/ open-webui.git / app / env / lib64 / python3.11 / site-packages / rapidfuzz / process.pyi

from __future__ import annotations from collections.abc import Collection, Generator, Hashable, Iterable, Mapping, Sequence from typing import ( Any, Callable, Protocol, TypeVar, overload, ) from rapidfuzz.fuzz import WRatio, ratio _StringType = Sequence[Hashable] _StringType1 = TypeVar("_StringType1", bound=Sequence[Hashable]) _StringType2 = TypeVar("_StringType2", bound=Sequence[Hashable]) _UnprocessedType1 = TypeVar("_UnprocessedType1") _UnprocessedType2 = TypeVar("_UnprocessedType2") _KeyType = TypeVar("_KeyType") _ResultType = TypeVar("_ResultType", int, float) _StringType1_contra = TypeVar("_StringType1_contra", contravariant=True, bound=Sequence[Hashable]) _StringType2_contra = TypeVar("_StringType2_contra", contravariant=True, bound=Sequence[Hashable]) _ResultType_contra = TypeVar("_ResultType_contra", int, float, contravariant=True) _ResultType_co = TypeVar("_ResultType_co", int, float, covariant=True) class _Scorer(Protocol[_StringType1_contra, _StringType2_contra, _ResultType_contra, _ResultType_co]): def __call__( self, __s1: _StringType1_contra, __s2: _StringType2_contra, *, score_cutoff: _ResultType_contra | None ) -> _ResultType_co: ... # mypy wants defaults to be valid for every possible parameterization of a generic function # so add separate overloads for the default version @overload def extractOne( query: Sequence[Hashable] | None, choices: Mapping[_KeyType, _StringType2 | None], *, scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = WRatio, processor: None = None, score_cutoff: float | None = None, score_hint: float | None = None, scorer_kwargs: dict[str, Any] | None = None, ) -> tuple[_StringType2, float, _KeyType]: ... @overload def extractOne( query: Sequence[Hashable] | None, choices: Iterable[_StringType2 | None], *, scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = WRatio, processor: None = None, score_cutoff: float | None = None, score_hint: float | None = None, scorer_kwargs: dict[str, Any] | None = None, ) -> tuple[_StringType2, float, int]: ... @overload def extractOne( query: _UnprocessedType1 | None, choices: Mapping[_KeyType, _UnprocessedType2 | None], *, scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = WRatio, processor: Callable[[_UnprocessedType1 | _UnprocessedType2], Sequence[Hashable]], score_cutoff: float | None = None, score_hint: float | None = None, scorer_kwargs: dict[str, Any] | None = None, ) -> tuple[_UnprocessedType2, float, _KeyType]: ... @overload def extractOne( query: _UnprocessedType1 | None, choices: Iterable[_UnprocessedType2 | None], *, scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = WRatio, processor: Callable[[_UnprocessedType1 | _UnprocessedType2], Sequence[Hashable]], score_cutoff: float | None = None, score_hint: float | None = None, scorer_kwargs: dict[str, Any] | None = None, ) -> tuple[_UnprocessedType2, float, int]: ... @overload def extractOne( query: _StringType1 | None, choices: Mapping[_KeyType, _StringType2 | None], *, scorer: _Scorer[_StringType1, _StringType2, _ResultType, _ResultType], processor: None = None, score_cutoff: _ResultType | None = None, score_hint: _ResultType | None = None, scorer_kwargs: dict[str, Any] | None = None, ) -> tuple[_StringType2, _ResultType, _KeyType]: ... @overload def extractOne( query: _StringType1 | None, choices: Iterable[_StringType2 | None], *, scorer: _Scorer[_StringType1, _StringType2, _ResultType, _ResultType], processor: None = None, score_cutoff: _ResultType | None = None, score_hint: _ResultType | None = None, scorer_kwargs: dict[str, Any] | None = None, ) -> tuple[_StringType2, _ResultType, int]: ... @overload def extractOne( query: _UnprocessedType1 | None, choices: Mapping[_KeyType, _UnprocessedType2 | None], *, scorer: _Scorer[_StringType1, _StringType1, _ResultType, _ResultType], processor: Callable[[_UnprocessedType1 | _UnprocessedType2], _StringType1], score_cutoff: _ResultType | None = None, score_hint: _ResultType | None = None, scorer_kwargs: dict[str, Any] | None = None, ) -> tuple[_UnprocessedType2, _ResultType, _KeyType]: ... @overload def extractOne( query: _UnprocessedType1 | None, choices: Iterable[_UnprocessedType2 | None], *, scorer: _Scorer[_StringType1, _StringType1, _ResultType, _ResultType], processor: Callable[[_UnprocessedType1 | _UnprocessedType2], _StringType1], score_cutoff: _ResultType | None = None, score_hint: _ResultType | None = None, scorer_kwargs: dict[str, Any] | None = None, ) -> tuple[_UnprocessedType2, _ResultType, int]: ... # mypy wants defaults to be valid for every possible parameterization of a generic function # so add separate overloads for the default version @overload def extract( query: Sequence[Hashable] | None, choices: Mapping[_KeyType, _StringType2 | None], *, scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = WRatio, processor: None = None, limit: int | None = 5, score_cutoff: float | None = None, score_hint: float | None = None, scorer_kwargs: dict[str, Any] | None = None, ) -> list[tuple[_StringType2, float, _KeyType]]: ... @overload def extract( query: Sequence[Hashable] | None, choices: Iterable[_StringType2 | None], *, scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = WRatio, processor: None = None, limit: int | None = 5, score_cutoff: float | None = None, score_hint: float | None = None, scorer_kwargs: dict[str, Any] | None = None, ) -> list[tuple[_StringType2, float, int]]: ... @overload def extract( query: _UnprocessedType1 | None, choices: Mapping[_KeyType, _UnprocessedType2 | None], *, scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = WRatio, processor: Callable[[_UnprocessedType1 | _UnprocessedType2], Sequence[Hashable]], limit: int | None = 5, score_cutoff: float | None = None, score_hint: float | None = None, scorer_kwargs: dict[str, Any] | None = None, ) -> list[tuple[_UnprocessedType2, float, _KeyType]]: ... @overload def extract( query: _UnprocessedType1 | None, choices: Iterable[_UnprocessedType2 | None], *, scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = WRatio, processor: Callable[[_UnprocessedType1 | _UnprocessedType2], Sequence[Hashable]], limit: int | None = 5, score_cutoff: float | None = None, score_hint: float | None = None, scorer_kwargs: dict[str, Any] | None = None, ) -> list[tuple[_UnprocessedType2, float, int]]: ... @overload def extract( query: _StringType1 | None, choices: Mapping[_KeyType, _StringType2 | None], *, scorer: _Scorer[_StringType1, _StringType2, _ResultType, _ResultType], processor: None = None, limit: int | None = 5, score_cutoff: _ResultType | None = None, score_hint: _ResultType | None = None, scorer_kwargs: dict[str, Any] | None = None, ) -> list[tuple[_StringType2, _ResultType, _KeyType]]: ... @overload def extract( query: _StringType1 | None, choices: Collection[_StringType2 | None], *, scorer: _Scorer[_StringType1, _StringType2, _ResultType, _ResultType], processor: None = None, limit: int | None = 5, score_cutoff: _ResultType | None = None, score_hint: _ResultType | None = None, scorer_kwargs: dict[str, Any] | None = None, ) -> list[tuple[_StringType2, _ResultType, int]]: ... @overload def extract( query: _UnprocessedType1 | None, choices: Mapping[_KeyType, _UnprocessedType2 | None], *, scorer: _Scorer[_StringType1, _StringType1, _ResultType, _ResultType], processor: Callable[[_UnprocessedType1 | _UnprocessedType2], _StringType1], limit: int | None = 5, score_cutoff: _ResultType | None = None, score_hint: _ResultType | None = None, scorer_kwargs: dict[str, Any] | None = None, ) -> list[tuple[_UnprocessedType2, _ResultType, _KeyType]]: ... @overload def