0Ph0dZddlZddlZddlZddlZddlZddlZddlmZ ddl Z ddl Z ddl Z ddl Z ddlZddlZddlZddlmZmZddlmZddlmZddlmZddlmZddlmZdd lmZmZdd lm Z m!Z!m"Z"m#Z#ddl$Z$ddl%Z&ddl'Z(dd l)m*Z+dd l)m,Z,m-Z-m.Z.m/Z/ddl0Z0dd l1m2Z2m3Z3m4Z4m5Z5m6Z6ddl7m8Z8ddl9m:Z:m;Z;mZ>ddl?m@Z@ddlAmBZBmCZCmDZDgdZEejFjGZGdeHfdZIGddZJ dGdZ*dHdZKdIdZLdZM e8ddZNn #eO$rdZNYnwxYw ddlPZPePjQRe:d !ZSePjQTe<d"!ZUePjQRe$jVjW d#!ZXePjQReN d$!ZYePjQRe jZd%kd&!Z[ePjQReM d'!Z\n #eO$rYnwxYwd(Z]dJd)Z^Gd*d+Z_dKd-Z`dJd.Zad/ZbdLd0Zcd1Zdd2Zed3Zf dMd4Zg dNd5ZhdOd8Zi dPd9ZjdQd:ZkGd;dZnGd?d@ZoGdAdBZpdCZqdDZrdEZsdFZtdS)RzTesting utilities.N) defaultdict namedtuple)Iterable) dataclass) context_diffwraps) signature)chaingroupby)STDOUTCalledProcessErrorTimeoutExpired check_output)assert_allclose)assert_almost_equalassert_array_almost_equalassert_array_equalassert_array_less)ClassifierTags RegressorTagsTags TargetTagsTransformerTags)_check_array_api_dispatch) _IS_32BITVisibleDeprecationWarning#_in_unstable_openblas_configuration parse_version sp_version)check_classification_targets) check_arraycheck_is_fitted check_X_y)rrrrr'assert_run_python_script_without_outputSkipTestct|tr?t|tr*|j}t d|t|rt||St|S)a Context manager and decorator to ignore warnings. Note: Using this (in both variants) will clear all warnings from all python modules loaded. In case you need to test cross-module-warning-logging, this is not your tool of choice. Parameters ---------- obj : callable, default=None callable where you want to ignore the warnings. category : warning class, default=Warning The category to filter. If Warning, all categories will be muted. Examples -------- >>> import warnings >>> from sklearn.utils._testing import ignore_warnings >>> with ignore_warnings(): ... warnings.warn('buhuhuhu') >>> def nasty_warn(): ... warnings.warn('buhuhuhu') ... print(42) >>> ignore_warnings(nasty_warn)() 42 z'obj' should be a callable where you want to ignore warnings. You passed a warning class instead: 'obj={warning_name}'. If you want to pass a warning class to ignore_warnings, you should use 'category={warning_name}') warning_namecategory) isinstancetype issubclassWarning__name__ ValueErrorformatcallable_IgnoreWarnings)objr*r(s V/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/sklearn/utils/_testing.pyignore_warningsr6Ls8#t 2C!9!9 2|  78>v.wrappers(** + +%h >>>r4*6** + + + + + + + + + + + + + + + + + +s"AA Ar)rBrKrLs`` r5__call__z_IgnoreWarnings.__call__s; r + + + + +  + r7cg}|jr|d|jtjdur|d|jzt |j}|dd|dS)Nz record=Truer:z module=%r(z, ))r;appendr>r<r=r,r/join)rBrInames r5__repr__z_IgnoreWarnings.__repr__s{ < ' KK & & & filters_filters showwarning _showwarningr:rHr*rBs r5 __enter__z_IgnoreWarnings.__enter__sp = ?6=>> >  , #}QQQ/  L4h 66666r7c|jstd|z|j|j_|j|j_g|jdd<dS)Nz%Cannot exit %r without entering first)r?rVrXr>rWrZrYr@)rBexc_infos r5__exit__z_IgnoreWarnings.__exit__sM} OFMNN N#} #'#4   r7N) r/ __module__ __qualname____doc__rCrMrTr\r_r7r5r3r3xsi  !!!   222777r7r3Tc g}tj|tj|}}|j|jg}|d|D}t|}t |||||||dS)audtype-aware variant of numpy.testing.assert_allclose This variant introspects the least precise floating point dtype in the input argument and automatically sets the relative tolerance parameter to 1e-4 float32 and use 1e-7 otherwise (typically float64 in scikit-learn). `atol` is always left to 0. by default. It should be adjusted manually to an assertion-specific value in case there are null values expected in `desired`. The aggregate tolerance is `atol + rtol * abs(desired)`. Parameters ---------- actual : array_like Array