You can use the `__name__` attribute of any function object in Python to get its name as a string. When writing a decorator, access the decorated function's name using `func.__name__` inside the wrapper. This is useful for logging or debugging.


Note


import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib
import io
from contextlib import redirect_stdout

class TestTask(unittest.TestCase):
    def test_add_function_name_printed(self):
        import user_code
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.add(10, 5)
        output = normalize_text(f.getvalue())
        _dynamic_test(
            self,
            "calling function: add" in output,
            "Function name 'add' is printed before execution",
            f"Expected function name 'add' in output, got: {output}",
        )

    def test_greet_function_name_printed(self):
        import user_code
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.greet("Charlie")
        output = normalize_text(f.getvalue())
        _dynamic_test(
            self,
            "calling function: greet" in output,
            "Function name 'greet' is printed before execution",
            f"Expected function name 'greet' in output, got: {output}",
        )

    def test_add_prints_args(self):
        import user_code
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.add(1, 2)
        output = normalize_text(f.getvalue())
        _dynamic_test(
            self,
            "args=(1, 2)" in output,
            "Positional arguments are printed as tuple",
            f"Expected positional arguments '(1, 2)' in output, got: {output}",
        )

    def test_greet_prints_kwargs(self):
        import user_code
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.greet("Bob", greeting="Hi")
        output = normalize_text(f.getvalue())
        _dynamic_test(
            self,
            "kwargs={'greeting': 'hi'}" in output or 'kwargs={"greeting": "hi"}' in output,
            "Keyword arguments are printed as dictionary",
            f"Expected keyword arguments in output, got: {output}",
        )


    def test_decorator_return_value_unchanged(self):
        import user_code
        importlib.reload(user_code)
        res1 = user_code.add(7, 8)
        res2 = user_code.greet("Zoe", greeting="Hiya")
        _dynamic_test(
            self,
            res1 == 15 and res2 == "Hiya, Zoe!",
            "Decorator does not interfere with the function's return value",
            f"Expected 15 and 'Hiya, Zoe!', got {res1} and {res2}",
        )

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

Master packing/unpacking, flexible function arguments, higher-order functions, lambdas, variable scope, closures, iterators and decorators. Designed for learners who have completed an introductory Python course and want to write more efficient, expressive, and powerful code.

Explore the art of packing and unpacking lists, tuples, and dictionaries to write more expressive and efficient Python code.

Delve into the world of higher-order functions, passing functions as arguments, and writing concise anonymous functions with lambda.

Unravel the mysteries of variable scope, and learn how nested functions and closures enable powerful encapsulation.

Delve into Python's iteration protocol, custom iterators, and generator functions for efficient data processing.

Challenge: Logging Decorator

Solution