k*ih SSKJr SSKrSSKJrJrJr SSKJrJ r /SQr Sr \SSSSS.Sjr \SSSSS.S jr \SS SSSS .S jrS rSr\ SSSSSSSS.Sjr\ SSSSSSSS.Sjrg)) annotationsN) ScorerFlagis_none setupPandas)WRatioratio)cdistextract extractOne extract_itercN[USS5nUbUS"S0UD6nUSUS4$g)N _RF_ScorerPyget_scorer_flags worst_score optimal_score)rd)getattrscorer scorer_kwargsparamsflagss T/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/rapidfuzz/process_py.py_get_scorer_flags_pyr sA V^T 2F )*;];m$eO&<== )r processor score_cutoff score_hintrc## UnU=(d 0n[X&5upX:n [5 [U5(agUcUnUbU"U5n[US5(aUR 5O [ U5n U H[up[U 5(aMUc U"X 4SU0UD6nOU"UU"U 54SU0UD6nU (aX:aXU 4v MLMNX::dMUXU 4v M] g7f)a Find the best match in a list of choices Parameters ---------- query : Sequence[Hashable] string we want to find choices : Iterable[Sequence[Hashable]] | Mapping[Sequence[Hashable]] list of all strings the query should be compared with or dict with a mapping {: } scorer : Callable, optional Optional callable that is used to calculate the matching score between the query and each choice. This can be any of the scorers included in RapidFuzz (both scorers that calculate the edit distance or the normalized edit distance), or a custom function, which returns a normalized edit distance. fuzz.WRatio is used by default. processor : Callable, optional Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour. score_cutoff : Any, optional Optional argument for a score threshold. When an edit distance is used this represents the maximum edit distance and matches with a `distance > score_cutoff` are ignored. When a normalized edit distance is used this represents the minimal similarity and matches with a `similarity < score_cutoff` are ignored. Default is None, which deactivates this behaviour. score_hint : Any, optional Optional argument for an expected score to be passed to the scorer. This is used to select a faster implementation. Default is None, which deactivates this behaviour. scorer_kwargs : dict[str, Any], optional any other named parameters are passed to the scorer. This can be used to pass e.g. weights to `Levenshtein.distance` Yields ------- tuple[Sequence[Hashable], Any, Any] Yields similarity between the query and each choice in form of a Tuple with 3 elements. The values stored in the tuple depend on the types of the input arguments. * The first element is always the current `choice`, which is the value that's compared to the query. * The second value represents the similarity calculated by the scorer. This can be: * An edit distance (distance is 0 for a perfect match and > 0 for non perfect matches). In this case only choices which have a `distance <= score_cutoff` are yielded. An example of a scorer with this behavior is `Levenshtein.distance`. * A normalized edit distance (similarity is a score between 0 and 100, with 100 being a perfect match). In this case only choices which have a `similarity >= score_cutoff` are yielded. An example of a scorer with this behavior is `Levenshtein.normalized_similarity`. Note, that for all scorers, which are not provided by RapidFuzz, only normalized edit distances are supported. * The third parameter depends on the type of the `choices` argument it is: * The `index of choice` when choices is a simple iterable like a list * The `key of choice` when choices is a mapping like a dict, or a pandas Series Nitemsrrrrhasattrr! enumerate)querychoicesrrrrr_rrlowest_score_worst choices_iterkeychoicescores rr r sF A!'RM!5f!LK&4Mu~~" % &-gw&?&?7==?YwEWL#  6??   5U|U}UE&!