:iejSrSSKrSSKrSSKrSSKrSSKrSSKrSSKJ r SSK J r J r \ "\R5r\ "\R5rSSjr\\ "S5:aSSKJr O SS KJr S r\\ "S 5:aS rOSS KJr \\ "S 5:aS\4SjrOSSKJr SSKJr g!\a Srgf=f)zCompatibility fixes for older version of python, numpy, scipy, and scikit-learn. If you add content to this file, please give the version of the package at which the fix is no longer needed. N) parse_version)config_context get_configc[[S5:a[RR XSS9$[RR XS9$)Nz1.9.0T)axiskeepdims)r) sp_versionrscipystatsmode)ars W/srv/projetos/modelo_ml_acdoc/venv/lib/python3.13/site-packages/imblearn/utils/fixes.py_modersA]7++{{t<< ;;  A  ))z1.1)_is_arraylike_not_scalar) _is_arraylikec\[U5=(a [R"U5(+$)z3Return True if array is array-like and not a scalar)rnpisscalar)arrays rrr#sU#>BKK,>(>>rz1.3c^U4SjnU$)aDecorator to run the fit methods of estimators within context managers. Parameters ---------- prefer_skip_nested_validation : bool If True, the validation of parameters of inner estimators or functions called during fit will be skipped. This is useful to avoid validating many times the parameters passed by the user from the public facing API. It's also useful to avoid validating parameters that we pass internally to inner functions that are guaranteed to be valid by the test suite. It should be set to True for most estimators, except for those that receive non-validated objects as parameters, such as meta-estimators that are given estimator objects. Returns ------- decorated_fit : method The decorated fit method. cJ>^[R"T5UU4Sj5nU$)Nc>[5SnTRS:H=(a [U5nU(dU(dUR5 [ T=(d US9 T"U/UQ70UD6sSSS5 $!,(df  g=f)Nskip_parameter_validation partial_fit)r)r__name__ _is_fitted_validate_paramsr) estimatorargskwargsglobal_skip_validationpartial_fit_and_fitted fit_methodprefer_skip_nested_validations rwrapper0_fit_context..decorator..wrapperDsy)36Q)R&''=8RZ =R'.6L..0#5O9O &iA$A&A s A66 B) functoolswraps)r%r'r&s` r decorator_fit_context..decoratorCs' __Z ( B) B$Nr)r&r+s` r _fit_contextr.+s0 ,r)r.c Ub@[U[[45(dU/nU"UVs/sHn[X5PM sn5$[US5(aUR 5$[ U5Vs/sH4oDR S5(dMURS5(aM2UPM6 nn[U5S:$s snfs snf)aDetermine if an estimator is fitted Parameters ---------- estimator : estimator instance Estimator instance for which the check is performed. attributes : str, list or tuple of str, default=None Attribute name(s) given as string or a list/tuple of strings Eg.: ``["coef_", "estimator_", ...], "coef_"`` If `None`, `estimator` is considered fitted if there exist an attribute that ends with a underscore and does not start with double underscore. all_or_any : callable, {all, any}, default=all Specify whether all or any of the given attributes must exist. Returns ------- fitted : bool Whether the estimator is fitted. __sklearn_is_fitted_____r) isinstancelisttuplehasattrr0varsendswith startswithlen)r attributes all_or_anyattrv fitted_attrss rrras0  !j4-88(\ JOJDwy7JOP P 95 6 6224 4I &!**S/A!,,tBTA  < 1$$P  sB=2CC'C)r) _is_pandas_dfc[US5(a:[US5(a)[RSn[ XR 5$g![a gf=f)z+Return True if the X is a pandas dataframe.columnsilocpandasF)r6sysmodulesKeyErrorr3 DataFrame)Xpds rr@r@sV 1i WQ%7%7 [[*a. .  sA AA)r)__doc__r)rEnumpyrr scipy.statssklearnsklearn.utils.fixesr_configrr __version__r sklearn_versionrsklearn.utils.validationrrr. sklearn.baseallrr@ ImportErrorr-rrrWs   -0 5,, -  3 34*mE**A6? ]5)).b*]5)))-##%L4 6   sB&& B21B2