Wh.VUdZddlmZddlZddlmZmZmZmZddl m Z ddl m Z ddl mZmZmZmZmZdd lmZmZd d gZerddlZdd lmZmZmZdd lmZneZddZedZedZ iZ!de"d<ddddd ddZ# ddZ$GddeZ% ddZ&y)z] Public testing utilities. See also _lib._testing for additional private testing utilities. ) annotationsN)CallableIterableIteratorSequence)wraps) ModuleType) TYPE_CHECKINGAny ParamSpecTypeVarcast)is_dask_namespaceis_jax_namespacelazy_xp_functionpatch_lazy_xp_functions)GraphKeySchedulerGetCallable)overridec|SN)funcs c/var/www/html/jupyter_env/lib/python3.12/site-packages/sklearn/externals/array_api_extra/testing.pyrrs PTzdict[object, dict[str, Any]] _ufuncs_tagsTallow_dask_computejax_jitstatic_argnumsstatic_argnamescR||||d} ||_y#t$r |t|<YywxYw)a_ Tag a function to be tested on lazy backends. Tag a function so that when any tests are executed with ``xp=jax.numpy`` the function is replaced with a jitted version of itself, and when it is executed with ``xp=dask.array`` the function will raise if it attempts to materialize the graph. This will be later expanded to provide test coverage for other lazy backends. In order for the tag to be effective, the test or a fixture must call :func:`patch_lazy_xp_functions`. Parameters ---------- func : callable Function to be tested. allow_dask_compute : int, optional Number of times `func` is allowed to internally materialize the Dask graph. This is typically triggered by ``bool()``, ``float()``, or ``np.asarray()``. Set to 1 if you are aware that `func` converts the input parameters to NumPy and want to let it do so at least for the time being, knowing that it is going to be extremely detrimental for performance. If a test needs values higher than 1 to pass, it is a canary that the conversion to NumPy/bool/float is happening multiple times, which translates to multiple computations of the whole graph. Short of making the function fully lazy, you should at least add explicit calls to ``np.asarray()`` early in the function. *Note:* the counter of `allow_dask_compute` resets after each call to `func`, so a test function that invokes `func` multiple times should still work with this parameter set to 1. Default: 0, meaning that `func` must be fully lazy and never materialize the graph. jax_jit : bool, optional Set to True to replace `func` with ``jax.jit(func)`` after calling the :func:`patch_lazy_xp_functions` test helper with ``xp=jax.numpy``. Set to False if `func` is only compatible with eager (non-jitted) JAX. Default: True. static_argnums : int | Sequence[int], optional Passed to jax.jit. Positional arguments to treat as static (compile-time constant). Default: infer from `static_argnames` using `inspect.signature(func)`. static_argnames : str | Iterable[str], optional Passed to jax.jit. Named arguments to treat as static (compile-time constant). Default: infer from `static_argnums` using `inspect.signature(func)`. See Also -------- patch_lazy_xp_functions : Companion function to call from the test or fixture. jax.jit : JAX function to compile a function for performance. Examples -------- In ``test_mymodule.py``:: from array_api_extra.testing import lazy_xp_function from mymodule import myfunc lazy_xp_function(myfunc) def test_myfunc(xp): a = xp.asarray([1, 2]) # When xp=jax.numpy, this is the same as `b = jax.jit(myfunc)(a)` # When xp=dask.array, crash on compute() or persist() b = myfunc(a) Notes ----- In order for this tag to be effective, the test function must be imported into the test module globals without its namespace; alternatively its namespace must be declared in a ``lazy_xp_modules`` list in the test module globals. Example 1:: from mymodule import myfunc lazy_xp_function(myfunc) def test_myfunc(xp): x = myfunc(xp.asarray([1, 2])) Example 2:: import mymodule lazy_xp_modules = [mymodule] lazy_xp_function(mymodule.myfunc) def test_myfunc(xp): x = mymodule.myfunc(xp.asarray([1, 2])) A test function can circumvent this monkey-patching system by using a namespace outside of the two above patterns. You need to sanitize your code to make sure this only happens intentionally. Example 1:: import mymodule from mymodule import myfunc lazy_xp_function(myfunc) def test_myfunc(xp): a = xp.asarray([1, 2]) b = myfunc(a) # This is wrapped when xp=jax.numpy or xp=dask.array c = mymodule.myfunc(a) # This is not Example 2:: import mymodule class naked: myfunc = mymodule.myfunc lazy_xp_modules = [mymodule] lazy_xp_function(mymodule.myfunc) def test_myfunc(xp): a = xp.asarray([1, 2]) b = mymodule.myfunc(a) # This is wrapped when xp=jax.numpy or xp=dask.array c = naked.myfunc(a) # This is not r!N)_lazy_xp_functionAttributeErrorr )rr"r#r$r%tagss rrr's>B1(*  D "!