obtained. desired : array_like Array desired. rtol : float, optional, default=None Relative tolerance. If None, it is set based on the provided arrays' dtypes. atol : float, optional, default=0. Absolute tolerance. equal_nan : bool, optional, default=True If True, NaNs will compare equal. err_msg : str, optional, default='' The error message to be printed in case of failure. verbose : bool, optional, default=True If True, the conflicting values are appended to the error message. Raises ------ AssertionError If actual and desired are not equal up to specified precision. See Also -------- numpy.testing.assert_allclose Examples -------- >>> import numpy as np >>> from sklearn.utils._testing import assert_allclose >>> x = [1e-5, 1e-3, 1e-1] >>> y = np.arccos(np.cos(x)) >>> assert_allclose(x, y, rtol=1e-5, atol=0) >>> a = np.full(shape=10, fill_value=1e-5, dtype=np.float32) >>> assert_allclose(a, 1e-5) Nc6g|]}|tjkrdndS)g-C6?Hz>)npfloat32).0dtypes r5 z#assert_allclose..s(KKK5"*,,$KKKr7)rtolatol equal_nanerr_msgverbose)ri asanyarrayrlmaxnp_assert_allclose) actualdesiredrnrorprqrrdtypesrtolss r5rrsjFmF++R]7-C-CGFlGM *F |KKFKKK5zz  r7rh& .>ctj|rtj|r|}|}||t |j|j|t |j|j|t|j |j |||dStj|s5tj|st|||||dStd)aAssert allclose for sparse and dense data. Both x and y need to be either sparse or dense, they can't be mixed. Parameters ---------- x : {array-like, sparse matrix} First array to compare. y : {array-like, sparse matrix} Second array to compare. rtol : float, default=1e-07 relative tolerance; see numpy.allclose. atol : float, default=1e-9 absolute tolerance; see numpy.allclose. Note that the default here is more tolerant than the default for numpy.testing.assert_allclose, where atol=0. err_msg : str, default='' Error message to raise. )rq)rnrorqzGCan only compare two sparse matrices, not a sparse matrix and an array.N) spsparseissparsetocsrsum_duplicatesrindicesindptrrdatar0)xyrnrorqs r5assert_allclose_dense_sparsers)2 y! !3!3A!6!6 GGII GGII  19aiAAAA18QXw????TgNNNNNN Y   " " 29+=+=a+@+@ 14dGDDDDDD U   r7cbd|vr||dSdS)a~Set random state of an estimator if it has the `random_state` param. Parameters ---------- estimator : object The estimator. random_state : int, RandomState instance or None, default=0 Pseudo random number generator state. Pass an int for reproducible results across multiple function calls. See :term:`Glossary `. random_state)rN) get_params set_params) estimatorrs r5set_random_stater$s@--////,777770/r7c@ ddl}dS#ttf$rYdSwxYw)NrTF)numpydoc ImportErrorAssertionError)rs r5 _is_numpydocr4s@t  (uus Fzskipped on 32bit platforms)reasonz+OpenBLAS is unstable for this configurationzjoblib is in serial modezErequires array_api_compat installed and a new enough version of NumPydarwinz)Possible multi-process bug with some BLASz+numpydoc is required to test the docstringsc~ttjddrt ddS)NSKLEARN_SKIP_NETWORK_TESTSrz-Text tutorial requires large dataset download)intosenvirongetr&rcr7r5check_skip_networkrqs? 2:>>6 : :;;HFGGGHHr7c tj|rtj|dSdS#t $r |rt jd|zYdSYdSwxYw)zuUtility function to cleanup a temporary folder if still existing. Copy from joblib.pool (for independence). z$Could not delete temporary folder %sN)rpathexistsshutilrmtreeOSErrorr:warn) folder_pathrs r5_delete_folderrvs P 7>>+ & & ' M+ & & & & & ' ' PPP  P M@;N O O O O O O P P PPs39#A#"A#c&eZdZdZddZdZdZdS) TempMemmapzM Parameters ---------- data mmap_mode : str, default='r' rc"||_||_dSN) mmap_moder)rBrrs r5rCzTempMemmap.