# SPDX-License-Identifier: MIT
# Copyright (C) 2022 Max Bachmann

from __future__ import annotations

from typing import Callable, Hashable, Sequence

from rapidfuzz._common_py import conv_sequences
from rapidfuzz._utils import is_none


def distance(
    s1: Sequence[Hashable],
    s2: Sequence[Hashable],
    *,
    processor: Callable[..., Sequence[Hashable]] | None = None,
    score_cutoff: int | None = None,
) -> int:
    """
    Calculates the Prefix distance between two strings.

    Parameters
    ----------
    s1 : Sequence[Hashable]
        First string to compare.
    s2 : Sequence[Hashable]
        Second string to compare.
    processor: callable, optional
        Optional callable that is used to preprocess the strings before
        comparing them. Default is None, which deactivates this behaviour.
    score_cutoff : int or None, optional
        Maximum distance between s1 and s2, that is
        considered as a result. If the distance is bigger than score_cutoff,
        score_cutoff + 1 is returned instead. Default is None, which deactivates
        this behaviour.

    Returns
    -------
    distance : int
        distance between s1 and s2
    """
    if processor is not None:
        s1 = processor(s1)
        s2 = processor(s2)

    s1, s2 = conv_sequences(s1, s2)
    maximum = max(len(s1), len(s2))
    sim = similarity(s1, s2)
    dist = maximum - sim

    return dist if (score_cutoff is None or dist <= score_cutoff) else score_cutoff + 1


def similarity(
    s1: Sequence[Hashable],
    s2: Sequence[Hashable],
    *,
    processor: Callable[..., Sequence[Hashable]] | None = None,
    score_cutoff: int | None = None,
) -> int:
    """
    Calculates the prefix similarity between two strings.

    This is calculated as ``len1 - distance``.

    Parameters
    ----------
    s1 : Sequence[Hashable]
        First string to compare.
    s2 : Sequence[Hashable]
        Second string to compare.
    processor: callable, optional
        Optional callable that is used to preprocess the strings before
        comparing them. Default is None, which deactivates this behaviour.
    score_cutoff : int, optional
        Maximum distance between s1 and s2, that is
        considered as a result. If the similarity is smaller than score_cutoff,
        0 is returned instead. Default is None, which deactivates
        this behaviour.

    Returns
    -------
    distance : int
        distance between s1 and s2
    """
    if processor is not None:
        s1 = processor(s1)
        s2 = processor(s2)

    s1, s2 = conv_sequences(s1, s2)
    sim = 0
    for ch1, ch2 in zip(s1, s2):
        if ch1 != ch2:
            break
        sim += 1

    return sim if (score_cutoff is None or sim >= score_cutoff) else 0


def normalized_distance(
    s1: Sequence[Hashable],
    s2: Sequence[Hashable],
    *,
    processor: Callable[..., Sequence[Hashable]] | None = None,
    score_cutoff: float | None = None,
) -> float:
    """
    Calculates a normalized prefix similarity in the range [1, 0].

    This is calculated as ``distance / (len1 + len2)``.

    Parameters
    ----------
    s1 : Sequence[Hashable]
        First string to compare.
    s2 : Sequence[Hashable]
        Second string to compare.
    processor: callable, optional
        Optional callable that is used to preprocess the strings before
        comparing them. Default is None, which deactivates this behaviour.
    score_cutoff : float, optional
        Optional argument for a score threshold as a float between 0 and 1.0.
        For norm_dist > score_cutoff 1.0 is returned instead. Default is 1.0,
        which deactivates this behaviour.

    Returns
    -------
    norm_dist : float
        normalized distance between s1 and s2 as a float between 0 and 1.0
    """
    if is_none(s1) or is_none(s2):
        return 1.0

    norm_sim = normalized_similarity(s1, s2, processor=processor)
    norm_dist = 1.0 - norm_sim

    return norm_dist if (score_cutoff is None or norm_dist <= score_cutoff) else 1.0


def normalized_similarity(
    s1: Sequence[Hashable],
    s2: Sequence[Hashable],
    *,
    processor: Callable[..., Sequence[Hashable]] | None = None,
    score_cutoff: float | None = None,
) -> float:
    """
    Calculates a normalized prefix similarity in the range [0, 1].

    This is calculated as ``1 - normalized_distance``

    Parameters
    ----------
    s1 : Sequence[Hashable]
        First string to compare.
    s2 : Sequence[Hashable]
        Second string to compare.
    processor: callable, optional
        Optional callable that is used to preprocess the strings before
        comparing them. Default is None, which deactivates this behaviour.
    score_cutoff : float, optional
        Optional argument for a score threshold as a float between 0 and 1.0.
        For norm_sim < score_cutoff 0 is returned instead. Default is 0,
        which deactivates this behaviour.

    Returns
    -------
    norm_sim : float
        normalized similarity between s1 and s2 as a float between 0 and 1.0
    """
    if is_none(s1) or is_none(s2):
        return 0.0

    if processor is not None:
        s1 = processor(s1)
        s2 = processor(s2)

    s1, s2 = conv_sequences(s1, s2)
    maximum = max(len(s1), len(s2))
    sim = similarity(s1, s2)
    norm_sim = sim / maximum if maximum else 1.0

    return norm_sim if (score_cutoff is None or norm_sim >= score_cutoff) else 0.0