extract( query: _UnprocessedType1 | None, choices: Collection[_UnprocessedType2 | None], *, scorer: _Scorer[_StringType1, _StringType1, _ResultType, _ResultType], processor: Callable[[_UnprocessedType1 | _UnprocessedType2], _StringType1], limit: int | None = 5, score_cutoff: _ResultType | None = None, score_hint: _ResultType | None = None, scorer_kwargs: dict[str, Any] | None = None, ) -> list[tuple[_UnprocessedType2, _ResultType, int]]: ... # mypy wants defaults to be valid for every possible parameterization of a generic function # so add separate overloads for the default version @overload def extract_iter( query: Sequence[Hashable] | None, choices: Mapping[_KeyType, _StringType2 | None], *, scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = WRatio, processor: None = None, score_cutoff: float | None = None, score_hint: float | None = None, scorer_kwargs: dict[str, Any] | None = None, ) -> Generator[tuple[_StringType2, float, _KeyType], None, None]: ... @overload def extract_iter( query: Sequence[Hashable] | None, choices: Iterable[_StringType2 | None], *, scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = WRatio, processor: None = None, score_cutoff: float | None = None, score_hint: float | None = None, scorer_kwargs: dict[str, Any] | None = None, ) -> Generator[tuple[_StringType2, float, int], None, None]: ... @overload def extract_iter( query: _UnprocessedType1 | None, choices: Mapping[_KeyType, _UnprocessedType2 | None], *, scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = WRatio, processor: Callable[[_UnprocessedType1 | _UnprocessedType2], Sequence[Hashable]], score_cutoff: float | None = None, score_hint: float | None = None, scorer_kwargs: dict[str, Any] | None = None, ) -> Generator[tuple[_UnprocessedType2, float, _KeyType], None, None]: ... @overload def extract_iter( query: _UnprocessedType1 | None, choices: Iterable[_UnprocessedType2 | None], *, scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = WRatio, processor: Callable[[_UnprocessedType1 | _UnprocessedType2], Sequence[Hashable]], score_cutoff: float | None = None, score_hint: float | None = None, scorer_kwargs: dict[str, Any] | None = None, ) -> Generator[tuple[_UnprocessedType2, float, int], None, None]: ... @overload def extract_iter( query: _StringType1 | None, choices: Mapping[_KeyType, _StringType2 | None], *, scorer: _Scorer[_StringType1, _StringType2, _ResultType, _ResultType], processor: None = None, score_cutoff: _ResultType | None = None, score_hint: _ResultType | None = None, scorer_kwargs: dict[str, Any] | None = None, ) -> Generator[tuple[_StringType2, _ResultType, _KeyType], None, None]: ... @overload def extract_iter( query: _StringType1 | None, choices: Iterable[_StringType2 | None], *, scorer: _Scorer[_StringType1, _StringType2, _ResultType, _ResultType], processor: None = None, score_cutoff: _ResultType | None = None, score_hint: _ResultType | None = None, scorer_kwargs: dict[str, Any] | None = None, ) -> Generator[tuple[_StringType2, _ResultType, int], None, None]: ... @overload def extract_iter( query: _UnprocessedType1 | None, choices: Mapping[_KeyType, _UnprocessedType2 | None], *, scorer: _Scorer[_StringType1, _StringType1, _ResultType, _ResultType], processor: Callable[[_UnprocessedType1 | _UnprocessedType2], _StringType1], score_cutoff: _ResultType | None = None, score_hint: _ResultType | None = None, scorer_kwargs: dict[str, Any] | None = None, ) -> Generator[tuple[_UnprocessedType2, _ResultType, _KeyType], None, None]: ... @overload def extract_iter( query: _UnprocessedType1 | None, choices: Iterable[_UnprocessedType2 | None], *, scorer: _Scorer[_StringType1, _StringType1, _ResultType, _ResultType], processor: Callable[[_UnprocessedType1 | _UnprocessedType2], _StringType1], score_cutoff: _ResultType | None = None, score_hint: _ResultType | None = None, scorer_kwargs: dict[str, Any] | None = None, ) -> Generator[tuple[_UnprocessedType2, _ResultType, int], None, None]: ... try: import numpy as np from numpy.typing import DTypeLike @overload def cdist( queries: Iterable[Sequence[Hashable] | None], choices: Iterable[Sequence[Hashable] | None], *, scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = ratio, processor: None = None, score_cutoff: float | None = None, score_hint: float | None = None, score_multiplier: float = 1, dtype: DTypeLike | None = None, workers: int = 1, scorer_kwargs: dict[str, Any] | None = None, ) -> np.ndarray: ... @overload def cdist( queries: Iterable[_UnprocessedType1 | None], choices: Iterable[_UnprocessedType2 | None], *, scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = ratio, processor: Callable[[_UnprocessedType1 | _UnprocessedType2], Sequence[Hashable]], score_cutoff: float | None = None, score_hint: float | None = None, score_multiplier: float = 1, dtype: DTypeLike | None = None, workers: int = 1, scorer_kwargs: dict[str, Any] | None = None, ) -> np.ndarray: ... @overload def cdist( queries: Iterable[_StringType1 | None], choices: Iterable[_StringType2 | None], *, scorer: _Scorer[_StringType1, _StringType2, _ResultType, _ResultType], processor: None = None, score_cutoff: _ResultType | None = None, score_hint: _ResultType | None = None, score_multiplier: _ResultType = 1, dtype: DTypeLike | None = None, workers: int = 1, scorer_kwargs: dict[str, Any] | None = None, ) -> np.ndarray: ... @overload def cdist( queries: Iterable[_UnprocessedType1 | None], choices: Iterable[_UnprocessedType2 | None], *, scorer: _Scorer[_StringType1, _StringType1, _ResultType, _ResultType], processor: Callable[[_UnprocessedType1 | _UnprocessedType2], _StringType1], score_cutoff: _ResultType | None = None, score_hint: _ResultType | None = None, score_multiplier: _ResultType = 1, dtype: DTypeLike | None = None, workers: int = 1, scorer_kwargs: dict[str, Any] | None = None, ) -> np.ndarray: ... @overload def cpdist( queries: Iterable[Sequence[Hashable] | None], choices: Iterable[Sequence[Hashable] | None], *, scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = ratio, processor: None = None, score_cutoff: float | None = None, score_hint: float | None = None, score_multiplier: float = 1, dtype: DTypeLike | None = None, workers: int = 1, scorer_kwargs: dict[str, Any] | None = None, ) -> np.ndarray: ... @overload def cpdist( queries: Iterable[_UnprocessedType1 | None], choices: Iterable[_UnprocessedType2 | None], *, scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = ratio, processor: Callable[[_UnprocessedType1 | _UnprocessedType2], Sequence[Hashable]], score_cutoff: float | None = None, score_hint: float | None = None, score_multiplier: float = 1, dtype: DTypeLike | None = None, workers: int = 1, scorer_kwargs: dict[str, Any] | None = None, ) -> np.ndarray: ... @overload def cpdist( queries: Iterable[_StringType1 | None], choices: Iterable[_StringType2 | None], *, scorer: _Scorer[_StringType1, _StringType2, _ResultType, _ResultType], processor: None = None, score_cutoff: _ResultType | None = None, score_hint: _ResultType | None = None, score_multiplier: _ResultType = 1, dtype: DTypeLike | None = None, workers: int = 1, scorer_kwargs: dict[str, Any] | None = None, ) -> np.ndarray: ... @overload def cpdist( queries: Iterable[_UnprocessedType1 | None], choices: Iterable[_UnprocessedType2 | None], *, scorer: _Scorer[_StringType1, _StringType1, _ResultType, _ResultType], processor: Callable[[_UnprocessedType1 | _UnprocessedType2], _StringType1], score_cutoff: _ResultType | None = None, score_hint: _ResultType | None = None, score_multiplier: _ResultType = 1, dtype: DTypeLike | None = None, workers: int = 1, scorer_kwargs: dict[str, Any] | None = None, ) -> np.ndarray: ... except ImportError: pass