"""Stubs for more_itertools.recipes"""

from __future__ import annotations

from collections.abc import Iterable, Iterator, Sequence
from dataclasses import dataclass
from decimal import Decimal
from fractions import Fraction
from typing import (
    Any,
    Callable,
    TypeVar,
    overload,
)

__all__ = [
    'Stats',
    'all_equal',
    'batched',
    'before_and_after',
    'consume',
    'convolve',
    'dotproduct',
    'first_true',
    'factor',
    'flatten',
    'grouper',
    'is_prime',
    'iter_except',
    'iter_index',
    'loops',
    'matmul',
    'multinomial',
    'ncycles',
    'nth',
    'nth_combination',
    'padnone',
    'pad_none',
    'pairwise',
    'partition',
    'polynomial_eval',
    'polynomial_from_roots',
    'polynomial_derivative',
    'powerset',
    'prepend',
    'quantify',
    'reshape',
    'random_combination_with_replacement',
    'random_combination',
    'random_derangement',
    'random_permutation',
    'random_product',
    'repeatfunc',
    'roundrobin',
    'running_max',
    'running_mean',
    'running_median',
    'running_min',
    'running_statistics',
    'sieve',
    'sliding_window',
    'subslices',
    'sum_of_squares',
    'tabulate',
    'tail',
    'take',
    'totient',
    'transpose',
    'triplewise',
    'unique',
    'unique_everseen',
    'unique_justseen',
]

# Type and type variable definitions
_T = TypeVar('_T')
_T1 = TypeVar('_T1')
_T2 = TypeVar('_T2')
_U = TypeVar('_U')
_NumberT = TypeVar("_NumberT", float, Decimal, Fraction)

def take(n: int, iterable: Iterable[_T]) -> list[_T]: ...
def tabulate(
    function: Callable[[int], _T], start: int = ...
) -> Iterator[_T]: ...
def tail(n: int, iterable: Iterable[_T]) -> Iterator[_T]: ...
def consume(iterator: Iterable[_T], n: int | None = ...) -> None: ...
@overload
def nth(iterable: Iterable[_T], n: int) -> _T | None: ...
@overload
def nth(iterable: Iterable[_T], n: int, default: _U) -> _T | _U: ...
def all_equal(
    iterable: Iterable[_T], key: Callable[[_T], _U] | None = ...
) -> bool: ...
def quantify(
    iterable: Iterable[_T], pred: Callable[[_T], bool] = ...
) -> int: ...
def pad_none(iterable: Iterable[_T]) -> Iterator[_T | None]: ...
def padnone(iterable: Iterable[_T]) -> Iterator[_T | None]: ...
def ncycles(iterable: Iterable[_T], n: int) -> Iterator[_T]: ...
def dotproduct(vec1: Iterable[_T1], vec2: Iterable[_T2]) -> Any: ...
def flatten(list_of_lists: Iterable[Iterable[_T]]) -> Iterator[_T]: ...
def repeatfunc(
    function: Callable[..., _U], times: int | None = ..., *args: Any
) -> Iterator[_U]: ...
def pairwise(iterable: Iterable[_T]) -> Iterator[tuple[_T, _T]]: ...
def grouper(
    iterable: Iterable[_T],
    n: int,
    incomplete: str = ...,
    fillvalue: _U = ...,
) -> Iterator[tuple[_T | _U, ...]]: ...
def roundrobin(*iterables: Iterable[_T]) -> Iterator[_T]: ...
def partition(
    pred: Callable[[_T], object] | None, iterable: Iterable[_T]
) -> tuple[Iterator[_T], Iterator[_T]]: ...
def powerset(iterable: Iterable[_T]) -> Iterator[tuple[_T, ...]]: ...
def unique_everseen(
    iterable: Iterable[_T], key: Callable[[_T], _U] | None = ...
) -> Iterator[_T]: ...
def unique_justseen(
    iterable: Iterable[_T], key: Callable[[_T], object] | None = ...
) -> Iterator[_T]: ...
def unique(
    iterable: Iterable[_T],
    key: Callable[[_T], object] | None = ...,
    reverse: bool = False,
) -> Iterator[_T]: ...
@overload
def iter_except(
    function: Callable[[], _T],
    exception: type[BaseException] | tuple[type[BaseException], ...],
    first: None = ...,
) -> Iterator[_T]: ...
@overload
def iter_except(
    function: Callable[[], _T],
    exception: type[BaseException] | tuple[type[BaseException], ...],
    first: Callable[[], _U],
) -> Iterator[_T | _U]: ...
@overload
def first_true(
    iterable: Iterable[_T], *, pred: Callable[[_T], object] | None = ...
) -> _T | None: ...
@overload
def first_true(
    iterable: Iterable[_T],
    default: _U,
    pred: Callable[[_T], object] | None = ...,
) -> _T | _U: ...
def random_product(
    *iterables: Iterable[_T], repeat: int = ...
) -> tuple[_T, ...]: ...
def random_permutation(
    iterable: Iterable[_T], r: int | None = ...
) -> tuple[_T, ...]: ...
def random_combination(iterable: Iterable[_T], r: int) -> tuple[_T, ...]: ...
def random_combination_with_replacement(
    iterable: Iterable[_T], r: int
) -> tuple[_T, ...]: ...
def nth_combination(
    iterable: Iterable[_T], r: int, index: int
) -> tuple[_T, ...]: ...
def prepend(value: _T, iterable: Iterable[_U]) -> Iterator[_T | _U]: ...
def convolve(signal: Iterable[_T], kernel: Iterable[_T]) -> Iterator[_T]: ...
def before_and_after(
    predicate: Callable[[_T], bool], it: Iterable[_T]
) -> tuple[Iterator[_T], Iterator[_T]]: ...
def triplewise(iterable: Iterable[_T]) -> Iterator[tuple[_T, _T, _T]]: ...
def sliding_window(
    iterable: Iterable[_T], n: int
) -> Iterator[tuple[_T, ...]]: ...
def subslices(iterable: Iterable[_T]) -> Iterator[list[_T]]: ...
def polynomial_from_roots(roots: Sequence[_T]) -> list[_T]: ...
def iter_index(
    iterable: Iterable[_T],
    value: Any,
    start: int | None = ...,
    stop: int | None = ...,
) -> Iterator[int]: ...
def sieve(n: int) -> Iterator[int]: ...
def _batched(
    iterable: Iterable[_T], n: int, *, strict: bool = False
) -> Iterator[tuple[_T, ...]]: ...

