I have a pytest fixture that imports a specific module. This is needed as importing the module is very expensive, so we don't want to do it on import-time (i.e. during pytest test collection). This results in code like this:
#pytest.fixture
def my_module_fix():
import my_module
yield my_module
def test_something(my_module_fix):
assert my_module_fix.my_func() = 5
I am using PyCharm and would like to have type-checking and autocompletion in my tests. To achieve that, I would somehow have to annotate the my_module_fix parameter as having the type of the my_module module.
I have no idea how to achieve that. All I found is that I can annotate my_module_fix as being of type types.ModuleType, but that is not enough: It is not any module, it is always my_module.
If I get your question, you have two (or three) separate goals
Deferred import of slowmodule
Autocomplete to continue to work as if it was a standard import
(Potentially?) typing (e.g. mypy?) to continue to work
I can think of at least five different approaches, though I'll only briefly mention the last because it's insane.
Import the module inside your tests
This is (by far) the most common and IMHO preferred solution.
e.g. instead of
import slowmodule
def test_foo():
slowmodule.foo()
def test_bar():
slowmodule.bar()
you'd write:
def test_foo():
import slowmodule
slowmodule.foo()
def test_bar():
import slowmodule
slowmodule.bar()
[deferred importing] Here, the module will be imported on-demand/lazily. So if you have pytest setup to fail-fast, and another test fails before pytest gets to your (test_foo, test_bar) tests, the module will never be imported and you'll never incur the runtime cost.
Because of Python's module cache, subsequent import statements won't actually re-import the module, just grab a reference to the already-imported module.
[autocomplete/typing] Of course, autocomplete will continue to work as you expect in this case. This is a perfectly fine import pattern.
While it does require adding potentially many additional import statements (one inside each test function), it's immediately clear what is going on (regardless of whether it's clear why it's going on).
[3.7+] Proxy your module with module __getattr__
If you create a module (e.g. slowmodule_proxy.py) with the contents like:
def __getattr__(name):
import slowmodule
return getattr(slowmodule, name)
And in your tests, e.g.
import slowmodule
def test_foo():
slowmodule.foo()
def test_bar():
slowmodule.bar()
instead of:
import slowmodule
you write:
import slowmodule_proxy as slowmodule
[deferred import] Thanks to PEP-562, you can "request" any name from slowmodule_proxy and it will fetch and return the corresponding name from slowmodule. Just as above, including the import inside the function will cause slowmodule to be imported only when the function is called and executed instead of on module load. Module caching still applies here of course, so you're only incurring the import penalty once per interpreter session.
[autocomplete] However, while deferred importing will work (and your tests run without issue), this approach (as stated so far) will "break" autocomplete:
Now we're in the realm of PyCharm. Some IDEs will perform "live" analysis of modules and actually load up the module and inspect its members. (PyDev had this option). If PyCharm did this, implementing module.__dir__ (same PEP) or __all__ would allow your proxy module to masquerade as the actual slowmodule and autocomplete would work.† But, PyCharm does not do this.
Nonetheless, you can fool PyCharm into giving you autocomplete suggestions:
if False:
import slowmodule
else:
import slowmodule_proxy as slowmodule
The interpreter will only execute the else branch, importing the proxy and naming it slowmodule (so your test code can continue to reference slowmodule unchanged).
But PyCharm will now provide autocompletion for the underlying module:
† While live-analysis can be an incredibly helpful, there's also a (potential) security concern that comes with it that static syntax analysis doesn't have. And the maturation of type hinting and stub files has made it less of an issue still.
Proxy slowmodule explicitly
If you really hated the dynamic proxy approach (or the fact that you have to fool PyCharm in this way), you could proxy the module explicitly.
(You'd likely only want to consider this if the slowmodule API is stable.)
