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

class TestTask(unittest.TestCase):
    def test_merge_all_keys(self):
        import user_code
        importlib.reload(user_code)
        d1 = {'a': 1, 'b': 2}
        d2 = {'b': 3, 'c': 4}
        result = user_code.merge_dicts(d1, d2)
        expected = {'a': 1, 'b': 3, 'c': 4}
        _dynamic_test(
            self,
            isinstance(result, dict) and result == expected,
            "Merged dictionary contains all keys and correct values when keys overlap.",
            f"Expected {expected}, got {result}"
        )

    def test_merge_with_empty_first(self):
        import user_code
        importlib.reload(user_code)
        d1 = {}
        d2 = {'x': 10}
        result = user_code.merge_dicts(d1, d2)
        expected = {'x': 10}
        _dynamic_test(
            self,
            isinstance(result, dict) and result == expected,
            "Merging with an empty first dictionary works.",
            f"Expected {expected}, got {result}"
        )

    def test_merge_with_empty_second(self):
        import user_code
        importlib.reload(user_code)
        d1 = {'foo': 9}
        d2 = {}
        result = user_code.merge_dicts(d1, d2)
        expected = {'foo': 9}
        _dynamic_test(
            self,
            isinstance(result, dict) and result == expected,
            "Merging with an empty second dictionary works.",
            f"Expected {expected}, got {result}"
        )

    def test_no_modification_of_inputs(self):
        import user_code
        importlib.reload(user_code)
        d1 = {'a': 1, 'b': 2}
        d2 = {'b': 3, 'c': 4}
        d1_copy = d1.copy()
        d2_copy = d2.copy()
        result = user_code.merge_dicts(d1, d2)
        _dynamic_test(
            self,
            d1 == d1_copy and d2 == d2_copy,
            "Input dictionaries are not modified after merge.",
            f"Input dictionaries were modified. d1: {d1}, d2: {d2}"
        )

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: Merging Dictionaries with Unpacking

Solution