h1JSSKrSSKrSSKrSSKJr SSKJr SSKJrJ r J r J r J r J r JrJrJrJrJr SSKrSSKJr SSKJrJr SSKJr SS KJrJr SS KJr SS K J!r! SS K"J#r#J$r$ SS K%J&r& SSKJ'r'J(r(J)r) SSK*J+r+ \ \,\\,\ \ \,\444r-Sr.S\ \$S\ \$S\ \$4Sjr/Sr0S\ \$S\ \$S\ \$4Sjr1Sr2\.S.S\ \&S\ \,\44Sjjr3\.4S\,4Sjjr4"SS\+5r5S r6g)!N)partial)Path) AnyCallableDictIterableListOptionalSequenceSetTupleUnioncast)util)ErrorsWarnings)Language)Matcher PhraseMatcher)levenshtein_compare)Scorer)DocSpan)Example)SimpleFrozenList ensure_pathregistry)Piperulerentitiesspansreturnc^Sn[XSS9n[U5n/n[5mUHnURnURn[ U4SjU55(dM7UR U5 UVs/sH&owRU:aURU:aM$UPM( nnTR[XV55 M X-$s snf)aMerge entities and spans into one list without overlaps by allowing spans to overwrite any entities that they overlap with. Intended to replicate the overwrite_ents=True behavior from the EntityRuler. entities (Iterable[Span]): The entities, already filtered for overlaps. spans (Iterable[Span]): The spans to merge, may contain overlaps. RETURNS (List[Span]): Filtered list of non-overlapping spans. cNURUR- UR*4$Nendstartspans S/home/james-whalen/.local/lib/python3.13/site-packages/spacy/pipeline/span_ruler.py,prioritize_new_ents_filter..0DJJ!6 DTkeyreversec3@># UHoRT;v M g7fr'i.0token seen_tokenss r- -prioritize_new_ents_filter..8.Nr0r1Tr2c3b# UH%n[URUR5v M' g7fr')rFr*r))r9ents r-r<2prioritize_existing_ents_filter..Ss"Ghssyy#''22hs-/c3@># UHoRT;v M g7fr'r6r8s r-r<rSWr>r?) r@rArBrEr*r)rCrDrF)r"r#rGrHr,r*r)r;s @r-prioritize_existing_ents_filterrUCsEL 5D 9EH~HLEKGhGH hh URRU[T5S/5$r')r#getlen)docr3 attr_prefixs r-r.1overlapping_labeled_spans_score..js!399==S5E5G1H"#Mr1has_annotationc">TUR;$r')r#)rdrYs r-r.rflsI4Jr1)dict setdefaultr score_spans)rZrYkwargsres ` @r-overlapping_labeled_spans_scorermas&\FK f i[9: ot, i& M &(JK   h 1& 11r1rYc[[US9$)NrX)rrmrXs r-%make_overlapping_labeled_spans_scorerrops 2i HHr1c\rSrSrSrS3\SS\RS\SS\ "\ \S9S. S\ S\ S \ \ S \ \\\\\/\\4S \S \\\\\/\\4S \ \\\ 4S\S\S\S\ \SS4SjjjrS\4SjrS\ S\4Sjr\S\ \ 4Sj5rS\S\4SjrS\4SjrSr\S\\ S44Sj5r\S\\ S44Sj5rSSS.S \/\\ 4S\ \ S!\ \!\"4S"jjr#\S\$\"4S#j5r%S!\$\"SS4S$jr&S4S%jr'S\ SS4S&jr(S'\ SS4S(jr)S4S)jr*\+"5S*.S+\,S,\\ SS4S-jjr-\+"5S*.S,\\ S\,4S.jjr.\+"5S*.S/\\ \/4S,\\ SS4S0jjr0\+"5S*.S/\\ \/4S,\\ SS4S1jjr1S2r2g)5 SpanRulertzThe SpanRuler lets you add spans to the `Doc.spans` using token-based rules or exact phrase matches. DOCS: https://spacy.io/api/spanruler USAGE: https://spacy.io/usage/rule-based-matching#spanruler NFrX) rY spans_filter annotate_ents ents_filterphrase_matcher_attrmatcher_fuzzy_comparevalidate overwritescorernlpnamerYrsrtrurvrwrxryrzr$c XlX lX0lXPlXplXlXlX@lX`lXl Xl 0Ul UR5 g)aInitialize the span ruler. If patterns are supplied here, they need to be a list of dictionaries