__init__s" r7cNt|j|jd\}|_|S)NT)r return_folder)create_memmap_backed_datarr temp_folder)rBdata_read_onlys r5r\zTempMemmap.__enter__s1+D It, , , ((r7c.t|jdSr)rr)rBexc_typeexc_valexc_tbs r5r_zTempMemmap.__exit__st'(((((r7N)r)r/r`rarbrCr\r_rcr7r5rrsP )))))r7rrctjd}tjt jt |dtj|d}tj ||tj ||}|s|n||f}|S)zv Parameters ---------- data mmap_mode : str, default='r' return_folder : bool, default=False sklearn_testing_)prefixT)rzdata.pkl)r) tempfilemkdtempatexitregister functoolspartialroprRjoblibdumpload)rrrrfilenamememmap_backed_dataresults r5rrs"*<===K OI%nkMMMNNNw{J//H KhXCCC"/V6H+5V  Mr7c t|j}n#t$rgcYSwxYwd|D}|r7d|D}t |dkrd}||fS|S)z!Helper to get function arguments.cBg|]\}}|j|j|jfv|Src)kindVAR_POSITIONAL VAR_KEYWORD)rkkeyparams r5rmz_get_args..s>    C :e2E4EF F F F F Fr7c<g|]}|j|jk|jSrc)rrrS)rkrs r5rmz_get_args..s4   zU111 J111r7rN)r parametersr0itemsvalueslen)functionvarargsparamsrIs r5 _get_argsrs8$$/     ,,..   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Remove the colon*z` c |vSrrcrs r5rz,check_docstring_parameters..@s qr7z\There's a parameter name mismatch in function docstring w.r.t. function signature, at index z diff: z != zbParameters in function docstring have less items w.r.t. function signature, first missing item: %sz`Parameters in function docstring have more items w.r.t. function signature, first extra item: %sz Full diff:c3>K|]}|VdSr)striprklines r5 z-check_docstring_parameters..ks>  r7)"rrr startswithrisdatadescriptorsplitlistfilterrrremover:rGrH UserWarning FunctionDocstrrQ ExceptionrVrindexrstripendswithlstriprangemindifflibpprintpformat splitlinesextendndiff)rdocrFr incorrect func_nameparam_signaturerecordsexpmessage param_docsrStype_definition param_docirrparam_docs_formattedparam_signature_formatteds ` r5check_docstring_parametersrs"#"""""I>RRvFt$$I    + +y/C/C00%%sB#FFFsA"4446";";";";Yt__MMNNO ?aOA$6&$@$@v&&& {  $D 1 1 1 ! !  !'; 7 7 7 !++D11 ) ) )73s88CC"#hhnnT222A26G"=)"="=!>!HHI$$$$$$ ! ! ! ! ! ! ! !s3xx(((((((( ! ! !i*<>(oy$$&& d{{t$5djjoo$56rss;AACC{QTXXY '',, N{{}}&&  d??   djjooa066t<< = = = 9~~f6666 CCDDJ G 3s:O(<(<== > > 1 A . . G&'QQ(:(:(:JqMMK G E / ?c*oo-- :c*oo. /   _  J / / 8_--. /   7||q NNNMMM!>>*55@@BB & ? ? J J L L ~G NNMM";=QRR W!9,- 9I sgH&8E H& HAG H!H&2"GH& H&HHH&HH&&H*-H*cR|jdur ||jvrdS|j ||jvrdSdS)z7Helper to check if item should be included in checking.TF)includeexclude) item_namerIs r5_check_item_includedrxs> |4IT\$A$Au |I$=$=u 4r7c|drdS|drdS|drdS|drdSdS)z,Key for grouping output from `context_diff`.z z- z+ z! N)r)rs r5 _diff_keyrsk tt   t   t   t 4r7c bd}d}g}|D]\}}|s|s||z }||tt||t |t |d}|d|ddz }t|ddtD]K\}}||dd|zz }#|d|zdd |Dzz }L|d z }|S) aGet message showing the difference between type/desc docstrings of all objects. `docstrings_grouped` keys should be the type/desc docstrings and values are a list of objects with that docstring. 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If `regex` provided, match descriptions to all descriptions. c,g|]}t|Src)r)rkobjss r5rmz,_check_consistency_items..sBBBdD BBBr7 descriptionzThe description of Nrz 'z' in z' does not match 'descr_regex_pattern': r$z and c34K|]}t|VdSr)r)rkr,s r5rz+_check_consistency_items..s9&&$E &&&&&&r7zThe z of z' is inconsistent between rzChecking was skipped for z: z' as they were not found in all objects.)rsumrrQresearchrtextwrapfillrrkeysr/rRr:r) items_docs type_or_descsection n_objectsdescr_regex_patternskippedrr' not_matchedr+r,msgr( obj_groupss r5_check_consistency_itemsrEs|G)3)9)9););&&% % BB&8&?&?&A&ABBB C Ci O O NN9 % % % % ] * */B *K$6$<$<$>$> . . 5y!4i@@.