If slowmodule has methods foo and bar you'd create a proxy module like:
def foo(*args, **kwargs):
import slowmodule
return slowmodule.foo(*args, **kwargs)
def bar(*args, **kwargs):
import slowmodule
return slowmodule.bar(*args, **kwargs)
(Using args and kwargs to pass arguments through to the underlying callables. And you could add type hinting to these functions to mirror the slowmodule functions.)
And in your test,
import slowmodule_proxy as slowmodule
Same as before. Importing inside the method gives you the deferred importing you want and the module cache takes care of multiple import calls.
And since it's a real module whose contents can be statically analyzed, there's no need to "fool" PyCharm.
So the benefit of this solution is that you don't have a bizarre looking if False in your test imports. This, however, comes at the (substantial) cost of having to maintain a proxy file alongside your module -- which could prove painful in the case that slowmodule's API wasn't stable.
[3.5+] Use importlib's LazyLoader instead of a proxy module
Instead of the proxy module slowmodule_proxy, you could follow a pattern similar to the one shown in the importlib docs
>>> import importlib.util
>>> import sys
>>> def lazy_import(name):
... spec = importlib.util.find_spec(name)
... loader = importlib.util.LazyLoader(spec.loader)
... spec.loader = loader
... module = importlib.util.module_from_spec(spec)
... sys.modules[name] = module
... loader.exec_module(module)
... return module
...
>>> lazy_typing = lazy_import("typing")
>>> #lazy_typing is a real module object,
>>> #but it is not loaded in memory yet.
You'd still need to fool PyCharm though, so something like:
if False:
import slowmodule
else:
slowmodule = lazy_import('slowmodule')
would be necessary.
Outside of the single additional level of indirection on module member access (and the two minor version availability difference), it's not immediately clear to me what, if anything, there is to be gained from this approach over the previous proxy module method, however.
Use importlib's Finder/Loader machinery to hook import (don't do this)
You could create a custom module Finder/Loader that would (only) hook your slowmodule import and, instead load, for example your proxy module.
Then you could just import that "importhook" module before you imported slowmode in your tests, e.g.
import myimporthooks
import slowmodule
def test_foo():
...
(Here, myimporthooks would use importlib's finder and loader machinery to do something simlar to the importhook package but intercept and redirect the import attempt rather than just serving as an import callback.)
But this is crazy. Not only is what you want (seemingly) achievable through (infinitely) more common and supported methods, but it's incredibly fragile, error-prone and, without diving into the internals of PyTest (which may mess with module loaders itself), it's hard to say whether it'd even work.
When Pytest collects files to be tested, modules are only imported once, even if the same import statement appears in multiple files.
To observe when my_module is imported, add a print statement and then use the Pytest -s flag (short for --capture=no), to ensure that all standard output is displayed.
my_module.py
answer: int = 42
print("MODULE IMPORTED: my_module.py")
You could then add your test fixture to a conftest.py file:
conftest.py
import pytest
#pytest.fixture
def my_module_fix():
import my_module
yield my_module
Then in your test files, my_module.py may be imported to add type hints:
test_file_01.py
import my_module
def test_something(my_module_fix: my_module):
assert my_module_fix.answer == 42
test_file_02.py
import my_module
def test_something2(my_module_fix: my_module):
assert my_module_fix.answer == 42
Then run Pytest to display all standard output and verify that the module is only imported once at runtime.
pytest -s ./
Output from Pytest
platform linux -- Python 3.9.7, pytest-6.2.5
rootdir: /home/your_username/your_repo
collecting ... MODULE IMPORTED: my_module.py <--- Print statement executed once
collected 2 items
test_file_01.py .
test_file_02.py .
This is quick and naive, but can you possibly annotate your tests with the "quote-style" annotation that is meant for different purposes, but may suit here by skipping the import at runtime but still help your editor?
def test_something(my_module_fix: "my_module"):
In a quick test, this seems to accomplish it at least for my setup.
Although it might not be considered a 'best practice', to keep your specific use case simple, you could just lazily import the module directly in your test where you need it.
def test_something():
import my_module
assert my_module.my_func() = 5
I believe Pytest will only import the module when the applicable tests run. Pytest should also 'cache' the import as well so that if multiple tests import the module, it is only actually imported once. This may also solve your autocomplete issues for your editor.
