import warnings
from numbers import Integral as Integral
from typing import Callable, ClassVar, Literal
from typing_extensions import Self

import numpy as np
from numpy import ndarray

from .._typing import ArrayLike, Float, Int, MatrixLike
from ..base import ClassifierMixin
from ..utils._param_validation import StrOptions as StrOptions
from ..utils.extmath import weighted_mode as weighted_mode
from ._base import KNeighborsMixin, NeighborsBase, RadiusNeighborsMixin

class KNeighborsClassifier(KNeighborsMixin, ClassifierMixin, NeighborsBase):
    outputs_2d_: bool = ...
    n_samples_fit_: int = ...
    feature_names_in_: ndarray = ...
    n_features_in_: int = ...
    effective_metric_params_: dict = ...
    effective_metric_: str | Callable = ...
    classes_: ndarray = ...

    _parameter_constraints: ClassVar[dict] = ...

    def __init__(
        self,
        n_neighbors: Int = 5,
        *,
        weights: None | Literal["uniform", "distance"] | Callable = "uniform",
        algorithm: Literal["auto", "ball_tree", "kd_tree", "brute"] = "auto",
        leaf_size: Int = 30,
        p: Int = 2,
        metric: str | Callable = "minkowski",
        metric_params: None | dict = None,
        n_jobs: None | Int = None,
    ) -> None: ...
    def fit(self, X: MatrixLike, y: MatrixLike | ArrayLike) -> Self: ...
    def predict(self, X: MatrixLike) -> ndarray: ...
    def predict_proba(self, X: MatrixLike) -> ndarray | list[ndarray]: ...

class RadiusNeighborsClassifier(RadiusNeighborsMixin, ClassifierMixin, NeighborsBase):
    outputs_2d_: bool = ...
    outlier_label_: ArrayLike | int = ...
    n_samples_fit_: int = ...
    feature_names_in_: ndarray = ...
    n_features_in_: int = ...
    effective_metric_params_: dict = ...
    effective_metric_: str | Callable = ...
    classes_: ndarray = ...

    _parameter_constraints: ClassVar[dict] = ...

    def __init__(
        self,
        radius: Float = 1.0,
        *,
        weights: None | Literal["uniform", "distance"] | Callable = "uniform",
        algorithm: Literal["auto", "ball_tree", "kd_tree", "brute"] = "auto",
        leaf_size: Int = 30,
        p: Int = 2,
        metric: str | Callable = "minkowski",
        outlier_label: None | str = None,
        metric_params: None | dict = None,
        n_jobs: None | Int = None,
    ) -> None: ...
    def fit(self, X: MatrixLike, y: MatrixLike | ArrayLike) -> Self: ...
    def predict(self, X: MatrixLike) -> ndarray: ...
    def predict_proba(self, X: MatrixLike) -> ndarray: ...