* E $c**%$c**'$s C C CcUnU=(d 0n[X&5upX:n [5 [U5(agUcUnUbU"U5nSn [US5(aUR 5O [ U5n U Htup[U5(aMUc U"X4SU0UD6nOU"UU"U54SU0UD6nU (aX:aU bXS:aUnXU 4n OX::aU bXS:aUnXU 4n X:XdMs U $ U $)aU Find the best match in a list of choices. When multiple elements have the same similarity, the first element is returned. Parameters ---------- query : Sequence[Hashable] string we want to find choices : Iterable[Sequence[Hashable]] | Mapping[Sequence[Hashable]] list of all strings the query should be compared with or dict with a mapping {: } scorer : Callable, optional Optional callable that is used to calculate the matching score between the query and each choice. This can be any of the scorers included in RapidFuzz (both scorers that calculate the edit distance or the normalized edit distance), or a custom function, which returns a normalized edit distance. fuzz.WRatio is used by default. processor : Callable, optional Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour. score_cutoff : Any, optional Optional argument for a score threshold. When an edit distance is used this represents the maximum edit distance and matches with a `distance > score_cutoff` are ignored. When a normalized edit distance is used this represents the minimal similarity and matches with a `similarity < score_cutoff` are ignored. Default is None, which deactivates this behaviour. score_hint : Any, optional Optional argument for an expected score to be passed to the scorer. This is used to select a faster implementation. Default is None, which deactivates this behaviour. scorer_kwargs : dict[str, Any], optional any other named parameters are passed to the scorer. This can be used to pass e.g. weights to `Levenshtein.distance` Returns ------- tuple[Sequence[Hashable], Any, Any] Returns the best match in form of a Tuple with 3 elements. The values stored in the tuple depend on the types of the input arguments. * The first element is always the `choice`, which is the value that's compared to the query. * The second value represents the similarity calculated by the scorer. This can be: * An edit distance (distance is 0 for a perfect match and > 0 for non perfect matches). In this case only choices which have a `distance <= score_cutoff` are returned. An example of a scorer with this behavior is `Levenshtein.distance`. * A normalized edit distance (similarity is a score between 0 and 100, with 100 being a perfect match). In this case only choices which have a `similarity >= score_cutoff` are returned. An example of a scorer with this behavior is `Levenshtein.normalized_similarity`. Note, that for all scorers, which are not provided by RapidFuzz, only normalized edit distances are supported. * The third parameter depends on the type of the `choices` argument it is: * The `index of choice` when choices is a simple iterable like a list * The `key of choice` when choices is a mapping like a dict, or a pandas Series None When no choice has a `similarity >= score_cutoff`/`distance <= score_cutoff` None is returned Examples -------- >>> from rapidfuzz.process import extractOne >>> from rapidfuzz.distance import Levenshtein >>> from rapidfuzz.fuzz import ratio extractOne can be used with normalized edit distances. >>> extractOne("abcd", ["abce"], scorer=ratio) ("abcd", 75.0, 1) >>> extractOne("abcd", ["abce"], scorer=Levenshtein.normalized_similarity) ("abcd", 0.75, 1) extractOne can be used with edit distances as well. >>> extractOne("abcd", ["abce"], scorer=Levenshtein.distance) ("abce", 1, 0) additional settings of the scorer can be passed via the scorer_kwargs argument to extractOne >>> extractOne("abcd", ["abce"], scorer=Levenshtein.distance, scorer_kwargs={"weights":(1,1,2)}) ("abcde", 2, 1) when a mapping is used for the choices the key of the choice is returned instead of the List index >>> extractOne("abcd", {"key": "abce"}, scorer=ratio) ("abcd", 75.0, "key") It is possible to specify a processor function which is used to preprocess the strings before comparing them. >>> extractOne("abcd", ["abcD"], scorer=ratio) ("abcD", 75.0, 0) >>> extractOne("abcd", ["abcD"], scorer=ratio, processor=utils.default_process) ("abcD", 100.0, 0) >>> extractOne("abcd", ["abcD"], scorer=ratio, processor=lambda s: s.upper()) ("abcD", 100.0, 0) When only results with a similarity above a certain threshold are relevant, the parameter score_cutoff can be used to filter out results with a lower similarity. This