% "! T"s &&c  tt|j}|gtttt |dg d fd }t |r6|D]+\}}}}|d}t ||} |j||| -yt|r`ddl } |D]Q\}}}}|dsttdtf| j||d|d  } |j||| Syy) a Test lazy execution of functions tagged with :func:`lazy_xp_function`. If ``xp==jax.numpy``, search for all functions which have been tagged with :func:`lazy_xp_function` in the globals of the module that defines the current test, as well as in the ``lazy_xp_modules`` list in the globals of the same module, and wrap them with :func:`jax.jit`. Unwrap them at the end of the test. If ``xp==dask.array``, wrap the functions with a decorator that disables ``compute()`` and ``persist()`` and ensures that exceptions and warnings are raised eagerly. This function should be typically called by your library's `xp` fixture that runs tests on multiple backends:: @pytest.fixture(params=[numpy, array_api_strict, jax.numpy, dask.array]) def xp(request, monkeypatch): patch_lazy_xp_functions(request, monkeypatch, xp=request.param) return request.param but it can be otherwise be called by the test itself too. Parameters ---------- request : pytest.FixtureRequest Pytest fixture, as acquired by the test itself or by one of its fixtures. monkeypatch : pytest.MonkeyPatch Pytest fixture, as acquired by the test itself or by one of its fixtures. xp : array_namespace Array namespace to be tested. See Also -------- lazy_xp_function : Tag a function to be tested on lazy backends. pytest.FixtureRequest : `request` test function parameter. lazy_xp_modulesc3jKD]}|jjD]r\}}d}tjt5|j }ddd|0tjt t5t|}ddd|k||||fty#1swYKxYw#1swY%xYwwr) __dict__items contextlibsuppressr(r'KeyError TypeErrorr )modnamerr)modss r iter_taggedz,patch_lazy_xp_functions..iter_taggeds 0C!ll002 0 d.2((8211D2<#,,XyA2+D12#tT4// 0 02222s<AB3 B(B39 B' B3 B3B$ B3'B0 ,B3r"rNr#.r$r%)r$r%)returnzDIterator[tuple[ModuleType, str, Callable[..., Any], dict[str, Any]]]) rr modulelistgetattrr _dask_wrapsetattrrjaxrr jit) request monkeypatchxpr3r6r4rr)nwrappedr=r5s @rrrsN z7>> *C  N$tJ'6G)LM ND 0L 0%0] 4 !CtT)*A q)G   T7 3 4 " %0] 8 !CtTIS#X&GG'+,<'=(,->(?##Cw7 8 rcJeZdZUdZded<ded<ded<d dZed dZy ) CountingDaskSchedulera Dask scheduler that counts how many times `dask.compute` is called. If the number of times exceeds 'max_count', it raises an error. This is a wrapper around Dask's own 'synchronous' scheduler. Parameters ---------- max_count : int Maximum number of allowed calls to `dask.compute`. msg : str Assertion to raise when the count exceeds `max_count`. intcount max_countstrmsgc.d|_||_||_y)Nr)rGrHrJ)selfrHrJs r__init__zCountingDaskScheduler.__init__s "rc ddl}|xjdz c_|j|jksJ|j|j||fi|S)Nrr)daskrGrHrJget)rLdskkeyskwargsrOs r__call__zCountingDaskScheduler.__call__sK a zzT^^+5TXX5+txxT,V,,rN)rHrFrJrI)rQrrRzSequence[Key] | KeyrSr r7r )__name__ __module__ __qualname____doc____annotations__rMrrTrrrrErEs1  JN H --rrEc ddltdt}rdnd}ddzd|d |d dzd td fd }|S)z Wrap `func` to raise if it attempts to call `dask.compute` more than `n` times. After the function returns, materialize the graph in order to re-raise exceptions. rNrUz only up to noz,Called `dask.compute()` or `dask.persist()` rz times, but z* calls are allowed. Set `lazy_xp_function(z, allow_dask_compute=zA)` to allow for more (but note that this will harm performance). ct}jjd|i5|i|}dddjddS#1swYxYw)N schedulerthreads)r]r)rEconfigsetpersist)argsrSr]outrOrrJrBs rwrapperz_dask_wrap..wrapper9sb)!S1 [[__k95 6 (''C ( ||C9|5a88  ( (s AA)rbzP.argsrSzP.kwargsr7r)rOr:rIr)rrB func_namen_strrdrOrJs`` @@rr;r;&sj#d)4I!"k! E 6q1ug>g&K'rus #BB??D 8 9<<*"cN CL-/ */  1526 I" I"I" I" / I" 0 I" I"XL8 "L81CL8LVL8 L8^!-0!-H  r