Merge branch 'master' of github.com:numpy/numpy into einsum-dot

7 files changed, 49 insertions, 23 deletions

diff --git a/numpy/typing/_array_like.py b/numpy/typing/_array_like.py
index a1a604239..63b67b33a 100644
--- a/numpy/typing/_array_like.py
+++ b/numpy/typing/_array_like.py
@@ -1,7 +1,9 @@
+from __future__ import annotations
+
 import sys
-from typing import Any, overload, Sequence, TYPE_CHECKING, Union
+from typing import Any, overload, Sequence, TYPE_CHECKING, Union, TypeVar
 
-from numpy import ndarray
+from numpy import ndarray, dtype
 from ._scalars import _ScalarLike
 from ._dtype_like import DTypeLike
 
@@ -16,12 +18,15 @@ else:
     else:
         HAVE_PROTOCOL = True
 
+_DType = TypeVar("_DType", bound="dtype[Any]")
+
 if TYPE_CHECKING or HAVE_PROTOCOL:
-    class _SupportsArray(Protocol):
-        @overload
-        def __array__(self, __dtype: DTypeLike = ...) -> ndarray: ...
-        @overload
-        def __array__(self, dtype: DTypeLike = ...) -> ndarray: ...
+    # The `_SupportsArray` protocol only cares about the default dtype
+    # (i.e. `dtype=None`) of the to-be returned array.
+    # Concrete implementations of the protocol are responsible for adding
+    # any and all remaining overloads
+    class _SupportsArray(Protocol[_DType]):
+        def __array__(self, dtype: None = ...) -> ndarray[Any, _DType]: ...
 else:
     _SupportsArray = Any
 
@@ -36,5 +41,5 @@ ArrayLike = Union[
     _ScalarLike,
     Sequence[_ScalarLike],
     Sequence[Sequence[Any]],  # TODO: Wait for support for recursive types
-    _SupportsArray,
+    "_SupportsArray[Any]",
 ]
diff --git a/numpy/typing/tests/data/fail/array_like.py b/numpy/typing/tests/data/fail/array_like.py
index a97e72dc7..3bbd29061 100644
--- a/numpy/typing/tests/data/fail/array_like.py
+++ b/numpy/typing/tests/data/fail/array_like.py
@@ -11,6 +11,6 @@ x2: ArrayLike = A()  # E: Incompatible types in assignment
 x3: ArrayLike = {1: "foo", 2: "bar"}  # E: Incompatible types in assignment
 
 scalar = np.int64(1)
-scalar.__array__(dtype=np.float64)  # E: Unexpected keyword argument
+scalar.__array__(dtype=np.float64)  # E: No overload variant
 array = np.array([1])
-array.__array__(dtype=np.float64)  # E: Unexpected keyword argument
+array.__array__(dtype=np.float64)  # E: No overload variant
diff --git a/numpy/typing/tests/data/pass/array_like.py b/numpy/typing/tests/data/pass/array_like.py
index f85724267..563fc08c7 100644
--- a/numpy/typing/tests/data/pass/array_like.py
+++ b/numpy/typing/tests/data/pass/array_like.py
@@ -25,13 +25,13 @@ class A:
 x13: ArrayLike = A()
 
 scalar: _SupportsArray = np.int64(1)
-scalar.__array__(np.float64)
+scalar.__array__(None)
 array: _SupportsArray = np.array(1)
-array.__array__(np.float64)
+array.__array__(None)
 
 a: _SupportsArray = A()
-a.__array__(np.int64)
-a.__array__(dtype=np.int64)
+a.__array__(None)
+a.__array__(dtype=None)
 
 # Escape hatch for when you mean to make something like an object
 # array.
diff --git a/numpy/typing/tests/data/pass/dtype.py b/numpy/typing/tests/data/pass/dtype.py
index cbae8c078..a97edc302 100644
--- a/numpy/typing/tests/data/pass/dtype.py
+++ b/numpy/typing/tests/data/pass/dtype.py
@@ -1,5 +1,7 @@
 import numpy as np
 