threshold is used by some of the scorers to exit early, when they are sure, that the similarity is below the threshold. For normalized edit distances all results with a similarity below score_cutoff are filtered out >>> extractOne("abcd", ["abce"], scorer=ratio) ("abce", 75.0, 0) >>> extractOne("abcd", ["abce"], scorer=ratio, score_cutoff=80) None For edit distances all results with an edit distance above the score_cutoff are filtered out >>> extractOne("abcd", ["abce"], scorer=Levenshtein.distance, scorer_kwargs={"weights":(1,1,2)}) ("abce", 2, 0) >>> extractOne("abcd", ["abce"], scorer=Levenshtein.distance, scorer_kwargs={"weights":(1,1,2)}, score_cutoff=1) None Nr!rr")r%r&rrrrrr'rrr(resultr)r*r+r,s rr r s5| A!'RM!5f!LK&4Mu~~" %  F&-gw&?&?7==?YwEWL#  6??   5U|U}UE&!* E $&.E1I: } scorer : Callable, optional Optional callable that is used to calculate the matching score between the query and each choice. This can be any of the scorers included in RapidFuzz (both scorers that calculate the edit distance or the normalized edit distance), or a custom function, which returns a normalized edit distance. fuzz.WRatio is used by default. processor : Callable, optional Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour. limit : int, optional maximum amount of results to return. None can be passed to disable this behavior. Default is 5. score_cutoff : Any, optional Optional argument for a score threshold. When an edit distance is used this represents the maximum edit distance and matches with a `distance > score_cutoff` are ignored. When a normalized edit distance is used this represents the minimal similarity and matches with a `similarity < score_cutoff` are ignored. Default is None, which deactivates this behaviour. score_hint : Any, optional Optional argument for an expected score to be passed to the scorer. This is used to select a faster implementation. Default is None, which deactivates this behaviour. scorer_kwargs : dict[str, Any], optional any other named parameters are passed to the scorer. This can be used to pass e.g. weights to `Levenshtein.distance` Returns ------- list[tuple[Sequence[Hashable], Any, Any]] The return type is always a List of Tuples with 3 elements. However the values stored in the tuple depend on the types of the input arguments. * The first element is always the `choice`, which is the value that's compared to the query. * The second value represents the similarity calculated by the scorer. This can be: * An edit distance (distance is 0 for a perfect match and > 0 for non perfect matches). In this case only choices which have a `distance <= score_cutoff` are returned. An example of a scorer with this behavior is `Levenshtein.distance`. * A normalized edit distance (similarity is a score between 0 and 100, with 100 being a perfect match). In this case only choices which have a `similarity >= score_cutoff` are returned. An example of a scorer with this behavior is `Levenshtein.normalized_similarity`. Note, that for all scorers, which are not provided by RapidFuzz, only normalized edit distances are supported. * The third parameter depends on the type of the `choices` argument it is: * The `index of choice` when choices is a simple iterable like a list * The `key of choice` when choices is a mapping like a dict, or a pandas Series The list is sorted by similarity or distance depending on the scorer used. The first element in the list has the `highest similarity`/`smallest distance`. r.)rrrrrc US$Nr.ris rextract..s1r)r*reversec US$r4rr5s rr7r8s!r)r*c US$r4rr5s rr7r8sQqTr)rr r sortedheapqnlargest nsmallest) r%r&rrr1rrrrrr(res result_iters rr r /sV"'RM!5f!LK&4 z  %!'  ;Iu  !