[TYP] return array dtype is always unkown but it's actually specified as parameter

AUTHORS.rst

@@ -60,6 +60,7 @@ their individual contributions.
 * `Felix Sheldon <https://www.github.com/darkpaw>`_
 * `Florian Bruhin <https://www.github.com/The-Compiler>`_
 * `follower <https://www.github.com/follower>`_
+* `Francesc Elies <https://www.github.com/FrancescElies>`_
 * `Gabe Joseph <https://github.com/gjoseph92>`_
 * `Gary Donovan <https://www.github.com/garyd203>`_
 * `George Macon <https://www.github.com/gmacon>`_

CONTRIBUTING.rst

@@ -8,7 +8,7 @@ First off: It's great that you want to contribute to Hypothesis! Thanks!
 Just tell me how to make a pull request
 ---------------------------------------
 
-1. Make you change and ensure it has adequate tests
+1. Make your change and ensure it has adequate tests
 2. Create ``hypothesis-python/RELEASE.rst`` with ``RELEASE_TYPE: patch``
    for small bugfixes, or ``minor`` for new features.  See recent PRs for examples.
 3. Add yourself to the list in ``AUTHORS.rst`` and open a PR!

hypothesis-python/RELEASE.rst

@@ -0,0 +1,4 @@
+RELEASE_TYPE: patch
+
+This patch fixes type annotations for the :func:`~hypothesis.extra.numpy.arrays`
+strategy.  Thanks to Francesc Elies for :pull:`3602`.

hypothesis-python/src/hypothesis/extra/numpy.py

@@ -9,9 +9,10 @@
 # obtain one at https://mozilla.org/MPL/2.0/.
 
 import math
-from typing import Any, Mapping, Optional, Sequence, Tuple, Union
+from typing import Any, Mapping, Optional, Sequence, Tuple, TypeVar, Union
 
 import numpy as np
+from numpy.typing import DTypeLike, NDArray
 
 from hypothesis import strategies as st
 from hypothesis._settings import note_deprecation
@@ -374,15 +375,18 @@ def fill_for(elements, unique, fill, name=""):
     return fill
 
 
+D = TypeVar("D", bound=DTypeLike)
+
+
 @defines_strategy(force_reusable_values=True)
 def arrays(
-    dtype: Any,
+    dtype: Union[D, st.SearchStrategy[D]],
     shape: Union[int, st.SearchStrategy[int], Shape, st.SearchStrategy[Shape]],
     *,
-    elements: Optional[Union[st.SearchStrategy, Mapping[str, Any]]] = None,
+    elements: Optional[Union[st.SearchStrategy[Any], Mapping[str, Any]]] = None,
     fill: Optional[st.SearchStrategy[Any]] = None,
     unique: bool = False,
-) -> st.SearchStrategy[np.ndarray]:
+) -> st.SearchStrategy[NDArray[D]]:
     r"""Returns a strategy for generating :class:`numpy:numpy.ndarray`\ s.
 
     * ``dtype`` may be any valid input to :class:`~numpy:numpy.dtype`
@@ -460,6 +464,7 @@ def arrays(
         )
     # From here on, we're only dealing with values and it's relatively simple.
     dtype = np.dtype(dtype)
+    assert isinstance(dtype, np.dtype)  # help mypy out a bit...
     if elements is None or isinstance(elements, Mapping):
         if dtype.kind in ("m", "M") and "[" not in dtype.str:
             # For datetime and timedelta dtypes, we have a tricky situation -
@@ -900,8 +905,8 @@ def integer_array_indices(
     shape: Shape,
     *,
     result_shape: st.SearchStrategy[Shape] = array_shapes(),
-    dtype: np.dtype = "int",
-) -> st.SearchStrategy[Tuple[np.ndarray, ...]]:
+    dtype: D = np.int_,
+) -> st.SearchStrategy[Tuple[NDArray[D], ...]]:
     """Return a search strategy for tuples of integer-arrays that, when used
     to index into an array of shape ``shape``, given an array whose shape
     was drawn from ``result_shape``.