracted_array, stats_dict, ax",
        "status": "fail",
        "details": {
          "test_case_1": "Traceback (most recent call last):\n  File \"__test__.py\", line 53, in test_case_1\nAssertionError: {'mean': 2.5, 'std_dev': 1.118033988749895, 'min': 1, 'max': 4} != {'mean': 2.5, 'std': 1.118033988749895, 'min': 1, 'max': 4}\n- {'max': 4, 'mean': 2.5, 'min': 1, 'std_dev': 1.118033988749895}\n?                                       ----\n\n+ {'max': 4, 'mean': 2.5, 'min': 1, 'std': 1.118033988749895}\n",
          "test_case_2": "Traceback (most recent call last):\n  File \"__test__.py\", line 60, in test_case_2\nAssertionError: {'mean': 15.0, 'std_dev': 5.0, 'min': 10, 'max': 20} != {'mean': 15.0, 'std': 5.0, 'min': 10, 'max': 20}\n- {'max': 20, 'mean': 15.0, 'min': 10, 'std_dev': 5.0}\n?                                          ----\n\n+ {'max': 20, 'mean': 15.0, 'min': 10, 'std': 5.0}\n",
          "test_case_3": "Traceback (most recent call last):\n  File \"__test__.py\", line 67, in test_case_3\nAssertionError: {'mean': -10.0, 'std_dev': 4.08248290463863, 'min': -15, 'max': -5} != {'mean': -10.0, 'std': 4.08248290463863, 'min': -15, 'max': -5}\n- {'max': -5, 'mean': -10.0, 'min': -15, 'std_dev': 4.08248290463863}\n?                                            ----\n\n+ {'max': -5, 'mean': -10.0, 'min': -15, 'std': 4.08248290463863}\n",
          "test_case_5": "Traceback (most recent call last):\n  File \"__test__.py\", line 81, in test_case_5\nAssertionError: {'mean': 6.5, 'std_dev': 0.816496580927726, 'min': 5.5, 'max': 7.5} != {'mean': 6.5, 'std': 0.816496580927726, 'min': 5.5, 'max': 7.5}\n- {'max': 7.5, 'mean': 6.5, 'min': 5.5, 'std_dev': 0.816496580927726}\n?                                           ----\n\n+ {'max': 7.5, 'mean': 6.5, 'min': 5.5, 'std': 0.816496580927726}\n",
          "test_case_4": "Traceback (most recent call last):\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/scipy/stats/_kde.py\", line 223, in __init__\n    self.set_bandwidth(bw_method=bw_method)\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/scipy/stats/_kde.py\", line 571, in set_bandwidth\n    self._compute_covariance()\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/scipy/stats/_kde.py\", line 583, in _compute_covariance\n    self._data_cho_cov = linalg.cholesky(self._data_covariance,\n                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/scipy/linalg/_decomp_cholesky.py\", line 90, in cholesky\n    c, lower = _cholesky(a, lower=lower, overwrite_a=overwrite_a, clean=True,\n               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/scipy/linalg/_decomp_cholesky.py\", line 38, in _cholesky\n    raise LinAlgError(\"%d-th leading minor of the array is not positive \"\nnumpy.linalg.LinAlgError: 1-th leading minor of the array is not positive definite\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File \"__test__.py\", line 71, in test_case_4\n  File \"__test__.py\", line 40, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/scipy/stats/_kde.py\", line 232, in __init__\n    raise linalg.LinAlgError(msg) from e\nnumpy.linalg.LinAlgError: The data appears to lie in a lower-dimensional subspace of the space in which it is expressed. This has resulted in a singular data covariance matrix, which cannot be treated using the algorithms implemented in `gaussian_kde`. Consider performing principle component analysis / dimensionality reduction and using `gaussian_kde` with the transformed data.\n"
        }
      }
    ],
    "BigCodeBench/274": [
      {
        "task_id": "BigCodeBench/274",
        "solution": "import cgi\nimport http.server\nimport smtplib\nfrom email.mime.text import MIMEText\nimport json\ndef task_func(smtp_server, smtp_port, smtp_username, smtp_password):\n\n    pass\nimport cgi\nimport http.server\nimport smtplib\nfrom email.mime.text import MIMEText\nimport json\n\nclass EmailHandler(http.server.BaseHTTPRequestHandler):\n    def do_POST(self):\n        # Step 1: Get the content length and read the request body\n        content_length = int(self.headers['Content-Length'])\n        post_data = self.rfile.read(content_length)\n\n        try:\n            # Step 2: Parse JSON data\n            email_data = json.loads(post_data)\n        except json.JSONDecodeError:\n            self.send_response(400)\n            self.end_headers()\n            self.wfile.write(b'Invalid JSON data.')\n            return\n\n        # Step 3: Validate required fields\n        try:\n            subject = email_data['subject']\n            message = email_data['message']\n            to_email = email_data['to']\n        except KeyError:\n            self.send_response(400)\n            self.end_headers()\n            self.wfile.write(b'Missing \"subject\", \"message\", or \"to\" in the request.')\n            return\n\n        # Step 4: Send email\n        try:\n            self.send_email(subject, message, to_email)\n            self.send_response(200)\n            self.end_headers()\n            self.wfile.write(b'Email sent successfully.')\n        except smtplib.SMTPAuthenticationError:\n            self.send_response(535)\n            self.end_headers()\n            self.wfile.write(b'Authentication Failed.')\n        except Exception as e:\n            self.send_response(500)\n            self.end_headers()\n            self.wfile.write(f'Error: {str(e)}'.encode())\n\n    def send_email(self, subject, message, to_email):\n        # SMTP server credentials -- ideally passed as parameters\n        smtp_server = 'smtp.example.com'\n        smtp_port = 587\n        smtp_username = 'your_username'\n        smtp_password = 'your_password'\n\n        msg = MIMEText(message)\n        msg['Subject'] = subject\n        msg['From'] = smtp_username\n        msg['To'] = to_email\n\n        with smtplib.SMTP(smtp_server, smtp_port) as server:\n            server.starttls()\n            server.login(smtp_username, smtp_password)\n            server.sendmail(smtp_username, to_email, msg.as_string())\n\ndef run(server_class=http.server.HTTPServer, handler_class=EmailHandler, port=8000):\n    server_address = ('', port)\n    httpd = server_class(server_address, handler_class)\n    print(f'Starting httpd on port {port}...')\n    httpd.serve_forever()\n\nif __name__ == \"__main__\":\n    run()",
        "status": "fail",
        "details": {
          "test_email_sending_exception": "Traceback (most recent call last):\n  File \"__test__.py\", line 94, in setUp\nTypeError: 'NoneType' object is not callable\n",
          "test_empty_json_data": "Traceback (most recent call last):\n  File \"__test__.py\", line 94, in setUp\nTypeError: 'NoneType' object is not callable\n",
          "test_invalid_content_type": "Traceback (most recent call last):\n  File \"__test__.py\", line 94, in setUp\nTypeError: 'NoneType' object is not callable\n",
          "test_invalid_json_format": "Traceback (most recent call last):\n  File \"__test__.py\", line 94, in setUp\nTypeError: 'NoneType' object is not callable\n",
          "test_missing_key_in_json_data": "Traceback (most recent call last):\n  File \"__test__.py\", line 94, in setUp\nTypeError: 'NoneType' object is not callable\n",
          "test_valid_json_request": "Traceback (most recent call last):\n  File \"__test__.py\", line 94, in setUp\nTypeError: 'NoneType' object is not callable\n"
        }
      }
    ],
    "BigCodeBench/147": [
      {
        "task_id": "BigCodeBench/147",
        "solution": "import socket\nfrom ipaddress import IPv4Network\nfrom threading import Thread\ndef task_func(ip_range, port):\n\n    pass\nimport socket\nfrom ipaddress import IPv4Network\nfrom threading import Thread\n\ndef check_port(ip, port, results):\n    \"\"\"Check if a specified port is open on a given IP address.\"\"\"\n    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:\n        sock.settimeout(1)  # Set a timeout for the connection\n        try:\n            sock.connect((str(ip), port))\n            results[ip] = True  # Port is open\n        except (socket.timeout, ConnectionRefusedError, OSError):\n            results[ip] = False  # Port is closed or host is unreachable\n\ndef task_func(ip_range, port):\n    \"\"\"Scan the specified IP address range for the specified port.\"\"\"\n    network = IPv4Network(ip_range)\n    results = {}\n    threads = []\n\n    for ip in network.hosts():  # Iterate through each IP in the network\n        thread = Thread(target=check_port, args=(ip, port, results))\n        threads.append(thread)\n        thread.start()\n\n    for thread in threads:  # Wait for all threads to finish\n        thread.join()\n\n    return results",
        "status": "fail",
        "details": {
          "test_all_ips_checked": "Traceback (most recent call last):\n  File \"__test__.py\", line 65, in test_all_ips_checked\nAssertionError: Items in the first set but not the second:\nIPv4Address('192.168.0.1')\nIPv4Address('192.168.0.2')\nItems in the second set but not the first:\n'192.168.0.3'\n'192.168.0.1'\n'192.168.0.0'\n'192.168.0.2' : All IPs in the range should be checked.\n",
          "test_closed_port": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 58, in test_closed_port\nAssertionError: False is not true : All ports should be closed for the test range.\n",
          "test_return_value_structure": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 75, in test_return_value_structure\nAssertionError: IPv4Address('192.168.0.1') is not an instance of <class 'str'> : All keys should be strings representing IP addresses.\n"
        }
      }
    ],
    "BigCodeBench/273": [
      {
        "task_id": "BigCodeBench/273",
        "solution": "import cgi\nimport http.server\nimport json\nSUCCESS_RESPONSE = {\n    'status': 'success',\n    'message': 'Data received successfully.'\n}\nERROR_RESPONSE = {\n    'status': 'error',\n    'message': 'Invalid data received.'\n}\ndef task_func():\n\n    pass\nimport cgi  \nimport http.server  \nimport json  \n\nSUCCESS_RESPONSE = {  \n    'status': 'success',  \n    'message': 'Data received successfully.'  \n}  \n\nERROR_RESPONSE = {  \n    'status': 'error',  \n    'message': 'Invalid data received.'  \n}  \n\nclass RequestHandler(http.server.BaseHTTPRequestHandler):  \n    def do_POST(self):  \n        # Step 3: Validate Content-Type  \n        if self.headers['Content-Type'] != 'application/json':  \n            self.send_response(400)  \n            self.send_header('Content-Type', 'application/json')  \n            self.send_header('Content-Length', str(len(ERROR_RESPONSE['message'])) + len(\"Content-Type header is not application/json\"))  \n            self.end_headers()  \n            self.wfile.write(json.dumps({'message': \"Content-Type header is not application/json\"}).encode())  \n            return  \n\n        # Step 4: Read and parse the JSON body  \n        content_length = int(self.headers['Content-Length'])  # Length of data  \n        post_data = self.rfile.read(content_length)  # Read the data  \n        try:  \n            json_data = json.loads(post_data)  # Try to parse JSON  \n        except json.JSONDecodeError:  \n            self.send_response(400)  \n            self.send_header('Content-Type', 'application/json')  \n            self.end_headers()  \n            self.wfile.write(json.dumps({'message': \"Invalid JSON\"}).encode())  \n            return  \n\n        # Step 5: Validate the 'data' key  \n        if 'data' not in json_data:  \n            self.send_response(400)  \n            self.send_header('Content-Type', 'application/json')  \n            self.end_headers()  \n            self.wfile.write(json.dumps({'message': \"No data key in request\"}).encode())  \n            return  \n\n        # Step 6: Send a success response  \n        self.send_response(200)  \n        self.send_header('Content-Type', 'application/json')  \n        self.end_headers()  \n        self.wfile.write(json.dumps(SUCCESS_RESPONSE).encode())  \n\ndef run(server_class=http.server.HTTPServer, handler_class=RequestHandler):  \n    server_address = ('', 8000)  # Listen on all interfaces  \n    httpd = server_class(server_address, handler_class)  \n    print('Starting httpd...')  \n    httpd.serve_forever()  \n\nif __name__ == \"__main__\":  \n    run()",
        "status": "fail",
        "details": {
          "test_class_properties": "Traceback (most recent call last):\n  File \"__test__.py\", line 93, in test_class_properties\nAssertionError: False is not true\n",
          "test_invalid_content_type": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 85, in test_invalid_content_type\nTypeError: 'NoneType' object is not callable\n",
          "test_invalid_json": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 117, in test_invalid_json\nTypeError: 'NoneType' object is not callable\n",
          "test_missing_data_key": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 128, in test_missing_data_key\nTypeError: 'NoneType' object is not callable\n",
          "test_valid_json_data": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 100, in test_valid_json_data\nTypeError: 'NoneType' object is not callable\n"
        }
      }
    ],
    "BigCodeBench/326": [
      {
        "task_id": "BigCodeBench/326",
        "solution": "import subprocess\nimport os\nimport sys\nimport glob\ndef task_func(directory_path):\n\n    pass\nimport subprocess\nimport os\nimport glob\n\ndef task_func(directory_path):\n    # Find all .bat files in the specified directory\n    bat_files = glob.glob(os.path.join(directory_path, '*.bat'))\n    results = []\n    \n    for bat_file in bat_files:\n        try:\n            # Execute the batch file and wait for it to complete\n            result = subprocess.run(bat_file, check=True)\n            exit_code = result.returncode\n        except subprocess.CalledProcessError:\n            # If the batch file fails during execution, set exit code to its error code\n            exit_code = result.returncode if 'result' in locals() else None\n        except Exception:\n            # Catch any other exceptions and set exit code to None\n            exit_code = None\n            \n        # Append a tuple of the file name and its exit code\n        results.append((os.path.basename(bat_file), exit_code))\n\n    return results",
        "status": "fail",
        "details": {
          "test_multiple_bat_files_mixed_results": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 73, in test_multiple_bat_files_mixed_results\nAssertionError: Lists differ: [('file1.bat', None), ('file2.bat', None), ('file3.bat', None)] != [('file1.bat', 0), ('file2.bat', 1), ('file3.bat', None)]\n\nFirst differing element 0:\n('file1.bat', None)\n('file1.bat', 0)\n\n- [('file1.bat', None), ('file2.bat', None), ('file3.bat', None)]\n?                ^^^^                 ^^^^\n\n+ [('file1.bat', 0), ('file2.bat', 1), ('file3.bat', None)]\n?                ^                 ^\n\n",
          "test_single_bat_file_failure": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 60, in test_single_bat_file_failure\nAssertionError: Lists differ: [('file1.bat', None)] != [('file1.bat', 1)]\n\nFirst differing element 0:\n('file1.bat', None)\n('file1.bat', 1)\n\n- [('file1.bat', None)]\n?                ^^^^\n\n+ [('file1.bat', 1)]\n?                ^\n\n",
          "test_single_bat_file_success": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 51, in test_single_bat_file_success\nAssertionError: Lists differ: [('file1.bat', None)] != [('file1.bat', 0)]\n\nFirst differing element 0:\n('file1.bat', None)\n('file1.bat', 0)\n\n- [('file1.bat', None)]\n?                ^^^^\n\n+ [('file1.bat', 0)]\n?                ^\n\n"
        }
      }
    ],
    "BigCodeBench/310": [
      {
        "task_id": "BigCodeBench/310",
        "solution": "import os\nimport csv\nimport random\nfrom statistics import mean\n# Constants\nCOLUMNS = ['Name', 'Age', 'Height', 'Weight']\nPEOPLE_COUNT = 100\ndef task_func(filename):\n\n    pass\nimport os\nimport csv\nimport random\nfrom statistics import mean\n\n# Constants\nCOLUMNS = ['Name', 'Age', 'Height', 'Weight']\nPEOPLE_COUNT = 100\n\ndef task_func(filename):\n    # Data Generation\n    names = [f'Person {i+1}' for i in range(PEOPLE_COUNT)]  # Sample names\n    age_list = [random.randint(18, 65) for _ in range(PEOPLE_COUNT)]\n    height_list = [random.randint(150, 200) for _ in range(PEOPLE_COUNT)]\n    weight_list = [random.randint(50, 100) for _ in range(PEOPLE_COUNT)]\n    \n    # CSV File Creation\n    try:\n        with open(filename, 'w', newline='') as csvfile:\n            writer = csv.writer(csvfile)\n            writer.writerow(COLUMNS)\n            \n            for i in range(PEOPLE_COUNT):\n                writer.writerow([names[i], age_list[i], height_list[i], weight_list[i]])\n                \n        # Calculating Averages\n        avg_age = mean(age_list)\n        avg_height = mean(height_list)\n        avg_weight = mean(weight_list)\n\n        # Appending Averages to CSV\n        with open(filename, 'a', newline='') as csvfile:\n            writer = csv.writer(csvfile)\n            writer.writerow(['Average', avg_age, avg_height, avg_weight])\n\n    except Exception as e:\n        print(f\"An error occurred: {e}\")\n        return None\n\n    return os.path.abspath(filename)",
        "status": "pass",
        "details": {}
      }
    ],
    "BigCodeBench/313": [
      {
        "task_id": "BigCodeBench/313",
        "solution": "import re\nimport os\nimport shutil\nfrom datetime import datetime\ndef task_func(directory):\n\n    pass\nimport re\nimport os\nimport shutil\n\ndef task_func(directory):\n    # Step 1: List all files in the directory\n    files = os.listdir(directory)\n    \n    # Step 2: Prepare a dictionary to hold subdirectory and file associations\n    organized_files = {}\n    \n    # Step 3: Process each file\n    for file in files:\n        # Skip if it is a directory\n        if os.path.isdir(os.path.join(directory, file)):\n            continue\n        \n        # Get the base name without extension to extract text\n        base_name = os.path.splitext(file)[0]\n        # Step 4: Find the first non-bracketed text using regex\n        match = re.search(r'([^\\[\\]]+)(?=\\s*\\[|$)', base_name)\n        \n        if match:\n            subdirectory_name = match.group(1).strip()\n            # Step 5: Create a new directory if it doesn't exist\n            subdirectory_path = os.path.join(directory, subdirectory_name)\n            if subdirectory_name not in organized_files:\n                os.makedirs(subdirectory_path, exist_ok=True)\n                organized_files[subdirectory_name] = []\n            \n            # Step 6: Move the file to the respective subdirectory\n            shutil.move(os.path.join(directory, file), os.path.join(subdirectory_path, file))\n            organized_files[subdirectory_name].append(file)\n\n    # Step 7: Return the organized files and the directory path\n    return directory, organized_files",
        "status": "fail",
        "details": {
          "test_case_1": "Traceback (most recent call last):\n  File \"__test__.py\", line 75, in test_case_1\nAssertionError: 'example' not found in {'test_file1': ['test_file1.txt']}\n",
          "test_case_2": "Traceback (most recent call last):\n  File \"__test__.py\", line 85, in test_case_2\nAssertionError: 'example' not found in {'test_file1': ['test_file1.txt'], 'test_file2': ['test_file2.txt'], 'test_file3': ['test_file3.txt']}\n",
          "test_case_4": "Traceback (most recent call last):\n  File \"__test__.py\", line 98, in test_case_4\nAssertionError: {'test_file1': ['test_file1.txt']} != {}\n- {'test_file1': ['test_file1.txt']}\n+ {}\n"
        }
      }
    ],
    "BigCodeBench/287": [
      {
        "task_id": "BigCodeBench/287",
        "solution": "from collections import Counter\nimport os\nimport json\ndef task_func(filename, directory):\n\n    pass\nfrom collections import Counter\nimport os\nimport json\n\ndef task_func(filename, directory):\n    total_words = 0\n    word_counts = {}\n\n    # Step 1: Directory Traversal\n    for file in os.listdir(directory):\n        if file.endswith('.txt'):\n            file_path = os.path.join(directory, file)\n            \n            try:\n                # Step 2: Word Counting\n                with open(file_path, 'r', encoding='utf-8') as f:\n                    content = f.read()\n                    # Splitting on whitespace and handling punctuation\n                    words = content.split()  # More robust methods could be used.\n                    word_count = len(words)\n\n                    # Update total word count\n                    total_words += word_count\n\n                    # Step 3: Data Aggregation\n                    word_counts[file] = word_count\n            except (IOError, UnicodeDecodeError) as e:\n                print(f\"Error reading {file_path}: {e}\")\n\n    # Step 4: JSON Export\n    try:\n        with open(filename, 'w', encoding='utf-8') as json_file:\n            json.dump(word_counts, json_file)\n    except IOError as e:\n        print(f\"Error writing to {filename}: {e}\")\n\n    # Step 5: Return Value\n    return total_words",
        "status": "fail",
        "details": {
          "test_empty_files": "Traceback (most recent call last):\n  File \"__test__.py\", line 94, in test_empty_files\nAssertionError: {'empty_file.txt': 0} != {}\n- {'empty_file.txt': 0}\n+ {}\n",
          "test_files_with_special_characters": "Traceback (most recent call last):\n  File \"__test__.py\", line 107, in test_files_with_special_characters\nAssertionError: {'special_chars.txt': 2} != {'hello-world': 1, 'hello_python': 1}\n- {'special_chars.txt': 2}\n+ {'hello-world': 1, 'hello_python': 1}\n",
          "test_multiple_files": "Traceback (most recent call last):\n  File \"__test__.py\", line 83, in test_multiple_files\nAssertionError: {'first.txt': 2, 'third.txt': 2, 'second.txt': 3} != {'hello': 2, 'world': 2, 'python': 2, 'coding': 1}\n- {'first.txt': 2, 'second.txt': 3, 'third.txt': 2}\n+ {'coding': 1, 'hello': 2, 'python': 2, 'world': 2}\n",
          "test_nested_directories": "Traceback (most recent call last):\n  File \"__test__.py\", line 121, in test_nested_directories\nAssertionError: {'nested_file.txt': 3} != {'hello': 2, 'world': 1}\n- {'nested_file.txt': 3}\n+ {'hello': 2, 'world': 1}\n",
          "test_single_file_few_words": "Traceback (most recent call last):\n  File \"__test__.py\", line 68, in test_single_file_few_words\nAssertionError: {'single_file.txt': 3} != {'hello': 2, 'world': 1}\n- {'single_file.txt': 3}\n+ {'hello': 2, 'world': 1}\n"
        }
      }
    ],
    "BigCodeBench/368": [
      {
        "task_id": "BigCodeBench/368",
        "solution": "import os\nimport shutil\nimport random\ndef task_func(src_dir: str, dest_dir: str, seed:int = 100) -> str:\n\n    pass\nimport os\nimport shutil\nimport random\n\ndef task_func(src_dir: str, dest_dir: str, seed:int = 100) -> str:\n    # Step 1: List all files in the source directory\n    files = [f for f in os.listdir(src_dir) if os.path.isfile(os.path.join(src_dir, f))]\n    \n    # Step 2: Set the seed for randomness\n    random.seed(seed)\n    \n    # Step 3: Randomly select a file\n    if not files:  # Check if there are any files to move\n        raise ValueError(\"No files found in the source directory.\")\n    \n    selected_file = random.choice(files)\n    \n    # Step 4: Move the file to the destination directory\n    shutil.move(os.path.join(src_dir, selected_file), os.path.join(dest_dir, selected_file))\n    \n    # Step 5: Return the name of the moved file\n    return selected_file",
        "status": "fail",
        "details": {
          "test_case_3": "Traceback (most recent call last):\n  File \"__test__.py\", line 80, in test_case_3\n  File \"__test__.py\", line 20, in task_func\nValueError: No files found in the source directory.\n"
        }
      }
    ],
    "BigCodeBench/399": [
      {
        "task_id": "BigCodeBench/399",
        "solution": "import numpy as np\nimport matplotlib.pyplot as plt\nimport math\ndef task_func(frequency, sample_size=10000):\n\n    pass\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef task_func(frequency, sample_size=10000):\n    # Step 1: Input Validation\n    if frequency < 0:\n        raise ValueError(\"Frequency must be non-negative.\")\n    if sample_size <= 0:\n        raise ValueError(\"Sample size must be a positive integer.\")\n    \n    # Step 2: Time Vector Creation\n    t = np.linspace(0, 1 / frequency, sample_size)  # Time vector for one period of the wave\n    \n    # Step 3: Wave Computation\n    y1 = np.sin(2 * np.pi * frequency * t)  # Sine wave\n    y2 = np.cos(2 * np.pi * frequency * t)  # Cosine wave\n    \n    # Step 4: Plotting\n    fig, ax = plt.subplots()\n    ax.plot(t, y1, label='Sine Wave', color='blue')\n    ax.plot(t, y2, label='Cosine Wave', color='orange')\n    \n    # Adding titles and labels\n    ax.set_title('Sine and Cosine Waves')\n    ax.set_xlabel('Time (s)')\n    ax.set_ylabel('Amplitude')\n    ax.legend()\n    \n    # Step 5: Returning Results\n    return fig, ax",
        "status": "fail",
        "details": {
          "test_case_1": "Traceback (most recent call last):\n  File \"__test__.py\", line 43, in test_case_1\nAssertionError: False is not true\n",
          "test_case_2": "Traceback (most recent call last):\n  File \"__test__.py\", line 45, in test_case_2\n  File \"__test__.py\", line 18, in task_func\nZeroDivisionError: division by zero\n"
        }
      }
    ],
    "BigCodeBench/37": [
      {
        "task_id": "BigCodeBench/37",
        "solution": "import pandas as pd\nfrom sklearn.ensemble import RandomForestClassifier\nimport seaborn as sns\nimport matplotlib.pyplot as plt\ndef task_func(df, target_column):\n\n    pass\nfrom sklearn.ensemble import RandomForestClassifier\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\ndef task_func(df, target_column):\n    # Sub-problem 1: Data Preparation\n    X = df.drop(columns=[target_column])\n    y = df[target_column]\n    \n    # Handle missing values (optional, can be customized)\n    X.fillna(method='ffill', inplace=True)  # forward fill for missing values\n\n    # Sub-problem 2: Model Training\n    model = RandomForestClassifier(random_state=42)  # Setting a random state for reproducibility\n    model.fit(X, y)\n    \n    # Sub-problem 3: Feature Importance Extraction\n    feature_importances = model.feature_importances_\n    feature_names = X.columns\n\n    # Sub-problem 4: Bar Plot Visualization\n    # Create a DataFrame for a more convenient handling of feature importances\n    importance_df = pd.DataFrame({\n        'Feature': feature_names,\n        'Importance': feature_importances\n    })\n    importance_df = importance_df.sort_values(by='Importance', ascending=False)\n\n    plt.figure(figsize=(10, 6))\n    ax = sns.barplot(x='Importance', y='Feature', data=importance_df)\n    ax.set_xlabel('Feature Importance Score')\n    ax.set_ylabel('Features')\n    ax.set_title('Visualizing Important Features')\n    \n    # Sub-problem 5: Output results\n    plt.show()  # Show the plot\n    return model, ax  # Return model and Axes object",
        "status": "pass",
        "details": {}
      }
    ],
    "BigCodeBench/367": [
      {
        "task_id": "BigCodeBench/367",
        "solution": "from datetime import datetime\nfrom collections import defaultdict\nimport matplotlib.pyplot as plt\ndef task_func(activities):\n\n    pass\nfrom datetime import datetime\nfrom collections import defaultdict\nimport matplotlib.pyplot as plt\n\ndef task_func(activities):\n    # Input Validation: Check if all elements are datetime objects\n    for activity in activities:\n        if not isinstance(activity, datetime):\n            raise TypeError(\"All activities must be datetime objects.\")\n\n    # Counting Activities by Day\n    activity_count = defaultdict(int)\n    for activity in activities:\n        # Get the day of the week for each activity\n        day_of_week = activity.strftime('%A')  # Convert to day name\n        activity_count[day_of_week] += 1\n\n    # Preparing Data for Plotting\n    days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n    counts = [activity_count[day] for day in days]\n\n    # Creating the Bar Chart\n    fig, ax = plt.subplots()\n    ax.bar(days, counts)\n    ax.set_xlabel('Day of the Week')\n    ax.set_ylabel('Number of Activities')\n    ax.set_title('Weekly Activity')\n\n    # Return the Axes object representing the bar chart\n    return ax",
        "status": "pass",
        "details": {}
      }
    ],
    "BigCodeBench/214": [
      {
        "task_id": "BigCodeBench/214",
        "solution": "import random\nimport numpy as np\nimport cv2\nimport matplotlib.pyplot as plt\ndef task_func(seed=42, image_size=(100, 100, 3), range_low=0, range_high=255):\n\n    pass\nimport random\nimport numpy as np\nimport cv2\nimport matplotlib.pyplot as plt\n\ndef task_func(seed=42, image_size=(100, 100, 3), range_low=0, range_high=255):\n    # Input Validation\n    if range_low >= range_high:\n        raise ValueError(\"range_low must be less than range_high.\")\n    \n    # Set seed for reproducibility\n    np.random.seed(seed)\n    \n    # Random Image Generation\n    image = np.random.randint(range_low, range_high + 1, size=image_size, dtype=np.uint8)\n    \n    # Visualization\n    fig, ax = plt.subplots()\n    ax.imshow(image)\n    ax.axis('off')  # Hide axes\n    plt.show()  # Display the image\n    \n    # Return the axes object and the image array\n    return ax, image",
        "status": "fail",
        "details": {
          "test_image_size_and_type": "Traceback (most recent call last):\n  File \"__test__.py\", line 47, in test_image_size_and_type\nAssertionError: Lists differ: [[[102, 220, 225], [95, 179, 61], [234, 203,[6230 chars]88]]] != [[[57, 12, 140], [125, 114, 71], [52, 44, 21[6305 chars]45]]]\n\nFirst differing element 0:\n[[102, 220, 225], [95, 179, 61], [234, 203,[269 chars]221]]\n[[57, 12, 140], [125, 114, 71], [52, 44, 21[266 chars] 36]]\n\nDiff is 16643 characters long. Set self.maxDiff to None to see it. : DataFrame contents should match the expected output\n"
        }
      }
    ],
    "BigCodeBench/324": [
      {
        "task_id": "BigCodeBench/324",
        "solution": "import subprocess\nimport time\nimport threading\ndef task_func(file_list):\n\n    pass\nimport subprocess\nimport threading\n\ndef run_file(file_name, exit_codes, index):\n    try:\n        # Run the subprocess and capture the exit code\n        result = subprocess.run(file_name, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n        exit_codes[index] = result.returncode  # Store exit code\n    except subprocess.CalledProcessError as e:\n        exit_codes[index] = e.returncode  # Store exit code on error\n\ndef task_func(file_list):\n    threads = []\n    exit_codes = [None] * len(file_list)  # Prepare a list to hold exit codes\n    \n    for index, file_name in enumerate(file_list):\n        # Create a thread for each file execution\n        thread = threading.Thread(target=run_file, args=(file_name, exit_codes, index))\n        threads.append(thread)\n        thread.start()  # Start the thread\n\n    # Wait for all threads to complete\n    for thread in threads:\n        thread.join()\n\n    return exit_codes  # Return the list of exit codes",
        "status": "fail",
        "details": {
          "test_multiple_processes_with_different_exit_codes": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 69, in test_multiple_processes_with_different_exit_codes\nAssertionError: Lists differ: [None, None, None] != [0, 1, None]\n\nFirst differing element 0:\nNone\n0\n\n- [None, None, None]\n+ [0, 1, None]\n",
          "test_valid_directory_and_files": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 56, in test_valid_directory_and_files\nAssertionError: Lists differ: [None, None] != [0, 0]\n\nFirst differing element 0:\nNone\n0\n\n- [None, None]\n+ [0, 0]\n",
          "test_valid_files": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 48, in test_valid_files\nAssertionError: Lists differ: [None, None] != [0, 0]\n\nFirst differing element 0:\nNone\n0\n\n- [None, None]\n+ [0, 0]\n"
        }
      }
    ],
    "BigCodeBench/360": [
      {
        "task_id": "BigCodeBench/360",
        "solution": "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\ndef task_func(file_location, sheet_name):\n\n    pass\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\n\ndef task_func(file_location, sheet_name):\n    # Step 1: Load the Excel file\n    if not os.path.exists(file_location):\n        raise FileNotFoundError(f\"The file {file_location} does not exist.\")\n    \n    try:\n        df = pd.read_excel(file_location, sheet_name=sheet_name)\n    except ValueError:\n        raise ValueError(f\"The sheet {sheet_name} does not exist in the Excel file.\")\n\n    # Step 2: Filter numeric columns and calculate mean and standard deviation\n    df_numeric = df.select_dtypes(include=[np.number])\n    means = df_numeric.mean().to_dict()\n    stds = df_numeric.std().to_dict()\n    statistics = {column: {\"mean\": means[column], \"std\": stds[column]} for column in means}\n\n    # Step 3: Visualize the results\n    columns = list(statistics.keys())\n    mean_values = [statistics[column][\"mean\"] for column in columns]\n    std_values = [statistics[column][\"std\"] for column in columns]\n\n    x = np.arange(len(columns))  # the label locations\n    width = 0.35  # the width of the bars\n\n    fig, ax = plt.subplots()\n    ax.bar(x - width/2, mean_values, width, label='Mean', color='blue')\n    ax.bar(x + width/2, std_values, width, label='Standard Deviation', color='orange')\n\n    # Add some text for labels, title and custom x-axis tick labels, etc.\n    ax.set_ylabel('Values')\n    ax.set_title('Mean and Standard Deviation')\n    ax.set_xticks(x)\n    ax.set_xticklabels(columns)\n    ax.legend()\n\n    # Step 4: Return results\n    return statistics, fig",
        "status": "fail",
        "details": {
          "test_bar_chart_labels": "Traceback (most recent call last):\n  File \"__test__.py\", line 95, in test_bar_chart_labels\nAssertionError: '' != 'Columns'\n+ Columns\n\n",
          "test_correct_mean_and_std_values": "Traceback (most recent call last):\n  File \"__test__.py\", line 91, in test_correct_mean_and_std_values\nAssertionError: {'A':[19 chars]d': 14.142135623730951}, 'B': {'mean': 30.0, '[21 chars]951}} != {'A':[19 chars]d': 10.0}, 'B': {'mean': 30.0, 'std': 10.0}}\n+ {'A': {'mean': 20.0, 'std': 10.0}, 'B': {'mean': 30.0, 'std': 10.0}}\n- {'A': {'mean': 20.0, 'std': 14.142135623730951},\n-  'B': {'mean': 30.0, 'std': 14.142135623730951}}\n",
          "test_value": "Traceback (most recent call last):\n  File \"__test__.py\", line 101, in test_value\nAssertionError: {'A':[19 chars]d': 10.0}, 'B': {'mean': 30.0, 'std': 10.0}} != {'A':[19 chars]d': 14.142135623730951}, 'B': {'mean': 30.0, '[21 chars]951}}\n- {'A': {'mean': 20.0, 'std': 10.0}, 'B': {'mean': 30.0, 'std': 10.0}}\n+ {'A': {'mean': 20.0, 'std': 14.142135623730951},\n+  'B': {'mean': 30.0, 'std': 14.142135623730951}}\n"
        }
      }
    ],
    "BigCodeBench/374": [
      {
        "task_id": "BigCodeBench/374",
        "solution": "import regex as re\nimport glob\nimport os\nfrom openpyxl import load_workbook\ndef task_func(directory_path='./xlsx_files/'):\n\n    pass\nimport regex as re\nimport glob\nimport os\nfrom openpyxl import load_workbook\n\ndef task_func(directory_path='./xlsx_files/'):\n    # Step 1: Locate Excel Files\n    file_pattern = os.path.join(directory_path, '*.xlsx')\n    excel_files = glob.glob(file_pattern)\n    processed_count = 0\n    \n    for file_path in excel_files:\n        # Step 2: Load Each Workbook\n        workbook = load_workbook(filename=file_path)\n        modified = False\n        \n        # Step 3: Modify Cell Values\n        for sheet in workbook.worksheets:\n            for row in sheet.iter_rows():\n                for cell in row:\n                    if isinstance(cell.value, str):  # Check if cell value is a string\n                        original_value = cell.value\n                        # Prefix double quotes with double backslashes\n                        modified_value = re.sub(r'\"', r'\\\\\\\\\\\\\"', original_value)\n                        if modified_value != original_value:\n                            cell.value = modified_value\n                            modified = True\n        \n        # Step 4: Save Modifications\n        if modified:\n            workbook.save(file_path)\n            processed_count += 1  # Increment the processed count\n    \n    # Step 5: Count Processed Files\n    return processed_count",
        "status": "fail",
        "details": {
          "test_case_1": "Traceback (most recent call last):\n  File \"__test__.py\", line 104, in test_case_1\nAssertionError: 2 != 3\n",
          "test_case_4": "Traceback (most recent call last):\n  File \"__test__.py\", line 130, in test_case_4\nAssertionError: FileNotFoundError not raised\n"
        }
      }
    ],
    "BigCodeBench/454": [
      {
        "task_id": "BigCodeBench/454",
