[pylint consider-using-any-or-all] Fix or disable all detected issues (…

…#12954)

* Avoid constructing a list of names
* noqa a a suggestion in already nested for

AUTHORS

@@ -256,6 +256,7 @@ lovetheguitar
 Lukas Bednar
 Luke Murphy
 Maciek Fijalkowski
+Maggie Chung
 Maho
 Maik Figura
 Mandeep Bhutani

changelog/12842.doc.rst

@@ -0,0 +1,2 @@
+Added dedicated page about using types with pytest
+See :ref:`types` for detailed usage.

doc/en/how-to/types.rst

@@ -0,0 +1,137 @@
+.. _types:
+
+How to use Types with Pytest
+===========================================
+
+You can add typing in pytest functions that helps to specify what type of data a function expects and returns.
+This guide explains how to apply typing in pytest to improve code readability and prevent type-related errors.
+
+
+1. Introduction to Typing
+-----------------
+
+Typing in Python helps developers specify the expected data types of variables, function parameters, and return values.
+It improves code clarity and prevents type errors.
+
+For example:
+
+
+.. code-block:: python
+    def add(x: int, y: int) -> int:
+        return x + y
+
+In this example:
+`x: int` and `y: int` mean that `x` and `y` should be integers.
+`-> int` shows that the function returns an integer.
+
+It makes the intentions clear that both the input and output are expected to be integers.
+
+2. Typing Test Functions
+-----------------
+Test functions in pytest check whether the code runs correctly.
+While test functions do not return values, we can add `-> None` as the return type.
+
+For example:
+
+.. code-block:: python
+    import pytest
+
+
+    def test_add() -> None:
+        result = add(2, 3)
+        assert result == 5
+
+In this function:
+`test_add` is typed as `-> None` because it does not return anything.
+The assertion `assert result == 5` checks if the result is correct.
+
+Example result:
+If the input data type is incorrect, like passing `add("2", 3)`, a `TypeError` will occur.
+
+3. Typing Fixtures
+-----------------
+Fixtures in pytest helps set up data or provides resources needed for tests.
+Adding type hints helps clarify what kind of data the fixtures returns, making code easier to read and easier to debug.
+
+* Basic Fixture Typing:
+
+If a fixture returns a number, you can specify it returns an `int`:
+
+.. code-block:: python
+    import pytest
+
+
+    @pytest.fixture
+    def sample_fixture() -> int:
+        return 38
+
+
+    def test_sample_fixture(sample_fixture: int) -> None:
+        assert sample_fixture == 38
+
+In this example:
+`sample_fixture()` is typed to return an `int`.
+In `test_sample_fixture`, using typing with fixtures, it ensures the return is an integer.
+If you change the return from an integer to a string (``"42"``), the test will fail.
+
+
+* Typing Fixtures with Lists and Dictionaries:
+Here are the examples showing how to use List and Dict types in pytest.
+When you want to use complex data structures like lists or dictionaries, import `List` and `Dict` from Python's `typing` module to specify the types.
+Note: From Python 3.5 or later, typing module is built-in module in Python.
+
+
+.. code-block:: python
+    from typing import List
+    import pytest
+
+
+    # Fixture returning a list of integers
+    @pytest.fixture
+    def sample_list() -> List[int]:
+        return [5, 10, 15]
+
+
+    def test_sample_list(sample_list: List[int]) -> None:
+        assert sum(sample_list) == 30
+In this example, `List[int]` ensures that the list contains only integers, allowing functions like sum() to operate.
+
+
+.. code-block:: python
+    from typing import Dict
+    import pytest
+
+
+    # Fixture returning a dictionary
+    @pytest.fixture
+    def sample_dict() -> Dict[str, int]:
+        return {"a": 50, "b": 100}
+
+
+    def test_sample_dict(sample_dict: Dict[str, int]) -> None:
+        assert sample_dict["a"] == 50
+In this example, `Dict[str, int]` ensures that each key is a string and each value is an integer, ensuring clarity and consistency when accessing dictionary elements by key.
+
+
+4. Typing Parameterized Tests
+----------
+`@pytest.mark.parametrize` allows developer to run the test multiple times with different sets of arguments.
+Adding types helps to maintain consistency of values, especially for complex inputs.
+
+For example, you are testing if adding 1 to `input_value` results in `expected_output` for each set of arguments.
+
+.. code-block:: python
+    import pytest
+
+
+    @pytest.mark.parametrize("input_value, expected_output", [(1, 2), (5, 6), (100, 101)])
+    def test_increment(input_value: int, expected_output: int) -> None:
+        assert input_value + 1 == expected_output
+
+In this example:
+You are expecting the type of both `input_value` and `expected_output` are integers.
+
+
+Conclusion
+----------
+Using typing in pytest helps improve readability of the tests and fixture returns, and prevent errors.

pyproject.toml

@@ -213,7 +213,6 @@ disable = [
     "condition-evals-to-constant",
     "consider-alternative-union-syntax",
     "confusing-consecutive-elif",
-    "consider-using-any-or-all",
     "consider-using-assignment-expr",
     "consider-using-dict-items",
     "consider-using-from-import",

src/_pytest/mark/__init__.py

@@ -193,12 +193,7 @@ def __call__(self, subname: str, /, **kwargs: str | int | bool | None) -> bool:
         if kwargs:
             raise UsageError("Keyword expressions do not support call parameters.")
         subname = subname.lower()
-        names = (name.lower() for name in self._names)
-
-        for name in names:
-            if subname in name:
-                return True
-        return False
+        return any(subname in name.lower() for name in self._names)
 
 
 def deselect_by_keyword(items: list[Item], config: Config) -> None:
@@ -243,10 +238,9 @@ def __call__(self, name: str, /, **kwargs: str | int | bool | None) -> bool:
         if not (matches := self.own_mark_name_mapping.get(name, [])):
             return False
 
-        for mark in matches:
+        for mark in matches:  # pylint: disable=consider-using-any-or-all
             if all(mark.kwargs.get(k, NOT_SET) == v for k, v in kwargs.items()):
                 return True
-
         return False