from collections.abc import Iterable, Mapping
from typing import Callable, Literal

from numpy import ndarray
from numpy.random import RandomState

from .._typing import ArrayLike, Float, Int, MatrixLike
from ..base import BaseEstimator
from ..svm._classes import SVC, LinearSVC
from . import BaseCrossValidator

# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#         Gael Varoquaux <gael.varoquaux@normalesup.org>
#         Olivier Grisel <olivier.grisel@ensta.org>
#         Raghav RV <rvraghav93@gmail.com>
#         Michal Karbownik <michakarbownik@gmail.com>
# License: BSD 3 clause

__all__ = [
    "cross_validate",
    "cross_val_score",
    "cross_val_predict",
    "permutation_test_score",
    "learning_curve",
    "validation_curve",
]

def cross_validate(
    estimator: BaseEstimator,
    X: MatrixLike,
    y: None | MatrixLike | ArrayLike = None,
    *,
    groups: None | ArrayLike = None,
    scoring: ArrayLike | None | tuple | Callable | Mapping | str = None,
    cv: int | BaseCrossValidator | Iterable | None = None,
    n_jobs: None | Int = None,
    verbose: Int = 0,
    fit_params: None | dict = None,
    pre_dispatch: str | Int = "2*n_jobs",
    return_train_score: bool = False,
    return_estimator: bool = False,
    error_score: str | Float = ...,
): ...
def cross_val_score(
    estimator: BaseEstimator,
    X: MatrixLike,
    y: None | MatrixLike | ArrayLike = None,
    *,
    groups: None | ArrayLike = None,
    scoring: None | str | Callable = None,
    cv: int | BaseCrossValidator | Iterable | None = None,
    n_jobs: None | Int = None,
    verbose: Int = 0,
    fit_params: None | dict = None,
    pre_dispatch: str | Int = "2*n_jobs",
    error_score: str | Float = ...,
) -> ndarray: ...
def cross_val_predict(
    estimator: BaseEstimator,
    X: MatrixLike,
    y: None | MatrixLike | ArrayLike = None,
    *,
    groups: None | ArrayLike = None,
    cv: int | BaseCrossValidator | Iterable | None = None,
    n_jobs: None | Int = None,
    verbose: Int = 0,
    fit_params: None | dict = None,
    pre_dispatch: str | Int = "2*n_jobs",
    method: Literal["predict", "predict_proba", "predict_log_proba", "decision_function"] = "predict",
) -> ndarray: ...
def permutation_test_score(
    estimator: BaseEstimator | SVC,
    X: MatrixLike,
    y: None | MatrixLike | ArrayLike,
    *,
    groups: None | ArrayLike = None,
    cv: int | BaseCrossValidator | Iterable | None = None,
    n_permutations: Int = 100,
    n_jobs: None | Int = None,
    random_state: RandomState | None | Int = 0,
    verbose: Int = 0,
    scoring: None | str | Callable = None,
    fit_params: None | dict = None,
) -> tuple[Float, ndarray, Float] | tuple[float, ndarray, float]: ...
def learning_curve(
    estimator,
    X: MatrixLike,
    y: MatrixLike | ArrayLike,
    *,
    groups: None | ArrayLike = None,
    train_sizes: ArrayLike = ...,
    cv: int | BaseCrossValidator | Iterable | None = None,
    scoring: None | str | Callable = None,
    exploit_incremental_learning: bool = False,
    n_jobs: None | Int = None,
    pre_dispatch: str | Int = "all",
    verbose: Int = 0,
    shuffle: bool = False,
    random_state: RandomState | None | Int = None,
    error_score: str | Float = ...,
    return_times: bool = False,
    fit_params: None | dict = None,
) -> tuple[ndarray, ndarray, ndarray, ndarray, ndarray]: ...
def validation_curve(
    estimator: SVC | LinearSVC,
    X: MatrixLike,
    y: None | MatrixLike | ArrayLike,
    *,
    param_name: str,
    param_range: ArrayLike,
    groups: None | ArrayLike = None,
    cv: int | BaseCrossValidator | Iterable | None = None,
    scoring: None | str | Callable = None,
    n_jobs: None | Int = None,
    pre_dispatch: str | Int = "all",
    verbose: Int = 0,
    error_score: str | Float = ...,
    fit_params: None | dict = None,
) -> tuple[ndarray, ndarray]: ...