        "solution": "import os\nimport shutil\nimport glob\ndef task_func(src_dir, dest_dir, ext):\n\n    pass\nimport os\nimport shutil\nimport glob\n\ndef task_func(src_dir, dest_dir, ext):\n    # Validate Directories\n    if not os.path.exists(src_dir):  # Check if source directory exists\n        raise FileNotFoundError(f\"The source directory '{src_dir}' does not exist.\")\n    if not os.path.exists(dest_dir):  # Check if destination directory exists\n        raise FileNotFoundError(f\"The destination directory '{dest_dir}' does not exist.\")\n    \n    # Search for Files\n    pattern = os.path.join(src_dir, f'*.{ext}')  # Create search pattern\n    files_to_move = glob.glob(pattern)  # Get list of files with specified extension\n    \n    moved_files = []  # List to track moved files\n    \n    # Move Files\n    for file_path in files_to_move:\n        file_name = os.path.basename(file_path)  # Extract the file name\n        destination_file_path = os.path.join(dest_dir, file_name)  # Construct the destination file path\n        \n        if not os.path.exists(destination_file_path):  # Check if file already exists in destination\n            shutil.move(file_path, destination_file_path)  # Move the file\n            moved_files.append(destination_file_path)  # Add the moved file to the list\n    \n    # Output Result\n    return moved_files  # Return the list of moved files",
        "status": "pass",
        "details": {}
      }
    ],
    "BigCodeBench/418": [
      {
        "task_id": "BigCodeBench/418",
        "solution": "from tensorflow import keras\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import roc_curve, auc\nimport matplotlib.pyplot as plt\ndef task_func(X, Y):\n\n    pass\nfrom tensorflow import keras\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import roc_curve, auc\nimport matplotlib.pyplot as plt\n\ndef task_func(X, Y):\n    # 1. Data Splitting\n    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=42)\n\n    # 2. Model Creation\n    model = keras.Sequential([\n        keras.layers.Dense(10, activation='sigmoid', input_shape=(X_train.shape[1],)),\n        keras.layers.Dense(1, activation='sigmoid')  # Output layer\n    ])\n\n    # 3. Model Compilation and Fitting\n    model.compile(loss='binary_crossentropy', optimizer=keras.optimizers.SGD(learning_rate=0.01), metrics=['accuracy'])\n    model.fit(X_train, Y_train, epochs=100, verbose=0)  # Optionally, you can add validation_data=(X_val, Y_val)\n\n    # 4. ROC Curve Plotting\n    Y_pred = model.predict(X_test).ravel()\n    fpr, tpr, thresholds = roc_curve(Y_test, Y_pred)\n    roc_auc = auc(fpr, tpr)\n\n    plt.figure()\n    plt.plot(fpr, tpr, color='blue', label='ROC curve (area = %0.2f)' % roc_auc)\n    plt.plot([0, 1], [0, 1], color='red', linestyle='--')\n    plt.xlim([0.0, 1.0])\n    plt.ylim([0.0, 1.05])\n    plt.xlabel('False positive rate')\n    plt.ylabel('True positive rate')\n    plt.title('ROC curve')\n    plt.legend(loc='lower right')\n    plt.grid()\n\n    # Return the model and the Axes object for the plot\n    return model, plt.gca()",
        "status": "fail",
        "details": {
          "ALL": "No module named 'tensorflow'"
        }
      }
    ],
    "BigCodeBench/401": [
      {
        "task_id": "BigCodeBench/401",
        "solution": "from flask import Flask\nimport os\nfrom flask_mail import Mail\ndef task_func(app_name):\n\n    pass\nfrom flask import Flask\nimport os\nfrom flask_mail import Mail\n\ndef task_func(app_name):\n    # Step 1: Set up the Flask application\n    app = Flask(app_name)\n\n    # Step 2: Retrieve environment variables or use defaults\n    mail_server = os.getenv('MAIL_SERVER', 'localhost')\n    mail_port = int(os.getenv('MAIL_PORT', 25))\n    mail_use_tls = os.getenv('MAIL_USE_TLS', 'False') == 'True'\n    mail_username = os.getenv('MAIL_USERNAME', None)\n    mail_password = os.getenv('MAIL_PASSWORD', None)\n\n    # Step 3: Configure the Flask application for Flask-Mail\n    app.config['MAIL_SERVER'] = mail_server\n    app.config['MAIL_PORT'] = mail_port\n    app.config['MAIL_USE_TLS'] = mail_use_tls\n    app.config['MAIL_USERNAME'] = mail_username\n    app.config['MAIL_PASSWORD'] = mail_password\n\n    # Step 4: Create and initialize the Flask-Mail instance\n    mail = Mail(app)\n\n    # Step 5: Return the Mail instance and configurations\n    return mail, {\n        'MAIL_SERVER': mail_server,\n        'MAIL_PORT': mail_port,\n        'MAIL_USE_TLS': mail_use_tls,\n        'MAIL_USERNAME': mail_username,\n        'MAIL_PASSWORD': mail_password\n    }",
        "status": "pass",
        "details": {}
      }
    ],
    "BigCodeBench/417": [
      {
        "task_id": "BigCodeBench/417",
        "solution": "from sklearn.model_selection import train_test_split\nimport matplotlib.pyplot as plt\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.optimizers import SGD\ndef task_func(X, Y):\n\n    pass\nfrom sklearn.model_selection import train_test_split  \nimport matplotlib.pyplot as plt  \nfrom keras.models import Sequential  \nfrom keras.layers import Dense  \nfrom keras.optimizers import SGD  \n  \ndef task_func(X, Y, learning_rate=0.01, epochs=100):  \n    # Validate input dimension  \n    if X.shape[1] != 2:  \n        raise ValueError(\"Input dimension must always be 2.\")  \n    \n    # 1. Data Splitting  \n    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25, random_state=42)  \n  \n    # 2. Model Construction  \n    model = Sequential()  \n    model.add(Dense(10, activation='sigmoid', input_dim=2))  # One hidden layer with 10 neurons  \n  \n    # 3. Model Compilation  \n    model.compile(loss='binary_crossentropy', optimizer=SGD(learning_rate=learning_rate), metrics=['accuracy'])  \n  \n    # 4. Model Fitting  \n    history = model.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=epochs, verbose=0)  \n  \n    # 5. Loss Visualization  \n    plt.figure(figsize=(10, 5))  \n    plt.plot(history.history['loss'], label='Train loss')  \n    plt.plot(history.history['val_loss'], label='Test loss')  \n    plt.title('Model loss')  \n    plt.xlabel('Epoch')  \n    plt.ylabel('Loss')  \n    plt.legend()  \n    ax = plt.gca()  # Get current Axes  \n  \n    # 6. Output  \n    return model, ax",
        "status": "fail",
        "details": {
          "ALL": "No module named 'tensorflow'"
        }
      }
    ],
    "BigCodeBench/93": [
      {
        "task_id": "BigCodeBench/93",
        "solution": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.decomposition import PCA\ndef task_func(data, n_components=2):\n\n    pass\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.decomposition import PCA\n\ndef task_func(data, n_components=2):\n    # Step 1: Input Validation\n    if not isinstance(n_components, int) or n_components <= 0:\n        raise ValueError(\"n_components must be a positive integer.\")\n    \n    # Step 2: Data Preparation\n    df = pd.DataFrame(data)\n    \n    # Ensure all data is numeric\n    if not np.issubdtype(df.dtypes, np.number).all():\n        raise ValueError(\"All columns in data must be numeric.\")\n\n    # Step 3: Apply PCA\n    pca = PCA(n_components=n_components)\n    transformed_data = pca.fit_transform(df",
        "status": "fail",
        "details": {
          "ALL": "'(' was never closed (__test__.py, line 27)"
        }
      }
    ],
    "BigCodeBench/92": [
      {
        "task_id": "BigCodeBench/92",
        "solution": "import pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.cluster import KMeans\nfrom matplotlib.collections import PathCollection\ndef task_func(data, n_clusters=3):\n\n    pass\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.cluster import KMeans\nimport numpy as np\n\ndef task_func(data, n_clusters=3):\n    # Input validation\n    if not isinstance(data, pd.DataFrame):\n        raise ValueError(\"Input 'data' must be a pd.DataFrame.\")\n    if not isinstance(n_clusters, int) or n_clusters <= 1:\n        raise ValueError(\"'n_clusters' must be an integer greater than 1.\")\n    if data.shape[1] < 2:\n        raise ValueError(\"Input 'data' must have at least two columns for visualization.\")\n\n    # K-means clustering\n    kmeans = KMeans(n_clusters=n_clusters, random_state=0)\n    cluster_labels = kmeans.fit_predict(data)\n\n    # Plotting the clusters and centroids\n    plt.figure(figsize=(8, 6))\n    scatter = plt.scatter(data.iloc[:, 0], data.iloc[:, 1], c=cluster_labels, cmap='viridis', label='Clusters')\n    plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=300, c='red', label='Centroids', marker='X')\n    plt.title('K-means Clustering')\n    plt.xlabel(data.columns[0])\n    plt.ylabel(data.columns[1])\n    plt.legend()\n\n    # Set ax to return\n    ax = plt.gca()\n\n    return cluster_labels, ax",
        "status": "pass",
        "details": {}
      }
    ],
    "BigCodeBench/409": [
      {
        "task_id": "BigCodeBench/409",
        "solution": "import os\nimport pandas as pd\nimport numpy as np\ndef task_func(excel_file_path, file_name, column_name):\n\n    pass\nimport os\nimport pandas as pd\nimport numpy as np\n\ndef task_func(excel_file_path, file_name, column_name):\n    # Step 1: Check if the file exists\n    full_path = os.path.join(excel_file_path, file_name)\n    if not os.path.exists(full_path):\n        raise FileNotFoundError(\"The specified Excel file does not exist.\")\n    \n    # Step 2: Read the Excel file\n    df = pd.read_excel(full_path)\n\n    # Step 3: Check if the column exists\n    if column_name not in df.columns:\n        raise ValueError(f\"The specified column '{column_name}' was not found in the Excel file.\")\n    \n    # Step 4: Calculate statistics\n    data = df[column_name].dropna()  # Dropping NaN values to avoid errors in calculations\n    mean_value = np.mean(data)\n    median_value = np.median(data)\n    std_dev_value = np.std(data)\n\n    # Step 5: Prepare the output\n    return {\n        'mean': mean_value,\n        'median': median_value,\n        'standard_deviation': std_dev_value\n    }",
        "status": "fail",
        "details": {
          "test_normal_functionality": "Traceback (most recent call last):\n  File \"__test__.py\", line 60, in test_normal_functionality\nKeyError: 'std_dev'\n"
        }
      }
    ],
    "BigCodeBench/445": [
      {
        "task_id": "BigCodeBench/445",
        "solution": "import numpy as np\nfrom scipy.spatial import Voronoi, voronoi_plot_2d\nimport matplotlib.pyplot as plt\ndef task_func(points, seed=0):\n\n    pass\nimport numpy as np\nfrom scipy.spatial import Voronoi, voronoi_plot_2d\nimport matplotlib.pyplot as plt\n\ndef task_func(points, seed=0, apply_jitter=True):\n    # 1. Input validation\n    if not isinstance(points, np.ndarray):\n        raise ValueError(\"Input should be a NumPy array.\")\n    \n    if points.ndim != 2 or points.shape[1] != 2:\n        raise ValueError(\"Input should be a 2D array of shape (n_points, 2).\")\n    \n    # 2. Jittering points\n    if apply_jitter:\n        np.random.seed(seed)\n        jitter = np.random.normal(0, 0.1, points.shape)  # Small jitter\n        jittered_points = points + jitter\n    else:\n        jittered_points = points\n    \n    # 3. Voronoi diagram calculation\n    vor = Voronoi(jittered_points)\n    \n    # 4. Plotting the Voronoi diagram\n    fig, ax = plt.subplots()\n    voronoi_plot_2d(vor, ax=ax, show_vertices=False)\n\n    # 5. Returning output\n    return (vor, ax)",
        "status": "pass",
        "details": {}
      }
    ],
    "BigCodeBench/501": [
      {
        "task_id": "BigCodeBench/501",
        "solution": "import xlwt\nimport os\nimport pandas as pd\ndef task_func(json_str, filename, sheet_name=\"sheet1\"):\n\n    pass\nimport os\nimport json\nimport pandas as pd\n\ndef task_func(json_str, filename, sheet_name=\"sheet1\"):\n    # Input Validation\n    if not isinstance(json_str, (str, bytes, bytearray)):\n        raise TypeError(\"Input should be a string, bytes, or bytearray.\")\n    \n    # Attempt to parse the JSON string\n    try:\n        data = json.loads(json_str)\n    except json.JSONDecodeError:\n        raise ValueError(\"Invalid JSON string provided.\")\n    \n    # Handle Empty JSON Array\n    if isinstance(data, list) and not data:\n        # Create an Excel file with no data rows (truly empty)\n        df = pd.DataFrame()\n    else:\n        # Convert JSON data to a DataFrame\n        df = pd.json_normalize(data)\n    \n    # Excel File Creation\n    try:\n        # Create the directory if it does not exist\n        os.makedirs(os.path.dirname(filename), exist_ok=True)\n        # Write the DataFrame to an Excel file\n        df.to_excel(filename, sheet_name=sheet_name, index=False)\n    except Exception as e:\n        raise Exception(f\"An error occurred while writing the Excel file: {e}\")\n    \n    # Output the Absolute Path\n    return os.path.abspath(filename)",
        "status": "fail",
        "details": {
          "test_custom_sheet_name": "Traceback (most recent call last):\n  File \"__test__.py\", line 33, in task_func\n  File \"<frozen os>\", line 225, in makedirs\nFileNotFoundError: [Errno 2] No such file or directory: ''\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n  File \"__test__.py\", line 66, in test_custom_sheet_name\n  File \"__test__.py\", line 37, in task_func\nException: An error occurred while writing the Excel file: [Errno 2] No such file or directory: ''\n",
          "test_empty_json": "Traceback (most recent call last):\n  File \"__test__.py\", line 33, in task_func\n  File \"<frozen os>\", line 225, in makedirs\nFileNotFoundError: [Errno 2] No such file or directory: ''\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n  File \"__test__.py\", line 54, in test_empty_json\n  File \"__test__.py\", line 37, in task_func\nException: An error occurred while writing the Excel file: [Errno 2] No such file or directory: ''\n",
          "test_file_content": "Traceback (most recent call last):\n  File \"__test__.py\", line 33, in task_func\n  File \"<frozen os>\", line 225, in makedirs\nFileNotFoundError: [Errno 2] No such file or directory: ''\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n  File \"__test__.py\", line 72, in test_file_content\n  File \"__test__.py\", line 37, in task_func\nException: An error occurred while writing the Excel file: [Errno 2] No such file or directory: ''\n",
          "test_valid_json": "Traceback (most recent call last):\n  File \"__test__.py\", line 33, in task_func\n  File \"<frozen os>\", line 225, in makedirs\nFileNotFoundError: [Errno 2] No such file or directory: ''\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n  File \"__test__.py\", line 47, in test_valid_json\n  File \"__test__.py\", line 37, in task_func\nException: An error occurred while writing the Excel file: [Errno 2] No such file or directory: ''\n"
        }
      }
    ],
    "BigCodeBench/302": [
      {
        "task_id": "BigCodeBench/302",
        "solution": "import pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n# Constants\nCOLUMNS = ['Date', 'Value']\ndef task_func(df, plot=False):\n\n    pass\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Constants\nCOLUMNS = ['Date', 'Value']\n\ndef task_func(df, plot=False):\n    # Input Validation\n    if df.empty:\n        raise ValueError(\"Input DataFrame is empty.\")\n        \n    if 'Value' not in df.columns or not all(isinstance(x, list) for x in df['Value']):\n        raise ValueError(\"The 'Value' column must be present and contain only lists.\")\n    \n    # Data Preparation\n    value_df = pd.DataFrame(df['Value'].tolist())\n    \n    # Correlation Calculation\n    correlation_matrix = value_df.corr(method='pearson')\n    \n    # Optional Visualization\n    axes = None\n    if plot:\n        plt.figure(figsize=(10, 8))\n        axes = sns.heatmap(correlation_matrix, annot=True, fmt='.2f', cmap='coolwarm')\n        plt.title(\"Correlation Heatmap\")\n        plt.show()\n    \n    return correlation_matrix, axes\n\n# Example usage (commented out for clarity):\n# df = pd.DataFrame({'Date': ['2023-01-01', '2023-01-02'], 'Value': [[1, 2, 3], [4, 5, 6]]})\n# correlation, ax = task_func(df, plot=True)",
        "status": "fail",
        "details": {
          "test_basic_functionality": "Traceback (most recent call last):\n  File \"__test__.py\", line 53, in test_basic_functionality\nAttributeError: 'tuple' object has no attribute 'equals'\n"
        }
      }
    ],
    "BigCodeBench/492": [
      {
        "task_id": "BigCodeBench/492",
        "solution": "import pandas as pd\nfrom datetime import datetime\nimport random\ndef task_func(\n    epoch_milliseconds,\n    random_seed=0,\n    products=[\"Product1\", \"Product2\", \"Product3\", \"Product4\", \"Product5\"],\n):\n\n    pass\nimport pandas as pd\nfrom datetime import datetime, timedelta\nimport random\n\ndef task_func(\n    epoch_milliseconds,\n    random_seed=0,\n    products=[\"Product1\", \"Product2\", \"Product3\", \"Product4\", \"Product5\"],\n):\n    # Set random seed for reproducibility\n    random.seed(random_seed)\n\n    # Input validation\n    if not isinstance(epoch_milliseconds, int):\n        raise ValueError(\"epoch_milliseconds must be an integer.\")\n    \n    current_time = int(datetime.now().timestamp() * 1000)\n    if epoch_milliseconds >= current_time:\n        raise ValueError(\"epoch_milliseconds must be less than the current time.\")\n\n    # Convert epoch milliseconds to datetime\n    start_date = datetime.fromtimestamp(epoch_milliseconds / 1000)\n    end_date = datetime.now()\n\n    # List to store sales data\n    sales_data = []\n\n    # Iterate through each day in the range\n    current_date = start_date\n    while current_date <= end_date:\n        for product in products:\n            sales_quantity = random.randint(10, 50)\n            sales_data.append({\"Product\": product, \"Date\": current_date, \"Sales\": sales_quantity})\n        current_date += timedelta(days=1)\n\n    # Create a DataFrame from the sales data\n    sales_df = pd.DataFrame(sales_data)\n    return sales_df",
        "status": "fail",
        "details": {
          "test_case_6": "Traceback (most recent call last):\n  File \"__test__.py\", line 104, in test_case_6\nAssertionError: ValueError not raised\n",
          "test_case_7": "Traceback (most recent call last):\n  File \"__test__.py\", line 108, in test_case_7\nAssertionError: ValueError not raised\n",
          "test_case_2": "Traceback (most recent call last):\n  File \"__test__.py\", line 66, in test_case_2\n  File \"__test__.py\", line 25, in task_func\nValueError: epoch_milliseconds must be an integer.\n",
          "test_case_3": "Traceback (most recent call last):\n  File \"__test__.py\", line 78, in test_case_3\n  File \"__test__.py\", line 25, in task_func\nValueError: epoch_milliseconds must be an integer.\n"
        }
      }
    ],
    "BigCodeBench/458": [
      {
        "task_id": "BigCodeBench/458",
        "solution": "import json\nimport re\nimport pandas as pd\ndef task_func(json_str):\n\n    pass\nimport json\nimport re\nimport pandas as pd\n\ndef task_func(json_str):\n    if not json_str:\n        return pd.DataFrame()  # Return empty DataFrame if input is empty\n    \n    try:\n        data = json.loads(json_str)  # Load JSON string into dictionary\n    except json.JSONDecodeError:\n        return pd.DataFrame()  # Return empty DataFrame if JSON is invalid\n    \n    def normalize_value(value):\n        if isinstance(value, (int, float)):\n            return value * 2.0  # Double numerical values\n        elif isinstance(value, list):\n            return [normalize_value(v) for v in value]  # Normalize elements in lists\n        elif isinstance(value, str):  # For strings, check if they can be interpreted as numbers\n            numbers = re.findall(r\"[-+]?\\\\d*\\\\.\\\\d+|[-+]?\\\\d+\", value)  # Extract numbers\n            if numbers:\n                for number in numbers:\n                    value = value.replace(number, str(float(number) * 2))  # Double the number in the string\n            return value  # Return the modified string\n        return value  # Return the value as-is if it doesn't match any conditions\n\n    normalized_data = {k: normalize_value(v) for k, v in data.items()}  # Normalize all values in the dictionary\n    \n    # Create DataFrame from the normalized dictionary\n    return pd.DataFrame.from_dict(normalized_data, orient='index').T",
        "status": "fail",
        "details": {
          "test_case_4": "Traceback (most recent call last):\n  File \"__test__.py\", line 64, in test_case_4\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/_testing/asserters.py\", line 1224, in assert_frame_equal\n    assert_series_equal(\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/_testing/asserters.py\", line 1014, in assert_series_equal\n    _testing.assert_almost_equal(\n  File \"pandas/_libs/testing.pyx\", line 52, in pandas._libs.testing.assert_almost_equal\n  File \"pandas/_libs/testing.pyx\", line 172, in pandas._libs.testing.assert_almost_equal\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/_testing/asserters.py\", line 599, in raise_assert_detail\n    raise AssertionError(msg)\nAssertionError: DataFrame.iloc[:, 0] (column name=\"a\") are different\n\nDataFrame.iloc[:, 0] (column name=\"a\") values are different (100.0 %)\n[index]: [0]\n[left]:  [1]\n[right]: [2]\nAt positional index 0, first diff: 1 != 2\n",
          "test_case_1": "Traceback (most recent call last):\n  File \"__test__.py\", line 45, in test_case_1\n  File \"__test__.py\", line 36, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 1760, in from_dict\n    return cls(data, index=index, columns=columns, dtype=dtype)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 782, in __init__\n    arrays, columns, index = nested_data_to_arrays(\n                             ^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/internals/construction.py\", line 498, in nested_data_to_arrays\n    arrays, columns = to_arrays(data, columns, dtype=dtype)\n                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/internals/construction.py\", line 830, in to_arrays\n    arr = _list_to_arrays(data)\n          ^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/internals/construction.py\", line 851, in _list_to_arrays\n    content = lib.to_object_array(data)\n              ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"pandas/_libs/lib.pyx\", line 2879, in pandas._libs.lib.to_object_array\nTypeError: object of type 'float' has no len()\n",
          "test_case_3": "Traceback (most recent call last):\n  File \"__test__.py\", line 60, in test_case_3\n  File \"__test__.py\", line 36, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 1760, in from_dict\n    return cls(data, index=index, columns=columns, dtype=dtype)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 782, in __init__\n    arrays, columns, index = nested_data_to_arrays(\n                             ^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/internals/construction.py\", line 498, in nested_data_to_arrays\n    arrays, columns = to_arrays(data, columns, dtype=dtype)\n                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/internals/construction.py\", line 830, in to_arrays\n    arr = _list_to_arrays(data)\n          ^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/internals/construction.py\", line 851, in _list_to_arrays\n    content = lib.to_object_array(data)\n              ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"pandas/_libs/lib.pyx\", line 2879, in pandas._libs.lib.to_object_array\nTypeError: object of type 'float' has no len()\n",
          "test_case_5": "Traceback (most recent call last):\n  File \"__test__.py\", line 68, in test_case_5\n  File \"__test__.py\", line 36, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 1760, in from_dict\n    return cls(data, index=index, columns=columns, dtype=dtype)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 782, in __init__\n    arrays, columns, index = nested_data_to_arrays(\n                             ^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/internals/construction.py\", line 498, in nested_data_to_arrays\n    arrays, columns = to_arrays(data, columns, dtype=dtype)\n                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/internals/construction.py\", line 830, in to_arrays\n    arr = _list_to_arrays(data)\n          ^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/internals/construction.py\", line 851, in _list_to_arrays\n    content = lib.to_object_array(data)\n              ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"pandas/_libs/lib.pyx\", line 2879, in pandas._libs.lib.to_object_array\nTypeError: object of type 'float' has no len()\n"
        }
      }
    ],
    "BigCodeBench/503": [
      {
        "task_id": "BigCodeBench/503",
        "solution": "import numpy as np\nimport pandas as pd\nfrom datetime import datetime\ndef task_func(\n    days_in_past=7, stock_names=[\"AAPL\", \"GOOGL\", \"MSFT\", \"AMZN\", \"FB\"], random_seed=0\n):\n\n    pass\nimport numpy as np\nimport pandas as pd\nfrom datetime import datetime, timedelta\n\ndef task_func(days_in_past=7, stock_names=[\"AAPL\", \"GOOGL\", \"MSFT\", \"AMZN\", \"FB\"], random_seed=0):\n    # Check for valid input\n    if days_in_past <= 0:\n        raise ValueError(\"days_in_past must be a positive integer.\")\n    \n    # 1. Input Handling\n    np.random.seed(random_seed)\n    \n    # 2. Date Generation\n    end_date = datetime.now()\n    start_date = end_date - timedelta(days=days_in_past)\n    date_range = [start_date + timedelta(days=i) for i in range(days_in_past)]\n    \n    # 3. Random Data Generation\n    stock_prices = np.random.rand(days_in_past, len(stock_names))\n    \n    # 4. DataFrame Creation\n    df = pd.DataFrame(stock_prices, index=date_range, columns=stock_names)\n    \n    # 5. Return Value\n    return df",
        "status": "fail",
        "details": {
          "test_case_1": "Traceback (most recent call last):\n  File \"__test__.py\", line 51, in test_case_1\nAssertionError: datetime.date(2025, 1, 15) != datetime.date(2025, 1, 16) : Last date should be today's date.\n",
          "test_case_2": "Traceback (most recent call last):\n  File \"__test__.py\", line 67, in test_case_2\nAssertionError: datetime.date(2025, 1, 15) != datetime.date(2025, 1, 16) : Last date should be today's date.\n",
          "test_case_3": "Traceback (most recent call last):\n  File \"__test__.py\", line 83, in test_case_3\nAssertionError: datetime.date(2025, 1, 15) != datetime.date(2025, 1, 16) : Last date should be today's date.\n",
          "test_case_5": "Traceback (most recent call last):\n  File \"__test__.py\", line 102, in test_case_5\nAssertionError: ValueError not raised\n",
          "test_case_6": "Traceback (most recent call last):\n  File \"__test__.py\", line 111, in test_case_6\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/_testing/asserters.py\", line 1171, in assert_frame_equal\n    assert_index_equal(\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/_testing/asserters.py\", line 322, in assert_index_equal\n    _testing.assert_almost_equal(\n  File \"pandas/_libs/testing.pyx\", line 52, in pandas._libs.testing.assert_almost_equal\n  File \"pandas/_libs/testing.pyx\", line 172, in pandas._libs.testing.assert_almost_equal\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/_testing/asserters.py\", line 599, in raise_assert_detail\n    raise AssertionError(msg)\nAssertionError: DataFrame.index are different\n\nDataFrame.index values are different (100.0 %)\n[left]:  DatetimeIndex(['2025-01-09 07:29:04.082596', '2025-01-10 07:29:04.082596',\n               '2025-01-11 07:29:04.082596', '2025-01-12 07:29:04.082596',\n               '2025-01-13 07:29:04.082596', '2025-01-14 07:29:04.082596',\n               '2025-01-15 07:29:04.082596'],\n              dtype='datetime64[ns]', freq=None)\n[right]: DatetimeIndex(['2025-01-09 07:29:04.082747', '2025-01-10 07:29:04.082747',\n               '2025-01-11 07:29:04.082747', '2025-01-12 07:29:04.082747',\n               '2025-01-13 07:29:04.082747', '2025-01-14 07:29:04.082747',\n               '2025-01-15 07:29:04.082747'],\n              dtype='datetime64[ns]', freq=None)\nAt positional index 0, first diff: 2025-01-09T07:29:04.082596000 != 2025-01-09T07:29:04.082747000\n",
          "test_case_4": "Traceback (most recent call last):\n  File \"__test__.py\", line 99, in test_case_4\n  File \"__test__.py\", line 24, in task_func\nTypeError: 'float' object cannot be interpreted as an integer\n"
        }
      }
    ],
    "BigCodeBench/486": [
      {
        "task_id": "BigCodeBench/486",
        "solution": "from datetime import datetime\nimport pandas as pd\nimport numpy as np\ndef task_func(start_time, end_time, step, trend, seed=42):\n\n    pass\nfrom datetime import datetime\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef task_func(start_time, end_time, step, trend, seed=42):\n    # 1. Parse Input Parameters\n    # Ensure inputs are in datetime format and validate step\n    start_dt = pd.to_datetime(start_time)\n    end_dt = pd.to_datetime(end_time)\n    \n    # Validate step\n    if not isinstance(step, (int, float)) or step <= 0:\n        raise ValueError(\"Step must be a positive number.\")\n\n    # 2. Generate Datetime Index\n    time_index = pd.date_range(start=start_dt, end=end_dt, freq=pd.DateOffset(seconds=step))\n\n    # 3. Generate Values from Normal Distribution\n    np.random.seed(seed)  # for reproducibility\n    num_points = len(time_index)\n    values = np.random.normal(loc=0.0, scale=1.0, size=num_points)  # mean=0, std deviation=1\n\n    # 4. Add Linear Trend to Generated Values\n    linear_trend = trend * np.arange(num_points)  # Create a linear trend\n    values_with_trend = values + linear_trend\n\n    # 5. Plotting the Time Series\n    fig, ax = plt.subplots(figsize=(10, 5))  # Set figsize for better readability\n    ax.plot(time_index, values_with_trend, marker='o')  # Plotting with points\n    ax.set_xlabel('Time')\n    ax.set_ylabel('Value')\n    ax.set_title('Time Series with Trend')\n\n    # 6. Return the Axes Object\n    return ax",
        "status": "fail",
        "details": {
          "test_case_6": "Traceback (most recent call last):\n  File \"__test__.py\", line 110, in test_case_6\nAssertionError: Exception not raised\n"
        }
      }
    ],
    "BigCodeBench/511": [
      {
        "task_id": "BigCodeBench/511",
        "solution": "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\ndef task_func(column, data):\n\n    pass\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef task_func(column, data):\n    # Step 1: Input Validation\n    if data.empty:\n        return ({\"sum\": 0, \"mean\": np.nan, \"min\": np.nan, \"max\": np.nan}, None)\n    \n    # Step 2: Statistical Calculations\n    stats = {\n        \"sum\": data[column].sum(),\n        \"mean\": data[column].mean(),\n        \"min\": data[column].min(),\n        \"max\": data[column].max()\n    }\n    \n    # Step 3: Visualization\n    fig, ax = plt.subplots()\n    ax.pie(data[column], labels=data['Age'], autopct='%1.1f%%')\n    ax.axis('equal')  # Equal aspect ratio ensures the pie chart is circular.\n    \n    # Step 4: Output Construction\n    return (stats, ax)",
        "status": "fail",
        "details": {
          "test_case_1": "Traceback (most recent call last):\n  File \"__test__.py\", line 44, in test_case_1\n  File \"__test__.py\", line 13, in task_func\nAttributeError: 'list' object has no attribute 'empty'\n",
          "test_case_2": "Traceback (most recent call last):\n  File \"__test__.py\", line 57, in test_case_2\n  File \"__test__.py\", line 13, in task_func\nAttributeError: 'list' object has no attribute 'empty'\n",
          "test_case_3": "Traceback (most recent call last):\n  File \"__test__.py\", line 68, in test_case_3\n  File \"__test__.py\", line 13, in task_func\nAttributeError: 'list' object has no attribute 'empty'\n",
          "test_case_4": "Traceback (most recent call last):\n  File \"__test__.py\", line 73, in test_case_4\n  File \"__test__.py\", line 13, in task_func\nAttributeError: 'list' object has no attribute 'empty'\n",
          "test_case_5": "Traceback (most recent call last):\n  File \"__test__.py\", line 80, in test_case_5\n  File \"__test__.py\", line 13, in task_func\nAttributeError: 'list' object has no attribute 'empty'\n",
          "test_case_6": "Traceback (most recent call last):\n  File \"__test__.py\", line 90, in test_case_6\n  File \"__test__.py\", line 13, in task_func\nAttributeError: 'list' object has no attribute 'empty'\n",
          "test_case_7": "Traceback (most recent call last):\n  File \"__test__.py\", line 100, in test_case_7\n  File \"__test__.py\", line 13, in task_func\nAttributeError: 'list' object has no attribute 'empty'\n"
        }
      }
    ],
    "BigCodeBench/477": [
      {
        "task_id": "BigCodeBench/477",
        "solution": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\ndef task_func(N=100, CATEGORIES=[\"A\", \"B\", \"C\", \"D\", \"E\"], seed=42):\n\n    pass\nimport numpy as np  \nimport pandas as pd  \nimport matplotlib.pyplot as plt  \n\ndef task_func(N=100, CATEGORIES=[\"A\", \"B\", \"C\", \"D\", \"E\"], seed=42):  \n    # Set the random seed for reproducibility  \n    np.random.seed(seed)  \n    \n    # 1. Generate random values for \"x\" and \"y\"  \n    x = np.random.rand(N)  \n    y = np.random.rand(N)  \n    \n    # 2. Generate \"category\" values  \n    if N >= len(CATEGORIES):  \n        # Ensure all categories appear at least once  \n        categories = np.array(CATEGORIES)  \n        np.random.shuffle(categories)  # Shuffle categories for random selection  \n        categories_assigned = np.tile(categories, N // len(categories) + 1)[:N]  # Repeat and cut to size  \n        np.random.shuffle(categories_assigned)  # Shuffle to randomize the order  \n    else:  \n        # Sample without replacement  \n        categories_assigned = np.random.choice(CATEGORIES, N, replace=False)  \n    \n    # 3. Create the DataFrame  \n    df = pd.DataFrame({  \n        'x': x,  \n        'y': y,  \n        'category': categories_assigned  \n    })  \n    \n    # 4. Create scatter plot  \n    fig, ax = plt.subplots()  \n    scatter = ax.scatter(df['x'], df['y'], c=pd.factorize(df['category'])[0], cmap='viridis')  \n    legend1 = ax.legend(*scatter.legend_elements(), title=\"Categories\")  \n    ax.add_artist(legend1)  \n    ax.set_xlabel('x')  \n    ax.set_ylabel('y')  \n    ax.set_title('Scatter Plot of x vs y Colored by Category')  \n    \n    # 5. Return the DataFrame and the Axes object  \n    return df, ax",
        "status": "fail",
        "details": {
          "test_case_6": "Traceback (most recent call last):\n  File \"__test__.py\", line 89, in test_case_6\n  File \"__test__.py\", line 24, in task_func\nZeroDivisionError: integer division or modulo by zero\n"
        }
      }
    ],
    "BigCodeBench/509": [
      {
        "task_id": "BigCodeBench/509",
        "solution": "import pandas as pd\nimport csv\nfrom difflib import ndiff\ndef task_func(file_path1, file_path2, delimiter=',', quotechar='\"'):\n\n    pass\nimport pandas as pd\n\ndef task_func(file_path1, file_path2, delimiter=',', quotechar='\"'):\n    # Step 1: File Reading and Validation\n    try:\n        with open(file_path1, 'r', encoding='utf-8') as file1, \\\n             open(file_path2, 'r', encoding='utf-8') as file2:\n            lines_file1 = file1.readlines()\n            lines_file2 = file2.readlines()\n        \n        if not lines_file1:\n            raise ValueError(f\"The file {file_path1} is empty.\")\n        if not lines_file2:\n            raise ValueError(f\"The file {file_path2} is empty.\")\n            \n    except FileNotFoundError as e:\n        raise FileNotFoundError(str(e))\n    except Exception as e:\n        raise Exception(f\"An IO error occurred: {str(e)}\")\n\n    # Step 2: Line-by-Line Comparison\n    max_length = max(len(lines_file1), len(lines_file2))\n    differences = []\n    \n    for i in range(max_length):\n        content1 = lines_file1[i].rstrip('\\n') if i < len(lines_file1) else None\n        content2 = lines_file2[i].rstrip('\\n') if i < len(lines_file2) else None\n\n        if content1 == content2:\n            differences.append({'Line Number': i + 1, 'Status': ' ', 'Content': content1})\n        elif content1 is not None and content2 is None:\n            differences.append({'Line Number': i + 1, 'Status': '-', 'Content': content1})\n        elif content1 is None and content2 is not None:\n            differences.append({'Line Number': i + 1, 'Status': '+', 'Content': content2})\n\n    # Step 3: Generating the Difference Report\n    diff_df = pd.DataFrame(differences)\n    \n    return diff_df",
        "status": "fail",
        "details": {
          "test_difference_report": "Traceback (most recent call last):\n  File \"__test__.py\", line 99, in test_difference_report\nAssertionError: Lists differ: ['1, ,name,age', '3, ,Bob,25'] != [\"1, ,('name', 'age')\", \"2,-,('Alice', '30[43 chars]5')\"]\n\nFirst differing element 0:\n'1, ,name,age'\n\"1, ,('name', 'age')\"\n\nSecond list contains 2 additional elements.\nFirst extra element 2:\n\"3,+,('Alice', '31')\"\n\n- ['1, ,name,age', '3, ,Bob,25']\n+ [\"1, ,('name', 'age')\",\n+  \"2,-,('Alice', '30')\",\n+  \"3,+,('Alice', '31')\",\n+  \"4, ,('Bob', '25')\"]\n",
          "test_empty_files": "Traceback (most recent call last):\n  File \"__test__.py\", line 18, in task_func\nValueError: The file empty1.csv is empty.\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n  File \"__test__.py\", line 119, in test_empty_files\n  File \"__test__.py\", line 25, in task_func\nException: An IO error occurred: The file empty1.csv is empty.\n"
        }
      }
    ],
    "BigCodeBench/553": [
      {
        "task_id": "BigCodeBench/553",