#K }k~?QRR~~enEE ??5> BBrc4SSKnUbURU5$[USS5nUbeUS"S0UD6nUS[R-(a UR $US[R -(a UR$UR$UR$)Nrrrrr) numpydtyperr RESULT_I64int32 RESULT_SIZE_Tuint32float32)rDrrnprrs r_dtype_to_type_numrKs  xx V^T 2F )*;]; >J11 188O >J44 499 zz ::rcr[USS5nUb'US"S0UD6nUS[R-(agg)NrrrTFr)rr SYMMETRICrs r _is_symmetricrNsA V^T 2F )*;]; >J00 0 rr.)rrrrscore_multiplierrDworkersrcSSKn X4n U =(d 0n [XrU 5nU R[U5[U54US9n [ 5 Uc [ U5n O*UVs/sHn[ U5(aUOU"U5PM n nXLa[X)5(a[U 5HunnU"UU4SU0U D6U-nU RXzR5(a [U5nUXU4'[US-[U 55HMnU"UU U4SU0U D6U-nU RXzR5(a [U5nU=XU4'U UU4'MO M U $[U5H|unnU(a[ U5(dU"U5OUn[U 5HFunnU"UU4SU0U D6U-nU RXzR5(a [U5nUXU4'MH M~ U $s snf)a Compute distance/similarity between each pair of the two collections of inputs. Parameters ---------- queries : Collection[Sequence[Hashable]] list of all strings the queries choices : Collection[Sequence[Hashable]] list of all strings the query should be compared scorer : Callable, optional Optional callable that is used to calculate the matching score between the query and each choice. This can be any of the scorers included in RapidFuzz (both scorers that calculate the edit distance or the normalized edit distance), or a custom function, which returns a normalized edit distance. fuzz.ratio is used by default. processor : Callable, optional Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour. score_cutoff : Any, optional Optional argument for a score threshold to be passed to the scorer. Default is None, which deactivates this behaviour. score_hint : Any, optional Optional argument for an expected score to be passed to the scorer. This is used to select a faster implementation. Default is None, which deactivates this behaviour. score_multiplier: Any, optional Optional argument to multiply the calculated score with. This is applied as the final step, so e.g. score_cutoff is applied on the unmodified score. This is mostly useful to map from a floating point range to an integer to reduce the memory usage. Default is 1, which deactivates this behaviour. dtype : data-type, optional The desired data-type for the result array. Depending on the scorer type the following dtypes are supported: - similarity: - np.float32, np.float64 - np.uint8 -> stores fixed point representation of the result scaled to a range 0-100 - distance: - np.int8, np.int16, np.int32, np.int64 If not given, then the type will be np.float32 for similarities and np.int32 for distances. workers : int, optional The calculation is subdivided into workers sections and evaluated in parallel. Supply -1 to use all available CPU cores. This argument is only available for scorers using the RapidFuzz C-API so far, since it releases the Python GIL. scorer_kwargs : dict[str, Any], optional any other named parameters are passed to the scorer. This can be used to pass e.g. weights to `Levenshtein.distance` Returns ------- ndarray Returns a matrix of dtype with the distance/similarity between each pair of the two collections of inputs. rNrDrr.) rCrKzeroslenrlistrrNr$ issubdtypeintegerroundrange)queriesr&rrrrrOrDrPrrJr'results proc_choicesxr6 proc_queryr,jr%r+s rr r sJA!'RM um stores fixed point representation of the result scaled to a range 0-100 - distance: - np.int8, np.int16, np.int32, np.int64 If not given, then the type will be np.float32 for similarities and np.int32 for distances. workers : int, optional The calculation is subdivided into workers sections and evaluated in parallel. Supply -1 to use all available CPU cores. This argument is only available for scorers using the RapidFuzz C-API so far, since it releases the Python GIL. scorer_kwargs : dict[str, Any], optional any other named parameters are passed to the scorer. This can be used to pass e.g. weights to `Levenshtein.distance` Returns ------- ndarray Returns a matrix of size (n x 1) of dtype with the distance/similarity between each pair of the two collections of inputs. rNz/Length of queries and choices must be the same!rRr) rCrT ValueErrorrKrSrr$ziprrVrWrX)rZr&rrrrrOrDrPrrJ len_queries len_choices error_messager'r[r6r%r+r^ proc_choicer,s rcpdistrg>sJg,Kg,K!I ''A!'RM um ?E6*3GENNYu%QV ,5gfooi'TZ    &    !! == + +%LE %?( Nr) __future__rr=rapidfuzz._utilsrrrrapidfuzz.fuzzrr__all__rr r r rKrNr rgrrrrls# ==( < h+^ lf  lC^,   }H   ir