with a `"label"` and `"pattern"` key. A pattern can either be a token pattern (list) or a phrase pattern (string). For example: `{'label': 'ORG', 'pattern': 'Apple'}`. nlp (Language): The shared nlp object to pass the vocab to the matchers and process phrase patterns. name (str): Instance name of the current pipeline component. Typically passed in automatically from the factory when the component is added. Used to disable the current span ruler while creating phrase patterns with the nlp object. spans_key (Optional[str]): The spans key to save the spans under. If `None`, no spans are saved. Defaults to "ruler". spans_filter (Optional[Callable[[Iterable[Span], Iterable[Span]], List[Span]]): The optional method to filter spans before they are assigned to doc.spans. Defaults to `None`. annotate_ents (bool): Whether to save spans to doc.ents. Defaults to `False`. ents_filter (Callable[[Iterable[Span], Iterable[Span]], List[Span]]): The method to filter spans before they are assigned to doc.ents. Defaults to `util.filter_chain_spans`. phrase_matcher_attr (Optional[Union[int, str]]): Token attribute to match on, passed to the internal PhraseMatcher as `attr`. Defaults to `None`. matcher_fuzzy_compare (Callable): The fuzzy comparison method for the internal Matcher. Defaults to spacy.matcher.levenshtein.levenshtein_compare. validate (bool): Whether patterns should be validated, passed to Matcher and PhraseMatcher as `validate`. overwrite (bool): Whether to remove any existing spans under this spans key if `spans_key` is set, and/or to remove any ents under `doc.ents` if `annotate_ents` is set. Defaults to `True`. scorer (Optional[Callable]): The scoring method. Defaults to spacy.pipeline.span_ruler.overlapping_labeled_spans_score. DOCS: https://spacy.io/api/spanruler#init N) r{r|rYrtrvrxryrsrurzrw_match_label_id_mapclear) selfr{r|rYrsrtrurvrwrxryrzs r-__init__SpanRuler.__init__|sRt "*#6  "(& %:">@  r1c,[UR5$)z1The number of all labels added to the span ruler.)rc _patternsrs r-__len__SpanRuler.__len__s4>>""r1labelc\URR5HnUSU:XdM g g)z+Whether a label is present in the patterns.rTF)r~values)rrlabel_ids r- __contains__SpanRuler.__contains__s000779H E):r1cUR$)z2Key of the doc.spans dict to save the spans under.rXrs r-r3 SpanRuler.keys~~r1rdcUR5nURU5nURX5 U$![anU"URX/U5sSnA$SnAff=f)zFind matches in document and add them as entities. doc (Doc): The Doc object in the pipeline. RETURNS (Doc): The Doc with added entities, if available. DOCS: https://spacy.io/api/spanruler#call N)get_error_handlermatchset_annotations Exceptionr|)rrd error_handlermatchesrIs r-__call__SpanRuler.