        "solution": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n# Constants\nCOLUMNS = ['A', 'B', 'C', 'D', 'E']\ndef task_func(a, b):\n\n    pass\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n# Constants\nCOLUMNS = ['A', 'B', 'C', 'D', 'E']\n\ndef task_func(a, b):\n    # Check if lists are empty\n    if not a or not b:\n        raise ValueError(\"Both lists 'a' and 'b' must be non-empty.\")\n\n    # Step 1: Generate random values\n    num_rows = len(a)\n    num_columns = min(len(b), len(COLUMNS))  # Ensure we don't exceed the number of available columns\n    random_values = np.random.rand(num_rows, num_columns)  # Generate random values\n\n    # Step 2: Create DataFrame\n    df = pd.DataFrame(random_values, index=a, columns=COLUMNS[:num_columns])  # Use the chosen columns\n\n    # Step 3: Plot the bar chart\n    ax = df.plot(kind='bar')  # Create a bar chart\n    plt.ylabel('Values')  # Add y-label for clarity\n    plt.title('Bar Chart of Random Values')  # Add a title\n\n    # Step 4: Return the Axes object\n    return ax",
        "status": "fail",
        "details": {
          "test_both_lists_empty": "Traceback (most recent call last):\n  File \"__test__.py\", line 53, in test_both_lists_empty\n  File \"__test__.py\", line 19, in task_func\nValueError: Both lists 'a' and 'b' must be non-empty.\n",
          "test_empty_a_list": "Traceback (most recent call last):\n  File \"__test__.py\", line 45, in test_empty_a_list\n  File \"__test__.py\", line 19, in task_func\nValueError: Both lists 'a' and 'b' must be non-empty.\n",
          "test_empty_b_list": "Traceback (most recent call last):\n  File \"__test__.py\", line 49, in test_empty_b_list\n  File \"__test__.py\", line 19, in task_func\nValueError: Both lists 'a' and 'b' must be non-empty.\n"
        }
      }
    ],
    "BigCodeBench/567": [
      {
        "task_id": "BigCodeBench/567",
        "solution": "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\ndef task_func(data):\n\n    pass\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef task_func(data):\n    # Step 1: Parse Input Data\n    try:\n        # Convert input string into a list of floats\n        numeric_values = np.array([float(x) for x in data.split(',')])\n        if len(numeric_values) == 0:\n            raise ValueError(\"No numeric values provided.\")\n    except ValueError as e:\n        print(f\"Error parsing data: {e}\")\n        return None\n\n    # Step 2: Calculate Bins for Histogram\n    bins = np.arange(numeric_values.min(), numeric_values.max() + 2) - 0.5\n\n    # Step 3: Plot the Histogram\n    fig, ax = plt.subplots()\n    ax.hist(numeric_values, bins=bins, edgecolor='black')\n\n    # Step 4: Add Labels and Title\n    ax.set_xlabel('Value')\n    ax.set_ylabel('Frequency')\n    ax.set_title('Histogram of Values')\n\n    # Step 5: Return the Axes object\n    return ax",
        "status": "fail",
        "details": {
          "test_case_3": "Traceback (most recent call last):\n  File \"__test__.py\", line 58, in test_case_3\nAssertionError: Lists differ: [6.4, 6.6000000000000005, 6.800000000000001, 7.0, 7.2, 7.4, 7.6000000000000005] != [7]\n\nFirst differing element 0:\n6.4\n7\n\nFirst list contains 6 additional elements.\nFirst extra element 1:\n6.6000000000000005\n\n- [6.4, 6.6000000000000005, 6.800000000000001, 7.0, 7.2, 7.4, 7.6000000000000005]\n+ [7]\n",
          "test_case_1": "Traceback (most recent call last):\n  File \"__test__.py\", line 41, in test_case_1\nAttributeError: 'NoneType' object has no attribute 'get_title'\n",
          "test_case_2": "Traceback (most recent call last):\n  File \"__test__.py\", line 48, in test_case_2\nAttributeError: 'NoneType' object has no attribute 'get_title'\n",
          "test_case_4": "Traceback (most recent call last):\n  File \"__test__.py\", line 62, in test_case_4\nAttributeError: 'NoneType' object has no attribute 'get_title'\n",
          "test_case_5": "Traceback (most recent call last):\n  File \"__test__.py\", line 69, in test_case_5\nAttributeError: 'NoneType' object has no attribute 'get_title'\n"
        }
      }
    ],
    "BigCodeBench/526": [
      {
        "task_id": "BigCodeBench/526",
        "solution": "import json\nimport pandas as pd\nimport numpy as np\nfrom collections import defaultdict\ndef task_func(input_file=\"data.json\"):\n\n    pass\nimport json\nimport pandas as pd\nimport numpy as np\nfrom collections import defaultdict\n\ndef task_func(input_file=\"data.json\"):\n    # Step 1: Read the JSON file\n    try:\n        with open(input_file, 'r') as f:\n            data = json.load(f)\n    except (FileNotFoundError, json.JSONDecodeError) as e:\n        print(f\"Error reading the JSON file: {e}\")\n        return pd.DataFrame()  # Return an empty DataFrame on error\n    \n    # Step 2: Initialize a defaultdict to collect numeric values for each key\n    data_dict = defaultdict(list)\n    \n    # Step 3: Extract numeric values\n    for entry in data:\n        for key, value in entry.items():\n            if isinstance(value, (int, float)):  # Check if the value is numeric\n                data_dict[key].append(value)\n                \n    # Step 4: Calculate mean and median for each key\n    results = {}\n    for key, values in data_dict.items():\n        if values:  # Check if we have any values\n            mean = np.mean(values)\n            median = np.median(values)\n            results[key] = {'mean': mean, 'median': median}\n\n    # Step 5: Convert results into a DataFrame\n    results_df = pd.DataFrame.from_dict(results, orient='index')\n    results_df.index.name = 'key'  # Name the index\n    results_df = results_df.sort_index()  # Sort by index (key names)\n    \n    return results_df",
        "status": "fail",
        "details": {
          "test_case_3": "Traceback (most recent call last):\n  File \"__test__.py\", line 80, in test_case_3\nAssertionError: Lists differ: ['a'] != ['a', 'b']\n\nSecond list contains 1 additional elements.\nFirst extra element 1:\n'b'\n\n- ['a']\n+ ['a', 'b']\n",
          "test_case_5": "Traceback (most recent call last):\n  File \"__test__.py\", line 92, in test_case_5\nAssertionError: Lists differ: ['a', 'c'] != ['a', 'b', 'c']\n\nFirst differing element 1:\n'c'\n'b'\n\nSecond list contains 1 additional elements.\nFirst extra element 2:\n'c'\n\n- ['a', 'c']\n+ ['a', 'b', 'c']\n?       +++++\n\n",
          "test_case_8": "Traceback (most recent call last):\n  File \"__test__.py\", line 131, in test_case_8\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1096, in __getitem__\n    return self.obj._get_value(*key, takeable=self._takeable)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 3870, in _get_value\n    series = self._get_item_cache(col)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 4255, in _get_item_cache\n    loc = self.columns.get_loc(item)\n          ^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/indexes/range.py\", line 349, in get_loc\n    raise KeyError(key)\nKeyError: 'mean'\n"
        }
      }
    ],
    "BigCodeBench/528": [
      {
        "task_id": "BigCodeBench/528",
        "solution": "import csv\nfrom collections import Counter\nimport pandas as pd\nimport matplotlib.pyplot as plt\ndef task_func(file_path):\n\n    pass\nimport csv\nfrom collections import Counter\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport os\n\ndef task_func(file_path):\n    # Step 1: Validate CSV file\n    if not isinstance(file_path, str) or not file_path.endswith('.csv'):\n        raise ValueError(\"Invalid file format. Please provide a valid CSV file.\")\n    \n    # Step 2: Read CSV file\n    rows = []\n    try:\n        with open(file_path, mode='r', newline='', encoding='utf-8') as csvfile:\n            csv_reader = csv.reader(csvfile)\n            header = next(csv_reader)  # Reading header\n            for row in csv_reader:\n                if row:  # Ensure the row is not empty\n                    rows.append(tuple(row))  # Convert to tuple for immutability and hashability           \n    except FileNotFoundError:\n        raise ValueError(\"File not found. Please provide a valid file path.\")\n    except Exception as e:\n        raise ValueError(f\"An error occurred while reading the file: {e}\")\n    \n    # Step 3: Identify duplicates\n    row_counts = Counter(rows)\n    duplicates = {row: count for row, count in row_counts.items() if count > 1}\n    \n    # Convert tuple keys to list for better readability\n    duplicate_display = {list(row): count for row, count in duplicates.items()}\n    \n    # Step 4: Create a DataFrame from duplicates\n    duplicate_df = pd.DataFrame(list(duplicate_display.items()), columns=['Row', 'Count'])\n    \n    # Step 5: Plot the duplicates\n    fig, ax = plt.subplots()\n    duplicate_df.plot(kind='bar', x='Row', y='Count', ax=ax, legend=False)\n    plt.title('Duplicate Rows and Their Counts')\n    plt.xlabel('Duplicate Row')\n    plt.ylabel('Count')\n    plt.xticks(rotation=45, ha='right')\n    \n    plt.tight_layout()  # Improve layout for better appearance\n    return duplicate_display, ax",
        "status": "fail",
        "details": {
          "test_case_1": "Traceback (most recent call last):\n  File \"__test__.py\", line 74, in test_case_1\n  File \"__test__.py\", line 38, in task_func\nTypeError: unhashable type: 'list'\n",
          "test_case_2": "Traceback (most recent call last):\n  File \"__test__.py\", line 83, in test_case_2\n  File \"__test__.py\", line 38, in task_func\nTypeError: unhashable type: 'list'\n",
          "test_case_3": "Traceback (most recent call last):\n  File \"__test__.py\", line 93, in test_case_3\n  File \"__test__.py\", line 45, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/plotting/_core.py\", line 975, in __call__\n    return plot_backend.plot(data, kind=kind, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/plotting/_matplotlib/__init__.py\", line 71, in plot\n    plot_obj.generate()\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/plotting/_matplotlib/core.py\", line 446, in generate\n    self._compute_plot_data()\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/plotting/_matplotlib/core.py\", line 632, in _compute_plot_data\n    raise TypeError(\"no numeric data to plot\")\nTypeError: no numeric data to plot\n",
          "test_case_5": "Traceback (most recent call last):\n  File \"__test__.py\", line 22, in task_func\nFileNotFoundError: [Errno 2] No such file or directory: '/tmp/tmphoif8z2j/non_existent_file.csv'\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n  File \"__test__.py\", line 101, in test_case_5\n  File \"__test__.py\", line 29, in task_func\nValueError: File not found. Please provide a valid file path.\n"
        }
      }
    ],
    "BigCodeBench/560": [
      {
        "task_id": "BigCodeBench/560",
        "solution": "from datetime import datetime\nimport matplotlib.pyplot as plt\nimport pandas as pd\ndef task_func(data):\n\n    pass\nfrom datetime import datetime\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\ndef task_func(data):\n    # Check if data is valid\n    if not isinstance(data, pd.DataFrame) or not all(col in data.columns for col in ['month', 'value']):\n        raise ValueError(\"Data must be a DataFrame with 'month' and 'value' columns\")\n    \n    # Extract year from the first month value\n    first_date = pd.to_datetime(data['month'].iloc[0])  # Assumes 'month' is a parseable date string\n    year = first_date.year\n    \n    # Ensure all months are present (1 to 12)\n    all_months = pd.DataFrame({'month': range(1, 13)})\n    merged_data = all_months.merge(data, on='month', how='left').fillna(0)\n    \n    # Create the bar chart\n    fig, ax = plt.subplots()\n    ax.bar(merged_data['month'], merged_data['value'])\n    \n    # Customizing the plot\n    ax.set_title(f'Monthly Data for {year}')\n    ax.set_xlabel('Month')\n    ax.set_ylabel('Value')\n    ax.set_xticks(merged_data['month'])\n    ax.set_xticklabels(merged_data['month'].apply(lambda x: datetime(2000, x, 1).strftime('%B')), rotation=45)\n    \n    # Return the Axes object\n    return ax",
        "status": "fail",
        "details": {
          "test_basic_functionality": "Traceback (most recent call last):\n  File \"__test__.py\", line 41, in test_basic_functionality\n  File \"__test__.py\", line 14, in task_func\nValueError: Data must be a DataFrame with 'month' and 'value' columns\n",
          "test_full_year_data": "Traceback (most recent call last):\n  File \"__test__.py\", line 48, in test_full_year_data\n  File \"__test__.py\", line 14, in task_func\nValueError: Data must be a DataFrame with 'month' and 'value' columns\n",
          "test_partial_year_data": "Traceback (most recent call last):\n  File \"__test__.py\", line 52, in test_partial_year_data\n  File \"__test__.py\", line 14, in task_func\nValueError: Data must be a DataFrame with 'month' and 'value' columns\n"
        }
      }
    ],
    "BigCodeBench/579": [
      {
        "task_id": "BigCodeBench/579",
        "solution": "import unicodedata\nimport csv\nfrom collections import Counter\nimport matplotlib.pyplot as plt\ndef task_func(csv_file):\n\n    pass\nimport unicodedata\nimport csv\nfrom collections import Counter\nimport matplotlib.pyplot as plt\n\ndef task_func(csv_file):\n    # Step 1: File Reading\n    try:\n        with open(csv_file, mode='r', encoding='utf-8') as file:\n            reader = csv.reader(file)\n            text_data = ' '.join(row[0] for row in reader if row)  # Handle empty rows\n    except FileNotFoundError:\n        raise FileNotFoundError(f\"The file {csv_file} was not found.\")\n    except IOError:\n        raise IOError(f\"An error occurred while reading the file {csv_file}.\")\n    \n    if not text_data.strip():  # Check if the text_data is empty\n        raise ValueError(\"The CSV file does not contain any text data.\")\n    \n    # Step 2: Text Normalization\n    normalized_text = unicodedata.normalize('NFKD', text_data).encode('ascii', 'ignore').decode('utf-8')\n    \n    # Step 3: Word Counting\n    words = normalized_text.split()\n    word_counts = Counter(words)\n    \n    # Step 4: Identifying Common Words\n    common_words = word_counts.most_common(10)\n    \n    # Step 5: Bar Plot Visualization\n    words, frequencies = zip(*common_words)\n    fig, ax = plt.subplots()\n    ax.bar(words, frequencies)\n    ax.set_xlabel('Words')\n    ax.set_ylabel('Frequencies')\n    ax.set_title('10 Most Common Words')\n    \n    # Step 6: Returning Results\n    return ax, common_words",
        "status": "fail",
        "details": {
          "test_normalized_text": "Traceback (most recent call last):\n  File \"__test__.py\", line 90, in test_normalized_text\nAssertionError: ('Cafe', 2) not found in [('Cafe', 1), ('Nino', 1)]\n"
        }
      }
    ],
    "BigCodeBench/341": [
      {
        "task_id": "BigCodeBench/341",
        "solution": "import pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\ndef task_func(df, col):\n\n    pass\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\ndef task_func(df, col):\n    # Step 1: Input validation\n    if not isinstance(df, pd.DataFrame):\n        raise ValueError(\"The input df must be a DataFrame.\")\n    if df.empty:\n        raise ValueError(\"The DataFrame must not be empty.\")\n    if col not in df.columns:\n        raise ValueError(f\"The column '{col}' does not exist in the DataFrame.\")\n    \n    # Check if the column data is numeric\n    if not pd.api.types.is_numeric_dtype(df[col]):\n        raise ValueError(f\"The column '{col}' must contain numeric data.\")\n    \n    # Step 2: Creating the subplots\n    fig, axes = plt.subplots(1, 2, figsize=(12, 5))  # Create a figure with 2 subplots\n    \n    # Histogram with Kernel Density Estimate for the first subplot\n    sns.histplot(df[col], kde=True, ax=axes[0])\n    axes[0].set_title(f'Histogram and KDE of {col}')\n    \n    # Box plot for the second subplot\n    sns.boxplot(x=df[col], ax=axes[1])\n    axes[1].set_title(f'Box Plot of {col}')\n    \n    # Step 3: Return the figure\n    return fig",
        "status": "fail",
        "details": {
          "test_categorical_data": "Traceback (most recent call last):\n  File \"__test__.py\", line 59, in test_categorical_data\n  File \"__test__.py\", line 22, in task_func\nValueError: The column 'categorical' must contain numeric data.\n"
        }
      }
    ],
    "BigCodeBench/532": [
      {
        "task_id": "BigCodeBench/532",
        "solution": "import numpy as np\nfrom collections import Counter\nfrom scipy.stats import norm\nimport matplotlib.pyplot as plt\ndef task_func(df, bins=4):\n\n    pass\nimport numpy as np\nfrom collections import Counter\nfrom scipy.stats import norm\nimport matplotlib.pyplot as plt\n\ndef task_func(df, bins=4):\n    # Step 1: Extract the 'value' column\n    values = df['value']\n    \n    # Step 2: Count duplicate values\n    duplicates_count = Counter(values)\n    \n    # Step 3: Plotting the histogram\n    fig, ax = plt.subplots()\n    counts, _, _ = ax.hist(values, bins=bins, color='green', alpha=0.6, edgecolor='black')\n    \n    # Step 4: Fitting and overlaying a normal distribution curve\n    mu, std = norm.fit(values)\n    xmin, xmax = ax.get_xlim()\n    x = np.linspace(xmin, xmax, 100)\n    p = norm.pdf(x, mu, std)\n    \n    # Normalize the probability density function to the histogram\n    p_normalized = p * (np.diff(ax.get_ylim())[0]) * len(values) / bins\n    ax.plot(x, p_normalized, 'k', linewidth=2)\n    \n    # Step 5: Finalize the plot\n    ax.set_title('Distribution')\n    ax.set_xlabel('Value')\n    ax.set_ylabel('Frequency')\n\n    # Step 6: Return Counter and Axes object\n    return duplicates_count, ax",
        "status": "fail",
        "details": {
          "test_case_1": "Traceback (most recent call last):\n  File \"__test__.py\", line 56, in test_case_1\nAssertionError: Counter({1: 1, 2: 1, 3: 1, 4: 1, 5: 1}) != Counter()\n",
          "test_case_4": "Traceback (most recent call last):\n  File \"__test__.py\", line 74, in test_case_4\nAssertionError: Counter({5: 4, 1: 4, 2: 3, 3: 2, 4: 1}) != Counter({5: 4, 1: 4, 2: 3, 3: 2})\n"
        }
      }
    ],
    "BigCodeBench/513": [
      {
        "task_id": "BigCodeBench/513",
        "solution": "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\ndef task_func(column, data):\n\n    pass\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef task_func(column, data):\n    # 1. Input Validation\n    if not data:\n        raise ValueError(\"Data list cannot be empty.\")\n\n    # Check for valid column \n    if column not in data[0]:\n        raise KeyError(f\"Column '{column}' is not valid.\")\n    \n    # Check for non-negative values\n    for entry in data:\n        if entry['steps'] < 0 or entry['calories burned'] < 0 or entry['distance walked'] < 0:\n            raise ValueError(\"Steps, calories burned, and distance walked must be non-negative.\")\n    \n    # 2. Data Extraction\n    column_values = [entry[column] for entry in data]\n\n    # 3. Statistical Calculation\n    statistics = {\n        'sum': np.sum(column_values),\n        'mean': np.mean(column_values),\n        'min': np.min(column_values),\n        'max': np.max(column_values)\n    }\n\n    # 4. Visualization\n    dates = [entry['date'] for entry in data]  # Assuming there is a 'date' field in data\n    fig, ax = plt.subplots()\n    ax.plot(dates, column_values, label=column)\n    ax.set_xlabel('Date')\n    ax.set_ylabel(column)\n    ax.set_title(f'Line Chart of {column}')\n    ax.legend()\n\n    # 5. Output Formation\n    return statistics, ax",
        "status": "fail",
        "details": {
          "test_case_1": "Traceback (most recent call last):\n  File \"__test__.py\", line 57, in test_case_1\n  File \"__test__.py\", line 18, in task_func\nKeyError: \"Column 'Steps' is not valid.\"\n",
          "test_case_11": "Traceback (most recent call last):\n  File \"__test__.py\", line 139, in test_case_11\n  File \"__test__.py\", line 18, in task_func\nKeyError: \"Column 'Steps' is not valid.\"\n",
          "test_case_12": "Traceback (most recent call last):\n  File \"__test__.py\", line 149, in test_case_12\n  File \"__test__.py\", line 18, in task_func\nKeyError: \"Column 'Distance Walked' is not valid.\"\n",
          "test_case_2": "Traceback (most recent call last):\n  File \"__test__.py\", line 68, in test_case_2\n  File \"__test__.py\", line 18, in task_func\nKeyError: \"Column 'Calories Burned' is not valid.\"\n",
          "test_case_3": "Traceback (most recent call last):\n  File \"__test__.py\", line 76, in test_case_3\n  File \"__test__.py\", line 18, in task_func\nKeyError: \"Column 'Distance Walked' is not valid.\"\n",
          "test_case_4": "Traceback (most recent call last):\n  File \"__test__.py\", line 86, in test_case_4\n  File \"__test__.py\", line 18, in task_func\nKeyError: \"Column 'Steps' is not valid.\"\n",
          "test_case_5": "Traceback (most recent call last):\n  File \"__test__.py\", line 96, in test_case_5\n  File \"__test__.py\", line 18, in task_func\nKeyError: \"Column 'Calories Burned' is not valid.\"\n",
          "test_case_7": "Traceback (most recent call last):\n  File \"__test__.py\", line 110, in test_case_7\n  File \"__test__.py\", line 18, in task_func\nKeyError: \"Column 'Steps' is not valid.\"\n",
          "test_case_8": "Traceback (most recent call last):\n  File \"__test__.py\", line 114, in test_case_8\n  File \"__test__.py\", line 18, in task_func\nKeyError: \"Column 'Steps' is not valid.\"\n",
          "test_case_9": "Traceback (most recent call last):\n  File \"__test__.py\", line 123, in test_case_9\n  File \"__test__.py\", line 18, in task_func\nKeyError: \"Column 'Steps' is not valid.\"\n"
        }
      }
    ],
    "BigCodeBench/594": [
      {
        "task_id": "BigCodeBench/594",
        "solution": "import csv\nimport os\nimport shutil\nfrom datetime import datetime\nfrom random import randint\n# Constants\nWEATHER_CONDITIONS = ['Sunny', 'Cloudy', 'Rainy', 'Snowy', 'Stormy']\nOUTPUT_DIR = './output'\ndef task_func(hours, output_dir=OUTPUT_DIR):\n\n    pass\nimport csv\nimport os\nimport shutil\nfrom datetime import datetime, timedelta\nfrom random import choice\n\n# Constants\nWEATHER_CONDITIONS = ['Sunny', 'Cloudy', 'Rainy', 'Snowy', 'Stormy']\nOUTPUT_DIR = './output'\nBACKUP_DIR = './backup'\n\ndef task_func(hours, output_dir=OUTPUT_DIR, backup_dir=BACKUP_DIR):\n    # Step 1: Generate Weather Data\n    weather_data = []\n    start_time = datetime.now()\n    for hour in range(hours):\n        time_stamp = start_time + timedelta(hours=hour)\n        condition = choice(WEATHER_CONDITIONS)\n        weather_data.append((time_stamp.strftime('%Y-%m-%d %H:%M:%S'), condition))\n    \n    # Step 2: Create and Write to a CSV File\n    os.makedirs(output_dir, exist_ok=True)\n    csv_file_path = os.path.join(output_dir, 'weather_data.csv')\n    \n    with open(csv_file_path, mode='w', newline='') as file:\n        writer = csv.writer(file)\n        writer.writerow(['Time', 'Condition'])  # Write header\n        writer.writerows(weather_data)  # Write data\n\n    # Step 3: Back Up the CSV File\n    os.makedirs(backup_dir, exist_ok=True)\n    shutil.copy(csv_file_path, backup_dir)\n    \n    # Step 4: Return the File Path\n    return csv_file_path",
        "status": "fail",
        "details": {
          "test_backup_file_creation": "Traceback (most recent call last):\n  File \"__test__.py\", line 107, in test_backup_file_creation\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 956, in assert_called_once_with\n    return self.assert_called_with(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 944, in assert_called_with\n    raise AssertionError(_error_message()) from cause\nAssertionError: expected call not found.\nExpected: copy('./output/weather_data.csv', './output/backup/')\n  Actual: copy('./output/weather_data.csv', './backup')\n",
          "test_task_func_checks_backup_directory_exists": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 77, in test_task_func_checks_backup_directory_exists\nAssertionError: 'output/backup' != '.'\n- output/backup\n+ .\n : Expected output/backup, got .\n",
          "test_task_func_copies_to_backup_directory": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 87, in test_task_func_copies_to_backup_directory\nAssertionError: 'output/backup' != '.'\n- output/backup\n+ .\n : The backup directory path does not match the expected directory path.\n"
        }
      }
    ],
    "BigCodeBench/574": [
      {
        "task_id": "BigCodeBench/574",
        "solution": "from scipy.optimize import curve_fit\nimport matplotlib.pyplot as plt\nimport numpy as np\ndef task_func(array_length=100, noise_level=0.2):\n\n    pass\nfrom scipy.optimize import curve_fit  \nimport matplotlib.pyplot as plt  \nimport numpy as np  \n  \ndef task_func(array_length=100, noise_level=0.2):  \n    # Step 1: Generate a Time Array  \n    x = np.linspace(0, 2 * np.pi, array_length)  \n    \n    # Step 2: Generate the Noisy Sine Wave  \n    y = np.sin(x) + noise_level * np.random.normal(size=array_length)  \n  \n    # Step 3: Define the Sine Function for Curve Fitting  \n    def sine_wave(x, amplitude, frequency, phase, offset):  \n        return amplitude * np.sin(frequency * x + phase) + offset  \n    \n    # Initial guess for the parameters  \n    initial_guess = [1, 1, 0, 0]  \n    \n    # Step 4: Fit the Sine Function to the Noisy Data  \n    popt, pcov = curve_fit(sine_wave, x, y, p0=initial_guess)  \n    \n    # Step 5: Plotting the Results  \n    plt.figure(figsize=(10, 6))  \n    plt.scatter(x, y, label='Noisy Sine Wave', color='red', s=10)  \n    \n    # Generate a smooth curve using the fitted parameters  \n    y_fit = sine_wave(x, *popt)  \n    plt.plot(x, y_fit, label='Fitted Curve', color='blue')  \n    \n    plt.title('Noisy Sine Wave and Fitted Curve')  \n    plt.xlabel('X Values')  \n    plt.ylabel('Y Values')  \n    plt.legend()  \n    plt.grid()  \n    \n    # Show the plot  \n    plt.show()  \n  \n# Call the function to execute  \ntask_func()",
        "status": "fail",
        "details": {
          "test_case_1": "Traceback (most recent call last):\n  File \"__test__.py\", line 52, in test_case_1\nAssertionError: None is not an instance of <class 'matplotlib.axes._axes.Axes'>\n",
          "test_case_4": "Traceback (most recent call last):\n  File \"__test__.py\", line 60, in test_case_4\nAssertionError: None is not an instance of <class 'matplotlib.axes._axes.Axes'>\n",
          "test_case_5": "Traceback (most recent call last):\n  File \"__test__.py\", line 67, in test_case_5\nAssertionError: None is not an instance of <class 'matplotlib.axes._axes.Axes'>\n",
          "test_plot_outputs": "Traceback (most recent call last):\n  File \"__test__.py\", line 78, in test_plot_outputs\nAssertionError: False is not true : Plot does not have associated figure attribute\n",
          "test_varying_noise_levels": "Traceback (most recent call last):\n  File \"__test__.py\", line 74, in test_varying_noise_levels\nAssertionError: None is not an instance of <class 'matplotlib.axes._axes.Axes'>\n"
        }
      }
    ],
    "BigCodeBench/15": [
      {
        "task_id": "BigCodeBench/15",
        "solution": "import subprocess\nimport csv\nimport os\ndef task_func(commands_file_path, output_dir_path):\n\n    pass\nimport subprocess\nimport csv\nimport os\n\ndef task_func(commands_file_path, output_dir_path):\n    # Step 1: Check if the commands file exists\n    if not os.path.exists(commands_file_path):\n        raise FileNotFoundError(f\"The commands file does not exist: {commands_file_path}\")\n    \n    # Check if output directory exists, if not create it\n    if not os.path.exists(output_dir_path):\n        os.makedirs(output_dir_path)\n\n    # Step 2: Read commands from the CSV file\n    with open(commands_file_path, mode='r', newline='') as csvfile:\n        reader = csv.reader(csvfile)\n        commands = [row[0] for row in reader if row]  # assuming each command is in the first column\n    \n    # Step 3: Prepare to hold the output file paths\n    output_file_paths = []\n    \n    # Step 4: Execute each command\n    for index, command in enumerate(commands):\n        output_file_name = f'command_{index}_output.txt'\n        output_file_path = os.path.join(output_dir_path, output_file_name)\n        \n        try:\n            # Execute the command\n            result = subprocess.run(command, shell=True, capture_output=True, text=True)\n            result.check_returncode()  # Raise an error if the command failed\n            \n            # Write the command output to the file\n            with open(output_file_path, 'w') as outfile:\n                outfile.write(result.stdout)\n\n        except subprocess.CalledProcessError as e:\n            # Handle command failure\n            with open(output_file_path, 'w') as outfile:\n                outfile.write(f\"Error: {e.stderr}\\nExit Code: {e.returncode}\")\n        \n        except Exception as e:\n            # Handle any other exceptions\n            with open(output_file_path, 'w') as outfile:\n                outfile.write(f\"Unexpected Error: {str(e)}\\n\")\n        \n        # Store the path of the output file\n        output_file_paths.append(output_file_path)\n    \n    # Step 5: Return the list of output files\n    return output_file_paths",
        "status": "fail",
        "details": {
          "test_command_failure_with_specific_exit_code": "Traceback (most recent call last):\n  File \"__test__.py\", line 130, in test_command_failure_with_specific_exit_code\nAssertionError: 'Error executing command' not found in 'Error: \\nExit Code: 1'\n"
        }
      }
    ],
    "BigCodeBench/582": [
      {
        "task_id": "BigCodeBench/582",
        "solution": "import numpy as np\nfrom scipy import stats\nimport matplotlib.pyplot as plt\ndef task_func(size=1000):\n\n    pass\nimport numpy as np\nfrom scipy import stats\nimport matplotlib.pyplot as plt\n\ndef task_func(size=1000):\n    # Step 1: Generate Normally Distributed Random Numbers\n    mu, sigma = 0, 1  # mean and standard deviation\n    data = np.random.normal(mu, sigma, size)\n\n    # Step 2: Create a Histogram\n    plt.figure(figsize=(10, 6))\n    count, bins, ignored = plt.hist(data, bins=30, density=True, alpha=0.5, color='g', label='Histogram')\n\n    # Step 3: Calculate the PDF\n    pdf_x = np.linspace(mu - 4*sigma, mu + 4*sigma, 100)\n    pdf_y = stats.norm.pdf(pdf_x, mu, sigma)\n\n    # Step 4: Plot the PDF\n    plt.plot(pdf_x, pdf_y, 'k', linewidth=2, label='PDF')\n    \n    # Additional plot features\n    plt.title('Histogram and Probability Density Function')\n    plt.xlabel('Value')\n    plt.ylabel('Density')\n    plt.legend()\n    \n    # Step 5: Return the figure\n    return plt.gcf()",
        "status": "fail",
        "details": {
          "test_histogram_pdf_overlay": "Traceback (most recent call last):\n  File \"__test__.py\", line 64, in test_histogram_pdf_overlay\nAssertionError: 30 != 28 : Number of histogram bins does not match expected\n"
        }
      }
    ],
    "BigCodeBench/13": [
      {
        "task_id": "BigCodeBench/13",
        "solution": "import subprocess\nimport ftplib\nimport os\ndef task_func(ftp_server='ftp.dlptest.com', ftp_user='dlpuser', ftp_password='rNrKYTX9g7z3RgJRmxWuGHbeu', ftp_dir='/ftp/test'):\n\n    pass\nimport subprocess\nimport ftplib\nimport os\n\ndef task_func(ftp_server='ftp.dlptest.com', ftp_user='dlpuser', ftp_password='rNrKYTX9g7z3RgJRmxWuGHbeu', ftp_dir='/ftp/test'):\n    ftp = None\n    attempted_downloads = []  \n  \n    try:\n        # Connecting to the FTP server\n        ftp = ftplib.FTP(ftp_server)\n    except Exception as e:\n        raise Exception(f\"Failed to connect to FTP server {ftp_server}: {str(e)}\")\n    \n    try:\n        # Logging into the FTP server\n        ftp.login(ftp_user, ftp_password)\n    except Exception as e:\n        raise Exception(f\"Failed to log into FTP server {ftp_server} with user {ftp_user}: {str(e)}\")\n    \n    try:\n        # Changing to the desired directory\n        ftp.cwd(ftp_dir)\n    except Exception as e:\n        raise Exception(f\"Failed to change to directory {ftp_dir} on server {ftp_server}: {str(e)}\")\n    \n    # Listing files in the directory\n    try:\n        filenames = ftp.nlst()\n    except Exception as e:\n        raise Exception(f\"Failed to list files in directory {ftp_dir} on server {ftp_server}: {str(e)}\")\n\n    for filename in filenames:\n        # Building the complete FTP URL for wget\n        ftp_file_url = f\"ftp://{ftp_user}:{ftp_password}@{ftp_server}{ftp_dir}/{filename}\"\n        try:\n            # Using subprocess to download the file with wget\n            result = subprocess.run(['wget', ftp_file_url], capture_output=True, text=True)\n            # Check if the download was successful\n            if result.returncode == 0:\n                attempted_downloads.append(filename)\n            else:\n                print(f\"Failed to download {filename}: {result.stderr}\")\n        except FileNotFoundError:\n            print(f\"wget command not found. Please ensure it is installed.\")\n        except Exception as e:\n            print(f\"An error occurred while downloading {filename}: {str(e)}\")\n    \n    # Ensure FTP connection is closed\n    if ftp:\n        ftp.quit()\n    \n    return attempted_downloads",
        "status": "fail",
        "details": {
          "test_case_1": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 81, in test_case_1\nAssertionError: 0 != 2\n"
        }
      }
    ],
    "BigCodeBench/636": [
      {
        "task_id": "BigCodeBench/636",
        "solution": "import matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n# Constants\nCOLUMNS = ['Column1', 'Column2', 'Column3', 'Column4', 'Column5']\ndef task_func(rows):\n\n    pass\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\n# Constants\nCOLUMNS = ['Column1', 'Column2', 'Column3', 'Column4', 'Column5']\n\ndef task_func(rows):\n    # Step 1: Generate the DataFrame with random values\n    df = pd.DataFrame(np.random.randint(0, 10, size=(rows, len(COLUMNS))), columns=COLUMNS)\n    \n    # Step 2: Count non-zero values in each column\n    non_zero_counts = (df != 0).sum()\n    \n    # Step 3: Visualize the counts using a bar plot\n    fig, ax = plt.subplots()\n    non_zero_counts.plot(kind='bar', ax=ax)\n    ax.set_title('Non-zero Counts per Column')\n    ax.set_xlabel('Columns')\n    ax.set_ylabel('Count of Non-Zero Values')\n    \n    # Step 4: Return the DataFrame and Axes object\n    return df, ax",
        "status": "fail",
        "details": {
          "test_case_1": "Traceback (most recent call last):\n  File \"__test__.py\", line 39, in test_case_1\nAssertionError: 5 != 0\n",
          "test_case_4": "Traceback (most recent call last):\n  File \"__test__.py\", line 52, in test_case_4\n  File \"__test__.py\", line 18, in task_func\n  File \"numpy/random/mtrand.pyx\", line 782, in numpy.random.mtrand.RandomState.randint\n  File \"numpy/random/_bounded_integers.pyx\", line 1343, in numpy.random._bounded_integers._rand_int64\nValueError: negative dimensions are not allowed\n"
        }
      }
    ],
    "BigCodeBench/530": [
      {
        "task_id": "BigCodeBench/530",
        "solution": "import pandas as pd\nimport numpy as np\nfrom collections import Counter\nimport seaborn as sns\nimport matplotlib.pyplot as plt\ndef task_func(df: pd.DataFrame) -> (Counter, plt.Axes):\n\n    pass\nimport pandas as pd\nimport numpy as np\nfrom collections import Counter\nimport matplotlib.pyplot as plt\n\ndef task_func(df: pd.DataFrame) -> (Counter, plt.Axes):\n    # Data validation\n    if df.empty:\n        raise ValueError(\"The DataFrame is empty.\")\n    \n    df['age'] = np.floor(df['age']).astype(int)  # Round down floats to nearest integer\n    if (df['age'] < 0).any():\n        raise ValueError(\"Age values must not be negative.\")\n    \n    # Identify duplicate names\n    duplicates = df[df.duplicated('name', keep=False)]\n    \n    if duplicates.empty:\n        return Counter(), None  # Return empty Counter and None if there are no duplicates\n    \n    # Record age distribution\n    age_distribution = Counter(duplicates['age'])\n    \n    # Create histogram\n    plt.figure(figsize=(10, 6))\n    \n    # Use min and max directly from duplicates DataFrame\n    min_age = duplicates['age'].min()  # Minimum age from the duplicates\n    max_age = duplicates['age'].max()  # Maximum age from the duplicates\n    bins = np.arange(min_age - 0.5, max_age + 1.5, 1)  # Adjust bins for proper age distribution\n    \n    plt.hist(duplicates['age'], bins=bins, edgecolor='black')\n    plt.xlabel('Age')\n    plt.ylabel('Count')\n    plt.title('Age Distribution of Duplicate Names')\n    \n    plt.gca()  # Get current Axes after setup\n    plt.show()  # Show plot\n    return age_distribution, plt.gca()  # Return Counter and the Axes of the plot",
        "status": "pass",
        "details": {}
      }
    ],
    "BigCodeBench/637": [
      {
        "task_id": "BigCodeBench/637",
        "solution": "from random import sample\nfrom typing import Tuple\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\ndef task_func(num_students):\n\n    pass\nfrom random import sample\nfrom typing import Tuple\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\ndef task_func(num_students: int) -> Tuple[pd.DataFrame, plt.Axes]:\n    if num_students <= 0:\n        raise ValueError(\"Number of students must be a positive integer.\")\n\n    # Step 1: Generate random student names and grades\n    courses = ['Math', 'Science', 'English', 'History']\n    students = [f'Student {i+1}' for i in range(num_students)]\n    grades = np.random.randint(0, 101, size=(num_students, len(courses)))  # Grades between 0 and 100\n\n    # Step 2: Create a DataFrame\n    df = pd.DataFrame(grades, columns=courses, index=students)\n\n    # Step 3: Calculate average grades and number of passing students\n    average_grades = df.mean()\n    passing_counts = (df >= 60).sum()\n\n    # Step 4: Prepare data for visualization\n    summary_df = pd.DataFrame({\n        'Average Grade': average_grades,\n        'Passing Students': passing_counts\n    })\n\n    # Step 5: Visualize the data\n    ax = summary_df.plot(kind='bar', figsize=(10, 6), title='Course-wise Average and Passing Grade Counts')\n    plt.xlabel('Courses')\n    plt.ylabel('Count / Average Grade')\n    plt.xticks(rotation=45)\n    plt.grid(axis='y')\n    \n    # Step 6: Return the DataFrame and Axes object\n    return (df, ax)",
