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

class TestTask(unittest.TestCase):
    def test_tom_average(self):
        import user_code
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.average_mark("Tom", 4.0, 3.5, 3.0, 3.3, 3.8)
        output = normalize_text(f.getvalue())
        expected = normalize_text("Tom got 3.5")
        _dynamic_test(
            self,
            expected in output,
            f"Output correctly includes: {expected}",
            f"Expected output: '{expected}' not found in: '{output}'",
        )

    def test_alex_average(self):
        import user_code
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.average_mark("Alex", 2.5, 3.8)
        output = normalize_text(f.getvalue())
        expected = normalize_text("Alex got 3.1")
        _dynamic_test(
            self,
            expected in output,
            f"Output correctly includes: {expected}",
            f"Expected output: '{expected}' not found in: '{output}'",
        )

    def test_single_mark(self):
        import user_code
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.average_mark("Harry", 4.0)
        output = normalize_text(f.getvalue())
        expected = normalize_text("Harry got 4.0")
        _dynamic_test(
            self,
            expected in output,
            f"Output correctly includes: {expected}",
            f"Expected output: '{expected}' not found in: '{output}'",
        )

    def test_multiple_marks(self):
        import user_code
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.average_mark("Sally", 5.0, 4.0, 3.0)
        output = normalize_text(f.getvalue())
        expected = normalize_text("Sally got 4.0")
        _dynamic_test(
            self,
            expected in output,
            f"Output correctly includes: {expected}",
            f"Expected output: '{expected}' not found in: '{output}'",
        )

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.

You'll find this topic enjoyable, and you'll learn how to pack and unpack values in a single assignment.

Explore the power of Python's function arguments, *args, **kwargs, and learn how to effectively use functions as arguments.

Discover the higher-order functions and how functions can be passed as arguments to other functions in programming.

What is variable scope, and how did we manage without it before? This topic is essential for understanding the following subjects: Closures and Decorators. I assure you that this topic will be both enjoyable and practical. You will gain knowledge about local, nonlocal, and global variables.

This section provides an in-depth exploration of Python decorators, a powerful feature that allows for the modification and enhancement of function behavior. Participants will learn how to write their own decorators, understand built-in decorators, and apply decorators in practical scenarios.

Challenge: Calculating the Average Mark with *args

Solución

Challenge: Calculating the Average Mark with *args

Solución