Merge pull request #4625 from HypothesisWorks/create-pull-request/patch

Update pinned dependencies

Author: Zac-HD

hypothesis-python/RELEASE.rst

@@ -0,0 +1,3 @@
+RELEASE_TYPE: patch
+
+Fixes :ref:`Ghostwriter <ghostwriter>` output for :pypi:`numpy` >= 2.4.0. Also adds support |st.from_type| for :pypi:`numpy` 2.5.0 nightly (which has not yet been released).

hypothesis-python/docs/changelog.rst

@@ -4347,7 +4347,7 @@ Thanks to Felix Divo for the new feature!
 -------------------
 
 This patch fixes invalid annotations detected for the tests generated by
-:ref:`Ghostwritter <ghostwriter>`. It will now correctly generate ``Optional``
+:ref:`Ghostwriter <ghostwriter>`. It will now correctly generate ``Optional``
 types with just one type argument and handle union expressions inside of type
 arguments correctly. Additionally, it now supports code with the
 ``from __future__ import annotations`` marker for Python 3.10 and newer.
@@ -4381,7 +4381,7 @@ For now, we capture calls made via :func:`~hypothesis.strategies.builds`, and vi
 6.64.0 - 2023-01-23
 -------------------
 
-The :ref:`Ghostwritter <ghostwriter>` will now include type annotations on tests
+The :ref:`Ghostwriter <ghostwriter>` will now include type annotations on tests
 for type-annotated code.  If you want to force this to happen (or not happen),
 pass a boolean to the new ``annotate=`` argument to the Python functions, or
 the ``--[no-]annotate`` CLI flag.
@@ -4924,7 +4924,7 @@ This PR was kindly supported by `Ordina Pythoneers
 6.47.0 - 2022-06-07
 -------------------
 
-The :ref:`Ghostwritter <ghostwriter>` can now write tests for
+The :ref:`Ghostwriter <ghostwriter>` can now write tests for
 :obj:`@classmethod <classmethod>` or :obj:`@staticmethod <staticmethod>`
 methods, in addition to the existing support for functions and other callables
 (:issue:`3318`).  Thanks to Cheuk Ting Ho for the patch.

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

@@ -491,11 +491,16 @@ def _get_params(func: Callable) -> dict[str, inspect.Parameter]:
             # we're out of ideas and should just re-raise the exception.
             raise
     else:
-        # If the params we got look like an uninformative placeholder, try fallbacks.
         P = inspect.Parameter
         placeholder = [("args", P.VAR_POSITIONAL), ("kwargs", P.VAR_KEYWORD)]
-        if [(p.name, p.kind) for p in params] == placeholder:
-            params = _get_params_ufunc(func) or _get_params_builtin_fn(func) or params
+        if ufunc_params := _get_params_ufunc(func):
+            # If func is a ufunc, prefer _get_params_ufunc over get_signature,
+            # as the latter includes keyword arguments we aren't well-equipped
+            # to ghostwrite.
+            params = ufunc_params
+        elif [(p.name, p.kind) for p in params] == placeholder:
+            # If the params we got look like an uninformative placeholder, try fallbacks.
+            params = _get_params_builtin_fn(func) or params
     return _params_to_dict(params)
 
 

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

@@ -1433,7 +1433,9 @@ def _from_type(thing: type[Ex]) -> st.SearchStrategy[Ex] | None:
             st.recursive(st.tuples(base_strat, base_strat), st.tuples),
         )
 
-    if origin in [np.ndarray, _SupportsArray]:
+    # note: get_origin(np.typing.NDArray[np.int64]) is np.ndarray in numpy < 2.5.0,
+    # but is np.typing.NDArray in numpy >= 2.5.0. Support both here.
+    if origin in [np.typing.NDArray, np.ndarray, _SupportsArray]:
         dtype = _dtype_from_args(args)
         return arrays(dtype, array_shapes(max_dims=2))  # type: ignore[return-value]
 

hypothesis-python/src/hypothesis/strategies/_internal/core.py

@@ -1585,7 +1585,7 @@ def _get_typeddict_qualifiers(key, annotation_type):
                 f"{from_type_repr} resolved to {builds_repr}, because we could not "
                 "find any (non-varargs) arguments. Use st.register_type_strategy() "
                 "to resolve to a strategy which can generate more than one value, "
-                "or silence this warning.",
+                "or to silence this warning.",
                 SmallSearchSpaceWarning,
                 stacklevel=2,
             )

hypothesis-python/tests/numpy/test_floor_ceil.py

@@ -9,7 +9,6 @@
 # obtain one at https://mozilla.org/MPL/2.0/.
 