        "status": "fail",
        "details": {
          "test_case_1": "Traceback (most recent call last):\n  File \"__test__.py\", line 54, in test_case_1\nAssertionError: Tuples differ: (10, 4) != (10, 5)\n\nFirst differing element 1:\n4\n5\n\n- (10, 4)\n?      ^\n\n+ (10, 5)\n?      ^\n\n",
          "test_case_2": "Traceback (most recent call last):\n  File \"__test__.py\", line 64, in test_case_2\nAssertionError: Tuples differ: (50, 4) != (50, 5)\n\nFirst differing element 1:\n4\n5\n\n- (50, 4)\n?      ^\n\n+ (50, 5)\n?      ^\n\n",
          "test_case_3": "Traceback (most recent call last):\n  File \"__test__.py\", line 74, in test_case_3\nAssertionError: Tuples differ: (100, 4) != (100, 5)\n\nFirst differing element 1:\n4\n5\n\n- (100, 4)\n?       ^\n\n+ (100, 5)\n?       ^\n\n",
          "test_case_4": "Traceback (most recent call last):\n  File \"__test__.py\", line 84, in test_case_4\nAssertionError: Tuples differ: (1, 4) != (1, 5)\n\nFirst differing element 1:\n4\n5\n\n- (1, 4)\n?     ^\n\n+ (1, 5)\n?     ^\n\n",
          "test_case_5": "Traceback (most recent call last):\n  File \"__test__.py\", line 94, in test_case_5\nAssertionError: Tuples differ: (5, 4) != (5, 5)\n\nFirst differing element 1:\n4\n5\n\n- (5, 4)\n?     ^\n\n+ (5, 5)\n?     ^\n\n"
        }
      }
    ],
    "BigCodeBench/424": [
      {
        "task_id": "BigCodeBench/424",
        "solution": "import cv2\nimport numpy as np\nimport os\nfrom sklearn.cluster import KMeans\ndef task_func(image_path='image.jpg', n_clusters=3, random_seed=42):\n\n    pass\nimport cv2\nimport numpy as np\nimport os\nfrom sklearn.cluster import KMeans\n\ndef task_func(image_path='image.jpg', n_clusters=3, random_seed=42):\n    # Step 1: Image Reading\n    if not os.path.isfile(image_path):\n        raise FileNotFoundError(f\"The image file {image_path} does not exist.\")\n\n    original_image = cv2.imread(image_path)\n    if original_image is None:\n        raise ValueError(\"Error reading the image file.\")\n    \n    # Step 2: Input Validation\n    if not isinstance(n_clusters, int) or n_clusters <= 0:\n        raise ValueError(\"'n_clusters' must be a positive integer.\")\n    \n    # Step 3: Image Reshaping\n    pixel_values = original_image.reshape((-1, 3))\n    pixel_values = np.float32(pixel_values)  # Convert to float for K-means\n    \n    # Step 4: K-means Clustering\n    kmeans = KMeans(n_clusters=n_clusters, random_state=random_seed)\n    kmeans.fit(pixel_values)\n    \n    # Step 5: Image Reconstruction\n    segmented_image = kmeans.cluster_centers_[kmeans.labels_]\n    segmented_image = segmented_image.reshape(original_image.shape)\n    segmented_image = np.uint8(segmented_image)  # Convert back to uint8\n    \n    # Step 6: Output Preparation\n    return (original_image, segmented_image)",
        "status": "fail",
        "details": {
          "test_single_cluster_returns_original_image": "Traceback (most recent call last):\n  File \"__test__.py\", line 100, in test_single_cluster_returns_original_image\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/numpy/testing/_private/utils.py\", line 920, in assert_array_equal\n    assert_array_compare(operator.__eq__, x, y, err_msg=err_msg,\n  File \"/usr/lib/python3.12/contextlib.py\", line 81, in inner\n    return func(*args, **kwds)\n           ^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/numpy/testing/_private/utils.py\", line 797, in assert_array_compare\n    raise AssertionError(msg)\nAssertionError: \nArrays are not equal\nThe original and segmented images should be identical when n_clusters is set to 1.\nMismatched elements: 296 / 300 (98.7%)\nMax absolute difference: 107\nMax relative difference: 0.43319838\n x: array([[[253, 252, 255],\n        [243, 246, 251],\n        [248, 254, 255],...\n y: array([[[244, 247, 247],\n        [244, 247, 247],\n        [244, 247, 247],...\n"
        }
      }
    ],
    "BigCodeBench/678": [
      {
        "task_id": "BigCodeBench/678",
        "solution": "import pandas as pd\nimport json\nimport os\nimport shutil\ndef task_func(path):\n\n    pass\nimport pandas as pd\nimport json\nimport os\nimport shutil\n\ndef task_func(path):\n    # Sub-problem 1: Directory management\n    processed_dir = os.path.join(path, 'processed')\n    if not os.path.exists(processed_dir):\n        os.makedirs(processed_dir)\n\n    # Sub-problem 2: File handling\n    json_files = sorted([f for f in os.listdir(path) if f.endswith('.json')])\n\n    # Sub-problem 3: Data processing\n    dataframes = []\n    for json_file in json_files:\n        try:\n            with open(os.path.join(path, json_file), 'r') as f:\n                data = json.load(f)\n                df = pd.DataFrame(data)\n                df['Source'] = json_file.replace('.json', '')\n                dataframes.append(df)\n        except (json.JSONDecodeError, FileNotFoundError) as e:\n            print(f\"Error processing {json_file}: {e}\")\n            continue\n\n    # Sub-problem 4: DataFrame concatenation\n    if dataframes:\n        final_df = pd.concat(dataframes, ignore_index=True)\n    else:\n        final_df = pd.DataFrame()  # Return an empty DataFrame if no files were processed\n\n    # Sub-problem 5: File moving\n    for json_file in json_files:\n        try:\n            shutil.move(os.path.join(path, json_file), os.path.join(processed_dir, json_file))\n        except Exception as e:\n            print(f\"Error moving {json_file}: {e}\")\n\n    # Sub-problem 6: Return result\n    return final_df",
        "status": "pass",
        "details": {}
      }
    ],
    "BigCodeBench/592": [
      {
        "task_id": "BigCodeBench/592",
        "solution": "import csv\nimport os\nfrom datetime import datetime\nfrom random import randint\n# Constants\nSENSORS = ['Temperature', 'Humidity', 'Pressure']\nOUTPUT_DIR = './output'\ndef task_func(hours, output_dir=OUTPUT_DIR):\n\n    pass\nimport csv\nimport os\nfrom datetime import datetime, timedelta\nfrom random import uniform\n\n# Constants\nSENSORS = ['Temperature', 'Humidity', 'Pressure']\nOUTPUT_DIR = './output'\n\ndef task_func(hours, output_dir=OUTPUT_DIR):\n    # Step 1: Handle output directory\n    if not os.path.exists(output_dir):\n        os.makedirs(output_dir)\n\n    # Step 2: Prepare CSV file\n    filename = os.path.join(output_dir, f'sensor_data_{hours}_hours.csv')\n    \n    with open(filename, mode='w', newline='') as file:\n        writer = csv.writer(file)\n        \n        # Step 3: Write header\n        writer.writerow(['Time', 'Temperature', 'Humidity', 'Pressure'])\n        \n        # Step 4: Generate sensor data\n        start_time = datetime.now()\n        for hour in range(hours):\n            # Generate timestamp\n            timestamp = start_time + timedelta(hours=hour)\n            # Generate random sensor values\n            temperature = round(uniform(-10.0, 50.0), 2)  # Example range for temperature\n            humidity = round(uniform(0.0, 100.0), 2)       # Example range for humidity\n            pressure = round(uniform(950.0, 1050.0), 2)    # Example range for pressure\n            \n            # Step 5: Write data row\n            writer.writerow([timestamp.strftime('%Y-%m-%d %H:%M:%S'), temperature, humidity, pressure])\n    \n    print(f\"Sensor data for {hours} hours saved to {filename}.\")",
        "status": "fail",
        "details": {
          "test_csv_file_creation": "Traceback (most recent call last):\n  File \"__test__.py\", line 63, in test_csv_file_creation\nAssertionError: False is not true\n",
          "test_file_path_return": "Traceback (most recent call last):\n  File \"__test__.py\", line 79, in test_file_path_return\nAssertionError: None != './output/sensor_data.csv'\n",
          "test_csv_file_header": "Traceback (most recent call last):\n  File \"__test__.py\", line 72, in test_csv_file_header\nFileNotFoundError: [Errno 2] No such file or directory: './output/sensor_data.csv'\n",
          "test_csv_file_rows": "Traceback (most recent call last):\n  File \"__test__.py\", line 67, in test_csv_file_rows\nFileNotFoundError: [Errno 2] No such file or directory: './output/sensor_data.csv'\n",
          "test_no_hours_data": "Traceback (most recent call last):\n  File \"__test__.py\", line 83, in test_no_hours_data\nFileNotFoundError: [Errno 2] No such file or directory: './output/sensor_data.csv'\n"
        }
      }
    ],
    "BigCodeBench/723": [
      {
        "task_id": "BigCodeBench/723",
        "solution": "import urllib.request\nfrom bs4 import BeautifulSoup\nimport csv\nimport os\n# Constants\nCSV_FILE_PATH = 'scraped_data.csv'\ndef task_func(url):\n\n    pass\nimport urllib.request\nfrom bs4 import BeautifulSoup\nimport csv\nimport os\n\n# Constants\nCSV_FILE_PATH = 'scraped_data.csv'\n\ndef task_func(url):\n    try:\n        # 1. Fetch HTML Content\n        response = urllib.request.urlopen(url)\n        html_content = response.read()\n\n        # 2. Parse HTML Content\n        soup = BeautifulSoup(html_content, 'html.parser')\n\n        # 3. Extract Desired Data\n        # Example: Let's say we want to extract titles from <h2> tags.\n        data_to_scrape = soup.find_all('h2')  # Adjust based on the actual webpage structure\n\n        # Extract text from the <h2> tags\n        scraped_data = [title.get_text(strip=True) for title in data_to_scrape]\n\n        # 4. Write Data to CSV File\n        with open(CSV_FILE_PATH, mode='w', newline='', encoding='utf-8') as file:\n            writer = csv.writer(file)\n            writer.writerow(['Title'])  # Writing the header\n            for title in scraped_data:\n                writer.writerow([title])  # Writing each title row\n\n        # 5. Return Output\n        return CSV_FILE_PATH\n\n    except Exception as e:\n        print(f\"An error occurred: {e}\")\n        return None\n\n# Example usage:\n# Place the URL you want to scrape below\n# url = 'https://example.com'\n# print(task_func(url))",
        "status": "fail",
        "details": {
          "test_correct_scraping": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 70, in test_correct_scraping\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 913, in assert_called\n    raise AssertionError(msg)\nAssertionError: Expected 'writerows' to have been called.\n",
          "test_invalid_url": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 73, in test_invalid_url\nAssertionError: Exception not raised\n",
          "test_no_table": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 89, in test_no_table\nAssertionError: Exception not raised\n",
          "test_overwrite_existing_csv": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 104, in test_overwrite_existing_csv\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 956, in assert_called_once_with\n    return self.assert_called_with(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 944, in assert_called_with\n    raise AssertionError(_error_message()) from cause\nAssertionError: expected call not found.\nExpected: open('scraped_data.csv', 'w')\n  Actual: open('scraped_data.csv', mode='w', newline='', encoding='utf-8')\n"
        }
      }
    ],
    "BigCodeBench/763": [
      {
        "task_id": "BigCodeBench/763",
        "solution": "import numpy as np\nfrom collections import defaultdict\nimport json\nimport csv\n# Constants\ndef task_func(input_file, output_file):\n\n    pass\nimport numpy as np\nfrom collections import defaultdict\nimport json\nimport csv\n\n# Constants\ndef task_func(input_file: str, output_file: str) -> dict:\n    # Step 1: Read JSON File\n    with open(input_file, 'r') as f:\n        data = json.load(f)\n\n    # Step 2: Calculate Mean and Median\n    results = defaultdict(list)\n    \n    for entry in data:\n        for key, value in entry.items():\n            results[key].append(value)\n\n    summary = {}\n    for key, values in results.items():\n        # Only calculate mean and median for numerical fields\n        if all(isinstance(x, (int, float)) for x in values):\n            mean_value = np.mean(values)\n            median_value = np.median(values)\n            summary[key] = {\n                'mean': mean_value,\n                'median': median_value\n            }\n    \n    # Step 3: Write to CSV\n    with open(output_file, 'w', newline='') as csvfile:\n        fieldnames = ['Field', 'Mean', 'Median']\n        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n\n        writer.writeheader()\n        for key, stats in summary.items():\n            writer.writerow({'Field': key, 'Mean': stats['mean'], 'Median': stats['median']})\n\n    return summary",
        "status": "fail",
        "details": {
          "test_case_1": "Traceback (most recent call last):\n  File \"__test__.py\", line 103, in test_case_1\n  File \"__test__.py\", line 97, in validate_data\nKeyError: 'key'\n",
          "test_case_2": "Traceback (most recent call last):\n  File \"__test__.py\", line 106, in test_case_2\n  File \"__test__.py\", line 97, in validate_data\nKeyError: 'key'\n",
          "test_case_3": "Traceback (most recent call last):\n  File \"__test__.py\", line 109, in test_case_3\n  File \"__test__.py\", line 97, in validate_data\nKeyError: 'key'\n",
          "test_case_4": "Traceback (most recent call last):\n  File \"__test__.py\", line 112, in test_case_4\n  File \"__test__.py\", line 97, in validate_data\nKeyError: 'key'\n",
          "test_case_5": "Traceback (most recent call last):\n  File \"__test__.py\", line 115, in test_case_5\n  File \"__test__.py\", line 97, in validate_data\nKeyError: 'key'\n"
        }
      }
    ],
    "BigCodeBench/765": [
      {
        "task_id": "BigCodeBench/765",
        "solution": "import os\nfrom pathlib import Path\nimport shutil\ndef task_func(kwargs, target_dir=\"non_none_files\"):\n\n    pass\nimport os\nfrom pathlib import Path\nimport shutil\n\ndef task_func(kwargs, target_dir=\"non_none_files\"):\n    copied_files = []\n    # Ensure the target directory exists\n    os.makedirs(target_dir, exist_ok=True)\n\n    for file_path in kwargs.values():\n        file_path = Path(file_path)  # Convert to Path object for easier manipulation\n        if file_path.exists() and file_path.is_file() and os.path.getsize(file_path) > 0:  # Check if file exists and is non-empty\n            target_path = Path(target_dir) / file_path.name  # Full target path\n            try:\n                shutil.copy(file_path, target_path)  # Copy the file\n                copied_files.append(str(target_path))  # Add the copied file path to the list\n            except Exception as e:\n                print(f\"Error copying {file_path}: {e}\")  # Print out the error if copying fails\n                \n    return copied_files",
        "status": "fail",
        "details": {
          "test_files_with_content": "Traceback (most recent call last):\n  File \"__test__.py\", line 48, in test_files_with_content\nAssertionError: 0 != 2\n",
          "test_files_with_no_content": "Traceback (most recent call last):\n  File \"__test__.py\", line 59, in test_files_with_no_content\n  File \"__test__.py\", line 17, in task_func\n  File \"/usr/lib/python3.12/pathlib.py\", line 1164, in __init__\n    super().__init__(*args)\n  File \"/usr/lib/python3.12/pathlib.py\", line 373, in __init__\n    raise TypeError(\nTypeError: argument should be a str or an os.PathLike object where __fspath__ returns a str, not 'NoneType'\n",
          "test_mixed_case": "Traceback (most recent call last):\n  File \"__test__.py\", line 75, in test_mixed_case\n  File \"__test__.py\", line 17, in task_func\n  File \"/usr/lib/python3.12/pathlib.py\", line 1164, in __init__\n    super().__init__(*args)\n  File \"/usr/lib/python3.12/pathlib.py\", line 373, in __init__\n    raise TypeError(\nTypeError: argument should be a str or an os.PathLike object where __fspath__ returns a str, not 'NoneType'\n"
        }
      }
    ],
    "BigCodeBench/657": [
      {
        "task_id": "BigCodeBench/657",
        "solution": "import re\nimport nltk\nfrom gensim.models import Word2Vec\n# Constants\nALPHANUMERIC = re.compile('[\\W_]+')\ndef task_func(texts, stopwords=None):\n\n    pass\nimport re\nimport nltk\nfrom gensim.models import Word2Vec\nfrom nltk.corpus import stopwords\nfrom typing import List, Optional\n\n# Ensure the nltk stopwords are downloaded\nnltk.download('stopwords')\n\n# Constants\nALPHANUMERIC = re.compile('[\\\\W_]+')\n\ndef task_func(texts: List[str], stopwords: Optional[set] = None) -> Word2Vec:\n    # Validate input\n    if not all(isinstance(text, str) for text in texts):\n        raise ValueError(\"All items in 'texts' should be strings.\")\n\n    if stopwords is None:\n        stopwords = set(nltk.corpus.stopwords.words('english'))\n\n    # Step 1: Clean the texts\n    cleaned_texts = []\n    for text in texts:\n        # Remove non-alphanumeric characters and lower case\n        cleaned_text = ALPHANUMERIC.sub(' ', text).lower()\n        # Tokenize and remove stopwords\n        tokens = [word for word in cleaned_text.split() if word not in stopwords]\n        cleaned_texts.append(tokens)\n\n    # Step 3: Prepare data for Word2Vec\n    # In 'cleaned_texts', each entry is a list of words\n\n    # Step 4: Train the Word2Vec model\n    model = Word2Vec(cleaned_texts, vector_size=100, window=5, min_count=1, workers=4)\n\n    # Step 5: Return the trained model\n    return model",
        "status": "fail",
        "details": {
          "ALL": "cannot import name 'triu' from 'scipy.linalg' (/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/scipy/linalg/__init__.py)"
        }
      }
    ],
    "BigCodeBench/443": [
      {
        "task_id": "BigCodeBench/443",
        "solution": "import numpy as np\nfrom sklearn.cluster import KMeans\nimport matplotlib.pyplot as plt\ndef task_func(\n    P: np.ndarray,\n    T: np.ndarray,\n    n_clusters: int = 3,\n    random_state: int = 0,\n    n_init: int = 10,\n) -> (np.ndarray, plt.Axes):\n\n    pass\nimport numpy as np\nfrom sklearn.cluster import KMeans\nimport matplotlib.pyplot as plt\n\ndef task_func(\n    P: np.ndarray,\n    T: np.ndarray,\n    n_clusters: int = 3,\n    random_state: int = 0,\n    n_init: int = 10,\n) -> (np.ndarray, plt.Axes):\n    # Step 1: Matrix-Tensor Multiplication\n    # Using np.tensordot to multiply P and T, ensuring correct axis alignment.\n    # Here we assume P.shape is (m, n) and T.shape is (n, p, q) to yield (m, p, q)\n    result = np.tensordot(P, T, axes=(1, 0))  # Adjust axes based on P and T shapes\n\n    # Step 2: Flattening the Result\n    flattened_result = result.reshape(result.shape[0], -1)  # Flatten to 2D array for KMeans\n\n    # Step 3: KMeans Clustering\n    kmeans = KMeans(n_clusters=n_clusters, random_state=random_state, n_init=n_init)\n    cluster_labels = kmeans.fit_predict(flattened_result)  # Fit without additional reshape\n\n    # Step 4: Visualization of Clusters\n    fig, ax = plt.subplots()\n    scatter = ax.scatter(np.arange(len(cluster_labels)), flattened_result.flatten(), c=cluster_labels, cmap='viridis')\n    ax.set_title('KMeans Clustering Visualization')\n    ax.set_xlabel('Data Point Index')\n    ax.set_ylabel('Flattened Result Values')\n    plt.colorbar(scatter, ax=ax)  # Optional: Add colorbar for better visualization\n\n    # Step 5: Return the clustering results and Axes object\n    return cluster_labels, ax",
        "status": "fail",
        "details": {
          "test_case_1": "Traceback (most recent call last):\n  File \"__test__.py\", line 66, in test_case_1\n  File \"__test__.py\", line 38, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/matplotlib/__init__.py\", line 1442, in inner\n    return func(ax, *map(sanitize_sequence, args), **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/matplotlib/axes/_axes.py\", line 4584, in scatter\n    raise ValueError(\"x and y must be the same size\")\nValueError: x and y must be the same size\n",
          "test_case_2": "Traceback (most recent call last):\n  File \"__test__.py\", line 73, in test_case_2\n  File \"__test__.py\", line 38, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/matplotlib/__init__.py\", line 1442, in inner\n    return func(ax, *map(sanitize_sequence, args), **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/matplotlib/axes/_axes.py\", line 4584, in scatter\n    raise ValueError(\"x and y must be the same size\")\nValueError: x and y must be the same size\n",
          "test_case_3": "Traceback (most recent call last):\n  File \"__test__.py\", line 89, in test_case_3\n  File \"__test__.py\", line 38, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/matplotlib/__init__.py\", line 1442, in inner\n    return func(ax, *map(sanitize_sequence, args), **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/matplotlib/axes/_axes.py\", line 4584, in scatter\n    raise ValueError(\"x and y must be the same size\")\nValueError: x and y must be the same size\n",
          "test_case_4": "Traceback (most recent call last):\n  File \"__test__.py\", line 97, in test_case_4\n  File \"__test__.py\", line 38, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/matplotlib/__init__.py\", line 1442, in inner\n    return func(ax, *map(sanitize_sequence, args), **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/matplotlib/axes/_axes.py\", line 4584, in scatter\n    raise ValueError(\"x and y must be the same size\")\nValueError: x and y must be the same size\n"
        }
      }
    ],
    "BigCodeBench/593": [
      {
        "task_id": "BigCodeBench/593",
        "solution": "import csv\nimport os\nfrom datetime import datetime\nfrom random import randint\nimport matplotlib.pyplot as plt\nimport pandas as pd\n# Constants\nVEHICLE_TYPES = ['Car', 'Bus', 'Truck', 'Bike']\nOUTPUT_DIR = './output'\ndef task_func(hours, output_dir=OUTPUT_DIR):\n\n    pass\nimport csv\nimport os\nfrom datetime import datetime, timedelta\nfrom random import randint\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# Constants\nVEHICLE_TYPES = ['Car', 'Bus', 'Truck', 'Bike']\nOUTPUT_DIR = './output'\n\ndef task_func(hours, output_dir=OUTPUT_DIR):\n    # Step 1: Data Generation\n    data = []\n    for hour in range(hours):\n        time = datetime.now() + timedelta(hours=hour)\n        time_str = time.strftime('%Y-%m-%d %H:%M:%S')\n        car_count = randint(50, 200)\n        bus_count = randint(10, 50)\n        truck_count = randint(5, 20)\n        bike_count = randint(30, 100)\n        data.append([time_str, car_count, bus_count, truck_count, bike_count])\n    \n    # Step 2: Create Output Directory\n    if not os.path.exists(output_dir):\n        os.makedirs(output_dir)\n    \n    # Step 3: Save Data to CSV\n    csv_file_path = os.path.join(output_dir, 'traffic_data.csv')\n    with open(csv_file_path, mode='w', newline='') as file:\n        writer = csv.writer(file)\n        writer.writerow(['Time', 'Car', 'Bus', 'Truck', 'Bike'])\n        writer.writerows(data)\n\n    # Step 4: Plotting the Data\n    df = pd.DataFrame(data, columns=['Time', 'Car', 'Bus', 'Truck', 'Bike'])\n    plt.figure(figsize=(12, 6))\n    plt.plot(df['Time'], df['Car'], label='Car', marker='o')\n    plt.plot(df['Time'], df['Bus'], label='Bus', marker='o')\n    plt.plot(df['Time'], df['Truck'], label='Truck', marker='o')\n    plt.plot(df['Time'], df['Bike'], label='Bike', marker='o')\n    plt.title('Traffic Data Over Time')\n    plt.xlabel('Time')\n    plt.ylabel('Vehicle Count')\n    plt.xticks(rotation=45)\n    plt.legend()\n    plt.tight_layout()\n    \n    # Return the path to the CSV file and the axes object\n    return csv_file_path, plt.gca()\n\n# Example call:\n# task_func(5)",
        "status": "fail",
        "details": {
          "test_dataframe_content": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 94, in test_dataframe_content\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 935, in assert_called_with\n    raise AssertionError(error_message)\nAssertionError: expected call not found.\nExpected: read_csv('./output/traffic_data.csv')\n  Actual: not called.\n",
          "test_empty_dataframe_on_zero_hours": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 100, in test_empty_dataframe_on_zero_hours\nAssertionError: <Axes: title={'center': 'Traffic Data Over Time'}, xlabel='Time', ylabel='Vehicle Count'> is not None\n",
          "test_plot_generation": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 113, in test_plot_generation\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 913, in assert_called\n    raise AssertionError(msg)\nAssertionError: Expected 'show' to have been called.\n"
        }
      }
    ],
    "BigCodeBench/654": [
      {
        "task_id": "BigCodeBench/654",
        "solution": "import matplotlib.pyplot as plt\nimport scipy.optimize as optimize\nimport numpy as np\ndef task_func(array, target_value):\n\n    pass\nimport matplotlib.pyplot as plt\nimport scipy.optimize as optimize\nimport numpy as np\n\ndef task_func(array, target_value):\n    # Step 1: Data Filtering\n    indices = np.where(array[:, 0] == target_value)[0]\n    x_data = indices  # We use the indices as x-values\n    y_data = array[indices, 1]  # Corresponding y-values\n    \n    # Step 2: Defining the Exponential Decay Function\n    def exp_decay(x, a, b):\n        return a * np.exp(-b * x)\n\n    # Step 3: Parameter Optimization\n    initial_guess = [1, 1]  # Initial guesses for a and b\n    popt, _ = optimize.curve_fit(exp_decay, x_data, y_data, p0=initial_guess)\n\n    # Step 4: Plotting the Results\n    plt.figure()\n    plt.scatter(x_data, y_data, label='Data Points', color='red')\n    x_fit = np.linspace(np.min(x_data), np.max(x_data), 100)\n    y_fit = exp_decay(x_fit, *popt)\n    plt.plot(x_fit, y_fit, label='Fitted Exponential Curve', color='blue')\n    plt.title('Exponential Decay Fit')\n    plt.xlabel('Index')\n    plt.ylabel('Value')\n    plt.legend()\n    ax = plt.gca()  # Get the current Axes object\n    \n    # Step 5: Return Values\n    return popt, ax",
        "status": "fail",
        "details": {
          "test_functionality": "Traceback (most recent call last):\n  File \"__test__.py\", line 71, in test_functionality\nAssertionError: Tuples differ: (2,) != (3,)\n\nFirst differing element 0:\n2\n3\n\n- (2,)\n?  ^\n\n+ (3,)\n?  ^\n : Should return three coefficients.\n",
          "test_not_enough_points": "Traceback (most recent call last):\n  File \"__test__.py\", line 67, in test_not_enough_points\n  File \"__test__.py\", line 15, in task_func\nIndexError: index 1 is out of bounds for axis 1 with size 1\n"
        }
      }
    ],
    "BigCodeBench/720": [
      {
        "task_id": "BigCodeBench/720",
        "solution": "import os\nimport csv\nimport random\nfrom datetime import datetime\ndef task_func():\n\n    pass\nimport os\nimport csv\nimport random\nfrom datetime import datetime\n\ndef task_func():\n    # Step 1: Ensure the directory exists\n    dir_path = 'task_func_data'\n    if not os.path.exists(dir_path):\n        os.makedirs(dir_path)\n\n    # Step 2: Path to the output file\n    file_path = os.path.join(dir_path, 'Output.csv')\n\n    # Step 3: Generate random sensor data\n    temperature = random.uniform(-10, 50)  # random temperature between -10\u00b0C and 50\u00b0C\n    humidity = random.uniform(0, 100)       # random humidity between 0% and 100%\n\n    # Step 4: Write to the CSV file in append mode\n    with open(file_path, mode='a', newline='') as file:\n        writer = csv.writer(file)\n        writer.writerow([datetime.now(), temperature, humidity])  # Write timestamp, temperature, humidity\n    \n    # Step 5: Clean up - delete the file after use\n    os.remove(file_path)  # This will delete the file\n\n    # Step 6: Return the file path\n    return file_path",
        "status": "fail",
        "details": {
          "test_data_appending": "Traceback (most recent call last):\n  File \"__test__.py\", line 77, in test_data_appending\nAssertionError: 1 != 2\n",
          "test_return_value": "Traceback (most recent call last):\n  File \"__test__.py\", line 55, in test_return_value\nAssertionError: 'task_func_data/Output.csv' != 'task_func_data/Output.txt'\n- task_func_data/Output.csv\n?                       ^^^\n+ task_func_data/Output.txt\n?                       ^^^\n\n",
          "test_file_content": "Traceback (most recent call last):\n  File \"__test__.py\", line 67, in test_file_content\nStopIteration\n"
        }
      }
    ],
    "BigCodeBench/502": [
      {
        "task_id": "BigCodeBench/502",
        "solution": "from datetime import datetime, timedelta\nimport pandas as pd\nimport random\nimport seaborn as sns\ndef task_func(days_in_past=7, random_seed=0):\n\n    pass\nfrom datetime import datetime, timedelta  \nimport pandas as pd  \nimport random  \nimport seaborn as sns  \nimport matplotlib.pyplot as plt  \n\ndef task_func(days_in_past=7, random_seed=0):  \n    # Set the random seed for reproducibility  \n    random.seed(random_seed)  \n\n    # Step 1: Generate random dates for the specified number of days in the past  \n    today = datetime.now().date()  \n    dates = [today - timedelta(days=i) for i in range(days_in_past)]  \n\n    # Step 2: Define activities and prepare a list for the DataFrame  \n    activities = [\"Running\", \"Swimming\", \"Cycling\", \"Yoga\", \"Weight Training\"]  \n    data = []  \n\n    # Step 3: Generate random durations for each date and each activity  \n    for date in dates:  \n        for activity in activities:  \n            duration = random.randint(0, 120)  # Duration between 0 and 120 minutes  \n            data.append({\"Date\": date, \"Activity\": activity, \"Duration\": duration})  \n\n    # Step 4: Create a DataFrame  \n    df = pd.DataFrame(data)  \n    df.sort_values(by='Date', inplace=True)  # Ensure the DataFrame is sorted by date  \n\n    # Step 5: Plot the data  \n    plt.figure(figsize=(10, 6))  \n    ax = sns.lineplot(data=df, x='Date', y='Duration', hue='Activity', marker='o')  \n    plt.title('Daily Activity Durations')  \n    plt.xlabel('Date')  \n    plt.ylabel('Duration (minutes)')  \n    plt.xticks(rotation=45)  \n    plt.tight_layout()  \n    plt.grid(True)  \n\n    # Return the Axes and DataFrame as a tuple (for further customization if needed)  \n    return ax, df",
        "status": "fail",
        "details": {
          "test_case_5": "Traceback (most recent call last):\n  File \"__test__.py\", line 94, in test_case_5\n  File \"__test__.py\", line 34, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 6758, in sort_values\n    k = self._get_label_or_level_values(by, axis=axis)\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/generic.py\", line 1778, in _get_label_or_level_values\n    raise KeyError(key)\nKeyError: 'Date'\n"
        }
      }
    ],
    "BigCodeBench/771": [
      {
        "task_id": "BigCodeBench/771",
        "solution": "import re\nimport os\nfrom pathlib import Path\nimport csv\ndef task_func(directory: str, pattern: str = r'^(.*?)-\\d+\\.csv$') -> list:\n\n    pass\nimport re  \nimport os  \nfrom pathlib import Path  \nimport csv  \n  \ndef task_func(directory: str, pattern: str = r'^(.*?)-\\\\d+\\\\.csv$') -> list:  \n    new_files = []  # To store the names of new files created  \n    regex = re.compile(pattern)  # Compile the regex pattern for efficiency  \n  \n    # Iterate over files in the specified directory  \n    for filepath in Path(directory).iterdir():  \n        if filepath.is_file() and regex.match(filepath.name):  \n            # Extract base filename  \n            base_name = regex.match(filepath.name).group(1)  \n            new_filename = f\"{base_name}-processed.csv\"  # New filename logic  \n  \n            # Create the new file path  \n            new_file_path = filepath.parent / new_filename  \n  \n            # Read the content of the original CSV file and write to the new file  \n            try:  \n                with open(filepath, mode='r', newline='', encoding='utf-8') as infile, \\  \n                     open(new_file_path, mode='w', newline='', encoding='utf-8') as outfile:  \n                      \n                    reader = csv.reader(infile)  \n                    writer = csv.writer(outfile)  \n                      \n                    for row in reader:  \n                        writer.writerow(row)  # Copy each row to the new file  \n                      \n                new_files.append(new_filename)  # Add the new filename to the list  \n            except Exception as e:  \n                print(f\"Error processing file {filepath.name}: {e}\")  # Log the error\n  \n    return new_files  # Finally return the list of new files created",
        "status": "fail",
        "details": {
          "ALL": "unexpected character after line continuation character (__test__.py, line 29)"
        }
      }
    ],
    "BigCodeBench/826": [
      {
        "task_id": "BigCodeBench/826",
        "solution": "import re\nimport os\nimport shutil\ndef task_func(source_dir, target_dir, file_pattern=r'\\b[A-Za-z0-9]+\\.(txt|doc|docx)\\b'):\n\n    pass\nimport re\nimport os\nimport shutil\n\ndef task_func(source_dir, target_dir, file_pattern=r'[A-Za-z0-9]+\\.(txt|doc|docx)$'):\n    # Compile the regex pattern for efficiency\n    pattern = re.compile(file_pattern)\n    \n    # Variable to count moved files\n    moved_files_count = 0\n    \n    # Iterate through each file in the source directory\n    for filename in os.listdir(source_dir):\n        # Check if the file matches the specified pattern\n        if pattern.match(filename):\n            # Construct full file path\n            source_file_path = os.path.join(source_dir, filename)\n            target_file_path = os.path.join(target_dir, filename)\n\n            try:\n                # Move the file\n                shutil.move(source_file_path, target_file_path)\n                # Increment the moved files count\n                moved_files_count += 1\n            except Exception as e:\n                print(f\"Error moving file {filename}: {e}\")\n\n    # Return the number of moved files\n    return moved_files_count",
        "status": "fail",
        "details": {
          "test_target_directory_creation": "Traceback (most recent call last):\n  File \"__test__.py\", line 90, in test_target_directory_creation\nAssertionError: False is not true\n"
        }
      }
    ],
    "BigCodeBench/857": [
      {
        "task_id": "BigCodeBench/857",
        "solution": "import warnings\nimport os\nimport glob\nimport shutil\nimport time\ndef task_func(SOURCE_DIR, DEST_DIR, EXTENSIONS):\n\n    pass\nimport warnings\nimport os\nimport glob\nimport shutil\n\ndef task_func(SOURCE_DIR, DEST_DIR, EXTENSIONS):\n    # 1. Input Validation\n    if not os.path.isdir(SOURCE_DIR):\n        raise ValueError(f\"Source directory '{SOURCE_DIR}' is not valid or does not exist.\")\n    if not os.path.isdir(DEST_DIR):\n        raise ValueError(f\"Destination directory '{DEST_DIR}' is not valid or does not exist.\")\n    if not isinstance(EXTENSIONS, list) or not all(isinstance(ext, str) for ext in EXTENSIONS):\n        raise ValueError(\"EXTENSIONS must be a list of strings representing file extensions.\")\n    \n    # Handle case where no extensions are provided\n    if not EXTENSIONS:\n        warnings.warn(\"No file extensions provided; no files will be transferred.\")\n        return []\n\n    transferred_files = []\n\n    # 2. File Filtering\n    for ext in EXTENSIONS:\n        pattern = os.path.join(SOURCE_DIR, f\"*{ext}\")\n        files = glob.glob(pattern)\n        \n        # 3. File Transfer\n        for file in files:\n            try:\n                shutil.move(file, DEST_DIR)\n                transferred_files.append(os.path.basename(file))\n            except (FileNotFoundError, PermissionError) as e:\n                warnings.warn(f\"Could not transfer '{file}': {str(e)}\")\n\n    return transferred_files",
        "status": "fail",
        "details": {
          "test_all_extensions": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 98, in test_all_extensions\n  File \"__test__.py\", line 38, in task_func\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1134, in __call__\n    return self._mock_call(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1138, in _mock_call\n    return self._execute_mock_call(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1195, in _execute_mock_call\n    result = next(effect)\n             ^^^^^^^^^^^^\nStopIteration\n",
          "test_exception_handling": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 91, in test_exception_handling\n  File \"__test__.py\", line 38, in task_func\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1134, in __call__\n    return self._mock_call(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1138, in _mock_call\n    return self._execute_mock_call(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1193, in _execute_mock_call\n    raise effect\nException: Permission denied\n",
          "test_successful_transfer": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 70, in test_successful_transfer\n  File \"__test__.py\", line 38, in task_func\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1134, in __call__\n    return self._mock_call(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1138, in _mock_call\n    return self._execute_mock_call(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1195, in _execute_mock_call\n    result = next(effect)\n             ^^^^^^^^^^^^\nStopIteration\n"
        }
      }
    ],
    "BigCodeBench/760": [
      {
        "task_id": "BigCodeBench/760",
