Instruction: Практическая работа: "Матрицы в машинном обучении" Условие: Для выполнения всех заданий в этой практической работе используйте только стандартные возможности Python. Никакие сторонние библиотеки или модули (например, numpy) использовать нельзя. Задание 1: Создание и отображение матрицы 1. Описание задачи: Реализуйте функцию create_matrix(rows, cols, fill_value=0), которая создает матрицу размера и заполняет её значениями fill_value. 2. Функция: def create_matrix(rows: int, cols: int, fill_value=0) -> list:  """  Создает матрицу размера rows x cols, заполненную значением fill_value.  Параметры:  rows (int): количество строк.  cols (int): количество столбцов.  fill_value (любой тип): значение для заполнения матрицы.  Возвращает:  list: матрица в виде списка списков.  """  pass 3. Пример использования: matrix = create_matrix(2, 3, 1) print(matrix) # [[1, 1, 1], [1, 1, 1]] Задание 2: Сложение матриц 1. Описание задачи: Реализуйте функцию add_matrices(matrix_a, matrix_b), которая выполняет сложение двух матриц одинакового размера. Сложение происходит поэлементно. Формула: 2. Функция: m × n Cij = Aij + Bij 2/5 def add_matrices(matrix_a: list, matrix_b: list) -> list:  """  Складывает две матрицы одинакового размера.  Параметры:  matrix_a (list): первая матрица.  matrix_b (list): вторая матрица.  Возвращает:  list: результат сложения матриц.  """  pass 3. Пример использования: matrix_a = [[1, 2], [3, 4]] matrix_b = [[5, 6], [7, 8]] result = add_matrices(matrix_a, matrix_b) print(result) # [[6, 8], [10, 12]] Задание 3: Умножение матрицы на число 1. Описание задачи: Реализуйте функцию scalar_multiply(matrix, scalar), которая умножает все элементы матрицы на заданное число. Формула: 2. Функция: def scalar_multiply(matrix: list, scalar: float) -> list:  """  Умножает все элементы матрицы на скаляр.  Параметры:  matrix (list): исходная матрица.  scalar (float): число, на которое умножаются элементы матрицы.  Возвращает:  list: результат умножения матрицы на скаляр.  """  pass 3. Пример использования: matrix = [[1, 2], [3, 4]] result = scalar_multiply(matrix, 3) print(result) # [[3, 6], [9, 12]] Cij = Aij × scalar 3/5 Задание 4: Умножение матриц 1. Описание задачи: Реализуйте функцию multiply_matrices(matrix_a, matrix_b), которая выполняет умножение двух матриц ( A ) и ( B ). Умножение возможно, если количество столбцов матрицы ( A ) совпадает с количеством строк матрицы ( B ). Формула: 2. Функция: def multiply_matrices(matrix_a: list, matrix_b: list) -> list:  """  Перемножает две матрицы.  Параметры:  matrix_a (list): первая матрица.  matrix_b (list): вторая матрица.  Возвращает:  list: результат умножения матриц.  """  pass 3. Пример использования: matrix_a = [[1, 2], [3, 4]] matrix_b = [[2, 0], [1, 3]] result = multiply_matrices(matrix_a, matrix_b) print(result) # [[4, 6], [10, 12]] Задание 5: Транспонирование матрицы 1. Описание задачи: Реализуйте функцию transpose_matrix(matrix), которая транспонирует заданную матрицу. Это означает, что строки исходной матрицы становятся столбцами, а столбцы — строками. Формула: 2. Функция: Cij = A × k=1 ∑ n ik Bkj Cij = Aji 4/5 def transpose_matrix(matrix: list) -> list:  """  Транспонирует матрицу.  Параметры:  matrix (list): исходная матрица.  Возвращает:  list: транспонированная матрица.  """  pass 3. Пример использования: matrix = [[1, 2], [3, 4], [5, 6]] result = transpose_matrix(matrix) print(result) # [[1, 3, 5], [2, 4, 6]] Задание 6: Определитель матрицы 1. Описание задачи: Реализуйте функцию determinant_3x3(matrix), которая вычисляет определитель для матрицы размером . Формула: 2. Функция: def determinant_3x3(matrix: list) -> float:  """  Вычисляет определитель матрицы 3x3.  Параметры:  matrix (list): исходная матрица размером 3x3.  Возвращает:  float: определитель матрицы.  """  pass 3. Пример использования: matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] result = determinant_3x3(matrix) print(result) # 0.0 3 × 3 3 × 3 det(A) = a11 (a22a33 − a23a32) − a12 (a21a33 − a23a31) + a13 (a21a32 − a22a31) 5/5 После выполнения всех заданий у вас будут функции, реализующие основные операции над матрицами, которые широко используются в Data Science и машинном обучении. Вы сможете самостоятельно создавать, складывать, умножать матрицы, а также вычислять их определитель и ранг. Убедитесь, что каждая функция корректно работает с заданными входными данными и возвращает ожидаемые результаты. Загрузите .py файл с реализованными функциями. Внутри файла не надо вызывать функции.

import pytest
from typing import Tuple, Any, List

def test_create_matrix_api(implementation):
    """Test that create_matrix function has the correct signature and returns a matrix"""
    impl_name, module = implementation

    # Check function existence
    assert hasattr(module, 'create_matrix'), f"{impl_name} is missing create_matrix function"

    # Test basic creation
    matrix = module.create_matrix(2, 3, 1)
    assert isinstance(matrix, list), f"{impl_name}: create_matrix should return a list"
    assert len(matrix) == 2, f"{impl_name}: create_matrix(2, 3, 1) should have 2 rows"
    assert all(len(row) == 3 for row in matrix), f"{impl_name}: create_matrix(2, 3, 1) should have 3 columns"
    assert all(all(cell == 1 for cell in row) for row in matrix), f"{impl_name}: create_matrix with fill_value=1 should fill matrix with 1s"

    # Test with default fill value
    matrix = module.create_matrix(2, 2)
    assert all(all(cell == 0 for cell in row) for row in matrix), f"{impl_name}: create_matrix with default fill_value should fill matrix with 0s"

def test_add_matrices_api(implementation):
    """Test that add_matrices function has the correct signature and behavior"""
    impl_name, module = implementation

    # Check function existence
    assert hasattr(module, 'add_matrices'), f"{impl_name} is missing add_matrices function"

    # Test addition
    matrix_a = [[1, 2], [3, 4]]
    matrix_b = [[5, 6], [7, 8]]
    result = module.add_matrices(matrix_a, matrix_b)

    assert isinstance(result, list), f"{impl_name}: add_matrices should return a list"
    assert len(result) == len(matrix_a), f"{impl_name}: add_matrices result should have same rows as input"
    assert all(len(row) == len(matrix_a[0]) for row in result), f"{impl_name}: add_matrices result should have same columns as input"

    expected = [[6, 8], [10, 12]]
    assert result == expected, f"{impl_name}: add_matrices({matrix_a}, {matrix_b}) returned {result} instead of {expected}"

def test_scalar_multiply_api(implementation):
    """Test that scalar_multiply function has the correct signature and behavior"""
    impl_name, module = implementation

    # Check function existence
    assert hasattr(module, 'scalar_multiply'), f"{impl_name} is missing scalar_multiply function"

    # Test scalar multiplication
    matrix = [[1, 2], [3, 4]]
    scalar = 3
    result = module.scalar_multiply(matrix, scalar)

    assert isinstance(result, list), f"{impl_name}: scalar_multiply should return a list"
    assert len(result) == len(matrix), f"{impl_name}: scalar_multiply result should have same rows as input"
    assert all(len(row) == len(matrix[0]) for row in result), f"{impl_name}: scalar_multiply result should have same columns as input"

    expected = [[3, 6], [9, 12]]
    assert result == expected, f"{impl_name}: scalar_multiply({matrix}, {scalar}) returned {result} instead of {expected}"

def test_multiply_matrices_api(implementation):
    """Test that multiply_matrices function has the correct signature and behavior"""
    impl_name, module = implementation

    # Check function existence
    assert hasattr(module, 'multiply_matrices'), f"{impl_name} is missing multiply_matrices function"

    # Test matrix multiplication
    matrix_a = [[1, 2], [3, 4]]
    matrix_b = [[2, 0], [1, 3]]
    result = module.multiply_matrices(matrix_a, matrix_b)

    assert isinstance(result, list), f"{impl_name}: multiply_matrices should return a list"
    assert len(result) == len(matrix_a), f"{impl_name}: multiply_matrices result should have same rows as matrix_a"
    assert all(len(row) == len(matrix_b[0]) for row in result), f"{impl_name}: multiply_matrices result columns should match matrix_b columns"

    expected = [[4, 6], [10, 12]]
    assert result == expected, f"{impl_name}: multiply_matrices({matrix_a}, {matrix_b}) returned {result} instead of {expected}"

def test_transpose_matrix_api(implementation):
    """Test that transpose_matrix function has the correct signature and behavior"""
    impl_name, module = implementation

    # Check function existence
    assert hasattr(module, 'transpose_matrix'), f"{impl_name} is missing transpose_matrix function"

    # Test transposition
    matrix = [[1, 2], [3, 4], [5, 6]]
    result = module.transpose_matrix(matrix)

    assert isinstance(result, list), f"{impl_name}: transpose_matrix should return a list"
    assert len(result) == len(matrix[0]), f"{impl_name}: transpose_matrix result rows should match input columns"
    assert all(len(row) == len(matrix) for row in result), f"{impl_name}: transpose_matrix result columns should match input rows"

    expected = [[1, 3, 5], [2, 4, 6]]
    assert result == expected, f"{impl_name}: transpose_matrix({matrix}) returned {result} instead of {expected}"

def test_determinant_3x3_api(implementation):
    """Test that determinant_3x3 function has the correct signature and behavior"""
    impl_name, module = implementation

    # Check function existence
    assert hasattr(module, 'determinant_3x3'), f"{impl_name} is missing determinant_3x3 function"

    # Test determinant calculation
    matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
    result = module.determinant_3x3(matrix)

    assert isinstance(result, (int, float)), f"{impl_name}: determinant_3x3 should return a number"

    expected = 0.0
    assert abs(result - expected) < 1e-10, f"{impl_name}: determinant_3x3({matrix}) returned {result} instead of {expected}"

    # Test non-zero determinant
    matrix = [[1, 2, 3], [0, 1, 4], [5, 6, 0]]
    result = module.determinant_3x3(matrix)
    expected = 1 * (1 * 0 - 4 * 6) - 2 * (0 * 0 - 4 * 5) + 3 * (0 * 6 - 1 * 5)
    assert abs(result - expected) < 1e-10, f"{impl_name}: determinant_3x3 calculation is incorrect"

def test_create_matrix_edge_cases(implementation):
    """Test create_matrix function with edge cases"""
    impl_name, module = implementation

