U 9%eN5@sdZddlZddlmZddlZddlmZddlmZddl m Z dd Z d d Z d d Z ddZddZddZGdddZddZddZGdddZeZddZddZd'dd d!d"Zd#d$Zd%d&ZdS)(zTools to support array_api.Nwraps) get_config) parse_versioncCs\|rXz ddl}Wntk r,tdYnXttj}d}|t|krXtd|ddS)zCheck that array_api_compat is installed and NumPy version is compatible. array_api_compat follows NEP29, which has a higher minimum NumPy version than scikit-learn. rNzKarray_api_compat is required to dispatch arrays using the API specificationz1.21zNumPy must be z7 or newer to dispatch array using the API specification)array_api_compat ImportErrorrnumpy __version__)array_api_dispatchrZ numpy_versionZmin_numpy_versionr W/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/sklearn/utils/_array_api.py_check_array_api_dispatch s     rcCst|tjtjfrdS|jS)aHardware device the array data resides on. Parameters ---------- x : array Array instance from NumPy or an array API compatible library. Returns ------- out : device `device` object (see the "Device Support" section of the array API spec). cpu) isinstancer ndarrayZgenericdevicexr r rr$s rcCs t|jS)zReturn the total number of elements of x. Parameters ---------- x : array Array instance from NumPy or an array API compatible library. Returns ------- out : int Total number of elements. )mathprodshaperr r rsize6s rcCs |jdkS)z%Return True if xp is backed by NumPy.>r numpy.array_apiarray_api_compat.numpy)__name__xpr r r_is_numpy_namespaceFsrcs4t|tr"tfdd|DSt|dSdS)zReturns a boolean indicating whether a provided dtype is of type "kind". Included in the v2022.12 of the Array API spec. https://data-apis.org/array-api/latest/API_specification/generated/array_api.isdtype.html c3s|]}t|dVqdSrN_isdtype_single.0kdtyperr r Rszisdtype..rN)rtupleanyr")r'kindrr r&risdtypeKs r,cst|tr |dkrjkS|dkr>jjjjhkS|dkr^jjj j hkS|dkr~t fdddDS|dkrj j hkS|d krt}td r|jtd r|j|kS|d krt fd ddDStd|n|kSdS)Nboolsigned integerunsigned integerintegralc3s|]}t|dVqdSr r!r#r&r rr(`sz"_isdtype_single..)r.r/ real floatingcomplex floating complex64 complex128numericc3s|]}t|dVqdSr r!r#r&r rr(ps)r0r1r2zUnrecognized data type kind: )rstrr-int8int16int32int64uint8uint16uint32uint64r*float32float64sethasattraddr3r4 ValueError)r'r+rZcomplex_dtypesr r&rr"Ws4       r"c@s6eZdZdZddZddZdddd Zd d Zd S) _ArrayAPIWrapperasklearn specific Array API compatibility wrapper This wrapper makes it possible for scikit-learn maintainers to deal with discrepancies between different implementations of the Python array API standard and its evolution over time. The Python array API standard specification: https://data-apis.org/array-api/latest/ Documentation of the NumPy implementation: https://numpy.org/neps/nep-0047-array-api-standard.html cCs ||_dSN) _namespace)selfZarray_namespacer r r__init__sz_ArrayAPIWrapper.__init__cCs t|j|SrF)getattrrG)rHnamer r r __getattr__sz_ArrayAPIWrapper.__getattr__raxiscs|jjdkr(tj||d}|j|S|dkr>td|jdkrXtdj|dkrjdkr~fd d |D}qfd d |D}nfd d |D}|jj||dS) NrrM>rrz&Only axis in (0, 1) is supported. Got >rrz(Only X.ndim in (1, 2) is supported. Got rrcsg|] }|qSr r r$iXr r sz)_ArrayAPIWrapper.take..csg|]}|ddfqSrFr rOrQr rrSscsg|]}dd|fqSrFr rOrQr rrSs)rGrr takeasarrayrDndimstack)rHrRindicesrNZX_npselectedr rQrrTs    z_ArrayAPIWrapper.takecCst|||jdSNr)r,rGrHr'r+r r rr,sz_ArrayAPIWrapper.isdtypeN)r __module__ __qualname____doc__rIrLrTr,r r r rrEzs  rEcCs|dkrtd|dS)N>rNzUnsupported device for NumPy: )rD)rr r r_check_device_cpusr_cstfdd}|S)Ncst|dd||S)Nr)r_pop)argskwargsfuncr r wrapped_funcsz(_accept_device_cpu..wrapped_funcr)rdrer rcr_accept_device_cpusrfc @seZdZdZdddddddd d d d h Zd dddddddddddh ZddZeddZdddd d!Z d"d"d"d#d$d%Z d&d'Z d(d)Z d*d+Z d"d,d-d.Zd"d/d0d1Zd2d3Zd"S)4_NumPyAPIWrapperaRArray API compat wrapper for any numpy version NumPy < 1.22 does not expose the numpy.array_api namespace. This wrapper makes it possible to write code that uses the standard Array API while working with any version of NumPy supported by scikit-learn. See the `get_namespace()` public function for more details. ZarangeemptyZ empty_likeeyefullZ full_likeZlinspaceZonesZ ones_likeZzerosZ zeros_liker7r8r9r:r;r<r=r>r?r@r3r4cCs4tt|}||jkrt|S||jkr0t|S|SrF)rJr _CREATION_FUNCSrf_DTYPESr')rHrKattrr r rrLs     z_NumPyAPIWrapper.