
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

Maîtrise de l'emballage/déballage, des arguments de fonction flexibles, des fonctions d'ordre supérieur, des lambdas, de la portée des variables, des closures, des itérateurs et des décorateurs. Conçu pour les apprenants ayant suivi un cours d'introduction à Python et souhaitant écrire un code plus efficace, expressif et puissant.

Explorez l'art du packing et unpacking des listes, tuples et dictionnaires pour écrire un code Python plus expressif et efficace.

Plongez dans le monde des fonctions d'ordre supérieur, du passage de fonctions en tant qu'arguments, et de l'écriture de fonctions anonymes concises avec lambda.

Démystifier la portée des variables et découvrir comment les fonctions imbriquées et les closures permettent une encapsulation puissante.

Plongez dans le protocole d'itération de Python, les itérateurs personnalisés et les fonctions génératrices pour un traitement efficace des données.

Défi : Lambda avec Map

Solution