    # Test with 0x0 matrix
    matrix = module.create_matrix(0, 0)
    assert matrix == [], f"{impl_name}: create_matrix(0, 0) should return an empty list"

    # Test with non-numeric fill value
    fill_value = "test"
    matrix = module.create_matrix(2, 2, fill_value)
    assert all(all(cell == fill_value for cell in row) for row in matrix), f"{impl_name}: create_matrix should work with non-numeric fill values"

def test_add_matrices_edge_cases(implementation):
    """Test add_matrices function with edge cases"""
    impl_name, module = implementation

    # Test with empty matrices
    if len(module.create_matrix(0, 0)) == 0:  # Only test if create_matrix(0,0) works
        try:
            result = module.add_matrices([], [])
            assert result == [], f"{impl_name}: add_matrices([], []) should return an empty list"
        except (IndexError, ValueError):
            # Some implementations might reject empty matrices
            pass

    # Test with matrices of different dimensions
    try:
        module.add_matrices([[1, 2]], [[3]])
        # If we reach here, the function didn't raise an error for different sized matrices
        # Check if the implementation handles this case in a different way
        result = module.add_matrices([[1, 2]], [[3]])
        # If there's a result, it should maintain some logical structure
        assert isinstance(result, list), f"{impl_name}: add_matrices should return a list even with invalid inputs"
    except (ValueError, IndexError):
        # This is acceptable - the function might validate dimensions
        pass

def test_scalar_multiply_edge_cases(implementation):
    """Test scalar_multiply function with edge cases"""
    impl_name, module = implementation

    # Test with empty matrix
    if len(module.create_matrix(0, 0)) == 0:  # Only test if create_matrix(0,0) works
        try:
            result = module.scalar_multiply([], 5)
            assert result == [], f"{impl_name}: scalar_multiply([], 5) should return an empty list"
        except (IndexError, ValueError):
            # Some implementations might reject empty matrices
            pass

    # Test with scalar = 0
    matrix = [[1, 2], [3, 4]]
    result = module.scalar_multiply(matrix, 0)
    expected = [[0, 0], [0, 0]]
    assert result == expected, f"{impl_name}: scalar_multiply({matrix}, 0) should return a matrix of zeros"

def test_multiply_matrices_edge_cases(implementation):
    """Test multiply_matrices function with edge cases"""
    impl_name, module = implementation

    # Test with matrices that can be multiplied but have special dimensions
    matrix_a = [[1, 2, 3]]  # 1x3
    matrix_b = [[4], [5], [6]]  # 3x1

    try:
        result = module.multiply_matrices(matrix_a, matrix_b)
        expected = [[32]]  # Result of 1x3 * 3x1 = 1x1
        assert result == expected, f"{impl_name}: multiply_matrices with 1x3 and 3x1 matrices should return [[32]]"
    except Exception as e:
        pytest.fail(f"{impl_name}: multiply_matrices failed with valid input: {str(e)}")

    # Test with incompatible matrices (should either raise error or handle gracefully)
    try:
        result = module.multiply_matrices([[1, 2]], [[3, 4, 5]])
        # If no error is raised, the implementation should handle this in some way
        # We won't assert on the specific result, as implementations may vary
    except (ValueError, IndexError):
        # This is acceptable - the function should validate dimensions
        pass

def test_transpose_matrix_edge_cases(implementation):
    """Test transpose_matrix function with edge cases"""
    impl_name, module = implementation

    # Test with empty matrix
    try:
        result = module.transpose_matrix([])
        assert result == [], f"{impl_name}: transpose_matrix([]) should return an empty list"
    except IndexError:
        # Some implementations might not handle empty matrices well
        pass

    # Test with 1x1 matrix
    matrix = [[5]]
    result = module.transpose_matrix(matrix)
    assert result == matrix, f"{impl_name}: transpose_matrix([[5]]) should return [[5]]"

    # Test with row vector
    matrix = [[1, 2, 3]]
    expected = [[1], [2], [3]]
    result = module.transpose_matrix(matrix)
    assert result == expected, f"{impl_name}: transpose_matrix({matrix}) returned {result} instead of {expected}"

    # Test with column vector
    matrix = [[1], [2], [3]]
    expected = [[1, 2, 3]]
    result = module.transpose_matrix(matrix)
    assert result == expected, f"{impl_name}: transpose_matrix({matrix}) returned {result} instead of {expected}"

def test_determinant_3x3_edge_cases(implementation):
    """Test determinant_3x3 function with edge cases"""
    impl_name, module = implementation

    # Test with identity matrix
    matrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]
    result = module.determinant_3x3(matrix)
    assert result == 1, f"{impl_name}: determinant_3x3 of identity matrix should be 1"

    # Test with matrix where determinant is negative
    # Corrected expected value based on actual determinant calculation
    matrix = [[2, 3, 1], [4, 1, 3], [2, 5, 2]]
    result = module.determinant_3x3(matrix)
    expected = -14  # Corrected from -27 to -14 based on the actual implementations
    assert result == expected, f"{impl_name}: determinant_3x3({matrix}) returned {result} instead of {expected}"

    # Test invalid matrix size (if the implementation validates)
    try:
        module.determinant_3x3([[1, 2], [3, 4]])
        # If we get here, the function didn't validate the matrix size
        # Some implementations might not validate
    except ValueError:
        # This is the expected behavior for implementations that validate
        pass

def test_comprehensive_matrix_operations(implementation):
    """Test a comprehensive workflow combining multiple matrix operations"""
    impl_name, module = implementation

    # Create two matrices
    matrix_a = module.create_matrix(2, 3, 1)  # [[1, 1, 1], [1, 1, 1]]
    matrix_b = module.create_matrix(2, 3, 2)  # [[2, 2, 2], [2, 2, 2]]

    # Add matrices
    sum_matrix = module.add_matrices(matrix_a, matrix_b)  # [[3, 3, 3], [3, 3, 3]]
    assert sum_matrix == [[3, 3, 3], [3, 3, 3]], f"{impl_name}: Matrix addition incorrect in workflow"

    # Multiply by scalar
    scaled_matrix = module.scalar_multiply(sum_matrix, 2)  # [[6, 6, 6], [6, 6, 6]]
    assert scaled_matrix == [[6, 6, 6], [6, 6, 6]], f"{impl_name}: Scalar multiplication incorrect in workflow"

    # Transpose
    transposed = module.transpose_matrix(scaled_matrix)  # [[6, 6], [6, 6], [6, 6]]
    assert transposed == [[6, 6], [6, 6], [6, 6]], f"{impl_name}: Matrix transposition incorrect in workflow"

    # Create a 3x2 matrix for multiplication
    matrix_c = module.create_matrix(3, 2, 1)  # [[1, 1], [1, 1], [1, 1]]

    # Multiply matrices: transposed (3x2) * matrix_c_transposed (2x3)
    matrix_c_transposed = module.transpose_matrix(matrix_c)  # [[1, 1, 1], [1, 1, 1]]
    product = module.multiply_matrices(transposed, matrix_c_transposed)

    # Corrected expectation: The product of 3x2 and 2x3 matrices is 3x3, where each element is

import pytest
import os
import sys
import json
from typing import Dict, List, Optional, Any

# Import from local test_utils.py in the same directory
from test_utils import TestUtils, TestResultsManager

# Load all implementations in the current sandbox
implementations = TestUtils.load_all_implementations()
test_results = TestResultsManager()

@pytest.fixture(scope="session")
def sandbox_dir():
    """Fixture to provide the sandbox directory path."""
    return os.path.dirname(os.path.abspath(__file__))

@pytest.fixture(scope="session")
def sandbox_name():
    """Fixture to provide the sandbox name."""
    return os.path.basename(os.path.dirname(os.path.abspath(__file__)))

@pytest.fixture(scope="session")
def all_implementations():
    """Fixture to provide all implementations as a dictionary."""
    return implementations

@pytest.fixture(params=list(implementations.items()))
def implementation(request):
    """Fixture to provide each implementation to tests one at a time."""
    return request.param

@pytest.fixture(scope="session")
def results_manager():
    """Fixture to provide access to the test results manager."""
    return test_results

# Hook for collecting test results
@pytest.hookimpl(tryfirst=True, hookwrapper=True)
def pytest_runtest_makereport(item, call):
    """Pytest hook to collect test results."""
    # Execute all other hooks to obtain the report object
    outcome = yield
    rep = outcome.get_result()

    # We're only interested in the call outcome
    if rep.when == "call":
        if hasattr(item, "callspec") and "implementation" in item.callspec.params:
            # Get implementation name and module
            impl_name, _ = item.callspec.params["implementation"]

            # Get test name
            test_name = item.nodeid.split("::")[-1]

            # Record result
            if rep.passed:
                test_results.record_result(impl_name, test_name, True)
            elif rep.failed:
                error_msg = str(rep.longrepr) if rep.longrepr else "Test failed"
                test_results.record_result(impl_name, test_name, False, error_msg)
            elif rep.skipped:
                skip_reason = rep.longrepr[2] if rep.longrepr else "Test skipped"
                test_results.record_skip(impl_name, test_name, skip_reason)

# Hook to save results at the end of testing
@pytest.hookimpl(trylast=True)
def pytest_sessionfinish(session, exitstatus):
    """Save test results at the end of the test session."""
    test_results.save_results()

import os
import sys
import glob
import re
import importlib.util
import traceback
import types
from typing import Dict, List, Optional, Any, Tuple

class TestUtils:
    @staticmethod
    def discover_implementation_files(directory: str = None) -> List[str]:
        """Find all implementation files in the current sandbox directory."""
        if directory is None:
            directory = os.path.dirname(os.path.abspath(__file__))

        patterns = [
            r'modified_code\d+\.py',
            r'new_code\d+\.py',
            # r'original_code\.py',
            r'implementation\d*\.py'
        ]

        pattern = re.compile('|'.join(f'({p})' for p in patterns))
        implementations = []

        for file_path in glob.glob(os.path.join(directory, '*.py')):
            if pattern.search(os.path.basename(file_path)):
                implementations.append(file_path)

        # Sort files numerically
        def sort_key(path):
            filename = os.path.basename(path)
            match = re.search(r'(\d+)', filename)
            return int(match.group(1)) if match else 0

        return sorted(implementations, key=sort_key)

    @staticmethod
    def create_mock_module(file_path: str, module_name: str, error_info: str) -> types.ModuleType:
        """Create a mock module that contains error information but can still be tested."""
        # Create a new module object
        mock_module = types.ModuleType(module_name)

        # Add basic attributes
        mock_module.__file__ = file_path
        mock_module.__name__ = module_name
        mock_module.__display_name__ = module_name
        mock_module.__error__ = error_info