 import math
-import warnings
 
 import numpy as np
 import pytest
@@ -33,22 +32,16 @@
     ],
 )
 def test_our_floor_and_ceil_avoid_numpy_rounding(value):
-    a = np.array([eval(value)])
-
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore", DeprecationWarning)
-        # See https://numpy.org/doc/stable/release/1.25.0-notes.html#deprecations
-        f = floor(a)
-        c = ceil(a)
+    a = np.array(eval(value))
+    f = floor(a)
+    c = ceil(a)
 
     # Check *exact* type - we don't want to allow a subclass of int here
     assert type(f) == int
     assert type(c) == int
 
     # Using math.floor or math.ceil for these values would give an incorrect result.
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore", DeprecationWarning)
-        assert (math.floor(a) > a) or (math.ceil(a) < a)
+    assert (math.floor(a) > a) or (math.ceil(a) < a)
 
     assert f <= a <= c
     assert f + 1 > a > c - 1

hypothesis-python/tests/numpy/test_from_type.py

@@ -9,13 +9,14 @@
 # obtain one at https://mozilla.org/MPL/2.0/.
 
 import typing
+import warnings
 
 import numpy as np
 import pytest
 
-from hypothesis import given
+from hypothesis import given, strategies as st
+from hypothesis.errors import SmallSearchSpaceWarning
 from hypothesis.extra.numpy import ArrayLike, NDArray, _NestedSequence, _SupportsArray
-from hypothesis.strategies import builds, from_type
 
 from .test_from_dtype import STANDARD_TYPES
 from tests.common.debug import assert_simple_property, find_any
@@ -26,29 +27,37 @@
 needs_np_private_typing = {"reason": "numpy._typing is not available"}
 
 
-@given(dtype=from_type(np.dtype))
+@given(dtype=st.from_type(np.dtype))
 def test_resolves_dtype_type(dtype):
     assert isinstance(dtype, np.dtype)
 
 
-@pytest.mark.parametrize("typ", [np.object_, np.void])
-def test_does_not_resolve_nonscalar_types(typ):
-    # Comparing the objects directly fails on Windows,
-    # so compare their reprs instead.
-    assert repr(from_type(typ)) == repr(builds(typ))
+def test_does_not_resolve_nonscalar_types():
+    # this was previously a parametrized test over np.object_ and np.void which
+    # used the same repr code path for the test. But then numpy changed their types
+    # such that we defer evaluation for st.from_type and are no longer identical
+    # to st.builds, but rather something morally equivalent to it. So we have
+    # these slightly more complicated checks.
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", SmallSearchSpaceWarning)
+        assert_simple_property(st.from_type(np.object_), lambda value: value is None)
+
+    with pytest.raises(TypeError):
+        # np.void() requires an argument, and so throws when instantiated
+        assert_simple_property(st.from_type(np.void))
 
 
 @pytest.mark.parametrize("typ", STANDARD_TYPES_TYPE)
 def test_resolves_and_varies_numpy_scalar_type(typ):
     # Check that we find an instance that is not equal to the default
-    x = find_any(from_type(typ), lambda x: x != type(x)())
+    x = find_any(st.from_type(typ), lambda x: x != type(x)())
     assert isinstance(x, typ)
 
 
 @pytest.mark.parametrize("atype", [np.ndarray, NDArray])
 def test_resolves_unspecified_array_type(atype):
     if atype is not None:
-        assert_simple_property(from_type(atype), lambda v: isinstance(v, np.ndarray))
+        assert_simple_property(st.from_type(atype), lambda v: isinstance(v, np.ndarray))
 
 
 def workaround(dtype):
@@ -65,12 +74,12 @@ def workaround(dtype):
 @pytest.mark.parametrize("typ", [workaround(t) for t in STANDARD_TYPES_TYPE])
 def test_resolves_specified_ndarray_type(typ):
     assert_simple_property(
-        from_type(np.ndarray[typ]),
+        st.from_type(np.ndarray[typ]),
         lambda arr: isinstance(arr, np.ndarray) and arr.dtype.type == typ,
     )
 
     assert_simple_property(
-        from_type(np.ndarray[typing.Any, typ]),
+        st.from_type(np.ndarray[typing.Any, typ]),
         lambda arr: isinstance(arr, np.ndarray) and arr.dtype.type == typ,
     )
 
@@ -79,20 +88,20 @@ def test_resolves_specified_ndarray_type(typ):
 @pytest.mark.parametrize("typ", [workaround(t) for t in STANDARD_TYPES_TYPE])
 def test_resolves_specified_NDArray_type(typ):
     assert_simple_property(
-        from_type(NDArray[typ]),
+        st.from_type(NDArray[typ]),
         lambda arr: isinstance(arr, np.ndarray) and arr.dtype.type == typ,
     )
 