        "solution": "import pandas as pd\nimport numpy as np\nimport codecs\nimport re\nimport datetime\ndef task_func(start_year=1980, end_year=2000, email_domain='example.com',\n           latin_names=['Sopet\u00f3n', 'M\u00e9ndez', 'G\u00f3mez', 'P\u00e9rez', 'Mu\u00f1oz'],\n           other_names=['Smith', 'Johnson', 'Williams', 'Brown', 'Jones'], \n           rng_seed=None):\n\n    pass\nimport pandas as pd\nimport numpy as np\nimport re\nimport datetime\n\ndef task_func(start_year=1980, end_year=2000, email_domain='example.com',\n               latin_names=['Sopet\u00f3n', 'M\u00e9ndez', 'G\u00f3mez', 'P\u00e9rez', 'Mu\u00f1oz'],\n               other_names=['Smith', 'Johnson', 'Williams', 'Brown', 'Jones'], \n               rng_seed=None):\n    # Set random seed for reproducibility\n    if rng_seed is not None:\n        np.random.seed(rng_seed)\n\n    # 1. ID Generation\n    ids = np.arange(1, 101)\n\n    # 2. Name Generation\n    all_names = latin_names + other_names\n    names = np.random.choice(all_names, size=100)\n\n    # 3. Date of Birth Generation\n    birth_years = np.random.randint(start_year, end_year + 1, size=100)\n    birth_months = np.random.randint(1, 13, size=100)\n    birth_days = []\n\n    # Generate valid days for the corresponding months\n    for year, month in zip(birth_years, birth_months):\n        if month in [1, 3, 5, 7, 8, 10, 12]:\n            day = np.random.randint(1, 32)\n        elif month in [4, 6, 9, 11]:\n            day = np.random.randint(1, 31)\n        else:  # February\n            if (year % 4 == 0 and year % 100 != 0) or (year % 400 == 0):\n                day = np.random.randint(1, 30)  # Leap year\n            else:\n                day = np.random.randint(1, 29)  # Non-leap year\n        birth_days.append(day)\n\n    dates_of_birth = [datetime.date(year, month, day) for year, month, day in zip(birth_years, birth_months, birth_days)]\n\n    # 4. Email Construction\n    emails = []\n    for name, dob in zip(names, dates_of_birth):\n        year_of_birth = dob.year\n        # Correct encoding\n        normalized_name = re.sub(r'[^a-zA-Z]', '', name).lower()  # Remove special characters and lowercase\n        email = f\"{normalized_name}{year_of_birth}@{email_domain}\"\n        emails.append(email)\n\n    # 5. Compile into a DataFrame\n    df = pd.DataFrame({\n        'ID': ids,\n        'Name': names,\n        'Date of Birth': dates_of_birth,\n        'Email': emails\n    })\n\n    return df\n\n# Example usage\ndf = task_func(start_year=0, end_year=1200, email_domain='test.at', rng_seed=3)\nprint(df)",
        "status": "pass",
        "details": {}
      }
    ],
    "BigCodeBench/777": [
      {
        "task_id": "BigCodeBench/777",
        "solution": "import re\nimport os\nimport zipfile\ndef task_func(directory, pattern=r'^(.*?)-\\d+\\.zip$'):\n\n    pass\nimport re\nimport os\nimport zipfile\n\ndef task_func(directory, pattern=r'^(.*?)-\\d+\\.zip$'):\n    extracted_dirs = []  # List to hold the paths of extracted directories\n    # Step 1: List all files in the directory\n    for filename in os.listdir(directory):\n        # Step 2: Check if the file matches the zip pattern using regex\n        match = re.match(pattern, filename)\n        if match:\n            # Step 3: Extract the prefix part from the filename\n            prefix = filename.rsplit('-', 1)[0]\n            # Step 4: Create a new directory based on the prefix\n            new_dir = os.path.join(directory, prefix)\n            os.makedirs(new_dir, exist_ok=True)\n            # Step 5: Unzip the contents into the new directory\n            try:\n                with zipfile.ZipFile(os.path.join(directory, filename), 'r') as zip_ref:\n                    zip_ref.extractall(new_dir)\n                # Step 6: Record the directory where files were extracted\n                extracted_dirs.append(new_dir)\n            except zipfile.BadZipFile:\n                print(f\"Error: The file {filename} is not a zip file or it is corrupted.\")\n            except Exception as e:\n                print(f\"An error occurred while extracting {filename}: {e}\")\n    return extracted_dirs",
        "status": "pass",
        "details": {}
      }
    ],
    "BigCodeBench/854": [
      {
        "task_id": "BigCodeBench/854",
        "solution": "from functools import reduce\nfrom itertools import permutations\nimport math\ndef task_func(numbers):\n\n    pass\nfrom functools import reduce\nfrom itertools import permutations\nimport math\n\ndef task_func(numbers):\n    # Input validation\n    if not isinstance(numbers, list):\n        raise TypeError(\"Input must be a list of integers.\")\n    if any(not isinstance(num, int) for num in numbers):\n        raise TypeError(\"All elements of the list must be integers.\")\n    if any(num < 0 for num in numbers):\n        raise ValueError(\"Input numbers must be non-negative.\")\n    \n    # Generate permutations\n    perms = list(permutations(numbers))\n    \n    # Precompute factorials to optimize\n    factorials = {num: math.factorial(num) for num in set(numbers)}\n    \n    # Calculate the sum of factorials for each permutation\n    factorial_sums = []\n    for perm in perms:\n        sum_of_factorials = sum(factorials[num] for num in perm)\n        factorial_sums.append(sum_of_factorials)\n    \n    return factorial_sums, perms",
        "status": "fail",
        "details": {
          "test_case_4": "Traceback (most recent call last):\n  File \"__test__.py\", line 57, in test_case_4\nAssertionError: Lists differ: [0] != []\n\nFirst list contains 1 additional elements.\nFirst extra element 0:\n0\n\n- [0]\n?  -\n\n+ []\n"
        }
      }
    ],
    "BigCodeBench/752": [
      {
        "task_id": "BigCodeBench/752",
        "solution": "import pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\nimport numpy as np\ndef task_func(data, target_column, test_size=0.2, random_state = 0) -> float:\n\n    pass\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\nimport numpy as np\n\ndef task_func(data, target_column, test_size=0.2, random_state=0) -> float:\n    # 1. Data Validation\n    if not isinstance(data, pd.DataFrame):\n        raise ValueError(\"The provided data is not a DataFrame.\")\n    if data.empty:\n        raise ValueError(\"The provided DataFrame is empty.\")\n\n    # 2. Target Column Validation\n    if target_column not in data.columns:\n        raise ValueError(f\"The target_column '{target_column}' is not in the DataFrame.\")\n\n    # 3. Type Checking for Numeric Values\n    if not pd.api.types.is_numeric_dtype(data[target_column]):\n        raise ValueError(f\"The target_column '{target_column}' must be numeric.\")\n    for col in data.columns:\n        if not pd.api.types.is_numeric_dtype(data[col]):\n            raise ValueError(f\"Column '{col}' must contain numeric values.\")\n\n    # 4. Random State Validation\n    if not isinstance(random_state, int):\n        raise ValueError(\"random_state must be an integer.\")\n\n    # 5. Test Size Validation\n    if not (0 < test_size < 1):\n        raise ValueError(\"test_size must be a float between 0 and 1.\")\n\n    # 6. Data Splitting\n    X = data.drop(columns=[target_column])\n    y = data[target_column]\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=random_state)\n\n    # 7. Model Training\n    model = LinearRegression()\n    model.fit(X_train, y_train)\n\n    # 8. Model Evaluation\n    score = model.score(X_test, y_test)\n    return score",
        "status": "pass",
        "details": {}
      }
    ],
    "BigCodeBench/865": [
      {
        "task_id": "BigCodeBench/865",
        "solution": "import pandas as pd\nimport numpy as np\nfrom scipy.stats import zscore\nfrom sklearn.preprocessing import MinMaxScaler\ndef task_func(data):\n\n    pass\nimport pandas as pd\nimport numpy as np\nfrom scipy.stats import zscore\nfrom sklearn.preprocessing import MinMaxScaler\n\ndef task_func(data):\n    # Convert input data into a DataFrame\n    df = pd.DataFrame(data, columns=['Item', 'Count', 'Weight'])\n    \n    # Normalize counts using z-score normalization\n    df['Normalized Count'] = zscore(df['Count'])\n    \n    # Normalize weights using min-max scaling\n    scaler = MinMaxScaler()\n    df['Normalized Weight'] = scaler.fit_transform(df[['Weight']])\n    \n    # Select the relevant columns for the result\n    result_df = df[['Item', 'Normalized Count', 'Normalized Weight']]\n    \n    return result_df",
        "status": "fail",
        "details": {
          "test_return_value": "Traceback (most recent call last):\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/internals/construction.py\", line 934, in _finalize_columns_and_data\n    columns = _validate_or_indexify_columns(contents, columns)\n              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/internals/construction.py\", line 981, in _validate_or_indexify_columns\n    raise AssertionError(\nAssertionError: 3 columns passed, passed data had 4 columns\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File \"__test__.py\", line 84, in test_return_value\n  File \"__test__.py\", line 15, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 782, in __init__\n    arrays, columns, index = nested_data_to_arrays(\n                             ^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/internals/construction.py\", line 498, in nested_data_to_arrays\n    arrays, columns = to_arrays(data, columns, dtype=dtype)\n                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/internals/construction.py\", line 840, in to_arrays\n    content, columns = _finalize_columns_and_data(arr, columns, dtype)\n                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/internals/construction.py\", line 937, in _finalize_columns_and_data\n    raise ValueError(err) from err\nValueError: 3 columns passed, passed data had 4 columns\n"
        }
      }
    ],
    "BigCodeBench/870": [
      {
        "task_id": "BigCodeBench/870",
        "solution": "import pandas as pd\nimport numpy as np\nimport itertools\ndef task_func(data_list=[('a', 1, 2.1), ('b', 2, 3.2), ('c', 3, 4.3), ('d', 4, 5.4), ('e', 5, 6.5)]):\n\n    pass\nimport pandas as pd\nimport numpy as np\n\ndef task_func(data_list=[('a', 1, 2.1), ('b', 2, 3.2), ('c', 3, 4.3), ('d', 4, 5.4), ('e', 5, 6.5)]):\n    # Step 4: Handle Edge Cases\n    if not data_list:\n        return pd.DataFrame(columns=['Mean Value'])\n\n    # Step 1: Extracting numeric values\n    position_values = {}\n\n    for tup in data_list:\n        for i, value in enumerate(tup):\n            if i not in position_values:\n                position_values[i] = []\n            if isinstance(value, (int, float)):\n                position_values[i].append(value)\n\n    # Step 2: Calculating means\n    mean_values = []\n    max_position = max(position_values.keys()) if position_values else -1\n\n    for i in range(max_position + 1):\n        if i in position_values and position_values[i]:\n            mean_values.append(np.mean(position_values[i]))\n        else:\n            mean_values.append(np.nan)\n\n    # Step 3: Constructing DataFrame\n    df = pd.DataFrame(mean_values, index=[f'Position {i}' for i in range(len(mean_values))], columns=['Mean Value'])\n\n    return df",
        "status": "pass",
        "details": {}
      }
    ],
    "BigCodeBench/906": [
      {
        "task_id": "BigCodeBench/906",
        "solution": "import zipfile\nimport os\nimport re\nimport shutil\ndef task_func(source_dir: str, target_dir: str, archive_name: str = 'archive.zip') -> str:\n\n    pass\n\\nimport zipfile\\nimport os\\nimport re\\n\\ndef task_func(source_dir: str, target_dir: str, archive_name: str = 'archive.zip') -> str:\\n    # Step 1: Identify processed files\\n    processed_files = [f for f in os.listdir(source_dir) if f.endswith('_processed') and os.path.isfile(os.path.join(source_dir, f))]\\n    \\n    # Step 2: Create target directory if it does not exist\\n    if not os.path.exists(target_dir):\\n        os.makedirs(target_dir)\\n\\n    # Step 3: Archive files if there are any processed files\\n    if processed_files:\\n        archive_path = os.path.join(target_dir, archive_name)\\n        with zipfile.ZipFile(archive_path, 'w') as archive:\\n            for processed_file in processed_files:\\n                file_path = os.path.join(source_dir, processed_file)\\n                archive.write(file_path, arcname=processed_file)  # Add file to archive\\n    else:\\n        raise FileNotFoundError('No processed files found to archive.')\\n\\n    # Step 4: Return archive path\\n    return archive_path\\n",
        "status": "fail",
        "details": {
          "ALL": "unexpected character after line continuation character (__test__.py, line 8)"
        }
      }
    ],
    "BigCodeBench/845": [
      {
        "task_id": "BigCodeBench/845",
        "solution": "import re\nimport numpy as np\nfrom collections import Counter\nfrom Levenshtein import ratio\n# Constants\nALPHANUMERIC = re.compile('[\\W_]+')\ndef task_func(text1, text2):\n\n    pass\nimport re\nimport numpy as np\nfrom collections import Counter\nfrom Levenshtein import ratio\n\n# Constants\nALPHANUMERIC = re.compile('[\\\\W_]+')\n\ndef task_func(text1, text2):\n    # Step 1: Clean the texts\n    cleaned_text1 = ALPHANUMERIC.sub(' ', text1).lower().strip()\n    cleaned_text2 = ALPHANUMERIC.sub(' ', text2).lower().strip()\n    \n    # Step 2: Tokenize and count term frequency\n    tokens1 = cleaned_text1.split()\n    tokens2 = cleaned_text2.split()\n    \n    freq1 = Counter(tokens1)\n    freq2 = Counter(tokens2)\n    \n    # Get all unique terms\n    all_terms = set(freq1.keys()).union(set(freq2.keys()))\n    \n    # Create term frequency vectors\n    vector1 = np.array([freq1[term] for term in all_terms])\n    vector2 = np.array([freq2[term] for term in all_terms])\n    \n    # Step 3: Calculate cosine similarity\n    dot_product = np.dot(vector1, vector2)\n    norm_a = np.linalg.norm(vector1)\n    norm_b = np.linalg.norm(vector2)\n    \n    # Handle cases where one or both vectors have no magnitude\n    if norm_a == 0 or norm_b == 0:\n        cosine_similarity = 0.0  # indicates no similarity possible\n    else:\n        cosine_similarity = dot_product / (norm_a * norm_b)\n    \n    # Step 4: Calculate Levenshtein ratio\n    levenshtein_ratio = ratio(text1, text2)\n    \n    # Return both similarity values as a tuple\n    return (cosine_similarity, levenshtein_ratio)",
        "status": "pass",
        "details": {}
      }
    ],
    "BigCodeBench/800": [
      {
        "task_id": "BigCodeBench/800",
        "solution": "import csv\nimport os\nfrom collections import Counter\n# Constants\nCSV_FILE_PATH = 'match_data.csv'\ndef create_test_csv(filename, content):\n    with open(filename, 'w', newline='') as file:\n        writer = csv.writer(file)\n        writer.writerows(content)\n# Example usage in a test setup:\ndef setup_csv():\n    content = [\n        ['team', 'goals', 'penalties'],\n        ['Team A', '2', '1'],\n        ['Team B', '1', '2'],\n        ['Team C', '3', '0']\n    ]\n    create_test_csv('test_data/test_case_2.csv', content)\ndef task_func(goals, penalties, csv_file_path=CSV_FILE_PATH):\n\n    pass\nimport csv\nimport os\nfrom collections import Counter\n\n# Constants\nCSV_FILE_PATH = 'match_data.csv'\n\ndef create_test_csv(filename, content):\n    with open(filename, 'w', newline='') as file:\n        writer = csv.writer(file)\n        writer.writerows(content)\n\n# Example usage in a test setup:\ndef setup_csv():\n    content = [\n        ['team', 'goals', 'penalties'],\n        ['Team A', '2', '1'],\n        ['Team B', '1', '2'],\n        ['Team C', '3', '0']\n    ]\n    create_test_csv(CSV_FILE_PATH, content)\n\ndef read_csv_file(csv_file_path):\n    goals = 0\n    penalties = 0\n    with open(csv_file_path, newline='') as file:\n        reader = csv.DictReader(file)\n        for row in reader:\n            goals += int(row['goals'])\n            penalties += int(row['penalties'])\n    return goals, penalties\n\ndef update_csv_file(csv_file_path, updated_goals, updated_penalties):\n    # Read existing data\n    with open(csv_file_path, newline='') as file:\n        reader = list(csv.reader(file))\n    \n    # Update goals and penalties\n    for row in reader[1:]:\n        row[1] = str(int(row[1]) + updated_goals)  # update the total goals\n        row[2] = str(int(row[2]) + updated_penalties)  # update the total penalties\n\n    # Write back to the CSV\n    with open(csv_file_path, 'w', newline='') as file:\n        writer = csv.writer(file)\n        writer.writerows(reader)\n\ndef task_func(goals, penalties, csv_file_path=CSV_FILE_PATH):\n    # Read current totals from the CSV\n    current_goals, current_penalties = read_csv_file(csv_file_path)\n\n    # Update totals with new goals and penalties\n    updated_goals = current_goals + goals\n    updated_penalties = current_penalties + penalties\n\n    # Update the CSV file with new totals\n    update_csv_file(csv_file_path, updated_goals, updated_penalties)\n\n    # Prepare the Counter object\n    count = Counter({'goals': updated_goals, 'penalties': updated_penalties})\n\n    return count\n\n# Setup the CSV for the first time\nsetup_csv()\n\n# Example call to the task function\nresult = task_func(1, 1)  # updating with 1 goal and 1 penalty\nprint(result)",
        "status": "fail",
        "details": {
          "test_case_1": "Traceback (most recent call last):\n  File \"__test__.py\", line 105, in test_case_1\n  File \"__test__.py\", line 74, in task_func\nTypeError: unsupported operand type(s) for +: 'int' and 'dict'\n",
          "test_case_2": "Traceback (most recent call last):\n  File \"__test__.py\", line 115, in test_case_2\n  File \"__test__.py\", line 74, in task_func\nTypeError: unsupported operand type(s) for +: 'int' and 'dict'\n",
          "test_case_3": "Traceback (most recent call last):\n  File \"__test__.py\", line 125, in test_case_3\n  File \"__test__.py\", line 74, in task_func\nTypeError: unsupported operand type(s) for +: 'int' and 'dict'\n",
          "test_case_4": "Traceback (most recent call last):\n  File \"__test__.py\", line 136, in test_case_4\n  File \"__test__.py\", line 74, in task_func\nTypeError: unsupported operand type(s) for +: 'int' and 'dict'\n",
          "test_case_5": "Traceback (most recent call last):\n  File \"__test__.py\", line 146, in test_case_5\n  File \"__test__.py\", line 74, in task_func\nTypeError: unsupported operand type(s) for +: 'int' and 'dict'\n"
        }
      }
    ],
    "BigCodeBench/897": [
      {
        "task_id": "BigCodeBench/897",
        "solution": "import numpy as np\nimport matplotlib.pyplot as plt\nimport random\n# Constants\nNUMBERS = list(range(1, 7))  # Adjusting for dice rolls (1 to 6)\ndef task_func(rolls, seed=None):\n\n    pass\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport random\n\n# Constants\nNUMBERS = list(range(1, 7))  # Adjusting for dice rolls (1 to 6)\n\ndef task_func(rolls, seed=None):\n    if seed is not None:\n        random.seed(seed)\n\n    if rolls < 1:\n        return np.zeros(6, dtype=int), None  # Return empty frequency and no plot if non-positive rolls\n\n    # Step 1: Simulate the dice rolls\n    results = [random.choice(NUMBERS) for _ in range(rolls)]\n\n    # Step 2: Calculate frequencies using numpy\n    frequency = np.bincount(results)[1:7]  # Count occurrences of 1 to 6\n\n    # Step 3: Create histogram\n    fig, ax = plt.subplots()\n    ax.hist(results, bins=np.arange(1, 8) - 0.5, align='center', rwidth=0.8)\n    ax.set_title(\"Histogram of Dice Rolls\")\n    ax.set_xlabel(\"Dice Value\")\n    ax.set_ylabel(\"Frequency\")\n\n    # Step 4: Return the frequency array and the Axes object\n    return frequency, ax",
        "status": "fail",
        "details": {
          "test_case_1": "Traceback (most recent call last):\n  File \"__test__.py\", line 43, in test_case_1\n  File \"__test__.py\", line 31, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/matplotlib/__init__.py\", line 1442, in inner\n    return func(ax, *map(sanitize_sequence, args), **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/matplotlib/axes/_axes.py\", line 6696, in hist\n    _api.check_in_list(['left', 'mid', 'right'], align=align)\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/matplotlib/_api/__init__.py\", line 131, in check_in_list\n    raise ValueError(msg)\nValueError: 'center' is not a valid value for align; supported values are 'left', 'mid', 'right'\n",
          "test_case_2": "Traceback (most recent call last):\n  File \"__test__.py\", line 53, in test_case_2\nAttributeError: 'NoneType' object has no attribute 'get_title'\n",
          "test_case_3": "Traceback (most recent call last):\n  File \"__test__.py\", line 57, in test_case_3\n  File \"__test__.py\", line 31, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/matplotlib/__init__.py\", line 1442, in inner\n    return func(ax, *map(sanitize_sequence, args), **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/matplotlib/axes/_axes.py\", line 6696, in hist\n    _api.check_in_list(['left', 'mid', 'right'], align=align)\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/matplotlib/_api/__init__.py\", line 131, in check_in_list\n    raise ValueError(msg)\nValueError: 'center' is not a valid value for align; supported values are 'left', 'mid', 'right'\n",
          "test_case_4": "Traceback (most recent call last):\n  File \"__test__.py\", line 64, in test_case_4\n  File \"__test__.py\", line 31, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/matplotlib/__init__.py\", line 1442, in inner\n    return func(ax, *map(sanitize_sequence, args), **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/matplotlib/axes/_axes.py\", line 6696, in hist\n    _api.check_in_list(['left', 'mid', 'right'], align=align)\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/matplotlib/_api/__init__.py\", line 131, in check_in_list\n    raise ValueError(msg)\nValueError: 'center' is not a valid value for align; supported values are 'left', 'mid', 'right'\n",
          "test_case_5": "Traceback (most recent call last):\n  File \"__test__.py\", line 71, in test_case_5\n  File \"__test__.py\", line 31, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/matplotlib/__init__.py\", line 1442, in inner\n    return func(ax, *map(sanitize_sequence, args), **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/matplotlib/axes/_axes.py\", line 6696, in hist\n    _api.check_in_list(['left', 'mid', 'right'], align=align)\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/matplotlib/_api/__init__.py\", line 131, in check_in_list\n    raise ValueError(msg)\nValueError: 'center' is not a valid value for align; supported values are 'left', 'mid', 'right'\n"
        }
      }
    ],
    "BigCodeBench/928": [
      {
        "task_id": "BigCodeBench/928",
        "solution": "from collections import Counter\nimport itertools\nimport string\ndef task_func(word: str) -> dict:\n\n    pass\nfrom collections import Counter\nimport itertools\nimport string\n\ndef task_func(word: str) -> dict:\n    # Step 1: Generate all two-letter combinations from lowercase English letters\n    letters = string.ascii_lowercase\n    combinations = [''.join(pair) for pair in itertools.product(letters, repeat=2)]\n    \n    # Step 2: Count frequencies of each two-letter combination in the word\n    word_length = len(word)\n    two_letter_counts = Counter(word[i:i+2] for i in range(word_length - 1))\n    \n    # Step 3: Create the result dictionary with all combinations and their frequencies\n    result_dict = {combination: two_letter_counts.get(combination, 0) for combination in combinations}\n    \n    # Step 4: Return the populated dictionary\n    return result_dict",
        "status": "pass",
        "details": {}
      }
    ],
    "BigCodeBench/655": [
      {
        "task_id": "BigCodeBench/655",
        "solution": "import re\nimport nltk\nfrom sklearn.decomposition import NMF\nfrom sklearn.feature_extraction.text import TfidfVectorizer\n# Ensure nltk's stopwords are downloaded\nnltk.download('stopwords')\n# Constants\nALPHANUMERIC = re.compile('[\\W_]+')\nSTOPWORDS = nltk.corpus.stopwords.words('english')\ndef task_func(texts, num_topics):\n\n    pass\nimport re\nimport nltk\nfrom sklearn.decomposition import NMF\nfrom sklearn.feature_extraction.text import TfidfVectorizer\n\n# Ensure nltk's stopwords are downloaded\nnltk.download('stopwords')\n\n# Constants\nALPHANUMERIC = re.compile(r'[^\\w\\s]')  # More efficient regex for alphanumeric characters\nSTOPWORDS = nltk.corpus.stopwords.words('english')\n\ndef task_func(texts, num_topics):\n    # Step 1: Preprocess the texts\n    cleaned_texts = []\n    for text in texts:\n        # Remove non-alphanumeric characters, convert to lowercase, and remove stopwords\n        cleaned_text = ALPHANUMERIC.sub(' ', text).lower().strip()\n        cleaned_text = ' '.join(word for word in cleaned_text.split() if word not in STOPWORDS)\n        cleaned_texts.append(cleaned_text)\n\n    # Step 2: Vectorize the cleaned texts using TF-IDF\n    vectorizer = TfidfVectorizer()\n    tfidf_matrix = vectorizer.fit_transform(cleaned_texts)\n\n    # Step 3: Apply NMF to extract topics\n    nmf_model = NMF(n_components=num_topics, random_state=1)\n    nmf_model.fit(tfidf_matrix)\n\n    # Step 4: Extract significant words for each topic\n    topic_words = []\n    feature_names = vectorizer.get_feature_names_out()\n    for topic_idx, topic in enumerate(nmf_model.components_):\n        top_words_indices = topic.argsort()[:-11:-1]  # Get top 10 words\n        top_words = [feature_names[i] for i in top_words_indices]\n        topic_words.append(top_words)\n\n    # Step 5: Return the list of topics\n    return topic_words",
        "status": "fail",
        "details": {
          "test_all_stopwords": "Traceback (most recent call last):\n  File \"__test__.py\", line 81, in test_all_stopwords\n  File \"__test__.py\", line 36, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/feature_extraction/text.py\", line 2139, in fit_transform\n    X = super().fit_transform(raw_documents)\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/base.py\", line 1152, in wrapper\n    return fit_method(estimator, *args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/feature_extraction/text.py\", line 1389, in fit_transform\n    vocabulary, X = self._count_vocab(raw_documents, self.fixed_vocabulary_)\n                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/feature_extraction/text.py\", line 1295, in _count_vocab\n    raise ValueError(\nValueError: empty vocabulary; perhaps the documents only contain stop words\n",
          "test_empty_texts": "Traceback (most recent call last):\n  File \"__test__.py\", line 71, in test_empty_texts\n  File \"__test__.py\", line 36, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/feature_extraction/text.py\", line 2139, in fit_transform\n    X = super().fit_transform(raw_documents)\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/base.py\", line 1152, in wrapper\n    return fit_method(estimator, *args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/feature_extraction/text.py\", line 1389, in fit_transform\n    vocabulary, X = self._count_vocab(raw_documents, self.fixed_vocabulary_)\n                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/sklearn/feature_extraction/text.py\", line 1295, in _count_vocab\n    raise ValueError(\nValueError: empty vocabulary; perhaps the documents only contain stop words\n"
        }
      }
    ],
    "BigCodeBench/963": [
      {
        "task_id": "BigCodeBench/963",
        "solution": "import os\nimport glob\nfrom pathlib import Path\nimport zipfile\ndef task_func(source_directory, target_directory, zip_name):\n\n    pass\nimport os\nimport glob\nfrom pathlib import Path\nimport zipfile\n\ndef task_func(source_directory, target_directory, zip_name):\n    # Step 1: Check if the source directory exists\n    if not os.path.exists(source_directory):\n        raise OSError(f\"The source directory '{source_directory}' does not exist.\")\n    \n    # Check if the target directory exists; create if it doesn't\n    if not os.path.exists(target_directory):\n        os.makedirs(target_directory)\n    \n    # Step 2: Collect files with valid extensions\n    valid_extensions = ['.txt', '.docx', '.xlsx', '.csv']\n    collected_files = []\n    \n    for ext in valid_extensions:\n        collected_files.extend(glob.glob(os.path.join(source_directory, f'*{ext}')))\n    \n    # Step 3: Create the zip file path\n    zip_file_path = os.path.join(target_directory, f\"{zip_name}.zip\")\n    \n    # Step 4: Create zip file and add files\n    with zipfile.ZipFile(zip_file_path, 'w') as zip_file:\n        for file_path in collected_files:\n            zip_file.write(file_path, os.path.basename(file_path))\n    \n    # Step 5: Return the full path to the created zip file\n    return zip_file_path",
        "status": "fail",
        "details": {
          "test_case_5": "Traceback (most recent call last):\n  File \"__test__.py\", line 123, in test_case_5\nAssertionError: 0 != 2\n",
          "test_case_7": "Traceback (most recent call last):\n  File \"__test__.py\", line 139, in test_case_7\nAssertionError: 0 != 4\n"
        }
      }
    ],
    "BigCodeBench/915": [
      {
        "task_id": "BigCodeBench/915",
        "solution": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.stats import zscore\ndef task_func(df, z_threshold=2):\n\n    pass\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.stats import zscore\n\ndef task_func(df, z_threshold=2):\n    # Validate input DataFrame\n    if df.empty:\n        raise ValueError(\"Input DataFrame is empty.\")\n    if 'closing_price' not in df.columns:\n        raise ValueError(\"The DataFrame must contain a 'closing_price' column.\")\n    \n    # Step 1: Calculate Z-Scores for the 'closing_price' column\n    z_scores = zscore(df['closing_price'])\n    \n    # Step 2: Identify outliers based on the z_threshold\n    outlier_mask = np.abs(z_scores) > z_threshold\n    \n    # Step 3: Prepare the DataFrame containing outliers\n    outliers_df = df[outlier_mask]\n    \n    # Step 4: Plot the outliers\n    fig, ax = plt.subplots()\n    ax.scatter(df.index, df['closing_price'], label='Closing Prices', color='blue')\n    ax.scatter(outliers_df.index, outliers_df['closing_price'], label='Outliers', color='red')\n    \n    ax.set_xlabel('Index')\n    ax.set_ylabel('Closing Price')\n    ax.set_title('Outliers in Closing Prices')\n    ax.legend()\n    \n    # Step 5: Return the outliers DataFrame and the plot\n    return outliers_df, ax",
        "status": "fail",
        "details": {
          "test_case_2": "Traceback (most recent call last):\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 3653, in get_loc\n    return self._engine.get_loc(casted_key)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"pandas/_libs/index.pyx\", line 147, in pandas._libs.index.IndexEngine.get_loc\n  File \"pandas/_libs/index.pyx\", line 176, in pandas._libs.index.IndexEngine.get_loc\n  File \"pandas/_libs/hashtable_class_helper.pxi\", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item\n  File \"pandas/_libs/hashtable_class_helper.pxi\", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item\nKeyError: 'Z_score'\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File \"__test__.py\", line 59, in test_case_2\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 3761, in __getitem__\n    indexer = self.columns.get_loc(key)\n              ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 3655, in get_loc\n    raise KeyError(key) from err\nKeyError: 'Z_score'\n",
          "test_case_3": "Traceback (most recent call last):\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 3653, in get_loc\n    return self._engine.get_loc(casted_key)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"pandas/_libs/index.pyx\", line 147, in pandas._libs.index.IndexEngine.get_loc\n  File \"pandas/_libs/index.pyx\", line 176, in pandas._libs.index.IndexEngine.get_loc\n  File \"pandas/_libs/hashtable_class_helper.pxi\", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item\n  File \"pandas/_libs/hashtable_class_helper.pxi\", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item\nKeyError: 'Z_score'\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File \"__test__.py\", line 67, in test_case_3\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 3761, in __getitem__\n    indexer = self.columns.get_loc(key)\n              ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 3655, in get_loc\n    raise KeyError(key) from err\nKeyError: 'Z_score'\n",
          "test_case_4": "Traceback (most recent call last):\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 3653, in get_loc\n    return self._engine.get_loc(casted_key)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"pandas/_libs/index.pyx\", line 147, in pandas._libs.index.IndexEngine.get_loc\n  File \"pandas/_libs/index.pyx\", line 176, in pandas._libs.index.IndexEngine.get_loc\n  File \"pandas/_libs/hashtable_class_helper.pxi\", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item\n  File \"pandas/_libs/hashtable_class_helper.pxi\", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item\nKeyError: 'Z_score'\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File \"__test__.py\", line 74, in test_case_4\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 3761, in __getitem__\n    indexer = self.columns.get_loc(key)\n              ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 3655, in get_loc\n    raise KeyError(key) from err\nKeyError: 'Z_score'\n",
          "test_case_5": "Traceback (most recent call last):\n  File \"__test__.py\", line 80, in test_case_5\n  File \"__test__.py\", line 14, in task_func\nValueError: Input DataFrame is empty.\n"
        }
      }
    ],
    "BigCodeBench/879": [
      {
        "task_id": "BigCodeBench/879",
        "solution": "import pandas as pd\nimport numpy as np\nfrom scipy.stats import chi2_contingency\ndef task_func(data, col1, col2):\n\n    pass\nimport pandas as pd\nimport numpy as np\nfrom scipy.stats import chi2_contingency\n\ndef task_func(data, col1, col2):\n    # 1. Input Validation\n    if data.empty:\n        raise ValueError(\"DataFrame is empty.\")\n    if col1 not in data.columns or col2 not in data.columns:\n        raise ValueError(f\"Columns {col1} or {col2} are not in the DataFrame.\")\n    \n    # Check if the specified columns are categorical and have multiple categories\n    if data[col1].dtype.name != 'category' and data[col1].dtype != 'object':\n        raise TypeError(f\"Column {col1} contains non-categorical data.\")\n    if data[col2].dtype.name != 'category' and data[col2].dtype != 'object':\n        raise TypeError(f\"Column {col2} contains non-categorical data.\")\n    \n    if data[col1].nunique() < 2 or data[col2].nunique() < 2:\n        raise ValueError(f\"Columns {col1} and {col2} must have multiple categories.\")\n    \n    # Create the contingency table\n    contingency_table = pd.crosstab(data[col1], data[col2])\n    \n    # Check that no category has less than 5 observations\n    if (contingency_table < 5).any().any():\n        raise ValueError(\"Some categories have less than 5 observations, violating chi-square assumptions.\")\n    \n    # 2. Chi-Square Test Execution\n    chi2_stat, p_value, _, _ = chi2_contingency(contingency_table)\n    \n    # 3. Return the Result\n    return p_value",
        "status": "pass",
        "details": {}
      }
    ],
    "BigCodeBench/618": [
      {
        "task_id": "BigCodeBench/618",
        "solution": "from random import randint\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\n# Constants\nTEAMS = ['Team A', 'Team B', 'Team C', 'Team D', 'Team E']\nPENALTY_COST = 1000  # in dollars\ndef task_func(goals, penalties):\n\n    pass\nfrom random import randint\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\n\n# Constants\nTEAMS = ['Team A', 'Team B', 'Team C', 'Team D', 'Team E']\nPENALTY_COST = 1000  # in dollars\n\ndef task_func():\n    # Step 1: Generate Random Goals\n    goals = [randint(0, 5) for _ in TEAMS]\n    \n    # Step 2: Generate Random Penalties\n    # Let's assume each team could get 0 to 3 penalties\n    penalties = [randint(0, 3) for _ in TEAMS]\n    \n    # Step 3: Calculate Penalty Fines\n    penalty_fines = [penalty * PENALTY_COST for penalty in penalties]\n    \n    # Step 4: Construct the DataFrame\n    results_df = pd.DataFrame({\n        'Team': TEAMS,\n        'Goals': goals,\n        'Penalty Count': penalties,\n        'Penalty Fine ($)': penalty_fines\n    })\n    \n    # Step 5: Visualize the Data\n    fig, axes = plt.subplots(1, 2, figsize=(12, 6))\n    \n    # Goals plot\n    sns.barplot(x='Team', y='Goals', data=results_df, ax=axes[0], palette='viridis')\n    axes[0].set_title('Goals Scored by Each Team')\n    axes[0].set_ylabel('Goals')\n    \n    # Penalty Fine plot\n    sns.barplot(x='Team', y='Penalty Fine ($)', data=results_df, ax=axes[1], palette='rocket')\n    axes[1].set_title('Penalty Fines Incurred by Each Team')\n    axes[1].set_ylabel('Penalty Fine ($)')\n    \n    plt.tight_layout()\n    plt.show()\n    \n    # Step 6: Return results\n    return results_df, axes\n\n# You can call the function to test it\nresults, plots = task_func()",
        "status": "fail",
        "details": {
          "test_case_1": "Traceback (most recent call last):\n  File \"__test__.py\", line 67, in test_case_1\nTypeError: task_func() takes 0 positional arguments but 2 were given\n",
          "test_case_2": "Traceback (most recent call last):\n  File \"__test__.py\", line 82, in test_case_2\nTypeError: task_func() takes 0 positional arguments but 2 were given\n",
