
import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib

class TestTask(unittest.TestCase):
    def test_lambda_cubes_argument(self):
        import user_code
        importlib.reload(user_code)
        cube = getattr(user_code, 'cube', None)
        _dynamic_test(
            self,
            callable(cube),
            "cube is defined and callable",
            "cube is not defined as a callable lambda function",
        )
        for val in [0, 1, 2, 5, -3]:
            expected = val ** 3
            actual = cube(val) if cube is not None else None
            _dynamic_test(
                self,
                actual == expected,
                f"cube({val}) returns {expected}",
                f"cube({val}) should return {expected}, got {actual}",
            )

    def test_cubed_numbers_list(self):
        import user_code
        importlib.reload(user_code)
        numbers = [1, 2, 3, 4, 5]
        cubed_numbers = getattr(user_code, 'cubed_numbers', None)
        _dynamic_test(
            self,
            isinstance(cubed_numbers, list),
            "cubed_numbers is a list",
            f"cubed_numbers should be a list, got {type(cubed_numbers)}",
        )
        expected = [n ** 3 for n in numbers]
        _dynamic_test(
            self,
            cubed_numbers == expected,
            f"cubed_numbers contains cubes: {expected}",
            f"cubed_numbers should be {expected}, got {cubed_numbers}",
        )

    def test_lambda_used_with_map(self):
        code = ""
        with open("user_code.py", "r") as f:
            code = f.read()
        found_lambda = "lambda" in code
        found_map = "map(" in code
        _dynamic_test(
            self,
            found_lambda and found_map,
            "Used lambda function with map as required",
            "Solution must use a lambda function with map",
        )

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: Lambda with Map

Solution