from array import array as array
from collections.abc import Iterable, Mapping
from numbers import Number as Number
from operator import itemgetter as itemgetter
from typing import Any, ClassVar, Iterable, Iterator, Mapping, TypeVar

from numpy import ndarray
from scipy.sparse import spmatrix

from .._typing import ArrayLike, MatrixLike
from ..base import BaseEstimator, TransformerMixin
from ..utils import check_array as check_array

DictVectorizer_Self = TypeVar("DictVectorizer_Self", bound="DictVectorizer")

# Authors: Lars Buitinck
#          Dan Blanchard <dblanchard@ets.org>
# License: BSD 3 clause

import numpy as np
import scipy.sparse as sp

class DictVectorizer(TransformerMixin, BaseEstimator):
    feature_names_: list = ...
    vocabulary_: dict = ...

    _parameter_constraints: ClassVar[dict] = ...

    def __init__(self, *, dtype=..., separator: str = "=", sparse: bool = True, sort: bool = True) -> None: ...
    def fit(self: DictVectorizer_Self, X: Mapping | Iterable[Mapping], y: Any = None) -> DictVectorizer_Self: ...
    def fit_transform(
        self,
        X: Iterator[Any] | Mapping | Iterable[Mapping] | list[dict[str, int]],
        y: Any = None,
    ) -> ndarray | spmatrix: ...
    def inverse_transform(self, X: MatrixLike | ArrayLike, dict_type=...) -> list[Mapping]: ...
    def transform(self, X: ArrayLike | Mapping[str, ArrayLike] | Iterator[Mapping[str, ArrayLike]]) -> ndarray | spmatrix: ...
    def get_feature_names_out(self, input_features: None | ArrayLike = None) -> ndarray: ...
    def restrict(self: DictVectorizer_Self, support: ArrayLike, indices: bool = False) -> DictVectorizer_Self: ...