        # Add a dummy function that can be detected by test functions
        def dummy_function(*args, **kwargs):
            return f"Error in module: {error_info}"

        setattr(mock_module, "implementation_error", dummy_function)

        return mock_module

    @staticmethod
    def load_module(file_path: str, module_name: Optional[str] = None) -> Any:
        """
        Safely load a module from a file path with proper error handling.
        If the module has errors, return a mock module that can still be tested.
        """
        if module_name is None:
            module_name = os.path.basename(file_path).replace('.py', '')

        # Create a unique module name to avoid conflicts
        sandbox_id = os.path.basename(os.path.dirname(file_path))
        unique_module_name = f"{sandbox_id}_{module_name}"

        try:
            # First, try to read the file to check for syntax errors
            with open(file_path, 'r') as f:
                source_code = f.read()

            # Check for syntax errors by compiling the code
            try:
                compiled = compile(source_code, file_path, 'exec')
            except SyntaxError as e:
                error_msg = f"Syntax error: {str(e)}"
                print(f"Syntax error in {file_path}: {e}")
                print(f"  Line {e.lineno}, column {e.offset}: {e.text}")
                return TestUtils.create_mock_module(file_path, unique_module_name, error_msg)

            # Create the module spec
            spec = importlib.util.spec_from_file_location(unique_module_name, file_path)
            if spec is None or spec.loader is None:
                error_msg = f"Could not create spec for {file_path}"
                print(f"Error: {error_msg}")
                return TestUtils.create_mock_module(file_path, unique_module_name, error_msg)

            # Create the module object
            module = importlib.util.module_from_spec(spec)
            sys.modules[unique_module_name] = module

            # Special handling for execution errors
            try:
                # Execute the module code in a safe way
                spec.loader.exec_module(module)
                # Store the original name for reference
                module.__display_name__ = module_name
                return module
            except Exception as e:
                error_msg = f"Runtime error: {str(e)}"
                traceback_str = traceback.format_exc()
                print(f"Error executing module {file_path}: {e}")
                print(traceback_str)

                # Create a partial module that contains what we loaded before the error
                mock_module = TestUtils.create_mock_module(file_path, unique_module_name, error_msg)

                # Copy any attributes that might have been defined before the error
                for attr_name in dir(module):
                    if not attr_name.startswith('__'):
                        try:
                            setattr(mock_module, attr_name, getattr(module, attr_name))
                        except Exception:
                            pass  # Skip attributes that can't be copied

                return mock_module

        except FileNotFoundError as e:
            error_msg = f"File not found: {str(e)}"
            print(f"Error: {error_msg}")
            return TestUtils.create_mock_module(file_path, unique_module_name, error_msg)
        except Exception as e:
            error_msg = f"Unexpected error: {str(e)}"
            print(f"Error loading module {file_path}: {e}")
            return TestUtils.create_mock_module(file_path, unique_module_name, error_msg)

    @classmethod
    def load_all_implementations(cls, directory: str = None) -> Dict[str, Any]:
        """Load all implementation files in the directory, including those with errors."""
        if directory is None:
            directory = os.path.dirname(os.path.abspath(__file__))

        implementations = {}

        implementation_files = cls.discover_implementation_files(directory)
        if not implementation_files:
            print("WARNING: No implementation files found. Check your file naming patterns.")

        for file_path in implementation_files:
            module_name = os.path.basename(file_path).replace('.py', '')
            module = cls.load_module(file_path, module_name)

            # Always add the module, even if it has errors
            implementations[module_name] = module

            if hasattr(module, '__error__'):
                print(f"Loaded with errors: {module_name} - {module.__error__}")
            else:
                print(f"Successfully loaded: {module_name}")

        return implementations

class TestResultsManager:
    def __init__(self):
        self.results = {}
        self.sandbox_name = os.path.basename(os.path.dirname(os.path.abspath(__file__)))

    def record_result(self, impl_name: str, test_name: str, passed: bool,
                     error_msg: Optional[str] = None) -> None:
        """Record a test result for an implementation."""
        if impl_name not in self.results:
            self.results[impl_name] = {"passed": 0, "failed": 0, "skipped": 0, "errors": []}

        if passed:
            self.results[impl_name]["passed"] += 1
        else:
            self.results[impl_name]["failed"] += 1
            if error_msg:
                self.results[impl_name]["errors"].append({
                    "test": test_name,
                    "error": error_msg
                })

    def record_skip(self, impl_name: str, test_name: str,
                   reason: Optional[str] = None) -> None:
        """Record a skipped test for an implementation."""
        if impl_name not in self.results:
            self.results[impl_name] = {"passed": 0, "failed": 0, "skipped": 0, "errors": []}

        self.results[impl_name]["skipped"] += 1
        if reason:
            self.results[impl_name]["errors"].append({
                "test": test_name,
                "error": f"SKIPPED: {reason}"
            })

    def get_winner(self) -> Tuple[Optional[int], Dict]:
        """Determine the winner based on test results."""
        winner = None
        max_passed = -1

        for impl_name, results in self.results.items():
            if impl_name == "original_code":
                continue  # Skip original code when determining winner

            if results["passed"] > max_passed:
                max_passed = results["passed"]
                winner = impl_name
            # Break ties by looking at failure count
            elif results["passed"] == max_passed and winner is not None:
                if results["failed"] < self.results[winner]["failed"]:
                    winner = impl_name

        # Convert winner to numeric index if possible
        winner_index = -1
        if winner and re.match(r'modified_code\d+', winner):
            try:
                winner_index = int(re.search(r'(\d+)', winner).group(1))
            except (AttributeError, ValueError):
                pass

        return winner_index, self.results

    def save_results(self, filename: str = "test_results.json") -> None:
        """Save test results to a JSON file."""
        import json

        winner_index, results = self.get_winner()

        # Check if all tests were skipped
        all_skipped = all(
            stats["skipped"] == stats["passed"] + stats["failed"] + stats["skipped"]
            for impl_name, stats in results.items()
            if impl_name != "original_code"
        )

        output = {
            "winner": winner_index,
            "all_skipped": all_skipped,
            "results": {
                name: {
                    "passed": stats["passed"],
                    "failed": stats["failed"],
                    "skipped": stats["skipped"],
                    "total": stats["passed"] + stats["failed"] + stats["skipped"]
                }
                for name, stats in results.items()
                if not name.startswith("_")  # Skip internal items
            }
        }

        with open(filename, "w") as f:
            json.dump(output, f, indent=2)

        print(f"Test results saved to {filename}")

        return output


class Token:
    start: int
    end: int

class IntegerToken(Token):
    value: int

    def is_digit(input: str, start_pos) -> bool:
        '''Returns the ending position of the token if the input is a valid integer token, otherwise returns -1'''
        DIGITS = set("0123456789")
        # print(DIGITS)

        return input[start_pos] in DIGITS

def is_int(input: str, start_pos) -> int:
        max_pos = len(input)
        current_pos = start_pos
        digit_found = False

        while current_pos < max_pos:
            if not IntegerToken.is_digit(input, current_pos):
            # if IntegerToken.is_digit(input, current_pos):
                break
            digit_found = True
            current_pos += 1

        assert digit_found, "Not an integer"
        return current_pos

if __name__=="__main__":
    # print(IntegerToken.is_digit("1234", 0))
    # print(IntegerToken.is_digit("a123", 0))
    print(IntegerToken.is_int("1234", 0))
    print(IntegerToken.is_int("a123", 0))

Instruction: Rewrite this to use python''s inbuilt is_digit

import pytest
import inspect
import re
import time
from typing import Callable

def test_implementation_exists(implementation):
    """Test that the implementation exists and has the required functions"""
    impl_name, module = implementation

    # Check if the IntegerToken class exists
    assert hasattr(module, 'IntegerToken'), f"{impl_name}: IntegerToken class is missing"

    # Check if the is_int function exists (either as standalone or part of IntegerToken)
    is_int_func = None
    if hasattr(module, 'is_int'):
        is_int_func = module.is_int
    elif hasattr(module.IntegerToken, 'is_int'):
        is_int_func = module.IntegerToken.is_int

    assert is_int_func is not None, f"{impl_name}: is_int function is missing"

def test_uses_isdigit(implementation):
    """Test that the implementation uses the built-in isdigit() method"""
    impl_name, module = implementation

    # Get the is_int function (either standalone or part of IntegerToken)
    is_int_func = get_is_int_function(module)

    # Get the source code of the is_int function
    source_code = inspect.getsource(is_int_func)

    # Check if the isdigit() method is used in the code
    assert '.isdigit()' in source_code, f"{impl_name}: Implementation does not use Python's built-in isdigit() method"

    # Make sure we're not using the custom is_digit function anymore
    # This is a bit tricky because we can't just check for "is_digit" since the function name itself contains it,
    # so we'll check for specific patterns that would indicate using the custom function
    patterns = [
        r'IntegerToken\.is_digit\(',
        r'self\.is_digit\(',
        r'is_digit\(input'
    ]

    for pattern in patterns:
        matches = re.search(pattern, source_code)
        assert not matches, f"{impl_name}: Implementation appears to still use the custom is_digit function"

def test_valid_integer_parsing(implementation):
    """Test that the implementation correctly parses valid integers"""
    impl_name, module = implementation

    # Get the is_int function
    is_int_func = get_is_int_function(module)

    test_cases = [
        # (input_str, start_pos, expected_end_pos)
        ("123", 0, 3),
        ("123abc", 0, 3),
        ("abc123", 3, 6),
        ("0", 0, 1),
        ("9876543210", 0, 10),
        ("  123", 2, 5)
    ]

    for input_str, start_pos, expected_end_pos in test_cases:
        result = is_int_func(input_str, start_pos)
        assert result == expected_end_pos, f"{impl_name}: Failed on '{input_str}' starting at {start_pos}. Expected {expected_end_pos}, got {result}"

def test_invalid_integer_parsing(implementation):
    """Test that the implementation correctly handles invalid integers"""
    impl_name, module = implementation

    # Get the is_int function
    is_int_func = get_is_int_function(module)

    test_cases = [
        # (input_str, start_pos)
        ("abc", 0),
        ("", 0),
        ("abc123", 0),  # Starts with non-digit
    ]

    for input_str, start_pos in test_cases:
        with pytest.raises(AssertionError) as exc_info:
            is_int_func(input_str, start_pos)
        assert "Not an integer" in str(exc_info.value), f"{impl_name}: Did not raise appropriate AssertionError for '{input_str}' at position {start_pos}"

def test_boundary_conditions(implementation):
    """Test that the implementation correctly handles boundary conditions"""
    impl_name, module = implementation

    # Get the is_int function
    is_int_func = get_is_int_function(module)

    # Test with position at the end of the string
    with pytest.raises(AssertionError) as exc_info:
        is_int_func("123", 3)  # Position is at the end of the string
    assert "Not an integer" in str(exc_info.value), f"{impl_name}: Did not raise appropriate AssertionError when position is at end of string"

