chore(internal): add internal helpers

src/openai/_compat.py

@@ -1,13 +1,15 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, Any, Union, TypeVar, cast
+from typing import TYPE_CHECKING, Any, Union, Generic, TypeVar, Callable, cast, overload
 from datetime import date, datetime
+from typing_extensions import Self
 
 import pydantic
 from pydantic.fields import FieldInfo
 
 from ._types import StrBytesIntFloat
 
+_T = TypeVar("_T")
 _ModelT = TypeVar("_ModelT", bound=pydantic.BaseModel)
 
 # --------------- Pydantic v2 compatibility ---------------
@@ -178,8 +180,43 @@ class GenericModel(pydantic.generics.GenericModel, pydantic.BaseModel):
 # cached properties
 if TYPE_CHECKING:
     cached_property = property
+
+    # we define a separate type (copied from typeshed)
+    # that represents that `cached_property` is `set`able
+    # at runtime, which differs from `@property`.
+    #
+    # this is a separate type as editors likely special case
+    # `@property` and we don't want to cause issues just to have
+    # more helpful internal types.
+
+    class typed_cached_property(Generic[_T]):
+        func: Callable[[Any], _T]
+        attrname: str | None
+
+        def __init__(self, func: Callable[[Any], _T]) -> None:
+            ...
+
+        @overload
+        def __get__(self, instance: None, owner: type[Any] | None = None) -> Self:
+            ...
+
+        @overload
+        def __get__(self, instance: object, owner: type[Any] | None = None) -> _T:
+            ...
+
+        def __get__(self, instance: object, owner: type[Any] | None = None) -> _T | Self:
+            raise NotImplementedError()
+
+        def __set_name__(self, owner: type[Any], name: str) -> None:
+            ...
+
+        # __set__ is not defined at runtime, but @cached_property is designed to be settable
+        def __set__(self, instance: object, value: _T) -> None:
+            ...
 else:
     try:
         from functools import cached_property as cached_property
     except ImportError:
         from cached_property import cached_property as cached_property
+
+    typed_cached_property = cached_property

src/openai/_utils/__init__.py

@@ -1,3 +1,4 @@
+from ._sync import asyncify as asyncify
 from ._proxy import LazyProxy as LazyProxy
 from ._utils import (
     flatten as flatten,

src/openai/_utils/_sync.py

@@ -0,0 +1,64 @@
+from __future__ import annotations
+
+import functools
+from typing import TypeVar, Callable, Awaitable
+from typing_extensions import ParamSpec
+
+import anyio
+import anyio.to_thread
+
+T_Retval = TypeVar("T_Retval")
+T_ParamSpec = ParamSpec("T_ParamSpec")
+
+
+# copied from `asyncer`, https://github.com/tiangolo/asyncer
+def asyncify(
+    function: Callable[T_ParamSpec, T_Retval],
+    *,
+    cancellable: bool = False,
+    limiter: anyio.CapacityLimiter | None = None,
+) -> Callable[T_ParamSpec, Awaitable[T_Retval]]:
+    """
+    Take a blocking function and create an async one that receives the same
+    positional and keyword arguments, and that when called, calls the original function
+    in a worker thread using `anyio.to_thread.run_sync()`. Internally,
+    `asyncer.asyncify()` uses the same `anyio.to_thread.run_sync()`, but it supports
+    keyword arguments additional to positional arguments and it adds better support for
+    autocompletion and inline errors for the arguments of the function called and the
+    return value.
+
+    If the `cancellable` option is enabled and the task waiting for its completion is
+    cancelled, the thread will still run its course but its return value (or any raised
+    exception) will be ignored.
+
+    Use it like this:
+
+    ```Python
+    def do_work(arg1, arg2, kwarg1="", kwarg2="") -> str:
+        # Do work
+        return "Some result"
+
+
+    result = await to_thread.asyncify(do_work)("spam", "ham", kwarg1="a", kwarg2="b")
+    print(result)
+    ```
+
+    ## Arguments
+
+    `function`: a blocking regular callable (e.g. a function)
+    `cancellable`: `True` to allow cancellation of the operation
+    `limiter`: capacity limiter to use to limit the total amount of threads running
+        (if omitted, the default limiter is used)
+
+    ## Return
+
+    An async function that takes the same positional and keyword arguments as the
+    original one, that when called runs the same original function in a thread worker
+    and returns the result.
+    """
+
+    async def wrapper(*args: T_ParamSpec.args, **kwargs: T_ParamSpec.kwargs) -> T_Retval:
+        partial_f = functools.partial(function, *args, **kwargs)
+        return await anyio.to_thread.run_sync(partial_f, cancellable=cancellable, limiter=limiter)
+
+    return wrapper