Side-note: Avoid writing code in a specific way to cater for a specific editor. Keep it simple, not everyone who looks at your code will use Pycharm.
would like to have type-checking and autocompletion in my tests
It sounds like you want Something that fits as the type symbol of your test function:
def test_something(my_module_fix: Something):
assert my_module_fix.my_func() = 5
... and from this, [hopefully] your IDE can make some inferences about my_module_fix. I'm not a PyCharm user, so I can't speak to what it can tell you from type signatures, but I can say this isn't something that's readily available.
For some intuition, in this example -- Something is a ModuleType like 3 is an int. Analogously, accessing a nonexistent attribute of Something is like doing something not allowed with 3. Perhaps, like accessing an attribute of it (3.__name__).
But really, I seems like you're thinking about this from the wrong direction. The question a type signature answers is: what contract must this [these] argument[s] satisfy for this function to use it [them]. Using the example above, a type of 3 is the kind of thing that is too specific to make useful functions:
def add_to(i: 3):
return i
Perhaps a better name for your type is:
def test_something(my_module_fix: SomethingThatHasMyFuncMethod):
assert my_module_fix.my_func() = 5
So the type you probably want is something like this:
class SomethingThatHasMyFuncMethod:
def my_func(self) -> int: ...
You'll need to define it (maybe in a .pyi file). See here for info.
Finally, here's some unsolicited advice regarding:
importing the module is very expensive, so we don't want to do it on import-time
You should probably employ some method of making this module do it's thing lazily. Some of the utils in the django framework could serve as a reference point. There's also the descriptor protocol which is a bit harder to grok but may suit your needs.
You want to add type hinting for the arguments of test_something, in particular to my_module_fix. This question is hard because we can't import my_module at the top.
Well, what is the type of my_module? I'm going to assume that if you do import my_module and then type(my_module) you will get <class 'module'>.
If you're okay with not telling users exactly which module it has to be, then you could try this.
from types import ModuleType
#pytest.fixture
def my_module_fix():
import my_module
yield my_module
def test_something(my_module_fix: ModuleType):
assert my_module_fix.my_func() = 5
The downside is that a user might infer that any old module would be suitable for my_module_fix.
You want it to be more specific? There's a cost to that, but we can get around some of it (it's hard to speak on performance in VS Code vs PyCharm vs others).
In that case, let's use TYPE_CHECKING. I think this was introduced in Python 3.6.
from types import ModuleType
from typing import TYPE_CHECKING
if TYPE_CHECKING:
import my_module
#pytest.fixture
def my_module_fix():
import my_module
yield my_module
def test_something(my_module_fix: my_module):
assert my_module_fix.my_func() = 5
You won't pay these costs during normal run time. You will pay the cost of importing my_module when your type checker is doing what it does.
If you're on an earlier verison of Python, you might need to do this instead.
from types import ModuleType
from typing import TYPE_CHECKING
if TYPE_CHECKING:
import my_module
#pytest.fixture
def my_module_fix():
import my_module
yield my_module
def test_something(my_module_fix: "my_module"):
assert my_module_fix.my_func() = 5
The type checker in VS Code is smart enough to know what's going on here. I can't speak for other type checkers.
We have a Python 3.7 application that has a declared constants.py file that has this form:
APP_CONSTANT_1 = os.environ.get('app-constant-1-value')
In a test.py we were hoping to test the setting of these constants using something like this (this is highly simplified but represents the core issue):
class TestConfig:
"""General config tests"""
#pytest.fixture
def mock_os_environ(self, monkeypatch):
""" """
def mock_get(*args, **kwargs):
return 'test_config_value'
monkeypatch.setattr(os.environ, "get", mock_get)
def test_mock_env_vars(self, mock_os_environ):
import constants
assert os.environ.get('app-constant-1-value') == 'test_config_value' #passes
assert constants.APP_CONSTANT_1 == 'test_config_value' #fails
The second assertion fails as constants.constants.APP_CONSTANT_1 is None. Turns out that the constants.py seems to be loaded during pytest's 'collecting' phase and thus is already set by the time the test is run.