    # Test with position beyond the end of the string
    # Based on the implementation behavior, it also raises AssertionError (not IndexError)
    # for positions beyond the end of the string
    with pytest.raises(AssertionError) as exc_info:
        is_int_func("123", 4)  # Position is beyond the end of the string
    assert "Not an integer" in str(exc_info.value), f"{impl_name}: Did not raise appropriate AssertionError when position is beyond end of string"

    # Test with a very long integer
    long_int = "1" * 1000
    result = is_int_func(long_int, 0)
    assert result == 1000, f"{impl_name}: Failed on very long integer. Expected 1000, got {result}"

def test_empty_string(implementation):
    """Test that the implementation correctly handles empty strings"""
    impl_name, module = implementation

    # Get the is_int function
    is_int_func = get_is_int_function(module)

    with pytest.raises(AssertionError) as exc_info:
        is_int_func("", 0)
    assert "Not an integer" in str(exc_info.value), f"{impl_name}: Did not raise appropriate AssertionError for empty string"

def get_is_int_function(module) -> Callable:
    """Helper function to get the is_int function from the module"""
    if hasattr(module, 'is_int'):
        return module.is_int
    elif hasattr(module.IntegerToken, 'is_int'):
        return module.IntegerToken.is_int
    else:
        raise AttributeError("is_int function not found in module")

import pytest
import os
import sys
import json
from typing import Dict, List, Optional, Any

# Import from local test_utils.py in the same directory
from test_utils import TestUtils, TestResultsManager

# Load all implementations in the current sandbox
implementations = TestUtils.load_all_implementations()
test_results = TestResultsManager()

@pytest.fixture(scope="session")
def sandbox_dir():
    """Fixture to provide the sandbox directory path."""
    return os.path.dirname(os.path.abspath(__file__))

@pytest.fixture(scope="session")
def sandbox_name():
    """Fixture to provide the sandbox name."""
    return os.path.basename(os.path.dirname(os.path.abspath(__file__)))

@pytest.fixture(scope="session")
def all_implementations():
    """Fixture to provide all implementations as a dictionary."""
    return implementations

@pytest.fixture(params=list(implementations.items()))
def implementation(request):
    """Fixture to provide each implementation to tests one at a time."""
    return request.param

@pytest.fixture(scope="session")
def results_manager():
    """Fixture to provide access to the test results manager."""
    return test_results

# Hook for collecting test results
@pytest.hookimpl(tryfirst=True, hookwrapper=True)
def pytest_runtest_makereport(item, call):
    """Pytest hook to collect test results."""
    # Execute all other hooks to obtain the report object
    outcome = yield
    rep = outcome.get_result()

    # We're only interested in the call outcome
    if rep.when == "call":
        if hasattr(item, "callspec") and "implementation" in item.callspec.params:
            # Get implementation name and module
            impl_name, _ = item.callspec.params["implementation"]

            # Get test name
            test_name = item.nodeid.split("::")[-1]

            # Record result
            if rep.passed:
                test_results.record_result(impl_name, test_name, True)
            elif rep.failed:
                error_msg = str(rep.longrepr) if rep.longrepr else "Test failed"
                test_results.record_result(impl_name, test_name, False, error_msg)
            elif rep.skipped:
                skip_reason = rep.longrepr[2] if rep.longrepr else "Test skipped"
                test_results.record_skip(impl_name, test_name, skip_reason)

# Hook to save results at the end of testing
@pytest.hookimpl(trylast=True)
def pytest_sessionfinish(session, exitstatus):
    """Save test results at the end of the test session."""
    test_results.save_results()

import os
import sys
import glob
import re
import importlib.util
import traceback
import types
from typing import Dict, List, Optional, Any, Tuple

class TestUtils:
    @staticmethod
    def discover_implementation_files(directory: str = None) -> List[str]:
        """Find all implementation files in the current sandbox directory."""
        if directory is None:
            directory = os.path.dirname(os.path.abspath(__file__))

        patterns = [
            r'modified_code\d+\.py',
            r'new_code\d+\.py',
            # r'original_code\.py',
            r'implementation\d*\.py'
        ]

        pattern = re.compile('|'.join(f'({p})' for p in patterns))
        implementations = []

        for file_path in glob.glob(os.path.join(directory, '*.py')):
            if pattern.search(os.path.basename(file_path)):
                implementations.append(file_path)

        # Sort files numerically
        def sort_key(path):
            filename = os.path.basename(path)
            match = re.search(r'(\d+)', filename)
            return int(match.group(1)) if match else 0

        return sorted(implementations, key=sort_key)

    @staticmethod
    def create_mock_module(file_path: str, module_name: str, error_info: str) -> types.ModuleType:
        """Create a mock module that contains error information but can still be tested."""
        # Create a new module object
        mock_module = types.ModuleType(module_name)

        # Add basic attributes
        mock_module.__file__ = file_path
        mock_module.__name__ = module_name
        mock_module.__display_name__ = module_name
        mock_module.__error__ = error_info

        # Add a dummy function that can be detected by test functions
        def dummy_function(*args, **kwargs):
            return f"Error in module: {error_info}"

        setattr(mock_module, "implementation_error", dummy_function)

        return mock_module

    @staticmethod
    def load_module(file_path: str, module_name: Optional[str] = None) -> Any:
        """
        Safely load a module from a file path with proper error handling.
        If the module has errors, return a mock module that can still be tested.
        """
        if module_name is None:
            module_name = os.path.basename(file_path).replace('.py', '')

        # Create a unique module name to avoid conflicts
        sandbox_id = os.path.basename(os.path.dirname(file_path))
        unique_module_name = f"{sandbox_id}_{module_name}"

        try:
            # First, try to read the file to check for syntax errors
            with open(file_path, 'r') as f:
                source_code = f.read()

            # Check for syntax errors by compiling the code
            try:
                compiled = compile(source_code, file_path, 'exec')
            except SyntaxError as e:
                error_msg = f"Syntax error: {str(e)}"
                print(f"Syntax error in {file_path}: {e}")
                print(f"  Line {e.lineno}, column {e.offset}: {e.text}")
                return TestUtils.create_mock_module(file_path, unique_module_name, error_msg)

            # Create the module spec
            spec = importlib.util.spec_from_file_location(unique_module_name, file_path)
            if spec is None or spec.loader is None:
                error_msg = f"Could not create spec for {file_path}"
                print(f"Error: {error_msg}")
                return TestUtils.create_mock_module(file_path, unique_module_name, error_msg)

            # Create the module object
            module = importlib.util.module_from_spec(spec)
            sys.modules[unique_module_name] = module

            # Special handling for execution errors
            try:
                # Execute the module code in a safe way
                spec.loader.exec_module(module)
                # Store the original name for reference
                module.__display_name__ = module_name
                return module
            except Exception as e:
                error_msg = f"Runtime error: {str(e)}"
                traceback_str = traceback.format_exc()
                print(f"Error executing module {file_path}: {e}")
                print(traceback_str)

                # Create a partial module that contains what we loaded before the error
                mock_module = TestUtils.create_mock_module(file_path, unique_module_name, error_msg)

                # Copy any attributes that might have been defined before the error
                for attr_name in dir(module):
                    if not attr_name.startswith('__'):
                        try:
                            setattr(mock_module, attr_name, getattr(module, attr_name))
                        except Exception:
                            pass  # Skip attributes that can't be copied

                return mock_module

        except FileNotFoundError as e:
            error_msg = f"File not found: {str(e)}"
            print(f"Error: {error_msg}")
            return TestUtils.create_mock_module(file_path, unique_module_name, error_msg)
        except Exception as e:
            error_msg = f"Unexpected error: {str(e)}"
            print(f"Error loading module {file_path}: {e}")
            return TestUtils.create_mock_module(file_path, unique_module_name, error_msg)

    @classmethod
    def load_all_implementations(cls, directory: str = None) -> Dict[str, Any]:
        """Load all implementation files in the directory, including those with errors."""
        if directory is None:
            directory = os.path.dirname(os.path.abspath(__file__))

        implementations = {}

        implementation_files = cls.discover_implementation_files(directory)
        if not implementation_files:
            print("WARNING: No implementation files found. Check your file naming patterns.")

        for file_path in implementation_files:
            module_name = os.path.basename(file_path).replace('.py', '')
            module = cls.load_module(file_path, module_name)

            # Always add the module, even if it has errors
            implementations[module_name] = module

            if hasattr(module, '__error__'):
                print(f"Loaded with errors: {module_name} - {module.__error__}")
            else:
                print(f"Successfully loaded: {module_name}")

        return implementations

class TestResultsManager:
    def __init__(self):
        self.results = {}
        self.sandbox_name = os.path.basename(os.path.dirname(os.path.abspath(__file__)))

    def record_result(self, impl_name: str, test_name: str, passed: bool,
                     error_msg: Optional[str] = None) -> None:
        """Record a test result for an implementation."""
        if impl_name not in self.results:
            self.results[impl_name] = {"passed": 0, "failed": 0, "skipped": 0, "errors": []}

        if passed:
            self.results[impl_name]["passed"] += 1
        else:
            self.results[impl_name]["failed"] += 1
            if error_msg:
                self.results[impl_name]["errors"].append({
                    "test": test_name,
                    "error": error_msg
                })

    def record_skip(self, impl_name: str, test_name: str,
                   reason: Optional[str] = None) -> None:
        """Record a skipped test for an implementation."""
        if impl_name not in self.results:
            self.results[impl_name] = {"passed": 0, "failed": 0, "skipped": 0, "errors": []}

        self.results[impl_name]["skipped"] += 1
        if reason:
            self.results[impl_name]["errors"].append({
                "test": test_name,
                "error": f"SKIPPED: {reason}"
            })

    def get_winner(self) -> Tuple[Optional[int], Dict]:
        """Determine the winner based on test results."""
        winner = None
        max_passed = -1

        for impl_name, results in self.results.items():
            if impl_name == "original_code":
                continue  # Skip original code when determining winner

            if results["passed"] > max_passed:
                max_passed = results["passed"]
                winner = impl_name
            # Break ties by looking at failure count
            elif results["passed"] == max_passed and winner is not None:
                if results["failed"] < self.results[winner]["failed"]:
                    winner = impl_name

        # Convert winner to numeric index if possible
        winner_index = -1
        if winner and re.match(r'modified_code\d+', winner):
            try:
                winner_index = int(re.search(r'(\d+)', winner).group(1))
            except (AttributeError, ValueError):
                pass

        return winner_index, self.results

    def save_results(self, filename: str = "test_results.json") -> None:
        """Save test results to a JSON file."""
        import json

        winner_index, results = self.get_winner()

