a dg@sddlZddlmZddlmZmZddlmZmZm Z erTddl m Z ddl m Z eeeeeffZeeeddd ZeGd d d Zdee d deedddZdS)N defaultdict) dataclassfield) TYPE_CHECKINGDictList)RecognizerResult) EngineResult) operator_namecountreturncCsTtdd|}tdd|}|drB|drBd|d|dS|d|SdS)z(Format the operator name with the count.z[<>]z_\d+$<>_N)resub startswithendswith)r r Zclean_operator_namer/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain_experimental/data_anonymizer/deanonymizer_mapping.pyformat_duplicated_operator s rc@sJeZdZUdZedddZeed<eedddZ ed d d d Z d S) DeanonymizerMappingzDeanonymizer mapping.cCs tddS)NcSsttSN)rstrrrrrz.DeanonymizerMapping...rrrrrrrzDeanonymizerMapping.)default_factorymapping)r cCsdd|jDS)z Return the deanonymizer mapping.cSsi|]\}}|t|qSr)dict).0kvrrr $rz,DeanonymizerMapping.data..)ritems)selfrrrdata!szDeanonymizerMapping.dataN) new_mappingr c Cst}|D]\}}t|j|d}|D]^\}}||vr0||j|vr0||j|vrjt||n|}||j||<|||d7}q0qdS)aJUpdate the deanonymizer mapping with new values. Duplicated values will not be added If there are multiple entities of the same type, the mapping will include a count to differentiate them. For example, if there are two names in the input text, the mapping will include NAME_1 and NAME_2. N)setr%lenrvaluesradd) r&r(Z seen_values entity_typer,r keyvalueZnew_keyrrrupdate&s  zDeanonymizerMapping.update) __name__ __module__ __qualname____doc__rrMappingDataType__annotations__propertyr'r1rrrrrs rFr r ) original_textanalyzer_resultsanonymizer_results is_reversedr cCs|jddd|jjdddtt}tt}t||jD]\}}||j|j}|j} |rr|||  v} n ||| v} | rq>|j ||  vs|j || vrt |j || d} || d7<n|j } |r| |fn|| f\} } | || | <q>|S)a]Create or update the mapping used to anonymize and/or deanonymize a text. This method exploits the results returned by the analysis and anonymization processes. If is_reversed is True, it constructs a mapping from each original entity to its anonymized value. If is_reversed is False, it constructs a mapping from each anonymized entity back to its original text value. If there are multiple entities of the same type, the mapping will include a count to differentiate them. For example, if there are two names in the input text, the mapping will include NAME_1 and NAME_2. Example of mapping: { "PERSON": { "": "", "John Doe": "Slim Shady" }, "PHONE_NUMBER": { "111-111-1111": "555-555-5555" } ... } cSs|jSrstartdrrrrkrz+create_anonymizer_mapping..)r/cSs|jSrr=r?rrrrlrr)) sortr%rr intzipr>endr.r,textr)r9r:r;r<rr ZanalyzedZ anonymizedoriginal_valuer.ZcondZanonymized_valueZ mapping_keyZ mapping_valuerrrcreate_anonymizer_mappingCs6(   rH)F)r collectionsr dataclassesrrtypingrrrZpresidio_analyzerr Zpresidio_anonymizer.entitiesr rr6rCrrboolrHrrrrs$    -