 
 @pytest.mark.skipif(NDArray is None, **needs_np_typing)
 def test_resolves_NDArray_with_dtype_union():
-    strat = from_type(NDArray[np.float64 | np.complex128])
+    strat = st.from_type(NDArray[np.float64 | np.complex128])
     find_any(strat, lambda arr: arr.dtype == np.dtype("float64"))
     find_any(strat, lambda arr: arr.dtype == np.dtype("complex128"))
 
 
 @pytest.mark.skipif(ArrayLike is None, **needs_np_typing)
-@given(arr_like=from_type(ArrayLike))
+@given(arr_like=st.from_type(ArrayLike))
 def test_resolves_ArrayLike_type(arr_like):
     arr = np.array(arr_like)
     assert isinstance(arr, np.ndarray)
@@ -103,7 +112,7 @@ def test_resolves_ArrayLike_type(arr_like):
 
 @pytest.mark.skipif(_NestedSequence is None, **needs_np_private_typing)
 def test_resolves_specified_NestedSequence():
-    @given(seq=from_type(_NestedSequence[int]))
+    @given(seq=st.from_type(_NestedSequence[int]))
     def test(seq):
         assert hasattr(seq, "__iter__")
 
@@ -120,20 +129,20 @@ def flatten(lst):
 
 
 @pytest.mark.skipif(_NestedSequence is None, **needs_np_private_typing)
-@given(seq=from_type(_NestedSequence))
+@given(seq=st.from_type(_NestedSequence))
 def test_resolves_unspecified_NestedSequence(seq):
     assert hasattr(seq, "__iter__")
 
 
 @pytest.mark.skipif(_SupportsArray is None, **needs_np_private_typing)
-@given(arr=from_type(_SupportsArray))
+@given(arr=st.from_type(_SupportsArray))
 def test_resolves_unspecified_SupportsArray(arr):
     assert hasattr(arr, "__array__")
 
 
 @pytest.mark.skipif(_SupportsArray is None, **needs_np_private_typing)
 def test_resolves_SupportsArray():
-    @given(arr=from_type(_SupportsArray[int]))
+    @given(arr=st.from_type(_SupportsArray[int]))
     def test(arr):
         assert hasattr(arr, "__array__")
         assert np.asarray(arr).dtype.kind == "i"
@@ -162,7 +171,7 @@ def test_resolve_ArrayLike_equivalent():
         ]
     )
 
-    @given(arr_like=from_type(ArrayLike_like))
+    @given(arr_like=st.from_type(ArrayLike_like))
     def test(arr_like):
         arr = np.array(arr_like)
         assert isinstance(arr, np.ndarray)

hypothesis-python/tox.ini

@@ -76,7 +76,7 @@ allowlist_externals =
 commands_pre =
     bash -c "pip install --upgrade --pre --only-binary :all: -i https://pypi.anaconda.org/scientific-python-nightly-wheels/simple numpy"
 commands =
-    python -bb -X dev -m pytest tests/numpy/ tests/array_api/ tests/pandas/ tests/ghostwriter/ tests/conjecture/ tests/cover -n auto
+    python -bb -X dev -m pytest tests/numpy/ tests/array_api/ tests/pandas/ tests/ghostwriter/ tests/conjecture/ tests/cover -n auto {posargs}
 
 # Note: when adding or removing tested Pandas versions, make sure to update the
 # docs in numpy.rst too.  To see current download rates of each minor version:

requirements/crosshair.txt

@@ -25,11 +25,11 @@ exceptiongroup==1.3.1 ; python_version < "3.11"
     #   pytest
 execnet==2.1.2
     # via pytest-xdist
-hypothesis==6.148.7
+hypothesis==6.148.8
     # via hypothesis-crosshair
 hypothesis-crosshair==0.0.27
     # via -r requirements/crosshair.in
-importlib-metadata==8.7.0
+importlib-metadata==8.7.1
     # via crosshair-tool
 importlib-resources==6.5.2
     # via typeshed-client

requirements/fuzzing.txt

@@ -57,7 +57,7 @@ hyperframe==6.1.0
     # via h2
 hypofuzz==25.11.1
     # via -r requirements/fuzzing.in
-hypothesis[cli,watchdog]==6.148.7
+hypothesis[cli,watchdog]==6.148.8
     # via hypofuzz
 idna==3.11
     # via
@@ -101,7 +101,7 @@ pluggy==1.6.0
     #   pytest-cov
 priority==2.0.0
     # via hypercorn
-psutil==7.1.3
+psutil==7.2.0
     # via hypofuzz
 ptyprocess==0.7.0
     # via pexpect