        # Check if all tests were skipped
        all_skipped = all(
            stats["skipped"] == stats["passed"] + stats["failed"] + stats["skipped"]
            for impl_name, stats in results.items()
            if impl_name != "original_code"
        )

        output = {
            "winner": winner_index,
            "all_skipped": all_skipped,
            "results": {
                name: {
                    "passed": stats["passed"],
                    "failed": stats["failed"],
                    "skipped": stats["skipped"],
                    "total": stats["passed"] + stats["failed"] + stats["skipped"]
                }
                for name, stats in results.items()
                if not name.startswith("_")  # Skip internal items
            }
        }

        with open(filename, "w") as f:
            json.dump(output, f, indent=2)

        print(f"Test results saved to {filename}")

        return output


import sqlite3
import datetime
import logging
import plotly.express as px
import pandas as pd

def connect_to_db():
    conn = None
    try:
        conn = sqlite3.connect('dns_monitor.db')
        logging.info("Successfully connected to the database.")
        return conn
    except sqlite3.Error as e:
        logging.error(f"Failed to connect to database: {e}")
        raise ValueError(f"Failed to connect to database: {e}")  # Re-raise the exception with a more specific error message
    except Exception as e:
        logging.exception(f"Unexpected error connecting to database: {e}")
        raise ValueError(f"Unexpected error connecting to database: {e}")  # Re-raise the exception with a more specific error message

def create_tables(conn):
    try:
        if conn is None:
            logging.error("Database connection is None")
            raise ValueError("Database connection is None")
        cursor = conn.cursor()

        table_definitions = [
            """
            CREATE TABLE IF NOT EXISTS root_servers (
                id INTEGER PRIMARY KEY,
                server_name TEXT,
                ip_address TEXT
            )
            """,
            """
            CREATE TABLE IF NOT EXISTS dns_tests (
                id INTEGER PRIMARY KEY,
                timestamp TIMESTAMP,
                server_id INTEGER,
                query_type TEXT,
                response_time REAL,
                success BOOLEAN,
                error_message TEXT,
                FOREIGN KEY (server_id) REFERENCES root_servers (id)
            )
            """,
            """
            CREATE TABLE IF NOT EXISTS events (
                id INTEGER PRIMARY KEY,
                timestamp TIMESTAMP,
                event_type TEXT,
                severity TEXT,
                message TEXT,
                source TEXT
            )
            """,
            """
            CREATE TABLE IF NOT EXISTS service_status (
                id INTEGER PRIMARY KEY,
                service_name TEXT,
                status TEXT,
                last_checked TIMESTAMP,
                uptime REAL,
                error_count INTEGER
            )
            """,
            """
            CREATE TABLE IF NOT EXISTS dns_queries (
                id INTEGER PRIMARY KEY,
                timestamp TIMESTAMP,
                query TEXT,
                response_time REAL
            )
            """
        ]

        for table_definition in table_definitions:
            try:
                cursor.execute(table_definition)
                logging.info(f"Table created or already exists: {table_definition.split()[5]}")
            except sqlite3.Error as e:
                logging.error(f"Error creating table: {e}")
                raise ValueError(f"Error creating table: {e}")  # Re-raise the exception with a more specific error message
            except Exception as e:
                logging.exception(f"Unexpected error creating table: {e}")
                raise ValueError(f"Unexpected error creating table: {e}")  # Re-raise the exception with a more specific error message

        conn.commit()
    except sqlite3.Error as e:
        logging.error(f"Error creating tables: {e}")
        raise ValueError(f"Error creating tables: {e}")  # Re-raise the exception with a more specific error message
    except Exception as e:
        logging.exception(f"Unexpected error creating tables: {e}")
        raise ValueError(f"Unexpected error creating tables: {e}")  # Re-raise the exception with a more specific error message

def check_database_tables(conn):
    try:
        if conn is None:
            logging.error("Database connection is None")
            raise ValueError("Database connection is None")
        cursor = conn.cursor()

        table_names = ["root_servers", "dns_tests", "events", "service_status", "dns_queries"]
        for table_name in table_names:
            cursor.execute(f"SELECT name FROM sqlite_master WHERE type='table' AND name='{table_name}'")
            if cursor.fetchone() is None:
                logging.error(f"Table {table_name} does not exist")
                raise ValueError(f"Table {table_name} does not exist")  # Re-raise the exception with a more specific error message
            else:
                logging.info(f"Table {table_name} exists.")

    except sqlite3.Error as e:
        logging.error(f"Error checking database tables: {e}")
        raise ValueError(f"Error checking database tables: {e}")  # Re-raise the exception with a more specific error message
    except Exception as e:
        logging.exception(f"Unexpected error checking database tables: {e}")
        raise ValueError(f"Unexpected error checking database tables: {e}")  # Re-raise the exception with a more specific error message

def retrieve_data(conn):
    try:
        if conn is None:
            logging.error("Database connection is None")
            raise ValueError("Database connection is None")
        cursor = conn.cursor()

        response_times = get_response_times(cursor)
        event_log_data = get_event_log_data(cursor)
        service_status_data = get_service_status_data(cursor)

        return response_times, event_log_data, service_status_data
    except sqlite3.Error as e:
        logging.error(f"Error retrieving data: {e}")
        raise ValueError(f"Error retrieving data: {e}")  # Re-raise the exception with a more specific error message
    except Exception as e:
        logging.exception(f"Unexpected error retrieving data: {e}")
        raise ValueError(f"Unexpected error retrieving data: {e}")  # Re-raise the exception with a more specific error message

def get_response_times(cursor):
    try:
        if cursor is None:
            logging.error("Cursor is None")
            raise ValueError("Cursor is None")
        cursor.execute("SELECT timestamp, response_time FROM dns_tests ORDER BY timestamp DESC LIMIT 100")
        response_times = cursor.fetchall()
        logging.info(f"Retrieved {len(response_times)} response times.")
        return response_times
    except sqlite3.Error as e:
        logging.error(f"Error retrieving response times: {e}")
        raise ValueError(f"Error retrieving response times: {e}")  # Re-raise the exception with a more specific error message
    except Exception as e:
        logging.exception(f"Unexpected error retrieving response times: {e}")
        raise ValueError(f"Unexpected error retrieving response times: {e}")  # Re-raise the exception with a more specific error message

def get_event_log_data(cursor):
    try:
        if cursor is None:
            logging.error("Cursor is None")
            raise ValueError("Cursor is None")
        cursor.execute("SELECT timestamp, event_type, severity, message, source FROM events ORDER BY timestamp DESC LIMIT 100")
        event_log_data = cursor.fetchall()
        logging.info(f"Retrieved {len(event_log_data)} event log entries.")
        return event_log_data
    except sqlite3.Error as e:
        logging.error(f"Error retrieving event log data: {e}")
        raise ValueError(f"Error retrieving event log data: {e}")  # Re-raise the exception with a more specific error message
    except Exception as e:
        logging.exception(f"Unexpected error retrieving event log data: {e}")
        raise ValueError(f"Unexpected error retrieving event log data: {e}")  # Re-raise the exception with a more specific error message

def get_service_status_data(cursor):
    try:
        if cursor is None:
            logging.error("Cursor is None")
            raise ValueError("Cursor is None")
        cursor.execute("SELECT service_name, status, last_checked, uptime, error_count FROM service_status ORDER BY service_name")
        service_status_data = cursor.fetchall()
        logging.info(f"Retrieved {len(service_status_data)} service status entries.")
        return service_status_data
    except sqlite3.Error as e:
        logging.error(f"Error retrieving service status data: {e}")
        raise ValueError(f"Error retrieving service status data: {e}")  # Re-raise the exception with a more specific error message
    except Exception as e:
        logging.exception(f"Unexpected error retrieving service status data: {e}")
        raise ValueError(f"Unexpected error retrieving service status data: {e}")  # Re-raise the exception with a more specific error message

def create_visualizations(response_times, event_log_data, service_status_data):
    if response_times is None or not isinstance(response_times, list):
        logging.error("Invalid response_times data")
        return "Invalid response_times data"
    if event_log_data is None or not isinstance(event_log_data, list):
        logging.error("Invalid event_log_data data")
        return "Invalid event_log_data data"
    if service_status_data is None or not isinstance(service_status_data, list):
        logging.error("Invalid service_status_data data")
        return "Invalid service_status_data data"

    if not response_times:
        logging.warning("No response times data to visualize")
        response_time_df = pd.DataFrame(columns=['timestamp', 'response_time'])
    else:
        response_time_df = pd.DataFrame(response_times, columns=['timestamp', 'response_time'])

    if not event_log_data:
        logging.warning("No event log data to visualize")
        event_log_df = pd.DataFrame(columns=['timestamp', 'event_type', 'severity', 'message', 'source'])
    else:
        event_log_df = pd.DataFrame(event_log_data, columns=['timestamp', 'event_type', 'severity', 'message', 'source'])

 if not service_status_data:
        logging.warning("No service status data to visualize")
        service_status_df = pd.DataFrame(columns=['service_name', 'status', 'last_checked', 'uptime', 'error_count'])
    else:
        service_status_df = pd.DataFrame(service_status_data, columns=['service_name', 'status', 'last_checked', 'uptime', 'error_count'])

    fig = px.line(response_time_df, x='timestamp',

Instruction: complete this function

import inspect
import pytest
import pandas as pd
import plotly.express as px
import sqlite3
from unittest.mock import patch, MagicMock, create_autospec

def test_implementation_structure(implementation):
    """Test if the implementation has the create_visualizations function completed"""
    impl_name, module = implementation

    # Check if create_visualizations function exists
    assert hasattr(module, "create_visualizations"), f"{impl_name} is missing create_visualizations function"

    # Check if the function has the expected parameters
    signature = inspect.signature(module.create_visualizations)
    params = list(signature.parameters.keys())
    assert "response_times" in params, f"{impl_name} create_visualizations function is missing parameter: response_times"
    assert "event_log_data" in params, f"{impl_name} create_visualizations function is missing parameter: event_log_data"
    assert "service_status_data" in params, f"{impl_name} create_visualizations function is missing parameter: service_status_data"

def test_basic_input_validation(implementation):
    """Test if the implementation properly validates inputs"""
    impl_name, module = implementation

    # Test with invalid inputs - instead of checking for exceptions, check that the function
    # returns an error message since the implementations log errors but don't raise exceptions
    result1 = module.create_visualizations(None, [], [])
    assert result1 is not None, f"{impl_name} doesn't properly handle None response_times"
    assert isinstance(result1, str) and "invalid" in result1.lower(), f"{impl_name} doesn't return error message for None response_times"

    result2 = module.create_visualizations([], None, [])
    assert result2 is not None, f"{impl_name} doesn't properly handle None event_log_data"
    assert isinstance(result2, str) and "invalid" in result2.lower(), f"{impl_name} doesn't return error message for None event_log_data"

    result3 = module.create_visualizations([], [], None)
    assert result3 is not None, f"{impl_name} doesn't properly handle None service_status_data"
    assert isinstance(result3, str) and "invalid" in result3.lower(), f"{impl_name} doesn't return error message for None service_status_data"

def test_empty_data_handling(implementation):
    """Test if the implementation handles empty data gracefully"""
    impl_name, module = implementation

