from numbers import Integral as Integral, Real as Real
from typing import ClassVar, TypeVar

from numpy import ndarray
from numpy.random import RandomState
from scipy.special import expit as expit

from .._typing import ArrayLike, Float, Int, MatrixLike
from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, TransformerMixin
from ..utils import check_random_state as check_random_state, gen_even_slices as gen_even_slices
from ..utils._param_validation import Interval as Interval
from ..utils.extmath import log_logistic as log_logistic, safe_sparse_dot as safe_sparse_dot
from ..utils.validation import check_is_fitted as check_is_fitted

BernoulliRBM_Self = TypeVar("BernoulliRBM_Self", bound=BernoulliRBM)

# Authors: Yann N. Dauphin <dauphiya@iro.umontreal.ca>
#          Vlad Niculae
#          Gabriel Synnaeve
#          Lars Buitinck
# License: BSD 3 clause

import time

import numpy as np
import scipy.sparse as sp

class BernoulliRBM(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):
    feature_names_in_: ndarray = ...
    n_features_in_: int = ...
    h_samples_: ArrayLike = ...
    components_: ArrayLike = ...
    intercept_visible_: ArrayLike = ...
    intercept_hidden_: ArrayLike = ...

    _parameter_constraints: ClassVar[dict] = ...

    def __init__(
        self,
        n_components: Int = 256,
        *,
        learning_rate: Float = 0.1,
        batch_size: Int = 10,
        n_iter: Int = 10,
        verbose: Int = 0,
        random_state: RandomState | None | Int = None,
    ) -> None: ...
    def transform(self, X: MatrixLike | ArrayLike) -> ndarray: ...
    def gibbs(self, v: ArrayLike) -> ndarray: ...
    def partial_fit(self: BernoulliRBM_Self, X: ArrayLike, y: None | MatrixLike | ArrayLike = None) -> BernoulliRBM_Self: ...
    def score_samples(self, X: MatrixLike | ArrayLike) -> ndarray: ...
    def fit(
        self: BernoulliRBM_Self,
        X: MatrixLike | ArrayLike,
        y: None | MatrixLike | ArrayLike = None,
    ) -> BernoulliRBM_Self: ...