What are we missing here? I feel like there is a simple way to resolve this in pytest but haven't yet discovered the secret. Is there some way to avoid loading the constants file prior to the tests being run? Any ideas are appreciated.
The problem is most likely that constants has been loaded before. To make sure it gets the patched value, you have to reload it:
import os
from importlib import reload
import pytest
import constants
class TestConfig:
"""General config tests"""
#pytest.fixture
def mock_os_environ(self, monkeypatch):
""" """
monkeypatch.setenv('app-constant-1-value', 'test_config_value')
reload(constants)
def test_mock_env_vars(self, mock_os_environ):
assert os.environ.get('app-constant-1-value') == 'test_config_value'
assert app.APP_CONSTANT_1 == 'test_config_value'
Note that I used monkeypatch.setenv to specifically set the variable you need. If you don't need to change all environment variables, this is easier to use.
Erm, I would avoid using constants. You can subclass os.environment for a start, and then use a mocked subclass for your unit tests, so you can have my_env.unique_env as a member variable. You can then use eg. import json to use a json configuration file without getting involved with hard coded python.
The subclass can then hold the relevant variables (or methods if you prefer)
Being able to add a facade to os.environment provides you with the abstraction you are looking for, without any of the problems.
Even is one is using a legacy/larger project, the advantage of using an adapter for access to the environment must be apparent.
Since you are writing unit tests, there is an opportunity to use an adapter class in both the tests and the functions being tested.
Let's say I have the following modules:
# src/myapp/utils.py
import thirdparty
from myapp.secrets import get_super_secret_stuff
def get_thirdparty_client() -> return thirdparty.Client:
thirdparty.Client(**get_super_secret_stuff())
# src/myapp/things.py
from myapp.utils import get_thirdparty_client
from myapp.transformations import apply_fairydust, make_it_sparkle
def do_something(x):
thirdparty_client = get_thirdparty_client()
y = thidparty_client.query(apply_fairydust(x))
return make_it_sparkle(y)
Assume that myapp is lightly-tested legacy code, and refactoring is out of the question. Also assume (annoyingly) that thirdparty.Client does non-deterministic network I/O in its __init__ method. Therefore I intend to mock the thirdparty.Client class itself so as to make this do_something function testable.
Assume also that I must use unittest and cannot use another test framework like Pytest.
It seems like the patch function from unittest.mock is the right tool for the job. However, I'm unsure of how to apply the usual admonition to "patch where it is used."
Ideally I want to write a test that looks something like this:
# tests/test_myapp/test_things.py
from unittest import TestCase
from unittest.mock import patch
from myapp.things import do_something
def gen_test_pairs():
# Generate pairs of expected inputs and outputs
...
class ThingTest(unittest.TestCase):
#patch('????')
def test_do_something(self, mock_thirdparty_client):
for x, y in gen_test_pairs:
with self.subTest(params={'x': x, 'y': y}):
mock_thirdparty_client.query.return_value = y
self.assertEqual(do_something(x), y)
My problem is that I don't know what to write in place of the ????, because I never actually import thirdparty.Client in src/myapp/things.py.
Options I considered:
Apply the patch at myapp.utils.thirdparty.Client, which makes my test fragile and dependent on implementation details.
"Break the rules" and apply the patch at thirdparty.Client.
Import get_thirdparty_client in the test, use patch.object on it, and set its return_value to another MagicMock that I create separately, and this second mock would stand in for thirdparty.Client. This makes for more verbose testing code that can't easily be applied as a single decorator.
None of these options sounds particularly appealing, but I don't know which is considered the least bad.
Or is there another option available to me that I am not seeing?
The correct answer is 2: apply the patch to thirdparty.Client.
