from numpy import ndarray
from scipy.sparse import spmatrix

from ..._typing import ArrayLike, Float, MatrixLike
from ._expected_mutual_info_fast import expected_mutual_information as expected_mutual_information

# Authors: Olivier Grisel <olivier.grisel@ensta.org>
#          Wei LI <kuantkid@gmail.com>
#          Diego Molla <dmolla-aliod@gmail.com>
#          Arnaud Fouchet <foucheta@gmail.com>
#          Thierry Guillemot <thierry.guillemot.work@gmail.com>
#          Gregory Stupp <stuppie@gmail.com>
#          Joel Nothman <joel.nothman@gmail.com>
#          Arya McCarthy <arya@jhu.edu>
#          Uwe F Mayer <uwe_f_mayer@yahoo.com>
# License: BSD 3 clause

def check_clusterings(labels_true: ArrayLike, labels_pred: ArrayLike) -> tuple[ndarray, ndarray]: ...
def contingency_matrix(
    labels_true: ArrayLike, labels_pred: ArrayLike, *, eps: None | Float = None, sparse: bool = False, dtype: float = ...
) -> ndarray | spmatrix: ...

# clustering measures

def pair_confusion_matrix(labels_true: ArrayLike, labels_pred: ArrayLike) -> ndarray: ...
def rand_score(labels_true: ArrayLike, labels_pred: ArrayLike) -> Float: ...
def adjusted_rand_score(labels_true: ArrayLike, labels_pred: ArrayLike) -> float: ...
def homogeneity_completeness_v_measure(
    labels_true: ArrayLike, labels_pred: ArrayLike, *, beta: Float = 1.0
) -> tuple[float, float, float] | tuple[Float, Float, Float]: ...
def homogeneity_score(labels_true: ArrayLike, labels_pred: ArrayLike) -> Float: ...
def completeness_score(labels_true: ArrayLike, labels_pred: ArrayLike) -> Float: ...
def v_measure_score(labels_true: ArrayLike, labels_pred: ArrayLike, *, beta: Float = 1.0) -> Float: ...
def mutual_info_score(
    labels_true: None | ArrayLike, labels_pred: None | ArrayLike, *, contingency: None | MatrixLike = None
) -> Float: ...
def adjusted_mutual_info_score(
    labels_true: ArrayLike, labels_pred: ArrayLike, *, average_method: str = "arithmetic"
) -> Float: ...
def normalized_mutual_info_score(
    labels_true: ArrayLike, labels_pred: ArrayLike, *, average_method: str = "arithmetic"
) -> Float: ...
def fowlkes_mallows_score(labels_true: ArrayLike, labels_pred: ArrayLike, *, sparse: bool = False) -> float: ...
def entropy(labels: ArrayLike) -> Float: ...