__call__s\..0  <jjoG   .J < D%; ; # UH<upnX#:wdM [TUUTRUSTRUSS9v M> g7f)rid)rspan_idN)rr~)r9m_idr*r)rdrs r-r<"SpanRuler.match..s\ # %, S| D..t4W=006t<  %,s A4A) _require_patternswarningscatch_warningsfilterwarningsrr r intrAmatcherphrase_matcherrBr@)rrdrdeduplicated_matchess`` r-rSpanRuler.matchs   $ $ &  # #Hi @U3S=)*T\\#&'$t/B/B3/G*HHG' # # %, # d/011#' &s A2C  CcNUR(a/nURUR;a*UR(dURURnURUR(aUR X25OU5 X1RUR'UR (aK/nUR(d[ UR5nURX25n[U5Ulgg![a [[R5ef=f)zModify the document in placeN) r3r#ryextendrsrtrAentsrur@ ValueErrorrE854)rrdrr#s r-rSpanRuler.set_annotationss 88Exx399$T^^ $((+ LL595F5F!!%1G #(IIdhh    E>>SXX$$U4E .!%=  . -- .s .D$D$.c[[[URVs/sHn[ [ US5PM sn555$s snf)zzAll labels present in the match patterns. RETURNS (set): The string labels. DOCS: https://spacy.io/api/spanruler#labels r)tupler@rBrrstrrps r-labelsSpanRuler.labelss:VC O1c1W:!6 OPQRR OsA c[[[URVs/sH"n[ [ UR S55PM$ sn5[S/5- 55$s snf)zqAll IDs present in the match patterns. RETURNS (set): The string IDs. DOCS: https://spacy.io/api/spanruler#ids rN)rr@rBrrrrbrs r-ids SpanRuler.idssO 3G1S!%%+.GH3PTv;V W  Gs)A#)r{patterns get_examplesrcVUR5 U(aURU5 gg)a[Initialize the pipe for training. get_examples (Callable[[], Iterable[Example]]): Function that returns a representative sample of gold-standard Example objects. nlp (Language): The current nlp object the component is part of. patterns (Optional[Iterable[PatternType]]): The list of patterns. DOCS: https://spacy.io/api/spanruler#initialize N)r add_patterns)rrr{rs r- initializeSpanRuler.initialize%s#    h ' r1cUR$)zGet all patterns that were added to the span ruler. RETURNS (list): The original patterns, one dictionary per pattern. DOCS: https://spacy.io/api/spanruler#patterns )rrs r-rSpanRuler.patterns9s~~r1c zSn[URR5HunupEX:XdMUn O URRUSVs/sHoUPM nnURR US9 /n/nUGH<n [ [U S5n [ [U RSS55n [X45n U U S.URURRRRU 5'[U S[5(a&URU 5 URU S5 Oa[U S[ 5(a UR"R%XS/5 O)[ [&R(R+U SS 95eUR,RU 5 GM? [/UURR1U55H!upUR2R%X/5 M# SSS5 gs snf![a /nGNf=f!,(df  g=f) a~Add patterns to the span ruler. A pattern can either be a token pattern (list of dicts) or a phrase pattern (string). For example: {'label': 'ORG', 'pattern': 'Apple'} {'label': 'ORG', 'pattern': 'Apple', 'id': 'apple'} {'label': 'GPE', 'pattern': [{'lower': 'san'}, {'lower': 'francisco'}]} patterns (list): The patterns to add. DOCS: https://spacy.io/api/spanruler#add_patterns N)disablerr)rrpattern)r) enumerater{pipeline pipe_namesr select_pipesrrrbreprr~vocabstringsas_int isinstancerDrAraddrE097formatrzippiper)rr current_indexr7r|rsubsequent_pipesphrase_pattern_labelsphrase_pattern_textsentryp_labelp_idrrs r-rSpanRuler.add_patternsCs "M#,TXX->->#??$V[[%7%7i@P%7%QRR%%e," #&% 23###''y9 #'= < V "!  " < >wG "5"55''..