from typing import Any, ClassVar, Iterable, Iterator, Mapping, TypeVar
from ..utils import check_array as check_array
from operator import itemgetter as itemgetter
from array import array as array
from numpy import ndarray
from numbers import Number as Number
from ..base import BaseEstimator, TransformerMixin
from scipy.sparse import spmatrix
from .._typing import MatrixLike, ArrayLike
from collections.abc import Mapping, Iterable

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:
        ...