          "test_case_3": "Traceback (most recent call last):\n  File \"__test__.py\", line 91, in test_case_3\nTypeError: task_func() takes 0 positional arguments but 2 were given\n",
          "test_case_4": "Traceback (most recent call last):\n  File \"__test__.py\", line 101, in test_case_4\nTypeError: task_func() takes 0 positional arguments but 2 were given\n",
          "test_case_5": "Traceback (most recent call last):\n  File \"__test__.py\", line 109, in test_case_5\nTypeError: task_func() takes 0 positional arguments but 2 were given\n"
        }
      }
    ],
    "BigCodeBench/971": [
      {
        "task_id": "BigCodeBench/971",
        "solution": "import os\nfrom pathlib import Path\nfrom datetime import datetime, timezone\ndef task_func(directory_path: str):\n\n    pass\nimport os\nfrom datetime import datetime, timezone\n\ndef task_func(directory_path: str):\n    # Step 1: Directory Existence Check\n    if not os.path.exists(directory_path):\n        raise ValueError(\"The provided directory does not exist.\")\n    \n    # Step 2: File Listing\n    items = os.listdir(directory_path)\n    files = [item for item in items if os.path.isfile(os.path.join(directory_path, item))]\n\n    results = []\n    \n    # Step 3: File Metadata Retrieval\n    for file in files:\n        file_path = os.path.join(directory_path, file)\n        file_size = os.path.getsize(file_path)\n        creation_time = os.path.getctime(file_path)\n        modification_time = os.path.getmtime(file_path)\n        \n        # Convert timestamps to ISO format\n        creation_time_iso = datetime.fromtimestamp(creation_time, timezone.utc).isoformat()\n        modification_time_iso = datetime.fromtimestamp(modification_time, timezone.utc).isoformat()\n        \n        # Step 4: Result Compilation\n        results.append((file, file_size, creation_time_iso, modification_time_iso))\n    \n    # Step 5: Return Statement\n    return results",
        "status": "fail",
        "details": {
          "test_case_9": "Traceback (most recent call last):\n  File \"__test__.py\", line 118, in test_case_9\n  File \"__test__.py\", line 16, in task_func\nNotADirectoryError: [Errno 20] Not a directory: '/tmp/tmpjbgbybjj'\n"
        }
      }
    ],
    "BigCodeBench/955": [
      {
        "task_id": "BigCodeBench/955",
        "solution": "import numpy as np\nimport matplotlib.pyplot as plt\nimport re\nfrom collections import Counter\ndef task_func(mystrings, text):\n\n    pass\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport re\nfrom collections import Counter\n\ndef task_func(mystrings, text):\n    # Step 1: Input Validation\n    if not text:\n        raise ValueError(\"Input text cannot be empty.\")\n    \n    # Step 2: Normalization\n    text = text.lower()\n    \n    # Step 3: Replacement\n    modified_text = re.sub(r'\\s+', '_', text.strip())  # Also strip leading/trailing spaces\n    \n    # Step 4: Frequency Calculation\n    words = modified_text.split('_')\n    frequency = Counter(words)\n    \n    # Step 5: Plotting\n    unique_words = list(frequency.keys())\n    counts = list(frequency.values())\n\n    fig, ax = plt.subplots()\n    ax.bar(unique_words, counts)\n    ax.set_xlabel('Words')\n    ax.set_ylabel('Frequency')\n    ax.set_title('Frequency of Unique Words')\n    plt.xticks(rotation=45)\n    \n    # Step 6: Return the Axes object\n    return ax",
        "status": "fail",
        "details": {
          "test_case_2": "Traceback (most recent call last):\n  File \"__test__.py\", line 56, in test_case_2\nAssertionError: 'hello' != 'hello_world!'\n- hello\n+ hello_world!\n\n",
          "test_case_3": "Traceback (most recent call last):\n  File \"__test__.py\", line 63, in test_case_3\nAssertionError: False is not true\n",
          "test_case_4": "Traceback (most recent call last):\n  File \"__test__.py\", line 72, in test_case_4\nAssertionError: False is not true\n",
          "test_case_5": "Traceback (most recent call last):\n  File \"__test__.py\", line 77, in test_case_5\nAssertionError: 'hello_world!' not found in ['hello', 'world!']\n",
          "test_case_6": "Traceback (most recent call last):\n  File \"__test__.py\", line 84, in test_case_6\nAssertionError: 'Hello_World!' not found in ['hello', 'world!']\n",
          "test_case_9": "Traceback (most recent call last):\n  File \"__test__.py\", line 101, in test_case_9\nAssertionError: 'test_123' not found in ['test', '123', 'is', 'fun.', '#$%!']\n"
        }
      }
    ],
    "BigCodeBench/914": [
      {
        "task_id": "BigCodeBench/914",
        "solution": "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.linear_model import LinearRegression\ndef task_func(df):\n\n    pass\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.linear_model import LinearRegression\n\ndef task_func(df):\n    # Step 1: Prepare the Input Data\n    if df.empty or 'Date' not in df.columns or 'Close' not in df.columns:\n        raise ValueError(\"DataFrame must contain 'Date' and 'Close' columns.\")\n    \n    df['Date'] = pd.to_datetime(df['Date'])\n    df = df.sort_values('Date')\n    \n    # Extracting dates and prices\n    X = df['Date'].map(pd.Timestamp.toordinal).values.reshape(-1, 1)  # Convert dates to ordinal format\n    y = df['Close'].values  # Prices\n    \n    # Step 2: Generate Future Timestamps\n    last_date = df['Date'].max()\n    future_dates = [last_date + pd.Timedelta(days=i+1) for i in range(7)]\n    future_X = np.array([pd.Timestamp(date).toordinal() for date in future_dates]).reshape(-1, 1)\n    \n    # Step 3: Train the Linear Regression Model\n    model = LinearRegression()\n    model.fit(X, y)\n    \n    # Step 4: Make Predictions\n    predicted_prices = model.predict(future_X)\n    \n    # Step 5: Plot the Data\n    plt.figure(figsize=(10, 5))\n    plt.plot(df['Date'], df['Close'], label='Historical Prices', marker='o')\n    plt.plot(future_dates, predicted_prices, label='Predicted Prices', marker='x', linestyle='--')\n    plt.title('Stock Closing Prices Prediction')\n    plt.xlabel('Date')\n    plt.ylabel('Closing Price')\n    plt.xticks(rotation=45)\n    plt.legend()\n    plt.tight_layout()\n    ax = plt.gca()  # Get the current Axes object\n    \n    # Step 6: Return Output\n    return list(predicted_prices), ax\n\n# Example of using the function with a DataFrame\n# df = pd.DataFrame({\n#     'Date': pd.date_range(start='2023-01-01', periods=30, freq='D'),\n#     'Close': np.random.rand(30) * 100  # Random prices for example\n# })\n# predicted_prices, plot_ax = task_func(df)\n# plt.show()  # To display the plot",
        "status": "fail",
        "details": {
          "test_case_1": "Traceback (most recent call last):\n  File \"__test__.py\", line 67, in test_case_1\n  File \"__test__.py\", line 16, in task_func\nValueError: DataFrame must contain 'Date' and 'Close' columns.\n",
          "test_case_2": "Traceback (most recent call last):\n  File \"__test__.py\", line 75, in test_case_2\n  File \"__test__.py\", line 16, in task_func\nValueError: DataFrame must contain 'Date' and 'Close' columns.\n",
          "test_case_3": "Traceback (most recent call last):\n  File \"__test__.py\", line 83, in test_case_3\n  File \"__test__.py\", line 16, in task_func\nValueError: DataFrame must contain 'Date' and 'Close' columns.\n",
          "test_case_4": "Traceback (most recent call last):\n  File \"__test__.py\", line 91, in test_case_4\n  File \"__test__.py\", line 16, in task_func\nValueError: DataFrame must contain 'Date' and 'Close' columns.\n",
          "test_case_5": "Traceback (most recent call last):\n  File \"__test__.py\", line 99, in test_case_5\n  File \"__test__.py\", line 16, in task_func\nValueError: DataFrame must contain 'Date' and 'Close' columns.\n"
        }
      }
    ],
    "BigCodeBench/945": [
      {
        "task_id": "BigCodeBench/945",
        "solution": "import pandas as pd\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression\ndef task_func(start_date='2016-01-01', periods=13, freq='WOM-2FRI', sales_data=None):\n\n    pass\nimport pandas as pd\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression\n\ndef task_func(start_date='2016-01-01', periods=13, freq='WOM-2FRI', sales_data=None):\n    # 1. Generating Sales Data\n    if sales_data is None:\n        date_range = pd.date_range(start=start_date, periods=periods, freq=freq)\n        sales_data = np.random.randint(100, 1000, size=periods)  # Generating random sales between 100 and 1000\n\n    # Creating a DataFrame for ease of use\n    df = pd.DataFrame({'date': date_range, 'sales': sales_data})\n    \n    # 2. Preparing Data for Linear Regression\n    df['time'] = np.arange(len(df))  # Convert date index to a numerical feature\n    X = df[['time']]  # Feature\n    y = df['sales']   # Target variable\n\n    # 3. Fitting the Linear Regression Model\n    model = LinearRegression()\n    model.fit(X, y)\n\n    # 4. Forecasting Future Sales\n    future_time = np.arange(len(df), len(df) + periods).reshape(-1, 1)  # Future time points\n    future_sales = model.predict(future_time)  # Predicting future sales\n\n    # 5. Outputting the Result\n    return future_sales\n\n# Example usage\nforecasted_sales = task_func()\nprint(forecasted_sales)",
        "status": "fail",
        "details": {
          "test_forecasted_values_increasing": "Traceback (most recent call last):\n  File \"__test__.py\", line 63, in test_forecasted_values_increasing\n  File \"__test__.py\", line 18, in task_func\nUnboundLocalError: cannot access local variable 'date_range' where it is not associated with a value\n",
          "test_with_custom_parameters": "Traceback (most recent call last):\n  File \"__test__.py\", line 50, in test_with_custom_parameters\n  File \"__test__.py\", line 18, in task_func\nUnboundLocalError: cannot access local variable 'date_range' where it is not associated with a value\n",
          "test_with_specific_sales_data": "Traceback (most recent call last):\n  File \"__test__.py\", line 69, in test_with_specific_sales_data\n  File \"__test__.py\", line 18, in task_func\nUnboundLocalError: cannot access local variable 'date_range' where it is not associated with a value\n"
        }
      }
    ],
    "BigCodeBench/969": [
      {
        "task_id": "BigCodeBench/969",
        "solution": "import numpy as np\nfrom sklearn.preprocessing import MinMaxScaler\nimport pandas as pd\ndef task_func(df: pd.DataFrame) -> pd.DataFrame:\n\n    pass\nimport numpy as np\nfrom sklearn.preprocessing import MinMaxScaler\nimport pandas as pd\n\ndef task_func(df: pd.DataFrame) -> pd.DataFrame:\n    # Step 1: Input Validation\n    if df.empty:\n        raise ValueError(\"DataFrame is empty\")\n    if df.isna().any().any():\n        raise ValueError(\"DataFrame contains NaN values\")\n    if not np.issubdtype(df.dtypes, np.number).all():\n        raise TypeError(\"DataFrame contains non-numeric data types\")\n    \n    # Step 2: Compute cumulative sums\n    cum_sum_df = df.cumsum()\n\n    # Step 3: Normalization using MinMaxScaler\n    scaler = MinMaxScaler()\n    normalized_data = scaler.fit_transform(cum_sum_df)\n\n    # Step 4: Create a new DataFrame with the same column names\n    normalized_df = pd.DataFrame(normalized_data, columns=df.columns)\n\n    # Step 5: Return the normalized cumulative sum DataFrame\n    return normalized_df",
        "status": "fail",
        "details": {
          "test_all_zeros": "Traceback (most recent call last):\n  File \"__test__.py\", line 54, in test_all_zeros\n  File \"__test__.py\", line 37, in check_cumsum_and_scaling\n  File \"__test__.py\", line 17, in task_func\nAttributeError: 'bool' object has no attribute 'all'\n",
          "test_incremental_values": "Traceback (most recent call last):\n  File \"__test__.py\", line 44, in test_incremental_values\n  File \"__test__.py\", line 37, in check_cumsum_and_scaling\n  File \"__test__.py\", line 17, in task_func\nAttributeError: 'bool' object has no attribute 'all'\n",
          "test_negative_numbers": "Traceback (most recent call last):\n  File \"__test__.py\", line 49, in test_negative_numbers\n  File \"__test__.py\", line 37, in check_cumsum_and_scaling\n  File \"__test__.py\", line 17, in task_func\nAttributeError: 'bool' object has no attribute 'all'\n",
          "test_non_numeric_data_raises": "Traceback (most recent call last):\n  File \"__test__.py\", line 63, in test_non_numeric_data_raises\n  File \"__test__.py\", line 17, in task_func\nAttributeError: 'bool' object has no attribute 'all'\n",
          "test_same_numbers": "Traceback (most recent call last):\n  File \"__test__.py\", line 59, in test_same_numbers\n  File \"__test__.py\", line 37, in check_cumsum_and_scaling\n  File \"__test__.py\", line 17, in task_func\nAttributeError: 'bool' object has no attribute 'all'\n"
        }
      }
    ],
    "BigCodeBench/988": [
      {
        "task_id": "BigCodeBench/988",
        "solution": "import os\nimport re\nfrom pathlib import Path\ndef task_func(dir_path: str, predicates: list) -> dict:\n\n    pass\nimport os\nimport re\nfrom pathlib import Path\n\ndef task_func(dir_path: str, predicates: list) -> dict:\n    # Step 1: Validate the Input Directory\n    if not os.path.exists(dir_path) or not os.path.isdir(dir_path):\n        raise FileNotFoundError(f\"{dir_path} does not exist or is not a directory.\")\n    \n    # Step 2: Validate the Predicates\n    if not predicates:\n        raise ValueError(\"No valid predicates provided.\")\n    \n    # Step 3: Deduplicate Predicates\n    unique_predicates = set(predicates)\n    \n    # Step 4: Evaluate Directory Contents\n    result = {}\n    for item in os.listdir(dir_path):\n        matches = []\n        for predicate in unique_predicates:\n            if re.match(predicate, item):  # Check if the item matches the predicate\n                matches.append(predicate)\n        result[item] = matches  # Store matches for the item\n    \n    # Step 5: Return the Results\n    return result",
        "status": "fail",
        "details": {
          "test_deduplicate_predicates": "Traceback (most recent call last):\n  File \"__test__.py\", line 143, in test_deduplicate_predicates\n  File \"__test__.py\", line 66, in helper_assert_predicate\nAssertionError: False is not true\n",
          "test_invalid_predicates": "Traceback (most recent call last):\n  File \"__test__.py\", line 148, in test_invalid_predicates\nAssertionError: ValueError not raised\n",
          "test_multiple_predicates": "Traceback (most recent call last):\n  File \"__test__.py\", line 133, in test_multiple_predicates\n  File \"__test__.py\", line 66, in helper_assert_predicate\nAssertionError: False is not true\n",
          "test_dir_is_dir": "Traceback (most recent call last):\n  File \"__test__.py\", line 92, in test_dir_is_dir\nTypeError: list indices must be integers or slices, not str\n",
          "test_dir_is_not_file": "Traceback (most recent call last):\n  File \"__test__.py\", line 100, in test_dir_is_not_file\nTypeError: list indices must be integers or slices, not str\n",
          "test_file_is_file": "Traceback (most recent call last):\n  File \"__test__.py\", line 76, in test_file_is_file\nTypeError: list indices must be integers or slices, not str\n",
          "test_file_is_not_dir": "Traceback (most recent call last):\n  File \"__test__.py\", line 84, in test_file_is_not_dir\nTypeError: list indices must be integers or slices, not str\n",
          "test_has_no_special_char": "Traceback (most recent call last):\n  File \"__test__.py\", line 118, in test_has_no_special_char\nTypeError: list indices must be integers or slices, not str\n",
          "test_has_numbers": "Traceback (most recent call last):\n  File \"__test__.py\", line 127, in test_has_numbers\nTypeError: list indices must be integers or slices, not str\n",
          "test_has_special_char": "Traceback (most recent call last):\n  File \"__test__.py\", line 109, in test_has_special_char\nTypeError: list indices must be integers or slices, not str\n"
        }
      }
    ],
    "BigCodeBench/985": [
      {
        "task_id": "BigCodeBench/985",
        "solution": "import pandas as pd\nimport json\nimport os\nimport math\ndef task_func(json_data, output_dir=\".\", file_name=\"country_population_report.csv\"):\n\n    pass\nimport pandas as pd\nimport json\nimport os\nimport math\n\ndef task_func(json_data, output_dir=\".\", file_name=\"country_population_report.csv\"):\n    # Step 1: Data Validation\n    try:\n        data = json.loads(json_data)\n        \n        if not data or not isinstance(data, list):\n            raise ValueError(\"Input JSON data is malformed or empty.\")\n        \n        for entry in data:\n            # Validate country name\n            if not isinstance(entry.get(\"Country\"), str):\n                raise ValueError(\"Country names must be strings.\")\n            # Validate population\n            if not isinstance(entry.get(\"Population\"), (int, float)) or entry[\"Population\"] < 0:\n                raise ValueError(\"Population values must be non-negative numbers.\")\n\n    except json.JSONDecodeError:\n        raise ValueError(\"Input JSON data is malformed.\")\n    \n    # Step 2: Data Processing\n    processed_data = []\n    for entry in data:\n        country = entry[\"Country\"]\n        population = math.floor(entry[\"Population\"])\n        processed_data.append({\"Country\": country, \"Population\": population})\n    \n    # Step 3: Creating the DataFrame\n    df = pd.DataFrame(processed_data)\n    \n    # Step 4: CSV File Output\n    output_path = os.path.join(output_dir, file_name)\n    \n    try:\n        df.to_csv(output_path, index=False)\n    except IOError as e:\n        raise IOError(f\"Unable to write to directory '{output_dir}': {e}\")\n    \n    # Step 5: Returning Results\n    return output_path, df",
        "status": "fail",
        "details": {
          "test_case_1": "Traceback (most recent call last):\n  File \"__test__.py\", line 70, in test_case_1\n  File \"__test__.py\", line 19, in task_func\nValueError: Input JSON data is malformed or empty.\n",
          "test_case_4": "Traceback (most recent call last):\n  File \"__test__.py\", line 99, in test_case_4\n  File \"__test__.py\", line 19, in task_func\nValueError: Input JSON data is malformed or empty.\n",
          "test_case_5": "Traceback (most recent call last):\n  File \"__test__.py\", line 107, in test_case_5\n  File \"__test__.py\", line 19, in task_func\nValueError: Input JSON data is malformed or empty.\n",
          "test_case_6": "Traceback (most recent call last):\n  File \"__test__.py\", line 115, in test_case_6\n  File \"__test__.py\", line 19, in task_func\nValueError: Input JSON data is malformed or empty.\n",
          "test_case_8": "Traceback (most recent call last):\n  File \"__test__.py\", line 139, in test_case_8\n  File \"__test__.py\", line 19, in task_func\nValueError: Input JSON data is malformed or empty.\n",
          "test_case_9": "Traceback (most recent call last):\n  File \"__test__.py\", line 149, in test_case_9\n  File \"__test__.py\", line 19, in task_func\nValueError: Input JSON data is malformed or empty.\n"
        }
      }
    ],
    "BigCodeBench/942": [
      {
        "task_id": "BigCodeBench/942",
        "solution": "import pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\n# Constants\nSTART_DATE = '2016-01-01'\nPERIODS = 13\nFREQ = 'WOM-2FRI'\nCATEGORIES = ['Electronics', 'Fashion', 'Home & Kitchen', 'Automotive', 'Sports']\ndef task_func(start_date=START_DATE, periods=PERIODS, freq=FREQ, categories=CATEGORIES):\n\n    pass\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# Constants\nSTART_DATE = '2016-01-01'\nPERIODS = 13\nFREQ = 'WOM-2FRI'\nCATEGORIES = ['Electronics', 'Fashion', 'Home & Kitchen', 'Automotive', 'Sports']\n\ndef task_func(start_date=START_DATE, periods=PERIODS, freq=FREQ, categories=CATEGORIES):\n    # Step 1: Generate Date Range\n    date_range = pd.date_range(start=start_date, periods=periods, freq=freq)\n\n    # Step 2: Generate Sales Data\n    sales_data = {}\n    for category in categories:\n        # Randomize sales numbers for each category, possibly influencing the range\n        sales_data[category] = np.random.randint(1000, 5000, size=periods)\n\n    # Step 3: Create DataFrame\n    sales_records = []\n    for i, date in enumerate(date_range):\n        for category in categories:\n            sales_records.append({'Date': date, 'Category': category, 'Sales': sales_data[category][i]})\n\n    sales_df = pd.DataFrame(sales_records)\n\n    # Step 4: Visualize Sales Data\n    fig, ax = plt.subplots(figsize=(10, 6))\n    for category in categories:\n        category_data = sales_df[sales_df['Category'] == category]\n        ax.plot(category_data['Date'], category_data['Sales'], marker='o', label=category)\n\n    # Enhancing the plot with labels and title\n    ax.set_title('Sales Report by Category Over Time')\n    ax.set_xlabel('Date')\n    ax.set_ylabel('Sales')\n    ax.legend(title='Categories')\n    plt.xticks(rotation=45)\n    plt.tight_layout()\n\n    # Step 5: Return Values\n    return sales_df, ax\n\n# Example usage\nsales_report, sales_plot = task_func()\nplt.show()  # Display the plot",
        "status": "fail",
        "details": {
          "test_case_1": "Traceback (most recent call last):\n  File \"__test__.py\", line 70, in test_case_1\nAssertionError: 'Sales Report by Category Over Time' != 'Category-wise Sales Trends'\n- Sales Report by Category Over Time\n+ Category-wise Sales Trends\n\n"
        }
      }
    ],
    "BigCodeBench/1004": [
      {
        "task_id": "BigCodeBench/1004",
        "solution": "import urllib.request\nimport re\nfrom collections import Counter\nimport matplotlib.pyplot as plt\ndef task_func(url):\n\n    pass\nimport urllib.request\nimport re\nfrom collections import Counter\nimport matplotlib.pyplot as plt\n\ndef task_func(url):\n    # Step 1: Download the text file\n    try:\n        response = urllib.request.urlopen(url)\n        text = response.read().decode('utf-8')\n    except Exception as e:\n        print(f\"Error downloading the file: {e}\")\n        return None, None\n\n    # Step 2: Process the text to count word frequencies\n    words = re.findall(r'\\b\\w+\\b', text)\n    word_counter = Counter(words)\n\n    # Step 3: Plot the frequencies\n    most_common_words = word_counter.most_common(10)\n    words, counts = zip(*most_common_words)  # Unzip into two lists\n\n    fig, ax = plt.subplots()\n    ax.bar(words, counts)\n    ax.set_xlabel('Words')\n    ax.set_ylabel('Frequency')\n    ax.set_title('Top 10 Most Frequent Words')\n    plt.xticks(rotation=45)\n    \n    # Return the Counter and the Axes object\n    return word_counter, ax",
        "status": "fail",
        "details": {
          "test_non_text_file": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 69, in test_non_text_file\nAssertionError: Exception not raised\n",
          "test_empty_file": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 60, in test_empty_file\n  File \"__test__.py\", line 23, in task_func\n  File \"/usr/lib/python3.12/re/__init__.py\", line 217, in findall\n    return _compile(pattern, flags).findall(string)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nTypeError: expected string or bytes-like object, got 'MagicMock'\n",
          "test_large_input": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 89, in test_large_input\n  File \"__test__.py\", line 23, in task_func\n  File \"/usr/lib/python3.12/re/__init__.py\", line 217, in findall\n    return _compile(pattern, flags).findall(string)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nTypeError: expected string or bytes-like object, got 'MagicMock'\n",
          "test_special_characters": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 77, in test_special_characters\n  File \"__test__.py\", line 23, in task_func\n  File \"/usr/lib/python3.12/re/__init__.py\", line 217, in findall\n    return _compile(pattern, flags).findall(string)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nTypeError: expected string or bytes-like object, got 'MagicMock'\n",
          "test_word_frequencies": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 51, in test_word_frequencies\n  File \"__test__.py\", line 23, in task_func\n  File \"/usr/lib/python3.12/re/__init__.py\", line 217, in findall\n    return _compile(pattern, flags).findall(string)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nTypeError: expected string or bytes-like object, got 'MagicMock'\n"
        }
      }
    ],
    "BigCodeBench/952": [
      {
        "task_id": "BigCodeBench/952",
        "solution": "import pandas as pd\nimport random\nfrom datetime import datetime\ndef task_func(\n    task_list,\n    n_tasks,\n    employees=[\"John Doe\", \"Jane Smith\", \"James Brown\", \"Mary Johnson\", \"Robert Davis\"],\n    seed=None,\n):\n\n    pass\nimport pandas as pd\nimport random\nfrom datetime import datetime\n\ndef task_func(\n    task_list,\n    n_tasks,\n    employees=[\"John Doe\", \"Jane Smith\", \"James Brown\", \"Mary Johnson\", \"Robert Davis\"],\n    seed=None,\n):\n    # Step 1: Input Validation\n    if n_tasks < 0:\n        raise ValueError(\"The number of tasks cannot be negative.\")\n    \n    # Handle case where n_tasks exceeds the length of task_list\n    if n_tasks > len(task_list):\n        raise ValueError(\"The number of tasks exceeds the available task names.\")\n    \n    # Step 2: Sanitizing task names\n    sanitized_tasks = [task.replace(\" \", \"_\") for task in task_list]\n    \n    # Step 3: Randomization of assignments\n    if seed is not None:\n        random.seed(seed)  # for reproducibility\n    assigned_employees = random.choices(employees, k=n_tasks)\n\n    # Step 4: Creating the Due Date\n    due_date = datetime.now().date()\n\n    # Step 5: Constructing the DataFrame\n    data = {\n        'Task Name': sanitized_tasks[:n_tasks],  # Ensure we only take n_tasks or less\n        'Assigned To': assigned_employees,\n        'Due Date': [due_date] * n_tasks  # Repeat the due date for each task\n    }\n    \n    assignments_df = pd.DataFrame(data)\n\n    # Step 6: Returning the DataFrame\n    return assignments_df",
        "status": "fail",
        "details": {
          "test_case_1": "Traceback (most recent call last):\n  File \"__test__.py\", line 67, in test_case_1\nAssertionError: False is not true\n",
          "test_case_3": "Traceback (most recent call last):\n  File \"__test__.py\", line 80, in test_case_3\n  File \"__test__.py\", line 28, in task_func\nValueError: The number of tasks exceeds the available task names.\n",
          "test_case_6": "Traceback (most recent call last):\n  File \"__test__.py\", line 96, in test_case_6\n  File \"__test__.py\", line 28, in task_func\nValueError: The number of tasks exceeds the available task names.\n",
          "test_case_7": "Traceback (most recent call last):\n  File \"__test__.py\", line 102, in test_case_7\n  File \"__test__.py\", line 28, in task_func\nValueError: The number of tasks exceeds the available task names.\n",
          "test_case_8": "Traceback (most recent call last):\n  File \"__test__.py\", line 110, in test_case_8\n  File \"__test__.py\", line 28, in task_func\nValueError: The number of tasks exceeds the available task names.\n"
        }
      }
    ],
    "BigCodeBench/990": [
      {
        "task_id": "BigCodeBench/990",
        "solution": "import binascii\nimport base64\nimport urllib.parse\nimport codecs\ndef task_func(hex_string):\n\n    pass\nimport binascii\nimport base64\nimport urllib.parse\nimport codecs\n\ndef task_func(hex_string):\n    # Step 1: Convert hex string to bytes\n    byte_data = bytes.fromhex(hex_string)\n    \n    # Step 2: Convert bytes to UTF-8 string\n    utf8_string = byte_data.decode('utf-8')\n    \n    # Step 3: Create a dictionary to hold the different encodings\n    result = {\n        'hex': hex_string,\n        'base64': base64.b64encode(utf8_string.encode('utf-8')).decode('utf-8'),\n        'utf-8': utf8_string,\n        'utf-16': utf8_string.encode('utf-16').decode('utf-16'),  # just encoding\n        'utf-32': utf8_string.encode('utf-32').decode('utf-32'),  # just encoding\n        'ASCII': utf8_string if all(ord(c) < 128 for c in utf8_string) else 'Not representable in ASCII',\n        'URL': urllib.parse.quote(utf8_string),\n        'ROT13': codecs.encode(utf8_string, 'rot_13')\n    }\n    \n    return result",
        "status": "pass",
        "details": {}
      }
    ],
    "BigCodeBench/998": [
      {
        "task_id": "BigCodeBench/998",
        "solution": "import urllib.request\nimport os\nimport hashlib\nimport tarfile\n# Constants\nTARGET_TAR_FILE = \"downloaded_files.tar.gz\"\nEXPECTED_MD5_CHECKSUM = \"d41d8cd98f00b204e9800998ecf8427e\"\ndef task_func(url):\n\n    pass\nimport urllib.request\nimport os\nimport hashlib\nimport tarfile\n\n# Constants\nTARGET_TAR_FILE = \"downloaded_files.tar.gz\"\nEXPECTED_MD5_CHECKSUM = \"d41d8cd98f00b204e9800998ecf8427e\"\n\ndef task_func(url, target_file=TARGET_TAR_FILE):\n    # Step 1: Download the file\n    try:\n        urllib.request.urlretrieve(url, target_file)\n    except Exception as e:\n        print(f\"Error downloading the file: {e}\")\n        return False\n\n    # Step 2: Validate the MD5 checksum\n    def calculate_md5(file_path):\n        hash_md5 = hashlib.md5()\n        with open(file_path, \"rb\") as f:\n            for chunk in iter(lambda: f.read(4096), b\"\"):\n                hash_md5.update(chunk)\n        return hash_md5.hexdigest()\n\n    downloaded_md5 = calculate_md5(target_file)\n    \n    if downloaded_md5 != EXPECTED_MD5_CHECKSUM:\n        os.remove(target_file)  # Delete the file if checksum does not match\n        print(\"MD5 checksum does not match. File deleted.\")\n        return False\n\n    # Step 3: Extract the file\n    try:\n        with tarfile.open(target_file, \"r:gz\") as tar:\n            tar.extractall()  # Extract all contents\n    except Exception as e:\n        print(f\"Error extracting the file: {e}\")\n        return False\n\n    # Step 4: Return True if all steps were successful\n    return True",
        "status": "fail",
        "details": {
          "test_valid_file": "Traceback (most recent call last):\n  File \"__test__.py\", line 77, in test_valid_file\nAssertionError: True is not false\n"
        }
      }
    ],
    "BigCodeBench/964": [
      {
        "task_id": "BigCodeBench/964",
        "solution": "import os\nfrom pathlib import Path\nimport pandas as pd\nimport docx\ndef task_func(source_directory: str, target_directory: str) -> int:\n\n    pass\nimport os\nfrom pathlib import Path\nimport pandas as pd\nimport docx\n\ndef task_func(source_directory: str, target_directory: str) -> int:\n    # Check if source directory exists\n    if not os.path.exists(source_directory):\n        raise FileNotFoundError(f\"The source directory '{source_directory}' does not exist.\")\n    \n    # Initialize count of converted files\n    converted_count = 0\n    \n    # Define the supported file extensions\n    file_extensions = ['*.txt', '*.docx', '*.xlsx', '*.csv']\n    \n    # Function to extract text from different file types\n    def extract_text(file_path):\n        text_content = \"\"\n        if file_path.suffix == '.txt':\n            with open(file_path, 'r', encoding='utf-8') as file:\n                text_content = file.read()\n        elif file_path.suffix == '.docx':\n            doc = docx.Document(file_path)\n            text_content = '\\n'.join([para.text for para in doc.paragraphs])\n        elif file_path.suffix == '.xlsx':\n            df = pd.read_excel(file_path)\n            text_content = '\\n'.join(df.astype(str).agg(' '.join, axis=1).tolist())\n        elif file_path.suffix == '.csv':\n            df = pd.read_csv(file_path)\n            text_content = '\\n'.join(df.astype(str).agg(' '.join, axis=1).tolist())\n        return text_content\n\n    # Iterate through each file type\n    for ext in file_extensions:\n        for file_path in Path(source_directory).glob(ext):\n            # Extract content based on file type\n            text_content = extract_text(file_path)\n            \n            # Create a DataFrame and save to a CSV file in the target directory\n            output_file_path = Path(target_directory) / (file_path.stem + '.csv')\n            df_to_save = pd.DataFrame({'Text': [text_content]})\n            df_to_save.to_csv(output_file_path, index=False)\n            \n            # Increment the count of converted files\n            converted_count += 1\n    \n    return converted_count",
        "status": "fail",
        "details": {
          "test_case_9": "Traceback (most recent call last):\n  File \"__test__.py\", line 144, in test_case_9\nAssertionError: 0 != 1\n"
        }
      }
    ],
    "BigCodeBench/916": [
      {
        "task_id": "BigCodeBench/916",
        "solution": "import pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\ndef task_func(df: pd.DataFrame) -> tuple:\n\n    pass\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\ndef task_func(df: pd.DataFrame) -> tuple:\n    # Ensure the DataFrame contains the 'Close' column for stock closing prices\n    if 'Close' not in df.columns:\n        raise ValueError(\"DataFrame must contain a 'Close' column with stock closing prices.\")\n    \n    # Extract stock closing prices\n    closing_prices = df['Close']\n    \n    # Create a figure with 2 subplots (1 row, 2 columns)\n    fig, (ax_box, ax_hist) = plt.subplots(1, 2, figsize=(12, 6))\n    \n    # Create the box plot\n    sns.boxplot(y=closing_prices, ax=ax_box)\n    ax_box.set_title('Box Plot of Closing Prices')\n    \n    # Create the histogram\n    sns.histplot(closing_prices, bins=30, kde=True, ax=ax_hist)\n    ax_hist.set_title('Histogram of Closing Prices')\n    \n    # Adjust layout to prevent overlap\n    plt.tight_layout()\n    \n    # Return Axes objects\n    return ax_box, ax_hist",
        "status": "fail",
        "details": {
          "test_case_1": "Traceback (most recent call last):\n  File \"__test__.py\", line 46, in test_case_1\n  File \"__test__.py\", line 14, in task_func\nValueError: DataFrame must contain a 'Close' column with stock closing prices.\n",
          "test_empty_df": "Traceback (most recent call last):\n  File \"__test__.py\", line 59, in test_empty_df\n  File \"__test__.py\", line 14, in task_func\nValueError: DataFrame must contain a 'Close' column with stock closing prices.\n",
          "test_invalid_column": "Traceback (most recent call last):\n  File \"__test__.py\", line 69, in test_invalid_column\n  File \"__test__.py\", line 14, in task_func\nValueError: DataFrame must contain a 'Close' column with stock closing prices.\n",
          "test_large_values_df": "Traceback (most recent call last):\n  File \"__test__.py\", line 80, in test_large_values_df\n  File \"__test__.py\", line 14, in task_func\nValueError: DataFrame must contain a 'Close' column with stock closing prices.\n",
          "test_single_value_df": "Traceback (most recent call last):\n  File \"__test__.py\", line 72, in test_single_value_df\n  File \"__test__.py\", line 14, in task_func\nValueError: DataFrame must contain a 'Close' column with stock closing prices.\n"
        }
      }
    ],
    "BigCodeBench/1003": [
      {
        "task_id": "BigCodeBench/1003",
        "solution": "import urllib.request\nfrom lxml import etree\nimport pandas as pd\ndef task_func(url):\n\n    pass\nimport urllib.request\nfrom lxml import etree\nimport pandas as pd\n\ndef task_func(url):\n    try:\n        # Step 1: Fetch the XML file\n        with urllib.request.urlopen(url) as response:\n            xml_content = response.read()\n        \n        # Step 2: Validate XML syntax\n        try:\n            root = etree.fromstring(xml_content)\n        except etree.XMLSyntaxError:\n            raise ValueError(\"XML structure does not match expected format.\")\n        \n        # Step 3: Parse XML structure\n        items = []\n        for item in root.findall('item'):\n            item_data = {}\n            for child in item:\n                item_data[child.tag] = child.text\n            items.append(item_data)\n        \n        # Step 4: Create DataFrame\n        df = pd.DataFrame(items)\n        \n        # Step 5: Error handling for expected XML structure\n        if df.empty or not all(col in df.columns for col in ['some_column']):\n            raise ValueError(\"XML structure does not match expected format.\")\n        \n        return df\n\n    except urllib.error.URLError:\n        raise ValueError(\"Invalid URL or unable to fetch XML file.\")\n    except Exception as e:\n        raise ValueError(f\"An error occurred: {e}\")",
        "status": "fail",
        "details": {
          "test_valid_xml": "Traceback (most recent call last):\n  File \"__test__.py\", line 36, in task_func\nValueError: XML structure does not match expected format.\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 59, in test_valid_xml\n  File \"__test__.py\", line 43, in task_func\nValueError: An error occurred: XML structure does not match expected format.\n"
        }
      }
    ],
    "BigCodeBench/1006": [
      {
        "task_id": "BigCodeBench/1006",