{;,,.LL'' 4 0Ls  DD pattern_idct[U5nURVs/sHo3RS5U:wdMUPM snUlU[U5:Xa1[[R R SXRS95eURHnURUSU:XdMURRRRU5nXPR;aURRU5 XPR;dMURRU5 M gs snf)zRemove a pattern by its pattern ID. pattern_id (str): ID of the pattern to be removed. RETURNS: None DOCS: https://spacy.io/api/spanruler#remove_by_id rIDrN)rcrrbrrrrr|r~r{rrrrrr)rrorig_lenrrrs r- remove_by_idSpanRuler.remove_by_idst9%)^^Q^uuT{j7P!^Q s4y  ##"* $  //G''06*D"hhnn44>>wG "5"55''..{;,,.LL'' 4 0Rs D5D5c[U5S:Xa<[R"[RR UR S95 gg)z:Raise a warning if this component has no patterns defined.r)r|N)rcrwarnrW036rr|rs r-rSpanRuler._require_patternss4 t9> MM(--..DII.> ? r1)exclude bytes_datarcf^TR5 SU4Sj0n[R"XU5 T$)zLoad the span ruler from a bytestring. bytes_data (bytes): The bytestring to load. RETURNS (SpanRuler): The loaded span ruler. DOCS: https://spacy.io/api/spanruler#from_bytes rcN>TR[R"U55$r')rsrsly json_loads)brs r-r.&SpanRuler.from_bytes..$"3"3E4D4DQ4G"Hr1)rr from_bytes)rrr deserializerss` r-rSpanRuler.from_bytess0 H   7; r1c@^SU4Sj0n[R"X!5$)zSerialize the span ruler to a bytestring. RETURNS (bytes): The serialized patterns. DOCS: https://spacy.io/api/spanruler#to_bytes rcD>[R"TR5$r')r json_dumpsrrsr-r.$SpanRuler.to_bytes..s 0 0 ?r1)rto_bytes)rr serializerss` r-rSpanRuler.to_bytess# ? }}[22r1pathc|^TR5 [U5nSU4Sj0n[R"X05 T$)zLoad the span ruler from a directory. path (Union[str, Path]): A path to a directory. RETURNS (SpanRuler): The loaded span ruler. DOCS: https://spacy.io/api/spanruler#from_disk rcN>TR[R"U55$r')rr read_jsonlrrs r-r.%SpanRuler.from_disk..rr1)rrr from_disk)rrrrs` r-r SpanRuler.from_disks: 4  H  tB/ r1cZ^[U5nSU4Sj0n[R"X05 g)zSave the span ruler patterns to a directory. path (Union[str, Path]): A path to a directory. DOCS: https://spacy.io/api/spanruler#to_disk rcF>[R"UTR5$r')r write_jsonlrr s r-r.#SpanRuler.to_disk..s%"3"3At}}"Er1N)rrto_disk)rrrrs` r-rSpanRuler.to_disks,4  E  T+r1)r~rrtrurrwr|r{ryrrvrzrsrYrx) span_ruler)r$N)3__name__ __module__ __qualname____firstlineno____doc__DEFAULT_SPANS_KEYrfilter_chain_spansrrrmrrr rrrboolrrrrrpropertyr3rrrrr rrrr PatternTyperr rrrrrrrbytesrrrr r__static_attributes__rLr1r-rqrqts7!F $5 #  # #9=*=%, +7H& #F FF C= F  htnhtn5x~E F FF d^Xd^ ,htn < F&eCHo6F (FF !F""#F( )FP###$Xc]N=O-5c] "4D3E 38C= 3% 3CSBT#t)$2:3- &CSBT ,#t)$ ,2:3- ,  , ,r1rqcUS:Xa"[R"S5nUR$US:Xa"[R"S5nUR$[ S[ SU35e)Nmake_span_rulerzspacy.pipeline.factoriesmake_entity_rulerzmodule z has no attribute ) importlib import_moduler#make_future_entity_rulerAttributeErrorr)r|modules r- __getattr__r*sg   (()CD%%% $ $(()CD... 78*,>tfE FFr1)7r%sysr functoolsrpathlibrtypingrrrrr r r r r rrrrrerrorsrrlanguagerrrrmatcher.levenshteinrrzrtokensrrtrainingrrrrrr rrrrJrMrUrWrmrorqr*rLr1r-r4s'      %,5::3c4S#X#77889 #tn#%-d^# $Z#4&#tn#%-d^# $Z#4+ /@ 2w 2 #s(^ 2