&&u--- *mU'#2#,UU)UU+UU>QUUU%S)))  *#((** + +a / /$%788H &&(:(A(A(C(C&&&J-)|))"))))%)))C 8OC %% %0   ( ( (7 ( ( (       r7c nddlm}tdgdfd} i| ||dd| ||dd | ||d d } t} |D]} t j| s(t j| st j| r&|t j| | | j <dtd t| d t|} | D]1\}}td}td}| D]\}}||D]\}}}t||rd|}dt'jd|D}||||||||Ìt-|d|| t-|d|| |3dS)aX Check consistency between docstring parameters/attributes/returns of objects. Checks if parameters/attributes/returns have the same type specification and description (ignoring whitespace) across `objects`. Intended to be used for related classes/functions/data descriptors. Entries that do not appear across all `objects` are ignored. Parameters ---------- objects : list of {classes, functions, data descriptors} Objects to check. Objects may be classes, functions or data descriptors with docstrings that can be parsed by numpydoc. include_params : list of str or bool, default=False List of parameters to be included. If True, all parameters are included, if False, checking is skipped for parameters. Can only be set if `exclude_params` is None. exclude_params : list of str or None, default=None List of parameters to be excluded. If None, no parameters are excluded. Can only be set if `include_params` is True. include_attrs : list of str or bool, default=False List of attributes to be included. If True, all attributes are included, if False, checking is skipped for attributes. Can only be set if `exclude_attrs` is None. exclude_attrs : list of str or None, default=None List of attributes to be excluded. If None, no attributes are excluded. Can only be set if `include_attrs` is True. include_returns : list of str or bool, default=False List of returns to be included. If True, all returns are included, if False, checking is skipped for returns. Can only be set if `exclude_returns` is None. exclude_returns : list of str or None, default=None List of returns to be excluded. If None, no returns are excluded. Can only be set if `include_returns` is True. descr_regex_pattern : str, default=None Regular expression to match to all descriptions of included parameters/attributes/returns. If None, will revert to default behavior of comparing descriptions between objects. Examples -------- >>> from sklearn.metrics import (accuracy_score, classification_report, ... mean_absolute_error, mean_squared_error, median_absolute_error) >>> from sklearn.utils._testing import assert_docstring_consistency ... # doctest: +SKIP >>> assert_docstring_consistency([mean_absolute_error, mean_squared_error], ... include_params=['y_true', 'y_pred', 'sample_weight']) # doctest: +SKIP >>> assert_docstring_consistency([median_absolute_error, mean_squared_error], ... include_params=True) # doctest: +SKIP >>> assert_docstring_consistency([accuracy_score, classification_report], ... include_params=["y_true"], ... descr_regex_pattern=r"Ground truth \(correct\) (labels|target values)") ... # doctest: +SKIP r)NumpyDocStringrI)rrarg_namecf|r|durtd|d|d|duriS||||iS)NTz The 'exclude_z-' argument can be set only when the 'include_z' argument is True.F) TypeError)rrrH section_nameArgss r5 _create_argsz2assert_docstring_consistency.._create_args*su  wd**:::$:::  e  Idd7GX>>??r7rrattrs AttributesreturnsReturnszDAll 'objects' must be one of: function, class or descriptor, got a: rc*ttSrrrrcr7r5rz.assert_docstring_consistency..JT):):r7c*ttSrrSrcr7r5rz.assert_docstring_consistency..KrTr7r$c3>K|]}|VdSr)rrs r5rz/assert_docstring_consistency..Qs*7V7V 7V7V7V7V7V7Vr7ztype specificationr3)r@N)numpydoc.docscraperGrdictrr isfunctionisclassgetdocr/rJr,rrrrrRrrr from_iterablerQrE)objectsinclude_paramsexclude_params include_attrs exclude_attrsinclude_returnsexclude_returnsr@rGrM section_args objects_docr4r?r>rI type_items desc_itemsobj_nameobj_docrtype_defdescrLs @r5assert_docstring_consistencyrlsP211111 f@@@ A AD@@@@@ ,~~x N N ,}mWl K K ,I N NL &&K    $S ) ) !