        "solution": "import os\nimport requests\nfrom zipfile import ZipFile, BadZipFile\ndef task_func(url, download_path=\"mnt/data/downloads/\"):\n\n    pass\nimport os\nimport requests\nfrom zipfile import ZipFile, BadZipFile\nfrom urllib.parse import urlparse\n\ndef task_func(url, download_path=\"mnt/data/downloads/\"):\n    # Step 1: Validate URL\n    parsed_url = urlparse(url)\n    if not all([parsed_url.scheme, parsed_url.netloc]):\n        return \"Error: Invalid URL format.\"\n    \n    # Ensure the download path ends with a /\n    if not download_path.endswith('/'):\n        download_path += '/'\n\n    # Ensure the directory exists\n    os.makedirs(download_path, exist_ok=True)\n\n    # Step 2: Download the ZIP file\n    try:\n        response = requests.get(url)\n        response.raise_for_status()\n        \n        # Check the content type\n        if response.headers['Content-Type'] != 'application/zip':\n            return \"Error: The URL does not point to a ZIP file.\"\n        \n        # Step 3: Save the ZIP file with a unique name\n        zip_file_path = os.path.join(download_path, 'downloaded.zip')\n        with open(zip_file_path, 'wb') as file:\n            file.write(response.content)\n\n    except requests.exceptions.RequestException:\n        return \"Error: Unable to download the file from the provided URL.\"\n    \n    # Step 4: Extract the ZIP file\n    try:\n        with ZipFile(zip_file_path, 'r') as zip_ref:\n            zip_ref.extractall(download_path)\n\n    except BadZipFile:\n        return \"Error: The downloaded file is not a valid ZIP file.\"\n    except Exception as e:\n        return f\"Error: {str(e)}\"\n    \n    # Step 5: Return results\n    return download_path",
        "status": "fail",
        "details": {
          "test_general_error": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 101, in test_general_error\n  File \"__test__.py\", line 27, in task_func\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1134, in __call__\n    return self._mock_call(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1138, in _mock_call\n    return self._execute_mock_call(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1193, in _execute_mock_call\n    raise effect\nRuntimeError: Unexpected error\n"
        }
      }
    ],
    "BigCodeBench/1019": [
      {
        "task_id": "BigCodeBench/1019",
        "solution": "from PIL import Image\nimport codecs\nimport pytesseract\nIMAGE_PATH = \"image.png\"\ndef task_func(filename=IMAGE_PATH, from_encoding=\"cp1251\", to_encoding=\"utf8\"):\n\n    pass\nfrom PIL import Image\nimport pytesseract\n\nIMAGE_PATH = \"image.png\"\n\ndef task_func(filename=IMAGE_PATH, from_encoding=\"cp1251\", to_encoding=\"utf8\"):\n    try:\n        # Step 1: Open an Image File\n        image = Image.open(filename)\n\n        # Step 2: Extract Text Using OCR\n        ocr_text = pytesseract.image_to_string(image)\n\n        # Step 3: Text Encoding Conversion\n        try:\n            # Decode from the original encoding and then encode to the target encoding\n            converted_text = ocr_text.encode(to_encoding).decode(from_encoding)\n            return converted_text\n        except (ValueError, UnicodeDecodeError, LookupError) as e:\n            raise ValueError(\"Encoding error occurred during OCR text conversion: \" + str(e))\n\n    except Exception:\n        # If OCR fails, attempt fallback to image comments\n        try:\n            comment = image.info.get('comment', '')\n            try:\n                converted_comment = comment.encode(to_encoding).decode(from_encoding)\n                return converted_comment\n            except (ValueError, UnicodeDecodeError, LookupError) as e:\n                raise ValueError(\"Encoding error occurred during comment conversion: \" + str(e))\n        except Exception:\n            # If both OCR and comment processing fail, return an empty string\n            return \"\"",
        "status": "fail",
        "details": {
          "test_ocr_and_comment_extraction_fail": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 85, in test_ocr_and_comment_extraction_fail\nAssertionError: <MagicMock name='open().info.get().encode().decode()' id='131500902720400'> != ''\n",
          "test_ocr_fails_comment_extraction_succeeds": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 69, in test_ocr_fails_comment_extraction_succeeds\nAssertionError: <MagicMock name='open().info.get().encode().decode()' id='131500902295616'> != 'Mocked Comment in cp1251'\n",
          "test_ocr_succeeds_encoding_fails": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 76, in test_ocr_succeeds_encoding_fails\nAssertionError: ValueError not raised\n"
        }
      }
    ],
    "BigCodeBench/999": [
      {
        "task_id": "BigCodeBench/999",
        "solution": "import urllib.request\nimport os\nimport csv\nimport collections\ndef task_func(url, column_name, csv_file_path):\n\n    pass\nimport urllib.request\nimport os\nimport csv\nimport collections\n\ndef task_func(url, column_name, csv_file_path):\n    # Step 1: Download the CSV file from the given URL\n    try:\n        urllib.request.urlretrieve(url, csv_file_path)\n    except Exception as e:\n        raise RuntimeError(f\"Failed to download the file from {url}: {e}\")\n\n    # Step 2: Read the content of the downloaded CSV file\n    try:\n        with open(csv_file_path, mode='r', newline='', encoding='utf-8') as csvfile:\n            csv_reader = csv.DictReader(csvfile)\n\n            # Step 3: Check if the specified column exists\n            if column_name not in csv_reader.fieldnames:\n                os.remove(csv_file_path)  # Cleanup by deleting the file\n                raise ValueError(f\"The provided column_name '{column_name}' does not exist in the CSV file.\")\n            \n            # Step 4: Count occurrences of each unique value in the specified column\n            counts = collections.Counter()\n            for row in csv_reader:\n                value = row[column_name].strip()  # Strip whitespace\n                counts[value] += 1\n            \n    except FileNotFoundError:\n        raise RuntimeError(f\"The file was not found: {csv_file_path}\")\n    except Exception as e:\n        raise RuntimeError(f\"Failed to process the CSV file: {e}\")\n    finally:\n        # Step 5: Cleanup by deleting the file after processing\n        if os.path.exists(csv_file_path):\n            os.remove(csv_file_path)\n\n    return dict(counts)  # Return the dictionary of counts",
        "status": "fail",
        "details": {
          "test_non_existent_column": "Traceback (most recent call last):\n  File \"__test__.py\", line 28, in task_func\nValueError: The provided column_name 'non_existent_column' does not exist in the CSV file.\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 105, in test_non_existent_column\n  File \"__test__.py\", line 39, in task_func\nRuntimeError: Failed to process the CSV file: The provided column_name 'non_existent_column' does not exist in the CSV file.\n"
        }
      }
    ],
    "BigCodeBench/917": [
      {
        "task_id": "BigCodeBench/917",
        "solution": "import pandas as pd\nimport matplotlib.pyplot as plt\nfrom matplotlib.axes import Axes\nfrom statsmodels.tsa.arima.model import ARIMA\nfrom typing import List, Tuple\ndef task_func(df: pd.DataFrame) -> Tuple[List[float], Axes]:\n\n    pass\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom matplotlib.axes import Axes\nfrom statsmodels.tsa.arima.model import ARIMA\nfrom typing import List, Tuple\n\ndef task_func(df: pd.DataFrame) -> Tuple[List[float], Axes]:\n    # 1. Data Preparation\n    df['date'] = pd.to_datetime(df['date'])\n    df.set_index('date', inplace=True)\n    \n    # Ensuring the time series is stationary (differencing if needed)\n    if df['prices'].isnull().any():\n        df['prices'].fillna(method='ffill', inplace=True)  # Fill forward any missing prices\n    \n    # 2. Model Selection and Fitting\n    model = ARIMA(df['prices'], order=(1, 1, 1))\n    model_fit = model.fit()\n\n    # 3. Forecasting\n    forecast = model_fit.forecast(steps=7)\n    \n    # 4. Plotting\n    plt.figure(figsize=(10, 5))\n    plt.plot(df['prices'], label='Historical Prices')\n    future_dates = pd.date_range(start=df.index[-1] + pd.Timedelta(days=1), periods=7, freq='D')\n    plt.plot(future_dates, forecast, label='Forecasted Prices', color='orange')\n    plt.title('Price Forecast for Next 7 Days')\n    plt.xlabel('Date')\n    plt.ylabel('Prices')\n    plt.xticks(rotation=45)\n    plt.legend()\n    \n    ax = plt.gca()  # Get current Axes\n    plt.tight_layout()\n    plt.show()      # Show the plot\n    \n    return list(forecast), ax",
        "status": "fail",
        "details": {
          "test_case_1": "Traceback (most recent call last):\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 3653, in get_loc\n    return self._engine.get_loc(casted_key)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"pandas/_libs/index.pyx\", line 147, in pandas._libs.index.IndexEngine.get_loc\n  File \"pandas/_libs/index.pyx\", line 176, in pandas._libs.index.IndexEngine.get_loc\n  File \"pandas/_libs/hashtable_class_helper.pxi\", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item\n  File \"pandas/_libs/hashtable_class_helper.pxi\", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item\nKeyError: 'prices'\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File \"__test__.py\", line 61, in test_case_1\n  File \"__test__.py\", line 21, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 3761, in __getitem__\n    indexer = self.columns.get_loc(key)\n              ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 3655, in get_loc\n    raise KeyError(key) from err\nKeyError: 'prices'\n",
          "test_case_2": "Traceback (most recent call last):\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 3653, in get_loc\n    return self._engine.get_loc(casted_key)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"pandas/_libs/index.pyx\", line 147, in pandas._libs.index.IndexEngine.get_loc\n  File \"pandas/_libs/index.pyx\", line 176, in pandas._libs.index.IndexEngine.get_loc\n  File \"pandas/_libs/hashtable_class_helper.pxi\", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item\n  File \"pandas/_libs/hashtable_class_helper.pxi\", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item\nKeyError: 'prices'\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File \"__test__.py\", line 82, in test_case_2\n  File \"__test__.py\", line 21, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 3761, in __getitem__\n    indexer = self.columns.get_loc(key)\n              ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 3655, in get_loc\n    raise KeyError(key) from err\nKeyError: 'prices'\n",
          "test_case_3": "Traceback (most recent call last):\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 3653, in get_loc\n    return self._engine.get_loc(casted_key)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"pandas/_libs/index.pyx\", line 147, in pandas._libs.index.IndexEngine.get_loc\n  File \"pandas/_libs/index.pyx\", line 176, in pandas._libs.index.IndexEngine.get_loc\n  File \"pandas/_libs/hashtable_class_helper.pxi\", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item\n  File \"pandas/_libs/hashtable_class_helper.pxi\", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item\nKeyError: 'prices'\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File \"__test__.py\", line 102, in test_case_3\n  File \"__test__.py\", line 21, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 3761, in __getitem__\n    indexer = self.columns.get_loc(key)\n              ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 3655, in get_loc\n    raise KeyError(key) from err\nKeyError: 'prices'\n",
          "test_case_4": "Traceback (most recent call last):\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 3653, in get_loc\n    return self._engine.get_loc(casted_key)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"pandas/_libs/index.pyx\", line 147, in pandas._libs.index.IndexEngine.get_loc\n  File \"pandas/_libs/index.pyx\", line 176, in pandas._libs.index.IndexEngine.get_loc\n  File \"pandas/_libs/hashtable_class_helper.pxi\", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item\n  File \"pandas/_libs/hashtable_class_helper.pxi\", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item\nKeyError: 'prices'\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File \"__test__.py\", line 123, in test_case_4\n  File \"__test__.py\", line 21, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 3761, in __getitem__\n    indexer = self.columns.get_loc(key)\n              ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 3655, in get_loc\n    raise KeyError(key) from err\nKeyError: 'prices'\n",
          "test_case_5": "Traceback (most recent call last):\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 3653, in get_loc\n    return self._engine.get_loc(casted_key)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"pandas/_libs/index.pyx\", line 147, in pandas._libs.index.IndexEngine.get_loc\n  File \"pandas/_libs/index.pyx\", line 176, in pandas._libs.index.IndexEngine.get_loc\n  File \"pandas/_libs/hashtable_class_helper.pxi\", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item\n  File \"pandas/_libs/hashtable_class_helper.pxi\", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item\nKeyError: 'prices'\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File \"__test__.py\", line 143, in test_case_5\n  File \"__test__.py\", line 21, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 3761, in __getitem__\n    indexer = self.columns.get_loc(key)\n              ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 3655, in get_loc\n    raise KeyError(key) from err\nKeyError: 'prices'\n"
        }
      }
    ],
    "BigCodeBench/1013": [
      {
        "task_id": "BigCodeBench/1013",
        "solution": "import requests\nfrom urllib.parse import urljoin\nfrom bs4 import BeautifulSoup\nimport csv\ndef task_func(\n    url: str,\n    base_url: str = \"https://www.example.com\",\n    csv_file: str = \"scraped_data.csv\",\n) -> int:\n\n    pass\nimport requests  \nfrom urllib.parse import urljoin  \nfrom bs4 import BeautifulSoup  \nimport csv  \n  \ndef task_func(  \n    url: str,  \n    base_url: str = \"https://www.example.com\",  \n    csv_file: str = \"scraped_data.csv\",  \n) -> int:  \n    # Step 1: Send HTTP request  \n    response = requests.get(url)  \n    response.raise_for_status()  # Check for any request errors  \n  \n    # Step 2: Parse HTML content  \n    soup = BeautifulSoup(response.text, 'html.parser')  \n    links = soup.find_all('a')  # Find all <a> tags  \n  \n    # Step 3: Extract and convert to absolute URLs  \n    unique_links = set()  \n    for link in links:  \n        href = link.get('href')  \n        if href:  # Check if href is not None  \n            absolute_url = urljoin(base_url, href)  \n            # Optional: Check for valid URLs before adding  \n            if absolute_url.startswith(('http://', 'https://')):  \n                unique_links.add(absolute_url)  \n  \n    # Step 4: Save unique URLs to CSV  \n    try:  \n        with open(csv_file, mode='w', newline='') as file:  \n            writer = csv.writer(file)  \n            for url in unique_links:  \n                writer.writerow([url])  \n    except Exception as e:  \n        print(f\"Error writing to file: {e}\")  \n  \n    # Return the number of unique URLs  \n    return len(unique_links)",
        "status": "pass",
        "details": {}
      }
    ],
    "BigCodeBench/1020": [
      {
        "task_id": "BigCodeBench/1020",
        "solution": "import json\nimport requests\nimport chardet\n# Constants\nAPI_URL = \"http://api.example.com/data\"\ndef task_func(url=API_URL, from_encoding=None, to_encoding=\"utf8\"):\n\n    pass\nimport json\nimport requests\nimport chardet\n\n# Constants\nAPI_URL = \"http://api.example.com/data\"\n\ndef task_func(url=API_URL, from_encoding=None, to_encoding=\"utf8\"):\n    # Step 1: Make HTTP GET request\n    try:\n        response = requests.get(url, timeout=5)\n        response_content = response.content\n    except requests.RequestException as e:\n        raise Exception(f\"Error during requests to {url}: {str(e)}\")\n\n    # Step 2: Handle empty content\n    if not response_content:\n        return {}\n\n    # Step 3: Detect encoding\n    if from_encoding is None:\n        detected = chardet.detect(response_content)\n        from_encoding = detected['encoding']\n    \n    if from_encoding is None:\n        raise ValueError(\"Unable to detect encoding for non-empty content\")\n\n    # Step 4: Decode content\n    decoded_content = response_content.decode(from_encoding)\n\n    # Step 5: Re-encode content to the desired encoding\n    re_encoded_content = decoded_content.encode(to_encoding).decode(to_encoding)\n\n    # Step 6: Parse JSON\n    return json.loads(re_encoded_content)",
        "status": "pass",
        "details": {}
      }
    ],
    "BigCodeBench/1039": [
      {
        "task_id": "BigCodeBench/1039",
        "solution": "import ssl\nimport os\nimport hashlib\ndef task_func(client_socket, cert_file, key_file, buffer_size=1024):\n\n    pass\nimport ssl\nimport os\nimport hashlib\n\ndef task_func(client_socket, cert_file, key_file, buffer_size=1024):\n    secure_socket = None\n    try:\n        # Step 1: Establish SSL/TLS communication\n        context = ssl.create_default_context(ssl.Purpose.CLIENT_AUTH)\n        context.load_cert_chain(certfile=cert_file, keyfile=key_file)\n        \n        # Wrap the socket with SSL\n        secure_socket = context.wrap_socket(client_socket, server_side=True)\n\n        # Step 2: Receive the requested file path\n        data = secure_socket.recv(buffer_size).decode('utf-8').strip()\n        \n        if not data:\n            secure_socket.sendall(\"No file requested.\".encode('utf-8'))\n            return\n        \n        # Step 3: Verify file existence (basic validation to prevent directory traversal)\n        safe_data = os.path.normpath(data)  # Normalize the path\n        if not os.path.isfile(safe_data) or not safe_data.startswith(os.getcwd()):\n            secure_socket.sendall('File not found'.encode('utf-8'))\n            return\n        \n        # Step 4: Calculate the SHA256 hash\n        sha256_hash = hashlib.sha256()\n        \n        with open(safe_data, \"rb\") as f:\n            # Read the file in chunks\n            for byte_block in iter(lambda: f.read(buffer_size), b\"\"):\n                sha256_hash.update(byte_block)\n        \n        # Send the hash back to the client\n        hash_value = sha256_hash.hexdigest()\n        secure_socket.sendall(hash_value.encode('utf-8'))\n\n    except Exception as e:\n        secure_socket.sendall(f'Error: {str(e)}'.encode('utf-8'))\n\n    finally:\n        if secure_socket:\n            secure_socket.close()",
        "status": "fail",
        "details": {
          "test_file_found": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 83, in test_file_found\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 935, in assert_called_with\n    raise AssertionError(error_message)\nAssertionError: expected call not found.\nExpected: open('path/to/requested_file.txt', 'rb')\n  Actual: not called.\n",
          "test_file_not_found": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 114, in test_file_not_found\nAssertionError: None != 'File not found'\n",
          "test_task_func_empty_file": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 164, in test_task_func_empty_file\nAssertionError: None != 'e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855'\n",
          "test_task_func_large_file": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 191, in test_task_func_large_file\nAssertionError: None != 'cdc76e5c9914fb9281a1c7e284d73e67f1809a48a497200e046d39ccc7112cd0'\n",
          "test_exception_handling": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 137, in test_exception_handling\nTypeError: argument of type 'NoneType' is not iterable\n"
        }
      }
    ],
    "BigCodeBench/1057": [
      {
        "task_id": "BigCodeBench/1057",
        "solution": "import pandas as pd\nimport itertools\nimport numpy as np\ndef task_func(animals=None, foods=None):\n\n    pass\nimport pandas as pd\nimport itertools\nimport numpy as np\n\ndef task_func(animals=None, foods=None):\n    # Special cases handling\n    if animals is None and foods is None:\n        return pd.DataFrame()\n    \n    if animals is None or len(animals) == 0:\n        animals = ['cat', 'dog', 'bird']\n        \n    if foods is None or len(foods) == 0:\n        foods = ['fish', 'meat', 'seeds']\n    \n    # Generate combinations\n    combinations = list(itertools.product(animals, foods))\n    \n    # Format the combinations into 'animal:food' strings\n    formatted_combinations = [f\"{animal}:{food}\" for animal, food in combinations]\n    \n    # Shuffle the formatted combinations\n    np.random.shuffle(formatted_combinations)\n    \n    # Create the DataFrame\n    df = pd.DataFrame(np.array(formatted_combinations).reshape(len(animals), len(foods)), \n                      columns=foods, \n                      index=animals)\n    \n    return df",
        "status": "fail",
        "details": {
          "test_default_input": "Traceback (most recent call last):\n  File \"__test__.py\", line 47, in test_default_input\nAssertionError: Tuples differ: (0, 0) != (10, 7)\n\nFirst differing element 0:\n0\n10\n\n- (0, 0)\n?     ^\n\n+ (10, 7)\n?  +   ^\n : The shape of the DataFrame with default inputs is not as expected.\n",
          "test_empty_input": "Traceback (most recent call last):\n  File \"__test__.py\", line 73, in test_empty_input\nAssertionError: Tuples differ: (3, 3) != (0, 0)\n\nFirst differing element 0:\n3\n0\n\n- (3, 3)\n+ (0, 0) : The shape of the DataFrame with empty inputs is not as expected.\n",
          "test_partial_default": "Traceback (most recent call last):\n  File \"__test__.py\", line 104, in test_partial_default\nAssertionError: Tuples differ: (3, 3) != (3, 7)\n\nFirst differing element 1:\n3\n7\n\n- (3, 3)\n?     ^\n\n+ (3, 7)\n?     ^\n : The shape of the DataFrame with partial default inputs is not as expected.\n"
        }
      }
    ],
    "BigCodeBench/1022": [
      {
        "task_id": "BigCodeBench/1022",
        "solution": "import pandas as pd\nimport os\nfrom datetime import datetime\nfrom pandas.errors import EmptyDataError\ndef task_func(csv_file_path, column_name, date_format=\"%Y-%m-%d\"):\n\n    pass\nimport pandas as pd\nimport os\nfrom datetime import datetime\nfrom pandas.errors import EmptyDataError\n\ndef task_func(csv_file_path: str, column_name: str, date_format: str = \"%Y-%m-%d\") -> pd.DataFrame:\n    # Step 1: Check File Existence\n    if not os.path.exists(csv_file_path):\n        raise FileNotFoundError(f\"The specified file \\'{csv_file_path}\\' does not exist.\")\n    \n    # Step 2: Read the CSV File\n    try:\n        df = pd.read_csv(csv_file_path)\n    except EmptyDataError:\n        return pd.DataFrame()  # Return empty DataFrame if the file is empty\n    \n    # Step 3: Validate Column Existence\n    if column_name not in df.columns:\n        raise ValueError(f\"The specified column \\'{column_name}\\' is not present in the CSV file.\")\n    \n    # Step 4: Convert Date Values\n    df[column_name] = pd.to_datetime(df[column_name], format=date_format)\n    \n    # Step 5: Filter Rows Based on Current Date\n    current_date = datetime.now()\n    filtered_df = df[df[column_name] <= current_date]\n    \n    # Step 6: Sort the Data\n    sorted_df = filtered_df.sort_values(by=column_name)\n    \n    # Step 7: Return the Processed DataFrame\n    return sorted_df",
        "status": "fail",
        "details": {
          "test_no_future_dates": "Traceback (most recent call last):\n  File \"__test__.py\", line 101, in test_no_future_dates\nAssertionError: False is not true\n",
          "test_valid_input": "Traceback (most recent call last):\n  File \"__test__.py\", line 66, in test_valid_input\nAssertionError: True is not false\n"
        }
      }
    ],
    "BigCodeBench/1042": [
      {
        "task_id": "BigCodeBench/1042",
        "solution": "import smtplib\nfrom email.message import EmailMessage\nimport getpass\nSERVER_ADDRESS = \"localhost\"\nSERVER_PORT = 25\nBUFFER_SIZE = 1024\nSMTP_SERVER = \"smtp.gmail.com\"\nSMTP_PORT = 587\ndef task_func(client_socket):\n\n    pass\nimport smtplib\nfrom email.message import EmailMessage\nimport getpass\nimport socket\n\n# Constants for server address and SMTP configurations\nSERVER_ADDRESS = \"localhost\"\nSERVER_PORT = 25  # For the server socket\nBUFFER_SIZE = 1024\nSMTP_SERVER = \"smtp.gmail.com\"\nSMTP_PORT = 587\n\ndef task_func(client_socket):\n    try:\n        # Step 1: Receive message from the client socket\n        print(\"Waiting for a client connection...\")\n        conn, addr = client_socket.accept()\n        print(f\"Connection established with {addr}\")\n        \n        # Reading the incoming message\n        message = conn.recv(BUFFER_SIZE).decode('utf-8')\n        if not message:\n            print(\"No message received.\")\n            return\n        print(\"Received message:\", message)\n\n        # Step 2: Gather email details\n        sender_email = input(\"Enter sender's email: \")\n        recipient_email = input(\"Enter recipient's email: \")\n        sender_password = getpass.getpass(\"Enter sender's email password: \")\n        \n        # Step 3: Set up the email message\n        email_message = EmailMessage()\n        email_message.set_content(message)\n        email_message['Subject'] = \"Message from Client Socket\"\n        email_message['From'] = sender_email\n        email_message['To'] = recipient_email\n\n        # Step 4: Send email via SMTP\n        with smtplib.SMTP(SMTP_SERVER, SMTP_PORT) as smtp:\n            smtp.starttls()  # Upgrade the connection to a secure encrypted SSL/TLS\n            smtp.login(sender_email, sender_password)  # Login to the SMTP server\n            smtp.send_message(email_message)  # Send the email\n\n        print(\"Email sent successfully!\")\n    except Exception as e:\n        print(f\"An error occurred: {e}\")\n    finally:\n        conn.close()  # Ensure the client connection is closed",
        "status": "fail",
        "details": {
          "test_email_with_empty_message": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 105, in test_email_with_empty_message\n  File \"__test__.py\", line 60, in task_func\nUnboundLocalError: cannot access local variable 'conn' where it is not associated with a value\n",
          "test_smtp_server_connection_error": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 127, in test_smtp_server_connection_error\n  File \"__test__.py\", line 60, in task_func\nUnboundLocalError: cannot access local variable 'conn' where it is not associated with a value\n",
          "test_socket_closes_after_operation": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 143, in test_socket_closes_after_operation\n  File \"__test__.py\", line 60, in task_func\nUnboundLocalError: cannot access local variable 'conn' where it is not associated with a value\n",
          "test_successful_email_dispatch": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 162, in test_successful_email_dispatch\n  File \"__test__.py\", line 60, in task_func\nUnboundLocalError: cannot access local variable 'conn' where it is not associated with a value\n",
          "test_successful_email_send": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 83, in test_successful_email_send\n  File \"__test__.py\", line 60, in task_func\nUnboundLocalError: cannot access local variable 'conn' where it is not associated with a value\n"
        }
      }
    ],
    "BigCodeBench/1008": [
      {
        "task_id": "BigCodeBench/1008",
        "solution": "import requests\nfrom bs4 import BeautifulSoup\nimport pandas as pd\nfrom io import StringIO\ndef task_func(url, table_id):\n\n    pass\nimport requests\nfrom bs4 import BeautifulSoup\nimport pandas as pd\n\ndef task_func(url, table_id):\n    # Step 1: Fetch the HTML content\n    response = requests.get(url)\n    response.raise_for_status()  # Raises HTTPError for bad responses\n    \n    # Step 2: Parse the HTML\n    soup = BeautifulSoup(response.content, 'html.parser')\n    table = soup.find('table', id=table_id)\n\n    # Step 3: Check if the table is present\n    if table is None:\n        raise ValueError(\"Table with the specified ID not found.\")\n    \n    rows = table.find_all('tr')\n    \n    # Step 4: Check for empty table\n    if len(rows) == 0:\n        return pd.DataFrame()  # Return empty DataFrame\n\n    # Step 5: Extract headers and rows\n    headers = [th.get_text(strip=True) for th in rows[0].find_all(['th', 'td'])]\n    data = []\n    \n    for row in rows[1:]:\n        cols = [td.get_text(strip=True) for td in row.find_all('td')]\n        if cols:  # Only append to data if there are columns\n            data.append(cols)\n            \n    # Step 6: Create DataFrame\n    df = pd.DataFrame(data, columns=headers)\n\n    return df",
        "status": "fail",
        "details": {
          "test_empty_table": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 113, in test_empty_table\n  File \"__test__.py\", line 18, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bs4/__init__.py\", line 319, in __init__\n    for (self.markup, self.original_encoding, self.declared_html_encoding,\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bs4/builder/_htmlparser.py\", line 325, in prepare_markup\n    dammit = UnicodeDammit(markup, try_encodings, is_html=True,\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bs4/dammit.py\", line 445, in __init__\n    for encoding in self.detector.encodings:\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bs4/dammit.py\", line 301, in encodings\n    self.declared_encoding = self.find_declared_encoding(\n                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bs4/dammit.py\", line 378, in find_declared_encoding\n    declared_encoding_match = xml_re.search(markup, endpos=xml_endpos)\n                              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nTypeError: expected string or bytes-like object, got 'MagicMock'\n",
          "test_successful_scrape": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 68, in test_successful_scrape\n  File \"__test__.py\", line 18, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bs4/__init__.py\", line 319, in __init__\n    for (self.markup, self.original_encoding, self.declared_html_encoding,\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bs4/builder/_htmlparser.py\", line 325, in prepare_markup\n    dammit = UnicodeDammit(markup, try_encodings, is_html=True,\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bs4/dammit.py\", line 445, in __init__\n    for encoding in self.detector.encodings:\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bs4/dammit.py\", line 301, in encodings\n    self.declared_encoding = self.find_declared_encoding(\n                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bs4/dammit.py\", line 378, in find_declared_encoding\n    declared_encoding_match = xml_re.search(markup, endpos=xml_endpos)\n                              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nTypeError: expected string or bytes-like object, got 'MagicMock'\n",
          "test_table_not_found": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 82, in test_table_not_found\n  File \"__test__.py\", line 18, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bs4/__init__.py\", line 319, in __init__\n    for (self.markup, self.original_encoding, self.declared_html_encoding,\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bs4/builder/_htmlparser.py\", line 325, in prepare_markup\n    dammit = UnicodeDammit(markup, try_encodings, is_html=True,\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bs4/dammit.py\", line 445, in __init__\n    for encoding in self.detector.encodings:\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bs4/dammit.py\", line 301, in encodings\n    self.declared_encoding = self.find_declared_encoding(\n                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bs4/dammit.py\", line 378, in find_declared_encoding\n    declared_encoding_match = xml_re.search(markup, endpos=xml_endpos)\n                              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nTypeError: expected string or bytes-like object, got 'MagicMock'\n"
        }
      }
    ],
    "BigCodeBench/346": [
      {
        "task_id": "BigCodeBench/346",
        "solution": "import subprocess\nimport os\nimport sys\nimport time\ndef task_func(script_path, wait=True, *args):\n\n    pass\nimport subprocess\nimport os\nimport sys\n\ndef task_func(script_path, wait=True, *args):\n    # Sub-Problem 1: Check if the script exists.\n    if not os.path.exists(script_path):\n        raise ValueError(f\"The script '{script_path}' does not exist.\")\n\n    # Sub-Problem 2: Running the subprocess.\n    try:\n        result = subprocess.run(\n            [sys.executable, script_path] + list(args),\n            check=True,  # raises CalledProcessError if the command fails\n            capture_output=True,  # capture output to prevent cluttering stdout\n            text=True  # capture output as string\n        )\n    except subprocess.CalledProcessError as e:\n        # Sub-Problem 3: Handle subprocess call errors.\n        print(f\"Error occurred: {e.stderr}\")  # Optional: log the error message\n        raise subprocess.CalledProcessError(e.returncode, e.cmd) from e\n\n    # Control output based on the `wait` flag.\n    return result.returncode if wait else None",
        "status": "fail",
        "details": {
          "test_case_2": "Traceback (most recent call last):\n  File \"__test__.py\", line 19, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bigcodebench/eval/utils.py\", line 61, in _run_patch\n    return original_run(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bigcodebench/eval/utils.py\", line 174, in safe_subprocess_run\n    return original_subprocess_run(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/lib/python3.12/subprocess.py\", line 571, in run\n    raise CalledProcessError(retcode, process.args,\nsubprocess.CalledProcessError: Command '['/home/mark/Documents/CURA-v2/CURA/.venv/bin/python3', '/tmp/tmpgpmrikap/test/script2.py']' returned non-zero exit status 1.\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File \"__test__.py\", line 75, in test_case_2\n  File \"__test__.py\", line 28, in task_func\nsubprocess.CalledProcessError: Command '['/home/mark/Documents/CURA-v2/CURA/.venv/bin/python3', '/tmp/tmpgpmrikap/test/script2.py']' returned non-zero exit status 1.\n"
        }
      }
    ],
    "BigCodeBench/1085": [
      {
        "task_id": "BigCodeBench/1085",
        "solution": "import re\nfrom collections import Counter\nimport matplotlib.pyplot as plt\ndef task_func(text):\n\n    pass\nimport re\nfrom collections import Counter\nimport matplotlib.pyplot as plt\n\ndef task_func(text):\n    # Step 1: Preprocessing the Text\n    # Lowercasing\n    text = text.lower()\n    \n    # Removing Punctuation\n    text = re.sub(r'[^\\w\\s]', '', text)  # Removes punctuation\n\n    # Splitting into Words\n    words = text.split()\n    \n    # Step 2: Counting Word Frequencies\n    word_counts = Counter(words)\n    \n    # Step 3: Selecting Top 10 Words\n    top_10_words = word_counts.most_common(10)\n\n    # Step 4: Plotting\n    words, counts = zip(*top_10_words)  # Unzip into two lists\n    fig, ax = plt.subplots()\n    ax.bar(words, counts)\n    ax.set_title('Top 10 Most Common Words')\n    ax.set_xlabel('Words')\n    ax.set_ylabel('Counts')\n    plt.xticks(rotation=45)\n    \n    # Step 5: Returning Results\n    return top_10_words, ax",
        "status": "fail",
        "details": {
          "test_empty_text": "Traceback (most recent call last):\n  File \"__test__.py\", line 47, in test_empty_text\n  File \"__test__.py\", line 29, in task_func\nValueError: not enough values to unpack (expected 2, got 0)\n"
        }
      }
    ],
    "BigCodeBench/1015": [
      {
        "task_id": "BigCodeBench/1015",
        "solution": "import requests\nfrom lxml import html\nimport pandas as pd\nimport sqlite3\ndef task_func(webpage_url: str, database_name: str = \"my_database.db\") -> int:\n\n    pass\nimport requests\nfrom lxml import html\nimport pandas as pd\nimport sqlite3\n\ndef task_func(webpage_url: str, database_name: str = \"my_database.db\") -> int:\n    # Step 1: Fetch HTML Content\n    try:\n        if webpage_url.startswith('http://') or webpage_url.startswith('https://'):\n            response = requests.get(webpage_url)\n            response.raise_for_status()  # Raise an exception for HTTP errors\n            html_content = response.content\n        else:\n            with open(webpage_url, 'r', encoding='utf-8') as file:\n                html_content = file.read()\n    except requests.RequestException as e:\n        raise e  # Raise network-related exceptions\n\n    # Step 2: Parse HTML to Extract Table\n    tree = html.fromstring(html_content)\n    tables = tree.xpath('//table')\n    if not tables:\n        return 0  # No table found\n\n    # Assuming we want the first table\n    table = tables[0]\n\n    # Step 3: Convert Table to DataFrame\n    rows = []\n    header = []\n    \n    for i, row in enumerate(table.xpath('.//tr')):\n        cols = row.xpath('.//td|.//th')  # Get data cells\n        if i == 0:  # Capture the header from the first row, if available\n            header = [col.text_content().strip() for col in cols]\n        else:\n            rows.append([col.text_content().strip() for col in cols])\n\n    df = pd.DataFrame(rows, columns=header)  # Include header in DataFrame\n    if df.empty:  # Check if DataFrame is empty\n        return 0\n\n    # Step 4: Store Data in SQLite Database\n    try:\n        conn = sqlite3.connect(database_name)\n        df.to_sql('my_table', conn, if_exists='replace', index=False)\n        conn.commit()\n    except sqlite3.DatabaseError as e:\n        raise e  # Raise database-related exceptions\n    finally:\n        conn.close()\n\n    # Step 5: Return the Number of Rows\n    return df.shape[0]",