+dtype_obj = np.dtype(np.str_)
+
 np.dtype(dtype=np.int64)
 np.dtype(int)
 np.dtype("int")
@@ -33,3 +35,9 @@ class Test:
 
 
 np.dtype(Test())
+
+# Methods and attributes
+dtype_obj.base
+dtype_obj.subdtype
+dtype_obj.newbyteorder()
+dtype_obj.type
diff --git a/numpy/typing/tests/data/pass/flatiter.py b/numpy/typing/tests/data/pass/flatiter.py
index c0219eb2b..4fdf25299 100644
--- a/numpy/typing/tests/data/pass/flatiter.py
+++ b/numpy/typing/tests/data/pass/flatiter.py
@@ -12,3 +12,5 @@ a[0]
 a[[0, 1, 2]]
 a[...]
 a[:]
+a.__array__()
+a.__array__(np.float64)
diff --git a/numpy/typing/tests/data/reveal/dtype.py b/numpy/typing/tests/data/reveal/dtype.py
index d414f2c49..626a15270 100644
--- a/numpy/typing/tests/data/reveal/dtype.py
+++ b/numpy/typing/tests/data/reveal/dtype.py
@@ -1,5 +1,7 @@
 import numpy as np
 
+dtype_obj: np.dtype[np.str_]
+
 reveal_type(np.dtype(np.float64))  # E: numpy.dtype[numpy.floating[numpy.typing._64Bit]]
 reveal_type(np.dtype(np.int64))  # E: numpy.dtype[numpy.signedinteger[numpy.typing._64Bit]]
 
@@ -31,3 +33,9 @@ reveal_type(np.dtype("S8"))  # E: numpy.dtype
 
 # Void
 reveal_type(np.dtype(("U", 10)))  # E: numpy.dtype[numpy.void]
+
+# Methods and attributes
+reveal_type(dtype_obj.base)  # E: numpy.dtype[numpy.str_]
+reveal_type(dtype_obj.subdtype)  # E: Union[Tuple[numpy.dtype[numpy.str_], builtins.tuple[builtins.int]], None]
+reveal_type(dtype_obj.newbyteorder())  # E: numpy.dtype[numpy.str_]
+reveal_type(dtype_obj.type)  # E: Type[numpy.str_]
diff --git a/numpy/typing/tests/data/reveal/flatiter.py b/numpy/typing/tests/data/reveal/flatiter.py
index 56cdc7a0e..221101ebb 100644
--- a/numpy/typing/tests/data/reveal/flatiter.py
+++ b/numpy/typing/tests/data/reveal/flatiter.py
@@ -1,14 +1,17 @@
+from typing import Any
 import numpy as np
 
-a: "np.flatiter[np.ndarray]"
+a: np.flatiter[np.ndarray[Any, np.dtype[np.str_]]]
 
-reveal_type(a.base)  # E: numpy.ndarray*
-reveal_type(a.copy())  # E: numpy.ndarray*
+reveal_type(a.base)  # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]]
+reveal_type(a.copy())  # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]]
 reveal_type(a.coords)  # E: tuple[builtins.int]
 reveal_type(a.index)  # E: int
-reveal_type(iter(a))  # E: Iterator[numpy.generic*]
-reveal_type(next(a))  # E: numpy.generic
-reveal_type(a[0])  # E: numpy.generic
-reveal_type(a[[0, 1, 2]])  # E: numpy.ndarray*
-reveal_type(a[...])  # E: numpy.ndarray*
-reveal_type(a[:])  # E: numpy.ndarray*
+reveal_type(iter(a))  # E: Iterator[numpy.str_]
+reveal_type(next(a))  # E: numpy.str_
+reveal_type(a[0])  # E: numpy.str_
+reveal_type(a[[0, 1, 2]])  # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]]
+reveal_type(a[...])  # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]]
+reveal_type(a[:])  # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]]
+reveal_type(a.__array__())  # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]]
+reveal_type(a.__array__(np.float64))  # E: numpy.ndarray[Any, numpy.dtype[Any]]