#&& s##  )7w~c7J7J(K(KK % %'s))'''  G I%++--   !:!:;;  !:!:;; !,!2!2!4!4 A A Hg-4W-= A A) 8T' 488A"xx(8(8(>(>(@(@AAH88E$77V7VQU7V7V7V$V$VWWDy)(3::8DDDy)$/66x@@@ A !-A7IVVV     3        r7.+<ctjd\}}tj| t |d5}||ddddn #1swxYwYtj|g}tj tj tj tjd}tj} tj ||dg|d<n#t$$r||d<YnwxYw|t&|d} tjd } | r | | d d <|| d < t+|fi| } n<#t,$r/} t/d | jdzd} ~ wwxYw| d} t5j|| r$|d krd} nd|} | d| }t9|n<#t:$r/} t/d| jdzd} ~ wwxYw tj|dS#tj|wxYw)a[Utility to check assertions in an independent Python subprocess. The script provided in the source code should return 0 and the stdtout + stderr should not match the pattern `pattern`. This is a port from cloudpickle https://github.com/cloudpipe/cloudpickle Parameters ---------- source_code : str The Python source code to execute. pattern : str Pattern that the stdout + stderr should not match. By default, unless stdout + stderr are both empty, an error will be raised. timeout : int, default=60 Time in seconds before timeout. z_src_test_sklearn.py)suffixwbzutf-8Nz.. PYTHONPATH)cwdstderrenvCOVERAGE_PROCESS_STARTrutimeoutzscript errored with output: %srmzExpected no outputz%The output was not supposed to match z$, got the following output instead: z!script timeout, output so far: %s)rmkstemprcloseopenwriteencoder< executablernormpathrRdirnamesklearn__file__rcopypathsepKeyErrorr rrrrVoutputdecoder7r8rrunlink) source_codepatternrwfd source_filefcmdrsrurJ coverage_rcoute expectationrs r5r%r%`s$&.DEEEOB HRLLL' +t $ $ 1 GGK&&w// 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1~{+k"'"*W-=">">EEFFjoo $ " c,6G0H I IC   $ $ $ #C    $s;;jnn%=>>  B6AF5M2 3#y  "311&11%   "58P8PP  **W%%Cy#&& .d??"6KK"U'"U"UK(UUcUU$W--- .   4qxw7O7OO   . + +sI)A3' I3A77I:A7;A2I.*DID+(I*D++?I, E:9H: F3*F..F33AHI H>*H99H>>II.c\ |dkr9|t|Stj||S|dkrF|t |St tj||S|dkrtj||S|dvrVt jd|}||||d }|#|D] }||d ||<!|S|d krt jd |} tj| |$d t j dD} fdt|D} | j | }|Zt|jD]E\} }||vr<|| |||}F|S|dkrwt jd|} | ||d}|E|D]B}|| || j}C|S|dkr-t jd|}|||S|dkr,t jd|} | |S|dkr-t jd|}|||S|dkrt1|d|dSd|vrt2j|stj|}d|vr1t:t=dkrt?|dt:|dvr!t2j ||S|dkr!t2j!||S|dkr!t2j"||S|dkr#t2j#||SdSdS) aConvert a given container to a specific array-like with a dtype. Parameters ---------- container : array-like The container to convert. constructor_name : {"list", "tuple", "array", "sparse", "dataframe", "series", "index", "slice", "sparse_csr", "sparse_csc", "sparse_csr_array", "sparse_csc_array", "pyarrow", "polars", "polars_series"} The type of the returned container. columns_name : index or array-like, default=None For pandas container supporting `columns_names`, it will affect specific names. dtype : dtype, default=None Force the dtype of the container. Does not apply to `"slice"` container. minversion : str, default=None Minimum version for package to install. categorical_feature_names : list of str, default=None List of column names to cast to categorical dtype. Returns ------- converted_container rN)rltuplearray)pandas dataframer) minversionF)columnsrlrr*pyarrowcg|]}d|S)colrc)rkrs r5rmz&_convert_container..sEEE!I!IIEEEr7r4c0i|]\}}|dd|fSrrc)rkrrSrs r5 z&_convert_container..s+IIIgaeAAAqDkIIIr7polarsrow)schemaorientseries polars_series)rrslicerr}z1.8z* is