# SPDX-License-Identifier: MIT
# Copyright (C) 2022 Max Bachmann
"""
The Levenshtein (edit) distance is a string metric to measure the
difference between two strings/sequences s1 and s2.
It's defined as the minimum number of insertions, deletions or
substitutions required to transform s1 into s2.
"""
from __future__ import annotations
from collections.abc import Hashable, Sequence
from typing import Callable, TypeVar, overload
from rapidfuzz.distance import Editops, Opcodes
_UnprocessedType1 = TypeVar("_UnprocessedType1")
_UnprocessedType2 = TypeVar("_UnprocessedType2")
@overload
def distance(
s1: Sequence[Hashable],
s2: Sequence[Hashable],
*,
weights: tuple[int, int, int] | None = (1, 1, 1),
processor: None = None,
score_cutoff: int | None = None,
score_hint: int | None = None,
) -> int: ...
@overload
def distance(
s1: _UnprocessedType1,
s2: _UnprocessedType2,
*,
weights: tuple[int, int, int] | None = (1, 1, 1),
processor: Callable[[_UnprocessedType1 | _UnprocessedType2], Sequence[Hashable]],
score_cutoff: int | None = None,
score_hint: int | None = None,
) -> int: ...
@overload
def normalized_distance(
s1: Sequence[Hashable],
s2: Sequence[Hashable],
*,
weights: tuple[int, int, int] | None = (1, 1, 1),
processor: None = None,
score_cutoff: float | None = 0,
score_hint: float | None = 0,
) -> float: ...
@overload
def normalized_distance(
s1: _UnprocessedType1,
s2: _UnprocessedType2,
*,
weights: tuple[int, int, int] | None = (1, 1, 1),
processor: Callable[[_UnprocessedType1 | _UnprocessedType2], Sequence[Hashable]],
score_cutoff: float | None = 0,
score_hint: float | None = 0,
) -> float: ...
@overload
def similarity(
s1: Sequence[Hashable],
s2: Sequence[Hashable],
*,
weights: tuple[int, int, int] | None = (1, 1, 1),
processor: None = None,
score_cutoff: int | None = None,
score_hint: int | None = None,
) -> int: ...
@overload
def similarity(
s1: _UnprocessedType1,
s2: _UnprocessedType2,
*,
weights: tuple[int, int, int] | None = (1, 1, 1),
processor: Callable[[_UnprocessedType1 | _UnprocessedType2], Sequence[Hashable]],
score_cutoff: int | None = None,
score_hint: int | None = None,
) -> int: ...
@overload
def normalized_similarity(
s1: Sequence[Hashable],
s2: Sequence[Hashable],
*,
weights: tuple[int, int, int] | None = (1, 1, 1),
processor: None = None,
score_cutoff: float | None = 0,
score_hint: float | None = 0,
) -> float: ...
@overload
def normalized_similarity(
s1: _UnprocessedType1,
s2: _UnprocessedType2,
*,
weights: tuple[int, int, int] | None = (1, 1, 1),
processor: Callable[[_UnprocessedType1 | _UnprocessedType2], Sequence[Hashable]],
score_cutoff: float | None = 0,
score_hint: float | None = 0,
) -> float: ...
@overload
def editops(
s1: Sequence[Hashable],
s2: Sequence[Hashable],
*,
processor: None = None,
score_hint: int | None = None,
) -> Editops: ...
@overload
def editops(
s1: _UnprocessedType1,
s2: _UnprocessedType2,
*,
processor: Callable[[_UnprocessedType1 | _UnprocessedType2], Sequence[Hashable]],
score_hint: int | None = None,
) -> Editops: ...
@overload
def opcodes(
s1: Sequence[Hashable],
s2: Sequence[Hashable],
*,
processor: None = None,
score_hint: int | None = None,
) -> Opcodes: ...
@overload
def opcodes(
s1: _UnprocessedType1,
s2: _UnprocessedType2,
*,
processor: Callable[[_UnprocessedType1 | _UnprocessedType2], Sequence[Hashable]],
score_hint: int | None = None,
) -> Opcodes: ...