mirror of
https://github.com/Tautulli/Tautulli.git
synced 2025-01-06 11:09:57 -08:00
a528f052b9
* Bump cherrypy from 18.9.0 to 18.10.0 Bumps [cherrypy](https://github.com/cherrypy/cherrypy) from 18.9.0 to 18.10.0. - [Changelog](https://github.com/cherrypy/cherrypy/blob/main/CHANGES.rst) - [Commits](https://github.com/cherrypy/cherrypy/compare/v18.9.0...v18.10.0) --- updated-dependencies: - dependency-name: cherrypy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] <support@github.com> * Update cherrypy==18.10.0 --------- Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: JonnyWong16 <9099342+JonnyWong16@users.noreply.github.com> [skip ci]
137 lines
4.5 KiB
Python
137 lines
4.5 KiB
Python
"""Stubs for more_itertools.recipes"""
|
|
|
|
from __future__ import annotations
|
|
|
|
from typing import (
|
|
Any,
|
|
Callable,
|
|
Iterable,
|
|
Iterator,
|
|
overload,
|
|
Sequence,
|
|
Type,
|
|
TypeVar,
|
|
)
|
|
|
|
# Type and type variable definitions
|
|
_T = TypeVar('_T')
|
|
_T1 = TypeVar('_T1')
|
|
_T2 = TypeVar('_T2')
|
|
_U = TypeVar('_U')
|
|
|
|
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(listOfLists: Iterable[Iterable[_T]]) -> Iterator[_T]: ...
|
|
def repeatfunc(
|
|
func: 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(
|
|
func: Callable[[], _T],
|
|
exception: Type[BaseException] | tuple[Type[BaseException], ...],
|
|
first: None = ...,
|
|
) -> Iterator[_T]: ...
|
|
@overload
|
|
def iter_except(
|
|
func: 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(
|
|
*args: 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, iterator: 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]]: ...
|
|
def transpose(
|
|
it: Iterable[Iterable[_T]],
|
|
) -> Iterator[tuple[_T, ...]]: ...
|
|
def reshape(
|
|
matrix: Iterable[Iterable[_T]], cols: int
|
|
) -> Iterator[tuple[_T, ...]]: ...
|
|
def matmul(m1: Sequence[_T], m2: Sequence[_T]) -> Iterator[tuple[_T]]: ...
|
|
def factor(n: int) -> Iterator[int]: ...
|
|
def polynomial_eval(coefficients: Sequence[_T], x: _U) -> _U: ...
|
|
def sum_of_squares(it: Iterable[_T]) -> _T: ...
|
|
def polynomial_derivative(coefficients: Sequence[_T]) -> list[_T]: ...
|
|
def totient(n: int) -> int: ...
|