        "status": "fail",
        "details": {
          "test_database_error": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 128, in test_database_error\nAssertionError: DatabaseError not raised\n",
          "test_local_file_url": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 92, in test_local_file_url\nAssertionError: 0 != 1\n",
          "test_valid_webpage_url": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 81, in test_valid_webpage_url\nAssertionError: 0 != 1\n"
        }
      }
    ],
    "BigCodeBench/1077": [
      {
        "task_id": "BigCodeBench/1077",
        "solution": "from datetime import datetime\nimport pytz\nimport numpy as np\ndef task_func(time_strings, timezone):\n\n    pass\nfrom datetime import datetime\nimport pytz\nimport numpy as np\n\ndef task_func(time_strings, timezone):\n    # Convert strings to datetime objects and to the specified timezone\n    tz = pytz.timezone(timezone)\n    timestamps = []\n    \n    for time_str in time_strings:\n        try:\n            dt = datetime.fromisoformat(time_str)  # Assumes ISO format input\n            dt = dt.replace(tzinfo=pytz.utc)  # Assuming input timestamps are in UTC\n            dt = dt.astimezone(tz)  # Convert to specified timezone\n            timestamps.append(dt)\n        except (ValueError, pytz.UnknownTimeZoneError) as e:\n            print(f\"Error processing timestamp '{time_str}': {e}\")\n            return 0.0\n    \n    # Calculate time differences in seconds\n    if len(timestamps) < 2:\n        return 0.0\n    \n    time_differences = []\n    for i in range(1, len(timestamps)):\n        diff = abs((timestamps[i] - timestamps[i - 1]).total_seconds())\n        time_differences.append(diff)\n    \n    if not time_differences:\n        return 0.0\n    \n    # Calculate mean average of time differences\n    average_difference = np.mean(time_differences)\n    return average_difference",
        "status": "fail",
        "details": {
          "test_different_timezones": "Traceback (most recent call last):\n  File \"__test__.py\", line 59, in test_different_timezones\nAssertionError: 0.0 != 61.0 within 7 places (61.0 difference)\n",
          "test_example_case": "Traceback (most recent call last):\n  File \"__test__.py\", line 51, in test_example_case\nAssertionError: 0.0 != 61.0 within 7 places (61.0 difference)\n",
          "test_out_of_order_strings": "Traceback (most recent call last):\n  File \"__test__.py\", line 84, in test_out_of_order_strings\nAssertionError: 0.0 != 91.5 within 7 places (91.5 difference)\n",
          "test_span_across_days": "Traceback (most recent call last):\n  File \"__test__.py\", line 76, in test_span_across_days\nAssertionError: 0.0 != 120.0 within 7 places (120.0 difference)\n",
          "test_varying_differences": "Traceback (most recent call last):\n  File \"__test__.py\", line 68, in test_varying_differences\nAssertionError: 0.0 != 91.5 within 7 places (91.5 difference)\n"
        }
      }
    ],
    "BigCodeBench/785": [
      {
        "task_id": "BigCodeBench/785",
        "solution": "import subprocess\nimport os\nimport glob\n# Constants\nARCHIVE_DIR = '/tmp/archive'\ndef task_func(pattern):\n\n    pass\nimport subprocess\nimport os\nimport glob\n\n# Constants\nARCHIVE_DIR = '/tmp/archive'\n\ndef task_func(pattern):\n    # Ensure archive directory exists\n    os.makedirs(ARCHIVE_DIR, exist_ok=True)\n    \n    # Step 1: Define the target directory\n    directory = os.getcwd()  # Get current working directory or specify a directory\n    \n    # Step 2: Find all files matching the pattern\n    files_to_archive = glob.glob(os.path.join(directory, pattern))\n    \n    if not files_to_archive:\n        raise FileNotFoundError(\"No files found matching the pattern.\")\n    \n    # Step 3: Create an archive file name\n    archive_file = os.path.join(ARCHIVE_DIR, 'archived_files.tar.gz')\n\n    # Create the archive using tar\n    subprocess.run(['tar', '-czf', archive_file] + files_to_archive, check=True)\n    \n    # Step 4: Delete the original files\n    for file in files_to_archive:\n        os.remove(file)\n\n    # Step 5: Return the archive file path\n    return archive_file",
        "status": "fail",
        "details": {
          "test_archiving_in_nonexistent_directory": "Traceback (most recent call last):\n  File \"__test__.py\", line 91, in test_archiving_in_nonexistent_directory\n  File \"__test__.py\", line 27, in task_func\nFileNotFoundError: No files found matching the pattern.\n",
          "test_multiple_archiving_unique_naming": "Traceback (most recent call last):\n  File \"__test__.py\", line 84, in test_multiple_archiving_unique_naming\n  File \"__test__.py\", line 27, in task_func\nFileNotFoundError: No files found matching the pattern.\n",
          "test_no_matching_files": "Traceback (most recent call last):\n  File \"__test__.py\", line 79, in test_no_matching_files\n  File \"__test__.py\", line 27, in task_func\nFileNotFoundError: No files found matching the pattern.\n"
        }
      }
    ],
    "BigCodeBench/1129": [
      {
        "task_id": "BigCodeBench/1129",
        "solution": "import json\nimport requests\nimport os\nfrom datetime import datetime\ndef task_func(json_data, unknown_key, save_dir=None):\n\n    pass\nimport json\nimport requests\nimport os\nfrom datetime import datetime\n\ndef task_func(json_data, unknown_key, save_dir=None):\n    # Parse the JSON data to retrieve the URL\n    data = json.loads(json_data)\n    if unknown_key not in data:\n        raise KeyError(f\"The key '{unknown_key}' is not found in the JSON data.\")\n    \n    url = data[unknown_key]\n    \n    # Validating the URL format could be added here.\n    \n    # Download the file\n    try:\n        response = requests.get(url)\n        response.raise_for_status()  # Raises an error for bad responses (4xx and 5xx)\n    except requests.RequestException as e:\n        raise ConnectionError(f\"Failed to download file from URL: {url}. Error: {str(e)}\")\n    \n    # Create the filename with a timestamp\n    timestamp = datetime.now().strftime('%Y%m%d%H%M%S%f')\n    filename = f\"{unknown_key}_{timestamp}.txt\"\n    \n    # Determine save directory\n    if save_dir is None:\n        save_dir = os.getcwd()\n    \n    # Saving the file\n    file_path = os.path.join(save_dir, filename)\n    try:\n        with open(file_path, 'wb') as file:\n            file.write(response.content)\n    except IOError as e:\n        raise IOError(f\"Failed to save file to {file_path}. Error: {str(e)}\")\n\n    # Return the absolute path of the saved file\n    return os.path.abspath(file_path)",
        "status": "fail",
        "details": {
          "test_download_to_default_directory": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 94, in test_download_to_default_directory\n  File \"__test__.py\", line 26, in task_func\nAttributeError: 'MockResponse' object has no attribute 'raise_for_status'\n",
          "test_download_with_direct_key": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 69, in test_download_with_direct_key\n  File \"__test__.py\", line 26, in task_func\nAttributeError: 'MockResponse' object has no attribute 'raise_for_status'\n",
          "test_download_with_specified_directory": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 85, in test_download_with_specified_directory\n  File \"__test__.py\", line 26, in task_func\nAttributeError: 'MockResponse' object has no attribute 'raise_for_status'\n",
          "test_multiple_downloads": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 105, in test_multiple_downloads\n  File \"__test__.py\", line 26, in task_func\nAttributeError: 'MockResponse' object has no attribute 'raise_for_status'\n"
        }
      }
    ],
    "BigCodeBench/1124": [
      {
        "task_id": "BigCodeBench/1124",
        "solution": "import re\nfrom urllib.parse import urlparse\nfrom bs4 import BeautifulSoup\nimport requests\ndef task_func(myString):\n\n    pass\nimport re\nfrom urllib.parse import urlparse\nfrom bs4 import BeautifulSoup\nimport requests\n\ndef task_func(myString):\n    # Step 1: Extract the URL\n    url_pattern = r'https?://[^\\s/$.?#].[^\\s]*'  # Updated regex pattern for better URL matching\n    match = re.search(url_pattern, myString)\n    \n    if not match:\n        return \"No valid URL found in the provided string.\"\n    \n    url = match.group(0)\n    \n    # Step 2: Fetch the content of the URL\n    try:\n        response = requests.get(url)\n        response.raise_for_status()  # Raise an error for bad responses\n    except requests.RequestException:\n        return f\"Unable to fetch the content of the URL: {url}\"\n    \n    # Step 3: Parse the webpage and retrieve the title\n    soup = BeautifulSoup(response.content, 'html.parser')\n    title = soup.title.string if soup.title else None\n    \n    if not title:\n        return \"No title tag found in the webpage.\"\n    \n    return title",
        "status": "fail",
        "details": {
          "test_multiple_urls_in_string": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 67, in test_multiple_urls_in_string\n  File \"__test__.py\", line 31, in task_func\nAttributeError: 'MockResponse' object has no attribute 'content'\n",
          "test_url_with_no_title_tag": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 73, in test_url_with_no_title_tag\n  File \"__test__.py\", line 31, in task_func\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bs4/__init__.py\", line 319, in __init__\n    for (self.markup, self.original_encoding, self.declared_html_encoding,\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bs4/builder/_htmlparser.py\", line 325, in prepare_markup\n    dammit = UnicodeDammit(markup, try_encodings, is_html=True,\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bs4/dammit.py\", line 430, in __init__\n    self.detector = EncodingDetector(\n                    ^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bs4/dammit.py\", line 264, in __init__\n    self.markup, self.sniffed_encoding = self.strip_byte_order_mark(markup)\n                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/bs4/dammit.py\", line 329, in strip_byte_order_mark\n    if (len(data) >= 4) and (data[:2] == b'\\xfe\\xff') \\\n        ^^^^^^^^^\nTypeError: object of type 'Mock' has no len()\n",
          "test_valid_url_with_title": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 53, in test_valid_url_with_title\n  File \"__test__.py\", line 31, in task_func\nAttributeError: 'MockResponse' object has no attribute 'content'\n"
        }
      }
    ],
    "BigCodeBench/1137": [
      {
        "task_id": "BigCodeBench/1137",
        "solution": "import bs4\nimport requests\nimport re\nimport json\ndef task_func(url: str, output_path: str) -> list:\n\n    pass\nimport bs4\nimport requests\nimport re\nimport json\n\ndef task_func(url: str, output_path: str) -> list:\n    # Step 1: Fetching the Content\n    content = \"\"\n    if url.startswith(\"http://\") or url.startswith(\"https://\"):\n        # Fetch content from a URL\n        response = requests.get(url)\n        if response.status_code == 200:\n            content = response.text\n        else:\n            print(f\"Error fetching URL: {response.status_code}\")\n            return []\n    else:\n        # Read content from a local file\n        try:\n            with open(url, 'r') as file:\n                content = file.read()\n        except Exception as e:\n            print(f\"Error reading file: {e}\")\n            return []\n\n    # Step 2: Parsing the Content\n    soup = bs4.BeautifulSoup(content, 'html.parser')\n    text = soup.get_text()\n\n    # Step 3: Extracting Phone Numbers\n    phone_pattern = r'\\b(\\+?\\d{1,3}[-.\\s]?)?(\\(?\\d{2,4}\\)?[-.\\s]?)?\\d{3}[-.\\s]?\\d{4}\\b'\n    found_numbers = re.findall(phone_pattern, text)\n    phone_numbers = [''.join(number).strip() for number in found_numbers if any(number)]  # Join tuples to get full phone numbers\n\n    # Step 4: Saving the Results\n    try:\n        with open(output_path, 'w') as json_file:\n            json.dump(phone_numbers, json_file)\n    except Exception as e:\n        print(f\"Error writing to file: {e}\")\n\n    return phone_numbers",
        "status": "fail",
        "details": {
          "test_phone_numbers_page_1": "Traceback (most recent call last):\n  File \"__test__.py\", line 77, in test_phone_numbers_page_1\n  File \"__test__.py\", line 103, in run_test_case\nAssertionError: Lists differ: [] != ['+1234567890']\n\nSecond list contains 1 additional elements.\nFirst extra element 0:\n'+1234567890'\n\n- []\n+ ['+1234567890']\n",
          "test_phone_numbers_page_2": "Traceback (most recent call last):\n  File \"__test__.py\", line 82, in test_phone_numbers_page_2\n  File \"__test__.py\", line 103, in run_test_case\nAssertionError: Lists differ: [] != ['+9876543210', '+1122334455']\n\nSecond list contains 2 additional elements.\nFirst extra element 0:\n'+9876543210'\n\n- []\n+ ['+9876543210', '+1122334455']\n",
          "test_phone_numbers_page_4": "Traceback (most recent call last):\n  File \"__test__.py\", line 92, in test_phone_numbers_page_4\n  File \"__test__.py\", line 103, in run_test_case\nAssertionError: Lists differ: [] != ['+919876543210']\n\nSecond list contains 1 additional elements.\nFirst extra element 0:\n'+919876543210'\n\n- []\n+ ['+919876543210']\n",
          "test_phone_numbers_page_5": "Traceback (most recent call last):\n  File \"__test__.py\", line 97, in test_phone_numbers_page_5\n  File \"__test__.py\", line 103, in run_test_case\nAssertionError: Lists differ: [] != ['+14151234567', '+13171234567']\n\nSecond list contains 2 additional elements.\nFirst extra element 0:\n'+14151234567'\n\n- []\n+ ['+14151234567', '+13171234567']\n",
          "test_phone_numbers_page_3": "Traceback (most recent call last):\n  File \"__test__.py\", line 87, in test_phone_numbers_page_3\n  File \"__test__.py\", line 104, in run_test_case\nFileNotFoundError: [Errno 2] No such file or directory: '/tmp/tmp9dqrbjqu/output_test_page_3.html'\n"
        }
      }
    ],
    "BigCodeBench/1053": [
      {
        "task_id": "BigCodeBench/1053",
        "solution": "import pandas as pd\nfrom sklearn.feature_extraction.text import CountVectorizer\nimport matplotlib.pyplot as plt\n# Constants\nSTOP_WORDS = [\"a\", \"an\", \"the\", \"in\", \"on\", \"at\", \"and\", \"or\"]\ndef task_func(file_path, save_path=None):\n\n    pass\nimport pandas as pd\nfrom sklearn.feature_extraction.text import CountVectorizer\nimport matplotlib.pyplot as plt\n\n# Constants\nSTOP_WORDS = [\"a\", \"an\", \"the\", \"in\", \"on\", \"at\", \"and\", \"or\"]\n\ndef task_func(file_path, save_path=None):\n    try:\n        # Step 1: File Reading\n        data = pd.read_csv(file_path)\n        \n        # Step 2: Data Extraction - extracting the 'Text' column or using the first column if no header\n        if 'Text' in data.columns:\n            text_data = data['Text']\n        elif data.shape[1] > 0:\n            text_data = data.iloc[:, 0]\n        else:\n            raise ValueError(\"The CSV file does not contain any data.\")\n\n        # Step 3: Text Preprocessing\n        text = ' '.join(text_data.astype(str))  # Ensure all data is string type\n        vectorizer = CountVectorizer(stop_words=STOP_WORDS)\n        word_counts = vectorizer.fit_transform([text])\n        \n        # Step 4: Word Frequency Calculation\n        counts = word_counts.toarray().flatten()\n        words = vectorizer.get_feature_names_out()  # Ensure using correct method for sklearn version\n        word_freq = pd.Series(counts, index=words)\n        top_words = word_freq.nlargest(10)\n        \n        # Step 5: Histogram Generation\n        fig, ax = plt.subplots()\n        top_words.plot(kind='bar', ax=ax)\n        ax.set_title('Top 10 Most Common Words')\n        ax.set_ylabel('Frequency')\n        \n        # Display or save the plot\n        if save_path:\n            plt.savefig(save_path)\n            plt.close(fig)\n            return None\n        else:\n            plt.show()\n            return ax  # Return Axes object for further customization\n        \n    except FileNotFoundError:\n        raise FileNotFoundError(f\"The file at {file_path} was not found.\")\n    except Exception as e:\n        print(f\"An error occurred: {e}\")\n        return None",
        "status": "pass",
        "details": {}
      }
    ],
    "BigCodeBench/1012": [
      {
        "task_id": "BigCodeBench/1012",
        "solution": "import requests\nfrom pathlib import Path\nimport zipfile\n# Constants\nDOWNLOAD_DIR = Path(\"downloads\")\nZIP_DIR = Path(\"unzipped_files\")\ndef task_func(url, filename):\n\n    pass\nimport requests\nfrom pathlib import Path\nimport zipfile\n\n# Constants\nDOWNLOAD_DIR = Path(\"downloads\")\nZIP_DIR = Path(\"unzipped_files\")\n\ndef task_func(url, filename):\n    # Ensure the download and unzip directories exist\n    DOWNLOAD_DIR.mkdir(exist_ok=True)\n    ZIP_DIR.mkdir(exist_ok=True)\n\n    zip_file_path = DOWNLOAD_DIR / filename\n\n    # Step 1: Download the ZIP file\n    try:\n        response = requests.get(url)\n        response.raise_for_status()  # Raise an error for bad responses\n        with open(zip_file_path, 'wb') as f:\n            f.write(response.content)\n    except requests.exceptions.RequestException as e:\n        return (\"Error: \" + str(e), [])\n\n    # Step 2: Extract the ZIP file\n    try:\n        with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:\n            zip_ref.extractall(ZIP_DIR)\n            # List of filenames in the unzipped directory\n            extracted_files = zip_ref.namelist()\n    except (zipfile.BadZipFile, OSError) as e:\n        return (\"Error: \" + str(e), [])\n\n    # Step 3: Return the result\n    return (\"Success\", extracted_files)",
        "status": "fail",
        "details": {
          "test_successful_download_and_extraction": "Traceback (most recent call last):\n  File \"__test__.py\", line 57, in test_successful_download_and_extraction\nAssertionError: 'Download and extraction successful' not found in 'Success'\n",
          "test_invalid_url": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 63, in test_invalid_url\n  File \"__test__.py\", line 30, in task_func\nTypeError: a bytes-like object is required, not 'MagicMock'\n",
          "test_non_200_http_response": "Traceback (most recent call last):\n  File \"/usr/lib/python3.12/unittest/mock.py\", line 1390, in patched\n    return func(*newargs, **newkeywargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"__test__.py\", line 70, in test_non_200_http_response\n  File \"__test__.py\", line 30, in task_func\nTypeError: a bytes-like object is required, not 'MagicMock'\n"
        }
      }
    ],
    "BigCodeBench/590": [
      {
        "task_id": "BigCodeBench/590",
        "solution": "import urllib.request\nfrom pyquery import PyQuery as pq\nfrom datetime import datetime\nimport pandas as pd\ndef task_func(url):\n\n    pass\nimport urllib.request\nfrom pyquery import PyQuery as pq\nfrom datetime import datetime\nimport pandas as pd\nfrom urllib.parse import urlparse\n\ndef task_func(url):\n    # 1. Validate the URL\n    if not url or not isinstance(url, str) or not urlparse(url).scheme:\n        raise ValueError(\"The provided URL is invalid or empty.\")\n    \n    # 2. Fetch HTML Content\n    try:\n        response = urllib.request.urlopen(url)\n        html_content = response.read()\n    except urllib.error.URLError as e:\n        raise urllib.error.URLError(f\"Network error occurred: {e.reason}\")\n    \n    # 3. Parse HTML to Extract Anchor Tags\n    document = pq(html_content)\n    anchors = document('a')\n    \n    # 4. Prepare Data for Output\n    data = []\n    fetch_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n    \n    for anchor in anchors.items():\n        text = anchor.text()\n        href = anchor.attr('href')\n        data.append({'text': text, 'href': href, 'fetch_time': fetch_time})\n    \n    # 5. Convert to DataFrame\n    df = pd.DataFrame(data)\n    return df",
        "status": "fail",
        "details": {
          "test_dynamic_comparison": "Traceback (most recent call last):\n  File \"__test__.py\", line 84, in test_dynamic_comparison\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/_testing/asserters.py\", line 1224, in assert_frame_equal\n    assert_series_equal(\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/_testing/asserters.py\", line 1014, in assert_series_equal\n    _testing.assert_almost_equal(\n  File \"pandas/_libs/testing.pyx\", line 52, in pandas._libs.testing.assert_almost_equal\n  File \"pandas/_libs/testing.pyx\", line 172, in pandas._libs.testing.assert_almost_equal\n  File \"/home/mark/Documents/CURA-v2/CURA/.venv/lib/python3.12/site-packages/pandas/_testing/asserters.py\", line 599, in raise_assert_detail\n    raise AssertionError(msg)\nAssertionError: DataFrame.iloc[:, 0] (column name=\"text\") are different\n\nDataFrame.iloc[:, 0] (column name=\"text\") values are different (66.90391 %)\n[index]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...]\n[left]:  [Jump to content, Main Page, Help, Browse, Cookbook, Wikijunior, Featured books, Recent changes, Random book, Using Wikibooks, Reading room forum, Community portal, Bulletin Board, Help out!, Policies and guidelines, Contact us, , Search, Donations, Create account, Log in, Donations, Create account, Log in, learn more, Contributions, Discussion for this IP address, Main Page, Discussion, Read, View source, View history, Read, View source, View history, What links here, Related changes, Upload file, Special pages, Permanent link, Page information, Cite this page, Get shortened URL, Download QR code, Wikipedia, Wikiversity, Wiktionary, Wikiquote, Wikisource, Wikinews, Wikivoyage, Commons, Wikidata, MediaWiki, Meta-Wiki, Create a collection, Download as PDF, Printable version, Wikimedia Commons, Wikimedia Foundation, MediaWiki, Meta-Wiki, Wikimedia Outreach, Multilingual Wikisource, Wikispecies, Wikidata, Wikifunctions, Wikimania, Wikinews, Wikipedia, Wikiquote, Wikisource, Wikiversity, Wikivoyage, Wiktionary, Wikidata item, Featured books, Wikijunior, Cookbook, Browse books, Help, Forum, Using Wikibooks, Welcome, Wikibooks, anyone can edit, 3,359 books, 97,780 pages, Computing, Engineering, Humanities, Languages, Mathematics, Miscellaneous, Recreational activities, Science, Social sciences, Standard curricula, Wikijunior, All subjects, ...]\n[right]: [Jump to content, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, \n\t, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, Featured books, Wikijunior, Cookbook, Browse books, Help, Forum, Using Wikibooks, Welcome, Wikibooks, anyone can edit, 3,359 books, 97,780 pages, Computing, Engineering, Humanities, Languages, Mathematics, Miscellaneous, Recreational activities, Science, Social sciences, Standard curricula, Wikijunior, All subjects, ...]\nAt positional index 1, first diff: Main Page != None\n"
        }
      }
    ],
    "BigCodeBench/101": [
      {
        "task_id": "BigCodeBench/101",
        "solution": "import matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\nimport numpy as np\ndef task_func(data_url=\"http://lib.stat.cmu.edu/datasets/boston\", seed=42):\n\n    pass\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\nimport numpy as np\n\ndef task_func(data_url=\"http://lib.stat.cmu.edu/datasets/boston\", save_path=\"boston_housing_correlation_heatmap.png\", seed=42):\n    try:\n        # Step 1: Data Retrieval\n        # Read the dataset into a pandas DataFrame\n        df = pd.read_csv(data_url, delim_whitespace=True, skiprows=22, header=None)  # Adjust based on actual format\n        \n        # Step 2: Data Preparation\n        # Calculate the correlation matrix\n        corr_matrix = df.corr()\n        \n        # Step 3: Heatmap Generation\n        plt.figure(figsize=(10, 8))\n        ax = sns.heatmap(corr_matrix, annot=True, fmt=\".2f\", cmap='coolwarm', square=True)\n        \n        # Title for the heatmap\n        plt.title('Correlation Heatmap of Boston Housing Dataset')\n        \n        # Step 4: File Saving\n        # Save the figure\n        plt.savefig(save_path)\n        \n        # Returning the plot Axes\n        return ax\n        \n    except Exception as e:\n        raise ValueError(f\"An error occurred while generating or saving the heatmap: {str(e)}\")",
        "status": "fail",
        "details": {
          "test_heatmap_features": "Traceback (most recent call last):\n  File \"__test__.py\", line 48, in test_heatmap_features\nAssertionError: Tuples differ: (121,) != (169,)\n\nFirst differing element 0:\n121\n169\n\n- (121,)\n+ (169,)\n",
          "test_plot_appearance": "Traceback (most recent call last):\n  File \"__test__.py\", line 60, in test_plot_appearance\nAssertionError: 'Correlation Heatmap of Boston Housing Dataset' != ''\n- Correlation Heatmap of Boston Housing Dataset\n\n",
          "test_heatmap_values": "Traceback (most recent call last):\n  File \"__test__.py\", line 55, in test_heatmap_values\n  File \"/usr/lib/python3.12/unittest/case.py\", line 916, in assertAlmostEqual\n    diff = abs(first - second)\n               ~~~~~~^~~~~~~~\nTypeError: unsupported operand type(s) for -: 'list' and 'list'\n"
        }
      }
    ],
    "BigCodeBench/99": [
      {
        "task_id": "BigCodeBench/99",
        "solution": "import matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\nfrom sklearn.datasets import load_iris\ndef task_func():\n\n    pass\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\nfrom sklearn.datasets import load_iris\n\ndef task_func():\n    # Step 1: Set global font to Arial\n    plt.rcParams['font.family'] = 'Arial'\n\n    # Step 2: Load the iris dataset\n    iris_data = load_iris()\n    iris_df = pd.DataFrame(data=iris_data.data, columns=iris_data.feature_names)\n    iris_df['species'] = iris_data.target\n\n    # Step 3: Generate the pair plot\n    pair_plot = sns.pairplot(iris_df, hue='species', palette='Set1')\n\n    # Step 4: Add title\n    pair_plot.fig.suptitle('Iris Dataset Pair Plot', y=1.02)\n\n    # Step 5: Show the plot\n    plt.show()\n\n    return pair_plot.fig\n\n# Call the function to see the output\ntask_func()",
        "status": "pass",
        "details": {}
      }
    ],
    "BigCodeBench/587": [
      {
        "task_id": "BigCodeBench/587",
        "solution": "import rsa\nimport os\nfrom cryptography.hazmat.backends import default_backend\nfrom cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes\nfrom cryptography.hazmat.primitives import padding\nfrom base64 import b64encode\ndef task_func(file_path):\n\n    pass\nimport rsa\nimport os\nfrom cryptography.hazmat.backends import default_backend\nfrom cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes\nfrom cryptography.hazmat.primitives import padding\nfrom base64 import b64encode\nimport secrets\n\ndef task_func(file_path):\n    # 1. Generate RSA Keys\n    public_key, private_key = rsa.newkeys(2048)\n\n    # 2. Read the content of the file with error handling\n    try:\n        with open(file_path, 'rb') as file:\n            file_data = file.read()\n    except FileNotFoundError:\n        raise Exception(f\"File {file_path} not found.\")\n    except Exception as e:\n        raise Exception(f\"Error reading file: {str(e)}\")\n\n    # 3. Generate a random AES key\n    aes_key = secrets.token_bytes(32)  # AES-256 requires a 32-byte key\n\n    # 4. Encrypt the file using AES\n    iv = os.urandom(16)  # Random initialization vector\n    cipher = Cipher(algorithms.AES(aes_key), modes.CBC(iv), backend=default_backend())\n    encryptor = cipher.encryptor()\n\n    # Pad the data to be a multiple of the block size\n    padder = padding.PKCS7(algorithms.AES.block_size).padder()\n    padded_data = padder.update(file_data) + padder.finalize()\n\n    # Encrypt the file data\n    encrypted_data = encryptor.update(padded_data) + encryptor.finalize()\n\n    # Save the encrypted file\n    encrypted_file_name = file_path + '.enc'\n    with open(encrypted_file_name, 'wb') as enc_file:\n        enc_file.write(iv + encrypted_data)  # Prepend IV for Decryption\n\n    # 5. Encrypt the AES key with RSA Public Key\n    encrypted_aes_key = rsa.encrypt(aes_key, public_key)\n\n    # Save the encrypted AES key\n    encrypted_aes_key_file_name = 'encrypted_aes_key.bin'\n    with open(encrypted_aes_key_file_name, 'wb') as aes_key_file:\n        aes_key_file.write(encrypted_aes_key)\n\n    # 6. Output the results\n    return public_key, encrypted_file_name, encrypted_aes_key_file_name",
        "status": "fail",
        "details": {
          "test_non_existent_file": "Traceback (most recent call last):\n  File \"__test__.py\", line 24, in task_func\nFileNotFoundError: [Errno 2] No such file or directory: 'non_existent_file.txt'\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n  File \"__test__.py\", line 91, in test_non_existent_file\n  File \"__test__.py\", line 27, in task_func\nException: File non_existent_file.txt not found.\n"
        }
      }
    ],
    "BigCodeBench/461": [
      {
        "task_id": "BigCodeBench/461",
        "solution": "import subprocess\nimport psutil\nimport time\nimport os\ndef task_func(script_path: str, timeout=10) -> dict:\n\n    pass\nimport subprocess\nimport psutil\nimport time\nimport os\n\ndef task_func(script_path: str, timeout=10) -> dict:\n    # Step 1: Check Script Path\n    if not os.path.isfile(script_path) or not os.access(script_path, os.X_OK):\n        raise ValueError(\"The provided script path does not exist or is not executable.\")\n    \n    # Step 2: Execute Script in a Subprocess\n    process = subprocess.Popen([script_path], stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n    \n    # Step 3: Monitor Process Resource Usage\n    cpu_usage_total = 0.0\n    memory_usage_max = 0  # Track maximum RSS memory usage\n    process_found = True\n    start_time = time.time()\n\n    try:\n        while True:\n            # Check if process is alive\n            if process.poll() is not None:  # Process has finished\n                break\n            \n            # Step 4: Collect CPU and Memory usage\n            p = psutil.Process(process.pid)\n            cpu_usage_total += p.cpu_percent(interval=0.1)  # Cumulative CPU usage over the interval\n            memory_usage_max = max(memory_usage_max, p.memory_info().rss)  # Track max RSS memory\n            \n            # Timeout handling\n            if time.time() - start_time > timeout:\n                process_found = False\n                process.terminate()  # Terminate the process if timeout is reached\n                break\n            \n            time.sleep(0.1)  # Sleep to avoid excessive CPU usage during monitoring\n\n    except psutil.NoSuchProcess:\n        process_found = False  # Handle case where the process is no longer found\n    except psutil.ZombieProcess:\n        pass  # A zombie process will not affect usage readings\n\n    # Final cleanup after the process terminates\n    if process_found:\n        process.wait()  # Wait for the process to finish execution\n    \n    return {\n        'CPU Usage': cpu_usage_total,\n        'Memory Usage': memory_usage_max\n    }",
        "status": "fail",
        "details": {
          "test_case_3": "Traceback (most recent call last):\n  File \"__test__.py\", line 92, in test_case_3\n  File \"__test__.py\", line 16, in task_func\nValueError: The provided script path does not exist or is not executable.\n",
          "test_case_9": "Traceback (most recent call last):\n  File \"__test__.py\", line 120, in test_case_9\n  File \"__test__.py\", line 16, in task_func\nValueError: The provided script path does not exist or is not executable.\n"
        }
      }
    ],
    "BigCodeBench/583": [
      {
        "task_id": "BigCodeBench/583",
        "solution": "import rsa\nimport os\nfrom Crypto.Random import get_random_bytes\nfrom Crypto.Cipher import AES\nfrom base64 import b64encode, b64decode\ndef task_func():\n\n    pass\nimport rsa\nimport os\nfrom Crypto.Random import get_random_bytes\nfrom Crypto.Cipher import AES\nfrom Crypto.Util.Padding import pad\nfrom base64 import b64encode\n\ndef task_func():\n    # Step 1: Generate RSA Key Pair\n    public_key, private_key = rsa.newkeys(2048)  # Generate RSA keys\n\n    # Step 2: Generate AES Encryption Parameters\n    aes_password = get_random_bytes(16)  # Generate 16-byte random AES password\n    nonce = get_random_bytes(16)  # Generate 16-byte nonce for AES \n\n    # Step 3: Encrypt the Private Key\n    cipher = AES.new(aes_password, AES.MODE_CBC, nonce)\n    \n    encrypted_private_key = cipher.encrypt(pad(private_key.save_pkcs1(format='PEM'), AES.block_size))\n    \n    # Step 4: Create Output Filename\n    hex_number = get_random_bytes(8).hex()  # Generate random 8 bytes and convert to hex\n    filename = f\"private_key_{hex_number}.txt\"  # Format the filename\n\n    # Step 5: Save Encrypted Private Key to File\n    with open(filename, 'wb') as f:\n        f.write(nonce + encrypted_private_key)  # Store nonce followed by encrypted key\n\n    # Step 6: Return Necessary Details\n    return public_key, filename, aes_password, nonce\n\n# Example usage:\nif __name__ == \"__main__\":\n    public_key, filename, password, nonce = task_func()\n    print(f\"Public Key: {public_key}\")\n    print(f\"Filename: {filename}\")\n    print(f\"Password: {b64encode(password).decode()}\")\n    print(f\"Nonce: {b64encode(nonce).decode()}\")",
        "status": "fail",
        "details": {
          "test_key_size": "Traceback (most recent call last):\n  File \"__test__.py\", line 72, in test_key_size\nAssertionError: 2048 != 512\n",
          "test_encryption_decryption": "Traceback (most recent call last):\n  File \"__test__.py\", line 83, in test_encryption_decryption\n  File \"<frozen codecs>\", line 322, in decode\nUnicodeDecodeError: 'utf-8' codec can't decode byte 0x8f in position 1: invalid start byte\n",
          "test_file_content": "Traceback (most recent call last):\n  File \"__test__.py\", line 67, in test_file_content\n  File \"<frozen codecs>\", line 322, in decode\nUnicodeDecodeError: 'utf-8' codec can't decode byte 0xcf in position 1: invalid continuation byte\n"
        }
      }
    ],
    "BigCodeBench/1040": [
      {
        "task_id": "BigCodeBench/1040",
        "solution": "import socket\nimport select\nimport queue\nfrom datetime import datetime, timedelta\ndef task_func(\n    server_address=\"localhost\", server_port=12345, buffer_size=1024, run_duration=5\n):\n\n    pass\nimport socket\nimport select\nimport queue\nfrom datetime import datetime, timedelta\n\ndef task_func(server_address=\"localhost\", server_port=12345, buffer_size=1024, run_duration=5):\n    # Step 1: Initialize server socket\n    server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    server_socket.bind((server_address, server_port))\n    server_socket.listen()\n    server_socket.setblocking(0)\n\n    # Step 2: Prepare to handle connections\n    inputs = [server_socket]\n    outputs = []\n    message_queues = {}\n\n    start_time = datetime.now()\n    end_time = start_time + timedelta(seconds=run_duration)\n\n    while datetime.now() < end_time:\n        readable, writable, exceptional = select.select(inputs, outputs, inputs)\n\n        for s in readable:\n            if s is server_socket:\n                # Accept new client connection\n                client_socket, client_address = s.accept()\n                client_socket.setblocking(0)\n                inputs.append(client_socket)\n                message_queues[client_socket] = queue.Queue()\n                print(f\"Accepted connection from {client_address}\")\n            else:\n                # Handle incoming data from clients\n                try:\n                    data = s.recv(buffer_size)\n                    if data:\n                        current_time = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n                        response = data.decode() + \" \" + current_time\n                        message_queues[s].put(response)\n                        if s not in outputs:\n                            outputs.append(s)\n                    else:\n                        # Client disconnected\n                        print(f\"Connection closed by {s.getpeername()}\")\n                        if s in outputs:\n                            outputs.remove(s)\n                        inputs.remove(s)\n                        s.close()\n                        del message_queues[s]\n                except Exception as e:\n                    print(f\"Error handling data from {s.getpeername()}: {str(e)}\")\n                    # Clean up on error\n                    if s in outputs:\n                        outputs.remove(s)\n                    inputs.remove(s)\n                    s.close()\n                    del message_queues[s]\n\n        for s in writable:\n            try:\n                next_msg = message_queues[s].get_nowait()\n            except queue.Empty:\n                outputs.remove(s)\n            else:\n                s.send(next_msg.encode())\n\n        for s in exceptional:\n            print(f\"Exception on connection {s.getpeername()}\")\n            inputs.remove(s)\n            if s in outputs:\n                outputs.remove(s)\n            s.close()\n            del message_queues[s]\n\n    # Step 6: Output server status and duration\n    run_duration_message = f\"Server ran for {run_duration} seconds.\"\n    print(run_duration_message)\n    return run_duration_message\n\n# Example usage of the task_func\nif __name__ == \"__main__\":\n    task_func()",
        "status": "timeout",
        "details": {}
      }
    ]
  }
}