import copy
import inspect
import platform
import re
import warnings
from collections import defaultdict as defaultdict
from collections.abc import Iterable
from typing import Any, ClassVar
from typing_extensions import Self

import numpy as np
from numpy import ndarray

from ._config import get_config as get_config
from ._typing import ArrayLike, Float, Int, MatrixLike
from .metrics import accuracy_score as accuracy_score, r2_score as r2_score
from .utils._estimator_html_repr import estimator_html_repr as estimator_html_repr
from .utils._metadata_requests import _MetadataRequester
from .utils._param_validation import validate_parameter_constraints as validate_parameter_constraints
from .utils._set_output import _SetOutputMixin
from .utils.validation import check_array as check_array, check_is_fitted as check_is_fitted, check_X_y as check_X_y

# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
# License: BSD 3 clause

def clone(estimator: BaseEstimator | Iterable[BaseEstimator], *, safe: bool = True) -> Any: ...

class BaseEstimator(_MetadataRequester):
    def get_params(self, deep: bool = True) -> dict: ...
    def set_params(self, **params) -> Self: ...
    def __repr__(self, N_CHAR_MAX: int = 700) -> str: ...
    def __getstate__(self): ...
    def __setstate__(self, state) -> None: ...

class ClassifierMixin:
    _estimator_type: ClassVar[str] = ...

    def score(
        self,
        X: MatrixLike,
        y: MatrixLike | ArrayLike,
        sample_weight: None | ArrayLike = None,
    ) -> Float: ...

class RegressorMixin:
    _estimator_type: ClassVar[str] = ...

    def score(
        self,
        X: MatrixLike,
        y: MatrixLike | ArrayLike,
        sample_weight: None | ArrayLike = None,
    ) -> Float: ...

class ClusterMixin:
    _estimator_type: ClassVar[str] = ...

    def fit_predict(self, X: MatrixLike, y: Any = None) -> ndarray: ...

class BiclusterMixin:
    def biclusters_(self): ...
    def get_indices(self, i: Int) -> tuple[ndarray, ndarray]: ...
    def get_shape(self, i: Int) -> tuple[int, int]: ...
    def get_submatrix(self, i: Int, data: MatrixLike) -> ndarray: ...

class TransformerMixin(_SetOutputMixin):
    def fit_transform(self, X: MatrixLike, y: None | MatrixLike | ArrayLike = None, **fit_params) -> ndarray: ...

class OneToOneFeatureMixin:
    def get_feature_names_out(self, input_features: None | ArrayLike = None) -> ndarray: ...

class ClassNamePrefixFeaturesOutMixin:
    def get_feature_names_out(self, input_features: None | ArrayLike = None) -> ndarray: ...

class DensityMixin:
    _estimator_type: ClassVar[str] = ...

    def score(self, X: MatrixLike, y: Any = None) -> float: ...

class OutlierMixin:
    _estimator_type: ClassVar[str] = ...

    def fit_predict(self, X: MatrixLike | ArrayLike, y: Any = None) -> ndarray: ...

class MetaEstimatorMixin:
    _required_parameters: ClassVar[list] = ...

class MultiOutputMixin: ...
class _UnstableArchMixin: ...

def is_classifier(estimator: Any) -> bool: ...
def is_regressor(estimator: BaseEstimator) -> bool: ...
def is_outlier_detector(estimator: BaseEstimator) -> bool: ...