ydb/contrib/python/pytest-lazy-fixtures/.dist-info/METADATA

Metadata-Version: 2.1
Name: pytest-lazy-fixtures
Version: 1.1.2
Summary: Allows you to use fixtures in @pytest.mark.parametrize.
Home-page: https://github.com/dev-petrov/pytest-lazy-fixtures
License: MIT
Keywords: tests,pytest,lazy,fixture
Author: Petrov Anton
Author-email: antonp2@yandex.ru
Requires-Python: >=3.8,<4.0
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Dist: pytest (>=7)
Project-URL: Repository, https://github.com/dev-petrov/pytest-lazy-fixtures
Description-Content-Type: text/markdown

# pytest-lazy-fixtures

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Use your fixtures in `@pytest.mark.parametrize`.

This project was inspired by [pytest-lazy-fixture](https://github.com/TvoroG/pytest-lazy-fixture).

Improvements that have been made in this project:

1. You can use fixtures in any data structures
2. You can access the attributes of fixtures
3. You can use functions in fixtures

## Installation

```shell
pip install pytest-lazy-fixtures
```

## Usage

To use your fixtures inside `@pytest.mark.parametrize` you can use `lf` (`lazy_fixture`).

```python
import pytest
from pytest_lazy_fixtures import lf

@pytest.fixture()
def one():
    return 1

@pytest.mark.parametrize('arg1,arg2', [('val1', lf('one'))])
def test_func(arg1, arg2):
    assert arg2 == 1
```

`lf` can be used with any data structures. For example, in the following example, `lf` is used in the dictionary:

```python
import pytest
from pytest_lazy_fixtures import lf

@pytest.fixture()
def one():
    return 1

@pytest.mark.parametrize('arg1,arg2', [('val1', {"value": lf('one')})])
def test_func(arg1, arg2):
    assert arg2 == {"value": 1}
```

You can also specify getting an attribute through a dot:

```python
import pytest
from pytest_lazy_fixtures import lf

class Entity:
    def __init__(self, value):
        self.value = value

@pytest.fixture()
def one():
    return Entity(1)

@pytest.mark.parametrize('arg1,arg2', [('val1', lf('one.value'))])
def test_func(arg1, arg2):
    assert arg2 == 1
```

And there is some useful wrapper called `lfc` (`lazy_fixture_callable`).
It can work with any callable and your fixtures, e.g.

```python
import pytest
from pytest_lazy_fixtures import lf, lfc

class Entity:
    def __init__(self, value):
        self.value = value

    def __str__(self) -> str:
        return str(self.value)

    def sum(self, value: int) -> int:
        return self.value + value

@pytest.fixture()
def entity():
    return Entity(1)

@pytest.fixture()
def two():
    return 2

@pytest.fixture()
def three():
    return 3

@pytest.fixture()
def entity_format():
    def _entity_format(entity: Entity):
        return {"value": entity.value}

    return _entity_format

@pytest.mark.parametrize(
    "message",
    [
        lfc(
            "There is two lazy fixture args, {} and {}! And one kwarg {field}! And also simple value {simple}".format,
            lf("entity"),
            lf("two"),
            field=lf("three"),
            simple="value",
        ),
    ],
)
def test_lazy_fixture_callable_with_func(message):
    assert message == "There is two lazy fixture args, 1 and 2! And one kwarg 3! And also simple value value"


@pytest.mark.parametrize("formatted", [lfc("entity_format", lf("entity"))])
def test_lazy_fixture_callable_with_lf(formatted, entity):
    assert formatted == {"value": entity.value}


@pytest.mark.parametrize("result", [lfc("entity.sum", lf("two"))])
def test_lazy_fixture_callable_with_attr_lf(result):
    assert result == 3
```

## Contributing

Contributions are very welcome. Tests can be run with `pytest`.

## License

Distributed under the terms of the `MIT` license, `pytest-lazy-fixtures` is free and open source software

## Issues

If you encounter any problems, please file an issue along with a detailed description.