batched = _batched

def transpose(
    matrix: Iterable[Iterable[_T]],
) -> Iterator[tuple[_T, ...]]: ...
@overload
def reshape(
    matrix: Iterable[Iterable[_T]], shape: int
) -> Iterator[tuple[_T, ...]]: ...
@overload
def reshape(matrix: Iterable[Any], shape: Iterable[int]) -> Iterator[Any]: ...
def matmul(m1: Sequence[_T], m2: Sequence[_T]) -> Iterator[tuple[_T]]: ...
def _factor_trial(n: int) -> Iterator[int]: ...
def _factor_pollard(n: int) -> int: ...
def factor(n: int) -> Iterator[int]: ...
def polynomial_eval(coefficients: Sequence[_T], x: _U) -> _U: ...
def sum_of_squares(iterable: Iterable[_T]) -> _T: ...
def polynomial_derivative(coefficients: Sequence[_T]) -> list[_T]: ...
def totient(n: int) -> int: ...
def _shift_to_odd(n: int) -> tuple[int, int]: ...
def _strong_probable_prime(n: int, base: int) -> bool: ...
def is_prime(n: int) -> bool: ...
def loops(n: int) -> Iterator[None]: ...
def multinomial(*counts: int) -> int: ...
def random_derangement(iterable: Iterable[_T]) -> tuple[_T, ...]: ...
def running_mean(
    iterable: Iterable[_NumberT], *, maxlen: int | None = ...
) -> Iterator[_NumberT]: ...
def running_median(
    iterable: Iterable[_NumberT], *, maxlen: int | None = ...
) -> Iterator[_NumberT]: ...
def running_min(
    iterable: Iterable[_NumberT], *, maxlen: int | None = ...
) -> Iterator[_NumberT]: ...
def running_max(
    iterable: Iterable[_NumberT], *, maxlen: int | None = ...
) -> Iterator[_NumberT]: ...

@dataclass(frozen=True, slots=True)
class Stats:
    size: int
    minimum: float
    median: float
    maximum: float
    mean: float

def running_statistics(
    iterable: Iterable[_NumberT], *, maxlen: int | None = ...
) -> Iterator[Stats]: ...
