hJddlmZddlZddlmZddlmZddl m Z dZ dZ dS) )suppressN)sparse) is_scalar_nan)_object_dtype_isnancttt5ddl}||jur!||cdddS dddn #1swxYwYt |rd|jjdkrtj |}nE|jjdvr!tj |j t}nt|}n||k}|S)Nrf)iu)dtype)r ImportErrorAttributeErrorpandasNAisnarr kindnpisnanzerosshapeboolr)X value_to_maskrXts [/var/www/html/services/rag-service/venv/lib/python3.11/site-packages/sklearn/utils/_mask.py_get_dense_maskr s0 +~ . .""  FI % %;;q>> """""""" & """""""""""""""]##  7<3  !BB W\Z ' '!'...BB%Q''BB -  Is"AAAcPtj|st||St|j|}|jdkr tjn tj}|||j|j f|j t}|S)aCompute the boolean mask X == value_to_mask. Parameters ---------- X : {ndarray, sparse matrix} of shape (n_samples, n_features) Input data, where ``n_samples`` is the number of samples and ``n_features`` is the number of features. value_to_mask : {int, float} The value which is to be masked in X. Returns ------- X_mask : {ndarray, sparse matrix} of shape (n_samples, n_features) Missing mask. csr)rr ) spissparserdataformat csr_matrix csc_matrixindicescopyindptrrr)rrrsparse_constructor Xt_sparses r _get_maskr*"s" ;q>>1q-000  / /B*+(e*;*;"" QY^^  qx}}/qwdI ) contextlibrnumpyrscipyrrrfixesrrr*r+rr2s&&&&&&0r+