This is correct because it does NOT in fact break the "patch where it is used" rule.
The rule is intended to be descriptive, not literal. In this case, thirdparty.Client is considered to be "used" in the thirdparty module.
This concept is described in more detail in the 2018 PyCon talk "Demystifying the Patch Function" by Lisa Roach. The full recording is available on YouTube here: https://youtu.be/ww1UsGZV8fQ?t=537. The explanation of this particular case starts at approximately 9 minutes into the video.
I haven't been able to found a good explanation of this in the net, I'm guessing that i'm missing something trivial but I haven't been able to find it, so I came here to ask the experts :)
I have a test were I need to patch a constructor call, reading the docs as I understand, something like this should work, but:
import unittest.mock as mocker
import some_module
mock_class1 = mocker.patch('some_module.some_class')
print(mock_class1 is some_module.some_class) # Returns False
print(mock_class1) # <unittest.mock._patch>
mock_instance1 = mock_class1.return_value # _patch object has no attr return_value
Instead I get a different output if I do this
with mocker.patch('some_module.some_class') as mock_class2:
print(mock_class2 is some_module.some_class) # Returns True
print(mock_class2) # <MagicMock name=...>
mock_instance2 = mock_class2.return_value # No problem
print(mock_instance2) # <NonCallableMagicMock name=...>
Now, for the test itself, i'm using pytest-mock module which gives a mocker fixture that behaves like the first code block.
I would like to know:
why the behavior differs depending on the way that one call the mock framework
is there a clean way to trigger the behavior of the second code block without the with clause?
1) pytest mocker plugin is being developer to avoid use of context managers; and probably not everybody fond of the way the standard mock plays with functions parameter 2) not really. It is intended to be used either as content manager or function decorator.
I think it is possible use mocker package without pytest
References
https://github.com/pytest-dev/pytest-mock
https://www.packtpub.com/mapt/book/application_development/9781847198846/5/ch05lvl1sec45/integrating-with-python-mocker
What about installing pytest-mock and creating a test like this
import itertools
def test1(mocker):
mock_class1 = mocker.patch('itertools.count')
print(mock_class1 is itertools.count)
print(mock_class1)
mock_instance1 = mock_class1.return_value # Magic staff...
or may be using monkeypatching? Just do not use the standard unittest.mock with pytest
So let's say I have this bit of code:
import coolObject
def doSomething():
x = coolObject()
x.coolOperation()
Now it's a simple enough method, and as you can see we are using an external library(coolObject).
In unit tests, I have to create a mock of this object that roughly replicates it. Let's call this mock object coolMock.
My question is how would I tell the code when to use coolMock or coolObject? I've looked it up online, and a few people have suggested dependency injection, but I'm not sure I understand it correctly.
Thanks in advance!
def doSomething(cool_object=None):
cool_object = cool_object or coolObject()
...
In you test:
def test_do_something(self):
cool_mock = mock.create_autospec(coolObject, ...)
cool_mock.coolOperation.side_effect = ...
doSomthing(cool_object=cool_mock)
...
self.assertEqual(cool_mock.coolOperation.call_count, ...)
As Dan's answer says, one option is to use dependency injection: have the function accept an optional argument, if it's not passed in use the default class, so that a test can pass in a moc.
Another option is to use the mock library (here or here) to replace your coolObject.
Let's say you have a foo.py that looks like
from somewhere.else import coolObject
def doSomething():
x = coolObject()
x.coolOperation()
In your test_foo.py you can do:
import mock
def test_thing():
path = 'foo.coolObject' # The fully-qualified path to the module, class, function, whatever you want to mock.
with mock.patch('foo.coolObject') as m:
doSomething()
# Whatever you want to assert here.
assert m.called
The path you use can include properties on objects, e.g. module1.module2.MyClass.my_class_method. A big gotcha is that you need to mock the object in the module being tested, not where it is defined. In the example above, that means using a path of foo.coolObject and not somwhere.else.coolObject.