    # Mock plotly express functions to avoid actual visualization creation
    with patch('plotly.express.line') as mock_line, \
         patch('plotly.express.bar') as mock_bar, \
         patch('plotly.express.pie') as mock_pie:

        # Create mock figures to return
        mock_fig = MagicMock()
        mock_line.return_value = mock_fig
        mock_bar.return_value = mock_fig
        mock_pie.return_value = mock_fig

        # Test with empty lists
        result = module.create_visualizations([], [], [])

        # Should either return a valid figure, a dict of figures, or a message
        assert result is not None or mock_line.called, f"{impl_name} doesn't handle empty data correctly"

@patch('plotly.express.line')
def test_response_time_visualization(mock_px_line, implementation):
    """Test if response time visualization is created correctly"""
    impl_name, module = implementation

    # Create mock data
    response_times = [
        ('2023-01-01 10:00:00', 0.5),
        ('2023-01-01 10:01:00', 0.6)
    ]

    # Create a mock figure
    mock_fig = MagicMock()
    mock_px_line.return_value = mock_fig

    # Call function with empty event_log and service_status to focus on response_time
    result = module.create_visualizations(response_times, [], [])

    # Verify px.line was called
    mock_px_line.assert_called_once()

    # Check that first argument to px.line was a dataframe with expected columns
    args, kwargs = mock_px_line.call_args
    assert isinstance(args[0], pd.DataFrame), f"{impl_name} doesn't pass a DataFrame to px.line"
    assert 'timestamp' in args[0].columns, f"{impl_name} DataFrame missing 'timestamp' column"
    assert 'response_time' in args[0].columns, f"{impl_name} DataFrame missing 'response_time' column"

@patch('plotly.express.line')
@patch('plotly.express.bar')
@patch('plotly.express.pie')
def test_comprehensive_visualization(mock_px_pie, mock_px_bar, mock_px_line, implementation):
    """Test if the implementation creates comprehensive visualizations with all data types"""
    impl_name, module = implementation

    # Create mock data
    response_times = [
        ('2023-01-01 10:00:00', 0.5),
        ('2023-01-01 10:01:00', 0.6)
    ]

    event_log_data = [
        ('2023-01-01 10:00:00', 'ERROR', 'HIGH', 'DNS lookup failed', 'monitor'),
        ('2023-01-01 10:01:00', 'WARNING', 'MEDIUM', 'Slow response', 'monitor')
    ]

    service_status_data = [
        ('DNS', 'UP', '2023-01-01 10:00:00', 99.9, 2),
        ('HTTP', 'DOWN', '2023-01-01 10:01:00', 95.5, 10)
    ]

    # Create mock figures
    mock_line_fig = MagicMock()
    mock_bar_fig = MagicMock()
    mock_pie_fig = MagicMock()

    mock_px_line.return_value = mock_line_fig
    mock_px_bar.return_value = mock_bar_fig
    mock_px_pie.return_value = mock_pie_fig

    # Call the function
    result = module.create_visualizations(response_times, event_log_data, service_status_data)

    # Verify that at least one visualization was created
    assert mock_px_line.called, f"{impl_name} doesn't create line visualization"

    # Since different implementations might return different result types,
    # we just check that the function does something useful (either returns figures, shows them, or returns a dict)
    assert (result is not None or
            mock_line_fig.show.called or
            mock_bar_fig.show.called or
            mock_pie_fig.show.called), f"{impl_name} doesn't produce any visualizations"

def test_implementation_completeness(implementation):
    """Test if the implementation has a complete function that doesn't end abruptly"""
    impl_name, module = implementation

    # Get the source code of the function
    source = inspect.getsource(module.create_visualizations)

    # Check for key visualization components
    assert "pd.DataFrame" in source or "pandas.DataFrame" in source, f"{impl_name} doesn't create DataFrames"
    assert "px.line" in source or "plotly.express.line" in source, f"{impl_name} doesn't use plotly.express.line"
    assert "response_time" in source, f"{impl_name} doesn't process response_time data"

def test_dataframe_creation(implementation):
    """Test if DataFrames are created correctly for the visualization"""
    impl_name, module = implementation

    # Create mock data
    response_times = [
        ('2023-01-01', 0.5)
    ]
    event_log_data = [
        ('2023-01-01', 'ERROR', 'HIGH', 'DNS lookup failed', 'monitor')
    ]
    service_status_data = [
        ('DNS', 'UP', '2023-01-01', 99.9, 2)
    ]

    # Instead of mocking pandas.DataFrame directly, which causes recursion,
    # patch plotly.express to avoid actually creating visualizations
    with patch('plotly.express.line') as mock_line, \
         patch('plotly.express.bar') as mock_bar, \
         patch('plotly.express.pie') as mock_pie:

        # Set up mock figures
        mock_line.return_value = MagicMock()
        mock_bar.return_value = MagicMock()
        mock_pie.return_value = MagicMock()

        # Call function
        module.create_visualizations(
            response_times,
            event_log_data,
            service_status_data
        )

        # Check that plotly.express functions were called at least once
        assert mock_line.called, f"{impl_name} doesn't create line visualization"

        # Check that DataFrame was passed to plotly function
        args, kwargs = mock_line.call_args
        assert isinstance(args[0], pd.DataFrame), f"{impl_name} doesn't pass a DataFrame to px.line"

@patch('plotly.express.line')
def test_visualization_parameters(mock_px_line, implementation):
    """Test if visualizations are created with the right parameters"""
    impl_name, module = implementation

    # Create mock data
    response_times = [
        ('2023-01-01 10:00:00', 0.5),
        ('2023-01-01 10:01:00', 0.6)
    ]

    # Create a mock figure
    mock_fig = MagicMock()
    mock_px_line.return_value = mock_fig

    module.create_visualizations(response_times, [], [])

    # Check that the visualization was created with the right parameters
    args, kwargs = mock_px_line.call_args
    assert 'x' in kwargs and kwargs['x'] == 'timestamp', f"{impl_name} doesn't use 'timestamp' as x-axis"
    assert 'y' in kwargs and kwargs['y'] == 'response_time', f"{impl_name} doesn't use 'response_time' as y-axis"
    assert 'title' in kwargs, f"{impl_name} doesn't set a title for the visualization"

@pytest.mark.parametrize("func_name", [
    "connect_to_db", "create_tables", "check_database_tables",
    "retrieve_data", "get_response_times", "get_event_log_data",
    "get_service_status_data"
])
def test_original_functions_preserved(implementation, func_name):
    """Test if the original functions are preserved"""
    impl_name, module = implementation

    assert hasattr(module, func_name), f"{impl_name} is missing the original function: {func_name}"

def test_exception_handling_with_invalid_types(implementation):
    """Test how the implementation handles unexpected input types"""
    impl_name, module = implementation

    # Test with data of incorrect types
    result1 = module.create_visualizations("not a list", [], [])
    assert isinstance(result1, str) and "invalid" in result1.lower(), f"{impl_name} doesn't return error for invalid response_times type"

    result2 = module.create_visualizations([], {}, [])
    assert isinstance(result2, str) and "invalid" in result2.lower(), f"{impl_name} doesn't return error for invalid event_log_data type"

    result3 = module.create_visualizations([], [], 123)
    assert isinstance(result3, str) and "invalid" in result3.lower(), f"{impl_name} doesn't return error for invalid service_status_data type"

def test_no_side_effects(implementation):
    """Test that the function does not modify the input data"""
    impl_name, module = implementation

    # Create data
    response_times = [
        ('2023-01-01 10:00:00', 0.5),
        ('2023-01-01 10:01:00', 0.6)
    ]
    event_log_data = [
        ('2023-01-01 10:00:00', 'ERROR', 'HIGH', 'DNS lookup failed', 'monitor')
    ]
    service_status_data = [
        ('DNS', 'UP', '2023-01-01 10:00:00', 99.9, 2)
    ]

    # Create copies to check they aren't modified
    response_times_copy = response_times.copy()
    event_log_data_copy = event_log_data.copy()
    service_status_data_copy = service_status_data.copy()

    # Patch plotly to avoid actual visualization creation
    with patch('plotly.express.line') as mock_line, \
         patch('plotly.express.bar') as mock_bar, \
         patch('plotly.express.pie') as mock_pie:

        mock_line.return_value = MagicMock()
        mock_bar.return_value = MagicMock()
        mock_pie.return_value = MagicMock()

        # Call the function
        module.create_visualizations(response_times, event_log_data, service_status_data)

    # Check data wasn't modified
    assert response_times == response_times_copy, f"{impl_name} modifies input response_times"
    assert event_log_data == event_log_data_copy, f"{impl_name} modifies input event_log_data"
    assert service_status_data == service_status_data_copy, f"{impl_name} modifies input service_status_data"

import pytest
import os
import sys
import json
from typing import Dict, List, Optional, Any

# Import from local test_utils.py in the same directory
from test_utils import TestUtils, TestResultsManager

# Load all implementations in the current sandbox
implementations = TestUtils.load_all_implementations()
test_results = TestResultsManager()

@pytest.fixture(scope="session")
def sandbox_dir():
    """Fixture to provide the sandbox directory path."""
    return os.path.dirname(os.path.abspath(__file__))

@pytest.fixture(scope="session")
def sandbox_name():
    """Fixture to provide the sandbox name."""
    return os.path.basename(os.path.dirname(os.path.abspath(__file__)))

@pytest.fixture(scope="session")
def all_implementations():
    """Fixture to provide all implementations as a dictionary."""
    return implementations

@pytest.fixture(params=list(implementations.items()))
def implementation(request):
    """Fixture to provide each implementation to tests one at a time."""
    return request.param

@pytest.fixture(scope="session")
def results_manager():
    """Fixture to provide access to the test results manager."""
    return test_results

# Hook for collecting test results
@pytest.hookimpl(tryfirst=True, hookwrapper=True)
def pytest_runtest_makereport(item, call):
    """Pytest hook to collect test results."""
    # Execute all other hooks to obtain the report object
    outcome = yield
    rep = outcome.get_result()

    # We're only interested in the call outcome
    if rep.when == "call":
        if hasattr(item, "callspec") and "implementation" in item.callspec.params:
            # Get implementation name and module
            impl_name, _ = item.callspec.params["implementation"]

