from collections.abc import Sequence
from typing import Callable, Literal
from typing_extensions import Self

from numpy import ndarray

from .._typing import ArrayLike, Int, MatrixLike
from ..base import BaseEstimator, ClassifierMixin

class ArraySlicingWrapper:
    def __init__(self, array) -> None: ...
    def __getitem__(self, aslice): ...

class MockDataFrame:
    # have shape and length but don't support indexing.

    def __init__(self, array) -> None: ...
    def __len__(self) -> int: ...
    def __array__(self, dtype=None): ...
    def __eq__(self, other) -> bool: ...
    def __ne__(self, other) -> bool: ...
    def take(self, indices, axis: int = 0): ...

class CheckingClassifier(ClassifierMixin, BaseEstimator):
    n_features_in_: int = ...
    classes_: int = ...

    def __init__(
        self,
        *,
        check_y: None | Callable = None,
        check_y_params: None | dict = None,
        check_X: None | Callable = None,
        check_X_params: None | dict = None,
        methods_to_check: Literal["all"] | Sequence[str] = "all",
        foo_param: Int = 0,
        expected_sample_weight: None | bool = None,
        expected_fit_params: None | Sequence[str] = None,
    ) -> None: ...
    def fit(
        self,
        X: MatrixLike,
        y: MatrixLike | ArrayLike,
        sample_weight: None | ArrayLike = None,
        **fit_params,
    ) -> Self: ...
    def predict(self, X: MatrixLike) -> ndarray: ...
    def predict_proba(self, X: MatrixLike) -> ndarray: ...
    def decision_function(self, X: MatrixLike) -> ndarray: ...
    def score(self, X: None | MatrixLike = None, Y: None | MatrixLike | ArrayLike = None) -> float: ...

class NoSampleWeightWrapper(BaseEstimator):
    def __init__(self, est: None | BaseEstimator = None) -> None: ...
    def fit(self, X, y): ...
    def predict(self, X): ...
    def predict_proba(self, X): ...