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

class TestTask(unittest.TestCase):
    def test_returns_tuple_of_three(self):
        import user_code
        importlib.reload(user_code)
        result = getattr(user_code, 'result', None)
        _dynamic_test(
            self,
            isinstance(result, tuple) and len(result) == 3,
            "Returned value is a tuple of three elements",
            f"Expected a tuple of three elements, got: {result}",
        )

    def test_tuple_contains_exact_values(self):
        import user_code
        importlib.reload(user_code)
        result = getattr(user_code, 'result', None)
        expected = (10, 20, 30)
        _dynamic_test(
            self,
            result == expected,
            "Returned tuple contains the correct values (10, 20, 30)",
            f"Expected tuple {expected}, got: {result}",
        )

    def test_unpacking_works(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, 'pack_to_tuple', None)
        test_values = [1, 2, 3]
        result = None
        if func:
            try:
                result = func(*test_values)
            except Exception as e:
                result = None
        _dynamic_test(
            self,
            isinstance(result, tuple) and result == (1, 2, 3),
            "Function works when called by unpacking a list of three elements.",
            f"Calling function with unpacked list [1, 2, 3] should return (1, 2, 3), got: {result}",
        )

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

In this course, you will learn advanced topics such as function arguments, iterators, generators, closures, and decorators. Dozens of practice exercises will help you grasp these topics.

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: Packing and Unpacking Lists and Tuples

Ratkaisu