            # Get test name
            test_name = item.nodeid.split("::")[-1]

            # Record result
            if rep.passed:
                test_results.record_result(impl_name, test_name, True)
            elif rep.failed:
                error_msg = str(rep.longrepr) if rep.longrepr else "Test failed"
                test_results.record_result(impl_name, test_name, False, error_msg)
            elif rep.skipped:
                skip_reason = rep.longrepr[2] if rep.longrepr else "Test skipped"
                test_results.record_skip(impl_name, test_name, skip_reason)

# Hook to save results at the end of testing
@pytest.hookimpl(trylast=True)
def pytest_sessionfinish(session, exitstatus):
    """Save test results at the end of the test session."""
    test_results.save_results()

import os
import sys
import glob
import re
import importlib.util
import traceback
import types
from typing import Dict, List, Optional, Any, Tuple

class TestUtils:
    @staticmethod
    def discover_implementation_files(directory: str = None) -> List[str]:
        """Find all implementation files in the current sandbox directory."""
        if directory is None:
            directory = os.path.dirname(os.path.abspath(__file__))

        patterns = [
            r'modified_code\d+\.py',
            r'new_code\d+\.py',
            # r'original_code\.py',
            r'implementation\d*\.py'
        ]

        pattern = re.compile('|'.join(f'({p})' for p in patterns))
        implementations = []

        for file_path in glob.glob(os.path.join(directory, '*.py')):
            if pattern.search(os.path.basename(file_path)):
                implementations.append(file_path)

        # Sort files numerically
        def sort_key(path):
            filename = os.path.basename(path)
            match = re.search(r'(\d+)', filename)
            return int(match.group(1)) if match else 0

        return sorted(implementations, key=sort_key)

    @staticmethod
    def create_mock_module(file_path: str, module_name: str, error_info: str) -> types.ModuleType:
        """Create a mock module that contains error information but can still be tested."""
        # Create a new module object
        mock_module = types.ModuleType(module_name)

        # Add basic attributes
        mock_module.__file__ = file_path
        mock_module.__name__ = module_name
        mock_module.__display_name__ = module_name
        mock_module.__error__ = error_info

        # Add a dummy function that can be detected by test functions
        def dummy_function(*args, **kwargs):
            return f"Error in module: {error_info}"

        setattr(mock_module, "implementation_error", dummy_function)

        return mock_module

    @staticmethod
    def load_module(file_path: str, module_name: Optional[str] = None) -> Any:
        """
        Safely load a module from a file path with proper error handling.
        If the module has errors, return a mock module that can still be tested.
        """
        if module_name is None:
            module_name = os.path.basename(file_path).replace('.py', '')

        # Create a unique module name to avoid conflicts
        sandbox_id = os.path.basename(os.path.dirname(file_path))
        unique_module_name = f"{sandbox_id}_{module_name}"

        try:
            # First, try to read the file to check for syntax errors
            with open(file_path, 'r') as f:
                source_code = f.read()

            # Check for syntax errors by compiling the code
            try:
                compiled = compile(source_code, file_path, 'exec')
            except SyntaxError as e:
                error_msg = f"Syntax error: {str(e)}"
                print(f"Syntax error in {file_path}: {e}")
                print(f"  Line {e.lineno}, column {e.offset}: {e.text}")
                return TestUtils.create_mock_module(file_path, unique_module_name, error_msg)

            # Create the module spec
            spec = importlib.util.spec_from_file_location(unique_module_name, file_path)
            if spec is None or spec.loader is None:
                error_msg = f"Could not create spec for {file_path}"
                print(f"Error: {error_msg}")
                return TestUtils.create_mock_module(file_path, unique_module_name, error_msg)

            # Create the module object
            module = importlib.util.module_from_spec(spec)
            sys.modules[unique_module_name] = module

            # Special handling for execution errors
            try:
                # Execute the module code in a safe way
                spec.loader.exec_module(module)
                # Store the original name for reference
                module.__display_name__ = module_name
                return module
            except Exception as e:
                error_msg = f"Runtime error: {str(e)}"
                traceback_str = traceback.format_exc()
                print(f"Error executing module {file_path}: {e}")
                print(traceback_str)

                # Create a partial module that contains what we loaded before the error
                mock_module = TestUtils.create_mock_module(file_path, unique_module_name, error_msg)

                # Copy any attributes that might have been defined before the error
                for attr_name in dir(module):
                    if not attr_name.startswith('__'):
                        try:
                            setattr(mock_module, attr_name, getattr(module, attr_name))
                        except Exception:
                            pass  # Skip attributes that can't be copied

                return mock_module

        except FileNotFoundError as e:
            error_msg = f"File not found: {str(e)}"
            print(f"Error: {error_msg}")
            return TestUtils.create_mock_module(file_path, unique_module_name, error_msg)
        except Exception as e:
            error_msg = f"Unexpected error: {str(e)}"
            print(f"Error loading module {file_path}: {e}")
            return TestUtils.create_mock_module(file_path, unique_module_name, error_msg)

    @classmethod
    def load_all_implementations(cls, directory: str = None) -> Dict[str, Any]:
        """Load all implementation files in the directory, including those with errors."""
        if directory is None:
            directory = os.path.dirname(os.path.abspath(__file__))

        implementations = {}

        implementation_files = cls.discover_implementation_files(directory)
        if not implementation_files:
            print("WARNING: No implementation files found. Check your file naming patterns.")

        for file_path in implementation_files:
            module_name = os.path.basename(file_path).replace('.py', '')
            module = cls.load_module(file_path, module_name)

            # Always add the module, even if it has errors
            implementations[module_name] = module

            if hasattr(module, '__error__'):
                print(f"Loaded with errors: {module_name} - {module.__error__}")
            else:
                print(f"Successfully loaded: {module_name}")

        return implementations

class TestResultsManager:
    def __init__(self):
        self.results = {}
        self.sandbox_name = os.path.basename(os.path.dirname(os.path.abspath(__file__)))

    def record_result(self, impl_name: str, test_name: str, passed: bool,
                     error_msg: Optional[str] = None) -> None:
        """Record a test result for an implementation."""
        if impl_name not in self.results:
            self.results[impl_name] = {"passed": 0, "failed": 0, "skipped": 0, "errors": []}

        if passed:
            self.results[impl_name]["passed"] += 1
        else:
            self.results[impl_name]["failed"] += 1
            if error_msg:
                self.results[impl_name]["errors"].append({
                    "test": test_name,
                    "error": error_msg
                })

    def record_skip(self, impl_name: str, test_name: str,
                   reason: Optional[str] = None) -> None:
        """Record a skipped test for an implementation."""
        if impl_name not in self.results:
            self.results[impl_name] = {"passed": 0, "failed": 0, "skipped": 0, "errors": []}

        self.results[impl_name]["skipped"] += 1
        if reason:
            self.results[impl_name]["errors"].append({
                "test": test_name,
                "error": f"SKIPPED: {reason}"
            })

    def get_winner(self) -> Tuple[Optional[int], Dict]:
        """Determine the winner based on test results."""
        winner = None
        max_passed = -1

        for impl_name, results in self.results.items():
            if impl_name == "original_code":
                continue  # Skip original code when determining winner

            if results["passed"] > max_passed:
                max_passed = results["passed"]
                winner = impl_name
            # Break ties by looking at failure count
            elif results["passed"] == max_passed and winner is not None:
                if results["failed"] < self.results[winner]["failed"]:
                    winner = impl_name

        # Convert winner to numeric index if possible
        winner_index = -1
        if winner and re.match(r'modified_code\d+', winner):
            try:
                winner_index = int(re.search(r'(\d+)', winner).group(1))
            except (AttributeError, ValueError):
                pass

        return winner_index, self.results

    def save_results(self, filename: str = "test_results.json") -> None:
        """Save test results to a JSON file."""
        import json

        winner_index, results = self.get_winner()

        # Check if all tests were skipped
        all_skipped = all(
            stats["skipped"] == stats["passed"] + stats["failed"] + stats["skipped"]
            for impl_name, stats in results.items()
            if impl_name != "original_code"
        )

        output = {
            "winner": winner_index,
            "all_skipped": all_skipped,
            "results": {
                name: {
                    "passed": stats["passed"],
                    "failed": stats["failed"],
                    "skipped": stats["skipped"],
                    "total": stats["passed"] + stats["failed"] + stats["skipped"]
                }
                for name, stats in results.items()
                if not name.startswith("_")  # Skip internal items
            }
        }

        with open(filename, "w") as f:
            json.dump(output, f, indent=2)

        print(f"Test results saved to {filename}")

        return output


import google.generativeai as genai

genai.configure(api_key="MASKED")  # Replace with your actual API key
model = genai.GenerativeModel("gemini-1.5-flash")
response = model.generate_content("Explain how AI works")
print(response.text)

import pandas as pd

data = pd.read_csv('file_path.csv',
                   delimiter=',')

Instruction: write me a sql where you select the first 10 results

import pytest
import inspect
import pandas as pd
from unittest.mock import patch, MagicMock
import ast
import importlib.util

def get_assignment_targets_after_line(module, match_text):
    """Find variables assigned in lines after a match like 'data ='."""
    source_lines, _ = inspect.getsourcelines(module)
    source = ''.join(source_lines)
    tree = ast.parse(source)

    found_match = False
    targets = []

    for node in tree.body:
        if isinstance(node, ast.Assign):
            line_text = source_lines[node.lineno - 1].strip()
            if match_text in line_text and not found_match:
                found_match = True
                continue

            if found_match:
                targets.extend(get_names_from_targets(node.targets))

    return targets

def get_names_from_targets(targets):
    """Extract variable names from assignment targets."""
    names = []
    for t in targets:
        if isinstance(t, ast.Name):
            names.append(t.id)
        elif isinstance(t, ast.Tuple):
            names.extend([elt.id for elt in t.elts if isinstance(elt, ast.Name)])
    return names

def create_mock_df():
    """Creates a mock DataFrame with standard structure for testing models."""
    df = pd.DataFrame({'column1': range(10), 'column2': range(10)})
    return df

def test_data_variable_exists(implementation):
    name, module = implementation

    with patch('pandas.read_csv', return_value=create_mock_df()), patch('google.generativeai.GenerativeModel', return_value=MagicMock(text="Mock response")), patch('google.generativeai.configure', return_value=None):
        spec = importlib.util.spec_from_file_location("dynamic_module", name + ".py")
        module = importlib.util.module_from_spec(spec)
        exec_namespace = {}  # Capture top-level variable assignments

        # This executes the module in our namespace
        with open(name + ".py") as f:
            code = f.read()
            exec(code, exec_namespace)

        assigned_vars = get_assignment_targets_after_line(module, "pd.read_csv")

        found_df = False
        found_valid_df = False
        for var_name in assigned_vars:
            if var_name in exec_namespace:
                val = exec_namespace[var_name]
                if isinstance(val, pd.DataFrame):
                    found_valid_df = True

                    if len(val) == 10:
                        found_valid_df = True

        assert found_df, f"{name}: Should assign a variable to a DataFrame"
        assert found_valid_df, f"{name}: Should assign a variable to a DataFrame, and the DataFrame should be of length 10"

