
import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib
import types
import sys

class TestTask(unittest.TestCase):
    def test_function_returns_correct_values(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, 'process_numbers', None)
        _dynamic_test(
            self,
            callable(func),
            "Function 'process_numbers' is defined",
            "Function 'process_numbers' is not defined or not callable",
        )
        result = func([1, 2, 3]) if func else None
        expected = [6, 9, 14]
        _dynamic_test(
            self,
            isinstance(result, list) and result == expected,
            "Returned list is correct for input [1, 2, 3]",
            f"Expected {expected}, got {result}",
        )

    def test_function_returns_same_length(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, 'process_numbers', None)
        in_list = [5, 10, -2, 0]
        result = func(in_list) if func else None
        _dynamic_test(
            self,
            isinstance(result, list) and len(result) == len(in_list),
            "Returned list has the same length as input",
            f"Expected length {len(in_list)}, got {len(result) if isinstance(result, list) else result}",
        )

    def test_function_works_for_empty_list(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, 'process_numbers', None)
        result = func([]) if func else None
        _dynamic_test(
            self,
            isinstance(result, list) and result == [],
            "Function returns empty list for empty input",
            f"Expected [], got {result}",
        )

    def test_function_works_for_negative_numbers(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, 'process_numbers', None)
        result = func([-1, -3]) if func else None
        expected = [6, 14]
        _dynamic_test(
            self,
            isinstance(result, list) and result == expected,
            "Function works correctly for negative numbers",
            f"Expected {expected}, got {result}",
        )

    def test_helper_function_defined_inside(self):
        import user_code
        import ast
        import inspect
        importlib.reload(user_code)
        source = inspect.getsource(user_code.process_numbers)
        tree = ast.parse(source)
        class HelperFinder(ast.NodeVisitor):
            def __init__(self):
                self.found = False
            def visit_FunctionDef(self, node):
                if node.name == 'process_numbers':
                    for child in node.body:
                        if isinstance(child, ast.FunctionDef) and child.name == 'square_and_add_five':
                            self.found = True
        finder = HelperFinder()
        finder.visit(tree)
        _dynamic_test(
            self,
            finder.found,
            "Helper function 'square_and_add_five' is defined inside 'process_numbers'",
            "Helper function 'square_and_add_five' is not defined inside 'process_numbers'",
        )

def _dynamic_test(test_case, condition, success_message, failure_message):
    if condition:
        test_case._testMethodName = success_message
        test_case.assertTrue(True, success_message)
    else:
        test_case._testMethodName = failure_message
        test_case.fail(failure_message)

def normalize_text(text):
    text = text.lower()
    text = re.sub(r"\\s{2,}", " ", text)
    text = re.sub(r"\\s*([,:?])\\s*", r"\\1 ", text)
    return text.strip()

def change_var(code: str, var_name: str, value: str) -> str:
    tree = ast.parse(code)
    lines = code.splitlines()
    changed = False
    # Collect all assignment nodes to modify
    assign_nodes = [
        (i, node)
        for i, node in enumerate(tree.body)
        if isinstance(node, ast.Assign)
        and any(isinstance(target, ast.Name) and target.id == var_name for target in node.targets)
    ]

    # If nothing to change, return unmodified code
    if not assign_nodes:
        return code

    # Perform replacements for all matching assignments (from last to first to not break line offsets)
    for i, node in reversed(assign_nodes):
        start_line = node.lineno - 1
        line = lines[start_line]
        indent = ' ' * (len(line) - len(line.lstrip()))
        lines[start_line] = f"{indent}{var_name} = {value}"
        next_line = len(lines)
        for next_node in tree.body[i+1:]:
            if hasattr(next_node, 'lineno'):
                next_line = next_node.lineno - 1
                break
        if next_line > start_line + 1:
            lines[start_line+1:next_line] = []
        changed = True

    return '\\n'.join(lines) if changed else code

if __name__ == "__main__":
    unittest.main()


test_main.py

Beherrschung von Packing/Unpacking, flexiblen Funktionsargumenten, Higher-Order-Funktionen, Lambdas, Variablenbereich, Closures, Iteratoren und Dekoratoren. Konzipiert für Lernende, die einen Einführungskurs in Python abgeschlossen haben und effizienteren, ausdrucksstärkeren und leistungsfähigeren Code schreiben möchten.

Erkunden Sie die Kunst des Packens und Entpackens von Listen, Tupeln und Dictionaries, um ausdrucksstärkeren und effizienteren Python-Code zu schreiben.

Tauchen Sie ein in die Welt der höherwertigen Funktionen, das Übergeben von Funktionen als Argumente und das Schreiben prägnanter anonymer Funktionen mit Lambda.

Entschlüsseln der Geheimnisse des Variablenbereichs und Erlernen, wie verschachtelte Funktionen und Closures eine leistungsstarke Kapselung ermöglichen.

Vertiefung in das Iterationsprotokoll von Python, benutzerdefinierte Iteratoren und Generatorfunktionen für effiziente Datenverarbeitung.

Challenge: Hilfsfunktion Innerhalb Einer Funktion

Lösung