__getattr__cCstjSrF)r Zbool_)rHr r rr-sz_NumPyAPIWrapper.boolTunsafecopycastingcCs|j|||dS)Nro)astype)rHrr'rprqr r rrrsz_NumPyAPIWrapper.astypeN)r'rrpcCs2t||dkr tj|d|dStj||dSdS)NT)rpr')r')r_r arrayrU)rHrr'rrpr r rrUsz_NumPyAPIWrapper.asarraycCstj|ddS)NT)Zreturn_inverser uniquerHrr r runique_inversesz_NumPyAPIWrapper.unique_inversecCstj|ddS)NT)Z return_countsrtrvr r r unique_countssz_NumPyAPIWrapper.unique_countscCs t|SrFrtrvr r r unique_valuessz_NumPyAPIWrapper.unique_valuesrMcCstj||dS)NrM)r Z concatenate)rHarraysrNr r rconcatsz_NumPyAPIWrapper.concat)rpcCs>t|ts"td|dt||dkr2|}t||S)zGives a new shape to an array without changing its data. The Array API specification requires shape to be a tuple. https://data-apis.org/array-api/latest/API_specification/generated/array_api.reshape.html zshape must be a tuple, got z of type T)rr) TypeErrortyperpr reshape)rHrrrpr r rr~ s z_NumPyAPIWrapper.reshapecCst|||dSrZ)r,r[r r rr,sz_NumPyAPIWrapper.isdtype)rr\r]r^rkrlrLpropertyr-rrrUrwrxryr{r~r,r r r rrgsJ   rgcGsPtd}|stdfSt|ddl}|j|d}}|jdkrHt|}||fS)aGet namespace of arrays. Introspect `arrays` arguments and return their common Array API compatible namespace object, if any. NumPy 1.22 and later can construct such containers using the `numpy.array_api` namespace for instance. See: https://numpy.org/neps/nep-0047-array-api-standard.html If `arrays` are regular numpy arrays, an instance of the `_NumPyAPIWrapper` compatibility wrapper is returned instead. Namespace support is not enabled by default. To enabled it call: sklearn.set_config(array_api_dispatch=True) or: with sklearn.config_context(array_api_dispatch=True): # your code here Otherwise an instance of the `_NumPyAPIWrapper` compatibility wrapper is always returned irrespective of the fact that arrays implement the `__array_namespace__` protocol or not. Parameters ---------- *arrays : array objects Array objects. Returns ------- namespace : module Namespace shared by array objects. If any of the `arrays` are not arrays, the namespace defaults to NumPy. is_array_api_compliant : bool True if the arrays are containers that implement the Array API spec. Always False when array_api_dispatch=False. r FrNT>cupy.array_apir)r_NUMPY_API_WRAPPER_INSTANCErr get_namespacerrE)rzr r namespaceZis_array_api_compliantr r rr!s+  rcCs>t|\}}t|r*|tt|Sdd|| S)Ng?)rrrUspecialZexpitr exp)rRr_r r r_expit`s rrcCsd|dkrt|\}}t|rP|dkr6tj|||d}ntj|||d}||S|j|||dSdS)aHelper to support the order kwarg only for NumPy-backed arrays Memory layout parameter `order` is not exposed in the Array API standard, however some input validation code in scikit-learn needs to work both for classes and functions that will leverage Array API only operations and for code that inherently relies on NumPy backed data containers with specific memory layout constraints (e.g. our own Cython code). The purpose of this helper is to make it possible to share code for data container validation without memory copies for both downstream use cases: the `order` parameter is only enforced if the input array implementation is NumPy based, otherwise `order` is just silently ignored. NT)orderr')r'rp)rrr rsrU)rsr'rrprrr r r_asarray_with_orderhs   rcCsF|j}|dkr|S|dkr,|jS|dkr<|St|S)z*Convert X into a NumPy ndarray on the CPU.>array_api_compat.torchtorchr>array_api_compat.cupycupy)rrr Z_arraygetrU)rsrZxp_namer r r_convert_to_numpys  rcCsXddlm}||}t|D]2\}}t|ds>t|tjrF||}t|||q |S)aCreate new estimator which converting all attributes that are arrays. The converter is called on all NumPy arrays and arrays that support the `DLPack interface `__. Parameters ---------- estimator : Estimator Estimator to convert converter : callable Callable that takes an array attribute and returns the converted array. Returns ------- new_estimator : Estimator Convert estimator r)cloneZ __dlpack__) Z sklearn.baservarsitemsrBrr rsetattr)Z estimator converterrZ new_estimatorkey attributer r r _estimator_with_converted_arrayss r)NNN)r^r functoolsrr Z scipy.specialr_configrfixesrrrrrr,r"rEr_rfrgrrrrrrr r r rs,     #0 f?