@pytest.fixture
def mock_generative_model():
    """Fixture to mock GenerativeModel class"""
    with patch('google.generativeai.GenerativeModel') as mock_class:
        mock_instance = MagicMock()
        mock_instance.generate_content.return_value = MagicMock(text="Mocked AI response")
        mock_class.return_value = mock_instance
        yield mock_class

@pytest.fixture
def mock_genai_configure():
    """Fixture to mock genai.configure"""
    with patch('google.generativeai.configure') as mock_configure:
        yield mock_configure

pytest
pytest-mock
pandas
google-generativeai
pandasql

import pytest
import os
import sys
import json
from typing import Dict, List, Optional, Any

# Import from local test_utils.py in the same directory
from test_utils import TestUtils, TestResultsManager

# Load all implementations in the current sandbox
implementations = TestUtils.load_all_implementations()
test_results = TestResultsManager()

@pytest.fixture(scope="session")
def sandbox_dir():
    """Fixture to provide the sandbox directory path."""
    return os.path.dirname(os.path.abspath(__file__))

@pytest.fixture(scope="session")
def sandbox_name():
    """Fixture to provide the sandbox name."""
    return os.path.basename(os.path.dirname(os.path.abspath(__file__)))

@pytest.fixture(scope="session")
def all_implementations():
    """Fixture to provide all implementations as a dictionary."""
    return implementations

@pytest.fixture(params=list(implementations.items()))
def implementation(request):
    """Fixture to provide each implementation to tests one at a time."""
    return request.param

@pytest.fixture(scope="session")
def results_manager():
    """Fixture to provide access to the test results manager."""
    return test_results

# Hook for collecting test results
@pytest.hookimpl(tryfirst=True, hookwrapper=True)
def pytest_runtest_makereport(item, call):
    """Pytest hook to collect test results."""
    # Execute all other hooks to obtain the report object
    outcome = yield
    rep = outcome.get_result()

    # We're only interested in the call outcome
    if rep.when == "call":
        if hasattr(item, "callspec") and "implementation" in item.callspec.params:
            # Get implementation name and module
            impl_name, _ = item.callspec.params["implementation"]

            # Get test name
            test_name = item.nodeid.split("::")[-1]

            # Record result
            if rep.passed:
                test_results.record_result(impl_name, test_name, True)
            elif rep.failed:
                error_msg = str(rep.longrepr) if rep.longrepr else "Test failed"
                test_results.record_result(impl_name, test_name, False, error_msg)
            elif rep.skipped:
                skip_reason = rep.longrepr[2] if rep.longrepr else "Test skipped"
                test_results.record_skip(impl_name, test_name, skip_reason)

# Hook to save results at the end of testing
@pytest.hookimpl(trylast=True)
def pytest_sessionfinish(session, exitstatus):
    """Save test results at the end of the test session."""
    test_results.save_results()

import os
import sys
import glob
import re
import importlib.util
import traceback
import types
from typing import Dict, List, Optional, Any, Tuple

class TestUtils:
    @staticmethod
    def discover_implementation_files(directory: str = None) -> List[str]:
        """Find all implementation files in the current sandbox directory."""
        if directory is None:
            directory = os.path.dirname(os.path.abspath(__file__))

        patterns = [
            r'modified_code\d+\.py',
            r'new_code\d+\.py',
            # r'original_code\.py',
            r'implementation\d*\.py'
        ]

        pattern = re.compile('|'.join(f'({p})' for p in patterns))
        implementations = []

        for file_path in glob.glob(os.path.join(directory, '*.py')):
            if pattern.search(os.path.basename(file_path)):
                implementations.append(file_path)

        # Sort files numerically
        def sort_key(path):
            filename = os.path.basename(path)
            match = re.search(r'(\d+)', filename)
            return int(match.group(1)) if match else 0

        return sorted(implementations, key=sort_key)

    @staticmethod
    def create_mock_module(file_path: str, module_name: str, error_info: str) -> types.ModuleType:
        """Create a mock module that contains error information but can still be tested."""
        # Create a new module object
        mock_module = types.ModuleType(module_name)

        # Add basic attributes
        mock_module.__file__ = file_path
        mock_module.__name__ = module_name
        mock_module.__display_name__ = module_name
        mock_module.__error__ = error_info

        # Add a dummy function that can be detected by test functions
        def dummy_function(*args, **kwargs):
            return f"Error in module: {error_info}"

        setattr(mock_module, "implementation_error", dummy_function)

        return mock_module

    @staticmethod
    def load_module(file_path: str, module_name: Optional[str] = None) -> Any:
        """
        Safely load a module from a file path with proper error handling.
        If the module has errors, return a mock module that can still be tested.
        """
        if module_name is None:
            module_name = os.path.basename(file_path).replace('.py', '')

        # Create a unique module name to avoid conflicts
        sandbox_id = os.path.basename(os.path.dirname(file_path))
        unique_module_name = f"{sandbox_id}_{module_name}"

        try:
            # First, try to read the file to check for syntax errors
            with open(file_path, 'r') as f:
                source_code = f.read()

            # Check for syntax errors by compiling the code
            try:
                compiled = compile(source_code, file_path, 'exec')
            except SyntaxError as e:
                error_msg = f"Syntax error: {str(e)}"
                print(f"Syntax error in {file_path}: {e}")
                print(f"  Line {e.lineno}, column {e.offset}: {e.text}")
                return TestUtils.create_mock_module(file_path, unique_module_name, error_msg)

            # Create the module spec
            spec = importlib.util.spec_from_file_location(unique_module_name, file_path)
            if spec is None or spec.loader is None:
                error_msg = f"Could not create spec for {file_path}"
                print(f"Error: {error_msg}")
                return TestUtils.create_mock_module(file_path, unique_module_name, error_msg)

            # Create the module object
            module = importlib.util.module_from_spec(spec)
            sys.modules[unique_module_name] = module

            # Special handling for execution errors
            try:
                # Execute the module code in a safe way
                spec.loader.exec_module(module)
                # Store the original name for reference
                module.__display_name__ = module_name
                return module
            except Exception as e:
                error_msg = f"Runtime error: {str(e)}"
                traceback_str = traceback.format_exc()
                print(f"Error executing module {file_path}: {e}")
                print(traceback_str)

                # Create a partial module that contains what we loaded before the error
                mock_module = TestUtils.create_mock_module(file_path, unique_module_name, error_msg)

                # Copy any attributes that might have been defined before the error
                for attr_name in dir(module):
                    if not attr_name.startswith('__'):
                        try:
                            setattr(mock_module, attr_name, getattr(module, attr_name))
                        except Exception:
                            pass  # Skip attributes that can't be copied

                return mock_module

        except FileNotFoundError as e:
            error_msg = f"File not found: {str(e)}"
            print(f"Error: {error_msg}")
            return TestUtils.create_mock_module(file_path, unique_module_name, error_msg)
        except Exception as e:
            error_msg = f"Unexpected error: {str(e)}"
            print(f"Error loading module {file_path}: {e}")
            return TestUtils.create_mock_module(file_path, unique_module_name, error_msg)

    @classmethod
    def load_all_implementations(cls, directory: str = None) -> Dict[str, Any]:
        """Load all implementation files in the directory, including those with errors."""
        if directory is None:
            directory = os.path.dirname(os.path.abspath(__file__))

        implementations = {}

        implementation_files = cls.discover_implementation_files(directory)
        if not implementation_files:
            print("WARNING: No implementation files found. Check your file naming patterns.")

        for file_path in implementation_files:
            module_name = os.path.basename(file_path).replace('.py', '')
            module = cls.load_module(file_path, module_name)

            # Always add the module, even if it has errors
            implementations[module_name] = module

            if hasattr(module, '__error__'):
                print(f"Loaded with errors: {module_name} - {module.__error__}")
            else:
                print(f"Successfully loaded: {module_name}")

        return implementations

class TestResultsManager:
    def __init__(self):
        self.results = {}
        self.sandbox_name = os.path.basename(os.path.dirname(os.path.abspath(__file__)))

    def record_result(self, impl_name: str, test_name: str, passed: bool,
                     error_msg: Optional[str] = None) -> None:
        """Record a test result for an implementation."""
        if impl_name not in self.results:
            self.results[impl_name] = {"passed": 0, "failed": 0, "skipped": 0, "errors": []}

        if passed:
            self.results[impl_name]["passed"] += 1
        else:
            self.results[impl_name]["failed"] += 1
            if error_msg:
                self.results[impl_name]["errors"].append({
                    "test": test_name,
                    "error": error_msg
                })

    def record_skip(self, impl_name: str, test_name: str,
                   reason: Optional[str] = None) -> None:
        """Record a skipped test for an implementation."""
        if impl_name not in self.results:
            self.results[impl_name] = {"passed": 0, "failed": 0, "skipped": 0, "errors": []}

        self.results[impl_name]["skipped"] += 1
        if reason:
            self.results[impl_name]["errors"].append({
                "test": test_name,
                "error": f"SKIPPED: {reason}"
            })

    def get_winner(self) -> Tuple[Optional[int], Dict]:
        """Determine the winner based on test results."""
        winner = None
        max_passed = -1

        for impl_name, results in self.results.items():
            if impl_name == "original_code":
                continue  # Skip original code when determining winner

            if results["passed"] > max_passed:
                max_passed = results["passed"]
                winner = impl_name
            # Break ties by looking at failure count
            elif results["passed"] == max_passed and winner is not None:
                if results["failed"] < self.results[winner]["failed"]:
                    winner = impl_name

        # Convert winner to numeric index if possible
        winner_index = -1
        if winner and re.match(r'modified_code\d+', winner):
            try:
                winner_index = int(re.search(r'(\d+)', winner).group(1))
            except (AttributeError, ValueError):
                pass

        return winner_index, self.results

    def save_results(self, filename: str = "test_results.json") -> None:
        """Save test results to a JSON file."""
        import json

        winner_index, results = self.get_winner()

        # Check if all tests were skipped
        all_skipped = all(
            stats["skipped"] == stats["passed"] + stats["failed"] + stats["skipped"]
            for impl_name, stats in results.items()
            if impl_name != "original_code"
        )

        output = {
            "winner": winner_index,
            "all_skipped": all_skipped,
            "results": {
                name: {
                    "passed": stats["passed"],
                    "failed": stats["failed"],
                    "skipped": stats["skipped"],
                    "total": stats["passed"] + stats["failed"] + stats["skipped"]
                }
                for name, stats in results.items()
                if not name.startswith("_")  # Skip internal items
            }
        }

        with open(filename, "w") as f:
            json.dump(output, f, indent=2)

        print(f"Test results saved to {filename}")

        return output