only available with scipy>=1.8.0, got )r} sparse_csrsparse_csr_array sparse_cscsparse_csc_array)$rriasarraytolistrpytest importorskip DataFrameastypershape enumerateTable from_pydict column_names set_columncolumndictionary_encode with_columnsrcast CategoricalSeriesIndexrr|r}r~ atleast_2dr rr0 csr_matrix csr_array csc_matrix csc_array) containerconstructor_name columns_namerlrcategorical_feature_namespdrcol_nameparcol_idxplrs @r5_convert_containerrsD6!! = ?? ":iu555<<>> > W $ $ =## #IU;;;BBDDEE E W $ $z)51111 4 4 4  j A A AiUQVWW $ 05 G G#)(#3#:#::#F#Fx   Y & &  z B B B 9%%  EEuU[^/D/DEEELIIII<1H1HIII%%d++ $ 0%.v/B%C%C  !888#..6==+B+B+T+T+V+VF X % %  j A A Ai UKK $ 05 T T,,RVVH-=-=-B-B2>-R-RSS X % %  j A A Ayy%y000 _ , ,  j A A Ayy y*** W $ $  j A A Axx x/// W $ $Yq\9Q<000 % % %y!!),, 1  i00I & & &: e8L8L+L+L#      7 7 79'' '?? ? !3 3 39&&y&>> >  - -9'' '?? ? !3 3 39&&y&>> >+ & %(4 3r7c&t||||S)a[Context manager to ensure exceptions are raised within a code block. This is similar to and inspired from pytest.raises, but supports a few other cases. This is only intended to be used in estimator_checks.py where we don't want to use pytest. In the rest of the code base, just use pytest.raises instead. Parameters ---------- excepted_exc_type : Exception or list of Exception The exception that should be raised by the block. If a list, the block should raise one of the exceptions. match : str or list of str, default=None A regex that the exception message should match. If a list, one of the entries must match. If None, match isn't enforced. may_pass : bool, default=False If True, the block is allowed to not raise an exception. Useful in cases where some estimators may support a feature but others must fail with an appropriate error message. By default, the context manager will raise an exception if the block does not raise an exception. err_msg : str, default=None If the context manager fails (e.g. the block fails to raise the proper exception, or fails to match), then an AssertionError is raised with this message. By default, an AssertionError is raised with a default error message (depends on the kind of failure). Use this to indicate how users should fix their estimators to pass the checks. 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A A  5   r7cxt}|D](}tj|j|j|j)dS)NrN)rQr:filterwarningsrHrr*)warnings_filters_info_listrTs r5turn_warnings_into_errorsrZwsX!@!B!B2     (!*       r7)NrdTreT)rhrzre)r)F)rF)NN)re)FNFNFNN)rmrn)NNNN)NFN)urbr contextlibrr6rros.pathrrr7rr<rr9unittestr: collectionsrrcollections.abcr dataclassesrrrr r itertoolsr r subprocessr rrrrnumpyriscipyr| numpy.testingrrurrrrr sklearn.utilsrrrrrsklearn.utils._array_apirsklearn.utils.fixesrrrrr sklearn.utils.multiclassr!sklearn.utils.validationr"r#r$__all__caser&r.r6r3rrrARRAY_API_COMPAT_FUNCTIONALrrmarkskipif skip_if_32bitxfailfails_if_unstable_openblasparallelmpskip_if_no_parallel'skip_if_array_api_compat_not_configuredplatform!if_safe_multiprocessing_with_blasskip_if_no_numpydocrrrrrrrrrr/rErlr%rrAbstractContextManagerrrrr$rDrQrVrZrcr7r5r{s;    ////////$$$$$$!!!!!! $$$$$$$$OOOOOOOOOOOO ???????>>>>>BAAAAA     = !w)2)2)2)2X66666666tOSFFFFR' ' ' ' T 8 8 8 8 (d###"&((("'(+ MMMK&&y9U&VVM!'!2!2++--<"3""!+,, O 'A-/5k.@.@ ''V/A//+0)/ (:(:  )T);))%!+,, LNN<-   D HHH P P P P)))))))),,44PPPPf   !!!JGI) ) ) ) \    D;;;;B " j?j?j?j?Z%@%@%@%@P*****j/***Z5 5 5 5 5 5 5 5 p, , , , , , , , ^* * * * * * * * Z+++\JJJZ     s%? 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