py-mocks 0.3.0

Testing utility decorators for mocking functions, variables, and HTTP endpoints

pymocks

Testing utility decorators and context managers for mocking functions, variables, and HTTP endpoints in Python.

Works with both synchronous and asynchronous test functions. Sync/async is detected automatically. All mocking utilities can be used as decorators or context managers.

Installation

pip install pymocks

Or with uv:

uv add pymocks

Usage

Mocking Functions and Variables

Use Mock with with_mock to monkeypatch module attributes for the duration of a test. Accepts one or more Mock objects. Works as a decorator or context manager:

import my_module
from pymocks import Mock, with_mock


# Mock a function — the replacement must have the same signature
def fake_function(x: int, y: str) -> bool:
    return True


mock = Mock(
    module_where_used=my_module,
    current_value=my_module.some_function,
    new_value=fake_function,
)


@with_mock(mock)
def test_with_mocked_function():
    result = my_module.some_function(1, "a")
    assert result is True


# Mock a variable — the replacement must have the same type
var_mock = Mock(
    module_where_used=my_module,
    current_value=my_module.API_URL,
    new_value="https://mock.example.com",
)


@with_mock(var_mock)
def test_with_mocked_variable():
    assert my_module.API_URL == "https://mock.example.com"

Pass multiple mocks to apply them all at once — they are all reverted together when the scope exits:

@with_mock(mock, var_mock)
def test_with_multiple_mocks():
    result = my_module.some_function(1, "a")
    assert result is True
    assert my_module.API_URL == "https://mock.example.com"

The same works for async tests:

@with_mock(mock)
async def test_async_with_mock():
    result = my_module.some_function(1, "a")
    assert result is True

Or use it as a context manager for more flexible scoping:

def test_with_context_manager():
    with with_mock(mock, var_mock):
        result = my_module.some_function(1, "a")
        assert result is True
    # mocks are reverted here


async def test_async_with_context_manager():
    async with with_mock(mock):
        result = my_module.some_function(1, "a")
        assert result is True

Mocking Classes

Use Mock to replace a class with a subclass. The replacement must be a subclass of the original:

import my_module
from pymocks import Mock, with_mock


class FakeService(my_module.Service):
    def fetch(self) -> str:
        return "fake data"


mock = Mock(
    module_where_used=my_module,
    current_value=my_module.Service,
    new_value=FakeService,
)


@with_mock(mock)
def test_with_mocked_class():
    svc = my_module.Service()
    assert svc.fetch() == "fake data"

Replacing a class with an unrelated class raises TypeError:

class Unrelated:
    pass


# Raises TypeError — Unrelated is not a subclass of Service
Mock(
    module_where_used=my_module,
    current_value=my_module.Service,
    new_value=Unrelated,
)

Signature and Type Validation

Mock validates compatibility between current_value and new_value at construction time:

• Callables: signatures must match exactly (parameter count, names, kinds, annotations, and return annotation)
• Classes: new_value must be a subclass of current_value
• Non-callables: type(current_value) must be the same as type(new_value)
• Mixed: replacing a callable with a non-callable (or vice versa) raises TypeError

def original(x: int) -> str:
    return str(x)


# Signature mismatch — raises TypeError immediately
Mock(
    module_where_used=my_module,
    current_value=original,
    new_value=lambda x: str(x),  # missing annotations
)

# Type mismatch — raises TypeError immediately
Mock(
    module_where_used=my_module,
    current_value="a string",
    new_value=42,
)

Mocking HTTP Endpoints

Use MockEndpoint with with_endpoints to mock HTTP calls via aioresponses. Works as a decorator or context manager:

import aiohttp
from pymocks import MockEndpoint, with_endpoints

endpoints = (
    MockEndpoint(
        url="https://api.example.com/users",
        method="GET",
        json_response={"users": [{"id": 1, "name": "Alice"}]},
    ),
    MockEndpoint(
        url="https://api.example.com/users",
        method="POST",
        json_response={"id": 2, "name": "Bob"},
    ),
)


# As a decorator
@with_endpoints(endpoints)
async def test_api_calls():
    async with aiohttp.ClientSession() as session:
        async with session.get("https://api.example.com/users") as resp:
            data = await resp.json()
            assert len(data["users"]) == 1


# As a context manager
async def test_api_calls_ctx():
    async with with_endpoints(endpoints):
        async with aiohttp.ClientSession() as session:
            async with session.get("https://api.example.com/users") as resp:
                data = await resp.json()
                assert len(data["users"]) == 1

API Reference

Mock[T_mocked]

A dataclass that defines a monkeypatch specification. Validates compatibility on construction.

Field	Type	Description
module_where_used	ModuleType	The module containing the attribute to patch
current_value	T_mocked	The current value (used to find its name)
new_value	T_mocked	The replacement value during the test

MockEndpoint

A frozen dataclass defining an HTTP endpoint mock.

Field	Type	Description
url	str	The URL to mock
method	Literal["GET", "POST", "PUT", "DELETE"]	HTTP method
json_response	dict[str, JsonValue] | None	JSON response body (optional)
body	str | None	Raw string body (optional)

with_mock(*mocks) / with_endpoints(endpoints)

Both can be used as decorators or context managers (sync and async):

# Decorator — single mock
@with_mock(mock)
def test_decorated(): ...

# Decorator — multiple mocks
@with_mock(mock, var_mock)
def test_decorated(): ...

# Sync context manager
with with_mock(mock, var_mock):
    ...

# Async context manager
async with with_mock(mock):
    ...

When used as decorators, sync/async is detected automatically.

Requirements

• Python >= 3.12
• pytest
• aioresponses

License

MIT